diff --git a/.gitignore b/.gitignore index c697761..d16756b 100644 --- a/.gitignore +++ b/.gitignore @@ -5,3 +5,4 @@ *.pyo *__pycache__* *.ipynb_checkpoints* +environ diff --git a/.scripts/requirements/python-basics-requirements.txt b/.scripts/requirements/python-basics-requirements.txt index 708f657..5537598 100644 --- a/.scripts/requirements/python-basics-requirements.txt +++ b/.scripts/requirements/python-basics-requirements.txt @@ -1,3 +1,3 @@ # These should match the versions in the version of jaspy which will be used for the course. -matplotlib==3.5.3 -pandas==2.0.3 +matplotlib==3.8.4 +pandas==2.2.2 \ No newline at end of file diff --git a/README.md b/README.md index 26d57a1..5ea12a0 100644 --- a/README.md +++ b/README.md @@ -1,26 +1,6 @@ -# Introduction to Scientific Computing course +# Introduction to Scientific Computing course This repository holds teaching materials for the NCAS Introduction to Scientific Computing course. -## Overview - -The course covers: -- Introduction to the Linux shell - - [Presentations and Exercises](https://ncasuk.github.io/ncas-isc-shell/) -- Python Setup - - [Logging in to the JASMIN Notebook Service ](https://github.com/ncasuk/ncas-isc/blob/main/setup/Logging_in_to_the_JASMIN_Notebook_Service.pdf) -- Git and GitHub - - [Presentation](https://github.com/ncasuk/ncas-isc/tree/main/version_control) - - [Exercise](https://github.com/ncasuk/ncas-isc/tree/main/version_control) -- Introduction to Python - - [Python Introduction Slides](https://github.com/ncasuk/ncas-isc/blob/main/python-intro/presentations.md) - - [Exercises - Jupyter Notebooks](https://github.com/ncasuk/ncas-isc/tree/main/python-intro/exercises) | [Solutions](https://github.com/ncasuk/ncas-isc/tree/main/python-intro/solutions) -- Data manipulation and visualisation in Python (Working with Data) - - [Python working with data Slides](https://github.com/ncasuk/ncas-isc/tree/main/python-data/slides) - - [Exercises - Jupyter Notebooks](https://github.com/ncasuk/ncas-isc/tree/main/python-data/notebooks) | [Solutions](https://github.com/ncasuk/ncas-isc/tree/main/python-data/solutions) -- Example code for all python modules - - [Example code for Python (Working with Data)](https://github.com/ncasuk/ncas-isc/tree/main/python-data/example_code) - - [Example data for Python (Working with Data)](https://github.com/ncasuk/ncas-isc/tree/main/python-data/example_data) - ## Index ### Overview Presentations * [Algorithmic thinking](https://github.com/ncasuk/ncas-isc/blob/main/working_practices/Algorithmic_thinking.pdf) @@ -37,50 +17,41 @@ The course covers: * [Exercise and solutions](https://github.com/ncasuk/ncas-isc/blob/main/version_control/01_git_exercise.md) ## Introduction to Python -1. [Running and Quitting](http://swcarpentry.github.io/python-novice-gapminder/01-run-quit.html) -2. [Variables and Assignment](http://swcarpentry.github.io/python-novice-gapminder/02-variables.html) -3. [Data Types and Type Conversion](http://swcarpentry.github.io/python-novice-gapminder/03-types-conversion.html) -4. [Built-in Functions and Help](http://swcarpentry.github.io/python-novice-gapminder/04-built-in.html) -5. [Libraries](http://swcarpentry.github.io/python-novice-gapminder/06-libraries.html) -6. [Reading Tabular Data into DataFrames](http://swcarpentry.github.io/python-novice-gapminder/07-reading-tabular.html) -7. [Pandas DataFrames](http://swcarpentry.github.io/python-novice-gapminder/08-data-frames.html) -8. [Plotting](http://swcarpentry.github.io/python-novice-gapminder/09-plotting.html) -9. [Lists](http://swcarpentry.github.io/python-novice-gapminder/11-lists.html) -10. [For Loops](http://swcarpentry.github.io/python-novice-gapminder/12-for-loops.html) -11. [Conditionals](http://swcarpentry.github.io/python-novice-gapminder/13-conditionals.html) -12. [Looping Over Data Sets](http://swcarpentry.github.io/python-novice-gapminder/14-looping-data-sets.html) -13. [Writing Functions](http://swcarpentry.github.io/python-novice-gapminder/16-writing-functions.html) -14. [Variable Scope](http://swcarpentry.github.io/python-novice-gapminder/17-scope.html) -15. [Programming Style](http://swcarpentry.github.io/python-novice-gapminder/18-style.html) -* [Exercises](https://github.com/ncasuk/ncas-isc/blob/main/python-intro/notebooks) and [Solutions](https://github.com/ncasuk/ncas-isc/blob/main/python-intro/solutions) +| Lesson | Exercise | Solution | +| ------ | -------- | -------- | +| [Running and quitting](https://swcarpentry.github.io/python-novice-gapminder/01-run-quit.html) | [Exercise 01](/python-intro/exercises/ex01_running_notebooks.ipynb) | [Solution 01](/python-intro/solutions/ex01_running_notebooks.ipynb) | +| [Variables and assignment](https://swcarpentry.github.io/python-novice-gapminder/02-variables.html) | [Exercise 02](/python-intro/exercises/ex02_variables_assignment.ipynb) | [Solution 02](/python-intro/solutions/ex02_variables_assignment.ipynb) | +| [Data types and type conversion](https://swcarpentry.github.io/python-novice-gapminder/03-types-conversion.html) | [Exercise 03](/python-intro/exercises/ex03_data_types.ipynb) | [Solution 03](/python-intro/solutions/ex03_data_types.ipynb) | +| [Built-in functions and Help](https://swcarpentry.github.io/python-novice-gapminder/04-built-in.html) | [Exercise 04](/python-intro/exercises/ex04_built_in_functions.ipynb) | [Solution 04](/python-intro/solutions/ex04_built_in_functions.ipynb) | +| [Libraries](https://swcarpentry.github.io/python-novice-gapminder/06-libraries.html) | [Exercise 05](/python-intro/exercises/ex05_libraries.ipynb) | [Solution 05](/python-intro/solutions/ex05_libraries.ipynb) | +| [Reading tabular data into data frames](https://swcarpentry.github.io/python-novice-gapminder/07-reading-tabular.html) | [Exercise 06](/python-intro/exercises/ex06_dataframes.ipynb) | [Solution 06](/python-intro/solutions/ex06_dataframes.ipynb) | +| [Pandas data frames](https://swcarpentry.github.io/python-novice-gapminder/08-data-frames.html) | [Exercise 07](/python-intro/exercises/ex07_pandas_dataframes.ipynb) | [Solution 07](/python-intro/solutions/ex07_pandas_dataframes.ipynb) | +| [Plotting](https://swcarpentry.github.io/python-novice-gapminder/09-plotting.html) | [Exercise 08](/python-intro/exercises/ex08_plotting.ipynb) | [Solution 08](/python-intro/solutions/ex08_plotting.ipynb) | +| [Lists](https://swcarpentry.github.io/python-novice-gapminder/11-lists.html) | [Exercise 09](/python-intro/exercises/ex09_lists.ipynb) | [Solution 09](/python-intro/solutions/ex09_lists.ipynb) | +| [For loops](https://swcarpentry.github.io/python-novice-gapminder/12-for-loops.html) | [Exercise 10](/python-intro/exercises/ex10_for_loops.ipynb) | [Solution 10](/python-intro/solutions/ex10_for_loops.ipynb) | +| [Conditionals](https://swcarpentry.github.io/python-novice-gapminder/13-conditionals.html) | [Exercise 11](/python-intro/exercises/ex11_conditionals.ipynb) | [Solution 11](/python-intro/solutions/ex11_conditionals.ipynb) | +| [Looping over data sets](https://swcarpentry.github.io/python-novice-gapminder/14-looping-data-sets.html) | [Exercise 12](/python-intro/exercises/ex12_looping_data_sets.ipynb) | [Solution 12](/python-intro/solutions/ex12_looping_data_sets.ipynb) | +| [Writing functions](https://swcarpentry.github.io/python-novice-gapminder/16-writing-functions.html) | [Exercise 13](/python-intro/exercises/ex13_writing_functions.ipynb) | [Solution 13](/python-intro/solutions/ex13_writing_functions.ipynb) | +| [Variable scope](https://swcarpentry.github.io/python-novice-gapminder/17-scope.html) | [Exercise 14](/python-intro/exercises/ex14_variable_scope.ipynb) | [Solution 14](/python-intro/solutions/ex14_variable_scope.ipynb) | +| [Programming style](https://swcarpentry.github.io/python-novice-gapminder/18-style.html) | [Exercise 15](/python-intro/exercises/ex15_programming_style.ipynb) | [Solution 15](/python-intro/solutions/ex15_programming_style.ipynb) | +| [Wrap Up / Summary](/python-intro/exercises/ex16_wrap_up.ipynb) | ## Python - Working with Data -### Handling arrays -* [Numpy](https://github.com/ncasuk/ncas-isc/blob/main/python-data/slides/numpy.pdf) -* [Exercises](https://github.com/ncasuk/ncas-isc/blob/main/python-data/notebooks/ex01_numpy_arrays.ipynb) and [Solutions](https://github.com/ncasuk/ncas-isc/blob/main/python-data/solutions/ex01_numpy_arrays_solutions.ipynb) - -### Visualisation -* [Matplotlib and cartopy](https://github.com/ncasuk/ncas-isc/blob/main/python-data/slides/matplotlib_and_cartopy.pdf) -* [Exercises](https://github.com/ncasuk/ncas-isc/blob/main/python-data/notebooks/ex02_matplotlib.ipynb) and [Solutions](https://github.com/ncasuk/ncas-isc/blob/main/python-data/solutions/ex02_matplotlib_solutions.ipynb) +| Lesson | Exercise | Solution | +| ------ | -------- | -------- | +| __xarray:__ Introduction to [multidimensional arrays](https://tutorial.xarray.dev/fundamentals/01_data_structures.html), [xarray data structures](https://tutorial.xarray.dev/fundamentals/01_datastructures.html) and [indexing](https://tutorial.xarray.dev/fundamentals/02.1_indexing_Basic.html) | [Exercise 01](/python-data/exercises/ex01_xr_intro.ipynb) [Exercise 01.5](/python-data/exercises/ex01.5_xr_label_based_indexing.ipynb)| [Solution 01](/python-data/solutions/ex01_xarray_intro.ipynb) [Solution 01.5](/python-data/solutions/ex01.5_xr_label_based_indexing.ipynb)| +| __xarray:__ [Plotting](https://tutorial.xarray.dev/fundamentals/04.1_basic_plotting.html) and [Aggregation](https://tutorial.xarray.dev/fundamentals/03.1_computation_with_xarray.html) | [Exercise 02](/python-data/exercises/ex02_xr_plotting.ipynb) [Exercise 02.5](/python-data/exercises/ex02.5_xr_aggregation.ipynb)| [Solution 02](/python-data/solutions/ex02_plotting.ipynb) [Solution 02.5](/python-data/solutions/ex02.5_xr_aggregation.ipynb)| +| __xarray:__ [GroupBy processing](https://tutorial.xarray.dev/fundamentals/03.2_groupby_with_xarray.html) and [masking](https://tutorial.xarray.dev/intermediate/indexing/boolean-masking-indexing.html) | [Exercise 03](/python-data/exercises/ex03_xr_groupby.ipynb) [Exercise 03.5](/python-data/exercises/ex03.5_xr_masking.ipynb)| [Solution 03](/python-data/solutions/ex03_groupby.ipynb) [Solution 03.5](/python-data/solutions/ex03.5_masking.ipynb)| +| [cf-python]() | [Exercise 04](/python-data/exercises/ex04_cf_python.ipynb) | [Solution 04](/python-data/solutions/ex04_cf_python.ipynb) | +| [matplotlib](https://matplotlib.org/stable/users/explain/quick_start.html) | [Exercise 05](/python-data/exercises/ex05_matplotlib.ipynb) | [Solution 05](/python-data/solutions/ex05_matplotlib.ipynb) | +| [numpy](https://numpy.org/doc/stable/user/quickstart.html) | [Exercise 06](/python-data/exercises/ex06_numpy.ipynb) | [Solution 06](/python-data/solutions/ex06_numpy.ipynb) | +| [netCDF4 basics](https://unidata.github.io/netcdf4-python/#tutorial) | [Exercise 07](/python-data/exercises/ex07_netcdf4_basics.ipynb) | [Solution 07](/python-data/solutions/ex07_netcdf4_basics.ipynb) | +| [netCDF advanced](https://unidata.github.io/netcdf4-python/#dealing-with-time-coordinates) | [Exercise 08](/python-data/exercises/ex08_netcdf4_advanced.ipynb) | [Exercise 08](/python-data/solutions/ex08_netcdf4_advanced.ipynb) | +| Weather Exercise | [Exercise 09a](/python-data/exercises/ex09a_weather_api.ipynb) | [Solution 09b](/python-data/solutions/ex09a_weather_api.ipynb) | +| Sentinel Data Exercise | [Exercise 09b](/python-data/exercises/ex09b_satellite_data.ipynb) | [Solution 09b](/python-data/solutions/ex09b_satellite_data.ipynb) | -### Read and Write data -1. [Data formats and metadata - why?](https://github.com/ncasuk/ncas-isc/blob/main/python-data/slides/01_data_formats.pdf) -2. [Text formats](https://github.com/ncasuk/ncas-isc/blob/main/python-data/slides/02_python_text_formats.pdf) -3. [Some more common text formats (at CEDA)](https://github.com/ncasuk/ncas-isc/blob/main/python-data/slides/03_text_formats_ceda.pdf) -4. [Binary formats](https://github.com/ncasuk/ncas-isc/blob/main/python-data/slides/04_binary_formats.pdf) -5. [Overview of NetCDF](https://github.com/ncasuk/ncas-isc/blob/main/python-data/slides/05_netcdf_overview.pdf) -6. [The structure of "Classic" NetCDF files](https://github.com/ncasuk/ncas-isc/blob/main/python-data/slides/06_netcdf_structure.pdf) -7. [`ncgen` and `ncdump` to create/export NetCDF and CDL](https://github.com/ncasuk/ncas-isc/blob/main/python-data/slides/07_ncgen_ncdump_cdl.pdf) -8. [The CF Metadata Conventions (for NetCDF)](https://github.com/ncasuk/ncas-isc/blob/main/python-data/slides/08_cf_metadata_conventions.pdf) -9. [Checking CF-compliance: `cf-checker`](https://github.com/ncasuk/ncas-isc/blob/main/python-data/slides/09_cfchecker.pdf) -10. [Reading NetCDF files with Python: `netCDF4`](https://github.com/ncasuk/ncas-isc/blob/main/python-data/slides/10_read_netcdf_python.pdf) -11. [Creating NetCDF files with Python](https://github.com/ncasuk/ncas-isc/blob/main/python-data/slides/11_create_netcdf_python.pdf) -12. [Reading and writing other formats](https://github.com/ncasuk/ncas-isc/blob/main/python-data/slides/12_python_and_other_formats.pdf) -13. [Viewing NetCDF: `Ncview` and `ncBrowse`](https://github.com/ncasuk/ncas-isc/blob/main/python-data/slides/13_ncview_ncbrowse.pdf) -* [Exercises](https://github.com/ncasuk/ncas-isc/blob/main/python-data/notebooks/ex03_netcdf.ipynb) and [Solutions](https://github.com/ncasuk/ncas-isc/blob/main/python-data/solutions/ex03_netcdf_solutions.ipynb) -* [Weather API Exercise](https://github.com/ncasuk/ncas-isc/blob/main/python-data/notebooks/ex04_weather_api.ipynb) and [Solution](https://github.com/ncasuk/ncas-isc/blob/main/python-data/solutions/ex04_weather_api_solutions.ipynb) ## Useful materials and resources @@ -91,4 +62,4 @@ See the [Resources page](resources.md) for links to useful related sites and mat Feel free to fork this repository on GitHub and re-use these materials however you like. ### Acknowledgements -The foundations of our course are based on the superb materials provided by [Software Carpentry](https://software-carpentry.org/) who we are eternally grateful to. +The foundations 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b/old_material/data_old_materials/notebooks/ex01_numpy_arrays.ipynb similarity index 96% rename from python-data/notebooks/ex01_numpy_arrays.ipynb rename to old_material/data_old_materials/notebooks/ex01_numpy_arrays.ipynb index c74be41..b8e0fdb 100644 --- a/python-data/notebooks/ex01_numpy_arrays.ipynb +++ b/old_material/data_old_materials/notebooks/ex01_numpy_arrays.ipynb @@ -53,7 +53,7 @@ "source": [ "### Let's create a numpy array from a list.\n", "\n", - "Create a with values 1 to 10 and assign it to the variable `x`" + "Create a range with values 1 to 10 and assign it to the variable `x`" ] }, { @@ -179,7 +179,7 @@ "Create an array from the list `[2, 3.2, 5.5, -6.4, -2.2, 2.4]` and assign it to the variable `a`\n", "\n", "- Do you know what `a[1]` will equal? Print to see.\n", - "- Try print `a[1:4]` to see what that equals." + "- Try printing `a[1:4]` to see what that equals." ] }, { @@ -261,7 +261,7 @@ "\n", "### Let's interrogate an array to find out it's characteristics\n", "\n", - "Create a 2-D array of shape (2, 4) containing two lists `range(4)` and `range(10, 14)`, assign it to the vairable `arr`\n", + "Create a 2-D array of shape (2, 4) containing two lists `range(4)` and `range(10, 14)`, assign it to the variable `arr`\n", "\n", "- Print the shape of the array\n", "- Print the size of the array\n", @@ -342,7 +342,7 @@ "\n", "### Let's perform some array calculations\n", "\n", - "Create a 2-D array of shape (2, 4) containing two lists `range(4)` and `range(10, 14)`, assign it to the vairable `a`\n", + "Create a 2-D array of shape (2, 4) containing two lists `range(4)` and `range(10, 14)`, assign it to the variable `a`\n", "\n", "Create an array from a list `[2, -1, 1, 0]` and assign it to the variable `b`\n", "\n", @@ -385,7 +385,7 @@ "\n", "Create an array of values 0 to 9 and assign it to the variable `arr`\n", "\n", - "- Print two different way of expressing the condition where the array is less than 3.\n", + "- Print two different ways of expressing the condition where the array is less than 3.\n", "- Create a numpy condition where `arr` is less than 3 OR greater than 8.\n", "- Use the `where` function to create a new array where the value is `arr*5` if the above condition is `True` and `arr-5` where the condition is `False`" ] @@ -418,7 +418,7 @@ "source": [ "### Let's implement a mathematical function that works on arrays.\n", "\n", - "Write a function that takes a 2-D array of horizontal zonal (east-west) wind components (`u`, in m/s) and a 2-D array of horizontal meridional (north-south) wind componenets (`v`, in m/s)\n", + "Write a function that takes a 2-D array of horizontal zonal (east-west) wind components (`u`, in m/s) and a 2-D array of horizontal meridional (north-south) wind components (`v`, in m/s)\n", "and returns an array of the magnitudes of the total wind.\n", "Include a test for the overall magnitude: if it is less than 0.1 then set it equal to 0.1 (We might presume this particular domain has no non-zero winds and that only winds above 0.1 m/s constitute \"good\" data while those below are indistinguishable from the minimum due to noise)\n", "\n", @@ -534,7 +534,7 @@ "- Print the array to view its values.\n", "- Print its missing value (i.e. `narr.fill_value`)\n", "- Print an array that converts `narr` so that the missing values are represented by the missing value (i.e. `MA.filled`). Assign it to `farr`\n", - "- What is the type of the `farr`" + "- What is the type of `farr`?" ] }, { diff --git a/python-data/notebooks/ex02_matplotlib.ipynb b/old_material/data_old_materials/notebooks/ex02_matplotlib.ipynb similarity index 99% rename from python-data/notebooks/ex02_matplotlib.ipynb rename to old_material/data_old_materials/notebooks/ex02_matplotlib.ipynb index 9ead188..6956562 100644 --- a/python-data/notebooks/ex02_matplotlib.ipynb +++ b/old_material/data_old_materials/notebooks/ex02_matplotlib.ipynb @@ -237,7 +237,7 @@ "source": [ "## 3. Plotting gridded data on a map\n", "\n", - "In this section, we will use `cartopy` - a python module that supports maps and usage with `matplotlib`.\n", + "In this section, we will use `cartopy` - a Python module that supports maps and usage with `matplotlib`.\n", "\n", "First, let's grab some data from a NetCDF file and quickly plot it.\n", "\n", diff --git a/python-data/notebooks/ex03_netcdf.ipynb b/old_material/data_old_materials/notebooks/ex03_netcdf.ipynb similarity index 98% rename from python-data/notebooks/ex03_netcdf.ipynb rename to old_material/data_old_materials/notebooks/ex03_netcdf.ipynb index ae75e70..4666438 100644 --- a/python-data/notebooks/ex03_netcdf.ipynb +++ b/old_material/data_old_materials/notebooks/ex03_netcdf.ipynb @@ -70,7 +70,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Loop through and print Dataset `variables` names, this is the ID that act like the key to access the Variable." + "Loop through and print Dataset `variables` names, this is the ID that acts like the key to access the variable." ] }, { @@ -314,7 +314,7 @@ "metadata": {}, "source": [ "Create four new Dimensions to `myds` from your previous slices. Re-use the names from the last section.\n", - "Note that the \"level\" and \"time\" Dimensions should have length, \"lat\" length 10 and \"lon\" length 5.\n", + "Note that the \"level\" and \"time\" dimensions should have length 1, \"lat\" length 10 and \"lon\" length 5.\n", "To create a new Dimension use `myds.createDimension(name, size)`" ] }, diff --git a/python-data/notebooks/ex04_weather_api.ipynb b/old_material/data_old_materials/notebooks/ex04_weather_api.ipynb similarity index 100% rename from python-data/notebooks/ex04_weather_api.ipynb rename to old_material/data_old_materials/notebooks/ex04_weather_api.ipynb diff --git a/python-data/notebooks/ex04b_satellite_data.ipynb b/old_material/data_old_materials/notebooks/ex04b_satellite_data.ipynb similarity index 100% rename from python-data/notebooks/ex04b_satellite_data.ipynb rename to old_material/data_old_materials/notebooks/ex04b_satellite_data.ipynb diff --git a/python-data/notebooks/satellite_setup_instructions.md 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b/old_material/data_old_materials/slides/matplotlib_and_cartopy.pptx similarity index 100% rename from python-data/slides/matplotlib_and_cartopy.pptx rename to old_material/data_old_materials/slides/matplotlib_and_cartopy.pptx diff --git a/python-data/slides/notes.txt b/old_material/data_old_materials/slides/notes.txt similarity index 100% rename from python-data/slides/notes.txt rename to old_material/data_old_materials/slides/notes.txt diff --git a/python-data/slides/numpy.pdf b/old_material/data_old_materials/slides/numpy.pdf similarity index 100% rename from python-data/slides/numpy.pdf rename to old_material/data_old_materials/slides/numpy.pdf diff --git a/python-data/slides/numpy.pptx b/old_material/data_old_materials/slides/numpy.pptx similarity index 100% rename from python-data/slides/numpy.pptx rename to old_material/data_old_materials/slides/numpy.pptx diff --git a/python-data/solutions/ex01_numpy_arrays_solutions.ipynb b/old_material/data_old_materials/solutions/ex01_numpy_arrays_solutions.ipynb similarity index 98% rename from python-data/solutions/ex01_numpy_arrays_solutions.ipynb rename to old_material/data_old_materials/solutions/ex01_numpy_arrays_solutions.ipynb index 09ec90c..5dc6336 100644 --- a/python-data/solutions/ex01_numpy_arrays_solutions.ipynb +++ b/old_material/data_old_materials/solutions/ex01_numpy_arrays_solutions.ipynb @@ -57,7 +57,7 @@ "source": [ "### Let's create a numpy array from a list.\n", "\n", - "Create a with values 1 to 10 and assign it to the variable `x`" + "Create a range with values 1 to 10 and assign it to the variable `x`" ] }, { @@ -291,7 +291,7 @@ "Create an array from the list `[2, 3.2, 5.5, -6.4, -2.2, 2.4]` and assign it to the variable `a`\n", "\n", "- Do you know what `a[1]` will equal? Print to see.\n", - "- Try print `a[1:4]` to see what that equals." + "- Try printing `a[1:4]` to see what that equals." ] }, { @@ -406,7 +406,7 @@ "\n", "### Let's interrogate an array to find out it's characteristics\n", "\n", - "Create a 2-D array of shape (2, 4) containing two lists `range(4)` and `range(10, 14)`, assign it to the vairable `arr`\n", + "Create a 2-D array of shape (2, 4) containing two lists `range(4)` and `range(10, 14)`, assign it to the variable `arr`\n", "\n", "- Print the shape of the array\n", "- Print the size of the array\n", @@ -533,7 +533,7 @@ "\n", "### Let's perform some array calculations\n", "\n", - "Create a 2-D array of shape (2, 4) containing two lists `range(4)` and `range(10, 14)`, assign it to the vairable `a`\n", + "Create a 2-D array of shape (2, 4) containing two lists `range(4)` and `range(10, 14)`, assign it to the variable `a`\n", "\n", "Create an array from a list `[2, -1, 1, 0]` and assign it to the variable `b`\n", "\n", @@ -602,7 +602,7 @@ "\n", "Create an array of values 0 to 9 and assign it to the variable `arr`\n", "\n", - "- Print two different way of expressing the condition where the array is less than 3.\n", + "- Print two different ways of expressing the condition where the array is less than 3.\n", "- Create a numpy condition where `arr` is less than 3 OR greater than 8.\n", "- Use the `where` function to create a new array where the value is `arr*5` if the above condition is `True` and `arr-5` where the condition is `False`" ] @@ -652,7 +652,7 @@ "source": [ "### Let's implement a mathematical function that works on arrays.\n", "\n", - "Write a function that takes a 2-D array of horizontal zonal (east-west) wind components (`u`, in m/s) and a 2-D array of horizontal meridional (north-south) wind componenets (`v`, in m/s)\n", + "Write a function that takes a 2-D array of horizontal zonal (east-west) wind components (`u`, in m/s) and a 2-D array of horizontal meridional (north-south) wind components (`v`, in m/s)\n", "and returns an array of the magnitudes of the total wind.\n", "Include a test for the overall magnitude: if it is less than 0.1 then set it equal to 0.1 (We might presume this particular domain has no non-zero winds and that only winds above 0.1 m/s constitute \"good\" data while those below are indistinguishable from the minimum due to noise)\n", "\n", @@ -807,7 +807,7 @@ "- Print the array to view its values.\n", "- Print its missing value (i.e. `narr.fill_value`)\n", "- Print an array that converts `narr` so that the missing values are represented by the missing value (i.e. `MA.filled`). Assign it to `farr`\n", - "- What is the type of the `farr`" + "- What is the type of `farr`?" ] }, { diff --git a/python-data/solutions/ex02_matplotlib_solutions.ipynb b/old_material/data_old_materials/solutions/ex02_matplotlib_solutions.ipynb similarity index 99% rename from python-data/solutions/ex02_matplotlib_solutions.ipynb rename to old_material/data_old_materials/solutions/ex02_matplotlib_solutions.ipynb index 9312148..5bca81a 100644 --- a/python-data/solutions/ex02_matplotlib_solutions.ipynb +++ b/old_material/data_old_materials/solutions/ex02_matplotlib_solutions.ipynb @@ -78,7 +78,7 @@ }, { "data": { - "image/png": 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\n", 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", 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\n", 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", 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\n", 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", 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" ] @@ -237,7 +237,7 @@ "outputs": [ { "data": { - "image/png": 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", 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\n", 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", 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\n", + "image/png": 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", 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" ] @@ -398,7 +398,7 @@ "source": [ "## 3. Plotting gridded data on a map\n", "\n", - "In this section, we will use `cartopy` - a python module that supports maps and usage with `matplotlib`.\n", + "In this section, we will use `cartopy` - a Python module that supports maps and usage with `matplotlib`.\n", "\n", "First, let's grab some data from a NetCDF file and quickly plot it.\n", "\n", @@ -467,7 +467,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", 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" ] @@ -554,7 +554,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", 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" ] diff --git a/python-data/solutions/ex03_netcdf_solutions.ipynb b/old_material/data_old_materials/solutions/ex03_netcdf_solutions.ipynb similarity index 99% rename from python-data/solutions/ex03_netcdf_solutions.ipynb rename to old_material/data_old_materials/solutions/ex03_netcdf_solutions.ipynb index 39eae62..8347ae4 100644 --- a/python-data/solutions/ex03_netcdf_solutions.ipynb +++ b/old_material/data_old_materials/solutions/ex03_netcdf_solutions.ipynb @@ -74,7 +74,7 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "Loop through and print Dataset `variables` names, this is the ID that act like the key to access the Variable." + "Loop through and print Dataset `variables` names, this is the ID that acts like the key to access the variable." ] }, { diff --git a/python-data/solutions/ex04_weather_api_solutions.ipynb b/old_material/data_old_materials/solutions/ex04a_weather_api_solutions.ipynb similarity index 100% rename from python-data/solutions/ex04_weather_api_solutions.ipynb rename to old_material/data_old_materials/solutions/ex04a_weather_api_solutions.ipynb diff --git a/python-data/solutions/ex04b_satellite_data_solutions.ipynb b/old_material/data_old_materials/solutions/ex04b_satellite_data_solutions.ipynb similarity index 100% rename from python-data/solutions/ex04b_satellite_data_solutions.ipynb rename to old_material/data_old_materials/solutions/ex04b_satellite_data_solutions.ipynb diff --git a/python-data/solutions/ex05_pandas.ipynb b/old_material/data_old_materials/solutions/ex05_pandas.ipynb similarity index 100% rename from python-data/solutions/ex05_pandas.ipynb rename to old_material/data_old_materials/solutions/ex05_pandas.ipynb diff --git a/python-data/solutions/ex06_pandas_rainfall.ipynb b/old_material/data_old_materials/solutions/ex06_pandas_rainfall.ipynb similarity index 100% rename from python-data/solutions/ex06_pandas_rainfall.ipynb rename to old_material/data_old_materials/solutions/ex06_pandas_rainfall.ipynb diff --git a/environments/conda/isc-environment.yml b/old_material/environments/conda/isc-environment.yml similarity index 100% rename from environments/conda/isc-environment.yml rename to old_material/environments/conda/isc-environment.yml diff --git a/python-data/example_code/__init__.py b/old_material/intro_old_materials/__init__.py similarity index 100% rename from python-data/example_code/__init__.py rename to old_material/intro_old_materials/__init__.py diff --git a/python-intro/z_old_materials/cheat_sheets/beginners_python_cheat_sheet_pcc.pdf b/old_material/intro_old_materials/cheat_sheets/beginners_python_cheat_sheet_pcc.pdf similarity index 100% rename from python-intro/z_old_materials/cheat_sheets/beginners_python_cheat_sheet_pcc.pdf rename to old_material/intro_old_materials/cheat_sheets/beginners_python_cheat_sheet_pcc.pdf diff --git a/python-intro/z_old_materials/cheat_sheets/python-cheat-sheet-basic.pdf 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/dev/null +++ b/python-data/README.md @@ -0,0 +1,22 @@ +# Intro to Python Data + +This part of the course is based on some different materials around data libraries in Python, with additional exercises and materials. + +In this folder you will find all the material used to run the course. +* Links to the presentation materials +* The jupyter-notebook based exercises we will complete as part of the course +* Solutions to the jupyter-notebook based exercises + +Presentation material is used from the links listed below: + +1. xarray: [Introduction to multidimensional arrays](https://tutorial.xarray.dev/fundamentals/01_data_structures.html), and [intro to xarray data structures](https://tutorial.xarray.dev/fundamentals/01_datastructures.html) and[label-based indexing](https://tutorial.xarray.dev/fundamentals/02.1_indexing_Basic.html). +2. xarray: [Plotting](https://tutorial.xarray.dev/fundamentals/04.1_basic_plotting.html) and [aggregation](https://tutorial.xarray.dev/fundamentals/03.1_computation_with_xarray.html) +3. xarray: [Groupby processing](https://tutorial.xarray.dev/fundamentals/03.2_groupby_with_xarray.html) and [masking](https://tutorial.xarray.dev/intermediate/indexing/boolean-masking-indexing.html) +4. [cf-python]() +5. [matplotlib](https://matplotlib.org/stable/users/explain/quick_start.html) +6. [numpy](https://numpy.org/doc/stable/user/quickstart.html) +7. [NetCDF4 basics](https://unidata.github.io/netcdf4-python/#tutorial) +8. [NetCDF4 advanced](https://unidata.github.io/netcdf4-python/#dealing-with-time-coordinates) +9. [Weather Exercise](./exercises/ex09a_weather_api.ipynb) and [Satellite Exercise](./exercises/ex09b_satellite_data.ipynb) + +Each of these has an equivalent notebook in the [exercises](./exercises) folder with the solutions in the [solutions](./solutions) folder. \ No newline at end of file diff --git a/python-data/data/cv-noxy_capeverde_20080301.nc b/python-data/data/cv-noxy_capeverde_20080301.nc new file mode 100644 index 0000000..85ff028 Binary files /dev/null and b/python-data/data/cv-noxy_capeverde_20080301.nc differ diff --git a/python-data/data/ggas2014121200_00-18.nc b/python-data/data/ggas2014121200_00-18.nc new file mode 100644 index 0000000..960605c Binary files /dev/null and b/python-data/data/ggas2014121200_00-18.nc differ diff --git a/python-data/data/n2o_emissions.nc b/python-data/data/n2o_emissions.nc new file mode 100644 index 0000000..dd00bde Binary files /dev/null and b/python-data/data/n2o_emissions.nc differ diff --git a/python-data/data/sensor_data.nc b/python-data/data/sensor_data.nc new file mode 100644 index 0000000..dae3f2f Binary files /dev/null and b/python-data/data/sensor_data.nc differ diff --git a/python-data/data/tas.nc b/python-data/data/tas.nc new file mode 100644 index 0000000..2d30cba Binary files /dev/null and b/python-data/data/tas.nc differ diff --git a/python-data/data/tas_hadcm3_cf.nc b/python-data/data/tas_hadcm3_cf.nc new file mode 100644 index 0000000..1468fdf Binary files /dev/null and b/python-data/data/tas_hadcm3_cf.nc differ diff --git a/python-data/data/tas_rcp45_2055_ann_avg_change.nc b/python-data/data/tas_rcp45_2055_ann_avg_change.nc new file mode 100644 index 0000000..bf6a905 Binary files /dev/null and b/python-data/data/tas_rcp45_2055_ann_avg_change.nc differ diff --git a/python-data/data/tas_rcp45_2055_mon_avg_change.nc b/python-data/data/tas_rcp45_2055_mon_avg_change.nc new file mode 100644 index 0000000..2d30cba Binary files /dev/null and b/python-data/data/tas_rcp45_2055_mon_avg_change.nc differ diff --git a/python-data/data/xbhubo.pgc0apr.nc b/python-data/data/xbhubo.pgc0apr.nc new file mode 100644 index 0000000..4a58420 Binary files /dev/null and b/python-data/data/xbhubo.pgc0apr.nc differ diff --git a/python-data/exercises/ex01.5_xr_label_based_indexing.ipynb b/python-data/exercises/ex01.5_xr_label_based_indexing.ipynb new file mode 100644 index 0000000..32dc8e9 --- /dev/null +++ b/python-data/exercises/ex01.5_xr_label_based_indexing.ipynb @@ -0,0 +1,239 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "77fa81b4-1601-4743-b297-ae21ac49fd49", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "# Exercise 1.5: Label-based indexing" + ] + }, + { + "cell_type": "markdown", + "id": "c1f847cc-4003-42a1-b484-7f7fc283b4c5", + "metadata": {}, + "source": [ + "## Aim: Learn how to index data arrays" + ] + }, + { + "cell_type": "markdown", + "id": "6374f110-5ea1-4959-b226-4eeb18cf4899", + "metadata": {}, + "source": [ + "Find the teaching resources here: https://tutorial.xarray.dev/fundamentals/02.1_indexing_Basic.html." + ] + }, + { + "cell_type": "markdown", + "id": "e0cc034f-61e8-430f-b5ea-511594ff8d42", + "metadata": {}, + "source": [ + "### Issues Covered: \n", + "- Indexing, using `.loc()`, `.isel()` and `.sel()`" + ] + }, + { + "cell_type": "markdown", + "id": "a33d279d-ad68-4790-8c97-f32cf1faa019", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q1. Open the `'../data/tas_rcp45_2055_mon_avg_change.nc'` dataset and load it into an xarray `Dataset` called `ds`.