From 740c2ad29d57dd3a2b8cb09258d36b0085116a9c Mon Sep 17 00:00:00 2001 From: iulia-grb Date: Wed, 13 Nov 2024 11:15:33 +0000 Subject: [PATCH 01/40] copy changes --- optimization202/Modeling_Session_1/exercise_set1.ipynb | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/optimization202/Modeling_Session_1/exercise_set1.ipynb b/optimization202/Modeling_Session_1/exercise_set1.ipynb index 57297a2..dd34c25 100755 --- a/optimization202/Modeling_Session_1/exercise_set1.ipynb +++ b/optimization202/Modeling_Session_1/exercise_set1.ipynb @@ -23,7 +23,7 @@ "source": [ "## Simple modeling / coding\n", "\n", - "1. Show these constraints are either compatible (find a feasible solution) or incompatible (prove a contradiction) algebraically\n", + "1. Show these constraints are either compatible (find a feasible solution) or incompatible (prove a contradiction) algebraically:\n", "\n", "\\begin{align*}\n", "x + y + z &\\ge 3 \\\\\n", @@ -49,7 +49,7 @@ "metadata": {}, "source": [ "## Back to the widget production problem\n", - "Use the code below for as the base model for this problem. " + "Use the code below as the base model for this problem. " ] }, { @@ -236,7 +236,7 @@ "source": [ "### Quadratic expression\n", "\n", - "In order to produce more widgets, production facilities must run their machines at faster speeds. This consumes more energy and produces more waste at a level that grows with each widget produced. Running their machines costs a production facility $0.01 times the square of the number of widgets produced. Modify the objective function to include this additional production cost. Here is the some code to get you started - it includes the original transportation cost expression that you will be adding to:\n", + "In order to produce more widgets, production facilities must run their machines at faster speeds. This consumes more energy and produces more waste at a level that grows with each widget produced. Running their machines costs a production facility $0.01 times the square of the number of widgets produced. Modify the objective function to include this additional production cost. Here is some code to get you started - it includes the original transportation cost expression that you will be adding to:\n", "\n", "```python\n", "m.setObjective(gp.quicksum(transp_cost[p,d]*x[p,d] for p in production for d in distribution) + ..., GRB.MINIMIZE)\n", From 73b6a888ae146213be4191a8a512af9e218858f1 Mon Sep 17 00:00:00 2001 From: iulia-t Date: Wed, 13 Nov 2024 12:53:44 +0000 Subject: [PATCH 02/40] copy changes --- .../Modeling_Session_2/exercise_set2.ipynb | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/optimization202/Modeling_Session_2/exercise_set2.ipynb b/optimization202/Modeling_Session_2/exercise_set2.ipynb index f59ab06..2bbff94 100644 --- a/optimization202/Modeling_Session_2/exercise_set2.ipynb +++ b/optimization202/Modeling_Session_2/exercise_set2.ipynb @@ -5,7 +5,7 @@ "metadata": {}, "source": [ "# Back to the widget production problem\n", - "Let's go back to the Opti 101/201 classic: Widget production and distribution. Use the code below for as the base model for this problem. " + "Let's go back to the Opti 101/201 classic: Widget production and distribution. Use the code below as the base model for this problem. " ] }, { @@ -223,11 +223,11 @@ "source": [ "## Piecewise Linear Modeling\n", "### Scenario 1\n", - "Let's go back to the Opti 101/201 classic: Widget production and distribution. We have a new cost structure for transporting widgets we make. \n", + "Let's go back to the Opti 101/201 classic: Widget production and distribution. We have a new cost structure for transporting the widgets we make. \n", "- There will be a flat 0.50 increase for all transportation costs. \n", "- If we decide to bulk ship at least half of a production facility's max to any one distribution location, every widget over that half capacity number costs 60% of the new transportation cost. \n", "\n", - " So if the original cost is 2, the new cost of shipping from $p$ to $d$ for the first half of capacity is 2.5 (0.50 flat increase), and anything produces after that ships at 1.5. Update the model to reflect this (0.60 * 2.5). " + " So, if the original cost is 2, the new cost of shipping from $p$ to $d$ for the first half of capacity is 2.5 (0.50 flat increase), and anything produced after that ships at 1.5. Update the model to reflect this (0.60 * 2.5). " ] }, { @@ -944,7 +944,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Fill in the blanks in the code below to simulate an uncertain transportation cost from Cleveland to Indianapolis. We will use assume the cost is normally distributed with mean 2.3 and standard deviation 0.2. Generate 1000 samples. " + "Fill in the blanks in the code below to simulate an uncertain transportation cost from Cleveland to Indianapolis. We will assume the cost is normally distributed with mean 2.3 and standard deviation 0.2. Generate 1000 samples. " ] }, { @@ -1039,7 +1039,7 @@ "source": [ "### Individual chance constraint\n", "\n", - "Meeting widget demand in Nashville is now an extremely high priority. Historically, our data says that the demand here for the next sales period follow a [Poisson distribution](https://en.wikipedia.org/wiki/Poisson_distribution) that has a mean of 100 (in the original model, demand was 101). Fill in the code blanks below to make sure we meet the demand with probability 0.95." + "Meeting widget demand in Nashville is now an extremely high priority. Historically, our data says that the demand here for the next sales period follows a [Poisson distribution](https://en.wikipedia.org/wiki/Poisson_distribution) that has a mean of 100 (in the original model, demand was 101). Fill in the code blanks below to make sure we meet the demand with probability 0.95." ] }, { @@ -1285,7 +1285,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Create an optimization model where we decide the percentage of our budget to invest to **maximize the minimum** total estimated returns considering all scenarios. **HINT**: Define a single new auxillary variable." + "Create an optimization model where we decide the percentage of our budget to invest to **maximize the minimum** total estimated returns considering all scenarios. **HINT**: Define a single new auxiliary variable." ] }, { @@ -1408,7 +1408,7 @@ "metadata": {}, "source": [ "You now decide that your investments need to be a little more diversified. Add any necessary variables and constraints that will:\n", - "- Make sure you invest in at least four sectors, \n", + "- Make sure you invest in at least four sectors. \n", "- And any investment will need to be at least 10% of the budget." ] }, From 51ba3e86c05f53946201c5b60b45844719b98fc4 Mon Sep 17 00:00:00 2001 From: iulia-t Date: Wed, 13 Nov 2024 13:27:50 +0000 Subject: [PATCH 03/40] copy changes --- .../opti202_disaster_prepositioning.ipynb | 44 +++++++++---------- 1 file changed, 22 insertions(+), 22 deletions(-) diff --git a/optimization202/ESUPS_case_study/opti202_disaster_prepositioning.ipynb b/optimization202/ESUPS_case_study/opti202_disaster_prepositioning.ipynb index 62e533b..1dfee01 100644 --- a/optimization202/ESUPS_case_study/opti202_disaster_prepositioning.ipynb +++ b/optimization202/ESUPS_case_study/opti202_disaster_prepositioning.ipynb @@ -51,7 +51,7 @@ "Before we begin, I wanted to make a quick note about a line of code you'll see repeated throughout the notebook: %%script false --no-raise-error\n", "\n", "\n", - "- **NOTE:** This is just cell magic (if you're unfamiliar, think of it as using the cmd line) telling the notebook not to run this cell. We'll use it in various places to demonstrate ideas or code snippets that are not meant to produce an output" + "- **NOTE:** This is just cell magic (if you're unfamiliar, think of it as using the cmd line) telling the notebook not to run this cell. We'll use it in various places to demonstrate ideas or code snippets that are not meant to produce an output." ] }, { @@ -215,7 +215,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "As you can see, the dataset fields are mainly data structures holding other object. Now we won't go into all these here, but it can be useful to see how they are set up. Let's take a look at one such object. " + "As you can see, the dataset fields are mainly data structures holding other objects. Now we won't go into all these here, but it can be useful to see how they are set up. Let's take a look at one such object. " ] }, { @@ -1306,7 +1306,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can see that the warehouses are aligned closely with the most prominent disaster sights*, so assuming they are built to a level that can survive and protect against those disasters, Madagascar should be in a strong position in terms of coverage. However, there's more than meets the eye. Let's dive a little deeper!\n", + "We can see that the warehouses are aligned closely with the most prominent disaster sites*, so assuming they are built to a level that can survive and protect against those disasters, Madagascar should be in a strong position in terms of coverage. However, there's more than meets the eye. Let's dive a little deeper!\n", "\n", "Hopefully, now that you've gotten a sense of the country and the layout, we'll turn off some of the more detailed parts (such as roads and urbanization) so it's simpler to see what’s going on.\n", "\n", @@ -2423,7 +2423,7 @@ "source": [ "As we can see from above, the supplies skew heavily towards buckets, water containers, and mosquito nets, which makes sense for an island nation. But while it's great that we have a lot of buckets, it won't do us too much good if none of them are at the coast, for instance.\n", "\n", - "- **NOTE:** From here out in the case study, we're going to focus on just buckets as they're the most prevalent item and, for the purposes of this case, it's time consuming and repetitive to analyze all 15 item types of supplies available. \n", + "- **NOTE:** From here on out in the case study, we're going to focus on just buckets as they're the most prevalent item and, for the purposes of this case, it's time consuming and repetitive to analyze all 15 item types of supplies available. \n", "\n", "As you'll see later in the notebook, it's a simple matter to apply the analysis of any one of the supply items to all 15 of them." ] @@ -3407,7 +3407,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "We can see that the supplies are largely located on the eastern coast line of the country" + "We can see that the supplies are largely located on the eastern coastline of the country." ] }, { @@ -5556,7 +5556,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "ESUPS's platform [Stockholm](https://www.esups-stockholm.org/#/private/signin), which is their platform for hosting this model (you can apply for access by visiting the homepage linked above), has two main pages, the first is the context page, and explains the context of the problem that we've been discussing in the past few sections, along with additional details about their mission and collaborating organizations. We'll look at this one first!" + "ESUPS' platform [Stockholm](https://www.esups-stockholm.org/#/private/signin), which is hosting this model (you can apply for access by visiting the homepage linked above), has two main pages, the first is the context page, and explains the context of the problem that we've been discussing in the past few sections, along with additional details about their mission and collaborating organizations. We'll look at this one first!" ] }, { @@ -5610,7 +5610,7 @@ "\n", "The design, though basic, allowed for rapid deployment and iteration, proving that in the early stages of development, it's more important to have a working solution than to strive for perfection. The dashboard's ability to deliver key insights swiftly and effectively earned it significant merit, as it laid the groundwork for future improvements and set the stage for more advanced iterations. This approach underscores a vital lesson in development: sometimes, a good solution delivered quickly can be more valuable than a perfect one delivered too late.\n", "\n", - "The initial success provided a strong foundation upon which ESUPS could build. The insights gained from this deployment informed the development of a new version, which addresses the limitations of the first while enhancing user experience and data communication. The new dashboard takes these lessons to heart, incorporating more advanced features and a more intuitive design, ensuring that ESUPS continues to meet the needs of its partners more effectively. Now, let’s explore how this new version improves upon the original and further advances ESUPS's mission." + "The initial success provided a strong foundation upon which ESUPS could build. The insights gained from this deployment informed the development of a new version, which addresses the limitations of the first while enhancing user experience and data communication. The new dashboard takes these lessons to heart, incorporating more advanced features and a more intuitive design, ensuring that ESUPS continues to meet the needs of its partners more effectively. Now, let’s explore how this new version improves upon the original and further advances ESUPS' mission." ] }, { @@ -5701,7 +5701,7 @@ "source": [ "
\n", " Note!\n", - "

Decision variables in mathematical optimization problems are typically assumed to be nonnegative. So while you'll see these constraints in formulations (ei.e. the algebraic representations), you may not see the code for it since it's likely assumed to be nonnegative.

\n", + "

Decision variables in mathematical optimization problems are typically assumed to be nonnegative. So while you'll see these constraints in formulations (i.e. the algebraic representations), you may not see the code for it since it's likely assumed to be nonnegative.

\n", "
" ] }, @@ -5812,7 +5812,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Now Let's Analyze the results!" + "Now let's analyze the results!" ] }, { @@ -6009,7 +6009,7 @@ "\n", "Now that we've got the initial problem outlined, let's start making it more realistic with two additions:\n", "1.\tInstead of preparing for only one disaster, let's prepare for all the disasters that might occur.\n", - "2.\tInstead of being an omniscient observer, let's say we aren't sure where the next disaster will be" + "2.\tInstead of being an omniscient observer, let's say we aren't sure where the next disaster will be." ] }, { @@ -6023,11 +6023,11 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Before we begin, let's explicitly define our new problem with the additional requirements outlined in the previous section so we're all on the same page. Our first step is to add in the fact that there are more disasters than just one. We can do that by including a variable to denote which disaster we're talking about\n", + "Before we begin, let's explicitly define our new problem with the additional requirements outlined in the previous section so we're all on the same page. Our first step is to add in the fact that there are more disasters than just one. We can do that by including a variable to denote which disaster we're talking about.\n", "\n", "Let:\n", "\n", - "- $k$ the disaster scenario at hand i.e. a storm, earthquake, or epidemic\n", + "- $k$ be the disaster scenario at hand i.e. a storm, earthquake, or epidemic\n", "- $j$ be the location of the disaster" ] }, @@ -6035,7 +6035,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "In scenario $k$ with the disaster located at $j$, the time for a warehouse $i$ to send $y_i$ items is " + "In scenario $k$ with the disaster located at $j$, the time for a warehouse $i$ to send $y_i$ items is: " ] }, { @@ -6116,14 +6116,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "If you haven't seen expected value before or if it's just a been a while and you'd like a refresher, try some of the practice problems below!" + "If you haven't seen expected value before or if it's just been a while and you'd like a refresher, try some of the practice problems below!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ - "When we solve expected value for discrete outcomes, we take the value/outcome/payoff of each possible event and discount it by the probability that it actually occurs. So it follows the form\n", + "When we solve expected value for discrete outcomes, we take the value/outcome/payoff of each possible event and discount it by the probability that it actually occurs. So it follows the form:\n", "\n", "$$\n", "E[X]=p_1(x_1)+p_2(x_2)+ ... + p_n(x_n)\n", @@ -6515,7 +6515,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "How do we calculate the probability though? Well, we have good long-term data on what disasters have affected which countries. For now, we can go through that data and calculate the probability of disaster k by counting how many times it has occurred and dividing by the number of total disasters i.e.\n", + "How do we calculate the probability though? Well, we have good long-term data on what disasters have affected which countries. For now, we can go through that data and calculate the probability of disaster k by counting how many times it has occurred and dividing by the number of total disasters:\n", "\n", "$$ P^k = \\frac{k}{\\|K\\|}$$" ] @@ -6804,11 +6804,11 @@ "y^k_i \\leq x_i,\\space \\forall i \\in I \\hspace{.2cm}\\text{(you can't send more than a warehouse has)}\\\\\n", "$$\n", "\n", - "So far, we've just been using the actual allocation we have at each warehouse for $x_i$. But what if those changes? Suddenly we would have an entirely new solution. So, if we say that the output of Gurobi (i.e. the allocations $y^k_i \\in Y$) is some function based on our starting amount warehouses ($X$ where $x_i\\in X$), then we can say\n", + "So far, we've just been using the actual allocation we have at each warehouse for $x_i$. But what if those change? Suddenly we would have an entirely new solution. So, if we say that the output of Gurobi (i.e. the allocations $y^k_i \\in Y$) is some function based on our starting amount of warehouses ($X$ where $x_i\\in X$), then we can say:\n", "\n", "$$Y=f(X)$$ \n", "\n", - "And when we frame it this way, it becomes much more simple to solve! All we need to do is minimize the travel times $Y$. In other words, our problem becomes \n", + "And when we frame it this way, it becomes much more simple to solve! All we need to do is minimize the travel times $Y$. In other words, our problem becomes: \n", "\n", "\n", "$$ \\min_{X} f(X) $$\n", @@ -6827,7 +6827,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "While this may initially look intimidating, it is one of the easiest changes to make to our current code. All we are doing is making a decision variable instead of a constant and then constraining it. Let’s substitute back in our equation from the last section with the new constraints to see this firsthand. To denote a new decision variable (i.e. a variable that can be changed), all we need to do is add it under the minimization sign. This means minimizing with respect to $x$ and $y$\n", + "While this may initially look intimidating, it is one of the easiest changes to make to our current code. All we are doing is making a decision variable instead of a constant and then constraining it. Let’s substitute back in our equation from the last section the new constraints to see this firsthand. To denote a new decision variable (i.e. a variable that can be changed), all we need to do is add it under the minimization sign. This means minimizing with respect to $x$ and $y$:\n", "\n", "$$\n", "\\min_{x,y} \\sum_k P^k \\sum_i \\tau_{ij}\\cdot y^k_i\n", @@ -6835,7 +6835,7 @@ "\n", "Then all we need to do is update the constraints. I've included the line to make it easier to see what's new as our list grows. It has no mathematical significance. \n", "\n", - "So how do we Implement this in Gurobi?\n", + "So how do we implement this in Gurobi?\n", "\n", "$$\n", "\\begin{aligned}\n", @@ -6980,9 +6980,9 @@ "\n", "The second additional facet considered in our real-life model that we haven't encountered yet is transportation mode. We alluded to it a little in the cost section, but often there is the option to fly or ship goods into a region, which can be especially useful when far away or the roads are clogged or otherwise unusable (such is often the case after a disaster).\n", "\n", - "So how do we implement this? Well let's take a look back at $y_i^k$, our variable which says how many goods to send from warehouse $i$ to disaster $k$. All we want to do is reflect and updated description: how many goods to send from warehouse $i$ to disaster $k$ via mode $r$. This can easily be represented as $y_{ir}^k$, let's explain what's happened. Before is $y$ was an array of length $K$ with each index holding sub array of length $I$ (which we could also write as size $K \\times I$), now each index in our subarrays also have an array of length $3$ to represent how much is sent via truck, plane, or boat. So our final array is of dimensions $K \\times I \\times R $. This may seem intimidating at first, but remember, adding a dimension just means adding one more nested for loop!\n", + "So how do we implement this? Well let's take a look back at $y_i^k$, our variable which says how many goods to send from warehouse $i$ to disaster $k$. All we want to do is reflect and update the description: how many goods to send from warehouse $i$ to disaster $k$ via mode $r$. This can easily be represented as $y_{ir}^k$, let's explain what's happened. Before this $y$ was an array of length $K$ with each index holding sub array of length $I$ (which we could also write as size $K \\times I$), now each index in our subarrays also have an array of length $3$ to represent how much is sent via truck, plane, or boat. So, our final array is of dimensions $K \\times I \\times R $. This may seem intimidating at first, but remember, adding a dimension just means adding one more nested for loop!\n", "\n", - "Let's look at how we would implement this. Remember, from a math point of view, all we've done is say $y_i^k$ can be broken down into $3$ modes instead of 1. So, it's rewritten as" + "Let's look at how we would implement this. Remember, from a math point of view, all we've done is say $y_i^k$ can be broken down into $3$ modes instead of 1. So, it's rewritten as:" ] }, { From 695a5525fe8405ea8067fd3561256e6b61c0789f Mon Sep 17 00:00:00 2001 From: Jerry Yurchisin Date: Wed, 13 Nov 2024 13:17:15 -0500 Subject: [PATCH 04/40] exercise update --- .../completed_exercise_set1.ipynb | 312 +++- ...pleted_nonlinear_regression_exercise.ipynb | 361 ++-- .../nonlinear_regression_exercise.ipynb | 1645 +---------------- .../completed_exercise_set2.ipynb | 268 +-- .../Modeling_Session_2/exercise_set2.ipynb | 268 +-- optimization202/next_level_modeling/cost.csv | 31 + 6 files changed, 819 insertions(+), 2066 deletions(-) create mode 100644 optimization202/next_level_modeling/cost.csv diff --git a/optimization202/Modeling_Session_1/completed_exercise_set1.ipynb b/optimization202/Modeling_Session_1/completed_exercise_set1.ipynb index 57297a2..a675622 100755 --- a/optimization202/Modeling_Session_1/completed_exercise_set1.ipynb +++ b/optimization202/Modeling_Session_1/completed_exercise_set1.ipynb @@ -13,8 +13,73 @@ "source": [ "## Multiple Choice / True-False\n", "\n", - "(Ehsan and Dan) Some simple questions based on the prior sessions\n", - "...." + "Correct answers are `marked like this`.\n", + "\n", + "**Q1.** When dealing with numerical instability, which of the following is NOT a recommended approach?\n", + "\n", + "> a. Using well-scaled and precise data\n", + "\n", + "> b. Ensuring matrix coefficients are within 6 orders of magnitude\n", + "\n", + "> c. `Increasing the Big M value as much as possible`\n", + "\n", + "> d. Setting the NumericFocus parameter to prioritize stability\n", + "\n", + "**Q2.** Which of the following statements about MIP Gap is accurate?\n", + "\n", + "> a. A lower MIP Gap always leads to significant solution improvements\n", + "\n", + "> b. `A higher MIP Gap can still yield a solution close to optimal, depending on the problem`\n", + "\n", + "> c. Setting MIP Gap to zero is ideal for all real-world applications\n", + "\n", + "> d. MIP Gap only affects runtime but not solution quality\n", + "\n", + "**Q3.** Which of the following is a potential benefit of using the Big M method in an optimization model?\n", + "\n", + "> a. `It helps establish conditional constraints based on binary variables`\n", + "\n", + "> b. It eliminates the need for slack variables\n", + "\n", + "> c. It reduces the numerical range required for coefficients\n", + "\n", + "> d. It ensures the model is solved to exact optimality\n", + "\n", + "**Q4.** An optimization model can sometimes be infeasible in exact arithmetic but feasible within the solver’s tolerances.\n", + "\n", + "> `True` \n", + "\n", + "> False\n", + "\n", + "**Q5.** For which of the following application areas is non-linear optimization used in practice?\n", + "\n", + "> a. Finance\n", + "\n", + "> b. Utilities\n", + "\n", + "> c. Engineering\n", + "\n", + "> d. `All of the above`\n", + "\n", + "**Q6.** Which type of optimization problem could have local optimal solutions that are not globally optimal?\n", + "\n", + "> a. Linear programs\n", + "\n", + "> b. Convex Quadratic Programs\n", + "\n", + "> c. `Non-convex Quadratic Programs`\n", + "\n", + "> d. None of the above\n", + "\n", + "**Q7.** Which of the following constraints defines a convex feasible region?\n", + "\n", + "> a. `Correct:` $y \\geq x^2$\n", + "\n", + "> b. $y \\leq x^2$\n", + "\n", + "> c. $y = x^2$\n", + "\n", + "> d. $y \\neq x^2$" ] }, { @@ -23,7 +88,8 @@ "source": [ "## Simple modeling / coding\n", "\n", - "1. Show these constraints are either compatible (find a feasible solution) or incompatible (prove a contradiction) algebraically\n", + "**Q8.** Show these constraints are either compatible (find a feasible solution) or incompatible (prove a contradiction) algebraically.\n", + "> **Q8a.** \n", "\n", "\\begin{align*}\n", "x + y + z &\\ge 3 \\\\\n", @@ -31,17 +97,30 @@ "x, y, z &\\ge 0\n", "\\end{align*}\n", "\n", + "`Solution:`\n", + "Select $x = y = z = 1$\n", + "\n", + "> **Q8b.** \n", + "\n", "\\begin{align*}\n", "x_1 + x_2 + x_3 &= 6 \\\\\n", "3x_1 + 2x_2 + x_3 &\\ge 20 \\\\\n", "x_1, x_2, x_3, &\\ge 0\n", "\\end{align*}\n", "\n", - "2. Translate the following into constraints using binary variables for deciding to invest in three stocks. Then determine if they are compatible as above. \n", + "`Solution:`\n", "\n", - "- I will invest in exactly two stocks. \n", - "- If I invest in stock A, then I will invest in stock B\n", - "- If I invest in stock B, then I will invest in stock C\n" + "$3x_1 + 2x_2 + x_3 = 2x_1 + x_2 + (x_1 + x_2 + x_3) = 2x_1 + x_2 + 6$\n", + "\n", + "Note that since $x_1$ and $x_2$ and both nonnegative, the first equation implies $x_1 \\le 6$ and $x_1 \\le 6$. \n", + "\n", + "Thus $2x_1 + x_2 + 6 \\le 18$ and cannot be 20 or more. \n", + "\n", + "**Q9.** Translate the following into constraints using binary variables for deciding to invest in three stocks. Then determine if they are compatible as above. \n", + "\n", + "- I will invest in exactly two of the three stocks.\n", + "- If I invest in stock A, then I will invest in stock B.\n", + "- If I invest in stock B, then I will invest in stock C." ] }, { @@ -54,10 +133,21 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: gurobipy>=12.0.0 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (12.0.0)\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], "source": [ + "%pip install 'gurobipy>=12.0.0'\n", + "\n", "# Import packages\n", "import pandas as pd\n", "import gurobipy as gp\n", @@ -66,7 +156,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -81,20 +171,22 @@ "\n", "max_prod = pd.Series([180,200,140,80,180], index = production, name = \"max_production\")\n", "n_demand = pd.Series([89,95,121,101,116,181], index = distribution, name = \"demand\")\n", + "\n", "# the min production is a fraction of the max\n", "frac = 0.75\n", - "C = 30\n" + "C = 30" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 3, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ + "Restricted license - for non-production use only - expires 2026-11-23\n", "The original model had a total cost of 1748.42\n" ] }, @@ -193,7 +285,7 @@ " St. Louis 4.60 41.0" ] }, - "execution_count": 7, + "execution_count": 3, "metadata": {}, "output_type": "execute_result" } @@ -203,7 +295,7 @@ "m = gp.Model('widgets')\n", "m.setParam('OutputFlag',0)\n", "\n", - "# decision vars\n", + "# decision variables - reminder that we ended on x being a semi-continuous variable type\n", "x = m.addVars(production, distribution, vtype=GRB.SEMICONT, lb = C, name = 'prod_ship')\n", "\n", "# constraints\n", @@ -240,17 +332,28 @@ "\n", "```python\n", "m.setObjective(gp.quicksum(transp_cost[p,d]*x[p,d] for p in production for d in distribution) + ..., GRB.MINIMIZE)\n", - "```" + "```\n", + "Solve the model with the updated objective and see how the cost has changed." ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 4, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The updated model had a total cost of 2808.99\n" + ] + } + ], "source": [ "# One solution\n", "m.setObjective(gp.quicksum(transp_cost[p,d]*x[p,d] for p in production for d in distribution) + 0.01 * gp.quicksum(x.sum(p, '*') * x.sum(p, '*') for p in production), GRB.MINIMIZE)\n", + "m.optimize()\n", + "print(f\"The updated model had a total cost of {round(m.ObjVal,2)}\")\n", "\n", "# Another solution\n", "# Introduces auxiliary variables representing total production for each facility\n", @@ -271,19 +374,30 @@ "prod_cost = m.addVar(name='prod_cost')\n", "m.addConstr(prod_cost ==..., name='set_prod_cost')\n", "m.setObjective(prod_cost, GRB.MINIMIZE)\n", - "```" + "```\n", + "Again, resolve the model and see how the cost has changed." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The updated model had a total cost of 97.47\n" + ] + } + ], "source": [ "# One solution\n", "prod_cost = m.addVar(name='prod_cost')\n", "m.addConstr(prod_cost == gp.quicksum(transp_cost[p,d] * gp.nlfunc.log(1 + x[p, d]) for p in production for d in distribution), name='set_prod_cost')\n", - "m.setObjective(prod_cost, GRB.MINIMIZE)" + "m.setObjective(prod_cost, GRB.MINIMIZE)\n", + "m.optimize()\n", + "print(f\"The updated model had a total cost of {round(m.ObjVal,2)}\")" ] }, { @@ -294,19 +408,34 @@ "\n", "A very strange company rule stipulates that the following mathematical relationship must hold:\n", "\n", - "$ x_{\\textrm{Cleveland,Richmond}} = \\log\\Large(\\frac{10 \\cdot x_{\\textrm{Baltimore,Indianapolis}}}{\\sum_d x_{\\textrm{Baltimore},d}})\\normalsize + x_{\\textrm{Cleveland,Richmond}}$\n", + "$ \\log\\Large(\\frac{4 \\cdot x_{\\textrm{Baltimore,Indianapolis}}}{\\sum_d x_{\\textrm{Baltimore},d}})\\normalsize \\geq 0$\n", + "\n", + "Add this constraint to the model using a nonlinear expression.\n", + "\n", + "Hint: Start by adding a non-negative slack variable $s$ and writing the constraint as $s = \\log\\Large(\\frac{4 \\cdot x_{\\textrm{Baltimore,Indianapolis}}}{\\sum_d x_{\\textrm{Baltimore},d}})$\n", "\n", - "Add this exact constraint to the model using a nonlinear expression." + "Solve the model and see how the cost has changed." ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The updated model had a total cost of 106.92\n" + ] + } + ], "source": [ "# One solution\n", - "m.addConstr(x['Baltimore', 'Richmond'] == gp.nlfunc.log(10 * x['Baltimore', 'Richmond'] / x.sum('Baltimore', '*')) + x['Cleveland', 'Richmond'], name='strange_company_rule')" + "s = m.addVar(name=\"s\")\n", + "NL2 = m.addConstr(s == gp.nlfunc.log(4 * x['Baltimore', 'Indianapolis'] / x.sum('Baltimore', '*')), name='strange_company_rule')\n", + "m.optimize()\n", + "print(f\"The updated model had a total cost of {round(m.ObjVal,2)}\")" ] }, { @@ -315,18 +444,139 @@ "source": [ " ### Linearizing a nonlinear expression\n", "\n", - "Wait a minute...is the nonlinear constraint we just added unnecessarily complicated? Rewrite the nonlinear constraint using only linear expressions. Describe the purpose of the constraint in words." + "Wait a minute...is the nonlinear constraint we just added unnecessarily complicated? Rewrite the nonlinear constraint using only linear expressions. Describe the purpose of the constraint in words.\n", + "\n", + "Solve the model again and inspect the solution. Verify by inspection that it satisfies this newly added constraint.\n", + "\n", + "Using the properties of logarithms, the constraint can be rewritten as:\n", + "$ x_{\\textrm{Baltimore,Indianapolis}}\\geq \\frac 14\\sum_d x_{\\textrm{Baltimore},d}$" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "The updated model had a total cost of 106.92\n" + ] + }, + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " cost shipment\n", + "production distribution \n", + "Baltimore Indianapolis 5.09 38.666667\n", + " Richmond 1.96 116.000000\n", + "Cleveland Columbia 2.43 89.000000\n", + " Nashville 4.13 101.000000\n", + "Little Rock St. Louis 2.92 121.000000\n", + "Birmingham St. Louis 4.01 60.000000\n", + "Charleston Indianapolis 2.61 56.333333\n", + " Lexington 1.61 121.000000" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "# One solution\n", - "# Purpose: exactly 10% of Baltimore's production must be sent to Indianapolis\n", - "m.addConstr(x['Baltimore', 'Indianapolis'] == 0.1 * x.sum('Baltimore', '*'), name='strange_company_rule_linearized')" + "# First, remove the complicated form of the constraint\n", + "m.remove(NL2)\n", + "\n", + "# Meaning of the constraint: at least 25% of Baltimore's total production must be sent to Indianapolis\n", + "m.addConstr(x['Baltimore', 'Indianapolis'] >= 0.25 * x.sum('Baltimore', '*'), name='strange_company_rule_linearized')\n", + "m.optimize()\n", + "x_values = pd.Series(m.getAttr('X', x), name = \"shipment\", index = transp_cost.index)\n", + "sol = pd.concat([transp_cost, x_values], axis=1)\n", + "\n", + "print(f\"The updated model had a total cost of {round(m.ObjVal,2)}\")\n", + "sol[sol.shipment > 0]" ] } ], diff --git a/optimization202/Modeling_Session_1/completed_nonlinear_regression_exercise.ipynb b/optimization202/Modeling_Session_1/completed_nonlinear_regression_exercise.ipynb index cd93622..5b385ce 100755 --- a/optimization202/Modeling_Session_1/completed_nonlinear_regression_exercise.ipynb +++ b/optimization202/Modeling_Session_1/completed_nonlinear_regression_exercise.ipynb @@ -14,8 +14,7 @@ "id": "e46ce635-ec5c-4bf5-b72d-8073962d5cce", "metadata": {}, "source": [ - "## Motivation\n", - "TODO: I got a bit wordy and perhaps redundant. I would not be offended if it got trimmed :)" + "## Motivation" ] }, { @@ -34,12 +33,10 @@ "\n", "- **Biology and Medicine**: Understanding growth rates and enzyme kinetics.\n", "- **Economics**: Analyzing complex demand and price curves.\n", - "- [TODO: I would welcome 1-2 more examples ot be added by someone]\n", + "- **Environmental Science**: Uncovering relationships between crop yields and soil conditions.\n", + "- **Physics and Engineering**: Understanding radioactive decay or modeling properties of materials.\n", "\n", - "These applications benefit from nonlinear regression's ability to deliver more accurate models and retain known model form for predictions and insights, especially in situations where linear regression approaches may fall short.\n", - "\n", - "\n", - "\n" + "These applications benefit from nonlinear regression's ability to deliver more accurate models and retain known model form for predictions and insights, especially in situations where linear regression approaches may fall short." ] }, { @@ -85,7 +82,7 @@ "\n", "This is because, in the optimization model, the regression coefficients $a$, $b$, and $c$ are the actual variables, while the independent and dependent variables, $x$ and $y$ are simply data points rather than variables themselves. So, the use of the word 'variable' in this notebook can get a little tricky! To keep things clear, we’ll refer to the optimization variables as 'decision variables' wherever possible.\n", "\n", - "Linear regression offers a closed-form solution, allowing parameters to be solved directly—an advantage that makes it fast in tools like Excel, R, and Python.\n", + "Linear regression offers a closed-form solution, allowing parameters to be calculated directly—an advantage that makes it fast in tools like Excel, R, and Python.\n", "\n", "Nonlinear regression, however, lacks this simplicity. Instead, we solve an optimization problem to find the parameters. Though more complex, this approach is essential for modeling curved or intricate data relationships, providing a flexible and accurate fit.\n" ] @@ -95,11 +92,11 @@ "id": "ad9c53cd-0f12-4099-8f03-542dd3cd7961", "metadata": {}, "source": [ - "In this notebook, we will learn how to write a nonlinear regression model where the goal is to determin the regression parameters that minimize the least squares error.\n", + "In this notebook, we will learn how to write a nonlinear regression model where the goal is to determine the regression parameters that minimize the least squares error.\n", "\n", "Here are a few handy resources to have ready:\n", "- [Gurobi Python Documentation](https://docs.gurobi.com/projects/optimizer/en/current/reference/python.html)\n", - "- [Gurobi Python Examples](https://www.gurobi.com/documentation/current/examples/python_examples.html)" + "- [Gurobi Python Examples](https://docs.gurobi.com/projects/examples/en/current/examples/python.html)" ] }, { @@ -122,7 +119,7 @@ "source": [ "## Problem description\n", "### Michaelis Menton Enzyme Kinetics\n", - "Michaelis-Menten kinetics describes how quickly a reaction happens when an enzyme—a type of molecule that speeds up processes in the body or other reactions—interacts with other molecules. Initially, as the amount of molecules $x$ increases, the reaction speeds up $y$. But at a certain point, adding more molecules doesn’t increase the speed much because the enzyme is already working as fast as it can.\n", + "Michaelis-Menten kinetics describes how quickly a reaction happens when an enzyme—a type of molecule that speeds up processes in the body or other reactions—interacts with other molecules. Initially, as the amount of molecules $x$ increases, the reaction speed, $y$, increases. But at a certain point, adding more molecules doesn’t increase the speed much because the enzyme is already working as fast as it can.\n", "\n", "![image.png](attachment:dfac2263-b4f3-4d9f-808d-c0d7b34ad8e6.png)\n", "\n", @@ -185,7 +182,8 @@ "!pip install gurobipy\n", "!pip install plotly\n", "!pip install numpy\n", - "!pip install pandas" + "!pip install pandas\n", + "!pip install openpyxl" ] }, { @@ -240,10 +238,10 @@ "output_type": "stream", "text": [ "Here are the x values:\n", - "{0.0: 0.05, 1.0: 0.1, 2.0: 0.25, 3.0: 0.5, 4.0: 1.0, 5.0: 2.5, 6.0: 5.0, 7.0: 8.0, 8.0: 20.0, 9.0: 30.0}\n", + "{np.float64(0.0): 0.05, np.float64(1.0): 0.1, np.float64(2.0): 0.25, np.float64(3.0): 0.5, np.float64(4.0): 1.0, np.float64(5.0): 2.5, np.float64(6.0): 5.0, np.float64(7.0): 8.0, np.float64(8.0): 20.0, np.float64(9.0): 30.0}\n", "\n", "Here are the y values:\n", - "{0.0: 2.98, 1.0: 5.2, 2.0: 14.38, 3.0: 30.3, 4.0: 48.99, 5.0: 86.25, 6.0: 112.57, 7.0: 136.24, 8.0: 169.97, 9.0: 177.71}\n" + "{np.float64(0.0): 2.98, np.float64(1.0): 5.2, np.float64(2.0): 14.38, np.float64(3.0): 30.3, np.float64(4.0): 48.99, np.float64(5.0): 86.25, np.float64(6.0): 112.57, np.float64(7.0): 136.24, np.float64(8.0): 169.97, np.float64(9.0): 177.71}\n" ] } ], @@ -289,13 +287,13 @@ " require.undef(\"plotly\");\n", " define('plotly', function(require, exports, module) {\n", " /**\n", - "* plotly.js v2.20.0\n", - "* Copyright 2012-2023, Plotly, Inc.\n", + "* plotly.js v2.35.2\n", + "* Copyright 2012-2024, Plotly, Inc.\n", "* All rights reserved.\n", "* Licensed under the MIT license\n", "*/\n", "/*! 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e?e.xpx:_[0]._length/2,lt=\"ypx\"in e?e.ypx:w[0]._length/2;if(e.pointerX=st+_[0]._offset,e.pointerY=lt+w[0]._offset,q=\"xval\"in e?y.flat(l,e.xval):y.p2c(_,st),G=\"yval\"in e?y.flat(l,e.yval):y.p2c(w,lt),!i(q[0])||!i(G[0]))return o.warn(\"Fx.hover failed\",e,t),d.unhoverRaw(t,e)}var ft=1/0;function ht(t,r){for(Y=0;Yrt&&(nt.splice(0,rt),ft=nt[0].distance),m&&0!==H&&0===nt.length){et.distance=H,et.index=!1;var f=X._module.hoverPoints(et,Q,tt,\"closest\",{hoverLayer:c._hoverlayer});if(f&&(f=f.filter((function(t){return t.spikeDistance<=H}))),f&&f.length){var h,d=f.filter((function(t){return t.xa.showspikes&&\"hovered data\"!==t.xa.spikesnap}));if(d.length){var v=d[0];i(v.x0)&&i(v.y0)&&(h=dt(v),(!at.vLinePoint||at.vLinePoint.spikeDistance>h.spikeDistance)&&(at.vLinePoint=h))}var g=f.filter((function(t){return t.ya.showspikes&&\"hovered data\"!==t.ya.spikesnap}));if(g.length){var x=g[0];i(x.x0)&&i(x.y0)&&(h=dt(x),(!at.hLinePoint||at.hLinePoint.spikeDistance>h.spikeDistance)&&(at.hLinePoint=h))}}}}}function pt(t,e,r){for(var n,i=null,a=1/0,o=0;o0&&Math.abs(t.distance)At-1;Mt--)Ct(nt[Mt]);nt=St,mt()}var Pt=t._hoverdata,Ot=[],It=U(t),Dt=V(t);for(Z=0;Z1||nt.length>1)||\"closest\"===I&&ot&&nt.length>1,Yt=p.combine(c.plot_bgcolor||p.background,c.paper_bgcolor),Wt=O(nt,{gd:t,hovermode:I,rotateLabels:Zt,bgColor:Yt,container:c._hoverlayer,outerContainer:c._paper.node(),commonLabelOpts:c.hoverlabel,hoverdistance:c.hoverdistance}),Xt=Wt.hoverLabels;if(y.isUnifiedHover(I)||(function(t,e,r,n){var i,a,o,s,l,u,c,f=e?\"xa\":\"ya\",h=e?\"ya\":\"xa\",p=0,d=1,v=t.size(),g=new Array(v),y=0,m=n.minX,x=n.maxX,b=n.minY,_=n.maxY,w=function(t){return t*r._invScaleX},k=function(t){return t*r._invScaleY};function A(t){var e=t[0],r=t[t.length-1];if(a=e.pmin-e.pos-e.dp+e.size,o=r.pos+r.dp+r.size-e.pmax,a>.01){for(l=t.length-1;l>=0;l--)t[l].dp+=a;i=!1}if(!(o<.01)){if(a<-.01){for(l=t.length-1;l>=0;l--)t[l].dp-=o;i=!1}if(i){var n=0;for(s=0;se.pmax&&n++;for(s=t.length-1;s>=0&&!(n<=0);s--)(u=t[s]).pos>e.pmax-1&&(u.del=!0,n--);for(s=0;s=0;l--)t[l].dp-=o;for(s=t.length-1;s>=0&&!(n<=0);s--)(u=t[s]).pos+u.dp+u.size>e.pmax&&(u.del=!0,n--)}}}for(t.each((function(t){var n=t[f],i=t[h],a=\"x\"===n._id.charAt(0),o=n.range;0===y&&o&&o[0]>o[1]!==a&&(d=-1);var s=0,l=a?r.width:r.height;if(\"x\"===r.hovermode||\"y\"===r.hovermode){var u,c,p=D(t,e),v=t.anchor,A=\"end\"===v?-1:1;if(\"middle\"===v)c=(u=t.crossPos+(a?k(p.y-t.by/2):w(t.bx/2+t.tx2width/2)))+(a?k(t.by):w(t.bx));else if(a)c=(u=t.crossPos+k(M+p.y)-k(t.by/2-M))+k(t.by);else{var S=w(A*M+p.x),E=S+w(A*t.bx);u=t.crossPos+Math.min(S,E),c=t.crossPos+Math.max(S,E)}a?void 0!==b&&void 0!==_&&Math.min(c,_)-Math.max(u,b)>1&&(\"left\"===i.side?(s=i._mainLinePosition,l=r.width):l=i._mainLinePosition):void 0!==m&&void 0!==x&&Math.min(c,x)-Math.max(u,m)>1&&(\"top\"===i.side?(s=i._mainLinePosition,l=r.height):l=i._mainLinePosition)}g[y++]=[{datum:t,traceIndex:t.trace.index,dp:0,pos:t.pos,posref:t.posref,size:t.by*(a?T:1)/2,pmin:s,pmax:l}]})),g.sort((function(t,e){return t[0].posref-e[0].posref||d*(e[0].traceIndex-t[0].traceIndex)}));!i&&p<=v;){for(p++,i=!0,s=0;s.01&&L.pmin===C.pmin&&L.pmax===C.pmax){for(l=E.length-1;l>=0;l--)E[l].dp+=a;for(S.push.apply(S,E),g.splice(s+1,1),c=0,l=S.length-1;l>=0;l--)c+=S[l].dp;for(o=c/S.length,l=S.length-1;l>=0;l--)S[l].dp-=o;i=!1}else s++}g.forEach(A)}for(s=g.length-1;s>=0;s--){var P=g[s];for(l=P.length-1;l>=0;l--){var O=P[l],I=O.datum;I.offset=O.dp,I.del=O.del}}}(Xt,Zt,c,Wt.commonLabelBoundingBox),z(Xt,Zt,c._invScaleX,c._invScaleY)),s&&s.tagName){var Jt=g.getComponentMethod(\"annotations\",\"hasClickToShow\")(t,Ot);f(n.select(s),Jt?\"pointer\":\"\")}s&&!a&&function(t,e,r){if(!r||r.length!==t._hoverdata.length)return!0;for(var n=r.length-1;n>=0;n--){var i=r[n],a=t._hoverdata[n];if(i.curveNumber!==a.curveNumber||String(i.pointNumber)!==String(a.pointNumber)||String(i.pointNumbers)!==String(a.pointNumbers))return!0}return!1}(t,0,Pt)&&(Pt&&t.emit(\"plotly_unhover\",{event:e,points:Pt}),t.emit(\"plotly_hover\",{event:e,points:t._hoverdata,xaxes:_,yaxes:w,xvals:q,yvals:G}))}(t,e,r,a,s)}))},e.loneHover=function(t,e){var r=!0;Array.isArray(t)||(r=!1,t=[t]);var i=e.gd,a=U(i),o=V(i),s=O(t.map((function(t){var r=t._x0||t.x0||t.x||0,n=t._x1||t.x1||t.x||0,s=t._y0||t.y0||t.y||0,l=t._y1||t.y1||t.y||0,u=t.eventData;if(u){var c=Math.min(r,n),f=Math.max(r,n),h=Math.min(s,l),d=Math.max(s,l),v=t.trace;if(g.traceIs(v,\"gl3d\")){var y=i._fullLayout[v.scene]._scene.container,m=y.offsetLeft,x=y.offsetTop;c+=m,f+=m,h+=x,d+=x}u.bbox={x0:c+o,x1:f+o,y0:h+a,y1:d+a},e.inOut_bbox&&e.inOut_bbox.push(u.bbox)}else u=!1;return{color:t.color||p.defaultLine,x0:t.x0||t.x||0,x1:t.x1||t.x||0,y0:t.y0||t.y||0,y1:t.y1||t.y||0,xLabel:t.xLabel,yLabel:t.yLabel,zLabel:t.zLabel,text:t.text,name:t.name,idealAlign:t.idealAlign,borderColor:t.borderColor,fontFamily:t.fontFamily,fontSize:t.fontSize,fontColor:t.fontColor,nameLength:t.nameLength,textAlign:t.textAlign,trace:t.trace||{index:0,hoverinfo:\"\"},xa:{_offset:0},ya:{_offset:0},index:0,hovertemplate:t.hovertemplate||!1,hovertemplateLabels:t.hovertemplateLabels||!1,eventData:u}})),{gd:i,hovermode:\"closest\",rotateLabels:!1,bgColor:e.bgColor||p.background,container:n.select(e.container),outerContainer:e.outerContainer||e.container}).hoverLabels,l=0,u=0;return s.sort((function(t,e){return t.y0-e.y0})).each((function(t,r){var n=t.y0-t.by/2;t.offset=n-5([\\s\\S]*)<\\/extra>/;function O(t,e){var r=e.gd,i=r._fullLayout,a=e.hovermode,u=e.rotateLabels,f=e.bgColor,d=e.container,v=e.outerContainer,w=e.commonLabelOpts||{};if(0===t.length)return[[]];var T=e.fontFamily||m.HOVERFONT,k=e.fontSize||m.HOVERFONTSIZE,A=t[0],E=A.xa,L=A.ya,P=a.charAt(0),O=P+\"Label\",D=A[O];if(void 0===D&&\"multicategory\"===E.type)for(var z=0;zi.width-b?(g=i.width-b,e.attr(\"d\",\"M\"+(b-M)+\",0L\"+b+\",\"+x+M+\"v\"+x+(2*S+m.height)+\"H-\"+b+\"V\"+x+M+\"H\"+(b-2*M)+\"Z\")):e.attr(\"d\",\"M0,0L\"+M+\",\"+x+M+\"H\"+b+\"v\"+x+(2*S+m.height)+\"H-\"+b+\"V\"+x+M+\"H-\"+M+\"Z\"),Y.minX=g-b,Y.maxX=g+b,\"top\"===E.side?(Y.minY=y-(2*S+m.height),Y.maxY=y-S):(Y.minY=y+S,Y.maxY=y+(2*S+m.height))}else{var _,C,P;\"right\"===L.side?(_=\"start\",C=1,P=\"\",g=E._offset+E._length):(_=\"end\",C=-1,P=\"-\",g=E._offset),y=L._offset+(A.y0+A.y1)/2,l.attr(\"text-anchor\",_),e.attr(\"d\",\"M0,0L\"+P+M+\",\"+M+\"V\"+(S+m.height/2)+\"h\"+P+(2*S+m.width)+\"V-\"+(S+m.height/2)+\"H\"+P+M+\"V-\"+M+\"Z\"),Y.minY=y-(S+m.height/2),Y.maxY=y+(S+m.height/2),\"right\"===L.side?(Y.minX=g+M,Y.maxX=g+M+(2*S+m.width)):(Y.minX=g-M-(2*S+m.width),Y.maxX=g-M);var O,I=m.height/2,z=F-m.top-I,R=\"clip\"+i._uid+\"commonlabel\"+L._id;if(g=0?lt:ut+ht=0?ut:bt+ht=0?ot:st+pt=0?st:_t+pt=0,\"top\"!==t.idealAlign&&G||!Z?G?(O+=R/2,t.anchor=\"start\"):t.anchor=\"middle\":(O-=R/2,t.anchor=\"end\"),t.crossPos=O;else{if(t.pos=O,G=P+z/2+Y<=B,Z=P-z/2-Y>=0,\"left\"!==t.idealAlign&&G||!Z)if(G)P+=z/2,t.anchor=\"start\";else{t.anchor=\"middle\";var W=Y/2,X=P+W-B,J=P-W;X>0&&(P-=X),J<0&&(P+=-J)}else P-=z/2,t.anchor=\"end\";t.crossPos=P}w.attr(\"text-anchor\",t.anchor),E&&A.attr(\"text-anchor\",t.anchor),e.attr(\"transform\",s(P,O)+(u?l(_):\"\"))})),{hoverLabels:wt,commonLabelBoundingBox:Y}}function I(t,e,r,n,i,a){var s=\"\",l=\"\";void 0!==t.nameOverride&&(t.name=t.nameOverride),t.name&&(t.trace._meta&&(t.name=o.templateString(t.name,t.trace._meta)),s=N(t.name,t.nameLength));var u=r.charAt(0),c=\"x\"===u?\"y\":\"x\";void 0!==t.zLabel?(void 0!==t.xLabel&&(l+=\"x: \"+t.xLabel+\"
\"),void 0!==t.yLabel&&(l+=\"y: \"+t.yLabel+\"
\"),\"choropleth\"!==t.trace.type&&\"choroplethmapbox\"!==t.trace.type&&(l+=(l?\"z: \":\"\")+t.zLabel)):e&&t[u+\"Label\"]===i?l=t[c+\"Label\"]||\"\":void 0===t.xLabel?void 0!==t.yLabel&&\"scattercarpet\"!==t.trace.type&&(l=t.yLabel):l=void 0===t.yLabel?t.xLabel:\"(\"+t.xLabel+\", \"+t.yLabel+\")\",!t.text&&0!==t.text||Array.isArray(t.text)||(l+=(l?\"
\":\"\")+t.text),void 0!==t.extraText&&(l+=(l?\"
\":\"\")+t.extraText),a&&\"\"===l&&!t.hovertemplate&&(\"\"===s&&a.remove(),l=s);var f=t.hovertemplate||!1;if(f){var h=t.hovertemplateLabels||t;t[u+\"Label\"]!==i&&(h[u+\"other\"]=h[u+\"Val\"],h[u+\"otherLabel\"]=h[u+\"Label\"]),l=(l=o.hovertemplateString(f,h,n._d3locale,t.eventData[0]||{},t.trace._meta)).replace(P,(function(e,r){return s=N(r,t.nameLength),\"\"}))}return[l,s]}function D(t,e){var r=0,n=t.offset;return e&&(n*=-A,r=t.offset*k),{x:r,y:n}}function z(t,e,r,i){var a=function(t){return t*r},o=function(t){return t*i};t.each((function(t){var r=n.select(this);if(t.del)return r.remove();var i,s,l,u,f=r.select(\"text.nums\"),p=t.anchor,d=\"end\"===p?-1:1,v=(u=(l=(s={start:1,end:-1,middle:0}[(i=t).anchor])*(M+S))+s*(i.txwidth+S),\"middle\"===i.anchor&&(l-=i.tx2width/2,u+=i.txwidth/2+S),{alignShift:s,textShiftX:l,text2ShiftX:u}),g=D(t,e),y=g.x,m=g.y,x=\"middle\"===p;r.select(\"path\").attr(\"d\",x?\"M-\"+a(t.bx/2+t.tx2width/2)+\",\"+o(m-t.by/2)+\"h\"+a(t.bx)+\"v\"+o(t.by)+\"h-\"+a(t.bx)+\"Z\":\"M0,0L\"+a(d*M+y)+\",\"+o(M+m)+\"v\"+o(t.by/2-M)+\"h\"+a(d*t.bx)+\"v-\"+o(t.by)+\"H\"+a(d*M+y)+\"V\"+o(m-M)+\"Z\");var b=y+v.textShiftX,_=m+t.ty0-t.by/2+S,w=t.textAlign||\"auto\";\"auto\"!==w&&(\"left\"===w&&\"start\"!==p?(f.attr(\"text-anchor\",\"start\"),b=x?-t.bx/2-t.tx2width/2+S:-t.bx-S):\"right\"===w&&\"end\"!==p&&(f.attr(\"text-anchor\",\"end\"),b=x?t.bx/2-t.tx2width/2-S:t.bx+S)),f.call(c.positionText,a(b),o(_)),t.tx2width&&(r.select(\"text.name\").call(c.positionText,a(v.text2ShiftX+v.alignShift*S+y),o(m+t.ty0-t.by/2+S)),r.select(\"rect\").call(h.setRect,a(v.text2ShiftX+(v.alignShift-1)*t.tx2width/2+y),o(m-t.by/2-1),a(t.tx2width),o(t.by+2)))}))}function R(t,e){var r=t.index,n=t.trace||{},a=t.cd[0],s=t.cd[r]||{};function l(t){return t||i(t)&&0===t}var u=Array.isArray(r)?function(t,e){var i=o.castOption(a,r,t);return l(i)?i:o.extractOption({},n,\"\",e)}:function(t,e){return o.extractOption(s,n,t,e)};function c(e,r,n){var i=u(r,n);l(i)&&(t[e]=i)}if(c(\"hoverinfo\",\"hi\",\"hoverinfo\"),c(\"bgcolor\",\"hbg\",\"hoverlabel.bgcolor\"),c(\"borderColor\",\"hbc\",\"hoverlabel.bordercolor\"),c(\"fontFamily\",\"htf\",\"hoverlabel.font.family\"),c(\"fontSize\",\"hts\",\"hoverlabel.font.size\"),c(\"fontColor\",\"htc\",\"hoverlabel.font.color\"),c(\"nameLength\",\"hnl\",\"hoverlabel.namelength\"),c(\"textAlign\",\"hta\",\"hoverlabel.align\"),t.posref=\"y\"===e||\"closest\"===e&&\"h\"===n.orientation?t.xa._offset+(t.x0+t.x1)/2:t.ya._offset+(t.y0+t.y1)/2,t.x0=o.constrain(t.x0,0,t.xa._length),t.x1=o.constrain(t.x1,0,t.xa._length),t.y0=o.constrain(t.y0,0,t.ya._length),t.y1=o.constrain(t.y1,0,t.ya._length),void 0!==t.xLabelVal&&(t.xLabel=\"xLabel\"in t?t.xLabel:v.hoverLabelText(t.xa,t.xLabelVal,n.xhoverformat),t.xVal=t.xa.c2d(t.xLabelVal)),void 0!==t.yLabelVal&&(t.yLabel=\"yLabel\"in t?t.yLabel:v.hoverLabelText(t.ya,t.yLabelVal,n.yhoverformat),t.yVal=t.ya.c2d(t.yLabelVal)),void 0!==t.zLabelVal&&void 0===t.zLabel&&(t.zLabel=String(t.zLabelVal)),!(isNaN(t.xerr)||\"log\"===t.xa.type&&t.xerr<=0)){var f=v.tickText(t.xa,t.xa.c2l(t.xerr),\"hover\").text;void 0!==t.xerrneg?t.xLabel+=\" +\"+f+\" / -\"+v.tickText(t.xa,t.xa.c2l(t.xerrneg),\"hover\").text:t.xLabel+=\" ± \"+f,\"x\"===e&&(t.distance+=1)}if(!(isNaN(t.yerr)||\"log\"===t.ya.type&&t.yerr<=0)){var h=v.tickText(t.ya,t.ya.c2l(t.yerr),\"hover\").text;void 0!==t.yerrneg?t.yLabel+=\" +\"+h+\" / -\"+v.tickText(t.ya,t.ya.c2l(t.yerrneg),\"hover\").text:t.yLabel+=\" ± \"+h,\"y\"===e&&(t.distance+=1)}var p=t.hoverinfo||t.trace.hoverinfo;return p&&\"all\"!==p&&(-1===(p=Array.isArray(p)?p:p.split(\"+\")).indexOf(\"x\")&&(t.xLabel=void 0),-1===p.indexOf(\"y\")&&(t.yLabel=void 0),-1===p.indexOf(\"z\")&&(t.zLabel=void 0),-1===p.indexOf(\"text\")&&(t.text=void 0),-1===p.indexOf(\"name\")&&(t.name=void 0)),t}function F(t,e,r){var 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t._fullLayout._activeShapeIndex,b(t))}t.exports={draw:b,drawOne:T,eraseActiveShape:function(t){if(w(t)){u(t);var e=t._fullLayout._activeShapeIndex,r=(t.layout||{}).shapes||[];if(e0&&lp&&(t=\"X\"),t}));return a>p&&(d=d.replace(/[\\s,]*X.*/,\"\"),i.log(\"Ignoring extra params in segment \"+t)),c+d}))}(r,s,u);if(\"pixel\"===r.xsizemode){var b=s(r.xanchor);c=b+r.x0,f=b+r.x1}else c=s(r.x0),f=s(r.x1);if(\"pixel\"===r.ysizemode){var _=u(r.yanchor);h=_-r.y0,p=_-r.y1}else h=u(r.y0),p=u(r.y1);if(\"line\"===d)return\"M\"+c+\",\"+h+\"L\"+f+\",\"+p;if(\"rect\"===d)return\"M\"+c+\",\"+h+\"H\"+f+\"V\"+p+\"H\"+c+\"Z\";var w=(c+f)/2,T=(h+p)/2,k=Math.abs(w-c),A=Math.abs(T-h),M=\"A\"+k+\",\"+A,S=w+k+\",\"+T;return\"M\"+S+M+\" 0 1,1 \"+w+\",\"+(T-A)+M+\" 0 0,1 \"+S+\"Z\"}},89853:function(t,e,r){\"use strict\";var n=r(34031);t.exports={moduleType:\"component\",name:\"shapes\",layoutAttributes:r(89827),supplyLayoutDefaults:r(84726),supplyDrawNewShapeDefaults:r(45547),includeBasePlot:r(76325)(\"shapes\"),calcAutorange:r(5627),draw:n.draw,drawOne:n.drawOne}},75067:function(t,e,r){\"use strict\";var n=r(41940),i=r(35025),a=r(1426).extendDeepAll,o=r(30962).overrideAll,s=r(85594),l=r(44467).templatedArray,u=r(98292),c=l(\"step\",{visible:{valType:\"boolean\",dflt:!0},method:{valType:\"enumerated\",values:[\"restyle\",\"relayout\",\"animate\",\"update\",\"skip\"],dflt:\"restyle\"},args:{valType:\"info_array\",freeLength:!0,items:[{valType:\"any\"},{valType:\"any\"},{valType:\"any\"}]},label:{valType:\"string\"},value:{valType:\"string\"},execute:{valType:\"boolean\",dflt:!0}});t.exports=o(l(\"slider\",{visible:{valType:\"boolean\",dflt:!0},active:{valType:\"number\",min:0,dflt:0},steps:c,lenmode:{valType:\"enumerated\",values:[\"fraction\",\"pixels\"],dflt:\"fraction\"},len:{valType:\"number\",min:0,dflt:1},x:{valType:\"number\",min:-2,max:3,dflt:0},pad:a(i({editType:\"arraydraw\"}),{},{t:{dflt:20}}),xanchor:{valType:\"enumerated\",values:[\"auto\",\"left\",\"center\",\"right\"],dflt:\"left\"},y:{valType:\"number\",min:-2,max:3,dflt:0},yanchor:{valType:\"enumerated\",values:[\"auto\",\"top\",\"middle\",\"bottom\"],dflt:\"top\"},transition:{duration:{valType:\"number\",min:0,dflt:150},easing:{valType:\"enumerated\",values:s.transition.easing.values,dflt:\"cubic-in-out\"}},currentvalue:{visible:{valType:\"boolean\",dflt:!0},xanchor:{valType:\"enumerated\",values:[\"left\",\"center\",\"right\"],dflt:\"left\"},offset:{valType:\"number\",dflt:10},prefix:{valType:\"string\"},suffix:{valType:\"string\"},font:n({})},font:n({}),activebgcolor:{valType:\"color\",dflt:u.gripBgActiveColor},bgcolor:{valType:\"color\",dflt:u.railBgColor},bordercolor:{valType:\"color\",dflt:u.railBorderColor},borderwidth:{valType:\"number\",min:0,dflt:u.railBorderWidth},ticklen:{valType:\"number\",min:0,dflt:u.tickLength},tickcolor:{valType:\"color\",dflt:u.tickColor},tickwidth:{valType:\"number\",min:0,dflt:1},minorticklen:{valType:\"number\",min:0,dflt:u.minorTickLength}}),\"arraydraw\",\"from-root\")},98292:function(t){\"use strict\";t.exports={name:\"sliders\",containerClassName:\"slider-container\",groupClassName:\"slider-group\",inputAreaClass:\"slider-input-area\",railRectClass:\"slider-rail-rect\",railTouchRectClass:\"slider-rail-touch-rect\",gripRectClass:\"slider-grip-rect\",tickRectClass:\"slider-tick-rect\",inputProxyClass:\"slider-input-proxy\",labelsClass:\"slider-labels\",labelGroupClass:\"slider-label-group\",labelClass:\"slider-label\",currentValueClass:\"slider-current-value\",railHeight:5,menuIndexAttrName:\"slider-active-index\",autoMarginIdRoot:\"slider-\",minWidth:30,minHeight:30,textPadX:40,arrowOffsetX:4,railRadius:2,railWidth:5,railBorder:4,railBorderWidth:1,railBorderColor:\"#bec8d9\",railBgColor:\"#f8fafc\",railInset:8,stepInset:10,gripRadius:10,gripWidth:20,gripHeight:20,gripBorder:20,gripBorderWidth:1,gripBorderColor:\"#bec8d9\",gripBgColor:\"#f6f8fa\",gripBgActiveColor:\"#dbdde0\",labelPadding:8,labelOffset:0,tickWidth:1,tickColor:\"#333\",tickOffset:25,tickLength:7,minorTickOffset:25,minorTickColor:\"#333\",minorTickLength:4,currentValuePadding:8,currentValueInset:0}},12343:function(t,e,r){\"use strict\";var n=r(71828),i=r(85501),a=r(75067),o=r(98292).name,s=a.steps;function l(t,e,r){function o(r,i){return n.coerce(t,e,a,r,i)}for(var s=i(t,e,{name:\"steps\",handleItemDefaults:u}),l=0,c=0;c0&&(s=s.transition().duration(e.transition.duration).ease(e.transition.easing)),s.attr(\"transform\",l(o-.5*f.gripWidth,e._dims.currentValueTotalHeight))}}function E(t,e){var r=t._dims;return r.inputAreaStart+f.stepInset+(r.inputAreaLength-2*f.stepInset)*Math.min(1,Math.max(0,e))}function L(t,e){var r=t._dims;return Math.min(1,Math.max(0,(e-f.stepInset-r.inputAreaStart)/(r.inputAreaLength-2*f.stepInset-2*r.inputAreaStart)))}function C(t,e,r){var n=r._dims,i=s.ensureSingle(t,\"rect\",f.railTouchRectClass,(function(n){n.call(A,e,t,r).style(\"pointer-events\",\"all\")}));i.attr({width:n.inputAreaLength,height:Math.max(n.inputAreaWidth,f.tickOffset+r.ticklen+n.labelHeight)}).call(a.fill,r.bgcolor).attr(\"opacity\",0),o.setTranslate(i,0,n.currentValueTotalHeight)}function P(t,e){var r=e._dims,n=r.inputAreaLength-2*f.railInset,i=s.ensureSingle(t,\"rect\",f.railRectClass);i.attr({width:n,height:f.railWidth,rx:f.railRadius,ry:f.railRadius,\"shape-rendering\":\"crispEdges\"}).call(a.stroke,e.bordercolor).call(a.fill,e.bgcolor).style(\"stroke-width\",e.borderwidth+\"px\"),o.setTranslate(i,f.railInset,.5*(r.inputAreaWidth-f.railWidth)+r.currentValueTotalHeight)}t.exports=function(t){var e=t._context.staticPlot,r=t._fullLayout,a=function(t,e){for(var r=t[f.name],n=[],i=0;i0?[0]:[]);function l(e){e._commandObserver&&(e._commandObserver.remove(),delete e._commandObserver),i.autoMargin(t,g(e))}if(s.enter().append(\"g\").classed(f.containerClassName,!0).style(\"cursor\",e?null:\"ew-resize\"),s.exit().each((function(){n.select(this).selectAll(\"g.\"+f.groupClassName).each(l)})).remove(),0!==a.length){var u=s.selectAll(\"g.\"+f.groupClassName).data(a,y);u.enter().append(\"g\").classed(f.groupClassName,!0),u.exit().each(l).remove();for(var c=0;c0||_<0){var M={left:[-w,0],right:[w,0],top:[0,-w],bottom:[0,w]}[b.side];r.attr(\"transform\",l(M[0],M[1]))}}}return R.call(F),I&&(E?R.on(\".opacity\",null):(A=0,M=!0,R.text(m).on(\"mouseover.opacity\",(function(){n.select(this).transition().duration(h.SHOW_PLACEHOLDER).style(\"opacity\",1)})).on(\"mouseout.opacity\",(function(){n.select(this).transition().duration(h.HIDE_PLACEHOLDER).style(\"opacity\",0)}))),R.call(f.makeEditable,{gd:t}).on(\"edit\",(function(e){void 0!==x?o.call(\"_guiRestyle\",t,y,e,x):o.call(\"_guiRelayout\",t,y,e)})).on(\"cancel\",(function(){this.text(this.attr(\"data-unformatted\")).call(F)})).on(\"input\",(function(t){this.text(t||\" \").call(f.positionText,_.x,_.y)}))),R.classed(\"js-placeholder\",M),T}}},7163:function(t,e,r){\"use strict\";var n=r(41940),i=r(22399),a=r(1426).extendFlat,o=r(30962).overrideAll,s=r(35025),l=r(44467).templatedArray,u=l(\"button\",{visible:{valType:\"boolean\"},method:{valType:\"enumerated\",values:[\"restyle\",\"relayout\",\"animate\",\"update\",\"skip\"],dflt:\"restyle\"},args:{valType:\"info_array\",freeLength:!0,items:[{valType:\"any\"},{valType:\"any\"},{valType:\"any\"}]},args2:{valType:\"info_array\",freeLength:!0,items:[{valType:\"any\"},{valType:\"any\"},{valType:\"any\"}]},label:{valType:\"string\",dflt:\"\"},execute:{valType:\"boolean\",dflt:!0}});t.exports=o(l(\"updatemenu\",{_arrayAttrRegexps:[/^updatemenus\\[(0|[1-9][0-9]+)\\]\\.buttons/],visible:{valType:\"boolean\"},type:{valType:\"enumerated\",values:[\"dropdown\",\"buttons\"],dflt:\"dropdown\"},direction:{valType:\"enumerated\",values:[\"left\",\"right\",\"up\",\"down\"],dflt:\"down\"},active:{valType:\"integer\",min:-1,dflt:0},showactive:{valType:\"boolean\",dflt:!0},buttons:u,x:{valType:\"number\",min:-2,max:3,dflt:-.05},xanchor:{valType:\"enumerated\",values:[\"auto\",\"left\",\"center\",\"right\"],dflt:\"right\"},y:{valType:\"number\",min:-2,max:3,dflt:1},yanchor:{valType:\"enumerated\",values:[\"auto\",\"top\",\"middle\",\"bottom\"],dflt:\"top\"},pad:a(s({editType:\"arraydraw\"}),{}),font:n({}),bgcolor:{valType:\"color\"},bordercolor:{valType:\"color\",dflt:i.borderLine},borderwidth:{valType:\"number\",min:0,dflt:1,editType:\"arraydraw\"}}),\"arraydraw\",\"from-root\")},75909:function(t){\"use strict\";t.exports={name:\"updatemenus\",containerClassName:\"updatemenu-container\",headerGroupClassName:\"updatemenu-header-group\",headerClassName:\"updatemenu-header\",headerArrowClassName:\"updatemenu-header-arrow\",dropdownButtonGroupClassName:\"updatemenu-dropdown-button-group\",dropdownButtonClassName:\"updatemenu-dropdown-button\",buttonClassName:\"updatemenu-button\",itemRectClassName:\"updatemenu-item-rect\",itemTextClassName:\"updatemenu-item-text\",menuIndexAttrName:\"updatemenu-active-index\",autoMarginIdRoot:\"updatemenu-\",blankHeaderOpts:{label:\" \"},minWidth:30,minHeight:30,textPadX:24,arrowPadX:16,rx:2,ry:2,textOffsetX:12,textOffsetY:3,arrowOffsetX:4,gapButtonHeader:5,gapButton:2,activeColor:\"#F4FAFF\",hoverColor:\"#F4FAFF\",arrowSymbol:{left:\"◄\",right:\"►\",up:\"▲\",down:\"▼\"}}},64897:function(t,e,r){\"use strict\";var n=r(71828),i=r(85501),a=r(7163),o=r(75909).name,s=a.buttons;function l(t,e,r){function o(r,i){return n.coerce(t,e,a,r,i)}o(\"visible\",i(t,e,{name:\"buttons\",handleItemDefaults:u}).length>0)&&(o(\"active\"),o(\"direction\"),o(\"type\"),o(\"showactive\"),o(\"x\"),o(\"y\"),n.noneOrAll(t,e,[\"x\",\"y\"]),o(\"xanchor\"),o(\"yanchor\"),o(\"pad.t\"),o(\"pad.r\"),o(\"pad.b\"),o(\"pad.l\"),n.coerceFont(o,\"font\",r.font),o(\"bgcolor\",r.paper_bgcolor),o(\"bordercolor\"),o(\"borderwidth\"))}function u(t,e){function r(r,i){return n.coerce(t,e,s,r,i)}r(\"visible\",\"skip\"===t.method||Array.isArray(t.args))&&(r(\"method\"),r(\"args\"),r(\"args2\"),r(\"label\"),r(\"execute\"))}t.exports=function(t,e){i(t,e,{name:o,handleItemDefaults:l})}},13689:function(t,e,r){\"use strict\";var n=r(39898),i=r(74875),a=r(7901),o=r(91424),s=r(71828),l=r(63893),u=r(44467).arrayEditor,c=r(18783).LINE_SPACING,f=r(75909),h=r(25849);function p(t){return t._index}function d(t,e){return+t.attr(f.menuIndexAttrName)===e._index}function v(t,e,r,n,i,a,o,s){e.active=o,u(t.layout,f.name,e).applyUpdate(\"active\",o),\"buttons\"===e.type?y(t,n,null,null,e):\"dropdown\"===e.type&&(i.attr(f.menuIndexAttrName,\"-1\"),g(t,n,i,a,e),s||y(t,n,i,a,e))}function g(t,e,r,n,i){var a=s.ensureSingle(e,\"g\",f.headerClassName,(function(t){t.style(\"pointer-events\",\"all\")})),l=i._dims,u=i.active,c=i.buttons[u]||f.blankHeaderOpts,h={y:i.pad.t,yPad:0,x:i.pad.l,xPad:0,index:0},p={width:l.headerWidth,height:l.headerHeight};a.call(m,i,c,t).call(M,i,h,p),s.ensureSingle(e,\"text\",f.headerArrowClassName,(function(t){t.attr(\"text-anchor\",\"end\").call(o.font,i.font).text(f.arrowSymbol[i.direction])})).attr({x:l.headerWidth-f.arrowOffsetX+i.pad.l,y:l.headerHeight/2+f.textOffsetY+i.pad.t}),a.on(\"click\",(function(){r.call(S,String(d(r,i)?-1:i._index)),y(t,e,r,n,i)})),a.on(\"mouseover\",(function(){a.call(w)})),a.on(\"mouseout\",(function(){a.call(T,i)})),o.setTranslate(e,l.lx,l.ly)}function y(t,e,r,a,o){r||(r=e).attr(\"pointer-events\",\"all\");var l=function(t){return-1==+t.attr(f.menuIndexAttrName)}(r)&&\"buttons\"!==o.type?[]:o.buttons,u=\"dropdown\"===o.type?f.dropdownButtonClassName:f.buttonClassName,c=r.selectAll(\"g.\"+u).data(s.filterVisible(l)),h=c.enter().append(\"g\").classed(u,!0),p=c.exit();\"dropdown\"===o.type?(h.attr(\"opacity\",\"0\").transition().attr(\"opacity\",\"1\"),p.transition().attr(\"opacity\",\"0\").remove()):p.remove();var d=0,g=0,y=o._dims,x=-1!==[\"up\",\"down\"].indexOf(o.direction);\"dropdown\"===o.type&&(x?g=y.headerHeight+f.gapButtonHeader:d=y.headerWidth+f.gapButtonHeader),\"dropdown\"===o.type&&\"up\"===o.direction&&(g=-f.gapButtonHeader+f.gapButton-y.openHeight),\"dropdown\"===o.type&&\"left\"===o.direction&&(d=-f.gapButtonHeader+f.gapButton-y.openWidth);var b={x:y.lx+d+o.pad.l,y:y.ly+g+o.pad.t,yPad:f.gapButton,xPad:f.gapButton,index:0},k={l:b.x+o.borderwidth,t:b.y+o.borderwidth};c.each((function(s,l){var u=n.select(this);u.call(m,o,s,t).call(M,o,b),u.on(\"click\",(function(){n.event.defaultPrevented||(s.execute&&(s.args2&&o.active===l?(v(t,o,0,e,r,a,-1),i.executeAPICommand(t,s.method,s.args2)):(v(t,o,0,e,r,a,l),i.executeAPICommand(t,s.method,s.args))),t.emit(\"plotly_buttonclicked\",{menu:o,button:s,active:o.active}))})),u.on(\"mouseover\",(function(){u.call(w)})),u.on(\"mouseout\",(function(){u.call(T,o),c.call(_,o)}))})),c.call(_,o),x?(k.w=Math.max(y.openWidth,y.headerWidth),k.h=b.y-k.t):(k.w=b.x-k.l,k.h=Math.max(y.openHeight,y.headerHeight)),k.direction=o.direction,a&&(c.size()?function(t,e,r,n,i,a){var o,s,l,u=i.direction,c=\"up\"===u||\"down\"===u,h=i._dims,p=i.active;if(c)for(s=0,l=0;l0?[0]:[]);if(o.enter().append(\"g\").classed(f.containerClassName,!0).style(\"cursor\",\"pointer\"),o.exit().each((function(){n.select(this).selectAll(\"g.\"+f.headerGroupClassName).each(a)})).remove(),0!==r.length){var l=o.selectAll(\"g.\"+f.headerGroupClassName).data(r,p);l.enter().append(\"g\").classed(f.headerGroupClassName,!0);for(var u=s.ensureSingle(o,\"g\",f.dropdownButtonGroupClassName,(function(t){t.style(\"pointer-events\",\"all\")})),c=0;cw,A=s.barLength+2*s.barPad,M=s.barWidth+2*s.barPad,S=d,E=g+y;E+M>u&&(E=u-M);var L=this.container.selectAll(\"rect.scrollbar-horizontal\").data(k?[0]:[]);L.exit().on(\".drag\",null).remove(),L.enter().append(\"rect\").classed(\"scrollbar-horizontal\",!0).call(i.fill,s.barColor),k?(this.hbar=L.attr({rx:s.barRadius,ry:s.barRadius,x:S,y:E,width:A,height:M}),this._hbarXMin=S+A/2,this._hbarTranslateMax=w-A):(delete this.hbar,delete this._hbarXMin,delete this._hbarTranslateMax);var C=y>T,P=s.barWidth+2*s.barPad,O=s.barLength+2*s.barPad,I=d+v,D=g;I+P>l&&(I=l-P);var z=this.container.selectAll(\"rect.scrollbar-vertical\").data(C?[0]:[]);z.exit().on(\".drag\",null).remove(),z.enter().append(\"rect\").classed(\"scrollbar-vertical\",!0).call(i.fill,s.barColor),C?(this.vbar=z.attr({rx:s.barRadius,ry:s.barRadius,x:I,y:D,width:P,height:O}),this._vbarYMin=D+O/2,this._vbarTranslateMax=T-O):(delete this.vbar,delete this._vbarYMin,delete this._vbarTranslateMax);var R=this.id,F=c-.5,B=C?f+P+.5:f+.5,N=h-.5,j=k?p+M+.5:p+.5,U=o._topdefs.selectAll(\"#\"+R).data(k||C?[0]:[]);if(U.exit().remove(),U.enter().append(\"clipPath\").attr(\"id\",R).append(\"rect\"),k||C?(this._clipRect=U.select(\"rect\").attr({x:Math.floor(F),y:Math.floor(N),width:Math.ceil(B)-Math.floor(F),height:Math.ceil(j)-Math.floor(N)}),this.container.call(a.setClipUrl,R,this.gd),this.bg.attr({x:d,y:g,width:v,height:y})):(this.bg.attr({width:0,height:0}),this.container.on(\"wheel\",null).on(\".drag\",null).call(a.setClipUrl,null),delete this._clipRect),k||C){var V=n.behavior.drag().on(\"dragstart\",(function(){n.event.sourceEvent.preventDefault()})).on(\"drag\",this._onBoxDrag.bind(this));this.container.on(\"wheel\",null).on(\"wheel\",this._onBoxWheel.bind(this)).on(\".drag\",null).call(V);var H=n.behavior.drag().on(\"dragstart\",(function(){n.event.sourceEvent.preventDefault(),n.event.sourceEvent.stopPropagation()})).on(\"drag\",this._onBarDrag.bind(this));k&&this.hbar.on(\".drag\",null).call(H),C&&this.vbar.on(\".drag\",null).call(H)}this.setTranslate(e,r)},s.prototype.disable=function(){(this.hbar||this.vbar)&&(this.bg.attr({width:0,height:0}),this.container.on(\"wheel\",null).on(\".drag\",null).call(a.setClipUrl,null),delete this._clipRect),this.hbar&&(this.hbar.on(\".drag\",null),this.hbar.remove(),delete this.hbar,delete this._hbarXMin,delete this._hbarTranslateMax),this.vbar&&(this.vbar.on(\".drag\",null),this.vbar.remove(),delete this.vbar,delete this._vbarYMin,delete this._vbarTranslateMax)},s.prototype._onBoxDrag=function(){var t=this.translateX,e=this.translateY;this.hbar&&(t-=n.event.dx),this.vbar&&(e-=n.event.dy),this.setTranslate(t,e)},s.prototype._onBoxWheel=function(){var t=this.translateX,e=this.translateY;this.hbar&&(t+=n.event.deltaY),this.vbar&&(e+=n.event.deltaY),this.setTranslate(t,e)},s.prototype._onBarDrag=function(){var t=this.translateX,e=this.translateY;if(this.hbar){var r=t+this._hbarXMin,i=r+this._hbarTranslateMax;t=(o.constrain(n.event.x,r,i)-r)/(i-r)*(this.position.w-this._box.w)}if(this.vbar){var a=e+this._vbarYMin,s=a+this._vbarTranslateMax;e=(o.constrain(n.event.y,a,s)-a)/(s-a)*(this.position.h-this._box.h)}this.setTranslate(t,e)},s.prototype.setTranslate=function(t,e){var r=this.position.w-this._box.w,n=this.position.h-this._box.h;if(t=o.constrain(t||0,0,r),e=o.constrain(e||0,0,n),this.translateX=t,this.translateY=e,this.container.call(a.setTranslate,this._box.l-this.position.l-t,this._box.t-this.position.t-e),this._clipRect&&this._clipRect.attr({x:Math.floor(this.position.l+t-.5),y:Math.floor(this.position.t+e-.5)}),this.hbar){var i=t/r;this.hbar.call(a.setTranslate,t+i*this._hbarTranslateMax,e)}if(this.vbar){var s=e/n;this.vbar.call(a.setTranslate,t,e+s*this._vbarTranslateMax)}}},18783:function(t){\"use strict\";t.exports={FROM_BL:{left:0,center:.5,right:1,bottom:0,middle:.5,top:1},FROM_TL:{left:0,center:.5,right:1,bottom:1,middle:.5,top:0},FROM_BR:{left:1,center:.5,right:0,bottom:0,middle:.5,top:1},LINE_SPACING:1.3,CAP_SHIFT:.7,MID_SHIFT:.35,OPPOSITE_SIDE:{left:\"right\",right:\"left\",top:\"bottom\",bottom:\"top\"}}},24695:function(t){\"use strict\";t.exports={axisRefDescription:function(t,e,r){return[\"If set to a\",t,\"axis id (e.g. *\"+t+\"* or\",\"*\"+t+\"2*), the `\"+t+\"` position refers to a\",t,\"coordinate. 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L=(v+.5)/84;e.maskBreaks(i*(1-L)+L*p)!==O&&d++}(h*=d/84)||(t[n].drop=!0),s&&c>A&&(h=c)}(h>0||0===n)&&(t[n].periodX=i+h/2)}}(C,t,t._definedDelta),t.rangebreaks){var ot=\"y\"===t._id.charAt(0),st=1;\"auto\"===t.tickmode&&(st=t.tickfont?t.tickfont.size:12);var lt=NaN;for(r=C.length-1;r>-1;r--)if(C[r].drop)C.splice(r,1);else{C[r].value=Dt(C[r].value,t);var ut=t.c2p(C[r].value);(ot?lt>ut-st:ltp||ftp&&(ct.periodX=p),ft10||\"01-01\"!==n.substr(5)?t._tickround=\"d\":t._tickround=+e.substr(1)%12==0?\"y\":\"m\";else if(e>=M&&a<=10||e>=15*M)t._tickround=\"d\";else if(e>=L&&a<=16||e>=E)t._tickround=\"M\";else if(e>=C&&a<=19||e>=L)t._tickround=\"S\";else{var o=t.l2r(r+e).replace(/^-/,\"\").length;t._tickround=Math.max(a,o)-20,t._tickround<0&&(t._tickround=4)}}else if(i(e)||\"L\"===e.charAt(0)){var s=t.range.map(t.r2d||Number);i(e)||(e=Number(e.substr(1))),t._tickround=2-Math.floor(Math.log(e)/Math.LN10+.01);var l=Math.max(Math.abs(s[0]),Math.abs(s[1])),u=Math.floor(Math.log(l)/Math.LN10+.01),c=void 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c=s.simpleMap(t.range,t.r2l);if(t._isMinor&&(e*=1.5),e>.7)t.dtick=Math.ceil(e);else if(Math.abs(c[1]-c[0])<1){var f=1.5*Math.abs((c[1]-c[0])/e);e=Math.abs(Math.pow(10,c[1])-Math.pow(10,c[0]))/f,n=a(10),t.dtick=\"L\"+pt(e,n,ot)}else t.dtick=e>.3?\"D2\":\"D1\"}else\"category\"===t.type||\"multicategory\"===t.type?(t.tick0=0,t.dtick=Math.ceil(Math.max(e,1))):It(t)?(t.tick0=0,n=1,t.dtick=pt(e,n,ht)):(t.tick0=0,n=a(10),t.dtick=pt(e,n,ot));if(0===t.dtick&&(t.dtick=1),!i(t.dtick)&&\"string\"!=typeof t.dtick){var h=t.dtick;throw t.dtick=1,\"ax.dtick error: \"+String(h)}},q.tickIncrement=function(t,e,r,a){var o=r?-1:1;if(i(e))return s.increment(t,o*e);var l=e.charAt(0),u=o*Number(e.substr(1));if(\"M\"===l)return s.incrementMonth(t,u,a);if(\"L\"===l)return Math.log(Math.pow(10,t)+u)/Math.LN10;if(\"D\"===l){var c=\"D2\"===e?ft:ct,f=t+.01*o,h=s.roundUp(s.mod(f,1),c,r);return Math.floor(f)+Math.log(n.round(Math.pow(10,h),1))/Math.LN10}throw\"unrecognized dtick 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n,i,a,o=e[t],s=w.scopeDefaults[e.scope];\"lonaxis\"===t?(n=s.lonaxisRange,i=s.lataxisRange,a=function(t,e){return[t,e]}):\"lataxis\"===t&&(n=s.lataxisRange,i=s.lonaxisRange,a=function(t,e){return[e,t]});var l={type:\"linear\",range:[n[0],n[1]-1e-6],tick0:o.tick0,dtick:o.dtick};v.setConvert(l,r);var u=v.calcTicks(l);e.isScoped||\"lonaxis\"!==t||u.pop();for(var c=u.length,f=new Array(c),h=0;h-1&&b(n.event,i,[r.xaxis],[r.yaxis],r.id,c),s.indexOf(\"event\")>-1&&p.click(i,n.event))}))}function f(t){return r.projection.invert([t[0]+r.xaxis._offset,t[1]+r.yaxis._offset])}},S.makeFramework=function(){var t=this,e=t.graphDiv,r=e._fullLayout,i=\"clip\"+r._uid+t.id;t.clipDef=r._clips.append(\"clipPath\").attr(\"id\",i),t.clipRect=t.clipDef.append(\"rect\"),t.framework=n.select(t.container).append(\"g\").attr(\"class\",\"geo \"+t.id).call(h.setClipUrl,i,e),t.project=function(e){var r=t.projection(e);return r?[r[0]-t.xaxis._offset,r[1]-t.yaxis._offset]:[null,null]},t.xaxis={_id:\"x\",c2p:function(e){return t.project(e)[0]}},t.yaxis={_id:\"y\",c2p:function(e){return t.project(e)[1]}},t.mockAxis={type:\"linear\",showexponent:\"all\",exponentformat:\"B\"},v.setConvert(t.mockAxis,r)},S.saveViewInitial=function(t){var e,r=t.center||{},n=t.projection,i=n.rotation||{};this.viewInitial={fitbounds:t.fitbounds,\"projection.scale\":n.scale},e=t._isScoped?{\"center.lon\":r.lon,\"center.lat\":r.lat}:t._isClipped?{\"projection.rotation.lon\":i.lon,\"projection.rotation.lat\":i.lat}:{\"center.lon\":r.lon,\"center.lat\":r.lat,\"projection.rotation.lon\":i.lon},u.extendFlat(this.viewInitial,e)},S.render=function(t){this._hasMarkerAngles&&t?this.plot(this._geoCalcData,this._fullLayout,[],!0):this._render()},S._render=function(){var t,e=this.projection,r=e.getPath();function n(t){var r=e(t.lonlat);return r?c(r[0],r[1]):null}function i(t){return e.isLonLatOverEdges(t.lonlat)?\"none\":null}for(t in this.basePaths)this.basePaths[t].attr(\"d\",r);for(t in this.dataPaths)this.dataPaths[t].attr(\"d\",(function(t){return r(t.geojson)}));for(t in this.dataPoints)this.dataPoints[t].attr(\"display\",i).attr(\"transform\",n)}},44622:function(t,e,r){\"use strict\";var n=r(27659).AU,i=r(71828).counterRegex,a=r(69082),o=\"geo\",s=i(o),l={};l.geo={valType:\"subplotid\",dflt:o,editType:\"calc\"},t.exports={attr:o,name:o,idRoot:o,idRegex:s,attrRegex:s,attributes:l,layoutAttributes:r(77519),supplyLayoutDefaults:r(82161),plot:function(t){for(var e=t._fullLayout,r=t.calcdata,i=e._subplots.geo,s=0;s0&&P<0&&(P+=360);var O,I,D,z=(C+P)/2;if(!p){var R=d?f.projRotate:[z,0,0];O=r(\"projection.rotation.lon\",R[0]),r(\"projection.rotation.lat\",R[1]),r(\"projection.rotation.roll\",R[2]),r(\"showcoastlines\",!d&&x)&&(r(\"coastlinecolor\"),r(\"coastlinewidth\")),r(\"showocean\",!!x&&void 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e.projection.rotation.lon))}t.exports=function(t,e,r){i(t,e,r,{type:\"geo\",attributes:s,handleDefaults:u,fullData:r,partition:\"y\"})}},74455:function(t,e,r){\"use strict\";var n=r(39898),i=r(71828),a=r(73972),o=Math.PI/180,s=180/Math.PI,l={cursor:\"pointer\"},u={cursor:\"auto\"};function c(t,e){return n.behavior.zoom().translate(e.translate()).scale(e.scale())}function f(t,e,r){var n=t.id,o=t.graphDiv,s=o.layout,l=s[n],u=o._fullLayout,c=u[n],f={},h={};function p(t,e){f[n+\".\"+t]=i.nestedProperty(l,t).get(),a.call(\"_storeDirectGUIEdit\",s,u._preGUI,f);var r=i.nestedProperty(c,t);r.get()!==e&&(r.set(e),i.nestedProperty(l,t).set(e),h[n+\".\"+t]=e)}r(p),p(\"projection.scale\",e.scale()/t.fitScale),p(\"fitbounds\",!1),o.emit(\"plotly_relayout\",h)}function h(t,e){var r=c(0,e);function i(r){var n=e.invert(t.midPt);r(\"center.lon\",n[0]),r(\"center.lat\",n[1])}return r.on(\"zoomstart\",(function(){n.select(this).style(l)})).on(\"zoom\",(function(){e.scale(n.event.scale).translate(n.event.translate),t.render(!0);var r=e.invert(t.midPt);t.graphDiv.emit(\"plotly_relayouting\",{\"geo.projection.scale\":e.scale()/t.fitScale,\"geo.center.lon\":r[0],\"geo.center.lat\":r[1]})})).on(\"zoomend\",(function(){n.select(this).style(u),f(t,e,i)})),r}function p(t,e){var r,i,a,o,s,h,p,d,v,g=c(0,e);function y(t){return e.invert(t)}function m(r){var n=e.rotate(),i=e.invert(t.midPt);r(\"projection.rotation.lon\",-n[0]),r(\"center.lon\",i[0]),r(\"center.lat\",i[1])}return g.on(\"zoomstart\",(function(){n.select(this).style(l),r=n.mouse(this),i=e.rotate(),a=e.translate(),o=i,s=y(r)})).on(\"zoom\",(function(){if(h=n.mouse(this),function(t){var r=y(t);if(!r)return!0;var n=e(r);return Math.abs(n[0]-t[0])>2||Math.abs(n[1]-t[1])>2}(r))return g.scale(e.scale()),void g.translate(e.translate());e.scale(n.event.scale),e.translate([a[0],n.event.translate[1]]),s?y(h)&&(d=y(h),p=[o[0]+(d[0]-s[0]),i[1],i[2]],e.rotate(p),o=p):s=y(r=h),v=!0,t.render(!0);var l=e.rotate(),u=e.invert(t.midPt);t.graphDiv.emit(\"plotly_relayouting\",{\"geo.projection.scale\":e.scale()/t.fitScale,\"geo.center.lon\":u[0],\"geo.center.lat\":u[1],\"geo.projection.rotation.lon\":-l[0]})})).on(\"zoomend\",(function(){n.select(this).style(u),v&&f(t,e,m)})),g}function d(t,e){var r,i={r:e.rotate(),k:e.scale()},a=c(0,e),o=function(t){for(var e=0,r=arguments.length,i=[];++ed?(a=(f>0?90:-90)-p,i=0):(a=Math.asin(f/d)*s-p,i=Math.sqrt(d*d-f*f));var v=180-a-2*p,g=(Math.atan2(h,c)-Math.atan2(u,i))*s,y=(Math.atan2(h,c)-Math.atan2(u,-i))*s;return b(r[0],r[1],a,g)<=b(r[0],r[1],v,y)?[a,g,r[2]]:[v,y,r[2]]}function b(t,e,r,n){var i=_(r-t),a=_(n-e);return Math.sqrt(i*i+a*a)}function _(t){return(t%360+540)%360-180}function w(t,e,r){var n=r*o,i=t.slice(),a=0===e?1:0,s=2===e?1:2,l=Math.cos(n),u=Math.sin(n);return i[a]=t[a]*l-t[s]*u,i[s]=t[s]*l+t[a]*u,i}function T(t){return[Math.atan2(2*(t[0]*t[1]+t[2]*t[3]),1-2*(t[1]*t[1]+t[2]*t[2]))*s,Math.asin(Math.max(-1,Math.min(1,2*(t[0]*t[2]-t[3]*t[1]))))*s,Math.atan2(2*(t[0]*t[3]+t[1]*t[2]),1-2*(t[2]*t[2]+t[3]*t[3]))*s]}function k(t,e){for(var r=0,n=0,i=t.length;nMath.abs(s)?(u.boxEnd[1]=u.boxStart[1]+Math.abs(a)*_*(s>=0?1:-1),u.boxEnd[1]l[3]&&(u.boxEnd[1]=l[3],u.boxEnd[0]=u.boxStart[0]+(l[3]-u.boxStart[1])/Math.abs(_))):(u.boxEnd[0]=u.boxStart[0]+Math.abs(s)/_*(a>=0?1:-1),u.boxEnd[0]l[2]&&(u.boxEnd[0]=l[2],u.boxEnd[1]=u.boxStart[1]+(l[2]-u.boxStart[0])*Math.abs(_)))}}else u.boxEnabled?(a=u.boxStart[0]!==u.boxEnd[0],s=u.boxStart[1]!==u.boxEnd[1],a||s?(a&&(g(0,u.boxStart[0],u.boxEnd[0]),t.xaxis.autorange=!1),s&&(g(1,u.boxStart[1],u.boxEnd[1]),t.yaxis.autorange=!1),t.relayoutCallback()):t.glplot.setDirty(),u.boxEnabled=!1,u.boxInited=!1):u.boxInited&&(u.boxInited=!1);break;case\"pan\":u.boxEnabled=!1,u.boxInited=!1,e?(u.panning||(u.dragStart[0]=n,u.dragStart[1]=i),Math.abs(u.dragStart[0]-n).999&&(g=\"turntable\"):g=\"turntable\")}else g=\"turntable\";r(\"dragmode\",g),r(\"hovermode\",n.getDfltFromLayout(\"hovermode\"))}t.exports=function(t,e,r){var i=e._basePlotModules.length>1;o(t,e,r,{type:c,attributes:l,handleDefaults:f,fullLayout:e,font:e.font,fullData:r,getDfltFromLayout:function(e){if(!i)return n.validate(t[e],l[e])?t[e]:void 0},autotypenumbersDflt:e.autotypenumbers,paper_bgcolor:e.paper_bgcolor,calendar:e.calendar})}},65500:function(t,e,r){\"use strict\";var n=r(77894),i=r(27670).Y,a=r(1426).extendFlat,o=r(71828).counterRegex;function s(t,e,r){return{x:{valType:\"number\",dflt:t,editType:\"camera\"},y:{valType:\"number\",dflt:e,editType:\"camera\"},z:{valType:\"number\",dflt:r,editType:\"camera\"},editType:\"camera\"}}t.exports={_arrayAttrRegexps:[o(\"scene\",\".annotations\",!0)],bgcolor:{valType:\"color\",dflt:\"rgba(0,0,0,0)\",editType:\"plot\"},camera:{up:a(s(0,0,1),{}),center:a(s(0,0,0),{}),eye:a(s(1.25,1.25,1.25),{}),projection:{type:{valType:\"enumerated\",values:[\"perspective\",\"orthographic\"],dflt:\"perspective\",editType:\"calc\"},editType:\"calc\"},editType:\"camera\"},domain:i({name:\"scene\",editType:\"plot\"}),aspectmode:{valType:\"enumerated\",values:[\"auto\",\"cube\",\"data\",\"manual\"],dflt:\"auto\",editType:\"plot\",impliedEdits:{\"aspectratio.x\":void 0,\"aspectratio.y\":void 0,\"aspectratio.z\":void 0}},aspectratio:{x:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},y:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},z:{valType:\"number\",min:0,editType:\"plot\",impliedEdits:{\"^aspectmode\":\"manual\"}},editType:\"plot\",impliedEdits:{aspectmode:\"manual\"}},xaxis:n,yaxis:n,zaxis:n,dragmode:{valType:\"enumerated\",values:[\"orbit\",\"turntable\",\"zoom\",\"pan\",!1],editType:\"plot\"},hovermode:{valType:\"enumerated\",values:[\"closest\",!1],dflt:\"closest\",editType:\"modebar\"},uirevision:{valType:\"any\",editType:\"none\"},editType:\"plot\",_deprecated:{cameraposition:{valType:\"info_array\",editType:\"camera\"}}}},13133:function(t,e,r){\"use strict\";var n=r(78614),i=[\"xaxis\",\"yaxis\",\"zaxis\"];function a(){this.enabled=[!0,!0,!0],this.colors=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.drawSides=[!0,!0,!0],this.lineWidth=[1,1,1]}a.prototype.merge=function(t){for(var e=0;e<3;++e){var r=t[i[e]];r.visible?(this.enabled[e]=r.showspikes,this.colors[e]=n(r.spikecolor),this.drawSides[e]=r.spikesides,this.lineWidth[e]=r.spikethickness):(this.enabled[e]=!1,this.drawSides[e]=!1)}},t.exports=function(t){var e=new a;return e.merge(t),e}},96085:function(t,e,r){\"use strict\";t.exports=function(t){for(var e=t.axesOptions,r=t.glplot.axesPixels,s=t.fullSceneLayout,l=[[],[],[]],u=0;u<3;++u){var c=s[a[u]];if(c._length=(r[u].hi-r[u].lo)*r[u].pixelsPerDataUnit/t.dataScale[u],Math.abs(c._length)===1/0||isNaN(c._length))l[u]=[];else{c._input_range=c.range.slice(),c.range[0]=r[u].lo/t.dataScale[u],c.range[1]=r[u].hi/t.dataScale[u],c._m=1/(t.dataScale[u]*r[u].pixelsPerDataUnit),c.range[0]===c.range[1]&&(c.range[0]-=1,c.range[1]+=1);var f=c.tickmode;if(\"auto\"===c.tickmode){c.tickmode=\"linear\";var h=c.nticks||i.constrain(c._length/40,4,9);n.autoTicks(c,Math.abs(c.range[1]-c.range[0])/h)}for(var p=n.calcTicks(c,{msUTC:!0}),d=0;d/g,\" 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a=r._fullLayout._invScaleX,o=r._fullLayout._invScaleY,s=i.width*a,l=i.height*o;n.setAttributeNS(null,\"viewBox\",\"0 0 \"+s+\" \"+l),n.setAttributeNS(null,\"width\",s),n.setAttributeNS(null,\"height\",l),b(e),e.glplot.axes.update(e.axesOptions);for(var u=Object.keys(e.traces),c=null,h=e.glplot.selection,v=0;v\")):\"isosurface\"===t.type||\"volume\"===t.type?(T.valueLabel=p.hoverLabelText(e._mockAxis,e._mockAxis.d2l(h.traceCoordinate[3]),t.valuehoverformat),S.push(\"value: \"+T.valueLabel),h.textLabel&&S.push(h.textLabel),x=S.join(\"
\")):x=h.textLabel;var E={x:h.traceCoordinate[0],y:h.traceCoordinate[1],z:h.traceCoordinate[2],data:_._input,fullData:_,curveNumber:_.index,pointNumber:w};d.appendArrayPointValue(E,_,w),t._module.eventData&&(E=_._module.eventData(E,h,_,{},w));var L={points:[E]};if(e.fullSceneLayout.hovermode){var C=[];d.loneHover({trace:_,x:(.5+.5*m[0]/m[3])*s,y:(.5-.5*m[1]/m[3])*l,xLabel:T.xLabel,yLabel:T.yLabel,zLabel:T.zLabel,text:x,name:c.name,color:d.castHoverOption(_,w,\"bgcolor\")||c.color,borderColor:d.castHoverOption(_,w,\"bordercolor\"),fontFamily:d.castHoverOption(_,w,\"font.family\"),fontSize:d.castHoverOption(_,w,\"font.size\"),fontColor:d.castHoverOption(_,w,\"font.color\"),nameLength:d.castHoverOption(_,w,\"namelength\"),textAlign:d.castHoverOption(_,w,\"align\"),hovertemplate:f.castOption(_,w,\"hovertemplate\"),hovertemplateLabels:f.extendFlat({},E,T),eventData:[E]},{container:n,gd:r,inOut_bbox:C}),E.bbox=C[0]}h.buttons&&h.distance<5?r.emit(\"plotly_click\",L):r.emit(\"plotly_hover\",L),this.oldEventData=L}else d.loneUnhover(n),this.oldEventData&&r.emit(\"plotly_unhover\",this.oldEventData),this.oldEventData=void 0;e.drawAnnotations(e)},w.recoverContext=function(){var t=this;t.glplot.dispose();var e=function(){t.glplot.gl.isContextLost()?requestAnimationFrame(e):t.initializeGLPlot()?t.plot.apply(t,t.plotArgs):f.error(\"Catastrophic and unrecoverable WebGL error. Context lost.\")};requestAnimationFrame(e)};var k=[\"xaxis\",\"yaxis\",\"zaxis\"];function A(t,e,r){for(var n=t.fullSceneLayout,i=0;i<3;i++){var a=k[i],o=a.charAt(0),s=n[a],l=e[o],u=e[o+\"calendar\"],c=e[\"_\"+o+\"length\"];if(f.isArrayOrTypedArray(l))for(var h,p=0;p<(c||l.length);p++)if(f.isArrayOrTypedArray(l[p]))for(var d=0;dy[1][o])y[0][o]=-1,y[1][o]=1;else{var C=y[1][o]-y[0][o];y[0][o]-=C/32,y[1][o]+=C/32}if(\"reversed\"===l.autorange){var P=y[0][o];y[0][o]=y[1][o],y[1][o]=P}}else{var O=l.range;y[0][o]=l.r2l(O[0]),y[1][o]=l.r2l(O[1])}y[0][o]===y[1][o]&&(y[0][o]-=1,y[1][o]+=1),m[o]=y[1][o]-y[0][o],n.glplot.setBounds(o,{min:y[0][o]*p[o],max:y[1][o]*p[o]})}var I=c.aspectmode;if(\"cube\"===I)g=[1,1,1];else if(\"manual\"===I){var D=c.aspectratio;g=[D.x,D.y,D.z]}else{if(\"auto\"!==I&&\"data\"!==I)throw new Error(\"scene.js aspectRatio was not one of the enumerated types\");var z=[1,1,1];for(o=0;o<3;++o){var R=x[u=(l=c[k[o]]).type];z[o]=Math.pow(R.acc,1/R.count)/p[o]}g=\"data\"===I||Math.max.apply(null,z)/Math.min.apply(null,z)<=4?z:[1,1,1]}c.aspectratio.x=f.aspectratio.x=g[0],c.aspectratio.y=f.aspectratio.y=g[1],c.aspectratio.z=f.aspectratio.z=g[2],n.glplot.setAspectratio(c.aspectratio),n.viewInitial.aspectratio||(n.viewInitial.aspectratio={x:c.aspectratio.x,y:c.aspectratio.y,z:c.aspectratio.z}),n.viewInitial.aspectmode||(n.viewInitial.aspectmode=c.aspectmode);var F=c.domain||null,B=e._size||null;if(F&&B){var N=n.container.style;N.position=\"absolute\",N.left=B.l+F.x[0]*B.w+\"px\",N.top=B.t+(1-F.y[1])*B.h+\"px\",N.width=B.w*(F.x[1]-F.x[0])+\"px\",N.height=B.h*(F.y[1]-F.y[0])+\"px\"}n.glplot.redraw()}},w.destroy=function(){var t=this;t.glplot&&(t.camera.mouseListener.enabled=!1,t.container.removeEventListener(\"wheel\",t.camera.wheelListener),t.camera=null,t.glplot.dispose(),t.container.parentNode.removeChild(t.container),t.glplot=null)},w.getCamera=function(){var t,e=this;return e.camera.view.recalcMatrix(e.camera.view.lastT()),{up:{x:(t=e.camera).up[0],y:t.up[1],z:t.up[2]},center:{x:t.center[0],y:t.center[1],z:t.center[2]},eye:{x:t.eye[0],y:t.eye[1],z:t.eye[2]},projection:{type:!0===t._ortho?\"orthographic\":\"perspective\"}}},w.setViewport=function(t){var e,r=this,n=t.camera;r.camera.lookAt.apply(this,[[(e=n).eye.x,e.eye.y,e.eye.z],[e.center.x,e.center.y,e.center.z],[e.up.x,e.up.y,e.up.z]]),r.glplot.setAspectratio(t.aspectratio),\"orthographic\"===n.projection.type!==r.camera._ortho&&(r.glplot.redraw(),r.glplot.clearRGBA(),r.glplot.dispose(),r.initializeGLPlot())},w.isCameraChanged=function(t){var e=this.getCamera(),r=f.nestedProperty(t,this.id+\".camera\").get();function n(t,e,r,n){var i=[\"up\",\"center\",\"eye\"],a=[\"x\",\"y\",\"z\"];return e[i[r]]&&t[i[r]][a[n]]===e[i[r]][a[n]]}var i=!1;if(void 0===r)i=!0;else{for(var a=0;a<3;a++)for(var o=0;o<3;o++)if(!n(e,r,a,o)){i=!0;break}(!r.projection||e.projection&&e.projection.type!==r.projection.type)&&(i=!0)}return i},w.isAspectChanged=function(t){var e=this.glplot.getAspectratio(),r=f.nestedProperty(t,this.id+\".aspectratio\").get();return void 0===r||r.x!==e.x||r.y!==e.y||r.z!==e.z},w.saveLayout=function(t){var e,r,n,i,a,o,s=this,l=s.fullLayout,u=s.isCameraChanged(t),h=s.isAspectChanged(t),p=u||h;if(p){var d={};u&&(e=s.getCamera(),n=(r=f.nestedProperty(t,s.id+\".camera\")).get(),d[s.id+\".camera\"]=n),h&&(i=s.glplot.getAspectratio(),o=(a=f.nestedProperty(t,s.id+\".aspectratio\")).get(),d[s.id+\".aspectratio\"]=o),c.call(\"_storeDirectGUIEdit\",t,l._preGUI,d),u&&(r.set(e),f.nestedProperty(l,s.id+\".camera\").set(e)),h&&(a.set(i),f.nestedProperty(l,s.id+\".aspectratio\").set(i),s.glplot.redraw())}return p},w.updateFx=function(t,e){var r=this,n=r.camera;if(n)if(\"orbit\"===t)n.mode=\"orbit\",n.keyBindingMode=\"rotate\";else if(\"turntable\"===t){n.up=[0,0,1],n.mode=\"turntable\",n.keyBindingMode=\"rotate\";var i=r.graphDiv,a=i._fullLayout,o=r.fullSceneLayout.camera,s=o.up.x,l=o.up.y,u=o.up.z;if(u/Math.sqrt(s*s+l*l+u*u)<.999){var h=r.id+\".camera.up\",p={x:0,y:0,z:1},d={};d[h]=p;var v=i.layout;c.call(\"_storeDirectGUIEdit\",v,a._preGUI,d),o.up=p,f.nestedProperty(v,h).set(p)}}else n.keyBindingMode=t;r.fullSceneLayout.hovermode=e},w.toImage=function(t){var e=this;t||(t=\"png\"),e.staticMode&&e.container.appendChild(n),e.glplot.redraw();var r=e.glplot.gl,i=r.drawingBufferWidth,a=r.drawingBufferHeight;r.bindFramebuffer(r.FRAMEBUFFER,null);var o=new Uint8Array(i*a*4);r.readPixels(0,0,i,a,r.RGBA,r.UNSIGNED_BYTE,o),function(t,e,r){for(var n=0,i=r-1;n0)for(var s=255/o,l=0;l<3;++l)t[a+l]=Math.min(s*t[a+l],255)}}(o,i,a);var s=document.createElement(\"canvas\");s.width=i,s.height=a;var l,u=s.getContext(\"2d\",{willReadFrequently:!0}),c=u.createImageData(i,a);switch(c.data.set(o),u.putImageData(c,0,0),t){case\"jpeg\":l=s.toDataURL(\"image/jpeg\");break;case\"webp\":l=s.toDataURL(\"image/webp\");break;default:l=s.toDataURL(\"image/png\")}return e.staticMode&&e.container.removeChild(n),l},w.setConvert=function(){for(var t=0;t<3;t++){var e=this.fullSceneLayout[k[t]];p.setConvert(e,this.fullLayout),e.setScale=f.noop}},w.make4thDimension=function(){var t=this,e=t.graphDiv._fullLayout;t._mockAxis={type:\"linear\",showexponent:\"all\",exponentformat:\"B\"},p.setConvert(t._mockAxis,e)},t.exports=_},90060:function(t){\"use strict\";t.exports=function(t,e,r,n){n=n||t.length;for(var i=new Array(n),a=0;aOpenStreetMap contributors',a=['© Carto',i].join(\" \"),o=['Map tiles by Stamen Design','under CC BY 3.0',\"|\",'Data by OpenStreetMap contributors','under ODbL'].join(\" 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this.subplot.map.getSource(this.idSource)&&\"image\"===this.sourceType&&\"image\"===t.sourcetype&&(this.source!==t.source||JSON.stringify(this.coordinates)!==JSON.stringify(t.coordinates))},l.needsNewSource=function(t){return this.sourceType!==t.sourcetype||JSON.stringify(this.source)!==JSON.stringify(t.source)||this.layerType!==t.type},l.needsNewLayer=function(t){return this.layerType!==t.type||this.below!==this.subplot.belowLookup[\"layout-\"+this.index]},l.lookupBelow=function(){return this.subplot.belowLookup[\"layout-\"+this.index]},l.updateImage=function(t){this.subplot.map.getSource(this.idSource).updateImage({url:t.source,coordinates:t.coordinates});var e=this.findFollowingMapboxLayerId(this.lookupBelow());null!==e&&this.subplot.map.moveLayer(this.idLayer,e)},l.updateSource=function(t){var e=this.subplot.map;if(e.getSource(this.idSource)&&e.removeSource(this.idSource),this.sourceType=t.sourcetype,this.source=t.source,u(t)){var r=function(t){var e,r=t.sourcetype,n=t.source,a={type:r};return\"geojson\"===r?e=\"data\":\"vector\"===r?e=\"string\"==typeof n?\"url\":\"tiles\":\"raster\"===r?(e=\"tiles\",a.tileSize=256):\"image\"===r&&(e=\"url\",a.coordinates=t.coordinates),a[e]=n,t.sourceattribution&&(a.attribution=i(t.sourceattribution)),a}(t);e.addSource(this.idSource,r)}},l.findFollowingMapboxLayerId=function(t){if(\"traces\"===t)for(var e=this.subplot.getMapLayers(),r=0;r1)for(r=0;r-1&&g(e.originalEvent,n,[r.xaxis],[r.yaxis],r.id,t),i.indexOf(\"event\")>-1&&u.click(n,e.originalEvent)}}},b.updateFx=function(t){var e=this,r=e.map,n=e.gd;if(!e.isStatic){var a,o=t.dragmode;a=function(t,r){r.isRect?(t.range={})[e.id]=[u([r.xmin,r.ymin]),u([r.xmax,r.ymax])]:(t.lassoPoints={})[e.id]=r.map(u)};var s=e.dragOptions;e.dragOptions=i.extendDeep(s||{},{dragmode:t.dragmode,element:e.div,gd:n,plotinfo:{id:e.id,domain:t[e.id].domain,xaxis:e.xaxis,yaxis:e.yaxis,fillRangeItems:a},xaxes:[e.xaxis],yaxes:[e.yaxis],subplot:e.id}),r.off(\"click\",e.onClickInPanHandler),h(o)||f(o)?(r.dragPan.disable(),r.on(\"zoomstart\",e.clearOutline),e.dragOptions.prepFn=function(t,r,n){p(t,r,n,e.dragOptions,o)},l.init(e.dragOptions)):(r.dragPan.enable(),r.off(\"zoomstart\",e.clearOutline),e.div.onmousedown=null,e.div.ontouchstart=null,e.div.removeEventListener(\"touchstart\",e.div._ontouchstart),e.onClickInPanHandler=e.onClickInPanFn(e.dragOptions),r.on(\"click\",e.onClickInPanHandler))}function u(t){var r=e.map.unproject(t);return[r.lng,r.lat]}},b.updateFramework=function(t){var 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y=E.findPolygonOffset(v,L[0],L[1],C);B+=A+y[0],N+=M+y[1]}switch(l){case\"zoom\":Y.clickFn=st,u||(Y.moveFn=C?it:rt,Y.doneFn=at,function(){j=null,U=null,V=s.pathSubplot(),H=!1;var t=c._fullLayout[s.id];q=i(t.bgcolor).getLuminance(),(G=g.makeZoombox(h,q,T,k,V)).attr(\"fill-rule\",\"evenodd\"),Z=g.makeCorners(h,T,k),w(c)}());break;case\"select\":case\"lasso\":b(t,n,a,Y,l)}},y.init(Y)},N.updateRadialDrag=function(t,e,r){var i=this,u=i.gd,c=i.layers,f=i.radius,h=i.innerRadius,p=i.cx,d=i.cy,v=i.radialAxis,m=S.radialDragBoxSize,x=m/2;if(v.visible){var b,_,T,M=R(i.radialAxisAngle),E=v._rl,L=E[0],C=E[1],P=E[r],O=.75*(E[1]-E[0])/(1-i.getHole(e))/f;r?(b=p+(f+x)*Math.cos(M),_=d-(f+x)*Math.sin(M),T=\"radialdrag\"):(b=p+(h-x)*Math.cos(M),_=d-(h-x)*Math.sin(M),T=\"radialdrag-inner\");var I,D,z,B=g.makeRectDragger(c,T,\"crosshair\",-x,-x,m,m),N={element:B,gd:u};!1===t.dragmode&&(N.dragmode=!1),V(n.select(B),v.visible&&h0==(r?z>L:zn?function(t){return t<=0}:function(t){return t>=0};t.c2g=function(r){var n=t.c2l(r)-e;return(s(n)?n:0)+o},t.g2c=function(r){return t.l2c(r+e-o)},t.g2p=function(t){return t*a},t.c2p=function(e){return t.g2p(t.c2g(e))}}}(t,e);break;case\"angularaxis\":!function(t,e){var r=t.type;if(\"linear\"===r){var i=t.d2c,s=t.c2d;t.d2c=function(t,e){return function(t,e){return\"degrees\"===e?a(t):t}(i(t),e)},t.c2d=function(t,e){return s(function(t,e){return\"degrees\"===e?o(t):t}(t,e))}}t.makeCalcdata=function(e,i){var a,o,s=e[i],l=e._length,u=function(r){return t.d2c(r,e.thetaunit)};if(s){if(n.isTypedArray(s)&&\"linear\"===r){if(l===s.length)return s;if(s.subarray)return s.subarray(0,l)}for(a=new Array(l),o=0;o0?1:0}function r(t){var e=t[0],r=t[1];if(!isFinite(e)||!isFinite(r))return[1,0];var n=(e+1)*(e+1)+r*r;return[(e*e+r*r-1)/n,2*r/n]}function n(t,e){var r=e[0],n=e[1];return[r*t.radius+t.cx,-n*t.radius+t.cy]}function i(t,e){return e*t.radius}t.exports={smith:r,reactanceArc:function(t,e,a,o){var s=n(t,r([a,e])),l=s[0],u=s[1],c=n(t,r([o,e])),f=c[0],h=c[1];if(0===e)return[\"M\"+l+\",\"+u,\"L\"+f+\",\"+h].join(\" \");var p=i(t,1/Math.abs(e));return[\"M\"+l+\",\"+u,\"A\"+p+\",\"+p+\" 0 0,\"+(e<0?1:0)+\" \"+f+\",\"+h].join(\" \")},resistanceArc:function(t,a,o,s){var l=i(t,1/(a+1)),u=n(t,r([a,o])),c=u[0],f=u[1],h=n(t,r([a,s])),p=h[0],d=h[1];if(e(o)!==e(s)){var v=n(t,r([a,0]));return[\"M\"+c+\",\"+f,\"A\"+l+\",\"+l+\" 0 0,\"+(00){for(var n=[],i=0;i=c&&(h.min=0,d.min=0,g.min=0,t.aaxis&&delete t.aaxis.min,t.baxis&&delete t.baxis.min,t.caxis&&delete t.caxis.min)}function v(t,e,r,n){var i=h[e._name];function o(r,n){return a.coerce(t,e,i,r,n)}o(\"uirevision\",n.uirevision),e.type=\"linear\";var p=o(\"color\"),d=p!==i.color.dflt?p:r.font.color,v=e._name.charAt(0).toUpperCase(),g=\"Component 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n=r(39898),i=r(84267),a=r(73972),o=r(71828),s=o.strTranslate,l=o._,u=r(7901),c=r(91424),f=r(21994),h=r(1426).extendFlat,p=r(74875),d=r(89298),v=r(28569),g=r(30211),y=r(64505),m=y.freeMode,x=y.rectMode,b=r(92998),_=r(47322).prepSelect,w=r(47322).selectOnClick,T=r(47322).clearOutline,k=r(47322).clearSelectionsCache,A=r(85555);function M(t,e){this.id=t.id,this.graphDiv=t.graphDiv,this.init(e),this.makeFramework(e),this.aTickLayout=null,this.bTickLayout=null,this.cTickLayout=null}t.exports=M;var S=M.prototype;S.init=function(t){this.container=t._ternarylayer,this.defs=t._defs,this.layoutId=t._uid,this.traceHash={},this.layers={}},S.plot=function(t,e){var r=this,n=e[r.id],i=e._size;r._hasClipOnAxisFalse=!1;for(var a=0;aE*b?i=(a=b)*E:a=(i=x)/E,o=y*i/x,l=m*a/b,r=e.l+e.w*v-i/2,n=e.t+e.h*(1-g)-a/2,p.x0=r,p.y0=n,p.w=i,p.h=a,p.sum=_,p.xaxis={type:\"linear\",range:[w+2*k-_,_-w-2*T],domain:[v-o/2,v+o/2],_id:\"x\"},f(p.xaxis,p.graphDiv._fullLayout),p.xaxis.setScale(),p.xaxis.isPtWithinRange=function(t){return t.a>=p.aaxis.range[0]&&t.a<=p.aaxis.range[1]&&t.b>=p.baxis.range[1]&&t.b<=p.baxis.range[0]&&t.c>=p.caxis.range[1]&&t.c<=p.caxis.range[0]},p.yaxis={type:\"linear\",range:[w,_-T-k],domain:[g-l/2,g+l/2],_id:\"y\"},f(p.yaxis,p.graphDiv._fullLayout),p.yaxis.setScale(),p.yaxis.isPtWithinRange=function(){return!0};var A=p.yaxis.domain[0],M=p.aaxis=h({},t.aaxis,{range:[w,_-T-k],side:\"left\",tickangle:(+t.aaxis.tickangle||0)-30,domain:[A,A+l*E],anchor:\"free\",position:0,_id:\"y\",_length:i});f(M,p.graphDiv._fullLayout),M.setScale();var S=p.baxis=h({},t.baxis,{range:[_-w-k,T],side:\"bottom\",domain:p.xaxis.domain,anchor:\"free\",position:0,_id:\"x\",_length:i});f(S,p.graphDiv._fullLayout),S.setScale();var L=p.caxis=h({},t.caxis,{range:[_-w-T,k],side:\"right\",tickangle:(+t.caxis.tickangle||0)+30,domain:[A,A+l*E],anchor:\"free\",position:0,_id:\"y\",_length:i});f(L,p.graphDiv._fullLayout),L.setScale();var C=\"M\"+r+\",\"+(n+a)+\"h\"+i+\"l-\"+i/2+\",-\"+a+\"Z\";p.clipDef.select(\"path\").attr(\"d\",C),p.layers.plotbg.select(\"path\").attr(\"d\",C);var P=\"M0,\"+a+\"h\"+i+\"l-\"+i/2+\",-\"+a+\"Z\";p.clipDefRelative.select(\"path\").attr(\"d\",P);var O=s(r,n);p.plotContainer.selectAll(\".scatterlayer,.maplayer\").attr(\"transform\",O),p.clipDefRelative.select(\"path\").attr(\"transform\",null);var I=s(r-S._offset,n+a);p.layers.baxis.attr(\"transform\",I),p.layers.bgrid.attr(\"transform\",I);var D=s(r+i/2,n)+\"rotate(30)\"+s(0,-M._offset);p.layers.aaxis.attr(\"transform\",D),p.layers.agrid.attr(\"transform\",D);var z=s(r+i/2,n)+\"rotate(-30)\"+s(0,-L._offset);p.layers.caxis.attr(\"transform\",z),p.layers.cgrid.attr(\"transform\",z),p.drawAxes(!0),p.layers.aline.select(\"path\").attr(\"d\",M.showline?\"M\"+r+\",\"+(n+a)+\"l\"+i/2+\",-\"+a:\"M0,0\").call(u.stroke,M.linecolor||\"#000\").style(\"stroke-width\",(M.linewidth||0)+\"px\"),p.layers.bline.select(\"path\").attr(\"d\",S.showline?\"M\"+r+\",\"+(n+a)+\"h\"+i:\"M0,0\").call(u.stroke,S.linecolor||\"#000\").style(\"stroke-width\",(S.linewidth||0)+\"px\"),p.layers.cline.select(\"path\").attr(\"d\",L.showline?\"M\"+(r+i/2)+\",\"+n+\"l\"+i/2+\",\"+a:\"M0,0\").call(u.stroke,L.linecolor||\"#000\").style(\"stroke-width\",(L.linewidth||0)+\"px\"),p.graphDiv._context.staticPlot||p.initInteractions(),c.setClipUrl(p.layers.frontplot,p._hasClipOnAxisFalse?null:p.clipId,p.graphDiv)},S.drawAxes=function(t){var e=this,r=e.graphDiv,n=e.id.substr(7)+\"title\",i=e.layers,a=e.aaxis,o=e.baxis,s=e.caxis;if(e.drawAx(a),e.drawAx(o),e.drawAx(s),t){var u=Math.max(a.showticklabels?a.tickfont.size/2:0,(s.showticklabels?.75*s.tickfont.size:0)+(\"outside\"===s.ticks?.87*s.ticklen:0)),c=(o.showticklabels?o.tickfont.size:0)+(\"outside\"===o.ticks?o.ticklen:0)+3;i[\"a-title\"]=b.draw(r,\"a\"+n,{propContainer:a,propName:e.id+\".aaxis.title\",placeholder:l(r,\"Click to enter Component A title\"),attributes:{x:e.x0+e.w/2,y:e.y0-a.title.font.size/3-u,\"text-anchor\":\"middle\"}}),i[\"b-title\"]=b.draw(r,\"b\"+n,{propContainer:o,propName:e.id+\".baxis.title\",placeholder:l(r,\"Click to enter Component B title\"),attributes:{x:e.x0-c,y:e.y0+e.h+.83*o.title.font.size+c,\"text-anchor\":\"middle\"}}),i[\"c-title\"]=b.draw(r,\"c\"+n,{propContainer:s,propName:e.id+\".caxis.title\",placeholder:l(r,\"Click to enter Component C title\"),attributes:{x:e.x0+e.w+c,y:e.y0+e.h+.83*s.title.font.size+c,\"text-anchor\":\"middle\"}})}},S.drawAx=function(t){var e,r=this,n=r.graphDiv,i=t._name,a=i.charAt(0),s=t._id,l=r.layers[i],u=a+\"tickLayout\",c=(e=t).ticks+String(e.ticklen)+String(e.showticklabels);r[u]!==c&&(l.selectAll(\".\"+s+\"tick\").remove(),r[u]=c),t.setScale();var f=d.calcTicks(t),h=d.clipEnds(t,f),p=d.makeTransTickFn(t),v=d.getTickSigns(t)[2],g=o.deg2rad(30),y=v*(t.linewidth||1)/2,m=v*t.ticklen,x=r.w,b=r.h,_=\"b\"===a?\"M0,\"+y+\"l\"+Math.sin(g)*m+\",\"+Math.cos(g)*m:\"M\"+y+\",0l\"+Math.cos(g)*m+\",\"+-Math.sin(g)*m,w={a:\"M0,0l\"+b+\",-\"+x/2,b:\"M0,0l-\"+x/2+\",-\"+b,c:\"M0,0l-\"+b+\",\"+x/2}[a];d.drawTicks(n,t,{vals:\"inside\"===t.ticks?h:f,layer:l,path:_,transFn:p,crisp:!1}),d.drawGrid(n,t,{vals:h,layer:r.layers[a+\"grid\"],path:w,transFn:p,crisp:!1}),d.drawLabels(n,t,{vals:f,layer:l,transFn:p,labelFns:d.makeLabelFns(t,0,30)})};var L=A.MINZOOM/2+.87,C=\"m-0.87,.5h\"+L+\"v3h-\"+(L+5.2)+\"l\"+(L/2+2.6)+\",-\"+(.87*L+4.5)+\"l2.6,1.5l-\"+L/2+\",\"+.87*L+\"Z\",P=\"m0.87,.5h-\"+L+\"v3h\"+(L+5.2)+\"l-\"+(L/2+2.6)+\",-\"+(.87*L+4.5)+\"l-2.6,1.5l\"+L/2+\",\"+.87*L+\"Z\",O=\"m0,1l\"+L/2+\",\"+.87*L+\"l2.6,-1.5l-\"+(L/2+2.6)+\",-\"+(.87*L+4.5)+\"l-\"+(L/2+2.6)+\",\"+(.87*L+4.5)+\"l2.6,1.5l\"+L/2+\",-\"+.87*L+\"Z\",I=!0;function D(t){n.select(t).selectAll(\".zoombox,.js-zoombox-backdrop,.js-zoombox-menu,.zoombox-corners\").remove()}S.clearOutline=function(){k(this.dragOptions),T(this.dragOptions.gd)},S.initInteractions=function(){var t,e,r,n,f,h,p,d,y,b,T,k,M=this,S=M.layers.plotbg.select(\"path\").node(),L=M.graphDiv,z=L._fullLayout._zoomlayer;function R(t){var e={};return e[M.id+\".aaxis.min\"]=t.a,e[M.id+\".baxis.min\"]=t.b,e[M.id+\".caxis.min\"]=t.c,e}function F(t,e){var r=L._fullLayout.clickmode;D(L),2===t&&(L.emit(\"plotly_doubleclick\",null),a.call(\"_guiRelayout\",L,R({a:0,b:0,c:0}))),r.indexOf(\"select\")>-1&&1===t&&w(e,L,[M.xaxis],[M.yaxis],M.id,M.dragOptions),r.indexOf(\"event\")>-1&&g.click(L,e,M.id)}function B(t,e){return 1-e/M.h}function N(t,e){return 1-(t+(M.h-e)/Math.sqrt(3))/M.w}function j(t,e){return(t-(M.h-e)/Math.sqrt(3))/M.w}function U(i,a){var o=r+i*t,s=n+a*e,l=Math.max(0,Math.min(1,B(0,n),B(0,s))),u=Math.max(0,Math.min(1,N(r,n),N(o,s))),c=Math.max(0,Math.min(1,j(r,n),j(o,s))),v=(l/2+c)*M.w,g=(1-l/2-u)*M.w,m=(v+g)/2,x=g-v,_=(1-l)*M.h,w=_-x/E;x.2?\"rgba(0,0,0,0.4)\":\"rgba(255,255,255,0.3)\").duration(200),k.transition().style(\"opacity\",1).duration(200),b=!0),L.emit(\"plotly_relayouting\",R(p))}function V(){D(L),p!==f&&(a.call(\"_guiRelayout\",L,R(p)),I&&L.data&&L._context.showTips&&(o.notifier(l(L,\"Double-click to zoom back out\"),\"long\"),I=!1))}function H(t,e){var r=t/M.xaxis._m,n=e/M.yaxis._m,i=[(p={a:f.a-n,b:f.b+(r+n)/2,c:f.c-(r-n)/2}).a,p.b,p.c].sort(o.sorterAsc),a=i.indexOf(p.a),l=i.indexOf(p.b),u=i.indexOf(p.c);i[0]<0&&(i[1]+i[0]/2<0?(i[2]+=i[0]+i[1],i[0]=i[1]=0):(i[2]+=i[0]/2,i[1]+=i[0]/2,i[0]=0),p={a:i[a],b:i[l],c:i[u]},e=(f.a-p.a)*M.yaxis._m,t=(f.c-p.c-f.b+p.b)*M.xaxis._m);var h=s(M.x0+t,M.y0+e);M.plotContainer.selectAll(\".scatterlayer,.maplayer\").attr(\"transform\",h);var d=s(-t,-e);M.clipDefRelative.select(\"path\").attr(\"transform\",d),M.aaxis.range=[p.a,M.sum-p.b-p.c],M.baxis.range=[M.sum-p.a-p.c,p.b],M.caxis.range=[M.sum-p.a-p.b,p.c],M.drawAxes(!1),M._hasClipOnAxisFalse&&M.plotContainer.select(\".scatterlayer\").selectAll(\".trace\").call(c.hideOutsideRangePoints,M),L.emit(\"plotly_relayouting\",R(p))}function 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n=r(50693),i=r(12663).axisHoverFormat,a=r(5386).f,o=r(54532),s=r(9012),l=r(1426).extendFlat;t.exports=l({x:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},y:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},z:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},i:{valType:\"data_array\",editType:\"calc\"},j:{valType:\"data_array\",editType:\"calc\"},k:{valType:\"data_array\",editType:\"calc\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"calc\"},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"calc\"},hovertemplate:a({editType:\"calc\"}),xhoverformat:i(\"x\"),yhoverformat:i(\"y\"),zhoverformat:i(\"z\"),delaunayaxis:{valType:\"enumerated\",values:[\"x\",\"y\",\"z\"],dflt:\"z\",editType:\"calc\"},alphahull:{valType:\"number\",dflt:-1,editType:\"calc\"},intensity:{valType:\"data_array\",editType:\"calc\"},intensitymode:{valType:\"enumerated\",values:[\"vertex\",\"cell\"],dflt:\"vertex\",editType:\"calc\"},color:{valType:\"color\",editType:\"calc\"},vertexcolor:{valType:\"data_array\",editType:\"calc\"},facecolor:{valType:\"data_array\",editType:\"calc\"},transforms:void 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n=r(9330).gl_mesh3d,i=r(9330).delaunay_triangulate,a=r(9330).alpha_shape,o=r(9330).convex_hull,s=r(81697).parseColorScale,l=r(78614),u=r(21081).extractOpts,c=r(90060);function f(t,e,r){this.scene=t,this.uid=r,this.mesh=e,this.name=\"\",this.color=\"#fff\",this.data=null,this.showContour=!1}var h=f.prototype;function p(t){for(var e=[],r=t.length,n=0;n=e-.5)return!1;return!0}h.handlePick=function(t){if(t.object===this.mesh){var e=t.index=t.data.index;t.data._cellCenter?t.traceCoordinate=t.data.dataCoordinate:t.traceCoordinate=[this.data.x[e],this.data.y[e],this.data.z[e]];var r=this.data.hovertext||this.data.text;return Array.isArray(r)&&void 0!==r[e]?t.textLabel=r[e]:r&&(t.textLabel=r),!0}},h.update=function(t){var e=this.scene,r=e.fullSceneLayout;this.data=t;var 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n=f(e),i=f(r);return\"backward\"===t.sortpaths&&(n.reverse(),i.reverse()),n.push(e.valueInds[0]),i.push(r.valueInds[0]),t.bundlecolors&&(n.unshift(e.rawColor),i.unshift(r.rawColor)),ni?1:0}));for(var h=new Array(u.length),p=e[0].model.count,d=e[0].categories.map((function(t){return t.height})).reduce((function(t,e){return t+e})),v=0;v0?d*(y.count/p):0;for(var m,x=new Array(n.length),b=0;b1?(t.width-80-16)/(n-1):0)*i;var a,o,s,l,u,c=[],f=t.model.maxCats,h=e.categories.length,p=e.count,d=t.height-8*(f-1),v=8*(f-h)/2,g=e.categories.map((function(t){return{displayInd:t.displayInd,categoryInd:t.categoryInd}}));for(g.sort((function(t,e){return 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n=r(9012),i=r(27670).Y,a=r(41940),o=r(22399),s=r(5386).f,l=r(5386).s,u=r(1426).extendFlat,c=a({editType:\"plot\",arrayOk:!0,colorEditType:\"plot\"});t.exports={labels:{valType:\"data_array\",editType:\"calc\"},label0:{valType:\"number\",dflt:0,editType:\"calc\"},dlabel:{valType:\"number\",dflt:1,editType:\"calc\"},values:{valType:\"data_array\",editType:\"calc\"},marker:{colors:{valType:\"data_array\",editType:\"calc\"},line:{color:{valType:\"color\",dflt:o.defaultLine,arrayOk:!0,editType:\"style\"},width:{valType:\"number\",min:0,dflt:0,arrayOk:!0,editType:\"style\"},editType:\"calc\"},editType:\"calc\"},text:{valType:\"data_array\",editType:\"plot\"},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"style\"},scalegroup:{valType:\"string\",dflt:\"\",editType:\"calc\"},textinfo:{valType:\"flaglist\",flags:[\"label\",\"text\",\"value\",\"percent\"],extras:[\"none\"],editType:\"calc\"},hoverinfo:u({},n.hoverinfo,{flags:[\"label\",\"text\",\"value\",\"percent\",\"name\"]}),hovertemplate:s({},{keys:[\"label\",\"color\",\"value\",\"percent\",\"text\"]}),texttemplate:l({editType:\"plot\"},{keys:[\"label\",\"color\",\"value\",\"percent\",\"text\"]}),textposition:{valType:\"enumerated\",values:[\"inside\",\"outside\",\"auto\",\"none\"],dflt:\"auto\",arrayOk:!0,editType:\"plot\"},textfont:u({},c,{}),insidetextorientation:{valType:\"enumerated\",values:[\"horizontal\",\"radial\",\"tangential\",\"auto\"],dflt:\"auto\",editType:\"plot\"},insidetextfont:u({},c,{}),outsidetextfont:u({},c,{}),automargin:{valType:\"boolean\",dflt:!1,editType:\"plot\"},title:{text:{valType:\"string\",dflt:\"\",editType:\"plot\"},font:u({},c,{}),position:{valType:\"enumerated\",values:[\"top 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n=r(74875);e.name=\"pie\",e.plot=function(t,r,i,a){n.plotBasePlot(e.name,t,r,i,a)},e.clean=function(t,r,i,a){n.cleanBasePlot(e.name,t,r,i,a)}},32354:function(t,e,r){\"use strict\";var n=r(92770),i=r(84267),a=r(7901),o={};function s(t){return function(e,r){return!!e&&!!(e=i(e)).isValid()&&(e=a.addOpacity(e,e.getAlpha()),t[r]||(t[r]=e),e)}}function l(t,e){var r,n=JSON.stringify(t),a=e[n];if(!a){for(a=t.slice(),r=0;r=0})),(\"funnelarea\"===e.type?y:e.sort)&&a.sort((function(t,e){return e.v-t.v})),a[0]&&(a[0].vTotal=g),a},crossTraceCalc:function(t,e){var r=(e||{}).type;r||(r=\"pie\");var n=t._fullLayout,i=t.calcdata,a=n[r+\"colorway\"],s=n[\"_\"+r+\"colormap\"];n[\"extend\"+r+\"colors\"]&&(a=l(a,o));for(var u=0,c=0;c0){s=!0;break}}s||(o=0)}return{hasLabels:r,hasValues:a,len:o}}t.exports={handleLabelsAndValues:l,supplyDefaults:function(t,e,r,n){function u(r,n){return i.coerce(t,e,a,r,n)}var c=l(u(\"labels\"),u(\"values\")),f=c.len;if(e._hasLabels=c.hasLabels,e._hasValues=c.hasValues,!e._hasLabels&&e._hasValues&&(u(\"label0\"),u(\"dlabel\")),f){e._length=f,u(\"marker.line.width\")&&u(\"marker.line.color\"),u(\"marker.colors\"),u(\"scalegroup\");var h,p=u(\"text\"),d=u(\"texttemplate\");if(d||(h=u(\"textinfo\",Array.isArray(p)?\"text+percent\":\"percent\")),u(\"hovertext\"),u(\"hovertemplate\"),d||h&&\"none\"!==h){var v=u(\"textposition\");s(t,e,n,u,v,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1}),(Array.isArray(v)||\"auto\"===v||\"outside\"===v)&&u(\"automargin\"),(\"inside\"===v||\"auto\"===v||Array.isArray(v))&&u(\"insidetextorientation\")}o(e,n,u);var g=u(\"hole\");if(u(\"title.text\")){var y=u(\"title.position\",g?\"middle center\":\"top center\");g||\"middle center\"!==y||(e.title.position=\"top center\"),i.coerceFont(u,\"title.font\",n.font)}u(\"sort\"),u(\"direction\"),u(\"rotation\"),u(\"pull\")}else e.visible=!1}}},20007:function(t,e,r){\"use strict\";var n=r(23469).appendArrayMultiPointValues;t.exports=function(t,e){var r={curveNumber:e.index,pointNumbers:t.pts,data:e._input,fullData:e,label:t.label,color:t.color,value:t.v,percent:t.percent,text:t.text,bbox:t.bbox,v:t.v};return 1===t.pts.length&&(r.pointNumber=r.i=t.pts[0]),n(r,e,t.pts),\"funnelarea\"===e.type&&(delete r.v,delete r.i),r}},53581:function(t,e,r){\"use strict\";var n=r(71828);function i(t){return-1!==t.indexOf(\"e\")?t.replace(/[.]?0+e/,\"e\"):-1!==t.indexOf(\".\")?t.replace(/[.]?0+$/,\"\"):t}e.formatPiePercent=function(t,e){var r=i((100*t).toPrecision(3));return n.numSeparate(r,e)+\"%\"},e.formatPieValue=function(t,e){var r=i(t.toPrecision(10));return n.numSeparate(r,e)},e.getFirstFilled=function(t,e){if(Array.isArray(t))for(var r=0;r\"),name:f.hovertemplate||-1!==h.indexOf(\"name\")?f.name:void 0,idealAlign:t.pxmid[0]<0?\"left\":\"right\",color:g.castOption(_.bgcolor,t.pts)||t.color,borderColor:g.castOption(_.bordercolor,t.pts),fontFamily:g.castOption(w.family,t.pts),fontSize:g.castOption(w.size,t.pts),fontColor:g.castOption(w.color,t.pts),nameLength:g.castOption(_.namelength,t.pts),textAlign:g.castOption(_.align,t.pts),hovertemplate:g.castOption(f.hovertemplate,t.pts),hovertemplateLabels:t,eventData:[y(t,f)]},{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:e,inOut_bbox:T}),t.bbox=T[0],u._hasHoverLabel=!0}u._hasHoverEvent=!0,e.emit(\"plotly_hover\",{points:[y(t,f)],event:n.event})}})),t.on(\"mouseout\",(function(t){var r=e._fullLayout,i=e._fullData[u.index],o=n.select(this).datum();u._hasHoverEvent&&(t.originalEvent=n.event,e.emit(\"plotly_unhover\",{points:[y(o,i)],event:n.event}),u._hasHoverEvent=!1),u._hasHoverLabel&&(a.loneUnhover(r._hoverlayer.node()),u._hasHoverLabel=!1)})),t.on(\"click\",(function(t){var r=e._fullLayout,i=e._fullData[u.index];e._dragging||!1===r.hovermode||(e._hoverdata=[y(t,i)],a.click(e,n.event))}))}function b(t,e,r){var n=g.castOption(t.insidetextfont.color,e.pts);!n&&t._input.textfont&&(n=g.castOption(t._input.textfont.color,e.pts));var i=g.castOption(t.insidetextfont.family,e.pts)||g.castOption(t.textfont.family,e.pts)||r.family,a=g.castOption(t.insidetextfont.size,e.pts)||g.castOption(t.textfont.size,e.pts)||r.size;return{color:n||o.contrast(e.color),family:i,size:a}}function _(t,e){for(var r,n,i=0;ie&&e>n||r=-4;g-=2)y(Math.PI*g,\"tan\");for(g=4;g>=-4;g-=2)y(Math.PI*(g+1),\"tan\")}if(f||p){for(g=4;g>=-4;g-=2)y(Math.PI*(g+1.5),\"rad\");for(g=4;g>=-4;g-=2)y(Math.PI*(g+.5),\"rad\")}}if(s||d||f){var m=Math.sqrt(t.width*t.width+t.height*t.height);if((a={scale:i*n*2/m,rCenter:1-i,rotate:0}).textPosAngle=(e.startangle+e.stopangle)/2,a.scale>=1)return a;v.push(a)}(d||p)&&((a=T(t,n,o,l,u)).textPosAngle=(e.startangle+e.stopangle)/2,v.push(a)),(d||h)&&((a=k(t,n,o,l,u)).textPosAngle=(e.startangle+e.stopangle)/2,v.push(a));for(var x=0,b=0,_=0;_=1)break}return v[x]}function T(t,e,r,n,i){e=Math.max(0,e-2*v);var a=t.width/t.height,o=S(a,n,e,r);return{scale:2*o/t.height,rCenter:A(a,o/e),rotate:M(i)}}function k(t,e,r,n,i){e=Math.max(0,e-2*v);var a=t.height/t.width,o=S(a,n,e,r);return{scale:2*o/t.width,rCenter:A(a,o/e),rotate:M(i+Math.PI/2)}}function A(t,e){return Math.cos(e)-t*e}function M(t){return(180/Math.PI*t+720)%180-90}function S(t,e,r,n){var i=t+1/(2*Math.tan(e));return r*Math.min(1/(Math.sqrt(i*i+.5)+i),n/(Math.sqrt(t*t+n/2)+t))}function E(t,e){return t.v!==e.vTotal||e.trace.hole?Math.min(1/(1+1/Math.sin(t.halfangle)),t.ring/2):1}function L(t,e){var r=e.pxmid[0],n=e.pxmid[1],i=t.width/2,a=t.height/2;return r<0&&(i*=-1),n<0&&(a*=-1),{scale:1,rCenter:1,rotate:0,x:i+Math.abs(a)*(i>0?1:-1)/2,y:a/(1+r*r/(n*n)),outside:!0}}function C(t,e){var r,n,i,a=t.trace,o={x:t.cx,y:t.cy},s={tx:0,ty:0};s.ty+=a.title.font.size,i=O(a),-1!==a.title.position.indexOf(\"top\")?(o.y-=(1+i)*t.r,s.ty-=t.titleBox.height):-1!==a.title.position.indexOf(\"bottom\")&&(o.y+=(1+i)*t.r);var l,u=t.r/(void 0===(l=t.trace.aspectratio)?1:l),c=e.w*(a.domain.x[1]-a.domain.x[0])/2;return-1!==a.title.position.indexOf(\"left\")?(c+=u,o.x-=(1+i)*u,s.tx+=t.titleBox.width/2):-1!==a.title.position.indexOf(\"center\")?c*=2:-1!==a.title.position.indexOf(\"right\")&&(c+=u,o.x+=(1+i)*u,s.tx-=t.titleBox.width/2),r=c/t.titleBox.width,n=P(t,e)/t.titleBox.height,{x:o.x,y:o.y,scale:Math.min(r,n),tx:s.tx,ty:s.ty}}function P(t,e){var r=t.trace,n=e.h*(r.domain.y[1]-r.domain.y[0]);return Math.min(t.titleBox.height,n/2)}function O(t){var e,r=t.pull;if(!r)return 0;if(Array.isArray(r))for(r=0,e=0;er&&(r=t.pull[e]);return r}function I(t,e){for(var r=[],n=0;n1?c=(u=r.r)/i.aspectratio:u=(c=r.r)*i.aspectratio,l=(u*=(1+i.baseratio)/2)*c}o=Math.min(o,l/r.vTotal)}for(n=0;n\")}if(a){var x=l.castOption(i,e.i,\"texttemplate\");if(x){var b=function(t){return{label:t.label,value:t.v,valueLabel:g.formatPieValue(t.v,n.separators),percent:t.v/r.vTotal,percentLabel:g.formatPiePercent(t.v/r.vTotal,n.separators),color:t.color,text:t.text,customdata:l.castOption(i,t.i,\"customdata\")}}(e),_=g.getFirstFilled(i.text,e.pts);(m(_)||\"\"===_)&&(b.text=_),e.text=l.texttemplateString(x,b,t._fullLayout._d3locale,b,i._meta||{})}else e.text=\"\"}}function R(t,e){var r=t.rotate*Math.PI/180,n=Math.cos(r),i=Math.sin(r),a=(e.left+e.right)/2,o=(e.top+e.bottom)/2;t.textX=a*n-o*i,t.textY=a*i+o*n,t.noCenter=!0}t.exports={plot:function(t,e){var r=t._context.staticPlot,a=t._fullLayout,h=a._size;d(\"pie\",a),_(e,t),I(e,h);var v=l.makeTraceGroups(a._pielayer,e,\"trace\").each((function(e){var d=n.select(this),v=e[0],y=v.trace;!function(t){var e,r,n,i=t[0],a=i.r,o=i.trace,s=g.getRotationAngle(o.rotation),l=2*Math.PI/i.vTotal,u=\"px0\",c=\"px1\";if(\"counterclockwise\"===o.direction){for(e=0;ei.vTotal/2?1:0,r.halfangle=Math.PI*Math.min(r.v/i.vTotal,.5),r.ring=1-o.hole,r.rInscribed=E(r,i))}(e),d.attr(\"stroke-linejoin\",\"round\"),d.each((function(){var m=n.select(this).selectAll(\"g.slice\").data(e);m.enter().append(\"g\").classed(\"slice\",!0),m.exit().remove();var _=[[[],[]],[[],[]]],T=!1;m.each((function(i,o){if(i.hidden)n.select(this).selectAll(\"path,g\").remove();else{i.pointNumber=i.i,i.curveNumber=y.index,_[i.pxmid[1]<0?0:1][i.pxmid[0]<0?0:1].push(i);var u=v.cx,c=v.cy,h=n.select(this),d=h.selectAll(\"path.surface\").data([i]);if(d.enter().append(\"path\").classed(\"surface\",!0).style({\"pointer-events\":r?\"none\":\"all\"}),h.call(x,t,e),y.pull){var m=+g.castOption(y.pull,i.pts)||0;m>0&&(u+=m*i.pxmid[0],c+=m*i.pxmid[1])}i.cxFinal=u,i.cyFinal=c;var k=y.hole;if(i.v===v.vTotal){var A=\"M\"+(u+i.px0[0])+\",\"+(c+i.px0[1])+P(i.px0,i.pxmid,!0,1)+P(i.pxmid,i.px0,!0,1)+\"Z\";k?d.attr(\"d\",\"M\"+(u+k*i.px0[0])+\",\"+(c+k*i.px0[1])+P(i.px0,i.pxmid,!1,k)+P(i.pxmid,i.px0,!1,k)+\"Z\"+A):d.attr(\"d\",A)}else{var M=P(i.px0,i.px1,!0,1);if(k){var S=1-k;d.attr(\"d\",\"M\"+(u+k*i.px1[0])+\",\"+(c+k*i.px1[1])+P(i.px1,i.px0,!1,k)+\"l\"+S*i.px0[0]+\",\"+S*i.px0[1]+M+\"Z\")}else d.attr(\"d\",\"M\"+u+\",\"+c+\"l\"+i.px0[0]+\",\"+i.px0[1]+M+\"Z\")}z(t,i,v);var E=g.castOption(y.textposition,i.pts),C=h.selectAll(\"g.slicetext\").data(i.text&&\"none\"!==E?[0]:[]);C.enter().append(\"g\").classed(\"slicetext\",!0),C.exit().remove(),C.each((function(){var r=l.ensureSingle(n.select(this),\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),h=l.ensureUniformFontSize(t,\"outside\"===E?function(t,e,r){return{color:g.castOption(t.outsidetextfont.color,e.pts)||g.castOption(t.textfont.color,e.pts)||r.color,family:g.castOption(t.outsidetextfont.family,e.pts)||g.castOption(t.textfont.family,e.pts)||r.family,size:g.castOption(t.outsidetextfont.size,e.pts)||g.castOption(t.textfont.size,e.pts)||r.size}}(y,i,a.font):b(y,i,a.font));r.text(i.text).attr({class:\"slicetext\",transform:\"\",\"text-anchor\":\"middle\"}).call(s.font,h).call(f.convertToTspans,t);var d,m=s.bBox(r.node());if(\"outside\"===E)d=L(m,i);else if(d=w(m,i,v),\"auto\"===E&&d.scale<1){var x=l.ensureUniformFontSize(t,y.outsidetextfont);r.call(s.font,x),d=L(m=s.bBox(r.node()),i)}var _=d.textPosAngle,k=void 0===_?i.pxmid:D(v.r,_);if(d.targetX=u+k[0]*d.rCenter+(d.x||0),d.targetY=c+k[1]*d.rCenter+(d.y||0),R(d,m),d.outside){var A=d.targetY;i.yLabelMin=A-m.height/2,i.yLabelMid=A,i.yLabelMax=A+m.height/2,i.labelExtraX=0,i.labelExtraY=0,T=!0}d.fontSize=h.size,p(y.type,d,a),e[o].transform=d,l.setTransormAndDisplay(r,d)}))}function P(t,e,r,n){var a=n*(e[0]-t[0]),o=n*(e[1]-t[1]);return\"a\"+n*v.r+\",\"+n*v.r+\" 0 \"+i.largeArc+(r?\" 1 \":\" 0 \")+a+\",\"+o}}));var k=n.select(this).selectAll(\"g.titletext\").data(y.title.text?[0]:[]);if(k.enter().append(\"g\").classed(\"titletext\",!0),k.exit().remove(),k.each((function(){var e,r=l.ensureSingle(n.select(this),\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),i=y.title.text;y._meta&&(i=l.templateString(i,y._meta)),r.text(i).attr({class:\"titletext\",transform:\"\",\"text-anchor\":\"middle\"}).call(s.font,y.title.font).call(f.convertToTspans,t),e=\"middle center\"===y.title.position?function(t){var e=Math.sqrt(t.titleBox.width*t.titleBox.width+t.titleBox.height*t.titleBox.height);return{x:t.cx,y:t.cy,scale:t.trace.hole*t.r*2/e,tx:0,ty:-t.titleBox.height/2+t.trace.title.font.size}}(v):C(v,h),r.attr(\"transform\",c(e.x,e.y)+u(Math.min(1,e.scale))+c(e.tx,e.ty))})),T&&function(t,e){var r,n,i,a,o,s,l,u,c,f,h,p,d;function v(t,e){return t.pxmid[1]-e.pxmid[1]}function y(t,e){return e.pxmid[1]-t.pxmid[1]}function m(t,r){r||(r={});var i,u,c,h,p=r.labelExtraY+(n?r.yLabelMax:r.yLabelMin),d=n?t.yLabelMin:t.yLabelMax,v=n?t.yLabelMax:t.yLabelMin,y=t.cyFinal+o(t.px0[1],t.px1[1]),m=p-d;if(m*l>0&&(t.labelExtraY=m),Array.isArray(e.pull))for(u=0;u=(g.castOption(e.pull,c.pts)||0)||((t.pxmid[1]-c.pxmid[1])*l>0?(m=c.cyFinal+o(c.px0[1],c.px1[1])-d-t.labelExtraY)*l>0&&(t.labelExtraY+=m):(v+t.labelExtraY-y)*l>0&&(i=3*s*Math.abs(u-f.indexOf(t)),(h=c.cxFinal+a(c.px0[0],c.px1[0])+i-(t.cxFinal+t.pxmid[0])-t.labelExtraX)*s>0&&(t.labelExtraX+=h)))}for(n=0;n<2;n++)for(i=n?v:y,o=n?Math.max:Math.min,l=n?1:-1,r=0;r<2;r++){for(a=r?Math.max:Math.min,s=r?1:-1,(u=t[n][r]).sort(i),c=t[1-n][r],f=c.concat(u),p=[],h=0;hMath.abs(f)?s+=\"l\"+f*t.pxmid[0]/t.pxmid[1]+\",\"+f+\"H\"+(a+t.labelExtraX+u):s+=\"l\"+t.labelExtraX+\",\"+c+\"v\"+(f-c)+\"h\"+u}else 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n=r(39898),i=r(63463),a=r(72597).resizeText;t.exports=function(t){var e=t._fullLayout._pielayer.selectAll(\".trace\");a(t,e,\"pie\"),e.each((function(t){var e=t[0].trace,r=n.select(this);r.style({opacity:e.opacity}),r.selectAll(\"path.surface\").each((function(t){n.select(this).call(i,t,e)}))}))}},63463:function(t,e,r){\"use strict\";var n=r(7901),i=r(53581).castOption;t.exports=function(t,e,r){var a=r.marker.line,o=i(a.color,e.pts)||n.defaultLine,s=i(a.width,e.pts)||0;t.style(\"stroke-width\",s).call(n.fill,e.color).call(n.stroke,o)}},10959:function(t,e,r){\"use strict\";var 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p=s.ensureSingle(u,\"g\",\"slicetext\"),w=s.ensureSingle(p,\"text\",\"\",(function(t){t.attr(\"data-notex\",1)})),T=s.ensureUniformFontSize(t,b.determineTextFont(A,i,y.font));w.text(e.formatSliceLabel(i,S,A,r,y)).classed(\"slicetext\",!0).attr(\"text-anchor\",\"middle\").call(o.font,T).call(l.convertToTspans,t);var M=o.bBox(w.node());i.transform=v(M,i,k),i.transform.targetX=Y(i),i.transform.targetY=W(i);var E=function(t,e){var r=t.transform;return d(r,e),r.fontSize=T.size,c(A.type,r,y),s.getTextTransform(r)};_?w.transition().attrTween(\"transform\",(function(t){var e=function(t){var e,r=F[b.getPtId(t)],n=t.transform;if(r)e=r;else if(e={rpx1:t.rpx1,transform:{textPosAngle:n.textPosAngle,scale:0,rotate:n.rotate,rCenter:n.rCenter,x:n.x,y:n.y}},R)if(t.parent)if(X){var i=t.x1>X?2*Math.PI:0;e.x0=e.x1=i}else s.extendFlat(e,$(t));else e.x0=e.x1=V;else e.x0=e.x1=V;var o=a(e.transform.textPosAngle,t.transform.textPosAngle),l=a(e.rpx1,t.rpx1),u=a(e.x0,t.x0),f=a(e.x1,t.x1),h=a(e.transform.scale,n.scale),p=a(e.transform.rotate,n.rotate),d=0===n.rCenter?3:0===e.transform.rCenter?1/3:1,v=a(e.transform.rCenter,n.rCenter);return function(t){var e=l(t),r=u(t),i=f(t),a=function(t){return v(Math.pow(t,d))}(t),s={pxmid:G(e,(r+i)/2),rpx1:e,transform:{textPosAngle:o(t),rCenter:a,x:n.x,y:n.y}};return c(A.type,n,y),{transform:{targetX:Y(s),targetY:W(s),scale:h(t),rotate:p(t),rCenter:a}}}}(t);return function(t){return E(e(t),M)}})):w.attr(\"transform\",E(i,M))}))}function w(t){return e=t.rpx1,r=t.transform.textPosAngle,[e*Math.sin(r),-e*Math.cos(r)];var e,r}e.plot=function(t,e,r,i){var a,o,s=t._fullLayout,l=s._sunburstlayer,u=!r,c=!s.uniformtext.mode&&b.hasTransition(r);f(\"sunburst\",s),(a=l.selectAll(\"g.trace.sunburst\").data(e,(function(t){return 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f=t.exports=u(l({z:{valType:\"data_array\"},x:{valType:\"data_array\"},y:{valType:\"data_array\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertemplate:o(),xhoverformat:a(\"x\"),yhoverformat:a(\"y\"),zhoverformat:a(\"z\"),connectgaps:{valType:\"boolean\",dflt:!1,editType:\"calc\"},surfacecolor:{valType:\"data_array\"}},i(\"\",{colorAttr:\"z or 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n=r(78803);t.exports=function(t,e){e.surfacecolor?n(t,e,{vals:e.surfacecolor,containerStr:\"\",cLetter:\"c\"}):n(t,e,{vals:e.z,containerStr:\"\",cLetter:\"c\"})}},43768:function(t,e,r){\"use strict\";var n=r(9330).gl_surface3d,i=r(9330).ndarray,a=r(9330).ndarray_linear_interpolate.d2,o=r(824),s=r(43907),l=r(71828).isArrayOrTypedArray,u=r(81697).parseColorScale,c=r(78614),f=r(21081).extractOpts;function h(t,e,r){this.scene=t,this.uid=r,this.surface=e,this.data=null,this.showContour=[!1,!1,!1],this.contourStart=[null,null,null],this.contourEnd=[null,null,null],this.contourSize=[0,0,0],this.minValues=[1/0,1/0,1/0],this.maxValues=[-1/0,-1/0,-1/0],this.dataScaleX=1,this.dataScaleY=1,this.refineData=!0,this.objectOffset=[0,0,0]}var p=h.prototype;p.getXat=function(t,e,r,n){var i=l(this.data.x)?l(this.data.x[0])?this.data.x[e][t]:this.data.x[t]:t;return void 0===r?i:n.d2l(i,0,r)},p.getYat=function(t,e,r,n){var i=l(this.data.y)?l(this.data.y[0])?this.data.y[e][t]:this.data.y[e]:e;return void 0===r?i:n.d2l(i,0,r)},p.getZat=function(t,e,r,n){var i=this.data.z[e][t];return null===i&&this.data.connectgaps&&this.data._interpolatedZ&&(i=this.data._interpolatedZ[e][t]),void 0===r?i:n.d2l(i,0,r)},p.handlePick=function(t){if(t.object===this.surface){var e=(t.data.index[0]-1)/this.dataScaleX-1,r=(t.data.index[1]-1)/this.dataScaleY-1,n=Math.max(Math.min(Math.round(e),this.data.z[0].length-1),0),i=Math.max(Math.min(Math.round(r),this.data._ylength-1),0);t.index=[n,i],t.traceCoordinate=[this.getXat(n,i),this.getYat(n,i),this.getZat(n,i)],t.dataCoordinate=[this.getXat(n,i,this.data.xcalendar,this.scene.fullSceneLayout.xaxis),this.getYat(n,i,this.data.ycalendar,this.scene.fullSceneLayout.yaxis),this.getZat(n,i,this.data.zcalendar,this.scene.fullSceneLayout.zaxis)];for(var a=0;a<3;a++){null!=t.dataCoordinate[a]&&(t.dataCoordinate[a]*=this.scene.dataScale[a])}var o=this.data.hovertext||this.data.text;return Array.isArray(o)&&o[i]&&void 0!==o[i][n]?t.textLabel=o[i][n]:t.textLabel=o||\"\",t.data.dataCoordinate=t.dataCoordinate.slice(),this.surface.highlight(t.data),this.scene.glplot.spikes.position=t.dataCoordinate,!0}};var 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v(t,e){if(t0){r=d[n];break}return r}function m(t,e){if(!(t<1||e<1)){for(var r=g(t),n=g(e),i=1,a=0;a_;)r--,r/=y(r),++r1?n:1},p.refineCoords=function(t){for(var e=this.dataScaleX,r=this.dataScaleY,n=t[0].shape[0],a=t[0].shape[1],o=0|Math.floor(t[0].shape[0]*e+1),s=0|Math.floor(t[0].shape[1]*r+1),l=1+n+1,u=1+a+1,c=i(new Float32Array(l*u),[l,u]),f=[1/e,0,0,0,1/r,0,0,0,1],h=0;h0&&null!==this.contourStart[t]&&null!==this.contourEnd[t]&&this.contourEnd[t]>this.contourStart[t]))for(i[t]=!0,e=this.contourStart[t];ea&&(this.minValues[e]=a),this.maxValues[e]\",maxDimensionCount:60,overdrag:45,releaseTransitionDuration:120,releaseTransitionEase:\"cubic-out\",scrollbarCaptureWidth:18,scrollbarHideDelay:1e3,scrollbarHideDuration:1e3,scrollbarOffset:5,scrollbarWidth:8,transitionDuration:100,transitionEase:\"cubic-out\",uplift:5,wrapSpacer:\" \",wrapSplitCharacter:\" \",cn:{table:\"table\",tableControlView:\"table-control-view\",scrollBackground:\"scroll-background\",yColumn:\"y-column\",columnBlock:\"column-block\",scrollAreaClip:\"scroll-area-clip\",scrollAreaClipRect:\"scroll-area-clip-rect\",columnBoundary:\"column-boundary\",columnBoundaryClippath:\"column-boundary-clippath\",columnBoundaryRect:\"column-boundary-rect\",columnCells:\"column-cells\",columnCell:\"column-cell\",cellRect:\"cell-rect\",cellText:\"cell-text\",cellTextHolder:\"cell-text-holder\",scrollbarKit:\"scrollbar-kit\",scrollbar:\"scrollbar\",scrollbarSlider:\"scrollbar-slider\",scrollbarGlyph:\"scrollbar-glyph\",scrollbarCaptureZone:\"scrollbar-capture-zone\"}}},51018:function(t,e,r){\"use strict\";var n=r(49850),i=r(1426).extendFlat,a=r(92770);function o(t){if(Array.isArray(t)){for(var e=0,r=0;r=e||u===t.length-1)&&(n[i]=o,o.key=l++,o.firstRowIndex=s,o.lastRowIndex=u,o={firstRowIndex:null,lastRowIndex:null,rows:[]},i+=a,s=u+1,a=0);return n}t.exports=function(t,e){var r=l(e.cells.values),p=function(t){return t.slice(e.header.values.length,t.length)},d=l(e.header.values);d.length&&!d[0].length&&(d[0]=[\"\"],d=l(d));var v=d.concat(p(r).map((function(){return u((d[0]||[\"\"]).length)}))),g=e.domain,y=Math.floor(t._fullLayout._size.w*(g.x[1]-g.x[0])),m=Math.floor(t._fullLayout._size.h*(g.y[1]-g.y[0])),x=e.header.values.length?v[0].map((function(){return e.header.height})):[n.emptyHeaderHeight],b=r.length?r[0].map((function(){return e.cells.height})):[],_=x.reduce(s,0),w=h(b,m-_+n.uplift),T=f(h(x,_),[]),k=f(w,T),A={},M=e._fullInput.columnorder.concat(p(r.map((function(t,e){return e})))),S=v.map((function(t,r){var n=Array.isArray(e.columnwidth)?e.columnwidth[Math.min(r,e.columnwidth.length-1)]:e.columnwidth;return a(n)?Number(n):1})),E=S.reduce(s,0);S=S.map((function(t){return t/E*y}));var L=Math.max(o(e.header.line.width),o(e.cells.line.width)),C={key:e.uid+t._context.staticPlot,translateX:g.x[0]*t._fullLayout._size.w,translateY:t._fullLayout._size.h*(1-g.y[1]),size:t._fullLayout._size,width:y,maxLineWidth:L,height:m,columnOrder:M,groupHeight:m,rowBlocks:k,headerRowBlocks:T,scrollY:0,cells:i({},e.cells,{values:r}),headerCells:i({},e.header,{values:v}),gdColumns:v.map((function(t){return t[0]})),gdColumnsOriginalOrder:v.map((function(t){return t[0]})),prevPages:[0,0],scrollbarState:{scrollbarScrollInProgress:!1},columns:v.map((function(t,e){var r=A[t];return A[t]=(r||0)+1,{key:t+\"__\"+A[t],label:t,specIndex:e,xIndex:M[e],xScale:c,x:void 0,calcdata:void 0,columnWidth:S[e]}}))};return C.columns.forEach((function(t){t.calcdata=C,t.x=c(t)})),C}},56269:function(t,e,r){\"use strict\";var n=r(1426).extendFlat;e.splitToPanels=function(t){var 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a=0,o=0;o>26,this.words[o]=67108863&e;for(;0!==a&&o>26,this.words[o]=67108863&e;if(0===a&&o>>13,p=0|o[1],d=8191&p,v=p>>>13,g=0|o[2],y=8191&g,m=g>>>13,x=0|o[3],b=8191&x,_=x>>>13,w=0|o[4],T=8191&w,k=w>>>13,A=0|o[5],M=8191&A,S=A>>>13,E=0|o[6],L=8191&E,C=E>>>13,P=0|o[7],O=8191&P,I=P>>>13,D=0|o[8],z=8191&D,R=D>>>13,F=0|o[9],B=8191&F,N=F>>>13,j=0|s[0],U=8191&j,V=j>>>13,H=0|s[1],q=8191&H,G=H>>>13,Z=0|s[2],Y=8191&Z,W=Z>>>13,X=0|s[3],J=8191&X,K=X>>>13,$=0|s[4],Q=8191&$,tt=$>>>13,et=0|s[5],rt=8191&et,nt=et>>>13,it=0|s[6],at=8191&it,ot=it>>>13,st=0|s[7],lt=8191&st,ut=st>>>13,ct=0|s[8],ft=8191&ct,ht=ct>>>13,pt=0|s[9],dt=8191&pt,vt=pt>>>13;r.negative=t.negative^e.negative,r.length=19;var gt=(u+(n=Math.imul(f,U))|0)+((8191&(i=(i=Math.imul(f,V))+Math.imul(h,U)|0))<<13)|0;u=((a=Math.imul(h,V))+(i>>>13)|0)+(gt>>>26)|0,gt&=67108863,n=Math.imul(d,U),i=(i=Math.imul(d,V))+Math.imul(v,U)|0,a=Math.imul(v,V);var yt=(u+(n=n+Math.imul(f,q)|0)|0)+((8191&(i=(i=i+Math.imul(f,G)|0)+Math.imul(h,q)|0))<<13)|0;u=((a=a+Math.imul(h,G)|0)+(i>>>13)|0)+(yt>>>26)|0,yt&=67108863,n=Math.imul(y,U),i=(i=Math.imul(y,V))+Math.imul(m,U)|0,a=Math.imul(m,V),n=n+Math.imul(d,q)|0,i=(i=i+Math.imul(d,G)|0)+Math.imul(v,q)|0,a=a+Math.imul(v,G)|0;var mt=(u+(n=n+Math.imul(f,Y)|0)|0)+((8191&(i=(i=i+Math.imul(f,W)|0)+Math.imul(h,Y)|0))<<13)|0;u=((a=a+Math.imul(h,W)|0)+(i>>>13)|0)+(mt>>>26)|0,mt&=67108863,n=Math.imul(b,U),i=(i=Math.imul(b,V))+Math.imul(_,U)|0,a=Math.imul(_,V),n=n+Math.imul(y,q)|0,i=(i=i+Math.imul(y,G)|0)+Math.imul(m,q)|0,a=a+Math.imul(m,G)|0,n=n+Math.imul(d,Y)|0,i=(i=i+Math.imul(d,W)|0)+Math.imul(v,Y)|0,a=a+Math.imul(v,W)|0;var xt=(u+(n=n+Math.imul(f,J)|0)|0)+((8191&(i=(i=i+Math.imul(f,K)|0)+Math.imul(h,J)|0))<<13)|0;u=((a=a+Math.imul(h,K)|0)+(i>>>13)|0)+(xt>>>26)|0,xt&=67108863,n=Math.imul(T,U),i=(i=Math.imul(T,V))+Math.imul(k,U)|0,a=Math.imul(k,V),n=n+Math.imul(b,q)|0,i=(i=i+Math.imul(b,G)|0)+Math.imul(_,q)|0,a=a+Math.imul(_,G)|0,n=n+Math.imul(y,Y)|0,i=(i=i+Math.imul(y,W)|0)+Math.imul(m,Y)|0,a=a+Math.imul(m,W)|0,n=n+Math.imul(d,J)|0,i=(i=i+Math.imul(d,K)|0)+Math.imul(v,J)|0,a=a+Math.imul(v,K)|0;var bt=(u+(n=n+Math.imul(f,Q)|0)|0)+((8191&(i=(i=i+Math.imul(f,tt)|0)+Math.imul(h,Q)|0))<<13)|0;u=((a=a+Math.imul(h,tt)|0)+(i>>>13)|0)+(bt>>>26)|0,bt&=67108863,n=Math.imul(M,U),i=(i=Math.imul(M,V))+Math.imul(S,U)|0,a=Math.imul(S,V),n=n+Math.imul(T,q)|0,i=(i=i+Math.imul(T,G)|0)+Math.imul(k,q)|0,a=a+Math.imul(k,G)|0,n=n+Math.imul(b,Y)|0,i=(i=i+Math.imul(b,W)|0)+Math.imul(_,Y)|0,a=a+Math.imul(_,W)|0,n=n+Math.imul(y,J)|0,i=(i=i+Math.imul(y,K)|0)+Math.imul(m,J)|0,a=a+Math.imul(m,K)|0,n=n+Math.imul(d,Q)|0,i=(i=i+Math.imul(d,tt)|0)+Math.imul(v,Q)|0,a=a+Math.imul(v,tt)|0;var _t=(u+(n=n+Math.imul(f,rt)|0)|0)+((8191&(i=(i=i+Math.imul(f,nt)|0)+Math.imul(h,rt)|0))<<13)|0;u=((a=a+Math.imul(h,nt)|0)+(i>>>13)|0)+(_t>>>26)|0,_t&=67108863,n=Math.imul(L,U),i=(i=Math.imul(L,V))+Math.imul(C,U)|0,a=Math.imul(C,V),n=n+Math.imul(M,q)|0,i=(i=i+Math.imul(M,G)|0)+Math.imul(S,q)|0,a=a+Math.imul(S,G)|0,n=n+Math.imul(T,Y)|0,i=(i=i+Math.imul(T,W)|0)+Math.imul(k,Y)|0,a=a+Math.imul(k,W)|0,n=n+Math.imul(b,J)|0,i=(i=i+Math.imul(b,K)|0)+Math.imul(_,J)|0,a=a+Math.imul(_,K)|0,n=n+Math.imul(y,Q)|0,i=(i=i+Math.imul(y,tt)|0)+Math.imul(m,Q)|0,a=a+Math.imul(m,tt)|0,n=n+Math.imul(d,rt)|0,i=(i=i+Math.imul(d,nt)|0)+Math.imul(v,rt)|0,a=a+Math.imul(v,nt)|0;var wt=(u+(n=n+Math.imul(f,at)|0)|0)+((8191&(i=(i=i+Math.imul(f,ot)|0)+Math.imul(h,at)|0))<<13)|0;u=((a=a+Math.imul(h,ot)|0)+(i>>>13)|0)+(wt>>>26)|0,wt&=67108863,n=Math.imul(O,U),i=(i=Math.imul(O,V))+Math.imul(I,U)|0,a=Math.imul(I,V),n=n+Math.imul(L,q)|0,i=(i=i+Math.imul(L,G)|0)+Math.imul(C,q)|0,a=a+Math.imul(C,G)|0,n=n+Math.imul(M,Y)|0,i=(i=i+Math.imul(M,W)|0)+Math.imul(S,Y)|0,a=a+Math.imul(S,W)|0,n=n+Math.imul(T,J)|0,i=(i=i+Math.imul(T,K)|0)+Math.imul(k,J)|0,a=a+Math.imul(k,K)|0,n=n+Math.imul(b,Q)|0,i=(i=i+Math.imul(b,tt)|0)+Math.imul(_,Q)|0,a=a+Math.imul(_,tt)|0,n=n+Math.imul(y,rt)|0,i=(i=i+Math.imul(y,nt)|0)+Math.imul(m,rt)|0,a=a+Math.imul(m,nt)|0,n=n+Math.imul(d,at)|0,i=(i=i+Math.imul(d,ot)|0)+Math.imul(v,at)|0,a=a+Math.imul(v,ot)|0;var Tt=(u+(n=n+Math.imul(f,lt)|0)|0)+((8191&(i=(i=i+Math.imul(f,ut)|0)+Math.imul(h,lt)|0))<<13)|0;u=((a=a+Math.imul(h,ut)|0)+(i>>>13)|0)+(Tt>>>26)|0,Tt&=67108863,n=Math.imul(z,U),i=(i=Math.imul(z,V))+Math.imul(R,U)|0,a=Math.imul(R,V),n=n+Math.imul(O,q)|0,i=(i=i+Math.imul(O,G)|0)+Math.imul(I,q)|0,a=a+Math.imul(I,G)|0,n=n+Math.imul(L,Y)|0,i=(i=i+Math.imul(L,W)|0)+Math.imul(C,Y)|0,a=a+Math.imul(C,W)|0,n=n+Math.imul(M,J)|0,i=(i=i+Math.imul(M,K)|0)+Math.imul(S,J)|0,a=a+Math.imul(S,K)|0,n=n+Math.imul(T,Q)|0,i=(i=i+Math.imul(T,tt)|0)+Math.imul(k,Q)|0,a=a+Math.imul(k,tt)|0,n=n+Math.imul(b,rt)|0,i=(i=i+Math.imul(b,nt)|0)+Math.imul(_,rt)|0,a=a+Math.imul(_,nt)|0,n=n+Math.imul(y,at)|0,i=(i=i+Math.imul(y,ot)|0)+Math.imul(m,at)|0,a=a+Math.imul(m,ot)|0,n=n+Math.imul(d,lt)|0,i=(i=i+Math.imul(d,ut)|0)+Math.imul(v,lt)|0,a=a+Math.imul(v,ut)|0;var kt=(u+(n=n+Math.imul(f,ft)|0)|0)+((8191&(i=(i=i+Math.imul(f,ht)|0)+Math.imul(h,ft)|0))<<13)|0;u=((a=a+Math.imul(h,ht)|0)+(i>>>13)|0)+(kt>>>26)|0,kt&=67108863,n=Math.imul(B,U),i=(i=Math.imul(B,V))+Math.imul(N,U)|0,a=Math.imul(N,V),n=n+Math.imul(z,q)|0,i=(i=i+Math.imul(z,G)|0)+Math.imul(R,q)|0,a=a+Math.imul(R,G)|0,n=n+Math.imul(O,Y)|0,i=(i=i+Math.imul(O,W)|0)+Math.imul(I,Y)|0,a=a+Math.imul(I,W)|0,n=n+Math.imul(L,J)|0,i=(i=i+Math.imul(L,K)|0)+Math.imul(C,J)|0,a=a+Math.imul(C,K)|0,n=n+Math.imul(M,Q)|0,i=(i=i+Math.imul(M,tt)|0)+Math.imul(S,Q)|0,a=a+Math.imul(S,tt)|0,n=n+Math.imul(T,rt)|0,i=(i=i+Math.imul(T,nt)|0)+Math.imul(k,rt)|0,a=a+Math.imul(k,nt)|0,n=n+Math.imul(b,at)|0,i=(i=i+Math.imul(b,ot)|0)+Math.imul(_,at)|0,a=a+Math.imul(_,ot)|0,n=n+Math.imul(y,lt)|0,i=(i=i+Math.imul(y,ut)|0)+Math.imul(m,lt)|0,a=a+Math.imul(m,ut)|0,n=n+Math.imul(d,ft)|0,i=(i=i+Math.imul(d,ht)|0)+Math.imul(v,ft)|0,a=a+Math.imul(v,ht)|0;var At=(u+(n=n+Math.imul(f,dt)|0)|0)+((8191&(i=(i=i+Math.imul(f,vt)|0)+Math.imul(h,dt)|0))<<13)|0;u=((a=a+Math.imul(h,vt)|0)+(i>>>13)|0)+(At>>>26)|0,At&=67108863,n=Math.imul(B,q),i=(i=Math.imul(B,G))+Math.imul(N,q)|0,a=Math.imul(N,G),n=n+Math.imul(z,Y)|0,i=(i=i+Math.imul(z,W)|0)+Math.imul(R,Y)|0,a=a+Math.imul(R,W)|0,n=n+Math.imul(O,J)|0,i=(i=i+Math.imul(O,K)|0)+Math.imul(I,J)|0,a=a+Math.imul(I,K)|0,n=n+Math.imul(L,Q)|0,i=(i=i+Math.imul(L,tt)|0)+Math.imul(C,Q)|0,a=a+Math.imul(C,tt)|0,n=n+Math.imul(M,rt)|0,i=(i=i+Math.imul(M,nt)|0)+Math.imul(S,rt)|0,a=a+Math.imul(S,nt)|0,n=n+Math.imul(T,at)|0,i=(i=i+Math.imul(T,ot)|0)+Math.imul(k,at)|0,a=a+Math.imul(k,ot)|0,n=n+Math.imul(b,lt)|0,i=(i=i+Math.imul(b,ut)|0)+Math.imul(_,lt)|0,a=a+Math.imul(_,ut)|0,n=n+Math.imul(y,ft)|0,i=(i=i+Math.imul(y,ht)|0)+Math.imul(m,ft)|0,a=a+Math.imul(m,ht)|0;var Mt=(u+(n=n+Math.imul(d,dt)|0)|0)+((8191&(i=(i=i+Math.imul(d,vt)|0)+Math.imul(v,dt)|0))<<13)|0;u=((a=a+Math.imul(v,vt)|0)+(i>>>13)|0)+(Mt>>>26)|0,Mt&=67108863,n=Math.imul(B,Y),i=(i=Math.imul(B,W))+Math.imul(N,Y)|0,a=Math.imul(N,W),n=n+Math.imul(z,J)|0,i=(i=i+Math.imul(z,K)|0)+Math.imul(R,J)|0,a=a+Math.imul(R,K)|0,n=n+Math.imul(O,Q)|0,i=(i=i+Math.imul(O,tt)|0)+Math.imul(I,Q)|0,a=a+Math.imul(I,tt)|0,n=n+Math.imul(L,rt)|0,i=(i=i+Math.imul(L,nt)|0)+Math.imul(C,rt)|0,a=a+Math.imul(C,nt)|0,n=n+Math.imul(M,at)|0,i=(i=i+Math.imul(M,ot)|0)+Math.imul(S,at)|0,a=a+Math.imul(S,ot)|0,n=n+Math.imul(T,lt)|0,i=(i=i+Math.imul(T,ut)|0)+Math.imul(k,lt)|0,a=a+Math.imul(k,ut)|0,n=n+Math.imul(b,ft)|0,i=(i=i+Math.imul(b,ht)|0)+Math.imul(_,ft)|0,a=a+Math.imul(_,ht)|0;var St=(u+(n=n+Math.imul(y,dt)|0)|0)+((8191&(i=(i=i+Math.imul(y,vt)|0)+Math.imul(m,dt)|0))<<13)|0;u=((a=a+Math.imul(m,vt)|0)+(i>>>13)|0)+(St>>>26)|0,St&=67108863,n=Math.imul(B,J),i=(i=Math.imul(B,K))+Math.imul(N,J)|0,a=Math.imul(N,K),n=n+Math.imul(z,Q)|0,i=(i=i+Math.imul(z,tt)|0)+Math.imul(R,Q)|0,a=a+Math.imul(R,tt)|0,n=n+Math.imul(O,rt)|0,i=(i=i+Math.imul(O,nt)|0)+Math.imul(I,rt)|0,a=a+Math.imul(I,nt)|0,n=n+Math.imul(L,at)|0,i=(i=i+Math.imul(L,ot)|0)+Math.imul(C,at)|0,a=a+Math.imul(C,ot)|0,n=n+Math.imul(M,lt)|0,i=(i=i+Math.imul(M,ut)|0)+Math.imul(S,lt)|0,a=a+Math.imul(S,ut)|0,n=n+Math.imul(T,ft)|0,i=(i=i+Math.imul(T,ht)|0)+Math.imul(k,ft)|0,a=a+Math.imul(k,ht)|0;var Et=(u+(n=n+Math.imul(b,dt)|0)|0)+((8191&(i=(i=i+Math.imul(b,vt)|0)+Math.imul(_,dt)|0))<<13)|0;u=((a=a+Math.imul(_,vt)|0)+(i>>>13)|0)+(Et>>>26)|0,Et&=67108863,n=Math.imul(B,Q),i=(i=Math.imul(B,tt))+Math.imul(N,Q)|0,a=Math.imul(N,tt),n=n+Math.imul(z,rt)|0,i=(i=i+Math.imul(z,nt)|0)+Math.imul(R,rt)|0,a=a+Math.imul(R,nt)|0,n=n+Math.imul(O,at)|0,i=(i=i+Math.imul(O,ot)|0)+Math.imul(I,at)|0,a=a+Math.imul(I,ot)|0,n=n+Math.imul(L,lt)|0,i=(i=i+Math.imul(L,ut)|0)+Math.imul(C,lt)|0,a=a+Math.imul(C,ut)|0,n=n+Math.imul(M,ft)|0,i=(i=i+Math.imul(M,ht)|0)+Math.imul(S,ft)|0,a=a+Math.imul(S,ht)|0;var Lt=(u+(n=n+Math.imul(T,dt)|0)|0)+((8191&(i=(i=i+Math.imul(T,vt)|0)+Math.imul(k,dt)|0))<<13)|0;u=((a=a+Math.imul(k,vt)|0)+(i>>>13)|0)+(Lt>>>26)|0,Lt&=67108863,n=Math.imul(B,rt),i=(i=Math.imul(B,nt))+Math.imul(N,rt)|0,a=Math.imul(N,nt),n=n+Math.imul(z,at)|0,i=(i=i+Math.imul(z,ot)|0)+Math.imul(R,at)|0,a=a+Math.imul(R,ot)|0,n=n+Math.imul(O,lt)|0,i=(i=i+Math.imul(O,ut)|0)+Math.imul(I,lt)|0,a=a+Math.imul(I,ut)|0,n=n+Math.imul(L,ft)|0,i=(i=i+Math.imul(L,ht)|0)+Math.imul(C,ft)|0,a=a+Math.imul(C,ht)|0;var Ct=(u+(n=n+Math.imul(M,dt)|0)|0)+((8191&(i=(i=i+Math.imul(M,vt)|0)+Math.imul(S,dt)|0))<<13)|0;u=((a=a+Math.imul(S,vt)|0)+(i>>>13)|0)+(Ct>>>26)|0,Ct&=67108863,n=Math.imul(B,at),i=(i=Math.imul(B,ot))+Math.imul(N,at)|0,a=Math.imul(N,ot),n=n+Math.imul(z,lt)|0,i=(i=i+Math.imul(z,ut)|0)+Math.imul(R,lt)|0,a=a+Math.imul(R,ut)|0,n=n+Math.imul(O,ft)|0,i=(i=i+Math.imul(O,ht)|0)+Math.imul(I,ft)|0,a=a+Math.imul(I,ht)|0;var Pt=(u+(n=n+Math.imul(L,dt)|0)|0)+((8191&(i=(i=i+Math.imul(L,vt)|0)+Math.imul(C,dt)|0))<<13)|0;u=((a=a+Math.imul(C,vt)|0)+(i>>>13)|0)+(Pt>>>26)|0,Pt&=67108863,n=Math.imul(B,lt),i=(i=Math.imul(B,ut))+Math.imul(N,lt)|0,a=Math.imul(N,ut),n=n+Math.imul(z,ft)|0,i=(i=i+Math.imul(z,ht)|0)+Math.imul(R,ft)|0,a=a+Math.imul(R,ht)|0;var Ot=(u+(n=n+Math.imul(O,dt)|0)|0)+((8191&(i=(i=i+Math.imul(O,vt)|0)+Math.imul(I,dt)|0))<<13)|0;u=((a=a+Math.imul(I,vt)|0)+(i>>>13)|0)+(Ot>>>26)|0,Ot&=67108863,n=Math.imul(B,ft),i=(i=Math.imul(B,ht))+Math.imul(N,ft)|0,a=Math.imul(N,ht);var It=(u+(n=n+Math.imul(z,dt)|0)|0)+((8191&(i=(i=i+Math.imul(z,vt)|0)+Math.imul(R,dt)|0))<<13)|0;u=((a=a+Math.imul(R,vt)|0)+(i>>>13)|0)+(It>>>26)|0,It&=67108863;var Dt=(u+(n=Math.imul(B,dt))|0)+((8191&(i=(i=Math.imul(B,vt))+Math.imul(N,dt)|0))<<13)|0;return u=((a=Math.imul(N,vt))+(i>>>13)|0)+(Dt>>>26)|0,Dt&=67108863,l[0]=gt,l[1]=yt,l[2]=mt,l[3]=xt,l[4]=bt,l[5]=_t,l[6]=wt,l[7]=Tt,l[8]=kt,l[9]=At,l[10]=Mt,l[11]=St,l[12]=Et,l[13]=Lt,l[14]=Ct,l[15]=Pt,l[16]=Ot,l[17]=It,l[18]=Dt,0!==u&&(l[19]=u,r.length++),r};function v(t,e,r){return(new g).mulp(t,e,r)}function g(t,e){this.x=t,this.y=e}Math.imul||(d=p),a.prototype.mulTo=function(t,e){var r,n=this.length+t.length;return r=10===this.length&&10===t.length?d(this,t,e):n<63?p(this,t,e):n<1024?function(t,e,r){r.negative=e.negative^t.negative,r.length=t.length+e.length;for(var n=0,i=0,a=0;a>>26)|0)>>>26,o&=67108863}r.words[a]=s,n=o,o=i}return 0!==n?r.words[a]=n:r.length--,r.strip()}(this,t,e):v(this,t,e),r},g.prototype.makeRBT=function(t){for(var e=new Array(t),r=a.prototype._countBits(t)-1,n=0;n>=1;return n},g.prototype.permute=function(t,e,r,n,i,a){for(var o=0;o>>=1)i++;return 1<>>=13,r[2*o+1]=8191&a,a>>>=13;for(o=2*e;o>=26,e+=i/67108864|0,e+=a>>>26,this.words[r]=67108863&a}return 0!==e&&(this.words[r]=e,this.length++),this},a.prototype.muln=function(t){return this.clone().imuln(t)},a.prototype.sqr=function(){return this.mul(this)},a.prototype.isqr=function(){return this.imul(this.clone())},a.prototype.pow=function(t){var e=function(t){for(var e=new Array(t.bitLength()),r=0;r>>i}return e}(t);if(0===e.length)return new a(1);for(var r=this,n=0;n=0);var e,r=t%26,i=(t-r)/26,a=67108863>>>26-r<<26-r;if(0!==r){var o=0;for(e=0;e>>26-r}o&&(this.words[e]=o,this.length++)}if(0!==i){for(e=this.length-1;e>=0;e--)this.words[e+i]=this.words[e];for(e=0;e=0),i=e?(e-e%26)/26:0;var a=t%26,o=Math.min((t-a)/26,this.length),s=67108863^67108863>>>a<o)for(this.length-=o,u=0;u=0&&(0!==c||u>=i);u--){var f=0|this.words[u];this.words[u]=c<<26-a|f>>>a,c=f&s}return l&&0!==c&&(l.words[l.length++]=c),0===this.length&&(this.words[0]=0,this.length=1),this.strip()},a.prototype.ishrn=function(t,e,r){return n(0===this.negative),this.iushrn(t,e,r)},a.prototype.shln=function(t){return this.clone().ishln(t)},a.prototype.ushln=function(t){return this.clone().iushln(t)},a.prototype.shrn=function(t){return this.clone().ishrn(t)},a.prototype.ushrn=function(t){return this.clone().iushrn(t)},a.prototype.testn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26,i=1<=0);var e=t%26,r=(t-e)/26;if(n(0===this.negative,\"imaskn works only with positive numbers\"),this.length<=r)return this;if(0!==e&&r++,this.length=Math.min(r,this.length),0!==e){var i=67108863^67108863>>>e<=67108864;e++)this.words[e]-=67108864,e===this.length-1?this.words[e+1]=1:this.words[e+1]++;return this.length=Math.max(this.length,e+1),this},a.prototype.isubn=function(t){if(n(\"number\"==typeof t),n(t<67108864),t<0)return this.iaddn(-t);if(0!==this.negative)return this.negative=0,this.iaddn(t),this.negative=1,this;if(this.words[0]-=t,1===this.length&&this.words[0]<0)this.words[0]=-this.words[0],this.negative=1;else for(var e=0;e>26)-(l/67108864|0),this.words[i+r]=67108863&a}for(;i>26,this.words[i+r]=67108863&a;if(0===s)return this.strip();for(n(-1===s),s=0,i=0;i>26,this.words[i]=67108863&a;return this.negative=1,this.strip()},a.prototype._wordDiv=function(t,e){var r=(this.length,t.length),n=this.clone(),i=t,o=0|i.words[i.length-1];0!=(r=26-this._countBits(o))&&(i=i.ushln(r),n.iushln(r),o=0|i.words[i.length-1]);var s,l=n.length-i.length;if(\"mod\"!==e){(s=new a(null)).length=l+1,s.words=new Array(s.length);for(var u=0;u=0;f--){var h=67108864*(0|n.words[i.length+f])+(0|n.words[i.length+f-1]);for(h=Math.min(h/o|0,67108863),n._ishlnsubmul(i,h,f);0!==n.negative;)h--,n.negative=0,n._ishlnsubmul(i,1,f),n.isZero()||(n.negative^=1);s&&(s.words[f]=h)}return s&&s.strip(),n.strip(),\"div\"!==e&&0!==r&&n.iushrn(r),{div:s||null,mod:n}},a.prototype.divmod=function(t,e,r){return n(!t.isZero()),this.isZero()?{div:new a(0),mod:new a(0)}:0!==this.negative&&0===t.negative?(s=this.neg().divmod(t,e),\"mod\"!==e&&(i=s.div.neg()),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.iadd(t)),{div:i,mod:o}):0===this.negative&&0!==t.negative?(s=this.divmod(t.neg(),e),\"mod\"!==e&&(i=s.div.neg()),{div:i,mod:s.mod}):0!=(this.negative&t.negative)?(s=this.neg().divmod(t.neg(),e),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.isub(t)),{div:s.div,mod:o}):t.length>this.length||this.cmp(t)<0?{div:new a(0),mod:this}:1===t.length?\"div\"===e?{div:this.divn(t.words[0]),mod:null}:\"mod\"===e?{div:null,mod:new a(this.modn(t.words[0]))}:{div:this.divn(t.words[0]),mod:new a(this.modn(t.words[0]))}:this._wordDiv(t,e);var i,o,s},a.prototype.div=function(t){return this.divmod(t,\"div\",!1).div},a.prototype.mod=function(t){return this.divmod(t,\"mod\",!1).mod},a.prototype.umod=function(t){return this.divmod(t,\"mod\",!0).mod},a.prototype.divRound=function(t){var e=this.divmod(t);if(e.mod.isZero())return e.div;var r=0!==e.div.negative?e.mod.isub(t):e.mod,n=t.ushrn(1),i=t.andln(1),a=r.cmp(n);return a<0||1===i&&0===a?e.div:0!==e.div.negative?e.div.isubn(1):e.div.iaddn(1)},a.prototype.modn=function(t){n(t<=67108863);for(var e=(1<<26)%t,r=0,i=this.length-1;i>=0;i--)r=(e*r+(0|this.words[i]))%t;return r},a.prototype.idivn=function(t){n(t<=67108863);for(var e=0,r=this.length-1;r>=0;r--){var i=(0|this.words[r])+67108864*e;this.words[r]=i/t|0,e=i%t}return this.strip()},a.prototype.divn=function(t){return this.clone().idivn(t)},a.prototype.egcd=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i=new a(1),o=new a(0),s=new a(0),l=new a(1),u=0;e.isEven()&&r.isEven();)e.iushrn(1),r.iushrn(1),++u;for(var c=r.clone(),f=e.clone();!e.isZero();){for(var h=0,p=1;0==(e.words[0]&p)&&h<26;++h,p<<=1);if(h>0)for(e.iushrn(h);h-- >0;)(i.isOdd()||o.isOdd())&&(i.iadd(c),o.isub(f)),i.iushrn(1),o.iushrn(1);for(var d=0,v=1;0==(r.words[0]&v)&&d<26;++d,v<<=1);if(d>0)for(r.iushrn(d);d-- >0;)(s.isOdd()||l.isOdd())&&(s.iadd(c),l.isub(f)),s.iushrn(1),l.iushrn(1);e.cmp(r)>=0?(e.isub(r),i.isub(s),o.isub(l)):(r.isub(e),s.isub(i),l.isub(o))}return{a:s,b:l,gcd:r.iushln(u)}},a.prototype._invmp=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i,o=new a(1),s=new a(0),l=r.clone();e.cmpn(1)>0&&r.cmpn(1)>0;){for(var u=0,c=1;0==(e.words[0]&c)&&u<26;++u,c<<=1);if(u>0)for(e.iushrn(u);u-- >0;)o.isOdd()&&o.iadd(l),o.iushrn(1);for(var f=0,h=1;0==(r.words[0]&h)&&f<26;++f,h<<=1);if(f>0)for(r.iushrn(f);f-- >0;)s.isOdd()&&s.iadd(l),s.iushrn(1);e.cmp(r)>=0?(e.isub(r),o.isub(s)):(r.isub(e),s.isub(o))}return(i=0===e.cmpn(1)?o:s).cmpn(0)<0&&i.iadd(t),i},a.prototype.gcd=function(t){if(this.isZero())return t.abs();if(t.isZero())return this.abs();var e=this.clone(),r=t.clone();e.negative=0,r.negative=0;for(var n=0;e.isEven()&&r.isEven();n++)e.iushrn(1),r.iushrn(1);for(;;){for(;e.isEven();)e.iushrn(1);for(;r.isEven();)r.iushrn(1);var i=e.cmp(r);if(i<0){var a=e;e=r,r=a}else if(0===i||0===r.cmpn(1))break;e.isub(r)}return r.iushln(n)},a.prototype.invm=function(t){return this.egcd(t).a.umod(t)},a.prototype.isEven=function(){return 0==(1&this.words[0])},a.prototype.isOdd=function(){return 1==(1&this.words[0])},a.prototype.andln=function(t){return this.words[0]&t},a.prototype.bincn=function(t){n(\"number\"==typeof t);var e=t%26,r=(t-e)/26,i=1<>>26,s&=67108863,this.words[o]=s}return 0!==a&&(this.words[o]=a,this.length++),this},a.prototype.isZero=function(){return 1===this.length&&0===this.words[0]},a.prototype.cmpn=function(t){var e,r=t<0;if(0!==this.negative&&!r)return-1;if(0===this.negative&&r)return 1;if(this.strip(),this.length>1)e=1;else{r&&(t=-t),n(t<=67108863,\"Number is too big\");var i=0|this.words[0];e=i===t?0:it.length)return 1;if(this.length=0;r--){var n=0|this.words[r],i=0|t.words[r];if(n!==i){ni&&(e=1);break}}return e},a.prototype.gtn=function(t){return 1===this.cmpn(t)},a.prototype.gt=function(t){return 1===this.cmp(t)},a.prototype.gten=function(t){return this.cmpn(t)>=0},a.prototype.gte=function(t){return this.cmp(t)>=0},a.prototype.ltn=function(t){return-1===this.cmpn(t)},a.prototype.lt=function(t){return-1===this.cmp(t)},a.prototype.lten=function(t){return this.cmpn(t)<=0},a.prototype.lte=function(t){return this.cmp(t)<=0},a.prototype.eqn=function(t){return 0===this.cmpn(t)},a.prototype.eq=function(t){return 0===this.cmp(t)},a.red=function(t){return new T(t)},a.prototype.toRed=function(t){return n(!this.red,\"Already a number in reduction context\"),n(0===this.negative,\"red works only with positives\"),t.convertTo(this)._forceRed(t)},a.prototype.fromRed=function(){return n(this.red,\"fromRed works only with numbers in reduction context\"),this.red.convertFrom(this)},a.prototype._forceRed=function(t){return this.red=t,this},a.prototype.forceRed=function(t){return n(!this.red,\"Already a number in reduction context\"),this._forceRed(t)},a.prototype.redAdd=function(t){return n(this.red,\"redAdd works only with red numbers\"),this.red.add(this,t)},a.prototype.redIAdd=function(t){return n(this.red,\"redIAdd works only with red numbers\"),this.red.iadd(this,t)},a.prototype.redSub=function(t){return n(this.red,\"redSub works only with red numbers\"),this.red.sub(this,t)},a.prototype.redISub=function(t){return n(this.red,\"redISub works only with red numbers\"),this.red.isub(this,t)},a.prototype.redShl=function(t){return n(this.red,\"redShl works only with red numbers\"),this.red.shl(this,t)},a.prototype.redMul=function(t){return n(this.red,\"redMul works only with red numbers\"),this.red._verify2(this,t),this.red.mul(this,t)},a.prototype.redIMul=function(t){return n(this.red,\"redMul works only with red numbers\"),this.red._verify2(this,t),this.red.imul(this,t)},a.prototype.redSqr=function(){return n(this.red,\"redSqr works only with red numbers\"),this.red._verify1(this),this.red.sqr(this)},a.prototype.redISqr=function(){return n(this.red,\"redISqr works only with red numbers\"),this.red._verify1(this),this.red.isqr(this)},a.prototype.redSqrt=function(){return n(this.red,\"redSqrt works only with red numbers\"),this.red._verify1(this),this.red.sqrt(this)},a.prototype.redInvm=function(){return n(this.red,\"redInvm works only with red numbers\"),this.red._verify1(this),this.red.invm(this)},a.prototype.redNeg=function(){return n(this.red,\"redNeg works only with red numbers\"),this.red._verify1(this),this.red.neg(this)},a.prototype.redPow=function(t){return n(this.red&&!t.red,\"redPow(normalNum)\"),this.red._verify1(this),this.red.pow(this,t)};var y={k256:null,p224:null,p192:null,p25519:null};function m(t,e){this.name=t,this.p=new a(e,16),this.n=this.p.bitLength(),this.k=new a(1).iushln(this.n).isub(this.p),this.tmp=this._tmp()}function x(){m.call(this,\"k256\",\"ffffffff ffffffff ffffffff ffffffff ffffffff ffffffff fffffffe fffffc2f\")}function b(){m.call(this,\"p224\",\"ffffffff ffffffff ffffffff ffffffff 00000000 00000000 00000001\")}function _(){m.call(this,\"p192\",\"ffffffff ffffffff ffffffff fffffffe ffffffff ffffffff\")}function w(){m.call(this,\"25519\",\"7fffffffffffffff ffffffffffffffff ffffffffffffffff ffffffffffffffed\")}function T(t){if(\"string\"==typeof t){var e=a._prime(t);this.m=e.p,this.prime=e}else n(t.gtn(1),\"modulus must be greater than 1\"),this.m=t,this.prime=null}function k(t){T.call(this,t),this.shift=this.m.bitLength(),this.shift%26!=0&&(this.shift+=26-this.shift%26),this.r=new a(1).iushln(this.shift),this.r2=this.imod(this.r.sqr()),this.rinv=this.r._invmp(this.m),this.minv=this.rinv.mul(this.r).isubn(1).div(this.m),this.minv=this.minv.umod(this.r),this.minv=this.r.sub(this.minv)}m.prototype._tmp=function(){var t=new a(null);return t.words=new Array(Math.ceil(this.n/13)),t},m.prototype.ireduce=function(t){var e,r=t;do{this.split(r,this.tmp),e=(r=(r=this.imulK(r)).iadd(this.tmp)).bitLength()}while(e>this.n);var n=e0?r.isub(this.p):void 0!==r.strip?r.strip():r._strip(),r},m.prototype.split=function(t,e){t.iushrn(this.n,0,e)},m.prototype.imulK=function(t){return t.imul(this.k)},i(x,m),x.prototype.split=function(t,e){for(var r=4194303,n=Math.min(t.length,9),i=0;i>>22,a=o}a>>>=22,t.words[i-10]=a,0===a&&t.length>10?t.length-=10:t.length-=9},x.prototype.imulK=function(t){t.words[t.length]=0,t.words[t.length+1]=0,t.length+=2;for(var e=0,r=0;r>>=26,t.words[r]=i,e=n}return 0!==e&&(t.words[t.length++]=e),t},a._prime=function(t){if(y[t])return y[t];var e;if(\"k256\"===t)e=new x;else if(\"p224\"===t)e=new b;else if(\"p192\"===t)e=new _;else{if(\"p25519\"!==t)throw new Error(\"Unknown prime \"+t);e=new w}return y[t]=e,e},T.prototype._verify1=function(t){n(0===t.negative,\"red works only with positives\"),n(t.red,\"red works only with red numbers\")},T.prototype._verify2=function(t,e){n(0==(t.negative|e.negative),\"red works only with positives\"),n(t.red&&t.red===e.red,\"red works only with red numbers\")},T.prototype.imod=function(t){return this.prime?this.prime.ireduce(t)._forceRed(this):t.umod(this.m)._forceRed(this)},T.prototype.neg=function(t){return t.isZero()?t.clone():this.m.sub(t)._forceRed(this)},T.prototype.add=function(t,e){this._verify2(t,e);var r=t.add(e);return r.cmp(this.m)>=0&&r.isub(this.m),r._forceRed(this)},T.prototype.iadd=function(t,e){this._verify2(t,e);var r=t.iadd(e);return r.cmp(this.m)>=0&&r.isub(this.m),r},T.prototype.sub=function(t,e){this._verify2(t,e);var r=t.sub(e);return r.cmpn(0)<0&&r.iadd(this.m),r._forceRed(this)},T.prototype.isub=function(t,e){this._verify2(t,e);var r=t.isub(e);return r.cmpn(0)<0&&r.iadd(this.m),r},T.prototype.shl=function(t,e){return this._verify1(t),this.imod(t.ushln(e))},T.prototype.imul=function(t,e){return this._verify2(t,e),this.imod(t.imul(e))},T.prototype.mul=function(t,e){return this._verify2(t,e),this.imod(t.mul(e))},T.prototype.isqr=function(t){return this.imul(t,t.clone())},T.prototype.sqr=function(t){return this.mul(t,t)},T.prototype.sqrt=function(t){if(t.isZero())return t.clone();var e=this.m.andln(3);if(n(e%2==1),3===e){var r=this.m.add(new a(1)).iushrn(2);return this.pow(t,r)}for(var i=this.m.subn(1),o=0;!i.isZero()&&0===i.andln(1);)o++,i.iushrn(1);n(!i.isZero());var s=new a(1).toRed(this),l=s.redNeg(),u=this.m.subn(1).iushrn(1),c=this.m.bitLength();for(c=new a(2*c*c).toRed(this);0!==this.pow(c,u).cmp(l);)c.redIAdd(l);for(var f=this.pow(c,i),h=this.pow(t,i.addn(1).iushrn(1)),p=this.pow(t,i),d=o;0!==p.cmp(s);){for(var v=p,g=0;0!==v.cmp(s);g++)v=v.redSqr();n(g=0;n--){for(var u=e.words[n],c=l-1;c>=0;c--){var f=u>>c&1;i!==r[0]&&(i=this.sqr(i)),0!==f||0!==o?(o<<=1,o|=f,(4==++s||0===n&&0===c)&&(i=this.mul(i,r[o]),s=0,o=0)):s=0}l=26}return i},T.prototype.convertTo=function(t){var 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e(t._color,t.key,t.value,t.left,t.right,t._count)}function n(t,r){return new e(t,r.key,r.value,r.left,r.right,r._count)}function i(t){t._count=1+(t.left?t.left._count:0)+(t.right?t.right._count:0)}function a(t,e){this._compare=t,this.root=e}t.exports=function(t){return new a(t||p,null)};var o=a.prototype;function s(t,e){var r;return e.left&&(r=s(t,e.left))?r:(r=t(e.key,e.value))||(e.right?s(t,e.right):void 0)}function l(t,e,r,n){if(e(t,n.key)<=0){var i;if(n.left&&(i=l(t,e,r,n.left)))return i;if(i=r(n.key,n.value))return i}if(n.right)return l(t,e,r,n.right)}function u(t,e,r,n,i){var a,o=r(t,i.key),s=r(e,i.key);if(o<=0){if(i.left&&(a=u(t,e,r,n,i.left)))return a;if(s>0&&(a=n(i.key,i.value)))return a}if(s>0&&i.right)return u(t,e,r,n,i.right)}function c(t,e){this.tree=t,this._stack=e}Object.defineProperty(o,\"keys\",{get:function(){var t=[];return this.forEach((function(e,r){t.push(e)})),t}}),Object.defineProperty(o,\"values\",{get:function(){var t=[];return this.forEach((function(e,r){t.push(r)})),t}}),Object.defineProperty(o,\"length\",{get:function(){return this.root?this.root._count:0}}),o.insert=function(t,r){for(var o=this._compare,s=this.root,l=[],u=[];s;){var c=o(t,s.key);l.push(s),u.push(c),s=c<=0?s.left:s.right}l.push(new e(0,t,r,null,null,1));for(var f=l.length-2;f>=0;--f)s=l[f],u[f]<=0?l[f]=new e(s._color,s.key,s.value,l[f+1],s.right,s._count+1):l[f]=new e(s._color,s.key,s.value,s.left,l[f+1],s._count+1);for(f=l.length-1;f>1;--f){var h=l[f-1];if(s=l[f],1===h._color||1===s._color)break;var p=l[f-2];if(p.left===h)if(h.left===s){if(!(d=p.right)||0!==d._color){p._color=0,p.left=h.right,h._color=1,h.right=p,l[f-2]=h,l[f-1]=s,i(p),i(h),f>=3&&((v=l[f-3]).left===p?v.left=h:v.right=h);break}h._color=1,p.right=n(1,d),p._color=0,f-=1}else{if(!(d=p.right)||0!==d._color){h.right=s.left,p._color=0,p.left=s.right,s._color=1,s.left=h,s.right=p,l[f-2]=s,l[f-1]=h,i(p),i(h),i(s),f>=3&&((v=l[f-3]).left===p?v.left=s:v.right=s);break}h._color=1,p.right=n(1,d),p._color=0,f-=1}else if(h.right===s){if(!(d=p.left)||0!==d._color){p._color=0,p.right=h.left,h._color=1,h.left=p,l[f-2]=h,l[f-1]=s,i(p),i(h),f>=3&&((v=l[f-3]).right===p?v.right=h:v.left=h);break}h._color=1,p.left=n(1,d),p._color=0,f-=1}else{var d;if(!(d=p.left)||0!==d._color){var v;h.left=s.right,p._color=0,p.right=s.left,s._color=1,s.right=h,s.left=p,l[f-2]=s,l[f-1]=h,i(p),i(h),i(s),f>=3&&((v=l[f-3]).right===p?v.right=s:v.left=s);break}h._color=1,p.left=n(1,d),p._color=0,f-=1}}return l[0]._color=1,new a(o,l[0])},o.forEach=function(t,e,r){if(this.root)switch(arguments.length){case 1:return s(t,this.root);case 2:return l(e,this._compare,t,this.root);case 3:if(this._compare(e,r)>=0)return;return u(e,r,this._compare,t,this.root)}},Object.defineProperty(o,\"begin\",{get:function(){for(var t=[],e=this.root;e;)t.push(e),e=e.left;return new c(this,t)}}),Object.defineProperty(o,\"end\",{get:function(){for(var t=[],e=this.root;e;)t.push(e),e=e.right;return new c(this,t)}}),o.at=function(t){if(t<0)return new c(this,[]);for(var e=this.root,r=[];;){if(r.push(e),e.left){if(t=e.right._count)break;e=e.right}return new c(this,[])},o.ge=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a<=0&&(i=n.length),r=a<=0?r.left:r.right}return n.length=i,new c(this,n)},o.gt=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a<0&&(i=n.length),r=a<0?r.left:r.right}return n.length=i,new c(this,n)},o.lt=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a>0&&(i=n.length),r=a<=0?r.left:r.right}return n.length=i,new c(this,n)},o.le=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a>=0&&(i=n.length),r=a<0?r.left:r.right}return n.length=i,new c(this,n)},o.find=function(t){for(var e=this._compare,r=this.root,n=[];r;){var i=e(t,r.key);if(n.push(r),0===i)return new c(this,n);r=i<=0?r.left:r.right}return new c(this,[])},o.remove=function(t){var e=this.find(t);return e?e.remove():this},o.get=function(t){for(var e=this._compare,r=this.root;r;){var n=e(t,r.key);if(0===n)return r.value;r=n<=0?r.left:r.right}};var f=c.prototype;function h(t,e){t.key=e.key,t.value=e.value,t.left=e.left,t.right=e.right,t._color=e._color,t._count=e._count}function p(t,e){return te?1:0}Object.defineProperty(f,\"valid\",{get:function(){return this._stack.length>0}}),Object.defineProperty(f,\"node\",{get:function(){return this._stack.length>0?this._stack[this._stack.length-1]:null},enumerable:!0}),f.clone=function(){return new c(this.tree,this._stack.slice())},f.remove=function(){var t=this._stack;if(0===t.length)return this.tree;var o=new Array(t.length),s=t[t.length-1];o[o.length-1]=new e(s._color,s.key,s.value,s.left,s.right,s._count);for(var l=t.length-2;l>=0;--l)(s=t[l]).left===t[l+1]?o[l]=new e(s._color,s.key,s.value,o[l+1],s.right,s._count):o[l]=new e(s._color,s.key,s.value,s.left,o[l+1],s._count);if((s=o[o.length-1]).left&&s.right){var u=o.length;for(s=s.left;s.right;)o.push(s),s=s.right;var c=o[u-1];for(o.push(new e(s._color,c.key,c.value,s.left,s.right,s._count)),o[u-1].key=s.key,o[u-1].value=s.value,l=o.length-2;l>=u;--l)s=o[l],o[l]=new e(s._color,s.key,s.value,s.left,o[l+1],s._count);o[u-1].left=o[u]}if(0===(s=o[o.length-1])._color){var f=o[o.length-2];for(f.left===s?f.left=null:f.right===s&&(f.right=null),o.pop(),l=0;l=0;--l){if(e=t[l],0===l)return void(e._color=1);if((a=t[l-1]).left===e){if((o=a.right).right&&0===o.right._color)return s=(o=a.right=r(o)).right=r(o.right),a.right=o.left,o.left=a,o.right=s,o._color=a._color,e._color=1,a._color=1,s._color=1,i(a),i(o),l>1&&((u=t[l-2]).left===a?u.left=o:u.right=o),void(t[l-1]=o);if(o.left&&0===o.left._color)return s=(o=a.right=r(o)).left=r(o.left),a.right=s.left,o.left=s.right,s.left=a,s.right=o,s._color=a._color,a._color=1,o._color=1,e._color=1,i(a),i(o),i(s),l>1&&((u=t[l-2]).left===a?u.left=s:u.right=s),void(t[l-1]=s);if(1===o._color){if(0===a._color)return a._color=1,void(a.right=n(0,o));a.right=n(0,o);continue}o=r(o),a.right=o.left,o.left=a,o._color=a._color,a._color=0,i(a),i(o),l>1&&((u=t[l-2]).left===a?u.left=o:u.right=o),t[l-1]=o,t[l]=a,l+11&&((u=t[l-2]).right===a?u.right=o:u.left=o),void(t[l-1]=o);if(o.right&&0===o.right._color)return s=(o=a.left=r(o)).right=r(o.right),a.left=s.right,o.right=s.left,s.right=a,s.left=o,s._color=a._color,a._color=1,o._color=1,e._color=1,i(a),i(o),i(s),l>1&&((u=t[l-2]).right===a?u.right=s:u.left=s),void(t[l-1]=s);if(1===o._color){if(0===a._color)return a._color=1,void(a.left=n(0,o));a.left=n(0,o);continue}var u;o=r(o),a.left=o.right,o.right=a,o._color=a._color,a._color=0,i(a),i(o),l>1&&((u=t[l-2]).right===a?u.right=o:u.left=o),t[l-1]=o,t[l]=a,l+10)return this._stack[this._stack.length-1].key},enumerable:!0}),Object.defineProperty(f,\"value\",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].value},enumerable:!0}),Object.defineProperty(f,\"index\",{get:function(){var t=0,e=this._stack;if(0===e.length){var r=this.tree.root;return r?r._count:0}e[e.length-1].left&&(t=e[e.length-1].left._count);for(var n=e.length-2;n>=0;--n)e[n+1]===e[n].right&&(++t,e[n].left&&(t+=e[n].left._count));return t},enumerable:!0}),f.next=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.right)for(e=e.right;e;)t.push(e),e=e.left;else for(t.pop();t.length>0&&t[t.length-1].right===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(f,\"hasNext\",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].right)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].left===t[e])return!0;return!1}}),f.update=function(t){var r=this._stack;if(0===r.length)throw new Error(\"Can't update empty node!\");var n=new Array(r.length),i=r[r.length-1];n[n.length-1]=new e(i._color,i.key,t,i.left,i.right,i._count);for(var o=r.length-2;o>=0;--o)(i=r[o]).left===r[o+1]?n[o]=new e(i._color,i.key,i.value,n[o+1],i.right,i._count):n[o]=new e(i._color,i.key,i.value,i.left,n[o+1],i._count);return new a(this.tree._compare,n[0])},f.prev=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.left)for(e=e.left;e;)t.push(e),e=e.right;else for(t.pop();t.length>0&&t[t.length-1].left===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(f,\"hasPrev\",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].left)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].right===t[e])return!0;return!1}})},7453:function(t,e,r){\"use strict\";t.exports=function(t,e){var r=new c(t);return r.update(e),r};var n=r(9557),i=r(1681),a=r(1011),o=r(2864),s=r(8468),l=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]);function u(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function c(t){this.gl=t,this.pixelRatio=1,this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.autoTicks=!0,this.tickSpacing=[1,1,1],this.tickEnable=[!0,!0,!0],this.tickFont=[\"sans-serif\",\"sans-serif\",\"sans-serif\"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickAlign=[\"auto\",\"auto\",\"auto\"],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[10,10,10],this.lastCubeProps={cubeEdges:[0,0,0],axis:[0,0,0]},this.labels=[\"x\",\"y\",\"z\"],this.labelEnable=[!0,!0,!0],this.labelFont=\"sans-serif\",this.labelSize=[20,20,20],this.labelAngle=[0,0,0],this.labelAlign=[\"auto\",\"auto\",\"auto\"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[10,10,10],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[0,0,0],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!1,!1,!1],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._firstInit=!0,this._text=null,this._lines=null,this._background=a(t)}var f=c.prototype;function h(){this.primalOffset=[0,0,0],this.primalMinor=[0,0,0],this.mirrorOffset=[0,0,0],this.mirrorMinor=[0,0,0]}f.update=function(t){function e(e,r,n){if(n in t){var i,a=t[n],o=this[n];(e?Array.isArray(a)&&Array.isArray(a[0]):Array.isArray(a))?this[n]=i=[r(a[0]),r(a[1]),r(a[2])]:this[n]=i=[r(a),r(a),r(a)];for(var s=0;s<3;++s)if(i[s]!==o[s])return!0}return!1}t=t||{};var r,a=e.bind(this,!1,Number),o=e.bind(this,!1,Boolean),l=e.bind(this,!1,String),u=e.bind(this,!0,(function(t){if(Array.isArray(t)){if(3===t.length)return[+t[0],+t[1],+t[2],1];if(4===t.length)return[+t[0],+t[1],+t[2],+t[3]]}return[0,0,0,1]})),c=!1,f=!1;if(\"bounds\"in t)for(var h=t.bounds,p=0;p<2;++p)for(var d=0;d<3;++d)h[p][d]!==this.bounds[p][d]&&(f=!0),this.bounds[p][d]=h[p][d];if(\"ticks\"in t)for(r=t.ticks,c=!0,this.autoTicks=!1,p=0;p<3;++p)this.tickSpacing[p]=0;else a(\"tickSpacing\")&&(this.autoTicks=!0,f=!0);if(this._firstInit&&(\"ticks\"in t||\"tickSpacing\"in 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v=l(\"labels\");l(\"labelFont\")&&(v=!0),o(\"labelEnable\"),a(\"labelSize\"),a(\"labelPad\"),u(\"labelColor\"),o(\"lineEnable\"),o(\"lineMirror\"),a(\"lineWidth\"),u(\"lineColor\"),o(\"lineTickEnable\"),o(\"lineTickMirror\"),a(\"lineTickLength\"),a(\"lineTickWidth\"),u(\"lineTickColor\"),o(\"gridEnable\"),a(\"gridWidth\"),u(\"gridColor\"),o(\"zeroEnable\"),u(\"zeroLineColor\"),a(\"zeroLineWidth\"),o(\"backgroundEnable\"),u(\"backgroundColor\"),this._text?this._text&&(v||c)&&this._text.update(this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont):this._text=n(this.gl,this.bounds,this.labels,this.labelFont,this.ticks,this.tickFont),this._lines&&c&&(this._lines.dispose(),this._lines=null),this._lines||(this._lines=i(this.gl,this.bounds,this.ticks))};var p=[new h,new h,new h];function d(t,e,r,n,i){for(var a=t.primalOffset,o=t.primalMinor,s=t.mirrorOffset,l=t.mirrorMinor,u=n[e],c=0;c<3;++c)if(e!==c){var f=a,h=s,p=o,d=l;u&1<0?(p[c]=-1,d[c]=0):(p[c]=0,d[c]=1)}}var v=[0,0,0],g={model:l,view:l,projection:l,_ortho:!1};f.isOpaque=function(){return!0},f.isTransparent=function(){return!1},f.drawTransparent=function(t){};var y=[0,0,0],m=[0,0,0],x=[0,0,0];f.draw=function(t){t=t||g;for(var e=this.gl,r=t.model||l,n=t.view||l,i=t.projection||l,a=this.bounds,s=t._ortho||!1,c=o(r,n,i,a,s),f=c.cubeEdges,h=c.axis,b=n[12],_=n[13],w=n[14],T=n[15],k=(s?2:1)*this.pixelRatio*(i[3]*b+i[7]*_+i[11]*w+i[15]*T)/e.drawingBufferHeight,A=0;A<3;++A)this.lastCubeProps.cubeEdges[A]=f[A],this.lastCubeProps.axis[A]=h[A];var M=p;for(A=0;A<3;++A)d(p[A],A,this.bounds,f,h);e=this.gl;var S,E,L,C=v;for(A=0;A<3;++A)this.backgroundEnable[A]?C[A]=h[A]:C[A]=0;for(this._background.draw(r,n,i,a,C,this.backgroundColor),this._lines.bind(r,n,i,this),A=0;A<3;++A){var P=[0,0,0];h[A]>0?P[A]=a[1][A]:P[A]=a[0][A];for(var O=0;O<2;++O){var I=(A+1+O)%3,D=(A+1+(1^O))%3;this.gridEnable[I]&&this._lines.drawGrid(I,D,this.bounds,P,this.gridColor[I],this.gridWidth[I]*this.pixelRatio)}for(O=0;O<2;++O)I=(A+1+O)%3,D=(A+1+(1^O))%3,this.zeroEnable[D]&&Math.min(a[0][D],a[1][D])<=0&&Math.max(a[0][D],a[1][D])>=0&&this._lines.drawZero(I,D,this.bounds,P,this.zeroLineColor[D],this.zeroLineWidth[D]*this.pixelRatio)}for(A=0;A<3;++A){this.lineEnable[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].primalOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio),this.lineMirror[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].mirrorOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio);var z=u(y,M[A].primalMinor),R=u(m,M[A].mirrorMinor),F=this.lineTickLength;for(O=0;O<3;++O){var B=k/r[5*O];z[O]*=F[O]*B,R[O]*=F[O]*B}this.lineTickEnable[A]&&this._lines.drawAxisTicks(A,M[A].primalOffset,z,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio),this.lineTickMirror[A]&&this._lines.drawAxisTicks(A,M[A].mirrorOffset,R,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio)}function N(t){(L=[0,0,0])[t]=1}function j(t,e,r){var n=(t+1)%3,i=(t+2)%3,a=e[n],o=e[i],s=r[n],l=r[i];a>0&&l>0||a>0&&l<0||a<0&&l>0||a<0&&l<0?N(n):(o>0&&s>0||o>0&&s<0||o<0&&s>0||o<0&&s<0)&&N(i)}for(this._lines.unbind(),this._text.bind(r,n,i,this.pixelRatio),A=0;A<3;++A){var U=M[A].primalMinor,V=M[A].mirrorMinor,H=u(x,M[A].primalOffset);for(O=0;O<3;++O)this.lineTickEnable[A]&&(H[O]+=k*U[O]*Math.max(this.lineTickLength[O],0)/r[5*O]);var q=[0,0,0];if(q[A]=1,this.tickEnable[A]){for(-3600===this.tickAngle[A]?(this.tickAngle[A]=0,this.tickAlign[A]=\"auto\"):this.tickAlign[A]=-1,E=1,\"auto\"===(S=[this.tickAlign[A],.5,E])[0]?S[0]=0:S[0]=parseInt(\"\"+S[0]),L=[0,0,0],j(A,U,V),O=0;O<3;++O)H[O]+=k*U[O]*this.tickPad[O]/r[5*O];this._text.drawTicks(A,this.tickSize[A],this.tickAngle[A],H,this.tickColor[A],q,L,S)}if(this.labelEnable[A]){for(E=0,L=[0,0,0],this.labels[A].length>4&&(N(A),E=1),\"auto\"===(S=[this.labelAlign[A],.5,E])[0]?S[0]=0:S[0]=parseInt(\"\"+S[0]),O=0;O<3;++O)H[O]+=k*U[O]*this.labelPad[O]/r[5*O];H[A]+=.5*(a[0][A]+a[1][A]),this._text.drawLabel(A,this.labelSize[A],this.labelAngle[A],H,this.labelColor[A],[0,0,0],L,S)}}this._text.unbind()},f.dispose=function(){this._text.dispose(),this._lines.dispose(),this._background.dispose(),this._lines=null,this._text=null,this._background=null,this.gl=null}},1011:function(t,e,r){\"use strict\";t.exports=function(t){for(var e=[],r=[],s=0,l=0;l<3;++l)for(var u=(l+1)%3,c=(l+2)%3,f=[0,0,0],h=[0,0,0],p=-1;p<=1;p+=2){r.push(s,s+2,s+1,s+1,s+2,s+3),f[l]=p,h[l]=p;for(var d=-1;d<=1;d+=2){f[u]=d;for(var v=-1;v<=1;v+=2)f[c]=v,e.push(f[0],f[1],f[2],h[0],h[1],h[2]),s+=1}var g=u;u=c,c=g}var y=n(t,new Float32Array(e)),m=n(t,new Uint16Array(r),t.ELEMENT_ARRAY_BUFFER),x=i(t,[{buffer:y,type:t.FLOAT,size:3,offset:0,stride:24},{buffer:y,type:t.FLOAT,size:3,offset:12,stride:24}],m),b=a(t);return b.attributes.position.location=0,b.attributes.normal.location=1,new o(t,y,x,b)};var n=r(5827),i=r(2944),a=r(1943).bg;function o(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n}var s=o.prototype;s.draw=function(t,e,r,n,i,a){for(var o=!1,s=0;s<3;++s)o=o||i[s];if(o){var l=this.gl;l.enable(l.POLYGON_OFFSET_FILL),l.polygonOffset(1,2),this.shader.bind(),this.shader.uniforms={model:t,view:e,projection:r,bounds:n,enable:i,colors:a},this.vao.bind(),this.vao.draw(this.gl.TRIANGLES,36),this.vao.unbind(),l.disable(l.POLYGON_OFFSET_FILL)}},s.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},2864:function(t,e,r){\"use strict\";t.exports=function(t,e,r,a,p){i(s,e,t),i(s,r,s);for(var m=0,x=0;x<2;++x){c[2]=a[x][2];for(var b=0;b<2;++b){c[1]=a[b][1];for(var _=0;_<2;++_)c[0]=a[_][0],h(l[m],c,s),m+=1}}var w=-1;for(x=0;x<8;++x){for(var T=l[x][3],k=0;k<3;++k)u[x][k]=l[x][k]/T;p&&(u[x][2]*=-1),T<0&&(w<0||u[x][2]E&&(w|=1<E&&(w|=1<u[x][1])&&(R=x);var F=-1;for(x=0;x<3;++x)(N=R^1<u[B][0]&&(B=N))}var j=v;j[0]=j[1]=j[2]=0,j[n.log2(F^R)]=R&F,j[n.log2(R^B)]=R&B;var U=7^B;U===w||U===z?(U=7^F,j[n.log2(B^U)]=U&B):j[n.log2(F^U)]=U&F;var V=g,H=w;for(A=0;A<3;++A)V[A]=H&1< HALF_PI) && (b <= ONE_AND_HALF_PI)) ?\\n b - PI :\\n b;\\n}\\n\\nfloat look_horizontal_or_vertical(float a, float ratio) {\\n // ratio controls the ratio between being horizontal to (vertical + horizontal)\\n // if ratio is set to 0.5 then it is 50%, 50%.\\n // when using a higher ratio e.g. 0.75 the result would\\n // likely be more horizontal than vertical.\\n\\n float b = positive_angle(a);\\n\\n return\\n (b < ( ratio) * HALF_PI) ? 0.0 :\\n (b < (2.0 - ratio) * HALF_PI) ? -HALF_PI :\\n (b < (2.0 + ratio) * HALF_PI) ? 0.0 :\\n (b < (4.0 - ratio) * HALF_PI) ? HALF_PI :\\n 0.0;\\n}\\n\\nfloat roundTo(float a, float b) {\\n return float(b * floor((a + 0.5 * b) / b));\\n}\\n\\nfloat look_round_n_directions(float a, int n) {\\n float b = positive_angle(a);\\n float div = TWO_PI / float(n);\\n float c = roundTo(b, div);\\n return look_upwards(c);\\n}\\n\\nfloat applyAlignOption(float rawAngle, float delta) {\\n return\\n (option > 2) ? look_round_n_directions(rawAngle + delta, option) : // option 3-n: round to n directions\\n (option == 2) ? look_horizontal_or_vertical(rawAngle + delta, hv_ratio) : // horizontal or vertical\\n (option == 1) ? rawAngle + delta : // use free angle, and flip to align with one direction of the axis\\n (option == 0) ? look_upwards(rawAngle) : // use free angle, and stay upwards\\n (option ==-1) ? 0.0 : // useful for backward compatibility, all texts remains horizontal\\n rawAngle; // otherwise return back raw input angle\\n}\\n\\nbool isAxisTitle = (axis.x == 0.0) &&\\n (axis.y == 0.0) &&\\n (axis.z == 0.0);\\n\\nvoid main() {\\n //Compute world offset\\n float axisDistance = position.z;\\n vec3 dataPosition = axisDistance * axis + offset;\\n\\n float beta = angle; // i.e. user defined attributes for each tick\\n\\n float axisAngle;\\n float clipAngle;\\n float flip;\\n\\n if (enableAlign) {\\n axisAngle = (isAxisTitle) ? HALF_PI :\\n computeViewAngle(dataPosition, dataPosition + axis);\\n clipAngle = computeViewAngle(dataPosition, dataPosition + alignDir);\\n\\n axisAngle += (sin(axisAngle) < 0.0) ? PI : 0.0;\\n clipAngle += (sin(clipAngle) < 0.0) ? PI : 0.0;\\n\\n flip = (dot(vec2(cos(axisAngle), sin(axisAngle)),\\n vec2(sin(clipAngle),-cos(clipAngle))) > 0.0) ? 1.0 : 0.0;\\n\\n beta += applyAlignOption(clipAngle, flip * PI);\\n }\\n\\n //Compute plane offset\\n vec2 planeCoord = position.xy * pixelScale;\\n\\n mat2 planeXform = scale * mat2(\\n cos(beta), sin(beta),\\n -sin(beta), cos(beta)\\n );\\n\\n vec2 viewOffset = 2.0 * planeXform * planeCoord / resolution;\\n\\n //Compute clip position\\n vec3 clipPosition = project(dataPosition);\\n\\n //Apply text offset in clip coordinates\\n clipPosition += vec3(viewOffset, 0.0);\\n\\n //Done\\n gl_Position = vec4(clipPosition, 1.0);\\n}\"]),l=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\nvoid main() {\\n gl_FragColor = color;\\n}\"]);e.f=function(t){return i(t,s,l,null,[{name:\"position\",type:\"vec3\"}])};var u=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec3 normal;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 enable;\\nuniform vec3 bounds[2];\\n\\nvarying vec3 colorChannel;\\n\\nvoid main() {\\n\\n vec3 signAxis = sign(bounds[1] - bounds[0]);\\n\\n vec3 realNormal = signAxis * normal;\\n\\n if(dot(realNormal, enable) > 0.0) {\\n vec3 minRange = min(bounds[0], bounds[1]);\\n vec3 maxRange = max(bounds[0], bounds[1]);\\n vec3 nPosition = mix(minRange, maxRange, 0.5 * (position + 1.0));\\n gl_Position = projection * view * model * vec4(nPosition, 1.0);\\n } else {\\n gl_Position = vec4(0,0,0,0);\\n }\\n\\n colorChannel = abs(realNormal);\\n}\"]),c=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 colors[3];\\n\\nvarying vec3 colorChannel;\\n\\nvoid main() {\\n gl_FragColor = colorChannel.x * colors[0] +\\n colorChannel.y * colors[1] +\\n colorChannel.z * colors[2];\\n}\"]);e.bg=function(t){return i(t,u,c,null,[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}])}},9557:function(t,e,r){\"use strict\";t.exports=function(t,e,r,i,o,l){var u=n(t),f=a(t,[{buffer:u,size:3}]),h=s(t);h.attributes.position.location=0;var p=new c(t,h,u,f);return p.update(e,r,i,o,l),p};var n=r(5827),a=r(2944),o=r(875),s=r(1943).f,l=window||i.global||{},u=l.__TEXT_CACHE||{};function c(t,e,r,n){this.gl=t,this.shader=e,this.buffer=r,this.vao=n,this.tickOffset=this.tickCount=this.labelOffset=this.labelCount=null}l.__TEXT_CACHE={};var f=c.prototype,h=[0,0];f.bind=function(t,e,r,n){this.vao.bind(),this.shader.bind();var i=this.shader.uniforms;i.model=t,i.view=e,i.projection=r,i.pixelScale=n,h[0]=this.gl.drawingBufferWidth,h[1]=this.gl.drawingBufferHeight,this.shader.uniforms.resolution=h},f.unbind=function(){this.vao.unbind()},f.update=function(t,e,r,n,i){var a=[];function s(t,e,r,n,i,s){var l=u[r];l||(l=u[r]={});var c=l[e];c||(c=l[e]=function(t,e){try{return o(t,e)}catch(e){return console.warn('error vectorizing text:\"'+t+'\" error:',e),{cells:[],positions:[]}}}(e,{triangles:!0,font:r,textAlign:\"center\",textBaseline:\"middle\",lineSpacing:i,styletags:s}));for(var f=(n||12)/12,h=c.positions,p=c.cells,d=0,v=p.length;d=0;--y){var m=h[g[y]];a.push(f*m[0],-f*m[1],t)}}for(var l=[0,0,0],c=[0,0,0],f=[0,0,0],h=[0,0,0],p={breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},d=0;d<3;++d){f[d]=a.length/3|0,s(.5*(t[0][d]+t[1][d]),e[d],r[d],12,1.25,p),h[d]=(a.length/3|0)-f[d],l[d]=a.length/3|0;for(var v=0;v=0&&(i=r.length-n-1);var a=Math.pow(10,i),o=Math.round(t*e*a),s=o+\"\";if(s.indexOf(\"e\")>=0)return s;var l=o/a,u=o%a;o<0?(l=0|-Math.ceil(l),u=0|-u):(l=0|Math.floor(l),u|=0);var c=\"\"+l;if(o<0&&(c=\"-\"+c),i){for(var f=\"\"+u;f.length=t[0][i];--o)a.push({x:o*e[i],text:r(e[i],o)});n.push(a)}return n},e.equal=function(t,e){for(var r=0;r<3;++r){if(t[r].length!==e[r].length)return!1;for(var n=0;nr)throw new Error(\"gl-buffer: If resizing buffer, must not specify offset\");return t.bufferSubData(e,a,i),r}function c(t,e){for(var r=n.malloc(t.length,e),i=t.length,a=0;a=0;--n){if(e[n]!==r)return!1;r*=t[n]}return!0}(t.shape,t.stride))0===t.offset&&t.data.length===t.shape[0]?this.length=u(this.gl,this.type,this.length,this.usage,t.data,e):this.length=u(this.gl,this.type,this.length,this.usage,t.data.subarray(t.offset,t.shape[0]),e);else{var s=n.malloc(t.size,r),l=a(s,t.shape);i.assign(l,t),this.length=u(this.gl,this.type,this.length,this.usage,e<0?s:s.subarray(0,t.size),e),n.free(s)}}else if(Array.isArray(t)){var f;f=this.type===this.gl.ELEMENT_ARRAY_BUFFER?c(t,\"uint16\"):c(t,\"float32\"),this.length=u(this.gl,this.type,this.length,this.usage,e<0?f:f.subarray(0,t.length),e),n.free(f)}else if(\"object\"==typeof t&&\"number\"==typeof t.length)this.length=u(this.gl,this.type,this.length,this.usage,t,e);else{if(\"number\"!=typeof t&&void 0!==t)throw new Error(\"gl-buffer: Invalid data type\");if(e>=0)throw new Error(\"gl-buffer: Cannot specify offset when resizing buffer\");(t|=0)<=0&&(t=1),this.gl.bufferData(this.type,0|t,this.usage),this.length=t}},t.exports=function(t,e,r,n){if(r=r||t.ARRAY_BUFFER,n=n||t.DYNAMIC_DRAW,r!==t.ARRAY_BUFFER&&r!==t.ELEMENT_ARRAY_BUFFER)throw new Error(\"gl-buffer: Invalid type for webgl buffer, must be either gl.ARRAY_BUFFER or gl.ELEMENT_ARRAY_BUFFER\");if(n!==t.DYNAMIC_DRAW&&n!==t.STATIC_DRAW&&n!==t.STREAM_DRAW)throw new Error(\"gl-buffer: Invalid usage for buffer, must be either gl.DYNAMIC_DRAW, gl.STATIC_DRAW or gl.STREAM_DRAW\");var i=t.createBuffer(),a=new s(t,r,i,0,n);return a.update(e),a}},1140:function(t,e,r){\"use strict\";var n=r(2858);t.exports=function(t,e){var r=t.positions,i=t.vectors,a={positions:[],vertexIntensity:[],vertexIntensityBounds:t.vertexIntensityBounds,vectors:[],cells:[],coneOffset:t.coneOffset,colormap:t.colormap};if(0===t.positions.length)return e&&(e[0]=[0,0,0],e[1]=[0,0,0]),a;for(var o=0,s=1/0,l=-1/0,u=1/0,c=-1/0,f=1/0,h=-1/0,p=null,d=null,v=[],g=1/0,y=!1,m=0;mo&&(o=n.length(b)),m){var _=2*n.distance(p,x)/(n.length(d)+n.length(b));_?(g=Math.min(g,_),y=!1):y=!0}y||(p=x,d=b),v.push(b)}var w=[s,u,f],T=[l,c,h];e&&(e[0]=w,e[1]=T),0===o&&(o=1);var k=1/o;isFinite(g)||(g=1),a.vectorScale=g;var A=t.coneSize||.5;t.absoluteConeSize&&(A=t.absoluteConeSize*k),a.coneScale=A,m=0;for(var M=0;m=1},p.isTransparent=function(){return this.opacity<1},p.pickSlots=1,p.setPickBase=function(t){this.pickId=t},p.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,\"lightPosition\"in t&&(this.lightPosition=t.lightPosition),\"opacity\"in t&&(this.opacity=t.opacity),\"ambient\"in t&&(this.ambientLight=t.ambient),\"diffuse\"in t&&(this.diffuseLight=t.diffuse),\"specular\"in t&&(this.specularLight=t.specular),\"roughness\"in t&&(this.roughness=t.roughness),\"fresnel\"in t&&(this.fresnel=t.fresnel),void 0!==t.tubeScale&&(this.tubeScale=t.tubeScale),void 0!==t.vectorScale&&(this.vectorScale=t.vectorScale),void 0!==t.coneScale&&(this.coneScale=t.coneScale),void 0!==t.coneOffset&&(this.coneOffset=t.coneOffset),t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t){for(var e=c({colormap:t,nshades:256,format:\"rgba\"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4*n+a]=i[a];r[4*n+3]=255*i[3]}return u(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions,i=t.vectors;if(n&&r&&i){var a=[],o=[],s=[],l=[],f=[];this.cells=r,this.positions=n,this.vectors=i;var h=t.meshColor||[1,1,1,1],p=t.vertexIntensity,d=1/0,v=-1/0;if(p)if(t.vertexIntensityBounds)d=+t.vertexIntensityBounds[0],v=+t.vertexIntensityBounds[1];else for(var g=0;g0){var v=this.triShader;v.bind(),v.uniforms=u,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()}},p.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||f,n=t.view||f,i=t.projection||f,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s={model:r,view:n,projection:i,clipBounds:a,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,pickId:this.pickId/255},l=this.pickShader;l.bind(),l.uniforms=s,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind())},p.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions[r[1]].slice(0,3),i={position:n,dataCoordinate:n,index:Math.floor(r[1]/48)};return\"cone\"===this.traceType?i.index=Math.floor(r[1]/48):\"streamtube\"===this.traceType&&(i.intensity=this.intensity[r[1]],i.velocity=this.vectors[r[1]].slice(0,3),i.divergence=this.vectors[r[1]][3],i.index=e),i},p.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.pickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleVectors.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleIds.dispose()},t.exports=function(t,e,r){var n=r.shaders;1===arguments.length&&(t=(e=t).gl);var s=d(t,n),l=v(t,n),c=o(t,u(new Uint8Array([255,255,255,255]),[1,1,4]));c.generateMipmap(),c.minFilter=t.LINEAR_MIPMAP_LINEAR,c.magFilter=t.LINEAR;var f=i(t),p=i(t),g=i(t),y=i(t),m=i(t),x=a(t,[{buffer:f,type:t.FLOAT,size:4},{buffer:m,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:g,type:t.FLOAT,size:4},{buffer:y,type:t.FLOAT,size:2},{buffer:p,type:t.FLOAT,size:4}]),b=new h(t,c,s,l,f,p,m,g,y,x,r.traceType||\"cone\");return b.update(e),b}},7234:function(t,e,r){var n=r(6832),i=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n // Return up-vector for only-z vector.\\n // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n // From the above if-statement we have ||a|| > 0 U ||b|| > 0.\\n // Assign z = 0, x = -b, y = a:\\n // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n return normalize(vec3(-v.y, v.x, 0.0));\\n } else {\\n return normalize(vec3(0.0, v.z, -v.y));\\n }\\n}\\n\\n// Calculate the cone vertex and normal at the given index.\\n//\\n// The returned vertex is for a cone with its top at origin and height of 1.0,\\n// pointing in the direction of the vector attribute.\\n//\\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\\n// These vertices are used to make up the triangles of the cone by the following:\\n// segment + 0 top vertex\\n// segment + 1 perimeter vertex a+1\\n// segment + 2 perimeter vertex a\\n// segment + 3 center base vertex\\n// segment + 4 perimeter vertex a\\n// segment + 5 perimeter vertex a+1\\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\\n// To go from index to segment, floor(index / 6)\\n// To go from segment to angle, 2*pi * (segment/segmentCount)\\n// To go from index to segment index, index - (segment*6)\\n//\\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\\n\\n const float segmentCount = 8.0;\\n\\n float index = rawIndex - floor(rawIndex /\\n (segmentCount * 6.0)) *\\n (segmentCount * 6.0);\\n\\n float segment = floor(0.001 + index/6.0);\\n float segmentIndex = index - (segment*6.0);\\n\\n normal = -normalize(d);\\n\\n if (segmentIndex > 2.99 && segmentIndex < 3.01) {\\n return mix(vec3(0.0), -d, coneOffset);\\n }\\n\\n float nextAngle = (\\n (segmentIndex > 0.99 && segmentIndex < 1.01) ||\\n (segmentIndex > 4.99 && segmentIndex < 5.01)\\n ) ? 1.0 : 0.0;\\n float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\\n\\n vec3 v1 = mix(d, vec3(0.0), coneOffset);\\n vec3 v2 = v1 - d;\\n\\n vec3 u = getOrthogonalVector(d);\\n vec3 v = normalize(cross(u, d));\\n\\n vec3 x = u * cos(angle) * length(d)*0.25;\\n vec3 y = v * sin(angle) * length(d)*0.25;\\n vec3 v3 = v2 + x + y;\\n if (segmentIndex < 3.0) {\\n vec3 tx = u * sin(angle);\\n vec3 ty = v * -cos(angle);\\n vec3 tangent = tx + ty;\\n normal = normalize(cross(v3 - v1, tangent));\\n }\\n\\n if (segmentIndex == 0.0) {\\n return mix(d, vec3(0.0), coneOffset);\\n }\\n return v3;\\n}\\n\\nattribute vec3 vector;\\nattribute vec4 color, position;\\nattribute vec2 uv;\\n\\nuniform float vectorScale, coneScale, coneOffset;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 eyePosition, lightPosition;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n // Scale the vector magnitude to stay constant with\\n // model & view changes.\\n vec3 normal;\\n vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector), position.w, coneOffset, normal);\\n vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n //Lighting geometry parameters\\n vec4 cameraCoordinate = view * conePosition;\\n cameraCoordinate.xyz /= cameraCoordinate.w;\\n f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n f_eyeDirection = eyePosition - cameraCoordinate.xyz;\\n f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n // vec4 m_position = model * vec4(conePosition, 1.0);\\n vec4 t_position = view * conePosition;\\n gl_Position = projection * t_position;\\n\\n f_color = color;\\n f_data = conePosition.xyz;\\n f_position = position.xyz;\\n f_uv = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n float NdotH = max(x, 0.0001);\\n float cos2Alpha = NdotH * NdotH;\\n float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n float roughness2 = roughness * roughness;\\n float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n vec3 lightDirection,\\n vec3 viewDirection,\\n vec3 surfaceNormal,\\n float roughness,\\n float fresnel) {\\n\\n float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n //Half angle vector\\n vec3 H = normalize(lightDirection + viewDirection);\\n\\n //Geometric term\\n float NdotH = max(dot(surfaceNormal, H), 0.0);\\n float VdotH = max(dot(viewDirection, H), 0.000001);\\n float LdotH = max(dot(lightDirection, H), 0.000001);\\n float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n float G = min(1.0, min(G1, G2));\\n \\n //Distribution term\\n float D = beckmannDistribution(NdotH, roughness);\\n\\n //Fresnel term\\n float F = pow(1.0 - VdotN, fresnel);\\n\\n //Multiply terms and done\\n return G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n vec3 N = normalize(f_normal);\\n vec3 L = normalize(f_lightDirection);\\n vec3 V = normalize(f_eyeDirection);\\n\\n if(gl_FrontFacing) {\\n N = -N;\\n }\\n\\n float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n float diffuse = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n vec4 surfaceColor = f_color * texture2D(texture, f_uv);\\n vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular, 1.0);\\n\\n gl_FragColor = litColor * opacity;\\n}\\n\"]),o=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n // Return up-vector for only-z vector.\\n // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n // From the above if-statement we have ||a|| > 0 U ||b|| > 0.\\n // Assign z = 0, x = -b, y = a:\\n // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n return normalize(vec3(-v.y, v.x, 0.0));\\n } else {\\n return normalize(vec3(0.0, v.z, -v.y));\\n }\\n}\\n\\n// Calculate the cone vertex and normal at the given index.\\n//\\n// The returned vertex is for a cone with its top at origin and height of 1.0,\\n// pointing in the direction of the vector attribute.\\n//\\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\\n// These vertices are used to make up the triangles of the cone by the following:\\n// segment + 0 top vertex\\n// segment + 1 perimeter vertex a+1\\n// segment + 2 perimeter vertex a\\n// segment + 3 center base vertex\\n// segment + 4 perimeter vertex a\\n// segment + 5 perimeter vertex a+1\\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\\n// To go from index to segment, floor(index / 6)\\n// To go from segment to angle, 2*pi * (segment/segmentCount)\\n// To go from index to segment index, index - (segment*6)\\n//\\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\\n\\n const float segmentCount = 8.0;\\n\\n float index = rawIndex - floor(rawIndex /\\n (segmentCount * 6.0)) *\\n (segmentCount * 6.0);\\n\\n float segment = floor(0.001 + index/6.0);\\n float segmentIndex = index - (segment*6.0);\\n\\n normal = -normalize(d);\\n\\n if (segmentIndex > 2.99 && segmentIndex < 3.01) {\\n return mix(vec3(0.0), -d, coneOffset);\\n }\\n\\n float nextAngle = (\\n (segmentIndex > 0.99 && segmentIndex < 1.01) ||\\n (segmentIndex > 4.99 && segmentIndex < 5.01)\\n ) ? 1.0 : 0.0;\\n float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\\n\\n vec3 v1 = mix(d, vec3(0.0), coneOffset);\\n vec3 v2 = v1 - d;\\n\\n vec3 u = getOrthogonalVector(d);\\n vec3 v = normalize(cross(u, d));\\n\\n vec3 x = u * cos(angle) * length(d)*0.25;\\n vec3 y = v * sin(angle) * length(d)*0.25;\\n vec3 v3 = v2 + x + y;\\n if (segmentIndex < 3.0) {\\n vec3 tx = u * sin(angle);\\n vec3 ty = v * -cos(angle);\\n vec3 tangent = tx + ty;\\n normal = normalize(cross(v3 - v1, tangent));\\n }\\n\\n if (segmentIndex == 0.0) {\\n return mix(d, vec3(0.0), coneOffset);\\n }\\n return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform float vectorScale, coneScale, coneOffset;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n vec3 normal;\\n vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector.xyz), position.w, coneOffset, normal);\\n vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n gl_Position = projection * view * conePosition;\\n f_id = id;\\n f_position = position.xyz;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]);e.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec4\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"vector\",type:\"vec3\"}]},e.pickShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec4\"},{name:\"id\",type:\"vec4\"},{name:\"vector\",type:\"vec3\"}]}},1950:function(t){t.exports={0:\"NONE\",1:\"ONE\",2:\"LINE_LOOP\",3:\"LINE_STRIP\",4:\"TRIANGLES\",5:\"TRIANGLE_STRIP\",6:\"TRIANGLE_FAN\",256:\"DEPTH_BUFFER_BIT\",512:\"NEVER\",513:\"LESS\",514:\"EQUAL\",515:\"LEQUAL\",516:\"GREATER\",517:\"NOTEQUAL\",518:\"GEQUAL\",519:\"ALWAYS\",768:\"SRC_COLOR\",769:\"ONE_MINUS_SRC_COLOR\",770:\"SRC_ALPHA\",771:\"ONE_MINUS_SRC_ALPHA\",772:\"DST_ALPHA\",773:\"ONE_MINUS_DST_ALPHA\",774:\"DST_COLOR\",775:\"ONE_MINUS_DST_COLOR\",776:\"SRC_ALPHA_SATURATE\",1024:\"STENCIL_BUFFER_BIT\",1028:\"FRONT\",1029:\"BACK\",1032:\"FRONT_AND_BACK\",1280:\"INVALID_ENUM\",1281:\"INVALID_VALUE\",1282:\"INVALID_OPERATION\",1285:\"OUT_OF_MEMORY\",1286:\"INVALID_FRAMEBUFFER_OPERATION\",2304:\"CW\",2305:\"CCW\",2849:\"LINE_WIDTH\",2884:\"CULL_FACE\",2885:\"CULL_FACE_MODE\",2886:\"FRONT_FACE\",2928:\"DEPTH_RANGE\",2929:\"DEPTH_TEST\",2930:\"DEPTH_WRITEMASK\",2931:\"DEPTH_CLEAR_VALUE\",2932:\"DEPTH_FUNC\",2960:\"STENCIL_TEST\",2961:\"STENCIL_CLEAR_VALUE\",2962:\"STENCIL_FUNC\",2963:\"STENCIL_VALUE_MASK\",2964:\"STENCIL_FAIL\",2965:\"STENCIL_PASS_DEPTH_FAIL\",2966:\"STENCIL_PASS_DEPTH_PASS\",2967:\"STENCIL_REF\",2968:\"STENCIL_WRITEMASK\",2978:\"VIEWPORT\",3024:\"DITHER\",3042:\"BLEND\",3088:\"SCISSOR_BOX\",3089:\"SCISSOR_TEST\",3106:\"COLOR_CLEAR_VALUE\",3107:\"COLOR_WRITEMASK\",3317:\"UNPACK_ALIGNMENT\",3333:\"PACK_ALIGNMENT\",3379:\"MAX_TEXTURE_SIZE\",3386:\"MAX_VIEWPORT_DIMS\",3408:\"SUBPIXEL_BITS\",3410:\"RED_BITS\",3411:\"GREEN_BITS\",3412:\"BLUE_BITS\",3413:\"ALPHA_BITS\",3414:\"DEPTH_BITS\",3415:\"STENCIL_BITS\",3553:\"TEXTURE_2D\",4352:\"DONT_CARE\",4353:\"FASTEST\",4354:\"NICEST\",5120:\"BYTE\",5121:\"UNSIGNED_BYTE\",5122:\"SHORT\",5123:\"UNSIGNED_SHORT\",5124:\"INT\",5125:\"UNSIGNED_INT\",5126:\"FLOAT\",5386:\"INVERT\",5890:\"TEXTURE\",6401:\"STENCIL_INDEX\",6402:\"DEPTH_COMPONENT\",6406:\"ALPHA\",6407:\"RGB\",6408:\"RGBA\",6409:\"LUMINANCE\",6410:\"LUMINANCE_ALPHA\",7680:\"KEEP\",7681:\"REPLACE\",7682:\"INCR\",7683:\"DECR\",7936:\"VENDOR\",7937:\"RENDERER\",7938:\"VERSION\",9728:\"NEAREST\",9729:\"LINEAR\",9984:\"NEAREST_MIPMAP_NEAREST\",9985:\"LINEAR_MIPMAP_NEAREST\",9986:\"NEAREST_MIPMAP_LINEAR\",9987:\"LINEAR_MIPMAP_LINEAR\",10240:\"TEXTURE_MAG_FILTER\",10241:\"TEXTURE_MIN_FILTER\",10242:\"TEXTURE_WRAP_S\",10243:\"TEXTURE_WRAP_T\",10497:\"REPEAT\",10752:\"POLYGON_OFFSET_UNITS\",16384:\"COLOR_BUFFER_BIT\",32769:\"CONSTANT_COLOR\",32770:\"ONE_MINUS_CONSTANT_COLOR\",32771:\"CONSTANT_ALPHA\",32772:\"ONE_MINUS_CONSTANT_ALPHA\",32773:\"BLEND_COLOR\",32774:\"FUNC_ADD\",32777:\"BLEND_EQUATION_RGB\",32778:\"FUNC_SUBTRACT\",32779:\"FUNC_REVERSE_SUBTRACT\",32819:\"UNSIGNED_SHORT_4_4_4_4\",32820:\"UNSIGNED_SHORT_5_5_5_1\",32823:\"POLYGON_OFFSET_FILL\",32824:\"POLYGON_OFFSET_FACTOR\",32854:\"RGBA4\",32855:\"RGB5_A1\",32873:\"TEXTURE_BINDING_2D\",32926:\"SAMPLE_ALPHA_TO_COVERAGE\",32928:\"SAMPLE_COVERAGE\",32936:\"SAMPLE_BUFFERS\",32937:\"SAMPLES\",32938:\"SAMPLE_COVERAGE_VALUE\",32939:\"SAMPLE_COVERAGE_INVERT\",32968:\"BLEND_DST_RGB\",32969:\"BLEND_SRC_RGB\",32970:\"BLEND_DST_ALPHA\",32971:\"BLEND_SRC_ALPHA\",33071:\"CLAMP_TO_EDGE\",33170:\"GENERATE_MIPMAP_HINT\",33189:\"DEPTH_COMPONENT16\",33306:\"DEPTH_STENCIL_ATTACHMENT\",33635:\"UNSIGNED_SHORT_5_6_5\",33648:\"MIRRORED_REPEAT\",33901:\"ALIASED_POINT_SIZE_RANGE\",33902:\"ALIASED_LINE_WIDTH_RANGE\",33984:\"TEXTURE0\",33985:\"TEXTURE1\",33986:\"TEXTURE2\",33987:\"TEXTURE3\",33988:\"TEXTURE4\",33989:\"TEXTURE5\",33990:\"TEXTURE6\",33991:\"TEXTURE7\",33992:\"TEXTURE8\",33993:\"TEXTURE9\",33994:\"TEXTURE10\",33995:\"TEXTURE11\",33996:\"TEXTURE12\",33997:\"TEXTURE13\",33998:\"TEXTURE14\",33999:\"TEXTURE15\",34e3:\"TEXTURE16\",34001:\"TEXTURE17\",34002:\"TEXTURE18\",34003:\"TEXTURE19\",34004:\"TEXTURE20\",34005:\"TEXTURE21\",34006:\"TEXTURE22\",34007:\"TEXTURE23\",34008:\"TEXTURE24\",34009:\"TEXTURE25\",34010:\"TEXTURE26\",34011:\"TEXTURE27\",34012:\"TEXTURE28\",34013:\"TEXTURE29\",34014:\"TEXTURE30\",34015:\"TEXTURE31\",34016:\"ACTIVE_TEXTURE\",34024:\"MAX_RENDERBUFFER_SIZE\",34041:\"DEPTH_STENCIL\",34055:\"INCR_WRAP\",34056:\"DECR_WRAP\",34067:\"TEXTURE_CUBE_MAP\",34068:\"TEXTURE_BINDING_CUBE_MAP\",34069:\"TEXTURE_CUBE_MAP_POSITIVE_X\",34070:\"TEXTURE_CUBE_MAP_NEGATIVE_X\",34071:\"TEXTURE_CUBE_MAP_POSITIVE_Y\",34072:\"TEXTURE_CUBE_MAP_NEGATIVE_Y\",34073:\"TEXTURE_CUBE_MAP_POSITIVE_Z\",34074:\"TEXTURE_CUBE_MAP_NEGATIVE_Z\",34076:\"MAX_CUBE_MAP_TEXTURE_SIZE\",34338:\"VERTEX_ATTRIB_ARRAY_ENABLED\",34339:\"VERTEX_ATTRIB_ARRAY_SIZE\",34340:\"VERTEX_ATTRIB_ARRAY_STRIDE\",34341:\"VERTEX_ATTRIB_ARRAY_TYPE\",34342:\"CURRENT_VERTEX_ATTRIB\",34373:\"VERTEX_ATTRIB_ARRAY_POINTER\",34466:\"NUM_COMPRESSED_TEXTURE_FORMATS\",34467:\"COMPRESSED_TEXTURE_FORMATS\",34660:\"BUFFER_SIZE\",34661:\"BUFFER_USAGE\",34816:\"STENCIL_BACK_FUNC\",34817:\"STENCIL_BACK_FAIL\",34818:\"STENCIL_BACK_PASS_DEPTH_FAIL\",34819:\"STENCIL_BACK_PASS_DEPTH_PASS\",34877:\"BLEND_EQUATION_ALPHA\",34921:\"MAX_VERTEX_ATTRIBS\",34922:\"VERTEX_ATTRIB_ARRAY_NORMALIZED\",34930:\"MAX_TEXTURE_IMAGE_UNITS\",34962:\"ARRAY_BUFFER\",34963:\"ELEMENT_ARRAY_BUFFER\",34964:\"ARRAY_BUFFER_BINDING\",34965:\"ELEMENT_ARRAY_BUFFER_BINDING\",34975:\"VERTEX_ATTRIB_ARRAY_BUFFER_BINDING\",35040:\"STREAM_DRAW\",35044:\"STATIC_DRAW\",35048:\"DYNAMIC_DRAW\",35632:\"FRAGMENT_SHADER\",35633:\"VERTEX_SHADER\",35660:\"MAX_VERTEX_TEXTURE_IMAGE_UNITS\",35661:\"MAX_COMBINED_TEXTURE_IMAGE_UNITS\",35663:\"SHADER_TYPE\",35664:\"FLOAT_VEC2\",35665:\"FLOAT_VEC3\",35666:\"FLOAT_VEC4\",35667:\"INT_VEC2\",35668:\"INT_VEC3\",35669:\"INT_VEC4\",35670:\"BOOL\",35671:\"BOOL_VEC2\",35672:\"BOOL_VEC3\",35673:\"BOOL_VEC4\",35674:\"FLOAT_MAT2\",35675:\"FLOAT_MAT3\",35676:\"FLOAT_MAT4\",35678:\"SAMPLER_2D\",35680:\"SAMPLER_CUBE\",35712:\"DELETE_STATUS\",35713:\"COMPILE_STATUS\",35714:\"LINK_STATUS\",35715:\"VALIDATE_STATUS\",35716:\"INFO_LOG_LENGTH\",35717:\"ATTACHED_SHADERS\",35718:\"ACTIVE_UNIFORMS\",35719:\"ACTIVE_UNIFORM_MAX_LENGTH\",35720:\"SHADER_SOURCE_LENGTH\",35721:\"ACTIVE_ATTRIBUTES\",35722:\"ACTIVE_ATTRIBUTE_MAX_LENGTH\",35724:\"SHADING_LANGUAGE_VERSION\",35725:\"CURRENT_PROGRAM\",36003:\"STENCIL_BACK_REF\",36004:\"STENCIL_BACK_VALUE_MASK\",36005:\"STENCIL_BACK_WRITEMASK\",36006:\"FRAMEBUFFER_BINDING\",36007:\"RENDERBUFFER_BINDING\",36048:\"FRAMEBUFFER_ATTACHMENT_OBJECT_TYPE\",36049:\"FRAMEBUFFER_ATTACHMENT_OBJECT_NAME\",36050:\"FRAMEBUFFER_ATTACHMENT_TEXTURE_LEVEL\",36051:\"FRAMEBUFFER_ATTACHMENT_TEXTURE_CUBE_MAP_FACE\",36053:\"FRAMEBUFFER_COMPLETE\",36054:\"FRAMEBUFFER_INCOMPLETE_ATTACHMENT\",36055:\"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT\",36057:\"FRAMEBUFFER_INCOMPLETE_DIMENSIONS\",36061:\"FRAMEBUFFER_UNSUPPORTED\",36064:\"COLOR_ATTACHMENT0\",36096:\"DEPTH_ATTACHMENT\",36128:\"STENCIL_ATTACHMENT\",36160:\"FRAMEBUFFER\",36161:\"RENDERBUFFER\",36162:\"RENDERBUFFER_WIDTH\",36163:\"RENDERBUFFER_HEIGHT\",36164:\"RENDERBUFFER_INTERNAL_FORMAT\",36168:\"STENCIL_INDEX8\",36176:\"RENDERBUFFER_RED_SIZE\",36177:\"RENDERBUFFER_GREEN_SIZE\",36178:\"RENDERBUFFER_BLUE_SIZE\",36179:\"RENDERBUFFER_ALPHA_SIZE\",36180:\"RENDERBUFFER_DEPTH_SIZE\",36181:\"RENDERBUFFER_STENCIL_SIZE\",36194:\"RGB565\",36336:\"LOW_FLOAT\",36337:\"MEDIUM_FLOAT\",36338:\"HIGH_FLOAT\",36339:\"LOW_INT\",36340:\"MEDIUM_INT\",36341:\"HIGH_INT\",36346:\"SHADER_COMPILER\",36347:\"MAX_VERTEX_UNIFORM_VECTORS\",36348:\"MAX_VARYING_VECTORS\",36349:\"MAX_FRAGMENT_UNIFORM_VECTORS\",37440:\"UNPACK_FLIP_Y_WEBGL\",37441:\"UNPACK_PREMULTIPLY_ALPHA_WEBGL\",37442:\"CONTEXT_LOST_WEBGL\",37443:\"UNPACK_COLORSPACE_CONVERSION_WEBGL\",37444:\"BROWSER_DEFAULT_WEBGL\"}},6603:function(t,e,r){var 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n=r(8931);t.exports=function(t,e,r,n){i||(i=t.FRAMEBUFFER_UNSUPPORTED,a=t.FRAMEBUFFER_INCOMPLETE_ATTACHMENT,o=t.FRAMEBUFFER_INCOMPLETE_DIMENSIONS,s=t.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT);var u=t.getExtension(\"WEBGL_draw_buffers\");if(!l&&u&&function(t,e){var r=t.getParameter(e.MAX_COLOR_ATTACHMENTS_WEBGL);l=new Array(r+1);for(var n=0;n<=r;++n){for(var i=new Array(r),a=0;ac||r<0||r>c)throw new Error(\"gl-fbo: Parameters are too large for FBO\");var f=1;if(\"color\"in(n=n||{})){if((f=Math.max(0|n.color,0))<0)throw new Error(\"gl-fbo: Must specify a nonnegative number of colors\");if(f>1){if(!u)throw new Error(\"gl-fbo: Multiple draw buffer extension not supported\");if(f>t.getParameter(u.MAX_COLOR_ATTACHMENTS_WEBGL))throw new Error(\"gl-fbo: Context does not support \"+f+\" draw buffers\")}}var h=t.UNSIGNED_BYTE,p=t.getExtension(\"OES_texture_float\");if(n.float&&f>0){if(!p)throw new Error(\"gl-fbo: Context does not support floating point textures\");h=t.FLOAT}else 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x=r.checkFramebufferStatus(r.FRAMEBUFFER);if(x!==r.FRAMEBUFFER_COMPLETE){for(t._destroyed=!0,r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteFramebuffer(t.handle),t.handle=null,t.depth&&(t.depth.dispose(),t.depth=null),t._depth_rb&&(r.deleteRenderbuffer(t._depth_rb),t._depth_rb=null),y=0;yi||r<0||r>i)throw new Error(\"gl-fbo: Can't resize FBO, invalid dimensions\");t._shape[0]=e,t._shape[1]=r;for(var a=u(n),o=0;o>8*p&255;this.pickOffset=r,i.bind();var d=i.uniforms;d.viewTransform=t,d.pickOffset=e,d.shape=this.shape;var v=i.attributes;return this.positionBuffer.bind(),v.position.pointer(),this.weightBuffer.bind(),v.weight.pointer(s.UNSIGNED_BYTE,!1),this.idBuffer.bind(),v.pickId.pointer(s.UNSIGNED_BYTE,!1),s.drawArrays(s.TRIANGLES,0,o),r+this.shape[0]*this.shape[1]}}}(),f.pick=function(t,e,r){var n=this.pickOffset,i=this.shape[0]*this.shape[1];if(r=n+i)return null;var a=r-n,o=this.xData,s=this.yData;return{object:this,pointId:a,dataCoord:[o[a%this.shape[0]],s[a/this.shape[0]|0]]}},f.update=function(t){var e=(t=t||{}).shape||[0,0],r=t.x||i(e[0]),o=t.y||i(e[1]),s=t.z||new Float32Array(e[0]*e[1]),l=!1!==t.zsmooth;this.xData=r,this.yData=o;var u,c,f,p,d=t.colorLevels||[0],v=t.colorValues||[0,0,0,1],g=d.length,y=this.bounds;l?(u=y[0]=r[0],c=y[1]=o[0],f=y[2]=r[r.length-1],p=y[3]=o[o.length-1]):(u=y[0]=r[0]+(r[1]-r[0])/2,c=y[1]=o[0]+(o[1]-o[0])/2,f=y[2]=r[r.length-1]+(r[r.length-1]-r[r.length-2])/2,p=y[3]=o[o.length-1]+(o[o.length-1]-o[o.length-2])/2);var m=1/(f-u),x=1/(p-c),b=e[0],_=e[1];this.shape=[b,_];var w=(l?(b-1)*(_-1):b*_)*(h.length>>>1);this.numVertices=w;for(var T=a.mallocUint8(4*w),k=a.mallocFloat32(2*w),A=a.mallocUint8(2*w),M=a.mallocUint32(w),S=0,E=l?b-1:b,L=l?_-1:_,C=0;C max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform sampler2D dashTexture;\\nuniform float dashScale;\\nuniform float opacity;\\n\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n if (\\n outOfRange(clipBounds[0], clipBounds[1], worldPosition) ||\\n fragColor.a * opacity == 0.\\n ) discard;\\n\\n float dashWeight = texture2D(dashTexture, vec2(dashScale * pixelArcLength, 0)).r;\\n if(dashWeight < 0.5) {\\n discard;\\n }\\n gl_FragColor = fragColor * opacity;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\n#define FLOAT_MAX 1.70141184e38\\n#define FLOAT_MIN 1.17549435e-38\\n\\n// https://github.com/mikolalysenko/glsl-read-float/blob/master/index.glsl\\nvec4 packFloat(float v) {\\n float av = abs(v);\\n\\n //Handle special cases\\n if(av < FLOAT_MIN) {\\n return vec4(0.0, 0.0, 0.0, 0.0);\\n } else if(v > FLOAT_MAX) {\\n return vec4(127.0, 128.0, 0.0, 0.0) / 255.0;\\n } else if(v < -FLOAT_MAX) {\\n return vec4(255.0, 128.0, 0.0, 0.0) / 255.0;\\n }\\n\\n vec4 c = vec4(0,0,0,0);\\n\\n //Compute exponent and mantissa\\n float e = floor(log2(av));\\n float m = av * pow(2.0, -e) - 1.0;\\n\\n //Unpack mantissa\\n c[1] = floor(128.0 * m);\\n m -= c[1] / 128.0;\\n c[2] = floor(32768.0 * m);\\n m -= c[2] / 32768.0;\\n c[3] = floor(8388608.0 * m);\\n\\n //Unpack exponent\\n float ebias = e + 127.0;\\n c[0] = floor(ebias / 2.0);\\n ebias -= c[0] * 2.0;\\n c[1] += floor(ebias) * 128.0;\\n\\n //Unpack sign bit\\n c[0] += 128.0 * step(0.0, -v);\\n\\n //Scale back to range\\n return c / 255.0;\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform float pickId;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], worldPosition)) discard;\\n\\n gl_FragColor = vec4(pickId/255.0, packFloat(pixelArcLength).xyz);\\n}\"]),l=[{name:\"position\",type:\"vec3\"},{name:\"nextPosition\",type:\"vec3\"},{name:\"arcLength\",type:\"float\"},{name:\"lineWidth\",type:\"float\"},{name:\"color\",type:\"vec4\"}];e.createShader=function(t){return i(t,a,o,null,l)},e.createPickShader=function(t){return i(t,a,s,null,l)}},6086:function(t,e,r){\"use strict\";t.exports=function(t){var e=t.gl||t.scene&&t.scene.gl,r=f(e);r.attributes.position.location=0,r.attributes.nextPosition.location=1,r.attributes.arcLength.location=2,r.attributes.lineWidth.location=3,r.attributes.color.location=4;var o=h(e);o.attributes.position.location=0,o.attributes.nextPosition.location=1,o.attributes.arcLength.location=2,o.attributes.lineWidth.location=3,o.attributes.color.location=4;for(var s=n(e),l=i(e,[{buffer:s,size:3,offset:0,stride:48},{buffer:s,size:3,offset:12,stride:48},{buffer:s,size:1,offset:24,stride:48},{buffer:s,size:1,offset:28,stride:48},{buffer:s,size:4,offset:32,stride:48}]),c=u(new Array(1024),[256,1,4]),p=0;p<1024;++p)c.data[p]=255;var d=a(e,c);d.wrap=e.REPEAT;var v=new y(e,r,o,s,l,d);return v.update(t),v};var n=r(5827),i=r(2944),a=r(8931),o=new Uint8Array(4),s=new Float32Array(o.buffer),l=r(5070),u=r(5050),c=r(248),f=c.createShader,h=c.createPickShader,p=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function d(t,e){for(var r=0,n=0;n<3;++n){var i=t[n]-e[n];r+=i*i}return Math.sqrt(r)}function v(t){for(var e=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],r=0;r<3;++r)e[0][r]=Math.max(t[0][r],e[0][r]),e[1][r]=Math.min(t[1][r],e[1][r]);return e}function g(t,e,r,n){this.arcLength=t,this.position=e,this.index=r,this.dataCoordinate=n}function y(t,e,r,n,i,a){this.gl=t,this.shader=e,this.pickShader=r,this.buffer=n,this.vao=i,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.points=[],this.arcLength=[],this.vertexCount=0,this.bounds=[[0,0,0],[0,0,0]],this.pickId=0,this.lineWidth=1,this.texture=a,this.dashScale=1,this.opacity=1,this.hasAlpha=!1,this.dirty=!0,this.pixelRatio=1}var m=y.prototype;m.isTransparent=function(){return this.hasAlpha},m.isOpaque=function(){return!this.hasAlpha},m.pickSlots=1,m.setPickBase=function(t){this.pickId=t},m.drawTransparent=m.draw=function(t){if(this.vertexCount){var e=this.gl,r=this.shader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,clipBounds:v(this.clipBounds),dashTexture:this.texture.bind(),dashScale:this.dashScale/this.arcLength[this.arcLength.length-1],opacity:this.opacity,screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},m.drawPick=function(t){if(this.vertexCount){var e=this.gl,r=this.pickShader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,pickId:this.pickId,clipBounds:v(this.clipBounds),screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},m.update=function(t){var e,r;this.dirty=!0;var n=!!t.connectGaps;\"dashScale\"in t&&(this.dashScale=t.dashScale),this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=+t.opacity,this.opacity<1&&(this.hasAlpha=!0));var i=[],a=[],o=[],s=0,c=0,f=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],h=t.position||t.positions;if(h){var p=t.color||t.colors||[0,0,0,1],v=t.lineWidth||1,g=!1;t:for(e=1;e0){for(var w=0;w<24;++w)i.push(i[i.length-12]);c+=2,g=!0}continue t}f[0][r]=Math.min(f[0][r],b[r],_[r]),f[1][r]=Math.max(f[1][r],b[r],_[r])}Array.isArray(p[0])?(y=p.length>e-1?p[e-1]:p.length>0?p[p.length-1]:[0,0,0,1],m=p.length>e?p[e]:p.length>0?p[p.length-1]:[0,0,0,1]):y=m=p,3===y.length&&(y=[y[0],y[1],y[2],1]),3===m.length&&(m=[m[0],m[1],m[2],1]),!this.hasAlpha&&y[3]<1&&(this.hasAlpha=!0),x=Array.isArray(v)?v.length>e-1?v[e-1]:v.length>0?v[v.length-1]:[0,0,0,1]:v;var T=s;if(s+=d(b,_),g){for(r=0;r<2;++r)i.push(b[0],b[1],b[2],_[0],_[1],_[2],T,x,y[0],y[1],y[2],y[3]);c+=2,g=!1}i.push(b[0],b[1],b[2],_[0],_[1],_[2],T,x,y[0],y[1],y[2],y[3],b[0],b[1],b[2],_[0],_[1],_[2],T,-x,y[0],y[1],y[2],y[3],_[0],_[1],_[2],b[0],b[1],b[2],s,-x,m[0],m[1],m[2],m[3],_[0],_[1],_[2],b[0],b[1],b[2],s,x,m[0],m[1],m[2],m[3]),c+=4}}if(this.buffer.update(i),a.push(s),o.push(h[h.length-1].slice()),this.bounds=f,this.vertexCount=c,this.points=o,this.arcLength=a,\"dashes\"in t){var k=t.dashes.slice();for(k.unshift(0),e=1;e1.0001)return null;y+=g[f]}return Math.abs(y-1)>.001?null:[h,s(t,g),g]}},2056:function(t,e,r){var n=r(6832),i=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, normal;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model\\n , view\\n , projection\\n , inverseModel;\\nuniform vec3 eyePosition\\n , lightPosition;\\n\\nvarying vec3 f_normal\\n , f_lightDirection\\n , f_eyeDirection\\n , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvec4 project(vec3 p) {\\n return projection * view * model * vec4(p, 1.0);\\n}\\n\\nvoid main() {\\n gl_Position = project(position);\\n\\n //Lighting geometry parameters\\n vec4 cameraCoordinate = view * vec4(position , 1.0);\\n cameraCoordinate.xyz /= cameraCoordinate.w;\\n f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n f_eyeDirection = eyePosition - cameraCoordinate.xyz;\\n f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n f_color = color;\\n f_data = position;\\n f_uv = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n float NdotH = max(x, 0.0001);\\n float cos2Alpha = NdotH * NdotH;\\n float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n float roughness2 = roughness * roughness;\\n float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n vec3 lightDirection,\\n vec3 viewDirection,\\n vec3 surfaceNormal,\\n float roughness,\\n float fresnel) {\\n\\n float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n //Half angle vector\\n vec3 H = normalize(lightDirection + viewDirection);\\n\\n //Geometric term\\n float NdotH = max(dot(surfaceNormal, H), 0.0);\\n float VdotH = max(dot(viewDirection, H), 0.000001);\\n float LdotH = max(dot(lightDirection, H), 0.000001);\\n float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n float G = min(1.0, min(G1, G2));\\n \\n //Distribution term\\n float D = beckmannDistribution(NdotH, roughness);\\n\\n //Fresnel term\\n float F = pow(1.0 - VdotN, fresnel);\\n\\n //Multiply terms and done\\n return G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\n//#pragma glslify: beckmann = require(glsl-specular-beckmann) // used in gl-surface3d\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness\\n , fresnel\\n , kambient\\n , kdiffuse\\n , kspecular;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal\\n , f_lightDirection\\n , f_eyeDirection\\n , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n if (f_color.a == 0.0 ||\\n outOfRange(clipBounds[0], clipBounds[1], f_data)\\n ) discard;\\n\\n vec3 N = normalize(f_normal);\\n vec3 L = normalize(f_lightDirection);\\n vec3 V = normalize(f_eyeDirection);\\n\\n if(gl_FrontFacing) {\\n N = -N;\\n }\\n\\n float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n //float specular = max(0.0, beckmann(L, V, N, roughness)); // used in gl-surface3d\\n\\n float diffuse = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n vec4 surfaceColor = vec4(f_color.rgb, 1.0) * texture2D(texture, f_uv);\\n vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular, 1.0);\\n\\n gl_FragColor = litColor * f_color.a;\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n gl_Position = projection * view * model * vec4(position, 1.0);\\n f_color = color;\\n f_data = position;\\n f_uv = uv;\\n}\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], f_data)) discard;\\n\\n gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\"]),l=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\nattribute float pointSize;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n gl_Position = vec4(0.0, 0.0 ,0.0 ,0.0);\\n } else {\\n gl_Position = projection * view * model * vec4(position, 1.0);\\n }\\n gl_PointSize = pointSize;\\n f_color = color;\\n f_uv = uv;\\n}\"]),u=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n vec2 pointR = gl_PointCoord.xy - vec2(0.5, 0.5);\\n if(dot(pointR, pointR) > 0.25) {\\n discard;\\n }\\n gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\"]),c=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n gl_Position = projection * view * model * vec4(position, 1.0);\\n f_id = id;\\n f_position = position;\\n}\"]),f=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]),h=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute float pointSize;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n gl_Position = vec4(0.0, 0.0, 0.0, 0.0);\\n } else {\\n gl_Position = projection * view * model * vec4(position, 1.0);\\n gl_PointSize = pointSize;\\n }\\n f_id = id;\\n f_position = position;\\n}\"]),p=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\n\\nvoid main() {\\n gl_Position = projection * view * model * vec4(position, 1.0);\\n}\"]),d=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec3 contourColor;\\n\\nvoid main() {\\n gl_FragColor = vec4(contourColor, 1.0);\\n}\\n\"]);e.