This work has been supported by the following grants:
+
+
National Science Foundation Graduate Research Fellowship (grant no. DGE-1610403)
+
Future Investigators in NASA Earth and Space Science and Technology (NASA FINESST, grant no. 21-EARTH21-0264)
+
Planet Texas 2050, a research grand challenge at the University of Texas at Austin.
+
U.S. Department of Energy, Office of Science, Biological and Environmental Research Program’s South-East Texas Urban Integrated Field Laboratory under Award Number DE-SC0023216.
SVInsight was developed to provide an open-source tool for creating social vulnerability indices or SVIs based on user defined study areas and input variables. SVInsight uses two methodologies to create indices based on existing scholarly research: (1) a factor analysis method and (2) a rank method. This tool’s purpose is to provide researchers with the ability to quickly create exploratory estimates of vulnerability based on existing and known relationships between socio-demographic variables and a community’s sensativity (a proxy for vulnerability).
+
While SVInsight is capable of rapidly producing tailor made estimates of relative vulnerability of a given region for researchers and/or practicitioners, it does not replace the in-depth investigation required to definitively define what makes someone more or less vulnerable. Indices created with SVInsight can be used as a starting point for understanding the complex relationship between socio-demographic variables and community characteristics including vulnerability, sensativity, and adaptive capacity by highlighting prominant geographic disparities. It is further important to note that vulnerability estimates are relative, and calculated scores should be taken in the context of the study area being examined.
+
This package was specifically written in conjunction with past and ongoing projects at the Univeristy of Texas at Austin [2][3][4]. More detailed infomration regarding funding sources can be found in the Acknolwedgements section.
All data comes from the American Community Survey 5-Year Estimates because they contain data at the Census block group and tract level. More information on data quality can be found on their website.
This methodology is heavily influenced by the original SoVI® [1] developed by Dr. Susan Cutter. By utilizing a data reduction methodology, a large subset of American Community Survey 5-Year Estimate variables can be dimensionally reduced and combined into a single index. Both principal component analyses and factor analyses are data reduction techniques that have been utilized to create vulnerability indices. A principal component analysis (PCA) reduces data by creating one or more index variables, or ‘components,’ by using a linear combination (i.e., a weighted average) from a larger set of measured variables. The purpose of a PCA is to determine the optimal number of components, the optimal choice of measured variables for each component, and the optimal weights. A factor analysis (FA) is a model of latent variables. A ‘latent variable’ cannot be directly measured with a single variable. A common example used to describe this is we cannot directly measure something like social anxiety, but we can measure whether someone has a high or low social anxiety based on a set of questions like “I am uncomfortable in large groups” or “ I get nervous talking to strangers.” We utilize a factor analysis approach because it is the data reduction methodology most often associated with creating composite indices, which is our end goal.
+
The follow are the steps in the iterative factor analysis approach:
+
+
+
Scale the data
+
We standardize the data to a range of 0-1. Since all of the variable have a different scale, this makes each variable have an equal weight going into the analysis.
+
+
+
+
+
+
Conduct Initial Factor Analysis
+
Inputs for a factor analysis include the number of factors we are attempting to reduce the data down to, the rotation method, and the fitting method. For this first factor analysis, we set the number of factors to the number of variables under investigation (the maximum number of possible factors) in order to determine if there are any variables at the start we can eliminate for having too low of an influence. The rotation method used is varimax, or orthogonal rotation, which maximizes the sum of the variances of the squared loadings. In simpler terms, ‘loadings’ refer to the correlations between variables and factors. With varimax rotation, the resultant factors are uncorrelated with each other. The fitting method we use is minimum residual (‘minres’).
+
+
+
+
+
+
Calculate Eigenvalues
-With our initial factors, we need to determine which ones are significant and which can be removed. If we stopped here, we would have reduced X variables to X factors, (i.e., we wouldn’t have reduced anything). Our goal is to find the weakest factors to eliminate so that while we are eliminating data, we maintain the highest level of reported variance in our final factors.
+-The first step in doing this is calculating the eigenvalues, which is defined as a measure of how much of the common variance of the observed variables a factor explains. A factor with an eigenvalue greater than or equal to 1 explains more variance than a single observed variable and is therefore beneficial. This is known as the Kaiser Criterion. The number of factors whose eigenvalues is greater than 1 now becomes our new number of factors.
+
+
+
+
+
Recompute Factor Analysis based on Kaiser Criterion
+
Similar to step 2, we run the factor analysis again with the new optimized number of factors.
+
+
+
+
+
+
Calculate Loading Factors
+
The loading factor is the correlation coefficient for the variable and factor. It shows the variance explained by the variable on that particular factor. This will become the weight of the variable on that factor. Various standards exist on what makes a significant loading factor. For the sake of this research, we identify any loading factor that is greater than 0.5 or less than -0.5 is significant.
+
+
+
+
+
+
Calculate Variance Statistics
+
-The variance statistics for each factor that we are interested in tracking are the SS Loadings, Proportion Variance, Cumulative Variance, and Ratio of Variance.
+
SS Loadings: The sum of the squared loadings for the factor. If a factor’s SS loading is greater than 1 it is worth keeping.
+
Proportion Variance: The proportion of the variance that a factor accounts for. The first factor will have the highest proportion, due to our rotation earlier, and subsequent factors will have a decreasing proportion of explained variance.
+
Cumulative Variance: The cumulative sum of the variance that is explained with each factor. The overall cumulative variance is how much of the original system’s variables are explained with this new reduced dimensionality. This is incredibly important because it shows us how much of the original data’s variance we are losing. If we are losing too much, then we need to reconsider how many factors we have reduced down to. If it is too close to 100% then we can theoretically reduce down more.
+
Ratio of Variance: Ratio of proportion of variance to cumulative variance.
+
+
+
+
+
+
+
+
Determine Significant Variables
-Any variable whose loading factor is greater than 0.5 or less than -0.5 at any point is significant and therefore needs to be included in the analysis. Any variable that is not significant in any factor can be eliminated. With the new dataset we can again re-run the factor analysis until only the variables that are significantly contributing to at least one factor are included.
+
+
+
+
+
Begin Iterative Loop
+
With the newly created list of variables that have a significant contribution to at least a single factor, we can eliminate those that are not contributing and re-run the factor analysis without it. This process is repeated until every variable is significantly contributing to at least one factor.
+
+
+
+
+
+
Compose the Final Index
-We first recalculate the final loadings and then for each factor, multiply the loading by the ratio of variance to scale the data. Therefore, factor 1 is rated higher than factor 2, factor 2 is rated higher than 3, and so on. From this, we can examine each factor to see which is the largest source of ‘vulnerability’ within the composite index (i.e., what is contributing the most). We create the composite index by adding each factor value together to calculate the unscaled composite index. The final composite index is minmax scaled so that the most vulnerable block group has a composite index of 1 and the least as a composite index of 0.
+
+
+
+
+
The purpose of the factor analysis and composite score index in the scope of social vulnerability is to determine which variables are most distinguishable across the study area. The variables with the highest variability are likely to influence the index the most and therefore they must be taken into context of the study area. For example, in Austin, TX a variable that is highly variant across the city is the percent of population that identifies as Hispanic. If this trait contributes to social vulnerability, then our index is working properly to identify areas of higher vulnerability. However, if we discovered that this trait does not contribute to social vulnerability, we cannot include it in this workflow because we are unintentionally weighting the index incorrectly. That is why it is imperative that indices are developed through collaborations with local experts to identify which variables are likely contributing to vulnerability.
Another commonly employed method to estimate social vulnerability is a ranking method, which was popularized by the Center for Disease Control’s Social Vulnerability Index[5]. This method is a more simplified way to produce an index for a given area. Each variable of interest is sorted from high to low and ranked. For each location within the study area, the ranks are summed so that locations with a higher overall rank have a greater vulnerability score. In our method, the final summed ranks are also minmax scaled so that the most vulnerable block group has a composite index of 1 and the least as a composite index of 0.
SVInsight comes with a standard set of 27 variables which come from the original principal component analysis method, SoVI®. We omit two variables from the original list of 29 due to data availability issues for all of the years and geographic boundaries of interest (the percentage of the population living in nursing facilities, and the number of hosptials per capita). It is important to note that the CDC SVI uses a list of 16 variables that are similar to those from the SoVI® method. Determining which or how many variables to use depends completely on the study area and objectives of the researcher/practitioner and can greatly influence the estimates. Special consideration must be taken to determine the most appropriate set of variables to use.
+
The variables, there definitions, and the American Community Survey 5-Year Estimate sources can be found below:
+
+
+
QAGDDEP: Percent of population under the age of 5 or over the age of 65
One of the outputs of the SVI workflow is an excel file containing documentation describing the characteristics of the index. This file can be found in the Documentation project folder and has the following naming convention:
Each tab of the excel sheet contains pertinent information regrading the iterative factor analysis SVI. The below subsections discusses what information is stored in each sheet and shows examples from an SVI run using the standard set of 27 variables (see background for more information) for Travis County, Texas in 2017 at the block group level.
This sheet shows what components are significantly contributing to each factor from the final factor analysis. The number of iterations can be determined based on the name of each factor. For example, in this run ‘F2’ means that this went through three rounds of factor analysis (zero-based numbering rules). As can be seen, there are 5 factors in the final index. Information on which variables contribute to which factor is useful in determining “themes” for the factors (e.g., wealth theme, social characteristics theme, etc.). In this example, one could identify the first factor as Social Status, and the second factor as Economic Status. These are for description only and will be unique based on the study area and year being analyzed. They may or may not be easily definiable into distinct themes, but do show what variables are correlated within a study area.
+
+
+
+
Table 1: Significant components for 2017 Travis County, Texas Block Group SVI estimate
Based on the variables within the final index, each has a loading factor associated with it for each factor. This information is critical in determining the weights for each variable for each factor when calculating the final index. For every boundary in the study area and for every factor, the variable is multipled by the loading factor and summed within each factor. This is how the factor scores are determined.
+
+
+
+
Table 2: Loading factors for 2017 Travis County, Texas Block Group SVI estimate
This shows the four main variance statistics associated with a factor analysis for each iteration.
+- SS Loadings: The sum of the squared loadings for the factor. If a factor’s SS loading is greater than 1 it is worth keeping.
+- Proportion Variance: The proportion of the variance that a factor accounts for. The first factor will have the highest proportion, due to our rotation earlier, and subsequent factors will have a decreasing proportion of explained variance. When calculating the final index, each factor is multipled by its proportion of variance to weight the variables.
+- Cumulative Variance: The cumulative sum of the variance that is explained with each factor. The overall cumulative variance is how much of the original system’s variables are explained with this new reduced dimensionality. This is important because it shows us how much of the original data’s variance we are losing. If we are losing too much, then we need to reconsider how many factors we have reduced down to. If it is too close to 100% then we can theoretically reduce down more.
+- Ratio Variance: Ratio of proportion of variance to cumulative variance.
+
For most studies, especially exploratory analyses, it is acceptable to have a cumulative variance as low as 60%, and sometimes as lowas 50% [1][2][3] .
+
+
+
+
Table 3: Iterative factor variance statistics for 2017 Travis County, Texas Block Group SVI estimate
contains the lists of what variables make the final index and which variables are excluded due to not significantly contributing to a any factors. This can be useful for double checking that the index is including variables you would likely expect to find given your study area.
This is a quick guide to using SVInsight. In this demonstration, we show how to load and create the SVInsight object, determine an area of interest, set a boundary level (e.g., tract or block groups), download the necessary boundary and raw data, calculate input variables, and compute the social vulnerability estimate.
+
First we load the SVInsight package, set the necessary variables, and create the SVInsight object.
+
>>> fromSVInsightimportSVInsightassvi
+>>> project_name='YOUR PROJECT NAME HERE'
+>>> file_path="YOUR PARENT FOLDER HERE"
+>>> api_key='YOUR CENSUS API KEY HERE'
+>>> geoids=['48453']
+>>> project=svi(project_name,file_path,api_key,geoids)
+
+
+
These variables create the instance of a project. Each have the following purpose:
+
+
project_name: This is the name of your project. In this case, it’s ‘Travis_County_SVI’. The project name is used when saving files throughout the project.
+
file_path: This is the path to the parent folder where your project files will be stored. Replace “YOUR PARENT FOLDER HERE” with the actual path. Creating the SVInsight object automatically creates the necessary file structure needed for your project based on the file_path and project_name.
+
api_key: This is your Census API key. Replace ‘YOUR CENSUS API KEY HERE’ with your actual API key. You can obtain an API key from the Census Bureau’s developer page.
+
geoids: This is a list of geographic identifiers for the areas you’re interested in. In this case, it’s [‘48453’], which represents Travis County, Texas. The necessary geographic identifiers for your state or county of interest can be obtained from this FCC page.
+
+
Each instance of SVInsight can be seen as a standalone project for a constant study area. Within each project, the user can explore different vulnerability estimates based on different years or boundaries (e.g., block group versus tract).
+
In this quickstart, we will examine a vulnerability estimate for Travis County using 2019 data at the block group level.
+
+
+
+
+
+
+
\ No newline at end of file
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diff --git a/_sources/Acknowledgements/acknowledgements.rst.txt b/_sources/Acknowledgements/acknowledgements.rst.txt
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+++ b/_sources/Acknowledgements/acknowledgements.rst.txt
@@ -0,0 +1,12 @@
+Acknowledgements
+================
+
+This work has been supported by the following grants:
+
++ National Science Foundation Graduate Research Fellowship (grant no. DGE-1610403)
++ Future Investigators in NASA Earth and Space Science and Technology (NASA FINESST, grant no. 21-EARTH21-0264)
++ Planet Texas 2050, a research grand challenge at the University of Texas at Austin.
++ U.S. Department of Energy, Office of Science, Biological and Environmental Research Program’s South-East Texas Urban Integrated Field Laboratory under Award Number DE-SC0023216.
+
+
+
diff --git a/_sources/Background/background.rst.txt b/_sources/Background/background.rst.txt
new file mode 100644
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--- /dev/null
+++ b/_sources/Background/background.rst.txt
@@ -0,0 +1,175 @@
+Background
+==========
+
+Motivation
+----------
+SVInsight was developed to provide an open-source tool for creating social vulnerability indices or SVIs based on user defined study areas and input variables. SVInsight uses two methodologies to create indices based on existing scholarly research: (1) a factor analysis method and (2) a rank method. This tool's purpose is to provide researchers with the ability to quickly create exploratory estimates of vulnerability based on existing and known relationships between socio-demographic variables and a community's sensativity (a proxy for vulnerability).