\n", + "(Hint: Don't forget to import any packages you need).\n", + "This file is a model run for HadCM3 run as part of the RAPID study: https://catalogue.ceda.ac.uk/uuid/6bbab8394124b252f8b1b036f9eb6b6b/" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "778346e3-ed07-408f-83e7-3b12632761e5", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "0a21a6b9-ebae-47fa-940b-3b7f92d3ad2b", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q2. Select a subset of the `temperature` array using label-based indexing to get data at the position [0,0,0]." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "d0818a4f-7255-4b90-9cb5-c8e1810cd7e3", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:33.495959Z", + "iopub.status.busy": "2024-11-08T14:55:33.495440Z", + "iopub.status.idle": "2024-11-08T14:55:33.510961Z", + "shell.execute_reply": "2024-11-08T14:55:33.510269Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "b706418f-ed69-4d32-8a2c-b9fd6accf9b6", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q3. Use `.loc` to find the temperature 5 meters below the sea surface in south atlantic where latitiude is -50.625 and longitude is 0." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "84ef9cec-9839-41fe-98e7-caaeb6e6e147", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:33.513642Z", + "iopub.status.busy": "2024-11-08T14:55:33.513261Z", + "iopub.status.idle": "2024-11-08T14:55:33.523273Z", + "shell.execute_reply": "2024-11-08T14:55:33.522720Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "09285680-d82a-44c7-b40a-092a47b568e9", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q4. It's not ideal to have to keep track of which dimension is in which position. Instead, use `.isel` to use the dimension names to get the data in the same place: this is depth position 0, latitude position 31 and longitude position 0." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "41ce3da8-ab39-4e62-b1f6-e27b13527f42", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:33.525735Z", + "iopub.status.busy": "2024-11-08T14:55:33.525438Z", + "iopub.status.idle": "2024-11-08T14:55:33.536074Z", + "shell.execute_reply": "2024-11-08T14:55:33.535518Z" + }, + "scrolled": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "422eddac-26f7-4c9b-b6ad-4e7386117b25", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q5. The previous method is still referring to a selection by integer position. Use `.sel` to give a labelled index with the named dimension to find the data at `time=2065-12-30`, `lat=-78.5`, `lon=11.0`." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "09dac653-01d8-4e60-9444-42ef7743cf99", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:33.538580Z", + "iopub.status.busy": "2024-11-08T14:55:33.538287Z", + "iopub.status.idle": "2024-11-08T14:55:33.550492Z", + "shell.execute_reply": "2024-11-08T14:55:33.549854Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 + Jaspy", + "language": "python", + "name": "jaspy" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/python-data/exercises/ex01_xr_intro.ipynb b/python-data/exercises/ex01_xr_intro.ipynb new file mode 100644 index 0000000..2372a4e --- /dev/null +++ b/python-data/exercises/ex01_xr_intro.ipynb @@ -0,0 +1,342 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "2713343a-c0f0-4da3-979f-bb0d9f8a5e4a", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "# Exercise 1: Introduction to xarray\n" + ] + }, + { + "cell_type": "markdown", + "id": "bbe08d6c-c85d-4798-9343-b3e958cf39d4", + "metadata": {}, + "source": [ + "## Aim: Learn about what xarray is and how to create and look at a `DataArray`." + ] + }, + { + "cell_type": "markdown", + "id": "c6bbe825-2d51-4e8a-bc9d-8ca75ba0d2c5", + "metadata": {}, + "source": [ + "Find the teaching resources here: https://tutorial.xarray.dev/fundamentals/01_data_structures.html and https://tutorial.xarray.dev/fundamentals/01_datastructures.html." + ] + }, + { + "cell_type": "markdown", + "id": "f98aa85b-8e65-4894-9b61-13acdb9938dc", + "metadata": {}, + "source": [ + "### Issues Covered:\n", + "- Importing `xarray`\n", + "- Loading a dataset using `xr.open_dataset()`\n", + "- Creating a `DataArray`" + ] + }, + { + "cell_type": "markdown", + "id": "adb09498-99fc-4174-8661-4b29858975d3", + "metadata": {}, + "source": [ + "## 1. Introduction to multidimensional arrays" + ] + }, + { + "cell_type": "markdown", + "id": "308be498-f527-4886-b5a8-474e43e7a1f9", + "metadata": {}, + "source": [ + "- Unlabelled N dimensional arrays of numbers are the most widely used data structure in scientific computing\n", + "- These arrays lack meaningful metadata so users must track indices in an arbitrary fashion" + ] + }, + { + "cell_type": "markdown", + "id": "3d4d8868-a7d4-4651-a07a-ec10104f34b2", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "id": "adabc378-941e-4520-a787-b02a563c6956", + "metadata": {}, + "source": [ + "Q1. Can you think of any reasons why xarray might be preferred to pandas when working with multi-dimensional data like climate models?\n", + "(Hint: how many dimensions does a pandas dataframe have?)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "14157c3e-af21-444e-9e8d-5a78eeb9edad", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:53:54.796581Z", + "iopub.status.busy": "2024-11-08T14:53:54.796266Z", + "iopub.status.idle": "2024-11-08T14:53:54.799454Z", + "shell.execute_reply": "2024-11-08T14:53:54.798949Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "4352c46a-c179-439e-b1fe-3953dc8ee41c", + "metadata": {}, + "source": [ + "## 2. Opening and Exploring Datasets" + ] + }, + { + "cell_type": "markdown", + "id": "48d19019-2546-46d3-9da1-64d8e7c363e8", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q2. Open the `'../data/tas_rcp45_2055_mon_avg_change.nc'` dataset and load it into an xarray `Dataset` called `ds`.\n", + "(Hint: Don't forget to import any packages you need).\n", + "This file is a model run for HadCM3 run as part of the RAPID study: https://catalogue.ceda.ac.uk/uuid/6bbab8394124b252f8b1b036f9eb6b6b/" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "397feb59-dd4a-42bf-bf95-f093d75d28b3", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:53:54.801981Z", + "iopub.status.busy": "2024-11-08T14:53:54.801656Z", + "iopub.status.idle": "2024-11-08T14:54:03.266840Z", + "shell.execute_reply": "2024-11-08T14:54:03.266140Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "9c34363a-9168-4478-885a-7bd2ec669f3a", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q3. Look at the parameters of the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "1257eab9-ded3-4e4b-b703-0b39236f5d23", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:03.270257Z", + "iopub.status.busy": "2024-11-08T14:54:03.269693Z", + "iopub.status.idle": "2024-11-08T14:54:03.295793Z", + "shell.execute_reply": "2024-11-08T14:54:03.295117Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "3cee6429-dbf8-4a10-b384-ad9de719a0d0", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q4. What are the dimensions and variables in this dataset? What does each represent? " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "67ff7eb4-4058-4041-beb7-66e294542887", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:03.298211Z", + "iopub.status.busy": "2024-11-08T14:54:03.297936Z", + "iopub.status.idle": "2024-11-08T14:54:03.300747Z", + "shell.execute_reply": "2024-11-08T14:54:03.300254Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "81207a17-eeaf-4de3-a3f6-9a0fd782c252", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q5. Find the name of the temperature data variable, and use it to extract a `DataArray` called `temperature`." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "3d0218b3-df73-4037-9fb9-eea80e1a70d2", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:03.303199Z", + "iopub.status.busy": "2024-11-08T14:54:03.302875Z", + "iopub.status.idle": "2024-11-08T14:54:03.362497Z", + "shell.execute_reply": "2024-11-08T14:54:03.361816Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "6ee4a984-305e-44da-87ab-75cf88d71f22", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q6. Take a look at the `temperature` data array and inspect its dimensions, coordinates and attributes. What are the specific dimensions and coordinates associated with it? What metadata (attributes) is provided?" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "cc8a03b5-d7ce-406b-9602-47a153d2d7ec", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:03.365566Z", + "iopub.status.busy": "2024-11-08T14:54:03.365299Z", + "iopub.status.idle": "2024-11-08T14:54:03.393288Z", + "shell.execute_reply": "2024-11-08T14:54:03.392731Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "e36ca3a3-8387-480a-8dfe-161bc6292681", + "metadata": {}, + "source": [ + "Q7. Find out what dimensions and coordinates exist in your dataset. Which latitude and longitude variables are associated with the ocean temperature variable?" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "32775beb-ec9b-4cc2-a8bb-ca59f8cf6bad", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:03.396388Z", + "iopub.status.busy": "2024-11-08T14:54:03.395886Z", + "iopub.status.idle": "2024-11-08T14:54:03.403873Z", + "shell.execute_reply": "2024-11-08T14:54:03.403369Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 + Jaspy", + "language": "python", + "name": "jaspy" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/python-data/exercises/ex02.5_xr_aggregation.ipynb b/python-data/exercises/ex02.5_xr_aggregation.ipynb new file mode 100644 index 0000000..7540cc5 --- /dev/null +++ b/python-data/exercises/ex02.5_xr_aggregation.ipynb @@ -0,0 +1,277 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0794ae77-cf09-45ba-8b05-ad591cee6b4d", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "# Exercise 2.5: Arithmetic and Aggregation" + ] + }, + { + "cell_type": "markdown", + "id": "7e654d07-1447-4684-9a69-0e0f7df8e54d", + "metadata": {}, + "source": [ + "## Aim: Learn to do computation with xarray" + ] + }, + { + "cell_type": "markdown", + "id": "870b0f69-7bfd-4ca1-b1f9-876ad665f3cb", + "metadata": {}, + "source": [ + "Find the teaching materials here: https://tutorial.xarray.dev/fundamentals/03.1_computation_with_xarray.html" + ] + }, + { + "cell_type": "markdown", + "id": "3d8da800-9ba2-440b-b13b-30caf7027300", + "metadata": {}, + "source": [ + "### Issues covered: \n", + "- Doing arithmetic on data arrays\n", + "- Using `.mean()`, `.std()`, `.max()` and `.min()`" + ] + }, + { + "cell_type": "markdown", + "id": "dc51608d-76da-4c7b-be20-089df1a52f9b", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q1. Import the `'../data/xbhubo.pgc0apr.nc'` dataset and create the temperature data array as in the last lesson." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "371d4e04-d765-4c34-b75c-b235a1165d6a", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:53.364943Z", + "iopub.status.busy": "2024-11-08T14:54:53.364387Z", + "iopub.status.idle": "2024-11-08T14:55:01.739075Z", + "shell.execute_reply": "2024-11-08T14:55:01.738157Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "dbc7c274-51f9-40e8-9cf8-4d4f5e25707c", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q2. Let's compare the data between the sea surface and further down. Create two temperature datasets and extract the temperature change data the sea surface and the sea bottom" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "37a7a666-942a-446e-ae54-6aab318dc084", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:01.743464Z", + "iopub.status.busy": "2024-11-08T14:55:01.742652Z", + "iopub.status.idle": "2024-11-08T14:55:01.754012Z", + "shell.execute_reply": "2024-11-08T14:55:01.752769Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "87224117-8b77-4a8a-ac4d-5c7749d263ad", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q3. Calculate the difference in temperature the bottom of the ocean and the surface." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "143821a5-c18c-4cd0-8ba6-18015a82c396", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "6726f384-403d-4614-a15b-4431ca62dd5c", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q4. Plot the difference in these temperatures using xarrays built-in features." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a4f06f85-2c15-47da-abcb-87cbfe19e852", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "02e22bb4-7c61-4b00-996f-cf03f3aff595", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q5. Calculate the **minimum** temperature across the water depth in all locations." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "24988c19-820d-426a-87f9-fa350f9c1d10", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "5cc23d19-2f5d-4dd9-9db0-807c47d83cc5", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q6. Compute the global average ocean temperature change (averaged over all depths) for the entire time period in the dataset. Then display the result as a 2D depth profile." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cf55c6cf-295d-4a02-956b-609a89815105", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "74ecf24e-9293-4c0b-98c6-6fc4805025f5", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q7. Calculate the zonal average temperature change for each latitude. Plot the result as a 2d contour with depth on the y axis and latitude on x." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e5044676-8cb0-46e7-a880-b9033f6ff53c", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 + Jaspy", + "language": "python", + "name": "jaspy" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/python-data/exercises/ex02_xr_plotting.ipynb b/python-data/exercises/ex02_xr_plotting.ipynb new file mode 100644 index 0000000..63fbe72 --- /dev/null +++ b/python-data/exercises/ex02_xr_plotting.ipynb @@ -0,0 +1,409 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "38e495fd-dc7a-4e98-a2ab-d7b28560db48", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "# Exercise 2: Plotting" + ] + }, + { + "cell_type": "markdown", + "id": "d991cb4d-6063-4e30-9c7d-c9bc1d6d1857", + "metadata": {}, + "source": [ + "## Aim: Learn to create plots with the inbuilt `.plot()` function" + ] + }, + { + "cell_type": "markdown", + "id": "bdddad9f-f9c1-4765-b79b-7f7968772894", + "metadata": {}, + "source": [ + "Find the teaching resources here: https://tutorial.xarray.dev/fundamentals/04.1_basic_plotting.html" + ] + }, + { + "cell_type": "markdown", + "id": "c0834ce1-ed90-4963-adb1-838bef5233c5", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Issues Covered: \n", + "- Creating and customising plots using the inbuilt xarray `.plot()` function\n", + "- Creating a time-series using `.sel()` and `.isel()` and plotting these." + ] + }, + { + "cell_type": "markdown", + "id": "f1f5a3da-837a-4aec-9562-3df9f416cb23", + "metadata": {}, + "source": [ + "## 1. Plotting" + ] + }, + { + "cell_type": "markdown", + "id": "dc7d1b36-cc80-44a4-9adf-5f02901b28f6", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q1. Import the `'../data/tas_rcp45_2055_mon_avg_change.nc'` dataset and create the temperature data array as in the last lesson." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "e129c00a-5a55-47a1-8df1-5e1ce8b28c53", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:08.637819Z", + "iopub.status.busy": "2024-11-08T14:54:08.637405Z", + "iopub.status.idle": "2024-11-08T14:54:16.985867Z", + "shell.execute_reply": "2024-11-08T14:54:16.985233Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "7d28e98c-52ac-452a-aff5-6ba8c78aa22f", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q2. Try a simple .plot() on your temperature dataarray, to see what xarray does. Why has it done this?" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "bd52476f-5b35-4c46-b242-9fe1da16062c", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:16.988828Z", + "iopub.status.busy": "2024-11-08T14:54:16.988376Z", + "iopub.status.idle": "2024-11-08T14:54:17.290362Z", + "shell.execute_reply": "2024-11-08T14:54:17.289849Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "a5652dd7-6176-4d90-b081-969ce7aec4f8", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q3. Next, try the same with a 2-dimensional view of your dataset. Try selecting sea surface temperature values and plotting those." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "81abc16a-8a76-4a79-8be7-4d567d582b2c", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:17.292801Z", + "iopub.status.busy": "2024-11-08T14:54:17.292520Z", + "iopub.status.idle": "2024-11-08T14:54:17.297118Z", + "shell.execute_reply": "2024-11-08T14:54:17.296578Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "9a047319-7af4-4b42-8231-d88ddcda20ef", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q4. Finally, create a depth profile from `temperature` by using `sel` to select data for the same latitude and longitude values (31,0)." + ] + }, + { + "cell_type": "markdown", + "id": "02ff3450-f7d4-4753-ad8e-3fc6e286655e", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Hint: The latitude value is `-50.625` and the longitude value is `0`. All 3 of these methods will return the same dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "98a422d3-7137-437d-b698-98e3cd85c321", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:17.687203Z", + "iopub.status.busy": "2024-11-08T14:54:17.686804Z", + "iopub.status.idle": "2024-11-08T14:54:18.151213Z", + "shell.execute_reply": "2024-11-08T14:54:18.150649Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "bbb5c8e1-74fc-4ec0-b4e1-f8b6e52e8388", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q5. Create a plot from this time series." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "e29cc9ca-07ca-46d9-ac5c-50a2c84bfe0f", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:18.153969Z", + "iopub.status.busy": "2024-11-08T14:54:18.153697Z", + "iopub.status.idle": "2024-11-08T14:54:18.492668Z", + "shell.execute_reply": "2024-11-08T14:54:18.492087Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "4f2d3902-45c9-4eb1-b4b0-9d39baa421ac", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q6. Make the plot red with 'x' marking the points." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "fdfb363f-e819-47a1-a571-5a74f6711196", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:18.495009Z", + "iopub.status.busy": "2024-11-08T14:54:18.494744Z", + "iopub.status.idle": "2024-11-08T14:54:18.692994Z", + "shell.execute_reply": "2024-11-08T14:54:18.692024Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "7a947799-3b5a-4b8b-bf4e-0c58986b58ed", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q7. Create a time series plot comparing the temperature profile at three different grid cells:\n", + " - lat = 0, lon = 0\n", + " - lat = 10, lon = 10\n", + " - lat = 20, lon = 20\n", + "\n", + "Make sure each time series has a different colour and include a legend. As an extension, give them different linestyles too.\n", + "Hint: use `.isel` to index the lat and lon." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "682cfc89-a2cd-454e-a124-d6df5f399208", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:18.695662Z", + "iopub.status.busy": "2024-11-08T14:54:18.695361Z", + "iopub.status.idle": "2024-11-08T14:54:18.907691Z", + "shell.execute_reply": "2024-11-08T14:54:18.907187Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "4cbb0586-67e2-4a6f-bbdf-552e51f46085", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q8. Let's plot some data in 2D. Use `sel` to select data for 200 meters below the surface." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "7c5f72d0-ebe2-4f19-887d-65b3b837002a", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:18.910028Z", + "iopub.status.busy": "2024-11-08T14:54:18.909756Z", + "iopub.status.idle": "2024-11-08T14:54:19.277468Z", + "shell.execute_reply": "2024-11-08T14:54:19.276783Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "specific_time = temperature.sel(depth='200', method='nearest')\n", + "specific_time.plot()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 + Jaspy", + "language": "python", + "name": "jaspy" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/python-data/exercises/ex03.5_xr_masking.ipynb b/python-data/exercises/ex03.5_xr_masking.ipynb new file mode 100644 index 0000000..70e3366 --- /dev/null +++ b/python-data/exercises/ex03.5_xr_masking.ipynb @@ -0,0 +1,245 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "3058f71b-e62e-4301-ab43-d93a996e7cd1", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "# Exercise 3.5: Masking" + ] + }, + { + "cell_type": "markdown", + "id": "000d2f40-7df5-4425-9869-d41d9b5cb356", + "metadata": {}, + "source": [ + "## Aim: Learn to mask data in xarray" + ] + }, + { + "cell_type": "markdown", + "id": "18b86830-4c51-4561-b228-00b893296566", + "metadata": {}, + "source": [ + "Find the teaching material here: https://tutorial.xarray.dev/intermediate/indexing/boolean-masking-indexing.html" + ] + }, + { + "cell_type": "markdown", + "id": "ee494cac-6ae1-4c16-a730-cbbd277e4744", + "metadata": {}, + "source": [ + "### Issues covered: \n", + "- Create re-usable masks for data\n", + "- Plot masked data" + ] + }, + { + "cell_type": "markdown", + "id": "372af117-a045-4830-b91f-9124107cff6e", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q1. For masking, we're back to using our ocean dataset. Load it now from `../data/vbhubo.pgc0apr.nc`." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "5bbad328-b6fe-4d9e-aacd-d5d2bcaac2c3", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:24.404131Z", + "iopub.status.busy": "2024-11-08T14:54:24.403855Z", + "iopub.status.idle": "2024-11-08T14:54:32.860391Z", + "shell.execute_reply": "2024-11-08T14:54:32.859756Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "36531d5e-1969-46cd-a093-a0f1d538827e", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q2. Determine which grid cells sea surface temperaturevis more than the mean." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b6358593-814b-4574-9dd1-a44c4c4cec9c", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:32.863370Z", + "iopub.status.busy": "2024-11-08T14:54:32.862901Z", + "iopub.status.idle": "2024-11-08T14:54:33.291924Z", + "shell.execute_reply": "2024-11-08T14:54:33.291420Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "9bc58a98-fb38-4703-a313-5263c0773183", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q3. Combine this with another .where() lookup to show only cells where the temperature is more than the mean and salinity is more than the mean." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d8fb7ec8-b129-443a-b469-95a61ff1c5db", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:33.294676Z", + "iopub.status.busy": "2024-11-08T14:54:33.294332Z", + "iopub.status.idle": "2024-11-08T14:54:33.805169Z", + "shell.execute_reply": "2024-11-08T14:54:33.804649Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "b3322279-7489-43b2-b9e8-dc41093c34a6", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q4. Masks are just boolean arrays. Create a re-usuable mask for the temperature and sst criteia above, and a combined one." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "61e20222-b105-4438-8ba2-c54bd9aa1d54", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:33.807832Z", + "iopub.status.busy": "2024-11-08T14:54:33.807570Z", + "iopub.status.idle": "2024-11-08T14:54:33.814723Z", + "shell.execute_reply": "2024-11-08T14:54:33.814173Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "aa4f780b-b22a-48bf-b165-7bedd9a4c011", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q5. Use this mask to make the same temperature plot, and a similar one for sst." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "850cffc7-f47e-432d-a0cd-25f40370f5cd", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:33.817140Z", + "iopub.status.busy": "2024-11-08T14:54:33.816637Z", + "iopub.status.idle": "2024-11-08T14:54:34.193250Z", + "shell.execute_reply": "2024-11-08T14:54:34.192692Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 + Jaspy", + "language": "python", + "name": "jaspy" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/python-data/exercises/ex03_xr_groupby.ipynb b/python-data/exercises/ex03_xr_groupby.ipynb new file mode 100644 index 0000000..106d28a --- /dev/null +++ b/python-data/exercises/ex03_xr_groupby.ipynb @@ -0,0 +1,292 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7e3a36bd-b713-4a08-9f6c-a6a0260c42c0", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "# Exercise 3: Grouping" + ] + }, + { + "cell_type": "markdown", + "id": "1770f8c8-7d27-4536-aac8-f1e773dc0c86", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Aim: Work with data which has been grouped." + ] + }, + { + "cell_type": "markdown", + "id": "b5a92dc3-6d27-4723-bba5-cdc30b5f2b0d", + "metadata": {}, + "source": [ + "Find the teaching resources here: https://tutorial.xarray.dev/fundamentals/03.2_groupby_with_xarray.html" + ] + }, + { + "cell_type": "markdown", + "id": "06944527-8b86-4881-9c21-67013b11af9f", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Issues Covered:\n", + "- Grouping data with `.groupby()`\n", + "- Finding the mean of grouped data." + ] + }, + { + "cell_type": "markdown", + "id": "1fbfc303-a5c7-490f-887a-30ed1d93ffb6", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## GroupBy processing" + ] + }, + { + "cell_type": "markdown", + "id": "3ad0ac92-b9c2-43e5-9235-dae178be3186", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Our ocean model dataset has no time dimension, so for this exercise we are going to use the NOAA ERSSST dataset from the tutorial. Load it using the command below." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "39c8e656-1f55-49c3-ba9b-51bb4004191e", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:08.296789Z", + "iopub.status.busy": "2024-11-08T14:55:08.296544Z", + "iopub.status.idle": "2024-11-08T14:55:18.999081Z", + "shell.execute_reply": "2024-11-08T14:55:18.998377Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import xarray as xr\n", + "ds = xr.tutorial.load_dataset(\"ersstv5\")" + ] + }, + { + "cell_type": "markdown", + "id": "db58d1ca-84dd-4933-9498-551bd60f21a6", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Have a quick explore of the dataset and see what it contains." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "181322e6-af04-42c4-94f4-f0d8c00a9c66", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:19.003385Z", + "iopub.status.busy": "2024-11-08T14:55:19.002653Z", + "iopub.status.idle": "2024-11-08T14:55:19.032353Z", + "shell.execute_reply": "2024-11-08T14:55:19.031755Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "bda96f3c-3fef-4aa4-b801-53e34c647f71", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q1. First, lets group our dataset by year." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "976836af-ec0b-49b8-8dbc-349139e5db77", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:19.035872Z", + "iopub.status.busy": "2024-11-08T14:55:19.035560Z", + "iopub.status.idle": "2024-11-08T14:55:19.042702Z", + "shell.execute_reply": "2024-11-08T14:55:19.041969Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "1c47c5a9-7c59-470b-b54c-00fb328dc1f0", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q2. Lets take the mean of each group, to give the annual mean." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ef15b52c-8cd8-46cf-87e6-814936ce280c", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:19.045489Z", + "iopub.status.busy": "2024-11-08T14:55:19.044962Z", + "iopub.status.idle": "2024-11-08T14:55:19.178623Z", + "shell.execute_reply": "2024-11-08T14:55:19.177989Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "2c7da30c-5f09-4173-b306-10e26def81e0", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q3. To see what we've done, lets plot the mean for the year 1960." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2c4dfcd3-1d9e-4f2c-843b-ebd934fe324a", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "3a6924bf-ebef-4b91-bfb7-95d286b2972e", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q4. Next, lets plot an annual mean time seties for the point in the Atlantic ocean latitude=-50.625, longitude=0." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b8797acf-2e92-48ea-9a77-0118cd6e552f", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 + Jaspy", + "language": "python", + "name": "jaspy" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/python-intro/exercises/ex20_feedback.ipynb b/python-data/exercises/ex04_cf_python.ipynb similarity index 63% rename from python-intro/exercises/ex20_feedback.ipynb rename to python-data/exercises/ex04_cf_python.ipynb index 15cf386..e93539d 100644 --- a/python-intro/exercises/ex20_feedback.ipynb +++ b/python-data/exercises/ex04_cf_python.ipynb @@ -2,18 +2,10 @@ "cells": [ { "cell_type": "markdown", - "id": "e28978d6-c4de-4532-b36e-2136c3b8cd13", + "id": "2762201c-7657-4894-9e07-aaa62c49efec", "metadata": {}, "source": [ - "# Exercise 20: Feedback" - ] - }, - { - "cell_type": "markdown", - "id": "5ab0f731-a958-4307-8119-ab5b1cc20f2e", - "metadata": {}, - "source": [ - "If you've got any feedback, we'd be happy to hear it!" + "# Exercise 4" ] } ], @@ -33,7 +25,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.5" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/python-data/exercises/ex05_matplotlib.ipynb b/python-data/exercises/ex05_matplotlib.ipynb new file mode 100644 index 0000000..441dbd6 --- /dev/null +++ b/python-data/exercises/ex05_matplotlib.ipynb @@ -0,0 +1,1017 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "e2574b49-2028-4bdf-a012-60d339dca6ac", + "metadata": {}, + "source": [ + "# Exercise 5: matplotlib" + ] + }, + { + "cell_type": "markdown", + "id": "4c39c2af-dc7c-4905-8535-f7280c4e9a37", + "metadata": {}, + "source": [ + "## Aim: Get to grips with how to create plots and customise them with matplotlib" + ] + }, + { + "cell_type": "markdown", + "id": "315d3123-78fd-44b4-874b-5638d478d112", + "metadata": {}, + "source": [ + "Find the teaching resources here: https://matplotlib.org/stable/users/explain/quick_start.html" + ] + }, + { + "cell_type": "markdown", + "id": "10599a64-226b-4789-89c1-093f0564bedf", + "metadata": {}, + "source": [ + "### Issues covered:\n", + "\n", + "- Creating plots\n", + "- Parts of a figure\n", + "- Styling the colours, linestyles, linewidths, markersizes etc\n", + "- Labelling plots: axis labels, titles, annotations and legends\n", + "- Axes properties: scales, ticks, plotting dates and strings\n", + "- Multiple figures, multiple axes,\n", + "- Colour-mapped data: colormaps, colorbars, normalizations" + ] + }, + { + "cell_type": "markdown", + "id": "f3612836-233d-479f-a2a8-e7ed99dc1133", + "metadata": {}, + "source": [ + "## Simple example" + ] + }, + { + "cell_type": "markdown", + "id": "6bc7192f-0962-4168-b6c6-5fd44daa24b2", + "metadata": {}, + "source": [ + "Q1. Let's create some sample data to plot. Create an array called `xaxis` with the value `[1,2,3,4,5]` and an array called `yaxis` with the value `[2, 16, 4, 8, 7]`. Plot this data on a single axes. Don't forget to import matplotlib!" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "df631937-9c0c-4453-80f0-c44da68ac71a", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:44.502584Z", + "iopub.status.busy": "2024-11-08T14:55:44.502263Z", + "iopub.status.idle": "2024-11-08T14:55:46.365779Z", + "shell.execute_reply": "2024-11-08T14:55:46.365119Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "5a84dc80-d94a-4440-8806-c51a2fbf9077", + "metadata": {}, + "source": [ + "## Parts of a figure" + ] + }, + { + "cell_type": "markdown", + "id": "9cfacc0a-5af7-4912-b79e-0ac0743e20e5", + "metadata": {}, + "source": [ + "Q2. Create 6 empty plots in a 2x3 grid." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "71cd4946-e8bd-40b0-9d21-c7a6fedb11bd", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:46.368639Z", + "iopub.status.busy": "2024-11-08T14:55:46.368283Z", + "iopub.status.idle": "2024-11-08T14:55:46.910842Z", + "shell.execute_reply": "2024-11-08T14:55:46.910332Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "f2e84578-b714-46db-b276-9e11029552dc", + "metadata": {}, + "source": [ + "## Types of inputs to plotting functions" + ] + }, + { + "cell_type": "markdown", + "id": "97d9019c-750c-4a23-b023-2c9ed472f154", + "metadata": {}, + "source": [ + "Q3. Some inputs won't work as intended. Run the following cell to create a pandas dataframe:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d2a59056-927b-4f16-b0b0-6ce0f0262fd4", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:46.913475Z", + "iopub.status.busy": "2024-11-08T14:55:46.913187Z", + "iopub.status.idle": "2024-11-08T14:55:48.226287Z", + "shell.execute_reply": "2024-11-08T14:55:48.225657Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "df = pd.DataFrame({\n", + " 'A': [1, 2, 3, 4],\n", + " 'B': [4, 5, 6, 7],\n", + " 'C': ['a', 'b', 'c', 'd']\n", + "})" + ] + }, + { + "cell_type": "markdown", + "id": "f5b75a4b-7810-4b08-8b3c-f1f2c3bb75d9", + "metadata": {}, + "source": [ + "Try plotting the dataframe directly. Do you know why there is an error?" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "e15026a5-a78f-4033-bc80-017e539f7d64", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:48.229435Z", + "iopub.status.busy": "2024-11-08T14:55:48.229042Z", + "iopub.status.idle": "2024-11-08T14:55:49.609311Z", + "shell.execute_reply": "2024-11-08T14:55:49.608644Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell", + "allow_errors" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "8d0a5420-bd47-4771-aebb-125ab192f458", + "metadata": {}, + "source": [ + "Q4. We need to extract only the numeric values to plot. Let's extract them as a numpy array and try again. Use `np.asarray(df[['A', 'B']])` to create a numpy array from the numeric data. Try plotting it now." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "58bf6119-5e1a-45af-8e3a-03ed80d381a5", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:49.611989Z", + "iopub.status.busy": "2024-11-08T14:55:49.611712Z", + "iopub.status.idle": "2024-11-08T14:55:49.772084Z", + "shell.execute_reply": "2024-11-08T14:55:49.771535Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "43bcbb06-d568-4091-a7a3-1e6ab4db0358", + "metadata": {}, + "source": [ + "Q5. Let's unpack the example given in the tutorial of using matplotlib with string-indexable objects. Instead of passing numpy arrays directly, we'll pass the names of the variables as strings.\n", + "\n", + "\n", + "Run the following cell to create a dictionary where `a` is a numpy array of integers from 0 to 49, `c` is random integers between 0 and 50 we can use as the colour, and `d` is the absolute value of randomly generated numbers which we will use as the size of each scatter point. Then `b` is set to a noisy version of `a`. Finally, `d` is scaled to be larger." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "734cb070-a7d1-425a-ba47-cd3d64bc8882", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:49.774679Z", + "iopub.status.busy": "2024-11-08T14:55:49.774396Z", + "iopub.status.idle": "2024-11-08T14:55:49.778904Z", + "shell.execute_reply": "2024-11-08T14:55:49.778398Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# Step 1: Create the dictionary\n", + "data = {'a': np.arange(50),\n", + " 'c': np.random.randint(0, 50, 50),\n", + " 'd': np.random.randn(50)}\n", + "# Step 2: Add b - the noisy version of a, and scale d to be bigger\n", + "data['b'] = data['a'] + 10 * np.random.randn(50)\n", + "data['d'] = np.abs(data['d']) * 100" + ] + }, + { + "cell_type": "markdown", + "id": "c6e0aaa3-a75b-4493-8828-67ea004480a2", + "metadata": {}, + "source": [ + "Now we can plot the scatter plot using the syntax `ax.scatter(xvalues, yvalues, c=colours, s=scatter_point_size, data=data)`. Hint: the x and y data is 'a' and 'b' and you should know what the colour and size is." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "e0bb152c-7a3e-4cfa-9dce-49e19ed62d81", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:49.781816Z", + "iopub.status.busy": "2024-11-08T14:55:49.781290Z", + "iopub.status.idle": "2024-11-08T14:55:49.935648Z", + "shell.execute_reply": "2024-11-08T14:55:49.935110Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "b057c40a-511b-43ba-840a-bae423a697e9", + "metadata": {}, + "source": [ + "## Coding styles" + ] + }, + { + "cell_type": "markdown", + "id": "ce3d4439-773f-4a61-8556-9967942f7300", + "metadata": {}, + "source": [ + "Q6. So far, we've been creating plots in the object oriented way: explicitly creating figures and axes. The pyplot-style is very subtly different - we just don't need to create the axis or subplots.\n", + "- Create x axis data using `np.linspace(min, max, num)`. Create 10 values between 0 and 10.\n", + "- Create y axis data using `np.linspace` to create 10 values between 0 and 100.\n", + "- Plot this data on the implicit axes using `plt.plot()` - don't worry about seeting the figsize and layout." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "3467e2c1-ab23-43ac-9bf0-3b34852d87f5", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:49.938331Z", + "iopub.status.busy": "2024-11-08T14:55:49.938015Z", + "iopub.status.idle": "2024-11-08T14:55:50.089870Z", + "shell.execute_reply": "2024-11-08T14:55:50.089123Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "8f6ba121-ea31-441c-8ac6-83ad85605684", + "metadata": {}, + "source": [ + "## Styling" + ] + }, + { + "cell_type": "markdown", + "id": "1d18ed69-464a-4f6f-af12-4b43de5fb010", + "metadata": {}, + "source": [ + "Q7. Let's use the x and y values from before and create some new y values to practice styling plots.\n", + "- Create `y2 = np.linspace(0, -100, 10)`.\n", + "- Plot both of these sets of data on the same axes using the [Styling Artists example](https://matplotlib.org/stable/users/explain/quick_start.html#styling-artists).\n", + "- Plot the original y data in purple with the `--` linestyle and the new y data in green with the `:` linestyle.\n", + "- What other values can you give for linestyle? Try editing them to be any character you want and see what happens! Can you make the lines thicker?" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "30c1faed-af70-4784-9c82-0c6758d2083a", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:50.092794Z", + "iopub.status.busy": "2024-11-08T14:55:50.092463Z", + "iopub.status.idle": "2024-11-08T14:55:50.245875Z", + "shell.execute_reply": "2024-11-08T14:55:50.245318Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "d3bba7b2-208b-4927-a246-b1ff18cadbf9", + "metadata": {}, + "source": [ + "Q8. There are lots of different customisation options in matplotlib for colour! You can even have different colours for the markers and outlines in a scatter plot. Use the following to generate some data for a scatter plot:\n", + "```\n", + "data1, data2 = np.random.randn(2,100)\n", + "```\n", + "Plot this data as an `ax.scatter` plot, using a magenta outline with a green marker. Hint: you'll need to visit the [allowable color definitions](https://matplotlib.org/stable/users/explain/colors/colors.html#colors-def) page to see how colours are defined." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "0746263b-80fb-4e68-a215-34872daff4f8", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:50.248847Z", + "iopub.status.busy": "2024-11-08T14:55:50.248580Z", + "iopub.status.idle": "2024-11-08T14:55:50.392506Z", + "shell.execute_reply": "2024-11-08T14:55:50.392014Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "e1308dc3-fc3c-4a49-a762-3fdb0a54eff4", + "metadata": {}, + "source": [ + "Q9. Generate two more scatter plot datasets as we did above then plot all 4 on one graph. Give each dataset a label and a different marker style - e.g. stars (`*`), plus (`P`) or diamonds (`D`). You can see more options for markers [in the documentation](https://matplotlib.org/stable/gallery/lines_bars_and_markers/marker_reference.html)." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "0658cc3b-c291-404a-b446-a388edfef3d4", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:50.394985Z", + "iopub.status.busy": "2024-11-08T14:55:50.394725Z", + "iopub.status.idle": "2024-11-08T14:55:50.547987Z", + "shell.execute_reply": "2024-11-08T14:55:50.547463Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "007ed496-b9c6-4c28-84b3-fa72264bb9e4", + "metadata": {}, + "source": [ + "## Labelling" + ] + }, + { + "cell_type": "markdown", + "id": "a912305e-861a-428b-8223-9d245004baf7", + "metadata": {}, + "source": [ + "Q10. Take the plot we just created in the previous question and give it `xlabel`, `ylabel` and a `title` of your choice. Add some text to the plot saying `some text` at `50, 0`. Add an annotation at top saying `some annotation` with a black arrow pointing to some data using `xy=(40,2)` and `xytext=(3,1.5)`. Also add a legend identifying each data set." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "2de354bb-2ef6-4c1a-a7d4-ec408a0e444e", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:50.550870Z", + "iopub.status.busy": "2024-11-08T14:55:50.550603Z", + "iopub.status.idle": "2024-11-08T14:55:50.975569Z", + "shell.execute_reply": "2024-11-08T14:55:50.975074Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "59e3abfd-3389-4ee9-87be-029af1fce401", + "metadata": {}, + "source": [ + "## Axes" + ] + }, + { + "cell_type": "markdown", + "id": "27c4fc57-9ca3-442b-a62b-469a5f7d73c5", + "metadata": {}, + "source": [ + "Q11. Let's practice plotting some log scale data.\n", + "- Create the xdata using `xdata = np.arange(5)`.\n", + "- Create the ydata using `ydata = np.array([0.1, 0.5, 1, 5, 10])`.\n", + "- Transform the y data by raising it to the power of 10 using `ydata = 10**ydata`.\n", + "- Plot two suplots - plot the x and y data in both plots. Set the y-axis of the second subplot to a logairthmic scale.\n", + "- Try experimenting with different datasets by changing the values in ydata. Try changing the base of the log scale - e.g. using `base=2`." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "8749ab24-0c45-4935-90e2-3df1186cf611", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:50.978562Z", + "iopub.status.busy": "2024-11-08T14:55:50.978294Z", + "iopub.status.idle": "2024-11-08T14:55:51.374280Z", + "shell.execute_reply": "2024-11-08T14:55:51.373658Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "447fc1f2-81e1-4034-bbcb-b00d57ab88b3", + "metadata": {}, + "source": [ + "Q12. To demonstrate the difference between automatic and manual ticks, let's create two subplots. Follow the following steps:\n", + "\n", + "- Create some data using `xdata = np.linspace(0, 99, 100)`, and `ydata = np.sin(xdata / 10)`\n", + "- Create a figure with 2 subplots arranged vertically.\n", + "- For the first subplot, plot the data and allow matplotlib to automatically place the ticks on the y and x axes\n", + "- For the second subplot, manually set the x-axis ticks at intervals of 30 using `np.arange(0,100,30)`, provide custom labels for these x-ticks using `('zero', '30', 'sixty', '90')` and manually set the yaxis ticks at `[-1.5, 0, 1.5]` without specifying labels so that default labels are used.\n", + "- Add titles to both subplots to distinguish between automatic and manual ticks." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "886e54a6-54e6-47fd-bec2-74c1eded9274", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:51.376817Z", + "iopub.status.busy": "2024-11-08T14:55:51.376549Z", + "iopub.status.idle": "2024-11-08T14:55:51.759482Z", + "shell.execute_reply": "2024-11-08T14:55:51.758935Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "ecc8e915-c9ad-44a3-9964-2d047516a3bb", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q13. Let's see how matplotlib handles plotting dates. We'll create a time series plot using an array of dates and random cumulative data:\n", + "- Run the following cell to generate a numpy array of dates starting from `2022-01-01` to `2022-01-10` at intervals of 3 hours then create a cumulative sum of random numbers for the same length of the array of dates." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "0d9bfaa3-723b-4e77-a109-5754e231527c", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:51.762161Z", + "iopub.status.busy": "2024-11-08T14:55:51.761888Z", + "iopub.status.idle": "2024-11-08T14:55:51.765450Z", + "shell.execute_reply": "2024-11-08T14:55:51.764965Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "# Step 1: Generate the numpy dates\n", + "dates = np.arange(np.datetime64('2022-01-01'), np.datetime64('2022-01-10'), np.timedelta64(3, 'h'))\n", + "\n", + "# Step 2: Create the cumulative sum\n", + "data = np.cumsum(np.random.randn(len(dates)))" + ] + }, + { + "cell_type": "markdown", + "id": "19cd7611-93d3-4b18-813e-fd0ac144e115", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "- Plot the data with the dates on the x axis and `data` on the y axis.\n", + "- Take a look at the dates if we don't format the axis - do they look all bunched up?\n", + "- Format the x-axis with `ConciseDateFormatter` for better readability of the date ticks" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "b44349e4-73cd-450f-b61e-304fb727b80b", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:51.767596Z", + "iopub.status.busy": "2024-11-08T14:55:51.767361Z", + "iopub.status.idle": "2024-11-08T14:55:52.044766Z", + "shell.execute_reply": "2024-11-08T14:55:52.044268Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "0f022a40-f65d-4649-a63e-bcdbd4cdf4a1", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q14. Let's have a go at plotting some categorical data. We'll create a bar chart using a list of categories and random values:\n", + "- Run the following cell to define a list of four fruit names `['apple', 'banana', 'cherry', 'date']` and generate random data for these categories." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "d78e6837-6fa3-41cd-bc4b-f897dc0935ec", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:52.047329Z", + "iopub.status.busy": "2024-11-08T14:55:52.047063Z", + "iopub.status.idle": "2024-11-08T14:55:52.050131Z", + "shell.execute_reply": "2024-11-08T14:55:52.049658Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# Step 1: Define fruit list\n", + "categories = ['apple', 'banana', 'cherry', 'date']\n", + "\n", + "# Step 2: Generate random data\n", + "values = np.random.rand(len(categories))" + ] + }, + { + "cell_type": "markdown", + "id": "56f95cac-6d0d-4635-b8ee-25728b2a7995", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Create a bar plot using these categories and their corresponding random values using `ax.bar()`" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "491a7449-45a9-4ef9-88a5-ab6d1244c3ca", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:52.052449Z", + "iopub.status.busy": "2024-11-08T14:55:52.052056Z", + "iopub.status.idle": "2024-11-08T14:55:52.222108Z", + "shell.execute_reply": "2024-11-08T14:55:52.221570Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "9d86f4d0-fd73-40b4-aa36-beb40ffbab10", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q15. Let's create a plot that demonstrates the use of both a secondary y-axis and a secondary x-axis with different scales:\n", + "- Run the following cell to generate a time series `t` ranging from 0 to 2π with 100 points and create two datasets: `s` for a sine wave and `l` for a linearly increasing dataset between 0 and the length of `t`." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "3b550c94-f3c6-49e9-a336-af67c2bb5679", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:52.224657Z", + "iopub.status.busy": "2024-11-08T14:55:52.224400Z", + "iopub.status.idle": "2024-11-08T14:55:52.227759Z", + "shell.execute_reply": "2024-11-08T14:55:52.227277Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# Step 1: Generate the time series\n", + "t = np.linspace(0, 2 * np.pi, 100)\n", + "\n", + "# Step 2: Create the s and l datasets\n", + "s = np.sin(t)\n", + "l = np.arange(len(t))" + ] + }, + { + "cell_type": "markdown", + "id": "0ae2c7cf-12d0-404c-80b5-4e8c16111727", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "- Plot both datasets on the same figure\n", + " - On the first subplot plot the sine wave on the left y axis and the linear data on the right y axis using `twinx()`\n", + " - On the second subplot plot the sine wave with a secondary x axis that converts radians to degrees using `secondary_xaxis()`" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "eb48d19a-3a71-45f4-961f-73e529185796", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:52.229862Z", + "iopub.status.busy": "2024-11-08T14:55:52.229636Z", + "iopub.status.idle": "2024-11-08T14:55:53.110273Z", + "shell.execute_reply": "2024-11-08T14:55:53.109779Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "ef2b0027-33a2-4c69-8bff-fada5fcc0daa", + "metadata": { + "editable": true, + "jupyter": { + "source_hidden": true + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Colour mapped data" + ] + }, + { + "cell_type": "markdown", + "id": "4a4d9f48-0dbc-4b5e-a54a-f440c6c9f46b", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q16. Let's create a series of suplots to practice visualizing data with colormaps:\n", + "- Run the following cell to generate x, y, and z data. Then generate 2 datsets to use for our scatter plot and a third dataset to use for the colors." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "10d56aef-9a1d-406d-89ea-5fe657dbb932", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:53.112929Z", + "iopub.status.busy": "2024-11-08T14:55:53.112669Z", + "iopub.status.idle": "2024-11-08T14:55:53.121109Z", + "shell.execute_reply": "2024-11-08T14:55:53.120598Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# Step 1: Generate x and y data\n", + "x,y = np.meshgrid(np.linspace(-3, 3, 128), np.linspace(-3, 3, 128))\n", + "\n", + "# Step 2: Generate z data\n", + "z = (1 - x/2 + x**5 + y**3) * np.exp(-x**2 - y**2)\n", + "\n", + "# Step 3: Create 3 datasets\n", + "data1 = np.random.randn(100)\n", + "data2 = np.random.randn(100)\n", + "data3 = np.random.rand(100)" + ] + }, + { + "cell_type": "markdown", + "id": "03263f96-ddd6-4cc6-beca-0f9abd222533", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "- Create 4 subplots in a 2x2 grid\n", + "- First plot: use `pcolormesh()` to display z values with a colormap\n", + "- Second plot: use `contourf()` to create a filled contour plot\n", + "- Third plot: use `imshow()` with a logarithmic color scale (`LogNorm`) to represent the square of z values\n", + "- Fourth plot: create a scatter plot where the colour of each point depends on that third datset we made\n", + "- Add colorbars to each plot to indicate the mapping between data and colours" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "75e37d2d-a0cd-4694-a7dd-5f1c2fb8e468", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:53.123288Z", + "iopub.status.busy": "2024-11-08T14:55:53.123038Z", + "iopub.status.idle": "2024-11-08T14:55:54.808669Z", + "shell.execute_reply": "2024-11-08T14:55:54.808122Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "71a90323-7b93-4bda-b519-6a945e812e3a", + "metadata": {}, + "source": [ + "## Multiple figures/axes" + ] + }, + { + "cell_type": "markdown", + "id": "4d431ef9-295d-4c21-b9ef-2f4b118b11c6", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q17. Let's create a figure with multiple subplots using the `suplot_mosiac` method. Each subplot should have its own distinct data and be customized with titles, labels, and a legend. You will also need to manipulate different axes in a single figure and work with multiple figures in a single program:\n", + "- Run the following cell to create data for the plots." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "cc1b0ee4-744b-4265-97c0-a6c71e8a4b1d", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:54.812190Z", + "iopub.status.busy": "2024-11-08T14:55:54.811895Z", + "iopub.status.idle": "2024-11-08T14:55:54.816798Z", + "shell.execute_reply": "2024-11-08T14:55:54.816065Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# Step 1: Generate the data\n", + "x = np.linspace(0,2 * np.pi, 100)\n", + "y_sin = np.sin(x)\n", + "y_cos = np.cos(x)\n", + "categories = ['A', 'B', 'C']\n", + "values = np.random.rand(len(categories))\n", + "random_x = np.random.rand(50)\n", + "random_y = np.random.rand(50)\n", + "x_exp = np.arange(0, 10, 0.1)\n", + "y_exp = np.exp(x_exp)" + ] + }, + { + "cell_type": "markdown", + "id": "a60c1d9c-0546-47b6-9d65-7a13bbd5d6c7", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "- Create a figure using `plt.subplot_mosaic()` with the following layout: `[['top', 'top', 'right'], ['bottom-left', 'bottom-right', 'right']]` with the layout set to `constrained` so the figure is properly spaced\n", + "- In the `top` subplot, plot `x` and `y_sin`. Label the plot and give it the color `blue`. Label the x and y axes and give a legend.\n", + "- In the `bottom-left` subplot, plot `x` and `y_cos`. Label the plot and give it the color `orange`. Label the x and y axes and give a legend.\n", + "- In the `bottom-right` subplot, plot `random_x` and `random_y`. Label the plot and give it the color `green`.Label the x and y axes.\n", + "- In the `right` subplot, plot `categories` and `values` as a bar chart and give it the colors `['purple', 'red', 'yellow']`. Label the x and y axes.\n", + "- Give appropriate titles to each subplot using `set_title()`\n", + "- Create a new figure using `plt.figure()` and plot a simple line graph of exponential growth in a single subplot using `x_exp` and `y_exp`. Give the plot x and y axis labels, a title, and a legend.\n", + "- Ensure both the mosaic figure and extra figure are displayed properly " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "3d3d277c-3440-484a-a9a4-69f1d0168ec4", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:54.820377Z", + "iopub.status.busy": "2024-11-08T14:55:54.819717Z", + "iopub.status.idle": "2024-11-08T14:55:55.981606Z", + "shell.execute_reply": "2024-11-08T14:55:55.980878Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 + Jaspy", + "language": "python", + "name": "jaspy" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/python-data/exercises/ex06_numpy.ipynb b/python-data/exercises/ex06_numpy.ipynb new file mode 100644 index 0000000..034d6ab --- /dev/null +++ b/python-data/exercises/ex06_numpy.ipynb @@ -0,0 +1,97 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "de2a613e-7102-4e10-8fee-4d07c0e1f9eb", + "metadata": {}, + "source": [ + "# Exercise 6: numpy" + ] + }, + { + "cell_type": "markdown", + "id": "91d90486-6b89-495c-b5bb-fb610dc73e15", + "metadata": {}, + "source": [ + "## Basics" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "58ccfac7-b803-4d9d-a728-c3dc08ca8db5", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "79f773e8-ab4e-4273-b398-b1a6f5682677", + "metadata": {}, + "source": [ + "## Shape manipulation" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "8b7141e5-f59f-47bd-a26c-19d342df5d18", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "288fc0f9-7014-4d9b-9b02-475b9c99d4e7", + "metadata": {}, + "source": [ + "## Copies and views" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "182c4af3-c08d-40f2-a08b-4b3fe427b155", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "5e6e8f7e-6d3b-4405-bc1c-4afa9d6188d4", + "metadata": {}, + "source": [ + "## Advanced" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dc6e841f-074d-4d8d-9f6e-63e4f2032ff1", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 + Jaspy", + "language": "python", + "name": "jaspy" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/python-data/exercises/ex06a_numpy.ipynb b/python-data/exercises/ex06a_numpy.ipynb new file mode 100644 index 0000000..4ea97ed --- /dev/null +++ b/python-data/exercises/ex06a_numpy.ipynb @@ -0,0 +1,896 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "de2a613e-7102-4e10-8fee-4d07c0e1f9eb", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "# Exercise 6a: numpy" + ] + }, + { + "cell_type": "markdown", + "id": "ea1f5d14-d8ac-4c83-a0fa-54b2309f8cd1", + "metadata": {}, + "source": [ + "## Aim: Get an overview of NumPy and some useful functions." + ] + }, + { + "cell_type": "markdown", + "id": "bd5f0117-b46f-475a-b6da-f4918a40f284", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "You can find the teaching resources for this lesson here: https://numpy.org/doc/stable/user/quickstart.html" + ] + }, + { + "cell_type": "markdown", + "id": "8e3500eb-400a-45e4-b108-27a337b2fb84", + "metadata": {}, + "source": [ + "### Issues covered:\n", + "- Importing NumPy\n", + "- Array creation\n", + "- Array indexing and slicing\n", + "- Array operations" + ] + }, + { + "cell_type": "markdown", + "id": "91d90486-6b89-495c-b5bb-fb610dc73e15", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## 1. Basics" + ] + }, + { + "cell_type": "markdown", + "id": "564704e3-b77a-45d3-a3ee-cb00feac6275", + "metadata": {}, + "source": [ + "### Importing NumPy" + ] + }, + { + "cell_type": "markdown", + "id": "cb507a6c-6946-4916-80ba-6996f79b32a4", + "metadata": {}, + "source": [ + "Q1. First, let's use the conventional way to import NumPy into our notebook. You'll need to run this cell to get the rest of the notebook to work!" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "b7b2c772-9185-4fa7-9cf7-61f5b53d56aa", + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-07T16:45:34.152395Z", + "iopub.status.busy": "2024-11-07T16:45:34.151744Z", + "iopub.status.idle": "2024-11-07T16:45:34.605734Z", + "shell.execute_reply": "2024-11-07T16:45:34.604361Z" + } + }, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "id": "743d7668-3ea1-4d4f-82d6-89ba93d5915f", + "metadata": {}, + "source": [ + "### Array creation" + ] + }, + { + "cell_type": "markdown", + "id": "f78525c3-6a0a-44be-a898-84f5af46a25e", + "metadata": {}, + "source": [ + "Q2. Let's start by creating some arrays - try to create an array using `a = np.array(1, 2, 3, 4)`. Does this work? Can you edit it to make it work?" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "56419349-939c-48df-b696-2f7c08ae7f41", + "metadata": { + "allow_errors": true, + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:45:34.612679Z", + "iopub.status.busy": "2024-11-07T16:45:34.611936Z", + "iopub.status.idle": "2024-11-07T16:45:34.787723Z", + "shell.execute_reply": "2024-11-07T16:45:34.786851Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "allow_errors", + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "c7db7e8a-cdf5-4eaa-b1a5-301d89d170c6", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q3. Take a look at the following numpy array:\n", + "```\n", + "[[7.0, 8.0, 4.0, 2.0],\n", + " [12.0, 1.0, 0.0, 10.0],\n", + " [0.0, 0.0, 0.0, 0.0]]\n", + "```\n", + "- How many axes does it have?\n", + "- What is the length of the array?\n", + "\n", + "Hint: you can use `.ndim` and `.shape` to help if you enclose the array in `np.array()` to define the array." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "0c33dc0e-fd79-4839-ac13-79ba785e78d3", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:45:34.793395Z", + "iopub.status.busy": "2024-11-07T16:45:34.793168Z", + "iopub.status.idle": "2024-11-07T16:45:34.799661Z", + "shell.execute_reply": "2024-11-07T16:45:34.798857Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "861d15c0-c5fa-4d34-84bf-154980a66088", + "metadata": {}, + "source": [ + "Q4. Can you come up with an example of what a 3D array would look like? Use `np.zeros` to make it then print it out and have a look at `.ndim` and `.shape`" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "1756a861-f70d-4889-9244-a229d9b2e7b7", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:45:34.803567Z", + "iopub.status.busy": "2024-11-07T16:45:34.803346Z", + "iopub.status.idle": "2024-11-07T16:45:34.814135Z", + "shell.execute_reply": "2024-11-07T16:45:34.813304Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "73d932df-887b-455f-93b2-ec265546b6cf", + "metadata": {}, + "source": [ + "Q5.\n", + "- How many elements are in your array? Use `.size` to check.\n", + "- What type are the elements in the array? Use `.dtype` to check.\n", + "- How many bites are in each element of the array? Use `.itemsize` to check." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "3d3c5463-aa6a-4167-9154-7b7f133e7bc3", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:45:34.818082Z", + "iopub.status.busy": "2024-11-07T16:45:34.817384Z", + "iopub.status.idle": "2024-11-07T16:45:34.830374Z", + "shell.execute_reply": "2024-11-07T16:45:34.829130Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "cb62bfcd-e67a-42ed-9980-1578002f9d3f", + "metadata": {}, + "source": [ + "Q6. Create a 1D array of nine numbers 1-9 using `a = np.linspace(1, 9, 9)`. Reshape this to be a 3x3 array and print it." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "aacebcbe-ef1a-4813-bc35-a65b061c4f8b", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:45:34.835664Z", + "iopub.status.busy": "2024-11-07T16:45:34.835106Z", + "iopub.status.idle": "2024-11-07T16:45:34.844780Z", + "shell.execute_reply": "2024-11-07T16:45:34.843863Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "2f2f3bfe-4776-46a1-ad0c-8995322aba55", + "metadata": {}, + "source": [ + "### Basic operations" + ] + }, + { + "cell_type": "markdown", + "id": "63700027-be94-4a47-9600-ef5ea449e1b4", + "metadata": {}, + "source": [ + "Q7. What happens if you multiply the previous array by 2 using `b = a*2`?" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "13b608bb-39cb-47cc-a97e-87c33cb4fe2a", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:45:34.848560Z", + "iopub.status.busy": "2024-11-07T16:45:34.848102Z", + "iopub.status.idle": "2024-11-07T16:45:34.861932Z", + "shell.execute_reply": "2024-11-07T16:45:34.860524Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "6ac2f29e-b42c-4e0b-9809-07938ae3aa5e", + "metadata": {}, + "source": [ + "Q8. How do you do matrix multiplication? Try doing the matrix product of `a` and `b`." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "80b1fc56-e095-435c-b3c6-25afce442071", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:45:34.867769Z", + "iopub.status.busy": "2024-11-07T16:45:34.866526Z", + "iopub.status.idle": "2024-11-07T16:45:34.877181Z", + "shell.execute_reply": "2024-11-07T16:45:34.875920Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "6b76ea97-318a-4bfd-964c-f72d85459f85", + "metadata": {}, + "source": [ + "Q9. When performing operations between arrays of different data types, numpy automatically converts the result to the more precise type - this is called upcasting. Let's demonstrate this concept:\n", + "- Create an array with 3 elements all set to one using `a = np.ones(3, dtype=np.int32)` and set the data type to `np.int32`\n", + "- Create a float array of 3 elements evenly spaced between 0 and π using `b = np.linspace(0, np.pi, 3)`. The data type will be `float64` by default\n", + "- Check the data type of both arrays\n", + "- Add the arrays `a` and `b` to make a new array `c`. Print the resulting array `c` and its data type." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "8ec1bb60-82f6-4152-9e47-c1d45ab955b0", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:45:34.881234Z", + "iopub.status.busy": "2024-11-07T16:45:34.880863Z", + "iopub.status.idle": "2024-11-07T16:45:34.898208Z", + "shell.execute_reply": "2024-11-07T16:45:34.897270Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "b824fbf3-7184-45fc-8219-a50712941091", + "metadata": {}, + "source": [ + "Q10. For matrix `a` in the previous question, what do you think `a.sum()` would be? Check your answer." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "91507ae1-8b08-45f1-bd8d-7fbeb5b9dd2c", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:45:34.902680Z", + "iopub.status.busy": "2024-11-07T16:45:34.902077Z", + "iopub.status.idle": "2024-11-07T16:45:34.906767Z", + "shell.execute_reply": "2024-11-07T16:45:34.906211Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "f0fedf38-dff2-43df-8057-4998d7219e31", + "metadata": {}, + "source": [ + "Q11. Create an array using `np.ones(6).reshape(3,2)`. If we only want to sum each column, how would we do that?" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "84d7b08c-5cfe-4662-b7c2-ee9a1fd5cf1d", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:45:34.911338Z", + "iopub.status.busy": "2024-11-07T16:45:34.911068Z", + "iopub.status.idle": "2024-11-07T16:45:34.921502Z", + "shell.execute_reply": "2024-11-07T16:45:34.920616Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "c6d39222-c0a5-4013-83ce-182dfab3ea2e", + "metadata": {}, + "source": [ + "### Indexing" + ] + }, + { + "cell_type": "markdown", + "id": "7b2b1a58-8c3e-480f-950a-ccf3a813b8cd", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q12.\n", + "- Create a 1D array of size 20 where each element is the cube of its index.\n", + "- Print the 5th element of `a`. Hint: your answer should be 64 - remember where we start indexing." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "f50807ff-0025-4e89-8a17-1ea2988cc367", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:45:34.925203Z", + "iopub.status.busy": "2024-11-07T16:45:34.924904Z", + "iopub.status.idle": "2024-11-07T16:45:34.938051Z", + "shell.execute_reply": "2024-11-07T16:45:34.937079Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "8793e6ef-144a-440f-aa9e-5b49dc4a583d", + "metadata": {}, + "source": [ + "Q13. Slice the array to get elements from index 3 to index 7 (inclusive)." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "7c45834d-1a39-4037-9bf1-ca97c8dab09d", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:45:34.942117Z", + "iopub.status.busy": "2024-11-07T16:45:34.941465Z", + "iopub.status.idle": "2024-11-07T16:45:34.955086Z", + "shell.execute_reply": "2024-11-07T16:45:34.953727Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "3be02020-6449-4a8c-a1f6-bf6fafb6ec12", + "metadata": {}, + "source": [ + "Q14. Change every 3rd element to -1. This should give: `[ -1, 1, 8, -1, 64, 125, -1, ... ]`" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "f16bfe38-1f6f-47fd-a7f2-2a9da0333a8b", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:45:34.961165Z", + "iopub.status.busy": "2024-11-07T16:45:34.960785Z", + "iopub.status.idle": "2024-11-07T16:45:34.971049Z", + "shell.execute_reply": "2024-11-07T16:45:34.970204Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "eb80452e-cd4f-4971-825d-86cb4747d9da", + "metadata": {}, + "source": [ + "Q15. Reverse the array and print the result." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "169e63ce-9d74-4a35-b386-d2bd07bad3a7", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:45:34.976296Z", + "iopub.status.busy": "2024-11-07T16:45:34.975378Z", + "iopub.status.idle": "2024-11-07T16:45:34.987276Z", + "shell.execute_reply": "2024-11-07T16:45:34.986061Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "1cd5271c-8a90-49ae-979b-d89575491b01", + "metadata": {}, + "source": [ + "Q16. Create a 3x4 numpy array `b` using `b = np.array([[2 * i + j for j in range(4)] for i in range(3)])`." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "5536dff6-d6c2-4dab-a4e6-869629e0edeb", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:45:34.991370Z", + "iopub.status.busy": "2024-11-07T16:45:34.990844Z", + "iopub.status.idle": "2024-11-07T16:45:35.005952Z", + "shell.execute_reply": "2024-11-07T16:45:35.003808Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "3983a14f-de1d-490b-b6fd-4f4d7eda14fb", + "metadata": {}, + "source": [ + "Q17. Print the element in the second row and third column. This should be 4." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "a734ee36-b794-4260-808c-575518b4a34e", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:45:35.010659Z", + "iopub.status.busy": "2024-11-07T16:45:35.009978Z", + "iopub.status.idle": "2024-11-07T16:45:35.021059Z", + "shell.execute_reply": "2024-11-07T16:45:35.020149Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "3e4d5ba7-1abe-4285-bd7b-29bca9febbf1", + "metadata": {}, + "source": [ + "Q18. Extract and print the second column as a 1D array." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "e190b233-124d-40c3-b928-c66f015217df", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:45:35.024197Z", + "iopub.status.busy": "2024-11-07T16:45:35.023920Z", + "iopub.status.idle": "2024-11-07T16:45:35.035750Z", + "shell.execute_reply": "2024-11-07T16:45:35.034822Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "24b839b8-a010-4bd5-afdc-f7046c7acc26", + "metadata": {}, + "source": [ + "Q19. Extract and print a sub-array containing the last two rows." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "6efa59b9-0bc1-4a8b-ae32-4ffbe75512cb", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:45:35.038908Z", + "iopub.status.busy": "2024-11-07T16:45:35.038379Z", + "iopub.status.idle": "2024-11-07T16:45:35.051161Z", + "shell.execute_reply": "2024-11-07T16:45:35.050287Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "d4fb0210-2031-4435-bdae-b567a6010c42", + "metadata": {}, + "source": [ + "Q20. Use slicing to replace the last row with the values `[7, 7, 7, 7]`." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "d2e59188-020a-4c03-a373-9d50b0def22f", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:45:35.054713Z", + "iopub.status.busy": "2024-11-07T16:45:35.054443Z", + "iopub.status.idle": "2024-11-07T16:45:35.068244Z", + "shell.execute_reply": "2024-11-07T16:45:35.067085Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "161545bc-06dd-40cb-b598-36de4f5378e3", + "metadata": {}, + "source": [ + "Q21. Iterate over the elements of the array using the `.flat` attribute and print them." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "78081cb7-6c73-466b-a736-1aca4c652f49", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:45:35.074455Z", + "iopub.status.busy": "2024-11-07T16:45:35.073677Z", + "iopub.status.idle": "2024-11-07T16:45:35.082625Z", + "shell.execute_reply": "2024-11-07T16:45:35.081523Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "0ea93892-c626-4e4e-b91c-763daf6bb7e1", + "metadata": {}, + "source": [ + "Q22. Create a 3D array `c` using `c = np.array([[[i * 10 + j * 5 + k for k in range(4)] for j in range(3)] for i in range(2)])`" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "15632b1d-f44e-4a1e-9efb-e6fde75e266b", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:45:35.087438Z", + "iopub.status.busy": "2024-11-07T16:45:35.086993Z", + "iopub.status.idle": "2024-11-07T16:45:35.099686Z", + "shell.execute_reply": "2024-11-07T16:45:35.098567Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "d48d62f7-5ccc-4dc6-a441-cac5ebc4cb10", + "metadata": {}, + "source": [ + "Q23. Print all elements of the first layer of the array." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "53d8f855-bba0-40a4-aeba-2b719b87b363", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:45:35.103756Z", + "iopub.status.busy": "2024-11-07T16:45:35.103053Z", + "iopub.status.idle": "2024-11-07T16:45:35.118641Z", + "shell.execute_reply": "2024-11-07T16:45:35.117258Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "96fcfb26-8c72-476b-b030-9e1f61a143ab", + "metadata": {}, + "source": [ + "Q24. Use `...` to print the last element of each 1D array contained within c." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "59fcba5b-3772-4326-9f82-da576e8e6f5d", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:45:35.124400Z", + "iopub.status.busy": "2024-11-07T16:45:35.123593Z", + "iopub.status.idle": "2024-11-07T16:45:35.132851Z", + "shell.execute_reply": "2024-11-07T16:45:35.131940Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "c96b71dd-bb47-41aa-9403-cc51f660aa0d", + "metadata": {}, + "source": [ + "Q25. Modify the first column of the second layer to `[0, 0, 0]`." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "495e40c1-5584-4e58-9db3-70b380b1fae5", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:45:35.137623Z", + "iopub.status.busy": "2024-11-07T16:45:35.137006Z", + "iopub.status.idle": "2024-11-07T16:45:35.148197Z", + "shell.execute_reply": "2024-11-07T16:45:35.147116Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 + Jaspy", + "language": "python", + "name": "jaspy" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/python-data/exercises/ex06b_numpy.ipynb b/python-data/exercises/ex06b_numpy.ipynb new file mode 100644 index 0000000..9746565 --- /dev/null +++ b/python-data/exercises/ex06b_numpy.ipynb @@ -0,0 +1,559 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "93699150-662a-4a21-bc2b-7836c39d0e0d", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "# Exercise 6b: numpy (continued)" + ] + }, + { + "cell_type": "markdown", + "id": "0465143d-b368-4fcb-baec-017e5aaf801b", + "metadata": {}, + "source": [ + "## Aim: Get an overview of NumPy and some useful functions." + ] + }, + { + "cell_type": "markdown", + "id": "0364bf4f-20d1-49bc-8e2e-90f636e579ea", + "metadata": {}, + "source": [ + "You can find the teaching resources for this lesson here: https://numpy.org/doc/stable/user/quickstart.html" + ] + }, + { + "cell_type": "markdown", + "id": "b169c5eb-8467-4830-ba53-8707ad9642d0", + "metadata": {}, + "source": [ + "### Issues covered:\n", + "- Shape manipulation: changing shape, stacking, splitting\n", + "- Copies and views" + ] + }, + { + "cell_type": "markdown", + "id": "bd16b618-3891-4613-9257-a3fbaba8f53f", + "metadata": {}, + "source": [ + "## 2. Shape manipulation" + ] + }, + { + "cell_type": "markdown", + "id": "776d6a74-7fd7-4a76-9388-694a241fcfdf", + "metadata": {}, + "source": [ + "### Changing the shape" + ] + }, + { + "cell_type": "markdown", + "id": "34b119db-ac4b-44cb-96a8-ca00e89cb2db", + "metadata": {}, + "source": [ + "Q1. Use the following to create a 3x4 array: \n", + "```\n", + "rg = np.random.default_rng(1)\n", + "a = np.floor(10 * rg.random((3, 4)))\n", + "```\n", + "Then use the `ravel` method to flatten the array and print it." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "addaf639-10ae-402b-a6e8-6dbbdab747f7", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:44:43.926389Z", + "iopub.status.busy": "2024-11-07T16:44:43.925221Z", + "iopub.status.idle": "2024-11-07T16:44:44.456971Z", + "shell.execute_reply": "2024-11-07T16:44:44.455705Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "4d060d03-973b-4916-80d9-29382400419f", + "metadata": {}, + "source": [ + "Q2. Reshape the array so it has the shape (2,6) and print it." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ddd29757-7974-4a25-a3ad-34fbe2910a62", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:44:44.460969Z", + "iopub.status.busy": "2024-11-07T16:44:44.460546Z", + "iopub.status.idle": "2024-11-07T16:44:44.467996Z", + "shell.execute_reply": "2024-11-07T16:44:44.467190Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "78cd0758-225c-4b58-8e84-1e4e3872293f", + "metadata": {}, + "source": [ + "Q3. Transpose the array and print it." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ff5c18e1-ea8a-4d78-8b6c-38ac268fe6dc", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:44:44.470550Z", + "iopub.status.busy": "2024-11-07T16:44:44.470296Z", + "iopub.status.idle": "2024-11-07T16:44:44.489171Z", + "shell.execute_reply": "2024-11-07T16:44:44.487883Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "bf99d355-263c-4448-9160-cb433c6297fd", + "metadata": {}, + "source": [ + "Q4. Use the resize method to change the shape of the array to (6,2). Notice the difference between reshape and resize." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "65805c3d-bfef-4cd2-9a20-d123078f9672", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:44:44.494484Z", + "iopub.status.busy": "2024-11-07T16:44:44.494103Z", + "iopub.status.idle": "2024-11-07T16:44:44.508456Z", + "shell.execute_reply": "2024-11-07T16:44:44.507225Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "0254d87c-8cf2-42e9-9193-fb0a57b99f71", + "metadata": {}, + "source": [ + "Q5. Reshape the array to a shape of (3, -1) and print the reshaped array. Note what `-1` does here." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ecd467a6-738c-4b3a-b37a-5f7ded7bb544", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:44:44.514020Z", + "iopub.status.busy": "2024-11-07T16:44:44.513435Z", + "iopub.status.idle": "2024-11-07T16:44:44.532472Z", + "shell.execute_reply": "2024-11-07T16:44:44.530794Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "e24b0985-7938-4d08-aa8a-ffe5b6741823", + "metadata": {}, + "source": [ + "### Stacking" + ] + }, + { + "cell_type": "markdown", + "id": "9150a86b-b60c-4235-99f1-df8a72ea383d", + "metadata": {}, + "source": [ + "Q6.\n", + "- Write a function `stack_arrays` that takes two 2D arrays `a` and `b`, an axis argument and returns the arrays stacked along the specified axis. The function should handle the following cases:\n", + " - Vertical stacking (`axis=0`): Stack the arrays along rows\n", + " - Horizontal stacking (`axis=1`): Stack the arrays along columns\n", + " - Column stacking (`axis=column`): Stack 1D arrays as columns of a 2D array if both a and b are 1D, if they are 2D stack them horizontally\n", + " - If `axis` is set to any other value raise a `ValueError`\n", + "- Once you're happy with your function, try the test cases in the solutions to check your working!" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "21527484-619e-47c0-a222-092ee88fb478", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:44:44.538232Z", + "iopub.status.busy": "2024-11-07T16:44:44.537362Z", + "iopub.status.idle": "2024-11-07T16:44:44.559570Z", + "shell.execute_reply": "2024-11-07T16:44:44.558050Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Vertical stacking:\n", + " [[9. 7.]\n", + " [5. 2.]\n", + " [1. 9.]\n", + " [5. 1.]]\n", + "\n", + "Horizontal stacking:\n", + " [[9. 7. 1. 9.]\n", + " [5. 2. 5. 1.]]\n", + "\n", + "Column stacking (1D arrays):\n", + " [[4. 3.]\n", + " [2. 8.]]\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "def stack_arrays(a, b, axis):\n", + " if axis == 0:\n", + " return np.vstack((a, b))\n", + " elif axis == 1:\n", + " return np.hstack((a,b))\n", + " elif axis == 'column':\n", + " return np.column_stack((a,b))\n", + " else:\n", + " raise ValueError(\"Invalid axis specified. Use 0, 1 or 'column'.\")\n", + "\n", + "# Test cases\n", + "a = np.array([[9,7], [5,2]])\n", + "b = np.array([[1., 9.], [5., 1.]])\n", + "c = np.array([4., 2.])\n", + "d = np.array([3., 8.])\n", + "\n", + "# Vertical stacking\n", + "print(\"Vertical stacking:\\n\", stack_arrays(a, b, axis=0))\n", + "\n", + "# Horizontal stacking\n", + "print(\"\\nHorizontal stacking:\\n\", stack_arrays(a, b, axis=1))\n", + "\n", + "# Column stacking for 1D arrays\n", + "print(\"\\nColumn stacking (1D arrays):\\n\", stack_arrays(c, d, axis='column'))" + ] + }, + { + "cell_type": "markdown", + "id": "54b6c868-dfc8-4113-bf8c-4ec92b72d6c4", + "metadata": {}, + "source": [ + "### Splitting" + ] + }, + { + "cell_type": "markdown", + "id": "9150e1f6-639b-4bce-9777-c35e96e49de8", + "metadata": {}, + "source": [ + "Q7.\n", + "Given the following 2D array `b`:\n", + "```\n", + "rg = np.random.default_rng(42)\n", + "b = np.floor(10 * rg.random((2, 10)))\n", + "```\n", + "- Split the array equally using `np.hsplit` into 5 parts along the horizontal axis. Assign the resulting sub-arrays to a variable named `equal_splits`\n", + "- Split the array after the second and fifth columns using `np.hsplit`. Assign the resulting sub-arrays to a variable called `column_splits`." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "c7d0c083-8f9d-4431-a68e-a57ee23fa4ff", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:44:44.565408Z", + "iopub.status.busy": "2024-11-07T16:44:44.564948Z", + "iopub.status.idle": "2024-11-07T16:44:44.582739Z", + "shell.execute_reply": "2024-11-07T16:44:44.581210Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "f5c2d6e1-e758-459c-bd22-192651a96507", + "metadata": {}, + "source": [ + "## 3. Copies and views" + ] + }, + { + "cell_type": "markdown", + "id": "dff98f88-d2e0-4b90-ba24-9752ffe36888", + "metadata": {}, + "source": [ + "Q8. Let's demonstrate No Copy:\n", + "- Write a function `test_no_copy()` that:\n", + " - Creates a `3x3` Numpy array `a`.\n", + " - Assigns `b=a` and checks if modifying `b` affects `a`.\n", + " - Returns `True` if `b` is a reference to `a` i.e. no copy is made, and `False` otherwise\n", + " - Hint: use `is` to verify if `a` and `b` are the same object." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "509b0f88-170f-49de-9e25-672bde58738e", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:44:44.588398Z", + "iopub.status.busy": "2024-11-07T16:44:44.587869Z", + "iopub.status.idle": "2024-11-07T16:44:44.603287Z", + "shell.execute_reply": "2024-11-07T16:44:44.602046Z" + }, + "scrolled": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Testing no copy behavior: True\n" + ] + } + ], + "source": [ + "def test_no_copy():\n", + " a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\n", + " b = a\n", + " b[0, 0] = 999\n", + " return b is a # Check if 'b' is a reference to 'a'\n", + "\n", + "print(\"Testing no copy behavior:\", test_no_copy()) # Expected: True" + ] + }, + { + "cell_type": "markdown", + "id": "c1094086-cb82-4541-b9a1-811773050bf8", + "metadata": {}, + "source": [ + "Q9. Let's demonstrate Shallow Copy:\n", + "- Write a function `test_shallow_copy()` that:\n", + " - Creates a `3x3` Numpy array `a`.\n", + " - Creates a shallow copy of `a` using `a.view()` and assigns it to `c`.\n", + " - Modifies an element in `c` and checks if the change is reflected in `a`\n", + " - Verifies that `a` and `c` are not the same object but share data\n", + " - Returns `True` if the modification in `c` also modifies `a` and `False` otherwise\n", + " - Hint: Use `is` to confirm `a` and `c` are different objects, and `c.base is a` to confirm shared data. " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "4788a3fb-9483-4417-9e6d-52a5cc15de64", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:44:44.609160Z", + "iopub.status.busy": "2024-11-07T16:44:44.608733Z", + "iopub.status.idle": "2024-11-07T16:44:44.631397Z", + "shell.execute_reply": "2024-11-07T16:44:44.629854Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "fd0dc68d-9a11-4f86-a4c9-c1e8fd10946c", + "metadata": {}, + "source": [ + "Q10. Let's demonstrate Deep Copy: \n", + "- Write a function `test_deep_copy()`that:\n", + " - Creates a `3x3` Numpy array `a`\n", + " - Creates a deep copy of `a` using `a.copy()` and assigns it to `d`\n", + " - Modifies an element in `d` and checks if the change is reflected in `a`\n", + " - Verifies that `a` and `d` do not share data\n", + " - Returns `True` if `a` and `d` do not share data and `False` if they do. " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "899f1587-f732-487a-a06c-b4f58d9d9af7", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:44:44.637952Z", + "iopub.status.busy": "2024-11-07T16:44:44.637302Z", + "iopub.status.idle": "2024-11-07T16:44:44.657136Z", + "shell.execute_reply": "2024-11-07T16:44:44.655518Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Testing deep copy behavior: True\n" + ] + } + ], + "source": [ + "def test_deep_copy():\n", + " a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\n", + " d = a.copy()\n", + " d[0, 0] = 999\n", + " return d.base is None and np.any(a != d) # Check if 'd' is a true deep copy\n", + "\n", + "print(\"Testing deep copy behavior:\", test_deep_copy()) # Expected: True" + ] + }, + { + "cell_type": "markdown", + "id": "d2493f55-f912-44bf-8ea2-90bb588d490a", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q11. Let's demonstrate Memory Management: \n", + "- Write a function `memory_management_example()` that:\n", + " - Creates a large array `a` of 10 million elements\n", + " - Creates a slice of `a` containing the first 10 elements and assigns it to `b`\n", + " - Deletes `a` and observes what happens to `b`\n", + " - Creates another slice of `a` containing the first 1- elements but it copies it deeply this time assigning it to `c`\n", + " - Deletes `a` and observes if `c` is still accessible\n", + " - Returns `True` if `b` cannot be accessed after deleting `a`, but `c` can and `False` otherwise\n", + " - Hint: use a try-except block to handle errors from accessing `b` after deleting `a`" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "91110ee3-d12d-41ba-8394-82b32f5f2e6f", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:44:44.663498Z", + "iopub.status.busy": "2024-11-07T16:44:44.662919Z", + "iopub.status.idle": "2024-11-07T16:44:44.725925Z", + "shell.execute_reply": "2024-11-07T16:44:44.724218Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 + Jaspy", + "language": "python", + "name": "jaspy" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/python-data/exercises/ex07_netcdf4_basics.ipynb b/python-data/exercises/ex07_netcdf4_basics.ipynb new file mode 100644 index 0000000..840969f --- /dev/null +++ b/python-data/exercises/ex07_netcdf4_basics.ipynb @@ -0,0 +1,468 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "4fc1b0c3-f3ec-4724-b24c-0e42bfdb2cb4", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "# Exercise 7: NetCDF4 Basics" + ] + }, + { + "cell_type": "markdown", + "id": "0ac81b88-7771-4404-89cd-d9ec233651d7", + "metadata": {}, + "source": [ + "## Aim: Introduce the netCDF4 library in Python to read and create NetCDF4 Files." + ] + }, + { + "cell_type": "markdown", + "id": "6457c36c-b7ba-44a7-861e-0ae678d5412c", + "metadata": {}, + "source": [ + "Find the teaching material here: https://unidata.github.io/netcdf4-python/" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "5512bea4-24d3-415b-8572-d770111ba0b6", + "metadata": {}, + "source": [ + "### Issues covered:\n", + "- Importing netCDF4\n", + "- Groups, dimensions, variables and attributes\n", + "- Writing data and retrieving it from variables" + ] + }, + { + "cell_type": "markdown", + "id": "81916b14-ec1c-4e8b-af3f-93d00377e9ff", + "metadata": {}, + "source": [ + "## Creating/opening/closing netCDF files" + ] + }, + { + "cell_type": "markdown", + "id": "a47cb092-895b-411d-8ac2-2b7df0d1138d", + "metadata": {}, + "source": [ + "Q1.\n", + "- Import the `netCDF4` library\n", + "- Let's create a new NetCDF file called `test.nc` in appending mode (`a`) with the `NETCDF4` format. This mode will allow us to edit the dataset later. Save this to a variable called `new_file`.\n", + "- Inspect the new file to see what its `data_model` is." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "261f60af-c7ec-4cb3-8859-5d173a534a1d", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:38.682331Z", + "iopub.status.busy": "2024-11-08T14:55:38.681747Z", + "iopub.status.idle": "2024-11-08T14:55:39.246105Z", + "shell.execute_reply": "2024-11-08T14:55:39.245107Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "1c4e8491-204a-4bcf-9696-3535a37c7b8d", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Groups, dimensions, variables and attributes" + ] + }, + { + "cell_type": "markdown", + "id": "4649d782-c96b-4bc5-840b-231345ed4c79", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Groups" + ] + }, + { + "cell_type": "markdown", + "id": "c5ecfa40-dcd9-4728-aba7-57fef3dd089e", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q2. Groups act as containers for variables, dimensions and attributes.\n", + "- Add a new group to the dataset we just made called \"forecasts\".\n", + "- Create a new group within forecasts called `model1`.\n", + "- List the groups of your dataset using `.groups`\n", + "- What happens if you do `group3 = new_file.createGroup(\"/analyses/model2\")`?\n", + "- What happens if you do `group4 = new_file.createGroup(\"analyses\")`?" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "6c3c7a16-5d5f-4f01-b83e-a20fe1c96392", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:39.250340Z", + "iopub.status.busy": "2024-11-08T14:55:39.249830Z", + "iopub.status.idle": "2024-11-08T14:55:39.257372Z", + "shell.execute_reply": "2024-11-08T14:55:39.256627Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "b898110e-14e8-464d-9a4f-bd19a273c7cb", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Dimensions" + ] + }, + { + "cell_type": "markdown", + "id": "4bdf87db-5455-45a2-ae47-22febfa0a20e", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q3.\n", + "- Create some dimensions for the `new_file` dataset:\n", + " - `time` dimension with unlimited size\n", + " - `level` dimension with unlimited size\n", + " - `lat` dimension with unlimited size\n", + " - `lon` dimension with unlimited size\n", + "- Print out the dimensions you just created.\n", + "- Check the length of the latitude dimension to make sure it is 0.\n", + "- Check that the level dimension is unlimited.\n", + "- Let's take a look at an overview using \n", + "```\n", + "for dim in new_file.dimensions.values():\n", + " print(dim)\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "037ca268-58fa-4657-a527-112d08cc16b8", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:39.260641Z", + "iopub.status.busy": "2024-11-08T14:55:39.260294Z", + "iopub.status.idle": "2024-11-08T14:55:39.269039Z", + "shell.execute_reply": "2024-11-08T14:55:39.268334Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "56511ca9-39a1-466a-9516-d801ab53407f", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Variables" + ] + }, + { + "cell_type": "markdown", + "id": "68e7d217-ea7c-4ea2-af69-942239aebb29", + "metadata": {}, + "source": [ + "Remember that the data types are as follows:\n", + "- `f4`: 32-bit floting point \n", + "- `f8`: 64-bit floating point \n", + "- `i4`: 32-bit signed integer \n", + "- `i2`: 16-bit signed integer\n", + "- `i8`: 64-bit unsigned integer\n", + "- `i1`: 8-bit signed integer\n", + "- `u1`: 8-bit unsigned integer\n", + "- `u2`: 16-bit unsigned integer\n", + "- `u4`: 32-bit unsigned integer\n", + "- `u8`: 64-bit unsigned integer\n", + "- `S1`: single-character string" + ] + }, + { + "cell_type": "markdown", + "id": "1b7176b7-79b9-4e38-99f5-5491d91de3a2", + "metadata": {}, + "source": [ + "Q4.\n", + "- Create a scalar variable called `times` with the type set to `f8`.\n", + "- Create a scalar variable called `levels` but this time set the type to `np.float64`. (You'll need to import numpy as np)\n", + "- Print out the variables using `new_file.variables`. What do you notice about the types?\n", + "- Create a variable in the `model2` group we made earlier called `temp`, with the `float64` type and this time give it dimensions: (`time`, `level`, `lat`, `lon`). Print it out.\n", + "- Create two values: \n", + " - `longitudes` with the name `lon`, type `float64` and dimension `lon`\n", + " - `latitudes` with the name `lat`, type `float64` and dimension `lat`" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "292ca4fe-3681-4b5e-8b2e-c681a3aa9249", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:39.272376Z", + "iopub.status.busy": "2024-11-08T14:55:39.271846Z", + "iopub.status.idle": "2024-11-08T14:55:39.282042Z", + "shell.execute_reply": "2024-11-08T14:55:39.281363Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "cf76552d-79d8-41c4-95fc-4616c02248b3", + "metadata": {}, + "source": [ + "### Attributes" + ] + }, + { + "cell_type": "markdown", + "id": "0be859b4-16a3-48d0-9242-b8c590d8508a", + "metadata": {}, + "source": [ + "Q5.\n", + "- Let's create a global attribute. Create an attribute on the `new_file` object called `.description` with the value `This is a test description.`.\n", + "- Let's create a variable attribute. Create an attribute on the `times` variable called `units` and put `hours`.\n", + "- Take a look at the attrs on `new_file` using `new_file.ncattrs()`. What does this show?\n", + "- To get the name AND description, use the following loop:\n", + "```\n", + "for name in new_file.ncattrs():\n", + " print(name, \":\", getattr(new_file, name))\n", + "```\n", + "- There is an easier way of doing this - using `new_file.__dict__`. Try it out!" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "5ae8dcf6-0184-4f83-b75c-9684a852d140", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:39.284955Z", + "iopub.status.busy": "2024-11-08T14:55:39.284674Z", + "iopub.status.idle": "2024-11-08T14:55:39.295226Z", + "shell.execute_reply": "2024-11-08T14:55:39.294641Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "df7ec8fe-81b3-4768-9f4b-c643d4a8c254", + "metadata": {}, + "source": [ + "## Writing data to and receiving data from netCDF variables" + ] + }, + { + "cell_type": "markdown", + "id": "d636cdaa-c646-406a-96e9-85810cea39ec", + "metadata": {}, + "source": [ + "Q6. \n", + "- Create an array to populate a new variable `lats` with using `lats = np.arange(-100, 100, 2)` and an array to populate the `lons` variable with using `lons = np.arange(-200, 200, 2)`.\n", + "- Print out the `latitudes` and `longitudes` variables we created earlier to see what they look like before we populate them.\n", + "- Populate the two variables with our data using `latitudes[:] = lats` and the same for longitudes.\n", + "- Print the data out and take a look." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f14444cf-ff2b-4b90-b4fd-3af73ad640b4", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:39.297995Z", + "iopub.status.busy": "2024-11-08T14:55:39.297706Z", + "iopub.status.idle": "2024-11-08T14:55:39.310135Z", + "shell.execute_reply": "2024-11-08T14:55:39.309547Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "ce5d22bf-d201-424e-babc-11d9d431f834", + "metadata": {}, + "source": [ + "Q7.\n", + "- Extend `new_file` to have the dimension `pressure` with size 10.