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},e.wireShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},e.pointShader={vertex:l,fragment:u,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"pointSize\",type:\"float\"}]},e.pickShader={vertex:c,fragment:f,attributes:[{name:\"position\",type:\"vec3\"},{name:\"id\",type:\"vec4\"}]},e.pointPickShader={vertex:h,fragment:f,attributes:[{name:\"position\",type:\"vec3\"},{name:\"pointSize\",type:\"float\"},{name:\"id\",type:\"vec4\"}]},e.contourShader={vertex:p,fragment:d,attributes:[{name:\"position\",type:\"vec3\"}]}},8116:function(t,e,r){\"use strict\";var n=r(5158),i=r(5827),a=r(2944),o=r(8931),s=r(115),l=r(104),u=r(7437),c=r(5050),f=r(9156),h=r(7212),p=r(5306),d=r(2056),v=r(4340),g=d.meshShader,y=d.wireShader,m=d.pointShader,x=d.pickShader,b=d.pointPickShader,_=d.contourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function T(t,e,r,n,i,a,o,s,l,u,c,f,h,p,d,v,g,y,m,x,b,_,T,k,A,M,S){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleColors=c,this.triangleNormals=h,this.triangleUVs=f,this.triangleIds=u,this.triangleVAO=p,this.triangleCount=0,this.lineWidth=1,this.edgePositions=d,this.edgeColors=g,this.edgeUVs=y,this.edgeIds=v,this.edgeVAO=m,this.edgeCount=0,this.pointPositions=x,this.pointColors=_,this.pointUVs=T,this.pointSizes=k,this.pointIds=b,this.pointVAO=A,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=M,this.contourVAO=S,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickVertex=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.hasAlpha=!1,this.opacityscale=!1,this._model=w,this._view=w,this._projection=w,this._resolution=[1,1]}var k=T.prototype;function A(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;rt&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}function M(t){var e=n(t,g.vertex,g.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.normal.location=4,e}function S(t){var e=n(t,y.vertex,y.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e}function E(t){var e=n(t,m.vertex,m.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.pointSize.location=4,e}function L(t){var e=n(t,x.vertex,x.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e}function C(t){var e=n(t,b.vertex,b.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.pointSize.location=4,e}function P(t){var e=n(t,_.vertex,_.fragment);return e.attributes.position.location=0,e}k.isOpaque=function(){return!this.hasAlpha},k.isTransparent=function(){return this.hasAlpha},k.pickSlots=1,k.setPickBase=function(t){this.pickId=t},k.highlight=function(t){if(t&&this.contourEnable){for(var e=h(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=p.mallocFloat32(6*a),s=0,l=0;l0&&((f=this.triShader).bind(),f.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&this.lineWidth>0&&((f=this.lineShader).bind(),f.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0&&((f=this.pointShader).bind(),f.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind()),this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0&&((f=this.contourShader).bind(),f.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind())},k.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s,l={model:r,view:n,projection:i,clipBounds:a,pickId:this.pickId/255};(s=this.pickShader).bind(),s.uniforms=l,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0&&((s=this.pointPickShader).bind(),s.uniforms=l,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind())},k.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;for(var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions,i=new Array(r.length),a=0;ai[k]&&(r.uniforms.dataAxis=u,r.uniforms.screenOffset=c,r.uniforms.color=g[t],r.uniforms.angle=y[t],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),m[t]&&T&&(c[1^t]-=M*p*x[t],r.uniforms.dataAxis=f,r.uniforms.screenOffset=c,r.uniforms.color=b[t],r.uniforms.angle=_[t],a.drawArrays(a.TRIANGLES,w,T)),c[1^t]=M*s[2+(1^t)]-1,d[t+2]&&(c[1^t]+=M*p*v[t+2],ki[k]&&(r.uniforms.dataAxis=u,r.uniforms.screenOffset=c,r.uniforms.color=g[t+2],r.uniforms.angle=y[t+2],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),m[t+2]&&T&&(c[1^t]+=M*p*x[t+2],r.uniforms.dataAxis=f,r.uniforms.screenOffset=c,r.uniforms.color=b[t+2],r.uniforms.angle=_[t+2],a.drawArrays(a.TRIANGLES,w,T))}),v.drawTitle=function(){var t=[0,0],e=[0,0];return function(){var r=this.plot,n=this.shader,i=r.gl,a=r.screenBox,o=r.titleCenter,s=r.titleAngle,l=r.titleColor,u=r.pixelRatio;if(this.titleCount){for(var c=0;c<2;++c)e[c]=2*(o[c]*u-a[c])/(a[2+c]-a[c])-1;n.bind(),n.uniforms.dataAxis=t,n.uniforms.screenOffset=e,n.uniforms.angle=s,n.uniforms.color=l,i.drawArrays(i.TRIANGLES,this.titleOffset,this.titleCount)}}}(),v.bind=(h=[0,0],p=[0,0],d=[0,0],function(){var t=this.plot,e=this.shader,r=t._tickBounds,n=t.dataBox,i=t.screenBox,a=t.viewBox;e.bind();for(var o=0;o<2;++o){var s=r[o],l=r[o+2]-s,u=.5*(n[o+2]+n[o]),c=n[o+2]-n[o],f=a[o],v=a[o+2]-f,g=i[o],y=i[o+2]-g;p[o]=2*l/c*v/y,h[o]=2*(s-u)/c*v/y}d[1]=2*t.pixelRatio/(i[3]-i[1]),d[0]=d[1]*(i[3]-i[1])/(i[2]-i[0]),e.uniforms.dataScale=p,e.uniforms.dataShift=h,e.uniforms.textScale=d,this.vbo.bind(),e.attributes.textCoordinate.pointer()}),v.update=function(t){var e,r,n,i,o,s=[],l=t.ticks,u=t.bounds;for(o=0;o<2;++o){var c=[Math.floor(s.length/3)],f=[-1/0],h=l[o];for(e=0;e=0){var v=e[d]-n[d]*(e[d+2]-e[d])/(n[d+2]-n[d]);0===d?o.drawLine(v,e[1],v,e[3],p[d],h[d]):o.drawLine(e[0],v,e[2],v,p[d],h[d])}}for(d=0;d=0;--t)this.objects[t].dispose();for(this.objects.length=0,t=this.overlays.length-1;t>=0;--t)this.overlays[t].dispose();this.overlays.length=0,this.gl=null},u.addObject=function(t){this.objects.indexOf(t)<0&&(this.objects.push(t),this.setDirty())},u.removeObject=function(t){for(var e=this.objects,r=0;rMath.abs(e))u.rotate(a,0,0,-t*r*Math.PI*d.rotateSpeed/window.innerWidth);else if(!d._ortho){var o=-d.zoomSpeed*i*e/window.innerHeight*(a-u.lastT())/20;u.pan(a,0,0,f*(Math.exp(o)-1))}}}),!0)},d.enableMouseListeners(),d};var n=r(8161),i=r(1152),a=r(6145),o=r(6475),s=r(2565),l=r(5233)},8245:function(t,e,r){var n=r(6832),i=r(5158),a=n([\"precision mediump float;\\n#define GLSLIFY 1\\nattribute vec2 position;\\nvarying vec2 uv;\\nvoid main() {\\n uv = position;\\n gl_Position = vec4(position, 0, 1);\\n}\"]),o=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D accumBuffer;\\nvarying vec2 uv;\\n\\nvoid main() {\\n vec4 accum = texture2D(accumBuffer, 0.5 * (uv + 1.0));\\n gl_FragColor = min(vec4(1,1,1,1), accum);\\n}\"]);t.exports=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec2\"}])}},1059:function(t,e,r){\"use strict\";var n=r(4296),i=r(7453),a=r(2771),o=r(6496),s=r(2611),l=r(4234),u=r(8126),c=r(6145),f=r(1120),h=r(5268),p=r(8245),d=r(2321)({tablet:!0,featureDetect:!0});function v(){this.mouse=[-1,-1],this.screen=null,this.distance=1/0,this.index=null,this.dataCoordinate=null,this.dataPosition=null,this.object=null,this.data=null}function g(t){var e=Math.round(Math.log(Math.abs(t))/Math.log(10));if(e<0){var r=Math.round(Math.pow(10,-e));return Math.ceil(t*r)/r}return e>0?(r=Math.round(Math.pow(10,e)),Math.ceil(t/r)*r):Math.ceil(t)}function y(t){return\"boolean\"!=typeof t||t}t.exports={createScene:function(t){(t=t||{}).camera=t.camera||{};var e=t.canvas;e||(e=document.createElement(\"canvas\"),t.container?t.container.appendChild(e):document.body.appendChild(e));var r=t.gl;if(r||(t.glOptions&&(d=!!t.glOptions.preserveDrawingBuffer),r=function(t,e){var r=null;try{(r=t.getContext(\"webgl\",e))||(r=t.getContext(\"experimental-webgl\",e))}catch(t){return null}return r}(e,t.glOptions||{premultipliedAlpha:!0,antialias:!0,preserveDrawingBuffer:d})),!r)throw new Error(\"webgl not supported\");var m=t.bounds||[[-10,-10,-10],[10,10,10]],x=new v,b=l(r,r.drawingBufferWidth,r.drawingBufferHeight,{preferFloat:!d}),_=p(r),w=t.cameraObject&&!0===t.cameraObject._ortho||t.camera.projection&&\"orthographic\"===t.camera.projection.type||!1,T={eye:t.camera.eye||[2,0,0],center:t.camera.center||[0,0,0],up:t.camera.up||[0,1,0],zoomMin:t.camera.zoomMax||.1,zoomMax:t.camera.zoomMin||100,mode:t.camera.mode||\"turntable\",_ortho:w},k=t.axes||{},A=i(r,k);A.enable=!k.disable;var M=t.spikes||{},S=o(r,M),E=[],L=[],C=[],P=[],O=!0,I=!0,D={view:null,projection:new Array(16),model:new Array(16),_ortho:!1},z=(I=!0,[r.drawingBufferWidth,r.drawingBufferHeight]),R=t.cameraObject||n(e,T),F={gl:r,contextLost:!1,pixelRatio:t.pixelRatio||1,canvas:e,selection:x,camera:R,axes:A,axesPixels:null,spikes:S,bounds:m,objects:E,shape:z,aspect:t.aspectRatio||[1,1,1],pickRadius:t.pickRadius||10,zNear:t.zNear||.01,zFar:t.zFar||1e3,fovy:t.fovy||Math.PI/4,clearColor:t.clearColor||[0,0,0,0],autoResize:y(t.autoResize),autoBounds:y(t.autoBounds),autoScale:!!t.autoScale,autoCenter:y(t.autoCenter),clipToBounds:y(t.clipToBounds),snapToData:!!t.snapToData,onselect:t.onselect||null,onrender:t.onrender||null,onclick:t.onclick||null,cameraParams:D,oncontextloss:null,mouseListener:null,_stopped:!1,getAspectratio:function(){return{x:this.aspect[0],y:this.aspect[1],z:this.aspect[2]}},setAspectratio:function(t){this.aspect[0]=t.x,this.aspect[1]=t.y,this.aspect[2]=t.z,I=!0},setBounds:function(t,e){this.bounds[0][t]=e.min,this.bounds[1][t]=e.max},setClearColor:function(t){this.clearColor=t},clearRGBA:function(){this.gl.clearColor(this.clearColor[0],this.clearColor[1],this.clearColor[2],this.clearColor[3]),this.gl.clear(this.gl.COLOR_BUFFER_BIT|this.gl.DEPTH_BUFFER_BIT)}},B=[r.drawingBufferWidth/F.pixelRatio|0,r.drawingBufferHeight/F.pixelRatio|0];function N(){if(!F._stopped&&F.autoResize){var t=e.parentNode,r=1,n=1;t&&t!==document.body?(r=t.clientWidth,n=t.clientHeight):(r=window.innerWidth,n=window.innerHeight);var i=0|Math.ceil(r*F.pixelRatio),a=0|Math.ceil(n*F.pixelRatio);if(i!==e.width||a!==e.height){e.width=i,e.height=a;var o=e.style;o.position=o.position||\"absolute\",o.left=\"0px\",o.top=\"0px\",o.width=r+\"px\",o.height=n+\"px\",O=!0}}}function j(){for(var t=E.length,e=P.length,n=0;n0&&0===C[e-1];)C.pop(),P.pop().dispose()}function U(){if(F.contextLost)return!0;r.isContextLost()&&(F.contextLost=!0,F.mouseListener.enabled=!1,F.selection.object=null,F.oncontextloss&&F.oncontextloss())}F.autoResize&&N(),window.addEventListener(\"resize\",N),F.update=function(t){F._stopped||(t=t||{},O=!0,I=!0)},F.add=function(t){F._stopped||(t.axes=A,E.push(t),L.push(-1),O=!0,I=!0,j())},F.remove=function(t){if(!F._stopped){var e=E.indexOf(t);e<0||(E.splice(e,1),L.pop(),O=!0,I=!0,j())}},F.dispose=function(){if(!F._stopped&&(F._stopped=!0,window.removeEventListener(\"resize\",N),e.removeEventListener(\"webglcontextlost\",U),F.mouseListener.enabled=!1,!F.contextLost)){A.dispose(),S.dispose();for(var t=0;tx.distance)continue;for(var u=0;u 1.0) {\\n discard;\\n }\\n baseColor = mix(borderColor, color, step(radius, centerFraction));\\n gl_FragColor = vec4(baseColor.rgb * baseColor.a, baseColor.a);\\n }\\n}\\n\"]),e.pickVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 pickId;\\n\\nuniform mat3 matrix;\\nuniform float pointSize;\\nuniform vec4 pickOffset;\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n vec3 hgPosition = matrix * vec3(position, 1);\\n gl_Position = vec4(hgPosition.xy, 0, hgPosition.z);\\n gl_PointSize = pointSize;\\n\\n vec4 id = pickId + pickOffset;\\n id.y += floor(id.x / 256.0);\\n id.x -= floor(id.x / 256.0) * 256.0;\\n\\n id.z += floor(id.y / 256.0);\\n id.y -= floor(id.y / 256.0) * 256.0;\\n\\n id.w += floor(id.z / 256.0);\\n id.z -= floor(id.z / 256.0) * 256.0;\\n\\n fragId = id;\\n}\\n\"]),e.pickFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n float radius = length(2.0 * gl_PointCoord.xy - 1.0);\\n if(radius > 1.0) {\\n discard;\\n }\\n gl_FragColor = fragId / 255.0;\\n}\\n\"])},8271:function(t,e,r){\"use strict\";var n=r(5158),i=r(5827),a=r(5306),o=r(8023);function s(t,e,r,n,i){this.plot=t,this.offsetBuffer=e,this.pickBuffer=r,this.shader=n,this.pickShader=i,this.sizeMin=.5,this.sizeMinCap=2,this.sizeMax=20,this.areaRatio=1,this.pointCount=0,this.color=[1,0,0,1],this.borderColor=[0,0,0,1],this.blend=!1,this.pickOffset=0,this.points=null}t.exports=function(t,e){var r=t.gl,a=new s(t,i(r),i(r),n(r,o.pointVertex,o.pointFragment),n(r,o.pickVertex,o.pickFragment));return a.update(e),t.addObject(a),a};var l,u,c=s.prototype;c.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.offsetBuffer.dispose(),this.pickBuffer.dispose(),this.plot.removeObject(this)},c.update=function(t){var e;function r(e,r){return e in t?t[e]:r}t=t||{},this.sizeMin=r(\"sizeMin\",.5),this.sizeMax=r(\"sizeMax\",20),this.color=r(\"color\",[1,0,0,1]).slice(),this.areaRatio=r(\"areaRatio\",1),this.borderColor=r(\"borderColor\",[0,0,0,1]).slice(),this.blend=r(\"blend\",!1);var n=t.positions.length>>>1,i=t.positions instanceof Float32Array,o=t.idToIndex instanceof Int32Array&&t.idToIndex.length>=n,s=t.positions,l=i?s:a.mallocFloat32(s.length),u=o?t.idToIndex:a.mallocInt32(n);if(i||l.set(s),!o)for(l.set(s),e=0;e>>1;for(r=0;r=e[0]&&a<=e[2]&&o>=e[1]&&o<=e[3]&&n++}return n}(this.points,i),c=this.plot.pickPixelRatio*Math.max(Math.min(this.sizeMinCap,this.sizeMin),Math.min(this.sizeMax,this.sizeMax/Math.pow(s,.33333)));l[0]=2/a,l[4]=2/o,l[6]=-2*i[0]/a-1,l[7]=-2*i[1]/o-1,this.offsetBuffer.bind(),r.bind(),r.attributes.position.pointer(),r.uniforms.matrix=l,r.uniforms.color=this.color,r.uniforms.borderColor=this.borderColor,r.uniforms.pointCloud=c<5,r.uniforms.pointSize=c,r.uniforms.centerFraction=Math.min(1,Math.max(0,Math.sqrt(1-this.areaRatio))),e&&(u[0]=255&t,u[1]=t>>8&255,u[2]=t>>16&255,u[3]=t>>24&255,this.pickBuffer.bind(),r.attributes.pickId.pointer(n.UNSIGNED_BYTE),r.uniforms.pickOffset=u,this.pickOffset=t);var f=n.getParameter(n.BLEND),h=n.getParameter(n.DITHER);return f&&!this.blend&&n.disable(n.BLEND),h&&n.disable(n.DITHER),n.drawArrays(n.POINTS,0,this.pointCount),f&&!this.blend&&n.enable(n.BLEND),h&&n.enable(n.DITHER),t+this.pointCount}),c.draw=c.unifiedDraw,c.drawPick=c.unifiedDraw,c.pick=function(t,e,r){var n=this.pickOffset,i=this.pointCount;if(r=n+i)return null;var a=r-n,o=this.points;return{object:this,pointId:a,dataCoord:[o[2*a],o[2*a+1]]}}},6093:function(t){t.exports=function(t,e,r,n){var i,a,o,s,l,u=e[0],c=e[1],f=e[2],h=e[3],p=r[0],d=r[1],v=r[2],g=r[3];return(a=u*p+c*d+f*v+h*g)<0&&(a=-a,p=-p,d=-d,v=-v,g=-g),1-a>1e-6?(i=Math.acos(a),o=Math.sin(i),s=Math.sin((1-n)*i)/o,l=Math.sin(n*i)/o):(s=1-n,l=n),t[0]=s*u+l*p,t[1]=s*c+l*d,t[2]=s*f+l*v,t[3]=s*h+l*g,t}},8240:function(t){\"use strict\";t.exports=function(t){return t||0===t?t.toString():\"\"}},4123:function(t,e,r){\"use strict\";var n=r(875);t.exports=function(t,e,r){var a=i[e];if(a||(a=i[e]={}),t in a)return a[t];var o={textAlign:\"center\",textBaseline:\"middle\",lineHeight:1,font:e,lineSpacing:1.25,styletags:{breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},triangles:!0},s=n(t,o);o.triangles=!1;var l,u,c=n(t,o);if(r&&1!==r){for(l=0;l max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform vec4 highlightId;\\nuniform float highlightScale;\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n gl_Position = vec4(0,0,0,0);\\n } else {\\n float scale = 1.0;\\n if(distance(highlightId, id) < 0.0001) {\\n scale = highlightScale;\\n }\\n\\n vec4 worldPosition = model * vec4(position, 1);\\n vec4 viewPosition = view * worldPosition;\\n viewPosition = viewPosition / viewPosition.w;\\n vec4 clipPosition = projection * (viewPosition + scale * vec4(glyph.x, -glyph.y, 0, 0));\\n\\n gl_Position = clipPosition;\\n interpColor = color;\\n pickId = id;\\n dataCoordinate = position;\\n }\\n}\"]),o=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float highlightScale, pixelRatio;\\nuniform vec4 highlightId;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n gl_Position = vec4(0,0,0,0);\\n } else {\\n float scale = pixelRatio;\\n if(distance(highlightId.bgr, id.bgr) < 0.001) {\\n scale *= highlightScale;\\n }\\n\\n vec4 worldPosition = model * vec4(position, 1.0);\\n vec4 viewPosition = view * worldPosition;\\n vec4 clipPosition = projection * viewPosition;\\n clipPosition /= clipPosition.w;\\n\\n gl_Position = clipPosition + vec4(screenSize * scale * vec2(glyph.x, -glyph.y), 0.0, 0.0);\\n interpColor = color;\\n pickId = id;\\n dataCoordinate = position;\\n }\\n}\"]),s=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform float highlightScale;\\nuniform vec4 highlightId;\\nuniform vec3 axes[2];\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float scale, pixelRatio;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n gl_Position = vec4(0,0,0,0);\\n } else {\\n float lscale = pixelRatio * scale;\\n if(distance(highlightId, id) < 0.0001) {\\n lscale *= highlightScale;\\n }\\n\\n vec4 clipCenter = projection * view * model * vec4(position, 1);\\n vec3 dataPosition = position + 0.5*lscale*(axes[0] * glyph.x + axes[1] * glyph.y) * clipCenter.w * screenSize.y;\\n vec4 clipPosition = projection * view * model * vec4(dataPosition, 1);\\n\\n gl_Position = clipPosition;\\n interpColor = color;\\n pickId = id;\\n dataCoordinate = dataPosition;\\n }\\n}\\n\"]),l=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float opacity;\\n\\nvarying vec4 interpColor;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if (\\n outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate) ||\\n interpColor.a * opacity == 0.\\n ) discard;\\n gl_FragColor = interpColor * opacity;\\n}\\n\"]),u=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float pickGroup;\\n\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if (outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate)) discard;\\n\\n gl_FragColor = vec4(pickGroup, pickId.bgr);\\n}\"]),c=[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"glyph\",type:\"vec2\"},{name:\"id\",type:\"vec4\"}],f={vertex:a,fragment:l,attributes:c},h={vertex:o,fragment:l,attributes:c},p={vertex:s,fragment:l,attributes:c},d={vertex:a,fragment:u,attributes:c},v={vertex:o,fragment:u,attributes:c},g={vertex:s,fragment:u,attributes:c};function y(t,e){var r=n(t,e),i=r.attributes;return i.position.location=0,i.color.location=1,i.glyph.location=2,i.id.location=3,r}e.createPerspective=function(t){return y(t,f)},e.createOrtho=function(t){return y(t,h)},e.createProject=function(t){return y(t,p)},e.createPickPerspective=function(t){return y(t,d)},e.createPickOrtho=function(t){return y(t,v)},e.createPickProject=function(t){return y(t,g)}},2182:function(t,e,r){\"use strict\";var n=r(3596),i=r(5827),a=r(2944),o=r(5306),s=r(104),l=r(9282),u=r(4123),c=r(8240),f=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function h(t,e){var r=t[0],n=t[1],i=t[2],a=t[3];return t[0]=e[0]*r+e[4]*n+e[8]*i+e[12]*a,t[1]=e[1]*r+e[5]*n+e[9]*i+e[13]*a,t[2]=e[2]*r+e[6]*n+e[10]*i+e[14]*a,t[3]=e[3]*r+e[7]*n+e[11]*i+e[15]*a,t}function p(t,e,r,n){return h(n,n),h(n,n),h(n,n)}function d(t,e){this.index=t,this.dataCoordinate=this.position=e}function v(t){return!0===t||t>1?1:t}function g(t,e,r,n,i,a,o,s,l,u,c,f){this.gl=t,this.pixelRatio=1,this.shader=e,this.orthoShader=r,this.projectShader=n,this.pointBuffer=i,this.colorBuffer=a,this.glyphBuffer=o,this.idBuffer=s,this.vao=l,this.vertexCount=0,this.lineVertexCount=0,this.opacity=1,this.hasAlpha=!1,this.lineWidth=0,this.projectScale=[2/3,2/3,2/3],this.projectOpacity=[1,1,1],this.projectHasAlpha=!1,this.pickId=0,this.pickPerspectiveShader=u,this.pickOrthoShader=c,this.pickProjectShader=f,this.points=[],this._selectResult=new d(0,[0,0,0]),this.useOrtho=!0,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.axesProject=[!0,!0,!0],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.highlightId=[1,1,1,1],this.highlightScale=2,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.dirty=!0}t.exports=function(t){var e=t.gl,r=l.createPerspective(e),n=l.createOrtho(e),o=l.createProject(e),s=l.createPickPerspective(e),u=l.createPickOrtho(e),c=l.createPickProject(e),f=i(e),h=i(e),p=i(e),d=i(e),v=new g(e,r,n,o,f,h,p,d,a(e,[{buffer:f,size:3,type:e.FLOAT},{buffer:h,size:4,type:e.FLOAT},{buffer:p,size:2,type:e.FLOAT},{buffer:d,size:4,type:e.UNSIGNED_BYTE,normalized:!0}]),s,u,c);return v.update(t),v};var y=g.prototype;y.pickSlots=1,y.setPickBase=function(t){this.pickId=t},y.isTransparent=function(){if(this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectHasAlpha)return!0;return!1},y.isOpaque=function(){if(!this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&!this.projectHasAlpha)return!0;return!1};var m=[0,0],x=[0,0,0],b=[0,0,0],_=[0,0,0,1],w=[0,0,0,1],T=f.slice(),k=[0,0,0],A=[[0,0,0],[0,0,0]];function M(t){return t[0]=t[1]=t[2]=0,t}function S(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=1,t}function E(t,e,r,n){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[r]=n,t}var L=[[-1e8,-1e8,-1e8],[1e8,1e8,1e8]];function C(t,e,r,n,i,a,o){var l=r.gl;if((a===r.projectHasAlpha||o)&&function(t,e,r,n){var i,a=e.axesProject,o=e.gl,l=t.uniforms,u=r.model||f,c=r.view||f,h=r.projection||f,d=e.axesBounds,v=function(t){for(var e=A,r=0;r<2;++r)for(var n=0;n<3;++n)e[r][n]=Math.max(Math.min(t[r][n],1e8),-1e8);return e}(e.clipBounds);i=e.axes&&e.axes.lastCubeProps?e.axes.lastCubeProps.axis:[1,1,1],m[0]=2/o.drawingBufferWidth,m[1]=2/o.drawingBufferHeight,t.bind(),l.view=c,l.projection=h,l.screenSize=m,l.highlightId=e.highlightId,l.highlightScale=e.highlightScale,l.clipBounds=v,l.pickGroup=e.pickId/255,l.pixelRatio=n;for(var g=0;g<3;++g)if(a[g]){l.scale=e.projectScale[g],l.opacity=e.projectOpacity[g];for(var y=T,L=0;L<16;++L)y[L]=0;for(L=0;L<4;++L)y[5*L]=1;y[5*g]=0,i[g]<0?y[12+g]=d[0][g]:y[12+g]=d[1][g],s(y,u,y),l.model=y;var C=(g+1)%3,P=(g+2)%3,O=M(x),I=M(b);O[C]=1,I[P]=1;var D=p(0,0,0,S(_,O)),z=p(0,0,0,S(w,I));if(Math.abs(D[1])>Math.abs(z[1])){var R=D;D=z,z=R,R=O,O=I,I=R;var F=C;C=P,P=F}D[0]<0&&(O[C]=-1),z[1]>0&&(I[P]=-1);var B=0,N=0;for(L=0;L<4;++L)B+=Math.pow(u[4*C+L],2),N+=Math.pow(u[4*P+L],2);O[C]/=Math.sqrt(B),I[P]/=Math.sqrt(N),l.axes[0]=O,l.axes[1]=I,l.fragClipBounds[0]=E(k,v[0],g,-1e8),l.fragClipBounds[1]=E(k,v[1],g,1e8),e.vao.bind(),e.vao.draw(o.TRIANGLES,e.vertexCount),e.lineWidth>0&&(o.lineWidth(e.lineWidth*n),e.vao.draw(o.LINES,e.lineVertexCount,e.vertexCount)),e.vao.unbind()}}(e,r,n,i),a===r.hasAlpha||o){t.bind();var u=t.uniforms;u.model=n.model||f,u.view=n.view||f,u.projection=n.projection||f,m[0]=2/l.drawingBufferWidth,m[1]=2/l.drawingBufferHeight,u.screenSize=m,u.highlightId=r.highlightId,u.highlightScale=r.highlightScale,u.fragClipBounds=L,u.clipBounds=r.axes.bounds,u.opacity=r.opacity,u.pickGroup=r.pickId/255,u.pixelRatio=i,r.vao.bind(),r.vao.draw(l.TRIANGLES,r.vertexCount),r.lineWidth>0&&(l.lineWidth(r.lineWidth*i),r.vao.draw(l.LINES,r.lineVertexCount,r.vertexCount)),r.vao.unbind()}}function P(t,e,r,i){var a;a=Array.isArray(t)?e=this.pointCount||e<0)return null;var r=this.points[e],n=this._selectResult;n.index=e;for(var i=0;i<3;++i)n.position[i]=n.dataCoordinate[i]=r[i];return n},y.highlight=function(t){if(t){var e=t.index,r=255&e,n=e>>8&255,i=e>>16&255;this.highlightId=[r/255,n/255,i/255,0]}else this.highlightId=[1,1,1,1]},y.update=function(t){if(\"perspective\"in(t=t||{})&&(this.useOrtho=!t.perspective),\"orthographic\"in t&&(this.useOrtho=!!t.orthographic),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"project\"in t)if(Array.isArray(t.project))this.axesProject=t.project;else{var e=!!t.project;this.axesProject=[e,e,e]}if(\"projectScale\"in t)if(Array.isArray(t.projectScale))this.projectScale=t.projectScale.slice();else{var r=+t.projectScale;this.projectScale=[r,r,r]}if(this.projectHasAlpha=!1,\"projectOpacity\"in t){Array.isArray(t.projectOpacity)?this.projectOpacity=t.projectOpacity.slice():(r=+t.projectOpacity,this.projectOpacity=[r,r,r]);for(var n=0;n<3;++n)this.projectOpacity[n]=v(this.projectOpacity[n]),this.projectOpacity[n]<1&&(this.projectHasAlpha=!0)}this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=v(t.opacity),this.opacity<1&&(this.hasAlpha=!0)),this.dirty=!0;var i,a,s=t.position,l=t.font||\"normal\",u=t.alignment||[0,0];if(2===u.length)i=u[0],a=u[1];else for(i=[],a=[],n=0;n0){var I=0,D=x,z=[0,0,0,1],R=[0,0,0,1],F=Array.isArray(p)&&Array.isArray(p[0]),B=Array.isArray(y)&&Array.isArray(y[0]);t:for(n=0;n<_;++n){for(m+=1,w=s[n],T=0;T<3;++T){if(isNaN(w[T])||!isFinite(w[T]))continue t;f[T]=Math.max(f[T],w[T]),c[T]=Math.min(c[T],w[T])}k=(N=P(h,n,l,this.pixelRatio)).mesh,A=N.lines,M=N.bounds;var N,j=N.visible;if(j)if(Array.isArray(p)){if(3===(U=F?n0?1-M[0][0]:Z<0?1+M[1][0]:1,Y*=Y>0?1-M[0][1]:Y<0?1+M[1][1]:1],X=k.cells||[],J=k.positions||[];for(T=0;T0){var y=r*c;o.drawBox(f-y,h-y,p+y,h+y,a),o.drawBox(f-y,d-y,p+y,d+y,a),o.drawBox(f-y,h-y,f+y,d+y,a),o.drawBox(p-y,h-y,p+y,d+y,a)}}}},s.update=function(t){t=t||{},this.innerFill=!!t.innerFill,this.outerFill=!!t.outerFill,this.innerColor=(t.innerColor||[0,0,0,.5]).slice(),this.outerColor=(t.outerColor||[0,0,0,.5]).slice(),this.borderColor=(t.borderColor||[0,0,0,1]).slice(),this.borderWidth=t.borderWidth||0,this.selectBox=(t.selectBox||this.selectBox).slice()},s.dispose=function(){this.boxBuffer.dispose(),this.boxShader.dispose(),this.plot.removeOverlay(this)}},2611:function(t,e,r){\"use strict\";t.exports=function(t,e){var r=e[0],a=e[1];return new l(t,n(t,r,a,{}),i.mallocUint8(r*a*4))};var n=r(4234),i=r(5306),a=r(5050),o=r(2288).nextPow2;function s(t,e,r,n,i){this.coord=[t,e],this.id=r,this.value=n,this.distance=i}function l(t,e,r){this.gl=t,this.fbo=e,this.buffer=r,this._readTimeout=null;var n=this;this._readCallback=function(){n.gl&&(e.bind(),t.readPixels(0,0,e.shape[0],e.shape[1],t.RGBA,t.UNSIGNED_BYTE,n.buffer),n._readTimeout=null)}}var u=l.prototype;Object.defineProperty(u,\"shape\",{get:function(){return this.gl?this.fbo.shape.slice():[0,0]},set:function(t){if(this.gl){this.fbo.shape=t;var e=this.fbo.shape[0],r=this.fbo.shape[1];if(r*e*4>this.buffer.length){i.free(this.buffer);for(var n=this.buffer=i.mallocUint8(o(r*e*4)),a=0;ar)for(t=r;te)for(t=e;t=0){for(var T=0|w.type.charAt(w.type.length-1),k=new Array(T),A=0;A=0;)M+=1;_[m]=M}var S=new Array(r.length);function E(){h.program=o.program(p,h._vref,h._fref,b,_);for(var t=0;t=0){if((d=h.charCodeAt(h.length-1)-48)<2||d>4)throw new n(\"\",\"Invalid data type for attribute \"+f+\": \"+h);s(t,e,p[0],i,d,a,f)}else{if(!(h.indexOf(\"mat\")>=0))throw new n(\"\",\"Unknown data type for attribute \"+f+\": \"+h);var d;if((d=h.charCodeAt(h.length-1)-48)<2||d>4)throw new n(\"\",\"Invalid data type for attribute \"+f+\": 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a||(a[s[0]]=[]),a=a[s[0]];for(var l=1;l1)for(var l=0;l 0 U ||b|| > 0.\\n // Assign z = 0, x = -b, y = a:\\n // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n return normalize(vec3(-v.y, v.x, 0.0));\\n } else {\\n return normalize(vec3(0.0, v.z, -v.y));\\n }\\n}\\n\\n// Calculate the tube vertex and normal at the given index.\\n//\\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\\n//\\n// Each tube segment is made up of a ring of vertices.\\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\\n// The indexes of tube segments run from 0 to 8.\\n//\\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\\n float segmentCount = 8.0;\\n\\n float angle = 2.0 * 3.14159 * (index / segmentCount);\\n\\n vec3 u = getOrthogonalVector(d);\\n vec3 v = normalize(cross(u, d));\\n\\n vec3 x = u * cos(angle) * length(d);\\n vec3 y = v * sin(angle) * length(d);\\n vec3 v3 = x + y;\\n\\n normal = normalize(v3);\\n\\n return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 color, position;\\nattribute vec2 uv;\\n\\nuniform float vectorScale, tubeScale;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 eyePosition, lightPosition;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n // Scale the vector magnitude to stay constant with\\n // model & view changes.\\n vec3 normal;\\n vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\\n vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n //Lighting geometry parameters\\n vec4 cameraCoordinate = view * tubePosition;\\n cameraCoordinate.xyz /= cameraCoordinate.w;\\n f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n f_eyeDirection = eyePosition - cameraCoordinate.xyz;\\n f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n // vec4 m_position = model * vec4(tubePosition, 1.0);\\n vec4 t_position = view * tubePosition;\\n gl_Position = projection * t_position;\\n\\n f_color = color;\\n f_data = tubePosition.xyz;\\n f_position = position.xyz;\\n f_uv = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n float NdotH = max(x, 0.0001);\\n float cos2Alpha = NdotH * NdotH;\\n float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n float roughness2 = roughness * roughness;\\n float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n vec3 lightDirection,\\n vec3 viewDirection,\\n vec3 surfaceNormal,\\n float roughness,\\n float fresnel) {\\n\\n float VdotN = max(dot(viewDirection, surfaceNormal), 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e=t[0],r=t[1],n=t[2];return!(eb||r_||nw)},k=10*n.distance(e[0],e[1])/u,A=k*k,M=1,S=0,E=r.length;E>1&&(M=function(t){for(var e=[],r=[],n=[],i={},a={},o={},s=t.length,u=0;uS&&(S=F),z.push(F),g.push({points:P,velocities:O,divergences:z});for(var B=0;B<100*u&&P.lengthA&&n.scale(N,N,k/Math.sqrt(j)),n.add(N,N,C),I=d(N),n.squaredDistance(D,N)-A>-1e-4*A&&(P.push(N),D=N,O.push(I),R=v(N,I),F=n.length(R),isFinite(F)&&F>S&&(S=F),z.push(F)),C=N}}var U=function(t,e,r,a){for(var o=0,s=0;s0)for(T=0;T<8;T++){var k=(T+1)%8;u.push(h[T],p[T],p[k],p[k],h[k],h[T]),f.push(m,y,y,y,m,m),d.push(v,g,g,g,v,v);var A=u.length;c.push([A-6,A-5,A-4],[A-3,A-2,A-1])}var M=h;h=p,p=M;var S=m;m=y,y=S;var E=v;v=g,g=E}return{positions:u,cells:c,vectors:f,vertexIntensity:d}}(t,r,a,o)})),f=[],h=[],p=[],d=[];for(s=0;s max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n 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dataCoordinate;\\n vec4 worldPosition = model * vec4(worldCoordinate, 1.0);\\n\\n vec4 clipPosition = projection * view * worldPosition;\\n clipPosition.z += zOffset;\\n\\n gl_Position = clipPosition;\\n value = f + objectOffset.z;\\n kill = -1.0;\\n planeCoordinate = uv.zw;\\n\\n vColor = texture2D(colormap, vec2(value, value));\\n\\n //Don't do lighting for contours\\n surfaceNormal = vec3(1,0,0);\\n eyeDirection = vec3(0,1,0);\\n lightDirection = vec3(0,0,1);\\n}\\n\"]),l=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec2 shape;\\nuniform vec3 clipBounds[2];\\nuniform float pickId;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 surfaceNormal;\\n\\nvec2 splitFloat(float v) {\\n float vh = 255.0 * v;\\n float upper = floor(vh);\\n float lower = fract(vh);\\n return vec2(upper / 255.0, floor(lower * 16.0) / 16.0);\\n}\\n\\nvoid main() {\\n if ((kill > 0.0) ||\\n (outOfRange(clipBounds[0], clipBounds[1], worldCoordinate))) discard;\\n\\n vec2 ux = splitFloat(planeCoordinate.x / shape.x);\\n vec2 uy = splitFloat(planeCoordinate.y / shape.y);\\n gl_FragColor = vec4(pickId, ux.x, uy.x, ux.y + (uy.y/16.0));\\n}\\n\"]);e.createShader=function(t){var e=n(t,a,o,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},e.createPickShader=function(t){var e=n(t,a,l,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e.attributes.normal.location=2,e},e.createContourShader=function(t){var e=n(t,s,o,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e},e.createPickContourShader=function(t){var e=n(t,s,l,null,[{name:\"uv\",type:\"vec4\"},{name:\"f\",type:\"float\"}]);return e.attributes.uv.location=0,e.attributes.f.location=1,e}},3754:function(t,e,r){\"use strict\";t.exports=function(t){var e=t.gl,r=m(e),n=b(e),s=x(e),l=_(e),u=i(e),c=a(e,[{buffer:u,size:4,stride:40,offset:0},{buffer:u,size:3,stride:40,offset:16},{buffer:u,size:3,stride:40,offset:28}]),f=i(e),h=a(e,[{buffer:f,size:4,stride:20,offset:0},{buffer:f,size:1,stride:20,offset:16}]),p=i(e),d=a(e,[{buffer:p,size:2,type:e.FLOAT}]),v=o(e,1,256,e.RGBA,e.UNSIGNED_BYTE);v.minFilter=e.LINEAR,v.magFilter=e.LINEAR;var g=new M(e,[0,0],[[0,0,0],[0,0,0]],r,n,u,c,v,s,l,f,h,p,d,[0,0,0]),y={levels:[[],[],[]]};for(var w in t)y[w]=t[w];return y.colormap=y.colormap||\"jet\",g.update(y),g};var n=r(2288),i=r(5827),a=r(2944),o=r(8931),s=r(5306),l=r(9156),u=r(7498),c=r(7382),f=r(5050),h=r(4162),p=r(104),d=r(7437),v=r(5070),g=r(9144),y=r(9054),m=y.createShader,x=y.createContourShader,b=y.createPickShader,_=y.createPickContourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],T=[[0,0],[0,1],[1,0],[1,1],[1,0],[0,1]],k=[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0]];function A(t,e,r,n,i){this.position=t,this.index=e,this.uv=r,this.level=n,this.dataCoordinate=i}function M(t,e,r,n,i,a,o,l,u,c,h,p,d,v,g){this.gl=t,this.shape=e,this.bounds=r,this.objectOffset=g,this.intensityBounds=[],this._shader=n,this._pickShader=i,this._coordinateBuffer=a,this._vao=o,this._colorMap=l,this._contourShader=u,this._contourPickShader=c,this._contourBuffer=h,this._contourVAO=p,this._contourOffsets=[[],[],[]],this._contourCounts=[[],[],[]],this._vertexCount=0,this._pickResult=new A([0,0,0],[0,0],[0,0],[0,0,0],[0,0,0]),this._dynamicBuffer=d,this._dynamicVAO=v,this._dynamicOffsets=[0,0,0],this._dynamicCounts=[0,0,0],this.contourWidth=[1,1,1],this.contourLevels=[[1],[1],[1]],this.contourTint=[0,0,0],this.contourColor=[[.5,.5,.5,1],[.5,.5,.5,1],[.5,.5,.5,1]],this.showContour=!0,this.showSurface=!0,this.enableHighlight=[!0,!0,!0],this.highlightColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.highlightTint=[1,1,1],this.highlightLevel=[-1,-1,-1],this.enableDynamic=[!0,!0,!0],this.dynamicLevel=[NaN,NaN,NaN],this.dynamicColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.dynamicTint=[1,1,1],this.dynamicWidth=[1,1,1],this.axesBounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.surfaceProject=[!1,!1,!1],this.contourProject=[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],this.colorBounds=[!1,!1],this._field=[f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0]),f(s.mallocFloat(1024),[0,0])],this.pickId=1,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.snapToData=!1,this.pixelRatio=1,this.opacity=1,this.lightPosition=[10,1e4,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.vertexColor=0,this.dirty=!0}!function(){for(var t=0;t<3;++t){var e=k[t],r=(t+2)%3;e[(t+1)%3+0]=1,e[r+3]=1,e[t+6]=1}}();var S=M.prototype;S.genColormap=function(t,e){var r=!1,n=c([l({colormap:t,nshades:256,format:\"rgba\"}).map((function(t,n){var i=e?function(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;rt&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}(n/255,e):t[3];return i<1&&(r=!0),[t[0],t[1],t[2],255*i]}))]);return u.divseq(n,255),this.hasAlphaScale=r,n},S.isTransparent=function(){return this.opacity<1||this.hasAlphaScale},S.isOpaque=function(){return!this.isTransparent()},S.pickSlots=1,S.setPickBase=function(t){this.pickId=t};var E=[0,0,0],L={showSurface:!1,showContour:!1,projections:[w.slice(),w.slice(),w.slice()],clipBounds:[[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]]]};function C(t,e){var r,n,i,a=e.axes&&e.axes.lastCubeProps.axis||E,o=e.showSurface,s=e.showContour;for(r=0;r<3;++r)for(o=o||e.surfaceProject[r],n=0;n<3;++n)s=s||e.contourProject[r][n];for(r=0;r<3;++r){var l=L.projections[r];for(n=0;n<16;++n)l[n]=0;for(n=0;n<4;++n)l[5*n]=1;l[5*r]=0,l[12+r]=e.axesBounds[+(a[r]>0)][r],p(l,t.model,l);var u=L.clipBounds[r];for(i=0;i<2;++i)for(n=0;n<3;++n)u[i][n]=t.clipBounds[i][n];u[0][r]=-1e8,u[1][r]=1e8}return L.showSurface=o,L.showContour=s,L}var P={model:w,view:w,projection:w,inverseModel:w.slice(),lowerBound:[0,0,0],upperBound:[0,0,0],colorMap:0,clipBounds:[[0,0,0],[0,0,0]],height:0,contourTint:0,contourColor:[0,0,0,1],permutation:[1,0,0,0,1,0,0,0,1],zOffset:-1e-4,objectOffset:[0,0,0],kambient:1,kdiffuse:1,kspecular:1,lightPosition:[1e3,1e3,1e3],eyePosition:[0,0,0],roughness:1,fresnel:1,opacity:1,vertexColor:0},O=w.slice(),I=[1,0,0,0,1,0,0,0,1];function D(t,e){t=t||{};var r=this.gl;r.disable(r.CULL_FACE),this._colorMap.bind(0);var n=P;n.model=t.model||w,n.view=t.view||w,n.projection=t.projection||w,n.lowerBound=[this.bounds[0][0],this.bounds[0][1],this.colorBounds[0]||this.bounds[0][2]],n.upperBound=[this.bounds[1][0],this.bounds[1][1],this.colorBounds[1]||this.bounds[1][2]],n.objectOffset=this.objectOffset,n.contourColor=this.contourColor[0],n.inverseModel=d(n.inverseModel,n.model);for(var i=0;i<2;++i)for(var a=n.clipBounds[i],o=0;o<3;++o)a[o]=Math.min(Math.max(this.clipBounds[i][o],-1e8),1e8);n.kambient=this.ambientLight,n.kdiffuse=this.diffuseLight,n.kspecular=this.specularLight,n.roughness=this.roughness,n.fresnel=this.fresnel,n.opacity=this.opacity,n.height=0,n.permutation=I,n.vertexColor=this.vertexColor;var s=O;for(p(s,n.view,n.model),p(s,n.projection,s),d(s,s),i=0;i<3;++i)n.eyePosition[i]=s[12+i]/s[15];var l=s[15];for(i=0;i<3;++i)l+=this.lightPosition[i]*s[4*i+3];for(i=0;i<3;++i){var u=s[12+i];for(o=0;o<3;++o)u+=s[4*o+i]*this.lightPosition[o];n.lightPosition[i]=u/l}var c=C(n,this);if(c.showSurface){for(this._shader.bind(),this._shader.uniforms=n,this._vao.bind(),this.showSurface&&this._vertexCount&&this._vao.draw(r.TRIANGLES,this._vertexCount),i=0;i<3;++i)this.surfaceProject[i]&&this.vertexCount&&(this._shader.uniforms.model=c.projections[i],this._shader.uniforms.clipBounds=c.clipBounds[i],this._vao.draw(r.TRIANGLES,this._vertexCount));this._vao.unbind()}if(c.showContour){var f=this._contourShader;n.kambient=1,n.kdiffuse=0,n.kspecular=0,n.opacity=1,f.bind(),f.uniforms=n;var h=this._contourVAO;for(h.bind(),i=0;i<3;++i)for(f.uniforms.permutation=k[i],r.lineWidth(this.contourWidth[i]*this.pixelRatio),o=0;o>4)/16)/255,i=Math.floor(n),a=n-i,o=e[1]*(t.value[1]+(15&t.value[2])/16)/255,s=Math.floor(o),l=o-s;i+=1,s+=1;var u=r.position;u[0]=u[1]=u[2]=0;for(var c=0;c<2;++c)for(var f=c?a:1-a,h=0;h<2;++h)for(var p=i+c,d=s+h,g=f*(h?l:1-l),y=0;y<3;++y)u[y]+=this._field[y].get(p,d)*g;for(var m=this._pickResult.level,x=0;x<3;++x)if(m[x]=v.le(this.contourLevels[x],u[x]),m[x]<0)this.contourLevels[x].length>0&&(m[x]=0);else if(m[x]Math.abs(_-u[x])&&(m[x]+=1)}for(r.index[0]=a<.5?i:i+1,r.index[1]=l<.5?s:s+1,r.uv[0]=n/e[0],r.uv[1]=o/e[1],y=0;y<3;++y)r.dataCoordinate[y]=this._field[y].get(r.index[0],r.index[1]);return r},S.padField=function(t,e){var r=e.shape.slice(),n=t.shape.slice();u.assign(t.lo(1,1).hi(r[0],r[1]),e),u.assign(t.lo(1).hi(r[0],1),e.hi(r[0],1)),u.assign(t.lo(1,n[1]-1).hi(r[0],1),e.lo(0,r[1]-1).hi(r[0],1)),u.assign(t.lo(0,1).hi(1,r[1]),e.hi(1)),u.assign(t.lo(n[0]-1,1).hi(1,r[1]),e.lo(r[0]-1)),t.set(0,0,e.get(0,0)),t.set(0,n[1]-1,e.get(0,r[1]-1)),t.set(n[0]-1,0,e.get(r[0]-1,0)),t.set(n[0]-1,n[1]-1,e.get(r[0]-1,r[1]-1))},S.update=function(t){t=t||{},this.objectOffset=t.objectOffset||this.objectOffset,this.dirty=!0,\"contourWidth\"in t&&(this.contourWidth=R(t.contourWidth,Number)),\"showContour\"in t&&(this.showContour=R(t.showContour,Boolean)),\"showSurface\"in t&&(this.showSurface=!!t.showSurface),\"contourTint\"in t&&(this.contourTint=R(t.contourTint,Boolean)),\"contourColor\"in t&&(this.contourColor=B(t.contourColor)),\"contourProject\"in t&&(this.contourProject=R(t.contourProject,(function(t){return R(t,Boolean)}))),\"surfaceProject\"in t&&(this.surfaceProject=t.surfaceProject),\"dynamicColor\"in t&&(this.dynamicColor=B(t.dynamicColor)),\"dynamicTint\"in t&&(this.dynamicTint=R(t.dynamicTint,Number)),\"dynamicWidth\"in t&&(this.dynamicWidth=R(t.dynamicWidth,Number)),\"opacity\"in t&&(this.opacity=t.opacity),\"opacityscale\"in t&&(this.opacityscale=t.opacityscale),\"colorBounds\"in t&&(this.colorBounds=t.colorBounds),\"vertexColor\"in t&&(this.vertexColor=t.vertexColor?1:0),\"colormap\"in t&&this._colorMap.setPixels(this.genColormap(t.colormap,this.opacityscale));var e=t.field||t.coords&&t.coords[2]||null,r=!1;if(e||(e=this._field[2].shape[0]||this._field[2].shape[2]?this._field[2].lo(1,1).hi(this._field[2].shape[0]-2,this._field[2].shape[1]-2):this._field[2].hi(0,0)),\"field\"in t||\"coords\"in t){var i=(e.shape[0]+2)*(e.shape[1]+2);i>this._field[2].data.length&&(s.freeFloat(this._field[2].data),this._field[2].data=s.mallocFloat(n.nextPow2(i))),this._field[2]=f(this._field[2].data,[e.shape[0]+2,e.shape[1]+2]),this.padField(this._field[2],e),this.shape=e.shape.slice();for(var a=this.shape,o=0;o<2;++o)this._field[2].size>this._field[o].data.length&&(s.freeFloat(this._field[o].data),this._field[o].data=s.mallocFloat(this._field[2].size)),this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2]);if(t.coords){var l=t.coords;if(!Array.isArray(l)||3!==l.length)throw new Error(\"gl-surface: invalid coordinates for x/y\");for(o=0;o<2;++o){var u=l[o];for(y=0;y<2;++y)if(u.shape[y]!==a[y])throw new Error(\"gl-surface: coords have incorrect shape\");this.padField(this._field[o],u)}}else if(t.ticks){var c=t.ticks;if(!Array.isArray(c)||2!==c.length)throw new Error(\"gl-surface: invalid ticks\");for(o=0;o<2;++o){var p=c[o];if((Array.isArray(p)||p.length)&&(p=f(p)),p.shape[0]!==a[o])throw new Error(\"gl-surface: invalid tick length\");var d=f(p.data,a);d.stride[o]=p.stride[0],d.stride[1^o]=0,this.padField(this._field[o],d)}}else{for(o=0;o<2;++o){var v=[0,0];v[o]=1,this._field[o]=f(this._field[o].data,[a[0]+2,a[1]+2],v,0)}this._field[0].set(0,0,0);for(var y=0;y0){for(var xt=0;xt<5;++xt)$.pop();U-=1}continue t}$.push(nt[0],nt[1],ot[0],ot[1],nt[2]),U+=1}}rt.push(U)}this._contourOffsets[Q]=et,this._contourCounts[Q]=rt}var bt=s.mallocFloat($.length);for(o=0;o<$.length;++o)bt[o]=$[o];this._contourBuffer.update(bt),s.freeFloat(bt)}},S.dispose=function(){this._shader.dispose(),this._vao.dispose(),this._coordinateBuffer.dispose(),this._colorMap.dispose(),this._contourBuffer.dispose(),this._contourVAO.dispose(),this._contourShader.dispose(),this._contourPickShader.dispose(),this._dynamicBuffer.dispose(),this._dynamicVAO.dispose();for(var t=0;t<3;++t)s.freeFloat(this._field[t].data)},S.highlight=function(t){var e,r;if(!t)return this._dynamicCounts=[0,0,0],this.dyanamicLevel=[NaN,NaN,NaN],void(this.highlightLevel=[-1,-1,-1]);for(e=0;e<3;++e)this.enableHighlight[e]?this.highlightLevel[e]=t.level[e]:this.highlightLevel[e]=-1;for(r=this.snapToData?t.dataCoordinate:t.position,e=0;e<3;++e)r[e]-=this.objectOffset[e];if(this.enableDynamic[0]&&r[0]!==this.dynamicLevel[0]||this.enableDynamic[1]&&r[1]!==this.dynamicLevel[1]||this.enableDynamic[2]&&r[2]!==this.dynamicLevel[2]){for(var n=0,i=this.shape,a=s.mallocFloat(12*i[0]*i[1]),o=0;o<3;++o)if(this.enableDynamic[o]){this.dynamicLevel[o]=r[o];var l=(o+1)%3,u=(o+2)%3,c=this._field[o],f=this._field[l],p=this._field[u],d=h(c,r[o]),v=d.cells,g=d.positions;for(this._dynamicOffsets[o]=n,e=0;ei||r<0||r>i)throw new Error(\"gl-texture2d: Invalid texture size\");return t._shape=[e,r],t.bind(),n.texImage2D(n.TEXTURE_2D,0,t.format,e,r,0,t.format,t.type,null),t._mipLevels=[0],t}function p(t,e,r,n,i,a){this.gl=t,this.handle=e,this.format=i,this.type=a,this._shape=[r,n],this._mipLevels=[0],this._magFilter=t.NEAREST,this._minFilter=t.NEAREST,this._wrapS=t.CLAMP_TO_EDGE,this._wrapT=t.CLAMP_TO_EDGE,this._anisoSamples=1;var o=this,s=[this._wrapS,this._wrapT];Object.defineProperties(s,[{get:function(){return o._wrapS},set:function(t){return o.wrapS=t}},{get:function(){return o._wrapT},set:function(t){return o.wrapT=t}}]),this._wrapVector=s;var l=[this._shape[0],this._shape[1]];Object.defineProperties(l,[{get:function(){return o._shape[0]},set:function(t){return o.width=t}},{get:function(){return o._shape[1]},set:function(t){return o.height=t}}]),this._shapeVector=l}var d=p.prototype;function v(t,e){return 3===t.length?1===e[2]&&e[1]===t[0]*t[2]&&e[0]===t[2]:1===e[0]&&e[1]===t[0]}function g(t){var e=t.createTexture();return t.bindTexture(t.TEXTURE_2D,e),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),e}function y(t,e,r,n,i){var a=t.getParameter(t.MAX_TEXTURE_SIZE);if(e<0||e>a||r<0||r>a)throw new Error(\"gl-texture2d: Invalid texture shape\");if(i===t.FLOAT&&!t.getExtension(\"OES_texture_float\"))throw new Error(\"gl-texture2d: Floating point textures not supported on this platform\");var o=g(t);return t.texImage2D(t.TEXTURE_2D,0,n,e,r,0,n,i,null),new p(t,o,e,r,n,i)}function m(t,e,r,n,i,a){var o=g(t);return t.texImage2D(t.TEXTURE_2D,0,i,i,a,e),new p(t,o,r,n,i,a)}function x(t,e){var r=e.dtype,o=e.shape.slice(),s=t.getParameter(t.MAX_TEXTURE_SIZE);if(o[0]<0||o[0]>s||o[1]<0||o[1]>s)throw new Error(\"gl-texture2d: Invalid texture size\");var l=v(o,e.stride.slice()),u=0;\"float32\"===r?u=t.FLOAT:\"float64\"===r?(u=t.FLOAT,l=!1,r=\"float32\"):\"uint8\"===r?u=t.UNSIGNED_BYTE:(u=t.UNSIGNED_BYTE,l=!1,r=\"uint8\");var c,h,d=0;if(2===o.length)d=t.LUMINANCE,o=[o[0],o[1],1],e=n(e.data,o,[e.stride[0],e.stride[1],1],e.offset);else{if(3!==o.length)throw new Error(\"gl-texture2d: Invalid shape for texture\");if(1===o[2])d=t.ALPHA;else if(2===o[2])d=t.LUMINANCE_ALPHA;else if(3===o[2])d=t.RGB;else{if(4!==o[2])throw new Error(\"gl-texture2d: Invalid shape for pixel coords\");d=t.RGBA}}u!==t.FLOAT||t.getExtension(\"OES_texture_float\")||(u=t.UNSIGNED_BYTE,l=!1);var y=e.size;if(l)c=0===e.offset&&e.data.length===y?e.data:e.data.subarray(e.offset,e.offset+y);else{var m=[o[2],o[2]*o[0],1];h=a.malloc(y,r);var 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e>t==t>0?o===a?(r+=1,o=0):o+=1:0===o?(o=a,r-=1):o-=1,n.pack(o,r)}},115:function(t,e){e.vertexNormals=function(t,e,r){for(var n=e.length,i=new Array(n),a=void 0===r?1e-6:r,o=0;oa){var b=i[u],_=1/Math.sqrt(g*m);for(x=0;x<3;++x){var w=(x+1)%3,T=(x+2)%3;b[x]+=_*(y[w]*v[T]-y[T]*v[w])}}}for(o=0;oa)for(_=1/Math.sqrt(k),x=0;x<3;++x)b[x]*=_;else for(x=0;x<3;++x)b[x]=0}return i},e.faceNormals=function(t,e,r){for(var n=t.length,i=new Array(n),a=void 0===r?1e-6:r,o=0;oa?1/Math.sqrt(p):0,u=0;u<3;++u)h[u]*=p;i[o]=h}return i}},567:function(t){\"use strict\";t.exports=function(t,e,r,n,i,a,o,s,l,u){var c=e+a+u;if(f>0){var f=Math.sqrt(c+1);t[0]=.5*(o-l)/f,t[1]=.5*(s-n)/f,t[2]=.5*(r-a)/f,t[3]=.5*f}else{var h=Math.max(e,a,u);f=Math.sqrt(2*h-c+1),e>=h?(t[0]=.5*f,t[1]=.5*(i+r)/f,t[2]=.5*(s+n)/f,t[3]=.5*(o-l)/f):a>=h?(t[0]=.5*(r+i)/f,t[1]=.5*f,t[2]=.5*(l+o)/f,t[3]=.5*(s-n)/f):(t[0]=.5*(n+s)/f,t[1]=.5*(o+l)/f,t[2]=.5*f,t[3]=.5*(r-i)/f)}return t}},7774:function(t,e,r){\"use strict\";t.exports=function(t){var 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u=0;u<16;++u)this.computedMatrix[u]=.5;this.recalcMatrix(0)}var p=h.prototype;p.setDistanceLimits=function(t,e){t=t>0?Math.log(t):-1/0,e=e>0?Math.log(e):1/0,e=Math.max(e,t),this.radius.bounds[0][0]=t,this.radius.bounds[1][0]=e},p.getDistanceLimits=function(t){var e=this.radius.bounds[0];return t?(t[0]=Math.exp(e[0][0]),t[1]=Math.exp(e[1][0]),t):[Math.exp(e[0][0]),Math.exp(e[1][0])]},p.recalcMatrix=function(t){this.center.curve(t),this.up.curve(t),this.right.curve(t),this.radius.curve(t),this.angle.curve(t);for(var e=this.computedUp,r=this.computedRight,n=0,i=0,a=0;a<3;++a)i+=e[a]*r[a],n+=e[a]*e[a];var l=Math.sqrt(n),c=0;for(a=0;a<3;++a)r[a]-=e[a]*i/n,c+=r[a]*r[a],e[a]/=l;var f=Math.sqrt(c);for(a=0;a<3;++a)r[a]/=f;var h=this.computedToward;o(h,e,r),s(h,h);var p=Math.exp(this.computedRadius[0]),d=this.computedAngle[0],v=this.computedAngle[1],g=Math.cos(d),y=Math.sin(d),m=Math.cos(v),x=Math.sin(v),b=this.computedCenter,_=g*m,w=y*m,T=x,k=-g*x,A=-y*x,M=m,S=this.computedEye,E=this.computedMatrix;for(a=0;a<3;++a){var L=_*r[a]+w*h[a]+T*e[a];E[4*a+1]=k*r[a]+A*h[a]+M*e[a],E[4*a+2]=L,E[4*a+3]=0}var C=E[1],P=E[5],O=E[9],I=E[2],D=E[6],z=E[10],R=P*z-O*D,F=O*I-C*z,B=C*D-P*I,N=u(R,F,B);for(R/=N,F/=N,B/=N,E[0]=R,E[4]=F,E[8]=B,a=0;a<3;++a)S[a]=b[a]+E[2+4*a]*p;for(a=0;a<3;++a){c=0;for(var j=0;j<3;++j)c+=E[a+4*j]*S[j];E[12+a]=-c}E[15]=1},p.getMatrix=function(t,e){this.recalcMatrix(t);var r=this.computedMatrix;if(e){for(var n=0;n<16;++n)e[n]=r[n];return e}return r};var d=[0,0,0];p.rotate=function(t,e,r,n){if(this.angle.move(t,e,r),n){this.recalcMatrix(t);var i=this.computedMatrix;d[0]=i[2],d[1]=i[6],d[2]=i[10];for(var o=this.computedUp,s=this.computedRight,l=this.computedToward,u=0;u<3;++u)i[4*u]=o[u],i[4*u+1]=s[u],i[4*u+2]=l[u];for(a(i,i,n,d),u=0;u<3;++u)o[u]=i[4*u],s[u]=i[4*u+1];this.up.set(t,o[0],o[1],o[2]),this.right.set(t,s[0],s[1],s[2])}},p.pan=function(t,e,r,n){e=e||0,r=r||0,n=n||0,this.recalcMatrix(t);var i=this.computedMatrix,a=(Math.exp(this.computedRadius[0]),i[1]),o=i[5],s=i[9],l=u(a,o,s);a/=l,o/=l,s/=l;var c=i[0],f=i[4],h=i[8],p=c*a+f*o+h*s,d=u(c-=a*p,f-=o*p,h-=s*p),v=(c/=d)*e+a*r,g=(f/=d)*e+o*r,y=(h/=d)*e+s*r;this.center.move(t,v,g,y);var m=Math.exp(this.computedRadius[0]);m=Math.max(1e-4,m+n),this.radius.set(t,Math.log(m))},p.translate=function(t,e,r,n){this.center.move(t,e||0,r||0,n||0)},p.setMatrix=function(t,e,r,n){var a=1;\"number\"==typeof r&&(a=0|r),(a<0||a>3)&&(a=1);var o=(a+2)%3;e||(this.recalcMatrix(t),e=this.computedMatrix);var s=e[a],l=e[a+4],f=e[a+8];if(n){var h=Math.abs(s),p=Math.abs(l),d=Math.abs(f),v=Math.max(h,p,d);h===v?(s=s<0?-1:1,l=f=0):d===v?(f=f<0?-1:1,s=l=0):(l=l<0?-1:1,s=f=0)}else{var g=u(s,l,f);s/=g,l/=g,f/=g}var y,m,x=e[o],b=e[o+4],_=e[o+8],w=x*s+b*l+_*f,T=u(x-=s*w,b-=l*w,_-=f*w),k=l*(_/=T)-f*(b/=T),A=f*(x/=T)-s*_,M=s*b-l*x,S=u(k,A,M);if(k/=S,A/=S,M/=S,this.center.jump(t,q,G,Z),this.radius.idle(t),this.up.jump(t,s,l,f),this.right.jump(t,x,b,_),2===a){var E=e[1],L=e[5],C=e[9],P=E*x+L*b+C*_,O=E*k+L*A+C*M;y=R<0?-Math.PI/2:Math.PI/2,m=Math.atan2(O,P)}else{var I=e[2],D=e[6],z=e[10],R=I*s+D*l+z*f,F=I*x+D*b+z*_,B=I*k+D*A+z*M;y=Math.asin(c(R)),m=Math.atan2(B,F)}this.angle.jump(t,m,y),this.recalcMatrix(t);var N=e[2],j=e[6],U=e[10],V=this.computedMatrix;i(V,e);var H=V[15],q=V[12]/H,G=V[13]/H,Z=V[14]/H,Y=Math.exp(this.computedRadius[0]);this.center.jump(t,q-N*Y,G-j*Y,Z-U*Y)},p.lastT=function(){return 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l=this._convertToCellCoord(t),u=this._convertToCellCoord(e),c=this._convertToCellCoord(r),f=this._convertToCellCoord(n),h=l;h<=c;h++)for(var p=u;p<=f;p++){var d=this.d*p+h;if((!s||s(this._convertFromCellCoord(h),this._convertFromCellCoord(p),this._convertFromCellCoord(h+1),this._convertFromCellCoord(p+1)))&&i.call(this,t,e,r,n,d,a,o,s))return}},Xn.prototype._convertFromCellCoord=function(t){return(t-this.padding)/this.scale},Xn.prototype._convertToCellCoord=function(t){return Math.max(0,Math.min(this.d-1,Math.floor(t*this.scale)+this.padding))},Xn.prototype.toArrayBuffer=function(){if(this.arrayBuffer)return this.arrayBuffer;for(var t=this.cells,e=3+this.cells.length+1+1,r=0,n=0;n=0)){var f=t[c];u[c]=$n[l].shallow.indexOf(c)>=0?f:ni(f,e)}t instanceof Error&&(u.message=t.message)}if(u.$name)throw new Error(\"$name property is reserved for worker serialization logic.\");return\"Object\"!==l&&(u.$name=l),u}throw new Error(\"can't serialize object of type \"+typeof t)}function ii(t){if(null==t||\"boolean\"==typeof t||\"number\"==typeof t||\"string\"==typeof t||t instanceof Boolean||t instanceof Number||t instanceof String||t instanceof Date||t instanceof RegExp||ei(t)||ri(t)||ArrayBuffer.isView(t)||t instanceof Jn)return t;if(Array.isArray(t))return t.map(ii);if(\"object\"==typeof t){var e=t.$name||\"Object\",r=$n[e].klass;if(!r)throw new Error(\"can't deserialize unregistered class \"+e);if(r.deserialize)return r.deserialize(t);for(var n=Object.create(r.prototype),i=0,a=Object.keys(t);i=0?s:ii(s)}}return n}throw new Error(\"can't deserialize object of type \"+typeof t)}var ai=function(){this.first=!0};ai.prototype.update=function(t,e){var r=Math.floor(t);return this.first?(this.first=!1,this.lastIntegerZoom=r,this.lastIntegerZoomTime=0,this.lastZoom=t,this.lastFloorZoom=r,!0):(this.lastFloorZoom>r?(this.lastIntegerZoom=r+1,this.lastIntegerZoomTime=e):this.lastFloorZoom=128&&t<=255},Arabic:function(t){return t>=1536&&t<=1791},\"Arabic Supplement\":function(t){return t>=1872&&t<=1919},\"Arabic Extended-A\":function(t){return t>=2208&&t<=2303},\"Hangul Jamo\":function(t){return t>=4352&&t<=4607},\"Unified Canadian Aboriginal Syllabics\":function(t){return t>=5120&&t<=5759},Khmer:function(t){return t>=6016&&t<=6143},\"Unified Canadian Aboriginal Syllabics Extended\":function(t){return t>=6320&&t<=6399},\"General Punctuation\":function(t){return t>=8192&&t<=8303},\"Letterlike Symbols\":function(t){return t>=8448&&t<=8527},\"Number Forms\":function(t){return t>=8528&&t<=8591},\"Miscellaneous Technical\":function(t){return t>=8960&&t<=9215},\"Control Pictures\":function(t){return t>=9216&&t<=9279},\"Optical Character Recognition\":function(t){return t>=9280&&t<=9311},\"Enclosed Alphanumerics\":function(t){return t>=9312&&t<=9471},\"Geometric Shapes\":function(t){return t>=9632&&t<=9727},\"Miscellaneous Symbols\":function(t){return t>=9728&&t<=9983},\"Miscellaneous Symbols and Arrows\":function(t){return t>=11008&&t<=11263},\"CJK Radicals Supplement\":function(t){return t>=11904&&t<=12031},\"Kangxi Radicals\":function(t){return t>=12032&&t<=12255},\"Ideographic Description Characters\":function(t){return t>=12272&&t<=12287},\"CJK Symbols and Punctuation\":function(t){return t>=12288&&t<=12351},Hiragana:function(t){return t>=12352&&t<=12447},Katakana:function(t){return t>=12448&&t<=12543},Bopomofo:function(t){return t>=12544&&t<=12591},\"Hangul Compatibility Jamo\":function(t){return t>=12592&&t<=12687},Kanbun:function(t){return t>=12688&&t<=12703},\"Bopomofo Extended\":function(t){return t>=12704&&t<=12735},\"CJK Strokes\":function(t){return t>=12736&&t<=12783},\"Katakana Phonetic Extensions\":function(t){return t>=12784&&t<=12799},\"Enclosed CJK Letters and Months\":function(t){return t>=12800&&t<=13055},\"CJK Compatibility\":function(t){return t>=13056&&t<=13311},\"CJK Unified Ideographs Extension A\":function(t){return t>=13312&&t<=19903},\"Yijing Hexagram 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e=0,r=t;e=65097&&t<=65103)||oi[\"CJK Compatibility Ideographs\"](t)||oi[\"CJK Compatibility\"](t)||oi[\"CJK Radicals Supplement\"](t)||oi[\"CJK Strokes\"](t)||!(!oi[\"CJK Symbols and Punctuation\"](t)||t>=12296&&t<=12305||t>=12308&&t<=12319||12336===t)||oi[\"CJK Unified Ideographs Extension A\"](t)||oi[\"CJK Unified Ideographs\"](t)||oi[\"Enclosed CJK Letters and Months\"](t)||oi[\"Hangul Compatibility Jamo\"](t)||oi[\"Hangul Jamo Extended-A\"](t)||oi[\"Hangul Jamo Extended-B\"](t)||oi[\"Hangul Jamo\"](t)||oi[\"Hangul Syllables\"](t)||oi.Hiragana(t)||oi[\"Ideographic Description Characters\"](t)||oi.Kanbun(t)||oi[\"Kangxi Radicals\"](t)||oi[\"Katakana Phonetic Extensions\"](t)||oi.Katakana(t)&&12540!==t||!(!oi[\"Halfwidth and Fullwidth Forms\"](t)||65288===t||65289===t||65293===t||t>=65306&&t<=65310||65339===t||65341===t||65343===t||t>=65371&&t<=65503||65507===t||t>=65512&&t<=65519)||!(!oi[\"Small Form Variants\"](t)||t>=65112&&t<=65118||t>=65123&&t<=65126)||oi[\"Unified Canadian 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this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(Zi);ia.prototype.bytesPerElement=12,Qn(\"StructArrayLayout2i2i2i12\",ia);var aa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i){var a=this.length;return this.resize(a+1),this.emplace(a,t,e,r,n,i)},e.prototype.emplace=function(t,e,r,n,i,a){var o=4*t,s=8*t;return this.float32[o+0]=e,this.float32[o+1]=r,this.float32[o+2]=n,this.int16[s+6]=i,this.int16[s+7]=a,t},e}(Zi);aa.prototype.bytesPerElement=16,Qn(\"StructArrayLayout2f1f2i16\",aa);var oa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=12*t,o=3*t;return this.uint8[a+0]=e,this.uint8[a+1]=r,this.float32[o+1]=n,this.float32[o+2]=i,t},e}(Zi);oa.prototype.bytesPerElement=12,Qn(\"StructArrayLayout2ub2f12\",oa);var sa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.uint16[i+0]=e,this.uint16[i+1]=r,this.uint16[i+2]=n,t},e}(Zi);sa.prototype.bytesPerElement=6,Qn(\"StructArrayLayout3ui6\",sa);var la=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,u,c,f,h,p,d,v,g){var y=this.length;return this.resize(y+1),this.emplace(y,t,e,r,n,i,a,o,s,l,u,c,f,h,p,d,v,g)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,u,c,f,h,p,d,v,g,y){var m=24*t,x=12*t,b=48*t;return this.int16[m+0]=e,this.int16[m+1]=r,this.uint16[m+2]=n,this.uint16[m+3]=i,this.uint32[x+2]=a,this.uint32[x+3]=o,this.uint32[x+4]=s,this.uint16[m+10]=l,this.uint16[m+11]=u,this.uint16[m+12]=c,this.float32[x+7]=f,this.float32[x+8]=h,this.uint8[b+36]=p,this.uint8[b+37]=d,this.uint8[b+38]=v,this.uint32[x+10]=g,this.int16[m+22]=y,t},e}(Zi);la.prototype.bytesPerElement=48,Qn(\"StructArrayLayout2i2ui3ul3ui2f3ub1ul1i48\",la);var ua=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,u,c,f,h,p,d,v,g,y,m,x,b,_,w,T,k,A,M,S){var E=this.length;return this.resize(E+1),this.emplace(E,t,e,r,n,i,a,o,s,l,u,c,f,h,p,d,v,g,y,m,x,b,_,w,T,k,A,M,S)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,u,c,f,h,p,d,v,g,y,m,x,b,_,w,T,k,A,M,S,E){var L=34*t,C=17*t;return this.int16[L+0]=e,this.int16[L+1]=r,this.int16[L+2]=n,this.int16[L+3]=i,this.int16[L+4]=a,this.int16[L+5]=o,this.int16[L+6]=s,this.int16[L+7]=l,this.uint16[L+8]=u,this.uint16[L+9]=c,this.uint16[L+10]=f,this.uint16[L+11]=h,this.uint16[L+12]=p,this.uint16[L+13]=d,this.uint16[L+14]=v,this.uint16[L+15]=g,this.uint16[L+16]=y,this.uint16[L+17]=m,this.uint16[L+18]=x,this.uint16[L+19]=b,this.uint16[L+20]=_,this.uint16[L+21]=w,this.uint16[L+22]=T,this.uint32[C+12]=k,this.float32[C+13]=A,this.float32[C+14]=M,this.float32[C+15]=S,this.float32[C+16]=E,t},e}(Zi);ua.prototype.bytesPerElement=68,Qn(\"StructArrayLayout8i15ui1ul4f68\",ua);var ca=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.float32[r+0]=e,t},e}(Zi);ca.prototype.bytesPerElement=4,Qn(\"StructArrayLayout1f4\",ca);var fa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.int16[i+0]=e,this.int16[i+1]=r,this.int16[i+2]=n,t},e}(Zi);fa.prototype.bytesPerElement=6,Qn(\"StructArrayLayout3i6\",fa);var ha=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=2*t,a=4*t;return this.uint32[i+0]=e,this.uint16[a+2]=r,this.uint16[a+3]=n,t},e}(Zi);ha.prototype.bytesPerElement=8,Qn(\"StructArrayLayout1ul2ui8\",ha);var pa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.uint16[n+0]=e,this.uint16[n+1]=r,t},e}(Zi);pa.prototype.bytesPerElement=4,Qn(\"StructArrayLayout2ui4\",pa);var da=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.uint16[r+0]=e,t},e}(Zi);da.prototype.bytesPerElement=2,Qn(\"StructArrayLayout1ui2\",da);var va=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.float32[n+0]=e,this.float32[n+1]=r,t},e}(Zi);va.prototype.bytesPerElement=8,Qn(\"StructArrayLayout2f8\",va);var ga=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=4*t;return this.float32[a+0]=e,this.float32[a+1]=r,this.float32[a+2]=n,this.float32[a+3]=i,t},e}(Zi);ga.prototype.bytesPerElement=16,Qn(\"StructArrayLayout4f16\",ga);var ya=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var r={anchorPointX:{configurable:!0},anchorPointY:{configurable:!0},x1:{configurable:!0},y1:{configurable:!0},x2:{configurable:!0},y2:{configurable:!0},featureIndex:{configurable:!0},sourceLayerIndex:{configurable:!0},bucketIndex:{configurable:!0},anchorPoint:{configurable:!0}};return r.anchorPointX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorPointY.get=function(){return this._structArray.int16[this._pos2+1]},r.x1.get=function(){return this._structArray.int16[this._pos2+2]},r.y1.get=function(){return this._structArray.int16[this._pos2+3]},r.x2.get=function(){return this._structArray.int16[this._pos2+4]},r.y2.get=function(){return this._structArray.int16[this._pos2+5]},r.featureIndex.get=function(){return this._structArray.uint32[this._pos4+3]},r.sourceLayerIndex.get=function(){return this._structArray.uint16[this._pos2+8]},r.bucketIndex.get=function(){return this._structArray.uint16[this._pos2+9]},r.anchorPoint.get=function(){return new a(this.anchorPointX,this.anchorPointY)},Object.defineProperties(e.prototype,r),e}(Gi);ya.prototype.size=20;var ma=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.get=function(t){return new ya(this,t)},e}(na);Qn(\"CollisionBoxArray\",ma);var xa=function(t){function e(){t.apply(this,arguments)}t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e;var 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S=o.content;x=Su(h,0,S[0]),_=Su(p,0,S[1]),b=Su(h,S[0],S[2]),w=Su(p,S[1],S[3]),T=S[0]-x,A=S[1]-_,k=S[2]-S[0]-b,M=S[3]-S[1]-w}var E=function(n,i,l,u){var h=Lu(n.stretch-x,b,c,t.left),p=Cu(n.fixed-T,k,n.stretch,v),d=Lu(i.stretch-_,w,f,t.top),y=Cu(i.fixed-A,M,i.stretch,g),m=Lu(l.stretch-x,b,c,t.left),S=Cu(l.fixed-T,k,l.stretch,v),E=Lu(u.stretch-_,w,f,t.top),L=Cu(u.fixed-A,M,u.stretch,g),C=new a(h,d),P=new a(m,d),O=new a(m,E),I=new a(h,E),D=new a(p/s,y/s),z=new a(S/s,L/s),R=e*Math.PI/180;if(R){var F=Math.sin(R),B=Math.cos(R),N=[B,-F,F,B];C._matMult(N),P._matMult(N),I._matMult(N),O._matMult(N)}var j=n.stretch+n.fixed,U=l.stretch+l.fixed,V=i.stretch+i.fixed,H=u.stretch+u.fixed;return{tl:C,tr:P,bl:I,br:O,tex:{x:o.paddedRect.x+1+j,y:o.paddedRect.y+1+V,w:U-j,h:H-V},writingMode:void 0,glyphOffset:[0,0],sectionIndex:0,pixelOffsetTL:D,pixelOffsetBR:z,minFontScaleX:k/s/c,minFontScaleY:M/s/f,isSDF:r}};if(n&&(o.stretchX||o.stretchY))for(var L=Eu(h,y,v),C=Eu(p,m,g),P=0;P0&&(d=Math.max(10,d),this.circleDiameter=d)}else{var v=o.top*s-l,g=o.bottom*s+l,y=o.left*s-l,m=o.right*s+l,x=o.collisionPadding;if(x&&(y-=x[0]*s,v-=x[1]*s,m+=x[2]*s,g+=x[3]*s),c){var b=new a(y,v),_=new a(m,v),w=new a(y,g),T=new a(m,g),k=c*Math.PI/180;b._rotate(k),_._rotate(k),w._rotate(k),T._rotate(k),y=Math.min(b.x,_.x,w.x,T.x),m=Math.max(b.x,_.x,w.x,T.x),v=Math.min(b.y,_.y,w.y,T.y),g=Math.max(b.y,_.y,w.y,T.y)}t.emplaceBack(e.x,e.y,y,v,m,g,r,n,i)}this.boxEndIndex=t.length},Ou=function(t,e){if(void 0===t&&(t=[]),void 0===e&&(e=Iu),this.data=t,this.length=this.data.length,this.compare=e,this.length>0)for(var r=(this.length>>1)-1;r>=0;r--)this._down(r)};function Iu(t,e){return te?1:0}function Du(t,e,r){void 0===e&&(e=1),void 0===r&&(r=!1);for(var n=1/0,i=1/0,o=-1/0,s=-1/0,l=t[0],u=0;uo)&&(o=c.x),(!u||c.y>s)&&(s=c.y)}var f=o-n,h=s-i,p=Math.min(f,h),d=p/2,v=new Ou([],zu);if(0===p)return new a(n,i);for(var g=n;gm.d||!m.d)&&(m=b,r&&console.log(\"found best %d 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i=t-1>>1,a=e[i];if(r(n,a)>=0)break;e[t]=a,t=i}e[t]=n},Ou.prototype._down=function(t){for(var e=this.data,r=this.compare,n=this.length>>1,i=e[t];t=0)break;e[t]=o,t=a}e[t]=i};var Fu=Number.POSITIVE_INFINITY;function Bu(t,e){return e[1]!==Fu?function(t,e,r){var n=0,i=0;switch(e=Math.abs(e),r=Math.abs(r),t){case\"top-right\":case\"top-left\":case\"top\":i=r-7;break;case\"bottom-right\":case\"bottom-left\":case\"bottom\":i=7-r}switch(t){case\"top-right\":case\"bottom-right\":case\"right\":n=-e;break;case\"top-left\":case\"bottom-left\":case\"left\":n=e}return[n,i]}(t,e[0],e[1]):function(t,e){var r=0,n=0;e<0&&(e=0);var i=e/Math.sqrt(2);switch(t){case\"top-right\":case\"top-left\":n=i-7;break;case\"bottom-right\":case\"bottom-left\":n=7-i;break;case\"bottom\":n=7-e;break;case\"top\":n=e-7}switch(t){case\"top-right\":case\"bottom-right\":r=-i;break;case\"top-left\":case\"bottom-left\":r=i;break;case\"left\":r=e;break;case\"right\":r=-e}return[r,n]}(t,e[0])}function Nu(t){switch(t){case\"right\":case\"top-right\":case\"bottom-right\":return\"right\";case\"left\":case\"top-left\":case\"bottom-left\":return\"left\"}return\"center\"}var ju=32640;function Uu(t,e,r,n,i,o,s,l,u,c,f,h,p,d,v){var g=function(t,e,r,n,i,o,s,l){for(var u=n.layout.get(\"text-rotate\").evaluate(o,{})*Math.PI/180,c=[],f=0,h=e.positionedLines;fju&&w(t.layerIds[0]+': Value for \"text-size\" is >= '+'255. 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this.hasDebugData()&&this.iconCollisionBox.segments.get().length>0},Ku.prototype.addIndicesForPlacedSymbol=function(t,e){for(var r=t.placedSymbolArray.get(e),n=r.vertexStartIndex+4*r.numGlyphs,i=r.vertexStartIndex;i1||this.icon.segments.get().length>1)){this.symbolInstanceIndexes=this.getSortedSymbolIndexes(t),this.sortedAngle=t,this.text.indexArray.clear(),this.icon.indexArray.clear(),this.featureSortOrder=[];for(var r=0,n=this.symbolInstanceIndexes;r=0&&n.indexOf(t)===r&&e.addIndicesForPlacedSymbol(e.text,t)})),a.verticalPlacedTextSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.text,a.verticalPlacedTextSymbolIndex),a.placedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.placedIconSymbolIndex),a.verticalPlacedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,a.verticalPlacedIconSymbolIndex)}this.text.indexBuffer&&this.text.indexBuffer.updateData(this.text.indexArray),this.icon.indexBuffer&&this.icon.indexBuffer.updateData(this.icon.indexArray)}},Qn(\"SymbolBucket\",Ku,{omit:[\"layers\",\"collisionBoxArray\",\"features\",\"compareText\"]}),Ku.MAX_GLYPHS=65535,Ku.addDynamicAttributes=Yu;var $u=new Ui({\"symbol-placement\":new Ri(It.layout_symbol[\"symbol-placement\"]),\"symbol-spacing\":new Ri(It.layout_symbol[\"symbol-spacing\"]),\"symbol-avoid-edges\":new Ri(It.layout_symbol[\"symbol-avoid-edges\"]),\"symbol-sort-key\":new Fi(It.layout_symbol[\"symbol-sort-key\"]),\"symbol-z-order\":new Ri(It.layout_symbol[\"symbol-z-order\"]),\"icon-allow-overlap\":new Ri(It.layout_symbol[\"icon-allow-overlap\"]),\"icon-ignore-placement\":new Ri(It.layout_symbol[\"icon-ignore-placement\"]),\"icon-optional\":new Ri(It.layout_symbol[\"icon-optional\"]),\"icon-rotation-alignment\":new Ri(It.layout_symbol[\"icon-rotation-alignment\"]),\"icon-size\":new Fi(It.layout_symbol[\"icon-size\"]),\"icon-text-fit\":new Ri(It.layout_symbol[\"icon-text-fit\"]),\"icon-text-fit-padding\":new Ri(It.layout_symbol[\"icon-text-fit-padding\"]),\"icon-image\":new Fi(It.layout_symbol[\"icon-image\"]),\"icon-rotate\":new Fi(It.layout_symbol[\"icon-rotate\"]),\"icon-padding\":new Ri(It.layout_symbol[\"icon-padding\"]),\"icon-keep-upright\":new Ri(It.layout_symbol[\"icon-keep-upright\"]),\"icon-offset\":new Fi(It.layout_symbol[\"icon-offset\"]),\"icon-anchor\":new Fi(It.layout_symbol[\"icon-anchor\"]),\"icon-pitch-alignment\":new Ri(It.layout_symbol[\"icon-pitch-alignment\"]),\"text-pitch-alignment\":new Ri(It.layout_symbol[\"text-pitch-alignment\"]),\"text-rotation-alignment\":new Ri(It.layout_symbol[\"text-rotation-alignment\"]),\"text-field\":new Fi(It.layout_symbol[\"text-field\"]),\"text-font\":new Fi(It.layout_symbol[\"text-font\"]),\"text-size\":new Fi(It.layout_symbol[\"text-size\"]),\"text-max-width\":new Fi(It.layout_symbol[\"text-max-width\"]),\"text-line-height\":new Ri(It.layout_symbol[\"text-line-height\"]),\"text-letter-spacing\":new Fi(It.layout_symbol[\"text-letter-spacing\"]),\"text-justify\":new Fi(It.layout_symbol[\"text-justify\"]),\"text-radial-offset\":new Fi(It.layout_symbol[\"text-radial-offset\"]),\"text-variable-anchor\":new Ri(It.layout_symbol[\"text-variable-anchor\"]),\"text-anchor\":new Fi(It.layout_symbol[\"text-anchor\"]),\"text-max-angle\":new Ri(It.layout_symbol[\"text-max-angle\"]),\"text-writing-mode\":new Ri(It.layout_symbol[\"text-writing-mode\"]),\"text-rotate\":new Fi(It.layout_symbol[\"text-rotate\"]),\"text-padding\":new Ri(It.layout_symbol[\"text-padding\"]),\"text-keep-upright\":new Ri(It.layout_symbol[\"text-keep-upright\"]),\"text-transform\":new Fi(It.layout_symbol[\"text-transform\"]),\"text-offset\":new Fi(It.layout_symbol[\"text-offset\"]),\"text-allow-overlap\":new Ri(It.layout_symbol[\"text-allow-overlap\"]),\"text-ignore-placement\":new Ri(It.layout_symbol[\"text-ignore-placement\"]),\"text-optional\":new Ri(It.layout_symbol[\"text-optional\"])}),Qu={paint:new Ui({\"icon-opacity\":new Fi(It.paint_symbol[\"icon-opacity\"]),\"icon-color\":new Fi(It.paint_symbol[\"icon-color\"]),\"icon-halo-color\":new Fi(It.paint_symbol[\"icon-halo-color\"]),\"icon-halo-width\":new Fi(It.paint_symbol[\"icon-halo-width\"]),\"icon-halo-blur\":new Fi(It.paint_symbol[\"icon-halo-blur\"]),\"icon-translate\":new Ri(It.paint_symbol[\"icon-translate\"]),\"icon-translate-anchor\":new Ri(It.paint_symbol[\"icon-translate-anchor\"]),\"text-opacity\":new Fi(It.paint_symbol[\"text-opacity\"]),\"text-color\":new Fi(It.paint_symbol[\"text-color\"],{runtimeType:Gt,getOverride:function(t){return t.textColor},hasOverride:function(t){return!!t.textColor}}),\"text-halo-color\":new Fi(It.paint_symbol[\"text-halo-color\"]),\"text-halo-width\":new Fi(It.paint_symbol[\"text-halo-width\"]),\"text-halo-blur\":new Fi(It.paint_symbol[\"text-halo-blur\"]),\"text-translate\":new Ri(It.paint_symbol[\"text-translate\"]),\"text-translate-anchor\":new Ri(It.paint_symbol[\"text-translate-anchor\"])}),layout:$u},tc=function(t){this.type=t.property.overrides?t.property.overrides.runtimeType:Ut,this.defaultValue=t};tc.prototype.evaluate=function(t){if(t.formattedSection){var e=this.defaultValue.property.overrides;if(e&&e.hasOverride(t.formattedSection))return e.getOverride(t.formattedSection)}return t.feature&&t.featureState?this.defaultValue.evaluate(t.feature,t.featureState):this.defaultValue.property.specification.default},tc.prototype.eachChild=function(t){this.defaultValue.isConstant()||t(this.defaultValue.value._styleExpression.expression)},tc.prototype.outputDefined=function(){return!1},tc.prototype.serialize=function(){return null},Qn(\"FormatSectionOverride\",tc,{omit:[\"defaultValue\"]});var ec=function(t){function e(e){t.call(this,e,Qu)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.recalculate=function(e,r){if(t.prototype.recalculate.call(this,e,r),\"auto\"===this.layout.get(\"icon-rotation-alignment\")&&(\"point\"!==this.layout.get(\"symbol-placement\")?this.layout._values[\"icon-rotation-alignment\"]=\"map\":this.layout._values[\"icon-rotation-alignment\"]=\"viewport\"),\"auto\"===this.layout.get(\"text-rotation-alignment\")&&(\"point\"!==this.layout.get(\"symbol-placement\")?this.layout._values[\"text-rotation-alignment\"]=\"map\":this.layout._values[\"text-rotation-alignment\"]=\"viewport\"),\"auto\"===this.layout.get(\"text-pitch-alignment\")&&(this.layout._values[\"text-pitch-alignment\"]=this.layout.get(\"text-rotation-alignment\")),\"auto\"===this.layout.get(\"icon-pitch-alignment\")&&(this.layout._values[\"icon-pitch-alignment\"]=this.layout.get(\"icon-rotation-alignment\")),\"point\"===this.layout.get(\"symbol-placement\")){var n=this.layout.get(\"text-writing-mode\");if(n){for(var i=[],a=0,o=n;a\",targetMapId:n,sourceMapId:a.mapId})}}},vc.prototype.receive=function(t){var e=t.data,r=e.id;if(r&&(!e.targetMapId||this.mapId===e.targetMapId))if(\"\"===e.type){delete this.tasks[r];var n=this.cancelCallbacks[r];delete this.cancelCallbacks[r],n&&n()}else A()||e.mustQueue?(this.tasks[r]=e,this.taskQueue.push(r),this.invoker.trigger()):this.processTask(r,e)},vc.prototype.process=function(){if(this.taskQueue.length){var t=this.taskQueue.shift(),e=this.tasks[t];delete this.tasks[t],this.taskQueue.length&&this.invoker.trigger(),e&&this.processTask(t,e)}},vc.prototype.processTask=function(t,e){var r=this;if(\"\"===e.type){var n=this.callbacks[t];delete this.callbacks[t],n&&(e.error?n(ii(e.error)):n(null,ii(e.data)))}else{var i=!1,a=E(this.globalScope)?void 0:[],o=e.hasCallback?function(e,n){i=!0,delete r.cancelCallbacks[t],r.target.postMessage({id:t,type:\"\",sourceMapId:r.mapId,error:e?ni(e):null,data:ni(n,a)},a)}:function(t){i=!0},s=null,l=ii(e.data);if(this.parent[e.type])s=this.parent[e.type](e.sourceMapId,l,o);else if(this.parent.getWorkerSource){var u=e.type.split(\".\");s=this.parent.getWorkerSource(e.sourceMapId,u[0],l.source)[u[1]](l,o)}else o(new Error(\"Could not find function \"+e.type));!i&&s&&s.cancel&&(this.cancelCallbacks[t]=s.cancel)}},vc.prototype.remove=function(){this.invoker.remove(),this.target.removeEventListener(\"message\",this.receive,!1)};var yc=function(t,e){t&&(e?this.setSouthWest(t).setNorthEast(e):4===t.length?this.setSouthWest([t[0],t[1]]).setNorthEast([t[2],t[3]]):this.setSouthWest(t[0]).setNorthEast(t[1]))};yc.prototype.setNorthEast=function(t){return this._ne=t instanceof xc?new xc(t.lng,t.lat):xc.convert(t),this},yc.prototype.setSouthWest=function(t){return this._sw=t instanceof xc?new xc(t.lng,t.lat):xc.convert(t),this},yc.prototype.extend=function(t){var e,r,n=this._sw,i=this._ne;if(t instanceof xc)e=t,r=t;else{if(!(t instanceof yc)){if(Array.isArray(t)){if(4===t.length||t.every(Array.isArray)){var a=t;return this.extend(yc.convert(a))}var o=t;return this.extend(xc.convert(o))}return this}if(e=t._sw,r=t._ne,!e||!r)return this}return n||i?(n.lng=Math.min(e.lng,n.lng),n.lat=Math.min(e.lat,n.lat),i.lng=Math.max(r.lng,i.lng),i.lat=Math.max(r.lat,i.lat)):(this._sw=new xc(e.lng,e.lat),this._ne=new xc(r.lng,r.lat)),this},yc.prototype.getCenter=function(){return new xc((this._sw.lng+this._ne.lng)/2,(this._sw.lat+this._ne.lat)/2)},yc.prototype.getSouthWest=function(){return this._sw},yc.prototype.getNorthEast=function(){return this._ne},yc.prototype.getNorthWest=function(){return new xc(this.getWest(),this.getNorth())},yc.prototype.getSouthEast=function(){return new xc(this.getEast(),this.getSouth())},yc.prototype.getWest=function(){return this._sw.lng},yc.prototype.getSouth=function(){return this._sw.lat},yc.prototype.getEast=function(){return this._ne.lng},yc.prototype.getNorth=function(){return this._ne.lat},yc.prototype.toArray=function(){return[this._sw.toArray(),this._ne.toArray()]},yc.prototype.toString=function(){return\"LngLatBounds(\"+this._sw.toString()+\", \"+this._ne.toString()+\")\"},yc.prototype.isEmpty=function(){return!(this._sw&&this._ne)},yc.prototype.contains=function(t){var e=xc.convert(t),r=e.lng,n=e.lat,i=this._sw.lat<=n&&n<=this._ne.lat,a=this._sw.lng<=r&&r<=this._ne.lng;return this._sw.lng>this._ne.lng&&(a=this._sw.lng>=r&&r>=this._ne.lng),i&&a},yc.convert=function(t){return!t||t instanceof yc?t:new yc(t)};var mc=6371008.8,xc=function(t,e){if(isNaN(t)||isNaN(e))throw new Error(\"Invalid LngLat object: (\"+t+\", \"+e+\")\");if(this.lng=+t,this.lat=+e,this.lat>90||this.lat<-90)throw new Error(\"Invalid LngLat latitude value: must be between -90 and 90\")};xc.prototype.wrap=function(){return new xc(c(this.lng,-180,180),this.lat)},xc.prototype.toArray=function(){return[this.lng,this.lat]},xc.prototype.toString=function(){return\"LngLat(\"+this.lng+\", \"+this.lat+\")\"},xc.prototype.distanceTo=function(t){var e=Math.PI/180,r=this.lat*e,n=t.lat*e,i=Math.sin(r)*Math.sin(n)+Math.cos(r)*Math.cos(n)*Math.cos((t.lng-this.lng)*e);return mc*Math.acos(Math.min(i,1))},xc.prototype.toBounds=function(t){void 0===t&&(t=0);var e=360*t/40075017,r=e/Math.cos(Math.PI/180*this.lat);return new yc(new xc(this.lng-r,this.lat-e),new xc(this.lng+r,this.lat+e))},xc.convert=function(t){if(t instanceof xc)return t;if(Array.isArray(t)&&(2===t.length||3===t.length))return new xc(Number(t[0]),Number(t[1]));if(!Array.isArray(t)&&\"object\"==typeof t&&null!==t)return new xc(Number(\"lng\"in t?t.lng:t.lon),Number(t.lat));throw new Error(\"`LngLatLike` argument must be specified as a LngLat instance, an object {lng: , lat: }, an object {lon: , lat: }, or an array of [, ]\")};var bc=2*Math.PI*mc;function _c(t){return bc*Math.cos(t*Math.PI/180)}function wc(t){return(180+t)/360}function Tc(t){return(180-180/Math.PI*Math.log(Math.tan(Math.PI/4+t*Math.PI/360)))/360}function kc(t,e){return t/_c(e)}function Ac(t){var e=180-360*t;return 360/Math.PI*Math.atan(Math.exp(e*Math.PI/180))-90}var Mc=function(t,e,r){void 0===r&&(r=0),this.x=+t,this.y=+e,this.z=+r};Mc.fromLngLat=function(t,e){void 0===e&&(e=0);var r=xc.convert(t);return new Mc(wc(r.lng),Tc(r.lat),kc(e,r.lat))},Mc.prototype.toLngLat=function(){return new xc(360*this.x-180,Ac(this.y))},Mc.prototype.toAltitude=function(){return t=this.z,e=this.y,t*_c(Ac(e));var t,e},Mc.prototype.meterInMercatorCoordinateUnits=function(){return 1/bc*(t=Ac(this.y),1/Math.cos(t*Math.PI/180));var t};var Sc=function(t,e,r){this.z=t,this.x=e,this.y=r,this.key=Cc(0,t,t,e,r)};Sc.prototype.equals=function(t){return this.z===t.z&&this.x===t.x&&this.y===t.y},Sc.prototype.url=function(t,e){var r,n,i,a,o,s=(r=this.x,n=this.y,i=this.z,a=gc(256*r,256*(n=Math.pow(2,i)-n-1),i),o=gc(256*(r+1),256*(n+1),i),a[0]+\",\"+a[1]+\",\"+o[0]+\",\"+o[1]),l=function(t,e,r){for(var n,i=\"\",a=t;a>0;a--)i+=(e&(n=1<this.canonical.z?new Lc(t,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y):new Lc(t,this.wrap,t,this.canonical.x>>e,this.canonical.y>>e)},Lc.prototype.calculateScaledKey=function(t,e){var r=this.canonical.z-t;return t>this.canonical.z?Cc(this.wrap*+e,t,this.canonical.z,this.canonical.x,this.canonical.y):Cc(this.wrap*+e,t,t,this.canonical.x>>r,this.canonical.y>>r)},Lc.prototype.isChildOf=function(t){if(t.wrap!==this.wrap)return!1;var e=this.canonical.z-t.canonical.z;return 0===t.overscaledZ||t.overscaledZ>e&&t.canonical.y===this.canonical.y>>e},Lc.prototype.children=function(t){if(this.overscaledZ>=t)return[new Lc(this.overscaledZ+1,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)];var e=this.canonical.z+1,r=2*this.canonical.x,n=2*this.canonical.y;return[new Lc(e,this.wrap,e,r,n),new Lc(e,this.wrap,e,r+1,n),new Lc(e,this.wrap,e,r,n+1),new Lc(e,this.wrap,e,r+1,n+1)]},Lc.prototype.isLessThan=function(t){return this.wrapt.wrap)&&(this.overscaledZt.overscaledZ)&&(this.canonical.xt.canonical.x)&&this.canonical.y=this.dim+1||e<-1||e>=this.dim+1)throw new RangeError(\"out of range source coordinates for DEM data\");return(e+1)*this.stride+(t+1)},Pc.prototype._unpackMapbox=function(t,e,r){return(256*t*256+256*e+r)/10-1e4},Pc.prototype._unpackTerrarium=function(t,e,r){return 256*t+e+r/256-32768},Pc.prototype.getPixels=function(){return new qo({width:this.stride,height:this.stride},new Uint8Array(this.data.buffer))},Pc.prototype.backfillBorder=function(t,e,r){if(this.dim!==t.dim)throw new Error(\"dem dimension mismatch\");var n=e*this.dim,i=e*this.dim+this.dim,a=r*this.dim,o=r*this.dim+this.dim;switch(e){case-1:n=i-1;break;case 1:i=n+1}switch(r){case-1:a=o-1;break;case 1:o=a+1}for(var s=-e*this.dim,l=-r*this.dim,u=a;u=0&&c[3]>=0&&s.insert(o,c[0],c[1],c[2],c[3])}},Rc.prototype.loadVTLayers=function(){return this.vtLayers||(this.vtLayers=new Zs.VectorTile(new Tl(this.rawTileData)).layers,this.sourceLayerCoder=new Oc(this.vtLayers?Object.keys(this.vtLayers).sort():[\"_geojsonTileLayer\"])),this.vtLayers},Rc.prototype.query=function(t,e,r,n){var i=this;this.loadVTLayers();for(var o=t.params||{},s=oo/t.tileSize/t.scale,l=xn(o.filter),u=t.queryGeometry,c=t.queryPadding*s,f=Bc(u),h=this.grid.query(f.minX-c,f.minY-c,f.maxX+c,f.maxY+c),p=Bc(t.cameraQueryGeometry),d=0,v=this.grid3D.query(p.minX-c,p.minY-c,p.maxX+c,p.maxY+c,(function(e,r,n,i){return function(t,e,r,n,i){for(var o=0,s=t;o=l.x&&i>=l.y)return!0}var u=[new a(e,r),new a(e,i),new a(n,i),new a(n,r)];if(t.length>2)for(var c=0,f=u;c=0)return!0;return!1}(a,f)){var h=this.sourceLayerCoder.decode(r),p=this.vtLayers[h].feature(n);if(i.filter(new Si(this.tileID.overscaledZ),p))for(var d=this.getId(p,h),v=0;vn)i=!1;else if(e)if(this.expirationTimect&&(t.getActor().send(\"enforceCacheSizeLimit\",ut),gt=0)},t.clamp=u,t.clearTileCache=function(t){var e=self.caches.delete(lt);t&&e.catch(t).then((function(){return t()}))},t.clipLine=Au,t.clone=function(t){var e=new Lo(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e},t.clone$1=b,t.clone$2=function(t){var e=new Lo(3);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e},t.collisionCircleLayout=yl,t.config=B,t.create=function(){var t=new Lo(16);return Lo!=Float32Array&&(t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[11]=0,t[12]=0,t[13]=0,t[14]=0),t[0]=1,t[5]=1,t[10]=1,t[15]=1,t},t.create$1=function(){var t=new Lo(9);return Lo!=Float32Array&&(t[1]=0,t[2]=0,t[3]=0,t[5]=0,t[6]=0,t[7]=0),t[0]=1,t[4]=1,t[8]=1,t},t.create$2=function(){var t=new Lo(4);return Lo!=Float32Array&&(t[1]=0,t[2]=0),t[0]=1,t[3]=1,t},t.createCommonjsModule=e,t.createExpression=an,t.createLayout=Yi,t.createStyleLayer=function(t){return\"custom\"===t.type?new oc(t):new sc[t.type](t)},t.cross=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=r[0],s=r[1],l=r[2];return t[0]=i*l-a*s,t[1]=a*o-n*l,t[2]=n*s-i*o,t},t.deepEqual=function t(e,r){if(Array.isArray(e)){if(!Array.isArray(r)||e.length!==r.length)return!1;for(var n=0;n0&&(a=1/Math.sqrt(a)),t[0]=e[0]*a,t[1]=e[1]*a,t[2]=e[2]*a,t},t.number=Ke,t.offscreenCanvasSupported=yt,t.ortho=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),u=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*u,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*u,t[15]=1,t},t.parseGlyphPBF=function(t){return new Tl(t).readFields(Hl,[])},t.pbf=Tl,t.performSymbolLayout=function(t,e,r,n,i,a,o){t.createArrays();var s=512*t.overscaling;t.tilePixelRatio=oo/s,t.compareText={},t.iconsNeedLinear=!1;var l=t.layers[0].layout,u=t.layers[0]._unevaluatedLayout._values,c={};if(\"composite\"===t.textSizeData.kind){var f=t.textSizeData,h=f.minZoom,p=f.maxZoom;c.compositeTextSizes=[u[\"text-size\"].possiblyEvaluate(new Si(h),o),u[\"text-size\"].possiblyEvaluate(new Si(p),o)]}if(\"composite\"===t.iconSizeData.kind){var d=t.iconSizeData,v=d.minZoom,g=d.maxZoom;c.compositeIconSizes=[u[\"icon-size\"].possiblyEvaluate(new Si(v),o),u[\"icon-size\"].possiblyEvaluate(new Si(g),o)]}c.layoutTextSize=u[\"text-size\"].possiblyEvaluate(new Si(t.zoom+1),o),c.layoutIconSize=u[\"icon-size\"].possiblyEvaluate(new Si(t.zoom+1),o),c.textMaxSize=u[\"text-size\"].possiblyEvaluate(new Si(18));for(var y=l.get(\"text-line-height\")*bl,m=\"map\"===l.get(\"text-rotation-alignment\")&&\"point\"!==l.get(\"symbol-placement\"),x=l.get(\"text-keep-upright\"),b=l.get(\"text-size\"),_=function(){var a=k[T],s=l.get(\"text-font\").evaluate(a,{},o).join(\",\"),u=b.evaluate(a,{},o),f=c.layoutTextSize.evaluate(a,{},o),h=c.layoutIconSize.evaluate(a,{},o),p={horizontal:{},vertical:void 0},d=a.text,v=[0,0];if(d){var g=d.toString(),_=l.get(\"text-letter-spacing\").evaluate(a,{},o)*bl,A=function(t){for(var e=0,r=t;e=oo||f.y<0||f.y>=oo||function(t,e,r,n,i,a,o,s,l,u,c,f,h,p,d,v,g,y,m,x,b,_,T,k,A){var M,S,E,L,C,P=t.addToLineVertexArray(e,r),O=0,I=0,D=0,z=0,R=-1,F=-1,B={},N=Ia(\"\"),j=0,U=0;if(void 0===s._unevaluatedLayout.getValue(\"text-radial-offset\")?(j=(M=s.layout.get(\"text-offset\").evaluate(b,{},k).map((function(t){return t*bl})))[0],U=M[1]):(j=s.layout.get(\"text-radial-offset\").evaluate(b,{},k)*bl,U=Fu),t.allowVerticalPlacement&&n.vertical){var V=s.layout.get(\"text-rotate\").evaluate(b,{},k)+90,H=n.vertical;L=new Pu(l,e,u,c,f,H,h,p,d,V),o&&(C=new Pu(l,e,u,c,f,o,g,y,d,V))}if(i){var q=s.layout.get(\"icon-rotate\").evaluate(b,{}),G=\"none\"!==s.layout.get(\"icon-text-fit\"),Z=Mu(i,q,T,G),Y=o?Mu(o,q,T,G):void 0;E=new Pu(l,e,u,c,f,i,g,y,!1,q),O=4*Z.length;var W=t.iconSizeData,X=null;\"source\"===W.kind?(X=[pu*s.layout.get(\"icon-size\").evaluate(b,{})])[0]>ju&&w(t.layerIds[0]+': Value for \"icon-size\" is >= '+'255. Reduce your \"icon-size\".'):\"composite\"===W.kind&&((X=[pu*_.compositeIconSizes[0].evaluate(b,{},k),pu*_.compositeIconSizes[1].evaluate(b,{},k)])[0]>ju||X[1]>ju)&&w(t.layerIds[0]+': Value for \"icon-size\" is >= '+'255. Reduce your \"icon-size\".'),t.addSymbols(t.icon,Z,X,x,m,b,!1,e,P.lineStartIndex,P.lineLength,-1,k),R=t.icon.placedSymbolArray.length-1,Y&&(I=4*Y.length,t.addSymbols(t.icon,Y,X,x,m,b,Jl.vertical,e,P.lineStartIndex,P.lineLength,-1,k),F=t.icon.placedSymbolArray.length-1)}for(var J in n.horizontal){var K=n.horizontal[J];if(!S){N=Ia(K.text);var $=s.layout.get(\"text-rotate\").evaluate(b,{},k);S=new Pu(l,e,u,c,f,K,h,p,d,$)}var Q=1===K.positionedLines.length;if(D+=Uu(t,e,K,a,s,d,b,v,P,n.vertical?Jl.horizontal:Jl.horizontalOnly,Q?Object.keys(n.horizontal):[J],B,R,_,k),Q)break}n.vertical&&(z+=Uu(t,e,n.vertical,a,s,d,b,v,P,Jl.vertical,[\"vertical\"],B,F,_,k));var tt=S?S.boxStartIndex:t.collisionBoxArray.length,et=S?S.boxEndIndex:t.collisionBoxArray.length,rt=L?L.boxStartIndex:t.collisionBoxArray.length,nt=L?L.boxEndIndex:t.collisionBoxArray.length,it=E?E.boxStartIndex:t.collisionBoxArray.length,at=E?E.boxEndIndex:t.collisionBoxArray.length,ot=C?C.boxStartIndex:t.collisionBoxArray.length,st=C?C.boxEndIndex:t.collisionBoxArray.length,lt=-1,ut=function(t,e){return t&&t.circleDiameter?Math.max(t.circleDiameter,e):e};lt=ut(S,lt),lt=ut(L,lt),lt=ut(E,lt);var ct=(lt=ut(C,lt))>-1?1:0;ct&&(lt*=A/bl),t.glyphOffsetArray.length>=Ku.MAX_GLYPHS&&w(\"Too many glyphs being rendered in a tile. See https://github.com/mapbox/mapbox-gl-js/issues/2907\"),void 0!==b.sortKey&&t.addToSortKeyRanges(t.symbolInstances.length,b.sortKey),t.symbolInstances.emplaceBack(e.x,e.y,B.right>=0?B.right:-1,B.center>=0?B.center:-1,B.left>=0?B.left:-1,B.vertical||-1,R,F,N,tt,et,rt,nt,it,at,ot,st,u,D,z,O,I,ct,0,h,j,U,lt)}(t,f,s,r,n,i,h,t.layers[0],t.collisionBoxArray,e.index,e.sourceLayerIndex,t.index,m,T,M,l,b,k,S,d,e,a,u,c,o)};if(\"line\"===E)for(var O=0,I=Au(e.geometry,0,0,oo,oo);O1){var U=wu(j,A,r.vertical||v,n,g,x);U&&P(j,U)}}else if(\"Polygon\"===e.type)for(var V=0,H=Ls(e.geometry,0);V=E.maxzoom||\"none\"!==E.visibility&&(o(S,this.zoom,n),(v[E.id]=E.createBucket({index:c.bucketLayerIDs.length,layers:S,zoom:this.zoom,pixelRatio:this.pixelRatio,overscaling:this.overscaling,collisionBoxArray:this.collisionBoxArray,sourceLayerIndex:b,sourceID:this.source})).populate(_,g,this.tileID.canonical),c.bucketLayerIDs.push(S.map((function(t){return t.id}))))}}}var L=t.mapObject(g.glyphDependencies,(function(t){return Object.keys(t).map(Number)}));Object.keys(L).length?a.send(\"getGlyphs\",{uid:this.uid,stacks:L},(function(t,e){f||(f=t,h=e,O.call(l))})):h={};var C=Object.keys(g.iconDependencies);C.length?a.send(\"getImages\",{icons:C,source:this.source,tileID:this.tileID,type:\"icons\"},(function(t,e){f||(f=t,p=e,O.call(l))})):p={};var P=Object.keys(g.patternDependencies);function O(){if(f)return s(f);if(h&&p&&d){var e=new i(h),r=new t.ImageAtlas(p,d);for(var a in v){var l=v[a];l instanceof t.SymbolBucket?(o(l.layers,this.zoom,n),t.performSymbolLayout(l,h,e.positions,p,r.iconPositions,this.showCollisionBoxes,this.tileID.canonical)):l.hasPattern&&(l instanceof t.LineBucket||l instanceof t.FillBucket||l instanceof t.FillExtrusionBucket)&&(o(l.layers,this.zoom,n),l.addFeatures(g,this.tileID.canonical,r.patternPositions))}this.status=\"done\",s(null,{buckets:t.values(v).filter((function(t){return!t.isEmpty()})),featureIndex:c,collisionBoxArray:this.collisionBoxArray,glyphAtlasImage:e.image,imageAtlas:r,glyphMap:this.returnDependencies?h:null,iconMap:this.returnDependencies?p:null,glyphPositions:this.returnDependencies?e.positions:null})}}P.length?a.send(\"getImages\",{icons:P,source:this.source,tileID:this.tileID,type:\"patterns\"},(function(t,e){f||(f=t,d=e,O.call(l))})):d={},O.call(this)};var l=function(t,e,r,n){this.actor=t,this.layerIndex=e,this.availableImages=r,this.loadVectorData=n||s,this.loading={},this.loaded={}};l.prototype.loadTile=function(e,r){var n=this,i=e.uid;this.loading||(this.loading={});var o=!!(e&&e.request&&e.request.collectResourceTiming)&&new t.RequestPerformance(e.request),s=this.loading[i]=new a(e);s.abort=this.loadVectorData(e,(function(e,a){if(delete n.loading[i],e||!a)return s.status=\"done\",n.loaded[i]=s,r(e);var l=a.rawData,u={};a.expires&&(u.expires=a.expires),a.cacheControl&&(u.cacheControl=a.cacheControl);var c={};if(o){var f=o.finish();f&&(c.resourceTiming=JSON.parse(JSON.stringify(f)))}s.vectorTile=a.vectorTile,s.parse(a.vectorTile,n.layerIndex,n.availableImages,n.actor,(function(e,n){if(e||!n)return r(e);r(null,t.extend({rawTileData:l.slice(0)},n,u,c))})),n.loaded=n.loaded||{},n.loaded[i]=s}))},l.prototype.reloadTile=function(t,e){var r=this,n=this.loaded,i=t.uid,a=this;if(n&&n[i]){var o=n[i];o.showCollisionBoxes=t.showCollisionBoxes;var s=function(t,n){var i=o.reloadCallback;i&&(delete o.reloadCallback,o.parse(o.vectorTile,a.layerIndex,r.availableImages,a.actor,i)),e(t,n)};\"parsing\"===o.status?o.reloadCallback=s:\"done\"===o.status&&(o.vectorTile?o.parse(o.vectorTile,this.layerIndex,this.availableImages,this.actor,s):s())}},l.prototype.abortTile=function(t,e){var r=this.loading,n=t.uid;r&&r[n]&&r[n].abort&&(r[n].abort(),delete r[n]),e()},l.prototype.removeTile=function(t,e){var r=this.loaded,n=t.uid;r&&r[n]&&delete r[n],e()};var u=t.window.ImageBitmap,c=function(){this.loaded={}};c.prototype.loadTile=function(e,r){var n=e.uid,i=e.encoding,a=e.rawImageData,o=u&&a instanceof u?this.getImageData(a):a,s=new t.DEMData(n,o,i);this.loaded=this.loaded||{},this.loaded[n]=s,r(null,s)},c.prototype.getImageData=function(e){this.offscreenCanvas&&this.offscreenCanvasContext||(this.offscreenCanvas=new OffscreenCanvas(e.width,e.height),this.offscreenCanvasContext=this.offscreenCanvas.getContext(\"2d\")),this.offscreenCanvas.width=e.width,this.offscreenCanvas.height=e.height,this.offscreenCanvasContext.drawImage(e,0,0,e.width,e.height);var r=this.offscreenCanvasContext.getImageData(-1,-1,e.width+2,e.height+2);return this.offscreenCanvasContext.clearRect(0,0,this.offscreenCanvas.width,this.offscreenCanvas.height),new t.RGBAImage({width:r.width,height:r.height},r.data)},c.prototype.removeTile=function(t){var e=this.loaded,r=t.uid;e&&e[r]&&delete e[r]};var f=function t(e,r){var n,i=e&&e.type;if(\"FeatureCollection\"===i)for(n=0;n=0!=!!e&&t.reverse()}var d=t.vectorTile.VectorTileFeature.prototype.toGeoJSON,v=function(e){this._feature=e,this.extent=t.EXTENT,this.type=e.type,this.properties=e.tags,\"id\"in e&&!isNaN(e.id)&&(this.id=parseInt(e.id,10))};v.prototype.loadGeometry=function(){if(1===this._feature.type){for(var e=[],r=0,n=this._feature.geometry;r>31}function P(t,e){for(var r=t.loadGeometry(),n=t.type,i=0,a=0,o=r.length,s=0;s>1;D(t,e,o,n,i,a%2),I(t,e,r,n,o-1,a+1),I(t,e,r,o+1,i,a+1)}}function D(t,e,r,n,i,a){for(;i>n;){if(i-n>600){var o=i-n+1,s=r-n+1,l=Math.log(o),u=.5*Math.exp(2*l/3),c=.5*Math.sqrt(l*u*(o-u)/o)*(s-o/2<0?-1:1);D(t,e,r,Math.max(n,Math.floor(r-s*u/o+c)),Math.min(i,Math.floor(r+(o-s)*u/o+c)),a)}var f=e[2*r+a],h=n,p=i;for(z(t,e,n,r),e[2*i+a]>f&&z(t,e,n,i);hf;)p--}e[2*n+a]===f?z(t,e,n,p):z(t,e,++p,i),p<=r&&(n=p+1),r<=p&&(i=p-1)}}function z(t,e,r,n){R(t,r,n),R(e,2*r,2*n),R(e,2*r+1,2*n+1)}function R(t,e,r){var n=t[e];t[e]=t[r],t[r]=n}function F(t,e,r,n){var i=t-r,a=e-n;return i*i+a*a}_.fromVectorTileJs=w,_.fromGeojsonVt=T,_.GeoJSONWrapper=k;var B=function(t){return t[0]},N=function(t){return t[1]},j=function(t,e,r,n,i){void 0===e&&(e=B),void 0===r&&(r=N),void 0===n&&(n=64),void 0===i&&(i=Float64Array),this.nodeSize=n,this.points=t;for(var a=t.length<65536?Uint16Array:Uint32Array,o=this.ids=new a(t.length),s=this.coords=new i(2*t.length),l=0;l=r&&s<=i&&l>=n&&l<=a&&c.push(t[d]);else{var v=Math.floor((p+h)/2);s=e[2*v],l=e[2*v+1],s>=r&&s<=i&&l>=n&&l<=a&&c.push(t[v]);var g=(f+1)%2;(0===f?r<=s:n<=l)&&(u.push(p),u.push(v-1),u.push(g)),(0===f?i>=s:a>=l)&&(u.push(v+1),u.push(h),u.push(g))}}return c}(this.ids,this.coords,t,e,r,n,this.nodeSize)},j.prototype.within=function(t,e,r){return function(t,e,r,n,i,a){for(var o=[0,t.length-1,0],s=[],l=i*i;o.length;){var u=o.pop(),c=o.pop(),f=o.pop();if(c-f<=a)for(var h=f;h<=c;h++)F(e[2*h],e[2*h+1],r,n)<=l&&s.push(t[h]);else{var p=Math.floor((f+c)/2),d=e[2*p],v=e[2*p+1];F(d,v,r,n)<=l&&s.push(t[p]);var g=(u+1)%2;(0===u?r-i<=d:n-i<=v)&&(o.push(f),o.push(p-1),o.push(g)),(0===u?r+i>=d:n+i>=v)&&(o.push(p+1),o.push(c),o.push(g))}}return s}(this.ids,this.coords,t,e,r,this.nodeSize)};var U={minZoom:0,maxZoom:16,radius:40,extent:512,nodeSize:64,log:!1,generateId:!1,reduce:null,map:function(t){return t}},V=function(t){this.options=X(Object.create(U),t),this.trees=new Array(this.options.maxZoom+1)};function H(t,e,r,n,i){return{x:t,y:e,zoom:1/0,id:r,parentId:-1,numPoints:n,properties:i}}function q(t,e){var r=t.geometry.coordinates,n=r[0],i=r[1];return{x:Y(n),y:W(i),zoom:1/0,index:e,parentId:-1}}function G(t){return{type:\"Feature\",id:t.id,properties:Z(t),geometry:{type:\"Point\",coordinates:[(n=t.x,360*(n-.5)),(e=t.y,r=(180-360*e)*Math.PI/180,360*Math.atan(Math.exp(r))/Math.PI-90)]}};var e,r,n}function Z(t){var e=t.numPoints,r=e>=1e4?Math.round(e/1e3)+\"k\":e>=1e3?Math.round(e/100)/10+\"k\":e;return X(X({},t.properties),{cluster:!0,cluster_id:t.id,point_count:e,point_count_abbreviated:r})}function Y(t){return t/360+.5}function W(t){var e=Math.sin(t*Math.PI/180),r=.5-.25*Math.log((1+e)/(1-e))/Math.PI;return r<0?0:r>1?1:r}function X(t,e){for(var r in e)t[r]=e[r];return t}function J(t){return t.x}function K(t){return t.y}function $(t,e,r,n){for(var i,a=n,o=r-e>>1,s=r-e,l=t[e],u=t[e+1],c=t[r],f=t[r+1],h=e+3;ha)i=h,a=p;else if(p===a){var d=Math.abs(h-o);dn&&(i-e>3&&$(t,e,i,n),t[i+2]=a,r-i>3&&$(t,i,r,n))}function Q(t,e,r,n,i,a){var o=i-r,s=a-n;if(0!==o||0!==s){var l=((t-r)*o+(e-n)*s)/(o*o+s*s);l>1?(r=i,n=a):l>0&&(r+=o*l,n+=s*l)}return(o=t-r)*o+(s=e-n)*s}function tt(t,e,r,n){var i={id:void 0===t?null:t,type:e,geometry:r,tags:n,minX:1/0,minY:1/0,maxX:-1/0,maxY:-1/0};return function(t){var e=t.geometry,r=t.type;if(\"Point\"===r||\"MultiPoint\"===r||\"LineString\"===r)et(t,e);else if(\"Polygon\"===r||\"MultiLineString\"===r)for(var n=0;n0&&(o+=n?(i*u-l*a)/2:Math.sqrt(Math.pow(l-i,2)+Math.pow(u-a,2))),i=l,a=u}var c=e.length-3;e[2]=1,$(e,0,c,r),e[c+2]=1,e.size=Math.abs(o),e.start=0,e.end=e.size}function at(t,e,r,n){for(var i=0;i1?1:r}function lt(t,e,r,n,i,a,o,s){if(n/=e,a>=(r/=e)&&o=n)return null;for(var l=[],u=0;u=r&&d=n)){var v=[];if(\"Point\"===h||\"MultiPoint\"===h)ut(f,v,r,n,i);else if(\"LineString\"===h)ct(f,v,r,n,i,!1,s.lineMetrics);else if(\"MultiLineString\"===h)ht(f,v,r,n,i,!1);else if(\"Polygon\"===h)ht(f,v,r,n,i,!0);else if(\"MultiPolygon\"===h)for(var g=0;g=r&&o<=n&&(e.push(t[a]),e.push(t[a+1]),e.push(t[a+2]))}}function ct(t,e,r,n,i,a,o){for(var s,l,u=ft(t),c=0===i?dt:vt,f=t.start,h=0;hr&&(l=c(u,p,d,g,y,r),o&&(u.start=f+s*l)):m>n?x=r&&(l=c(u,p,d,g,y,r),b=!0),x>n&&m<=n&&(l=c(u,p,d,g,y,n),b=!0),!a&&b&&(o&&(u.end=f+s*l),e.push(u),u=ft(t)),o&&(f+=s)}var _=t.length-3;p=t[_],d=t[_+1],v=t[_+2],(m=0===i?p:d)>=r&&m<=n&&pt(u,p,d,v),_=u.length-3,a&&_>=3&&(u[_]!==u[0]||u[_+1]!==u[1])&&pt(u,u[0],u[1],u[2]),u.length&&e.push(u)}function ft(t){var e=[];return e.size=t.size,e.start=t.start,e.end=t.end,e}function ht(t,e,r,n,i,a){for(var o=0;oo.maxX&&(o.maxX=c),f>o.maxY&&(o.maxY=f)}return o}function _t(t,e,r,n){var i=e.geometry,a=e.type,o=[];if(\"Point\"===a||\"MultiPoint\"===a)for(var s=0;s0&&e.size<(i?o:n))r.numPoints+=e.length/3;else{for(var s=[],l=0;lo)&&(r.numSimplified++,s.push(e[l]),s.push(e[l+1])),r.numPoints++;i&&function(t,e){for(var r=0,n=0,i=t.length,a=i-2;n0===e)for(n=0,i=t.length;n24)throw new Error(\"maxZoom should be in the 0-24 range\");if(e.promoteId&&e.generateId)throw new Error(\"promoteId and generateId cannot be used together.\");var n=function(t,e){var r=[];if(\"FeatureCollection\"===t.type)for(var n=0;n=n;u--){var c=+Date.now();s=this._cluster(s,u),this.trees[u]=new j(s,J,K,a,Float32Array),r&&console.log(\"z%d: %d clusters in %dms\",u,s.length,+Date.now()-c)}return r&&console.timeEnd(\"total time\"),this},V.prototype.getClusters=function(t,e){var r=((t[0]+180)%360+360)%360-180,n=Math.max(-90,Math.min(90,t[1])),i=180===t[2]?180:((t[2]+180)%360+360)%360-180,a=Math.max(-90,Math.min(90,t[3]));if(t[2]-t[0]>=360)r=-180,i=180;else if(r>i){var o=this.getClusters([r,n,180,a],e),s=this.getClusters([-180,n,i,a],e);return o.concat(s)}for(var l=this.trees[this._limitZoom(e)],u=[],c=0,f=l.range(Y(r),W(a),Y(i),W(n));c1?this._map(u,!0):null,g=(l<<5)+(e+1)+this.points.length,y=0,m=f;y>5},V.prototype._getOriginZoom=function(t){return(t-this.points.length)%32},V.prototype._map=function(t,e){if(t.numPoints)return e?X({},t.properties):t.properties;var r=this.points[t.index].properties,n=this.options.map(r);return e&&n===r?X({},n):n},Tt.prototype.options={maxZoom:14,indexMaxZoom:5,indexMaxPoints:1e5,tolerance:3,extent:4096,buffer:64,lineMetrics:!1,promoteId:null,generateId:!1,debug:0},Tt.prototype.splitTile=function(t,e,r,n,i,a,o){for(var s=[t,e,r,n],l=this.options,u=l.debug;s.length;){n=s.pop(),r=s.pop(),e=s.pop(),t=s.pop();var c=1<1&&console.time(\"creation\"),h=this.tiles[f]=bt(t,e,r,n,l),this.tileCoords.push({z:e,x:r,y:n}),u)){u>1&&(console.log(\"tile z%d-%d-%d (features: %d, points: %d, simplified: %d)\",e,r,n,h.numFeatures,h.numPoints,h.numSimplified),console.timeEnd(\"creation\"));var p=\"z\"+e;this.stats[p]=(this.stats[p]||0)+1,this.total++}if(h.source=t,i){if(e===l.maxZoom||e===i)continue;var d=1<1&&console.time(\"clipping\");var v,g,y,m,x,b,_=.5*l.buffer/l.extent,w=.5-_,T=.5+_,k=1+_;v=g=y=m=null,x=lt(t,c,r-_,r+T,0,h.minX,h.maxX,l),b=lt(t,c,r+w,r+k,0,h.minX,h.maxX,l),t=null,x&&(v=lt(x,c,n-_,n+T,1,h.minY,h.maxY,l),g=lt(x,c,n+w,n+k,1,h.minY,h.maxY,l),x=null),b&&(y=lt(b,c,n-_,n+T,1,h.minY,h.maxY,l),m=lt(b,c,n+w,n+k,1,h.minY,h.maxY,l),b=null),u>1&&console.timeEnd(\"clipping\"),s.push(v||[],e+1,2*r,2*n),s.push(g||[],e+1,2*r,2*n+1),s.push(y||[],e+1,2*r+1,2*n),s.push(m||[],e+1,2*r+1,2*n+1)}}},Tt.prototype.getTile=function(t,e,r){var n=this.options,i=n.extent,a=n.debug;if(t<0||t>24)return null;var o=1<1&&console.log(\"drilling down to z%d-%d-%d\",t,e,r);for(var l,u=t,c=e,f=r;!l&&u>0;)u--,c=Math.floor(c/2),f=Math.floor(f/2),l=this.tiles[kt(u,c,f)];return l&&l.source?(a>1&&console.log(\"found parent tile z%d-%d-%d\",u,c,f),a>1&&console.time(\"drilling down\"),this.splitTile(l.source,u,c,f,t,e,r),a>1&&console.timeEnd(\"drilling down\"),this.tiles[s]?mt(this.tiles[s],i):null):null};var Mt=function(e){function r(t,r,n,i){e.call(this,t,r,n,At),i&&(this.loadGeoJSON=i)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadData=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),this._pendingCallback=e,this._pendingLoadDataParams=t,this._state&&\"Idle\"!==this._state?this._state=\"NeedsLoadData\":(this._state=\"Coalescing\",this._loadData())},r.prototype._loadData=function(){var e=this;if(this._pendingCallback&&this._pendingLoadDataParams){var r=this._pendingCallback,n=this._pendingLoadDataParams;delete this._pendingCallback,delete this._pendingLoadDataParams;var i=!!(n&&n.request&&n.request.collectResourceTiming)&&new t.RequestPerformance(n.request);this.loadGeoJSON(n,(function(a,o){if(a||!o)return r(a);if(\"object\"!=typeof o)return r(new Error(\"Input data given to '\"+n.source+\"' is not a valid GeoJSON object.\"));f(o,!0);try{e._geoJSONIndex=n.cluster?new V(function(e){var r=e.superclusterOptions,n=e.clusterProperties;if(!n||!r)return r;for(var i={},a={},o={accumulated:null,zoom:0},s={properties:null},l=Object.keys(n),u=0,c=l;u=0?0:e.button},r.remove=function(t){t.parentNode&&t.parentNode.removeChild(t)};var h=function(e){function r(){e.call(this),this.images={},this.updatedImages={},this.callbackDispatchedThisFrame={},this.loaded=!1,this.requestors=[],this.patterns={},this.atlasImage=new t.RGBAImage({width:1,height:1}),this.dirty=!0}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.isLoaded=function(){return this.loaded},r.prototype.setLoaded=function(t){if(this.loaded!==t&&(this.loaded=t,t)){for(var e=0,r=this.requestors;e=0?1.2:1))}function y(t,e,r,n,i,a,o){for(var s=0;s65535)e(new Error(\"glyphs > 65535 not supported\"));else if(a.ranges[s])e(null,{stack:r,id:i,glyph:o});else{var l=a.requests[s];l||(l=a.requests[s]=[],x.loadGlyphRange(r,s,n.url,n.requestManager,(function(t,e){if(e){for(var r in e)n._doesCharSupportLocalGlyph(+r)||(a.glyphs[+r]=e[+r]);a.ranges[s]=!0}for(var i=0,o=l;i1&&(l=t[++s]);var c=Math.abs(u-l.left),f=Math.abs(u-l.right),h=Math.min(c,f),p=void 0,d=i/r*(n+1);if(l.isDash){var v=n-Math.abs(d);p=Math.sqrt(h*h+v*v)}else p=n-Math.sqrt(h*h+d*d);this.data[o+u]=Math.max(0,Math.min(255,p+128))}},k.prototype.addRegularDash=function(t){for(var e=t.length-1;e>=0;--e){var r=t[e],n=t[e+1];r.zeroLength?t.splice(e,1):n&&n.isDash===r.isDash&&(n.left=r.left,t.splice(e,1))}var i=t[0],a=t[t.length-1];i.isDash===a.isDash&&(i.left=a.left-this.width,a.right=i.right+this.width);for(var o=this.width*this.nextRow,s=0,l=t[s],u=0;u1&&(l=t[++s]);var c=Math.abs(u-l.left),f=Math.abs(u-l.right),h=Math.min(c,f),p=l.isDash?h:-h;this.data[o+u]=Math.max(0,Math.min(255,p+128))}},k.prototype.addDash=function(e,r){var n=r?7:0,i=2*n+1;if(this.nextRow+i>this.height)return t.warnOnce(\"LineAtlas out of space\"),null;for(var a=0,o=0;o=n&&e.x=i&&e.y0&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y-1).key]={backfilled:!1}),r.y+10&&(n.resourceTiming=e._resourceTiming,e._resourceTiming=[]),e.fire(new t.Event(\"data\",n))}}))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setData=function(e){var r=this;return this._data=e,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._updateWorkerData((function(e){if(e)r.fire(new t.ErrorEvent(e));else{var n={dataType:\"source\",sourceDataType:\"content\"};r._collectResourceTiming&&r._resourceTiming&&r._resourceTiming.length>0&&(n.resourceTiming=r._resourceTiming,r._resourceTiming=[]),r.fire(new t.Event(\"data\",n))}})),this},r.prototype.getClusterExpansionZoom=function(t,e){return this.actor.send(\"geojson.getClusterExpansionZoom\",{clusterId:t,source:this.id},e),this},r.prototype.getClusterChildren=function(t,e){return this.actor.send(\"geojson.getClusterChildren\",{clusterId:t,source:this.id},e),this},r.prototype.getClusterLeaves=function(t,e,r,n){return this.actor.send(\"geojson.getClusterLeaves\",{source:this.id,clusterId:t,limit:e,offset:r},n),this},r.prototype._updateWorkerData=function(e){var r=this;this._loaded=!1;var n=t.extend({},this.workerOptions),i=this._data;\"string\"==typeof i?(n.request=this.map._requestManager.transformRequest(t.browser.resolveURL(i),t.ResourceType.Source),n.request.collectResourceTiming=this._collectResourceTiming):n.data=JSON.stringify(i),this.actor.send(this.type+\".loadData\",n,(function(t,i){r._removed||i&&i.abandoned||(r._loaded=!0,i&&i.resourceTiming&&i.resourceTiming[r.id]&&(r._resourceTiming=i.resourceTiming[r.id].slice(0)),r.actor.send(r.type+\".coalesce\",{source:n.source},null),e(t))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.loadTile=function(e,r){var n=this,i=e.actor?\"reloadTile\":\"loadTile\";e.actor=this.actor;var a={type:this.type,uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};e.request=this.actor.send(i,a,(function(t,a){return delete e.request,e.unloadVectorData(),e.aborted?r(null):t?r(t):(e.loadVectorData(a,n.map.painter,\"reloadTile\"===i),r(null))}))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.aborted=!0},r.prototype.unloadTile=function(t){t.unloadVectorData(),this.actor.send(\"removeTile\",{uid:t.uid,type:this.type,source:this.id})},r.prototype.onRemove=function(){this._removed=!0,this.actor.send(\"removeSource\",{type:this.type,source:this.id})},r.prototype.serialize=function(){return t.extend({},this._options,{type:this.type,data:this._data})},r.prototype.hasTransition=function(){return!1},r}(t.Evented),O=t.createLayout([{name:\"a_pos\",type:\"Int16\",components:2},{name:\"a_texture_pos\",type:\"Int16\",components:2}]),I=function(e){function r(t,r,n,i){e.call(this),this.id=t,this.dispatcher=n,this.coordinates=r.coordinates,this.type=\"image\",this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.tiles={},this._loaded=!1,this.setEventedParent(i),this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(e,r){var n=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this.url=this.options.url,t.getImage(this.map._requestManager.transformRequest(this.url,t.ResourceType.Image),(function(i,a){n._loaded=!0,i?n.fire(new t.ErrorEvent(i)):a&&(n.image=a,e&&(n.coordinates=e),r&&r(),n._finishLoading())}))},r.prototype.loaded=function(){return this._loaded},r.prototype.updateImage=function(t){var e=this;return this.image&&t.url?(this.options.url=t.url,this.load(t.coordinates,(function(){e.texture=null})),this):this},r.prototype._finishLoading=function(){this.map&&(this.setCoordinates(this.coordinates),this.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setCoordinates=function(e){var r=this;this.coordinates=e;var n=e.map(t.MercatorCoordinate.fromLngLat);this.tileID=function(e){for(var r=1/0,n=1/0,i=-1/0,a=-1/0,o=0,s=e;or.end(0)?this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+this.id,null,\"Playback for this video can be set only between the \"+r.start(0)+\" and \"+r.end(0)+\"-second mark.\"))):this.video.currentTime=e}},r.prototype.getVideo=function(){return this.video},r.prototype.onAdd=function(t){this.map||(this.map=t,this.load(),this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))},r.prototype.prepare=function(){if(!(0===Object.keys(this.tiles).length||this.video.readyState<2)){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,O.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?this.video.paused||(this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE),r.texSubImage2D(r.TEXTURE_2D,0,0,0,r.RGBA,r.UNSIGNED_BYTE,this.video)):(this.texture=new t.Texture(e,this.video,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];\"loaded\"!==i.state&&(i.state=\"loaded\",i.texture=this.texture)}}},r.prototype.serialize=function(){return{type:\"video\",urls:this.urls,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this.video&&!this.video.paused},r}(I),z=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),n.coordinates?Array.isArray(n.coordinates)&&4===n.coordinates.length&&!n.coordinates.some((function(t){return!Array.isArray(t)||2!==t.length||t.some((function(t){return\"number\"!=typeof t}))}))||this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'\"coordinates\" property must be an array of 4 longitude/latitude array pairs'))):this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'missing required property \"coordinates\"'))),n.animate&&\"boolean\"!=typeof n.animate&&this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'optional \"animate\" property must be a boolean value'))),n.canvas?\"string\"==typeof n.canvas||n.canvas instanceof t.window.HTMLCanvasElement||this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'\"canvas\" must be either a string representing the ID of the canvas element from which to read, or an HTMLCanvasElement instance'))):this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'missing required property \"canvas\"'))),this.options=n,this.animate=void 0===n.animate||n.animate}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){this._loaded=!0,this.canvas||(this.canvas=this.options.canvas instanceof t.window.HTMLCanvasElement?this.options.canvas:t.window.document.getElementById(this.options.canvas)),this.width=this.canvas.width,this.height=this.canvas.height,this._hasInvalidDimensions()?this.fire(new t.ErrorEvent(new Error(\"Canvas dimensions cannot be less than or equal to zero.\"))):(this.play=function(){this._playing=!0,this.map.triggerRepaint()},this.pause=function(){this._playing&&(this.prepare(),this._playing=!1)},this._finishLoading())},r.prototype.getCanvas=function(){return this.canvas},r.prototype.onAdd=function(t){this.map=t,this.load(),this.canvas&&this.animate&&this.play()},r.prototype.onRemove=function(){this.pause()},r.prototype.prepare=function(){var e=!1;if(this.canvas.width!==this.width&&(this.width=this.canvas.width,e=!0),this.canvas.height!==this.height&&(this.height=this.canvas.height,e=!0),!this._hasInvalidDimensions()&&0!==Object.keys(this.tiles).length){var r=this.map.painter.context,n=r.gl;for(var i in this.boundsBuffer||(this.boundsBuffer=r.createVertexBuffer(this._boundsArray,O.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?(e||this._playing)&&this.texture.update(this.canvas,{premultiply:!0}):this.texture=new t.Texture(r,this.canvas,n.RGBA,{premultiply:!0}),this.tiles){var a=this.tiles[i];\"loaded\"!==a.state&&(a.state=\"loaded\",a.texture=this.texture)}}},r.prototype.serialize=function(){return{type:\"canvas\",coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this._playing},r.prototype._hasInvalidDimensions=function(){for(var t=0,e=[this.canvas.width,this.canvas.height];tthis.max){var o=this._getAndRemoveByKey(this.order[0]);o&&this.onRemove(o)}return this},j.prototype.has=function(t){return t.wrapped().key in this.data},j.prototype.getAndRemove=function(t){return this.has(t)?this._getAndRemoveByKey(t.wrapped().key):null},j.prototype._getAndRemoveByKey=function(t){var e=this.data[t].shift();return e.timeout&&clearTimeout(e.timeout),0===this.data[t].length&&delete this.data[t],this.order.splice(this.order.indexOf(t),1),e.value},j.prototype.getByKey=function(t){var e=this.data[t];return e?e[0].value:null},j.prototype.get=function(t){return this.has(t)?this.data[t.wrapped().key][0].value:null},j.prototype.remove=function(t,e){if(!this.has(t))return this;var r=t.wrapped().key,n=void 0===e?0:this.data[r].indexOf(e),i=this.data[r][n];return this.data[r].splice(n,1),i.timeout&&clearTimeout(i.timeout),0===this.data[r].length&&delete this.data[r],this.onRemove(i.value),this.order.splice(this.order.indexOf(r),1),this},j.prototype.setMaxSize=function(t){for(this.max=t;this.order.length>this.max;){var e=this._getAndRemoveByKey(this.order[0]);e&&this.onRemove(e)}return this},j.prototype.filter=function(t){var e=[];for(var r in this.data)for(var n=0,i=this.data[r];n1||(Math.abs(r)>1&&(1===Math.abs(r+i)?r+=i:1===Math.abs(r-i)&&(r-=i)),e.dem&&t.dem&&(t.dem.backfillBorder(e.dem,r,n),t.neighboringTiles&&t.neighboringTiles[a]&&(t.neighboringTiles[a].backfilled=!0)))}},r.prototype.getTile=function(t){return this.getTileByID(t.key)},r.prototype.getTileByID=function(t){return this._tiles[t]},r.prototype._retainLoadedChildren=function(t,e,r,n){for(var i in this._tiles){var a=this._tiles[i];if(!(n[i]||!a.hasData()||a.tileID.overscaledZ<=e||a.tileID.overscaledZ>r)){for(var o=a.tileID;a&&a.tileID.overscaledZ>e+1;){var s=a.tileID.scaledTo(a.tileID.overscaledZ-1);(a=this._tiles[s.key])&&a.hasData()&&(o=s)}for(var l=o;l.overscaledZ>e;)if(t[(l=l.scaledTo(l.overscaledZ-1)).key]){n[o.key]=o;break}}}},r.prototype.findLoadedParent=function(t,e){if(t.key in this._loadedParentTiles){var r=this._loadedParentTiles[t.key];return r&&r.tileID.overscaledZ>=e?r:null}for(var n=t.overscaledZ-1;n>=e;n--){var i=t.scaledTo(n),a=this._getLoadedTile(i);if(a)return a}},r.prototype._getLoadedTile=function(t){var e=this._tiles[t.key];return e&&e.hasData()?e:this._cache.getByKey(t.wrapped().key)},r.prototype.updateCacheSize=function(t){var e=(Math.ceil(t.width/this._source.tileSize)+1)*(Math.ceil(t.height/this._source.tileSize)+1),r=Math.floor(5*e),n=\"number\"==typeof this._maxTileCacheSize?Math.min(this._maxTileCacheSize,r):r;this._cache.setMaxSize(n)},r.prototype.handleWrapJump=function(t){var e=(t-(void 0===this._prevLng?t:this._prevLng))/360,r=Math.round(e);if(this._prevLng=t,r){var n={};for(var i in this._tiles){var a=this._tiles[i];a.tileID=a.tileID.unwrapTo(a.tileID.wrap+r),n[a.tileID.key]=a}for(var o in this._tiles=n,this._timers)clearTimeout(this._timers[o]),delete this._timers[o];for(var s in this._tiles){var l=this._tiles[s];this._setTileReloadTimer(s,l)}}},r.prototype.update=function(e){var n=this;if(this.transform=e,this._sourceLoaded&&!this._paused){var i;this.updateCacheSize(e),this.handleWrapJump(this.transform.center.lng),this._coveredTiles={},this.used?this._source.tileID?i=e.getVisibleUnwrappedCoordinates(this._source.tileID).map((function(e){return new t.OverscaledTileID(e.canonical.z,e.wrap,e.canonical.z,e.canonical.x,e.canonical.y)})):(i=e.coveringTiles({tileSize:this._source.tileSize,minzoom:this._source.minzoom,maxzoom:this._source.maxzoom,roundZoom:this._source.roundZoom,reparseOverscaled:this._source.reparseOverscaled}),this._source.hasTile&&(i=i.filter((function(t){return n._source.hasTile(t)})))):i=[];var a=e.coveringZoomLevel(this._source),o=Math.max(a-r.maxOverzooming,this._source.minzoom),s=Math.max(a+r.maxUnderzooming,this._source.minzoom),l=this._updateRetainedTiles(i,a);if(Dt(this._source.type)){for(var u={},c={},f=0,h=Object.keys(l);fthis._source.maxzoom){var g=d.children(this._source.maxzoom)[0],y=this.getTile(g);if(y&&y.hasData()){n[g.key]=g;continue}}else{var m=d.children(this._source.maxzoom);if(n[m[0].key]&&n[m[1].key]&&n[m[2].key]&&n[m[3].key])continue}for(var x=v.wasRequested(),b=d.overscaledZ-1;b>=a;--b){var _=d.scaledTo(b);if(i[_.key])break;if(i[_.key]=!0,!(v=this.getTile(_))&&x&&(v=this._addTile(_)),v&&(n[_.key]=_,x=v.wasRequested(),v.hasData()))break}}}return n},r.prototype._updateLoadedParentTileCache=function(){for(var t in this._loadedParentTiles={},this._tiles){for(var e=[],r=void 0,n=this._tiles[t].tileID;n.overscaledZ>0;){if(n.key in this._loadedParentTiles){r=this._loadedParentTiles[n.key];break}e.push(n.key);var i=n.scaledTo(n.overscaledZ-1);if(r=this._getLoadedTile(i))break;n=i}for(var a=0,o=e;a0||(e.hasData()&&\"reloading\"!==e.state?this._cache.add(e.tileID,e,e.getExpiryTimeout()):(e.aborted=!0,this._abortTile(e),this._unloadTile(e))))},r.prototype.clearTiles=function(){for(var t in this._shouldReloadOnResume=!1,this._paused=!1,this._tiles)this._removeTile(t);this._cache.reset()},r.prototype.tilesIn=function(e,r,n){var i=this,a=[],o=this.transform;if(!o)return a;for(var s=n?o.getCameraQueryGeometry(e):e,l=e.map((function(t){return o.pointCoordinate(t)})),u=s.map((function(t){return o.pointCoordinate(t)})),c=this.getIds(),f=1/0,h=1/0,p=-1/0,d=-1/0,v=0,g=u;v=0&&y[1].y+g>=0){var m=l.map((function(t){return s.getTilePoint(t)})),x=u.map((function(t){return s.getTilePoint(t)}));a.push({tile:n,tileID:s,queryGeometry:m,cameraQueryGeometry:x,scale:v})}}},x=0;x=t.browser.now())return!0}return!1},r.prototype.setFeatureState=function(t,e,r){t=t||\"_geojsonTileLayer\",this._state.updateState(t,e,r)},r.prototype.removeFeatureState=function(t,e,r){t=t||\"_geojsonTileLayer\",this._state.removeFeatureState(t,e,r)},r.prototype.getFeatureState=function(t,e){return t=t||\"_geojsonTileLayer\",this._state.getState(t,e)},r.prototype.setDependencies=function(t,e,r){var n=this._tiles[t];n&&n.setDependencies(e,r)},r.prototype.reloadTilesForDependencies=function(t,e){for(var r in this._tiles)this._tiles[r].hasDependency(t,e)&&this._reloadTile(r,\"reloading\");this._cache.filter((function(r){return!r.hasDependency(t,e)}))},r}(t.Evented);function It(t,e){var r=Math.abs(2*t.wrap)-+(t.wrap<0),n=Math.abs(2*e.wrap)-+(e.wrap<0);return t.overscaledZ-e.overscaledZ||n-r||e.canonical.y-t.canonical.y||e.canonical.x-t.canonical.x}function Dt(t){return\"raster\"===t||\"image\"===t||\"video\"===t}function zt(){return new t.window.Worker(ta.workerUrl)}Ot.maxOverzooming=10,Ot.maxUnderzooming=3;var Rt=\"mapboxgl_preloaded_worker_pool\",Ft=function(){this.active={}};Ft.prototype.acquire=function(t){if(!this.workers)for(this.workers=[];this.workers.length0?(i-o)/s:0;return this.points[a].mult(1-l).add(this.points[r].mult(l))};var Qt=function(t,e,r){var n=this.boxCells=[],i=this.circleCells=[];this.xCellCount=Math.ceil(t/r),this.yCellCount=Math.ceil(e/r);for(var a=0;a=-e[0]&&r<=e[0]&&n>=-e[1]&&n<=e[1]}function ae(e,r,n,i,a,o,s,l){var u=i?e.textSizeData:e.iconSizeData,c=t.evaluateSizeForZoom(u,n.transform.zoom),f=[256/n.width*2+1,256/n.height*2+1],h=i?e.text.dynamicLayoutVertexArray:e.icon.dynamicLayoutVertexArray;h.clear();for(var p=e.lineVertexArray,d=i?e.text.placedSymbolArray:e.icon.placedSymbolArray,v=n.transform.width/n.transform.height,g=!1,y=0;yMath.abs(n.x-r.x)*i?{useVertical:!0}:(e===t.WritingMode.vertical?r.yn.x)?{needsFlipping:!0}:null}function le(e,r,n,i,a,o,s,l,u,c,f,h,p,d){var v,g=r/24,y=e.lineOffsetX*g,m=e.lineOffsetY*g;if(e.numGlyphs>1){var x=e.glyphStartIndex+e.numGlyphs,b=e.lineStartIndex,_=e.lineStartIndex+e.lineLength,w=oe(g,l,y,m,n,f,h,e,u,o,p);if(!w)return{notEnoughRoom:!0};var T=re(w.first.point,s).point,k=re(w.last.point,s).point;if(i&&!n){var A=se(e.writingMode,T,k,d);if(A)return A}v=[w.first];for(var M=e.glyphStartIndex+1;M0?C.point:ue(h,L,S,1,a),O=se(e.writingMode,S,P,d);if(O)return O}var I=ce(g*l.getoffsetX(e.glyphStartIndex),y,m,n,f,h,e.segment,e.lineStartIndex,e.lineStartIndex+e.lineLength,u,o,p);if(!I)return{notEnoughRoom:!0};v=[I]}for(var D=0,z=v;D0?1:-1,v=0;i&&(d*=-1,v=Math.PI),d<0&&(v+=Math.PI);for(var g=d>0?l+s:l+s+1,y=a,m=a,x=0,b=0,_=Math.abs(p),w=[];x+b<=_;){if((g+=d)=u)return null;if(m=y,w.push(y),void 0===(y=h[g])){var T=new t.Point(c.getx(g),c.gety(g)),k=re(T,f);if(k.signedDistanceFromCamera>0)y=h[g]=k.point;else{var A=g-d;y=ue(0===x?o:new t.Point(c.getx(A),c.gety(A)),T,m,_-x+1,f)}}x+=b,b=m.dist(y)}var M=(_-x)/b,S=y.sub(m),E=S.mult(M)._add(m);E._add(S._unit()._perp()._mult(n*d));var L=v+Math.atan2(y.y-m.y,y.x-m.x);return w.push(E),{point:E,angle:L,path:w}}Qt.prototype.keysLength=function(){return this.boxKeys.length+this.circleKeys.length},Qt.prototype.insert=function(t,e,r,n,i){this._forEachCell(e,r,n,i,this._insertBoxCell,this.boxUid++),this.boxKeys.push(t),this.bboxes.push(e),this.bboxes.push(r),this.bboxes.push(n),this.bboxes.push(i)},Qt.prototype.insertCircle=function(t,e,r,n){this._forEachCell(e-n,r-n,e+n,r+n,this._insertCircleCell,this.circleUid++),this.circleKeys.push(t),this.circles.push(e),this.circles.push(r),this.circles.push(n)},Qt.prototype._insertBoxCell=function(t,e,r,n,i,a){this.boxCells[i].push(a)},Qt.prototype._insertCircleCell=function(t,e,r,n,i,a){this.circleCells[i].push(a)},Qt.prototype._query=function(t,e,r,n,i,a){if(r<0||t>this.width||n<0||e>this.height)return!i&&[];var o=[];if(t<=0&&e<=0&&this.width<=r&&this.height<=n){if(i)return!0;for(var s=0;s0:o},Qt.prototype._queryCircle=function(t,e,r,n,i){var a=t-r,o=t+r,s=e-r,l=e+r;if(o<0||a>this.width||l<0||s>this.height)return!n&&[];var u=[],c={hitTest:n,circle:{x:t,y:e,radius:r},seenUids:{box:{},circle:{}}};return this._forEachCell(a,s,o,l,this._queryCellCircle,u,c,i),n?u.length>0:u},Qt.prototype.query=function(t,e,r,n,i){return this._query(t,e,r,n,!1,i)},Qt.prototype.hitTest=function(t,e,r,n,i){return this._query(t,e,r,n,!0,i)},Qt.prototype.hitTestCircle=function(t,e,r,n){return this._queryCircle(t,e,r,!0,n)},Qt.prototype._queryCell=function(t,e,r,n,i,a,o,s){var l=o.seenUids,u=this.boxCells[i];if(null!==u)for(var c=this.bboxes,f=0,h=u;f=c[d+0]&&n>=c[d+1]&&(!s||s(this.boxKeys[p]))){if(o.hitTest)return a.push(!0),!0;a.push({key:this.boxKeys[p],x1:c[d],y1:c[d+1],x2:c[d+2],y2:c[d+3]})}}}var v=this.circleCells[i];if(null!==v)for(var g=this.circles,y=0,m=v;yo*o+s*s},Qt.prototype._circleAndRectCollide=function(t,e,r,n,i,a,o){var s=(a-n)/2,l=Math.abs(t-(n+s));if(l>s+r)return!1;var u=(o-i)/2,c=Math.abs(e-(i+u));if(c>u+r)return!1;if(l<=s||c<=u)return!0;var f=l-s,h=c-u;return f*f+h*h<=r*r};var fe=new Float32Array([-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0]);function he(t,e){for(var r=0;r=1;P--)C.push(E.path[P]);for(var O=1;O0){for(var R=C[0].clone(),F=C[0].clone(),B=1;B=A.x&&F.x<=M.x&&R.y>=A.y&&F.y<=M.y?[C]:F.xM.x||F.yM.y?[]:t.clipLine([C],A.x,A.y,M.x,M.y)}for(var N=0,j=z;N=this.screenRightBoundary||nthis.screenBottomBoundary},ve.prototype.isInsideGrid=function(t,e,r,n){return r>=0&&t=0&&e0?(this.prevPlacement&&this.prevPlacement.variableOffsets[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID]&&this.prevPlacement.placements[f.crossTileID].text&&(v=this.prevPlacement.variableOffsets[f.crossTileID].anchor),this.variableOffsets[f.crossTileID]={textOffset:g,width:r,height:n,anchor:t,textBoxScale:i,prevAnchor:v},this.markUsedJustification(h,t,f,p),h.allowVerticalPlacement&&(this.markUsedOrientation(h,p,f),this.placedOrientations[f.crossTileID]=p),{shift:y,placedGlyphBoxes:m}):void 0},Ae.prototype.placeLayerBucketPart=function(e,r,n){var i=this,a=e.parameters,o=a.bucket,s=a.layout,l=a.posMatrix,u=a.textLabelPlaneMatrix,c=a.labelToScreenMatrix,f=a.textPixelRatio,h=a.holdingForFade,p=a.collisionBoxArray,d=a.partiallyEvaluatedTextSize,v=a.collisionGroup,g=s.get(\"text-optional\"),y=s.get(\"icon-optional\"),m=s.get(\"text-allow-overlap\"),x=s.get(\"icon-allow-overlap\"),b=\"map\"===s.get(\"text-rotation-alignment\"),_=\"map\"===s.get(\"text-pitch-alignment\"),w=\"none\"!==s.get(\"icon-text-fit\"),T=\"viewport-y\"===s.get(\"symbol-z-order\"),k=m&&(x||!o.hasIconData()||y),A=x&&(m||!o.hasTextData()||g);!o.collisionArrays&&p&&o.deserializeCollisionBoxes(p);var M=function(e,a){if(!r[e.crossTileID])if(h)i.placements[e.crossTileID]=new xe(!1,!1,!1);else{var p,T=!1,M=!1,S=!0,E=null,L={box:null,offscreen:null},C={box:null,offscreen:null},P=null,O=null,I=0,D=0,z=0;a.textFeatureIndex?I=a.textFeatureIndex:e.useRuntimeCollisionCircles&&(I=e.featureIndex),a.verticalTextFeatureIndex&&(D=a.verticalTextFeatureIndex);var R=a.textBox;if(R){var F=function(r){var n=t.WritingMode.horizontal;if(o.allowVerticalPlacement&&!r&&i.prevPlacement){var a=i.prevPlacement.placedOrientations[e.crossTileID];a&&(i.placedOrientations[e.crossTileID]=a,n=a,i.markUsedOrientation(o,n,e))}return n},B=function(r,n){if(o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&a.verticalTextBox)for(var i=0,s=o.writingModes;i0&&(N=N.filter((function(t){return t!==j.anchor}))).unshift(j.anchor)}var U=function(t,r,n){for(var a=t.x2-t.x1,s=t.y2-t.y1,u=e.textBoxScale,c=w&&!x?r:null,h={box:[],offscreen:!1},p=m?2*N.length:N.length,d=0;d=N.length,k=i.attemptAnchorPlacement(g,t,a,s,u,b,_,f,l,v,y,e,o,n,c);if(k&&(h=k.placedGlyphBoxes)&&h.box&&h.box.length){T=!0,E=k.shift;break}}return h};B((function(){return U(R,a.iconBox,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox,n=L&&L.box&&L.box.length;return o.allowVerticalPlacement&&!n&&e.numVerticalGlyphVertices>0&&r?U(r,a.verticalIconBox,t.WritingMode.vertical):{box:null,offscreen:null}})),L&&(T=L.box,S=L.offscreen);var V=F(L&&L.box);if(!T&&i.prevPlacement){var H=i.prevPlacement.variableOffsets[e.crossTileID];H&&(i.variableOffsets[e.crossTileID]=H,i.markUsedJustification(o,H.anchor,e,V))}}else{var q=function(t,r){var n=i.collisionIndex.placeCollisionBox(t,m,f,l,v.predicate);return n&&n.box&&n.box.length&&(i.markUsedOrientation(o,r,e),i.placedOrientations[e.crossTileID]=r),n};B((function(){return q(R,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox;return o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&r?q(r,t.WritingMode.vertical):{box:null,offscreen:null}})),F(L&&L.box&&L.box.length)}}if(T=(p=L)&&p.box&&p.box.length>0,S=p&&p.offscreen,e.useRuntimeCollisionCircles){var G=o.text.placedSymbolArray.get(e.centerJustifiedTextSymbolIndex),Z=t.evaluateSizeForFeature(o.textSizeData,d,G),Y=s.get(\"text-padding\"),W=e.collisionCircleDiameter;P=i.collisionIndex.placeCollisionCircles(m,G,o.lineVertexArray,o.glyphOffsetArray,Z,l,u,c,n,_,v.predicate,W,Y),T=m||P.circles.length>0&&!P.collisionDetected,S=S&&P.offscreen}if(a.iconFeatureIndex&&(z=a.iconFeatureIndex),a.iconBox){var X=function(t){var e=w&&E?ke(t,E.x,E.y,b,_,i.transform.angle):t;return i.collisionIndex.placeCollisionBox(e,x,f,l,v.predicate)};M=C&&C.box&&C.box.length&&a.verticalIconBox?(O=X(a.verticalIconBox)).box.length>0:(O=X(a.iconBox)).box.length>0,S=S&&O.offscreen}var J=g||0===e.numHorizontalGlyphVertices&&0===e.numVerticalGlyphVertices,K=y||0===e.numIconVertices;if(J||K?K?J||(M=M&&T):T=M&&T:M=T=M&&T,T&&p&&p.box&&(C&&C.box&&D?i.collisionIndex.insertCollisionBox(p.box,s.get(\"text-ignore-placement\"),o.bucketInstanceId,D,v.ID):i.collisionIndex.insertCollisionBox(p.box,s.get(\"text-ignore-placement\"),o.bucketInstanceId,I,v.ID)),M&&O&&i.collisionIndex.insertCollisionBox(O.box,s.get(\"icon-ignore-placement\"),o.bucketInstanceId,z,v.ID),P&&(T&&i.collisionIndex.insertCollisionCircles(P.circles,s.get(\"text-ignore-placement\"),o.bucketInstanceId,I,v.ID),n)){var $=o.bucketInstanceId,Q=i.collisionCircleArrays[$];void 0===Q&&(Q=i.collisionCircleArrays[$]=new be);for(var tt=0;tt=0;--E){var L=S[E];M(o.symbolInstances.get(L),o.collisionArrays[L])}else for(var C=e.symbolInstanceStart;C=0&&(e.text.placedSymbolArray.get(u).crossTileID=a>=0&&u!==a?0:n.crossTileID)}},Ae.prototype.markUsedOrientation=function(e,r,n){for(var i=r===t.WritingMode.horizontal||r===t.WritingMode.horizontalOnly?r:0,a=r===t.WritingMode.vertical?r:0,o=0,s=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex];o0||l>0,x=a.numIconVertices>0,b=i.placedOrientations[a.crossTileID],_=b===t.WritingMode.vertical,w=b===t.WritingMode.horizontal||b===t.WritingMode.horizontalOnly;if(m){var T=De(y.text),k=_?ze:T;d(e.text,s,k);var A=w?ze:T;d(e.text,l,A);var M=y.text.isHidden();[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t){t>=0&&(e.text.placedSymbolArray.get(t).hidden=M||_?1:0)})),a.verticalPlacedTextSymbolIndex>=0&&(e.text.placedSymbolArray.get(a.verticalPlacedTextSymbolIndex).hidden=M||w?1:0);var S=i.variableOffsets[a.crossTileID];S&&i.markUsedJustification(e,S.anchor,a,b);var E=i.placedOrientations[a.crossTileID];E&&(i.markUsedJustification(e,\"left\",a,E),i.markUsedOrientation(e,E,a))}if(x){var L=De(y.icon),C=!(h&&a.verticalPlacedIconSymbolIndex&&_);if(a.placedIconSymbolIndex>=0){var P=C?L:ze;d(e.icon,a.numIconVertices,P),e.icon.placedSymbolArray.get(a.placedIconSymbolIndex).hidden=y.icon.isHidden()}if(a.verticalPlacedIconSymbolIndex>=0){var O=C?ze:L;d(e.icon,a.numVerticalIconVertices,O),e.icon.placedSymbolArray.get(a.verticalPlacedIconSymbolIndex).hidden=y.icon.isHidden()}}if(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData()){var I=e.collisionArrays[n];if(I){var D=new t.Point(0,0);if(I.textBox||I.verticalTextBox){var z=!0;if(u){var R=i.variableOffsets[v];R?(D=Te(R.anchor,R.width,R.height,R.textOffset,R.textBoxScale),c&&D._rotate(f?i.transform.angle:-i.transform.angle)):z=!1}I.textBox&&Me(e.textCollisionBox.collisionVertexArray,y.text.placed,!z||_,D.x,D.y),I.verticalTextBox&&Me(e.textCollisionBox.collisionVertexArray,y.text.placed,!z||w,D.x,D.y)}var F=Boolean(!w&&I.verticalIconBox);I.iconBox&&Me(e.iconCollisionBox.collisionVertexArray,y.icon.placed,F,h?D.x:0,h?D.y:0),I.verticalIconBox&&Me(e.iconCollisionBox.collisionVertexArray,y.icon.placed,!F,h?D.x:0,h?D.y:0)}}},g=0;gt},Ae.prototype.setStale=function(){this.stale=!0};var Se=Math.pow(2,25),Ee=Math.pow(2,24),Le=Math.pow(2,17),Ce=Math.pow(2,16),Pe=Math.pow(2,9),Oe=Math.pow(2,8),Ie=Math.pow(2,1);function De(t){if(0===t.opacity&&!t.placed)return 0;if(1===t.opacity&&t.placed)return 4294967295;var e=t.placed?1:0,r=Math.floor(127*t.opacity);return r*Se+e*Ee+r*Le+e*Ce+r*Pe+e*Oe+r*Ie+e}var ze=0,Re=function(t){this._sortAcrossTiles=\"viewport-y\"!==t.layout.get(\"symbol-z-order\")&&void 0!==t.layout.get(\"symbol-sort-key\").constantOr(1),this._currentTileIndex=0,this._currentPartIndex=0,this._seenCrossTileIDs={},this._bucketParts=[]};Re.prototype.continuePlacement=function(t,e,r,n,i){for(var a=this._bucketParts;this._currentTileIndex2};this._currentPlacementIndex>=0;){var s=r[e[this._currentPlacementIndex]],l=this.placement.collisionIndex.transform.zoom;if(\"symbol\"===s.type&&(!s.minzoom||s.minzoom<=l)&&(!s.maxzoom||s.maxzoom>l)){if(this._inProgressLayer||(this._inProgressLayer=new Re(s)),this._inProgressLayer.continuePlacement(n[s.source],this.placement,this._showCollisionBoxes,s,o))return;delete this._inProgressLayer}this._currentPlacementIndex--}this._done=!0},Fe.prototype.commit=function(t){return this.placement.commit(t),this.placement};var Be=512/t.EXTENT/2,Ne=function(t,e,r){this.tileID=t,this.indexedSymbolInstances={},this.bucketInstanceId=r;for(var n=0;nt.overscaledZ)for(var s in o){var l=o[s];l.tileID.isChildOf(t)&&l.findMatches(e.symbolInstances,t,i)}else{var u=o[t.scaledTo(Number(a)).key];u&&u.findMatches(e.symbolInstances,t,i)}}for(var c=0;c1?\"@2x\":\"\",l=t.getJSON(r.transformRequest(r.normalizeSpriteURL(e,s,\".json\"),t.ResourceType.SpriteJSON),(function(t,e){l=null,o||(o=t,i=e,c())})),u=t.getImage(r.transformRequest(r.normalizeSpriteURL(e,s,\".png\"),t.ResourceType.SpriteImage),(function(t,e){u=null,o||(o=t,a=e,c())}));function c(){if(o)n(o);else if(i&&a){var e=t.browser.getImageData(a),r={};for(var s in i){var l=i[s],u=l.width,c=l.height,f=l.x,h=l.y,p=l.sdf,d=l.pixelRatio,v=l.stretchX,g=l.stretchY,y=l.content,m=new t.RGBAImage({width:u,height:c});t.RGBAImage.copy(e,m,{x:f,y:h},{x:0,y:0},{width:u,height:c}),r[s]={data:m,pixelRatio:d,sdf:p,stretchX:v,stretchY:g,content:y}}n(null,r)}}return{cancel:function(){l&&(l.cancel(),l=null),u&&(u.cancel(),u=null)}}}(e,this.map._requestManager,(function(e,n){if(r._spriteRequest=null,e)r.fire(new t.ErrorEvent(e));else if(n)for(var i in n)r.imageManager.addImage(i,n[i]);r.imageManager.setLoaded(!0),r._availableImages=r.imageManager.listImages(),r.dispatcher.broadcast(\"setImages\",r._availableImages),r.fire(new t.Event(\"data\",{dataType:\"style\"}))}))},r.prototype._validateLayer=function(e){var r=this.sourceCaches[e.source];if(r){var n=e.sourceLayer;if(n){var i=r.getSource();(\"geojson\"===i.type||i.vectorLayerIds&&-1===i.vectorLayerIds.indexOf(n))&&this.fire(new t.ErrorEvent(new Error('Source layer \"'+n+'\" does not exist on source \"'+i.id+'\" as specified by style layer \"'+e.id+'\"')))}}},r.prototype.loaded=function(){if(!this._loaded)return!1;if(Object.keys(this._updatedSources).length)return!1;for(var t in this.sourceCaches)if(!this.sourceCaches[t].loaded())return!1;return!!this.imageManager.isLoaded()},r.prototype._serializeLayers=function(t){for(var e=[],r=0,n=t;r0)throw new Error(\"Unimplemented: \"+i.map((function(t){return t.command})).join(\", \")+\".\");return n.forEach((function(t){\"setTransition\"!==t.command&&r[t.command].apply(r,t.args)})),this.stylesheet=e,!0},r.prototype.addImage=function(e,r){if(this.getImage(e))return this.fire(new t.ErrorEvent(new Error(\"An image with this name already exists.\")));this.imageManager.addImage(e,r),this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.fire(new t.Event(\"data\",{dataType:\"style\"}))},r.prototype.updateImage=function(t,e){this.imageManager.updateImage(t,e)},r.prototype.getImage=function(t){return this.imageManager.getImage(t)},r.prototype.removeImage=function(e){if(!this.getImage(e))return this.fire(new t.ErrorEvent(new Error(\"No image with this name exists.\")));this.imageManager.removeImage(e),this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.fire(new t.Event(\"data\",{dataType:\"style\"}))},r.prototype.listImages=function(){return this._checkLoaded(),this.imageManager.listImages()},r.prototype.addSource=function(e,r,n){var i=this;if(void 0===n&&(n={}),this._checkLoaded(),void 0!==this.sourceCaches[e])throw new Error(\"There is already a source with this ID\");if(!r.type)throw new Error(\"The type property must be defined, but the only the following properties were given: \"+Object.keys(r).join(\", \")+\".\");if(!([\"vector\",\"raster\",\"geojson\",\"video\",\"image\"].indexOf(r.type)>=0&&this._validate(t.validateStyle.source,\"sources.\"+e,r,null,n))){this.map&&this.map._collectResourceTiming&&(r.collectResourceTiming=!0);var a=this.sourceCaches[e]=new Ot(e,r,this.dispatcher);a.style=this,a.setEventedParent(this,(function(){return{isSourceLoaded:i.loaded(),source:a.serialize(),sourceId:e}})),a.onAdd(this.map),this._changed=!0}},r.prototype.removeSource=function(e){if(this._checkLoaded(),void 0===this.sourceCaches[e])throw new Error(\"There is no source with this ID\");for(var r in this._layers)if(this._layers[r].source===e)return this.fire(new t.ErrorEvent(new Error('Source \"'+e+'\" cannot be removed while layer \"'+r+'\" is using it.')));var n=this.sourceCaches[e];delete this.sourceCaches[e],delete this._updatedSources[e],n.fire(new t.Event(\"data\",{sourceDataType:\"metadata\",dataType:\"source\",sourceId:e})),n.setEventedParent(null),n.clearTiles(),n.onRemove&&n.onRemove(this.map),this._changed=!0},r.prototype.setGeoJSONSourceData=function(t,e){this._checkLoaded(),this.sourceCaches[t].getSource().setData(e),this._changed=!0},r.prototype.getSource=function(t){return this.sourceCaches[t]&&this.sourceCaches[t].getSource()},r.prototype.addLayer=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=e.id;if(this.getLayer(i))this.fire(new t.ErrorEvent(new Error('Layer with id \"'+i+'\" already exists on this map')));else{var a;if(\"custom\"===e.type){if(He(this,t.validateCustomStyleLayer(e)))return;a=t.createStyleLayer(e)}else{if(\"object\"==typeof e.source&&(this.addSource(i,e.source),e=t.clone$1(e),e=t.extend(e,{source:i})),this._validate(t.validateStyle.layer,\"layers.\"+i,e,{arrayIndex:-1},n))return;a=t.createStyleLayer(e),this._validateLayer(a),a.setEventedParent(this,{layer:{id:i}}),this._serializedLayers[a.id]=a.serialize()}var o=r?this._order.indexOf(r):this._order.length;if(r&&-1===o)this.fire(new t.ErrorEvent(new Error('Layer with id \"'+r+'\" does not exist on this map.')));else{if(this._order.splice(o,0,i),this._layerOrderChanged=!0,this._layers[i]=a,this._removedLayers[i]&&a.source&&\"custom\"!==a.type){var s=this._removedLayers[i];delete this._removedLayers[i],s.type!==a.type?this._updatedSources[a.source]=\"clear\":(this._updatedSources[a.source]=\"reload\",this.sourceCaches[a.source].pause())}this._updateLayer(a),a.onAdd&&a.onAdd(this.map)}}},r.prototype.moveLayer=function(e,r){if(this._checkLoaded(),this._changed=!0,this._layers[e]){if(e!==r){var n=this._order.indexOf(e);this._order.splice(n,1);var i=r?this._order.indexOf(r):this._order.length;r&&-1===i?this.fire(new t.ErrorEvent(new Error('Layer with id \"'+r+'\" does not exist on this map.'))):(this._order.splice(i,0,e),this._layerOrderChanged=!0)}}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be moved.\")))},r.prototype.removeLayer=function(e){this._checkLoaded();var r=this._layers[e];if(r){r.setEventedParent(null);var n=this._order.indexOf(e);this._order.splice(n,1),this._layerOrderChanged=!0,this._changed=!0,this._removedLayers[e]=r,delete this._layers[e],delete this._serializedLayers[e],delete this._updatedLayers[e],delete this._updatedPaintProps[e],r.onRemove&&r.onRemove(this.map)}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be removed.\")))},r.prototype.getLayer=function(t){return this._layers[t]},r.prototype.hasLayer=function(t){return t in this._layers},r.prototype.setLayerZoomRange=function(e,r,n){this._checkLoaded();var i=this.getLayer(e);i?i.minzoom===r&&i.maxzoom===n||(null!=r&&(i.minzoom=r),null!=n&&(i.maxzoom=n),this._updateLayer(i)):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot have zoom extent.\")))},r.prototype.setFilter=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=this.getLayer(e);if(i){if(!t.deepEqual(i.filter,r))return null==r?(i.filter=void 0,void this._updateLayer(i)):void(this._validate(t.validateStyle.filter,\"layers.\"+i.id+\".filter\",r,null,n)||(i.filter=t.clone$1(r),this._updateLayer(i)))}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be filtered.\")))},r.prototype.getFilter=function(e){return t.clone$1(this.getLayer(e).filter)},r.prototype.setLayoutProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getLayoutProperty(r),n)||(a.setLayoutProperty(r,n,i),this._updateLayer(a)):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be styled.\")))},r.prototype.getLayoutProperty=function(e,r){var n=this.getLayer(e);if(n)return n.getLayoutProperty(r);this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style.\")))},r.prototype.setPaintProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getPaintProperty(r),n)||(a.setPaintProperty(r,n,i)&&this._updateLayer(a),this._changed=!0,this._updatedPaintProps[e]=!0):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be styled.\")))},r.prototype.getPaintProperty=function(t,e){return this.getLayer(t).getPaintProperty(e)},r.prototype.setFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=e.sourceLayer,a=this.sourceCaches[n];if(void 0!==a){var o=a.getSource().type;\"geojson\"===o&&i?this.fire(new t.ErrorEvent(new Error(\"GeoJSON sources cannot have a sourceLayer parameter.\"))):\"vector\"!==o||i?(void 0===e.id&&this.fire(new t.ErrorEvent(new Error(\"The feature id parameter must be provided.\"))),a.setFeatureState(i,e.id,r)):this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+n+\"' does not exist in the map's style.\")))},r.prototype.removeFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=this.sourceCaches[n];if(void 0!==i){var a=i.getSource().type,o=\"vector\"===a?e.sourceLayer:void 0;\"vector\"!==a||o?r&&\"string\"!=typeof e.id&&\"number\"!=typeof e.id?this.fire(new t.ErrorEvent(new Error(\"A feature id is requred to remove its specific state property.\"))):i.removeFeatureState(o,e.id,r):this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+n+\"' does not exist in the map's style.\")))},r.prototype.getFeatureState=function(e){this._checkLoaded();var r=e.source,n=e.sourceLayer,i=this.sourceCaches[r];if(void 0!==i){if(\"vector\"!==i.getSource().type||n)return void 0===e.id&&this.fire(new t.ErrorEvent(new Error(\"The feature id parameter must be provided.\"))),i.getFeatureState(n,e.id);this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+r+\"' does not exist in the map's style.\")))},r.prototype.getTransition=function(){return t.extend({duration:300,delay:0},this.stylesheet&&this.stylesheet.transition)},r.prototype.serialize=function(){return t.filterObject({version:this.stylesheet.version,name:this.stylesheet.name,metadata:this.stylesheet.metadata,light:this.stylesheet.light,center:this.stylesheet.center,zoom:this.stylesheet.zoom,bearing:this.stylesheet.bearing,pitch:this.stylesheet.pitch,sprite:this.stylesheet.sprite,glyphs:this.stylesheet.glyphs,transition:this.stylesheet.transition,sources:t.mapObject(this.sourceCaches,(function(t){return t.serialize()})),layers:this._serializeLayers(this._order)},(function(t){return void 0!==t}))},r.prototype._updateLayer=function(t){this._updatedLayers[t.id]=!0,t.source&&!this._updatedSources[t.source]&&\"raster\"!==this.sourceCaches[t.source].getSource().type&&(this._updatedSources[t.source]=\"reload\",this.sourceCaches[t.source].pause()),this._changed=!0},r.prototype._flattenAndSortRenderedFeatures=function(t){for(var e=this,r=function(t){return\"fill-extrusion\"===e._layers[t].type},n={},i=[],a=this._order.length-1;a>=0;a--){var o=this._order[a];if(r(o)){n[o]=a;for(var s=0,l=t;s=0;d--){var v=this._order[d];if(r(v))for(var g=i.length-1;g>=0;g--){var y=i[g].feature;if(n[y.layer.id] 0.5) {gl_FragColor=vec4(0.0,0.0,1.0,0.5)*alpha;}if (v_notUsed > 0.5) {gl_FragColor*=.1;}}\",\"attribute vec2 a_pos;attribute vec2 a_anchor_pos;attribute vec2 a_extrude;attribute vec2 a_placed;attribute vec2 a_shift;uniform mat4 u_matrix;uniform vec2 u_extrude_scale;uniform float u_camera_to_center_distance;varying float v_placed;varying float v_notUsed;void main() {vec4 projectedPoint=u_matrix*vec4(a_anchor_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);gl_Position=u_matrix*vec4(a_pos,0.0,1.0);gl_Position.xy+=(a_extrude+a_shift)*u_extrude_scale*gl_Position.w*collision_perspective_ratio;v_placed=a_placed.x;v_notUsed=a_placed.y;}\"),nr=_r(\"varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;void main() {float alpha=0.5*min(v_perspective_ratio,1.0);float stroke_radius=0.9*max(v_perspective_ratio,1.0);float distance_to_center=length(v_extrude);float distance_to_edge=abs(distance_to_center-v_radius);float opacity_t=smoothstep(-stroke_radius,0.0,-distance_to_edge);vec4 color=mix(vec4(0.0,0.0,1.0,0.5),vec4(1.0,0.0,0.0,1.0),v_collision);gl_FragColor=color*alpha*opacity_t;}\",\"attribute vec2 a_pos;attribute float a_radius;attribute vec2 a_flags;uniform mat4 u_matrix;uniform mat4 u_inv_matrix;uniform vec2 u_viewport_size;uniform float u_camera_to_center_distance;varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;vec3 toTilePosition(vec2 screenPos) {vec4 rayStart=u_inv_matrix*vec4(screenPos,-1.0,1.0);vec4 rayEnd =u_inv_matrix*vec4(screenPos, 1.0,1.0);rayStart.xyz/=rayStart.w;rayEnd.xyz /=rayEnd.w;highp float t=(0.0-rayStart.z)/(rayEnd.z-rayStart.z);return mix(rayStart.xyz,rayEnd.xyz,t);}void main() {vec2 quadCenterPos=a_pos;float radius=a_radius;float collision=a_flags.x;float vertexIdx=a_flags.y;vec2 quadVertexOffset=vec2(mix(-1.0,1.0,float(vertexIdx >=2.0)),mix(-1.0,1.0,float(vertexIdx >=1.0 && vertexIdx <=2.0)));vec2 quadVertexExtent=quadVertexOffset*radius;vec3 tilePos=toTilePosition(quadCenterPos);vec4 clipPos=u_matrix*vec4(tilePos,1.0);highp float camera_to_anchor_distance=clipPos.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);float padding_factor=1.2;v_radius=radius;v_extrude=quadVertexExtent*padding_factor;v_perspective_ratio=collision_perspective_ratio;v_collision=collision;gl_Position=vec4(clipPos.xyz/clipPos.w,1.0)+vec4(quadVertexExtent*padding_factor/u_viewport_size*2.0,0.0,0.0);}\"),ir=_r(\"uniform highp vec4 u_color;uniform sampler2D u_overlay;varying vec2 v_uv;void main() {vec4 overlay_color=texture2D(u_overlay,v_uv);gl_FragColor=mix(u_color,overlay_color,overlay_color.a);}\",\"attribute vec2 a_pos;varying vec2 v_uv;uniform mat4 u_matrix;uniform float u_overlay_scale;void main() {v_uv=a_pos/8192.0;gl_Position=u_matrix*vec4(a_pos*u_overlay_scale,0,1);}\"),ar=_r(\"#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float opacity\\ngl_FragColor=color*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float opacity\\ngl_Position=u_matrix*vec4(a_pos,0,1);}\"),or=_r(\"varying vec2 v_pos;\\n#pragma mapbox: define highp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 outline_color\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=outline_color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;uniform vec2 u_world;varying vec2 v_pos;\\n#pragma mapbox: define highp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 outline_color\\n#pragma mapbox: initialize lowp float opacity\\ngl_Position=u_matrix*vec4(a_pos,0,1);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}\"),sr=_r(\"uniform vec2 u_texsize;uniform sampler2D u_image;uniform float u_fade;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=mix(color1,color2,u_fade)*alpha*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_world;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;gl_Position=u_matrix*vec4(a_pos,0,1);vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,a_pos);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}\"),lr=_r(\"uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_fade)*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileZoomRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileZoomRatio,a_pos);}\"),ur=_r(\"varying vec4 v_color;void main() {gl_FragColor=v_color;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;uniform float u_vertical_gradient;uniform lowp float u_opacity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec4 v_color;\\n#pragma mapbox: define highp float base\\n#pragma mapbox: define highp float height\\n#pragma mapbox: define highp vec4 color\\nvoid main() {\\n#pragma mapbox: initialize highp float base\\n#pragma mapbox: initialize highp float height\\n#pragma mapbox: initialize highp vec4 color\\nvec3 normal=a_normal_ed.xyz;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);gl_Position=u_matrix*vec4(a_pos,t > 0.0 ? height : base,1);float colorvalue=color.r*0.2126+color.g*0.7152+color.b*0.0722;v_color=vec4(0.0,0.0,0.0,1.0);vec4 ambientlight=vec4(0.03,0.03,0.03,1.0);color+=ambientlight;float directional=clamp(dot(normal/16384.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((1.0-colorvalue+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_color.r+=clamp(color.r*directional*u_lightcolor.r,mix(0.0,0.3,1.0-u_lightcolor.r),1.0);v_color.g+=clamp(color.g*directional*u_lightcolor.g,mix(0.0,0.3,1.0-u_lightcolor.g),1.0);v_color.b+=clamp(color.b*directional*u_lightcolor.b,mix(0.0,0.3,1.0-u_lightcolor.b),1.0);v_color*=u_opacity;}\"),cr=_r(\"uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float base\\n#pragma mapbox: initialize lowp float height\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);vec4 mixedColor=mix(color1,color2,u_fade);gl_FragColor=mixedColor*v_lighting;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_height_factor;uniform vec3 u_scale;uniform float u_vertical_gradient;uniform lowp float u_opacity;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float base\\n#pragma mapbox: initialize lowp float height\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec3 normal=a_normal_ed.xyz;float edgedistance=a_normal_ed.w;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);float z=t > 0.0 ? height : base;gl_Position=u_matrix*vec4(a_pos,z,1);vec2 pos=normal.x==1.0 && normal.y==0.0 && normal.z==16384.0\\n? a_pos\\n: vec2(edgedistance,z*u_height_factor);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,pos);v_lighting=vec4(0.0,0.0,0.0,1.0);float directional=clamp(dot(normal/16383.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((0.5+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_lighting.rgb+=clamp(directional*u_lightcolor,mix(vec3(0.0),vec3(0.3),1.0-u_lightcolor),vec3(1.0));v_lighting*=u_opacity;}\"),fr=_r(\"#ifdef GL_ES\\nprecision highp float;\\n#endif\\nuniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_dimension;uniform float u_zoom;uniform float u_maxzoom;uniform vec4 u_unpack;float getElevation(vec2 coord,float bias) {vec4 data=texture2D(u_image,coord)*255.0;data.a=-1.0;return dot(data,u_unpack)/4.0;}void main() {vec2 epsilon=1.0/u_dimension;float a=getElevation(v_pos+vec2(-epsilon.x,-epsilon.y),0.0);float b=getElevation(v_pos+vec2(0,-epsilon.y),0.0);float c=getElevation(v_pos+vec2(epsilon.x,-epsilon.y),0.0);float d=getElevation(v_pos+vec2(-epsilon.x,0),0.0);float e=getElevation(v_pos,0.0);float f=getElevation(v_pos+vec2(epsilon.x,0),0.0);float g=getElevation(v_pos+vec2(-epsilon.x,epsilon.y),0.0);float h=getElevation(v_pos+vec2(0,epsilon.y),0.0);float i=getElevation(v_pos+vec2(epsilon.x,epsilon.y),0.0);float exaggeration=u_zoom < 2.0 ? 0.4 : u_zoom < 4.5 ? 0.35 : 0.3;vec2 deriv=vec2((c+f+f+i)-(a+d+d+g),(g+h+h+i)-(a+b+b+c))/ pow(2.0,(u_zoom-u_maxzoom)*exaggeration+19.2562-u_zoom);gl_FragColor=clamp(vec4(deriv.x/2.0+0.5,deriv.y/2.0+0.5,1.0,1.0),0.0,1.0);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_dimension;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);highp vec2 epsilon=1.0/u_dimension;float scale=(u_dimension.x-2.0)/u_dimension.x;v_pos=(a_texture_pos/8192.0)*scale+epsilon;}\"),hr=_r(\"uniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_latrange;uniform vec2 u_light;uniform vec4 u_shadow;uniform vec4 u_highlight;uniform vec4 u_accent;\\n#define PI 3.141592653589793\\nvoid main() {vec4 pixel=texture2D(u_image,v_pos);vec2 deriv=((pixel.rg*2.0)-1.0);float scaleFactor=cos(radians((u_latrange[0]-u_latrange[1])*(1.0-v_pos.y)+u_latrange[1]));float slope=atan(1.25*length(deriv)/scaleFactor);float aspect=deriv.x !=0.0 ? atan(deriv.y,-deriv.x) : PI/2.0*(deriv.y > 0.0 ? 1.0 :-1.0);float intensity=u_light.x;float azimuth=u_light.y+PI;float base=1.875-intensity*1.75;float maxValue=0.5*PI;float scaledSlope=intensity !=0.5 ? ((pow(base,slope)-1.0)/(pow(base,maxValue)-1.0))*maxValue : slope;float accent=cos(scaledSlope);vec4 accent_color=(1.0-accent)*u_accent*clamp(intensity*2.0,0.0,1.0);float shade=abs(mod((aspect+azimuth)/PI+0.5,2.0)-1.0);vec4 shade_color=mix(u_shadow,u_highlight,shade)*sin(scaledSlope)*clamp(intensity*2.0,0.0,1.0);gl_FragColor=accent_color*(1.0-shade_color.a)+shade_color;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos=a_texture_pos/8192.0;}\"),pr=_r(\"uniform lowp float u_device_pixel_ratio;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform vec2 u_units_to_pixels;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_linesofar;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}\"),dr=_r(\"uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;varying highp float v_lineprogress;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);vec4 color=texture2D(u_image,vec2(v_lineprogress,0.5));gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define MAX_LINE_DISTANCE 32767.0\\n#define scale 0.015873016\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_lineprogress;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_lineprogress=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0/MAX_LINE_DISTANCE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}\"),vr=_r(\"uniform lowp float u_device_pixel_ratio;uniform vec2 u_texsize;uniform float u_fade;uniform mediump vec3 u_scale;uniform sampler2D u_image;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;vec2 pattern_size_a=vec2(display_size_a.x*fromScale/tileZoomRatio,display_size_a.y);vec2 pattern_size_b=vec2(display_size_b.x*toScale/tileZoomRatio,display_size_b.y);float aspect_a=display_size_a.y/v_width;float aspect_b=display_size_b.y/v_width;float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float x_a=mod(v_linesofar/pattern_size_a.x*aspect_a,1.0);float x_b=mod(v_linesofar/pattern_size_b.x*aspect_b,1.0);float y=0.5*v_normal.y+0.5;vec2 texel_size=1.0/u_texsize;vec2 pos_a=mix(pattern_tl_a*texel_size-texel_size,pattern_br_a*texel_size+texel_size,vec2(x_a,y));vec2 pos_b=mix(pattern_tl_b*texel_size-texel_size,pattern_br_b*texel_size+texel_size,vec2(x_b,y));vec4 color=mix(texture2D(u_image,pos_a),texture2D(u_image,pos_b),u_fade);gl_FragColor=color*alpha*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\n#define LINE_DISTANCE_SCALE 2.0\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform vec2 u_units_to_pixels;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_linesofar=a_linesofar;v_width2=vec2(outset,inset);v_width=floorwidth;}\"),gr=_r(\"uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;uniform float u_sdfgamma;uniform float u_mix;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float sdfdist_a=texture2D(u_image,v_tex_a).a;float sdfdist_b=texture2D(u_image,v_tex_b).a;float sdfdist=mix(sdfdist_a,sdfdist_b,u_mix);alpha*=smoothstep(0.5-u_sdfgamma/floorwidth,0.5+u_sdfgamma/floorwidth,sdfdist);gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\n#define LINE_DISTANCE_SCALE 2.0\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_patternscale_a;uniform float u_tex_y_a;uniform vec2 u_patternscale_b;uniform float u_tex_y_b;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_tex_a=vec2(a_linesofar*u_patternscale_a.x/floorwidth,normal.y*u_patternscale_a.y+u_tex_y_a);v_tex_b=vec2(a_linesofar*u_patternscale_b.x/floorwidth,normal.y*u_patternscale_b.y+u_tex_y_b);v_width2=vec2(outset,inset);}\"),yr=_r(\"uniform float u_fade_t;uniform float u_opacity;uniform sampler2D u_image0;uniform sampler2D u_image1;varying vec2 v_pos0;varying vec2 v_pos1;uniform float u_brightness_low;uniform float u_brightness_high;uniform float u_saturation_factor;uniform float u_contrast_factor;uniform vec3 u_spin_weights;void main() {vec4 color0=texture2D(u_image0,v_pos0);vec4 color1=texture2D(u_image1,v_pos1);if (color0.a > 0.0) {color0.rgb=color0.rgb/color0.a;}if (color1.a > 0.0) {color1.rgb=color1.rgb/color1.a;}vec4 color=mix(color0,color1,u_fade_t);color.a*=u_opacity;vec3 rgb=color.rgb;rgb=vec3(dot(rgb,u_spin_weights.xyz),dot(rgb,u_spin_weights.zxy),dot(rgb,u_spin_weights.yzx));float average=(color.r+color.g+color.b)/3.0;rgb+=(average-rgb)*u_saturation_factor;rgb=(rgb-0.5)*u_contrast_factor+0.5;vec3 u_high_vec=vec3(u_brightness_low,u_brightness_low,u_brightness_low);vec3 u_low_vec=vec3(u_brightness_high,u_brightness_high,u_brightness_high);gl_FragColor=vec4(mix(u_high_vec,u_low_vec,rgb)*color.a,color.a);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_tl_parent;uniform float u_scale_parent;uniform float u_buffer_scale;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos0;varying vec2 v_pos1;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos0=(((a_texture_pos/8192.0)-0.5)/u_buffer_scale )+0.5;v_pos1=(v_pos0*u_scale_parent)+u_tl_parent;}\"),mr=_r(\"uniform sampler2D u_texture;varying vec2 v_tex;varying float v_fade_opacity;\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\nlowp float alpha=opacity*v_fade_opacity;gl_FragColor=texture2D(u_texture,v_tex)*alpha;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform highp float u_camera_to_center_distance;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform float u_fade_change;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform vec2 u_texsize;varying vec2 v_tex;varying float v_fade_opacity;\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;vec2 a_minFontScale=a_pixeloffset.zw/256.0;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*max(a_minFontScale,fontScale)+a_pxoffset/16.0),0.0,1.0);v_tex=a_tex/u_texsize;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;v_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));}\"),xr=_r(\"#define SDF_PX 8.0\\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;uniform bool u_is_text;varying vec2 v_data0;varying vec3 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nfloat EDGE_GAMMA=0.105/u_device_pixel_ratio;vec2 tex=v_data0.xy;float gamma_scale=v_data1.x;float size=v_data1.y;float fade_opacity=v_data1[2];float fontScale=u_is_text ? size/24.0 : size;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;varying vec2 v_data0;varying vec3 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale+a_pxoffset),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0=a_tex/u_texsize;v_data1=vec3(gamma_scale,size,interpolated_fade_opacity);}\"),br=_r(\"#define SDF_PX 8.0\\n#define SDF 1.0\\n#define ICON 0.0\\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform sampler2D u_texture_icon;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;varying vec4 v_data0;varying vec4 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nfloat fade_opacity=v_data1[2];if (v_data1.w==ICON) {vec2 tex_icon=v_data0.zw;lowp float alpha=opacity*fade_opacity;gl_FragColor=texture2D(u_texture_icon,tex_icon)*alpha;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\nreturn;}vec2 tex=v_data0.xy;float EDGE_GAMMA=0.105/u_device_pixel_ratio;float gamma_scale=v_data1.x;float size=v_data1.y;float fontScale=size/24.0;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_texsize_icon;varying vec4 v_data0;varying vec4 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);float is_sdf=a_size[0]-2.0*a_size_min;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=size/24.0;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0.xy=a_tex/u_texsize;v_data0.zw=a_tex/u_texsize_icon;v_data1=vec4(gamma_scale,size,interpolated_fade_opacity,is_sdf);}\");function _r(t,e){var r=/#pragma mapbox: ([\\w]+) ([\\w]+) ([\\w]+) ([\\w]+)/g,n={};return{fragmentSource:t=t.replace(r,(function(t,e,r,i,a){return n[a]=!0,\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nvarying \"+r+\" \"+i+\" \"+a+\";\\n#else\\nuniform \"+r+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"\\n#ifdef HAS_UNIFORM_u_\"+a+\"\\n \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\"})),vertexSource:e=e.replace(r,(function(t,e,r,i,a){var o=\"float\"===i?\"vec2\":\"vec4\",s=a.match(/color/)?\"color\":o;return n[a]?\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nuniform lowp float u_\"+a+\"_t;\\nattribute \"+r+\" \"+o+\" a_\"+a+\";\\nvarying \"+r+\" \"+i+\" \"+a+\";\\n#else\\nuniform \"+r+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"vec4\"===s?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n \"+a+\" = a_\"+a+\";\\n#else\\n \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\":\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n \"+a+\" = unpack_mix_\"+s+\"(a_\"+a+\", u_\"+a+\"_t);\\n#else\\n \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\":\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\nuniform lowp float u_\"+a+\"_t;\\nattribute \"+r+\" \"+o+\" a_\"+a+\";\\n#else\\nuniform \"+r+\" \"+i+\" u_\"+a+\";\\n#endif\\n\":\"vec4\"===s?\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n \"+r+\" \"+i+\" \"+a+\" = a_\"+a+\";\\n#else\\n \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\":\"\\n#ifndef HAS_UNIFORM_u_\"+a+\"\\n \"+r+\" \"+i+\" \"+a+\" = unpack_mix_\"+s+\"(a_\"+a+\", u_\"+a+\"_t);\\n#else\\n \"+r+\" \"+i+\" \"+a+\" = u_\"+a+\";\\n#endif\\n\"}))}}var wr=Object.freeze({__proto__:null,prelude:Xe,background:Je,backgroundPattern:Ke,circle:$e,clippingMask:Qe,heatmap:tr,heatmapTexture:er,collisionBox:rr,collisionCircle:nr,debug:ir,fill:ar,fillOutline:or,fillOutlinePattern:sr,fillPattern:lr,fillExtrusion:ur,fillExtrusionPattern:cr,hillshadePrepare:fr,hillshade:hr,line:pr,lineGradient:dr,linePattern:vr,lineSDF:gr,raster:yr,symbolIcon:mr,symbolSDF:xr,symbolTextAndIcon:br}),Tr=function(){this.boundProgram=null,this.boundLayoutVertexBuffer=null,this.boundPaintVertexBuffers=[],this.boundIndexBuffer=null,this.boundVertexOffset=null,this.boundDynamicVertexBuffer=null,this.vao=null};Tr.prototype.bind=function(t,e,r,n,i,a,o,s){this.context=t;for(var l=this.boundPaintVertexBuffers.length!==n.length,u=0;!l&&u>16,s>>16],u_pixel_coord_lower:[65535&o,65535&s]}}kr.prototype.draw=function(t,e,r,n,i,a,o,s,l,u,c,f,h,p,d,v){var g,y=t.gl;if(!this.failedToCreate){for(var m in t.program.set(this.program),t.setDepthMode(r),t.setStencilMode(n),t.setColorMode(i),t.setCullFace(a),this.fixedUniforms)this.fixedUniforms[m].set(o[m]);p&&p.setUniforms(t,this.binderUniforms,f,{zoom:h});for(var x=(g={},g[y.LINES]=2,g[y.TRIANGLES]=3,g[y.LINE_STRIP]=1,g)[e],b=0,_=c.get();b<_.length;b+=1){var w=_[b],T=w.vaos||(w.vaos={});(T[s]||(T[s]=new Tr)).bind(t,this,l,p?p.getPaintVertexBuffers():[],u,w.vertexOffset,d,v),y.drawElements(e,w.primitiveLength*x,y.UNSIGNED_SHORT,w.primitiveOffset*x*2)}}};var Mr=function(e,r,n,i){var a=r.style.light,o=a.properties.get(\"position\"),s=[o.x,o.y,o.z],l=t.create$1();\"viewport\"===a.properties.get(\"anchor\")&&t.fromRotation(l,-r.transform.angle),t.transformMat3(s,s,l);var u=a.properties.get(\"color\");return{u_matrix:e,u_lightpos:s,u_lightintensity:a.properties.get(\"intensity\"),u_lightcolor:[u.r,u.g,u.b],u_vertical_gradient:+n,u_opacity:i}},Sr=function(e,r,n,i,a,o,s){return t.extend(Mr(e,r,n,i),Ar(o,r,s),{u_height_factor:-Math.pow(2,a.overscaledZ)/s.tileSize/8})},Er=function(t){return{u_matrix:t}},Lr=function(e,r,n,i){return t.extend(Er(e),Ar(n,r,i))},Cr=function(t,e){return{u_matrix:t,u_world:e}},Pr=function(e,r,n,i,a){return t.extend(Lr(e,r,n,i),{u_world:a})},Or=function(e,r,n,i){var a,o,s=e.transform;if(\"map\"===i.paint.get(\"circle-pitch-alignment\")){var l=ge(n,1,s.zoom);a=!0,o=[l,l]}else a=!1,o=s.pixelsToGLUnits;return{u_camera_to_center_distance:s.cameraToCenterDistance,u_scale_with_map:+(\"map\"===i.paint.get(\"circle-pitch-scale\")),u_matrix:e.translatePosMatrix(r.posMatrix,n,i.paint.get(\"circle-translate\"),i.paint.get(\"circle-translate-anchor\")),u_pitch_with_map:+a,u_device_pixel_ratio:t.browser.devicePixelRatio,u_extrude_scale:o}},Ir=function(t,e,r){var n=ge(r,1,e.zoom),i=Math.pow(2,e.zoom-r.tileID.overscaledZ),a=r.tileID.overscaleFactor();return{u_matrix:t,u_camera_to_center_distance:e.cameraToCenterDistance,u_pixels_to_tile_units:n,u_extrude_scale:[e.pixelsToGLUnits[0]/(n*i),e.pixelsToGLUnits[1]/(n*i)],u_overscale_factor:a}},Dr=function(t,e,r){return{u_matrix:t,u_inv_matrix:e,u_camera_to_center_distance:r.cameraToCenterDistance,u_viewport_size:[r.width,r.height]}},zr=function(t,e,r){return void 0===r&&(r=1),{u_matrix:t,u_color:e,u_overlay:0,u_overlay_scale:r}},Rr=function(t){return{u_matrix:t}},Fr=function(t,e,r,n){return{u_matrix:t,u_extrude_scale:ge(e,1,r),u_intensity:n}};var Br=function(e,r,n){var i=e.transform;return{u_matrix:Hr(e,r,n),u_ratio:1/ge(r,1,i.zoom),u_device_pixel_ratio:t.browser.devicePixelRatio,u_units_to_pixels:[1/i.pixelsToGLUnits[0],1/i.pixelsToGLUnits[1]]}},Nr=function(e,r,n){return t.extend(Br(e,r,n),{u_image:0})},jr=function(e,r,n,i){var a=e.transform,o=Vr(r,a);return{u_matrix:Hr(e,r,n),u_texsize:r.imageAtlasTexture.size,u_ratio:1/ge(r,1,a.zoom),u_device_pixel_ratio:t.browser.devicePixelRatio,u_image:0,u_scale:[o,i.fromScale,i.toScale],u_fade:i.t,u_units_to_pixels:[1/a.pixelsToGLUnits[0],1/a.pixelsToGLUnits[1]]}},Ur=function(e,r,n,i,a){var o=e.transform,s=e.lineAtlas,l=Vr(r,o),u=\"round\"===n.layout.get(\"line-cap\"),c=s.getDash(i.from,u),f=s.getDash(i.to,u),h=c.width*a.fromScale,p=f.width*a.toScale;return t.extend(Br(e,r,n),{u_patternscale_a:[l/h,-c.height/2],u_patternscale_b:[l/p,-f.height/2],u_sdfgamma:s.width/(256*Math.min(h,p)*t.browser.devicePixelRatio)/2,u_image:0,u_tex_y_a:c.y,u_tex_y_b:f.y,u_mix:a.t})};function Vr(t,e){return 1/ge(t,1,e.tileZoom)}function Hr(t,e,r){return t.translatePosMatrix(e.tileID.posMatrix,e,r.paint.get(\"line-translate\"),r.paint.get(\"line-translate-anchor\"))}var qr=function(t,e,r,n,i){return{u_matrix:t,u_tl_parent:e,u_scale_parent:r,u_buffer_scale:1,u_fade_t:n.mix,u_opacity:n.opacity*i.paint.get(\"raster-opacity\"),u_image0:0,u_image1:1,u_brightness_low:i.paint.get(\"raster-brightness-min\"),u_brightness_high:i.paint.get(\"raster-brightness-max\"),u_saturation_factor:(o=i.paint.get(\"raster-saturation\"),o>0?1-1/(1.001-o):-o),u_contrast_factor:(a=i.paint.get(\"raster-contrast\"),a>0?1/(1-a):1+a),u_spin_weights:Gr(i.paint.get(\"raster-hue-rotate\"))};var a,o};function Gr(t){t*=Math.PI/180;var e=Math.sin(t),r=Math.cos(t);return[(2*r+1)/3,(-Math.sqrt(3)*e-r+1)/3,(Math.sqrt(3)*e-r+1)/3]}var Zr,Yr=function(t,e,r,n,i,a,o,s,l,u){var c=i.transform;return{u_is_size_zoom_constant:+(\"constant\"===t||\"source\"===t),u_is_size_feature_constant:+(\"constant\"===t||\"camera\"===t),u_size_t:e?e.uSizeT:0,u_size:e?e.uSize:0,u_camera_to_center_distance:c.cameraToCenterDistance,u_pitch:c.pitch/360*2*Math.PI,u_rotate_symbol:+r,u_aspect_ratio:c.width/c.height,u_fade_change:i.options.fadeDuration?i.symbolFadeChange:1,u_matrix:a,u_label_plane_matrix:o,u_coord_matrix:s,u_is_text:+l,u_pitch_with_map:+n,u_texsize:u,u_texture:0}},Wr=function(e,r,n,i,a,o,s,l,u,c,f){var h=a.transform;return t.extend(Yr(e,r,n,i,a,o,s,l,u,c),{u_gamma_scale:i?Math.cos(h._pitch)*h.cameraToCenterDistance:1,u_device_pixel_ratio:t.browser.devicePixelRatio,u_is_halo:+f})},Xr=function(e,r,n,i,a,o,s,l,u,c){return t.extend(Wr(e,r,n,i,a,o,s,l,!0,u,!0),{u_texsize_icon:c,u_texture_icon:1})},Jr=function(t,e,r){return{u_matrix:t,u_opacity:e,u_color:r}},Kr=function(e,r,n,i,a,o){return t.extend(function(t,e,r,n){var i=r.imageManager.getPattern(t.from.toString()),a=r.imageManager.getPattern(t.to.toString()),o=r.imageManager.getPixelSize(),s=o.width,l=o.height,u=Math.pow(2,n.tileID.overscaledZ),c=n.tileSize*Math.pow(2,r.transform.tileZoom)/u,f=c*(n.tileID.canonical.x+n.tileID.wrap*u),h=c*n.tileID.canonical.y;return{u_image:0,u_pattern_tl_a:i.tl,u_pattern_br_a:i.br,u_pattern_tl_b:a.tl,u_pattern_br_b:a.br,u_texsize:[s,l],u_mix:e.t,u_pattern_size_a:i.displaySize,u_pattern_size_b:a.displaySize,u_scale_a:e.fromScale,u_scale_b:e.toScale,u_tile_units_to_pixels:1/ge(n,1,r.transform.tileZoom),u_pixel_coord_upper:[f>>16,h>>16],u_pixel_coord_lower:[65535&f,65535&h]}}(i,o,n,a),{u_matrix:e,u_opacity:r})},$r={fillExtrusion:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fillExtrusionPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_height_factor:new t.Uniform1f(e,r.u_height_factor),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fill:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},fillPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},fillOutline:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world)}},fillOutlinePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},circle:function(e,r){return{u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_scale_with_map:new t.Uniform1i(e,r.u_scale_with_map),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},collisionBox:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pixels_to_tile_units:new t.Uniform1f(e,r.u_pixels_to_tile_units),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_overscale_factor:new t.Uniform1f(e,r.u_overscale_factor)}},collisionCircle:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_inv_matrix:new t.UniformMatrix4f(e,r.u_inv_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_viewport_size:new t.Uniform2f(e,r.u_viewport_size)}},debug:function(e,r){return{u_color:new t.UniformColor(e,r.u_color),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_overlay:new t.Uniform1i(e,r.u_overlay),u_overlay_scale:new t.Uniform1f(e,r.u_overlay_scale)}},clippingMask:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmap:function(e,r){return{u_extrude_scale:new t.Uniform1f(e,r.u_extrude_scale),u_intensity:new t.Uniform1f(e,r.u_intensity),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmapTexture:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_color_ramp:new t.Uniform1i(e,r.u_color_ramp),u_opacity:new t.Uniform1f(e,r.u_opacity)}},hillshade:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_latrange:new t.Uniform2f(e,r.u_latrange),u_light:new t.Uniform2f(e,r.u_light),u_shadow:new t.UniformColor(e,r.u_shadow),u_highlight:new t.UniformColor(e,r.u_highlight),u_accent:new t.UniformColor(e,r.u_accent)}},hillshadePrepare:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_dimension:new t.Uniform2f(e,r.u_dimension),u_zoom:new t.Uniform1f(e,r.u_zoom),u_maxzoom:new t.Uniform1f(e,r.u_maxzoom),u_unpack:new t.Uniform4f(e,r.u_unpack)}},line:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels)}},lineGradient:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_image:new t.Uniform1i(e,r.u_image)}},linePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_texsize:new t.Uniform2f(e,r.u_texsize),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_image:new t.Uniform1i(e,r.u_image),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},lineSDF:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_patternscale_a:new t.Uniform2f(e,r.u_patternscale_a),u_patternscale_b:new t.Uniform2f(e,r.u_patternscale_b),u_sdfgamma:new t.Uniform1f(e,r.u_sdfgamma),u_image:new t.Uniform1i(e,r.u_image),u_tex_y_a:new t.Uniform1f(e,r.u_tex_y_a),u_tex_y_b:new t.Uniform1f(e,r.u_tex_y_b),u_mix:new t.Uniform1f(e,r.u_mix)}},raster:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_tl_parent:new t.Uniform2f(e,r.u_tl_parent),u_scale_parent:new t.Uniform1f(e,r.u_scale_parent),u_buffer_scale:new t.Uniform1f(e,r.u_buffer_scale),u_fade_t:new t.Uniform1f(e,r.u_fade_t),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image0:new t.Uniform1i(e,r.u_image0),u_image1:new t.Uniform1i(e,r.u_image1),u_brightness_low:new t.Uniform1f(e,r.u_brightness_low),u_brightness_high:new t.Uniform1f(e,r.u_brightness_high),u_saturation_factor:new t.Uniform1f(e,r.u_saturation_factor),u_contrast_factor:new t.Uniform1f(e,r.u_contrast_factor),u_spin_weights:new t.Uniform3f(e,r.u_spin_weights)}},symbolIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture)}},symbolSDF:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},symbolTextAndIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texsize_icon:new t.Uniform2f(e,r.u_texsize_icon),u_texture:new t.Uniform1i(e,r.u_texture),u_texture_icon:new t.Uniform1i(e,r.u_texture_icon),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},background:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_color:new t.UniformColor(e,r.u_color)}},backgroundPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image:new t.Uniform1i(e,r.u_image),u_pattern_tl_a:new t.Uniform2f(e,r.u_pattern_tl_a),u_pattern_br_a:new t.Uniform2f(e,r.u_pattern_br_a),u_pattern_tl_b:new t.Uniform2f(e,r.u_pattern_tl_b),u_pattern_br_b:new t.Uniform2f(e,r.u_pattern_br_b),u_texsize:new t.Uniform2f(e,r.u_texsize),u_mix:new t.Uniform1f(e,r.u_mix),u_pattern_size_a:new t.Uniform2f(e,r.u_pattern_size_a),u_pattern_size_b:new t.Uniform2f(e,r.u_pattern_size_b),u_scale_a:new t.Uniform1f(e,r.u_scale_a),u_scale_b:new t.Uniform1f(e,r.u_scale_b),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_tile_units_to_pixels:new t.Uniform1f(e,r.u_tile_units_to_pixels)}}};function Qr(e,r,n,i,a,o,s){for(var l=e.context,u=l.gl,c=e.useProgram(\"collisionBox\"),f=[],h=0,p=0,d=0;d0){var _=t.create(),w=m;t.mul(_,y.placementInvProjMatrix,e.transform.glCoordMatrix),t.mul(_,_,y.placementViewportMatrix),f.push({circleArray:b,circleOffset:p,transform:w,invTransform:_}),p=h+=b.length/4}x&&c.draw(l,u.LINES,Mt.disabled,Et.disabled,e.colorModeForRenderPass(),Ct.disabled,Ir(m,e.transform,g),n.id,x.layoutVertexBuffer,x.indexBuffer,x.segments,null,e.transform.zoom,null,null,x.collisionVertexBuffer)}}if(s&&f.length){var T=e.useProgram(\"collisionCircle\"),k=new t.StructArrayLayout2f1f2i16;k.resize(4*h),k._trim();for(var A=0,M=0,S=f;M=0&&(v[y.associatedIconIndex]={shiftedAnchor:S,angle:E})}else he(y.numGlyphs,p)}if(f){d.clear();for(var C=e.icon.placedSymbolArray,P=0;P0){var s=t.browser.now(),l=(s-e.timeAdded)/o,u=r?(s-r.timeAdded)/o:-1,c=n.getSource(),f=a.coveringZoomLevel({tileSize:c.tileSize,roundZoom:c.roundZoom}),h=!r||Math.abs(r.tileID.overscaledZ-f)>Math.abs(e.tileID.overscaledZ-f),p=h&&e.refreshedUponExpiration?1:t.clamp(h?l:1-u,0,1);return e.refreshedUponExpiration&&l>=1&&(e.refreshedUponExpiration=!1),r?{opacity:1,mix:1-p}:{opacity:p,mix:0}}return{opacity:1,mix:0}}var hn=new t.Color(1,0,0,1),pn=new t.Color(0,1,0,1),dn=new t.Color(0,0,1,1),vn=new t.Color(1,0,1,1),gn=new t.Color(0,1,1,1);function yn(t){var e=t.transform.padding;mn(t,t.transform.height-(e.top||0),3,hn),mn(t,e.bottom||0,3,pn),xn(t,e.left||0,3,dn),xn(t,t.transform.width-(e.right||0),3,vn);var r=t.transform.centerPoint;!function(t,e,r,n){var i=20,a=2;bn(t,e-a/2,r-i/2,a,i,n),bn(t,e-i/2,r-a/2,i,a,n)}(t,r.x,t.transform.height-r.y,gn)}function mn(t,e,r,n){bn(t,0,e+r/2,t.transform.width,r,n)}function xn(t,e,r,n){bn(t,e-r/2,0,r,t.transform.height,n)}function bn(e,r,n,i,a,o){var s=e.context,l=s.gl;l.enable(l.SCISSOR_TEST),l.scissor(r*t.browser.devicePixelRatio,n*t.browser.devicePixelRatio,i*t.browser.devicePixelRatio,a*t.browser.devicePixelRatio),s.clear({color:o}),l.disable(l.SCISSOR_TEST)}function _n(e,r,n){var i=e.context,a=i.gl,o=n.posMatrix,s=e.useProgram(\"debug\"),l=Mt.disabled,u=Et.disabled,c=e.colorModeForRenderPass(),f=\"$debug\";i.activeTexture.set(a.TEXTURE0),e.emptyTexture.bind(a.LINEAR,a.CLAMP_TO_EDGE),s.draw(i,a.LINE_STRIP,l,u,c,Ct.disabled,zr(o,t.Color.red),f,e.debugBuffer,e.tileBorderIndexBuffer,e.debugSegments);var h=r.getTileByID(n.key).latestRawTileData,p=h&&h.byteLength||0,d=Math.floor(p/1024),v=r.getTile(n).tileSize,g=512/Math.min(v,512)*(n.overscaledZ/e.transform.zoom)*.5,y=n.canonical.toString();n.overscaledZ!==n.canonical.z&&(y+=\" => \"+n.overscaledZ),function(t,e){t.initDebugOverlayCanvas();var r=t.debugOverlayCanvas,n=t.context.gl,i=t.debugOverlayCanvas.getContext(\"2d\");i.clearRect(0,0,r.width,r.height),i.shadowColor=\"white\",i.shadowBlur=2,i.lineWidth=1.5,i.strokeStyle=\"white\",i.textBaseline=\"top\",i.font=\"bold 36px Open Sans, sans-serif\",i.fillText(e,5,5),i.strokeText(e,5,5),t.debugOverlayTexture.update(r),t.debugOverlayTexture.bind(n.LINEAR,n.CLAMP_TO_EDGE)}(e,y+\" \"+d+\"kb\"),s.draw(i,a.TRIANGLES,l,u,Lt.alphaBlended,Ct.disabled,zr(o,t.Color.transparent,g),f,e.debugBuffer,e.quadTriangleIndexBuffer,e.debugSegments)}var wn={symbol:function(e,r,n,i,a){if(\"translucent\"===e.renderPass){var o=Et.disabled,s=e.colorModeForRenderPass();n.layout.get(\"text-variable-anchor\")&&function(e,r,n,i,a,o,s){for(var l=r.transform,u=\"map\"===a,c=\"map\"===o,f=0,h=e;f256&&this.clearStencil(),r.setColorMode(Lt.disabled),r.setDepthMode(Mt.disabled);var i=this.useProgram(\"clippingMask\");this._tileClippingMaskIDs={};for(var a=0,o=e;a256&&this.clearStencil();var t=this.nextStencilID++,e=this.context.gl;return new Et({func:e.NOTEQUAL,mask:255},t,255,e.KEEP,e.KEEP,e.REPLACE)},Tn.prototype.stencilModeForClipping=function(t){var e=this.context.gl;return new Et({func:e.EQUAL,mask:255},this._tileClippingMaskIDs[t.key],0,e.KEEP,e.KEEP,e.REPLACE)},Tn.prototype.stencilConfigForOverlap=function(t){var e,r=this.context.gl,n=t.sort((function(t,e){return e.overscaledZ-t.overscaledZ})),i=n[n.length-1].overscaledZ,a=n[0].overscaledZ-i+1;if(a>1){this.currentStencilSource=void 0,this.nextStencilID+a>256&&this.clearStencil();for(var o={},s=0;s=0;this.currentLayer--){var w=this.style._layers[i[this.currentLayer]],T=a[w.source],k=c[w.source];this._renderTileClippingMasks(w,k),this.renderLayer(this,T,w,k)}for(this.renderPass=\"translucent\",this.currentLayer=0;this.currentLayer0?e.pop():null},Tn.prototype.isPatternMissing=function(t){if(!t)return!1;if(!t.from||!t.to)return!0;var e=this.imageManager.getPattern(t.from.toString()),r=this.imageManager.getPattern(t.to.toString());return!e||!r},Tn.prototype.useProgram=function(t,e){this.cache=this.cache||{};var r=\"\"+t+(e?e.cacheKey:\"\")+(this._showOverdrawInspector?\"/overdraw\":\"\");return this.cache[r]||(this.cache[r]=new kr(this.context,wr[t],e,$r[t],this._showOverdrawInspector)),this.cache[r]},Tn.prototype.setCustomLayerDefaults=function(){this.context.unbindVAO(),this.context.cullFace.setDefault(),this.context.activeTexture.setDefault(),this.context.pixelStoreUnpack.setDefault(),this.context.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.context.pixelStoreUnpackFlipY.setDefault()},Tn.prototype.setBaseState=function(){var t=this.context.gl;this.context.cullFace.set(!1),this.context.viewport.set([0,0,this.width,this.height]),this.context.blendEquation.set(t.FUNC_ADD)},Tn.prototype.initDebugOverlayCanvas=function(){if(null==this.debugOverlayCanvas){this.debugOverlayCanvas=t.window.document.createElement(\"canvas\"),this.debugOverlayCanvas.width=512,this.debugOverlayCanvas.height=512;var e=this.context.gl;this.debugOverlayTexture=new t.Texture(this.context,this.debugOverlayCanvas,e.RGBA)}},Tn.prototype.destroy=function(){this.emptyTexture.destroy(),this.debugOverlayTexture&&this.debugOverlayTexture.destroy()};var kn=function(t,e){this.points=t,this.planes=e};kn.fromInvProjectionMatrix=function(e,r,n){var i=Math.pow(2,n),a=[[-1,1,-1,1],[1,1,-1,1],[1,-1,-1,1],[-1,-1,-1,1],[-1,1,1,1],[1,1,1,1],[1,-1,1,1],[-1,-1,1,1]].map((function(r){return t.transformMat4([],r,e)})).map((function(e){return t.scale$1([],e,1/e[3]/r*i)})),o=[[0,1,2],[6,5,4],[0,3,7],[2,1,5],[3,2,6],[0,4,5]].map((function(e){var r=t.sub([],a[e[0]],a[e[1]]),n=t.sub([],a[e[2]],a[e[1]]),i=t.normalize([],t.cross([],r,n)),o=-t.dot(i,a[e[1]]);return i.concat(o)}));return new kn(a,o)};var An=function(e,r){this.min=e,this.max=r,this.center=t.scale$2([],t.add([],this.min,this.max),.5)};An.prototype.quadrant=function(e){for(var r=[e%2==0,e<2],n=t.clone$2(this.min),i=t.clone$2(this.max),a=0;a=0;if(0===o)return 0;o!==r.length&&(n=!1)}if(n)return 2;for(var l=0;l<3;l++){for(var u=Number.MAX_VALUE,c=-Number.MAX_VALUE,f=0;fthis.max[l]-this.min[l])return 0}return 1};var Mn=function(t,e,r,n){if(void 0===t&&(t=0),void 0===e&&(e=0),void 0===r&&(r=0),void 0===n&&(n=0),isNaN(t)||t<0||isNaN(e)||e<0||isNaN(r)||r<0||isNaN(n)||n<0)throw new Error(\"Invalid value for edge-insets, top, bottom, left and right must all be numbers\");this.top=t,this.bottom=e,this.left=r,this.right=n};Mn.prototype.interpolate=function(e,r,n){return null!=r.top&&null!=e.top&&(this.top=t.number(e.top,r.top,n)),null!=r.bottom&&null!=e.bottom&&(this.bottom=t.number(e.bottom,r.bottom,n)),null!=r.left&&null!=e.left&&(this.left=t.number(e.left,r.left,n)),null!=r.right&&null!=e.right&&(this.right=t.number(e.right,r.right,n)),this},Mn.prototype.getCenter=function(e,r){var n=t.clamp((this.left+e-this.right)/2,0,e),i=t.clamp((this.top+r-this.bottom)/2,0,r);return new t.Point(n,i)},Mn.prototype.equals=function(t){return this.top===t.top&&this.bottom===t.bottom&&this.left===t.left&&this.right===t.right},Mn.prototype.clone=function(){return new Mn(this.top,this.bottom,this.left,this.right)},Mn.prototype.toJSON=function(){return{top:this.top,bottom:this.bottom,left:this.left,right:this.right}};var Sn=function(e,r,n,i,a){this.tileSize=512,this.maxValidLatitude=85.051129,this._renderWorldCopies=void 0===a||a,this._minZoom=e||0,this._maxZoom=r||22,this._minPitch=null==n?0:n,this._maxPitch=null==i?60:i,this.setMaxBounds(),this.width=0,this.height=0,this._center=new t.LngLat(0,0),this.zoom=0,this.angle=0,this._fov=.6435011087932844,this._pitch=0,this._unmodified=!0,this._edgeInsets=new Mn,this._posMatrixCache={},this._alignedPosMatrixCache={}},En={minZoom:{configurable:!0},maxZoom:{configurable:!0},minPitch:{configurable:!0},maxPitch:{configurable:!0},renderWorldCopies:{configurable:!0},worldSize:{configurable:!0},centerOffset:{configurable:!0},size:{configurable:!0},bearing:{configurable:!0},pitch:{configurable:!0},fov:{configurable:!0},zoom:{configurable:!0},center:{configurable:!0},padding:{configurable:!0},centerPoint:{configurable:!0},unmodified:{configurable:!0},point:{configurable:!0}};Sn.prototype.clone=function(){var t=new Sn(this._minZoom,this._maxZoom,this._minPitch,this.maxPitch,this._renderWorldCopies);return t.tileSize=this.tileSize,t.latRange=this.latRange,t.width=this.width,t.height=this.height,t._center=this._center,t.zoom=this.zoom,t.angle=this.angle,t._fov=this._fov,t._pitch=this._pitch,t._unmodified=this._unmodified,t._edgeInsets=this._edgeInsets.clone(),t._calcMatrices(),t},En.minZoom.get=function(){return this._minZoom},En.minZoom.set=function(t){this._minZoom!==t&&(this._minZoom=t,this.zoom=Math.max(this.zoom,t))},En.maxZoom.get=function(){return this._maxZoom},En.maxZoom.set=function(t){this._maxZoom!==t&&(this._maxZoom=t,this.zoom=Math.min(this.zoom,t))},En.minPitch.get=function(){return this._minPitch},En.minPitch.set=function(t){this._minPitch!==t&&(this._minPitch=t,this.pitch=Math.max(this.pitch,t))},En.maxPitch.get=function(){return this._maxPitch},En.maxPitch.set=function(t){this._maxPitch!==t&&(this._maxPitch=t,this.pitch=Math.min(this.pitch,t))},En.renderWorldCopies.get=function(){return this._renderWorldCopies},En.renderWorldCopies.set=function(t){void 0===t?t=!0:null===t&&(t=!1),this._renderWorldCopies=t},En.worldSize.get=function(){return this.tileSize*this.scale},En.centerOffset.get=function(){return this.centerPoint._sub(this.size._div(2))},En.size.get=function(){return new t.Point(this.width,this.height)},En.bearing.get=function(){return-this.angle/Math.PI*180},En.bearing.set=function(e){var r=-t.wrap(e,-180,180)*Math.PI/180;this.angle!==r&&(this._unmodified=!1,this.angle=r,this._calcMatrices(),this.rotationMatrix=t.create$2(),t.rotate(this.rotationMatrix,this.rotationMatrix,this.angle))},En.pitch.get=function(){return this._pitch/Math.PI*180},En.pitch.set=function(e){var r=t.clamp(e,this.minPitch,this.maxPitch)/180*Math.PI;this._pitch!==r&&(this._unmodified=!1,this._pitch=r,this._calcMatrices())},En.fov.get=function(){return this._fov/Math.PI*180},En.fov.set=function(t){t=Math.max(.01,Math.min(60,t)),this._fov!==t&&(this._unmodified=!1,this._fov=t/180*Math.PI,this._calcMatrices())},En.zoom.get=function(){return this._zoom},En.zoom.set=function(t){var e=Math.min(Math.max(t,this.minZoom),this.maxZoom);this._zoom!==e&&(this._unmodified=!1,this._zoom=e,this.scale=this.zoomScale(e),this.tileZoom=Math.floor(e),this.zoomFraction=e-this.tileZoom,this._constrain(),this._calcMatrices())},En.center.get=function(){return this._center},En.center.set=function(t){t.lat===this._center.lat&&t.lng===this._center.lng||(this._unmodified=!1,this._center=t,this._constrain(),this._calcMatrices())},En.padding.get=function(){return this._edgeInsets.toJSON()},En.padding.set=function(t){this._edgeInsets.equals(t)||(this._unmodified=!1,this._edgeInsets.interpolate(this._edgeInsets,t,1),this._calcMatrices())},En.centerPoint.get=function(){return this._edgeInsets.getCenter(this.width,this.height)},Sn.prototype.isPaddingEqual=function(t){return this._edgeInsets.equals(t)},Sn.prototype.interpolatePadding=function(t,e,r){this._unmodified=!1,this._edgeInsets.interpolate(t,e,r),this._constrain(),this._calcMatrices()},Sn.prototype.coveringZoomLevel=function(t){var e=(t.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/t.tileSize));return Math.max(0,e)},Sn.prototype.getVisibleUnwrappedCoordinates=function(e){var r=[new t.UnwrappedTileID(0,e)];if(this._renderWorldCopies)for(var n=this.pointCoordinate(new t.Point(0,0)),i=this.pointCoordinate(new t.Point(this.width,0)),a=this.pointCoordinate(new t.Point(this.width,this.height)),o=this.pointCoordinate(new t.Point(0,this.height)),s=Math.floor(Math.min(n.x,i.x,a.x,o.x)),l=Math.floor(Math.max(n.x,i.x,a.x,o.x)),u=s-1;u<=l+1;u++)0!==u&&r.push(new t.UnwrappedTileID(u,e));return r},Sn.prototype.coveringTiles=function(e){var r=this.coveringZoomLevel(e),n=r;if(void 0!==e.minzoom&&re.maxzoom&&(r=e.maxzoom);var i=t.MercatorCoordinate.fromLngLat(this.center),a=Math.pow(2,r),o=[a*i.x,a*i.y,0],s=kn.fromInvProjectionMatrix(this.invProjMatrix,this.worldSize,r),l=e.minzoom||0;this.pitch<=60&&this._edgeInsets.top<.1&&(l=r);var u=function(t){return{aabb:new An([t*a,0,0],[(t+1)*a,a,0]),zoom:0,x:0,y:0,wrap:t,fullyVisible:!1}},c=[],f=[],h=r,p=e.reparseOverscaled?n:r;if(this._renderWorldCopies)for(var d=1;d<=3;d++)c.push(u(-d)),c.push(u(d));for(c.push(u(0));c.length>0;){var v=c.pop(),g=v.x,y=v.y,m=v.fullyVisible;if(!m){var x=v.aabb.intersects(s);if(0===x)continue;m=2===x}var b=v.aabb.distanceX(o),_=v.aabb.distanceY(o),w=Math.max(Math.abs(b),Math.abs(_)),T=3+(1<T&&v.zoom>=l)f.push({tileID:new t.OverscaledTileID(v.zoom===h?p:v.zoom,v.wrap,v.zoom,g,y),distanceSq:t.sqrLen([o[0]-.5-g,o[1]-.5-y])});else for(var k=0;k<4;k++){var A=(g<<1)+k%2,M=(y<<1)+(k>>1);c.push({aabb:v.aabb.quadrant(k),zoom:v.zoom+1,x:A,y:M,wrap:v.wrap,fullyVisible:m})}}return f.sort((function(t,e){return t.distanceSq-e.distanceSq})).map((function(t){return t.tileID}))},Sn.prototype.resize=function(t,e){this.width=t,this.height=e,this.pixelsToGLUnits=[2/t,-2/e],this._constrain(),this._calcMatrices()},En.unmodified.get=function(){return this._unmodified},Sn.prototype.zoomScale=function(t){return Math.pow(2,t)},Sn.prototype.scaleZoom=function(t){return Math.log(t)/Math.LN2},Sn.prototype.project=function(e){var r=t.clamp(e.lat,-this.maxValidLatitude,this.maxValidLatitude);return new t.Point(t.mercatorXfromLng(e.lng)*this.worldSize,t.mercatorYfromLat(r)*this.worldSize)},Sn.prototype.unproject=function(e){return new t.MercatorCoordinate(e.x/this.worldSize,e.y/this.worldSize).toLngLat()},En.point.get=function(){return this.project(this.center)},Sn.prototype.setLocationAtPoint=function(e,r){var n=this.pointCoordinate(r),i=this.pointCoordinate(this.centerPoint),a=this.locationCoordinate(e),o=new t.MercatorCoordinate(a.x-(n.x-i.x),a.y-(n.y-i.y));this.center=this.coordinateLocation(o),this._renderWorldCopies&&(this.center=this.center.wrap())},Sn.prototype.locationPoint=function(t){return this.coordinatePoint(this.locationCoordinate(t))},Sn.prototype.pointLocation=function(t){return this.coordinateLocation(this.pointCoordinate(t))},Sn.prototype.locationCoordinate=function(e){return t.MercatorCoordinate.fromLngLat(e)},Sn.prototype.coordinateLocation=function(t){return t.toLngLat()},Sn.prototype.pointCoordinate=function(e){var r=[e.x,e.y,0,1],n=[e.x,e.y,1,1];t.transformMat4(r,r,this.pixelMatrixInverse),t.transformMat4(n,n,this.pixelMatrixInverse);var i=r[3],a=n[3],o=r[0]/i,s=n[0]/a,l=r[1]/i,u=n[1]/a,c=r[2]/i,f=n[2]/a,h=c===f?0:(0-c)/(f-c);return new t.MercatorCoordinate(t.number(o,s,h)/this.worldSize,t.number(l,u,h)/this.worldSize)},Sn.prototype.coordinatePoint=function(e){var r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix),new t.Point(r[0]/r[3],r[1]/r[3])},Sn.prototype.getBounds=function(){return(new t.LngLatBounds).extend(this.pointLocation(new t.Point(0,0))).extend(this.pointLocation(new t.Point(this.width,0))).extend(this.pointLocation(new t.Point(this.width,this.height))).extend(this.pointLocation(new t.Point(0,this.height)))},Sn.prototype.getMaxBounds=function(){return this.latRange&&2===this.latRange.length&&this.lngRange&&2===this.lngRange.length?new t.LngLatBounds([this.lngRange[0],this.latRange[0]],[this.lngRange[1],this.latRange[1]]):null},Sn.prototype.setMaxBounds=function(t){t?(this.lngRange=[t.getWest(),t.getEast()],this.latRange=[t.getSouth(),t.getNorth()],this._constrain()):(this.lngRange=null,this.latRange=[-this.maxValidLatitude,this.maxValidLatitude])},Sn.prototype.calculatePosMatrix=function(e,r){void 0===r&&(r=!1);var n=e.key,i=r?this._alignedPosMatrixCache:this._posMatrixCache;if(i[n])return i[n];var a=e.canonical,o=this.worldSize/this.zoomScale(a.z),s=a.x+Math.pow(2,a.z)*e.wrap,l=t.identity(new Float64Array(16));return t.translate(l,l,[s*o,a.y*o,0]),t.scale(l,l,[o/t.EXTENT,o/t.EXTENT,1]),t.multiply(l,r?this.alignedProjMatrix:this.projMatrix,l),i[n]=new Float32Array(l),i[n]},Sn.prototype.customLayerMatrix=function(){return this.mercatorMatrix.slice()},Sn.prototype._constrain=function(){if(this.center&&this.width&&this.height&&!this._constraining){this._constraining=!0;var e,r,n,i,a=-90,o=90,s=-180,l=180,u=this.size,c=this._unmodified;if(this.latRange){var f=this.latRange;a=t.mercatorYfromLat(f[1])*this.worldSize,e=(o=t.mercatorYfromLat(f[0])*this.worldSize)-ao&&(i=o-g)}if(this.lngRange){var y=p.x,m=u.x/2;y-ml&&(n=l-m)}void 0===n&&void 0===i||(this.center=this.unproject(new t.Point(void 0!==n?n:p.x,void 0!==i?i:p.y))),this._unmodified=c,this._constraining=!1}},Sn.prototype._calcMatrices=function(){if(this.height){var e=this._fov/2,r=this.centerOffset;this.cameraToCenterDistance=.5/Math.tan(e)*this.height;var n=Math.PI/2+this._pitch,i=this._fov*(.5+r.y/this.height),a=Math.sin(i)*this.cameraToCenterDistance/Math.sin(t.clamp(Math.PI-n-i,.01,Math.PI-.01)),o=this.point,s=o.x,l=o.y,u=1.01*(Math.cos(Math.PI/2-this._pitch)*a+this.cameraToCenterDistance),c=this.height/50,f=new Float64Array(16);t.perspective(f,this._fov,this.width/this.height,c,u),f[8]=2*-r.x/this.width,f[9]=2*r.y/this.height,t.scale(f,f,[1,-1,1]),t.translate(f,f,[0,0,-this.cameraToCenterDistance]),t.rotateX(f,f,this._pitch),t.rotateZ(f,f,this.angle),t.translate(f,f,[-s,-l,0]),this.mercatorMatrix=t.scale([],f,[this.worldSize,this.worldSize,this.worldSize]),t.scale(f,f,[1,1,t.mercatorZfromAltitude(1,this.center.lat)*this.worldSize,1]),this.projMatrix=f,this.invProjMatrix=t.invert([],this.projMatrix);var h=this.width%2/2,p=this.height%2/2,d=Math.cos(this.angle),v=Math.sin(this.angle),g=s-Math.round(s)+d*h+v*p,y=l-Math.round(l)+d*p+v*h,m=new Float64Array(f);if(t.translate(m,m,[g>.5?g-1:g,y>.5?y-1:y,0]),this.alignedProjMatrix=m,f=t.create(),t.scale(f,f,[this.width/2,-this.height/2,1]),t.translate(f,f,[1,-1,0]),this.labelPlaneMatrix=f,f=t.create(),t.scale(f,f,[1,-1,1]),t.translate(f,f,[-1,-1,0]),t.scale(f,f,[2/this.width,2/this.height,1]),this.glCoordMatrix=f,this.pixelMatrix=t.multiply(new Float64Array(16),this.labelPlaneMatrix,this.projMatrix),!(f=t.invert(new Float64Array(16),this.pixelMatrix)))throw new Error(\"failed to invert matrix\");this.pixelMatrixInverse=f,this._posMatrixCache={},this._alignedPosMatrixCache={}}},Sn.prototype.maxPitchScaleFactor=function(){if(!this.pixelMatrixInverse)return 1;var e=this.pointCoordinate(new t.Point(0,0)),r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix)[3]/this.cameraToCenterDistance},Sn.prototype.getCameraPoint=function(){var e=this._pitch,r=Math.tan(e)*(this.cameraToCenterDistance||1);return this.centerPoint.add(new t.Point(0,r))},Sn.prototype.getCameraQueryGeometry=function(e){var r=this.getCameraPoint();if(1===e.length)return[e[0],r];for(var n=r.x,i=r.y,a=r.x,o=r.y,s=0,l=e;s=3&&!t.some((function(t){return isNaN(t)}))){var e=this._map.dragRotate.isEnabled()&&this._map.touchZoomRotate.isEnabled()?+(t[3]||0):this._map.getBearing();return this._map.jumpTo({center:[+t[2],+t[1]],zoom:+t[0],bearing:e,pitch:+(t[4]||0)}),!0}return!1},Ln.prototype._updateHashUnthrottled=function(){var e=this.getHashString();try{t.window.history.replaceState(t.window.history.state,\"\",e)}catch(t){}};var Cn={linearity:.3,easing:t.bezier(0,0,.3,1)},Pn=t.extend({deceleration:2500,maxSpeed:1400},Cn),On=t.extend({deceleration:20,maxSpeed:1400},Cn),In=t.extend({deceleration:1e3,maxSpeed:360},Cn),Dn=t.extend({deceleration:1e3,maxSpeed:90},Cn),zn=function(t){this._map=t,this.clear()};function Rn(t,e){(!t.duration||t.duration0&&r-e[0].time>160;)e.shift()},zn.prototype._onMoveEnd=function(e){if(this._drainInertiaBuffer(),!(this._inertiaBuffer.length<2)){for(var r={zoom:0,bearing:0,pitch:0,pan:new t.Point(0,0),pinchAround:void 0,around:void 0},n=0,i=this._inertiaBuffer;n=this._clickTolerance||this._map.fire(new Bn(t.type,this._map,t))},Un.prototype.dblclick=function(t){return this._firePreventable(new Bn(t.type,this._map,t))},Un.prototype.mouseover=function(t){this._map.fire(new Bn(t.type,this._map,t))},Un.prototype.mouseout=function(t){this._map.fire(new Bn(t.type,this._map,t))},Un.prototype.touchstart=function(t){return this._firePreventable(new Nn(t.type,this._map,t))},Un.prototype.touchmove=function(t){this._map.fire(new Nn(t.type,this._map,t))},Un.prototype.touchend=function(t){this._map.fire(new Nn(t.type,this._map,t))},Un.prototype.touchcancel=function(t){this._map.fire(new Nn(t.type,this._map,t))},Un.prototype._firePreventable=function(t){if(this._map.fire(t),t.defaultPrevented)return{}},Un.prototype.isEnabled=function(){return!0},Un.prototype.isActive=function(){return!1},Un.prototype.enable=function(){},Un.prototype.disable=function(){};var Vn=function(t){this._map=t};Vn.prototype.reset=function(){this._delayContextMenu=!1,delete this._contextMenuEvent},Vn.prototype.mousemove=function(t){this._map.fire(new Bn(t.type,this._map,t))},Vn.prototype.mousedown=function(){this._delayContextMenu=!0},Vn.prototype.mouseup=function(){this._delayContextMenu=!1,this._contextMenuEvent&&(this._map.fire(new Bn(\"contextmenu\",this._map,this._contextMenuEvent)),delete this._contextMenuEvent)},Vn.prototype.contextmenu=function(t){this._delayContextMenu?this._contextMenuEvent=t:this._map.fire(new Bn(t.type,this._map,t)),this._map.listens(\"contextmenu\")&&t.preventDefault()},Vn.prototype.isEnabled=function(){return!0},Vn.prototype.isActive=function(){return!1},Vn.prototype.enable=function(){},Vn.prototype.disable=function(){};var Hn=function(t,e){this._map=t,this._el=t.getCanvasContainer(),this._container=t.getContainer(),this._clickTolerance=e.clickTolerance||1};function qn(t,e){for(var r={},n=0;nthis.numTouches)&&(this.aborted=!0),this.aborted||(void 0===this.startTime&&(this.startTime=e.timeStamp),n.length===this.numTouches&&(this.centroid=function(e){for(var r=new t.Point(0,0),n=0,i=e;n30)&&(this.aborted=!0)}}},Gn.prototype.touchend=function(t,e,r){if((!this.centroid||t.timeStamp-this.startTime>500)&&(this.aborted=!0),0===r.length){var n=!this.aborted&&this.centroid;if(this.reset(),n)return n}};var Zn=function(t){this.singleTap=new Gn(t),this.numTaps=t.numTaps,this.reset()};Zn.prototype.reset=function(){this.lastTime=1/0,delete this.lastTap,this.count=0,this.singleTap.reset()},Zn.prototype.touchstart=function(t,e,r){this.singleTap.touchstart(t,e,r)},Zn.prototype.touchmove=function(t,e,r){this.singleTap.touchmove(t,e,r)},Zn.prototype.touchend=function(t,e,r){var n=this.singleTap.touchend(t,e,r);if(n){var i=t.timeStamp-this.lastTime<500,a=!this.lastTap||this.lastTap.dist(n)<30;if(i&&a||this.reset(),this.count++,this.lastTime=t.timeStamp,this.lastTap=n,this.count===this.numTaps)return this.reset(),n}};var Yn=function(){this._zoomIn=new Zn({numTouches:1,numTaps:2}),this._zoomOut=new Zn({numTouches:2,numTaps:1}),this.reset()};Yn.prototype.reset=function(){this._active=!1,this._zoomIn.reset(),this._zoomOut.reset()},Yn.prototype.touchstart=function(t,e,r){this._zoomIn.touchstart(t,e,r),this._zoomOut.touchstart(t,e,r)},Yn.prototype.touchmove=function(t,e,r){this._zoomIn.touchmove(t,e,r),this._zoomOut.touchmove(t,e,r)},Yn.prototype.touchend=function(t,e,r){var n=this,i=this._zoomIn.touchend(t,e,r),a=this._zoomOut.touchend(t,e,r);return i?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()+1,around:e.unproject(i)},{originalEvent:t})}}):a?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()-1,around:e.unproject(a)},{originalEvent:t})}}):void 0},Yn.prototype.touchcancel=function(){this.reset()},Yn.prototype.enable=function(){this._enabled=!0},Yn.prototype.disable=function(){this._enabled=!1,this.reset()},Yn.prototype.isEnabled=function(){return this._enabled},Yn.prototype.isActive=function(){return this._active};var Wn=function(t){this.reset(),this._clickTolerance=t.clickTolerance||1};Wn.prototype.reset=function(){this._active=!1,this._moved=!1,delete this._lastPoint,delete this._eventButton},Wn.prototype._correctButton=function(t,e){return!1},Wn.prototype._move=function(t,e){return{}},Wn.prototype.mousedown=function(t,e){if(!this._lastPoint){var n=r.mouseButton(t);this._correctButton(t,n)&&(this._lastPoint=e,this._eventButton=n)}},Wn.prototype.mousemoveWindow=function(t,e){var r=this._lastPoint;if(r&&(t.preventDefault(),this._moved||!(e.dist(r)0&&(this._active=!0);var i=qn(n,r),a=new t.Point(0,0),o=new t.Point(0,0),s=0;for(var l in i){var u=i[l],c=this._touches[l];c&&(a._add(u),o._add(u.sub(c)),s++,i[l]=u)}if(this._touches=i,!(sMath.abs(t.x)}var oi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),this._valid=void 0,delete this._firstMove,delete this._lastPoints},e.prototype._start=function(t){this._lastPoints=t,ai(t[0].sub(t[1]))&&(this._valid=!1)},e.prototype._move=function(t,e,r){var n=t[0].sub(this._lastPoints[0]),i=t[1].sub(this._lastPoints[1]);if(this._valid=this.gestureBeginsVertically(n,i,r.timeStamp),this._valid)return this._lastPoints=t,this._active=!0,{pitchDelta:(n.y+i.y)/2*-.5}},e.prototype.gestureBeginsVertically=function(t,e,r){if(void 0!==this._valid)return this._valid;var n=t.mag()>=2,i=e.mag()>=2;if(n||i){if(!n||!i)return void 0===this._firstMove&&(this._firstMove=r),r-this._firstMove<100&&void 0;var a=t.y>0==e.y>0;return ai(t)&&ai(e)&&a}},e}(Qn),si={panStep:100,bearingStep:15,pitchStep:10},li=function(){var t=si;this._panStep=t.panStep,this._bearingStep=t.bearingStep,this._pitchStep=t.pitchStep};function ui(t){return t*(2-t)}li.prototype.reset=function(){this._active=!1},li.prototype.keydown=function(t){var e=this;if(!(t.altKey||t.ctrlKey||t.metaKey)){var r=0,n=0,i=0,a=0,o=0;switch(t.keyCode){case 61:case 107:case 171:case 187:r=1;break;case 189:case 109:case 173:r=-1;break;case 37:t.shiftKey?n=-1:(t.preventDefault(),a=-1);break;case 39:t.shiftKey?n=1:(t.preventDefault(),a=1);break;case 38:t.shiftKey?i=1:(t.preventDefault(),o=-1);break;case 40:t.shiftKey?i=-1:(t.preventDefault(),o=1);break;default:return}return{cameraAnimation:function(s){var l=s.getZoom();s.easeTo({duration:300,easeId:\"keyboardHandler\",easing:ui,zoom:r?Math.round(l)+r*(t.shiftKey?2:1):l,bearing:s.getBearing()+n*e._bearingStep,pitch:s.getPitch()+i*e._pitchStep,offset:[-a*e._panStep,-o*e._panStep],center:s.getCenter()},{originalEvent:t})}}}},li.prototype.enable=function(){this._enabled=!0},li.prototype.disable=function(){this._enabled=!1,this.reset()},li.prototype.isEnabled=function(){return this._enabled},li.prototype.isActive=function(){return this._active};var ci=4.000244140625,fi=function(e,r){this._map=e,this._el=e.getCanvasContainer(),this._handler=r,this._delta=0,this._defaultZoomRate=.01,this._wheelZoomRate=.0022222222222222222,t.bindAll([\"_onWheel\",\"_onTimeout\",\"_onScrollFrame\",\"_onScrollFinished\"],this)};fi.prototype.setZoomRate=function(t){this._defaultZoomRate=t},fi.prototype.setWheelZoomRate=function(t){this._wheelZoomRate=t},fi.prototype.isEnabled=function(){return!!this._enabled},fi.prototype.isActive=function(){return!!this._active||void 0!==this._finishTimeout},fi.prototype.isZooming=function(){return!!this._zooming},fi.prototype.enable=function(t){this.isEnabled()||(this._enabled=!0,this._aroundCenter=t&&\"center\"===t.around)},fi.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},fi.prototype.wheel=function(e){if(this.isEnabled()){var r=e.deltaMode===t.window.WheelEvent.DOM_DELTA_LINE?40*e.deltaY:e.deltaY,n=t.browser.now(),i=n-(this._lastWheelEventTime||0);this._lastWheelEventTime=n,0!==r&&r%ci==0?this._type=\"wheel\":0!==r&&Math.abs(r)<4?this._type=\"trackpad\":i>400?(this._type=null,this._lastValue=r,this._timeout=setTimeout(this._onTimeout,40,e)):this._type||(this._type=Math.abs(i*r)<200?\"trackpad\":\"wheel\",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,r+=this._lastValue)),e.shiftKey&&r&&(r/=4),this._type&&(this._lastWheelEvent=e,this._delta-=r,this._active||this._start(e)),e.preventDefault()}},fi.prototype._onTimeout=function(t){this._type=\"wheel\",this._delta-=this._lastValue,this._active||this._start(t)},fi.prototype._start=function(e){if(this._delta){this._frameId&&(this._frameId=null),this._active=!0,this.isZooming()||(this._zooming=!0),this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout);var n=r.mousePos(this._el,e);this._around=t.LngLat.convert(this._aroundCenter?this._map.getCenter():this._map.unproject(n)),this._aroundPoint=this._map.transform.locationPoint(this._around),this._frameId||(this._frameId=!0,this._handler._triggerRenderFrame())}},fi.prototype.renderFrame=function(){return this._onScrollFrame()},fi.prototype._onScrollFrame=function(){var e=this;if(this._frameId&&(this._frameId=null,this.isActive())){var r=this._map.transform;if(0!==this._delta){var n=\"wheel\"===this._type&&Math.abs(this._delta)>ci?this._wheelZoomRate:this._defaultZoomRate,i=2/(1+Math.exp(-Math.abs(this._delta*n)));this._delta<0&&0!==i&&(i=1/i);var a=\"number\"==typeof this._targetZoom?r.zoomScale(this._targetZoom):r.scale;this._targetZoom=Math.min(r.maxZoom,Math.max(r.minZoom,r.scaleZoom(a*i))),\"wheel\"===this._type&&(this._startZoom=r.zoom,this._easing=this._smoothOutEasing(200)),this._delta=0}var o,s=\"number\"==typeof this._targetZoom?this._targetZoom:r.zoom,l=this._startZoom,u=this._easing,c=!1;if(\"wheel\"===this._type&&l&&u){var f=Math.min((t.browser.now()-this._lastWheelEventTime)/200,1),h=u(f);o=t.number(l,s,h),f<1?this._frameId||(this._frameId=!0):c=!0}else o=s,c=!0;return this._active=!0,c&&(this._active=!1,this._finishTimeout=setTimeout((function(){e._zooming=!1,e._handler._triggerRenderFrame(),delete e._targetZoom,delete e._finishTimeout}),200)),{noInertia:!0,needsRenderFrame:!c,zoomDelta:o-r.zoom,around:this._aroundPoint,originalEvent:this._lastWheelEvent}}},fi.prototype._smoothOutEasing=function(e){var r=t.ease;if(this._prevEase){var n=this._prevEase,i=(t.browser.now()-n.start)/n.duration,a=n.easing(i+.01)-n.easing(i),o=.27/Math.sqrt(a*a+1e-4)*.01,s=Math.sqrt(.0729-o*o);r=t.bezier(o,s,.25,1)}return this._prevEase={start:t.browser.now(),duration:e,easing:r},r},fi.prototype.reset=function(){this._active=!1};var hi=function(t,e){this._clickZoom=t,this._tapZoom=e};hi.prototype.enable=function(){this._clickZoom.enable(),this._tapZoom.enable()},hi.prototype.disable=function(){this._clickZoom.disable(),this._tapZoom.disable()},hi.prototype.isEnabled=function(){return this._clickZoom.isEnabled()&&this._tapZoom.isEnabled()},hi.prototype.isActive=function(){return this._clickZoom.isActive()||this._tapZoom.isActive()};var pi=function(){this.reset()};pi.prototype.reset=function(){this._active=!1},pi.prototype.dblclick=function(t,e){return t.preventDefault(),{cameraAnimation:function(r){r.easeTo({duration:300,zoom:r.getZoom()+(t.shiftKey?-1:1),around:r.unproject(e)},{originalEvent:t})}}},pi.prototype.enable=function(){this._enabled=!0},pi.prototype.disable=function(){this._enabled=!1,this.reset()},pi.prototype.isEnabled=function(){return this._enabled},pi.prototype.isActive=function(){return this._active};var di=function(){this._tap=new Zn({numTouches:1,numTaps:1}),this.reset()};di.prototype.reset=function(){this._active=!1,delete this._swipePoint,delete this._swipeTouch,delete this._tapTime,this._tap.reset()},di.prototype.touchstart=function(t,e,r){this._swipePoint||(this._tapTime&&t.timeStamp-this._tapTime>500&&this.reset(),this._tapTime?r.length>0&&(this._swipePoint=e[0],this._swipeTouch=r[0].identifier):this._tap.touchstart(t,e,r))},di.prototype.touchmove=function(t,e,r){if(this._tapTime){if(this._swipePoint){if(r[0].identifier!==this._swipeTouch)return;var n=e[0],i=n.y-this._swipePoint.y;return this._swipePoint=n,t.preventDefault(),this._active=!0,{zoomDelta:i/128}}}else this._tap.touchmove(t,e,r)},di.prototype.touchend=function(t,e,r){this._tapTime?this._swipePoint&&0===r.length&&this.reset():this._tap.touchend(t,e,r)&&(this._tapTime=t.timeStamp)},di.prototype.touchcancel=function(){this.reset()},di.prototype.enable=function(){this._enabled=!0},di.prototype.disable=function(){this._enabled=!1,this.reset()},di.prototype.isEnabled=function(){return this._enabled},di.prototype.isActive=function(){return this._active};var vi=function(t,e,r){this._el=t,this._mousePan=e,this._touchPan=r};vi.prototype.enable=function(t){this._inertiaOptions=t||{},this._mousePan.enable(),this._touchPan.enable(),this._el.classList.add(\"mapboxgl-touch-drag-pan\")},vi.prototype.disable=function(){this._mousePan.disable(),this._touchPan.disable(),this._el.classList.remove(\"mapboxgl-touch-drag-pan\")},vi.prototype.isEnabled=function(){return this._mousePan.isEnabled()&&this._touchPan.isEnabled()},vi.prototype.isActive=function(){return this._mousePan.isActive()||this._touchPan.isActive()};var gi=function(t,e,r){this._pitchWithRotate=t.pitchWithRotate,this._mouseRotate=e,this._mousePitch=r};gi.prototype.enable=function(){this._mouseRotate.enable(),this._pitchWithRotate&&this._mousePitch.enable()},gi.prototype.disable=function(){this._mouseRotate.disable(),this._mousePitch.disable()},gi.prototype.isEnabled=function(){return this._mouseRotate.isEnabled()&&(!this._pitchWithRotate||this._mousePitch.isEnabled())},gi.prototype.isActive=function(){return this._mouseRotate.isActive()||this._mousePitch.isActive()};var yi=function(t,e,r,n){this._el=t,this._touchZoom=e,this._touchRotate=r,this._tapDragZoom=n,this._rotationDisabled=!1,this._enabled=!0};yi.prototype.enable=function(t){this._touchZoom.enable(t),this._rotationDisabled||this._touchRotate.enable(t),this._tapDragZoom.enable(),this._el.classList.add(\"mapboxgl-touch-zoom-rotate\")},yi.prototype.disable=function(){this._touchZoom.disable(),this._touchRotate.disable(),this._tapDragZoom.disable(),this._el.classList.remove(\"mapboxgl-touch-zoom-rotate\")},yi.prototype.isEnabled=function(){return this._touchZoom.isEnabled()&&(this._rotationDisabled||this._touchRotate.isEnabled())&&this._tapDragZoom.isEnabled()},yi.prototype.isActive=function(){return this._touchZoom.isActive()||this._touchRotate.isActive()||this._tapDragZoom.isActive()},yi.prototype.disableRotation=function(){this._rotationDisabled=!0,this._touchRotate.disable()},yi.prototype.enableRotation=function(){this._rotationDisabled=!1,this._touchZoom.isEnabled()&&this._touchRotate.enable()};var mi=function(t){return t.zoom||t.drag||t.pitch||t.rotate},xi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(t.Event);function bi(t){return t.panDelta&&t.panDelta.mag()||t.zoomDelta||t.bearingDelta||t.pitchDelta}var _i=function(e,n){this._map=e,this._el=this._map.getCanvasContainer(),this._handlers=[],this._handlersById={},this._changes=[],this._inertia=new zn(e),this._bearingSnap=n.bearingSnap,this._previousActiveHandlers={},this._eventsInProgress={},this._addDefaultHandlers(n),t.bindAll([\"handleEvent\",\"handleWindowEvent\"],this);var i=this._el;this._listeners=[[i,\"touchstart\",{passive:!1}],[i,\"touchmove\",{passive:!1}],[i,\"touchend\",void 0],[i,\"touchcancel\",void 0],[i,\"mousedown\",void 0],[i,\"mousemove\",void 0],[i,\"mouseup\",void 0],[t.window.document,\"mousemove\",{capture:!0}],[t.window.document,\"mouseup\",void 0],[i,\"mouseover\",void 0],[i,\"mouseout\",void 0],[i,\"dblclick\",void 0],[i,\"click\",void 0],[i,\"keydown\",{capture:!1}],[i,\"keyup\",void 0],[i,\"wheel\",{passive:!1}],[i,\"contextmenu\",void 0],[t.window,\"blur\",void 0]];for(var a=0,o=this._listeners;aa?Math.min(2,_):Math.max(.5,_),w=Math.pow(g,1-e),T=i.unproject(x.add(b.mult(e*w)).mult(v));i.setLocationAtPoint(i.renderWorldCopies?T.wrap():T,d)}n._fireMoveEvents(r)}),(function(t){n._afterEase(r,t)}),e),this},r.prototype._prepareEase=function(e,r,n){void 0===n&&(n={}),this._moving=!0,r||n.moving||this.fire(new t.Event(\"movestart\",e)),this._zooming&&!n.zooming&&this.fire(new t.Event(\"zoomstart\",e)),this._rotating&&!n.rotating&&this.fire(new t.Event(\"rotatestart\",e)),this._pitching&&!n.pitching&&this.fire(new t.Event(\"pitchstart\",e))},r.prototype._fireMoveEvents=function(e){this.fire(new t.Event(\"move\",e)),this._zooming&&this.fire(new t.Event(\"zoom\",e)),this._rotating&&this.fire(new t.Event(\"rotate\",e)),this._pitching&&this.fire(new t.Event(\"pitch\",e))},r.prototype._afterEase=function(e,r){if(!this._easeId||!r||this._easeId!==r){delete this._easeId;var n=this._zooming,i=this._rotating,a=this._pitching;this._moving=!1,this._zooming=!1,this._rotating=!1,this._pitching=!1,this._padding=!1,n&&this.fire(new t.Event(\"zoomend\",e)),i&&this.fire(new t.Event(\"rotateend\",e)),a&&this.fire(new t.Event(\"pitchend\",e)),this.fire(new t.Event(\"moveend\",e))}},r.prototype.flyTo=function(e,r){var n=this;if(!e.essential&&t.browser.prefersReducedMotion){var i=t.pick(e,[\"center\",\"zoom\",\"bearing\",\"pitch\",\"around\"]);return this.jumpTo(i,r)}this.stop(),e=t.extend({offset:[0,0],speed:1.2,curve:1.42,easing:t.ease},e);var a=this.transform,o=this.getZoom(),s=this.getBearing(),l=this.getPitch(),u=this.getPadding(),c=\"zoom\"in e?t.clamp(+e.zoom,a.minZoom,a.maxZoom):o,f=\"bearing\"in e?this._normalizeBearing(e.bearing,s):s,h=\"pitch\"in e?