+
+
+While SVInsight is capable of rapidly producing tailor made estimates of relative vulnerability of a given region for researchers and/or practicitioners, it does not replace the in-depth investigation required to definitively define what makes someone more or less vulnerable. Indices created with SVInsight can be used as a starting point for understanding the complex relationship between socio-demographic variables and community characteristics including vulnerability, sensativity, and adaptive capacity by highlighting prominant geographic disparities. It is further important to note that vulnerability estimates are relative, and calculated scores should be taken in the context of the study area being examined.
+
+This package was specifically written in conjunction with past and ongoing projects at the Univeristy of Texas at Austin [2]_ [3]_ [4]_. More detailed infomration regarding funding sources can be found in the :doc:`Acknolwedgements section <../Acknowledgements/acknowledgements>`.
+
+
+Methodology
+-----------
+
+Data Source
+^^^^^^^^^^^
+All data comes from the American Community Survey 5-Year Estimates because they contain data at the Census block group and tract level. More information on data quality can be found on their `website `_.
+
+Iterative Factor Analysis
+^^^^^^^^^^^^^^^^^^^^^^^^^
+
+This methodology is heavily influenced by the original SoVI® [1]_ developed by Dr. Susan Cutter. By utilizing a data reduction methodology, a large subset of American Community Survey 5-Year Estimate variables can be dimensionally reduced and combined into a single index. Both principal component analyses and factor analyses are data reduction techniques that have been utilized to create vulnerability indices. A principal component analysis (PCA) reduces data by creating one or more index variables, or ‘components,’ by using a linear combination (i.e., a weighted average) from a larger set of measured variables. The purpose of a PCA is to determine the optimal number of components, the optimal choice of measured variables for each component, and the optimal weights. A factor analysis (FA) is a model of latent variables. A ‘latent variable’ cannot be directly measured with a single variable. A common example used to describe this is we cannot directly measure something like social anxiety, but we can measure whether someone has a high or low social anxiety based on a set of questions like “I am uncomfortable in large groups” or “ I get nervous talking to strangers.” We utilize a factor analysis approach because it is the data reduction methodology most often associated with creating composite indices, which is our end goal.
+
+The follow are the steps in the iterative factor analysis approach:
+
+
+1. **Scale the data**
+ - We standardize the data to a range of 0-1. Since all of the variable have a different scale, this makes each variable have an equal weight going into the analysis.
+
+2. **Conduct Initial Factor Analysis**
+ - Inputs for a factor analysis include the number of factors we are attempting to reduce the data down to, the rotation method, and the fitting method. For this first factor analysis, we set the number of factors to the number of variables under investigation (the maximum number of possible factors) in order to determine if there are any variables at the start we can eliminate for having too low of an influence. The rotation method used is varimax, or orthogonal rotation, which maximizes the sum of the variances of the squared loadings. In simpler terms, ‘loadings’ refer to the correlations between variables and factors. With varimax rotation, the resultant factors are uncorrelated with each other. The fitting method we use is minimum residual (‘minres’).
+
+3. **Calculate Eigenvalues**
+ -With our initial factors, we need to determine which ones are significant and which can be removed. If we stopped here, we would have reduced X variables to X factors, (i.e., we wouldn’t have reduced anything). Our goal is to find the weakest factors to eliminate so that while we are eliminating data, we maintain the highest level of reported variance in our final factors.
+ -The first step in doing this is calculating the eigenvalues, which is defined as a measure of how much of the common variance of the observed variables a factor explains. A factor with an eigenvalue greater than or equal to 1 explains more variance than a single observed variable and is therefore beneficial. This is known as the Kaiser Criterion. The number of factors whose eigenvalues is greater than 1 now becomes our new number of factors.
+
+4. **Recompute Factor Analysis based on Kaiser Criterion**
+ - Similar to step 2, we run the factor analysis again with the new optimized number of factors.
+
+5. **Calculate Loading Factors**
+ - The loading factor is the correlation coefficient for the variable and factor. It shows the variance explained by the variable on that particular factor. This will become the weight of the variable on that factor. Various standards exist on what makes a significant loading factor. For the sake of this research, we identify any loading factor that is greater than 0.5 or less than -0.5 is significant.
+
+6. **Calculate Variance Statistics**
+ -The variance statistics for each factor that we are interested in tracking are the SS Loadings, Proportion Variance, Cumulative Variance, and Ratio of Variance.
+ + *SS Loadings:* The sum of the squared loadings for the factor. If a factor’s SS loading is greater than 1 it is worth keeping.
+ + *Proportion Variance:* The proportion of the variance that a factor accounts for. The first factor will have the highest proportion, due to our rotation earlier, and subsequent factors will have a decreasing proportion of explained variance.
+ + *Cumulative Variance:* The cumulative sum of the variance that is explained with each factor. The overall cumulative variance is how much of the original system’s variables are explained with this new reduced dimensionality. This is incredibly important because it shows us how much of the original data’s variance we are losing. If we are losing too much, then we need to reconsider how many factors we have reduced down to. If it is too close to 100% then we can theoretically reduce down more.
+ + *Ratio of Variance:* Ratio of proportion of variance to cumulative variance.
+
+7. **Determine Significant Variables**
+ -Any variable whose loading factor is greater than 0.5 or less than -0.5 at any point is significant and therefore needs to be included in the analysis. Any variable that is not significant in any factor can be eliminated. With the new dataset we can again re-run the factor analysis until only the variables that are significantly contributing to at least one factor are included.
+
+8. **Begin Iterative Loop**
+ - With the newly created list of variables that have a significant contribution to at least a single factor, we can eliminate those that are not contributing and re-run the factor analysis without it. This process is repeated until every variable is significantly contributing to at least one factor.
+
+9. **Compose the Final Index**
+ -We first recalculate the final loadings and then for each factor, multiply the loading by the ratio of variance to scale the data. Therefore, factor 1 is rated higher than factor 2, factor 2 is rated higher than 3, and so on. From this, we can examine each factor to see which is the largest source of ‘vulnerability’ within the composite index (i.e., what is contributing the most). We create the composite index by adding each factor value together to calculate the unscaled composite index. The final composite index is minmax scaled so that the most vulnerable block group has a composite index of 1 and the least as a composite index of 0.
+
+The purpose of the factor analysis and composite score index in the scope of social vulnerability is to determine which variables are most distinguishable across the study area. The variables with the highest variability are likely to influence the index the most and therefore they must be taken into context of the study area. For example, in Austin, TX a variable that is highly variant across the city is the percent of population that identifies as Hispanic. If this trait contributes to social vulnerability, then our index is working properly to identify areas of higher vulnerability. However, if we discovered that this trait does not contribute to social vulnerability, we cannot include it in this workflow because we are unintentionally weighting the index incorrectly. That is why it is *imperative that indices are developed through collaborations with local experts* to identify which variables are likely contributing to vulnerability.
+
+
+
+Rank Method
+^^^^^^^^^^^
+Another commonly employed method to estimate social vulnerability is a ranking method, which was popularized by the `Center for Disease Control's Social Vulnerability Index `_ [5]_. This method is a more simplified way to produce an index for a given area. Each variable of interest is sorted from high to low and ranked. For each location within the study area, the ranks are summed so that locations with a higher overall rank have a greater vulnerability score. In our method, the final summed ranks are also minmax scaled so that the most vulnerable block group has a composite index of 1 and the least as a composite index of 0.
+
+
+
+Variables
+---------
+SVInsight comes with a standard set of 27 variables which come from the original principal component analysis method, SoVI®. We omit two variables from the original list of 29 due to data availability issues for all of the years and geographic boundaries of interest (the percentage of the population living in nursing facilities, and the number of hosptials per capita). It is important to note that the CDC SVI uses a list of 16 variables that are similar to those from the SoVI® method. Determining which or how many variables to use depends completely on the study area and objectives of the researcher/practitioner and can greatly influence the estimates. Special consideration must be taken to determine the most appropriate set of variables to use.
+
+The variables, there definitions, and the American Community Survey 5-Year Estimate sources can be found below:
+
+
++ QAGDDEP: Percent of population under the age of 5 or over the age of 65
+ - ['B01001_001E', 'B01001_026E', 'B01001_003E', 'B01001_020E', 'B01001_021E', 'B01001_022E', 'B01001_023E', 'B01001_024E', 'B01001_025E', 'B01001_027E', 'B01001_044E', 'B01001_045E', 'B01001_046E', 'B01001_047E', 'B01001_048E', 'B01001_049E']
+
++ QFEMALE: Percent of population that is female
+ - ['B01001_001E', 'B01001_026E']
+
++ MEDAGE: Median age
+ - ['B01002_001E']
+
++ QBLACK: Percent of population that is non-Hispanic Black/African-American
+ - ['B03002_001E', 'B03002_004E']
+
++ QNATIVE: Percent of population that is non-Hispanic Native American
+ - ['B03002_001E', 'B03002_005E']
+
++ QASIAN: Percent of population that is non-Hispanic Asian
+ - ['B03002_001E', 'B03002_006E']
+
++ QHISPC: Percent of population that is Hispanic
+ - ['B03002_001E', 'B03002_012E']
+
++ QFAM: Percent of families where only one spouse is present in the household
+ - ['B11005_003E', 'B11005_005E']
+
++ PPUNIT: People per unit, or average household size
+ - ['B25010_001E']
+
++ QFHH: Percent of households with Female householder and no spouse present
+ - ['B11001_001E', 'B11001_006E']
+
++ QEDLESHI: Percent of population over the age of 25 with less than a high school diploma (or equivalent)
+ - ['B15003_001E', 'B15003_002E', 'B15003_003E', 'B15003_004E', 'B15003_005E', 'B15003_006E', 'B15003_007E', 'B15003_008E', 'B15003_009E', 'B15003_010E', 'B15003_011E', 'B15003_012E', 'B15003_013E', 'B15003_014E', 'B15003_015E', 'B15003_016E']
+
++ QCVLUN: Percent of civilian population over the age of 15 that is unemployed
+ - ['B23025_003E', 'B23025_005E']
+
++ QRICH: Percent of households earning over $200,000 annually (inversely related to vulnerability)
+ - ['B19001_001E', 'B19001_017E']
+
++ QSSBEN: Percent of houseolds with social security income
+ - ['B19055_001E', 'B19055_002E']
+
++ PERCAP: Per capita income in the past 12 months (inversely related to vulnerability)
+ - ['B19301_001E']
+
++ QRENTER: Percent of households that are renters
+ - ['B25003_001E', 'B25003_003E']
+
++ QUNOCCHU: Percent of housing units that are unoccupied
+ - ['B25002_001E', 'B25002_003E']
+
++ QMOHO: Percent of housing unts that are mobile homes
+ - ['B25024_001E', 'B25024_010E']
+
++ MDHSEVAL: Median housing value (inversely related to vulnerability)
+ - ['B25077_001E']
+
++ MDGRENT: Median gross rent
+ - ['B25064_001E']
+
++ QPOVTY: Percent of population whose income in the past 12 months was below the poverty level
+ - ['B17021_001E', 'B17021_002E']
+
++ QNOAUTO: Percent of households without access to a car
+ - ['B25044_001E', 'B25044_003E', 'B25044_010E']
+
++ QNOHLTH: Percent of population without health insurance
+ - ['B27001_001E', 'B27001_005E', 'B27001_008E', 'B27001_011E', 'B27001_014E', 'B27001_017E', 'B27001_020E', 'B27001_023E', 'B27001_026E', 'B27001_029E', 'B27001_033E', 'B27001_036E', 'B27001_039E', 'B27001_042E', 'B27001_045E', 'B27001_048E', 'B27001_051E', 'B27001_054E', 'B27001_057E']
+
++ QESL: Percent of population who speaks English "not well" or "not at all"
+ - ['B16004_001E', 'B16004_007E', 'B16004_008E', 'B16004_012E', 'B16004_013E', 'B16004_017E', 'B16004_018E', 'B16004_022E', 'B16004_023E', 'B16004_029E', 'B16004_030E', 'B16004_034E', 'B16004_035E', 'B16004_039E', 'B16004_040E', 'B16004_044E', 'B16004_045E', 'B16004_051E', 'B16004_052E', 'B16004_056E', 'B16004_057E', 'B16004_061E', 'B16004_062E', 'B16004_066E', 'B16004_067E']
+
++ QFEMLBR: Percent of the civilian employed population over the age of 16 that is female
+ - ['C24010_001E', 'C24010_038E']
+
++ QSERV: Percent of the civilian employed population that has a service occupation
+ - ['C24010_001E', 'C24010_019E', 'C24010_055E']
+
++ QEXTRCT: Percent of the civilian employed population that has a construction and extraction occupation
+ - ['C24010_001E', 'C24010_032E', 'C24010_068E']
+
+
+
+
+
+
+References
+----------
+
+.. [1] Cutter, S. L., Boruff, B. J., & Shirley, W. L. (2012). Social vulnerability to environmental hazards. In Hazards vulnerability and environmental justice (pp. 143-160). Routledge.
+
+.. [2] Bixler, R. P., Yang, E., Richter, S. M., & Coudert, M. (2021). Boundary crossing for urban community resilience: A social vulnerability and multi-hazard approach in Austin, Texas, USA. International Journal of Disaster Risk Reduction, 66, 102613.
+
+.. [3] Preisser, M., Passalacqua, P., Bixler, R. P., & Hofmann, J. (2022). Intersecting near-real time fluvial and pluvial inundation estimates with sociodemographic vulnerability to quantify a household flood impact index. Hydrology and Earth System Sciences, 26(15), 3941-3964.
+
+.. [4] Preisser, M., Passalacqua, P., Bixler, R. P., & Boyles, S. (2023). A network-based analysis of critical resource accessibility during floods. Frontiers in Water, 5, 1278205.
+
+.. [5] Flanagan, B. E., Gregory, E. W., Hallisey, E. J., Heitgerd, J. L., & Lewis, B. (2011). A social vulnerability index for disaster management. Journal of homeland security and emergency management, 8(1), 0000102202154773551792.