\n", + "- Define a 4D variable `temperature` with dimensions (time, pressure, latitude, longitude). Print the shape of the temperature variable to look at the size before populating with data.\n", + "- Generate random temperature data for a subset of time and pressure values - start by creating `nlats` and `nlons` equal to the length of the `lat` and `lon` dimensions. Assign random data to `temperature[0:10, 0:3, :, :]` using `np.random.uniform(size=(10,3, nlats, nlons))`.\n", + "- After assigning the data, print the shape of the `temperature` variable. Take a look at the size of it now." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "f4c4384f-a966-4615-a2da-77168568ae77", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:39.312687Z", + "iopub.status.busy": "2024-11-08T14:55:39.312397Z", + "iopub.status.idle": "2024-11-08T14:55:39.741786Z", + "shell.execute_reply": "2024-11-08T14:55:39.741066Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "12c17e3e-ad67-4c29-a426-e6390626ce25", + "metadata": {}, + "source": [ + "Q8. \n", + "- Define the `pressure` variable with type `f4` and the `pressure` dimension.\n", + "- Populate the `pressure` variable with the values [1000, 850, 700, 500, 300, 250, 200, 150, 100, 50].\n", + "- Extract the tempearture variable using `temperature = new_file.variables[\"temperature\"]`, the latitudes using `latitudes = new_file.variables[\"lat\"][:]` and the longitudes using `longitudes = new_file.variables[\"lon\"][:]`.\n", + "- Use fancy indexing to slice the temperature variable: select times 0, 2 and 4. Index the 2nd, 4th and 7th values of the pressures and select only positive latitudes and longitudes.\n", + "- Print the shape of the resulting subset array." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "769c08e7-6db7-4112-b49e-8003787b6882", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:39.745009Z", + "iopub.status.busy": "2024-11-08T14:55:39.744663Z", + "iopub.status.idle": "2024-11-08T14:55:39.882617Z", + "shell.execute_reply": "2024-11-08T14:55:39.881968Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 + Jaspy", + "language": "python", + "name": "jaspy" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/python-data/exercises/ex08_netcdf4_advanced.ipynb b/python-data/exercises/ex08_netcdf4_advanced.ipynb new file mode 100644 index 0000000..3533ffe --- /dev/null +++ b/python-data/exercises/ex08_netcdf4_advanced.ipynb @@ -0,0 +1,491 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "22542fd5-6792-4df8-9122-fe35f3e4ddf5", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "# Exercise 8: NetCDF4 Advanced" + ] + }, + { + "cell_type": "markdown", + "id": "b8bc8ade-8ef9-4caa-b734-d0a0df52a450", + "metadata": {}, + "source": [ + "## Aim: Introduce more advanced uses of the netCDF4 library in Python to read and create NetCDF4 Files." + ] + }, + { + "cell_type": "markdown", + "id": "e92fc14e-6e03-49c3-99c1-7a7b1c2e52cf", + "metadata": {}, + "source": [ + "Find the teaching material here: https://unidata.github.io/netcdf4-python/" + ] + }, + { + "cell_type": "markdown", + "id": "c2a56e34-d2ba-451d-900c-09e33c404e24", + "metadata": {}, + "source": [ + "### Issues covered:\n", + "- Working with time coordinates\n", + "- Multi-file datasets\n", + "- Compression of variables\n", + "- Compound datatypes\n", + "- Enum data type" + ] + }, + { + "cell_type": "markdown", + "id": "b9ba87e0-e96e-490d-a0a6-1da86bae8084", + "metadata": {}, + "source": [ + "## Time-coordinates" + ] + }, + { + "cell_type": "markdown", + "id": "1c4acf3e-dc07-442e-a421-63efb78e0f79", + "metadata": {}, + "source": [ + "Most metadata standards specify that time should be measured relative to a fixed date with units such as `hours since YY-MM-DD hh:mm:ss`. We can convert values to and from calendar dates using `num2date` and `date2num` from the `cftime` library. Two other helpful functions are `datetime` and `timedelta` from the `datetime` library." + ] + }, + { + "cell_type": "markdown", + "id": "2fecb97e-5775-4575-9739-9ad8cb7c3f97", + "metadata": {}, + "source": [ + "Q1. \n", + "- Let's generate a list of data and time values: create a list called `dates` containing date and time values, starting from January 1st 2022, and incrementing by 6 hours for a total of 5 entries. \n", + "- Use `date2num` to convert your list of dates to numeric values using: `units=\"hours since 2022-01-01 00:00:00\"` amd `calendar=\"gregorian\"`. Store these in an array called `times`.\n", + "- Print the numeric times values to confirm the numeric representation.\n", + "- Use `num2date` to convert times back to datetime objects using the same units and calendar. Store these in a list called `converted_dates`\n", + "- Print the converted dates to verify they match the original dates list. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "477bd19f-2833-4bb6-a2ae-83ebb7fc4a3d", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:39.567060Z", + "iopub.status.busy": "2024-11-08T14:54:39.566736Z", + "iopub.status.idle": "2024-11-08T14:54:40.032446Z", + "shell.execute_reply": "2024-11-08T14:54:40.031854Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "7271d566-91d2-4d24-9213-39d6d28d2a0d", + "metadata": {}, + "source": [ + "## Multi-file datasets" + ] + }, + { + "cell_type": "markdown", + "id": "95cb2707-b27e-42ac-b80f-cf255b1a3c0f", + "metadata": {}, + "source": [ + "Q2. Let's create multiple netCDF files with a shared variable and unlimited dimension, and use `MFDataset` to read the aggregated data as if it were contained in a single file.\n", + "- Create 5 netCDF files named `data/datafile0.nc` through to `data/datafile4.nc`. Each file should contain:\n", + " - A single unlimited dimension named `time`.\n", + " - A variable named `temperature` with 10 integer values ranging from `file_index * 10` to `(file_index+1) * 10 - 1`.\n", + " - Ensure each file is saved in the `NETCDF4_CLASSIC` format.\n", + " - **Hint: Use a loop such as `for .. in range(..):` to do this task.**\n", + "- Using `MFDataset` read all the `temperature` data from the 5 files at once by specifying a wildcard string `datafile*.nc` - store this in a variable `f`. Assign this data to a new variable using `temperature_data = f.variables[\"temperature\"][:]`\n", + "- Print the aggregated `temperature` values to verify that they span from 0 to 49." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "3c6df18f-87e8-4051-a701-60d169656701", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:40.035145Z", + "iopub.status.busy": "2024-11-08T14:54:40.034779Z", + "iopub.status.idle": "2024-11-08T14:54:40.104560Z", + "shell.execute_reply": "2024-11-08T14:54:40.103872Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "28e3eb27-a38b-4de7-beec-a04f95922561", + "metadata": {}, + "source": [ + "## Compression of variables" + ] + }, + { + "cell_type": "markdown", + "id": "c2727e4a-5009-4678-b227-e40d49b576e6", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q3. Let's explore various compression options available in netCDF. \n", + "- Run the following cell to create an array of random temperature data and create a function to create NetCDF files with given compression settings. Take a look at the function and figure out what it's doing." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f9fea430-9b8f-4f65-ba2e-ee600cb0e0e2", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:40.109135Z", + "iopub.status.busy": "2024-11-08T14:54:40.108718Z", + "iopub.status.idle": "2024-11-08T14:54:40.118891Z", + "shell.execute_reply": "2024-11-08T14:54:40.118326Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import os\n", + "\n", + "# Step 1: Create a random dataset \n", + "time_dim, level_dim, lat_dim, lon_dim = 10, 5, 50, 100\n", + "data = np.random.rand(time_dim, level_dim, lat_dim, lon_dim) * 30 + 273.15\n", + "\n", + "# Step 2: Create a function to create NetCDF files with the given compression settings:\n", + "file_path = \"data/temperature_data.nc\"\n", + "def create_netcdf(file_path, compression=None, least_significant_digit=None, significant_digits=None):\n", + " with Dataset(file_path, 'w', format=\"NETCDF4\") as rootgrp:\n", + " # Create dimensions\n", + " rootgrp.createDimension(\"time\", time_dim)\n", + " rootgrp.createDimension(\"level\", level_dim)\n", + " rootgrp.createDimension(\"lat\", lat_dim)\n", + " rootgrp.createDimension(\"lon\", lon_dim)\n", + " # Define variable with compression settings\n", + " temp = rootgrp.createVariable(\"temp\", \"f4\", (\"time\", \"level\", \"lat\", \"lon\"), compression=compression, least_significant_digit=least_significant_digit, significant_digits=significant_digits)\n", + " #Assign data to the variable\n", + " temp[:] = data\n", + " # Check and print file size\n", + " print(f\"File size with compression={compression}, \"\n", + " f\"least_significant_digit={least_significant_digit}, \"\n", + " f\"significant_digits={significant_digits}: {os.path.getsize(file_path) / 1024:.2f} kB\")" + ] + }, + { + "cell_type": "markdown", + "id": "8e50f691-f784-4e3c-bffc-9520cc08f703", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "- Use this function to test the following cases:\n", + " - First, create the temperature variable without compression and observe the file size. Use the file path `data/temperature_data_no_compress.nc`.\n", + " - Then, enable zlib compression and observe the change in file size. Use the file path `data/temperature_data_zlib.nc`.\n", + " - Next, add zlib and least signigicant digit quantization (`least_significant_digit=3`) and check the file size again. Use the file path `data/temperature_data_zlib_lsd.nc`.\n", + " - Finally, add zlib and significant digits quantization (`significant_digits=4`) and check the file size again. Use the file path `data/temperature_data_zlib_sig.nc`.\n", + " - Hint: call the function using: `create_netcdf(filepath, compression, least_significant_digit, significant_digit)`. Note that the default for the compression/signigificant digits arguments is None so if you don't need them you can omit them when calling the function." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "73365563-9cf4-4b3b-9be8-c9814332b90a", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:40.121272Z", + "iopub.status.busy": "2024-11-08T14:54:40.121015Z", + "iopub.status.idle": "2024-11-08T14:54:40.299169Z", + "shell.execute_reply": "2024-11-08T14:54:40.298642Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "393a6184-658e-48fb-a10b-1c8d302bade6", + "metadata": {}, + "source": [ + "## Compound data types" + ] + }, + { + "cell_type": "markdown", + "id": "19c598d5-5d89-430e-9178-ec9cac5efe3f", + "metadata": {}, + "source": [ + "Q4. Let's work with compound data types and structured arrays.\n", + "- Create a netCDF file called `data/vectors.nc` in write mode with `NETCDF4` format assigned to the variable `f`.\n", + "- Define a compound data type that represents a 3D vector. Each vector should have 3 components:\n", + " - `x`: a `float33` representing the x-coordinate\n", + " - `y`: a `float32` representing the y-coordinate\n", + " - `z`: a `float32` representing the z-coordinate\n", + " - Hint: use `np.dtype([('x', type), ('y'..), (...)])` to define x,y,z then `f.createCompoundType()` to create the compound data type.\n", + "- Create a dimension named `num_vectors` to store an unlimited number of vectors.\n", + "- Create a variable called `vector_data` in the file using the compound data type from step 2, with the dimension from step 3.\n", + "- Generate a numpy structured array with 5 sample 3D vectors:\n", + " - Each vector should have random values for `x`, `y` and `z` components (use `np.random.rand(num_samples)`).\n", + " - Store these in the structured array (initialize the array with `np.empty(num_samples, dtype)` then use `data[\"x\"]` etc to assign the data.\n", + " - Write them to the netCDF variable.\n", + "- Close the file and then reopen it in read mode.\n", + "- Read the data back into a new numpy structured array and print each vector.\n", + " - Hint: use `f.variables['var_name']` to read in the variable data.\n", + " - Hint: Use `data_in = vector_var[:]` to extract the data for the variables.\n", + " - Hint: Use `for i, vev in enumerate(data_in):` to loop through the data so you can print each vector." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "c92e87db-f092-4802-a346-aaeb158b6395", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:40.301564Z", + "iopub.status.busy": "2024-11-08T14:54:40.301300Z", + "iopub.status.idle": "2024-11-08T14:54:40.313735Z", + "shell.execute_reply": "2024-11-08T14:54:40.313229Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "1a537d6b-f892-444b-b63d-6bdbe4963e83", + "metadata": {}, + "source": [ + "## Variable-length data types" + ] + }, + { + "cell_type": "markdown", + "id": "faa9d0ba-0352-45bb-98cb-0443cecbc198", + "metadata": {}, + "source": [ + "Q5. Let's create and manipulate variable-length (vlen) arrays\n", + "- Create a netCDF file named `data/exercise_vlen.nc` in write mode.\n", + "- Define dimensions:\n", + " - Create a dimension `a` with a size of `5`.\n", + " - Create a dimension `b` with a size of `4`.\n", + "- Create a variable-length data type using `f.createVLType()` named `my_vlen_int` using `np.int32` as the datatype.\n", + "- Use the vlen type you defined to create a variable `vlen_var` with dimensions `(\"a\", \"b\")`.\n", + "- Populate `vlen_var` with random data:\n", + " - Use the following to generate the random data:\n", + " ```\n", + " data = np.empty((5,4), dtype=object)\n", + " for i in range(5):\n", + " for j in range(4):\n", + " random_length = random.randint(2, 8)\n", + " data[i,j] = np.random.randint(1, 101, size=random_length, dtype=np.int32)\n", + " ```\n", + " - Assign the data to the netCDF variable\n", + "- Create a new dimension `c` with a size of `7`.\n", + "- Define a variable `vlen_str_var` along dimension `c`.\n", + "- Populate this variable with random strings of lengths between 3 and 10 using uppercase and lowercase alphabetic characters using the following:\n", + " ```\n", + " chars = string.ascii_letters\n", + " string_data = np.empty(7, dtype=object)\n", + " for i in range(7):\n", + " random_length = random.randint(3,10)\n", + " string_data[i] = ''.join(random.choice(chars) for _ in range(random_length))\n", + " # Assign the string data to the netCDF variable\n", + " str_var[:] = string_data\n", + " ```\n", + "- Print the contents of `vlen_var` and `vlen_str_var`. Print the structure of the netCDF4 file to show defined dimensions, variables, and data types." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "70ba5df2-e554-47de-b164-11d2c926f63d", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:40.316032Z", + "iopub.status.busy": "2024-11-08T14:54:40.315783Z", + "iopub.status.idle": "2024-11-08T14:54:40.347230Z", + "shell.execute_reply": "2024-11-08T14:54:40.346679Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [] + }, + { + "cell_type": "markdown", + "id": "55abd572-29b3-47ea-9b9f-0e25364297d5", + "metadata": {}, + "source": [ + "## Enum data type" + ] + }, + { + "cell_type": "markdown", + "id": "6f972a56-42e9-499c-ab1e-940b022c616a", + "metadata": {}, + "source": [ + "Q6. Let's create a netCDF file to store weather observation data including an enumerated type representing different types of precipiation.\n", + "- Create a new netCDF file called `data/weather_data.nc` in write mode with the `NETCDF4` format.\n", + "- Create a Python dictionary `precip_dict` where:\n", + " - `None` maps to `0`\n", + " - `Rain` maps to `1`\n", + " - `Snow` maps to `2`\n", + " - `Sleet` maps to `3`\n", + " - `Hail` maps to `4`\n", + " - `Unknown` maps to `255`\n", + "- Use this dictionary to define an Enum data type using `.createEnumType()` called `precip_t` with a base type of `np.uint8`\n", + "- Define a dimension called `time` with an unlimited length for observations over time\n", + "- Create a 1D variable named `precipitation` of type `precip_type` that uses the `time` dimension and has `fill_value=precip_dict['Unknown']`. The fill value indicates missing data.\n", + "- Write the following precipiatation observations to the `precipitation` variable: `precip_var[:] = [precip_dict[k] for k in ['None', 'Rain', 'Snow', 'Unknown', 'Sleet']]`.\n", + "- Close and reopen the file in read mode, then print the contents of the `precipitation` variable, inlcuding: the data values confirming they match the written values, the enum dictionary associated with the enum data type, verifying the precipitation mapping. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "f08fe449-acc3-41e3-bb36-3f6a2e63b98b", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:40.349575Z", + "iopub.status.busy": "2024-11-08T14:54:40.349315Z", + "iopub.status.idle": "2024-11-08T14:54:40.374465Z", + "shell.execute_reply": "2024-11-08T14:54:40.373923Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Enum dictionary: {'None': 0, 'Rain': 1, 'Snow': 2, 'Sleet': 3, 'Hail': 4, 'Unknown': 255}\n", + "Precipitation data: [0 1 2 -- 3]\n" + ] + } + ], + "source": [ + "# Step 1: Create a new netCDF file\n", + "nc = Dataset('data/weather_data.nc', 'w', format='NETCDF4')\n", + "\n", + "# Step 2: Define the Enum dictionary and create the Enum type\n", + "precip_dict = {\n", + " 'None': 0,\n", + " 'Rain': 1,\n", + " 'Snow': 2,\n", + " 'Sleet': 3,\n", + " 'Hail': 4,\n", + " 'Unknown': 255\n", + "}\n", + "\n", + "# Step 3: Create an Enum type called 'precip_t' with base type uint8\n", + "precip_type = nc.createEnumType(np.uint8, 'precip_t', precip_dict)\n", + "\n", + "# Step 4: Create a time dimension\n", + "nc.createDimension('time', None)\n", + "\n", + "# Step 5: Create the precipitation variable, setting the fill_value to 'Unknown'\n", + "precip_var = nc.createVariable('precipitation', precip_type, ('time',),\n", + " fill_value=precip_dict['Unknown'])\n", + "\n", + "# Step 6: Write data to the variable\n", + "precip_var[:] = [precip_dict[k] for k in ['None', 'Rain', 'Snow', 'Unknown', 'Sleet']]\n", + "\n", + "# Step 7: Close the file\n", + "nc.close()\n", + "# Reopen the file, read and print the data\n", + "nc = Dataset('data/weather_data.nc', 'r')\n", + "precip_var = nc.variables['precipitation']\n", + "# Print the Enum dictionary\n", + "print(\"Enum dictionary:\", precip_var.datatype.enum_dict)\n", + "# Print the data stored in the variable\n", + "print(\"Precipitation data:\", precip_var[:])\n", + "\n", + "# Close the file\n", + "nc.close()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 + Jaspy", + "language": "python", + "name": "jaspy" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/python-data/exercises/ex09a_weather_api.ipynb b/python-data/exercises/ex09a_weather_api.ipynb new file mode 100644 index 0000000..fe7f97d --- /dev/null +++ b/python-data/exercises/ex09a_weather_api.ipynb @@ -0,0 +1,953 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# Exercise 9a: Weather API\n", + "\n", + "## Aim: Use a Weather API to create and graph NetCDF files\n", + "\n", + "### Issues covered:\n", + "\n", + "- Request and get data from a weather API service\n", + "- Read and retrieve information from a JSON response\n", + "- Write contents to a NetCDF file\n", + "- Read a collection of NetCDF files and plot a time series graph\n", + "\n", + "## 1. Let's get data from a web API on the internet\n", + "\n", + "We will use the NOAA National Weather Service in the US as our data source:\n", + "\n", + "![](https://www.weather.gov/css/images/header.png)\n", + "\n", + "The service has a web API that allows you to request forecast data for a given grid point in the USA. Details of the API are documented at:\n", + "\n", + "https://www.weather.gov/documentation/services-web-api\n", + "\n", + "Use the endpoint `https://api.weather.gov/` as the base URL.\n", + "\n", + "Firstly, we want to get a grid ID and based on some latitude/longitude coordinates. To do so we will use the `points/{latitude,longitude}` endpoint of the API.\n", + "\n", + "**Choose the latitude and longitude of your favourite US location (this API is US only and in latitude North, longitude East). The extent of the USA is approximately:**\n", + "- Longitude: -120, -80\n", + "- Latitude: 30, 48\n", + "\n", + "Once you have queried the `points` API you will get back a `grid ID` (`GridId`). The `grid ID`h can be used to get a weather forecast for your location of interest, using the `gridpoints/{grid ID}/{grid co-ordinates}` endpoint." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Import the `requests` library which is great for downloading content from external URLs." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "import requests" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "You can use the requests library to access the web API. Fill in the elipses with the `latitude` (degrees North) and `longitude` (degrees East, so use negative value) of a location in the US. \n", + "If successful, the response code should be 200." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "url = 'https://api.weather.gov/'\n", + "latitude = ...\n", + "longitude = ...\n", + "\n", + "# Hint: use the requests library to GET from the url: https://api.weather.gov/points/{LAT},{LON}\n", + "response = requests.get(f'{url}points/{latitude},{longitude}')\n", + "response.status_code" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the requests library, the results from the webAPI can be extracted into in JSON format. A JSON document behaves exactly like a dictionary.\n", + "\n", + "Use dictionary indexing to extract the values of the grid ID and the X/Y coordinates:\n", + "\n", + "- get `gridID`\n", + "- get `gridX`\n", + "- get `gridY`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# hint: you can view the JSON by pasting the URL directly into your browser address bar\n", + "\n", + "response = response.json()\n", + "\n", + "gridID = ...\n", + "gridX = ...\n", + "gridY = ..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With your `gridID`, `gridX`, and `gridY`, use the `gridpoints` API endpoint to request a weather forecast for that location. Print the status code.\n", + "If everything is working, you should get another 200 status code." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "response = requests.get(f'{url}gridpoints/{gridID}/{gridX},{gridY}')\n", + "response.status_code" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Can you use the JSON response data to get the forecast temperature values? Use dictionary indexing to get the `values` from `temperature` in `properties`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "data = response.json()\n", + "forecast = ..." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The below code extracts the coordinates of the grid box you have chosen." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "coords = data['geometry']['coordinates'][0][0]\n", + "x = coords[1]\n", + "y = coords[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Let's format that data and write it to NetCDF\n", + "\n", + "### Formatting the data\n", + "\n", + "First, format your forecast data to get the datetime and air temperature as separate\n", + "lists." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from datetime import datetime as dt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Loop through your `forecast` values and get the temperatures (`value`) and datetimes (`validTime`) into a list.\n", + "`forecast` is a list of dictionaries, where each dictionary is of one time instance.\n", + "Fill in the ellipses to format each `validTime` string to a python `datetime` object and assign and set to the variable `date`. Get each `value` and assign to the variable `temp`. These values will then be appended to the `temps` and `timeseries` lists." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "pycharm": { + "name": "#%%\n" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# Use the datetime module to convert the times from the data to a datetime object.\n", + "# Hint: look at the validTime string and see how you can turn the string to datetime\n", + "# using strptime, the format of the datetime is: '%Y-%m-%dT%H:%M:%Sz'.\n", + "\n", + "timeseries = []\n", + "temps = []\n", + "\n", + "for item in forecast:\n", + " ...\n", + " timeseries.append(date)\n", + " temps.append(temp)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Format the `timeseries` list and convert it to relative time in seconds from the start of the timeseries. When using NetCDF and the CF Metadata Conventions time is stored as an offset from a base time rather than an absolute times.\n", + "\n", + "If you are stuck, take look at the 'Time series' slide in the [logging data from serial ports](https://github.com/ncasuk/ncas-isc/raw/68abbfd3a573e576c32fc127fafc874bfff98b1e/python/presentations/logging-data-from-serial-ports/LDFSP_Slides.pdf) presentation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "base_time = timeseries[0]\n", + "time_values = []\n", + "\n", + "for t in timeseries:\n", + " ...\n", + "\n", + "time_units = ..." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Convert the `temps` list from degrees C to Kelvin. As per the CF Conventions, the canonical units for Air Temperature is K. Create a new list, called `temp_values`, which is the temperature in Kelvin." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "temp_values = []\n", + "\n", + "..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create a netCDF4 Dataset and write the contents to a file\n", + "\n", + "Import the `Dataset` class from the `netCDF4` library. You can go on to create an *instance* of this class which will contain:\n", + "- variables\n", + "- coordinate variables\n", + "- dimensions\n", + "- global attributes\n", + "\n", + "When you create the instance of `Dataset`, you will give it a file name which will be written to when you close the `Dataset`.\n", + "\n", + "Also import `numpy` as `np`. This will be used to construct the data arrays from the existing lists that currently hold the weather data and coordinate information.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from netCDF4 import Dataset\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Quick aside, let's make sure we have a `DATA_DIR` to write to\n", + "\n", + "Since this is a group exercise, everyone should be writing to the same output directory. Let's set some python variables that can be used below:\n", + "1. `USER` - used in the output file names to ensure every NetCDF file is unique.\n", + "2. `HOME_DIR` - your `$HOME` directory\n", + "2. `MY_DATA_DIR` - the directory where you will write your NetCDF file.\n", + "3. `GROUP_DATA_DIR` - the directory where all the NetCDF files will eventually be collected/available.\n", + "\n", + "Since `GROUP_DATA_DIR` is not writeable directly from the Notebook Service, we have set up a job to replicate files from `MY_DATA_DIR` to `GROUP_DATA_DIR` (which runs once per minute)." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "USER = os.environ[\"JUPYTERHUB_USER\"]\n", + "\n", + "HOME_DIR = f\"/home/users/{USER}\"\n", + "MY_DATA_DIR = os.path.join(HOME_DIR, \"weather-api-outputs\")\n", + "\n", + "# Create MY_DATA_DIR if it doesn't exist\n", + "if not os.path.isdir(MY_DATA_DIR):\n", + " os.mkdir(MY_DATA_DIR)\n", + "\n", + "# All NetCDF will be automatically copied here (once per minute)\n", + "GROUP_DATA_DIR = \"/gws/pw/j07/workshop/weather-api-data\"\n", + "\n", + "# The output file will initially be written to your HOME_DIR (then you will move\n", + "# it when complete)\n", + "filename = f\"{gridID}-{USER}-temps.nc\"\n", + "outfile = f\"{HOME_DIR}/{filename}\"" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Back to our NetCDF file\n", + "\n", + "Create the output file, as a `netCDF4 Dataset` instance, using the `outfile` defined above.\n", + "\n", + "If you need help, have a look at the 'Create the NetCDF dimensions & variables' slide in the [logging data from serial ports](https://github.com/ncasuk/ncas-isc/raw/68abbfd3a573e576c32fc127fafc874bfff98b1e/python/presentations/logging-data-from-serial-ports/LDFSP_Slides.pdf) presentation." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "dataset = ..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Start by defining some dimensions\n", + "\n", + "Create NetCDF *dimensions*:\n", + "- `time_dim`: *unlimited* length\n", + "- `lat_dim`: length 1\n", + "- `lon_dim`: length 1" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [], + "source": [ + "time_dim = ...\n", + "lat_dim = ...\n", + "lon_dim = ..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Now define the coordinate variables and then temperature variable\n", + "\n", + "Create the `time` *variable* with the following properties:\n", + "- type: numpy float (`np.float64`)\n", + "- variable id: `time`\n", + "- dimensions: (`time`,)\n", + "- set the array using the `time_values` list\n", + "- `units`: `time_units` defined earlier\n", + "- `standard_name`: `time`\n", + "- `calendar`: `standard`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "time_var = ...\n", + "time_var[:] = ...\n", + "time_var.units = ...\n", + "time_var.standard_name = ...\n", + "time_var.calendar = ..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create the `lat` *variable* with the following properties:\n", + "- type: numpy float (`np.float64`)\n", + "- variable id: `lat`\n", + "- dimensions: (`lat`,)\n", + "- set the array of length 1 using the `gridY` value\n", + "- `units`: `degrees_north`\n", + "- `standard_name`: `latitude`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "lat_var = ...\n", + "lat_var[:] = ...\n", + "lat_var.units = ...\n", + "lat_var.standard_name = ..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create the `lon` *variable* with the following properties:\n", + "- type: numpy float (`np.float64`)\n", + "- variable id: `lon`\n", + "- dimensions: (`lon`,)\n", + "- set the array of length 1 using the `gridX` value\n", + "- `units`: `degrees_east`\n", + "- `standard_name`: `longitude`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "lon_var = ...\n", + "lon_var[:] = ...\n", + "lon_var.units = ...\n", + "lon_var.standard_name = ..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create the `temp` *variable* with the following properties:\n", + "- type: numpy float (`np.float64`)\n", + "- variable id: `temp`\n", + "- dimensions: (`time`,)\n", + "- set the array using the `temp_values` list\n", + "- `long_name`: `air temperature (K)`\n", + "- `units`: `K`\n", + "- `standard_name`: `air_temperature`\n", + "- `coordinates`: `lon lat` - to relate the longitude and latitude to this variable" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "temp_var = ...\n", + "temp_var[:] = ...\n", + "temp_var.var_id = ...\n", + "temp_var.long_name = ...\n", + "temp_var.units = ...\n", + "temp_var.standard_name = ...\n", + "temp_var.coordinates = ..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Add some global attributes\n", + "\n", + "The [CF Metadata Conventions](https://cfconventions.org/cf-conventions/cf-conventions.html#_overview) recommends a set of global attributes to \"provide human readable documentation of the file contents\":\n", + "- title\n", + "- history\n", + "- institution\n", + "- source\n", + "- references\n", + "- comment\n", + "\n", + "Add each of the above to your `Dataset` instance. Here are some suggested values (but you can say whatever you like):\n", + "- title: Air Temperature forecasts for ``\n", + "- history: File created on: ``\n", + "- institution: NCAS-ISC\n", + "- source: NOAA Weather API Service\n", + "- references: https://www.weather.gov/documentation/services-web-api\n", + "- comment: The ISC course is teaching me about Python and NetCDF!\n", + "\n", + "You can add any other global attributes that you wish to." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset.title = ...\n", + "dataset.history = ...\n", + "dataset.institution = ...\n", + "dataset.source = ...\n", + "dataset.references = ...\n", + "dataset.comment = ..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Finally, close the `Dataset` to save the file\n", + "\n", + "Save your NetCDF file by closing the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "dataset.