+e.pitch:l,p=\"padding\"in e?e.padding:a.padding,d=a.zoomScale(c-o),v=t.Point.convert(e.offset),g=a.centerPoint.add(v),y=a.pointLocation(g),m=t.LngLat.convert(e.center||y);this._normalizeCenter(m);var x=a.project(y),b=a.project(m).sub(x),_=e.curve,w=Math.max(a.width,a.height),T=w/d,k=b.mag();if(\"minZoom\"in e){var A=t.clamp(Math.min(e.minZoom,o,c),a.minZoom,a.maxZoom),M=w/a.zoomScale(A-o);_=Math.sqrt(M/k*2)}var S=_*_;function E(t){var e=(T*T-w*w+(t?-1:1)*S*S*k*k)/(2*(t?T:w)*S*k);return Math.log(Math.sqrt(e*e+1)-e)}function L(t){return(Math.exp(t)-Math.exp(-t))/2}function C(t){return(Math.exp(t)+Math.exp(-t))/2}var P=E(0),O=function(t){return C(P)/C(P+_*t)},I=function(t){return w*((C(P)*(L(e=P+_*t)/C(e))-L(P))/S)/k;var e},D=(E(1)-P)/_;if(Math.abs(k)<1e-6||!isFinite(D)){if(Math.abs(w-T)<1e-6)return this.easeTo(e,r);var z=Te.maxDuration&&(e.duration=0),this._zooming=!0,this._rotating=s!==f,this._pitching=h!==l,this._padding=!a.isPaddingEqual(p),this._prepareEase(r,!1),this._ease((function(e){var i=e*D,d=1/O(i);a.zoom=1===e?c:o+a.scaleZoom(d),n._rotating&&(a.bearing=t.number(s,f,e)),n._pitching&&(a.pitch=t.number(l,h,e)),n._padding&&(a.interpolatePadding(u,p,e),g=a.centerPoint.add(v));var y=1===e?m:a.unproject(x.add(b.mult(I(i))).mult(d));a.setLocationAtPoint(a.renderWorldCopies?y.wrap():y,g),n._fireMoveEvents(r)}),(function(){return n._afterEase(r)}),e),this},r.prototype.isEasing=function(){return!!this._easeFrameId},r.prototype.stop=function(){return this._stop()},r.prototype._stop=function(t,e){if(this._easeFrameId&&(this._cancelRenderFrame(this._easeFrameId),delete this._easeFrameId,delete this._onEaseFrame),this._onEaseEnd){var r=this._onEaseEnd;delete this._onEaseEnd,r.call(this,e)}if(!t){var n=this.handlers;n&&n.stop()}return this},r.prototype._ease=function(e,r,n){!1===n.animate||0===n.duration?(e(1),r()):(this._easeStart=t.browser.now(),this._easeOptions=n,this._onEaseFrame=e,this._onEaseEnd=r,this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback))},r.prototype._renderFrameCallback=function(){var e=Math.min((t.browser.now()-this._easeStart)/this._easeOptions.duration,1);this._onEaseFrame(this._easeOptions.easing(e)),e<1?this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback):this.stop()},r.prototype._normalizeBearing=function(e,r){e=t.wrap(e,-180,180);var n=Math.abs(e-r);return Math.abs(e-360-r)180?-360:r<-180?360:0}},r}(t.Evented),Ti=function(e){void 0===e&&(e={}),this.options=e,t.bindAll([\"_updateEditLink\",\"_updateData\",\"_updateCompact\"],this)};Ti.prototype.getDefaultPosition=function(){return\"bottom-right\"},Ti.prototype.onAdd=function(t){var e=this.options&&this.options.compact;return this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-attrib\"),this._innerContainer=r.create(\"div\",\"mapboxgl-ctrl-attrib-inner\",this._container),e&&this._container.classList.add(\"mapboxgl-compact\"),this._updateAttributions(),this._updateEditLink(),this._map.on(\"styledata\",this._updateData),this._map.on(\"sourcedata\",this._updateData),this._map.on(\"moveend\",this._updateEditLink),void 0===e&&(this._map.on(\"resize\",this._updateCompact),this._updateCompact()),this._container},Ti.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"styledata\",this._updateData),this._map.off(\"sourcedata\",this._updateData),this._map.off(\"moveend\",this._updateEditLink),this._map.off(\"resize\",this._updateCompact),this._map=void 0,this._attribHTML=void 0},Ti.prototype._updateEditLink=function(){var e=this._editLink;e||(e=this._editLink=this._container.querySelector(\".mapbox-improve-map\"));var r=[{key:\"owner\",value:this.styleOwner},{key:\"id\",value:this.styleId},{key:\"access_token\",value:this._map._requestManager._customAccessToken||t.config.ACCESS_TOKEN}];if(e){var n=r.reduce((function(t,e,n){return e.value&&(t+=e.key+\"=\"+e.value+(n=0)return!1;return!0}))).join(\" | \");o!==this._attribHTML&&(this._attribHTML=o,t.length?(this._innerContainer.innerHTML=o,this._container.classList.remove(\"mapboxgl-attrib-empty\")):this._container.classList.add(\"mapboxgl-attrib-empty\"),this._editLink=null)}},Ti.prototype._updateCompact=function(){this._map.getCanvasContainer().offsetWidth<=640?this._container.classList.add(\"mapboxgl-compact\"):this._container.classList.remove(\"mapboxgl-compact\")};var ki=function(){t.bindAll([\"_updateLogo\"],this),t.bindAll([\"_updateCompact\"],this)};ki.prototype.onAdd=function(t){this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl\");var e=r.create(\"a\",\"mapboxgl-ctrl-logo\");return e.target=\"_blank\",e.rel=\"noopener nofollow\",e.href=\"https://www.mapbox.com/\",e.setAttribute(\"aria-label\",this._map._getUIString(\"LogoControl.Title\")),e.setAttribute(\"rel\",\"noopener nofollow\"),this._container.appendChild(e),this._container.style.display=\"none\",this._map.on(\"sourcedata\",this._updateLogo),this._updateLogo(),this._map.on(\"resize\",this._updateCompact),this._updateCompact(),this._container},ki.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"sourcedata\",this._updateLogo),this._map.off(\"resize\",this._updateCompact)},ki.prototype.getDefaultPosition=function(){return\"bottom-left\"},ki.prototype._updateLogo=function(t){t&&\"metadata\"!==t.sourceDataType||(this._container.style.display=this._logoRequired()?\"block\":\"none\")},ki.prototype._logoRequired=function(){if(this._map.style){var t=this._map.style.sourceCaches;for(var e in t)if(t[e].getSource().mapbox_logo)return!0;return!1}},ki.prototype._updateCompact=function(){var t=this._container.children;if(t.length){var e=t[0];this._map.getCanvasContainer().offsetWidth<250?e.classList.add(\"mapboxgl-compact\"):e.classList.remove(\"mapboxgl-compact\")}};var Ai=function(){this._queue=[],this._id=0,this._cleared=!1,this._currentlyRunning=!1};Ai.prototype.add=function(t){var e=++this._id;return this._queue.push({callback:t,id:e,cancelled:!1}),e},Ai.prototype.remove=function(t){for(var e=this._currentlyRunning,r=0,n=e?this._queue.concat(e):this._queue;re.maxZoom)throw new Error(\"maxZoom must be greater than or equal to minZoom\");if(null!=e.minPitch&&null!=e.maxPitch&&e.minPitch>e.maxPitch)throw new Error(\"maxPitch must be greater than or equal to minPitch\");if(null!=e.minPitch&&e.minPitch<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(null!=e.maxPitch&&e.maxPitch>Ci)throw new Error(\"maxPitch must be less than or equal to 60\");var i=new Sn(e.minZoom,e.maxZoom,e.minPitch,e.maxPitch,e.renderWorldCopies);if(n.call(this,i,e),this._interactive=e.interactive,this._maxTileCacheSize=e.maxTileCacheSize,this._failIfMajorPerformanceCaveat=e.failIfMajorPerformanceCaveat,this._preserveDrawingBuffer=e.preserveDrawingBuffer,this._antialias=e.antialias,this._trackResize=e.trackResize,this._bearingSnap=e.bearingSnap,this._refreshExpiredTiles=e.refreshExpiredTiles,this._fadeDuration=e.fadeDuration,this._crossSourceCollisions=e.crossSourceCollisions,this._crossFadingFactor=1,this._collectResourceTiming=e.collectResourceTiming,this._renderTaskQueue=new Ai,this._controls=[],this._mapId=t.uniqueId(),this._locale=t.extend({},Mi,e.locale),this._requestManager=new t.RequestManager(e.transformRequest,e.accessToken),\"string\"==typeof e.container){if(this._container=t.window.document.getElementById(e.container),!this._container)throw new Error(\"Container '\"+e.container+\"' not found.\")}else{if(!(e.container instanceof Ei))throw new Error(\"Invalid type: 'container' must be a String or HTMLElement.\");this._container=e.container}if(e.maxBounds&&this.setMaxBounds(e.maxBounds),t.bindAll([\"_onWindowOnline\",\"_onWindowResize\",\"_contextLost\",\"_contextRestored\"],this),this._setupContainer(),this._setupPainter(),void 0===this.painter)throw new Error(\"Failed to initialize WebGL.\");this.on(\"move\",(function(){return r._update(!1)})),this.on(\"moveend\",(function(){return r._update(!1)})),this.on(\"zoom\",(function(){return r._update(!0)})),void 0!==t.window&&(t.window.addEventListener(\"online\",this._onWindowOnline,!1),t.window.addEventListener(\"resize\",this._onWindowResize,!1)),this.handlers=new _i(this,e);var a=\"string\"==typeof e.hash&&e.hash||void 0;this._hash=e.hash&&new Ln(a).addTo(this),this._hash&&this._hash._onHashChange()||(this.jumpTo({center:e.center,zoom:e.zoom,bearing:e.bearing,pitch:e.pitch}),e.bounds&&(this.resize(),this.fitBounds(e.bounds,t.extend({},e.fitBoundsOptions,{duration:0})))),this.resize(),this._localIdeographFontFamily=e.localIdeographFontFamily,e.style&&this.setStyle(e.style,{localIdeographFontFamily:e.localIdeographFontFamily}),e.attributionControl&&this.addControl(new Ti({customAttribution:e.customAttribution})),this.addControl(new ki,e.logoPosition),this.on(\"style.load\",(function(){r.transform.unmodified&&r.jumpTo(r.style.stylesheet)})),this.on(\"data\",(function(e){r._update(\"style\"===e.dataType),r.fire(new t.Event(e.dataType+\"data\",e))})),this.on(\"dataloading\",(function(e){r.fire(new t.Event(e.dataType+\"dataloading\",e))}))}n&&(i.__proto__=n),i.prototype=Object.create(n&&n.prototype),i.prototype.constructor=i;var a={showTileBoundaries:{configurable:!0},showPadding:{configurable:!0},showCollisionBoxes:{configurable:!0},showOverdrawInspector:{configurable:!0},repaint:{configurable:!0},vertices:{configurable:!0},version:{configurable:!0}};return i.prototype._getMapId=function(){return this._mapId},i.prototype.addControl=function(e,r){if(void 0===r&&e.getDefaultPosition&&(r=e.getDefaultPosition()),void 0===r&&(r=\"top-right\"),!e||!e.onAdd)return this.fire(new t.ErrorEvent(new Error(\"Invalid argument to map.addControl(). Argument must be a control with onAdd and onRemove methods.\")));var n=e.onAdd(this);this._controls.push(e);var i=this._controlPositions[r];return-1!==r.indexOf(\"bottom\")?i.insertBefore(n,i.firstChild):i.appendChild(n),this},i.prototype.removeControl=function(e){if(!e||!e.onRemove)return this.fire(new t.ErrorEvent(new Error(\"Invalid argument to map.removeControl(). Argument must be a control with onAdd and onRemove methods.\")));var r=this._controls.indexOf(e);return r>-1&&this._controls.splice(r,1),e.onRemove(this),this},i.prototype.resize=function(e){var r=this._containerDimensions(),n=r[0],i=r[1];this._resizeCanvas(n,i),this.transform.resize(n,i),this.painter.resize(n,i);var a=!this._moving;return a&&(this.stop(),this.fire(new t.Event(\"movestart\",e)).fire(new t.Event(\"move\",e))),this.fire(new t.Event(\"resize\",e)),a&&this.fire(new t.Event(\"moveend\",e)),this},i.prototype.getBounds=function(){return this.transform.getBounds()},i.prototype.getMaxBounds=function(){return this.transform.getMaxBounds()},i.prototype.setMaxBounds=function(e){return this.transform.setMaxBounds(t.LngLatBounds.convert(e)),this._update()},i.prototype.setMinZoom=function(t){if((t=null==t?-2:t)>=-2&&t<=this.transform.maxZoom)return this.transform.minZoom=t,this._update(),this.getZoom()=this.transform.minZoom)return this.transform.maxZoom=t,this._update(),this.getZoom()>t&&this.setZoom(t),this;throw new Error(\"maxZoom must be greater than the current minZoom\")},i.prototype.getMaxZoom=function(){return this.transform.maxZoom},i.prototype.setMinPitch=function(t){if((t=null==t?0:t)<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(t>=0&&t<=this.transform.maxPitch)return this.transform.minPitch=t,this._update(),this.getPitch()Ci)throw new Error(\"maxPitch must be less than or equal to 60\");if(t>=this.transform.minPitch)return this.transform.maxPitch=t,this._update(),this.getPitch()>t&&this.setPitch(t),this;throw new Error(\"maxPitch must be greater than the current minPitch\")},i.prototype.getMaxPitch=function(){return this.transform.maxPitch},i.prototype.getRenderWorldCopies=function(){return this.transform.renderWorldCopies},i.prototype.setRenderWorldCopies=function(t){return this.transform.renderWorldCopies=t,this._update()},i.prototype.project=function(e){return this.transform.locationPoint(t.LngLat.convert(e))},i.prototype.unproject=function(e){return this.transform.pointLocation(t.Point.convert(e))},i.prototype.isMoving=function(){return this._moving||this.handlers.isMoving()},i.prototype.isZooming=function(){return this._zooming||this.handlers.isZooming()},i.prototype.isRotating=function(){return this._rotating||this.handlers.isRotating()},i.prototype._createDelegatedListener=function(t,e,r){var n,i=this;if(\"mouseenter\"===t||\"mouseover\"===t){var a=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){var o=i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[];o.length?a||(a=!0,r.call(i,new Bn(t,i,n.originalEvent,{features:o}))):a=!1},mouseout:function(){a=!1}}}}if(\"mouseleave\"===t||\"mouseout\"===t){var o=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){(i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[]).length?o=!0:o&&(o=!1,r.call(i,new Bn(t,i,n.originalEvent)))},mouseout:function(e){o&&(o=!1,r.call(i,new Bn(t,i,e.originalEvent)))}}}}return{layer:e,listener:r,delegates:(n={},n[t]=function(t){var n=i.getLayer(e)?i.queryRenderedFeatures(t.point,{layers:[e]}):[];n.length&&(t.features=n,r.call(i,t),delete t.features)},n)}},i.prototype.on=function(t,e,r){if(void 0===r)return n.prototype.on.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in this._delegatedListeners=this._delegatedListeners||{},this._delegatedListeners[t]=this._delegatedListeners[t]||[],this._delegatedListeners[t].push(i),i.delegates)this.on(a,i.delegates[a]);return this},i.prototype.once=function(t,e,r){if(void 0===r)return n.prototype.once.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in i.delegates)this.once(a,i.delegates[a]);return this},i.prototype.off=function(t,e,r){var i=this;if(void 0===r)return n.prototype.off.call(this,t,e);return this._delegatedListeners&&this._delegatedListeners[t]&&function(n){for(var a=n[t],o=0;o180;){var s=n.locationPoint(e);if(s.x>=0&&s.y>=0&&s.x<=n.width&&s.y<=n.height)break;e.lng>n.center.lng?e.lng-=360:e.lng+=360}return e}Ri.prototype.down=function(t,e){this.mouseRotate.mousedown(t,e),this.mousePitch&&this.mousePitch.mousedown(t,e),r.disableDrag()},Ri.prototype.move=function(t,e){var r=this.map,n=this.mouseRotate.mousemoveWindow(t,e);if(n&&n.bearingDelta&&r.setBearing(r.getBearing()+n.bearingDelta),this.mousePitch){var i=this.mousePitch.mousemoveWindow(t,e);i&&i.pitchDelta&&r.setPitch(r.getPitch()+i.pitchDelta)}},Ri.prototype.off=function(){var t=this.element;r.removeEventListener(t,\"mousedown\",this.mousedown),r.removeEventListener(t,\"touchstart\",this.touchstart,{passive:!1}),r.removeEventListener(t,\"touchmove\",this.touchmove),r.removeEventListener(t,\"touchend\",this.touchend),r.removeEventListener(t,\"touchcancel\",this.reset),this.offTemp()},Ri.prototype.offTemp=function(){r.enableDrag(),r.removeEventListener(t.window,\"mousemove\",this.mousemove),r.removeEventListener(t.window,\"mouseup\",this.mouseup)},Ri.prototype.mousedown=function(e){this.down(t.extend({},e,{ctrlKey:!0,preventDefault:function(){return e.preventDefault()}}),r.mousePos(this.element,e)),r.addEventListener(t.window,\"mousemove\",this.mousemove),r.addEventListener(t.window,\"mouseup\",this.mouseup)},Ri.prototype.mousemove=function(t){this.move(t,r.mousePos(this.element,t))},Ri.prototype.mouseup=function(t){this.mouseRotate.mouseupWindow(t),this.mousePitch&&this.mousePitch.mouseupWindow(t),this.offTemp()},Ri.prototype.touchstart=function(t){1!==t.targetTouches.length?this.reset():(this._startPos=this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.down({type:\"mousedown\",button:0,ctrlKey:!0,preventDefault:function(){return t.preventDefault()}},this._startPos))},Ri.prototype.touchmove=function(t){1!==t.targetTouches.length?this.reset():(this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.move({preventDefault:function(){return t.preventDefault()}},this._lastPos))},Ri.prototype.touchend=function(t){0===t.targetTouches.length&&this._startPos&&this._lastPos&&this._startPos.dist(this._lastPos)e.getEast()||r.latitudee.getNorth())},n.prototype._setErrorState=function(){switch(this._watchState){case\"WAITING_ACTIVE\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\");break;case\"ACTIVE_LOCK\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\");break;case\"BACKGROUND\":this._watchState=\"BACKGROUND_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\")}},n.prototype._onSuccess=function(e){if(this._map){if(this._isOutOfMapMaxBounds(e))return this._setErrorState(),this.fire(new t.Event(\"outofmaxbounds\",e)),this._updateMarker(),void this._finish();if(this.options.trackUserLocation)switch(this._lastKnownPosition=e,this._watchState){case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"BACKGROUND\":case\"BACKGROUND_ERROR\":this._watchState=\"BACKGROUND\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\")}this.options.showUserLocation&&\"OFF\"!==this._watchState&&this._updateMarker(e),this.options.trackUserLocation&&\"ACTIVE_LOCK\"!==this._watchState||this._updateCamera(e),this.options.showUserLocation&&this._dotElement.classList.remove(\"mapboxgl-user-location-dot-stale\"),this.fire(new t.Event(\"geolocate\",e)),this._finish()}},n.prototype._updateCamera=function(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude),n=e.coords.accuracy,i=this._map.getBearing(),a=t.extend({bearing:i},this.options.fitBoundsOptions);this._map.fitBounds(r.toBounds(n),a,{geolocateSource:!0})},n.prototype._updateMarker=function(e){if(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude);this._accuracyCircleMarker.setLngLat(r).addTo(this._map),this._userLocationDotMarker.setLngLat(r).addTo(this._map),this._accuracy=e.coords.accuracy,this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()}else this._userLocationDotMarker.remove(),this._accuracyCircleMarker.remove()},n.prototype._updateCircleRadius=function(){var t=this._map._container.clientHeight/2,e=this._map.unproject([0,t]),r=this._map.unproject([1,t]),n=e.distanceTo(r),i=Math.ceil(2*this._accuracy/n);this._circleElement.style.width=i+\"px\",this._circleElement.style.height=i+\"px\"},n.prototype._onZoom=function(){this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()},n.prototype._onError=function(e){if(this._map){if(this.options.trackUserLocation)if(1===e.code){this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.disabled=!0;var r=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.title=r,this._geolocateButton.setAttribute(\"aria-label\",r),void 0!==this._geolocationWatchID&&this._clearWatch()}else{if(3===e.code&&qi)return;this._setErrorState()}\"OFF\"!==this._watchState&&this.options.showUserLocation&&this._dotElement.classList.add(\"mapboxgl-user-location-dot-stale\"),this.fire(new t.Event(\"error\",e)),this._finish()}},n.prototype._finish=function(){this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0},n.prototype._setupUI=function(e){var n=this;if(this._container.addEventListener(\"contextmenu\",(function(t){return t.preventDefault()})),this._geolocateButton=r.create(\"button\",\"mapboxgl-ctrl-geolocate\",this._container),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._geolocateButton).setAttribute(\"aria-hidden\",!0),this._geolocateButton.type=\"button\",!1===e){t.warnOnce(\"Geolocation support is not available so the GeolocateControl will be disabled.\");var i=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.disabled=!0,this._geolocateButton.title=i,this._geolocateButton.setAttribute(\"aria-label\",i)}else{var a=this._map._getUIString(\"GeolocateControl.FindMyLocation\");this._geolocateButton.title=a,this._geolocateButton.setAttribute(\"aria-label\",a)}this.options.trackUserLocation&&(this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this._watchState=\"OFF\"),this.options.showUserLocation&&(this._dotElement=r.create(\"div\",\"mapboxgl-user-location-dot\"),this._userLocationDotMarker=new Ui(this._dotElement),this._circleElement=r.create(\"div\",\"mapboxgl-user-location-accuracy-circle\"),this._accuracyCircleMarker=new Ui({element:this._circleElement,pitchAlignment:\"map\"}),this.options.trackUserLocation&&(this._watchState=\"OFF\"),this._map.on(\"zoom\",this._onZoom)),this._geolocateButton.addEventListener(\"click\",this.trigger.bind(this)),this._setup=!0,this.options.trackUserLocation&&this._map.on(\"movestart\",(function(e){var r=e.originalEvent&&\"resize\"===e.originalEvent.type;e.geolocateSource||\"ACTIVE_LOCK\"!==n._watchState||r||(n._watchState=\"BACKGROUND\",n._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\"),n._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),n.fire(new t.Event(\"trackuserlocationend\")))}))},n.prototype.trigger=function(){if(!this._setup)return t.warnOnce(\"Geolocate control triggered before added to a map\"),!1;if(this.options.trackUserLocation){switch(this._watchState){case\"OFF\":this._watchState=\"WAITING_ACTIVE\",this.fire(new t.Event(\"trackuserlocationstart\"));break;case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":case\"BACKGROUND_ERROR\":Hi--,qi=!1,this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this.fire(new t.Event(\"trackuserlocationend\"));break;case\"BACKGROUND\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._lastKnownPosition&&this._updateCamera(this._lastKnownPosition),this.fire(new t.Event(\"trackuserlocationstart\"))}switch(this._watchState){case\"WAITING_ACTIVE\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"ACTIVE_LOCK\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"ACTIVE_ERROR\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\");break;case\"BACKGROUND\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\");break;case\"BACKGROUND_ERROR\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background-error\")}if(\"OFF\"===this._watchState&&void 0!==this._geolocationWatchID)this._clearWatch();else if(void 0===this._geolocationWatchID){var e;this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"true\"),++Hi>1?(e={maximumAge:6e5,timeout:0},qi=!0):(e=this.options.positionOptions,qi=!1),this._geolocationWatchID=t.window.navigator.geolocation.watchPosition(this._onSuccess,this._onError,e)}}else t.window.navigator.geolocation.getCurrentPosition(this._onSuccess,this._onError,this.options.positionOptions),this._timeoutId=setTimeout(this._finish,1e4);return!0},n.prototype._clearWatch=function(){t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0,this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this.options.showUserLocation&&this._updateMarker(null)},n}(t.Evented),Zi={maxWidth:100,unit:\"metric\"},Yi=function(e){this.options=t.extend({},Zi,e),t.bindAll([\"_onMove\",\"setUnit\"],this)};function Wi(t,e,r){var n=r&&r.maxWidth||100,i=t._container.clientHeight/2,a=t.unproject([0,i]),o=t.unproject([n,i]),s=a.distanceTo(o);if(r&&\"imperial\"===r.unit){var l=3.2808*s;l>5280?Xi(e,n,l/5280,t._getUIString(\"ScaleControl.Miles\")):Xi(e,n,l,t._getUIString(\"ScaleControl.Feet\"))}else r&&\"nautical\"===r.unit?Xi(e,n,s/1852,t._getUIString(\"ScaleControl.NauticalMiles\")):s>=1e3?Xi(e,n,s/1e3,t._getUIString(\"ScaleControl.Kilometers\")):Xi(e,n,s,t._getUIString(\"ScaleControl.Meters\"))}function Xi(t,e,r,n){var i,a,o,s=(i=r,(a=Math.pow(10,(\"\"+Math.floor(i)).length-1))*((o=i/a)>=10?10:o>=5?5:o>=3?3:o>=2?2:o>=1?1:function(t){var e=Math.pow(10,Math.ceil(-Math.log(t)/Math.LN10));return Math.round(t*e)/e}(o))),l=s/r;t.style.width=e*l+\"px\",t.innerHTML=s+\" \"+n}Yi.prototype.getDefaultPosition=function(){return\"bottom-left\"},Yi.prototype._onMove=function(){Wi(this._map,this._container,this.options)},Yi.prototype.onAdd=function(t){return this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl 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this._map=e,this._container||(this._container=this._map.getContainer()),this._controlContainer=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),this._checkFullscreenSupport()?this._setupUI():(this._controlContainer.style.display=\"none\",t.warnOnce(\"This device does not support fullscreen mode.\")),this._controlContainer},Ji.prototype.onRemove=function(){r.remove(this._controlContainer),this._map=null,t.window.document.removeEventListener(this._fullscreenchange,this._changeIcon)},Ji.prototype._checkFullscreenSupport=function(){return!!(t.window.document.fullscreenEnabled||t.window.document.mozFullScreenEnabled||t.window.document.msFullscreenEnabled||t.window.document.webkitFullscreenEnabled)},Ji.prototype._setupUI=function(){var e=this._fullscreenButton=r.create(\"button\",\"mapboxgl-ctrl-fullscreen\",this._controlContainer);r.create(\"span\",\"mapboxgl-ctrl-icon\",e).setAttribute(\"aria-hidden\",!0),e.type=\"button\",this._updateTitle(),this._fullscreenButton.addEventListener(\"click\",this._onClickFullscreen),t.window.document.addEventListener(this._fullscreenchange,this._changeIcon)},Ji.prototype._updateTitle=function(){var t=this._getTitle();this._fullscreenButton.setAttribute(\"aria-label\",t),this._fullscreenButton.title=t},Ji.prototype._getTitle=function(){return this._map._getUIString(this._isFullscreen()?\"FullscreenControl.Exit\":\"FullscreenControl.Enter\")},Ji.prototype._isFullscreen=function(){return this._fullscreen},Ji.prototype._changeIcon=function(){(t.window.document.fullscreenElement||t.window.document.mozFullScreenElement||t.window.document.webkitFullscreenElement||t.window.document.msFullscreenElement)===this._container!==this._fullscreen&&(this._fullscreen=!this._fullscreen,this._fullscreenButton.classList.toggle(\"mapboxgl-ctrl-shrink\"),this._fullscreenButton.classList.toggle(\"mapboxgl-ctrl-fullscreen\"),this._updateTitle())},Ji.prototype._onClickFullscreen=function(){this._isFullscreen()?t.window.document.exitFullscreen?t.window.document.exitFullscreen():t.window.document.mozCancelFullScreen?t.window.document.mozCancelFullScreen():t.window.document.msExitFullscreen?t.window.document.msExitFullscreen():t.window.document.webkitCancelFullScreen&&t.window.document.webkitCancelFullScreen():this._container.requestFullscreen?this._container.requestFullscreen():this._container.mozRequestFullScreen?this._container.mozRequestFullScreen():this._container.msRequestFullscreen?this._container.msRequestFullscreen():this._container.webkitRequestFullscreen&&this._container.webkitRequestFullscreen()};var Ki={closeButton:!0,closeOnClick:!0,className:\"\",maxWidth:\"240px\"},$i=function(e){function n(r){e.call(this),this.options=t.extend(Object.create(Ki),r),t.bindAll([\"_update\",\"_onClose\",\"remove\",\"_onMouseMove\",\"_onMouseUp\",\"_onDrag\"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.addTo=function(e){return this._map&&this.remove(),this._map=e,this.options.closeOnClick&&this._map.on(\"click\",this._onClose),this.options.closeOnMove&&this._map.on(\"move\",this._onClose),this._map.on(\"remove\",this.remove),this._update(),this._trackPointer?(this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"mouseup\",this._onMouseUp),this._container&&this._container.classList.add(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"mapboxgl-track-pointer\")):this._map.on(\"move\",this._update),this.fire(new t.Event(\"open\")),this},n.prototype.isOpen=function(){return!!this._map},n.prototype.remove=function(){return this._content&&r.remove(this._content),this._container&&(r.remove(this._container),delete this._container),this._map&&(this._map.off(\"move\",this._update),this._map.off(\"move\",this._onClose),this._map.off(\"click\",this._onClose),this._map.off(\"remove\",this.remove),this._map.off(\"mousemove\",this._onMouseMove),this._map.off(\"mouseup\",this._onMouseUp),this._map.off(\"drag\",this._onDrag),delete this._map),this.fire(new t.Event(\"close\")),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._trackPointer=!1,this._update(),this._map&&(this._map.on(\"move\",this._update),this._map.off(\"mousemove\",this._onMouseMove),this._container&&this._container.classList.remove(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.remove(\"mapboxgl-track-pointer\")),this},n.prototype.trackPointer=function(){return this._trackPointer=!0,this._pos=null,this._update(),this._map&&(this._map.off(\"move\",this._update),this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"drag\",this._onDrag),this._container&&this._container.classList.add(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"mapboxgl-track-pointer\")),this},n.prototype.getElement=function(){return this._container},n.prototype.setText=function(e){return this.setDOMContent(t.window.document.createTextNode(e))},n.prototype.setHTML=function(e){var r,n=t.window.document.createDocumentFragment(),i=t.window.document.createElement(\"body\");for(i.innerHTML=e;r=i.firstChild;)n.appendChild(r);return this.setDOMContent(n)},n.prototype.getMaxWidth=function(){return this._container&&this._container.style.maxWidth},n.prototype.setMaxWidth=function(t){return this.options.maxWidth=t,this._update(),this},n.prototype.setDOMContent=function(t){return this._createContent(),this._content.appendChild(t),this._update(),this},n.prototype.addClassName=function(t){this._container&&this._container.classList.add(t)},n.prototype.removeClassName=function(t){this._container&&this._container.classList.remove(t)},n.prototype.toggleClassName=function(t){if(this._container)return this._container.classList.toggle(t)},n.prototype._createContent=function(){this._content&&r.remove(this._content),this._content=r.create(\"div\",\"mapboxgl-popup-content\",this._container),this.options.closeButton&&(this._closeButton=r.create(\"button\",\"mapboxgl-popup-close-button\",this._content),this._closeButton.type=\"button\",this._closeButton.setAttribute(\"aria-label\",\"Close popup\"),this._closeButton.innerHTML=\"×\",this._closeButton.addEventListener(\"click\",this._onClose))},n.prototype._onMouseUp=function(t){this._update(t.point)},n.prototype._onMouseMove=function(t){this._update(t.point)},n.prototype._onDrag=function(t){this._update(t.point)},n.prototype._update=function(t){var e=this,n=this._lngLat||this._trackPointer;if(this._map&&n&&this._content&&(this._container||(this._container=r.create(\"div\",\"mapboxgl-popup\",this._map.getContainer()),this._tip=r.create(\"div\",\"mapboxgl-popup-tip\",this._container),this._container.appendChild(this._content),this.options.className&&this.options.className.split(\" \").forEach((function(t){return e._container.classList.add(t)})),this._trackPointer&&this._container.classList.add(\"mapboxgl-popup-track-pointer\")),this.options.maxWidth&&this._container.style.maxWidth!==this.options.maxWidth&&(this._container.style.maxWidth=this.options.maxWidth),this._map.transform.renderWorldCopies&&!this._trackPointer&&(this._lngLat=Fi(this._lngLat,this._pos,this._map.transform)),!this._trackPointer||t)){var i=this._pos=this._trackPointer&&t?t:this._map.project(this._lngLat),a=this.options.anchor,o=Qi(this.options.offset);if(!a){var s,l=this._container.offsetWidth,u=this._container.offsetHeight;s=i.y+o.bottom.ythis._map.transform.height-u?[\"bottom\"]:[],i.xthis._map.transform.width-l/2&&s.push(\"right\"),a=0===s.length?\"bottom\":s.join(\"-\")}var c=i.add(o[a]).round();r.setTransform(this._container,Bi[a]+\" translate(\"+c.x+\"px,\"+c.y+\"px)\"),Ni(this._container,a,\"popup\")}},n.prototype._onClose=function(){this.remove()},n}(t.Evented);function Qi(e){if(e){if(\"number\"==typeof e){var r=Math.round(Math.sqrt(.5*Math.pow(e,2)));return{center:new t.Point(0,0),top:new t.Point(0,e),\"top-left\":new t.Point(r,r),\"top-right\":new t.Point(-r,r),bottom:new t.Point(0,-e),\"bottom-left\":new t.Point(r,-r),\"bottom-right\":new t.Point(-r,-r),left:new t.Point(e,0),right:new t.Point(-e,0)}}if(e instanceof t.Point||Array.isArray(e)){var n=t.Point.convert(e);return{center:n,top:n,\"top-left\":n,\"top-right\":n,bottom:n,\"bottom-left\":n,\"bottom-right\":n,left:n,right:n}}return{center:t.Point.convert(e.center||[0,0]),top:t.Point.convert(e.top||[0,0]),\"top-left\":t.Point.convert(e[\"top-left\"]||[0,0]),\"top-right\":t.Point.convert(e[\"top-right\"]||[0,0]),bottom:t.Point.convert(e.bottom||[0,0]),\"bottom-left\":t.Point.convert(e[\"bottom-left\"]||[0,0]),\"bottom-right\":t.Point.convert(e[\"bottom-right\"]||[0,0]),left:t.Point.convert(e.left||[0,0]),right:t.Point.convert(e.right||[0,0])}}return Qi(new t.Point(0,0))}var ta={version:t.version,supported:e,setRTLTextPlugin:t.setRTLTextPlugin,getRTLTextPluginStatus:t.getRTLTextPluginStatus,Map:Oi,NavigationControl:zi,GeolocateControl:Gi,AttributionControl:Ti,ScaleControl:Yi,FullscreenControl:Ji,Popup:$i,Marker:Ui,Style:Ye,LngLat:t.LngLat,LngLatBounds:t.LngLatBounds,Point:t.Point,MercatorCoordinate:t.MercatorCoordinate,Evented:t.Evented,config:t.config,prewarm:function(){jt().acquire(Rt)},clearPrewarmedResources:function(){var t=Bt;t&&(t.isPreloaded()&&1===t.numActive()?(t.release(Rt),Bt=null):console.warn(\"Could not clear WebWorkers since there are active Map instances that still reference it. 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{\\n\\t\\t\\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\\n\\t\\t\\tif (distToEnd < 0.) {\\n\\t\\t\\t\\tfloat radius = length(gl_FragCoord.xy - endCoord);\\n\\n\\t\\t\\t\\tif(radius > cutoff + .5) {\\n\\t\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\t\\treturn;\\n\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\\n\\t\\t\\t}\\n\\t\\t}\\n\\t}\\n\\n\\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashLength) * .5 + .25;\\n\\tfloat dash = texture2D(dashTexture, vec2(t, .5)).r;\\n\\n\\tgl_FragColor = fragColor;\\n\\tgl_FragColor.a *= alpha * opacity * 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this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),!1},formatYear:function(t){t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year();var e=Math.floor(t/400);return t%=400,t+=t<0?400:0,e+\".\"+Math.floor(t/20)+\".\"+t%20},forYear:function(t){if((t=t.split(\".\")).length<3)throw\"Invalid Mayan year\";for(var e=0,r=0;r19||r>0&&n<0)throw\"Invalid Mayan year\";e=20*e+n}return e},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),18},weekOfYear:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),0},daysInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),360},daysInMonth:function(t,e){return this._validate(t,e,this.minDay,n.local.invalidMonth),20},daysInWeek:function(){return 5},dayOfWeek:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate).day()},weekDay:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),!0},extraInfo:function(t,e,r){var 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n.baseCalendar,i(a.prototype,{name:\"UmmAlQura\",hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Umm al-Qura\",epochs:[\"BH\",\"AH\"],monthNames:[\"Al-Muharram\",\"Safar\",\"Rabi' al-awwal\",\"Rabi' Al-Thani\",\"Jumada Al-Awwal\",\"Jumada Al-Thani\",\"Rajab\",\"Sha'aban\",\"Ramadan\",\"Shawwal\",\"Dhu al-Qi'dah\",\"Dhu al-Hijjah\"],monthNamesShort:[\"Muh\",\"Saf\",\"Rab1\",\"Rab2\",\"Jum1\",\"Jum2\",\"Raj\",\"Sha'\",\"Ram\",\"Shaw\",\"DhuQ\",\"DhuH\"],dayNames:[\"Yawm al-Ahad\",\"Yawm al-Ithnain\",\"Yawm al-Thalāthā’\",\"Yawm al-Arba‘ā’\",\"Yawm al-Khamīs\",\"Yawm al-Jum‘a\",\"Yawm al-Sabt\"],dayNamesMin:[\"Ah\",\"Ith\",\"Th\",\"Ar\",\"Kh\",\"Ju\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:6,isRTL:!0}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return 355===this.daysInYear(e.year())},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){for(var e=0,r=1;r<=12;r++)e+=this.daysInMonth(t,r);return e},daysInMonth:function(t,e){for(var r=this._validate(t,e,this.minDay,n.local.invalidMonth).toJD()-24e5+.5,i=0,a=0;ar)return o[i]-o[i-1];i++}return 30},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate),a=12*(i.year()-1)+i.month()-15292;return i.day()+o[a-1]-1+24e5-.5},fromJD:function(t){for(var e=t-24e5+.5,r=0,n=0;ne);n++)r++;var i=r+15292,a=Math.floor((i-1)/12),s=a+1,l=i-12*a,u=e-o[r-1]+1;return this.newDate(s,l,u)},isValid:function(t,e,r){var i=n.baseCalendar.prototype.isValid.apply(this,arguments);return i&&(i=(t=null!=t.year?t.year:t)>=1276&&t<=1500),i},_validate:function(t,e,r,i){var a=n.baseCalendar.prototype._validate.apply(this,arguments);if(a.year<1276||a.year>1500)throw i.replace(/\\{0\\}/,this.local.name);return a}}),n.calendars.ummalqura=a;var 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n=r(56131);function i(){this.regionalOptions=[],this.regionalOptions[\"\"]={invalidCalendar:\"Calendar {0} not found\",invalidDate:\"Invalid {0} date\",invalidMonth:\"Invalid {0} month\",invalidYear:\"Invalid {0} year\",differentCalendars:\"Cannot mix {0} and {1} dates\"},this.local=this.regionalOptions[\"\"],this.calendars={},this._localCals={}}function a(t,e,r,n){if(this._calendar=t,this._year=e,this._month=r,this._day=n,0===this._calendar._validateLevel&&!this._calendar.isValid(this._year,this._month,this._day))throw(u.local.invalidDate||u.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name)}function o(t,e){return\"000000\".substring(0,e-(t=\"\"+t).length)+t}function s(){this.shortYearCutoff=\"+10\"}function l(t){this.local=this.regionalOptions[t]||this.regionalOptions[\"\"]}n(i.prototype,{instance:function(t,e){t=(t||\"gregorian\").toLowerCase(),e=e||\"\";var r=this._localCals[t+\"-\"+e];if(!r&&this.calendars[t]&&(r=new this.calendars[t](e),this._localCals[t+\"-\"+e]=r),!r)throw(this.local.invalidCalendar||this.regionalOptions[\"\"].invalidCalendar).replace(/\\{0\\}/,t);return r},newDate:function(t,e,r,n,i){return(n=(null!=t&&t.year?t.calendar():\"string\"==typeof n?this.instance(n,i):n)||this.instance()).newDate(t,e,r)},substituteDigits:function(t){return function(e){return(e+\"\").replace(/[0-9]/g,(function(e){return t[e]}))}},substituteChineseDigits:function(t,e){return function(r){for(var n=\"\",i=0;r>0;){var a=r%10;n=(0===a?\"\":t[a]+e[i])+n,i++,r=Math.floor(r/10)}return 0===n.indexOf(t[1]+e[1])&&(n=n.substr(1)),n||t[0]}}}),n(a.prototype,{newDate:function(t,e,r){return this._calendar.newDate(null==t?this:t,e,r)},year:function(t){return 0===arguments.length?this._year:this.set(t,\"y\")},month:function(t){return 0===arguments.length?this._month:this.set(t,\"m\")},day:function(t){return 0===arguments.length?this._day:this.set(t,\"d\")},date:function(t,e,r){if(!this._calendar.isValid(t,e,r))throw(u.local.invalidDate||u.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name);return this._year=t,this._month=e,this._day=r,this},leapYear:function(){return this._calendar.leapYear(this)},epoch:function(){return this._calendar.epoch(this)},formatYear:function(){return this._calendar.formatYear(this)},monthOfYear:function(){return this._calendar.monthOfYear(this)},weekOfYear:function(){return this._calendar.weekOfYear(this)},daysInYear:function(){return this._calendar.daysInYear(this)},dayOfYear:function(){return this._calendar.dayOfYear(this)},daysInMonth:function(){return this._calendar.daysInMonth(this)},dayOfWeek:function(){return this._calendar.dayOfWeek(this)},weekDay:function(){return this._calendar.weekDay(this)},extraInfo:function(){return this._calendar.extraInfo(this)},add:function(t,e){return this._calendar.add(this,t,e)},set:function(t,e){return this._calendar.set(this,t,e)},compareTo:function(t){if(this._calendar.name!==t._calendar.name)throw(u.local.differentCalendars||u.regionalOptions[\"\"].differentCalendars).replace(/\\{0\\}/,this._calendar.local.name).replace(/\\{1\\}/,t._calendar.local.name);var e=this._year!==t._year?this._year-t._year:this._month!==t._month?this.monthOfYear()-t.monthOfYear():this._day-t._day;return 0===e?0:e<0?-1:1},calendar:function(){return this._calendar},toJD:function(){return this._calendar.toJD(this)},fromJD:function(t){return this._calendar.fromJD(t)},toJSDate:function(){return this._calendar.toJSDate(this)},fromJSDate:function(t){return this._calendar.fromJSDate(t)},toString:function(){return(this.year()<0?\"-\":\"\")+o(Math.abs(this.year()),4)+\"-\"+o(this.month(),2)+\"-\"+o(this.day(),2)}}),n(s.prototype,{_validateLevel:0,newDate:function(t,e,r){return null==t?this.today():(t.year&&(this._validate(t,e,r,u.local.invalidDate||u.regionalOptions[\"\"].invalidDate),r=t.day(),e=t.month(),t=t.year()),new a(this,t,e,r))},today:function(){return this.fromJSDate(new Date)},epoch:function(t){return this._validate(t,this.minMonth,this.minDay,u.local.invalidYear||u.regionalOptions[\"\"].invalidYear).year()<0?this.local.epochs[0]:this.local.epochs[1]},formatYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,u.local.invalidYear||u.regionalOptions[\"\"].invalidYear);return(e.year()<0?\"-\":\"\")+o(Math.abs(e.year()),4)},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,u.local.invalidYear||u.regionalOptions[\"\"].invalidYear),12},monthOfYear:function(t,e){var r=this._validate(t,e,this.minDay,u.local.invalidMonth||u.regionalOptions[\"\"].invalidMonth);return(r.month()+this.monthsInYear(r)-this.firstMonth)%this.monthsInYear(r)+this.minMonth},fromMonthOfYear:function(t,e){var 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n=r(28231);t.exports={moduleType:\"component\",name:\"shapes\",layoutAttributes:r(43144),supplyLayoutDefaults:r(74367),supplyDrawNewShapeDefaults:r(85522),includeBasePlot:r(20706)(\"shapes\"),calcAutorange:r(44959),draw:n.draw,drawOne:n.drawOne}},41235:function(t){\"use strict\";function e(t,e){return e?e.d2l(t):t}function r(t,e){return e?e.l2d(t):t}function n(t){return t.x0shift||0}function i(t){return t.x1shift||0}function a(t){return t.y0shift||0}function o(t){return t.y1shift||0}function s(t,r){return e(t.x1,r)+i(t)-e(t.x0,r)-n(t)}function l(t,r,n){return e(t.y1,n)+o(t)-e(t.y0,n)-a(t)}t.exports={x0:function(t){return t.x0},x1:function(t){return t.x1},y0:function(t){return t.y0},y1:function(t){return t.y1},slope:function(t,e,r){return\"line\"!==t.type?void 0:l(t,0,r)/s(t,e)},dx:s,dy:l,width:function(t,e){return Math.abs(s(t,e))},height:function(t,e,r){return Math.abs(l(t,0,r))},length:function(t,e,r){return\"line\"!==t.type?void 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r=e._dims,n=r.inputAreaLength-2*h.railInset,i=s.ensureSingle(t,\"rect\",h.railRectClass);i.attr({width:n,height:h.railWidth,rx:h.railRadius,ry:h.railRadius,\"shape-rendering\":\"crispEdges\"}).call(a.stroke,e.bordercolor).call(a.fill,e.bgcolor).style(\"stroke-width\",e.borderwidth+\"px\"),o.setTranslate(i,h.railInset,.5*(r.inputAreaWidth-h.railWidth)+r.currentValueTotalHeight)}t.exports=function(t){var e=t._context.staticPlot,r=t._fullLayout,a=function(t,e){for(var r=t[h.name],n=[],i=0;i0?[0]:[]);function l(e){e._commandObserver&&(e._commandObserver.remove(),delete e._commandObserver),i.autoMargin(t,g(e))}if(s.enter().append(\"g\").classed(h.containerClassName,!0).style(\"cursor\",e?null:\"ew-resize\"),s.exit().each((function(){n.select(this).selectAll(\"g.\"+h.groupClassName).each(l)})).remove(),0!==a.length){var c=s.selectAll(\"g.\"+h.groupClassName).data(a,y);c.enter().append(\"g\").classed(h.groupClassName,!0),c.exit().each(l).remove();for(var u=0;u0||T<0){var 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r?[r[0]-t.xaxis._offset,r[1]-t.yaxis._offset]:[null,null]},t.xaxis={_id:\"x\",c2p:function(e){return t.project(e)[0]}},t.yaxis={_id:\"y\",c2p:function(e){return t.project(e)[1]}},t.mockAxis={type:\"linear\",showexponent:\"all\",exponentformat:\"B\"},m.setConvert(t.mockAxis,r)},S.saveViewInitial=function(t){var e,r=t.center||{},n=t.projection,i=n.rotation||{};this.viewInitial={fitbounds:t.fitbounds,\"projection.scale\":n.scale},e=t._isScoped?{\"center.lon\":r.lon,\"center.lat\":r.lat}:t._isClipped?{\"projection.rotation.lon\":i.lon,\"projection.rotation.lat\":i.lat}:{\"center.lon\":r.lon,\"center.lat\":r.lat,\"projection.rotation.lon\":i.lon},c.extendFlat(this.viewInitial,e)},S.render=function(t){this._hasMarkerAngles&&t?this.plot(this._geoCalcData,this._fullLayout,[],!0):this._render()},S._render=function(){var t,e=this.projection,r=e.getPath();function n(t){var r=e(t.lonlat);return r?u(r[0],r[1]):null}function i(t){return e.isLonLatOverEdges(t.lonlat)?\"none\":null}for(t in 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r=t[i[e]];r.visible?(this.enabled[e]=r.showspikes,this.colors[e]=n(r.spikecolor),this.drawSides[e]=r.spikesides,this.lineWidth[e]=r.spikethickness):(this.enabled[e]=!1,this.drawSides[e]=!1)}},t.exports=function(t){var e=new a;return e.merge(t),e}},32412:function(t,e,r){\"use strict\";t.exports=function(t){for(var e=t.axesOptions,r=t.glplot.axesPixels,s=t.fullSceneLayout,l=[[],[],[]],c=0;c<3;++c){var u=s[a[c]];if(u._length=(r[c].hi-r[c].lo)*r[c].pixelsPerDataUnit/t.dataScale[c],Math.abs(u._length)===1/0||isNaN(u._length))l[c]=[];else{u._input_range=u.range.slice(),u.range[0]=r[c].lo/t.dataScale[c],u.range[1]=r[c].hi/t.dataScale[c],u._m=1/(t.dataScale[c]*r[c].pixelsPerDataUnit),u.range[0]===u.range[1]&&(u.range[0]-=1,u.range[1]+=1);var h=u.tickmode;if(\"auto\"===u.tickmode){u.tickmode=\"linear\";var f=u.nticks||i.constrain(u._length/40,4,9);n.autoTicks(u,Math.abs(u.range[1]-u.range[0])/f)}for(var p=n.calcTicks(u,{msUTC:!0}),d=0;d/g,\" 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a=r._fullLayout._invScaleX,o=r._fullLayout._invScaleY,s=i.width*a,l=i.height*o;n.setAttributeNS(null,\"viewBox\",\"0 0 \"+s+\" \"+l),n.setAttributeNS(null,\"width\",s),n.setAttributeNS(null,\"height\",l),_(e),e.glplot.axes.update(e.axesOptions);for(var c=Object.keys(e.traces),u=null,f=e.glplot.selection,m=0;m\")):\"isosurface\"===t.type||\"volume\"===t.type?(k.valueLabel=p.hoverLabelText(e._mockAxis,e._mockAxis.d2l(f.traceCoordinate[3]),t.valuehoverformat),E.push(\"value: \"+k.valueLabel),f.textLabel&&E.push(f.textLabel),x=E.join(\"
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R=[1,1,1];for(o=0;o<3;++o){var F=x[c=(l=u[M[o]]).type];R[o]=Math.pow(F.acc,1/F.count)/p[o]}g=\"data\"===O||Math.max.apply(null,R)/Math.min.apply(null,R)<=4?R:[1,1,1]}u.aspectratio.x=h.aspectratio.x=g[0],u.aspectratio.y=h.aspectratio.y=g[1],u.aspectratio.z=h.aspectratio.z=g[2],n.glplot.setAspectratio(u.aspectratio),n.viewInitial.aspectratio||(n.viewInitial.aspectratio={x:u.aspectratio.x,y:u.aspectratio.y,z:u.aspectratio.z}),n.viewInitial.aspectmode||(n.viewInitial.aspectmode=u.aspectmode);var B=u.domain||null,N=e._size||null;if(B&&N){var j=n.container.style;j.position=\"absolute\",j.left=N.l+B.x[0]*N.w+\"px\",j.top=N.t+(1-B.y[1])*N.h+\"px\",j.width=N.w*(B.x[1]-B.x[0])+\"px\",j.height=N.h*(B.y[1]-B.y[0])+\"px\"}n.glplot.redraw()}},k.destroy=function(){var t=this;t.glplot&&(t.camera.mouseListener.enabled=!1,t.container.removeEventListener(\"wheel\",t.camera.wheelListener),t.camera=null,t.glplot.dispose(),t.container.parentNode.removeChild(t.container),t.glplot=null)},k.getCamera=function(){var t,e=this;return e.camera.view.recalcMatrix(e.camera.view.lastT()),{up:{x:(t=e.camera).up[0],y:t.up[1],z:t.up[2]},center:{x:t.center[0],y:t.center[1],z:t.center[2]},eye:{x:t.eye[0],y:t.eye[1],z:t.eye[2]},projection:{type:!0===t._ortho?\"orthographic\":\"perspective\"}}},k.setViewport=function(t){var e,r=this,n=t.camera;r.camera.lookAt.apply(this,[[(e=n).eye.x,e.eye.y,e.eye.z],[e.center.x,e.center.y,e.center.z],[e.up.x,e.up.y,e.up.z]]),r.glplot.setAspectratio(t.aspectratio),\"orthographic\"===n.projection.type!==r.camera._ortho&&(r.glplot.redraw(),r.glplot.clearRGBA(),r.glplot.dispose(),r.initializeGLPlot())},k.isCameraChanged=function(t){var e=this.getCamera(),r=h.nestedProperty(t,this.id+\".camera\").get();function n(t,e,r,n){var i=[\"up\",\"center\",\"eye\"],a=[\"x\",\"y\",\"z\"];return e[i[r]]&&t[i[r]][a[n]]===e[i[r]][a[n]]}var i=!1;if(void 0===r)i=!0;else{for(var a=0;a<3;a++)for(var o=0;o<3;o++)if(!n(e,r,a,o)){i=!0;break}(!r.projection||e.projection&&e.projection.type!==r.projection.type)&&(i=!0)}return i},k.isAspectChanged=function(t){var e=this.glplot.getAspectratio(),r=h.nestedProperty(t,this.id+\".aspectratio\").get();return void 0===r||r.x!==e.x||r.y!==e.y||r.z!==e.z},k.saveLayout=function(t){var e,r,n,i,a,o,s=this,l=s.fullLayout,c=s.isCameraChanged(t),f=s.isAspectChanged(t),p=c||f;if(p){var d={};c&&(e=s.getCamera(),n=(r=h.nestedProperty(t,s.id+\".camera\")).get(),d[s.id+\".camera\"]=n),f&&(i=s.glplot.getAspectratio(),o=(a=h.nestedProperty(t,s.id+\".aspectratio\")).get(),d[s.id+\".aspectratio\"]=o),u.call(\"_storeDirectGUIEdit\",t,l._preGUI,d),c&&(r.set(e),h.nestedProperty(l,s.id+\".camera\").set(e)),f&&(a.set(i),h.nestedProperty(l,s.id+\".aspectratio\").set(i),s.glplot.redraw())}return p},k.updateFx=function(t,e){var r=this,n=r.camera;if(n)if(\"orbit\"===t)n.mode=\"orbit\",n.keyBindingMode=\"rotate\";else if(\"turntable\"===t){n.up=[0,0,1],n.mode=\"turntable\",n.keyBindingMode=\"rotate\";var i=r.graphDiv,a=i._fullLayout,o=r.fullSceneLayout.camera,s=o.up.x,l=o.up.y,c=o.up.z;if(c/Math.sqrt(s*s+l*l+c*c)<.999){var f=r.id+\".camera.up\",p={x:0,y:0,z:1},d={};d[f]=p;var m=i.layout;u.call(\"_storeDirectGUIEdit\",m,a._preGUI,d),o.up=p,h.nestedProperty(m,f).set(p)}}else n.keyBindingMode=t;r.fullSceneLayout.hovermode=e},k.toImage=function(t){var e=this;t||(t=\"png\"),e.staticMode&&e.container.appendChild(n),e.glplot.redraw();var r=e.glplot.gl,i=r.drawingBufferWidth,a=r.drawingBufferHeight;r.bindFramebuffer(r.FRAMEBUFFER,null);var o=new Uint8Array(i*a*4);r.readPixels(0,0,i,a,r.RGBA,r.UNSIGNED_BYTE,o),function(t,e,r){for(var n=0,i=r-1;n0)for(var s=255/o,l=0;l<3;++l)t[a+l]=Math.min(s*t[a+l],255)}}(o,i,a);var s=document.createElement(\"canvas\");s.width=i,s.height=a;var l,c=s.getContext(\"2d\",{willReadFrequently:!0}),u=c.createImageData(i,a);switch(u.data.set(o),c.putImageData(u,0,0),t){case\"jpeg\":l=s.toDataURL(\"image/jpeg\");break;case\"webp\":l=s.toDataURL(\"image/webp\");break;default:l=s.toDataURL(\"image/png\")}return e.staticMode&&e.container.removeChild(n),l},k.setConvert=function(){for(var t=0;t<3;t++){var e=this.fullSceneLayout[M[t]];p.setConvert(e,this.fullLayout),e.setScale=h.noop}},k.make4thDimension=function(){var t=this,e=t.graphDiv._fullLayout;t._mockAxis={type:\"linear\",showexponent:\"all\",exponentformat:\"B\"},p.setConvert(t._mockAxis,e)},t.exports=T},88239:function(t){\"use strict\";t.exports=function(t,e,r,n){n=n||t.length;for(var i=new Array(n),a=0;aOpenStreetMap contributors',tiles:[\"https://tile.openstreetmap.org/{z}/{x}/{y}.png\"],tileSize:256}},layers:[{id:\"plotly-osm-tiles\",type:\"raster\",source:\"plotly-osm-tiles\",minzoom:0,maxzoom:22}],glyphs:\"https://fonts.openmaptiles.org/{fontstack}/{range}.pbf\"},\"white-bg\":{id:\"white-bg\",version:8,sources:{},layers:[{id:\"white-bg\",type:\"background\",paint:{\"background-color\":\"#FFFFFF\"},minzoom:0,maxzoom:22}],glyphs:\"https://fonts.openmaptiles.org/{fontstack}/{range}.pbf\"},\"carto-positron\":a,\"carto-darkmatter\":o,\"carto-voyager\":s,\"carto-positron-nolabels\":\"https://basemaps.cartocdn.com/gl/positron-nolabels-gl-style/style.json\",\"carto-darkmatter-nolabels\":\"https://basemaps.cartocdn.com/gl/dark-matter-nolabels-gl-style/style.json\",\"carto-voyager-nolabels\":\"https://basemaps.cartocdn.com/gl/voyager-nolabels-gl-style/style.json\"},c=n(l);t.exports={styleValueDflt:\"basic\",stylesMap:l,styleValuesMap:c,traceLayerPrefix:\"plotly-trace-layer-\",layoutLayerPrefix:\"plotly-layout-layer-\",missingStyleErrorMsg:[\"No valid maplibre style found, please set `map.style` to one of:\",c.join(\", \"),\"or use a tile service.\"].join(\"\\n\"),mapOnErrorMsg:\"Map error.\"}},4657:function(t,e,r){\"use strict\";var n=r(34809);t.exports=function(t,e){var r=t.split(\" \"),i=r[0],a=r[1],o=n.isArrayOrTypedArray(e)?n.mean(e):e,s=.5+o/100,l=1.5+o/100,c=[\"\",\"\"],u=[0,0];switch(i){case\"top\":c[0]=\"top\",u[1]=-l;break;case\"bottom\":c[0]=\"bottom\",u[1]=l}switch(a){case\"left\":c[1]=\"right\",u[0]=-s;break;case\"right\":c[1]=\"left\",u[0]=s}return{anchor:c[0]&&c[1]?c.join(\"-\"):c[0]?c[0]:c[1]?c[1]:\"center\",offset:u}}},34091:function(t,e,r){\"use strict\";var n=r(34809),i=n.strTranslate,a=n.strScale,o=r(4173).fX,s=r(62972),l=r(45568),c=r(62203),u=r(30635),h=r(38793),f=\"map\";e.name=f,e.attr=\"subplot\",e.idRoot=f,e.idRegex=e.attrRegex=n.counterRegex(f),e.attributes={subplot:{valType:\"subplotid\",dflt:\"map\",editType:\"calc\"}},e.layoutAttributes=r(8257),e.supplyLayoutDefaults=r(97446),e.plot=function(t){for(var e=t._fullLayout,r=t.calcdata,i=e._subplots[f],a=0;ax/2){var _=m.split(\"|\").join(\"
\");y.text(_).attr(\"data-unformatted\",_).call(u.convertToTspans,t),v=c.bBox(y.node())}y.attr(\"transform\",i(-3,8-v.height)),g.insert(\"rect\",\".static-attribution\").attr({x:-v.width-6,y:-v.height-3,width:v.width+6,height:v.height+3,fill:\"rgba(255, 255, 255, 0.75)\"});var b=1;v.width+6>x&&(b=x/(v.width+6));var w=[n.l+n.w*p.x[1],n.t+n.h*(1-p.y[0])];g.attr(\"transform\",i(w[0],w[1])+a(b))}},e.updateFx=function(t){for(var e=t._fullLayout,r=e._subplots[f],n=0;n0){for(var r=0;r0}function u(t){var e={},r={};switch(t.type){case\"circle\":n.extendFlat(r,{\"circle-radius\":t.circle.radius,\"circle-color\":t.color,\"circle-opacity\":t.opacity});break;case\"line\":n.extendFlat(r,{\"line-width\":t.line.width,\"line-color\":t.color,\"line-opacity\":t.opacity,\"line-dasharray\":t.line.dash});break;case\"fill\":n.extendFlat(r,{\"fill-color\":t.color,\"fill-outline-color\":t.fill.outlinecolor,\"fill-opacity\":t.opacity});break;case\"symbol\":var i=t.symbol,o=a(i.textposition,i.iconsize);n.extendFlat(e,{\"icon-image\":i.icon+\"-15\",\"icon-size\":i.iconsize/10,\"text-field\":i.text,\"text-size\":i.textfont.size,\"text-anchor\":o.anchor,\"text-offset\":o.offset,\"symbol-placement\":i.placement}),n.extendFlat(r,{\"icon-color\":t.color,\"text-color\":i.textfont.color,\"text-opacity\":t.opacity});break;case\"raster\":n.extendFlat(r,{\"raster-fade-duration\":0,\"raster-opacity\":t.opacity})}return{layout:e,paint:r}}l.update=function(t){this.visible?this.needsNewImage(t)?this.updateImage(t):this.needsNewSource(t)?(this.removeLayer(),this.updateSource(t),this.updateLayer(t)):this.needsNewLayer(t)?this.updateLayer(t):this.updateStyle(t):(this.updateSource(t),this.updateLayer(t)),this.visible=c(t)},l.needsNewImage=function(t){return this.subplot.map.getSource(this.idSource)&&\"image\"===this.sourceType&&\"image\"===t.sourcetype&&(this.source!==t.source||JSON.stringify(this.coordinates)!==JSON.stringify(t.coordinates))},l.needsNewSource=function(t){return this.sourceType!==t.sourcetype||JSON.stringify(this.source)!==JSON.stringify(t.source)||this.layerType!==t.type},l.needsNewLayer=function(t){return this.layerType!==t.type||this.below!==this.subplot.belowLookup[\"layout-\"+this.index]},l.lookupBelow=function(){return this.subplot.belowLookup[\"layout-\"+this.index]},l.updateImage=function(t){this.subplot.map.getSource(this.idSource).updateImage({url:t.source,coordinates:t.coordinates});var e=this.findFollowingMapLayerId(this.lookupBelow());null!==e&&this.subplot.map.moveLayer(this.idLayer,e)},l.updateSource=function(t){var e=this.subplot.map;if(e.getSource(this.idSource)&&e.removeSource(this.idSource),this.sourceType=t.sourcetype,this.source=t.source,c(t)){var r=function(t){var e,r=t.sourcetype,n=t.source,a={type:r};return\"geojson\"===r?e=\"data\":\"vector\"===r?e=\"string\"==typeof n?\"url\":\"tiles\":\"raster\"===r?(e=\"tiles\",a.tileSize=256):\"image\"===r&&(e=\"url\",a.coordinates=t.coordinates),a[e]=n,t.sourceattribution&&(a.attribution=i(t.sourceattribution)),a}(t);e.addSource(this.idSource,r)}},l.findFollowingMapLayerId=function(t){if(\"traces\"===t)for(var e=this.subplot.getMapLayers(),r=0;r1)for(r=0;r-1&&g(e.originalEvent,n,[r.xaxis],[r.yaxis],r.id,t),i.indexOf(\"event\")>-1&&c.click(n,e.originalEvent)}}},_.updateFx=function(t){var e=this,r=e.map,n=e.gd;if(!e.isStatic){var a,o=t.dragmode;a=function(t,r){r.isRect?(t.range={})[e.id]=[c([r.xmin,r.ymin]),c([r.xmax,r.ymax])]:(t.lassoPoints={})[e.id]=r.map(c)};var s=e.dragOptions;e.dragOptions=i.extendDeep(s||{},{dragmode:t.dragmode,element:e.div,gd:n,plotinfo:{id:e.id,domain:t[e.id].domain,xaxis:e.xaxis,yaxis:e.yaxis,fillRangeItems:a},xaxes:[e.xaxis],yaxes:[e.yaxis],subplot:e.id}),r.off(\"click\",e.onClickInPanHandler),f(o)||h(o)?(r.dragPan.disable(),r.on(\"zoomstart\",e.clearOutline),e.dragOptions.prepFn=function(t,r,n){p(t,r,n,e.dragOptions,o)},l.init(e.dragOptions)):(r.dragPan.enable(),r.off(\"zoomstart\",e.clearOutline),e.div.onmousedown=null,e.div.ontouchstart=null,e.div.removeEventListener(\"touchstart\",e.div._ontouchstart),e.onClickInPanHandler=e.onClickInPanFn(e.dragOptions),r.on(\"click\",e.onClickInPanHandler))}function c(t){var r=e.map.unproject(t);return[r.lng,r.lat]}},_.updateFramework=function(t){var e=t[this.id].domain,r=t._size,n=this.div.style;n.width=r.w*(e.x[1]-e.x[0])+\"px\",n.height=r.h*(e.y[1]-e.y[0])+\"px\",n.left=r.l+e.x[0]*r.w+\"px\",n.top=r.t+(1-e.y[1])*r.h+\"px\",this.xaxis._offset=r.l+e.x[0]*r.w,this.xaxis._length=r.w*(e.x[1]-e.x[0]),this.yaxis._offset=r.t+(1-e.y[1])*r.h,this.yaxis._length=r.h*(e.y[1]-e.y[0])},_.updateLayers=function(t){var e,r=t[this.id].layers,n=this.layerList;if(r.length!==n.length){for(e=0;eOpenStreetMap contributors',o=['© Carto',a].join(\" \"),s=['Map tiles by Stamen Design','under CC BY 3.0',\"|\",'Data by OpenStreetMap contributors','under ODbL'].join(\" \"),l={\"open-street-map\":{id:\"osm\",version:8,sources:{\"plotly-osm-tiles\":{type:\"raster\",attribution:a,tiles:[\"https://a.tile.openstreetmap.org/{z}/{x}/{y}.png\",\"https://b.tile.openstreetmap.org/{z}/{x}/{y}.png\"],tileSize:256}},layers:[{id:\"plotly-osm-tiles\",type:\"raster\",source:\"plotly-osm-tiles\",minzoom:0,maxzoom:22}],glyphs:\"https://fonts.openmaptiles.org/{fontstack}/{range}.pbf\"},\"white-bg\":{id:\"white-bg\",version:8,sources:{},layers:[{id:\"white-bg\",type:\"background\",paint:{\"background-color\":\"#FFFFFF\"},minzoom:0,maxzoom:22}],glyphs:\"https://fonts.openmaptiles.org/{fontstack}/{range}.pbf\"},\"carto-positron\":{id:\"carto-positron\",version:8,sources:{\"plotly-carto-positron\":{type:\"raster\",attribution:o,tiles:[\"https://cartodb-basemaps-c.global.ssl.fastly.net/light_all/{z}/{x}/{y}.png\"],tileSize:256}},layers:[{id:\"plotly-carto-positron\",type:\"raster\",source:\"plotly-carto-positron\",minzoom:0,maxzoom:22}],glyphs:\"https://fonts.openmaptiles.org/{fontstack}/{range}.pbf\"},\"carto-darkmatter\":{id:\"carto-darkmatter\",version:8,sources:{\"plotly-carto-darkmatter\":{type:\"raster\",attribution:o,tiles:[\"https://cartodb-basemaps-c.global.ssl.fastly.net/dark_all/{z}/{x}/{y}.png\"],tileSize:256}},layers:[{id:\"plotly-carto-darkmatter\",type:\"raster\",source:\"plotly-carto-darkmatter\",minzoom:0,maxzoom:22}],glyphs:\"https://fonts.openmaptiles.org/{fontstack}/{range}.pbf\"},\"stamen-terrain\":{id:\"stamen-terrain\",version:8,sources:{\"plotly-stamen-terrain\":{type:\"raster\",attribution:s,tiles:[\"https://tiles.stadiamaps.com/tiles/stamen_terrain/{z}/{x}/{y}.png?api_key=\"],tileSize:256}},layers:[{id:\"plotly-stamen-terrain\",type:\"raster\",source:\"plotly-stamen-terrain\",minzoom:0,maxzoom:22}],glyphs:\"https://fonts.openmaptiles.org/{fontstack}/{range}.pbf\"},\"stamen-toner\":{id:\"stamen-toner\",version:8,sources:{\"plotly-stamen-toner\":{type:\"raster\",attribution:s,tiles:[\"https://tiles.stadiamaps.com/tiles/stamen_toner/{z}/{x}/{y}.png?api_key=\"],tileSize:256}},layers:[{id:\"plotly-stamen-toner\",type:\"raster\",source:\"plotly-stamen-toner\",minzoom:0,maxzoom:22}],glyphs:\"https://fonts.openmaptiles.org/{fontstack}/{range}.pbf\"},\"stamen-watercolor\":{id:\"stamen-watercolor\",version:8,sources:{\"plotly-stamen-watercolor\":{type:\"raster\",attribution:['Map tiles by Stamen Design','under CC BY 3.0',\"|\",'Data by OpenStreetMap contributors','under CC BY SA'].join(\" \"),tiles:[\"https://tiles.stadiamaps.com/tiles/stamen_watercolor/{z}/{x}/{y}.jpg?api_key=\"],tileSize:256}},layers:[{id:\"plotly-stamen-watercolor\",type:\"raster\",source:\"plotly-stamen-watercolor\",minzoom:0,maxzoom:22}],glyphs:\"https://fonts.openmaptiles.org/{fontstack}/{range}.pbf\"}},c=n(l);t.exports={requiredVersion:i,styleUrlPrefix:\"mapbox://styles/mapbox/\",styleUrlSuffix:\"v9\",styleValuesMapbox:[\"basic\",\"streets\",\"outdoors\",\"light\",\"dark\",\"satellite\",\"satellite-streets\"],styleValueDflt:\"basic\",stylesNonMapbox:l,styleValuesNonMapbox:c,traceLayerPrefix:\"plotly-trace-layer-\",layoutLayerPrefix:\"plotly-layout-layer-\",wrongVersionErrorMsg:[\"Your custom plotly.js bundle is not using the correct mapbox-gl version\",\"Please install @plotly/mapbox-gl@\"+i+\".\"].join(\"\\n\"),noAccessTokenErrorMsg:[\"Missing Mapbox access token.\",\"Mapbox trace type 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e,r,s=this,c=s.isSmith,u=s.gd,h=s.layers,f=t._zoomlayer,p=S.MINZOOM,d=S.OFFEDGE,m=s.radius,x=s.innerRadius,T=s.cx,k=s.cy,A=s.cxx,M=s.cyy,C=s.sectorInRad,L=s.vangles,I=s.radialAxis,P=E.clampTiny,z=E.findXYatLength,O=E.findEnclosingVertexAngles,D=S.cornerHalfWidth,R=S.cornerLen/2,F=g.makeDragger(h,\"path\",\"maindrag\",!1===t.dragmode?\"none\":\"crosshair\");n.select(F).attr(\"d\",s.pathSubplot()).attr(\"transform\",l(T,k)),F.onmousemove=function(t){v.hover(u,t,s.id),u._fullLayout._lasthover=F,u._fullLayout._hoversubplot=s.id},F.onmouseout=function(t){u._dragging||y.unhover(u,t)};var B,N,j,U,V,q,H,G,Z,W={element:F,gd:u,subplot:s.id,plotinfo:{id:s.id,xaxis:s.xaxis,yaxis:s.yaxis},xaxes:[s.xaxis],yaxes:[s.yaxis]};function Y(t,e){return Math.sqrt(t*t+e*e)}function X(t,e){return Y(t-A,e-M)}function $(t,e){return Math.atan2(M-e,t-A)}function J(t,e){return[t*Math.cos(e),t*Math.sin(-e)]}function K(t,e){if(0===t)return s.pathSector(2*D);var 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y=E.findPolygonOffset(m,C[0],C[1],L);B+=A+y[0],N+=M+y[1]}switch(l){case\"zoom\":W.clickFn=st,c||(W.moveFn=L?it:rt,W.doneFn=at,function(){j=null,U=null,V=s.pathSubplot(),q=!1;var t=u._fullLayout[s.id];H=i(t.bgcolor).getLuminance(),(G=g.makeZoombox(f,H,T,k,V)).attr(\"fill-rule\",\"evenodd\"),Z=g.makeCorners(f,T,k),w(u)}());break;case\"select\":case\"lasso\":_(t,n,a,W,l)}},y.init(W)},N.updateRadialDrag=function(t,e,r){var i=this,c=i.gd,u=i.layers,h=i.radius,f=i.innerRadius,p=i.cx,d=i.cy,m=i.radialAxis,v=S.radialDragBoxSize,x=v/2;if(m.visible){var _,b,T,M=R(i.radialAxisAngle),E=m._rl,C=E[0],L=E[1],I=E[r],P=.75*(E[1]-E[0])/(1-i.getHole(e))/h;r?(_=p+(h+x)*Math.cos(M),b=d-(h+x)*Math.sin(M),T=\"radialdrag\"):(_=p+(f-x)*Math.cos(M),b=d-(f-x)*Math.sin(M),T=\"radialdrag-inner\");var z,O,D,B=g.makeRectDragger(u,T,\"crosshair\",-x,-x,v,v),N={element:B,gd:c};!1===t.dragmode&&(N.dragmode=!1),V(n.select(B),m.visible&&f0==(r?D>C:Dn?function(t){return t<=0}:function(t){return t>=0};t.c2g=function(r){var n=t.c2l(r)-e;return(s(n)?n:0)+o},t.g2c=function(r){return t.l2c(r+e-o)},t.g2p=function(t){return t*a},t.c2p=function(e){return t.g2p(t.c2g(e))}}}(t,e);break;case\"angularaxis\":!function(t,e){var r=t.type;if(\"linear\"===r){var i=t.d2c,s=t.c2d;t.d2c=function(t,e){return function(t,e){return\"degrees\"===e?a(t):t}(i(t),e)},t.c2d=function(t,e){return s(function(t,e){return\"degrees\"===e?o(t):t}(t,e))}}t.makeCalcdata=function(e,r){var n,i,a=e[r],o=e._length,s=function(r){return t.d2c(r,e.thetaunit)};if(a)for(n=new Array(o),i=0;i0?1:0}function r(t){var e=t[0],r=t[1];if(!isFinite(e)||!isFinite(r))return[1,0];var n=(e+1)*(e+1)+r*r;return[(e*e+r*r-1)/n,2*r/n]}function n(t,e){var r=e[0],n=e[1];return[r*t.radius+t.cx,-n*t.radius+t.cy]}function i(t,e){return e*t.radius}t.exports={smith:r,reactanceArc:function(t,e,a,o){var s=n(t,r([a,e])),l=s[0],c=s[1],u=n(t,r([o,e])),h=u[0],f=u[1];if(0===e)return[\"M\"+l+\",\"+c,\"L\"+h+\",\"+f].join(\" \");var p=i(t,1/Math.abs(e));return[\"M\"+l+\",\"+c,\"A\"+p+\",\"+p+\" 0 0,\"+(e<0?1:0)+\" \"+h+\",\"+f].join(\" \")},resistanceArc:function(t,a,o,s){var l=i(t,1/(a+1)),c=n(t,r([a,o])),u=c[0],h=c[1],f=n(t,r([a,s])),p=f[0],d=f[1];if(e(o)!==e(s)){var m=n(t,r([a,0]));return[\"M\"+u+\",\"+h,\"A\"+l+\",\"+l+\" 0 0,\"+(00){for(var n=[],i=0;i=u&&(f.min=0,d.min=0,g.min=0,t.aaxis&&delete t.aaxis.min,t.baxis&&delete t.baxis.min,t.caxis&&delete t.caxis.min)}function m(t,e,r,n){var i=f[e._name];function o(r,n){return a.coerce(t,e,i,r,n)}o(\"uirevision\",n.uirevision),e.type=\"linear\";var p=o(\"color\"),d=p!==i.color.dflt?p:r.font.color,m=e._name.charAt(0).toUpperCase(),g=\"Component 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n=r(45568),i=r(65657),a=r(33626),o=r(34809),s=o.strTranslate,l=o._,c=r(78766),u=r(62203),h=r(19091),f=r(93049).extendFlat,p=r(44122),d=r(29714),m=r(14751),g=r(32141),y=r(70414),v=y.freeMode,x=y.rectMode,_=r(17240),b=r(44844).prepSelect,w=r(44844).selectOnClick,T=r(44844).clearOutline,k=r(44844).clearSelectionsCache,A=r(54826);function M(t,e){this.id=t.id,this.graphDiv=t.graphDiv,this.init(e),this.makeFramework(e),this.updateFx(e),this.aTickLayout=null,this.bTickLayout=null,this.cTickLayout=null}t.exports=M;var S=M.prototype;S.init=function(t){this.container=t._ternarylayer,this.defs=t._defs,this.layoutId=t._uid,this.traceHash={},this.layers={}},S.plot=function(t,e){var r=this,n=e[r.id],i=e._size;r._hasClipOnAxisFalse=!1;for(var a=0;aE*_?i=(a=_)*E:a=(i=x)/E,o=y*i/x,l=v*a/_,r=e.l+e.w*m-i/2,n=e.t+e.h*(1-g)-a/2,p.x0=r,p.y0=n,p.w=i,p.h=a,p.sum=b,p.xaxis={type:\"linear\",range:[w+2*k-b,b-w-2*T],domain:[m-o/2,m+o/2],_id:\"x\"},h(p.xaxis,p.graphDiv._fullLayout),p.xaxis.setScale(),p.xaxis.isPtWithinRange=function(t){return t.a>=p.aaxis.range[0]&&t.a<=p.aaxis.range[1]&&t.b>=p.baxis.range[1]&&t.b<=p.baxis.range[0]&&t.c>=p.caxis.range[1]&&t.c<=p.caxis.range[0]},p.yaxis={type:\"linear\",range:[w,b-T-k],domain:[g-l/2,g+l/2],_id:\"y\"},h(p.yaxis,p.graphDiv._fullLayout),p.yaxis.setScale(),p.yaxis.isPtWithinRange=function(){return!0};var A=p.yaxis.domain[0],M=p.aaxis=f({},t.aaxis,{range:[w,b-T-k],side:\"left\",tickangle:(+t.aaxis.tickangle||0)-30,domain:[A,A+l*E],anchor:\"free\",position:0,_id:\"y\",_length:i});h(M,p.graphDiv._fullLayout),M.setScale();var S=p.baxis=f({},t.baxis,{range:[b-w-k,T],side:\"bottom\",domain:p.xaxis.domain,anchor:\"free\",position:0,_id:\"x\",_length:i});h(S,p.graphDiv._fullLayout),S.setScale();var C=p.caxis=f({},t.caxis,{range:[b-w-T,k],side:\"right\",tickangle:(+t.caxis.tickangle||0)+30,domain:[A,A+l*E],anchor:\"free\",position:0,_id:\"y\",_length:i});h(C,p.graphDiv._fullLayout),C.setScale();var L=\"M\"+r+\",\"+(n+a)+\"h\"+i+\"l-\"+i/2+\",-\"+a+\"Z\";p.clipDef.select(\"path\").attr(\"d\",L),p.layers.plotbg.select(\"path\").attr(\"d\",L);var I=\"M0,\"+a+\"h\"+i+\"l-\"+i/2+\",-\"+a+\"Z\";p.clipDefRelative.select(\"path\").attr(\"d\",I);var P=s(r,n);p.plotContainer.selectAll(\".scatterlayer,.maplayer\").attr(\"transform\",P),p.clipDefRelative.select(\"path\").attr(\"transform\",null);var z=s(r-S._offset,n+a);p.layers.baxis.attr(\"transform\",z),p.layers.bgrid.attr(\"transform\",z);var O=s(r+i/2,n)+\"rotate(30)\"+s(0,-M._offset);p.layers.aaxis.attr(\"transform\",O),p.layers.agrid.attr(\"transform\",O);var D=s(r+i/2,n)+\"rotate(-30)\"+s(0,-C._offset);p.layers.caxis.attr(\"transform\",D),p.layers.cgrid.attr(\"transform\",D),p.drawAxes(!0),p.layers.aline.select(\"path\").attr(\"d\",M.showline?\"M\"+r+\",\"+(n+a)+\"l\"+i/2+\",-\"+a:\"M0,0\").call(c.stroke,M.linecolor||\"#000\").style(\"stroke-width\",(M.linewidth||0)+\"px\"),p.layers.bline.select(\"path\").attr(\"d\",S.showline?\"M\"+r+\",\"+(n+a)+\"h\"+i:\"M0,0\").call(c.stroke,S.linecolor||\"#000\").style(\"stroke-width\",(S.linewidth||0)+\"px\"),p.layers.cline.select(\"path\").attr(\"d\",C.showline?\"M\"+(r+i/2)+\",\"+n+\"l\"+i/2+\",\"+a:\"M0,0\").call(c.stroke,C.linecolor||\"#000\").style(\"stroke-width\",(C.linewidth||0)+\"px\"),p.graphDiv._context.staticPlot||p.initInteractions(),u.setClipUrl(p.layers.frontplot,p._hasClipOnAxisFalse?null:p.clipId,p.graphDiv)},S.drawAxes=function(t){var e=this,r=e.graphDiv,n=e.id.substr(7)+\"title\",i=e.layers,a=e.aaxis,o=e.baxis,s=e.caxis;if(e.drawAx(a),e.drawAx(o),e.drawAx(s),t){var c=Math.max(a.showticklabels?a.tickfont.size/2:0,(s.showticklabels?.75*s.tickfont.size:0)+(\"outside\"===s.ticks?.87*s.ticklen:0)),u=(o.showticklabels?o.tickfont.size:0)+(\"outside\"===o.ticks?o.ticklen:0)+3;i[\"a-title\"]=_.draw(r,\"a\"+n,{propContainer:a,propName:e.id+\".aaxis.title\",placeholder:l(r,\"Click to enter Component A title\"),attributes:{x:e.x0+e.w/2,y:e.y0-a.title.font.size/3-c,\"text-anchor\":\"middle\"}}),i[\"b-title\"]=_.draw(r,\"b\"+n,{propContainer:o,propName:e.id+\".baxis.title\",placeholder:l(r,\"Click to enter Component B title\"),attributes:{x:e.x0-u,y:e.y0+e.h+.83*o.title.font.size+u,\"text-anchor\":\"middle\"}}),i[\"c-title\"]=_.draw(r,\"c\"+n,{propContainer:s,propName:e.id+\".caxis.title\",placeholder:l(r,\"Click to enter Component C title\"),attributes:{x:e.x0+e.w+u,y:e.y0+e.h+.83*s.title.font.size+u,\"text-anchor\":\"middle\"}})}},S.drawAx=function(t){var e,r=this,n=r.graphDiv,i=t._name,a=i.charAt(0),s=t._id,l=r.layers[i],c=a+\"tickLayout\",u=(e=t).ticks+String(e.ticklen)+String(e.showticklabels);r[c]!==u&&(l.selectAll(\".\"+s+\"tick\").remove(),r[c]=u),t.setScale();var h=d.calcTicks(t),f=d.clipEnds(t,h),p=d.makeTransTickFn(t),m=d.getTickSigns(t)[2],g=o.deg2rad(30),y=m*(t.linewidth||1)/2,v=m*t.ticklen,x=r.w,_=r.h,b=\"b\"===a?\"M0,\"+y+\"l\"+Math.sin(g)*v+\",\"+Math.cos(g)*v:\"M\"+y+\",0l\"+Math.cos(g)*v+\",\"+-Math.sin(g)*v,w={a:\"M0,0l\"+_+\",-\"+x/2,b:\"M0,0l-\"+x/2+\",-\"+_,c:\"M0,0l-\"+_+\",\"+x/2}[a];d.drawTicks(n,t,{vals:\"inside\"===t.ticks?f:h,layer:l,path:b,transFn:p,crisp:!1}),d.drawGrid(n,t,{vals:f,layer:r.layers[a+\"grid\"],path:w,transFn:p,crisp:!1}),d.drawLabels(n,t,{vals:h,layer:l,transFn:p,labelFns:d.makeLabelFns(t,0,30)})};var C=A.MINZOOM/2+.87,L=\"m-0.87,.5h\"+C+\"v3h-\"+(C+5.2)+\"l\"+(C/2+2.6)+\",-\"+(.87*C+4.5)+\"l2.6,1.5l-\"+C/2+\",\"+.87*C+\"Z\",I=\"m0.87,.5h-\"+C+\"v3h\"+(C+5.2)+\"l-\"+(C/2+2.6)+\",-\"+(.87*C+4.5)+\"l-2.6,1.5l\"+C/2+\",\"+.87*C+\"Z\",P=\"m0,1l\"+C/2+\",\"+.87*C+\"l2.6,-1.5l-\"+(C/2+2.6)+\",-\"+(.87*C+4.5)+\"l-\"+(C/2+2.6)+\",\"+(.87*C+4.5)+\"l2.6,1.5l\"+C/2+\",-\"+.87*C+\"Z\",z=!0;function O(t){n.select(t).selectAll(\".zoombox,.js-zoombox-backdrop,.js-zoombox-menu,.zoombox-corners\").remove()}S.clearOutline=function(){k(this.dragOptions),T(this.dragOptions.gd)},S.initInteractions=function(){var t,e,r,n,h,f,p,d,y,_,T,k,M=this,S=M.layers.plotbg.select(\"path\").node(),C=M.graphDiv,D=C._fullLayout._zoomlayer;function R(t){var e={};return e[M.id+\".aaxis.min\"]=t.a,e[M.id+\".baxis.min\"]=t.b,e[M.id+\".caxis.min\"]=t.c,e}function F(t,e){var r=C._fullLayout.clickmode;O(C),2===t&&(C.emit(\"plotly_doubleclick\",null),a.call(\"_guiRelayout\",C,R({a:0,b:0,c:0}))),r.indexOf(\"select\")>-1&&1===t&&w(e,C,[M.xaxis],[M.yaxis],M.id,M.dragOptions),r.indexOf(\"event\")>-1&&g.click(C,e,M.id)}function B(t,e){return 1-e/M.h}function N(t,e){return 1-(t+(M.h-e)/Math.sqrt(3))/M.w}function j(t,e){return(t-(M.h-e)/Math.sqrt(3))/M.w}function U(i,a){var o=r+i*t,s=n+a*e,l=Math.max(0,Math.min(1,B(0,n),B(0,s))),c=Math.max(0,Math.min(1,N(r,n),N(o,s))),u=Math.max(0,Math.min(1,j(r,n),j(o,s))),m=(l/2+u)*M.w,g=(1-l/2-c)*M.w,v=(m+g)/2,x=g-m,b=(1-l)*M.h,w=b-x/E;x.2?\"rgba(0,0,0,0.4)\":\"rgba(255,255,255,0.3)\").duration(200),k.transition().style(\"opacity\",1).duration(200),_=!0),C.emit(\"plotly_relayouting\",R(p))}function V(){O(C),p!==h&&(a.call(\"_guiRelayout\",C,R(p)),z&&C.data&&C._context.showTips&&(o.notifier(l(C,\"Double-click to zoom back out\"),\"long\"),z=!1))}function q(t,e){var r=t/M.xaxis._m,n=e/M.yaxis._m,i=[(p={a:h.a-n,b:h.b+(r+n)/2,c:h.c-(r-n)/2}).a,p.b,p.c].sort(o.sorterAsc),a=i.indexOf(p.a),l=i.indexOf(p.b),c=i.indexOf(p.c);i[0]<0&&(i[1]+i[0]/2<0?(i[2]+=i[0]+i[1],i[0]=i[1]=0):(i[2]+=i[0]/2,i[1]+=i[0]/2,i[0]=0),p={a:i[a],b:i[l],c:i[c]},e=(h.a-p.a)*M.yaxis._m,t=(h.c-p.c-h.b+p.b)*M.xaxis._m);var f=s(M.x0+t,M.y0+e);M.plotContainer.selectAll(\".scatterlayer,.maplayer\").attr(\"transform\",f);var d=s(-t,-e);M.clipDefRelative.select(\"path\").attr(\"transform\",d),M.aaxis.range=[p.a,M.sum-p.b-p.c],M.baxis.range=[M.sum-p.a-p.c,p.b],M.caxis.range=[M.sum-p.a-p.b,p.c],M.drawAxes(!1),M._hasClipOnAxisFalse&&M.plotContainer.select(\".scatterlayer\").selectAll(\".trace\").call(u.hideOutsideRangePoints,M),C.emit(\"plotly_relayouting\",R(p))}function H(){a.call(\"_guiRelayout\",C,R(p))}this.dragOptions={element:S,gd:C,plotinfo:{id:M.id,domain:C._fullLayout[M.id].domain,xaxis:M.xaxis,yaxis:M.yaxis},subplot:M.id,prepFn:function(a,l,u){M.dragOptions.xaxes=[M.xaxis],M.dragOptions.yaxes=[M.yaxis],t=C._fullLayout._invScaleX,e=C._fullLayout._invScaleY;var m=M.dragOptions.dragmode=C._fullLayout.dragmode;v(m)?M.dragOptions.minDrag=1:M.dragOptions.minDrag=void 0,\"zoom\"===m?(M.dragOptions.moveFn=U,M.dragOptions.clickFn=F,M.dragOptions.doneFn=V,function(t,e,a){var l=S.getBoundingClientRect();r=e-l.left,n=a-l.top,C._fullLayout._calcInverseTransform(C);var u=C._fullLayout._invTransform,m=o.apply3DTransform(u)(r,n);r=m[0],n=m[1],h={a:M.aaxis.range[0],b:M.baxis.range[1],c:M.caxis.range[1]},p=h,f=M.aaxis.range[1]-h.a,d=i(M.graphDiv._fullLayout[M.id].bgcolor).getLuminance(),y=\"M0,\"+M.h+\"L\"+M.w/2+\", 0L\"+M.w+\",\"+M.h+\"Z\",_=!1,T=D.append(\"path\").attr(\"class\",\"zoombox\").attr(\"transform\",s(M.x0,M.y0)).style({fill:d>.2?\"rgba(0,0,0,0)\":\"rgba(255,255,255,0)\",\"stroke-width\":0}).attr(\"d\",y),k=D.append(\"path\").attr(\"class\",\"zoombox-corners\").attr(\"transform\",s(M.x0,M.y0)).style({fill:c.background,stroke:c.defaultLine,\"stroke-width\":1,opacity:0}).attr(\"d\",\"M0,0Z\"),M.clearOutline(C)}(0,l,u)):\"pan\"===m?(M.dragOptions.moveFn=q,M.dragOptions.clickFn=F,M.dragOptions.doneFn=H,h={a:M.aaxis.range[0],b:M.baxis.range[1],c:M.caxis.range[1]},p=h,M.clearOutline(C)):(x(m)||v(m))&&b(a,l,u,M.dragOptions,m)}},S.onmousemove=function(t){g.hover(C,t,M.id),C._fullLayout._lasthover=S,C._fullLayout._hoversubplot=M.id},S.onmouseout=function(t){C._dragging||m.unhover(C,t)},m.init(this.dragOptions)}},33626:function(t,e,r){\"use strict\";var n=r(48636),i=r(4969),a=r(36539),o=r(56174),s=r(95425).addStyleRule,l=r(93049),c=r(9829),u=r(6704),h=l.extendFlat,f=l.extendDeepAll;function p(t){var i=t.name,a=t.categories,o=t.meta;if(e.modules[i])n.log(\"Type \"+i+\" already registered\");else{e.subplotsRegistry[t.basePlotModule.name]||function(t){var r=t.name;if(e.subplotsRegistry[r])n.log(\"Plot type \"+r+\" already registered.\");else for(var i in y(t),e.subplotsRegistry[r]=t,e.componentsRegistry)_(i,t.name)}(t.basePlotModule);for(var l={},c=0;c-1&&(h[p[r]].title={text:\"\"});for(r=0;r\")?\"\":e.html(t).text()}));return e.remove(),r}(w)).replace(/&(?!\\w+;|\\#[0-9]+;| \\#x[0-9A-F]+;)/g,\"&\")).replace(u,\"'\"),i.isIE()&&(w=(w=(w=w.replace(/\"/gi,\"'\")).replace(/(\\('#)([^']*)('\\))/gi,'(\"#$2\")')).replace(/(\\\\')/gi,'\"')),w}},35374:function(t,e,r){\"use strict\";var n=r(34809);t.exports=function(t,e){for(var r=0;rh+c||!n(u))}for(var p=0;p=0)return t}else if(\"string\"==typeof t&&\"%\"===(t=t.trim()).slice(-1)&&n(t.slice(0,-1))&&(t=+t.slice(0,-1))>=0)return t+\"%\"}function d(t,e,r,n,a,o){var s=!(!1===(o=o||{}).moduleHasSelected),l=!(!1===o.moduleHasUnselected),c=!(!1===o.moduleHasConstrain),u=!(!1===o.moduleHasCliponaxis),h=!(!1===o.moduleHasTextangle),p=!(!1===o.moduleHasInsideanchor),d=!!o.hasPathbar,m=Array.isArray(a)||\"auto\"===a,g=m||\"inside\"===a,y=m||\"outside\"===a;if(g||y){var v=f(n,\"textfont\",r.font),x=i.extendFlat({},v),_=!(t.textfont&&t.textfont.color);if(_&&delete x.color,f(n,\"insidetextfont\",x),d){var b=i.extendFlat({},v);_&&delete b.color,f(n,\"pathbar.textfont\",b)}y&&f(n,\"outsidetextfont\",v),s&&n(\"selected.textfont.color\"),l&&n(\"unselected.textfont.color\"),c&&n(\"constraintext\"),u&&n(\"cliponaxis\"),h&&n(\"textangle\"),n(\"texttemplate\")}g&&p&&n(\"insidetextanchor\")}t.exports={supplyDefaults:function(t,e,r,n){function u(r,n){return 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n.inbox(r-e,i-e,w+(i-e)/(i-r)-1)}(B)+function(t){return I(A(t),M(t),w)}(B))/2,t[h+\"Spike\"]=D.c2p(B.p,!0),o(B,y,t),t.hovertemplate=y.hovertemplate,t}}function h(t,e){var r=e.mcc||t.marker.color,n=e.mlcc||t.marker.line.color,i=s(t,e);return a.opacity(r)?r:a.opacity(n)&&i?n:void 0}t.exports={hoverPoints:function(t,e,r,n,a){var o=u(t,e,r,n,a);if(o){var s=o.cd,l=s[0].trace,c=s[o.index];return o.color=h(l,c),i.getComponentMethod(\"errorbars\",\"hoverInfo\")(c,l,o),[o]}},hoverOnBars:u,getTraceColor:h}},58218:function(t,e,r){\"use 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o=!!a.isHorizontal,s=!!a.constrained,l=a.angle||0,c=a.anchor,u=\"end\"===c,h=\"start\"===c,f=((a.leftToRight||0)+1)/2,p=1-f,d=a.hasB,m=a.r,g=a.overhead,y=i.width,v=i.height,x=Math.abs(e-t),_=Math.abs(n-r),w=x>2*b&&_>2*b?b:0;x-=2*w,_-=2*w;var T=C(l);\"auto\"!==l||y<=x&&v<=_||!(y>x||v>_)||(y>_||v>x)&&yb){var E=function(t,e,r,n,i,a,o,s,l){var c,u,h,f,p=Math.max(0,Math.abs(e-t)-2*b),d=Math.max(0,Math.abs(n-r)-2*b),m=a-b,g=o?m-Math.sqrt(m*m-(m-o)*(m-o)):m,y=l?2*m:s?m-o:2*g,v=l?2*m:s?2*g:m-o;return 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n=r(87163),i=r(80712).axisHoverFormat,a=r(3208).rb,o=r(42450),s=r(9829),l=r(93049).extendFlat,c=r(13582).overrideAll,u=t.exports=c(l({x:{valType:\"data_array\"},y:{valType:\"data_array\"},z:{valType:\"data_array\"},value:{valType:\"data_array\"},isomin:{valType:\"number\"},isomax:{valType:\"number\"},surface:{show:{valType:\"boolean\",dflt:!0},count:{valType:\"integer\",dflt:2,min:1},fill:{valType:\"number\",min:0,max:1,dflt:1},pattern:{valType:\"flaglist\",flags:[\"A\",\"B\",\"C\",\"D\",\"E\"],extras:[\"all\",\"odd\",\"even\"],dflt:\"all\"}},spaceframe:{show:{valType:\"boolean\",dflt:!1},fill:{valType:\"number\",min:0,max:1,dflt:.15}},slices:{x:{show:{valType:\"boolean\",dflt:!1},locations:{valType:\"data_array\",dflt:[]},fill:{valType:\"number\",min:0,max:1,dflt:1}},y:{show:{valType:\"boolean\",dflt:!1},locations:{valType:\"data_array\",dflt:[]},fill:{valType:\"number\",min:0,max:1,dflt:1}},z:{show:{valType:\"boolean\",dflt:!1},locations:{valType:\"data_array\",dflt:[]},fill:{valType:\"number\",min:0,max:1,dflt:1}}},caps:{x:{show:{valType:\"boolean\",dflt:!0},fill:{valType:\"number\",min:0,max:1,dflt:1}},y:{show:{valType:\"boolean\",dflt:!0},fill:{valType:\"number\",min:0,max:1,dflt:1}},z:{show:{valType:\"boolean\",dflt:!0},fill:{valType:\"number\",min:0,max:1,dflt:1}}},text:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0},hovertemplate:a(),xhoverformat:i(\"x\"),yhoverformat:i(\"y\"),zhoverformat:i(\"z\"),valuehoverformat:i(\"value\",1),showlegend:l({},s.showlegend,{dflt:!1})},n(\"\",{colorAttr:\"`value`\",showScaleDflt:!0,editTypeOverride:\"calc\"}),{opacity:o.opacity,lightposition:o.lightposition,lighting:o.lighting,flatshading:o.flatshading,contour:o.contour,hoverinfo:l({},s.hoverinfo)}),\"calc\",\"nested\");u.flatshading.dflt=!0,u.lighting.facenormalsepsilon.dflt=0,u.x.editType=u.y.editType=u.z.editType=u.value.editType=\"calc+clearAxisTypes\",u.transforms=void 0},58988:function(t,e,r){\"use strict\";var n=r(28379),i=r(36402).processGrid,a=r(36402).filter;t.exports=function(t,e){e._len=Math.min(e.x.length,e.y.length,e.z.length,e.value.length),e._x=a(e.x,e._len),e._y=a(e.y,e._len),e._z=a(e.z,e._len),e._value=a(e.value,e._len);var r=i(e);e._gridFill=r.fill,e._Xs=r.Xs,e._Ys=r.Ys,e._Zs=r.Zs,e._len=r.len;for(var o=1/0,s=-1/0,l=0;l0;r--){var n=Math.min(e[r],e[r-1]),i=Math.max(e[r],e[r-1]);if(i>n&&n-1}function R(t,e){return null===t?e:t}function F(e,r,n){L();var i,a,o,l=[r],c=[n];if(s>=1)l=[r],c=[n];else if(s>0){var u=function(t,e){var r=t[0],n=t[1],i=t[2],a=function(t,e,r){for(var n=[],i=0;i-1?n[p]:C(d,m,y);f[p]=x>-1?x:P(d,m,y,R(e,v))}i=f[0],a=f[1],o=f[2],t._meshI.push(i),t._meshJ.push(a),t._meshK.push(o),++g}}function B(t,e,r,n){var i=t[3];in&&(i=n);for(var a=(t[3]-i)/(t[3]-e[3]+1e-9),o=[],s=0;s<4;s++)o[s]=(1-a)*t[s]+a*e[s];return o}function N(t,e,r){return t>=e&&t<=r}function j(t){var e=.001*(E-S);return t>=S-e&&t<=E+e}function U(e){for(var 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n=r(87163),i=r(80712).axisHoverFormat,a=r(3208).rb,o=r(16131),s=r(9829),l=r(93049).extendFlat;t.exports=l({x:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},y:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},z:{valType:\"data_array\",editType:\"calc+clearAxisTypes\"},i:{valType:\"data_array\",editType:\"calc\"},j:{valType:\"data_array\",editType:\"calc\"},k:{valType:\"data_array\",editType:\"calc\"},text:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"calc\"},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"calc\"},hovertemplate:a({editType:\"calc\"}),xhoverformat:i(\"x\"),yhoverformat:i(\"y\"),zhoverformat:i(\"z\"),delaunayaxis:{valType:\"enumerated\",values:[\"x\",\"y\",\"z\"],dflt:\"z\",editType:\"calc\"},alphahull:{valType:\"number\",dflt:-1,editType:\"calc\"},intensity:{valType:\"data_array\",editType:\"calc\"},intensitymode:{valType:\"enumerated\",values:[\"vertex\",\"cell\"],dflt:\"vertex\",editType:\"calc\"},color:{valType:\"color\",editType:\"calc\"},vertexcolor:{valType:\"data_array\",editType:\"calc\"},facecolor:{valType:\"data_array\",editType:\"calc\"},transforms:void 0},n(\"\",{colorAttr:\"`intensity`\",showScaleDflt:!0,editTypeOverride:\"calc\"}),{opacity:o.opacity,flatshading:{valType:\"boolean\",dflt:!1,editType:\"calc\"},contour:{show:l({},o.contours.x.show,{}),color:o.contours.x.color,width:o.contours.x.width,editType:\"calc\"},lightposition:{x:l({},o.lightposition.x,{dflt:1e5}),y:l({},o.lightposition.y,{dflt:1e5}),z:l({},o.lightposition.z,{dflt:0}),editType:\"calc\"},lighting:l({vertexnormalsepsilon:{valType:\"number\",min:0,max:1,dflt:1e-12,editType:\"calc\"},facenormalsepsilon:{valType:\"number\",min:0,max:1,dflt:1e-6,editType:\"calc\"},editType:\"calc\"},o.lighting),hoverinfo:l({},s.hoverinfo,{editType:\"calc\"}),showlegend:l({},s.showlegend,{dflt:!1})})},44878:function(t,e,r){\"use strict\";var n=r(28379);t.exports=function(t,e){e.intensity&&n(t,e,{vals:e.intensity,containerStr:\"\",cLetter:\"c\"})}},82836:function(t,e,r){\"use strict\";var n=r(99098).gl_mesh3d,i=r(99098).delaunay_triangulate,a=r(99098).alpha_shape,o=r(99098).convex_hull,s=r(46998).parseColorScale,l=r(34809).isArrayOrTypedArray,c=r(55010),u=r(88856).extractOpts,h=r(88239);function f(t,e,r){this.scene=t,this.uid=r,this.mesh=e,this.name=\"\",this.color=\"#fff\",this.data=null,this.showContour=!1}var p=f.prototype;function d(t){for(var e=[],r=t.length,n=0;n=e-.5)return!1;return!0}p.handlePick=function(t){if(t.object===this.mesh){var e=t.index=t.data.index;t.data._cellCenter?t.traceCoordinate=t.data.dataCoordinate:t.traceCoordinate=[this.data.x[e],this.data.y[e],this.data.z[e]];var r=this.data.hovertext||this.data.text;return l(r)&&void 0!==r[e]?t.textLabel=r[e]:r&&(t.textLabel=r),!0}},p.update=function(t){var e=this.scene,r=e.fullSceneLayout;this.data=t;var n,l=t.x.length,f=h(m(r.xaxis,t.x,e.dataScale[0],t.xcalendar),m(r.yaxis,t.y,e.dataScale[1],t.ycalendar),m(r.zaxis,t.z,e.dataScale[2],t.zcalendar));if(t.i&&t.j&&t.k){if(t.i.length!==t.j.length||t.j.length!==t.k.length||!y(t.i,l)||!y(t.j,l)||!y(t.k,l))return;n=h(g(t.i),g(t.j),g(t.k))}else n=0===t.alphahull?o(f):t.alphahull>0?a(t.alphahull,f):function(t,e){for(var r=[\"x\",\"y\",\"z\"].indexOf(t),n=[],a=e.length,o=0;oy):g=A>w,y=A;var M=c(w,T,k,A);M.pos=b,M.yc=(w+A)/2,M.i=_,M.dir=g?\"increasing\":\"decreasing\",M.x=M.pos,M.y=[k,T],v&&(M.orig_p=r[_]),d&&(M.tx=e.text[_]),m&&(M.htx=e.hovertext[_]),x.push(M)}else x.push({pos:b,empty:!0})}return e._extremes[l._id]=a.findExtremes(l,n.concat(f,h),{padded:!0}),x.length&&(x[0].t={labels:{open:i(t,\"open:\")+\" \",high:i(t,\"high:\")+\" \",low:i(t,\"low:\")+\" \",close:i(t,\"close:\")+\" \"}}),x}t.exports={calc:function(t,e){var r=a.getFromId(t,e.xaxis),i=a.getFromId(t,e.yaxis),s=function(t,e,r){var i=r._minDiff;if(!i){var 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o=e.yaxis,s=e.xaxis,l=!!s.rangebreaks;i.makeTraceGroups(a,r,\"trace ohlc\").each((function(t){var e=n.select(this),r=t[0],a=r.t;if(!0!==r.trace.visible||a.empty)e.remove();else{var c=a.tickLen,u=e.selectAll(\"path\").data(i.identity);u.enter().append(\"path\"),u.exit().remove(),u.attr(\"d\",(function(t){if(t.empty)return\"M0,0Z\";var e=s.c2p(t.pos-c,!0),r=s.c2p(t.pos+c,!0),n=l?(e+r)/2:s.c2p(t.pos,!0);return\"M\"+e+\",\"+o.c2p(t.o,!0)+\"H\"+n+\"M\"+n+\",\"+o.c2p(t.h,!0)+\"V\"+o.c2p(t.l,!0)+\"M\"+r+\",\"+o.c2p(t.c,!0)+\"H\"+n}))}}))}},49343:function(t){\"use strict\";t.exports=function(t,e){var r,n=t.cd,i=t.xaxis,a=t.yaxis,o=[],s=n[0].t.bPos||0;if(!1===e)for(r=0;r=t.length)return!1;if(void 0!==e[t[r]])return!1;e[t[r]]=!0}return!0}(r))for(e=0;e0||u(s);c&&(o=\"array\");var h=r(\"categoryorder\",o);\"array\"===h?(r(\"categoryarray\"),r(\"ticktext\")):(delete t.categoryarray,delete t.ticktext),c||\"array\"!==h||(e.categoryorder=\"trace\")}}t.exports=function(t,e,r,u){function f(r,i){return 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strict\";t.exports={attributes:r(11660),supplyDefaults:r(62651),calc:r(95564),plot:r(37822),colorbar:{container:\"line\",min:\"cmin\",max:\"cmax\"},moduleType:\"trace\",name:\"parcats\",basePlotModule:r(83260),categories:[\"noOpacity\"],meta:{}}},27219:function(t,e,r){\"use strict\";var n=r(45568),i=r(88640).Dj,a=r(31420),o=r(32141),s=r(34809),l=s.strTranslate,c=r(62203),u=r(65657),h=r(30635);function f(t,e,r,i){var a=e._context.staticPlot,o=t.map(F.bind(0,e,r)),u=i.selectAll(\"g.parcatslayer\").data([null]);u.enter().append(\"g\").attr(\"class\",\"parcatslayer\").style(\"pointer-events\",a?\"none\":\"all\");var f=u.selectAll(\"g.trace.parcats\").data(o,p),v=f.enter().append(\"g\").attr(\"class\",\"trace parcats\");f.attr(\"transform\",(function(t){return l(t.x,t.y)})),v.append(\"g\").attr(\"class\",\"paths\");var x=f.select(\"g.paths\").selectAll(\"path.path\").data((function(t){return t.paths}),p);x.attr(\"fill\",(function(t){return t.model.color}));var w=x.enter().append(\"path\").attr(\"class\",\"path\").attr(\"stroke-opacity\",0).attr(\"fill\",(function(t){return t.model.color})).attr(\"fill-opacity\",0);b(w),x.attr(\"d\",(function(t){return t.svgD})),w.empty()||x.sort(m),x.exit().remove(),x.on(\"mouseover\",g).on(\"mouseout\",y).on(\"click\",_),v.append(\"g\").attr(\"class\",\"dimensions\");var A=f.select(\"g.dimensions\").selectAll(\"g.dimension\").data((function(t){return t.dimensions}),p);A.enter().append(\"g\").attr(\"class\",\"dimension\"),A.attr(\"transform\",(function(t){return l(t.x,0)})),A.exit().remove();var M=A.selectAll(\"g.category\").data((function(t){return t.categories}),p),S=M.enter().append(\"g\").attr(\"class\",\"category\");M.attr(\"transform\",(function(t){return l(0,t.y)})),S.append(\"rect\").attr(\"class\",\"catrect\").attr(\"pointer-events\",\"none\"),M.select(\"rect.catrect\").attr(\"fill\",\"none\").attr(\"width\",(function(t){return t.width})).attr(\"height\",(function(t){return t.height})),T(S);var E=M.selectAll(\"rect.bandrect\").data((function(t){return t.bands}),p);E.each((function(){s.raiseToTop(this)})),E.attr(\"fill\",(function(t){return t.color}));var O=E.enter().append(\"rect\").attr(\"class\",\"bandrect\").attr(\"stroke-opacity\",0).attr(\"fill\",(function(t){return t.color})).attr(\"fill-opacity\",0);E.attr(\"fill\",(function(t){return t.color})).attr(\"width\",(function(t){return t.width})).attr(\"height\",(function(t){return t.height})).attr(\"y\",(function(t){return t.y})).attr(\"cursor\",(function(t){return\"fixed\"===t.parcatsViewModel.arrangement?\"default\":\"perpendicular\"===t.parcatsViewModel.arrangement?\"ns-resize\":\"move\"})),k(O),E.exit().remove(),S.append(\"text\").attr(\"class\",\"catlabel\").attr(\"pointer-events\",\"none\"),M.select(\"text.catlabel\").attr(\"text-anchor\",(function(t){return d(t)?\"start\":\"end\"})).attr(\"alignment-baseline\",\"middle\").style(\"fill\",\"rgb(0, 0, 0)\").attr(\"x\",(function(t){return d(t)?t.width+5:-5})).attr(\"y\",(function(t){return t.height/2})).text((function(t){return t.model.categoryLabel})).each((function(t){c.font(n.select(this),t.parcatsViewModel.categorylabelfont),h.convertToTspans(n.select(this),e)})),S.append(\"text\").attr(\"class\",\"dimlabel\"),M.select(\"text.dimlabel\").attr(\"text-anchor\",\"middle\").attr(\"alignment-baseline\",\"baseline\").attr(\"cursor\",(function(t){return\"fixed\"===t.parcatsViewModel.arrangement?\"default\":\"ew-resize\"})).attr(\"x\",(function(t){return t.width/2})).attr(\"y\",-5).text((function(t,e){return 0===e?t.parcatsViewModel.model.dimensions[t.model.dimensionInd].dimensionLabel:null})).each((function(t){c.font(n.select(this),t.parcatsViewModel.labelfont)})),M.selectAll(\"rect.bandrect\").on(\"mouseover\",C).on(\"mouseout\",L),M.exit().remove(),A.call(n.behavior.drag().origin((function(t){return{x:t.x,y:0}})).on(\"dragstart\",I).on(\"drag\",P).on(\"dragend\",z)),f.each((function(t){t.traceSelection=n.select(this),t.pathSelection=n.select(this).selectAll(\"g.paths\").selectAll(\"path.path\"),t.dimensionSelection=n.select(this).selectAll(\"g.dimensions\").selectAll(\"g.dimension\")})),f.exit().remove()}function p(t){return t.key}function d(t){var e=t.parcatsViewModel.dimensions.length,r=t.parcatsViewModel.dimensions[e-1].model.dimensionInd;return t.model.dimensionInd===r}function m(t,e){return 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m=u.model.count,g=u.model.categoryLabel,y=m/u.parcatsViewModel.model.count,v={countLabel:m,categoryLabel:g,probabilityLabel:y.toFixed(3)},x=[];-1!==u.parcatsViewModel.hoverinfoItems.indexOf(\"count\")&&x.push([\"Count:\",v.countLabel].join(\" \")),-1!==u.parcatsViewModel.hoverinfoItems.indexOf(\"probability\")&&x.push([\"P(\"+v.categoryLabel+\"):\",v.probabilityLabel].join(\" \"));var _=x.join(\"
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e=t.parcatsViewModel;e.dragDimension||(b(e.pathSelection),T(e.dimensionSelection.selectAll(\"g.category\")),k(e.dimensionSelection.selectAll(\"g.category\").selectAll(\"rect.bandrect\")),o.loneUnhover(e.graphDiv._fullLayout._hoverlayer.node()),e.pathSelection.sort(m),-1!==e.hoverinfoItems.indexOf(\"skip\"))||(\"color\"===t.parcatsViewModel.hoveron?S(this,\"plotly_unhover\",n.event):M(this,\"plotly_unhover\",n.event))}function I(t){\"fixed\"!==t.parcatsViewModel.arrangement&&(t.dragDimensionDisplayInd=t.model.displayInd,t.initialDragDimensionDisplayInds=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),t.dragHasMoved=!1,t.dragCategoryDisplayInd=null,n.select(this).selectAll(\"g.category\").select(\"rect.catrect\").each((function(e){var r=n.mouse(this)[0],i=n.mouse(this)[1];-2<=r&&r<=e.width+2&&-2<=i&&i<=e.height+2&&(t.dragCategoryDisplayInd=e.model.displayInd,t.initialDragCategoryDisplayInds=t.model.categories.map((function(t){return t.displayInd})),e.model.dragY=e.y,s.raiseToTop(this.parentNode),n.select(this.parentNode).selectAll(\"rect.bandrect\").each((function(e){e.yh.y+h.height/2&&(o.model.displayInd=h.model.displayInd,h.model.displayInd=l),t.dragCategoryDisplayInd=o.model.displayInd}if(null===t.dragCategoryDisplayInd||\"freeform\"===t.parcatsViewModel.arrangement){a.model.dragX=n.event.x;var f=t.parcatsViewModel.dimensions[r],p=t.parcatsViewModel.dimensions[i];void 0!==f&&a.model.dragXp.x&&(a.model.displayInd=p.model.displayInd,p.model.displayInd=t.dragDimensionDisplayInd),t.dragDimensionDisplayInd=a.model.displayInd}j(t.parcatsViewModel),N(t.parcatsViewModel),R(t.parcatsViewModel),D(t.parcatsViewModel)}}function z(t){if(\"fixed\"!==t.parcatsViewModel.arrangement&&null!==t.dragDimensionDisplayInd){n.select(this).selectAll(\"text\").attr(\"font-weight\",\"normal\");var e={},r=O(t.parcatsViewModel),i=t.parcatsViewModel.model.dimensions.map((function(t){return t.displayInd})),o=t.initialDragDimensionDisplayInds.some((function(t,e){return t!==i[e]}));o&&i.forEach((function(r,n){var i=t.parcatsViewModel.model.dimensions[n].containerInd;e[\"dimensions[\"+i+\"].displayindex\"]=r}));var s=!1;if(null!==t.dragCategoryDisplayInd){var l=t.model.categories.map((function(t){return t.displayInd}));if(s=t.initialDragCategoryDisplayInds.some((function(t,e){return t!==l[e]}))){var c=t.model.categories.slice().sort((function(t,e){return t.displayInd-e.displayInd})),u=c.map((function(t){return t.categoryValue})),h=c.map((function(t){return t.categoryLabel}));e[\"dimensions[\"+t.model.containerInd+\"].categoryarray\"]=[u],e[\"dimensions[\"+t.model.containerInd+\"].ticktext\"]=[h],e[\"dimensions[\"+t.model.containerInd+\"].categoryorder\"]=\"array\"}}-1===t.parcatsViewModel.hoverinfoItems.indexOf(\"skip\")&&!t.dragHasMoved&&t.potentialClickBand&&(\"color\"===t.parcatsViewModel.hoveron?S(t.potentialClickBand,\"plotly_click\",n.event.sourceEvent):M(t.potentialClickBand,\"plotly_click\",n.event.sourceEvent)),t.model.dragX=null,null!==t.dragCategoryDisplayInd&&(t.parcatsViewModel.dimensions[t.dragDimensionDisplayInd].categories[t.dragCategoryDisplayInd].model.dragY=null,t.dragCategoryDisplayInd=null),t.dragDimensionDisplayInd=null,t.parcatsViewModel.dragDimension=null,t.dragHasMoved=null,t.potentialClickBand=null,j(t.parcatsViewModel),N(t.parcatsViewModel),n.transition().duration(300).ease(\"cubic-in-out\").each((function(){R(t.parcatsViewModel,!0),D(t.parcatsViewModel,!0)})).each(\"end\",(function(){(o||s)&&a.restyle(t.parcatsViewModel.graphDiv,e,[r])}))}}function O(t){for(var e,r=t.graphDiv._fullData,n=0;n=0;s--)u+=\"C\"+c[s]+\",\"+(e[s+1]+n)+\" \"+l[s]+\",\"+(e[s]+n)+\" \"+(t[s]+r[s])+\",\"+(e[s]+n),u+=\"l-\"+r[s]+\",0 \";return u+\"Z\"}function N(t){var e=t.dimensions,r=t.model,n=e.map((function(t){return t.categories.map((function(t){return t.y}))})),i=t.model.dimensions.map((function(t){return t.categories.map((function(t){return t.displayInd}))})),a=t.model.dimensions.map((function(t){return t.displayInd})),o=t.dimensions.map((function(t){return t.model.dimensionInd})),s=e.map((function(t){return t.x})),l=e.map((function(t){return t.width})),c=[];for(var u in r.paths)r.paths.hasOwnProperty(u)&&c.push(r.paths[u]);function h(t){var e=t.categoryInds.map((function(t,e){return i[e][t]}));return o.map((function(t){return e[t]}))}c.sort((function(e,r){var n=h(e),i=h(r);return\"backward\"===t.sortpaths&&(n.reverse(),i.reverse()),n.push(e.valueInds[0]),i.push(r.valueInds[0]),t.bundlecolors&&(n.unshift(e.rawColor),i.unshift(r.rawColor)),ni?1:0}));for(var f=new Array(c.length),p=e[0].model.count,d=e[0].categories.map((function(t){return t.height})).reduce((function(t,e){return t+e})),m=0;m0?d*(y.count/p):0;for(var v,x=new Array(n.length),_=0;_1?(t.width-80-16)/(n-1):0)*i;var a,o,s,l,c,u=[],h=t.model.maxCats,f=e.categories.length,p=e.count,d=t.height-8*(h-1),m=8*(h-f)/2,g=e.categories.map((function(t){return{displayInd:t.displayInd,categoryInd:t.categoryInd}}));for(g.sort((function(t,e){return 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r=e.height-i.mouse(t)[1]-2*n.verticalPadding,a=e.unitToPaddedPx.invert(r),o=e.brush,s=b(e,r),l=s.interval,c=o.svgBrush;if(c.wasDragged=!1,c.grabbingBar=\"ns\"===s.region,c.grabbingBar){var u=l.map(e.unitToPaddedPx);c.grabPoint=r-u[0]-n.verticalPadding,c.barLength=u[1]-u[0]}c.clickableOrdinalRange=s.clickableOrdinalRange,c.stayingIntervals=e.multiselect&&o.filterSpecified?o.filter.getConsolidated():[],l&&(c.stayingIntervals=c.stayingIntervals.filter((function(t){return t[0]!==l[0]&&t[1]!==l[1]}))),c.startExtent=s.region?l[\"s\"===s.region?1:0]:a,e.parent.inBrushDrag=!0,c.brushStartCallback()}(this,t)})).on(\"drag\",(function(t){w(this,t)})).on(\"dragend\",(function(t){!function(t,e){var r=e.brush,n=r.filter,a=r.svgBrush;a._dragging||(T(t,e),w(t,e),e.brush.svgBrush.wasDragged=!1),a._dragging=!1,i.event.sourceEvent.stopPropagation();var o=a.grabbingBar;if(a.grabbingBar=!1,a.grabLocation=void 0,e.parent.inBrushDrag=!1,v(),!a.wasDragged)return a.wasDragged=void 0,a.clickableOrdinalRange?r.filterSpecified&&e.multiselect?a.extent.push(a.clickableOrdinalRange):(a.extent=[a.clickableOrdinalRange],r.filterSpecified=!0):o?(a.extent=a.stayingIntervals,0===a.extent.length&&M(r)):M(r),a.brushCallback(e),_(t.parentNode),void a.brushEndCallback(r.filterSpecified?n.getConsolidated():[]);var s=function(){n.set(n.getConsolidated())};if(e.ordinal){var l=e.unitTickvals;l[l.length-1]a.newExtent[0];a.extent=a.stayingIntervals.concat(c?[a.newExtent]:[]),a.extent.length||M(r),a.brushCallback(e),c?_(t.parentNode,s):(s(),_(t.parentNode))}else s();a.brushEndCallback(r.filterSpecified?n.getConsolidated():[])}(this,t)})))}function A(t,e){return t[0]-e[0]}function M(t){t.filterSpecified=!1,t.svgBrush.extent=[[-1/0,1/0]]}function S(t){for(var e,r=t.slice(),n=[],i=r.shift();i;){for(e=i.slice();(i=r.shift())&&i[0]<=e[1];)e[1]=Math.max(e[1],i[1]);n.push(e)}return 1===n.length&&n[0][0]>n[0][1]&&(n=[]),n}t.exports={makeBrush:function(t,e,r,n,i,a){var o,l=function(){var t,e,r=[];return{set:function(n){1===(r=n.map((function(t){return t.slice().sort(s)})).sort(A)).length&&r[0][0]===-1/0&&r[0][1]===1/0&&(r=[[0,-1]]),t=S(r),e=r.reduce((function(t,e){return[Math.min(t[0],e[0]),Math.max(t[1],e[1])]}),[1/0,-1/0])},get:function(){return r.slice()},getConsolidated:function(){return t},getBounds:function(){return e}}}();return l.set(r),{filter:l,filterSpecified:e,svgBrush:{extent:[],brushStartCallback:n,brushCallback:(o=i,function(t){var e=t.brush,r=function(t){return t.svgBrush.extent.map((function(t){return t.slice()}))}(e),n=r.slice();e.filter.set(n),o()}),brushEndCallback:a}}},ensureAxisBrush:function(t,e,r){var i=t.selectAll(\".\"+n.cn.axisBrush).data(o,a);i.enter().append(\"g\").classed(n.cn.axisBrush,!0),function(t,e,r){var i=r._context.staticPlot,a=t.selectAll(\".background\").data(o);a.enter().append(\"rect\").classed(\"background\",!0).call(d).call(m).style(\"pointer-events\",i?\"none\":\"auto\").attr(\"transform\",l(0,n.verticalPadding)),a.call(k).attr(\"height\",(function(t){return t.height-n.verticalPadding}));var s=t.selectAll(\".highlight-shadow\").data(o);s.enter().append(\"line\").classed(\"highlight-shadow\",!0).attr(\"x\",-n.bar.width/2).attr(\"stroke-width\",n.bar.width+n.bar.strokeWidth).attr(\"stroke\",e).attr(\"opacity\",n.bar.strokeOpacity).attr(\"stroke-linecap\",\"butt\"),s.attr(\"y1\",(function(t){return t.height})).call(x);var c=t.selectAll(\".highlight\").data(o);c.enter().append(\"line\").classed(\"highlight\",!0).attr(\"x\",-n.bar.width/2).attr(\"stroke-width\",n.bar.width-n.bar.strokeWidth).attr(\"stroke\",n.bar.fillColor).attr(\"opacity\",n.bar.fillOpacity).attr(\"stroke-linecap\",\"butt\"),c.attr(\"y1\",(function(t){return t.height})).call(x)}(i,e,r)},cleanRanges:function(t,e){if(Array.isArray(t[0])?(t=t.map((function(t){return t.sort(s)})),t=e.multiselect?S(t.sort(A)):[t[0]]):t=[t.sort(s)],e.tickvals){var r=e.tickvals.slice().sort(s);if(!(t=t.map((function(t){var e=[p(0,r,t[0],[]),p(1,r,t[1],[])];if(e[1]>e[0])return e})).filter((function(t){return t}))).length)return}return t.length>1?t:t[0]}}},79846:function(t,e,r){\"use strict\";t.exports={attributes:r(59549),supplyDefaults:r(12842),calc:r(20113),colorbar:{container:\"line\",min:\"cmin\",max:\"cmax\"},moduleType:\"trace\",name:\"parcoords\",basePlotModule:r(67207),categories:[\"gl\",\"regl\",\"noOpacity\",\"noHover\"],meta:{}}},67207:function(t,e,r){\"use strict\";var n=r(45568),i=r(4173).eV,a=r(58823),o=r(62972);e.name=\"parcoords\",e.plot=function(t){var e=i(t.calcdata,\"parcoords\")[0];e.length&&a(t,e)},e.clean=function(t,e,r,n){var i=n._has&&n._has(\"parcoords\"),a=e._has&&e._has(\"parcoords\");i&&!a&&(n._paperdiv.selectAll(\".parcoords\").remove(),n._glimages.selectAll(\"*\").remove())},e.toSVG=function(t){var e=t._fullLayout._glimages,r=n.select(t).selectAll(\".svg-container\");r.filter((function(t,e){return e===r.size()-1})).selectAll(\".gl-canvas-context, .gl-canvas-focus\").each((function(){var t=this,r=t.toDataURL(\"image/png\");e.append(\"svg:image\").attr({xmlns:o.svg,\"xlink:href\":r,preserveAspectRatio:\"none\",x:0,y:0,width:t.style.width,height:t.style.height})})),window.setTimeout((function(){n.selectAll(\"#filterBarPattern\").attr(\"id\",\"filterBarPattern\")}),60)}},20113:function(t,e,r){\"use strict\";var n=r(34809).isArrayOrTypedArray,i=r(88856),a=r(71293).wrap;t.exports=function(t,e){var r,o;return i.hasColorscale(e,\"line\")&&n(e.line.color)?(r=e.line.color,o=i.extractOpts(e.line).colorscale,i.calc(t,e,{vals:r,containerStr:\"line\",cLetter:\"c\"})):(r=function(t){for(var e=new Array(t),r=0;rh&&(n.log(\"parcoords traces support up to \"+h+\" dimensions at the moment\"),d.splice(h));var m=s(t,e,{name:\"dimensions\",layout:l,handleItemDefaults:p}),g=function(t,e,r,o,s){var l=s(\"line.color\",r);if(i(t,\"line\")&&n.isArrayOrTypedArray(l)){if(l.length)return s(\"line.colorscale\"),a(t,e,o,s,{prefix:\"line.\",cLetter:\"c\"}),l.length;e.line.color=r}return 1/0}(t,e,r,l,u);o(e,l,u),Array.isArray(m)&&m.length||(e.visible=!1),f(e,m,\"values\",g);var y=n.extendFlat({},l.font,{size:Math.round(l.font.size/1.2)});n.coerceFont(u,\"labelfont\",y),n.coerceFont(u,\"tickfont\",y,{autoShadowDflt:!0}),n.coerceFont(u,\"rangefont\",y),u(\"labelangle\"),u(\"labelside\"),u(\"unselected.line.color\"),u(\"unselected.line.opacity\")}},62935:function(t,e,r){\"use strict\";var n=r(34809).isTypedArray;e.convertTypedArray=function(t){return n(t)?Array.prototype.slice.call(t):t},e.isOrdinal=function(t){return!!t.tickvals},e.isVisible=function(t){return t.visible||!(\"visible\"in 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axisY(float ratio, mat4 A, mat4 B, mat4 C, mat4 D) {\",\" float y1 = val(A, dim0A) + val(B, dim0B) + val(C, dim0C) + val(D, dim0D);\",\" float y2 = val(A, dim1A) + val(B, dim1B) + val(C, dim1C) + val(D, dim1D);\",\" return y1 * (1.0 - ratio) + y2 * ratio;\",\"}\",\"\",\"int iMod(int a, int b) {\",\" return a - b * (a / b);\",\"}\",\"\",\"bool fOutside(float p, float lo, float hi) {\",\" return (lo < hi) && (lo > p || p > hi);\",\"}\",\"\",\"bool vOutside(vec4 p, vec4 lo, vec4 hi) {\",\" return (\",\" fOutside(p[0], lo[0], hi[0]) ||\",\" fOutside(p[1], lo[1], hi[1]) ||\",\" fOutside(p[2], lo[2], hi[2]) ||\",\" fOutside(p[3], lo[3], hi[3])\",\" );\",\"}\",\"\",\"bool mOutside(mat4 p, mat4 lo, mat4 hi) {\",\" return (\",\" vOutside(p[0], lo[0], hi[0]) ||\",\" vOutside(p[1], lo[1], hi[1]) ||\",\" vOutside(p[2], lo[2], hi[2]) ||\",\" vOutside(p[3], lo[3], hi[3])\",\" );\",\"}\",\"\",\"bool outsideBoundingBox(mat4 A, mat4 B, mat4 C, mat4 D) {\",\" return mOutside(A, loA, hiA) ||\",\" 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a=t._gl;a.enable(a.SCISSOR_TEST),a.scissor(e,r,n,i),t.clear({color:[0,0,0,0],depth:1})}function f(t,e,r,n,i,a){var o=a.key;r.drawCompleted||(function(t){t.read({x:0,y:0,width:1,height:1,data:l})}(t),r.drawCompleted=!0),function s(l){var c=Math.min(n,i-l*n);0===l&&(window.cancelAnimationFrame(r.currentRafs[o]),delete r.currentRafs[o],h(t,a.scissorX,a.scissorY,a.scissorWidth,a.viewBoxSize[1])),r.clearOnly||(a.count=2*c,a.offset=2*l*n,e(a),l*n+c>>8*e)%256/255}function m(t,e,r){for(var n=new Array(8*e),i=0,a=0;ac&&(c=t[i].dim1.canvasX,o=i);0===s&&h(k,0,0,r.canvasWidth,r.canvasHeight);var u=function(t){var e,r,n,i=[[],[]];for(n=0;n<64;n++){var a=!t&&ns._length&&(E=E.slice(0,s._length));var L,I=s.tickvals;function P(t,e){return{val:t,text:L[e]}}function z(t,e){return t.val-e.val}if(a(I)&&I.length){i.isTypedArray(I)&&(I=Array.from(I)),L=s.ticktext,a(L)&&L.length?L.length>I.length?L=L.slice(0,I.length):I.length>L.length&&(I=I.slice(0,L.length)):L=I.map(o(s.tickformat));for(var O=1;O=r||l>=i)return;var c=t.lineLayer.readPixel(s,i-1-l),u=0!==c[3],h=u?c[2]+256*(c[1]+256*c[0]):null,f={x:s,y:l,clientX:e.clientX,clientY:e.clientY,dataIndex:t.model.key,curveNumber:h};h!==N&&(u?a.hover(f):a.unhover&&a.unhover(f),N=h)}})),B.style(\"opacity\",(function(t){return t.pick?0:1})),p.style(\"background\",\"rgba(255, 255, 255, 0)\");var j=p.selectAll(\".\"+x.cn.parcoords).data(F,m);j.exit().remove(),j.enter().append(\"g\").classed(x.cn.parcoords,!0).style(\"shape-rendering\",\"crispEdges\").style(\"pointer-events\",\"none\"),j.attr(\"transform\",(function(t){return u(t.model.translateX,t.model.translateY)}));var U=j.selectAll(\".\"+x.cn.parcoordsControlView).data(g,m);U.enter().append(\"g\").classed(x.cn.parcoordsControlView,!0),U.attr(\"transform\",(function(t){return u(t.model.pad.l,t.model.pad.t)}));var V=U.selectAll(\".\"+x.cn.yAxis).data((function(t){return t.dimensions}),m);V.enter().append(\"g\").classed(x.cn.yAxis,!0),U.each((function(t){O(V,t,w)})),B.each((function(t){if(t.viewModel){!t.lineLayer||a?t.lineLayer=b(this,t):t.lineLayer.update(t),(t.key||0===t.key)&&(t.viewModel[t.key]=t.lineLayer);var e=!t.context||a;t.lineLayer.render(t.viewModel.panels,e)}})),V.attr(\"transform\",(function(t){return u(t.xScale(t.xIndex),0)})),V.call(n.behavior.drag().origin((function(t){return t})).on(\"drag\",(function(t){var e=t.parent;E.linePickActive(!1),t.x=Math.max(-x.overdrag,Math.min(t.model.width+x.overdrag,n.event.x)),t.canvasX=t.x*t.model.canvasPixelRatio,V.sort((function(t,e){return t.x-e.x})).each((function(e,r){e.xIndex=r,e.x=t===e?e.x:e.xScale(e.xIndex),e.canvasX=e.x*e.model.canvasPixelRatio})),O(V,e,w),V.filter((function(e){return 0!==Math.abs(t.xIndex-e.xIndex)})).attr(\"transform\",(function(t){return u(t.xScale(t.xIndex),0)})),n.select(this).attr(\"transform\",u(t.x,0)),V.each((function(r,n,i){i===t.parent.key&&(e.dimensions[n]=r)})),e.contextLayer&&e.contextLayer.render(e.panels,!1,!C(e)),e.focusLayer.render&&e.focusLayer.render(e.panels)})).on(\"dragend\",(function(t){var e=t.parent;t.x=t.xScale(t.xIndex),t.canvasX=t.x*t.model.canvasPixelRatio,O(V,e,w),n.select(this).attr(\"transform\",(function(t){return u(t.x,0)})),e.contextLayer&&e.contextLayer.render(e.panels,!1,!C(e)),e.focusLayer&&e.focusLayer.render(e.panels),e.pickLayer&&e.pickLayer.render(e.panels,!0),E.linePickActive(!0),a&&a.axesMoved&&a.axesMoved(e.key,e.dimensions.map((function(t){return t.crossfilterDimensionIndex})))}))),V.exit().remove();var q=V.selectAll(\".\"+x.cn.axisOverlays).data(g,m);q.enter().append(\"g\").classed(x.cn.axisOverlays,!0),q.selectAll(\".\"+x.cn.axis).remove();var H=q.selectAll(\".\"+x.cn.axis).data(g,m);H.enter().append(\"g\").classed(x.cn.axis,!0),H.each((function(t){var e=t.model.height/t.model.tickDistance,r=t.domainScale,i=r.domain();n.select(this).call(n.svg.axis().orient(\"left\").tickSize(4).outerTickSize(2).ticks(e,t.tickFormat).tickValues(t.ordinal?i:null).tickFormat((function(e){return v.isOrdinal(t)?e:D(t.model.dimensions[t.visibleIndex],e)})).scale(r)),f.font(H.selectAll(\"text\"),t.model.tickFont)})),H.selectAll(\".domain, .tick>line\").attr(\"fill\",\"none\").attr(\"stroke\",\"black\").attr(\"stroke-opacity\",.25).attr(\"stroke-width\",\"1px\"),H.selectAll(\"text\").style(\"cursor\",\"default\");var G=q.selectAll(\".\"+x.cn.axisHeading).data(g,m);G.enter().append(\"g\").classed(x.cn.axisHeading,!0);var Z=G.selectAll(\".\"+x.cn.axisTitle).data(g,m);Z.enter().append(\"text\").classed(x.cn.axisTitle,!0).attr(\"text-anchor\",\"middle\").style(\"cursor\",\"ew-resize\").style(\"pointer-events\",o?\"none\":\"auto\"),Z.text((function(t){return t.label})).each((function(e){var r=n.select(this);f.font(r,e.model.labelFont),h.convertToTspans(r,t)})).attr(\"transform\",(function(t){var e=z(t.model.labelAngle,t.model.labelSide),r=x.axisTitleOffset;return(e.dir>0?\"\":u(0,2*r+t.model.height))+c(e.degrees)+u(-r*e.dx,-r*e.dy)})).attr(\"text-anchor\",(function(t){var e=z(t.model.labelAngle,t.model.labelSide);return 2*Math.abs(e.dx)>Math.abs(e.dy)?e.dir*e.dx<0?\"start\":\"end\":\"middle\"}));var W=q.selectAll(\".\"+x.cn.axisExtent).data(g,m);W.enter().append(\"g\").classed(x.cn.axisExtent,!0);var Y=W.selectAll(\".\"+x.cn.axisExtentTop).data(g,m);Y.enter().append(\"g\").classed(x.cn.axisExtentTop,!0),Y.attr(\"transform\",u(0,-x.axisExtentOffset));var X=Y.selectAll(\".\"+x.cn.axisExtentTopText).data(g,m);X.enter().append(\"text\").classed(x.cn.axisExtentTopText,!0).call(P),X.text((function(t){return R(t,!0)})).each((function(t){f.font(n.select(this),t.model.rangeFont)}));var 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a=c[e][n],o=i.map((function(t){return t.slice()})),s=\"dimensions[\"+n+\"].constraintrange\",l=r._tracePreGUI[t._fullData[u[e]]._fullInput.uid];if(void 0===l[s]){var f=a.constraintrange;l[s]=f||null}var p=t._fullData[u[e]].dimensions[n];o.length?(1===o.length&&(o=o[0]),a.constraintrange=o,p.constraintrange=o.slice(),o=[o]):(delete a.constraintrange,delete p.constraintrange,o=null);var d={};d[s]=o,t.emit(\"plotly_restyle\",[d,[h[e]]])},hover:function(e){t.emit(\"plotly_hover\",e)},unhover:function(e){t.emit(\"plotly_unhover\",e)},axesMoved:function(e,r){var n=function(t,e){return function(r,n){return s(t,e,r)-s(t,e,n)}}(r,c[e].filter(a));l[e].sort(n),c[e].filter((function(t){return!a(t)})).sort((function(t){return c[e].indexOf(t)})).forEach((function(t){l[e].splice(l[e].indexOf(t),1),l[e].splice(c[e].indexOf(t),0,t)})),t.emit(\"plotly_restyle\",[{dimensions:[l[e]]},[h[e]]])}})}}).reglPrecompiled=o},55412:function(t,e,r){\"use strict\";var n=r(9829),i=r(13792).u,a=r(80337),o=r(10229),s=r(3208).rb,l=r(3208).ay,c=r(93049).extendFlat,u=r(94850).k,h=a({editType:\"plot\",arrayOk:!0,colorEditType:\"plot\"});t.exports={labels:{valType:\"data_array\",editType:\"calc\"},label0:{valType:\"number\",dflt:0,editType:\"calc\"},dlabel:{valType:\"number\",dflt:1,editType:\"calc\"},values:{valType:\"data_array\",editType:\"calc\"},marker:{colors:{valType:\"data_array\",editType:\"calc\"},line:{color:{valType:\"color\",dflt:o.defaultLine,arrayOk:!0,editType:\"style\"},width:{valType:\"number\",min:0,dflt:0,arrayOk:!0,editType:\"style\"},editType:\"calc\"},pattern:u,editType:\"calc\"},text:{valType:\"data_array\",editType:\"plot\"},hovertext:{valType:\"string\",dflt:\"\",arrayOk:!0,editType:\"style\"},scalegroup:{valType:\"string\",dflt:\"\",editType:\"calc\"},textinfo:{valType:\"flaglist\",flags:[\"label\",\"text\",\"value\",\"percent\"],extras:[\"none\"],editType:\"calc\"},hoverinfo:c({},n.hoverinfo,{flags:[\"label\",\"text\",\"value\",\"percent\",\"name\"]}),hovertemplate:s({},{keys:[\"label\",\"color\",\"value\",\"percent\",\"text\"]}),texttemplate:l({editType:\"plot\"},{keys:[\"label\",\"color\",\"value\",\"percent\",\"text\"]}),textposition:{valType:\"enumerated\",values:[\"inside\",\"outside\",\"auto\",\"none\"],dflt:\"auto\",arrayOk:!0,editType:\"plot\"},textfont:c({},h,{}),insidetextorientation:{valType:\"enumerated\",values:[\"horizontal\",\"radial\",\"tangential\",\"auto\"],dflt:\"auto\",editType:\"plot\"},insidetextfont:c({},h,{}),outsidetextfont:c({},h,{}),automargin:{valType:\"boolean\",dflt:!1,editType:\"plot\"},title:{text:{valType:\"string\",dflt:\"\",editType:\"plot\"},font:c({},h,{}),position:{valType:\"enumerated\",values:[\"top 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n=r(44122);e.name=\"pie\",e.plot=function(t,r,i,a){n.plotBasePlot(e.name,t,r,i,a)},e.clean=function(t,r,i,a){n.cleanBasePlot(e.name,t,r,i,a)}},44148:function(t,e,r){\"use strict\";var n=r(10721),i=r(65657),a=r(78766),o={};function s(t){return function(e,r){return!!e&&!!(e=i(e)).isValid()&&(e=a.addOpacity(e,e.getAlpha()),t[r]||(t[r]=e),e)}}function l(t,e){var r,n=JSON.stringify(t),a=e[n];if(!a){for(a=t.slice(),r=0;r=0})),(\"funnelarea\"===e.type?y:e.sort)&&a.sort((function(t,e){return e.v-t.v})),a[0]&&(a[0].vTotal=g),a},crossTraceCalc:function(t,e){var r=(e||{}).type;r||(r=\"pie\");var n=t._fullLayout,i=t.calcdata,a=n[r+\"colorway\"],s=n[\"_\"+r+\"colormap\"];n[\"extend\"+r+\"colors\"]&&(a=l(a,o));for(var c=0,u=0;u0){s=!0;break}}s||(o=0)}return{hasLabels:r,hasValues:a,len:o}}function u(t,e,r,n,i){n(\"marker.line.width\")&&n(\"marker.line.color\",i?void 0:r.paper_bgcolor);var a=n(\"marker.colors\");l(n,\"marker.pattern\",a),t.marker&&!e.marker.pattern.fgcolor&&(e.marker.pattern.fgcolor=t.marker.colors),e.marker.pattern.bgcolor||(e.marker.pattern.bgcolor=r.paper_bgcolor)}t.exports={handleLabelsAndValues:c,handleMarkerDefaults:u,supplyDefaults:function(t,e,r,n){function l(r,n){return i.coerce(t,e,a,r,n)}var h=c(l(\"labels\"),l(\"values\")),f=h.len;if(e._hasLabels=h.hasLabels,e._hasValues=h.hasValues,!e._hasLabels&&e._hasValues&&(l(\"label0\"),l(\"dlabel\")),f){e._length=f,u(t,e,n,l,!0),l(\"scalegroup\");var p,d=l(\"text\"),m=l(\"texttemplate\");if(m||(p=l(\"textinfo\",i.isArrayOrTypedArray(d)?\"text+percent\":\"percent\")),l(\"hovertext\"),l(\"hovertemplate\"),m||p&&\"none\"!==p){var g=l(\"textposition\");s(t,e,n,l,g,{moduleHasSelected:!1,moduleHasUnselected:!1,moduleHasConstrain:!1,moduleHasCliponaxis:!1,moduleHasTextangle:!1,moduleHasInsideanchor:!1}),(Array.isArray(g)||\"auto\"===g||\"outside\"===g)&&l(\"automargin\"),(\"inside\"===g||\"auto\"===g||Array.isArray(g))&&l(\"insidetextorientation\")}else\"none\"===p&&l(\"textposition\",\"none\");o(e,n,l);var y=l(\"hole\");if(l(\"title.text\")){var v=l(\"title.position\",y?\"middle center\":\"top center\");y||\"middle center\"!==v||(e.title.position=\"top center\"),i.coerceFont(l,\"title.font\",n.font)}l(\"sort\"),l(\"direction\"),l(\"rotation\"),l(\"pull\")}else e.visible=!1}}},50568:function(t,e,r){\"use strict\";var n=r(36040).appendArrayMultiPointValues;t.exports=function(t,e){var r={curveNumber:e.index,pointNumbers:t.pts,data:e._input,fullData:e,label:t.label,color:t.color,value:t.v,percent:t.percent,text:t.text,bbox:t.bbox,v:t.v};return 1===t.pts.length&&(r.pointNumber=r.i=t.pts[0]),n(r,e,t.pts),\"funnelarea\"===e.type&&(delete r.v,delete r.i),r}},75067:function(t,e,r){\"use strict\";var n=r(62203),i=r(78766);t.exports=function(t,e,r,a){var o=r.marker.pattern;o&&o.shape?n.pointStyle(t,r,a,e):i.fill(t,e.color)}},37252:function(t,e,r){\"use strict\";var n=r(34809);function i(t){return-1!==t.indexOf(\"e\")?t.replace(/[.]?0+e/,\"e\"):-1!==t.indexOf(\".\")?t.replace(/[.]?0+$/,\"\"):t}e.formatPiePercent=function(t,e){var r=i((100*t).toPrecision(3));return n.numSeparate(r,e)+\"%\"},e.formatPieValue=function(t,e){var r=i(t.toPrecision(10));return n.numSeparate(r,e)},e.getFirstFilled=function(t,e){if(n.isArrayOrTypedArray(t))for(var r=0;r\"),name:h.hovertemplate||-1!==f.indexOf(\"name\")?h.name:void 0,idealAlign:t.pxmid[0]<0?\"left\":\"right\",color:g.castOption(b.bgcolor,t.pts)||t.color,borderColor:g.castOption(b.bordercolor,t.pts),fontFamily:g.castOption(w.family,t.pts),fontSize:g.castOption(w.size,t.pts),fontColor:g.castOption(w.color,t.pts),nameLength:g.castOption(b.namelength,t.pts),textAlign:g.castOption(b.align,t.pts),hovertemplate:g.castOption(h.hovertemplate,t.pts),hovertemplateLabels:t,eventData:[y(t,h)]},{container:r._hoverlayer.node(),outerContainer:r._paper.node(),gd:e,inOut_bbox:T}),t.bbox=T[0],c._hasHoverLabel=!0}c._hasHoverEvent=!0,e.emit(\"plotly_hover\",{points:[y(t,h)],event:n.event})}})),t.on(\"mouseout\",(function(t){var r=e._fullLayout,i=e._fullData[c.index],o=n.select(this).datum();c._hasHoverEvent&&(t.originalEvent=n.event,e.emit(\"plotly_unhover\",{points:[y(o,i)],event:n.event}),c._hasHoverEvent=!1),c._hasHoverLabel&&(a.loneUnhover(r._hoverlayer.node()),c._hasHoverLabel=!1)})),t.on(\"click\",(function(t){var r=e._fullLayout,i=e._fullData[c.index];e._dragging||!1===r.hovermode||(e._hoverdata=[y(t,i)],a.click(e,n.event))}))}function _(t,e,r){var n=g.castOption(t.insidetextfont.color,e.pts);!n&&t._input.textfont&&(n=g.castOption(t._input.textfont.color,e.pts));var i=g.castOption(t.insidetextfont.family,e.pts)||g.castOption(t.textfont.family,e.pts)||r.family,a=g.castOption(t.insidetextfont.size,e.pts)||g.castOption(t.textfont.size,e.pts)||r.size,s=g.castOption(t.insidetextfont.weight,e.pts)||g.castOption(t.textfont.weight,e.pts)||r.weight,l=g.castOption(t.insidetextfont.style,e.pts)||g.castOption(t.textfont.style,e.pts)||r.style,c=g.castOption(t.insidetextfont.variant,e.pts)||g.castOption(t.textfont.variant,e.pts)||r.variant,u=g.castOption(t.insidetextfont.textcase,e.pts)||g.castOption(t.textfont.textcase,e.pts)||r.textcase,h=g.castOption(t.insidetextfont.lineposition,e.pts)||g.castOption(t.textfont.lineposition,e.pts)||r.lineposition,f=g.castOption(t.insidetextfont.shadow,e.pts)||g.castOption(t.textfont.shadow,e.pts)||r.shadow;return{color:n||o.contrast(e.color),family:i,size:a,weight:s,style:l,variant:c,textcase:u,lineposition:h,shadow:f}}function b(t,e){for(var r,n,i=0;ie&&e>n||r=-4;g-=2)y(Math.PI*g,\"tan\");for(g=4;g>=-4;g-=2)y(Math.PI*(g+1),\"tan\")}if(h||p){for(g=4;g>=-4;g-=2)y(Math.PI*(g+1.5),\"rad\");for(g=4;g>=-4;g-=2)y(Math.PI*(g+.5),\"rad\")}}if(s||d||h){var v=Math.sqrt(t.width*t.width+t.height*t.height);if((a={scale:i*n*2/v,rCenter:1-i,rotate:0}).textPosAngle=(e.startangle+e.stopangle)/2,a.scale>=1)return a;m.push(a)}(d||p)&&((a=T(t,n,o,l,c)).textPosAngle=(e.startangle+e.stopangle)/2,m.push(a)),(d||f)&&((a=k(t,n,o,l,c)).textPosAngle=(e.startangle+e.stopangle)/2,m.push(a));for(var x=0,_=0,b=0;b=1)break}return m[x]}function T(t,e,r,n,i){e=Math.max(0,e-2*m);var a=t.width/t.height,o=S(a,n,e,r);return{scale:2*o/t.height,rCenter:A(a,o/e),rotate:M(i)}}function k(t,e,r,n,i){e=Math.max(0,e-2*m);var a=t.height/t.width,o=S(a,n,e,r);return{scale:2*o/t.width,rCenter:A(a,o/e),rotate:M(i+Math.PI/2)}}function A(t,e){return Math.cos(e)-t*e}function M(t){return(180/Math.PI*t+720)%180-90}function S(t,e,r,n){var i=t+1/(2*Math.tan(e));return 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0===(l=t.trace.aspectratio)?1:l),u=e.w*(a.domain.x[1]-a.domain.x[0])/2;return-1!==a.title.position.indexOf(\"left\")?(u+=c,o.x-=(1+i)*c,s.tx+=t.titleBox.width/2):-1!==a.title.position.indexOf(\"center\")?u*=2:-1!==a.title.position.indexOf(\"right\")&&(u+=c,o.x+=(1+i)*c,s.tx-=t.titleBox.width/2),r=u/t.titleBox.width,n=I(t,e)/t.titleBox.height,{x:o.x,y:o.y,scale:Math.min(r,n),tx:s.tx,ty:s.ty}}function I(t,e){var r=t.trace,n=e.h*(r.domain.y[1]-r.domain.y[0]);return Math.min(t.titleBox.height,n/2)}function P(t){var e,r=t.pull;if(!r)return 0;if(l.isArrayOrTypedArray(r))for(r=0,e=0;er&&(r=t.pull[e]);return r}function z(t,e){for(var r=[],n=0;n1?u=(c=r.r)/i.aspectratio:c=(u=r.r)*i.aspectratio,l=(c*=(1+i.baseratio)/2)*u}o=Math.min(o,l/r.vTotal)}for(n=0;n\")}if(a){var x=l.castOption(i,e.i,\"texttemplate\");if(x){var 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e,r,n,i=t[0],a=i.r,o=i.trace,s=g.getRotationAngle(o.rotation),l=2*Math.PI/i.vTotal,c=\"px0\",u=\"px1\";if(\"counterclockwise\"===o.direction){for(e=0;ei.vTotal/2?1:0,r.halfangle=Math.PI*Math.min(r.v/i.vTotal,.5),r.ring=1-o.hole,r.rInscribed=E(r,i))}(e),d.attr(\"stroke-linejoin\",\"round\"),d.each((function(){var v=n.select(this).selectAll(\"g.slice\").data(e);v.enter().append(\"g\").classed(\"slice\",!0),v.exit().remove();var b=[[[],[]],[[],[]]],T=!1;v.each((function(i,o){if(i.hidden)n.select(this).selectAll(\"path,g\").remove();else{i.pointNumber=i.i,i.curveNumber=y.index,b[i.pxmid[1]<0?0:1][i.pxmid[0]<0?0:1].push(i);var c=m.cx,u=m.cy,f=n.select(this),d=f.selectAll(\"path.surface\").data([i]);if(d.enter().append(\"path\").classed(\"surface\",!0).style({\"pointer-events\":r?\"none\":\"all\"}),f.call(x,t,e),y.pull){var v=+g.castOption(y.pull,i.pts)||0;v>0&&(c+=v*i.pxmid[0],u+=v*i.pxmid[1])}i.cxFinal=c,i.cyFinal=u;var k=y.hole;if(i.v===m.vTotal){var A=\"M\"+(c+i.px0[0])+\",\"+(u+i.px0[1])+I(i.px0,i.pxmid,!0,1)+I(i.pxmid,i.px0,!0,1)+\"Z\";k?d.attr(\"d\",\"M\"+(c+k*i.px0[0])+\",\"+(u+k*i.px0[1])+I(i.px0,i.pxmid,!1,k)+I(i.pxmid,i.px0,!1,k)+\"Z\"+A):d.attr(\"d\",A)}else{var M=I(i.px0,i.px1,!0,1);if(k){var S=1-k;d.attr(\"d\",\"M\"+(c+k*i.px1[0])+\",\"+(u+k*i.px1[1])+I(i.px1,i.px0,!1,k)+\"l\"+S*i.px0[0]+\",\"+S*i.px0[1]+M+\"Z\")}else d.attr(\"d\",\"M\"+c+\",\"+u+\"l\"+i.px0[0]+\",\"+i.px0[1]+M+\"Z\")}D(t,i,m);var E=g.castOption(y.textposition,i.pts),L=f.selectAll(\"g.slicetext\").data(i.text&&\"none\"!==E?[0]:[]);L.enter().append(\"g\").classed(\"slicetext\",!0),L.exit().remove(),L.each((function(){var 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y=s.c2p(f.x,!0),v=l.c2p(f.y,!0),x=f.mrc||1,_=o.hoverlabel;_&&(f.hbg=Array.isArray(_.bgcolor)?_.bgcolor[h]:_.bgcolor,f.hbc=Array.isArray(_.bordercolor)?_.bordercolor[h]:_.bordercolor,f.hts=i.isArrayOrTypedArray(_.font.size)?_.font.size[h]:_.font.size,f.htc=Array.isArray(_.font.color)?_.font.color[h]:_.font.color,f.htf=Array.isArray(_.font.family)?_.font.family[h]:_.font.family,f.hnl=i.isArrayOrTypedArray(_.namelength)?_.namelength[h]:_.namelength);var b=o.hoverinfo;b&&(f.hi=Array.isArray(b)?b[h]:b);var w=o.hovertemplate;w&&(f.ht=Array.isArray(w)?w[h]:w);var T={};T[t.index]=f;var k=o._origX,A=o._origY,M=i.extendFlat({},t,{color:a(o,f),x0:y-x,x1:y+x,xLabelVal:k?k[h]:f.x,y0:v-x,y1:v+x,yLabelVal:A?A[h]:f.y,cd:T,distance:c,spikeDistance:u,hovertemplate:f.ht});return f.htx?M.text=f.htx:f.tx?M.text=f.tx:o.text&&(M.text=o.text),i.fillText(f,o,M),n.getComponentMethod(\"errorbars\",\"hoverInfo\")(f,o,M),M}t.exports={hoverPoints:function(t,e,r,n){var i,a,s,l,c,u,h,f,p,d,m=t.cd,g=m[0].t,y=m[0].trace,v=t.xa,x=t.ya,_=g.x,b=g.y,w=v.c2p(e),T=x.c2p(r),k=t.distance;if(g.tree){var A=v.p2c(w-k),M=v.p2c(w+k),S=x.p2c(T-k),E=x.p2c(T+k);i=\"x\"===n?g.tree.range(Math.min(A,M),Math.min(x._rl[0],x._rl[1]),Math.max(A,M),Math.max(x._rl[0],x._rl[1])):g.tree.range(Math.min(A,M),Math.min(S,E),Math.max(A,M),Math.max(S,E))}else i=g.ids;var C=k;if(\"x\"===n){var L=!!y.xperiodalignment,I=!!y.yperiodalignment;for(u=0;u=Math.min(P,z)&&w<=Math.max(P,z)?0:1/0}if(h=Math.min(O,D)&&T<=Math.max(O,D)?0:1/0}d=Math.sqrt(h*h+f*f),s=i[u]}}}else for(u=i.length-1;u>-1;u--)l=_[a=i[u]],c=b[a],h=v.c2p(l)-w,f=x.c2p(c)-T,(p=Math.sqrt(h*h+f*f))v.glText.length){var T=b-v.glText.length;for(m=0;mr&&(isNaN(e[n])||isNaN(e[n+1]));)n-=2;t.positions=e.slice(r,n+2)}return t})),v.line2d.update(v.lineOptions)),v.error2d){var A=(v.errorXOptions||[]).concat(v.errorYOptions||[]);v.error2d.update(A)}v.scatter2d&&v.scatter2d.update(v.markerOptions),v.fillOrder=s.repeat(null,b),v.fill2d&&(v.fillOptions=v.fillOptions.map((function(t,e){var n=r[e];if(t&&n&&n[0]&&n[0].trace){var i,a,o=n[0],s=o.trace,l=o.t,c=v.lineOptions[e],u=[];s._ownfill&&u.push(e),s._nexttrace&&u.push(e+1),u.length&&(v.fillOrder[e]=u);var h,f,p=[],d=c&&c.positions||l.positions;if(\"tozeroy\"===s.fill){for(h=0;hh&&isNaN(d[f+1]);)f-=2;0!==d[h+1]&&(p=[d[h],0]),p=p.concat(d.slice(h,f+2)),0!==d[f+1]&&(p=p.concat([d[f],0]))}else if(\"tozerox\"===s.fill){for(h=0;hh&&isNaN(d[f]);)f-=2;0!==d[h]&&(p=[0,d[h+1]]),p=p.concat(d.slice(h,f+2)),0!==d[f]&&(p=p.concat([0,d[f+1]]))}else if(\"toself\"===s.fill||\"tonext\"===s.fill){for(p=[],i=0,t.splitNull=!0,a=0;a-1;for(m=0;m850?\" Black\":i>750?\" Extra Bold\":i>650?\" Bold\":i>550?\" Semi Bold\":i>450?\" Medium\":i>350?\" Regular\":i>250?\" Light\":i>150?\" Extra Light\":\" Thin\"):\"Open 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z;if((w||T)&&(r=o.calcTraceToLineCoords(e)),w&&(C.geojson=o.makePolygon(r),C.layout.visibility=\"visible\",i.extendFlat(C.paint,{\"fill-color\":a.fillcolor})),T&&(L.geojson=o.makeLine(r),L.layout.visibility=\"visible\",i.extendFlat(L.paint,{\"line-width\":a.line.width,\"line-color\":a.line.color,\"line-opacity\":a.opacity})),M){var O=function(t){var e,r,a,o,u=t[0].trace,h=u.marker,f=u.selectedpoints,p=i.isArrayOrTypedArray(h.color),d=i.isArrayOrTypedArray(h.size),m=i.isArrayOrTypedArray(h.opacity);function g(t){return u.opacity*t}p&&(r=s.hasColorscale(u,\"marker\")?s.makeColorScaleFuncFromTrace(h):i.identity),d&&(a=c(u)),m&&(o=function(t){return g(n(t)?+i.constrain(t,0,1):0)});var y,v,_=[];for(e=0;e\")}function u(t){return t+\"°\"}}t.exports={hoverPoints:function(t,e,r){var o=t.cd,u=o[0].trace,h=t.xa,f=t.ya,p=t.subplot,d=[],m=l+u.uid+\"-circle\",g=u.cluster&&u.cluster.enabled;if(g){var y=p.map.queryRenderedFeatures(null,{layers:[m]});d=y.map((function(t){return t.id}))}var v=360*(e>=0?Math.floor((e+180)/360):Math.ceil((e-180)/360)),x=e-v;if(n.getClosest(o,(function(t){var e=t.lonlat;if(e[0]===s)return 1/0;if(g&&-1===d.indexOf(t.i+1))return 1/0;var n=i.modHalf(e[0],360),a=e[1],o=p.project([n,a]),l=o.x-h.c2p([x,a]),c=o.y-f.c2p([n,r]),u=Math.max(3,t.mrc||0);return Math.max(Math.sqrt(l*l+c*c)-u,1-3/u)}),t),!1!==t.index){var _=o[t.index],b=_.lonlat,w=[i.modHalf(b[0],360)+v,b[1]],T=h.c2p(w),k=f.c2p(w),A=_.mrc||1;t.x0=T-A,t.x1=T+A,t.y0=k-A,t.y1=k+A;var M={};M[u.subplot]={_subplot:p};var S=u._module.formatLabels(_,u,M);return t.lonLabel=S.lonLabel,t.latLabel=S.latLabel,t.color=a(u,_),t.extraText=c(u,_,o[0].t.labels),t.hovertemplate=u.hovertemplate,[t]}},getExtraText:c}},30929:function(t,e,r){\"use 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s(t,e,r,n){this.type=\"scattermap\",this.subplot=t,this.uid=e,this.clusterEnabled=r,this.isHidden=n,this.sourceIds={fill:\"source-\"+e+\"-fill\",line:\"source-\"+e+\"-line\",circle:\"source-\"+e+\"-circle\",symbol:\"source-\"+e+\"-symbol\",cluster:\"source-\"+e+\"-circle\",clusterCount:\"source-\"+e+\"-circle\"},this.layerIds={fill:a+e+\"-fill\",line:a+e+\"-line\",circle:a+e+\"-circle\",symbol:a+e+\"-symbol\",cluster:a+e+\"-cluster\",clusterCount:a+e+\"-cluster-count\"},this.below=null}var l=s.prototype;l.addSource=function(t,e,r){var i={type:\"geojson\",data:e.geojson};r&&r.enabled&&n.extendFlat(i,{cluster:!0,clusterMaxZoom:r.maxzoom});var a=this.subplot.map.getSource(this.sourceIds[t]);a?a.setData(e.geojson):this.subplot.map.addSource(this.sourceIds[t],i)},l.setSourceData=function(t,e){this.subplot.map.getSource(this.sourceIds[t]).setData(e.geojson)},l.addLayer=function(t,e,r){var n={type:e.type,id:this.layerIds[t],source:this.sourceIds[t],layout:e.layout,paint:e.paint};e.filter&&(n.filter=e.filter);for(var i,a=this.layerIds[t],o=this.subplot.getMapLayers(),s=0;s=0;r--){var i=e[r];n.removeLayer(u.layerIds[i])}t||n.removeSource(u.sourceIds.circle)}(t):function(t){for(var e=o.nonCluster,r=e.length-1;r>=0;r--){var i=e[r];n.removeLayer(u.layerIds[i]),t||n.removeSource(u.sourceIds[i])}}(t)}function f(t){l?function(t){t||u.addSource(\"circle\",a.circle,e.cluster);for(var r=o.cluster,n=0;n=0;r--){var n=e[r];t.removeLayer(this.layerIds[n]),t.removeSource(this.sourceIds[n])}},t.exports=function(t,e){var r,n,a,l=e[0].trace,c=l.cluster&&l.cluster.enabled,u=!0!==l.visible,h=new s(t,l.uid,c,u),f=i(t.gd,e),p=h.below=t.belowLookup[\"trace-\"+l.uid];if(c)for(h.addSource(\"circle\",f.circle,l.cluster),r=0;r850?\" Black\":i>750?\" Extra Bold\":i>650?\" Bold\":i>550?\" Semi Bold\":i>450?\" Medium\":i>350?\" Regular\":i>250?\" Light\":i>150?\" Extra Light\":\" Thin\"):\"Open Sans\"===a.slice(0,2).join(\" \")?(s=\"Open Sans\",s+=i>750?\" Extrabold\":i>650?\" Bold\":i>550?\" Semibold\":i>350?\" Regular\":\" Light\"):\"Klokantech Noto Sans\"===a.slice(0,3).join(\" \")&&(s=\"Klokantech Noto Sans\",\"CJK\"===a[3]&&(s+=\" CJK\"),s+=i>500?\" Bold\":\" Regular\")),o&&(s+=\" Italic\"),\"Open Sans Regular Italic\"===s?s=\"Open Sans Italic\":\"Open Sans Regular Bold\"===s?s=\"Open Sans Bold\":\"Open Sans Regular Bold Italic\"===s?s=\"Open Sans Bold Italic\":\"Klokantech Noto Sans Regular Italic\"===s&&(s=\"Klokantech Noto Sans Italic\"),h(s)||(s=r),s.split(\", \")}t.exports=function(t,e){var r,a=e[0].trace,h=!0===a.visible&&0!==a._length,w=\"none\"!==a.fill,T=u.hasLines(a),k=u.hasMarkers(a),A=u.hasText(a),M=k&&\"circle\"===a.marker.symbol,S=k&&\"circle\"!==a.marker.symbol,E=a.cluster&&a.cluster.enabled,C=g(\"fill\"),L=g(\"line\"),I=g(\"circle\"),P=g(\"symbol\"),z={fill:C,line:L,circle:I,symbol:P};if(!h)return z;if((w||T)&&(r=o.calcTraceToLineCoords(e)),w&&(C.geojson=o.makePolygon(r),C.layout.visibility=\"visible\",i.extendFlat(C.paint,{\"fill-color\":a.fillcolor})),T&&(L.geojson=o.makeLine(r),L.layout.visibility=\"visible\",i.extendFlat(L.paint,{\"line-width\":a.line.width,\"line-color\":a.line.color,\"line-opacity\":a.opacity})),M){var O=function(t){var e,r,a,o,u=t[0].trace,h=u.marker,f=u.selectedpoints,p=i.isArrayOrTypedArray(h.color),d=i.isArrayOrTypedArray(h.size),m=i.isArrayOrTypedArray(h.opacity);function g(t){return u.opacity*t}p&&(r=s.hasColorscale(u,\"marker\")?s.makeColorScaleFuncFromTrace(h):i.identity),d&&(a=c(u)),m&&(o=function(t){return g(n(t)?+i.constrain(t,0,1):0)});var y,v,_=[];for(e=0;e\")}function u(t){return t+\"°\"}}t.exports={hoverPoints:function(t,e,r){var o=t.cd,u=o[0].trace,h=t.xa,f=t.ya,p=t.subplot,d=[],m=l+u.uid+\"-circle\",g=u.cluster&&u.cluster.enabled;if(g){var y=p.map.queryRenderedFeatures(null,{layers:[m]});d=y.map((function(t){return t.id}))}var v=360*(e>=0?Math.floor((e+180)/360):Math.ceil((e-180)/360)),x=e-v;if(n.getClosest(o,(function(t){var e=t.lonlat;if(e[0]===s)return 1/0;if(g&&-1===d.indexOf(t.i+1))return 1/0;var n=i.modHalf(e[0],360),a=e[1],o=p.project([n,a]),l=o.x-h.c2p([x,a]),c=o.y-f.c2p([n,r]),u=Math.max(3,t.mrc||0);return Math.max(Math.sqrt(l*l+c*c)-u,1-3/u)}),t),!1!==t.index){var _=o[t.index],b=_.lonlat,w=[i.modHalf(b[0],360)+v,b[1]],T=h.c2p(w),k=f.c2p(w),A=_.mrc||1;t.x0=T-A,t.x1=T+A,t.y0=k-A,t.y1=k+A;var M={};M[u.subplot]={_subplot:p};var S=u._module.formatLabels(_,u,M);return t.lonLabel=S.lonLabel,t.latLabel=S.latLabel,t.color=a(u,_),t.extraText=c(u,_,o[0].t.labels),t.hovertemplate=u.hovertemplate,[t]}},getExtraText:c}},83866:function(t,e,r){\"use strict\";[\"*scattermapbox* trace is deprecated!\",\"Please consider switching to the *scattermap* trace type and `map` subplots.\",\"Learn more at: https://plotly.com/javascript/maplibre-migration/\"].join(\" 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s(t,e,r,n){this.type=\"scattermapbox\",this.subplot=t,this.uid=e,this.clusterEnabled=r,this.isHidden=n,this.sourceIds={fill:\"source-\"+e+\"-fill\",line:\"source-\"+e+\"-line\",circle:\"source-\"+e+\"-circle\",symbol:\"source-\"+e+\"-symbol\",cluster:\"source-\"+e+\"-circle\",clusterCount:\"source-\"+e+\"-circle\"},this.layerIds={fill:a+e+\"-fill\",line:a+e+\"-line\",circle:a+e+\"-circle\",symbol:a+e+\"-symbol\",cluster:a+e+\"-cluster\",clusterCount:a+e+\"-cluster-count\"},this.below=null}var l=s.prototype;l.addSource=function(t,e,r){var i={type:\"geojson\",data:e.geojson};r&&r.enabled&&n.extendFlat(i,{cluster:!0,clusterMaxZoom:r.maxzoom});var a=this.subplot.map.getSource(this.sourceIds[t]);a?a.setData(e.geojson):this.subplot.map.addSource(this.sourceIds[t],i)},l.setSourceData=function(t,e){this.subplot.map.getSource(this.sourceIds[t]).setData(e.geojson)},l.addLayer=function(t,e,r){var 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a,o,s=t._fullLayout,l=s._sunburstlayer,c=!r,u=!s.uniformtext.mode&&_.hasTransition(r);h(\"sunburst\",s),(a=l.selectAll(\"g.trace.sunburst\").data(e,(function(t){return t[0].trace.uid}))).enter().append(\"g\").classed(\"trace\",!0).classed(\"sunburst\",!0).attr(\"stroke-linejoin\",\"round\"),a.order(),u?(i&&(o=i()),n.transition().duration(r.duration).ease(r.easing).each(\"end\",(function(){o&&o()})).each(\"interrupt\",(function(){o&&o()})).each((function(){l.selectAll(\"g.trace\").each((function(e){b(t,e,this,r)}))}))):(a.each((function(e){b(t,e,this,r)})),s.uniformtext.mode&&y(t,s._sunburstlayer.selectAll(\".trace\"),\"sunburst\")),c&&a.exit().remove()},e.formatSliceLabel=function(t,e,r,n,i){var a=r.texttemplate,o=r.textinfo;if(!(a||o&&\"none\"!==o))return\"\";var l=i.separators,c=n[0],u=t.data.data,h=c.hierarchy,f=_.isHierarchyRoot(t),p=_.getParent(h,t),d=_.getValue(t);if(!a){var 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n=r(28379);t.exports=function(t,e){e.surfacecolor?n(t,e,{vals:e.surfacecolor,containerStr:\"\",cLetter:\"c\"}):n(t,e,{vals:e.z,containerStr:\"\",cLetter:\"c\"})}},27159:function(t,e,r){\"use strict\";var n=r(99098).gl_surface3d,i=r(99098).ndarray,a=r(99098).ndarray_linear_interpolate.d2,o=r(69295),s=r(78106),l=r(34809).isArrayOrTypedArray,c=r(46998).parseColorScale,u=r(55010),h=r(88856).extractOpts;function f(t,e,r){this.scene=t,this.uid=r,this.surface=e,this.data=null,this.showContour=[!1,!1,!1],this.contourStart=[null,null,null],this.contourEnd=[null,null,null],this.contourSize=[0,0,0],this.minValues=[1/0,1/0,1/0],this.maxValues=[-1/0,-1/0,-1/0],this.dataScaleX=1,this.dataScaleY=1,this.refineData=!0,this.objectOffset=[0,0,0]}var p=f.prototype;p.getXat=function(t,e,r,n){var i=l(this.data.x)?l(this.data.x[0])?this.data.x[e][t]:this.data.x[t]:t;return void 0===r?i:n.d2l(i,0,r)},p.getYat=function(t,e,r,n){var i=l(this.data.y)?l(this.data.y[0])?this.data.y[e][t]:this.data.y[e]:e;return void 0===r?i:n.d2l(i,0,r)},p.getZat=function(t,e,r,n){var i=this.data.z[e][t];return null===i&&this.data.connectgaps&&this.data._interpolatedZ&&(i=this.data._interpolatedZ[e][t]),void 0===r?i:n.d2l(i,0,r)},p.handlePick=function(t){if(t.object===this.surface){var e=(t.data.index[0]-1)/this.dataScaleX-1,r=(t.data.index[1]-1)/this.dataScaleY-1,n=Math.max(Math.min(Math.round(e),this.data.z[0].length-1),0),i=Math.max(Math.min(Math.round(r),this.data._ylength-1),0);t.index=[n,i],t.traceCoordinate=[this.getXat(n,i),this.getYat(n,i),this.getZat(n,i)],t.dataCoordinate=[this.getXat(n,i,this.data.xcalendar,this.scene.fullSceneLayout.xaxis),this.getYat(n,i,this.data.ycalendar,this.scene.fullSceneLayout.yaxis),this.getZat(n,i,this.data.zcalendar,this.scene.fullSceneLayout.zaxis)];for(var a=0;a<3;a++){null!=t.dataCoordinate[a]&&(t.dataCoordinate[a]*=this.scene.dataScale[a])}var o=this.data.hovertext||this.data.text;return l(o)&&o[i]&&void 0!==o[i][n]?t.textLabel=o[i][n]:t.textLabel=o||\"\",t.data.dataCoordinate=t.dataCoordinate.slice(),this.surface.highlight(t.data),this.scene.glplot.spikes.position=t.dataCoordinate,!0}};var 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m(t,e){if(t0){r=d[n];break}return r}function v(t,e){if(!(t<1||e<1)){for(var r=g(t),n=g(e),i=1,a=0;ab;)r--,r/=y(r),++r<_&&(r=b);var n=Math.round(r/t);return n>1?n:1},p.refineCoords=function(t){for(var e=this.dataScaleX,r=this.dataScaleY,n=t[0].shape[0],a=t[0].shape[1],o=0|Math.floor(t[0].shape[0]*e+1),s=0|Math.floor(t[0].shape[1]*r+1),l=1+n+1,c=1+a+1,u=i(new Float32Array(l*c),[l,c]),h=[1/e,0,0,0,1/r,0,0,0,1],f=0;f0&&null!==this.contourStart[t]&&null!==this.contourEnd[t]&&this.contourEnd[t]>this.contourStart[t]))for(i[t]=!0,e=this.contourStart[t];ea&&(this.minValues[e]=a),this.maxValues[e]\",maxDimensionCount:60,overdrag:45,releaseTransitionDuration:120,releaseTransitionEase:\"cubic-out\",scrollbarCaptureWidth:18,scrollbarHideDelay:1e3,scrollbarHideDuration:1e3,scrollbarOffset:5,scrollbarWidth:8,transitionDuration:100,transitionEase:\"cubic-out\",uplift:5,wrapSpacer:\" \",wrapSplitCharacter:\" 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e=0,r=0;r=e||c===t.length-1)&&(n[i]=o,o.key=l++,o.firstRowIndex=s,o.lastRowIndex=c,o={firstRowIndex:null,lastRowIndex:null,rows:[]},i+=a,s=c+1,a=0);return n}t.exports=function(t,e){var r=u(e.cells.values),o=function(t){return t.slice(e.header.values.length,t.length)},m=u(e.header.values);m.length&&!m[0].length&&(m[0]=[\"\"],m=u(m));var g=m.concat(o(r).map((function(){return h((m[0]||[\"\"]).length)}))),y=e.domain,v=Math.floor(t._fullLayout._size.w*(y.x[1]-y.x[0])),x=Math.floor(t._fullLayout._size.h*(y.y[1]-y.y[0])),_=e.header.values.length?g[0].map((function(){return e.header.height})):[n.emptyHeaderHeight],b=r.length?r[0].map((function(){return e.cells.height})):[],w=_.reduce(c,0),T=d(b,x-w+n.uplift),k=p(d(_,w),[]),A=p(T,k),M={},S=e._fullInput.columnorder;s(S)&&(S=Array.from(S)),S=S.concat(o(r.map((function(t,e){return e}))));var E=g.map((function(t,r){var n=s(e.columnwidth)?e.columnwidth[Math.min(r,e.columnwidth.length-1)]:e.columnwidth;return a(n)?Number(n):1})),C=E.reduce(c,0);E=E.map((function(t){return t/C*v}));var L=Math.max(l(e.header.line.width),l(e.cells.line.width)),I={key:e.uid+t._context.staticPlot,translateX:y.x[0]*t._fullLayout._size.w,translateY:t._fullLayout._size.h*(1-y.y[1]),size:t._fullLayout._size,width:v,maxLineWidth:L,height:x,columnOrder:S,groupHeight:x,rowBlocks:A,headerRowBlocks:k,scrollY:0,cells:i({},e.cells,{values:r}),headerCells:i({},e.header,{values:g}),gdColumns:g.map((function(t){return t[0]})),gdColumnsOriginalOrder:g.map((function(t){return t[0]})),prevPages:[0,0],scrollbarState:{scrollbarScrollInProgress:!1},columns:g.map((function(t,e){var r=M[t];return M[t]=(r||0)+1,{key:t+\"__\"+M[t],label:t,specIndex:e,xIndex:S[e],xScale:f,x:void 0,calcdata:void 0,columnWidth:E[e]}}))};return I.columns.forEach((function(t){t.calcdata=I,t.x=f(t)})),I}},49618:function(t,e,r){\"use strict\";var n=r(93049).extendFlat;e.splitToPanels=function(t){var e=[0,0],r=n({},t,{key:\"header\",type:\"header\",page:0,prevPages:e,currentRepaint:[null,null],dragHandle:!0,values:t.calcdata.headerCells.values[t.specIndex],rowBlocks:t.calcdata.headerRowBlocks,calcdata:n({},t.calcdata,{cells:t.calcdata.headerCells})});return[n({},t,{key:\"cells1\",type:\"cells\",page:0,prevPages:e,currentRepaint:[null,null],dragHandle:!1,values:t.calcdata.cells.values[t.specIndex],rowBlocks:t.calcdata.rowBlocks}),n({},t,{key:\"cells2\",type:\"cells\",page:1,prevPages:e,currentRepaint:[null,null],dragHandle:!1,values:t.calcdata.cells.values[t.specIndex],rowBlocks:t.calcdata.rowBlocks}),r]},e.splitToCells=function(t){var e=function(t){var e=t.rowBlocks[t.page],r=e?e.rows[0].rowIndex:0;return[r,e?r+e.rows.length:0]}(t);return(t.values||[]).slice(e[0],e[1]).map((function(r,n){return{keyWithinBlock:n+(\"string\"==typeof r&&r.match(/[<$&> 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_t=(c+(n=n+Math.imul(h,Q)|0)|0)+((8191&(i=(i=i+Math.imul(h,tt)|0)+Math.imul(f,Q)|0))<<13)|0;c=((a=a+Math.imul(f,tt)|0)+(i>>>13)|0)+(_t>>>26)|0,_t&=67108863,n=Math.imul(M,U),i=(i=Math.imul(M,V))+Math.imul(S,U)|0,a=Math.imul(S,V),n=n+Math.imul(T,H)|0,i=(i=i+Math.imul(T,G)|0)+Math.imul(k,H)|0,a=a+Math.imul(k,G)|0,n=n+Math.imul(_,W)|0,i=(i=i+Math.imul(_,Y)|0)+Math.imul(b,W)|0,a=a+Math.imul(b,Y)|0,n=n+Math.imul(y,$)|0,i=(i=i+Math.imul(y,J)|0)+Math.imul(v,$)|0,a=a+Math.imul(v,J)|0,n=n+Math.imul(d,Q)|0,i=(i=i+Math.imul(d,tt)|0)+Math.imul(m,Q)|0,a=a+Math.imul(m,tt)|0;var bt=(c+(n=n+Math.imul(h,rt)|0)|0)+((8191&(i=(i=i+Math.imul(h,nt)|0)+Math.imul(f,rt)|0))<<13)|0;c=((a=a+Math.imul(f,nt)|0)+(i>>>13)|0)+(bt>>>26)|0,bt&=67108863,n=Math.imul(C,U),i=(i=Math.imul(C,V))+Math.imul(L,U)|0,a=Math.imul(L,V),n=n+Math.imul(M,H)|0,i=(i=i+Math.imul(M,G)|0)+Math.imul(S,H)|0,a=a+Math.imul(S,G)|0,n=n+Math.imul(T,W)|0,i=(i=i+Math.imul(T,Y)|0)+Math.imul(k,W)|0,a=a+Math.imul(k,Y)|0,n=n+Math.imul(_,$)|0,i=(i=i+Math.imul(_,J)|0)+Math.imul(b,$)|0,a=a+Math.imul(b,J)|0,n=n+Math.imul(y,Q)|0,i=(i=i+Math.imul(y,tt)|0)+Math.imul(v,Q)|0,a=a+Math.imul(v,tt)|0,n=n+Math.imul(d,rt)|0,i=(i=i+Math.imul(d,nt)|0)+Math.imul(m,rt)|0,a=a+Math.imul(m,nt)|0;var wt=(c+(n=n+Math.imul(h,at)|0)|0)+((8191&(i=(i=i+Math.imul(h,ot)|0)+Math.imul(f,at)|0))<<13)|0;c=((a=a+Math.imul(f,ot)|0)+(i>>>13)|0)+(wt>>>26)|0,wt&=67108863,n=Math.imul(P,U),i=(i=Math.imul(P,V))+Math.imul(z,U)|0,a=Math.imul(z,V),n=n+Math.imul(C,H)|0,i=(i=i+Math.imul(C,G)|0)+Math.imul(L,H)|0,a=a+Math.imul(L,G)|0,n=n+Math.imul(M,W)|0,i=(i=i+Math.imul(M,Y)|0)+Math.imul(S,W)|0,a=a+Math.imul(S,Y)|0,n=n+Math.imul(T,$)|0,i=(i=i+Math.imul(T,J)|0)+Math.imul(k,$)|0,a=a+Math.imul(k,J)|0,n=n+Math.imul(_,Q)|0,i=(i=i+Math.imul(_,tt)|0)+Math.imul(b,Q)|0,a=a+Math.imul(b,tt)|0,n=n+Math.imul(y,rt)|0,i=(i=i+Math.imul(y,nt)|0)+Math.imul(v,rt)|0,a=a+Math.imul(v,nt)|0,n=n+Math.imul(d,at)|0,i=(i=i+Math.imul(d,ot)|0)+Math.imul(m,at)|0,a=a+Math.imul(m,ot)|0;var Tt=(c+(n=n+Math.imul(h,lt)|0)|0)+((8191&(i=(i=i+Math.imul(h,ct)|0)+Math.imul(f,lt)|0))<<13)|0;c=((a=a+Math.imul(f,ct)|0)+(i>>>13)|0)+(Tt>>>26)|0,Tt&=67108863,n=Math.imul(D,U),i=(i=Math.imul(D,V))+Math.imul(R,U)|0,a=Math.imul(R,V),n=n+Math.imul(P,H)|0,i=(i=i+Math.imul(P,G)|0)+Math.imul(z,H)|0,a=a+Math.imul(z,G)|0,n=n+Math.imul(C,W)|0,i=(i=i+Math.imul(C,Y)|0)+Math.imul(L,W)|0,a=a+Math.imul(L,Y)|0,n=n+Math.imul(M,$)|0,i=(i=i+Math.imul(M,J)|0)+Math.imul(S,$)|0,a=a+Math.imul(S,J)|0,n=n+Math.imul(T,Q)|0,i=(i=i+Math.imul(T,tt)|0)+Math.imul(k,Q)|0,a=a+Math.imul(k,tt)|0,n=n+Math.imul(_,rt)|0,i=(i=i+Math.imul(_,nt)|0)+Math.imul(b,rt)|0,a=a+Math.imul(b,nt)|0,n=n+Math.imul(y,at)|0,i=(i=i+Math.imul(y,ot)|0)+Math.imul(v,at)|0,a=a+Math.imul(v,ot)|0,n=n+Math.imul(d,lt)|0,i=(i=i+Math.imul(d,ct)|0)+Math.imul(m,lt)|0,a=a+Math.imul(m,ct)|0;var kt=(c+(n=n+Math.imul(h,ht)|0)|0)+((8191&(i=(i=i+Math.imul(h,ft)|0)+Math.imul(f,ht)|0))<<13)|0;c=((a=a+Math.imul(f,ft)|0)+(i>>>13)|0)+(kt>>>26)|0,kt&=67108863,n=Math.imul(B,U),i=(i=Math.imul(B,V))+Math.imul(N,U)|0,a=Math.imul(N,V),n=n+Math.imul(D,H)|0,i=(i=i+Math.imul(D,G)|0)+Math.imul(R,H)|0,a=a+Math.imul(R,G)|0,n=n+Math.imul(P,W)|0,i=(i=i+Math.imul(P,Y)|0)+Math.imul(z,W)|0,a=a+Math.imul(z,Y)|0,n=n+Math.imul(C,$)|0,i=(i=i+Math.imul(C,J)|0)+Math.imul(L,$)|0,a=a+Math.imul(L,J)|0,n=n+Math.imul(M,Q)|0,i=(i=i+Math.imul(M,tt)|0)+Math.imul(S,Q)|0,a=a+Math.imul(S,tt)|0,n=n+Math.imul(T,rt)|0,i=(i=i+Math.imul(T,nt)|0)+Math.imul(k,rt)|0,a=a+Math.imul(k,nt)|0,n=n+Math.imul(_,at)|0,i=(i=i+Math.imul(_,ot)|0)+Math.imul(b,at)|0,a=a+Math.imul(b,ot)|0,n=n+Math.imul(y,lt)|0,i=(i=i+Math.imul(y,ct)|0)+Math.imul(v,lt)|0,a=a+Math.imul(v,ct)|0,n=n+Math.imul(d,ht)|0,i=(i=i+Math.imul(d,ft)|0)+Math.imul(m,ht)|0,a=a+Math.imul(m,ft)|0;var At=(c+(n=n+Math.imul(h,dt)|0)|0)+((8191&(i=(i=i+Math.imul(h,mt)|0)+Math.imul(f,dt)|0))<<13)|0;c=((a=a+Math.imul(f,mt)|0)+(i>>>13)|0)+(At>>>26)|0,At&=67108863,n=Math.imul(B,H),i=(i=Math.imul(B,G))+Math.imul(N,H)|0,a=Math.imul(N,G),n=n+Math.imul(D,W)|0,i=(i=i+Math.imul(D,Y)|0)+Math.imul(R,W)|0,a=a+Math.imul(R,Y)|0,n=n+Math.imul(P,$)|0,i=(i=i+Math.imul(P,J)|0)+Math.imul(z,$)|0,a=a+Math.imul(z,J)|0,n=n+Math.imul(C,Q)|0,i=(i=i+Math.imul(C,tt)|0)+Math.imul(L,Q)|0,a=a+Math.imul(L,tt)|0,n=n+Math.imul(M,rt)|0,i=(i=i+Math.imul(M,nt)|0)+Math.imul(S,rt)|0,a=a+Math.imul(S,nt)|0,n=n+Math.imul(T,at)|0,i=(i=i+Math.imul(T,ot)|0)+Math.imul(k,at)|0,a=a+Math.imul(k,ot)|0,n=n+Math.imul(_,lt)|0,i=(i=i+Math.imul(_,ct)|0)+Math.imul(b,lt)|0,a=a+Math.imul(b,ct)|0,n=n+Math.imul(y,ht)|0,i=(i=i+Math.imul(y,ft)|0)+Math.imul(v,ht)|0,a=a+Math.imul(v,ft)|0;var Mt=(c+(n=n+Math.imul(d,dt)|0)|0)+((8191&(i=(i=i+Math.imul(d,mt)|0)+Math.imul(m,dt)|0))<<13)|0;c=((a=a+Math.imul(m,mt)|0)+(i>>>13)|0)+(Mt>>>26)|0,Mt&=67108863,n=Math.imul(B,W),i=(i=Math.imul(B,Y))+Math.imul(N,W)|0,a=Math.imul(N,Y),n=n+Math.imul(D,$)|0,i=(i=i+Math.imul(D,J)|0)+Math.imul(R,$)|0,a=a+Math.imul(R,J)|0,n=n+Math.imul(P,Q)|0,i=(i=i+Math.imul(P,tt)|0)+Math.imul(z,Q)|0,a=a+Math.imul(z,tt)|0,n=n+Math.imul(C,rt)|0,i=(i=i+Math.imul(C,nt)|0)+Math.imul(L,rt)|0,a=a+Math.imul(L,nt)|0,n=n+Math.imul(M,at)|0,i=(i=i+Math.imul(M,ot)|0)+Math.imul(S,at)|0,a=a+Math.imul(S,ot)|0,n=n+Math.imul(T,lt)|0,i=(i=i+Math.imul(T,ct)|0)+Math.imul(k,lt)|0,a=a+Math.imul(k,ct)|0,n=n+Math.imul(_,ht)|0,i=(i=i+Math.imul(_,ft)|0)+Math.imul(b,ht)|0,a=a+Math.imul(b,ft)|0;var St=(c+(n=n+Math.imul(y,dt)|0)|0)+((8191&(i=(i=i+Math.imul(y,mt)|0)+Math.imul(v,dt)|0))<<13)|0;c=((a=a+Math.imul(v,mt)|0)+(i>>>13)|0)+(St>>>26)|0,St&=67108863,n=Math.imul(B,$),i=(i=Math.imul(B,J))+Math.imul(N,$)|0,a=Math.imul(N,J),n=n+Math.imul(D,Q)|0,i=(i=i+Math.imul(D,tt)|0)+Math.imul(R,Q)|0,a=a+Math.imul(R,tt)|0,n=n+Math.imul(P,rt)|0,i=(i=i+Math.imul(P,nt)|0)+Math.imul(z,rt)|0,a=a+Math.imul(z,nt)|0,n=n+Math.imul(C,at)|0,i=(i=i+Math.imul(C,ot)|0)+Math.imul(L,at)|0,a=a+Math.imul(L,ot)|0,n=n+Math.imul(M,lt)|0,i=(i=i+Math.imul(M,ct)|0)+Math.imul(S,lt)|0,a=a+Math.imul(S,ct)|0,n=n+Math.imul(T,ht)|0,i=(i=i+Math.imul(T,ft)|0)+Math.imul(k,ht)|0,a=a+Math.imul(k,ft)|0;var Et=(c+(n=n+Math.imul(_,dt)|0)|0)+((8191&(i=(i=i+Math.imul(_,mt)|0)+Math.imul(b,dt)|0))<<13)|0;c=((a=a+Math.imul(b,mt)|0)+(i>>>13)|0)+(Et>>>26)|0,Et&=67108863,n=Math.imul(B,Q),i=(i=Math.imul(B,tt))+Math.imul(N,Q)|0,a=Math.imul(N,tt),n=n+Math.imul(D,rt)|0,i=(i=i+Math.imul(D,nt)|0)+Math.imul(R,rt)|0,a=a+Math.imul(R,nt)|0,n=n+Math.imul(P,at)|0,i=(i=i+Math.imul(P,ot)|0)+Math.imul(z,at)|0,a=a+Math.imul(z,ot)|0,n=n+Math.imul(C,lt)|0,i=(i=i+Math.imul(C,ct)|0)+Math.imul(L,lt)|0,a=a+Math.imul(L,ct)|0,n=n+Math.imul(M,ht)|0,i=(i=i+Math.imul(M,ft)|0)+Math.imul(S,ht)|0,a=a+Math.imul(S,ft)|0;var Ct=(c+(n=n+Math.imul(T,dt)|0)|0)+((8191&(i=(i=i+Math.imul(T,mt)|0)+Math.imul(k,dt)|0))<<13)|0;c=((a=a+Math.imul(k,mt)|0)+(i>>>13)|0)+(Ct>>>26)|0,Ct&=67108863,n=Math.imul(B,rt),i=(i=Math.imul(B,nt))+Math.imul(N,rt)|0,a=Math.imul(N,nt),n=n+Math.imul(D,at)|0,i=(i=i+Math.imul(D,ot)|0)+Math.imul(R,at)|0,a=a+Math.imul(R,ot)|0,n=n+Math.imul(P,lt)|0,i=(i=i+Math.imul(P,ct)|0)+Math.imul(z,lt)|0,a=a+Math.imul(z,ct)|0,n=n+Math.imul(C,ht)|0,i=(i=i+Math.imul(C,ft)|0)+Math.imul(L,ht)|0,a=a+Math.imul(L,ft)|0;var Lt=(c+(n=n+Math.imul(M,dt)|0)|0)+((8191&(i=(i=i+Math.imul(M,mt)|0)+Math.imul(S,dt)|0))<<13)|0;c=((a=a+Math.imul(S,mt)|0)+(i>>>13)|0)+(Lt>>>26)|0,Lt&=67108863,n=Math.imul(B,at),i=(i=Math.imul(B,ot))+Math.imul(N,at)|0,a=Math.imul(N,ot),n=n+Math.imul(D,lt)|0,i=(i=i+Math.imul(D,ct)|0)+Math.imul(R,lt)|0,a=a+Math.imul(R,ct)|0,n=n+Math.imul(P,ht)|0,i=(i=i+Math.imul(P,ft)|0)+Math.imul(z,ht)|0,a=a+Math.imul(z,ft)|0;var It=(c+(n=n+Math.imul(C,dt)|0)|0)+((8191&(i=(i=i+Math.imul(C,mt)|0)+Math.imul(L,dt)|0))<<13)|0;c=((a=a+Math.imul(L,mt)|0)+(i>>>13)|0)+(It>>>26)|0,It&=67108863,n=Math.imul(B,lt),i=(i=Math.imul(B,ct))+Math.imul(N,lt)|0,a=Math.imul(N,ct),n=n+Math.imul(D,ht)|0,i=(i=i+Math.imul(D,ft)|0)+Math.imul(R,ht)|0,a=a+Math.imul(R,ft)|0;var Pt=(c+(n=n+Math.imul(P,dt)|0)|0)+((8191&(i=(i=i+Math.imul(P,mt)|0)+Math.imul(z,dt)|0))<<13)|0;c=((a=a+Math.imul(z,mt)|0)+(i>>>13)|0)+(Pt>>>26)|0,Pt&=67108863,n=Math.imul(B,ht),i=(i=Math.imul(B,ft))+Math.imul(N,ht)|0,a=Math.imul(N,ft);var zt=(c+(n=n+Math.imul(D,dt)|0)|0)+((8191&(i=(i=i+Math.imul(D,mt)|0)+Math.imul(R,dt)|0))<<13)|0;c=((a=a+Math.imul(R,mt)|0)+(i>>>13)|0)+(zt>>>26)|0,zt&=67108863;var Ot=(c+(n=Math.imul(B,dt))|0)+((8191&(i=(i=Math.imul(B,mt))+Math.imul(N,dt)|0))<<13)|0;return c=((a=Math.imul(N,mt))+(i>>>13)|0)+(Ot>>>26)|0,Ot&=67108863,l[0]=gt,l[1]=yt,l[2]=vt,l[3]=xt,l[4]=_t,l[5]=bt,l[6]=wt,l[7]=Tt,l[8]=kt,l[9]=At,l[10]=Mt,l[11]=St,l[12]=Et,l[13]=Ct,l[14]=Lt,l[15]=It,l[16]=Pt,l[17]=zt,l[18]=Ot,0!==c&&(l[19]=c,r.length++),r};function m(t,e,r){return(new g).mulp(t,e,r)}function g(t,e){this.x=t,this.y=e}Math.imul||(d=p),a.prototype.mulTo=function(t,e){var r,n=this.length+t.length;return r=10===this.length&&10===t.length?d(this,t,e):n<63?p(this,t,e):n<1024?function(t,e,r){r.negative=e.negative^t.negative,r.length=t.length+e.length;for(var n=0,i=0,a=0;a>>26)|0)>>>26,o&=67108863}r.words[a]=s,n=o,o=i}return 0!==n?r.words[a]=n:r.length--,r.strip()}(this,t,e):m(this,t,e),r},g.prototype.makeRBT=function(t){for(var e=new Array(t),r=a.prototype._countBits(t)-1,n=0;n>=1;return n},g.prototype.permute=function(t,e,r,n,i,a){for(var o=0;o>>=1)i++;return 1<>>=13,r[2*o+1]=8191&a,a>>>=13;for(o=2*e;o>=26,e+=i/67108864|0,e+=a>>>26,this.words[r]=67108863&a}return 0!==e&&(this.words[r]=e,this.length++),this},a.prototype.muln=function(t){return this.clone().imuln(t)},a.prototype.sqr=function(){return this.mul(this)},a.prototype.isqr=function(){return this.imul(this.clone())},a.prototype.pow=function(t){var e=function(t){for(var e=new Array(t.bitLength()),r=0;r>>i}return e}(t);if(0===e.length)return new a(1);for(var r=this,n=0;n=0);var e,r=t%26,i=(t-r)/26,a=67108863>>>26-r<<26-r;if(0!==r){var o=0;for(e=0;e>>26-r}o&&(this.words[e]=o,this.length++)}if(0!==i){for(e=this.length-1;e>=0;e--)this.words[e+i]=this.words[e];for(e=0;e=0),i=e?(e-e%26)/26:0;var a=t%26,o=Math.min((t-a)/26,this.length),s=67108863^67108863>>>a<o)for(this.length-=o,c=0;c=0&&(0!==u||c>=i);c--){var h=0|this.words[c];this.words[c]=u<<26-a|h>>>a,u=h&s}return l&&0!==u&&(l.words[l.length++]=u),0===this.length&&(this.words[0]=0,this.length=1),this.strip()},a.prototype.ishrn=function(t,e,r){return n(0===this.negative),this.iushrn(t,e,r)},a.prototype.shln=function(t){return this.clone().ishln(t)},a.prototype.ushln=function(t){return this.clone().iushln(t)},a.prototype.shrn=function(t){return this.clone().ishrn(t)},a.prototype.ushrn=function(t){return this.clone().iushrn(t)},a.prototype.testn=function(t){n(\"number\"==typeof t&&t>=0);var e=t%26,r=(t-e)/26,i=1<=0);var e=t%26,r=(t-e)/26;if(n(0===this.negative,\"imaskn works only with positive numbers\"),this.length<=r)return this;if(0!==e&&r++,this.length=Math.min(r,this.length),0!==e){var i=67108863^67108863>>>e<=67108864;e++)this.words[e]-=67108864,e===this.length-1?this.words[e+1]=1:this.words[e+1]++;return this.length=Math.max(this.length,e+1),this},a.prototype.isubn=function(t){if(n(\"number\"==typeof t),n(t<67108864),t<0)return this.iaddn(-t);if(0!==this.negative)return this.negative=0,this.iaddn(t),this.negative=1,this;if(this.words[0]-=t,1===this.length&&this.words[0]<0)this.words[0]=-this.words[0],this.negative=1;else for(var e=0;e>26)-(l/67108864|0),this.words[i+r]=67108863&a}for(;i>26,this.words[i+r]=67108863&a;if(0===s)return this.strip();for(n(-1===s),s=0,i=0;i>26,this.words[i]=67108863&a;return this.negative=1,this.strip()},a.prototype._wordDiv=function(t,e){var r=(this.length,t.length),n=this.clone(),i=t,o=0|i.words[i.length-1];0!=(r=26-this._countBits(o))&&(i=i.ushln(r),n.iushln(r),o=0|i.words[i.length-1]);var s,l=n.length-i.length;if(\"mod\"!==e){(s=new a(null)).length=l+1,s.words=new Array(s.length);for(var c=0;c=0;h--){var f=67108864*(0|n.words[i.length+h])+(0|n.words[i.length+h-1]);for(f=Math.min(f/o|0,67108863),n._ishlnsubmul(i,f,h);0!==n.negative;)f--,n.negative=0,n._ishlnsubmul(i,1,h),n.isZero()||(n.negative^=1);s&&(s.words[h]=f)}return s&&s.strip(),n.strip(),\"div\"!==e&&0!==r&&n.iushrn(r),{div:s||null,mod:n}},a.prototype.divmod=function(t,e,r){return n(!t.isZero()),this.isZero()?{div:new a(0),mod:new a(0)}:0!==this.negative&&0===t.negative?(s=this.neg().divmod(t,e),\"mod\"!==e&&(i=s.div.neg()),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.iadd(t)),{div:i,mod:o}):0===this.negative&&0!==t.negative?(s=this.divmod(t.neg(),e),\"mod\"!==e&&(i=s.div.neg()),{div:i,mod:s.mod}):0!=(this.negative&t.negative)?(s=this.neg().divmod(t.neg(),e),\"div\"!==e&&(o=s.mod.neg(),r&&0!==o.negative&&o.isub(t)),{div:s.div,mod:o}):t.length>this.length||this.cmp(t)<0?{div:new a(0),mod:this}:1===t.length?\"div\"===e?{div:this.divn(t.words[0]),mod:null}:\"mod\"===e?{div:null,mod:new a(this.modn(t.words[0]))}:{div:this.divn(t.words[0]),mod:new a(this.modn(t.words[0]))}:this._wordDiv(t,e);var i,o,s},a.prototype.div=function(t){return this.divmod(t,\"div\",!1).div},a.prototype.mod=function(t){return this.divmod(t,\"mod\",!1).mod},a.prototype.umod=function(t){return this.divmod(t,\"mod\",!0).mod},a.prototype.divRound=function(t){var e=this.divmod(t);if(e.mod.isZero())return e.div;var r=0!==e.div.negative?e.mod.isub(t):e.mod,n=t.ushrn(1),i=t.andln(1),a=r.cmp(n);return a<0||1===i&&0===a?e.div:0!==e.div.negative?e.div.isubn(1):e.div.iaddn(1)},a.prototype.modn=function(t){n(t<=67108863);for(var e=(1<<26)%t,r=0,i=this.length-1;i>=0;i--)r=(e*r+(0|this.words[i]))%t;return r},a.prototype.idivn=function(t){n(t<=67108863);for(var e=0,r=this.length-1;r>=0;r--){var i=(0|this.words[r])+67108864*e;this.words[r]=i/t|0,e=i%t}return this.strip()},a.prototype.divn=function(t){return this.clone().idivn(t)},a.prototype.egcd=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i=new a(1),o=new a(0),s=new a(0),l=new a(1),c=0;e.isEven()&&r.isEven();)e.iushrn(1),r.iushrn(1),++c;for(var u=r.clone(),h=e.clone();!e.isZero();){for(var f=0,p=1;0==(e.words[0]&p)&&f<26;++f,p<<=1);if(f>0)for(e.iushrn(f);f-- >0;)(i.isOdd()||o.isOdd())&&(i.iadd(u),o.isub(h)),i.iushrn(1),o.iushrn(1);for(var d=0,m=1;0==(r.words[0]&m)&&d<26;++d,m<<=1);if(d>0)for(r.iushrn(d);d-- >0;)(s.isOdd()||l.isOdd())&&(s.iadd(u),l.isub(h)),s.iushrn(1),l.iushrn(1);e.cmp(r)>=0?(e.isub(r),i.isub(s),o.isub(l)):(r.isub(e),s.isub(i),l.isub(o))}return{a:s,b:l,gcd:r.iushln(c)}},a.prototype._invmp=function(t){n(0===t.negative),n(!t.isZero());var e=this,r=t.clone();e=0!==e.negative?e.umod(t):e.clone();for(var i,o=new a(1),s=new a(0),l=r.clone();e.cmpn(1)>0&&r.cmpn(1)>0;){for(var c=0,u=1;0==(e.words[0]&u)&&c<26;++c,u<<=1);if(c>0)for(e.iushrn(c);c-- >0;)o.isOdd()&&o.iadd(l),o.iushrn(1);for(var h=0,f=1;0==(r.words[0]&f)&&h<26;++h,f<<=1);if(h>0)for(r.iushrn(h);h-- >0;)s.isOdd()&&s.iadd(l),s.iushrn(1);e.cmp(r)>=0?(e.isub(r),o.isub(s)):(r.isub(e),s.isub(o))}return(i=0===e.cmpn(1)?o:s).cmpn(0)<0&&i.iadd(t),i},a.prototype.gcd=function(t){if(this.isZero())return t.abs();if(t.isZero())return this.abs();var e=this.clone(),r=t.clone();e.negative=0,r.negative=0;for(var n=0;e.isEven()&&r.isEven();n++)e.iushrn(1),r.iushrn(1);for(;;){for(;e.isEven();)e.iushrn(1);for(;r.isEven();)r.iushrn(1);var i=e.cmp(r);if(i<0){var a=e;e=r,r=a}else if(0===i||0===r.cmpn(1))break;e.isub(r)}return r.iushln(n)},a.prototype.invm=function(t){return this.egcd(t).a.umod(t)},a.prototype.isEven=function(){return 0==(1&this.words[0])},a.prototype.isOdd=function(){return 1==(1&this.words[0])},a.prototype.andln=function(t){return this.words[0]&t},a.prototype.bincn=function(t){n(\"number\"==typeof t);var e=t%26,r=(t-e)/26,i=1<>>26,s&=67108863,this.words[o]=s}return 0!==a&&(this.words[o]=a,this.length++),this},a.prototype.isZero=function(){return 1===this.length&&0===this.words[0]},a.prototype.cmpn=function(t){var e,r=t<0;if(0!==this.negative&&!r)return-1;if(0===this.negative&&r)return 1;if(this.strip(),this.length>1)e=1;else{r&&(t=-t),n(t<=67108863,\"Number is too big\");var i=0|this.words[0];e=i===t?0:it.length)return 1;if(this.length=0;r--){var n=0|this.words[r],i=0|t.words[r];if(n!==i){ni&&(e=1);break}}return e},a.prototype.gtn=function(t){return 1===this.cmpn(t)},a.prototype.gt=function(t){return 1===this.cmp(t)},a.prototype.gten=function(t){return this.cmpn(t)>=0},a.prototype.gte=function(t){return this.cmp(t)>=0},a.prototype.ltn=function(t){return-1===this.cmpn(t)},a.prototype.lt=function(t){return-1===this.cmp(t)},a.prototype.lten=function(t){return this.cmpn(t)<=0},a.prototype.lte=function(t){return this.cmp(t)<=0},a.prototype.eqn=function(t){return 0===this.cmpn(t)},a.prototype.eq=function(t){return 0===this.cmp(t)},a.red=function(t){return new T(t)},a.prototype.toRed=function(t){return n(!this.red,\"Already a number in reduction context\"),n(0===this.negative,\"red works only with positives\"),t.convertTo(this)._forceRed(t)},a.prototype.fromRed=function(){return n(this.red,\"fromRed works only with numbers in reduction context\"),this.red.convertFrom(this)},a.prototype._forceRed=function(t){return this.red=t,this},a.prototype.forceRed=function(t){return n(!this.red,\"Already a number in reduction context\"),this._forceRed(t)},a.prototype.redAdd=function(t){return n(this.red,\"redAdd works only with red numbers\"),this.red.add(this,t)},a.prototype.redIAdd=function(t){return n(this.red,\"redIAdd works only with red numbers\"),this.red.iadd(this,t)},a.prototype.redSub=function(t){return n(this.red,\"redSub works only with red numbers\"),this.red.sub(this,t)},a.prototype.redISub=function(t){return n(this.red,\"redISub works only with red numbers\"),this.red.isub(this,t)},a.prototype.redShl=function(t){return n(this.red,\"redShl works only with red numbers\"),this.red.shl(this,t)},a.prototype.redMul=function(t){return n(this.red,\"redMul works only with red numbers\"),this.red._verify2(this,t),this.red.mul(this,t)},a.prototype.redIMul=function(t){return n(this.red,\"redMul works only with red numbers\"),this.red._verify2(this,t),this.red.imul(this,t)},a.prototype.redSqr=function(){return n(this.red,\"redSqr works only with red numbers\"),this.red._verify1(this),this.red.sqr(this)},a.prototype.redISqr=function(){return n(this.red,\"redISqr works only with red numbers\"),this.red._verify1(this),this.red.isqr(this)},a.prototype.redSqrt=function(){return n(this.red,\"redSqrt works only with red numbers\"),this.red._verify1(this),this.red.sqrt(this)},a.prototype.redInvm=function(){return n(this.red,\"redInvm works only with red numbers\"),this.red._verify1(this),this.red.invm(this)},a.prototype.redNeg=function(){return n(this.red,\"redNeg works only with red numbers\"),this.red._verify1(this),this.red.neg(this)},a.prototype.redPow=function(t){return n(this.red&&!t.red,\"redPow(normalNum)\"),this.red._verify1(this),this.red.pow(this,t)};var y={k256:null,p224:null,p192:null,p25519:null};function v(t,e){this.name=t,this.p=new a(e,16),this.n=this.p.bitLength(),this.k=new a(1).iushln(this.n).isub(this.p),this.tmp=this._tmp()}function x(){v.call(this,\"k256\",\"ffffffff ffffffff ffffffff ffffffff ffffffff ffffffff fffffffe fffffc2f\")}function _(){v.call(this,\"p224\",\"ffffffff ffffffff ffffffff ffffffff 00000000 00000000 00000001\")}function b(){v.call(this,\"p192\",\"ffffffff ffffffff ffffffff fffffffe ffffffff ffffffff\")}function w(){v.call(this,\"25519\",\"7fffffffffffffff ffffffffffffffff ffffffffffffffff ffffffffffffffed\")}function T(t){if(\"string\"==typeof t){var e=a._prime(t);this.m=e.p,this.prime=e}else n(t.gtn(1),\"modulus must be greater than 1\"),this.m=t,this.prime=null}function k(t){T.call(this,t),this.shift=this.m.bitLength(),this.shift%26!=0&&(this.shift+=26-this.shift%26),this.r=new a(1).iushln(this.shift),this.r2=this.imod(this.r.sqr()),this.rinv=this.r._invmp(this.m),this.minv=this.rinv.mul(this.r).isubn(1).div(this.m),this.minv=this.minv.umod(this.r),this.minv=this.r.sub(this.minv)}v.prototype._tmp=function(){var t=new a(null);return t.words=new Array(Math.ceil(this.n/13)),t},v.prototype.ireduce=function(t){var e,r=t;do{this.split(r,this.tmp),e=(r=(r=this.imulK(r)).iadd(this.tmp)).bitLength()}while(e>this.n);var n=e0?r.isub(this.p):void 0!==r.strip?r.strip():r._strip(),r},v.prototype.split=function(t,e){t.iushrn(this.n,0,e)},v.prototype.imulK=function(t){return t.imul(this.k)},i(x,v),x.prototype.split=function(t,e){for(var r=4194303,n=Math.min(t.length,9),i=0;i>>22,a=o}a>>>=22,t.words[i-10]=a,0===a&&t.length>10?t.length-=10:t.length-=9},x.prototype.imulK=function(t){t.words[t.length]=0,t.words[t.length+1]=0,t.length+=2;for(var e=0,r=0;r>>=26,t.words[r]=i,e=n}return 0!==e&&(t.words[t.length++]=e),t},a._prime=function(t){if(y[t])return y[t];var e;if(\"k256\"===t)e=new x;else if(\"p224\"===t)e=new _;else if(\"p192\"===t)e=new b;else{if(\"p25519\"!==t)throw new Error(\"Unknown prime \"+t);e=new w}return y[t]=e,e},T.prototype._verify1=function(t){n(0===t.negative,\"red works only with positives\"),n(t.red,\"red works only with red numbers\")},T.prototype._verify2=function(t,e){n(0==(t.negative|e.negative),\"red works only with positives\"),n(t.red&&t.red===e.red,\"red works only with red numbers\")},T.prototype.imod=function(t){return this.prime?this.prime.ireduce(t)._forceRed(this):t.umod(this.m)._forceRed(this)},T.prototype.neg=function(t){return t.isZero()?t.clone():this.m.sub(t)._forceRed(this)},T.prototype.add=function(t,e){this._verify2(t,e);var r=t.add(e);return r.cmp(this.m)>=0&&r.isub(this.m),r._forceRed(this)},T.prototype.iadd=function(t,e){this._verify2(t,e);var r=t.iadd(e);return r.cmp(this.m)>=0&&r.isub(this.m),r},T.prototype.sub=function(t,e){this._verify2(t,e);var r=t.sub(e);return r.cmpn(0)<0&&r.iadd(this.m),r._forceRed(this)},T.prototype.isub=function(t,e){this._verify2(t,e);var r=t.isub(e);return r.cmpn(0)<0&&r.iadd(this.m),r},T.prototype.shl=function(t,e){return this._verify1(t),this.imod(t.ushln(e))},T.prototype.imul=function(t,e){return this._verify2(t,e),this.imod(t.imul(e))},T.prototype.mul=function(t,e){return this._verify2(t,e),this.imod(t.mul(e))},T.prototype.isqr=function(t){return this.imul(t,t.clone())},T.prototype.sqr=function(t){return this.mul(t,t)},T.prototype.sqrt=function(t){if(t.isZero())return t.clone();var e=this.m.andln(3);if(n(e%2==1),3===e){var r=this.m.add(new a(1)).iushrn(2);return this.pow(t,r)}for(var i=this.m.subn(1),o=0;!i.isZero()&&0===i.andln(1);)o++,i.iushrn(1);n(!i.isZero());var s=new a(1).toRed(this),l=s.redNeg(),c=this.m.subn(1).iushrn(1),u=this.m.bitLength();for(u=new a(2*u*u).toRed(this);0!==this.pow(u,c).cmp(l);)u.redIAdd(l);for(var h=this.pow(u,i),f=this.pow(t,i.addn(1).iushrn(1)),p=this.pow(t,i),d=o;0!==p.cmp(s);){for(var m=p,g=0;0!==m.cmp(s);g++)m=m.redSqr();n(g=0;n--){for(var c=e.words[n],u=l-1;u>=0;u--){var h=c>>u&1;i!==r[0]&&(i=this.sqr(i)),0!==h||0!==o?(o<<=1,o|=h,(4==++s||0===n&&0===u)&&(i=this.mul(i,r[o]),s=0,o=0)):s=0}l=26}return i},T.prototype.convertTo=function(t){var e=t.umod(this.m);return e===t?e.clone():e},T.prototype.convertFrom=function(t){var e=t.clone();return e.red=null,e},a.mont=function(t){return new k(t)},i(k,T),k.prototype.convertTo=function(t){return this.imod(t.ushln(this.shift))},k.prototype.convertFrom=function(t){var e=this.imod(t.mul(this.rinv));return e.red=null,e},k.prototype.imul=function(t,e){if(t.isZero()||e.isZero())return t.words[0]=0,t.length=1,t;var r=t.imul(e),n=r.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),i=r.isub(n).iushrn(this.shift),a=i;return i.cmp(this.m)>=0?a=i.isub(this.m):i.cmpn(0)<0&&(a=i.iadd(this.m)),a._forceRed(this)},k.prototype.mul=function(t,e){if(t.isZero()||e.isZero())return new a(0)._forceRed(this);var r=t.mul(e),n=r.maskn(this.shift).mul(this.minv).imaskn(this.shift).mul(this.m),i=r.isub(n).iushrn(this.shift),o=i;return i.cmp(this.m)>=0?o=i.isub(this.m):i.cmpn(0)<0&&(o=i.iadd(this.m)),o._forceRed(this)},k.prototype.invm=function(t){return this.imod(t._invmp(this.m).mul(this.r2))._forceRed(this)}}(t=r.nmd(t),this)},6204:function(t){\"use strict\";t.exports=function(t){var e,r,n,i=t.length,a=0;for(e=0;e>>1;if(!(u<=0)){var h,f=i.mallocDouble(2*u*s),p=i.mallocInt32(s);if((s=l(t,u,f,p))>0){if(1===u&&n)a.init(s),h=a.sweepComplete(u,r,0,s,f,p,0,s,f,p);else{var d=i.mallocDouble(2*u*c),m=i.mallocInt32(c);(c=l(e,u,d,m))>0&&(a.init(s+c),h=1===u?a.sweepBipartite(u,r,0,s,f,p,0,c,d,m):o(u,r,n,s,f,p,c,d,m),i.free(d),i.free(m))}i.free(f),i.free(p)}return h}}}function u(t,e){n.push([t,e])}},2455:function(t,e){\"use strict\";function r(t){return t?function(t,e,r,n,i,a,o,s,l,c,u){return i-n>l-s?function(t,e,r,n,i,a,o,s,l,c,u){for(var h=2*t,f=n,p=h*n;fc-l?n?function(t,e,r,n,i,a,o,s,l,c,u){for(var h=2*t,f=n,p=h*n;f0;){var O=(P-=1)*_,D=w[O],R=w[O+1],F=w[O+2],B=w[O+3],N=w[O+4],j=w[O+5],U=P*b,V=T[U],q=T[U+1],H=1&j,G=!!(16&j),Z=u,W=S,Y=C,X=L;if(H&&(Z=C,W=L,Y=u,X=S),!(2&j&&R>=(F=g(t,D,R,F,Z,W,q))||4&j&&(R=y(t,D,R,F,Z,W,V))>=F)){var 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if(f.right===s){if(!(d=p.left)||0!==d._color){p._color=0,p.right=f.left,f._color=1,f.left=p,l[h-2]=f,l[h-1]=s,i(p),i(f),h>=3&&((m=l[h-3]).right===p?m.right=f:m.left=f);break}f._color=1,p.left=n(1,d),p._color=0,h-=1}else{var d;if(!(d=p.left)||0!==d._color){var m;f.left=s.right,p._color=0,p.right=s.left,s._color=1,s.right=f,s.left=p,l[h-2]=s,l[h-1]=f,i(p),i(f),i(s),h>=3&&((m=l[h-3]).right===p?m.right=s:m.left=s);break}f._color=1,p.left=n(1,d),p._color=0,h-=1}}return l[0]._color=1,new a(o,l[0])},o.forEach=function(t,e,r){if(this.root)switch(arguments.length){case 1:return s(t,this.root);case 2:return l(e,this._compare,t,this.root);case 3:if(this._compare(e,r)>=0)return;return c(e,r,this._compare,t,this.root)}},Object.defineProperty(o,\"begin\",{get:function(){for(var t=[],e=this.root;e;)t.push(e),e=e.left;return new u(this,t)}}),Object.defineProperty(o,\"end\",{get:function(){for(var t=[],e=this.root;e;)t.push(e),e=e.right;return new u(this,t)}}),o.at=function(t){if(t<0)return new u(this,[]);for(var e=this.root,r=[];;){if(r.push(e),e.left){if(t=e.right._count)break;e=e.right}return new u(this,[])},o.ge=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a<=0&&(i=n.length),r=a<=0?r.left:r.right}return n.length=i,new u(this,n)},o.gt=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a<0&&(i=n.length),r=a<0?r.left:r.right}return n.length=i,new u(this,n)},o.lt=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a>0&&(i=n.length),r=a<=0?r.left:r.right}return n.length=i,new u(this,n)},o.le=function(t){for(var e=this._compare,r=this.root,n=[],i=0;r;){var a=e(t,r.key);n.push(r),a>=0&&(i=n.length),r=a<0?r.left:r.right}return n.length=i,new u(this,n)},o.find=function(t){for(var e=this._compare,r=this.root,n=[];r;){var i=e(t,r.key);if(n.push(r),0===i)return new u(this,n);r=i<=0?r.left:r.right}return new u(this,[])},o.remove=function(t){var e=this.find(t);return e?e.remove():this},o.get=function(t){for(var e=this._compare,r=this.root;r;){var n=e(t,r.key);if(0===n)return r.value;r=n<=0?r.left:r.right}};var h=u.prototype;function f(t,e){t.key=e.key,t.value=e.value,t.left=e.left,t.right=e.right,t._color=e._color,t._count=e._count}function p(t,e){return te?1:0}Object.defineProperty(h,\"valid\",{get:function(){return this._stack.length>0}}),Object.defineProperty(h,\"node\",{get:function(){return this._stack.length>0?this._stack[this._stack.length-1]:null},enumerable:!0}),h.clone=function(){return new u(this.tree,this._stack.slice())},h.remove=function(){var t=this._stack;if(0===t.length)return this.tree;var o=new Array(t.length),s=t[t.length-1];o[o.length-1]=new e(s._color,s.key,s.value,s.left,s.right,s._count);for(var l=t.length-2;l>=0;--l)(s=t[l]).left===t[l+1]?o[l]=new e(s._color,s.key,s.value,o[l+1],s.right,s._count):o[l]=new e(s._color,s.key,s.value,s.left,o[l+1],s._count);if((s=o[o.length-1]).left&&s.right){var c=o.length;for(s=s.left;s.right;)o.push(s),s=s.right;var u=o[c-1];for(o.push(new e(s._color,u.key,u.value,s.left,s.right,s._count)),o[c-1].key=s.key,o[c-1].value=s.value,l=o.length-2;l>=c;--l)s=o[l],o[l]=new e(s._color,s.key,s.value,s.left,o[l+1],s._count);o[c-1].left=o[c]}if(0===(s=o[o.length-1])._color){var h=o[o.length-2];for(h.left===s?h.left=null:h.right===s&&(h.right=null),o.pop(),l=0;l=0;--l){if(e=t[l],0===l)return void(e._color=1);if((a=t[l-1]).left===e){if((o=a.right).right&&0===o.right._color)return s=(o=a.right=r(o)).right=r(o.right),a.right=o.left,o.left=a,o.right=s,o._color=a._color,e._color=1,a._color=1,s._color=1,i(a),i(o),l>1&&((c=t[l-2]).left===a?c.left=o:c.right=o),void(t[l-1]=o);if(o.left&&0===o.left._color)return s=(o=a.right=r(o)).left=r(o.left),a.right=s.left,o.left=s.right,s.left=a,s.right=o,s._color=a._color,a._color=1,o._color=1,e._color=1,i(a),i(o),i(s),l>1&&((c=t[l-2]).left===a?c.left=s:c.right=s),void(t[l-1]=s);if(1===o._color){if(0===a._color)return a._color=1,void(a.right=n(0,o));a.right=n(0,o);continue}o=r(o),a.right=o.left,o.left=a,o._color=a._color,a._color=0,i(a),i(o),l>1&&((c=t[l-2]).left===a?c.left=o:c.right=o),t[l-1]=o,t[l]=a,l+11&&((c=t[l-2]).right===a?c.right=o:c.left=o),void(t[l-1]=o);if(o.right&&0===o.right._color)return s=(o=a.left=r(o)).right=r(o.right),a.left=s.right,o.right=s.left,s.right=a,s.left=o,s._color=a._color,a._color=1,o._color=1,e._color=1,i(a),i(o),i(s),l>1&&((c=t[l-2]).right===a?c.right=s:c.left=s),void(t[l-1]=s);if(1===o._color){if(0===a._color)return a._color=1,void(a.left=n(0,o));a.left=n(0,o);continue}var c;o=r(o),a.left=o.right,o.right=a,o._color=a._color,a._color=0,i(a),i(o),l>1&&((c=t[l-2]).right===a?c.right=o:c.left=o),t[l-1]=o,t[l]=a,l+10)return this._stack[this._stack.length-1].key},enumerable:!0}),Object.defineProperty(h,\"value\",{get:function(){if(this._stack.length>0)return this._stack[this._stack.length-1].value},enumerable:!0}),Object.defineProperty(h,\"index\",{get:function(){var t=0,e=this._stack;if(0===e.length){var r=this.tree.root;return r?r._count:0}e[e.length-1].left&&(t=e[e.length-1].left._count);for(var n=e.length-2;n>=0;--n)e[n+1]===e[n].right&&(++t,e[n].left&&(t+=e[n].left._count));return t},enumerable:!0}),h.next=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.right)for(e=e.right;e;)t.push(e),e=e.left;else for(t.pop();t.length>0&&t[t.length-1].right===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(h,\"hasNext\",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].right)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].left===t[e])return!0;return!1}}),h.update=function(t){var r=this._stack;if(0===r.length)throw new Error(\"Can't update empty node!\");var n=new Array(r.length),i=r[r.length-1];n[n.length-1]=new e(i._color,i.key,t,i.left,i.right,i._count);for(var o=r.length-2;o>=0;--o)(i=r[o]).left===r[o+1]?n[o]=new e(i._color,i.key,i.value,n[o+1],i.right,i._count):n[o]=new e(i._color,i.key,i.value,i.left,n[o+1],i._count);return new a(this.tree._compare,n[0])},h.prev=function(){var t=this._stack;if(0!==t.length){var e=t[t.length-1];if(e.left)for(e=e.left;e;)t.push(e),e=e.right;else for(t.pop();t.length>0&&t[t.length-1].left===e;)e=t[t.length-1],t.pop()}},Object.defineProperty(h,\"hasPrev\",{get:function(){var t=this._stack;if(0===t.length)return!1;if(t[t.length-1].left)return!0;for(var e=t.length-1;e>0;--e)if(t[e-1].right===t[e])return!0;return!1}})},3837:function(t,e,r){\"use strict\";t.exports=function(t,e){var r=new p(t);return r.update(e),r};var n=r(4935),i=r(501),a=r(5304),o=r(6429),s=r(6444),l=new Float32Array([1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1]),c=ArrayBuffer,u=DataView;function h(t){return Array.isArray(t)||function(t){return c.isView(t)&&!(t instanceof u)}(t)}function f(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t}function p(t){this.gl=t,this.pixelRatio=1,this.bounds=[[-10,-10,-10],[10,10,10]],this.ticks=[[],[],[]],this.autoTicks=!0,this.tickSpacing=[1,1,1],this.tickEnable=[!0,!0,!0],this.tickFont=[\"sans-serif\",\"sans-serif\",\"sans-serif\"],this.tickFontStyle=[\"normal\",\"normal\",\"normal\"],this.tickFontWeight=[\"normal\",\"normal\",\"normal\"],this.tickFontVariant=[\"normal\",\"normal\",\"normal\"],this.tickSize=[12,12,12],this.tickAngle=[0,0,0],this.tickAlign=[\"auto\",\"auto\",\"auto\"],this.tickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.tickPad=[10,10,10],this.lastCubeProps={cubeEdges:[0,0,0],axis:[0,0,0]},this.labels=[\"x\",\"y\",\"z\"],this.labelEnable=[!0,!0,!0],this.labelFont=[\"sans-serif\",\"sans-serif\",\"sans-serif\"],this.labelFontStyle=[\"normal\",\"normal\",\"normal\"],this.labelFontWeight=[\"normal\",\"normal\",\"normal\"],this.labelFontVariant=[\"normal\",\"normal\",\"normal\"],this.labelSize=[20,20,20],this.labelAngle=[0,0,0],this.labelAlign=[\"auto\",\"auto\",\"auto\"],this.labelColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.labelPad=[10,10,10],this.lineEnable=[!0,!0,!0],this.lineMirror=[!1,!1,!1],this.lineWidth=[1,1,1],this.lineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.lineTickEnable=[!0,!0,!0],this.lineTickMirror=[!1,!1,!1],this.lineTickLength=[0,0,0],this.lineTickWidth=[1,1,1],this.lineTickColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.gridEnable=[!0,!0,!0],this.gridWidth=[1,1,1],this.gridColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroEnable=[!0,!0,!0],this.zeroLineColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.zeroLineWidth=[2,2,2],this.backgroundEnable=[!1,!1,!1],this.backgroundColor=[[.8,.8,.8,.5],[.8,.8,.8,.5],[.8,.8,.8,.5]],this._firstInit=!0,this._text=null,this._lines=null,this._background=a(t)}var d=p.prototype;function m(){this.primalOffset=[0,0,0],this.primalMinor=[0,0,0],this.mirrorOffset=[0,0,0],this.mirrorMinor=[0,0,0]}d.update=function(t){function e(e,r,n){if(n in t){var i,a=t[n],o=this[n];(e?h(a)&&h(a[0]):h(a))?this[n]=i=[r(a[0]),r(a[1]),r(a[2])]:this[n]=i=[r(a),r(a),r(a)];for(var s=0;s<3;++s)if(i[s]!==o[s])return!0}return!1}t=t||{};var r,a=e.bind(this,!1,Number),o=e.bind(this,!1,Boolean),l=e.bind(this,!1,String),c=e.bind(this,!0,(function(t){if(h(t)){if(3===t.length)return[+t[0],+t[1],+t[2],1];if(4===t.length)return[+t[0],+t[1],+t[2],+t[3]]}return[0,0,0,1]})),u=!1,f=!1;if(\"bounds\"in t)for(var p=t.bounds,d=0;d<2;++d)for(var m=0;m<3;++m)p[d][m]!==this.bounds[d][m]&&(f=!0),this.bounds[d][m]=p[d][m];if(\"ticks\"in t)for(r=t.ticks,u=!0,this.autoTicks=!1,d=0;d<3;++d)this.tickSpacing[d]=0;else a(\"tickSpacing\")&&(this.autoTicks=!0,f=!0);if(this._firstInit&&(\"ticks\"in t||\"tickSpacing\"in t||(this.autoTicks=!0),f=!0,u=!0,this._firstInit=!1),f&&this.autoTicks&&(r=s.create(this.bounds,this.tickSpacing),u=!0),u){for(d=0;d<3;++d)r[d].sort((function(t,e){return t.x-e.x}));s.equal(r,this.ticks)?u=!1:this.ticks=r}o(\"tickEnable\"),l(\"tickFont\")&&(u=!0),l(\"tickFontStyle\")&&(u=!0),l(\"tickFontWeight\")&&(u=!0),l(\"tickFontVariant\")&&(u=!0),a(\"tickSize\"),a(\"tickAngle\"),a(\"tickPad\"),c(\"tickColor\");var g=l(\"labels\");l(\"labelFont\")&&(g=!0),l(\"labelFontStyle\")&&(g=!0),l(\"labelFontWeight\")&&(g=!0),l(\"labelFontVariant\")&&(g=!0),o(\"labelEnable\"),a(\"labelSize\"),a(\"labelPad\"),c(\"labelColor\"),o(\"lineEnable\"),o(\"lineMirror\"),a(\"lineWidth\"),c(\"lineColor\"),o(\"lineTickEnable\"),o(\"lineTickMirror\"),a(\"lineTickLength\"),a(\"lineTickWidth\"),c(\"lineTickColor\"),o(\"gridEnable\"),a(\"gridWidth\"),c(\"gridColor\"),o(\"zeroEnable\"),c(\"zeroLineColor\"),a(\"zeroLineWidth\"),o(\"backgroundEnable\"),c(\"backgroundColor\");var y=[{family:this.labelFont[0],style:this.labelFontStyle[0],weight:this.labelFontWeight[0],variant:this.labelFontVariant[0]},{family:this.labelFont[1],style:this.labelFontStyle[1],weight:this.labelFontWeight[1],variant:this.labelFontVariant[1]},{family:this.labelFont[2],style:this.labelFontStyle[2],weight:this.labelFontWeight[2],variant:this.labelFontVariant[2]}],v=[{family:this.tickFont[0],style:this.tickFontStyle[0],weight:this.tickFontWeight[0],variant:this.tickFontVariant[0]},{family:this.tickFont[1],style:this.tickFontStyle[1],weight:this.tickFontWeight[1],variant:this.tickFontVariant[1]},{family:this.tickFont[2],style:this.tickFontStyle[2],weight:this.tickFontWeight[2],variant:this.tickFontVariant[2]}];this._text?this._text&&(g||u)&&this._text.update(this.bounds,this.labels,y,this.ticks,v):this._text=n(this.gl,this.bounds,this.labels,y,this.ticks,v),this._lines&&u&&(this._lines.dispose(),this._lines=null),this._lines||(this._lines=i(this.gl,this.bounds,this.ticks))};var g=[new m,new m,new m];function y(t,e,r,n,i){for(var a=t.primalOffset,o=t.primalMinor,s=t.mirrorOffset,l=t.mirrorMinor,c=n[e],u=0;u<3;++u)if(e!==u){var h=a,f=s,p=o,d=l;c&1<0?(p[u]=-1,d[u]=0):(p[u]=0,d[u]=1)}}var v=[0,0,0],x={model:l,view:l,projection:l,_ortho:!1};d.isOpaque=function(){return!0},d.isTransparent=function(){return!1},d.drawTransparent=function(t){};var _=[0,0,0],b=[0,0,0],w=[0,0,0];d.draw=function(t){t=t||x;for(var e=this.gl,r=t.model||l,n=t.view||l,i=t.projection||l,a=this.bounds,s=t._ortho||!1,c=o(r,n,i,a,s),u=c.cubeEdges,h=c.axis,p=n[12],d=n[13],m=n[14],T=n[15],k=(s?2:1)*this.pixelRatio*(i[3]*p+i[7]*d+i[11]*m+i[15]*T)/e.drawingBufferHeight,A=0;A<3;++A)this.lastCubeProps.cubeEdges[A]=u[A],this.lastCubeProps.axis[A]=h[A];var M=g;for(A=0;A<3;++A)y(g[A],A,this.bounds,u,h);e=this.gl;var S,E,C,L=v;for(A=0;A<3;++A)this.backgroundEnable[A]?L[A]=h[A]:L[A]=0;for(this._background.draw(r,n,i,a,L,this.backgroundColor),this._lines.bind(r,n,i,this),A=0;A<3;++A){var I=[0,0,0];h[A]>0?I[A]=a[1][A]:I[A]=a[0][A];for(var P=0;P<2;++P){var z=(A+1+P)%3,O=(A+1+(1^P))%3;this.gridEnable[z]&&this._lines.drawGrid(z,O,this.bounds,I,this.gridColor[z],this.gridWidth[z]*this.pixelRatio)}for(P=0;P<2;++P)z=(A+1+P)%3,O=(A+1+(1^P))%3,this.zeroEnable[O]&&Math.min(a[0][O],a[1][O])<=0&&Math.max(a[0][O],a[1][O])>=0&&this._lines.drawZero(z,O,this.bounds,I,this.zeroLineColor[O],this.zeroLineWidth[O]*this.pixelRatio)}for(A=0;A<3;++A){this.lineEnable[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].primalOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio),this.lineMirror[A]&&this._lines.drawAxisLine(A,this.bounds,M[A].mirrorOffset,this.lineColor[A],this.lineWidth[A]*this.pixelRatio);var D=f(_,M[A].primalMinor),R=f(b,M[A].mirrorMinor),F=this.lineTickLength;for(P=0;P<3;++P){var B=k/r[5*P];D[P]*=F[P]*B,R[P]*=F[P]*B}this.lineTickEnable[A]&&this._lines.drawAxisTicks(A,M[A].primalOffset,D,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio),this.lineTickMirror[A]&&this._lines.drawAxisTicks(A,M[A].mirrorOffset,R,this.lineTickColor[A],this.lineTickWidth[A]*this.pixelRatio)}function N(t){(C=[0,0,0])[t]=1}function j(t,e,r){var n=(t+1)%3,i=(t+2)%3,a=e[n],o=e[i],s=r[n],l=r[i];a>0&&l>0||a>0&&l<0||a<0&&l>0||a<0&&l<0?N(n):(o>0&&s>0||o>0&&s<0||o<0&&s>0||o<0&&s<0)&&N(i)}for(this._lines.unbind(),this._text.bind(r,n,i,this.pixelRatio),A=0;A<3;++A){var U=M[A].primalMinor,V=M[A].mirrorMinor,q=f(w,M[A].primalOffset);for(P=0;P<3;++P)this.lineTickEnable[A]&&(q[P]+=k*U[P]*Math.max(this.lineTickLength[P],0)/r[5*P]);var H=[0,0,0];if(H[A]=1,this.tickEnable[A]){for(-3600===this.tickAngle[A]?(this.tickAngle[A]=0,this.tickAlign[A]=\"auto\"):this.tickAlign[A]=-1,E=1,\"auto\"===(S=[this.tickAlign[A],.5,E])[0]?S[0]=0:S[0]=parseInt(\"\"+S[0]),C=[0,0,0],j(A,U,V),P=0;P<3;++P)q[P]+=k*U[P]*this.tickPad[P]/r[5*P];this._text.drawTicks(A,this.tickSize[A],this.tickAngle[A],q,this.tickColor[A],H,C,S)}if(this.labelEnable[A]){for(E=0,C=[0,0,0],this.labels[A].length>4&&(N(A),E=1),\"auto\"===(S=[this.labelAlign[A],.5,E])[0]?S[0]=0:S[0]=parseInt(\"\"+S[0]),P=0;P<3;++P)q[P]+=k*U[P]*this.labelPad[P]/r[5*P];q[A]+=.5*(a[0][A]+a[1][A]),this._text.drawLabel(A,this.labelSize[A],this.labelAngle[A],q,this.labelColor[A],[0,0,0],C,S)}}this._text.unbind()},d.dispose=function(){this._text.dispose(),this._lines.dispose(),this._background.dispose(),this._lines=null,this._text=null,this._background=null,this.gl=null}},5304:function(t,e,r){\"use strict\";t.exports=function(t){for(var e=[],r=[],s=0,l=0;l<3;++l)for(var c=(l+1)%3,u=(l+2)%3,h=[0,0,0],f=[0,0,0],p=-1;p<=1;p+=2){r.push(s,s+2,s+1,s+1,s+2,s+3),h[l]=p,f[l]=p;for(var d=-1;d<=1;d+=2){h[c]=d;for(var m=-1;m<=1;m+=2)h[u]=m,e.push(h[0],h[1],h[2],f[0],f[1],f[2]),s+=1}var g=c;c=u,u=g}var y=n(t,new Float32Array(e)),v=n(t,new Uint16Array(r),t.ELEMENT_ARRAY_BUFFER),x=i(t,[{buffer:y,type:t.FLOAT,size:3,offset:0,stride:24},{buffer:y,type:t.FLOAT,size:3,offset:12,stride:24}],v),_=a(t);return _.attributes.position.location=0,_.attributes.normal.location=1,new o(t,y,x,_)};var n=r(2762),i=r(8116),a=r(1879).bg;function o(t,e,r,n){this.gl=t,this.buffer=e,this.vao=r,this.shader=n}var s=o.prototype;s.draw=function(t,e,r,n,i,a){for(var o=!1,s=0;s<3;++s)o=o||i[s];if(o){var l=this.gl;l.enable(l.POLYGON_OFFSET_FILL),l.polygonOffset(1,2),this.shader.bind(),this.shader.uniforms={model:t,view:e,projection:r,bounds:n,enable:i,colors:a},this.vao.bind(),this.vao.draw(this.gl.TRIANGLES,36),this.vao.unbind(),l.disable(l.POLYGON_OFFSET_FILL)}},s.dispose=function(){this.vao.dispose(),this.buffer.dispose(),this.shader.dispose()}},6429:function(t,e,r){\"use strict\";t.exports=function(t,e,r,a,p){i(s,e,t),i(s,r,s);for(var v=0,x=0;x<2;++x){u[2]=a[x][2];for(var _=0;_<2;++_){u[1]=a[_][1];for(var b=0;b<2;++b)u[0]=a[b][0],f(l[v],u,s),v+=1}}var w=-1;for(x=0;x<8;++x){for(var T=l[x][3],k=0;k<3;++k)c[x][k]=l[x][k]/T;p&&(c[x][2]*=-1),T<0&&(w<0||c[x][2]E&&(w|=1<E&&(w|=1<c[x][1])&&(R=x);var F=-1;for(x=0;x<3;++x)(N=R^1<c[B][0]&&(B=N))}var j=m;j[0]=j[1]=j[2]=0,j[n.log2(F^R)]=R&F,j[n.log2(R^B)]=R&B;var U=7^B;U===w||U===D?(U=7^F,j[n.log2(B^U)]=U&B):j[n.log2(F^U)]=U&F;var V=g,q=w;for(A=0;A<3;++A)V[A]=q&1< HALF_PI) && (b <= ONE_AND_HALF_PI)) ?\\n b - PI :\\n b;\\n}\\n\\nfloat look_horizontal_or_vertical(float a, float ratio) {\\n // ratio controls the ratio between being horizontal to (vertical + horizontal)\\n // if ratio is set to 0.5 then it is 50%, 50%.\\n // when using a higher ratio e.g. 0.75 the result would\\n // likely be more horizontal than vertical.\\n\\n float b = positive_angle(a);\\n\\n return\\n (b < ( ratio) * HALF_PI) ? 0.0 :\\n (b < (2.0 - ratio) * HALF_PI) ? -HALF_PI :\\n (b < (2.0 + ratio) * HALF_PI) ? 0.0 :\\n (b < (4.0 - ratio) * HALF_PI) ? HALF_PI :\\n 0.0;\\n}\\n\\nfloat roundTo(float a, float b) {\\n return float(b * floor((a + 0.5 * b) / b));\\n}\\n\\nfloat look_round_n_directions(float a, int n) {\\n float b = positive_angle(a);\\n float div = TWO_PI / float(n);\\n float c = roundTo(b, div);\\n return look_upwards(c);\\n}\\n\\nfloat applyAlignOption(float rawAngle, float delta) {\\n return\\n (option > 2) ? look_round_n_directions(rawAngle + delta, option) : // option 3-n: round to n directions\\n (option == 2) ? look_horizontal_or_vertical(rawAngle + delta, hv_ratio) : // horizontal or vertical\\n (option == 1) ? rawAngle + delta : // use free angle, and flip to align with one direction of the axis\\n (option == 0) ? look_upwards(rawAngle) : // use free angle, and stay upwards\\n (option ==-1) ? 0.0 : // useful for backward compatibility, all texts remains horizontal\\n rawAngle; // otherwise return back raw input angle\\n}\\n\\nbool isAxisTitle = (axis.x == 0.0) &&\\n (axis.y == 0.0) &&\\n (axis.z == 0.0);\\n\\nvoid main() {\\n //Compute world offset\\n float axisDistance = position.z;\\n vec3 dataPosition = axisDistance * axis + offset;\\n\\n float beta = angle; // i.e. user defined attributes for each tick\\n\\n float axisAngle;\\n float clipAngle;\\n float flip;\\n\\n if (enableAlign) {\\n axisAngle = (isAxisTitle) ? HALF_PI :\\n computeViewAngle(dataPosition, dataPosition + axis);\\n clipAngle = computeViewAngle(dataPosition, dataPosition + alignDir);\\n\\n axisAngle += (sin(axisAngle) < 0.0) ? PI : 0.0;\\n clipAngle += (sin(clipAngle) < 0.0) ? PI : 0.0;\\n\\n flip = (dot(vec2(cos(axisAngle), sin(axisAngle)),\\n vec2(sin(clipAngle),-cos(clipAngle))) > 0.0) ? 1.0 : 0.0;\\n\\n beta += applyAlignOption(clipAngle, flip * PI);\\n }\\n\\n //Compute plane offset\\n vec2 planeCoord = position.xy * pixelScale;\\n\\n mat2 planeXform = scale * mat2(\\n cos(beta), sin(beta),\\n -sin(beta), cos(beta)\\n );\\n\\n vec2 viewOffset = 2.0 * planeXform * planeCoord / resolution;\\n\\n //Compute clip position\\n vec3 clipPosition = project(dataPosition);\\n\\n //Apply text offset in clip coordinates\\n clipPosition += vec3(viewOffset, 0.0);\\n\\n //Done\\n gl_Position = vec4(clipPosition, 1.0);\\n}\\n\"]),l=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\nvoid main() {\\n gl_FragColor = color;\\n}\"]);e.Q=function(t){return i(t,s,l,null,[{name:\"position\",type:\"vec3\"}])};var c=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec3 normal;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 enable;\\nuniform vec3 bounds[2];\\n\\nvarying vec3 colorChannel;\\n\\nvoid main() {\\n\\n vec3 signAxis = sign(bounds[1] - bounds[0]);\\n\\n vec3 realNormal = signAxis * normal;\\n\\n if(dot(realNormal, enable) > 0.0) {\\n vec3 minRange = min(bounds[0], bounds[1]);\\n vec3 maxRange = max(bounds[0], bounds[1]);\\n vec3 nPosition = mix(minRange, maxRange, 0.5 * (position + 1.0));\\n gl_Position = projection * (view * (model * vec4(nPosition, 1.0)));\\n } else {\\n gl_Position = vec4(0,0,0,0);\\n }\\n\\n colorChannel = abs(realNormal);\\n}\\n\"]),u=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec4 colors[3];\\n\\nvarying vec3 colorChannel;\\n\\nvoid main() {\\n gl_FragColor = colorChannel.x * colors[0] +\\n colorChannel.y * colors[1] +\\n colorChannel.z * colors[2];\\n}\"]);e.bg=function(t){return i(t,c,u,null,[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"}])}},4935:function(t,e,r){\"use strict\";t.exports=function(t,e,r,i,o,l){var c=n(t),h=a(t,[{buffer:c,size:3}]),f=s(t);f.attributes.position.location=0;var p=new u(t,f,c,h);return p.update(e,r,i,o,l),p};var n=r(2762),a=r(8116),o=r(4359),s=r(1879).Q,l=window||i.global||{},c=l.__TEXT_CACHE||{};function u(t,e,r,n){this.gl=t,this.shader=e,this.buffer=r,this.vao=n,this.tickOffset=this.tickCount=this.labelOffset=this.labelCount=null}l.__TEXT_CACHE={};var h=u.prototype,f=[0,0];h.bind=function(t,e,r,n){this.vao.bind(),this.shader.bind();var i=this.shader.uniforms;i.model=t,i.view=e,i.projection=r,i.pixelScale=n,f[0]=this.gl.drawingBufferWidth,f[1]=this.gl.drawingBufferHeight,this.shader.uniforms.resolution=f},h.unbind=function(){this.vao.unbind()},h.update=function(t,e,r,n,i){var a=[];function s(t,e,r,n,i,s){var l=[r.style,r.weight,r.variant,r.family].join(\"_\"),u=c[l];u||(u=c[l]={});var h=u[e];h||(h=u[e]=function(t,e){try{return o(t,e)}catch(e){return console.warn('error vectorizing text:\"'+t+'\" error:',e),{cells:[],positions:[]}}}(e,{triangles:!0,font:r.family,fontStyle:r.style,fontWeight:r.weight,fontVariant:r.variant,textAlign:\"center\",textBaseline:\"middle\",lineSpacing:i,styletags:s}));for(var f=(n||12)/12,p=h.positions,d=h.cells,m=0,g=d.length;m=0;--v){var x=p[y[v]];a.push(f*x[0],-f*x[1],t)}}for(var l=[0,0,0],u=[0,0,0],h=[0,0,0],f=[0,0,0],p={breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},d=0;d<3;++d){h[d]=a.length/3|0,s(.5*(t[0][d]+t[1][d]),e[d],r[d],12,1.25,p),f[d]=(a.length/3|0)-h[d],l[d]=a.length/3|0;for(var m=0;m=0&&(i=r.length-n-1);var a=Math.pow(10,i),o=Math.round(t*e*a),s=o+\"\";if(s.indexOf(\"e\")>=0)return s;var l=o/a,c=o%a;o<0?(l=0|-Math.ceil(l),c=0|-c):(l=0|Math.floor(l),c|=0);var u=\"\"+l;if(o<0&&(u=\"-\"+u),i){for(var h=\"\"+c;h.length=t[0][i];--o)a.push({x:o*e[i],text:r(e[i],o)});n.push(a)}return n},e.equal=function(t,e){for(var r=0;r<3;++r){if(t[r].length!==e[r].length)return!1;for(var n=0;nr)throw new Error(\"gl-buffer: If resizing buffer, must not specify offset\");return t.bufferSubData(e,a,i),r}function u(t,e){for(var r=n.malloc(t.length,e),i=t.length,a=0;a=0;--n){if(e[n]!==r)return!1;r*=t[n]}return!0}(t.shape,t.stride))0===t.offset&&t.data.length===t.shape[0]?this.length=c(this.gl,this.type,this.length,this.usage,t.data,e):this.length=c(this.gl,this.type,this.length,this.usage,t.data.subarray(t.offset,t.shape[0]),e);else{var s=n.malloc(t.size,r),l=a(s,t.shape);i.assign(l,t),this.length=c(this.gl,this.type,this.length,this.usage,e<0?s:s.subarray(0,t.size),e),n.free(s)}}else if(Array.isArray(t)){var h;h=this.type===this.gl.ELEMENT_ARRAY_BUFFER?u(t,\"uint16\"):u(t,\"float32\"),this.length=c(this.gl,this.type,this.length,this.usage,e<0?h:h.subarray(0,t.length),e),n.free(h)}else if(\"object\"==typeof t&&\"number\"==typeof t.length)this.length=c(this.gl,this.type,this.length,this.usage,t,e);else{if(\"number\"!=typeof t&&void 0!==t)throw new Error(\"gl-buffer: Invalid data type\");if(e>=0)throw new Error(\"gl-buffer: Cannot specify offset when resizing buffer\");(t|=0)<=0&&(t=1),this.gl.bufferData(this.type,0|t,this.usage),this.length=t}},t.exports=function(t,e,r,n){if(r=r||t.ARRAY_BUFFER,n=n||t.DYNAMIC_DRAW,r!==t.ARRAY_BUFFER&&r!==t.ELEMENT_ARRAY_BUFFER)throw new Error(\"gl-buffer: Invalid type for webgl buffer, must be either gl.ARRAY_BUFFER or gl.ELEMENT_ARRAY_BUFFER\");if(n!==t.DYNAMIC_DRAW&&n!==t.STATIC_DRAW&&n!==t.STREAM_DRAW)throw new Error(\"gl-buffer: Invalid usage for buffer, must be either gl.DYNAMIC_DRAW, gl.STATIC_DRAW or gl.STREAM_DRAW\");var i=t.createBuffer(),a=new s(t,r,i,0,n);return a.update(e),a}},6405:function(t,e,r){\"use strict\";var n=r(2931);t.exports=function(t,e){var r=t.positions,i=t.vectors,a={positions:[],vertexIntensity:[],vertexIntensityBounds:t.vertexIntensityBounds,vectors:[],cells:[],coneOffset:t.coneOffset,colormap:t.colormap};if(0===t.positions.length)return e&&(e[0]=[0,0,0],e[1]=[0,0,0]),a;for(var o=0,s=1/0,l=-1/0,c=1/0,u=-1/0,h=1/0,f=-1/0,p=null,d=null,m=[],g=1/0,y=!1,v=\"raw\"===t.coneSizemode,x=0;xo&&(o=n.length(b)),x&&!v){var w=2*n.distance(p,_)/(n.length(d)+n.length(b));w?(g=Math.min(g,w),y=!1):y=!0}y||(p=_,d=b),m.push(b)}var T=[s,c,h],k=[l,u,f];e&&(e[0]=T,e[1]=k),0===o&&(o=1);var A=1/o;isFinite(g)||(g=1),a.vectorScale=g;var M=t.coneSize||(v?1:.5);t.absoluteConeSize&&(M=t.absoluteConeSize*A),a.coneScale=M,x=0;for(var S=0;x=1},p.isTransparent=function(){return this.opacity<1},p.pickSlots=1,p.setPickBase=function(t){this.pickId=t},p.update=function(t){t=t||{};var e=this.gl;this.dirty=!0,\"lightPosition\"in t&&(this.lightPosition=t.lightPosition),\"opacity\"in t&&(this.opacity=t.opacity),\"ambient\"in t&&(this.ambientLight=t.ambient),\"diffuse\"in t&&(this.diffuseLight=t.diffuse),\"specular\"in t&&(this.specularLight=t.specular),\"roughness\"in t&&(this.roughness=t.roughness),\"fresnel\"in t&&(this.fresnel=t.fresnel),void 0!==t.tubeScale&&(this.tubeScale=t.tubeScale),void 0!==t.vectorScale&&(this.vectorScale=t.vectorScale),void 0!==t.coneScale&&(this.coneScale=t.coneScale),void 0!==t.coneOffset&&(this.coneOffset=t.coneOffset),t.colormap&&(this.texture.shape=[256,256],this.texture.minFilter=e.LINEAR_MIPMAP_LINEAR,this.texture.magFilter=e.LINEAR,this.texture.setPixels(function(t){for(var e=u({colormap:t,nshades:256,format:\"rgba\"}),r=new Uint8Array(1024),n=0;n<256;++n){for(var i=e[n],a=0;a<3;++a)r[4*n+a]=i[a];r[4*n+3]=255*i[3]}return c(r,[256,256,4],[4,0,1])}(t.colormap)),this.texture.generateMipmap());var r=t.cells,n=t.positions,i=t.vectors;if(n&&r&&i){var a=[],o=[],s=[],l=[],h=[];this.cells=r,this.positions=n,this.vectors=i;var f=t.meshColor||[1,1,1,1],p=t.vertexIntensity,d=1/0,m=-1/0;if(p)if(t.vertexIntensityBounds)d=+t.vertexIntensityBounds[0],m=+t.vertexIntensityBounds[1];else for(var g=0;g0){var m=this.triShader;m.bind(),m.uniforms=c,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()}},p.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||h,n=t.view||h,i=t.projection||h,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s={model:r,view:n,projection:i,clipBounds:a,tubeScale:this.tubeScale,vectorScale:this.vectorScale,coneScale:this.coneScale,coneOffset:this.coneOffset,pickId:this.pickId/255},l=this.pickShader;l.bind(),l.uniforms=s,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind())},p.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions[r[1]].slice(0,3),i={position:n,dataCoordinate:n,index:Math.floor(r[1]/48)};return\"cone\"===this.traceType?i.index=Math.floor(r[1]/48):\"streamtube\"===this.traceType&&(i.intensity=this.intensity[r[1]],i.velocity=this.vectors[r[1]].slice(0,3),i.divergence=this.vectors[r[1]][3],i.index=e),i},p.dispose=function(){this.texture.dispose(),this.triShader.dispose(),this.pickShader.dispose(),this.triangleVAO.dispose(),this.trianglePositions.dispose(),this.triangleVectors.dispose(),this.triangleColors.dispose(),this.triangleUVs.dispose(),this.triangleIds.dispose()},t.exports=function(t,e,r){var s=r.shaders;1===arguments.length&&(t=(e=t).gl);var l=function(t,e){var r=n(t,e.meshShader.vertex,e.meshShader.fragment,null,e.meshShader.attributes);return r.attributes.position.location=0,r.attributes.color.location=2,r.attributes.uv.location=3,r.attributes.vector.location=4,r}(t,s),u=function(t,e){var r=n(t,e.pickShader.vertex,e.pickShader.fragment,null,e.pickShader.attributes);return r.attributes.position.location=0,r.attributes.id.location=1,r.attributes.vector.location=4,r}(t,s),h=o(t,c(new Uint8Array([255,255,255,255]),[1,1,4]));h.generateMipmap(),h.minFilter=t.LINEAR_MIPMAP_LINEAR,h.magFilter=t.LINEAR;var p=i(t),d=i(t),m=i(t),g=i(t),y=i(t),v=new f(t,h,l,u,p,d,y,m,g,a(t,[{buffer:p,type:t.FLOAT,size:4},{buffer:y,type:t.UNSIGNED_BYTE,size:4,normalized:!0},{buffer:m,type:t.FLOAT,size:4},{buffer:g,type:t.FLOAT,size:2},{buffer:d,type:t.FLOAT,size:4}]),r.traceType||\"cone\");return v.update(e),v}},614:function(t,e,r){var n=r(3236),i=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n // Return up-vector for only-z vector.\\n // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n // From the above if-statement we have ||a|| > 0 U ||b|| > 0.\\n // Assign z = 0, x = -b, y = a:\\n // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n return normalize(vec3(-v.y, v.x, 0.0));\\n } else {\\n return normalize(vec3(0.0, v.z, -v.y));\\n }\\n}\\n\\n// Calculate the cone vertex and normal at the given index.\\n//\\n// The returned vertex is for a cone with its top at origin and height of 1.0,\\n// pointing in the direction of the vector attribute.\\n//\\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\\n// These vertices are used to make up the triangles of the cone by the following:\\n// segment + 0 top vertex\\n// segment + 1 perimeter vertex a+1\\n// segment + 2 perimeter vertex a\\n// segment + 3 center base vertex\\n// segment + 4 perimeter vertex a\\n// segment + 5 perimeter vertex a+1\\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\\n// To go from index to segment, floor(index / 6)\\n// To go from segment to angle, 2*pi * (segment/segmentCount)\\n// To go from index to segment index, index - (segment*6)\\n//\\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\\n\\n const float segmentCount = 8.0;\\n\\n float index = rawIndex - floor(rawIndex /\\n (segmentCount * 6.0)) *\\n (segmentCount * 6.0);\\n\\n float segment = floor(0.001 + index/6.0);\\n float segmentIndex = index - (segment*6.0);\\n\\n normal = -normalize(d);\\n\\n if (segmentIndex > 2.99 && segmentIndex < 3.01) {\\n return mix(vec3(0.0), -d, coneOffset);\\n }\\n\\n float nextAngle = (\\n (segmentIndex > 0.99 && segmentIndex < 1.01) ||\\n (segmentIndex > 4.99 && segmentIndex < 5.01)\\n ) ? 1.0 : 0.0;\\n float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\\n\\n vec3 v1 = mix(d, vec3(0.0), coneOffset);\\n vec3 v2 = v1 - d;\\n\\n vec3 u = getOrthogonalVector(d);\\n vec3 v = normalize(cross(u, d));\\n\\n vec3 x = u * cos(angle) * length(d)*0.25;\\n vec3 y = v * sin(angle) * length(d)*0.25;\\n vec3 v3 = v2 + x + y;\\n if (segmentIndex < 3.0) {\\n vec3 tx = u * sin(angle);\\n vec3 ty = v * -cos(angle);\\n vec3 tangent = tx + ty;\\n normal = normalize(cross(v3 - v1, tangent));\\n }\\n\\n if (segmentIndex == 0.0) {\\n return mix(d, vec3(0.0), coneOffset);\\n }\\n return v3;\\n}\\n\\nattribute vec3 vector;\\nattribute vec4 color, position;\\nattribute vec2 uv;\\n\\nuniform float vectorScale, coneScale, coneOffset;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 eyePosition, lightPosition;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n // Scale the vector magnitude to stay constant with\\n // model & view changes.\\n vec3 normal;\\n vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector), position.w, coneOffset, normal);\\n vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n //Lighting geometry parameters\\n vec4 cameraCoordinate = view * conePosition;\\n cameraCoordinate.xyz /= cameraCoordinate.w;\\n f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n f_eyeDirection = eyePosition - cameraCoordinate.xyz;\\n f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n // vec4 m_position = model * vec4(conePosition, 1.0);\\n vec4 t_position = view * conePosition;\\n gl_Position = projection * t_position;\\n\\n f_color = color;\\n f_data = conePosition.xyz;\\n f_position = position.xyz;\\n f_uv = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n float NdotH = max(x, 0.0001);\\n float cos2Alpha = NdotH * NdotH;\\n float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n float roughness2 = roughness * roughness;\\n float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n vec3 lightDirection,\\n vec3 viewDirection,\\n vec3 surfaceNormal,\\n float roughness,\\n float fresnel) {\\n\\n float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n //Half angle vector\\n vec3 H = normalize(lightDirection + viewDirection);\\n\\n //Geometric term\\n float NdotH = max(dot(surfaceNormal, H), 0.0);\\n float VdotH = max(dot(viewDirection, H), 0.000001);\\n float LdotH = max(dot(lightDirection, H), 0.000001);\\n float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n float G = min(1.0, min(G1, G2));\\n \\n //Distribution term\\n float D = beckmannDistribution(NdotH, roughness);\\n\\n //Fresnel term\\n float F = pow(1.0 - VdotN, fresnel);\\n\\n //Multiply terms and done\\n return G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n vec3 N = normalize(f_normal);\\n vec3 L = normalize(f_lightDirection);\\n vec3 V = normalize(f_eyeDirection);\\n\\n if(gl_FrontFacing) {\\n N = -N;\\n }\\n\\n float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n float diffuse = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n vec4 surfaceColor = f_color * texture2D(texture, f_uv);\\n vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular, 1.0);\\n\\n gl_FragColor = litColor * opacity;\\n}\\n\"]),o=n([\"precision highp float;\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nvec3 getOrthogonalVector(vec3 v) {\\n // Return up-vector for only-z vector.\\n // Return ax + by + cz = 0, a point that lies on the plane that has v as a normal and that isn't (0,0,0).\\n // From the above if-statement we have ||a|| > 0 U ||b|| > 0.\\n // Assign z = 0, x = -b, y = a:\\n // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n return normalize(vec3(-v.y, v.x, 0.0));\\n } else {\\n return normalize(vec3(0.0, v.z, -v.y));\\n }\\n}\\n\\n// Calculate the cone vertex and normal at the given index.\\n//\\n// The returned vertex is for a cone with its top at origin and height of 1.0,\\n// pointing in the direction of the vector attribute.\\n//\\n// Each cone is made up of a top vertex, a center base vertex and base perimeter vertices.\\n// These vertices are used to make up the triangles of the cone by the following:\\n// segment + 0 top vertex\\n// segment + 1 perimeter vertex a+1\\n// segment + 2 perimeter vertex a\\n// segment + 3 center base vertex\\n// segment + 4 perimeter vertex a\\n// segment + 5 perimeter vertex a+1\\n// Where segment is the number of the radial segment * 6 and a is the angle at that radial segment.\\n// To go from index to segment, floor(index / 6)\\n// To go from segment to angle, 2*pi * (segment/segmentCount)\\n// To go from index to segment index, index - (segment*6)\\n//\\nvec3 getConePosition(vec3 d, float rawIndex, float coneOffset, out vec3 normal) {\\n\\n const float segmentCount = 8.0;\\n\\n float index = rawIndex - floor(rawIndex /\\n (segmentCount * 6.0)) *\\n (segmentCount * 6.0);\\n\\n float segment = floor(0.001 + index/6.0);\\n float segmentIndex = index - (segment*6.0);\\n\\n normal = -normalize(d);\\n\\n if (segmentIndex > 2.99 && segmentIndex < 3.01) {\\n return mix(vec3(0.0), -d, coneOffset);\\n }\\n\\n float nextAngle = (\\n (segmentIndex > 0.99 && segmentIndex < 1.01) ||\\n (segmentIndex > 4.99 && segmentIndex < 5.01)\\n ) ? 1.0 : 0.0;\\n float angle = 2.0 * 3.14159 * ((segment + nextAngle) / segmentCount);\\n\\n vec3 v1 = mix(d, vec3(0.0), coneOffset);\\n vec3 v2 = v1 - d;\\n\\n vec3 u = getOrthogonalVector(d);\\n vec3 v = normalize(cross(u, d));\\n\\n vec3 x = u * cos(angle) * length(d)*0.25;\\n vec3 y = v * sin(angle) * length(d)*0.25;\\n vec3 v3 = v2 + x + y;\\n if (segmentIndex < 3.0) {\\n vec3 tx = u * sin(angle);\\n vec3 ty = v * -cos(angle);\\n vec3 tangent = tx + ty;\\n normal = normalize(cross(v3 - v1, tangent));\\n }\\n\\n if (segmentIndex == 0.0) {\\n return mix(d, vec3(0.0), coneOffset);\\n }\\n return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform float vectorScale, coneScale, coneOffset;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n vec3 normal;\\n vec3 XYZ = getConePosition(mat3(model) * ((vectorScale * coneScale) * vector.xyz), position.w, coneOffset, normal);\\n vec4 conePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n gl_Position = projection * (view * conePosition);\\n f_id = id;\\n f_position = position.xyz;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]);e.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec4\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"vector\",type:\"vec3\"}]},e.pickShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec4\"},{name:\"id\",type:\"vec4\"},{name:\"vector\",type:\"vec3\"}]}},737:function(t){t.exports={0:\"NONE\",1:\"ONE\",2:\"LINE_LOOP\",3:\"LINE_STRIP\",4:\"TRIANGLES\",5:\"TRIANGLE_STRIP\",6:\"TRIANGLE_FAN\",256:\"DEPTH_BUFFER_BIT\",512:\"NEVER\",513:\"LESS\",514:\"EQUAL\",515:\"LEQUAL\",516:\"GREATER\",517:\"NOTEQUAL\",518:\"GEQUAL\",519:\"ALWAYS\",768:\"SRC_COLOR\",769:\"ONE_MINUS_SRC_COLOR\",770:\"SRC_ALPHA\",771:\"ONE_MINUS_SRC_ALPHA\",772:\"DST_ALPHA\",773:\"ONE_MINUS_DST_ALPHA\",774:\"DST_COLOR\",775:\"ONE_MINUS_DST_COLOR\",776:\"SRC_ALPHA_SATURATE\",1024:\"STENCIL_BUFFER_BIT\",1028:\"FRONT\",1029:\"BACK\",1032:\"FRONT_AND_BACK\",1280:\"INVALID_ENUM\",1281:\"INVALID_VALUE\",1282:\"INVALID_OPERATION\",1285:\"OUT_OF_MEMORY\",1286:\"INVALID_FRAMEBUFFER_OPERATION\",2304:\"CW\",2305:\"CCW\",2849:\"LINE_WIDTH\",2884:\"CULL_FACE\",2885:\"CULL_FACE_MODE\",2886:\"FRONT_FACE\",2928:\"DEPTH_RANGE\",2929:\"DEPTH_TEST\",2930:\"DEPTH_WRITEMASK\",2931:\"DEPTH_CLEAR_VALUE\",2932:\"DEPTH_FUNC\",2960:\"STENCIL_TEST\",2961:\"STENCIL_CLEAR_VALUE\",2962:\"STENCIL_FUNC\",2963:\"STENCIL_VALUE_MASK\",2964:\"STENCIL_FAIL\",2965:\"STENCIL_PASS_DEPTH_FAIL\",2966:\"STENCIL_PASS_DEPTH_PASS\",2967:\"STENCIL_REF\",2968:\"STENCIL_WRITEMASK\",2978:\"VIEWPORT\",3024:\"DITHER\",3042:\"BLEND\",3088:\"SCISSOR_BOX\",3089:\"SCISSOR_TEST\",3106:\"COLOR_CLEAR_VALUE\",3107:\"COLOR_WRITEMASK\",3317:\"UNPACK_ALIGNMENT\",3333:\"PACK_ALIGNMENT\",3379:\"MAX_TEXTURE_SIZE\",3386:\"MAX_VIEWPORT_DIMS\",3408:\"SUBPIXEL_BITS\",3410:\"RED_BITS\",3411:\"GREEN_BITS\",3412:\"BLUE_BITS\",3413:\"ALPHA_BITS\",3414:\"DEPTH_BITS\",3415:\"STENCIL_BITS\",3553:\"TEXTURE_2D\",4352:\"DONT_CARE\",4353:\"FASTEST\",4354:\"NICEST\",5120:\"BYTE\",5121:\"UNSIGNED_BYTE\",5122:\"SHORT\",5123:\"UNSIGNED_SHORT\",5124:\"INT\",5125:\"UNSIGNED_INT\",5126:\"FLOAT\",5386:\"INVERT\",5890:\"TEXTURE\",6401:\"STENCIL_INDEX\",6402:\"DEPTH_COMPONENT\",6406:\"ALPHA\",6407:\"RGB\",6408:\"RGBA\",6409:\"LUMINANCE\",6410:\"LUMINANCE_ALPHA\",7680:\"KEEP\",7681:\"REPLACE\",7682:\"INCR\",7683:\"DECR\",7936:\"VENDOR\",7937:\"RENDERER\",7938:\"VERSION\",9728:\"NEAREST\",9729:\"LINEAR\",9984:\"NEAREST_MIPMAP_NEAREST\",9985:\"LINEAR_MIPMAP_NEAREST\",9986:\"NEAREST_MIPMAP_LINEAR\",9987:\"LINEAR_MIPMAP_LINEAR\",10240:\"TEXTURE_MAG_FILTER\",10241:\"TEXTURE_MIN_FILTER\",10242:\"TEXTURE_WRAP_S\",10243:\"TEXTURE_WRAP_T\",10497:\"REPEAT\",10752:\"POLYGON_OFFSET_UNITS\",16384:\"COLOR_BUFFER_BIT\",32769:\"CONSTANT_COLOR\",32770:\"ONE_MINUS_CONSTANT_COLOR\",32771:\"CONSTANT_ALPHA\",32772:\"ONE_MINUS_CONSTANT_ALPHA\",32773:\"BLEND_COLOR\",32774:\"FUNC_ADD\",32777:\"BLEND_EQUATION_RGB\",32778:\"FUNC_SUBTRACT\",32779:\"FUNC_REVERSE_SUBTRACT\",32819:\"UNSIGNED_SHORT_4_4_4_4\",32820:\"UNSIGNED_SHORT_5_5_5_1\",32823:\"POLYGON_OFFSET_FILL\",32824:\"POLYGON_OFFSET_FACTOR\",32854:\"RGBA4\",32855:\"RGB5_A1\",32873:\"TEXTURE_BINDING_2D\",32926:\"SAMPLE_ALPHA_TO_COVERAGE\",32928:\"SAMPLE_COVERAGE\",32936:\"SAMPLE_BUFFERS\",32937:\"SAMPLES\",32938:\"SAMPLE_COVERAGE_VALUE\",32939:\"SAMPLE_COVERAGE_INVERT\",32968:\"BLEND_DST_RGB\",32969:\"BLEND_SRC_RGB\",32970:\"BLEND_DST_ALPHA\",32971:\"BLEND_SRC_ALPHA\",33071:\"CLAMP_TO_EDGE\",33170:\"GENERATE_MIPMAP_HINT\",33189:\"DEPTH_COMPONENT16\",33306:\"DEPTH_STENCIL_ATTACHMENT\",33635:\"UNSIGNED_SHORT_5_6_5\",33648:\"MIRRORED_REPEAT\",33901:\"ALIASED_POINT_SIZE_RANGE\",33902:\"ALIASED_LINE_WIDTH_RANGE\",33984:\"TEXTURE0\",33985:\"TEXTURE1\",33986:\"TEXTURE2\",33987:\"TEXTURE3\",33988:\"TEXTURE4\",33989:\"TEXTURE5\",33990:\"TEXTURE6\",33991:\"TEXTURE7\",33992:\"TEXTURE8\",33993:\"TEXTURE9\",33994:\"TEXTURE10\",33995:\"TEXTURE11\",33996:\"TEXTURE12\",33997:\"TEXTURE13\",33998:\"TEXTURE14\",33999:\"TEXTURE15\",34e3:\"TEXTURE16\",34001:\"TEXTURE17\",34002:\"TEXTURE18\",34003:\"TEXTURE19\",34004:\"TEXTURE20\",34005:\"TEXTURE21\",34006:\"TEXTURE22\",34007:\"TEXTURE23\",34008:\"TEXTURE24\",34009:\"TEXTURE25\",34010:\"TEXTURE26\",34011:\"TEXTURE27\",34012:\"TEXTURE28\",34013:\"TEXTURE29\",34014:\"TEXTURE30\",34015:\"TEXTURE31\",34016:\"ACTIVE_TEXTURE\",34024:\"MAX_RENDERBUFFER_SIZE\",34041:\"DEPTH_STENCIL\",34055:\"INCR_WRAP\",34056:\"DECR_WRAP\",34067:\"TEXTURE_CUBE_MAP\",34068:\"TEXTURE_BINDING_CUBE_MAP\",34069:\"TEXTURE_CUBE_MAP_POSITIVE_X\",34070:\"TEXTURE_CUBE_MAP_NEGATIVE_X\",34071:\"TEXTURE_CUBE_MAP_POSITIVE_Y\",34072:\"TEXTURE_CUBE_MAP_NEGATIVE_Y\",34073:\"TEXTURE_CUBE_MAP_POSITIVE_Z\",34074:\"TEXTURE_CUBE_MAP_NEGATIVE_Z\",34076:\"MAX_CUBE_MAP_TEXTURE_SIZE\",34338:\"VERTEX_ATTRIB_ARRAY_ENABLED\",34339:\"VERTEX_ATTRIB_ARRAY_SIZE\",34340:\"VERTEX_ATTRIB_ARRAY_STRIDE\",34341:\"VERTEX_ATTRIB_ARRAY_TYPE\",34342:\"CURRENT_VERTEX_ATTRIB\",34373:\"VERTEX_ATTRIB_ARRAY_POINTER\",34466:\"NUM_COMPRESSED_TEXTURE_FORMATS\",34467:\"COMPRESSED_TEXTURE_FORMATS\",34660:\"BUFFER_SIZE\",34661:\"BUFFER_USAGE\",34816:\"STENCIL_BACK_FUNC\",34817:\"STENCIL_BACK_FAIL\",34818:\"STENCIL_BACK_PASS_DEPTH_FAIL\",34819:\"STENCIL_BACK_PASS_DEPTH_PASS\",34877:\"BLEND_EQUATION_ALPHA\",34921:\"MAX_VERTEX_ATTRIBS\",34922:\"VERTEX_ATTRIB_ARRAY_NORMALIZED\",34930:\"MAX_TEXTURE_IMAGE_UNITS\",34962:\"ARRAY_BUFFER\",34963:\"ELEMENT_ARRAY_BUFFER\",34964:\"ARRAY_BUFFER_BINDING\",34965:\"ELEMENT_ARRAY_BUFFER_BINDING\",34975:\"VERTEX_ATTRIB_ARRAY_BUFFER_BINDING\",35040:\"STREAM_DRAW\",35044:\"STATIC_DRAW\",35048:\"DYNAMIC_DRAW\",35632:\"FRAGMENT_SHADER\",35633:\"VERTEX_SHADER\",35660:\"MAX_VERTEX_TEXTURE_IMAGE_UNITS\",35661:\"MAX_COMBINED_TEXTURE_IMAGE_UNITS\",35663:\"SHADER_TYPE\",35664:\"FLOAT_VEC2\",35665:\"FLOAT_VEC3\",35666:\"FLOAT_VEC4\",35667:\"INT_VEC2\",35668:\"INT_VEC3\",35669:\"INT_VEC4\",35670:\"BOOL\",35671:\"BOOL_VEC2\",35672:\"BOOL_VEC3\",35673:\"BOOL_VEC4\",35674:\"FLOAT_MAT2\",35675:\"FLOAT_MAT3\",35676:\"FLOAT_MAT4\",35678:\"SAMPLER_2D\",35680:\"SAMPLER_CUBE\",35712:\"DELETE_STATUS\",35713:\"COMPILE_STATUS\",35714:\"LINK_STATUS\",35715:\"VALIDATE_STATUS\",35716:\"INFO_LOG_LENGTH\",35717:\"ATTACHED_SHADERS\",35718:\"ACTIVE_UNIFORMS\",35719:\"ACTIVE_UNIFORM_MAX_LENGTH\",35720:\"SHADER_SOURCE_LENGTH\",35721:\"ACTIVE_ATTRIBUTES\",35722:\"ACTIVE_ATTRIBUTE_MAX_LENGTH\",35724:\"SHADING_LANGUAGE_VERSION\",35725:\"CURRENT_PROGRAM\",36003:\"STENCIL_BACK_REF\",36004:\"STENCIL_BACK_VALUE_MASK\",36005:\"STENCIL_BACK_WRITEMASK\",36006:\"FRAMEBUFFER_BINDING\",36007:\"RENDERBUFFER_BINDING\",36048:\"FRAMEBUFFER_ATTACHMENT_OBJECT_TYPE\",36049:\"FRAMEBUFFER_ATTACHMENT_OBJECT_NAME\",36050:\"FRAMEBUFFER_ATTACHMENT_TEXTURE_LEVEL\",36051:\"FRAMEBUFFER_ATTACHMENT_TEXTURE_CUBE_MAP_FACE\",36053:\"FRAMEBUFFER_COMPLETE\",36054:\"FRAMEBUFFER_INCOMPLETE_ATTACHMENT\",36055:\"FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT\",36057:\"FRAMEBUFFER_INCOMPLETE_DIMENSIONS\",36061:\"FRAMEBUFFER_UNSUPPORTED\",36064:\"COLOR_ATTACHMENT0\",36096:\"DEPTH_ATTACHMENT\",36128:\"STENCIL_ATTACHMENT\",36160:\"FRAMEBUFFER\",36161:\"RENDERBUFFER\",36162:\"RENDERBUFFER_WIDTH\",36163:\"RENDERBUFFER_HEIGHT\",36164:\"RENDERBUFFER_INTERNAL_FORMAT\",36168:\"STENCIL_INDEX8\",36176:\"RENDERBUFFER_RED_SIZE\",36177:\"RENDERBUFFER_GREEN_SIZE\",36178:\"RENDERBUFFER_BLUE_SIZE\",36179:\"RENDERBUFFER_ALPHA_SIZE\",36180:\"RENDERBUFFER_DEPTH_SIZE\",36181:\"RENDERBUFFER_STENCIL_SIZE\",36194:\"RGB565\",36336:\"LOW_FLOAT\",36337:\"MEDIUM_FLOAT\",36338:\"HIGH_FLOAT\",36339:\"LOW_INT\",36340:\"MEDIUM_INT\",36341:\"HIGH_INT\",36346:\"SHADER_COMPILER\",36347:\"MAX_VERTEX_UNIFORM_VECTORS\",36348:\"MAX_VARYING_VECTORS\",36349:\"MAX_FRAGMENT_UNIFORM_VECTORS\",37440:\"UNPACK_FLIP_Y_WEBGL\",37441:\"UNPACK_PREMULTIPLY_ALPHA_WEBGL\",37442:\"CONTEXT_LOST_WEBGL\",37443:\"UNPACK_COLORSPACE_CONVERSION_WEBGL\",37444:\"BROWSER_DEFAULT_WEBGL\"}},5171:function(t,e,r){var n=r(737);t.exports=function(t){return n[t]}},9165:function(t,e,r){\"use strict\";t.exports=function(t){var e=t.gl,r=n(e),o=i(e,[{buffer:r,type:e.FLOAT,size:3,offset:0,stride:40},{buffer:r,type:e.FLOAT,size:4,offset:12,stride:40},{buffer:r,type:e.FLOAT,size:3,offset:28,stride:40}]),l=a(e);l.attributes.position.location=0,l.attributes.color.location=1,l.attributes.offset.location=2;var c=new s(e,r,o,l);return c.update(t),c};var n=r(2762),i=r(8116),a=r(3436),o=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function s(t,e,r,n){this.gl=t,this.shader=n,this.buffer=e,this.vao=r,this.pixelRatio=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lineWidth=[1,1,1],this.capSize=[10,10,10],this.lineCount=[0,0,0],this.lineOffset=[0,0,0],this.opacity=1,this.hasAlpha=!1}var l=s.prototype;function c(t,e){for(var r=0;r<3;++r)t[0][r]=Math.min(t[0][r],e[r]),t[1][r]=Math.max(t[1][r],e[r])}l.isOpaque=function(){return!this.hasAlpha},l.isTransparent=function(){return this.hasAlpha},l.drawTransparent=l.draw=function(t){var e=this.gl,r=this.shader.uniforms;this.shader.bind();var n=r.view=t.view||o,i=r.projection=t.projection||o;r.model=t.model||o,r.clipBounds=this.clipBounds,r.opacity=this.opacity;var a=n[12],s=n[13],l=n[14],c=n[15],u=(t._ortho?2:1)*this.pixelRatio*(i[3]*a+i[7]*s+i[11]*l+i[15]*c)/e.drawingBufferHeight;this.vao.bind();for(var h=0;h<3;++h)e.lineWidth(this.lineWidth[h]*this.pixelRatio),r.capSize=this.capSize[h]*u,this.lineCount[h]&&e.drawArrays(e.LINES,this.lineOffset[h],this.lineCount[h]);this.vao.unbind()};var u=function(){for(var t=new Array(3),e=0;e<3;++e){for(var r=[],n=1;n<=2;++n)for(var i=-1;i<=1;i+=2){var a=[0,0,0];a[(n+e)%3]=i,r.push(a)}t[e]=r}return t}();function h(t,e,r,n){for(var i=u[n],a=0;a0&&((p=u.slice())[s]+=d[1][s],i.push(u[0],u[1],u[2],m[0],m[1],m[2],m[3],0,0,0,p[0],p[1],p[2],m[0],m[1],m[2],m[3],0,0,0),c(this.bounds,p),o+=2+h(i,p,m,s))}this.lineCount[s]=o-this.lineOffset[s]}this.buffer.update(i)}},l.dispose=function(){this.shader.dispose(),this.buffer.dispose(),this.vao.dispose()}},3436:function(t,e,r){\"use strict\";var n=r(3236),i=r(9405),a=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, offset;\\nattribute vec4 color;\\nuniform mat4 model, view, projection;\\nuniform float capSize;\\nvarying vec4 fragColor;\\nvarying vec3 fragPosition;\\n\\nvoid main() {\\n vec4 worldPosition = model * vec4(position, 1.0);\\n worldPosition = (worldPosition / worldPosition.w) + vec4(capSize * offset, 0.0);\\n gl_Position = projection * (view * worldPosition);\\n fragColor = color;\\n fragPosition = position;\\n}\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float opacity;\\nvarying vec3 fragPosition;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n if (\\n outOfRange(clipBounds[0], clipBounds[1], fragPosition) ||\\n fragColor.a * opacity == 0.