\ No newline at end of file
diff --git a/_sources/Background/paper.rst.txt b/_sources/Background/paper.rst.txt
new file mode 100644
index 0000000..035061d
--- /dev/null
+++ b/_sources/Background/paper.rst.txt
@@ -0,0 +1,4 @@
+Paper
+=====
+
+A paper accompanying this software package will be published in the Journal of Open Source Software. Once published, it will be linked here.
\ No newline at end of file
diff --git a/_sources/Background/understanding.rst.txt b/_sources/Background/understanding.rst.txt
new file mode 100644
index 0000000..1645af8
--- /dev/null
+++ b/_sources/Background/understanding.rst.txt
@@ -0,0 +1,76 @@
+Understanding Iterative Factor Analysis Results
+===============================================
+
+One of the outputs of the SVI workflow is an excel file containing documentation describing the characteristics of the index. This file can be found in the Documentation project folder and has the following naming convention:
+
+- folder location: ``{file_path}/{project_name}/{Documentation}/``
+- file name: ``{project_name}_{year}_{boundadry}_{config_file}.xlsx``
+
+Each tab of the excel sheet contains pertinent information regrading the iterative factor analysis SVI. The below subsections discusses what information is stored in each sheet and shows examples from an SVI run using the standard set of 27 variables (see :doc:`background <../Background/background>` for more information) for Travis County, Texas in 2017 at the block group level.
+
+
+Significant Components
+----------------------
+This sheet shows what components are significantly contributing to each factor from the final factor analysis. The number of iterations can be determined based on the name of each factor. For example, in this run *'F2'* means that this went through three rounds of factor analysis (zero-based numbering rules). As can be seen, there are 5 factors in the final index. Information on which variables contribute to which factor is useful in determining "themes" for the factors (e.g., wealth theme, social characteristics theme, etc.). In this example, one could identify the first factor as Social Status, and the second factor as Economic Status. These are for description only and will be unique based on the study area and year being analyzed. They may or may not be easily definiable into distinct themes, but do show what variables are correlated within a study area.
+
+.. figure:: ../Background/sig_comp_.pdf
+ :alt: Table of significant components for 2017 Travis County, Texas Block Group SVI estimate
+
+*Table 1: Significant components for 2017 Travis County, Texas Block Group SVI estimate*
+
+
+
+Loading Factors
+---------------
+Based on the variables within the final index, each has a loading factor associated with it for each factor. This information is critical in determining the weights for each variable for each factor when calculating the final index. For every boundary in the study area and for every factor, the variable is multipled by the loading factor and summed within each factor. This is how the factor scores are determined.
+
+.. figure:: ../Background/loading_fac_.pdf
+ :alt: Table of loading factors for 2017 Travis County, Texas Block Group SVI estimate
+
+*Table 2: Loading factors for 2017 Travis County, Texas Block Group SVI estimate*
+
+
+
+All Refactor Variances
+----------------------
+This shows the four main variance statistics associated with a factor analysis for each iteration.
+- SS Loadings: The sum of the squared loadings for the factor. If a factor’s SS loading is greater than 1 it is worth keeping.
+- Proportion Variance: The proportion of the variance that a factor accounts for. The first factor will have the highest proportion, due to our rotation earlier, and subsequent factors will have a decreasing proportion of explained variance. **When calculating the final index, each factor is multipled by its proportion of variance to weight the variables.**
+- Cumulative Variance: The cumulative sum of the variance that is explained with each factor. The overall cumulative variance is how much of the original system’s variables are explained with this new reduced dimensionality. **This is important because it shows us how much of the original data’s variance we are losing.** If we are losing too much, then we need to reconsider how many factors we have reduced down to. If it is too close to 100% then we can theoretically reduce down more.
+- Ratio Variance: Ratio of proportion of variance to cumulative variance.
+
+For most studies, especially exploratory analyses, it is acceptable to have a cumulative variance as low as 60%, and sometimes as lowas 50% [1]_ [2]_ [3]_ .
+
+.. figure:: ../Background/all_refac_.pdf
+ :alt: Table of all factor variance statistics for 2017 Travis County, Texas Block Group SVI estimate
+
+*Table 3: Iterative factor variance statistics for 2017 Travis County, Texas Block Group SVI estimate*
+
+
+
+Final Variances
+---------------
+An abreviated version of All Refactor Variances, showing only the final table for the final iteration.
+
+.. figure:: ../Background/final_fac_.pdf
+ :alt: Table of Final factor analysis variance statistics for 2017 Travis County, Texas Block Group SVI estimate
+
+*Table 4: Final factor analysis variance statistics for 2017 Travis County, Texas Block Group SVI estimate*
+
+
+
+Included and Excluded
+---------------------
+contains the lists of what variables make the final index and which variables are excluded due to not significantly contributing to a any factors. This can be useful for double checking that the index is including variables you would likely expect to find given your study area.
+
+
+
+References
+----------
+
+.. [1] Hair, J. S., Black, W. C., Babin, B. J., Anderson, R. E. & Tatham, R. L. (2006). Multivariate Data Analysis. New Jersey: Prentice-Hall.
+
+.. [2] Peterson, R. A. (2000). A Meta-Analysis of Variance Accounted for and Factor Loadings in Exploratory Factor Analysis. Marketing Letters, 11(3), 261–275. http://www.jstor.org/stable/40239882
+
+.. [3] Advice for Exploratory Factor Analyses: `Link `_
+
diff --git a/_sources/Examples/examples.rst.txt b/_sources/Examples/examples.rst.txt
new file mode 100644
index 0000000..94ee91e
--- /dev/null
+++ b/_sources/Examples/examples.rst.txt
@@ -0,0 +1,4 @@
+Examples
+========
+
+Examples will go here.
\ No newline at end of file
diff --git a/_sources/Getting_Started/install.rst.txt b/_sources/Getting_Started/install.rst.txt
new file mode 100644
index 0000000..26595f4
--- /dev/null
+++ b/_sources/Getting_Started/install.rst.txt
@@ -0,0 +1,21 @@
+Install
+=======
+
+Installation via *pip*
+----------------------
+
+This package will be pip installable soon:
+
+.. code-block:: console
+
+ $ pip install SVInsight
+
+Installation via *conda*
+------------------------
+
+This package will be conda installable soon:
+
+
+.. code-block:: console
+
+ $ conda install SVInsight
\ No newline at end of file
diff --git a/_sources/Getting_Started/quickstart.rst.txt b/_sources/Getting_Started/quickstart.rst.txt
new file mode 100644
index 0000000..c044038
--- /dev/null
+++ b/_sources/Getting_Started/quickstart.rst.txt
@@ -0,0 +1,57 @@
+Getting Started
+===============
+
+Quickstart
+----------
+
+This is a quick guide to using SVInsight. In this demonstration, we show how to load and create the SVInsight object, determine an area of interest, set a boundary level (e.g., tract or block groups), download the necessary boundary and raw data, calculate input variables, and compute the social vulnerability estimate.
+
+First we load the SVInsight package, set the necessary variables, and create the SVInsight object.
+
+.. doctest::
+
+ >>> from SVInsight import SVInsight as svi
+ >>> project_name = 'YOUR PROJECT NAME HERE'
+ >>> file_path = "YOUR PARENT FOLDER HERE"
+ >>> api_key = 'YOUR CENSUS API KEY HERE'
+ >>> geoids = ['48453']
+ >>> project = svi(project_name, file_path, api_key, geoids)
+
+These variables create the instance of a project. Each have the following purpose:
+
+- ``project_name``: This is the name of your project. In this case, it's 'Travis_County_SVI'. The project name is used when saving files throughout the project.
+- ``file_path``: This is the path to the parent folder where your project files will be stored. Replace "YOUR PARENT FOLDER HERE" with the actual path. Creating the SVInsight object automatically creates the necessary file structure needed for your project based on the ``file_path`` and ``project_name``.
+- ``api_key``: This is your Census API key. Replace 'YOUR CENSUS API KEY HERE' with your actual API key. You can obtain an API key from the `Census Bureau's developer page `_.
+- ``geoids``: This is a list of geographic identifiers for the areas you're interested in. In this case, it's ['48453'], which represents Travis County, Texas. The necessary geographic identifiers for your state or county of interest can be obtained from this `FCC page `_.
+
+
+Each instance of SVInsight can be seen as a standalone project for a constant study area. Within each project, the user can explore different vulnerability estimates based on different years or boundaries (e.g., block group versus tract).
+
+In this quickstart, we will examine a vulnerability estimate for Travis County using 2019 data at the block group level.
+
+.. doctest::
+
+ >>> boundary = 'bg'
+ >>> year = 2019
+ >>> config_file = 'config_file'
+
+The simplest workflow is as follows:
+
+(1) Extract boundaries
+(2) Extract Census data
+(3) Configure variables
+(4) Calculate vulnerability estimate
+
+.. doctest::
+
+ >>> project.boundaries_data(boundary, year)
+ >>> project.census_data(boundary, year)
+ >>> project.configure_variables(config_file)
+ >>> project.calculate_svi(config_file, boundary, year)
+
+The output SVI .csv and .gpkg files built will be saved with the following file format:
+
+- folder location: ``{file_path}/{project_name}/{SVIs}/``
+- file name: ``{project_name}_{year}_{boundadry}_{config_file}_svi``
+
+More detailed examples of the complete functionality of SVInsight can be found under :doc:`Examples <../Examples/examples>`.
\ No newline at end of file
diff --git a/_sources/User_Guide/userguide.rst.txt b/_sources/User_Guide/userguide.rst.txt
new file mode 100644
index 0000000..85cf9ad
--- /dev/null
+++ b/_sources/User_Guide/userguide.rst.txt
@@ -0,0 +1,4 @@
+User Guide
+==========
+
+User guide will go here.
\ No newline at end of file
diff --git a/_sources/apiref/apiref.rst.txt b/_sources/apiref/apiref.rst.txt
new file mode 100644
index 0000000..dc345c8
--- /dev/null
+++ b/_sources/apiref/apiref.rst.txt
@@ -0,0 +1,7 @@
+SVInsight
+=========
+
+
+.. automodule:: SVInsight.svi
+ :members:
+ :special-members:
\ No newline at end of file
diff --git a/_sources/apiref/license.rst.txt b/_sources/apiref/license.rst.txt
new file mode 100644
index 0000000..a1c9299
--- /dev/null
+++ b/_sources/apiref/license.rst.txt
@@ -0,0 +1,6 @@
+License
+=======
+
+This project is provided under the `MIT License `_.
+
+
diff --git a/_sources/index.rst.txt b/_sources/index.rst.txt
new file mode 100644
index 0000000..e11ebed
--- /dev/null
+++ b/_sources/index.rst.txt
@@ -0,0 +1,72 @@
+.. SVInsight documentation master file, created by
+ sphinx-quickstart on Wed Apr 10 16:47:20 2024.
+ You can adapt this file completely to your liking, but it should at least
+ contain the root `toctree` directive.
+
+SVInsight
+=========
+
+**SVInsight** is a python package for calculating an exploratory social vulnerability index. This package calculates SVI using two methods: (1) an iterative factor analysis method and (2) a rank method, both of which have been heavily utilized in scholarly research. This package automates the creation and comparions of indices using U.S. American Community Survey 5-Year Data (ACS5) at the block group or tract level.
+
+.. note::
+
+ This project is under active development and this website is currently being developed. Please pardon our progress as we finish updating the documentation in the coming weeks (as of April 16th, 2024).
+
+Getting Started
+###############
+.. toctree::
+ :maxdepth: 2
+
+ Getting_Started/install
+ Getting_Started/quickstart
+
+
+Background
+##########
+.. toctree::
+ :maxdepth: 1
+
+ Background/background
+ Background/understanding
+ Background/paper
+
+
+User Guide
+##########
+.. toctree::
+ :maxdepth: 2
+
+ User_Guide/userguide
+
+API Reference
+#############
+.. toctree::
+ :maxdepth: 1
+
+ apiref/apiref
+ apiref/license
+
+
+Examples
+########
+.. toctree::
+ :maxdepth: 2
+
+ Examples/examples
+
+
+Acknowledgements
+################
+.. toctree::
+ :maxdepth: 2
+
+ Acknowledgements/acknowledgements
+
+
+
+Indices and tables
+==================
+
+* :ref:`genindex`
+* :ref:`modindex`
+* :ref:`search`
diff --git a/_static/_sphinx_javascript_frameworks_compat.js b/_static/_sphinx_javascript_frameworks_compat.js
new file mode 100644
index 0000000..8141580
--- /dev/null
+++ b/_static/_sphinx_javascript_frameworks_compat.js
@@ -0,0 +1,123 @@
+/* Compatability shim for jQuery and underscores.js.
+ *
+ * Copyright Sphinx contributors
+ * Released under the two clause BSD licence
+ */
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+/**
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+ var addItems = [];
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+
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+ */
+if (!jQuery.browser) {
+ jQuery.uaMatch = function(ua) {
+ ua = ua.toLowerCase();
+
+ var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
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+
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+ jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;
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new file mode 100644
index 0000000..f316efc
--- /dev/null
+++ b/_static/basic.css
@@ -0,0 +1,925 @@
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+ * basic.css
+ * ~~~~~~~~~
+ *
+ * Sphinx stylesheet -- basic theme.