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can check it is there using `os.path.isfile(...)`:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "os.path.isfile(outfile)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### IMPORTANT: Move the file to your MY_DATA_DIR so it gets copied to the GROUP_DATA_DIR\n", + "\n", + "Since we cannot write directly to the `GROUP_DATA_DIR`, move the file from your `HOME_DIR` to your `MY_DATA_DIR`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "os.rename(outfile, f\"{MY_DATA_DIR}/{filename}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## 3. Find all the NetCDF files written during this exercise\n", + "\n", + "To find all the `.nc` files in a group workspace, we will use the glob module in Python.\n", + "Glob let's us find all files matching a pattern, in our case:\n", + "\n", + "`{GROUP_DATA_DIR}/*.nc`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from glob import glob" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Can you use glob to make a list of file paths of all NetCDF files in the\n", + "group workspace?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "filepaths = glob(f\"{...}*temps.nc\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## 4. Create a time-series graph of all the forecasts\n", + "\n", + "Now that we have a list of NetCDF file paths, we can open them and extract their data.\n", + "\n", + "To start, let us make the a plot using matplotlib." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from netCDF4 import num2date\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.dates as mdates\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Create a subplots figure with figure and axis" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "fig, ax = ..." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Can you set the x-axis locator (ticks) using dates class from matplotlib?\n", + "- set the major locator to days.\n", + "- set the minor locator to every 6 hours.\n", + "- set the x-axis formatter to Day-Month for each day." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# In the matplotlib.dates module, as mdates, look at the DayLocator and HourLocator.\n", + "fmt_day = ...\n", + "fmt_six_hours = ...\n", + "\n", + "ax.xaxis.set_major_locator(fmt_day)\n", + "ax.xaxis.set_minor_locator(fmt_six_hours)\n", + "ax.xaxis.set_major_formatter(mdates.DateFormatter('%d-%m'))" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Label the axis, `ax`, on the plot:\n", + "- label the x-axis as `Date`\n", + "- label the y-axis as `Air Temperature / K`\n", + "- set a title on your plot" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "..." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Open each NetCDF file and extract the `temp`, `time`, `lat` and `lon` variables from the file. Then use the matplotlib `plot_date` function to plot the graph.\n", + "\n", + "- set the label of plot to the `, ` coordinates attribute of the `temp` variable.\n", + "\n", + "Replace the elipses with your plotting, the `for` loop works through all the shared NetCDF files in the workspace, where `f` is the file path and `filepaths` is a list of data files.\n", + "\n", + "If you need help, look at the 'Plotting data with matplotlib' slide in the [logging data from serial ports](https://github.com/ncasuk/ncas-isc/raw/68abbfd3a573e576c32fc127fafc874bfff98b1e/python/presentations/logging-data-from-serial-ports/LDFSP_Slides.pdf) presentation.\n", + "\n", + "Plot a line graph using matplotlib: \n", + "\n", + "- you will need to set the marker to `-` otherwise you will get a scatter graph.\n", + "- set the label of the plot to a string: `, `." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "for f in filepaths:\n", + " ..." + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Finally, show the plot with a legend, you might want to enable tight layout,\n", + "and save the plot to your `MY_DATA_DIR` directory." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "collapsed": false, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "..." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Save the graph to a PNG file" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig.savefig(f\"{MY_DATA_DIR}/{gridID}-{USER}-temps.png\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 + Jaspy", + "language": "python", + "name": "jaspy" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/python-data/exercises/ex09b_satellite_data.ipynb b/python-data/exercises/ex09b_satellite_data.ipynb new file mode 100644 index 0000000..17b8518 --- /dev/null +++ b/python-data/exercises/ex09b_satellite_data.ipynb @@ -0,0 +1,1232 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "73b81a5a-4fc6-4c33-849b-3b717a43b1c8", + "metadata": {}, + "source": [ + "# Exercise 9b: Working with Satellite Data\n", + "\n", + "## Aim: Use python tools to search for, download, and manipulate satellite data\n", + "\n", + "### Issues covered:\n", + "- Search for and request data from a public STAC catalogue of satellite imagery\n", + "- Download satellite imagery as raster data \n", + "- Read rasters into python using the rioxarray package\n", + "- Visualise single/multi-band raster data\n", + "\n", + "### Introduction\n", + "\n", + "A number of satellites take snapshots of the Earth’s surface from space. The images recorded by these remote sensors represent a very precious data source for any activity that involves monitoring changes on Earth. Satellite imagery is typically provided in the form of geospatial raster data, with the measurements in each grid cell (“pixel”) being associated to accurate geographic coordinate information.\n", + "\n", + "In this notebook exercise we will explore how to access open satellite data using Python. In particular, we will consider [the Sentinel-2 data collection that is hosted on AWS](https://registry.opendata.aws/sentinel-2-l2a-cogs). This dataset consists of multi-band optical images acquired by the two satellites of [the Sentinel-2 mission](https://sentinel.esa.int/web/sentinel/missions/sentinel-2) and it is continuously updated with new images.\n", + "\n", + "\n", + "# 1. Search for satellite imagery\n", + "\n", + "**The SpatioTemporal Asset Catalog (STAC) specification**\n", + "\n", + "Current sensor resolutions and satellite revisit periods are such that terabytes of data products are added daily to the corresponding collections. Such datasets cannot be made accessible to users via full-catalog download. Space agencies and other data providers often offer access to their data catalogs through interactive Graphical User Interfaces (GUIs), see for instance the [Copernicus Open Access Hub portal](https://scihub.copernicus.eu/dhus/#/home) for the Sentinel missions. Accessing data via a GUI is a nice way to explore a catalog and get familiar with its content, but it represents a heavy and error-prone task that should be avoided if carried out systematically to retrieve data.\n", + "\n", + "A service that offers programmatic access to the data enables users to reach the desired data in a more reliable, scalable and reproducible manner. An important element in the software interface exposed to the users, which is generally called the Application Programming Interface (API), is the use of standards. Standards, in fact, can significantly facilitate the reusability of tools and scripts across datasets and applications.\n", + "\n", + "The SpatioTemporal Asset Catalog (STAC) specification is an emerging standard for describing geospatial data. By organizing metadata in a form that adheres to the STAC specifications, data providers make it possible for users to access data from different missions, instruments and collections using the same set of tools.\n", + "\n", + "\n", + "![Views of the STAC browser](https://carpentries-incubator.github.io/geospatial-python/fig/E05/STAC-browser.jpg)\n", + "Views of the radiant earth STAC browser\n", + "\n", + "## More Resources on STAC\n", + "- [STAC specification](https://github.com/radiantearth/stac-spec#readme)\n", + "- [Tools based on STAC](https://stacindex.org/ecosystem)\n", + "- [STAC catalogs](https://stacindex.org/catalogs)\n", + "\n", + "## Search a STAC catalog\n", + "\n", + "The [STAC browser](https://radiantearth.github.io/stac-browser/#/) is a good starting point to discover available datasets, as it provides an up-to-date list of existing STAC catalogs. From the list, let's click on the \"Earth Search\" catalog, i.e. the access point to search the archive of Sentinel-2 images hosted on AWS.\n" + ] + }, + { + "cell_type": "markdown", + "id": "bb95dfdf-8721-4d47-af20-211e2f7bd491", + "metadata": {}, + "source": [ + "## Install some packages we will need\n", + "\n", + "We need to install some additional python packages which unfortunately aren't (yet) on Jaspy. To do this, we run:\n", + "\n", + "`pip install --user pystac_client rioxarray shapely pyproj`\n", + "\n", + "Which will install these python packages into your local python path, so we can use them with your account alongside all the packages in Jaspy. __NOTE: this command may take some time__ " + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4ab589c7-f462-4c6d-aaa6-b1dc630e5cf6", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# Type the pip command here" + ] + }, + { + "cell_type": "markdown", + "id": "517be10e-1c03-433c-b6b9-3722cc0d15b9", + "metadata": {}, + "source": [ + "## **Exercise:** Discover a STAC catalog\n", + "Let's take a moment to explore the Earth Search STAC catalog, which is the catalog indexing the Sentinel-2 collection\n", + "that is hosted on AWS. We can interactively browse this catalog using the STAC browser at [this link](https://radiantearth.github.io/stac-browser/#/external/earth-search.aws.element84.com/v1).\n", + "\n", + "1. Open the link in your web browser. Which (sub-)catalogs are available?\n", + "2. Open the Sentinel-2 Level 2A collection, and select one item from the list. Each item corresponds to a satellite\n", + "\"scene\", i.e. a portion of the footage recorded by the satellite at a given time. Have a look at the metadata fields\n", + "and the list of assets. What kind of data do the assets represent?" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3c869fc6-9581-4ad2-9a37-72d1d61b84d3", + "metadata": {}, + "outputs": [], + "source": [ + "# Try something in here" + ] + }, + { + "cell_type": "markdown", + "id": "dfdc9c0c-cad2-4cba-9e67-fbc7bdc99a50", + "metadata": {}, + "source": [ + "## **Solution:**\n", + "(press three dots to reveal)" + ] + }, + { + "cell_type": "markdown", + "id": "591f2ce6-52ca-45bb-b2b4-a504ef182515", + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "source": [ + "\n", + "![Views of the Earth Search STAC endpoint](https://carpentries-incubator.github.io/geospatial-python/fig/E05/STAC-browser-exercise.jpg)\n", + "\n", + "1. 7 subcatalogs are available, including a catalog for Landsat Collection 2, Level-2 and Sentinel-2 Level 2A (see left screenshot in the figure above).\n", + "2. When you select the Sentinel-2 Level 2A collection, and randomly choose one of the items from the list, you\n", + "should find yourself on a page similar to the right screenshot in the figure above. On the left side you will find\n", + "a list of the available assets: overview images (thumbnail and true color images), metadata files and the \"real\"\n", + "satellite images, one for each band captured by the Multispectral Instrument on board Sentinel-2." + ] + }, + { + "cell_type": "markdown", + "id": "462ea61f-3cc0-4793-be7a-b7f9886e1484", + "metadata": {}, + "source": [ + "When opening a catalog with the STAC browser, you can access the API URL by clicking on the \"Source\" button on the top\n", + "right of the page. By using this URL, we have access to the catalog content and, if supported by the catalog, to the\n", + "functionality of searching its items. For the Earth Search STAC catalog the API URL is:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ca27bf8b-05c4-4d6b-b8ee-d152487e6f06", + "metadata": {}, + "outputs": [], + "source": [ + "api_url = \"https://earth-search.aws.element84.com/v1\"" + ] + }, + { + "cell_type": "markdown", + "id": "d298328b-2f37-442e-996f-ea61c68eb039", + "metadata": {}, + "source": [ + "You can query a STAC API endpoint from Python using the `pystac_client` library:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "cac729fb-8cda-484b-9fd4-da68a2c8267c", + "metadata": {}, + "outputs": [], + "source": [ + "from pystac_client import Client\n", + "\n", + "client = Client.open(api_url)\n" + ] + }, + { + "cell_type": "markdown", + "id": "81f1cc52-814e-4af0-908f-6d4aa7cc10fe", + "metadata": {}, + "source": [ + "In the following, we ask for scenes belonging to the `sentinel-2-l2a` collection. This dataset includes Sentinel-2 data products pre-processed at level 2A (bottom-of-atmosphere reflectance) and saved in Cloud Optimized GeoTIFF (COG) format:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4e7e407d-721e-4187-8a47-e8e9634262c9", + "metadata": {}, + "outputs": [], + "source": [ + "collection = \"sentinel-2-l2a\" # Sentinel-2, Level 2A, Cloud Optimized GeoTiffs (COGs)" + ] + }, + { + "cell_type": "markdown", + "id": "57f5d59b-f60b-45d0-b191-f5e4d5e8939e", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## A note on cloud-optimized GeoTIFFs\n", + "\n", + "Cloud Optimized GeoTIFFs (COGs) are regular GeoTIFF files with some additional features that make them ideal to be employed in the context of cloud computing and other web-based services. This format builds on the widely-employed GeoTIFF format, which you can find out more about in [Episode 1: Introduction to Raster Data](01-intro-raster-data.md). In short, a GeoTIFF is a standard .tif image format with additional spatial (georeferencing) information embedded in the file as tags. These tags should include the following raster metadata:\n", + "- Extent\n", + "- Resolution\n", + "- Coordinate Reference System (CRS)\n", + "- Values that represent missing data (NoDataValue)\n", + "\n", + "COGs, by extension, are regular GeoTIFF files with a special internal structure. One of the features of COGs is that data is organized in \"blocks\" that can be accessed remotely via independent HTTP requests. Data users can thus access the only blocks of a GeoTIFF that are relevant for their analysis, without having to download the full file. In addition, COGs typically include multiple lower-resolution versions of the original image, called \"overviews\", which can also be accessed independently. By providing this \"pyramidal\" structure, users that are not interested in the details provided by a high-resolution raster can directly access the lower-resolution versions of the same image, significantly saving on the downloading time. More information on the COG format can be found [here](https://www.cogeo.org).\n", + "\n", + "---\n", + "\n", + "We also ask for scenes intersecting a geometry defined using the `shapely` library (in this case, a point):" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b58e48e4-2609-4e86-b434-ed664dafa6f6", + "metadata": {}, + "outputs": [], + "source": [ + "from shapely.geometry import Point\n", + "point = Point(4.89, 52.37) # AMS coordinates" + ] + }, + { + "cell_type": "markdown", + "id": "76b597f5-db31-4bad-b858-59e9f2961d92", + "metadata": {}, + "source": [ + "Note: at this stage, we are only dealing with metadata, so no image is going to be downloaded yet. But even metadata can be quite bulky if a large number of scenes match our search! For this reason, we limit the search result to 10 items:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "08d64392-75f9-442c-bcc5-e173e1448683", + "metadata": {}, + "outputs": [], + "source": [ + "search = client.search(\n", + " collections=[collection],\n", + " intersects=point,\n", + " max_items=10,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "71b0aa02-8e8c-44c2-974e-b49ef84c7163", + "metadata": {}, + "source": [ + "We submit the query and find out how many scenes match our search criteria (please note that this output can be different as more data is added to the catalog):\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2fd8d411-a898-452a-be69-f19a8cdb920c", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "print(search.matched())" + ] + }, + { + "cell_type": "markdown", + "id": "2fa2aeb2-0f3a-4a5c-bd2a-c8e1e5b704e3", + "metadata": {}, + "source": [ + "Finally, we retrieve the metadata of the search results:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2790d193-02a1-42e2-a51c-42adfc17041c", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "items = search.item_collection()" + ] + }, + { + "cell_type": "markdown", + "id": "500a6faf-0d21-421e-b8b3-60b0d0df6e64", + "metadata": {}, + "source": [ + "The variable `items` is an `ItemCollection` object. We can check its size by:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ebb1f4c0-104a-4c38-953d-f5b1f2117643", + "metadata": {}, + "outputs": [], + "source": [ + "print(len(items))" + ] + }, + { + "cell_type": "markdown", + "id": "e2ccac65-d1aa-4e66-8243-b4d27968d638", + "metadata": {}, + "source": [ + "which is consistent with the maximum number of items that we have set in the search criteria. We can iterate over the returned items and print these to show their IDs:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9c94ff82-849e-4164-89b2-8fb6bec6d622", + "metadata": {}, + "outputs": [], + "source": [ + "for item in items:\n", + " print(item)" + ] + }, + { + "cell_type": "markdown", + "id": "fbddf824-83d3-4789-a354-f1989936c438", + "metadata": {}, + "source": [ + "Each of the items contains information about the scene geometry, its acquisition time, and other metadata that can be accessed as a dictionary from the `properties` attribute.\n", + "\n", + "Let's inspect the metadata associated with the first item of the search results:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f32b169d-31df-4081-ba4d-9219f4efeb15", + "metadata": {}, + "outputs": [], + "source": [ + "item = items[0]\n", + "print(item.datetime)\n", + "print(item.geometry)\n", + "print(item.properties)" + ] + }, + { + "cell_type": "markdown", + "id": "ad300420-d4d9-4765-8d2c-1aa961ae77d3", + "metadata": {}, + "source": [ + "## **Exercise**: Search satellite scenes using metadata filters\n", + "Search for all the available Sentinel-2 scenes in the `sentinel-2-l2a` collection that satisfy the following criteria:\n", + "- intersect a provided bounding box, use ±0.01 deg in lat/lon from the previously defined point (hint: use the `buffer` and `bounds` methods on the shapely `point` object we saw above)\n", + "- have been recorded between 20 March 2020 and 30 March 2020;\n", + "- have a cloud coverage smaller than 10% (hint: use the `query` argument of `client.search` - there are two ways, info can be found [here](https://pystac-client.readthedocs.io/en/latest/usage.html#query-extension) and [here](https://github.com/stac-api-extensions/query)).\n", + "\n", + "How many scenes are available? Save the search results in GeoJSON format as `search.json`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b7b4d17e-2746-4546-9c83-a7c43342ce9f", + "metadata": {}, + "outputs": [], + "source": [ + "# Try something in here:\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "1b0566d9-fc33-48b9-9ca0-afa8a889add0", + "metadata": {}, + "source": [ + "## Access the assets\n", + "\n", + "So far we have only discussed metadata - but how can one get to the actual images of a satellite scene (the \"assets\" in the STAC nomenclature)? These can be reached via links that are made available through the item's attribute `assets`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "18778b46-71fd-4f21-872f-4112a5656439", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "assets = items[0].assets # first item's asset dictionary\n", + "\n", + "# Have a look at the keys\n", + "print(...)\n" + ] + }, + { + "cell_type": "markdown", + "id": "664aa928-bf54-4003-833e-a7e93bab27a7", + "metadata": { + "tags": [] + }, + "source": [ + "We can print a minimal description of the available assets:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5d4f2cb8-c392-4d43-b298-824529df3131", + "metadata": {}, + "outputs": [], + "source": [ + "for key, asset in assets.items():\n", + " print(f\"{key}: {asset.title}\")" + ] + }, + { + "cell_type": "markdown", + "id": "9012da82-cb57-455e-96ac-5c6df9eeb3ae", + "metadata": {}, + "source": [ + "Among the others, assets include multiple raster data files (one per optical band, as acquired by the multi-spectral instrument), a thumbnail, a true-color image (\"visual\"), instrument metadata and scene-classification information (\"SCL\"). Let's get the URL links to the actual asset:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "20a37bef-2d6e-41d8-acbf-3eb25fa88581", + "metadata": {}, + "outputs": [], + "source": [ + "print(assets[\"thumbnail\"].href)" + ] + }, + { + "cell_type": "markdown", + "id": "ae6b336c-18b6-48cb-8ded-309b0287bdf3", + "metadata": { + "tags": [] + }, + "source": [ + "This can be used to download the corresponding file:\n", + "\n", + "![Overview of the true-colour image](https://carpentries-incubator.github.io/geospatial-python/fig/E05/STAC-s2-preview.jpg)\n", + "\n", + "###### Overview of the true-colour image (\"thumbnail\")\n", + "\n", + "\n", + "Remote raster data can be directly opened via the `rioxarray` library. We will\n", + "learn more about this library in the next part of the notebook." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a78e28a3-2c80-41b8-be30-47941de2186b", + "metadata": {}, + "outputs": [], + "source": [ + "import rioxarray\n", + "nir_href = assets[\"nir\"].href\n", + "nir = rioxarray.open_rasterio(nir_href)\n", + "print(nir)\n" + ] + }, + { + "cell_type": "markdown", + "id": "baf3b34f-be9b-48ed-8647-0a592763311b", + "metadata": {}, + "source": [ + "We can then save the data to disk:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "45965a50-8df3-40f1-b3d4-b23906c84fd6", + "metadata": {}, + "outputs": [], + "source": [ + "# save whole image to disk\n", + "# NOTE: This might take a while\n", + "nir.rio.to_raster(\"nir.tif\")" + ] + }, + { + "cell_type": "markdown", + "id": "52993398-3216-4d12-808e-1b357b12f684", + "metadata": {}, + "source": [ + "Since that might take a while, given there are over 10000 x 10000 = a hundred million pixels in the 10 meter NIR band, you can take a smaller subset before downloading it. Becuase the raster is a COG, we can download just what we need!\n", + "\n", + "Here, we specify that we want to download the first (and only) band in the tif file, and a slice of the width and height dimensions.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "aa06ed25-cdcc-4efe-bee0-cbe6f37af9b6", + "metadata": {}, + "outputs": [], + "source": [ + "# save portion of an image to disk\n", + "nir[0,1500:2200,1500:2200].rio.to_raster(\"nir_subset.tif\")" + ] + }, + { + "cell_type": "markdown", + "id": "e5b47a49-3ade-40fe-899e-3b328cf78e0f", + "metadata": {}, + "source": [ + "The difference is 155 Megabytes for the large image vs about 1 Megabyte for the subset.\n", + "\n", + "\n", + "## **Exercise:** Downloading Landsat 8 Assets\n", + "In this exercise we put in practice all the skills we have learned thusfar to retrieve images from a different mission: [Landsat 8](https://www.usgs.gov/landsat-missions/landsat-8). In particular, we browse images from the [Harmonized Landsat Sentinel-2 (HLS) project](https://lpdaac.usgs.gov/products/hlsl30v002/), which provides images from NASA's Landsat 8 and ESA's Sentinel-2 that have been made consistent with each other. The HLS catalog is indexed in the NASA Common Metadata Repository (CMR) and it can be accessed from the STAC API endpoint at the following URL:\n", + "`https://cmr.earthdata.nasa.gov/stac/LPCLOUD`.\n", + "\n", + "1. Using `pystac_client`, search for all assets of the Landsat 8 collection (`HLSL30.v2.0`) from February to March\n", + " 2021, intersecting the point with longitude/latitute coordinates (-73.97, 40.78) deg.\n", + "2. Visualize an item's thumbnail (asset key `browse`).\n", + "\n", + "Note: we don't want to use the cloud cover query filter on this one" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "87816ef7-b896-47ea-a254-9b066434cf4d", + "metadata": {}, + "outputs": [], + "source": [ + "# Try something in here" + ] + }, + { + "cell_type": "markdown", + "id": "6ad7784e-5320-4bc1-a61f-dc714eb6e6d6", + "metadata": {}, + "source": [ + "## Public catalogs, protected data\n", + "\n", + "Publicly accessible catalogs and STAC endpoints do not necessarily imply publicly accessible data. Data providers, in\n", + "fact, may limit data access to specific infrastructures and/or require authentication. For instance, the NASA CMR STAC\n", + "endpoint considered in the last exercise offers publicly accessible metadata for the HLS collection, but most of the\n", + "linked assets are available only for registered users (the thumbnail is publicly accessible).\n", + "\n", + "The authentication procedure for dataset with restricted access might differ depending on the data provider. For the\n", + "NASA CMR, follow these steps in order to access data using Python:\n", + "\n", + "* Create a NASA Earthdata login account [here](https://urs.earthdata.nasa.gov);\n", + "* Set up a netrc file with your credentials, e.g. by using [this script](https://git.earthdata.nasa.gov/projects/LPDUR/repos/daac_data_download_python/browse/EarthdataLoginSetup.py);\n", + "* Define the following environment variables:\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "20eb8446-450a-4769-9e94-df0ff0f20373", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "os.environ[\"GDAL_HTTP_COOKIEFILE\"] = \"./cookies.txt\"\n", + "os.environ[\"GDAL_HTTP_COOKIEJAR\"] = \"./cookies.txt\"" + ] + }, + { + "cell_type": "markdown", + "id": "77abe4c0-fe83-4161-9269-13c9a6591ff4", + "metadata": {}, + "source": [ + "## Key takeaways:\n", + "\n", + "Accessing satellite images via the providers' API enables a more reliable and scalable data retrieval.\n", + "\n", + " - STAC catalogs can be browsed and searched using the same tools and scripts.\n", + " - `rioxarray` allows you to open and download remote raster files.\n", + " \n", + "---\n", + "\n", + "# 2. Read and visualise raster data\n", + "\n", + "Next, we introduce the fundamental principles, packages and metadata/raster attributes for working with raster data in Python. We will also explore how Python handles missing and bad data values.\n", + "\n", + "[`rioxarray`](https://corteva.github.io/rioxarray/stable/) is the Python package we will use throughout the rest of this notebook to work with raster data. It is based on the popular [`rasterio`](https://rasterio.readthedocs.io/en/latest/) package for working with rasters and [`xarray`](https://xarray.pydata.org/en/stable/) for working with multi-dimensional arrays.\n", + "`rioxarray` extends `xarray` by providing top-level functions (e.g. the `open_rasterio` function to open raster datasets) and by adding a set of methods to the main objects of the `xarray` package (the `Dataset` and the `DataArray`). These additional methods are made available via the `rio` accessor and become available from `xarray` objects after importing `rioxarray`.\n", + "\n", + "We will also use the [`pystac`](https://github.com/stac-utils/pystac) package to load rasters from the search results we created in the previous section.\n", + "\n", + "### About Raster Data\n", + "\n", + "Raster data is any pixelated (or gridded) data where each pixel is associated\n", + "with a specific geographic location. The value of a pixel can be\n", + "continuous (e.g. elevation) or categorical (e.g. land use). If this sounds\n", + "familiar, it is because this data structure is very common: it's how\n", + "we represent any digital image. A geospatial raster is only different\n", + "from a digital photo in that it is accompanied by spatial information\n", + "that connects the data to a particular location. This includes the\n", + "raster's extent and cell size, the number of rows and columns, and\n", + "its coordinate reference system (or CRS).\n", + "\n", + "![raster-concept](https://carpentries-incubator.github.io/geospatial-python/fig/E01/raster_concept.png)\n", + "###### Raster Concept (Source: National Ecological Observatory Network (NEON))\n", + "\n", + "Some examples of continuous rasters include:\n", + "\n", + "1. Precipitation maps.\n", + "2. Maps of tree height derived from LiDAR data.\n", + "3. Elevation values for a region.\n", + "\n", + "A map of elevation for Harvard Forest derived from the [NEON AOP LiDAR sensor](https://www.neonscience.org/data-collection/airborne-remote-sensing)\n", + "is below. Elevation is represented as a continuous numeric variable in this map. The legend\n", + "shows the continuous range of values in the data from around 300 to 420 meters.\n", + "\n", + "![elevation plot](https://carpentries-incubator.github.io/geospatial-python/fig/E01/continuous-elevation-HARV-plot-01.png)\n", + "###### Continuous Elevation Map: HARV Field Site\n", + "\n", + "For more information and further examples of raster data you can visit the [relevant lesson](01-intro-raster-data.md) in the software carpentry course this notebook is based off. \n", + "\n", + "\n", + "## Load a Raster and View Attributes\n", + "In the previous episode, we searched for Sentinel-2 images, and then saved the search results to a file: `search.json`. This contains the information on where and how to access the target images from a remote repository. We can use the function `pystac.ItemCollection.from_file()` to load the search results as an `Item` list.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "727637ca-374f-47cf-884d-d2b2766742c1", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "import pystac\n", + "items = pystac.ItemCollection.from_file(\"search.json\")\n", + "items" + ] + }, + { + "cell_type": "markdown", + "id": "94c70f55-7825-4083-af2d-c943b76f9cc2", + "metadata": {}, + "source": [ + "In the search results, we have 6 `Item` type objects, corresponding to several Sentinel-2 scenes from March 21th and 28th in 2020. We will focus on the scene `S2A_31UFU_20200328_0_L2A`, and load band `nir09` (central wavelength 945 nm). We can load this band using the function `rioxarray.open_rasterio()`, via the Hypertext Reference `href` (commonly referred to as a URL):\n", + "\n", + "## **Exercise:** finding the right item and asset\n", + "How do we go about selecting the correct item and asset from our ItemCollection we just loaded?\n", + "1. Find the item corresponding to scene S2A_31UFU_20200328_0_L2A\n", + "2. Find the asset `href` for the `nir09` band in the item's asset dictionary.\n", + "3. Load it using rioxarray's `open_rasterio` method into a variable called `raster_ams_b9`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "720cdf4b-6472-483c-9e48-2e7e6817392f", + "metadata": {}, + "outputs": [], + "source": [ + "# Try something in here\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "91e2911c-fa5d-473c-8d72-3430607da2c0", + "metadata": {}, + "source": [ + "By calling the variable name in the jupyter notebook we can get a quick look at the shape and attributes of the data." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "eedd0705-06ee-4490-9aa0-9677e495a390", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "raster_ams_b9" + ] + }, + { + "cell_type": "markdown", + "id": "9ba8bbc2-b368-4429-8bd9-32088e3b7e9a", + "metadata": {}, + "source": [ + "The first call to `rioxarray.open_rasterio()` opens the file from remote or local storage, and then returns a `xarray.DataArray` object. The object is stored in a variable, i.e. `raster_ams_b9`. Reading in the data with `xarray` instead of `rioxarray` also returns a `xarray.DataArray`, but the output will not contain the geospatial metadata (such as projection information). You can use numpy functions or built-in Python math operators on a `xarray.DataArray` just like a numpy array. Calling the variable name of the `DataArray` also prints out all of its metadata information.