\\n ) discard;\\n\\n gl_FragColor = opacity * fragColor;\\n}\"]);t.exports=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"offset\",type:\"vec3\"}])}},2260:function(t,e,r){\"use strict\";var n=r(7766);t.exports=function(t,e,r,n){i||(i=t.FRAMEBUFFER_UNSUPPORTED,a=t.FRAMEBUFFER_INCOMPLETE_ATTACHMENT,o=t.FRAMEBUFFER_INCOMPLETE_DIMENSIONS,s=t.FRAMEBUFFER_INCOMPLETE_MISSING_ATTACHMENT);var c=t.getExtension(\"WEBGL_draw_buffers\");if(!l&&c&&function(t,e){var r=t.getParameter(e.MAX_COLOR_ATTACHMENTS_WEBGL);l=new Array(r+1);for(var n=0;n<=r;++n){for(var i=new Array(r),a=0;au||r<0||r>u)throw new Error(\"gl-fbo: Parameters are too large for FBO\");var h=1;if(\"color\"in(n=n||{})){if((h=Math.max(0|n.color,0))<0)throw new Error(\"gl-fbo: Must specify a nonnegative number of colors\");if(h>1){if(!c)throw new Error(\"gl-fbo: Multiple draw buffer extension not supported\");if(h>t.getParameter(c.MAX_COLOR_ATTACHMENTS_WEBGL))throw new Error(\"gl-fbo: Context does not support \"+h+\" draw buffers\")}}var f=t.UNSIGNED_BYTE,p=t.getExtension(\"OES_texture_float\");if(n.float&&h>0){if(!p)throw new Error(\"gl-fbo: Context does not support floating point textures\");f=t.FLOAT}else n.preferFloat&&h>0&&p&&(f=t.FLOAT);var m=!0;\"depth\"in n&&(m=!!n.depth);var g=!1;return\"stencil\"in n&&(g=!!n.stencil),new d(t,e,r,f,h,m,g,c)};var i,a,o,s,l=null;function c(t){return[t.getParameter(t.FRAMEBUFFER_BINDING),t.getParameter(t.RENDERBUFFER_BINDING),t.getParameter(t.TEXTURE_BINDING_2D)]}function u(t,e){t.bindFramebuffer(t.FRAMEBUFFER,e[0]),t.bindRenderbuffer(t.RENDERBUFFER,e[1]),t.bindTexture(t.TEXTURE_2D,e[2])}function h(t){switch(t){case i:throw new Error(\"gl-fbo: Framebuffer unsupported\");case a:throw new Error(\"gl-fbo: Framebuffer incomplete attachment\");case o:throw new Error(\"gl-fbo: Framebuffer incomplete dimensions\");case s:throw new Error(\"gl-fbo: Framebuffer incomplete missing attachment\");default:throw new Error(\"gl-fbo: Framebuffer failed for unspecified reason\")}}function f(t,e,r,i,a,o){if(!i)return null;var s=n(t,e,r,a,i);return s.magFilter=t.NEAREST,s.minFilter=t.NEAREST,s.mipSamples=1,s.bind(),t.framebufferTexture2D(t.FRAMEBUFFER,o,t.TEXTURE_2D,s.handle,0),s}function p(t,e,r,n,i){var a=t.createRenderbuffer();return t.bindRenderbuffer(t.RENDERBUFFER,a),t.renderbufferStorage(t.RENDERBUFFER,n,e,r),t.framebufferRenderbuffer(t.FRAMEBUFFER,i,t.RENDERBUFFER,a),a}function d(t,e,r,n,i,a,o,s){this.gl=t,this._shape=[0|e,0|r],this._destroyed=!1,this._ext=s,this.color=new Array(i);for(var d=0;d1&&s.drawBuffersWEBGL(l[o]);var v=r.getExtension(\"WEBGL_depth_texture\");v?d?t.depth=f(r,i,a,v.UNSIGNED_INT_24_8_WEBGL,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):m&&(t.depth=f(r,i,a,r.UNSIGNED_SHORT,r.DEPTH_COMPONENT,r.DEPTH_ATTACHMENT)):m&&d?t._depth_rb=p(r,i,a,r.DEPTH_STENCIL,r.DEPTH_STENCIL_ATTACHMENT):m?t._depth_rb=p(r,i,a,r.DEPTH_COMPONENT16,r.DEPTH_ATTACHMENT):d&&(t._depth_rb=p(r,i,a,r.STENCIL_INDEX,r.STENCIL_ATTACHMENT));var x=r.checkFramebufferStatus(r.FRAMEBUFFER);if(x!==r.FRAMEBUFFER_COMPLETE){for(t._destroyed=!0,r.bindFramebuffer(r.FRAMEBUFFER,null),r.deleteFramebuffer(t.handle),t.handle=null,t.depth&&(t.depth.dispose(),t.depth=null),t._depth_rb&&(r.deleteRenderbuffer(t._depth_rb),t._depth_rb=null),y=0;yi||r<0||r>i)throw new Error(\"gl-fbo: Can't resize FBO, invalid dimensions\");t._shape[0]=e,t._shape[1]=r;for(var a=c(n),o=0;o>8*p&255;this.pickOffset=r,i.bind();var d=i.uniforms;d.viewTransform=t,d.pickOffset=e,d.shape=this.shape;var m=i.attributes;return this.positionBuffer.bind(),m.position.pointer(),this.weightBuffer.bind(),m.weight.pointer(s.UNSIGNED_BYTE,!1),this.idBuffer.bind(),m.pickId.pointer(s.UNSIGNED_BYTE,!1),s.drawArrays(s.TRIANGLES,0,o),r+this.shape[0]*this.shape[1]}}}(),h.pick=function(t,e,r){var n=this.pickOffset,i=this.shape[0]*this.shape[1];if(r=n+i)return null;var a=r-n,o=this.xData,s=this.yData;return{object:this,pointId:a,dataCoord:[o[a%this.shape[0]],s[a/this.shape[0]|0]]}},h.update=function(t){var e=(t=t||{}).shape||[0,0],r=t.x||i(e[0]),o=t.y||i(e[1]),s=t.z||new Float32Array(e[0]*e[1]),l=!1!==t.zsmooth;this.xData=r,this.yData=o;var c,u,h,p,d=t.colorLevels||[0],m=t.colorValues||[0,0,0,1],g=d.length,y=this.bounds;l?(c=y[0]=r[0],u=y[1]=o[0],h=y[2]=r[r.length-1],p=y[3]=o[o.length-1]):(c=y[0]=r[0]+(r[1]-r[0])/2,u=y[1]=o[0]+(o[1]-o[0])/2,h=y[2]=r[r.length-1]+(r[r.length-1]-r[r.length-2])/2,p=y[3]=o[o.length-1]+(o[o.length-1]-o[o.length-2])/2);var v=1/(h-c),x=1/(p-u),_=e[0],b=e[1];this.shape=[_,b];var w=(l?(_-1)*(b-1):_*b)*(f.length>>>1);this.numVertices=w;for(var T=a.mallocUint8(4*w),k=a.mallocFloat32(2*w),A=a.mallocUint8(2*w),M=a.mallocUint32(w),S=0,E=l?_-1:_,C=l?b-1:b,L=0;L max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform sampler2D dashTexture;\\nuniform float dashScale;\\nuniform float opacity;\\n\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n if (\\n outOfRange(clipBounds[0], clipBounds[1], worldPosition) ||\\n fragColor.a * opacity == 0.\\n ) discard;\\n\\n float dashWeight = texture2D(dashTexture, vec2(dashScale * pixelArcLength, 0)).r;\\n if(dashWeight < 0.5) {\\n discard;\\n }\\n gl_FragColor = fragColor * opacity;\\n}\\n\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\n#define FLOAT_MAX 1.70141184e38\\n#define FLOAT_MIN 1.17549435e-38\\n\\n// https://github.com/mikolalysenko/glsl-read-float/blob/master/index.glsl\\nvec4 packFloat(float v) {\\n float av = abs(v);\\n\\n //Handle special cases\\n if(av < FLOAT_MIN) {\\n return vec4(0.0, 0.0, 0.0, 0.0);\\n } else if(v > FLOAT_MAX) {\\n return vec4(127.0, 128.0, 0.0, 0.0) / 255.0;\\n } else if(v < -FLOAT_MAX) {\\n return vec4(255.0, 128.0, 0.0, 0.0) / 255.0;\\n }\\n\\n vec4 c = vec4(0,0,0,0);\\n\\n //Compute exponent and mantissa\\n float e = floor(log2(av));\\n float m = av * pow(2.0, -e) - 1.0;\\n\\n //Unpack mantissa\\n c[1] = floor(128.0 * m);\\n m -= c[1] / 128.0;\\n c[2] = floor(32768.0 * m);\\n m -= c[2] / 32768.0;\\n c[3] = floor(8388608.0 * m);\\n\\n //Unpack exponent\\n float ebias = e + 127.0;\\n c[0] = floor(ebias / 2.0);\\n ebias -= c[0] * 2.0;\\n c[1] += floor(ebias) * 128.0;\\n\\n //Unpack sign bit\\n c[0] += 128.0 * step(0.0, -v);\\n\\n //Scale back to range\\n return c / 255.0;\\n}\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform float pickId;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 worldPosition;\\nvarying float pixelArcLength;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], worldPosition)) discard;\\n\\n gl_FragColor = vec4(pickId/255.0, packFloat(pixelArcLength).xyz);\\n}\"]),l=[{name:\"position\",type:\"vec3\"},{name:\"nextPosition\",type:\"vec3\"},{name:\"arcLength\",type:\"float\"},{name:\"lineWidth\",type:\"float\"},{name:\"color\",type:\"vec4\"}];e.createShader=function(t){return i(t,a,o,null,l)},e.createPickShader=function(t){return i(t,a,s,null,l)}},5714:function(t,e,r){\"use strict\";t.exports=function(t){var e=t.gl||t.scene&&t.scene.gl,r=h(e);r.attributes.position.location=0,r.attributes.nextPosition.location=1,r.attributes.arcLength.location=2,r.attributes.lineWidth.location=3,r.attributes.color.location=4;var o=f(e);o.attributes.position.location=0,o.attributes.nextPosition.location=1,o.attributes.arcLength.location=2,o.attributes.lineWidth.location=3,o.attributes.color.location=4;for(var s=n(e),l=i(e,[{buffer:s,size:3,offset:0,stride:48},{buffer:s,size:3,offset:12,stride:48},{buffer:s,size:1,offset:24,stride:48},{buffer:s,size:1,offset:28,stride:48},{buffer:s,size:4,offset:32,stride:48}]),u=c(new Array(1024),[256,1,4]),p=0;p<1024;++p)u.data[p]=255;var d=a(e,u);d.wrap=e.REPEAT;var m=new y(e,r,o,s,l,d);return m.update(t),m};var n=r(2762),i=r(8116),a=r(7766),o=new Uint8Array(4),s=new Float32Array(o.buffer),l=r(2478),c=r(9618),u=r(7319),h=u.createShader,f=u.createPickShader,p=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function d(t,e){for(var r=0,n=0;n<3;++n){var i=t[n]-e[n];r+=i*i}return Math.sqrt(r)}function m(t){for(var e=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],r=0;r<3;++r)e[0][r]=Math.max(t[0][r],e[0][r]),e[1][r]=Math.min(t[1][r],e[1][r]);return e}function g(t,e,r,n){this.arcLength=t,this.position=e,this.index=r,this.dataCoordinate=n}function y(t,e,r,n,i,a){this.gl=t,this.shader=e,this.pickShader=r,this.buffer=n,this.vao=i,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.points=[],this.arcLength=[],this.vertexCount=0,this.bounds=[[0,0,0],[0,0,0]],this.pickId=0,this.lineWidth=1,this.texture=a,this.dashScale=1,this.opacity=1,this.hasAlpha=!1,this.dirty=!0,this.pixelRatio=1}var v=y.prototype;v.isTransparent=function(){return this.hasAlpha},v.isOpaque=function(){return!this.hasAlpha},v.pickSlots=1,v.setPickBase=function(t){this.pickId=t},v.drawTransparent=v.draw=function(t){if(this.vertexCount){var e=this.gl,r=this.shader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,clipBounds:m(this.clipBounds),dashTexture:this.texture.bind(),dashScale:this.dashScale/this.arcLength[this.arcLength.length-1],opacity:this.opacity,screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},v.drawPick=function(t){if(this.vertexCount){var e=this.gl,r=this.pickShader,n=this.vao;r.bind(),r.uniforms={model:t.model||p,view:t.view||p,projection:t.projection||p,pickId:this.pickId,clipBounds:m(this.clipBounds),screenShape:[e.drawingBufferWidth,e.drawingBufferHeight],pixelRatio:this.pixelRatio},n.bind(),n.draw(e.TRIANGLE_STRIP,this.vertexCount),n.unbind()}},v.update=function(t){var e,r;this.dirty=!0;var n=!!t.connectGaps;\"dashScale\"in t&&(this.dashScale=t.dashScale),this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=+t.opacity,this.opacity<1&&(this.hasAlpha=!0));var i=[],a=[],o=[],s=0,u=0,h=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],f=t.position||t.positions;if(f){var p=t.color||t.colors||[0,0,0,1],m=t.lineWidth||1,g=!1;t:for(e=1;e0){for(var w=0;w<24;++w)i.push(i[i.length-12]);u+=2,g=!0}continue t}h[0][r]=Math.min(h[0][r],_[r],b[r]),h[1][r]=Math.max(h[1][r],_[r],b[r])}Array.isArray(p[0])?(y=p.length>e-1?p[e-1]:p.length>0?p[p.length-1]:[0,0,0,1],v=p.length>e?p[e]:p.length>0?p[p.length-1]:[0,0,0,1]):y=v=p,3===y.length&&(y=[y[0],y[1],y[2],1]),3===v.length&&(v=[v[0],v[1],v[2],1]),!this.hasAlpha&&y[3]<1&&(this.hasAlpha=!0),x=Array.isArray(m)?m.length>e-1?m[e-1]:m.length>0?m[m.length-1]:[0,0,0,1]:m;var T=s;if(s+=d(_,b),g){for(r=0;r<2;++r)i.push(_[0],_[1],_[2],b[0],b[1],b[2],T,x,y[0],y[1],y[2],y[3]);u+=2,g=!1}i.push(_[0],_[1],_[2],b[0],b[1],b[2],T,x,y[0],y[1],y[2],y[3],_[0],_[1],_[2],b[0],b[1],b[2],T,-x,y[0],y[1],y[2],y[3],b[0],b[1],b[2],_[0],_[1],_[2],s,-x,v[0],v[1],v[2],v[3],b[0],b[1],b[2],_[0],_[1],_[2],s,x,v[0],v[1],v[2],v[3]),u+=4}}if(this.buffer.update(i),a.push(s),o.push(f[f.length-1].slice()),this.bounds=h,this.vertexCount=u,this.points=o,this.arcLength=a,\"dashes\"in t){var k=t.dashes.slice();for(k.unshift(0),e=1;e1.0001)return null;y+=g[h]}return Math.abs(y-1)>.001?null:[f,s(t,g),g]}},840:function(t,e,r){var n=r(3236),i=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position, normal;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model\\n , view\\n , projection\\n , inverseModel;\\nuniform vec3 eyePosition\\n , lightPosition;\\n\\nvarying vec3 f_normal\\n , f_lightDirection\\n , f_eyeDirection\\n , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvec4 project(vec3 p) {\\n return projection * (view * (model * vec4(p, 1.0)));\\n}\\n\\nvoid main() {\\n gl_Position = project(position);\\n\\n //Lighting geometry parameters\\n vec4 cameraCoordinate = view * vec4(position , 1.0);\\n cameraCoordinate.xyz /= cameraCoordinate.w;\\n f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n f_eyeDirection = eyePosition - cameraCoordinate.xyz;\\n f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n f_color = color;\\n f_data = position;\\n f_uv = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n float NdotH = max(x, 0.0001);\\n float cos2Alpha = NdotH * NdotH;\\n float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n float roughness2 = roughness * roughness;\\n float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n vec3 lightDirection,\\n vec3 viewDirection,\\n vec3 surfaceNormal,\\n float roughness,\\n float fresnel) {\\n\\n float VdotN = max(dot(viewDirection, surfaceNormal), 0.0);\\n float LdotN = max(dot(lightDirection, surfaceNormal), 0.0);\\n\\n //Half angle vector\\n vec3 H = normalize(lightDirection + viewDirection);\\n\\n //Geometric term\\n float NdotH = max(dot(surfaceNormal, H), 0.0);\\n float VdotH = max(dot(viewDirection, H), 0.000001);\\n float LdotH = max(dot(lightDirection, H), 0.000001);\\n float G1 = (2.0 * NdotH * VdotN) / VdotH;\\n float G2 = (2.0 * NdotH * LdotN) / LdotH;\\n float G = min(1.0, min(G1, G2));\\n \\n //Distribution term\\n float D = beckmannDistribution(NdotH, roughness);\\n\\n //Fresnel term\\n float F = pow(1.0 - VdotN, fresnel);\\n\\n //Multiply terms and done\\n return G * F * D / max(3.14159265 * VdotN, 0.000001);\\n}\\n\\n//#pragma glslify: beckmann = require(glsl-specular-beckmann) // used in gl-surface3d\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness\\n , fresnel\\n , kambient\\n , kdiffuse\\n , kspecular;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal\\n , f_lightDirection\\n , f_eyeDirection\\n , f_data;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n if (f_color.a == 0.0 ||\\n outOfRange(clipBounds[0], clipBounds[1], f_data)\\n ) discard;\\n\\n vec3 N = normalize(f_normal);\\n vec3 L = normalize(f_lightDirection);\\n vec3 V = normalize(f_eyeDirection);\\n\\n if(gl_FrontFacing) {\\n N = -N;\\n }\\n\\n float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n //float specular = max(0.0, beckmann(L, V, N, roughness)); // used in gl-surface3d\\n\\n float diffuse = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n vec4 surfaceColor = vec4(f_color.rgb, 1.0) * texture2D(texture, f_uv);\\n vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular, 1.0);\\n\\n gl_FragColor = litColor * f_color.a;\\n}\\n\"]),o=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n gl_Position = projection * (view * (model * vec4(position, 1.0)));\\n f_color = color;\\n f_data = position;\\n f_uv = uv;\\n}\"]),s=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec3 f_data;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], f_data)) discard;\\n\\n gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\"]),l=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 uv;\\nattribute float pointSize;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n gl_Position = vec4(0.0, 0.0 ,0.0 ,0.0);\\n } else {\\n gl_Position = projection * (view * (model * vec4(position, 1.0)));\\n }\\n gl_PointSize = pointSize;\\n f_color = color;\\n f_uv = uv;\\n}\"]),c=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D texture;\\nuniform float opacity;\\n\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n vec2 pointR = gl_PointCoord.xy - vec2(0.5, 0.5);\\n if(dot(pointR, pointR) > 0.25) {\\n discard;\\n }\\n gl_FragColor = f_color * texture2D(texture, f_uv) * opacity;\\n}\"]),u=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n gl_Position = projection * (view * (model * vec4(position, 1.0)));\\n f_id = id;\\n f_position = position;\\n}\"]),h=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float pickId;\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n\\n gl_FragColor = vec4(pickId, f_id.xyz);\\n}\"]),f=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute float pointSize;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec3 f_position;\\nvarying vec4 f_id;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n gl_Position = vec4(0.0, 0.0, 0.0, 0.0);\\n } else {\\n gl_Position = projection * (view * (model * vec4(position, 1.0)));\\n gl_PointSize = pointSize;\\n }\\n f_id = id;\\n f_position = position;\\n}\"]),p=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec3 position;\\n\\nuniform mat4 model, view, projection;\\n\\nvoid main() {\\n gl_Position = projection * (view * (model * vec4(position, 1.0)));\\n}\"]),d=n([\"precision highp float;\\n#define GLSLIFY 1\\n\\nuniform vec3 contourColor;\\n\\nvoid main() {\\n gl_FragColor = vec4(contourColor, 1.0);\\n}\\n\"]);e.meshShader={vertex:i,fragment:a,attributes:[{name:\"position\",type:\"vec3\"},{name:\"normal\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},e.wireShader={vertex:o,fragment:s,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"}]},e.pointShader={vertex:l,fragment:c,attributes:[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"uv\",type:\"vec2\"},{name:\"pointSize\",type:\"float\"}]},e.pickShader={vertex:u,fragment:h,attributes:[{name:\"position\",type:\"vec3\"},{name:\"id\",type:\"vec4\"}]},e.pointPickShader={vertex:f,fragment:h,attributes:[{name:\"position\",type:\"vec3\"},{name:\"pointSize\",type:\"float\"},{name:\"id\",type:\"vec4\"}]},e.contourShader={vertex:p,fragment:d,attributes:[{name:\"position\",type:\"vec3\"}]}},7201:function(t,e,r){\"use strict\";var n=r(9405),i=r(2762),a=r(8116),o=r(7766),s=r(8406),l=r(6760),c=r(7608),u=r(9618),h=r(6729),f=r(7765),p=r(1888),d=r(840),m=r(7626),g=d.meshShader,y=d.wireShader,v=d.pointShader,x=d.pickShader,_=d.pointPickShader,b=d.contourShader,w=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1];function T(t,e,r,n,i,a,o,s,l,c,u,h,f,p,d,m,g,y,v,x,_,b,T,k,A,M,S){this.gl=t,this.pixelRatio=1,this.cells=[],this.positions=[],this.intensity=[],this.texture=e,this.dirty=!0,this.triShader=r,this.lineShader=n,this.pointShader=i,this.pickShader=a,this.pointPickShader=o,this.contourShader=s,this.trianglePositions=l,this.triangleColors=u,this.triangleNormals=f,this.triangleUVs=h,this.triangleIds=c,this.triangleVAO=p,this.triangleCount=0,this.lineWidth=1,this.edgePositions=d,this.edgeColors=g,this.edgeUVs=y,this.edgeIds=m,this.edgeVAO=v,this.edgeCount=0,this.pointPositions=x,this.pointColors=b,this.pointUVs=T,this.pointSizes=k,this.pointIds=_,this.pointVAO=A,this.pointCount=0,this.contourLineWidth=1,this.contourPositions=M,this.contourVAO=S,this.contourCount=0,this.contourColor=[0,0,0],this.contourEnable=!0,this.pickVertex=!0,this.pickId=1,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.lightPosition=[1e5,1e5,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.opacity=1,this.hasAlpha=!1,this.opacityscale=!1,this._model=w,this._view=w,this._projection=w,this._resolution=[1,1]}var k=T.prototype;function A(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;rt&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}function M(t){var e=n(t,v.vertex,v.fragment);return e.attributes.position.location=0,e.attributes.color.location=2,e.attributes.uv.location=3,e.attributes.pointSize.location=4,e}function S(t){var e=n(t,x.vertex,x.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e}function E(t){var e=n(t,_.vertex,_.fragment);return e.attributes.position.location=0,e.attributes.id.location=1,e.attributes.pointSize.location=4,e}function C(t){var e=n(t,b.vertex,b.fragment);return e.attributes.position.location=0,e}k.isOpaque=function(){return!this.hasAlpha},k.isTransparent=function(){return this.hasAlpha},k.pickSlots=1,k.setPickBase=function(t){this.pickId=t},k.highlight=function(t){if(t&&this.contourEnable){for(var e=f(this.cells,this.intensity,t.intensity),r=e.cells,n=e.vertexIds,i=e.vertexWeights,a=r.length,o=p.mallocFloat32(6*a),s=0,l=0;l0&&((h=this.triShader).bind(),h.uniforms=s,this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&this.lineWidth>0&&((h=this.lineShader).bind(),h.uniforms=s,this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0&&((h=this.pointShader).bind(),h.uniforms=s,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind()),this.contourEnable&&this.contourCount>0&&this.contourLineWidth>0&&((h=this.contourShader).bind(),h.uniforms=s,this.contourVAO.bind(),e.drawArrays(e.LINES,0,this.contourCount),this.contourVAO.unbind())},k.drawPick=function(t){t=t||{};for(var e=this.gl,r=t.model||w,n=t.view||w,i=t.projection||w,a=[[-1e6,-1e6,-1e6],[1e6,1e6,1e6]],o=0;o<3;++o)a[0][o]=Math.max(a[0][o],this.clipBounds[0][o]),a[1][o]=Math.min(a[1][o],this.clipBounds[1][o]);this._model=[].slice.call(r),this._view=[].slice.call(n),this._projection=[].slice.call(i),this._resolution=[e.drawingBufferWidth,e.drawingBufferHeight];var s,l={model:r,view:n,projection:i,clipBounds:a,pickId:this.pickId/255};(s=this.pickShader).bind(),s.uniforms=l,this.triangleCount>0&&(this.triangleVAO.bind(),e.drawArrays(e.TRIANGLES,0,3*this.triangleCount),this.triangleVAO.unbind()),this.edgeCount>0&&(this.edgeVAO.bind(),e.lineWidth(this.lineWidth*this.pixelRatio),e.drawArrays(e.LINES,0,2*this.edgeCount),this.edgeVAO.unbind()),this.pointCount>0&&((s=this.pointPickShader).bind(),s.uniforms=l,this.pointVAO.bind(),e.drawArrays(e.POINTS,0,this.pointCount),this.pointVAO.unbind())},k.pick=function(t){if(!t)return null;if(t.id!==this.pickId)return null;for(var e=t.value[0]+256*t.value[1]+65536*t.value[2],r=this.cells[e],n=this.positions,i=new Array(r.length),a=0;ai[k]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=g[t],r.uniforms.angle=y[t],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),v[t]&&T&&(u[1^t]-=M*p*x[t],r.uniforms.dataAxis=h,r.uniforms.screenOffset=u,r.uniforms.color=_[t],r.uniforms.angle=b[t],a.drawArrays(a.TRIANGLES,w,T)),u[1^t]=M*s[2+(1^t)]-1,d[t+2]&&(u[1^t]+=M*p*m[t+2],ki[k]&&(r.uniforms.dataAxis=c,r.uniforms.screenOffset=u,r.uniforms.color=g[t+2],r.uniforms.angle=y[t+2],a.drawArrays(a.TRIANGLES,i[k],i[A]-i[k]))),v[t+2]&&T&&(u[1^t]+=M*p*x[t+2],r.uniforms.dataAxis=h,r.uniforms.screenOffset=u,r.uniforms.color=_[t+2],r.uniforms.angle=b[t+2],a.drawArrays(a.TRIANGLES,w,T))}),m.drawTitle=function(){var t=[0,0],e=[0,0];return function(){var r=this.plot,n=this.shader,i=r.gl,a=r.screenBox,o=r.titleCenter,s=r.titleAngle,l=r.titleColor,c=r.pixelRatio;if(this.titleCount){for(var u=0;u<2;++u)e[u]=2*(o[u]*c-a[u])/(a[2+u]-a[u])-1;n.bind(),n.uniforms.dataAxis=t,n.uniforms.screenOffset=e,n.uniforms.angle=s,n.uniforms.color=l,i.drawArrays(i.TRIANGLES,this.titleOffset,this.titleCount)}}}(),m.bind=(f=[0,0],p=[0,0],d=[0,0],function(){var t=this.plot,e=this.shader,r=t._tickBounds,n=t.dataBox,i=t.screenBox,a=t.viewBox;e.bind();for(var o=0;o<2;++o){var s=r[o],l=r[o+2]-s,c=.5*(n[o+2]+n[o]),u=n[o+2]-n[o],h=a[o],m=a[o+2]-h,g=i[o],y=i[o+2]-g;p[o]=2*l/u*m/y,f[o]=2*(s-c)/u*m/y}d[1]=2*t.pixelRatio/(i[3]-i[1]),d[0]=d[1]*(i[3]-i[1])/(i[2]-i[0]),e.uniforms.dataScale=p,e.uniforms.dataShift=f,e.uniforms.textScale=d,this.vbo.bind(),e.attributes.textCoordinate.pointer()}),m.update=function(t){var e,r,n,i,o,s=[],l=t.ticks,c=t.bounds;for(o=0;o<2;++o){var u=[Math.floor(s.length/3)],h=[-1/0],f=l[o];for(e=0;e=0){var m=e[d]-n[d]*(e[d+2]-e[d])/(n[d+2]-n[d]);0===d?o.drawLine(m,e[1],m,e[3],p[d],f[d]):o.drawLine(e[0],m,e[2],m,p[d],f[d])}}for(d=0;d=0;--t)this.objects[t].dispose();for(this.objects.length=0,t=this.overlays.length-1;t>=0;--t)this.overlays[t].dispose();this.overlays.length=0,this.gl=null},c.addObject=function(t){this.objects.indexOf(t)<0&&(this.objects.push(t),this.setDirty())},c.removeObject=function(t){for(var e=this.objects,r=0;rMath.abs(e))c.rotate(a,0,0,-t*r*Math.PI*d.rotateSpeed/window.innerWidth);else if(!d._ortho){var o=-d.zoomSpeed*i*e/window.innerHeight*(a-c.lastT())/20;c.pan(a,0,0,h*(Math.exp(o)-1))}}}),!0)},d.enableMouseListeners(),d};var n=r(3025),i=r(6296),a=r(351),o=r(8512),s=r(24),l=r(7520)},799:function(t,e,r){var n=r(3236),i=r(9405),a=n([\"precision mediump float;\\n#define GLSLIFY 1\\nattribute vec2 position;\\nvarying vec2 uv;\\nvoid main() {\\n uv = position;\\n gl_Position = vec4(position, 0, 1);\\n}\"]),o=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform sampler2D accumBuffer;\\nvarying vec2 uv;\\n\\nvoid main() {\\n vec4 accum = texture2D(accumBuffer, 0.5 * (uv + 1.0));\\n gl_FragColor = min(vec4(1,1,1,1), accum);\\n}\"]);t.exports=function(t){return i(t,a,o,null,[{name:\"position\",type:\"vec2\"}])}},4100:function(t,e,r){\"use strict\";var n=r(4437),i=r(3837),a=r(5445),o=r(4449),s=r(3589),l=r(2260),c=r(7169),u=r(351),h=r(4772),f=r(4040),p=r(799),d=r(9216)({tablet:!0,featureDetect:!0});function m(){this.mouse=[-1,-1],this.screen=null,this.distance=1/0,this.index=null,this.dataCoordinate=null,this.dataPosition=null,this.object=null,this.data=null}function g(t){var e=Math.round(Math.log(Math.abs(t))/Math.log(10));if(e<0){var r=Math.round(Math.pow(10,-e));return Math.ceil(t*r)/r}return e>0?(r=Math.round(Math.pow(10,e)),Math.ceil(t/r)*r):Math.ceil(t)}function y(t){return\"boolean\"!=typeof t||t}t.exports={createScene:function(t){(t=t||{}).camera=t.camera||{};var e=t.canvas;e||(e=document.createElement(\"canvas\"),t.container?t.container.appendChild(e):document.body.appendChild(e));var r=t.gl;if(r||(t.glOptions&&(d=!!t.glOptions.preserveDrawingBuffer),r=function(t,e){var r=null;try{(r=t.getContext(\"webgl\",e))||(r=t.getContext(\"experimental-webgl\",e))}catch(t){return null}return r}(e,t.glOptions||{premultipliedAlpha:!0,antialias:!0,preserveDrawingBuffer:d})),!r)throw new Error(\"webgl not supported\");var v=t.bounds||[[-10,-10,-10],[10,10,10]],x=new m,_=l(r,r.drawingBufferWidth,r.drawingBufferHeight,{preferFloat:!d}),b=p(r),w=t.cameraObject&&!0===t.cameraObject._ortho||t.camera.projection&&\"orthographic\"===t.camera.projection.type||!1,T={eye:t.camera.eye||[2,0,0],center:t.camera.center||[0,0,0],up:t.camera.up||[0,1,0],zoomMin:t.camera.zoomMax||.1,zoomMax:t.camera.zoomMin||100,mode:t.camera.mode||\"turntable\",_ortho:w},k=t.axes||{},A=i(r,k);A.enable=!k.disable;var M=t.spikes||{},S=o(r,M),E=[],C=[],L=[],I=[],P=!0,z=!0,O={view:null,projection:new Array(16),model:new Array(16),_ortho:!1},D=(z=!0,[r.drawingBufferWidth,r.drawingBufferHeight]),R=t.cameraObject||n(e,T),F={gl:r,contextLost:!1,pixelRatio:t.pixelRatio||1,canvas:e,selection:x,camera:R,axes:A,axesPixels:null,spikes:S,bounds:v,objects:E,shape:D,aspect:t.aspectRatio||[1,1,1],pickRadius:t.pickRadius||10,zNear:t.zNear||.01,zFar:t.zFar||1e3,fovy:t.fovy||Math.PI/4,clearColor:t.clearColor||[0,0,0,0],autoResize:y(t.autoResize),autoBounds:y(t.autoBounds),autoScale:!!t.autoScale,autoCenter:y(t.autoCenter),clipToBounds:y(t.clipToBounds),snapToData:!!t.snapToData,onselect:t.onselect||null,onrender:t.onrender||null,onclick:t.onclick||null,cameraParams:O,oncontextloss:null,mouseListener:null,_stopped:!1,getAspectratio:function(){return{x:this.aspect[0],y:this.aspect[1],z:this.aspect[2]}},setAspectratio:function(t){this.aspect[0]=t.x,this.aspect[1]=t.y,this.aspect[2]=t.z,z=!0},setBounds:function(t,e){this.bounds[0][t]=e.min,this.bounds[1][t]=e.max},setClearColor:function(t){this.clearColor=t},clearRGBA:function(){this.gl.clearColor(this.clearColor[0],this.clearColor[1],this.clearColor[2],this.clearColor[3]),this.gl.clear(this.gl.COLOR_BUFFER_BIT|this.gl.DEPTH_BUFFER_BIT)}},B=[r.drawingBufferWidth/F.pixelRatio|0,r.drawingBufferHeight/F.pixelRatio|0];function N(){if(!F._stopped&&F.autoResize){var t=e.parentNode,r=1,n=1;t&&t!==document.body?(r=t.clientWidth,n=t.clientHeight):(r=window.innerWidth,n=window.innerHeight);var i=0|Math.ceil(r*F.pixelRatio),a=0|Math.ceil(n*F.pixelRatio);if(i!==e.width||a!==e.height){e.width=i,e.height=a;var o=e.style;o.position=o.position||\"absolute\",o.left=\"0px\",o.top=\"0px\",o.width=r+\"px\",o.height=n+\"px\",P=!0}}}function j(){for(var t=E.length,e=I.length,n=0;n0&&0===L[e-1];)L.pop(),I.pop().dispose()}function U(){if(F.contextLost)return!0;r.isContextLost()&&(F.contextLost=!0,F.mouseListener.enabled=!1,F.selection.object=null,F.oncontextloss&&F.oncontextloss())}F.autoResize&&N(),window.addEventListener(\"resize\",N),F.update=function(t){F._stopped||(t=t||{},P=!0,z=!0)},F.add=function(t){F._stopped||(t.axes=A,E.push(t),C.push(-1),P=!0,z=!0,j())},F.remove=function(t){if(!F._stopped){var e=E.indexOf(t);e<0||(E.splice(e,1),C.pop(),P=!0,z=!0,j())}},F.dispose=function(){if(!F._stopped&&(F._stopped=!0,window.removeEventListener(\"resize\",N),e.removeEventListener(\"webglcontextlost\",U),F.mouseListener.enabled=!1,!F.contextLost)){A.dispose(),S.dispose();for(var t=0;tx.distance)continue;for(var c=0;c 1.0) {\\n discard;\\n }\\n baseColor = mix(borderColor, color, step(radius, centerFraction));\\n gl_FragColor = vec4(baseColor.rgb * baseColor.a, baseColor.a);\\n }\\n}\\n\"]),e.pickVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 position;\\nattribute vec4 pickId;\\n\\nuniform mat3 matrix;\\nuniform float pointSize;\\nuniform vec4 pickOffset;\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n vec3 hgPosition = matrix * vec3(position, 1);\\n gl_Position = vec4(hgPosition.xy, 0, hgPosition.z);\\n gl_PointSize = pointSize;\\n\\n vec4 id = pickId + pickOffset;\\n id.y += floor(id.x / 256.0);\\n id.x -= floor(id.x / 256.0) * 256.0;\\n\\n id.z += floor(id.y / 256.0);\\n id.y -= floor(id.y / 256.0) * 256.0;\\n\\n id.w += floor(id.z / 256.0);\\n id.z -= floor(id.z / 256.0) * 256.0;\\n\\n fragId = id;\\n}\\n\"]),e.pickFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nvarying vec4 fragId;\\n\\nvoid main() {\\n float radius = length(2.0 * gl_PointCoord.xy - 1.0);\\n if(radius > 1.0) {\\n discard;\\n }\\n gl_FragColor = fragId / 255.0;\\n}\\n\"])},4696:function(t,e,r){\"use strict\";var n=r(9405),i=r(2762),a=r(1888),o=r(6640);function s(t,e,r,n,i){this.plot=t,this.offsetBuffer=e,this.pickBuffer=r,this.shader=n,this.pickShader=i,this.sizeMin=.5,this.sizeMinCap=2,this.sizeMax=20,this.areaRatio=1,this.pointCount=0,this.color=[1,0,0,1],this.borderColor=[0,0,0,1],this.blend=!1,this.pickOffset=0,this.points=null}t.exports=function(t,e){var r=t.gl,a=new s(t,i(r),i(r),n(r,o.pointVertex,o.pointFragment),n(r,o.pickVertex,o.pickFragment));return a.update(e),t.addObject(a),a};var l,c,u=s.prototype;u.dispose=function(){this.shader.dispose(),this.pickShader.dispose(),this.offsetBuffer.dispose(),this.pickBuffer.dispose(),this.plot.removeObject(this)},u.update=function(t){var e;function r(e,r){return e in t?t[e]:r}t=t||{},this.sizeMin=r(\"sizeMin\",.5),this.sizeMax=r(\"sizeMax\",20),this.color=r(\"color\",[1,0,0,1]).slice(),this.areaRatio=r(\"areaRatio\",1),this.borderColor=r(\"borderColor\",[0,0,0,1]).slice(),this.blend=r(\"blend\",!1);var n=t.positions.length>>>1,i=t.positions instanceof Float32Array,o=t.idToIndex instanceof Int32Array&&t.idToIndex.length>=n,s=t.positions,l=i?s:a.mallocFloat32(s.length),c=o?t.idToIndex:a.mallocInt32(n);if(i||l.set(s),!o)for(l.set(s),e=0;e>>1;for(r=0;r=e[0]&&a<=e[2]&&o>=e[1]&&o<=e[3]&&n++}return n}(this.points,i),u=this.plot.pickPixelRatio*Math.max(Math.min(this.sizeMinCap,this.sizeMin),Math.min(this.sizeMax,this.sizeMax/Math.pow(s,.33333)));l[0]=2/a,l[4]=2/o,l[6]=-2*i[0]/a-1,l[7]=-2*i[1]/o-1,this.offsetBuffer.bind(),r.bind(),r.attributes.position.pointer(),r.uniforms.matrix=l,r.uniforms.color=this.color,r.uniforms.borderColor=this.borderColor,r.uniforms.pointCloud=u<5,r.uniforms.pointSize=u,r.uniforms.centerFraction=Math.min(1,Math.max(0,Math.sqrt(1-this.areaRatio))),e&&(c[0]=255&t,c[1]=t>>8&255,c[2]=t>>16&255,c[3]=t>>24&255,this.pickBuffer.bind(),r.attributes.pickId.pointer(n.UNSIGNED_BYTE),r.uniforms.pickOffset=c,this.pickOffset=t);var h=n.getParameter(n.BLEND),f=n.getParameter(n.DITHER);return h&&!this.blend&&n.disable(n.BLEND),f&&n.disable(n.DITHER),n.drawArrays(n.POINTS,0,this.pointCount),h&&!this.blend&&n.enable(n.BLEND),f&&n.enable(n.DITHER),t+this.pointCount}),u.draw=u.unifiedDraw,u.drawPick=u.unifiedDraw,u.pick=function(t,e,r){var n=this.pickOffset,i=this.pointCount;if(r=n+i)return null;var a=r-n,o=this.points;return{object:this,pointId:a,dataCoord:[o[2*a],o[2*a+1]]}}},783:function(t){t.exports=function(t,e,r,n){var i,a,o,s,l,c=e[0],u=e[1],h=e[2],f=e[3],p=r[0],d=r[1],m=r[2],g=r[3];return(a=c*p+u*d+h*m+f*g)<0&&(a=-a,p=-p,d=-d,m=-m,g=-g),1-a>1e-6?(i=Math.acos(a),o=Math.sin(i),s=Math.sin((1-n)*i)/o,l=Math.sin(n*i)/o):(s=1-n,l=n),t[0]=s*c+l*p,t[1]=s*u+l*d,t[2]=s*h+l*m,t[3]=s*f+l*g,t}},5964:function(t){\"use strict\";t.exports=function(t){return t||0===t?t.toString():\"\"}},9366:function(t,e,r){\"use strict\";var n=r(4359);t.exports=function(t,e,r){var a=[e.style,e.weight,e.variant,e.family].join(\"_\"),o=i[a];if(o||(o=i[a]={}),t in o)return o[t];var s={textAlign:\"center\",textBaseline:\"middle\",lineHeight:1,font:e.family,fontStyle:e.style,fontWeight:e.weight,fontVariant:e.variant,lineSpacing:1.25,styletags:{breaklines:!0,bolds:!0,italics:!0,subscripts:!0,superscripts:!0},triangles:!0},l=n(t,s);s.triangles=!1;var c,u,h=n(t,s);if(r&&1!==r){for(c=0;c max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform vec4 highlightId;\\nuniform float highlightScale;\\nuniform mat4 model, view, projection;\\nuniform vec3 clipBounds[2];\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n gl_Position = vec4(0,0,0,0);\\n } else {\\n float scale = 1.0;\\n if(distance(highlightId, id) < 0.0001) {\\n scale = highlightScale;\\n }\\n\\n vec4 worldPosition = model * vec4(position, 1);\\n vec4 viewPosition = view * worldPosition;\\n viewPosition = viewPosition / viewPosition.w;\\n vec4 clipPosition = projection * (viewPosition + scale * vec4(glyph.x, -glyph.y, 0, 0));\\n\\n gl_Position = clipPosition;\\n interpColor = color;\\n pickId = id;\\n dataCoordinate = position;\\n }\\n}\"]),o=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float highlightScale, pixelRatio;\\nuniform vec4 highlightId;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n gl_Position = vec4(0,0,0,0);\\n } else {\\n float scale = pixelRatio;\\n if(distance(highlightId.bgr, id.bgr) < 0.001) {\\n scale *= highlightScale;\\n }\\n\\n vec4 worldPosition = model * vec4(position, 1.0);\\n vec4 viewPosition = view * worldPosition;\\n vec4 clipPosition = projection * viewPosition;\\n clipPosition /= clipPosition.w;\\n\\n gl_Position = clipPosition + vec4(screenSize * scale * vec2(glyph.x, -glyph.y), 0.0, 0.0);\\n interpColor = color;\\n pickId = id;\\n dataCoordinate = position;\\n }\\n}\"]),s=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nattribute vec3 position;\\nattribute vec4 color;\\nattribute vec2 glyph;\\nattribute vec4 id;\\n\\nuniform float highlightScale;\\nuniform vec4 highlightId;\\nuniform vec3 axes[2];\\nuniform mat4 model, view, projection;\\nuniform vec2 screenSize;\\nuniform vec3 clipBounds[2];\\nuniform float scale, pixelRatio;\\n\\nvarying vec4 interpColor;\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], position)) {\\n\\n gl_Position = vec4(0,0,0,0);\\n } else {\\n float lscale = pixelRatio * scale;\\n if(distance(highlightId, id) < 0.0001) {\\n lscale *= highlightScale;\\n }\\n\\n vec4 clipCenter = projection * (view * (model * vec4(position, 1)));\\n vec3 dataPosition = position + 0.5*lscale*(axes[0] * glyph.x + axes[1] * glyph.y) * clipCenter.w * screenSize.y;\\n vec4 clipPosition = projection * (view * (model * vec4(dataPosition, 1)));\\n\\n gl_Position = clipPosition;\\n interpColor = color;\\n pickId = id;\\n dataCoordinate = dataPosition;\\n }\\n}\\n\"]),l=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float opacity;\\n\\nvarying vec4 interpColor;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if (\\n outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate) ||\\n interpColor.a * opacity == 0.\\n ) discard;\\n gl_FragColor = interpColor * opacity;\\n}\\n\"]),c=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nbool outOfRange(float a, float b, float p) {\\n return ((p > max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 fragClipBounds[2];\\nuniform float pickGroup;\\n\\nvarying vec4 pickId;\\nvarying vec3 dataCoordinate;\\n\\nvoid main() {\\n if (outOfRange(fragClipBounds[0], fragClipBounds[1], dataCoordinate)) discard;\\n\\n gl_FragColor = vec4(pickGroup, pickId.bgr);\\n}\"]),u=[{name:\"position\",type:\"vec3\"},{name:\"color\",type:\"vec4\"},{name:\"glyph\",type:\"vec2\"},{name:\"id\",type:\"vec4\"}],h={vertex:a,fragment:l,attributes:u},f={vertex:o,fragment:l,attributes:u},p={vertex:s,fragment:l,attributes:u},d={vertex:a,fragment:c,attributes:u},m={vertex:o,fragment:c,attributes:u},g={vertex:s,fragment:c,attributes:u};function y(t,e){var r=n(t,e),i=r.attributes;return i.position.location=0,i.color.location=1,i.glyph.location=2,i.id.location=3,r}e.createPerspective=function(t){return y(t,h)},e.createOrtho=function(t){return y(t,f)},e.createProject=function(t){return y(t,p)},e.createPickPerspective=function(t){return y(t,d)},e.createPickOrtho=function(t){return y(t,m)},e.createPickProject=function(t){return y(t,g)}},8418:function(t,e,r){\"use strict\";var n=r(5219),i=r(2762),a=r(8116),o=r(1888),s=r(6760),l=r(1283),c=r(9366),u=r(5964),h=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],f=ArrayBuffer,p=DataView;function d(t){return Array.isArray(t)||function(t){return f.isView(t)&&!(t instanceof p)}(t)}function m(t,e){var r=t[0],n=t[1],i=t[2],a=t[3];return t[0]=e[0]*r+e[4]*n+e[8]*i+e[12]*a,t[1]=e[1]*r+e[5]*n+e[9]*i+e[13]*a,t[2]=e[2]*r+e[6]*n+e[10]*i+e[14]*a,t[3]=e[3]*r+e[7]*n+e[11]*i+e[15]*a,t}function g(t,e,r,n){return m(n,n),m(n,n),m(n,n)}function y(t,e){this.index=t,this.dataCoordinate=this.position=e}function v(t){return!0===t||t>1?1:t}function x(t,e,r,n,i,a,o,s,l,c,u,h){this.gl=t,this.pixelRatio=1,this.shader=e,this.orthoShader=r,this.projectShader=n,this.pointBuffer=i,this.colorBuffer=a,this.glyphBuffer=o,this.idBuffer=s,this.vao=l,this.vertexCount=0,this.lineVertexCount=0,this.opacity=1,this.hasAlpha=!1,this.lineWidth=0,this.projectScale=[2/3,2/3,2/3],this.projectOpacity=[1,1,1],this.projectHasAlpha=!1,this.pickId=0,this.pickPerspectiveShader=c,this.pickOrthoShader=u,this.pickProjectShader=h,this.points=[],this._selectResult=new y(0,[0,0,0]),this.useOrtho=!0,this.bounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.axesProject=[!0,!0,!0],this.axesBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.highlightId=[1,1,1,1],this.highlightScale=2,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.dirty=!0}t.exports=function(t){var e=t.gl,r=l.createPerspective(e),n=l.createOrtho(e),o=l.createProject(e),s=l.createPickPerspective(e),c=l.createPickOrtho(e),u=l.createPickProject(e),h=i(e),f=i(e),p=i(e),d=i(e),m=new x(e,r,n,o,h,f,p,d,a(e,[{buffer:h,size:3,type:e.FLOAT},{buffer:f,size:4,type:e.FLOAT},{buffer:p,size:2,type:e.FLOAT},{buffer:d,size:4,type:e.UNSIGNED_BYTE,normalized:!0}]),s,c,u);return m.update(t),m};var _=x.prototype;_.pickSlots=1,_.setPickBase=function(t){this.pickId=t},_.isTransparent=function(){if(this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&this.projectHasAlpha)return!0;return!1},_.isOpaque=function(){if(!this.hasAlpha)return!0;for(var t=0;t<3;++t)if(this.axesProject[t]&&!this.projectHasAlpha)return!0;return!1};var b=[0,0],w=[0,0,0],T=[0,0,0],k=[0,0,0,1],A=[0,0,0,1],M=h.slice(),S=[0,0,0],E=[[0,0,0],[0,0,0]];function C(t){return t[0]=t[1]=t[2]=0,t}function L(t,e){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[3]=1,t}function I(t,e,r,n){return t[0]=e[0],t[1]=e[1],t[2]=e[2],t[r]=n,t}var P=[[-1e8,-1e8,-1e8],[1e8,1e8,1e8]];function z(t,e,r,n,i,a,o){var l=r.gl;if((a===r.projectHasAlpha||o)&&function(t,e,r,n){var i,a=e.axesProject,o=e.gl,l=t.uniforms,c=r.model||h,u=r.view||h,f=r.projection||h,p=e.axesBounds,d=function(t){for(var e=E,r=0;r<2;++r)for(var n=0;n<3;++n)e[r][n]=Math.max(Math.min(t[r][n],1e8),-1e8);return e}(e.clipBounds);i=e.axes&&e.axes.lastCubeProps?e.axes.lastCubeProps.axis:[1,1,1],b[0]=2/o.drawingBufferWidth,b[1]=2/o.drawingBufferHeight,t.bind(),l.view=u,l.projection=f,l.screenSize=b,l.highlightId=e.highlightId,l.highlightScale=e.highlightScale,l.clipBounds=d,l.pickGroup=e.pickId/255,l.pixelRatio=n;for(var m=0;m<3;++m)if(a[m]){l.scale=e.projectScale[m],l.opacity=e.projectOpacity[m];for(var y=M,v=0;v<16;++v)y[v]=0;for(v=0;v<4;++v)y[5*v]=1;y[5*m]=0,i[m]<0?y[12+m]=p[0][m]:y[12+m]=p[1][m],s(y,c,y),l.model=y;var x=(m+1)%3,_=(m+2)%3,P=C(w),z=C(T);P[x]=1,z[_]=1;var O=g(0,0,0,L(k,P)),D=g(0,0,0,L(A,z));if(Math.abs(O[1])>Math.abs(D[1])){var R=O;O=D,D=R,R=P,P=z,z=R;var F=x;x=_,_=F}O[0]<0&&(P[x]=-1),D[1]>0&&(z[_]=-1);var B=0,N=0;for(v=0;v<4;++v)B+=Math.pow(c[4*x+v],2),N+=Math.pow(c[4*_+v],2);P[x]/=Math.sqrt(B),z[_]/=Math.sqrt(N),l.axes[0]=P,l.axes[1]=z,l.fragClipBounds[0]=I(S,d[0],m,-1e8),l.fragClipBounds[1]=I(S,d[1],m,1e8),e.vao.bind(),e.vao.draw(o.TRIANGLES,e.vertexCount),e.lineWidth>0&&(o.lineWidth(e.lineWidth*n),e.vao.draw(o.LINES,e.lineVertexCount,e.vertexCount)),e.vao.unbind()}}(e,r,n,i),a===r.hasAlpha||o){t.bind();var c=t.uniforms;c.model=n.model||h,c.view=n.view||h,c.projection=n.projection||h,b[0]=2/l.drawingBufferWidth,b[1]=2/l.drawingBufferHeight,c.screenSize=b,c.highlightId=r.highlightId,c.highlightScale=r.highlightScale,c.fragClipBounds=P,c.clipBounds=r.axes.bounds,c.opacity=r.opacity,c.pickGroup=r.pickId/255,c.pixelRatio=i,r.vao.bind(),r.vao.draw(l.TRIANGLES,r.vertexCount),r.lineWidth>0&&(l.lineWidth(r.lineWidth*i),r.vao.draw(l.LINES,r.lineVertexCount,r.vertexCount)),r.vao.unbind()}}function O(t,e,r,i){var a;a=d(t)?e=this.pointCount||e<0)return null;var r=this.points[e],n=this._selectResult;n.index=e;for(var i=0;i<3;++i)n.position[i]=n.dataCoordinate[i]=r[i];return n},_.highlight=function(t){if(t){var e=t.index,r=255&e,n=e>>8&255,i=e>>16&255;this.highlightId=[r/255,n/255,i/255,0]}else this.highlightId=[1,1,1,1]},_.update=function(t){if(\"perspective\"in(t=t||{})&&(this.useOrtho=!t.perspective),\"orthographic\"in t&&(this.useOrtho=!!t.orthographic),\"lineWidth\"in t&&(this.lineWidth=t.lineWidth),\"project\"in t)if(d(t.project))this.axesProject=t.project;else{var e=!!t.project;this.axesProject=[e,e,e]}if(\"projectScale\"in t)if(d(t.projectScale))this.projectScale=t.projectScale.slice();else{var r=+t.projectScale;this.projectScale=[r,r,r]}if(this.projectHasAlpha=!1,\"projectOpacity\"in t){d(t.projectOpacity)?this.projectOpacity=t.projectOpacity.slice():(r=+t.projectOpacity,this.projectOpacity=[r,r,r]);for(var n=0;n<3;++n)this.projectOpacity[n]=v(this.projectOpacity[n]),this.projectOpacity[n]<1&&(this.projectHasAlpha=!0)}this.hasAlpha=!1,\"opacity\"in t&&(this.opacity=v(t.opacity),this.opacity<1&&(this.hasAlpha=!0)),this.dirty=!0;var i,a,s=t.position,l={family:t.font||\"normal\",style:t.fontStyle||\"normal\",weight:t.fontWeight||\"normal\",variant:t.fontVariant||\"normal\"},c=t.alignment||[0,0];if(2===c.length)i=c[0],a=c[1];else for(i=[],a=[],n=0;n0){var z=0,D=_,R=[0,0,0,1],F=[0,0,0,1],B=d(p)&&d(p[0]),N=d(y)&&d(y[0]);t:for(n=0;n0?1-S[0][0]:W<0?1+S[1][0]:1,Y*=Y>0?1-S[0][1]:Y<0?1+S[1][1]:1],$=A.cells||[],J=A.positions||[];for(k=0;k<$.length;++k)for(var K=$[k],Q=0;Q<3;++Q){for(var tt=0;tt<3;++tt)C[3*z+tt]=T[tt];for(tt=0;tt<4;++tt)L[4*z+tt]=R[tt];P[z]=x;var et=J[K[Q]];I[2*z]=q*(G*et[0]-Z*et[1]+X[0]),I[2*z+1]=q*(Z*et[0]+G*et[1]+X[1]),z+=1}for($=M.edges,J=M.positions,k=0;k<$.length;++k)for(K=$[k],Q=0;Q<2;++Q){for(tt=0;tt<3;++tt)C[3*D+tt]=T[tt];for(tt=0;tt<4;++tt)L[4*D+tt]=F[tt];P[D]=x,et=J[K[Q]],I[2*D]=q*(G*et[0]-Z*et[1]+X[0]),I[2*D+1]=q*(Z*et[0]+G*et[1]+X[1]),D+=1}}}this.bounds=[u,h],this.points=s,this.pointCount=s.length,this.vertexCount=_,this.lineVertexCount=b,this.pointBuffer.update(C),this.colorBuffer.update(L),this.glyphBuffer.update(I),this.idBuffer.update(P),o.free(C),o.free(L),o.free(I),o.free(P)},_.dispose=function(){this.shader.dispose(),this.orthoShader.dispose(),this.pickPerspectiveShader.dispose(),this.pickOrthoShader.dispose(),this.vao.dispose(),this.pointBuffer.dispose(),this.colorBuffer.dispose(),this.glyphBuffer.dispose(),this.idBuffer.dispose()}},4298:function(t,e,r){\"use strict\";var n=r(3236);e.boxVertex=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nattribute vec2 vertex;\\n\\nuniform vec2 cornerA, cornerB;\\n\\nvoid main() {\\n gl_Position = vec4(mix(cornerA, cornerB, vertex), 0, 1);\\n}\\n\"]),e.boxFragment=n([\"precision mediump float;\\n#define GLSLIFY 1\\n\\nuniform vec4 color;\\n\\nvoid main() {\\n gl_FragColor = color;\\n}\\n\"])},3161:function(t,e,r){\"use strict\";var n=r(9405),i=r(2762),a=r(4298);function o(t,e,r){this.plot=t,this.boxBuffer=e,this.boxShader=r,this.enabled=!0,this.selectBox=[1/0,1/0,-1/0,-1/0],this.borderColor=[0,0,0,1],this.innerFill=!1,this.innerColor=[0,0,0,.25],this.outerFill=!0,this.outerColor=[0,0,0,.5],this.borderWidth=10}t.exports=function(t,e){var r=t.gl,s=new o(t,i(r,[0,0,0,1,1,0,1,1]),n(r,a.boxVertex,a.boxFragment));return s.update(e),t.addOverlay(s),s};var s=o.prototype;s.draw=function(){if(this.enabled){var t=this.plot,e=this.selectBox,r=this.borderWidth,n=(this.innerFill,this.innerColor),i=(this.outerFill,this.outerColor),a=this.borderColor,o=t.box,s=t.screenBox,l=t.dataBox,c=t.viewBox,u=t.pixelRatio,h=(e[0]-l[0])*(c[2]-c[0])/(l[2]-l[0])+c[0],f=(e[1]-l[1])*(c[3]-c[1])/(l[3]-l[1])+c[1],p=(e[2]-l[0])*(c[2]-c[0])/(l[2]-l[0])+c[0],d=(e[3]-l[1])*(c[3]-c[1])/(l[3]-l[1])+c[1];if(h=Math.max(h,c[0]),f=Math.max(f,c[1]),p=Math.min(p,c[2]),d=Math.min(d,c[3]),!(p0){var y=r*u;o.drawBox(h-y,f-y,p+y,f+y,a),o.drawBox(h-y,d-y,p+y,d+y,a),o.drawBox(h-y,f-y,h+y,d+y,a),o.drawBox(p-y,f-y,p+y,d+y,a)}}}},s.update=function(t){t=t||{},this.innerFill=!!t.innerFill,this.outerFill=!!t.outerFill,this.innerColor=(t.innerColor||[0,0,0,.5]).slice(),this.outerColor=(t.outerColor||[0,0,0,.5]).slice(),this.borderColor=(t.borderColor||[0,0,0,1]).slice(),this.borderWidth=t.borderWidth||0,this.selectBox=(t.selectBox||this.selectBox).slice()},s.dispose=function(){this.boxBuffer.dispose(),this.boxShader.dispose(),this.plot.removeOverlay(this)}},3589:function(t,e,r){\"use strict\";t.exports=function(t,e){var r=e[0],a=e[1];return new l(t,n(t,r,a,{}),i.mallocUint8(r*a*4))};var n=r(2260),i=r(1888),a=r(9618),o=r(8828).nextPow2;function s(t,e,r,n,i){this.coord=[t,e],this.id=r,this.value=n,this.distance=i}function l(t,e,r){this.gl=t,this.fbo=e,this.buffer=r,this._readTimeout=null;var n=this;this._readCallback=function(){n.gl&&(e.bind(),t.readPixels(0,0,e.shape[0],e.shape[1],t.RGBA,t.UNSIGNED_BYTE,n.buffer),n._readTimeout=null)}}var c=l.prototype;Object.defineProperty(c,\"shape\",{get:function(){return this.gl?this.fbo.shape.slice():[0,0]},set:function(t){if(this.gl){this.fbo.shape=t;var e=this.fbo.shape[0],r=this.fbo.shape[1];if(r*e*4>this.buffer.length){i.free(this.buffer);for(var n=this.buffer=i.mallocUint8(o(r*e*4)),a=0;ar)for(t=r;te)for(t=e;t=0){for(var T=0|w.type.charAt(w.type.length-1),k=new Array(T),A=0;A=0;)M+=1;b[v]=M}var S=new Array(r.length);function E(){f.program=o.program(p,f._vref,f._fref,_,b);for(var t=0;t=0){if((d=f.charCodeAt(f.length-1)-48)<2||d>4)throw new n(\"\",\"Invalid data type for attribute \"+h+\": \"+f);s(t,e,p[0],i,d,a,h)}else{if(!(f.indexOf(\"mat\")>=0))throw new n(\"\",\"Unknown data type for attribute \"+h+\": \"+f);var d;if((d=f.charCodeAt(f.length-1)-48)<2||d>4)throw new n(\"\",\"Invalid data type for attribute \"+h+\": \"+f);l(t,e,p,i,d,a,h)}}}return a};var n=r(8866);function i(t,e,r,n,i,a){this._gl=t,this._wrapper=e,this._index=r,this._locations=n,this._dimension=i,this._constFunc=a}var a=i.prototype;a.pointer=function(t,e,r,n){var i=this,a=i._gl,o=i._locations[i._index];a.vertexAttribPointer(o,i._dimension,t||a.FLOAT,!!e,r||0,n||0),a.enableVertexAttribArray(o)},a.set=function(t,e,r,n){return this._constFunc(this._locations[this._index],t,e,r,n)},Object.defineProperty(a,\"location\",{get:function(){return this._locations[this._index]},set:function(t){return t!==this._locations[this._index]&&(this._locations[this._index]=0|t,this._wrapper.program=null),0|t}});var o=[function(t,e,r){return void 0===r.length?t.vertexAttrib1f(e,r):t.vertexAttrib1fv(e,r)},function(t,e,r,n){return void 0===r.length?t.vertexAttrib2f(e,r,n):t.vertexAttrib2fv(e,r)},function(t,e,r,n,i){return void 0===r.length?t.vertexAttrib3f(e,r,n,i):t.vertexAttrib3fv(e,r)},function(t,e,r,n,i,a){return void 0===r.length?t.vertexAttrib4f(e,r,n,i,a):t.vertexAttrib4fv(e,r)}];function s(t,e,r,n,a,s,l){var c=o[a],u=new i(t,e,r,n,a,c);Object.defineProperty(s,l,{set:function(e){return t.disableVertexAttribArray(n[r]),c(t,n[r],e),e},get:function(){return u},enumerable:!0})}function l(t,e,r,n,i,a,o){for(var l=new Array(i),c=new Array(i),u=0;u4)throw new i(\"\",\"Invalid uniform dimension type for matrix \"+name+\": \"+v);t[\"uniformMatrix\"+y+\"fv\"](s[h],!1,f);break}throw new i(\"\",\"Unknown uniform data type for \"+name+\": \"+v)}if((y=v.charCodeAt(v.length-1)-48)<2||y>4)throw new i(\"\",\"Invalid data type\");switch(v.charAt(0)){case\"b\":case\"i\":t[\"uniform\"+y+\"iv\"](s[h],f);break;case\"v\":t[\"uniform\"+y+\"fv\"](s[h],f);break;default:throw new i(\"\",\"Unrecognized data type for vector \"+name+\": \"+v)}}}}}}function c(t,e){if(\"object\"!=typeof e)return[[t,e]];var r=[];for(var n in e){var i=e[n],a=t;parseInt(n)+\"\"===n?a+=\"[\"+n+\"]\":a+=\".\"+n,\"object\"==typeof i?r.push.apply(r,c(a,i)):r.push([a,i])}return r}function u(t,e,n){if(\"object\"==typeof n){var c=h(n);Object.defineProperty(t,e,{get:a(c),set:l(n),enumerable:!0,configurable:!1})}else s[n]?Object.defineProperty(t,e,{get:(u=n,function(t,e,r){return t.getUniform(e.program,r[u])}),set:l(n),enumerable:!0,configurable:!1}):t[e]=function(t){switch(t){case\"bool\":return!1;case\"int\":case\"sampler2D\":case\"samplerCube\":case\"float\":return 0;default:var e=t.indexOf(\"vec\");if(0<=e&&e<=1&&t.length===4+e){if((r=t.charCodeAt(t.length-1)-48)<2||r>4)throw new i(\"\",\"Invalid data type\");return\"b\"===t.charAt(0)?o(r,!1):o(r,0)}if(0===t.indexOf(\"mat\")&&4===t.length){var r;if((r=t.charCodeAt(t.length-1)-48)<2||r>4)throw new i(\"\",\"Invalid uniform dimension type for matrix \"+name+\": \"+t);return o(r*r,0)}throw new i(\"\",\"Unknown uniform data type for \"+name+\": \"+t)}}(r[n].type);var u}function h(t){var e;if(Array.isArray(t)){e=new Array(t.length);for(var r=0;r1){s[0]in a||(a[s[0]]=[]),a=a[s[0]];for(var l=1;l1)for(var l=0;l 0 U ||b|| > 0.\\n // Assign z = 0, x = -b, y = a:\\n // a*-b + b*a + c*0 = -ba + ba + 0 = 0\\n if (v.x*v.x > v.z*v.z || v.y*v.y > v.z*v.z) {\\n return normalize(vec3(-v.y, v.x, 0.0));\\n } else {\\n return normalize(vec3(0.0, v.z, -v.y));\\n }\\n}\\n\\n// Calculate the tube vertex and normal at the given index.\\n//\\n// The returned vertex is for a tube ring with its center at origin, radius of length(d), pointing in the direction of d.\\n//\\n// Each tube segment is made up of a ring of vertices.\\n// These vertices are used to make up the triangles of the tube by connecting them together in the vertex array.\\n// The indexes of tube segments run from 0 to 8.\\n//\\nvec3 getTubePosition(vec3 d, float index, out vec3 normal) {\\n float segmentCount = 8.0;\\n\\n float angle = 2.0 * 3.14159 * (index / segmentCount);\\n\\n vec3 u = getOrthogonalVector(d);\\n vec3 v = normalize(cross(u, d));\\n\\n vec3 x = u * cos(angle) * length(d);\\n vec3 y = v * sin(angle) * length(d);\\n vec3 v3 = x + y;\\n\\n normal = normalize(v3);\\n\\n return v3;\\n}\\n\\nattribute vec4 vector;\\nattribute vec4 color, position;\\nattribute vec2 uv;\\n\\nuniform float vectorScale, tubeScale;\\nuniform mat4 model, view, projection, inverseModel;\\nuniform vec3 eyePosition, lightPosition;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n // Scale the vector magnitude to stay constant with\\n // model & view changes.\\n vec3 normal;\\n vec3 XYZ = getTubePosition(mat3(model) * (tubeScale * vector.w * normalize(vector.xyz)), position.w, normal);\\n vec4 tubePosition = model * vec4(position.xyz, 1.0) + vec4(XYZ, 0.0);\\n\\n //Lighting geometry parameters\\n vec4 cameraCoordinate = view * tubePosition;\\n cameraCoordinate.xyz /= cameraCoordinate.w;\\n f_lightDirection = lightPosition - cameraCoordinate.xyz;\\n f_eyeDirection = eyePosition - cameraCoordinate.xyz;\\n f_normal = normalize((vec4(normal, 0.0) * inverseModel).xyz);\\n\\n // vec4 m_position = model * vec4(tubePosition, 1.0);\\n vec4 t_position = view * tubePosition;\\n gl_Position = projection * t_position;\\n\\n f_color = color;\\n f_data = tubePosition.xyz;\\n f_position = position.xyz;\\n f_uv = uv;\\n}\\n\"]),a=n([\"#extension GL_OES_standard_derivatives : enable\\n\\nprecision highp float;\\n#define GLSLIFY 1\\n\\nfloat beckmannDistribution(float x, float roughness) {\\n float NdotH = max(x, 0.0001);\\n float cos2Alpha = NdotH * NdotH;\\n float tan2Alpha = (cos2Alpha - 1.0) / cos2Alpha;\\n float roughness2 = roughness * roughness;\\n float denom = 3.141592653589793 * roughness2 * cos2Alpha * cos2Alpha;\\n return exp(tan2Alpha / roughness2) / denom;\\n}\\n\\nfloat cookTorranceSpecular(\\n vec3 lightDirection,\\n vec3 viewDirection,\\n vec3 surfaceNormal,\\n float roughness,\\n float fresnel) {\\n\\n float VdotN = max(dot(viewDirection, surfaceNormal), 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p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform sampler2D texture;\\n\\nvarying vec3 f_normal, f_lightDirection, f_eyeDirection, f_data, f_position;\\nvarying vec4 f_color;\\nvarying vec2 f_uv;\\n\\nvoid main() {\\n if (outOfRange(clipBounds[0], clipBounds[1], f_position)) discard;\\n vec3 N = normalize(f_normal);\\n vec3 L = normalize(f_lightDirection);\\n vec3 V = normalize(f_eyeDirection);\\n\\n if(gl_FrontFacing) {\\n N = -N;\\n }\\n\\n float specular = min(1.0, max(0.0, cookTorranceSpecular(L, V, N, roughness, fresnel)));\\n float diffuse = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n vec4 surfaceColor = f_color * texture2D(texture, f_uv);\\n vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) 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e=t[0],r=t[1],n=t[2];return!(e_||rb||nw)},k=10*n.distance(e[0],e[1])/c,A=k*k,M=1,S=0,E=r.length;E>1&&(M=function(t){for(var e=[],r=[],n=[],i={},a={},o={},s=t.length,c=0;cS&&(S=F),D.push(F),g.push({points:I,velocities:P,divergences:D});for(var B=0;B<100*c&&I.lengthA&&n.scale(N,N,k/Math.sqrt(j)),n.add(N,N,L),z=d(N),n.squaredDistance(O,N)-A>-1e-4*A&&(I.push(N),O=N,P.push(z),R=m(N,z),F=n.length(R),isFinite(F)&&F>S&&(S=F),D.push(F)),L=N}}var U=function(t,e,r,a){for(var o=0,s=0;s0)for(T=0;T<8;T++){var k=(T+1)%8;c.push(f[T],p[T],p[k],p[k],f[k],f[T]),h.push(v,y,y,y,v,v),d.push(m,g,g,g,m,m);var A=c.length;u.push([A-6,A-5,A-4],[A-3,A-2,A-1])}var M=f;f=p,p=M;var S=v;v=y,y=S;var E=m;m=g,g=E}return{positions:c,cells:u,vectors:h,vertexIntensity:d}}(t,r,a,o)})),h=[],f=[],p=[],d=[];for(s=0;s max(a, b)) || \\n (p < min(a, b)));\\n}\\n\\nbool outOfRange(vec2 a, vec2 b, vec2 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y));\\n}\\n\\nbool outOfRange(vec3 a, vec3 b, vec3 p) {\\n return (outOfRange(a.x, b.x, p.x) ||\\n outOfRange(a.y, b.y, p.y) ||\\n outOfRange(a.z, b.z, p.z));\\n}\\n\\nbool outOfRange(vec4 a, vec4 b, vec4 p) {\\n return outOfRange(a.xyz, b.xyz, p.xyz);\\n}\\n\\nuniform vec3 lowerBound, upperBound;\\nuniform float contourTint;\\nuniform vec4 contourColor;\\nuniform sampler2D colormap;\\nuniform vec3 clipBounds[2];\\nuniform float roughness, fresnel, kambient, kdiffuse, kspecular, opacity;\\nuniform float vertexColor;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n if (\\n kill > 0.0 ||\\n vColor.a == 0.0 ||\\n outOfRange(clipBounds[0], clipBounds[1], worldCoordinate)\\n ) discard;\\n\\n vec3 N = normalize(surfaceNormal);\\n vec3 V = normalize(eyeDirection);\\n vec3 L = normalize(lightDirection);\\n\\n if(gl_FrontFacing) {\\n N = -N;\\n }\\n\\n float specular = max(beckmannSpecular(L, V, N, roughness), 0.);\\n float diffuse = min(kambient + kdiffuse * max(dot(N, L), 0.0), 1.0);\\n\\n //decide how to interpolate color — in vertex or in fragment\\n vec4 surfaceColor =\\n step(vertexColor, .5) * texture2D(colormap, vec2(value, value)) +\\n step(.5, vertexColor) * vColor;\\n\\n vec4 litColor = surfaceColor.a * vec4(diffuse * surfaceColor.rgb + kspecular * vec3(1,1,1) * specular, 1.0);\\n\\n gl_FragColor = mix(litColor, contourColor, contourTint) * opacity;\\n}\\n\"]),s=i([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute vec4 uv;\\nattribute float f;\\n\\nuniform vec3 objectOffset;\\nuniform mat3 permutation;\\nuniform mat4 model, view, projection;\\nuniform float height, zOffset;\\nuniform sampler2D colormap;\\n\\nvarying float value, kill;\\nvarying vec3 worldCoordinate;\\nvarying vec2 planeCoordinate;\\nvarying vec3 lightDirection, eyeDirection, surfaceNormal;\\nvarying vec4 vColor;\\n\\nvoid main() {\\n vec3 dataCoordinate = permutation * vec3(uv.xy, height);\\n worldCoordinate = objectOffset + 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e=t.gl,r=v(e),n=_(e),s=x(e),l=b(e),c=i(e),u=a(e,[{buffer:c,size:4,stride:w,offset:0},{buffer:c,size:3,stride:w,offset:16},{buffer:c,size:3,stride:w,offset:28}]),h=i(e),f=a(e,[{buffer:h,size:4,stride:20,offset:0},{buffer:h,size:1,stride:20,offset:16}]),p=i(e),d=a(e,[{buffer:p,size:2,type:e.FLOAT}]),m=o(e,1,S,e.RGBA,e.UNSIGNED_BYTE);m.minFilter=e.LINEAR,m.magFilter=e.LINEAR;var g=new E(e,[0,0],[[0,0,0],[0,0,0]],r,n,c,u,m,s,l,h,f,p,d,[0,0,0]),y={levels:[[],[],[]]};for(var T in t)y[T]=t[T];return y.colormap=y.colormap||\"jet\",g.update(y),g};var n=r(8828),i=r(2762),a=r(8116),o=r(7766),s=r(1888),l=r(6729),c=r(5298),u=r(9994),h=r(9618),f=r(3711),p=r(6760),d=r(7608),m=r(2478),g=r(6199),y=r(990),v=y.createShader,x=y.createContourShader,_=y.createPickShader,b=y.createPickContourShader,w=40,T=[1,0,0,0,0,1,0,0,0,0,1,0,0,0,0,1],k=[[0,0],[0,1],[1,0],[1,1],[1,0],[0,1]],A=[[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0],[0,0,0,0,0,0,0,0,0]];function M(t,e,r,n,i){this.position=t,this.index=e,this.uv=r,this.level=n,this.dataCoordinate=i}!function(){for(var t=0;t<3;++t){var e=A[t],r=(t+2)%3;e[(t+1)%3+0]=1,e[r+3]=1,e[t+6]=1}}();var S=256;function E(t,e,r,n,i,a,o,l,c,u,f,p,d,m,g){this.gl=t,this.shape=e,this.bounds=r,this.objectOffset=g,this.intensityBounds=[],this._shader=n,this._pickShader=i,this._coordinateBuffer=a,this._vao=o,this._colorMap=l,this._contourShader=c,this._contourPickShader=u,this._contourBuffer=f,this._contourVAO=p,this._contourOffsets=[[],[],[]],this._contourCounts=[[],[],[]],this._vertexCount=0,this._pickResult=new M([0,0,0],[0,0],[0,0],[0,0,0],[0,0,0]),this._dynamicBuffer=d,this._dynamicVAO=m,this._dynamicOffsets=[0,0,0],this._dynamicCounts=[0,0,0],this.contourWidth=[1,1,1],this.contourLevels=[[1],[1],[1]],this.contourTint=[0,0,0],this.contourColor=[[.5,.5,.5,1],[.5,.5,.5,1],[.5,.5,.5,1]],this.showContour=!0,this.showSurface=!0,this.enableHighlight=[!0,!0,!0],this.highlightColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.highlightTint=[1,1,1],this.highlightLevel=[-1,-1,-1],this.enableDynamic=[!0,!0,!0],this.dynamicLevel=[NaN,NaN,NaN],this.dynamicColor=[[0,0,0,1],[0,0,0,1],[0,0,0,1]],this.dynamicTint=[1,1,1],this.dynamicWidth=[1,1,1],this.axesBounds=[[1/0,1/0,1/0],[-1/0,-1/0,-1/0]],this.surfaceProject=[!1,!1,!1],this.contourProject=[[!1,!1,!1],[!1,!1,!1],[!1,!1,!1]],this.colorBounds=[!1,!1],this._field=[h(s.mallocFloat(1024),[0,0]),h(s.mallocFloat(1024),[0,0]),h(s.mallocFloat(1024),[0,0])],this.pickId=1,this.clipBounds=[[-1/0,-1/0,-1/0],[1/0,1/0,1/0]],this.snapToData=!1,this.pixelRatio=1,this.opacity=1,this.lightPosition=[10,1e4,0],this.ambientLight=.8,this.diffuseLight=.8,this.specularLight=2,this.roughness=.5,this.fresnel=1.5,this.vertexColor=0,this.dirty=!0}var C=E.prototype;C.genColormap=function(t,e){var r=!1,n=u([l({colormap:t,nshades:S,format:\"rgba\"}).map((function(t,n){var i=e?function(t,e){if(!e)return 1;if(!e.length)return 1;for(var r=0;rt&&r>0){var n=(e[r][0]-t)/(e[r][0]-e[r-1][0]);return e[r][1]*(1-n)+n*e[r-1][1]}}return 1}(n/255,e):t[3];return i<1&&(r=!0),[t[0],t[1],t[2],255*i]}))]);return c.divseq(n,255),this.hasAlphaScale=r,n},C.isTransparent=function(){return this.opacity<1||this.hasAlphaScale},C.isOpaque=function(){return!this.isTransparent()},C.pickSlots=1,C.setPickBase=function(t){this.pickId=t};var L=[0,0,0],I={showSurface:!1,showContour:!1,projections:[T.slice(),T.slice(),T.slice()],clipBounds:[[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]],[[0,0,0],[0,0,0]]]};function P(t,e){var r,n,i,a=e.axes&&e.axes.lastCubeProps.axis||L,o=e.showSurface,s=e.showContour;for(r=0;r<3;++r)for(o=o||e.surfaceProject[r],n=0;n<3;++n)s=s||e.contourProject[r][n];for(r=0;r<3;++r){var l=I.projections[r];for(n=0;n<16;++n)l[n]=0;for(n=0;n<4;++n)l[5*n]=1;l[5*r]=0,l[12+r]=e.axesBounds[+(a[r]>0)][r],p(l,t.model,l);var c=I.clipBounds[r];for(i=0;i<2;++i)for(n=0;n<3;++n)c[i][n]=t.clipBounds[i][n];c[0][r]=-1e8,c[1][r]=1e8}return I.showSurface=o,I.showContour=s,I}var z={model:T,view:T,projection:T,inverseModel:T.slice(),lowerBound:[0,0,0],upperBound:[0,0,0],colorMap:0,clipBounds:[[0,0,0],[0,0,0]],height:0,contourTint:0,contourColor:[0,0,0,1],permutation:[1,0,0,0,1,0,0,0,1],zOffset:-1e-4,objectOffset:[0,0,0],kambient:1,kdiffuse:1,kspecular:1,lightPosition:[1e3,1e3,1e3],eyePosition:[0,0,0],roughness:1,fresnel:1,opacity:1,vertexColor:0},O=T.slice(),D=[1,0,0,0,1,0,0,0,1];function R(t,e){t=t||{};var r=this.gl;r.disable(r.CULL_FACE),this._colorMap.bind(0);var n=z;n.model=t.model||T,n.view=t.view||T,n.projection=t.projection||T,n.lowerBound=[this.bounds[0][0],this.bounds[0][1],this.colorBounds[0]||this.bounds[0][2]],n.upperBound=[this.bounds[1][0],this.bounds[1][1],this.colorBounds[1]||this.bounds[1][2]],n.objectOffset=this.objectOffset,n.contourColor=this.contourColor[0],n.inverseModel=d(n.inverseModel,n.model);for(var i=0;i<2;++i)for(var a=n.clipBounds[i],o=0;o<3;++o)a[o]=Math.min(Math.max(this.clipBounds[i][o],-1e8),1e8);n.kambient=this.ambientLight,n.kdiffuse=this.diffuseLight,n.kspecular=this.specularLight,n.roughness=this.roughness,n.fresnel=this.fresnel,n.opacity=this.opacity,n.height=0,n.permutation=D,n.vertexColor=this.vertexColor;var s=O;for(p(s,n.view,n.model),p(s,n.projection,s),d(s,s),i=0;i<3;++i)n.eyePosition[i]=s[12+i]/s[15];var l=s[15];for(i=0;i<3;++i)l+=this.lightPosition[i]*s[4*i+3];for(i=0;i<3;++i){var c=s[12+i];for(o=0;o<3;++o)c+=s[4*o+i]*this.lightPosition[o];n.lightPosition[i]=c/l}var u=P(n,this);if(u.showSurface){for(this._shader.bind(),this._shader.uniforms=n,this._vao.bind(),this.showSurface&&this._vertexCount&&this._vao.draw(r.TRIANGLES,this._vertexCount),i=0;i<3;++i)this.surfaceProject[i]&&this.vertexCount&&(this._shader.uniforms.model=u.projections[i],this._shader.uniforms.clipBounds=u.clipBounds[i],this._vao.draw(r.TRIANGLES,this._vertexCount));this._vao.unbind()}if(u.showContour){var h=this._contourShader;n.kambient=1,n.kdiffuse=0,n.kspecular=0,n.opacity=1,h.bind(),h.uniforms=n;var f=this._contourVAO;for(f.bind(),i=0;i<3;++i)for(h.uniforms.permutation=A[i],r.lineWidth(this.contourWidth[i]*this.pixelRatio),o=0;o>4)/16)/255,i=Math.floor(n),a=n-i,o=e[1]*(t.value[1]+(15&t.value[2])/16)/255,s=Math.floor(o),l=o-s;i+=1,s+=1;var c=r.position;c[0]=c[1]=c[2]=0;for(var u=0;u<2;++u)for(var h=u?a:1-a,f=0;f<2;++f)for(var p=i+u,d=s+f,g=h*(f?l:1-l),y=0;y<3;++y)c[y]+=this._field[y].get(p,d)*g;for(var v=this._pickResult.level,x=0;x<3;++x)if(v[x]=m.le(this.contourLevels[x],c[x]),v[x]<0)this.contourLevels[x].length>0&&(v[x]=0);else if(v[x]Math.abs(b-c[x])&&(v[x]+=1)}for(r.index[0]=a<.5?i:i+1,r.index[1]=l<.5?s:s+1,r.uv[0]=n/e[0],r.uv[1]=o/e[1],y=0;y<3;++y)r.dataCoordinate[y]=this._field[y].get(r.index[0],r.index[1]);return r},C.padField=function(t,e){var r=e.shape.slice(),n=t.shape.slice();c.assign(t.lo(1,1).hi(r[0],r[1]),e),c.assign(t.lo(1).hi(r[0],1),e.hi(r[0],1)),c.assign(t.lo(1,n[1]-1).hi(r[0],1),e.lo(0,r[1]-1).hi(r[0],1)),c.assign(t.lo(0,1).hi(1,r[1]),e.hi(1)),c.assign(t.lo(n[0]-1,1).hi(1,r[1]),e.lo(r[0]-1)),t.set(0,0,e.get(0,0)),t.set(0,n[1]-1,e.get(0,r[1]-1)),t.set(n[0]-1,0,e.get(r[0]-1,0)),t.set(n[0]-1,n[1]-1,e.get(r[0]-1,r[1]-1))},C.update=function(t){t=t||{},this.objectOffset=t.objectOffset||this.objectOffset,this.dirty=!0,\"contourWidth\"in t&&(this.contourWidth=B(t.contourWidth,Number)),\"showContour\"in t&&(this.showContour=B(t.showContour,Boolean)),\"showSurface\"in t&&(this.showSurface=!!t.showSurface),\"contourTint\"in t&&(this.contourTint=B(t.contourTint,Boolean)),\"contourColor\"in t&&(this.contourColor=j(t.contourColor)),\"contourProject\"in t&&(this.contourProject=B(t.contourProject,(function(t){return B(t,Boolean)}))),\"surfaceProject\"in t&&(this.surfaceProject=t.surfaceProject),\"dynamicColor\"in t&&(this.dynamicColor=j(t.dynamicColor)),\"dynamicTint\"in t&&(this.dynamicTint=B(t.dynamicTint,Number)),\"dynamicWidth\"in t&&(this.dynamicWidth=B(t.dynamicWidth,Number)),\"opacity\"in t&&(this.opacity=t.opacity),\"opacityscale\"in t&&(this.opacityscale=t.opacityscale),\"colorBounds\"in t&&(this.colorBounds=t.colorBounds),\"vertexColor\"in t&&(this.vertexColor=t.vertexColor?1:0),\"colormap\"in t&&this._colorMap.setPixels(this.genColormap(t.colormap,this.opacityscale));var e=t.field||t.coords&&t.coords[2]||null,r=!1;if(e||(e=this._field[2].shape[0]||this._field[2].shape[2]?this._field[2].lo(1,1).hi(this._field[2].shape[0]-2,this._field[2].shape[1]-2):this._field[2].hi(0,0)),\"field\"in t||\"coords\"in t){var i=(e.shape[0]+2)*(e.shape[1]+2);i>this._field[2].data.length&&(s.freeFloat(this._field[2].data),this._field[2].data=s.mallocFloat(n.nextPow2(i))),this._field[2]=h(this._field[2].data,[e.shape[0]+2,e.shape[1]+2]),this.padField(this._field[2],e),this.shape=e.shape.slice();for(var a=this.shape,o=0;o<2;++o)this._field[2].size>this._field[o].data.length&&(s.freeFloat(this._field[o].data),this._field[o].data=s.mallocFloat(this._field[2].size)),this._field[o]=h(this._field[o].data,[a[0]+2,a[1]+2]);if(t.coords){var l=t.coords;if(!Array.isArray(l)||3!==l.length)throw new Error(\"gl-surface: invalid coordinates for x/y\");for(o=0;o<2;++o){var c=l[o];for(y=0;y<2;++y)if(c.shape[y]!==a[y])throw new Error(\"gl-surface: coords have incorrect shape\");this.padField(this._field[o],c)}}else if(t.ticks){var u=t.ticks;if(!Array.isArray(u)||2!==u.length)throw new Error(\"gl-surface: invalid ticks\");for(o=0;o<2;++o){var p=u[o];if((Array.isArray(p)||p.length)&&(p=h(p)),p.shape[0]!==a[o])throw new Error(\"gl-surface: invalid tick length\");var d=h(p.data,a);d.stride[o]=p.stride[0],d.stride[1^o]=0,this.padField(this._field[o],d)}}else{for(o=0;o<2;++o){var m=[0,0];m[o]=1,this._field[o]=h(this._field[o].data,[a[0]+2,a[1]+2],m,0)}this._field[0].set(0,0,0);for(var y=0;y0){for(var xt=0;xt<5;++xt)K.pop();U-=1}continue t}K.push(nt[0],nt[1],ot[0],ot[1],nt[2]),U+=1}}rt.push(U)}this._contourOffsets[Q]=et,this._contourCounts[Q]=rt}var _t=s.mallocFloat(K.length);for(o=0;os||o[1]<0||o[1]>s)throw new Error(\"gl-texture2d: Invalid texture size\");var l=d(o,e.stride.slice()),c=0;\"float32\"===r?c=t.FLOAT:\"float64\"===r?(c=t.FLOAT,l=!1,r=\"float32\"):\"uint8\"===r?c=t.UNSIGNED_BYTE:(c=t.UNSIGNED_BYTE,l=!1,r=\"uint8\");var h,p,g=0;if(2===o.length)g=t.LUMINANCE,o=[o[0],o[1],1],e=n(e.data,o,[e.stride[0],e.stride[1],1],e.offset);else{if(3!==o.length)throw new Error(\"gl-texture2d: Invalid shape for texture\");if(1===o[2])g=t.ALPHA;else if(2===o[2])g=t.LUMINANCE_ALPHA;else if(3===o[2])g=t.RGB;else{if(4!==o[2])throw new Error(\"gl-texture2d: Invalid shape for pixel coords\");g=t.RGBA}}c!==t.FLOAT||t.getExtension(\"OES_texture_float\")||(c=t.UNSIGNED_BYTE,l=!1);var y=e.size;if(l)h=0===e.offset&&e.data.length===y?e.data:e.data.subarray(e.offset,e.offset+y);else{var v=[o[2],o[2]*o[0],1];p=a.malloc(y,r);var x=n(p,o,v,0);\"float32\"!==r&&\"float64\"!==r||c!==t.UNSIGNED_BYTE?i.assign(x,e):u(x,e),h=p.subarray(0,y)}var _=m(t);return t.texImage2D(t.TEXTURE_2D,0,g,o[0],o[1],0,g,c,h),l||a.free(p),new f(t,_,o[0],o[1],g,c)}(t,e)}throw new Error(\"gl-texture2d: Invalid arguments for texture2d constructor\")};var o=null,s=null,l=null;function c(t){return\"undefined\"!=typeof HTMLCanvasElement&&t instanceof HTMLCanvasElement||\"undefined\"!=typeof HTMLImageElement&&t instanceof HTMLImageElement||\"undefined\"!=typeof HTMLVideoElement&&t instanceof HTMLVideoElement||\"undefined\"!=typeof ImageData&&t instanceof ImageData}var u=function(t,e){i.muls(t,e,255)};function h(t,e,r){var n=t.gl,i=n.getParameter(n.MAX_TEXTURE_SIZE);if(e<0||e>i||r<0||r>i)throw new Error(\"gl-texture2d: Invalid texture size\");return t._shape=[e,r],t.bind(),n.texImage2D(n.TEXTURE_2D,0,t.format,e,r,0,t.format,t.type,null),t._mipLevels=[0],t}function f(t,e,r,n,i,a){this.gl=t,this.handle=e,this.format=i,this.type=a,this._shape=[r,n],this._mipLevels=[0],this._magFilter=t.NEAREST,this._minFilter=t.NEAREST,this._wrapS=t.CLAMP_TO_EDGE,this._wrapT=t.CLAMP_TO_EDGE,this._anisoSamples=1;var o=this,s=[this._wrapS,this._wrapT];Object.defineProperties(s,[{get:function(){return o._wrapS},set:function(t){return o.wrapS=t}},{get:function(){return o._wrapT},set:function(t){return o.wrapT=t}}]),this._wrapVector=s;var l=[this._shape[0],this._shape[1]];Object.defineProperties(l,[{get:function(){return o._shape[0]},set:function(t){return o.width=t}},{get:function(){return o._shape[1]},set:function(t){return o.height=t}}]),this._shapeVector=l}var p=f.prototype;function d(t,e){return 3===t.length?1===e[2]&&e[1]===t[0]*t[2]&&e[0]===t[2]:1===e[0]&&e[1]===t[0]}function m(t){var e=t.createTexture();return t.bindTexture(t.TEXTURE_2D,e),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MIN_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_MAG_FILTER,t.NEAREST),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_S,t.CLAMP_TO_EDGE),t.texParameteri(t.TEXTURE_2D,t.TEXTURE_WRAP_T,t.CLAMP_TO_EDGE),e}function g(t,e,r,n,i){var a=t.getParameter(t.MAX_TEXTURE_SIZE);if(e<0||e>a||r<0||r>a)throw new Error(\"gl-texture2d: Invalid texture shape\");if(i===t.FLOAT&&!t.getExtension(\"OES_texture_float\"))throw new Error(\"gl-texture2d: Floating point textures not supported on this platform\");var o=m(t);return t.texImage2D(t.TEXTURE_2D,0,n,e,r,0,n,i,null),new f(t,o,e,r,n,i)}Object.defineProperties(p,{minFilter:{get:function(){return this._minFilter},set:function(t){this.bind();var e=this.gl;if(this.type===e.FLOAT&&o.indexOf(t)>=0&&(e.getExtension(\"OES_texture_float_linear\")||(t=e.NEAREST)),s.indexOf(t)<0)throw new Error(\"gl-texture2d: Unknown filter mode \"+t);return e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MIN_FILTER,t),this._minFilter=t}},magFilter:{get:function(){return this._magFilter},set:function(t){this.bind();var e=this.gl;if(this.type===e.FLOAT&&o.indexOf(t)>=0&&(e.getExtension(\"OES_texture_float_linear\")||(t=e.NEAREST)),s.indexOf(t)<0)throw new Error(\"gl-texture2d: Unknown filter mode \"+t);return e.texParameteri(e.TEXTURE_2D,e.TEXTURE_MAG_FILTER,t),this._magFilter=t}},mipSamples:{get:function(){return this._anisoSamples},set:function(t){var e=this._anisoSamples;if(this._anisoSamples=0|Math.max(t,1),e!==this._anisoSamples){var r=this.gl.getExtension(\"EXT_texture_filter_anisotropic\");r&&this.gl.texParameterf(this.gl.TEXTURE_2D,r.TEXTURE_MAX_ANISOTROPY_EXT,this._anisoSamples)}return this._anisoSamples}},wrapS:{get:function(){return this._wrapS},set:function(t){if(this.bind(),l.indexOf(t)<0)throw new Error(\"gl-texture2d: Unknown wrap mode \"+t);return this.gl.texParameteri(this.gl.TEXTURE_2D,this.gl.TEXTURE_WRAP_S,t),this._wrapS=t}},wrapT:{get:function(){return this._wrapT},set:function(t){if(this.bind(),l.indexOf(t)<0)throw new Error(\"gl-texture2d: Unknown wrap mode \"+t);return this.gl.texParameteri(this.gl.TEXTURE_2D,this.gl.TEXTURE_WRAP_T,t),this._wrapT=t}},wrap:{get:function(){return this._wrapVector},set:function(t){if(Array.isArray(t)||(t=[t,t]),2!==t.length)throw new Error(\"gl-texture2d: Must specify wrap mode for rows and columns\");for(var e=0;e<2;++e)if(l.indexOf(t[e])<0)throw new Error(\"gl-texture2d: Unknown wrap mode \"+t);this._wrapS=t[0],this._wrapT=t[1];var r=this.gl;return 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e>t==t>0?o===a?(r+=1,o=0):o+=1:0===o?(o=a,r-=1):o-=1,n.pack(o,r)}},8406:function(t,e){e.vertexNormals=function(t,e,r){for(var n=e.length,i=new Array(n),a=void 0===r?1e-6:r,o=0;oa){var _=i[c],b=1/Math.sqrt(g*v);for(x=0;x<3;++x){var w=(x+1)%3,T=(x+2)%3;_[x]+=b*(y[w]*m[T]-y[T]*m[w])}}}for(o=0;oa)for(b=1/Math.sqrt(k),x=0;x<3;++x)_[x]*=b;else for(x=0;x<3;++x)_[x]=0}return i},e.faceNormals=function(t,e,r){for(var n=t.length,i=new Array(n),a=void 0===r?1e-6:r,o=0;oa?1/Math.sqrt(p):0,c=0;c<3;++c)f[c]*=p;i[o]=f}return i}},4081:function(t){\"use strict\";t.exports=function(t,e,r,n,i,a,o,s,l,c){var u=e+a+c;if(h>0){var h=Math.sqrt(u+1);t[0]=.5*(o-l)/h,t[1]=.5*(s-n)/h,t[2]=.5*(r-a)/h,t[3]=.5*h}else{var f=Math.max(e,a,c);h=Math.sqrt(2*f-u+1),e>=f?(t[0]=.5*h,t[1]=.5*(i+r)/h,t[2]=.5*(s+n)/h,t[3]=.5*(o-l)/h):a>=f?(t[0]=.5*(r+i)/h,t[1]=.5*h,t[2]=.5*(l+o)/h,t[3]=.5*(s-n)/h):(t[0]=.5*(n+s)/h,t[1]=.5*(o+l)/h,t[2]=.5*h,t[3]=.5*(r-i)/h)}return t}},9977:function(t,e,r){\"use strict\";t.exports=function(t){var e=(t=t||{}).center||[0,0,0],r=t.rotation||[0,0,0,1],n=t.radius||1;e=[].slice.call(e,0,3),u(r=[].slice.call(r,0,4),r);var i=new h(r,e,Math.log(n));return i.setDistanceLimits(t.zoomMin,t.zoomMax),(\"eye\"in t||\"up\"in t)&&i.lookAt(0,t.eye,t.center,t.up),i};var n=r(9215),i=r(6582),a=r(7399),o=r(7608),s=r(4081);function l(t,e,r){return Math.sqrt(Math.pow(t,2)+Math.pow(e,2)+Math.pow(r,2))}function c(t,e,r,n){return Math.sqrt(Math.pow(t,2)+Math.pow(e,2)+Math.pow(r,2)+Math.pow(n,2))}function u(t,e){var r=e[0],n=e[1],i=e[2],a=e[3],o=c(r,n,i,a);o>1e-6?(t[0]=r/o,t[1]=n/o,t[2]=i/o,t[3]=a/o):(t[0]=t[1]=t[2]=0,t[3]=1)}function h(t,e,r){this.radius=n([r]),this.center=n(e),this.rotation=n(t),this.computedRadius=this.radius.curve(0),this.computedCenter=this.center.curve(0),this.computedRotation=this.rotation.curve(0),this.computedUp=[.1,0,0],this.computedEye=[.1,0,0],this.computedMatrix=[.1,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0],this.recalcMatrix(0)}var f=h.prototype;f.lastT=function(){return Math.max(this.radius.lastT(),this.center.lastT(),this.rotation.lastT())},f.recalcMatrix=function(t){this.radius.curve(t),this.center.curve(t),this.rotation.curve(t);var e=this.computedRotation;u(e,e);var r=this.computedMatrix;a(r,e);var n=this.computedCenter,i=this.computedEye,o=this.computedUp,s=Math.exp(this.computedRadius[0]);i[0]=n[0]+s*r[2],i[1]=n[1]+s*r[6],i[2]=n[2]+s*r[10],o[0]=r[1],o[1]=r[5],o[2]=r[9];for(var l=0;l<3;++l){for(var c=0,h=0;h<3;++h)c+=r[l+4*h]*i[h];r[12+l]=-c}},f.getMatrix=function(t,e){this.recalcMatrix(t);var r=this.computedMatrix;if(e){for(var n=0;n<16;++n)e[n]=r[n];return e}return r},f.idle=function(t){this.center.idle(t),this.radius.idle(t),this.rotation.idle(t)},f.flush=function(t){this.center.flush(t),this.radius.flush(t),this.rotation.flush(t)},f.pan=function(t,e,r,n){e=e||0,r=r||0,n=n||0,this.recalcMatrix(t);var i=this.computedMatrix,a=i[1],o=i[5],s=i[9],c=l(a,o,s);a/=c,o/=c,s/=c;var 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6:u.push([t-.5-.25*(-n-r+a+i)/(n-r+i-a),e-.5-.25*(-i-r+a+n)/(i-r+n-a)]);break;case 7:u.push([t-.75-.25*(a+i-2*f)/(i-a),e-.75-.25*(a+n-2*f)/(n-a)]);break;case 8:u.push([t-.75-.25*(-a-i+2*f)/(a-i),e-.75-.25*(-a-n+2*f)/(a-n)]);break;case 9:u.push([t-.5-.25*(n+r+-a-i)/(r-n+a-i),e-.5-.25*(i+r+-a-n)/(r-i+a-n)]);break;case 10:u.push([t-.5-.5*(-n-r-a-i+4*f)/(n-r+a-i),e-.5]);break;case 11:u.push([t-.25-.25*(-a-i+2*f)/(a-i),e-.75-.25*(i+r-2*f)/(r-i)]);break;case 12:u.push([t-.5,e-.5-.5*(-i-r-a-n+4*f)/(i-r+a-n)]);break;case 13:u.push([t-.75-.25*(n+r-2*f)/(r-n),e-.25-.25*(-a-n+2*f)/(a-n)]);break;case 14:u.push([t-.25-.25*(-n-r+2*f)/(n-r),e-.25-.25*(-i-r+2*f)/(i-r)])}},cell:function(t,e,r,n,i,a,o,s,l){i?s.push([t,e]):s.push([e,t])}});return function(t,e){var r=[],i=[];return n(t,r,i,e),{positions:r,cells:i}}}},o={}},529:function(t,e,r){\"use strict\";t.exports=function t(e,r,i){var a=(i=i||{}).fontStyle||\"normal\",s=i.fontWeight||\"normal\",l=i.fontVariant||\"normal\",c=[a,s,l,e].join(\"_\"),u=o[c];u||(u=o[c]={\" \":{data:new Float32Array(0),shape:.2}});var h=u[r];if(!h)if(r.length<=1||!/\\d/.test(r))h=u[r]=function(t){for(var e=t.cells,r=t.positions,n=new Float32Array(6*e.length),i=0,a=0,o=0;o0&&(m+=.02);var y=new Float32Array(d),v=0,x=-.5*m;for(g=0;gMath.max(r,n)?i[2]=1:r>Math.max(e,n)?i[0]=1:i[1]=1;for(var a=0,o=0,l=0;l<3;++l)a+=t[l]*t[l],o+=i[l]*t[l];for(l=0;l<3;++l)i[l]-=o/a*t[l];return s(i,i),i}function f(t,e,r,i,a,o,s,l){this.center=n(r),this.up=n(i),this.right=n(a),this.radius=n([o]),this.angle=n([s,l]),this.angle.bounds=[[-1/0,-Math.PI/2],[1/0,Math.PI/2]],this.setDistanceLimits(t,e),this.computedCenter=this.center.curve(0),this.computedUp=this.up.curve(0),this.computedRight=this.right.curve(0),this.computedRadius=this.radius.curve(0),this.computedAngle=this.angle.curve(0),this.computedToward=[0,0,0],this.computedEye=[0,0,0],this.computedMatrix=new Array(16);for(var c=0;c<16;++c)this.computedMatrix[c]=.5;this.recalcMatrix(0)}var p=f.prototype;p.setDistanceLimits=function(t,e){t=t>0?Math.log(t):-1/0,e=e>0?Math.log(e):1/0,e=Math.max(e,t),this.radius.bounds[0][0]=t,this.radius.bounds[1][0]=e},p.getDistanceLimits=function(t){var e=this.radius.bounds[0];return t?(t[0]=Math.exp(e[0][0]),t[1]=Math.exp(e[1][0]),t):[Math.exp(e[0][0]),Math.exp(e[1][0])]},p.recalcMatrix=function(t){this.center.curve(t),this.up.curve(t),this.right.curve(t),this.radius.curve(t),this.angle.curve(t);for(var e=this.computedUp,r=this.computedRight,n=0,i=0,a=0;a<3;++a)i+=e[a]*r[a],n+=e[a]*e[a];var l=Math.sqrt(n),u=0;for(a=0;a<3;++a)r[a]-=e[a]*i/n,u+=r[a]*r[a],e[a]/=l;var h=Math.sqrt(u);for(a=0;a<3;++a)r[a]/=h;var f=this.computedToward;o(f,e,r),s(f,f);var p=Math.exp(this.computedRadius[0]),d=this.computedAngle[0],m=this.computedAngle[1],g=Math.cos(d),y=Math.sin(d),v=Math.cos(m),x=Math.sin(m),_=this.computedCenter,b=g*v,w=y*v,T=x,k=-g*x,A=-y*x,M=v,S=this.computedEye,E=this.computedMatrix;for(a=0;a<3;++a){var C=b*r[a]+w*f[a]+T*e[a];E[4*a+1]=k*r[a]+A*f[a]+M*e[a],E[4*a+2]=C,E[4*a+3]=0}var L=E[1],I=E[5],P=E[9],z=E[2],O=E[6],D=E[10],R=I*D-P*O,F=P*z-L*D,B=L*O-I*z,N=c(R,F,B);for(R/=N,F/=N,B/=N,E[0]=R,E[4]=F,E[8]=B,a=0;a<3;++a)S[a]=_[a]+E[2+4*a]*p;for(a=0;a<3;++a){u=0;for(var j=0;j<3;++j)u+=E[a+4*j]*S[j];E[12+a]=-u}E[15]=1},p.getMatrix=function(t,e){this.recalcMatrix(t);var r=this.computedMatrix;if(e){for(var n=0;n<16;++n)e[n]=r[n];return e}return r};var d=[0,0,0];p.rotate=function(t,e,r,n){if(this.angle.move(t,e,r),n){this.recalcMatrix(t);var i=this.computedMatrix;d[0]=i[2],d[1]=i[6],d[2]=i[10];for(var o=this.computedUp,s=this.computedRight,l=this.computedToward,c=0;c<3;++c)i[4*c]=o[c],i[4*c+1]=s[c],i[4*c+2]=l[c];for(a(i,i,n,d),c=0;c<3;++c)o[c]=i[4*c],s[c]=i[4*c+1];this.up.set(t,o[0],o[1],o[2]),this.right.set(t,s[0],s[1],s[2])}},p.pan=function(t,e,r,n){e=e||0,r=r||0,n=n||0,this.recalcMatrix(t);var i=this.computedMatrix,a=(Math.exp(this.computedRadius[0]),i[1]),o=i[5],s=i[9],l=c(a,o,s);a/=l,o/=l,s/=l;var u=i[0],h=i[4],f=i[8],p=u*a+h*o+f*s,d=c(u-=a*p,h-=o*p,f-=s*p),m=(u/=d)*e+a*r,g=(h/=d)*e+o*r,y=(f/=d)*e+s*r;this.center.move(t,m,g,y);var v=Math.exp(this.computedRadius[0]);v=Math.max(1e-4,v+n),this.radius.set(t,Math.log(v))},p.translate=function(t,e,r,n){this.center.move(t,e||0,r||0,n||0)},p.setMatrix=function(t,e,r,n){var a=1;\"number\"==typeof r&&(a=0|r),(a<0||a>3)&&(a=1);var o=(a+2)%3;e||(this.recalcMatrix(t),e=this.computedMatrix);var s=e[a],l=e[a+4],h=e[a+8];if(n){var f=Math.abs(s),p=Math.abs(l),d=Math.abs(h),m=Math.max(f,p,d);f===m?(s=s<0?-1:1,l=h=0):d===m?(h=h<0?-1:1,s=l=0):(l=l<0?-1:1,s=h=0)}else{var g=c(s,l,h);s/=g,l/=g,h/=g}var y,v,x=e[o],_=e[o+4],b=e[o+8],w=x*s+_*l+b*h,T=c(x-=s*w,_-=l*w,b-=h*w),k=l*(b/=T)-h*(_/=T),A=h*(x/=T)-s*b,M=s*_-l*x,S=c(k,A,M);if(k/=S,A/=S,M/=S,this.center.jump(t,H,G,Z),this.radius.idle(t),this.up.jump(t,s,l,h),this.right.jump(t,x,_,b),2===a){var E=e[1],C=e[5],L=e[9],I=E*x+C*_+L*b,P=E*k+C*A+L*M;y=R<0?-Math.PI/2:Math.PI/2,v=Math.atan2(P,I)}else{var z=e[2],O=e[6],D=e[10],R=z*s+O*l+D*h,F=z*x+O*_+D*b,B=z*k+O*A+D*M;y=Math.asin(u(R)),v=Math.atan2(B,F)}this.angle.jump(t,v,y),this.recalcMatrix(t);var N=e[2],j=e[6],U=e[10],V=this.computedMatrix;i(V,e);var q=V[15],H=V[12]/q,G=V[13]/q,Z=V[14]/q,W=Math.exp(this.computedRadius[0]);this.center.jump(t,H-N*W,G-j*W,Z-U*W)},p.lastT=function(){return Math.max(this.center.lastT(),this.up.lastT(),this.right.lastT(),this.radius.lastT(),this.angle.lastT())},p.idle=function(t){this.center.idle(t),this.up.idle(t),this.right.idle(t),this.radius.idle(t),this.angle.idle(t)},p.flush=function(t){this.center.flush(t),this.up.flush(t),this.right.flush(t),this.radius.flush(t),this.angle.flush(t)},p.setDistance=function(t,e){e>0&&this.radius.set(t,Math.log(e))},p.lookAt=function(t,e,r,n){this.recalcMatrix(t),e=e||this.computedEye,r=r||this.computedCenter;var i=(n=n||this.computedUp)[0],a=n[1],o=n[2],s=c(i,a,o);if(!(s<1e-6)){i/=s,a/=s,o/=s;var l=e[0]-r[0],h=e[1]-r[1],f=e[2]-r[2],p=c(l,h,f);if(!(p<1e-6)){l/=p,h/=p,f/=p;var d=this.computedRight,m=d[0],g=d[1],y=d[2],v=i*m+a*g+o*y,x=c(m-=v*i,g-=v*a,y-=v*o);if(!(x<.01&&(x=c(m=a*f-o*h,g=o*l-i*f,y=i*h-a*l))<1e-6)){m/=x,g/=x,y/=x,this.up.set(t,i,a,o),this.right.set(t,m,g,y),this.center.set(t,r[0],r[1],r[2]),this.radius.set(t,Math.log(p));var 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Cr=function(t,e,r,n,i,a){this.inputType=t,this.type=e,this.input=r,this.cases=n,this.outputs=i,this.otherwise=a};Cr.parse=function(t,e){if(t.length<5)return e.error(\"Expected at least 4 arguments, but found only \"+(t.length-1)+\".\");if(t.length%2!=1)return e.error(\"Expected an even number of arguments.\");var r,n;e.expectedType&&\"value\"!==e.expectedType.kind&&(n=e.expectedType);for(var i={},a=[],o=2;oNumber.MAX_SAFE_INTEGER)return c.error(\"Branch labels must be integers no larger than \"+Number.MAX_SAFE_INTEGER+\".\");if(\"number\"==typeof f&&Math.floor(f)!==f)return c.error(\"Numeric branch labels must be integer values.\");if(r){if(c.checkSubtype(r,ge(f)))return null}else r=ge(f);if(void 0!==i[String(f)])return c.error(\"Branch labels must be unique.\");i[String(f)]=a.length}var p=e.parse(l,o,n);if(!p)return null;n=n||p.type,a.push(p)}var d=e.parse(t[1],1,Jt);if(!d)return null;var m=e.parse(t[t.length-1],t.length-1,n);return m?\"value\"!==d.type.kind&&e.concat(1).checkSubtype(r,d.type)?null:new Cr(r,n,d,i,a,m):null},Cr.prototype.evaluate=function(t){var e=this.input.evaluate(t);return(ge(e)===this.inputType&&this.outputs[this.cases[e]]||this.otherwise).evaluate(t)},Cr.prototype.eachChild=function(t){t(this.input),this.outputs.forEach(t),t(this.otherwise)},Cr.prototype.outputDefined=function(){return this.outputs.every((function(t){return t.outputDefined()}))&&this.otherwise.outputDefined()},Cr.prototype.serialize=function(){for(var t=this,e=[\"match\",this.input.serialize()],r=[],n={},i=0,a=Object.keys(this.cases).sort();i=5)return e.error(\"Expected 3 or 4 arguments, but found \"+(t.length-1)+\" instead.\");var r=e.parse(t[1],1,Jt),n=e.parse(t[2],2,Zt);if(!r||!n)return null;if(!ae(r.type,[ee(Jt),Wt,Jt]))return e.error(\"Expected first argument to be of type array or string, but found \"+re(r.type)+\" instead\");if(4===t.length){var i=e.parse(t[3],3,Zt);return i?new Ir(r.type,r,n,i):null}return new Ir(r.type,r,n)},Ir.prototype.evaluate=function(t){var e=this.input.evaluate(t),r=this.beginIndex.evaluate(t);if(!oe(e,[\"string\",\"array\"]))throw new xe(\"Expected first argument to be of type array or string, but found \"+re(ge(e))+\" instead.\");if(this.endIndex){var n=this.endIndex.evaluate(t);return e.slice(r,n)}return e.slice(r)},Ir.prototype.eachChild=function(t){t(this.input),t(this.beginIndex),this.endIndex&&t(this.endIndex)},Ir.prototype.outputDefined=function(){return!1},Ir.prototype.serialize=function(){if(null!=this.endIndex&&void 0!==this.endIndex){var t=this.endIndex.serialize();return[\"slice\",this.input.serialize(),this.beginIndex.serialize(),t]}return[\"slice\",this.input.serialize(),this.beginIndex.serialize()]};var Dr=Or(\"==\",(function(t,e,r){return e===r}),zr),Rr=Or(\"!=\",(function(t,e,r){return e!==r}),(function(t,e,r,n){return!zr(0,e,r,n)})),Fr=Or(\"<\",(function(t,e,r){return e\",(function(t,e,r){return e>r}),(function(t,e,r,n){return 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Intl.NumberFormat(this.locale?this.locale.evaluate(t):[],{style:this.currency?\"currency\":\"decimal\",currency:this.currency?this.currency.evaluate(t):void 0,minimumFractionDigits:this.minFractionDigits?this.minFractionDigits.evaluate(t):void 0,maximumFractionDigits:this.maxFractionDigits?this.maxFractionDigits.evaluate(t):void 0}).format(this.number.evaluate(t))},Ur.prototype.eachChild=function(t){t(this.number),this.locale&&t(this.locale),this.currency&&t(this.currency),this.minFractionDigits&&t(this.minFractionDigits),this.maxFractionDigits&&t(this.maxFractionDigits)},Ur.prototype.outputDefined=function(){return!1},Ur.prototype.serialize=function(){var t={};return this.locale&&(t.locale=this.locale.serialize()),this.currency&&(t.currency=this.currency.serialize()),this.minFractionDigits&&(t[\"min-fraction-digits\"]=this.minFractionDigits.serialize()),this.maxFractionDigits&&(t[\"max-fraction-digits\"]=this.maxFractionDigits.serialize()),[\"number-format\",this.number.serialize(),t]};var Vr=function(t){this.type=Zt,this.input=t};Vr.parse=function(t,e){if(2!==t.length)return e.error(\"Expected 1 argument, but found \"+(t.length-1)+\" instead.\");var r=e.parse(t[1],1);return r?\"array\"!==r.type.kind&&\"string\"!==r.type.kind&&\"value\"!==r.type.kind?e.error(\"Expected argument of type string or array, but found \"+re(r.type)+\" instead.\"):new Vr(r):null},Vr.prototype.evaluate=function(t){var e=this.input.evaluate(t);if(\"string\"==typeof e)return e.length;if(Array.isArray(e))return e.length;throw new xe(\"Expected value to be of type string or array, but found \"+re(ge(e))+\" 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cn(n,e)):Xr(r.errors)}cn.prototype.evaluateWithoutErrorHandling=function(t,e,r,n,i,a){return this._evaluator.globals=t,this._evaluator.feature=e,this._evaluator.featureState=r,this._evaluator.canonical=n,this._evaluator.availableImages=i||null,this._evaluator.formattedSection=a,this.expression.evaluate(this._evaluator)},cn.prototype.evaluate=function(t,e,r,n,i,a){this._evaluator.globals=t,this._evaluator.feature=e||null,this._evaluator.featureState=r||null,this._evaluator.canonical=n,this._evaluator.availableImages=i||null,this._evaluator.formattedSection=a||null;try{var o=this.expression.evaluate(this._evaluator);if(null==o||\"number\"==typeof o&&o!=o)return this._defaultValue;if(this._enumValues&&!(o in this._enumValues))throw new xe(\"Expected value to be one of \"+Object.keys(this._enumValues).map((function(t){return JSON.stringify(t)})).join(\", \")+\", but found \"+JSON.stringify(o)+\" instead.\");return o}catch(t){return this._warningHistory[t.message]||(this._warningHistory[t.message]=!0,\"undefined\"!=typeof console&&console.warn(t.message)),this._defaultValue}};var fn=function(t,e){this.kind=t,this._styleExpression=e,this.isStateDependent=\"constant\"!==t&&!Ye(e.expression)};fn.prototype.evaluateWithoutErrorHandling=function(t,e,r,n,i,a){return this._styleExpression.evaluateWithoutErrorHandling(t,e,r,n,i,a)},fn.prototype.evaluate=function(t,e,r,n,i,a){return this._styleExpression.evaluate(t,e,r,n,i,a)};var pn=function(t,e,r,n){this.kind=t,this.zoomStops=r,this._styleExpression=e,this.isStateDependent=\"camera\"!==t&&!Ye(e.expression),this.interpolationType=n};function dn(t,e){if(\"error\"===(t=hn(t,e)).result)return t;var r=t.value.expression,n=We(r);if(!n&&!$r(e))return Xr([new qt(\"\",\"data expressions not supported\")]);var i=Xe(r,[\"zoom\"]);if(!i&&!Jr(e))return Xr([new qt(\"\",\"zoom expressions not supported\")]);var a=gn(r);if(!a&&!i)return Xr([new qt(\"\",'\"zoom\" expression may only be 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Bt(e,r,r+\" is greater than the maximum value \"+n.maximum)]:[]}function _n(t){var e,r,n,i=t.valueSpec,a=Ut(t.value.type),o={},s=\"categorical\"!==a&&void 0===t.value.property,l=!s,c=\"array\"===Qr(t.value.stops)&&\"array\"===Qr(t.value.stops[0])&&\"object\"===Qr(t.value.stops[0][0]),u=yn({key:t.key,value:t.value,valueSpec:t.styleSpec.function,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{stops:function(t){if(\"identity\"===a)return[new Bt(t.key,t.value,'identity function may not have a \"stops\" property')];var e=[],r=t.value;return e=e.concat(vn({key:t.key,value:r,valueSpec:t.valueSpec,style:t.style,styleSpec:t.styleSpec,arrayElementValidator:h})),\"array\"===Qr(r)&&0===r.length&&e.push(new Bt(t.key,r,\"array must have at least one stop\")),e},default:function(t){return Hn({key:t.key,value:t.value,valueSpec:i,style:t.style,styleSpec:t.styleSpec})}}});return\"identity\"===a&&s&&u.push(new Bt(t.key,t.value,'missing required property 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expected, \"+s+\" found\";return $r(i)&&void 0===a&&(u+='\\nIf you intended to use a categorical function, specify `\"type\": \"categorical\"`.'),[new Bt(t.key,c,u)]}return\"categorical\"!==a||\"number\"!==s||isFinite(l)&&Math.floor(l)===l?\"categorical\"!==a&&\"number\"===s&&void 0!==r&&l=2&&\"$id\"!==t[1]&&\"$type\"!==t[1];case\"in\":return t.length>=3&&(\"string\"!=typeof t[1]||Array.isArray(t[2]));case\"!in\":case\"!has\":case\"none\":return!1;case\"==\":case\"!=\":case\">\":case\">=\":case\"<\":case\"<=\":return 3!==t.length||Array.isArray(t[1])||Array.isArray(t[2]);case\"any\":case\"all\":for(var e=0,r=t.slice(1);ee?1:0}function Sn(t){if(!Array.isArray(t))return!1;if(\"within\"===t[0])return!0;for(var e=1;e\"===r||\"<=\"===r||\">=\"===r?Cn(t[1],t[2],r):\"any\"===r?(e=t.slice(1),[\"any\"].concat(e.map(En))):\"all\"===r?[\"all\"].concat(t.slice(1).map(En)):\"none\"===r?[\"all\"].concat(t.slice(1).map(En).map(Pn)):\"in\"===r?Ln(t[1],t.slice(2)):\"!in\"===r?Pn(Ln(t[1],t.slice(2))):\"has\"===r?In(t[1]):\"!has\"===r?Pn(In(t[1])):\"within\"!==r||t}function Cn(t,e,r){switch(t){case\"$type\":return[\"filter-type-\"+r,e];case\"$id\":return[\"filter-id-\"+r,e];default:return[\"filter-\"+r,t,e]}}function Ln(t,e){if(0===e.length)return!1;switch(t){case\"$type\":return[\"filter-type-in\",[\"literal\",e]];case\"$id\":return[\"filter-id-in\",[\"literal\",e]];default:return e.length>200&&!e.some((function(t){return typeof t!=typeof e[0]}))?[\"filter-in-large\",t,[\"literal\",e.sort(Mn)]]:[\"filter-in-small\",t,[\"literal\",e]]}}function In(t){switch(t){case\"$type\":return!0;case\"$id\":return[\"filter-has-id\"];default:return[\"filter-has\",t]}}function Pn(t){return[\"!\",t]}function zn(t){return Tn(Vt(t.value))?bn(jt({},t,{expressionContext:\"filter\",valueSpec:{value:\"boolean\"}})):On(t)}function On(t){var e=t.value,r=t.key;if(\"array\"!==Qr(e))return[new Bt(r,e,\"array expected, \"+Qr(e)+\" found\")];var n,i=t.styleSpec,a=[];if(e.length<1)return[new Bt(r,e,\"filter array must have at least 1 element\")];switch(a=a.concat(wn({key:r+\"[0]\",value:e[0],valueSpec:i.filter_operator,style:t.style,styleSpec:t.styleSpec})),Ut(e[0])){case\"<\":case\"<=\":case\">\":case\">=\":e.length>=2&&\"$type\"===Ut(e[1])&&a.push(new Bt(r,e,'\"$type\" cannot be use with operator \"'+e[0]+'\"'));case\"==\":case\"!=\":3!==e.length&&a.push(new Bt(r,e,'filter array for operator \"'+e[0]+'\" must have 3 elements'));case\"in\":case\"!in\":e.length>=2&&\"string\"!==(n=Qr(e[1]))&&a.push(new Bt(r+\"[1]\",e[1],\"string expected, \"+n+\" found\"));for(var o=2;o=u[p+0]&&n>=u[p+1])?(o[f]=!0,a.push(c[f])):o[f]=!1}}},ri.prototype._forEachCell=function(t,e,r,n,i,a,o,s){for(var l=this._convertToCellCoord(t),c=this._convertToCellCoord(e),u=this._convertToCellCoord(r),h=this._convertToCellCoord(n),f=l;f<=u;f++)for(var p=c;p<=h;p++){var d=this.d*p+f;if((!s||s(this._convertFromCellCoord(f),this._convertFromCellCoord(p),this._convertFromCellCoord(f+1),this._convertFromCellCoord(p+1)))&&i.call(this,t,e,r,n,d,a,o,s))return}},ri.prototype._convertFromCellCoord=function(t){return(t-this.padding)/this.scale},ri.prototype._convertToCellCoord=function(t){return Math.max(0,Math.min(this.d-1,Math.floor(t*this.scale)+this.padding))},ri.prototype.toArrayBuffer=function(){if(this.arrayBuffer)return this.arrayBuffer;for(var t=this.cells,e=ei+this.cells.length+1+1,r=0,n=0;n=0)){var h=t[u];c[u]=ai[l].shallow.indexOf(u)>=0?h:ui(h,e)}t instanceof Error&&(c.message=t.message)}if(c.$name)throw new Error(\"$name property is reserved for worker serialization logic.\");return\"Object\"!==l&&(c.$name=l),c}throw new Error(\"can't serialize object of type \"+typeof t)}function hi(t){if(null==t||\"boolean\"==typeof t||\"number\"==typeof t||\"string\"==typeof t||t instanceof Boolean||t instanceof Number||t instanceof String||t instanceof Date||t instanceof RegExp||li(t)||ci(t)||ArrayBuffer.isView(t)||t instanceof ni)return t;if(Array.isArray(t))return t.map(hi);if(\"object\"==typeof t){var e=t.$name||\"Object\",r=ai[e].klass;if(!r)throw new Error(\"can't deserialize unregistered class \"+e);if(r.deserialize)return r.deserialize(t);for(var n=Object.create(r.prototype),i=0,a=Object.keys(t);i=0?s:hi(s)}}return n}throw new Error(\"can't deserialize object of type \"+typeof t)}var fi=function(){this.first=!0};fi.prototype.update=function(t,e){var r=Math.floor(t);return this.first?(this.first=!1,this.lastIntegerZoom=r,this.lastIntegerZoomTime=0,this.lastZoom=t,this.lastFloorZoom=r,!0):(this.lastFloorZoom>r?(this.lastIntegerZoom=r+1,this.lastIntegerZoomTime=e):this.lastFloorZoom=128&&t<=255},Arabic:function(t){return t>=1536&&t<=1791},\"Arabic 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t>=11008&&t<=11263},\"CJK Radicals Supplement\":function(t){return t>=11904&&t<=12031},\"Kangxi Radicals\":function(t){return t>=12032&&t<=12255},\"Ideographic Description Characters\":function(t){return t>=12272&&t<=12287},\"CJK Symbols and Punctuation\":function(t){return t>=12288&&t<=12351},Hiragana:function(t){return t>=12352&&t<=12447},Katakana:function(t){return t>=12448&&t<=12543},Bopomofo:function(t){return t>=12544&&t<=12591},\"Hangul Compatibility Jamo\":function(t){return t>=12592&&t<=12687},Kanbun:function(t){return t>=12688&&t<=12703},\"Bopomofo Extended\":function(t){return t>=12704&&t<=12735},\"CJK Strokes\":function(t){return t>=12736&&t<=12783},\"Katakana Phonetic Extensions\":function(t){return t>=12784&&t<=12799},\"Enclosed CJK Letters and Months\":function(t){return t>=12800&&t<=13055},\"CJK Compatibility\":function(t){return t>=13056&&t<=13311},\"CJK Unified Ideographs Extension A\":function(t){return t>=13312&&t<=19903},\"Yijing Hexagram 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e=0,r=t;e=65097&&t<=65103)||pi[\"CJK Compatibility Ideographs\"](t)||pi[\"CJK Compatibility\"](t)||pi[\"CJK Radicals Supplement\"](t)||pi[\"CJK Strokes\"](t)||!(!pi[\"CJK Symbols and Punctuation\"](t)||t>=12296&&t<=12305||t>=12308&&t<=12319||12336===t)||pi[\"CJK Unified Ideographs Extension A\"](t)||pi[\"CJK Unified Ideographs\"](t)||pi[\"Enclosed CJK Letters and Months\"](t)||pi[\"Hangul Compatibility Jamo\"](t)||pi[\"Hangul Jamo Extended-A\"](t)||pi[\"Hangul Jamo Extended-B\"](t)||pi[\"Hangul Jamo\"](t)||pi[\"Hangul Syllables\"](t)||pi.Hiragana(t)||pi[\"Ideographic Description Characters\"](t)||pi.Kanbun(t)||pi[\"Kangxi Radicals\"](t)||pi[\"Katakana Phonetic Extensions\"](t)||pi.Katakana(t)&&12540!==t||!(!pi[\"Halfwidth and Fullwidth Forms\"](t)||65288===t||65289===t||65293===t||t>=65306&&t<=65310||65339===t||65341===t||65343===t||t>=65371&&t<=65503||65507===t||t>=65512&&t<=65519)||!(!pi[\"Small Form Variants\"](t)||t>=65112&&t<=65118||t>=65123&&t<=65126)||pi[\"Unified Canadian 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Di=function(t,e){this.property=t,this.value=e,this.expression=function(t,e){if(tn(t))return new mn(t,e);if(un(t)){var r=dn(t,e);if(\"error\"===r.result)throw new Error(r.value.map((function(t){return t.key+\": \"+t.message})).join(\", \"));return r.value}var n=t;return\"string\"==typeof t&&\"color\"===e.type&&(n=ce.parse(t)),{kind:\"constant\",evaluate:function(){return n}}}(void 0===e?t.specification.default:e,t.specification)};Di.prototype.isDataDriven=function(){return\"source\"===this.expression.kind||\"composite\"===this.expression.kind},Di.prototype.possiblyEvaluate=function(t,e,r){return this.property.possiblyEvaluate(this,t,e,r)};var Ri=function(t){this.property=t,this.value=new Di(t,void 0)};Ri.prototype.transitioned=function(t,e){return new Bi(this.property,this.value,e,p({},t.transition,this.transition),t.now)},Ri.prototype.untransitioned=function(){return new Bi(this.property,this.value,null,{},0)};var 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Ni=function(t){this._properties=t,this._values=Object.create(t.defaultTransitioningPropertyValues)};Ni.prototype.possiblyEvaluate=function(t,e,r){for(var n=new Vi(this._properties),i=0,a=Object.keys(this._values);in.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}},e.prototype.interpolate=function(t){return t},e}(Hi),Zi=function(t){this.specification=t};Zi.prototype.possiblyEvaluate=function(t,e,r,n){if(void 0!==t.value){if(\"constant\"===t.expression.kind){var i=t.expression.evaluate(e,null,{},r,n);return this._calculate(i,i,i,e)}return this._calculate(t.expression.evaluate(new Oi(Math.floor(e.zoom-1),e)),t.expression.evaluate(new Oi(Math.floor(e.zoom),e)),t.expression.evaluate(new Oi(Math.floor(e.zoom+1),e)),e)}},Zi.prototype._calculate=function(t,e,r,n){return n.zoom>n.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}},Zi.prototype.interpolate=function(t){return t};var Wi=function(t){this.specification=t};Wi.prototype.possiblyEvaluate=function(t,e,r,n){return!!t.expression.evaluate(e,null,{},r,n)},Wi.prototype.interpolate=function(){return!1};var Yi=function(t){for(var e in this.properties=t,this.defaultPropertyValues={},this.defaultTransitionablePropertyValues={},this.defaultTransitioningPropertyValues={},this.defaultPossiblyEvaluatedValues={},this.overridableProperties=[],t){var r=t[e];r.specification.overridable&&this.overridableProperties.push(e);var n=this.defaultPropertyValues[e]=new Di(r,void 0),i=this.defaultTransitionablePropertyValues[e]=new Ri(r);this.defaultTransitioningPropertyValues[e]=i.untransitioned(),this.defaultPossiblyEvaluatedValues[e]=n.possiblyEvaluate({})}};oi(\"DataDrivenProperty\",Hi),oi(\"DataConstantProperty\",qi),oi(\"CrossFadedDataDrivenProperty\",Gi),oi(\"CrossFadedProperty\",Zi),oi(\"ColorRampProperty\",Wi);var Xi=\"-transition\",$i=function(t){function e(e,r){if(t.call(this),this.id=e.id,this.type=e.type,this._featureFilter={filter:function(){return!0},needGeometry:!1},\"custom\"!==e.type&&(this.metadata=e.metadata,this.minzoom=e.minzoom,this.maxzoom=e.maxzoom,\"background\"!==e.type&&(this.source=e.source,this.sourceLayer=e[\"source-layer\"],this.filter=e.filter),r.layout&&(this._unevaluatedLayout=new ji(r.layout)),r.paint)){for(var n in this._transitionablePaint=new Fi(r.paint),e.paint)this.setPaintProperty(n,e.paint[n],{validate:!1});for(var i in e.layout)this.setLayoutProperty(i,e.layout[i],{validate:!1});this._transitioningPaint=this._transitionablePaint.untransitioned(),this.paint=new Vi(r.paint)}}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.getCrossfadeParameters=function(){return this._crossfadeParameters},e.prototype.getLayoutProperty=function(t){return\"visibility\"===t?this.visibility:this._unevaluatedLayout.getValue(t)},e.prototype.setLayoutProperty=function(t,e,r){if(void 0===r&&(r={}),null!=e){var n=\"layers.\"+this.id+\".layout.\"+t;if(this._validate(Kn,n,t,e,r))return}\"visibility\"!==t?this._unevaluatedLayout.setValue(t,e):this.visibility=e},e.prototype.getPaintProperty=function(t){return x(t,Xi)?this._transitionablePaint.getTransition(t.slice(0,-11)):this._transitionablePaint.getValue(t)},e.prototype.setPaintProperty=function(t,e,r){if(void 0===r&&(r={}),null!=e){var n=\"layers.\"+this.id+\".paint.\"+t;if(this._validate(Jn,n,t,e,r))return!1}if(x(t,Xi))return this._transitionablePaint.setTransition(t.slice(0,-11),e||void 0),!1;var i=this._transitionablePaint._values[t],a=\"cross-faded-data-driven\"===i.property.specification[\"property-type\"],o=i.value.isDataDriven(),s=i.value;this._transitionablePaint.setValue(t,e),this._handleSpecialPaintPropertyUpdate(t);var l=this._transitionablePaint._values[t].value;return l.isDataDriven()||o||a||this._handleOverridablePaintPropertyUpdate(t,s,l)},e.prototype._handleSpecialPaintPropertyUpdate=function(t){},e.prototype._handleOverridablePaintPropertyUpdate=function(t,e,r){return!1},e.prototype.isHidden=function(t){return!!(this.minzoom&&t=this.maxzoom)||\"none\"===this.visibility},e.prototype.updateTransitions=function(t){this._transitioningPaint=this._transitionablePaint.transitioned(t,this._transitioningPaint)},e.prototype.hasTransition=function(){return this._transitioningPaint.hasTransition()},e.prototype.recalculate=function(t,e){t.getCrossfadeParameters&&(this._crossfadeParameters=t.getCrossfadeParameters()),this._unevaluatedLayout&&(this.layout=this._unevaluatedLayout.possiblyEvaluate(t,void 0,e)),this.paint=this._transitioningPaint.possiblyEvaluate(t,void 0,e)},e.prototype.serialize=function(){var t={id:this.id,type:this.type,source:this.source,\"source-layer\":this.sourceLayer,metadata:this.metadata,minzoom:this.minzoom,maxzoom:this.maxzoom,filter:this.filter,layout:this._unevaluatedLayout&&this._unevaluatedLayout.serialize(),paint:this._transitionablePaint&&this._transitionablePaint.serialize()};return this.visibility&&(t.layout=t.layout||{},t.layout.visibility=this.visibility),b(t,(function(t,e){return!(void 0===t||\"layout\"===e&&!Object.keys(t).length||\"paint\"===e&&!Object.keys(t).length)}))},e.prototype._validate=function(t,e,r,n,i){return void 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i,a=(i=t.type,Ji[i].BYTES_PER_ELEMENT),o=r=ea(r,Math.max(e,a)),s=t.components||1;return n=Math.max(n,a),r+=a*s,{name:t.name,type:t.type,components:s,offset:o}})),size:ea(r,Math.max(n,e)),alignment:e}}function ea(t,e){return Math.ceil(t/e)*e}Qi.serialize=function(t,e){return t._trim(),e&&(t.isTransferred=!0,e.push(t.arrayBuffer)),{length:t.length,arrayBuffer:t.arrayBuffer}},Qi.deserialize=function(t){var e=Object.create(this.prototype);return e.arrayBuffer=t.arrayBuffer,e.length=t.length,e.capacity=t.arrayBuffer.byteLength/e.bytesPerElement,e._refreshViews(),e},Qi.prototype._trim=function(){this.length!==this.capacity&&(this.capacity=this.length,this.arrayBuffer=this.arrayBuffer.slice(0,this.length*this.bytesPerElement),this._refreshViews())},Qi.prototype.clear=function(){this.length=0},Qi.prototype.resize=function(t){this.reserve(t),this.length=t},Qi.prototype.reserve=function(t){if(t>this.capacity){this.capacity=Math.max(t,Math.floor(5*this.capacity),128),this.arrayBuffer=new ArrayBuffer(this.capacity*this.bytesPerElement);var e=this.uint8;this._refreshViews(),e&&this.uint8.set(e)}},Qi.prototype._refreshViews=function(){throw new Error(\"_refreshViews() must be implemented by each concrete StructArray layout\")};var ra=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.int16[n+0]=e,this.int16[n+1]=r,t},e}(Qi);ra.prototype.bytesPerElement=4,oi(\"StructArrayLayout2i4\",ra);var na=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=4*t;return this.int16[a+0]=e,this.int16[a+1]=r,this.int16[a+2]=n,this.int16[a+3]=i,t},e}(Qi);na.prototype.bytesPerElement=8,oi(\"StructArrayLayout4i8\",na);var ia=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(Qi);ia.prototype.bytesPerElement=12,oi(\"StructArrayLayout2i4i12\",ia);var aa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=4*t,l=8*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.uint8[l+4]=n,this.uint8[l+5]=i,this.uint8[l+6]=a,this.uint8[l+7]=o,t},e}(Qi);aa.prototype.bytesPerElement=8,oi(\"StructArrayLayout2i4ub8\",aa);var oa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e){var r=this.length;return this.resize(r+1),this.emplace(r,t,e)},e.prototype.emplace=function(t,e,r){var n=2*t;return this.float32[n+0]=e,this.float32[n+1]=r,t},e}(Qi);oa.prototype.bytesPerElement=8,oi(\"StructArrayLayout2f8\",oa);var sa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c){var u=this.length;return this.resize(u+1),this.emplace(u,t,e,r,n,i,a,o,s,l,c)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u){var h=10*t;return this.uint16[h+0]=e,this.uint16[h+1]=r,this.uint16[h+2]=n,this.uint16[h+3]=i,this.uint16[h+4]=a,this.uint16[h+5]=o,this.uint16[h+6]=s,this.uint16[h+7]=l,this.uint16[h+8]=c,this.uint16[h+9]=u,t},e}(Qi);sa.prototype.bytesPerElement=20,oi(\"StructArrayLayout10ui20\",sa);var la=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l,c,u,h){var f=this.length;return this.resize(f+1),this.emplace(f,t,e,r,n,i,a,o,s,l,c,u,h)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c,u,h,f){var p=12*t;return this.int16[p+0]=e,this.int16[p+1]=r,this.int16[p+2]=n,this.int16[p+3]=i,this.uint16[p+4]=a,this.uint16[p+5]=o,this.uint16[p+6]=s,this.uint16[p+7]=l,this.int16[p+8]=c,this.int16[p+9]=u,this.int16[p+10]=h,this.int16[p+11]=f,t},e}(Qi);la.prototype.bytesPerElement=24,oi(\"StructArrayLayout4i4ui4i24\",la);var ca=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return this.float32[i+0]=e,this.float32[i+1]=r,this.float32[i+2]=n,t},e}(Qi);ca.prototype.bytesPerElement=12,oi(\"StructArrayLayout3f12\",ca);var ua=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t){var e=this.length;return this.resize(e+1),this.emplace(e,t)},e.prototype.emplace=function(t,e){var r=1*t;return this.uint32[r+0]=e,t},e}(Qi);ua.prototype.bytesPerElement=4,oi(\"StructArrayLayout1ul4\",ua);var ha=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a,o,s,l){var c=this.length;return this.resize(c+1),this.emplace(c,t,e,r,n,i,a,o,s,l)},e.prototype.emplace=function(t,e,r,n,i,a,o,s,l,c){var u=10*t,h=5*t;return this.int16[u+0]=e,this.int16[u+1]=r,this.int16[u+2]=n,this.int16[u+3]=i,this.int16[u+4]=a,this.int16[u+5]=o,this.uint32[h+3]=s,this.uint16[u+8]=l,this.uint16[u+9]=c,t},e}(Qi);ha.prototype.bytesPerElement=20,oi(\"StructArrayLayout6i1ul2ui20\",ha);var fa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i,a){var o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)},e.prototype.emplace=function(t,e,r,n,i,a,o){var s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t},e}(Qi);fa.prototype.bytesPerElement=12,oi(\"StructArrayLayout2i2i2i12\",fa);var pa=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n,i){var a=this.length;return this.resize(a+1),this.emplace(a,t,e,r,n,i)},e.prototype.emplace=function(t,e,r,n,i,a){var o=4*t,s=8*t;return this.float32[o+0]=e,this.float32[o+1]=r,this.float32[o+2]=n,this.int16[s+6]=i,this.int16[s+7]=a,t},e}(Qi);pa.prototype.bytesPerElement=16,oi(\"StructArrayLayout2f1f2i16\",pa);var da=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r,n){var i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)},e.prototype.emplace=function(t,e,r,n,i){var a=12*t,o=3*t;return this.uint8[a+0]=e,this.uint8[a+1]=r,this.float32[o+1]=n,this.float32[o+2]=i,t},e}(Qi);da.prototype.bytesPerElement=12,oi(\"StructArrayLayout2ub2f12\",da);var ma=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype._refreshViews=function(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)},e.prototype.emplaceBack=function(t,e,r){var n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)},e.prototype.emplace=function(t,e,r,n){var i=3*t;return 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r={anchorX:{configurable:!0},anchorY:{configurable:!0},rightJustifiedTextSymbolIndex:{configurable:!0},centerJustifiedTextSymbolIndex:{configurable:!0},leftJustifiedTextSymbolIndex:{configurable:!0},verticalPlacedTextSymbolIndex:{configurable:!0},placedIconSymbolIndex:{configurable:!0},verticalPlacedIconSymbolIndex:{configurable:!0},key:{configurable:!0},textBoxStartIndex:{configurable:!0},textBoxEndIndex:{configurable:!0},verticalTextBoxStartIndex:{configurable:!0},verticalTextBoxEndIndex:{configurable:!0},iconBoxStartIndex:{configurable:!0},iconBoxEndIndex:{configurable:!0},verticalIconBoxStartIndex:{configurable:!0},verticalIconBoxEndIndex:{configurable:!0},featureIndex:{configurable:!0},numHorizontalGlyphVertices:{configurable:!0},numVerticalGlyphVertices:{configurable:!0},numIconVertices:{configurable:!0},numVerticalIconVertices:{configurable:!0},useRuntimeCollisionCircles:{configurable:!0},crossTileID:{configurable:!0},textBoxScale:{configurable:!0},textOffset0:{configurable:!0},textOffset1:{configurable:!0},collisionCircleDiameter:{configurable:!0}};return r.anchorX.get=function(){return this._structArray.int16[this._pos2+0]},r.anchorY.get=function(){return this._structArray.int16[this._pos2+1]},r.rightJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+2]},r.centerJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+3]},r.leftJustifiedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+4]},r.verticalPlacedTextSymbolIndex.get=function(){return this._structArray.int16[this._pos2+5]},r.placedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+6]},r.verticalPlacedIconSymbolIndex.get=function(){return this._structArray.int16[this._pos2+7]},r.key.get=function(){return this._structArray.uint16[this._pos2+8]},r.textBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+9]},r.textBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+10]},r.verticalTextBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+11]},r.verticalTextBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+12]},r.iconBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+13]},r.iconBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+14]},r.verticalIconBoxStartIndex.get=function(){return this._structArray.uint16[this._pos2+15]},r.verticalIconBoxEndIndex.get=function(){return this._structArray.uint16[this._pos2+16]},r.featureIndex.get=function(){return this._structArray.uint16[this._pos2+17]},r.numHorizontalGlyphVertices.get=function(){return this._structArray.uint16[this._pos2+18]},r.numVerticalGlyphVertices.get=function(){return this._structArray.uint16[this._pos2+19]},r.numIconVertices.get=function(){return this._structArray.uint16[this._pos2+20]},r.numVerticalIconVertices.get=function(){return this._structArray.uint16[this._pos2+21]},r.useRuntimeCollisionCircles.get=function(){return 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qi(Ft.layout_symbol[\"icon-pitch-alignment\"]),\"text-pitch-alignment\":new qi(Ft.layout_symbol[\"text-pitch-alignment\"]),\"text-rotation-alignment\":new qi(Ft.layout_symbol[\"text-rotation-alignment\"]),\"text-field\":new Hi(Ft.layout_symbol[\"text-field\"]),\"text-font\":new Hi(Ft.layout_symbol[\"text-font\"]),\"text-size\":new Hi(Ft.layout_symbol[\"text-size\"]),\"text-max-width\":new Hi(Ft.layout_symbol[\"text-max-width\"]),\"text-line-height\":new qi(Ft.layout_symbol[\"text-line-height\"]),\"text-letter-spacing\":new Hi(Ft.layout_symbol[\"text-letter-spacing\"]),\"text-justify\":new Hi(Ft.layout_symbol[\"text-justify\"]),\"text-radial-offset\":new Hi(Ft.layout_symbol[\"text-radial-offset\"]),\"text-variable-anchor\":new qi(Ft.layout_symbol[\"text-variable-anchor\"]),\"text-anchor\":new Hi(Ft.layout_symbol[\"text-anchor\"]),\"text-max-angle\":new qi(Ft.layout_symbol[\"text-max-angle\"]),\"text-writing-mode\":new qi(Ft.layout_symbol[\"text-writing-mode\"]),\"text-rotate\":new Hi(Ft.layout_symbol[\"text-rotate\"]),\"text-padding\":new qi(Ft.layout_symbol[\"text-padding\"]),\"text-keep-upright\":new qi(Ft.layout_symbol[\"text-keep-upright\"]),\"text-transform\":new Hi(Ft.layout_symbol[\"text-transform\"]),\"text-offset\":new Hi(Ft.layout_symbol[\"text-offset\"]),\"text-allow-overlap\":new qi(Ft.layout_symbol[\"text-allow-overlap\"]),\"text-ignore-placement\":new qi(Ft.layout_symbol[\"text-ignore-placement\"]),\"text-optional\":new qi(Ft.layout_symbol[\"text-optional\"])}),du={paint:new Yi({\"icon-opacity\":new Hi(Ft.paint_symbol[\"icon-opacity\"]),\"icon-color\":new Hi(Ft.paint_symbol[\"icon-color\"]),\"icon-halo-color\":new Hi(Ft.paint_symbol[\"icon-halo-color\"]),\"icon-halo-width\":new Hi(Ft.paint_symbol[\"icon-halo-width\"]),\"icon-halo-blur\":new Hi(Ft.paint_symbol[\"icon-halo-blur\"]),\"icon-translate\":new qi(Ft.paint_symbol[\"icon-translate\"]),\"icon-translate-anchor\":new qi(Ft.paint_symbol[\"icon-translate-anchor\"]),\"text-opacity\":new Hi(Ft.paint_symbol[\"text-opacity\"]),\"text-color\":new Hi(Ft.paint_symbol[\"text-color\"],{runtimeType:Xt,getOverride:function(t){return t.textColor},hasOverride:function(t){return!!t.textColor}}),\"text-halo-color\":new Hi(Ft.paint_symbol[\"text-halo-color\"]),\"text-halo-width\":new Hi(Ft.paint_symbol[\"text-halo-width\"]),\"text-halo-blur\":new Hi(Ft.paint_symbol[\"text-halo-blur\"]),\"text-translate\":new qi(Ft.paint_symbol[\"text-translate\"]),\"text-translate-anchor\":new qi(Ft.paint_symbol[\"text-translate-anchor\"])}),layout:pu},mu=function(t){this.type=t.property.overrides?t.property.overrides.runtimeType:Gt,this.defaultValue=t};mu.prototype.evaluate=function(t){if(t.formattedSection){var e=this.defaultValue.property.overrides;if(e&&e.hasOverride(t.formattedSection))return e.getOverride(t.formattedSection)}return t.feature&&t.featureState?this.defaultValue.evaluate(t.feature,t.featureState):this.defaultValue.property.specification.default},mu.prototype.eachChild=function(t){this.defaultValue.isConstant()||t(this.defaultValue.value._styleExpression.expression)},mu.prototype.outputDefined=function(){return!1},mu.prototype.serialize=function(){return null},oi(\"FormatSectionOverride\",mu,{omit:[\"defaultValue\"]});var gu=function(t){function e(e){t.call(this,e,du)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.recalculate=function(e,r){if(t.prototype.recalculate.call(this,e,r),\"auto\"===this.layout.get(\"icon-rotation-alignment\")&&(\"point\"!==this.layout.get(\"symbol-placement\")?this.layout._values[\"icon-rotation-alignment\"]=\"map\":this.layout._values[\"icon-rotation-alignment\"]=\"viewport\"),\"auto\"===this.layout.get(\"text-rotation-alignment\")&&(\"point\"!==this.layout.get(\"symbol-placement\")?this.layout._values[\"text-rotation-alignment\"]=\"map\":this.layout._values[\"text-rotation-alignment\"]=\"viewport\"),\"auto\"===this.layout.get(\"text-pitch-alignment\")&&(this.layout._values[\"text-pitch-alignment\"]=this.layout.get(\"text-rotation-alignment\")),\"auto\"===this.layout.get(\"icon-pitch-alignment\")&&(this.layout._values[\"icon-pitch-alignment\"]=this.layout.get(\"icon-rotation-alignment\")),\"point\"===this.layout.get(\"symbol-placement\")){var n=this.layout.get(\"text-writing-mode\");if(n){for(var i=[],a=0,o=n;a\",targetMapId:n,sourceMapId:a.mapId})}}},Lu.prototype.receive=function(t){var e=t.data,r=e.id;if(r&&(!e.targetMapId||this.mapId===e.targetMapId))if(\"\"===e.type){delete this.tasks[r];var n=this.cancelCallbacks[r];delete this.cancelCallbacks[r],n&&n()}else S()||e.mustQueue?(this.tasks[r]=e,this.taskQueue.push(r),this.invoker.trigger()):this.processTask(r,e)},Lu.prototype.process=function(){if(this.taskQueue.length){var t=this.taskQueue.shift(),e=this.tasks[t];delete this.tasks[t],this.taskQueue.length&&this.invoker.trigger(),e&&this.processTask(t,e)}},Lu.prototype.processTask=function(t,e){var r=this;if(\"\"===e.type){var n=this.callbacks[t];delete this.callbacks[t],n&&(e.error?n(hi(e.error)):n(null,hi(e.data)))}else{var i=!1,a=L(this.globalScope)?void 0:[],o=e.hasCallback?function(e,n){i=!0,delete r.cancelCallbacks[t],r.target.postMessage({id:t,type:\"\",sourceMapId:r.mapId,error:e?ui(e):null,data:ui(n,a)},a)}:function(t){i=!0},s=null,l=hi(e.data);if(this.parent[e.type])s=this.parent[e.type](e.sourceMapId,l,o);else if(this.parent.getWorkerSource){var c=e.type.split(\".\");s=this.parent.getWorkerSource(e.sourceMapId,c[0],l.source)[c[1]](l,o)}else o(new Error(\"Could not find function \"+e.type));!i&&s&&s.cancel&&(this.cancelCallbacks[t]=s.cancel)}},Lu.prototype.remove=function(){this.invoker.remove(),this.target.removeEventListener(\"message\",this.receive,!1)};var Pu=function(t,e){t&&(e?this.setSouthWest(t).setNorthEast(e):4===t.length?this.setSouthWest([t[0],t[1]]).setNorthEast([t[2],t[3]]):this.setSouthWest(t[0]).setNorthEast(t[1]))};Pu.prototype.setNorthEast=function(t){return this._ne=t instanceof Ou?new Ou(t.lng,t.lat):Ou.convert(t),this},Pu.prototype.setSouthWest=function(t){return this._sw=t instanceof Ou?new Ou(t.lng,t.lat):Ou.convert(t),this},Pu.prototype.extend=function(t){var e,r,n=this._sw,i=this._ne;if(t instanceof Ou)e=t,r=t;else{if(!(t instanceof Pu)){if(Array.isArray(t)){if(4===t.length||t.every(Array.isArray)){var a=t;return this.extend(Pu.convert(a))}var o=t;return this.extend(Ou.convert(o))}return this}if(e=t._sw,r=t._ne,!e||!r)return this}return n||i?(n.lng=Math.min(e.lng,n.lng),n.lat=Math.min(e.lat,n.lat),i.lng=Math.max(r.lng,i.lng),i.lat=Math.max(r.lat,i.lat)):(this._sw=new Ou(e.lng,e.lat),this._ne=new Ou(r.lng,r.lat)),this},Pu.prototype.getCenter=function(){return new Ou((this._sw.lng+this._ne.lng)/2,(this._sw.lat+this._ne.lat)/2)},Pu.prototype.getSouthWest=function(){return this._sw},Pu.prototype.getNorthEast=function(){return this._ne},Pu.prototype.getNorthWest=function(){return new Ou(this.getWest(),this.getNorth())},Pu.prototype.getSouthEast=function(){return new Ou(this.getEast(),this.getSouth())},Pu.prototype.getWest=function(){return this._sw.lng},Pu.prototype.getSouth=function(){return this._sw.lat},Pu.prototype.getEast=function(){return this._ne.lng},Pu.prototype.getNorth=function(){return this._ne.lat},Pu.prototype.toArray=function(){return[this._sw.toArray(),this._ne.toArray()]},Pu.prototype.toString=function(){return\"LngLatBounds(\"+this._sw.toString()+\", \"+this._ne.toString()+\")\"},Pu.prototype.isEmpty=function(){return!(this._sw&&this._ne)},Pu.prototype.contains=function(t){var e=Ou.convert(t),r=e.lng,n=e.lat,i=this._sw.lat<=n&&n<=this._ne.lat,a=this._sw.lng<=r&&r<=this._ne.lng;return this._sw.lng>this._ne.lng&&(a=this._sw.lng>=r&&r>=this._ne.lng),i&&a},Pu.convert=function(t){return!t||t instanceof Pu?t:new Pu(t)};var zu=6371008.8,Ou=function(t,e){if(isNaN(t)||isNaN(e))throw new Error(\"Invalid LngLat object: (\"+t+\", \"+e+\")\");if(this.lng=+t,this.lat=+e,this.lat>90||this.lat<-90)throw new Error(\"Invalid LngLat latitude value: must be between -90 and 90\")};Ou.prototype.wrap=function(){return new Ou(f(this.lng,-180,180),this.lat)},Ou.prototype.toArray=function(){return[this.lng,this.lat]},Ou.prototype.toString=function(){return\"LngLat(\"+this.lng+\", \"+this.lat+\")\"},Ou.prototype.distanceTo=function(t){var e=Math.PI/180,r=this.lat*e,n=t.lat*e,i=Math.sin(r)*Math.sin(n)+Math.cos(r)*Math.cos(n)*Math.cos((t.lng-this.lng)*e);return zu*Math.acos(Math.min(i,1))},Ou.prototype.toBounds=function(t){void 0===t&&(t=0);var e=360*t/40075017,r=e/Math.cos(Math.PI/180*this.lat);return new Pu(new Ou(this.lng-r,this.lat-e),new Ou(this.lng+r,this.lat+e))},Ou.convert=function(t){if(t instanceof Ou)return t;if(Array.isArray(t)&&(2===t.length||3===t.length))return new Ou(Number(t[0]),Number(t[1]));if(!Array.isArray(t)&&\"object\"==typeof t&&null!==t)return new Ou(Number(\"lng\"in t?t.lng:t.lon),Number(t.lat));throw new Error(\"`LngLatLike` argument must be specified as a LngLat instance, an object {lng: , lat: }, an object {lon: , lat: }, or an array of [, ]\")};var Du=2*Math.PI*zu;function Ru(t){return Du*Math.cos(t*Math.PI/180)}function Fu(t){return(180+t)/360}function Bu(t){return(180-180/Math.PI*Math.log(Math.tan(Math.PI/4+t*Math.PI/360)))/360}function Nu(t,e){return t/Ru(e)}function ju(t){var e=180-360*t;return 360/Math.PI*Math.atan(Math.exp(e*Math.PI/180))-90}var Uu=function(t,e,r){void 0===r&&(r=0),this.x=+t,this.y=+e,this.z=+r};Uu.fromLngLat=function(t,e){void 0===e&&(e=0);var r=Ou.convert(t);return new Uu(Fu(r.lng),Bu(r.lat),Nu(e,r.lat))},Uu.prototype.toLngLat=function(){return new Ou(360*this.x-180,ju(this.y))},Uu.prototype.toAltitude=function(){return t=this.z,e=this.y,t*Ru(ju(e));var t,e},Uu.prototype.meterInMercatorCoordinateUnits=function(){return 1/Du*(t=ju(this.y),1/Math.cos(t*Math.PI/180));var t};var Vu=function(t,e,r){this.z=t,this.x=e,this.y=r,this.key=Gu(0,t,t,e,r)};Vu.prototype.equals=function(t){return this.z===t.z&&this.x===t.x&&this.y===t.y},Vu.prototype.url=function(t,e){var r,n,i,a,o,s=(r=this.x,n=this.y,i=this.z,a=Iu(256*r,256*(n=Math.pow(2,i)-n-1),i),o=Iu(256*(r+1),256*(n+1),i),a[0]+\",\"+a[1]+\",\"+o[0]+\",\"+o[1]),l=function(t,e,r){for(var n,i=\"\",a=t;a>0;a--)i+=(e&(n=1<this.canonical.z?new Hu(t,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y):new Hu(t,this.wrap,t,this.canonical.x>>e,this.canonical.y>>e)},Hu.prototype.calculateScaledKey=function(t,e){var r=this.canonical.z-t;return t>this.canonical.z?Gu(this.wrap*+e,t,this.canonical.z,this.canonical.x,this.canonical.y):Gu(this.wrap*+e,t,t,this.canonical.x>>r,this.canonical.y>>r)},Hu.prototype.isChildOf=function(t){if(t.wrap!==this.wrap)return!1;var e=this.canonical.z-t.canonical.z;return 0===t.overscaledZ||t.overscaledZ>e&&t.canonical.y===this.canonical.y>>e},Hu.prototype.children=function(t){if(this.overscaledZ>=t)return[new Hu(this.overscaledZ+1,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)];var e=this.canonical.z+1,r=2*this.canonical.x,n=2*this.canonical.y;return[new Hu(e,this.wrap,e,r,n),new Hu(e,this.wrap,e,r+1,n),new Hu(e,this.wrap,e,r,n+1),new Hu(e,this.wrap,e,r+1,n+1)]},Hu.prototype.isLessThan=function(t){return this.wrapt.wrap)&&(this.overscaledZt.overscaledZ)&&(this.canonical.xt.canonical.x)&&this.canonical.y=this.dim+1||e<-1||e>=this.dim+1)throw new RangeError(\"out of range source coordinates for DEM data\");return(e+1)*this.stride+(t+1)},Zu.prototype._unpackMapbox=function(t,e,r){return(256*t*256+256*e+r)/10-1e4},Zu.prototype._unpackTerrarium=function(t,e,r){return 256*t+e+r/256-32768},Zu.prototype.getPixels=function(){return new Ko({width:this.stride,height:this.stride},new Uint8Array(this.data.buffer))},Zu.prototype.backfillBorder=function(t,e,r){if(this.dim!==t.dim)throw new Error(\"dem dimension mismatch\");var n=e*this.dim,i=e*this.dim+this.dim,a=r*this.dim,o=r*this.dim+this.dim;switch(e){case-1:n=i-1;break;case 1:i=n+1}switch(r){case-1:a=o-1;break;case 1:o=a+1}for(var s=-e*this.dim,l=-r*this.dim,c=a;c=0&&u[3]>=0&&s.insert(o,u[0],u[1],u[2],u[3])}},Ju.prototype.loadVTLayers=function(){return this.vtLayers||(this.vtLayers=new tl.VectorTile(new Pl(this.rawTileData)).layers,this.sourceLayerCoder=new Wu(this.vtLayers?Object.keys(this.vtLayers).sort():[\"_geojsonTileLayer\"])),this.vtLayers},Ju.prototype.query=function(t,e,r,n){var i=this;this.loadVTLayers();for(var o=t.params||{},s=po/t.tileSize/t.scale,l=An(o.filter),c=t.queryGeometry,u=t.queryPadding*s,h=Qu(c),f=this.grid.query(h.minX-u,h.minY-u,h.maxX+u,h.maxY+u),p=Qu(t.cameraQueryGeometry),d=0,m=this.grid3D.query(p.minX-u,p.minY-u,p.maxX+u,p.maxY+u,(function(e,r,n,i){return function(t,e,r,n,i){for(var o=0,s=t;o=l.x&&i>=l.y)return!0}var c=[new a(e,r),new a(e,i),new a(n,i),new a(n,r)];if(t.length>2)for(var u=0,h=c;u=0)return!0;return!1}(a,h)){var f=this.sourceLayerCoder.decode(r),d=this.vtLayers[f].feature(n);if(i.needGeometry){var m=vo(d,!0);if(!i.filter(new Oi(this.tileID.overscaledZ),m,this.tileID.canonical))return}else if(!i.filter(new Oi(this.tileID.overscaledZ),d))return;for(var g=this.getId(d,f),y=0;yn)i=!1;else if(e)if(this.expirationTimeft&&(t.getActor().send(\"enforceCacheSizeLimit\",ht),xt=0)},t.clamp=h,t.clearTileCache=function(t){var e=s.caches.delete(ut);t&&e.catch(t).then((function(){return t()}))},t.clipLine=Fc,t.clone=function(t){var e=new Fo(16);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e[3]=t[3],e[4]=t[4],e[5]=t[5],e[6]=t[6],e[7]=t[7],e[8]=t[8],e[9]=t[9],e[10]=t[10],e[11]=t[11],e[12]=t[12],e[13]=t[13],e[14]=t[14],e[15]=t[15],e},t.clone$1=w,t.clone$2=function(t){var e=new Fo(3);return e[0]=t[0],e[1]=t[1],e[2]=t[2],e},t.collisionCircleLayout=Ml,t.config=j,t.create=function(){var t=new Fo(16);return Fo!=Float32Array&&(t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[11]=0,t[12]=0,t[13]=0,t[14]=0),t[0]=1,t[5]=1,t[10]=1,t[15]=1,t},t.create$1=function(){var t=new Fo(9);return Fo!=Float32Array&&(t[1]=0,t[2]=0,t[3]=0,t[5]=0,t[6]=0,t[7]=0),t[0]=1,t[4]=1,t[8]=1,t},t.create$2=function(){var t=new Fo(4);return Fo!=Float32Array&&(t[1]=0,t[2]=0),t[0]=1,t[3]=1,t},t.createCommonjsModule=e,t.createExpression=hn,t.createLayout=ta,t.createStyleLayer=function(t){return\"custom\"===t.type?new bu(t):new wu[t.type](t)},t.cross=function(t,e,r){var n=e[0],i=e[1],a=e[2],o=r[0],s=r[1],l=r[2];return t[0]=i*l-a*s,t[1]=a*o-n*l,t[2]=n*s-i*o,t},t.deepEqual=function t(e,r){if(Array.isArray(e)){if(!Array.isArray(r)||e.length!==r.length)return!1;for(var n=0;n0&&(a=1/Math.sqrt(a)),t[0]=e[0]*a,t[1]=e[1]*a,t[2]=e[2]*a,t},t.number=er,t.offscreenCanvasSupported=_t,t.ortho=function(t,e,r,n,i,a,o){var s=1/(e-r),l=1/(n-i),c=1/(a-o);return t[0]=-2*s,t[1]=0,t[2]=0,t[3]=0,t[4]=0,t[5]=-2*l,t[6]=0,t[7]=0,t[8]=0,t[9]=0,t[10]=2*c,t[11]=0,t[12]=(e+r)*s,t[13]=(i+n)*l,t[14]=(o+a)*c,t[15]=1,t},t.parseGlyphPBF=function(t){return new Pl(t).readFields(Ql,[])},t.pbf=Pl,t.performSymbolLayout=function(t,e,r,n,i,a,o){t.createArrays();var s=512*t.overscaling;t.tilePixelRatio=po/s,t.compareText={},t.iconsNeedLinear=!1;var l=t.layers[0].layout,c=t.layers[0]._unevaluatedLayout._values,u={};if(\"composite\"===t.textSizeData.kind){var h=t.textSizeData,f=h.minZoom,p=h.maxZoom;u.compositeTextSizes=[c[\"text-size\"].possiblyEvaluate(new Oi(f),o),c[\"text-size\"].possiblyEvaluate(new Oi(p),o)]}if(\"composite\"===t.iconSizeData.kind){var d=t.iconSizeData,m=d.minZoom,g=d.maxZoom;u.compositeIconSizes=[c[\"icon-size\"].possiblyEvaluate(new Oi(m),o),c[\"icon-size\"].possiblyEvaluate(new Oi(g),o)]}u.layoutTextSize=c[\"text-size\"].possiblyEvaluate(new Oi(t.zoom+1),o),u.layoutIconSize=c[\"icon-size\"].possiblyEvaluate(new Oi(t.zoom+1),o),u.textMaxSize=c[\"text-size\"].possiblyEvaluate(new Oi(18));for(var y=l.get(\"text-line-height\")*Cl,v=\"map\"===l.get(\"text-rotation-alignment\")&&\"point\"!==l.get(\"symbol-placement\"),x=l.get(\"text-keep-upright\"),_=l.get(\"text-size\"),b=function(){var a=T[w],s=l.get(\"text-font\").evaluate(a,{},o).join(\",\"),c=_.evaluate(a,{},o),h=u.layoutTextSize.evaluate(a,{},o),f=u.layoutIconSize.evaluate(a,{},o),p={horizontal:{},vertical:void 0},d=a.text,m=[0,0];if(d){var g=d.toString(),b=l.get(\"text-letter-spacing\").evaluate(a,{},o)*Cl,A=function(t){for(var e=0,r=t;e=po||h.y<0||h.y>=po||function(t,e,r,n,i,a,o,s,l,c,u,h,f,p,d,m,g,y,v,x,_,b,w,T,A){var M,S,E,C,L,I=t.addToLineVertexArray(e,r),P=0,z=0,O=0,D=0,R=-1,F=-1,B={},N=ja(\"\"),j=0,U=0;if(void 0===s._unevaluatedLayout.getValue(\"text-radial-offset\")?(j=(M=s.layout.get(\"text-offset\").evaluate(_,{},T).map((function(t){return t*Cl})))[0],U=M[1]):(j=s.layout.get(\"text-radial-offset\").evaluate(_,{},T)*Cl,U=Jc),t.allowVerticalPlacement&&n.vertical){var V=s.layout.get(\"text-rotate\").evaluate(_,{},T)+90,q=n.vertical;C=new Hc(l,e,c,u,h,q,f,p,d,V),o&&(L=new Hc(l,e,c,u,h,o,g,y,d,V))}if(i){var H=s.layout.get(\"icon-rotate\").evaluate(_,{}),G=\"none\"!==s.layout.get(\"icon-text-fit\"),Z=Nc(i,H,w,G),W=o?Nc(o,H,w,G):void 0;E=new Hc(l,e,c,u,h,i,g,y,!1,H),P=4*Z.length;var Y=t.iconSizeData,X=null;\"source\"===Y.kind?(X=[Ac*s.layout.get(\"icon-size\").evaluate(_,{})])[0]>eu&&k(t.layerIds[0]+': Value for \"icon-size\" is >= '+tu+'. Reduce your \"icon-size\".'):\"composite\"===Y.kind&&((X=[Ac*b.compositeIconSizes[0].evaluate(_,{},T),Ac*b.compositeIconSizes[1].evaluate(_,{},T)])[0]>eu||X[1]>eu)&&k(t.layerIds[0]+': Value for \"icon-size\" is >= '+tu+'. Reduce your \"icon-size\".'),t.addSymbols(t.icon,Z,X,x,v,_,!1,e,I.lineStartIndex,I.lineLength,-1,T),R=t.icon.placedSymbolArray.length-1,W&&(z=4*W.length,t.addSymbols(t.icon,W,X,x,v,_,lc.vertical,e,I.lineStartIndex,I.lineLength,-1,T),F=t.icon.placedSymbolArray.length-1)}for(var $ in n.horizontal){var J=n.horizontal[$];if(!S){N=ja(J.text);var K=s.layout.get(\"text-rotate\").evaluate(_,{},T);S=new Hc(l,e,c,u,h,J,f,p,d,K)}var Q=1===J.positionedLines.length;if(O+=ru(t,e,J,a,s,d,_,m,I,n.vertical?lc.horizontal:lc.horizontalOnly,Q?Object.keys(n.horizontal):[$],B,R,b,T),Q)break}n.vertical&&(D+=ru(t,e,n.vertical,a,s,d,_,m,I,lc.vertical,[\"vertical\"],B,F,b,T));var tt=S?S.boxStartIndex:t.collisionBoxArray.length,et=S?S.boxEndIndex:t.collisionBoxArray.length,rt=C?C.boxStartIndex:t.collisionBoxArray.length,nt=C?C.boxEndIndex:t.collisionBoxArray.length,it=E?E.boxStartIndex:t.collisionBoxArray.length,at=E?E.boxEndIndex:t.collisionBoxArray.length,ot=L?L.boxStartIndex:t.collisionBoxArray.length,st=L?L.boxEndIndex:t.collisionBoxArray.length,lt=-1,ct=function(t,e){return t&&t.circleDiameter?Math.max(t.circleDiameter,e):e};lt=ct(S,lt),lt=ct(C,lt),lt=ct(E,lt);var ut=(lt=ct(L,lt))>-1?1:0;ut&&(lt*=A/Cl),t.glyphOffsetArray.length>=fu.MAX_GLYPHS&&k(\"Too many glyphs being rendered in a tile. 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C=t.mapObject(g.glyphDependencies,(function(t){return Object.keys(t).map(Number)}));Object.keys(C).length?a.send(\"getGlyphs\",{uid:this.uid,stacks:C},(function(t,e){h||(h=t,f=e,P.call(l))})):f={};var L=Object.keys(g.iconDependencies);L.length?a.send(\"getImages\",{icons:L,source:this.source,tileID:this.tileID,type:\"icons\"},(function(t,e){h||(h=t,p=e,P.call(l))})):p={};var I=Object.keys(g.patternDependencies);function P(){if(h)return s(h);if(f&&p&&d){var e=new i(f),r=new t.ImageAtlas(p,d);for(var a in m){var l=m[a];l instanceof t.SymbolBucket?(o(l.layers,this.zoom,n),t.performSymbolLayout(l,f,e.positions,p,r.iconPositions,this.showCollisionBoxes,this.tileID.canonical)):l.hasPattern&&(l instanceof t.LineBucket||l instanceof t.FillBucket||l instanceof 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r=this.loading,n=t.uid;r&&r[n]&&r[n].abort&&(r[n].abort(),delete r[n]),e()},l.prototype.removeTile=function(t,e){var r=this.loaded,n=t.uid;r&&r[n]&&delete r[n],e()};var c=t.window.ImageBitmap,u=function(){this.loaded={}};u.prototype.loadTile=function(e,r){var n=e.uid,i=e.encoding,a=e.rawImageData,o=c&&a instanceof c?this.getImageData(a):a,s=new t.DEMData(n,o,i);this.loaded=this.loaded||{},this.loaded[n]=s,r(null,s)},u.prototype.getImageData=function(e){this.offscreenCanvas&&this.offscreenCanvasContext||(this.offscreenCanvas=new OffscreenCanvas(e.width,e.height),this.offscreenCanvasContext=this.offscreenCanvas.getContext(\"2d\")),this.offscreenCanvas.width=e.width,this.offscreenCanvas.height=e.height,this.offscreenCanvasContext.drawImage(e,0,0,e.width,e.height);var r=this.offscreenCanvasContext.getImageData(-1,-1,e.width+2,e.height+2);return this.offscreenCanvasContext.clearRect(0,0,this.offscreenCanvas.width,this.offscreenCanvas.height),new 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r(t,r,n,i){e.call(this,t,r,n,At),i&&(this.loadGeoJSON=i)}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.loadData=function(t,e){this._pendingCallback&&this._pendingCallback(null,{abandoned:!0}),this._pendingCallback=e,this._pendingLoadDataParams=t,this._state&&\"Idle\"!==this._state?this._state=\"NeedsLoadData\":(this._state=\"Coalescing\",this._loadData())},r.prototype._loadData=function(){var e=this;if(this._pendingCallback&&this._pendingLoadDataParams){var r=this._pendingCallback,n=this._pendingLoadDataParams;delete this._pendingCallback,delete this._pendingLoadDataParams;var i=!!(n&&n.request&&n.request.collectResourceTiming)&&new t.RequestPerformance(n.request);this.loadGeoJSON(n,(function(a,o){if(a||!o)return r(a);if(\"object\"!=typeof o)return r(new Error(\"Input data given to '\"+n.source+\"' is not a valid GeoJSON object.\"));h(o,!0);try{if(n.filter){var s=t.createExpression(n.filter,{type:\"boolean\",\"property-type\":\"data-driven\",overridable:!1,transition:!1});if(\"error\"===s.result)throw new Error(s.value.map((function(t){return t.key+\": \"+t.message})).join(\", \"));var l=o.features.filter((function(t){return s.value.evaluate({zoom:0},t)}));o={type:\"FeatureCollection\",features:l}}e._geoJSONIndex=n.cluster?new V(function(e){var r=e.superclusterOptions,n=e.clusterProperties;if(!n||!r)return r;for(var i={},a={},o={accumulated:null,zoom:0},s={properties:null},l=Object.keys(n),c=0,u=l;c=0?0:e.button},r.remove=function(t){t.parentNode&&t.parentNode.removeChild(t)};var f=function(e){function r(){e.call(this),this.images={},this.updatedImages={},this.callbackDispatchedThisFrame={},this.loaded=!1,this.requestors=[],this.patterns={},this.atlasImage=new t.RGBAImage({width:1,height:1}),this.dirty=!0}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.isLoaded=function(){return this.loaded},r.prototype.setLoaded=function(t){if(this.loaded!==t&&(this.loaded=t,t)){for(var e=0,r=this.requestors;e=0?1.2:1))}function y(t,e,r,n,i,a,o){for(var s=0;s65535)e(new Error(\"glyphs > 65535 not supported\"));else if(a.ranges[s])e(null,{stack:r,id:i,glyph:o});else{var l=a.requests[s];l||(l=a.requests[s]=[],x.loadGlyphRange(r,s,n.url,n.requestManager,(function(t,e){if(e){for(var r in e)n._doesCharSupportLocalGlyph(+r)||(a.glyphs[+r]=e[+r]);a.ranges[s]=!0}for(var i=0,o=l;i1&&(l=t[++s]);var u=Math.abs(c-l.left),h=Math.abs(c-l.right),f=Math.min(u,h),p=void 0,d=i/r*(n+1);if(l.isDash){var m=n-Math.abs(d);p=Math.sqrt(f*f+m*m)}else p=n-Math.sqrt(f*f+d*d);this.data[o+c]=Math.max(0,Math.min(255,p+128))}},k.prototype.addRegularDash=function(t){for(var e=t.length-1;e>=0;--e){var r=t[e],n=t[e+1];r.zeroLength?t.splice(e,1):n&&n.isDash===r.isDash&&(n.left=r.left,t.splice(e,1))}var i=t[0],a=t[t.length-1];i.isDash===a.isDash&&(i.left=a.left-this.width,a.right=i.right+this.width);for(var o=this.width*this.nextRow,s=0,l=t[s],c=0;c1&&(l=t[++s]);var u=Math.abs(c-l.left),h=Math.abs(c-l.right),f=Math.min(u,h),p=l.isDash?f:-f;this.data[o+c]=Math.max(0,Math.min(255,p+128))}},k.prototype.addDash=function(e,r){var n=r?7:0,i=2*n+1;if(this.nextRow+i>this.height)return t.warnOnce(\"LineAtlas out of space\"),null;for(var a=0,o=0;o=n&&e.x=i&&e.y0&&(l[new t.OverscaledTileID(e.overscaledZ,a,r.z,i,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,e.wrap,r.z,r.x,r.y-1).key]={backfilled:!1},l[new t.OverscaledTileID(e.overscaledZ,s,r.z,o,r.y-1).key]={backfilled:!1}),r.y+10&&(n.resourceTiming=e._resourceTiming,e._resourceTiming=[]),e.fire(new t.Event(\"data\",n))}}))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setData=function(e){var r=this;return this._data=e,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this._updateWorkerData((function(e){if(e)r.fire(new t.ErrorEvent(e));else{var n={dataType:\"source\",sourceDataType:\"content\"};r._collectResourceTiming&&r._resourceTiming&&r._resourceTiming.length>0&&(n.resourceTiming=r._resourceTiming,r._resourceTiming=[]),r.fire(new t.Event(\"data\",n))}})),this},r.prototype.getClusterExpansionZoom=function(t,e){return this.actor.send(\"geojson.getClusterExpansionZoom\",{clusterId:t,source:this.id},e),this},r.prototype.getClusterChildren=function(t,e){return this.actor.send(\"geojson.getClusterChildren\",{clusterId:t,source:this.id},e),this},r.prototype.getClusterLeaves=function(t,e,r,n){return this.actor.send(\"geojson.getClusterLeaves\",{source:this.id,clusterId:t,limit:e,offset:r},n),this},r.prototype._updateWorkerData=function(e){var r=this;this._loaded=!1;var n=t.extend({},this.workerOptions),i=this._data;\"string\"==typeof i?(n.request=this.map._requestManager.transformRequest(t.browser.resolveURL(i),t.ResourceType.Source),n.request.collectResourceTiming=this._collectResourceTiming):n.data=JSON.stringify(i),this.actor.send(this.type+\".loadData\",n,(function(t,i){r._removed||i&&i.abandoned||(r._loaded=!0,i&&i.resourceTiming&&i.resourceTiming[r.id]&&(r._resourceTiming=i.resourceTiming[r.id].slice(0)),r.actor.send(r.type+\".coalesce\",{source:n.source},null),e(t))}))},r.prototype.loaded=function(){return this._loaded},r.prototype.loadTile=function(e,r){var n=this,i=e.actor?\"reloadTile\":\"loadTile\";e.actor=this.actor;var a={type:this.type,uid:e.uid,tileID:e.tileID,zoom:e.tileID.overscaledZ,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,pixelRatio:t.browser.devicePixelRatio,showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};e.request=this.actor.send(i,a,(function(t,a){return delete e.request,e.unloadVectorData(),e.aborted?r(null):t?r(t):(e.loadVectorData(a,n.map.painter,\"reloadTile\"===i),r(null))}))},r.prototype.abortTile=function(t){t.request&&(t.request.cancel(),delete t.request),t.aborted=!0},r.prototype.unloadTile=function(t){t.unloadVectorData(),this.actor.send(\"removeTile\",{uid:t.uid,type:this.type,source:this.id})},r.prototype.onRemove=function(){this._removed=!0,this.actor.send(\"removeSource\",{type:this.type,source:this.id})},r.prototype.serialize=function(){return t.extend({},this._options,{type:this.type,data:this._data})},r.prototype.hasTransition=function(){return!1},r}(t.Evented),P=t.createLayout([{name:\"a_pos\",type:\"Int16\",components:2},{name:\"a_texture_pos\",type:\"Int16\",components:2}]),z=function(e){function r(t,r,n,i){e.call(this),this.id=t,this.dispatcher=n,this.coordinates=r.coordinates,this.type=\"image\",this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.tiles={},this._loaded=!1,this.setEventedParent(i),this.options=r}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(e,r){var n=this;this._loaded=!1,this.fire(new t.Event(\"dataloading\",{dataType:\"source\"})),this.url=this.options.url,t.getImage(this.map._requestManager.transformRequest(this.url,t.ResourceType.Image),(function(i,a){n._loaded=!0,i?n.fire(new t.ErrorEvent(i)):a&&(n.image=a,e&&(n.coordinates=e),r&&r(),n._finishLoading())}))},r.prototype.loaded=function(){return this._loaded},r.prototype.updateImage=function(t){var e=this;return this.image&&t.url?(this.options.url=t.url,this.load(t.coordinates,(function(){e.texture=null})),this):this},r.prototype._finishLoading=function(){this.map&&(this.setCoordinates(this.coordinates),this.fire(new t.Event(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})))},r.prototype.onAdd=function(t){this.map=t,this.load()},r.prototype.setCoordinates=function(e){var r=this;this.coordinates=e;var n=e.map(t.MercatorCoordinate.fromLngLat);this.tileID=function(e){for(var r=1/0,n=1/0,i=-1/0,a=-1/0,o=0,s=e;or.end(0)?this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+this.id,null,\"Playback for this video can be set only between the \"+r.start(0)+\" and \"+r.end(0)+\"-second mark.\"))):this.video.currentTime=e}},r.prototype.getVideo=function(){return this.video},r.prototype.onAdd=function(t){this.map||(this.map=t,this.load(),this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))},r.prototype.prepare=function(){if(!(0===Object.keys(this.tiles).length||this.video.readyState<2)){var e=this.map.painter.context,r=e.gl;for(var n in this.boundsBuffer||(this.boundsBuffer=e.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?this.video.paused||(this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE),r.texSubImage2D(r.TEXTURE_2D,0,0,0,r.RGBA,r.UNSIGNED_BYTE,this.video)):(this.texture=new t.Texture(e,this.video,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE)),this.tiles){var i=this.tiles[n];\"loaded\"!==i.state&&(i.state=\"loaded\",i.texture=this.texture)}}},r.prototype.serialize=function(){return{type:\"video\",urls:this.urls,coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this.video&&!this.video.paused},r}(z),D=function(e){function r(r,n,i,a){e.call(this,r,n,i,a),n.coordinates?Array.isArray(n.coordinates)&&4===n.coordinates.length&&!n.coordinates.some((function(t){return!Array.isArray(t)||2!==t.length||t.some((function(t){return\"number\"!=typeof t}))}))||this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'\"coordinates\" property must be an array of 4 longitude/latitude array pairs'))):this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'missing required property \"coordinates\"'))),n.animate&&\"boolean\"!=typeof n.animate&&this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'optional \"animate\" property must be a boolean value'))),n.canvas?\"string\"==typeof n.canvas||n.canvas instanceof t.window.HTMLCanvasElement||this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'\"canvas\" must be either a string representing the ID of the canvas element from which to read, or an HTMLCanvasElement instance'))):this.fire(new t.ErrorEvent(new t.ValidationError(\"sources.\"+r,null,'missing required property \"canvas\"'))),this.options=n,this.animate=void 0===n.animate||n.animate}return e&&(r.__proto__=e),r.prototype=Object.create(e&&e.prototype),r.prototype.constructor=r,r.prototype.load=function(){this._loaded=!0,this.canvas||(this.canvas=this.options.canvas instanceof t.window.HTMLCanvasElement?this.options.canvas:t.window.document.getElementById(this.options.canvas)),this.width=this.canvas.width,this.height=this.canvas.height,this._hasInvalidDimensions()?this.fire(new t.ErrorEvent(new Error(\"Canvas dimensions cannot be less than or equal to zero.\"))):(this.play=function(){this._playing=!0,this.map.triggerRepaint()},this.pause=function(){this._playing&&(this.prepare(),this._playing=!1)},this._finishLoading())},r.prototype.getCanvas=function(){return this.canvas},r.prototype.onAdd=function(t){this.map=t,this.load(),this.canvas&&this.animate&&this.play()},r.prototype.onRemove=function(){this.pause()},r.prototype.prepare=function(){var e=!1;if(this.canvas.width!==this.width&&(this.width=this.canvas.width,e=!0),this.canvas.height!==this.height&&(this.height=this.canvas.height,e=!0),!this._hasInvalidDimensions()&&0!==Object.keys(this.tiles).length){var r=this.map.painter.context,n=r.gl;for(var i in this.boundsBuffer||(this.boundsBuffer=r.createVertexBuffer(this._boundsArray,P.members)),this.boundsSegments||(this.boundsSegments=t.SegmentVector.simpleSegment(0,0,4,2)),this.texture?(e||this._playing)&&this.texture.update(this.canvas,{premultiply:!0}):this.texture=new t.Texture(r,this.canvas,n.RGBA,{premultiply:!0}),this.tiles){var a=this.tiles[i];\"loaded\"!==a.state&&(a.state=\"loaded\",a.texture=this.texture)}}},r.prototype.serialize=function(){return{type:\"canvas\",coordinates:this.coordinates}},r.prototype.hasTransition=function(){return this._playing},r.prototype._hasInvalidDimensions=function(){for(var t=0,e=[this.canvas.width,this.canvas.height];tthis.max){var o=this._getAndRemoveByKey(this.order[0]);o&&this.onRemove(o)}return this},j.prototype.has=function(t){return t.wrapped().key in this.data},j.prototype.getAndRemove=function(t){return this.has(t)?this._getAndRemoveByKey(t.wrapped().key):null},j.prototype._getAndRemoveByKey=function(t){var e=this.data[t].shift();return e.timeout&&clearTimeout(e.timeout),0===this.data[t].length&&delete this.data[t],this.order.splice(this.order.indexOf(t),1),e.value},j.prototype.getByKey=function(t){var e=this.data[t];return e?e[0].value:null},j.prototype.get=function(t){return this.has(t)?this.data[t.wrapped().key][0].value:null},j.prototype.remove=function(t,e){if(!this.has(t))return this;var r=t.wrapped().key,n=void 0===e?0:this.data[r].indexOf(e),i=this.data[r][n];return this.data[r].splice(n,1),i.timeout&&clearTimeout(i.timeout),0===this.data[r].length&&delete this.data[r],this.onRemove(i.value),this.order.splice(this.order.indexOf(r),1),this},j.prototype.setMaxSize=function(t){for(this.max=t;this.order.length>this.max;){var e=this._getAndRemoveByKey(this.order[0]);e&&this.onRemove(e)}return this},j.prototype.filter=function(t){var e=[];for(var r in this.data)for(var n=0,i=this.data[r];n1||(Math.abs(r)>1&&(1===Math.abs(r+i)?r+=i:1===Math.abs(r-i)&&(r-=i)),e.dem&&t.dem&&(t.dem.backfillBorder(e.dem,r,n),t.neighboringTiles&&t.neighboringTiles[a]&&(t.neighboringTiles[a].backfilled=!0)))}},r.prototype.getTile=function(t){return this.getTileByID(t.key)},r.prototype.getTileByID=function(t){return this._tiles[t]},r.prototype._retainLoadedChildren=function(t,e,r,n){for(var i in this._tiles){var a=this._tiles[i];if(!(n[i]||!a.hasData()||a.tileID.overscaledZ<=e||a.tileID.overscaledZ>r)){for(var o=a.tileID;a&&a.tileID.overscaledZ>e+1;){var s=a.tileID.scaledTo(a.tileID.overscaledZ-1);(a=this._tiles[s.key])&&a.hasData()&&(o=s)}for(var l=o;l.overscaledZ>e;)if(t[(l=l.scaledTo(l.overscaledZ-1)).key]){n[o.key]=o;break}}}},r.prototype.findLoadedParent=function(t,e){if(t.key in this._loadedParentTiles){var r=this._loadedParentTiles[t.key];return r&&r.tileID.overscaledZ>=e?r:null}for(var n=t.overscaledZ-1;n>=e;n--){var i=t.scaledTo(n),a=this._getLoadedTile(i);if(a)return a}},r.prototype._getLoadedTile=function(t){var e=this._tiles[t.key];return e&&e.hasData()?e:this._cache.getByKey(t.wrapped().key)},r.prototype.updateCacheSize=function(t){var e=(Math.ceil(t.width/this._source.tileSize)+1)*(Math.ceil(t.height/this._source.tileSize)+1),r=Math.floor(5*e),n=\"number\"==typeof this._maxTileCacheSize?Math.min(this._maxTileCacheSize,r):r;this._cache.setMaxSize(n)},r.prototype.handleWrapJump=function(t){var e=(t-(void 0===this._prevLng?t:this._prevLng))/360,r=Math.round(e);if(this._prevLng=t,r){var n={};for(var i in this._tiles){var a=this._tiles[i];a.tileID=a.tileID.unwrapTo(a.tileID.wrap+r),n[a.tileID.key]=a}for(var o in this._tiles=n,this._timers)clearTimeout(this._timers[o]),delete this._timers[o];for(var s in this._tiles){var l=this._tiles[s];this._setTileReloadTimer(s,l)}}},r.prototype.update=function(e){var n=this;if(this.transform=e,this._sourceLoaded&&!this._paused){var i;this.updateCacheSize(e),this.handleWrapJump(this.transform.center.lng),this._coveredTiles={},this.used?this._source.tileID?i=e.getVisibleUnwrappedCoordinates(this._source.tileID).map((function(e){return new t.OverscaledTileID(e.canonical.z,e.wrap,e.canonical.z,e.canonical.x,e.canonical.y)})):(i=e.coveringTiles({tileSize:this._source.tileSize,minzoom:this._source.minzoom,maxzoom:this._source.maxzoom,roundZoom:this._source.roundZoom,reparseOverscaled:this._source.reparseOverscaled}),this._source.hasTile&&(i=i.filter((function(t){return n._source.hasTile(t)})))):i=[];var a=e.coveringZoomLevel(this._source),o=Math.max(a-r.maxOverzooming,this._source.minzoom),s=Math.max(a+r.maxUnderzooming,this._source.minzoom),l=this._updateRetainedTiles(i,a);if(Ot(this._source.type)){for(var c={},u={},h=0,f=Object.keys(l);hthis._source.maxzoom){var g=d.children(this._source.maxzoom)[0],y=this.getTile(g);if(y&&y.hasData()){n[g.key]=g;continue}}else{var v=d.children(this._source.maxzoom);if(n[v[0].key]&&n[v[1].key]&&n[v[2].key]&&n[v[3].key])continue}for(var x=m.wasRequested(),_=d.overscaledZ-1;_>=a;--_){var b=d.scaledTo(_);if(i[b.key])break;if(i[b.key]=!0,!(m=this.getTile(b))&&x&&(m=this._addTile(b)),m&&(n[b.key]=b,x=m.wasRequested(),m.hasData()))break}}}return n},r.prototype._updateLoadedParentTileCache=function(){for(var t in this._loadedParentTiles={},this._tiles){for(var e=[],r=void 0,n=this._tiles[t].tileID;n.overscaledZ>0;){if(n.key in this._loadedParentTiles){r=this._loadedParentTiles[n.key];break}e.push(n.key);var i=n.scaledTo(n.overscaledZ-1);if(r=this._getLoadedTile(i))break;n=i}for(var a=0,o=e;a0||(e.hasData()&&\"reloading\"!==e.state?this._cache.add(e.tileID,e,e.getExpiryTimeout()):(e.aborted=!0,this._abortTile(e),this._unloadTile(e))))},r.prototype.clearTiles=function(){for(var t in this._shouldReloadOnResume=!1,this._paused=!1,this._tiles)this._removeTile(t);this._cache.reset()},r.prototype.tilesIn=function(e,r,n){var i=this,a=[],o=this.transform;if(!o)return a;for(var s=n?o.getCameraQueryGeometry(e):e,l=e.map((function(t){return o.pointCoordinate(t)})),c=s.map((function(t){return o.pointCoordinate(t)})),u=this.getIds(),h=1/0,f=1/0,p=-1/0,d=-1/0,m=0,g=c;m=0&&y[1].y+g>=0){var v=l.map((function(t){return s.getTilePoint(t)})),x=c.map((function(t){return s.getTilePoint(t)}));a.push({tile:n,tileID:s,queryGeometry:v,cameraQueryGeometry:x,scale:m})}}},x=0;x=t.browser.now())return!0}return!1},r.prototype.setFeatureState=function(t,e,r){t=t||\"_geojsonTileLayer\",this._state.updateState(t,e,r)},r.prototype.removeFeatureState=function(t,e,r){t=t||\"_geojsonTileLayer\",this._state.removeFeatureState(t,e,r)},r.prototype.getFeatureState=function(t,e){return t=t||\"_geojsonTileLayer\",this._state.getState(t,e)},r.prototype.setDependencies=function(t,e,r){var n=this._tiles[t];n&&n.setDependencies(e,r)},r.prototype.reloadTilesForDependencies=function(t,e){for(var r in this._tiles)this._tiles[r].hasDependency(t,e)&&this._reloadTile(r,\"reloading\");this._cache.filter((function(r){return!r.hasDependency(t,e)}))},r}(t.Evented);function zt(t,e){var r=Math.abs(2*t.wrap)-+(t.wrap<0),n=Math.abs(2*e.wrap)-+(e.wrap<0);return t.overscaledZ-e.overscaledZ||n-r||e.canonical.y-t.canonical.y||e.canonical.x-t.canonical.x}function Ot(t){return\"raster\"===t||\"image\"===t||\"video\"===t}function Dt(){return new t.window.Worker(oa.workerUrl)}Pt.maxOverzooming=10,Pt.maxUnderzooming=3;var Rt=\"mapboxgl_preloaded_worker_pool\",Ft=function(){this.active={}};Ft.prototype.acquire=function(t){if(!this.workers)for(this.workers=[];this.workers.length0?(i-o)/s:0;return this.points[a].mult(1-l).add(this.points[r].mult(l))};var Qt=function(t,e,r){var n=this.boxCells=[],i=this.circleCells=[];this.xCellCount=Math.ceil(t/r),this.yCellCount=Math.ceil(e/r);for(var a=0;a=-e[0]&&r<=e[0]&&n>=-e[1]&&n<=e[1]}function ae(e,r,n,i,a,o,s,l){var c=i?e.textSizeData:e.iconSizeData,u=t.evaluateSizeForZoom(c,n.transform.zoom),h=[256/n.width*2+1,256/n.height*2+1],f=i?e.text.dynamicLayoutVertexArray:e.icon.dynamicLayoutVertexArray;f.clear();for(var p=e.lineVertexArray,d=i?e.text.placedSymbolArray:e.icon.placedSymbolArray,m=n.transform.width/n.transform.height,g=!1,y=0;yMath.abs(n.x-r.x)*i?{useVertical:!0}:(e===t.WritingMode.vertical?r.yn.x)?{needsFlipping:!0}:null}function le(e,r,n,i,a,o,s,l,c,u,h,f,p,d){var m,g=r/24,y=e.lineOffsetX*g,v=e.lineOffsetY*g;if(e.numGlyphs>1){var x=e.glyphStartIndex+e.numGlyphs,_=e.lineStartIndex,b=e.lineStartIndex+e.lineLength,w=oe(g,l,y,v,n,h,f,e,c,o,p);if(!w)return{notEnoughRoom:!0};var T=re(w.first.point,s).point,k=re(w.last.point,s).point;if(i&&!n){var A=se(e.writingMode,T,k,d);if(A)return A}m=[w.first];for(var M=e.glyphStartIndex+1;M0?L.point:ce(f,C,S,1,a),P=se(e.writingMode,S,I,d);if(P)return P}var z=ue(g*l.getoffsetX(e.glyphStartIndex),y,v,n,h,f,e.segment,e.lineStartIndex,e.lineStartIndex+e.lineLength,c,o,p);if(!z)return{notEnoughRoom:!0};m=[z]}for(var O=0,D=m;O0?1:-1,m=0;i&&(d*=-1,m=Math.PI),d<0&&(m+=Math.PI);for(var g=d>0?l+s:l+s+1,y=a,v=a,x=0,_=0,b=Math.abs(p),w=[];x+_<=b;){if((g+=d)=c)return null;if(v=y,w.push(y),void 0===(y=f[g])){var T=new t.Point(u.getx(g),u.gety(g)),k=re(T,h);if(k.signedDistanceFromCamera>0)y=f[g]=k.point;else{var A=g-d;y=ce(0===x?o:new t.Point(u.getx(A),u.gety(A)),T,v,b-x+1,h)}}x+=_,_=v.dist(y)}var M=(b-x)/_,S=y.sub(v),E=S.mult(M)._add(v);E._add(S._unit()._perp()._mult(n*d));var C=m+Math.atan2(y.y-v.y,y.x-v.x);return w.push(E),{point:E,angle:C,path:w}}Qt.prototype.keysLength=function(){return this.boxKeys.length+this.circleKeys.length},Qt.prototype.insert=function(t,e,r,n,i){this._forEachCell(e,r,n,i,this._insertBoxCell,this.boxUid++),this.boxKeys.push(t),this.bboxes.push(e),this.bboxes.push(r),this.bboxes.push(n),this.bboxes.push(i)},Qt.prototype.insertCircle=function(t,e,r,n){this._forEachCell(e-n,r-n,e+n,r+n,this._insertCircleCell,this.circleUid++),this.circleKeys.push(t),this.circles.push(e),this.circles.push(r),this.circles.push(n)},Qt.prototype._insertBoxCell=function(t,e,r,n,i,a){this.boxCells[i].push(a)},Qt.prototype._insertCircleCell=function(t,e,r,n,i,a){this.circleCells[i].push(a)},Qt.prototype._query=function(t,e,r,n,i,a){if(r<0||t>this.width||n<0||e>this.height)return!i&&[];var o=[];if(t<=0&&e<=0&&this.width<=r&&this.height<=n){if(i)return!0;for(var s=0;s0:o},Qt.prototype._queryCircle=function(t,e,r,n,i){var a=t-r,o=t+r,s=e-r,l=e+r;if(o<0||a>this.width||l<0||s>this.height)return!n&&[];var c=[],u={hitTest:n,circle:{x:t,y:e,radius:r},seenUids:{box:{},circle:{}}};return this._forEachCell(a,s,o,l,this._queryCellCircle,c,u,i),n?c.length>0:c},Qt.prototype.query=function(t,e,r,n,i){return this._query(t,e,r,n,!1,i)},Qt.prototype.hitTest=function(t,e,r,n,i){return this._query(t,e,r,n,!0,i)},Qt.prototype.hitTestCircle=function(t,e,r,n){return this._queryCircle(t,e,r,!0,n)},Qt.prototype._queryCell=function(t,e,r,n,i,a,o,s){var l=o.seenUids,c=this.boxCells[i];if(null!==c)for(var u=this.bboxes,h=0,f=c;h=u[d+0]&&n>=u[d+1]&&(!s||s(this.boxKeys[p]))){if(o.hitTest)return a.push(!0),!0;a.push({key:this.boxKeys[p],x1:u[d],y1:u[d+1],x2:u[d+2],y2:u[d+3]})}}}var m=this.circleCells[i];if(null!==m)for(var g=this.circles,y=0,v=m;yo*o+s*s},Qt.prototype._circleAndRectCollide=function(t,e,r,n,i,a,o){var s=(a-n)/2,l=Math.abs(t-(n+s));if(l>s+r)return!1;var c=(o-i)/2,u=Math.abs(e-(i+c));if(u>c+r)return!1;if(l<=s||u<=c)return!0;var h=l-s,f=u-c;return h*h+f*f<=r*r};var he=new Float32Array([-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0]);function fe(t,e){for(var r=0;r=1;I--)L.push(E.path[I]);for(var P=1;P0){for(var R=L[0].clone(),F=L[0].clone(),B=1;B=A.x&&F.x<=M.x&&R.y>=A.y&&F.y<=M.y?[L]:F.xM.x||F.yM.y?[]:t.clipLine([L],A.x,A.y,M.x,M.y)}for(var N=0,j=D;N=this.screenRightBoundary||nthis.screenBottomBoundary},me.prototype.isInsideGrid=function(t,e,r,n){return r>=0&&t=0&&e0?(this.prevPlacement&&this.prevPlacement.variableOffsets[h.crossTileID]&&this.prevPlacement.placements[h.crossTileID]&&this.prevPlacement.placements[h.crossTileID].text&&(m=this.prevPlacement.variableOffsets[h.crossTileID].anchor),this.variableOffsets[h.crossTileID]={textOffset:g,width:r,height:n,anchor:t,textBoxScale:i,prevAnchor:m},this.markUsedJustification(f,t,h,p),f.allowVerticalPlacement&&(this.markUsedOrientation(f,p,h),this.placedOrientations[h.crossTileID]=p),{shift:y,placedGlyphBoxes:v}):void 0},Ae.prototype.placeLayerBucketPart=function(e,r,n){var i=this,a=e.parameters,o=a.bucket,s=a.layout,l=a.posMatrix,c=a.textLabelPlaneMatrix,u=a.labelToScreenMatrix,h=a.textPixelRatio,f=a.holdingForFade,p=a.collisionBoxArray,d=a.partiallyEvaluatedTextSize,m=a.collisionGroup,g=s.get(\"text-optional\"),y=s.get(\"icon-optional\"),v=s.get(\"text-allow-overlap\"),x=s.get(\"icon-allow-overlap\"),_=\"map\"===s.get(\"text-rotation-alignment\"),b=\"map\"===s.get(\"text-pitch-alignment\"),w=\"none\"!==s.get(\"icon-text-fit\"),T=\"viewport-y\"===s.get(\"symbol-z-order\"),k=v&&(x||!o.hasIconData()||y),A=x&&(v||!o.hasTextData()||g);!o.collisionArrays&&p&&o.deserializeCollisionBoxes(p);var M=function(e,a){if(!r[e.crossTileID])if(f)i.placements[e.crossTileID]=new xe(!1,!1,!1);else{var p,T=!1,M=!1,S=!0,E=null,C={box:null,offscreen:null},L={box:null,offscreen:null},I=null,P=null,z=0,O=0,D=0;a.textFeatureIndex?z=a.textFeatureIndex:e.useRuntimeCollisionCircles&&(z=e.featureIndex),a.verticalTextFeatureIndex&&(O=a.verticalTextFeatureIndex);var R=a.textBox;if(R){var F=function(r){var n=t.WritingMode.horizontal;if(o.allowVerticalPlacement&&!r&&i.prevPlacement){var a=i.prevPlacement.placedOrientations[e.crossTileID];a&&(i.placedOrientations[e.crossTileID]=a,n=a,i.markUsedOrientation(o,n,e))}return n},B=function(r,n){if(o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&a.verticalTextBox)for(var i=0,s=o.writingModes;i0&&(N=N.filter((function(t){return t!==j.anchor}))).unshift(j.anchor)}var U=function(t,r,n){for(var a=t.x2-t.x1,s=t.y2-t.y1,c=e.textBoxScale,u=w&&!x?r:null,f={box:[],offscreen:!1},p=v?2*N.length:N.length,d=0;d=N.length,k=i.attemptAnchorPlacement(g,t,a,s,c,_,b,h,l,m,y,e,o,n,u);if(k&&(f=k.placedGlyphBoxes)&&f.box&&f.box.length){T=!0,E=k.shift;break}}return f};B((function(){return U(R,a.iconBox,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox,n=C&&C.box&&C.box.length;return o.allowVerticalPlacement&&!n&&e.numVerticalGlyphVertices>0&&r?U(r,a.verticalIconBox,t.WritingMode.vertical):{box:null,offscreen:null}})),C&&(T=C.box,S=C.offscreen);var V=F(C&&C.box);if(!T&&i.prevPlacement){var q=i.prevPlacement.variableOffsets[e.crossTileID];q&&(i.variableOffsets[e.crossTileID]=q,i.markUsedJustification(o,q.anchor,e,V))}}else{var H=function(t,r){var n=i.collisionIndex.placeCollisionBox(t,v,h,l,m.predicate);return n&&n.box&&n.box.length&&(i.markUsedOrientation(o,r,e),i.placedOrientations[e.crossTileID]=r),n};B((function(){return H(R,t.WritingMode.horizontal)}),(function(){var r=a.verticalTextBox;return o.allowVerticalPlacement&&e.numVerticalGlyphVertices>0&&r?H(r,t.WritingMode.vertical):{box:null,offscreen:null}})),F(C&&C.box&&C.box.length)}}if(T=(p=C)&&p.box&&p.box.length>0,S=p&&p.offscreen,e.useRuntimeCollisionCircles){var G=o.text.placedSymbolArray.get(e.centerJustifiedTextSymbolIndex),Z=t.evaluateSizeForFeature(o.textSizeData,d,G),W=s.get(\"text-padding\"),Y=e.collisionCircleDiameter;I=i.collisionIndex.placeCollisionCircles(v,G,o.lineVertexArray,o.glyphOffsetArray,Z,l,c,u,n,b,m.predicate,Y,W),T=v||I.circles.length>0&&!I.collisionDetected,S=S&&I.offscreen}if(a.iconFeatureIndex&&(D=a.iconFeatureIndex),a.iconBox){var X=function(t){var e=w&&E?ke(t,E.x,E.y,_,b,i.transform.angle):t;return i.collisionIndex.placeCollisionBox(e,x,h,l,m.predicate)};M=L&&L.box&&L.box.length&&a.verticalIconBox?(P=X(a.verticalIconBox)).box.length>0:(P=X(a.iconBox)).box.length>0,S=S&&P.offscreen}var $=g||0===e.numHorizontalGlyphVertices&&0===e.numVerticalGlyphVertices,J=y||0===e.numIconVertices;if($||J?J?$||(M=M&&T):T=M&&T:M=T=M&&T,T&&p&&p.box&&(L&&L.box&&O?i.collisionIndex.insertCollisionBox(p.box,s.get(\"text-ignore-placement\"),o.bucketInstanceId,O,m.ID):i.collisionIndex.insertCollisionBox(p.box,s.get(\"text-ignore-placement\"),o.bucketInstanceId,z,m.ID)),M&&P&&i.collisionIndex.insertCollisionBox(P.box,s.get(\"icon-ignore-placement\"),o.bucketInstanceId,D,m.ID),I&&(T&&i.collisionIndex.insertCollisionCircles(I.circles,s.get(\"text-ignore-placement\"),o.bucketInstanceId,z,m.ID),n)){var K=o.bucketInstanceId,Q=i.collisionCircleArrays[K];void 0===Q&&(Q=i.collisionCircleArrays[K]=new _e);for(var tt=0;tt=0;--E){var C=S[E];M(o.symbolInstances.get(C),o.collisionArrays[C])}else for(var L=e.symbolInstanceStart;L=0&&(e.text.placedSymbolArray.get(c).crossTileID=a>=0&&c!==a?0:n.crossTileID)}},Ae.prototype.markUsedOrientation=function(e,r,n){for(var i=r===t.WritingMode.horizontal||r===t.WritingMode.horizontalOnly?r:0,a=r===t.WritingMode.vertical?r:0,o=0,s=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex];o0||l>0,x=a.numIconVertices>0,_=i.placedOrientations[a.crossTileID],b=_===t.WritingMode.vertical,w=_===t.WritingMode.horizontal||_===t.WritingMode.horizontalOnly;if(v){var T=Oe(y.text),k=b?De:T;d(e.text,s,k);var A=w?De:T;d(e.text,l,A);var M=y.text.isHidden();[a.rightJustifiedTextSymbolIndex,a.centerJustifiedTextSymbolIndex,a.leftJustifiedTextSymbolIndex].forEach((function(t){t>=0&&(e.text.placedSymbolArray.get(t).hidden=M||b?1:0)})),a.verticalPlacedTextSymbolIndex>=0&&(e.text.placedSymbolArray.get(a.verticalPlacedTextSymbolIndex).hidden=M||w?1:0);var S=i.variableOffsets[a.crossTileID];S&&i.markUsedJustification(e,S.anchor,a,_);var E=i.placedOrientations[a.crossTileID];E&&(i.markUsedJustification(e,\"left\",a,E),i.markUsedOrientation(e,E,a))}if(x){var C=Oe(y.icon),L=!(f&&a.verticalPlacedIconSymbolIndex&&b);if(a.placedIconSymbolIndex>=0){var I=L?C:De;d(e.icon,a.numIconVertices,I),e.icon.placedSymbolArray.get(a.placedIconSymbolIndex).hidden=y.icon.isHidden()}if(a.verticalPlacedIconSymbolIndex>=0){var P=L?De:C;d(e.icon,a.numVerticalIconVertices,P),e.icon.placedSymbolArray.get(a.verticalPlacedIconSymbolIndex).hidden=y.icon.isHidden()}}if(e.hasIconCollisionBoxData()||e.hasTextCollisionBoxData()){var z=e.collisionArrays[n];if(z){var O=new t.Point(0,0);if(z.textBox||z.verticalTextBox){var D=!0;if(c){var R=i.variableOffsets[m];R?(O=Te(R.anchor,R.width,R.height,R.textOffset,R.textBoxScale),u&&O._rotate(h?i.transform.angle:-i.transform.angle)):D=!1}z.textBox&&Me(e.textCollisionBox.collisionVertexArray,y.text.placed,!D||b,O.x,O.y),z.verticalTextBox&&Me(e.textCollisionBox.collisionVertexArray,y.text.placed,!D||w,O.x,O.y)}var F=Boolean(!w&&z.verticalIconBox);z.iconBox&&Me(e.iconCollisionBox.collisionVertexArray,y.icon.placed,F,f?O.x:0,f?O.y:0),z.verticalIconBox&&Me(e.iconCollisionBox.collisionVertexArray,y.icon.placed,!F,f?O.x:0,f?O.y:0)}}},g=0;gt},Ae.prototype.setStale=function(){this.stale=!0};var Se=Math.pow(2,25),Ee=Math.pow(2,24),Ce=Math.pow(2,17),Le=Math.pow(2,16),Ie=Math.pow(2,9),Pe=Math.pow(2,8),ze=Math.pow(2,1);function Oe(t){if(0===t.opacity&&!t.placed)return 0;if(1===t.opacity&&t.placed)return 4294967295;var e=t.placed?1:0,r=Math.floor(127*t.opacity);return r*Se+e*Ee+r*Ce+e*Le+r*Ie+e*Pe+r*ze+e}var De=0,Re=function(t){this._sortAcrossTiles=\"viewport-y\"!==t.layout.get(\"symbol-z-order\")&&void 0!==t.layout.get(\"symbol-sort-key\").constantOr(1),this._currentTileIndex=0,this._currentPartIndex=0,this._seenCrossTileIDs={},this._bucketParts=[]};Re.prototype.continuePlacement=function(t,e,r,n,i){for(var a=this._bucketParts;this._currentTileIndex2};this._currentPlacementIndex>=0;){var s=r[e[this._currentPlacementIndex]],l=this.placement.collisionIndex.transform.zoom;if(\"symbol\"===s.type&&(!s.minzoom||s.minzoom<=l)&&(!s.maxzoom||s.maxzoom>l)){if(this._inProgressLayer||(this._inProgressLayer=new Re(s)),this._inProgressLayer.continuePlacement(n[s.source],this.placement,this._showCollisionBoxes,s,o))return;delete this._inProgressLayer}this._currentPlacementIndex--}this._done=!0},Fe.prototype.commit=function(t){return this.placement.commit(t),this.placement};var Be=512/t.EXTENT/2,Ne=function(t,e,r){this.tileID=t,this.indexedSymbolInstances={},this.bucketInstanceId=r;for(var n=0;nt.overscaledZ)for(var s in o){var l=o[s];l.tileID.isChildOf(t)&&l.findMatches(e.symbolInstances,t,i)}else{var c=o[t.scaledTo(Number(a)).key];c&&c.findMatches(e.symbolInstances,t,i)}}for(var u=0;u1?\"@2x\":\"\",l=t.getJSON(r.transformRequest(r.normalizeSpriteURL(e,s,\".json\"),t.ResourceType.SpriteJSON),(function(t,e){l=null,o||(o=t,i=e,u())})),c=t.getImage(r.transformRequest(r.normalizeSpriteURL(e,s,\".png\"),t.ResourceType.SpriteImage),(function(t,e){c=null,o||(o=t,a=e,u())}));function u(){if(o)n(o);else if(i&&a){var e=t.browser.getImageData(a),r={};for(var s in i){var l=i[s],c=l.width,u=l.height,h=l.x,f=l.y,p=l.sdf,d=l.pixelRatio,m=l.stretchX,g=l.stretchY,y=l.content,v=new t.RGBAImage({width:c,height:u});t.RGBAImage.copy(e,v,{x:h,y:f},{x:0,y:0},{width:c,height:u}),r[s]={data:v,pixelRatio:d,sdf:p,stretchX:m,stretchY:g,content:y}}n(null,r)}}return{cancel:function(){l&&(l.cancel(),l=null),c&&(c.cancel(),c=null)}}}(e,this.map._requestManager,(function(e,n){if(r._spriteRequest=null,e)r.fire(new t.ErrorEvent(e));else if(n)for(var i in n)r.imageManager.addImage(i,n[i]);r.imageManager.setLoaded(!0),r._availableImages=r.imageManager.listImages(),r.dispatcher.broadcast(\"setImages\",r._availableImages),r.fire(new t.Event(\"data\",{dataType:\"style\"}))}))},r.prototype._validateLayer=function(e){var r=this.sourceCaches[e.source];if(r){var n=e.sourceLayer;if(n){var i=r.getSource();(\"geojson\"===i.type||i.vectorLayerIds&&-1===i.vectorLayerIds.indexOf(n))&&this.fire(new t.ErrorEvent(new Error('Source layer \"'+n+'\" does not exist on source \"'+i.id+'\" as specified by style layer \"'+e.id+'\"')))}}},r.prototype.loaded=function(){if(!this._loaded)return!1;if(Object.keys(this._updatedSources).length)return!1;for(var t in this.sourceCaches)if(!this.sourceCaches[t].loaded())return!1;return!!this.imageManager.isLoaded()},r.prototype._serializeLayers=function(t){for(var e=[],r=0,n=t;r0)throw new Error(\"Unimplemented: \"+i.map((function(t){return t.command})).join(\", \")+\".\");return n.forEach((function(t){\"setTransition\"!==t.command&&r[t.command].apply(r,t.args)})),this.stylesheet=e,!0},r.prototype.addImage=function(e,r){if(this.getImage(e))return this.fire(new t.ErrorEvent(new Error(\"An image with this name already exists.\")));this.imageManager.addImage(e,r),this._afterImageUpdated(e)},r.prototype.updateImage=function(t,e){this.imageManager.updateImage(t,e)},r.prototype.getImage=function(t){return this.imageManager.getImage(t)},r.prototype.removeImage=function(e){if(!this.getImage(e))return this.fire(new t.ErrorEvent(new Error(\"No image with this name exists.\")));this.imageManager.removeImage(e),this._afterImageUpdated(e)},r.prototype._afterImageUpdated=function(e){this._availableImages=this.imageManager.listImages(),this._changedImages[e]=!0,this._changed=!0,this.dispatcher.broadcast(\"setImages\",this._availableImages),this.fire(new t.Event(\"data\",{dataType:\"style\"}))},r.prototype.listImages=function(){return this._checkLoaded(),this.imageManager.listImages()},r.prototype.addSource=function(e,r,n){var i=this;if(void 0===n&&(n={}),this._checkLoaded(),void 0!==this.sourceCaches[e])throw new Error(\"There is already a source with this ID\");if(!r.type)throw new Error(\"The type property must be defined, but only the following properties were given: \"+Object.keys(r).join(\", \")+\".\");if(!([\"vector\",\"raster\",\"geojson\",\"video\",\"image\"].indexOf(r.type)>=0&&this._validate(t.validateStyle.source,\"sources.\"+e,r,null,n))){this.map&&this.map._collectResourceTiming&&(r.collectResourceTiming=!0);var a=this.sourceCaches[e]=new Pt(e,r,this.dispatcher);a.style=this,a.setEventedParent(this,(function(){return{isSourceLoaded:i.loaded(),source:a.serialize(),sourceId:e}})),a.onAdd(this.map),this._changed=!0}},r.prototype.removeSource=function(e){if(this._checkLoaded(),void 0===this.sourceCaches[e])throw new Error(\"There is no source with this ID\");for(var r in this._layers)if(this._layers[r].source===e)return this.fire(new t.ErrorEvent(new Error('Source \"'+e+'\" cannot be removed while layer \"'+r+'\" is using it.')));var n=this.sourceCaches[e];delete this.sourceCaches[e],delete this._updatedSources[e],n.fire(new t.Event(\"data\",{sourceDataType:\"metadata\",dataType:\"source\",sourceId:e})),n.setEventedParent(null),n.clearTiles(),n.onRemove&&n.onRemove(this.map),this._changed=!0},r.prototype.setGeoJSONSourceData=function(t,e){this._checkLoaded(),this.sourceCaches[t].getSource().setData(e),this._changed=!0},r.prototype.getSource=function(t){return this.sourceCaches[t]&&this.sourceCaches[t].getSource()},r.prototype.addLayer=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=e.id;if(this.getLayer(i))this.fire(new t.ErrorEvent(new Error('Layer with id \"'+i+'\" already exists on this map')));else{var a;if(\"custom\"===e.type){if(qe(this,t.validateCustomStyleLayer(e)))return;a=t.createStyleLayer(e)}else{if(\"object\"==typeof e.source&&(this.addSource(i,e.source),e=t.clone$1(e),e=t.extend(e,{source:i})),this._validate(t.validateStyle.layer,\"layers.\"+i,e,{arrayIndex:-1},n))return;a=t.createStyleLayer(e),this._validateLayer(a),a.setEventedParent(this,{layer:{id:i}}),this._serializedLayers[a.id]=a.serialize()}var o=r?this._order.indexOf(r):this._order.length;if(r&&-1===o)this.fire(new t.ErrorEvent(new Error('Layer with id \"'+r+'\" does not exist on this map.')));else{if(this._order.splice(o,0,i),this._layerOrderChanged=!0,this._layers[i]=a,this._removedLayers[i]&&a.source&&\"custom\"!==a.type){var s=this._removedLayers[i];delete this._removedLayers[i],s.type!==a.type?this._updatedSources[a.source]=\"clear\":(this._updatedSources[a.source]=\"reload\",this.sourceCaches[a.source].pause())}this._updateLayer(a),a.onAdd&&a.onAdd(this.map)}}},r.prototype.moveLayer=function(e,r){if(this._checkLoaded(),this._changed=!0,this._layers[e]){if(e!==r){var n=this._order.indexOf(e);this._order.splice(n,1);var i=r?this._order.indexOf(r):this._order.length;r&&-1===i?this.fire(new t.ErrorEvent(new Error('Layer with id \"'+r+'\" does not exist on this map.'))):(this._order.splice(i,0,e),this._layerOrderChanged=!0)}}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be moved.\")))},r.prototype.removeLayer=function(e){this._checkLoaded();var r=this._layers[e];if(r){r.setEventedParent(null);var n=this._order.indexOf(e);this._order.splice(n,1),this._layerOrderChanged=!0,this._changed=!0,this._removedLayers[e]=r,delete this._layers[e],delete this._serializedLayers[e],delete this._updatedLayers[e],delete this._updatedPaintProps[e],r.onRemove&&r.onRemove(this.map)}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be removed.\")))},r.prototype.getLayer=function(t){return this._layers[t]},r.prototype.hasLayer=function(t){return t in this._layers},r.prototype.setLayerZoomRange=function(e,r,n){this._checkLoaded();var i=this.getLayer(e);i?i.minzoom===r&&i.maxzoom===n||(null!=r&&(i.minzoom=r),null!=n&&(i.maxzoom=n),this._updateLayer(i)):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot have zoom extent.\")))},r.prototype.setFilter=function(e,r,n){void 0===n&&(n={}),this._checkLoaded();var i=this.getLayer(e);if(i){if(!t.deepEqual(i.filter,r))return null==r?(i.filter=void 0,void this._updateLayer(i)):void(this._validate(t.validateStyle.filter,\"layers.\"+i.id+\".filter\",r,null,n)||(i.filter=t.clone$1(r),this._updateLayer(i)))}else this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be filtered.\")))},r.prototype.getFilter=function(e){return t.clone$1(this.getLayer(e).filter)},r.prototype.setLayoutProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getLayoutProperty(r),n)||(a.setLayoutProperty(r,n,i),this._updateLayer(a)):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be styled.\")))},r.prototype.getLayoutProperty=function(e,r){var n=this.getLayer(e);if(n)return n.getLayoutProperty(r);this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style.\")))},r.prototype.setPaintProperty=function(e,r,n,i){void 0===i&&(i={}),this._checkLoaded();var a=this.getLayer(e);a?t.deepEqual(a.getPaintProperty(r),n)||(a.setPaintProperty(r,n,i)&&this._updateLayer(a),this._changed=!0,this._updatedPaintProps[e]=!0):this.fire(new t.ErrorEvent(new Error(\"The layer '\"+e+\"' does not exist in the map's style and cannot be styled.\")))},r.prototype.getPaintProperty=function(t,e){return this.getLayer(t).getPaintProperty(e)},r.prototype.setFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=e.sourceLayer,a=this.sourceCaches[n];if(void 0!==a){var o=a.getSource().type;\"geojson\"===o&&i?this.fire(new t.ErrorEvent(new Error(\"GeoJSON sources cannot have a sourceLayer parameter.\"))):\"vector\"!==o||i?(void 0===e.id&&this.fire(new t.ErrorEvent(new Error(\"The feature id parameter must be provided.\"))),a.setFeatureState(i,e.id,r)):this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+n+\"' does not exist in the map's style.\")))},r.prototype.removeFeatureState=function(e,r){this._checkLoaded();var n=e.source,i=this.sourceCaches[n];if(void 0!==i){var a=i.getSource().type,o=\"vector\"===a?e.sourceLayer:void 0;\"vector\"!==a||o?r&&\"string\"!=typeof e.id&&\"number\"!=typeof e.id?this.fire(new t.ErrorEvent(new Error(\"A feature id is required to remove its specific state property.\"))):i.removeFeatureState(o,e.id,r):this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+n+\"' does not exist in the map's style.\")))},r.prototype.getFeatureState=function(e){this._checkLoaded();var r=e.source,n=e.sourceLayer,i=this.sourceCaches[r];if(void 0!==i){if(\"vector\"!==i.getSource().type||n)return void 0===e.id&&this.fire(new t.ErrorEvent(new Error(\"The feature id parameter must be provided.\"))),i.getFeatureState(n,e.id);this.fire(new t.ErrorEvent(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}else this.fire(new t.ErrorEvent(new Error(\"The source '\"+r+\"' does not exist in the map's style.\")))},r.prototype.getTransition=function(){return t.extend({duration:300,delay:0},this.stylesheet&&this.stylesheet.transition)},r.prototype.serialize=function(){return t.filterObject({version:this.stylesheet.version,name:this.stylesheet.name,metadata:this.stylesheet.metadata,light:this.stylesheet.light,center:this.stylesheet.center,zoom:this.stylesheet.zoom,bearing:this.stylesheet.bearing,pitch:this.stylesheet.pitch,sprite:this.stylesheet.sprite,glyphs:this.stylesheet.glyphs,transition:this.stylesheet.transition,sources:t.mapObject(this.sourceCaches,(function(t){return t.serialize()})),layers:this._serializeLayers(this._order)},(function(t){return void 0!==t}))},r.prototype._updateLayer=function(t){this._updatedLayers[t.id]=!0,t.source&&!this._updatedSources[t.source]&&\"raster\"!==this.sourceCaches[t.source].getSource().type&&(this._updatedSources[t.source]=\"reload\",this.sourceCaches[t.source].pause()),this._changed=!0},r.prototype._flattenAndSortRenderedFeatures=function(t){for(var e=this,r=function(t){return\"fill-extrusion\"===e._layers[t].type},n={},i=[],a=this._order.length-1;a>=0;a--){var o=this._order[a];if(r(o)){n[o]=a;for(var s=0,l=t;s=0;d--){var m=this._order[d];if(r(m))for(var g=i.length-1;g>=0;g--){var y=i[g].feature;if(n[y.layer.id] 0.5) {gl_FragColor=vec4(0.0,0.0,1.0,0.5)*alpha;}if (v_notUsed > 0.5) {gl_FragColor*=.1;}}\",\"attribute vec2 a_pos;attribute vec2 a_anchor_pos;attribute vec2 a_extrude;attribute vec2 a_placed;attribute vec2 a_shift;uniform mat4 u_matrix;uniform vec2 u_extrude_scale;uniform float u_camera_to_center_distance;varying float v_placed;varying float v_notUsed;void main() {vec4 projectedPoint=u_matrix*vec4(a_anchor_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);gl_Position=u_matrix*vec4(a_pos,0.0,1.0);gl_Position.xy+=(a_extrude+a_shift)*u_extrude_scale*gl_Position.w*collision_perspective_ratio;v_placed=a_placed.x;v_notUsed=a_placed.y;}\"),nr=br(\"varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;void main() {float alpha=0.5*min(v_perspective_ratio,1.0);float stroke_radius=0.9*max(v_perspective_ratio,1.0);float distance_to_center=length(v_extrude);float distance_to_edge=abs(distance_to_center-v_radius);float opacity_t=smoothstep(-stroke_radius,0.0,-distance_to_edge);vec4 color=mix(vec4(0.0,0.0,1.0,0.5),vec4(1.0,0.0,0.0,1.0),v_collision);gl_FragColor=color*alpha*opacity_t;}\",\"attribute vec2 a_pos;attribute float a_radius;attribute vec2 a_flags;uniform mat4 u_matrix;uniform mat4 u_inv_matrix;uniform vec2 u_viewport_size;uniform float u_camera_to_center_distance;varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;vec3 toTilePosition(vec2 screenPos) {vec4 rayStart=u_inv_matrix*vec4(screenPos,-1.0,1.0);vec4 rayEnd =u_inv_matrix*vec4(screenPos, 1.0,1.0);rayStart.xyz/=rayStart.w;rayEnd.xyz /=rayEnd.w;highp float t=(0.0-rayStart.z)/(rayEnd.z-rayStart.z);return mix(rayStart.xyz,rayEnd.xyz,t);}void main() {vec2 quadCenterPos=a_pos;float radius=a_radius;float collision=a_flags.x;float vertexIdx=a_flags.y;vec2 quadVertexOffset=vec2(mix(-1.0,1.0,float(vertexIdx >=2.0)),mix(-1.0,1.0,float(vertexIdx >=1.0 && vertexIdx <=2.0)));vec2 quadVertexExtent=quadVertexOffset*radius;vec3 tilePos=toTilePosition(quadCenterPos);vec4 clipPos=u_matrix*vec4(tilePos,1.0);highp float camera_to_anchor_distance=clipPos.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);float padding_factor=1.2;v_radius=radius;v_extrude=quadVertexExtent*padding_factor;v_perspective_ratio=collision_perspective_ratio;v_collision=collision;gl_Position=vec4(clipPos.xyz/clipPos.w,1.0)+vec4(quadVertexExtent*padding_factor/u_viewport_size*2.0,0.0,0.0);}\"),ir=br(\"uniform highp vec4 u_color;uniform sampler2D u_overlay;varying vec2 v_uv;void main() {vec4 overlay_color=texture2D(u_overlay,v_uv);gl_FragColor=mix(u_color,overlay_color,overlay_color.a);}\",\"attribute vec2 a_pos;varying vec2 v_uv;uniform mat4 u_matrix;uniform float u_overlay_scale;void main() {v_uv=a_pos/8192.0;gl_Position=u_matrix*vec4(a_pos*u_overlay_scale,0,1);}\"),ar=br(\"#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float opacity\\ngl_FragColor=color*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float opacity\\ngl_Position=u_matrix*vec4(a_pos,0,1);}\"),or=br(\"varying vec2 v_pos;\\n#pragma mapbox: define highp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 outline_color\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=outline_color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;uniform vec2 u_world;varying vec2 v_pos;\\n#pragma mapbox: define highp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 outline_color\\n#pragma mapbox: initialize lowp float opacity\\ngl_Position=u_matrix*vec4(a_pos,0,1);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}\"),sr=br(\"uniform vec2 u_texsize;uniform sampler2D u_image;uniform float u_fade;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=mix(color1,color2,u_fade)*alpha*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_world;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;gl_Position=u_matrix*vec4(a_pos,0,1);vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,a_pos);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}\"),lr=br(\"uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_fade)*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileZoomRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileZoomRatio,a_pos);}\"),cr=br(\"varying vec4 v_color;void main() {gl_FragColor=v_color;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;uniform float u_vertical_gradient;uniform lowp float u_opacity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec4 v_color;\\n#pragma mapbox: define highp float base\\n#pragma mapbox: define highp float height\\n#pragma mapbox: define highp vec4 color\\nvoid main() {\\n#pragma mapbox: initialize highp float base\\n#pragma mapbox: initialize highp float height\\n#pragma mapbox: initialize highp vec4 color\\nvec3 normal=a_normal_ed.xyz;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);gl_Position=u_matrix*vec4(a_pos,t > 0.0 ? height : base,1);float colorvalue=color.r*0.2126+color.g*0.7152+color.b*0.0722;v_color=vec4(0.0,0.0,0.0,1.0);vec4 ambientlight=vec4(0.03,0.03,0.03,1.0);color+=ambientlight;float directional=clamp(dot(normal/16384.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((1.0-colorvalue+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_color.r+=clamp(color.r*directional*u_lightcolor.r,mix(0.0,0.3,1.0-u_lightcolor.r),1.0);v_color.g+=clamp(color.g*directional*u_lightcolor.g,mix(0.0,0.3,1.0-u_lightcolor.g),1.0);v_color.b+=clamp(color.b*directional*u_lightcolor.b,mix(0.0,0.3,1.0-u_lightcolor.b),1.0);v_color*=u_opacity;}\"),ur=br(\"uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float base\\n#pragma mapbox: initialize lowp float height\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);vec4 mixedColor=mix(color1,color2,u_fade);gl_FragColor=mixedColor*v_lighting;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_height_factor;uniform vec3 u_scale;uniform float u_vertical_gradient;uniform lowp float u_opacity;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;attribute vec2 a_pos;attribute vec4 a_normal_ed;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float base\\n#pragma mapbox: initialize lowp float height\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec3 normal=a_normal_ed.xyz;float edgedistance=a_normal_ed.w;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;base=max(0.0,base);height=max(0.0,height);float t=mod(normal.x,2.0);float z=t > 0.0 ? height : base;gl_Position=u_matrix*vec4(a_pos,z,1);vec2 pos=normal.x==1.0 && normal.y==0.0 && normal.z==16384.0\\n? a_pos\\n: vec2(edgedistance,z*u_height_factor);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,pos);v_lighting=vec4(0.0,0.0,0.0,1.0);float directional=clamp(dot(normal/16383.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((0.5+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_lighting.rgb+=clamp(directional*u_lightcolor,mix(vec3(0.0),vec3(0.3),1.0-u_lightcolor),vec3(1.0));v_lighting*=u_opacity;}\"),hr=br(\"#ifdef GL_ES\\nprecision highp float;\\n#endif\\nuniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_dimension;uniform float u_zoom;uniform vec4 u_unpack;float getElevation(vec2 coord,float bias) {vec4 data=texture2D(u_image,coord)*255.0;data.a=-1.0;return dot(data,u_unpack)/4.0;}void main() {vec2 epsilon=1.0/u_dimension;float a=getElevation(v_pos+vec2(-epsilon.x,-epsilon.y),0.0);float b=getElevation(v_pos+vec2(0,-epsilon.y),0.0);float c=getElevation(v_pos+vec2(epsilon.x,-epsilon.y),0.0);float d=getElevation(v_pos+vec2(-epsilon.x,0),0.0);float e=getElevation(v_pos,0.0);float f=getElevation(v_pos+vec2(epsilon.x,0),0.0);float g=getElevation(v_pos+vec2(-epsilon.x,epsilon.y),0.0);float h=getElevation(v_pos+vec2(0,epsilon.y),0.0);float i=getElevation(v_pos+vec2(epsilon.x,epsilon.y),0.0);float exaggerationFactor=u_zoom < 2.0 ? 0.4 : u_zoom < 4.5 ? 0.35 : 0.3;float exaggeration=u_zoom < 15.0 ? (u_zoom-15.0)*exaggerationFactor : 0.0;vec2 deriv=vec2((c+f+f+i)-(a+d+d+g),(g+h+h+i)-(a+b+b+c))/pow(2.0,exaggeration+(19.2562-u_zoom));gl_FragColor=clamp(vec4(deriv.x/2.0+0.5,deriv.y/2.0+0.5,1.0,1.0),0.0,1.0);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_dimension;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);highp vec2 epsilon=1.0/u_dimension;float scale=(u_dimension.x-2.0)/u_dimension.x;v_pos=(a_texture_pos/8192.0)*scale+epsilon;}\"),fr=br(\"uniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_latrange;uniform vec2 u_light;uniform vec4 u_shadow;uniform vec4 u_highlight;uniform vec4 u_accent;\\n#define PI 3.141592653589793\\nvoid main() {vec4 pixel=texture2D(u_image,v_pos);vec2 deriv=((pixel.rg*2.0)-1.0);float scaleFactor=cos(radians((u_latrange[0]-u_latrange[1])*(1.0-v_pos.y)+u_latrange[1]));float slope=atan(1.25*length(deriv)/scaleFactor);float aspect=deriv.x !=0.0 ? atan(deriv.y,-deriv.x) : PI/2.0*(deriv.y > 0.0 ? 1.0 :-1.0);float intensity=u_light.x;float azimuth=u_light.y+PI;float base=1.875-intensity*1.75;float maxValue=0.5*PI;float scaledSlope=intensity !=0.5 ? ((pow(base,slope)-1.0)/(pow(base,maxValue)-1.0))*maxValue : slope;float accent=cos(scaledSlope);vec4 accent_color=(1.0-accent)*u_accent*clamp(intensity*2.0,0.0,1.0);float shade=abs(mod((aspect+azimuth)/PI+0.5,2.0)-1.0);vec4 shade_color=mix(u_shadow,u_highlight,shade)*sin(scaledSlope)*clamp(intensity*2.0,0.0,1.0);gl_FragColor=accent_color*(1.0-shade_color.a)+shade_color;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos=a_texture_pos/8192.0;}\"),pr=br(\"uniform lowp float u_device_pixel_ratio;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform vec2 u_units_to_pixels;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_linesofar;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}\"),dr=br(\"uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;varying highp vec2 v_uv;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);vec4 color=texture2D(u_image,v_uv);gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\nattribute vec2 a_pos_normal;attribute vec4 a_data;attribute float a_uv_x;attribute float a_split_index;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_units_to_pixels;uniform float u_image_height;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp vec2 v_uv;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;highp float texel_height=1.0/u_image_height;highp float half_texel_height=0.5*texel_height;v_uv=vec2(a_uv_x,a_split_index*texel_height-half_texel_height);vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_width2=vec2(outset,inset);}\"),mr=br(\"uniform lowp float u_device_pixel_ratio;uniform vec2 u_texsize;uniform float u_fade;uniform mediump vec3 u_scale;uniform sampler2D u_image;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;vec2 pattern_size_a=vec2(display_size_a.x*fromScale/tileZoomRatio,display_size_a.y);vec2 pattern_size_b=vec2(display_size_b.x*toScale/tileZoomRatio,display_size_b.y);float aspect_a=display_size_a.y/v_width;float aspect_b=display_size_b.y/v_width;float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float x_a=mod(v_linesofar/pattern_size_a.x*aspect_a,1.0);float x_b=mod(v_linesofar/pattern_size_b.x*aspect_b,1.0);float y=0.5*v_normal.y+0.5;vec2 texel_size=1.0/u_texsize;vec2 pos_a=mix(pattern_tl_a*texel_size-texel_size,pattern_br_a*texel_size+texel_size,vec2(x_a,y));vec2 pos_b=mix(pattern_tl_b*texel_size-texel_size,pattern_br_b*texel_size+texel_size,vec2(x_b,y));vec4 color=mix(texture2D(u_image,pos_a),texture2D(u_image,pos_b),u_fade);gl_FragColor=color*alpha*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\n#define LINE_DISTANCE_SCALE 2.0\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform vec2 u_units_to_pixels;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_linesofar=a_linesofar;v_width2=vec2(outset,inset);v_width=floorwidth;}\"),gr=br(\"uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;uniform float u_sdfgamma;uniform float u_mix;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float sdfdist_a=texture2D(u_image,v_tex_a).a;float sdfdist_b=texture2D(u_image,v_tex_b).a;float sdfdist=mix(sdfdist_a,sdfdist_b,u_mix);alpha*=smoothstep(0.5-u_sdfgamma/floorwidth,0.5+u_sdfgamma/floorwidth,sdfdist);gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\n#define LINE_DISTANCE_SCALE 2.0\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_patternscale_a;uniform float u_tex_y_a;uniform vec2 u_patternscale_b;uniform float u_tex_y_b;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;float extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;v_tex_a=vec2(a_linesofar*u_patternscale_a.x/floorwidth,normal.y*u_patternscale_a.y+u_tex_y_a);v_tex_b=vec2(a_linesofar*u_patternscale_b.x/floorwidth,normal.y*u_patternscale_b.y+u_tex_y_b);v_width2=vec2(outset,inset);}\"),yr=br(\"uniform float u_fade_t;uniform float u_opacity;uniform sampler2D u_image0;uniform sampler2D u_image1;varying vec2 v_pos0;varying vec2 v_pos1;uniform float u_brightness_low;uniform float u_brightness_high;uniform float u_saturation_factor;uniform float u_contrast_factor;uniform vec3 u_spin_weights;void main() {vec4 color0=texture2D(u_image0,v_pos0);vec4 color1=texture2D(u_image1,v_pos1);if (color0.a > 0.0) {color0.rgb=color0.rgb/color0.a;}if (color1.a > 0.0) {color1.rgb=color1.rgb/color1.a;}vec4 color=mix(color0,color1,u_fade_t);color.a*=u_opacity;vec3 rgb=color.rgb;rgb=vec3(dot(rgb,u_spin_weights.xyz),dot(rgb,u_spin_weights.zxy),dot(rgb,u_spin_weights.yzx));float average=(color.r+color.g+color.b)/3.0;rgb+=(average-rgb)*u_saturation_factor;rgb=(rgb-0.5)*u_contrast_factor+0.5;vec3 u_high_vec=vec3(u_brightness_low,u_brightness_low,u_brightness_low);vec3 u_low_vec=vec3(u_brightness_high,u_brightness_high,u_brightness_high);gl_FragColor=vec4(mix(u_high_vec,u_low_vec,rgb)*color.a,color.a);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_tl_parent;uniform float u_scale_parent;uniform float u_buffer_scale;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos0;varying vec2 v_pos1;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos0=(((a_texture_pos/8192.0)-0.5)/u_buffer_scale )+0.5;v_pos1=(v_pos0*u_scale_parent)+u_tl_parent;}\"),vr=br(\"uniform sampler2D u_texture;varying vec2 v_tex;varying float v_fade_opacity;\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\nlowp float alpha=opacity*v_fade_opacity;gl_FragColor=texture2D(u_texture,v_tex)*alpha;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform highp float u_camera_to_center_distance;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform float u_fade_change;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform vec2 u_texsize;varying vec2 v_tex;varying float v_fade_opacity;\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;vec2 a_minFontScale=a_pixeloffset.zw/256.0;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*max(a_minFontScale,fontScale)+a_pxoffset/16.0),0.0,1.0);v_tex=a_tex/u_texsize;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;v_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));}\"),xr=br(\"#define SDF_PX 8.0\\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;uniform bool u_is_text;varying vec2 v_data0;varying vec3 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nfloat EDGE_GAMMA=0.105/u_device_pixel_ratio;vec2 tex=v_data0.xy;float gamma_scale=v_data1.x;float size=v_data1.y;float fade_opacity=v_data1[2];float fontScale=u_is_text ? size/24.0 : size;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;varying vec2 v_data0;varying vec3 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale+a_pxoffset),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0=a_tex/u_texsize;v_data1=vec3(gamma_scale,size,interpolated_fade_opacity);}\"),_r=br(\"#define SDF_PX 8.0\\n#define SDF 1.0\\n#define ICON 0.0\\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform sampler2D u_texture_icon;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;varying vec4 v_data0;varying vec4 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nfloat fade_opacity=v_data1[2];if (v_data1.w==ICON) {vec2 tex_icon=v_data0.zw;lowp float alpha=opacity*fade_opacity;gl_FragColor=texture2D(u_texture_icon,tex_icon)*alpha;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\nreturn;}vec2 tex=v_data0.xy;float EDGE_GAMMA=0.105/u_device_pixel_ratio;float gamma_scale=v_data1.x;float size=v_data1.y;float fontScale=size/24.0;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"const float PI=3.141592653589793;attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_texsize_icon;varying vec4 v_data0;varying vec4 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);float is_sdf=a_size[0]-2.0*a_size_min;highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec4 projectedPoint=u_matrix*vec4(a_pos,0,1);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=size/24.0;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=u_matrix*vec4(a_pos+vec2(1,0),0,1);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy,0.0,1.0);gl_Position=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale),0.0,1.0);float gamma_scale=gl_Position.w;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(1.0,fade_opacity[0]+fade_change));v_data0.xy=a_tex/u_texsize;v_data0.zw=a_tex/u_texsize_icon;v_data1=vec4(gamma_scale,size,interpolated_fade_opacity,is_sdf);}\");function br(t,e){var r=/#pragma mapbox: ([\\w]+) ([\\w]+) ([\\w]+) ([\\w]+)/g,n=e.match(/attribute ([\\w]+) ([\\w]+)/g),i=t.match(/uniform ([\\w]+) ([\\w]+)([\\s]*)([\\w]*)/g),a=e.match(/uniform ([\\w]+) ([\\w]+)([\\s]*)([\\w]*)/g),o=a?a.concat(i):i,s={};return{fragmentSource:t=t.replace(r,(function(t,e,r,n,i){return s[i]=!0,\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+i+\"\\nvarying \"+r+\" \"+n+\" \"+i+\";\\n#else\\nuniform \"+r+\" \"+n+\" u_\"+i+\";\\n#endif\\n\":\"\\n#ifdef HAS_UNIFORM_u_\"+i+\"\\n \"+r+\" \"+n+\" \"+i+\" = u_\"+i+\";\\n#endif\\n\"})),vertexSource:e=e.replace(r,(function(t,e,r,n,i){var a=\"float\"===n?\"vec2\":\"vec4\",o=i.match(/color/)?\"color\":a;return s[i]?\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+i+\"\\nuniform lowp float u_\"+i+\"_t;\\nattribute \"+r+\" \"+a+\" a_\"+i+\";\\nvarying \"+r+\" \"+n+\" \"+i+\";\\n#else\\nuniform \"+r+\" \"+n+\" u_\"+i+\";\\n#endif\\n\":\"vec4\"===o?\"\\n#ifndef HAS_UNIFORM_u_\"+i+\"\\n \"+i+\" = a_\"+i+\";\\n#else\\n \"+r+\" \"+n+\" \"+i+\" = u_\"+i+\";\\n#endif\\n\":\"\\n#ifndef HAS_UNIFORM_u_\"+i+\"\\n \"+i+\" = unpack_mix_\"+o+\"(a_\"+i+\", u_\"+i+\"_t);\\n#else\\n \"+r+\" \"+n+\" \"+i+\" = u_\"+i+\";\\n#endif\\n\":\"define\"===e?\"\\n#ifndef HAS_UNIFORM_u_\"+i+\"\\nuniform lowp float u_\"+i+\"_t;\\nattribute \"+r+\" \"+a+\" a_\"+i+\";\\n#else\\nuniform \"+r+\" \"+n+\" u_\"+i+\";\\n#endif\\n\":\"vec4\"===o?\"\\n#ifndef HAS_UNIFORM_u_\"+i+\"\\n \"+r+\" \"+n+\" \"+i+\" = a_\"+i+\";\\n#else\\n \"+r+\" \"+n+\" \"+i+\" = u_\"+i+\";\\n#endif\\n\":\"\\n#ifndef HAS_UNIFORM_u_\"+i+\"\\n \"+r+\" \"+n+\" \"+i+\" = unpack_mix_\"+o+\"(a_\"+i+\", u_\"+i+\"_t);\\n#else\\n \"+r+\" \"+n+\" \"+i+\" = u_\"+i+\";\\n#endif\\n\"})),staticAttributes:n,staticUniforms:o}}var wr=Object.freeze({__proto__:null,prelude:Xe,background:$e,backgroundPattern:Je,circle:Ke,clippingMask:Qe,heatmap:tr,heatmapTexture:er,collisionBox:rr,collisionCircle:nr,debug:ir,fill:ar,fillOutline:or,fillOutlinePattern:sr,fillPattern:lr,fillExtrusion:cr,fillExtrusionPattern:ur,hillshadePrepare:hr,hillshade:fr,line:pr,lineGradient:dr,linePattern:mr,lineSDF:gr,raster:yr,symbolIcon:vr,symbolSDF:xr,symbolTextAndIcon:_r}),Tr=function(){this.boundProgram=null,this.boundLayoutVertexBuffer=null,this.boundPaintVertexBuffers=[],this.boundIndexBuffer=null,this.boundVertexOffset=null,this.boundDynamicVertexBuffer=null,this.vao=null};function kr(t){for(var e=[],r=0;r>16,s>>16],u_pixel_coord_lower:[65535&o,65535&s]}}Ar.prototype.draw=function(t,e,r,n,i,a,o,s,l,c,u,h,f,p,d,m){var g,y=t.gl;if(!this.failedToCreate){for(var v in t.program.set(this.program),t.setDepthMode(r),t.setStencilMode(n),t.setColorMode(i),t.setCullFace(a),this.fixedUniforms)this.fixedUniforms[v].set(o[v]);p&&p.setUniforms(t,this.binderUniforms,h,{zoom:f});for(var x=(g={},g[y.LINES]=2,g[y.TRIANGLES]=3,g[y.LINE_STRIP]=1,g)[e],_=0,b=u.get();_0?1-1/(1.001-o):-o),u_contrast_factor:(a=i.paint.get(\"raster-contrast\"),a>0?1/(1-a):1+a),u_spin_weights:Xr(i.paint.get(\"raster-hue-rotate\"))};var a,o};function Xr(t){t*=Math.PI/180;var e=Math.sin(t),r=Math.cos(t);return[(2*r+1)/3,(-Math.sqrt(3)*e-r+1)/3,(Math.sqrt(3)*e-r+1)/3]}var $r,Jr=function(t,e,r,n,i,a,o,s,l,c){var u=i.transform;return{u_is_size_zoom_constant:+(\"constant\"===t||\"source\"===t),u_is_size_feature_constant:+(\"constant\"===t||\"camera\"===t),u_size_t:e?e.uSizeT:0,u_size:e?e.uSize:0,u_camera_to_center_distance:u.cameraToCenterDistance,u_pitch:u.pitch/360*2*Math.PI,u_rotate_symbol:+r,u_aspect_ratio:u.width/u.height,u_fade_change:i.options.fadeDuration?i.symbolFadeChange:1,u_matrix:a,u_label_plane_matrix:o,u_coord_matrix:s,u_is_text:+l,u_pitch_with_map:+n,u_texsize:c,u_texture:0}},Kr=function(e,r,n,i,a,o,s,l,c,u,h){var f=a.transform;return t.extend(Jr(e,r,n,i,a,o,s,l,c,u),{u_gamma_scale:i?Math.cos(f._pitch)*f.cameraToCenterDistance:1,u_device_pixel_ratio:t.browser.devicePixelRatio,u_is_halo:+h})},Qr=function(e,r,n,i,a,o,s,l,c,u){return t.extend(Kr(e,r,n,i,a,o,s,l,!0,c,!0),{u_texsize_icon:u,u_texture_icon:1})},tn=function(t,e,r){return{u_matrix:t,u_opacity:e,u_color:r}},en=function(e,r,n,i,a,o){return t.extend(function(t,e,r,n){var i=r.imageManager.getPattern(t.from.toString()),a=r.imageManager.getPattern(t.to.toString()),o=r.imageManager.getPixelSize(),s=o.width,l=o.height,c=Math.pow(2,n.tileID.overscaledZ),u=n.tileSize*Math.pow(2,r.transform.tileZoom)/c,h=u*(n.tileID.canonical.x+n.tileID.wrap*c),f=u*n.tileID.canonical.y;return{u_image:0,u_pattern_tl_a:i.tl,u_pattern_br_a:i.br,u_pattern_tl_b:a.tl,u_pattern_br_b:a.br,u_texsize:[s,l],u_mix:e.t,u_pattern_size_a:i.displaySize,u_pattern_size_b:a.displaySize,u_scale_a:e.fromScale,u_scale_b:e.toScale,u_tile_units_to_pixels:1/ge(n,1,r.transform.tileZoom),u_pixel_coord_upper:[h>>16,f>>16],u_pixel_coord_lower:[65535&h,65535&f]}}(i,o,n,a),{u_matrix:e,u_opacity:r})},rn={fillExtrusion:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fillExtrusionPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_lightpos:new t.Uniform3f(e,r.u_lightpos),u_lightintensity:new t.Uniform1f(e,r.u_lightintensity),u_lightcolor:new t.Uniform3f(e,r.u_lightcolor),u_vertical_gradient:new t.Uniform1f(e,r.u_vertical_gradient),u_height_factor:new t.Uniform1f(e,r.u_height_factor),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade),u_opacity:new t.Uniform1f(e,r.u_opacity)}},fill:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},fillPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},fillOutline:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world)}},fillOutlinePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_texsize:new t.Uniform2f(e,r.u_texsize),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},circle:function(e,r){return{u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_scale_with_map:new t.Uniform1i(e,r.u_scale_with_map),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},collisionBox:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pixels_to_tile_units:new t.Uniform1f(e,r.u_pixels_to_tile_units),u_extrude_scale:new t.Uniform2f(e,r.u_extrude_scale),u_overscale_factor:new t.Uniform1f(e,r.u_overscale_factor)}},collisionCircle:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_inv_matrix:new t.UniformMatrix4f(e,r.u_inv_matrix),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_viewport_size:new t.Uniform2f(e,r.u_viewport_size)}},debug:function(e,r){return{u_color:new t.UniformColor(e,r.u_color),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_overlay:new t.Uniform1i(e,r.u_overlay),u_overlay_scale:new t.Uniform1f(e,r.u_overlay_scale)}},clippingMask:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmap:function(e,r){return{u_extrude_scale:new t.Uniform1f(e,r.u_extrude_scale),u_intensity:new t.Uniform1f(e,r.u_intensity),u_matrix:new t.UniformMatrix4f(e,r.u_matrix)}},heatmapTexture:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_world:new t.Uniform2f(e,r.u_world),u_image:new t.Uniform1i(e,r.u_image),u_color_ramp:new t.Uniform1i(e,r.u_color_ramp),u_opacity:new t.Uniform1f(e,r.u_opacity)}},hillshade:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_latrange:new t.Uniform2f(e,r.u_latrange),u_light:new t.Uniform2f(e,r.u_light),u_shadow:new t.UniformColor(e,r.u_shadow),u_highlight:new t.UniformColor(e,r.u_highlight),u_accent:new t.UniformColor(e,r.u_accent)}},hillshadePrepare:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_image:new t.Uniform1i(e,r.u_image),u_dimension:new t.Uniform2f(e,r.u_dimension),u_zoom:new t.Uniform1f(e,r.u_zoom),u_unpack:new t.Uniform4f(e,r.u_unpack)}},line:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels)}},lineGradient:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_image:new t.Uniform1i(e,r.u_image),u_image_height:new t.Uniform1f(e,r.u_image_height)}},linePattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_texsize:new t.Uniform2f(e,r.u_texsize),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_image:new t.Uniform1i(e,r.u_image),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_scale:new t.Uniform3f(e,r.u_scale),u_fade:new t.Uniform1f(e,r.u_fade)}},lineSDF:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_ratio:new t.Uniform1f(e,r.u_ratio),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_units_to_pixels:new t.Uniform2f(e,r.u_units_to_pixels),u_patternscale_a:new t.Uniform2f(e,r.u_patternscale_a),u_patternscale_b:new t.Uniform2f(e,r.u_patternscale_b),u_sdfgamma:new t.Uniform1f(e,r.u_sdfgamma),u_image:new t.Uniform1i(e,r.u_image),u_tex_y_a:new t.Uniform1f(e,r.u_tex_y_a),u_tex_y_b:new t.Uniform1f(e,r.u_tex_y_b),u_mix:new t.Uniform1f(e,r.u_mix)}},raster:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_tl_parent:new t.Uniform2f(e,r.u_tl_parent),u_scale_parent:new t.Uniform1f(e,r.u_scale_parent),u_buffer_scale:new t.Uniform1f(e,r.u_buffer_scale),u_fade_t:new t.Uniform1f(e,r.u_fade_t),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image0:new t.Uniform1i(e,r.u_image0),u_image1:new t.Uniform1i(e,r.u_image1),u_brightness_low:new t.Uniform1f(e,r.u_brightness_low),u_brightness_high:new t.Uniform1f(e,r.u_brightness_high),u_saturation_factor:new t.Uniform1f(e,r.u_saturation_factor),u_contrast_factor:new t.Uniform1f(e,r.u_contrast_factor),u_spin_weights:new t.Uniform3f(e,r.u_spin_weights)}},symbolIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture)}},symbolSDF:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texture:new t.Uniform1i(e,r.u_texture),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},symbolTextAndIcon:function(e,r){return{u_is_size_zoom_constant:new t.Uniform1i(e,r.u_is_size_zoom_constant),u_is_size_feature_constant:new t.Uniform1i(e,r.u_is_size_feature_constant),u_size_t:new t.Uniform1f(e,r.u_size_t),u_size:new t.Uniform1f(e,r.u_size),u_camera_to_center_distance:new t.Uniform1f(e,r.u_camera_to_center_distance),u_pitch:new t.Uniform1f(e,r.u_pitch),u_rotate_symbol:new t.Uniform1i(e,r.u_rotate_symbol),u_aspect_ratio:new t.Uniform1f(e,r.u_aspect_ratio),u_fade_change:new t.Uniform1f(e,r.u_fade_change),u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_label_plane_matrix:new t.UniformMatrix4f(e,r.u_label_plane_matrix),u_coord_matrix:new t.UniformMatrix4f(e,r.u_coord_matrix),u_is_text:new t.Uniform1i(e,r.u_is_text),u_pitch_with_map:new t.Uniform1i(e,r.u_pitch_with_map),u_texsize:new t.Uniform2f(e,r.u_texsize),u_texsize_icon:new t.Uniform2f(e,r.u_texsize_icon),u_texture:new t.Uniform1i(e,r.u_texture),u_texture_icon:new t.Uniform1i(e,r.u_texture_icon),u_gamma_scale:new t.Uniform1f(e,r.u_gamma_scale),u_device_pixel_ratio:new t.Uniform1f(e,r.u_device_pixel_ratio),u_is_halo:new t.Uniform1i(e,r.u_is_halo)}},background:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_color:new t.UniformColor(e,r.u_color)}},backgroundPattern:function(e,r){return{u_matrix:new t.UniformMatrix4f(e,r.u_matrix),u_opacity:new t.Uniform1f(e,r.u_opacity),u_image:new t.Uniform1i(e,r.u_image),u_pattern_tl_a:new t.Uniform2f(e,r.u_pattern_tl_a),u_pattern_br_a:new t.Uniform2f(e,r.u_pattern_br_a),u_pattern_tl_b:new t.Uniform2f(e,r.u_pattern_tl_b),u_pattern_br_b:new t.Uniform2f(e,r.u_pattern_br_b),u_texsize:new t.Uniform2f(e,r.u_texsize),u_mix:new t.Uniform1f(e,r.u_mix),u_pattern_size_a:new t.Uniform2f(e,r.u_pattern_size_a),u_pattern_size_b:new t.Uniform2f(e,r.u_pattern_size_b),u_scale_a:new t.Uniform1f(e,r.u_scale_a),u_scale_b:new t.Uniform1f(e,r.u_scale_b),u_pixel_coord_upper:new t.Uniform2f(e,r.u_pixel_coord_upper),u_pixel_coord_lower:new t.Uniform2f(e,r.u_pixel_coord_lower),u_tile_units_to_pixels:new t.Uniform1f(e,r.u_tile_units_to_pixels)}}};function nn(e,r,n,i,a,o,s){for(var l=e.context,c=l.gl,u=e.useProgram(\"collisionBox\"),h=[],f=0,p=0,d=0;d0){var b=t.create(),w=v;t.mul(b,y.placementInvProjMatrix,e.transform.glCoordMatrix),t.mul(b,b,y.placementViewportMatrix),h.push({circleArray:_,circleOffset:p,transform:w,invTransform:b}),p=f+=_.length/4}x&&u.draw(l,c.LINES,Mt.disabled,Et.disabled,e.colorModeForRenderPass(),Lt.disabled,Or(v,e.transform,g),n.id,x.layoutVertexBuffer,x.indexBuffer,x.segments,null,e.transform.zoom,null,null,x.collisionVertexBuffer)}}if(s&&h.length){var T=e.useProgram(\"collisionCircle\"),k=new t.StructArrayLayout2f1f2i16;k.resize(4*f),k._trim();for(var A=0,M=0,S=h;M=0&&(m[y.associatedIconIndex]={shiftedAnchor:S,angle:E})}else fe(y.numGlyphs,p)}if(h){d.clear();for(var L=e.icon.placedSymbolArray,I=0;I0){var s=t.browser.now(),l=(s-e.timeAdded)/o,c=r?(s-r.timeAdded)/o:-1,u=n.getSource(),h=a.coveringZoomLevel({tileSize:u.tileSize,roundZoom:u.roundZoom}),f=!r||Math.abs(r.tileID.overscaledZ-h)>Math.abs(e.tileID.overscaledZ-h),p=f&&e.refreshedUponExpiration?1:t.clamp(f?l:1-c,0,1);return e.refreshedUponExpiration&&l>=1&&(e.refreshedUponExpiration=!1),r?{opacity:1,mix:1-p}:{opacity:p,mix:0}}return{opacity:1,mix:0}}var gn=new t.Color(1,0,0,1),yn=new t.Color(0,1,0,1),vn=new t.Color(0,0,1,1),xn=new t.Color(1,0,1,1),_n=new t.Color(0,1,1,1);function bn(t){var e=t.transform.padding;wn(t,t.transform.height-(e.top||0),3,gn),wn(t,e.bottom||0,3,yn),Tn(t,e.left||0,3,vn),Tn(t,t.transform.width-(e.right||0),3,xn);var r=t.transform.centerPoint;!function(t,e,r,n){var i=20,a=2;kn(t,e-a/2,r-i/2,a,i,n),kn(t,e-i/2,r-a/2,i,a,n)}(t,r.x,t.transform.height-r.y,_n)}function wn(t,e,r,n){kn(t,0,e+r/2,t.transform.width,r,n)}function Tn(t,e,r,n){kn(t,e-r/2,0,r,t.transform.height,n)}function kn(e,r,n,i,a,o){var s=e.context,l=s.gl;l.enable(l.SCISSOR_TEST),l.scissor(r*t.browser.devicePixelRatio,n*t.browser.devicePixelRatio,i*t.browser.devicePixelRatio,a*t.browser.devicePixelRatio),s.clear({color:o}),l.disable(l.SCISSOR_TEST)}function An(e,r,n){var i=e.context,a=i.gl,o=n.posMatrix,s=e.useProgram(\"debug\"),l=Mt.disabled,c=Et.disabled,u=e.colorModeForRenderPass(),h=\"$debug\";i.activeTexture.set(a.TEXTURE0),e.emptyTexture.bind(a.LINEAR,a.CLAMP_TO_EDGE),s.draw(i,a.LINE_STRIP,l,c,u,Lt.disabled,Rr(o,t.Color.red),h,e.debugBuffer,e.tileBorderIndexBuffer,e.debugSegments);var f=r.getTileByID(n.key).latestRawTileData,p=f&&f.byteLength||0,d=Math.floor(p/1024),m=r.getTile(n).tileSize,g=512/Math.min(m,512)*(n.overscaledZ/e.transform.zoom)*.5,y=n.canonical.toString();n.overscaledZ!