+ *
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
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+/* -- main layout ----------------------------------------------------------- */
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+ font-weight: bold;
+}
+
+img.align-left, figure.align-left, .figure.align-left, object.align-left {
+ clear: left;
+ float: left;
+ margin-right: 1em;
+}
+
+img.align-right, figure.align-right, .figure.align-right, object.align-right {
+ clear: right;
+ float: right;
+ margin-left: 1em;
+}
+
+img.align-center, figure.align-center, .figure.align-center, object.align-center {
+ display: block;
+ margin-left: auto;
+ margin-right: auto;
+}
+
+img.align-default, figure.align-default, .figure.align-default {
+ display: block;
+ margin-left: auto;
+ margin-right: auto;
+}
+
+.align-left {
+ text-align: left;
+}
+
+.align-center {
+ text-align: center;
+}
+
+.align-default {
+ text-align: center;
+}
+
+.align-right {
+ text-align: right;
+}
+
+/* -- sidebars -------------------------------------------------------------- */
+
+div.sidebar,
+aside.sidebar {
+ margin: 0 0 0.5em 1em;
+ border: 1px solid #ddb;
+ padding: 7px;
+ background-color: #ffe;
+ width: 40%;
+ float: right;
+ clear: right;
+ overflow-x: auto;
+}
+
+p.sidebar-title {
+ font-weight: bold;
+}
+
+nav.contents,
+aside.topic,
+div.admonition, div.topic, blockquote {
+ clear: left;
+}
+
+/* -- topics ---------------------------------------------------------------- */
+
+nav.contents,
+aside.topic,
+div.topic {
+ border: 1px solid #ccc;
+ padding: 7px;
+ margin: 10px 0 10px 0;
+}
+
+p.topic-title {
+ font-size: 1.1em;
+ font-weight: bold;
+ margin-top: 10px;
+}
+
+/* -- admonitions ----------------------------------------------------------- */
+
+div.admonition {
+ margin-top: 10px;
+ margin-bottom: 10px;
+ padding: 7px;
+}
+
+div.admonition dt {
+ font-weight: bold;
+}
+
+p.admonition-title {
+ margin: 0px 10px 5px 0px;
+ font-weight: bold;
+}
+
+div.body p.centered {
+ text-align: center;
+ margin-top: 25px;
+}
+
+/* -- content of sidebars/topics/admonitions -------------------------------- */
+
+div.sidebar > :last-child,
+aside.sidebar > :last-child,
+nav.contents > :last-child,
+aside.topic > :last-child,
+div.topic > :last-child,
+div.admonition > :last-child {
+ margin-bottom: 0;
+}
+
+div.sidebar::after,
+aside.sidebar::after,
+nav.contents::after,
+aside.topic::after,
+div.topic::after,
+div.admonition::after,
+blockquote::after {
+ display: block;
+ content: '';
+ clear: both;
+}
+
+/* -- tables ---------------------------------------------------------------- */
+
+table.docutils {
+ margin-top: 10px;
+ margin-bottom: 10px;
+ border: 0;
+ border-collapse: collapse;
+}
+
+table.align-center {
+ margin-left: auto;
+ margin-right: auto;
+}
+
+table.align-default {
+ margin-left: auto;
+ margin-right: auto;
+}
+
+table caption span.caption-number {
+ font-style: italic;
+}
+
+table caption span.caption-text {
+}
+
+table.docutils td, table.docutils th {
+ padding: 1px 8px 1px 5px;
+ border-top: 0;
+ border-left: 0;
+ border-right: 0;
+ border-bottom: 1px solid #aaa;
+}
+
+th {
+ text-align: left;
+ padding-right: 5px;
+}
+
+table.citation {
+ border-left: solid 1px gray;
+ margin-left: 1px;
+}
+
+table.citation td {
+ border-bottom: none;
+}
+
+th > :first-child,
+td > :first-child {
+ margin-top: 0px;
+}
+
+th > :last-child,
+td > :last-child {
+ margin-bottom: 0px;
+}
+
+/* -- figures --------------------------------------------------------------- */
+
+div.figure, figure {
+ margin: 0.5em;
+ padding: 0.5em;
+}
+
+div.figure p.caption, figcaption {
+ padding: 0.3em;
+}
+
+div.figure p.caption span.caption-number,
+figcaption span.caption-number {
+ font-style: italic;
+}
+
+div.figure p.caption span.caption-text,
+figcaption span.caption-text {
+}
+
+/* -- field list styles ----------------------------------------------------- */
+
+table.field-list td, table.field-list th {
+ border: 0 !important;
+}
+
+.field-list ul {
+ margin: 0;
+ padding-left: 1em;
+}
+
+.field-list p {
+ margin: 0;
+}
+
+.field-name {
+ -moz-hyphens: manual;
+ -ms-hyphens: manual;
+ -webkit-hyphens: manual;
+ hyphens: manual;
+}
+
+/* -- hlist styles ---------------------------------------------------------- */
+
+table.hlist {
+ margin: 1em 0;
+}
+
+table.hlist td {
+ vertical-align: top;
+}
+
+/* -- object description styles --------------------------------------------- */
+
+.sig {
+ font-family: 'Consolas', 'Menlo', 'DejaVu Sans Mono', 'Bitstream Vera Sans Mono', monospace;
+}
+
+.sig-name, code.descname {
+ background-color: transparent;
+ font-weight: bold;
+}
+
+.sig-name {
+ font-size: 1.1em;
+}
+
+code.descname {
+ font-size: 1.2em;
+}
+
+.sig-prename, code.descclassname {
+ background-color: transparent;
+}
+
+.optional {
+ font-size: 1.3em;
+}
+
+.sig-paren {
+ font-size: larger;
+}
+
+.sig-param.n {
+ font-style: italic;
+}
+
+/* C++ specific styling */
+
+.sig-inline.c-texpr,
+.sig-inline.cpp-texpr {
+ font-family: unset;
+}
+
+.sig.c .k, .sig.c .kt,
+.sig.cpp .k, .sig.cpp .kt {
+ color: #0033B3;
+}
+
+.sig.c .m,
+.sig.cpp .m {
+ color: #1750EB;
+}
+
+.sig.c .s, .sig.c .sc,
+.sig.cpp .s, .sig.cpp .sc {
+ color: #067D17;
+}
+
+
+/* -- other body styles ----------------------------------------------------- */
+
+ol.arabic {
+ list-style: decimal;
+}
+
+ol.loweralpha {
+ list-style: lower-alpha;
+}
+
+ol.upperalpha {
+ list-style: upper-alpha;
+}
+
+ol.lowerroman {
+ list-style: lower-roman;
+}
+
+ol.upperroman {
+ list-style: upper-roman;
+}
+
+:not(li) > ol > li:first-child > :first-child,
+:not(li) > ul > li:first-child > :first-child {
+ margin-top: 0px;
+}
+
+:not(li) > ol > li:last-child > :last-child,
+:not(li) > ul > li:last-child > :last-child {
+ margin-bottom: 0px;
+}
+
+ol.simple ol p,
+ol.simple ul p,
+ul.simple ol p,
+ul.simple ul p {
+ margin-top: 0;
+}
+
+ol.simple > li:not(:first-child) > p,
+ul.simple > li:not(:first-child) > p {
+ margin-top: 0;
+}
+
+ol.simple p,
+ul.simple p {
+ margin-bottom: 0;
+}
+
+aside.footnote > span,
+div.citation > span {
+ float: left;
+}
+aside.footnote > span:last-of-type,
+div.citation > span:last-of-type {
+ padding-right: 0.5em;
+}
+aside.footnote > p {
+ margin-left: 2em;
+}
+div.citation > p {
+ margin-left: 4em;
+}
+aside.footnote > p:last-of-type,
+div.citation > p:last-of-type {
+ margin-bottom: 0em;
+}
+aside.footnote > p:last-of-type:after,
+div.citation > p:last-of-type:after {
+ content: "";
+ clear: both;
+}
+
+dl.field-list {
+ display: grid;
+ grid-template-columns: fit-content(30%) auto;
+}
+
+dl.field-list > dt {
+ font-weight: bold;
+ word-break: break-word;
+ padding-left: 0.5em;
+ padding-right: 5px;
+}
+
+dl.field-list > dd {
+ padding-left: 0.5em;
+ margin-top: 0em;
+ margin-left: 0em;
+ margin-bottom: 0em;
+}
+
+dl {
+ margin-bottom: 15px;
+}
+
+dd > :first-child {
+ margin-top: 0px;
+}
+
+dd ul, dd table {
+ margin-bottom: 10px;
+}
+
+dd {
+ margin-top: 3px;
+ margin-bottom: 10px;
+ margin-left: 30px;
+}
+
+.sig dd {
+ margin-top: 0px;
+ margin-bottom: 0px;
+}
+
+.sig dl {
+ margin-top: 0px;
+ margin-bottom: 0px;
+}
+
+dl > dd:last-child,
+dl > dd:last-child > :last-child {
+ margin-bottom: 0;
+}
+
+dt:target, span.highlighted {
+ background-color: #fbe54e;
+}
+
+rect.highlighted {
+ fill: #fbe54e;
+}
+
+dl.glossary dt {
+ font-weight: bold;
+ font-size: 1.1em;
+}
+
+.versionmodified {
+ font-style: italic;
+}
+
+.system-message {
+ background-color: #fda;
+ padding: 5px;
+ border: 3px solid red;
+}
+
+.footnote:target {
+ background-color: #ffa;
+}
+
+.line-block {
+ display: block;
+ margin-top: 1em;
+ margin-bottom: 1em;
+}
+
+.line-block .line-block {
+ margin-top: 0;
+ margin-bottom: 0;
+ margin-left: 1.5em;
+}
+
+.guilabel, .menuselection {
+ font-family: sans-serif;
+}
+
+.accelerator {
+ text-decoration: underline;
+}
+
+.classifier {
+ font-style: oblique;
+}
+
+.classifier:before {
+ font-style: normal;
+ margin: 0 0.5em;
+ content: ":";
+ display: inline-block;
+}
+
+abbr, acronym {
+ border-bottom: dotted 1px;
+ cursor: help;
+}
+
+.translated {
+ background-color: rgba(207, 255, 207, 0.2)
+}
+
+.untranslated {
+ background-color: rgba(255, 207, 207, 0.2)
+}
+
+/* -- code displays --------------------------------------------------------- */
+
+pre {
+ overflow: auto;
+ overflow-y: hidden; /* fixes display issues on Chrome browsers */
+}
+
+pre, div[class*="highlight-"] {
+ clear: both;
+}
+
+span.pre {
+ -moz-hyphens: none;
+ -ms-hyphens: none;
+ -webkit-hyphens: none;
+ hyphens: none;
+ white-space: nowrap;
+}
+
+div[class*="highlight-"] {
+ margin: 1em 0;
+}
+
+td.linenos pre {
+ border: 0;
+ background-color: transparent;
+ color: #aaa;
+}
+
+table.highlighttable {
+ display: block;
+}
+
+table.highlighttable tbody {
+ display: block;
+}
+
+table.highlighttable tr {
+ display: flex;
+}
+
+table.highlighttable td {
+ margin: 0;
+ padding: 0;
+}
+
+table.highlighttable td.linenos {
+ padding-right: 0.5em;
+}
+
+table.highlighttable td.code {
+ flex: 1;
+ overflow: hidden;
+}
+
+.highlight .hll {
+ display: block;
+}
+
+div.highlight pre,
+table.highlighttable pre {
+ margin: 0;
+}
+
+div.code-block-caption + div {
+ margin-top: 0;
+}
+
+div.code-block-caption {
+ margin-top: 1em;
+ padding: 2px 5px;
+ font-size: small;
+}
+
+div.code-block-caption code {
+ background-color: transparent;
+}
+
+table.highlighttable td.linenos,
+span.linenos,
+div.highlight span.gp { /* gp: Generic.Prompt */
+ user-select: none;
+ -webkit-user-select: text; /* Safari fallback only */
+ -webkit-user-select: none; /* Chrome/Safari */
+ -moz-user-select: none; /* Firefox */
+ -ms-user-select: none; /* IE10+ */
+}
+
+div.code-block-caption span.caption-number {
+ padding: 0.1em 0.3em;
+ font-style: italic;
+}
+
+div.code-block-caption span.caption-text {
+}
+
+div.literal-block-wrapper {
+ margin: 1em 0;
+}
+
+code.xref, a code {
+ background-color: transparent;
+ font-weight: bold;
+}
+
+h1 code, h2 code, h3 code, h4 code, h5 code, h6 code {
+ background-color: transparent;
+}
+
+.viewcode-link {
+ float: right;
+}
+
+.viewcode-back {
+ float: right;
+ font-family: sans-serif;
+}
+
+div.viewcode-block:target {
+ margin: -1px -10px;
+ padding: 0 10px;
+}
+
+/* -- math display ---------------------------------------------------------- */
+
+img.math {
+ vertical-align: middle;
+}
+
+div.body div.math p {
+ text-align: center;
+}
+
+span.eqno {
+ float: right;
+}
+
+span.eqno a.headerlink {
+ position: absolute;
+ z-index: 1;
+}
+
+div.math:hover a.headerlink {
+ visibility: visible;
+}
+
+/* -- printout stylesheet --------------------------------------------------- */
+
+@media print {
+ div.document,
+ div.documentwrapper,
+ div.bodywrapper {
+ margin: 0 !important;
+ width: 100%;
+ }
+
+ div.sphinxsidebar,
+ div.related,
+ div.footer,
+ #top-link {
+ display: none;
+ }
+}
\ No newline at end of file
diff --git a/_static/debug.css b/_static/debug.css
new file mode 100644
index 0000000..74d4aec
--- /dev/null
+++ b/_static/debug.css
@@ -0,0 +1,69 @@
+/*
+ This CSS file should be overridden by the theme authors. It's
+ meant for debugging and developing the skeleton that this theme provides.
+*/
+body {
+ font-family: -apple-system, "Segoe UI", Roboto, Helvetica, Arial, sans-serif,
+ "Apple Color Emoji", "Segoe UI Emoji";
+ background: lavender;
+}
+.sb-announcement {
+ background: rgb(131, 131, 131);
+}
+.sb-announcement__inner {
+ background: black;
+ color: white;
+}
+.sb-header {
+ background: lightskyblue;
+}
+.sb-header__inner {
+ background: royalblue;
+ color: white;
+}
+.sb-header-secondary {
+ background: lightcyan;
+}
+.sb-header-secondary__inner {
+ background: cornflowerblue;
+ color: white;
+}
+.sb-sidebar-primary {
+ background: lightgreen;
+}
+.sb-main {
+ background: blanchedalmond;
+}
+.sb-main__inner {
+ background: antiquewhite;
+}
+.sb-header-article {
+ background: lightsteelblue;
+}
+.sb-article-container {
+ background: snow;
+}
+.sb-article-main {
+ background: white;
+}
+.sb-footer-article {
+ background: lightpink;
+}
+.sb-sidebar-secondary {
+ background: lightgoldenrodyellow;
+}
+.sb-footer-content {
+ background: plum;
+}
+.sb-footer-content__inner {
+ background: palevioletred;
+}
+.sb-footer {
+ background: pink;
+}
+.sb-footer__inner {
+ background: salmon;
+}
+.sb-article {
+ background: white;
+}
diff --git a/_static/doctools.js b/_static/doctools.js
new file mode 100644
index 0000000..4d67807
--- /dev/null
+++ b/_static/doctools.js
@@ -0,0 +1,156 @@
+/*
+ * doctools.js
+ * ~~~~~~~~~~~
+ *
+ * Base JavaScript utilities for all Sphinx HTML documentation.