\n", + "\n", + "The output tells us that we are looking at an `xarray.DataArray`, with `1` band, `1830` rows, and `1830` columns. We can also see the number of pixel values in the `DataArray`, and the type of those pixel values, which is unsigned integer (or `uint16`). The `DataArray` also stores different values for the coordinates of the `DataArray`. When using `rioxarray`, the term coordinates refers to spatial coordinates like `x` and `y` but also the `band` coordinate. Each of these sequences of values has its own data type, like `float64` for the spatial coordinates and `int64` for the `band` coordinate.\n", + "\n", + "This `DataArray` object also has a couple of attributes that are accessed like `.rio.crs`, `.rio.nodata`, and `.rio.bounds()`, which contain the metadata for the file we opened. Note that many of the metadata are accessed as attributes without `()`, but `bounds()` is a method (i.e. a function in an object) and needs parentheses.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "28143c46-f7dd-464b-94b5-431cf7cc81d3", + "metadata": {}, + "outputs": [], + "source": [ + "print(raster_ams_b9.rio.crs)\n", + "print(raster_ams_b9.rio.nodata)\n", + "print(raster_ams_b9.rio.bounds())\n", + "print(raster_ams_b9.rio.width)\n", + "print(raster_ams_b9.rio.height)" + ] + }, + { + "cell_type": "markdown", + "id": "93f6f2eb-ccfd-4512-a2a1-4ca420946b75", + "metadata": {}, + "source": [ + "The Coordinate Reference System, or `raster_ams_b9.rio.crs`, is reported as the string `EPSG:32631`. The `nodata` value is encoded as 0 and the bounding box corners of our raster are represented by the output of `.bounds()` as a `tuple` (like a list but you can't edit it). The height and width match what we saw when we printed the `DataArray`, but by using `.rio.width` and `.rio.height` we can access these values if we need them in calculations.\n", + "\n", + "We will be exploring this data throughout this episode. By the end of this episode, you will be able to understand and explain the metadata output.\n", + "\n", + "\n", + "*TIP: To improve code readability, file and object names should be used that make it clear what is in the file. The data for this episode covers Amsterdam, and is from Band 9, so we'll use a naming convention of `raster_ams_b9` for the variable name.*\n", + "\n", + "\n", + "## Visualize a Raster\n", + "\n", + "After viewing the attributes of our raster, we can examine the raw values of the array with `.values`:\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "7a8ac587-b7ab-4e59-9262-702790888e43", + "metadata": {}, + "outputs": [], + "source": [ + "raster_ams_b9.values" + ] + }, + { + "cell_type": "markdown", + "id": "2bb2fe6f-ea90-43d8-ba98-709860e2caed", + "metadata": {}, + "source": [ + "This can give us a quick view of the values of our array, but only at the corners. Since our raster is loaded in python as a `DataArray` type, we can plot this in one line similar to a pandas `DataFrame` with `DataArray.plot()`.\n", + "\n", + "__Exercise: plot our raster file using the plot() method__" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "959a6f86-4db2-4d14-bd10-f870d9796160", + "metadata": {}, + "outputs": [], + "source": [ + "raster_ams_b9..." + ] + }, + { + "cell_type": "markdown", + "id": "b6fae531-5581-49be-b40f-7264ce5f1844", + "metadata": {}, + "source": [ + "Notice that `rioxarray` helpfully allows us to plot this raster with spatial coordinates on the x and y axis (this is not the default in many cases with other functions or libraries).\n", + "\n", + "This plot shows the satellite measurement of the spectral band `nir09` for an area that covers part of the Netherlands. According to the [Sentinel-2 documentaion](https://sentinels.copernicus.eu/web/sentinel/technical-guides/sentinel-2-msi/msi-instrument), this is a band with the central wavelength of 945nm, which is sensitive to water vapor. It has a spatial resolution of 60m. Note that the `band=1` in the image title refers to the ordering of all the bands in the `DataArray`, not the Sentinel-2 band number `09` that we saw in the pystac search results.\n", + "\n", + "With a quick view of the image, we notice that half of the image is blank, no data is captured. We also see that the cloudy pixels at the top have high reflectance values, while the contrast of everything else is quite low. This is expected because this band is sensitive to the water vapor. However if one would like to have a better color contrast, one can add the option `robust=True`, which displays values between the 2nd and 98th percentile:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "ca2f2b9d-ed77-4325-8771-90cdcae636d6", + "metadata": {}, + "outputs": [], + "source": [ + "raster_ams_b9.plot(robust=True)" + ] + }, + { + "cell_type": "markdown", + "id": "ad3e3a1b-651d-4400-9be3-f246178bbf8e", + "metadata": {}, + "source": [ + "Now the color limit is set in a way fitting most of the values in the image. We have a better view of the ground pixels.\n", + "\n", + "---\n", + "\n", + "*NOTE: The option `robust=True` always forces displaying values between the 2nd and 98th percentile. Of course, this will not work for every case. For a customized displaying range, you can also manually specifying the keywords `vmin` and `vmax`. For example ploting between `100` and `7000`:*\n", + "\n", + "__Exercise: plot the raster with vmin and vmax arguments__" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a93139e6-8380-4b36-a45d-e6d8d4da5531", + "metadata": {}, + "outputs": [], + "source": [ + "raster_ams_b9 ..." + ] + }, + { + "cell_type": "markdown", + "id": "211a5bc3-749a-467e-a787-79dac2562851", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## View Raster Coordinate Reference System (CRS) in Python\n", + "Another information that we're interested in is the CRS, and it can be accessed with `.rio.crs`. To find out more about CRS look at [the earlier\n", + "episode](https://carpentries-incubator.github.io/geospatial-python/instructor/03-crs.html) in the software carpentry course.\n", + "Now we will see how features of the CRS appear in our data file and what\n", + "meanings they have. We can view the CRS string associated with our DataArray's `rio` object using the `crs`\n", + "attribute.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a2024c3e-ceb2-4b7b-9420-25e727f2047e", + "metadata": {}, + "outputs": [], + "source": [ + "print(raster_ams_b9.rio.crs)\n" + ] + }, + { + "cell_type": "markdown", + "id": "a06dc157-4d79-4e58-bdea-27c63c3c0ee8", + "metadata": {}, + "source": [ + "To print the EPSG code number as an `int`, we use the `.to_epsg()` method:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "86c0e9eb-5587-4e00-8ec2-09eb09d3248d", + "metadata": {}, + "outputs": [], + "source": [ + "raster_ams_b9.rio.crs.to_epsg()" + ] + }, + { + "cell_type": "markdown", + "id": "7d65ca6a-2d92-4aee-95ab-2e2db3f4762a", + "metadata": {}, + "source": [ + "EPSG codes are great for succinctly representing a particular coordinate reference system. But what if we want to see more details about the CRS, like the units? For that, we can use `pyproj`, a library for representing and working with coordinate reference systems." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5803b0ea-3ae6-41dc-95a4-da0d3231d604", + "metadata": {}, + "outputs": [], + "source": [ + "from pyproj import CRS\n", + "epsg = raster_ams_b9.rio.crs.to_epsg()\n", + "crs = CRS(epsg)\n", + "crs" + ] + }, + { + "cell_type": "markdown", + "id": "30c00eb9-a206-43ea-a467-59b9ef161b2d", + "metadata": {}, + "source": [ + "The `CRS` class from the `pyproj` library allows us to create a `CRS` object with methods and attributes for accessing specific information about a CRS, or the detailed summary shown above.\n", + "\n", + "A particularly useful attribute is `area_of_use`, which shows the geographic bounds that the CRS is intended to be used.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a5caeb4d-cfde-4726-b265-f316cef2896a", + "metadata": {}, + "outputs": [], + "source": [ + "crs.area_of_use" + ] + }, + { + "cell_type": "markdown", + "id": "396b9eac-2847-4073-9233-0746d578eb37", + "metadata": {}, + "source": [ + "## **Exercise**: find the axes units of the CRS\n", + "What units are our data in? See if you can find a method to examine this information using `help(crs)` or `dir(crs)`" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "5d1f760a-6fd9-496e-bfdc-531e7f6a1f95", + "metadata": {}, + "outputs": [], + "source": [ + "# Try something in here\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "f573adc5-b5de-4f8a-9441-d123722446d0", + "metadata": {}, + "source": [ + "Let's break down the pieces of the `pyproj` CRS summary. The string contains all of the individual CRS elements that Python or another GIS might need, separated into distinct sections, and datum." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "108a05cf-5faa-41fd-8451-03922edfe641", + "metadata": {}, + "outputs": [], + "source": [ + "crs" + ] + }, + { + "cell_type": "markdown", + "id": "b1adeed3-3871-4240-8e1f-468dc952ffce", + "metadata": {}, + "source": [ + "* **Name** of the projection is UTM zone 31N (UTM has 60 zones, each 6-degrees of longitude in width). The underlying datum is WGS84.\n", + "* **Axis Info**: the CRS shows a Cartesian system with two axes, easting and northing, in meter units.\n", + "* **Area of Use**: the projection is used for a particular range of longitudes `0°E to 6°E` in the northern hemisphere (`0.0°N to 84.0°N`)\n", + "* **Coordinate Operation**: the operation to project the coordinates (if it is projected) onto a cartesian (x, y) plane. Transverse Mercator is accurate for areas with longitudinal widths of a few degrees, hence the distinct UTM zones.\n", + "* **Datum**: Details about the datum, or the reference point for coordinates. `WGS 84` and `NAD 1983` are common datums. `NAD 1983` is [set to be replaced in 2022](https://en.wikipedia.org/wiki/Datum_of_2022).\n", + "\n", + "Note that the zone is unique to the UTM projection. Not all CRSs will have a\n", + "zone. Below is a simplified view of US UTM zones.\n", + "\n", + "![UTMZones](https://upload.wikimedia.org/wikipedia/commons/thumb/8/8d/Utm-zones-USA.svg/1920px-Utm-zones-USA.svg.png)\n", + "###### The UTM zones across the continental United States (Chrismurf at English Wikipedia, via [Wikimedia Commons](https://en.wikipedia.org/wiki/Universal_Transverse_Mercator_coordinate_system#/media/File:Utm-zones-USA.svg) (CC-BY))\n", + "\n", + "## Calculate Raster Statistics\n", + "\n", + "It is useful to know the minimum or maximum values of a raster dataset. __Exercise: compute these and other descriptive statistics with `min`, `max`, `mean`, and `std`.__\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c46bd232-2850-4cca-963d-5cf571d109d1", + "metadata": {}, + "outputs": [], + "source": [ + "print(raster_ams_b9...)\n", + "print(raster_ams_b9...)\n", + "print(raster_ams_b9...)\n", + "print(raster_ams_b9...)" + ] + }, + { + "cell_type": "markdown", + "id": "13dd8ed9-18a3-4466-b519-64546c66b7c9", + "metadata": {}, + "source": [ + "The information above includes a report of the min, max, mean, and standard deviation values, along with the data type. If we want to see specific quantiles, we can use xarray's `.quantile()` method. For example for the 25% and 75% quantiles:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3bd4db6c-374b-48d1-a1c8-db4f7472fb33", + "metadata": {}, + "outputs": [], + "source": [ + "print(raster_ams_b9.quantile([0.25, 0.75]))" + ] + }, + { + "cell_type": "markdown", + "id": "05138d80-9b26-4b92-8749-df2d69b1473b", + "metadata": {}, + "source": [ + "---\n", + "*NOTE: You could also get each of these values one by one using `numpy`.*\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "fe927ff4-6467-4488-a671-05e59a884d12", + "metadata": {}, + "outputs": [], + "source": [ + "import numpy\n", + "print(numpy.percentile(raster_ams_b9, 25))\n", + "print(numpy.percentile(raster_ams_b9, 75))" + ] + }, + { + "cell_type": "markdown", + "id": "d64fa87d-3e80-4fb7-b973-a6595d37b8f9", + "metadata": {}, + "source": [ + "You may notice that `raster_ams_b9.quantile` and `numpy.percentile` didn't require an argument specifying the axis or dimension along which to compute the quantile. This is because `axis=None` is the default for most numpy functions, and therefore `dim=None` is the default for most xarray methods. It's always good to check out the docs on a function to see what the default arguments are, particularly when working with multi-dimensional image data. To do so, we can use`help(raster_ams_b9.quantile)` (or `?raster_ams_b9.percentile` in jupyter notebook), e.g.\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "25334bd8-d927-4df3-987a-b2f0fc2fd443", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "?raster_ams_b9.quantile" + ] + }, + { + "cell_type": "markdown", + "id": "6f8a771b-b8c4-4096-b887-f23bf5fb7b15", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## Dealing with Missing Data\n", + "So far, we have visualized a band of a Sentinel-2 scene and calculated its statistics. However, we need to take missing data into account. Raster data often has a \"no data value\" associated with it and for raster datasets read in by `rioxarray`. This value is referred to as `nodata`. This is a value assigned to pixels where data is missing or no data were collected. There can be different cases that cause missing data, and it's common for other values in a raster to represent different cases. The most common example is missing data at the edges of rasters.\n", + "\n", + "By default the shape of a raster is always rectangular. So if we have a dataset that has a shape that isn't rectangular, some pixels at the edge of the raster will have no data values. This often happens when the data were collected by a sensor which only flew over some part of a defined region.\n", + "\n", + "As we have seen above, the `nodata` value of this dataset (`raster_ams_b9.rio.nodata`) is 0. When we have plotted the band data, or calculated statistics, the missing value was not distinguished from other values. Missing data may cause some unexpected results. For example, the 25th percentile we just calculated was 0, probably reflecting the presence of a lot of missing data in the raster.\n", + "\n", + "To distinguish missing data from real data, one possible way is to use `nan` to represent them. This can be done by specifying `masked=True` when loading the raster:\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3aefc091-a57f-48c3-aba0-aa415a516495", + "metadata": {}, + "outputs": [], + "source": [ + "raster_ams_b9 = rioxarray.open_rasterio(items[0].assets[\"nir09\"].href, masked=True)" + ] + }, + { + "cell_type": "markdown", + "id": "e1e9160d-db4c-4c49-89c8-1d5181bfe723", + "metadata": {}, + "source": [ + "One can also use the `where` function to select all the pixels which are different from the `nodata` value of the raster:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9704ddf0-8692-496d-82f5-d0dd53f0b069", + "metadata": {}, + "outputs": [], + "source": [ + "raster_ams_b9.where(raster_ams_b9!=raster_ams_b9.rio.nodata)" + ] + }, + { + "cell_type": "markdown", + "id": "caf54238-6a6c-49f4-9813-918c33a13f93", + "metadata": {}, + "source": [ + "Either way will change the `nodata` value from 0 to `nan`. Now if we compute the statistics again the missing data will not be considered.\n", + "\n", + "__Exercise: Compute the statistics (`min`, `max`, `mean`, `std`) again:__" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "08403853-b38c-49e8-b5f1-06ff6d885cdf", + "metadata": {}, + "outputs": [], + "source": [ + "print(raster_ams_b9...)\n", + "print(raster_ams_b9...)\n", + "print(raster_ams_b9...)\n", + "print(raster_ams_b9...)" + ] + }, + { + "cell_type": "markdown", + "id": "97bd2c01-819b-4e52-8e70-f0ec67a1f8a8", + "metadata": {}, + "source": [ + "And if we plot the image, the `nodata` pixels are not shown because they are not 0 anymore. \n", + "\n", + "__Exercise: plot the masked image with `robust` set to true__" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a827b027-9b97-4c4b-808d-0e87a9fa6b18", + "metadata": {}, + "outputs": [], + "source": [ + "raster_ams_b9..." + ] + }, + { + "cell_type": "markdown", + "id": "d3fd6acc-b9cf-4a87-abcb-86d0e1af6d39", + "metadata": {}, + "source": [ + "One should notice that there is a side effect of using `nan` instead of `0` to represent the missing data: the data type of the `DataArray` was changed from integers to float. This need to be taken into consideration when the data type matters in your application.\n", + "\n", + "## Raster Bands\n", + "So far we looked into a single band raster, i.e. the `nir09` band of a Sentinel-2 scene. However, to get a smaller, non georeferenced version of the scene, one may also want to visualize the true-color overview of the region. This is provided as a multi-band raster -- a raster dataset that contains more than one band.\n", + "\n", + "![Sketch of a multi-band raster image](https://carpentries-incubator.github.io/geospatial-python/fig/E06/single_multi_raster.png)\n", + "###### Sketch of a multi-band raster image\n", + "\n", + "The `overview` asset in the Sentinel-2 scene is a multiband asset. Similar to `nir09`, we can load it by:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "a63e401b-e438-4291-94ab-c45aa378732a", + "metadata": {}, + "outputs": [], + "source": [ + "raster_ams_overview = rioxarray.open_rasterio(items[0].assets['visual'].href, overview_level=3)\n", + "raster_ams_overview\n" + ] + }, + { + "cell_type": "markdown", + "id": "32ac1d52-a434-46a2-bbbb-5869e554a851", + "metadata": {}, + "source": [ + "The band number comes first when GeoTiffs are read with the `.open_rasterio()` function. As we can see in the `xarray.DataArray` object, the shape is now `(band: 3, y: 687, x: 687)`, with three bands in the `band` dimension. It's always a good idea to examine the shape of the raster array you are working with and make sure it's what you expect. Many functions, especially the ones that plot images, expect a raster array to have a particular shape. One can also check the shape using the `.shape` attribute:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e13291ec-3701-4bbd-96cf-b33c8b76f165", + "metadata": {}, + "outputs": [], + "source": [ + "raster_ams_overview.shape" + ] + }, + { + "cell_type": "markdown", + "id": "c5097c1d-f1ef-45da-9ac6-c2b84e408c99", + "metadata": {}, + "source": [ + "One can visualize the multi-band data with the `DataArray.plot.imshow()` function:" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "91c685c2-4375-4822-9829-bc6ae6a01cf1", + "metadata": {}, + "outputs": [], + "source": [ + "raster_ams_overview.plot.imshow()" + ] + }, + { + "cell_type": "markdown", + "id": "37ea12ba-fd14-4f5d-bf65-915334df90a9", + "metadata": {}, + "source": [ + "Note that the `DataArray.plot.imshow()` function makes assumptions about the shape of the input DataArray, that since it has three channels, the correct colormap for these channels is RGB. It does not work directly on image arrays with more than 3 channels. One can replace one of the RGB channels with another band, to make a false-color image.\n", + "\n", + "## **Exercise**: set the plotting aspect ratio\n", + "As seen in the figure above, the true-color image is stretched. Visualize it with the right aspect ratio. You can use the [documentation](https://xarray.pydata.org/en/stable/generated/xarray.DataArray.plot.imshow.html) of `DataArray.plot.imshow()`." + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "26d2ac75-1bd5-4594-8b97-538742660295", + "metadata": {}, + "outputs": [], + "source": [ + "# Try something in here\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "b15a23d3-8fd3-40e5-9846-a5ba9f4c7175", + "metadata": {}, + "source": [ + "## Key takeaways:\n", + "- `rioxarray` and `xarray` are for working with multidimensional arrays like pandas is for working with tabular data.\n", + "- `rioxarray` stores CRS information as a CRS object that can be converted to an EPSG code or PROJ4 string.\n", + "- Missing raster data are filled with nodata values, which should be handled with care for statistics and visualization." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 + Jaspy", + "language": "python", + "name": "jaspy" + }, + 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}, + "source": [ + "# Exercise 1.5: Label-based indexing" + ] + }, + { + "cell_type": "markdown", + "id": "c1f847cc-4003-42a1-b484-7f7fc283b4c5", + "metadata": {}, + "source": [ + "## Aim: Learn how to index data arrays" + ] + }, + { + "cell_type": "markdown", + "id": "6374f110-5ea1-4959-b226-4eeb18cf4899", + "metadata": {}, + "source": [ + "Find the teaching resources here: https://tutorial.xarray.dev/fundamentals/02.1_indexing_Basic.html." + ] + }, + { + "cell_type": "markdown", + "id": "e0cc034f-61e8-430f-b5ea-511594ff8d42", + "metadata": {}, + "source": [ + "### Issues Covered: \n", + "- Indexing, using `.loc()`, `.isel()` and `.sel()`" + ] + }, + { + "cell_type": "markdown", + "id": "a33d279d-ad68-4790-8c97-f32cf1faa019", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q1. Open the `'../data/tas_rcp45_2055_mon_avg_change.nc'` dataset and load it into an xarray `Dataset` called `ds`.\n", + "(Hint: Don't forget to import any packages you need).\n", + "This file is a model run for HadCM3 run as part of the RAPID study: https://catalogue.ceda.ac.uk/uuid/6bbab8394124b252f8b1b036f9eb6b6b/" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "778346e3-ed07-408f-83e7-3b12632761e5", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:25.099383Z", + "iopub.status.busy": "2024-11-08T14:55:25.099110Z", + "iopub.status.idle": "2024-11-08T14:55:33.492337Z", + "shell.execute_reply": "2024-11-08T14:55:33.491253Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [ + "import xarray as xr\n", + "ds = xr.open_dataset('../data/xbhubo.pgc0apr.nc')\n", + "temperature = ds[\"temp\"]" + ] + }, + { + "cell_type": "markdown", + "id": "0a21a6b9-ebae-47fa-940b-3b7f92d3ad2b", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q2. Select a subset of the `temperature` array using label-based indexing to get data at the position [0,0,0]." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "d0818a4f-7255-4b90-9cb5-c8e1810cd7e3", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:33.495959Z", + "iopub.status.busy": "2024-11-08T14:55:33.495440Z", + "iopub.status.idle": "2024-11-08T14:55:33.510961Z", + "shell.execute_reply": "2024-11-08T14:55:33.510269Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Size: 4B\n", + "[1 values with dtype=float32]\n", + "Coordinates:\n", + " longitude float32 4B 0.0\n", + " latitude float32 4B -50.62\n", + " depth float32 4B 5.0\n", + " * t (t) object 8B 1920-04-16 00:00:00\n", + "Attributes:\n", + " source: Unified Model Output:\n", + " name: temp\n", + " title: POTENTIAL TEMPERATURE (OCEAN) DEG.C\n", + " date: 01/12/99\n", + " time: 00:00\n", + " long_name: POTENTIAL TEMPERATURE (OCEAN) DEG.C\n", + " units: degC\n", + " valid_min: -1.7999878\n", + " valid_max: 35.0495" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "temperature.isel(depth=0, latitude=31, longitude=0)" + ] + }, + { + "cell_type": "markdown", + "id": "422eddac-26f7-4c9b-b6ad-4e7386117b25", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q5. The previous method is still referring to a selection by integer position. Use `.sel` to give a labelled index with the named dimension to find the data at `time=2065-12-30`, `lat=-78.5`, `lon=11.0`." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "09dac653-01d8-4e60-9444-42ef7743cf99", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:33.538580Z", + "iopub.status.busy": "2024-11-08T14:55:33.538287Z", + "iopub.status.idle": "2024-11-08T14:55:33.550492Z", + "shell.execute_reply": "2024-11-08T14:55:33.549854Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.DataArray 'temp' (t: 1)> Size: 4B\n",
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+       "  * t          (t) object 8B 1920-04-16 00:00:00\n",
+       "Attributes:\n",
+       "    source:     Unified Model Output:\n",
+       "    name:       temp\n",
+       "    title:      POTENTIAL TEMPERATURE (OCEAN)  DEG.C\n",
+       "    date:       01/12/99\n",
+       "    time:       00:00\n",
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" + ], + "text/plain": [ + " Size: 4B\n", + "[1 values with dtype=float32]\n", + "Coordinates:\n", + " longitude float32 4B 0.0\n", + " latitude float32 4B -50.62\n", + " depth float32 4B 5.0\n", + " * t (t) object 8B 1920-04-16 00:00:00\n", + "Attributes:\n", + " source: Unified Model Output:\n", + " name: temp\n", + " title: POTENTIAL TEMPERATURE (OCEAN) DEG.C\n", + " date: 01/12/99\n", + " time: 00:00\n", + " long_name: POTENTIAL TEMPERATURE (OCEAN) DEG.C\n", + " units: degC\n", + " valid_min: -1.7999878\n", + " valid_max: 35.0495" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "temperature.sel(depth=5, latitude=-50.625, longitude=0)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 + Jaspy", + "language": "python", + "name": "jaspy" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/python-data/solutions/ex01_xr_intro.ipynb b/python-data/solutions/ex01_xr_intro.ipynb new file mode 100644 index 0000000..bafc814 --- /dev/null +++ b/python-data/solutions/ex01_xr_intro.ipynb @@ -0,0 +1,1367 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "2713343a-c0f0-4da3-979f-bb0d9f8a5e4a", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "# Exercise 1: Introduction to xarray\n" + ] + }, + { + "cell_type": "markdown", + "id": "bbe08d6c-c85d-4798-9343-b3e958cf39d4", + "metadata": {}, + "source": [ + "## Aim: Learn about what xarray is and how to create and look at a `DataArray`." + ] + }, + { + "cell_type": "markdown", + "id": "c6bbe825-2d51-4e8a-bc9d-8ca75ba0d2c5", + "metadata": {}, + "source": [ + "Find the teaching resources here: https://tutorial.xarray.dev/fundamentals/01_data_structures.html and https://tutorial.xarray.dev/fundamentals/01_datastructures.html." + ] + }, + { + "cell_type": "markdown", + "id": "f98aa85b-8e65-4894-9b61-13acdb9938dc", + "metadata": {}, + "source": [ + "### Issues Covered:\n", + "- Importing `xarray`\n", + "- Loading a dataset using `xr.open_dataset()`\n", + "- Creating a `DataArray`" + ] + }, + { + "cell_type": "markdown", + "id": "adb09498-99fc-4174-8661-4b29858975d3", + "metadata": {}, + "source": [ + "## 1. Introduction to multidimensional arrays" + ] + }, + { + "cell_type": "markdown", + "id": "308be498-f527-4886-b5a8-474e43e7a1f9", + "metadata": {}, + "source": [ + "- Unlabelled N dimensional arrays of numbers are the most widely used data structure in scientific computing\n", + "- These arrays lack meaningful metadata so users must track indices in an arbitrary fashion" + ] + }, + { + "cell_type": "markdown", + "id": "3d4d8868-a7d4-4651-a07a-ec10104f34b2", + "metadata": {}, + "source": [ + "" + ] + }, + { + "cell_type": "markdown", + "id": "adabc378-941e-4520-a787-b02a563c6956", + "metadata": {}, + "source": [ + "Q1. Can you think of any reasons why xarray might be preferred to pandas when working with multi-dimensional data like climate models?\n", + "(Hint: how many dimensions does a pandas dataframe have?)" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "14157c3e-af21-444e-9e8d-5a78eeb9edad", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:53:54.796581Z", + "iopub.status.busy": "2024-11-08T14:53:54.796266Z", + "iopub.status.idle": "2024-11-08T14:53:54.799454Z", + "shell.execute_reply": "2024-11-08T14:53:54.798949Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [ + "# xarray is designed to handle data with multiple dimensions\n", + "# pandas is defined for 1D (series) and 2D (dataframe) structures.\n", + "# xarray allows you to work with labelled dimensions and coorfinates.\n", + "# pandas only offers labels through 'MultiIndex'\n", + "# xarray is built on netcdf model and understands CF conventions\n", + "# panfas doesn't natively support netcdf or cf conventions\n", + "# xarray supports metadata attached to datasets\n", + "# pandas metadata support is minimal" + ] + }, + { + "cell_type": "markdown", + "id": "4352c46a-c179-439e-b1fe-3953dc8ee41c", + "metadata": {}, + "source": [ + "## 2. Opening and Exploring Datasets" + ] + }, + { + "cell_type": "markdown", + "id": "48d19019-2546-46d3-9da1-64d8e7c363e8", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q2. Open the `'../data/tas_rcp45_2055_mon_avg_change.nc'` dataset and load it into an xarray `Dataset` called `ds`.\n", + "(Hint: Don't forget to import any packages you need).\n", + "This file is a model run for HadCM3 run as part of the RAPID study: https://catalogue.ceda.ac.uk/uuid/6bbab8394124b252f8b1b036f9eb6b6b/" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "397feb59-dd4a-42bf-bf95-f093d75d28b3", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:53:54.801981Z", + "iopub.status.busy": "2024-11-08T14:53:54.801656Z", + "iopub.status.idle": "2024-11-08T14:54:03.266840Z", + "shell.execute_reply": "2024-11-08T14:54:03.266140Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [ + "import xarray as xr\n", + "ds = xr.open_dataset('../data/xbhubo.pgc0apr.nc')" + ] + }, + { + "cell_type": "markdown", + "id": "9c34363a-9168-4478-885a-7bd2ec669f3a", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q3. Look at the parameters of the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "1257eab9-ded3-4e4b-b703-0b39236f5d23", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:03.270257Z", + "iopub.status.busy": "2024-11-08T14:54:03.269693Z", + "iopub.status.idle": "2024-11-08T14:54:03.295793Z", + "shell.execute_reply": "2024-11-08T14:54:03.295117Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.Dataset> Size: 13MB\n",
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+       "  * latitude_1   (latitude_1) float32 572B -88.75 -87.5 -86.25 ... 87.5 88.75\n",
+       "Data variables:\n",
+       "    temp         (t, depth, latitude, longitude) float32 3MB ...\n",
+       "    salinity     (t, depth, latitude, longitude) float32 3MB ...\n",
+       "    ucurr        (t, depth, latitude_1, longitude_1) float32 3MB ...\n",
+       "    vcurr        (t, depth, latitude_1, longitude_1) float32 3MB ...\n",
+       "Attributes:\n",
+       "    history:      Tue Sep 12 11:49:35 BST 2006 - CONVSH V1.91 16-February-2006\n",
+       "    Conventions:  CF-1.0