==n.canonical.z&&(y+=\" => \"+n.overscaledZ),function(t,e){t.initDebugOverlayCanvas();var r=t.debugOverlayCanvas,n=t.context.gl,i=t.debugOverlayCanvas.getContext(\"2d\");i.clearRect(0,0,r.width,r.height),i.shadowColor=\"white\",i.shadowBlur=2,i.lineWidth=1.5,i.strokeStyle=\"white\",i.textBaseline=\"top\",i.font=\"bold 36px Open Sans, sans-serif\",i.fillText(e,5,5),i.strokeText(e,5,5),t.debugOverlayTexture.update(r),t.debugOverlayTexture.bind(n.LINEAR,n.CLAMP_TO_EDGE)}(e,y+\" \"+d+\"kb\"),s.draw(i,a.TRIANGLES,l,c,Ct.alphaBlended,Lt.disabled,Rr(o,t.Color.transparent,g),h,e.debugBuffer,e.quadTriangleIndexBuffer,e.debugSegments)}var Mn={symbol:function(e,r,n,i,a){if(\"translucent\"===e.renderPass){var o=Et.disabled,s=e.colorModeForRenderPass();n.layout.get(\"text-variable-anchor\")&&function(e,r,n,i,a,o,s){for(var l=r.transform,c=\"map\"===a,u=\"map\"===o,h=0,f=e;h256&&this.clearStencil(),r.setColorMode(Ct.disabled),r.setDepthMode(Mt.disabled);var i=this.useProgram(\"clippingMask\");this._tileClippingMaskIDs={};for(var a=0,o=e;a256&&this.clearStencil();var t=this.nextStencilID++,e=this.context.gl;return new Et({func:e.NOTEQUAL,mask:255},t,255,e.KEEP,e.KEEP,e.REPLACE)},Sn.prototype.stencilModeForClipping=function(t){var e=this.context.gl;return new Et({func:e.EQUAL,mask:255},this._tileClippingMaskIDs[t.key],0,e.KEEP,e.KEEP,e.REPLACE)},Sn.prototype.stencilConfigForOverlap=function(t){var e,r=this.context.gl,n=t.sort((function(t,e){return e.overscaledZ-t.overscaledZ})),i=n[n.length-1].overscaledZ,a=n[0].overscaledZ-i+1;if(a>1){this.currentStencilSource=void 0,this.nextStencilID+a>256&&this.clearStencil();for(var o={},s=0;s=0;this.currentLayer--){var w=this.style._layers[i[this.currentLayer]],T=a[w.source],k=u[w.source];this._renderTileClippingMasks(w,k),this.renderLayer(this,T,w,k)}for(this.renderPass=\"translucent\",this.currentLayer=0;this.currentLayer0?e.pop():null},Sn.prototype.isPatternMissing=function(t){if(!t)return!1;if(!t.from||!t.to)return!0;var e=this.imageManager.getPattern(t.from.toString()),r=this.imageManager.getPattern(t.to.toString());return!e||!r},Sn.prototype.useProgram=function(t,e){this.cache=this.cache||{};var r=\"\"+t+(e?e.cacheKey:\"\")+(this._showOverdrawInspector?\"/overdraw\":\"\");return this.cache[r]||(this.cache[r]=new Ar(this.context,t,wr[t],e,rn[t],this._showOverdrawInspector)),this.cache[r]},Sn.prototype.setCustomLayerDefaults=function(){this.context.unbindVAO(),this.context.cullFace.setDefault(),this.context.activeTexture.setDefault(),this.context.pixelStoreUnpack.setDefault(),this.context.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.context.pixelStoreUnpackFlipY.setDefault()},Sn.prototype.setBaseState=function(){var t=this.context.gl;this.context.cullFace.set(!1),this.context.viewport.set([0,0,this.width,this.height]),this.context.blendEquation.set(t.FUNC_ADD)},Sn.prototype.initDebugOverlayCanvas=function(){if(null==this.debugOverlayCanvas){this.debugOverlayCanvas=t.window.document.createElement(\"canvas\"),this.debugOverlayCanvas.width=512,this.debugOverlayCanvas.height=512;var e=this.context.gl;this.debugOverlayTexture=new t.Texture(this.context,this.debugOverlayCanvas,e.RGBA)}},Sn.prototype.destroy=function(){this.emptyTexture.destroy(),this.debugOverlayTexture&&this.debugOverlayTexture.destroy()};var En=function(t,e){this.points=t,this.planes=e};En.fromInvProjectionMatrix=function(e,r,n){var i=Math.pow(2,n),a=[[-1,1,-1,1],[1,1,-1,1],[1,-1,-1,1],[-1,-1,-1,1],[-1,1,1,1],[1,1,1,1],[1,-1,1,1],[-1,-1,1,1]].map((function(r){return t.transformMat4([],r,e)})).map((function(e){return t.scale$1([],e,1/e[3]/r*i)})),o=[[0,1,2],[6,5,4],[0,3,7],[2,1,5],[3,2,6],[0,4,5]].map((function(e){var r=t.sub([],a[e[0]],a[e[1]]),n=t.sub([],a[e[2]],a[e[1]]),i=t.normalize([],t.cross([],r,n)),o=-t.dot(i,a[e[1]]);return i.concat(o)}));return new En(a,o)};var Cn=function(e,r){this.min=e,this.max=r,this.center=t.scale$2([],t.add([],this.min,this.max),.5)};Cn.prototype.quadrant=function(e){for(var r=[e%2==0,e<2],n=t.clone$2(this.min),i=t.clone$2(this.max),a=0;a=0;if(0===o)return 0;o!==r.length&&(n=!1)}if(n)return 2;for(var l=0;l<3;l++){for(var c=Number.MAX_VALUE,u=-Number.MAX_VALUE,h=0;hthis.max[l]-this.min[l])return 0}return 1};var Ln=function(t,e,r,n){if(void 0===t&&(t=0),void 0===e&&(e=0),void 0===r&&(r=0),void 0===n&&(n=0),isNaN(t)||t<0||isNaN(e)||e<0||isNaN(r)||r<0||isNaN(n)||n<0)throw new Error(\"Invalid value for edge-insets, top, bottom, left and right must all be numbers\");this.top=t,this.bottom=e,this.left=r,this.right=n};Ln.prototype.interpolate=function(e,r,n){return null!=r.top&&null!=e.top&&(this.top=t.number(e.top,r.top,n)),null!=r.bottom&&null!=e.bottom&&(this.bottom=t.number(e.bottom,r.bottom,n)),null!=r.left&&null!=e.left&&(this.left=t.number(e.left,r.left,n)),null!=r.right&&null!=e.right&&(this.right=t.number(e.right,r.right,n)),this},Ln.prototype.getCenter=function(e,r){var n=t.clamp((this.left+e-this.right)/2,0,e),i=t.clamp((this.top+r-this.bottom)/2,0,r);return new t.Point(n,i)},Ln.prototype.equals=function(t){return this.top===t.top&&this.bottom===t.bottom&&this.left===t.left&&this.right===t.right},Ln.prototype.clone=function(){return new Ln(this.top,this.bottom,this.left,this.right)},Ln.prototype.toJSON=function(){return{top:this.top,bottom:this.bottom,left:this.left,right:this.right}};var In=function(e,r,n,i,a){this.tileSize=512,this.maxValidLatitude=85.051129,this._renderWorldCopies=void 0===a||a,this._minZoom=e||0,this._maxZoom=r||22,this._minPitch=null==n?0:n,this._maxPitch=null==i?60:i,this.setMaxBounds(),this.width=0,this.height=0,this._center=new t.LngLat(0,0),this.zoom=0,this.angle=0,this._fov=.6435011087932844,this._pitch=0,this._unmodified=!0,this._edgeInsets=new Ln,this._posMatrixCache={},this._alignedPosMatrixCache={}},Pn={minZoom:{configurable:!0},maxZoom:{configurable:!0},minPitch:{configurable:!0},maxPitch:{configurable:!0},renderWorldCopies:{configurable:!0},worldSize:{configurable:!0},centerOffset:{configurable:!0},size:{configurable:!0},bearing:{configurable:!0},pitch:{configurable:!0},fov:{configurable:!0},zoom:{configurable:!0},center:{configurable:!0},padding:{configurable:!0},centerPoint:{configurable:!0},unmodified:{configurable:!0},point:{configurable:!0}};In.prototype.clone=function(){var t=new In(this._minZoom,this._maxZoom,this._minPitch,this.maxPitch,this._renderWorldCopies);return t.tileSize=this.tileSize,t.latRange=this.latRange,t.width=this.width,t.height=this.height,t._center=this._center,t.zoom=this.zoom,t.angle=this.angle,t._fov=this._fov,t._pitch=this._pitch,t._unmodified=this._unmodified,t._edgeInsets=this._edgeInsets.clone(),t._calcMatrices(),t},Pn.minZoom.get=function(){return this._minZoom},Pn.minZoom.set=function(t){this._minZoom!==t&&(this._minZoom=t,this.zoom=Math.max(this.zoom,t))},Pn.maxZoom.get=function(){return this._maxZoom},Pn.maxZoom.set=function(t){this._maxZoom!==t&&(this._maxZoom=t,this.zoom=Math.min(this.zoom,t))},Pn.minPitch.get=function(){return this._minPitch},Pn.minPitch.set=function(t){this._minPitch!==t&&(this._minPitch=t,this.pitch=Math.max(this.pitch,t))},Pn.maxPitch.get=function(){return this._maxPitch},Pn.maxPitch.set=function(t){this._maxPitch!==t&&(this._maxPitch=t,this.pitch=Math.min(this.pitch,t))},Pn.renderWorldCopies.get=function(){return this._renderWorldCopies},Pn.renderWorldCopies.set=function(t){void 0===t?t=!0:null===t&&(t=!1),this._renderWorldCopies=t},Pn.worldSize.get=function(){return this.tileSize*this.scale},Pn.centerOffset.get=function(){return this.centerPoint._sub(this.size._div(2))},Pn.size.get=function(){return new t.Point(this.width,this.height)},Pn.bearing.get=function(){return-this.angle/Math.PI*180},Pn.bearing.set=function(e){var r=-t.wrap(e,-180,180)*Math.PI/180;this.angle!==r&&(this._unmodified=!1,this.angle=r,this._calcMatrices(),this.rotationMatrix=t.create$2(),t.rotate(this.rotationMatrix,this.rotationMatrix,this.angle))},Pn.pitch.get=function(){return this._pitch/Math.PI*180},Pn.pitch.set=function(e){var r=t.clamp(e,this.minPitch,this.maxPitch)/180*Math.PI;this._pitch!==r&&(this._unmodified=!1,this._pitch=r,this._calcMatrices())},Pn.fov.get=function(){return this._fov/Math.PI*180},Pn.fov.set=function(t){t=Math.max(.01,Math.min(60,t)),this._fov!==t&&(this._unmodified=!1,this._fov=t/180*Math.PI,this._calcMatrices())},Pn.zoom.get=function(){return this._zoom},Pn.zoom.set=function(t){var e=Math.min(Math.max(t,this.minZoom),this.maxZoom);this._zoom!==e&&(this._unmodified=!1,this._zoom=e,this.scale=this.zoomScale(e),this.tileZoom=Math.floor(e),this.zoomFraction=e-this.tileZoom,this._constrain(),this._calcMatrices())},Pn.center.get=function(){return this._center},Pn.center.set=function(t){t.lat===this._center.lat&&t.lng===this._center.lng||(this._unmodified=!1,this._center=t,this._constrain(),this._calcMatrices())},Pn.padding.get=function(){return this._edgeInsets.toJSON()},Pn.padding.set=function(t){this._edgeInsets.equals(t)||(this._unmodified=!1,this._edgeInsets.interpolate(this._edgeInsets,t,1),this._calcMatrices())},Pn.centerPoint.get=function(){return this._edgeInsets.getCenter(this.width,this.height)},In.prototype.isPaddingEqual=function(t){return this._edgeInsets.equals(t)},In.prototype.interpolatePadding=function(t,e,r){this._unmodified=!1,this._edgeInsets.interpolate(t,e,r),this._constrain(),this._calcMatrices()},In.prototype.coveringZoomLevel=function(t){var e=(t.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/t.tileSize));return Math.max(0,e)},In.prototype.getVisibleUnwrappedCoordinates=function(e){var r=[new t.UnwrappedTileID(0,e)];if(this._renderWorldCopies)for(var n=this.pointCoordinate(new t.Point(0,0)),i=this.pointCoordinate(new t.Point(this.width,0)),a=this.pointCoordinate(new t.Point(this.width,this.height)),o=this.pointCoordinate(new t.Point(0,this.height)),s=Math.floor(Math.min(n.x,i.x,a.x,o.x)),l=Math.floor(Math.max(n.x,i.x,a.x,o.x)),c=s-1;c<=l+1;c++)0!==c&&r.push(new t.UnwrappedTileID(c,e));return r},In.prototype.coveringTiles=function(e){var r=this.coveringZoomLevel(e),n=r;if(void 0!==e.minzoom&&re.maxzoom&&(r=e.maxzoom);var i=t.MercatorCoordinate.fromLngLat(this.center),a=Math.pow(2,r),o=[a*i.x,a*i.y,0],s=En.fromInvProjectionMatrix(this.invProjMatrix,this.worldSize,r),l=e.minzoom||0;this.pitch<=60&&this._edgeInsets.top<.1&&(l=r);var c=function(t){return{aabb:new Cn([t*a,0,0],[(t+1)*a,a,0]),zoom:0,x:0,y:0,wrap:t,fullyVisible:!1}},u=[],h=[],f=r,p=e.reparseOverscaled?n:r;if(this._renderWorldCopies)for(var d=1;d<=3;d++)u.push(c(-d)),u.push(c(d));for(u.push(c(0));u.length>0;){var m=u.pop(),g=m.x,y=m.y,v=m.fullyVisible;if(!v){var x=m.aabb.intersects(s);if(0===x)continue;v=2===x}var _=m.aabb.distanceX(o),b=m.aabb.distanceY(o),w=Math.max(Math.abs(_),Math.abs(b)),T=3+(1<T&&m.zoom>=l)h.push({tileID:new t.OverscaledTileID(m.zoom===f?p:m.zoom,m.wrap,m.zoom,g,y),distanceSq:t.sqrLen([o[0]-.5-g,o[1]-.5-y])});else for(var k=0;k<4;k++){var A=(g<<1)+k%2,M=(y<<1)+(k>>1);u.push({aabb:m.aabb.quadrant(k),zoom:m.zoom+1,x:A,y:M,wrap:m.wrap,fullyVisible:v})}}return h.sort((function(t,e){return t.distanceSq-e.distanceSq})).map((function(t){return t.tileID}))},In.prototype.resize=function(t,e){this.width=t,this.height=e,this.pixelsToGLUnits=[2/t,-2/e],this._constrain(),this._calcMatrices()},Pn.unmodified.get=function(){return this._unmodified},In.prototype.zoomScale=function(t){return Math.pow(2,t)},In.prototype.scaleZoom=function(t){return Math.log(t)/Math.LN2},In.prototype.project=function(e){var r=t.clamp(e.lat,-this.maxValidLatitude,this.maxValidLatitude);return new t.Point(t.mercatorXfromLng(e.lng)*this.worldSize,t.mercatorYfromLat(r)*this.worldSize)},In.prototype.unproject=function(e){return new t.MercatorCoordinate(e.x/this.worldSize,e.y/this.worldSize).toLngLat()},Pn.point.get=function(){return this.project(this.center)},In.prototype.setLocationAtPoint=function(e,r){var n=this.pointCoordinate(r),i=this.pointCoordinate(this.centerPoint),a=this.locationCoordinate(e),o=new t.MercatorCoordinate(a.x-(n.x-i.x),a.y-(n.y-i.y));this.center=this.coordinateLocation(o),this._renderWorldCopies&&(this.center=this.center.wrap())},In.prototype.locationPoint=function(t){return this.coordinatePoint(this.locationCoordinate(t))},In.prototype.pointLocation=function(t){return this.coordinateLocation(this.pointCoordinate(t))},In.prototype.locationCoordinate=function(e){return t.MercatorCoordinate.fromLngLat(e)},In.prototype.coordinateLocation=function(t){return t.toLngLat()},In.prototype.pointCoordinate=function(e){var r=[e.x,e.y,0,1],n=[e.x,e.y,1,1];t.transformMat4(r,r,this.pixelMatrixInverse),t.transformMat4(n,n,this.pixelMatrixInverse);var i=r[3],a=n[3],o=r[0]/i,s=n[0]/a,l=r[1]/i,c=n[1]/a,u=r[2]/i,h=n[2]/a,f=u===h?0:(0-u)/(h-u);return new t.MercatorCoordinate(t.number(o,s,f)/this.worldSize,t.number(l,c,f)/this.worldSize)},In.prototype.coordinatePoint=function(e){var r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix),new t.Point(r[0]/r[3],r[1]/r[3])},In.prototype.getBounds=function(){return(new t.LngLatBounds).extend(this.pointLocation(new t.Point(0,0))).extend(this.pointLocation(new t.Point(this.width,0))).extend(this.pointLocation(new t.Point(this.width,this.height))).extend(this.pointLocation(new t.Point(0,this.height)))},In.prototype.getMaxBounds=function(){return this.latRange&&2===this.latRange.length&&this.lngRange&&2===this.lngRange.length?new t.LngLatBounds([this.lngRange[0],this.latRange[0]],[this.lngRange[1],this.latRange[1]]):null},In.prototype.setMaxBounds=function(t){t?(this.lngRange=[t.getWest(),t.getEast()],this.latRange=[t.getSouth(),t.getNorth()],this._constrain()):(this.lngRange=null,this.latRange=[-this.maxValidLatitude,this.maxValidLatitude])},In.prototype.calculatePosMatrix=function(e,r){void 0===r&&(r=!1);var n=e.key,i=r?this._alignedPosMatrixCache:this._posMatrixCache;if(i[n])return i[n];var a=e.canonical,o=this.worldSize/this.zoomScale(a.z),s=a.x+Math.pow(2,a.z)*e.wrap,l=t.identity(new Float64Array(16));return t.translate(l,l,[s*o,a.y*o,0]),t.scale(l,l,[o/t.EXTENT,o/t.EXTENT,1]),t.multiply(l,r?this.alignedProjMatrix:this.projMatrix,l),i[n]=new Float32Array(l),i[n]},In.prototype.customLayerMatrix=function(){return this.mercatorMatrix.slice()},In.prototype._constrain=function(){if(this.center&&this.width&&this.height&&!this._constraining){this._constraining=!0;var e,r,n,i,a=-90,o=90,s=-180,l=180,c=this.size,u=this._unmodified;if(this.latRange){var h=this.latRange;a=t.mercatorYfromLat(h[1])*this.worldSize,e=(o=t.mercatorYfromLat(h[0])*this.worldSize)-ao&&(i=o-g)}if(this.lngRange){var y=p.x,v=c.x/2;y-vl&&(n=l-v)}void 0===n&&void 0===i||(this.center=this.unproject(new t.Point(void 0!==n?n:p.x,void 0!==i?i:p.y))),this._unmodified=u,this._constraining=!1}},In.prototype._calcMatrices=function(){if(this.height){var e=this._fov/2,r=this.centerOffset;this.cameraToCenterDistance=.5/Math.tan(e)*this.height;var n=Math.PI/2+this._pitch,i=this._fov*(.5+r.y/this.height),a=Math.sin(i)*this.cameraToCenterDistance/Math.sin(t.clamp(Math.PI-n-i,.01,Math.PI-.01)),o=this.point,s=o.x,l=o.y,c=1.01*(Math.cos(Math.PI/2-this._pitch)*a+this.cameraToCenterDistance),u=this.height/50,h=new Float64Array(16);t.perspective(h,this._fov,this.width/this.height,u,c),h[8]=2*-r.x/this.width,h[9]=2*r.y/this.height,t.scale(h,h,[1,-1,1]),t.translate(h,h,[0,0,-this.cameraToCenterDistance]),t.rotateX(h,h,this._pitch),t.rotateZ(h,h,this.angle),t.translate(h,h,[-s,-l,0]),this.mercatorMatrix=t.scale([],h,[this.worldSize,this.worldSize,this.worldSize]),t.scale(h,h,[1,1,t.mercatorZfromAltitude(1,this.center.lat)*this.worldSize,1]),this.projMatrix=h,this.invProjMatrix=t.invert([],this.projMatrix);var f=this.width%2/2,p=this.height%2/2,d=Math.cos(this.angle),m=Math.sin(this.angle),g=s-Math.round(s)+d*f+m*p,y=l-Math.round(l)+d*p+m*f,v=new Float64Array(h);if(t.translate(v,v,[g>.5?g-1:g,y>.5?y-1:y,0]),this.alignedProjMatrix=v,h=t.create(),t.scale(h,h,[this.width/2,-this.height/2,1]),t.translate(h,h,[1,-1,0]),this.labelPlaneMatrix=h,h=t.create(),t.scale(h,h,[1,-1,1]),t.translate(h,h,[-1,-1,0]),t.scale(h,h,[2/this.width,2/this.height,1]),this.glCoordMatrix=h,this.pixelMatrix=t.multiply(new Float64Array(16),this.labelPlaneMatrix,this.projMatrix),!(h=t.invert(new Float64Array(16),this.pixelMatrix)))throw new Error(\"failed to invert matrix\");this.pixelMatrixInverse=h,this._posMatrixCache={},this._alignedPosMatrixCache={}}},In.prototype.maxPitchScaleFactor=function(){if(!this.pixelMatrixInverse)return 1;var e=this.pointCoordinate(new t.Point(0,0)),r=[e.x*this.worldSize,e.y*this.worldSize,0,1];return t.transformMat4(r,r,this.pixelMatrix)[3]/this.cameraToCenterDistance},In.prototype.getCameraPoint=function(){var e=this._pitch,r=Math.tan(e)*(this.cameraToCenterDistance||1);return this.centerPoint.add(new t.Point(0,r))},In.prototype.getCameraQueryGeometry=function(e){var r=this.getCameraPoint();if(1===e.length)return[e[0],r];for(var n=r.x,i=r.y,a=r.x,o=r.y,s=0,l=e;s=3&&!t.some((function(t){return isNaN(t)}))){var e=this._map.dragRotate.isEnabled()&&this._map.touchZoomRotate.isEnabled()?+(t[3]||0):this._map.getBearing();return this._map.jumpTo({center:[+t[2],+t[1]],zoom:+t[0],bearing:e,pitch:+(t[4]||0)}),!0}return!1},zn.prototype._updateHashUnthrottled=function(){var e=t.window.location.href.replace(/(#.+)?$/,this.getHashString());try{t.window.history.replaceState(t.window.history.state,null,e)}catch(t){}};var On={linearity:.3,easing:t.bezier(0,0,.3,1)},Dn=t.extend({deceleration:2500,maxSpeed:1400},On),Rn=t.extend({deceleration:20,maxSpeed:1400},On),Fn=t.extend({deceleration:1e3,maxSpeed:360},On),Bn=t.extend({deceleration:1e3,maxSpeed:90},On),Nn=function(t){this._map=t,this.clear()};function jn(t,e){(!t.duration||t.duration0&&r-e[0].time>160;)e.shift()},Nn.prototype._onMoveEnd=function(e){if(this._drainInertiaBuffer(),!(this._inertiaBuffer.length<2)){for(var r={zoom:0,bearing:0,pitch:0,pan:new t.Point(0,0),pinchAround:void 0,around:void 0},n=0,i=this._inertiaBuffer;n=this._clickTolerance||this._map.fire(new Vn(t.type,this._map,t))},Gn.prototype.dblclick=function(t){return this._firePreventable(new Vn(t.type,this._map,t))},Gn.prototype.mouseover=function(t){this._map.fire(new Vn(t.type,this._map,t))},Gn.prototype.mouseout=function(t){this._map.fire(new Vn(t.type,this._map,t))},Gn.prototype.touchstart=function(t){return this._firePreventable(new qn(t.type,this._map,t))},Gn.prototype.touchmove=function(t){this._map.fire(new qn(t.type,this._map,t))},Gn.prototype.touchend=function(t){this._map.fire(new qn(t.type,this._map,t))},Gn.prototype.touchcancel=function(t){this._map.fire(new qn(t.type,this._map,t))},Gn.prototype._firePreventable=function(t){if(this._map.fire(t),t.defaultPrevented)return{}},Gn.prototype.isEnabled=function(){return!0},Gn.prototype.isActive=function(){return!1},Gn.prototype.enable=function(){},Gn.prototype.disable=function(){};var Zn=function(t){this._map=t};Zn.prototype.reset=function(){this._delayContextMenu=!1,delete this._contextMenuEvent},Zn.prototype.mousemove=function(t){this._map.fire(new Vn(t.type,this._map,t))},Zn.prototype.mousedown=function(){this._delayContextMenu=!0},Zn.prototype.mouseup=function(){this._delayContextMenu=!1,this._contextMenuEvent&&(this._map.fire(new Vn(\"contextmenu\",this._map,this._contextMenuEvent)),delete this._contextMenuEvent)},Zn.prototype.contextmenu=function(t){this._delayContextMenu?this._contextMenuEvent=t:this._map.fire(new Vn(t.type,this._map,t)),this._map.listens(\"contextmenu\")&&t.preventDefault()},Zn.prototype.isEnabled=function(){return!0},Zn.prototype.isActive=function(){return!1},Zn.prototype.enable=function(){},Zn.prototype.disable=function(){};var Wn=function(t,e){this._map=t,this._el=t.getCanvasContainer(),this._container=t.getContainer(),this._clickTolerance=e.clickTolerance||1};function Yn(t,e){for(var r={},n=0;nthis.numTouches)&&(this.aborted=!0),this.aborted||(void 0===this.startTime&&(this.startTime=e.timeStamp),n.length===this.numTouches&&(this.centroid=function(e){for(var r=new t.Point(0,0),n=0,i=e;n30)&&(this.aborted=!0)}}},Xn.prototype.touchend=function(t,e,r){if((!this.centroid||t.timeStamp-this.startTime>500)&&(this.aborted=!0),0===r.length){var n=!this.aborted&&this.centroid;if(this.reset(),n)return n}};var $n=function(t){this.singleTap=new Xn(t),this.numTaps=t.numTaps,this.reset()};$n.prototype.reset=function(){this.lastTime=1/0,delete this.lastTap,this.count=0,this.singleTap.reset()},$n.prototype.touchstart=function(t,e,r){this.singleTap.touchstart(t,e,r)},$n.prototype.touchmove=function(t,e,r){this.singleTap.touchmove(t,e,r)},$n.prototype.touchend=function(t,e,r){var n=this.singleTap.touchend(t,e,r);if(n){var i=t.timeStamp-this.lastTime<500,a=!this.lastTap||this.lastTap.dist(n)<30;if(i&&a||this.reset(),this.count++,this.lastTime=t.timeStamp,this.lastTap=n,this.count===this.numTaps)return this.reset(),n}};var Jn=function(){this._zoomIn=new $n({numTouches:1,numTaps:2}),this._zoomOut=new $n({numTouches:2,numTaps:1}),this.reset()};Jn.prototype.reset=function(){this._active=!1,this._zoomIn.reset(),this._zoomOut.reset()},Jn.prototype.touchstart=function(t,e,r){this._zoomIn.touchstart(t,e,r),this._zoomOut.touchstart(t,e,r)},Jn.prototype.touchmove=function(t,e,r){this._zoomIn.touchmove(t,e,r),this._zoomOut.touchmove(t,e,r)},Jn.prototype.touchend=function(t,e,r){var n=this,i=this._zoomIn.touchend(t,e,r),a=this._zoomOut.touchend(t,e,r);return i?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()+1,around:e.unproject(i)},{originalEvent:t})}}):a?(this._active=!0,t.preventDefault(),setTimeout((function(){return n.reset()}),0),{cameraAnimation:function(e){return e.easeTo({duration:300,zoom:e.getZoom()-1,around:e.unproject(a)},{originalEvent:t})}}):void 0},Jn.prototype.touchcancel=function(){this.reset()},Jn.prototype.enable=function(){this._enabled=!0},Jn.prototype.disable=function(){this._enabled=!1,this.reset()},Jn.prototype.isEnabled=function(){return this._enabled},Jn.prototype.isActive=function(){return this._active};var Kn={};Kn[0]=1,Kn[2]=2;var Qn=function(t){this.reset(),this._clickTolerance=t.clickTolerance||1};Qn.prototype.reset=function(){this._active=!1,this._moved=!1,delete this._lastPoint,delete this._eventButton},Qn.prototype._correctButton=function(t,e){return!1},Qn.prototype._move=function(t,e){return{}},Qn.prototype.mousedown=function(t,e){if(!this._lastPoint){var n=r.mouseButton(t);this._correctButton(t,n)&&(this._lastPoint=e,this._eventButton=n)}},Qn.prototype.mousemoveWindow=function(t,e){var r=this._lastPoint;if(r)if(t.preventDefault(),function(t,e){var r=Kn[e];return void 0===t.buttons||(t.buttons&r)!==r}(t,this._eventButton))this.reset();else if(this._moved||!(e.dist(r)0&&(this._active=!0);var i=Yn(n,r),a=new t.Point(0,0),o=new t.Point(0,0),s=0;for(var l in i){var c=i[l],u=this._touches[l];u&&(a._add(c),o._add(c.sub(u)),s++,i[l]=c)}if(this._touches=i,!(sMath.abs(t.x)}var hi=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e.prototype.reset=function(){t.prototype.reset.call(this),this._valid=void 0,delete this._firstMove,delete this._lastPoints},e.prototype._start=function(t){this._lastPoints=t,ui(t[0].sub(t[1]))&&(this._valid=!1)},e.prototype._move=function(t,e,r){var n=t[0].sub(this._lastPoints[0]),i=t[1].sub(this._lastPoints[1]);if(this._valid=this.gestureBeginsVertically(n,i,r.timeStamp),this._valid)return this._lastPoints=t,this._active=!0,{pitchDelta:(n.y+i.y)/2*-.5}},e.prototype.gestureBeginsVertically=function(t,e,r){if(void 0!==this._valid)return this._valid;var n=t.mag()>=2,i=e.mag()>=2;if(n||i){if(!n||!i)return void 0===this._firstMove&&(this._firstMove=r),r-this._firstMove<100&&void 0;var a=t.y>0==e.y>0;return ui(t)&&ui(e)&&a}},e}(ii),fi={panStep:100,bearingStep:15,pitchStep:10},pi=function(){var t=fi;this._panStep=t.panStep,this._bearingStep=t.bearingStep,this._pitchStep=t.pitchStep,this._rotationDisabled=!1};function di(t){return t*(2-t)}pi.prototype.reset=function(){this._active=!1},pi.prototype.keydown=function(t){var e=this;if(!(t.altKey||t.ctrlKey||t.metaKey)){var r=0,n=0,i=0,a=0,o=0;switch(t.keyCode){case 61:case 107:case 171:case 187:r=1;break;case 189:case 109:case 173:r=-1;break;case 37:t.shiftKey?n=-1:(t.preventDefault(),a=-1);break;case 39:t.shiftKey?n=1:(t.preventDefault(),a=1);break;case 38:t.shiftKey?i=1:(t.preventDefault(),o=-1);break;case 40:t.shiftKey?i=-1:(t.preventDefault(),o=1);break;default:return}return this._rotationDisabled&&(n=0,i=0),{cameraAnimation:function(s){var l=s.getZoom();s.easeTo({duration:300,easeId:\"keyboardHandler\",easing:di,zoom:r?Math.round(l)+r*(t.shiftKey?2:1):l,bearing:s.getBearing()+n*e._bearingStep,pitch:s.getPitch()+i*e._pitchStep,offset:[-a*e._panStep,-o*e._panStep],center:s.getCenter()},{originalEvent:t})}}}},pi.prototype.enable=function(){this._enabled=!0},pi.prototype.disable=function(){this._enabled=!1,this.reset()},pi.prototype.isEnabled=function(){return this._enabled},pi.prototype.isActive=function(){return this._active},pi.prototype.disableRotation=function(){this._rotationDisabled=!0},pi.prototype.enableRotation=function(){this._rotationDisabled=!1};var mi=4.000244140625,gi=function(e,r){this._map=e,this._el=e.getCanvasContainer(),this._handler=r,this._delta=0,this._defaultZoomRate=.01,this._wheelZoomRate=.0022222222222222222,t.bindAll([\"_onTimeout\"],this)};gi.prototype.setZoomRate=function(t){this._defaultZoomRate=t},gi.prototype.setWheelZoomRate=function(t){this._wheelZoomRate=t},gi.prototype.isEnabled=function(){return!!this._enabled},gi.prototype.isActive=function(){return!!this._active||void 0!==this._finishTimeout},gi.prototype.isZooming=function(){return!!this._zooming},gi.prototype.enable=function(t){this.isEnabled()||(this._enabled=!0,this._aroundCenter=t&&\"center\"===t.around)},gi.prototype.disable=function(){this.isEnabled()&&(this._enabled=!1)},gi.prototype.wheel=function(e){if(this.isEnabled()){var r=e.deltaMode===t.window.WheelEvent.DOM_DELTA_LINE?40*e.deltaY:e.deltaY,n=t.browser.now(),i=n-(this._lastWheelEventTime||0);this._lastWheelEventTime=n,0!==r&&r%mi==0?this._type=\"wheel\":0!==r&&Math.abs(r)<4?this._type=\"trackpad\":i>400?(this._type=null,this._lastValue=r,this._timeout=setTimeout(this._onTimeout,40,e)):this._type||(this._type=Math.abs(i*r)<200?\"trackpad\":\"wheel\",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,r+=this._lastValue)),e.shiftKey&&r&&(r/=4),this._type&&(this._lastWheelEvent=e,this._delta-=r,this._active||this._start(e)),e.preventDefault()}},gi.prototype._onTimeout=function(t){this._type=\"wheel\",this._delta-=this._lastValue,this._active||this._start(t)},gi.prototype._start=function(e){if(this._delta){this._frameId&&(this._frameId=null),this._active=!0,this.isZooming()||(this._zooming=!0),this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout);var n=r.mousePos(this._el,e);this._around=t.LngLat.convert(this._aroundCenter?this._map.getCenter():this._map.unproject(n)),this._aroundPoint=this._map.transform.locationPoint(this._around),this._frameId||(this._frameId=!0,this._handler._triggerRenderFrame())}},gi.prototype.renderFrame=function(){var e=this;if(this._frameId&&(this._frameId=null,this.isActive())){var r=this._map.transform;if(0!==this._delta){var n=\"wheel\"===this._type&&Math.abs(this._delta)>mi?this._wheelZoomRate:this._defaultZoomRate,i=2/(1+Math.exp(-Math.abs(this._delta*n)));this._delta<0&&0!==i&&(i=1/i);var a=\"number\"==typeof this._targetZoom?r.zoomScale(this._targetZoom):r.scale;this._targetZoom=Math.min(r.maxZoom,Math.max(r.minZoom,r.scaleZoom(a*i))),\"wheel\"===this._type&&(this._startZoom=r.zoom,this._easing=this._smoothOutEasing(200)),this._delta=0}var o,s=\"number\"==typeof this._targetZoom?this._targetZoom:r.zoom,l=this._startZoom,c=this._easing,u=!1;if(\"wheel\"===this._type&&l&&c){var h=Math.min((t.browser.now()-this._lastWheelEventTime)/200,1),f=c(h);o=t.number(l,s,f),h<1?this._frameId||(this._frameId=!0):u=!0}else o=s,u=!0;return this._active=!0,u&&(this._active=!1,this._finishTimeout=setTimeout((function(){e._zooming=!1,e._handler._triggerRenderFrame(),delete e._targetZoom,delete e._finishTimeout}),200)),{noInertia:!0,needsRenderFrame:!u,zoomDelta:o-r.zoom,around:this._aroundPoint,originalEvent:this._lastWheelEvent}}},gi.prototype._smoothOutEasing=function(e){var r=t.ease;if(this._prevEase){var n=this._prevEase,i=(t.browser.now()-n.start)/n.duration,a=n.easing(i+.01)-n.easing(i),o=.27/Math.sqrt(a*a+1e-4)*.01,s=Math.sqrt(.0729-o*o);r=t.bezier(o,s,.25,1)}return this._prevEase={start:t.browser.now(),duration:e,easing:r},r},gi.prototype.reset=function(){this._active=!1};var yi=function(t,e){this._clickZoom=t,this._tapZoom=e};yi.prototype.enable=function(){this._clickZoom.enable(),this._tapZoom.enable()},yi.prototype.disable=function(){this._clickZoom.disable(),this._tapZoom.disable()},yi.prototype.isEnabled=function(){return this._clickZoom.isEnabled()&&this._tapZoom.isEnabled()},yi.prototype.isActive=function(){return this._clickZoom.isActive()||this._tapZoom.isActive()};var vi=function(){this.reset()};vi.prototype.reset=function(){this._active=!1},vi.prototype.dblclick=function(t,e){return t.preventDefault(),{cameraAnimation:function(r){r.easeTo({duration:300,zoom:r.getZoom()+(t.shiftKey?-1:1),around:r.unproject(e)},{originalEvent:t})}}},vi.prototype.enable=function(){this._enabled=!0},vi.prototype.disable=function(){this._enabled=!1,this.reset()},vi.prototype.isEnabled=function(){return this._enabled},vi.prototype.isActive=function(){return this._active};var xi=function(){this._tap=new $n({numTouches:1,numTaps:1}),this.reset()};xi.prototype.reset=function(){this._active=!1,delete this._swipePoint,delete this._swipeTouch,delete this._tapTime,this._tap.reset()},xi.prototype.touchstart=function(t,e,r){this._swipePoint||(this._tapTime&&t.timeStamp-this._tapTime>500&&this.reset(),this._tapTime?r.length>0&&(this._swipePoint=e[0],this._swipeTouch=r[0].identifier):this._tap.touchstart(t,e,r))},xi.prototype.touchmove=function(t,e,r){if(this._tapTime){if(this._swipePoint){if(r[0].identifier!==this._swipeTouch)return;var n=e[0],i=n.y-this._swipePoint.y;return this._swipePoint=n,t.preventDefault(),this._active=!0,{zoomDelta:i/128}}}else this._tap.touchmove(t,e,r)},xi.prototype.touchend=function(t,e,r){this._tapTime?this._swipePoint&&0===r.length&&this.reset():this._tap.touchend(t,e,r)&&(this._tapTime=t.timeStamp)},xi.prototype.touchcancel=function(){this.reset()},xi.prototype.enable=function(){this._enabled=!0},xi.prototype.disable=function(){this._enabled=!1,this.reset()},xi.prototype.isEnabled=function(){return this._enabled},xi.prototype.isActive=function(){return this._active};var _i=function(t,e,r){this._el=t,this._mousePan=e,this._touchPan=r};_i.prototype.enable=function(t){this._inertiaOptions=t||{},this._mousePan.enable(),this._touchPan.enable(),this._el.classList.add(\"mapboxgl-touch-drag-pan\")},_i.prototype.disable=function(){this._mousePan.disable(),this._touchPan.disable(),this._el.classList.remove(\"mapboxgl-touch-drag-pan\")},_i.prototype.isEnabled=function(){return this._mousePan.isEnabled()&&this._touchPan.isEnabled()},_i.prototype.isActive=function(){return this._mousePan.isActive()||this._touchPan.isActive()};var bi=function(t,e,r){this._pitchWithRotate=t.pitchWithRotate,this._mouseRotate=e,this._mousePitch=r};bi.prototype.enable=function(){this._mouseRotate.enable(),this._pitchWithRotate&&this._mousePitch.enable()},bi.prototype.disable=function(){this._mouseRotate.disable(),this._mousePitch.disable()},bi.prototype.isEnabled=function(){return this._mouseRotate.isEnabled()&&(!this._pitchWithRotate||this._mousePitch.isEnabled())},bi.prototype.isActive=function(){return this._mouseRotate.isActive()||this._mousePitch.isActive()};var wi=function(t,e,r,n){this._el=t,this._touchZoom=e,this._touchRotate=r,this._tapDragZoom=n,this._rotationDisabled=!1,this._enabled=!0};wi.prototype.enable=function(t){this._touchZoom.enable(t),this._rotationDisabled||this._touchRotate.enable(t),this._tapDragZoom.enable(),this._el.classList.add(\"mapboxgl-touch-zoom-rotate\")},wi.prototype.disable=function(){this._touchZoom.disable(),this._touchRotate.disable(),this._tapDragZoom.disable(),this._el.classList.remove(\"mapboxgl-touch-zoom-rotate\")},wi.prototype.isEnabled=function(){return this._touchZoom.isEnabled()&&(this._rotationDisabled||this._touchRotate.isEnabled())&&this._tapDragZoom.isEnabled()},wi.prototype.isActive=function(){return this._touchZoom.isActive()||this._touchRotate.isActive()||this._tapDragZoom.isActive()},wi.prototype.disableRotation=function(){this._rotationDisabled=!0,this._touchRotate.disable()},wi.prototype.enableRotation=function(){this._rotationDisabled=!1,this._touchZoom.isEnabled()&&this._touchRotate.enable()};var Ti=function(t){return t.zoom||t.drag||t.pitch||t.rotate},ki=function(t){function e(){t.apply(this,arguments)}return t&&(e.__proto__=t),e.prototype=Object.create(t&&t.prototype),e.prototype.constructor=e,e}(t.Event);function Ai(t){return t.panDelta&&t.panDelta.mag()||t.zoomDelta||t.bearingDelta||t.pitchDelta}var Mi=function(e,n){this._map=e,this._el=this._map.getCanvasContainer(),this._handlers=[],this._handlersById={},this._changes=[],this._inertia=new Nn(e),this._bearingSnap=n.bearingSnap,this._previousActiveHandlers={},this._eventsInProgress={},this._addDefaultHandlers(n),t.bindAll([\"handleEvent\",\"handleWindowEvent\"],this);var i=this._el;this._listeners=[[i,\"touchstart\",{passive:!0}],[i,\"touchmove\",{passive:!1}],[i,\"touchend\",void 0],[i,\"touchcancel\",void 0],[i,\"mousedown\",void 0],[i,\"mousemove\",void 0],[i,\"mouseup\",void 0],[t.window.document,\"mousemove\",{capture:!0}],[t.window.document,\"mouseup\",void 0],[i,\"mouseover\",void 0],[i,\"mouseout\",void 0],[i,\"dblclick\",void 0],[i,\"click\",void 0],[i,\"keydown\",{capture:!1}],[i,\"keyup\",void 0],[i,\"wheel\",{passive:!1}],[i,\"contextmenu\",void 0],[t.window,\"blur\",void 0]];for(var a=0,o=this._listeners;aa?Math.min(2,b):Math.max(.5,b),w=Math.pow(g,1-e),T=i.unproject(x.add(_.mult(e*w)).mult(m));i.setLocationAtPoint(i.renderWorldCopies?T.wrap():T,d)}n._fireMoveEvents(r)}),(function(t){n._afterEase(r,t)}),e),this},r.prototype._prepareEase=function(e,r,n){void 0===n&&(n={}),this._moving=!0,r||n.moving||this.fire(new t.Event(\"movestart\",e)),this._zooming&&!n.zooming&&this.fire(new t.Event(\"zoomstart\",e)),this._rotating&&!n.rotating&&this.fire(new t.Event(\"rotatestart\",e)),this._pitching&&!n.pitching&&this.fire(new t.Event(\"pitchstart\",e))},r.prototype._fireMoveEvents=function(e){this.fire(new t.Event(\"move\",e)),this._zooming&&this.fire(new t.Event(\"zoom\",e)),this._rotating&&this.fire(new t.Event(\"rotate\",e)),this._pitching&&this.fire(new t.Event(\"pitch\",e))},r.prototype._afterEase=function(e,r){if(!this._easeId||!r||this._easeId!==r){delete this._easeId;var n=this._zooming,i=this._rotating,a=this._pitching;this._moving=!1,this._zooming=!1,this._rotating=!1,this._pitching=!1,this._padding=!1,n&&this.fire(new t.Event(\"zoomend\",e)),i&&this.fire(new t.Event(\"rotateend\",e)),a&&this.fire(new t.Event(\"pitchend\",e)),this.fire(new t.Event(\"moveend\",e))}},r.prototype.flyTo=function(e,r){var n=this;if(!e.essential&&t.browser.prefersReducedMotion){var i=t.pick(e,[\"center\",\"zoom\",\"bearing\",\"pitch\",\"around\"]);return this.jumpTo(i,r)}this.stop(),e=t.extend({offset:[0,0],speed:1.2,curve:1.42,easing:t.ease},e);var a=this.transform,o=this.getZoom(),s=this.getBearing(),l=this.getPitch(),c=this.getPadding(),u=\"zoom\"in e?t.clamp(+e.zoom,a.minZoom,a.maxZoom):o,h=\"bearing\"in e?this._normalizeBearing(e.bearing,s):s,f=\"pitch\"in e?+e.pitch:l,p=\"padding\"in e?e.padding:a.padding,d=a.zoomScale(u-o),m=t.Point.convert(e.offset),g=a.centerPoint.add(m),y=a.pointLocation(g),v=t.LngLat.convert(e.center||y);this._normalizeCenter(v);var x=a.project(y),_=a.project(v).sub(x),b=e.curve,w=Math.max(a.width,a.height),T=w/d,k=_.mag();if(\"minZoom\"in e){var A=t.clamp(Math.min(e.minZoom,o,u),a.minZoom,a.maxZoom),M=w/a.zoomScale(A-o);b=Math.sqrt(M/k*2)}var S=b*b;function E(t){var e=(T*T-w*w+(t?-1:1)*S*S*k*k)/(2*(t?T:w)*S*k);return Math.log(Math.sqrt(e*e+1)-e)}function C(t){return(Math.exp(t)-Math.exp(-t))/2}function L(t){return(Math.exp(t)+Math.exp(-t))/2}var I=E(0),P=function(t){return L(I)/L(I+b*t)},z=function(t){return w*((L(I)*(C(e=I+b*t)/L(e))-C(I))/S)/k;var e},O=(E(1)-I)/b;if(Math.abs(k)<1e-6||!isFinite(O)){if(Math.abs(w-T)<1e-6)return this.easeTo(e,r);var D=Te.maxDuration&&(e.duration=0),this._zooming=!0,this._rotating=s!==h,this._pitching=f!==l,this._padding=!a.isPaddingEqual(p),this._prepareEase(r,!1),this._ease((function(e){var i=e*O,d=1/P(i);a.zoom=1===e?u:o+a.scaleZoom(d),n._rotating&&(a.bearing=t.number(s,h,e)),n._pitching&&(a.pitch=t.number(l,f,e)),n._padding&&(a.interpolatePadding(c,p,e),g=a.centerPoint.add(m));var y=1===e?v:a.unproject(x.add(_.mult(z(i))).mult(d));a.setLocationAtPoint(a.renderWorldCopies?y.wrap():y,g),n._fireMoveEvents(r)}),(function(){return n._afterEase(r)}),e),this},r.prototype.isEasing=function(){return!!this._easeFrameId},r.prototype.stop=function(){return this._stop()},r.prototype._stop=function(t,e){if(this._easeFrameId&&(this._cancelRenderFrame(this._easeFrameId),delete this._easeFrameId,delete this._onEaseFrame),this._onEaseEnd){var r=this._onEaseEnd;delete this._onEaseEnd,r.call(this,e)}if(!t){var n=this.handlers;n&&n.stop(!1)}return this},r.prototype._ease=function(e,r,n){!1===n.animate||0===n.duration?(e(1),r()):(this._easeStart=t.browser.now(),this._easeOptions=n,this._onEaseFrame=e,this._onEaseEnd=r,this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback))},r.prototype._renderFrameCallback=function(){var e=Math.min((t.browser.now()-this._easeStart)/this._easeOptions.duration,1);this._onEaseFrame(this._easeOptions.easing(e)),e<1?this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback):this.stop()},r.prototype._normalizeBearing=function(e,r){e=t.wrap(e,-180,180);var n=Math.abs(e-r);return Math.abs(e-360-r)180?-360:r<-180?360:0}},r}(t.Evented),Ei=function(e){void 0===e&&(e={}),this.options=e,t.bindAll([\"_toggleAttribution\",\"_updateEditLink\",\"_updateData\",\"_updateCompact\"],this)};Ei.prototype.getDefaultPosition=function(){return\"bottom-right\"},Ei.prototype.onAdd=function(t){var e=this.options&&this.options.compact;return this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-attrib\"),this._compactButton=r.create(\"button\",\"mapboxgl-ctrl-attrib-button\",this._container),this._compactButton.addEventListener(\"click\",this._toggleAttribution),this._setElementTitle(this._compactButton,\"ToggleAttribution\"),this._innerContainer=r.create(\"div\",\"mapboxgl-ctrl-attrib-inner\",this._container),this._innerContainer.setAttribute(\"role\",\"list\"),e&&this._container.classList.add(\"mapboxgl-compact\"),this._updateAttributions(),this._updateEditLink(),this._map.on(\"styledata\",this._updateData),this._map.on(\"sourcedata\",this._updateData),this._map.on(\"moveend\",this._updateEditLink),void 0===e&&(this._map.on(\"resize\",this._updateCompact),this._updateCompact()),this._container},Ei.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"styledata\",this._updateData),this._map.off(\"sourcedata\",this._updateData),this._map.off(\"moveend\",this._updateEditLink),this._map.off(\"resize\",this._updateCompact),this._map=void 0,this._attribHTML=void 0},Ei.prototype._setElementTitle=function(t,e){var r=this._map._getUIString(\"AttributionControl.\"+e);t.title=r,t.setAttribute(\"aria-label\",r)},Ei.prototype._toggleAttribution=function(){this._container.classList.contains(\"mapboxgl-compact-show\")?(this._container.classList.remove(\"mapboxgl-compact-show\"),this._compactButton.setAttribute(\"aria-pressed\",\"false\")):(this._container.classList.add(\"mapboxgl-compact-show\"),this._compactButton.setAttribute(\"aria-pressed\",\"true\"))},Ei.prototype._updateEditLink=function(){var e=this._editLink;e||(e=this._editLink=this._container.querySelector(\".mapbox-improve-map\"));var r=[{key:\"owner\",value:this.styleOwner},{key:\"id\",value:this.styleId},{key:\"access_token\",value:this._map._requestManager._customAccessToken||t.config.ACCESS_TOKEN}];if(e){var n=r.reduce((function(t,e,n){return e.value&&(t+=e.key+\"=\"+e.value+(n=0)return!1;return!0}))).join(\" | \");o!==this._attribHTML&&(this._attribHTML=o,t.length?(this._innerContainer.innerHTML=o,this._container.classList.remove(\"mapboxgl-attrib-empty\")):this._container.classList.add(\"mapboxgl-attrib-empty\"),this._editLink=null)}},Ei.prototype._updateCompact=function(){this._map.getCanvasContainer().offsetWidth<=640?this._container.classList.add(\"mapboxgl-compact\"):this._container.classList.remove(\"mapboxgl-compact\",\"mapboxgl-compact-show\")};var Ci=function(){t.bindAll([\"_updateLogo\"],this),t.bindAll([\"_updateCompact\"],this)};Ci.prototype.onAdd=function(t){this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl\");var e=r.create(\"a\",\"mapboxgl-ctrl-logo\");return e.target=\"_blank\",e.rel=\"noopener nofollow\",e.href=\"https://www.mapbox.com/\",e.setAttribute(\"aria-label\",this._map._getUIString(\"LogoControl.Title\")),e.setAttribute(\"rel\",\"noopener nofollow\"),this._container.appendChild(e),this._container.style.display=\"none\",this._map.on(\"sourcedata\",this._updateLogo),this._updateLogo(),this._map.on(\"resize\",this._updateCompact),this._updateCompact(),this._container},Ci.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"sourcedata\",this._updateLogo),this._map.off(\"resize\",this._updateCompact)},Ci.prototype.getDefaultPosition=function(){return\"bottom-left\"},Ci.prototype._updateLogo=function(t){t&&\"metadata\"!==t.sourceDataType||(this._container.style.display=this._logoRequired()?\"block\":\"none\")},Ci.prototype._logoRequired=function(){if(this._map.style){var t=this._map.style.sourceCaches;for(var e in t)if(t[e].getSource().mapbox_logo)return!0;return!1}},Ci.prototype._updateCompact=function(){var t=this._container.children;if(t.length){var e=t[0];this._map.getCanvasContainer().offsetWidth<250?e.classList.add(\"mapboxgl-compact\"):e.classList.remove(\"mapboxgl-compact\")}};var Li=function(){this._queue=[],this._id=0,this._cleared=!1,this._currentlyRunning=!1};Li.prototype.add=function(t){var e=++this._id;return this._queue.push({callback:t,id:e,cancelled:!1}),e},Li.prototype.remove=function(t){for(var e=this._currentlyRunning,r=0,n=e?this._queue.concat(e):this._queue;re.maxZoom)throw new Error(\"maxZoom must be greater than or equal to minZoom\");if(null!=e.minPitch&&null!=e.maxPitch&&e.minPitch>e.maxPitch)throw new Error(\"maxPitch must be greater than or equal to minPitch\");if(null!=e.minPitch&&e.minPitch<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(null!=e.maxPitch&&e.maxPitch>Di)throw new Error(\"maxPitch must be less than or equal to 60\");var i=new In(e.minZoom,e.maxZoom,e.minPitch,e.maxPitch,e.renderWorldCopies);if(n.call(this,i,e),this._interactive=e.interactive,this._maxTileCacheSize=e.maxTileCacheSize,this._failIfMajorPerformanceCaveat=e.failIfMajorPerformanceCaveat,this._preserveDrawingBuffer=e.preserveDrawingBuffer,this._antialias=e.antialias,this._trackResize=e.trackResize,this._bearingSnap=e.bearingSnap,this._refreshExpiredTiles=e.refreshExpiredTiles,this._fadeDuration=e.fadeDuration,this._crossSourceCollisions=e.crossSourceCollisions,this._crossFadingFactor=1,this._collectResourceTiming=e.collectResourceTiming,this._renderTaskQueue=new Li,this._controls=[],this._mapId=t.uniqueId(),this._locale=t.extend({},Ii,e.locale),this._clickTolerance=e.clickTolerance,this._requestManager=new t.RequestManager(e.transformRequest,e.accessToken),\"string\"==typeof e.container){if(this._container=t.window.document.getElementById(e.container),!this._container)throw new Error(\"Container '\"+e.container+\"' not found.\")}else{if(!(e.container instanceof zi))throw new Error(\"Invalid type: 'container' must be a String or HTMLElement.\");this._container=e.container}if(e.maxBounds&&this.setMaxBounds(e.maxBounds),t.bindAll([\"_onWindowOnline\",\"_onWindowResize\",\"_onMapScroll\",\"_contextLost\",\"_contextRestored\"],this),this._setupContainer(),this._setupPainter(),void 0===this.painter)throw new Error(\"Failed to initialize WebGL.\");this.on(\"move\",(function(){return r._update(!1)})),this.on(\"moveend\",(function(){return r._update(!1)})),this.on(\"zoom\",(function(){return r._update(!0)})),void 0!==t.window&&(t.window.addEventListener(\"online\",this._onWindowOnline,!1),t.window.addEventListener(\"resize\",this._onWindowResize,!1),t.window.addEventListener(\"orientationchange\",this._onWindowResize,!1)),this.handlers=new Mi(this,e);var a=\"string\"==typeof e.hash&&e.hash||void 0;this._hash=e.hash&&new zn(a).addTo(this),this._hash&&this._hash._onHashChange()||(this.jumpTo({center:e.center,zoom:e.zoom,bearing:e.bearing,pitch:e.pitch}),e.bounds&&(this.resize(),this.fitBounds(e.bounds,t.extend({},e.fitBoundsOptions,{duration:0})))),this.resize(),this._localIdeographFontFamily=e.localIdeographFontFamily,e.style&&this.setStyle(e.style,{localIdeographFontFamily:e.localIdeographFontFamily}),e.attributionControl&&this.addControl(new Ei({customAttribution:e.customAttribution})),this.addControl(new Ci,e.logoPosition),this.on(\"style.load\",(function(){r.transform.unmodified&&r.jumpTo(r.style.stylesheet)})),this.on(\"data\",(function(e){r._update(\"style\"===e.dataType),r.fire(new t.Event(e.dataType+\"data\",e))})),this.on(\"dataloading\",(function(e){r.fire(new t.Event(e.dataType+\"dataloading\",e))}))}n&&(i.__proto__=n),i.prototype=Object.create(n&&n.prototype),i.prototype.constructor=i;var a={showTileBoundaries:{configurable:!0},showPadding:{configurable:!0},showCollisionBoxes:{configurable:!0},showOverdrawInspector:{configurable:!0},repaint:{configurable:!0},vertices:{configurable:!0},version:{configurable:!0}};return i.prototype._getMapId=function(){return this._mapId},i.prototype.addControl=function(e,r){if(void 0===r&&(r=e.getDefaultPosition?e.getDefaultPosition():\"top-right\"),!e||!e.onAdd)return this.fire(new t.ErrorEvent(new Error(\"Invalid argument to map.addControl(). Argument must be a control with onAdd and onRemove methods.\")));var n=e.onAdd(this);this._controls.push(e);var i=this._controlPositions[r];return-1!==r.indexOf(\"bottom\")?i.insertBefore(n,i.firstChild):i.appendChild(n),this},i.prototype.removeControl=function(e){if(!e||!e.onRemove)return this.fire(new t.ErrorEvent(new Error(\"Invalid argument to map.removeControl(). Argument must be a control with onAdd and onRemove methods.\")));var r=this._controls.indexOf(e);return r>-1&&this._controls.splice(r,1),e.onRemove(this),this},i.prototype.hasControl=function(t){return this._controls.indexOf(t)>-1},i.prototype.resize=function(e){var r=this._containerDimensions(),n=r[0],i=r[1];this._resizeCanvas(n,i),this.transform.resize(n,i),this.painter.resize(n,i);var a=!this._moving;return a&&(this.stop(),this.fire(new t.Event(\"movestart\",e)).fire(new t.Event(\"move\",e))),this.fire(new t.Event(\"resize\",e)),a&&this.fire(new t.Event(\"moveend\",e)),this},i.prototype.getBounds=function(){return this.transform.getBounds()},i.prototype.getMaxBounds=function(){return this.transform.getMaxBounds()},i.prototype.setMaxBounds=function(e){return this.transform.setMaxBounds(t.LngLatBounds.convert(e)),this._update()},i.prototype.setMinZoom=function(t){if((t=null==t?-2:t)>=-2&&t<=this.transform.maxZoom)return this.transform.minZoom=t,this._update(),this.getZoom()=this.transform.minZoom)return this.transform.maxZoom=t,this._update(),this.getZoom()>t&&this.setZoom(t),this;throw new Error(\"maxZoom must be greater than the current minZoom\")},i.prototype.getMaxZoom=function(){return this.transform.maxZoom},i.prototype.setMinPitch=function(t){if((t=null==t?0:t)<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(t>=0&&t<=this.transform.maxPitch)return this.transform.minPitch=t,this._update(),this.getPitch()Di)throw new Error(\"maxPitch must be less than or equal to 60\");if(t>=this.transform.minPitch)return this.transform.maxPitch=t,this._update(),this.getPitch()>t&&this.setPitch(t),this;throw new Error(\"maxPitch must be greater than the current minPitch\")},i.prototype.getMaxPitch=function(){return this.transform.maxPitch},i.prototype.getRenderWorldCopies=function(){return this.transform.renderWorldCopies},i.prototype.setRenderWorldCopies=function(t){return this.transform.renderWorldCopies=t,this._update()},i.prototype.project=function(e){return this.transform.locationPoint(t.LngLat.convert(e))},i.prototype.unproject=function(e){return this.transform.pointLocation(t.Point.convert(e))},i.prototype.isMoving=function(){return this._moving||this.handlers.isMoving()},i.prototype.isZooming=function(){return this._zooming||this.handlers.isZooming()},i.prototype.isRotating=function(){return this._rotating||this.handlers.isRotating()},i.prototype._createDelegatedListener=function(t,e,r){var n,i=this;if(\"mouseenter\"===t||\"mouseover\"===t){var a=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){var o=i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[];o.length?a||(a=!0,r.call(i,new Vn(t,i,n.originalEvent,{features:o}))):a=!1},mouseout:function(){a=!1}}}}if(\"mouseleave\"===t||\"mouseout\"===t){var o=!1;return{layer:e,listener:r,delegates:{mousemove:function(n){(i.getLayer(e)?i.queryRenderedFeatures(n.point,{layers:[e]}):[]).length?o=!0:o&&(o=!1,r.call(i,new Vn(t,i,n.originalEvent)))},mouseout:function(e){o&&(o=!1,r.call(i,new Vn(t,i,e.originalEvent)))}}}}return{layer:e,listener:r,delegates:(n={},n[t]=function(t){var n=i.getLayer(e)?i.queryRenderedFeatures(t.point,{layers:[e]}):[];n.length&&(t.features=n,r.call(i,t),delete t.features)},n)}},i.prototype.on=function(t,e,r){if(void 0===r)return n.prototype.on.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in this._delegatedListeners=this._delegatedListeners||{},this._delegatedListeners[t]=this._delegatedListeners[t]||[],this._delegatedListeners[t].push(i),i.delegates)this.on(a,i.delegates[a]);return this},i.prototype.once=function(t,e,r){if(void 0===r)return n.prototype.once.call(this,t,e);var i=this._createDelegatedListener(t,e,r);for(var a in i.delegates)this.once(a,i.delegates[a]);return this},i.prototype.off=function(t,e,r){var i=this;if(void 0===r)return n.prototype.off.call(this,t,e);return this._delegatedListeners&&this._delegatedListeners[t]&&function(n){for(var a=n[t],o=0;o180;){var s=n.locationPoint(e);if(s.x>=0&&s.y>=0&&s.x<=n.width&&s.y<=n.height)break;e.lng>n.center.lng?e.lng-=360:e.lng+=360}return e}Ui.prototype.down=function(t,e){this.mouseRotate.mousedown(t,e),this.mousePitch&&this.mousePitch.mousedown(t,e),r.disableDrag()},Ui.prototype.move=function(t,e){var r=this.map,n=this.mouseRotate.mousemoveWindow(t,e);if(n&&n.bearingDelta&&r.setBearing(r.getBearing()+n.bearingDelta),this.mousePitch){var i=this.mousePitch.mousemoveWindow(t,e);i&&i.pitchDelta&&r.setPitch(r.getPitch()+i.pitchDelta)}},Ui.prototype.off=function(){var t=this.element;r.removeEventListener(t,\"mousedown\",this.mousedown),r.removeEventListener(t,\"touchstart\",this.touchstart,{passive:!1}),r.removeEventListener(t,\"touchmove\",this.touchmove),r.removeEventListener(t,\"touchend\",this.touchend),r.removeEventListener(t,\"touchcancel\",this.reset),this.offTemp()},Ui.prototype.offTemp=function(){r.enableDrag(),r.removeEventListener(t.window,\"mousemove\",this.mousemove),r.removeEventListener(t.window,\"mouseup\",this.mouseup)},Ui.prototype.mousedown=function(e){this.down(t.extend({},e,{ctrlKey:!0,preventDefault:function(){return e.preventDefault()}}),r.mousePos(this.element,e)),r.addEventListener(t.window,\"mousemove\",this.mousemove),r.addEventListener(t.window,\"mouseup\",this.mouseup)},Ui.prototype.mousemove=function(t){this.move(t,r.mousePos(this.element,t))},Ui.prototype.mouseup=function(t){this.mouseRotate.mouseupWindow(t),this.mousePitch&&this.mousePitch.mouseupWindow(t),this.offTemp()},Ui.prototype.touchstart=function(t){1!==t.targetTouches.length?this.reset():(this._startPos=this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.down({type:\"mousedown\",button:0,ctrlKey:!0,preventDefault:function(){return t.preventDefault()}},this._startPos))},Ui.prototype.touchmove=function(t){1!==t.targetTouches.length?this.reset():(this._lastPos=r.touchPos(this.element,t.targetTouches)[0],this.move({preventDefault:function(){return t.preventDefault()}},this._lastPos))},Ui.prototype.touchend=function(t){0===t.targetTouches.length&&this._startPos&&this._lastPos&&this._startPos.dist(this._lastPos)=r}this._isDragging&&(this._pos=e.point.sub(this._positionDelta),this._lngLat=this._map.unproject(this._pos),this.setLngLat(this._lngLat),this._element.style.pointerEvents=\"none\",\"pending\"===this._state&&(this._state=\"active\",this.fire(new t.Event(\"dragstart\"))),this.fire(new t.Event(\"drag\")))},n.prototype._onUp=function(){this._element.style.pointerEvents=\"auto\",this._positionDelta=null,this._pointerdownPos=null,this._isDragging=!1,this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),\"active\"===this._state&&this.fire(new t.Event(\"dragend\")),this._state=\"inactive\"},n.prototype._addDragHandler=function(t){this._element.contains(t.originalEvent.target)&&(t.preventDefault(),this._positionDelta=t.point.sub(this._pos).add(this._offset),this._pointerdownPos=t.point,this._state=\"pending\",this._map.on(\"mousemove\",this._onMove),this._map.on(\"touchmove\",this._onMove),this._map.once(\"mouseup\",this._onUp),this._map.once(\"touchend\",this._onUp))},n.prototype.setDraggable=function(t){return this._draggable=!!t,this._map&&(t?(this._map.on(\"mousedown\",this._addDragHandler),this._map.on(\"touchstart\",this._addDragHandler)):(this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler))),this},n.prototype.isDraggable=function(){return this._draggable},n.prototype.setRotation=function(t){return this._rotation=t||0,this._update(),this},n.prototype.getRotation=function(){return this._rotation},n.prototype.setRotationAlignment=function(t){return this._rotationAlignment=t||\"auto\",this._update(),this},n.prototype.getRotationAlignment=function(){return this._rotationAlignment},n.prototype.setPitchAlignment=function(t){return this._pitchAlignment=t&&\"auto\"!==t?t:this._rotationAlignment,this._update(),this},n.prototype.getPitchAlignment=function(){return this._pitchAlignment},n}(t.Evented),Wi={positionOptions:{enableHighAccuracy:!1,maximumAge:0,timeout:6e3},fitBoundsOptions:{maxZoom:15},trackUserLocation:!1,showAccuracyCircle:!0,showUserLocation:!0};var Yi=0,Xi=!1,$i=function(e){function n(r){e.call(this),this.options=t.extend({},Wi,r),t.bindAll([\"_onSuccess\",\"_onError\",\"_onZoom\",\"_finish\",\"_setupUI\",\"_updateCamera\",\"_updateMarker\"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.onAdd=function(e){return this._map=e,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),n=this._setupUI,void 0!==Gi?n(Gi):void 0!==t.window.navigator.permissions?t.window.navigator.permissions.query({name:\"geolocation\"}).then((function(t){Gi=\"denied\"!==t.state,n(Gi)})):(Gi=!!t.window.navigator.geolocation,n(Gi)),this._container;var n},n.prototype.onRemove=function(){void 0!==this._geolocationWatchID&&(t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0),this.options.showUserLocation&&this._userLocationDotMarker&&this._userLocationDotMarker.remove(),this.options.showAccuracyCircle&&this._accuracyCircleMarker&&this._accuracyCircleMarker.remove(),r.remove(this._container),this._map.off(\"zoom\",this._onZoom),this._map=void 0,Yi=0,Xi=!1},n.prototype._isOutOfMapMaxBounds=function(t){var e=this._map.getMaxBounds(),r=t.coords;return e&&(r.longitudee.getEast()||r.latitudee.getNorth())},n.prototype._setErrorState=function(){switch(this._watchState){case\"WAITING_ACTIVE\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\");break;case\"ACTIVE_LOCK\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\");break;case\"BACKGROUND\":this._watchState=\"BACKGROUND_ERROR\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\")}},n.prototype._onSuccess=function(e){if(this._map){if(this._isOutOfMapMaxBounds(e))return this._setErrorState(),this.fire(new t.Event(\"outofmaxbounds\",e)),this._updateMarker(),void this._finish();if(this.options.trackUserLocation)switch(this._lastKnownPosition=e,this._watchState){case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"BACKGROUND\":case\"BACKGROUND_ERROR\":this._watchState=\"BACKGROUND\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\")}this.options.showUserLocation&&\"OFF\"!==this._watchState&&this._updateMarker(e),this.options.trackUserLocation&&\"ACTIVE_LOCK\"!==this._watchState||this._updateCamera(e),this.options.showUserLocation&&this._dotElement.classList.remove(\"mapboxgl-user-location-dot-stale\"),this.fire(new t.Event(\"geolocate\",e)),this._finish()}},n.prototype._updateCamera=function(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude),n=e.coords.accuracy,i=this._map.getBearing(),a=t.extend({bearing:i},this.options.fitBoundsOptions);this._map.fitBounds(r.toBounds(n),a,{geolocateSource:!0})},n.prototype._updateMarker=function(e){if(e){var r=new t.LngLat(e.coords.longitude,e.coords.latitude);this._accuracyCircleMarker.setLngLat(r).addTo(this._map),this._userLocationDotMarker.setLngLat(r).addTo(this._map),this._accuracy=e.coords.accuracy,this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()}else this._userLocationDotMarker.remove(),this._accuracyCircleMarker.remove()},n.prototype._updateCircleRadius=function(){var t=this._map._container.clientHeight/2,e=this._map.unproject([0,t]),r=this._map.unproject([1,t]),n=e.distanceTo(r),i=Math.ceil(2*this._accuracy/n);this._circleElement.style.width=i+\"px\",this._circleElement.style.height=i+\"px\"},n.prototype._onZoom=function(){this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()},n.prototype._onError=function(e){if(this._map){if(this.options.trackUserLocation)if(1===e.code){this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this._geolocateButton.disabled=!0;var r=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.title=r,this._geolocateButton.setAttribute(\"aria-label\",r),void 0!==this._geolocationWatchID&&this._clearWatch()}else{if(3===e.code&&Xi)return;this._setErrorState()}\"OFF\"!==this._watchState&&this.options.showUserLocation&&this._dotElement.classList.add(\"mapboxgl-user-location-dot-stale\"),this.fire(new t.Event(\"error\",e)),this._finish()}},n.prototype._finish=function(){this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0},n.prototype._setupUI=function(e){var n=this;if(this._container.addEventListener(\"contextmenu\",(function(t){return t.preventDefault()})),this._geolocateButton=r.create(\"button\",\"mapboxgl-ctrl-geolocate\",this._container),r.create(\"span\",\"mapboxgl-ctrl-icon\",this._geolocateButton).setAttribute(\"aria-hidden\",!0),this._geolocateButton.type=\"button\",!1===e){t.warnOnce(\"Geolocation support is not available so the GeolocateControl will be disabled.\");var i=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.disabled=!0,this._geolocateButton.title=i,this._geolocateButton.setAttribute(\"aria-label\",i)}else{var a=this._map._getUIString(\"GeolocateControl.FindMyLocation\");this._geolocateButton.title=a,this._geolocateButton.setAttribute(\"aria-label\",a)}this.options.trackUserLocation&&(this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this._watchState=\"OFF\"),this.options.showUserLocation&&(this._dotElement=r.create(\"div\",\"mapboxgl-user-location-dot\"),this._userLocationDotMarker=new Zi(this._dotElement),this._circleElement=r.create(\"div\",\"mapboxgl-user-location-accuracy-circle\"),this._accuracyCircleMarker=new Zi({element:this._circleElement,pitchAlignment:\"map\"}),this.options.trackUserLocation&&(this._watchState=\"OFF\"),this._map.on(\"zoom\",this._onZoom)),this._geolocateButton.addEventListener(\"click\",this.trigger.bind(this)),this._setup=!0,this.options.trackUserLocation&&this._map.on(\"movestart\",(function(e){var r=e.originalEvent&&\"resize\"===e.originalEvent.type;e.geolocateSource||\"ACTIVE_LOCK\"!==n._watchState||r||(n._watchState=\"BACKGROUND\",n._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\"),n._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),n.fire(new t.Event(\"trackuserlocationend\")))}))},n.prototype.trigger=function(){if(!this._setup)return t.warnOnce(\"Geolocate control triggered before added to a map\"),!1;if(this.options.trackUserLocation){switch(this._watchState){case\"OFF\":this._watchState=\"WAITING_ACTIVE\",this.fire(new t.Event(\"trackuserlocationstart\"));break;case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":case\"BACKGROUND_ERROR\":Yi--,Xi=!1,this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background-error\"),this.fire(new t.Event(\"trackuserlocationend\"));break;case\"BACKGROUND\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-background\"),this._lastKnownPosition&&this._updateCamera(this._lastKnownPosition),this.fire(new t.Event(\"trackuserlocationstart\"))}switch(this._watchState){case\"WAITING_ACTIVE\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"ACTIVE_LOCK\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active\");break;case\"ACTIVE_ERROR\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-active-error\");break;case\"BACKGROUND\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background\");break;case\"BACKGROUND_ERROR\":this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-background-error\")}if(\"OFF\"===this._watchState&&void 0!==this._geolocationWatchID)this._clearWatch();else if(void 0===this._geolocationWatchID){var e;this._geolocateButton.classList.add(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"true\"),++Yi>1?(e={maximumAge:6e5,timeout:0},Xi=!0):(e=this.options.positionOptions,Xi=!1),this._geolocationWatchID=t.window.navigator.geolocation.watchPosition(this._onSuccess,this._onError,e)}}else t.window.navigator.geolocation.getCurrentPosition(this._onSuccess,this._onError,this.options.positionOptions),this._timeoutId=setTimeout(this._finish,1e4);return!0},n.prototype._clearWatch=function(){t.window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0,this._geolocateButton.classList.remove(\"mapboxgl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this.options.showUserLocation&&this._updateMarker(null)},n}(t.Evented),Ji={maxWidth:100,unit:\"metric\"},Ki=function(e){this.options=t.extend({},Ji,e),t.bindAll([\"_onMove\",\"setUnit\"],this)};function Qi(t,e,r){var n=r&&r.maxWidth||100,i=t._container.clientHeight/2,a=t.unproject([0,i]),o=t.unproject([n,i]),s=a.distanceTo(o);if(r&&\"imperial\"===r.unit){var l=3.2808*s;l>5280?ta(e,n,l/5280,t._getUIString(\"ScaleControl.Miles\")):ta(e,n,l,t._getUIString(\"ScaleControl.Feet\"))}else r&&\"nautical\"===r.unit?ta(e,n,s/1852,t._getUIString(\"ScaleControl.NauticalMiles\")):s>=1e3?ta(e,n,s/1e3,t._getUIString(\"ScaleControl.Kilometers\")):ta(e,n,s,t._getUIString(\"ScaleControl.Meters\"))}function ta(t,e,r,n){var i,a,o,s=(i=r,(a=Math.pow(10,(\"\"+Math.floor(i)).length-1))*((o=i/a)>=10?10:o>=5?5:o>=3?3:o>=2?2:o>=1?1:function(t){var e=Math.pow(10,Math.ceil(-Math.log(t)/Math.LN10));return Math.round(t*e)/e}(o))),l=s/r;t.style.width=e*l+\"px\",t.innerHTML=s+\" \"+n}Ki.prototype.getDefaultPosition=function(){return\"bottom-left\"},Ki.prototype._onMove=function(){Qi(this._map,this._container,this.options)},Ki.prototype.onAdd=function(t){return this._map=t,this._container=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-scale\",t.getContainer()),this._map.on(\"move\",this._onMove),this._onMove(),this._container},Ki.prototype.onRemove=function(){r.remove(this._container),this._map.off(\"move\",this._onMove),this._map=void 0},Ki.prototype.setUnit=function(t){this.options.unit=t,Qi(this._map,this._container,this.options)};var ea=function(e){this._fullscreen=!1,e&&e.container&&(e.container instanceof t.window.HTMLElement?this._container=e.container:t.warnOnce(\"Full screen control 'container' must be a DOM element.\")),t.bindAll([\"_onClickFullscreen\",\"_changeIcon\"],this),\"onfullscreenchange\"in t.window.document?this._fullscreenchange=\"fullscreenchange\":\"onmozfullscreenchange\"in t.window.document?this._fullscreenchange=\"mozfullscreenchange\":\"onwebkitfullscreenchange\"in t.window.document?this._fullscreenchange=\"webkitfullscreenchange\":\"onmsfullscreenchange\"in t.window.document&&(this._fullscreenchange=\"MSFullscreenChange\")};ea.prototype.onAdd=function(e){return this._map=e,this._container||(this._container=this._map.getContainer()),this._controlContainer=r.create(\"div\",\"mapboxgl-ctrl mapboxgl-ctrl-group\"),this._checkFullscreenSupport()?this._setupUI():(this._controlContainer.style.display=\"none\",t.warnOnce(\"This device does not support fullscreen mode.\")),this._controlContainer},ea.prototype.onRemove=function(){r.remove(this._controlContainer),this._map=null,t.window.document.removeEventListener(this._fullscreenchange,this._changeIcon)},ea.prototype._checkFullscreenSupport=function(){return!!(t.window.document.fullscreenEnabled||t.window.document.mozFullScreenEnabled||t.window.document.msFullscreenEnabled||t.window.document.webkitFullscreenEnabled)},ea.prototype._setupUI=function(){var e=this._fullscreenButton=r.create(\"button\",\"mapboxgl-ctrl-fullscreen\",this._controlContainer);r.create(\"span\",\"mapboxgl-ctrl-icon\",e).setAttribute(\"aria-hidden\",!0),e.type=\"button\",this._updateTitle(),this._fullscreenButton.addEventListener(\"click\",this._onClickFullscreen),t.window.document.addEventListener(this._fullscreenchange,this._changeIcon)},ea.prototype._updateTitle=function(){var t=this._getTitle();this._fullscreenButton.setAttribute(\"aria-label\",t),this._fullscreenButton.title=t},ea.prototype._getTitle=function(){return this._map._getUIString(this._isFullscreen()?\"FullscreenControl.Exit\":\"FullscreenControl.Enter\")},ea.prototype._isFullscreen=function(){return this._fullscreen},ea.prototype._changeIcon=function(){(t.window.document.fullscreenElement||t.window.document.mozFullScreenElement||t.window.document.webkitFullscreenElement||t.window.document.msFullscreenElement)===this._container!==this._fullscreen&&(this._fullscreen=!this._fullscreen,this._fullscreenButton.classList.toggle(\"mapboxgl-ctrl-shrink\"),this._fullscreenButton.classList.toggle(\"mapboxgl-ctrl-fullscreen\"),this._updateTitle())},ea.prototype._onClickFullscreen=function(){this._isFullscreen()?t.window.document.exitFullscreen?t.window.document.exitFullscreen():t.window.document.mozCancelFullScreen?t.window.document.mozCancelFullScreen():t.window.document.msExitFullscreen?t.window.document.msExitFullscreen():t.window.document.webkitCancelFullScreen&&t.window.document.webkitCancelFullScreen():this._container.requestFullscreen?this._container.requestFullscreen():this._container.mozRequestFullScreen?this._container.mozRequestFullScreen():this._container.msRequestFullscreen?this._container.msRequestFullscreen():this._container.webkitRequestFullscreen&&this._container.webkitRequestFullscreen()};var ra={closeButton:!0,closeOnClick:!0,focusAfterOpen:!0,className:\"\",maxWidth:\"240px\"},na=[\"a[href]\",\"[tabindex]:not([tabindex='-1'])\",\"[contenteditable]:not([contenteditable='false'])\",\"button:not([disabled])\",\"input:not([disabled])\",\"select:not([disabled])\",\"textarea:not([disabled])\"].join(\", \"),ia=function(e){function n(r){e.call(this),this.options=t.extend(Object.create(ra),r),t.bindAll([\"_update\",\"_onClose\",\"remove\",\"_onMouseMove\",\"_onMouseUp\",\"_onDrag\"],this)}return e&&(n.__proto__=e),n.prototype=Object.create(e&&e.prototype),n.prototype.constructor=n,n.prototype.addTo=function(e){return this._map&&this.remove(),this._map=e,this.options.closeOnClick&&this._map.on(\"click\",this._onClose),this.options.closeOnMove&&this._map.on(\"move\",this._onClose),this._map.on(\"remove\",this.remove),this._update(),this._focusFirstElement(),this._trackPointer?(this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"mouseup\",this._onMouseUp),this._container&&this._container.classList.add(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"mapboxgl-track-pointer\")):this._map.on(\"move\",this._update),this.fire(new t.Event(\"open\")),this},n.prototype.isOpen=function(){return!!this._map},n.prototype.remove=function(){return this._content&&r.remove(this._content),this._container&&(r.remove(this._container),delete this._container),this._map&&(this._map.off(\"move\",this._update),this._map.off(\"move\",this._onClose),this._map.off(\"click\",this._onClose),this._map.off(\"remove\",this.remove),this._map.off(\"mousemove\",this._onMouseMove),this._map.off(\"mouseup\",this._onMouseUp),this._map.off(\"drag\",this._onDrag),delete this._map),this.fire(new t.Event(\"close\")),this},n.prototype.getLngLat=function(){return this._lngLat},n.prototype.setLngLat=function(e){return this._lngLat=t.LngLat.convert(e),this._pos=null,this._trackPointer=!1,this._update(),this._map&&(this._map.on(\"move\",this._update),this._map.off(\"mousemove\",this._onMouseMove),this._container&&this._container.classList.remove(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.remove(\"mapboxgl-track-pointer\")),this},n.prototype.trackPointer=function(){return this._trackPointer=!0,this._pos=null,this._update(),this._map&&(this._map.off(\"move\",this._update),this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"drag\",this._onDrag),this._container&&this._container.classList.add(\"mapboxgl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"mapboxgl-track-pointer\")),this},n.prototype.getElement=function(){return this._container},n.prototype.setText=function(e){return this.setDOMContent(t.window.document.createTextNode(e))},n.prototype.setHTML=function(e){var r,n=t.window.document.createDocumentFragment(),i=t.window.document.createElement(\"body\");for(i.innerHTML=e;r=i.firstChild;)n.appendChild(r);return this.setDOMContent(n)},n.prototype.getMaxWidth=function(){return this._container&&this._container.style.maxWidth},n.prototype.setMaxWidth=function(t){return this.options.maxWidth=t,this._update(),this},n.prototype.setDOMContent=function(t){if(this._content)for(;this._content.hasChildNodes();)this._content.firstChild&&this._content.removeChild(this._content.firstChild);else this._content=r.create(\"div\",\"mapboxgl-popup-content\",this._container);return this._content.appendChild(t),this._createCloseButton(),this._update(),this._focusFirstElement(),this},n.prototype.addClassName=function(t){this._container&&this._container.classList.add(t)},n.prototype.removeClassName=function(t){this._container&&this._container.classList.remove(t)},n.prototype.setOffset=function(t){return this.options.offset=t,this._update(),this},n.prototype.toggleClassName=function(t){if(this._container)return this._container.classList.toggle(t)},n.prototype._createCloseButton=function(){this.options.closeButton&&(this._closeButton=r.create(\"button\",\"mapboxgl-popup-close-button\",this._content),this._closeButton.type=\"button\",this._closeButton.setAttribute(\"aria-label\",\"Close popup\"),this._closeButton.innerHTML=\"×\",this._closeButton.addEventListener(\"click\",this._onClose))},n.prototype._onMouseUp=function(t){this._update(t.point)},n.prototype._onMouseMove=function(t){this._update(t.point)},n.prototype._onDrag=function(t){this._update(t.point)},n.prototype._update=function(t){var e=this,n=this._lngLat||this._trackPointer;if(this._map&&n&&this._content&&(this._container||(this._container=r.create(\"div\",\"mapboxgl-popup\",this._map.getContainer()),this._tip=r.create(\"div\",\"mapboxgl-popup-tip\",this._container),this._container.appendChild(this._content),this.options.className&&this.options.className.split(\" \").forEach((function(t){return e._container.classList.add(t)})),this._trackPointer&&this._container.classList.add(\"mapboxgl-popup-track-pointer\")),this.options.maxWidth&&this._container.style.maxWidth!==this.options.maxWidth&&(this._container.style.maxWidth=this.options.maxWidth),this._map.transform.renderWorldCopies&&!this._trackPointer&&(this._lngLat=Vi(this._lngLat,this._pos,this._map.transform)),!this._trackPointer||t)){var i=this._pos=this._trackPointer&&t?t:this._map.project(this._lngLat),a=this.options.anchor,o=aa(this.options.offset);if(!a){var s,l=this._container.offsetWidth,c=this._container.offsetHeight;s=i.y+o.bottom.ythis._map.transform.height-c?[\"bottom\"]:[],i.xthis._map.transform.width-l/2&&s.push(\"right\"),a=0===s.length?\"bottom\":s.join(\"-\")}var u=i.add(o[a]).round();r.setTransform(this._container,qi[a]+\" translate(\"+u.x+\"px,\"+u.y+\"px)\"),Hi(this._container,a,\"popup\")}},n.prototype._focusFirstElement=function(){if(this.options.focusAfterOpen&&this._container){var t=this._container.querySelector(na);t&&t.focus()}},n.prototype._onClose=function(){this.remove()},n}(t.Evented);function aa(e){if(e){if(\"number\"==typeof e){var r=Math.round(Math.sqrt(.5*Math.pow(e,2)));return{center:new t.Point(0,0),top:new t.Point(0,e),\"top-left\":new t.Point(r,r),\"top-right\":new t.Point(-r,r),bottom:new t.Point(0,-e),\"bottom-left\":new t.Point(r,-r),\"bottom-right\":new t.Point(-r,-r),left:new t.Point(e,0),right:new t.Point(-e,0)}}if(e instanceof t.Point||Array.isArray(e)){var n=t.Point.convert(e);return{center:n,top:n,\"top-left\":n,\"top-right\":n,bottom:n,\"bottom-left\":n,\"bottom-right\":n,left:n,right:n}}return{center:t.Point.convert(e.center||[0,0]),top:t.Point.convert(e.top||[0,0]),\"top-left\":t.Point.convert(e[\"top-left\"]||[0,0]),\"top-right\":t.Point.convert(e[\"top-right\"]||[0,0]),bottom:t.Point.convert(e.bottom||[0,0]),\"bottom-left\":t.Point.convert(e[\"bottom-left\"]||[0,0]),\"bottom-right\":t.Point.convert(e[\"bottom-right\"]||[0,0]),left:t.Point.convert(e.left||[0,0]),right:t.Point.convert(e.right||[0,0])}}return aa(new t.Point(0,0))}var oa={version:t.version,supported:e,setRTLTextPlugin:t.setRTLTextPlugin,getRTLTextPluginStatus:t.getRTLTextPluginStatus,Map:Fi,NavigationControl:ji,GeolocateControl:$i,AttributionControl:Ei,ScaleControl:Ki,FullscreenControl:ea,Popup:ia,Marker:Zi,Style:We,LngLat:t.LngLat,LngLatBounds:t.LngLatBounds,Point:t.Point,MercatorCoordinate:t.MercatorCoordinate,Evented:t.Evented,config:t.config,prewarm:function(){jt().acquire(Rt)},clearPrewarmedResources:function(){var t=Bt;t&&(t.isPreloaded()&&1===t.numActive()?(t.release(Rt),Bt=null):console.warn(\"Could not clear WebWorkers since there are active Map instances that still reference it. The pre-warmed WebWorker pool can only be cleared when all map instances have been removed with map.remove()\"))},get accessToken(){return t.config.ACCESS_TOKEN},set accessToken(e){t.config.ACCESS_TOKEN=e},get baseApiUrl(){return t.config.API_URL},set baseApiUrl(e){t.config.API_URL=e},get workerCount(){return Ft.workerCount},set workerCount(t){Ft.workerCount=t},get maxParallelImageRequests(){return t.config.MAX_PARALLEL_IMAGE_REQUESTS},set maxParallelImageRequests(e){t.config.MAX_PARALLEL_IMAGE_REQUESTS=e},clearStorage:function(e){t.clearTileCache(e)},workerUrl:\"\"};return oa})),r}()},27549:function(t,e,r){\"use strict\";t.exports=r(55366)},55366:function(t,e,r){\"use strict\";var n=r(31625),i=r(75144),a=r(5137),o=r(78112),s=r(6807),l=r(68650),c=r(83473),u=r(60201),h=r(10275),f=r(62914);function p(t,e){for(var r=e[0],n=e[1],a=1/(e[2]-r),o=1/(e[3]-n),s=new Array(t.length),l=0,c=t.length/2;l>>1;e.dtype||(e.dtype=\"array\"),\"string\"==typeof e.dtype?d=new(h(e.dtype))(g):e.dtype&&(d=e.dtype,Array.isArray(d)&&(d.length=g));for(var y=0;yr||s>1073741824){for(var f=0;fr+i||M>n+i||S=L||o===s)){var l=v[a];void 0===s&&(s=l.length);for(var c=o;c=g&&h<=w&&f>=y&&f<=T&&I.push(u)}var p=x[a],d=p[4*o+0],m=p[4*o+1],_=p[4*o+2],b=p[4*o+3],k=function(t,e){for(var r=null,n=0;null===r;)if(r=t[4*e+n],++n>t.length)return null;return r}(p,o+1),E=.5*i,P=a+1;e(r,n,E,P,d,m||_||b||k),e(r,n+E,E,P,m,_||b||k),e(r+E,n,E,P,_,b||k),e(r+E,n+E,E,P,b,k)}}(0,0,1,0,0,1),I},d;function E(t,e,r){for(var n=1,i=.5,a=.5,o=.5,s=0;si&&(i=t[o]),t[o]1?r-1:0),i=1;i1?r-1:0),i=1;i1?r-1:0),i=1;i1?r-1:0),i=1;it.length)&&(r=t.length),t.substring(r-e.length,r)===e}var x=\"\",_=\"\",b=\"\",w=\"\",T={deepStrictEqual:\"Expected values to be strictly deep-equal:\",strictEqual:\"Expected values to be strictly equal:\",strictEqualObject:'Expected \"actual\" to be reference-equal to \"expected\":',deepEqual:\"Expected values to be loosely deep-equal:\",equal:\"Expected values to be 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float fontSize, charStep, em, align, baseline;\\n\\t\\t\\tuniform vec4 viewport;\\n\\t\\t\\tuniform vec4 color;\\n\\t\\t\\tuniform vec2 atlasSize, atlasDim, scale, translate, positionOffset;\\n\\t\\t\\tvarying vec2 charCoord, charId;\\n\\t\\t\\tvarying float charWidth;\\n\\t\\t\\tvarying vec4 fontColor;\\n\\t\\t\\tvoid main () {\\n\\t\\t\\t\\tvec2 offset = floor(em * (vec2(align + charOffset, baseline)\\n\\t\\t\\t\\t\\t+ vec2(positionOffset.x, -positionOffset.y)))\\n\\t\\t\\t\\t\\t/ (viewport.zw * scale.xy);\\n\\n\\t\\t\\t\\tvec2 position = (position + translate) * scale;\\n\\t\\t\\t\\tposition += offset * scale;\\n\\n\\t\\t\\t\\tcharCoord = position * viewport.zw + viewport.xy;\\n\\n\\t\\t\\t\\tgl_Position = vec4(position * 2. - 1., 0, 1);\\n\\n\\t\\t\\t\\tgl_PointSize = charStep;\\n\\n\\t\\t\\t\\tcharId.x = mod(char, atlasDim.x);\\n\\t\\t\\t\\tcharId.y = floor(char / atlasDim.x);\\n\\n\\t\\t\\t\\tcharWidth = width * em;\\n\\n\\t\\t\\t\\tfontColor = color / 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o=r.metrics;e.shader.atlas[a]=e.fontAtlas[i]={fontString:a,step:2*Math.ceil(e.fontSize[i]*o.bottom*.5),em:e.fontSize[i],cols:0,rows:0,height:0,width:0,chars:[],ids:{},texture:e.regl.texture()}}null==t.text&&(t.text=e.text)})),\"string\"==typeof t.text&&t.position&&t.position.length>2){for(var s=Array(.5*t.position.length),f=0;f2){for(var w=!t.position[0].length,k=u.mallocFloat(2*this.count),A=0,M=0;A1?e.align[r]:e.align[0]:e.align;if(\"number\"==typeof n)return n;switch(n){case\"right\":case\"end\":return-t;case\"center\":case\"centre\":case\"middle\":return.5*-t}return 0}))),null==this.baseline&&null==t.baseline&&(t.baseline=0),null!=t.baseline&&(this.baseline=t.baseline,Array.isArray(this.baseline)||(this.baseline=[this.baseline]),this.baselineOffset=this.baseline.map((function(t,r){var n=(e.font[r]||e.font[0]).metrics,i=0;return i+=.5*n.bottom,-1*(i+=\"number\"==typeof t?t-n.baseline:-n[t])}))),null!=t.color)if(t.color||(t.color=\"transparent\"),\"string\"!=typeof 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strict\";t.exports={avif:r(31149),bmp:r(78218),gif:r(37495),ico:r(88708),jpeg:r(13827),png:r(46594),psd:r(13198),svg:r(94203),tiff:r(46966),webp:r(88023)}},19490:function(t,e,r){\"use strict\";var n=r(43751);t.exports=function(t){return function(t){for(var e=Object.keys(n),r=0;r1)for(var r=1;r1&&(t.scaleRatio=[t.scale[0]*t.viewport.width,t.scale[1]*t.viewport.height],r(t),t.after&&t.after(t))}function T(t){if(t){null!=t.length?\"number\"==typeof t[0]&&(t=[{positions:t}]):Array.isArray(t)||(t=[t]);var e=0,r=0;if(_.groups=x=t.map((function(t,c){var u=x[c];return t?(\"function\"==typeof t?t={after:t}:\"number\"==typeof t[0]&&(t={positions:t}),t=o(t,{color:\"color colors fill\",capSize:\"capSize cap capsize cap-size\",lineWidth:\"lineWidth line-width width line thickness\",opacity:\"opacity alpha\",range:\"range dataBox\",viewport:\"viewport viewBox\",errors:\"errors error\",positions:\"positions position data points\"}),u||(x[c]=u={id:c,scale:null,translate:null,scaleFract:null,translateFract:null,draw:!0},t=s({},v,t)),a(u,t,[{lineWidth:function(t){return.5*+t},capSize:function(t){return.5*+t},opacity:parseFloat,errors:function(t){return t=l(t),r+=t.length,t},positions:function(t,r){return t=l(t,\"float64\"),r.count=Math.floor(t.length/2),r.bounds=n(t,2),r.offset=e,e+=r.count,t}},{color:function(t,e){var r=e.count;if(t||(t=\"transparent\"),!Array.isArray(t)||\"number\"==typeof t[0]){var n=t;t=Array(r);for(var a=0;a 0. && baClipping < length(normalWidth * endBotJoin)) {\\n\\t\\t//handle miter clipping\\n\\t\\tbTopCoord -= normalWidth * endTopJoin;\\n\\t\\tbTopCoord += normalize(endTopJoin * normalWidth) * baClipping;\\n\\t}\\n\\n\\tif (nextReverse) {\\n\\t\\t//make join rectangular\\n\\t\\tvec2 miterShift = normalWidth * endJoinDirection * miterLimit * .5;\\n\\t\\tfloat normalAdjust = 1. - min(miterLimit / endMiterRatio, 1.);\\n\\t\\tbBotCoord = bCoord + miterShift - normalAdjust * normalWidth * currNormal * .5;\\n\\t\\tbTopCoord = bCoord + miterShift + normalAdjust * normalWidth * currNormal * .5;\\n\\t}\\n\\telse if (!prevReverse && abClipping > 0. && abClipping < length(normalWidth * startBotJoin)) {\\n\\t\\t//handle miter clipping\\n\\t\\taBotCoord -= normalWidth * startBotJoin;\\n\\t\\taBotCoord += normalize(startBotJoin * normalWidth) * abClipping;\\n\\t}\\n\\n\\tvec2 aTopPosition = (aTopCoord) * adjustedScale + translate;\\n\\tvec2 aBotPosition = (aBotCoord) * adjustedScale + translate;\\n\\n\\tvec2 bTopPosition = (bTopCoord) * adjustedScale + translate;\\n\\tvec2 bBotPosition = (bBotCoord) * adjustedScale + translate;\\n\\n\\t//position is normalized 0..1 coord on the screen\\n\\tvec2 position = (aTopPosition * lineTop + aBotPosition * lineBot) * lineStart + (bTopPosition * lineTop + bBotPosition * lineBot) * lineEnd;\\n\\n\\tstartCoord = aCoord * scaleRatio + translate * viewport.zw + viewport.xy;\\n\\tendCoord = bCoord * scaleRatio + translate * viewport.zw + viewport.xy;\\n\\n\\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\\n\\n\\tenableStartMiter = step(dot(currTangent, prevTangent), .5);\\n\\tenableEndMiter = step(dot(currTangent, nextTangent), .5);\\n\\n\\t//bevel miter cutoffs\\n\\tif (miterMode == 1.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * miterLimit * .5;\\n\\t\\t\\tstartCutoff = vec4(aCoord, aCoord);\\n\\t\\t\\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\\n\\t\\t\\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tstartCutoff += viewport.xyxy;\\n\\t\\t\\tstartCutoff += startMiterWidth.xyxy;\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * miterLimit * .5;\\n\\t\\t\\tendCutoff = vec4(bCoord, bCoord);\\n\\t\\t\\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x) / scaleRatio;\\n\\t\\t\\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tendCutoff += viewport.xyxy;\\n\\t\\t\\tendCutoff += endMiterWidth.xyxy;\\n\\t\\t}\\n\\t}\\n\\n\\t//round miter cutoffs\\n\\telse if (miterMode == 2.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tvec2 startMiterWidth = vec2(startJoinDirection) * thickness * abs(dot(startJoinDirection, currNormal)) * .5;\\n\\t\\t\\tstartCutoff = vec4(aCoord, aCoord);\\n\\t\\t\\tstartCutoff.zw += vec2(-startJoinDirection.y, startJoinDirection.x) / scaleRatio;\\n\\t\\t\\tstartCutoff = startCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tstartCutoff += viewport.xyxy;\\n\\t\\t\\tstartCutoff += startMiterWidth.xyxy;\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tvec2 endMiterWidth = vec2(endJoinDirection) * thickness * abs(dot(endJoinDirection, currNormal)) * .5;\\n\\t\\t\\tendCutoff = vec4(bCoord, bCoord);\\n\\t\\t\\tendCutoff.zw += vec2(-endJoinDirection.y, endJoinDirection.x) / scaleRatio;\\n\\t\\t\\tendCutoff = endCutoff * scaleRatio.xyxy + translate.xyxy * viewport.zwzw;\\n\\t\\t\\tendCutoff += viewport.xyxy;\\n\\t\\t\\tendCutoff += endMiterWidth.xyxy;\\n\\t\\t}\\n\\t}\\n}\\n\",frag:\"\\nprecision highp float;\\n\\nuniform float dashLength, pixelRatio, thickness, opacity, id, miterMode;\\nuniform sampler2D dashTexture;\\n\\nvarying vec4 fragColor;\\nvarying vec2 tangent;\\nvarying vec4 startCutoff, endCutoff;\\nvarying vec2 startCoord, endCoord;\\nvarying float enableStartMiter, enableEndMiter;\\n\\nfloat distToLine(vec2 p, vec2 a, vec2 b) {\\n\\tvec2 diff = b - a;\\n\\tvec2 perp = normalize(vec2(-diff.y, diff.x));\\n\\treturn dot(p - a, perp);\\n}\\n\\nvoid main() {\\n\\tfloat alpha = 1., distToStart, distToEnd;\\n\\tfloat cutoff = thickness * .5;\\n\\n\\t//bevel miter\\n\\tif (miterMode == 1.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\\n\\t\\t\\tif (distToStart < -1.) {\\n\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\treturn;\\n\\t\\t\\t}\\n\\t\\t\\talpha *= min(max(distToStart + 1., 0.), 1.);\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\\n\\t\\t\\tif (distToEnd < -1.) {\\n\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\treturn;\\n\\t\\t\\t}\\n\\t\\t\\talpha *= min(max(distToEnd + 1., 0.), 1.);\\n\\t\\t}\\n\\t}\\n\\n\\t// round miter\\n\\telse if (miterMode == 2.) {\\n\\t\\tif (enableStartMiter == 1.) {\\n\\t\\t\\tdistToStart = distToLine(gl_FragCoord.xy, startCutoff.xy, startCutoff.zw);\\n\\t\\t\\tif (distToStart < 0.) {\\n\\t\\t\\t\\tfloat radius = length(gl_FragCoord.xy - startCoord);\\n\\n\\t\\t\\t\\tif(radius > cutoff + .5) {\\n\\t\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\t\\treturn;\\n\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\\n\\t\\t\\t}\\n\\t\\t}\\n\\n\\t\\tif (enableEndMiter == 1.) {\\n\\t\\t\\tdistToEnd = distToLine(gl_FragCoord.xy, endCutoff.xy, endCutoff.zw);\\n\\t\\t\\tif (distToEnd < 0.) {\\n\\t\\t\\t\\tfloat radius = length(gl_FragCoord.xy - endCoord);\\n\\n\\t\\t\\t\\tif(radius > cutoff + .5) {\\n\\t\\t\\t\\t\\tdiscard;\\n\\t\\t\\t\\t\\treturn;\\n\\t\\t\\t\\t}\\n\\n\\t\\t\\t\\talpha -= smoothstep(cutoff - .5, cutoff + .5, radius);\\n\\t\\t\\t}\\n\\t\\t}\\n\\t}\\n\\n\\tfloat t = fract(dot(tangent, gl_FragCoord.xy) / dashLength) * .5 + .25;\\n\\tfloat dash = texture2D(dashTexture, vec2(t, .5)).r;\\n\\n\\tgl_FragColor = fragColor;\\n\\tgl_FragColor.a *= alpha * opacity * dash;\\n}\\n\",attributes:{lineEnd:{buffer:r,divisor:0,stride:8,offset:0},lineTop:{buffer:r,divisor:0,stride:8,offset:4},aColor:{buffer:t.prop(\"colorBuffer\"),stride:4,offset:0,divisor:1},bColor:{buffer:t.prop(\"colorBuffer\"),stride:4,offset:4,divisor:1},prevCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:0,divisor:1},aCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:8,divisor:1},bCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:16,divisor:1},nextCoord:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:24,divisor:1}}},n))}catch(t){e=i}return{fill:t({primitive:\"triangle\",elements:function(t,e){return e.triangles},offset:0,vert:\"\\nprecision highp float;\\n\\nattribute vec2 position, positionFract;\\n\\nuniform vec4 color;\\nuniform vec2 scale, scaleFract, translate, translateFract;\\nuniform float pixelRatio, id;\\nuniform vec4 viewport;\\nuniform float opacity;\\n\\nvarying vec4 fragColor;\\n\\nconst float MAX_LINES = 256.;\\n\\nvoid main() {\\n\\tfloat depth = (MAX_LINES - 4. - id) / (MAX_LINES);\\n\\n\\tvec2 position = position * scale + translate\\n + positionFract * scale + translateFract\\n + position * scaleFract\\n + positionFract * scaleFract;\\n\\n\\tgl_Position = vec4(position * 2.0 - 1.0, depth, 1);\\n\\n\\tfragColor = color / 255.;\\n\\tfragColor.a *= opacity;\\n}\\n\",frag:\"\\nprecision highp float;\\nvarying vec4 fragColor;\\n\\nvoid main() {\\n\\tgl_FragColor = fragColor;\\n}\\n\",uniforms:{scale:t.prop(\"scale\"),color:t.prop(\"fill\"),scaleFract:t.prop(\"scaleFract\"),translateFract:t.prop(\"translateFract\"),translate:t.prop(\"translate\"),opacity:t.prop(\"opacity\"),pixelRatio:t.context(\"pixelRatio\"),id:t.prop(\"id\"),viewport:function(t,e){return[e.viewport.x,e.viewport.y,t.viewportWidth,t.viewportHeight]}},attributes:{position:{buffer:t.prop(\"positionBuffer\"),stride:8,offset:8},positionFract:{buffer:t.prop(\"positionFractBuffer\"),stride:8,offset:8}},blend:n.blend,depth:{enable:!1},scissor:n.scissor,stencil:n.stencil,viewport:n.viewport}),rect:i,miter:e}},g.defaults={dashes:null,join:\"miter\",miterLimit:1,thickness:10,cap:\"square\",color:\"black\",opacity:1,overlay:!1,viewport:null,range:null,close:!1,fill:null},g.prototype.render=function(){for(var t,e=[],r=arguments.length;r--;)e[r]=arguments[r];e.length&&(t=this).update.apply(t,e),this.draw()},g.prototype.draw=function(){for(var t=this,e=[],r=arguments.length;r--;)e[r]=arguments[r];return(e.length?e:this.passes).forEach((function(e,r){var n;if(e&&Array.isArray(e))return(n=t).draw.apply(n,e);\"number\"==typeof e&&(e=t.passes[e]),e&&e.count>1&&e.opacity&&(t.regl._refresh(),e.fill&&e.triangles&&e.triangles.length>2&&t.shaders.fill(e),e.thickness&&(e.scale[0]*e.viewport.width>g.precisionThreshold||e.scale[1]*e.viewport.height>g.precisionThreshold||\"rect\"===e.join||!e.join&&(e.thickness<=2||e.count>=g.maxPoints)?t.shaders.rect(e):t.shaders.miter(e)))})),this},g.prototype.update=function(t){var e=this;if(t){null!=t.length?\"number\"==typeof t[0]&&(t=[{positions:t}]):Array.isArray(t)||(t=[t]);var r=this.regl,u=this.gl;if(t.forEach((function(t,p){var y=e.passes[p];if(void 0!==t)if(null!==t){if(\"number\"==typeof t[0]&&(t={positions:t}),t=o(t,{positions:\"positions points data coords\",thickness:\"thickness lineWidth lineWidths line-width linewidth width stroke-width strokewidth strokeWidth\",join:\"lineJoin linejoin join type mode\",miterLimit:\"miterlimit miterLimit\",dashes:\"dash dashes dasharray dash-array dashArray\",color:\"color colour stroke colors colours stroke-color strokeColor\",fill:\"fill fill-color fillColor\",opacity:\"alpha opacity\",overlay:\"overlay crease overlap intersect\",close:\"closed close closed-path closePath\",range:\"range dataBox\",viewport:\"viewport viewBox\",hole:\"holes hole hollow\",splitNull:\"splitNull\"}),y||(e.passes[p]=y={id:p,scale:null,scaleFract:null,translate:null,translateFract:null,count:0,hole:[],depth:0,dashLength:1,dashTexture:r.texture({channels:1,data:new Uint8Array([255]),width:1,height:1,mag:\"linear\",min:\"linear\"}),colorBuffer:r.buffer({usage:\"dynamic\",type:\"uint8\",data:new Uint8Array}),positionBuffer:r.buffer({usage:\"dynamic\",type:\"float\",data:new Uint8Array}),positionFractBuffer:r.buffer({usage:\"dynamic\",type:\"float\",data:new Uint8Array})},t=a({},g.defaults,t)),null!=t.thickness&&(y.thickness=parseFloat(t.thickness)),null!=t.opacity&&(y.opacity=parseFloat(t.opacity)),null!=t.miterLimit&&(y.miterLimit=parseFloat(t.miterLimit)),null!=t.overlay&&(y.overlay=!!t.overlay,p=D}));(P=P.slice(0,R)).push(D)}for(var F=function(t){var e=k.slice(2*O,2*P[t]).concat(D?k.slice(2*D):[]),r=(y.hole||[]).map((function(e){return e-D+(P[t]-O)})),n=l(e,r);n=n.map((function(e){return e+O+(e+Ot.length)&&(e=t.length);for(var r=0,n=new Array(e);r 1.0 + delta) {\\n\\t\\tdiscard;\\n\\t}\\n\\n\\talpha -= smoothstep(1.0 - delta, 1.0 + delta, radius);\\n\\n\\tfloat borderRadius = fragBorderRadius;\\n\\tfloat ratio = smoothstep(borderRadius - delta, borderRadius + delta, radius);\\n\\tvec4 color = mix(fragColor, fragBorderColor, ratio);\\n\\tcolor.a *= alpha * opacity;\\n\\tgl_FragColor = color;\\n}\\n\"]),l.vert=h([\"precision highp float;\\n#define GLSLIFY 1\\n\\nattribute float x, y, xFract, yFract;\\nattribute float size, 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r=this._validateYear(t),n=t.month(),i=[\"一月\",\"二月\",\"三月\",\"四月\",\"五月\",\"六月\",\"七月\",\"八月\",\"九月\",\"十月\",\"十一月\",\"十二月\"][this.toChineseMonth(r,n)-1];return this.isIntercalaryMonth(r,n)&&(i=\"闰\"+i),i},monthNamesShort:function(t){if(\"string\"==typeof t){var e=t.match(u);return e?e[0]:\"\"}var r=this._validateYear(t),n=t.month(),i=[\"一\",\"二\",\"三\",\"四\",\"五\",\"六\",\"七\",\"八\",\"九\",\"十\",\"十一\",\"十二\"][this.toChineseMonth(r,n)-1];return this.isIntercalaryMonth(r,n)&&(i=\"闰\"+i),i},parseMonth:function(t,e){t=this._validateYear(t);var r,n=parseInt(e);if(isNaN(n))\"闰\"===e[0]&&(r=!0,e=e.substring(1)),\"月\"===e[e.length-1]&&(e=e.substring(0,e.length-1)),n=1+[\"一\",\"二\",\"三\",\"四\",\"五\",\"六\",\"七\",\"八\",\"九\",\"十\",\"十一\",\"十二\"].indexOf(e);else{var i=e[e.length-1];r=\"i\"===i||\"I\"===i}return this.toMonthIndex(t,n,r)},dayNames:[\"Sunday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:1,isRTL:!1}},_validateYear:function(t,e){if(t.year&&(t=t.year()),\"number\"!=typeof t||t<1888||t>2111)throw e.replace(/\\{0\\}/,this.local.name);return t},toMonthIndex:function(t,e,r){var i=this.intercalaryMonth(t);if(r&&e!==i||e<1||e>12)throw n.local.invalidMonth.replace(/\\{0\\}/,this.local.name);return i?!r&&e<=i?e-1:e:e-1},toChineseMonth:function(t,e){t.year&&(e=(t=t.year()).month());var r=this.intercalaryMonth(t);if(e<0||e>(r?12:11))throw n.local.invalidMonth.replace(/\\{0\\}/,this.local.name);return r?e>13},isIntercalaryMonth:function(t,e){t.year&&(e=(t=t.year()).month());var r=this.intercalaryMonth(t);return!!r&&r===e},leapYear:function(t){return 0!==this.intercalaryMonth(t)},weekOfYear:function(t,e,r){var i,o=this._validateYear(t,n.local.invalidyear),s=f[o-f[0]],l=s>>9&4095,c=s>>5&15,u=31&s;(i=a.newDate(l,c,u)).add(4-(i.dayOfWeek()||7),\"d\");var h=this.toJD(t,e,r)-i.toJD();return 1+Math.floor(h/7)},monthsInYear:function(t){return this.leapYear(t)?13:12},daysInMonth:function(t,e){t.year&&(e=t.month(),t=t.year()),t=this._validateYear(t);var r=h[t-h[0]];if(e>(r>>13?12:11))throw n.local.invalidMonth.replace(/\\{0\\}/,this.local.name);return r&1<<12-e?30:29},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,s,r,n.local.invalidDate);t=this._validateYear(i.year()),e=i.month(),r=i.day();var o=this.isIntercalaryMonth(t,e),s=this.toChineseMonth(t,e),l=function(t,e,r,n,i){var a,o,s;if(\"object\"==typeof t)o=t,a=e||{};else{var l;if(!(\"number\"==typeof t&&t>=1888&&t<=2111))throw new Error(\"Lunar year outside range 1888-2111\");if(!(\"number\"==typeof e&&e>=1&&e<=12))throw new Error(\"Lunar month outside range 1 - 12\");if(!(\"number\"==typeof r&&r>=1&&r<=30))throw new Error(\"Lunar day outside range 1 - 30\");\"object\"==typeof n?(l=!1,a=n):(l=!!n,a={}),o={year:t,month:e,day:r,isIntercalary:l}}s=o.day-1;var c,u=h[o.year-h[0]],p=u>>13;c=p&&(o.month>p||o.isIntercalary)?o.month:o.month-1;for(var d=0;d>9&4095,(m>>5&15)-1,(31&m)+s);return a.year=g.getFullYear(),a.month=1+g.getMonth(),a.day=g.getDate(),a}(t,s,r,o);return a.toJD(l.year,l.month,l.day)},fromJD:function(t){var e=a.fromJD(t),r=function(t,e,r,n){var i,a;if(\"object\"==typeof t)i=t,a=e||{};else{if(!(\"number\"==typeof t&&t>=1888&&t<=2111))throw new Error(\"Solar year outside range 1888-2111\");if(!(\"number\"==typeof e&&e>=1&&e<=12))throw new Error(\"Solar month outside range 1 - 12\");if(!(\"number\"==typeof r&&r>=1&&r<=31))throw new Error(\"Solar day outside range 1 - 31\");i={year:t,month:e,day:r},a={}}var o=f[i.year-f[0]],s=i.year<<9|i.month<<5|i.day;a.year=s>=o?i.year:i.year-1,o=f[a.year-f[0]];var l,c=new Date(o>>9&4095,(o>>5&15)-1,31&o),u=new Date(i.year,i.month-1,i.day);l=Math.round((u-c)/864e5);var p,d=h[a.year-h[0]];for(p=0;p<13;p++){var m=d&1<<12-p?30:29;if(l>13;return!g||p=2&&n<=6},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return{century:o[Math.floor((i.year()-1)/100)+1]||\"\"}},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=i.year()+(i.year()<0?1:0),e=i.month(),(r=i.day())+(e>1?16:0)+(e>2?32*(e-2):0)+400*(t-1)+this.jdEpoch-1},fromJD:function(t){t=Math.floor(t+.5)-Math.floor(this.jdEpoch)-1;var e=Math.floor(t/400)+1;t-=400*(e-1),t+=t>15?16:0;var r=Math.floor(t/32)+1,n=t-32*(r-1)+1;return this.newDate(e<=0?e-1:e,r,n)}});var o={20:\"Fruitbat\",21:\"Anchovy\"};n.calendars.discworld=a},81133:function(t,e,r){var n=r(24453),i=r(27976);function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Ethiopian\",jdEpoch:1724220.5,daysPerMonth:[30,30,30,30,30,30,30,30,30,30,30,30,5],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Ethiopian\",epochs:[\"BEE\",\"EE\"],monthNames:[\"Meskerem\",\"Tikemet\",\"Hidar\",\"Tahesas\",\"Tir\",\"Yekatit\",\"Megabit\",\"Miazia\",\"Genbot\",\"Sene\",\"Hamle\",\"Nehase\",\"Pagume\"],monthNamesShort:[\"Mes\",\"Tik\",\"Hid\",\"Tah\",\"Tir\",\"Yek\",\"Meg\",\"Mia\",\"Gen\",\"Sen\",\"Ham\",\"Neh\",\"Pag\"],dayNames:[\"Ehud\",\"Segno\",\"Maksegno\",\"Irob\",\"Hamus\",\"Arb\",\"Kidame\"],dayNamesShort:[\"Ehu\",\"Seg\",\"Mak\",\"Iro\",\"Ham\",\"Arb\",\"Kid\"],dayNamesMin:[\"Eh\",\"Se\",\"Ma\",\"Ir\",\"Ha\",\"Ar\",\"Ki\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return(t=e.year()+(e.year()<0?1:0))%4==3||t%4==-1},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear||n.regionalOptions[\"\"].invalidYear),13},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(13===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return(t=i.year())<0&&t++,i.day()+30*(i.month()-1)+365*(t-1)+Math.floor(t/4)+this.jdEpoch-1},fromJD:function(t){var e=Math.floor(t)+.5-this.jdEpoch,r=Math.floor((e-Math.floor((e+366)/1461))/365)+1;r<=0&&r--,e=Math.floor(t)+.5-this.newDate(r,1,1).toJD();var n=Math.floor(e/30)+1,i=e-30*(n-1)+1;return this.newDate(r,n,i)}}),n.calendars.ethiopian=a},78295:function(t,e,r){var n=r(24453),i=r(27976);function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}function o(t,e){return t-e*Math.floor(t/e)}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Hebrew\",jdEpoch:347995.5,daysPerMonth:[30,29,30,29,30,29,30,29,30,29,30,29,29],hasYearZero:!1,minMonth:1,firstMonth:7,minDay:1,regionalOptions:{\"\":{name:\"Hebrew\",epochs:[\"BAM\",\"AM\"],monthNames:[\"Nisan\",\"Iyar\",\"Sivan\",\"Tammuz\",\"Av\",\"Elul\",\"Tishrei\",\"Cheshvan\",\"Kislev\",\"Tevet\",\"Shevat\",\"Adar\",\"Adar II\"],monthNamesShort:[\"Nis\",\"Iya\",\"Siv\",\"Tam\",\"Av\",\"Elu\",\"Tis\",\"Che\",\"Kis\",\"Tev\",\"She\",\"Ada\",\"Ad2\"],dayNames:[\"Yom Rishon\",\"Yom Sheni\",\"Yom Shlishi\",\"Yom Revi'i\",\"Yom Chamishi\",\"Yom Shishi\",\"Yom Shabbat\"],dayNamesShort:[\"Ris\",\"She\",\"Shl\",\"Rev\",\"Cha\",\"Shi\",\"Sha\"],dayNamesMin:[\"Ri\",\"She\",\"Shl\",\"Re\",\"Ch\",\"Shi\",\"Sha\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return this._leapYear(e.year())},_leapYear:function(t){return o(7*(t=t<0?t+1:t)+1,19)<7},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),this._leapYear(t.year?t.year():t)?13:12},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){return t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year(),this.toJD(-1===t?1:t+1,7,1)-this.toJD(t,7,1)},daysInMonth:function(t,e){return t.year&&(e=t.month(),t=t.year()),this._validate(t,e,this.minDay,n.local.invalidMonth),12===e&&this.leapYear(t)||8===e&&5===o(this.daysInYear(t),10)?30:9===e&&3===o(this.daysInYear(t),10)?29:this.daysPerMonth[e-1]},weekDay:function(t,e,r){return 6!==this.dayOfWeek(t,e,r)},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return{yearType:(this.leapYear(i)?\"embolismic\":\"common\")+\" \"+[\"deficient\",\"regular\",\"complete\"][this.daysInYear(i)%10-3]}},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=t<=0?t+1:t,o=this.jdEpoch+this._delay1(a)+this._delay2(a)+r+1;if(e<7){for(var s=7;s<=this.monthsInYear(t);s++)o+=this.daysInMonth(t,s);for(s=1;s=this.toJD(-1===e?1:e+1,7,1);)e++;for(var r=tthis.toJD(e,r,this.daysInMonth(e,r));)r++;var n=t-this.toJD(e,r,1)+1;return this.newDate(e,r,n)}}),n.calendars.hebrew=a},25512:function(t,e,r){var n=r(24453),i=r(27976);function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Islamic\",jdEpoch:1948439.5,daysPerMonth:[30,29,30,29,30,29,30,29,30,29,30,29],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Islamic\",epochs:[\"BH\",\"AH\"],monthNames:[\"Muharram\",\"Safar\",\"Rabi' al-awwal\",\"Rabi' al-thani\",\"Jumada al-awwal\",\"Jumada al-thani\",\"Rajab\",\"Sha'aban\",\"Ramadan\",\"Shawwal\",\"Dhu al-Qi'dah\",\"Dhu al-Hijjah\"],monthNamesShort:[\"Muh\",\"Saf\",\"Rab1\",\"Rab2\",\"Jum1\",\"Jum2\",\"Raj\",\"Sha'\",\"Ram\",\"Shaw\",\"DhuQ\",\"DhuH\"],dayNames:[\"Yawm al-ahad\",\"Yawm al-ithnayn\",\"Yawm ath-thulaathaa'\",\"Yawm al-arbi'aa'\",\"Yawm al-khamīs\",\"Yawm al-jum'a\",\"Yawm as-sabt\"],dayNamesShort:[\"Aha\",\"Ith\",\"Thu\",\"Arb\",\"Kha\",\"Jum\",\"Sab\"],dayNamesMin:[\"Ah\",\"It\",\"Th\",\"Ar\",\"Kh\",\"Ju\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:6,isRTL:!1}},leapYear:function(t){return(11*this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year()+14)%30<11},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){return this.leapYear(t)?355:354},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=i.year(),e=i.month(),t=t<=0?t+1:t,(r=i.day())+Math.ceil(29.5*(e-1))+354*(t-1)+Math.floor((3+11*t)/30)+this.jdEpoch-1},fromJD:function(t){t=Math.floor(t)+.5;var e=Math.floor((30*(t-this.jdEpoch)+10646)/10631);e=e<=0?e-1:e;var r=Math.min(12,Math.ceil((t-29-this.toJD(e,1,1))/29.5)+1),n=t-this.toJD(e,r,1)+1;return this.newDate(e,r,n)}}),n.calendars.islamic=a},42645:function(t,e,r){var n=r(24453),i=r(27976);function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Julian\",jdEpoch:1721423.5,daysPerMonth:[31,28,31,30,31,30,31,31,30,31,30,31],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Julian\",epochs:[\"BC\",\"AD\"],monthNames:[\"January\",\"February\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\",\"November\",\"December\"],monthNamesShort:[\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\"],dayNames:[\"Sunday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"mm/dd/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return(t=e.year()<0?e.year()+1:e.year())%4==0},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(4-(n.dayOfWeek()||7),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=i.year(),e=i.month(),r=i.day(),t<0&&t++,e<=2&&(t--,e+=12),Math.floor(365.25*(t+4716))+Math.floor(30.6001*(e+1))+r-1524.5},fromJD:function(t){var e=Math.floor(t+.5)+1524,r=Math.floor((e-122.1)/365.25),n=Math.floor(365.25*r),i=Math.floor((e-n)/30.6001),a=i-Math.floor(i<14?1:13),o=r-Math.floor(a>2?4716:4715),s=e-n-Math.floor(30.6001*i);return o<=0&&o--,this.newDate(o,a,s)}}),n.calendars.julian=a},62324:function(t,e,r){var n=r(24453),i=r(27976);function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}function o(t,e){return t-e*Math.floor(t/e)}function s(t,e){return o(t-1,e)+1}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Mayan\",jdEpoch:584282.5,hasYearZero:!0,minMonth:0,firstMonth:0,minDay:0,regionalOptions:{\"\":{name:\"Mayan\",epochs:[\"\",\"\"],monthNames:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\"],monthNamesShort:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\"],dayNames:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\",\"19\"],dayNamesShort:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\",\"19\"],dayNamesMin:[\"0\",\"1\",\"2\",\"3\",\"4\",\"5\",\"6\",\"7\",\"8\",\"9\",\"10\",\"11\",\"12\",\"13\",\"14\",\"15\",\"16\",\"17\",\"18\",\"19\"],digits:null,dateFormat:\"YYYY.m.d\",firstDay:0,isRTL:!1,haabMonths:[\"Pop\",\"Uo\",\"Zip\",\"Zotz\",\"Tzec\",\"Xul\",\"Yaxkin\",\"Mol\",\"Chen\",\"Yax\",\"Zac\",\"Ceh\",\"Mac\",\"Kankin\",\"Muan\",\"Pax\",\"Kayab\",\"Cumku\",\"Uayeb\"],tzolkinMonths:[\"Imix\",\"Ik\",\"Akbal\",\"Kan\",\"Chicchan\",\"Cimi\",\"Manik\",\"Lamat\",\"Muluc\",\"Oc\",\"Chuen\",\"Eb\",\"Ben\",\"Ix\",\"Men\",\"Cib\",\"Caban\",\"Etznab\",\"Cauac\",\"Ahau\"]}},leapYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),!1},formatYear:function(t){t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year();var e=Math.floor(t/400);return t%=400,t+=t<0?400:0,e+\".\"+Math.floor(t/20)+\".\"+t%20},forYear:function(t){if((t=t.split(\".\")).length<3)throw\"Invalid Mayan year\";for(var e=0,r=0;r19||r>0&&n<0)throw\"Invalid Mayan year\";e=20*e+n}return e},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),18},weekOfYear:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),0},daysInYear:function(t){return this._validate(t,this.minMonth,this.minDay,n.local.invalidYear),360},daysInMonth:function(t,e){return this._validate(t,e,this.minDay,n.local.invalidMonth),20},daysInWeek:function(){return 5},dayOfWeek:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate).day()},weekDay:function(t,e,r){return this._validate(t,e,r,n.local.invalidDate),!0},extraInfo:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate).toJD(),a=this._toHaab(i),o=this._toTzolkin(i);return{haabMonthName:this.local.haabMonths[a[0]-1],haabMonth:a[0],haabDay:a[1],tzolkinDayName:this.local.tzolkinMonths[o[0]-1],tzolkinDay:o[0],tzolkinTrecena:o[1]}},_toHaab:function(t){var e=o(8+(t-=this.jdEpoch)+340,365);return[Math.floor(e/20)+1,o(e,20)]},_toTzolkin:function(t){return[s(20+(t-=this.jdEpoch),20),s(t+4,13)]},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return i.day()+20*i.month()+360*i.year()+this.jdEpoch},fromJD:function(t){t=Math.floor(t)+.5-this.jdEpoch;var e=Math.floor(t/360);t%=360,t+=t<0?360:0;var r=Math.floor(t/20),n=t%20;return this.newDate(e,r,n)}}),n.calendars.mayan=a},91662:function(t,e,r){var n=r(24453),i=r(27976);function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar;var o=n.instance(\"gregorian\");i(a.prototype,{name:\"Nanakshahi\",jdEpoch:2257673.5,daysPerMonth:[31,31,31,31,31,30,30,30,30,30,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Nanakshahi\",epochs:[\"BN\",\"AN\"],monthNames:[\"Chet\",\"Vaisakh\",\"Jeth\",\"Harh\",\"Sawan\",\"Bhadon\",\"Assu\",\"Katak\",\"Maghar\",\"Poh\",\"Magh\",\"Phagun\"],monthNamesShort:[\"Che\",\"Vai\",\"Jet\",\"Har\",\"Saw\",\"Bha\",\"Ass\",\"Kat\",\"Mgr\",\"Poh\",\"Mgh\",\"Pha\"],dayNames:[\"Somvaar\",\"Mangalvar\",\"Budhvaar\",\"Veervaar\",\"Shukarvaar\",\"Sanicharvaar\",\"Etvaar\"],dayNamesShort:[\"Som\",\"Mangal\",\"Budh\",\"Veer\",\"Shukar\",\"Sanichar\",\"Et\"],dayNamesMin:[\"So\",\"Ma\",\"Bu\",\"Ve\",\"Sh\",\"Sa\",\"Et\"],digits:null,dateFormat:\"dd-mm-yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear||n.regionalOptions[\"\"].invalidYear);return o.leapYear(e.year()+(e.year()<1?1:0)+1469)},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(1-(n.dayOfWeek()||7),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidMonth);(t=i.year())<0&&t++;for(var a=i.day(),s=1;s=this.toJD(e+1,1,1);)e++;for(var r=t-Math.floor(this.toJD(e,1,1)+.5)+1,n=1;r>this.daysInMonth(e,n);)r-=this.daysInMonth(e,n),n++;return this.newDate(e,n,r)}}),n.calendars.nanakshahi=a},66445:function(t,e,r){var n=r(24453),i=r(27976);function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"Nepali\",jdEpoch:1700709.5,daysPerMonth:[31,31,32,32,31,30,30,29,30,29,30,30],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,daysPerYear:365,regionalOptions:{\"\":{name:\"Nepali\",epochs:[\"BBS\",\"ABS\"],monthNames:[\"Baisakh\",\"Jestha\",\"Ashadh\",\"Shrawan\",\"Bhadra\",\"Ashwin\",\"Kartik\",\"Mangsir\",\"Paush\",\"Mangh\",\"Falgun\",\"Chaitra\"],monthNamesShort:[\"Bai\",\"Je\",\"As\",\"Shra\",\"Bha\",\"Ash\",\"Kar\",\"Mang\",\"Pau\",\"Ma\",\"Fal\",\"Chai\"],dayNames:[\"Aaitabaar\",\"Sombaar\",\"Manglbaar\",\"Budhabaar\",\"Bihibaar\",\"Shukrabaar\",\"Shanibaar\"],dayNamesShort:[\"Aaita\",\"Som\",\"Mangl\",\"Budha\",\"Bihi\",\"Shukra\",\"Shani\"],dayNamesMin:[\"Aai\",\"So\",\"Man\",\"Bu\",\"Bi\",\"Shu\",\"Sha\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:1,isRTL:!1}},leapYear:function(t){return this.daysInYear(t)!==this.daysPerYear},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){if(t=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear).year(),void 0===this.NEPALI_CALENDAR_DATA[t])return this.daysPerYear;for(var e=0,r=this.minMonth;r<=12;r++)e+=this.NEPALI_CALENDAR_DATA[t][r];return e},daysInMonth:function(t,e){return t.year&&(e=t.month(),t=t.year()),this._validate(t,e,this.minDay,n.local.invalidMonth),void 0===this.NEPALI_CALENDAR_DATA[t]?this.daysPerMonth[e-1]:this.NEPALI_CALENDAR_DATA[t][e]},weekDay:function(t,e,r){return 6!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=n.instance(),o=0,s=e,l=t;this._createMissingCalendarData(t);var c=t-(s>9||9===s&&r>=this.NEPALI_CALENDAR_DATA[l][0]?56:57);for(9!==e&&(o=r,s--);9!==s;)s<=0&&(s=12,l--),o+=this.NEPALI_CALENDAR_DATA[l][s],s--;return 9===e?(o+=r-this.NEPALI_CALENDAR_DATA[l][0])<0&&(o+=a.daysInYear(c)):o+=this.NEPALI_CALENDAR_DATA[l][9]-this.NEPALI_CALENDAR_DATA[l][0],a.newDate(c,1,1).add(o,\"d\").toJD()},fromJD:function(t){var e=n.instance().fromJD(t),r=e.year(),i=e.dayOfYear(),a=r+56;this._createMissingCalendarData(a);for(var o=9,s=this.NEPALI_CALENDAR_DATA[a][0],l=this.NEPALI_CALENDAR_DATA[a][o]-s+1;i>l;)++o>12&&(o=1,a++),l+=this.NEPALI_CALENDAR_DATA[a][o];var c=this.NEPALI_CALENDAR_DATA[a][o]-(l-i);return this.newDate(a,o,c)},_createMissingCalendarData:function(t){var e=this.daysPerMonth.slice(0);e.unshift(17);for(var r=t-1;r0?474:473))%2820+474+38)%2816<682},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-(n.dayOfWeek()+1)%7,\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(12===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);t=i.year(),e=i.month(),r=i.day();var a=t-(t>=0?474:473),s=474+o(a,2820);return r+(e<=7?31*(e-1):30*(e-1)+6)+Math.floor((682*s-110)/2816)+365*(s-1)+1029983*Math.floor(a/2820)+this.jdEpoch-1},fromJD:function(t){var e=(t=Math.floor(t)+.5)-this.toJD(475,1,1),r=Math.floor(e/1029983),n=o(e,1029983),i=2820;if(1029982!==n){var a=Math.floor(n/366),s=o(n,366);i=Math.floor((2134*a+2816*s+2815)/1028522)+a+1}var l=i+2820*r+474;l=l<=0?l-1:l;var c=t-this.toJD(l,1,1)+1,u=c<=186?Math.ceil(c/31):Math.ceil((c-6)/30),h=t-this.toJD(l,u,1)+1;return this.newDate(l,u,h)}}),n.calendars.persian=a,n.calendars.jalali=a},84756:function(t,e,r){var n=r(24453),i=r(27976),a=n.instance();function o(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}o.prototype=new n.baseCalendar,i(o.prototype,{name:\"Taiwan\",jdEpoch:2419402.5,yearsOffset:1911,daysPerMonth:[31,28,31,30,31,30,31,31,30,31,30,31],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Taiwan\",epochs:[\"BROC\",\"ROC\"],monthNames:[\"January\",\"February\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\",\"November\",\"December\"],monthNamesShort:[\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\"],dayNames:[\"Sunday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:1,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return t=this._t2gYear(e.year()),a.leapYear(t)},weekOfYear:function(t,e,r){var i=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return t=this._t2gYear(i.year()),a.weekOfYear(t,i.month(),i.day())},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=this._t2gYear(i.year()),a.toJD(t,i.month(),i.day())},fromJD:function(t){var e=a.fromJD(t),r=this._g2tYear(e.year());return this.newDate(r,e.month(),e.day())},_t2gYear:function(t){return t+this.yearsOffset+(t>=-this.yearsOffset&&t<=-1?1:0)},_g2tYear:function(t){return t-this.yearsOffset-(t>=1&&t<=this.yearsOffset?1:0)}}),n.calendars.taiwan=o},41858:function(t,e,r){var n=r(24453),i=r(27976),a=n.instance();function o(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}o.prototype=new n.baseCalendar,i(o.prototype,{name:\"Thai\",jdEpoch:1523098.5,yearsOffset:543,daysPerMonth:[31,28,31,30,31,30,31,31,30,31,30,31],hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Thai\",epochs:[\"BBE\",\"BE\"],monthNames:[\"January\",\"February\",\"March\",\"April\",\"May\",\"June\",\"July\",\"August\",\"September\",\"October\",\"November\",\"December\"],monthNamesShort:[\"Jan\",\"Feb\",\"Mar\",\"Apr\",\"May\",\"Jun\",\"Jul\",\"Aug\",\"Sep\",\"Oct\",\"Nov\",\"Dec\"],dayNames:[\"Sunday\",\"Monday\",\"Tuesday\",\"Wednesday\",\"Thursday\",\"Friday\",\"Saturday\"],dayNamesShort:[\"Sun\",\"Mon\",\"Tue\",\"Wed\",\"Thu\",\"Fri\",\"Sat\"],dayNamesMin:[\"Su\",\"Mo\",\"Tu\",\"We\",\"Th\",\"Fr\",\"Sa\"],digits:null,dateFormat:\"dd/mm/yyyy\",firstDay:0,isRTL:!1}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return t=this._t2gYear(e.year()),a.leapYear(t)},weekOfYear:function(t,e,r){var i=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return t=this._t2gYear(i.year()),a.weekOfYear(t,i.month(),i.day())},daysInMonth:function(t,e){var r=this._validate(t,e,this.minDay,n.local.invalidMonth);return this.daysPerMonth[r.month()-1]+(2===r.month()&&this.leapYear(r.year())?1:0)},weekDay:function(t,e,r){return(this.dayOfWeek(t,e,r)||7)<6},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate);return t=this._t2gYear(i.year()),a.toJD(t,i.month(),i.day())},fromJD:function(t){var e=a.fromJD(t),r=this._g2tYear(e.year());return this.newDate(r,e.month(),e.day())},_t2gYear:function(t){return t-this.yearsOffset-(t>=1&&t<=this.yearsOffset?1:0)},_g2tYear:function(t){return t+this.yearsOffset+(t>=-this.yearsOffset&&t<=-1?1:0)}}),n.calendars.thai=o},57985:function(t,e,r){var n=r(24453),i=r(27976);function a(t){this.local=this.regionalOptions[t||\"\"]||this.regionalOptions[\"\"]}a.prototype=new n.baseCalendar,i(a.prototype,{name:\"UmmAlQura\",hasYearZero:!1,minMonth:1,firstMonth:1,minDay:1,regionalOptions:{\"\":{name:\"Umm al-Qura\",epochs:[\"BH\",\"AH\"],monthNames:[\"Al-Muharram\",\"Safar\",\"Rabi' al-awwal\",\"Rabi' Al-Thani\",\"Jumada Al-Awwal\",\"Jumada Al-Thani\",\"Rajab\",\"Sha'aban\",\"Ramadan\",\"Shawwal\",\"Dhu al-Qi'dah\",\"Dhu al-Hijjah\"],monthNamesShort:[\"Muh\",\"Saf\",\"Rab1\",\"Rab2\",\"Jum1\",\"Jum2\",\"Raj\",\"Sha'\",\"Ram\",\"Shaw\",\"DhuQ\",\"DhuH\"],dayNames:[\"Yawm al-Ahad\",\"Yawm al-Ithnain\",\"Yawm al-Thalāthā’\",\"Yawm al-Arba‘ā’\",\"Yawm al-Khamīs\",\"Yawm al-Jum‘a\",\"Yawm al-Sabt\"],dayNamesMin:[\"Ah\",\"Ith\",\"Th\",\"Ar\",\"Kh\",\"Ju\",\"Sa\"],digits:null,dateFormat:\"yyyy/mm/dd\",firstDay:6,isRTL:!0}},leapYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,n.local.invalidYear);return 355===this.daysInYear(e.year())},weekOfYear:function(t,e,r){var n=this.newDate(t,e,r);return n.add(-n.dayOfWeek(),\"d\"),Math.floor((n.dayOfYear()-1)/7)+1},daysInYear:function(t){for(var e=0,r=1;r<=12;r++)e+=this.daysInMonth(t,r);return e},daysInMonth:function(t,e){for(var r=this._validate(t,e,this.minDay,n.local.invalidMonth).toJD()-24e5+.5,i=0,a=0;ar)return o[i]-o[i-1];i++}return 30},weekDay:function(t,e,r){return 5!==this.dayOfWeek(t,e,r)},toJD:function(t,e,r){var i=this._validate(t,e,r,n.local.invalidDate),a=12*(i.year()-1)+i.month()-15292;return i.day()+o[a-1]-1+24e5-.5},fromJD:function(t){for(var e=t-24e5+.5,r=0,n=0;ne);n++)r++;var i=r+15292,a=Math.floor((i-1)/12),s=a+1,l=i-12*a,c=e-o[r-1]+1;return this.newDate(s,l,c)},isValid:function(t,e,r){var i=n.baseCalendar.prototype.isValid.apply(this,arguments);return i&&(i=(t=null!=t.year?t.year:t)>=1276&&t<=1500),i},_validate:function(t,e,r,i){var a=n.baseCalendar.prototype._validate.apply(this,arguments);if(a.year<1276||a.year>1500)throw i.replace(/\\{0\\}/,this.local.name);return a}}),n.calendars.ummalqura=a;var 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n=r(27976);function i(){this.regionalOptions=[],this.regionalOptions[\"\"]={invalidCalendar:\"Calendar {0} not found\",invalidDate:\"Invalid {0} date\",invalidMonth:\"Invalid {0} month\",invalidYear:\"Invalid {0} year\",differentCalendars:\"Cannot mix {0} and {1} dates\"},this.local=this.regionalOptions[\"\"],this.calendars={},this._localCals={}}function a(t,e,r,n){if(this._calendar=t,this._year=e,this._month=r,this._day=n,0===this._calendar._validateLevel&&!this._calendar.isValid(this._year,this._month,this._day))throw(c.local.invalidDate||c.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name)}function o(t,e){return\"000000\".substring(0,e-(t=\"\"+t).length)+t}function s(){this.shortYearCutoff=\"+10\"}function l(t){this.local=this.regionalOptions[t]||this.regionalOptions[\"\"]}n(i.prototype,{instance:function(t,e){t=(t||\"gregorian\").toLowerCase(),e=e||\"\";var r=this._localCals[t+\"-\"+e];if(!r&&this.calendars[t]&&(r=new this.calendars[t](e),this._localCals[t+\"-\"+e]=r),!r)throw(this.local.invalidCalendar||this.regionalOptions[\"\"].invalidCalendar).replace(/\\{0\\}/,t);return r},newDate:function(t,e,r,n,i){return(n=(null!=t&&t.year?t.calendar():\"string\"==typeof n?this.instance(n,i):n)||this.instance()).newDate(t,e,r)},substituteDigits:function(t){return function(e){return(e+\"\").replace(/[0-9]/g,(function(e){return t[e]}))}},substituteChineseDigits:function(t,e){return function(r){for(var n=\"\",i=0;r>0;){var a=r%10;n=(0===a?\"\":t[a]+e[i])+n,i++,r=Math.floor(r/10)}return 0===n.indexOf(t[1]+e[1])&&(n=n.substr(1)),n||t[0]}}}),n(a.prototype,{newDate:function(t,e,r){return this._calendar.newDate(null==t?this:t,e,r)},year:function(t){return 0===arguments.length?this._year:this.set(t,\"y\")},month:function(t){return 0===arguments.length?this._month:this.set(t,\"m\")},day:function(t){return 0===arguments.length?this._day:this.set(t,\"d\")},date:function(t,e,r){if(!this._calendar.isValid(t,e,r))throw(c.local.invalidDate||c.regionalOptions[\"\"].invalidDate).replace(/\\{0\\}/,this._calendar.local.name);return this._year=t,this._month=e,this._day=r,this},leapYear:function(){return this._calendar.leapYear(this)},epoch:function(){return this._calendar.epoch(this)},formatYear:function(){return this._calendar.formatYear(this)},monthOfYear:function(){return this._calendar.monthOfYear(this)},weekOfYear:function(){return this._calendar.weekOfYear(this)},daysInYear:function(){return this._calendar.daysInYear(this)},dayOfYear:function(){return this._calendar.dayOfYear(this)},daysInMonth:function(){return this._calendar.daysInMonth(this)},dayOfWeek:function(){return this._calendar.dayOfWeek(this)},weekDay:function(){return this._calendar.weekDay(this)},extraInfo:function(){return this._calendar.extraInfo(this)},add:function(t,e){return this._calendar.add(this,t,e)},set:function(t,e){return this._calendar.set(this,t,e)},compareTo:function(t){if(this._calendar.name!==t._calendar.name)throw(c.local.differentCalendars||c.regionalOptions[\"\"].differentCalendars).replace(/\\{0\\}/,this._calendar.local.name).replace(/\\{1\\}/,t._calendar.local.name);var e=this._year!==t._year?this._year-t._year:this._month!==t._month?this.monthOfYear()-t.monthOfYear():this._day-t._day;return 0===e?0:e<0?-1:1},calendar:function(){return this._calendar},toJD:function(){return this._calendar.toJD(this)},fromJD:function(t){return this._calendar.fromJD(t)},toJSDate:function(){return this._calendar.toJSDate(this)},fromJSDate:function(t){return this._calendar.fromJSDate(t)},toString:function(){return(this.year()<0?\"-\":\"\")+o(Math.abs(this.year()),4)+\"-\"+o(this.month(),2)+\"-\"+o(this.day(),2)}}),n(s.prototype,{_validateLevel:0,newDate:function(t,e,r){return null==t?this.today():(t.year&&(this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate),r=t.day(),e=t.month(),t=t.year()),new a(this,t,e,r))},today:function(){return this.fromJSDate(new Date)},epoch:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear).year()<0?this.local.epochs[0]:this.local.epochs[1]},formatYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear);return(e.year()<0?\"-\":\"\")+o(Math.abs(e.year()),4)},monthsInYear:function(t){return this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear),12},monthOfYear:function(t,e){var r=this._validate(t,e,this.minDay,c.local.invalidMonth||c.regionalOptions[\"\"].invalidMonth);return(r.month()+this.monthsInYear(r)-this.firstMonth)%this.monthsInYear(r)+this.minMonth},fromMonthOfYear:function(t,e){var r=(e+this.firstMonth-2*this.minMonth)%this.monthsInYear(t)+this.minMonth;return this._validate(t,r,this.minDay,c.local.invalidMonth||c.regionalOptions[\"\"].invalidMonth),r},daysInYear:function(t){var e=this._validate(t,this.minMonth,this.minDay,c.local.invalidYear||c.regionalOptions[\"\"].invalidYear);return this.leapYear(e)?366:365},dayOfYear:function(t,e,r){var n=this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate);return n.toJD()-this.newDate(n.year(),this.fromMonthOfYear(n.year(),this.minMonth),this.minDay).toJD()+1},daysInWeek:function(){return 7},dayOfWeek:function(t,e,r){var n=this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate);return(Math.floor(this.toJD(n))+2)%this.daysInWeek()},extraInfo:function(t,e,r){return this._validate(t,e,r,c.local.invalidDate||c.regionalOptions[\"\"].invalidDate),{}},add:function(t,e,r){return 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0===i?0:1===e?a/i:(Math.pow(e,a)-1)/(Math.pow(e,i)-1)}(r,n,t.stops[a][0],t.stops[a+1][0]),s=t.stops[a][1],l=t.stops[a+1][1],c=Pe[e.type]||gn;return\"function\"==typeof s.evaluate?{evaluate(...e){const r=s.evaluate.apply(void 0,e),n=l.evaluate.apply(void 0,e);if(void 0!==r&&void 0!==n)return c(r,n,o,t.colorSpace)}}:c(s,l,o,t.colorSpace)}function wn(t,e,r){switch(e.type){case\"color\":r=Xt.parse(r);break;case\"formatted\":r=Kt.fromString(r.toString());break;case\"resolvedImage\":r=re.fromString(r.toString());break;case\"padding\":r=Qt.parse(r);break;default:dn(r)===e.type||\"enum\"===e.type&&e.values[r]||(r=void 0)}return vn(r,t.default,e.default)}Qr.register(Kr,{error:[{kind:\"error\"},[ut],(t,[e])=>{throw new le(e.evaluate(t))}],typeof:[ut,[dt],(t,[e])=>bt(ae(e.evaluate(t)))],\"to-rgba\":[_t(ct,4),[ft],(t,[e])=>{const[r,n,i,a]=e.evaluate(t).rgb;return[255*r,255*n,255*i,a]}],rgb:[ft,[ct,ct,ct],tn],rgba:[ft,[ct,ct,ct,ct],tn],has:{type:ht,overloads:[[[ut],(t,[e])=>en(e.evaluate(t),t.properties())],[[ut,pt],(t,[e,r])=>en(e.evaluate(t),r.evaluate(t))]]},get:{type:dt,overloads:[[[ut],(t,[e])=>rn(e.evaluate(t),t.properties())],[[ut,pt],(t,[e,r])=>rn(e.evaluate(t),r.evaluate(t))]]},\"feature-state\":[dt,[ut],(t,[e])=>rn(e.evaluate(t),t.featureState||{})],properties:[pt,[],t=>t.properties()],\"geometry-type\":[ut,[],t=>t.geometryType()],id:[dt,[],t=>t.id()],zoom:[ct,[],t=>t.globals.zoom],\"heatmap-density\":[ct,[],t=>t.globals.heatmapDensity||0],\"line-progress\":[ct,[],t=>t.globals.lineProgress||0],accumulated:[dt,[],t=>void 0===t.globals.accumulated?null:t.globals.accumulated],\"+\":[ct,nn(ct),(t,e)=>{let r=0;for(const n of e)r+=n.evaluate(t);return r}],\"*\":[ct,nn(ct),(t,e)=>{let r=1;for(const n of e)r*=n.evaluate(t);return r}],\"-\":{type:ct,overloads:[[[ct,ct],(t,[e,r])=>e.evaluate(t)-r.evaluate(t)],[[ct],(t,[e])=>-e.evaluate(t)]]},\"/\":[ct,[ct,ct],(t,[e,r])=>e.evaluate(t)/r.evaluate(t)],\"%\":[ct,[ct,ct],(t,[e,r])=>e.evaluate(t)%r.evaluate(t)],ln2:[ct,[],()=>Math.LN2],pi:[ct,[],()=>Math.PI],e:[ct,[],()=>Math.E],\"^\":[ct,[ct,ct],(t,[e,r])=>Math.pow(e.evaluate(t),r.evaluate(t))],sqrt:[ct,[ct],(t,[e])=>Math.sqrt(e.evaluate(t))],log10:[ct,[ct],(t,[e])=>Math.log(e.evaluate(t))/Math.LN10],ln:[ct,[ct],(t,[e])=>Math.log(e.evaluate(t))],log2:[ct,[ct],(t,[e])=>Math.log(e.evaluate(t))/Math.LN2],sin:[ct,[ct],(t,[e])=>Math.sin(e.evaluate(t))],cos:[ct,[ct],(t,[e])=>Math.cos(e.evaluate(t))],tan:[ct,[ct],(t,[e])=>Math.tan(e.evaluate(t))],asin:[ct,[ct],(t,[e])=>Math.asin(e.evaluate(t))],acos:[ct,[ct],(t,[e])=>Math.acos(e.evaluate(t))],atan:[ct,[ct],(t,[e])=>Math.atan(e.evaluate(t))],min:[ct,nn(ct),(t,e)=>Math.min(...e.map((e=>e.evaluate(t))))],max:[ct,nn(ct),(t,e)=>Math.max(...e.map((e=>e.evaluate(t))))],abs:[ct,[ct],(t,[e])=>Math.abs(e.evaluate(t))],round:[ct,[ct],(t,[e])=>{const r=e.evaluate(t);return r<0?-Math.round(-r):Math.round(r)}],floor:[ct,[ct],(t,[e])=>Math.floor(e.evaluate(t))],ceil:[ct,[ct],(t,[e])=>Math.ceil(e.evaluate(t))],\"filter-==\":[ht,[ut,dt],(t,[e,r])=>t.properties()[e.value]===r.value],\"filter-id-==\":[ht,[dt],(t,[e])=>t.id()===e.value],\"filter-type-==\":[ht,[ut],(t,[e])=>t.geometryType()===e.value],\"filter-<\":[ht,[ut,dt],(t,[e,r])=>{const n=t.properties()[e.value],i=r.value;return typeof n==typeof i&&n{const r=t.id(),n=e.value;return typeof r==typeof n&&r\":[ht,[ut,dt],(t,[e,r])=>{const n=t.properties()[e.value],i=r.value;return typeof n==typeof i&&n>i}],\"filter-id->\":[ht,[dt],(t,[e])=>{const r=t.id(),n=e.value;return typeof r==typeof n&&r>n}],\"filter-<=\":[ht,[ut,dt],(t,[e,r])=>{const n=t.properties()[e.value],i=r.value;return typeof n==typeof i&&n<=i}],\"filter-id-<=\":[ht,[dt],(t,[e])=>{const r=t.id(),n=e.value;return typeof r==typeof n&&r<=n}],\"filter->=\":[ht,[ut,dt],(t,[e,r])=>{const n=t.properties()[e.value],i=r.value;return typeof n==typeof i&&n>=i}],\"filter-id->=\":[ht,[dt],(t,[e])=>{const r=t.id(),n=e.value;return typeof r==typeof n&&r>=n}],\"filter-has\":[ht,[dt],(t,[e])=>e.value in t.properties()],\"filter-has-id\":[ht,[],t=>null!==t.id()&&void 0!==t.id()],\"filter-type-in\":[ht,[_t(ut)],(t,[e])=>e.value.indexOf(t.geometryType())>=0],\"filter-id-in\":[ht,[_t(dt)],(t,[e])=>e.value.indexOf(t.id())>=0],\"filter-in-small\":[ht,[ut,_t(dt)],(t,[e,r])=>r.value.indexOf(t.properties()[e.value])>=0],\"filter-in-large\":[ht,[ut,_t(dt)],(t,[e,r])=>function(t,e,r,n){for(;r<=n;){const i=r+n>>1;if(e[i]===t)return!0;e[i]>t?n=i-1:r=i+1}return!1}(t.properties()[e.value],r.value,0,r.value.length-1)],all:{type:ht,overloads:[[[ht,ht],(t,[e,r])=>e.evaluate(t)&&r.evaluate(t)],[nn(ht),(t,e)=>{for(const r of e)if(!r.evaluate(t))return!1;return!0}]]},any:{type:ht,overloads:[[[ht,ht],(t,[e,r])=>e.evaluate(t)||r.evaluate(t)],[nn(ht),(t,e)=>{for(const r of e)if(r.evaluate(t))return!0;return!1}]]},\"!\":[ht,[ht],(t,[e])=>!e.evaluate(t)],\"is-supported-script\":[ht,[ut],(t,[e])=>{const r=t.globals&&t.globals.isSupportedScript;return!r||r(e.evaluate(t))}],upcase:[ut,[ut],(t,[e])=>e.evaluate(t).toUpperCase()],downcase:[ut,[ut],(t,[e])=>e.evaluate(t).toLowerCase()],concat:[ut,nn(dt),(t,e)=>e.map((e=>oe(e.evaluate(t)))).join(\"\")],\"resolved-locale\":[ut,[mt],(t,[e])=>e.evaluate(t).resolvedLocale()]});class Tn{constructor(t,e){var r;this.expression=t,this._warningHistory={},this._evaluator=new de,this._defaultValue=e?\"color\"===(r=e).type&&mn(r.default)?new Xt(0,0,0,0):\"color\"===r.type?Xt.parse(r.default)||null:\"padding\"===r.type?Qt.parse(r.default)||null:\"variableAnchorOffsetCollection\"===r.type?ee.parse(r.default)||null:void 0===r.default?null:r.default:null,this._enumValues=e&&\"enum\"===e.type?e.values:null}evaluateWithoutErrorHandling(t,e,r,n,i,a){return this._evaluator.globals=t,this._evaluator.feature=e,this._evaluator.featureState=r,this._evaluator.canonical=n,this._evaluator.availableImages=i||null,this._evaluator.formattedSection=a,this.expression.evaluate(this._evaluator)}evaluate(t,e,r,n,i,a){this._evaluator.globals=t,this._evaluator.feature=e||null,this._evaluator.featureState=r||null,this._evaluator.canonical=n,this._evaluator.availableImages=i||null,this._evaluator.formattedSection=a||null;try{const t=this.expression.evaluate(this._evaluator);if(null==t||\"number\"==typeof t&&t!=t)return this._defaultValue;if(this._enumValues&&!(t in this._enumValues))throw new le(`Expected value to be one of ${Object.keys(this._enumValues).map((t=>JSON.stringify(t))).join(\", \")}, but found ${JSON.stringify(t)} instead.`);return t}catch(t){return this._warningHistory[t.message]||(this._warningHistory[t.message]=!0,\"undefined\"!=typeof console&&console.warn(t.message)),this._defaultValue}}}function kn(t){return Array.isArray(t)&&t.length>0&&\"string\"==typeof t[0]&&t[0]in Kr}function An(t,e){const r=new me(Kr,an,[],e?function(t){const e={color:ft,string:ut,number:ct,enum:ut,boolean:ht,formatted:gt,padding:yt,resolvedImage:vt,variableAnchorOffsetCollection:xt};return\"array\"===t.type?_t(e[t.value]||dt,t.length):e[t.type]}(e):void 0),n=r.parse(t,void 0,void 0,void 0,e&&\"string\"===e.type?{typeAnnotation:\"coerce\"}:void 0);return n?cn(new Tn(n,e)):un(r.errors)}class Mn{constructor(t,e){this.kind=t,this._styleExpression=e,this.isStateDependent=\"constant\"!==t&&!sn(e.expression)}evaluateWithoutErrorHandling(t,e,r,n,i,a){return this._styleExpression.evaluateWithoutErrorHandling(t,e,r,n,i,a)}evaluate(t,e,r,n,i,a){return this._styleExpression.evaluate(t,e,r,n,i,a)}}class Sn{constructor(t,e,r,n){this.kind=t,this.zoomStops=r,this._styleExpression=e,this.isStateDependent=\"camera\"!==t&&!sn(e.expression),this.interpolationType=n}evaluateWithoutErrorHandling(t,e,r,n,i,a){return this._styleExpression.evaluateWithoutErrorHandling(t,e,r,n,i,a)}evaluate(t,e,r,n,i,a){return this._styleExpression.evaluate(t,e,r,n,i,a)}interpolationFactor(t,e,r){return this.interpolationType?ze.interpolationFactor(this.interpolationType,t,e,r):0}}function En(t,e){const r=An(t,e);if(\"error\"===r.result)return r;const n=r.value.expression,i=on(n);if(!i&&!hn(e))return un([new ot(\"\",\"data expressions not supported\")]);const a=ln(n,[\"zoom\"]);if(!a&&!fn(e))return un([new ot(\"\",\"zoom expressions not supported\")]);const o=Ln(n);if(!o&&!a)return un([new ot(\"\",'\"zoom\" expression may only be used as input to a top-level \"step\" or \"interpolate\" expression.')]);if(o instanceof ot)return un([o]);if(o instanceof ze&&!pn(e))return un([new ot(\"\",'\"interpolate\" expressions cannot be used with this property')]);if(!o)return cn(new Mn(i?\"constant\":\"source\",r.value));const s=o instanceof ze?o.interpolation:void 0;return cn(new Sn(i?\"camera\":\"composite\",r.value,o.labels,s))}class Cn{constructor(t,e){this._parameters=t,this._specification=e,at(this,yn(this._parameters,this._specification))}static deserialize(t){return new Cn(t._parameters,t._specification)}static serialize(t){return{_parameters:t._parameters,_specification:t._specification}}}function Ln(t){let e=null;if(t instanceof ge)e=Ln(t.result);else if(t instanceof De){for(const r of t.args)if(e=Ln(r),e)break}else(t instanceof Ae||t instanceof ze)&&t.input instanceof Qr&&\"zoom\"===t.input.name&&(e=t);return e instanceof ot||t.eachChild((t=>{const r=Ln(t);r instanceof ot?e=r:!e&&r?e=new ot(\"\",'\"zoom\" expression may only be used as input to a top-level \"step\" or \"interpolate\" expression.'):e&&r&&e!==r&&(e=new ot(\"\",'Only one zoom-based \"step\" or \"interpolate\" subexpression may be used in an expression.'))})),e}function In(t){if(!0===t||!1===t)return!0;if(!Array.isArray(t)||0===t.length)return!1;switch(t[0]){case\"has\":return t.length>=2&&\"$id\"!==t[1]&&\"$type\"!==t[1];case\"in\":return t.length>=3&&(\"string\"!=typeof t[1]||Array.isArray(t[2]));case\"!in\":case\"!has\":case\"none\":return!1;case\"==\":case\"!=\":case\">\":case\">=\":case\"<\":case\"<=\":return 3!==t.length||Array.isArray(t[1])||Array.isArray(t[2]);case\"any\":case\"all\":for(const e of t.slice(1))if(!In(e)&&\"boolean\"!=typeof e)return!1;return!0;default:return!0}}const Pn={type:\"boolean\",default:!1,transition:!1,\"property-type\":\"data-driven\",expression:{interpolated:!1,parameters:[\"zoom\",\"feature\"]}};function zn(t){if(null==t)return{filter:()=>!0,needGeometry:!1};In(t)||(t=Rn(t));const e=An(t,Pn);if(\"error\"===e.result)throw new Error(e.value.map((t=>`${t.key}: ${t.message}`)).join(\", \"));return{filter:(t,r,n)=>e.value.evaluate(t,r,{},n),needGeometry:Dn(t)}}function On(t,e){return te?1:0}function Dn(t){if(!Array.isArray(t))return!1;if(\"within\"===t[0]||\"distance\"===t[0])return!0;for(let e=1;e\"===e||\"<=\"===e||\">=\"===e?Fn(t[1],t[2],e):\"any\"===e?(r=t.slice(1),[\"any\"].concat(r.map(Rn))):\"all\"===e?[\"all\"].concat(t.slice(1).map(Rn)):\"none\"===e?[\"all\"].concat(t.slice(1).map(Rn).map(jn)):\"in\"===e?Bn(t[1],t.slice(2)):\"!in\"===e?jn(Bn(t[1],t.slice(2))):\"has\"===e?Nn(t[1]):\"!has\"!==e||jn(Nn(t[1]));var r}function Fn(t,e,r){switch(t){case\"$type\":return[`filter-type-${r}`,e];case\"$id\":return[`filter-id-${r}`,e];default:return[`filter-${r}`,t,e]}}function Bn(t,e){if(0===e.length)return!1;switch(t){case\"$type\":return[\"filter-type-in\",[\"literal\",e]];case\"$id\":return[\"filter-id-in\",[\"literal\",e]];default:return e.length>200&&!e.some((t=>typeof t!=typeof e[0]))?[\"filter-in-large\",t,[\"literal\",e.sort(On)]]:[\"filter-in-small\",t,[\"literal\",e]]}}function Nn(t){switch(t){case\"$type\":return!0;case\"$id\":return[\"filter-has-id\"];default:return[\"filter-has\",t]}}function jn(t){return[\"!\",t]}function Un(t){const e=typeof t;if(\"number\"===e||\"boolean\"===e||\"string\"===e||null==t)return JSON.stringify(t);if(Array.isArray(t)){let e=\"[\";for(const r of t)e+=`${Un(r)},`;return`${e}]`}const r=Object.keys(t).sort();let n=\"{\";for(let e=0;en.maximum?[new it(e,r,`${r} is greater than the maximum value ${n.maximum}`)]:[]}function Xn(t){const e=t.valueSpec,r=Hn(t.value.type);let n,i,a,o={};const s=\"categorical\"!==r&&void 0===t.value.property,l=!s,c=\"array\"===dn(t.value.stops)&&\"array\"===dn(t.value.stops[0])&&\"object\"===dn(t.value.stops[0][0]),u=Zn({key:t.key,value:t.value,valueSpec:t.styleSpec.function,validateSpec:t.validateSpec,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{stops:function(t){if(\"identity\"===r)return[new it(t.key,t.value,'identity function may not have a \"stops\" property')];let e=[];const n=t.value;return e=e.concat(Wn({key:t.key,value:n,valueSpec:t.valueSpec,validateSpec:t.validateSpec,style:t.style,styleSpec:t.styleSpec,arrayElementValidator:h})),\"array\"===dn(n)&&0===n.length&&e.push(new it(t.key,n,\"array must have at least one stop\")),e},default:function(t){return t.validateSpec({key:t.key,value:t.value,valueSpec:e,validateSpec:t.validateSpec,style:t.style,styleSpec:t.styleSpec})}}});return\"identity\"===r&&s&&u.push(new it(t.key,t.value,'missing required property \"property\"')),\"identity\"===r||t.value.stops||u.push(new it(t.key,t.value,'missing required property \"stops\"')),\"exponential\"===r&&t.valueSpec.expression&&!pn(t.valueSpec)&&u.push(new it(t.key,t.value,\"exponential functions not supported\")),t.styleSpec.$version>=8&&(l&&!hn(t.valueSpec)?u.push(new it(t.key,t.value,\"property functions not supported\")):s&&!fn(t.valueSpec)&&u.push(new it(t.key,t.value,\"zoom functions not supported\"))),\"categorical\"!==r&&!c||void 0!==t.value.property||u.push(new it(t.key,t.value,'\"property\" property is required')),u;function h(t){let r=[];const n=t.value,s=t.key;if(\"array\"!==dn(n))return[new it(s,n,`array expected, ${dn(n)} found`)];if(2!==n.length)return[new it(s,n,`array length 2 expected, length ${n.length} found`)];if(c){if(\"object\"!==dn(n[0]))return[new it(s,n,`object expected, ${dn(n[0])} found`)];if(void 0===n[0].zoom)return[new it(s,n,\"object stop key must have zoom\")];if(void 0===n[0].value)return[new it(s,n,\"object stop key must have value\")];if(a&&a>Hn(n[0].zoom))return[new it(s,n[0].zoom,\"stop zoom values must appear in ascending order\")];Hn(n[0].zoom)!==a&&(a=Hn(n[0].zoom),i=void 0,o={}),r=r.concat(Zn({key:`${s}[0]`,value:n[0],valueSpec:{zoom:{}},validateSpec:t.validateSpec,style:t.style,styleSpec:t.styleSpec,objectElementValidators:{zoom:Yn,value:f}}))}else r=r.concat(f({key:`${s}[0]`,value:n[0],valueSpec:{},validateSpec:t.validateSpec,style:t.style,styleSpec:t.styleSpec},n));return kn(Gn(n[1]))?r.concat([new it(`${s}[1]`,n[1],\"expressions are not allowed in function stops.\")]):r.concat(t.validateSpec({key:`${s}[1]`,value:n[1],valueSpec:e,validateSpec:t.validateSpec,style:t.style,styleSpec:t.styleSpec}))}function f(t,a){const s=dn(t.value),l=Hn(t.value),c=null!==t.value?t.value:a;if(n){if(s!==n)return[new it(t.key,c,`${s} stop domain type must match previous stop domain type ${n}`)]}else n=s;if(\"number\"!==s&&\"string\"!==s&&\"boolean\"!==s)return[new it(t.key,c,\"stop domain value must be a number, string, or boolean\")];if(\"number\"!==s&&\"categorical\"!==r){let n=`number expected, ${s} found`;return hn(e)&&void 0===r&&(n+='\\nIf you intended to use a categorical function, specify `\"type\": \"categorical\"`.'),[new it(t.key,c,n)]}return\"categorical\"!==r||\"number\"!==s||isFinite(l)&&Math.floor(l)===l?\"categorical\"!==r&&\"number\"===s&&void 0!==i&&lnew it(`${t.key}${e.key}`,t.value,e.message)));const r=e.value.expression||e.value._styleExpression.expression;if(\"property\"===t.expressionContext&&\"text-font\"===t.propertyKey&&!r.outputDefined())return[new it(t.key,t.value,`Invalid data expression for \"${t.propertyKey}\". Output values must be contained as literals within the expression.`)];if(\"property\"===t.expressionContext&&\"layout\"===t.propertyType&&!sn(r))return[new it(t.key,t.value,'\"feature-state\" data expressions are not supported with layout properties.')];if(\"filter\"===t.expressionContext&&!sn(r))return[new it(t.key,t.value,'\"feature-state\" data expressions are not supported with filters.')];if(t.expressionContext&&0===t.expressionContext.indexOf(\"cluster\")){if(!ln(r,[\"zoom\",\"feature-state\"]))return[new it(t.key,t.value,'\"zoom\" and \"feature-state\" expressions are not supported with cluster properties.')];if(\"cluster-initial\"===t.expressionContext&&!on(r))return[new it(t.key,t.value,\"Feature data expressions are not supported with initial expression part of cluster properties.\")]}return[]}function Jn(t){const e=t.key,r=t.value,n=t.valueSpec,i=[];return Array.isArray(n.values)?-1===n.values.indexOf(Hn(r))&&i.push(new it(e,r,`expected one of [${n.values.join(\", \")}], 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Presentation Forms-A\"](t)||zi[\"Arabic Presentation Forms-B\"](t)}function Ui(t,e){return!(!e&&ji(t)||t>=2304&&t<=3583||t>=3840&&t<=4255||zi.Khmer(t))}function Vi(t){for(const e of t)if(ji(e.charCodeAt(0)))return!0;return!1}const qi=new class{constructor(){this.applyArabicShaping=null,this.processBidirectionalText=null,this.processStyledBidirectionalText=null,this.pluginStatus=\"unavailable\",this.pluginURL=null}setState(t){this.pluginStatus=t.pluginStatus,this.pluginURL=t.pluginURL}getState(){return{pluginStatus:this.pluginStatus,pluginURL:this.pluginURL}}setMethods(t){this.applyArabicShaping=t.applyArabicShaping,this.processBidirectionalText=t.processBidirectionalText,this.processStyledBidirectionalText=t.processStyledBidirectionalText}isParsed(){return null!=this.applyArabicShaping&&null!=this.processBidirectionalText&&null!=this.processStyledBidirectionalText}getPluginURL(){return this.pluginURL}getRTLTextPluginStatus(){return this.pluginStatus}};class Hi{constructor(t,e){this.zoom=t,e?(this.now=e.now,this.fadeDuration=e.fadeDuration,this.zoomHistory=e.zoomHistory,this.transition=e.transition):(this.now=0,this.fadeDuration=0,this.zoomHistory=new Pi,this.transition={})}isSupportedScript(t){return function(t,e){for(const r of t)if(!Ui(r.charCodeAt(0),e))return!1;return!0}(t,\"loaded\"===qi.getRTLTextPluginStatus())}crossFadingFactor(){return 0===this.fadeDuration?1:Math.min((this.now-this.zoomHistory.lastIntegerZoomTime)/this.fadeDuration,1)}getCrossfadeParameters(){const t=this.zoom,e=t-Math.floor(t),r=this.crossFadingFactor();return t>this.zoomHistory.lastIntegerZoom?{fromScale:2,toScale:1,t:e+(1-e)*r}:{fromScale:.5,toScale:1,t:1-(1-r)*e}}}class Gi{constructor(t,e){this.property=t,this.value=e,this.expression=function(t,e){if(mn(t))return new Cn(t,e);if(kn(t)){const r=En(t,e);if(\"error\"===r.result)throw new Error(r.value.map((t=>`${t.key}: ${t.message}`)).join(\", \"));return r.value}{let r=t;return\"color\"===e.type&&\"string\"==typeof t?r=Xt.parse(t):\"padding\"!==e.type||\"number\"!=typeof t&&!Array.isArray(t)?\"variableAnchorOffsetCollection\"===e.type&&Array.isArray(t)&&(r=ee.parse(t)):r=Qt.parse(t),{kind:\"constant\",evaluate:()=>r}}}(void 0===e?t.specification.default:e,t.specification)}isDataDriven(){return\"source\"===this.expression.kind||\"composite\"===this.expression.kind}possiblyEvaluate(t,e,r){return this.property.possiblyEvaluate(this,t,e,r)}}class Zi{constructor(t){this.property=t,this.value=new Gi(t,void 0)}transitioned(t,e){return new Yi(this.property,this.value,e,y({},t.transition,this.transition),t.now)}untransitioned(){return new Yi(this.property,this.value,null,{},0)}}class Wi{constructor(t){this._properties=t,this._values=Object.create(t.defaultTransitionablePropertyValues)}getValue(t){return b(this._values[t].value.value)}setValue(t,e){Object.prototype.hasOwnProperty.call(this._values,t)||(this._values[t]=new Zi(this._values[t].property)),this._values[t].value=new Gi(this._values[t].property,null===e?void 0:b(e))}getTransition(t){return b(this._values[t].transition)}setTransition(t,e){Object.prototype.hasOwnProperty.call(this._values,t)||(this._values[t]=new Zi(this._values[t].property)),this._values[t].transition=b(e)||void 0}serialize(){const t={};for(const e of Object.keys(this._values)){const r=this.getValue(e);void 0!==r&&(t[e]=r);const n=this.getTransition(e);void 0!==n&&(t[`${e}-transition`]=n)}return t}transitioned(t,e){const r=new Xi(this._properties);for(const n of Object.keys(this._values))r._values[n]=this._values[n].transitioned(t,e._values[n]);return r}untransitioned(){const t=new Xi(this._properties);for(const e of Object.keys(this._values))t._values[e]=this._values[e].untransitioned();return t}}class Yi{constructor(t,e,r,n,i){this.property=t,this.value=e,this.begin=i+n.delay||0,this.end=this.begin+n.duration||0,t.specification.transition&&(n.delay||n.duration)&&(this.prior=r)}possiblyEvaluate(t,e,r){const n=t.now||0,i=this.value.possiblyEvaluate(t,e,r),a=this.prior;if(a){if(n>this.end)return this.prior=null,i;if(this.value.isDataDriven())return this.prior=null,i;if(n=1)return 1;const e=t*t,r=e*t;return 4*(t<.5?r:3*(t-e)+r-.75)}(o))}}return i}}class Xi{constructor(t){this._properties=t,this._values=Object.create(t.defaultTransitioningPropertyValues)}possiblyEvaluate(t,e,r){const n=new Ki(this._properties);for(const i of Object.keys(this._values))n._values[i]=this._values[i].possiblyEvaluate(t,e,r);return n}hasTransition(){for(const t of Object.keys(this._values))if(this._values[t].prior)return!0;return!1}}class $i{constructor(t){this._properties=t,this._values=Object.create(t.defaultPropertyValues)}hasValue(t){return void 0!==this._values[t].value}getValue(t){return b(this._values[t].value)}setValue(t,e){this._values[t]=new Gi(this._values[t].property,null===e?void 0:b(e))}serialize(){const t={};for(const e of Object.keys(this._values)){const r=this.getValue(e);void 0!==r&&(t[e]=r)}return t}possiblyEvaluate(t,e,r){const n=new Ki(this._properties);for(const i of Object.keys(this._values))n._values[i]=this._values[i].possiblyEvaluate(t,e,r);return n}}class Ji{constructor(t,e,r){this.property=t,this.value=e,this.parameters=r}isConstant(){return\"constant\"===this.value.kind}constantOr(t){return\"constant\"===this.value.kind?this.value.value:t}evaluate(t,e,r,n){return this.property.evaluate(this.value,this.parameters,t,e,r,n)}}class Ki{constructor(t){this._properties=t,this._values=Object.create(t.defaultPossiblyEvaluatedValues)}get(t){return this._values[t]}}class Qi{constructor(t){this.specification=t}possiblyEvaluate(t,e){if(t.isDataDriven())throw new Error(\"Value should not be data driven\");return t.expression.evaluate(e)}interpolate(t,e,r){const n=this.specification.type,i=Pe[n];return i?i(t,e,r):t}}class ta{constructor(t,e){this.specification=t,this.overrides=e}possiblyEvaluate(t,e,r,n){return\"constant\"===t.expression.kind||\"camera\"===t.expression.kind?new Ji(this,{kind:\"constant\",value:t.expression.evaluate(e,null,{},r,n)},e):new Ji(this,t.expression,e)}interpolate(t,e,r){if(\"constant\"!==t.value.kind||\"constant\"!==e.value.kind)return t;if(void 0===t.value.value||void 0===e.value.value)return new Ji(this,{kind:\"constant\",value:void 0},t.parameters);const n=this.specification.type,i=Pe[n];if(i){const n=i(t.value.value,e.value.value,r);return new Ji(this,{kind:\"constant\",value:n},t.parameters)}return t}evaluate(t,e,r,n,i,a){return\"constant\"===t.kind?t.value:t.evaluate(e,r,n,i,a)}}class ea extends ta{possiblyEvaluate(t,e,r,n){if(void 0===t.value)return new Ji(this,{kind:\"constant\",value:void 0},e);if(\"constant\"===t.expression.kind){const i=t.expression.evaluate(e,null,{},r,n),a=\"resolvedImage\"===t.property.specification.type&&\"string\"!=typeof i?i.name:i,o=this._calculate(a,a,a,e);return new Ji(this,{kind:\"constant\",value:o},e)}if(\"camera\"===t.expression.kind){const r=this._calculate(t.expression.evaluate({zoom:e.zoom-1}),t.expression.evaluate({zoom:e.zoom}),t.expression.evaluate({zoom:e.zoom+1}),e);return new Ji(this,{kind:\"constant\",value:r},e)}return new Ji(this,t.expression,e)}evaluate(t,e,r,n,i,a){if(\"source\"===t.kind){const o=t.evaluate(e,r,n,i,a);return this._calculate(o,o,o,e)}return\"composite\"===t.kind?this._calculate(t.evaluate({zoom:Math.floor(e.zoom)-1},r,n),t.evaluate({zoom:Math.floor(e.zoom)},r,n),t.evaluate({zoom:Math.floor(e.zoom)+1},r,n),e):t.value}_calculate(t,e,r,n){return n.zoom>n.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}}interpolate(t){return t}}class ra{constructor(t){this.specification=t}possiblyEvaluate(t,e,r,n){if(void 0!==t.value){if(\"constant\"===t.expression.kind){const i=t.expression.evaluate(e,null,{},r,n);return this._calculate(i,i,i,e)}return this._calculate(t.expression.evaluate(new Hi(Math.floor(e.zoom-1),e)),t.expression.evaluate(new Hi(Math.floor(e.zoom),e)),t.expression.evaluate(new Hi(Math.floor(e.zoom+1),e)),e)}}_calculate(t,e,r,n){return n.zoom>n.zoomHistory.lastIntegerZoom?{from:t,to:e}:{from:r,to:e}}interpolate(t){return t}}class na{constructor(t){this.specification=t}possiblyEvaluate(t,e,r,n){return!!t.expression.evaluate(e,null,{},r,n)}interpolate(){return!1}}class ia{constructor(t){this.properties=t,this.defaultPropertyValues={},this.defaultTransitionablePropertyValues={},this.defaultTransitioningPropertyValues={},this.defaultPossiblyEvaluatedValues={},this.overridableProperties=[];for(const e in t){const r=t[e];r.specification.overridable&&this.overridableProperties.push(e);const n=this.defaultPropertyValues[e]=new Gi(r,void 0),i=this.defaultTransitionablePropertyValues[e]=new Zi(r);this.defaultTransitioningPropertyValues[e]=i.untransitioned(),this.defaultPossiblyEvaluatedValues[e]=n.possiblyEvaluate({})}}}Mi(\"DataDrivenProperty\",ta),Mi(\"DataConstantProperty\",Qi),Mi(\"CrossFadedDataDrivenProperty\",ea),Mi(\"CrossFadedProperty\",ra),Mi(\"ColorRampProperty\",na);const aa=\"-transition\";class oa extends G{constructor(t,e){if(super(),this.id=t.id,this.type=t.type,this._featureFilter={filter:()=>!0,needGeometry:!1},\"custom\"!==t.type&&(this.metadata=t.metadata,this.minzoom=t.minzoom,this.maxzoom=t.maxzoom,\"background\"!==t.type&&(this.source=t.source,this.sourceLayer=t[\"source-layer\"],this.filter=t.filter),e.layout&&(this._unevaluatedLayout=new $i(e.layout)),e.paint)){this._transitionablePaint=new Wi(e.paint);for(const e in t.paint)this.setPaintProperty(e,t.paint[e],{validate:!1});for(const e in t.layout)this.setLayoutProperty(e,t.layout[e],{validate:!1});this._transitioningPaint=this._transitionablePaint.untransitioned(),this.paint=new Ki(e.paint)}}getCrossfadeParameters(){return this._crossfadeParameters}getLayoutProperty(t){return\"visibility\"===t?this.visibility:this._unevaluatedLayout.getValue(t)}setLayoutProperty(t,e,r={}){if(null!=e){const n=`layers.${this.id}.layout.${t}`;if(this._validate(wi,n,t,e,r))return}\"visibility\"!==t?this._unevaluatedLayout.setValue(t,e):this.visibility=e}getPaintProperty(t){return t.endsWith(aa)?this._transitionablePaint.getTransition(t.slice(0,-11)):this._transitionablePaint.getValue(t)}setPaintProperty(t,e,r={}){if(null!=e){const n=`layers.${this.id}.paint.${t}`;if(this._validate(bi,n,t,e,r))return!1}if(t.endsWith(aa))return this._transitionablePaint.setTransition(t.slice(0,-11),e||void 0),!1;{const r=this._transitionablePaint._values[t],n=\"cross-faded-data-driven\"===r.property.specification[\"property-type\"],i=r.value.isDataDriven(),a=r.value;this._transitionablePaint.setValue(t,e),this._handleSpecialPaintPropertyUpdate(t);const o=this._transitionablePaint._values[t].value;return o.isDataDriven()||i||n||this._handleOverridablePaintPropertyUpdate(t,a,o)}}_handleSpecialPaintPropertyUpdate(t){}_handleOverridablePaintPropertyUpdate(t,e,r){return!1}isHidden(t){return!!(this.minzoom&&t=this.maxzoom)||\"none\"===this.visibility}updateTransitions(t){this._transitioningPaint=this._transitionablePaint.transitioned(t,this._transitioningPaint)}hasTransition(){return this._transitioningPaint.hasTransition()}recalculate(t,e){t.getCrossfadeParameters&&(this._crossfadeParameters=t.getCrossfadeParameters()),this._unevaluatedLayout&&(this.layout=this._unevaluatedLayout.possiblyEvaluate(t,void 0,e)),this.paint=this._transitioningPaint.possiblyEvaluate(t,void 0,e)}serialize(){const t={id:this.id,type:this.type,source:this.source,\"source-layer\":this.sourceLayer,metadata:this.metadata,minzoom:this.minzoom,maxzoom:this.maxzoom,filter:this.filter,layout:this._unevaluatedLayout&&this._unevaluatedLayout.serialize(),paint:this._transitionablePaint&&this._transitionablePaint.serialize()};return this.visibility&&(t.layout=t.layout||{},t.layout.visibility=this.visibility),_(t,((t,e)=>!(void 0===t||\"layout\"===e&&!Object.keys(t).length||\"paint\"===e&&!Object.keys(t).length)))}_validate(t,e,r,n,i={}){return(!i||!1!==i.validate)&&Ti(this,t.call(vi,{key:e,layerType:this.type,objectKey:r,value:n,styleSpec:Z,style:{glyphs:!0,sprite:!0}}))}is3D(){return!1}isTileClipped(){return!1}hasOffscreenPass(){return!1}resize(){}isStateDependent(){for(const t in this.paint._values){const e=this.paint.get(t);if(e instanceof Ji&&hn(e.property.specification)&&(\"source\"===e.value.kind||\"composite\"===e.value.kind)&&e.value.isStateDependent)return!0}return!1}}const sa={Int8:Int8Array,Uint8:Uint8Array,Int16:Int16Array,Uint16:Uint16Array,Int32:Int32Array,Uint32:Uint32Array,Float32:Float32Array};class la{constructor(t,e){this._structArray=t,this._pos1=e*this.size,this._pos2=this._pos1/2,this._pos4=this._pos1/4,this._pos8=this._pos1/8}}class ca{constructor(){this.isTransferred=!1,this.capacity=-1,this.resize(0)}static serialize(t,e){return t._trim(),e&&(t.isTransferred=!0,e.push(t.arrayBuffer)),{length:t.length,arrayBuffer:t.arrayBuffer}}static deserialize(t){const e=Object.create(this.prototype);return e.arrayBuffer=t.arrayBuffer,e.length=t.length,e.capacity=t.arrayBuffer.byteLength/e.bytesPerElement,e._refreshViews(),e}_trim(){this.length!==this.capacity&&(this.capacity=this.length,this.arrayBuffer=this.arrayBuffer.slice(0,this.length*this.bytesPerElement),this._refreshViews())}clear(){this.length=0}resize(t){this.reserve(t),this.length=t}reserve(t){if(t>this.capacity){this.capacity=Math.max(t,Math.floor(5*this.capacity),128),this.arrayBuffer=new ArrayBuffer(this.capacity*this.bytesPerElement);const e=this.uint8;this._refreshViews(),e&&this.uint8.set(e)}}_refreshViews(){throw new Error(\"_refreshViews() must be implemented by each concrete StructArray layout\")}}function ua(t,e=1){let r=0,n=0;return{members:t.map((t=>{const i=(s=t.type,sa[s].BYTES_PER_ELEMENT),a=r=ha(r,Math.max(e,i)),o=t.components||1;var s;return n=Math.max(n,i),r+=i*o,{name:t.name,type:t.type,components:o,offset:a}})),size:ha(r,Math.max(n,e)),alignment:e}}function ha(t,e){return Math.ceil(t/e)*e}class fa extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(t,e){const r=this.length;return this.resize(r+1),this.emplace(r,t,e)}emplace(t,e,r){const n=2*t;return this.int16[n+0]=e,this.int16[n+1]=r,t}}fa.prototype.bytesPerElement=4,Mi(\"StructArrayLayout2i4\",fa);class pa extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(t,e,r){const n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)}emplace(t,e,r,n){const i=3*t;return this.int16[i+0]=e,this.int16[i+1]=r,this.int16[i+2]=n,t}}pa.prototype.bytesPerElement=6,Mi(\"StructArrayLayout3i6\",pa);class da extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(t,e,r,n){const i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)}emplace(t,e,r,n,i){const a=4*t;return this.int16[a+0]=e,this.int16[a+1]=r,this.int16[a+2]=n,this.int16[a+3]=i,t}}da.prototype.bytesPerElement=8,Mi(\"StructArrayLayout4i8\",da);class ma extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(t,e,r,n,i,a){const o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)}emplace(t,e,r,n,i,a,o){const s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t}}ma.prototype.bytesPerElement=12,Mi(\"StructArrayLayout2i4i12\",ma);class ga extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(t,e,r,n,i,a){const o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)}emplace(t,e,r,n,i,a,o){const s=4*t,l=8*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.uint8[l+4]=n,this.uint8[l+5]=i,this.uint8[l+6]=a,this.uint8[l+7]=o,t}}ga.prototype.bytesPerElement=8,Mi(\"StructArrayLayout2i4ub8\",ga);class ya extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(t,e){const r=this.length;return this.resize(r+1),this.emplace(r,t,e)}emplace(t,e,r){const n=2*t;return this.float32[n+0]=e,this.float32[n+1]=r,t}}ya.prototype.bytesPerElement=8,Mi(\"StructArrayLayout2f8\",ya);class va extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(t,e,r,n,i,a,o,s,l,c){const u=this.length;return this.resize(u+1),this.emplace(u,t,e,r,n,i,a,o,s,l,c)}emplace(t,e,r,n,i,a,o,s,l,c,u){const h=10*t;return this.uint16[h+0]=e,this.uint16[h+1]=r,this.uint16[h+2]=n,this.uint16[h+3]=i,this.uint16[h+4]=a,this.uint16[h+5]=o,this.uint16[h+6]=s,this.uint16[h+7]=l,this.uint16[h+8]=c,this.uint16[h+9]=u,t}}va.prototype.bytesPerElement=20,Mi(\"StructArrayLayout10ui20\",va);class xa extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(t,e,r,n,i,a,o,s,l,c,u,h){const f=this.length;return this.resize(f+1),this.emplace(f,t,e,r,n,i,a,o,s,l,c,u,h)}emplace(t,e,r,n,i,a,o,s,l,c,u,h,f){const p=12*t;return this.int16[p+0]=e,this.int16[p+1]=r,this.int16[p+2]=n,this.int16[p+3]=i,this.uint16[p+4]=a,this.uint16[p+5]=o,this.uint16[p+6]=s,this.uint16[p+7]=l,this.int16[p+8]=c,this.int16[p+9]=u,this.int16[p+10]=h,this.int16[p+11]=f,t}}xa.prototype.bytesPerElement=24,Mi(\"StructArrayLayout4i4ui4i24\",xa);class _a extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(t,e,r){const n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)}emplace(t,e,r,n){const i=3*t;return this.float32[i+0]=e,this.float32[i+1]=r,this.float32[i+2]=n,t}}_a.prototype.bytesPerElement=12,Mi(\"StructArrayLayout3f12\",_a);class ba extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer)}emplaceBack(t){const e=this.length;return this.resize(e+1),this.emplace(e,t)}emplace(t,e){const r=1*t;return this.uint32[r+0]=e,t}}ba.prototype.bytesPerElement=4,Mi(\"StructArrayLayout1ul4\",ba);class wa extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(t,e,r,n,i,a,o,s,l){const c=this.length;return this.resize(c+1),this.emplace(c,t,e,r,n,i,a,o,s,l)}emplace(t,e,r,n,i,a,o,s,l,c){const u=10*t,h=5*t;return this.int16[u+0]=e,this.int16[u+1]=r,this.int16[u+2]=n,this.int16[u+3]=i,this.int16[u+4]=a,this.int16[u+5]=o,this.uint32[h+3]=s,this.uint16[u+8]=l,this.uint16[u+9]=c,t}}wa.prototype.bytesPerElement=20,Mi(\"StructArrayLayout6i1ul2ui20\",wa);class Ta extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(t,e,r,n,i,a){const o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)}emplace(t,e,r,n,i,a,o){const s=6*t;return this.int16[s+0]=e,this.int16[s+1]=r,this.int16[s+2]=n,this.int16[s+3]=i,this.int16[s+4]=a,this.int16[s+5]=o,t}}Ta.prototype.bytesPerElement=12,Mi(\"StructArrayLayout2i2i2i12\",Ta);class ka extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(t,e,r,n,i){const a=this.length;return this.resize(a+1),this.emplace(a,t,e,r,n,i)}emplace(t,e,r,n,i,a){const o=4*t,s=8*t;return this.float32[o+0]=e,this.float32[o+1]=r,this.float32[o+2]=n,this.int16[s+6]=i,this.int16[s+7]=a,t}}ka.prototype.bytesPerElement=16,Mi(\"StructArrayLayout2f1f2i16\",ka);class Aa extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer)}emplaceBack(t,e,r,n,i,a){const o=this.length;return this.resize(o+1),this.emplace(o,t,e,r,n,i,a)}emplace(t,e,r,n,i,a,o){const s=16*t,l=4*t,c=8*t;return this.uint8[s+0]=e,this.uint8[s+1]=r,this.float32[l+1]=n,this.float32[l+2]=i,this.int16[c+6]=a,this.int16[c+7]=o,t}}Aa.prototype.bytesPerElement=16,Mi(\"StructArrayLayout2ub2f2i16\",Aa);class Ma extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(t,e,r){const n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)}emplace(t,e,r,n){const i=3*t;return this.uint16[i+0]=e,this.uint16[i+1]=r,this.uint16[i+2]=n,t}}Ma.prototype.bytesPerElement=6,Mi(\"StructArrayLayout3ui6\",Ma);class Sa extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(t,e,r,n,i,a,o,s,l,c,u,h,f,p,d,m,g){const y=this.length;return this.resize(y+1),this.emplace(y,t,e,r,n,i,a,o,s,l,c,u,h,f,p,d,m,g)}emplace(t,e,r,n,i,a,o,s,l,c,u,h,f,p,d,m,g,y){const v=24*t,x=12*t,_=48*t;return this.int16[v+0]=e,this.int16[v+1]=r,this.uint16[v+2]=n,this.uint16[v+3]=i,this.uint32[x+2]=a,this.uint32[x+3]=o,this.uint32[x+4]=s,this.uint16[v+10]=l,this.uint16[v+11]=c,this.uint16[v+12]=u,this.float32[x+7]=h,this.float32[x+8]=f,this.uint8[_+36]=p,this.uint8[_+37]=d,this.uint8[_+38]=m,this.uint32[x+10]=g,this.int16[v+22]=y,t}}Sa.prototype.bytesPerElement=48,Mi(\"StructArrayLayout2i2ui3ul3ui2f3ub1ul1i48\",Sa);class Ea extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.int16=new Int16Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(t,e,r,n,i,a,o,s,l,c,u,h,f,p,d,m,g,y,v,x,_,b,w,T,k,A,M,S){const E=this.length;return this.resize(E+1),this.emplace(E,t,e,r,n,i,a,o,s,l,c,u,h,f,p,d,m,g,y,v,x,_,b,w,T,k,A,M,S)}emplace(t,e,r,n,i,a,o,s,l,c,u,h,f,p,d,m,g,y,v,x,_,b,w,T,k,A,M,S,E){const C=32*t,L=16*t;return this.int16[C+0]=e,this.int16[C+1]=r,this.int16[C+2]=n,this.int16[C+3]=i,this.int16[C+4]=a,this.int16[C+5]=o,this.int16[C+6]=s,this.int16[C+7]=l,this.uint16[C+8]=c,this.uint16[C+9]=u,this.uint16[C+10]=h,this.uint16[C+11]=f,this.uint16[C+12]=p,this.uint16[C+13]=d,this.uint16[C+14]=m,this.uint16[C+15]=g,this.uint16[C+16]=y,this.uint16[C+17]=v,this.uint16[C+18]=x,this.uint16[C+19]=_,this.uint16[C+20]=b,this.uint16[C+21]=w,this.uint16[C+22]=T,this.uint32[L+12]=k,this.float32[L+13]=A,this.float32[L+14]=M,this.uint16[C+30]=S,this.uint16[C+31]=E,t}}Ea.prototype.bytesPerElement=64,Mi(\"StructArrayLayout8i15ui1ul2f2ui64\",Ea);class Ca extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(t){const e=this.length;return this.resize(e+1),this.emplace(e,t)}emplace(t,e){const r=1*t;return this.float32[r+0]=e,t}}Ca.prototype.bytesPerElement=4,Mi(\"StructArrayLayout1f4\",Ca);class La extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(t,e,r){const n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)}emplace(t,e,r,n){const i=6*t,a=3*t;return this.uint16[i+0]=e,this.float32[a+1]=r,this.float32[a+2]=n,t}}La.prototype.bytesPerElement=12,Mi(\"StructArrayLayout1ui2f12\",La);class Ia extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint32=new Uint32Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(t,e,r){const n=this.length;return this.resize(n+1),this.emplace(n,t,e,r)}emplace(t,e,r,n){const i=2*t,a=4*t;return this.uint32[i+0]=e,this.uint16[a+2]=r,this.uint16[a+3]=n,t}}Ia.prototype.bytesPerElement=8,Mi(\"StructArrayLayout1ul2ui8\",Ia);class Pa extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(t,e){const r=this.length;return this.resize(r+1),this.emplace(r,t,e)}emplace(t,e,r){const n=2*t;return this.uint16[n+0]=e,this.uint16[n+1]=r,t}}Pa.prototype.bytesPerElement=4,Mi(\"StructArrayLayout2ui4\",Pa);class za extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.uint16=new Uint16Array(this.arrayBuffer)}emplaceBack(t){const e=this.length;return this.resize(e+1),this.emplace(e,t)}emplace(t,e){const r=1*t;return this.uint16[r+0]=e,t}}za.prototype.bytesPerElement=2,Mi(\"StructArrayLayout1ui2\",za);class Oa extends ca{_refreshViews(){this.uint8=new Uint8Array(this.arrayBuffer),this.float32=new Float32Array(this.arrayBuffer)}emplaceBack(t,e,r,n){const i=this.length;return this.resize(i+1),this.emplace(i,t,e,r,n)}emplace(t,e,r,n,i){const a=4*t;return this.float32[a+0]=e,this.float32[a+1]=r,this.float32[a+2]=n,this.float32[a+3]=i,t}}Oa.prototype.bytesPerElement=16,Mi(\"StructArrayLayout4f16\",Oa);class Da extends la{get anchorPointX(){return this._structArray.int16[this._pos2+0]}get anchorPointY(){return this._structArray.int16[this._pos2+1]}get x1(){return this._structArray.int16[this._pos2+2]}get y1(){return this._structArray.int16[this._pos2+3]}get x2(){return this._structArray.int16[this._pos2+4]}get y2(){return this._structArray.int16[this._pos2+5]}get featureIndex(){return this._structArray.uint32[this._pos4+3]}get sourceLayerIndex(){return this._structArray.uint16[this._pos2+8]}get bucketIndex(){return this._structArray.uint16[this._pos2+9]}get anchorPoint(){return new a(this.anchorPointX,this.anchorPointY)}}Da.prototype.size=20;class Ra extends wa{get(t){return new Da(this,t)}}Mi(\"CollisionBoxArray\",Ra);class Fa extends la{get anchorX(){return this._structArray.int16[this._pos2+0]}get anchorY(){return this._structArray.int16[this._pos2+1]}get glyphStartIndex(){return this._structArray.uint16[this._pos2+2]}get numGlyphs(){return this._structArray.uint16[this._pos2+3]}get vertexStartIndex(){return this._structArray.uint32[this._pos4+2]}get lineStartIndex(){return this._structArray.uint32[this._pos4+3]}get lineLength(){return this._structArray.uint32[this._pos4+4]}get segment(){return this._structArray.uint16[this._pos2+10]}get lowerSize(){return this._structArray.uint16[this._pos2+11]}get upperSize(){return this._structArray.uint16[this._pos2+12]}get lineOffsetX(){return this._structArray.float32[this._pos4+7]}get lineOffsetY(){return this._structArray.float32[this._pos4+8]}get writingMode(){return this._structArray.uint8[this._pos1+36]}get placedOrientation(){return this._structArray.uint8[this._pos1+37]}set placedOrientation(t){this._structArray.uint8[this._pos1+37]=t}get hidden(){return this._structArray.uint8[this._pos1+38]}set hidden(t){this._structArray.uint8[this._pos1+38]=t}get crossTileID(){return this._structArray.uint32[this._pos4+10]}set crossTileID(t){this._structArray.uint32[this._pos4+10]=t}get associatedIconIndex(){return this._structArray.int16[this._pos2+22]}}Fa.prototype.size=48;class Ba extends Sa{get(t){return new Fa(this,t)}}Mi(\"PlacedSymbolArray\",Ba);class Na extends la{get anchorX(){return this._structArray.int16[this._pos2+0]}get anchorY(){return this._structArray.int16[this._pos2+1]}get rightJustifiedTextSymbolIndex(){return this._structArray.int16[this._pos2+2]}get centerJustifiedTextSymbolIndex(){return this._structArray.int16[this._pos2+3]}get leftJustifiedTextSymbolIndex(){return this._structArray.int16[this._pos2+4]}get verticalPlacedTextSymbolIndex(){return this._structArray.int16[this._pos2+5]}get placedIconSymbolIndex(){return this._structArray.int16[this._pos2+6]}get verticalPlacedIconSymbolIndex(){return this._structArray.int16[this._pos2+7]}get key(){return this._structArray.uint16[this._pos2+8]}get textBoxStartIndex(){return this._structArray.uint16[this._pos2+9]}get textBoxEndIndex(){return this._structArray.uint16[this._pos2+10]}get verticalTextBoxStartIndex(){return this._structArray.uint16[this._pos2+11]}get verticalTextBoxEndIndex(){return this._structArray.uint16[this._pos2+12]}get iconBoxStartIndex(){return this._structArray.uint16[this._pos2+13]}get iconBoxEndIndex(){return this._structArray.uint16[this._pos2+14]}get verticalIconBoxStartIndex(){return this._structArray.uint16[this._pos2+15]}get verticalIconBoxEndIndex(){return this._structArray.uint16[this._pos2+16]}get featureIndex(){return this._structArray.uint16[this._pos2+17]}get numHorizontalGlyphVertices(){return this._structArray.uint16[this._pos2+18]}get numVerticalGlyphVertices(){return this._structArray.uint16[this._pos2+19]}get numIconVertices(){return this._structArray.uint16[this._pos2+20]}get numVerticalIconVertices(){return this._structArray.uint16[this._pos2+21]}get useRuntimeCollisionCircles(){return this._structArray.uint16[this._pos2+22]}get crossTileID(){return this._structArray.uint32[this._pos4+12]}set crossTileID(t){this._structArray.uint32[this._pos4+12]=t}get textBoxScale(){return this._structArray.float32[this._pos4+13]}get collisionCircleDiameter(){return this._structArray.float32[this._pos4+14]}get textAnchorOffsetStartIndex(){return this._structArray.uint16[this._pos2+30]}get textAnchorOffsetEndIndex(){return this._structArray.uint16[this._pos2+31]}}Na.prototype.size=64;class ja extends Ea{get(t){return new Na(this,t)}}Mi(\"SymbolInstanceArray\",ja);class Ua extends Ca{getoffsetX(t){return this.float32[1*t+0]}}Mi(\"GlyphOffsetArray\",Ua);class Va extends pa{getx(t){return this.int16[3*t+0]}gety(t){return this.int16[3*t+1]}gettileUnitDistanceFromAnchor(t){return this.int16[3*t+2]}}Mi(\"SymbolLineVertexArray\",Va);class qa extends la{get textAnchor(){return this._structArray.uint16[this._pos2+0]}get textOffset0(){return this._structArray.float32[this._pos4+1]}get textOffset1(){return this._structArray.float32[this._pos4+2]}}qa.prototype.size=12;class Ha extends La{get(t){return new qa(this,t)}}Mi(\"TextAnchorOffsetArray\",Ha);class Ga extends la{get featureIndex(){return this._structArray.uint32[this._pos4+0]}get sourceLayerIndex(){return this._structArray.uint16[this._pos2+2]}get bucketIndex(){return this._structArray.uint16[this._pos2+3]}}Ga.prototype.size=8;class Za extends Ia{get(t){return new Ga(this,t)}}Mi(\"FeatureIndexArray\",Za);class Wa extends fa{}class Ya extends fa{}class Xa extends fa{}class $a extends ma{}class Ja extends ga{}class Ka extends ya{}class Qa extends va{}class to extends xa{}class eo extends _a{}class ro extends ba{}class no extends Ta{}class io extends Aa{}class ao extends Ma{}class oo extends Pa{}const so=ua([{name:\"a_pos\",components:2,type:\"Int16\"}],4),{members:lo,size:co,alignment:uo}=so;class ho{constructor(t=[]){this.segments=t}prepareSegment(t,e,r,n){let i=this.segments[this.segments.length-1];return t>ho.MAX_VERTEX_ARRAY_LENGTH&&T(`Max vertices per segment is ${ho.MAX_VERTEX_ARRAY_LENGTH}: bucket requested ${t}`),(!i||i.vertexLength+t>ho.MAX_VERTEX_ARRAY_LENGTH||i.sortKey!==n)&&(i={vertexOffset:e.length,primitiveOffset:r.length,vertexLength:0,primitiveLength:0},void 0!==n&&(i.sortKey=n),this.segments.push(i)),i}get(){return this.segments}destroy(){for(const t of this.segments)for(const e in t.vaos)t.vaos[e].destroy()}static simpleSegment(t,e,r,n){return new ho([{vertexOffset:t,primitiveOffset:e,vertexLength:r,primitiveLength:n,vaos:{},sortKey:0}])}}function fo(t,e){return 256*(t=m(Math.floor(t),0,255))+m(Math.floor(e),0,255)}ho.MAX_VERTEX_ARRAY_LENGTH=Math.pow(2,16)-1,Mi(\"SegmentVector\",ho);const po=ua([{name:\"a_pattern_from\",components:4,type:\"Uint16\"},{name:\"a_pattern_to\",components:4,type:\"Uint16\"},{name:\"a_pixel_ratio_from\",components:1,type:\"Uint16\"},{name:\"a_pixel_ratio_to\",components:1,type:\"Uint16\"}]);var mo={exports:{}},go={exports:{}};!function(t){t.exports=function(t,e){var r,n,i,a,o,s,l,c;for(r=3&t.length,n=t.length-r,i=e,o=3432918353,s=461845907,c=0;c>>16)*o&65535)<<16)&4294967295)<<15|l>>>17))*s+(((l>>>16)*s&65535)<<16)&4294967295)<<13|i>>>19))+((5*(i>>>16)&65535)<<16)&4294967295))+((58964+(a>>>16)&65535)<<16);switch(l=0,r){case 3:l^=(255&t.charCodeAt(c+2))<<16;case 2:l^=(255&t.charCodeAt(c+1))<<8;case 1:i^=l=(65535&(l=(l=(65535&(l^=255&t.charCodeAt(c)))*o+(((l>>>16)*o&65535)<<16)&4294967295)<<15|l>>>17))*s+(((l>>>16)*s&65535)<<16)&4294967295}return i^=t.length,i=2246822507*(65535&(i^=i>>>16))+((2246822507*(i>>>16)&65535)<<16)&4294967295,i=3266489909*(65535&(i^=i>>>13))+((3266489909*(i>>>16)&65535)<<16)&4294967295,(i^=i>>>16)>>>0}}(go);var yo=go.exports,vo={exports:{}};!function(t){t.exports=function(t,e){for(var r,n=t.length,i=e^n,a=0;n>=4;)r=1540483477*(65535&(r=255&t.charCodeAt(a)|(255&t.charCodeAt(++a))<<8|(255&t.charCodeAt(++a))<<16|(255&t.charCodeAt(++a))<<24))+((1540483477*(r>>>16)&65535)<<16),i=1540483477*(65535&i)+((1540483477*(i>>>16)&65535)<<16)^(r=1540483477*(65535&(r^=r>>>24))+((1540483477*(r>>>16)&65535)<<16)),n-=4,++a;switch(n){case 3:i^=(255&t.charCodeAt(a+2))<<16;case 2:i^=(255&t.charCodeAt(a+1))<<8;case 1:i=1540483477*(65535&(i^=255&t.charCodeAt(a)))+((1540483477*(i>>>16)&65535)<<16)}return i=1540483477*(65535&(i^=i>>>13))+((1540483477*(i>>>16)&65535)<<16),(i^=i>>>15)>>>0}}(vo);var xo=yo,_o=vo.exports;mo.exports=xo,mo.exports.murmur3=xo,mo.exports.murmur2=_o;var bo=r(mo.exports);class wo{constructor(){this.ids=[],this.positions=[],this.indexed=!1}add(t,e,r,n){this.ids.push(To(t)),this.positions.push(e,r,n)}getPositions(t){if(!this.indexed)throw new Error(\"Trying to get index, but feature positions are not indexed\");const e=To(t);let r=0,n=this.ids.length-1;for(;r>1;this.ids[t]>=e?n=t:r=t+1}const i=[];for(;this.ids[r]===e;){const t=this.positions[3*r],e=this.positions[3*r+1],n=this.positions[3*r+2];i.push({index:t,start:e,end:n}),r++}return i}static serialize(t,e){const r=new Float64Array(t.ids),n=new Uint32Array(t.positions);return ko(r,n,0,r.length-1),e&&e.push(r.buffer,n.buffer),{ids:r,positions:n}}static deserialize(t){const e=new wo;return e.ids=t.ids,e.positions=t.positions,e.indexed=!0,e}}function To(t){const e=+t;return!isNaN(e)&&e<=Number.MAX_SAFE_INTEGER?e:bo(String(t))}function ko(t,e,r,n){for(;r>1];let a=r-1,o=n+1;for(;;){do{a++}while(t[a]i);if(a>=o)break;Ao(t,a,o),Ao(e,3*a,3*o),Ao(e,3*a+1,3*o+1),Ao(e,3*a+2,3*o+2)}o-r`u_${t}`)),this.type=r}setUniform(t,e,r){t.set(r.constantOr(this.value))}getBinding(t,e,r){return\"color\"===this.type?new Co(t,e):new So(t,e)}}class zo{constructor(t,e){this.uniformNames=e.map((t=>`u_${t}`)),this.patternFrom=null,this.patternTo=null,this.pixelRatioFrom=1,this.pixelRatioTo=1}setConstantPatternPositions(t,e){this.pixelRatioFrom=e.pixelRatio,this.pixelRatioTo=t.pixelRatio,this.patternFrom=e.tlbr,this.patternTo=t.tlbr}setUniform(t,e,r,n){const i=\"u_pattern_to\"===n?this.patternTo:\"u_pattern_from\"===n?this.patternFrom:\"u_pixel_ratio_to\"===n?this.pixelRatioTo:\"u_pixel_ratio_from\"===n?this.pixelRatioFrom:null;i&&t.set(i)}getBinding(t,e,r){return\"u_pattern\"===r.substr(0,9)?new Eo(t,e):new So(t,e)}}class Oo{constructor(t,e,r,n){this.expression=t,this.type=r,this.maxValue=0,this.paintVertexAttributes=e.map((t=>({name:`a_${t}`,type:\"Float32\",components:\"color\"===r?2:1,offset:0}))),this.paintVertexArray=new n}populatePaintArray(t,e,r,n,i){const a=this.paintVertexArray.length,o=this.expression.evaluate(new Hi(0),e,{},n,[],i);this.paintVertexArray.resize(t),this._setPaintValue(a,t,o)}updatePaintArray(t,e,r,n){const i=this.expression.evaluate({zoom:0},r,n);this._setPaintValue(t,e,i)}_setPaintValue(t,e,r){if(\"color\"===this.type){const n=Io(r);for(let r=t;r`u_${t}_t`)),this.type=r,this.useIntegerZoom=n,this.zoom=i,this.maxValue=0,this.paintVertexAttributes=e.map((t=>({name:`a_${t}`,type:\"Float32\",components:\"color\"===r?4:2,offset:0}))),this.paintVertexArray=new a}populatePaintArray(t,e,r,n,i){const a=this.expression.evaluate(new Hi(this.zoom),e,{},n,[],i),o=this.expression.evaluate(new Hi(this.zoom+1),e,{},n,[],i),s=this.paintVertexArray.length;this.paintVertexArray.resize(t),this._setPaintValue(s,t,a,o)}updatePaintArray(t,e,r,n){const i=this.expression.evaluate({zoom:this.zoom},r,n),a=this.expression.evaluate({zoom:this.zoom+1},r,n);this._setPaintValue(t,e,i,a)}_setPaintValue(t,e,r,n){if(\"color\"===this.type){const i=Io(r),a=Io(n);for(let r=t;r`#define HAS_UNIFORM_${t}`)))}return t}getBinderAttributes(){const t=[];for(const e in this.binders){const r=this.binders[e];if(r instanceof Oo||r instanceof Do)for(let e=0;e!0)){this.programConfigurations={};for(const n of t)this.programConfigurations[n.id]=new Fo(n,e,r);this.needsUpload=!1,this._featureMap=new wo,this._bufferOffset=0}populatePaintArrays(t,e,r,n,i,a){for(const r in this.programConfigurations)this.programConfigurations[r].populatePaintArrays(t,e,n,i,a);void 0!==e.id&&this._featureMap.add(e.id,r,this._bufferOffset,t),this._bufferOffset=t,this.needsUpload=!0}updatePaintArrays(t,e,r,n){for(const i of r)this.needsUpload=this.programConfigurations[i.id].updatePaintArrays(t,this._featureMap,e,i,n)||this.needsUpload}get(t){return this.programConfigurations[t]}upload(t){if(this.needsUpload){for(const e in this.programConfigurations)this.programConfigurations[e].upload(t);this.needsUpload=!1}}destroy(){for(const t in this.programConfigurations)this.programConfigurations[t].destroy()}}function 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ho,this.stateDependentLayerIds=this.layers.filter((t=>t.isStateDependent())).map((t=>t.id))}populate(t,e,r){this.hasPattern=hl(\"fill\",this.layers,e);const n=this.layers[0].layout.get(\"fill-sort-key\"),i=!n.isConstant(),a=[];for(const{feature:o,id:s,index:l,sourceLayerIndex:c}of t){const t=this.layers[0]._featureFilter.needGeometry,u=Go(o,t);if(!this.layers[0]._featureFilter.filter(new Hi(this.zoom),u,r))continue;const h=i?n.evaluate(u,{},r,e.availableImages):void 0,f={id:s,properties:o.properties,type:o.type,sourceLayerIndex:c,index:l,geometry:t?u.geometry:Ho(o),patterns:{},sortKey:h};a.push(f)}i&&a.sort(((t,e)=>t.sortKey-e.sortKey));for(const n of a){const{geometry:i,index:a,sourceLayerIndex:o}=n;if(this.hasPattern){const t=fl(\"fill\",this.layers,n,this.zoom,e);this.patternFeatures.push(t)}else this.addFeature(n,i,a,r,{});const s=t[a].feature;e.featureIndex.insert(s,i,a,o,this.index)}}update(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)}addFeatures(t,e,r){for(const t of this.patternFeatures)this.addFeature(t,t.geometry,t.index,e,r)}isEmpty(){return 0===this.layoutVertexArray.length}uploadPending(){return!this.uploaded||this.programConfigurations.needsUpload}upload(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,Fs),this.indexBuffer=t.createIndexBuffer(this.indexArray),this.indexBuffer2=t.createIndexBuffer(this.indexArray2)),this.programConfigurations.upload(t),this.uploaded=!0}destroy(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.indexBuffer2.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.segments2.destroy())}addFeature(t,e,r,n,i){for(const t of br(e,500)){let e=0;for(const r of t)e+=r.length;const r=this.segments.prepareSegment(e,this.layoutVertexArray,this.indexArray),n=r.vertexLength,i=[],a=[];for(const e of t){if(0===e.length)continue;e!==t[0]&&a.push(i.length/2);const r=this.segments2.prepareSegment(e.length,this.layoutVertexArray,this.indexArray2),n=r.vertexLength;this.layoutVertexArray.emplaceBack(e[0].x,e[0].y),this.indexArray2.emplaceBack(n+e.length-1,n),i.push(e[0].x),i.push(e[0].y);for(let t=1;t>3}if(i--,1===n||2===n)a+=t.readSVarint(),o+=t.readSVarint(),1===n&&(e&&s.push(e),e=[]),e.push(new kl(a,o));else{if(7!==n)throw new Error(\"unknown command \"+n);e&&e.push(e[0].clone())}}return e&&s.push(e),s},Ml.prototype.bbox=function(){var t=this._pbf;t.pos=this._geometry;for(var e=t.readVarint()+t.pos,r=1,n=0,i=0,a=0,o=1/0,s=-1/0,l=1/0,c=-1/0;t.pos>3}if(n--,1===r||2===r)(i+=t.readSVarint())s&&(s=i),(a+=t.readSVarint())c&&(c=a);else if(7!==r)throw new Error(\"unknown command \"+r)}return[o,l,s,c]},Ml.prototype.toGeoJSON=function(t,e,r){var n,i,a=this.extent*Math.pow(2,r),o=this.extent*t,s=this.extent*e,l=this.loadGeometry(),c=Ml.types[this.type];function u(t){for(var e=0;e>3;e=1===n?t.readString():2===n?t.readFloat():3===n?t.readDouble():4===n?t.readVarint64():5===n?t.readVarint():6===n?t.readSVarint():7===n?t.readBoolean():null}return e}(r))}Il.prototype.feature=function(t){if(t<0||t>=this._features.length)throw new Error(\"feature index out of bounds\");this._pbf.pos=this._features[t];var e=this._pbf.readVarint()+this._pbf.pos;return new Cl(this._pbf,e,this.extent,this._keys,this._values)};var zl=Ll,Ol=function(t,e){this.layers=t.readFields(Dl,{},e)};function Dl(t,e,r){if(3===t){var n=new zl(r,r.readVarint()+r.pos);n.length&&(e[n.name]=n)}}Tl.VectorTile=Ol,Tl.VectorTileFeature=Al,Tl.VectorTileLayer=Ll;const Rl=Tl.VectorTileFeature.types,Fl=Math.pow(2,13);function Bl(t,e,r,n,i,a,o,s){t.emplaceBack(e,r,2*Math.floor(n*Fl)+o,i*Fl*2,a*Fl*2,Math.round(s))}class Nl{constructor(t){this.zoom=t.zoom,this.overscaling=t.overscaling,this.layers=t.layers,this.layerIds=this.layers.map((t=>t.id)),this.index=t.index,this.hasPattern=!1,this.layoutVertexArray=new $a,this.centroidVertexArray=new Wa,this.indexArray=new ao,this.programConfigurations=new Bo(t.layers,t.zoom),this.segments=new ho,this.stateDependentLayerIds=this.layers.filter((t=>t.isStateDependent())).map((t=>t.id))}populate(t,e,r){this.features=[],this.hasPattern=hl(\"fill-extrusion\",this.layers,e);for(const{feature:n,id:i,index:a,sourceLayerIndex:o}of t){const t=this.layers[0]._featureFilter.needGeometry,s=Go(n,t);if(!this.layers[0]._featureFilter.filter(new Hi(this.zoom),s,r))continue;const l={id:i,sourceLayerIndex:o,index:a,geometry:t?s.geometry:Ho(n),properties:n.properties,type:n.type,patterns:{}};this.hasPattern?this.features.push(fl(\"fill-extrusion\",this.layers,l,this.zoom,e)):this.addFeature(l,l.geometry,a,r,{}),e.featureIndex.insert(n,l.geometry,a,o,this.index,!0)}}addFeatures(t,e,r){for(const t of this.features){const{geometry:n}=t;this.addFeature(t,n,t.index,e,r)}}update(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)}isEmpty(){return 0===this.layoutVertexArray.length&&0===this.centroidVertexArray.length}uploadPending(){return!this.uploaded||this.programConfigurations.needsUpload}upload(t){this.uploaded||(this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,_l),this.centroidVertexBuffer=t.createVertexBuffer(this.centroidVertexArray,xl.members,!0),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0}destroy(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy(),this.centroidVertexBuffer.destroy())}addFeature(t,e,r,n,i){for(const r of br(e,500)){const e={x:0,y:0,vertexCount:0};let n=0;for(const t of r)n+=t.length;let i=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray);for(const t of r){if(0===t.length)continue;if(Ul(t))continue;let r=0;for(let n=0;n=1){const o=t[n-1];if(!jl(a,o)){i.vertexLength+4>ho.MAX_VERTEX_ARRAY_LENGTH&&(i=this.segments.prepareSegment(4,this.layoutVertexArray,this.indexArray));const t=a.sub(o)._perp()._unit(),n=o.dist(a);r+n>32768&&(r=0),Bl(this.layoutVertexArray,a.x,a.y,t.x,t.y,0,0,r),Bl(this.layoutVertexArray,a.x,a.y,t.x,t.y,0,1,r),e.x+=2*a.x,e.y+=2*a.y,e.vertexCount+=2,r+=n,Bl(this.layoutVertexArray,o.x,o.y,t.x,t.y,0,0,r),Bl(this.layoutVertexArray,o.x,o.y,t.x,t.y,0,1,r),e.x+=2*o.x,e.y+=2*o.y,e.vertexCount+=2;const s=i.vertexLength;this.indexArray.emplaceBack(s,s+2,s+1),this.indexArray.emplaceBack(s+1,s+2,s+3),i.vertexLength+=4,i.primitiveLength+=2}}}}if(i.vertexLength+n>ho.MAX_VERTEX_ARRAY_LENGTH&&(i=this.segments.prepareSegment(n,this.layoutVertexArray,this.indexArray)),\"Polygon\"!==Rl[t.type])continue;const a=[],o=[],s=i.vertexLength;for(const t of r)if(0!==t.length){t!==r[0]&&o.push(a.length/2);for(let r=0;rUo)||t.y===e.y&&(t.y<0||t.y>Uo)}function Ul(t){return t.every((t=>t.x<0))||t.every((t=>t.x>Uo))||t.every((t=>t.y<0))||t.every((t=>t.y>Uo))}let Vl;Mi(\"FillExtrusionBucket\",Nl,{omit:[\"layers\",\"features\"]});var ql={get paint(){return Vl=Vl||new ia({\"fill-extrusion-opacity\":new Qi(Z[\"paint_fill-extrusion\"][\"fill-extrusion-opacity\"]),\"fill-extrusion-color\":new ta(Z[\"paint_fill-extrusion\"][\"fill-extrusion-color\"]),\"fill-extrusion-translate\":new Qi(Z[\"paint_fill-extrusion\"][\"fill-extrusion-translate\"]),\"fill-extrusion-translate-anchor\":new Qi(Z[\"paint_fill-extrusion\"][\"fill-extrusion-translate-anchor\"]),\"fill-extrusion-pattern\":new ea(Z[\"paint_fill-extrusion\"][\"fill-extrusion-pattern\"]),\"fill-extrusion-height\":new ta(Z[\"paint_fill-extrusion\"][\"fill-extrusion-height\"]),\"fill-extrusion-base\":new ta(Z[\"paint_fill-extrusion\"][\"fill-extrusion-base\"]),\"fill-extrusion-vertical-gradient\":new Qi(Z[\"paint_fill-extrusion\"][\"fill-extrusion-vertical-gradient\"])})}};class Hl extends oa{constructor(t){super(t,ql)}createBucket(t){return new Nl(t)}queryRadius(){return os(this.paint.get(\"fill-extrusion-translate\"))}is3D(){return!0}queryIntersectsFeature(t,e,r,n,i,o,s,l){const c=ss(t,this.paint.get(\"fill-extrusion-translate\"),this.paint.get(\"fill-extrusion-translate-anchor\"),o.angle,s),u=this.paint.get(\"fill-extrusion-height\").evaluate(e,r),h=this.paint.get(\"fill-extrusion-base\").evaluate(e,r),f=function(t,e,r,n){const i=[];for(const r of t){const t=[r.x,r.y,n,1];vs(t,t,e),i.push(new a(t[0]/t[3],t[1]/t[3]))}return i}(c,l,0,0),p=function(t,e,r,n){const i=[],o=[],s=n[8]*e,l=n[9]*e,c=n[10]*e,u=n[11]*e,h=n[8]*r,f=n[9]*r,p=n[10]*r,d=n[11]*r;for(const e of t){const t=[],r=[];for(const i of e){const e=i.x,o=i.y,m=n[0]*e+n[4]*o+n[12],g=n[1]*e+n[5]*o+n[13],y=n[2]*e+n[6]*o+n[14],v=n[3]*e+n[7]*o+n[15],x=y+c,_=v+u,b=m+h,w=g+f,T=y+p,k=v+d,A=new a((m+s)/_,(g+l)/_);A.z=x/_,t.push(A);const M=new a(b/k,w/k);M.z=T/k,r.push(M)}i.push(t),o.push(r)}return[i,o]}(n,h,u,l);return function(t,e,r){let n=1/0;$o(r,e)&&(n=Zl(r,e[0]));for(let i=0;it.id)),this.index=t.index,this.hasPattern=!1,this.patternFeatures=[],this.lineClipsArray=[],this.gradients={},this.layers.forEach((t=>{this.gradients[t.id]={}})),this.layoutVertexArray=new Ja,this.layoutVertexArray2=new Ka,this.indexArray=new ao,this.programConfigurations=new Bo(t.layers,t.zoom),this.segments=new ho,this.maxLineLength=0,this.stateDependentLayerIds=this.layers.filter((t=>t.isStateDependent())).map((t=>t.id))}populate(t,e,r){this.hasPattern=hl(\"line\",this.layers,e);const n=this.layers[0].layout.get(\"line-sort-key\"),i=!n.isConstant(),a=[];for(const{feature:e,id:o,index:s,sourceLayerIndex:l}of t){const t=this.layers[0]._featureFilter.needGeometry,c=Go(e,t);if(!this.layers[0]._featureFilter.filter(new Hi(this.zoom),c,r))continue;const u=i?n.evaluate(c,{},r):void 0,h={id:o,properties:e.properties,type:e.type,sourceLayerIndex:l,index:s,geometry:t?c.geometry:Ho(e),patterns:{},sortKey:u};a.push(h)}i&&a.sort(((t,e)=>t.sortKey-e.sortKey));for(const n of a){const{geometry:i,index:a,sourceLayerIndex:o}=n;if(this.hasPattern){const t=fl(\"line\",this.layers,n,this.zoom,e);this.patternFeatures.push(t)}else this.addFeature(n,i,a,r,{});const s=t[a].feature;e.featureIndex.insert(s,i,a,o,this.index)}}update(t,e,r){this.stateDependentLayers.length&&this.programConfigurations.updatePaintArrays(t,e,this.stateDependentLayers,r)}addFeatures(t,e,r){for(const t of this.patternFeatures)this.addFeature(t,t.geometry,t.index,e,r)}isEmpty(){return 0===this.layoutVertexArray.length}uploadPending(){return!this.uploaded||this.programConfigurations.needsUpload}upload(t){this.uploaded||(0!==this.layoutVertexArray2.length&&(this.layoutVertexBuffer2=t.createVertexBuffer(this.layoutVertexArray2,Kl)),this.layoutVertexBuffer=t.createVertexBuffer(this.layoutVertexArray,Yl),this.indexBuffer=t.createIndexBuffer(this.indexArray)),this.programConfigurations.upload(t),this.uploaded=!0}destroy(){this.layoutVertexBuffer&&(this.layoutVertexBuffer.destroy(),this.indexBuffer.destroy(),this.programConfigurations.destroy(),this.segments.destroy())}lineFeatureClips(t){if(t.properties&&Object.prototype.hasOwnProperty.call(t.properties,\"mapbox_clip_start\")&&Object.prototype.hasOwnProperty.call(t.properties,\"mapbox_clip_end\"))return{start:+t.properties.mapbox_clip_start,end:+t.properties.mapbox_clip_end}}addFeature(t,e,r,n,i){const a=this.layers[0].layout,o=a.get(\"line-join\").evaluate(t,{}),s=a.get(\"line-cap\"),l=a.get(\"line-miter-limit\"),c=a.get(\"line-round-limit\");this.lineClips=this.lineFeatureClips(t);for(const r of e)this.addLine(r,t,o,s,l,c);this.programConfigurations.populatePaintArrays(this.layoutVertexArray.length,t,r,i,n)}addLine(t,e,r,n,i,a){if(this.distance=0,this.scaledDistance=0,this.totalDistance=0,this.lineClips){this.lineClipsArray.push(this.lineClips);for(let e=0;e=2&&t[s-1].equals(t[s-2]);)s--;let l=0;for(;l0;if(b&&e>l){const t=h.dist(f);if(t>2*c){const e=h.sub(h.sub(f)._mult(c/t)._round());this.updateDistance(f,e),this.addCurrentVertex(e,d,0,0,u),f=e}}const T=f&&p;let k=T?r:o?\"butt\":n;if(T&&\"round\"===k&&(xi&&(k=\"bevel\"),\"bevel\"===k&&(x>2&&(k=\"flipbevel\"),x100)g=m.mult(-1);else{const t=x*d.add(m).mag()/d.sub(m).mag();g._perp()._mult(t*(w?-1:1))}this.addCurrentVertex(h,g,0,0,u),this.addCurrentVertex(h,g.mult(-1),0,0,u)}else if(\"bevel\"===k||\"fakeround\"===k){const t=-Math.sqrt(x*x-1),e=w?t:0,r=w?0:t;if(f&&this.addCurrentVertex(h,d,e,r,u),\"fakeround\"===k){const t=Math.round(180*_/Math.PI/20);for(let e=1;e2*c){const e=h.add(p.sub(h)._mult(c/t)._round());this.updateDistance(h,e),this.addCurrentVertex(e,m,0,0,u),h=e}}}}addCurrentVertex(t,e,r,n,i,a=!1){const o=e.x+e.y*r,s=e.y-e.x*r,l=-e.x+e.y*n,c=-e.y-e.x*n;this.addHalfVertex(t,o,s,a,!1,r,i),this.addHalfVertex(t,l,c,a,!0,-n,i),this.distance>nc/2&&0===this.totalDistance&&(this.distance=0,this.updateScaledDistance(),this.addCurrentVertex(t,e,r,n,i,a))}addHalfVertex({x:t,y:e},r,n,i,a,o,s){const l=.5*(this.lineClips?this.scaledDistance*(nc-1):this.scaledDistance);if(this.layoutVertexArray.emplaceBack((t<<1)+(i?1:0),(e<<1)+(a?1:0),Math.round(63*r)+128,Math.round(63*n)+128,1+(0===o?0:o<0?-1:1)|(63&l)<<2,l>>6),this.lineClips){const t=(this.scaledDistance-this.lineClips.start)/(this.lineClips.end-this.lineClips.start);this.layoutVertexArray2.emplaceBack(t,this.lineClipsArray.length)}const c=s.vertexLength++;this.e1>=0&&this.e2>=0&&(this.indexArray.emplaceBack(this.e1,this.e2,c),s.primitiveLength++),a?this.e2=c:this.e1=c}updateScaledDistance(){this.scaledDistance=this.lineClips?this.lineClips.start+(this.lineClips.end-this.lineClips.start)*this.distance/this.totalDistance:this.distance}updateDistance(t,e){this.distance+=t.dist(e),this.updateScaledDistance()}}let ac;Mi(\"LineBucket\",ic,{omit:[\"layers\",\"patternFeatures\"]});let oc;var sc={get paint(){return oc=oc||new ia({\"line-opacity\":new ta(Z.paint_line[\"line-opacity\"]),\"line-color\":new ta(Z.paint_line[\"line-color\"]),\"line-translate\":new Qi(Z.paint_line[\"line-translate\"]),\"line-translate-anchor\":new Qi(Z.paint_line[\"line-translate-anchor\"]),\"line-width\":new ta(Z.paint_line[\"line-width\"]),\"line-gap-width\":new ta(Z.paint_line[\"line-gap-width\"]),\"line-offset\":new ta(Z.paint_line[\"line-offset\"]),\"line-blur\":new ta(Z.paint_line[\"line-blur\"]),\"line-dasharray\":new ra(Z.paint_line[\"line-dasharray\"]),\"line-pattern\":new ea(Z.paint_line[\"line-pattern\"]),\"line-gradient\":new na(Z.paint_line[\"line-gradient\"])})},get layout(){return ac=ac||new ia({\"line-cap\":new Qi(Z.layout_line[\"line-cap\"]),\"line-join\":new ta(Z.layout_line[\"line-join\"]),\"line-miter-limit\":new Qi(Z.layout_line[\"line-miter-limit\"]),\"line-round-limit\":new Qi(Z.layout_line[\"line-round-limit\"]),\"line-sort-key\":new ta(Z.layout_line[\"line-sort-key\"])})}};class lc extends ta{possiblyEvaluate(t,e){return e=new Hi(Math.floor(e.zoom),{now:e.now,fadeDuration:e.fadeDuration,zoomHistory:e.zoomHistory,transition:e.transition}),super.possiblyEvaluate(t,e)}evaluate(t,e,r,n){return e=y({},e,{zoom:Math.floor(e.zoom)}),super.evaluate(t,e,r,n)}}let cc;class uc extends oa{constructor(t){super(t,sc),this.gradientVersion=0,cc||(cc=new lc(sc.paint.properties[\"line-width\"].specification),cc.useIntegerZoom=!0)}_handleSpecialPaintPropertyUpdate(t){if(\"line-gradient\"===t){const t=this.gradientExpression();!function(t){return void 0!==t._styleExpression}(t)?this.stepInterpolant=!1:this.stepInterpolant=t._styleExpression.expression instanceof Ae,this.gradientVersion=(this.gradientVersion+1)%Number.MAX_SAFE_INTEGER}}gradientExpression(){return this._transitionablePaint._values[\"line-gradient\"].value.expression}recalculate(t,e){super.recalculate(t,e),this.paint._values[\"line-floorwidth\"]=cc.possiblyEvaluate(this._transitioningPaint._values[\"line-width\"].value,t)}createBucket(t){return new ic(t)}queryRadius(t){const e=t,r=hc(as(\"line-width\",this,e),as(\"line-gap-width\",this,e)),n=as(\"line-offset\",this,e);return r/2+Math.abs(n)+os(this.paint.get(\"line-translate\"))}queryIntersectsFeature(t,e,r,n,i,o,s){const l=ss(t,this.paint.get(\"line-translate\"),this.paint.get(\"line-translate-anchor\"),o.angle,s),c=s/2*hc(this.paint.get(\"line-width\").evaluate(e,r),this.paint.get(\"line-gap-width\").evaluate(e,r)),u=this.paint.get(\"line-offset\").evaluate(e,r);return u&&(n=function(t,e){const r=[];for(let n=0;n=3)for(let e=0;e0?e+2*t:t}const fc=ua([{name:\"a_pos_offset\",components:4,type:\"Int16\"},{name:\"a_data\",components:4,type:\"Uint16\"},{name:\"a_pixeloffset\",components:4,type:\"Int16\"}],4),pc=ua([{name:\"a_projected_pos\",components:3,type:\"Float32\"}],4);ua([{name:\"a_fade_opacity\",components:1,type:\"Uint32\"}],4);const dc=ua([{name:\"a_placed\",components:2,type:\"Uint8\"},{name:\"a_shift\",components:2,type:\"Float32\"},{name:\"a_box_real\",components:2,type:\"Int16\"}]);ua([{type:\"Int16\",name:\"anchorPointX\"},{type:\"Int16\",name:\"anchorPointY\"},{type:\"Int16\",name:\"x1\"},{type:\"Int16\",name:\"y1\"},{type:\"Int16\",name:\"x2\"},{type:\"Int16\",name:\"y2\"},{type:\"Uint32\",name:\"featureIndex\"},{type:\"Uint16\",name:\"sourceLayerIndex\"},{type:\"Uint16\",name:\"bucketIndex\"}]);const mc=ua([{name:\"a_pos\",components:2,type:\"Int16\"},{name:\"a_anchor_pos\",components:2,type:\"Int16\"},{name:\"a_extrude\",components:2,type:\"Int16\"}],4),gc=ua([{name:\"a_pos\",components:2,type:\"Float32\"},{name:\"a_radius\",components:1,type:\"Float32\"},{name:\"a_flags\",components:2,type:\"Int16\"}],4);function yc(t,e,r){return t.sections.forEach((t=>{t.text=function(t,e,r){const 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t)if(0!==e.positionedGlyphs.length)return!1;return!0}(_)&&b}const ru={9:!0,10:!0,11:!0,12:!0,13:!0,32:!0},nu={10:!0,32:!0,38:!0,41:!0,43:!0,45:!0,47:!0,173:!0,183:!0,8203:!0,8208:!0,8211:!0,8231:!0},iu={40:!0};function au(t,e,r,n,i,a){if(e.imageName){const t=n[e.imageName];return t?t.displaySize[0]*e.scale*xc/a+i:0}{const n=r[e.fontStack],a=n&&n[t];return a?a.metrics.advance*e.scale+i:0}}function ou(t,e,r,n){const i=Math.pow(t-e,2);return n?t=0;let c=0;for(let r=0;rh){const t=Math.ceil(a/h);i*=t/o,o=t}return{x1:n,y1:i,x2:n+a,y2:i+o}}function mu(t,e,r,n,i,a){const o=t.image;let s;if(o.content){const t=o.content,e=o.pixelRatio||1;s=[t[0]/e,t[1]/e,o.displaySize[0]-t[2]/e,o.displaySize[1]-t[3]/e]}const l=e.left*a,c=e.right*a;let u,h,f,p;\"width\"===r||\"both\"===r?(p=i[0]+l-n[3],h=i[0]+c+n[1]):(p=i[0]+(l+c-o.displaySize[0])/2,h=p+o.displaySize[0]);const d=e.top*a,m=e.bottom*a;return\"height\"===r||\"both\"===r?(u=i[1]+d-n[0],f=i[1]+m+n[2]):(u=i[1]+(d+m-o.displaySize[1])/2,f=u+o.displaySize[1]),{image:o,top:u,right:h,bottom:f,left:p,collisionPadding:s}}const gu=255,yu=128,vu=gu*yu;function xu(t,e){const{expression:r}=e;if(\"constant\"===r.kind)return{kind:\"constant\",layoutSize:r.evaluate(new Hi(t+1))};if(\"source\"===r.kind)return{kind:\"source\"};{const{zoomStops:e,interpolationType:n}=r;let i=0;for(;it.id)),this.index=e.index,this.pixelRatio=e.pixelRatio,this.sourceLayerIndex=e.sourceLayerIndex,this.hasPattern=!1,this.hasRTLText=!1,this.sortKeyRanges=[],this.collisionCircleArray=[],this.placementInvProjMatrix=ps([]),this.placementViewportMatrix=ps([]);const r=this.layers[0]._unevaluatedLayout._values;this.textSizeData=xu(this.zoom,r[\"text-size\"]),this.iconSizeData=xu(this.zoom,r[\"icon-size\"]);const n=this.layers[0].layout,i=n.get(\"symbol-sort-key\"),a=n.get(\"symbol-z-order\");this.canOverlap=\"never\"!==_u(n,\"text-overlap\",\"text-allow-overlap\")||\"never\"!==_u(n,\"icon-overlap\",\"icon-allow-overlap\")||n.get(\"text-ignore-placement\")||n.get(\"icon-ignore-placement\"),this.sortFeaturesByKey=\"viewport-y\"!==a&&!i.isConstant();const o=\"viewport-y\"===a||\"auto\"===a&&!this.sortFeaturesByKey;this.sortFeaturesByY=o&&this.canOverlap,\"point\"===n.get(\"symbol-placement\")&&(this.writingModes=n.get(\"text-writing-mode\").map((e=>t.ai[e]))),this.stateDependentLayerIds=this.layers.filter((t=>t.isStateDependent())).map((t=>t.id)),this.sourceID=e.sourceID}createArrays(){this.text=new Mu(new Bo(this.layers,this.zoom,(t=>/^text/.test(t)))),this.icon=new Mu(new Bo(this.layers,this.zoom,(t=>/^icon/.test(t)))),this.glyphOffsetArray=new Ua,this.lineVertexArray=new Va,this.symbolInstances=new ja,this.textAnchorOffsets=new Ha}calculateGlyphDependencies(t,e,r,n,i){for(let a=0;a0)&&(\"constant\"!==o.value.kind||o.value.value.length>0),u=\"constant\"!==l.value.kind||!!l.value.value||Object.keys(l.parameters).length>0,h=a.get(\"symbol-sort-key\");if(this.features=[],!c&&!u)return;const f=r.iconDependencies,p=r.glyphDependencies,d=r.availableImages,m=new Hi(this.zoom);for(const{feature:r,id:s,index:l,sourceLayerIndex:g}of e){const e=i._featureFilter.needGeometry,y=Go(r,e);if(!i._featureFilter.filter(m,y,n))continue;let v,x;if(e||(y.geometry=Ho(r)),c){const t=i.getValueAndResolveTokens(\"text-field\",y,n,d),e=Kt.factory(t),r=this.hasRTLText=this.hasRTLText||Au(e);(!r||\"unavailable\"===qi.getRTLTextPluginStatus()||r&&qi.isParsed())&&(v=yc(e,i,y))}if(u){const t=i.getValueAndResolveTokens(\"icon-image\",y,n,d);x=t instanceof re?t:re.fromString(t)}if(!v&&!x)continue;const _=this.sortFeaturesByKey?h.evaluate(y,{},n):void 0,b={id:s,text:v,icon:x,index:l,sourceLayerIndex:g,geometry:y.geometry,properties:r.properties,type:bu[r.type],sortKey:_};if(this.features.push(b),x&&(f[x.name]=!0),v){const e=o.evaluate(y,{},n).join(\",\"),r=\"viewport\"!==a.get(\"text-rotation-alignment\")&&\"point\"!==a.get(\"symbol-placement\");this.allowVerticalPlacement=this.writingModes&&this.writingModes.indexOf(t.ai.vertical)>=0;for(const t of v.sections)if(t.image)f[t.image.name]=!0;else{const n=Oi(v.toString()),i=t.fontStack||e,a=p[i]=p[i]||{};this.calculateGlyphDependencies(t.text,a,r,this.allowVerticalPlacement,n)}}}\"line\"===a.get(\"symbol-placement\")&&(this.features=function(t){const e={},r={},n=[];let i=0;function a(e){n.push(t[e]),i++}function o(t,e,i){const a=r[t];return delete r[t],r[e]=a,n[a].geometry[0].pop(),n[a].geometry[0]=n[a].geometry[0].concat(i[0]),a}function s(t,r,i){const a=e[r];return delete e[r],e[t]=a,n[a].geometry[0].shift(),n[a].geometry[0]=i[0].concat(n[a].geometry[0]),a}function l(t,e,r){const n=r?e[0][e[0].length-1]:e[0][0];return`${t}:${n.x}:${n.y}`}for(let c=0;ct.geometry))}(this.features)),this.sortFeaturesByKey&&this.features.sort(((t,e)=>t.sortKey-e.sortKey))}update(t,e,r){this.stateDependentLayers.length&&(this.text.programConfigurations.updatePaintArrays(t,e,this.layers,r),this.icon.programConfigurations.updatePaintArrays(t,e,this.layers,r))}isEmpty(){return 0===this.symbolInstances.length&&!this.hasRTLText}uploadPending(){return!this.uploaded||this.text.programConfigurations.needsUpload||this.icon.programConfigurations.needsUpload}upload(t){!this.uploaded&&this.hasDebugData()&&(this.textCollisionBox.upload(t),this.iconCollisionBox.upload(t)),this.text.upload(t,this.sortFeaturesByY,!this.uploaded,this.text.programConfigurations.needsUpload),this.icon.upload(t,this.sortFeaturesByY,!this.uploaded,this.icon.programConfigurations.needsUpload),this.uploaded=!0}destroyDebugData(){this.textCollisionBox.destroy(),this.iconCollisionBox.destroy()}destroy(){this.text.destroy(),this.icon.destroy(),this.hasDebugData()&&this.destroyDebugData()}addToLineVertexArray(t,e){const r=this.lineVertexArray.length;if(void 0!==t.segment){let r=t.dist(e[t.segment+1]),n=t.dist(e[t.segment]);const i={};for(let n=t.segment+1;n=0;r--)i[r]={x:e[r].x,y:e[r].y,tileUnitDistanceFromAnchor:n},r>0&&(n+=e[r-1].dist(e[r]));for(let t=0;t0}hasIconData(){return this.icon.segments.get().length>0}hasDebugData(){return this.textCollisionBox&&this.iconCollisionBox}hasTextCollisionBoxData(){return this.hasDebugData()&&this.textCollisionBox.segments.get().length>0}hasIconCollisionBoxData(){return this.hasDebugData()&&this.iconCollisionBox.segments.get().length>0}addIndicesForPlacedSymbol(t,e){const r=t.placedSymbolArray.get(e),n=r.vertexStartIndex+4*r.numGlyphs;for(let e=r.vertexStartIndex;en[t]-n[e]||i[e]-i[t])),a}addToSortKeyRanges(t,e){const r=this.sortKeyRanges[this.sortKeyRanges.length-1];r&&r.sortKey===e?r.symbolInstanceEnd=t+1:this.sortKeyRanges.push({sortKey:e,symbolInstanceStart:t,symbolInstanceEnd:t+1})}sortFeatures(t){if(this.sortFeaturesByY&&this.sortedAngle!==t&&!(this.text.segments.get().length>1||this.icon.segments.get().length>1)){this.symbolInstanceIndexes=this.getSortedSymbolIndexes(t),this.sortedAngle=t,this.text.indexArray.clear(),this.icon.indexArray.clear(),this.featureSortOrder=[];for(const t of this.symbolInstanceIndexes){const e=this.symbolInstances.get(t);this.featureSortOrder.push(e.featureIndex),[e.rightJustifiedTextSymbolIndex,e.centerJustifiedTextSymbolIndex,e.leftJustifiedTextSymbolIndex].forEach(((t,e,r)=>{t>=0&&r.indexOf(t)===e&&this.addIndicesForPlacedSymbol(this.text,t)})),e.verticalPlacedTextSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.text,e.verticalPlacedTextSymbolIndex),e.placedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,e.placedIconSymbolIndex),e.verticalPlacedIconSymbolIndex>=0&&this.addIndicesForPlacedSymbol(this.icon,e.verticalPlacedIconSymbolIndex)}this.text.indexBuffer&&this.text.indexBuffer.updateData(this.text.indexArray),this.icon.indexBuffer&&this.icon.indexBuffer.updateData(this.icon.indexArray)}}}let Cu;Mi(\"SymbolBucket\",Eu,{omit:[\"layers\",\"collisionBoxArray\",\"features\",\"compareText\"]}),Eu.MAX_GLYPHS=65535,Eu.addDynamicAttributes=ku;let Lu;var Iu={get paint(){return Lu=Lu||new ia({\"icon-opacity\":new ta(Z.paint_symbol[\"icon-opacity\"]),\"icon-color\":new ta(Z.paint_symbol[\"icon-color\"]),\"icon-halo-color\":new ta(Z.paint_symbol[\"icon-halo-color\"]),\"icon-halo-width\":new ta(Z.paint_symbol[\"icon-halo-width\"]),\"icon-halo-blur\":new ta(Z.paint_symbol[\"icon-halo-blur\"]),\"icon-translate\":new Qi(Z.paint_symbol[\"icon-translate\"]),\"icon-translate-anchor\":new Qi(Z.paint_symbol[\"icon-translate-anchor\"]),\"text-opacity\":new ta(Z.paint_symbol[\"text-opacity\"]),\"text-color\":new ta(Z.paint_symbol[\"text-color\"],{runtimeType:ft,getOverride:t=>t.textColor,hasOverride:t=>!!t.textColor}),\"text-halo-color\":new ta(Z.paint_symbol[\"text-halo-color\"]),\"text-halo-width\":new ta(Z.paint_symbol[\"text-halo-width\"]),\"text-halo-blur\":new ta(Z.paint_symbol[\"text-halo-blur\"]),\"text-translate\":new Qi(Z.paint_symbol[\"text-translate\"]),\"text-translate-anchor\":new Qi(Z.paint_symbol[\"text-translate-anchor\"])})},get layout(){return Cu=Cu||new ia({\"symbol-placement\":new Qi(Z.layout_symbol[\"symbol-placement\"]),\"symbol-spacing\":new Qi(Z.layout_symbol[\"symbol-spacing\"]),\"symbol-avoid-edges\":new Qi(Z.layout_symbol[\"symbol-avoid-edges\"]),\"symbol-sort-key\":new ta(Z.layout_symbol[\"symbol-sort-key\"]),\"symbol-z-order\":new Qi(Z.layout_symbol[\"symbol-z-order\"]),\"icon-allow-overlap\":new Qi(Z.layout_symbol[\"icon-allow-overlap\"]),\"icon-overlap\":new Qi(Z.layout_symbol[\"icon-overlap\"]),\"icon-ignore-placement\":new Qi(Z.layout_symbol[\"icon-ignore-placement\"]),\"icon-optional\":new Qi(Z.layout_symbol[\"icon-optional\"]),\"icon-rotation-alignment\":new Qi(Z.layout_symbol[\"icon-rotation-alignment\"]),\"icon-size\":new ta(Z.layout_symbol[\"icon-size\"]),\"icon-text-fit\":new Qi(Z.layout_symbol[\"icon-text-fit\"]),\"icon-text-fit-padding\":new Qi(Z.layout_symbol[\"icon-text-fit-padding\"]),\"icon-image\":new ta(Z.layout_symbol[\"icon-image\"]),\"icon-rotate\":new ta(Z.layout_symbol[\"icon-rotate\"]),\"icon-padding\":new ta(Z.layout_symbol[\"icon-padding\"]),\"icon-keep-upright\":new Qi(Z.layout_symbol[\"icon-keep-upright\"]),\"icon-offset\":new ta(Z.layout_symbol[\"icon-offset\"]),\"icon-anchor\":new ta(Z.layout_symbol[\"icon-anchor\"]),\"icon-pitch-alignment\":new Qi(Z.layout_symbol[\"icon-pitch-alignment\"]),\"text-pitch-alignment\":new Qi(Z.layout_symbol[\"text-pitch-alignment\"]),\"text-rotation-alignment\":new Qi(Z.layout_symbol[\"text-rotation-alignment\"]),\"text-field\":new ta(Z.layout_symbol[\"text-field\"]),\"text-font\":new ta(Z.layout_symbol[\"text-font\"]),\"text-size\":new ta(Z.layout_symbol[\"text-size\"]),\"text-max-width\":new ta(Z.layout_symbol[\"text-max-width\"]),\"text-line-height\":new Qi(Z.layout_symbol[\"text-line-height\"]),\"text-letter-spacing\":new ta(Z.layout_symbol[\"text-letter-spacing\"]),\"text-justify\":new ta(Z.layout_symbol[\"text-justify\"]),\"text-radial-offset\":new ta(Z.layout_symbol[\"text-radial-offset\"]),\"text-variable-anchor\":new Qi(Z.layout_symbol[\"text-variable-anchor\"]),\"text-variable-anchor-offset\":new ta(Z.layout_symbol[\"text-variable-anchor-offset\"]),\"text-anchor\":new ta(Z.layout_symbol[\"text-anchor\"]),\"text-max-angle\":new Qi(Z.layout_symbol[\"text-max-angle\"]),\"text-writing-mode\":new Qi(Z.layout_symbol[\"text-writing-mode\"]),\"text-rotate\":new ta(Z.layout_symbol[\"text-rotate\"]),\"text-padding\":new Qi(Z.layout_symbol[\"text-padding\"]),\"text-keep-upright\":new Qi(Z.layout_symbol[\"text-keep-upright\"]),\"text-transform\":new ta(Z.layout_symbol[\"text-transform\"]),\"text-offset\":new ta(Z.layout_symbol[\"text-offset\"]),\"text-allow-overlap\":new Qi(Z.layout_symbol[\"text-allow-overlap\"]),\"text-overlap\":new Qi(Z.layout_symbol[\"text-overlap\"]),\"text-ignore-placement\":new Qi(Z.layout_symbol[\"text-ignore-placement\"]),\"text-optional\":new Qi(Z.layout_symbol[\"text-optional\"])})}};class Pu{constructor(t){if(void 0===t.property.overrides)throw new Error(\"overrides must be provided to instantiate FormatSectionOverride class\");this.type=t.property.overrides?t.property.overrides.runtimeType:lt,this.defaultValue=t}evaluate(t){if(t.formattedSection){const e=this.defaultValue.property.overrides;if(e&&e.hasOverride(t.formattedSection))return e.getOverride(t.formattedSection)}return t.feature&&t.featureState?this.defaultValue.evaluate(t.feature,t.featureState):this.defaultValue.property.specification.default}eachChild(t){this.defaultValue.isConstant()||t(this.defaultValue.value._styleExpression.expression)}outputDefined(){return!1}serialize(){return null}}Mi(\"FormatSectionOverride\",Pu,{omit:[\"defaultValue\"]});class zu extends oa{constructor(t){super(t,Iu)}recalculate(t,e){if(super.recalculate(t,e),\"auto\"===this.layout.get(\"icon-rotation-alignment\")&&(\"point\"!==this.layout.get(\"symbol-placement\")?this.layout._values[\"icon-rotation-alignment\"]=\"map\":this.layout._values[\"icon-rotation-alignment\"]=\"viewport\"),\"auto\"===this.layout.get(\"text-rotation-alignment\")&&(\"point\"!==this.layout.get(\"symbol-placement\")?this.layout._values[\"text-rotation-alignment\"]=\"map\":this.layout._values[\"text-rotation-alignment\"]=\"viewport\"),\"auto\"===this.layout.get(\"text-pitch-alignment\")&&(this.layout._values[\"text-pitch-alignment\"]=\"map\"===this.layout.get(\"text-rotation-alignment\")?\"map\":\"viewport\"),\"auto\"===this.layout.get(\"icon-pitch-alignment\")&&(this.layout._values[\"icon-pitch-alignment\"]=this.layout.get(\"icon-rotation-alignment\")),\"point\"===this.layout.get(\"symbol-placement\")){const t=this.layout.get(\"text-writing-mode\");if(t){const e=[];for(const r of t)e.indexOf(r)<0&&e.push(r);this.layout._values[\"text-writing-mode\"]=e}else this.layout._values[\"text-writing-mode\"]=[\"horizontal\"]}this._setPaintOverrides()}getValueAndResolveTokens(t,e,r,n){const i=this.layout.get(t).evaluate(e,{},r,n),a=this._unevaluatedLayout._values[t];return a.isDataDriven()||kn(a.value)||!i?i:function(t,e){return e.replace(/{([^{}]+)}/g,((e,r)=>t&&r in t?String(t[r]):\"\"))}(e.properties,i)}createBucket(t){return new Eu(t)}queryRadius(){return 0}queryIntersectsFeature(){throw new Error(\"Should take a different path in FeatureIndex\")}_setPaintOverrides(){for(const t of Iu.paint.overridableProperties){if(!zu.hasPaintOverride(this.layout,t))continue;const e=this.paint.get(t),r=new Pu(e),n=new Tn(r,e.property.specification);let i=null;i=\"constant\"===e.value.kind||\"source\"===e.value.kind?new Mn(\"source\",n):new Sn(\"composite\",n,e.value.zoomStops),this.paint._values[t]=new Ji(e.property,i,e.parameters)}}_handleOverridablePaintPropertyUpdate(t,e,r){return!(!this.layout||e.isDataDriven()||r.isDataDriven())&&zu.hasPaintOverride(this.layout,t)}static hasPaintOverride(t,e){const r=t.get(\"text-field\"),n=Iu.paint.properties[e];let i=!1;const a=t=>{for(const e of t)if(n.overrides&&n.overrides.hasOverride(e))return void(i=!0)};if(\"constant\"===r.value.kind&&r.value.value instanceof Kt)a(r.value.value.sections);else if(\"source\"===r.value.kind){const t=e=>{if(!i)if(e instanceof se&&ae(e.value)===gt){const t=e.value;a(t.sections)}else e instanceof We?a(e.sections):e.eachChild(t)},e=r.value;e._styleExpression&&t(e._styleExpression.expression)}return i}}let Ou;var Du={get paint(){return Ou=Ou||new ia({\"background-color\":new Qi(Z.paint_background[\"background-color\"]),\"background-pattern\":new ra(Z.paint_background[\"background-pattern\"]),\"background-opacity\":new Qi(Z.paint_background[\"background-opacity\"])})}};class Ru extends oa{constructor(t){super(t,Du)}}let Fu;var Bu={get paint(){return Fu=Fu||new ia({\"raster-opacity\":new Qi(Z.paint_raster[\"raster-opacity\"]),\"raster-hue-rotate\":new Qi(Z.paint_raster[\"raster-hue-rotate\"]),\"raster-brightness-min\":new Qi(Z.paint_raster[\"raster-brightness-min\"]),\"raster-brightness-max\":new Qi(Z.paint_raster[\"raster-brightness-max\"]),\"raster-saturation\":new Qi(Z.paint_raster[\"raster-saturation\"]),\"raster-contrast\":new Qi(Z.paint_raster[\"raster-contrast\"]),\"raster-resampling\":new Qi(Z.paint_raster[\"raster-resampling\"]),\"raster-fade-duration\":new Qi(Z.paint_raster[\"raster-fade-duration\"])})}};class Nu extends oa{constructor(t){super(t,Bu)}}class ju extends oa{constructor(t){super(t,{}),this.onAdd=t=>{this.implementation.onAdd&&this.implementation.onAdd(t,t.painter.context.gl)},this.onRemove=t=>{this.implementation.onRemove&&this.implementation.onRemove(t,t.painter.context.gl)},this.implementation=t}is3D(){return\"3d\"===this.implementation.renderingMode}hasOffscreenPass(){return void 0!==this.implementation.prerender}recalculate(){}updateTransitions(){}hasTransition(){return!1}serialize(){throw new Error(\"Custom layers cannot be serialized\")}}class Uu{constructor(t){this._methodToThrottle=t,this._triggered=!1,\"undefined\"!=typeof MessageChannel&&(this._channel=new MessageChannel,this._channel.port2.onmessage=()=>{this._triggered=!1,this._methodToThrottle()})}trigger(){this._triggered||(this._triggered=!0,this._channel?this._channel.port1.postMessage(!0):setTimeout((()=>{this._triggered=!1,this._methodToThrottle()}),0))}remove(){delete this._channel,this._methodToThrottle=()=>{}}}const Vu=6371008.8;class qu{constructor(t,e){if(isNaN(t)||isNaN(e))throw new Error(`Invalid LngLat object: (${t}, ${e})`);if(this.lng=+t,this.lat=+e,this.lat>90||this.lat<-90)throw new Error(\"Invalid LngLat latitude value: must be between -90 and 90\")}wrap(){return new qu(g(this.lng,-180,180),this.lat)}toArray(){return[this.lng,this.lat]}toString(){return`LngLat(${this.lng}, ${this.lat})`}distanceTo(t){const e=Math.PI/180,r=this.lat*e,n=t.lat*e,i=Math.sin(r)*Math.sin(n)+Math.cos(r)*Math.cos(n)*Math.cos((t.lng-this.lng)*e);return Vu*Math.acos(Math.min(i,1))}static convert(t){if(t instanceof qu)return t;if(Array.isArray(t)&&(2===t.length||3===t.length))return new qu(Number(t[0]),Number(t[1]));if(!Array.isArray(t)&&\"object\"==typeof t&&null!==t)return new qu(Number(\"lng\"in t?t.lng:t.lon),Number(t.lat));throw new Error(\"`LngLatLike` argument must be specified as a LngLat instance, an object {lng: , lat: }, an object {lon: , lat: }, or an array of [, ]\")}}const Hu=2*Math.PI*Vu;function Gu(t){return Hu*Math.cos(t*Math.PI/180)}function Zu(t){return(180+t)/360}function Wu(t){return(180-180/Math.PI*Math.log(Math.tan(Math.PI/4+t*Math.PI/360)))/360}function Yu(t,e){return t/Gu(e)}function Xu(t){const e=180-360*t;return 360/Math.PI*Math.atan(Math.exp(e*Math.PI/180))-90}class $u{constructor(t,e,r=0){this.x=+t,this.y=+e,this.z=+r}static fromLngLat(t,e=0){const r=qu.convert(t);return new $u(Zu(r.lng),Wu(r.lat),Yu(e,r.lat))}toLngLat(){return new qu(360*this.x-180,Xu(this.y))}toAltitude(){return t=this.z,e=this.y,t*Gu(Xu(e));var t,e}meterInMercatorCoordinateUnits(){return 1/Hu*(t=Xu(this.y),1/Math.cos(t*Math.PI/180));var t}}function Ju(t,e,r){var n=2*Math.PI*6378137/256/Math.pow(2,r);return[t*n-2*Math.PI*6378137/2,e*n-2*Math.PI*6378137/2]}class Ku{constructor(t,e,r){if(t<0||t>25||r<0||r>=Math.pow(2,t)||e<0||e>=Math.pow(2,t))throw new Error(`x=${e}, y=${r}, z=${t} outside of bounds. 