+ *
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+"use strict";
+
+const BLACKLISTED_KEY_CONTROL_ELEMENTS = new Set([
+ "TEXTAREA",
+ "INPUT",
+ "SELECT",
+ "BUTTON",
+]);
+
+const _ready = (callback) => {
+ if (document.readyState !== "loading") {
+ callback();
+ } else {
+ document.addEventListener("DOMContentLoaded", callback);
+ }
+};
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+const Documentation = {
+ init: () => {
+ Documentation.initDomainIndexTable();
+ Documentation.initOnKeyListeners();
+ },
+
+ /**
+ * i18n support
+ */
+ TRANSLATIONS: {},
+ PLURAL_EXPR: (n) => (n === 1 ? 0 : 1),
+ LOCALE: "unknown",
+
+ // gettext and ngettext don't access this so that the functions
+ // can safely bound to a different name (_ = Documentation.gettext)
+ gettext: (string) => {
+ const translated = Documentation.TRANSLATIONS[string];
+ switch (typeof translated) {
+ case "undefined":
+ return string; // no translation
+ case "string":
+ return translated; // translation exists
+ default:
+ return translated[0]; // (singular, plural) translation tuple exists
+ }
+ },
+
+ ngettext: (singular, plural, n) => {
+ const translated = Documentation.TRANSLATIONS[singular];
+ if (typeof translated !== "undefined")
+ return translated[Documentation.PLURAL_EXPR(n)];
+ return n === 1 ? singular : plural;
+ },
+
+ addTranslations: (catalog) => {
+ Object.assign(Documentation.TRANSLATIONS, catalog.messages);
+ Documentation.PLURAL_EXPR = new Function(
+ "n",
+ `return (${catalog.plural_expr})`
+ );
+ Documentation.LOCALE = catalog.locale;
+ },
+
+ /**
+ * helper function to focus on search bar
+ */
+ focusSearchBar: () => {
+ document.querySelectorAll("input[name=q]")[0]?.focus();
+ },
+
+ /**
+ * Initialise the domain index toggle buttons
+ */
+ initDomainIndexTable: () => {
+ const toggler = (el) => {
+ const idNumber = el.id.substr(7);
+ const toggledRows = document.querySelectorAll(`tr.cg-${idNumber}`);
+ if (el.src.substr(-9) === "minus.png") {
+ el.src = `${el.src.substr(0, el.src.length - 9)}plus.png`;
+ toggledRows.forEach((el) => (el.style.display = "none"));
+ } else {
+ el.src = `${el.src.substr(0, el.src.length - 8)}minus.png`;
+ toggledRows.forEach((el) => (el.style.display = ""));
+ }
+ };
+
+ const togglerElements = document.querySelectorAll("img.toggler");
+ togglerElements.forEach((el) =>
+ el.addEventListener("click", (event) => toggler(event.currentTarget))
+ );
+ togglerElements.forEach((el) => (el.style.display = ""));
+ if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) togglerElements.forEach(toggler);
+ },
+
+ initOnKeyListeners: () => {
+ // only install a listener if it is really needed
+ if (
+ !DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS &&
+ !DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS
+ )
+ return;
+
+ document.addEventListener("keydown", (event) => {
+ // bail for input elements
+ if (BLACKLISTED_KEY_CONTROL_ELEMENTS.has(document.activeElement.tagName)) return;
+ // bail with special keys
+ if (event.altKey || event.ctrlKey || event.metaKey) return;
+
+ if (!event.shiftKey) {
+ switch (event.key) {
+ case "ArrowLeft":
+ if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+ const prevLink = document.querySelector('link[rel="prev"]');
+ if (prevLink && prevLink.href) {
+ window.location.href = prevLink.href;
+ event.preventDefault();
+ }
+ break;
+ case "ArrowRight":
+ if (!DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) break;
+
+ const nextLink = document.querySelector('link[rel="next"]');
+ if (nextLink && nextLink.href) {
+ window.location.href = nextLink.href;
+ event.preventDefault();
+ }
+ break;
+ }
+ }
+
+ // some keyboard layouts may need Shift to get /
+ switch (event.key) {
+ case "/":
+ if (!DOCUMENTATION_OPTIONS.ENABLE_SEARCH_SHORTCUTS) break;
+ Documentation.focusSearchBar();
+ event.preventDefault();
+ }
+ });
+ },
+};
+
+// quick alias for translations
+const _ = Documentation.gettext;
+
+_ready(Documentation.init);
diff --git a/_static/documentation_options.js b/_static/documentation_options.js
new file mode 100644
index 0000000..7e4c114
--- /dev/null
+++ b/_static/documentation_options.js
@@ -0,0 +1,13 @@
+const DOCUMENTATION_OPTIONS = {
+ VERSION: '',
+ LANGUAGE: 'en',
+ COLLAPSE_INDEX: false,
+ BUILDER: 'html',
+ FILE_SUFFIX: '.html',
+ LINK_SUFFIX: '.html',
+ HAS_SOURCE: true,
+ SOURCELINK_SUFFIX: '.txt',
+ NAVIGATION_WITH_KEYS: false,
+ SHOW_SEARCH_SUMMARY: true,
+ ENABLE_SEARCH_SHORTCUTS: true,
+};
\ No newline at end of file
diff --git a/_static/file.png b/_static/file.png
new file mode 100644
index 0000000..a858a41
Binary files /dev/null and b/_static/file.png differ
diff --git a/_static/jquery.js b/_static/jquery.js
new file mode 100644
index 0000000..c4c6022
--- /dev/null
+++ b/_static/jquery.js
@@ -0,0 +1,2 @@
+/*! jQuery v3.6.0 | (c) OpenJS Foundation and other contributors | jquery.org/license */
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+ var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1
+ var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1
+ var s_v = "^(" + C + ")?" + v; // vowel in stem
+
+ this.stemWord = function (w) {
+ var stem;
+ var suffix;
+ var firstch;
+ var origword = w;
+
+ if (w.length < 3)
+ return w;
+
+ var re;
+ var re2;
+ var re3;
+ var re4;
+
+ firstch = w.substr(0,1);
+ if (firstch == "y")
+ w = firstch.toUpperCase() + w.substr(1);
+
+ // Step 1a
+ re = /^(.+?)(ss|i)es$/;
+ re2 = /^(.+?)([^s])s$/;
+
+ if (re.test(w))
+ w = w.replace(re,"$1$2");
+ else if (re2.test(w))
+ w = w.replace(re2,"$1$2");
+
+ // Step 1b
+ re = /^(.+?)eed$/;
+ re2 = /^(.+?)(ed|ing)$/;
+ if (re.test(w)) {
+ var fp = re.exec(w);
+ re = new RegExp(mgr0);
+ if (re.test(fp[1])) {
+ re = /.$/;
+ w = w.replace(re,"");
+ }
+ }
+ else if (re2.test(w)) {
+ var fp = re2.exec(w);
+ stem = fp[1];
+ re2 = new RegExp(s_v);
+ if (re2.test(stem)) {
+ w = stem;
+ re2 = /(at|bl|iz)$/;
+ re3 = new RegExp("([^aeiouylsz])\\1$");
+ re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+ if (re2.test(w))
+ w = w + "e";
+ else if (re3.test(w)) {
+ re = /.$/;
+ w = w.replace(re,"");
+ }
+ else if (re4.test(w))
+ w = w + "e";
+ }
+ }
+
+ // Step 1c
+ re = /^(.+?)y$/;
+ if (re.test(w)) {
+ var fp = re.exec(w);
+ stem = fp[1];
+ re = new RegExp(s_v);
+ if (re.test(stem))
+ w = stem + "i";
+ }
+
+ // Step 2
+ re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
+ if (re.test(w)) {
+ var fp = re.exec(w);
+ stem = fp[1];
+ suffix = fp[2];
+ re = new RegExp(mgr0);
+ if (re.test(stem))
+ w = stem + step2list[suffix];
+ }
+
+ // Step 3
+ re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
+ if (re.test(w)) {
+ var fp = re.exec(w);
+ stem = fp[1];
+ suffix = fp[2];
+ re = new RegExp(mgr0);
+ if (re.test(stem))
+ w = stem + step3list[suffix];
+ }
+
+ // Step 4
+ re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
+ re2 = /^(.+?)(s|t)(ion)$/;
+ if (re.test(w)) {
+ var fp = re.exec(w);
+ stem = fp[1];
+ re = new RegExp(mgr1);
+ if (re.test(stem))
+ w = stem;
+ }
+ else if (re2.test(w)) {
+ var fp = re2.exec(w);
+ stem = fp[1] + fp[2];
+ re2 = new RegExp(mgr1);
+ if (re2.test(stem))
+ w = stem;
+ }
+
+ // Step 5
+ re = /^(.+?)e$/;
+ if (re.test(w)) {
+ var fp = re.exec(w);
+ stem = fp[1];
+ re = new RegExp(mgr1);
+ re2 = new RegExp(meq1);
+ re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
+ if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
+ w = stem;
+ }
+ re = /ll$/;
+ re2 = new RegExp(mgr1);
+ if (re.test(w) && re2.test(w)) {
+ re = /.$/;
+ w = w.replace(re,"");
+ }
+
+ // and turn initial Y back to y
+ if (firstch == "y")
+ w = firstch.toLowerCase() + w.substr(1);
+ return w;
+ }
+}
+
diff --git a/_static/minus.png b/_static/minus.png
new file mode 100644
index 0000000..d96755f
Binary files /dev/null and b/_static/minus.png differ
diff --git a/_static/plus.png b/_static/plus.png
new file mode 100644
index 0000000..7107cec
Binary files /dev/null and b/_static/plus.png differ
diff --git a/_static/pygments.css b/_static/pygments.css
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index 0000000..c2e07c7
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diff --git a/_static/scripts/furo-extensions.js b/_static/scripts/furo-extensions.js
new file mode 100644
index 0000000..e69de29
diff --git a/_static/scripts/furo.js b/_static/scripts/furo.js
new file mode 100644
index 0000000..32e7c05
--- /dev/null
+++ b/_static/scripts/furo.js
@@ -0,0 +1,3 @@
+/*! For license information please see furo.js.LICENSE.txt */
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\ No newline at end of file
diff --git a/_static/scripts/furo.js.LICENSE.txt b/_static/scripts/furo.js.LICENSE.txt
new file mode 100644
index 0000000..1632189
--- /dev/null
+++ b/_static/scripts/furo.js.LICENSE.txt
@@ -0,0 +1,7 @@
+/*!
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+ * A simple, framework-agnostic scrollspy script.