0<=x<${Math.pow(2,t)}, 0<=y<${Math.pow(2,t)} 0<=z<=25 `);this.z=t,this.x=e,this.y=r,this.key=eh(0,t,t,e,r)}equals(t){return this.z===t.z&&this.x===t.x&&this.y===t.y}url(t,e,r){const n=(a=this.x,o=this.y,s=this.z,l=Ju(256*a,256*(o=Math.pow(2,s)-o-1),s),c=Ju(256*(a+1),256*(o+1),s),l[0]+\",\"+l[1]+\",\"+c[0]+\",\"+c[1]),i=function(t,e,r){let n,i=\"\";for(let a=t;a>0;a--)n=1<1?\"@2x\":\"\").replace(/{quadkey}/g,i).replace(/{bbox-epsg-3857}/g,n)}isChildOf(t){const e=this.z-t.z;return e>0&&t.x===this.x>>e&&t.y===this.y>>e}getTilePoint(t){const e=Math.pow(2,this.z);return new a((t.x*e-this.x)*Uo,(t.y*e-this.y)*Uo)}toString(){return`${this.z}/${this.x}/${this.y}`}}class Qu{constructor(t,e){this.wrap=t,this.canonical=e,this.key=eh(t,e.z,e.z,e.x,e.y)}}class th{constructor(t,e,r,n,i){if(t= z; overscaledZ = ${t}; z = ${r}`);this.overscaledZ=t,this.wrap=e,this.canonical=new Ku(r,+n,+i),this.key=eh(e,t,r,n,i)}clone(){return new th(this.overscaledZ,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)}equals(t){return this.overscaledZ===t.overscaledZ&&this.wrap===t.wrap&&this.canonical.equals(t.canonical)}scaledTo(t){if(t>this.overscaledZ)throw new Error(`targetZ > this.overscaledZ; targetZ = ${t}; overscaledZ = ${this.overscaledZ}`);const e=this.canonical.z-t;return t>this.canonical.z?new th(t,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y):new th(t,this.wrap,t,this.canonical.x>>e,this.canonical.y>>e)}calculateScaledKey(t,e){if(t>this.overscaledZ)throw new Error(`targetZ > this.overscaledZ; targetZ = ${t}; overscaledZ = ${this.overscaledZ}`);const r=this.canonical.z-t;return t>this.canonical.z?eh(this.wrap*+e,t,this.canonical.z,this.canonical.x,this.canonical.y):eh(this.wrap*+e,t,t,this.canonical.x>>r,this.canonical.y>>r)}isChildOf(t){if(t.wrap!==this.wrap)return!1;const e=this.canonical.z-t.canonical.z;return 0===t.overscaledZ||t.overscaledZ>e&&t.canonical.y===this.canonical.y>>e}children(t){if(this.overscaledZ>=t)return[new th(this.overscaledZ+1,this.wrap,this.canonical.z,this.canonical.x,this.canonical.y)];const e=this.canonical.z+1,r=2*this.canonical.x,n=2*this.canonical.y;return[new th(e,this.wrap,e,r,n),new th(e,this.wrap,e,r+1,n),new th(e,this.wrap,e,r,n+1),new th(e,this.wrap,e,r+1,n+1)]}isLessThan(t){return this.wrapt.wrap)&&(this.overscaledZt.overscaledZ)&&(this.canonical.xt.canonical.x)&&this.canonical.ythis.max&&(this.max=r),r=this.dim+1||e<-1||e>=this.dim+1)throw new RangeError(\"out of range source coordinates for DEM data\");return(e+1)*this.stride+(t+1)}unpack(t,e,r){return t*this.redFactor+e*this.greenFactor+r*this.blueFactor-this.baseShift}getPixels(){return new Ls({width:this.stride,height:this.stride},new Uint8Array(this.data.buffer))}backfillBorder(t,e,r){if(this.dim!==t.dim)throw new Error(\"dem dimension mismatch\");let n=e*this.dim,i=e*this.dim+this.dim,a=r*this.dim,o=r*this.dim+this.dim;switch(e){case-1:n=i-1;break;case 1:i=n+1}switch(r){case-1:a=o-1;break;case 1:o=a+1}const s=-e*this.dim,l=-r*this.dim;for(let e=a;e=this._numberToString.length)throw new Error(`Out of bounds. Index requested n=${t} can't be >= this._numberToString.length ${this._numberToString.length}`);return this._numberToString[t]}}class ih{constructor(t,e,r,n,i){this.type=\"Feature\",this._vectorTileFeature=t,t._z=e,t._x=r,t._y=n,this.properties=t.properties,this.id=i}get geometry(){return void 0===this._geometry&&(this._geometry=this._vectorTileFeature.toGeoJSON(this._vectorTileFeature._x,this._vectorTileFeature._y,this._vectorTileFeature._z).geometry),this._geometry}set geometry(t){this._geometry=t}toJSON(){const t={geometry:this.geometry};for(const e in this)\"_geometry\"!==e&&\"_vectorTileFeature\"!==e&&(t[e]=this[e]);return t}}class ah{constructor(t,e){this.tileID=t,this.x=t.canonical.x,this.y=t.canonical.y,this.z=t.canonical.z,this.grid=new ki(Uo,16,0),this.grid3D=new ki(Uo,16,0),this.featureIndexArray=new Za,this.promoteId=e}insert(t,e,r,n,i,a){const o=this.featureIndexArray.length;this.featureIndexArray.emplaceBack(r,n,i);const s=a?this.grid3D:this.grid;for(let t=0;t=0&&n[3]>=0&&s.insert(o,n[0],n[1],n[2],n[3])}}loadVTLayers(){return this.vtLayers||(this.vtLayers=new Tl.VectorTile(new Uc(this.rawTileData)).layers,this.sourceLayerCoder=new nh(this.vtLayers?Object.keys(this.vtLayers).sort():[\"_geojsonTileLayer\"])),this.vtLayers}query(t,e,r,n){this.loadVTLayers();const i=t.params||{},o=Uo/t.tileSize/t.scale,s=zn(i.filter),l=t.queryGeometry,c=t.queryPadding*o,u=sh(l),h=this.grid.query(u.minX-c,u.minY-c,u.maxX+c,u.maxY+c),f=sh(t.cameraQueryGeometry),p=this.grid3D.query(f.minX-c,f.minY-c,f.maxX+c,f.maxY+c,((e,r,n,i)=>function(t,e,r,n,i){for(const a of t)if(e<=a.x&&r<=a.y&&n>=a.x&&i>=a.y)return!0;const o=[new a(e,r),new a(e,i),new a(n,i),new a(n,r)];if(t.length>2)for(const e of o)if(ns(t,e))return!0;for(let e=0;e(f||(f=Ho(e)),r.queryIntersectsFeature(l,e,n,f,this.z,t.transform,o,t.pixelPosMatrix))))}return d}loadMatchingFeature(t,e,r,n,i,a,o,s,l,c,u){const h=this.bucketLayerIDs[e];if(a&&!function(t,e){for(let r=0;r=0)return!0;return!1}(a,h))return;const f=this.sourceLayerCoder.decode(r),p=this.vtLayers[f].feature(n);if(i.needGeometry){const t=Go(p,!0);if(!i.filter(new Hi(this.tileID.overscaledZ),t,this.tileID.canonical))return}else if(!i.filter(new Hi(this.tileID.overscaledZ),p))return;const d=this.getId(p,f);for(let e=0;e{const o=e instanceof Ki?e.get(a):null;return o&&o.evaluate?o.evaluate(r,n,i):o}))}function sh(t){let e=1/0,r=1/0,n=-1/0,i=-1/0;for(const a of t)e=Math.min(e,a.x),r=Math.min(r,a.y),n=Math.max(n,a.x),i=Math.max(i,a.y);return{minX:e,minY:r,maxX:n,maxY:i}}function lh(t,e){return e-t}function ch(t,e,r,n,i){const o=[];for(let s=0;s=n&&u.x>=n||(s.x>=n?s=new a(n,s.y+(u.y-s.y)*((n-s.x)/(u.x-s.x)))._round():u.x>=n&&(u=new a(n,s.y+(u.y-s.y)*((n-s.x)/(u.x-s.x)))._round()),s.y>=i&&u.y>=i||(s.y>=i?s=new a(s.x+(u.x-s.x)*((i-s.y)/(u.y-s.y)),i)._round():u.y>=i&&(u=new a(s.x+(u.x-s.x)*((i-s.y)/(u.y-s.y)),i)._round()),c&&s.equals(c[c.length-1])||(c=[s],o.push(c)),c.push(u)))))}}return o}Mi(\"FeatureIndex\",ah,{omit:[\"rawTileData\",\"sourceLayerCoder\"]});class uh extends a{constructor(t,e,r,n){super(t,e),this.angle=r,void 0!==n&&(this.segment=n)}clone(){return new uh(this.x,this.y,this.angle,this.segment)}}function hh(t,e,r,n,i){if(void 0===e.segment||0===r)return!0;let a=e,o=e.segment+1,s=0;for(;s>-r/2;){if(o--,o<0)return!1;s-=t[o].dist(a),a=t[o]}s+=t[o].dist(t[o+1]),o++;const l=[];let c=0;for(;sn;)c-=l.shift().angleDelta;if(c>i)return!1;o++,s+=r.dist(a)}return!0}function fh(t){let e=0;for(let r=0;rc){const u=(c-l)/a,h=Pe.number(n.x,i.x,u),f=Pe.number(n.y,i.y,u),p=new uh(h,f,i.angleTo(n),r);return p._round(),!o||hh(t,p,s,o,e)?p:void 0}l+=a}}function gh(t,e,r,n,i,a,o,s,l){const c=ph(n,a,o),u=dh(n,i),h=u*o,f=0===t[0].x||t[0].x===l||0===t[0].y||t[0].y===l;return e-h=0&&y=0&&v=0&&f+c<=u){const r=new uh(y,v,m,e);r._round(),n&&!hh(t,r,a,n,i)||p.push(r)}}h+=d}return s||p.length||o||(p=yh(t,h/2,r,n,i,a,o,!0,l)),p}Mi(\"Anchor\",uh);const vh=Yc;function xh(t,e,r,n){const i=[],o=t.image,s=o.pixelRatio,l=o.paddedRect.w-2*vh,c=o.paddedRect.h-2*vh;let u={x1:t.left,y1:t.top,x2:t.right,y2:t.bottom};const h=o.stretchX||[[0,l]],f=o.stretchY||[[0,c]],p=(t,e)=>t+e[1]-e[0],d=h.reduce(p,0),m=f.reduce(p,0),g=l-d,y=c-m;let v=0,x=d,_=0,b=m,w=0,T=g,k=0,A=y;if(o.content&&n){const e=o.content,r=e[2]-e[0],n=e[3]-e[1];(o.textFitWidth||o.textFitHeight)&&(u=du(t)),v=_h(h,0,e[0]),_=_h(f,0,e[1]),x=_h(h,e[0],e[2]),b=_h(f,e[1],e[3]),w=e[0]-v,k=e[1]-_,T=r-x,A=n-b}const M=u.x1,S=u.y1,E=u.x2-M,C=u.y2-S,L=(t,n,i,l)=>{const c=wh(t.stretch-v,x,E,M),u=Th(t.fixed-w,T,t.stretch,d),h=wh(n.stretch-_,b,C,S),f=Th(n.fixed-k,A,n.stretch,m),p=wh(i.stretch-v,x,E,M),g=Th(i.fixed-w,T,i.stretch,d),y=wh(l.stretch-_,b,C,S),L=Th(l.fixed-k,A,l.stretch,m),I=new a(c,h),P=new a(p,h),z=new a(p,y),O=new a(c,y),D=new a(u/s,f/s),R=new a(g/s,L/s),F=e*Math.PI/180;if(F){const t=Math.sin(F),e=Math.cos(F),r=[e,-t,t,e];I._matMult(r),P._matMult(r),O._matMult(r),z._matMult(r)}const B=t.stretch+t.fixed,N=i.stretch+i.fixed,j=n.stretch+n.fixed,U=l.stretch+l.fixed;return{tl:I,tr:P,bl:O,br:z,tex:{x:o.paddedRect.x+vh+B,y:o.paddedRect.y+vh+j,w:N-B,h:U-j},writingMode:void 0,glyphOffset:[0,0],sectionIndex:0,pixelOffsetTL:D,pixelOffsetBR:R,minFontScaleX:T/s/E,minFontScaleY:A/s/C,isSDF:r}};if(n&&(o.stretchX||o.stretchY)){const t=bh(h,g,d),e=bh(f,y,m);for(let r=0;r0&&(n=Math.max(10,n),this.circleDiameter=n)}else{const c=(null===(h=o.image)||void 0===h?void 0:h.content)&&(o.image.textFitWidth||o.image.textFitHeight)?du(o):{x1:o.left,y1:o.top,x2:o.right,y2:o.bottom};c.y1=c.y1*s-l[0],c.y2=c.y2*s+l[2],c.x1=c.x1*s-l[3],c.x2=c.x2*s+l[1];const f=o.collisionPadding;if(f&&(c.x1-=f[0]*s,c.y1-=f[1]*s,c.x2+=f[2]*s,c.y2+=f[3]*s),u){const t=new a(c.x1,c.y1),e=new a(c.x2,c.y1),r=new a(c.x1,c.y2),n=new a(c.x2,c.y2),i=u*Math.PI/180;t._rotate(i),e._rotate(i),r._rotate(i),n._rotate(i),c.x1=Math.min(t.x,e.x,r.x,n.x),c.x2=Math.max(t.x,e.x,r.x,n.x),c.y1=Math.min(t.y,e.y,r.y,n.y),c.y2=Math.max(t.y,e.y,r.y,n.y)}t.emplaceBack(e.x,e.y,c.x1,c.y1,c.x2,c.y2,r,n,i)}this.boxEndIndex=t.length}}class Ah{constructor(t=[],e=((t,e)=>te?1:0)){if(this.data=t,this.length=this.data.length,this.compare=e,this.length>0)for(let t=(this.length>>1)-1;t>=0;t--)this._down(t)}push(t){this.data.push(t),this._up(this.length++)}pop(){if(0===this.length)return;const t=this.data[0],e=this.data.pop();return--this.length>0&&(this.data[0]=e,this._down(0)),t}peek(){return this.data[0]}_up(t){const{data:e,compare:r}=this,n=e[t];for(;t>0;){const i=t-1>>1,a=e[i];if(r(n,a)>=0)break;e[t]=a,t=i}e[t]=n}_down(t){const{data:e,compare:r}=this,n=this.length>>1,i=e[t];for(;t=0)break;e[t]=e[n],t=n}e[t]=i}}function Mh(t,e=1,r=!1){let n=1/0,i=1/0,o=-1/0,s=-1/0;const l=t[0];for(let t=0;to)&&(o=e.x),(!t||e.y>s)&&(s=e.y)}const c=o-n,u=s-i,h=Math.min(c,u);let f=h/2;const p=new Ah([],Sh);if(0===h)return new a(n,i);for(let e=n;ed.d||!d.d)&&(d=n,r&&console.log(\"found best %d after %d probes\",Math.round(1e4*n.d)/1e4,m)),n.max-d.d<=e||(f=n.h/2,p.push(new Eh(n.p.x-f,n.p.y-f,f,t)),p.push(new Eh(n.p.x+f,n.p.y-f,f,t)),p.push(new Eh(n.p.x-f,n.p.y+f,f,t)),p.push(new Eh(n.p.x+f,n.p.y+f,f,t)),m+=4)}return r&&(console.log(`num probes: ${m}`),console.log(`best distance: ${d.d}`)),d.p}function Sh(t,e){return e.max-t.max}function Eh(t,e,r,n){this.p=new a(t,e),this.h=r,this.d=function(t,e){let r=!1,n=1/0;for(let i=0;it.y!=s.y>t.y&&t.x<(s.x-i.x)*(t.y-i.y)/(s.y-i.y)+i.x&&(r=!r),n=Math.min(n,es(t,i,s))}}return(r?1:-1)*Math.sqrt(n)}(this.p,n),this.max=this.d+this.h*Math.SQRT2}var Ch;t.ar=void 0,(Ch=t.ar||(t.ar={}))[Ch.center=1]=\"center\",Ch[Ch.left=2]=\"left\",Ch[Ch.right=3]=\"right\",Ch[Ch.top=4]=\"top\",Ch[Ch.bottom=5]=\"bottom\",Ch[Ch[\"top-left\"]=6]=\"top-left\",Ch[Ch[\"top-right\"]=7]=\"top-right\",Ch[Ch[\"bottom-left\"]=8]=\"bottom-left\",Ch[Ch[\"bottom-right\"]=9]=\"bottom-right\";const Lh=7,Ih=Number.POSITIVE_INFINITY;function Ph(t,e){return e[1]!==Ih?function(t,e,r){let n=0,i=0;switch(e=Math.abs(e),r=Math.abs(r),t){case\"top-right\":case\"top-left\":case\"top\":i=r-Lh;break;case\"bottom-right\":case\"bottom-left\":case\"bottom\":i=-r+Lh}switch(t){case\"top-right\":case\"bottom-right\":case\"right\":n=-e;break;case\"top-left\":case\"bottom-left\":case\"left\":n=e}return[n,i]}(t,e[0],e[1]):function(t,e){let r=0,n=0;e<0&&(e=0);const 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h.call(this,t,e,r)}},p=class{constructor(e){this.layers={_geojsonTileLayer:this},this.name=\"_geojsonTileLayer\",this.extent=t.X,this.length=e.length,this._features=e}feature(t){return new f(this._features[t])}};var d={exports:{}},m=t.by,g=t.bs.VectorTileFeature,y=v;function v(t,e){this.options=e||{},this.features=t,this.length=t.length}function x(t,e){this.id=\"number\"==typeof t.id?t.id:void 0,this.type=t.type,this.rawGeometry=1===t.type?[t.geometry]:t.geometry,this.properties=t.tags,this.extent=e||4096}v.prototype.feature=function(t){return new x(this.features[t],this.options.extent)},x.prototype.loadGeometry=function(){var t=this.rawGeometry;this.geometry=[];for(var e=0;e>31}function E(t,e){for(var r=t.loadGeometry(),n=t.type,i=0,a=0,o=r.length,s=0;st},z=Math.fround||(O=new Float32Array(1),t=>(O[0]=+t,O[0]));var O;const D=3,R=5,F=6;class B{constructor(t){this.options=Object.assign(Object.create(P),t),this.trees=new Array(this.options.maxZoom+1),this.stride=this.options.reduce?7:6,this.clusterProps=[]}load(t){const{log:e,minZoom:r,maxZoom:n}=this.options;e&&console.time(\"total time\");const i=`prepare ${t.length} points`;e&&console.time(i),this.points=t;const a=[];for(let e=0;e=r;t--){const r=+Date.now();o=this.trees[t]=this._createTree(this._cluster(o,t)),e&&console.log(\"z%d: %d clusters in %dms\",t,o.numItems,+Date.now()-r)}return e&&console.timeEnd(\"total time\"),this}getClusters(t,e){let r=((t[0]+180)%360+360)%360-180;const n=Math.max(-90,Math.min(90,t[1]));let i=180===t[2]?180:((t[2]+180)%360+360)%360-180;const a=Math.max(-90,Math.min(90,t[3]));if(t[2]-t[0]>=360)r=-180,i=180;else if(r>i){const t=this.getClusters([r,n,180,a],e),o=this.getClusters([-180,n,i,a],e);return t.concat(o)}const o=this.trees[this._limitZoom(e)],s=o.range(U(r),V(a),U(i),V(n)),l=o.data,c=[];for(const t of s){const e=this.stride*t;c.push(l[e+R]>1?N(l,e,this.clusterProps):this.points[l[e+D]])}return c}getChildren(t){const e=this._getOriginId(t),r=this._getOriginZoom(t),n=\"No cluster with the specified id.\",i=this.trees[r];if(!i)throw new Error(n);const a=i.data;if(e*this.stride>=a.length)throw new Error(n);const o=this.options.radius/(this.options.extent*Math.pow(2,r-1)),s=a[e*this.stride],l=a[e*this.stride+1],c=i.within(s,l,o),u=[];for(const e of c){const r=e*this.stride;a[r+4]===t&&u.push(a[r+R]>1?N(a,r,this.clusterProps):this.points[a[r+D]])}if(0===u.length)throw new Error(n);return u}getLeaves(t,e,r){e=e||10,r=r||0;const n=[];return this._appendLeaves(n,t,e,r,0),n}getTile(t,e,r){const n=this.trees[this._limitZoom(t)],i=Math.pow(2,t),{extent:a,radius:o}=this.options,s=o/a,l=(r-s)/i,c=(r+1+s)/i,u={features:[]};return this._addTileFeatures(n.range((e-s)/i,l,(e+1+s)/i,c),n.data,e,r,i,u),0===e&&this._addTileFeatures(n.range(1-s/i,l,1,c),n.data,i,r,i,u),e===i-1&&this._addTileFeatures(n.range(0,l,s/i,c),n.data,-1,r,i,u),u.features.length?u:null}getClusterExpansionZoom(t){let e=this._getOriginZoom(t)-1;for(;e<=this.options.maxZoom;){const r=this.getChildren(t);if(e++,1!==r.length)break;t=r[0].properties.cluster_id}return e}_appendLeaves(t,e,r,n,i){const a=this.getChildren(e);for(const e of a){const a=e.properties;if(a&&a.cluster?i+a.point_count<=n?i+=a.point_count:i=this._appendLeaves(t,a.cluster_id,r,n,i):i1;let l,c,u;if(s)l=j(e,t,this.clusterProps),c=e[t],u=e[t+1];else{const r=this.points[e[t+D]];l=r.properties;const[n,i]=r.geometry.coordinates;c=U(n),u=V(i)}const h={type:1,geometry:[[Math.round(this.options.extent*(c*i-r)),Math.round(this.options.extent*(u*i-n))]],tags:l};let f;f=s||this.options.generateId?e[t+D]:this.points[e[t+D]].id,void 0!==f&&(h.id=f),a.features.push(h)}}_limitZoom(t){return Math.max(this.options.minZoom,Math.min(Math.floor(+t),this.options.maxZoom+1))}_cluster(t,e){const{radius:r,extent:n,reduce:i,minPoints:a}=this.options,o=r/(n*Math.pow(2,e)),s=t.data,l=[],c=this.stride;for(let r=0;re&&(p+=s[r+R])}if(p>f&&p>=a){let t,a=n*f,o=u*f,d=-1;const m=((r/c|0)<<5)+(e+1)+this.points.length;for(const n of h){const l=n*c;if(s[l+2]<=e)continue;s[l+2]=e;const u=s[l+R];a+=s[l]*u,o+=s[l+1]*u,s[l+4]=m,i&&(t||(t=this._map(s,r,!0),d=this.clusterProps.length,this.clusterProps.push(t)),i(t,this._map(s,l)))}s[r+4]=m,l.push(a/p,o/p,1/0,m,-1,p),i&&l.push(d)}else{for(let t=0;t1)for(const t of h){const r=t*c;if(!(s[r+2]<=e)){s[r+2]=e;for(let t=0;t>5}_getOriginZoom(t){return(t-this.points.length)%32}_map(t,e,r){if(t[e+R]>1){const n=this.clusterProps[t[e+F]];return r?Object.assign({},n):n}const n=this.points[t[e+D]].properties,i=this.options.map(n);return r&&i===n?Object.assign({},i):i}}function N(t,e,r){return{type:\"Feature\",id:t[e+D],properties:j(t,e,r),geometry:{type:\"Point\",coordinates:[(n=t[e],360*(n-.5)),q(t[e+1])]}};var n}function j(t,e,r){const n=t[e+R],i=n>=1e4?`${Math.round(n/1e3)}k`:n>=1e3?Math.round(n/100)/10+\"k\":n,a=t[e+F],o=-1===a?{}:Object.assign({},r[a]);return 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at(t,a,r+(i-r)*o,1),o}function st(t,e,r,n,i,a){const o=(a-r)/(i-r);return at(t,e+(n-e)*o,a,1),o}function lt(t,e){const r=[];for(let n=0;n0&&e.size<(i?o:n))return void(r.numPoints+=e.length/3);const s=[];for(let t=0;to)&&(r.numSimplified++,s.push(e[t],e[t+1])),r.numPoints++;i&&function(t,e){let r=0;for(let e=0,n=t.length,i=n-2;e0===e)for(let e=0,r=t.length;e24)throw new Error(\"maxZoom should be in the 0-24 range\");if(e.promoteId&&e.generateId)throw new Error(\"promoteId and generateId cannot be used together.\");let n=function(t,e){const r=[];if(\"FeatureCollection\"===t.type)for(let n=0;n1&&console.time(\"creation\"),f=this.tiles[h]=ft(t,e,r,n,l),this.tileCoords.push({z:e,x:r,y:n}),c)){c>1&&(console.log(\"tile z%d-%d-%d (features: %d, points: %d, simplified: %d)\",e,r,n,f.numFeatures,f.numPoints,f.numSimplified),console.timeEnd(\"creation\"));const t=`z${e}`;this.stats[t]=(this.stats[t]||0)+1,this.total++}if(f.source=t,null==i){if(e===l.indexMaxZoom||f.numPoints<=l.indexMaxPoints)continue}else{if(e===l.maxZoom||e===i)continue;if(null!=i){const t=i-e;if(r!==a>>t||n!==o>>t)continue}}if(f.source=null,0===t.length)continue;c>1&&console.time(\"clipping\");const p=.5*l.buffer/l.extent,d=.5-p,m=.5+p,g=1+p;let y=null,v=null,x=null,_=null,b=tt(t,u,r-p,r+m,0,f.minX,f.maxX,l),w=tt(t,u,r+d,r+g,0,f.minX,f.maxX,l);t=null,b&&(y=tt(b,u,n-p,n+m,1,f.minY,f.maxY,l),v=tt(b,u,n+d,n+g,1,f.minY,f.maxY,l),b=null),w&&(x=tt(w,u,n-p,n+m,1,f.minY,f.maxY,l),_=tt(w,u,n+d,n+g,1,f.minY,f.maxY,l),w=null),c>1&&console.timeEnd(\"clipping\"),s.push(y||[],e+1,2*r,2*n),s.push(v||[],e+1,2*r,2*n+1),s.push(x||[],e+1,2*r+1,2*n),s.push(_||[],e+1,2*r+1,2*n+1)}}getTile(t,e,r){t=+t,e=+e,r=+r;const n=this.options,{extent:i,debug:a}=n;if(t<0||t>24)return null;const o=1<1&&console.log(\"drilling down to z%d-%d-%d\",t,e,r);let l,c=t,u=e,h=r;for(;!l&&c>0;)c--,u>>=1,h>>=1,l=this.tiles[yt(c,u,h)];return l&&l.source?(a>1&&(console.log(\"found parent tile z%d-%d-%d\",c,u,h),console.time(\"drilling down\")),this.splitTile(l.source,c,u,h,t,e,r),a>1&&console.timeEnd(\"drilling down\"),this.tiles[s]?ut(this.tiles[s],i):null):null}}function yt(t,e,r){return 32*((1<{o.properties=t;const e={};for(const t of s)e[t]=n[t].evaluate(a,o);return e},e.reduce=(t,e)=>{o.properties=e;for(const e of s)a.accumulated=t[e],t[e]=i[e].evaluate(a,o)},e}(e)).load((yield this._pendingData).features):(i=yield this._pendingData,a=e.geojsonVtOptions,new gt(i,a)),this.loaded={};const r={};if(n){const t=n.finish();t&&(r.resourceTiming={},r.resourceTiming[e.source]=JSON.parse(JSON.stringify(t)))}return r}catch(e){if(delete this._pendingRequest,t.bA(e))return{abandoned:!0};throw e}var i,a}))}getData(){return t._(this,void 0,void 0,(function*(){return this._pendingData}))}reloadTile(t){const e=this.loaded,r=t.uid;return e&&e[r]?super.reloadTile(t):this.loadTile(t)}loadAndProcessGeoJSON(e,r){return t._(this,void 0,void 0,(function*(){let n=yield this.loadGeoJSON(e,r);if(delete this._pendingRequest,\"object\"!=typeof n)throw new Error(`Input data given to '${e.source}' is not a valid GeoJSON object.`);if(u(n,!0),e.filter){const r=t.bB(e.filter,{type:\"boolean\",\"property-type\":\"data-driven\",overridable:!1,transition:!1});if(\"error\"===r.result)throw new Error(r.value.map((t=>`${t.key}: ${t.message}`)).join(\", \"));const i=n.features.filter((t=>r.value.evaluate({zoom:0},t)));n={type:\"FeatureCollection\",features:i}}return n}))}loadGeoJSON(e,r){return t._(this,void 0,void 0,(function*(){const{promoteId:n}=e;if(e.request){const i=yield t.h(e.request,r);return this._dataUpdateable=xt(i.data,n)?_t(i.data,n):void 0,i.data}if(\"string\"==typeof e.data)try{const t=JSON.parse(e.data);return this._dataUpdateable=xt(t,n)?_t(t,n):void 0,t}catch(t){throw new Error(`Input data given to '${e.source}' is not a 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r.removeProperties)Object.prototype.hasOwnProperty.call(e.properties,t)&&delete e.properties[t];if((null===(o=r.addOrUpdateProperties)||void 0===o?void 0:o.length)>0)for(const{key:t,value:n}of r.addOrUpdateProperties)e.properties[t]=n}}(this._dataUpdateable,e.dataDiff,n),{type:\"FeatureCollection\",features:Array.from(this._dataUpdateable.values())}}))}removeSource(e){return t._(this,void 0,void 0,(function*(){this._pendingRequest&&this._pendingRequest.abort()}))}getClusterExpansionZoom(t){return this._geoJSONIndex.getClusterExpansionZoom(t.clusterId)}getClusterChildren(t){return this._geoJSONIndex.getChildren(t.clusterId)}getClusterLeaves(t){return this._geoJSONIndex.getLeaves(t.clusterId,t.limit,t.offset)}}class wt{constructor(e){this.self=e,this.actor=new t.F(e),this.layerIndexes={},this.availableImages={},this.workerSources={},this.demWorkerSources={},this.externalWorkerSourceTypes={},this.self.registerWorkerSource=(t,e)=>{if(this.externalWorkerSourceTypes[t])throw new 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this._getWorkerSource(e,r.type,r.source).getClusterLeaves(r)})))),this.actor.registerMessageHandler(\"LD\",((t,e)=>this._getWorkerSource(t,e.type,e.source).loadData(e))),this.actor.registerMessageHandler(\"GD\",((t,e)=>this._getWorkerSource(t,e.type,e.source).getData())),this.actor.registerMessageHandler(\"LT\",((t,e)=>this._getWorkerSource(t,e.type,e.source).loadTile(e))),this.actor.registerMessageHandler(\"RT\",((t,e)=>this._getWorkerSource(t,e.type,e.source).reloadTile(e))),this.actor.registerMessageHandler(\"AT\",((t,e)=>this._getWorkerSource(t,e.type,e.source).abortTile(e))),this.actor.registerMessageHandler(\"RMT\",((t,e)=>this._getWorkerSource(t,e.type,e.source).removeTile(e))),this.actor.registerMessageHandler(\"RS\",((e,r)=>t._(this,void 0,void 0,(function*(){if(!this.workerSources[e]||!this.workerSources[e][r.type]||!this.workerSources[e][r.type][r.source])return;const t=this.workerSources[e][r.type][r.source];delete this.workerSources[e][r.type][r.source],void 0!==t.removeSource&&t.removeSource(r)})))),this.actor.registerMessageHandler(\"RM\",(e=>t._(this,void 0,void 0,(function*(){delete this.layerIndexes[e],delete this.availableImages[e],delete this.workerSources[e],delete this.demWorkerSources[e]})))),this.actor.registerMessageHandler(\"SR\",((e,r)=>t._(this,void 0,void 0,(function*(){this.referrer=r})))),this.actor.registerMessageHandler(\"SRPS\",((t,e)=>this._syncRTLPluginState(t,e))),this.actor.registerMessageHandler(\"IS\",((e,r)=>t._(this,void 0,void 0,(function*(){this.self.importScripts(r)})))),this.actor.registerMessageHandler(\"SI\",((t,e)=>this._setImages(t,e))),this.actor.registerMessageHandler(\"UL\",((e,r)=>t._(this,void 0,void 0,(function*(){this._getLayerIndex(e).update(r.layers,r.removedIds)})))),this.actor.registerMessageHandler(\"SL\",((e,r)=>t._(this,void 0,void 0,(function*(){this._getLayerIndex(e).replace(r)}))))}_setImages(e,r){return t._(this,void 0,void 0,(function*(){this.availableImages[e]=r;for(const t in this.workerSources[e]){const n=this.workerSources[e][t];for(const t in n)n[t].availableImages=r}}))}_syncRTLPluginState(e,r){return t._(this,void 0,void 0,(function*(){if(t.bC.isParsed())return t.bC.getState();if(\"loading\"!==r.pluginStatus)return t.bC.setState(r),r;const e=r.pluginURL;if(this.self.importScripts(e),t.bC.isParsed()){const r={pluginStatus:\"loaded\",pluginURL:e};return t.bC.setState(r),r}throw t.bC.setState({pluginStatus:\"error\",pluginURL:\"\"}),new Error(`RTL Text Plugin failed to import scripts from ${e}`)}))}_getAvailableImages(t){let e=this.availableImages[t];return e||(e=[]),e}_getLayerIndex(t){let r=this.layerIndexes[t];return r||(r=this.layerIndexes[t]=new e),r}_getWorkerSource(t,e,r){if(this.workerSources[t]||(this.workerSources[t]={}),this.workerSources[t][e]||(this.workerSources[t][e]={}),!this.workerSources[t][e][r]){const n={sendAsync:(e,r)=>(e.targetMapId=t,this.actor.sendAsync(e,r))};switch(e){case\"vector\":this.workerSources[t][e][r]=new a(n,this._getLayerIndex(t),this._getAvailableImages(t));break;case\"geojson\":this.workerSources[t][e][r]=new bt(n,this._getLayerIndex(t),this._getAvailableImages(t));break;default:this.workerSources[t][e][r]=new this.externalWorkerSourceTypes[e](n,this._getLayerIndex(t),this._getAvailableImages(t))}}return this.workerSources[t][e][r]}_getDEMWorkerSource(t,e){return this.demWorkerSources[t]||(this.demWorkerSources[t]={}),this.demWorkerSources[t][e]||(this.demWorkerSources[t][e]=new o),this.demWorkerSources[t][e]}}return t.i(self)&&(self.worker=new wt(self)),wt})),r(\"index\",0,(function(t,e){var r=\"4.5.2\";let n,i;const a={now:\"undefined\"!=typeof performance&&performance&&performance.now?performance.now.bind(performance):Date.now.bind(Date),frameAsync(t){return new Promise(((r,n)=>{const i=requestAnimationFrame(r);t.signal.addEventListener(\"abort\",(()=>{cancelAnimationFrame(i),n(e.c())}))}))},getImageData(t,e=0){return this.getImageCanvasContext(t).getImageData(-e,-e,t.width+2*e,t.height+2*e)},getImageCanvasContext(t){const e=window.document.createElement(\"canvas\"),r=e.getContext(\"2d\",{willReadFrequently:!0});if(!r)throw new Error(\"failed to create canvas 2d context\");return e.width=t.width,e.height=t.height,r.drawImage(t,0,0,t.width,t.height),r},resolveURL(t){return n||(n=document.createElement(\"a\")),n.href=t,n.href},hardwareConcurrency:\"undefined\"!=typeof navigator&&navigator.hardwareConcurrency||4,get prefersReducedMotion(){return!!matchMedia&&(null==i&&(i=matchMedia(\"(prefers-reduced-motion: reduce)\")),i.matches)}};class o{static testProp(t){if(!o.docStyle)return t[0];for(let e=0;e{window.removeEventListener(\"click\",o.suppressClickInternal,!0)}),0)}static getScale(t){const e=t.getBoundingClientRect();return{x:e.width/t.offsetWidth||1,y:e.height/t.offsetHeight||1,boundingClientRect:e}}static getPoint(t,r,n){const i=r.boundingClientRect;return new e.P((n.clientX-i.left)/r.x-t.clientLeft,(n.clientY-i.top)/r.y-t.clientTop)}static mousePos(t,e){const r=o.getScale(t);return o.getPoint(t,r,e)}static touchPos(t,e){const r=[],n=o.getScale(t);for(let i=0;i{l&&f(l),l=null,h=!0},c.onerror=()=>{u=!0,l=null},c.src=\"data:image/webp;base64,UklGRh4AAABXRUJQVlA4TBEAAAAvAQAAAAfQ//73v/+BiOh/AAA=\"),function(t){let r,n,i,a;t.resetRequestQueue=()=>{r=[],n=0,i=0,a={}},t.addThrottleControl=t=>{const e=i++;return a[e]=t,e},t.removeThrottleControl=t=>{delete a[t],l()};t.getImage=(t,n,i=!0)=>new Promise(((a,o)=>{s.supported&&(t.headers||(t.headers={}),t.headers.accept=\"image/webp,*/*\"),e.e(t,{type:\"image\"});const c={abortController:n,requestParameters:t,supportImageRefresh:i,state:\"queued\",onError:t=>{o(t)},onSuccess:t=>{a(t)}};r.push(c),l()}));const o=t=>e._(this,void 0,void 0,(function*(){t.state=\"running\";const{requestParameters:r,supportImageRefresh:i,onError:a,onSuccess:o,abortController:s}=t,u=!1===i&&!e.i(self)&&!e.g(r.url)&&(!r.headers||Object.keys(r.headers).reduce(((t,e)=>t&&\"accept\"===e),!0));n++;const h=u?c(r,s):e.m(r,s);try{const r=yield h;delete t.abortController,t.state=\"completed\",r.data instanceof HTMLImageElement||e.b(r.data)?o(r):r.data&&o({data:yield(f=r.data,\"function\"==typeof createImageBitmap?e.d(f):e.f(f)),cacheControl:r.cacheControl,expires:r.expires})}catch(e){delete t.abortController,a(e)}finally{n--,l()}var f})),l=()=>{const t=(()=>{for(const t of Object.keys(a))if(a[t]())return!0;return!1})()?e.a.MAX_PARALLEL_IMAGE_REQUESTS_PER_FRAME:e.a.MAX_PARALLEL_IMAGE_REQUESTS;for(let e=n;e0;e++){const t=r.shift();t.abortController.signal.aborted?e--:o(t)}},c=(t,r)=>new Promise(((n,i)=>{const a=new Image,o=t.url,s=t.credentials;s&&\"include\"===s?a.crossOrigin=\"use-credentials\":(s&&\"same-origin\"===s||!e.s(o))&&(a.crossOrigin=\"anonymous\"),r.signal.addEventListener(\"abort\",(()=>{a.src=\"\",i(e.c())})),a.fetchPriority=\"high\",a.onload=()=>{a.onerror=a.onload=null,n({data:a})},a.onerror=()=>{a.onerror=a.onload=null,r.signal.aborted||i(new Error(\"Could not load image. Please make sure to use a supported image type such as PNG or JPEG. Note that SVGs are not supported.\"))},a.src=o}))}(p||(p={})),p.resetRequestQueue();class d{constructor(t){this._transformRequestFn=t}transformRequest(t,e){return this._transformRequestFn&&this._transformRequestFn(t,e)||{url:t}}setTransformRequest(t){this._transformRequestFn=t}}function m(t){var r=new e.A(3);return r[0]=t[0],r[1]=t[1],r[2]=t[2],r}var g,y=function(t,e,r){return t[0]=e[0]-r[0],t[1]=e[1]-r[1],t[2]=e[2]-r[2],t};g=new e.A(3),e.A!=Float32Array&&(g[0]=0,g[1]=0,g[2]=0);var v=function(t){var e=t[0],r=t[1];return e*e+r*r};function x(t){const e=[];if(\"string\"==typeof t)e.push({id:\"default\",url:t});else if(t&&t.length>0){const r=[];for(const{id:n,url:i}of t){const t=`${n}${i}`;-1===r.indexOf(t)&&(r.push(t),e.push({id:n,url:i}))}}return e}function _(t,e,r){const n=t.split(\"?\");return n[0]+=`${e}${r}`,n.join(\"?\")}function b(t,r,n,i){return e._(this,void 0,void 0,(function*(){const o=x(t),s=n>1?\"@2x\":\"\",l={},c={};for(const{id:t,url:n}of o){const a=r.transformRequest(_(n,s,\".json\"),\"SpriteJSON\");l[t]=e.h(a,i);const o=r.transformRequest(_(n,s,\".png\"),\"SpriteImage\");c[t]=p.getImage(o,i)}return yield Promise.all([...Object.values(l),...Object.values(c)]),function(t,r){return e._(this,void 0,void 0,(function*(){const e={};for(const n in t){e[n]={};const i=a.getImageCanvasContext((yield r[n]).data),o=(yield t[n]).data;for(const t in o){const{width:r,height:a,x:s,y:l,sdf:c,pixelRatio:u,stretchX:h,stretchY:f,content:p,textFitWidth:d,textFitHeight:m}=o[t],g={width:r,height:a,x:s,y:l,context:i};e[n][t]={data:null,pixelRatio:u,sdf:c,stretchX:h,stretchY:f,content:p,textFitWidth:d,textFitHeight:m,spriteData:g}}}return e}))}(l,c)}))}!function(){var t=new e.A(2);e.A!=Float32Array&&(t[0]=0,t[1]=0)}();class w{constructor(t,e,r,n){this.context=t,this.format=r,this.texture=t.gl.createTexture(),this.update(e,n)}update(t,r,n){const{width:i,height:a}=t,o=!(this.size&&this.size[0]===i&&this.size[1]===a||n),{context:s}=this,{gl:l}=s;if(this.useMipmap=Boolean(r&&r.useMipmap),l.bindTexture(l.TEXTURE_2D,this.texture),s.pixelStoreUnpackFlipY.set(!1),s.pixelStoreUnpack.set(1),s.pixelStoreUnpackPremultiplyAlpha.set(this.format===l.RGBA&&(!r||!1!==r.premultiply)),o)this.size=[i,a],t instanceof HTMLImageElement||t instanceof HTMLCanvasElement||t instanceof HTMLVideoElement||t instanceof ImageData||e.b(t)?l.texImage2D(l.TEXTURE_2D,0,this.format,this.format,l.UNSIGNED_BYTE,t):l.texImage2D(l.TEXTURE_2D,0,this.format,i,a,0,this.format,l.UNSIGNED_BYTE,t.data);else{const{x:r,y:o}=n||{x:0,y:0};t instanceof HTMLImageElement||t instanceof HTMLCanvasElement||t instanceof HTMLVideoElement||t instanceof ImageData||e.b(t)?l.texSubImage2D(l.TEXTURE_2D,0,r,o,l.RGBA,l.UNSIGNED_BYTE,t):l.texSubImage2D(l.TEXTURE_2D,0,r,o,i,a,l.RGBA,l.UNSIGNED_BYTE,t.data)}this.useMipmap&&this.isSizePowerOfTwo()&&l.generateMipmap(l.TEXTURE_2D)}bind(t,e,r){const{context:n}=this,{gl:i}=n;i.bindTexture(i.TEXTURE_2D,this.texture),r!==i.LINEAR_MIPMAP_NEAREST||this.isSizePowerOfTwo()||(r=i.LINEAR),t!==this.filter&&(i.texParameteri(i.TEXTURE_2D,i.TEXTURE_MAG_FILTER,t),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_MIN_FILTER,r||t),this.filter=t),e!==this.wrap&&(i.texParameteri(i.TEXTURE_2D,i.TEXTURE_WRAP_S,e),i.texParameteri(i.TEXTURE_2D,i.TEXTURE_WRAP_T,e),this.wrap=e)}isSizePowerOfTwo(){return this.size[0]===this.size[1]&&Math.log(this.size[0])/Math.LN2%1==0}destroy(){const{gl:t}=this.context;t.deleteTexture(this.texture),this.texture=null}}function T(t){const{userImage:e}=t;return!!(e&&e.render&&e.render())&&(t.data.replace(new Uint8Array(e.data.buffer)),!0)}class k extends e.E{constructor(){super(),this.images={},this.updatedImages={},this.callbackDispatchedThisFrame={},this.loaded=!1,this.requestors=[],this.patterns={},this.atlasImage=new e.R({width:1,height:1}),this.dirty=!0}isLoaded(){return this.loaded}setLoaded(t){if(this.loaded!==t&&(this.loaded=t,t)){for(const{ids:t,promiseResolve:e}of this.requestors)e(this._getImagesForIds(t));this.requestors=[]}}getImage(t){const r=this.images[t];if(r&&!r.data&&r.spriteData){const t=r.spriteData;r.data=new e.R({width:t.width,height:t.height},t.context.getImageData(t.x,t.y,t.width,t.height).data),r.spriteData=null}return r}addImage(t,e){if(this.images[t])throw new Error(`Image id ${t} already exist, use updateImage instead`);this._validate(t,e)&&(this.images[t]=e)}_validate(t,r){let n=!0;const i=r.data||r.spriteData;return this._validateStretch(r.stretchX,i&&i.width)||(this.fire(new e.j(new Error(`Image \"${t}\" has invalid \"stretchX\" value`))),n=!1),this._validateStretch(r.stretchY,i&&i.height)||(this.fire(new e.j(new Error(`Image \"${t}\" has invalid \"stretchY\" value`))),n=!1),this._validateContent(r.content,r)||(this.fire(new e.j(new Error(`Image \"${t}\" has invalid \"content\" value`))),n=!1),n}_validateStretch(t,e){if(!t)return!0;let r=0;for(const n of t){if(n[0]{let n=!0;if(!this.isLoaded())for(const e of t)this.images[e]||(n=!1);this.isLoaded()||n?e(this._getImagesForIds(t)):this.requestors.push({ids:t,promiseResolve:e})}))}_getImagesForIds(t){const r={};for(const n of t){let t=this.getImage(n);t||(this.fire(new e.k(\"styleimagemissing\",{id:n})),t=this.getImage(n)),t?r[n]={data:t.data.clone(),pixelRatio:t.pixelRatio,sdf:t.sdf,version:t.version,stretchX:t.stretchX,stretchY:t.stretchY,content:t.content,textFitWidth:t.textFitWidth,textFitHeight:t.textFitHeight,hasRenderCallback:Boolean(t.userImage&&t.userImage.render)}:e.w(`Image \"${n}\" could not be loaded. Please make sure you have added the image with map.addImage() or a \"sprite\" property in your style. You can provide missing images by listening for the \"styleimagemissing\" map event.`)}return r}getPixelSize(){const{width:t,height:e}=this.atlasImage;return{width:t,height:e}}getPattern(t){const r=this.patterns[t],n=this.getImage(t);if(!n)return null;if(r&&r.position.version===n.version)return r.position;if(r)r.position.version=n.version;else{const r={w:n.data.width+2,h:n.data.height+2,x:0,y:0},i=new e.I(r,n);this.patterns[t]={bin:r,position:i}}return this._updatePatternAtlas(),this.patterns[t].position}bind(t){const e=t.gl;this.atlasTexture?this.dirty&&(this.atlasTexture.update(this.atlasImage),this.dirty=!1):this.atlasTexture=new w(t,this.atlasImage,e.RGBA),this.atlasTexture.bind(e.LINEAR,e.CLAMP_TO_EDGE)}_updatePatternAtlas(){const t=[];for(const e in this.patterns)t.push(this.patterns[e].bin);const{w:r,h:n}=e.p(t),i=this.atlasImage;i.resize({width:r||1,height:n||1});for(const t in this.patterns){const{bin:r}=this.patterns[t],n=r.x+1,a=r.y+1,o=this.getImage(t).data,s=o.width,l=o.height;e.R.copy(o,i,{x:0,y:0},{x:n,y:a},{width:s,height:l}),e.R.copy(o,i,{x:0,y:l-1},{x:n,y:a-1},{width:s,height:1}),e.R.copy(o,i,{x:0,y:0},{x:n,y:a+l},{width:s,height:1}),e.R.copy(o,i,{x:s-1,y:0},{x:n-1,y:a},{width:1,height:l}),e.R.copy(o,i,{x:0,y:0},{x:n+s,y:a},{width:1,height:l})}this.dirty=!0}beginFrame(){this.callbackDispatchedThisFrame={}}dispatchRenderCallbacks(t){for(const r of t){if(this.callbackDispatchedThisFrame[r])continue;this.callbackDispatchedThisFrame[r]=!0;const t=this.getImage(r);t||e.w(`Image with ID: \"${r}\" was not found`),T(t)&&this.updateImage(r,t)}}}const A=1e20;function M(t,e,r,n,i,a,o,s,l){for(let c=e;c-1);l++,a[l]=s,o[l]=c,o[l+1]=A}for(let s=0,l=0;s65535)throw new Error(\"glyphs > 65535 not supported\");if(e.ranges[i])return{stack:t,id:r,glyph:n};if(!this.url)throw new Error(\"glyphsUrl is not set\");if(!e.requests[i]){const r=E.loadGlyphRange(t,i,this.url,this.requestManager);e.requests[i]=r}const a=yield e.requests[i];for(const t in a)this._doesCharSupportLocalGlyph(+t)||(e.glyphs[+t]=a[+t]);return e.ranges[i]=!0,{stack:t,id:r,glyph:a[r]||null}}))}_doesCharSupportLocalGlyph(t){return!!this.localIdeographFontFamily&&(e.u[\"CJK Unified Ideographs\"](t)||e.u[\"Hangul Syllables\"](t)||e.u.Hiragana(t)||e.u.Katakana(t))}_tinySDF(t,r,n){const i=this.localIdeographFontFamily;if(!i)return;if(!this._doesCharSupportLocalGlyph(n))return;let a=t.tinySDF;if(!a){let e=\"400\";/bold/i.test(r)?e=\"900\":/medium/i.test(r)?e=\"500\":/light/i.test(r)&&(e=\"200\"),a=t.tinySDF=new E.TinySDF({fontSize:48,buffer:6,radius:16,cutoff:.25,fontFamily:i,fontWeight:e})}const o=a.draw(String.fromCharCode(n));return{id:n,bitmap:new e.o({width:o.width||60,height:o.height||60},o.data),metrics:{width:o.glyphWidth/2||24,height:o.glyphHeight/2||24,left:o.glyphLeft/2+.5||0,top:o.glyphTop/2-27.5||-8,advance:o.glyphAdvance/2||24,isDoubleResolution:!0}}}}E.loadGlyphRange=function(t,r,n,i){return e._(this,void 0,void 0,(function*(){const a=256*r,o=a+255,s=i.transformRequest(n.replace(\"{fontstack}\",t).replace(\"{range}\",`${a}-${o}`),\"Glyphs\"),l=yield e.l(s,new AbortController);if(!l||!l.data)throw new Error(`Could not load glyph range. range: ${r}, ${a}-${o}`);const c={};for(const t of e.n(l.data))c[t.id]=t;return c}))},E.TinySDF=class{constructor({fontSize:t=24,buffer:e=3,radius:r=8,cutoff:n=.25,fontFamily:i=\"sans-serif\",fontWeight:a=\"normal\",fontStyle:o=\"normal\"}={}){this.buffer=e,this.cutoff=n,this.radius=r;const s=this.size=t+4*e,l=this._createCanvas(s),c=this.ctx=l.getContext(\"2d\",{willReadFrequently:!0});c.font=`${o} ${a} ${t}px ${i}`,c.textBaseline=\"alphabetic\",c.textAlign=\"left\",c.fillStyle=\"black\",this.gridOuter=new Float64Array(s*s),this.gridInner=new Float64Array(s*s),this.f=new Float64Array(s),this.z=new Float64Array(s+1),this.v=new Uint16Array(s)}_createCanvas(t){const e=document.createElement(\"canvas\");return e.width=e.height=t,e}draw(t){const{width:e,actualBoundingBoxAscent:r,actualBoundingBoxDescent:n,actualBoundingBoxLeft:i,actualBoundingBoxRight:a}=this.ctx.measureText(t),o=Math.ceil(r),s=Math.max(0,Math.min(this.size-this.buffer,Math.ceil(a-i))),l=Math.min(this.size-this.buffer,o+Math.ceil(n)),c=s+2*this.buffer,u=l+2*this.buffer,h=Math.max(c*u,0),f=new Uint8ClampedArray(h),p={data:f,width:c,height:u,glyphWidth:s,glyphHeight:l,glyphTop:o,glyphLeft:0,glyphAdvance:e};if(0===s||0===l)return p;const{ctx:d,buffer:m,gridInner:g,gridOuter:y}=this;d.clearRect(m,m,s,l),d.fillText(t,m,m+o);const v=d.getImageData(m,m,s,l);y.fill(A,0,h),g.fill(0,0,h);for(let t=0;t0?t*t:0,g[n]=t<0?t*t:0}}M(y,0,0,c,u,c,this.f,this.v,this.z),M(g,m,m,s,l,c,this.f,this.v,this.z);for(let t=0;t1&&(s=t[++o]);const l=Math.abs(i-s.left),c=Math.abs(i-s.right),u=Math.min(l,c);let h;const f=e/r*(n+1);if(s.isDash){const t=n-Math.abs(f);h=Math.sqrt(u*u+t*t)}else h=n-Math.sqrt(u*u+f*f);this.data[a+i]=Math.max(0,Math.min(255,h+128))}}}addRegularDash(t){for(let e=t.length-1;e>=0;--e){const r=t[e],n=t[e+1];r.zeroLength?t.splice(e,1):n&&n.isDash===r.isDash&&(n.left=r.left,t.splice(e,1))}const e=t[0],r=t[t.length-1];e.isDash===r.isDash&&(e.left=r.left-this.width,r.right=e.right+this.width);const n=this.width*this.nextRow;let i=0,a=t[i];for(let e=0;e1&&(a=t[++i]);const r=Math.abs(e-a.left),o=Math.abs(e-a.right),s=Math.min(r,o),l=a.isDash?s:-s;this.data[n+e]=Math.max(0,Math.min(255,l+128))}}addDash(t,r){const n=r?7:0,i=2*n+1;if(this.nextRow+i>this.height)return e.w(\"LineAtlas out of space\"),null;let a=0;for(let e=0;e{t.terminate()})),this.workers=null)}isPreloaded(){return!!this.active[F]}numActive(){return Object.keys(this.active).length}}const N=Math.floor(a.hardwareConcurrency/2);let j,U;function V(){return j||(j=new B),j}B.workerCount=e.C(globalThis)?Math.max(Math.min(N,3),1):1;class q{constructor(t,r){this.workerPool=t,this.actors=[],this.currentActor=0,this.id=r;const n=this.workerPool.acquire(r);for(let t=0;t{t.remove()})),this.actors=[],t&&this.workerPool.release(this.id)}registerMessageHandler(t,e){for(const r of this.actors)r.registerMessageHandler(t,e)}}function H(){return U||(U=new q(V(),e.G),U.registerMessageHandler(\"GR\",((t,r,n)=>e.m(r,n)))),U}function G(t,r){const n=e.H();return e.J(n,n,[1,1,0]),e.K(n,n,[.5*t.width,.5*t.height,1]),e.L(n,n,t.calculatePosMatrix(r.toUnwrapped()))}function Z(t,e,r,n,i,a){const o=function(t,e,r){if(t)for(const n of t){const t=e[n];if(t&&t.source===r&&\"fill-extrusion\"===t.type)return!0}else for(const t in e){const n=e[t];if(n.source===r&&\"fill-extrusion\"===n.type)return!0}return!1}(i&&i.layers,e,t.id),s=a.maxPitchScaleFactor(),l=t.tilesIn(n,s,o);l.sort(W);const c=[];for(const n of l)c.push({wrappedTileID:n.tileID.wrapped().key,queryResults:n.tile.queryRenderedFeatures(e,r,t._state,n.queryGeometry,n.cameraQueryGeometry,n.scale,i,a,s,G(t.transform,n.tileID))});const u=function(t){const e={},r={};for(const n of t){const t=n.queryResults,i=n.wrappedTileID,a=r[i]=r[i]||{};for(const r in t){const n=t[r],i=a[r]=a[r]||{},o=e[r]=e[r]||[];for(const t of n)i[t.featureIndex]||(i[t.featureIndex]=!0,o.push(t))}}return e}(c);for(const e in u)u[e].forEach((e=>{const r=e.feature,n=t.getFeatureState(r.layer[\"source-layer\"],r.id);r.source=r.layer.source,r.layer[\"source-layer\"]&&(r.sourceLayer=r.layer[\"source-layer\"]),r.state=n}));return u}function W(t,e){const r=t.tileID,n=e.tileID;return r.overscaledZ-n.overscaledZ||r.canonical.y-n.canonical.y||r.wrap-n.wrap||r.canonical.x-n.canonical.x}function Y(t,r,n){return e._(this,void 0,void 0,(function*(){let i=t;if(t.url?i=(yield e.h(r.transformRequest(t.url,\"Source\"),n)).data:yield a.frameAsync(n),!i)return null;const o=e.M(e.e(i,t),[\"tiles\",\"minzoom\",\"maxzoom\",\"attribution\",\"bounds\",\"scheme\",\"tileSize\",\"encoding\"]);return\"vector_layers\"in i&&i.vector_layers&&(o.vectorLayerIds=i.vector_layers.map((t=>t.id))),o}))}class X{constructor(t,e){t&&(e?this.setSouthWest(t).setNorthEast(e):Array.isArray(t)&&(4===t.length?this.setSouthWest([t[0],t[1]]).setNorthEast([t[2],t[3]]):this.setSouthWest(t[0]).setNorthEast(t[1])))}setNorthEast(t){return this._ne=t instanceof e.N?new e.N(t.lng,t.lat):e.N.convert(t),this}setSouthWest(t){return this._sw=t instanceof e.N?new e.N(t.lng,t.lat):e.N.convert(t),this}extend(t){const r=this._sw,n=this._ne;let i,a;if(t instanceof e.N)i=t,a=t;else{if(!(t instanceof X)){if(Array.isArray(t)){if(4===t.length||t.every(Array.isArray)){const e=t;return this.extend(X.convert(e))}{const r=t;return this.extend(e.N.convert(r))}}return t&&(\"lng\"in t||\"lon\"in t)&&\"lat\"in t?this.extend(e.N.convert(t)):this}if(i=t._sw,a=t._ne,!i||!a)return this}return r||n?(r.lng=Math.min(i.lng,r.lng),r.lat=Math.min(i.lat,r.lat),n.lng=Math.max(a.lng,n.lng),n.lat=Math.max(a.lat,n.lat)):(this._sw=new e.N(i.lng,i.lat),this._ne=new e.N(a.lng,a.lat)),this}getCenter(){return new e.N((this._sw.lng+this._ne.lng)/2,(this._sw.lat+this._ne.lat)/2)}getSouthWest(){return this._sw}getNorthEast(){return this._ne}getNorthWest(){return new e.N(this.getWest(),this.getNorth())}getSouthEast(){return new e.N(this.getEast(),this.getSouth())}getWest(){return this._sw.lng}getSouth(){return this._sw.lat}getEast(){return this._ne.lng}getNorth(){return this._ne.lat}toArray(){return[this._sw.toArray(),this._ne.toArray()]}toString(){return`LngLatBounds(${this._sw.toString()}, ${this._ne.toString()})`}isEmpty(){return!(this._sw&&this._ne)}contains(t){const{lng:r,lat:n}=e.N.convert(t),i=this._sw.lat<=n&&n<=this._ne.lat;let a=this._sw.lng<=r&&r<=this._ne.lng;return this._sw.lng>this._ne.lng&&(a=this._sw.lng>=r&&r>=this._ne.lng),i&&a}static convert(t){return t instanceof X?t:t?new X(t):t}static fromLngLat(t,r=0){const n=360*r/40075017,i=n/Math.cos(Math.PI/180*t.lat);return new X(new e.N(t.lng-i,t.lat-n),new e.N(t.lng+i,t.lat+n))}}class ${constructor(t,e,r){this.bounds=X.convert(this.validateBounds(t)),this.minzoom=e||0,this.maxzoom=r||24}validateBounds(t){return Array.isArray(t)&&4===t.length?[Math.max(-180,t[0]),Math.max(-90,t[1]),Math.min(180,t[2]),Math.min(90,t[3])]:[-180,-90,180,90]}contains(t){const r=Math.pow(2,t.z),n=Math.floor(e.O(this.bounds.getWest())*r),i=Math.floor(e.Q(this.bounds.getNorth())*r),a=Math.ceil(e.O(this.bounds.getEast())*r),o=Math.ceil(e.Q(this.bounds.getSouth())*r);return t.x>=n&&t.x=i&&t.y{this._options.tiles=t})),this}setUrl(t){return this.setSourceProperty((()=>{this.url=t,this._options.url=t})),this}onRemove(){this._tileJSONRequest&&(this._tileJSONRequest.abort(),this._tileJSONRequest=null)}serialize(){return e.e({},this._options)}loadTile(t){return e._(this,void 0,void 0,(function*(){const e=t.tileID.canonical.url(this.tiles,this.map.getPixelRatio(),this.scheme),r={request:this.map._requestManager.transformRequest(e,\"Tile\"),uid:t.uid,tileID:t.tileID,zoom:t.tileID.overscaledZ,tileSize:this.tileSize*t.tileID.overscaleFactor(),type:this.type,source:this.id,pixelRatio:this.map.getPixelRatio(),showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};r.request.collectResourceTiming=this._collectResourceTiming;let n=\"RT\";if(t.actor&&\"expired\"!==t.state){if(\"loading\"===t.state)return new Promise(((e,r)=>{t.reloadPromise={resolve:e,reject:r}}))}else t.actor=this.dispatcher.getActor(),n=\"LT\";t.abortController=new AbortController;try{const e=yield t.actor.sendAsync({type:n,data:r},t.abortController);if(delete t.abortController,t.aborted)return;this._afterTileLoadWorkerResponse(t,e)}catch(e){if(delete t.abortController,t.aborted)return;if(e&&404!==e.status)throw e;this._afterTileLoadWorkerResponse(t,null)}}))}_afterTileLoadWorkerResponse(t,e){if(e&&e.resourceTiming&&(t.resourceTiming=e.resourceTiming),e&&this.map._refreshExpiredTiles&&t.setExpiryData(e),t.loadVectorData(e,this.map.painter),t.reloadPromise){const e=t.reloadPromise;t.reloadPromise=null,this.loadTile(t).then(e.resolve).catch(e.reject)}}abortTile(t){return e._(this,void 0,void 0,(function*(){t.abortController&&(t.abortController.abort(),delete t.abortController),t.actor&&(yield t.actor.sendAsync({type:\"AT\",data:{uid:t.uid,type:this.type,source:this.id}}))}))}unloadTile(t){return e._(this,void 0,void 0,(function*(){t.unloadVectorData(),t.actor&&(yield t.actor.sendAsync({type:\"RMT\",data:{uid:t.uid,type:this.type,source:this.id}}))}))}hasTransition(){return!1}}class K extends e.E{constructor(t,r,n,i){super(),this.id=t,this.dispatcher=n,this.setEventedParent(i),this.type=\"raster\",this.minzoom=0,this.maxzoom=22,this.roundZoom=!0,this.scheme=\"xyz\",this.tileSize=512,this._loaded=!1,this._options=e.e({type:\"raster\"},r),e.e(this,e.M(r,[\"url\",\"scheme\",\"tileSize\"]))}load(){return e._(this,void 0,void 0,(function*(){this._loaded=!1,this.fire(new e.k(\"dataloading\",{dataType:\"source\"})),this._tileJSONRequest=new AbortController;try{const t=yield Y(this._options,this.map._requestManager,this._tileJSONRequest);this._tileJSONRequest=null,this._loaded=!0,t&&(e.e(this,t),t.bounds&&(this.tileBounds=new $(t.bounds,this.minzoom,this.maxzoom)),this.fire(new e.k(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})),this.fire(new e.k(\"data\",{dataType:\"source\",sourceDataType:\"content\"})))}catch(t){this._tileJSONRequest=null,this.fire(new e.j(t))}}))}loaded(){return this._loaded}onAdd(t){this.map=t,this.load()}onRemove(){this._tileJSONRequest&&(this._tileJSONRequest.abort(),this._tileJSONRequest=null)}setSourceProperty(t){this._tileJSONRequest&&(this._tileJSONRequest.abort(),this._tileJSONRequest=null),t(),this.load()}setTiles(t){return this.setSourceProperty((()=>{this._options.tiles=t})),this}setUrl(t){return this.setSourceProperty((()=>{this.url=t,this._options.url=t})),this}serialize(){return e.e({},this._options)}hasTile(t){return!this.tileBounds||this.tileBounds.contains(t.canonical)}loadTile(t){return e._(this,void 0,void 0,(function*(){const e=t.tileID.canonical.url(this.tiles,this.map.getPixelRatio(),this.scheme);t.abortController=new AbortController;try{const r=yield p.getImage(this.map._requestManager.transformRequest(e,\"Tile\"),t.abortController,this.map._refreshExpiredTiles);if(delete t.abortController,t.aborted)return void(t.state=\"unloaded\");if(r&&r.data){this.map._refreshExpiredTiles&&r.cacheControl&&r.expires&&t.setExpiryData({cacheControl:r.cacheControl,expires:r.expires});const e=this.map.painter.context,n=e.gl,i=r.data;t.texture=this.map.painter.getTileTexture(i.width),t.texture?t.texture.update(i,{useMipmap:!0}):(t.texture=new w(e,i,n.RGBA,{useMipmap:!0}),t.texture.bind(n.LINEAR,n.CLAMP_TO_EDGE,n.LINEAR_MIPMAP_NEAREST)),t.state=\"loaded\"}}catch(e){if(delete t.abortController,t.aborted)t.state=\"unloaded\";else if(e)throw t.state=\"errored\",e}}))}abortTile(t){return e._(this,void 0,void 0,(function*(){t.abortController&&(t.abortController.abort(),delete t.abortController)}))}unloadTile(t){return e._(this,void 0,void 0,(function*(){t.texture&&this.map.painter.saveTileTexture(t.texture)}))}hasTransition(){return!1}}class Q extends K{constructor(t,r,n,i){super(t,r,n,i),this.type=\"raster-dem\",this.maxzoom=22,this._options=e.e({type:\"raster-dem\"},r),this.encoding=r.encoding||\"mapbox\",this.redFactor=r.redFactor,this.greenFactor=r.greenFactor,this.blueFactor=r.blueFactor,this.baseShift=r.baseShift}loadTile(t){return e._(this,void 0,void 0,(function*(){const r=t.tileID.canonical.url(this.tiles,this.map.getPixelRatio(),this.scheme),n=this.map._requestManager.transformRequest(r,\"Tile\");t.neighboringTiles=this._getNeighboringTiles(t.tileID),t.abortController=new AbortController;try{const r=yield p.getImage(n,t.abortController,this.map._refreshExpiredTiles);if(delete t.abortController,t.aborted)return void(t.state=\"unloaded\");if(r&&r.data){const n=r.data;this.map._refreshExpiredTiles&&r.cacheControl&&r.expires&&t.setExpiryData({cacheControl:r.cacheControl,expires:r.expires});const i=e.b(n)&&e.U()?n:yield this.readImageNow(n),a={type:this.type,uid:t.uid,source:this.id,rawImageData:i,encoding:this.encoding,redFactor:this.redFactor,greenFactor:this.greenFactor,blueFactor:this.blueFactor,baseShift:this.baseShift};if(!t.actor||\"expired\"===t.state){t.actor=this.dispatcher.getActor();const e=yield t.actor.sendAsync({type:\"LDT\",data:a});t.dem=e,t.needsHillshadePrepare=!0,t.needsTerrainPrepare=!0,t.state=\"loaded\"}}}catch(e){if(delete t.abortController,t.aborted)t.state=\"unloaded\";else if(e)throw t.state=\"errored\",e}}))}readImageNow(t){return e._(this,void 0,void 0,(function*(){if(\"undefined\"!=typeof VideoFrame&&e.V()){const r=t.width+2,n=t.height+2;try{return new e.R({width:r,height:n},yield e.W(t,-1,-1,r,n))}catch(t){}}return a.getImageData(t,1)}))}_getNeighboringTiles(t){const r=t.canonical,n=Math.pow(2,r.z),i=(r.x-1+n)%n,a=0===r.x?t.wrap-1:t.wrap,o=(r.x+1+n)%n,s=r.x+1===n?t.wrap+1:t.wrap,l={};return l[new e.S(t.overscaledZ,a,r.z,i,r.y).key]={backfilled:!1},l[new e.S(t.overscaledZ,s,r.z,o,r.y).key]={backfilled:!1},r.y>0&&(l[new e.S(t.overscaledZ,a,r.z,i,r.y-1).key]={backfilled:!1},l[new e.S(t.overscaledZ,t.wrap,r.z,r.x,r.y-1).key]={backfilled:!1},l[new e.S(t.overscaledZ,s,r.z,o,r.y-1).key]={backfilled:!1}),r.y+10&&e.e(i,{resourceTiming:n}),this.fire(new e.k(\"data\",Object.assign(Object.assign({},i),{sourceDataType:\"metadata\"}))),this.fire(new e.k(\"data\",Object.assign(Object.assign({},i),{sourceDataType:\"content\"})))}catch(t){if(this._pendingLoads--,this._removed)return void this.fire(new e.k(\"dataabort\",{dataType:\"source\"}));this.fire(new e.j(t))}}))}loaded(){return 0===this._pendingLoads}loadTile(t){return e._(this,void 0,void 0,(function*(){const e=t.actor?\"RT\":\"LT\";t.actor=this.actor;const r={type:this.type,uid:t.uid,tileID:t.tileID,zoom:t.tileID.overscaledZ,maxZoom:this.maxzoom,tileSize:this.tileSize,source:this.id,pixelRatio:this.map.getPixelRatio(),showCollisionBoxes:this.map.showCollisionBoxes,promoteId:this.promoteId};t.abortController=new AbortController;const n=yield this.actor.sendAsync({type:e,data:r},t.abortController);delete t.abortController,t.unloadVectorData(),t.aborted||t.loadVectorData(n,this.map.painter,\"RT\"===e)}))}abortTile(t){return e._(this,void 0,void 0,(function*(){t.abortController&&(t.abortController.abort(),delete t.abortController),t.aborted=!0}))}unloadTile(t){return e._(this,void 0,void 0,(function*(){t.unloadVectorData(),yield this.actor.sendAsync({type:\"RMT\",data:{uid:t.uid,type:this.type,source:this.id}})}))}onRemove(){this._removed=!0,this.actor.sendAsync({type:\"RS\",data:{type:this.type,source:this.id}})}serialize(){return e.e({},this._options,{type:this.type,data:this._data})}hasTransition(){return!1}}var et=e.Y([{name:\"a_pos\",type:\"Int16\",components:2},{name:\"a_texture_pos\",type:\"Int16\",components:2}]);class rt extends e.E{constructor(t,e,r,n){super(),this.id=t,this.dispatcher=r,this.coordinates=e.coordinates,this.type=\"image\",this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.tiles={},this._loaded=!1,this.setEventedParent(n),this.options=e}load(t){return e._(this,void 0,void 0,(function*(){this._loaded=!1,this.fire(new e.k(\"dataloading\",{dataType:\"source\"})),this.url=this.options.url,this._request=new AbortController;try{const e=yield p.getImage(this.map._requestManager.transformRequest(this.url,\"Image\"),this._request);this._request=null,this._loaded=!0,e&&e.data&&(this.image=e.data,t&&(this.coordinates=t),this._finishLoading())}catch(t){this._request=null,this._loaded=!0,this.fire(new e.j(t))}}))}loaded(){return this._loaded}updateImage(t){return t.url?(this._request&&(this._request.abort(),this._request=null),this.options.url=t.url,this.load(t.coordinates).finally((()=>{this.texture=null})),this):this}_finishLoading(){this.map&&(this.setCoordinates(this.coordinates),this.fire(new e.k(\"data\",{dataType:\"source\",sourceDataType:\"metadata\"})))}onAdd(t){this.map=t,this.load()}onRemove(){this._request&&(this._request.abort(),this._request=null)}setCoordinates(t){this.coordinates=t;const r=t.map(e.Z.fromLngLat);this.tileID=function(t){let r=1/0,n=1/0,i=-1/0,a=-1/0;for(const e of t)r=Math.min(r,e.x),n=Math.min(n,e.y),i=Math.max(i,e.x),a=Math.max(a,e.y);const o=i-r,s=a-n,l=Math.max(o,s),c=Math.max(0,Math.floor(-Math.log(l)/Math.LN2)),u=Math.pow(2,c);return new e.a1(c,Math.floor((r+i)/2*u),Math.floor((n+a)/2*u))}(r),this.minzoom=this.maxzoom=this.tileID.z;const n=r.map((t=>this.tileID.getTilePoint(t)._round()));return this._boundsArray=new e.$,this._boundsArray.emplaceBack(n[0].x,n[0].y,0,0),this._boundsArray.emplaceBack(n[1].x,n[1].y,e.X,0),this._boundsArray.emplaceBack(n[3].x,n[3].y,0,e.X),this._boundsArray.emplaceBack(n[2].x,n[2].y,e.X,e.X),this.boundsBuffer&&(this.boundsBuffer.destroy(),delete this.boundsBuffer),this.fire(new e.k(\"data\",{dataType:\"source\",sourceDataType:\"content\"})),this}prepare(){if(0===Object.keys(this.tiles).length||!this.image)return;const t=this.map.painter.context,r=t.gl;this.boundsBuffer||(this.boundsBuffer=t.createVertexBuffer(this._boundsArray,et.members)),this.boundsSegments||(this.boundsSegments=e.a0.simpleSegment(0,0,4,2)),this.texture||(this.texture=new w(t,this.image,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE));let n=!1;for(const t in this.tiles){const e=this.tiles[t];\"loaded\"!==e.state&&(e.state=\"loaded\",e.texture=this.texture,n=!0)}n&&this.fire(new e.k(\"data\",{dataType:\"source\",sourceDataType:\"idle\",sourceId:this.id}))}loadTile(t){return e._(this,void 0,void 0,(function*(){this.tileID&&this.tileID.equals(t.tileID.canonical)?(this.tiles[String(t.tileID.wrap)]=t,t.buckets={}):t.state=\"errored\"}))}serialize(){return{type:\"image\",url:this.options.url,coordinates:this.coordinates}}hasTransition(){return!1}}class nt extends rt{constructor(t,e,r,n){super(t,e,r,n),this.roundZoom=!0,this.type=\"video\",this.options=e}load(){return e._(this,void 0,void 0,(function*(){this._loaded=!1;const t=this.options;this.urls=[];for(const e of t.urls)this.urls.push(this.map._requestManager.transformRequest(e,\"Source\").url);try{const t=yield e.a3(this.urls);if(this._loaded=!0,!t)return;this.video=t,this.video.loop=!0,this.video.addEventListener(\"playing\",(()=>{this.map.triggerRepaint()})),this.map&&this.video.play(),this._finishLoading()}catch(t){this.fire(new e.j(t))}}))}pause(){this.video&&this.video.pause()}play(){this.video&&this.video.play()}seek(t){if(this.video){const r=this.video.seekable;tr.end(0)?this.fire(new e.j(new e.a2(`sources.${this.id}`,null,`Playback for this video can be set only between the ${r.start(0)} and ${r.end(0)}-second mark.`))):this.video.currentTime=t}}getVideo(){return this.video}onAdd(t){this.map||(this.map=t,this.load(),this.video&&(this.video.play(),this.setCoordinates(this.coordinates)))}prepare(){if(0===Object.keys(this.tiles).length||this.video.readyState<2)return;const t=this.map.painter.context,r=t.gl;this.boundsBuffer||(this.boundsBuffer=t.createVertexBuffer(this._boundsArray,et.members)),this.boundsSegments||(this.boundsSegments=e.a0.simpleSegment(0,0,4,2)),this.texture?this.video.paused||(this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE),r.texSubImage2D(r.TEXTURE_2D,0,0,0,r.RGBA,r.UNSIGNED_BYTE,this.video)):(this.texture=new w(t,this.video,r.RGBA),this.texture.bind(r.LINEAR,r.CLAMP_TO_EDGE));let n=!1;for(const t in this.tiles){const e=this.tiles[t];\"loaded\"!==e.state&&(e.state=\"loaded\",e.texture=this.texture,n=!0)}n&&this.fire(new e.k(\"data\",{dataType:\"source\",sourceDataType:\"idle\",sourceId:this.id}))}serialize(){return{type:\"video\",urls:this.urls,coordinates:this.coordinates}}hasTransition(){return this.video&&!this.video.paused}}class it extends rt{constructor(t,r,n,i){super(t,r,n,i),r.coordinates?Array.isArray(r.coordinates)&&4===r.coordinates.length&&!r.coordinates.some((t=>!Array.isArray(t)||2!==t.length||t.some((t=>\"number\"!=typeof t))))||this.fire(new e.j(new e.a2(`sources.${t}`,null,'\"coordinates\" property must be an array of 4 longitude/latitude array pairs'))):this.fire(new e.j(new e.a2(`sources.${t}`,null,'missing required property \"coordinates\"'))),r.animate&&\"boolean\"!=typeof r.animate&&this.fire(new e.j(new e.a2(`sources.${t}`,null,'optional \"animate\" property must be a boolean value'))),r.canvas?\"string\"==typeof r.canvas||r.canvas instanceof HTMLCanvasElement||this.fire(new e.j(new e.a2(`sources.${t}`,null,'\"canvas\" must be either a string representing the ID of the canvas element from which to read, or an HTMLCanvasElement instance'))):this.fire(new e.j(new e.a2(`sources.${t}`,null,'missing required property \"canvas\"'))),this.options=r,this.animate=void 0===r.animate||r.animate}load(){return e._(this,void 0,void 0,(function*(){this._loaded=!0,this.canvas||(this.canvas=this.options.canvas instanceof HTMLCanvasElement?this.options.canvas:document.getElementById(this.options.canvas)),this.width=this.canvas.width,this.height=this.canvas.height,this._hasInvalidDimensions()?this.fire(new e.j(new Error(\"Canvas dimensions cannot be less than or equal to zero.\"))):(this.play=function(){this._playing=!0,this.map.triggerRepaint()},this.pause=function(){this._playing&&(this.prepare(),this._playing=!1)},this._finishLoading())}))}getCanvas(){return this.canvas}onAdd(t){this.map=t,this.load(),this.canvas&&this.animate&&this.play()}onRemove(){this.pause()}prepare(){let t=!1;if(this.canvas.width!==this.width&&(this.width=this.canvas.width,t=!0),this.canvas.height!==this.height&&(this.height=this.canvas.height,t=!0),this._hasInvalidDimensions())return;if(0===Object.keys(this.tiles).length)return;const r=this.map.painter.context,n=r.gl;this.boundsBuffer||(this.boundsBuffer=r.createVertexBuffer(this._boundsArray,et.members)),this.boundsSegments||(this.boundsSegments=e.a0.simpleSegment(0,0,4,2)),this.texture?(t||this._playing)&&this.texture.update(this.canvas,{premultiply:!0}):this.texture=new w(r,this.canvas,n.RGBA,{premultiply:!0});let i=!1;for(const t in this.tiles){const e=this.tiles[t];\"loaded\"!==e.state&&(e.state=\"loaded\",e.texture=this.texture,i=!0)}i&&this.fire(new e.k(\"data\",{dataType:\"source\",sourceDataType:\"idle\",sourceId:this.id}))}serialize(){return{type:\"canvas\",coordinates:this.coordinates}}hasTransition(){return this._playing}_hasInvalidDimensions(){for(const t of[this.canvas.width,this.canvas.height])if(isNaN(t)||t<=0)return!0;return!1}}const at={},ot=t=>{switch(t){case\"geojson\":return tt;case\"image\":return rt;case\"raster\":return K;case\"raster-dem\":return Q;case\"vector\":return J;case\"video\":return nt;case\"canvas\":return it}return at[t]};const st=\"RTLPluginLoaded\";class lt extends e.E{constructor(){super(...arguments),this.status=\"unavailable\",this.url=null,this.dispatcher=H()}_syncState(t){return this.status=t,this.dispatcher.broadcast(\"SRPS\",{pluginStatus:t,pluginURL:this.url}).catch((t=>{throw this.status=\"error\",t}))}getRTLTextPluginStatus(){return this.status}clearRTLTextPlugin(){this.status=\"unavailable\",this.url=null}setRTLTextPlugin(t){return e._(this,arguments,void 0,(function*(t,e=!1){if(this.url)throw new Error(\"setRTLTextPlugin cannot be called multiple times.\");if(this.url=a.resolveURL(t),!this.url)throw new Error(`requested url ${t} is invalid`);if(\"unavailable\"===this.status){if(!e)return this._requestImport();this.status=\"deferred\",this._syncState(this.status)}else if(\"requested\"===this.status)return this._requestImport()}))}_requestImport(){return e._(this,void 0,void 0,(function*(){yield this._syncState(\"loading\"),this.status=\"loaded\",this.fire(new e.k(st))}))}lazyLoad(){\"unavailable\"===this.status?this.status=\"requested\":\"deferred\"===this.status&&this._requestImport()}}let ct=null;function ut(){return ct||(ct=new lt),ct}class ht{constructor(t,r){this.timeAdded=0,this.fadeEndTime=0,this.tileID=t,this.uid=e.a4(),this.uses=0,this.tileSize=r,this.buckets={},this.expirationTime=null,this.queryPadding=0,this.hasSymbolBuckets=!1,this.hasRTLText=!1,this.dependencies={},this.rtt=[],this.rttCoords={},this.expiredRequestCount=0,this.state=\"loading\"}registerFadeDuration(t){const e=t+this.timeAdded;ee.getLayer(t))).filter(Boolean);if(0!==t.length){n.layers=t,n.stateDependentLayerIds&&(n.stateDependentLayers=n.stateDependentLayerIds.map((e=>t.filter((t=>t.id===e))[0])));for(const e of t)r[e.id]=n}}return r}(t.buckets,r.style),this.hasSymbolBuckets=!1;for(const t in this.buckets){const r=this.buckets[t];if(r instanceof e.a6){if(this.hasSymbolBuckets=!0,!n)break;r.justReloaded=!0}}if(this.hasRTLText=!1,this.hasSymbolBuckets)for(const t in this.buckets){const r=this.buckets[t];if(r instanceof e.a6&&r.hasRTLText){this.hasRTLText=!0,ut().lazyLoad();break}}this.queryPadding=0;for(const t in this.buckets){const e=this.buckets[t];this.queryPadding=Math.max(this.queryPadding,r.style.getLayer(t).queryRadius(e))}t.imageAtlas&&(this.imageAtlas=t.imageAtlas),t.glyphAtlasImage&&(this.glyphAtlasImage=t.glyphAtlasImage)}else this.collisionBoxArray=new e.a5}unloadVectorData(){for(const t in this.buckets)this.buckets[t].destroy();this.buckets={},this.imageAtlasTexture&&this.imageAtlasTexture.destroy(),this.imageAtlas&&(this.imageAtlas=null),this.glyphAtlasTexture&&this.glyphAtlasTexture.destroy(),this.latestFeatureIndex=null,this.state=\"unloaded\"}getBucket(t){return this.buckets[t.id]}upload(t){for(const e in this.buckets){const r=this.buckets[e];r.uploadPending()&&r.upload(t)}const e=t.gl;this.imageAtlas&&!this.imageAtlas.uploaded&&(this.imageAtlasTexture=new w(t,this.imageAtlas.image,e.RGBA),this.imageAtlas.uploaded=!0),this.glyphAtlasImage&&(this.glyphAtlasTexture=new w(t,this.glyphAtlasImage,e.ALPHA),this.glyphAtlasImage=null)}prepare(t){this.imageAtlas&&this.imageAtlas.patchUpdatedImages(t,this.imageAtlasTexture)}queryRenderedFeatures(t,e,r,n,i,a,o,s,l,c){return this.latestFeatureIndex&&this.latestFeatureIndex.rawTileData?this.latestFeatureIndex.query({queryGeometry:n,cameraQueryGeometry:i,scale:a,tileSize:this.tileSize,pixelPosMatrix:c,transform:s,params:o,queryPadding:this.queryPadding*l},t,e,r):{}}querySourceFeatures(t,r){const n=this.latestFeatureIndex;if(!n||!n.rawTileData)return;const i=n.loadVTLayers(),a=r&&r.sourceLayer?r.sourceLayer:\"\",o=i._geojsonTileLayer||i[a];if(!o)return;const s=e.a7(r&&r.filter),{z:l,x:c,y:u}=this.tileID.canonical,h={z:l,x:c,y:u};for(let r=0;rt)e=!1;else if(r)if(this.expirationTime{this.remove(t,i)}),r)),this.data[n].push(i),this.order.push(n),this.order.length>this.max){const t=this._getAndRemoveByKey(this.order[0]);t&&this.onRemove(t)}return this}has(t){return t.wrapped().key in this.data}getAndRemove(t){return this.has(t)?this._getAndRemoveByKey(t.wrapped().key):null}_getAndRemoveByKey(t){const e=this.data[t].shift();return e.timeout&&clearTimeout(e.timeout),0===this.data[t].length&&delete this.data[t],this.order.splice(this.order.indexOf(t),1),e.value}getByKey(t){const e=this.data[t];return e?e[0].value:null}get(t){return this.has(t)?this.data[t.wrapped().key][0].value:null}remove(t,e){if(!this.has(t))return this;const r=t.wrapped().key,n=void 0===e?0:this.data[r].indexOf(e),i=this.data[r][n];return this.data[r].splice(n,1),i.timeout&&clearTimeout(i.timeout),0===this.data[r].length&&delete this.data[r],this.onRemove(i.value),this.order.splice(this.order.indexOf(r),1),this}setMaxSize(t){for(this.max=t;this.order.length>this.max;){const t=this._getAndRemoveByKey(this.order[0]);t&&this.onRemove(t)}return this}filter(t){const e=[];for(const r in this.data)for(const n of this.data[r])t(n.value)||e.push(n);for(const t of e)this.remove(t.value.tileID,t)}}class pt{constructor(){this.state={},this.stateChanges={},this.deletedStates={}}updateState(t,r,n){const i=String(r);if(this.stateChanges[t]=this.stateChanges[t]||{},this.stateChanges[t][i]=this.stateChanges[t][i]||{},e.e(this.stateChanges[t][i],n),null===this.deletedStates[t]){this.deletedStates[t]={};for(const e in this.state[t])e!==i&&(this.deletedStates[t][e]=null)}else if(this.deletedStates[t]&&null===this.deletedStates[t][i]){this.deletedStates[t][i]={};for(const e in this.state[t][i])n[e]||(this.deletedStates[t][i][e]=null)}else for(const e in n)this.deletedStates[t]&&this.deletedStates[t][i]&&null===this.deletedStates[t][i][e]&&delete this.deletedStates[t][i][e]}removeFeatureState(t,e,r){if(null===this.deletedStates[t])return;const n=String(e);if(this.deletedStates[t]=this.deletedStates[t]||{},r&&void 0!==e)null!==this.deletedStates[t][n]&&(this.deletedStates[t][n]=this.deletedStates[t][n]||{},this.deletedStates[t][n][r]=null);else if(void 0!==e)if(this.stateChanges[t]&&this.stateChanges[t][n])for(r in this.deletedStates[t][n]={},this.stateChanges[t][n])this.deletedStates[t][n][r]=null;else this.deletedStates[t][n]=null;else this.deletedStates[t]=null}getState(t,r){const n=String(r),i=this.state[t]||{},a=this.stateChanges[t]||{},o=e.e({},i[n],a[n]);if(null===this.deletedStates[t])return{};if(this.deletedStates[t]){const e=this.deletedStates[t][r];if(null===e)return{};for(const t in e)delete o[t]}return o}initializeTileState(t,e){t.setFeatureState(this.state,e)}coalesceChanges(t,r){const n={};for(const t in this.stateChanges){this.state[t]=this.state[t]||{};const r={};for(const n in this.stateChanges[t])this.state[t][n]||(this.state[t][n]={}),e.e(this.state[t][n],this.stateChanges[t][n]),r[n]=this.state[t][n];n[t]=r}for(const t in this.deletedStates){this.state[t]=this.state[t]||{};const r={};if(null===this.deletedStates[t])for(const e in this.state[t])r[e]={},this.state[t][e]={};else for(const e in this.deletedStates[t]){if(null===this.deletedStates[t][e])this.state[t][e]={};else for(const r of Object.keys(this.deletedStates[t][e]))delete this.state[t][e][r];r[e]=this.state[t][e]}n[t]=n[t]||{},e.e(n[t],r)}if(this.stateChanges={},this.deletedStates={},0!==Object.keys(n).length)for(const e in t)t[e].setFeatureState(n,r)}}class dt extends e.E{constructor(t,e,r){super(),this.id=t,this.dispatcher=r,this.on(\"data\",(t=>this._dataHandler(t))),this.on(\"dataloading\",(()=>{this._sourceErrored=!1})),this.on(\"error\",(()=>{this._sourceErrored=this._source.loaded()})),this._source=((t,e,r,n)=>{const i=new(ot(e.type))(t,e,r,n);if(i.id!==t)throw new Error(`Expected Source id to be ${t} instead of ${i.id}`);return i})(t,e,r,this),this._tiles={},this._cache=new ft(0,(t=>this._unloadTile(t))),this._timers={},this._cacheTimers={},this._maxTileCacheSize=null,this._maxTileCacheZoomLevels=null,this._loadedParentTiles={},this._coveredTiles={},this._state=new pt,this._didEmitContent=!1,this._updated=!1}onAdd(t){this.map=t,this._maxTileCacheSize=t?t._maxTileCacheSize:null,this._maxTileCacheZoomLevels=t?t._maxTileCacheZoomLevels:null,this._source&&this._source.onAdd&&this._source.onAdd(t)}onRemove(t){this.clearTiles(),this._source&&this._source.onRemove&&this._source.onRemove(t)}loaded(){if(this._sourceErrored)return!0;if(!this._sourceLoaded)return!1;if(!this._source.loaded())return!1;if(!(void 0===this.used&&void 0===this.usedForTerrain||this.used||this.usedForTerrain))return!0;if(!this._updated)return!1;for(const t in this._tiles){const e=this._tiles[t];if(\"loaded\"!==e.state&&\"errored\"!==e.state)return!1}return!0}getSource(){return this._source}pause(){this._paused=!0}resume(){if(!this._paused)return;const t=this._shouldReloadOnResume;this._paused=!1,this._shouldReloadOnResume=!1,t&&this.reload(),this.transform&&this.update(this.transform,this.terrain)}_loadTile(t,r,n){return e._(this,void 0,void 0,(function*(){try{yield this._source.loadTile(t),this._tileLoaded(t,r,n)}catch(r){t.state=\"errored\",404!==r.status?this._source.fire(new e.j(r,{tile:t})):this.update(this.transform,this.terrain)}}))}_unloadTile(t){this._source.unloadTile&&this._source.unloadTile(t)}_abortTile(t){this._source.abortTile&&this._source.abortTile(t),this._source.fire(new e.k(\"dataabort\",{tile:t,coord:t.tileID,dataType:\"source\"}))}serialize(){return this._source.serialize()}prepare(t){this._source.prepare&&this._source.prepare(),this._state.coalesceChanges(this._tiles,this.map?this.map.painter:null);for(const e in this._tiles){const r=this._tiles[e];r.upload(t),r.prepare(this.map.style.imageManager)}}getIds(){return Object.values(this._tiles).map((t=>t.tileID)).sort(mt).map((t=>t.key))}getRenderableIds(t){const r=[];for(const e in this._tiles)this._isIdRenderable(e,t)&&r.push(this._tiles[e]);return t?r.sort(((t,r)=>{const n=t.tileID,i=r.tileID,a=new e.P(n.canonical.x,n.canonical.y)._rotate(this.transform.angle),o=new e.P(i.canonical.x,i.canonical.y)._rotate(this.transform.angle);return n.overscaledZ-i.overscaledZ||o.y-a.y||o.x-a.x})).map((t=>t.tileID.key)):r.map((t=>t.tileID)).sort(mt).map((t=>t.key))}hasRenderableParent(t){const e=this.findLoadedParent(t,0);return!!e&&this._isIdRenderable(e.tileID.key)}_isIdRenderable(t,e){return this._tiles[t]&&this._tiles[t].hasData()&&!this._coveredTiles[t]&&(e||!this._tiles[t].holdingForFade())}reload(){if(this._paused)this._shouldReloadOnResume=!0;else{this._cache.reset();for(const t in this._tiles)\"errored\"!==this._tiles[t].state&&this._reloadTile(t,\"reloading\")}}_reloadTile(t,r){return e._(this,void 0,void 0,(function*(){const e=this._tiles[t];e&&(\"loading\"!==e.state&&(e.state=r),yield this._loadTile(e,t,r))}))}_tileLoaded(t,r,n){t.timeAdded=a.now(),\"expired\"===n&&(t.refreshedUponExpiration=!0),this._setTileReloadTimer(r,t),\"raster-dem\"===this.getSource().type&&t.dem&&this._backfillDEM(t),this._state.initializeTileState(t,this.map?this.map.painter:null),t.aborted||this._source.fire(new e.k(\"data\",{dataType:\"source\",tile:t,coord:t.tileID}))}_backfillDEM(t){const e=this.getRenderableIds();for(let n=0;n1||(Math.abs(r)>1&&(1===Math.abs(r+i)?r+=i:1===Math.abs(r-i)&&(r-=i)),e.dem&&t.dem&&(t.dem.backfillBorder(e.dem,r,n),t.neighboringTiles&&t.neighboringTiles[a]&&(t.neighboringTiles[a].backfilled=!0)))}}getTile(t){return this.getTileByID(t.key)}getTileByID(t){return this._tiles[t]}_retainLoadedChildren(t,e,r,n){for(const i in this._tiles){let a=this._tiles[i];if(n[i]||!a.hasData()||a.tileID.overscaledZ<=e||a.tileID.overscaledZ>r)continue;let o=a.tileID;for(;a&&a.tileID.overscaledZ>e+1;){const t=a.tileID.scaledTo(a.tileID.overscaledZ-1);a=this._tiles[t.key],a&&a.hasData()&&(o=t)}let s=o;for(;s.overscaledZ>e;)if(s=s.scaledTo(s.overscaledZ-1),t[s.key]){n[o.key]=o;break}}}findLoadedParent(t,e){if(t.key in this._loadedParentTiles){const r=this._loadedParentTiles[t.key];return r&&r.tileID.overscaledZ>=e?r:null}for(let r=t.overscaledZ-1;r>=e;r--){const e=t.scaledTo(r),n=this._getLoadedTile(e);if(n)return n}}findLoadedSibling(t){return this._getLoadedTile(t)}_getLoadedTile(t){const e=this._tiles[t.key];return e&&e.hasData()?e:this._cache.getByKey(t.wrapped().key)}updateCacheSize(t){const r=(Math.ceil(t.width/this._source.tileSize)+1)*(Math.ceil(t.height/this._source.tileSize)+1),n=null===this._maxTileCacheZoomLevels?e.a.MAX_TILE_CACHE_ZOOM_LEVELS:this._maxTileCacheZoomLevels,i=Math.floor(r*n),a=\"number\"==typeof this._maxTileCacheSize?Math.min(this._maxTileCacheSize,i):i;this._cache.setMaxSize(a)}handleWrapJump(t){const e=(t-(void 0===this._prevLng?t:this._prevLng))/360,r=Math.round(e);if(this._prevLng=t,r){const t={};for(const e in this._tiles){const n=this._tiles[e];n.tileID=n.tileID.unwrapTo(n.tileID.wrap+r),t[n.tileID.key]=n}this._tiles=t;for(const t in this._timers)clearTimeout(this._timers[t]),delete this._timers[t];for(const t in this._tiles){const e=this._tiles[t];this._setTileReloadTimer(t,e)}}}_updateCoveredAndRetainedTiles(t,e,r,n,i,o){const s={},l={},c=Object.keys(t),u=a.now();for(const r of c){const n=t[r],i=this._tiles[r];if(!i||0!==i.fadeEndTime&&i.fadeEndTime<=u)continue;const a=this.findLoadedParent(n,e),o=this.findLoadedSibling(n),c=a||o||null;c&&(this._addTile(c.tileID),s[c.tileID.key]=c.tileID),l[r]=n}this._retainLoadedChildren(l,n,r,t);for(const e in s)t[e]||(this._coveredTiles[e]=!0,t[e]=s[e]);if(o){const e={},r={};for(const t of i)this._tiles[t.key].hasData()?e[t.key]=t:r[t.key]=t;for(const n in r){const i=r[n].children(this._source.maxzoom);this._tiles[i[0].key]&&this._tiles[i[1].key]&&this._tiles[i[2].key]&&this._tiles[i[3].key]&&(e[i[0].key]=t[i[0].key]=i[0],e[i[1].key]=t[i[1].key]=i[1],e[i[2].key]=t[i[2].key]=i[2],e[i[3].key]=t[i[3].key]=i[3],delete r[n])}for(const n in r){const i=r[n],a=this.findLoadedParent(i,this._source.minzoom),o=this.findLoadedSibling(i),s=a||o||null;if(s){e[s.tileID.key]=t[s.tileID.key]=s.tileID;for(const t in e)e[t].isChildOf(s.tileID)&&delete e[t]}}for(const t in this._tiles)e[t]||(this._coveredTiles[t]=!0)}}update(t,r){if(!this._sourceLoaded||this._paused)return;let n;this.transform=t,this.terrain=r,this.updateCacheSize(t),this.handleWrapJump(this.transform.center.lng),this._coveredTiles={},this.used||this.usedForTerrain?this._source.tileID?n=t.getVisibleUnwrappedCoordinates(this._source.tileID).map((t=>new e.S(t.canonical.z,t.wrap,t.canonical.z,t.canonical.x,t.canonical.y))):(n=t.coveringTiles({tileSize:this.usedForTerrain?this.tileSize:this._source.tileSize,minzoom:this._source.minzoom,maxzoom:this._source.maxzoom,roundZoom:!this.usedForTerrain&&this._source.roundZoom,reparseOverscaled:this._source.reparseOverscaled,terrain:r}),this._source.hasTile&&(n=n.filter((t=>this._source.hasTile(t))))):n=[];const i=t.coveringZoomLevel(this._source),a=Math.max(i-dt.maxOverzooming,this._source.minzoom),o=Math.max(i+dt.maxUnderzooming,this._source.minzoom);if(this.usedForTerrain){const t={};for(const e of n)if(e.canonical.z>this._source.minzoom){const r=e.scaledTo(e.canonical.z-1);t[r.key]=r;const n=e.scaledTo(Math.max(this._source.minzoom,Math.min(e.canonical.z,5)));t[n.key]=n}n=n.concat(Object.values(t))}const s=0===n.length&&!this._updated&&this._didEmitContent;this._updated=!0,s&&this.fire(new e.k(\"data\",{sourceDataType:\"idle\",dataType:\"source\",sourceId:this.id}));const l=this._updateRetainedTiles(n,i);gt(this._source.type)&&this._updateCoveredAndRetainedTiles(l,a,o,i,n,r);for(const t in l)this._tiles[t].clearFadeHold();const c=e.ac(this._tiles,l);for(const t of c){const e=this._tiles[t];e.hasSymbolBuckets&&!e.holdingForFade()?e.setHoldDuration(this.map._fadeDuration):e.hasSymbolBuckets&&!e.symbolFadeFinished()||this._removeTile(t)}this._updateLoadedParentTileCache(),this._updateLoadedSiblingTileCache()}releaseSymbolFadeTiles(){for(const t in this._tiles)this._tiles[t].holdingForFade()&&this._removeTile(t)}_updateRetainedTiles(t,e){var r;const n={},i={},a=Math.max(e-dt.maxOverzooming,this._source.minzoom),o=Math.max(e+dt.maxUnderzooming,this._source.minzoom),s={};for(const r of t){const t=this._addTile(r);n[r.key]=r,t.hasData()||ethis._source.maxzoom){const t=o.children(this._source.maxzoom)[0],e=this.getTile(t);if(e&&e.hasData()){n[t.key]=t;continue}}else{const t=o.children(this._source.maxzoom);if(n[t[0].key]&&n[t[1].key]&&n[t[2].key]&&n[t[3].key])continue}let s=t.wasRequested();for(let e=o.overscaledZ-1;e>=a;--e){const a=o.scaledTo(e);if(i[a.key])break;if(i[a.key]=!0,t=this.getTile(a),!t&&s&&(t=this._addTile(a)),t){const e=t.hasData();if((e||!(null===(r=this.map)||void 0===r?void 0:r.cancelPendingTileRequestsWhileZooming)||s)&&(n[a.key]=a),s=t.wasRequested(),e)break}}}return n}_updateLoadedParentTileCache(){this._loadedParentTiles={};for(const t in this._tiles){const e=[];let r,n=this._tiles[t].tileID;for(;n.overscaledZ>0;){if(n.key in this._loadedParentTiles){r=this._loadedParentTiles[n.key];break}e.push(n.key);const t=n.scaledTo(n.overscaledZ-1);if(r=this._getLoadedTile(t),r)break;n=t}for(const t of e)this._loadedParentTiles[t]=r}}_updateLoadedSiblingTileCache(){this._loadedSiblingTiles={};for(const t in this._tiles){const e=this._tiles[t].tileID,r=this._getLoadedTile(e);this._loadedSiblingTiles[e.key]=r}}_addTile(t){let r=this._tiles[t.key];if(r)return r;r=this._cache.getAndRemove(t),r&&(this._setTileReloadTimer(t.key,r),r.tileID=t,this._state.initializeTileState(r,this.map?this.map.painter:null),this._cacheTimers[t.key]&&(clearTimeout(this._cacheTimers[t.key]),delete this._cacheTimers[t.key],this._setTileReloadTimer(t.key,r)));const n=r;return r||(r=new ht(t,this._source.tileSize*t.overscaleFactor()),this._loadTile(r,t.key,r.state)),r.uses++,this._tiles[t.key]=r,n||this._source.fire(new e.k(\"dataloading\",{tile:r,coord:r.tileID,dataType:\"source\"})),r}_setTileReloadTimer(t,e){t in this._timers&&(clearTimeout(this._timers[t]),delete this._timers[t]);const r=e.getExpiryTimeout();r&&(this._timers[t]=setTimeout((()=>{this._reloadTile(t,\"expired\"),delete this._timers[t]}),r))}_removeTile(t){const e=this._tiles[t];e&&(e.uses--,delete this._tiles[t],this._timers[t]&&(clearTimeout(this._timers[t]),delete this._timers[t]),e.uses>0||(e.hasData()&&\"reloading\"!==e.state?this._cache.add(e.tileID,e,e.getExpiryTimeout()):(e.aborted=!0,this._abortTile(e),this._unloadTile(e))))}_dataHandler(t){const e=t.sourceDataType;\"source\"===t.dataType&&\"metadata\"===e&&(this._sourceLoaded=!0),this._sourceLoaded&&!this._paused&&\"source\"===t.dataType&&\"content\"===e&&(this.reload(),this.transform&&this.update(this.transform,this.terrain),this._didEmitContent=!0)}clearTiles(){this._shouldReloadOnResume=!1,this._paused=!1;for(const t in this._tiles)this._removeTile(t);this._cache.reset()}tilesIn(t,r,n){const i=[],a=this.transform;if(!a)return i;const o=n?a.getCameraQueryGeometry(t):t,s=t.map((t=>a.pointCoordinate(t,this.terrain))),l=o.map((t=>a.pointCoordinate(t,this.terrain))),c=this.getIds();let u=1/0,h=1/0,f=-1/0,p=-1/0;for(const t of l)u=Math.min(u,t.x),h=Math.min(h,t.y),f=Math.max(f,t.x),p=Math.max(p,t.y);for(let t=0;t=0&&g[1].y+m>=0){const t=s.map((t=>o.getTilePoint(t))),e=l.map((t=>o.getTilePoint(t)));i.push({tile:n,tileID:o,queryGeometry:t,cameraQueryGeometry:e,scale:d})}}return i}getVisibleCoordinates(t){const e=this.getRenderableIds(t).map((t=>this._tiles[t].tileID));for(const t of e)t.posMatrix=this.transform.calculatePosMatrix(t.toUnwrapped());return e}hasTransition(){if(this._source.hasTransition())return!0;if(gt(this._source.type)){const t=a.now();for(const e in this._tiles)if(this._tiles[e].fadeEndTime>=t)return!0}return!1}setFeatureState(t,e,r){t=t||\"_geojsonTileLayer\",this._state.updateState(t,e,r)}removeFeatureState(t,e,r){t=t||\"_geojsonTileLayer\",this._state.removeFeatureState(t,e,r)}getFeatureState(t,e){return t=t||\"_geojsonTileLayer\",this._state.getState(t,e)}setDependencies(t,e,r){const n=this._tiles[t];n&&n.setDependencies(e,r)}reloadTilesForDependencies(t,e){for(const r in this._tiles)this._tiles[r].hasDependency(t,e)&&this._reloadTile(r,\"reloading\");this._cache.filter((r=>!r.hasDependency(t,e)))}}function mt(t,e){const r=Math.abs(2*t.wrap)-+(t.wrap<0),n=Math.abs(2*e.wrap)-+(e.wrap<0);return t.overscaledZ-e.overscaledZ||n-r||e.canonical.y-t.canonical.y||e.canonical.x-t.canonical.x}function gt(t){return\"raster\"===t||\"image\"===t||\"video\"===t}dt.maxOverzooming=10,dt.maxUnderzooming=3;class yt{constructor(t,e){this.reset(t,e)}reset(t,e){this.points=t||[],this._distances=[0];for(let t=1;t0?(i-o)/s:0;return this.points[a].mult(1-l).add(this.points[r].mult(l))}}function vt(t,e){let r=!0;return\"always\"===t||\"never\"!==t&&\"never\"!==e||(r=!1),r}class xt{constructor(t,e,r){const n=this.boxCells=[],i=this.circleCells=[];this.xCellCount=Math.ceil(t/r),this.yCellCount=Math.ceil(e/r);for(let t=0;tthis.width||n<0||e>this.height)return[];const s=[];if(t<=0&&e<=0&&this.width<=r&&this.height<=n){if(i)return[{key:null,x1:t,y1:e,x2:r,y2:n}];for(let t=0;t0}hitTestCircle(t,e,r,n,i){const a=t-r,o=t+r,s=e-r,l=e+r;if(o<0||a>this.width||l<0||s>this.height)return!1;const c=[],u={hitTest:!0,overlapMode:n,circle:{x:t,y:e,radius:r},seenUids:{box:{},circle:{}}};return this._forEachCell(a,s,o,l,this._queryCellCircle,c,u,i),c.length>0}_queryCell(t,e,r,n,i,a,o,s){const{seenUids:l,hitTest:c,overlapMode:u}=o,h=this.boxCells[i];if(null!==h){const i=this.bboxes;for(const o of h)if(!l.box[o]){l.box[o]=!0;const h=4*o,f=this.boxKeys[o];if(t<=i[h+2]&&e<=i[h+3]&&r>=i[h+0]&&n>=i[h+1]&&(!s||s(f))&&(!c||!vt(u,f.overlapMode))&&(a.push({key:f,x1:i[h],y1:i[h+1],x2:i[h+2],y2:i[h+3]}),c))return!0}}const f=this.circleCells[i];if(null!==f){const i=this.circles;for(const o of f)if(!l.circle[o]){l.circle[o]=!0;const h=3*o,f=this.circleKeys[o];if(this._circleAndRectCollide(i[h],i[h+1],i[h+2],t,e,r,n)&&(!s||s(f))&&(!c||!vt(u,f.overlapMode))){const t=i[h],e=i[h+1],r=i[h+2];if(a.push({key:f,x1:t-r,y1:e-r,x2:t+r,y2:e+r}),c)return!0}}}return!1}_queryCellCircle(t,e,r,n,i,a,o,s){const{circle:l,seenUids:c,overlapMode:u}=o,h=this.boxCells[i];if(null!==h){const t=this.bboxes;for(const e of h)if(!c.box[e]){c.box[e]=!0;const r=4*e,n=this.boxKeys[e];if(this._circleAndRectCollide(l.x,l.y,l.radius,t[r+0],t[r+1],t[r+2],t[r+3])&&(!s||s(n))&&!vt(u,n.overlapMode))return a.push(!0),!0}}const f=this.circleCells[i];if(null!==f){const t=this.circles;for(const e of f)if(!c.circle[e]){c.circle[e]=!0;const r=3*e,n=this.circleKeys[e];if(this._circlesCollide(t[r],t[r+1],t[r+2],l.x,l.y,l.radius)&&(!s||s(n))&&!vt(u,n.overlapMode))return a.push(!0),!0}}}_forEachCell(t,e,r,n,i,a,o,s){const l=this._convertToXCellCoord(t),c=this._convertToYCellCoord(e),u=this._convertToXCellCoord(r),h=this._convertToYCellCoord(n);for(let f=l;f<=u;f++)for(let l=c;l<=h;l++){const c=this.xCellCount*l+f;if(i.call(this,t,e,r,n,c,a,o,s))return}}_convertToXCellCoord(t){return Math.max(0,Math.min(this.xCellCount-1,Math.floor(t*this.xScale)))}_convertToYCellCoord(t){return Math.max(0,Math.min(this.yCellCount-1,Math.floor(t*this.yScale)))}_circlesCollide(t,e,r,n,i,a){const o=n-t,s=i-e,l=r+a;return l*l>o*o+s*s}_circleAndRectCollide(t,e,r,n,i,a,o){const s=(a-n)/2,l=Math.abs(t-(n+s));if(l>s+r)return!1;const c=(o-i)/2,u=Math.abs(e-(i+c));if(u>c+r)return!1;if(l<=s||u<=c)return!0;const h=l-s,f=u-c;return h*h+f*f<=r*r}}function _t(t,r,n,i,a){const o=e.H();return r?(e.K(o,o,[1/a,1/a,1]),n||e.ae(o,o,i.angle)):e.L(o,i.labelPlaneMatrix,t),o}function bt(t,r,n,i,a){if(r){const r=e.af(t);return e.K(r,r,[a,a,1]),n||e.ae(r,r,-i.angle),r}return i.glCoordMatrix}function wt(t,r,n){let i;n?(i=[t.x,t.y,n(t.x,t.y),1],e.ag(i,i,r)):(i=[t.x,t.y,0,1],function(t,e,r){const n=e[0],i=e[1];t[0]=r[0]*n+r[4]*i+r[12],t[1]=r[1]*n+r[5]*i+r[13],t[3]=r[3]*n+r[7]*i+r[15]}(i,i,r));const a=i[3];return{point:new e.P(i[0]/a,i[1]/a),signedDistanceFromCamera:a,isOccluded:!1}}function Tt(t,e){return.5+t/e*.5}function kt(t,e){return t.x>=-e[0]&&t.x<=e[0]&&t.y>=-e[1]&&t.y<=e[1]}function At(t,r,n,i,a,o,s,l,c,u,h,f,p,d,m){const g=i?t.textSizeData:t.iconSizeData,y=e.ah(g,n.transform.zoom),v=[256/n.width*2+1,256/n.height*2+1],x=i?t.text.dynamicLayoutVertexArray:t.icon.dynamicLayoutVertexArray;x.clear();const _=t.lineVertexArray,b=i?t.text.placedSymbolArray:t.icon.placedSymbolArray,w=n.transform.width/n.transform.height;let T=!1;for(let i=0;iMath.abs(n.x-r.x)*i?{useVertical:!0}:(t===e.ai.vertical?r.yn.x)?{needsFlipping:!0}:null}function Et(t,r,n,i,a,o,s,l,c,u,h){const f=n/24,p=r.lineOffsetX*f,d=r.lineOffsetY*f;let m;if(r.numGlyphs>1){const e=r.glyphStartIndex+r.numGlyphs,n=r.lineStartIndex,o=r.lineStartIndex+r.lineLength,c=Mt(f,l,p,d,i,r,h,t);if(!c)return{notEnoughRoom:!0};const g=wt(c.first.point,s,t.getElevation).point,y=wt(c.last.point,s,t.getElevation).point;if(a&&!i){const t=St(r.writingMode,g,y,u);if(t)return t}m=[c.first];for(let a=r.glyphStartIndex+1;a0?s.point:function(t,e,r,n,i,a){return Ct(t,e,r,n,i,a)}(t.tileAnchorPoint,a,n,1,o,t),c=St(r.writingMode,n,l,u);if(c)return c}const n=Ot(f*l.getoffsetX(r.glyphStartIndex),p,d,i,r.segment,r.lineStartIndex,r.lineStartIndex+r.lineLength,t,h);if(!n||t.projectionCache.anyProjectionOccluded)return{notEnoughRoom:!0};m=[n]}for(const t of m)e.ak(c,t.point,t.angle);return{}}function Ct(t,e,r,n,i,a){const o=t.add(t.sub(e)._unit()),s=void 0!==i?wt(o,i,a.getElevation).point:It(o.x,o.y,a).point,l=r.sub(s);return r.add(l._mult(n/l.mag()))}function Lt(t,r,n){const i=r.projectionCache;if(i.projections[t])return i.projections[t];const a=new e.P(r.lineVertexArray.getx(t),r.lineVertexArray.gety(t)),o=It(a.x,a.y,r);if(o.signedDistanceFromCamera>0)return i.projections[t]=o.point,i.anyProjectionOccluded=i.anyProjectionOccluded||o.isOccluded,o.point;const s=t-n.direction,l=0===n.distanceFromAnchor?r.tileAnchorPoint:new e.P(r.lineVertexArray.getx(s),r.lineVertexArray.gety(s)),c=n.absOffsetX-n.distanceFromAnchor+1;return function(t,e,r,n,i){return Ct(t,e,r,n,void 0,i)}(l,a,n.previousVertex,c,r)}function It(t,r,n){const i=t+n.translation[0],a=r+n.translation[1];let o;return!n.pitchWithMap&&n.projection.useSpecialProjectionForSymbols?(o=n.projection.projectTileCoordinates(i,a,n.unwrappedTileID,n.getElevation),o.point.x=(.5*o.point.x+.5)*n.width,o.point.y=(.5*-o.point.y+.5)*n.height):(o=wt(new e.P(i,a),n.labelPlaneMatrix,n.getElevation),o.isOccluded=!1),o}function Pt(t,e,r){return t._unit()._perp()._mult(e*r)}function zt(t,r,n,i,a,o,s,l,c){if(l.projectionCache.offsets[t])return l.projectionCache.offsets[t];const u=n.add(r);if(t+c.direction=a)return l.projectionCache.offsets[t]=u,u;const h=Lt(t+c.direction,l,c),f=Pt(h.sub(n),s,c.direction),p=n.add(f),d=h.add(f);return l.projectionCache.offsets[t]=e.al(o,u,p,d)||u,l.projectionCache.offsets[t]}function Ot(t,e,r,n,i,a,o,s,l){const c=n?t-e:t+e;let u=c>0?1:-1,h=0;n&&(u*=-1,h=Math.PI),u<0&&(h+=Math.PI);let f,p=u>0?a+i:a+i+1;s.projectionCache.cachedAnchorPoint?f=s.projectionCache.cachedAnchorPoint:(f=It(s.tileAnchorPoint.x,s.tileAnchorPoint.y,s).point,s.projectionCache.cachedAnchorPoint=f);let d,m,g=f,y=f,v=0,x=0;const _=Math.abs(c),b=[];let w;for(;v+x<=_;){if(p+=u,p=o)return null;v+=x,y=g,m=d;const t={absOffsetX:_,direction:u,distanceFromAnchor:v,previousVertex:y};if(g=Lt(p,s,t),0===r)b.push(y),w=g.sub(y);else{let e;const n=g.sub(y);e=0===n.mag()?Pt(Lt(p+u,s,t).sub(g),r,u):Pt(n,r,u),m||(m=y.add(e)),d=zt(p,e,g,a,o,m,r,s,t),b.push(m),w=d.sub(m)}x=w.mag()}const T=(_-v)/x,k=w._mult(T)._add(m||y),A=h+Math.atan2(g.y-y.y,g.x-y.x);return b.push(k),{point:k,angle:l?A:0,path:b}}const Dt=new Float32Array([-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0,-1/0,-1/0,0]);function Rt(t,e){for(let r=0;r=1;t--)l.push(o.path[t]);for(let t=1;tt.signedDistanceFromCamera<=0))?[]:t.map((t=>t.point))}let m=[];if(l.length>0){const t=l[0].clone(),r=l[0].clone();for(let e=1;e=n.x&&r.x<=i.x&&t.y>=n.y&&r.y<=i.y?[l]:r.xi.x||r.yi.y?[]:e.am([l],n.x,n.y,i.x,i.y)}for(const e of m){a.reset(e,.25*r);let n=0;n=a.length<=.5*r?1:Math.ceil(a.paddedLength/h)+1;for(let e=0;ewt(t,r,e.getElevation)))}queryRenderedSymbols(t){if(0===t.length||0===this.grid.keysLength()&&0===this.ignoredGrid.keysLength())return{};const r=[];let n=1/0,i=1/0,a=-1/0,o=-1/0;for(const s of t){const t=new e.P(s.x+Ft,s.y+Ft);n=Math.min(n,t.x),i=Math.min(i,t.y),a=Math.max(a,t.x),o=Math.max(o,t.y),r.push(t)}const s=this.grid.query(n,i,a,o).concat(this.ignoredGrid.query(n,i,a,o)),l={},c={};for(const t of s){const n=t.key;if(void 0===l[n.bucketInstanceId]&&(l[n.bucketInstanceId]={}),l[n.bucketInstanceId][n.featureIndex])continue;const i=[new e.P(t.x1,t.y1),new e.P(t.x2,t.y1),new e.P(t.x2,t.y2),new e.P(t.x1,t.y2)];e.an(r,i)&&(l[n.bucketInstanceId][n.featureIndex]=!0,void 0===c[n.bucketInstanceId]&&(c[n.bucketInstanceId]=[]),c[n.bucketInstanceId].push(n.featureIndex))}return c}insertCollisionBox(t,e,r,n,i,a){const o={bucketInstanceId:n,featureIndex:i,collisionGroupID:a,overlapMode:e};(r?this.ignoredGrid:this.grid).insert(o,t[0],t[1],t[2],t[3])}insertCollisionCircles(t,e,r,n,i,a){const o=r?this.ignoredGrid:this.grid,s={bucketInstanceId:n,featureIndex:i,collisionGroupID:a,overlapMode:e};for(let e=0;e=this.screenRightBoundary||nthis.screenBottomBoundary}isInsideGrid(t,e,r,n){return r>=0&&t=0&&ethis.projectAndGetPerspectiveRatio(n,t.x,t.y,i,c)));A=t.some((t=>!t.isOccluded)),k=t.map((t=>t.point))}else A=!0;return{box:e.ap(k),allPointsOccluded:!A}}}function Nt(t,r,n){return r*(e.X/(t.tileSize*Math.pow(2,n-t.tileID.overscaledZ)))}class jt{constructor(t,e,r,n){this.opacity=t?Math.max(0,Math.min(1,t.opacity+(t.placed?e:-e))):n&&r?1:0,this.placed=r}isHidden(){return 0===this.opacity&&!this.placed}}class Ut{constructor(t,e,r,n,i){this.text=new jt(t?t.text:null,e,r,i),this.icon=new jt(t?t.icon:null,e,n,i)}isHidden(){return this.text.isHidden()&&this.icon.isHidden()}}class Vt{constructor(t,e,r){this.text=t,this.icon=e,this.skipFade=r}}class qt{constructor(){this.invProjMatrix=e.H(),this.viewportMatrix=e.H(),this.circles=[]}}class Ht{constructor(t,e,r,n,i){this.bucketInstanceId=t,this.featureIndex=e,this.sourceLayerIndex=r,this.bucketIndex=n,this.tileID=i}}class Gt{constructor(t){this.crossSourceCollisions=t,this.maxGroupID=0,this.collisionGroups={}}get(t){if(this.crossSourceCollisions)return{ID:0,predicate:null};if(!this.collisionGroups[t]){const e=++this.maxGroupID;this.collisionGroups[t]={ID:e,predicate:t=>t.collisionGroupID===e}}return this.collisionGroups[t]}}function Zt(t,r,n,i,a){const{horizontalAlign:o,verticalAlign:s}=e.av(t),l=-(o-.5)*r,c=-(s-.5)*n;return new e.P(l+i[0]*a,c+i[1]*a)}class Wt{constructor(t,e,r,n,i,a){this.transform=t.clone(),this.terrain=r,this.collisionIndex=new Bt(this.transform,e),this.placements={},this.opacities={},this.variableOffsets={},this.stale=!1,this.commitTime=0,this.fadeDuration=n,this.retainedQueryData={},this.collisionGroups=new Gt(i),this.collisionCircleArrays={},this.collisionBoxArrays=new Map,this.prevPlacement=a,a&&(a.prevPlacement=void 0),this.placedOrientations={}}_getTerrainElevationFunc(t){const e=this.terrain;return e?(r,n)=>e.getElevation(t,r,n):null}getBucketParts(t,r,n,i){const a=n.getBucket(r),o=n.latestFeatureIndex;if(!a||!o||r.id!==a.layerIds[0])return;const s=n.collisionBoxArray,l=a.layers[0].layout,c=a.layers[0].paint,u=Math.pow(2,this.transform.zoom-n.tileID.overscaledZ),h=n.tileSize/e.X,f=n.tileID.toUnwrapped(),p=this.transform.calculatePosMatrix(f),d=\"map\"===l.get(\"text-pitch-alignment\"),m=\"map\"===l.get(\"text-rotation-alignment\"),g=Nt(n,1,this.transform.zoom),y=this.collisionIndex.mapProjection.translatePosition(this.transform,n,c.get(\"text-translate\"),c.get(\"text-translate-anchor\")),v=this.collisionIndex.mapProjection.translatePosition(this.transform,n,c.get(\"icon-translate\"),c.get(\"icon-translate-anchor\")),x=_t(p,d,m,this.transform,g);let _=null;if(d){const t=bt(p,d,m,this.transform,g);_=e.L([],this.transform.labelPlaneMatrix,t)}this.retainedQueryData[a.bucketInstanceId]=new Ht(a.bucketInstanceId,o,a.sourceLayerIndex,a.index,n.tileID);const b={bucket:a,layout:l,translationText:y,translationIcon:v,posMatrix:p,unwrappedTileID:f,textLabelPlaneMatrix:x,labelToScreenMatrix:_,scale:u,textPixelRatio:h,holdingForFade:n.holdingForFade(),collisionBoxArray:s,partiallyEvaluatedTextSize:e.ah(a.textSizeData,this.transform.zoom),collisionGroup:this.collisionGroups.get(a.sourceID)};if(i)for(const e of a.sortKeyRanges){const{sortKey:r,symbolInstanceStart:n,symbolInstanceEnd:i}=e;t.push({sortKey:r,symbolInstanceStart:n,symbolInstanceEnd:i,parameters:b})}else t.push({symbolInstanceStart:0,symbolInstanceEnd:a.symbolInstances.length,parameters:b})}attemptAnchorPlacement(t,r,n,i,a,o,s,l,c,u,h,f,p,d,m,g,y,v,x){const _=e.ar[t.textAnchor],b=[t.textOffset0,t.textOffset1],w=Zt(_,n,i,b,a),T=this.collisionIndex.placeCollisionBox(r,f,l,c,u,s,o,g,h.predicate,x,w);if((!v||this.collisionIndex.placeCollisionBox(v,f,l,c,u,s,o,y,h.predicate,x,w).placeable)&&T.placeable){let t;if(this.prevPlacement&&this.prevPlacement.variableOffsets[p.crossTileID]&&this.prevPlacement.placements[p.crossTileID]&&this.prevPlacement.placements[p.crossTileID].text&&(t=this.prevPlacement.variableOffsets[p.crossTileID].anchor),0===p.crossTileID)throw new Error(\"symbolInstance.crossTileID can't be 0\");return this.variableOffsets[p.crossTileID]={textOffset:b,width:n,height:i,anchor:_,textBoxScale:a,prevAnchor:t},this.markUsedJustification(d,_,p,m),d.allowVerticalPlacement&&(this.markUsedOrientation(d,m,p),this.placedOrientations[p.crossTileID]=m),{shift:w,placedGlyphBoxes:T}}}placeLayerBucketPart(t,r,n){const{bucket:i,layout:a,translationText:o,translationIcon:s,posMatrix:l,unwrappedTileID:c,textLabelPlaneMatrix:u,labelToScreenMatrix:h,textPixelRatio:f,holdingForFade:p,collisionBoxArray:d,partiallyEvaluatedTextSize:m,collisionGroup:g}=t.parameters,y=a.get(\"text-optional\"),v=a.get(\"icon-optional\"),x=e.as(a,\"text-overlap\",\"text-allow-overlap\"),_=\"always\"===x,b=e.as(a,\"icon-overlap\",\"icon-allow-overlap\"),w=\"always\"===b,T=\"map\"===a.get(\"text-rotation-alignment\"),k=\"map\"===a.get(\"text-pitch-alignment\"),A=\"none\"!==a.get(\"icon-text-fit\"),M=\"viewport-y\"===a.get(\"symbol-z-order\"),S=_&&(w||!i.hasIconData()||v),E=w&&(_||!i.hasTextData()||y);!i.collisionArrays&&d&&i.deserializeCollisionBoxes(d);const C=this.retainedQueryData[i.bucketInstanceId].tileID,L=this._getTerrainElevationFunc(C),I=(t,d,w)=>{var M,C;if(r[t.crossTileID])return;if(p)return void(this.placements[t.crossTileID]=new Vt(!1,!1,!1));let I=!1,P=!1,z=!0,O=null,D={box:null,placeable:!1,offscreen:null},R={box:null,placeable:!1,offscreen:null},F=null,B=null,N=null,j=0,U=0,V=0;d.textFeatureIndex?j=d.textFeatureIndex:t.useRuntimeCollisionCircles&&(j=t.featureIndex),d.verticalTextFeatureIndex&&(U=d.verticalTextFeatureIndex);const q=d.textBox;if(q){const r=r=>{let n=e.ai.horizontal;if(i.allowVerticalPlacement&&!r&&this.prevPlacement){const e=this.prevPlacement.placedOrientations[t.crossTileID];e&&(this.placedOrientations[t.crossTileID]=e,n=e,this.markUsedOrientation(i,n,t))}return n},a=(r,n)=>{if(i.allowVerticalPlacement&&t.numVerticalGlyphVertices>0&&d.verticalTextBox){for(const t of i.writingModes)if(t===e.ai.vertical?(D=n(),R=D):D=r(),D&&D.placeable)break}else D=r()},u=t.textAnchorOffsetStartIndex,h=t.textAnchorOffsetEndIndex;if(h===u){const n=(e,r)=>{const n=this.collisionIndex.placeCollisionBox(e,x,f,l,c,k,T,o,g.predicate,L);return n&&n.placeable&&(this.markUsedOrientation(i,r,t),this.placedOrientations[t.crossTileID]=r),n};a((()=>n(q,e.ai.horizontal)),(()=>{const r=d.verticalTextBox;return i.allowVerticalPlacement&&t.numVerticalGlyphVertices>0&&r?n(r,e.ai.vertical):{box:null,offscreen:null}})),r(D&&D.placeable)}else{let p=e.ar[null===(C=null===(M=this.prevPlacement)||void 0===M?void 0:M.variableOffsets[t.crossTileID])||void 0===C?void 0:C.anchor];const m=(r,a,d)=>{const m=r.x2-r.x1,y=r.y2-r.y1,v=t.textBoxScale,_=A&&\"never\"===b?a:null;let w=null,M=\"never\"===x?1:2,S=\"never\";p&&M++;for(let e=0;em(q,d.iconBox,e.ai.horizontal)),(()=>{const r=d.verticalTextBox,n=D&&D.placeable;return i.allowVerticalPlacement&&!n&&t.numVerticalGlyphVertices>0&&r?m(r,d.verticalIconBox,e.ai.vertical):{box:null,occluded:!0,offscreen:null}})),D&&(I=D.placeable,z=D.offscreen);const y=r(D&&D.placeable);if(!I&&this.prevPlacement){const e=this.prevPlacement.variableOffsets[t.crossTileID];e&&(this.variableOffsets[t.crossTileID]=e,this.markUsedJustification(i,e.anchor,t,y))}}}if(F=D,I=F&&F.placeable,z=F&&F.offscreen,t.useRuntimeCollisionCircles){const r=i.text.placedSymbolArray.get(t.centerJustifiedTextSymbolIndex),s=e.aj(i.textSizeData,m,r),f=a.get(\"text-padding\"),p=t.collisionCircleDiameter;B=this.collisionIndex.placeCollisionCircles(x,r,i.lineVertexArray,i.glyphOffsetArray,s,l,c,u,h,n,k,g.predicate,p,f,o,L),B.circles.length&&B.collisionDetected&&!n&&e.w(\"Collisions detected, but collision boxes are not shown\"),I=_||B.circles.length>0&&!B.collisionDetected,z=z&&B.offscreen}if(d.iconFeatureIndex&&(V=d.iconFeatureIndex),d.iconBox){const t=t=>this.collisionIndex.placeCollisionBox(t,b,f,l,c,k,T,s,g.predicate,L,A&&O?O:void 0);R&&R.placeable&&d.verticalIconBox?(N=t(d.verticalIconBox),P=N.placeable):(N=t(d.iconBox),P=N.placeable),z=z&&N.offscreen}const H=y||0===t.numHorizontalGlyphVertices&&0===t.numVerticalGlyphVertices,G=v||0===t.numIconVertices;H||G?G?H||(P=P&&I):I=P&&I:P=I=P&&I;const Z=I&&F.placeable,W=P&&N.placeable;if(Z&&(R&&R.placeable&&U?this.collisionIndex.insertCollisionBox(F.box,x,a.get(\"text-ignore-placement\"),i.bucketInstanceId,U,g.ID):this.collisionIndex.insertCollisionBox(F.box,x,a.get(\"text-ignore-placement\"),i.bucketInstanceId,j,g.ID)),W&&this.collisionIndex.insertCollisionBox(N.box,b,a.get(\"icon-ignore-placement\"),i.bucketInstanceId,V,g.ID),B&&I&&this.collisionIndex.insertCollisionCircles(B.circles,x,a.get(\"text-ignore-placement\"),i.bucketInstanceId,j,g.ID),n&&this.storeCollisionData(i.bucketInstanceId,w,d,F,N,B),0===t.crossTileID)throw new Error(\"symbolInstance.crossTileID can't be 0\");if(0===i.bucketInstanceId)throw new Error(\"bucket.bucketInstanceId can't be 0\");this.placements[t.crossTileID]=new Vt(I||S,P||E,z||i.justReloaded),r[t.crossTileID]=!0};if(M){if(0!==t.symbolInstanceStart)throw new Error(\"bucket.bucketInstanceId should be 0\");const e=i.getSortedSymbolIndexes(this.transform.angle);for(let t=e.length-1;t>=0;--t){const r=e[t];I(i.symbolInstances.get(r),i.collisionArrays[r],r)}}else for(let e=t.symbolInstanceStart;e=0&&(t.text.placedSymbolArray.get(e).crossTileID=o>=0&&e!==o?0:n.crossTileID)}markUsedOrientation(t,r,n){const i=r===e.ai.horizontal||r===e.ai.horizontalOnly?r:0,a=r===e.ai.vertical?r:0,o=[n.leftJustifiedTextSymbolIndex,n.centerJustifiedTextSymbolIndex,n.rightJustifiedTextSymbolIndex];for(const e of o)t.text.placedSymbolArray.get(e).placedOrientation=i;n.verticalPlacedTextSymbolIndex&&(t.text.placedSymbolArray.get(n.verticalPlacedTextSymbolIndex).placedOrientation=a)}commit(t){this.commitTime=t,this.zoomAtLastRecencyCheck=this.transform.zoom;const e=this.prevPlacement;let r=!1;this.prevZoomAdjustment=e?e.zoomAdjustment(this.transform.zoom):0;const n=e?e.symbolFadeChange(t):1,i=e?e.opacities:{},a=e?e.variableOffsets:{},o=e?e.placedOrientations:{};for(const t in this.placements){const e=this.placements[t],a=i[t];a?(this.opacities[t]=new Ut(a,n,e.text,e.icon),r=r||e.text!==a.text.placed||e.icon!==a.icon.placed):(this.opacities[t]=new Ut(null,n,e.text,e.icon,e.skipFade),r=r||e.text||e.icon)}for(const t in i){const e=i[t];if(!this.opacities[t]){const i=new Ut(e,n,!1,!1);i.isHidden()||(this.opacities[t]=i,r=r||e.text.placed||e.icon.placed)}}for(const t in a)this.variableOffsets[t]||!this.opacities[t]||this.opacities[t].isHidden()||(this.variableOffsets[t]=a[t]);for(const t in o)this.placedOrientations[t]||!this.opacities[t]||this.opacities[t].isHidden()||(this.placedOrientations[t]=o[t]);if(e&&void 0===e.lastPlacementChangeTime)throw new Error(\"Last placement time for previous placement is not defined\");r?this.lastPlacementChangeTime=t:\"number\"!=typeof this.lastPlacementChangeTime&&(this.lastPlacementChangeTime=e?e.lastPlacementChangeTime:t)}updateLayerOpacities(t,e){const r={};for(const n of e){const e=n.getBucket(t);e&&n.latestFeatureIndex&&t.id===e.layerIds[0]&&this.updateBucketOpacities(e,n.tileID,r,n.collisionBoxArray)}}updateBucketOpacities(t,r,n,i){t.hasTextData()&&(t.text.opacityVertexArray.clear(),t.text.hasVisibleVertices=!1),t.hasIconData()&&(t.icon.opacityVertexArray.clear(),t.icon.hasVisibleVertices=!1),t.hasIconCollisionBoxData()&&t.iconCollisionBox.collisionVertexArray.clear(),t.hasTextCollisionBoxData()&&t.textCollisionBox.collisionVertexArray.clear();const a=t.layers[0],o=a.layout,s=new Ut(null,0,!1,!1,!0),l=o.get(\"text-allow-overlap\"),c=o.get(\"icon-allow-overlap\"),u=a._unevaluatedLayout.hasValue(\"text-variable-anchor\")||a._unevaluatedLayout.hasValue(\"text-variable-anchor-offset\"),h=\"map\"===o.get(\"text-rotation-alignment\"),f=\"map\"===o.get(\"text-pitch-alignment\"),p=\"none\"!==o.get(\"icon-text-fit\"),d=new Ut(null,0,l&&(c||!t.hasIconData()||o.get(\"icon-optional\")),c&&(l||!t.hasTextData()||o.get(\"text-optional\")),!0);!t.collisionArrays&&i&&(t.hasIconCollisionBoxData()||t.hasTextCollisionBoxData())&&t.deserializeCollisionBoxes(i);const m=(t,e,r)=>{for(let n=0;n0||o>0,x=i.numIconVertices>0,_=this.placedOrientations[i.crossTileID],b=_===e.ai.vertical,w=_===e.ai.horizontal||_===e.ai.horizontalOnly;if(v){const e=re(y.text),r=b?ne:e;m(t.text,a,r);const n=w?ne:e;m(t.text,o,n);const s=y.text.isHidden();[i.rightJustifiedTextSymbolIndex,i.centerJustifiedTextSymbolIndex,i.leftJustifiedTextSymbolIndex].forEach((e=>{e>=0&&(t.text.placedSymbolArray.get(e).hidden=s||b?1:0)})),i.verticalPlacedTextSymbolIndex>=0&&(t.text.placedSymbolArray.get(i.verticalPlacedTextSymbolIndex).hidden=s||w?1:0);const l=this.variableOffsets[i.crossTileID];l&&this.markUsedJustification(t,l.anchor,i,_);const c=this.placedOrientations[i.crossTileID];c&&(this.markUsedJustification(t,\"left\",i,c),this.markUsedOrientation(t,c,i))}if(x){const e=re(y.icon),r=!(p&&i.verticalPlacedIconSymbolIndex&&b);if(i.placedIconSymbolIndex>=0){const n=r?e:ne;m(t.icon,i.numIconVertices,n),t.icon.placedSymbolArray.get(i.placedIconSymbolIndex).hidden=y.icon.isHidden()}if(i.verticalPlacedIconSymbolIndex>=0){const n=r?ne:e;m(t.icon,i.numVerticalIconVertices,n),t.icon.placedSymbolArray.get(i.verticalPlacedIconSymbolIndex).hidden=y.icon.isHidden()}}const T=g&&g.has(r)?g.get(r):{text:null,icon:null};if(t.hasIconCollisionBoxData()||t.hasTextCollisionBoxData()){const n=t.collisionArrays[r];if(n){let r=new e.P(0,0);if(n.textBox||n.verticalTextBox){let e=!0;if(u){const t=this.variableOffsets[l];t?(r=Zt(t.anchor,t.width,t.height,t.textOffset,t.textBoxScale),h&&r._rotate(f?this.transform.angle:-this.transform.angle)):e=!1}if(n.textBox||n.verticalTextBox){let i;n.textBox&&(i=b),n.verticalTextBox&&(i=w),Yt(t.textCollisionBox.collisionVertexArray,y.text.placed,!e||i,T.text,r.x,r.y)}}if(n.iconBox||n.verticalIconBox){const e=Boolean(!w&&n.verticalIconBox);let i;n.iconBox&&(i=e),n.verticalIconBox&&(i=!e),Yt(t.iconCollisionBox.collisionVertexArray,y.icon.placed,i,T.icon,p?r.x:0,p?r.y:0)}}}}if(t.sortFeatures(this.transform.angle),this.retainedQueryData[t.bucketInstanceId]&&(this.retainedQueryData[t.bucketInstanceId].featureSortOrder=t.featureSortOrder),t.hasTextData()&&t.text.opacityVertexBuffer&&t.text.opacityVertexBuffer.updateData(t.text.opacityVertexArray),t.hasIconData()&&t.icon.opacityVertexBuffer&&t.icon.opacityVertexBuffer.updateData(t.icon.opacityVertexArray),t.hasIconCollisionBoxData()&&t.iconCollisionBox.collisionVertexBuffer&&t.iconCollisionBox.collisionVertexBuffer.updateData(t.iconCollisionBox.collisionVertexArray),t.hasTextCollisionBoxData()&&t.textCollisionBox.collisionVertexBuffer&&t.textCollisionBox.collisionVertexBuffer.updateData(t.textCollisionBox.collisionVertexArray),t.text.opacityVertexArray.length!==t.text.layoutVertexArray.length/4)throw new Error(`bucket.text.opacityVertexArray.length (= ${t.text.opacityVertexArray.length}) !== bucket.text.layoutVertexArray.length (= ${t.text.layoutVertexArray.length}) / 4`);if(t.icon.opacityVertexArray.length!==t.icon.layoutVertexArray.length/4)throw new Error(`bucket.icon.opacityVertexArray.length (= ${t.icon.opacityVertexArray.length}) !== bucket.icon.layoutVertexArray.length (= ${t.icon.layoutVertexArray.length}) / 4`);if(t.bucketInstanceId in this.collisionCircleArrays){const e=this.collisionCircleArrays[t.bucketInstanceId];t.placementInvProjMatrix=e.invProjMatrix,t.placementViewportMatrix=e.viewportMatrix,t.collisionCircleArray=e.circles,delete this.collisionCircleArrays[t.bucketInstanceId]}}symbolFadeChange(t){return 0===this.fadeDuration?1:(t-this.commitTime)/this.fadeDuration+this.prevZoomAdjustment}zoomAdjustment(t){return Math.max(0,(this.transform.zoom-t)/1.5)}hasTransitions(t){return this.stale||t-this.lastPlacementChangeTimet}setStale(){this.stale=!0}}function Yt(t,e,r,n,i,a){n&&0!==n.length||(n=[0,0,0,0]);const o=n[0]-Ft,s=n[1]-Ft,l=n[2]-Ft,c=n[3]-Ft;t.emplaceBack(e?1:0,r?1:0,i||0,a||0,o,s),t.emplaceBack(e?1:0,r?1:0,i||0,a||0,l,s),t.emplaceBack(e?1:0,r?1:0,i||0,a||0,l,c),t.emplaceBack(e?1:0,r?1:0,i||0,a||0,o,c)}const Xt=Math.pow(2,25),$t=Math.pow(2,24),Jt=Math.pow(2,17),Kt=Math.pow(2,16),Qt=Math.pow(2,9),te=Math.pow(2,8),ee=Math.pow(2,1);function re(t){if(0===t.opacity&&!t.placed)return 0;if(1===t.opacity&&t.placed)return 4294967295;const e=t.placed?1:0,r=Math.floor(127*t.opacity);return r*Xt+e*$t+r*Jt+e*Kt+r*Qt+e*te+r*ee+e}const ne=0;function ie(){return{isOccluded(t,e,r){return!1},getPitchedTextCorrection(t,e,r){return 1},get useSpecialProjectionForSymbols(){return!1},projectTileCoordinates(t,e,r,n){throw new Error(\"Not implemented.\")},translatePosition(t,e,r,n){return function(t,e,r,n,i=!1){if(!r[0]&&!r[1])return[0,0];const a=i?\"map\"===n?t.angle:0:\"viewport\"===n?-t.angle:0;if(a){const t=Math.sin(a),e=Math.cos(a);r=[r[0]*e-r[1]*t,r[0]*t+r[1]*e]}return[i?r[0]:Nt(e,r[0],t.zoom),i?r[1]:Nt(e,r[1],t.zoom)]}(t,e,r,n)},getCircleRadiusCorrection(t){return 1}}}class ae{constructor(t){this._sortAcrossTiles=\"viewport-y\"!==t.layout.get(\"symbol-z-order\")&&!t.layout.get(\"symbol-sort-key\").isConstant(),this._currentTileIndex=0,this._currentPartIndex=0,this._seenCrossTileIDs={},this._bucketParts=[]}continuePlacement(t,e,r,n,i){const a=this._bucketParts;for(;this._currentTileIndext.sortKey-e.sortKey)));this._currentPartIndex!this._forceFullPlacement&&a.now()-n>2;for(;this._currentPlacementIndex>=0;){const n=e[t[this._currentPlacementIndex]],a=this.placement.collisionIndex.transform.zoom;if(\"symbol\"===n.type&&(!n.minzoom||n.minzoom<=a)&&(!n.maxzoom||n.maxzoom>a)){if(this._inProgressLayer||(this._inProgressLayer=new ae(n)),this._inProgressLayer.continuePlacement(r[n.source],this.placement,this._showCollisionBoxes,n,i))return;delete this._inProgressLayer}this._currentPlacementIndex--}this._done=!0}commit(t){return this.placement.commit(t),this.placement}}const se=512/e.X/2;class le{constructor(t,r,n){this.tileID=t,this.bucketInstanceId=n,this._symbolsByKey={};const i=new Map;for(let t=0;t({x:Math.floor(t.anchorX*se),y:Math.floor(t.anchorY*se)}))),crossTileIDs:r.map((t=>t.crossTileID))};if(n.positions.length>128){const t=new e.aw(n.positions.length,16,Uint16Array);for(const{x:e,y:r}of n.positions)t.add(e,r);t.finish(),delete n.positions,n.index=t}this._symbolsByKey[t]=n}}getScaledCoordinates(t,r){const{x:n,y:i,z:a}=this.tileID.canonical,{x:o,y:s,z:l}=r.canonical,c=l-a,u=se/Math.pow(2,c),h=(o*e.X+t.anchorX)*u,f=(s*e.X+t.anchorY)*u,p=n*e.X*se,d=i*e.X*se;return{x:Math.floor(h-p),y:Math.floor(f-d)}}findMatches(t,e,r){const n=this.tileID.canonical.zt))}}class ce{constructor(){this.maxCrossTileID=0}generate(){return++this.maxCrossTileID}}class ue{constructor(){this.indexes={},this.usedCrossTileIDs={},this.lng=0}handleWrapJump(t){const e=Math.round((t-this.lng)/360);if(0!==e)for(const t in this.indexes){const r=this.indexes[t],n={};for(const t in r){const i=r[t];i.tileID=i.tileID.unwrapTo(i.tileID.wrap+e),n[i.tileID.key]=i}this.indexes[t]=n}this.lng=t}addBucket(t,e,r){if(this.indexes[t.overscaledZ]&&this.indexes[t.overscaledZ][t.key]){if(this.indexes[t.overscaledZ][t.key].bucketInstanceId===e.bucketInstanceId)return!1;this.removeBucketCrossTileIDs(t.overscaledZ,this.indexes[t.overscaledZ][t.key])}for(let t=0;tt.overscaledZ)for(const r in i){const a=i[r];a.tileID.isChildOf(t)&&a.findMatches(e.symbolInstances,t,n)}else{const a=i[t.scaledTo(Number(r)).key];a&&a.findMatches(e.symbolInstances,t,n)}}for(let t=0;t{e[t]=!0}));for(const t in this.layerIndexes)e[t]||delete this.layerIndexes[t]}}const fe=(t,r)=>e.t(t,r&&r.filter((t=>\"source.canvas\"!==t.identifier))),pe=e.ax();class de extends e.E{constructor(t,r={}){super(),this._rtlPluginLoaded=()=>{for(const t in this.sourceCaches){const e=this.sourceCaches[t].getSource().type;\"vector\"!==e&&\"geojson\"!==e||this.sourceCaches[t].reload()}},this.map=t,this.dispatcher=new q(V(),t._getMapId()),this.dispatcher.registerMessageHandler(\"GG\",((t,e)=>this.getGlyphs(t,e))),this.dispatcher.registerMessageHandler(\"GI\",((t,e)=>this.getImages(t,e))),this.imageManager=new k,this.imageManager.setEventedParent(this),this.glyphManager=new E(t._requestManager,r.localIdeographFontFamily),this.lineAtlas=new R(256,512),this.crossTileSymbolIndex=new he,this._spritesImagesIds={},this._layers={},this._order=[],this.sourceCaches={},this.zoomHistory=new e.ay,this._loaded=!1,this._availableImages=[],this._resetUpdates(),this.dispatcher.broadcast(\"SR\",e.az()),ut().on(st,this._rtlPluginLoaded),this.on(\"data\",(t=>{if(\"source\"!==t.dataType||\"metadata\"!==t.sourceDataType)return;const e=this.sourceCaches[t.sourceId];if(!e)return;const r=e.getSource();if(r&&r.vectorLayerIds)for(const t in this._layers){const e=this._layers[t];e.source===r.id&&this._validateLayer(e)}}))}loadURL(t,r={},n){this.fire(new e.k(\"dataloading\",{dataType:\"style\"})),r.validate=\"boolean\"!=typeof r.validate||r.validate;const i=this.map._requestManager.transformRequest(t,\"Style\");this._loadStyleRequest=new AbortController;const a=this._loadStyleRequest;e.h(i,this._loadStyleRequest).then((t=>{this._loadStyleRequest=null,this._load(t.data,r,n)})).catch((t=>{this._loadStyleRequest=null,t&&!a.signal.aborted&&this.fire(new e.j(t))}))}loadJSON(t,r={},n){this.fire(new e.k(\"dataloading\",{dataType:\"style\"})),this._frameRequest=new AbortController,a.frameAsync(this._frameRequest).then((()=>{this._frameRequest=null,r.validate=!1!==r.validate,this._load(t,r,n)})).catch((()=>{}))}loadEmpty(){this.fire(new e.k(\"dataloading\",{dataType:\"style\"})),this._load(pe,{validate:!1})}_load(t,r,n){var i;const a=r.transformStyle?r.transformStyle(n,t):t;if(!r.validate||!fe(this,e.x(a))){this._loaded=!0,this.stylesheet=a;for(const t in a.sources)this.addSource(t,a.sources[t],{validate:!1});a.sprite?this._loadSprite(a.sprite):this.imageManager.setLoaded(!0),this.glyphManager.setURL(a.glyphs),this._createLayers(),this.light=new P(this.stylesheet.light),this.sky=new D(this.stylesheet.sky),this.map.setTerrain(null!==(i=this.stylesheet.terrain)&&void 0!==i?i:null),this.fire(new e.k(\"data\",{dataType:\"style\"})),this.fire(new e.k(\"style.load\"))}}_createLayers(){const t=e.aA(this.stylesheet.layers);this.dispatcher.broadcast(\"SL\",t),this._order=t.map((t=>t.id)),this._layers={},this._serializedLayers=null;for(const r of t){const t=e.aB(r);t.setEventedParent(this,{layer:{id:r.id}}),this._layers[r.id]=t}}_loadSprite(t,r=!1,n=void 0){let i;this.imageManager.setLoaded(!1),this._spriteRequest=new AbortController,b(t,this.map._requestManager,this.map.getPixelRatio(),this._spriteRequest).then((t=>{if(this._spriteRequest=null,t)for(const e in t){this._spritesImagesIds[e]=[];const n=this._spritesImagesIds[e]?this._spritesImagesIds[e].filter((e=>!(e in t))):[];for(const t of n)this.imageManager.removeImage(t),this._changedImages[t]=!0;for(const n in t[e]){const i=\"default\"===e?n:`${e}:${n}`;this._spritesImagesIds[e].push(i),i in this.imageManager.images?this.imageManager.updateImage(i,t[e][n],!1):this.imageManager.addImage(i,t[e][n]),r&&(this._changedImages[i]=!0)}}})).catch((t=>{this._spriteRequest=null,i=t,this.fire(new e.j(i))})).finally((()=>{this.imageManager.setLoaded(!0),this._availableImages=this.imageManager.listImages(),r&&(this._changed=!0),this.dispatcher.broadcast(\"SI\",this._availableImages),this.fire(new e.k(\"data\",{dataType:\"style\"})),n&&n(i)}))}_unloadSprite(){for(const t of Object.values(this._spritesImagesIds).flat())this.imageManager.removeImage(t),this._changedImages[t]=!0;this._spritesImagesIds={},this._availableImages=this.imageManager.listImages(),this._changed=!0,this.dispatcher.broadcast(\"SI\",this._availableImages),this.fire(new e.k(\"data\",{dataType:\"style\"}))}_validateLayer(t){const r=this.sourceCaches[t.source];if(!r)return;const n=t.sourceLayer;if(!n)return;const i=r.getSource();(\"geojson\"===i.type||i.vectorLayerIds&&-1===i.vectorLayerIds.indexOf(n))&&this.fire(new e.j(new Error(`Source layer \"${n}\" does not exist on source \"${i.id}\" as specified by style layer \"${t.id}\".`)))}loaded(){if(!this._loaded)return!1;if(Object.keys(this._updatedSources).length)return!1;for(const t in this.sourceCaches)if(!this.sourceCaches[t].loaded())return!1;return!!this.imageManager.isLoaded()}_serializeByIds(t){const e=this._serializedAllLayers();if(!t||0===t.length)return Object.values(e);const r=[];for(const n of t)e[n]&&r.push(e[n]);return r}_serializedAllLayers(){let t=this._serializedLayers;if(t)return t;t=this._serializedLayers={};const e=Object.keys(this._layers);for(const r of e){const e=this._layers[r];\"custom\"!==e.type&&(t[r]=e.serialize())}return t}hasTransitions(){if(this.light&&this.light.hasTransition())return!0;if(this.sky&&this.sky.hasTransition())return!0;for(const t in this.sourceCaches)if(this.sourceCaches[t].hasTransition())return!0;for(const t in this._layers)if(this._layers[t].hasTransition())return!0;return!1}_checkLoaded(){if(!this._loaded)throw new Error(\"Style is not done loading.\")}update(t){if(!this._loaded)return;const r=this._changed;if(r){const e=Object.keys(this._updatedLayers),r=Object.keys(this._removedLayers);(e.length||r.length)&&this._updateWorkerLayers(e,r);for(const t in this._updatedSources){const e=this._updatedSources[t];if(\"reload\"===e)this._reloadSource(t);else{if(\"clear\"!==e)throw new Error(`Invalid action ${e}`);this._clearSource(t)}}this._updateTilesForChangedImages(),this._updateTilesForChangedGlyphs();for(const e in this._updatedPaintProps)this._layers[e].updateTransitions(t);this.light.updateTransitions(t),this.sky.updateTransitions(t),this._resetUpdates()}const n={};for(const t in this.sourceCaches){const e=this.sourceCaches[t];n[t]=e.used,e.used=!1}for(const e of this._order){const r=this._layers[e];r.recalculate(t,this._availableImages),!r.isHidden(t.zoom)&&r.source&&(this.sourceCaches[r.source].used=!0)}for(const t in n){const r=this.sourceCaches[t];!!n[t]!=!!r.used&&r.fire(new e.k(\"data\",{sourceDataType:\"visibility\",dataType:\"source\",sourceId:t}))}this.light.recalculate(t),this.sky.recalculate(t),this.z=t.zoom,r&&this.fire(new e.k(\"data\",{dataType:\"style\"}))}_updateTilesForChangedImages(){const t=Object.keys(this._changedImages);if(t.length){for(const e in this.sourceCaches)this.sourceCaches[e].reloadTilesForDependencies([\"icons\",\"patterns\"],t);this._changedImages={}}}_updateTilesForChangedGlyphs(){if(this._glyphsDidChange){for(const t in this.sourceCaches)this.sourceCaches[t].reloadTilesForDependencies([\"glyphs\"],[\"\"]);this._glyphsDidChange=!1}}_updateWorkerLayers(t,e){this.dispatcher.broadcast(\"UL\",{layers:this._serializeByIds(t),removedIds:e})}_resetUpdates(){this._changed=!1,this._updatedLayers={},this._removedLayers={},this._updatedSources={},this._updatedPaintProps={},this._changedImages={},this._glyphsDidChange=!1}setState(t,r={}){var n;this._checkLoaded();const i=this.serialize();if(t=r.transformStyle?r.transformStyle(i,t):t,(null===(n=r.validate)||void 0===n||n)&&fe(this,e.x(t)))return!1;(t=e.aC(t)).layers=e.aA(t.layers);const a=e.aD(i,t),o=this._getOperationsToPerform(a);if(o.unimplemented.length>0)throw new Error(`Unimplemented: ${o.unimplemented.join(\", \")}.`);if(0===o.operations.length)return!1;for(const t of o.operations)t();return this.stylesheet=t,this._serializedLayers=null,!0}_getOperationsToPerform(t){const e=[],r=[];for(const n of t)switch(n.command){case\"setCenter\":case\"setZoom\":case\"setBearing\":case\"setPitch\":continue;case\"addLayer\":e.push((()=>this.addLayer.apply(this,n.args)));break;case\"removeLayer\":e.push((()=>this.removeLayer.apply(this,n.args)));break;case\"setPaintProperty\":e.push((()=>this.setPaintProperty.apply(this,n.args)));break;case\"setLayoutProperty\":e.push((()=>this.setLayoutProperty.apply(this,n.args)));break;case\"setFilter\":e.push((()=>this.setFilter.apply(this,n.args)));break;case\"addSource\":e.push((()=>this.addSource.apply(this,n.args)));break;case\"removeSource\":e.push((()=>this.removeSource.apply(this,n.args)));break;case\"setLayerZoomRange\":e.push((()=>this.setLayerZoomRange.apply(this,n.args)));break;case\"setLight\":e.push((()=>this.setLight.apply(this,n.args)));break;case\"setGeoJSONSourceData\":e.push((()=>this.setGeoJSONSourceData.apply(this,n.args)));break;case\"setGlyphs\":e.push((()=>this.setGlyphs.apply(this,n.args)));break;case\"setSprite\":e.push((()=>this.setSprite.apply(this,n.args)));break;case\"setSky\":e.push((()=>this.setSky.apply(this,n.args)));break;case\"setTerrain\":e.push((()=>this.map.setTerrain.apply(this,n.args)));break;case\"setTransition\":e.push((()=>{}));break;default:r.push(n.command)}return{operations:e,unimplemented:r}}addImage(t,r){if(this.getImage(t))return this.fire(new e.j(new Error(`An image named \"${t}\" already exists.`)));this.imageManager.addImage(t,r),this._afterImageUpdated(t)}updateImage(t,e){this.imageManager.updateImage(t,e)}getImage(t){return this.imageManager.getImage(t)}removeImage(t){if(!this.getImage(t))return this.fire(new e.j(new Error(`An image named \"${t}\" does not exist.`)));this.imageManager.removeImage(t),this._afterImageUpdated(t)}_afterImageUpdated(t){this._availableImages=this.imageManager.listImages(),this._changedImages[t]=!0,this._changed=!0,this.dispatcher.broadcast(\"SI\",this._availableImages),this.fire(new e.k(\"data\",{dataType:\"style\"}))}listImages(){return this._checkLoaded(),this.imageManager.listImages()}addSource(t,r,n={}){if(this._checkLoaded(),void 0!==this.sourceCaches[t])throw new Error(`Source \"${t}\" already exists.`);if(!r.type)throw new Error(`The type property must be defined, but only the following properties were given: ${Object.keys(r).join(\", \")}.`);if([\"vector\",\"raster\",\"geojson\",\"video\",\"image\"].indexOf(r.type)>=0&&this._validate(e.x.source,`sources.${t}`,r,null,n))return;this.map&&this.map._collectResourceTiming&&(r.collectResourceTiming=!0);const i=this.sourceCaches[t]=new dt(t,r,this.dispatcher);i.style=this,i.setEventedParent(this,(()=>({isSourceLoaded:i.loaded(),source:i.serialize(),sourceId:t}))),i.onAdd(this.map),this._changed=!0}removeSource(t){if(this._checkLoaded(),void 0===this.sourceCaches[t])throw new Error(\"There is no source with this ID\");for(const r in this._layers)if(this._layers[r].source===t)return this.fire(new e.j(new Error(`Source \"${t}\" cannot be removed while layer \"${r}\" is using it.`)));const r=this.sourceCaches[t];delete this.sourceCaches[t],delete this._updatedSources[t],r.fire(new e.k(\"data\",{sourceDataType:\"metadata\",dataType:\"source\",sourceId:t})),r.setEventedParent(null),r.onRemove(this.map),this._changed=!0}setGeoJSONSourceData(t,e){if(this._checkLoaded(),void 0===this.sourceCaches[t])throw new Error(`There is no source with this ID=${t}`);const r=this.sourceCaches[t].getSource();if(\"geojson\"!==r.type)throw new Error(`geojsonSource.type is ${r.type}, which is !== 'geojson`);r.setData(e),this._changed=!0}getSource(t){return this.sourceCaches[t]&&this.sourceCaches[t].getSource()}addLayer(t,r,n={}){this._checkLoaded();const i=t.id;if(this.getLayer(i))return void this.fire(new e.j(new Error(`Layer \"${i}\" already exists on this map.`)));let a;if(\"custom\"===t.type){if(fe(this,e.aE(t)))return;a=e.aB(t)}else{if(\"source\"in t&&\"object\"==typeof t.source&&(this.addSource(i,t.source),t=e.aC(t),t=e.e(t,{source:i})),this._validate(e.x.layer,`layers.${i}`,t,{arrayIndex:-1},n))return;a=e.aB(t),this._validateLayer(a),a.setEventedParent(this,{layer:{id:i}})}const o=r?this._order.indexOf(r):this._order.length;if(r&&-1===o)this.fire(new e.j(new Error(`Cannot add layer \"${i}\" before non-existing layer \"${r}\".`)));else{if(this._order.splice(o,0,i),this._layerOrderChanged=!0,this._layers[i]=a,this._removedLayers[i]&&a.source&&\"custom\"!==a.type){const t=this._removedLayers[i];delete this._removedLayers[i],t.type!==a.type?this._updatedSources[a.source]=\"clear\":(this._updatedSources[a.source]=\"reload\",this.sourceCaches[a.source].pause())}this._updateLayer(a),a.onAdd&&a.onAdd(this.map)}}moveLayer(t,r){if(this._checkLoaded(),this._changed=!0,!this._layers[t])return void this.fire(new e.j(new Error(`The layer '${t}' does not exist in the map's style and cannot be moved.`)));if(t===r)return;const n=this._order.indexOf(t);this._order.splice(n,1);const i=r?this._order.indexOf(r):this._order.length;r&&-1===i?this.fire(new e.j(new Error(`Cannot move layer \"${t}\" before non-existing layer \"${r}\".`))):(this._order.splice(i,0,t),this._layerOrderChanged=!0)}removeLayer(t){this._checkLoaded();const r=this._layers[t];if(!r)return void this.fire(new e.j(new Error(`Cannot remove non-existing layer \"${t}\".`)));r.setEventedParent(null);const n=this._order.indexOf(t);this._order.splice(n,1),this._layerOrderChanged=!0,this._changed=!0,this._removedLayers[t]=r,delete this._layers[t],this._serializedLayers&&delete this._serializedLayers[t],delete this._updatedLayers[t],delete this._updatedPaintProps[t],r.onRemove&&r.onRemove(this.map)}getLayer(t){return this._layers[t]}getLayersOrder(){return[...this._order]}hasLayer(t){return t in this._layers}setLayerZoomRange(t,r,n){this._checkLoaded();const i=this.getLayer(t);i?i.minzoom===r&&i.maxzoom===n||(null!=r&&(i.minzoom=r),null!=n&&(i.maxzoom=n),this._updateLayer(i)):this.fire(new e.j(new Error(`Cannot set the zoom range of non-existing layer \"${t}\".`)))}setFilter(t,r,n={}){this._checkLoaded();const i=this.getLayer(t);if(i){if(!e.aF(i.filter,r))return null==r?(i.filter=void 0,void this._updateLayer(i)):void(this._validate(e.x.filter,`layers.${i.id}.filter`,r,null,n)||(i.filter=e.aC(r),this._updateLayer(i)))}else this.fire(new e.j(new Error(`Cannot filter non-existing layer \"${t}\".`)))}getFilter(t){return e.aC(this.getLayer(t).filter)}setLayoutProperty(t,r,n,i={}){this._checkLoaded();const a=this.getLayer(t);a?e.aF(a.getLayoutProperty(r),n)||(a.setLayoutProperty(r,n,i),this._updateLayer(a)):this.fire(new e.j(new Error(`Cannot style non-existing layer \"${t}\".`)))}getLayoutProperty(t,r){const n=this.getLayer(t);if(n)return n.getLayoutProperty(r);this.fire(new e.j(new Error(`Cannot get style of non-existing layer \"${t}\".`)))}setPaintProperty(t,r,n,i={}){this._checkLoaded();const a=this.getLayer(t);a?e.aF(a.getPaintProperty(r),n)||(a.setPaintProperty(r,n,i)&&this._updateLayer(a),this._changed=!0,this._updatedPaintProps[t]=!0,this._serializedLayers=null):this.fire(new e.j(new Error(`Cannot style non-existing layer \"${t}\".`)))}getPaintProperty(t,e){return this.getLayer(t).getPaintProperty(e)}setFeatureState(t,r){this._checkLoaded();const n=t.source,i=t.sourceLayer,a=this.sourceCaches[n];if(void 0===a)return void this.fire(new e.j(new Error(`The source '${n}' does not exist in the map's style.`)));const o=a.getSource().type;\"geojson\"===o&&i?this.fire(new e.j(new Error(\"GeoJSON sources cannot have a sourceLayer parameter.\"))):\"vector\"!==o||i?(void 0===t.id&&this.fire(new e.j(new Error(\"The feature id parameter must be provided.\"))),a.setFeatureState(i,t.id,r)):this.fire(new e.j(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}removeFeatureState(t,r){this._checkLoaded();const n=t.source,i=this.sourceCaches[n];if(void 0===i)return void this.fire(new e.j(new Error(`The source '${n}' does not exist in the map's style.`)));const a=i.getSource().type,o=\"vector\"===a?t.sourceLayer:void 0;\"vector\"!==a||o?r&&\"string\"!=typeof t.id&&\"number\"!=typeof t.id?this.fire(new e.j(new Error(\"A feature id is required to remove its specific state property.\"))):i.removeFeatureState(o,t.id,r):this.fire(new e.j(new Error(\"The sourceLayer parameter must be provided for vector source types.\")))}getFeatureState(t){this._checkLoaded();const r=t.source,n=t.sourceLayer,i=this.sourceCaches[r];if(void 0!==i)return\"vector\"!==i.getSource().type||n?(void 0===t.id&&this.fire(new e.j(new Error(\"The feature id parameter must be provided.\"))),i.getFeatureState(n,t.id)):void this.fire(new e.j(new Error(\"The sourceLayer parameter must be provided for vector source types.\")));this.fire(new e.j(new Error(`The source '${r}' does not exist in the map's style.`)))}getTransition(){return e.e({duration:300,delay:0},this.stylesheet&&this.stylesheet.transition)}serialize(){if(!this._loaded)return;const t=e.aG(this.sourceCaches,(t=>t.serialize())),r=this._serializeByIds(this._order),n=this.map.getTerrain()||void 0,i=this.stylesheet;return e.aH({version:i.version,name:i.name,metadata:i.metadata,light:i.light,sky:i.sky,center:i.center,zoom:i.zoom,bearing:i.bearing,pitch:i.pitch,sprite:i.sprite,glyphs:i.glyphs,transition:i.transition,sources:t,layers:r,terrain:n},(t=>void 0!==t))}_updateLayer(t){this._updatedLayers[t.id]=!0,t.source&&!this._updatedSources[t.source]&&\"raster\"!==this.sourceCaches[t.source].getSource().type&&(this._updatedSources[t.source]=\"reload\",this.sourceCaches[t.source].pause()),this._serializedLayers=null,this._changed=!0}_flattenAndSortRenderedFeatures(t){const e=t=>\"fill-extrusion\"===this._layers[t].type,r={},n=[];for(let i=this._order.length-1;i>=0;i--){const a=this._order[i];if(e(a)){r[a]=i;for(const e of t){const t=e[a];if(t)for(const e of t)n.push(e)}}}n.sort(((t,e)=>e.intersectionZ-t.intersectionZ));const i=[];for(let a=this._order.length-1;a>=0;a--){const o=this._order[a];if(e(o))for(let t=n.length-1;t>=0;t--){const e=n[t].feature;if(r[e.layer.id]{const n=r.featureSortOrder;if(n){const r=n.indexOf(t.featureIndex);return n.indexOf(e.featureIndex)-r}return e.featureIndex-t.featureIndex}));for(const t of i)e.push(t)}}for(const e in s)s[e].forEach((n=>{const i=n.feature,a=t[e],o=r[a.source].getFeatureState(i.layer[\"source-layer\"],i.id);i.source=i.layer.source,i.layer[\"source-layer\"]&&(i.sourceLayer=i.layer[\"source-layer\"]),i.state=o}));return s}(this._layers,o,this.sourceCaches,t,r,this.placement.collisionIndex,this.placement.retainedQueryData)),this._flattenAndSortRenderedFeatures(a)}querySourceFeatures(t,r){r&&r.filter&&this._validate(e.x.filter,\"querySourceFeatures.filter\",r.filter,null,r);const n=this.sourceCaches[t];return n?function(t,e){const r=t.getRenderableIds().map((e=>t.getTileByID(e))),n=[],i={};for(let t=0;tt.getTileByID(e))).sort(((t,e)=>e.tileID.overscaledZ-t.tileID.overscaledZ||(t.tileID.isLessThan(e.tileID)?-1:1)))}const n=this.crossTileSymbolIndex.addLayer(r,l[r.source],t.center.lng);o=o||n}if(this.crossTileSymbolIndex.pruneUnusedLayers(this._order),((i=i||this._layerOrderChanged||0===r)||!this.pauseablePlacement||this.pauseablePlacement.isDone()&&!this.placement.stillRecent(a.now(),t.zoom))&&(this.pauseablePlacement=new oe(t,this.map.terrain,this._order,i,e,r,n,this.placement),this._layerOrderChanged=!1),this.pauseablePlacement.isDone()?this.placement.setStale():(this.pauseablePlacement.continuePlacement(this._order,this._layers,l),this.pauseablePlacement.isDone()&&(this.placement=this.pauseablePlacement.commit(a.now()),s=!0),o&&this.pauseablePlacement.placement.setStale()),s||o)for(const t of this._order){const e=this._layers[t];\"symbol\"===e.type&&this.placement.updateLayerOpacities(e,l[e.source])}return!this.pauseablePlacement.isDone()||this.placement.hasTransitions(a.now())}_releaseSymbolFadeTiles(){for(const t in this.sourceCaches)this.sourceCaches[t].releaseSymbolFadeTiles()}getImages(t,r){return e._(this,void 0,void 0,(function*(){const t=yield this.imageManager.getImages(r.icons);this._updateTilesForChangedImages();const e=this.sourceCaches[r.source];return e&&e.setDependencies(r.tileID.key,r.type,r.icons),t}))}getGlyphs(t,r){return e._(this,void 0,void 0,(function*(){const t=yield this.glyphManager.getGlyphs(r.stacks),e=this.sourceCaches[r.source];return e&&e.setDependencies(r.tileID.key,r.type,[\"\"]),t}))}getGlyphsUrl(){return this.stylesheet.glyphs||null}setGlyphs(t,r={}){this._checkLoaded(),t&&this._validate(e.x.glyphs,\"glyphs\",t,null,r)||(this._glyphsDidChange=!0,this.stylesheet.glyphs=t,this.glyphManager.entries={},this.glyphManager.setURL(t))}addSprite(t,r,n={},i){this._checkLoaded();const a=[{id:t,url:r}],o=[...x(this.stylesheet.sprite),...a];this._validate(e.x.sprite,\"sprite\",o,null,n)||(this.stylesheet.sprite=o,this._loadSprite(a,!0,i))}removeSprite(t){this._checkLoaded();const r=x(this.stylesheet.sprite);if(r.find((e=>e.id===t))){if(this._spritesImagesIds[t])for(const e of this._spritesImagesIds[t])this.imageManager.removeImage(e),this._changedImages[e]=!0;r.splice(r.findIndex((e=>e.id===t)),1),this.stylesheet.sprite=r.length>0?r:void 0,delete this._spritesImagesIds[t],this._availableImages=this.imageManager.listImages(),this._changed=!0,this.dispatcher.broadcast(\"SI\",this._availableImages),this.fire(new e.k(\"data\",{dataType:\"style\"}))}else this.fire(new e.j(new Error(`Sprite \"${t}\" doesn't exists on this map.`)))}getSprite(){return x(this.stylesheet.sprite)}setSprite(t,r={},n){this._checkLoaded(),t&&this._validate(e.x.sprite,\"sprite\",t,null,r)||(this.stylesheet.sprite=t,t?this._loadSprite(t,!0,n):(this._unloadSprite(),n&&n(null)))}}var me=e.Y([{name:\"a_pos\",type:\"Int16\",components:2}]);const ge={prelude:ye(\"#ifdef GL_ES\\nprecision mediump float;\\n#else\\n#if !defined(lowp)\\n#define lowp\\n#endif\\n#if !defined(mediump)\\n#define mediump\\n#endif\\n#if !defined(highp)\\n#define highp\\n#endif\\n#endif\\n\",\"#ifdef GL_ES\\nprecision highp float;\\n#else\\n#if !defined(lowp)\\n#define lowp\\n#endif\\n#if !defined(mediump)\\n#define mediump\\n#endif\\n#if !defined(highp)\\n#define highp\\n#endif\\n#endif\\nvec2 unpack_float(const float packedValue) {int packedIntValue=int(packedValue);int v0=packedIntValue/256;return vec2(v0,packedIntValue-v0*256);}vec2 unpack_opacity(const float packedOpacity) {int intOpacity=int(packedOpacity)/2;return vec2(float(intOpacity)/127.0,mod(packedOpacity,2.0));}vec4 decode_color(const vec2 encodedColor) {return vec4(unpack_float(encodedColor[0])/255.0,unpack_float(encodedColor[1])/255.0\\n);}float unpack_mix_vec2(const vec2 packedValue,const float t) {return mix(packedValue[0],packedValue[1],t);}vec4 unpack_mix_color(const vec4 packedColors,const float t) {vec4 minColor=decode_color(vec2(packedColors[0],packedColors[1]));vec4 maxColor=decode_color(vec2(packedColors[2],packedColors[3]));return mix(minColor,maxColor,t);}vec2 get_pattern_pos(const vec2 pixel_coord_upper,const vec2 pixel_coord_lower,const vec2 pattern_size,const float tile_units_to_pixels,const vec2 pos) {vec2 offset=mod(mod(mod(pixel_coord_upper,pattern_size)*256.0,pattern_size)*256.0+pixel_coord_lower,pattern_size);return (tile_units_to_pixels*pos+offset)/pattern_size;}\\n#ifdef TERRAIN3D\\nuniform sampler2D u_terrain;uniform float u_terrain_dim;uniform mat4 u_terrain_matrix;uniform vec4 u_terrain_unpack;uniform float u_terrain_exaggeration;uniform highp sampler2D u_depth;\\n#endif\\nconst highp vec4 bitSh=vec4(256.*256.*256.,256.*256.,256.,1.);const highp vec4 bitShifts=vec4(1.)/bitSh;highp float unpack(highp vec4 color) {return dot(color,bitShifts);}highp float depthOpacity(vec3 frag) {\\n#ifdef TERRAIN3D\\nhighp float d=unpack(texture2D(u_depth,frag.xy*0.5+0.5))+0.0001-frag.z;return 1.0-max(0.0,min(1.0,-d*500.0));\\n#else\\nreturn 1.0;\\n#endif\\n}float calculate_visibility(vec4 pos) {\\n#ifdef TERRAIN3D\\nvec3 frag=pos.xyz/pos.w;highp float d=depthOpacity(frag);if (d > 0.95) return 1.0;return (d+depthOpacity(frag+vec3(0.0,0.01,0.0)))/2.0;\\n#else\\nreturn 1.0;\\n#endif\\n}float ele(vec2 pos) {\\n#ifdef TERRAIN3D\\nvec4 rgb=(texture2D(u_terrain,pos)*255.0)*u_terrain_unpack;return rgb.r+rgb.g+rgb.b-u_terrain_unpack.a;\\n#else\\nreturn 0.0;\\n#endif\\n}float get_elevation(vec2 pos) {\\n#ifdef TERRAIN3D\\nvec2 coord=(u_terrain_matrix*vec4(pos,0.0,1.0)).xy*u_terrain_dim+1.0;vec2 f=fract(coord);vec2 c=(floor(coord)+0.5)/(u_terrain_dim+2.0);float d=1.0/(u_terrain_dim+2.0);float tl=ele(c);float tr=ele(c+vec2(d,0.0));float bl=ele(c+vec2(0.0,d));float br=ele(c+vec2(d,d));float elevation=mix(mix(tl,tr,f.x),mix(bl,br,f.x),f.y);return elevation*u_terrain_exaggeration;\\n#else\\nreturn 0.0;\\n#endif\\n}\"),background:ye(\"uniform vec4 u_color;uniform float u_opacity;void main() {gl_FragColor=u_color*u_opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}\"),backgroundPattern:ye(\"uniform vec2 u_pattern_tl_a;uniform vec2 u_pattern_br_a;uniform vec2 u_pattern_tl_b;uniform vec2 u_pattern_br_b;uniform vec2 u_texsize;uniform float u_mix;uniform float u_opacity;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(u_pattern_tl_a/u_texsize,u_pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(u_pattern_tl_b/u_texsize,u_pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_mix)*u_opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pattern_size_a;uniform vec2 u_pattern_size_b;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_scale_a;uniform float u_scale_b;uniform float u_tile_units_to_pixels;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_a*u_pattern_size_a,u_tile_units_to_pixels,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,u_scale_b*u_pattern_size_b,u_tile_units_to_pixels,a_pos);}\"),circle:ye(\"varying vec3 v_data;varying float v_visibility;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define mediump float radius\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define highp vec4 stroke_color\\n#pragma mapbox: define mediump float stroke_width\\n#pragma mapbox: define lowp float stroke_opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize mediump float radius\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize highp vec4 stroke_color\\n#pragma mapbox: initialize mediump float stroke_width\\n#pragma mapbox: initialize lowp float stroke_opacity\\nvec2 extrude=v_data.xy;float extrude_length=length(extrude);lowp float antialiasblur=v_data.z;float antialiased_blur=-max(blur,antialiasblur);float opacity_t=smoothstep(0.0,antialiased_blur,extrude_length-1.0);float color_t=stroke_width < 0.01 ? 0.0 : smoothstep(antialiased_blur,0.0,extrude_length-radius/(radius+stroke_width));gl_FragColor=v_visibility*opacity_t*mix(color*opacity,stroke_color*stroke_opacity,color_t);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform bool u_scale_with_map;uniform bool u_pitch_with_map;uniform vec2 u_extrude_scale;uniform lowp float u_device_pixel_ratio;uniform highp float u_camera_to_center_distance;attribute vec2 a_pos;varying vec3 v_data;varying float v_visibility;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define mediump float radius\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define highp vec4 stroke_color\\n#pragma mapbox: define mediump float stroke_width\\n#pragma mapbox: define lowp float stroke_opacity\\nvoid main(void) {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize mediump float radius\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize highp vec4 stroke_color\\n#pragma mapbox: initialize mediump float stroke_width\\n#pragma mapbox: initialize lowp float stroke_opacity\\nvec2 extrude=vec2(mod(a_pos,2.0)*2.0-1.0);vec2 circle_center=floor(a_pos*0.5);float ele=get_elevation(circle_center);v_visibility=calculate_visibility(u_matrix*vec4(circle_center,ele,1.0));if (u_pitch_with_map) {vec2 corner_position=circle_center;if (u_scale_with_map) {corner_position+=extrude*(radius+stroke_width)*u_extrude_scale;} else {vec4 projected_center=u_matrix*vec4(circle_center,0,1);corner_position+=extrude*(radius+stroke_width)*u_extrude_scale*(projected_center.w/u_camera_to_center_distance);}gl_Position=u_matrix*vec4(corner_position,ele,1);} else {gl_Position=u_matrix*vec4(circle_center,ele,1);if (u_scale_with_map) {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*u_camera_to_center_distance;} else {gl_Position.xy+=extrude*(radius+stroke_width)*u_extrude_scale*gl_Position.w;}}lowp float antialiasblur=1.0/u_device_pixel_ratio/(radius+stroke_width);v_data=vec3(extrude.x,extrude.y,antialiasblur);}\"),clippingMask:ye(\"void main() {gl_FragColor=vec4(1.0);}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);}\"),heatmap:ye(\"uniform highp float u_intensity;varying vec2 v_extrude;\\n#pragma mapbox: define highp float weight\\n#define GAUSS_COEF 0.3989422804014327\\nvoid main() {\\n#pragma mapbox: initialize highp float weight\\nfloat d=-0.5*3.0*3.0*dot(v_extrude,v_extrude);float val=weight*u_intensity*GAUSS_COEF*exp(d);gl_FragColor=vec4(val,1.0,1.0,1.0);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform float u_extrude_scale;uniform float u_opacity;uniform float u_intensity;attribute vec2 a_pos;varying vec2 v_extrude;\\n#pragma mapbox: define highp float weight\\n#pragma mapbox: define mediump float radius\\nconst highp float ZERO=1.0/255.0/16.0;\\n#define GAUSS_COEF 0.3989422804014327\\nvoid main(void) {\\n#pragma mapbox: initialize highp float weight\\n#pragma mapbox: initialize mediump float radius\\nvec2 unscaled_extrude=vec2(mod(a_pos,2.0)*2.0-1.0);float S=sqrt(-2.0*log(ZERO/weight/u_intensity/GAUSS_COEF))/3.0;v_extrude=S*unscaled_extrude;vec2 extrude=v_extrude*radius*u_extrude_scale;vec4 pos=vec4(floor(a_pos*0.5)+extrude,0,1);gl_Position=u_matrix*pos;}\"),heatmapTexture:ye(\"uniform sampler2D u_image;uniform sampler2D u_color_ramp;uniform float u_opacity;varying vec2 v_pos;void main() {float t=texture2D(u_image,v_pos).r;vec4 color=texture2D(u_color_ramp,vec2(t,0.5));gl_FragColor=color*u_opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(0.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_world;attribute vec2 a_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos*u_world,0,1);v_pos.x=a_pos.x;v_pos.y=1.0-a_pos.y;}\"),collisionBox:ye(\"varying float v_placed;varying float v_notUsed;void main() {float alpha=0.5;gl_FragColor=vec4(1.0,0.0,0.0,1.0)*alpha;if (v_placed > 0.5) {gl_FragColor=vec4(0.0,0.0,1.0,0.5)*alpha;}if (v_notUsed > 0.5) {gl_FragColor*=.1;}}\",\"attribute vec2 a_anchor_pos;attribute vec2 a_placed;attribute vec2 a_box_real;uniform mat4 u_matrix;uniform vec2 u_pixel_extrude_scale;varying float v_placed;varying float v_notUsed;vec4 projectTileWithElevation(vec2 posInTile,float elevation) {return u_matrix*vec4(posInTile,elevation,1.0);}void main() {gl_Position=projectTileWithElevation(a_anchor_pos,get_elevation(a_anchor_pos));gl_Position.xy=((a_box_real+0.5)*u_pixel_extrude_scale*2.0-1.0)*vec2(1.0,-1.0)*gl_Position.w;if (gl_Position.z/gl_Position.w < 1.1) {gl_Position.z=0.5;}v_placed=a_placed.x;v_notUsed=a_placed.y;}\"),collisionCircle:ye(\"varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;void main() {float alpha=0.5*min(v_perspective_ratio,1.0);float stroke_radius=0.9*max(v_perspective_ratio,1.0);float distance_to_center=length(v_extrude);float distance_to_edge=abs(distance_to_center-v_radius);float opacity_t=smoothstep(-stroke_radius,0.0,-distance_to_edge);vec4 color=mix(vec4(0.0,0.0,1.0,0.5),vec4(1.0,0.0,0.0,1.0),v_collision);gl_FragColor=color*alpha*opacity_t;}\",\"attribute vec2 a_pos;attribute float a_radius;attribute vec2 a_flags;uniform mat4 u_matrix;uniform mat4 u_inv_matrix;uniform vec2 u_viewport_size;uniform float u_camera_to_center_distance;varying float v_radius;varying vec2 v_extrude;varying float v_perspective_ratio;varying float v_collision;vec3 toTilePosition(vec2 screenPos) {vec4 rayStart=u_inv_matrix*vec4(screenPos,-1.0,1.0);vec4 rayEnd =u_inv_matrix*vec4(screenPos, 1.0,1.0);rayStart.xyz/=rayStart.w;rayEnd.xyz /=rayEnd.w;highp float t=(0.0-rayStart.z)/(rayEnd.z-rayStart.z);return mix(rayStart.xyz,rayEnd.xyz,t);}void main() {vec2 quadCenterPos=a_pos;float radius=a_radius;float collision=a_flags.x;float vertexIdx=a_flags.y;vec2 quadVertexOffset=vec2(mix(-1.0,1.0,float(vertexIdx >=2.0)),mix(-1.0,1.0,float(vertexIdx >=1.0 && vertexIdx <=2.0)));vec2 quadVertexExtent=quadVertexOffset*radius;vec3 tilePos=toTilePosition(quadCenterPos);vec4 clipPos=u_matrix*vec4(tilePos,1.0);highp float camera_to_anchor_distance=clipPos.w;highp float collision_perspective_ratio=clamp(0.5+0.5*(u_camera_to_center_distance/camera_to_anchor_distance),0.0,4.0);float padding_factor=1.2;v_radius=radius;v_extrude=quadVertexExtent*padding_factor;v_perspective_ratio=collision_perspective_ratio;v_collision=collision;gl_Position=vec4(clipPos.xyz/clipPos.w,1.0)+vec4(quadVertexExtent*padding_factor/u_viewport_size*2.0,0.0,0.0);}\"),debug:ye(\"uniform highp vec4 u_color;uniform sampler2D u_overlay;varying vec2 v_uv;void main() {vec4 overlay_color=texture2D(u_overlay,v_uv);gl_FragColor=mix(u_color,overlay_color,overlay_color.a);}\",\"attribute vec2 a_pos;varying vec2 v_uv;uniform mat4 u_matrix;uniform float u_overlay_scale;void main() {v_uv=a_pos/8192.0;gl_Position=u_matrix*vec4(a_pos*u_overlay_scale,get_elevation(a_pos),1);}\"),fill:ye(\"#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float opacity\\ngl_FragColor=color*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float opacity\\ngl_Position=u_matrix*vec4(a_pos,0,1);}\"),fillOutline:ye(\"varying vec2 v_pos;\\n#pragma mapbox: define highp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 outline_color\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=outline_color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec2 a_pos;uniform mat4 u_matrix;uniform vec2 u_world;varying vec2 v_pos;\\n#pragma mapbox: define highp vec4 outline_color\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 outline_color\\n#pragma mapbox: initialize lowp float opacity\\ngl_Position=u_matrix*vec4(a_pos,0,1);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}\"),fillOutlinePattern:ye(\"uniform vec2 u_texsize;uniform sampler2D u_image;uniform float u_fade;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);float dist=length(v_pos-gl_FragCoord.xy);float alpha=1.0-smoothstep(0.0,1.0,dist);gl_FragColor=mix(color1,color2,u_fade)*alpha*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_world;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec2 v_pos;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;gl_Position=u_matrix*vec4(a_pos,0,1);vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,a_pos);v_pos=(gl_Position.xy/gl_Position.w+1.0)/2.0*u_world;}\"),fillPattern:ye(\"#ifdef GL_ES\\nprecision highp float;\\n#endif\\nuniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);gl_FragColor=mix(color1,color2,u_fade)*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform vec3 u_scale;attribute vec2 a_pos;varying vec2 v_pos_a;varying vec2 v_pos_b;\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;gl_Position=u_matrix*vec4(a_pos,0,1);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileZoomRatio,a_pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileZoomRatio,a_pos);}\"),fillExtrusion:ye(\"varying vec4 v_color;void main() {gl_FragColor=v_color;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;uniform float u_vertical_gradient;uniform lowp float u_opacity;attribute vec2 a_pos;attribute vec4 a_normal_ed;\\n#ifdef TERRAIN3D\\nattribute vec2 a_centroid;\\n#endif\\nvarying vec4 v_color;\\n#pragma mapbox: define highp float base\\n#pragma mapbox: define highp float height\\n#pragma mapbox: define highp vec4 color\\nvoid main() {\\n#pragma mapbox: initialize highp float base\\n#pragma mapbox: initialize highp float height\\n#pragma mapbox: initialize highp vec4 color\\nvec3 normal=a_normal_ed.xyz;\\n#ifdef TERRAIN3D\\nfloat height_terrain3d_offset=get_elevation(a_centroid);float base_terrain3d_offset=height_terrain3d_offset-(base > 0.0 ? 0.0 : 10.0);\\n#else\\nfloat height_terrain3d_offset=0.0;float base_terrain3d_offset=0.0;\\n#endif\\nbase=max(0.0,base)+base_terrain3d_offset;height=max(0.0,height)+height_terrain3d_offset;float t=mod(normal.x,2.0);gl_Position=u_matrix*vec4(a_pos,t > 0.0 ? height : base,1);float colorvalue=color.r*0.2126+color.g*0.7152+color.b*0.0722;v_color=vec4(0.0,0.0,0.0,1.0);vec4 ambientlight=vec4(0.03,0.03,0.03,1.0);color+=ambientlight;float directional=clamp(dot(normal/16384.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((1.0-colorvalue+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_color.r+=clamp(color.r*directional*u_lightcolor.r,mix(0.0,0.3,1.0-u_lightcolor.r),1.0);v_color.g+=clamp(color.g*directional*u_lightcolor.g,mix(0.0,0.3,1.0-u_lightcolor.g),1.0);v_color.b+=clamp(color.b*directional*u_lightcolor.b,mix(0.0,0.3,1.0-u_lightcolor.b),1.0);v_color*=u_opacity;}\"),fillExtrusionPattern:ye(\"uniform vec2 u_texsize;uniform float u_fade;uniform sampler2D u_image;varying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float base\\n#pragma mapbox: initialize lowp float height\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;vec2 imagecoord=mod(v_pos_a,1.0);vec2 pos=mix(pattern_tl_a/u_texsize,pattern_br_a/u_texsize,imagecoord);vec4 color1=texture2D(u_image,pos);vec2 imagecoord_b=mod(v_pos_b,1.0);vec2 pos2=mix(pattern_tl_b/u_texsize,pattern_br_b/u_texsize,imagecoord_b);vec4 color2=texture2D(u_image,pos2);vec4 mixedColor=mix(color1,color2,u_fade);gl_FragColor=mixedColor*v_lighting;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_pixel_coord_upper;uniform vec2 u_pixel_coord_lower;uniform float u_height_factor;uniform vec3 u_scale;uniform float u_vertical_gradient;uniform lowp float u_opacity;uniform vec3 u_lightcolor;uniform lowp vec3 u_lightpos;uniform lowp float u_lightintensity;attribute vec2 a_pos;attribute vec4 a_normal_ed;\\n#ifdef TERRAIN3D\\nattribute vec2 a_centroid;\\n#endif\\nvarying vec2 v_pos_a;varying vec2 v_pos_b;varying vec4 v_lighting;\\n#pragma mapbox: define lowp float base\\n#pragma mapbox: define lowp float height\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float base\\n#pragma mapbox: initialize lowp float height\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec3 normal=a_normal_ed.xyz;float edgedistance=a_normal_ed.w;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;\\n#ifdef TERRAIN3D\\nfloat height_terrain3d_offset=get_elevation(a_centroid);float base_terrain3d_offset=height_terrain3d_offset-(base > 0.0 ? 0.0 : 10.0);\\n#else\\nfloat height_terrain3d_offset=0.0;float base_terrain3d_offset=0.0;\\n#endif\\nbase=max(0.0,base)+base_terrain3d_offset;height=max(0.0,height)+height_terrain3d_offset;float t=mod(normal.x,2.0);float z=t > 0.0 ? height : base;gl_Position=u_matrix*vec4(a_pos,z,1);vec2 pos=normal.x==1.0 && normal.y==0.0 && normal.z==16384.0\\n? a_pos\\n: vec2(edgedistance,z*u_height_factor);v_pos_a=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,fromScale*display_size_a,tileRatio,pos);v_pos_b=get_pattern_pos(u_pixel_coord_upper,u_pixel_coord_lower,toScale*display_size_b,tileRatio,pos);v_lighting=vec4(0.0,0.0,0.0,1.0);float directional=clamp(dot(normal/16383.0,u_lightpos),0.0,1.0);directional=mix((1.0-u_lightintensity),max((0.5+u_lightintensity),1.0),directional);if (normal.y !=0.0) {directional*=((1.0-u_vertical_gradient)+(u_vertical_gradient*clamp((t+base)*pow(height/150.0,0.5),mix(0.7,0.98,1.0-u_lightintensity),1.0)));}v_lighting.rgb+=clamp(directional*u_lightcolor,mix(vec3(0.0),vec3(0.3),1.0-u_lightcolor),vec3(1.0));v_lighting*=u_opacity;}\"),hillshadePrepare:ye(\"#ifdef GL_ES\\nprecision highp float;\\n#endif\\nuniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_dimension;uniform float u_zoom;uniform vec4 u_unpack;float getElevation(vec2 coord,float bias) {vec4 data=texture2D(u_image,coord)*255.0;data.a=-1.0;return dot(data,u_unpack)/4.0;}void main() {vec2 epsilon=1.0/u_dimension;float a=getElevation(v_pos+vec2(-epsilon.x,-epsilon.y),0.0);float b=getElevation(v_pos+vec2(0,-epsilon.y),0.0);float c=getElevation(v_pos+vec2(epsilon.x,-epsilon.y),0.0);float d=getElevation(v_pos+vec2(-epsilon.x,0),0.0);float e=getElevation(v_pos,0.0);float f=getElevation(v_pos+vec2(epsilon.x,0),0.0);float g=getElevation(v_pos+vec2(-epsilon.x,epsilon.y),0.0);float h=getElevation(v_pos+vec2(0,epsilon.y),0.0);float i=getElevation(v_pos+vec2(epsilon.x,epsilon.y),0.0);float exaggerationFactor=u_zoom < 2.0 ? 0.4 : u_zoom < 4.5 ? 0.35 : 0.3;float exaggeration=u_zoom < 15.0 ? (u_zoom-15.0)*exaggerationFactor : 0.0;vec2 deriv=vec2((c+f+f+i)-(a+d+d+g),(g+h+h+i)-(a+b+b+c))/pow(2.0,exaggeration+(19.2562-u_zoom));gl_FragColor=clamp(vec4(deriv.x/2.0+0.5,deriv.y/2.0+0.5,1.0,1.0),0.0,1.0);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_dimension;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);highp vec2 epsilon=1.0/u_dimension;float scale=(u_dimension.x-2.0)/u_dimension.x;v_pos=(a_texture_pos/8192.0)*scale+epsilon;}\"),hillshade:ye(\"uniform sampler2D u_image;varying vec2 v_pos;uniform vec2 u_latrange;uniform vec2 u_light;uniform vec4 u_shadow;uniform vec4 u_highlight;uniform vec4 u_accent;\\n#define PI 3.141592653589793\\nvoid main() {vec4 pixel=texture2D(u_image,v_pos);vec2 deriv=((pixel.rg*2.0)-1.0);float scaleFactor=cos(radians((u_latrange[0]-u_latrange[1])*(1.0-v_pos.y)+u_latrange[1]));float slope=atan(1.25*length(deriv)/scaleFactor);float aspect=deriv.x !=0.0 ? atan(deriv.y,-deriv.x) : PI/2.0*(deriv.y > 0.0 ? 1.0 :-1.0);float intensity=u_light.x;float azimuth=u_light.y+PI;float base=1.875-intensity*1.75;float maxValue=0.5*PI;float scaledSlope=intensity !=0.5 ? ((pow(base,slope)-1.0)/(pow(base,maxValue)-1.0))*maxValue : slope;float accent=cos(scaledSlope);vec4 accent_color=(1.0-accent)*u_accent*clamp(intensity*2.0,0.0,1.0);float shade=abs(mod((aspect+azimuth)/PI+0.5,2.0)-1.0);vec4 shade_color=mix(u_shadow,u_highlight,shade)*sin(scaledSlope)*clamp(intensity*2.0,0.0,1.0);gl_FragColor=accent_color*(1.0-shade_color.a)+shade_color;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos=a_texture_pos/8192.0;}\"),line:ye(\"uniform lowp float u_device_pixel_ratio;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform vec2 u_units_to_pixels;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp float v_linesofar;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;v_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*2.0;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;\\n#ifdef TERRAIN3D\\nv_gamma_scale=1.0;\\n#else\\nfloat extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;\\n#endif\\nv_width2=vec2(outset,inset);}\"),lineGradient:ye(\"uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;varying vec2 v_width2;varying vec2 v_normal;varying float v_gamma_scale;varying highp vec2 v_uv;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);vec4 color=texture2D(u_image,v_uv);gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\nattribute vec2 a_pos_normal;attribute vec4 a_data;attribute float a_uv_x;attribute float a_split_index;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_units_to_pixels;uniform float u_image_height;varying vec2 v_normal;varying vec2 v_width2;varying float v_gamma_scale;varying highp vec2 v_uv;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;highp float texel_height=1.0/u_image_height;highp float half_texel_height=0.5*texel_height;v_uv=vec2(a_uv_x,a_split_index*texel_height-half_texel_height);vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;\\n#ifdef TERRAIN3D\\nv_gamma_scale=1.0;\\n#else\\nfloat extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;\\n#endif\\nv_width2=vec2(outset,inset);}\"),linePattern:ye(\"#ifdef GL_ES\\nprecision highp float;\\n#endif\\nuniform lowp float u_device_pixel_ratio;uniform vec2 u_texsize;uniform float u_fade;uniform mediump vec3 u_scale;uniform sampler2D u_image;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\nvec2 pattern_tl_a=pattern_from.xy;vec2 pattern_br_a=pattern_from.zw;vec2 pattern_tl_b=pattern_to.xy;vec2 pattern_br_b=pattern_to.zw;float tileZoomRatio=u_scale.x;float fromScale=u_scale.y;float toScale=u_scale.z;vec2 display_size_a=(pattern_br_a-pattern_tl_a)/pixel_ratio_from;vec2 display_size_b=(pattern_br_b-pattern_tl_b)/pixel_ratio_to;vec2 pattern_size_a=vec2(display_size_a.x*fromScale/tileZoomRatio,display_size_a.y);vec2 pattern_size_b=vec2(display_size_b.x*toScale/tileZoomRatio,display_size_b.y);float aspect_a=display_size_a.y/v_width;float aspect_b=display_size_b.y/v_width;float dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float x_a=mod(v_linesofar/pattern_size_a.x*aspect_a,1.0);float x_b=mod(v_linesofar/pattern_size_b.x*aspect_b,1.0);float y=0.5*v_normal.y+0.5;vec2 texel_size=1.0/u_texsize;vec2 pos_a=mix(pattern_tl_a*texel_size-texel_size,pattern_br_a*texel_size+texel_size,vec2(x_a,y));vec2 pos_b=mix(pattern_tl_b*texel_size-texel_size,pattern_br_b*texel_size+texel_size,vec2(x_b,y));vec4 color=mix(texture2D(u_image,pos_a),texture2D(u_image,pos_b),u_fade);gl_FragColor=color*alpha*opacity;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\n#define LINE_DISTANCE_SCALE 2.0\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform vec2 u_units_to_pixels;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;varying vec2 v_normal;varying vec2 v_width2;varying float v_linesofar;varying float v_gamma_scale;varying float v_width;\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\n#pragma mapbox: define lowp vec4 pattern_from\\n#pragma mapbox: define lowp vec4 pattern_to\\n#pragma mapbox: define lowp float pixel_ratio_from\\n#pragma mapbox: define lowp float pixel_ratio_to\\nvoid main() {\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\n#pragma mapbox: initialize mediump vec4 pattern_from\\n#pragma mapbox: initialize mediump vec4 pattern_to\\n#pragma mapbox: initialize lowp float pixel_ratio_from\\n#pragma mapbox: initialize lowp float pixel_ratio_to\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;\\n#ifdef TERRAIN3D\\nv_gamma_scale=1.0;\\n#else\\nfloat extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;\\n#endif\\nv_linesofar=a_linesofar;v_width2=vec2(outset,inset);v_width=floorwidth;}\"),lineSDF:ye(\"uniform lowp float u_device_pixel_ratio;uniform sampler2D u_image;uniform float u_sdfgamma;uniform float u_mix;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\nfloat dist=length(v_normal)*v_width2.s;float blur2=(blur+1.0/u_device_pixel_ratio)*v_gamma_scale;float alpha=clamp(min(dist-(v_width2.t-blur2),v_width2.s-dist)/blur2,0.0,1.0);float sdfdist_a=texture2D(u_image,v_tex_a).a;float sdfdist_b=texture2D(u_image,v_tex_b).a;float sdfdist=mix(sdfdist_a,sdfdist_b,u_mix);alpha*=smoothstep(0.5-u_sdfgamma/floorwidth,0.5+u_sdfgamma/floorwidth,sdfdist);gl_FragColor=color*(alpha*opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"\\n#define scale 0.015873016\\n#define LINE_DISTANCE_SCALE 2.0\\nattribute vec2 a_pos_normal;attribute vec4 a_data;uniform mat4 u_matrix;uniform mediump float u_ratio;uniform lowp float u_device_pixel_ratio;uniform vec2 u_patternscale_a;uniform float u_tex_y_a;uniform vec2 u_patternscale_b;uniform float u_tex_y_b;uniform vec2 u_units_to_pixels;varying vec2 v_normal;varying vec2 v_width2;varying vec2 v_tex_a;varying vec2 v_tex_b;varying float v_gamma_scale;\\n#pragma mapbox: define highp vec4 color\\n#pragma mapbox: define lowp float blur\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define mediump float gapwidth\\n#pragma mapbox: define lowp float offset\\n#pragma mapbox: define mediump float width\\n#pragma mapbox: define lowp float floorwidth\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 color\\n#pragma mapbox: initialize lowp float blur\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize mediump float gapwidth\\n#pragma mapbox: initialize lowp float offset\\n#pragma mapbox: initialize mediump float width\\n#pragma mapbox: initialize lowp float floorwidth\\nfloat ANTIALIASING=1.0/u_device_pixel_ratio/2.0;vec2 a_extrude=a_data.xy-128.0;float a_direction=mod(a_data.z,4.0)-1.0;float a_linesofar=(floor(a_data.z/4.0)+a_data.w*64.0)*LINE_DISTANCE_SCALE;vec2 pos=floor(a_pos_normal*0.5);mediump vec2 normal=a_pos_normal-2.0*pos;normal.y=normal.y*2.0-1.0;v_normal=normal;gapwidth=gapwidth/2.0;float halfwidth=width/2.0;offset=-1.0*offset;float inset=gapwidth+(gapwidth > 0.0 ? ANTIALIASING : 0.0);float outset=gapwidth+halfwidth*(gapwidth > 0.0 ? 2.0 : 1.0)+(halfwidth==0.0 ? 0.0 : ANTIALIASING);mediump vec2 dist=outset*a_extrude*scale;mediump float u=0.5*a_direction;mediump float t=1.0-abs(u);mediump vec2 offset2=offset*a_extrude*scale*normal.y*mat2(t,-u,u,t);vec4 projected_extrude=u_matrix*vec4(dist/u_ratio,0.0,0.0);gl_Position=u_matrix*vec4(pos+offset2/u_ratio,0.0,1.0)+projected_extrude;\\n#ifdef TERRAIN3D\\nv_gamma_scale=1.0;\\n#else\\nfloat extrude_length_without_perspective=length(dist);float extrude_length_with_perspective=length(projected_extrude.xy/gl_Position.w*u_units_to_pixels);v_gamma_scale=extrude_length_without_perspective/extrude_length_with_perspective;\\n#endif\\nv_tex_a=vec2(a_linesofar*u_patternscale_a.x/floorwidth,normal.y*u_patternscale_a.y+u_tex_y_a);v_tex_b=vec2(a_linesofar*u_patternscale_b.x/floorwidth,normal.y*u_patternscale_b.y+u_tex_y_b);v_width2=vec2(outset,inset);}\"),raster:ye(\"uniform float u_fade_t;uniform float u_opacity;uniform sampler2D u_image0;uniform sampler2D u_image1;varying vec2 v_pos0;varying vec2 v_pos1;uniform float u_brightness_low;uniform float u_brightness_high;uniform float u_saturation_factor;uniform float u_contrast_factor;uniform vec3 u_spin_weights;void main() {vec4 color0=texture2D(u_image0,v_pos0);vec4 color1=texture2D(u_image1,v_pos1);if (color0.a > 0.0) {color0.rgb=color0.rgb/color0.a;}if (color1.a > 0.0) {color1.rgb=color1.rgb/color1.a;}vec4 color=mix(color0,color1,u_fade_t);color.a*=u_opacity;vec3 rgb=color.rgb;rgb=vec3(dot(rgb,u_spin_weights.xyz),dot(rgb,u_spin_weights.zxy),dot(rgb,u_spin_weights.yzx));float average=(color.r+color.g+color.b)/3.0;rgb+=(average-rgb)*u_saturation_factor;rgb=(rgb-0.5)*u_contrast_factor+0.5;vec3 u_high_vec=vec3(u_brightness_low,u_brightness_low,u_brightness_low);vec3 u_low_vec=vec3(u_brightness_high,u_brightness_high,u_brightness_high);gl_FragColor=vec4(mix(u_high_vec,u_low_vec,rgb)*color.a,color.a);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"uniform mat4 u_matrix;uniform vec2 u_tl_parent;uniform float u_scale_parent;uniform float u_buffer_scale;attribute vec2 a_pos;attribute vec2 a_texture_pos;varying vec2 v_pos0;varying vec2 v_pos1;void main() {gl_Position=u_matrix*vec4(a_pos,0,1);v_pos0=(((a_texture_pos/8192.0)-0.5)/u_buffer_scale )+0.5;v_pos1=(v_pos0*u_scale_parent)+u_tl_parent;}\"),symbolIcon:ye(\"uniform sampler2D u_texture;varying vec2 v_tex;varying float v_fade_opacity;\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\nlowp float alpha=opacity*v_fade_opacity;gl_FragColor=texture2D(u_texture,v_tex)*alpha;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform highp float u_camera_to_center_distance;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform float u_fade_change;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform vec2 u_texsize;uniform bool u_is_along_line;uniform bool u_is_variable_anchor;uniform vec2 u_translation;uniform float u_pitched_scale;varying vec2 v_tex;varying float v_fade_opacity;vec4 projectTileWithElevation(vec2 posInTile,float elevation) {return u_matrix*vec4(posInTile,elevation,1.0);}\\n#pragma mapbox: define lowp float opacity\\nvoid main() {\\n#pragma mapbox: initialize lowp float opacity\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;vec2 a_minFontScale=a_pixeloffset.zw/256.0;float ele=get_elevation(a_pos);highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec2 translated_a_pos=a_pos+u_translation;vec4 projectedPoint=projectTileWithElevation(translated_a_pos,ele);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=projectTileWithElevation(translated_a_pos+vec2(1,0),ele);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos;if (u_is_along_line || u_is_variable_anchor) {projected_pos=vec4(a_projected_pos.xy,ele,1.0);} else if (u_pitch_with_map) {projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy+u_translation,ele,1.0);} else {projected_pos=u_label_plane_matrix*projectTileWithElevation(a_projected_pos.xy+u_translation,ele);}float z=float(u_pitch_with_map)*projected_pos.z/projected_pos.w;float projectionScaling=1.0;vec4 finalPos=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*max(a_minFontScale,fontScale)+a_pxoffset/16.0)*projectionScaling,z,1.0);if(u_pitch_with_map) {finalPos=projectTileWithElevation(finalPos.xy,finalPos.z);}gl_Position=finalPos;v_tex=a_tex/u_texsize;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float visibility=calculate_visibility(projectedPoint);v_fade_opacity=max(0.0,min(visibility,fade_opacity[0]+fade_change));}\"),symbolSDF:ye(\"#define SDF_PX 8.0\\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;uniform bool u_is_text;varying vec2 v_data0;varying vec3 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nfloat EDGE_GAMMA=0.105/u_device_pixel_ratio;vec2 tex=v_data0.xy;float gamma_scale=v_data1.x;float size=v_data1.y;float fade_opacity=v_data1[2];float fontScale=u_is_text ? size/24.0 : size;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float inner_edge=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);inner_edge=inner_edge+gamma*gamma_scale;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(inner_edge-gamma_scaled,inner_edge+gamma_scaled,dist);if (u_is_halo) {lowp float halo_edge=(6.0-halo_width/fontScale)/SDF_PX;alpha=min(smoothstep(halo_edge-gamma_scaled,halo_edge+gamma_scaled,dist),1.0-alpha);}gl_FragColor=color*(alpha*opacity*fade_opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec4 a_pixeloffset;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform bool u_is_along_line;uniform bool u_is_variable_anchor;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_translation;uniform float u_pitched_scale;varying vec2 v_data0;varying vec3 v_data1;vec4 projectTileWithElevation(vec2 posInTile,float elevation) {return u_matrix*vec4(posInTile,elevation,1.0);}\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);vec2 a_pxoffset=a_pixeloffset.xy;float ele=get_elevation(a_pos);highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec2 translated_a_pos=a_pos+u_translation;vec4 projectedPoint=projectTileWithElevation(translated_a_pos,ele);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=u_is_text ? size/24.0 : size;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=projectTileWithElevation(translated_a_pos+vec2(1,0),ele);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos;if (u_is_along_line || u_is_variable_anchor) {projected_pos=vec4(a_projected_pos.xy,ele,1.0);} else if (u_pitch_with_map) {projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy+u_translation,ele,1.0);} else {projected_pos=u_label_plane_matrix*projectTileWithElevation(a_projected_pos.xy+u_translation,ele);}float z=float(u_pitch_with_map)*projected_pos.z/projected_pos.w;float projectionScaling=1.0;vec4 finalPos=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale+a_pxoffset)*projectionScaling,z,1.0);if(u_pitch_with_map) {finalPos=projectTileWithElevation(finalPos.xy,finalPos.z);}float gamma_scale=finalPos.w;gl_Position=finalPos;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float visibility=calculate_visibility(projectedPoint);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(visibility,fade_opacity[0]+fade_change));v_data0=a_tex/u_texsize;v_data1=vec3(gamma_scale,size,interpolated_fade_opacity);}\"),symbolTextAndIcon:ye(\"#define SDF_PX 8.0\\n#define SDF 1.0\\n#define ICON 0.0\\nuniform bool u_is_halo;uniform sampler2D u_texture;uniform sampler2D u_texture_icon;uniform highp float u_gamma_scale;uniform lowp float u_device_pixel_ratio;varying vec4 v_data0;varying vec4 v_data1;\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nfloat fade_opacity=v_data1[2];if (v_data1.w==ICON) {vec2 tex_icon=v_data0.zw;lowp float alpha=opacity*fade_opacity;gl_FragColor=texture2D(u_texture_icon,tex_icon)*alpha;\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\nreturn;}vec2 tex=v_data0.xy;float EDGE_GAMMA=0.105/u_device_pixel_ratio;float gamma_scale=v_data1.x;float size=v_data1.y;float fontScale=size/24.0;lowp vec4 color=fill_color;highp float gamma=EDGE_GAMMA/(fontScale*u_gamma_scale);lowp float buff=(256.0-64.0)/256.0;if (u_is_halo) {color=halo_color;gamma=(halo_blur*1.19/SDF_PX+EDGE_GAMMA)/(fontScale*u_gamma_scale);buff=(6.0-halo_width/fontScale)/SDF_PX;}lowp float dist=texture2D(u_texture,tex).a;highp float gamma_scaled=gamma*gamma_scale;highp float alpha=smoothstep(buff-gamma_scaled,buff+gamma_scaled,dist);gl_FragColor=color*(alpha*opacity*fade_opacity);\\n#ifdef OVERDRAW_INSPECTOR\\ngl_FragColor=vec4(1.0);\\n#endif\\n}\",\"attribute vec4 a_pos_offset;attribute vec4 a_data;attribute vec3 a_projected_pos;attribute float a_fade_opacity;uniform bool u_is_size_zoom_constant;uniform bool u_is_size_feature_constant;uniform highp float u_size_t;uniform highp float u_size;uniform mat4 u_matrix;uniform mat4 u_label_plane_matrix;uniform mat4 u_coord_matrix;uniform bool u_is_text;uniform bool u_pitch_with_map;uniform highp float u_pitch;uniform bool u_rotate_symbol;uniform highp float u_aspect_ratio;uniform highp float u_camera_to_center_distance;uniform float u_fade_change;uniform vec2 u_texsize;uniform vec2 u_texsize_icon;uniform bool u_is_along_line;uniform bool u_is_variable_anchor;uniform vec2 u_translation;uniform float u_pitched_scale;varying vec4 v_data0;varying vec4 v_data1;vec4 projectTileWithElevation(vec2 posInTile,float elevation) {return u_matrix*vec4(posInTile,elevation,1.0);}\\n#pragma mapbox: define highp vec4 fill_color\\n#pragma mapbox: define highp vec4 halo_color\\n#pragma mapbox: define lowp float opacity\\n#pragma mapbox: define lowp float halo_width\\n#pragma mapbox: define lowp float halo_blur\\nvoid main() {\\n#pragma mapbox: initialize highp vec4 fill_color\\n#pragma mapbox: initialize highp vec4 halo_color\\n#pragma mapbox: initialize lowp float opacity\\n#pragma mapbox: initialize lowp float halo_width\\n#pragma mapbox: initialize lowp float halo_blur\\nvec2 a_pos=a_pos_offset.xy;vec2 a_offset=a_pos_offset.zw;vec2 a_tex=a_data.xy;vec2 a_size=a_data.zw;float a_size_min=floor(a_size[0]*0.5);float is_sdf=a_size[0]-2.0*a_size_min;float ele=get_elevation(a_pos);highp float segment_angle=-a_projected_pos[2];float size;if (!u_is_size_zoom_constant && !u_is_size_feature_constant) {size=mix(a_size_min,a_size[1],u_size_t)/128.0;} else if (u_is_size_zoom_constant && !u_is_size_feature_constant) {size=a_size_min/128.0;} else {size=u_size;}vec2 translated_a_pos=a_pos+u_translation;vec4 projectedPoint=projectTileWithElevation(translated_a_pos,ele);highp float camera_to_anchor_distance=projectedPoint.w;highp float distance_ratio=u_pitch_with_map ?\\ncamera_to_anchor_distance/u_camera_to_center_distance :\\nu_camera_to_center_distance/camera_to_anchor_distance;highp float perspective_ratio=clamp(0.5+0.5*distance_ratio,0.0,4.0);size*=perspective_ratio;float fontScale=size/24.0;highp float symbol_rotation=0.0;if (u_rotate_symbol) {vec4 offsetProjectedPoint=projectTileWithElevation(translated_a_pos+vec2(1,0),ele);vec2 a=projectedPoint.xy/projectedPoint.w;vec2 b=offsetProjectedPoint.xy/offsetProjectedPoint.w;symbol_rotation=atan((b.y-a.y)/u_aspect_ratio,b.x-a.x);}highp float angle_sin=sin(segment_angle+symbol_rotation);highp float angle_cos=cos(segment_angle+symbol_rotation);mat2 rotation_matrix=mat2(angle_cos,-1.0*angle_sin,angle_sin,angle_cos);vec4 projected_pos;if (u_is_along_line || u_is_variable_anchor) {projected_pos=vec4(a_projected_pos.xy,ele,1.0);} else if (u_pitch_with_map) {projected_pos=u_label_plane_matrix*vec4(a_projected_pos.xy+u_translation,ele,1.0);} else {projected_pos=u_label_plane_matrix*projectTileWithElevation(a_projected_pos.xy+u_translation,ele);}float z=float(u_pitch_with_map)*projected_pos.z/projected_pos.w;float projectionScaling=1.0;vec4 finalPos=u_coord_matrix*vec4(projected_pos.xy/projected_pos.w+rotation_matrix*(a_offset/32.0*fontScale)*projectionScaling,z,1.0);if(u_pitch_with_map) {finalPos=projectTileWithElevation(finalPos.xy,finalPos.z);}float gamma_scale=finalPos.w;gl_Position=finalPos;vec2 fade_opacity=unpack_opacity(a_fade_opacity);float visibility=calculate_visibility(projectedPoint);float fade_change=fade_opacity[1] > 0.5 ? u_fade_change :-u_fade_change;float interpolated_fade_opacity=max(0.0,min(visibility,fade_opacity[0]+fade_change));v_data0.xy=a_tex/u_texsize;v_data0.zw=a_tex/u_texsize_icon;v_data1=vec4(gamma_scale,size,interpolated_fade_opacity,is_sdf);}\"),terrain:ye(\"uniform sampler2D u_texture;uniform vec4 u_fog_color;uniform vec4 u_horizon_color;uniform float u_fog_ground_blend;uniform float u_fog_ground_blend_opacity;uniform float u_horizon_fog_blend;varying vec2 v_texture_pos;varying float v_fog_depth;const float gamma=2.2;vec4 gammaToLinear(vec4 color) {return pow(color,vec4(gamma));}vec4 linearToGamma(vec4 color) {return pow(color,vec4(1.0/gamma));}void main() {vec4 surface_color=texture2D(u_texture,v_texture_pos);if (v_fog_depth > u_fog_ground_blend) {vec4 surface_color_linear=gammaToLinear(surface_color);float blend_color=smoothstep(0.0,1.0,max((v_fog_depth-u_horizon_fog_blend)/(1.0-u_horizon_fog_blend),0.0));vec4 fog_horizon_color_linear=mix(gammaToLinear(u_fog_color),gammaToLinear(u_horizon_color),blend_color);float factor_fog=max(v_fog_depth-u_fog_ground_blend,0.0)/(1.0-u_fog_ground_blend);gl_FragColor=linearToGamma(mix(surface_color_linear,fog_horizon_color_linear,pow(factor_fog,2.0)*u_fog_ground_blend_opacity));} else {gl_FragColor=surface_color;}}\",\"attribute vec3 a_pos3d;uniform mat4 u_matrix;uniform mat4 u_fog_matrix;uniform float u_ele_delta;varying vec2 v_texture_pos;varying float v_fog_depth;void main() {float ele=get_elevation(a_pos3d.xy);float ele_delta=a_pos3d.z==1.0 ? u_ele_delta : 0.0;v_texture_pos=a_pos3d.xy/8192.0;gl_Position=u_matrix*vec4(a_pos3d.xy,ele-ele_delta,1.0);vec4 pos=u_fog_matrix*vec4(a_pos3d.xy,ele,1.0);v_fog_depth=pos.z/pos.w*0.5+0.5;}\"),terrainDepth:ye(\"varying float v_depth;const highp vec4 bitSh=vec4(256.*256.*256.,256.*256.,256.,1.);const highp vec4 bitMsk=vec4(0.,vec3(1./256.0));highp vec4 pack(highp float value) {highp vec4 comp=fract(value*bitSh);comp-=comp.xxyz*bitMsk;return comp;}void main() {gl_FragColor=pack(v_depth);}\",\"attribute vec3 a_pos3d;uniform mat4 u_matrix;uniform float u_ele_delta;varying float v_depth;void main() {float ele=get_elevation(a_pos3d.xy);float ele_delta=a_pos3d.z==1.0 ? u_ele_delta : 0.0;gl_Position=u_matrix*vec4(a_pos3d.xy,ele-ele_delta,1.0);v_depth=gl_Position.z/gl_Position.w;}\"),terrainCoords:ye(\"precision mediump float;uniform sampler2D u_texture;uniform float u_terrain_coords_id;varying vec2 v_texture_pos;void main() {vec4 rgba=texture2D(u_texture,v_texture_pos);gl_FragColor=vec4(rgba.r,rgba.g,rgba.b,u_terrain_coords_id);}\",\"attribute vec3 a_pos3d;uniform mat4 u_matrix;uniform float u_ele_delta;varying vec2 v_texture_pos;void main() {float ele=get_elevation(a_pos3d.xy);float ele_delta=a_pos3d.z==1.0 ? u_ele_delta : 0.0;v_texture_pos=a_pos3d.xy/8192.0;gl_Position=u_matrix*vec4(a_pos3d.xy,ele-ele_delta,1.0);}\"),sky:ye(\"uniform vec4 u_sky_color;uniform vec4 u_horizon_color;uniform float u_horizon;uniform float u_sky_horizon_blend;void main() {float y=gl_FragCoord.y;if (y > u_horizon) {float blend=y-u_horizon;if (blend < u_sky_horizon_blend) {gl_FragColor=mix(u_sky_color,u_horizon_color,pow(1.0-blend/u_sky_horizon_blend,2.0));} else {gl_FragColor=u_sky_color;}}}\",\"attribute vec2 a_pos;void main() {gl_Position=vec4(a_pos,1.0,1.0);}\")};function ye(t,e){const r=/#pragma mapbox: ([\\w]+) ([\\w]+) ([\\w]+) ([\\w]+)/g,n=e.match(/attribute ([\\w]+) ([\\w]+)/g),i=t.match(/uniform ([\\w]+) ([\\w]+)([\\s]*)([\\w]*)/g),a=e.match(/uniform ([\\w]+) ([\\w]+)([\\s]*)([\\w]*)/g),o=a?a.concat(i):i,s={};return{fragmentSource:t=t.replace(r,((t,e,r,n,i)=>(s[i]=!0,\"define\"===e?`\\n#ifndef HAS_UNIFORM_u_${i}\\nvarying ${r} ${n} ${i};\\n#else\\nuniform ${r} ${n} u_${i};\\n#endif\\n`:`\\n#ifdef HAS_UNIFORM_u_${i}\\n ${r} ${n} ${i} = u_${i};\\n#endif\\n`))),vertexSource:e=e.replace(r,((t,e,r,n,i)=>{const a=\"float\"===n?\"vec2\":\"vec4\",o=i.match(/color/)?\"color\":a;return s[i]?\"define\"===e?`\\n#ifndef HAS_UNIFORM_u_${i}\\nuniform lowp float u_${i}_t;\\nattribute ${r} ${a} a_${i};\\nvarying ${r} ${n} ${i};\\n#else\\nuniform ${r} ${n} u_${i};\\n#endif\\n`:\"vec4\"===o?`\\n#ifndef HAS_UNIFORM_u_${i}\\n ${i} = a_${i};\\n#else\\n ${r} ${n} ${i} = u_${i};\\n#endif\\n`:`\\n#ifndef HAS_UNIFORM_u_${i}\\n ${i} = unpack_mix_${o}(a_${i}, u_${i}_t);\\n#else\\n ${r} ${n} ${i} = u_${i};\\n#endif\\n`:\"define\"===e?`\\n#ifndef HAS_UNIFORM_u_${i}\\nuniform lowp float u_${i}_t;\\nattribute ${r} ${a} a_${i};\\n#else\\nuniform ${r} ${n} u_${i};\\n#endif\\n`:\"vec4\"===o?`\\n#ifndef HAS_UNIFORM_u_${i}\\n ${r} ${n} ${i} = a_${i};\\n#else\\n ${r} ${n} ${i} = u_${i};\\n#endif\\n`:`\\n#ifndef HAS_UNIFORM_u_${i}\\n ${r} ${n} ${i} = unpack_mix_${o}(a_${i}, u_${i}_t);\\n#else\\n ${r} ${n} ${i} = u_${i};\\n#endif\\n`})),staticAttributes:n,staticUniforms:o}}class ve{constructor(){this.boundProgram=null,this.boundLayoutVertexBuffer=null,this.boundPaintVertexBuffers=[],this.boundIndexBuffer=null,this.boundVertexOffset=null,this.boundDynamicVertexBuffer=null,this.vao=null}bind(t,e,r,n,i,a,o,s,l){this.context=t;let c=this.boundPaintVertexBuffers.length!==n.length;for(let t=0;!c&&t({u_matrix:t,u_texture:0,u_ele_delta:r,u_fog_matrix:n,u_fog_color:i?i.properties.get(\"fog-color\"):e.aN.white,u_fog_ground_blend:i?i.properties.get(\"fog-ground-blend\"):1,u_fog_ground_blend_opacity:i?i.calculateFogBlendOpacity(a):0,u_horizon_color:i?i.properties.get(\"horizon-color\"):e.aN.white,u_horizon_fog_blend:i?i.properties.get(\"horizon-fog-blend\"):1});function _e(t){const e=[];for(let r=0;r({u_depth:new e.aI(t,r.u_depth),u_terrain:new e.aI(t,r.u_terrain),u_terrain_dim:new e.aJ(t,r.u_terrain_dim),u_terrain_matrix:new e.aK(t,r.u_terrain_matrix),u_terrain_unpack:new e.aL(t,r.u_terrain_unpack),u_terrain_exaggeration:new e.aJ(t,r.u_terrain_exaggeration)}))(t,b),this.binderUniforms=n?n.getUniforms(t,b):[]}draw(t,e,r,n,i,a,o,s,l,c,u,h,f,p,d,m,g,y){const v=t.gl;if(this.failedToCreate)return;if(t.program.set(this.program),t.setDepthMode(r),t.setStencilMode(n),t.setColorMode(i),t.setCullFace(a),s){t.activeTexture.set(v.TEXTURE2),v.bindTexture(v.TEXTURE_2D,s.depthTexture),t.activeTexture.set(v.TEXTURE3),v.bindTexture(v.TEXTURE_2D,s.texture);for(const t in this.terrainUniforms)this.terrainUniforms[t].set(s[t])}for(const t in this.fixedUniforms)this.fixedUniforms[t].set(o[t]);d&&d.setUniforms(t,this.binderUniforms,f,{zoom:p});let x=0;switch(e){case v.LINES:x=2;break;case v.TRIANGLES:x=3;break;case v.LINE_STRIP:x=1}for(const r of h.get()){const n=r.vaos||(r.vaos={});(n[l]||(n[l]=new ve)).bind(t,this,c,d?d.getPaintVertexBuffers():[],u,r.vertexOffset,m,g,y),v.drawElements(e,r.primitiveLength*x,v.UNSIGNED_SHORT,r.primitiveOffset*x*2)}}}function we(t,e,r){const n=1/Nt(r,1,e.transform.tileZoom),i=Math.pow(2,r.tileID.overscaledZ),a=r.tileSize*Math.pow(2,e.transform.tileZoom)/i,o=a*(r.tileID.canonical.x+r.tileID.wrap*i),s=a*r.tileID.canonical.y;return{u_image:0,u_texsize:r.imageAtlasTexture.size,u_scale:[n,t.fromScale,t.toScale],u_fade:t.t,u_pixel_coord_upper:[o>>16,s>>16],u_pixel_coord_lower:[65535&o,65535&s]}}const Te=(t,r,n,i)=>{const a=r.style.light,o=a.properties.get(\"position\"),s=[o.x,o.y,o.z],l=function(){var t=new e.A(9);return e.A!=Float32Array&&(t[1]=0,t[2]=0,t[3]=0,t[5]=0,t[6]=0,t[7]=0),t[0]=1,t[4]=1,t[8]=1,t}();\"viewport\"===a.properties.get(\"anchor\")&&function(t,e){var r=Math.sin(e),n=Math.cos(e);t[0]=n,t[1]=r,t[2]=0,t[3]=-r,t[4]=n,t[5]=0,t[6]=0,t[7]=0,t[8]=1}(l,-r.transform.angle),function(t,e,r){var n=e[0],i=e[1],a=e[2];t[0]=n*r[0]+i*r[3]+a*r[6],t[1]=n*r[1]+i*r[4]+a*r[7],t[2]=n*r[2]+i*r[5]+a*r[8]}(s,s,l);const c=a.properties.get(\"color\");return{u_matrix:t,u_lightpos:s,u_lightintensity:a.properties.get(\"intensity\"),u_lightcolor:[c.r,c.g,c.b],u_vertical_gradient:+n,u_opacity:i}},ke=(t,r,n,i,a,o,s)=>e.e(Te(t,r,n,i),we(o,r,s),{u_height_factor:-Math.pow(2,a.overscaledZ)/s.tileSize/8}),Ae=t=>({u_matrix:t}),Me=(t,r,n,i)=>e.e(Ae(t),we(n,r,i)),Se=(t,e)=>({u_matrix:t,u_world:e}),Ee=(t,r,n,i,a)=>e.e(Me(t,r,n,i),{u_world:a}),Ce=(t,e,r,n)=>{const i=t.transform;let a,o;if(\"map\"===n.paint.get(\"circle-pitch-alignment\")){const t=Nt(r,1,i.zoom);a=!0,o=[t,t]}else a=!1,o=i.pixelsToGLUnits;return{u_camera_to_center_distance:i.cameraToCenterDistance,u_scale_with_map:+(\"map\"===n.paint.get(\"circle-pitch-scale\")),u_matrix:t.translatePosMatrix(e.posMatrix,r,n.paint.get(\"circle-translate\"),n.paint.get(\"circle-translate-anchor\")),u_pitch_with_map:+a,u_device_pixel_ratio:t.pixelRatio,u_extrude_scale:o}},Le=(t,e)=>({u_matrix:e,u_pixel_extrude_scale:[1/t.width,1/t.height]}),Ie=(t,e,r)=>({u_matrix:t,u_inv_matrix:e,u_camera_to_center_distance:r.cameraToCenterDistance,u_viewport_size:[r.width,r.height]}),Pe=(t,e,r=1)=>({u_matrix:t,u_color:e,u_overlay:0,u_overlay_scale:r}),ze=t=>({u_matrix:t}),Oe=(t,e,r,n)=>({u_matrix:t,u_extrude_scale:Nt(e,1,r),u_intensity:n}),De=(t,r,n,i)=>{const a=e.H();e.aQ(a,0,t.width,t.height,0,0,1);const o=t.context.gl;return{u_matrix:a,u_world:[o.drawingBufferWidth,o.drawingBufferHeight],u_image:n,u_color_ramp:i,u_opacity:r.paint.get(\"heatmap-opacity\")}},Re=(t,e,r,n)=>{const i=r.paint.get(\"hillshade-shadow-color\"),a=r.paint.get(\"hillshade-highlight-color\"),o=r.paint.get(\"hillshade-accent-color\");let s=r.paint.get(\"hillshade-illumination-direction\")*(Math.PI/180);\"viewport\"===r.paint.get(\"hillshade-illumination-anchor\")&&(s-=t.transform.angle);const l=!t.options.moving;return{u_matrix:n?n.posMatrix:t.transform.calculatePosMatrix(e.tileID.toUnwrapped(),l),u_image:0,u_latrange:Be(0,e.tileID),u_light:[r.paint.get(\"hillshade-exaggeration\"),s],u_shadow:i,u_highlight:a,u_accent:o}},Fe=(t,r)=>{const n=r.stride,i=e.H();return e.aQ(i,0,e.X,-e.X,0,0,1),e.J(i,i,[0,-e.X,0]),{u_matrix:i,u_image:1,u_dimension:[n,n],u_zoom:t.overscaledZ,u_unpack:r.getUnpackVector()}};function Be(t,r){const n=Math.pow(2,r.canonical.z),i=r.canonical.y;return[new e.Z(0,i/n).toLngLat().lat,new e.Z(0,(i+1)/n).toLngLat().lat]}const Ne=(t,e,r,n)=>{const i=t.transform;return{u_matrix:He(t,e,r,n),u_ratio:1/Nt(e,1,i.zoom),u_device_pixel_ratio:t.pixelRatio,u_units_to_pixels:[1/i.pixelsToGLUnits[0],1/i.pixelsToGLUnits[1]]}},je=(t,r,n,i,a)=>e.e(Ne(t,r,n,a),{u_image:0,u_image_height:i}),Ue=(t,e,r,n,i)=>{const a=t.transform,o=qe(e,a);return{u_matrix:He(t,e,r,i),u_texsize:e.imageAtlasTexture.size,u_ratio:1/Nt(e,1,a.zoom),u_device_pixel_ratio:t.pixelRatio,u_image:0,u_scale:[o,n.fromScale,n.toScale],u_fade:n.t,u_units_to_pixels:[1/a.pixelsToGLUnits[0],1/a.pixelsToGLUnits[1]]}},Ve=(t,r,n,i,a,o)=>{const s=t.transform,l=t.lineAtlas,c=qe(r,s),u=\"round\"===n.layout.get(\"line-cap\"),h=l.getDash(i.from,u),f=l.getDash(i.to,u),p=h.width*a.fromScale,d=f.width*a.toScale;return e.e(Ne(t,r,n,o),{u_patternscale_a:[c/p,-h.height/2],u_patternscale_b:[c/d,-f.height/2],u_sdfgamma:l.width/(256*Math.min(p,d)*t.pixelRatio)/2,u_image:0,u_tex_y_a:h.y,u_tex_y_b:f.y,u_mix:a.t})};function qe(t,e){return 1/Nt(t,1,e.tileZoom)}function He(t,e,r,n){return t.translatePosMatrix(n?n.posMatrix:e.tileID.posMatrix,e,r.paint.get(\"line-translate\"),r.paint.get(\"line-translate-anchor\"))}const Ge=(t,e,r,n,i)=>{return{u_matrix:t,u_tl_parent:e,u_scale_parent:r,u_buffer_scale:1,u_fade_t:n.mix,u_opacity:n.opacity*i.paint.get(\"raster-opacity\"),u_image0:0,u_image1:1,u_brightness_low:i.paint.get(\"raster-brightness-min\"),u_brightness_high:i.paint.get(\"raster-brightness-max\"),u_saturation_factor:(o=i.paint.get(\"raster-saturation\"),o>0?1-1/(1.001-o):-o),u_contrast_factor:(a=i.paint.get(\"raster-contrast\"),a>0?1/(1-a):1+a),u_spin_weights:Ze(i.paint.get(\"raster-hue-rotate\"))};var a,o};function Ze(t){t*=Math.PI/180;const e=Math.sin(t),r=Math.cos(t);return[(2*r+1)/3,(-Math.sqrt(3)*e-r+1)/3,(Math.sqrt(3)*e-r+1)/3]}const We=(t,e,r,n,i,a,o,s,l,c,u,h,f,p)=>{const d=o.transform;return{u_is_size_zoom_constant:+(\"constant\"===t||\"source\"===t),u_is_size_feature_constant:+(\"constant\"===t||\"camera\"===t),u_size_t:e?e.uSizeT:0,u_size:e?e.uSize:0,u_camera_to_center_distance:d.cameraToCenterDistance,u_pitch:d.pitch/360*2*Math.PI,u_rotate_symbol:+r,u_aspect_ratio:d.width/d.height,u_fade_change:o.options.fadeDuration?o.symbolFadeChange:1,u_matrix:s,u_label_plane_matrix:l,u_coord_matrix:c,u_is_text:+h,u_pitch_with_map:+n,u_is_along_line:i,u_is_variable_anchor:a,u_texsize:f,u_texture:0,u_translation:u,u_pitched_scale:p}},Ye=(t,r,n,i,a,o,s,l,c,u,h,f,p,d,m)=>{const g=s.transform;return e.e(We(t,r,n,i,a,o,s,l,c,u,h,f,p,m),{u_gamma_scale:i?Math.cos(g._pitch)*g.cameraToCenterDistance:1,u_device_pixel_ratio:s.pixelRatio,u_is_halo:+d})},Xe=(t,r,n,i,a,o,s,l,c,u,h,f,p,d)=>e.e(Ye(t,r,n,i,a,o,s,l,c,u,h,!0,f,!0,d),{u_texsize_icon:p,u_texture_icon:1}),$e=(t,e,r)=>({u_matrix:t,u_opacity:e,u_color:r}),Je=(t,r,n,i,a,o)=>e.e(function(t,e,r,n){const i=r.imageManager.getPattern(t.from.toString()),a=r.imageManager.getPattern(t.to.toString()),{width:o,height:s}=r.imageManager.getPixelSize(),l=Math.pow(2,n.tileID.overscaledZ),c=n.tileSize*Math.pow(2,r.transform.tileZoom)/l,u=c*(n.tileID.canonical.x+n.tileID.wrap*l),h=c*n.tileID.canonical.y;return{u_image:0,u_pattern_tl_a:i.tl,u_pattern_br_a:i.br,u_pattern_tl_b:a.tl,u_pattern_br_b:a.br,u_texsize:[o,s],u_mix:e.t,u_pattern_size_a:i.displaySize,u_pattern_size_b:a.displaySize,u_scale_a:e.fromScale,u_scale_b:e.toScale,u_tile_units_to_pixels:1/Nt(n,1,r.transform.tileZoom),u_pixel_coord_upper:[u>>16,h>>16],u_pixel_coord_lower:[65535&u,65535&h]}}(i,o,n,a),{u_matrix:t,u_opacity:r}),Ke={fillExtrusion:(t,r)=>({u_matrix:new e.aK(t,r.u_matrix),u_lightpos:new e.aO(t,r.u_lightpos),u_lightintensity:new e.aJ(t,r.u_lightintensity),u_lightcolor:new e.aO(t,r.u_lightcolor),u_vertical_gradient:new e.aJ(t,r.u_vertical_gradient),u_opacity:new e.aJ(t,r.u_opacity)}),fillExtrusionPattern:(t,r)=>({u_matrix:new e.aK(t,r.u_matrix),u_lightpos:new e.aO(t,r.u_lightpos),u_lightintensity:new e.aJ(t,r.u_lightintensity),u_lightcolor:new e.aO(t,r.u_lightcolor),u_vertical_gradient:new e.aJ(t,r.u_vertical_gradient),u_height_factor:new e.aJ(t,r.u_height_factor),u_image:new e.aI(t,r.u_image),u_texsize:new e.aP(t,r.u_texsize),u_pixel_coord_upper:new e.aP(t,r.u_pixel_coord_upper),u_pixel_coord_lower:new e.aP(t,r.u_pixel_coord_lower),u_scale:new e.aO(t,r.u_scale),u_fade:new e.aJ(t,r.u_fade),u_opacity:new e.aJ(t,r.u_opacity)}),fill:(t,r)=>({u_matrix:new e.aK(t,r.u_matrix)}),fillPattern:(t,r)=>({u_matrix:new e.aK(t,r.u_matrix),u_image:new e.aI(t,r.u_image),u_texsize:new e.aP(t,r.u_texsize),u_pixel_coord_upper:new e.aP(t,r.u_pixel_coord_upper),u_pixel_coord_lower:new e.aP(t,r.u_pixel_coord_lower),u_scale:new e.aO(t,r.u_scale),u_fade:new e.aJ(t,r.u_fade)}),fillOutline:(t,r)=>({u_matrix:new e.aK(t,r.u_matrix),u_world:new e.aP(t,r.u_world)}),fillOutlinePattern:(t,r)=>({u_matrix:new e.aK(t,r.u_matrix),u_world:new e.aP(t,r.u_world),u_image:new e.aI(t,r.u_image),u_texsize:new e.aP(t,r.u_texsize),u_pixel_coord_upper:new e.aP(t,r.u_pixel_coord_upper),u_pixel_coord_lower:new e.aP(t,r.u_pixel_coord_lower),u_scale:new e.aO(t,r.u_scale),u_fade:new e.aJ(t,r.u_fade)}),circle:(t,r)=>({u_camera_to_center_distance:new e.aJ(t,r.u_camera_to_center_distance),u_scale_with_map:new e.aI(t,r.u_scale_with_map),u_pitch_with_map:new e.aI(t,r.u_pitch_with_map),u_extrude_scale:new e.aP(t,r.u_extrude_scale),u_device_pixel_ratio:new e.aJ(t,r.u_device_pixel_ratio),u_matrix:new e.aK(t,r.u_matrix)}),collisionBox:(t,r)=>({u_matrix:new e.aK(t,r.u_matrix),u_pixel_extrude_scale:new e.aP(t,r.u_pixel_extrude_scale)}),collisionCircle:(t,r)=>({u_matrix:new e.aK(t,r.u_matrix),u_inv_matrix:new e.aK(t,r.u_inv_matrix),u_camera_to_center_distance:new e.aJ(t,r.u_camera_to_center_distance),u_viewport_size:new e.aP(t,r.u_viewport_size)}),debug:(t,r)=>({u_color:new e.aM(t,r.u_color),u_matrix:new e.aK(t,r.u_matrix),u_overlay:new e.aI(t,r.u_overlay),u_overlay_scale:new e.aJ(t,r.u_overlay_scale)}),clippingMask:(t,r)=>({u_matrix:new e.aK(t,r.u_matrix)}),heatmap:(t,r)=>({u_extrude_scale:new e.aJ(t,r.u_extrude_scale),u_intensity:new e.aJ(t,r.u_intensity),u_matrix:new e.aK(t,r.u_matrix)}),heatmapTexture:(t,r)=>({u_matrix:new e.aK(t,r.u_matrix),u_world:new e.aP(t,r.u_world),u_image:new e.aI(t,r.u_image),u_color_ramp:new e.aI(t,r.u_color_ramp),u_opacity:new e.aJ(t,r.u_opacity)}),hillshade:(t,r)=>({u_matrix:new e.aK(t,r.u_matrix),u_image:new e.aI(t,r.u_image),u_latrange:new e.aP(t,r.u_latrange),u_light:new e.aP(t,r.u_light),u_shadow:new e.aM(t,r.u_shadow),u_highlight:new e.aM(t,r.u_highlight),u_accent:new e.aM(t,r.u_accent)}),hillshadePrepare:(t,r)=>({u_matrix:new e.aK(t,r.u_matrix),u_image:new e.aI(t,r.u_image),u_dimension:new e.aP(t,r.u_dimension),u_zoom:new e.aJ(t,r.u_zoom),u_unpack:new e.aL(t,r.u_unpack)}),line:(t,r)=>({u_matrix:new e.aK(t,r.u_matrix),u_ratio:new e.aJ(t,r.u_ratio),u_device_pixel_ratio:new e.aJ(t,r.u_device_pixel_ratio),u_units_to_pixels:new e.aP(t,r.u_units_to_pixels)}),lineGradient:(t,r)=>({u_matrix:new e.aK(t,r.u_matrix),u_ratio:new e.aJ(t,r.u_ratio),u_device_pixel_ratio:new e.aJ(t,r.u_device_pixel_ratio),u_units_to_pixels:new e.aP(t,r.u_units_to_pixels),u_image:new e.aI(t,r.u_image),u_image_height:new e.aJ(t,r.u_image_height)}),linePattern:(t,r)=>({u_matrix:new e.aK(t,r.u_matrix),u_texsize:new e.aP(t,r.u_texsize),u_ratio:new e.aJ(t,r.u_ratio),u_device_pixel_ratio:new e.aJ(t,r.u_device_pixel_ratio),u_image:new e.aI(t,r.u_image),u_units_to_pixels:new e.aP(t,r.u_units_to_pixels),u_scale:new e.aO(t,r.u_scale),u_fade:new e.aJ(t,r.u_fade)}),lineSDF:(t,r)=>({u_matrix:new e.aK(t,r.u_matrix),u_ratio:new e.aJ(t,r.u_ratio),u_device_pixel_ratio:new e.aJ(t,r.u_device_pixel_ratio),u_units_to_pixels:new e.aP(t,r.u_units_to_pixels),u_patternscale_a:new e.aP(t,r.u_patternscale_a),u_patternscale_b:new e.aP(t,r.u_patternscale_b),u_sdfgamma:new e.aJ(t,r.u_sdfgamma),u_image:new e.aI(t,r.u_image),u_tex_y_a:new e.aJ(t,r.u_tex_y_a),u_tex_y_b:new e.aJ(t,r.u_tex_y_b),u_mix:new e.aJ(t,r.u_mix)}),raster:(t,r)=>({u_matrix:new e.aK(t,r.u_matrix),u_tl_parent:new e.aP(t,r.u_tl_parent),u_scale_parent:new e.aJ(t,r.u_scale_parent),u_buffer_scale:new e.aJ(t,r.u_buffer_scale),u_fade_t:new e.aJ(t,r.u_fade_t),u_opacity:new e.aJ(t,r.u_opacity),u_image0:new e.aI(t,r.u_image0),u_image1:new e.aI(t,r.u_image1),u_brightness_low:new e.aJ(t,r.u_brightness_low),u_brightness_high:new e.aJ(t,r.u_brightness_high),u_saturation_factor:new e.aJ(t,r.u_saturation_factor),u_contrast_factor:new e.aJ(t,r.u_contrast_factor),u_spin_weights:new e.aO(t,r.u_spin_weights)}),symbolIcon:(t,r)=>({u_is_size_zoom_constant:new e.aI(t,r.u_is_size_zoom_constant),u_is_size_feature_constant:new e.aI(t,r.u_is_size_feature_constant),u_size_t:new e.aJ(t,r.u_size_t),u_size:new e.aJ(t,r.u_size),u_camera_to_center_distance:new e.aJ(t,r.u_camera_to_center_distance),u_pitch:new e.aJ(t,r.u_pitch),u_rotate_symbol:new e.aI(t,r.u_rotate_symbol),u_aspect_ratio:new e.aJ(t,r.u_aspect_ratio),u_fade_change:new e.aJ(t,r.u_fade_change),u_matrix:new e.aK(t,r.u_matrix),u_label_plane_matrix:new e.aK(t,r.u_label_plane_matrix),u_coord_matrix:new e.aK(t,r.u_coord_matrix),u_is_text:new e.aI(t,r.u_is_text),u_pitch_with_map:new e.aI(t,r.u_pitch_with_map),u_is_along_line:new e.aI(t,r.u_is_along_line),u_is_variable_anchor:new e.aI(t,r.u_is_variable_anchor),u_texsize:new e.aP(t,r.u_texsize),u_texture:new e.aI(t,r.u_texture),u_translation:new e.aP(t,r.u_translation),u_pitched_scale:new e.aJ(t,r.u_pitched_scale)}),symbolSDF:(t,r)=>({u_is_size_zoom_constant:new e.aI(t,r.u_is_size_zoom_constant),u_is_size_feature_constant:new e.aI(t,r.u_is_size_feature_constant),u_size_t:new e.aJ(t,r.u_size_t),u_size:new e.aJ(t,r.u_size),u_camera_to_center_distance:new e.aJ(t,r.u_camera_to_center_distance),u_pitch:new e.aJ(t,r.u_pitch),u_rotate_symbol:new e.aI(t,r.u_rotate_symbol),u_aspect_ratio:new e.aJ(t,r.u_aspect_ratio),u_fade_change:new e.aJ(t,r.u_fade_change),u_matrix:new e.aK(t,r.u_matrix),u_label_plane_matrix:new e.aK(t,r.u_label_plane_matrix),u_coord_matrix:new e.aK(t,r.u_coord_matrix),u_is_text:new e.aI(t,r.u_is_text),u_pitch_with_map:new e.aI(t,r.u_pitch_with_map),u_is_along_line:new e.aI(t,r.u_is_along_line),u_is_variable_anchor:new e.aI(t,r.u_is_variable_anchor),u_texsize:new e.aP(t,r.u_texsize),u_texture:new e.aI(t,r.u_texture),u_gamma_scale:new e.aJ(t,r.u_gamma_scale),u_device_pixel_ratio:new e.aJ(t,r.u_device_pixel_ratio),u_is_halo:new e.aI(t,r.u_is_halo),u_translation:new e.aP(t,r.u_translation),u_pitched_scale:new e.aJ(t,r.u_pitched_scale)}),symbolTextAndIcon:(t,r)=>({u_is_size_zoom_constant:new e.aI(t,r.u_is_size_zoom_constant),u_is_size_feature_constant:new e.aI(t,r.u_is_size_feature_constant),u_size_t:new e.aJ(t,r.u_size_t),u_size:new e.aJ(t,r.u_size),u_camera_to_center_distance:new e.aJ(t,r.u_camera_to_center_distance),u_pitch:new e.aJ(t,r.u_pitch),u_rotate_symbol:new e.aI(t,r.u_rotate_symbol),u_aspect_ratio:new e.aJ(t,r.u_aspect_ratio),u_fade_change:new e.aJ(t,r.u_fade_change),u_matrix:new e.aK(t,r.u_matrix),u_label_plane_matrix:new e.aK(t,r.u_label_plane_matrix),u_coord_matrix:new e.aK(t,r.u_coord_matrix),u_is_text:new e.aI(t,r.u_is_text),u_pitch_with_map:new e.aI(t,r.u_pitch_with_map),u_is_along_line:new e.aI(t,r.u_is_along_line),u_is_variable_anchor:new e.aI(t,r.u_is_variable_anchor),u_texsize:new e.aP(t,r.u_texsize),u_texsize_icon:new e.aP(t,r.u_texsize_icon),u_texture:new e.aI(t,r.u_texture),u_texture_icon:new e.aI(t,r.u_texture_icon),u_gamma_scale:new e.aJ(t,r.u_gamma_scale),u_device_pixel_ratio:new e.aJ(t,r.u_device_pixel_ratio),u_is_halo:new e.aI(t,r.u_is_halo),u_translation:new e.aP(t,r.u_translation),u_pitched_scale:new e.aJ(t,r.u_pitched_scale)}),background:(t,r)=>({u_matrix:new e.aK(t,r.u_matrix),u_opacity:new e.aJ(t,r.u_opacity),u_color:new e.aM(t,r.u_color)}),backgroundPattern:(t,r)=>({u_matrix:new e.aK(t,r.u_matrix),u_opacity:new e.aJ(t,r.u_opacity),u_image:new e.aI(t,r.u_image),u_pattern_tl_a:new e.aP(t,r.u_pattern_tl_a),u_pattern_br_a:new e.aP(t,r.u_pattern_br_a),u_pattern_tl_b:new e.aP(t,r.u_pattern_tl_b),u_pattern_br_b:new e.aP(t,r.u_pattern_br_b),u_texsize:new e.aP(t,r.u_texsize),u_mix:new e.aJ(t,r.u_mix),u_pattern_size_a:new e.aP(t,r.u_pattern_size_a),u_pattern_size_b:new e.aP(t,r.u_pattern_size_b),u_scale_a:new e.aJ(t,r.u_scale_a),u_scale_b:new e.aJ(t,r.u_scale_b),u_pixel_coord_upper:new e.aP(t,r.u_pixel_coord_upper),u_pixel_coord_lower:new e.aP(t,r.u_pixel_coord_lower),u_tile_units_to_pixels:new e.aJ(t,r.u_tile_units_to_pixels)}),terrain:(t,r)=>({u_matrix:new e.aK(t,r.u_matrix),u_texture:new e.aI(t,r.u_texture),u_ele_delta:new e.aJ(t,r.u_ele_delta),u_fog_matrix:new e.aK(t,r.u_fog_matrix),u_fog_color:new e.aM(t,r.u_fog_color),u_fog_ground_blend:new e.aJ(t,r.u_fog_ground_blend),u_fog_ground_blend_opacity:new e.aJ(t,r.u_fog_ground_blend_opacity),u_horizon_color:new e.aM(t,r.u_horizon_color),u_horizon_fog_blend:new e.aJ(t,r.u_horizon_fog_blend)}),terrainDepth:(t,r)=>({u_matrix:new e.aK(t,r.u_matrix),u_ele_delta:new e.aJ(t,r.u_ele_delta)}),terrainCoords:(t,r)=>({u_matrix:new e.aK(t,r.u_matrix),u_texture:new e.aI(t,r.u_texture),u_terrain_coords_id:new e.aJ(t,r.u_terrain_coords_id),u_ele_delta:new e.aJ(t,r.u_ele_delta)}),sky:(t,r)=>({u_sky_color:new e.aM(t,r.u_sky_color),u_horizon_color:new e.aM(t,r.u_horizon_color),u_horizon:new e.aJ(t,r.u_horizon),u_sky_horizon_blend:new e.aJ(t,r.u_sky_horizon_blend)})};class Qe{constructor(t,e,r){this.context=t;const n=t.gl;this.buffer=n.createBuffer(),this.dynamicDraw=Boolean(r),this.context.unbindVAO(),t.bindElementBuffer.set(this.buffer),n.bufferData(n.ELEMENT_ARRAY_BUFFER,e.arrayBuffer,this.dynamicDraw?n.DYNAMIC_DRAW:n.STATIC_DRAW),this.dynamicDraw||delete e.arrayBuffer}bind(){this.context.bindElementBuffer.set(this.buffer)}updateData(t){const e=this.context.gl;if(!this.dynamicDraw)throw new Error(\"Attempted to update data while not in dynamic mode.\");this.context.unbindVAO(),this.bind(),e.bufferSubData(e.ELEMENT_ARRAY_BUFFER,0,t.arrayBuffer)}destroy(){const t=this.context.gl;this.buffer&&(t.deleteBuffer(this.buffer),delete this.buffer)}}const tr={Int8:\"BYTE\",Uint8:\"UNSIGNED_BYTE\",Int16:\"SHORT\",Uint16:\"UNSIGNED_SHORT\",Int32:\"INT\",Uint32:\"UNSIGNED_INT\",Float32:\"FLOAT\"};class er{constructor(t,e,r,n){this.length=e.length,this.attributes=r,this.itemSize=e.bytesPerElement,this.dynamicDraw=n,this.context=t;const i=t.gl;this.buffer=i.createBuffer(),t.bindVertexBuffer.set(this.buffer),i.bufferData(i.ARRAY_BUFFER,e.arrayBuffer,this.dynamicDraw?i.DYNAMIC_DRAW:i.STATIC_DRAW),this.dynamicDraw||delete e.arrayBuffer}bind(){this.context.bindVertexBuffer.set(this.buffer)}updateData(t){if(t.length!==this.length)throw new Error(`Length of new data is ${t.length}, which doesn't match current length of ${this.length}`);const e=this.context.gl;this.bind(),e.bufferSubData(e.ARRAY_BUFFER,0,t.arrayBuffer)}enableAttributes(t,e){for(let r=0;r0){const r=e.H();e.aR(r,d.placementInvProjMatrix,t.transform.glCoordMatrix),e.aR(r,r,d.placementViewportMatrix),c.push({circleArray:g,circleOffset:h,transform:p.posMatrix,invTransform:r,coord:p}),u+=g.length/4,h=u}m&&l.draw(o,s.LINES,qr.disabled,Gr.disabled,t.colorModeForRenderPass(),Zr.disabled,Le(t.transform,p.posMatrix),t.style.map.terrain&&t.style.map.terrain.getTerrainData(p),n.id,m.layoutVertexBuffer,m.indexBuffer,m.segments,null,t.transform.zoom,null,null,m.collisionVertexBuffer)}if(!a||!c.length)return;const f=t.useProgram(\"collisionCircle\"),p=new e.aS;p.resize(4*u),p._trim();let d=0;for(const t of c)for(let e=0;er.style.map.terrain.getElevation(a,t,e):null,i=h.translatePosition(u,t,s,l);Qr(o,f,p,c,u,y,a.posMatrix,e,m,v,h,i,a.toUnwrapped(),n)}}}(i,t,n,r,n.layout.get(\"text-rotation-alignment\"),n.layout.get(\"text-pitch-alignment\"),n.paint.get(\"text-translate\"),n.paint.get(\"text-translate-anchor\"),a),0!==n.paint.get(\"icon-opacity\").constantOr(1)&&en(t,r,n,i,!1,n.paint.get(\"icon-translate\"),n.paint.get(\"icon-translate-anchor\"),n.layout.get(\"icon-rotation-alignment\"),n.layout.get(\"icon-pitch-alignment\"),n.layout.get(\"icon-keep-upright\"),o,s),0!==n.paint.get(\"text-opacity\").constantOr(1)&&en(t,r,n,i,!0,n.paint.get(\"text-translate\"),n.paint.get(\"text-translate-anchor\"),n.layout.get(\"text-rotation-alignment\"),n.layout.get(\"text-pitch-alignment\"),n.layout.get(\"text-keep-upright\"),o,s),r.map.showCollisionBoxes&&(Yr(t,r,n,i,!0),Yr(t,r,n,i,!1))}function Jr(t,r,n,i,a,o){const{horizontalAlign:s,verticalAlign:l}=e.av(t),c=-(s-.5)*r,u=-(l-.5)*n;return new e.P((c/a+i[0])*o,(u/a+i[1])*o)}function Kr(t,r,n,i,a,o){const s=r.tileAnchorPoint.add(new e.P(r.translation[0],r.translation[1]));if(r.pitchWithMap){let t=i.mult(o);return n||(t=t.rotate(-a)),wt(s.add(t),r.labelPlaneMatrix,r.getElevation).point}if(n){const e=It(r.tileAnchorPoint.x+1,r.tileAnchorPoint.y,r).point.sub(t),n=Math.atan(e.y/e.x)+(e.x<0?Math.PI:0);return t.add(i.rotate(n))}return t.add(i)}function Qr(t,r,n,i,a,o,s,l,c,u,h,f,p,d){const m=t.text.placedSymbolArray,g=t.text.dynamicLayoutVertexArray,y=t.icon.dynamicLayoutVertexArray,v={};g.clear();for(let y=0;y=0&&(v[x.associatedIconIndex]={shiftedAnchor:L,angle:I})}else Rt(x.numGlyphs,g)}if(u){y.clear();const r=t.icon.placedSymbolArray;for(let t=0;tt.style.map.terrain.getElevation(l,e,r):null,r=\"map\"===n.layout.get(\"text-rotation-alignment\");At(c,l.posMatrix,t,a,j,V,v,u,r,g,l.toUnwrapped(),m.width,m.height,q,e)}const Z=l.posMatrix,W=a&&A||G,Y=x||W?Xr:j,X=U,$=p&&0!==n.paint.get(a?\"text-halo-width\":\"icon-halo-width\").constantOr(1);let J;J=p?c.iconsInText?Xe(k.kind,L,_,v,x,W,t,Z,Y,X,q,P,R,S):Ye(k.kind,L,_,v,x,W,t,Z,Y,X,q,a,P,!0,S):We(k.kind,L,_,v,x,W,t,Z,Y,X,q,a,P,S);const K={program:C,buffers:h,uniformValues:J,atlasTexture:z,atlasTextureIcon:F,atlasInterpolation:O,atlasInterpolationIcon:D,isSDF:p,hasHalo:$};if(w&&c.canOverlap){T=!0;const t=h.segments.get();for(const r of t)M.push({segments:new e.a0([r]),sortKey:r.sortKey,state:K,terrainData:I})}else M.push({segments:h.segments,sortKey:0,state:K,terrainData:I})}T&&M.sort(((t,e)=>t.sortKey-e.sortKey));for(const e of M){const r=e.state;if(p.activeTexture.set(d.TEXTURE0),r.atlasTexture.bind(r.atlasInterpolation,d.CLAMP_TO_EDGE),r.atlasTextureIcon&&(p.activeTexture.set(d.TEXTURE1),r.atlasTextureIcon&&r.atlasTextureIcon.bind(r.atlasInterpolationIcon,d.CLAMP_TO_EDGE)),r.isSDF){const i=r.uniformValues;r.hasHalo&&(i.u_is_halo=1,rn(r.buffers,e.segments,n,t,r.program,k,h,f,i,e.terrainData)),i.u_is_halo=0}rn(r.buffers,e.segments,n,t,r.program,k,h,f,r.uniformValues,e.terrainData)}}function rn(t,e,r,n,i,a,o,s,l,c){const u=n.context,h=u.gl;i.draw(u,h.TRIANGLES,a,o,s,Zr.disabled,l,c,r.id,t.layoutVertexBuffer,t.indexBuffer,e,r.paint,n.transform.zoom,t.programConfigurations.get(r.id),t.dynamicLayoutVertexBuffer,t.opacityVertexBuffer)}function nn(t,r,n,i){if(0!==n.paint.get(\"heatmap-opacity\"))if(\"offscreen\"===t.renderPass){const a=t.context,o=a.gl,s=Gr.disabled,l=new Ur([o.ONE,o.ONE],e.aN.transparent,[!0,!0,!0,!0]);(function(t,e,r){const n=t.gl;t.activeTexture.set(n.TEXTURE1),t.viewport.set([0,0,e.width/4,e.height/4]);let i=r.heatmapFbo;if(i)n.bindTexture(n.TEXTURE_2D,i.colorAttachment.get()),t.bindFramebuffer.set(i.framebuffer);else{const a=n.createTexture();n.bindTexture(n.TEXTURE_2D,a),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_S,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_WRAP_T,n.CLAMP_TO_EDGE),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MIN_FILTER,n.LINEAR),n.texParameteri(n.TEXTURE_2D,n.TEXTURE_MAG_FILTER,n.LINEAR),i=r.heatmapFbo=t.createFramebuffer(e.width/4,e.height/4,!1,!1),function(t,e,r,n){var i,a;const o=t.gl,s=null!==(i=t.HALF_FLOAT)&&void 0!==i?i:o.UNSIGNED_BYTE,l=null!==(a=t.RGBA16F)&&void 0!==a?a:o.RGBA;o.texImage2D(o.TEXTURE_2D,0,l,e.width/4,e.height/4,0,o.RGBA,s,null),n.colorAttachment.set(r)}(t,e,a,i)}})(a,t,n),a.clear({color:e.aN.transparent});for(let e=0;e0){const i=a.now(),s=(i-t.timeAdded)/l,c=r?(i-r.timeAdded)/l:-1,u=n.getSource(),h=o.coveringZoomLevel({tileSize:u.tileSize,roundZoom:u.roundZoom}),f=!r||Math.abs(r.tileID.overscaledZ-h)>Math.abs(t.tileID.overscaledZ-h),p=f&&t.refreshedUponExpiration?1:e.ad(f?s:1-c,0,1);return t.refreshedUponExpiration&&s>=1&&(t.refreshedUponExpiration=!1),r?{opacity:1,mix:1-p}:{opacity:p,mix:0}}return{opacity:1,mix:0}}const hn=new e.aN(1,0,0,1),fn=new e.aN(0,1,0,1),pn=new e.aN(0,0,1,1),dn=new e.aN(1,0,1,1),mn=new e.aN(0,1,1,1);function gn(t){const e=t.transform.padding;yn(t,t.transform.height-(e.top||0),3,hn),yn(t,e.bottom||0,3,fn),vn(t,e.left||0,3,pn),vn(t,t.transform.width-(e.right||0),3,dn);const r=t.transform.centerPoint;!function(t,e,r,n){const i=20,a=2;xn(t,e-a/2,r-i/2,a,i,n),xn(t,e-i/2,r-a/2,i,a,n)}(t,r.x,t.transform.height-r.y,mn)}function yn(t,e,r,n){xn(t,0,e+r/2,t.transform.width,r,n)}function vn(t,e,r,n){xn(t,e-r/2,0,r,t.transform.height,n)}function xn(t,e,r,n,i,a){const o=t.context,s=o.gl;s.enable(s.SCISSOR_TEST),s.scissor(e*t.pixelRatio,r*t.pixelRatio,n*t.pixelRatio,i*t.pixelRatio),o.clear({color:a}),s.disable(s.SCISSOR_TEST)}function _n(t,r,n){const i=t.context,a=i.gl,o=n.posMatrix,s=t.useProgram(\"debug\"),l=qr.disabled,c=Gr.disabled,u=t.colorModeForRenderPass(),h=\"$debug\",f=t.style.map.terrain&&t.style.map.terrain.getTerrainData(n);i.activeTexture.set(a.TEXTURE0);const p=r.getTileByID(n.key).latestRawTileData,d=p&&p.byteLength||0,m=Math.floor(d/1024),g=r.getTile(n).tileSize,y=512/Math.min(g,512)*(n.overscaledZ/t.transform.zoom)*.5;let v=n.canonical.toString();n.overscaledZ!==n.canonical.z&&(v+=` => ${n.overscaledZ}`),function(t,e){t.initDebugOverlayCanvas();const r=t.debugOverlayCanvas,n=t.context.gl,i=t.debugOverlayCanvas.getContext(\"2d\");i.clearRect(0,0,r.width,r.height),i.shadowColor=\"white\",i.shadowBlur=2,i.lineWidth=1.5,i.strokeStyle=\"white\",i.textBaseline=\"top\",i.font=\"bold 36px Open Sans, sans-serif\",i.fillText(e,5,5),i.strokeText(e,5,5),t.debugOverlayTexture.update(r),t.debugOverlayTexture.bind(n.LINEAR,n.CLAMP_TO_EDGE)}(t,`${v} ${m}kB`),s.draw(i,a.TRIANGLES,l,c,Ur.alphaBlended,Zr.disabled,Pe(o,e.aN.transparent,y),null,h,t.debugBuffer,t.quadTriangleIndexBuffer,t.debugSegments),s.draw(i,a.LINE_STRIP,l,c,u,Zr.disabled,Pe(o,e.aN.red),f,h,t.debugBuffer,t.tileBorderIndexBuffer,t.debugSegments)}function bn(t,e,r){const n=t.context,i=n.gl,a=t.colorModeForRenderPass(),o=new qr(i.LEQUAL,qr.ReadWrite,t.depthRangeFor3D),s=t.useProgram(\"terrain\"),l=e.getTerrainMesh();n.bindFramebuffer.set(null),n.viewport.set([0,0,t.width,t.height]);for(const c of r){const r=t.renderToTexture.getTexture(c),u=e.getTerrainData(c.tileID);n.activeTexture.set(i.TEXTURE0),i.bindTexture(i.TEXTURE_2D,r.texture);const h=t.transform.calculatePosMatrix(c.tileID.toUnwrapped()),f=e.getMeshFrameDelta(t.transform.zoom),p=t.transform.calculateFogMatrix(c.tileID.toUnwrapped()),d=xe(h,f,p,t.style.sky,t.transform.pitch);s.draw(n,i.TRIANGLES,o,Gr.disabled,a,Zr.backCCW,d,u,\"terrain\",l.vertexBuffer,l.indexBuffer,l.segments)}}class wn{constructor(t,e,r){this.vertexBuffer=t,this.indexBuffer=e,this.segments=r}destroy(){this.vertexBuffer.destroy(),this.indexBuffer.destroy(),this.segments.destroy(),this.vertexBuffer=null,this.indexBuffer=null,this.segments=null}}class Tn{constructor(t,r){this.context=new Vr(t),this.transform=r,this._tileTextures={},this.terrainFacilitator={dirty:!0,matrix:e.ao(new Float64Array(16)),renderTime:0},this.setup(),this.numSublayers=dt.maxUnderzooming+dt.maxOverzooming+1,this.depthEpsilon=1/Math.pow(2,16),this.crossTileSymbolIndex=new he}resize(t,e,r){if(this.width=Math.floor(t*r),this.height=Math.floor(e*r),this.pixelRatio=r,this.context.viewport.set([0,0,this.width,this.height]),this.style)for(const t of this.style._order)this.style._layers[t].resize()}setup(){const t=this.context,r=new e.aX;r.emplaceBack(0,0),r.emplaceBack(e.X,0),r.emplaceBack(0,e.X),r.emplaceBack(e.X,e.X),this.tileExtentBuffer=t.createVertexBuffer(r,me.members),this.tileExtentSegments=e.a0.simpleSegment(0,0,4,2);const n=new e.aX;n.emplaceBack(0,0),n.emplaceBack(e.X,0),n.emplaceBack(0,e.X),n.emplaceBack(e.X,e.X),this.debugBuffer=t.createVertexBuffer(n,me.members),this.debugSegments=e.a0.simpleSegment(0,0,4,5);const i=new e.$;i.emplaceBack(0,0,0,0),i.emplaceBack(e.X,0,e.X,0),i.emplaceBack(0,e.X,0,e.X),i.emplaceBack(e.X,e.X,e.X,e.X),this.rasterBoundsBuffer=t.createVertexBuffer(i,et.members),this.rasterBoundsSegments=e.a0.simpleSegment(0,0,4,2);const a=new e.aX;a.emplaceBack(0,0),a.emplaceBack(1,0),a.emplaceBack(0,1),a.emplaceBack(1,1),this.viewportBuffer=t.createVertexBuffer(a,me.members),this.viewportSegments=e.a0.simpleSegment(0,0,4,2);const o=new e.aZ;o.emplaceBack(0),o.emplaceBack(1),o.emplaceBack(3),o.emplaceBack(2),o.emplaceBack(0),this.tileBorderIndexBuffer=t.createIndexBuffer(o);const s=new e.aY;s.emplaceBack(0,1,2),s.emplaceBack(2,1,3),this.quadTriangleIndexBuffer=t.createIndexBuffer(s);const l=this.context.gl;this.stencilClearMode=new Gr({func:l.ALWAYS,mask:0},0,255,l.ZERO,l.ZERO,l.ZERO)}clearStencil(){const t=this.context,r=t.gl;this.nextStencilID=1,this.currentStencilSource=void 0;const n=e.H();e.aQ(n,0,this.width,this.height,0,0,1),e.K(n,n,[r.drawingBufferWidth,r.drawingBufferHeight,0]),this.useProgram(\"clippingMask\").draw(t,r.TRIANGLES,qr.disabled,this.stencilClearMode,Ur.disabled,Zr.disabled,ze(n),null,\"$clipping\",this.viewportBuffer,this.quadTriangleIndexBuffer,this.viewportSegments)}_renderTileClippingMasks(t,e){if(this.currentStencilSource===t.source||!t.isTileClipped()||!e||!e.length)return;this.currentStencilSource=t.source;const r=this.context,n=r.gl;this.nextStencilID+e.length>256&&this.clearStencil(),r.setColorMode(Ur.disabled),r.setDepthMode(qr.disabled);const i=this.useProgram(\"clippingMask\");this._tileClippingMaskIDs={};for(const t of e){const e=this._tileClippingMaskIDs[t.key]=this.nextStencilID++,a=this.style.map.terrain&&this.style.map.terrain.getTerrainData(t);i.draw(r,n.TRIANGLES,qr.disabled,new Gr({func:n.ALWAYS,mask:0},e,255,n.KEEP,n.KEEP,n.REPLACE),Ur.disabled,Zr.disabled,ze(t.posMatrix),a,\"$clipping\",this.tileExtentBuffer,this.quadTriangleIndexBuffer,this.tileExtentSegments)}}stencilModeFor3D(){this.currentStencilSource=void 0,this.nextStencilID+1>256&&this.clearStencil();const t=this.nextStencilID++,e=this.context.gl;return new Gr({func:e.NOTEQUAL,mask:255},t,255,e.KEEP,e.KEEP,e.REPLACE)}stencilModeForClipping(t){const e=this.context.gl;return new Gr({func:e.EQUAL,mask:255},this._tileClippingMaskIDs[t.key],0,e.KEEP,e.KEEP,e.REPLACE)}stencilConfigForOverlap(t){const e=this.context.gl,r=t.sort(((t,e)=>e.overscaledZ-t.overscaledZ)),n=r[r.length-1].overscaledZ,i=r[0].overscaledZ-n+1;if(i>1){this.currentStencilSource=void 0,this.nextStencilID+i>256&&this.clearStencil();const t={};for(let r=0;r({u_sky_color:t.properties.get(\"sky-color\"),u_horizon_color:t.properties.get(\"horizon-color\"),u_horizon:(e.height/2+e.getHorizon())*r,u_sky_horizon_blend:t.properties.get(\"sky-horizon-blend\")*e.height/2*r}))(r,t.style.map.transform,t.pixelRatio),o=new qr(i.LEQUAL,qr.ReadWrite,[0,1]),s=Gr.disabled,l=t.colorModeForRenderPass(),c=t.useProgram(\"sky\");if(!r.mesh){const t=new e.aX;t.emplaceBack(-1,-1),t.emplaceBack(1,-1),t.emplaceBack(1,1),t.emplaceBack(-1,1);const i=new e.aY;i.emplaceBack(0,1,2),i.emplaceBack(0,2,3),r.mesh=new wn(n.createVertexBuffer(t,me.members),n.createIndexBuffer(i),e.a0.simpleSegment(0,0,t.length,i.length))}c.draw(n,i.TRIANGLES,o,s,l,Zr.disabled,a,void 0,\"sky\",r.mesh.vertexBuffer,r.mesh.indexBuffer,r.mesh.segments)}(this,this.style.sky),this._showOverdrawInspector=r.showOverdrawInspector,this.depthRangeFor3D=[0,1-(t._order.length+2)*this.numSublayers*this.depthEpsilon],!this.renderToTexture)for(this.renderPass=\"opaque\",this.currentLayer=i.length-1;this.currentLayer>=0;this.currentLayer--){const t=this.style._layers[i[this.currentLayer]],e=o[t.source],r=s[t.source];this._renderTileClippingMasks(t,r),this.renderLayer(this,e,t,r)}for(this.renderPass=\"translucent\",this.currentLayer=0;this.currentLayerr.source&&!r.isHidden(e)?