+ * (c) 2019 Chris Ferdinandi
+ * MIT License
+ * http://github.com/cferdinandi/gumshoe
+ */
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new file mode 100644
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false,\n nestedClass: \"active\",\n\n // Offset & reflow\n offset: 0,\n reflow: false,\n\n // Event support\n events: true,\n };\n\n //\n // Methods\n //\n\n /**\n * Merge two or more objects together.\n * @param {Object} objects The objects to merge together\n * @returns {Object} Merged values of defaults and options\n */\n var extend = function () {\n var merged = {};\n Array.prototype.forEach.call(arguments, function (obj) {\n for (var key in obj) {\n if (!obj.hasOwnProperty(key)) return;\n merged[key] = obj[key];\n }\n });\n return merged;\n };\n\n /**\n * Emit a custom event\n * @param {String} type The event type\n * @param {Node} elem The element to attach the event to\n * @param {Object} detail Any details to pass along with the event\n */\n var emitEvent = function (type, elem, detail) {\n // Make sure events are enabled\n if (!detail.settings.events) return;\n\n // Create a new event\n var event = new CustomEvent(type, {\n bubbles: true,\n cancelable: true,\n detail: detail,\n });\n\n // Dispatch the event\n elem.dispatchEvent(event);\n };\n\n /**\n * Get an element's distance from the top of the Document.\n * @param {Node} elem The element\n * @return {Number} Distance from the top in pixels\n */\n var getOffsetTop = function (elem) {\n var location = 0;\n if (elem.offsetParent) {\n while (elem) {\n location += elem.offsetTop;\n elem = elem.offsetParent;\n }\n }\n return location >= 0 ? location : 0;\n };\n\n /**\n * Sort content from first to last in the DOM\n * @param {Array} contents The content areas\n */\n var sortContents = function (contents) {\n if (contents) {\n contents.sort(function (item1, item2) {\n var offset1 = getOffsetTop(item1.content);\n var offset2 = getOffsetTop(item2.content);\n if (offset1 < offset2) return -1;\n return 1;\n });\n }\n };\n\n /**\n * Get the offset to use for calculating position\n * @param {Object} settings The settings for this instantiation\n * @return {Float} The number of pixels to offset the 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function (nav, settings) {\n // If nesting isn't activated, bail\n if (!settings.nested) return;\n\n // Get the parent navigation\n var li = nav.parentNode.closest(\"li\");\n if (!li) return;\n\n // Add the active class\n li.classList.add(settings.nestedClass);\n\n // Apply recursively to any parent navigation elements\n activateNested(li, settings);\n };\n\n /**\n * Activate a nav and content area\n * @param {Object} items The nav item and content to activate\n * @param {Object} settings The settings for this instantiation\n */\n var activate = function (items, settings) {\n // Make sure there are items to activate\n if (!items) return;\n\n // Get the parent list item\n var li = items.nav.closest(\"li\");\n if (!li) return;\n\n // Add the active class to the nav and content\n li.classList.add(settings.navClass);\n items.content.classList.add(settings.contentClass);\n\n // Activate any parent navs in a nested navigation\n activateNested(li, settings);\n\n // Emit a custom event\n emitEvent(\"gumshoeActivate\", li, {\n link: items.nav,\n content: items.content,\n settings: settings,\n });\n };\n\n /**\n * Create the Constructor object\n * @param {String} selector The selector to use for navigation items\n * @param {Object} options User options and settings\n */\n var Constructor = function (selector, options) {\n //\n // Variables\n //\n\n var publicAPIs = {};\n var navItems, contents, current, timeout, settings;\n\n //\n // Methods\n //\n\n /**\n * Set variables from DOM elements\n */\n publicAPIs.setup = function () {\n // Get all nav items\n navItems = document.querySelectorAll(selector);\n\n // Create contents array\n contents = [];\n\n // Loop through each item, get it's matching content, and push to the array\n Array.prototype.forEach.call(navItems, function (item) {\n // Get the content for the nav item\n var content = document.getElementById(\n decodeURIComponent(item.hash.substr(1)),\n );\n if (!content) return;\n\n // Push to the contents array\n 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window.cancelAnimationFrame(timeout);\n }\n\n // Setup debounce callback\n timeout = window.requestAnimationFrame(publicAPIs.detect);\n };\n\n /**\n * Update content sorting on resize\n * Debounced for performance\n */\n var resizeHandler = function (event) {\n // If there's a timer, cancel it\n if (timeout) {\n window.cancelAnimationFrame(timeout);\n }\n\n // Setup debounce callback\n timeout = window.requestAnimationFrame(function () {\n sortContents(contents);\n publicAPIs.detect();\n });\n };\n\n /**\n * Destroy the current instantiation\n */\n publicAPIs.destroy = function () {\n // Undo DOM changes\n if (current) {\n deactivate(current, settings);\n }\n\n // Remove event listeners\n window.removeEventListener(\"scroll\", scrollHandler, false);\n if (settings.reflow) {\n window.removeEventListener(\"resize\", resizeHandler, false);\n }\n\n // Reset variables\n contents = null;\n navItems = null;\n current = null;\n timeout = null;\n settings = null;\n };\n\n /**\n * Initialize the 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globalThis === 'object') return globalThis;\n\ttry {\n\t\treturn this || new Function('return this')();\n\t} catch (e) {\n\t\tif (typeof window === 'object') return window;\n\t}\n})();","__webpack_require__.o = (obj, prop) => (Object.prototype.hasOwnProperty.call(obj, prop))","import Gumshoe from \"./gumshoe-patched.js\";\n\n////////////////////////////////////////////////////////////////////////////////\n// Scroll Handling\n////////////////////////////////////////////////////////////////////////////////\nvar tocScroll = null;\nvar header = null;\nvar lastScrollTop = window.pageYOffset || document.documentElement.scrollTop;\nconst GO_TO_TOP_OFFSET = 64;\n\nfunction scrollHandlerForHeader() {\n if (Math.floor(header.getBoundingClientRect().top) == 0) {\n header.classList.add(\"scrolled\");\n } else {\n header.classList.remove(\"scrolled\");\n }\n}\n\nfunction scrollHandlerForBackToTop(positionY) {\n if (positionY < GO_TO_TOP_OFFSET) {\n document.documentElement.classList.remove(\"show-back-to-top\");\n } else {\n if (positionY < lastScrollTop) {\n document.documentElement.classList.add(\"show-back-to-top\");\n } else if (positionY > lastScrollTop) {\n document.documentElement.classList.remove(\"show-back-to-top\");\n }\n }\n lastScrollTop = positionY;\n}\n\nfunction scrollHandlerForTOC(positionY) {\n if (tocScroll === null) {\n return;\n }\n\n // top of page.\n if (positionY == 0) {\n tocScroll.scrollTo(0, 0);\n } else if (\n // bottom of page.\n Math.ceil(positionY) >=\n Math.floor(document.documentElement.scrollHeight - window.innerHeight)\n ) {\n tocScroll.scrollTo(0, tocScroll.scrollHeight);\n } else {\n // somewhere in the middle.\n const current = document.querySelector(\".scroll-current\");\n if (current == null) {\n return;\n }\n\n // https://github.com/pypa/pip/issues/9159 This breaks scroll behaviours.\n // // scroll the currently \"active\" heading in toc, into view.\n // const rect = current.getBoundingClientRect();\n // if (0 > rect.top) {\n // current.scrollIntoView(true); // the argument is \"alignTop\"\n // } else if (rect.bottom > window.innerHeight) {\n // current.scrollIntoView(false);\n // }\n }\n}\n\nfunction scrollHandler(positionY) {\n scrollHandlerForHeader();\n scrollHandlerForBackToTop(positionY);\n scrollHandlerForTOC(positionY);\n}\n\n////////////////////////////////////////////////////////////////////////////////\n// Theme Toggle\n////////////////////////////////////////////////////////////////////////////////\nfunction setTheme(mode) {\n if (mode !== \"light\" && mode !== \"dark\" && mode !== \"auto\") {\n console.error(`Got invalid theme mode: ${mode}. Resetting to auto.`);\n mode = \"auto\";\n }\n\n document.body.dataset.theme = mode;\n localStorage.setItem(\"theme\", mode);\n console.log(`Changed to ${mode} mode.`);\n}\n\nfunction cycleThemeOnce() {\n const currentTheme = localStorage.getItem(\"theme\") || \"auto\";\n const prefersDark = window.matchMedia(\"(prefers-color-scheme: dark)\").matches;\n\n if (prefersDark) {\n // Auto (dark) -> Light -> Dark\n if (currentTheme === \"auto\") {\n setTheme(\"light\");\n } else if (currentTheme == \"light\") {\n setTheme(\"dark\");\n } else {\n setTheme(\"auto\");\n }\n } else {\n // Auto (light) -> Dark -> Light\n if (currentTheme === \"auto\") {\n setTheme(\"dark\");\n } else if (currentTheme == \"dark\") {\n setTheme(\"light\");\n } else {\n setTheme(\"auto\");\n }\n }\n}\n\n////////////////////////////////////////////////////////////////////////////////\n// Setup\n////////////////////////////////////////////////////////////////////////////////\nfunction setupScrollHandler() {\n // Taken from https://developer.mozilla.org/en-US/docs/Web/API/Document/scroll_event\n let last_known_scroll_position = 0;\n let ticking = false;\n\n window.addEventListener(\"scroll\", function (e) {\n last_known_scroll_position = window.scrollY;\n\n if (!ticking) {\n window.requestAnimationFrame(function () {\n scrollHandler(last_known_scroll_position);\n ticking = false;\n });\n\n ticking = true;\n }\n });\n window.scroll();\n}\n\nfunction setupScrollSpy() {\n if (tocScroll === null) {\n return;\n }\n\n // Scrollspy -- highlight table on contents, based on scroll\n new Gumshoe(\".toc-tree a\", {\n reflow: true,\n recursive: true,\n navClass: \"scroll-current\",\n offset: () => {\n let rem = parseFloat(getComputedStyle(document.documentElement).fontSize);\n return header.getBoundingClientRect().height + 0.5 * rem + 1;\n },\n });\n}\n\nfunction setupTheme() {\n // Attach event handlers for toggling themes\n const buttons = document.getElementsByClassName(\"theme-toggle\");\n Array.from(buttons).forEach((btn) => {\n btn.addEventListener(\"click\", cycleThemeOnce);\n });\n}\n\nfunction setup() {\n setupTheme();\n setupScrollHandler();\n setupScrollSpy();\n}\n\n////////////////////////////////////////////////////////////////////////////////\n// Main entrypoint\n////////////////////////////////////////////////////////////////////////////////\nfunction main() {\n document.body.parentNode.classList.remove(\"no-js\");\n\n header = document.querySelector(\"header\");\n tocScroll = document.querySelector(\".toc-scroll\");\n\n setup();\n}\n\ndocument.addEventListener(\"DOMContentLoaded\", main);\n"],"names":["root","g","window","this","defaults","navClass","contentClass","nested","nestedClass","offset","reflow","events","emitEvent","type","elem","detail","settings","event","CustomEvent","bubbles","cancelable","dispatchEvent","getOffsetTop","location","offsetParent","offsetTop","sortContents","contents","sort","item1","item2","content","isInView","bottom","bounds","getBoundingClientRect","parseFloat","getOffset","parseInt","innerHeight","document","documentElement","clientHeight","top","isAtBottom","Math","ceil","pageYOffset","max","body","scrollHeight","offsetHeight","getActive","last","length","item","useLastItem","i","deactivateNested","nav","parentNode","li","closest","classList","remove","deactivate","items","link","activateNested","add","selector","options","navItems","current","timeout","publicAPIs","querySelectorAll","Array","prototype","forEach","call","getElementById","decodeURIComponent","hash","substr","push","active","activate","scrollHandler","cancelAnimationFrame","requestAnimationFrame","detect","resizeHandler","destroy","removeEventListener","merged","arguments","obj","key","hasOwnProperty","extend","setup","addEventListener","factory","__webpack_module_cache__","__webpack_require__","moduleId","cachedModule","undefined","exports","module","__webpack_modules__","n","getter","__esModule","d","a","definition","o","Object","defineProperty","enumerable","get","globalThis","Function","e","prop","tocScroll","header","lastScrollTop","scrollTop","GO_TO_TOP_OFFSET","cycleThemeOnce","currentTheme","localStorage","getItem","mode","matchMedia","matches","console","error","dataset","theme","setItem","log","buttons","getElementsByClassName","from","btn","setupTheme","last_known_scroll_position","ticking","scrollY","positionY","floor","scrollHandlerForBackToTop","scrollTo","querySelector","scrollHandlerForTOC","scroll","setupScrollHandler","recursive","rem","getComputedStyle","fontSize","height"],"sourceRoot":""}
\ No newline at end of file
diff --git a/_static/searchtools.js b/_static/searchtools.js
new file mode 100644
index 0000000..92da3f8
--- /dev/null
+++ b/_static/searchtools.js
@@ -0,0 +1,619 @@
+/*
+ * searchtools.js
+ * ~~~~~~~~~~~~~~~~
+ *
+ * Sphinx JavaScript utilities for the full-text search.
+ *
+ * :copyright: Copyright 2007-2024 by the Sphinx team, see AUTHORS.
+ * :license: BSD, see LICENSE for details.
+ *
+ */
+"use strict";
+
+/**
+ * Simple result scoring code.
+ */
+if (typeof Scorer === "undefined") {
+ var Scorer = {
+ // Implement the following function to further tweak the score for each result
+ // The function takes a result array [docname, title, anchor, descr, score, filename]
+ // and returns the new score.
+ /*
+ score: result => {
+ const [docname, title, anchor, descr, score, filename] = result
+ return score
+ },
+ */
+
+ // query matches the full name of an object
+ objNameMatch: 11,
+ // or matches in the last dotted part of the object name
+ objPartialMatch: 6,
+ // Additive scores depending on the priority of the object
+ objPrio: {
+ 0: 15, // used to be importantResults
+ 1: 5, // used to be objectResults
+ 2: -5, // used to be unimportantResults
+ },
+ // Used when the priority is not in the mapping.
+ objPrioDefault: 0,
+
+ // query found in title
+ title: 15,
+ partialTitle: 7,
+ // query found in terms
+ term: 5,
+ partialTerm: 2,
+ };
+}
+
+const _removeChildren = (element) => {
+ while (element && element.lastChild) element.removeChild(element.lastChild);
+};
+
+/**
+ * See https://developer.mozilla.org/en-US/docs/Web/JavaScript/Guide/Regular_Expressions#escaping
+ */
+const _escapeRegExp = (string) =>
+ string.replace(/[.*+\-?^${}()|[\]\\]/g, "\\$&"); // $& means the whole matched string
+
+const _displayItem = (item, searchTerms, highlightTerms) => {
+ const docBuilder = DOCUMENTATION_OPTIONS.BUILDER;
+ const docFileSuffix = DOCUMENTATION_OPTIONS.FILE_SUFFIX;
+ const docLinkSuffix = DOCUMENTATION_OPTIONS.LINK_SUFFIX;
+ const showSearchSummary = DOCUMENTATION_OPTIONS.SHOW_SEARCH_SUMMARY;
+ const contentRoot = document.documentElement.dataset.content_root;
+
+ const [docName, title, anchor, descr, score, _filename] = item;
+
+ let listItem = document.createElement("li");
+ let requestUrl;
+ let linkUrl;
+ if (docBuilder === "dirhtml") {
+ // dirhtml builder
+ let dirname = docName + "/";
+ if (dirname.match(/\/index\/$/))
+ dirname = dirname.substring(0, dirname.length - 6);
+ else if (dirname === "index/") dirname = "";
+ requestUrl = contentRoot + dirname;
+ linkUrl = requestUrl;
+ } else {
+ // normal html builders
+ requestUrl = contentRoot + docName + docFileSuffix;
+ linkUrl = docName + docLinkSuffix;
+ }
+ let linkEl = listItem.appendChild(document.createElement("a"));
+ linkEl.href = linkUrl + anchor;
+ linkEl.dataset.score = score;
+ linkEl.innerHTML = title;
+ if (descr) {
+ listItem.appendChild(document.createElement("span")).innerHTML =
+ " (" + descr + ")";
+ // highlight search terms in the description
+ if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js
+ highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
+ }
+ else if (showSearchSummary)
+ fetch(requestUrl)
+ .then((responseData) => responseData.text())
+ .then((data) => {
+ if (data)
+ listItem.appendChild(
+ Search.makeSearchSummary(data, searchTerms, anchor)
+ );
+ // highlight search terms in the summary
+ if (SPHINX_HIGHLIGHT_ENABLED) // set in sphinx_highlight.js
+ highlightTerms.forEach((term) => _highlightText(listItem, term, "highlighted"));
+ });
+ Search.output.appendChild(listItem);
+};
+const _finishSearch = (resultCount) => {
+ Search.stopPulse();
+ Search.title.innerText = _("Search Results");
+ if (!resultCount)
+ Search.status.innerText = Documentation.gettext(
+ "Your search did not match any documents. Please make sure that all words are spelled correctly and that you've selected enough categories."
+ );
+ else
+ Search.status.innerText = _(
+ "Search finished, found ${resultCount} page(s) matching the search query."
+ ).replace('${resultCount}', resultCount);
+};
+const _displayNextItem = (
+ results,
+ resultCount,
+ searchTerms,
+ highlightTerms,
+) => {
+ // results left, load the summary and display it
+ // this is intended to be dynamic (don't sub resultsCount)
+ if (results.length) {
+ _displayItem(results.pop(), searchTerms, highlightTerms);
+ setTimeout(
+ () => _displayNextItem(results, resultCount, searchTerms, highlightTerms),
+ 5
+ );
+ }
+ // search finished, update title and status message
+ else _finishSearch(resultCount);
+};
+// Helper function used by query() to order search results.
+// Each input is an array of [docname, title, anchor, descr, score, filename].
+// Order the results by score (in opposite order of appearance, since the
+// `_displayNextItem` function uses pop() to retrieve items) and then alphabetically.
+const _orderResultsByScoreThenName = (a, b) => {
+ const leftScore = a[4];
+ const rightScore = b[4];
+ if (leftScore === rightScore) {
+ // same score: sort alphabetically
+ const leftTitle = a[1].toLowerCase();
+ const rightTitle = b[1].toLowerCase();
+ if (leftTitle === rightTitle) return 0;
+ return leftTitle > rightTitle ? -1 : 1; // inverted is intentional
+ }
+ return leftScore > rightScore ? 1 : -1;
+};
+
+/**
+ * Default splitQuery function. Can be overridden in ``sphinx.search`` with a
+ * custom function per language.
+ *
+ * The regular expression works by splitting the string on consecutive characters
+ * that are not Unicode letters, numbers, underscores, or emoji characters.
+ * This is the same as ``\W+`` in Python, preserving the surrogate pair area.
+ */
+if (typeof splitQuery === "undefined") {
+ var splitQuery = (query) => query
+ .split(/[^\p{Letter}\p{Number}_\p{Emoji_Presentation}]+/gu)
+ .filter(term => term) // remove remaining empty strings
+}
+
+/**
+ * Search Module
+ */
+const Search = {
+ _index: null,
+ _queued_query: null,
+ _pulse_status: -1,
+
+ htmlToText: (htmlString, anchor) => {
+ const htmlElement = new DOMParser().parseFromString(htmlString, 'text/html');
+ for (const removalQuery of [".headerlinks", "script", "style"]) {
+ htmlElement.querySelectorAll(removalQuery).forEach((el) => { el.remove() });
+ }
+ if (anchor) {
+ const anchorContent = htmlElement.querySelector(`[role="main"] ${anchor}`);
+ if (anchorContent) return anchorContent.textContent;
+
+ console.warn(
+ `Anchored content block not found. Sphinx search tries to obtain it via DOM query '[role=main] ${anchor}'. Check your theme or template.`
+ );
+ }
+
+ // if anchor not specified or not found, fall back to main content
+ const docContent = htmlElement.querySelector('[role="main"]');
+ if (docContent) return docContent.textContent;
+
+ console.warn(
+ "Content block not found. Sphinx search tries to obtain it via DOM query '[role=main]'. Check your theme or template."
+ );
+ return "";
+ },
+
+ init: () => {
+ const query = new URLSearchParams(window.location.search).get("q");
+ document
+ .querySelectorAll('input[name="q"]')
+ .forEach((el) => (el.value = query));
+ if (query) Search.performSearch(query);
+ },
+
+ loadIndex: (url) =>
+ (document.body.appendChild(document.createElement("script")).src = url),
+
+ setIndex: (index) => {
+ Search._index = index;
+ if (Search._queued_query !== null) {
+ const query = Search._queued_query;
+ Search._queued_query = null;
+ Search.query(query);
+ }
+ },
+
+ hasIndex: () => Search._index !== null,
+
+ deferQuery: (query) => (Search._queued_query = query),
+
+ stopPulse: () => (Search._pulse_status = -1),
+
+ startPulse: () => {
+ if (Search._pulse_status >= 0) return;
+
+ const pulse = () => {
+ Search._pulse_status = (Search._pulse_status + 1) % 4;
+ Search.dots.innerText = ".".repeat(Search._pulse_status);
+ if (Search._pulse_status >= 0) window.setTimeout(pulse, 500);
+ };
+ pulse();
+ },
+
+ /**
+ * perform a search for something (or wait until index is loaded)
+ */
+ performSearch: (query) => {
+ // create the required interface elements
+ const searchText = document.createElement("h2");
+ searchText.textContent = _("Searching");
+ const searchSummary = document.createElement("p");
+ searchSummary.classList.add("search-summary");
+ searchSummary.innerText = "";
+ const searchList = document.createElement("ul");
+ searchList.classList.add("search");
+
+ const out = document.getElementById("search-results");
+ Search.title = out.appendChild(searchText);
+ Search.dots = Search.title.appendChild(document.createElement("span"));
+ Search.status = out.appendChild(searchSummary);
+ Search.output = out.appendChild(searchList);
+
+ const searchProgress = document.getElementById("search-progress");
+ // Some themes don't use the search progress node
+ if (searchProgress) {
+ searchProgress.innerText = _("Preparing search...");
+ }
+ Search.startPulse();
+
+ // index already loaded, the browser was quick!
+ if (Search.hasIndex()) Search.query(query);
+ else Search.deferQuery(query);
+ },
+
+ _parseQuery: (query) => {
+ // stem the search terms and add them to the correct list
+ const stemmer = new Stemmer();
+ const searchTerms = new Set();
+ const excludedTerms = new Set();
+ const highlightTerms = new Set();
+ const objectTerms = new Set(splitQuery(query.toLowerCase().trim()));
+ splitQuery(query.trim()).forEach((queryTerm) => {
+ const queryTermLower = queryTerm.toLowerCase();
+
+ // maybe skip this "word"
+ // stopwords array is from language_data.js
+ if (
+ stopwords.indexOf(queryTermLower) !== -1 ||
+ queryTerm.match(/^\d+$/)
+ )
+ return;
+
+ // stem the word
+ let word = stemmer.stemWord(queryTermLower);
+ // select the correct list
+ if (word[0] === "-") excludedTerms.add(word.substr(1));
+ else {
+ searchTerms.add(word);
+ highlightTerms.add(queryTermLower);
+ }
+ });
+
+ if (SPHINX_HIGHLIGHT_ENABLED) { // set in sphinx_highlight.js
+ localStorage.setItem("sphinx_highlight_terms", [...highlightTerms].join(" "))
+ }
+
+ // console.debug("SEARCH: searching for:");
+ // console.info("required: ", [...searchTerms]);
+ // console.info("excluded: ", [...excludedTerms]);
+
+ return [query, searchTerms, excludedTerms, highlightTerms, objectTerms];
+ },
+
+ /**
+ * execute search (requires search index to be loaded)
+ */
+ _performSearch: (query, searchTerms, excludedTerms, highlightTerms, objectTerms) => {
+ const filenames = Search._index.filenames;
+ const docNames = Search._index.docnames;
+ const titles = Search._index.titles;
+ const allTitles = Search._index.alltitles;
+ const indexEntries = Search._index.indexentries;
+
+ // Collect multiple result groups to be sorted separately and then ordered.
+ // Each is an array of [docname, title, anchor, descr, score, filename].
+ const normalResults = [];
+ const nonMainIndexResults = [];
+
+ _removeChildren(document.getElementById("search-progress"));
+
+ const queryLower = query.toLowerCase().trim();
+ for (const [title, foundTitles] of Object.entries(allTitles)) {
+ if (title.toLowerCase().trim().includes(queryLower) && (queryLower.length >= title.length/2)) {
+ for (const [file, id] of foundTitles) {
+ let score = Math.round(100 * queryLower.length / title.length)
+ normalResults.push([
+ docNames[file],
+ titles[file] !== title ? `${titles[file]} > ${title}` : title,
+ id !== null ? "#" + id : "",
+ null,
+ score,
+ filenames[file],
+ ]);
+ }
+ }
+ }
+
+ // search for explicit entries in index directives
+ for (const [entry, foundEntries] of Object.entries(indexEntries)) {
+ if (entry.includes(queryLower) && (queryLower.length >= entry.length/2)) {
+ for (const [file, id, isMain] of foundEntries) {
+ const score = Math.round(100 * queryLower.length / entry.length);
+ const result = [
+ docNames[file],
+ titles[file],
+ id ? "#" + id : "",
+ null,
+ score,
+ filenames[file],
+ ];
+ if (isMain) {
+ normalResults.push(result);
+ } else {
+ nonMainIndexResults.push(result);
+ }
+ }
+ }
+ }
+
+ // lookup as object
+ objectTerms.forEach((term) =>
+ normalResults.push(...Search.performObjectSearch(term, objectTerms))
+ );
+
+ // lookup as search terms in fulltext
+ normalResults.push(...Search.performTermsSearch(searchTerms, excludedTerms));
+
+ // let the scorer override scores with a custom scoring function
+ if (Scorer.score) {
+ normalResults.forEach((item) => (item[4] = Scorer.score(item)));
+ nonMainIndexResults.forEach((item) => (item[4] = Scorer.score(item)));
+ }
+
+ // Sort each group of results by score and then alphabetically by name.
+ normalResults.sort(_orderResultsByScoreThenName);
+ nonMainIndexResults.sort(_orderResultsByScoreThenName);
+
+ // Combine the result groups in (reverse) order.
+ // Non-main index entries are typically arbitrary cross-references,
+ // so display them after other results.
+ let results = [...nonMainIndexResults, ...normalResults];
+
+ // remove duplicate search results
+ // note the reversing of results, so that in the case of duplicates, the highest-scoring entry is kept
+ let seen = new Set();
+ results = results.reverse().reduce((acc, result) => {
+ let resultStr = result.slice(0, 4).concat([result[5]]).map(v => String(v)).join(',');
+ if (!seen.has(resultStr)) {
+ acc.push(result);
+ seen.add(resultStr);
+ }
+ return acc;
+ }, []);
+
+ return results.reverse();
+ },
+
+ query: (query) => {
+ const [searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms] = Search._parseQuery(query);
+ const results = Search._performSearch(searchQuery, searchTerms, excludedTerms, highlightTerms, objectTerms);
+
+ // for debugging
+ //Search.lastresults = results.slice(); // a copy
+ // console.info("search results:", Search.lastresults);
+
+ // print the results
+ _displayNextItem(results, results.length, searchTerms, highlightTerms);
+ },
+
+ /**
+ * search for object names
+ */
+ performObjectSearch: (object, objectTerms) => {
+ const filenames = Search._index.filenames;
+ const docNames = Search._index.docnames;
+ const objects = Search._index.objects;
+ const objNames = Search._index.objnames;
+ const titles = Search._index.titles;
+
+ const results = [];
+
+ const objectSearchCallback = (prefix, match) => {
+ const name = match[4]
+ const fullname = (prefix ? prefix + "." : "") + name;
+ const fullnameLower = fullname.toLowerCase();
+ if (fullnameLower.indexOf(object) < 0) return;
+
+ let score = 0;
+ const parts = fullnameLower.split(".");
+
+ // check for different match types: exact matches of full name or
+ // "last name" (i.e. last dotted part)
+ if (fullnameLower === object || parts.slice(-1)[0] === object)
+ score += Scorer.objNameMatch;
+ else if (parts.slice(-1)[0].indexOf(object) > -1)
+ score += Scorer.objPartialMatch; // matches in last name
+
+ const objName = objNames[match[1]][2];
+ const title = titles[match[0]];
+
+ // If more than one term searched for, we require other words to be
+ // found in the name/title/description
+ const otherTerms = new Set(objectTerms);
+ otherTerms.delete(object);
+ if (otherTerms.size > 0) {
+ const haystack = `${prefix} ${name} ${objName} ${title}`.toLowerCase();
+ if (
+ [...otherTerms].some((otherTerm) => haystack.indexOf(otherTerm) < 0)
+ )
+ return;
+ }
+
+ let anchor = match[3];
+ if (anchor === "") anchor = fullname;
+ else if (anchor === "-") anchor = objNames[match[1]][1] + "-" + fullname;
+
+ const descr = objName + _(", in ") + title;
+
+ // add custom score for some objects according to scorer
+ if (Scorer.objPrio.hasOwnProperty(match[2]))
+ score += Scorer.objPrio[match[2]];
+ else score += Scorer.objPrioDefault;
+
+ results.push([
+ docNames[match[0]],
+ fullname,
+ "#" + anchor,
+ descr,
+ score,
+ filenames[match[0]],
+ ]);
+ };
+ Object.keys(objects).forEach((prefix) =>
+ objects[prefix].forEach((array) =>
+ objectSearchCallback(prefix, array)
+ )
+ );
+ return results;
+ },
+
+ /**
+ * search for full-text terms in the index
+ */
+ performTermsSearch: (searchTerms, excludedTerms) => {
+ // prepare search
+ const terms = Search._index.terms;
+ const titleTerms = Search._index.titleterms;
+ const filenames = Search._index.filenames;
+ const docNames = Search._index.docnames;
+ const titles = Search._index.titles;
+
+ const scoreMap = new Map();
+ const fileMap = new Map();
+
+ // perform the search on the required terms
+ searchTerms.forEach((word) => {
+ const files = [];
+ const arr = [
+ { files: terms[word], score: Scorer.term },
+ { files: titleTerms[word], score: Scorer.title },
+ ];
+ // add support for partial matches
+ if (word.length > 2) {
+ const escapedWord = _escapeRegExp(word);
+ if (!terms.hasOwnProperty(word)) {
+ Object.keys(terms).forEach((term) => {
+ if (term.match(escapedWord))
+ arr.push({ files: terms[term], score: Scorer.partialTerm });
+ });
+ }
+ if (!titleTerms.hasOwnProperty(word)) {
+ Object.keys(titleTerms).forEach((term) => {
+ if (term.match(escapedWord))
+ arr.push({ files: titleTerms[term], score: Scorer.partialTitle });
+ });
+ }
+ }
+
+ // no match but word was a required one
+ if (arr.every((record) => record.files === undefined)) return;
+
+ // found search word in contents
+ arr.forEach((record) => {
+ if (record.files === undefined) return;
+
+ let recordFiles = record.files;
+ if (recordFiles.length === undefined) recordFiles = [recordFiles];
+ files.push(...recordFiles);
+
+ // set score for the word in each file
+ recordFiles.forEach((file) => {
+ if (!scoreMap.has(file)) scoreMap.set(file, {});
+ scoreMap.get(file)[word] = record.score;
+ });
+ });
+
+ // create the mapping
+ files.forEach((file) => {
+ if (!fileMap.has(file)) fileMap.set(file, [word]);
+ else if (fileMap.get(file).indexOf(word) === -1) fileMap.get(file).push(word);
+ });
+ });
+
+ // now check if the files don't contain excluded terms
+ const results = [];
+ for (const [file, wordList] of fileMap) {
+ // check if all requirements are matched
+
+ // as search terms with length < 3 are discarded
+ const filteredTermCount = [...searchTerms].filter(
+ (term) => term.length > 2
+ ).length;
+ if (
+ wordList.length !== searchTerms.size &&
+ wordList.length !== filteredTermCount
+ )
+ continue;
+
+ // ensure that none of the excluded terms is in the search result
+ if (
+ [...excludedTerms].some(
+ (term) =>
+ terms[term] === file ||
+ titleTerms[term] === file ||
+ (terms[term] || []).includes(file) ||
+ (titleTerms[term] || []).includes(file)
+ )
+ )
+ break;
+
+ // select one (max) score for the file.