[t.sourceCaches[r.source]]:[])),i=n.filter((t=>\"vector\"===t.getSource().type)),a=n.filter((t=>\"vector\"!==t.getSource().type)),o=t=>{(!r||r.getSource().maxzoomo(t))),r||a.forEach((t=>o(t))),r}(this.style,this.transform.zoom);t&&function(t,e,r){for(let n=0;n0),i&&(e.b0(r,n),this.terrainFacilitator.renderTime=Date.now(),this.terrainFacilitator.dirty=!1,function(t,r){const n=t.context,i=n.gl,a=Ur.unblended,o=new qr(i.LEQUAL,qr.ReadWrite,[0,1]),s=r.getTerrainMesh(),l=r.sourceCache.getRenderableTiles(),c=t.useProgram(\"terrainDepth\");n.bindFramebuffer.set(r.getFramebuffer(\"depth\").framebuffer),n.viewport.set([0,0,t.width/devicePixelRatio,t.height/devicePixelRatio]),n.clear({color:e.aN.transparent,depth:1});for(const e of l){const l=r.getTerrainData(e.tileID),u={u_matrix:t.transform.calculatePosMatrix(e.tileID.toUnwrapped()),u_ele_delta:r.getMeshFrameDelta(t.transform.zoom)};c.draw(n,i.TRIANGLES,o,Gr.disabled,a,Zr.backCCW,u,l,\"terrain\",s.vertexBuffer,s.indexBuffer,s.segments)}n.bindFramebuffer.set(null),n.viewport.set([0,0,t.width,t.height])}(this,this.style.map.terrain),function(t,r){const n=t.context,i=n.gl,a=Ur.unblended,o=new qr(i.LEQUAL,qr.ReadWrite,[0,1]),s=r.getTerrainMesh(),l=r.getCoordsTexture(),c=r.sourceCache.getRenderableTiles(),u=t.useProgram(\"terrainCoords\");n.bindFramebuffer.set(r.getFramebuffer(\"coords\").framebuffer),n.viewport.set([0,0,t.width/devicePixelRatio,t.height/devicePixelRatio]),n.clear({color:e.aN.transparent,depth:1}),r.coordsIndex=[];for(const e of c){const c=r.getTerrainData(e.tileID);n.activeTexture.set(i.TEXTURE0),i.bindTexture(i.TEXTURE_2D,l.texture);const h={u_matrix:t.transform.calculatePosMatrix(e.tileID.toUnwrapped()),u_terrain_coords_id:(255-r.coordsIndex.length)/255,u_texture:0,u_ele_delta:r.getMeshFrameDelta(t.transform.zoom)};u.draw(n,i.TRIANGLES,o,Gr.disabled,a,Zr.backCCW,h,c,\"terrain\",s.vertexBuffer,s.indexBuffer,s.segments),r.coordsIndex.push(e.tileID.key)}n.bindFramebuffer.set(null),n.viewport.set([0,0,t.width,t.height])}(this,this.style.map.terrain))}renderLayer(t,r,n,i){if(!n.isHidden(this.transform.zoom)&&(\"background\"===n.type||\"custom\"===n.type||(i||[]).length))switch(this.id=n.id,n.type){case\"symbol\":$r(t,r,n,i,this.style.placement.variableOffsets);break;case\"circle\":!function(t,r,n,i){if(\"translucent\"!==t.renderPass)return;const a=n.paint.get(\"circle-opacity\"),o=n.paint.get(\"circle-stroke-width\"),s=n.paint.get(\"circle-stroke-opacity\"),l=!n.layout.get(\"circle-sort-key\").isConstant();if(0===a.constantOr(1)&&(0===o.constantOr(1)||0===s.constantOr(1)))return;const c=t.context,u=c.gl,h=t.depthModeForSublayer(0,qr.ReadOnly),f=Gr.disabled,p=t.colorModeForRenderPass(),d=[];for(let a=0;at.sortKey-e.sortKey));for(const e of d){const{programConfiguration:r,program:i,layoutVertexBuffer:a,indexBuffer:o,uniformValues:s,terrainData:l}=e.state,d=e.segments;i.draw(c,u.TRIANGLES,h,f,p,Zr.disabled,s,l,n.id,a,o,d,n.paint,t.transform.zoom,r)}}(t,r,n,i);break;case\"heatmap\":nn(t,r,n,i);break;case\"line\":!function(t,r,n,i){if(\"translucent\"!==t.renderPass)return;const a=n.paint.get(\"line-opacity\"),o=n.paint.get(\"line-width\");if(0===a.constantOr(1)||0===o.constantOr(1))return;const s=t.depthModeForSublayer(0,qr.ReadOnly),l=t.colorModeForRenderPass(),c=n.paint.get(\"line-dasharray\"),u=n.paint.get(\"line-pattern\"),h=u.constantOr(1),f=n.paint.get(\"line-gradient\"),p=n.getCrossfadeParameters(),d=h?\"linePattern\":c?\"lineSDF\":f?\"lineGradient\":\"line\",m=t.context,g=m.gl;let y=!0;for(const a of i){const i=r.getTile(a);if(h&&!i.patternsLoaded())continue;const o=i.getBucket(n);if(!o)continue;const v=o.programConfigurations.get(n.id),x=t.context.program.get(),_=t.useProgram(d,v),b=y||_.program!==x,T=t.style.map.terrain&&t.style.map.terrain.getTerrainData(a),k=u.constantOr(null);if(k&&i.imageAtlas){const t=i.imageAtlas,e=t.patternPositions[k.to.toString()],r=t.patternPositions[k.from.toString()];e&&r&&v.setConstantPatternPositions(e,r)}const A=T?a:null,M=h?Ue(t,i,n,p,A):c?Ve(t,i,n,c,p,A):f?je(t,i,n,o.lineClipsArray.length,A):Ne(t,i,n,A);if(h)m.activeTexture.set(g.TEXTURE0),i.imageAtlasTexture.bind(g.LINEAR,g.CLAMP_TO_EDGE),v.updatePaintBuffers(p);else if(c&&(b||t.lineAtlas.dirty))m.activeTexture.set(g.TEXTURE0),t.lineAtlas.bind(m);else if(f){const i=o.gradients[n.id];let s=i.texture;if(n.gradientVersion!==i.version){let l=256;if(n.stepInterpolant){const n=r.getSource().maxzoom,i=a.canonical.z===n?Math.ceil(1<20&&a.texParameterf(a.TEXTURE_2D,i.extTextureFilterAnisotropic.TEXTURE_MAX_ANISOTROPY_EXT,i.extTextureFilterAnisotropicMax);const _=t.style.map.terrain&&t.style.map.terrain.getTerrainData(n),b=_?n:null,w=b?b.posMatrix:t.transform.calculatePosMatrix(n.toUnwrapped(),f),T=Ge(w,v||[0,0],y||1,g,r);o instanceof rt?s.draw(i,a.TRIANGLES,u,Gr.disabled,l,Zr.disabled,T,_,r.id,o.boundsBuffer,t.quadTriangleIndexBuffer,o.boundsSegments):s.draw(i,a.TRIANGLES,u,c[n.overscaledZ],l,Zr.disabled,T,_,r.id,t.rasterBoundsBuffer,t.quadTriangleIndexBuffer,t.rasterBoundsSegments)}}(t,r,n,i);break;case\"background\":!function(t,e,r,n){const i=r.paint.get(\"background-color\"),a=r.paint.get(\"background-opacity\");if(0===a)return;const o=t.context,s=o.gl,l=t.transform,c=l.tileSize,u=r.paint.get(\"background-pattern\");if(t.isPatternMissing(u))return;const h=!u&&1===i.a&&1===a&&t.opaquePassEnabledForLayer()?\"opaque\":\"translucent\";if(t.renderPass!==h)return;const f=Gr.disabled,p=t.depthModeForSublayer(0,\"opaque\"===h?qr.ReadWrite:qr.ReadOnly),d=t.colorModeForRenderPass(),m=t.useProgram(u?\"backgroundPattern\":\"background\"),g=n||l.coveringTiles({tileSize:c,terrain:t.style.map.terrain});u&&(o.activeTexture.set(s.TEXTURE0),t.imageManager.bind(t.context));const y=r.getCrossfadeParameters();for(const e of g){const l=n?e.posMatrix:t.transform.calculatePosMatrix(e.toUnwrapped()),h=u?Je(l,a,t,u,{tileID:e,tileSize:c},y):$e(l,a,i),g=t.style.map.terrain&&t.style.map.terrain.getTerrainData(e);m.draw(o,s.TRIANGLES,p,f,d,Zr.disabled,h,g,r.id,t.tileExtentBuffer,t.quadTriangleIndexBuffer,t.tileExtentSegments)}}(t,0,n,i);break;case\"custom\":!function(t,e,r){const n=t.context,i=r.implementation;if(\"offscreen\"===t.renderPass){const e=i.prerender;e&&(t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),e.call(i,n.gl,t.transform.customLayerMatrix()),n.setDirty(),t.setBaseState())}else if(\"translucent\"===t.renderPass){t.setCustomLayerDefaults(),n.setColorMode(t.colorModeForRenderPass()),n.setStencilMode(Gr.disabled);const e=\"3d\"===i.renderingMode?new qr(t.context.gl.LEQUAL,qr.ReadWrite,t.depthRangeFor3D):t.depthModeForSublayer(0,qr.ReadOnly);n.setDepthMode(e),i.render(n.gl,t.transform.customLayerMatrix(),{farZ:t.transform.farZ,nearZ:t.transform.nearZ,fov:t.transform._fov,modelViewProjectionMatrix:t.transform.modelViewProjectionMatrix,projectionMatrix:t.transform.projectionMatrix}),n.setDirty(),t.setBaseState(),n.bindFramebuffer.set(null)}}(t,0,n)}}translatePosMatrix(t,r,n,i,a){if(!n[0]&&!n[1])return t;const o=a?\"map\"===i?this.transform.angle:0:\"viewport\"===i?-this.transform.angle:0;if(o){const t=Math.sin(o),e=Math.cos(o);n=[n[0]*e-n[1]*t,n[0]*t+n[1]*e]}const s=[a?n[0]:Nt(r,n[0],this.transform.zoom),a?n[1]:Nt(r,n[1],this.transform.zoom),0],l=new Float32Array(16);return e.J(l,t,s),l}saveTileTexture(t){const e=this._tileTextures[t.size[0]];e?e.push(t):this._tileTextures[t.size[0]]=[t]}getTileTexture(t){const e=this._tileTextures[t];return e&&e.length>0?e.pop():null}isPatternMissing(t){if(!t)return!1;if(!t.from||!t.to)return!0;const e=this.imageManager.getPattern(t.from.toString()),r=this.imageManager.getPattern(t.to.toString());return!e||!r}useProgram(t,e){this.cache=this.cache||{};const r=t+(e?e.cacheKey:\"\")+(this._showOverdrawInspector?\"/overdraw\":\"\")+(this.style.map.terrain?\"/terrain\":\"\");return this.cache[r]||(this.cache[r]=new be(this.context,ge[t],e,Ke[t],this._showOverdrawInspector,this.style.map.terrain)),this.cache[r]}setCustomLayerDefaults(){this.context.unbindVAO(),this.context.cullFace.setDefault(),this.context.activeTexture.setDefault(),this.context.pixelStoreUnpack.setDefault(),this.context.pixelStoreUnpackPremultiplyAlpha.setDefault(),this.context.pixelStoreUnpackFlipY.setDefault()}setBaseState(){const t=this.context.gl;this.context.cullFace.set(!1),this.context.viewport.set([0,0,this.width,this.height]),this.context.blendEquation.set(t.FUNC_ADD)}initDebugOverlayCanvas(){if(null==this.debugOverlayCanvas){this.debugOverlayCanvas=document.createElement(\"canvas\"),this.debugOverlayCanvas.width=512,this.debugOverlayCanvas.height=512;const t=this.context.gl;this.debugOverlayTexture=new w(this.context,this.debugOverlayCanvas,t.RGBA)}}destroy(){this.debugOverlayTexture&&this.debugOverlayTexture.destroy()}overLimit(){const{drawingBufferWidth:t,drawingBufferHeight:e}=this.context.gl;return this.width!==t||this.height!==e}}class kn{constructor(t,e){this.points=t,this.planes=e}static fromInvProjectionMatrix(t,r,n){const i=Math.pow(2,n),a=[[-1,1,-1,1],[1,1,-1,1],[1,-1,-1,1],[-1,-1,-1,1],[-1,1,1,1],[1,1,1,1],[1,-1,1,1],[-1,-1,1,1]].map((n=>{const a=1/(n=e.ag([],n,t))[3]/r*i;return e.b1(n,n,[a,a,1/n[3],a])})),o=[[0,1,2],[6,5,4],[0,3,7],[2,1,5],[3,2,6],[0,4,5]].map((t=>{const e=function(t,e){var r=e[0],n=e[1],i=e[2],a=r*r+n*n+i*i;return a>0&&(a=1/Math.sqrt(a)),t[0]=e[0]*a,t[1]=e[1]*a,t[2]=e[2]*a,t}([],function(t,e,r){var n=e[0],i=e[1],a=e[2],o=r[0],s=r[1],l=r[2];return t[0]=i*l-a*s,t[1]=a*o-n*l,t[2]=n*s-i*o,t}([],y([],a[t[0]],a[t[1]]),y([],a[t[2]],a[t[1]]))),r=(n=e,i=a[t[1]],-(n[0]*i[0]+n[1]*i[1]+n[2]*i[2]));var n,i;return e.concat(r)}));return new kn(a,o)}}class An{constructor(t,e){this.min=t,this.max=e,this.center=function(t,e,r){return t[0]=e[0]*r,t[1]=e[1]*r,t[2]=e[2]*r,t}([],function(t,e,r){return t[0]=e[0]+r[0],t[1]=e[1]+r[1],t[2]=e[2]+r[2],t}([],this.min,this.max),.5)}quadrant(t){const e=[t%2==0,t<2],r=m(this.min),n=m(this.max);for(let t=0;t=0&&o++;if(0===o)return 0;o!==r.length&&(n=!1)}if(n)return 2;for(let e=0;e<3;e++){let r=Number.MAX_VALUE,n=-Number.MAX_VALUE;for(let i=0;ithis.max[e]-this.min[e])return 0}return 1}}class Mn{constructor(t=0,e=0,r=0,n=0){if(isNaN(t)||t<0||isNaN(e)||e<0||isNaN(r)||r<0||isNaN(n)||n<0)throw new Error(\"Invalid value for edge-insets, top, bottom, left and right must all be numbers\");this.top=t,this.bottom=e,this.left=r,this.right=n}interpolate(t,r,n){return null!=r.top&&null!=t.top&&(this.top=e.z.number(t.top,r.top,n)),null!=r.bottom&&null!=t.bottom&&(this.bottom=e.z.number(t.bottom,r.bottom,n)),null!=r.left&&null!=t.left&&(this.left=e.z.number(t.left,r.left,n)),null!=r.right&&null!=t.right&&(this.right=e.z.number(t.right,r.right,n)),this}getCenter(t,r){const n=e.ad((this.left+t-this.right)/2,0,t),i=e.ad((this.top+r-this.bottom)/2,0,r);return new e.P(n,i)}equals(t){return this.top===t.top&&this.bottom===t.bottom&&this.left===t.left&&this.right===t.right}clone(){return new Mn(this.top,this.bottom,this.left,this.right)}toJSON(){return{top:this.top,bottom:this.bottom,left:this.left,right:this.right}}}const Sn=85.051129;class En{constructor(t,r,n,i,a){this.tileSize=512,this._renderWorldCopies=void 0===a||!!a,this._minZoom=t||0,this._maxZoom=r||22,this._minPitch=null==n?0:n,this._maxPitch=null==i?60:i,this.setMaxBounds(),this.width=0,this.height=0,this._center=new e.N(0,0),this._elevation=0,this.zoom=0,this.angle=0,this._fov=.6435011087932844,this._pitch=0,this._unmodified=!0,this._edgeInsets=new Mn,this._posMatrixCache={},this._alignedPosMatrixCache={},this._fogMatrixCache={},this.minElevationForCurrentTile=0}clone(){const t=new En(this._minZoom,this._maxZoom,this._minPitch,this.maxPitch,this._renderWorldCopies);return t.apply(this),t}apply(t){this.tileSize=t.tileSize,this.latRange=t.latRange,this.width=t.width,this.height=t.height,this._center=t._center,this._elevation=t._elevation,this.minElevationForCurrentTile=t.minElevationForCurrentTile,this.zoom=t.zoom,this.angle=t.angle,this._fov=t._fov,this._pitch=t._pitch,this._unmodified=t._unmodified,this._edgeInsets=t._edgeInsets.clone(),this._calcMatrices()}get minZoom(){return this._minZoom}set minZoom(t){this._minZoom!==t&&(this._minZoom=t,this.zoom=Math.max(this.zoom,t))}get maxZoom(){return this._maxZoom}set maxZoom(t){this._maxZoom!==t&&(this._maxZoom=t,this.zoom=Math.min(this.zoom,t))}get minPitch(){return this._minPitch}set minPitch(t){this._minPitch!==t&&(this._minPitch=t,this.pitch=Math.max(this.pitch,t))}get maxPitch(){return this._maxPitch}set maxPitch(t){this._maxPitch!==t&&(this._maxPitch=t,this.pitch=Math.min(this.pitch,t))}get renderWorldCopies(){return this._renderWorldCopies}set renderWorldCopies(t){void 0===t?t=!0:null===t&&(t=!1),this._renderWorldCopies=t}get worldSize(){return this.tileSize*this.scale}get centerOffset(){return this.centerPoint._sub(this.size._div(2))}get size(){return new e.P(this.width,this.height)}get bearing(){return-this.angle/Math.PI*180}set bearing(t){const r=-e.b3(t,-180,180)*Math.PI/180;this.angle!==r&&(this._unmodified=!1,this.angle=r,this._calcMatrices(),this.rotationMatrix=function(){var t=new e.A(4);return e.A!=Float32Array&&(t[1]=0,t[2]=0),t[0]=1,t[3]=1,t}(),function(t,e,r){var n=e[0],i=e[1],a=e[2],o=e[3],s=Math.sin(r),l=Math.cos(r);t[0]=n*l+a*s,t[1]=i*l+o*s,t[2]=n*-s+a*l,t[3]=i*-s+o*l}(this.rotationMatrix,this.rotationMatrix,this.angle))}get pitch(){return this._pitch/Math.PI*180}set pitch(t){const r=e.ad(t,this.minPitch,this.maxPitch)/180*Math.PI;this._pitch!==r&&(this._unmodified=!1,this._pitch=r,this._calcMatrices())}get fov(){return this._fov/Math.PI*180}set fov(t){t=Math.max(.01,Math.min(60,t)),this._fov!==t&&(this._unmodified=!1,this._fov=t/180*Math.PI,this._calcMatrices())}get zoom(){return this._zoom}set zoom(t){const e=Math.min(Math.max(t,this.minZoom),this.maxZoom);this._zoom!==e&&(this._unmodified=!1,this._zoom=e,this.tileZoom=Math.max(0,Math.floor(e)),this.scale=this.zoomScale(e),this._constrain(),this._calcMatrices())}get center(){return this._center}set center(t){t.lat===this._center.lat&&t.lng===this._center.lng||(this._unmodified=!1,this._center=t,this._constrain(),this._calcMatrices())}get elevation(){return this._elevation}set elevation(t){t!==this._elevation&&(this._elevation=t,this._constrain(),this._calcMatrices())}get padding(){return this._edgeInsets.toJSON()}set padding(t){this._edgeInsets.equals(t)||(this._unmodified=!1,this._edgeInsets.interpolate(this._edgeInsets,t,1),this._calcMatrices())}get centerPoint(){return this._edgeInsets.getCenter(this.width,this.height)}isPaddingEqual(t){return this._edgeInsets.equals(t)}interpolatePadding(t,e,r){this._unmodified=!1,this._edgeInsets.interpolate(t,e,r),this._constrain(),this._calcMatrices()}coveringZoomLevel(t){const e=(t.roundZoom?Math.round:Math.floor)(this.zoom+this.scaleZoom(this.tileSize/t.tileSize));return Math.max(0,e)}getVisibleUnwrappedCoordinates(t){const r=[new e.b4(0,t)];if(this._renderWorldCopies){const n=this.pointCoordinate(new e.P(0,0)),i=this.pointCoordinate(new e.P(this.width,0)),a=this.pointCoordinate(new e.P(this.width,this.height)),o=this.pointCoordinate(new e.P(0,this.height)),s=Math.floor(Math.min(n.x,i.x,a.x,o.x)),l=Math.floor(Math.max(n.x,i.x,a.x,o.x)),c=1;for(let n=s-c;n<=l+c;n++)0!==n&&r.push(new e.b4(n,t))}return r}coveringTiles(t){var r,n;let i=this.coveringZoomLevel(t);const a=i;if(void 0!==t.minzoom&&it.maxzoom&&(i=t.maxzoom);const o=this.pointCoordinate(this.getCameraPoint()),s=e.Z.fromLngLat(this.center),l=Math.pow(2,i),c=[l*o.x,l*o.y,0],u=[l*s.x,l*s.y,0],h=kn.fromInvProjectionMatrix(this.invModelViewProjectionMatrix,this.worldSize,i);let f=t.minzoom||0;!t.terrain&&this.pitch<=60&&this._edgeInsets.top<.1&&(f=i);const p=t.terrain?2/Math.min(this.tileSize,t.tileSize)*this.tileSize:3,d=t=>({aabb:new An([t*l,0,0],[(t+1)*l,l,0]),zoom:0,x:0,y:0,wrap:t,fullyVisible:!1}),m=[],g=[],y=i,x=t.reparseOverscaled?a:i;if(this._renderWorldCopies)for(let t=1;t<=3;t++)m.push(d(-t)),m.push(d(t));for(m.push(d(0));m.length>0;){const i=m.pop(),a=i.x,o=i.y;let s=i.fullyVisible;if(!s){const t=i.aabb.intersects(h);if(0===t)continue;s=2===t}const l=t.terrain?c:u,d=i.aabb.distanceX(l),_=i.aabb.distanceY(l),b=Math.max(Math.abs(d),Math.abs(_)),w=p+(1<w&&i.zoom>=f){const t=y-i.zoom,r=c[0]-.5-(a<>1),h=i.zoom+1;let f=i.aabb.quadrant(l);if(t.terrain){const a=new e.S(h,i.wrap,h,c,u),o=t.terrain.getMinMaxElevation(a),s=null!==(r=o.minElevation)&&void 0!==r?r:this.elevation,l=null!==(n=o.maxElevation)&&void 0!==n?n:this.elevation;f=new An([f.min[0],f.min[1],s],[f.max[0],f.max[1],l])}m.push({aabb:f,zoom:h,x:c,y:u,wrap:i.wrap,fullyVisible:s})}}return g.sort(((t,e)=>t.distanceSq-e.distanceSq)).map((t=>t.tileID))}resize(t,e){this.width=t,this.height=e,this.pixelsToGLUnits=[2/t,-2/e],this._constrain(),this._calcMatrices()}get unmodified(){return this._unmodified}zoomScale(t){return Math.pow(2,t)}scaleZoom(t){return Math.log(t)/Math.LN2}project(t){const r=e.ad(t.lat,-85.051129,Sn);return new e.P(e.O(t.lng)*this.worldSize,e.Q(r)*this.worldSize)}unproject(t){return new e.Z(t.x/this.worldSize,t.y/this.worldSize).toLngLat()}get point(){return this.project(this.center)}getCameraPosition(){return{lngLat:this.pointLocation(this.getCameraPoint()),altitude:Math.cos(this._pitch)*this.cameraToCenterDistance/this._pixelPerMeter+this.elevation}}recalculateZoom(t){const r=this.elevation,n=Math.cos(this._pitch)*this.cameraToCenterDistance/this._pixelPerMeter,i=this.pointLocation(this.centerPoint,t),a=t.getElevationForLngLatZoom(i,this.tileZoom);if(!(this.elevation-a))return;const o=n+r-a,s=Math.cos(this._pitch)*this.cameraToCenterDistance/o/e.b5(1,i.lat)/this.tileSize,l=this.scaleZoom(s);this._elevation=a,this._center=i,this.zoom=l}setLocationAtPoint(t,r){const n=this.pointCoordinate(r),i=this.pointCoordinate(this.centerPoint),a=this.locationCoordinate(t),o=new e.Z(a.x-(n.x-i.x),a.y-(n.y-i.y));this.center=this.coordinateLocation(o),this._renderWorldCopies&&(this.center=this.center.wrap())}locationPoint(t,e){return e?this.coordinatePoint(this.locationCoordinate(t),e.getElevationForLngLatZoom(t,this.tileZoom),this.pixelMatrix3D):this.coordinatePoint(this.locationCoordinate(t))}pointLocation(t,e){return this.coordinateLocation(this.pointCoordinate(t,e))}locationCoordinate(t){return e.Z.fromLngLat(t)}coordinateLocation(t){return t&&t.toLngLat()}pointCoordinate(t,r){if(r){const e=r.pointCoordinate(t);if(null!=e)return e}const n=[t.x,t.y,0,1],i=[t.x,t.y,1,1];e.ag(n,n,this.pixelMatrixInverse),e.ag(i,i,this.pixelMatrixInverse);const a=n[3],o=i[3],s=n[0]/a,l=i[0]/o,c=n[1]/a,u=i[1]/o,h=n[2]/a,f=i[2]/o,p=h===f?0:(0-h)/(f-h);return new e.Z(e.z.number(s,l,p)/this.worldSize,e.z.number(c,u,p)/this.worldSize)}coordinatePoint(t,r=0,n=this.pixelMatrix){const i=[t.x*this.worldSize,t.y*this.worldSize,r,1];return e.ag(i,i,n),new e.P(i[0]/i[3],i[1]/i[3])}getBounds(){const t=Math.max(0,this.height/2-this.getHorizon());return(new X).extend(this.pointLocation(new e.P(0,t))).extend(this.pointLocation(new e.P(this.width,t))).extend(this.pointLocation(new e.P(this.width,this.height))).extend(this.pointLocation(new e.P(0,this.height)))}getMaxBounds(){return this.latRange&&2===this.latRange.length&&this.lngRange&&2===this.lngRange.length?new X([this.lngRange[0],this.latRange[0]],[this.lngRange[1],this.latRange[1]]):null}getHorizon(){return Math.tan(Math.PI/2-this._pitch)*this.cameraToCenterDistance*.85}setMaxBounds(t){t?(this.lngRange=[t.getWest(),t.getEast()],this.latRange=[t.getSouth(),t.getNorth()],this._constrain()):(this.lngRange=null,this.latRange=[-85.051129,Sn])}calculateTileMatrix(t){const r=t.canonical,n=this.worldSize/this.zoomScale(r.z),i=r.x+Math.pow(2,r.z)*t.wrap,a=e.ao(new Float64Array(16));return e.J(a,a,[i*n,r.y*n,0]),e.K(a,a,[n/e.X,n/e.X,1]),a}calculatePosMatrix(t,r=!1){const n=t.key,i=r?this._alignedPosMatrixCache:this._posMatrixCache;if(i[n])return i[n];const a=this.calculateTileMatrix(t);return e.L(a,r?this.alignedModelViewProjectionMatrix:this.modelViewProjectionMatrix,a),i[n]=new Float32Array(a),i[n]}calculateFogMatrix(t){const r=t.key,n=this._fogMatrixCache;if(n[r])return n[r];const i=this.calculateTileMatrix(t);return e.L(i,this.fogMatrix,i),n[r]=new Float32Array(i),n[r]}customLayerMatrix(){return this.mercatorMatrix.slice()}getConstrained(t,r){r=e.ad(+r,this.minZoom,this.maxZoom);const n={center:new e.N(t.lng,t.lat),zoom:r};let i=this.lngRange;if(!this._renderWorldCopies&&null===i){const t=180-1e-10;i=[-t,t]}const a=this.tileSize*this.zoomScale(n.zoom);let o=0,s=a,l=0,c=a,u=0,h=0;const{x:f,y:p}=this.size;if(this.latRange){const t=this.latRange;o=e.Q(t[1])*a,s=e.Q(t[0])*a,s-os&&(y=s-t)}if(i){const t=(l+c)/2;let r=d;this._renderWorldCopies&&(r=e.b3(d,t-a/2,t+a/2));const n=f/2;r-nc&&(g=c-n)}if(void 0!==g||void 0!==y){const t=new e.P(null!=g?g:d,null!=y?y:m);n.center=this.unproject.call({worldSize:a},t).wrap()}return n}_constrain(){if(!this.center||!this.width||!this.height||this._constraining)return;this._constraining=!0;const t=this._unmodified,{center:e,zoom:r}=this.getConstrained(this.center,this.zoom);this.center=e,this.zoom=r,this._unmodified=t,this._constraining=!1}_calcMatrices(){if(!this.height)return;const t=this._fov/2,r=this.centerOffset,n=this.point.x,i=this.point.y;this.cameraToCenterDistance=.5/Math.tan(t)*this.height,this._pixelPerMeter=e.b5(1,this.center.lat)*this.worldSize;let a=e.ao(new Float64Array(16));e.K(a,a,[this.width/2,-this.height/2,1]),e.J(a,a,[1,-1,0]),this.labelPlaneMatrix=a,a=e.ao(new Float64Array(16)),e.K(a,a,[1,-1,1]),e.J(a,a,[-1,-1,0]),e.K(a,a,[2/this.width,2/this.height,1]),this.glCoordMatrix=a;const o=this.cameraToCenterDistance+this._elevation*this._pixelPerMeter/Math.cos(this._pitch),s=Math.min(this.elevation,this.minElevationForCurrentTile),l=o-s*this._pixelPerMeter/Math.cos(this._pitch),c=s<0?l:o,u=Math.PI/2+this._pitch,h=this._fov*(.5+r.y/this.height),f=Math.sin(h)*c/Math.sin(e.ad(Math.PI-u-h,.01,Math.PI-.01)),p=this.getHorizon(),d=2*Math.atan(p/this.cameraToCenterDistance)*(.5+r.y/(2*p)),m=Math.sin(d)*c/Math.sin(e.ad(Math.PI-u-d,.01,Math.PI-.01)),g=Math.min(f,m);this.farZ=1.01*(Math.cos(Math.PI/2-this._pitch)*g+c),this.nearZ=this.height/50,a=new Float64Array(16),e.b6(a,this._fov,this.width/this.height,this.nearZ,this.farZ),a[8]=2*-r.x/this.width,a[9]=2*r.y/this.height,this.projectionMatrix=e.af(a),e.K(a,a,[1,-1,1]),e.J(a,a,[0,0,-this.cameraToCenterDistance]),e.b7(a,a,this._pitch),e.ae(a,a,this.angle),e.J(a,a,[-n,-i,0]),this.mercatorMatrix=e.K([],a,[this.worldSize,this.worldSize,this.worldSize]),e.K(a,a,[1,1,this._pixelPerMeter]),this.pixelMatrix=e.L(new Float64Array(16),this.labelPlaneMatrix,a),e.J(a,a,[0,0,-this.elevation]),this.modelViewProjectionMatrix=a,this.invModelViewProjectionMatrix=e.at([],a),this.fogMatrix=new Float64Array(16),e.b6(this.fogMatrix,this._fov,this.width/this.height,o,this.farZ),this.fogMatrix[8]=2*-r.x/this.width,this.fogMatrix[9]=2*r.y/this.height,e.K(this.fogMatrix,this.fogMatrix,[1,-1,1]),e.J(this.fogMatrix,this.fogMatrix,[0,0,-this.cameraToCenterDistance]),e.b7(this.fogMatrix,this.fogMatrix,this._pitch),e.ae(this.fogMatrix,this.fogMatrix,this.angle),e.J(this.fogMatrix,this.fogMatrix,[-n,-i,0]),e.K(this.fogMatrix,this.fogMatrix,[1,1,this._pixelPerMeter]),e.J(this.fogMatrix,this.fogMatrix,[0,0,-this.elevation]),this.pixelMatrix3D=e.L(new Float64Array(16),this.labelPlaneMatrix,a);const y=this.width%2/2,v=this.height%2/2,x=Math.cos(this.angle),_=Math.sin(this.angle),b=n-Math.round(n)+x*y+_*v,w=i-Math.round(i)+x*v+_*y,T=new Float64Array(a);if(e.J(T,T,[b>.5?b-1:b,w>.5?w-1:w,0]),this.alignedModelViewProjectionMatrix=T,a=e.at(new Float64Array(16),this.pixelMatrix),!a)throw new Error(\"failed to invert matrix\");this.pixelMatrixInverse=a,this._posMatrixCache={},this._alignedPosMatrixCache={},this._fogMatrixCache={}}maxPitchScaleFactor(){if(!this.pixelMatrixInverse)return 1;const t=this.pointCoordinate(new e.P(0,0)),r=[t.x*this.worldSize,t.y*this.worldSize,0,1];return e.ag(r,r,this.pixelMatrix)[3]/this.cameraToCenterDistance}getCameraPoint(){const t=this._pitch,r=Math.tan(t)*(this.cameraToCenterDistance||1);return this.centerPoint.add(new e.P(0,r))}getCameraQueryGeometry(t){const r=this.getCameraPoint();if(1===t.length)return[t[0],r];{let n=r.x,i=r.y,a=r.x,o=r.y;for(const e of t)n=Math.min(n,e.x),i=Math.min(i,e.y),a=Math.max(a,e.x),o=Math.max(o,e.y);return[new e.P(n,i),new e.P(a,i),new e.P(a,o),new e.P(n,o),new e.P(n,i)]}}lngLatToCameraDepth(t,r){const n=this.locationCoordinate(t),i=[n.x*this.worldSize,n.y*this.worldSize,r,1];return e.ag(i,i,this.modelViewProjectionMatrix),i[2]/i[3]}}function Cn(t,e){let r,n=!1,i=null,a=null;const o=()=>{i=null,n&&(t.apply(a,r),i=setTimeout(o,e),n=!1)};return(...t)=>(n=!0,a=this,r=t,i||o(),i)}class Ln{constructor(t){this._getCurrentHash=()=>{const t=window.location.hash.replace(\"#\",\"\");if(this._hashName){let e;return t.split(\"&\").map((t=>t.split(\"=\"))).forEach((t=>{t[0]===this._hashName&&(e=t)})),(e&&e[1]||\"\").split(\"/\")}return t.split(\"/\")},this._onHashChange=()=>{const t=this._getCurrentHash();if(t.length>=3&&!t.some((t=>isNaN(t)))){const e=this._map.dragRotate.isEnabled()&&this._map.touchZoomRotate.isEnabled()?+(t[3]||0):this._map.getBearing();return this._map.jumpTo({center:[+t[2],+t[1]],zoom:+t[0],bearing:e,pitch:+(t[4]||0)}),!0}return!1},this._updateHashUnthrottled=()=>{const t=window.location.href.replace(/(#.+)?$/,this.getHashString());window.history.replaceState(window.history.state,null,t)},this._removeHash=()=>{const t=this._getCurrentHash();if(0===t.length)return;const e=t.join(\"/\");let r=e;r.split(\"&\").length>0&&(r=r.split(\"&\")[0]),this._hashName&&(r=`${this._hashName}=${e}`);let n=window.location.hash.replace(r,\"\");n.startsWith(\"#&\")?n=n.slice(0,1)+n.slice(2):\"#\"===n&&(n=\"\");let i=window.location.href.replace(/(#.+)?$/,n);i=i.replace(\"&&\",\"&\"),window.history.replaceState(window.history.state,null,i)},this._updateHash=Cn(this._updateHashUnthrottled,300),this._hashName=t&&encodeURIComponent(t)}addTo(t){return this._map=t,addEventListener(\"hashchange\",this._onHashChange,!1),this._map.on(\"moveend\",this._updateHash),this}remove(){return removeEventListener(\"hashchange\",this._onHashChange,!1),this._map.off(\"moveend\",this._updateHash),clearTimeout(this._updateHash()),this._removeHash(),delete this._map,this}getHashString(t){const e=this._map.getCenter(),r=Math.round(100*this._map.getZoom())/100,n=Math.ceil((r*Math.LN2+Math.log(512/360/.5))/Math.LN10),i=Math.pow(10,n),a=Math.round(e.lng*i)/i,o=Math.round(e.lat*i)/i,s=this._map.getBearing(),l=this._map.getPitch();let c=\"\";if(c+=t?`/${a}/${o}/${r}`:`${r}/${o}/${a}`,(s||l)&&(c+=\"/\"+Math.round(10*s)/10),l&&(c+=`/${Math.round(l)}`),this._hashName){const t=this._hashName;let e=!1;const r=window.location.hash.slice(1).split(\"&\").map((r=>{const n=r.split(\"=\")[0];return n===t?(e=!0,`${n}=${c}`):r})).filter((t=>t));return e||r.push(`${t}=${c}`),`#${r.join(\"&\")}`}return`#${c}`}}const In={linearity:.3,easing:e.b8(0,0,.3,1)},Pn=e.e({deceleration:2500,maxSpeed:1400},In),zn=e.e({deceleration:20,maxSpeed:1400},In),On=e.e({deceleration:1e3,maxSpeed:360},In),Dn=e.e({deceleration:1e3,maxSpeed:90},In);class Rn{constructor(t){this._map=t,this.clear()}clear(){this._inertiaBuffer=[]}record(t){this._drainInertiaBuffer(),this._inertiaBuffer.push({time:a.now(),settings:t})}_drainInertiaBuffer(){const t=this._inertiaBuffer,e=a.now();for(;t.length>0&&e-t[0].time>160;)t.shift()}_onMoveEnd(t){if(this._drainInertiaBuffer(),this._inertiaBuffer.length<2)return;const r={zoom:0,bearing:0,pitch:0,pan:new e.P(0,0),pinchAround:void 0,around:void 0};for(const{settings:t}of this._inertiaBuffer)r.zoom+=t.zoomDelta||0,r.bearing+=t.bearingDelta||0,r.pitch+=t.pitchDelta||0,t.panDelta&&r.pan._add(t.panDelta),t.around&&(r.around=t.around),t.pinchAround&&(r.pinchAround=t.pinchAround);const n=this._inertiaBuffer[this._inertiaBuffer.length-1].time-this._inertiaBuffer[0].time,i={};if(r.pan.mag()){const a=Bn(r.pan.mag(),n,e.e({},Pn,t||{}));i.offset=r.pan.mult(a.amount/r.pan.mag()),i.center=this._map.transform.center,Fn(i,a)}if(r.zoom){const t=Bn(r.zoom,n,zn);i.zoom=this._map.transform.zoom+t.amount,Fn(i,t)}if(r.bearing){const t=Bn(r.bearing,n,On);i.bearing=this._map.transform.bearing+e.ad(t.amount,-179,179),Fn(i,t)}if(r.pitch){const t=Bn(r.pitch,n,Dn);i.pitch=this._map.transform.pitch+t.amount,Fn(i,t)}if(i.zoom||i.bearing){const t=void 0===r.pinchAround?r.around:r.pinchAround;i.around=t?this._map.unproject(t):this._map.getCenter()}return this.clear(),e.e(i,{noMoveStart:!0})}}function Fn(t,e){(!t.duration||t.durationr.unproject(t))),l=a.reduce(((t,e,r,n)=>t.add(e.div(n.length))),new e.P(0,0));super(t,{points:a,point:l,lngLats:s,lngLat:r.unproject(l),originalEvent:n}),this._defaultPrevented=!1}}class Un extends e.k{preventDefault(){this._defaultPrevented=!0}get defaultPrevented(){return this._defaultPrevented}constructor(t,e,r){super(t,{originalEvent:r}),this._defaultPrevented=!1}}class Vn{constructor(t,e){this._map=t,this._clickTolerance=e.clickTolerance}reset(){delete this._mousedownPos}wheel(t){return this._firePreventable(new Un(t.type,this._map,t))}mousedown(t,e){return this._mousedownPos=e,this._firePreventable(new Nn(t.type,this._map,t))}mouseup(t){this._map.fire(new Nn(t.type,this._map,t))}click(t,e){this._mousedownPos&&this._mousedownPos.dist(e)>=this._clickTolerance||this._map.fire(new Nn(t.type,this._map,t))}dblclick(t){return this._firePreventable(new Nn(t.type,this._map,t))}mouseover(t){this._map.fire(new Nn(t.type,this._map,t))}mouseout(t){this._map.fire(new Nn(t.type,this._map,t))}touchstart(t){return this._firePreventable(new jn(t.type,this._map,t))}touchmove(t){this._map.fire(new jn(t.type,this._map,t))}touchend(t){this._map.fire(new jn(t.type,this._map,t))}touchcancel(t){this._map.fire(new jn(t.type,this._map,t))}_firePreventable(t){if(this._map.fire(t),t.defaultPrevented)return{}}isEnabled(){return!0}isActive(){return!1}enable(){}disable(){}}class qn{constructor(t){this._map=t}reset(){this._delayContextMenu=!1,this._ignoreContextMenu=!0,delete this._contextMenuEvent}mousemove(t){this._map.fire(new Nn(t.type,this._map,t))}mousedown(){this._delayContextMenu=!0,this._ignoreContextMenu=!1}mouseup(){this._delayContextMenu=!1,this._contextMenuEvent&&(this._map.fire(new Nn(\"contextmenu\",this._map,this._contextMenuEvent)),delete this._contextMenuEvent)}contextmenu(t){this._delayContextMenu?this._contextMenuEvent=t:this._ignoreContextMenu||this._map.fire(new Nn(t.type,this._map,t)),this._map.listens(\"contextmenu\")&&t.preventDefault()}isEnabled(){return!0}isActive(){return!1}enable(){}disable(){}}class Hn{constructor(t){this._map=t}get transform(){return this._map._requestedCameraState||this._map.transform}get center(){return{lng:this.transform.center.lng,lat:this.transform.center.lat}}get zoom(){return this.transform.zoom}get pitch(){return this.transform.pitch}get bearing(){return this.transform.bearing}unproject(t){return this.transform.pointLocation(e.P.convert(t),this._map.terrain)}}class Gn{constructor(t,e){this._map=t,this._tr=new Hn(t),this._el=t.getCanvasContainer(),this._container=t.getContainer(),this._clickTolerance=e.clickTolerance||1}isEnabled(){return!!this._enabled}isActive(){return!!this._active}enable(){this.isEnabled()||(this._enabled=!0)}disable(){this.isEnabled()&&(this._enabled=!1)}mousedown(t,e){this.isEnabled()&&t.shiftKey&&0===t.button&&(o.disableDrag(),this._startPos=this._lastPos=e,this._active=!0)}mousemoveWindow(t,e){if(!this._active)return;const r=e;if(this._lastPos.equals(r)||!this._box&&r.dist(this._startPos)t.fitScreenCoordinates(n,i,this._tr.bearing,{linear:!0})};this._fireEvent(\"boxzoomcancel\",t)}keydown(t){this._active&&27===t.keyCode&&(this.reset(),this._fireEvent(\"boxzoomcancel\",t))}reset(){this._active=!1,this._container.classList.remove(\"maplibregl-crosshair\"),this._box&&(o.remove(this._box),this._box=null),o.enableDrag(),delete this._startPos,delete this._lastPos}_fireEvent(t,r){return this._map.fire(new e.k(t,{originalEvent:r}))}}function Zn(t,e){if(t.length!==e.length)throw new Error(`The number of touches and points are not equal - touches ${t.length}, points ${e.length}`);const r={};for(let n=0;nthis.numTouches)&&(this.aborted=!0),this.aborted||(void 0===this.startTime&&(this.startTime=t.timeStamp),n.length===this.numTouches&&(this.centroid=function(t){const r=new e.P(0,0);for(const e of t)r._add(e);return r.div(t.length)}(r),this.touches=Zn(n,r)))}touchmove(t,e,r){if(this.aborted||!this.centroid)return;const n=Zn(r,e);for(const t in this.touches){const e=this.touches[t],r=n[t];(!r||r.dist(e)>30)&&(this.aborted=!0)}}touchend(t,e,r){if((!this.centroid||t.timeStamp-this.startTime>500)&&(this.aborted=!0),0===r.length){const t=!this.aborted&&this.centroid;if(this.reset(),t)return t}}}class Yn{constructor(t){this.singleTap=new Wn(t),this.numTaps=t.numTaps,this.reset()}reset(){this.lastTime=1/0,delete this.lastTap,this.count=0,this.singleTap.reset()}touchstart(t,e,r){this.singleTap.touchstart(t,e,r)}touchmove(t,e,r){this.singleTap.touchmove(t,e,r)}touchend(t,e,r){const n=this.singleTap.touchend(t,e,r);if(n){const e=t.timeStamp-this.lastTime<500,r=!this.lastTap||this.lastTap.dist(n)<30;if(e&&r||this.reset(),this.count++,this.lastTime=t.timeStamp,this.lastTap=n,this.count===this.numTaps)return this.reset(),n}}}class Xn{constructor(t){this._tr=new Hn(t),this._zoomIn=new Yn({numTouches:1,numTaps:2}),this._zoomOut=new Yn({numTouches:2,numTaps:1}),this.reset()}reset(){this._active=!1,this._zoomIn.reset(),this._zoomOut.reset()}touchstart(t,e,r){this._zoomIn.touchstart(t,e,r),this._zoomOut.touchstart(t,e,r)}touchmove(t,e,r){this._zoomIn.touchmove(t,e,r),this._zoomOut.touchmove(t,e,r)}touchend(t,e,r){const n=this._zoomIn.touchend(t,e,r),i=this._zoomOut.touchend(t,e,r),a=this._tr;return n?(this._active=!0,t.preventDefault(),setTimeout((()=>this.reset()),0),{cameraAnimation:e=>e.easeTo({duration:300,zoom:a.zoom+1,around:a.unproject(n)},{originalEvent:t})}):i?(this._active=!0,t.preventDefault(),setTimeout((()=>this.reset()),0),{cameraAnimation:e=>e.easeTo({duration:300,zoom:a.zoom-1,around:a.unproject(i)},{originalEvent:t})}):void 0}touchcancel(){this.reset()}enable(){this._enabled=!0}disable(){this._enabled=!1,this.reset()}isEnabled(){return this._enabled}isActive(){return this._active}}class $n{constructor(t){this._enabled=!!t.enable,this._moveStateManager=t.moveStateManager,this._clickTolerance=t.clickTolerance||1,this._moveFunction=t.move,this._activateOnStart=!!t.activateOnStart,t.assignEvents(this),this.reset()}reset(t){this._active=!1,this._moved=!1,delete this._lastPoint,this._moveStateManager.endMove(t)}_move(...t){const e=this._moveFunction(...t);if(e.bearingDelta||e.pitchDelta||e.around||e.panDelta)return this._active=!0,e}dragStart(t,e){this.isEnabled()&&!this._lastPoint&&this._moveStateManager.isValidStartEvent(t)&&(this._moveStateManager.startMove(t),this._lastPoint=e.length?e[0]:e,this._activateOnStart&&this._lastPoint&&(this._active=!0))}dragMove(t,e){if(!this.isEnabled())return;const r=this._lastPoint;if(!r)return;if(t.preventDefault(),!this._moveStateManager.isValidMoveEvent(t))return void this.reset(t);const n=e.length?e[0]:e;return!this._moved&&n.dist(r){t.mousedown=t.dragStart,t.mousemoveWindow=t.dragMove,t.mouseup=t.dragEnd,t.contextmenu=t=>{t.preventDefault()}},ei=({enable:t,clickTolerance:e,bearingDegreesPerPixelMoved:r=.8})=>{const n=new Kn({checkCorrectEvent:t=>0===o.mouseButton(t)&&t.ctrlKey||2===o.mouseButton(t)});return new $n({clickTolerance:e,move:(t,e)=>({bearingDelta:(e.x-t.x)*r}),moveStateManager:n,enable:t,assignEvents:ti})},ri=({enable:t,clickTolerance:e,pitchDegreesPerPixelMoved:r=-.5})=>{const n=new Kn({checkCorrectEvent:t=>0===o.mouseButton(t)&&t.ctrlKey||2===o.mouseButton(t)});return new $n({clickTolerance:e,move:(t,e)=>({pitchDelta:(e.y-t.y)*r}),moveStateManager:n,enable:t,assignEvents:ti})};class ni{constructor(t,e){this._clickTolerance=t.clickTolerance||1,this._map=e,this.reset()}reset(){this._active=!1,this._touches={},this._sum=new e.P(0,0)}_shouldBePrevented(t){return t<(this._map.cooperativeGestures.isEnabled()?2:1)}touchstart(t,e,r){return this._calculateTransform(t,e,r)}touchmove(t,e,r){if(this._active){if(!this._shouldBePrevented(r.length))return t.preventDefault(),this._calculateTransform(t,e,r);this._map.cooperativeGestures.notifyGestureBlocked(\"touch_pan\",t)}}touchend(t,e,r){this._calculateTransform(t,e,r),this._active&&this._shouldBePrevented(r.length)&&this.reset()}touchcancel(){this.reset()}_calculateTransform(t,r,n){n.length>0&&(this._active=!0);const i=Zn(n,r),a=new e.P(0,0),o=new e.P(0,0);let s=0;for(const t in i){const e=i[t],r=this._touches[t];r&&(a._add(e),o._add(e.sub(r)),s++,i[t]=e)}if(this._touches=i,this._shouldBePrevented(s)||!o.mag())return;const l=o.div(s);return this._sum._add(l),this._sum.mag()Math.abs(t.x)}class hi extends ii{constructor(t){super(),this._currentTouchCount=0,this._map=t}reset(){super.reset(),this._valid=void 0,delete this._firstMove,delete this._lastPoints}touchstart(t,e,r){super.touchstart(t,e,r),this._currentTouchCount=r.length}_start(t){this._lastPoints=t,ui(t[0].sub(t[1]))&&(this._valid=!1)}_move(t,e,r){if(this._map.cooperativeGestures.isEnabled()&&this._currentTouchCount<3)return;const n=t[0].sub(this._lastPoints[0]),i=t[1].sub(this._lastPoints[1]);return this._valid=this.gestureBeginsVertically(n,i,r.timeStamp),this._valid?(this._lastPoints=t,this._active=!0,{pitchDelta:(n.y+i.y)/2*-.5}):void 0}gestureBeginsVertically(t,e,r){if(void 0!==this._valid)return this._valid;const n=t.mag()>=2,i=e.mag()>=2;if(!n&&!i)return;if(!n||!i)return void 0===this._firstMove&&(this._firstMove=r),r-this._firstMove<100&&void 0;const a=t.y>0==e.y>0;return ui(t)&&ui(e)&&a}}const fi={panStep:100,bearingStep:15,pitchStep:10};class pi{constructor(t){this._tr=new Hn(t);const e=fi;this._panStep=e.panStep,this._bearingStep=e.bearingStep,this._pitchStep=e.pitchStep,this._rotationDisabled=!1}reset(){this._active=!1}keydown(t){if(t.altKey||t.ctrlKey||t.metaKey)return;let e=0,r=0,n=0,i=0,a=0;switch(t.keyCode){case 61:case 107:case 171:case 187:e=1;break;case 189:case 109:case 173:e=-1;break;case 37:t.shiftKey?r=-1:(t.preventDefault(),i=-1);break;case 39:t.shiftKey?r=1:(t.preventDefault(),i=1);break;case 38:t.shiftKey?n=1:(t.preventDefault(),a=-1);break;case 40:t.shiftKey?n=-1:(t.preventDefault(),a=1);break;default:return}return this._rotationDisabled&&(r=0,n=0),{cameraAnimation:o=>{const s=this._tr;o.easeTo({duration:300,easeId:\"keyboardHandler\",easing:di,zoom:e?Math.round(s.zoom)+e*(t.shiftKey?2:1):s.zoom,bearing:s.bearing+r*this._bearingStep,pitch:s.pitch+n*this._pitchStep,offset:[-i*this._panStep,-a*this._panStep],center:s.center},{originalEvent:t})}}}enable(){this._enabled=!0}disable(){this._enabled=!1,this.reset()}isEnabled(){return this._enabled}isActive(){return this._active}disableRotation(){this._rotationDisabled=!0}enableRotation(){this._rotationDisabled=!1}}function di(t){return t*(2-t)}const mi=4.000244140625;class gi{constructor(t,e){this._onTimeout=t=>{this._type=\"wheel\",this._delta-=this._lastValue,this._active||this._start(t)},this._map=t,this._tr=new Hn(t),this._triggerRenderFrame=e,this._delta=0,this._defaultZoomRate=.01,this._wheelZoomRate=.0022222222222222222}setZoomRate(t){this._defaultZoomRate=t}setWheelZoomRate(t){this._wheelZoomRate=t}isEnabled(){return!!this._enabled}isActive(){return!!this._active||void 0!==this._finishTimeout}isZooming(){return!!this._zooming}enable(t){this.isEnabled()||(this._enabled=!0,this._aroundCenter=!!t&&\"center\"===t.around)}disable(){this.isEnabled()&&(this._enabled=!1)}_shouldBePrevented(t){return!!this._map.cooperativeGestures.isEnabled()&&!(t.ctrlKey||this._map.cooperativeGestures.isBypassed(t))}wheel(t){if(!this.isEnabled())return;if(this._shouldBePrevented(t))return void this._map.cooperativeGestures.notifyGestureBlocked(\"wheel_zoom\",t);let e=t.deltaMode===WheelEvent.DOM_DELTA_LINE?40*t.deltaY:t.deltaY;const r=a.now(),n=r-(this._lastWheelEventTime||0);this._lastWheelEventTime=r,0!==e&&e%mi==0?this._type=\"wheel\":0!==e&&Math.abs(e)<4?this._type=\"trackpad\":n>400?(this._type=null,this._lastValue=e,this._timeout=setTimeout(this._onTimeout,40,t)):this._type||(this._type=Math.abs(n*e)<200?\"trackpad\":\"wheel\",this._timeout&&(clearTimeout(this._timeout),this._timeout=null,e+=this._lastValue)),t.shiftKey&&e&&(e/=4),this._type&&(this._lastWheelEvent=t,this._delta-=e,this._active||this._start(t)),t.preventDefault()}_start(t){if(!this._delta)return;this._frameId&&(this._frameId=null),this._active=!0,this.isZooming()||(this._zooming=!0),this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout);const r=o.mousePos(this._map.getCanvas(),t),n=this._tr;r.y>n.transform.height/2-n.transform.getHorizon()?this._around=e.N.convert(this._aroundCenter?n.center:n.unproject(r)):this._around=e.N.convert(n.center),this._aroundPoint=n.transform.locationPoint(this._around),this._frameId||(this._frameId=!0,this._triggerRenderFrame())}renderFrame(){if(!this._frameId)return;if(this._frameId=null,!this.isActive())return;const t=this._tr.transform;if(0!==this._delta){const e=\"wheel\"===this._type&&Math.abs(this._delta)>mi?this._wheelZoomRate:this._defaultZoomRate;let r=2/(1+Math.exp(-Math.abs(this._delta*e)));this._delta<0&&0!==r&&(r=1/r);const n=\"number\"==typeof this._targetZoom?t.zoomScale(this._targetZoom):t.scale;this._targetZoom=Math.min(t.maxZoom,Math.max(t.minZoom,t.scaleZoom(n*r))),\"wheel\"===this._type&&(this._startZoom=t.zoom,this._easing=this._smoothOutEasing(200)),this._delta=0}const r=\"number\"==typeof this._targetZoom?this._targetZoom:t.zoom,n=this._startZoom,i=this._easing;let o,s=!1;const l=a.now()-this._lastWheelEventTime;if(\"wheel\"===this._type&&n&&i&&l){const t=Math.min(l/200,1),a=i(t);o=e.z.number(n,r,a),t<1?this._frameId||(this._frameId=!0):s=!0}else o=r,s=!0;return this._active=!0,s&&(this._active=!1,this._finishTimeout=setTimeout((()=>{this._zooming=!1,this._triggerRenderFrame(),delete this._targetZoom,delete this._finishTimeout}),200)),{noInertia:!0,needsRenderFrame:!s,zoomDelta:o-t.zoom,around:this._aroundPoint,originalEvent:this._lastWheelEvent}}_smoothOutEasing(t){let r=e.b9;if(this._prevEase){const t=this._prevEase,n=(a.now()-t.start)/t.duration,i=t.easing(n+.01)-t.easing(n),o=.27/Math.sqrt(i*i+1e-4)*.01,s=Math.sqrt(.0729-o*o);r=e.b8(o,s,.25,1)}return this._prevEase={start:a.now(),duration:t,easing:r},r}reset(){this._active=!1,this._zooming=!1,delete this._targetZoom,this._finishTimeout&&(clearTimeout(this._finishTimeout),delete this._finishTimeout)}}class yi{constructor(t,e){this._clickZoom=t,this._tapZoom=e}enable(){this._clickZoom.enable(),this._tapZoom.enable()}disable(){this._clickZoom.disable(),this._tapZoom.disable()}isEnabled(){return this._clickZoom.isEnabled()&&this._tapZoom.isEnabled()}isActive(){return this._clickZoom.isActive()||this._tapZoom.isActive()}}class vi{constructor(t){this._tr=new Hn(t),this.reset()}reset(){this._active=!1}dblclick(t,e){return t.preventDefault(),{cameraAnimation:r=>{r.easeTo({duration:300,zoom:this._tr.zoom+(t.shiftKey?-1:1),around:this._tr.unproject(e)},{originalEvent:t})}}}enable(){this._enabled=!0}disable(){this._enabled=!1,this.reset()}isEnabled(){return this._enabled}isActive(){return this._active}}class xi{constructor(){this._tap=new Yn({numTouches:1,numTaps:1}),this.reset()}reset(){this._active=!1,delete this._swipePoint,delete this._swipeTouch,delete this._tapTime,delete this._tapPoint,this._tap.reset()}touchstart(t,e,r){if(!this._swipePoint)if(this._tapTime){const n=e[0],i=t.timeStamp-this._tapTime<500,a=this._tapPoint.dist(n)<30;i&&a?r.length>0&&(this._swipePoint=n,this._swipeTouch=r[0].identifier):this.reset()}else this._tap.touchstart(t,e,r)}touchmove(t,e,r){if(this._tapTime){if(this._swipePoint){if(r[0].identifier!==this._swipeTouch)return;const n=e[0],i=n.y-this._swipePoint.y;return this._swipePoint=n,t.preventDefault(),this._active=!0,{zoomDelta:i/128}}}else this._tap.touchmove(t,e,r)}touchend(t,e,r){if(this._tapTime)this._swipePoint&&0===r.length&&this.reset();else{const n=this._tap.touchend(t,e,r);n&&(this._tapTime=t.timeStamp,this._tapPoint=n)}}touchcancel(){this.reset()}enable(){this._enabled=!0}disable(){this._enabled=!1,this.reset()}isEnabled(){return this._enabled}isActive(){return this._active}}class _i{constructor(t,e,r){this._el=t,this._mousePan=e,this._touchPan=r}enable(t){this._inertiaOptions=t||{},this._mousePan.enable(),this._touchPan.enable(),this._el.classList.add(\"maplibregl-touch-drag-pan\")}disable(){this._mousePan.disable(),this._touchPan.disable(),this._el.classList.remove(\"maplibregl-touch-drag-pan\")}isEnabled(){return this._mousePan.isEnabled()&&this._touchPan.isEnabled()}isActive(){return this._mousePan.isActive()||this._touchPan.isActive()}}class bi{constructor(t,e,r){this._pitchWithRotate=t.pitchWithRotate,this._mouseRotate=e,this._mousePitch=r}enable(){this._mouseRotate.enable(),this._pitchWithRotate&&this._mousePitch.enable()}disable(){this._mouseRotate.disable(),this._mousePitch.disable()}isEnabled(){return this._mouseRotate.isEnabled()&&(!this._pitchWithRotate||this._mousePitch.isEnabled())}isActive(){return this._mouseRotate.isActive()||this._mousePitch.isActive()}}class wi{constructor(t,e,r,n){this._el=t,this._touchZoom=e,this._touchRotate=r,this._tapDragZoom=n,this._rotationDisabled=!1,this._enabled=!0}enable(t){this._touchZoom.enable(t),this._rotationDisabled||this._touchRotate.enable(t),this._tapDragZoom.enable(),this._el.classList.add(\"maplibregl-touch-zoom-rotate\")}disable(){this._touchZoom.disable(),this._touchRotate.disable(),this._tapDragZoom.disable(),this._el.classList.remove(\"maplibregl-touch-zoom-rotate\")}isEnabled(){return this._touchZoom.isEnabled()&&(this._rotationDisabled||this._touchRotate.isEnabled())&&this._tapDragZoom.isEnabled()}isActive(){return this._touchZoom.isActive()||this._touchRotate.isActive()||this._tapDragZoom.isActive()}disableRotation(){this._rotationDisabled=!0,this._touchRotate.disable()}enableRotation(){this._rotationDisabled=!1,this._touchZoom.isEnabled()&&this._touchRotate.enable()}}class Ti{constructor(t,e){this._bypassKey=-1!==navigator.userAgent.indexOf(\"Mac\")?\"metaKey\":\"ctrlKey\",this._map=t,this._options=e,this._enabled=!1}isActive(){return!1}reset(){}_setupUI(){if(this._container)return;const t=this._map.getCanvasContainer();t.classList.add(\"maplibregl-cooperative-gestures\"),this._container=o.create(\"div\",\"maplibregl-cooperative-gesture-screen\",t);let e=this._map._getUIString(\"CooperativeGesturesHandler.WindowsHelpText\");\"metaKey\"===this._bypassKey&&(e=this._map._getUIString(\"CooperativeGesturesHandler.MacHelpText\"));const r=this._map._getUIString(\"CooperativeGesturesHandler.MobileHelpText\"),n=document.createElement(\"div\");n.className=\"maplibregl-desktop-message\",n.textContent=e,this._container.appendChild(n);const i=document.createElement(\"div\");i.className=\"maplibregl-mobile-message\",i.textContent=r,this._container.appendChild(i),this._container.setAttribute(\"aria-hidden\",\"true\")}_destroyUI(){this._container&&(o.remove(this._container),this._map.getCanvasContainer().classList.remove(\"maplibregl-cooperative-gestures\")),delete this._container}enable(){this._setupUI(),this._enabled=!0}disable(){this._enabled=!1,this._destroyUI()}isEnabled(){return this._enabled}isBypassed(t){return t[this._bypassKey]}notifyGestureBlocked(t,r){this._enabled&&(this._map.fire(new e.k(\"cooperativegestureprevented\",{gestureType:t,originalEvent:r})),this._container.classList.add(\"maplibregl-show\"),setTimeout((()=>{this._container.classList.remove(\"maplibregl-show\")}),100))}}const ki=t=>t.zoom||t.drag||t.pitch||t.rotate;class Ai extends e.k{}function Mi(t){return t.panDelta&&t.panDelta.mag()||t.zoomDelta||t.bearingDelta||t.pitchDelta}class Si{constructor(t,e){this.handleWindowEvent=t=>{this.handleEvent(t,`${t.type}Window`)},this.handleEvent=(t,e)=>{if(\"blur\"===t.type)return void this.stop(!0);this._updatingCamera=!0;const r=\"renderFrame\"===t.type?void 0:t,n={needsRenderFrame:!1},i={},a={},s=t.touches,l=s?this._getMapTouches(s):void 0,c=l?o.touchPos(this._map.getCanvas(),l):o.mousePos(this._map.getCanvas(),t);for(const{handlerName:o,handler:s,allowed:u}of this._handlers){if(!s.isEnabled())continue;let h;this._blockedByActive(a,u,o)?s.reset():s[e||t.type]&&(h=s[e||t.type](t,c,l),this.mergeHandlerResult(n,i,h,o,r),h&&h.needsRenderFrame&&this._triggerRenderFrame()),(h||s.isActive())&&(a[o]=s)}const u={};for(const t in this._previousActiveHandlers)a[t]||(u[t]=r);this._previousActiveHandlers=a,(Object.keys(u).length||Mi(n))&&(this._changes.push([n,i,u]),this._triggerRenderFrame()),(Object.keys(a).length||Mi(n))&&this._map._stop(!0),this._updatingCamera=!1;const{cameraAnimation:h}=n;h&&(this._inertia.clear(),this._fireEvents({},{},!0),this._changes=[],h(this._map))},this._map=t,this._el=this._map.getCanvasContainer(),this._handlers=[],this._handlersById={},this._changes=[],this._inertia=new Rn(t),this._bearingSnap=e.bearingSnap,this._previousActiveHandlers={},this._eventsInProgress={},this._addDefaultHandlers(e);const r=this._el;this._listeners=[[r,\"touchstart\",{passive:!0}],[r,\"touchmove\",{passive:!1}],[r,\"touchend\",void 0],[r,\"touchcancel\",void 0],[r,\"mousedown\",void 0],[r,\"mousemove\",void 0],[r,\"mouseup\",void 0],[document,\"mousemove\",{capture:!0}],[document,\"mouseup\",void 0],[r,\"mouseover\",void 0],[r,\"mouseout\",void 0],[r,\"dblclick\",void 0],[r,\"click\",void 0],[r,\"keydown\",{capture:!1}],[r,\"keyup\",void 0],[r,\"wheel\",{passive:!1}],[r,\"contextmenu\",void 0],[window,\"blur\",void 0]];for(const[t,e,r]of this._listeners)o.addEventListener(t,e,t===document?this.handleWindowEvent:this.handleEvent,r)}destroy(){for(const[t,e,r]of this._listeners)o.removeEventListener(t,e,t===document?this.handleWindowEvent:this.handleEvent,r)}_addDefaultHandlers(t){const e=this._map,r=e.getCanvasContainer();this._add(\"mapEvent\",new Vn(e,t));const n=e.boxZoom=new Gn(e,t);this._add(\"boxZoom\",n),t.interactive&&t.boxZoom&&n.enable();const i=e.cooperativeGestures=new Ti(e,t.cooperativeGestures);this._add(\"cooperativeGestures\",i),t.cooperativeGestures&&i.enable();const a=new Xn(e),s=new vi(e);e.doubleClickZoom=new yi(s,a),this._add(\"tapZoom\",a),this._add(\"clickZoom\",s),t.interactive&&t.doubleClickZoom&&e.doubleClickZoom.enable();const l=new xi;this._add(\"tapDragZoom\",l);const c=e.touchPitch=new hi(e);this._add(\"touchPitch\",c),t.interactive&&t.touchPitch&&e.touchPitch.enable(t.touchPitch);const u=ei(t),h=ri(t);e.dragRotate=new bi(t,u,h),this._add(\"mouseRotate\",u,[\"mousePitch\"]),this._add(\"mousePitch\",h,[\"mouseRotate\"]),t.interactive&&t.dragRotate&&e.dragRotate.enable();const f=(({enable:t,clickTolerance:e})=>{const r=new Kn({checkCorrectEvent:t=>0===o.mouseButton(t)&&!t.ctrlKey});return new $n({clickTolerance:e,move:(t,e)=>({around:e,panDelta:e.sub(t)}),activateOnStart:!0,moveStateManager:r,enable:t,assignEvents:ti})})(t),p=new ni(t,e);e.dragPan=new _i(r,f,p),this._add(\"mousePan\",f),this._add(\"touchPan\",p,[\"touchZoom\",\"touchRotate\"]),t.interactive&&t.dragPan&&e.dragPan.enable(t.dragPan);const d=new ci,m=new si;e.touchZoomRotate=new wi(r,m,d,l),this._add(\"touchRotate\",d,[\"touchPan\",\"touchZoom\"]),this._add(\"touchZoom\",m,[\"touchPan\",\"touchRotate\"]),t.interactive&&t.touchZoomRotate&&e.touchZoomRotate.enable(t.touchZoomRotate);const g=e.scrollZoom=new gi(e,(()=>this._triggerRenderFrame()));this._add(\"scrollZoom\",g,[\"mousePan\"]),t.interactive&&t.scrollZoom&&e.scrollZoom.enable(t.scrollZoom);const y=e.keyboard=new pi(e);this._add(\"keyboard\",y),t.interactive&&t.keyboard&&e.keyboard.enable(),this._add(\"blockableMapEvent\",new qn(e))}_add(t,e,r){this._handlers.push({handlerName:t,handler:e,allowed:r}),this._handlersById[t]=e}stop(t){if(!this._updatingCamera){for(const{handler:t}of this._handlers)t.reset();this._inertia.clear(),this._fireEvents({},{},t),this._changes=[]}}isActive(){for(const{handler:t}of this._handlers)if(t.isActive())return!0;return!1}isZooming(){return!!this._eventsInProgress.zoom||this._map.scrollZoom.isZooming()}isRotating(){return!!this._eventsInProgress.rotate}isMoving(){return Boolean(ki(this._eventsInProgress))||this.isZooming()}_blockedByActive(t,e,r){for(const n in t)if(n!==r&&(!e||e.indexOf(n)<0))return!0;return!1}_getMapTouches(t){const e=[];for(const r of t){const t=r.target;this._el.contains(t)&&e.push(r)}return e}mergeHandlerResult(t,r,n,i,a){if(!n)return;e.e(t,n);const o={handlerName:i,originalEvent:n.originalEvent||a};void 0!==n.zoomDelta&&(r.zoom=o),void 0!==n.panDelta&&(r.drag=o),void 0!==n.pitchDelta&&(r.pitch=o),void 0!==n.bearingDelta&&(r.rotate=o)}_applyChanges(){const t={},r={},n={};for(const[i,a,o]of this._changes)i.panDelta&&(t.panDelta=(t.panDelta||new e.P(0,0))._add(i.panDelta)),i.zoomDelta&&(t.zoomDelta=(t.zoomDelta||0)+i.zoomDelta),i.bearingDelta&&(t.bearingDelta=(t.bearingDelta||0)+i.bearingDelta),i.pitchDelta&&(t.pitchDelta=(t.pitchDelta||0)+i.pitchDelta),void 0!==i.around&&(t.around=i.around),void 0!==i.pinchAround&&(t.pinchAround=i.pinchAround),i.noInertia&&(t.noInertia=i.noInertia),e.e(r,a),e.e(n,o);this._updateMapTransform(t,r,n),this._changes=[]}_updateMapTransform(t,e,r){const n=this._map,i=n._getTransformForUpdate(),a=n.terrain;if(!(Mi(t)||a&&this._terrainMovement))return this._fireEvents(e,r,!0);let{panDelta:o,zoomDelta:s,bearingDelta:l,pitchDelta:c,around:u,pinchAround:h}=t;void 0!==h&&(u=h),n._stop(!0),u=u||n.transform.centerPoint;const f=i.pointLocation(o?u.sub(o):u);l&&(i.bearing+=l),c&&(i.pitch+=c),s&&(i.zoom+=s),a?this._terrainMovement||!e.drag&&!e.zoom?e.drag&&this._terrainMovement?i.center=i.pointLocation(i.centerPoint.sub(o)):i.setLocationAtPoint(f,u):(this._terrainMovement=!0,this._map._elevationFreeze=!0,i.setLocationAtPoint(f,u)):i.setLocationAtPoint(f,u),n._applyUpdatedTransform(i),this._map._update(),t.noInertia||this._inertia.record(t),this._fireEvents(e,r,!0)}_fireEvents(t,r,n){const i=ki(this._eventsInProgress),o=ki(t),s={};for(const e in t){const{originalEvent:r}=t[e];this._eventsInProgress[e]||(s[`${e}start`]=r),this._eventsInProgress[e]=t[e]}!i&&o&&this._fireEvent(\"movestart\",o.originalEvent);for(const t in s)this._fireEvent(t,s[t]);o&&this._fireEvent(\"move\",o.originalEvent);for(const e in t){const{originalEvent:r}=t[e];this._fireEvent(e,r)}const l={};let c;for(const t in this._eventsInProgress){const{handlerName:e,originalEvent:n}=this._eventsInProgress[t];this._handlersById[e].isActive()||(delete this._eventsInProgress[t],c=r[e]||n,l[`${t}end`]=c)}for(const t in l)this._fireEvent(t,l[t]);const u=ki(this._eventsInProgress),h=(i||o)&&!u;if(h&&this._terrainMovement){this._map._elevationFreeze=!1,this._terrainMovement=!1;const t=this._map._getTransformForUpdate();t.recalculateZoom(this._map.terrain),this._map._applyUpdatedTransform(t)}if(n&&h){this._updatingCamera=!0;const t=this._inertia._onMoveEnd(this._map.dragPan._inertiaOptions),r=t=>0!==t&&-this._bearingSnap{delete this._frameId,this.handleEvent(new Ai(\"renderFrame\",{timeStamp:t})),this._applyChanges()}))}_triggerRenderFrame(){void 0===this._frameId&&(this._frameId=this._requestFrame())}}class Ei extends e.E{constructor(t,e){super(),this._renderFrameCallback=()=>{const t=Math.min((a.now()-this._easeStart)/this._easeOptions.duration,1);this._onEaseFrame(this._easeOptions.easing(t)),t<1&&this._easeFrameId?this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback):this.stop()},this._moving=!1,this._zooming=!1,this.transform=t,this._bearingSnap=e.bearingSnap,this.on(\"moveend\",(()=>{delete this._requestedCameraState}))}getCenter(){return new e.N(this.transform.center.lng,this.transform.center.lat)}setCenter(t,e){return this.jumpTo({center:t},e)}panBy(t,r,n){return t=e.P.convert(t).mult(-1),this.panTo(this.transform.center,e.e({offset:t},r),n)}panTo(t,r,n){return this.easeTo(e.e({center:t},r),n)}getZoom(){return this.transform.zoom}setZoom(t,e){return this.jumpTo({zoom:t},e),this}zoomTo(t,r,n){return this.easeTo(e.e({zoom:t},r),n)}zoomIn(t,e){return this.zoomTo(this.getZoom()+1,t,e),this}zoomOut(t,e){return this.zoomTo(this.getZoom()-1,t,e),this}getBearing(){return this.transform.bearing}setBearing(t,e){return this.jumpTo({bearing:t},e),this}getPadding(){return this.transform.padding}setPadding(t,e){return this.jumpTo({padding:t},e),this}rotateTo(t,r,n){return this.easeTo(e.e({bearing:t},r),n)}resetNorth(t,r){return this.rotateTo(0,e.e({duration:1e3},t),r),this}resetNorthPitch(t,r){return this.easeTo(e.e({bearing:0,pitch:0,duration:1e3},t),r),this}snapToNorth(t,e){return Math.abs(this.getBearing()){if(this._zooming&&(i.zoom=e.z.number(o,y,n)),this._rotating&&(i.bearing=e.z.number(s,u,n)),this._pitching&&(i.pitch=e.z.number(l,h,n)),this._padding&&(i.interpolatePadding(c,f,n),d=i.centerPoint.add(p)),this.terrain&&!t.freezeElevation&&this._updateElevation(n),b)i.setLocationAtPoint(b,w);else{const t=i.zoomScale(i.zoom-o),e=y>o?Math.min(2,_):Math.max(.5,_),r=Math.pow(e,1-n),a=i.unproject(v.add(x.mult(n*r)).mult(t));i.setLocationAtPoint(i.renderWorldCopies?a.wrap():a,d)}this._applyUpdatedTransform(i),this._fireMoveEvents(r)}),(e=>{this.terrain&&t.freezeElevation&&this._finalizeElevation(),this._afterEase(r,e)}),t),this}_prepareEase(t,r,n={}){this._moving=!0,r||n.moving||this.fire(new e.k(\"movestart\",t)),this._zooming&&!n.zooming&&this.fire(new e.k(\"zoomstart\",t)),this._rotating&&!n.rotating&&this.fire(new e.k(\"rotatestart\",t)),this._pitching&&!n.pitching&&this.fire(new e.k(\"pitchstart\",t))}_prepareElevation(t){this._elevationCenter=t,this._elevationStart=this.transform.elevation,this._elevationTarget=this.terrain.getElevationForLngLatZoom(t,this.transform.tileZoom),this._elevationFreeze=!0}_updateElevation(t){this.transform.minElevationForCurrentTile=this.terrain.getMinTileElevationForLngLatZoom(this._elevationCenter,this.transform.tileZoom);const r=this.terrain.getElevationForLngLatZoom(this._elevationCenter,this.transform.tileZoom);if(t<1&&r!==this._elevationTarget){const e=this._elevationTarget-this._elevationStart,n=(r-(e*t+this._elevationStart))/(1-t);this._elevationStart+=t*(e-n),this._elevationTarget=r}this.transform.elevation=e.z.number(this._elevationStart,this._elevationTarget,t)}_finalizeElevation(){this._elevationFreeze=!1,this.transform.recalculateZoom(this.terrain)}_getTransformForUpdate(){return this.transformCameraUpdate||this.terrain?(this._requestedCameraState||(this._requestedCameraState=this.transform.clone()),this._requestedCameraState):this.transform}_elevateCameraIfInsideTerrain(t){const e=t.getCameraPosition(),r=this.terrain.getElevationForLngLatZoom(e.lngLat,t.zoom);if(e.altitudethis._elevateCameraIfInsideTerrain(t))),this.transformCameraUpdate&&e.push((t=>this.transformCameraUpdate(t))),!e.length)return;const r=t.clone();for(const t of e){const e=r.clone(),{center:n,zoom:i,pitch:a,bearing:o,elevation:s}=t(e);n&&(e.center=n),void 0!==i&&(e.zoom=i),void 0!==a&&(e.pitch=a),void 0!==o&&(e.bearing=o),void 0!==s&&(e.elevation=s),r.apply(e)}this.transform.apply(r)}_fireMoveEvents(t){this.fire(new e.k(\"move\",t)),this._zooming&&this.fire(new e.k(\"zoom\",t)),this._rotating&&this.fire(new e.k(\"rotate\",t)),this._pitching&&this.fire(new e.k(\"pitch\",t))}_afterEase(t,r){if(this._easeId&&r&&this._easeId===r)return;delete this._easeId;const n=this._zooming,i=this._rotating,a=this._pitching;this._moving=!1,this._zooming=!1,this._rotating=!1,this._pitching=!1,this._padding=!1,n&&this.fire(new e.k(\"zoomend\",t)),i&&this.fire(new e.k(\"rotateend\",t)),a&&this.fire(new e.k(\"pitchend\",t)),this.fire(new e.k(\"moveend\",t))}flyTo(t,r){var n;if(!t.essential&&a.prefersReducedMotion){const n=e.M(t,[\"center\",\"zoom\",\"bearing\",\"pitch\",\"around\"]);return this.jumpTo(n,r)}this.stop(),t=e.e({offset:[0,0],speed:1.2,curve:1.42,easing:e.b9},t);const i=this._getTransformForUpdate(),o=i.zoom,s=i.bearing,l=i.pitch,c=i.padding,u=\"bearing\"in t?this._normalizeBearing(t.bearing,s):s,h=\"pitch\"in t?+t.pitch:l,f=\"padding\"in t?t.padding:i.padding,p=e.P.convert(t.offset);let d=i.centerPoint.add(p);const m=i.pointLocation(d),{center:g,zoom:y}=i.getConstrained(e.N.convert(t.center||m),null!==(n=t.zoom)&&void 0!==n?n:o);this._normalizeCenter(g,i);const v=i.zoomScale(y-o),x=i.project(m),_=i.project(g).sub(x);let b=t.curve;const w=Math.max(i.width,i.height),T=w/v,k=_.mag();if(\"minZoom\"in t){const r=e.ad(Math.min(t.minZoom,o,y),i.minZoom,i.maxZoom),n=w/i.zoomScale(r-o);b=Math.sqrt(n/k*2)}const A=b*b;function M(t){const e=(T*T-w*w+(t?-1:1)*A*A*k*k)/(2*(t?T:w)*A*k);return Math.log(Math.sqrt(e*e+1)-e)}function S(t){return(Math.exp(t)-Math.exp(-t))/2}function E(t){return(Math.exp(t)+Math.exp(-t))/2}const C=M(!1);let L=function(t){return E(C)/E(C+b*t)},I=function(t){return w*((E(C)*(S(e=C+b*t)/E(e))-S(C))/A)/k;var e},P=(M(!0)-C)/b;if(Math.abs(k)<1e-6||!isFinite(P)){if(Math.abs(w-T)<1e-6)return this.easeTo(t,r);const e=T0,L=t=>Math.exp(e*b*t)}if(\"duration\"in t)t.duration=+t.duration;else{const e=\"screenSpeed\"in t?+t.screenSpeed/b:+t.speed;t.duration=1e3*P/e}return t.maxDuration&&t.duration>t.maxDuration&&(t.duration=0),this._zooming=!0,this._rotating=s!==u,this._pitching=h!==l,this._padding=!i.isPaddingEqual(f),this._prepareEase(r,!1),this.terrain&&this._prepareElevation(g),this._ease((n=>{const a=n*P,m=1/L(a);i.zoom=1===n?y:o+i.scaleZoom(m),this._rotating&&(i.bearing=e.z.number(s,u,n)),this._pitching&&(i.pitch=e.z.number(l,h,n)),this._padding&&(i.interpolatePadding(c,f,n),d=i.centerPoint.add(p)),this.terrain&&!t.freezeElevation&&this._updateElevation(n);const v=1===n?g:i.unproject(x.add(_.mult(I(a))).mult(m));i.setLocationAtPoint(i.renderWorldCopies?v.wrap():v,d),this._applyUpdatedTransform(i),this._fireMoveEvents(r)}),(()=>{this.terrain&&t.freezeElevation&&this._finalizeElevation(),this._afterEase(r)}),t),this}isEasing(){return!!this._easeFrameId}stop(){return this._stop()}_stop(t,e){var r;if(this._easeFrameId&&(this._cancelRenderFrame(this._easeFrameId),delete this._easeFrameId,delete this._onEaseFrame),this._onEaseEnd){const t=this._onEaseEnd;delete this._onEaseEnd,t.call(this,e)}return t||null===(r=this.handlers)||void 0===r||r.stop(!1),this}_ease(t,e,r){!1===r.animate||0===r.duration?(t(1),e()):(this._easeStart=a.now(),this._easeOptions=r,this._onEaseFrame=t,this._onEaseEnd=e,this._easeFrameId=this._requestRenderFrame(this._renderFrameCallback))}_normalizeBearing(t,r){t=e.b3(t,-180,180);const n=Math.abs(t-r);return Math.abs(t-360-r)180?-360:r<-180?360:0}queryTerrainElevation(t){return this.terrain?this.terrain.getElevationForLngLatZoom(e.N.convert(t),this.transform.tileZoom)-this.transform.elevation:null}}const Ci={compact:!0,customAttribution:'MapLibre'};class Li{constructor(t=Ci){this._toggleAttribution=()=>{this._container.classList.contains(\"maplibregl-compact\")&&(this._container.classList.contains(\"maplibregl-compact-show\")?(this._container.setAttribute(\"open\",\"\"),this._container.classList.remove(\"maplibregl-compact-show\")):(this._container.classList.add(\"maplibregl-compact-show\"),this._container.removeAttribute(\"open\")))},this._updateData=t=>{!t||\"metadata\"!==t.sourceDataType&&\"visibility\"!==t.sourceDataType&&\"style\"!==t.dataType&&\"terrain\"!==t.type||this._updateAttributions()},this._updateCompact=()=>{this._map.getCanvasContainer().offsetWidth<=640||this._compact?!1===this._compact?this._container.setAttribute(\"open\",\"\"):this._container.classList.contains(\"maplibregl-compact\")||this._container.classList.contains(\"maplibregl-attrib-empty\")||(this._container.setAttribute(\"open\",\"\"),this._container.classList.add(\"maplibregl-compact\",\"maplibregl-compact-show\")):(this._container.setAttribute(\"open\",\"\"),this._container.classList.contains(\"maplibregl-compact\")&&this._container.classList.remove(\"maplibregl-compact\",\"maplibregl-compact-show\"))},this._updateCompactMinimize=()=>{this._container.classList.contains(\"maplibregl-compact\")&&this._container.classList.contains(\"maplibregl-compact-show\")&&this._container.classList.remove(\"maplibregl-compact-show\")},this.options=t}getDefaultPosition(){return\"bottom-right\"}onAdd(t){return this._map=t,this._compact=this.options.compact,this._container=o.create(\"details\",\"maplibregl-ctrl maplibregl-ctrl-attrib\"),this._compactButton=o.create(\"summary\",\"maplibregl-ctrl-attrib-button\",this._container),this._compactButton.addEventListener(\"click\",this._toggleAttribution),this._setElementTitle(this._compactButton,\"ToggleAttribution\"),this._innerContainer=o.create(\"div\",\"maplibregl-ctrl-attrib-inner\",this._container),this._updateAttributions(),this._updateCompact(),this._map.on(\"styledata\",this._updateData),this._map.on(\"sourcedata\",this._updateData),this._map.on(\"terrain\",this._updateData),this._map.on(\"resize\",this._updateCompact),this._map.on(\"drag\",this._updateCompactMinimize),this._container}onRemove(){o.remove(this._container),this._map.off(\"styledata\",this._updateData),this._map.off(\"sourcedata\",this._updateData),this._map.off(\"terrain\",this._updateData),this._map.off(\"resize\",this._updateCompact),this._map.off(\"drag\",this._updateCompactMinimize),this._map=void 0,this._compact=void 0,this._attribHTML=void 0}_setElementTitle(t,e){const r=this._map._getUIString(`AttributionControl.${e}`);t.title=r,t.setAttribute(\"aria-label\",r)}_updateAttributions(){if(!this._map.style)return;let t=[];if(this.options.customAttribution&&(Array.isArray(this.options.customAttribution)?t=t.concat(this.options.customAttribution.map((t=>\"string\"!=typeof t?\"\":t))):\"string\"==typeof this.options.customAttribution&&t.push(this.options.customAttribution)),this._map.style.stylesheet){const t=this._map.style.stylesheet;this.styleOwner=t.owner,this.styleId=t.id}const e=this._map.style.sourceCaches;for(const r in e){const n=e[r];if(n.used||n.usedForTerrain){const e=n.getSource();e.attribution&&t.indexOf(e.attribution)<0&&t.push(e.attribution)}}t=t.filter((t=>String(t).trim())),t.sort(((t,e)=>t.length-e.length)),t=t.filter(((e,r)=>{for(let n=r+1;n=0)return!1;return!0}));const r=t.join(\" | \");r!==this._attribHTML&&(this._attribHTML=r,t.length?(this._innerContainer.innerHTML=r,this._container.classList.remove(\"maplibregl-attrib-empty\")):this._container.classList.add(\"maplibregl-attrib-empty\"),this._updateCompact(),this._editLink=null)}}class Ii{constructor(t={}){this._updateCompact=()=>{const t=this._container.children;if(t.length){const e=t[0];this._map.getCanvasContainer().offsetWidth<=640||this._compact?!1!==this._compact&&e.classList.add(\"maplibregl-compact\"):e.classList.remove(\"maplibregl-compact\")}},this.options=t}getDefaultPosition(){return\"bottom-left\"}onAdd(t){this._map=t,this._compact=this.options&&this.options.compact,this._container=o.create(\"div\",\"maplibregl-ctrl\");const e=o.create(\"a\",\"maplibregl-ctrl-logo\");return e.target=\"_blank\",e.rel=\"noopener nofollow\",e.href=\"https://maplibre.org/\",e.setAttribute(\"aria-label\",this._map._getUIString(\"LogoControl.Title\")),e.setAttribute(\"rel\",\"noopener nofollow\"),this._container.appendChild(e),this._container.style.display=\"block\",this._map.on(\"resize\",this._updateCompact),this._updateCompact(),this._container}onRemove(){o.remove(this._container),this._map.off(\"resize\",this._updateCompact),this._map=void 0,this._compact=void 0}}class Pi{constructor(){this._queue=[],this._id=0,this._cleared=!1,this._currentlyRunning=!1}add(t){const e=++this._id;return this._queue.push({callback:t,id:e,cancelled:!1}),e}remove(t){const e=this._currentlyRunning,r=e?this._queue.concat(e):this._queue;for(const e of r)if(e.id===t)return void(e.cancelled=!0)}run(t=0){if(this._currentlyRunning)throw new Error(\"Attempting to run(), but is already running.\");const e=this._currentlyRunning=this._queue;this._queue=[];for(const r of e)if(!r.cancelled&&(r.callback(t),this._cleared))break;this._cleared=!1,this._currentlyRunning=!1}clear(){this._currentlyRunning&&(this._cleared=!0),this._queue=[]}}var zi=e.Y([{name:\"a_pos3d\",type:\"Int16\",components:3}]);class Oi extends e.E{constructor(t){super(),this.sourceCache=t,this._tiles={},this._renderableTilesKeys=[],this._sourceTileCache={},this.minzoom=0,this.maxzoom=22,this.tileSize=512,this.deltaZoom=1,t.usedForTerrain=!0,t.tileSize=this.tileSize*2**this.deltaZoom}destruct(){this.sourceCache.usedForTerrain=!1,this.sourceCache.tileSize=null}update(t,r){this.sourceCache.update(t,r),this._renderableTilesKeys=[];const n={};for(const i of t.coveringTiles({tileSize:this.tileSize,minzoom:this.minzoom,maxzoom:this.maxzoom,reparseOverscaled:!1,terrain:r}))n[i.key]=!0,this._renderableTilesKeys.push(i.key),this._tiles[i.key]||(i.posMatrix=new Float64Array(16),e.aQ(i.posMatrix,0,e.X,0,e.X,0,1),this._tiles[i.key]=new ht(i,this.tileSize));for(const t in this._tiles)n[t]||delete this._tiles[t]}freeRtt(t){for(const e in this._tiles){const r=this._tiles[e];(!t||r.tileID.equals(t)||r.tileID.isChildOf(t)||t.isChildOf(r.tileID))&&(r.rtt=[])}}getRenderableTiles(){return this._renderableTilesKeys.map((t=>this.getTileByID(t)))}getTileByID(t){return this._tiles[t]}getTerrainCoords(t){const r={};for(const n of this._renderableTilesKeys){const i=this._tiles[n].tileID;if(i.canonical.equals(t.canonical)){const i=t.clone();i.posMatrix=new Float64Array(16),e.aQ(i.posMatrix,0,e.X,0,e.X,0,1),r[n]=i}else if(i.canonical.isChildOf(t.canonical)){const a=t.clone();a.posMatrix=new Float64Array(16);const o=i.canonical.z-t.canonical.z,s=i.canonical.x-(i.canonical.x>>o<>o<>o;e.aQ(a.posMatrix,0,c,0,c,0,1),e.J(a.posMatrix,a.posMatrix,[-s*c,-l*c,0]),r[n]=a}else if(t.canonical.isChildOf(i.canonical)){const a=t.clone();a.posMatrix=new Float64Array(16);const o=t.canonical.z-i.canonical.z,s=t.canonical.x-(t.canonical.x>>o<>o<>o;e.aQ(a.posMatrix,0,e.X,0,e.X,0,1),e.J(a.posMatrix,a.posMatrix,[s*c,l*c,0]),e.K(a.posMatrix,a.posMatrix,[1/2**o,1/2**o,0]),r[n]=a}}return r}getSourceTile(t,e){const r=this.sourceCache._source;let n=t.overscaledZ-this.deltaZoom;if(n>r.maxzoom&&(n=r.maxzoom),n=r.minzoom&&(!i||!i.dem);)i=this.sourceCache.getTileByID(t.scaledTo(n--).key);return i}tilesAfterTime(t=Date.now()){return Object.values(this._tiles).filter((e=>e.timeAdded>=t))}}class Di{constructor(t,e,r){this.painter=t,this.sourceCache=new Oi(e),this.options=r,this.exaggeration=\"number\"==typeof r.exaggeration?r.exaggeration:1,this.qualityFactor=2,this.meshSize=128,this._demMatrixCache={},this.coordsIndex=[],this._coordsTextureSize=1024}getDEMElevation(t,r,n,i=e.X){var a;if(!(r>=0&&r=0&&nt.canonical.z&&(t.canonical.z>=n?i=t.canonical.z-n:e.w(\"cannot calculate elevation if elevation maxzoom > source.maxzoom\"));const a=t.canonical.x-(t.canonical.x>>i<>i<>8<<4|t>>8,r[e+3]=0;const n=new e.R({width:this._coordsTextureSize,height:this._coordsTextureSize},new Uint8Array(r.buffer)),i=new w(t,n,t.gl.RGBA,{premultiply:!1});return i.bind(t.gl.NEAREST,t.gl.CLAMP_TO_EDGE),this._coordsTexture=i,i}pointCoordinate(t){this.painter.maybeDrawDepthAndCoords(!0);const r=new Uint8Array(4),n=this.painter.context,i=n.gl,a=Math.round(t.x*this.painter.pixelRatio/devicePixelRatio),o=Math.round(t.y*this.painter.pixelRatio/devicePixelRatio),s=Math.round(this.painter.height/devicePixelRatio);n.bindFramebuffer.set(this.getFramebuffer(\"coords\").framebuffer),i.readPixels(a,s-o-1,1,1,i.RGBA,i.UNSIGNED_BYTE,r),n.bindFramebuffer.set(null);const l=r[0]+(r[2]>>4<<8),c=r[1]+((15&r[2])<<8),u=this.coordsIndex[255-r[3]],h=u&&this.sourceCache.getTileByID(u);if(!h)return null;const f=this._coordsTextureSize,p=(1<t.id!==e)),this._recentlyUsed.push(t.id)}stampObject(t){t.stamp=++this._stamp}getOrCreateFreeObject(){for(const t of this._recentlyUsed)if(!this._objects[t].inUse)return this._objects[t];if(this._objects.length>=this._size)throw new Error(\"No free RenderPool available, call freeAllObjects() required!\");const t=this._createObject(this._objects.length);return this._objects.push(t),t}freeObject(t){t.inUse=!1}freeAllObjects(){for(const t of this._objects)this.freeObject(t)}isFull(){return!(this._objects.length!t.inUse))}}const Fi={background:!0,fill:!0,line:!0,raster:!0,hillshade:!0};class Bi{constructor(t,e){this.painter=t,this.terrain=e,this.pool=new Ri(t.context,30,e.sourceCache.tileSize*e.qualityFactor)}destruct(){this.pool.destruct()}getTexture(t){return this.pool.getObjectForId(t.rtt[this._stacks.length-1].id).texture}prepareForRender(t,e){this._stacks=[],this._prevType=null,this._rttTiles=[],this._renderableTiles=this.terrain.sourceCache.getRenderableTiles(),this._renderableLayerIds=t._order.filter((r=>!t._layers[r].isHidden(e))),this._coordsDescendingInv={};for(const e in t.sourceCaches){this._coordsDescendingInv[e]={};const r=t.sourceCaches[e].getVisibleCoordinates();for(const t of r){const r=this.terrain.sourceCache.getTerrainCoords(t);for(const t in r)this._coordsDescendingInv[e][t]||(this._coordsDescendingInv[e][t]=[]),this._coordsDescendingInv[e][t].push(r[t])}}this._coordsDescendingInvStr={};for(const e of t._order){const r=t._layers[e],n=r.source;if(Fi[r.type]&&!this._coordsDescendingInvStr[n]){this._coordsDescendingInvStr[n]={};for(const t in this._coordsDescendingInv[n])this._coordsDescendingInvStr[n][t]=this._coordsDescendingInv[n][t].map((t=>t.key)).sort().join()}}for(const t of this._renderableTiles)for(const e in this._coordsDescendingInvStr){const r=this._coordsDescendingInvStr[e][t.tileID.key];r&&r!==t.rttCoords[e]&&(t.rtt=[])}}renderLayer(t){if(t.isHidden(this.painter.transform.zoom))return!1;const r=t.type,n=this.painter,i=this._renderableLayerIds[this._renderableLayerIds.length-1]===t.id;if(Fi[r]&&(this._prevType&&Fi[this._prevType]||this._stacks.push([]),this._prevType=r,this._stacks[this._stacks.length-1].push(t.id),!i))return!0;if(Fi[this._prevType]||Fi[r]&&i){this._prevType=r;const t=this._stacks.length-1,i=this._stacks[t]||[];for(const r of this._renderableTiles){if(this.pool.isFull()&&(bn(this.painter,this.terrain,this._rttTiles),this._rttTiles=[],this.pool.freeAllObjects()),this._rttTiles.push(r),r.rtt[t]){const e=this.pool.getObjectForId(r.rtt[t].id);if(e.stamp===r.rtt[t].stamp){this.pool.useObject(e);continue}}const a=this.pool.getOrCreateFreeObject();this.pool.useObject(a),this.pool.stampObject(a),r.rtt[t]={id:a.id,stamp:a.stamp},n.context.bindFramebuffer.set(a.fbo.framebuffer),n.context.clear({color:e.aN.transparent,stencil:0}),n.currentStencilSource=void 0;for(let t=0;t{t.touchstart=t.dragStart,t.touchmoveWindow=t.dragMove,t.touchend=t.dragEnd},qi={showCompass:!0,showZoom:!0,visualizePitch:!1};class Hi{constructor(t,r,n=!1){this.mousedown=t=>{this.startMouse(e.e({},t,{ctrlKey:!0,preventDefault:()=>t.preventDefault()}),o.mousePos(this.element,t)),o.addEventListener(window,\"mousemove\",this.mousemove),o.addEventListener(window,\"mouseup\",this.mouseup)},this.mousemove=t=>{this.moveMouse(t,o.mousePos(this.element,t))},this.mouseup=t=>{this.mouseRotate.dragEnd(t),this.mousePitch&&this.mousePitch.dragEnd(t),this.offTemp()},this.touchstart=t=>{1!==t.targetTouches.length?this.reset():(this._startPos=this._lastPos=o.touchPos(this.element,t.targetTouches)[0],this.startTouch(t,this._startPos),o.addEventListener(window,\"touchmove\",this.touchmove,{passive:!1}),o.addEventListener(window,\"touchend\",this.touchend))},this.touchmove=t=>{1!==t.targetTouches.length?this.reset():(this._lastPos=o.touchPos(this.element,t.targetTouches)[0],this.moveTouch(t,this._lastPos))},this.touchend=t=>{0===t.targetTouches.length&&this._startPos&&this._lastPos&&this._startPos.dist(this._lastPos){this.mouseRotate.reset(),this.mousePitch&&this.mousePitch.reset(),this.touchRotate.reset(),this.touchPitch&&this.touchPitch.reset(),delete this._startPos,delete this._lastPos,this.offTemp()},this._clickTolerance=10;const i=t.dragRotate._mouseRotate.getClickTolerance(),a=t.dragRotate._mousePitch.getClickTolerance();this.element=r,this.mouseRotate=ei({clickTolerance:i,enable:!0}),this.touchRotate=(({enable:t,clickTolerance:e,bearingDegreesPerPixelMoved:r=.8})=>{const n=new Qn;return new $n({clickTolerance:e,move:(t,e)=>({bearingDelta:(e.x-t.x)*r}),moveStateManager:n,enable:t,assignEvents:Vi})})({clickTolerance:i,enable:!0}),this.map=t,n&&(this.mousePitch=ri({clickTolerance:a,enable:!0}),this.touchPitch=(({enable:t,clickTolerance:e,pitchDegreesPerPixelMoved:r=-.5})=>{const n=new Qn;return new $n({clickTolerance:e,move:(t,e)=>({pitchDelta:(e.y-t.y)*r}),moveStateManager:n,enable:t,assignEvents:Vi})})({clickTolerance:a,enable:!0})),o.addEventListener(r,\"mousedown\",this.mousedown),o.addEventListener(r,\"touchstart\",this.touchstart,{passive:!1}),o.addEventListener(r,\"touchcancel\",this.reset)}startMouse(t,e){this.mouseRotate.dragStart(t,e),this.mousePitch&&this.mousePitch.dragStart(t,e),o.disableDrag()}startTouch(t,e){this.touchRotate.dragStart(t,e),this.touchPitch&&this.touchPitch.dragStart(t,e),o.disableDrag()}moveMouse(t,e){const r=this.map,{bearingDelta:n}=this.mouseRotate.dragMove(t,e)||{};if(n&&r.setBearing(r.getBearing()+n),this.mousePitch){const{pitchDelta:n}=this.mousePitch.dragMove(t,e)||{};n&&r.setPitch(r.getPitch()+n)}}moveTouch(t,e){const r=this.map,{bearingDelta:n}=this.touchRotate.dragMove(t,e)||{};if(n&&r.setBearing(r.getBearing()+n),this.touchPitch){const{pitchDelta:n}=this.touchPitch.dragMove(t,e)||{};n&&r.setPitch(r.getPitch()+n)}}off(){const t=this.element;o.removeEventListener(t,\"mousedown\",this.mousedown),o.removeEventListener(t,\"touchstart\",this.touchstart,{passive:!1}),o.removeEventListener(window,\"touchmove\",this.touchmove,{passive:!1}),o.removeEventListener(window,\"touchend\",this.touchend),o.removeEventListener(t,\"touchcancel\",this.reset),this.offTemp()}offTemp(){o.enableDrag(),o.removeEventListener(window,\"mousemove\",this.mousemove),o.removeEventListener(window,\"mouseup\",this.mouseup),o.removeEventListener(window,\"touchmove\",this.touchmove,{passive:!1}),o.removeEventListener(window,\"touchend\",this.touchend)}}let Gi;function Zi(t,r,n){const i=new e.N(t.lng,t.lat);if(t=new e.N(t.lng,t.lat),r){const i=new e.N(t.lng-360,t.lat),a=new e.N(t.lng+360,t.lat),o=n.locationPoint(t).distSqr(r);n.locationPoint(i).distSqr(r)180;){const e=n.locationPoint(t);if(e.x>=0&&e.y>=0&&e.x<=n.width&&e.y<=n.height)break;t.lng>n.center.lng?t.lng-=360:t.lng+=360}return t.lng!==i.lng&&n.locationPoint(t).y>n.height/2-n.getHorizon()?t:i}const Wi={center:\"translate(-50%,-50%)\",top:\"translate(-50%,0)\",\"top-left\":\"translate(0,0)\",\"top-right\":\"translate(-100%,0)\",bottom:\"translate(-50%,-100%)\",\"bottom-left\":\"translate(0,-100%)\",\"bottom-right\":\"translate(-100%,-100%)\",left:\"translate(0,-50%)\",right:\"translate(-100%,-50%)\"};function Yi(t,e,r){const n=t.classList;for(const t in Wi)n.remove(`maplibregl-${r}-anchor-${t}`);n.add(`maplibregl-${r}-anchor-${e}`)}class Xi extends e.E{constructor(t){if(super(),this._onKeyPress=t=>{const e=t.code,r=t.charCode||t.keyCode;\"Space\"!==e&&\"Enter\"!==e&&32!==r&&13!==r||this.togglePopup()},this._onMapClick=t=>{const e=t.originalEvent.target,r=this._element;this._popup&&(e===r||r.contains(e))&&this.togglePopup()},this._update=t=>{var e;if(!this._map)return;const r=this._map.loaded()&&!this._map.isMoving();(\"terrain\"===(null==t?void 0:t.type)||\"render\"===(null==t?void 0:t.type)&&!r)&&this._map.once(\"render\",this._update),this._map.transform.renderWorldCopies?this._lngLat=Zi(this._lngLat,this._flatPos,this._map.transform):this._lngLat=null===(e=this._lngLat)||void 0===e?void 0:e.wrap(),this._flatPos=this._pos=this._map.project(this._lngLat)._add(this._offset),this._map.terrain&&(this._flatPos=this._map.transform.locationPoint(this._lngLat)._add(this._offset));let n=\"\";\"viewport\"===this._rotationAlignment||\"auto\"===this._rotationAlignment?n=`rotateZ(${this._rotation}deg)`:\"map\"===this._rotationAlignment&&(n=`rotateZ(${this._rotation-this._map.getBearing()}deg)`);let i=\"\";\"viewport\"===this._pitchAlignment||\"auto\"===this._pitchAlignment?i=\"rotateX(0deg)\":\"map\"===this._pitchAlignment&&(i=`rotateX(${this._map.getPitch()}deg)`),this._subpixelPositioning||t&&\"moveend\"!==t.type||(this._pos=this._pos.round()),o.setTransform(this._element,`${Wi[this._anchor]} translate(${this._pos.x}px, ${this._pos.y}px) ${i} ${n}`),a.frameAsync(new AbortController).then((()=>{this._updateOpacity(t&&\"moveend\"===t.type)})).catch((()=>{}))},this._onMove=t=>{if(!this._isDragging){const e=this._clickTolerance||this._map._clickTolerance;this._isDragging=t.point.dist(this._pointerdownPos)>=e}this._isDragging&&(this._pos=t.point.sub(this._positionDelta),this._lngLat=this._map.unproject(this._pos),this.setLngLat(this._lngLat),this._element.style.pointerEvents=\"none\",\"pending\"===this._state&&(this._state=\"active\",this.fire(new e.k(\"dragstart\"))),this.fire(new e.k(\"drag\")))},this._onUp=()=>{this._element.style.pointerEvents=\"auto\",this._positionDelta=null,this._pointerdownPos=null,this._isDragging=!1,this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),\"active\"===this._state&&this.fire(new e.k(\"dragend\")),this._state=\"inactive\"},this._addDragHandler=t=>{this._element.contains(t.originalEvent.target)&&(t.preventDefault(),this._positionDelta=t.point.sub(this._pos).add(this._offset),this._pointerdownPos=t.point,this._state=\"pending\",this._map.on(\"mousemove\",this._onMove),this._map.on(\"touchmove\",this._onMove),this._map.once(\"mouseup\",this._onUp),this._map.once(\"touchend\",this._onUp))},this._anchor=t&&t.anchor||\"center\",this._color=t&&t.color||\"#3FB1CE\",this._scale=t&&t.scale||1,this._draggable=t&&t.draggable||!1,this._clickTolerance=t&&t.clickTolerance||0,this._subpixelPositioning=t&&t.subpixelPositioning||!1,this._isDragging=!1,this._state=\"inactive\",this._rotation=t&&t.rotation||0,this._rotationAlignment=t&&t.rotationAlignment||\"auto\",this._pitchAlignment=t&&t.pitchAlignment&&\"auto\"!==t.pitchAlignment?t.pitchAlignment:this._rotationAlignment,this.setOpacity(),this.setOpacity(null==t?void 0:t.opacity,null==t?void 0:t.opacityWhenCovered),t&&t.element)this._element=t.element,this._offset=e.P.convert(t&&t.offset||[0,0]);else{this._defaultMarker=!0,this._element=o.create(\"div\");const r=o.createNS(\"http://www.w3.org/2000/svg\",\"svg\"),n=41,i=27;r.setAttributeNS(null,\"display\",\"block\"),r.setAttributeNS(null,\"height\",`${n}px`),r.setAttributeNS(null,\"width\",`${i}px`),r.setAttributeNS(null,\"viewBox\",`0 0 ${i} ${n}`);const a=o.createNS(\"http://www.w3.org/2000/svg\",\"g\");a.setAttributeNS(null,\"stroke\",\"none\"),a.setAttributeNS(null,\"stroke-width\",\"1\"),a.setAttributeNS(null,\"fill\",\"none\"),a.setAttributeNS(null,\"fill-rule\",\"evenodd\");const s=o.createNS(\"http://www.w3.org/2000/svg\",\"g\");s.setAttributeNS(null,\"fill-rule\",\"nonzero\");const l=o.createNS(\"http://www.w3.org/2000/svg\",\"g\");l.setAttributeNS(null,\"transform\",\"translate(3.0, 29.0)\"),l.setAttributeNS(null,\"fill\",\"#000000\");const c=[{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"10.5\",ry:\"5.25002273\"},{rx:\"9.5\",ry:\"4.77275007\"},{rx:\"8.5\",ry:\"4.29549936\"},{rx:\"7.5\",ry:\"3.81822308\"},{rx:\"6.5\",ry:\"3.34094679\"},{rx:\"5.5\",ry:\"2.86367051\"},{rx:\"4.5\",ry:\"2.38636864\"}];for(const t of c){const e=o.createNS(\"http://www.w3.org/2000/svg\",\"ellipse\");e.setAttributeNS(null,\"opacity\",\"0.04\"),e.setAttributeNS(null,\"cx\",\"10.5\"),e.setAttributeNS(null,\"cy\",\"5.80029008\"),e.setAttributeNS(null,\"rx\",t.rx),e.setAttributeNS(null,\"ry\",t.ry),l.appendChild(e)}const u=o.createNS(\"http://www.w3.org/2000/svg\",\"g\");u.setAttributeNS(null,\"fill\",this._color);const h=o.createNS(\"http://www.w3.org/2000/svg\",\"path\");h.setAttributeNS(null,\"d\",\"M27,13.5 C27,19.074644 20.250001,27.000002 14.75,34.500002 C14.016665,35.500004 12.983335,35.500004 12.25,34.500002 C6.7499993,27.000002 0,19.222562 0,13.5 C0,6.0441559 6.0441559,0 13.5,0 C20.955844,0 27,6.0441559 27,13.5 Z\"),u.appendChild(h);const f=o.createNS(\"http://www.w3.org/2000/svg\",\"g\");f.setAttributeNS(null,\"opacity\",\"0.25\"),f.setAttributeNS(null,\"fill\",\"#000000\");const p=o.createNS(\"http://www.w3.org/2000/svg\",\"path\");p.setAttributeNS(null,\"d\",\"M13.5,0 C6.0441559,0 0,6.0441559 0,13.5 C0,19.222562 6.7499993,27 12.25,34.5 C13,35.522727 14.016664,35.500004 14.75,34.5 C20.250001,27 27,19.074644 27,13.5 C27,6.0441559 20.955844,0 13.5,0 Z M13.5,1 C20.415404,1 26,6.584596 26,13.5 C26,15.898657 24.495584,19.181431 22.220703,22.738281 C19.945823,26.295132 16.705119,30.142167 13.943359,33.908203 C13.743445,34.180814 13.612715,34.322738 13.5,34.441406 C13.387285,34.322738 13.256555,34.180814 13.056641,33.908203 C10.284481,30.127985 7.4148684,26.314159 5.015625,22.773438 C2.6163816,19.232715 1,15.953538 1,13.5 C1,6.584596 6.584596,1 13.5,1 Z\"),f.appendChild(p);const d=o.createNS(\"http://www.w3.org/2000/svg\",\"g\");d.setAttributeNS(null,\"transform\",\"translate(6.0, 7.0)\"),d.setAttributeNS(null,\"fill\",\"#FFFFFF\");const m=o.createNS(\"http://www.w3.org/2000/svg\",\"g\");m.setAttributeNS(null,\"transform\",\"translate(8.0, 8.0)\");const g=o.createNS(\"http://www.w3.org/2000/svg\",\"circle\");g.setAttributeNS(null,\"fill\",\"#000000\"),g.setAttributeNS(null,\"opacity\",\"0.25\"),g.setAttributeNS(null,\"cx\",\"5.5\"),g.setAttributeNS(null,\"cy\",\"5.5\"),g.setAttributeNS(null,\"r\",\"5.4999962\");const y=o.createNS(\"http://www.w3.org/2000/svg\",\"circle\");y.setAttributeNS(null,\"fill\",\"#FFFFFF\"),y.setAttributeNS(null,\"cx\",\"5.5\"),y.setAttributeNS(null,\"cy\",\"5.5\"),y.setAttributeNS(null,\"r\",\"5.4999962\"),m.appendChild(g),m.appendChild(y),s.appendChild(l),s.appendChild(u),s.appendChild(f),s.appendChild(d),s.appendChild(m),r.appendChild(s),r.setAttributeNS(null,\"height\",n*this._scale+\"px\"),r.setAttributeNS(null,\"width\",i*this._scale+\"px\"),this._element.appendChild(r),this._offset=e.P.convert(t&&t.offset||[0,-14])}if(this._element.classList.add(\"maplibregl-marker\"),this._element.addEventListener(\"dragstart\",(t=>{t.preventDefault()})),this._element.addEventListener(\"mousedown\",(t=>{t.preventDefault()})),Yi(this._element,this._anchor,\"marker\"),t&&t.className)for(const e of t.className.split(\" \"))this._element.classList.add(e);this._popup=null}addTo(t){return this.remove(),this._map=t,this._element.setAttribute(\"aria-label\",t._getUIString(\"Marker.Title\")),t.getCanvasContainer().appendChild(this._element),t.on(\"move\",this._update),t.on(\"moveend\",this._update),t.on(\"terrain\",this._update),this.setDraggable(this._draggable),this._update(),this._map.on(\"click\",this._onMapClick),this}remove(){return this._opacityTimeout&&(clearTimeout(this._opacityTimeout),delete this._opacityTimeout),this._map&&(this._map.off(\"click\",this._onMapClick),this._map.off(\"move\",this._update),this._map.off(\"moveend\",this._update),this._map.off(\"terrain\",this._update),this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler),this._map.off(\"mouseup\",this._onUp),this._map.off(\"touchend\",this._onUp),this._map.off(\"mousemove\",this._onMove),this._map.off(\"touchmove\",this._onMove),delete this._map),o.remove(this._element),this._popup&&this._popup.remove(),this}getLngLat(){return this._lngLat}setLngLat(t){return this._lngLat=e.N.convert(t),this._pos=null,this._popup&&this._popup.setLngLat(this._lngLat),this._update(),this}getElement(){return this._element}setPopup(t){if(this._popup&&(this._popup.remove(),this._popup=null,this._element.removeEventListener(\"keypress\",this._onKeyPress),this._originalTabIndex||this._element.removeAttribute(\"tabindex\")),t){if(!(\"offset\"in t.options)){const e=38.1,r=13.5,n=Math.abs(r)/Math.SQRT2;t.options.offset=this._defaultMarker?{top:[0,0],\"top-left\":[0,0],\"top-right\":[0,0],bottom:[0,-e],\"bottom-left\":[n,-1*(e-r+n)],\"bottom-right\":[-n,-1*(e-r+n)],left:[r,-1*(e-r)],right:[-r,-1*(e-r)]}:this._offset}this._popup=t,this._originalTabIndex=this._element.getAttribute(\"tabindex\"),this._originalTabIndex||this._element.setAttribute(\"tabindex\",\"0\"),this._element.addEventListener(\"keypress\",this._onKeyPress)}return this}setSubpixelPositioning(t){return this._subpixelPositioning=t,this}getPopup(){return this._popup}togglePopup(){const t=this._popup;return this._element.style.opacity===this._opacityWhenCovered?this:t?(t.isOpen()?t.remove():(t.setLngLat(this._lngLat),t.addTo(this._map)),this):this}_updateOpacity(t=!1){var r,n;if(!(null===(r=this._map)||void 0===r?void 0:r.terrain))return void(this._element.style.opacity!==this._opacity&&(this._element.style.opacity=this._opacity));if(t)this._opacityTimeout=null;else{if(this._opacityTimeout)return;this._opacityTimeout=setTimeout((()=>{this._opacityTimeout=null}),100)}const i=this._map,a=i.terrain.depthAtPoint(this._pos),o=i.terrain.getElevationForLngLatZoom(this._lngLat,i.transform.tileZoom);if(i.transform.lngLatToCameraDepth(this._lngLat,o)-a<.006)return void(this._element.style.opacity=this._opacity);const s=-this._offset.y/i.transform._pixelPerMeter,l=Math.sin(i.getPitch()*Math.PI/180)*s,c=i.terrain.depthAtPoint(new e.P(this._pos.x,this._pos.y-this._offset.y)),u=i.transform.lngLatToCameraDepth(this._lngLat,o+l)-c>.006;(null===(n=this._popup)||void 0===n?void 0:n.isOpen())&&u&&this._popup.remove(),this._element.style.opacity=u?this._opacityWhenCovered:this._opacity}getOffset(){return this._offset}setOffset(t){return this._offset=e.P.convert(t),this._update(),this}addClassName(t){this._element.classList.add(t)}removeClassName(t){this._element.classList.remove(t)}toggleClassName(t){return this._element.classList.toggle(t)}setDraggable(t){return this._draggable=!!t,this._map&&(t?(this._map.on(\"mousedown\",this._addDragHandler),this._map.on(\"touchstart\",this._addDragHandler)):(this._map.off(\"mousedown\",this._addDragHandler),this._map.off(\"touchstart\",this._addDragHandler))),this}isDraggable(){return this._draggable}setRotation(t){return this._rotation=t||0,this._update(),this}getRotation(){return this._rotation}setRotationAlignment(t){return this._rotationAlignment=t||\"auto\",this._update(),this}getRotationAlignment(){return this._rotationAlignment}setPitchAlignment(t){return this._pitchAlignment=t&&\"auto\"!==t?t:this._rotationAlignment,this._update(),this}getPitchAlignment(){return this._pitchAlignment}setOpacity(t,e){return void 0===t&&void 0===e&&(this._opacity=\"1\",this._opacityWhenCovered=\"0.2\"),void 0!==t&&(this._opacity=t),void 0!==e&&(this._opacityWhenCovered=e),this._map&&this._updateOpacity(!0),this}}const $i={positionOptions:{enableHighAccuracy:!1,maximumAge:0,timeout:6e3},fitBoundsOptions:{maxZoom:15},trackUserLocation:!1,showAccuracyCircle:!0,showUserLocation:!0};let Ji=0,Ki=!1;class Qi extends e.E{constructor(t){super(),this._onSuccess=t=>{if(this._map){if(this._isOutOfMapMaxBounds(t))return this._setErrorState(),this.fire(new e.k(\"outofmaxbounds\",t)),this._updateMarker(),void this._finish();if(this.options.trackUserLocation)switch(this._lastKnownPosition=t,this._watchState){case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-active\");break;case\"BACKGROUND\":case\"BACKGROUND_ERROR\":this._watchState=\"BACKGROUND\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-background\");break;default:throw new Error(`Unexpected watchState ${this._watchState}`)}this.options.showUserLocation&&\"OFF\"!==this._watchState&&this._updateMarker(t),this.options.trackUserLocation&&\"ACTIVE_LOCK\"!==this._watchState||this._updateCamera(t),this.options.showUserLocation&&this._dotElement.classList.remove(\"maplibregl-user-location-dot-stale\"),this.fire(new e.k(\"geolocate\",t)),this._finish()}},this._updateCamera=t=>{const r=new e.N(t.coords.longitude,t.coords.latitude),n=t.coords.accuracy,i=this._map.getBearing(),a=e.e({bearing:i},this.options.fitBoundsOptions),o=X.fromLngLat(r,n);this._map.fitBounds(o,a,{geolocateSource:!0})},this._updateMarker=t=>{if(t){const r=new e.N(t.coords.longitude,t.coords.latitude);this._accuracyCircleMarker.setLngLat(r).addTo(this._map),this._userLocationDotMarker.setLngLat(r).addTo(this._map),this._accuracy=t.coords.accuracy,this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()}else this._userLocationDotMarker.remove(),this._accuracyCircleMarker.remove()},this._onZoom=()=>{this.options.showUserLocation&&this.options.showAccuracyCircle&&this._updateCircleRadius()},this._onError=t=>{if(this._map){if(this.options.trackUserLocation)if(1===t.code){this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-background-error\"),this._geolocateButton.disabled=!0;const t=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.title=t,this._geolocateButton.setAttribute(\"aria-label\",t),void 0!==this._geolocationWatchID&&this._clearWatch()}else{if(3===t.code&&Ki)return;this._setErrorState()}\"OFF\"!==this._watchState&&this.options.showUserLocation&&this._dotElement.classList.add(\"maplibregl-user-location-dot-stale\"),this.fire(new e.k(\"error\",t)),this._finish()}},this._finish=()=>{this._timeoutId&&clearTimeout(this._timeoutId),this._timeoutId=void 0},this._setupUI=()=>{this._map&&(this._container.addEventListener(\"contextmenu\",(t=>t.preventDefault())),this._geolocateButton=o.create(\"button\",\"maplibregl-ctrl-geolocate\",this._container),o.create(\"span\",\"maplibregl-ctrl-icon\",this._geolocateButton).setAttribute(\"aria-hidden\",\"true\"),this._geolocateButton.type=\"button\",this._geolocateButton.disabled=!0)},this._finishSetupUI=t=>{if(this._map){if(!1===t){e.w(\"Geolocation support is not available so the GeolocateControl will be disabled.\");const t=this._map._getUIString(\"GeolocateControl.LocationNotAvailable\");this._geolocateButton.disabled=!0,this._geolocateButton.title=t,this._geolocateButton.setAttribute(\"aria-label\",t)}else{const t=this._map._getUIString(\"GeolocateControl.FindMyLocation\");this._geolocateButton.disabled=!1,this._geolocateButton.title=t,this._geolocateButton.setAttribute(\"aria-label\",t)}this.options.trackUserLocation&&(this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this._watchState=\"OFF\"),this.options.showUserLocation&&(this._dotElement=o.create(\"div\",\"maplibregl-user-location-dot\"),this._userLocationDotMarker=new Xi({element:this._dotElement}),this._circleElement=o.create(\"div\",\"maplibregl-user-location-accuracy-circle\"),this._accuracyCircleMarker=new Xi({element:this._circleElement,pitchAlignment:\"map\"}),this.options.trackUserLocation&&(this._watchState=\"OFF\"),this._map.on(\"zoom\",this._onZoom)),this._geolocateButton.addEventListener(\"click\",(()=>this.trigger())),this._setup=!0,this.options.trackUserLocation&&this._map.on(\"movestart\",(t=>{const r=t.originalEvent&&\"resize\"===t.originalEvent.type;t.geolocateSource||\"ACTIVE_LOCK\"!==this._watchState||r||(this._watchState=\"BACKGROUND\",this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active\"),this.fire(new e.k(\"trackuserlocationend\")),this.fire(new e.k(\"userlocationlostfocus\")))}))}},this.options=e.e({},$i,t)}onAdd(t){return this._map=t,this._container=o.create(\"div\",\"maplibregl-ctrl maplibregl-ctrl-group\"),this._setupUI(),function(){return e._(this,arguments,void 0,(function*(t=!1){if(void 0!==Gi&&!t)return Gi;if(void 0===window.navigator.permissions)return Gi=!!window.navigator.geolocation,Gi;try{const t=yield window.navigator.permissions.query({name:\"geolocation\"});Gi=\"denied\"!==t.state}catch(t){Gi=!!window.navigator.geolocation}return Gi}))}().then((t=>this._finishSetupUI(t))),this._container}onRemove(){void 0!==this._geolocationWatchID&&(window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0),this.options.showUserLocation&&this._userLocationDotMarker&&this._userLocationDotMarker.remove(),this.options.showAccuracyCircle&&this._accuracyCircleMarker&&this._accuracyCircleMarker.remove(),o.remove(this._container),this._map.off(\"zoom\",this._onZoom),this._map=void 0,Ji=0,Ki=!1}_isOutOfMapMaxBounds(t){const e=this._map.getMaxBounds(),r=t.coords;return e&&(r.longitudee.getEast()||r.latitudee.getNorth())}_setErrorState(){switch(this._watchState){case\"WAITING_ACTIVE\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-active-error\");break;case\"ACTIVE_LOCK\":this._watchState=\"ACTIVE_ERROR\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-waiting\");break;case\"BACKGROUND\":this._watchState=\"BACKGROUND_ERROR\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-background\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-background-error\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-waiting\");break;case\"ACTIVE_ERROR\":break;default:throw new Error(`Unexpected watchState ${this._watchState}`)}}_updateCircleRadius(){const t=this._map.getBounds(),e=t.getSouthEast(),r=t.getNorthEast(),n=e.distanceTo(r),i=this._map._container.clientHeight,a=Math.ceil(this._accuracy/(n/i)*2);this._circleElement.style.width=`${a}px`,this._circleElement.style.height=`${a}px`}trigger(){if(!this._setup)return e.w(\"Geolocate control triggered before added to a map\"),!1;if(this.options.trackUserLocation){switch(this._watchState){case\"OFF\":this._watchState=\"WAITING_ACTIVE\",this.fire(new e.k(\"trackuserlocationstart\"));break;case\"WAITING_ACTIVE\":case\"ACTIVE_LOCK\":case\"ACTIVE_ERROR\":case\"BACKGROUND_ERROR\":Ji--,Ki=!1,this._watchState=\"OFF\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-active-error\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-background\"),this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-background-error\"),this.fire(new e.k(\"trackuserlocationend\"));break;case\"BACKGROUND\":this._watchState=\"ACTIVE_LOCK\",this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-background\"),this._lastKnownPosition&&this._updateCamera(this._lastKnownPosition),this.fire(new e.k(\"trackuserlocationstart\")),this.fire(new e.k(\"userlocationfocus\"));break;default:throw new Error(`Unexpected watchState ${this._watchState}`)}switch(this._watchState){case\"WAITING_ACTIVE\":this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-waiting\"),this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-active\");break;case\"ACTIVE_LOCK\":this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-active\");break;case\"OFF\":break;default:throw new Error(`Unexpected watchState ${this._watchState}`)}if(\"OFF\"===this._watchState&&void 0!==this._geolocationWatchID)this._clearWatch();else if(void 0===this._geolocationWatchID){let t;this._geolocateButton.classList.add(\"maplibregl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"true\"),Ji++,Ji>1?(t={maximumAge:6e5,timeout:0},Ki=!0):(t=this.options.positionOptions,Ki=!1),this._geolocationWatchID=window.navigator.geolocation.watchPosition(this._onSuccess,this._onError,t)}}else window.navigator.geolocation.getCurrentPosition(this._onSuccess,this._onError,this.options.positionOptions),this._timeoutId=setTimeout(this._finish,1e4);return!0}_clearWatch(){window.navigator.geolocation.clearWatch(this._geolocationWatchID),this._geolocationWatchID=void 0,this._geolocateButton.classList.remove(\"maplibregl-ctrl-geolocate-waiting\"),this._geolocateButton.setAttribute(\"aria-pressed\",\"false\"),this.options.showUserLocation&&this._updateMarker(null)}}const ta={maxWidth:100,unit:\"metric\"};function ea(t,e,r){const n=r&&r.maxWidth||100,i=t._container.clientHeight/2,a=t.unproject([0,i]),o=t.unproject([n,i]),s=a.distanceTo(o);if(r&&\"imperial\"===r.unit){const r=3.2808*s;r>5280?ra(e,n,r/5280,t._getUIString(\"ScaleControl.Miles\")):ra(e,n,r,t._getUIString(\"ScaleControl.Feet\"))}else r&&\"nautical\"===r.unit?ra(e,n,s/1852,t._getUIString(\"ScaleControl.NauticalMiles\")):s>=1e3?ra(e,n,s/1e3,t._getUIString(\"ScaleControl.Kilometers\")):ra(e,n,s,t._getUIString(\"ScaleControl.Meters\"))}function ra(t,e,r,n){const i=function(t){const e=Math.pow(10,`${Math.floor(t)}`.length-1);let r=t/e;return r=r>=10?10:r>=5?5:r>=3?3:r>=2?2:r>=1?1:function(t){const e=Math.pow(10,Math.ceil(-Math.log(t)/Math.LN10));return Math.round(t*e)/e}(r),e*r}(r),a=i/r;t.style.width=e*a+\"px\",t.innerHTML=`${i} ${n}`}class na extends e.E{constructor(t={}){super(),this._onFullscreenChange=()=>{var t;let e=window.document.fullscreenElement||window.document.mozFullScreenElement||window.document.webkitFullscreenElement||window.document.msFullscreenElement;for(;null===(t=null==e?void 0:e.shadowRoot)||void 0===t?void 0:t.fullscreenElement;)e=e.shadowRoot.fullscreenElement;e===this._container!==this._fullscreen&&this._handleFullscreenChange()},this._onClickFullscreen=()=>{this._isFullscreen()?this._exitFullscreen():this._requestFullscreen()},this._fullscreen=!1,t&&t.container&&(t.container instanceof HTMLElement?this._container=t.container:e.w(\"Full screen control 'container' must be a DOM element.\")),\"onfullscreenchange\"in document?this._fullscreenchange=\"fullscreenchange\":\"onmozfullscreenchange\"in document?this._fullscreenchange=\"mozfullscreenchange\":\"onwebkitfullscreenchange\"in document?this._fullscreenchange=\"webkitfullscreenchange\":\"onmsfullscreenchange\"in document&&(this._fullscreenchange=\"MSFullscreenChange\")}onAdd(t){return this._map=t,this._container||(this._container=this._map.getContainer()),this._controlContainer=o.create(\"div\",\"maplibregl-ctrl maplibregl-ctrl-group\"),this._setupUI(),this._controlContainer}onRemove(){o.remove(this._controlContainer),this._map=null,window.document.removeEventListener(this._fullscreenchange,this._onFullscreenChange)}_setupUI(){const t=this._fullscreenButton=o.create(\"button\",\"maplibregl-ctrl-fullscreen\",this._controlContainer);o.create(\"span\",\"maplibregl-ctrl-icon\",t).setAttribute(\"aria-hidden\",\"true\"),t.type=\"button\",this._updateTitle(),this._fullscreenButton.addEventListener(\"click\",this._onClickFullscreen),window.document.addEventListener(this._fullscreenchange,this._onFullscreenChange)}_updateTitle(){const t=this._getTitle();this._fullscreenButton.setAttribute(\"aria-label\",t),this._fullscreenButton.title=t}_getTitle(){return this._map._getUIString(this._isFullscreen()?\"FullscreenControl.Exit\":\"FullscreenControl.Enter\")}_isFullscreen(){return this._fullscreen}_handleFullscreenChange(){this._fullscreen=!this._fullscreen,this._fullscreenButton.classList.toggle(\"maplibregl-ctrl-shrink\"),this._fullscreenButton.classList.toggle(\"maplibregl-ctrl-fullscreen\"),this._updateTitle(),this._fullscreen?(this.fire(new e.k(\"fullscreenstart\")),this._prevCooperativeGesturesEnabled=this._map.cooperativeGestures.isEnabled(),this._map.cooperativeGestures.disable()):(this.fire(new e.k(\"fullscreenend\")),this._prevCooperativeGesturesEnabled&&this._map.cooperativeGestures.enable())}_exitFullscreen(){window.document.exitFullscreen?window.document.exitFullscreen():window.document.mozCancelFullScreen?window.document.mozCancelFullScreen():window.document.msExitFullscreen?window.document.msExitFullscreen():window.document.webkitCancelFullScreen?window.document.webkitCancelFullScreen():this._togglePseudoFullScreen()}_requestFullscreen(){this._container.requestFullscreen?this._container.requestFullscreen():this._container.mozRequestFullScreen?this._container.mozRequestFullScreen():this._container.msRequestFullscreen?this._container.msRequestFullscreen():this._container.webkitRequestFullscreen?this._container.webkitRequestFullscreen():this._togglePseudoFullScreen()}_togglePseudoFullScreen(){this._container.classList.toggle(\"maplibregl-pseudo-fullscreen\"),this._handleFullscreenChange(),this._map.resize()}}const ia={closeButton:!0,closeOnClick:!0,focusAfterOpen:!0,className:\"\",maxWidth:\"240px\",subpixelPositioning:!1},aa=[\"a[href]\",\"[tabindex]:not([tabindex='-1'])\",\"[contenteditable]:not([contenteditable='false'])\",\"button:not([disabled])\",\"input:not([disabled])\",\"select:not([disabled])\",\"textarea:not([disabled])\"].join(\", \");class oa extends e.E{constructor(t){super(),this.remove=()=>(this._content&&o.remove(this._content),this._container&&(o.remove(this._container),delete this._container),this._map&&(this._map.off(\"move\",this._update),this._map.off(\"move\",this._onClose),this._map.off(\"click\",this._onClose),this._map.off(\"remove\",this.remove),this._map.off(\"mousemove\",this._onMouseMove),this._map.off(\"mouseup\",this._onMouseUp),this._map.off(\"drag\",this._onDrag),this._map._canvasContainer.classList.remove(\"maplibregl-track-pointer\"),delete this._map,this.fire(new e.k(\"close\"))),this),this._onMouseUp=t=>{this._update(t.point)},this._onMouseMove=t=>{this._update(t.point)},this._onDrag=t=>{this._update(t.point)},this._update=t=>{var e;const r=this._lngLat||this._trackPointer;if(!this._map||!r||!this._content)return;if(!this._container){if(this._container=o.create(\"div\",\"maplibregl-popup\",this._map.getContainer()),this._tip=o.create(\"div\",\"maplibregl-popup-tip\",this._container),this._container.appendChild(this._content),this.options.className)for(const t of this.options.className.split(\" \"))this._container.classList.add(t);this._closeButton&&this._closeButton.setAttribute(\"aria-label\",this._map._getUIString(\"Popup.Close\")),this._trackPointer&&this._container.classList.add(\"maplibregl-popup-track-pointer\")}if(this.options.maxWidth&&this._container.style.maxWidth!==this.options.maxWidth&&(this._container.style.maxWidth=this.options.maxWidth),this._map.transform.renderWorldCopies&&!this._trackPointer?this._lngLat=Zi(this._lngLat,this._flatPos,this._map.transform):this._lngLat=null===(e=this._lngLat)||void 0===e?void 0:e.wrap(),this._trackPointer&&!t)return;const n=this._flatPos=this._pos=this._trackPointer&&t?t:this._map.project(this._lngLat);this._map.terrain&&(this._flatPos=this._trackPointer&&t?t:this._map.transform.locationPoint(this._lngLat));let i=this.options.anchor;const a=sa(this.options.offset);if(!i){const t=this._container.offsetWidth,e=this._container.offsetHeight;let r;r=n.y+a.bottom.ythis._map.transform.height-e?[\"bottom\"]:[],n.xthis._map.transform.width-t/2&&r.push(\"right\"),i=0===r.length?\"bottom\":r.join(\"-\")}let s=n.add(a[i]);this.options.subpixelPositioning||(s=s.round()),o.setTransform(this._container,`${Wi[i]} translate(${s.x}px,${s.y}px)`),Yi(this._container,i,\"popup\")},this._onClose=()=>{this.remove()},this.options=e.e(Object.create(ia),t)}addTo(t){return this._map&&this.remove(),this._map=t,this.options.closeOnClick&&this._map.on(\"click\",this._onClose),this.options.closeOnMove&&this._map.on(\"move\",this._onClose),this._map.on(\"remove\",this.remove),this._update(),this._focusFirstElement(),this._trackPointer?(this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"mouseup\",this._onMouseUp),this._container&&this._container.classList.add(\"maplibregl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"maplibregl-track-pointer\")):this._map.on(\"move\",this._update),this.fire(new e.k(\"open\")),this}isOpen(){return!!this._map}getLngLat(){return this._lngLat}setLngLat(t){return this._lngLat=e.N.convert(t),this._pos=null,this._flatPos=null,this._trackPointer=!1,this._update(),this._map&&(this._map.on(\"move\",this._update),this._map.off(\"mousemove\",this._onMouseMove),this._container&&this._container.classList.remove(\"maplibregl-popup-track-pointer\"),this._map._canvasContainer.classList.remove(\"maplibregl-track-pointer\")),this}trackPointer(){return this._trackPointer=!0,this._pos=null,this._flatPos=null,this._update(),this._map&&(this._map.off(\"move\",this._update),this._map.on(\"mousemove\",this._onMouseMove),this._map.on(\"drag\",this._onDrag),this._container&&this._container.classList.add(\"maplibregl-popup-track-pointer\"),this._map._canvasContainer.classList.add(\"maplibregl-track-pointer\")),this}getElement(){return this._container}setText(t){return this.setDOMContent(document.createTextNode(t))}setHTML(t){const e=document.createDocumentFragment(),r=document.createElement(\"body\");let n;for(r.innerHTML=t;n=r.firstChild,n;)e.appendChild(n);return this.setDOMContent(e)}getMaxWidth(){var t;return null===(t=this._container)||void 0===t?void 0:t.style.maxWidth}setMaxWidth(t){return this.options.maxWidth=t,this._update(),this}setDOMContent(t){if(this._content)for(;this._content.hasChildNodes();)this._content.firstChild&&this._content.removeChild(this._content.firstChild);else this._content=o.create(\"div\",\"maplibregl-popup-content\",this._container);return this._content.appendChild(t),this._createCloseButton(),this._update(),this._focusFirstElement(),this}addClassName(t){return this._container&&this._container.classList.add(t),this}removeClassName(t){return this._container&&this._container.classList.remove(t),this}setOffset(t){return this.options.offset=t,this._update(),this}toggleClassName(t){if(this._container)return this._container.classList.toggle(t)}setSubpixelPositioning(t){this.options.subpixelPositioning=t}_createCloseButton(){this.options.closeButton&&(this._closeButton=o.create(\"button\",\"maplibregl-popup-close-button\",this._content),this._closeButton.type=\"button\",this._closeButton.innerHTML=\"×\",this._closeButton.addEventListener(\"click\",this._onClose))}_focusFirstElement(){if(!this.options.focusAfterOpen||!this._container)return;const t=this._container.querySelector(aa);t&&t.focus()}}function sa(t){if(t){if(\"number\"==typeof t){const r=Math.round(Math.abs(t)/Math.SQRT2);return{center:new e.P(0,0),top:new e.P(0,t),\"top-left\":new e.P(r,r),\"top-right\":new e.P(-r,r),bottom:new e.P(0,-t),\"bottom-left\":new e.P(r,-r),\"bottom-right\":new e.P(-r,-r),left:new e.P(t,0),right:new e.P(-t,0)}}if(t instanceof e.P||Array.isArray(t)){const r=e.P.convert(t);return{center:r,top:r,\"top-left\":r,\"top-right\":r,bottom:r,\"bottom-left\":r,\"bottom-right\":r,left:r,right:r}}return{center:e.P.convert(t.center||[0,0]),top:e.P.convert(t.top||[0,0]),\"top-left\":e.P.convert(t[\"top-left\"]||[0,0]),\"top-right\":e.P.convert(t[\"top-right\"]||[0,0]),bottom:e.P.convert(t.bottom||[0,0]),\"bottom-left\":e.P.convert(t[\"bottom-left\"]||[0,0]),\"bottom-right\":e.P.convert(t[\"bottom-right\"]||[0,0]),left:e.P.convert(t.left||[0,0]),right:e.P.convert(t.right||[0,0])}}return sa(new e.P(0,0))}const la=r;t.AJAXError=e.bg,t.Evented=e.E,t.LngLat=e.N,t.MercatorCoordinate=e.Z,t.Point=e.P,t.addProtocol=e.bh,t.config=e.a,t.removeProtocol=e.bi,t.AttributionControl=Li,t.BoxZoomHandler=Gn,t.CanvasSource=it,t.CooperativeGesturesHandler=Ti,t.DoubleClickZoomHandler=yi,t.DragPanHandler=_i,t.DragRotateHandler=bi,t.EdgeInsets=Mn,t.FullscreenControl=na,t.GeoJSONSource=tt,t.GeolocateControl=Qi,t.Hash=Ln,t.ImageSource=rt,t.KeyboardHandler=pi,t.LngLatBounds=X,t.LogoControl=Ii,t.Map=class extends Ei{constructor(t){e.be.mark(e.bf.create);const r=Object.assign(Object.assign({},Ui),t);if(null!=r.minZoom&&null!=r.maxZoom&&r.minZoom>r.maxZoom)throw new Error(\"maxZoom must be greater than or equal to minZoom\");if(null!=r.minPitch&&null!=r.maxPitch&&r.minPitch>r.maxPitch)throw new Error(\"maxPitch must be greater than or equal to minPitch\");if(null!=r.minPitch&&r.minPitch<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(null!=r.maxPitch&&r.maxPitch>85)throw new Error(\"maxPitch must be less than or equal to 85\");if(super(new En(r.minZoom,r.maxZoom,r.minPitch,r.maxPitch,r.renderWorldCopies),{bearingSnap:r.bearingSnap}),this._idleTriggered=!1,this._crossFadingFactor=1,this._renderTaskQueue=new Pi,this._controls=[],this._mapId=e.a4(),this._contextLost=t=>{t.preventDefault(),this._frameRequest&&(this._frameRequest.abort(),this._frameRequest=null),this.fire(new e.k(\"webglcontextlost\",{originalEvent:t}))},this._contextRestored=t=>{this._setupPainter(),this.resize(),this._update(),this.fire(new e.k(\"webglcontextrestored\",{originalEvent:t}))},this._onMapScroll=t=>{if(t.target===this._container)return this._container.scrollTop=0,this._container.scrollLeft=0,!1},this._onWindowOnline=()=>{this._update()},this._interactive=r.interactive,this._maxTileCacheSize=r.maxTileCacheSize,this._maxTileCacheZoomLevels=r.maxTileCacheZoomLevels,this._failIfMajorPerformanceCaveat=!0===r.failIfMajorPerformanceCaveat,this._preserveDrawingBuffer=!0===r.preserveDrawingBuffer,this._antialias=!0===r.antialias,this._trackResize=!0===r.trackResize,this._bearingSnap=r.bearingSnap,this._refreshExpiredTiles=!0===r.refreshExpiredTiles,this._fadeDuration=r.fadeDuration,this._crossSourceCollisions=!0===r.crossSourceCollisions,this._collectResourceTiming=!0===r.collectResourceTiming,this._locale=Object.assign(Object.assign({},Ni),r.locale),this._clickTolerance=r.clickTolerance,this._overridePixelRatio=r.pixelRatio,this._maxCanvasSize=r.maxCanvasSize,this.transformCameraUpdate=r.transformCameraUpdate,this.cancelPendingTileRequestsWhileZooming=!0===r.cancelPendingTileRequestsWhileZooming,this._imageQueueHandle=p.addThrottleControl((()=>this.isMoving())),this._requestManager=new d(r.transformRequest),\"string\"==typeof r.container){if(this._container=document.getElementById(r.container),!this._container)throw new Error(`Container '${r.container}' not found.`)}else{if(!(r.container instanceof HTMLElement))throw new Error(\"Invalid type: 'container' must be a String or HTMLElement.\");this._container=r.container}if(r.maxBounds&&this.setMaxBounds(r.maxBounds),this._setupContainer(),this._setupPainter(),this.on(\"move\",(()=>this._update(!1))).on(\"moveend\",(()=>this._update(!1))).on(\"zoom\",(()=>this._update(!0))).on(\"terrain\",(()=>{this.painter.terrainFacilitator.dirty=!0,this._update(!0)})).once(\"idle\",(()=>{this._idleTriggered=!0})),\"undefined\"!=typeof window){addEventListener(\"online\",this._onWindowOnline,!1);let t=!1;const e=Cn((t=>{this._trackResize&&!this._removed&&this.resize(t)._update()}),50);this._resizeObserver=new ResizeObserver((r=>{t?e(r):t=!0})),this._resizeObserver.observe(this._container)}this.handlers=new Si(this,r);const n=\"string\"==typeof r.hash&&r.hash||void 0;this._hash=r.hash&&new Ln(n).addTo(this),this._hash&&this._hash._onHashChange()||(this.jumpTo({center:r.center,zoom:r.zoom,bearing:r.bearing,pitch:r.pitch}),r.bounds&&(this.resize(),this.fitBounds(r.bounds,e.e({},r.fitBoundsOptions,{duration:0})))),this.resize(),this._localIdeographFontFamily=r.localIdeographFontFamily,this._validateStyle=r.validateStyle,r.style&&this.setStyle(r.style,{localIdeographFontFamily:r.localIdeographFontFamily}),r.attributionControl&&this.addControl(new Li(\"boolean\"==typeof r.attributionControl?void 0:r.attributionControl)),r.maplibreLogo&&this.addControl(new Ii,r.logoPosition),this.on(\"style.load\",(()=>{this.transform.unmodified&&this.jumpTo(this.style.stylesheet)})),this.on(\"data\",(t=>{this._update(\"style\"===t.dataType),this.fire(new e.k(`${t.dataType}data`,t))})),this.on(\"dataloading\",(t=>{this.fire(new e.k(`${t.dataType}dataloading`,t))})),this.on(\"dataabort\",(t=>{this.fire(new e.k(\"sourcedataabort\",t))}))}_getMapId(){return this._mapId}addControl(t,r){if(void 0===r&&(r=t.getDefaultPosition?t.getDefaultPosition():\"top-right\"),!t||!t.onAdd)return this.fire(new e.j(new Error(\"Invalid argument to map.addControl(). Argument must be a control with onAdd and onRemove methods.\")));const n=t.onAdd(this);this._controls.push(t);const i=this._controlPositions[r];return-1!==r.indexOf(\"bottom\")?i.insertBefore(n,i.firstChild):i.appendChild(n),this}removeControl(t){if(!t||!t.onRemove)return this.fire(new e.j(new Error(\"Invalid argument to map.removeControl(). Argument must be a control with onAdd and onRemove methods.\")));const r=this._controls.indexOf(t);return r>-1&&this._controls.splice(r,1),t.onRemove(this),this}hasControl(t){return this._controls.indexOf(t)>-1}calculateCameraOptionsFromTo(t,e,r,n){return null==n&&this.terrain&&(n=this.terrain.getElevationForLngLatZoom(r,this.transform.tileZoom)),super.calculateCameraOptionsFromTo(t,e,r,n)}resize(t){var r;const n=this._containerDimensions(),i=n[0],a=n[1],o=this._getClampedPixelRatio(i,a);if(this._resizeCanvas(i,a,o),this.painter.resize(i,a,o),this.painter.overLimit()){const t=this.painter.context.gl;this._maxCanvasSize=[t.drawingBufferWidth,t.drawingBufferHeight];const e=this._getClampedPixelRatio(i,a);this._resizeCanvas(i,a,e),this.painter.resize(i,a,e)}this.transform.resize(i,a),null===(r=this._requestedCameraState)||void 0===r||r.resize(i,a);const s=!this._moving;return s&&(this.stop(),this.fire(new e.k(\"movestart\",t)).fire(new e.k(\"move\",t))),this.fire(new e.k(\"resize\",t)),s&&this.fire(new e.k(\"moveend\",t)),this}_getClampedPixelRatio(t,e){const{0:r,1:n}=this._maxCanvasSize,i=this.getPixelRatio(),a=t*i,o=e*i,s=a>r?r/a:1,l=o>n?n/o:1;return Math.min(s,l)*i}getPixelRatio(){var t;return null!==(t=this._overridePixelRatio)&&void 0!==t?t:devicePixelRatio}setPixelRatio(t){this._overridePixelRatio=t,this.resize()}getBounds(){return this.transform.getBounds()}getMaxBounds(){return this.transform.getMaxBounds()}setMaxBounds(t){return this.transform.setMaxBounds(X.convert(t)),this._update()}setMinZoom(t){if((t=null==t?-2:t)>=-2&&t<=this.transform.maxZoom)return this.transform.minZoom=t,this._update(),this.getZoom()=this.transform.minZoom)return this.transform.maxZoom=t,this._update(),this.getZoom()>t&&this.setZoom(t),this;throw new Error(\"maxZoom must be greater than the current minZoom\")}getMaxZoom(){return this.transform.maxZoom}setMinPitch(t){if((t=null==t?0:t)<0)throw new Error(\"minPitch must be greater than or equal to 0\");if(t>=0&&t<=this.transform.maxPitch)return this.transform.minPitch=t,this._update(),this.getPitch()85)throw new Error(\"maxPitch must be less than or equal to 85\");if(t>=this.transform.minPitch)return this.transform.maxPitch=t,this._update(),this.getPitch()>t&&this.setPitch(t),this;throw new Error(\"maxPitch must be greater than the current minPitch\")}getMaxPitch(){return this.transform.maxPitch}getRenderWorldCopies(){return this.transform.renderWorldCopies}setRenderWorldCopies(t){return this.transform.renderWorldCopies=t,this._update()}project(t){return this.transform.locationPoint(e.N.convert(t),this.style&&this.terrain)}unproject(t){return this.transform.pointLocation(e.P.convert(t),this.terrain)}isMoving(){var t;return this._moving||(null===(t=this.handlers)||void 0===t?void 0:t.isMoving())}isZooming(){var t;return this._zooming||(null===(t=this.handlers)||void 0===t?void 0:t.isZooming())}isRotating(){var t;return this._rotating||(null===(t=this.handlers)||void 0===t?void 0:t.isRotating())}_createDelegatedListener(t,e,r){if(\"mouseenter\"===t||\"mouseover\"===t){let n=!1;const i=i=>{const a=this.getLayer(e)?this.queryRenderedFeatures(i.point,{layers:[e]}):[];a.length?n||(n=!0,r.call(this,new Nn(t,this,i.originalEvent,{features:a}))):n=!1};return{layer:e,listener:r,delegates:{mousemove:i,mouseout:()=>{n=!1}}}}if(\"mouseleave\"===t||\"mouseout\"===t){let n=!1;const i=i=>{(this.getLayer(e)?this.queryRenderedFeatures(i.point,{layers:[e]}):[]).length?n=!0:n&&(n=!1,r.call(this,new Nn(t,this,i.originalEvent)))},a=e=>{n&&(n=!1,r.call(this,new Nn(t,this,e.originalEvent)))};return{layer:e,listener:r,delegates:{mousemove:i,mouseout:a}}}{const n=t=>{const n=this.getLayer(e)?this.queryRenderedFeatures(t.point,{layers:[e]}):[];n.length&&(t.features=n,r.call(this,t),delete t.features)};return{layer:e,listener:r,delegates:{[t]:n}}}}on(t,e,r){if(void 0===r)return super.on(t,e);const n=this._createDelegatedListener(t,e,r);this._delegatedListeners=this._delegatedListeners||{},this._delegatedListeners[t]=this._delegatedListeners[t]||[],this._delegatedListeners[t].push(n);for(const t in n.delegates)this.on(t,n.delegates[t]);return this}once(t,e,r){if(void 0===r)return super.once(t,e);const n=this._createDelegatedListener(t,e,r);for(const t in n.delegates)this.once(t,n.delegates[t]);return this}off(t,e,r){if(void 0===r)return super.off(t,e);return this._delegatedListeners&&this._delegatedListeners[t]&&(n=>{const i=n[t];for(let t=0;tthis._updateStyle(t,e)));const r=this.style&&e.transformStyle?this.style.serialize():void 0;return this.style&&(this.style.setEventedParent(null),this.style._remove(!t)),t?(this.style=new de(this,e||{}),this.style.setEventedParent(this,{style:this.style}),\"string\"==typeof t?this.style.loadURL(t,e,r):this.style.loadJSON(t,e,r),this):(delete this.style,this)}_lazyInitEmptyStyle(){this.style||(this.style=new de(this,{}),this.style.setEventedParent(this,{style:this.style}),this.style.loadEmpty())}_diffStyle(t,r){if(\"string\"==typeof t){const n=t,i=this._requestManager.transformRequest(n,\"Style\");e.h(i,new AbortController).then((t=>{this._updateDiff(t.data,r)})).catch((t=>{t&&this.fire(new e.j(t))}))}else\"object\"==typeof t&&this._updateDiff(t,r)}_updateDiff(t,r){try{this.style.setState(t,r)&&this._update(!0)}catch(n){e.w(`Unable to perform style diff: ${n.message||n.error||n}. Rebuilding the style from scratch.`),this._updateStyle(t,r)}}getStyle(){if(this.style)return this.style.serialize()}isStyleLoaded(){return this.style?this.style.loaded():e.w(\"There is no style added to the map.\")}addSource(t,e){return this._lazyInitEmptyStyle(),this.style.addSource(t,e),this._update(!0)}isSourceLoaded(t){const r=this.style&&this.style.sourceCaches[t];if(void 0!==r)return r.loaded();this.fire(new e.j(new Error(`There is no source with ID '${t}'`)))}setTerrain(t){if(this.style._checkLoaded(),this._terrainDataCallback&&this.style.off(\"data\",this._terrainDataCallback),t){const r=this.style.sourceCaches[t.source];if(!r)throw new Error(`cannot load terrain, because there exists no source with ID: ${t.source}`);null===this.terrain&&r.reload();for(const r in this.style._layers){const n=this.style._layers[r];\"hillshade\"===n.type&&n.source===t.source&&e.w(\"You are using the same source for a hillshade layer and for 3D terrain. Please consider using two separate sources to improve rendering quality.\")}this.terrain=new Di(this.painter,r,t),this.painter.renderToTexture=new Bi(this.painter,this.terrain),this.transform.minElevationForCurrentTile=this.terrain.getMinTileElevationForLngLatZoom(this.transform.center,this.transform.tileZoom),this.transform.elevation=this.terrain.getElevationForLngLatZoom(this.transform.center,this.transform.tileZoom),this._terrainDataCallback=e=>{\"style\"===e.dataType?this.terrain.sourceCache.freeRtt():\"source\"===e.dataType&&e.tile&&(e.sourceId!==t.source||this._elevationFreeze||(this.transform.minElevationForCurrentTile=this.terrain.getMinTileElevationForLngLatZoom(this.transform.center,this.transform.tileZoom),this.transform.elevation=this.terrain.getElevationForLngLatZoom(this.transform.center,this.transform.tileZoom)),this.terrain.sourceCache.freeRtt(e.tile.tileID))},this.style.on(\"data\",this._terrainDataCallback)}else this.terrain&&this.terrain.sourceCache.destruct(),this.terrain=null,this.painter.renderToTexture&&this.painter.renderToTexture.destruct(),this.painter.renderToTexture=null,this.transform.minElevationForCurrentTile=0,this.transform.elevation=0;return this.fire(new e.k(\"terrain\",{terrain:t})),this}getTerrain(){var t,e;return null!==(e=null===(t=this.terrain)||void 0===t?void 0:t.options)&&void 0!==e?e:null}areTilesLoaded(){const t=this.style&&this.style.sourceCaches;for(const e in t){const r=t[e]._tiles;for(const t in r){const e=r[t];if(\"loaded\"!==e.state&&\"errored\"!==e.state)return!1}}return!0}removeSource(t){return this.style.removeSource(t),this._update(!0)}getSource(t){return this.style.getSource(t)}addImage(t,r,n={}){const{pixelRatio:i=1,sdf:o=!1,stretchX:s,stretchY:l,content:c,textFitWidth:u,textFitHeight:h}=n;this._lazyInitEmptyStyle();if(!(r instanceof HTMLImageElement||e.b(r))){if(void 0===r.width||void 0===r.height)return this.fire(new e.j(new Error(\"Invalid arguments to map.addImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\")));{const{width:n,height:a,data:f}=r,p=r;return this.style.addImage(t,{data:new e.R({width:n,height:a},new Uint8Array(f)),pixelRatio:i,stretchX:s,stretchY:l,content:c,textFitWidth:u,textFitHeight:h,sdf:o,version:0,userImage:p}),p.onAdd&&p.onAdd(this,t),this}}{const{width:n,height:f,data:p}=a.getImageData(r);this.style.addImage(t,{data:new e.R({width:n,height:f},p),pixelRatio:i,stretchX:s,stretchY:l,content:c,textFitWidth:u,textFitHeight:h,sdf:o,version:0})}}updateImage(t,r){const n=this.style.getImage(t);if(!n)return this.fire(new e.j(new Error(\"The map has no image with that id. If you are adding a new image use `map.addImage(...)` instead.\")));const i=r instanceof HTMLImageElement||e.b(r)?a.getImageData(r):r,{width:o,height:s,data:l}=i;if(void 0===o||void 0===s)return this.fire(new e.j(new Error(\"Invalid arguments to map.updateImage(). The second argument must be an `HTMLImageElement`, `ImageData`, `ImageBitmap`, or object with `width`, `height`, and `data` properties with the same format as `ImageData`\")));if(o!==n.data.width||s!==n.data.height)return this.fire(new e.j(new Error(\"The width and height of the updated image must be that same as the previous version of the image\")));const c=!(r instanceof HTMLImageElement||e.b(r));return n.data.replace(l,c),this.style.updateImage(t,n),this}getImage(t){return this.style.getImage(t)}hasImage(t){return t?!!this.style.getImage(t):(this.fire(new e.j(new Error(\"Missing required image id\"))),!1)}removeImage(t){this.style.removeImage(t)}loadImage(t){return p.getImage(this._requestManager.transformRequest(t,\"Image\"),new AbortController)}listImages(){return this.style.listImages()}addLayer(t,e){return this._lazyInitEmptyStyle(),this.style.addLayer(t,e),this._update(!0)}moveLayer(t,e){return this.style.moveLayer(t,e),this._update(!0)}removeLayer(t){return this.style.removeLayer(t),this._update(!0)}getLayer(t){return this.style.getLayer(t)}getLayersOrder(){return this.style.getLayersOrder()}setLayerZoomRange(t,e,r){return this.style.setLayerZoomRange(t,e,r),this._update(!0)}setFilter(t,e,r={}){return this.style.setFilter(t,e,r),this._update(!0)}getFilter(t){return this.style.getFilter(t)}setPaintProperty(t,e,r,n={}){return this.style.setPaintProperty(t,e,r,n),this._update(!0)}getPaintProperty(t,e){return this.style.getPaintProperty(t,e)}setLayoutProperty(t,e,r,n={}){return this.style.setLayoutProperty(t,e,r,n),this._update(!0)}getLayoutProperty(t,e){return this.style.getLayoutProperty(t,e)}setGlyphs(t,e={}){return this._lazyInitEmptyStyle(),this.style.setGlyphs(t,e),this._update(!0)}getGlyphs(){return this.style.getGlyphsUrl()}addSprite(t,e,r={}){return this._lazyInitEmptyStyle(),this.style.addSprite(t,e,r,(t=>{t||this._update(!0)})),this}removeSprite(t){return this._lazyInitEmptyStyle(),this.style.removeSprite(t),this._update(!0)}getSprite(){return this.style.getSprite()}setSprite(t,e={}){return this._lazyInitEmptyStyle(),this.style.setSprite(t,e,(t=>{t||this._update(!0)})),this}setLight(t,e={}){return this._lazyInitEmptyStyle(),this.style.setLight(t,e),this._update(!0)}getLight(){return this.style.getLight()}setSky(t){return this._lazyInitEmptyStyle(),this.style.setSky(t),this._update(!0)}getSky(){return this.style.getSky()}setFeatureState(t,e){return this.style.setFeatureState(t,e),this._update()}removeFeatureState(t,e){return this.style.removeFeatureState(t,e),this._update()}getFeatureState(t){return this.style.getFeatureState(t)}getContainer(){return this._container}getCanvasContainer(){return this._canvasContainer}getCanvas(){return this._canvas}_containerDimensions(){let t=0,e=0;return this._container&&(t=this._container.clientWidth||400,e=this._container.clientHeight||300),[t,e]}_setupContainer(){const t=this._container;t.classList.add(\"maplibregl-map\");const e=this._canvasContainer=o.create(\"div\",\"maplibregl-canvas-container\",t);this._interactive&&e.classList.add(\"maplibregl-interactive\"),this._canvas=o.create(\"canvas\",\"maplibregl-canvas\",e),this._canvas.addEventListener(\"webglcontextlost\",this._contextLost,!1),this._canvas.addEventListener(\"webglcontextrestored\",this._contextRestored,!1),this._canvas.setAttribute(\"tabindex\",this._interactive?\"0\":\"-1\"),this._canvas.setAttribute(\"aria-label\",this._getUIString(\"Map.Title\")),this._canvas.setAttribute(\"role\",\"region\");const r=this._containerDimensions(),n=this._getClampedPixelRatio(r[0],r[1]);this._resizeCanvas(r[0],r[1],n);const i=this._controlContainer=o.create(\"div\",\"maplibregl-control-container\",t),a=this._controlPositions={};[\"top-left\",\"top-right\",\"bottom-left\",\"bottom-right\"].forEach((t=>{a[t]=o.create(\"div\",`maplibregl-ctrl-${t} `,i)})),this._container.addEventListener(\"scroll\",this._onMapScroll,!1)}_resizeCanvas(t,e,r){this._canvas.width=Math.floor(r*t),this._canvas.height=Math.floor(r*e),this._canvas.style.width=`${t}px`,this._canvas.style.height=`${e}px`}_setupPainter(){const t={alpha:!0,stencil:!0,depth:!0,failIfMajorPerformanceCaveat:this._failIfMajorPerformanceCaveat,preserveDrawingBuffer:this._preserveDrawingBuffer,antialias:this._antialias||!1};let e=null;this._canvas.addEventListener(\"webglcontextcreationerror\",(r=>{e={requestedAttributes:t},r&&(e.statusMessage=r.statusMessage,e.type=r.type)}),{once:!0});const r=this._canvas.getContext(\"webgl2\",t)||this._canvas.getContext(\"webgl\",t);if(!r){const t=\"Failed to initialize WebGL\";throw e?(e.message=t,new Error(JSON.stringify(e))):new Error(t)}this.painter=new Tn(r,this.transform),s.testSupport(r)}loaded(){return!this._styleDirty&&!this._sourcesDirty&&!!this.style&&this.style.loaded()}_update(t){return this.style&&this.style._loaded?(this._styleDirty=this._styleDirty||t,this._sourcesDirty=!0,this.triggerRepaint(),this):this}_requestRenderFrame(t){return this._update(),this._renderTaskQueue.add(t)}_cancelRenderFrame(t){this._renderTaskQueue.remove(t)}_render(t){const r=this._idleTriggered?this._fadeDuration:0;if(this.painter.context.setDirty(),this.painter.setBaseState(),this._renderTaskQueue.run(t),this._removed)return;let n=!1;if(this.style&&this._styleDirty){this._styleDirty=!1;const t=this.transform.zoom,i=a.now();this.style.zoomHistory.update(t,i);const o=new e.a9(t,{now:i,fadeDuration:r,zoomHistory:this.style.zoomHistory,transition:this.style.getTransition()}),s=o.crossFadingFactor();1===s&&s===this._crossFadingFactor||(n=!0,this._crossFadingFactor=s),this.style.update(o)}this.style&&this._sourcesDirty&&(this._sourcesDirty=!1,this.style._updateSources(this.transform)),this.terrain?(this.terrain.sourceCache.update(this.transform,this.terrain),this.transform.minElevationForCurrentTile=this.terrain.getMinTileElevationForLngLatZoom(this.transform.center,this.transform.tileZoom),this._elevationFreeze||(this.transform.elevation=this.terrain.getElevationForLngLatZoom(this.transform.center,this.transform.tileZoom))):(this.transform.minElevationForCurrentTile=0,this.transform.elevation=0),this._placementDirty=this.style&&this.style._updatePlacement(this.painter.transform,this.showCollisionBoxes,r,this._crossSourceCollisions),this.painter.render(this.style,{showTileBoundaries:this.showTileBoundaries,showOverdrawInspector:this._showOverdrawInspector,rotating:this.isRotating(),zooming:this.isZooming(),moving:this.isMoving(),fadeDuration:r,showPadding:this.showPadding}),this.fire(new e.k(\"render\")),this.loaded()&&!this._loaded&&(this._loaded=!0,e.be.mark(e.bf.load),this.fire(new e.k(\"load\"))),this.style&&(this.style.hasTransitions()||n)&&(this._styleDirty=!0),this.style&&!this._placementDirty&&this.style._releaseSymbolFadeTiles();const i=this._sourcesDirty||this._styleDirty||this._placementDirty;return i||this._repaint?this.triggerRepaint():!this.isMoving()&&this.loaded()&&this.fire(new e.k(\"idle\")),!this._loaded||this._fullyLoaded||i||(this._fullyLoaded=!0,e.be.mark(e.bf.fullLoad)),this}redraw(){return this.style&&(this._frameRequest&&(this._frameRequest.abort(),this._frameRequest=null),this._render(0)),this}remove(){var t;this._hash&&this._hash.remove();for(const t of this._controls)t.onRemove(this);this._controls=[],this._frameRequest&&(this._frameRequest.abort(),this._frameRequest=null),this._renderTaskQueue.clear(),this.painter.destroy(),this.handlers.destroy(),delete this.handlers,this.setStyle(null),\"undefined\"!=typeof window&&removeEventListener(\"online\",this._onWindowOnline,!1),p.removeThrottleControl(this._imageQueueHandle),null===(t=this._resizeObserver)||void 0===t||t.disconnect();const r=this.painter.context.gl.getExtension(\"WEBGL_lose_context\");(null==r?void 0:r.loseContext)&&r.loseContext(),this._canvas.removeEventListener(\"webglcontextrestored\",this._contextRestored,!1),this._canvas.removeEventListener(\"webglcontextlost\",this._contextLost,!1),o.remove(this._canvasContainer),o.remove(this._controlContainer),this._container.classList.remove(\"maplibregl-map\"),e.be.clearMetrics(),this._removed=!0,this.fire(new e.k(\"remove\"))}triggerRepaint(){this.style&&!this._frameRequest&&(this._frameRequest=new AbortController,a.frameAsync(this._frameRequest).then((t=>{e.be.frame(t),this._frameRequest=null,this._render(t)})).catch((()=>{})))}get showTileBoundaries(){return!!this._showTileBoundaries}set showTileBoundaries(t){this._showTileBoundaries!==t&&(this._showTileBoundaries=t,this._update())}get showPadding(){return!!this._showPadding}set showPadding(t){this._showPadding!==t&&(this._showPadding=t,this._update())}get showCollisionBoxes(){return!!this._showCollisionBoxes}set showCollisionBoxes(t){this._showCollisionBoxes!==t&&(this._showCollisionBoxes=t,t?this.style._generateCollisionBoxes():this._update())}get showOverdrawInspector(){return!!this._showOverdrawInspector}set showOverdrawInspector(t){this._showOverdrawInspector!==t&&(this._showOverdrawInspector=t,this._update())}get repaint(){return!!this._repaint}set repaint(t){this._repaint!==t&&(this._repaint=t,this.triggerRepaint())}get vertices(){return!!this._vertices}set vertices(t){this._vertices=t,this._update()}get version(){return ji}getCameraTargetElevation(){return this.transform.elevation}},t.MapMouseEvent=Nn,t.MapTouchEvent=jn,t.MapWheelEvent=Un,t.Marker=Xi,t.NavigationControl=class{constructor(t){this._updateZoomButtons=()=>{const t=this._map.getZoom(),e=t===this._map.getMaxZoom(),r=t===this._map.getMinZoom();this._zoomInButton.disabled=e,this._zoomOutButton.disabled=r,this._zoomInButton.setAttribute(\"aria-disabled\",e.toString()),this._zoomOutButton.setAttribute(\"aria-disabled\",r.toString())},this._rotateCompassArrow=()=>{const t=this.options.visualizePitch?`scale(${1/Math.pow(Math.cos(this._map.transform.pitch*(Math.PI/180)),.5)}) rotateX(${this._map.transform.pitch}deg) rotateZ(${this._map.transform.angle*(180/Math.PI)}deg)`:`rotate(${this._map.transform.angle*(180/Math.PI)}deg)`;this._compassIcon.style.transform=t},this._setButtonTitle=(t,e)=>{const r=this._map._getUIString(`NavigationControl.${e}`);t.title=r,t.setAttribute(\"aria-label\",r)},this.options=e.e({},qi,t),this._container=o.create(\"div\",\"maplibregl-ctrl 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r.type=\"button\",r.addEventListener(\"click\",e),r}},t.Popup=oa,t.RasterDEMTileSource=Q,t.RasterTileSource=K,t.ScaleControl=class{constructor(t){this._onMove=()=>{ea(this._map,this._container,this.options)},this.setUnit=t=>{this.options.unit=t,ea(this._map,this._container,this.options)},this.options=Object.assign(Object.assign({},ta),t)}getDefaultPosition(){return\"bottom-left\"}onAdd(t){return this._map=t,this._container=o.create(\"div\",\"maplibregl-ctrl maplibregl-ctrl-scale\",t.getContainer()),this._map.on(\"move\",this._onMove),this._onMove(),this._container}onRemove(){o.remove(this._container),this._map.off(\"move\",this._onMove),this._map=void 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- -9.818694267515925, - 190.50869426751592 + -11.439466019417475, + 192.1294660194175 ], "title": { "text": "y" @@ -1234,9 +1232,9 @@ } }, "text/html": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Create a figure\n", "def ylist_MM(xlist, Vmax, Km):\n", @@ -3088,46 +1585,6 @@ "fig.show()" ] }, - { - "cell_type": "markdown", - "id": "ec8c0a51-6cc2-485e-81ea-dde62d857eb6", - "metadata": {}, - "source": [ - "
\n", - " ×\n", - " Refresher: Whole model at a glance \n", - "

We created this model over several different cells so we could dig into how and why each step is done. When we put all the code together, the model is written in only a few lines of code. Here is the complete code to write and solve this model after you have the data available:

\n", - " 
# Write and solve the nonlinear regression example:\n",
-    "import gurobipy as gp\n",
-    "from gurobipy import GRB\n",
-    "#\n",
-    "# Declare and initialize model\n",
-    "model = gp.Model(\"Nonlinear regression\")\n",
-    "#\n",
-    "# Add variables\n",
-    "# Create decision variables \n",
-    "Vmax = model.addVar(name='Vmax', ub=1000)\n",
-    "Km = model.addVar(name='Km', ub=1000)\n",
-    "ymodel = model.addVars(points, lb=-2*ymax, ub=2*ymax, name=\"ymodel\")\n",
-    "#\n",
-    "# Add constraints\n",
-    "constrs = model.addConstrs((ymodel[i]*(Km + xtrain[i]) == Vmax*xtrain[i] for i in points), name=\"Define_ymodel\")\n",
-    "#\n",
-    "# Add the objective\n",
-    "model.setObjective(sum((ymodel[i] - ytrain[i])**2 for i in points), sense=GRB.MINIMIZE)\n",
-    "#\n",
-    "# Solve the model\n",
-    "model.optimize()
" - ] - }, - { - "cell_type": "markdown", - "id": "7e7f3b53-8449-42fc-bf08-c4aea196e4d6", - "metadata": {}, - "source": [ - "TODO: I give the answers above, feel free to remove." - ] - }, { "cell_type": "markdown", "id": "e8085a38-9f4c-4f7b-a1a9-ac65b781e422", @@ -3141,18 +1598,12 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "id": "f98c1ae2-f9e8-4a9e-b600-45906b50224d", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Freeing default Gurobi environment\n" - ] - } - ], + "metadata": { + "scrolled": true + }, + "outputs": [], "source": [ "# Free Gurobi resources: Model and environment\n", "model.dispose()\n", @@ -3176,7 +1627,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.9.2" + "version": "3.12.7" } }, "nbformat": 4, diff --git a/optimization202/Modeling_Session_2/completed_exercise_set2.ipynb b/optimization202/Modeling_Session_2/completed_exercise_set2.ipynb index f59ab06..4850463 100644 --- a/optimization202/Modeling_Session_2/completed_exercise_set2.ipynb +++ b/optimization202/Modeling_Session_2/completed_exercise_set2.ipynb @@ -10,13 +10,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: gurobipy in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (12.0.0)\n", + "Note: you may need to restart the kernel to use updated packages.\n", + "Requirement already satisfied: seaborn in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (0.13.0)\n", + "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from seaborn) (1.26.4)\n", + "Requirement already satisfied: pandas>=1.2 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from seaborn) (2.2.2)\n", + "Requirement already satisfied: matplotlib!=3.6.1,>=3.3 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from seaborn) (3.8.0)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (1.1.1)\n", + "Requirement already satisfied: cycler>=0.10 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (0.12.1)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (4.43.1)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (1.4.5)\n", + "Requirement already satisfied: packaging>=20.0 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (23.2)\n", + "Requirement already satisfied: pillow>=6.2.0 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (10.0.1)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (3.1.1)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from pandas>=1.2->seaborn) (2023.3.post1)\n", + "Requirement already satisfied: tzdata>=2022.7 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from pandas>=1.2->seaborn) (2023.3)\n", + "Requirement already satisfied: six>=1.5 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.3->seaborn) (1.16.0)\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], "source": [ - "#%pip install gurobipy\n", + "%pip install gurobipy\n", + "%pip install seaborn\n", "\n", "import pandas as pd\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", "import gurobipy as gp\n", "from gurobipy import GRB" ] @@ -48,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": 221, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -78,7 +107,7 @@ }, { "cell_type": "code", - "execution_count": 314, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -183,7 +212,7 @@ " St. Louis 4.60 41.0" ] }, - "execution_count": 314, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -232,7 +261,7 @@ }, { "cell_type": "code", - "execution_count": 206, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -365,28 +394,27 @@ " Lexington 1.61 2.11 1.27 121.0" ] }, - "execution_count": 206, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "import gurobipy as gp\n", - "from gurobipy import GRB\n", - "\n", "m_pwl = gp.Model('widgets')\n", "m_pwl.setParam('OutputFlag', 0)\n", "\n", - "x = m_pwl.addVars(production, distribution, vtype=GRB.SEMICONT, lb = 30, name = 'prod_ship')\n", + "x_pwl = m_pwl.addVars(production, distribution, vtype=GRB.SEMICONT, lb = 30, name = 'prod_ship')\n", "pwl_cost = m_pwl.addVars(production, distribution, name = 'pwl_cost')\n", "\n", "# constraints\n", - "can_produce = m_pwl.addConstrs((gp.quicksum(x[p,d] for d in distribution) <= max_prod[p] for p in production), name = 'can_produce')\n", - "must_produce = m_pwl.addConstrs((gp.quicksum(x[p,d] for d in distribution) >= frac*max_prod[p] for p in production), name = 'must_produce')\n", - "meet_demand = m_pwl.addConstrs((x.sum('*', d) >= n_demand[d] for d in distribution), name = 'demand')\n", + "can_produce = m_pwl.addConstrs((gp.quicksum(x_pwl[p,d] for d in distribution) <= max_prod[p] for p in production), name = 'can_produce')\n", + "must_produce = m_pwl.addConstrs((gp.quicksum(x_pwl[p,d] for d in distribution) >= frac*max_prod[p] for p in production), name = 'must_produce')\n", + "meet_demand = m_pwl.addConstrs((x_pwl.sum('*', d) >= n_demand[d] for d in distribution), name = 'demand')\n", "\n", + "# create empty series for analysis \n", "bulk_costs = pd.Series(index=transp_cost.keys(), name='bulk_costs')\n", "base_costs = pd.Series(index=transp_cost.keys(), name='base_costs')\n", + "\n", "# Create piecewise cost functions\n", "for p in production:\n", " for d in distribution:\n", @@ -409,7 +437,7 @@ " 0, base_cost * frac_produce, base_cost * frac_produce + bulk_cost * (max_produce - frac_produce)\n", " ]\n", "\n", - " m_pwl.addGenConstrPWL(x[p, d], pwl_cost[p, d], pwl_points_x, pwl_points_y, name=f\"pwl_{p}_{d}\")\n", + " m_pwl.addGenConstrPWL(x_pwl[p, d], pwl_cost[p, d], pwl_points_x, pwl_points_y, name=f\"pwl_{p}_{d}\")\n", "\n", "# Objective: minimize the total cost\n", "m_pwl.setObjective(gp.quicksum(pwl_cost[p, d] for p, d in transp_cost.keys()), GRB.MINIMIZE)\n", @@ -417,7 +445,7 @@ "# Optimize\n", "m_pwl.optimize()\n", "\n", - "x_values_pwl = pd.Series(m_pwl.getAttr('X', x), name = \"shipment_pwl\", index = transp_cost.index)\n", + "x_values_pwl = pd.Series(m_pwl.getAttr('X', x_pwl), name = \"shipment_pwl\", index = transp_cost.index)\n", "sol_pwl = pd.concat([transp_cost, base_costs, bulk_costs, x_values_pwl], axis=1)\n", "\n", "print(f\"This model has a total cost of {round(m_pwl.ObjVal,2)}\")\n", @@ -426,7 +454,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -473,42 +501,58 @@ " Nashville\n", " 5.96\n", " 30.0\n", - " 5.96\n", - " 5.96\n", - " 30.0\n", + " 6.46\n", + " 3.88\n", + " 0.0\n", " \n", " \n", " Richmond\n", " 1.96\n", " 116.0\n", - " 1.96\n", - " 1.96\n", - " 116.0\n", + " 2.46\n", + " 1.48\n", + " 135.0\n", " \n", " \n", - " Cleveland\n", + " Cleveland\n", " Columbia\n", " 2.43\n", " 89.0\n", - " 2.43\n", - " 2.43\n", + " 2.93\n", + " 1.76\n", " 89.0\n", " \n", " \n", " Indianapolis\n", " 2.37\n", " 95.0\n", - " 2.37\n", - " 2.37\n", - " 95.0\n", + " 2.87\n", + " 1.72\n", + " 40.0\n", + " \n", + " \n", + " Nashville\n", + " 4.13\n", + " 0.0\n", + " 4.63\n", + " 2.78\n", + " 30.0\n", + " \n", + " \n", + " St. Louis\n", + " 4.88\n", + " 0.0\n", + " 5.38\n", + " 3.23\n", + " 41.0\n", " \n", " \n", " Little Rock\n", " St. Louis\n", " 2.92\n", " 140.0\n", - " 2.92\n", - " 2.92\n", + " 3.42\n", + " 2.05\n", " 140.0\n", " \n", " \n", @@ -516,26 +560,34 @@ " Nashville\n", " 1.53\n", " 71.0\n", - " 1.53\n", - " 1.53\n", + " 2.03\n", + " 1.22\n", " 71.0\n", " \n", " \n", - " Charleston\n", + " Charleston\n", + " Indianapolis\n", + " 2.61\n", + " 0.0\n", + " 3.11\n", + " 1.87\n", + " 55.0\n", + " \n", + " \n", " Lexington\n", " 1.61\n", " 121.0\n", - " 1.61\n", - " 1.61\n", + " 2.11\n", + " 1.27\n", " 121.0\n", " \n", " \n", " St. Louis\n", " 4.60\n", " 41.0\n", - " 4.60\n", - " 4.60\n", - " 41.0\n", + " 5.10\n", + " 3.06\n", + " 0.0\n", " \n", " \n", "\n", @@ -544,17 +596,20 @@ "text/plain": [ " cost shipment base_costs bulk_costs shipment_pwl\n", "production distribution \n", - "Baltimore Nashville 5.96 30.0 5.96 5.96 30.0\n", - " Richmond 1.96 116.0 1.96 1.96 116.0\n", - "Cleveland Columbia 2.43 89.0 2.43 2.43 89.0\n", - " Indianapolis 2.37 95.0 2.37 2.37 95.0\n", - "Little Rock St. Louis 2.92 140.0 2.92 2.92 140.0\n", - "Birmingham Nashville 1.53 71.0 1.53 1.53 71.0\n", - "Charleston Lexington 1.61 121.0 1.61 1.61 121.0\n", - " St. Louis 4.60 41.0 4.60 4.60 41.0" + "Baltimore Nashville 5.96 30.0 6.46 3.88 0.0\n", + " Richmond 1.96 116.0 2.46 1.48 135.0\n", + "Cleveland Columbia 2.43 89.0 2.93 1.76 89.0\n", + " Indianapolis 2.37 95.0 2.87 1.72 40.0\n", + " Nashville 4.13 0.0 4.63 2.78 30.0\n", + " St. Louis 4.88 0.0 5.38 3.23 41.0\n", + "Little Rock St. Louis 2.92 140.0 3.42 2.05 140.0\n", + "Birmingham Nashville 1.53 71.0 2.03 1.22 71.0\n", + "Charleston Indianapolis 2.61 0.0 3.11 1.87 55.0\n", + " Lexington 1.61 121.0 2.11 1.27 121.0\n", + " St. Louis 4.60 41.0 5.10 3.06 0.0" ] }, - "execution_count": 23, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -569,26 +624,27 @@ "metadata": {}, "source": [ "### Scenario 2\n", - "The original model does not reflect any production costs to make a widget in different locations. Suppose we have costs of $c^1_p$ per widget for making up to half of a production facility's capacity and a cost of $c^2_d$ for everything after. Update the model to reflect this and solve. " + "The original model does not reflect any production costs to make a widget in different locations. Suppose we have a given production cost per widget for making up to 25% of a production facility's capacity. After that level of production is reached, then the cost *increases* by 25%. For example, if a facility can produce 100 widgets and the initial cost is $1, then the first 25 widgets made will cost $1 each and up to the next 75 widgets qill cost $1.25 each to make. " ] }, { "cell_type": "code", - "execution_count": 290, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Gurobi Optimizer version 11.0.3 build v11.0.3rc0 (mac64[rosetta2] - Darwin 23.6.0 23G80)\n", + "Gurobi Optimizer version 12.0.0 build v12.0.0rc1 (mac64[rosetta2] - Darwin 24.1.0 24B83)\n", "\n", "CPU model: Apple M1\n", "Thread count: 8 physical cores, 8 logical processors, using up to 8 threads\n", "\n", "Optimize a model with 21 rows, 40 columns and 125 nonzeros\n", - "Model fingerprint: 0xdf7fc01f\n", - "Model has 5 general constraints\n", + "Model fingerprint: 0x4405fcbf\n", + "Model has 5 simple general constraints\n", + " 5 PWL\n", "Variable types: 10 continuous, 0 integer (0 binary)\n", "Semi-Variable types: 30 continuous, 0 integer\n", "Coefficient statistics:\n", @@ -614,12 +670,13 @@ "\n", "Cutting planes:\n", " Gomory: 2\n", + " Cover: 1\n", " Implied bound: 2\n", " MIR: 3\n", " Flow cover: 6\n", " Relax-and-lift: 2\n", "\n", - "Explored 1 nodes (42 simplex iterations) in 0.09 seconds (0.00 work units)\n", + "Explored 1 nodes (44 simplex iterations) in 0.03 seconds (0.00 work units)\n", "Thread count was 8 (of 8 available processors)\n", "\n", "Solution count 1: 3121.29 \n", @@ -651,60 +708,50 @@ " \n", " \n", " \n", - " cost\n", " shipment\n", " \n", " \n", " production\n", " distribution\n", " \n", - " \n", " \n", " \n", " \n", " \n", " Baltimore\n", " Nashville\n", - " 5.96\n", " 30.0\n", " \n", " \n", " Richmond\n", - " 1.96\n", " 116.0\n", " \n", " \n", " Cleveland\n", " Columbia\n", - " 2.43\n", " 89.0\n", " \n", " \n", " Indianapolis\n", - " 2.37\n", " 95.0\n", " \n", " \n", " Little Rock\n", " St. Louis\n", - " 2.92\n", " 140.0\n", " \n", " \n", " Birmingham\n", " Nashville\n", - " 1.53\n", " 71.0\n", " \n", " \n", " Charleston\n", " Lexington\n", - " 1.61\n", " 121.0\n", " \n", " \n", " St. Louis\n", - " 4.60\n", " 41.0\n", " \n", " \n", @@ -712,19 +759,19 @@ "
" ], "text/plain": [ - " cost shipment\n", - "production distribution \n", - "Baltimore Nashville 5.96 30.0\n", - " Richmond 1.96 116.0\n", - "Cleveland Columbia 2.43 89.0\n", - " Indianapolis 2.37 95.0\n", - "Little Rock St. Louis 2.92 140.0\n", - "Birmingham Nashville 1.53 71.0\n", - "Charleston Lexington 1.61 121.0\n", - " St. Louis 4.60 41.0" + " shipment\n", + "production distribution \n", + "Baltimore Nashville 30.0\n", + " Richmond 116.0\n", + "Cleveland Columbia 89.0\n", + " Indianapolis 95.0\n", + "Little Rock St. Louis 140.0\n", + "Birmingham Nashville 71.0\n", + "Charleston Lexington 121.0\n", + " St. Louis 41.0" ] }, - "execution_count": 290, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -734,12 +781,12 @@ "#m_pwl2.setParam('OutputFlag',0)\n", "\n", "# decision vars\n", - "x = m_pwl2.addVars(production, distribution, vtype=GRB.SEMICONT, lb = C, name = 'prod_ship')\n", + "x_pwl2 = m_pwl2.addVars(production, distribution, vtype=GRB.SEMICONT, lb = C, name = 'prod_ship')\n", "\n", "# constraints\n", - "can_produce = m_pwl2.addConstrs((gp.quicksum(x[p,d] for d in distribution) <= max_prod[p] for p in production), name = 'can_produce')\n", - "must_produce = m_pwl2.addConstrs((gp.quicksum(x[p,d] for d in distribution) >= frac*max_prod[p] for p in production), name = 'must_produce')\n", - "meet_demand = m_pwl2.addConstrs(x.sum('*', d) >= n_demand[d] for d in distribution)\n", + "can_produce = m_pwl2.addConstrs((gp.quicksum(x_pwl2[p,d] for d in distribution) <= max_prod[p] for p in production), name = 'can_produce')\n", + "must_produce = m_pwl2.addConstrs((gp.quicksum(x_pwl2[p,d] for d in distribution) >= frac*max_prod[p] for p in production), name = 'must_produce')\n", + "meet_demand = m_pwl2.addConstrs(x_pwl2.sum('*', d) >= n_demand[d] for d in distribution)\n", "\n", "# Total production for each facility\n", "base_prod_cost = pd.Series([1,1.2,.8,.8,.9], index = production, name = \"max_production\")\n", @@ -747,28 +794,28 @@ "# Add new variables and/or constraints\n", "q = m_pwl2.addVars(production, name='q')\n", "pwl_prod = m_pwl2.addVars(production, name='pwl_prod')\n", - "m_pwl2.addConstrs((q[p] == gp.quicksum(x[p, d] for d in distribution) for p in production), name='total_production')\n", + "m_pwl2.addConstrs((q[p] == gp.quicksum(x_pwl2[p, d] for d in distribution) for p in production), name='total_production')\n", "\n", "for p in production:\n", " m_pwl2.addGenConstrPWL(\n", " q[p],\n", " pwl_prod[p],\n", " [0, 0.25 * max_prod[p], max_prod[p]],\n", - " [0, base_prod_cost[p]*max_prod[p], base_prod_cost[p]*max_prod[p] + 1.25*base_prod_cost[p]* (max_prod[p] - 0.25*max_prod[p])],\n", + " [0, base_prod_cost[p]*max_prod[p], base_prod_cost[p]*max_prod[p] + 1.25*base_prod_cost[p]* (max_prod[p] - 0.25*max_prod[p])],\n", " name=f'pwl_cost_{p}'\n", " )\n", "\n", "# objective\n", - "total_transp_cost = gp.quicksum(transp_cost[p,d]*x[p,d] for p in production for d in distribution)\n", + "total_transp_cost = gp.quicksum(transp_cost[p,d]*x_pwl2[p,d] for p in production for d in distribution)\n", "m_pwl2.setObjective(total_transp_cost + pwl_prod.sum(), GRB.MINIMIZE)\n", "\n", "# optimize and extract and show solution\n", "m_pwl2.optimize()\n", "\n", - "x_values_prod = pd.Series(m_pwl2.getAttr('X', x), name = \"shipment\", index = transp_cost.index)\n", + "x_values_prod = pd.Series(m_pwl2.getAttr('X', x_pwl2), name = \"shipment\", index = transp_cost.index)\n", "sol_prod = pd.concat([transp_cost, x_values_prod], axis=1)\n", "print(f\"This model has a total cost of {round(m_pwl2.ObjVal,2)}\")\n", - "sol_prod[sol_prod.shipment > 0]" + "sol_prod[sol_prod.shipment > 0].drop('cost',axis=1)" ] }, { @@ -789,7 +836,7 @@ }, { "cell_type": "code", - "execution_count": 313, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -925,7 +972,7 @@ " St. Louis 4.60 41.0 0.0" ] }, - "execution_count": 313, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -949,7 +996,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -964,11 +1011,6 @@ } ], "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "\n", "np.random.seed(2579)\n", "\n", "# Number of samples to generate\n", @@ -1044,16 +1086,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "101" + "117.0" ] }, - "execution_count": 319, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -1074,7 +1116,7 @@ }, { "cell_type": "code", - "execution_count": 317, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -1203,7 +1245,7 @@ " St. Louis 4.60 41.0 41.0" ] }, - "execution_count": 317, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -1233,14 +1275,10 @@ }, { "cell_type": "code", - "execution_count": 202, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ - "import pandas as pd\n", - "import gurobipy as gp\n", - "from gurobipy import GRB\n", - "\n", "# Define sectors\n", "sectors = [\n", " 'Tech',\n", @@ -1290,7 +1328,7 @@ }, { "cell_type": "code", - "execution_count": 203, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -1368,7 +1406,7 @@ "TradHealth 0.000" ] }, - "execution_count": 203, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -1414,7 +1452,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -1444,15 +1482,15 @@ " \n", " \n", " Tech\n", - " 0.276\n", + " 0.307\n", " \n", " \n", " TradRetailUtil\n", - " 0.100\n", + " 0.000\n", " \n", " \n", " Renewable\n", - " 0.223\n", + " 0.257\n", " \n", " \n", " FossilFuel\n", @@ -1464,7 +1502,7 @@ " \n", " \n", " GenHealth\n", - " 0.301\n", + " 0.336\n", " \n", " \n", " TradHealth\n", @@ -1476,16 +1514,16 @@ ], "text/plain": [ " Investment Allocation\n", - "Tech 0.276\n", - "TradRetailUtil 0.100\n", - "Renewable 0.223\n", + "Tech 0.307\n", + "TradRetailUtil 0.000\n", + "Renewable 0.257\n", "FossilFuel 0.000\n", "Auto 0.100\n", - "GenHealth 0.301\n", + "GenHealth 0.336\n", "TradHealth 0.000" ] }, - "execution_count": 205, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } diff --git a/optimization202/Modeling_Session_2/exercise_set2.ipynb b/optimization202/Modeling_Session_2/exercise_set2.ipynb index 2bbff94..ee34750 100644 --- a/optimization202/Modeling_Session_2/exercise_set2.ipynb +++ b/optimization202/Modeling_Session_2/exercise_set2.ipynb @@ -10,13 +10,42 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: gurobipy in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (12.0.0)\n", + "Note: you may need to restart the kernel to use updated packages.\n", + "Requirement already satisfied: seaborn in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (0.13.0)\n", + "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from seaborn) (1.26.4)\n", + "Requirement already satisfied: pandas>=1.2 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from seaborn) (2.2.2)\n", + "Requirement already satisfied: matplotlib!=3.6.1,>=3.3 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from seaborn) (3.8.0)\n", + "Requirement already satisfied: contourpy>=1.0.1 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (1.1.1)\n", + "Requirement already satisfied: cycler>=0.10 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (0.12.1)\n", + "Requirement already satisfied: fonttools>=4.22.0 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (4.43.1)\n", + "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (1.4.5)\n", + "Requirement already satisfied: packaging>=20.0 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (23.2)\n", + "Requirement already satisfied: pillow>=6.2.0 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (10.0.1)\n", + "Requirement already satisfied: pyparsing>=2.3.1 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (3.1.1)\n", + "Requirement already satisfied: python-dateutil>=2.7 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from pandas>=1.2->seaborn) (2023.3.post1)\n", + "Requirement already satisfied: tzdata>=2022.7 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from pandas>=1.2->seaborn) (2023.3)\n", + "Requirement already satisfied: six>=1.5 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.3->seaborn) (1.16.0)\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], "source": [ - "#%pip install gurobipy\n", + "%pip install gurobipy\n", + "%pip install seaborn\n", "\n", "import pandas as pd\n", + "import numpy as np\n", + "import seaborn as sns\n", + "import matplotlib.pyplot as plt\n", "import gurobipy as gp\n", "from gurobipy import GRB" ] @@ -48,7 +77,7 @@ }, { "cell_type": "code", - "execution_count": 221, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -78,7 +107,7 @@ }, { "cell_type": "code", - "execution_count": 314, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -183,7 +212,7 @@ " St. Louis 4.60 41.0" ] }, - "execution_count": 314, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -232,7 +261,7 @@ }, { "cell_type": "code", - "execution_count": 206, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -365,28 +394,27 @@ " Lexington 1.61 2.11 1.27 121.0" ] }, - "execution_count": 206, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "import gurobipy as gp\n", - "from gurobipy import GRB\n", - "\n", "m_pwl = gp.Model('widgets')\n", "m_pwl.setParam('OutputFlag', 0)\n", "\n", - "x = m_pwl.addVars(production, distribution, vtype=GRB.SEMICONT, lb = 30, name = 'prod_ship')\n", + "x_pwl = m_pwl.addVars(production, distribution, vtype=GRB.SEMICONT, lb = 30, name = 'prod_ship')\n", "pwl_cost = m_pwl.addVars(production, distribution, name = 'pwl_cost')\n", "\n", "# constraints\n", - "can_produce = m_pwl.addConstrs((gp.quicksum(x[p,d] for d in distribution) <= max_prod[p] for p in production), name = 'can_produce')\n", - "must_produce = m_pwl.addConstrs((gp.quicksum(x[p,d] for d in distribution) >= frac*max_prod[p] for p in production), name = 'must_produce')\n", - "meet_demand = m_pwl.addConstrs((x.sum('*', d) >= n_demand[d] for d in distribution), name = 'demand')\n", + "can_produce = m_pwl.addConstrs((gp.quicksum(x_pwl[p,d] for d in distribution) <= max_prod[p] for p in production), name = 'can_produce')\n", + "must_produce = m_pwl.addConstrs((gp.quicksum(x_pwl[p,d] for d in distribution) >= frac*max_prod[p] for p in production), name = 'must_produce')\n", + "meet_demand = m_pwl.addConstrs((x_pwl.sum('*', d) >= n_demand[d] for d in distribution), name = 'demand')\n", "\n", + "# create empty series for analysis \n", "bulk_costs = pd.Series(index=transp_cost.keys(), name='bulk_costs')\n", "base_costs = pd.Series(index=transp_cost.keys(), name='base_costs')\n", + "\n", "# Create piecewise cost functions\n", "for p in production:\n", " for d in distribution:\n", @@ -409,7 +437,7 @@ " 0, base_cost * frac_produce, base_cost * frac_produce + bulk_cost * (max_produce - frac_produce)\n", " ]\n", "\n", - " m_pwl.addGenConstrPWL(x[p, d], pwl_cost[p, d], pwl_points_x, pwl_points_y, name=f\"pwl_{p}_{d}\")\n", + " m_pwl.addGenConstrPWL(x_pwl[p, d], pwl_cost[p, d], pwl_points_x, pwl_points_y, name=f\"pwl_{p}_{d}\")\n", "\n", "# Objective: minimize the total cost\n", "m_pwl.setObjective(gp.quicksum(pwl_cost[p, d] for p, d in transp_cost.keys()), GRB.MINIMIZE)\n", @@ -417,7 +445,7 @@ "# Optimize\n", "m_pwl.optimize()\n", "\n", - "x_values_pwl = pd.Series(m_pwl.getAttr('X', x), name = \"shipment_pwl\", index = transp_cost.index)\n", + "x_values_pwl = pd.Series(m_pwl.getAttr('X', x_pwl), name = \"shipment_pwl\", index = transp_cost.index)\n", "sol_pwl = pd.concat([transp_cost, base_costs, bulk_costs, x_values_pwl], axis=1)\n", "\n", "print(f\"This model has a total cost of {round(m_pwl.ObjVal,2)}\")\n", @@ -426,7 +454,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -473,42 +501,58 @@ " Nashville\n", " 5.96\n", " 30.0\n", - " 5.96\n", - " 5.96\n", - " 30.0\n", + " 6.46\n", + " 3.88\n", + " 0.0\n", " \n", " \n", " Richmond\n", " 1.96\n", " 116.0\n", - " 1.96\n", - " 1.96\n", - " 116.0\n", + " 2.46\n", + " 1.48\n", + " 135.0\n", " \n", " \n", - " Cleveland\n", + " Cleveland\n", " Columbia\n", " 2.43\n", " 89.0\n", - " 2.43\n", - " 2.43\n", + " 2.93\n", + " 1.76\n", " 89.0\n", " \n", " \n", " Indianapolis\n", " 2.37\n", " 95.0\n", - " 2.37\n", - " 2.37\n", - " 95.0\n", + " 2.87\n", + " 1.72\n", + " 40.0\n", + " \n", + " \n", + " Nashville\n", + " 4.13\n", + " 0.0\n", + " 4.63\n", + " 2.78\n", + " 30.0\n", + " \n", + " \n", + " St. Louis\n", + " 4.88\n", + " 0.0\n", + " 5.38\n", + " 3.23\n", + " 41.0\n", " \n", " \n", " Little Rock\n", " St. Louis\n", " 2.92\n", " 140.0\n", - " 2.92\n", - " 2.92\n", + " 3.42\n", + " 2.05\n", " 140.0\n", " \n", " \n", @@ -516,26 +560,34 @@ " Nashville\n", " 1.53\n", " 71.0\n", - " 1.53\n", - " 1.53\n", + " 2.03\n", + " 1.22\n", " 71.0\n", " \n", " \n", - " Charleston\n", + " Charleston\n", + " Indianapolis\n", + " 2.61\n", + " 0.0\n", + " 3.11\n", + " 1.87\n", + " 55.0\n", + " \n", + " \n", " Lexington\n", " 1.61\n", " 121.0\n", - " 1.61\n", - " 1.61\n", + " 2.11\n", + " 1.27\n", " 121.0\n", " \n", " \n", " St. Louis\n", " 4.60\n", " 41.0\n", - " 4.60\n", - " 4.60\n", - " 41.0\n", + " 5.10\n", + " 3.06\n", + " 0.0\n", " \n", " \n", "\n", @@ -544,17 +596,20 @@ "text/plain": [ " cost shipment base_costs bulk_costs shipment_pwl\n", "production distribution \n", - "Baltimore Nashville 5.96 30.0 5.96 5.96 30.0\n", - " Richmond 1.96 116.0 1.96 1.96 116.0\n", - "Cleveland Columbia 2.43 89.0 2.43 2.43 89.0\n", - " Indianapolis 2.37 95.0 2.37 2.37 95.0\n", - "Little Rock St. Louis 2.92 140.0 2.92 2.92 140.0\n", - "Birmingham Nashville 1.53 71.0 1.53 1.53 71.0\n", - "Charleston Lexington 1.61 121.0 1.61 1.61 121.0\n", - " St. Louis 4.60 41.0 4.60 4.60 41.0" + "Baltimore Nashville 5.96 30.0 6.46 3.88 0.0\n", + " Richmond 1.96 116.0 2.46 1.48 135.0\n", + "Cleveland Columbia 2.43 89.0 2.93 1.76 89.0\n", + " Indianapolis 2.37 95.0 2.87 1.72 40.0\n", + " Nashville 4.13 0.0 4.63 2.78 30.0\n", + " St. Louis 4.88 0.0 5.38 3.23 41.0\n", + "Little Rock St. Louis 2.92 140.0 3.42 2.05 140.0\n", + "Birmingham Nashville 1.53 71.0 2.03 1.22 71.0\n", + "Charleston Indianapolis 2.61 0.0 3.11 1.87 55.0\n", + " Lexington 1.61 121.0 2.11 1.27 121.0\n", + " St. Louis 4.60 41.0 5.10 3.06 0.0" ] }, - "execution_count": 23, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -569,26 +624,27 @@ "metadata": {}, "source": [ "### Scenario 2\n", - "The original model does not reflect any production costs to make a widget in different locations. Suppose we have costs of $c^1_p$ per widget for making up to half of a production facility's capacity and a cost of $c^2_d$ for everything after. Update the model to reflect this and solve. " + "The original model does not reflect any production costs to make a widget in different locations. Suppose we have a given production cost per widget for making up to 25% of a production facility's capacity. After that level of production is reached, then the cost *increases* by 25%. For example, if a facility can produce 100 widgets and the initial cost is $1, then the first 25 widgets made will cost $1 each and up to the next 75 widgets qill cost $1.25 each to make. " ] }, { "cell_type": "code", - "execution_count": 290, + "execution_count": null, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Gurobi Optimizer version 11.0.3 build v11.0.3rc0 (mac64[rosetta2] - Darwin 23.6.0 23G80)\n", + "Gurobi Optimizer version 12.0.0 build v12.0.0rc1 (mac64[rosetta2] - Darwin 24.1.0 24B83)\n", "\n", "CPU model: Apple M1\n", "Thread count: 8 physical cores, 8 logical processors, using up to 8 threads\n", "\n", "Optimize a model with 21 rows, 40 columns and 125 nonzeros\n", - "Model fingerprint: 0xdf7fc01f\n", - "Model has 5 general constraints\n", + "Model fingerprint: 0x4405fcbf\n", + "Model has 5 simple general constraints\n", + " 5 PWL\n", "Variable types: 10 continuous, 0 integer (0 binary)\n", "Semi-Variable types: 30 continuous, 0 integer\n", "Coefficient statistics:\n", @@ -614,12 +670,13 @@ "\n", "Cutting planes:\n", " Gomory: 2\n", + " Cover: 1\n", " Implied bound: 2\n", " MIR: 3\n", " Flow cover: 6\n", " Relax-and-lift: 2\n", "\n", - "Explored 1 nodes (42 simplex iterations) in 0.09 seconds (0.00 work units)\n", + "Explored 1 nodes (44 simplex iterations) in 0.03 seconds (0.00 work units)\n", "Thread count was 8 (of 8 available processors)\n", "\n", "Solution count 1: 3121.29 \n", @@ -651,60 +708,50 @@ " \n", " \n", " \n", - " cost\n", " shipment\n", " \n", " \n", " production\n", " distribution\n", " \n", - " \n", " \n", " \n", " \n", " \n", " Baltimore\n", " Nashville\n", - " 5.96\n", " 30.0\n", " \n", " \n", " Richmond\n", - " 1.96\n", " 116.0\n", " \n", " \n", " Cleveland\n", " Columbia\n", - " 2.43\n", " 89.0\n", " \n", " \n", " Indianapolis\n", - " 2.37\n", " 95.0\n", " \n", " \n", " Little Rock\n", " St. Louis\n", - " 2.92\n", " 140.0\n", " \n", " \n", " Birmingham\n", " Nashville\n", - " 1.53\n", " 71.0\n", " \n", " \n", " Charleston\n", " Lexington\n", - " 1.61\n", " 121.0\n", " \n", " \n", " St. Louis\n", - " 4.60\n", " 41.0\n", " \n", " \n", @@ -712,19 +759,19 @@ "" ], "text/plain": [ - " cost shipment\n", - "production distribution \n", - "Baltimore Nashville 5.96 30.0\n", - " Richmond 1.96 116.0\n", - "Cleveland Columbia 2.43 89.0\n", - " Indianapolis 2.37 95.0\n", - "Little Rock St. Louis 2.92 140.0\n", - "Birmingham Nashville 1.53 71.0\n", - "Charleston Lexington 1.61 121.0\n", - " St. Louis 4.60 41.0" + " shipment\n", + "production distribution \n", + "Baltimore Nashville 30.0\n", + " Richmond 116.0\n", + "Cleveland Columbia 89.0\n", + " Indianapolis 95.0\n", + "Little Rock St. Louis 140.0\n", + "Birmingham Nashville 71.0\n", + "Charleston Lexington 121.0\n", + " St. Louis 41.0" ] }, - "execution_count": 290, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -734,12 +781,12 @@ "#m_pwl2.setParam('OutputFlag',0)\n", "\n", "# decision vars\n", - "x = m_pwl2.addVars(production, distribution, vtype=GRB.SEMICONT, lb = C, name = 'prod_ship')\n", + "x_pwl2 = m_pwl2.addVars(production, distribution, vtype=GRB.SEMICONT, lb = C, name = 'prod_ship')\n", "\n", "# constraints\n", - "can_produce = m_pwl2.addConstrs((gp.quicksum(x[p,d] for d in distribution) <= max_prod[p] for p in production), name = 'can_produce')\n", - "must_produce = m_pwl2.addConstrs((gp.quicksum(x[p,d] for d in distribution) >= frac*max_prod[p] for p in production), name = 'must_produce')\n", - "meet_demand = m_pwl2.addConstrs(x.sum('*', d) >= n_demand[d] for d in distribution)\n", + "can_produce = m_pwl2.addConstrs((gp.quicksum(x_pwl2[p,d] for d in distribution) <= max_prod[p] for p in production), name = 'can_produce')\n", + "must_produce = m_pwl2.addConstrs((gp.quicksum(x_pwl2[p,d] for d in distribution) >= frac*max_prod[p] for p in production), name = 'must_produce')\n", + "meet_demand = m_pwl2.addConstrs(x_pwl2.sum('*', d) >= n_demand[d] for d in distribution)\n", "\n", "# Total production for each facility\n", "base_prod_cost = pd.Series([1,1.2,.8,.8,.9], index = production, name = \"max_production\")\n", @@ -747,28 +794,28 @@ "# Add new variables and/or constraints\n", "q = m_pwl2.addVars(production, name='q')\n", "pwl_prod = m_pwl2.addVars(production, name='pwl_prod')\n", - "m_pwl2.addConstrs((q[p] == gp.quicksum(x[p, d] for d in distribution) for p in production), name='total_production')\n", + "m_pwl2.addConstrs((q[p] == gp.quicksum(x_pwl2[p, d] for d in distribution) for p in production), name='total_production')\n", "\n", "for p in production:\n", " m_pwl2.addGenConstrPWL(\n", " q[p],\n", " pwl_prod[p],\n", " [0, 0.25 * max_prod[p], max_prod[p]],\n", - " [0, base_prod_cost[p]*max_prod[p], base_prod_cost[p]*max_prod[p] + 1.25*base_prod_cost[p]* (max_prod[p] - 0.25*max_prod[p])],\n", + " [0, base_prod_cost[p]*max_prod[p], base_prod_cost[p]*max_prod[p] + 1.25*base_prod_cost[p]* (max_prod[p] - 0.25*max_prod[p])],\n", " name=f'pwl_cost_{p}'\n", " )\n", "\n", "# objective\n", - "total_transp_cost = gp.quicksum(transp_cost[p,d]*x[p,d] for p in production for d in distribution)\n", + "total_transp_cost = gp.quicksum(transp_cost[p,d]*x_pwl2[p,d] for p in production for d in distribution)\n", "m_pwl2.setObjective(total_transp_cost + pwl_prod.sum(), GRB.MINIMIZE)\n", "\n", "# optimize and extract and show solution\n", "m_pwl2.optimize()\n", "\n", - "x_values_prod = pd.Series(m_pwl2.getAttr('X', x), name = \"shipment\", index = transp_cost.index)\n", + "x_values_prod = pd.Series(m_pwl2.getAttr('X', x_pwl2), name = \"shipment\", index = transp_cost.index)\n", "sol_prod = pd.concat([transp_cost, x_values_prod], axis=1)\n", "print(f\"This model has a total cost of {round(m_pwl2.ObjVal,2)}\")\n", - "sol_prod[sol_prod.shipment > 0]" + "sol_prod[sol_prod.shipment > 0].drop('cost',axis=1)" ] }, { @@ -789,7 +836,7 @@ }, { "cell_type": "code", - "execution_count": 313, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -925,7 +972,7 @@ " St. Louis 4.60 41.0 0.0" ] }, - "execution_count": 313, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -949,7 +996,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [ { @@ -964,11 +1011,6 @@ } ], "source": [ - "import numpy as np\n", - "import pandas as pd\n", - "import seaborn as sns\n", - "import matplotlib.pyplot as plt\n", - "\n", "np.random.seed(2579)\n", "\n", "# Number of samples to generate\n", @@ -1044,16 +1086,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "101" + "117.0" ] }, - "execution_count": 319, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -1074,7 +1116,7 @@ }, { "cell_type": "code", - "execution_count": 317, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -1203,7 +1245,7 @@ " St. Louis 4.60 41.0 41.0" ] }, - "execution_count": 317, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -1233,14 +1275,10 @@ }, { "cell_type": "code", - "execution_count": 202, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ - "import pandas as pd\n", - "import gurobipy as gp\n", - "from gurobipy import GRB\n", - "\n", "# Define sectors\n", "sectors = [\n", " 'Tech',\n", @@ -1290,7 +1328,7 @@ }, { "cell_type": "code", - "execution_count": 203, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -1368,7 +1406,7 @@ "TradHealth 0.000" ] }, - "execution_count": 203, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -1414,7 +1452,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, "outputs": [ { @@ -1444,15 +1482,15 @@ " \n", " \n", " Tech\n", - " 0.276\n", + " 0.307\n", " \n", " \n", " TradRetailUtil\n", - " 0.100\n", + " 0.000\n", " \n", " \n", " Renewable\n", - " 0.223\n", + " 0.257\n", " \n", " \n", " FossilFuel\n", @@ -1464,7 +1502,7 @@ " \n", " \n", " GenHealth\n", - " 0.301\n", + " 0.336\n", " \n", " \n", " TradHealth\n", @@ -1476,16 +1514,16 @@ ], "text/plain": [ " Investment Allocation\n", - "Tech 0.276\n", - "TradRetailUtil 0.100\n", - "Renewable 0.223\n", + "Tech 0.307\n", + "TradRetailUtil 0.000\n", + "Renewable 0.257\n", "FossilFuel 0.000\n", "Auto 0.100\n", - "GenHealth 0.301\n", + "GenHealth 0.336\n", "TradHealth 0.000" ] }, - "execution_count": 205, + "execution_count": 17, "metadata": {}, "output_type": "execute_result" } diff --git a/optimization202/next_level_modeling/cost.csv b/optimization202/next_level_modeling/cost.csv new file mode 100644 index 0000000..c45798f --- /dev/null +++ b/optimization202/next_level_modeling/cost.csv @@ -0,0 +1,31 @@ +production,distribution,cost +Baltimore,Columbia,4.5 +Baltimore,Indianapolis,5.09 +Baltimore,Lexington,4.33 +Baltimore,Nashville,5.96 +Baltimore,Richmond,1.96 +Baltimore,St. Louis,7.3 +Cleveland,Columbia,2.43 +Cleveland,Indianapolis,2.37 +Cleveland,Lexington,2.54 +Cleveland,Nashville,4.13 +Cleveland,Richmond,3.2 +Cleveland,St. Louis,4.88 +Little Rock,Columbia,6.42 +Little Rock,Indianapolis,4.83 +Little Rock,Lexington,3.39 +Little Rock,Nashville,4.4 +Little Rock,Richmond,7.44 +Little Rock,St. Louis,2.92 +Birmingham,Columbia,3.33 +Birmingham,Indianapolis,4.33 +Birmingham,Lexington,3.38 +Birmingham,Nashville,1.53 +Birmingham,Richmond,5.95 +Birmingham,St. Louis,4.01 +Charleston,Columbia,3.02 +Charleston,Indianapolis,2.61 +Charleston,Lexington,1.61 +Charleston,Nashville,4.44 +Charleston,Richmond,2.36 +Charleston,St. Louis,4.6 \ No newline at end of file From 196037f3227b634f741b2d0d4653f5ea0cc1877c Mon Sep 17 00:00:00 2001 From: Jerry Yurchisin Date: Wed, 13 Nov 2024 13:26:01 -0500 Subject: [PATCH 05/40] nlreg update --- ...pleted_nonlinear_regression_exercise.ipynb | 420 +++---- .../nonlinear_regression_exercise.ipynb | 1011 +---------------- 2 files changed, 196 insertions(+), 1235 deletions(-) diff --git a/optimization202/Modeling_Session_1/completed_nonlinear_regression_exercise.ipynb b/optimization202/Modeling_Session_1/completed_nonlinear_regression_exercise.ipynb index 5b385ce..42fbf8b 100755 --- a/optimization202/Modeling_Session_1/completed_nonlinear_regression_exercise.ipynb +++ b/optimization202/Modeling_Session_1/completed_nonlinear_regression_exercise.ipynb @@ -117,15 +117,14 @@ "id": "f6ad2892-6047-4bb5-bfea-0c66e01cc80e", "metadata": {}, "source": [ - "## Problem description\n", - "### Michaelis Menton Enzyme Kinetics\n", - "Michaelis-Menten kinetics describes how quickly a reaction happens when an enzyme—a type of molecule that speeds up processes in the body or other reactions—interacts with other molecules. Initially, as the amount of molecules $x$ increases, the reaction speed, $y$, increases. But at a certain point, adding more molecules doesn’t increase the speed much because the enzyme is already working as fast as it can.\n", + "### Michaelis-Menten Enzyme Kinetics\n", + "[Michaelis-Menten](https://en.wikipedia.org/wiki/Michaelis%E2%80%93Menten_kinetics) kinetics describes how quickly a reaction happens when an enzyme—a type of molecule that speeds up processes in the body or other reactions—interacts with other molecules. Initially, as the amount of molecules $x$ increases, the reaction speed, $y$, increases. But at a certain point, adding more molecules doesn’t increase the speed much because the enzyme is already working as fast as it can.\n", "\n", "![image.png](attachment:dfac2263-b4f3-4d9f-808d-c0d7b34ad8e6.png)\n", "\n", "Two key things come from this: the maximum speed the enzyme can reach $V_{max}$ and the concentration level at which it’s working at half that maximum speed $K_m$. These insights help us understand efficiency and limitations, which is valuable for making decisions in fields like drug development, where knowing an enzyme's limits can guide dosage or predict interactions.\n", "\n", - "In Michaelis Menten kinetics, we can model the speed of an enzymatic reaction can be with the following equations:\n", + "In Michaelis-Menten kinetics, we can model the speed of an enzymatic reaction can be with the following equations:\n", "\n", "$$\n", "[\\text{Speed of reaction}] = \\frac{{\\bf V_{max}}\\cdot [\\text{Initial concentration}]}{K_m + [\\text{Initial concentration}]}\n", @@ -137,7 +136,7 @@ "\n", "where the regression parameters that we will find in this notebook are $V_{max}$ and $K_m$. We will be given data points $i$ for \n", "- The independent variable $x$ which is the concentration. Each data point will be $x_i$.\n", - "- The dependent variable $y(x)$ which is the speed of the reaction that we are trying to model. Each data point will be $y_i$.\n" + "- The dependent variable $y(x)$ which is the speed of the reaction that we are trying to model. Each data point will be $y_i$." ] }, { @@ -174,10 +173,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "bf53b323-9e7b-40c7-a8d8-28723d77ae3a", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Requirement already satisfied: gurobipy in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (12.0.0)\n", + "Requirement already satisfied: plotly in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (5.23.0)\n", + "Requirement already satisfied: tenacity>=6.2.0 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from plotly) (8.2.3)\n", + "Requirement already satisfied: packaging in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from plotly) (23.2)\n", + "Requirement already satisfied: numpy in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (1.26.4)\n", + "Requirement already satisfied: pandas in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (2.2.2)\n", + "Requirement already satisfied: numpy>=1.23.2 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from pandas) (1.26.4)\n", + "Requirement already satisfied: python-dateutil>=2.8.2 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from pandas) (2.8.2)\n", + "Requirement already satisfied: pytz>=2020.1 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from pandas) (2023.3.post1)\n", + "Requirement already satisfied: tzdata>=2022.7 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from pandas) (2023.3)\n", + "Requirement already satisfied: six>=1.5 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n", + "Requirement already satisfied: openpyxl in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (3.1.2)\n", + "Requirement already satisfied: et-xmlfile in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from openpyxl) (1.1.0)\n" + ] + } + ], "source": [ "!pip install gurobipy\n", "!pip install plotly\n", @@ -238,10 +257,10 @@ "output_type": "stream", "text": [ "Here are the x values:\n", - "{np.float64(0.0): 0.05, np.float64(1.0): 0.1, np.float64(2.0): 0.25, np.float64(3.0): 0.5, np.float64(4.0): 1.0, np.float64(5.0): 2.5, np.float64(6.0): 5.0, np.float64(7.0): 8.0, np.float64(8.0): 20.0, np.float64(9.0): 30.0}\n", + "{0.0: 0.05, 1.0: 0.1, 2.0: 0.25, 3.0: 0.5, 4.0: 1.0, 5.0: 2.5, 6.0: 5.0, 7.0: 8.0, 8.0: 20.0, 9.0: 30.0}\n", "\n", "Here are the y values:\n", - "{np.float64(0.0): 2.98, np.float64(1.0): 5.2, np.float64(2.0): 14.38, np.float64(3.0): 30.3, np.float64(4.0): 48.99, np.float64(5.0): 86.25, np.float64(6.0): 112.57, np.float64(7.0): 136.24, np.float64(8.0): 169.97, np.float64(9.0): 177.71}\n" + "{0.0: 2.98, 1.0: 5.2, 2.0: 14.38, 3.0: 30.3, 4.0: 48.99, 5.0: 86.25, 6.0: 112.57, 7.0: 136.24, 8.0: 169.97, 9.0: 177.71}\n" ] } ], @@ -277,35 +296,6 @@ "id": "0693fe8b-08ac-4bd2-af7f-dcbd531888e3", "metadata": {}, "outputs": [ - { - "data": { - "text/html": [ - " \n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, { "data": { "application/vnd.plotly.v1+json": { @@ -344,7 +334,6 @@ } ], "layout": { - "autosize": true, "template": { "data": { "bar": [ @@ -1208,56 +1197,17 @@ "text": "Nonlinear regression example: Reaction Speed vs Concentration" }, "xaxis": { - "autorange": true, - "range": [ - -1.8746865203761753, - 31.924686520376177 - ], "title": { "text": "x" - }, - "type": "linear" + } }, "yaxis": { - "autorange": true, - "range": [ - -11.439466019417475, - 192.1294660194175 - ], "title": { "text": "y" - }, - "type": "linear" + } } } - }, - "text/html": [ - "
" - ] + } }, "metadata": {}, "output_type": "display_data" @@ -1320,7 +1270,15 @@ "execution_count": 5, "id": "a4d15290-83c7-46c9-bd7d-de202a18a2cf", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Restricted license - for non-production use only - expires 2026-11-23\n" + ] + } + ], "source": [ "# Declare and initialize model\n", "model = gp.Model(\"Nonlinear regression\")" @@ -1521,7 +1479,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 10, "id": "e4b41f36-17ff-4748-95c5-d8d101e80231", "metadata": {}, "outputs": [ @@ -1529,9 +1487,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "Gurobi Optimizer version 12.0.0 build v12.0.0rc1 (mac64[arm] - Darwin 23.6.0 23G93)\n", + "Gurobi Optimizer version 12.0.0 build v12.0.0rc1 (mac64[rosetta2] - Darwin 24.1.0 24B83)\n", "\n", - "CPU model: Apple M1 Pro\n", + "CPU model: Apple M1\n", "Thread count: 8 physical cores, 8 logical processors, using up to 8 threads\n", "\n", "Optimize a model with 0 rows, 12 columns and 0 nonzeros\n", @@ -1556,7 +1514,7 @@ "Variable types: 13 continuous, 0 integer (0 binary)\n", "Found heuristic solution: objective 76.2644466\n", "\n", - "Root relaxation: objective 0.000000e+00, 5 iterations, 0.00 seconds (0.00 work units)\n", + "Root relaxation: objective 0.000000e+00, 5 iterations, 0.01 seconds (0.00 work units)\n", "\n", " Nodes | Current Node | Objective Bounds | Work\n", " Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time\n", @@ -1569,13 +1527,13 @@ "* 1917 51 26 76.2644456 76.22556 0.05% 2.2 0s\n", "* 2134 4 26 76.2644455 76.25478 0.01% 2.2 0s\n", "\n", - "Explored 2299 nodes (4752 simplex iterations) in 0.06 seconds (0.01 work units)\n", + "Explored 2299 nodes (4747 simplex iterations) in 0.13 seconds (0.01 work units)\n", "Thread count was 8 (of 8 available processors)\n", "\n", "Solution count 4: 76.2644 76.2644 76.2644 76.2644 \n", "\n", "Optimal solution found (tolerance 1.00e-04)\n", - "Best objective 7.626444550832e+01, best bound 7.626444409353e+01, gap 0.0000%\n" + "Best objective 7.626444550834e+01, best bound 7.626444409354e+01, gap 0.0000%\n" ] } ], @@ -1601,10 +1559,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "31f7a434-c49f-4ab1-a788-3dcae22bbd48", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "An optimial solution was found with objective 76.26444550833548\n", + "\n", + " Variable X \n", + "-------------------------\n", + " Vmax 195.345 \n", + " Km 3.26518 \n", + " ymodel[0.0] 2.94622 \n", + " ymodel[1.0] 5.80489 \n", + " ymodel[2.0] 13.893 \n", + " ymodel[3.0] 25.941 \n", + " ymodel[4.0] 45.8 \n", + " ymodel[5.0] 84.709 \n", + " ymodel[6.0] 118.174 \n", + " ymodel[7.0] 138.725 \n", + " ymodel[8.0] 167.929 \n", + " ymodel[9.0] 176.171 \n" + ] + } + ], "source": [ "status = model.status\n", "if status == GRB.OPTIMAL:\n", 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- "text/html": [ - "
" - ] + } }, "metadata": {}, "output_type": "display_data" @@ -3092,12 +3032,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "id": "f98c1ae2-f9e8-4a9e-b600-45906b50224d", "metadata": { "scrolled": true }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Freeing default Gurobi environment\n" + ] + } + ], "source": [ "# Free Gurobi resources: Model and environment\n", "model.dispose()\n", @@ -3107,7 +3055,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "gurobi_ml", "language": "python", "name": "python3" }, @@ -3121,7 +3069,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.7" + "version": "3.11.6" } }, "nbformat": 4, diff --git a/optimization202/Modeling_Session_1/nonlinear_regression_exercise.ipynb b/optimization202/Modeling_Session_1/nonlinear_regression_exercise.ipynb index 089e92e..2c50abf 100755 --- a/optimization202/Modeling_Session_1/nonlinear_regression_exercise.ipynb +++ b/optimization202/Modeling_Session_1/nonlinear_regression_exercise.ipynb @@ -117,15 +117,14 @@ "id": "f6ad2892-6047-4bb5-bfea-0c66e01cc80e", "metadata": {}, "source": [ - "## Problem description\n", - "### Michaelis Menton Enzyme Kinetics\n", - "Michaelis-Menten kinetics describes how quickly a reaction happens when an enzyme—a type of molecule that speeds up processes in the body or other reactions—interacts with other molecules. Initially, as the amount of molecules $x$ increases, the reaction speed, $y$, increases. But at a certain point, adding more molecules doesn’t increase the speed much because the enzyme is already working as fast as it can.\n", + "### Michaelis-Menten Enzyme Kinetics\n", + "[Michaelis-Menten](https://en.wikipedia.org/wiki/Michaelis%E2%80%93Menten_kinetics) kinetics describes how quickly a reaction happens when an enzyme—a type of molecule that speeds up processes in the body or other reactions—interacts with other molecules. Initially, as the amount of molecules $x$ increases, the reaction speed, $y$, increases. But at a certain point, adding more molecules doesn’t increase the speed much because the enzyme is already working as fast as it can.\n", "\n", "![image.png](attachment:dfac2263-b4f3-4d9f-808d-c0d7b34ad8e6.png)\n", "\n", "Two key things come from this: the maximum speed the enzyme can reach $V_{max}$ and the concentration level at which it’s working at half that maximum speed $K_m$. These insights help us understand efficiency and limitations, which is valuable for making decisions in fields like drug development, where knowing an enzyme's limits can guide dosage or predict interactions.\n", "\n", - "In Michaelis Menten kinetics, we can model the speed of an enzymatic reaction can be with the following equations:\n", + "In Michaelis-Menten kinetics, we can model the speed of an enzymatic reaction can be with the following equations:\n", "\n", "$$\n", "[\\text{Speed of reaction}] = \\frac{{\\bf V_{max}}\\cdot [\\text{Initial concentration}]}{K_m + [\\text{Initial concentration}]}\n", @@ -137,7 +136,7 @@ "\n", "where the regression parameters that we will find in this notebook are $V_{max}$ and $K_m$. We will be given data points $i$ for \n", "- The independent variable $x$ which is the concentration. Each data point will be $x_i$.\n", - "- The dependent variable $y(x)$ which is the speed of the reaction that we are trying to model. Each data point will be $y_i$.\n" + "- The dependent variable $y(x)$ which is the speed of the reaction that we are trying to model. Each data point will be $y_i$." ] }, { @@ -188,7 +187,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "id": "b2a60227-b5f8-44cb-9b0b-08fbcc7e873c", "metadata": {}, "outputs": [], @@ -261,996 +260,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "id": "0693fe8b-08ac-4bd2-af7f-dcbd531888e3", "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - " \n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "mode": "markers", - "name": "Training data", - "type": "scatter", - "x": [ - 0.05, - 0.1, - 0.25, - 0.5, - 1, - 2.5, - 5, - 8, - 20, - 30 - ], - "y": [ - 2.98, - 5.2, - 14.38, - 30.3, - 48.99, - 86.25, - 112.57, - 136.24, - 169.97, - 177.71 - ] - } - ], - "layout": { - "autosize": true, - "template": { - "data": { - "bar": [ - { - "error_x": { - "color": "rgb(36,36,36)" - 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}, - "zeroline": false, - "zerolinecolor": "rgb(36,36,36)" - } - } - }, - "title": { - "text": "Nonlinear regression example: Reaction Speed vs Concentration" - }, - "xaxis": { - "autorange": true, - "range": [ - -1.8746865203761753, - 31.924686520376177 - ], - "title": { - "text": "x" - }, - "type": "linear" - }, - "yaxis": { - "autorange": true, - "range": [ - -11.439466019417475, - 192.1294660194175 - ], - "title": { - "text": "y" - }, - "type": "linear" - } - } - }, - "text/html": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# Create a figure\n", "fig = go.Figure()\n", @@ -1305,7 +318,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "id": "a4d15290-83c7-46c9-bd7d-de202a18a2cf", "metadata": {}, "outputs": [], @@ -1329,7 +342,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "id": "7b93ec63-f6a6-49dc-a4a6-a7be0cb21c5e", "metadata": {}, "outputs": [], @@ -1390,7 +403,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "id": "38341cae-b427-4855-a4a8-064ba50e3cd5", "metadata": {}, "outputs": [], @@ -1436,7 +449,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "id": "2fb54b23-d80e-4a37-8ccf-d5c78d31c50e", "metadata": {}, "outputs": [], @@ -1455,7 +468,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "id": "7ed492af-3d18-44ba-afb8-7e8b53efaec0", "metadata": {}, "outputs": [], From 98719bfe77d167bfb472eb56f052fb4639e53d1c Mon Sep 17 00:00:00 2001 From: Jerry Yurchisin Date: Wed, 13 Nov 2024 13:33:51 -0500 Subject: [PATCH 06/40] Update completed_nonlinear_regression_exercise.ipynb --- ...pleted_nonlinear_regression_exercise.ipynb | 24 ++----------------- 1 file changed, 2 insertions(+), 22 deletions(-) diff --git a/optimization202/Modeling_Session_1/completed_nonlinear_regression_exercise.ipynb b/optimization202/Modeling_Session_1/completed_nonlinear_regression_exercise.ipynb index 42fbf8b..f907d89 100755 --- a/optimization202/Modeling_Session_1/completed_nonlinear_regression_exercise.ipynb +++ b/optimization202/Modeling_Session_1/completed_nonlinear_regression_exercise.ipynb @@ -173,30 +173,10 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "id": "bf53b323-9e7b-40c7-a8d8-28723d77ae3a", "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: gurobipy in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (12.0.0)\n", - "Requirement already satisfied: plotly in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (5.23.0)\n", - "Requirement already satisfied: tenacity>=6.2.0 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from plotly) (8.2.3)\n", - "Requirement already satisfied: packaging in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from plotly) (23.2)\n", - "Requirement already satisfied: numpy in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (1.26.4)\n", - "Requirement already satisfied: pandas in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (2.2.2)\n", - "Requirement already satisfied: numpy>=1.23.2 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from pandas) (1.26.4)\n", - "Requirement already satisfied: python-dateutil>=2.8.2 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from pandas) (2.8.2)\n", - "Requirement already satisfied: pytz>=2020.1 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from pandas) (2023.3.post1)\n", - "Requirement already satisfied: tzdata>=2022.7 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from pandas) (2023.3)\n", - "Requirement already satisfied: six>=1.5 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from python-dateutil>=2.8.2->pandas) (1.16.0)\n", - "Requirement already satisfied: openpyxl in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (3.1.2)\n", - "Requirement already satisfied: et-xmlfile in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from openpyxl) (1.1.0)\n" - ] - } - ], + "outputs": [], "source": [ "!pip install gurobipy\n", "!pip install plotly\n", From 273a8e7b03aa268fe84c1b6907332fff6dac5b7e Mon Sep 17 00:00:00 2001 From: Jerry Yurchisin Date: Wed, 13 Nov 2024 14:33:18 -0500 Subject: [PATCH 07/40] comment typos --- ...pleted_nonlinear_regression_exercise.ipynb | 4 +- .../nonlinear_regression_exercise.ipynb | 4 +- .../Modeling_Session_2/exercise_set2.ipynb | 1126 +---------------- 3 files changed, 57 insertions(+), 1077 deletions(-) diff --git a/optimization202/Modeling_Session_1/completed_nonlinear_regression_exercise.ipynb b/optimization202/Modeling_Session_1/completed_nonlinear_regression_exercise.ipynb index f907d89..dc391b7 100755 --- a/optimization202/Modeling_Session_1/completed_nonlinear_regression_exercise.ipynb +++ b/optimization202/Modeling_Session_1/completed_nonlinear_regression_exercise.ipynb @@ -1539,7 +1539,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "id": "31f7a434-c49f-4ab1-a788-3dcae22bbd48", "metadata": {}, "outputs": [ @@ -1569,7 +1569,7 @@ "source": [ "status = model.status\n", "if status == GRB.OPTIMAL:\n", - " print(f\"An optimial solution was found with objective {model.objVal}\")\n", + " print(f\"An optimal solution was found with objective {model.objVal}\")\n", " model.printAttr('X')\n", "else:\n", " print(f\"Model is not optimal, status = {status}\")" diff --git a/optimization202/Modeling_Session_1/nonlinear_regression_exercise.ipynb b/optimization202/Modeling_Session_1/nonlinear_regression_exercise.ipynb index 2c50abf..953116c 100755 --- a/optimization202/Modeling_Session_1/nonlinear_regression_exercise.ipynb +++ b/optimization202/Modeling_Session_1/nonlinear_regression_exercise.ipynb @@ -408,7 +408,7 @@ "metadata": {}, "outputs": [], "source": [ - "# Write out your constraint to define ymodel[i] for each data poitn in points\n", + "# Write out your constraint to define ymodel[i] for each data point in points\n", "# (your code goes here)" ] }, @@ -535,7 +535,7 @@ "source": [ "status = model.status\n", "if status == GRB.OPTIMAL:\n", - " print(f\"An optimial solution was found with objective {model.objVal}\")\n", + " print(f\"An optimal solution was found with objective {model.objVal}\")\n", " model.printAttr('X')\n", "else:\n", " print(f\"Model is not optimal, status = {status}\")" diff --git a/optimization202/Modeling_Session_2/exercise_set2.ipynb b/optimization202/Modeling_Session_2/exercise_set2.ipynb index ee34750..17c8cd2 100644 --- a/optimization202/Modeling_Session_2/exercise_set2.ipynb +++ b/optimization202/Modeling_Session_2/exercise_set2.ipynb @@ -10,34 +10,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: gurobipy in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (12.0.0)\n", - "Note: you may need to restart the kernel to use updated packages.\n", - "Requirement already satisfied: seaborn in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (0.13.0)\n", - "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from seaborn) (1.26.4)\n", - "Requirement already satisfied: pandas>=1.2 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from seaborn) (2.2.2)\n", - "Requirement already satisfied: matplotlib!=3.6.1,>=3.3 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from seaborn) (3.8.0)\n", - "Requirement already satisfied: contourpy>=1.0.1 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (1.1.1)\n", - "Requirement already satisfied: cycler>=0.10 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (0.12.1)\n", - "Requirement already satisfied: fonttools>=4.22.0 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (4.43.1)\n", - "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (1.4.5)\n", - "Requirement already satisfied: packaging>=20.0 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (23.2)\n", - "Requirement already satisfied: pillow>=6.2.0 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (10.0.1)\n", - "Requirement already satisfied: pyparsing>=2.3.1 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (3.1.1)\n", - "Requirement already satisfied: python-dateutil>=2.7 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (2.8.2)\n", - "Requirement already satisfied: pytz>=2020.1 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from pandas>=1.2->seaborn) (2023.3.post1)\n", - "Requirement already satisfied: tzdata>=2022.7 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from pandas>=1.2->seaborn) (2023.3)\n", - "Requirement already satisfied: six>=1.5 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.3->seaborn) (1.16.0)\n", - "Note: you may need to restart the kernel to use updated packages.\n" - ] - } - ], + "outputs": [], "source": [ "%pip install gurobipy\n", "%pip install seaborn\n", @@ -77,7 +52,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -107,116 +82,9 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "This model has a total cost of 1748.42\n" - ] - }, - { - "data": { - "text/html": [ - "
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costshipment
productiondistribution
BaltimoreNashville5.9630.0
Richmond1.96116.0
ClevelandColumbia2.4389.0
Indianapolis2.3795.0
Little RockSt. Louis2.92140.0
BirminghamNashville1.5371.0
CharlestonLexington1.61121.0
St. Louis4.6041.0
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" - ], - "text/plain": [ - " cost shipment\n", - "production distribution \n", - "Baltimore Nashville 5.96 30.0\n", - " Richmond 1.96 116.0\n", - "Cleveland Columbia 2.43 89.0\n", - " Indianapolis 2.37 95.0\n", - "Little Rock St. Louis 2.92 140.0\n", - "Birmingham Nashville 1.53 71.0\n", - "Charleston Lexington 1.61 121.0\n", - " St. Louis 4.60 41.0" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "m = gp.Model('widgets')\n", "m.setParam('OutputFlag',0)\n", @@ -263,142 +131,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "This model has a total cost of 1925.29\n" - ] - }, - { - "data": { - "text/html": [ - "
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costbase_costsbulk_costsshipment_pwl
productiondistribution
BaltimoreRichmond1.962.461.48135.0
ClevelandColumbia2.432.931.7689.0
Indianapolis2.372.871.7240.0
Nashville4.134.632.7830.0
St. Louis4.885.383.2341.0
Little RockSt. Louis2.923.422.05140.0
BirminghamNashville1.532.031.2271.0
CharlestonIndianapolis2.613.111.8755.0
Lexington1.612.111.27121.0
\n", - "
" - ], - "text/plain": [ - " cost base_costs bulk_costs shipment_pwl\n", - "production distribution \n", - "Baltimore Richmond 1.96 2.46 1.48 135.0\n", - "Cleveland Columbia 2.43 2.93 1.76 89.0\n", - " Indianapolis 2.37 2.87 1.72 40.0\n", - " Nashville 4.13 4.63 2.78 30.0\n", - " St. Louis 4.88 5.38 3.23 41.0\n", - "Little Rock St. Louis 2.92 3.42 2.05 140.0\n", - "Birmingham Nashville 1.53 2.03 1.22 71.0\n", - "Charleston Indianapolis 2.61 3.11 1.87 55.0\n", - " Lexington 1.61 2.11 1.27 121.0" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "m_pwl = gp.Model('widgets')\n", "m_pwl.setParam('OutputFlag', 0)\n", @@ -418,26 +151,26 @@ "# Create piecewise cost functions\n", "for p in production:\n", " for d in distribution:\n", - " max_produce = max_prod[p]\n", - " frac_produce = 0.5*max_produce\n", + " max_produce = ???\n", + " frac_produce = ???\n", " \n", " # Adjusted costs\n", - " base_cost = transp_cost[p,d] + 0.5\n", - " bulk_cost = round(0.6*base_cost, 2) # 25% increase for the first half\n", + " base_cost = ???\n", + " bulk_cost = round(???, 2) # 25% increase for the first half\n", "\n", " base_costs[p,d] = base_cost\n", " bulk_costs[p,d] = bulk_cost\n", "\n", " # Define the piecewise-linear function points\n", " pwl_points_x = [\n", - " 0, frac_produce, max_produce\n", + " 0, ???, ???\n", " ]\n", "\n", " pwl_points_y = [\n", - " 0, base_cost * frac_produce, base_cost * frac_produce + bulk_cost * (max_produce - frac_produce)\n", + " 0, ???, ???\n", " ]\n", "\n", - " m_pwl.addGenConstrPWL(x_pwl[p, d], pwl_cost[p, d], pwl_points_x, pwl_points_y, name=f\"pwl_{p}_{d}\")\n", + " m_pwl.addGenConstrPWL(???, ???, ???, ???, name=f\"pwl_{p}_{d}\")\n", "\n", "# Objective: minimize the total cost\n", "m_pwl.setObjective(gp.quicksum(pwl_cost[p, d] for p, d in transp_cost.keys()), GRB.MINIMIZE)\n", @@ -454,166 +187,9 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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costshipmentbase_costsbulk_costsshipment_pwl
productiondistribution
BaltimoreNashville5.9630.06.463.880.0
Richmond1.96116.02.461.48135.0
ClevelandColumbia2.4389.02.931.7689.0
Indianapolis2.3795.02.871.7240.0
Nashville4.130.04.632.7830.0
St. Louis4.880.05.383.2341.0
Little RockSt. Louis2.92140.03.422.05140.0
BirminghamNashville1.5371.02.031.2271.0
CharlestonIndianapolis2.610.03.111.8755.0
Lexington1.61121.02.111.27121.0
St. Louis4.6041.05.103.060.0
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" - ], - "text/plain": [ - " cost shipment base_costs bulk_costs shipment_pwl\n", - "production distribution \n", - "Baltimore Nashville 5.96 30.0 6.46 3.88 0.0\n", - " Richmond 1.96 116.0 2.46 1.48 135.0\n", - "Cleveland Columbia 2.43 89.0 2.93 1.76 89.0\n", - " Indianapolis 2.37 95.0 2.87 1.72 40.0\n", - " Nashville 4.13 0.0 4.63 2.78 30.0\n", - " St. Louis 4.88 0.0 5.38 3.23 41.0\n", - "Little Rock St. Louis 2.92 140.0 3.42 2.05 140.0\n", - "Birmingham Nashville 1.53 71.0 2.03 1.22 71.0\n", - "Charleston Indianapolis 2.61 0.0 3.11 1.87 55.0\n", - " Lexington 1.61 121.0 2.11 1.27 121.0\n", - " St. Louis 4.60 41.0 5.10 3.06 0.0" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "sol_comp = pd.concat([sol, sol_pwl.drop('cost', axis=1)], axis=1)\n", "sol_comp[(sol_comp.shipment > 0) | (sol_comp.shipment_pwl > 0)]" @@ -631,151 +207,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Gurobi Optimizer version 12.0.0 build v12.0.0rc1 (mac64[rosetta2] - Darwin 24.1.0 24B83)\n", - "\n", - "CPU model: Apple M1\n", - "Thread count: 8 physical cores, 8 logical processors, using up to 8 threads\n", - "\n", - "Optimize a model with 21 rows, 40 columns and 125 nonzeros\n", - "Model fingerprint: 0x4405fcbf\n", - "Model has 5 simple general constraints\n", - " 5 PWL\n", - "Variable types: 10 continuous, 0 integer (0 binary)\n", - "Semi-Variable types: 30 continuous, 0 integer\n", - "Coefficient statistics:\n", - " Matrix range [1e+00, 1e+00]\n", - " Objective range [1e+00, 7e+00]\n", - " Bounds range [3e+01, 3e+01]\n", - " RHS range [6e+01, 2e+02]\n", - " PWLCon x range [2e+01, 5e+01]\n", - " PWLCon y range [6e+01, 2e+02]\n", - "Presolve removed 10 rows and 5 columns\n", - "Presolve time: 0.00s\n", - "Presolved: 71 rows, 65 columns, 185 nonzeros\n", - "Variable types: 35 continuous, 30 integer (30 binary)\n", - "\n", - "Root relaxation: objective 3.073585e+03, 27 iterations, 0.00 seconds (0.00 work units)\n", - "\n", - " Nodes | Current Node | Objective Bounds | Work\n", - " Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time\n", - "\n", - " 0 0 3073.58500 0 3 - 3073.58500 - - 0s\n", - "H 0 0 3121.2950000 3073.58500 1.53% - 0s\n", - " 0 0 cutoff 0 3121.29500 3121.29500 0.00% - 0s\n", - "\n", - "Cutting planes:\n", - " Gomory: 2\n", - " Cover: 1\n", - " Implied bound: 2\n", - " MIR: 3\n", - " Flow cover: 6\n", - " Relax-and-lift: 2\n", - "\n", - "Explored 1 nodes (44 simplex iterations) in 0.03 seconds (0.00 work units)\n", - "Thread count was 8 (of 8 available processors)\n", - "\n", - "Solution count 1: 3121.29 \n", - "\n", - "Optimal solution found (tolerance 1.00e-04)\n", - "Best objective 3.121295000000e+03, best bound 3.121295000000e+03, gap 0.0000%\n", - "This model has a total cost of 3121.29\n" - ] - }, - { - "data": { - "text/html": [ - "
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shipment
productiondistribution
BaltimoreNashville30.0
Richmond116.0
ClevelandColumbia89.0
Indianapolis95.0
Little RockSt. Louis140.0
BirminghamNashville71.0
CharlestonLexington121.0
St. Louis41.0
\n", - "
" - ], - "text/plain": [ - " shipment\n", - "production distribution \n", - "Baltimore Nashville 30.0\n", - " Richmond 116.0\n", - "Cleveland Columbia 89.0\n", - " Indianapolis 95.0\n", - "Little Rock St. Louis 140.0\n", - "Birmingham Nashville 71.0\n", - "Charleston Lexington 121.0\n", - " St. Louis 41.0" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "m_pwl2 = gp.Model('widgets')\n", "#m_pwl2.setParam('OutputFlag',0)\n", @@ -798,16 +230,16 @@ "\n", "for p in production:\n", " m_pwl2.addGenConstrPWL(\n", - " q[p],\n", - " pwl_prod[p],\n", - " [0, 0.25 * max_prod[p], max_prod[p]],\n", - " [0, base_prod_cost[p]*max_prod[p], base_prod_cost[p]*max_prod[p] + 1.25*base_prod_cost[p]* (max_prod[p] - 0.25*max_prod[p])],\n", + " ???,\n", + " ???,\n", + " ???,\n", + " ???,\n", " name=f'pwl_cost_{p}'\n", " )\n", "\n", "# objective\n", - "total_transp_cost = gp.quicksum(transp_cost[p,d]*x_pwl2[p,d] for p in production for d in distribution)\n", - "m_pwl2.setObjective(total_transp_cost + pwl_prod.sum(), GRB.MINIMIZE)\n", + "total_transp_cost = gp.quicksum(??? for p in production for d in distribution)\n", + "m_pwl2.setObjective(total_transp_cost + ???, GRB.MINIMIZE)\n", "\n", "# optimize and extract and show solution\n", "m_pwl2.optimize()\n", @@ -836,147 +268,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "This model has a total cost of 1755.44. The original cost was 1748.42\n" - ] - }, - { - "data": { - "text/html": [ - "
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costshipmentshipment2
productiondistribution
BaltimoreNashville5.9630.00.0
Richmond1.96116.0135.0
ClevelandColumbia2.4389.089.0
Indianapolis2.3795.040.0
Nashville4.130.030.0
St. Louis4.880.041.0
Little RockSt. Louis2.92140.0140.0
BirminghamNashville1.5371.071.0
CharlestonIndianapolis2.610.055.0
Lexington1.61121.0121.0
St. Louis4.6041.00.0
\n", - "
" - ], - "text/plain": [ - " cost shipment shipment2\n", - "production distribution \n", - "Baltimore Nashville 5.96 30.0 0.0\n", - " Richmond 1.96 116.0 135.0\n", - "Cleveland Columbia 2.43 89.0 89.0\n", - " Indianapolis 2.37 95.0 40.0\n", - " Nashville 4.13 0.0 30.0\n", - " St. Louis 4.88 0.0 41.0\n", - "Little Rock St. Louis 2.92 140.0 140.0\n", - "Birmingham Nashville 1.53 71.0 71.0\n", - "Charleston Indianapolis 2.61 0.0 55.0\n", - " Lexington 1.61 121.0 121.0\n", - " St. Louis 4.60 41.0 0.0" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "m.Params.SolutionNumber = 1\n", "m_ObjVal2 = m.PoolObjVal\n", @@ -996,20 +290,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "np.random.seed(2579)\n", "\n", @@ -1020,7 +303,7 @@ "mean_cost = 2.3\n", "std_sev = 0.2\n", "# Generate random costs using normal distribution\n", - "normal_costs = np.random.normal(loc=mean_cost, scale=std_sev, size=n_samples)\n", + "normal_costs = np.random.normal(loc=???, scale=???, size=???)\n", "\n", "# Extract the shipment amount from Cleveland to Indianapolis for each solution\n", "shipment_amount = sol_sim_comp.loc[('Cleveland', 'Indianapolis'), 'shipment']\n", @@ -1032,8 +315,8 @@ "\n", "# Create a DataFrame to hold total costs including the new simulated costs \n", "total_costs_df = pd.DataFrame({\n", - " 'Total_Cost_Sol1': obj1 + normal_costs * shipment_amount,\n", - " 'Total_Cost_Sol2': obj2 + normal_costs * shipment_amount2\n", + " 'Total_Cost_Sol1': ???\n", + " 'Total_Cost_Sol2': ???\n", "})\n", "\n", "# Determine the common range for both datasets\n", @@ -1065,7 +348,7 @@ "plt.ylabel('Frequency')\n", "plt.figtext(0.5, 0.01, 'Dashed lines show respective means', ha='center', fontsize=10)\n", "plt.legend()\n", - "plt.show()\n" + "plt.show()" ] }, { @@ -1086,24 +369,13 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "117.0" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "from scipy.stats import poisson\n", "\n", - "q95 = poisson.ppf(0.95, 100)\n", + "q95 = poisson.ppf(???, ???)\n", "q95" ] }, @@ -1116,142 +388,11 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "This model has a total cost of 1798.3. The original model has a cost of 1748.42\n" - ] - }, - { - "data": { - "text/html": [ - "
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costshipmentshipment_Nash
productiondistribution
BaltimoreColumbia4.500.030.0
Nashville5.9630.00.0
Richmond1.96116.0116.0
ClevelandColumbia2.4389.059.0
Indianapolis2.3795.095.0
Nashville4.130.037.0
Little RockSt. Louis2.92140.0140.0
BirminghamNashville1.5371.080.0
CharlestonLexington1.61121.0121.0
St. Louis4.6041.041.0
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" - ], - "text/plain": [ - " cost shipment shipment_Nash\n", - "production distribution \n", - "Baltimore Columbia 4.50 0.0 30.0\n", - " Nashville 5.96 30.0 0.0\n", - " Richmond 1.96 116.0 116.0\n", - "Cleveland Columbia 2.43 89.0 59.0\n", - " Indianapolis 2.37 95.0 95.0\n", - " Nashville 4.13 0.0 37.0\n", - "Little Rock St. Louis 2.92 140.0 140.0\n", - "Birmingham Nashville 1.53 71.0 80.0\n", - "Charleston Lexington 1.61 121.0 121.0\n", - " St. Louis 4.60 41.0 41.0" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "meet_demand['Nashville'].RHS = q95\n", + "meet_demand['Nashville'].RHS = ???\n", "m.update()\n", "m.optimize()\n", "\n", @@ -1275,7 +416,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1328,117 +469,35 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Optimal objective value: 5.93\n" - ] - }, - { - "data": { - "text/html": [ - "
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Investment Allocation
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GenHealth0.380
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" - ], - "text/plain": [ - " Investment Allocation\n", - "Tech 0.350\n", - "TradRetailUtil 0.000\n", - "Renewable 0.271\n", - "FossilFuel 0.000\n", - "Auto 0.000\n", - "GenHealth 0.380\n", - "TradHealth 0.000" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Create a new model\n", - "m_scen = gp.Model(\"PortfolioOptimization\")\n", - "m_scen.setParam('OutputFlag', 0)\n", + "???\n", "\n", "# Decision variables: Investment fractions in each sector\n", - "x = m_scen.addVars(sectors, name=\"Investment\", lb=0, ub=1)\n", + "???\n", "\n", "# Auxiliary variable: Minimum portfolio return\n", - "z = m_scen.addVar(name=\"MinReturn\")\n", + "???\n", "\n", "# Add investment allocation constraint\n", - "m_scen.addConstr(x.sum() == 1, name=\"TotalInvestment\")\n", + "???\n", "\n", "# Add portfolio return constraints for each scenario\n", - "portfolio_return = m_scen.addConstrs((gp.quicksum(estimated_returns[(i, s)] * x[i] for i in sectors) >= z for s in scenarios), name = 'ReturnConstraint_Scenario')\n", + "???\n", "\n", "# Set objective: Maximize the minimum portfolio return\n", - "m_scen.setObjective(z, GRB.MAXIMIZE)\n", + "???\n", "\n", "# Optimize the model\n", - "m_scen.optimize()\n", + "???\n", "\n", - "print(f\"Optimal objective value: {round(m_scen.ObjVal,2)}\")\n", + "print(f\"Optimal objective value: {round(???,2)}\")\n", "\n", "# Get the results\n", - "investment_allocation = pd.Series({i: round(x[i].X,3) for i in sectors}, name='Investment Allocation')\n", - "investment_allocation.to_frame()" + "???" ] }, { @@ -1452,96 +511,17 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Investment Allocation
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TradRetailUtil0.000
Renewable0.257
FossilFuel0.000
Auto0.100
GenHealth0.336
TradHealth0.000
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" - ], - "text/plain": [ - " Investment Allocation\n", - "Tech 0.307\n", - "TradRetailUtil 0.000\n", - "Renewable 0.257\n", - "FossilFuel 0.000\n", - "Auto 0.100\n", - "GenHealth 0.336\n", - "TradHealth 0.000" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# Add new variables and/or constraints\n", - "y = m_scen.addVars(sectors, vtype=GRB.BINARY,name='decision')\n", "\n", - "l = 0.1\n", - "m_scen.addConstrs((x[i] <= y[i] for i in sectors), name='xy_link1')\n", - "m_scen.addConstrs((x[i] >= l*y[i] for i in sectors), name='xy_link2')\n", - "m_scen.addConstr(y.sum() >= 4)\n", + "\n", "# Optimize the model\n", - "m_scen.optimize()\n", "\n", - "# Get the results\n", - "investment_allocation = pd.Series({i: round(x[i].X,3) for i in sectors}, name='Investment Allocation')\n", - "investment_allocation.to_frame()" + "\n", + "# Get the results\n" ] } ], From eddb41e3787ec5572ba82a969c7f4b6507a241be Mon Sep 17 00:00:00 2001 From: Jerry Yurchisin Date: Wed, 13 Nov 2024 14:50:57 -0500 Subject: [PATCH 08/40] ex2 notes --- .../completed_exercise_set2.ipynb | 51 +++++++++---------- .../Modeling_Session_2/exercise_set2.ipynb | 22 +++++++- 2 files changed, 44 insertions(+), 29 deletions(-) diff --git a/optimization202/Modeling_Session_2/completed_exercise_set2.ipynb b/optimization202/Modeling_Session_2/completed_exercise_set2.ipynb index 4850463..2c1f6cc 100644 --- a/optimization202/Modeling_Session_2/completed_exercise_set2.ipynb +++ b/optimization202/Modeling_Session_2/completed_exercise_set2.ipynb @@ -10,34 +10,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Requirement already satisfied: gurobipy in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (12.0.0)\n", - "Note: you may need to restart the kernel to use updated packages.\n", - "Requirement already satisfied: seaborn in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (0.13.0)\n", - "Requirement already satisfied: numpy!=1.24.0,>=1.20 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from seaborn) (1.26.4)\n", - "Requirement already satisfied: pandas>=1.2 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from seaborn) (2.2.2)\n", - "Requirement already satisfied: matplotlib!=3.6.1,>=3.3 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from seaborn) (3.8.0)\n", - "Requirement already satisfied: contourpy>=1.0.1 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (1.1.1)\n", - "Requirement already satisfied: cycler>=0.10 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (0.12.1)\n", - "Requirement already satisfied: fonttools>=4.22.0 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (4.43.1)\n", - "Requirement already satisfied: kiwisolver>=1.0.1 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (1.4.5)\n", - "Requirement already satisfied: packaging>=20.0 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (23.2)\n", - "Requirement already satisfied: pillow>=6.2.0 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (10.0.1)\n", - "Requirement already satisfied: pyparsing>=2.3.1 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (3.1.1)\n", - "Requirement already satisfied: python-dateutil>=2.7 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from matplotlib!=3.6.1,>=3.3->seaborn) (2.8.2)\n", - "Requirement already satisfied: pytz>=2020.1 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from pandas>=1.2->seaborn) (2023.3.post1)\n", - "Requirement already satisfied: tzdata>=2022.7 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from pandas>=1.2->seaborn) (2023.3)\n", - "Requirement already satisfied: six>=1.5 in /Users/yurchisin/opt/anaconda3/envs/gurobi_ml/lib/python3.11/site-packages (from python-dateutil>=2.7->matplotlib!=3.6.1,>=3.3->seaborn) (1.16.0)\n", - "Note: you may need to restart the kernel to use updated packages.\n" - ] - } - ], + "outputs": [], "source": [ "%pip install gurobipy\n", "%pip install seaborn\n", @@ -250,7 +225,27 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Piecewise Linear Modeling\n", + "## Piecewise Linear Modeling" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " ×\n", + " Here are a couple a good documentation links to help you use PWL in the code below. \n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Links to PWL in `gurobipy`:\n", + "- [Model.addGenConstrPWL](https://docs.gurobi.com/projects/optimizer/en/current/reference/python/model.html#Model.addGenConstrPWL) command\n", + "- [Visualization of PWL](https://docs.gurobi.com/projects/optimizer/en/current/concepts/modeling/objectives.html#piecewise-linear-objectives) and how it gets put together\n", + "\n", "### Scenario 1\n", "Let's go back to the Opti 101/201 classic: Widget production and distribution. We have a new cost structure for transporting widgets we make. \n", "- There will be a flat 0.50 increase for all transportation costs. \n", diff --git a/optimization202/Modeling_Session_2/exercise_set2.ipynb b/optimization202/Modeling_Session_2/exercise_set2.ipynb index 17c8cd2..b447396 100644 --- a/optimization202/Modeling_Session_2/exercise_set2.ipynb +++ b/optimization202/Modeling_Session_2/exercise_set2.ipynb @@ -118,7 +118,27 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Piecewise Linear Modeling\n", + "## Piecewise Linear Modeling" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "
\n", + " ×\n", + " Here are a couple a good documentation links to help you use PWL in the code below. \n", + "
" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Links to PWL in `gurobipy`:\n", + "- [Model.addGenConstrPWL](https://docs.gurobi.com/projects/optimizer/en/current/reference/python/model.html#Model.addGenConstrPWL) command\n", + "- [Visualization of PWL](https://docs.gurobi.com/projects/optimizer/en/current/concepts/modeling/objectives.html#piecewise-linear-objectives) and how it gets put together\n", + "\n", "### Scenario 1\n", "Let's go back to the Opti 101/201 classic: Widget production and distribution. We have a new cost structure for transporting the widgets we make. \n", "- There will be a flat 0.50 increase for all transportation costs. \n", From 0bfa20f8c50d0ff48ef970cc21b71ecf25d2c12a Mon Sep 17 00:00:00 2001 From: Jerry Yurchisin Date: Wed, 13 Nov 2024 15:40:01 -0500 Subject: [PATCH 09/40] link to xl data, csv as backup --- optimization202/Modeling_Session_1/MMdata.csv | 11 +++++++++++ .../completed_nonlinear_regression_exercise.ipynb | 5 +++-- .../nonlinear_regression_exercise.ipynb | 3 ++- 3 files changed, 16 insertions(+), 3 deletions(-) create mode 100644 optimization202/Modeling_Session_1/MMdata.csv diff --git a/optimization202/Modeling_Session_1/MMdata.csv b/optimization202/Modeling_Session_1/MMdata.csv new file mode 100644 index 0000000..552d18b --- /dev/null +++ b/optimization202/Modeling_Session_1/MMdata.csv @@ -0,0 +1,11 @@ +index,x,y +0,0.05,2.98 +1,0.1,5.2 +2,0.25,14.38 +3,0.5,30.3 +4,1,48.99 +5,2.5,86.25 +6,5,112.57 +7,8,136.24 +8,20,169.97 +9,30,177.71 \ No newline at end of file diff --git a/optimization202/Modeling_Session_1/completed_nonlinear_regression_exercise.ipynb b/optimization202/Modeling_Session_1/completed_nonlinear_regression_exercise.ipynb index dc391b7..f25a86b 100755 --- a/optimization202/Modeling_Session_1/completed_nonlinear_regression_exercise.ipynb +++ b/optimization202/Modeling_Session_1/completed_nonlinear_regression_exercise.ipynb @@ -228,7 +228,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "id": "2d903089-2524-487b-8639-2b9dfd395525", "metadata": {}, "outputs": [ @@ -246,7 +246,8 @@ ], "source": [ "# Data\n", - "df = pd.read_excel('MMdata.xlsx') \n", + "path = 'https://raw.githubusercontent.com/Gurobi/modeling-examples/master/optimization202/Modeling_Session_1/'\n", + "df = pd.read_excel(path + 'MMdata.xlsx') \n", "\n", "# Use the gurobipy multidict structure to unpack the data in our data frame\n", "# Data is from https://cran.r-project.org/web/packages/renz/vignettes/dirMM.html\n", diff --git a/optimization202/Modeling_Session_1/nonlinear_regression_exercise.ipynb b/optimization202/Modeling_Session_1/nonlinear_regression_exercise.ipynb index 953116c..3c63f6e 100755 --- a/optimization202/Modeling_Session_1/nonlinear_regression_exercise.ipynb +++ b/optimization202/Modeling_Session_1/nonlinear_regression_exercise.ipynb @@ -234,7 +234,8 @@ "outputs": [], "source": [ "# Data\n", - "df = pd.read_excel('MMdata.xlsx') \n", + "path = 'https://raw.githubusercontent.com/Gurobi/modeling-examples/master/optimization202/Modeling_Session_1/'\n", + "df = pd.read_excel(path + 'MMdata.xlsx') \n", "\n", "# Use the gurobipy multidict structure to unpack the data in our data frame\n", "# Data is from https://cran.r-project.org/web/packages/renz/vignettes/dirMM.html\n", From 44bf3d42a1e9b5dea3e27b1c92872a4ddb9a26d9 Mon Sep 17 00:00:00 2001 From: Jerry Yurchisin <97685128+yurchisin@users.noreply.github.com> Date: Wed, 13 Nov 2024 15:30:45 -0500 Subject: [PATCH 10/40] Update README.md --- optimization202/Modeling_Session_1/README.md | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/optimization202/Modeling_Session_1/README.md b/optimization202/Modeling_Session_1/README.md index 2cfe94e..c7cdb65 100644 --- a/optimization202/Modeling_Session_1/README.md +++ b/optimization202/Modeling_Session_1/README.md @@ -19,13 +19,13 @@ Note that you must sign in with a Google account to be able to run the code in C [Google Colab Link - Exercise *with* answers](https://colab.research.google.com/github/Gurobi/modeling-examples/blob/master/optimization202/Modeling_Session_1/completed_exercise_set1.ipynb) -[Google Colab Link - Exercise *without* answers](https://colab.research.google.com/github/Gurobi/modeling-examples/blob/master/optimization202/Modeling_Session_1/nonlinear_regression_exercise.ipynb) +[Google Colab Link - Nonlinear Regression Exercise *without* answers](https://colab.research.google.com/github/Gurobi/modeling-examples/blob/master/optimization202/Modeling_Session_1/nonlinear_regression_exercise.ipynb) -[Google Colab Link - Exercise *with* answers](https://colab.research.google.com/github/Gurobi/modeling-examples/blob/master/optimization202/Modeling_Session_1/completed_nonlinear_regression_exercise.ipynb) +[Google Colab Link - Nonlinear Regression Exercise *with* answers](https://colab.research.google.com/github/Gurobi/modeling-examples/blob/master/optimization202/Modeling_Session_1/completed_nonlinear_regression_exercise.ipynb) ---- For details on licensing or on running the notebooks, see the overview on [Modeling Examples](../../) -© Gurobi Optimization, LLC \ No newline at end of file +© Gurobi Optimization, LLC From 3c1fa83a7be7c3f36201f989bf804eb64c55eac4 Mon Sep 17 00:00:00 2001 From: Jerry Yurchisin Date: Wed, 13 Nov 2024 15:52:59 -0500 Subject: [PATCH 11/40] requirements --- optimization202/ESUPS_case_study/requirements.txt | 12 ++++++++++++ .../completed_nonlinear_regression_exercise.ipynb | 15 ++++++++++++--- 2 files changed, 24 insertions(+), 3 deletions(-) create mode 100644 optimization202/ESUPS_case_study/requirements.txt diff --git a/optimization202/ESUPS_case_study/requirements.txt b/optimization202/ESUPS_case_study/requirements.txt new file mode 100644 index 0000000..bcc7f65 --- /dev/null +++ b/optimization202/ESUPS_case_study/requirements.txt @@ -0,0 +1,12 @@ +gurobipy==11.0.3 +ipython==8.26.0 +ipywidgets==8.1.3 +numpy<2 +pandas==2.2.2 +pandera==0.20.3 +plotly==5.23.0 +pydantic==2.8.2 +scipy==1.14.1 +tqdm==4.66.4 +typing_extensions==4.12.2 +networkx \ No newline at end of file diff --git a/optimization202/Modeling_Session_1/completed_nonlinear_regression_exercise.ipynb b/optimization202/Modeling_Session_1/completed_nonlinear_regression_exercise.ipynb index f25a86b..5a8d648 100755 --- a/optimization202/Modeling_Session_1/completed_nonlinear_regression_exercise.ipynb +++ b/optimization202/Modeling_Session_1/completed_nonlinear_regression_exercise.ipynb @@ -2976,8 +2976,16 @@ "
\n", " ×\n", " Refresher: Whole model at a glance \n", - "

We created this model over several different cells so we could dig into how and why each step is done. When we put all the code together, the model is written in only a few lines of code. Here is the complete code to write and solve this model after you have the data available:

\n", - " 
# Write and solve the nonlinear regression example:\n",
+    "    
We created this model over several different cells so we could dig into how and why each step is done.  When we put all the code together, the model is written in only a few lines of code.  Here is the complete code to write and solve this model after you have the data available:
"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2c02b4f8",
+   "metadata": {},
+   "source": [
+    "```python\n",
+    "# Write and solve the nonlinear regression example:\n",
     "import gurobipy as gp\n",
     "from gurobipy import GRB\n",
     "#\n",
@@ -2997,7 +3005,8 @@
     "model.setObjective(sum((ymodel[i] - ytrain[i])**2 for i in points), sense=GRB.MINIMIZE)\n",
     "#\n",
     "# Solve the model\n",
-    "model.optimize()
" + "model.optimize()\n", + "```" ] }, { From baa41c08d9452fe238af96d7d4dd0de15630d712 Mon Sep 17 00:00:00 2001 From: Jerry Yurchisin Date: Wed, 13 Nov 2024 16:03:58 -0500 Subject: [PATCH 12/40] Update requirements.txt --- optimization202/ESUPS_case_study/requirements.txt | 5 ----- 1 file changed, 5 deletions(-) diff --git a/optimization202/ESUPS_case_study/requirements.txt b/optimization202/ESUPS_case_study/requirements.txt index bcc7f65..a01c2ca 100644 --- a/optimization202/ESUPS_case_study/requirements.txt +++ b/optimization202/ESUPS_case_study/requirements.txt @@ -1,12 +1,7 @@ gurobipy==11.0.3 -ipython==8.26.0 -ipywidgets==8.1.3 numpy<2 -pandas==2.2.2 pandera==0.20.3 -plotly==5.23.0 pydantic==2.8.2 -scipy==1.14.1 tqdm==4.66.4 typing_extensions==4.12.2 networkx \ No newline at end of file From 112445eae0c275d130055a04392e53e0b6f9ff8c Mon Sep 17 00:00:00 2001 From: Jerry Yurchisin Date: Wed, 13 Nov 2024 16:41:17 -0500 Subject: [PATCH 13/40] update workspace --- .../ESUPS_case_study/requirements.txt | 5 + .../workspaces/helper_scripts.py | 217 ++++++++++++++++++ .../workspaces/temp_Analyer_workspace.py | 115 ++++++++++ .../workspaces/temp_solver_workspace.py | 200 ++++++++++++++++ .../workspaces/temp_worker_workspace.py | 107 +++++++++ 5 files changed, 644 insertions(+) create mode 100644 optimization202/ESUPS_case_study/workspaces/helper_scripts.py create mode 100644 optimization202/ESUPS_case_study/workspaces/temp_Analyer_workspace.py create mode 100644 optimization202/ESUPS_case_study/workspaces/temp_solver_workspace.py create mode 100644 optimization202/ESUPS_case_study/workspaces/temp_worker_workspace.py diff --git a/optimization202/ESUPS_case_study/requirements.txt b/optimization202/ESUPS_case_study/requirements.txt index a01c2ca..bcc7f65 100644 --- a/optimization202/ESUPS_case_study/requirements.txt +++ b/optimization202/ESUPS_case_study/requirements.txt @@ -1,7 +1,12 @@ gurobipy==11.0.3 +ipython==8.26.0 +ipywidgets==8.1.3 numpy<2 +pandas==2.2.2 pandera==0.20.3 +plotly==5.23.0 pydantic==2.8.2 +scipy==1.14.1 tqdm==4.66.4 typing_extensions==4.12.2 networkx \ No newline at end of file diff --git a/optimization202/ESUPS_case_study/workspaces/helper_scripts.py b/optimization202/ESUPS_case_study/workspaces/helper_scripts.py new file mode 100644 index 0000000..236b15d --- /dev/null +++ b/optimization202/ESUPS_case_study/workspaces/helper_scripts.py @@ -0,0 +1,217 @@ + +#Called in analysis/analysis worker to filter the disasters by time +def _filter_dataset(self, dataset: Dataset) -> Dataset: + if self.parameters.disaster_month > -1: + months = range( + self.parameters.disaster_month, + self.parameters.disaster_month + + 1 + + self.parameters.num_months_to_average, + ) + months = [(month - 1) % 12 + 1 for month in months] + predicate: Callable[[Disaster], bool] = ( + lambda disaster: disaster.month in months + ) + dataset = dataset.take_disaster_subset(predicate) + + dataset = dataset.take_disaster_subset( + lambda disaster: disaster.year >= self.parameters.min_year + and disaster.year <= self.parameters.max_year + ) + + return dataset + + +def _post_process( + self, + dataset: Dataset, + item: Item, + costs: dict[SolverObjective, CostMatrix], + solutions: dict[SolutionTags, Solution], + ): + beta_source = ( + dataset.persons_per_item_monthly + if self.parameters.care_about_month_demand + else dataset.persons_per_item_general + ) + beta = { + location.id: beta_source[location, item] + for disaster in dataset.disasters + for location in disaster.impacted_locations + } + + solution_stats = pd.DataFrame.from_records( + [ + { + "objective": objective, + "strategy": strategy, + "coveredDemandExcDummy": solution.covered_demand_exc_dummy, + "dualTotInv": solution.dual_total_inventory, + "totalCostIncDummy": solution.total_cost_inc_dummy, + "totalCostExcDummy": solution.total_cost_exc_dummy, + "totalDemand": solution.total_demand, + "fractionOfDisastersUsingDummy": solution.fraction_of_disasters_using_dummy, + } + for (objective, strategy), solution in solutions.items() + ] + ).set_index(["objective", "strategy"]) + + df_flows = pd.DataFrame.from_records( + [ + { + "objective": objective, + "strategy": strategy, + "disaster": disaster.id, + "depot": depot.id, + "impact": impact.id, + "location": impact.location.id, + "mode": mode.id, + "flow": value, + "distance": dataset.distance[depot, impact.location, mode].distance + if depot.id != "DUMMY" + else None, + } + for (objective, strategy), solution in solutions.items() + for (disaster, depot, impact, mode), value in solution.flow.items() + ] + ) + + # Average unit cost + solution_stats["averageUnitCost"] = solution_stats["totalCostExcDummy"] / ( + solution_stats["coveredDemandExcDummy"] + 1e-7 + ) + + # Demand fulfillment fraction + solution_stats["demandFulfillmentFraction"] = solution_stats[ + "coveredDemandExcDummy" + ] / (solution_stats["totalDemand"] + 1e-7) + + # Balance metric + strategies = set(solution_stats.reset_index()["strategy"]) + pivoted = solution_stats.reset_index().pivot( + index="objective", columns="strategy", values="totalCostExcDummy" + ) + pivoted["balanceMetric"] = ( + pivoted[AllocationStrategy.MinimizeFixedInventory] + / (pivoted[AllocationStrategy.MinimizeTwoStage] + 1e-7) + if AllocationStrategy.MinimizeFixedInventory in strategies + and AllocationStrategy.MinimizeTwoStage in strategies + else None + ) + balance_metric = pivoted + + df_probabilities = pd.DataFrame.from_dict( + { + disaster.id: dataset.probabilities[disaster] + for disaster in dataset.disasters + }, + columns=["probability"], + orient="index", + ) + + # Units shipped + df_flow_no_dummy = df_flows.join(df_probabilities, on="disaster") + df_flow_no_dummy = df_flow_no_dummy[ + df_flow_no_dummy["depot"] != "DUMMY" + ] # TODO Replace hardcoded dummy ID + temp = df_flow_no_dummy.copy() + temp["unitsShipped"] = temp["probability"] * temp["flow"] + temp["unitsShippedWeighted"] = temp["unitsShipped"] * temp["distance"] + units_shipped = ( + temp.set_index(["objective", "strategy", "mode"])[ + ["unitsShipped", "unitsShippedWeighted"] + ] + .groupby(["objective", "strategy", "mode"]) + .sum() + ) + + # People served per item + temp = df_flow_no_dummy.copy() + temp["beta"] = temp["impact"].apply(lambda loc: beta[loc]) + temp["peopleServed"] = temp["probability"] * temp["beta"] * temp["flow"] + people_served = ( + temp.set_index(["objective", "strategy"])["peopleServed"] + .groupby(["objective", "strategy"]) + .sum() + ) + people_served_per_item = pd.DataFrame( + people_served / (solution_stats["coveredDemandExcDummy"] + 1e-7), + columns=["peopleServedPerItem"], + ) + + # Impact of optimizing one objective on another objective + impact = [] + for other in self.parameters.comparison_objectives: + cost = { + (depot.id, location.id, mode.id): value + for (depot, location, mode), value in costs[other].items() + } + temp = df_flow_no_dummy.copy() + if temp.empty: + raise RuntimeError("Empty flow matrix encountered") + temp["cost"] = temp.apply( + lambda row: cost[row["depot"], row["location"], row["mode"]], axis=1 + ) + temp["other"] = other + temp["impact"] = temp["cost"] * temp["probability"] * temp["flow"] + impact.append(temp.reset_index()) + cross_impact = ( + pd.concat(impact) + .groupby(["objective", "strategy", "other"])[["impact"]] + .sum() + ) + + return Analysis( + self.parameters, + dataset, + item, + solutions, + solution_stats, + balance_metric, + units_shipped, + people_served_per_item, + cross_impact, + ) + + + + + +def _get_arc_cost( + self, + cost: CostMatrix, + source: Location, + target: DisasterImpact, + mode: TransportMode, +): + if source == self._dummy: + return self._threshold_cost_dummy + cell = cost.get((source, target.location, mode)) + return self._threshold_cost_elim if cell == None else cell + +def _scale_demand(self, problem: Problem) -> dict[DisasterImpact, float]: + supply = sum(problem.inventory.values()) + result: dict[DisasterImpact, float] = {} + for disaster in problem.disasters: + total_demand = sum( + problem.demand[location] for location in disaster.impacted_locations + ) + factor = min(1, supply / total_demand) if total_demand > 1e-6 else 1 + for location in disaster.impacted_locations: + result[location] = factor * problem.demand[location] + return result + + + + + + + + + + + + + + + diff --git a/optimization202/ESUPS_case_study/workspaces/temp_Analyer_workspace.py b/optimization202/ESUPS_case_study/workspaces/temp_Analyer_workspace.py new file mode 100644 index 0000000..01299d1 --- /dev/null +++ b/optimization202/ESUPS_case_study/workspaces/temp_Analyer_workspace.py @@ -0,0 +1,115 @@ + + +#Code for the class + +from enum import IntEnum +from multiprocessing import Pool +from os import getenv +from typing import Callable + +import pandas as pd + +from src.data import Dataset, Disaster, DisasterImpact, DistanceInfo, Item +from src.solving import ( + AllocationStrategy, + CostMatrix, + Problem, + Solution, + SolverParameters, + StochasticSolver, +) + + +class AnalysisParameters: + """ + + Attributes + ---------- + expand_depot_set + Flag indicating whether inventory can be reallocated to depots that don't currently hold any stock + care_about_month_demand + Flag indicating whether we take month-by-month demand (True) or the general number (False) + disaster_month + Month from which to select disasters + num_months_to_average + Number of months to use for selecting disasters, when disasterMonth>=0 + optimization_objectives + Set of objectives to use for running the optimization model + comparison_objectives + Set of objectives to use for comparing results + allocation_strategies + Which strategies to test for (re)allocation inventory to depots in the first stage + min_year + First year from which disasters should be taken into account + max_year + Last year from which disasters should be taken into account + scale_demand + Whether demand must be scaled to not exceed total available inventory or not + + + """ + + expand_depot_set: bool = False + care_about_month_demand: bool = True + disaster_month: int = -1 + num_months_to_average: int = 3 + optimization_objectives: list[SolverObjective] = [ + SolverObjective.Cost, + SolverObjective.Time, + ] + comparison_objectives: list[SolverObjective] = list(SolverObjective) + allocation_strategies: list[AllocationStrategy] = list(AllocationStrategy) + min_year: int = 1900 + max_year: int = 2100 + scale_demand: bool = True + + + + + +#instanctiate object, might be better to just make the object + +parameters = AnalysisParameters() + + + +analyzer = Analyzer(parameters) + + +#def run_all(self, dataset: Dataset) -> dict[tuple[str, Item], Analysis]: +inventory_datasets = { + filename: dataset.take_inventory_scenario(filename) + for filename in dataset.inventory_scenarios +} +tasks = [ + (filename, inventory_dataset, item) + for (filename, inventory_dataset) in inventory_datasets.items() + for item in inventory_dataset.items +] + + + + +#def _run_tasks(self, tasks: list[tuple[str, Dataset, Item]]): + + + +_solver=StochasticSolver() +#worker = AnalyzerWorker(self.parameters) +result = [ + (filename, format_data(dataset, item,parameters)) + for (filename, dataset, item) in tasks +] + + + + + +PROCESSED_INFO= { + (filename, analysis.item): analysis + for (filename, analysis) in result + if analysis is not None +} + + + diff --git a/optimization202/ESUPS_case_study/workspaces/temp_solver_workspace.py b/optimization202/ESUPS_case_study/workspaces/temp_solver_workspace.py new file mode 100644 index 0000000..69b77ee --- /dev/null +++ b/optimization202/ESUPS_case_study/workspaces/temp_solver_workspace.py @@ -0,0 +1,200 @@ + +from dataclasses import dataclass +from enum import IntEnum +from typing import Tuple, Union + +import gurobipy as gp +from gurobipy import GRB, tupledict + +from src.data import Depot, Disaster, DisasterImpact, Location, TransportMode + + + +class StochasticSolver: + _threshold_cost_elim: float = 1e9 + _threshold_cost_dummy: float = 1e9 + + def __init__(self): + self._dummy = Location("DUMMY", "", "", 0, 0) + self._env = gp.Env(params={"OutputFlag": 0, "Threads": 1}) + + def dispose(self): + self._env.dispose() + self._env = None + + def solve(self, problem: Problem, parameters: SolverParameters) -> Solution: + sources = problem.depots + [self._dummy] + + demand = ( + self._scale_demand(problem) if parameters.scale_demand else problem.demand + ) + + arcs = gp.tuplelist( + [ + (k, i, j, v) + for i in sources + for k in problem.disasters + for j in k.impacted_locations + for v in problem.transport_modes + if ( + self._get_arc_cost(problem.cost, i, j, v) + < self._threshold_cost_elim + ) + or (i == self._dummy) + ] + ) + + arc_cost = { + (k, i, j, v): self._get_arc_cost(problem.cost, i, j, v) + * problem.probabilities[k] + for (k, i, j, v) in arcs + } + + model = gp.Model("StochLP", env=self._env) + + # First stage variable: Quantity to be allocated to each depot + x: tupledict[Depot, gp.Var] = model.addVars(problem.depots, lb=0, name="x") + + # Second stage variable: Quantity transported from (real or dummy) depot to disaster locations using each mode of transport + y: tupledict[ + Tuple[Disaster, Union[Depot, Location], DisasterImpact, TransportMode], + gp.Var, + ] = model.addVars(arcs, lb=0, obj=arc_cost, name="y") + + # Constraint: Total incoming arc flow must cover demand for each disaster location + model.addConstrs( + ( + y.sum(k, "*", j, "*") == demand[j] + for k in problem.disasters + for j in k.impacted_locations + ), + name="satisfyDemand", + ) + + # Constraint: Total outgoing arc flow must match initial or reallocated inventory + inventory_balance: tupledict[ + Tuple[Disaster, Depot], gp.Constr + ] = model.addConstrs( + ( + y.sum(k, i, "*", "*") <= x[i] + for k in problem.disasters + for i in problem.depots + ), + name="satisfySupply", + ) + + # Constraint: Ensure inventory reallocation matches total existing inventory + total_initial_inventory = sum(problem.inventory.values()) + match_total_inventory = model.addConstr(x.sum() == total_initial_inventory) + + def fix_inventory_balance(values: dict[Disaster, float]): + for key, value in values.items(): + x[key].LB = x[key].UB = value + + if parameters.allocation_strategy == AllocationStrategy.MinimizeFixedInventory: + fix_inventory_balance(problem.inventory) + elif parameters.allocation_strategy == AllocationStrategy.WorstDepot: + worst_depot = None + worst_objective = -1e100 + for depot in problem.depots: + centralized_inventory = { + other: total_initial_inventory if other == depot else 0 + for other in problem.depots + } + fix_inventory_balance(centralized_inventory) + model.optimize() + if model.Status != GRB.Status.OPTIMAL: + raise RuntimeError("Could not solve model to optimality") + if model.ObjVal > worst_objective: + worst_depot = depot + worst_objective = model.ObjVal + centralized_inventory = { + other: total_initial_inventory if other == worst_depot else 0 + for other in problem.depots + } + fix_inventory_balance(centralized_inventory) + + model.optimize() + if model.Status != GRB.Status.OPTIMAL: + raise RuntimeError("Could not solve model to optimality") + + # Total transport cost + total_cost_inc_dummy = model.ObjVal + dummy_cost = sum( + var.X * var.Obj for var in y.select("*", self._dummy, "*", "*") + ) + total_cost_exc_dummy = total_cost_inc_dummy - dummy_cost + + # Demand met without using the dummy node + covered_demand_by_dummy = sum( + y[k, i, j, v].X * problem.probabilities[k] + for (k, i, j, v) in arcs.select("*", self._dummy, "*", "*") + ) + total_demand = sum( + local_demand * problem.probabilities[j.disaster] + for (j, local_demand) in demand.items() + ) + covered_demand_exc_dummy = total_demand - covered_demand_by_dummy + + # Flow in solution + solution_y = {key: y[key].X for key in arcs} + + # Fraction of disaster scenarios in which the dummy supply is used + fraction_of_disasters_using_dummy = len( + [ + disaster + for disaster in problem.disasters + if sum( + solution_y[key] + for key in arcs.select(disaster, self._dummy, "*", "*") + ) + > 0 + ] + ) / len(problem.disasters) + + # Dual variables for the inventory balance constraints + dual_correction = fraction_of_disasters_using_dummy * self._threshold_cost_dummy + duals_inventory_exc_dummy_unadjusted = { + i: sum(inventory_balance[k, i].Pi for k in problem.disasters) + for i in problem.depots + } + duals_inventory_exc_dummy_plus_dummy_cost = { + i: dual_correction + pi + for (i, pi) in duals_inventory_exc_dummy_unadjusted.items() + } + duals_inventory_exc_dummy_all = { + (k, i): constr.Pi for (k, i), constr in inventory_balance.items() + } + + flow = {(k, i, j, v): var.X for (k, i, j, v), var in y.items() if var.X > 0} + + flow_exc_dummy = { + (k, i, j, v): value + for (k, i, j, v), value in flow.items() + if i != self._dummy + } + + optimal_inventory = {depot: var.X for depot, var in x.items()} + + dual_total_inventory = ( + match_total_inventory.Pi + if parameters.allocation_strategy + == AllocationStrategy.MinimizeFixedInventory + else None + ) + + return Solution( + total_cost_inc_dummy, + total_cost_exc_dummy, + total_demand, + covered_demand_exc_dummy, + fraction_of_disasters_using_dummy, + duals_inventory_exc_dummy_plus_dummy_cost, + duals_inventory_exc_dummy_unadjusted, + duals_inventory_exc_dummy_all, + flow_exc_dummy, + flow, + optimal_inventory, + dual_total_inventory, + self._dummy, + ) diff --git a/optimization202/ESUPS_case_study/workspaces/temp_worker_workspace.py b/optimization202/ESUPS_case_study/workspaces/temp_worker_workspace.py new file mode 100644 index 0000000..a1b5196 --- /dev/null +++ b/optimization202/ESUPS_case_study/workspaces/temp_worker_workspace.py @@ -0,0 +1,107 @@ +#This script will roughly fill in the job that a worker does, +#it will become a jupyter notebook +from enum import IntEnum +from multiprocessing import Pool +from os import getenv +from typing import Callable + +import pandas as pd + +from src.data import Dataset, Disaster, DisasterImpact, DistanceInfo, Item +from src.solving import ( + AllocationStrategy, + CostMatrix, + Problem, + Solution, + SolverParameters, + StochasticSolver, +) + + +def format_data(dataset,item,parameters): +#Calculate disaster prob + + #dataset = self._filter_dataset(dataset) + probabilities = { + disaster: 1 / len(dataset.disasters) for disaster in dataset.disasters + } + + + + #Let's derfine our dictionary to store all the dictionaries + + solutions: dict[SolutionTags, Solution] = {} + + #First we construct the chance each distaster will occur + probabilities = { + disaster: 1 / len(dataset.disasters) for disaster in dataset.disasters + } + + #Now we need to create our cost matrix to tell the solver how much each thing will + #reguire from out budget + + #To do this we will iterate over every item in our dataset and return the designated item + cost_matrices={} + for key, value in dataset.distance.items(): + match objective: + case SolverObjective.Cost: + aa = item.weight * value.cost_per_ton + + case SolverObjective.Time: + aa = value.time + + case SolverObjective.Distance: + aa = value.distance + cost_matrices[key]=aa + + + #Now we find the inventory in a nice formate + inventory = { + depot: dataset.inventory.get((depot, item), 0) + for depot in dataset.depots + if self.parameters.expand_depot_set + or dataset.inventory.get((depot, item), 0) > 0 + } + + + #Check that there is inventory + if sum(inventory.values()) == 0: + return None + + + #Now we get the demand for each item + + #One of the settings we can tweak, need to look more at this + source = ( + dataset.monthly_demand + if self.parameters.care_about_month_demand + else dataset.general_demand + ) + + #After the setting is tweeked, we now make the dictionary + demand={ + location: source.get((location, item), 0) + for disaster in dataset.disasters + for location in disaster.impacted_locations + } + + # Solve models for all objectives + for objective in self.parameters.optimization_objectives: + #Check all stratigies for each objective + for strategy in self.parameters.allocation_strategies: + problem = Problem( #from another file + dataset.depots, + inventory, + demand, + dataset.disasters, + probabilities, + dataset.transport_modes, + cost_matrices[objective], + ) + parameters = SolverParameters(strategy, self.parameters.scale_demand) + tags = (objective, strategy) + solutions[tags] = self._solver.solve(problem, parameters) + + return self._post_process(dataset, item, cost_matrices, solutions) + + #self._solver = StochasticSolver() From dcc9dc0d53c6bdc8a0ed7f599e5cfee23a71eec7 Mon Sep 17 00:00:00 2001 From: Jerry Yurchisin <97685128+yurchisin@users.noreply.github.com> Date: Wed, 13 Nov 2024 17:27:26 -0500 Subject: [PATCH 14/40] Update requirements.txt --- optimization202/ESUPS_case_study/requirements.txt | 12 ++++++------ 1 file changed, 6 insertions(+), 6 deletions(-) diff --git a/optimization202/ESUPS_case_study/requirements.txt b/optimization202/ESUPS_case_study/requirements.txt index bcc7f65..81cb37d 100644 --- a/optimization202/ESUPS_case_study/requirements.txt +++ b/optimization202/ESUPS_case_study/requirements.txt @@ -1,12 +1,12 @@ gurobipy==11.0.3 -ipython==8.26.0 -ipywidgets==8.1.3 +#ipython==8.26.0 +#ipywidgets==8.1.3 numpy<2 -pandas==2.2.2 +#pandas==2.2.2 pandera==0.20.3 -plotly==5.23.0 +#plotly==5.23.0 pydantic==2.8.2 -scipy==1.14.1 +#scipy==1.14.1 tqdm==4.66.4 typing_extensions==4.12.2 -networkx \ No newline at end of file +networkx From 5f71cb160284d2c5d4515d5ae09203a1ac9e4b86 Mon Sep 17 00:00:00 2001 From: Jerry Yurchisin <97685128+yurchisin@users.noreply.github.com> Date: Wed, 13 Nov 2024 17:36:16 -0500 Subject: [PATCH 15/40] Update requirements.txt --- optimization202/ESUPS_case_study/requirements.txt | 1 + 1 file changed, 1 insertion(+) diff --git a/optimization202/ESUPS_case_study/requirements.txt b/optimization202/ESUPS_case_study/requirements.txt index 81cb37d..5e5ad89 100644 --- a/optimization202/ESUPS_case_study/requirements.txt +++ b/optimization202/ESUPS_case_study/requirements.txt @@ -10,3 +10,4 @@ pydantic==2.8.2 tqdm==4.66.4 typing_extensions==4.12.2 networkx +src From 5ab04a976e6de76a2b1c4deab496649c3566af0e Mon Sep 17 00:00:00 2001 From: Jerry Yurchisin <97685128+yurchisin@users.noreply.github.com> Date: Wed, 13 Nov 2024 17:37:02 -0500 Subject: [PATCH 16/40] Update requirements.txt --- optimization202/ESUPS_case_study/requirements.txt | 1 - 1 file changed, 1 deletion(-) diff --git a/optimization202/ESUPS_case_study/requirements.txt b/optimization202/ESUPS_case_study/requirements.txt index 5e5ad89..81cb37d 100644 --- a/optimization202/ESUPS_case_study/requirements.txt +++ b/optimization202/ESUPS_case_study/requirements.txt @@ -10,4 +10,3 @@ pydantic==2.8.2 tqdm==4.66.4 typing_extensions==4.12.2 networkx -src From 090ee8acff6afaa1696ac2383a536384eff58133 Mon Sep 17 00:00:00 2001 From: Jerry Yurchisin Date: Wed, 13 Nov 2024 17:51:57 -0500 Subject: [PATCH 17/40] update data --- optimization202/ESUPS_case_study/README.md | 5 - .../ESUPS_case_study/data/Book3.xlsx | Bin 9900 -> 0 bytes .../data/madagascar/depotCoordinates.csv | 56 +- .../data/vanuatu/abilityToRespond.csv | 216 - .../data/vanuatu/countryContinents.csv | 216 - .../data/vanuatu/depotCoordinates.csv | 7 - .../data/vanuatu/depotMapping.csv | 7 - .../data/vanuatu/depotSelection.csv | 7 - .../data/vanuatu/disasterCoordinates.csv | 7 - .../data/vanuatu/disasterTypeSelection.csv | 22 - .../data/vanuatu/disasters.csv | 64 - .../data/vanuatu/distanceMatrix.csv | 37 - .../data/vanuatu/inventory/actual.csv | 56 - .../data/vanuatu/inventory/actual_50pct.csv | 14 - .../data/vanuatu/inventory/actual_55pct.csv | 14 - .../data/vanuatu/inventory/actual_60pct.csv | 14 - .../data/vanuatu/inventory/actual_65pct.csv | 14 - .../data/vanuatu/inventory/actual_70pct.csv | 14 - .../data/vanuatu/inventory/actual_75pct.csv | 14 - .../data/vanuatu/inventory/actual_80pct.csv | 14 - .../data/vanuatu/inventory/actual_85pct.csv | 14 - .../data/vanuatu/inventory/actual_90pct.csv | 14 - .../data/vanuatu/inventory/actual_95pct.csv | 14 - .../data/vanuatu/inventory/rm_data.py | 13 - .../ESUPS_case_study/data/vanuatu/items.csv | 14 - .../data/vanuatu/personsPerItem.csv | 29 - .../data/vanuatu/province_lookup.csv | 7 - .../data/vanuatu/transportModes.csv | 3 - .../data/vanuatu/transportParameters.csv | 12 - .../data/vanuatu_simple/abilityToRespond.csv | 216 - .../data/vanuatu_simple/countryContinents.csv | 216 - .../data/vanuatu_simple/depotCoordinates.csv | 7 - .../data/vanuatu_simple/depotMapping.csv | 7 - .../data/vanuatu_simple/depotSelection.csv | 7 - .../vanuatu_simple/disasterCoordinates.csv | 7 - .../vanuatu_simple/disasterTypeSelection.csv | 22 - .../data/vanuatu_simple/disasters.csv | 64 - .../data/vanuatu_simple/distanceMatrix.csv | 37 - .../data/vanuatu_simple/inventory/actual.csv | 7 - .../vanuatu_simple/inventory/actual_50pct.csv | 3 - .../vanuatu_simple/inventory/actual_55pct.csv | 3 - .../vanuatu_simple/inventory/actual_60pct.csv | 3 - .../vanuatu_simple/inventory/actual_65pct.csv | 3 - .../vanuatu_simple/inventory/actual_70pct.csv | 3 - .../vanuatu_simple/inventory/actual_75pct.csv | 3 - .../vanuatu_simple/inventory/actual_80pct.csv | 3 - .../vanuatu_simple/inventory/actual_85pct.csv | 3 - .../vanuatu_simple/inventory/actual_90pct.csv | 3 - .../vanuatu_simple/inventory/actual_95pct.csv | 3 - .../data/vanuatu_simple/inventory/rm_data.py | 13 - .../data/vanuatu_simple/items.csv | 3 - .../data/vanuatu_simple/personsPerItem.csv | 29 - .../data/vanuatu_simple/province_lookup.csv | 7 - .../data/vanuatu_simple/transportModes.csv | 3 - .../vanuatu_simple/transportParameters.csv | 12 - .../opti202_disaster_prepositioning.ipynb | 5601 +---------------- 56 files changed, 67 insertions(+), 7139 deletions(-) delete mode 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b/optimization202/ESUPS_case_study/README.md index e80447b..48b21bf 100644 --- a/optimization202/ESUPS_case_study/README.md +++ b/optimization202/ESUPS_case_study/README.md @@ -9,11 +9,6 @@ The goal is to decide: - How much of each supply to store in certain locations - How to respond to disaster events at minimal cost -## Google Colab -[Google Colab Link - ESUPS Case Study](https://colab.research.google.com/github/Gurobi/modeling-examples/blob/master/optimization202/ESUPS_case_study/opti202_disaster_prepositioning.ipynb) - -Note that you must sign in with a Google account to be able to run the code in Colab. - ---- For details on licensing or on running the notebooks, see the overview on [Modeling Examples](../../) diff --git a/optimization202/ESUPS_case_study/data/Book3.xlsx b/optimization202/ESUPS_case_study/data/Book3.xlsx deleted file mode 100644 index d2a5b35cff0ed7a0e46dc3822dc1b3adf3e802d9..0000000000000000000000000000000000000000 GIT binary patch literal 0 HcmV?d00001 literal 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null, "metadata": {}, "outputs": [], "source": [ @@ -185,7 +136,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -220,7 +171,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -264,1001 +215,9 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "hovertemplate": "Type=warehouses
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}, - "hovermode": "closest", - "mapbox": { - "style": "light" - }, - "paper_bgcolor": "white", - "plot_bgcolor": "#E5ECF6", - "polar": { - "angularaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "radialaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "scene": { - "xaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "yaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "zaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - } - }, - "shapedefaults": { - "line": { - "color": "#2a3f5f" - } - }, - "ternary": { - "aaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "baxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "caxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "title": { - "x": 0.05 - }, - "xaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - }, - "yaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - } - } - } - } - } - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "def graph_supplies():\n", " supplies=[[x[0].latitude,\n", @@ -3419,998 +456,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - 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People Impacted=%{marker.size}
Lat=%{lat}
Long=%{lon}", - "lat": [ - -12.2667, - -23.35, - -20.2847, - -14.2667, - -17.8237, - -18.9085, - -25.17613271, - -25.0316, - -22.8167, - -18.1499, - -21.447, - -22.3961, - -18.9608, - -18.0646, - -21.2367, - -22.15 - ], - "legendgroup": "Storm", - "lon": [ - 49.2833, - 43.6667, - 44.3175, - 50.1667, - 48.4263, - 47.5375, - 46.08937803, - 46.99, - 47.8167, - 49.4023, - 47.0872, - 46.1217, - 46.9036, - 44.0295, - 48.3461, - 48 - ], - "marker": { - "color": "rgb(136, 204, 238)", - "size": [ - 118000, - 70000, - 13561, - 100215, - 84309, - 55345, - 250000, - 540043, - 100000, - 600000, - 736938, - 369272, - 1900, - 526200, - 500, - 162086 - ], - "sizemode": "area", - "sizeref": 1842.345 - }, - "mode": "markers", - "name": "Storm", - "showlegend": true, - "subplot": "mapbox", - "type": "scattermapbox" - }, - { - "hovertemplate": "Type=Flood
People Impacted=%{marker.size}
Lat=%{lat}
Long=%{lon}", - "lat": [ - -17.3843, - -16.9504, - -19.8667 - ], - "legendgroup": "Flood", - "lon": [ - 49.4098, - 46.8281, - 47.0333 - ], - "marker": { - "color": "rgb(204, 102, 119)", - "size": [ - 28223, - 20000, - 23369 - ], - "sizemode": "area", - "sizeref": 1842.345 - }, - "mode": "markers", - "name": "Flood", - "showlegend": true, - "subplot": "mapbox", - "type": "scattermapbox" - }, - { - "hovertemplate": "Type=Epidemic
People Impacted=%{marker.size}
Lat=%{lat}
Long=%{lon}", - "lat": [ - -15.7167, - -18.7698, - -14.8762 - ], - "legendgroup": "Epidemic", - "lon": [ - 46.3167, - 46.05, - 47.9835 - ], - "marker": { - "color": "rgb(221, 204, 119)", - "size": [ - 15172, - 3055, - 21976 - ], - "sizemode": "area", - "sizeref": 1842.345 - }, - "mode": "markers", - "name": "Epidemic", - "showlegend": true, - "subplot": "mapbox", - "type": "scattermapbox" - } - ], - "layout": { - "legend": { - "itemsizing": "constant", - "title": { - "text": "Type" - }, - "tracegroupgap": 0 - }, - "mapbox": { - "center": { - "lat": -19.358815123181813, - "lon": 47.17299445590909 - }, - "domain": { - "x": [ - 0, - 1 - ], - "y": [ - 0, - 1 - ] - }, - "style": "carto-positron", - "zoom": 4.5 - }, - "margin": { - "b": 0, - "l": 0, - "r": 0, - "t": 0 - }, - "template": { - "data": { - "bar": [ - { - "error_x": { - "color": "#2a3f5f" - }, - "error_y": { - "color": "#2a3f5f" - }, - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - 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}, - "type": "scatter" - } - ], - "scatter3d": [ - { - "line": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatter3d" - } - ], - "scattercarpet": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattercarpet" - } - ], - "scattergeo": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattergeo" - } - ], - "scattergl": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattergl" - } - ], - "scattermapbox": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scattermapbox" - } - ], - "scatterpolar": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterpolar" - } - ], - "scatterpolargl": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterpolargl" - } - ], - "scatterternary": [ - { - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "type": "scatterternary" - } - ], - "surface": [ - { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "type": "surface" - } - ], - "table": [ - { - "cells": { - "fill": { - "color": "#EBF0F8" - }, - "line": { - "color": "white" - } - }, - "header": { - "fill": { - "color": "#C8D4E3" - }, - "line": { - "color": "white" - } - }, - "type": "table" - } - ] - }, - "layout": { - "annotationdefaults": { - "arrowcolor": "#2a3f5f", - "arrowhead": 0, - "arrowwidth": 1 - }, - "autotypenumbers": "strict", - "coloraxis": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "colorscale": { - "diverging": [ - [ - 0, - "#8e0152" - ], - [ - 0.1, - "#c51b7d" - ], - [ - 0.2, - "#de77ae" - ], - [ - 0.3, - "#f1b6da" - ], - [ - 0.4, - "#fde0ef" - ], - [ - 0.5, - "#f7f7f7" - ], - [ - 0.6, - "#e6f5d0" - ], - [ - 0.7, - "#b8e186" - ], - [ - 0.8, - "#7fbc41" - ], - [ - 0.9, - "#4d9221" - ], - [ - 1, - "#276419" - ] - ], - "sequential": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ], - "sequentialminus": [ - [ - 0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1, - "#f0f921" - ] - ] - }, - "colorway": [ - "#636efa", - "#EF553B", - "#00cc96", - "#ab63fa", - "#FFA15A", - "#19d3f3", - "#FF6692", - "#B6E880", - "#FF97FF", - "#FECB52" - ], - "font": { - "color": "#2a3f5f" - }, - "geo": { - "bgcolor": "white", - "lakecolor": "white", - "landcolor": "#E5ECF6", - "showlakes": true, - "showland": true, - "subunitcolor": "white" - }, - "hoverlabel": { - "align": "left" - }, - "hovermode": "closest", - "mapbox": { - "style": "light" - }, - "paper_bgcolor": "white", - "plot_bgcolor": "#E5ECF6", - "polar": { - "angularaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "radialaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "scene": { - "xaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "yaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "zaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - } - }, - "shapedefaults": { - "line": { - "color": "#2a3f5f" - } - }, - "ternary": { - "aaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "baxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "caxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "title": { - "x": 0.05 - }, - "xaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - }, - "yaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - } - } - } - } - } - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "def graph_impacts():\n", " \n", @@ -4469,1026 +517,9 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "application/vnd.plotly.v1+json": { - "config": { - "plotlyServerURL": "https://plot.ly" - }, - "data": [ - { - "hovertemplate": "Type=supplies
Scale=%{marker.size}
Lat=%{lat}
Long=%{lon}", - "lat": [ - -13.6804, - -17.8237, - -20.5167, - -25.17613271, - -18.9085, - -14.8762, - -22.8167, - -17.3843, - -15.7167, - -18.0646, - -22.15, - -18.9608, - -20.2847, - -14.2667, - -18.1499, - -23.35 - ], - "legendgroup": "supplies", - "lon": [ - 48.4555, - 48.4263, - 47.25, - 46.08937803, - 47.5375, - 47.9835, - 47.8167, - 49.4098, - 46.3167, - 44.0295, - 48, - 46.9036, - 44.3175, - 50.1667, - 49.4023, - 43.6667 - ], - "marker": { - "color": "rgb(136, 204, 238)", - "size": [ - 937.5, - 65, - 102.5, - 5805, - 22615, - 1525, - 13002.5, - 16722.5, - 375, - 6150, - 10587.5, - 7.5, - 1840, - 14250, - 3950, - 4092.5 - ], - "sizemode": "area", - "sizeref": 460.58625 - }, - "mode": "markers", - "name": "supplies", - "showlegend": true, - "subplot": "mapbox", - "type": "scattermapbox" - }, - { - "hovertemplate": "Type=disasters
Scale=%{marker.size}
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"linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - }, - "zaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "gridwidth": 2, - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white" - } - }, - "shapedefaults": { - "line": { - "color": "#2a3f5f" - } - }, - "ternary": { - "aaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "baxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "bgcolor": "#E5ECF6", - "caxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "title": { - "x": 0.05 - }, - "xaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - }, - "yaxis": { - "automargin": true, - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "zerolinewidth": 2 - } - } - } - } - } - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "def graph_overlap():\n", " supplies=[['supplies',\n", @@ -5579,25 +610,9 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "" - ] - }, - "execution_count": 20, - "metadata": { - "image/png": { - "width": 1200 - } - }, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "Image(filename='images/Old_Stockholm.png', width=1200)" ] @@ -5615,25 +630,9 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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", - "text/plain": [ - "" - ] - }, - "execution_count": 22, - "metadata": { - "image/png": { - "width": 1200 - } - }, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "Image(filename='images/Disaster_Dashboard.png',width=1200)" ] @@ -5721,17 +720,9 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Restricted license - for non-production use only - expires 2025-11-24\n" - ] - } - ], + "outputs": [], "source": [ "#prep distance matrix\n", "df_distance, relevant_warehouses, BucketsNeeded = get_distance_matrix(dataset)\n", @@ -5759,50 +750,9 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Gurobi Optimizer version 11.0.3 build v11.0.3rc0 (mac64[rosetta2] - Darwin 23.6.0 23G80)\n", - "\n", - "CPU model: Apple M1\n", - "Thread count: 8 physical cores, 8 logical processors, using up to 8 threads\n", - "\n", - "Optimize a model with 17 rows, 16 columns and 32 nonzeros\n", - "Model fingerprint: 0x0ab092cf\n", - "Variable types: 0 continuous, 16 integer (0 binary)\n", - "Coefficient statistics:\n", - " Matrix range [1e+00, 1e+00]\n", - " Objective range [6e+00, 3e+01]\n", - " Bounds range [0e+00, 0e+00]\n", - " RHS range [3e+00, 1e+04]\n", - "Found heuristic solution: objective 186156.00000\n", - "Presolve removed 16 rows and 2 columns\n", - "Presolve time: 0.00s\n", - "Presolved: 1 rows, 14 columns, 14 nonzeros\n", - "Variable types: 0 continuous, 14 integer (0 binary)\n", - "Found heuristic solution: objective 186086.75000\n", - "\n", - "Root relaxation: objective 9.829300e+04, 1 iterations, 0.00 seconds (0.00 work units)\n", - "\n", - " Nodes | Current Node | Objective Bounds | Work\n", - " Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time\n", - "\n", - "* 0 0 0 98293.000000 98293.0000 0.00% - 0s\n", - "\n", - "Explored 1 nodes (1 simplex iterations) in 0.03 seconds (0.00 work units)\n", - "Thread count was 8 (of 8 available processors)\n", - "\n", - "Solution count 3: 98293 186087 186156 \n", - "\n", - "Optimal solution found (tolerance 1.00e-04)\n", - "Best objective 9.829300000000e+04, best bound 9.829300000000e+04, gap 0.0000%\n" - ] - } - ], + "outputs": [], "source": [ "# Fire up the solver!\n", "model.optimize()" @@ -5817,178 +767,9 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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Varamountpossibledistance
1Warehouse_Allocation[1]26.0260.00
4Warehouse_Allocation[4]9046.090466.00
11Warehouse_Allocation[11]3.037.00
14Warehouse_Allocation[14]1580.015808.00
5Warehouse_Allocation[5]610.061010.00
2Warehouse_Allocation[2]-0.04111.00
7Warehouse_Allocation[7]2296.0668911.00
0Warehouse_Allocation[0]-0.037514.00
9Warehouse_Allocation[9]-0.0246014.25
8Warehouse_Allocation[8]0.015016.00
12Warehouse_Allocation[12]-0.073616.00
10Warehouse_Allocation[10]-0.0423517.00
6Warehouse_Allocation[6]-0.0520119.00
15Warehouse_Allocation[15]-0.0163722.00
13Warehouse_Allocation[13]-0.0570023.00
3Warehouse_Allocation[3]-0.0232226.00
\n", - "
" - ], - "text/plain": [ - " Var amount possible distance\n", - "1 Warehouse_Allocation[1] 26.0 26 0.00\n", - "4 Warehouse_Allocation[4] 9046.0 9046 6.00\n", - "11 Warehouse_Allocation[11] 3.0 3 7.00\n", - "14 Warehouse_Allocation[14] 1580.0 1580 8.00\n", - "5 Warehouse_Allocation[5] 610.0 610 10.00\n", - "2 Warehouse_Allocation[2] -0.0 41 11.00\n", - "7 Warehouse_Allocation[7] 2296.0 6689 11.00\n", - "0 Warehouse_Allocation[0] -0.0 375 14.00\n", - "9 Warehouse_Allocation[9] -0.0 2460 14.25\n", - "8 Warehouse_Allocation[8] 0.0 150 16.00\n", - "12 Warehouse_Allocation[12] -0.0 736 16.00\n", - "10 Warehouse_Allocation[10] -0.0 4235 17.00\n", - "6 Warehouse_Allocation[6] -0.0 5201 19.00\n", - "15 Warehouse_Allocation[15] -0.0 1637 22.00\n", - "13 Warehouse_Allocation[13] -0.0 5700 23.00\n", - "3 Warehouse_Allocation[3] -0.0 2322 26.00" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "#Show Solution\n", "b=[]\n", @@ -6158,59 +939,9 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1m 1) What is the Objective Function? \u001b[0m\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "5fdae22271cf48789dc0fd35180320c8", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "RadioButtons(options=(('-1', 1), ('0', 2), ('1', 3)), value=1)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "fa0898d5db0a4eb181b6acb3c3a78825", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Button(description='Check', style=ButtonStyle())" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "334b1d4f419344a28bb421d64457b720", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Output()" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "q_3_1()" ] @@ -6231,59 +962,9 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1m 1) What is the Objective Function? \u001b[0m\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "4fad507aa29242c189e56dfa436d6599", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "RadioButtons(options=(('0', 1), ('1/2', 2), ('1', 3)), value=1)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "3e40781cb87e476f8264dce0878a42eb", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Button(description='Check', style=ButtonStyle())" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "6f10f550a65b4565b18ea8c58f07d3c7", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Output()" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "q_3_2()" ] @@ -6529,33 +1210,9 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Gurobi Optimizer version 11.0.3 build v11.0.3rc0 (mac64[rosetta2] - Darwin 23.6.0 23G80)\n", - "\n", - "CPU model: Apple M1\n", - "Thread count: 8 physical cores, 8 logical processors, using up to 8 threads\n", - "\n" - ] - }, - { - "ename": "GurobiError", - "evalue": "Model too large for size-limited license; visit https://gurobi.com/unrestricted for more information", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mGurobiError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[28], line 31\u001b[0m\n\u001b[1;32m 29\u001b[0m \u001b[38;5;66;03m# Optimize model\u001b[39;00m\n\u001b[1;32m 30\u001b[0m model\u001b[38;5;241m.\u001b[39msetObjective(objective, GRB\u001b[38;5;241m.\u001b[39mMINIMIZE)\n\u001b[0;32m---> 31\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43moptimize\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 33\u001b[0m \u001b[38;5;66;03m# Store results in the list 'a'\u001b[39;00m\n\u001b[1;32m 34\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m v \u001b[38;5;129;01min\u001b[39;00m model\u001b[38;5;241m.\u001b[39mgetVars():\n", - "File \u001b[0;32msrc/gurobipy/model.pxi:893\u001b[0m, in \u001b[0;36mgurobipy.Model.optimize\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mGurobiError\u001b[0m: Model too large for size-limited license; visit https://gurobi.com/unrestricted for more information" - ] - } - ], + "outputs": [], "source": [ "demand, probs = get_probs(dataset)\n", "\n", @@ -6596,138 +1253,9 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
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01
0Warehouse_Allocation[0]375.0
1Warehouse_Allocation[1]26.0
2Warehouse_Allocation[2]41.0
3Warehouse_Allocation[3]2322.0
4Warehouse_Allocation[4]9046.0
5Warehouse_Allocation[5]610.0
6Warehouse_Allocation[6]-0.0
7Warehouse_Allocation[7]-0.0
8Warehouse_Allocation[8]-0.0
9Warehouse_Allocation[9]-0.0
10Warehouse_Allocation[10]-0.0
11Warehouse_Allocation[11]-0.0
12Warehouse_Allocation[12]-0.0
13Warehouse_Allocation[13]-0.0
14Warehouse_Allocation[14]-0.0
\n", - "
" - ], - "text/plain": [ - " 0 1\n", - "0 Warehouse_Allocation[0] 375.0\n", - "1 Warehouse_Allocation[1] 26.0\n", - "2 Warehouse_Allocation[2] 41.0\n", - "3 Warehouse_Allocation[3] 2322.0\n", - "4 Warehouse_Allocation[4] 9046.0\n", - "5 Warehouse_Allocation[5] 610.0\n", - "6 Warehouse_Allocation[6] -0.0\n", - "7 Warehouse_Allocation[7] -0.0\n", - "8 Warehouse_Allocation[8] -0.0\n", - "9 Warehouse_Allocation[9] -0.0\n", - "10 Warehouse_Allocation[10] -0.0\n", - "11 Warehouse_Allocation[11] -0.0\n", - "12 Warehouse_Allocation[12] -0.0\n", - "13 Warehouse_Allocation[13] -0.0\n", - "14 Warehouse_Allocation[14] -0.0" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "pd.DataFrame(a).iloc[:15]" ] @@ -6736,18 +1264,7 @@ "cell_type": "code", "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "40811" - ] - }, - "execution_count": 69, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [] }, { @@ -6866,49 +1383,9 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Gurobi Optimizer version 11.0.3 build v11.0.3rc0 (mac64[rosetta2] - Darwin 23.6.0 23G80)\n", - "\n", - "CPU model: Apple M1\n", - "Thread count: 8 physical cores, 8 logical processors, using up to 8 threads\n", - "\n", - "Optimize a model with 1153 rows, 1040 columns and 2192 nonzeros\n", - "Model fingerprint: 0x0e8cfb4c\n", - "Variable types: 0 continuous, 1040 integer (0 binary)\n", - "Coefficient statistics:\n", - " Matrix range [1e+00, 1e+00]\n", - " Objective range [9e-02, 4e-01]\n", - " Bounds range [0e+00, 0e+00]\n", - " RHS range [3e+00, 4e+04]\n", - "Presolve removed 1152 rows and 1026 columns\n", - "Presolve time: 0.02s\n", - "Presolved: 1 rows, 14 columns, 14 nonzeros\n", - "Variable types: 0 continuous, 14 integer (0 binary)\n", - "Found heuristic solution: objective 330422.47266\n", - "\n", - "Root relaxation: objective 3.274030e+05, 1 iterations, 0.00 seconds (0.00 work units)\n", - "\n", - " Nodes | Current Node | Objective Bounds | Work\n", - " Expl Unexpl | Obj Depth IntInf | Incumbent BestBd Gap | It/Node Time\n", - "\n", - "* 0 0 0 327403.00000 327403.000 0.00% - 0s\n", - "\n", - "Explored 1 nodes (1 simplex iterations) in 0.04 seconds (0.00 work units)\n", - "Thread count was 8 (of 8 available processors)\n", - "\n", - "Solution count 2: 327403 330422 \n", - "\n", - "Optimal solution found (tolerance 1.00e-04)\n", - "Best objective 3.274030000000e+05, best bound 3.274030000000e+05, gap 0.0000%\n" - ] - } - ], + "outputs": [], "source": [ "model = gp.Model(\"full_allocation\")\n", "\n", @@ -7025,7 +1502,7 @@ }, { "cell_type": "code", - "execution_count": 168, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ From e9232584f89dd218a2fb8af39a87831b8292a2ef Mon Sep 17 00:00:00 2001 From: Jerry Yurchisin Date: Wed, 13 Nov 2024 18:54:44 -0500 Subject: [PATCH 18/40] streamlining --- .../opti202_disaster_prepositioning.ipynb | 93 ++------ .../workspaces/helper_scripts.py | 217 ------------------ .../workspaces/temp_Analyer_workspace.py | 115 ---------- .../workspaces/temp_solver_workspace.py | 200 ---------------- .../workspaces/temp_worker_workspace.py | 107 --------- 5 files changed, 14 insertions(+), 718 deletions(-) delete mode 100644 optimization202/ESUPS_case_study/workspaces/helper_scripts.py delete mode 100644 optimization202/ESUPS_case_study/workspaces/temp_Analyer_workspace.py delete mode 100644 optimization202/ESUPS_case_study/workspaces/temp_solver_workspace.py delete mode 100644 optimization202/ESUPS_case_study/workspaces/temp_worker_workspace.py diff --git a/optimization202/ESUPS_case_study/opti202_disaster_prepositioning.ipynb b/optimization202/ESUPS_case_study/opti202_disaster_prepositioning.ipynb index 68a3bed..71f136d 100644 --- a/optimization202/ESUPS_case_study/opti202_disaster_prepositioning.ipynb +++ b/optimization202/ESUPS_case_study/opti202_disaster_prepositioning.ipynb @@ -11,25 +11,9 @@ }, { "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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WLYGpOi+q+qivFyYqzgMAAAAAg1RU9SoinmfnAEjyS1HVF2Mr/AAAQMeuJ8uv+jz5tEttmXoVmxL9UWwK9Mdh6vWYHMZmkvlRcg6+w7olMHF7sSnP9/Iir79kBwAAAAAAeKiiqucRcZKdAyDZqqjqg+wQAADQQ28j4u/NonzWLMr5VErzX2sW5UWzKOcRcRARv8Zm6j7jcNiWs+kZ65YAEbG5yOssO8S3KM4DAAAAAINSVPUsIl5n5wDogb3YTM8EAAA20+V/i4j/axblrFmUq+Q8vdEsyo/Nolw2i/IgIv4eCvRjcVJU9TI7BF9YtwT4k5Oiqk+zQ3xNcR4AAAAAGIx2srKSKMAXh0VV93J6EwAA7Mj7iPhHRBw0i/K0WZSXyXl6rb2g4Cg2FxowfL+0E85JVlT1s7BuCfC1V0VVH2WHuElxHgAAAAAYkvPYTFgG4IuXRVUfZ4cAAIAdex8Rf28W5UGzKM+aRfkxO9CAzML6ypic9a2UOFHr8HMF8C3n7cVFvaA4DwAAAAAMQjtR+TA7B0BPrdpTOQAAYOxuFuZX2WEGap4dgE7tRcS6T6XEqbFuCXCr/YhYZYe4pjgPAAAAAPReO0n5ZXYOgB7bix5tQAEAwBYozHegveD2RXYOOrcXm4nn7Jh1S4B7eVFU9Tw7RITiPAAAAADQc+2G7io5BsAQPC+q+jQ7BAAAdOwqIn5VmO/MPDsAW3PYTj5nR6xbAjzIWR9OzFScBwAAAAD6bhWbqVkA3O1VUdVH2SEAAKAjbyLioFmUy+wgI+Ji23F72U5AZzdWYd0S4L56cWKm4jwAAAAA0FtFVS8j4nl2DoCBWWUHAACAJ3oXEX9rFuW8WZQfs8OMRVHV81DynYJVHyb6jl174pt1S4CHST8xU3EeAAAAAOildmLyL9k5AAbosL3wCAAAhujXZlEeNYtynR1khObZAdiJvYg4zw4xZu265avsHAADtcy8wEtxHgAAAADoq1V2AIAB+8WEQQAABuZdRPy1WZTL7CBj1L4+MB17Og6Lqj7LDjFiq+wAAAO2F4n3o4rzAAAAAEDvtJOSD7NzAAzcKjsAAADc0/WU+YvsICN2mh2AnXtZVPUsO8TYWLcE6MTzoqrnGV9YcR4AAAAA6JX2qONfsnMAjEDaBhQAANzT+zBlflfm2QFIcV5U9bPsEGPRntxg3RKgG2cZj1GK8wAAAABA3zhGGqA7KRtQAABwD79HhCnzO9BeULuXnYMUe+E0si6tsgMAjMheRCx3/UUV5wEAAACA3iiq+jQinmfnABiRvXBBEgAA/fOPZlEeN4vyY3aQiZhnByDVi6Kqj7NDDF17AYp1S4BuvWxPId4ZxXkAAAAAoBfaicjL7BwAI3Sy6w0oAAD4jquI+FuzKF3cuSNFVR+Esi8RK6eRPV77d+d+C2A7dnr/qjgPAAAAAPTFWTg2HGBbbPADAJDtXUQcNYtynR1kYpbZAeiFvYhYZYcYsGVYtwTYluftqR47oTgPAAAAAKQrqnoWESfZOQBGbKcbUAAA8JW3ETFrFuVldpApaadkH2fnoDdeFFXt++GB2hPcXmbnABi55a5ORlGcBwAAAAD6YJkdAGACltkBAACYpDfNopw1i/JjdpAJOg5Tsvmzs10VE0fECW4A27cfEae7+EKK8wAAAABAqnYC8vPsHAATsF9U9TI7BAAAk/KmWZTz7BATtpMCGoOyHy6qvrd2Qr91S4DdON3FxV2K8wAAAABAtmV2AIAJ2ckGFAAARMTflebzFFV9FBGH2TnopZft9wd3M20eYHf2YgcX/SnOAwAAAABp2mnz+9k5ACZkJxtQAABM3t+bRbnKDjFxnvdzG4XwO1i3BEhxWlT1wTa/gOI8AAAAAJCinXhskw5g97a+AQUAwKQpzSdr11yOs3PQa8+LqvY9crtldgCACdqLLd//Ks4DAAAA7ALJAgAAIABJREFUAFlOY7MICsBubX0DCgCAyVKa74d5WHPhbmftRRZ8xbR5gFQn2xz6oTgPAAAAAOxcuynnyHCAPFvdgAIAYJJ+VZrvDWsu3Md++F75H07JBOiFrT0+/bCtTwwAAAAAcAvT5gHyLWMziRIAAJ7qTbMol9khiCiqehYmZXN/p0VVnzWL8mN2kB45DuuW9MfbG/99ERHf+ln93v9/FhFH3/m8R+3bb/7e9z19Mi+qermNxyfFeQAAAABgp0ybp6feR8Rl+98fY7PhFN/5/U0XD1m8b7//v96wuvn/bv738/t+Xnikk3YD6jI7CAAAg/amWZTz7BD8wZoLD7EXm+nq8+QcfbLMDsCkvI3NmuSfbh2u1Zw/9AOKqr5Zqp+1vx60twhrluzGXmye0yy7/sSK8wAAAADArpnaxK5dxab4ftne/ijCN4tyvcsgbcn+W1/zu5tY7bTA60L9UWw2qQ67T8dEzUMpAACAx3unNN8fRVUfRMSL7BwMjouqW0VVz8OJDWzH+9isR17EZm2wy3J8p5pFeXOAyPp772fNkh3YyqkoivMAAAAAwK4tswMwWtdT49fxZTrTOi9ON278Gf5Urm83p643pmZhY5fH2coGFAAAk/A+vkyipR9Mm+exlmHqfIR1S7rzLjZrlBcRse5rSf4p7liznMWXNUtDdHisvdgMYlp1+UkV5wEAAACAnTG1iQ59PaXpYmrF33Zzan39+3ay4Ky9OdmB+9rasccAAIzaVUQcT+112ADMswMwWJOfOl9U9XFYt+TxrmKzTnceIy3K39c31iyvC/SzcCoKD7cMxXkAAADgDu8iwoYV0Ffz7AAM1vWUpnVsSvKXmWH6qP07WbW3602peWxK9DZ+uY3iPAAADzVvFuVFdgi+aIcVuICap1jGtNfunNjAQ13Fpih/3izK87veeara5wsXEXEW8cdFKtc3j1vcZb+o6uMuf8YU5wEAAGB8Tm8cjwjQG+0Rrc+zczAY1xtP69hsPrko7IHaTanTiDi9UaKfhw0p/tdeUdXzZlGusoMAADAIvyoI9pLSL0812anz7Sl+1i25rzehLP9o7d/beYQSPfd2Gu33TBcU5wEAAACAXZlnB6D33sdmAXxlcmG3virRz2Pz82hDmJuW0fGxxwAAjNLbZlEus0PwZ+2wgsPsHIzCMqa5hrfMDkDvvYvNxHQDPjr0VYl+HtYs+bbnRVUfdHVhl+I8AAAAALB1RVU/i4iT7Bz00vVk+ZUTU3ajnSq+aqfQn4afTTb2i6qe+TkEAOAWV7GZCEv/mDZPVyY3db5dt3Tfxve8CeuWO3FjzfIgNhezmELPTafR0fMdxXkAAAAAYBfm2QHoHVOakrVT6OdFVS9jsxmlQM88ItbJGQAA6K9jr9/6py0YvsjOwagsY1precq5fO0qNqfynU3pIpK+aP/O5+1FLddlaT+jzKOj4vxfuvgkAAAAAAB3MPmMa79HxN+aRXnULMqV0kW+ZlFeNotyHhH/F5spWkzXSVu6AQCAr/1m2m5vLbMDMDonbWF1Kqxbcu0qIn6NiINmUZ4qzedqFuXHZlEuI+IgNv8uV6mByLZXVPW8i0+kOA8AAAAAbFVR1bOI2M/OQbo3EfF/zaI8VrbopxsF+r9FxNvkOOSZZwcAAKB33odydi+15ebj7ByM0iTK5EVVH0XEYXYOeuG6ML806KNfFOi5oZPnPIrzAAAAAMC2zbMDkOptRPy1WZRzU5qGoVmU62ZRziLi72Ejaorm2QEAAOiduRJhb51GxF52CEZpEsX58BqYzemY/6cw339fFeh/y01DkhddnJapOA8AAAAAbI3JZ5P2PiL+1izKWbMoL7LD8HDNolzFZiPq99wk7Nh+UdXutwEAuPabU8N6bZ4dgNHaK6p6nh1iB+bZAUhzvXZ5bNjHsLQF+tOI+Gs4NXOKnrxuqTgPAAAAAGzTcZh8NkW/NovyQLli+NqNqOMwfX5q5tkBAADohfcRscwOwbe1F7zuZ+dg1EY9db79GbJuOU3WLkegWZQX7amZ/wjrllMyf+onUJwHAAAAALbJ1OJpeRcRf22PzGVE2unzR7H5N2b8XrQnhgAAMG3zZlF+zA7Bd1lzYdsOi6qeZYfYIj9D02PtcoSaRXkWm3VL0+en4bCo6oOnfALFeQAAAABgK9rS5YvsHOzMb82iPGoW5UV2ELajWZSXzaI8iog32VnYCQUCAIBp+90k3t7znJ1dmGcH2CI/Q9PyW0TMrF2OU7tuOQvT56fiSSeiKM4DAAAAANti82kariLi/zWLctTHd/NFsyjnEfH37BxsnZ9pAIDpugrPB4dgLzsAk3AyxhPJiqo+Dj9DU3EVEX9vFuWpU1TGr50+PwunZo7dk/aefugqBQAAAADAVxTnx+9dRBw3i/IyOwi71SzKVVHVHyNiFTaax+qwqOoDP98AAJN05nng/RVVPYuIZxFx1P6v2T0+bN3+ehERH033p+fmEXGWHaJj1i2n4X1s1i5NmZ+QZlFetI/NZxFxkhyH7dh/yrql4jwAAAAA0Ll2EtWL7Bxs1dvYbDyZ1DRRzaI8bzeh1qE8P1bHMb5yBAAAt3sfngN+V1HVB7Epxs9iU5Q/fOSnev7V543Y/N2vY1OmX9+j6HkVXouxG6cxvvsFxfnxexcRM2uX09T+u8+Lqr6MiF+S47Adj163VJwHAAAAALZhlh2ArXrTLMp5dgjy3ZjgtA6FjTGax/jKEQAA3G6pZPhnRVUfxea58SweX5S/j/3YTMY9ab/uVUSct7f1N/5dVhHxcot54Np+UdVHY5na3a5jWMMYt98jYu7xjGZRLtvy/Fn4uR+beTxy3fIv3eYAAAAAAIgIU5vGTGmeP2k3zmexmXbIuBy2EzUBAJiG982iXGWH6IOiqp8VVT0vqvoiIv4dm4L6Nkvz37IXmxL9PyPiv0VVr756fr6MzURl2IXT7AAdsm45bm+aRemUTP7QPreZhbXLsXn0uqXiPAAAAACwDTagxklpnm9Snh819+cAANOxzA6Qrajqg6KqzyLiMiJex+7L8rc5bhbl5fVv2lLocSjPsxtjem04yw7A1li75JusXY7W7DEfpDgPAAAAAHSqPcLcsafj89bGE7exATVas+wAAADsxKSnzbcT5lcR8Z/YTJfv47rG6uv/0RbpZxHx246zMD17RVUPvjzfTifu0wUxdEdpnltZuxylRz0uKc4DAAAAAF0b/CYa/+Nd+HflHtoNqHl2Djr1oqjqZ9khAADYumV2gCxFVS9jM2H+JDfJnc6+9T+bRfmxWZSnEfHXiHi720hMzBjWhmbZAdgKAz+4F+X50Zk95oMU5wEAAACArs2yA9Cpq9gcB/8xOwjD0CzK84j4e3YOOjXLDgAAwFZNctp8UdWzoqovI+KX6OeE+Zt+b6fLf1ezKC+aRTmLiL+FAj3bcTKCC6tn2QHonIEfPIjy/KjsFVU9e+gHKc4DAAAAAF17nh2ATh3ftTkPX2tLN2+yc9AZG9AAAOO2zA6wa0VVn0XEvyJiPzvLPa3u+47Noly3Bfr/FxHvtxWIyRr668Oh5+fPDPzgUW6U5xm+2UM/QHEeAAAAAOjMY6Z70Gu/NotynR2CYWqPyH6XnYNOzLIDAACwNVdTmjZfVPVRUdUXEfEyO8sDvG9P9nqQZlGeN4vyICJ+DVN16c5gi+dFVR9F/0+X4GEM/ODR2vK8UzOHb/bQD1CcBwAAAAC6NMsOQGfeNYtymR2CwTsOBY0x2C+q+iA7BAAAW3GWHWBXiqo+joh1RBwmR3mo1VM+uH1tfxBOBaMbL7IDPMEsOwCdMvCDJ2svHvw1OwdP8uATkBXnAQAAAIAuzbID0Jl5dgCGr536NU+OQTdm2QEAANiKSRTni6qeR8Q/Y5jTpldP/QTNovzYngr2t4h4/9TPx7S1F6EM0VF2ADrz1sAPutJ+L/2enYPHe+hJyIrzAAAAAECXHjzdg176tT2qFp6sWZTnEfFbdg6ebJYdAACAzr1pFuXH7BDbVlT1KiJeZ+d4pDftBcmdaKczH4XpujzNUIvzs+wAdOIqDGmge/NwYdmQzR7yzorzAAAAAEAnHjrVg956HxOZOMhOLcPm09DNsgMAANC5VXaAbWtL8yfZOZ5g1fUnbKfPLyPirxHxruvPzyQMrjhfVPVBROxn56ATyy4vKIKIzWNjDPC+jT/MHvLOivMAAAAAQFccdzwOyylMHGS32u+peXYOnmS/qOpn2SEAAOjM+3b6+GiNoDS/1X+j9qS5WTghjIfbK6p6aOuAQ8vLt71tFqWBH2xF+7joRJZhetBJyIrzAAAAAEBXZtkBeLJ3zaJcZYdgnNrCx5vsHDzJLDsAAACdWWUH2Kaiqk9j2KX5iB2cBtdOnz+NiP8XEVfb/nqMytAmMyvOj8NpdgDGrT2RxWksA/SQC7oU5wEAAACArtiAGj6bT2zbaShjDJn7eQCA8VhlB9iWoqrnEfEqO8cTXcUO/42aRXkem+f7yoLc19CK87PsADzZm3YiOGzbPDsAjzK77zsqzgMAAAAAT1ZU9bOI2M/OwZNs9Qh4iNhMM4yIZXYOHm2WHQAAgE68axblZXaIbWinjb7OztGB8/b10840i/KyWZRHEfHbLr8ug3XYrgcOhQvBh2+ZHYBpaC/Q8Fg4PCbOAwAAAAA7ZfNp+JbZAZiGZlGehSmGQ+W+HgBgHFbZAbahLfGeZ+foyFnWF24W5WlE/D2cFsbdZtkB7qOo6oOI2MvOwZP8NtYLvuitZXgcHBrFeQAAAABgp5Qph+2qWZSr7BBMyml2AB5lb2ATBQEA+LaxlMu/topxnIb3tp12m6ZdI5iF0iC3O84OcE8H2QF4srSLiZgmp2YO0uF931FxHgAAAADoguL8sK2yAzAtzaJcR8Tb7Bw8ivt7AIBhez/Gqb1FVZ9GxIvsHB1ZZQeIiGjL+0fhxDC+b5Yd4J5m2QF4kjdjfNyi/9pTM99n5+D+iqqe3ef9FOcBAAAAgC4cZAfgSVbZAZikZXYAHkVxHgBg2EY3bb6o6oMYz+uLXp0I15ZVZ6E8z7fttz9/fXeQHYAnMW2eTMvsADzIvdYtFecBAAAAgC48zw7Ao73LPgKeaTJ1frAOsgMAAPAk6+wAW3AWEXvZITqyyg7wtWZRfoxNef5NchT6aZYd4B4OsgPwaNYtSdVezGbq/HAc3OedFOcBAAAAgCcZyGQpvm+VHYBJW2YH4MFMnAcAGLBmUY5q4nxR1bOIeJGdo0O9nKzcLMqPzaKch/I8/2sIrxEN/BiuXt4nMjnL7ADcm4nzAAAAAMBOHGQH4EnW2QGYLlPnB+kgOwAAAI82xufeq+wAHfq9WZSX2SFuozzPN8yyA9ymqOpn2Rl4klFd7MVgnUfEVXYI7kVxHgAAAADYiSFMluLb3jvumB5YZgfgQfazAwAA8Gjr7ABdKqp6HuN6frrKDnAfyvN85bDn5XTrlsP1e7MoP2aHgPb7cJWdg3vZu89jkuI8AAAAAPBUB9kBeLR1dgBop86/y87B/RVVfZCdAQCAR1lnB+jYMjtAh943i3Iwk5WV5/lKn8vpB9kBeLR1dgC44Sw7APd252OS4jwAAAAA8FR93hzjduvsANCy+TQsB9kBAAB4lNGcOGbafL62PP97dg56YZYd4BYH2QF4tMFcTMT4NYvyMiLeZufgXg7uegfFeQAAAADgqfp8HDO3W2cHgIiIZlGuIuIqOwf3dpAdAACAB3vXLMqP2SE6tMwO0LFVdoBHmocTxOj3UI2D7AA8yvu2qAx9ssoOwL0c3PUOivMAAAAAwFMdZgfgUa5sQNEzps4Px0F2AAAAHsy0+f56M9TX5+3FGLNQnp+6WXaAWxxkB+BRRvOYxag4BWEY7ryYS3EeAAAAAGCabEDRN6vsAAAAMGKX2QE6NM8O0LFVdoCnaMvz83CK2JTtFVV9kB3iO5yUOUzr7ADwtfbx7vfsHNzpzvt9xXkAAAAA4NGKqp5lZ+DR1tkB4KZ2wqLNp2G4c3ITAAC9s84O0IW2nPs8O0eH3jeLcp0d4qmaRXkREcfZOUh1kB3gO5yUOUwGftBXps73353PExXnAQAAAACm6TI7AHyDzadhMLEPAGB4LrMDdOQ0O0DHzrIDdKW9AOAf2TlIM8sOwKgoztNX6+wAPJ3iPAAAAADwFKYOD9dldgD4WrMoVxFxlZ0DAADGpj3haQzGNNX8KiJW2SG61CzKs4h4k52DFL1bI3RS5nA1i/Jjdgb4lvb51LvsHNzurvt/xXkAAAAA4ClMHR4uk5voK1Pn+899PwDAsIyi4FVU9VFE7Gfn6ND5SMuhpzGS7zke5CA7AKPxNjsA3GGdHYCnUZwHAAAAAJigkW7OMw6K8/13mB0AAIAHGcvrv3l2gI6dZQfYhna9YZ6dg53zOhGYCgNp+u/WU1AU5wEAAACAp5hlB+BRTH6jt5pFeR4R77NzAADAiKyzA3Rklh2gQ++aRTna4l37Z/tHdg52q6jqWXaGr8yyA/Aol9kB4A7r7ADc6dbTMhXnAQAAAACmZyzTBhkvU+cBAIA/FFX9LMY10XqU0+ZvahblWUS8zc7BTt1aVIR7uswOALdpFuVlRFxl5+DxFOcBAAAAAIC+WWcHAACAEbnMDtCBo+wAHbpqFuUqO8SOzEO5cErG9HMKcJvRnhozBYrzAAAAAMBT2BADOtcsyvNQrgAAgK5cZgfowCw7QIdW2QF2pZ3Ku0yOwe70bZ3QBHxgWxTnB0xxHgAAAAB4ir3sADzKZXYAuIfz7AAAAEBv9K2Q+xRn2QF2qVmUZxHxNjsHO9G3ovqY7jeAfrnMDsDjKc4DAAAAAEzPZXYAuAfFebp2kB0AAIBHO8gO0JHf2ynsU3OaHYCdeJ4dAGBHTJwfMMV5AAAAAIDpMXGLIVhnB2B09rMDAAAkGUO56zA7QEdW2QEyNIvyIiJ+y87B9hVV3bep8wCdaxblOiLeZefgcRTnAQAAAACmxyYmvdcsyo8R8TY7BwAADF373Jp875tFOeWTtZYRcZUdgq0zrIGnOsgOAPc0D49rg6Q4DwAAAAA8SlHVs+wMwOitswMAAAC5iqo+yM7QkVV2gEztBRzL7BxsnWENPNVBdgC4j/Y0lVmYPD84P2QHAAAAAAAA+I5VdgAAACDdQXaAjqyyA2RrFuVZUdWnEbGfnYWtOYqIKZ+swNO5+ILBaBblRTtg6DQijiPiMDcR96E4DwAAAAAwPQfZAeA+mkV5GSYSAgAAw/emfX3D5jXe6+wQbE2fSs9H2QF4FMVjBuXGiSrL3CTc11+yAwAAAAAAsHMmuwEAAMDuXGQH6ItmUa4i4n12DramT2X1vewAPE5R1QfZGYDxUpwHAAAAAJggG1AAAACwM6+Kql4XVT3LDtITy+wAQK8dZAcAxktxHgAAAABgmg6yAwAAAMA9XGYH6MjziPhXUdXnU7+Y3dT5UXueHYBRmGUHAMZLcR4AAAAAYJpm2QEAAADgLs2ivMzO0LEXEXFRVPUyO0iyZXYAoLeOsgMA46U4DwAAAAAwTTagAAAAJqCoaq//+mcvIn4pqvqyqOpZdpgM7dT5q+wcdG/qJyrQiVl2AGC8FOcBAAAAAKZplh0AAACAnXiWHaADb7MDbMl+RPyrqOqz7CBJpvrnHruD7ACtd9kBeLQ9F30B26I4DwAAAAAwTTagAAAAGIqP2QG27GVR1RcTfJ2uOM82jf1+Y+xm2QGAcVKcBwAAAAAey+bT8B1nBwAAAIB7uMgOsAOHEbEuqnqeHWRXmkX5MSLeZOegc1O7AITtsG4JbIXiPAAAAADwKM2inMKm9djZgAIAABi/WXaADqyzA+zIXkS8Lqp6SpPYV9kB6Nyz7ACMwvOiqn0vAZ1TnAcAAAAAmK7DoqoPskMAAADAbZpFuc7OsGMvi6o+n0JptP23fZ+dA+glQz+AzinOAwAAAABM22l2AAAAALbqIDtAR95mB9ixFxGxnkJ5PkydH5uD7ACMhnVLoHOK8wAAAAAA0zbPDgAAAMBWHWQH6Mh5doAEhzGN8vwqOwCdOsgO0PqYHYAnc1om0DnFeQAAAADgKRylPXx7RVXPs0MAAACwNQfZAToyxeJ8xATK882ivIyId9k5GJ2L7AB0YpkdABgXxXkAAAAA4CkuswPQiWV2AAAAALZmPztAF9py9dvsHElGX54PU+eBbzse+X0fsGOK8wAAAAAA7Js6DwAAMF5FVR9lZ+jIKjtAosMY959/qicKjNFY7m/oh72IOM0OAYyH4jwAAAAA8BSX2QHozDI7AAAAAFszimm9zaJcRcRVdo5EL4qqPssOsQ3tiQLvsnPQib3sAK11dgA6c2rqPNAVxXkAAAAA4CkuswPQmf2iqk1vAgAAGKdZdoAOjbI4/gAvi6o+zg6xJevsAEAvmToPdEZxHgAAAACAa0vTmwAAAEbpKDtAh85i2lPnIyJWRVUfZIfYgvPsAIzKx+wAdOp0pPd7wI4pzgMAAAAAT7HODkCn9iJilR0CAACAzh1kB+hKsyg/hqnzo3z93izKdbgogo40i/IiOwOd2gv3/UAHFOcBAAAAALjpxYiPfAcAAJiqw+wAHTN1PuJ5UdWn2SG2YJ0dgKczGZwteVFU9Sw7BDBsivMAAAAAwKO1k8AYn7Ee+Q4AADBZYyobmjr/h+UIX7+vswPQiYPsAK232QHo3Kqo6mfZIYDhUpwHAAAAAOBrexFxbhMKAABgVI6yA3TsLCLeZ4dIthcRy+wQHVtnB2BUPmYHoHP74cIp4AkU5wEAAACApzK5aZwOwyYUAADAmMyyA3SpnTo/z87RAycjO03gIiKusnMwGhfZAdiKk6Kq59khgGFSnAcAAAAAnuoyOwBbc1JU9Wl2CAAAADoxtonz0SzKdUT8np2jB5bZATqm7ExXLrMDsDVnRVWP7nEN2D7FeQAAAADgqS6zA7BVr0xwAgAAGIX9oqoPskNswTxMKH8+sgLpOjsAo3GZHYCt2YuI86Kqn2UHAYZFcR4AAAAAeKp1dgC27rXyPAAAwCjMsgN0rVmUH2NTnp+6MZ0YZ+I8XfG9NG77EbFWngce4ofsANAj859+/HmWHWIkZtkBAAAAgJ26zA7ATrwuqjqaRbnKDgIAAMCjzSJilZyhc82iPC+q+reIeJmdJdFJUdWn7YUEQ6fsTCeaRfmxqOqr2EwnZ5wOI+IsXEAF3JPiPHxxkh0AAAAAYIiaRXlpA2oylOcBAACG7Tg7wLY0i/K0qOpZbEqUUzWPTYF00Kw10bGLiHieHYKtOmnXLOfZQYD++0t2AAAAAABgFEwCm47XRVWP6fh3AACAKdkrqvooO8QWHUfEVXaIRPPsAB2y1kRX1tkB2ImToqpX2SGA/lOcBwAAAAC6sM4OwE69shEFAAAwWGOeOn8ZI/7z3cPhiC6MUJynK76XpuOkqOqLoqqfZQcB+uuH7AAAWzL/6cefZ99522V7+571LW+7/PT5w20fCwAAAFNlA2p6ToqqPoiI42ZRfswOAwAAwL0dR8QyO8S2NItyXVT13yPidXaWJLMYxzrNZXYARmMMPw/c32FErIuqPm4vpgL4E8V5YKz229u3PL/jY3+57Y0//fjz9950Fbc/2b6IiO9tIn+862M/ff5gAxoAAIA+swE1Tc8j4rKo6lmzKH0PAAAADMNhUdUHYy4UNotyVVT1LCJOsrMkOI6Is+wQHbDOQCeaRXlZVPVVROxlZ2FnDiPioi3Pr7PDAP2iOA/Qnb24vZR/V2H/VrcU9t/HEybo3/axnz5/uO1jAQAA4A/tBtT7+P6F7IzXXkT8u6jqX5tFucwOAwAAwL2MpVz9Xc2inBdVHTG98vzzoqqfjeB0uMvsAIzKOiJeZIdgp/Yi4l/WLIGvKc4DDN9t0/UjnlDYv6WsH3F7Yf/OCfpxy/T9T58/uHIcAABgmNYxvc1ovvilneY3H/PUQgAAgJGYx8iL8xF/lOcP4omD7gboKG4fstd77ZCG7BiMxzoU56fql6KqjyPi2JolEKE4D8Dj3VXYf/QLjjsK++/iltJ93F7YX9/ytstPnz9c3hoMAACAu6xDcX7qnsfmGORlsyhHX8AAAAAYsMOiqg8mUiI8js2axWFyjl2axcCL8y2nG9IVAxyn7TAi/mP6PBChOA/A8Ny1mHFbYf+X2z7wjsL+21vedhlPmL7/6fOHoR+RBwAAcG2dHYBe2IuIV+0kJ9PnAQAA+uu0vY1asyg/tiekrWM65fmj7AAduQzF+aG6zA5wU7Mo10VVX8Vm3Yrp+qWo6nls1izXyVmAJIrzAHA/tx3d96Rj/W4p7F/FHaX7eOT0/U+fP6zvkw0AAOAh2iO0TQLj2vX0+TOTnAAAAHrpOCZQnI+YZHl+LMV5BqqngxTWcfswRqZhPyL+VVT124hYKtDD9CjOA0B/7cWWCvt3TNd/H7df/b2+5W2Xt32swj4AAEzCeUS8zA5Bb+yFSU4AAAB9tV9U9XGzKM+zg+zCxMrzYxlqsI4nDrKDG9ahOM8Xz0OBHiZJcR4A+Np+3L6Qsq3C/rt45AT9uGP6/qfPH277WAAAoFvrUJznf11Pcvo9Ik57OnUMAABgiuaxuQh+Em6U589j5IXsoqqPmkVpnxS+OI+IV9kh6J2bBfpVsyhXyXmALVOcBwD64q6pDo++8vuOwv7bW952GU+Yvv/p84fbPhYAAEapWZTnRVVfxWbSOHztRUS8KKr6t9hMcvreRdAAAADsxouiqg+mdIFz+1p0VlT1KiJOkuNs07PsANAnzaK8LKr6fYznRAa69TwinhdVfRYRq4g4m9JjI0yJ4jwAMHW3TZK4a8rEL7e98QmF/Vsn6Mcd0/c/ff6geAIAQLZ1OPaY272MiHm7EXWmQA8AAJDqtL1NSrMo50VVR4y3PH8Utw8CG4LL7AA8yvvsALc4D6dlcruA5DIjAAAgAElEQVS92HyPvCyq+l1EnEXEufVLGA/FeQCAHE8p7N/qlsL+VdxRuo/vF/Yv45aFqU+fP6zvTgYAwISch+I8d9uLzQXJpwr0AAAAqeZFVU/yVLC2PL+OiNfZWbZgDBPnL7MD8CiX2QFusQ7Fee7vMDaPD6+Lqn4TmwL9eXIm4IkU5wEApmMvtlTYv2O6/vu4fXFkfcvbLm/52I+fPn+47UIAAADynMc4N5zZDgV6AACAXHsRMY/NVN3JaRblqp08fxabvwtgpJpFeV5U9VX4WefhTiLipP3+WUXEqlmU+gowQIrzAABs2357+55tFfbfxf9O0L/49PnD5I4aBQDYtWZRfiyq+vcwdZ6HuVmgP4+IZbMoL3MjAQAATMZpTLQ4H/FHef4iNgOfxlKoPcgOAD11HpsSNDzGXmxOLXhZVPX72Dx2nlvHhOH4S3YAAADYksPYlPJv3o5SEwEATIsja3msvdhsXv6nqOrzoqpnyXkAAACmYL+o6nl2iEzt5OCD2AxnGoOD7ABMVt+ncFu3pCv7EfEqNuuYF0VVz4uqfpYdCrid4jwAAAAAsA3nEXGVHYLBexER/yqq+rKo6lMbTwAAAFu1zA6QrVmUH5tFeRQRb7KzwIB9fSp4rzSL8jwi3mfnYHQOI+J1RPy3HQaiRA89pTgPAAAAAHSuWZQfw/QmunM9vem/RVWvTKEHAADYiv2iqk+zQ/RBsyjnoTwPY2bdkm16EV9K9Kuiqo+zAwFfKM4DAAAAANuyyg7AKJ3Elyn0Z0VVH2QHAgAAGJGlCbl/mGUHgIFaZwe4h1V2ACbjJCL+WVT1RyV66AfFeQAAAABgK5pFuQ7HHrM9+xHxMiL+U1T1RVHVp0r0AAAAT7YXEZOfOt8WG/ezcwDb0SzKi4h4l52DSdkLJXroBcV5AAAAAGCbzrIDMAmHEfEqlOgBAAC64DXVOC4eWGcHYLIuswPck3VLsijRQ6IfsgMAAAAAAKO2ik2hGXblukT/qqjqd7H5Hly3k8QAAAC4215sCqWTLPG1Fw08z84BQ9UsysvsDPd0Hpv7ur3sIEzadYn+pKjqiIjfY/O9ed4syo+ZwWCsTJwHAAAAALamXdx/k52Dybou0f+7qOrLoqrPTG8CAAC4lxdFVc+yQyQZw7T5sXiWHYAHu8oOcF/tuuUqOwd85UVEvI6I/xZVfV5U9dwpMNAtE+cBAAAAgG1bxmZqDmTaj4iXEfGyqOqr2BxZfx6bafSXibkAAAD6alVU9dEEJ97OswN0ZJ0doANH2QF4sKGd+HcWm/Ui6KMX7S2crAndUZyH/8/e3eS2raVrw37eg2omsF9kAFaNwK4RWNUhwFZcI4j2COLqE4g2wP52jWArIyinRYCdkkew7REceQDBayPpf19D9LaTsvyjv0VS1wUIJwmT+K6zY8tcvNezAAAAANioushnWVldhGPOaY+9+PHB03U0JfqYP3zatVIIAADAYw5iPn19nDjH1mRlNYr5PSOwnE6tqTTrlp/D0A/a7+5kzYdrmed1kU9ThoIuUpwHAAAAALZhHBH/SR0CFvhzGn3EnxOc7qbRTxPmAgAASO1TVlbnOzTd9jR1gHVxP0siXfxaMQnFebrl55M17waCnBsIAs/7n9QBAAAAAID+ax7WXqTOAS90GBGfIuI/WVn9f1lZTbOyGmdlNUycCwAAIIVJ6gDbkJXVUczvB/vgKnWANRmmDsCrzVIHeC3rlnTcXsw3fvweEf8vK6vLrKxOm/c04BEmzgMAAAAA2zKJiOPUIWAJx83rU1ZWEfOHqdMwkR4AANgNh1lZndVF3ptp7Av06X9fF6d+0w+z1AGWNA6nZdIPhxHxW0REVlbX0Uyij/k6pmn0ECbO0y4Xj7xukyYCAAAAYG3qIp9ExHXqHLAGx2EiPQAAsFs+9vmeJyur/ZhP7O2LvhTnB6kD8Gqd/Ldn6jw9dRDz97Z/x3wa/d0apmn07DQT52mNb9+/Dl/z+9++ebfo9+9HxKIv7oNY/E31IOZvFgAAAABszjjmx8ZCn/w8kf4qmon0YZoTAADQH+dZWQ16eo8zSh1gzc5TB1gTPZ5uue3414dxmDpPvz1cw7yNH6fRzxLmgq1SnKezvn3/On3i8lpvAN6+eXcU80L+Y4YLfv25Ar9v7gEAAICdUxf5JCurUcwX6KGvDpvXx4iIrKyuYj5xbRoeRAEAAN21F/M+xjBxjk04TR1gja77cN+ZldUgdQZerZPT5u/URT7NyuoirFuyG/Yi4n3ziqysrqMp0YdBIPSc4jy8wLfvX5/6xm66zo+1ZEn/qWv7MX9IBwAAANAW4zC9id1yV6T/EBHxYKLTNOYPojr9YBkAANgpx1lZndVF3puieVZWw+jX8MO+TJsfpA7Aq/VhfWMc1i3ZTQcxHwLycBDINCLO6yKfposF66c4Dy2z5ZL+IBbfaCw7ZV9JHwAAAHiS6U3wXxOdIiIuYr7+dxmmOgEAAO32MSury7rIJ6mDrElvNgE0+lKcP0odgFebpQ6wqmbd8nM0ww9gh/15ouZPa5dTRXq6TnEedti3719nsfib1uk6P9YKJf2nrnm4DgAAAN11GhF/pA4BLXIcD9a7mqlOl3H/QGqWJhYAAMCjzpryfKcnTGdlNYhmU3NPXPeo0DhIHYBX6/TXgwfGEXES88EHwNzd2uUnRXq6TnEe2Iotl/T3Y/HO40EsV+BX0gcAoEuOmkUrYEMsBK+uLvJL05vgSXdTnT5ERGRldRvNw6iIuPR1CAAASGwvIqZZWQ06fmLWKHWANZukDrBGJs53Ty+K83WRz7KyOouIT6mzQIsp0tNZivNA73z7/vUm1lzGX2SFkv5T147CrlUAAFbzW+oAsAP+T+oAPXEapjfBS+3FfAri+4iInx5IXcb8oVSXyyoAAED33JXnhx2+HxmlDrBmk9QB1siAw2657vDXgcecxfzrw0HiHNAVivR0huI8wAq2WdKPiHj75t1wwaVVpuwrJwAAAJBEXeQ3WVmNw4YfWNbdA6mIiMjK6ip+LNLP0sQCAAB2yGF0tDyfldVJ9KsU+7kv94FZWZk23z29mDZ/p1m3HEXEf1JngY5SpKe1FOcBOuTb96/TJy6fr/NjPVHSj4hYdO25An+fFh0AAABYg7rIz5qHUIeps0APHMaDz6WsrK7jxyJ9rx5iAwAArXEY99OZu+Q0dYA1G6cOsEaK893TuzWHusinWVl9ieb0P2AlivS0huI8AI96pqT/1LVXe/vm3VHMS/ePGS74dSV9AACA/hhFxB+pQ0APHUTEh+YVWVndRvNAKhTpAQCA9fqQlVXURT5KHeQlsrIaxIMTvHqgN9PmG4rz3TNNHWBDRhExi4i9tDGgdxTpSUZxHoDkvn3/+tRD2uk6P9YzJf1lC/ymIgIAAKygLvLLrKx+jYhPqbNAz+3FfEra+whFegAAYO26VJ43bb7dhqkD8Gq9XFOoi/ymOS3z36mzQM8p0rM1ivMA7JQtl/QHMZ9+/xglfQAAgAfqIh9nZXUS7ntgmxTpAQCAdetKeX6UOsAa/dqnafNZWXku3j1XdZHfpA6xKXWRn2dl9SWa9RNgKxYV6c+tWbIqxXkA2JBv37/OYn5k12Om6/xYb9+824/Fx9UNYrkCf5+OJgQAALphFBF/pA4BO+yxIv153BfpZ8mSAQAAXdLq8nwzPXovdY41uY6Is9Qh1myYOgCvtgsl1lHM+x99+doBXfOwSG/4BytRnAeAHvj2/etNrLmMv4iSPgAAsCl1kV9mZfVrRHxKnQWIiPnD4A/NK7Kyuo4fi/S9nSYHAACs7EMzOXzUwnuHUeoAa9TG//+uapg6AK82TR1g0+oiv2k23fw7dRbgv4Z/XMePRfpZqmB0g+I8APAq2yzpR0S8ffNuuODSKgV+u8ABAKCl6iIfZ2V1Eo7khjY6iIiPzSuysrqIpkhvshMAAPCI9xExzcpq2JZyd1ZWg+jPUK9/1UU+TR1iA05SB+DVpqkDbENd5OdZWf0rmnURoDUO4sfhH1cx/7p03tP3SVakOA8AtNq371+nT1w+X+fH2kBJfxDzb9ABAIDXOYn5Ec82vUK73R2R/HCy012RvhWlGAAAILnDmJfnRy3ZcHuaOsCaXEXEOHWIdWs2Nni+2i3XuzTduS7y06yshmHoB7TZYfP6mJVVRMSXuC/Sz9LFoi0U5wEAGlsu6R/FvJD/mOGCX3+uwG8RCQCAXqiLfOboY+icnyc7eSAFAADcuSvPD1tQnh8l/vjrcBsRo55uWDZtvnumqQMkYOgHdMv75vVbM/zjPEyj32mK8wAACXz7/vWpRcHpOj/WkiX9p67thx30AABsmKOPofMePpC6Ox550oKSDAAAkMZeRPyRldUvdZFPUgRoNun3oeh62uN7q1HqALzaNHWAbTP0AzrtIObPHD5mZXUb9ydonvd0QxqPUJwHAOi5LZf0BzGffv+Yo5jvvj9e58cEAKC/mqOPj8L3kNB1D49HvpvqpEQPAAC76fdm8vwowcdO8THX7ddUGw82LSurQRje1UXT1AFSaIZ+/BoRn1JnAZa2F/fDP37Pyuoi7kv0s5TB2CzFeQAA1ubb96+ziJgtuDx9++bdfig9AQDwOndHHx+kDgKsxcOpTtdxP4l+mjIUAACwVR+ajfIn2yqmNaXsrj+j+lwX+Th1iA06SR2AV7ve5XJpXeTj5mvLh9RZgLU4bl53J2hOQom+l/4ndQAAAAAAgEWa41FPIuI2dRZg7Q5i/nD5P1lZzbKyOmvKMwAAQP8dRsRlVlbbKkufbunjbMrnRFP6t2mUOgCvdp46QAucRsRV6hDA2h1GxG8R8b9ZWV1mZXXabJShBxTnAQAAAIBWq4v8Mrr/gBt42t0k+j8elOgHiTMBAACbtRcR/87K6mwLH2u0hY+xKb0vzTf3f4epc/Bq09QBUmuGfgzD0A/oMyX6nlGcBwAAAABary7ySUT8kjoHsBV3JfqHD6P2U4cCAAA25mPzvf9GTqDKymoU85J+F/W+NN8wMKGbpqkDtIHyPOyUx0r01i07RnEeAAAAAOiEpjz/OXUOYKvuHkb9v6yszrOyOkkdCAAA2IjDmJ9ANd7A3z3awN+5DbtSmo/o7n+jXfalKYwTf56YOQzledgl1i07SnEeAAAAAOiM5oGx8jzspvcR8e+srGZZWZ05EhkAAHrp0zqnzzf3Dcfr+Lu27JddKc13/ESAXTZNHaBtmvK80xNgN92tW94065YbOUWH9VCcBwAAAAA6pXlw/CV1DiCZg4j4GPMjkadNyQIAAOiPdU6f71qJ9TYi/t6curcrRqkDsJTz1AHaqPnc/SV1DiCZvZivW/7RbAQcZWW1nzoUP1KcBwAAAAC6aBQRV6lDAMkdR8TvzRT6sSn0AADQK5+a7/WHK/wdozVl2YaLiBjURT5NHWRbmom8XTwRYNdd1UU+Sx2irZTngcZhRPweEbOsrCam0LeH4jwAAAAA0Dl1kd9ExDCU54G5g4j4FPMp9JMVizUAAEB7HETEf7KyOn/tRtnmdKq9TYRas9uI+Gdd5MNmvWOXdO1EAOYmqQO0nfI88MBeRHyIB1PoE+fZeYrzAAAAAEAnKc8DC3yIebHGgygAAOiP9xFx2Zw0tf/CPzPaYJ51+RIRR3WRn6UOsm3NRogPqXOwlPPUAbpAeR54xGHMT8+8cXpmOorzAAAAAEBnKc8DT7h7EDV7ZbkGAABop72YnzQ1e26TbFNEO95CpmVdR8Tf6yI/qYt8ljpMIqbNd9PVDv+bfTXleWCBu+9p7k7PHCTOs1MU5wEAAACATlOeB55xEPflmjMPogAAoPP24n6T7GjB72lrKfs6In6pi3xQF/k0dZhUmo3No9Q5WMokdYCuUZ4HnvEh5gX6aVZWw9RhdoHiPAAAAADQeXWR39RFfhQRn1NnAVprLyI+hklOAADQFwexuEB/kyDPUy7ivjA/SR2mBU5jfo9G95ynDtBFzef93yPiNnEUoL2OI+I/TYF+lDpMnynOAwAAAAC9URf5KJTngefdTXJSoAcAgO57WKAfRkTURT6OiL9GxK8xn/Kewm3M1yj+Vhf5UGF+rrkHa+uJADztoi7yWeoQXdWcMjEM5Xngacfx/Mk6rEBxHgAAAADolaY8/2vqHEAnOAoZAAD64yAiBnc/qYt8Vhf5uC7yQUT8LSL+FZsv0V/HvCz/j7rI9+siH9VFfrnhj9k14zBtvqsmqQN0XfP1YBjpNvQA3fHUyTqsQHEeAAAAAOidZrLcL6lzAJ3x8CjkYeowAADAUm4j4vyxC3WRX9ZFftqU6P8a8zWDzxFxteLHvGj+nl8i4q91kQ+asvyjOXZdM23+Q+ocLM2/6zVoyvNHsfrXH2A3KNCv2V9SBwAAAAAA2IS6yCdZWc1i/lDPJDPgJe4K9J8jYuwIegAA6JRJXeQ3z/2m5vv8STyYnt0UugcRsR/zQusi0+b/ztwvLGWSOgBL+/ySzy9epi7ym2bj/iQi3qdNA3TEXYF+FPN1y2naON2lOA8AAAAA9FZd5NMHD6EO06YBOuRDRHxQoAcAgE45W/YPNt/zz5qfmqq9Ac36zHHqHCxtkjpA3zQbEU6yspqEkxiAl7sb/HEREafNKRa8wv+kDgAAAAAAsEnNwvEwIr4kjgJ0z4eI+N+srM6ystpPHQYAAFjowobX1pukDsDSrk023py6yEcR8UvqHEDnHEfEH1lZTZqTc3ghxXkAAAAAoPfqIr+pi/wkIn5NnQXopI8RMcvKapw6CAAA8KhJ6gAs1txLHaTOwdKWPs2Bl6mLfBIRf4uI28RRgO75EBGXWVmNDf54GcV5AAAAAGBn1EU+joh/hIdQwOvtRcSnrKxmWVmdpA4DAAD86bopndJCzRTcT6lzsJJJ6gC7oDk18ygirlJnATpnL+bvtZdZWY0SZ2k9xXkAAAAAYKfURX4eHkIByzuIiH9nZTXNyuoodRgAAECpt+UmqQOwks91kd+kDrEr6iKfRcQwIj6nTQJ01EFE/G7d8mmK8wAAAADAzqmLfFYX+VFE/Ct1FqCzjiPij6yszhyDDAAASU1SB+BxWVmdxvzeie6apA6wa+oiv6mLfBQRv4RTM4HlWLd8guI8AAAAALCz6iI/jYh/hIdQwPI+RsTMMcgAAJDEl2ZCMy3TTLodp87BSq7qIp+mDrGr6iKfxHz6/HXaJECHWbd8hOI8AAAAALDT6iI/j4hBRFwkjgJ01144BhkAAFI4Sx2AhSYxv1eiu3x+JVYX+WVEHEXE59RZgM56uG45SB2mDRTnAQAAAICd1xyBPIyIf4bp88Dy7o5BHqcOAgAAO+DaNOx2ysrqLCIOU+dgJdfNxHMSa9YtRxHxS1i3BJZ3HBH/a91ScR4AAAAA4E91kZ/FfIrTVeosQKd9ysrqMiurYeog/MDXdgCAfjENu4WysjqJiI+pc7CySeoA/KjZyGDdEljV3brlzp6aqTgPAAAAAPBAXeSzusiPIuLXMMUJWN5hRPwnK6uzrKz2U4chIiJuUgcAAGCtJqkD8KOsrAbhv0sf3IaNKa3007olwLIOY4dPzVScBwAAAAB4RF3k45hPcbpIHAXoto8RMd3lKU4AALABn+sitzGyfc4jYi91CFZ25vOr3Zp1y7+F6fPAanZy+rziPAAAAADAAs0Up2FE/DNMnweWt9NTnAAAYANMw26ZrKwmMb/3odtMm++IusgvTZ8H1uBu3fI0dZBtUZwHAAAAAHhGXeRnETGIiC+JowDddjfFaZA6CAAAdNhVXeSXqUNwrynbfUidg7U4N22+W0yfB9bkt6yspruwbqk4DwAAAADwAnWR39RFfhIRf4+I69R5gM46jIjLrKxGqYMAAEBHmYbdIs29zW+pc7A249QBeD3T54E1OY75uuVJ6iCbpDgPAAAAAPAKdZFP6yIfxPxB1G3iOEA37UXE71lZTbKy2k8dBgAAOuQ2Is5Th2AuK6ujsJGhTz7XRT5LHYLlNdPn/xoRF4mjAN21FxH/zsqqt+/vivMAAAAAAEtoHkQdRcSXxFGA7voQEdOmbAIAADzvvC7ym9Qh+LM0P415wY7uuw3T5nuhLvJZXeTDiPhHODUTWN7HrKwus7IapA6yborzAAAAAABLah5EnUTE3yPiKnUeoJMOY16e7/URyAAAsCbj1AGIaE7OOg+l+T45M22+X+oiP4/50I9fU2cBOuswIi77tm6pOA8AAAAAsKK6yKd1kR9FxC8xn9AF8Bq9PwIZAADW4EKxN72mND+NiIPEUVif24hwP9pDdZHfNKdm/jUiLhLHAbrpbt1ynDrIuijOAwAAAACsSV3kk4gYhElOwHI+ZmV13hRRAACAH01SB9h1D0rzh4mjsF5ndZHfpA7B5jSnZg5jfmrmdeI4QDd96su6peI8AAAAAMAa/TTJ6XPiOED3vI+IaVZWR6mDAABAi9w2m9VJRGm+t67DtPmd0ZyaOYj5qZkK9MBr9WLdUnEeAAAAAGADmklOo4j4WzgKGXidw+jBQygAAFgjxd6ElOZ7bWza/O5pNiIdxfzUzNu0aYCO6fy6peI8AAAAAMAG1UV++eAoZAV64KX2IuKPrKxGqYMAAEALTFIH2FVNMW4aSvN9dOUkh9314NTMQSjQA6/T6XVLxXkAAAAAgC1ojkIexrxA7yhk4KV+z8rqNHUIAABI6Etd5LPUIXaR0nzvuddEgR5Yxe9ZWY1Th3gtxXkAAAAAgC1qCvSDiPglFOiBl/ktK6tJ6hAAAJDIJHWAXZSV1UnMS/N7iaOwGV/qIp+mDkF7KNADS/rUtXVLxXkAAAAAgATqIp8o0AOv8KFrD6EAAGANrusiP08dYtdkZTWKiH+H0nxf3YZp8yygQA8s4UNWVpOsrPZTB3kJxXkAAAAAgIR+KtBfJY4DtJvyPAAAu+YsdYBd09xz/J46Bxt1Vhf5LHUI2u2RAr3BH8BTPkTEtAvlecV5AAAAAIAWaAr0RxHx94i4SJ0HaK0PWVldduEhFAAArMEkdYBdkZXVflZW05gX3+ivq6YMDS9yV6B3cibwAofRgfK84jwAAAAAQIvURT6ti3wYEX+LiM+J4wDt1ImHUAAAsKLPdZHfpA6xC7KyOoqIWUQcJ47C5p2mDkB3PTg58+8R8SVxHKCdWr9uqTgPAAAAANBCdZFf1kU+ioi/xvw45Nu0iYCWaf1DKAAAWNEkdYBdkJXVOCL+iIi9xFHYvH/VRT5NHYLuawZ/nMR83fJfYd0S+FGr1y3/kjoAAAAAAACL1UU+i4hxRIyzshrFfDLYYcJIQHscRsQ0Io4S5wAAgHW7UvDdrKbMdh6mzO+K65ivL8HaNOuWpxFxat0S+MldeX7YthOETJwHAAAAAOiI5jjko4j4W0R8DtOcgIjDrKwmqUMAAMCanaUO0GdZWZ1ExCyU5nfJqG3FRfrFuiXwiFZOnlecBwAAAADomLrIL+siH0XEICJ+iYirpIGA1D4ozwMA0CO3MZ+EzpplZbWfldV5RPw7IvZS52Fr/uUEB7blbt2yLvL9mK9bfkmdCUiqdeV5xXkAAAAAgI6qi/zmwTSnv0bEv2J+9Dawe5TnAQDoi3OTsdcvK6tRzKfMv0+bhC27johx6hDspmbd8iTm65b/DOuWsKsOI2KaOsQdxXkAAAAAgB6oi3xWF/lpXeSDiPhHOBIZdtGHrKxOU4cAAIAVjVMH6JOsrAZZWU0j4vcwZX4XjWxEIbVm3fKsWbf8Wxj+AbvosC1DP/6SOgAAAAAAAOtVF/l5NMfaNxPlTsJEOdgVv2VldVMX+SR1EAAAWMJFXeSz1CH6ICur/ZhvQviYOArp/FoX+TR1CHioLvLLiDiNiNOsrI4iYhTztcuDlLmArfiQlVXURT5KGcLEeQAAAACAHntwJPL/jYhfIuJL4kjA5v3ePHwGAICumaQO0AfNSVSzUJrfZVd1kY9Th4Cn1EV++eAETZPoYTd8aIb9JGPiPAAAAADADmiO5Z5ExKSZOncSJtFDn02zsjoyrRMAgA65dXLSapoi2jhMbt51tzFf84HOeGQS/d3a5WHSYMAm/N6cmHme4oMrzgMAAAAA7JhHSvTDuH8YtZcuGbBGexFxnpXVsPmcBwCAtjtLHaCrFOb5ycgmarqsKdFfRsQ4K6tB3K9dDsPaJfTFpFm3vNz2B1acBwAAAADYYU2h9rx5RVZWw7gv0XvgDt12GPPy0ShxDgAAeIlJ6gBdozDPI/6VaoIvbEKzCWTSvCIrq7sCvbVL6La7oR9H2x76oTgPAAAAAMCf6iKfRsQ05sciD+K+RH+cLhWwgg9ZWV3WRW56JwAAbfbFhOyXaU6OO435BlmlUR66qov8NHUI2KRmY8h5/Lh2OYyI9wljAcs5iPnn83CbH1RxHgAAAACARzWlhbOIOGsezA/j/mGUh/PQHb815flp6iAAALDAoJmefr7tqaNdkZXVUcwL8x9SZ6GVbmPLxUNI7eHaZcQPJ2kOY34KH9B+x1lZnW1z45fiPAAAAAAAz2qKC3cTne4e2A/DRCfoivOsrAZKSAAAtNRhRPweEb9nZXURzf3nrk+hfzBN+TRsYOdpQ/d77LoHJ2nenc5xV6Ifhq+h0GYfs7KaNidKbJziPAAAAAAAr1YX+WVEXIaJTtAVe5Hg6GMAAFjCcfP6LSurq7gv0V+mjbUdD8qeJ2GjOi/zy658fsBLNRtJJs3LEBBov0lWVkfb2DSpOA8AAAAAwMoemeg0jPsivYlO0A7HWVmd1kV+ljoIAAC80GHz+pSV1XXMS/Rbm0i6LQ8myw9DoZPX+VwX+SR1CGi7BUNA7l7HqXIBf7ob+nG06Q+kOA8AAAAAwFo1E53Om9ddAWAYjkaGNvitOfrYNEIAALrmICI+RsTHrKwiIr7EfAN3576/fbDhfBjzwrz7ZJbxpS7yUeoQ0EULhoDcvQQkMNQAACAASURBVJymCWkcZmV1Vhf56SY/iOI8AAAAAAAb1RyvOonHj0YexnyaDLA9k6yshs0mFwAA6Kr3zSuysrqN+SThafO6bNP3u8198N1rGEqZrO4qIkapQ0AfPDIExGmakM7HrKzOm80tG6E4DwAAAADAVj1yNLIiPWzXYUSMI2Kj05sAAGCL9iLiuHl9iojIyuo6ImYxL9LP7l7N5u6NyMpq2PxwGBGD5nW8qY/HzrqNCJuhYUOcpgnJTbKyOtrU+5ziPADAjnv75t1RROwvuLzKNUUXAADgRZ4p0r9PFgz6bePTmwAAILGD5vVDcT0rq4iIu1J9xPx+9Odi1uzB9Yj5/enPHj4rU45nW5TmYcseOU1zEIr0sEkHMf98O9nEX644DwDQMm/fvBvEfPrEY1a55mYNAADohEeK9MO4fxCljADrM4nFawkAANBnd6X6CPeZdMddaf4ydRDYZY8U6Z2mCev3Piur4SaGfijOAwA84+2bd/sxnxjxmGWvDUKRHQAA4EWaxfHp3c8fFOlPIuIwRSboiYOsrMZ1kY9TBwEAAOBZI6V5aJ9nTtMchiI9LGuSldXRuk9ZUZwHADrp7Zt3wycuL7r2XMld2QIAAKADHhTpx1lZ7cePD6Lc28HrfMrK6lz5AgAAoNV+qYv8PHUI4HmK9LA2BxExjojTdf6livMAwFq8ffPuKObl88cMn/ijT11zLCIAAABPaqbNnDevyMpqED8+iHLaFzzvLJ5eowEAACCdX+oin6QOASxHkR5W8jErq8k6h34ozgNATz1TZF/lmm/YAQAAaK26yGcRMWlePz+Iep8mFbTecVZWI0UMAACA1lGah555okh/EgZMwmMmMe+srYXiPABsyds37wYRMVhweZVrJucBAADAAo88iDqJ+yL9YbJg0D5nWVmdN6c4AAAAkJ7SPOyAR9Yvh3G/fqlIDxGH6xz6oTgPwM56++bdfizejbbstUEosgMAAEBr1UV+HhHnERFZWQ3i/iHUSThljd22FxGnETFOnAMAAACledhZdZFPI2J69/MHRfqTMAiE3bW2oR+K8wC0yts374ZPXF507bmSu28aAQAAgP9SF/ks5se8TiL+PBb5biK9aU7sok9ZWU2azw0AAADSUJoH/vSgSD/Oymo/7geBDEMnit2xtqEfivMAPOntm3dHMS+fP2b4xB996poHzwAAAEDrPDgWOX56CHUSTphjd4wjYpQ4AwAAwC66jYhTpXlgkWba9sMTNffjfhDIMKxh0m9rGfqhOA/QMc8U2Ve55ihyAID++Gc0pT8AYDk/PYQ6zcpqEPcPod6nSwYb96F5ADVNHWQDLsNQDwAAoJ1uI2LYbOoHeJFmDXMS9ydqDuLHIr0+GH0zjhWHfijOA6zo7Zt3g4gYLLi8yjU7AAEAWNZlT4tOAJBMM8XmrHlFVlbDuH8I5Uhk+mYcT58o2VU3qQMAAAA8QmkeWIsFa5jDmK9jWsOkD1Ye+qE4D/TO2zfv9mM+Qf0xy14bhCI7AAAAAI1mYX4a8eckp2HcF+lNcqLrjrOyGtqMCQAAsHFXEXHSlF0B1urBGuY4K6v9+HENUxeOrhrHCkM/FOeBrXj75t3wicuLrj1XcrcLDgAAAIDkmofbk7g/Evko5g+gTHKiy8bRz6nzAAAAbXER89K807GAjWu+1pw3r7s1zGHM1zCP0yWDV1tp6IfiPOyot2/eHcW8fP6Y4RN/9Klr3kABAAAA2HnN0eqXcT/J6W6K00mYRk93mDoPAACwOZ/rIh+lDgHsrgdrmGem0dNB41hy6IfiPLTEM0X2Va55EAcAAAAAiTSTnCZxP41+GPcPoEyjp+3GYeo8AADAuv2zLvKz1CEA7iyYRu9ETdps6aEfivOwwNs37wYRMVhweZVrdmMBAAAAwI5qFvKnERFZWQ3ifpLT+1SZ4AmmzgMAAKzPbUScuMcC2m7BiZrWMGmbUTRr7a+hOE9nvH3zbj/mE9Qfs+y1QSiyAwAAAAAJ1EU+ix+n0d89gBqGdUvaYxymzgMAAKzqKual+VnqIACv8ciJmndrmCcRsZcsGER8yMpq/Nr3VsV5WuPtm3fTmJfcHe0BAAAAAOycusgdh0wbmToPAACwms8RcdqUTwE67ac1TCV6UhvHfPL8iynO0ybHqQMAAAAAALTBT8chD+J+Er3jkEnhNJY49hgAAGDH3ca8MD9JHQRgE5ToaYGTrKz2X7M5TXEeAAAAAABarDlq9iwizrKy2o/7B1DD8BCK7XifldXgtcceAwAA7LCriBg1G+MBek+JnkT2Yj70Y/zSP6A4DwAAAAAAHdFMzpk0Lw+h2KZxvPLYYwAAgB31r4gYv2b6LUCf3JXofxoC4iRNNmUUivMAAAAAANB/JjmxRa8+9hgAAGDH3MZ8yvx56iAAbfBwCEhToh81r8N0qeihg6ysTl76/qs4DwAAAAAAPaBEz4btxfzB5lniHAAAAG30JealeZuNAR7RfH08i4izrKyOIuI0rF2yPqNo1safozgPAAAAAAA9o0TPhpyG4jwAAMBDtxExrovcvRLAC9VFfhnzonNkZTVqfnycLhE98D4rq0Fd5LPnfuP/bCEMAAAAAACQSF3k53WRj+oi34+If0TE55g/2IfXOmg2YgAAABBxERFHSvMAy6uLfFIX+TAi/hoR/wrrlizvReuWivMAAAAAALAj7kr0ETGIiF9ifpQ8vMYodQAAAIDEbiPiH3WRD18y2RaA59VFPquL/DTu1y2v0iaig05f8psU5wEAAAAAYMfURX7TTHM6iYj/GxH/DA+jeJn3WVkNUocAAABI5HNEDOoiP08dBKCPHqxbHkXE38PgD17uICuro+d+01+2kQQAAAAAAGinushvIuIsIs6aQvRpzI+1PUiZi1Y7ifm/GQAAgF1xFRGndZFPUwcB2BXN19zpgzXLUUTsJYxE+43imcnzJs4DAAAAAAARcX8kcl3kg4j4R0RcJI5EO73o2GMAAIAeuI2If9ZFfqQ0D5DG3ZplRAwi4teYf22Gx4ye+w2K8wAAAAAAwH+pi/y8LvJhRPw15kfRw50XHXsMAADQcZ8jYlAXuRO3AFqgLvKbusjHdZHvR8QvEXGdOhOts5eV1clTv0FxHgAAAAAAWKiZ6DSKeYHeRCfujFIHAAAA2JCLiPhbXeSjushvUocB4L/VRT5pTs1UoOdnTxbn/7KtFAAAsAHXETFbcG32yLVFvxcAAIBn1EU+i4hxVlZnETGOiI9JA5HaSUScpg4BAACwRlcRcVoX+TR1EABepi7ySURMsrIaxXzN8iBlHlpBcR4AgK25jYjLBddm8bqS+5/Xvn3/uugaAAAAW9ZM2zttCvRnEfE+cSTSOMjK6qQu8vPUQQAAAFZ0HRHjpnwJQAcp0PPAXlZWR3WRP9pfUpwHAOi/iwW/fhOLS+5PXvv2/euiawAAAOyIZgL9SVZWw5gX6A+TBiKFk4hQnAcAALpKYR6gZx4U6E9jXqDfSxqIVEax4LRMxXkAgO1bVGSPiJguc+3b969P/TkAAADYmOYI+6OsrMYR8SltGrZsmDoAAADAEhTmAXquLvKzrKwmMS/Pf0ybhgROQnEeAGChq5hPWH/M5bLXvn3/uugaAAAA9E5d5OOsrM4jYhKmz++Kg6eOPQYAAGgZhXmAHVIX+U1EnDYF+klYs9wlB1lZDZpTU3+gOA8AtNF1RMwWXJsteU2RHQAAADasKVAfZWV1FiY57YpRLJjeBEAy+6kDAEDLXETERGEeYDc9WLMchxMzd8lJRJz9/IuK8wDAS9zGfLr6Y26euDaLJ0ru375/XXQNAAAA6LC6yE+zsprGfJLTXto0bNgwdYBXMBkf2BVHqQMAQEt8iYizusinqYMAkJ4TM3fOMBTnAaBXLp64Nl3w60+V3G++ff/q4SEAAACwFnWRn2dlNQwPovrucNGxxy3kNMJuGcbidU4AAFjkNiLOI2LckfsUALaoLvLLB2uW79OmYcOGj/2i4jwArM9VLH74Nn3izy289u3716f+HAAAAECrPXgQNQ3l+T4bxvxhIwAAQCrXETGOiPO6yG2aBWCh5n3iJCurcUR8ShyHzdnLyuqoLvIfBskqzgPQZ08V2S+Xvfbt+1c32QAAAAAvVBf5jSlOvXcSivMAAEAanyNiUhf5NHUQALqlLvJxVlaziPg9dRY2ZhjzLuCfFOcB2KbriJgtuDZb9tq3718XXQMAAACgBR5McZpExIfEcVi/YeoAAPxgmDoAAGzYVcw3705MlwdgFXWRT5ry/HlE7CWOw/oNI+Ls4S8ozgPsttv4aUfVAzfLXvv2/euiawAAAADssLrIR1lZ7YfJ833z6LHHsKJh6gAAALTKbdyX5d17ALA2dZFPmxMzp6E83zfDn39BcR6gfS6euDZd4poiOwAAAABtMor5WtZh2his2TAWD9sAAABYxm3MJwCf10V+njoMAP1VF/llU57/I3UW1uq/Bn4ozgM87yrmE9Yfc/nEtemiv/Db968LrwEAAABAn9VFftM8hLqMiIPEcVifYfx07DEAyRynDgAAK1CWByCJpjz/S0T8njoLa3UUDwZ+KM4DfXUR/11cf6rkfvnt+9dF1wAAAACANWrK8yfh+OM+GaYOQO8o/gIA7I7rmJflp8ryAKRUF/kkK6v9iPgtdRbWZhgRk7ufKM4Dr3Ubi4/bnTWviPlxyymnRU2/ff86TvjxAQAAAIAnNBOcTsMEp77Yy8pqUBf5LHUQgF2WldVR6gwA8EIXcV+WX9RDAYCtq4v8rLm3+pA6C2vxw32y4jz0x8WCX7+JlxXdf7bSBPa3b94NwzHLAAAAAMATmglOJxHxPnUW1uIoFq85w6tlZXWkRAWvtp86AAAscBXzU8emMS/LL91JAYAtOI35Wtdh6iCs7If/horzsFmtKrMDAAAAALTQKOZrontpY7AGw5hPjIR1UQCG1zNxvnsWPVMG6DpFeQA6qy7ym6ysRjF/H7Nu2XFZWQ3rIp9GKM7DQ19CmR0AAAAAYKsePIT6d+osrExZk3U7ivkDauDlbDgBIIXrmHduLmNekp+mjQMAq6uL/DIrq3FE/JY6Cysb3P1AcR4a375/PUmdAQAAAABgF9VFfp6V1UVEHKfOwkoU51k3BWB4vWHqAAD03g8l+Yi4NE0egL6qi/wsK6uTsG7ZdYO7HyjOAwAAAAAAbXAaEX+kDsFK9rKyGtRFPksdhN4Ypg4AHTRIHQCA3riOiFnMy/GziJiZJA/AjrJu2X3Dux8ozgMAAAAAAMk1Rx9/jogPqbOwkkHMSzWwDibOw+sdpA4AQKdcRcRNzKfH38S8JH9TF/llylAA0CbWLXthcPcDxXkAAAAAAKAtxuEBVNcNY162aaOb1AF4tcPUAaBLsrIaps4AQGvcTYuPuC/F3xXkw+R4AHi104g4iYi91EFYyp+bzBXnAQAAAACAVqiLfJaV1ZeIeJ86C0sbpA6wSDMdLHUMXikrqyMTT+HFBqkDsJRp6gBAKz0svj80feLns7rIH/szAMCK6iK/ycrqLCI+pc7CcrKyGtZFPlWcBwAAAAAA2uQsFOe7bJA6AL1zFM1kVOBZR6kDAJ32a9xPJr+si7w3p/VkZXUUEfupc/xEyR0Auucs5pPnTZ3vpv0IE+cBAAAAAIAWqYt8mpXVdTw4PpdOUdpk3fybgpfz+QIsrS7yceoMm+L0GgBgHZqp8+cR8SF1FpZyFBHn/5M6BQAAAAAAwE/OUgdgaSZusW6KwPByx6kDsJTeTPUGAIAdME4dgKXtR0QozgMAAAAAAG1znjoAy8vKapg6A72iCAwv4Gtvp5mEDQAAHVEX+SwiLlLnYClHEYrzAAAAAABAyzQPoK5S56CX/LvqoKysTJ2H5w1TBwAAANgRk9QBWJ7iPAAAAAAA0EamzndXm0vON6kDsJRh6gDQAW3+2svTvDcBAEC3WLfspuOIiL+kTgEAAAAApJeV1SgiBolj8LRJM4UbdsU0Ij6lDsFS9lMHoHeGEXGWOgS03DB1AJZTF/ll6gwA0GbNCVTuM1uqLvJp6gywbXWR32Rl9SUi3qfOwuspzgMAAAAAEfPSvIJqu00jYpY4A2xNXeTTrKxSx2A5bS40mOrbTcPUAaDNsrIaRsRe6hwAABtyGhEfUofgcVlZ/V15nh01DcX5zsnKavA/qUMAAAAAAAAscJE6AEs5Sh3gCab6dtNeM2USeNwwdQCWdpU6AAB0wCx1AJ40TB0AEjlPHYClKM4DAAAAABFhAm8XDFMHgASUnIE7w9QBoMVOUgdgae7DAICuG6YOACnURT6LiNvUOXg9xXkAAAAAIEI5FWgnX5tYNwXF7hqlDgBtlJXVICIOU+dgabPUAQCgA9zHtdtx6gCQ0DR1AF7NxHkAAAAAgI4YpA4ACcxSB2ApbX5objNGdx1mZbWfOgS00DB1AFYySx0AADrAfVzLZWU1TJ0BEvH1qXsU5wEAAACAiFDY6IJB6gCwbXWRT1NnAFrlJHUAaCGfF91mgi4A0AfD1AEgEcX5DlKcBwAAAACiLvJZ6gw8a5A6AEAPeKDZbQrC8EBzCsP71DlYifclAHiejWbtN0wdABKZpQ7A6ynOAwAAAAB0w0HqAJDIReoA9Edd5AoX3fa+KQoDczaTdN8sdQAAaLu6yG00a7/j1AEgBV+fuklxHgAAAAC4c5s6AE/LymqYOgNAD1ynDsBKFIXh3ih1AFbj5C8AoC+sWwIdcaQ4DwAAAADcMR2l/QapAwC8RMungs9SB2Alo9QBoA2yshqEyZ5dd5U6AAB0iA3Q7TdMHQAScVpmt+wrzgMAAAAAdMcgdQBIYJY6AEs5Sh3gCbPUAVjJcVMYhl03Sh2Ald2kDgAAHTJLHYBnDVMHAHgJxXkAAAAA4I7iRvsNUweABGapA9A7s9QBWNkodQBogVHqAKxsmjoAAMAaOQ0J6ATFeQAAAADgzmXqADyrzROcAbpiljoAKxulDgApZWV1EhEHqXOwslnqAADQIdPUAXheVlbD1BkAnqM4DwAAAADQHXtZWQ1ShwDouFnqAKzsoCkOw64apQ7AWsxSBwAAWLNh6gAAz1GcBwAAAADu3KQOwIsMUwcA6DgnrPTDaeoAkEKzifJ96hysri7yaeoMANAhs9QBeJFh6gAAz1GcBwAAAADuKBJ2wzB1ANiy/dQBWEprN2PVRd7abLzKsVNY2FHj1AFYi+vUAQCgY2apA/Aix1lZWccBWk1xHgAAAACgW4apA8CWHaUOwOvVRd72zVgXqQOwFuPUAWCbmhLSSeocrEXb3ycBoG1sgO6OYeoAsGU2i3SM4jwAAAAAcGeWOgAvcmDCLsDKZqkDsBYfvCeyY04jYi91CNZCcR4AXqEDm7O5N0wdALbsMHUAXkdxHgAAAACIiIi6yGepM/BiJo0CrGaWOgBrM04dALahmTZ/mjoHazNNHQAAYEOsWwKtpjgPAAAAANA9w9QBYIuOUgegl6apA7A2ps6zK0yb7xdTcwHg9S5SB+BFDrKyspbDTsjKapg6A6+nOA8AAAAAPOQBVDe8b6aOwi5QEmQTZqkDsFbj1AFgk0yb753rushvUocAANigYeoAsCXW6LtnqjgPAAAAANBNjj2m90yR7qzr1AGeUxf5LCJuU+dgbUydp+9Mm+8X0+YBYDnT1AF4sVHqALAlTlfoIMV5AAAAAOAhJY7uUJxnFwxSB2Aps9QBXsh7Xr+cpQ4Am9BsCjFtvl+8/wAAfXdoczM7QnG+gxTnAQAAAICHblIH4MXeZ2XlKFj6bpg6AL02TR2AtXqfldUwdQjYgHGYNt8309QBAKCjpqkD8Cqj1AFgC4apA/B6ivMAAAAAwEOmH3bLKHUA2DBTm7qpK5uwvOf1j6nz9EqzGeRD6hysV13k09QZAKCjunKvydwodQDYpOZUBZucu2emOA8AAAAAPOQBVLecpg4AG6Y4301dKaR3JScvd5iVlfdG+sRmkP65SB0AALqqLnL3cN1y4FQwem6YOgBLUZwHAAAAAO6Zftg5HkDRW83UpoPUOeivushnEXGbOgdrN26+fkCnNZtADlPnYO2mqQMAQMddpw7Aq4xSB4ANOkkdgOUozgMAAAAAP1Mi7BaTdemrYeoALG2WOsArTFMHYO32wpRuOq7Z/DFOHIPNMCkXAFYzSx2AV/lgYzM9NkwdgKXcKM4DAAAAAD9T5uiW9x5A0VPD1AFY2ix1gFfwntdP77OyMvmNLjuL+SYQ+meaOgAAdNw0dQBebZQ6AKxbs+bgnq2D6iK/VJwHAAAAAH6mRNg949QBYAOUXrtrljrAK0xTB2BjJjaW0UVZWY0i4n3qHGzERV3kN6lDAEDHzVIH4NVOs7LaTx0C1sy6ZYcpzgMAAAAAP5ulDsCrOfaYXsnKahimNnVWXeSz1Bleqi7yaeoMbMxeRExSh4DXaL6fO0udg42Zpg4AAD0wSx2AV9uLiNPUIWBdmo0givPddBWhOA8AAAAA/DcT57tpnDoArNEodQCWdp06wBIuUgdgY46zshqnDgGvMAkbx/psmjoAAHSdzc+dZeo8fXIS7tu66iZCcR4AAAAA+IkHUJ1l6jx9YmpTd81SB1jCNHUANupTc4oFtFqzyeM4dQ425tZ9FgCsTRc3bO86U+fpE/+Wu2sWoTgPAAAAADzOA6huOksdAFaVldUoTG3qsi6eWjJNHYCNOzfdkDZrNnd8Sp2DjZqmDgAAPdLF+05MnacHmnu3w9Q5WNosQnEeAAAAAHicB1Dd9N5UXXrA1KZum6UO8FomAO+EvVBapaWa8tB56hxsnP/GALA+1i27ydR5+sC/4W67jFCcBwAAAAAe5wFUd5k6T2eZ2tQLXX3/uEgdgI07zMpqkjoEPGIaTlrZBdPUAQCgR7p630nEp6ysBqlDwDKaf7vvU+dgJTcRivMAAAAAwOOmqQOwtMOsrEy+oavGqQOwslnqAEsyCXg3fPAeSZs0mzlsGOu/q7rIZ6lDAECPKM53m6EfdNU4dQBWc3fqpOI8AAAAAPAYD6C6bWx6E13TTJs/Tp2Dldx2uBg4TR2ArfktK6tR6hDQbOL4kDoHWzFJHQAA+qS577xNnYOlvc/K6iR1CHiNZt3S/Vu3/fm+oTgPAAAAAPyXushvIuIqdQ6WthcKOnTPOHUAVtbZTVd1kV9GxHXqHGzN71lZHaUOwe5qNm/8ljoHWzNNHQAAemiaOgArOcvKaj91CHiFceoArOzPdUvFeQAAAABgkc4WIImIiONmkim0XjNpzLT57uv6+8Y0dQC2aqo8TwrNv7vfU+dga66bzVkAwHp5f+22g1BEpiOsW/aG4jwAAAAA8Kxp6gCs7DelQNqumTB2ljoHa9H14sJ56gBs1V4oz7Nlzb+3aeocbJX3FgDYjGnqAKzsY1NIhtaybtkrs7sfKM4DAAAAAIt0vQDJ3MTRx7TcOOaTxui+Tr9v1EV+HhG3qXOwVcrzbM2D0vxe4ihs1yR1AADoo7rIp6kzsBbWLWm7cVi37AsT5wEAAACAp9VFfhkKhH1wGKbi0FJZWQ0j4mPqHKzFbfO+0XXT1AHYOuV5Nk5pfmdd9+S9EQDa6iJ1AFa2F07ooaWa+zjrlj3xcMOV4jwAAAAA8JRp6gCsxYesrE5Th4CHmolik9Q5WJu+FAM9sN9NyvNsjNL8TvOeAv8/e/eT29aVbg973UI1ScgAB2D9RmDVCMzqEGAruiMIM4JS+gTCAOyXMoLQI7hKiwA7oUZwpRFcagDCZ0Hq19c4hxH9X5JJ7kPyeQBCsuLEq1SWjs4+a78bYLP25T700L3tjaeGftAo1i33zvXqLxTnAQAAAICvmZcOwNr8uzeenpYOASsmcdTxPpmXDrAmSo6Ha1me75YOwv5Qmj94CmAAsFnz0gFYm3/1xtNB6RCwYpTqJFf2wwcbrRTnAQAAAICvmZcOwFpNTNOlCeoTEH4onYO1mpcOsA6zYf99kj9K56CYoyR/KmywDvUmjHmU5g/V9WzYX5QOAQD7bDbs2/i8X86tW9IE9ZrAv0rnYK0U5wEAAACAp5kN+1dJbkrnYG2W03Q9hKKYukj479I5WLurb/+WnaF8we+98XRUOgS7qy5a/Bml+UM2KR0AAA7EZekArM1y3fK4dBAOV71u7uSo/TNf/YXiPAAAAADwLfPSAVir5UOoV6WDcHjqh09Kyfvnup7Uvhdmw/4kyV3pHBT3S288nbhe8lz1povfS+eguEnpAABwIOalA7BWR0ku3IdRQv337iI2QO+dekDUXxTnAQAAAIBvmZcOwNopz7N19d+3eTx82kfz0gE2wAYPkuTHmHjIE/XG01e98XSS5JfSWSju3T5tKAOAhnPvtn/exLolW7aybvm6cBTW75OTSRTnAQAAAIBv8QBqP3kIxdYoze+9eekAG+Dax9KbJFe98fS0dBCaq95cMU+12QJcQwBgS+opwk4M2z/WLdm281R/79g/848/oDgPAAAAAHxVPS3xk6kc7IU3SRa98fSkdBD210pp3sOnPTUb9veuIFj/b7opnYPGOEryP73x9Lx0EJqn3lRxFdc5Kjf7eF0EgIZz7d1PyvNsRX1ymE3Q+2v+8QcU5wEAAACAp/AAan8dpXoIpTzP2inNH4R93lg1KR2AxvlXbzy9qqeLQ+rNFP8TJ6rwaFI6AAAcIOuW+0t5no1Smt9/s2F//vHHFOcBAAAAgKfwAGq/Lcvzp6WDsD/qzRgm8O6/fb4+TEoHoJHeJLnqjadnpYNQTm88PemNp1dJ/lU6C40zKR0AAA6N01723vIezNAP1kpp/iB8duCH4jwAAAAA8E2zYX+R5KZ0DjbqKMn/KAKyDvXDzHmS14WjsHnz0gE2pb727fNEfV7uKMm/e+PphcmHh6f+WWkeG8P41Lv62gEAbN8fpQOwUa9TDf3olg7C7uuNp6+ULlS0GQAAIABJREFU5g/GZzdWKc4DAAAAAE9letNh+HdvPJ0oAvJSvfF0kOR/UxVL2W83s2H/qnSIDZuUDkCj/ZBkYdPZYeiNp8e98XSe5N9xjePzJqUDAMABs265/46S/FmvO8GL1Gve8yjNH4r55z6oOA8AAAAAPNWkdAC25sdUU5wcgcyz9MbT8yS/l87B1sxLB9i02bA/iRNX+Lrl9HnXzT3WG09HSf4vydvCUWiu69mwPy8dAgAOmOL84fjd0A9eor5nv4rTww7FFwd+KM4DAAAAAE9SLzIqDx6ON6nK84PSQWi+egrvVZJ/lc7CVh1KMWFSOgA74W2S/+2NpyMFjv3RG0+7vfF0keSX0llovPPSAQDgkM2G/fdJ3pXOwdYY+sGz1Gvc8ySvyyZhi+Zf+geK8wAAAADAcxxKSZLKUaopThdKgHxJbzw9jWlNh+huNuwfyjVBGZLn+CXJwsaz3VZvCJsn+TOKFXzbTX1CCQBQ1qHco1JZDv04Kx2E5uqNp69WTsg8Kp2HrfriNUFxHgAAAAB4DuXBw/RDkqveeNotHYTmqB88TZL8Tzx4OkQHU0gwuZAXWG48c+3cMXVhfpLk/1KdIgBPMSkdAABI6s3dd6VzsFVHSf7dG0/nvfH0uHQYmqU+kWAeJ2Qeoq8O/FCcBwAAAACebDbsL5Jcl85BEa+T/NkbTyemz1MXQa9SHY3NYTqY4nzNxjFe4k2qa+dcgb7Z6s1go7i28Xx3cY0AgCaZlA5AEW9TDf0wfZ4kSX1/979xQuahmn/tHyrOAwAAAADPNSkdgKJ+TLLojaeD0kHYvrpYeJHkz1SbKThMX53atI9mw/5VksvSOdhZb6NA30j1hPlRkkWSX+IEFZ7vvD6ZBABoBhvaDtdy+ryTvw5Ybzzt9sbTq1T3dxyur65bKs4DAAAAAM81KR2A4o6S/K4AeFjqqV2LJD8UjkJ5B1WaXzEqHYCdp0DfEHVhfpLk/6Iwz/eZlA4AADxyWiZ5PPlr0htPj0uHYTvqYR+TVMM+TJk/bHdRnAcAAAAA1qmeqPhH6Rw0wrIA6EHUHqsnNS2S/DuKhVQOsjg/G/bnUcBgPVYL9KelwxyS3nh62htP56kK8z8WjsPue1eX8wCAZjF1nqT6ef+qN56OeuPpq9Jh2Iy6MD9KNezDPR5JcvGtU8H+vq0kAAAAAMBemcTUaR79mOTH3nj6a5Lzby1Msxt64+lJqofNb0tnoVHuZsP+QRbna+dJfi8dgr3xNsnb3nh6k+rv1sQ1dP3qzX2D+vW6ZBb2zqh0AADgsy5S/Xxt8z9HqU6YOuuNp+exbrlXeuPpIL7W+dQ31y1NnAcAAAAAnq0uTd6UzkHj/JJkYZLTbuuNp8f10cb/G6V5PnXIpfnMhv1JXP9Yv9epTvX4/+pTXLqF8+y8eurgYGW6/C9Rmme9TJsHgIaqi9GT0jlolGWBfrlueVw4Dy+0nDBfn475e5Tm+dCTBn6YOA8AAAAAvNQk1QMHWLU6yekiyUipaDfUE+bP4lhjvs6R99WEYVPn2ZTlKS43qX7WmriOPk29ae+0fjkZiU0blQ4AAHzVeZJ/lQ5B4yzXLX/pjafvUk2gvyqciSdYOUnsLMryfNmTBn4ozgMAAAAAL3UexXm+7CiP5b93qYp/87KR+Jx6svEopsvzbTceKFdT53vj6SimV7NZr/NY6LhOVaK/UKL/UF2eOE3SjbI82/Obr0UAaLbZsL/ojaeXsdbBly3XLS9TrVtOCufhM+p1y7O43+NpnjTwQ3EeAAAAAHiR2bD/vi5Em07NtywfRF2nWry+qI/NppCVqbyjKP/ydKbNPxrF1Hm2502Sfyf5d30tnae6ls5Lhiqhvn5169dpXMPYvruYNg8Au2KU5M/SIWi8t0ne9sbT81TTqk2hL2xluvwg7vl4uuunfu0qzgMAAAAA3+M8ivM83ZtURdPz3nh6kar096TjU1mP3nh6kmpK02kca8zz+Xqt1VPnBzG9kO17U7/+1RtP71KV6OdJ5vtY7qivW8tXN9X/dijp3AZQANgNs2F/3htPb6J4y9Osnp55k2oNZLKP91lNtDLk4zSmy/MyTx74oTgPAAAAAM9UF3hePfX37/M00Nmwf+XYY17Ag6gtqqc0naYqzHtYzEv9MRv2F6VDNMwophdS1lGqQsEPSVIX6a/yWKa/2qWCb2887SY5zmNR3s+XNM1dnL4CALtmFKeF8Xyvk/wr1Ybl5drlQZ76tWm98XRZljech+9xl2cM/FCcBwAAgP3zZ288LZ0BWLHyNXmTqsQ02bNF9vMoNvFyHz+Imsck+u+2UpYfxHRe1mNSOkDT1NMLbR6jSY5S/X18m+SX5IMy/VWSxfJtqY0wKxtQl2+7qcryNnaxC852aTMKAPDXaWGj+HmTl1tdu1ye+nWR6tSvRcFcO8lkeTbk4jn3aorzAAAAALA9r/M4ZfuPJIN9KF7Mhv0Lxx6zJqtfI0nyR+qpuabRf1s9qfc0VQlRWZ51urGZ5YvOkvxv6RDwFatl+r/U19mbVGX6pLre5jPvf2yRquj+JasnMy0L8suPH30rLDTYzWzYn5QOAQC8yCT1xlL4Th+f+rUcAjKPIv0X1Ruol2V5a5ZswrNOBlOcBwAAAIAyfkj10Oa0cI51GcWxx6zf6oOo5USnq1QPoublYjVDXZRfvky8ZpMmpQM01WzYv+qNp+/iSHF20+s8bnxcvY4oFcGnBqUDAAAvdp5q07ONnKzbX0NAkk+K9FeHOgikLsp3V16+9tiky+d+rSnOAwAAAEA5P/TG05N9WEB37DFbsDrR6Zd6Uu51qiL9Io8PpHb+FIfPqR84rb4U5dmmZ01tOkBnqTbCeRAMsJ8ubdoEgN01G/bf98bT89ggyuZ9XKRPkstU65dX2cMyfW88Pc7jemU3Thtj+569bqk4DwAAAABlDVIV7vbBKKbOs11v8ni87y/JBw+kFh+/duG45HqK/KtUD5mO67eOMKakd/u6IWVdlDAA9t6gdAAA4LuZOk8pb7MyAOMzw0CuUq1bNr5QX69bHtevbpTkKe9mNuxfPPdfUpwHAAAAgLJOSgdYF1PnaZAPHkgt1Q+mkqpYvzRfef8qyWpB+P06HlrVD5WWlqX4j983QZ6mGpUOsAtmw/6oN54O4hoIsG9+3YXNlwDA19nwTMOsDgNJ8te65V0e1yeXa5Lz+u1Gh4L0xtPVdcqTPK5bLt8qyNNEo5f8S4rzAAAAAFDW3hTna6OYOk/zvf3C+59YKdvDIbpUFnyWQZI/S4cAYG1uUk2nBQD2g6nzNN1RHtcqf6jf/rXZY2Wdclmw/9j7L3x8tRS/6jgGALC7bmbD/uQl/6LiPAAAAACUtVcPakydB9gro9IBdsls2J/3xtM/8vhwG4DddjYb9t9/+7cBALvA1Hn2yGrB/mPWJDgUk5f+i39bYwgAAAAAgETREmAfXM6G/XnpEDtokGryGwC77Y/ZsH9ROgQAsHbncc8GsOvu8h2ngynOAwAAAABrVR+PeVM6BwDfZVQ6wC6qJxOPSucA4LvcJTkrHQIAWL/6ns11HmC3nX/P6WCK8wAAAADAJgxKBwDgxUyb/w6zYf88yWXpHAC82Gg27C9KhwAANsPQD4Cd9l3T5hPFeQAAAABgA+rCpdIgwG4alQ6wBwapHuQBsFsu6w1QAMB+G5QOAMCLjL5n2nyiOA8AAAAAbM6gdAAAns20+TWoJxWPCscA4Hnu4h4GAA6CoR8AO+lmHRudFecBAAAAgI2oS4O/lc4BwLOMSgfYF/WDPEUMgN0xqu9hAIDDMCgdAIBnGa3jP6I4DwAAAABs0ijV5EYAms+0+fUbxHUQYBdcrmNyIQCwOwz9ANgpl7Nhf7KO/5DiPAAAAACwMbNh/32Ss9I5AHgS36/XrC5iDArHAODr7uJ7NQAcqlFsdgbYBaN1/YcU5wEAAACgrOvSATatngJyWToHAF/1bjbsX5UOsY9mw/5FknelcwDwRYN6oxMAcGDqoR+D0jkA+Ko/1nlKpuI8AAAAAJT1vnSALTHFGKC57uL79KadJbkpHQKAT7yrNzgBAAeq/lnA0A+AZlr7uqXiPAAAAACwcfUU419L5wDgs87rKXtsSP35PS2dA4AP3MTGMQCgMkhVzgSgWc7XfUKY4jwAAAAAsBWzYX+U5Lp0DgA+cFN/f2bD6k1kP5fOAcBfTm0cAwCSpC5ljgrHAOBDG1m3VJwHAAAAALZpUDoAAB8YlA5wSGbD/nmSd6VzAJCf6w1NAABJ/rpfM/QDoDkGm/iPKs4DAAAAAFtTl1N+LZ0DgCTJu9mwPy8d4gCdRRkDoKR3dTEOAOBjgyR3pUMAsLl1S8V5AAAAAGCr6qM1FQYByrpLVeBmy2bD/vsoYwCUch3XPwDgC+qhH6PSOQAO3EbXLRXnAQAAAIASBlEYBChpUBe4KaAuYwxK5wA4MHdx/QMAvqE+meaydA6AA7bR+zbFeQAAAABg60xvAijqj9mwf1E6xKGr/z/4uXQOgAMyqO9DAAC+ZRBDPwBK2Pi6peI8AAAAAFBEPb3pj9I5AA7MXUw6b4z6WviudA6AA/CrTWMAwFPNhv1F3DsDbNtNtvC9V3EeAAAAAChpkGoxFIDt2OhRxzzfbNgfJLkunQNgj72bDfuj0iEAgN1Sb7qz0Rlge7aybqk4DwAAAAAUUy+CnpbOAXAg3pm221jdKM8DbMJ1krPSIQCAnXUW92oA2/DbbNifb+MPUpwHAAAAAIqaDftXSX4unQNgz91EcbCx6o1kgyR3haMA7JO7JF0nrQAAL+VeDWArrmfD/tbWLRXnAQAAAIDiZsP+eRx9DLBJp4qDzVZvJOtGIQNgHZTmAYC1qO/VBqVzAOypu2z5VGLFeQAAAACgKRx9DLAZP9cP+mm4+v+nrT4sBNhTp659AMC6zIb9iyS/lc4BsIcGs2F/sc0/UHEeAAAAAGiEehpkNybtAqzTH/WpHuyI2bA/T/JT6RwAO+yn+nspAMDazIb9sySXpXMA7JHf6o1JW6U4DwAAAAA0hvI8wFpdx3HyO2k27E+iPA/wEj/V30MBADbhNE7MBFiHy3pD0tYpzgMAAAAAjTIb9q+SFFkwBdgjd6mOOn5fOggvozwP8GzvlOYBgE2q77EHMfQD4HvcpNqIVITiPAAAAADQOHXh5efSOQB22KDeiMQOU54HeLJ3s2F/UDoEALD/6nvtYoVPgB13l+S05LAPxXk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- "text/plain": [ - "" - ] - }, - "execution_count": 21, - "metadata": { - "image/png": { - "width": 600 - } - }, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "from IPython.display import Image\n", "Image(filename='images/ESUPS-Logo.png', width=600)" @@ -220,16 +204,14 @@ "outputs": [], "source": [ "def graph_locations():\n", - " h = 0.1\n", - " l = -0.1\n", " warehouses = [['warehouses',\n", - " depot.latitude + np.random.uniform(low=l, high=h),\n", - " depot.longitude + np.random.uniform(low=l, high=h)] \n", + " depot.latitude,\n", + " depot.longitude] \n", " for depot in dataset.depots]\n", "\n", " disasters = [['disasters',\n", - " disaster_locations.latitude + np.random.uniform(low=l, high=h),\n", - " disaster_locations.longitude + np.random.uniform(low=l, high=h)] \n", + " disaster_locations.latitude,\n", + " disaster_locations.longitude] \n", " for disaster_locations in dataset.disaster_locations]\n", "\n", " dfw = pd.DataFrame(warehouses)\n", @@ -621,11 +603,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "The earlier version of the ESUPS dashboard, while not perfect, played a critical role in enabling the organization to quickly deliver a functional product that demonstrated tangible results to other non-profits. This initial version provided the essential tools needed to map stockpiles and showcase the value of the platform in real-world scenarios. By prioritizing functionality and speed, ESUPS was able to meet the immediate needs of its partners, proving the concept and gaining crucial buy-in from stakeholders.\n", + "Here is one of the earlier versions of the ESUPS dashboard; even in v1, it played a critical role in enabling the organization to quickly deliver a functional product that demonstrated tangible results to other non-profits. This initial version provided the essential tools needed to map stockpiles and showcase the value of the platform in real-world scenarios. By prioritizing functionality and speed, ESUPS was able to meet the immediate needs of its partners, proving the concept and gaining crucial buy-in from stakeholders.\n", "\n", - "The design, though basic, allowed for rapid deployment and iteration, proving that in the early stages of development, it's more important to have a working solution than to strive for perfection. The dashboard's ability to deliver key insights swiftly and effectively earned it significant merit, as it laid the groundwork for future improvements and set the stage for more advanced iterations. This approach underscores a vital lesson in development: sometimes, a good solution delivered quickly can be more valuable than a perfect one delivered too late.\n", - "\n", - "The initial success provided a strong foundation upon which ESUPS could build. The insights gained from this deployment informed the development of a new version, which addresses the limitations of the first while enhancing user experience and data communication. The new dashboard takes these lessons to heart, incorporating more advanced features and a more intuitive design, ensuring that ESUPS continues to meet the needs of its partners more effectively. Now, let’s explore how this new version improves upon the original and further advances ESUPS' mission." + "The lesson here: Start with the tools and skills available and always look for ways to improve. " ] }, { @@ -778,7 +758,7 @@ " #print('%s %g | total= %g | distance=%g' % (v.VarName, v.X,total[2],dis))\n", " b.append([v.VarName, v.X,total[2],dis])\n", "b=pd.DataFrame(b)\n", - "b.columns=['Var','amount','possible','distance' ]\n", + "b.columns=['Var','amount','possible','distance']\n", "b.sort_values('distance')" ] }, @@ -1054,34 +1034,6 @@ "##### Intermediate Problems" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "**Custom Die Game:**\n", - "Imagine a custom die with faces labeled 1, 2, 3, 4, 5, and 6. Each face has a different probability: \\(P(1) = 0.1\\), \\(P(2) = 0.2\\), \\(P(3) = 0.15\\), \\(P(4) = 0.25\\), \\(P(5) = 0.2\\), and \\(P(6) = 0.1\\). If the outcome of the die is 1, you win $2; for a 2, you win $4; for a 3, you win $6; for a 4, you lose $3; for a 5, you lose $7; and for a 6, you lose $5. What is the expected value of this game?" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Solution:\n", - "$$\n", - "\\begin{aligned}\n", - "E[X] &= (0.1 \\times 2) + (0.2 \\times 4) + (0.15 \\times 6) + (0.25 \\times -3) + (0.2 \\times -7) + (0.1 \\times -5)\n", - "\n", - "\\\\\n", - "E[X] &= (0.2) + (0.8) + (0.9) + (-0.75) + (-1.4) + (-0.5)\n", - "\\\\\n", - "\n", - "E[X] &= 1.9 - 2.65 = -0.75\n", - "\n", - "\\end{aligned}\n", - "$$\n", - "**Answer**:The expected value is $-\\$0.75$. On average, you lose $75$ cents per roll.\n" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -1122,23 +1074,6 @@ "**Answer**: The expected value is approximately \\$0.23. On average, you gain 23 cents per draw.\n" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "\n", - "##### Advanced Extension Problems \n", - "\n", - "5. **Roulette Game:**\n", - " You bet $1 on a single number in a game of American Roulette. If the ball lands on your number, you win $35; otherwise, you lose your bet. There are 38 slots on an American Roulette wheel (numbers 1-36, 0, and 00). What is the expected value of this bet?\n", - "\n", - "6. **Insurance Policy:**\n", - " An insurance company sells a one-year term life insurance policy for $500 to a healthy 30-year-old. The policy pays $100,000 in case of death within the year. The probability of a healthy 30-year-old dying within a year is 0.001. What is the expected profit for the insurance company from selling this policy?\n", - "\n", - "7. **Stock Investment Scenario:**\n", - " Suppose you invest in a stock with three possible outcomes over a year: there is a 50% chance it will increase by 10%, a 30% chance it will decrease by 5%, and a 20% chance it will decrease by 15%. What is the expected value of the return on investment after one year?\n" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -1475,11 +1410,11 @@ "\\begin{aligned}\n", "\\text{s.t.} \\\\\n", "\\sum_{i}\\sum_{r} & y^k_{ir}&=d^k & & \\hspace{.2cm} \\text{(total supplies sent must meet demand)}\\\\\n", - " \\sum_{r} & y^k_{ir} &\\leq x_i & \\forall i \\in I& \\hspace{.2cm}\\text{(you can't send more than a warehouse has)}\\\\\n", - " \\sum_{i} & x_i&=\\chi & & \\hspace{.2cm} \\text{(we allocate all supplies and no more)}\\\\\n", - "\\text{} & y^k_{ir} &\\geq 0 &\\forall r\\in R, i \\in I& \\hspace{.2cm} \\text{(you can't send negative supplies)}\\\\\n", + "\\sum_{r} & y^k_{ir} &\\leq x_i & \\forall i \\in I& \\hspace{.2cm}\\text{(you can't send more than a warehouse has)}\\\\\n", + "\\sum_{i} & x_i&=\\chi & & \\hspace{.2cm} \\text{(we allocate all supplies and no more)}\\\\\n", + "& y^k_{ir} &\\geq 0, \\space &\\forall r\\in R, i \\in I& \\hspace{.2cm} \\text{(you can't send negative supplies)}\\\\\n", "\\\\\n", - "& x_{i} &\\geq 0 &\\forall i \\in I& \\hspace{.2cm} \\text{(you can't allocate negative supplies)}\\\\\n", + "& x_{i} &\\geq 0 , \\space &\\forall i \\in I& \\hspace{.2cm} \\text{(you can't allocate negative supplies)}\\\\\n", "\n", "\n", "\\end{aligned}\n", diff --git a/optimization202/ESUPS_case_study/workspaces/helper_scripts.py b/optimization202/ESUPS_case_study/workspaces/helper_scripts.py deleted file mode 100644 index 236b15d..0000000 --- a/optimization202/ESUPS_case_study/workspaces/helper_scripts.py +++ /dev/null @@ -1,217 +0,0 @@ - -#Called in analysis/analysis worker to filter the disasters by time -def _filter_dataset(self, dataset: Dataset) -> Dataset: - if self.parameters.disaster_month > -1: - months = range( - self.parameters.disaster_month, - self.parameters.disaster_month - + 1 - + self.parameters.num_months_to_average, - ) - months = [(month - 1) % 12 + 1 for month in months] - predicate: Callable[[Disaster], bool] = ( - lambda disaster: disaster.month in months - ) - dataset = dataset.take_disaster_subset(predicate) - - dataset = dataset.take_disaster_subset( - lambda disaster: disaster.year >= self.parameters.min_year - and disaster.year <= self.parameters.max_year - ) - - return dataset - - -def _post_process( - self, - dataset: Dataset, - item: Item, - costs: dict[SolverObjective, CostMatrix], - solutions: dict[SolutionTags, Solution], - ): - beta_source = ( - dataset.persons_per_item_monthly - if self.parameters.care_about_month_demand - else dataset.persons_per_item_general - ) - beta = { - location.id: beta_source[location, item] - for disaster in dataset.disasters - for location in disaster.impacted_locations - } - - solution_stats = pd.DataFrame.from_records( - [ - { - "objective": objective, - "strategy": strategy, - "coveredDemandExcDummy": solution.covered_demand_exc_dummy, - "dualTotInv": solution.dual_total_inventory, - "totalCostIncDummy": solution.total_cost_inc_dummy, - "totalCostExcDummy": solution.total_cost_exc_dummy, - "totalDemand": solution.total_demand, - "fractionOfDisastersUsingDummy": solution.fraction_of_disasters_using_dummy, - } - for (objective, strategy), solution in solutions.items() - ] - ).set_index(["objective", "strategy"]) - - df_flows = pd.DataFrame.from_records( - [ - { - "objective": objective, - "strategy": strategy, - "disaster": disaster.id, - "depot": depot.id, - "impact": impact.id, - "location": impact.location.id, - "mode": mode.id, - "flow": value, - "distance": dataset.distance[depot, impact.location, mode].distance - if depot.id != "DUMMY" - else None, - } - for (objective, strategy), solution in solutions.items() - for (disaster, depot, impact, mode), value in solution.flow.items() - ] - ) - - # Average unit cost - solution_stats["averageUnitCost"] = solution_stats["totalCostExcDummy"] / ( - solution_stats["coveredDemandExcDummy"] + 1e-7 - ) - - # Demand fulfillment fraction - solution_stats["demandFulfillmentFraction"] = solution_stats[ - "coveredDemandExcDummy" - ] / (solution_stats["totalDemand"] + 1e-7) - - # Balance metric - strategies = set(solution_stats.reset_index()["strategy"]) - pivoted = solution_stats.reset_index().pivot( - index="objective", columns="strategy", values="totalCostExcDummy" - ) - pivoted["balanceMetric"] = ( - pivoted[AllocationStrategy.MinimizeFixedInventory] - / (pivoted[AllocationStrategy.MinimizeTwoStage] + 1e-7) - if AllocationStrategy.MinimizeFixedInventory in strategies - and AllocationStrategy.MinimizeTwoStage in strategies - else None - ) - balance_metric = pivoted - - df_probabilities = pd.DataFrame.from_dict( - { - disaster.id: dataset.probabilities[disaster] - for disaster in dataset.disasters - }, - columns=["probability"], - orient="index", - ) - - # Units shipped - df_flow_no_dummy = df_flows.join(df_probabilities, on="disaster") - df_flow_no_dummy = df_flow_no_dummy[ - df_flow_no_dummy["depot"] != "DUMMY" - ] # TODO Replace hardcoded dummy ID - temp = df_flow_no_dummy.copy() - temp["unitsShipped"] = temp["probability"] * temp["flow"] - temp["unitsShippedWeighted"] = temp["unitsShipped"] * temp["distance"] - units_shipped = ( - temp.set_index(["objective", "strategy", "mode"])[ - ["unitsShipped", "unitsShippedWeighted"] - ] - .groupby(["objective", "strategy", "mode"]) - .sum() - ) - - # People served per item - temp = df_flow_no_dummy.copy() - temp["beta"] = temp["impact"].apply(lambda loc: beta[loc]) - temp["peopleServed"] = temp["probability"] * temp["beta"] * temp["flow"] - people_served = ( - temp.set_index(["objective", "strategy"])["peopleServed"] - .groupby(["objective", "strategy"]) - .sum() - ) - people_served_per_item = pd.DataFrame( - people_served / (solution_stats["coveredDemandExcDummy"] + 1e-7), - columns=["peopleServedPerItem"], - ) - - # Impact of optimizing one objective on another objective - impact = [] - for other in self.parameters.comparison_objectives: - cost = { - (depot.id, location.id, mode.id): value - for (depot, location, mode), value in costs[other].items() - } - temp = df_flow_no_dummy.copy() - if temp.empty: - raise RuntimeError("Empty flow matrix encountered") - temp["cost"] = temp.apply( - lambda row: cost[row["depot"], row["location"], row["mode"]], axis=1 - ) - temp["other"] = other - temp["impact"] = temp["cost"] * temp["probability"] * temp["flow"] - impact.append(temp.reset_index()) - cross_impact = ( - pd.concat(impact) - .groupby(["objective", "strategy", "other"])[["impact"]] - .sum() - ) - - return Analysis( - self.parameters, - dataset, - item, - solutions, - solution_stats, - balance_metric, - units_shipped, - people_served_per_item, - cross_impact, - ) - - - - - -def _get_arc_cost( - self, - cost: CostMatrix, - source: Location, - target: DisasterImpact, - mode: TransportMode, -): - if source == self._dummy: - return self._threshold_cost_dummy - cell = cost.get((source, target.location, mode)) - return self._threshold_cost_elim if cell == None else cell - -def _scale_demand(self, problem: Problem) -> dict[DisasterImpact, float]: - supply = sum(problem.inventory.values()) - result: dict[DisasterImpact, float] = {} - for disaster in problem.disasters: - total_demand = sum( - problem.demand[location] for location in disaster.impacted_locations - ) - factor = min(1, supply / total_demand) if total_demand > 1e-6 else 1 - for location in disaster.impacted_locations: - result[location] = factor * problem.demand[location] - return result - - - - - - - - - - - - - - - diff --git a/optimization202/ESUPS_case_study/workspaces/temp_Analyer_workspace.py b/optimization202/ESUPS_case_study/workspaces/temp_Analyer_workspace.py deleted file mode 100644 index 01299d1..0000000 --- a/optimization202/ESUPS_case_study/workspaces/temp_Analyer_workspace.py +++ /dev/null @@ -1,115 +0,0 @@ - - -#Code for the class - -from enum import IntEnum -from multiprocessing import Pool -from os import getenv -from typing import Callable - -import pandas as pd - -from src.data import Dataset, Disaster, DisasterImpact, DistanceInfo, Item -from src.solving import ( - AllocationStrategy, - CostMatrix, - Problem, - Solution, - SolverParameters, - StochasticSolver, -) - - -class AnalysisParameters: - """ - - Attributes - ---------- - expand_depot_set - Flag indicating whether inventory can be reallocated to depots that don't currently hold any stock - care_about_month_demand - Flag indicating whether we take month-by-month demand (True) or the general number (False) - disaster_month - Month from which to select disasters - num_months_to_average - Number of months to use for selecting disasters, when disasterMonth>=0 - optimization_objectives - Set of objectives to use for running the optimization model - comparison_objectives - Set of objectives to use for comparing results - allocation_strategies - Which strategies to test for (re)allocation inventory to depots in the first stage - min_year - First year from which disasters should be taken into account - max_year - Last year from which disasters should be taken into account - scale_demand - Whether demand must be scaled to not exceed total available inventory or not - - - """ - - expand_depot_set: bool = False - care_about_month_demand: bool = True - disaster_month: int = -1 - num_months_to_average: int = 3 - optimization_objectives: list[SolverObjective] = [ - SolverObjective.Cost, - SolverObjective.Time, - ] - comparison_objectives: list[SolverObjective] = list(SolverObjective) - allocation_strategies: list[AllocationStrategy] = list(AllocationStrategy) - min_year: int = 1900 - max_year: int = 2100 - scale_demand: bool = True - - - - - -#instanctiate object, might be better to just make the object - -parameters = AnalysisParameters() - - - -analyzer = Analyzer(parameters) - - -#def run_all(self, dataset: Dataset) -> dict[tuple[str, Item], Analysis]: -inventory_datasets = { - filename: dataset.take_inventory_scenario(filename) - for filename in dataset.inventory_scenarios -} -tasks = [ - (filename, inventory_dataset, item) - for (filename, inventory_dataset) in inventory_datasets.items() - for item in inventory_dataset.items -] - - - - -#def _run_tasks(self, tasks: list[tuple[str, Dataset, Item]]): - - - -_solver=StochasticSolver() -#worker = AnalyzerWorker(self.parameters) -result = [ - (filename, format_data(dataset, item,parameters)) - for (filename, dataset, item) in tasks -] - - - - - -PROCESSED_INFO= { - (filename, analysis.item): analysis - for (filename, analysis) in result - if analysis is not None -} - - - diff --git a/optimization202/ESUPS_case_study/workspaces/temp_solver_workspace.py b/optimization202/ESUPS_case_study/workspaces/temp_solver_workspace.py deleted file mode 100644 index 69b77ee..0000000 --- a/optimization202/ESUPS_case_study/workspaces/temp_solver_workspace.py +++ /dev/null @@ -1,200 +0,0 @@ - -from dataclasses import dataclass -from enum import IntEnum -from typing import Tuple, Union - -import gurobipy as gp -from gurobipy import GRB, tupledict - -from src.data import Depot, Disaster, DisasterImpact, Location, TransportMode - - - -class StochasticSolver: - _threshold_cost_elim: float = 1e9 - _threshold_cost_dummy: float = 1e9 - - def __init__(self): - self._dummy = Location("DUMMY", "", "", 0, 0) - self._env = gp.Env(params={"OutputFlag": 0, "Threads": 1}) - - def dispose(self): - self._env.dispose() - self._env = None - - def solve(self, problem: Problem, parameters: SolverParameters) -> Solution: - sources = problem.depots + [self._dummy] - - demand = ( - self._scale_demand(problem) if parameters.scale_demand else problem.demand - ) - - arcs = gp.tuplelist( - [ - (k, i, j, v) - for i in sources - for k in problem.disasters - for j in k.impacted_locations - for v in problem.transport_modes - if ( - self._get_arc_cost(problem.cost, i, j, v) - < self._threshold_cost_elim - ) - or (i == self._dummy) - ] - ) - - arc_cost = { - (k, i, j, v): self._get_arc_cost(problem.cost, i, j, v) - * problem.probabilities[k] - for (k, i, j, v) in arcs - } - - model = gp.Model("StochLP", env=self._env) - - # First stage variable: Quantity to be allocated to each depot - x: tupledict[Depot, gp.Var] = model.addVars(problem.depots, lb=0, name="x") - - # Second stage variable: Quantity transported from (real or dummy) depot to disaster locations using each mode of transport - y: tupledict[ - Tuple[Disaster, Union[Depot, Location], DisasterImpact, TransportMode], - gp.Var, - ] = model.addVars(arcs, lb=0, obj=arc_cost, name="y") - - # Constraint: Total incoming arc flow must cover demand for each disaster location - model.addConstrs( - ( - y.sum(k, "*", j, "*") == demand[j] - for k in problem.disasters - for j in k.impacted_locations - ), - name="satisfyDemand", - ) - - # Constraint: Total outgoing arc flow must match initial or reallocated inventory - inventory_balance: tupledict[ - Tuple[Disaster, Depot], gp.Constr - ] = model.addConstrs( - ( - y.sum(k, i, "*", "*") <= x[i] - for k in problem.disasters - for i in problem.depots - ), - name="satisfySupply", - ) - - # Constraint: Ensure inventory reallocation matches total existing inventory - total_initial_inventory = sum(problem.inventory.values()) - match_total_inventory = model.addConstr(x.sum() == total_initial_inventory) - - def fix_inventory_balance(values: dict[Disaster, float]): - for key, value in values.items(): - x[key].LB = x[key].UB = value - - if parameters.allocation_strategy == AllocationStrategy.MinimizeFixedInventory: - fix_inventory_balance(problem.inventory) - elif parameters.allocation_strategy == AllocationStrategy.WorstDepot: - worst_depot = None - worst_objective = -1e100 - for depot in problem.depots: - centralized_inventory = { - other: total_initial_inventory if other == depot else 0 - for other in problem.depots - } - fix_inventory_balance(centralized_inventory) - model.optimize() - if model.Status != GRB.Status.OPTIMAL: - raise RuntimeError("Could not solve model to optimality") - if model.ObjVal > worst_objective: - worst_depot = depot - worst_objective = model.ObjVal - centralized_inventory = { - other: total_initial_inventory if other == worst_depot else 0 - for other in problem.depots - } - fix_inventory_balance(centralized_inventory) - - model.optimize() - if model.Status != GRB.Status.OPTIMAL: - raise RuntimeError("Could not solve model to optimality") - - # Total transport cost - total_cost_inc_dummy = model.ObjVal - dummy_cost = sum( - var.X * var.Obj for var in y.select("*", self._dummy, "*", "*") - ) - total_cost_exc_dummy = total_cost_inc_dummy - dummy_cost - - # Demand met without using the dummy node - covered_demand_by_dummy = sum( - y[k, i, j, v].X * problem.probabilities[k] - for (k, i, j, v) in arcs.select("*", self._dummy, "*", "*") - ) - total_demand = sum( - local_demand * problem.probabilities[j.disaster] - for (j, local_demand) in demand.items() - ) - covered_demand_exc_dummy = total_demand - covered_demand_by_dummy - - # Flow in solution - solution_y = {key: y[key].X for key in arcs} - - # Fraction of disaster scenarios in which the dummy supply is used - fraction_of_disasters_using_dummy = len( - [ - disaster - for disaster in problem.disasters - if sum( - solution_y[key] - for key in arcs.select(disaster, self._dummy, "*", "*") - ) - > 0 - ] - ) / len(problem.disasters) - - # Dual variables for the inventory balance constraints - dual_correction = fraction_of_disasters_using_dummy * self._threshold_cost_dummy - duals_inventory_exc_dummy_unadjusted = { - i: sum(inventory_balance[k, i].Pi for k in problem.disasters) - for i in problem.depots - } - duals_inventory_exc_dummy_plus_dummy_cost = { - i: dual_correction + pi - for (i, pi) in duals_inventory_exc_dummy_unadjusted.items() - } - duals_inventory_exc_dummy_all = { - (k, i): constr.Pi for (k, i), constr in inventory_balance.items() - } - - flow = {(k, i, j, v): var.X for (k, i, j, v), var in y.items() if var.X > 0} - - flow_exc_dummy = { - (k, i, j, v): value - for (k, i, j, v), value in flow.items() - if i != self._dummy - } - - optimal_inventory = {depot: var.X for depot, var in x.items()} - - dual_total_inventory = ( - match_total_inventory.Pi - if parameters.allocation_strategy - == AllocationStrategy.MinimizeFixedInventory - else None - ) - - return Solution( - total_cost_inc_dummy, - total_cost_exc_dummy, - total_demand, - covered_demand_exc_dummy, - fraction_of_disasters_using_dummy, - duals_inventory_exc_dummy_plus_dummy_cost, - duals_inventory_exc_dummy_unadjusted, - duals_inventory_exc_dummy_all, - flow_exc_dummy, - flow, - optimal_inventory, - dual_total_inventory, - self._dummy, - ) diff --git a/optimization202/ESUPS_case_study/workspaces/temp_worker_workspace.py b/optimization202/ESUPS_case_study/workspaces/temp_worker_workspace.py deleted file mode 100644 index a1b5196..0000000 --- a/optimization202/ESUPS_case_study/workspaces/temp_worker_workspace.py +++ /dev/null @@ -1,107 +0,0 @@ -#This script will roughly fill in the job that a worker does, -#it will become a jupyter notebook -from enum import IntEnum -from multiprocessing import Pool -from os import getenv -from typing import Callable - -import pandas as pd - -from src.data import Dataset, Disaster, DisasterImpact, DistanceInfo, Item -from src.solving import ( - AllocationStrategy, - CostMatrix, - Problem, - Solution, - SolverParameters, - StochasticSolver, -) - - -def format_data(dataset,item,parameters): -#Calculate disaster prob - - #dataset = self._filter_dataset(dataset) - probabilities = { - disaster: 1 / len(dataset.disasters) for disaster in dataset.disasters - } - - - - #Let's derfine our dictionary to store all the dictionaries - - solutions: dict[SolutionTags, Solution] = {} - - #First we construct the chance each distaster will occur - probabilities = { - disaster: 1 / len(dataset.disasters) for disaster in dataset.disasters - } - - #Now we need to create our cost matrix to tell the solver how much each thing will - #reguire from out budget - - #To do this we will iterate over every item in our dataset and return the designated item - cost_matrices={} - for key, value in dataset.distance.items(): - match objective: - case SolverObjective.Cost: - aa = item.weight * value.cost_per_ton - - case SolverObjective.Time: - aa = value.time - - case SolverObjective.Distance: - aa = value.distance - cost_matrices[key]=aa - - - #Now we find the inventory in a nice formate - inventory = { - depot: dataset.inventory.get((depot, item), 0) - for depot in dataset.depots - if self.parameters.expand_depot_set - or dataset.inventory.get((depot, item), 0) > 0 - } - - - #Check that there is inventory - if sum(inventory.values()) == 0: - return None - - - #Now we get the demand for each item - - #One of the settings we can tweak, need to look more at this - source = ( - dataset.monthly_demand - if self.parameters.care_about_month_demand - else dataset.general_demand - ) - - #After the setting is tweeked, we now make the dictionary - demand={ - location: source.get((location, item), 0) - for disaster in dataset.disasters - for location in disaster.impacted_locations - } - - # Solve models for all objectives - for objective in self.parameters.optimization_objectives: - #Check all stratigies for each objective - for strategy in self.parameters.allocation_strategies: - problem = Problem( #from another file - dataset.depots, - inventory, - demand, - dataset.disasters, - probabilities, - dataset.transport_modes, - cost_matrices[objective], - ) - parameters = SolverParameters(strategy, self.parameters.scale_demand) - tags = (objective, strategy) - solutions[tags] = self._solver.solve(problem, parameters) - - return self._post_process(dataset, item, cost_matrices, solutions) - - #self._solver = StochasticSolver() From 86d9b3a3209efe4e98266afe34cc6b07592755a5 Mon Sep 17 00:00:00 2001 From: Jerry Yurchisin Date: Wed, 13 Nov 2024 19:18:39 -0500 Subject: [PATCH 19/40] typo fix --- .../completed_exercise_set1.ipynb | 15 +- .../Modeling_Session_1/exercise_set1.ipynb | 144 +++++++++++++----- 2 files changed, 118 insertions(+), 41 deletions(-) diff --git a/optimization202/Modeling_Session_1/completed_exercise_set1.ipynb b/optimization202/Modeling_Session_1/completed_exercise_set1.ipynb index a675622..bf0268a 100755 --- a/optimization202/Modeling_Session_1/completed_exercise_set1.ipynb +++ b/optimization202/Modeling_Session_1/completed_exercise_set1.ipynb @@ -88,7 +88,7 @@ "source": [ "## Simple modeling / coding\n", "\n", - "**Q8.** Show these constraints are either compatible (find a feasible solution) or incompatible (prove a contradiction) algebraically.\n", + "**Q8.** Show these constraints are either compatible (find a feasible solution) or incompatible (prove a contradiction) algebraically:\n", "> **Q8a.** \n", "\n", "\\begin{align*}\n", @@ -120,7 +120,16 @@ "\n", "- I will invest in exactly two of the three stocks.\n", "- If I invest in stock A, then I will invest in stock B.\n", - "- If I invest in stock B, then I will invest in stock C." + "- If I invest in stock B, then I will invest in stock C.\n", + "\n", + "`Solution:`\n", + "Let $y_A, y_B, y_C$ be the three stocks.\n", + "\\begin{align*}\n", + "\\sum_{i \\in \\{A,B,C\\}} y_i &= 2 \\\\\n", + "y_A &\\le y_B \\\\\n", + "y_B &\\le y_C \\\\\n", + "\\end{align*}\n", + "$y_A = 0, y_B = 1, y_C = 1$ satisfies the constraints. \n" ] }, { @@ -128,7 +137,7 @@ "metadata": {}, "source": [ "## Back to the widget production problem\n", - "Use the code below for as the base model for this problem. " + "Use the code below as the base model for this problem. " ] }, { diff --git a/optimization202/Modeling_Session_1/exercise_set1.ipynb b/optimization202/Modeling_Session_1/exercise_set1.ipynb index dd34c25..d238e61 100755 --- a/optimization202/Modeling_Session_1/exercise_set1.ipynb +++ b/optimization202/Modeling_Session_1/exercise_set1.ipynb @@ -13,8 +13,71 @@ "source": [ "## Multiple Choice / True-False\n", "\n", - "(Ehsan and Dan) Some simple questions based on the prior sessions\n", - "...." + "**Q1.** When dealing with numerical instability, which of the following is NOT a recommended approach?\n", + "\n", + "> a. Using well-scaled and precise data\n", + "\n", + "> b. Ensuring matrix coefficients are within 6 orders of magnitude\n", + "\n", + "> c. Increasing the Big M value as much as possible\n", + "\n", + "> d. Setting the NumericFocus parameter to prioritize stability\n", + "\n", + "**Q2.** Which of the following statements about MIP Gap is accurate?\n", + "\n", + "> a. A lower MIP Gap always leads to significant solution improvements\n", + "\n", + "> b. A higher MIP Gap can still yield a solution close to optimal, depending on the problem\n", + "\n", + "> c. Setting MIP Gap to zero is ideal for all real-world applications\n", + "\n", + "> d. MIP Gap only affects runtime but not solution quality\n", + "\n", + "**Q3.** Which of the following is a potential benefit of using the Big M method in an optimization model?\n", + "\n", + "> a. It helps establish conditional constraints based on binary variables\n", + "\n", + "> b. It eliminates the need for slack variables\n", + "\n", + "> c. It reduces the numerical range required for coefficients\n", + "\n", + "> d. It ensures the model is solved to exact optimality\n", + "\n", + "**Q4.** An optimization model can sometimes be infeasible in exact arithmetic but feasible within the solver’s tolerances.\n", + "\n", + "> True\n", + "\n", + "> False\n", + "\n", + "**Q5.** For which of the following application areas is non-linear optimization used in practice?\n", + "\n", + "> a. Finance\n", + "\n", + "> b. Utilities\n", + "\n", + "> c. Engineering\n", + "\n", + "> d. All of the above\n", + "\n", + "**Q6.** Which type of optimization problem could have local optimal solutions that are not globally optimal?\n", + "\n", + "> a. Linear programs\n", + "\n", + "> b. Convex Quadratic Programs\n", + "\n", + "> c. Non-convex Quadratic Programs\n", + "\n", + "> d. None of the above\n", + "\n", + "**Q7.** Which of the following constraints defines a convex feasible region?\n", + "\n", + "> a. $y \\geq x^2$\n", + "\n", + "> b. $y \\leq x^2$\n", + "\n", + "> c. $y = x^2$\n", + "\n", + "> d. $y \\neq x^2$" ] }, { @@ -23,7 +86,8 @@ "source": [ "## Simple modeling / coding\n", "\n", - "1. Show these constraints are either compatible (find a feasible solution) or incompatible (prove a contradiction) algebraically:\n", + "**Q8.** Show these constraints are either compatible (find a feasible solution) or incompatible (prove a contradiction) algebraically:\n", + "> Part 1. \n", "\n", "\\begin{align*}\n", "x + y + z &\\ge 3 \\\\\n", @@ -31,17 +95,19 @@ "x, y, z &\\ge 0\n", "\\end{align*}\n", "\n", + "> Part 2. \n", + "\n", "\\begin{align*}\n", "x_1 + x_2 + x_3 &= 6 \\\\\n", "3x_1 + 2x_2 + x_3 &\\ge 20 \\\\\n", "x_1, x_2, x_3, &\\ge 0\n", "\\end{align*}\n", "\n", - "2. Translate the following into constraints using binary variables for deciding to invest in three stocks. Then determine if they are compatible as above. \n", + "**Q9.** Translate the following into constraints using binary variables for deciding to invest in three stocks. Then determine if they are compatible as above. \n", "\n", - "- I will invest in exactly two stocks. \n", - "- If I invest in stock A, then I will invest in stock B\n", - "- If I invest in stock B, then I will invest in stock C\n" + "- I will invest in exactly two of the three stocks.\n", + "- If I invest in stock A, then I will invest in stock B.\n", + "- If I invest in stock B, then I will invest in stock C." ] }, { @@ -54,10 +120,13 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ + "# Install any packages\n", + "%pip install gurobipy\n", + "\n", "# Import packages\n", "import pandas as pd\n", "import gurobipy as gp\n", @@ -66,7 +135,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 227, "metadata": {}, "outputs": [], "source": [ @@ -83,12 +152,12 @@ "n_demand = pd.Series([89,95,121,101,116,181], index = distribution, name = \"demand\")\n", "# the min production is a fraction of the max\n", "frac = 0.75\n", - "C = 30\n" + "C = 30" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -193,7 +262,7 @@ " St. Louis 4.60 41.0" ] }, - "execution_count": 7, + "execution_count": 228, "metadata": {}, "output_type": "execute_result" } @@ -203,7 +272,7 @@ "m = gp.Model('widgets')\n", "m.setParam('OutputFlag',0)\n", "\n", - "# decision vars\n", + "# decision variables - reminder that we ended on x being a semi-continuous variable type\n", "x = m.addVars(production, distribution, vtype=GRB.SEMICONT, lb = C, name = 'prod_ship')\n", "\n", "# constraints\n", @@ -240,23 +309,17 @@ "\n", "```python\n", "m.setObjective(gp.quicksum(transp_cost[p,d]*x[p,d] for p in production for d in distribution) + ..., GRB.MINIMIZE)\n", - "```" + "```\n", + "Solve the model with the updated objective and see how the cost has changed." ] }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "# One solution\n", - "m.setObjective(gp.quicksum(transp_cost[p,d]*x[p,d] for p in production for d in distribution) + 0.01 * gp.quicksum(x.sum(p, '*') * x.sum(p, '*') for p in production), GRB.MINIMIZE)\n", - "\n", - "# Another solution\n", - "# Introduces auxiliary variables representing total production for each facility\n", - "# produced = m.addVars(production, name='total_produced')\n", - "# m.addConstrs((produced[p] == x.sum(p, '*') for p in production), name='set_produced')\n", - "# m.setObjective(gp.quicksum(transp_cost[p,d]*x[p,d] for p in production for d in distribution) + 0.01 * gp.quicksum(produced[p] * produced[p] for p in production), GRB.MINIMIZE)" + "# Your code goes here" ] }, { @@ -271,7 +334,8 @@ "prod_cost = m.addVar(name='prod_cost')\n", "m.addConstr(prod_cost ==..., name='set_prod_cost')\n", "m.setObjective(prod_cost, GRB.MINIMIZE)\n", - "```" + "```\n", + "Again, resolve the model and see how the cost has changed." ] }, { @@ -280,10 +344,7 @@ "metadata": {}, "outputs": [], "source": [ - "# One solution\n", - "prod_cost = m.addVar(name='prod_cost')\n", - "m.addConstr(prod_cost == gp.quicksum(transp_cost[p,d] * gp.nlfunc.log(1 + x[p, d]) for p in production for d in distribution), name='set_prod_cost')\n", - "m.setObjective(prod_cost, GRB.MINIMIZE)" + "# Your code goes here" ] }, { @@ -294,9 +355,13 @@ "\n", "A very strange company rule stipulates that the following mathematical relationship must hold:\n", "\n", - "$ x_{\\textrm{Cleveland,Richmond}} = \\log\\Large(\\frac{10 \\cdot x_{\\textrm{Baltimore,Indianapolis}}}{\\sum_d x_{\\textrm{Baltimore},d}})\\normalsize + x_{\\textrm{Cleveland,Richmond}}$\n", + "$ \\log\\Large(\\frac{4 \\cdot x_{\\textrm{Baltimore,Indianapolis}}}{\\sum_d x_{\\textrm{Baltimore},d}})\\normalsize \\geq 0$\n", + "\n", + "Add this constraint to the model using a nonlinear expression.\n", "\n", - "Add this exact constraint to the model using a nonlinear expression." + "Hint: Start by adding a non-negative slack variable $s$ and writing the constraint as $s = \\log\\Large(\\frac{4 \\cdot x_{\\textrm{Baltimore,Indianapolis}}}{\\sum_d x_{\\textrm{Baltimore},d}})$\n", + "\n", + "Solve the model and see how the cost has changed." ] }, { @@ -305,8 +370,9 @@ "metadata": {}, "outputs": [], "source": [ - "# One solution\n", - "m.addConstr(x['Baltimore', 'Richmond'] == gp.nlfunc.log(10 * x['Baltimore', 'Richmond'] / x.sum('Baltimore', '*')) + x['Cleveland', 'Richmond'], name='strange_company_rule')" + "# Your code goes here\n", + "s = \n", + "NL2 = m.addConstr( ??? )" ] }, { @@ -315,7 +381,9 @@ "source": [ " ### Linearizing a nonlinear expression\n", "\n", - "Wait a minute...is the nonlinear constraint we just added unnecessarily complicated? Rewrite the nonlinear constraint using only linear expressions. Describe the purpose of the constraint in words." + "Wait a minute...is the nonlinear constraint we just added unnecessarily complicated? Rewrite the nonlinear constraint using only linear expressions. Describe the purpose of the constraint in words.\n", + "\n", + "Solve the model again and inspect the solution. Verify by inspection that it satisfies this newly added constraint." ] }, { @@ -324,15 +392,15 @@ "metadata": {}, "outputs": [], "source": [ - "# One solution\n", - "# Purpose: exactly 10% of Baltimore's production must be sent to Indianapolis\n", - "m.addConstr(x['Baltimore', 'Indianapolis'] == 0.1 * x.sum('Baltimore', '*'), name='strange_company_rule_linearized')" + "# First, remove the complicated form of the constraint\n", + "m.remove(NL2)\n", + "# Your code goes here" ] } ], "metadata": { "kernelspec": { - "display_name": "gurobi_ml", + "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, @@ -346,7 +414,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.11.6" + "version": "3.12.7" } }, "nbformat": 4, From 2fab8ed04eea65645947bc85d6da8d25f797cef5 Mon Sep 17 00:00:00 2001 From: Jerry Yurchisin <97685128+yurchisin@users.noreply.github.com> Date: Wed, 13 Nov 2024 18:58:41 -0500 Subject: [PATCH 20/40] Update README.md --- optimization202/ESUPS_case_study/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/optimization202/ESUPS_case_study/README.md b/optimization202/ESUPS_case_study/README.md index 48b21bf..2ccb26b 100644 --- a/optimization202/ESUPS_case_study/README.md +++ b/optimization202/ESUPS_case_study/README.md @@ -3,11 +3,11 @@ To reduce gaps and overlaps in emergency preparedness, [ESUPS](https://esups.org/) promotes coordinated prepositioning of emergency supplies and collective logistics strategies. ## Problem -By using scenario-based transportation linear programming model, the model resource allocation and logistics decisions, providing quantitative metrics to evaluate and improve humanitarian operations. +This model used a scenario-based transportation linear programming model with a probabalistic objective to show how to allocated resources at minimal expcted cost. Several important metrics are then derived to inform decision making. The goal is to decide: - How much of each supply to store in certain locations -- How to respond to disaster events at minimal cost +- And then how to respond to disaster events at minimal cost ---- For details on licensing or on running the notebooks, see the overview on [Modeling Examples](../../) From e48d0e994a0438230ed60755128ffdfeda676553 Mon Sep 17 00:00:00 2001 From: Jerry Yurchisin Date: Wed, 13 Nov 2024 20:14:43 -0500 Subject: [PATCH 21/40] more typos fixed --- .../opti202_disaster_prepositioning.ipynb | 135 ++++++++++++++++-- .../completed_exercise_set1.ipynb | 11 +- .../completed_exercise_set2.ipynb | 19 ++- .../Modeling_Session_2/exercise_set2.ipynb | 11 +- 4 files changed, 147 insertions(+), 29 deletions(-) diff --git a/optimization202/ESUPS_case_study/opti202_disaster_prepositioning.ipynb b/optimization202/ESUPS_case_study/opti202_disaster_prepositioning.ipynb index 71f136d..b351528 100644 --- a/optimization202/ESUPS_case_study/opti202_disaster_prepositioning.ipynb +++ b/optimization202/ESUPS_case_study/opti202_disaster_prepositioning.ipynb @@ -11,9 +11,25 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 26, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "" + ] + }, + "execution_count": 26, + "metadata": { + "image/png": { + "width": 600 + } + }, + "output_type": "execute_result" + } + ], "source": [ "from IPython.display import Image\n", "Image(filename='images/ESUPS-Logo.png', width=600)" @@ -58,6 +74,7 @@ "metadata": {}, "outputs": [], "source": [ + "%%capture\n", "!pip install -r requirements.txt \n", "\n", "import pandas as pd\n", @@ -919,9 +936,59 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m 1) What is the Objective Function? \u001b[0m\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "e6c3e86cfecb4185813d71b2f67453e6", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "RadioButtons(options=(('-1', 1), ('0', 2), ('1', 3)), value=1)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "8d4e445c314746d79c8c3640b7270023", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Button(description='Check', style=ButtonStyle())" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "ffa3ac05550b447b9ef11c38df0408a4", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Output()" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "q_3_1()" ] @@ -942,9 +1009,59 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], + "execution_count": 28, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\u001b[1m 1) What is the Objective Function? \u001b[0m\n" + ] + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "03a7cdc369c949958c6926605e545ff0", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "RadioButtons(options=(('0', 1), ('1/2', 2), ('1', 3)), value=1)" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "9a621cd24c84400a9efe4cbaf02d12ad", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Button(description='Check', style=ButtonStyle())" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.jupyter.widget-view+json": { + "model_id": "f90a6d606cab4b039daada2cdd7292aa", + "version_major": 2, + "version_minor": 0 + }, + "text/plain": [ + "Output()" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "q_3_2()" ] diff --git a/optimization202/Modeling_Session_1/completed_exercise_set1.ipynb b/optimization202/Modeling_Session_1/completed_exercise_set1.ipynb index bf0268a..08414b7 100755 --- a/optimization202/Modeling_Session_1/completed_exercise_set1.ipynb +++ b/optimization202/Modeling_Session_1/completed_exercise_set1.ipynb @@ -102,19 +102,22 @@ "\n", "> **Q8b.** \n", "\n", - "\\begin{align*}\n", + "\\begin{align}\n", "x_1 + x_2 + x_3 &= 6 \\\\\n", "3x_1 + 2x_2 + x_3 &\\ge 20 \\\\\n", "x_1, x_2, x_3, &\\ge 0\n", - "\\end{align*}\n", + "\\end{align}\n", "\n", "`Solution:`\n", "\n", + "Substitute $(1)$ into $(2)$:\n", "$3x_1 + 2x_2 + x_3 = 2x_1 + x_2 + (x_1 + x_2 + x_3) = 2x_1 + x_2 + 6$\n", "\n", - "Note that since $x_1$ and $x_2$ and both nonnegative, the first equation implies $x_1 \\le 6$ and $x_1 \\le 6$. \n", + "This simplifies $(2)$ as $2x_1 + x_2 \\ge 14$.\n", + "\n", + "Since $x_3$ is nonnegative, the first equation implies $x_1 + x_2\\le 6$, or $-x_1 - x_2 \\ge -6$.\n", "\n", - "Thus $2x_1 + x_2 + 6 \\le 18$ and cannot be 20 or more. \n", + "Adding the inequalities gives $x_1 \\ge 8$, which can't happen due to $(1)$. \n", "\n", "**Q9.** Translate the following into constraints using binary variables for deciding to invest in three stocks. Then determine if they are compatible as above. \n", "\n", diff --git a/optimization202/Modeling_Session_2/completed_exercise_set2.ipynb b/optimization202/Modeling_Session_2/completed_exercise_set2.ipynb index 2c1f6cc..cea719d 100644 --- a/optimization202/Modeling_Session_2/completed_exercise_set2.ipynb +++ b/optimization202/Modeling_Session_2/completed_exercise_set2.ipynb @@ -5,7 +5,7 @@ "metadata": {}, "source": [ "# Back to the widget production problem\n", - "Let's go back to the Opti 101/201 classic: Widget production and distribution. Use the code below for as the base model for this problem. " + "Let's go back to the Opti 101/201 classic: Widget production and distribution. Use the code below as the base model for this problem." ] }, { @@ -15,7 +15,6 @@ "outputs": [], "source": [ "%pip install gurobipy\n", - "%pip install seaborn\n", "\n", "import pandas as pd\n", "import numpy as np\n", @@ -244,14 +243,14 @@ "source": [ "Links to PWL in `gurobipy`:\n", "- [Model.addGenConstrPWL](https://docs.gurobi.com/projects/optimizer/en/current/reference/python/model.html#Model.addGenConstrPWL) command\n", - "- [Visualization of PWL](https://docs.gurobi.com/projects/optimizer/en/current/concepts/modeling/objectives.html#piecewise-linear-objectives) and how it gets put together\n", + "- [Visualization of PWL](https://docs.gurobi.com/projects/optimizer/en/current/concepts/modeling/objectives.html#piecewise-linear-objectives)\n", "\n", "### Scenario 1\n", - "Let's go back to the Opti 101/201 classic: Widget production and distribution. We have a new cost structure for transporting widgets we make. \n", + "We have a new cost structure for transporting widgets we make. \n", "- There will be a flat 0.50 increase for all transportation costs. \n", "- If we decide to bulk ship at least half of a production facility's max to any one distribution location, every widget over that half capacity number costs 60% of the new transportation cost. \n", "\n", - " So if the original cost is 2, the new cost of shipping from $p$ to $d$ for the first half of capacity is 2.5 (0.50 flat increase), and anything produces after that ships at 1.5. Update the model to reflect this (0.60 * 2.5). " + " So, if the original cost is 2, the new cost of shipping from $p$ to $d$ for the first half of capacity is 2.5 (0.50 flat increase), and anything produced after that ships at 1.5. Update the model to reflect this (0.60 * 2.5). " ] }, { @@ -986,7 +985,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Fill in the blanks in the code below to simulate an uncertain transportation cost from Cleveland to Indianapolis. We will use assume the cost is normally distributed with mean 2.3 and standard deviation 0.2. Generate 1000 samples. " + "Fill in the blanks in the code below to simulate an uncertain transportation cost from Cleveland to Indianapolis. We will assume the cost is normally distributed with mean 2.3 and standard deviation 0.2. Generate 1000 samples. " ] }, { @@ -1076,7 +1075,7 @@ "source": [ "### Individual chance constraint\n", "\n", - "Meeting widget demand in Nashville is now an extremely high priority. Historically, our data says that the demand here for the next sales period follow a [Poisson distribution](https://en.wikipedia.org/wiki/Poisson_distribution) that has a mean of 100 (in the original model, demand was 101). Fill in the code blanks below to make sure we meet the demand with probability 0.95." + "Meeting widget demand in Nashville is now an extremely high priority. Historically, our data says that the demand here for the next sales period follows a [Poisson distribution](https://en.wikipedia.org/wiki/Poisson_distribution) that has a mean of 100 (in the original model, demand was 101). Fill in the code blanks below to make sure we meet the demand with probability 0.95." ] }, { @@ -1318,7 +1317,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Create an optimization model where we decide the percentage of our budget to invest to **maximize the minimum** total estimated returns considering all scenarios. **HINT**: Define a single new auxillary variable." + "Create an optimization model where we decide the percentage of our budget to invest to **maximize the minimum** total estimated returns considering all scenarios. **HINT**: Define a single new auxiliary variable." ] }, { @@ -1441,8 +1440,8 @@ "metadata": {}, "source": [ "You now decide that your investments need to be a little more diversified. Add any necessary variables and constraints that will:\n", - "- Make sure you invest in at least four sectors, \n", - "- And any investment will need to be at least 10% of the budget." + "- Make sure you invest in at least four sectors.\n", + "- If an investment is made in a sector, then will need to be at least 10% of the budget." ] }, { diff --git a/optimization202/Modeling_Session_2/exercise_set2.ipynb b/optimization202/Modeling_Session_2/exercise_set2.ipynb index b447396..c1e7644 100644 --- a/optimization202/Modeling_Session_2/exercise_set2.ipynb +++ b/optimization202/Modeling_Session_2/exercise_set2.ipynb @@ -5,7 +5,7 @@ "metadata": {}, "source": [ "# Back to the widget production problem\n", - "Let's go back to the Opti 101/201 classic: Widget production and distribution. Use the code below as the base model for this problem. " + "Let's go back to the Opti 101/201 classic: Widget production and distribution. Use the code below as the base model for this problem." ] }, { @@ -15,7 +15,6 @@ "outputs": [], "source": [ "%pip install gurobipy\n", - "%pip install seaborn\n", "\n", "import pandas as pd\n", "import numpy as np\n", @@ -137,10 +136,10 @@ "source": [ "Links to PWL in `gurobipy`:\n", "- [Model.addGenConstrPWL](https://docs.gurobi.com/projects/optimizer/en/current/reference/python/model.html#Model.addGenConstrPWL) command\n", - "- [Visualization of PWL](https://docs.gurobi.com/projects/optimizer/en/current/concepts/modeling/objectives.html#piecewise-linear-objectives) and how it gets put together\n", + "- [Visualization of PWL](https://docs.gurobi.com/projects/optimizer/en/current/concepts/modeling/objectives.html#piecewise-linear-objectives)\n", "\n", "### Scenario 1\n", - "Let's go back to the Opti 101/201 classic: Widget production and distribution. We have a new cost structure for transporting the widgets we make. \n", + "We have a new cost structure for transporting the widgets we make. \n", "- There will be a flat 0.50 increase for all transportation costs. \n", "- If we decide to bulk ship at least half of a production facility's max to any one distribution location, every widget over that half capacity number costs 60% of the new transportation cost. \n", "\n", @@ -525,8 +524,8 @@ "metadata": {}, "source": [ "You now decide that your investments need to be a little more diversified. Add any necessary variables and constraints that will:\n", - "- Make sure you invest in at least four sectors. \n", - "- And any investment will need to be at least 10% of the budget." + "- Make sure you invest in at least four sectors.\n", + "- If an investment is made in a sector, then it will need to be at least 10% of the budget." ] }, { From 8470b43b04de060879d2be866f880fa155c2ea77 Mon Sep 17 00:00:00 2001 From: Jerry Yurchisin <97685128+yurchisin@users.noreply.github.com> Date: Wed, 13 Nov 2024 20:17:01 -0500 Subject: [PATCH 22/40] Update README.md --- optimization202/next_level_modeling/README.md | 2 ++ 1 file changed, 2 insertions(+) diff --git a/optimization202/next_level_modeling/README.md b/optimization202/next_level_modeling/README.md index 99d22bb..fe22b82 100644 --- a/optimization202/next_level_modeling/README.md +++ b/optimization202/next_level_modeling/README.md @@ -7,6 +7,8 @@ Gurobi AI Modeling is a way for people new to mathematical optimization to learn If you're an experienced Gurobi user, check out [Gurobot](https://chatgpt.com/g/g-vPqYcfN7M-gurobot), which helps with optimization modeling, Gurobi API questions, and troubleshooting. +[Google Colab Link - Modeling with GenAI links and MMO Code](https://colab.research.google.com/github/Gurobi/modeling-examples/blob/master/optimization202/next_level_modeling/modeling-with-genAI.ipynb) + ---- For details on licensing or on running the notebooks, see the overview on [Modeling Examples](../../) From 83d01aebf0b54b952cdde0c0bc6ec9dfb019c3ae Mon Sep 17 00:00:00 2001 From: Jerry Yurchisin <97685128+yurchisin@users.noreply.github.com> Date: Thu, 14 Nov 2024 09:23:58 -0500 Subject: [PATCH 23/40] Update README.md --- optimization202/Modeling_Session_1/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/optimization202/Modeling_Session_1/README.md b/optimization202/Modeling_Session_1/README.md index c7cdb65..b2f1c56 100644 --- a/optimization202/Modeling_Session_1/README.md +++ b/optimization202/Modeling_Session_1/README.md @@ -13,10 +13,10 @@ We also put **completed** versions of the modeling notebook and the exercise in ## Exercise Set 1 Notebook Links Note that you must sign in with a Google account to be able to run the code in Colab. -[Google Colab Link - Exercise *without* answers](https://colab.research.google.com/github/Gurobi/modeling-examples/blob/master/optimization202/Modeling_Session_1/exercise_set1.ipynb) +[Google Colab Link - Exercise Set 1 *without* answers](https://colab.research.google.com/github/Gurobi/modeling-examples/blob/master/optimization202/Modeling_Session_1/exercise_set1.ipynb) -[Google Colab Link - Exercise *with* answers](https://colab.research.google.com/github/Gurobi/modeling-examples/blob/master/optimization202/Modeling_Session_1/completed_exercise_set1.ipynb) +[Google Colab Link - Exercise Set 1 *with* answers](https://colab.research.google.com/github/Gurobi/modeling-examples/blob/master/optimization202/Modeling_Session_1/completed_exercise_set1.ipynb) [Google Colab Link - Nonlinear Regression Exercise *without* answers](https://colab.research.google.com/github/Gurobi/modeling-examples/blob/master/optimization202/Modeling_Session_1/nonlinear_regression_exercise.ipynb) From d4585c59b3462734d2d7114196300697812fd1b7 Mon Sep 17 00:00:00 2001 From: Jerry Yurchisin Date: Thu, 14 Nov 2024 09:47:47 -0500 Subject: [PATCH 24/40] Update exercise_set1.ipynb --- optimization202/Modeling_Session_1/exercise_set1.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/optimization202/Modeling_Session_1/exercise_set1.ipynb b/optimization202/Modeling_Session_1/exercise_set1.ipynb index d238e61..bb595d9 100755 --- a/optimization202/Modeling_Session_1/exercise_set1.ipynb +++ b/optimization202/Modeling_Session_1/exercise_set1.ipynb @@ -97,11 +97,11 @@ "\n", "> Part 2. \n", "\n", - "\\begin{align*}\n", + "\\begin{align}\n", "x_1 + x_2 + x_3 &= 6 \\\\\n", "3x_1 + 2x_2 + x_3 &\\ge 20 \\\\\n", "x_1, x_2, x_3, &\\ge 0\n", - "\\end{align*}\n", + "\\end{align}\n", "\n", "**Q9.** Translate the following into constraints using binary variables for deciding to invest in three stocks. Then determine if they are compatible as above. \n", "\n", From 9829f46ca4212a868d94692a567196d157f5f199 Mon Sep 17 00:00:00 2001 From: Jerry Yurchisin Date: Thu, 14 Nov 2024 09:49:35 -0500 Subject: [PATCH 25/40] Update exercise_set1.ipynb --- optimization202/Modeling_Session_1/exercise_set1.ipynb | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/optimization202/Modeling_Session_1/exercise_set1.ipynb b/optimization202/Modeling_Session_1/exercise_set1.ipynb index bb595d9..21219f4 100755 --- a/optimization202/Modeling_Session_1/exercise_set1.ipynb +++ b/optimization202/Modeling_Session_1/exercise_set1.ipynb @@ -97,11 +97,11 @@ "\n", "> Part 2. \n", "\n", - "\\begin{align}\n", - "x_1 + x_2 + x_3 &= 6 \\\\\n", - "3x_1 + 2x_2 + x_3 &\\ge 20 \\\\\n", + "\\begin{align*}\n", + "x_1 + x_2 + x_3 &= 6 \\quad &(1) \\\\\n", + "3x_1 + 2x_2 + x_3 &\\ge 20 \\quad &(2)\\\\\n", "x_1, x_2, x_3, &\\ge 0\n", - "\\end{align}\n", + "\\end{align*}\n", "\n", "**Q9.** Translate the following into constraints using binary variables for deciding to invest in three stocks. Then determine if they are compatible as above. \n", "\n", From c08b720e06a4a72aac6e5628c3c1844f7f4f623b Mon Sep 17 00:00:00 2001 From: Jerry Yurchisin Date: Thu, 14 Nov 2024 09:53:14 -0500 Subject: [PATCH 26/40] colab align fix --- .../Modeling_Session_1/completed_exercise_set1.ipynb | 12 ++++++------ .../Modeling_Session_1/exercise_set1.ipynb | 4 ++-- 2 files changed, 8 insertions(+), 8 deletions(-) diff --git a/optimization202/Modeling_Session_1/completed_exercise_set1.ipynb b/optimization202/Modeling_Session_1/completed_exercise_set1.ipynb index 08414b7..2af21d2 100755 --- a/optimization202/Modeling_Session_1/completed_exercise_set1.ipynb +++ b/optimization202/Modeling_Session_1/completed_exercise_set1.ipynb @@ -92,8 +92,8 @@ "> **Q8a.** \n", "\n", "\\begin{align*}\n", - "x + y + z &\\ge 3 \\\\\n", - "2x + 3y - z &\\le 4 \\\\\n", + "x + y + z &\\ge 3 \\quad &(1) \\\\\n", + "2x + 3y - z &\\le 4 \\quad &(2) \\\\\n", "x, y, z &\\ge 0\n", "\\end{align*}\n", "\n", @@ -102,11 +102,11 @@ "\n", "> **Q8b.** \n", "\n", - "\\begin{align}\n", - "x_1 + x_2 + x_3 &= 6 \\\\\n", - "3x_1 + 2x_2 + x_3 &\\ge 20 \\\\\n", + "\\begin{align*}\n", + "x_1 + x_2 + x_3 &= 6 \\quad &(1) \\\\\n", + "3x_1 + 2x_2 + x_3 &\\ge 20 \\quad &(2)\\\\\n", "x_1, x_2, x_3, &\\ge 0\n", - "\\end{align}\n", + "\\end{align*}\n", "\n", "`Solution:`\n", "\n", diff --git a/optimization202/Modeling_Session_1/exercise_set1.ipynb b/optimization202/Modeling_Session_1/exercise_set1.ipynb index 21219f4..76c3685 100755 --- a/optimization202/Modeling_Session_1/exercise_set1.ipynb +++ b/optimization202/Modeling_Session_1/exercise_set1.ipynb @@ -90,8 +90,8 @@ "> Part 1. \n", "\n", "\\begin{align*}\n", - "x + y + z &\\ge 3 \\\\\n", - "2x + 3y - z &\\le 4 \\\\\n", + "x + y + z &\\ge 3 \\quad &(1) \\\\\n", + "2x + 3y - z &\\le 4 \\quad &(2) \\\\\n", "x, y, z &\\ge 0\n", "\\end{align*}\n", "\n", From 2b0c73d83b789482c9a8fca548949f9c8d6888d1 Mon Sep 17 00:00:00 2001 From: Jerry Yurchisin Date: Thu, 14 Nov 2024 10:09:57 -0500 Subject: [PATCH 27/40] typos --- .../completed_exercise_set1.ipynb | 20 +++++++++++++------ ...pleted_nonlinear_regression_exercise.ipynb | 2 +- .../nonlinear_regression_exercise.ipynb | 2 +- 3 files changed, 16 insertions(+), 8 deletions(-) diff --git a/optimization202/Modeling_Session_1/completed_exercise_set1.ipynb b/optimization202/Modeling_Session_1/completed_exercise_set1.ipynb index 2af21d2..025f6a5 100755 --- a/optimization202/Modeling_Session_1/completed_exercise_set1.ipynb +++ b/optimization202/Modeling_Session_1/completed_exercise_set1.ipynb @@ -98,7 +98,7 @@ "\\end{align*}\n", "\n", "`Solution:`\n", - "Select $x = y = z = 1$\n", + "An example solution is to select $x = y = z = 1$.\n", "\n", "> **Q8b.** \n", "\n", @@ -110,14 +110,22 @@ "\n", "`Solution:`\n", "\n", - "Substitute $(1)$ into $(2)$:\n", - "$3x_1 + 2x_2 + x_3 = 2x_1 + x_2 + (x_1 + x_2 + x_3) = 2x_1 + x_2 + 6$\n", + "One way to derive a contradiction:\n", + "Multiply $(1)$ by 3, which gives $3x_1 + 3x_2 + 3x_3 = 18$. \n", "\n", - "This simplifies $(2)$ as $2x_1 + x_2 \\ge 14$.\n", + "Since $x_1, x_2, x_3$ are all nonnegative,\n", + "\\begin{align*}\n", + "3x_1 + 3x_2 + 3x_3 \\ge 3x_1 + 2x_2 + x_3 \\ge 20\n", + "\\end{align*}\n", + "\n", + "This reduces to:\n", "\n", - "Since $x_3$ is nonnegative, the first equation implies $x_1 + x_2\\le 6$, or $-x_1 - x_2 \\ge -6$.\n", + "\\begin{align*}\n", + "18 \\ge 3x_1 + &2x_2 + x_3 \\ge 20 \\\\\n", + "18 &\\ge 20\n", + "\\end{align*}\n", "\n", - "Adding the inequalities gives $x_1 \\ge 8$, which can't happen due to $(1)$. \n", + "Which is a contradiction. \n", "\n", "**Q9.** Translate the following into constraints using binary variables for deciding to invest in three stocks. Then determine if they are compatible as above. \n", "\n", diff --git a/optimization202/Modeling_Session_1/completed_nonlinear_regression_exercise.ipynb b/optimization202/Modeling_Session_1/completed_nonlinear_regression_exercise.ipynb index 5a8d648..f8a6aaf 100755 --- a/optimization202/Modeling_Session_1/completed_nonlinear_regression_exercise.ipynb +++ b/optimization202/Modeling_Session_1/completed_nonlinear_regression_exercise.ipynb @@ -63,7 +63,7 @@ "\n", "\n", "\n", - "In many applicaiton such problems are solved using a reduced gradient method that cannot garantee global optimality. In nonlienar regression, it is possible to have multiple local optima because they are typically nonconvex. " + "In many application such problems are solved using a reduced gradient method that cannot guarantee global optimality. In nonlinear regression, it is possible to have multiple local optima because they are typically nonconvex. " ] }, { diff --git a/optimization202/Modeling_Session_1/nonlinear_regression_exercise.ipynb b/optimization202/Modeling_Session_1/nonlinear_regression_exercise.ipynb index 3c63f6e..981029e 100755 --- a/optimization202/Modeling_Session_1/nonlinear_regression_exercise.ipynb +++ b/optimization202/Modeling_Session_1/nonlinear_regression_exercise.ipynb @@ -63,7 +63,7 @@ "\n", "\n", "\n", - "In many applicaiton such problems are solved using a reduced gradient method that cannot garantee global optimality. In nonlienar regression, it is possible to have multiple local optima because they are typically nonconvex. " + "In many application such problems are solved using a reduced gradient method that cannot guarantee global optimality. In nonlinear regression, it is possible to have multiple local optima because they are typically nonconvex. " ] }, { From fbddd073bdd31b31b82ddccba1e92c53d923a195 Mon Sep 17 00:00:00 2001 From: Jerry Yurchisin Date: Thu, 14 Nov 2024 10:11:10 -0500 Subject: [PATCH 28/40] Update completed_exercise_set1.ipynb --- .../Modeling_Session_1/completed_exercise_set1.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/optimization202/Modeling_Session_1/completed_exercise_set1.ipynb b/optimization202/Modeling_Session_1/completed_exercise_set1.ipynb index 025f6a5..3b34748 100755 --- a/optimization202/Modeling_Session_1/completed_exercise_set1.ipynb +++ b/optimization202/Modeling_Session_1/completed_exercise_set1.ipynb @@ -134,7 +134,7 @@ "- If I invest in stock B, then I will invest in stock C.\n", "\n", "`Solution:`\n", - "Let $y_A, y_B, y_C$ be the three stocks.\n", + "Let $y_A, y_B, y_C \\in \\{0,1\\}$ be the three stocks.\n", "\\begin{align*}\n", "\\sum_{i \\in \\{A,B,C\\}} y_i &= 2 \\\\\n", "y_A &\\le y_B \\\\\n", From 007ffc24d07201a1daaeaa48e97746dedac1e80e Mon Sep 17 00:00:00 2001 From: Jerry Yurchisin Date: Thu, 14 Nov 2024 10:35:00 -0500 Subject: [PATCH 29/40] typo fix --- .../completed_nonlinear_regression_exercise.ipynb | 4 ++-- .../nonlinear_regression_exercise.ipynb | 8 ++++---- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/optimization202/Modeling_Session_1/completed_nonlinear_regression_exercise.ipynb b/optimization202/Modeling_Session_1/completed_nonlinear_regression_exercise.ipynb index f8a6aaf..ef5791f 100755 --- a/optimization202/Modeling_Session_1/completed_nonlinear_regression_exercise.ipynb +++ b/optimization202/Modeling_Session_1/completed_nonlinear_regression_exercise.ipynb @@ -1397,7 +1397,7 @@ "\n", "---\n", "## 5. Set the objective\n", - "The goal is to minimize the least squared error. In this step, we will write the following objective\n", + "The goal is to minimize the squared error. In this step, we will write the following objective:\n", "$$\n", "\\begin{align*}\n", "{\\rm minimize} & \\quad \\displaystyle \\sum_{\\text{data points }i} \\left(y_{\\text{model},i} - y_i\\right)^2 \n", @@ -1583,7 +1583,7 @@ "source": [ "---\n", "## 7. View the solution\n", - "In the next section, we will plot our resulting regtession model alongside the data. \n", + "In the next section, we will plot our resulting regression model alongside the data. \n", "\n", "We pulled this data from [dirMM - Fitting the Michaelis-Menten Model](https://cran.r-project.org/web/packages/renz/vignettes/dirMM.html). This R package also gives its values for $V_\\text{max} = 181.182$ and $K_m=3.115$. As you can see our values may differ from this. Let's plot them and see which looks better. I have also calculated the Mean Squared Error (MSE) for both lines and added it to the chart." ] diff --git a/optimization202/Modeling_Session_1/nonlinear_regression_exercise.ipynb b/optimization202/Modeling_Session_1/nonlinear_regression_exercise.ipynb index 981029e..31e1676 100755 --- a/optimization202/Modeling_Session_1/nonlinear_regression_exercise.ipynb +++ b/optimization202/Modeling_Session_1/nonlinear_regression_exercise.ipynb @@ -440,7 +440,7 @@ "\n", "---\n", "## 5. Set the objective\n", - "The goal is to minimize the least squared error. In this step, we will write the following objective\n", + "The goal is to minimize the squared error. In this step, we will write the following objective:\n", "$$\n", "\\begin{align*}\n", "{\\rm minimize} & \\quad \\displaystyle \\sum_{\\text{data points }i} \\left(y_{\\text{model},i} - y_i\\right)^2 \n", @@ -549,7 +549,7 @@ "source": [ "---\n", "## 7. View the solution\n", - "In the next section, we will plot our resulting regtession model alongside the data. \n", + "In the next section, we will plot our resulting regression model alongside the data. \n", "\n", "We pulled this data from [dirMM - Fitting the Michaelis-Menten Model](https://cran.r-project.org/web/packages/renz/vignettes/dirMM.html). This R package also gives its values for $V_\\text{max} = 181.182$ and $K_m=3.115$. As you can see our values may differ from this. Let's plot them and see which looks better. I have also calculated the Mean Squared Error (MSE) for both lines and added it to the chart." ] @@ -627,7 +627,7 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "gurobi_ml", "language": "python", "name": "python3" }, @@ -641,7 +641,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.12.7" + "version": "3.11.6" } }, "nbformat": 4, From 3b5c6bd37aeb9e2a137164de11ae3d135589e3de Mon Sep 17 00:00:00 2001 From: Jerry Yurchisin Date: Thu, 14 Nov 2024 10:55:12 -0500 Subject: [PATCH 30/40] typo --- .../completed_nonlinear_regression_exercise.ipynb | 2 +- .../Modeling_Session_1/nonlinear_regression_exercise.ipynb | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/optimization202/Modeling_Session_1/completed_nonlinear_regression_exercise.ipynb b/optimization202/Modeling_Session_1/completed_nonlinear_regression_exercise.ipynb index ef5791f..f4fcd1e 100755 --- a/optimization202/Modeling_Session_1/completed_nonlinear_regression_exercise.ipynb +++ b/optimization202/Modeling_Session_1/completed_nonlinear_regression_exercise.ipynb @@ -1397,7 +1397,7 @@ "\n", "---\n", "## 5. Set the objective\n", - "The goal is to minimize the squared error. In this step, we will write the following objective:\n", + "The goal is to minimize the sum of the squared error. In this step, we will write the following objective:\n", "$$\n", "\\begin{align*}\n", "{\\rm minimize} & \\quad \\displaystyle \\sum_{\\text{data points }i} \\left(y_{\\text{model},i} - y_i\\right)^2 \n", diff --git a/optimization202/Modeling_Session_1/nonlinear_regression_exercise.ipynb b/optimization202/Modeling_Session_1/nonlinear_regression_exercise.ipynb index 31e1676..c584392 100755 --- a/optimization202/Modeling_Session_1/nonlinear_regression_exercise.ipynb +++ b/optimization202/Modeling_Session_1/nonlinear_regression_exercise.ipynb @@ -440,7 +440,7 @@ "\n", "---\n", "## 5. Set the objective\n", - "The goal is to minimize the squared error. In this step, we will write the following objective:\n", + "The goal is to minimize the sum of the squared error. In this step, we will write the following objective:\n", "$$\n", "\\begin{align*}\n", "{\\rm minimize} & \\quad \\displaystyle \\sum_{\\text{data points }i} \\left(y_{\\text{model},i} - y_i\\right)^2 \n", From df8a5db442b97dc14f773b138f1192780c4fbfae Mon Sep 17 00:00:00 2001 From: Jerry Yurchisin Date: Thu, 14 Nov 2024 12:43:18 -0500 Subject: [PATCH 31/40] Update completed_exercise_set1.ipynb --- .../Modeling_Session_1/completed_exercise_set1.ipynb | 8 +++++++- 1 file changed, 7 insertions(+), 1 deletion(-) diff --git a/optimization202/Modeling_Session_1/completed_exercise_set1.ipynb b/optimization202/Modeling_Session_1/completed_exercise_set1.ipynb index 3b34748..e9e9916 100755 --- a/optimization202/Modeling_Session_1/completed_exercise_set1.ipynb +++ b/optimization202/Modeling_Session_1/completed_exercise_set1.ipynb @@ -469,7 +469,13 @@ "Solve the model again and inspect the solution. Verify by inspection that it satisfies this newly added constraint.\n", "\n", "Using the properties of logarithms, the constraint can be rewritten as:\n", - "$ x_{\\textrm{Baltimore,Indianapolis}}\\geq \\frac 14\\sum_d x_{\\textrm{Baltimore},d}$" + "$ x_{\\textrm{Baltimore,Indianapolis}}\\geq \\frac 14\\sum_d x_{\\textrm{Baltimore},d}$\n", + "\n", + "\\begin{align*}\n", + "\\log(\\frac{4 \\cdot x_{\\textrm{Baltimore,Indianapolis}}}{\\sum_d x_{\\textrm{Baltimore},d}})\\normalsize &\\geq 0 \\\\\n", + "\\frac{4 \\cdot x_{\\textrm{Baltimore,Indianapolis}}}{\\sum_d x_{\\textrm{Baltimore},d}} &\\geq 1 \\\\\n", + "x_{\\textrm{Baltimore,Indianapolis}}&\\geq \\frac 14\\sum_d x_{\\textrm{Baltimore},d}\n", + "\\end{align*}" ] }, { From 48cf1e30b96258762aa0178faf886290618e726d Mon Sep 17 00:00:00 2001 From: Jerry Yurchisin Date: Thu, 14 Nov 2024 13:14:43 -0500 Subject: [PATCH 32/40] Create mm.png --- optimization202/Modeling_Session_1/mm.png | Bin 0 -> 134347 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create mode 100644 optimization202/Modeling_Session_1/mm.png diff --git a/optimization202/Modeling_Session_1/mm.png b/optimization202/Modeling_Session_1/mm.png new file mode 100644 index 0000000000000000000000000000000000000000..7976b1f89057272e2a56b1f12ea94bb97d733047 GIT binary patch literal 134347 zcmeFZcU)7=);5d?0-^@Ut%x8X3MgGbI)tXEbd`X#2#AOfdJjdKfGDU`X+e-8p_kAT zAs|Xsij)AMg$|*I5E9;apXWU1El1Dy=l910`xiEQX7;STW@gQ`uC;bzj0|*6pS*CA zj*jm1o!i>?=;#;+>FDUInNQHJoSZ>lqkWb;YH1nW(bD2K@^F3P=xk3%cRMC2h3P@U zD93ZcQGvWUe?LnzOFs+0;Ldj#FzF2ga|D}aSlH>L_OoWj544RQem8j;=5*|_cr+t6 zXQW*6YfiQrCu>gFLGTp24>6NC>F?S57$x&wS%#Pe>wC{fcQR9)gXIw;opdCJdjwoq zRYEN^j{C$F^%D<1(|O4W>$II>Wu=eq9z%CVHPWS3){5nOP{>C|CV$m8@tmPEI^X(j zoX0?@wSz5lF1n3_&M1lqQ;yi6$IDv`PfJUbm(evHmf zI$hj=@ws#4{!)F$gDU|4qU5AEU7P}z&&w^IJUPcXZ4jaV<~)1reG=EVkj{jIl#Fkc z8O}aMuN?NI#r<4^<8FWMC4=H;Lv-`il`JsljXlyvK=c(wU*A)*-k!Z`XX9~uQEa<@ zU~?ctXg5B~-XPAjRcwJ-;9*s_J&dVyi&-=5iDD!{GgjvX>yCH$KEv3TnRv@!z3AfFS6@HZpO<_O5qte+EmC-AI|H#IsvOmw z0b0xKdsmY5@g}LOH&k^PWcW3s>pf6-<#pHg-uu{?_@9j%Zhrq>8e5d&7{j*OEK9~E>B=nhngpO&7UgbG}neGGs$+mF8=g0fih5AX- zPhd6979N9Xs46hTJ(qLi$YA7bF>yOSehi=iI!`aza&@0hh@ZJtYYSaanAMzjx#3VU&Dvf7)4rPa;CTCCTk#Ae&2MU#sb~vK#1- zgIDX=Uo1DZmU6_5P9<`R{FU_lu11V8!nnQglC{Wz?dT*Ha&!c&p^-uov5FH>cyLR7`zuxxdyD+)kO7E4Xu53kDO zziZSPoEOkzI~(&w+tl{2>zQ@QW_O&0tohXhP?z7{4!9zGPa(!ogx}=dnFNWqCvL;P z-hC`n$*K5OOwd(;kKgwlApvK6^{>k>nFK=xJ~-#d8uc1y8L4#|R>7+X9Z6}lO4QVA6 zm=}-b1nBHmU9jrW5!Zsonx~K1c~>{Q52!nk-c?=Z9E3Ptb*g*nCj0V6+*6hB5jT2d zFUy)fT_0-7SIo~V!50YjkA3nsSV@x8$7(5C!P55}*Vr!>#igHtIHdP-Wpa9*RZ5V^ zHtp{l(DA~xORZuY248{?R6^bH28unmx|Pt z^U|jVPWhhq;f~|x;%>hHm3k&~K@NQ53D*L*JQqm%Luy_6+T2vz(RQsN%sX<{eD9FsMYSH!Sl5J@Ss zH|3AI!qyofC95S2U~)09x#&YCv$Cr7@?a;sCzy&)$a<)5#cqw29oB{XrFT5ep|)c^ zOLa(XBse@Mg~G|C!&ISFg^A7$wR<-!(V_N2`tmE|&bqk>_bo5;S+D-3gsSkWHd6F9 zpTs!{E{WF?7}nRETj$m-6c&pXI($LF=rlwUB*Jr;KiLEp>t?)c^72zvO5_lz;&N@3Ge&!f; zaAU_k)!oczL~?p$qkLm|!-4%LyXfVH%b(urUgo|G()PcVbW`<}TukyU-}bK^SH8%$ zz3K?)NbcC{5RbMv>wCjmHH#0I^;|dl&Inuz`PJda-c7BTy_lOZzMR~W)~Nk0j$Oep z!Gh#YNqb4vw1_(|^`GgbJ&vke8L|6TJZJSSmq)VqOs{S!lP|Y##6qX&f~ujr{xw4` z18@C_G^Ka*S>U&OFA42sGw3@+!?HVkeat-yZ#MPWM4x_Za}cu6ViRGJiCCzru1a>; zec65UW)9VPVRdaZcSe1a%5&Ah-JnsMiz~78j{Zm79VU+n4}>R3BKDz=SN)8~^p=zV z6MydQtOfG?Q8j&YSXGWw-g;+^C=>X)=}_qluyEPBc`iENt;Z-7qCqjA8GBWOF0C>* zMXgf~Ms_bonnyB5hT}*4~*FKRBFCTTl zYJzJ@yl;7*g;l^jiE3ERsxq?h7sJ!!A!5qvk%zipHd-x*8W?V;rIq`{?jdme3iN_v zs^7+!O9ZX8o^w4l;?>FTFjM6_8?o?&U=-}i#_eI_FF)q(UUc|eEZ~)Ni8S9f*TMFW z9bjj%Fhhon#bvi81!^o_lF48>t5_}1qi9uj(`$Ok)nTjemG?^}NT$B3@<>hf%5;65 zXKh~;oKVl^H{E{|7rW5#e!@Yb-df3jl(ZEi`brdPda)E~U4`~JG$ljgi3&T_*1k=X z!I-66I)v8A0r?5ntY&=B$_(`98p2m|eP{AD3RWgrQFg>joZJX%p~wR~`1Dl7{384x z9GXqLw*z~Gz0vQfKl=&aL!NzxO^pqFEdVenhD(O@k%Bx5H@NT*+dVTEPGjUS!a9LE zE=^`Twu^a-_%=jN$W^M{uv5Zt2X$kAcI@f+;&<-NbXQkh+^1&cjmh<(VY88u1i*Vf zp)awZqM+iVw*5hh;@C({VC99_bD~;Gjy_-g^Gc6AZ@_8L#L*je$T{SZqb#Ol;}A) zOfS_3-K~C0cyUvd?!))xA2XbcQ?Al714o|-Xbb+X15UD`&nD|CrlMDb24S`B&F(zW z*Qfh+&P+#tjDzkt?d%xsmoCbYj^XcfIyxcRUmEJorvK*^#=-34|2(I!{#AI(Bk=~U z3Zvr#GcPlJJtaF=sD#a9S6h1tKd9TU3Un%dO0-j`y_XHYAJo~!Q^`+N;O`nrwDVt= zB?b8ZuHxmSDqyB>#INP*Vb3ooAtfOtpmvggd0J{voHAn=)0j(+yeX4;NWdl%2Y+sG

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b/optimization202/Modeling_Session_1/completed_nonlinear_regression_exercise.ipynb index f4fcd1e..417f661 100755 --- a/optimization202/Modeling_Session_1/completed_nonlinear_regression_exercise.ipynb +++ b/optimization202/Modeling_Session_1/completed_nonlinear_regression_exercise.ipynb @@ -108,11 +108,6 @@ ] }, { - "attachments": { - "dfac2263-b4f3-4d9f-808d-c0d7b34ad8e6.png": { - "image/png": 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" - } - }, "cell_type": "markdown", "id": "f6ad2892-6047-4bb5-bfea-0c66e01cc80e", "metadata": {}, @@ -120,7 +115,7 @@ "### Michaelis-Menten Enzyme Kinetics\n", "[Michaelis-Menten](https://en.wikipedia.org/wiki/Michaelis%E2%80%93Menten_kinetics) kinetics describes how quickly a reaction happens when an enzyme—a type of molecule that speeds up processes in the body or other reactions—interacts with other molecules. Initially, as the amount of molecules $x$ increases, the reaction speed, $y$, increases. But at a certain point, adding more molecules doesn’t increase the speed much because the enzyme is already working as fast as it can.\n", "\n", - "![image.png](attachment:dfac2263-b4f3-4d9f-808d-c0d7b34ad8e6.png)\n", + "![image.png](https://raw.githubusercontent.com/Gurobi/modeling-examples/master/optimization202/Modeling_Session_1/mm.png)\n", "\n", "Two key things come from this: the maximum speed the enzyme can reach $V_{max}$ and the concentration level at which it’s working at half that maximum speed $K_m$. These insights help us understand efficiency and limitations, which is valuable for making decisions in fields like drug development, where knowing an enzyme's limits can guide dosage or predict interactions.\n", "\n", diff --git a/optimization202/Modeling_Session_1/nonlinear_regression_exercise.ipynb b/optimization202/Modeling_Session_1/nonlinear_regression_exercise.ipynb index c584392..7125659 100755 --- a/optimization202/Modeling_Session_1/nonlinear_regression_exercise.ipynb +++ b/optimization202/Modeling_Session_1/nonlinear_regression_exercise.ipynb @@ -108,11 +108,6 @@ ] }, { - "attachments": { - "dfac2263-b4f3-4d9f-808d-c0d7b34ad8e6.png": { - "image/png": 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" - } - }, "cell_type": "markdown", "id": "f6ad2892-6047-4bb5-bfea-0c66e01cc80e", "metadata": {}, @@ -120,7 +115,7 @@ "### Michaelis-Menten Enzyme Kinetics\n", "[Michaelis-Menten](https://en.wikipedia.org/wiki/Michaelis%E2%80%93Menten_kinetics) kinetics describes how quickly a reaction happens when an enzyme—a type of molecule that speeds up processes in the body or other reactions—interacts with other molecules. Initially, as the amount of molecules $x$ increases, the reaction speed, $y$, increases. But at a certain point, adding more molecules doesn’t increase the speed much because the enzyme is already working as fast as it can.\n", "\n", - "![image.png](attachment:dfac2263-b4f3-4d9f-808d-c0d7b34ad8e6.png)\n", + "![image.png](https://raw.githubusercontent.com/Gurobi/modeling-examples/master/optimization202/Modeling_Session_1/mm.png)\n", "\n", "Two key things come from this: the maximum speed the enzyme can reach $V_{max}$ and the concentration level at which it’s working at half that maximum speed $K_m$. These insights help us understand efficiency and limitations, which is valuable for making decisions in fields like drug development, where knowing an enzyme's limits can guide dosage or predict interactions.\n", "\n", From dc4fe49d5310203746b534bfbe2528ae635c600c Mon Sep 17 00:00:00 2001 From: Jerry Yurchisin Date: Thu, 14 Nov 2024 13:20:11 -0500 Subject: [PATCH 34/40] mm png --- .../completed_nonlinear_regression_exercise.ipynb | 2 +- .../Modeling_Session_1/nonlinear_regression_exercise.ipynb | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/optimization202/Modeling_Session_1/completed_nonlinear_regression_exercise.ipynb b/optimization202/Modeling_Session_1/completed_nonlinear_regression_exercise.ipynb index 417f661..0cba055 100755 --- a/optimization202/Modeling_Session_1/completed_nonlinear_regression_exercise.ipynb +++ b/optimization202/Modeling_Session_1/completed_nonlinear_regression_exercise.ipynb @@ -115,7 +115,7 @@ "### Michaelis-Menten Enzyme Kinetics\n", "[Michaelis-Menten](https://en.wikipedia.org/wiki/Michaelis%E2%80%93Menten_kinetics) kinetics describes how quickly a reaction happens when an enzyme—a type of molecule that speeds up processes in the body or other reactions—interacts with other molecules. Initially, as the amount of molecules $x$ increases, the reaction speed, $y$, increases. But at a certain point, adding more molecules doesn’t increase the speed much because the enzyme is already working as fast as it can.\n", "\n", - "![image.png](https://raw.githubusercontent.com/Gurobi/modeling-examples/master/optimization202/Modeling_Session_1/mm.png)\n", + "\"Image\"\n", "\n", "Two key things come from this: the maximum speed the enzyme can reach $V_{max}$ and the concentration level at which it’s working at half that maximum speed $K_m$. These insights help us understand efficiency and limitations, which is valuable for making decisions in fields like drug development, where knowing an enzyme's limits can guide dosage or predict interactions.\n", "\n", diff --git a/optimization202/Modeling_Session_1/nonlinear_regression_exercise.ipynb b/optimization202/Modeling_Session_1/nonlinear_regression_exercise.ipynb index 7125659..4d4c43b 100755 --- a/optimization202/Modeling_Session_1/nonlinear_regression_exercise.ipynb +++ b/optimization202/Modeling_Session_1/nonlinear_regression_exercise.ipynb @@ -115,7 +115,7 @@ "### Michaelis-Menten Enzyme Kinetics\n", "[Michaelis-Menten](https://en.wikipedia.org/wiki/Michaelis%E2%80%93Menten_kinetics) kinetics describes how quickly a reaction happens when an enzyme—a type of molecule that speeds up processes in the body or other reactions—interacts with other molecules. Initially, as the amount of molecules $x$ increases, the reaction speed, $y$, increases. But at a certain point, adding more molecules doesn’t increase the speed much because the enzyme is already working as fast as it can.\n", "\n", - "![image.png](https://raw.githubusercontent.com/Gurobi/modeling-examples/master/optimization202/Modeling_Session_1/mm.png)\n", + "\"Image\"\n", "\n", "Two key things come from this: the maximum speed the enzyme can reach $V_{max}$ and the concentration level at which it’s working at half that maximum speed $K_m$. These insights help us understand efficiency and limitations, which is valuable for making decisions in fields like drug development, where knowing an enzyme's limits can guide dosage or predict interactions.\n", "\n", From a689280371315eaa9bcf8f63b91dd2acae5813ab Mon Sep 17 00:00:00 2001 From: Jerry Yurchisin Date: Fri, 15 Nov 2024 10:12:24 -0500 Subject: [PATCH 35/40] Update opti202_disaster_prepositioning.ipynb --- .../opti202_disaster_prepositioning.ipynb | 5700 ++++++++++++++++- 1 file changed, 5508 insertions(+), 192 deletions(-) diff --git a/optimization202/ESUPS_case_study/opti202_disaster_prepositioning.ipynb b/optimization202/ESUPS_case_study/opti202_disaster_prepositioning.ipynb index b351528..4d370a4 100644 --- a/optimization202/ESUPS_case_study/opti202_disaster_prepositioning.ipynb +++ b/optimization202/ESUPS_case_study/opti202_disaster_prepositioning.ipynb @@ -11,7 +11,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 1, "metadata": {}, "outputs": [ { @@ -21,7 +21,7 @@ "" ] }, - "execution_count": 26, + "execution_count": 1, "metadata": { "image/png": { "width": 600 @@ -70,7 +70,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -99,7 +99,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -117,7 +117,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -137,7 +137,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -172,7 +172,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -216,9 +216,1001 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "Type=warehouses
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+ "showland": true, + "subunitcolor": "white" + }, + "hoverlabel": { + "align": "left" + }, + "hovermode": "closest", + "mapbox": { + "style": "light" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "def graph_supplies():\n", " supplies=[[x[0].latitude,\n", @@ -455,9 +3369,998 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "application/vnd.plotly.v1+json": { + "config": { + "plotlyServerURL": "https://plot.ly" + }, + "data": [ + { + "hovertemplate": "Type=Storm
People Impacted=%{marker.size}
Lat=%{lat}
Long=%{lon}", + "lat": [ + -12.2667, + -23.35, + -20.2847, + -14.2667, + -17.8237, + -18.9085, + -25.17613271, + -25.0316, + -22.8167, + -18.1499, + -21.447, + -22.3961, + -18.9608, + -18.0646, + -21.2367, + -22.15 + ], + "legendgroup": "Storm", + "lon": [ + 49.2833, + 43.6667, + 44.3175, + 50.1667, + 48.4263, + 47.5375, + 46.08937803, + 46.99, + 47.8167, + 49.4023, + 47.0872, + 46.1217, + 46.9036, + 44.0295, + 48.3461, + 48 + ], + "marker": { + "color": "rgb(136, 204, 238)", + "size": [ + 118000, + 70000, + 13561, + 100215, + 84309, + 55345, + 250000, + 540043, + 100000, + 600000, + 736938, + 369272, + 1900, + 526200, + 500, + 162086 + ], + "sizemode": "area", + "sizeref": 1842.345 + }, + "mode": "markers", + "name": "Storm", + "showlegend": true, + "subplot": "mapbox", + "type": "scattermapbox" + }, + { + "hovertemplate": "Type=Flood
People Impacted=%{marker.size}
Lat=%{lat}
Long=%{lon}", + "lat": [ + -17.3843, + -16.9504, + -19.8667 + ], + "legendgroup": "Flood", + "lon": [ + 49.4098, + 46.8281, + 47.0333 + ], + "marker": { + "color": "rgb(204, 102, 119)", + "size": [ + 28223, + 20000, + 23369 + ], + "sizemode": "area", + "sizeref": 1842.345 + }, + "mode": "markers", + "name": "Flood", + "showlegend": true, + "subplot": "mapbox", + "type": "scattermapbox" + }, + { + "hovertemplate": "Type=Epidemic
People Impacted=%{marker.size}
Lat=%{lat}
Long=%{lon}", + "lat": [ + -15.7167, + -18.7698, + -14.8762 + ], + "legendgroup": "Epidemic", + "lon": [ + 46.3167, + 46.05, + 47.9835 + ], + "marker": { + "color": "rgb(221, 204, 119)", + "size": [ + 15172, + 3055, + 21976 + ], + "sizemode": "area", + "sizeref": 1842.345 + }, + "mode": "markers", + "name": "Epidemic", + "showlegend": true, + "subplot": "mapbox", + "type": "scattermapbox" + } + ], + "layout": { + "legend": { + "itemsizing": "constant", + "title": { + "text": "Type" + }, + "tracegroupgap": 0 + }, + "mapbox": { + "center": { + "lat": -19.358815123181813, + "lon": 47.17299445590909 + }, + "domain": { + "x": [ + 0, + 1 + ], + "y": [ + 0, + 1 + ] + }, + "style": "carto-positron", + "zoom": 4.5 + }, + "margin": { + "b": 0, + "l": 0, + "r": 0, + "t": 0 + }, + "template": { + "data": { + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": 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Scale=%{marker.size}
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"linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "gridwidth": 2, + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white" + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "ternary": { + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "bgcolor": "#E5ECF6", + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "title": { + "x": 0.05 + }, + "xaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + }, + "yaxis": { + "automargin": true, + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "zerolinewidth": 2 + } + } + } + } + } + }, + "metadata": {}, + "output_type": "display_data" + } + ], "source": [ "def graph_overlap():\n", " supplies=[['supplies',\n", @@ -609,9 +5529,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 12, + "metadata": { + "image/png": { + "width": 1200 + } + }, + "output_type": "execute_result" + } + ], "source": [ "Image(filename='images/Old_Stockholm.png', width=1200)" ] @@ -627,9 +5563,25 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "" + ] + }, + "execution_count": 13, + "metadata": { + "image/png": { + "width": 1200 + } + }, + "output_type": "execute_result" + } + ], "source": [ "Image(filename='images/Disaster_Dashboard.png',width=1200)" ] @@ -717,9 +5669,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Restricted license - for non-production use only - expires 2025-11-24\n" + ] + } + ], "source": [ "#prep distance matrix\n", "df_distance, relevant_warehouses, BucketsNeeded = get_distance_matrix(dataset)\n", @@ -730,7 +5690,7 @@ "model = gp.Model(\"simple_Allocation\") \n", "\n", "# Add decision variables\n", - "y=model.addVars(n, vtype=GRB.INTEGER, name=\"Warehouse_Allocation\")\n", + "y=model.addVars(n, name=\"Warehouse_Allocation\")\n", "\n", "#Add constraint to meet demand\n", "model.addConstr(gp.quicksum(y[i] for i in range(n))==BucketsNeeded,name='Meet_Demand')\n", @@ -747,9 +5707,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Gurobi Optimizer version 11.0.3 build v11.0.3rc0 (mac64[rosetta2] - Darwin 24.1.0 24B83)\n", + "\n", + "CPU model: Apple M1\n", + "Thread count: 8 physical cores, 8 logical processors, using up to 8 threads\n", + "\n", + "Optimize a model with 17 rows, 16 columns and 32 nonzeros\n", + "Model fingerprint: 0x37d66425\n", + "Coefficient statistics:\n", + " Matrix range [1e+00, 1e+00]\n", + " Objective range [6e+00, 3e+01]\n", + " Bounds range [0e+00, 0e+00]\n", + " RHS range [3e+00, 1e+04]\n", + "Presolve removed 16 rows and 2 columns\n", + "Presolve time: 0.01s\n", + "Presolved: 1 rows, 14 columns, 14 nonzeros\n", + "\n", + "Iteration Objective Primal Inf. Dual Inf. Time\n", + " 0 0.0000000e+00 1.691875e+03 0.000000e+00 0s\n", + " 1 9.8293000e+04 0.000000e+00 0.000000e+00 0s\n", + "\n", + "Solved in 1 iterations and 0.01 seconds (0.00 work units)\n", + "Optimal objective 9.829300000e+04\n" + ] + } + ], "source": [ "# Fire up the solver!\n", "model.optimize()" @@ -764,9 +5753,178 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "

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Varamountpossibledistance
1Warehouse_Allocation[1]26.0260.00
4Warehouse_Allocation[4]9046.090466.00
11Warehouse_Allocation[11]3.037.00
14Warehouse_Allocation[14]1580.015808.00
5Warehouse_Allocation[5]610.061010.00
2Warehouse_Allocation[2]0.04111.00
7Warehouse_Allocation[7]2296.0668911.00
0Warehouse_Allocation[0]0.037514.00
9Warehouse_Allocation[9]0.0246014.25
8Warehouse_Allocation[8]0.015016.00
12Warehouse_Allocation[12]0.073616.00
10Warehouse_Allocation[10]0.0423517.00
6Warehouse_Allocation[6]0.0520119.00
15Warehouse_Allocation[15]0.0163722.00
13Warehouse_Allocation[13]0.0570023.00
3Warehouse_Allocation[3]0.0232226.00
\n", + "
" + ], + "text/plain": [ + " Var amount possible distance\n", + "1 Warehouse_Allocation[1] 26.0 26 0.00\n", + "4 Warehouse_Allocation[4] 9046.0 9046 6.00\n", + "11 Warehouse_Allocation[11] 3.0 3 7.00\n", + "14 Warehouse_Allocation[14] 1580.0 1580 8.00\n", + "5 Warehouse_Allocation[5] 610.0 610 10.00\n", + "2 Warehouse_Allocation[2] 0.0 41 11.00\n", + "7 Warehouse_Allocation[7] 2296.0 6689 11.00\n", + "0 Warehouse_Allocation[0] 0.0 375 14.00\n", + "9 Warehouse_Allocation[9] 0.0 2460 14.25\n", + "8 Warehouse_Allocation[8] 0.0 150 16.00\n", + "12 Warehouse_Allocation[12] 0.0 736 16.00\n", + "10 Warehouse_Allocation[10] 0.0 4235 17.00\n", + "6 Warehouse_Allocation[6] 0.0 5201 19.00\n", + "15 Warehouse_Allocation[15] 0.0 1637 22.00\n", + "13 Warehouse_Allocation[13] 0.0 5700 23.00\n", + "3 Warehouse_Allocation[3] 0.0 2322 26.00" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "#Show Solution\n", "b=[]\n", @@ -920,152 +6078,6 @@ "**Starter Problems**" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Coin Flip: " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Suppose we play a coin flipping game. Every time we flip the coin and it lands on heads, you get a dollar, and every time it lands on tails, you have to pay a dollar. How much are you expected to make or lose when playing this game?" - ] - }, - { - "cell_type": "code", - "execution_count": 27, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1m 1) What is the Objective Function? \u001b[0m\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "e6c3e86cfecb4185813d71b2f67453e6", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "RadioButtons(options=(('-1', 1), ('0', 2), ('1', 3)), value=1)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "8d4e445c314746d79c8c3640b7270023", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Button(description='Check', style=ButtonStyle())" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "ffa3ac05550b447b9ef11c38df0408a4", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Output()" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "q_3_1()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "##### Coin Flip Variant: " - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Now, let's change the game a little, say you now get 5 paid dollars when the coin lands on heads and only have to pay 3 dollars when it lands on tails. Would you play? How much are you expected to make or lose when playing this game?" - ] - }, - { - "cell_type": "code", - "execution_count": 28, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[1m 1) What is the Objective Function? \u001b[0m\n" - ] - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "03a7cdc369c949958c6926605e545ff0", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "RadioButtons(options=(('0', 1), ('1/2', 2), ('1', 3)), value=1)" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "9a621cd24c84400a9efe4cbaf02d12ad", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Button(description='Check', style=ButtonStyle())" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.jupyter.widget-view+json": { - "model_id": "f90a6d606cab4b039daada2cdd7292aa", - "version_major": 2, - "version_minor": 0 - }, - "text/plain": [ - "Output()" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "q_3_2()" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -1262,9 +6274,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Gurobi Optimizer version 11.0.3 build v11.0.3rc0 (mac64[rosetta2] - Darwin 24.1.0 24B83)\n", + "\n", + "CPU model: Apple M1\n", + "Thread count: 8 physical cores, 8 logical processors, using up to 8 threads\n", + "\n", + "Optimize a model with 1088 rows, 1024 columns and 2048 nonzeros\n", + "Model fingerprint: 0xd98f621f\n", + "Coefficient statistics:\n", + " Matrix range [1e+00, 1e+00]\n", + " Objective range [9e-02, 4e-01]\n", + " Bounds range [0e+00, 0e+00]\n", + " RHS range [3e+00, 4e+04]\n", + "Presolve removed 1079 rows and 899 columns\n", + "Presolve time: 0.01s\n", + "Presolved: 9 rows, 125 columns, 125 nonzeros\n", + "\n", + "Iteration Objective Primal Inf. Dual Inf. Time\n", + " 0 2.9962728e+05 2.378912e+04 0.000000e+00 0s\n", + " 9 3.2740300e+05 0.000000e+00 0.000000e+00 0s\n", + "\n", + "Solved in 9 iterations and 0.01 seconds (0.00 work units)\n", + "Optimal objective 3.274030000e+05\n" + ] + } + ], "source": [ "demand, probs = get_probs(dataset)\n", "\n", @@ -1272,12 +6313,14 @@ "m = len(demand)\n", "a = []\n", "\n", + "model = gp.Model(\"all_disaster_allocation\") \n", + "\n", "# Create an array of driving times based on the df_distance DataFrame\n", - "t = [int(row['drivingTime_hrs']) for i, row in df_distance.iterrows()]\n", - "c=pd.DataFrame(t)\n", + "t = df_distance.drivingTime_hrs\n", + "c = pd.DataFrame(t)\n", "\n", "# Amount to take per Warehouse\n", - "y = model.addVars(m, n, vtype=GRB.INTEGER, name=\"Warehouse_Allocation\")\n", + "y = model.addVars(m, n, name=\"Warehouse_Allocation\")\n", "\n", "# Add constraints to meet demand for each disaster scenario (k)\n", "for k in range(m):\n", @@ -1305,20 +6348,142 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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01
0Warehouse_Allocation[0,0]375.0
1Warehouse_Allocation[0,1]26.0
2Warehouse_Allocation[0,2]41.0
3Warehouse_Allocation[0,3]2322.0
4Warehouse_Allocation[0,4]9046.0
5Warehouse_Allocation[0,5]610.0
6Warehouse_Allocation[0,6]5201.0
7Warehouse_Allocation[0,7]6689.0
8Warehouse_Allocation[0,8]150.0
9Warehouse_Allocation[0,9]2460.0
10Warehouse_Allocation[0,10]4235.0
11Warehouse_Allocation[0,11]3.0
12Warehouse_Allocation[0,12]736.0
13Warehouse_Allocation[0,13]5700.0
14Warehouse_Allocation[0,14]1580.0
\n", + "
" + ], + "text/plain": [ + " 0 1\n", + "0 Warehouse_Allocation[0,0] 375.0\n", + "1 Warehouse_Allocation[0,1] 26.0\n", + "2 Warehouse_Allocation[0,2] 41.0\n", + "3 Warehouse_Allocation[0,3] 2322.0\n", + "4 Warehouse_Allocation[0,4] 9046.0\n", + "5 Warehouse_Allocation[0,5] 610.0\n", + "6 Warehouse_Allocation[0,6] 5201.0\n", + "7 Warehouse_Allocation[0,7] 6689.0\n", + "8 Warehouse_Allocation[0,8] 150.0\n", + "9 Warehouse_Allocation[0,9] 2460.0\n", + "10 Warehouse_Allocation[0,10] 4235.0\n", + "11 Warehouse_Allocation[0,11] 3.0\n", + "12 Warehouse_Allocation[0,12] 736.0\n", + "13 Warehouse_Allocation[0,13] 5700.0\n", + "14 Warehouse_Allocation[0,14] 1580.0" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "pd.DataFrame(a).iloc[:15]" ] }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, { "cell_type": "markdown", "metadata": {}, @@ -1435,9 +6600,38 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Gurobi Optimizer version 11.0.3 build v11.0.3rc0 (mac64[rosetta2] - Darwin 24.1.0 24B83)\n", + "\n", + "CPU model: Apple M1\n", + "Thread count: 8 physical cores, 8 logical processors, using up to 8 threads\n", + "\n", + "Optimize a model with 1153 rows, 1040 columns and 2192 nonzeros\n", + "Model fingerprint: 0x6a82a5d2\n", + "Coefficient statistics:\n", + " Matrix range [1e+00, 1e+00]\n", + " Objective range [9e-02, 4e-01]\n", + " Bounds range [0e+00, 0e+00]\n", + " RHS range [3e+00, 4e+04]\n", + "Presolve removed 1144 rows and 915 columns\n", + "Presolve time: 0.01s\n", + "Presolved: 9 rows, 125 columns, 125 nonzeros\n", + "\n", + "Iteration Objective Primal Inf. Dual Inf. Time\n", + " 0 2.9962728e+05 2.378912e+04 0.000000e+00 0s\n", + " 9 3.2740300e+05 0.000000e+00 0.000000e+00 0s\n", + "\n", + "Solved in 9 iterations and 0.01 seconds (0.00 work units)\n", + "Optimal objective 3.274030000e+05\n" + ] + } + ], "source": [ "model = gp.Model(\"full_allocation\")\n", "\n", @@ -1448,10 +6642,10 @@ "t = df_distance.drivingTime_hrs\n", "\n", "# Amount to take per Warehouse\n", - "y = model.addVars(m, n, vtype=GRB.INTEGER, name=\"Single_Warehouse_Allocation\")\n", + "y = model.addVars(m, n, name=\"Single_Warehouse_Allocation\")\n", "\n", "# National Allocation\n", - "X = model.addVars(n, vtype=GRB.INTEGER, name=\"National_Allocation\")\n", + "X = model.addVars(n, name=\"National_Allocation\")\n", "\n", "# Total national endowment constraint\n", "model.addConstr(gp.quicksum(X[i] for i in range(n)) == 40811, name=\"Total_National_Endowment\")\n", @@ -1476,6 +6670,128 @@ "model.optimize()" ] }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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VarNameX
0Single_Warehouse_Allocation[0,0]375.0
1Single_Warehouse_Allocation[0,1]26.0
2Single_Warehouse_Allocation[0,2]41.0
3Single_Warehouse_Allocation[0,3]2322.0
4Single_Warehouse_Allocation[0,4]9046.0
.........
980Single_Warehouse_Allocation[61,4]3174.0
993Single_Warehouse_Allocation[62,1]26.0
996Single_Warehouse_Allocation[62,4]974.0
1009Single_Warehouse_Allocation[63,1]26.0
1012Single_Warehouse_Allocation[63,4]1375.0
\n", + "

638 rows × 2 columns

\n", + "
" + ], + "text/plain": [ + " VarName X\n", + "0 Single_Warehouse_Allocation[0,0] 375.0\n", + "1 Single_Warehouse_Allocation[0,1] 26.0\n", + "2 Single_Warehouse_Allocation[0,2] 41.0\n", + "3 Single_Warehouse_Allocation[0,3] 2322.0\n", + "4 Single_Warehouse_Allocation[0,4] 9046.0\n", + "... ... ...\n", + "980 Single_Warehouse_Allocation[61,4] 3174.0\n", + "993 Single_Warehouse_Allocation[62,1] 26.0\n", + "996 Single_Warehouse_Allocation[62,4] 974.0\n", + "1009 Single_Warehouse_Allocation[63,1] 26.0\n", + "1012 Single_Warehouse_Allocation[63,4] 1375.0\n", + "\n", + "[638 rows x 2 columns]" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "y_values = []\n", + "for i in range(m):\n", + " for j in range(n):\n", + " y_values.append([y[(i,j)].VarName, y[(i,j)].X])\n", + "y_values = pd.DataFrame(y_values, columns=['VarName', 'X'])\n", + "y_values[y_values.X > 0]" + ] + }, { "cell_type": "markdown", "metadata": {}, From 27c47a6dc0542c914bf18ed7db861618c1c749b2 Mon Sep 17 00:00:00 2001 From: Jerry Yurchisin <97685128+yurchisin@users.noreply.github.com> Date: Fri, 15 Nov 2024 10:42:17 -0500 Subject: [PATCH 36/40] Update .gitignore --- optimization202/ESUPS_case_study/.gitignore | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/optimization202/ESUPS_case_study/.gitignore b/optimization202/ESUPS_case_study/.gitignore index 2bf7443..fbc72b5 100644 --- a/optimization202/ESUPS_case_study/.gitignore +++ b/optimization202/ESUPS_case_study/.gitignore @@ -10,7 +10,6 @@ *.coverage *htmlcov/ *.html -*.zip # Jupyter Notebook -.ipynb_checkpoints \ No newline at end of file +.ipynb_checkpoints From be930a1a710187a63959a5d90ad81e3493132768 Mon Sep 17 00:00:00 2001 From: Jerry Yurchisin Date: Fri, 15 Nov 2024 10:42:30 -0500 Subject: [PATCH 37/40] Create ESUPS_case_study.zip --- .../ESUPS_case_study/ESUPS_case_study.zip | Bin 0 -> 2537131 bytes 1 file changed, 0 insertions(+), 0 deletions(-) create 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zh6+u?D0JeJXoYxUiR-yFjxr6c27zbc!z&_U2Bl1q@AcuswE?hN#H-8!8C_N(s=@uemRMI!ogi8n;jY5Q4mxKNLm}=rl4D)9VY+CAc$njI6;>Hk z-NaCa`>kAaFmbbZQD2n7wn>~|?z2)TySSgk#41%C^j|2&iK9LgLhff8Cu5k3TP0d4 sk+YE)g$Jb;_j7?*E%I0YBP}>w2+gT}{ZX-vhX1Ua48V{mxXc0n2a%aaQ~&?~ literal 0 HcmV?d00001 From 073dfe7409c1573630bc3b8bce18c57ebff072bd Mon Sep 17 00:00:00 2001 From: Jerry Yurchisin Date: Fri, 15 Nov 2024 11:48:58 -0500 Subject: [PATCH 38/40] Update opti202_disaster_prepositioning.ipynb --- .../opti202_disaster_prepositioning.ipynb | 22 +++++++++---------- 1 file changed, 11 insertions(+), 11 deletions(-) diff --git a/optimization202/ESUPS_case_study/opti202_disaster_prepositioning.ipynb b/optimization202/ESUPS_case_study/opti202_disaster_prepositioning.ipynb index 4d370a4..30f322e 100644 --- a/optimization202/ESUPS_case_study/opti202_disaster_prepositioning.ipynb +++ b/optimization202/ESUPS_case_study/opti202_disaster_prepositioning.ipynb @@ -70,12 +70,12 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "%%capture\n", - "!pip install -r requirements.txt \n", + "%pip install -r requirements.txt \n", "\n", "import pandas as pd\n", "import numpy as np\n", @@ -117,13 +117,13 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "warnings.filterwarnings('ignore')\n", - "reader = CsvProblemReader()\n", - "dataset = reader.read(DATA_DIR / COUNTRY)" + "warnings.filterwarnings('ignore') # type: ignore\n", + "reader = CsvProblemReader() # type: ignore\n", + "dataset = reader.read(DATA_DIR / COUNTRY) # type: ignore" ] }, { @@ -216,7 +216,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -1226,7 +1226,7 @@ " dfw = pd.DataFrame(warehouses)\n", " dfw.columns = ['Type', 'Lat', 'Long']\n", "\n", - " dfd = pd.DataFrame(disasters)\n", + " dfd = pd.DataFrame(disasters) # type: ignore\n", " dfd.columns = ['Type', 'Lat', 'Long']\n", "\n", " df_combined = pd.concat([dfw[['Lat', 'Long', 'Type']], dfd[['Lat', 'Long', 'Type']]], ignore_index=True)\n", @@ -5669,7 +5669,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [ { @@ -5682,7 +5682,7 @@ ], "source": [ "#prep distance matrix\n", - "df_distance, relevant_warehouses, BucketsNeeded = get_distance_matrix(dataset)\n", + "df_distance, relevant_warehouses, BucketsNeeded = get_distance_matrix(dataset) # type: ignore\n", "t = df_distance.drivingTime_hrs\n", "n = len(relevant_warehouses)\n", "\n", @@ -6843,7 +6843,7 @@ "\\begin{aligned}\n", "\\text{s.t.} \\\\\n", "\\sum_{i}\\sum_{r} & y^k_{ir}&=d^k & & \\hspace{.2cm} \\text{(total supplies sent must meet demand)}\\\\\n", - "\\sum_{r} & y^k_{ir} &\\leq x_i & \\forall i \\in I& \\hspace{.2cm}\\text{(you can't send more than a warehouse has)}\\\\\n", + "\\sum_{r} & y^k_{ir} &\\leq x_i, & \\space \\forall i \\in I& \\hspace{.2cm}\\text{(you can't send more than a warehouse has)}\\\\\n", "\\sum_{i} & x_i&=\\chi & & \\hspace{.2cm} \\text{(we allocate all supplies and no more)}\\\\\n", "& y^k_{ir} &\\geq 0, \\space &\\forall r\\in R, i \\in I& \\hspace{.2cm} \\text{(you can't send negative supplies)}\\\\\n", "\\\\\n", From f54011b738d357d8f94134bf3b607f7656666ebf Mon Sep 17 00:00:00 2001 From: Jerry Yurchisin <97685128+yurchisin@users.noreply.github.com> Date: Fri, 15 Nov 2024 11:18:03 -0500 Subject: [PATCH 39/40] Update README.md --- optimization202/Modeling_Session_2/README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/optimization202/Modeling_Session_2/README.md b/optimization202/Modeling_Session_2/README.md index b31711e..4ff9c58 100755 --- a/optimization202/Modeling_Session_2/README.md +++ b/optimization202/Modeling_Session_2/README.md @@ -13,10 +13,10 @@ We also put **completed** versions of the modeling notebook and the exercise in ## Exercise Set 2 Notebook Links Note that you must sign in with a Google account to be able to run the code in Colab. -[Google Colab Link - Exercise *without* answers](https://colab.research.google.com/github/Gurobi/modeling-examples/blob/master/optimization202/Modeling_Session_2/exercise_set2.ipynb) +[Google Colab Link - Exercise Set 2 *without* answers](https://colab.research.google.com/github/Gurobi/modeling-examples/blob/master/optimization202/Modeling_Session_2/exercise_set2.ipynb) -[Google Colab Link - Exercise *with* answers](https://colab.research.google.com/github/Gurobi/modeling-examples/blob/master/optimization202/Modeling_Session_2/completed_exercise_set2.ipynb) +[Google Colab Link - Exercise Set 2 *with* answers](https://colab.research.google.com/github/Gurobi/modeling-examples/blob/master/optimization202/Modeling_Session_2/completed_exercise_set2.ipynb) ---- From 127e4607c6c4f51bd5d61fb004472e029550349a Mon Sep 17 00:00:00 2001 From: Jerry Yurchisin Date: Fri, 15 Nov 2024 11:51:01 -0500 Subject: [PATCH 40/40] Update opti202_disaster_prepositioning.ipynb --- .../ESUPS_case_study/opti202_disaster_prepositioning.ipynb | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/optimization202/ESUPS_case_study/opti202_disaster_prepositioning.ipynb b/optimization202/ESUPS_case_study/opti202_disaster_prepositioning.ipynb index 30f322e..d999335 100644 --- a/optimization202/ESUPS_case_study/opti202_disaster_prepositioning.ipynb +++ b/optimization202/ESUPS_case_study/opti202_disaster_prepositioning.ipynb @@ -6901,12 +6901,12 @@ "\n", "##### Definition and Calculation of the Balance Metric\n", "\n", - "The balance metric $(D)$ is defined as the ratio between the actual objective value (either cost or time) of the current inventory allocation $V(X)$ and the optimal objective value $V(A')$ given the same overall capacity:\n", + "The balance metric $(D)$ is defined as the ratio between the actual objective value (either cost or time) of the current inventory allocation $V(A)$ and the optimal objective value $V(A')$ given the same overall capacity:\n", "\n", "$$ D = \\frac{V(A)}{V(A')} $$\n", "\n", "Here:\n", - "- $V(A)$: Represents the current system-wide cost or time to meet demand based on the existing inventory allocation \\(X\\).\n", + "- $V(A)$: Represents the current system-wide cost or time to meet demand based on the existing inventory allocation \\(A\\).\n", "- $V(A')$: Represents the minimized cost or time if the inventory were optimally distributed across all depots.\n", "\n", "##### Interpretation of the Balance Metric\n",

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