+ const score = Math.max(...wordList.map((w) => scoreMap.get(file)[w]));
+ // add result to the result list
+ results.push([
+ docNames[file],
+ titles[file],
+ "",
+ null,
+ score,
+ filenames[file],
+ ]);
+ }
+ return results;
+ },
+
+ /**
+ * helper function to return a node containing the
+ * search summary for a given text. keywords is a list
+ * of stemmed words.
+ */
+ makeSearchSummary: (htmlText, keywords, anchor) => {
+ const text = Search.htmlToText(htmlText, anchor);
+ if (text === "") return null;
+
+ const textLower = text.toLowerCase();
+ const actualStartPosition = [...keywords]
+ .map((k) => textLower.indexOf(k.toLowerCase()))
+ .filter((i) => i > -1)
+ .slice(-1)[0];
+ const startWithContext = Math.max(actualStartPosition - 120, 0);
+
+ const top = startWithContext === 0 ? "" : "...";
+ const tail = startWithContext + 240 < text.length ? "..." : "";
+
+ let summary = document.createElement("p");
+ summary.classList.add("context");
+ summary.textContent = top + text.substr(startWithContext, 240).trim() + tail;
+
+ return summary;
+ },
+};
+
+_ready(Search.init);
diff --git a/_static/skeleton.css b/_static/skeleton.css
new file mode 100644
index 0000000..467c878
--- /dev/null
+++ b/_static/skeleton.css
@@ -0,0 +1,296 @@
+/* Some sane resets. */
+html {
+ height: 100%;
+}
+
+body {
+ margin: 0;
+ min-height: 100%;
+}
+
+/* All the flexbox magic! */
+body,
+.sb-announcement,
+.sb-content,
+.sb-main,
+.sb-container,
+.sb-container__inner,
+.sb-article-container,
+.sb-footer-content,
+.sb-header,
+.sb-header-secondary,
+.sb-footer {
+ display: flex;
+}
+
+/* These order things vertically */
+body,
+.sb-main,
+.sb-article-container {
+ flex-direction: column;
+}
+
+/* Put elements in the center */
+.sb-header,
+.sb-header-secondary,
+.sb-container,
+.sb-content,
+.sb-footer,
+.sb-footer-content {
+ justify-content: center;
+}
+/* Put elements at the ends */
+.sb-article-container {
+ justify-content: space-between;
+}
+
+/* These elements grow. */
+.sb-main,
+.sb-content,
+.sb-container,
+article {
+ flex-grow: 1;
+}
+
+/* Because padding making this wider is not fun */
+article {
+ box-sizing: border-box;
+}
+
+/* The announcements element should never be wider than the page. */
+.sb-announcement {
+ max-width: 100%;
+}
+
+.sb-sidebar-primary,
+.sb-sidebar-secondary {
+ flex-shrink: 0;
+ width: 17rem;
+}
+
+.sb-announcement__inner {
+ justify-content: center;
+
+ box-sizing: border-box;
+ height: 3rem;
+
+ overflow-x: auto;
+ white-space: nowrap;
+}
+
+/* Sidebars, with checkbox-based toggle */
+.sb-sidebar-primary,
+.sb-sidebar-secondary {
+ position: fixed;
+ height: 100%;
+ top: 0;
+}
+
+.sb-sidebar-primary {
+ left: -17rem;
+ transition: left 250ms ease-in-out;
+}
+.sb-sidebar-secondary {
+ right: -17rem;
+ transition: right 250ms ease-in-out;
+}
+
+.sb-sidebar-toggle {
+ display: none;
+}
+.sb-sidebar-overlay {
+ position: fixed;
+ top: 0;
+ width: 0;
+ height: 0;
+
+ transition: width 0ms ease 250ms, height 0ms ease 250ms, opacity 250ms ease;
+
+ opacity: 0;
+ background-color: rgba(0, 0, 0, 0.54);
+}
+
+#sb-sidebar-toggle--primary:checked
+ ~ .sb-sidebar-overlay[for="sb-sidebar-toggle--primary"],
+#sb-sidebar-toggle--secondary:checked
+ ~ .sb-sidebar-overlay[for="sb-sidebar-toggle--secondary"] {
+ width: 100%;
+ height: 100%;
+ opacity: 1;
+ transition: width 0ms ease, height 0ms ease, opacity 250ms ease;
+}
+
+#sb-sidebar-toggle--primary:checked ~ .sb-container .sb-sidebar-primary {
+ left: 0;
+}
+#sb-sidebar-toggle--secondary:checked ~ .sb-container .sb-sidebar-secondary {
+ right: 0;
+}
+
+/* Full-width mode */
+.drop-secondary-sidebar-for-full-width-content
+ .hide-when-secondary-sidebar-shown {
+ display: none !important;
+}
+.drop-secondary-sidebar-for-full-width-content .sb-sidebar-secondary {
+ display: none !important;
+}
+
+/* Mobile views */
+.sb-page-width {
+ width: 100%;
+}
+
+.sb-article-container,
+.sb-footer-content__inner,
+.drop-secondary-sidebar-for-full-width-content .sb-article,
+.drop-secondary-sidebar-for-full-width-content .match-content-width {
+ width: 100vw;
+}
+
+.sb-article,
+.match-content-width {
+ padding: 0 1rem;
+ box-sizing: border-box;
+}
+
+@media (min-width: 32rem) {
+ .sb-article,
+ .match-content-width {
+ padding: 0 2rem;
+ }
+}
+
+/* Tablet views */
+@media (min-width: 42rem) {
+ .sb-article-container {
+ width: auto;
+ }
+ .sb-footer-content__inner,
+ .drop-secondary-sidebar-for-full-width-content .sb-article,
+ .drop-secondary-sidebar-for-full-width-content .match-content-width {
+ width: 42rem;
+ }
+ .sb-article,
+ .match-content-width {
+ width: 42rem;
+ }
+}
+@media (min-width: 46rem) {
+ .sb-footer-content__inner,
+ .drop-secondary-sidebar-for-full-width-content .sb-article,
+ .drop-secondary-sidebar-for-full-width-content .match-content-width {
+ width: 46rem;
+ }
+ .sb-article,
+ .match-content-width {
+ width: 46rem;
+ }
+}
+@media (min-width: 50rem) {
+ .sb-footer-content__inner,
+ .drop-secondary-sidebar-for-full-width-content .sb-article,
+ .drop-secondary-sidebar-for-full-width-content .match-content-width {
+ width: 50rem;
+ }
+ .sb-article,
+ .match-content-width {
+ width: 50rem;
+ }
+}
+
+/* Tablet views */
+@media (min-width: 59rem) {
+ .sb-sidebar-secondary {
+ position: static;
+ }
+ .hide-when-secondary-sidebar-shown {
+ display: none !important;
+ }
+ .sb-footer-content__inner,
+ .drop-secondary-sidebar-for-full-width-content .sb-article,
+ .drop-secondary-sidebar-for-full-width-content .match-content-width {
+ width: 59rem;
+ }
+ .sb-article,
+ .match-content-width {
+ width: 42rem;
+ }
+}
+@media (min-width: 63rem) {
+ .sb-footer-content__inner,
+ .drop-secondary-sidebar-for-full-width-content .sb-article,
+ .drop-secondary-sidebar-for-full-width-content .match-content-width {
+ width: 63rem;
+ }
+ .sb-article,
+ .match-content-width {
+ width: 46rem;
+ }
+}
+@media (min-width: 67rem) {
+ .sb-footer-content__inner,
+ .drop-secondary-sidebar-for-full-width-content .sb-article,
+ .drop-secondary-sidebar-for-full-width-content .match-content-width {
+ width: 67rem;
+ }
+ .sb-article,
+ .match-content-width {
+ width: 50rem;
+ }
+}
+
+/* Desktop views */
+@media (min-width: 76rem) {
+ .sb-sidebar-primary {
+ position: static;
+ }
+ .hide-when-primary-sidebar-shown {
+ display: none !important;
+ }
+ .sb-footer-content__inner,
+ .drop-secondary-sidebar-for-full-width-content .sb-article,
+ .drop-secondary-sidebar-for-full-width-content .match-content-width {
+ width: 59rem;
+ }
+ .sb-article,
+ .match-content-width {
+ width: 42rem;
+ }
+}
+
+/* Full desktop views */
+@media (min-width: 80rem) {
+ .sb-article,
+ .match-content-width {
+ width: 46rem;
+ }
+ .sb-footer-content__inner,
+ .drop-secondary-sidebar-for-full-width-content .sb-article,
+ .drop-secondary-sidebar-for-full-width-content .match-content-width {
+ width: 63rem;
+ }
+}
+
+@media (min-width: 84rem) {
+ .sb-article,
+ .match-content-width {
+ width: 50rem;
+ }
+ .sb-footer-content__inner,
+ .drop-secondary-sidebar-for-full-width-content .sb-article,
+ .drop-secondary-sidebar-for-full-width-content .match-content-width {
+ width: 67rem;
+ }
+}
+
+@media (min-width: 88rem) {
+ .sb-footer-content__inner,
+ .drop-secondary-sidebar-for-full-width-content .sb-article,
+ .drop-secondary-sidebar-for-full-width-content .match-content-width {
+ width: 67rem;
+ }
+ .sb-page-width {
+ width: 88rem;
+ }
+}
diff --git a/_static/sphinx_highlight.js b/_static/sphinx_highlight.js
new file mode 100644
index 0000000..8a96c69
--- /dev/null
+++ b/_static/sphinx_highlight.js
@@ -0,0 +1,154 @@
+/* Highlighting utilities for Sphinx HTML documentation. */
+"use strict";
+
+const SPHINX_HIGHLIGHT_ENABLED = true
+
+/**
+ * highlight a given string on a node by wrapping it in
+ * span elements with the given class name.
+ */
+const _highlight = (node, addItems, text, className) => {
+ if (node.nodeType === Node.TEXT_NODE) {
+ const val = node.nodeValue;
+ const parent = node.parentNode;
+ const pos = val.toLowerCase().indexOf(text);
+ if (
+ pos >= 0 &&
+ !parent.classList.contains(className) &&
+ !parent.classList.contains("nohighlight")
+ ) {
+ let span;
+
+ const closestNode = parent.closest("body, svg, foreignObject");
+ const isInSVG = closestNode && closestNode.matches("svg");
+ if (isInSVG) {
+ span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
+ } else {
+ span = document.createElement("span");
+ span.classList.add(className);
+ }
+
+ span.appendChild(document.createTextNode(val.substr(pos, text.length)));
+ const rest = document.createTextNode(val.substr(pos + text.length));
+ parent.insertBefore(
+ span,
+ parent.insertBefore(
+ rest,
+ node.nextSibling
+ )
+ );
+ node.nodeValue = val.substr(0, pos);
+ /* There may be more occurrences of search term in this node. So call this
+ * function recursively on the remaining fragment.
+ */
+ _highlight(rest, addItems, text, className);
+
+ if (isInSVG) {
+ const rect = document.createElementNS(
+ "http://www.w3.org/2000/svg",
+ "rect"
+ );
+ const bbox = parent.getBBox();
+ rect.x.baseVal.value = bbox.x;
+ rect.y.baseVal.value = bbox.y;
+ rect.width.baseVal.value = bbox.width;
+ rect.height.baseVal.value = bbox.height;
+ rect.setAttribute("class", className);
+ addItems.push({ parent: parent, target: rect });
+ }
+ }
+ } else if (node.matches && !node.matches("button, select, textarea")) {
+ node.childNodes.forEach((el) => _highlight(el, addItems, text, className));
+ }
+};
+const _highlightText = (thisNode, text, className) => {
+ let addItems = [];
+ _highlight(thisNode, addItems, text, className);
+ addItems.forEach((obj) =>
+ obj.parent.insertAdjacentElement("beforebegin", obj.target)
+ );
+};
+
+/**
+ * Small JavaScript module for the documentation.
+ */
+const SphinxHighlight = {
+
+ /**
+ * highlight the search words provided in localstorage in the text
+ */
+ highlightSearchWords: () => {
+ if (!SPHINX_HIGHLIGHT_ENABLED) return; // bail if no highlight
+
+ // get and clear terms from localstorage
+ const url = new URL(window.location);
+ const highlight =
+ localStorage.getItem("sphinx_highlight_terms")
+ || url.searchParams.get("highlight")
+ || "";
+ localStorage.removeItem("sphinx_highlight_terms")
+ url.searchParams.delete("highlight");
+ window.history.replaceState({}, "", url);
+
+ // get individual terms from highlight string
+ const terms = highlight.toLowerCase().split(/\s+/).filter(x => x);
+ if (terms.length === 0) return; // nothing to do
+
+ // There should never be more than one element matching "div.body"
+ const divBody = document.querySelectorAll("div.body");
+ const body = divBody.length ? divBody[0] : document.querySelector("body");
+ window.setTimeout(() => {
+ terms.forEach((term) => _highlightText(body, term, "highlighted"));
+ }, 10);
+
+ const searchBox = document.getElementById("searchbox");
+ if (searchBox === null) return;
+ searchBox.appendChild(
+ document
+ .createRange()
+ .createContextualFragment(
+ '