" + ], + "text/plain": [ + " Size: 13MB\n", + "Dimensions: (longitude: 288, latitude: 144, depth: 20, t: 1,\n", + " longitude_1: 288, latitude_1: 143)\n", + "Coordinates:\n", + " * longitude (longitude) float32 1kB 0.0 1.25 2.5 3.75 ... 356.2 357.5 358.8\n", + " * latitude (latitude) float32 576B -89.38 -88.12 -86.88 ... 88.12 89.38\n", + " * depth (depth) float32 80B 5.0 15.0 25.0 ... 4.577e+03 5.192e+03\n", + " * t (t) object 8B 1920-04-16 00:00:00\n", + " * longitude_1 (longitude_1) float32 1kB 0.625 1.875 3.125 ... 358.1 359.4\n", + " * latitude_1 (latitude_1) float32 572B -88.75 -87.5 -86.25 ... 87.5 88.75\n", + "Data variables:\n", + " temp (t, depth, latitude, longitude) float32 3MB ...\n", + " salinity (t, depth, latitude, longitude) float32 3MB ...\n", + " ucurr (t, depth, latitude_1, longitude_1) float32 3MB ...\n", + " vcurr (t, depth, latitude_1, longitude_1) float32 3MB ...\n", + "Attributes:\n", + " history: Tue Sep 12 11:49:35 BST 2006 - CONVSH V1.91 16-February-2006\n", + " Conventions: CF-1.0" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds" + ] + }, + { + "cell_type": "markdown", + "id": "3cee6429-dbf8-4a10-b384-ad9de719a0d0", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q4. What are the dimensions and variables in this dataset? What does each represent? " + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "67ff7eb4-4058-4041-beb7-66e294542887", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:03.298211Z", + "iopub.status.busy": "2024-11-08T14:54:03.297936Z", + "iopub.status.idle": "2024-11-08T14:54:03.300747Z", + "shell.execute_reply": "2024-11-08T14:54:03.300254Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [ + "# There are four data variables, temp, salinity, ucurr and vcurr.\n", + "# t (time) and depth are dimensions used by all of these variables.\n", + "# temp and salinity have a a different grid to ucurr and vcurr. This is why there is longitude and longitude_1, which are dimensions which refer to differnt variables." + ] + }, + { + "cell_type": "markdown", + "id": "81207a17-eeaf-4de3-a3f6-9a0fd782c252", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q5. Find the name of the temperature data variable, and use it to extract a `DataArray` called `temperature`." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "3d0218b3-df73-4037-9fb9-eea80e1a70d2", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:03.303199Z", + "iopub.status.busy": "2024-11-08T14:54:03.302875Z", + "iopub.status.idle": "2024-11-08T14:54:03.362497Z", + "shell.execute_reply": "2024-11-08T14:54:03.361816Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [ + "# temp = sea surface temperature.\n", + "temperature = ds[\"temp\"]" + ] + }, + { + "cell_type": "markdown", + "id": "6ee4a984-305e-44da-87ab-75cf88d71f22", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q6. Take a look at the `temperature` data array and inspect its dimensions, coordinates and attributes. What are the specific dimensions and coordinates associated with it? What metadata (attributes) is provided?" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "cc8a03b5-d7ce-406b-9602-47a153d2d7ec", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:03.365566Z", + "iopub.status.busy": "2024-11-08T14:54:03.365299Z", + "iopub.status.idle": "2024-11-08T14:54:03.393288Z", + "shell.execute_reply": "2024-11-08T14:54:03.392731Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.DataArray 'temp' (t: 1, depth: 20, latitude: 144, longitude: 288)> Size: 3MB\n",
+       "[829440 values with dtype=float32]\n",
+       "Coordinates:\n",
+       "  * longitude  (longitude) float32 1kB 0.0 1.25 2.5 3.75 ... 356.2 357.5 358.8\n",
+       "  * latitude   (latitude) float32 576B -89.38 -88.12 -86.88 ... 88.12 89.38\n",
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+       "Attributes:\n",
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+       "    name:       temp\n",
+       "    title:      POTENTIAL TEMPERATURE (OCEAN)  DEG.C\n",
+       "    date:       01/12/99\n",
+       "    time:       00:00\n",
+       "    long_name:  POTENTIAL TEMPERATURE (OCEAN)  DEG.C\n",
+       "    units:      degC\n",
+       "    valid_min:  -1.7999878\n",
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" + ], + "text/plain": [ + " Size: 3MB\n", + "[829440 values with dtype=float32]\n", + "Coordinates:\n", + " * longitude (longitude) float32 1kB 0.0 1.25 2.5 3.75 ... 356.2 357.5 358.8\n", + " * latitude (latitude) float32 576B -89.38 -88.12 -86.88 ... 88.12 89.38\n", + " * depth (depth) float32 80B 5.0 15.0 25.0 ... 4.577e+03 5.192e+03\n", + " * t (t) object 8B 1920-04-16 00:00:00\n", + "Attributes:\n", + " source: Unified Model Output:\n", + " name: temp\n", + " title: POTENTIAL TEMPERATURE (OCEAN) DEG.C\n", + " date: 01/12/99\n", + " time: 00:00\n", + " long_name: POTENTIAL TEMPERATURE (OCEAN) DEG.C\n", + " units: degC\n", + " valid_min: -1.7999878\n", + " valid_max: 35.0495" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# It is a 3D array with dimensions (time, lat, lon)\n", + "temperature" + ] + }, + { + "cell_type": "markdown", + "id": "e36ca3a3-8387-480a-8dfe-161bc6292681", + "metadata": {}, + "source": [ + "Q7. Find out what dimensions and coordinates exist in your dataset. Which latitude and longitude variables are associated with the ocean temperature variable?" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "32775beb-ec9b-4cc2-a8bb-ca59f8cf6bad", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:03.396388Z", + "iopub.status.busy": "2024-11-08T14:54:03.395886Z", + "iopub.status.idle": "2024-11-08T14:54:03.403873Z", + "shell.execute_reply": "2024-11-08T14:54:03.403369Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Coordinates:\n", + " * longitude (longitude) float32 1kB 0.0 1.25 2.5 3.75 ... 356.2 357.5 358.8\n", + " * latitude (latitude) float32 576B -89.38 -88.12 -86.88 ... 88.12 89.38\n", + " * depth (depth) float32 80B 5.0 15.0 25.0 ... 4.577e+03 5.192e+03\n", + " * t (t) object 8B 1920-04-16 00:00:00\n", + " * longitude_1 (longitude_1) float32 1kB 0.625 1.875 3.125 ... 358.1 359.4\n", + " * latitude_1 (latitude_1) float32 572B -88.75 -87.5 -86.25 ... 87.5 88.75" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ds.coords" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "c9e02ab5-526c-4c7d-9fc4-2c8d7d3345f6", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:03.406856Z", + "iopub.status.busy": "2024-11-08T14:54:03.406273Z", + "iopub.status.idle": "2024-11-08T14:54:03.457188Z", + "shell.execute_reply": "2024-11-08T14:54:03.456654Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "('t', 'depth', 'latitude', 'longitude')" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "temperature.dims" + ] + }, + { + "cell_type": "markdown", + "id": "74a6650f-7949-4f38-ba4f-fb39f0232cb7", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove_answer_cell" + ] + }, + "source": [ + "The latitude and longitude coordinates (rather than those with _1) are associated with temperature." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 + Jaspy", + "language": "python", + "name": "jaspy" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/python-data/solutions/ex02.5_xr_aggregation.ipynb b/python-data/solutions/ex02.5_xr_aggregation.ipynb new file mode 100644 index 0000000..f6df2b3 --- /dev/null +++ b/python-data/solutions/ex02.5_xr_aggregation.ipynb @@ -0,0 +1,851 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "0794ae77-cf09-45ba-8b05-ad591cee6b4d", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "# Exercise 2.5: Arithmetic and Aggregation" + ] + }, + { + "cell_type": "markdown", + "id": "7e654d07-1447-4684-9a69-0e0f7df8e54d", + "metadata": {}, + "source": [ + "## Aim: Learn to do computation with xarray" + ] + }, + { + "cell_type": "markdown", + "id": "870b0f69-7bfd-4ca1-b1f9-876ad665f3cb", + "metadata": {}, + "source": [ + "Find the teaching materials here: https://tutorial.xarray.dev/fundamentals/03.1_computation_with_xarray.html" + ] + }, + { + "cell_type": "markdown", + "id": "3d8da800-9ba2-440b-b13b-30caf7027300", + "metadata": {}, + "source": [ + "### Issues covered: \n", + "- Doing arithmetic on data arrays\n", + "- Using `.mean()`, `.std()`, `.max()` and `.min()`" + ] + }, + { + "cell_type": "markdown", + "id": "dc51608d-76da-4c7b-be20-089df1a52f9b", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q1. Import the `'../data/xbhubo.pgc0apr.nc'` dataset and create the temperature data array as in the last lesson." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "371d4e04-d765-4c34-b75c-b235a1165d6a", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:53.364943Z", + "iopub.status.busy": "2024-11-08T14:54:53.364387Z", + "iopub.status.idle": "2024-11-08T14:55:01.739075Z", + "shell.execute_reply": "2024-11-08T14:55:01.738157Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [ + "import xarray as xr\n", + "ds = xr.open_dataset('../data/xbhubo.pgc0apr.nc')\n", + "temperature = ds[\"temp\"]" + ] + }, + { + "cell_type": "markdown", + "id": "dbc7c274-51f9-40e8-9cf8-4d4f5e25707c", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q2. Let's compare the data between the sea surface and further down. Create two temperature datasets and extract the temperature change data the sea surface and the sea bottom" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "37a7a666-942a-446e-ae54-6aab318dc084", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:01.743464Z", + "iopub.status.busy": "2024-11-08T14:55:01.742652Z", + "iopub.status.idle": "2024-11-08T14:55:01.754012Z", + "shell.execute_reply": "2024-11-08T14:55:01.752769Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(144, 288)\n", + "(144, 288)\n" + ] + } + ], + "source": [ + "surface = temperature.isel(depth=0).squeeze()\n", + "bottom = temperature.isel(depth=-1).squeeze()\n", + "print(surface.shape)\n", + "print(bottom.shape)" + ] + }, + { + "cell_type": "markdown", + "id": "87224117-8b77-4a8a-ac4d-5c7749d263ad", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q3. Calculate the difference in temperature the bottom of the ocean and the surface." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "143821a5-c18c-4cd0-8ba6-18015a82c396", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:01.757943Z", + "iopub.status.busy": "2024-11-08T14:55:01.757065Z", + "iopub.status.idle": "2024-11-08T14:55:01.803493Z", + "shell.execute_reply": "2024-11-08T14:55:01.802710Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "
<xarray.DataArray 'temp' (latitude: 144, longitude: 288)> Size: 166kB\n",
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+       "       [nan, nan, nan, ..., nan, nan, nan],\n",
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+       "       [nan, nan, nan, ..., nan, nan, nan]], dtype=float32)\n",
+       "Coordinates:\n",
+       "  * longitude  (longitude) float32 1kB 0.0 1.25 2.5 3.75 ... 356.2 357.5 358.8\n",
+       "  * latitude   (latitude) float32 576B -89.38 -88.12 -86.88 ... 88.12 89.38\n",
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" + ], + "text/plain": [ + " Size: 166kB\n", + "array([[nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " ...,\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan],\n", + " [nan, nan, nan, ..., nan, nan, nan]], dtype=float32)\n", + "Coordinates:\n", + " * longitude (longitude) float32 1kB 0.0 1.25 2.5 3.75 ... 356.2 357.5 358.8\n", + " * latitude (latitude) float32 576B -89.38 -88.12 -86.88 ... 88.12 89.38\n", + " t object 8B 1920-04-16 00:00:00" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "delta_temp = bottom - surface\n", + "delta_temp" + ] + }, + { + "cell_type": "markdown", + "id": "6726f384-403d-4614-a15b-4431ca62dd5c", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q4. Plot the difference in these temperatures using xarrays built-in features." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "a4f06f85-2c15-47da-abcb-87cbfe19e852", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:01.806315Z", + "iopub.status.busy": "2024-11-08T14:55:01.805873Z", + "iopub.status.idle": "2024-11-08T14:55:02.227273Z", + "shell.execute_reply": "2024-11-08T14:55:02.226666Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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Calculate the **minimum** temperature across the water depth in all locations." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "24988c19-820d-426a-87f9-fa350f9c1d10", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:02.229919Z", + "iopub.status.busy": "2024-11-08T14:55:02.229608Z", + "iopub.status.idle": "2024-11-08T14:55:02.782674Z", + "shell.execute_reply": "2024-11-08T14:55:02.782057Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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MJkwn6Morr8TNN9+MG2+8ES+88AKuuuoqXH311bjhhhtUmauuugrXXHMNbrzxRqxcuRIzZ87E4sWL0dc3Nll+CYIgCGI0CDoxbIR/W/osxh8Txh326KOP4phjjsGRRx4JAJg/fz5+8pOf4PHHHwcQWIGuu+46XHjhhTjuuOMAALfddhtmzJiBO+64A2eccUZzGyRNpYLXNovq25JcPrVMrmkuooTfVa6nenzH8X3jm/Xjq/MV1W0Ly6a2Ian++Hkz26xfrR/iPOLlt3bIFTZhaMQ9lvYOikyual2+pXVkDRsGhXx+zI85UqI2p7e93N8DxF179I5tU0wYS9D73vc+/P73v8ff//53AMBf/vIXPPzww/jgBz8IAFi1ahXWrl2Lww47TO2Ty+Vw8MEH45FHHtkibSYIgiCIZkDusNFhwliCvvSlL6Gnpwe77bYbbNuG7/u4/PLL8W//9m8AgLVrg+mQM2bMMPabMWMGXnvttdR6y+UyyuUoaK63t3cUWk8QBEEQw4dmh40OE6YT9NOf/hT/+Z//iTvuuAOLFi3C008/jXPOOQezZ8/GSSedpMqx2EMihKhap7N8+XJccskl1RuYVTtVQWgyZZ4buWRi7h/BLMCK1RF3g8l/ZYC3HdwSYTuAZVfXh5jrCclmdpHoMhv6BWCae0kAkZk47nayTAF2wbLVx6/h8mNaOf18Gp5Rw8x6UhF863Kd1eNOJcYVQ87ysrOJ5YRFyQ5GCx5zN9rl/uDbG8K4N/TMVZolNqGZMHfv/PPPx5e//GV84hOfwB577IETTzwRX/ziF7F8+XIAwMyZMwFEFiHJunXrqqxDOhdccAF6enrU3+rVq0fvJAiCIAhiGFhNcIVZDRqCbrrpJuy5557o6OhAR0cH9t9/f/z2t79V27eGGdkTxhI0ODgIK2ZVsW1bTZFfsGABZs6cifvuuw977703AMB1XTz44IO48sorU+vN5XLI5aqDD3VrT9J65ocWEu5H26wshNMC7gSBi2UO+DyyPlgMcOzgHGy/HFgmwlGesDIQjClLTC2rjYBtGjVY+khRrybJEBI/jHFcZqtRKEsJiA628aptDIElydiOaotNTStWLUtSPDDbryDXPqmqnE55QJslaAR9byELEWP1jyJle5kd28dOLgeYzyYx+hjW1/A9SrsHdjZ5PUJLr6alw4Soy4rbCKViEaxSVMvcaYEQAA/fhbaWQlOPN14YLJYARLfH8ivBj/C9iQeiC8EjB5K0iqdZ80eZLeEOmzNnDq644grstNNOAILJRscccwyeeuopLFq0SM3I/tGPfoRddtkFl112GRYvXowXX3wR7e3tI2rrWDFhOkFHHXUULr/8csybNw+LFi3CU089hWuuuQannnoqgMANds4552DZsmXYeeedsfPOO2PZsmVoaWnB8ccfv4VbTxAEQRATi6OOOspYvvzyy3HTTTfhsccew8KFC8d2RvYoMWE6QTfccAO+9rWv4cwzz8S6deswe/ZsnHHGGbjoootUmSVLlqBYLOLMM89Ed3c39ttvP6xYsWLC9EgJgiAIIolmzO4aSXSZ7/v42c9+hoGBAey///5DzsimTlCTaW9vx3XXXYfrrrsutQxjDEuXLsXSpUubd2DpFgvdbkzwQIJdmk91CXjBAe4p07UQHLbFEv2wvm2aXRkLvDIiNFdaunZFaB73whAuX5i+3Xj1GX2F4TZr7A1iif6zaveNqLENMF1gacHLiS6yuCy/EGbweYNmaB66IBj3jbYm6iGlUct1Fr++SddqDFIGqOfGjh3fr1S7bOLnM1qm/a0lkNuKuyNN5P1lvhus0M+bWdEzsoWvgzWwMfwRtE+eka3Obet0h0l3X5YnhDMEK9Q9Y+5AsEq5ohO+OWlhE6NAMztB8VnQqWEhAJ555hnsv//+KJVKaGtrw5133omFCxcq6ZlGZ2SPNyZMYDRBEARBbKuMXC06iimaO3cuOjs71Z+cYJTErrvuiqeffhqPPfYYPvvZz+Kkk07C888/r7Y3OiN7vDFhLEFjjgyArpSC0YI2TVXYWTWCYKH1BwBYGGxoe0GwndPaBZFxwFu6AACD2Q5U5EjEZshYUSC0MdVeHl9TXmaCIxOfbg/AYswwQFRZbwQHV2rMYSyuFuTMuBeNXhtRtK3DEiPrE8yqPwmkRr61uW7MelRvS8WiuaLGOernF620zJHjFkC3NunB9rXaI2UKkoLcAQTP+3CsObr1AzAtkxMFbfJCXM5Bv/fCsqOJDeE068giFJOUsDKGSrE+LbvZloViKQgGtsv9ap0lg6LVsxpYPeT5uJXB6H6Hz1PiNwDB93BLqFgPBwHA4a6xzipuhsjI++VF5xn/3spv8kS3ZgJYvXo1Ojo61HKaFQgAHMdRgdH77rsvVq5cieuvvx5f+tKXAAQzsmfNmqXKDzUje7xBliCCIAiCGOfYaIJidFiXnPIu/2p1guIIIVAul40Z2RI5I/uAAw5o7smPImQJIgiCIIhxjtWEKfJWg/t/5StfwQc+8AHMnTsXfX19+K//+i888MADuOeee7aaGdnUCUojdIdJM6mE+aGCqFR3BSBYEETIMnkwrxCZuX0fzC3C9gLza2u+CJENe9wV83DMcwFmQYSK0eBcqUcr9WorcMFlMjkVfM24D4gaqrLMggVN40fE3F4pSst1ua8090jcNZBax1akrpp4fkMl1B1jDPdorYDeqh1jZY0ga7t+naX4c8nExHAn6EmApcuae4AQYAiDaS07FlRvVen5SN0ZJszzNhN2xq5Jk5+fxAkOAOC7Vatk65lXqi6vB/UzCzwXusBEHsDEcIcxAOB+NFHDDd1+PMG1LVX8dbdYVeLn8fOujwZvv/02TjzxRKxZswadnZ3Yc889cc8992Dx4sUAto4Z2dQJIgiCIIhxTlNmhzW4/w9+8IOa20dlRvYYQ52gNMJp8UxOgYwpJjMRKY2qUQX3gtGDMXLUAim9kjnC0oMqbQfglWi6u+Dm6NzWAikZUyNMaQFSgz0ZCFvHVHR1qOGMZmL15GJBzFUBxjHyhfE5BXek7SoNDqRvbGQUOcYjThnUW/NZSLMgCt+wgAldDTkeSGqH+8mpyePRKmRnVXD5SJ8HGZQMwLyHQ8hP6BalegL6a6HOQTsXb2Bj40G+MckKazCwJPkds+D2bAAQToLIFsbt+50VHpjvBpb3FOLfyNqXiFdLeYwSTVGMnkCztsaKrduWRxAEQRAEkQJZggiCIAhinLMl3GHbAtQJSoH5FTBfi15OCWpkvqs0Pni2xdzuFgMXmNIKYVGgtZUJXA96AKYVuREMZWE7Y7gbmF+BpbRfgiScyo0RD3yuwbDcYEkkKSNvo2ZXYdlBsLokriE07IpjqrVxJeJa+6Vt15+pmHsm6f4xpASIWjb0YGkWdw/EdKIEs8CYlhA2fr22pItslJStqwKms5GLq56kyc3Gb5sGq9wfJVEdjksnvFbWYHcUAA7AD7+D4xG7d62ZJFU9s5F7Nq7/ZdyVuO4VEp73UYLcYaMDucMIgiAIgtgmIUtQCoz7sWms0Uic+S5YpRyVdYPRVFVeKN3SAwDcR2a7dzTcFnfzumA0bWlT5nUEr8ofZmwehaDaoRRiRxrMOVEZD+dtBKXXuvc1FaTTc4pVBY6GQdL1KI8z3aIlCaffx4Prh6Lcu6mh8skNMoO5h8px1yj1PA9G8PQYkeuYDGBy6nbvjeeGrkS/VqHVXNjZSJJjCyOvq8cFHC94J1iuFaxSVhNURLZgTJkH99R3n4XK/0ox3M4Yz4WwMsH/BzWCrJuJxVjDOj9JdRAm1AkiCIIgiHEOsxlYUjbuRuqgTlAV1AkiCIIgiHGOZTNYI+wEkSWoGuoEpSH1geRv6YriHphbhCgPhss+oFSly2CWBREqjbJMFrxnI6z2SUHR1ilI0XWu3ZRswQyihhlEyRKSW7IU98VouMaI8Y007zfr3sfrqceFlJhsdoh9hmxHtmC6rPUA61oB4ZZd7bqWu9H7ASDQ/rFKvQAAq9xXs6zgnvo+Mu4F5ceBTpB06RY2v66+i1apL9SAi54V+b0GAFEuQnjahJhMFiwbJle1bLCMljzbzkJkckGSbWLCQp0ggiAIghjv2BZYPLN9o7DxEa81nqBOEEEQBEGMc5jFwEYo9MNqTqHZNqFOUArMdwMNIGaB+R6YH6ZDCGdCsEyYQJVHDi5mWbB2OdCox0r53QhDzZopD/TVrWvCBG94Fg4xsRgPKQuMGWoxXSNh2U2ZRTcezrMZjPaMws39g0OWmdRmavvkOiYDHdHsMX/1M+k7698e7o9ZGgn+0mPRMTM5lZyauUWITA6OF87gZRYQ/uYDfWDZLFhLJwDA3/gW7M4p8Hs2BkUzTuQOsyygVIFwS2obstkoKbBbAtqc2gmEiXEPdYIIgiAIYpxj2QzWCC1BFlmCqqBOUBpeBfCChKasUlSjAVEJRwkysM7SQp0LtbVzCGJbIVJJNwP2A00hGjlPNHi+M5p04Q6A+ZpqvvABaNYmPYHuKCK/yeA+RN/maH0mC967CZa0ZOmBz3KfUj8AwJo2D/BdWOG3mxcHojIAhO+D5TRLnWXByreGxxlbTTBmjTwmiN69amgqBEEQBEEQ2yRkCSIIgiCIcQ65w0YH6gQNAeMeYGfBnGid8CONCZbNKp0gls3Fd6+i3N9jLOfaOkfcRgp0JkabwWJJpUNgMuA0LZWGnsYlTJ6qttvZVI0eojnUEwjdKOXWabBDob5M71qwgU2JyWaZ4EC5D2732mhnweFMnt3wMb0nfgPW2hHU6xQg3GJiOV4cCIKWNVeR1dIOPhjoG4lyCXbnFABAdr9jax5TPPEbwAncXMJzgXIRkO4wzgGvAj4Q6ie1jq1LjNk0O2w0IHcYQRAEQRDbJDQkS0H0rIfwB4FsFrw4YGxjlm0GyxWC0UpdIWekSEtMQDJuvzkVOqaibjzXurK5lQmCpJXVQICz4eimE1sS22KwQwVpnu8IgqP1JNKayjJjHHb/egDB/ee54VmqWaFVBQJLK5Ba1oKdmWUHE1X0oGHLhhVadFjnFHMCSy0yWfAwyFqUBpCZNR+iNBi2oRQcww08ARwAy1WA8kBKZc0lsASNMDAazUkMvDVBnSCCIAiCGOdQTNDoQJ0ggiAIghjnMNaELPKcOkFxqBOUAsvlg79MFqwc6UZk9jmyoXrcTW8Zy7lhBAgSxJaGuYPg+TBI1SsFAaEyia+dNZOXynVAoOGSjZSduZ2t029MpDEagc/xOuMK0gBUolCrUjTcXwBirlJESUZRgc09AHOGbINSgEawP3PyUT2ZbOCOkqrQFgd46Bpq7QA8N9JwC91ivBS4qUTfZlWPt/Z1CLcE3tcNAMjMnAdRLil3md0+ydAf8t58GVYYVC3KRUBPoOrkAc8N/ogJC3WCCIIgCGKcY9kWrBHGBFmCYlLjUCcoBb93E3y/FFiCMk4wXXI40HRgYiuA5zsMFWhWGYyCnX0zVxQTvCqXnZwWb4dyEiiMrdouMTKct1+MFoSoO1chgEB9vx6YFdVr2cFyJgq+Z7kWs2zYIWAWh0AYII1gSnwQxBwsM5tHxkfPBbgfKUQP9BlWTK80YFo1M05QBqE1qjig6uW9mwDLgj/QfMtcEk2ZIi/IHRZnQnUL33zzTfy///f/MGXKFLS0tGCvvfbCE088obYLIbB06VLMnj0bhUIBhxxyCJ577rkt2GKCIAiCIMYrE6YT1N3djQMPPBDZbBa//e1v8fzzz+Ob3/wmJk2apMpcddVVuOaaa3DjjTdi5cqVmDlzJhYvXoy+vr4t13CCIAiCGCHSEjTSP8JkwvhqrrzySsydOxe33nqrWjd//nz1WwiB6667DhdeeCGOO+44AMBtt92GGTNm4I477sAZZ5zR2AG5H5pFR5gMUHBDN8XdvM7Y5nTNHFn9xDZP/2CkpBuPOeYC6GwtGOuUmq+VgQifTavUC1ZJVuQFABsAC13CvNAZuLe0+ATmlSN3mZWJ3BrMAnzXUJAGs4BJ0xs5xW2anoEixDATXzJm/qcn60kKfNZxN6+LEqZ6LkT7NKULxCqDhkZQPfirnozaJJPp2llACDCvlLZbtTaVrkclt8WTWVsW7K5pSt9NuCUwpSvUCqG+7SF6gtVyEcI161NJS7lv6g1ZVrAvHxvtHYoJGh0mzBW56667sO++++KjH/0opk+fjr333hvf+9731PZVq1Zh7dq1OOyww9S6XC6Hgw8+GI888siWaDJBEARBEOOYCdMJeuWVV3DTTTdh5513xr333ovPfOYz+PznP4/bb78dALB2bTC6nTFjhrHfjBkz1LYkyuUyent7jT+CIAiCGFc0wxVG7rAqJow7jHOOfffdF8uWLQMA7L333njuuedw00034ZOf/KQql2T+ja/TWb58OS655JKq9Zmps5Fpa4XwKhAVV808aBjBo9kzlhWZc5kFSqFBNAvdWcKH8JxYAxuTN+izc5KOYQefC+YOgMFMmiqsTJRM0rJVPSLjgHmB2wMI3G4YpmtnW2W4rrAkan0LzYLat4n7QDYPyw2eG57vBPMqQXJpQLlUE6tJep6Um20Il1qNfcF9I3WGccxcAUJP4+GYMxFZDfeVX4lmAQs3mB0slJvNDp77MGWS1dIBq3MqMn39tc+jSViMwRqhWKJV7/3fhpgw/wvPmjULCxcuNNa94x3vwOuvvw4AmDkziK2JW33WrVtXZR3SueCCC9DT06P+Vq9e3eSWEwRBEAQxHpkwlqADDzwQL774orHu73//O7bffnsAwIIFCzBz5kzcd9992HvvvQEAruviwQcfxJVXXplaby6XQy6Xq95gZQA7C2ZnwZy8UhHlrzweJIV0w6A7zqPAOQDWLgca1TBNQ0VwGMGkBFEsRUGhfooJRwBGxp8sd8H8YKQr7CwyVkYlJbUgkG8xA6G9N+qUiRA8GrnL0bK0QugjSBkQqlkomO9HgdMtXRDZcPQdPv/CCdrEBYdV6oP/6tPBflx7P5hlWg70bV6lWpVap8Y24fsA58jsfUT6/uOYeBBzvYrRdVt9EnA6pyY0JAhmtwF4laKROFQFO0tdNLkcsxIxPbh5KK2hJGuPEdCs/U6w1DM71A3yY0HQ3A+Co2U5y4YfJk3l/ZshQmsQs20IHiVitbumV2UM8J/9faAdNAYw2xp5AlVO///EmTCdoC9+8Ys44IADsGzZMnzsYx/Dn//8Z9xyyy245ZZbAAQv/DnnnINly5Zh5513xs4774xly5ahpaUFxx9//BZuPUEQBEEMn6YkUKXcYVVMmE7Qu9/9btx555244IILcOmll2LBggW47rrrcMIJJ6gyS5YsQbFYxJlnnonu7m7st99+WLFiBdrb27dgywmCIAhiZDRFMZo6QVVMmE4QAHzoQx/Chz70odTtjDEsXboUS5cuHfnBuAf4FSCTC8y3hSB5pEBgwk8KyksL1FOBgIJDWJopmAKjt3mkRylT2gwrTDTKvDKE7SQGnjLuwepfHwUh51ohcu2wZGj0UC6GWPBzYuBqrYYC6WkQwiJW/wb1bItM1njOrcogUOqLXMiWrSXbDKuR9cddHPVOToi5xphtb1U5Wxt1c8nA6pG4x+Lwli7YUhfKq0TPaCPpNGoF4sfdmzFtH8PFBQDxZWNfrvYVXsXUCeIcvOKqcId4ELWVb4HdFbgB+SCJ7m6NTKhOEEEQBEFsi1BM0OhAnaChCC02alRuZYLgv3zgYrPn72UUr7y9yggiDaYIhyMm7sOZPHtMmk2MTwaLUSA0FwJZHgYT5zuNqetWecB85uxQudyyACsTqTN7WeS7WutvgB78XAulkhsLkNaoWY+0frphcHMpGEXzXCtY6yTADdSphZUBY+F5ShXiuFKv0S7NGqRbBvRlzbqk4/1lhbZv0L6JGCwdVwCP0zNgKn/rFqARzrBWOJOmG6rfuiJ0LaqC3+NIi4187pTFxoeoVKotOiEsruRs1MmjsqHkiT6FXrcyCa8CUY7eUZFvhd8TvJdyarxxPr5fbZUaJSwbTYgJalJjtiKoW0gQBEEQxDYJWYIIgiAIYpzDLAY2QlPeSPffGqFOUAqC81ADiAemfWVqz9QM/mO+G20P96s7+JTYZmAMsBmDzwKNqky5VynoxhNKMt8Fk9b7kSiNh4GoRqB1ip6LvotCBf7HAlrreb7DMtbgZrN+X1P2tazq4Oda52pH7WDQVLPjiS7ldj8Kht2W0P/fs0dJMVjqQDHupQfOy7LhPY1/F+NaUKLiqnvFSwORSwyocneK+P22bcOlK7V/1DF0t5u+rLnCWC4PlskqNxjLOFXnwmxb6RGNNpbVhASqPjl/4tAVIQiCIAhim4QsQSnYuxwAu6MD/O9/ikYLAOyYInSczOxdx6J5xASlpWAGV8pAaT/fATsMoFeWoDQriC6vkBQArCtEa5Yjxr0qi0+tvE/6duZ7UXB03PKTVEdaGSu2HCelfbVyUAX12tEU+6GmU2t4T/zGCLDNvvvo1LIThXjgdN9gECg9mo6QzHbvMJb91/4S/Kil8i1RQcuuYekRXgU8VGMWbqlKIdoInI+rS8etilqAtaxb7VcuGc+A1d4V/JtvQe6fo7yUSdi7vx/2GCXdbopOECVQrYIsQQRBEAQxzpFT5Ef61wjLly/Hu9/9brS3t2P69Ok49thjq9JXCSGwdOlSzJ49G4VCAYcccgiee67OVD3jAOoEEQRBEARRxYMPPoizzjoLjz32GO677z54nofDDjsMAwNRvrSrrroK11xzDW688UasXLkSM2fOxOLFi9HXNzHEJckdRhBbkEjpGYAdvo71BD7LgOBYEHXN/TkfOoFvA8q/uquqyq2WEHyd1Laa7jhNUbimWwxITrZpbK8xmaFeJeoJjkCgTTUW8HygsG8Ndg9ZVrqmRLkU/fZcYxmeG7u/lWo9IL1OIFlhXD4nMhA6dL+poGetzvGmEM0sK1k7q8E6GuGee+4xlm+99VZMnz4dTzzxBA466CAIIXDdddfhwgsvxHHHHQcAuO222zBjxgzccccdOOOMM0bU3rGALEEEQRAEMc6xbKspfwDQ29tr/JXL5bra0NPTAwCYPHkyAGDVqlVYu3YtDjvsMFUml8vh4IMPxiOPPNLkKzA6UCeIIAiCIMY7zYgHCjtBc+fORWdnp/pbvnz5kIcXQuDcc8/F+973Puy+++4AgLVr1wIAZsyYYZSdMWOG2jbeIXfYEFhDzAYjiJEgdN2WuCuqTj0g760Xg4S/8X1iSXpFI+62WFuEnYlcUZwZaTTqnWE2UlhcmyjNNSbdIMZsIqumS0zODqqsvMsol93v2OE2d9zQ3lI7zcaokHJvqu6hH6WpEF5FJTJVqS30exj/LZOdZpxk7R/U1isCED4XPoRMbQSo+8+yWRR/ea1WtjodS+HYLw59jHHI6tWr0dHRoZZzudyQ+5x99tn461//iocffrhqWzw5rxCiqQl7RxPqBBEEQRDEOIdZTUigGsYEdXR0GJ2gofjc5z6Hu+66C3/84x8xZ84ctX7mzJkAAovQrFmz1Pp169ZVWYfGK9QJIogtCAstKoIxiGwwYheZfHLAcxoxi0/6wYbxAdUtSfKHPUSQsk6j6sx64K5WvzpWI+rrMhErQq2geFJYdRh/m1ORHm2kgrTgHlh5oNoCBECUByHKpUANGqZaM7hvBkMnaT+FQdORPlRy2aq26bpRso7wWRGaVUj+y0KLE4vtO1ZK0ep4WyAwWgiBz33uc7jzzjvxwAMPYMGCBcb2BQsWYObMmbjvvvuw9957AwBc18WDDz6IK6+8ckRtHSuoE0QQBEEQRBVnnXUW7rjjDvzqV79Ce3u7ivPp7OxEoVAAYwznnHMOli1bhp133hk777wzli1bhpaWFhx//PFbuPX1QZ0ggiAIghjnBMHNI7M+MbsOBW+Nm266CQBwyCGHGOtvvfVWnHzyyQCAJUuWoFgs4swzz0R3dzf2228/rFixAu3t7SNq61hBnSCC2IJwacwXgLCC15HV694aiiYFJKfVV4/iTGoTktxPCWk5ktwoxu960jIgcF0oV4YeJB2m25DnIrzKkAlAxxvl/mDasrx+ufZJzT/GAz9WiURFOUhhIZetlg5k9j7C3CF8loWVActkgUo4BTsMhgYA3rcZvDiggqFZJguWdcJiNpBxwKRrSgY9a+4olRS14lZ1DmqlSjGevTDYWehB1SHMsoJlGayNUE9ohC6p4TIcxeekOhpB1KErxRjD0qVLsXTp0mG2astCU+QJgiAIgtgmIUsQQWxBLBGOQC0b9ua3gt/MAtPUksctNdSl05WdY+sTAqFrWn9ilp9ElWg9WaYsV8sykLBv3eXHAawSJEgVtgPmDgKjYAkCEAUR2zYEIrXlRMtIaMGxKoNg5YHIMlTsg9+9HgDAB3ohvEpgKQIAKw9RLiYfW8oeyPui33eZEFWWSQmAN9Cfjfgz5XP1L7Mt8xnzXKUuPTba2xGWZcEaoRVqpPtvjVAniCAIgiDGOVvCHbYtQJ0ggiAIghjnUCdodKBO0DjD/dN/Rwt6AkBNyyRuqs8dckJddVotHWDZLOzd39+cxhINUyoG5n4ZEN3S0hptnLkjAMBb848xb1fDxNxdhgurlstrqH3TficlSE0Lik5zaaW4znSdIOXimUjIgHqvBMsdUM8PcwcBAPb27xzxIez2Scpt5HevD9xdoVvIe3s1vBU/UGWZZcHa/aCwDUVU3nhZuceEHnQu9Xx48B+zGOzVzqlanRmWrQKnwX3jXgnuK72gpP3SnpW4KwzQ3GGcg/lRx0P+a6hLExMe6gQRBEEQxDiHsSaIJTZ7xuhWAHWCxjN68F4lyqujU89LYbV0RPVZ+aY1jxgGoXXD5h6E7dQss0Wo49hRDjGp4lw9tT2xfNIxYtaeYFX1utSRfL1ToVPqkFagKotAnVPvxwUybxxgPFMi3wHmN8dqwUsDyjJttU8C79usbdRztAVl+LN/DP4NrTfcDa0rpQFlQWKZLJiTbKVRFqOU3GHC13OFBVbC+PR29Vuvd4jAeuFz8EpwPZltgVc82CnvqQyQHivIHTY60BUhCIIgCGKbhCxBBEEQBDHOIUvQ6ECdoCHovunLyLSmu5DaP7nUWB786fIogLDkouOUSxs6nkoiGAuKFl5FBQ7qyQPrwVv/ZlBNz0Y4OyxqqD3EyFBqviEsfDaY1LwpFJJ3jLuQamjy1M0Q+zLfM1xbuhtLaLEEVclMh1BvTtTykcTcHYZ7K8ktZtWRNiDN7WWU4eZ63f1SzzHGEU7XzKbX6T//gEoc6v79KSO4WGryqMSiUudHYtmRO8urgA/2qU2iXNK0fvyqe64rPzfi6lTqznLfqiS5yc+grgkEALziqd8AMOnTy9LbMMZYtgVrhJ2Yke6/NUJXhCAIgiCIbRKyBBEEQRDEOIdZbOSzwyw2dKFtjAnbCVq+fDm+8pWv4Atf+AKuu+46AEGyt0suuQS33HKLymb77W9/G4sWDd8FlGnNK/l0AIapNBHLRqWnr76yCfD+zaoec3aYO+LEjva07VTSQ2KM0DRyGPeimTyCw/I9AJMT9qmRQLWRdBr1zNbikSuLcU8ltxScB2kBklxLCevr0uRJomq2jl97X2sYs7aqXCN+avuDNA7V7drW8DeuNdNhcF/NThXlEpDJRv8hO/nI3eS5wW/pOqu4wfXXr2f4HRNeBYL7ajZWHDuXS22fnBFmrIt9b9U3m/OqbWoGmBWVAQDfjb6x9jhzHVFM0OgwIa/IypUrccstt2DPPfc01l911VW45pprcOONN2LlypWYOXMmFi9ejL6+vpSaCIIgCILYVplwlqD+/n6ccMIJ+N73vofLLrtMrRdC4LrrrsOFF16I4447DgBw2223YcaMGbjjjjtwxhlnDOt45c19YJaFbKsZwCpHDj0/+KoxmmCWpUYdzLaw+ZavGKOLKWdfXfN4haM/P6x21iJ/2KeM5cGfLgcA9K9eC2ZbmPbFa5t+zG2VyvrXVRAx4x6Y06K2sUpJjYgZ98C8at0nuU0PRDasQnG1Za8SBDFrFiak7KvapQKZPS2A1QX3fcMyFFSXEqhcta0By0mtwONa9cSso6xGPUnWnugYvDoYVwVIu6kWsK2Z0j23gPdshD1tOwAAK7SCd68DEAZCc19N2uBeJbDmyGcnFhgt3JJh3WG2ZXwjuWtafpKClpllBceJEQ9kToPZllFG/ybrwc8qSaqmEi3bVzOgfwtAlqDRYcJdkbPOOgtHHnkk/uVf/sVYv2rVKqxduxaHHXaYWpfL5XDwwQfjkUceGetmEgRBEETTkIrRI/ojxegqJpQl6L/+67/w5JNPYuXKlVXb1q5dCwCYMWOGsX7GjBl47bXXUussl8sol8tqube3N7UsQRAEQWwJmG3Dskcm38BGuP/WSF2doHe9610NVcoYw1133YXttttuWI1KYvXq1fjCF76AFStWIJ9PD+5lzIx+F0JUrdNZvnw5LrnkktTtdjYw9fqlQH5emhOli8uybVhOmMAwplXhuxVTu2IYgdKjweZ/vA4AKK7vxrS9dtnCrdn6YGFwMfPd6Hd5AMwdiApZNvhALzB3j6r9+dpV4AN9sDunBEU7JpsuLu5FKVRCnRX1nBnBrNxcDhGay0u6w6rcRyn7ym162gJZV13EXVh1avIELiuzTUnJL432pRE7V8E1DRzOwTx3wmkFjRR3zRsoru9GLpzUkZs5MwhqBsBLg+Cup1xcvlup+u7J38Ln8EtuapBy/HctuOvVPyElhvB5ojtLusJ4+HxYtg1mW+p77pfcEc/AIiYWdXWCnn76afz7v/872trahiwrhMAVV1xhWFeawRNPPIF169Zhn332Uet838cf//hH3HjjjXjxxRcBBBahWbNmqTLr1q2rsg7pXHDBBTj33HPVcm9vL+bOndvUthMEQRDESKCYoNGhbnfY+eefj+nTp9dV9pvf/OawG5TG+9//fjzzzDPGulNOOQW77bYbvvSlL2GHHXbAzJkzcd9992HvvfcGALiuiwcffBBXXnllar25XA65hKmYm777VVQKOVi2jcpAUY0qmGXB1wLrsq15WOGownYCq5EcFcEFOKKRN7MtbPjWv6tjcNdTZf2SC+5zzLrg241emoaRL0Lnjtth099eQ8eoH3Hi4j//AETFBcsFgfEsk42SVDIL9vy9jPLMHYBVDi0+gkNUwsEA9yEA2AsPUWUtAIM//0awuTQY1WFZKK7vhtsbrMtNakPrDjsE+3RNh5VvjQ7IfcMiwjS1XhG3aMTVkLkPUdGUkhMSYQ6H2grR8W2V9JG31gYpNGAkw/RTLFEJ06eZYUGKTdm2OOC5UT1a2eKvv20kcS0c+8Xktk4wen7wVax7/G/oea0bANC18zQAQLm7HwCQ3dhTFSBsZUPLT6hcLJetbAZWKUycGlol4wHM8pujB0nHXTv6f9AqYDllGnz8P/O4pahqSryvW/5iU+bdyNLvFd3IqlVHYHTvrReht9jcAX8a1AkaHerqBK1atQrTpk2ru9Lnn38es2fPHnajkmhvb8fuu+9urGttbcWUKVPU+nPOOQfLli3DzjvvjJ133hnLli1DS0sLjj/++Ka2hSAIgiCIiU9dnaDtt9++oUq3lDtpyZIlKBaLOPPMM5VY4ooVK9De3r5F2kMQBEEQzUDO8BppHYTJsGaHbd68GX/+85+xbt068JjJ8JOf/GRTGlYPDzzwgLHMGMPSpUuxdOnSEdft9hfhej6yrXnDDMlsC3beUeV4xUsNfma2BQuBS0xt56aZWJqcKwOlMQucLkzpVO3LaOdCVCPKJTOA2LLBCoE7KumDYhV7goSTAHjfZrDWwNlotbSD5fKIO5m8cDZieXOfCjzlPofT3qLuk1cqo7wmSIKb6dkI5uRTE00y21ZuJFFxwWw72h4Lnja2xfYdyh3GLMssOwJ3mkjZL2k6g9BcU8GypmvETZeH4QIz6tDeUcsy7i+4D150YYX3WJSLURD6VoRXLCM3qR2Twu+aV3Rh2Rb8bKjh0zuo3EJeGOgsv4F2NmO4Vey8YyQgtbIZ2FrQtPH91O4Jjwev+77hIqv1PdS3xetJcqFVucu077Bwov8GeSUKALfr+DaWN/fBLZE7bCLTcCfof//3f3HCCSdgYGAA7e3txswrxtiYdoIIgiAIgiCGCxNCiKGLReyyyy744Ac/qOJttjZ6e3vR2dmJnp4edHQMHTK8+qufQqY1mLKfyTuG+qgMHEwKsIsHDsoylYGSWq8HAQqfww9HKAu+8R/DPj+iMSor7wp+aFYUlglGiDIPm1LS7d8Mq6Ud3sZAs2rg738bUiF8zfKzAASBmfJ5yU1qq5qCHJ9iLEfUzLKrAqMNYirLqVh2+pT4GMoKFbcayeM0YAWqsqbp+8bbnmDpSbMEJbZDWo8SrF9yOrjKcyVVkDkPAswBWIXWrSYwuvjLa/HGvX9C7xs9AIBMIYOpi+ap58ztG4QdPo+Z1nxgJQqt1n7JDazcWqB0PGDZill+6rFANGKlqBUIHf/e8gTlaL29tpNRZSq9g8q7kW3JGwrSnHPMv/K2qrY0+n9Go8j637zlAnS0jCz3Y+9gCdt9evmotXUi0rAl6M0338TnP//5rbIDRBAEQRDjEYoJGh0aviKHH344Hn/88dFoC0EQBEEQxJhRlyXorrvuUr+PPPJInH/++Xj++eexxx57IJs1k+cdffTRzW3hOEf4HN5AFDhpZzPKbWUPEegsOFdmWKmOagRzaqZbPpQKLjEq6AGzwb8cvLI52OaWVHJJAPD7NqO4rruh+mVguq6OCwRaU0nPArMtoyyzkwM+JcyKucpquaq8SpUKM0sIlFb+8/B5jAdYG/snyfQn1SXR289LpgssHgCrl08Mro4l4NSSwsrzCq5XpXp/Lt/L6N3emt6//lWvY2BdHwpdgXulf90A3L5BZFsjd4sXaufI75kVPo+cc4gSV+4y/VlV36y4QrR2K6w0t1d1vlQDPtT3NMHtBSToAmkwHgaGh99wt28gcumFbmh5/v4YBUCnwSy7ZtLgeusgTOrqBB177LFV6y699NKqdYwx+FvRh4IgCIIgxgVDDWLqrYMwqKsTFJ8GT0TMW36rsfzmJWeo37oiNABjmqlcltMxhc/VqESi582Roy5ibHH2/4ixXP7D7WrKtLdxrXGPi+u6YWUzKG8OVHd5xcPqr34KgDmNGAAyBXP6rV9ylWotAOS62quCOGWdwg9G4pJaeZn0oGorG1i20kbF8XxL+r5xK6Wxn/xXU1VXJOYKq0TT9LPadZCWGk3FmnuReSCIidCsSJoytPlu1TYpBOfvqfPSUVPmeWShlfcl5+Qx+NPlcMP8WpWBIqZ98dqaxxoPrPvGFwAAPS+/ibVPrwEQBEIDgFeU+cB8FNdthpvPVu1v2UFuMBFa/YXvh89hmH9Le2ak9UTeAmZbYL55jRv53yRNMbrmPtK6nvL/ljHdHxn4vovKYBFAYP3yikHj7Tk5zI9937collX35IWadRAGDV+R22+/PTEvmOu6uP3225vSKIIgCIIgiNGm4U7QKaecgp6enqr1fX19OOWUU5rSKIIgCIIgIphtN+WPMGnYxyKEMAQSJW+88QY6Ozub0qiJTGUwUn72BorItBZSXVl60J2dzcDKZpTpXdcF4pVK6JqgB3hLwwf71O+BN9cDgNJPKa7vRnFDHzrmBYmGpYkdAPjGYOCQ5rqqDJSUG8krlYP7Hz4LcU2WeGCp7n5Kqr8qeWgCzLJg2bbh/gKqdVUS962ZzNKLbY8lTNWCsWUAq64+nHQO+rlIDZdhq+nGPGcqwFe7Tl4YEGv19sLKZoz2TQSk9lhp8yBapgbSJpbNwH2hzsV2bBS7S7Cd4JnL5KNvlmVbsCoemOUa9SbdJx5+p9S9iF1fS7uPSe6qRlTzqwLl63B/xY9htbeiMljE4LpAuT2TzyDXUai7DWMKxQSNCnV3gvbee28wxsAYw/vf/35kMtGuvu9j1apVOOKII0alkQRBEARBEM2m7k6QnCH29NNP4/DDD0dbW5va5jgO5s+fj4985CMpexMEQRAEMWz0fH0jqYMwqLsTdPHFF8P3fWy//fY4/PDDMWvWrNFs14QlSVZ9S/PsCUcCAErdgVl837t/vyWbM7HxXJUmIzepHXbeweDajQDCJLhcKDdmuXsAzA5cx77rw3Zs+G5gwpcmd+nmapnWhaJymdkqESUAY4ZhUFfkhmG2BaHpBln6DMOKOcNLzv7SUxmkubLqcUsoVxU3XWdp5YBkd1OavguPJdTU0zP4lYrh5mC+ZZTVidcTX5brAMB25AyoSJdJ3gevWDa0vzIteWy88Xwj5c2sC76d2IYtSX/oti33lFEMvwGT5neGz2Jw/XyXw2nLws4mX8PkJKTRDCu9HOM8dYKe7sCqN0FqErpmkGVbiRpCVYlyNWSCVz+cbem0B/pIvOKhMrBl9YDSIMXo0aGhK2LbNj7zmc+gVNr6sioTBEEQBLFt0XBg9B577IFXXnkFCxYsGI32EKOIO1BB35r+Ld2MCU1pzdvoeTXQWtn4/FuY80+7YfNLbwIAul/pRjafge+uU+Wd1kAHp2V6J5htK4XoTGse3PVUcK1f8QztIH0krCuSW7Ege2kNUUGq2jbLDhKMpmn/6MfRNavqIa4C3AjxQFV9fdwCIC0HVYHaMoGqrqRdo/26BhMA8HhwuS0tIhVVPxBYmHT9I+FzZTWSbZCBx6lKyFuY0uYgQD8IfA4sPdwXhmXSdiwwiymLim5dsWwLwo+0vaV1U8J9DjQQ0CzRNZ7UOt/UENfboC/rJF13vY3MtqreAWVRLJXhu57WFqH2HUqhesxhTQiMZhQYHafht/byyy/Heeedh1//+tdYs2YNent7jT+CIAiCIJqMnB020j/CoGFLkJwBdvTRRxtT5eXUeUqbQRAEQRDERKDhTtD9998/Gu0gRhHBAxNzoSuPQlcej/7zwQACM7Lv+jjgwT9uyeZNKAbXdeOvtz8BAJix+zRsfPZVrH36bQCBCX76QQuUe0T4HO3zZgAIUmpkO1qVGT6eaFVwrvbLtgIiRVsqMRlkBYm6LNxKdvmkaaY0orMzEtdP3M3QmDZM5P7S/63So4knI46nX4gnmvXNYHHuVrvWpFslTSeI+xyvfumkoDm9A+A+x663/KLuc0vj2ROOVJMabMdGtjWr3un8pBYVyOt0FGDnc3B7B4I2hOtlezOFDIobiuE6gbbZbYZ7zK9wWDKQX0so63MfzIoGvBaka1KWqT3wreVWElyA+1VpdKvwK+l16O0GAGYx1cawBWZ7XF+1mPs8eFbC/Rfe/r9DtmVLQYHRo0PDnaCDDz54NNpBEARBEEQaJJY4KgwrK+fmzZvxgx/8AC+88AIYY1i4cCFOPfVUUowe53BfwLKZGsW7/e4QexBxXr77GbRNC1R3Jy3owsYXN6AwNZjuPuOd2yE3qR3l7mAknutqRSmc9m45GXgDRcOSoSclZZaFbGswTTfbUoBXKsMKp2tzd+gIZKkmHrfmxBV6rSaNBOu1GsUtN02jUh2crY9y9bG/LguQ1hZdYoCXeKKFzEbGUHJX9SdYhrjPUdw40Ph5JbDppW6UewOrTsuUFriv9Sjl5763+uG0Bs9J35p+tE5vQbY1BwDwSmaS2Gw+A9GVD9cxCJ+j3B+UyRQyEL6AsKuzATDbAjRrTS2rTJwo8D7Z2qOrVtdC3Z+wffHAZ+OYXBjWK90qxGwWnrt+Pj6YX33e444toBP0xz/+EVdffTWeeOIJrFmzBnfeeafSDASCMJhLLrkEt9xyC7q7u7Hffvvh29/+NhYtWjSydo4hDX+ZHn/8cey444649tprsWnTJmzYsAHXXHMNdtxxRzz55JOj0UaCIAiCIMaYgYEBvPOd78SNN96YuP2qq67CNddcgxtvvBErV67EzJkzsXjxYvT19SWWH480bAn64he/iKOPPhrf+973VOoMz/Nw2mmn4ZxzzsEf/0jxJQRBEATRTJqRALXR/T/wgQ/gAx/4QOI2IQSuu+46XHjhhTjuuOMAALfddhtmzJiBO+64A2ecccaI2jpWNNwJevzxx40OEABkMhksWbIE++67b1MbRzSHPX5yd8P73L/3fmGyzsBMnG2VrpnAdH3gww81r4HjmGf+7YNK7RkAch05dS02vrgB5d4ych2B++H1h1Zh1rtmwQnVoOO6N25vEVZYj1RztmwzIBcIEq9y14sCrOMaJ/GgXqs6qaqsM4mRuqZGK7hyqA90lavPN11ckZp04OKxUlxgjJv16Ofjh/fMuObacSztOGqfUIPI7RuAVwx+84oH4Qs8/ZHDg20DFXhFb1iTELyih5YpLWq5ZWqLcgfZjo1yb3jMsF3ts4L2Fqa0YtNLG5XrzK9wDIaB0YWuPIrdJbWNh3pBvlut3VN13cPkq1aC6ywe5By/VpwLbdvQAdES+WTw0HlhwXSJxdujB3L7lcg15peC9uQnBe8oHyiD+wLZFKXscYVljTztRbh/XM4ml8shl8s1VNWqVauwdu1aHHbYYUY9Bx98MB555JEJ0wlq+Ip2dHTg9ddfr1q/evVqtLe3N6VRBEEQBEGMDnPnzkVnZ6f6W758ecN1rF27FgAwY8YMY/2MGTPUtolAw5agj3/84/jUpz6Fb3zjGzjggAPAGMPDDz+M888/H//2b/82Gm0kthCVARfZUPG4MlBBJp+BX9m2dKDWP7cBHXOizv2Gv22E7wWjyZbJBZR7y5i5V/QRcPtKyOSD18odqKiRsO3YKPeUkSmEOYtcDtuJxiC2Y6u8Tdzn8CsJU+GRPCoHzJFv3CoUV/hNolHrUJI1qF5rVHydyidWQ2OM2XaV0jTXrDW8VK4KDpfbLcu03sTrEX56riu1HYFFjlvV6sMSO59TyzL3m18KKvbdyAozHGTOr0whA3fAVVPmWShzAQC+x9EyuQCvGFrCHAv9a/rV8uDGonrGyr3lMChZqOVMPqOm3mfyGRVwLQOs4+rNaXCf17TyxJ9Hvb6qbVb6syuPIZ8boYKmg0Bu/d2oDJg3WN6XgXWDyOQz6hqNa5o4O2z16tXo6OhQqxu1AunoeoFApBk4UWi4E/SNb3wDjDF88pOfhOcFD042m8VnP/tZXHHFFU1vIEEQBEFs6zDLBhthJ0ju39HRYXSChsPMmTMBBBYhPaH6unXrqqxD45mG3WGO4+D6669Hd3c3nn76aTz11FPYtGkTrr322hH1JgmCIAiCmBgsWLAAM2fOxH333afWua6LBx98EAcccMAWbFljDEsnCABaWlqwxx57NLMtRJ3IoGUgcIcc9H+PNP0YgxuKsGymzPuBGb6iTMx/3O8AZYbP5DP4p0f/1PQ2bAme/sjhGNxQRM9rUt8nGDm9/cx6AEB/2cM7j94FALDpH5uQ63BUUtQNL65Hy9QW5arQg0Rl8GqlFCVDxQBUwLVV9IwkobW0WHSXgWUxw7UTD1atpaeSVL4eWIqLQp6LcayK3Ec+r5bhjtJ/13LJ6QlN5XJagHZVMG5CmXpHf3G3WZKSNNcSgUptp/LmfpR7y8pl4w64sLPDC2rtXz+Iju0Cl2xxwyDyXXnVrkw+o52vhcqAq1yulY3BM5f0bt6z495gNsPAukDLiFkWfNdVri8AhhtY+EK5wwQXYBZTz6idtZQbbSiYxbTJAGGwcujOYjZT4tNSxVkGUsefc33/aDl2r7Qgaq7dB+5yFMKA8P3um0AZEFgTAqNZY/v39/fjpZdeUsurVq3C008/jcmTJ2PevHk455xzsGzZMuy8887YeeedsWzZMrS0tOD4448fWTvHkIY7QQMDA7jiiivw+9//HuvWrTP88gDwyiuvNK1xBEEQBEE01x1WL48//jgOPfRQtXzuuecCAE466ST86Ec/wpIlS1AsFnHmmWcqscQVK1ZMqElSDXeCTjvtNDz44IM48cQTMWvWrAkVAEUQBEEQRH0ccsghEKJGkDtjWLp0KZYuXTp2jWoyDXeCfvvb3+I3v/kNDjzwwNFoD1EHpe6SctNk88P2aNYkU8iEKTaCTi53/UjjBoDbX4nM7gNDp3WYKGz6Rzf61w+qZdsX2PDiJqzdHLix5i+YhPXPBa6xP/11HQ49YA7WP78OQHBfyr2ucU8qoXvBzlrwSl6VGyhN2yTuXkib/cUS0ggklUsqo8+8iaclkOVrudqk+ytpJlA8XYZ0Z/nScqw/MprLLK6BpNoSaiHpyTgtTVsJAFjKvlKTSUeWDAToLKUpFLdsCy0VBktw5fFKdCLMsuANBDPAyr1l+G6U2FP4wnA1NUI2Zysdn87tO0NtqsAFO7ihGN0H10PnDpOUO7YWtmOB+0LNZPRdDr/iq/tWKfvKVdYypQWbX+tB64xWdd6GLpNWb5J7lVnMeJ7VPQx3lNtsTatH2LHnX0v3o6/Tic8kk98nzxVwB9zwvO1AZ2kiJhLdAmkztgUaviJdXV2YPHnyaLSlJsuXL8e73/1utLe3Y/r06Tj22GPx4osvGmWEEFi6dClmz56NQqGAQw45BM8999yYt5UgCIIgmooUSxzpH2HQsBnh61//Oi666CLcdtttaGlpGXqHJvHggw/irLPOwrvf/W54nocLL7wQhx12GJ5//nm0tgYjFJnH5Ec/+hF22WUXXHbZZVi8eDFefPHFCeWjHIqBoofW8DcfJaVTO2vDK3lKQdZpzQYBu+HozXasSGW24uMPe75HBR96JQ9HvPxUQ8f7nxlBwj1fM70WYiO/o9c8O6xzkTy0f2C9lCNKaaUZeHsAxfBc8raFbM5GSVpwAKzaWMTu75gCAHj7lc2Yu+d0AMB+u0xG31v9alQuk1LKUadX8tSI0ysiRenZtLwMBbPNIFTmszDYWFTVwWMhwVZszOP7IirPfVgWgy7VE1g8pDYMM5JrMtsyEnRaNkvU2pEWLNnmtKDq4BhmwGwcP6bPw32eOopL1ALS2h7XJvLjliuV+NPULhK+r6w/cUVwACj3FsNtQWJQt5SuvFwvb/WWsXZdYJ3cozNX9R78Zu6eAIAK5yh1l9RzzV0fuY4cfjv/nQCA7oEK5KX9+Lrnax7zN3P3RLkneI4z+Qxsx44sPy6HHm4uuKcFvlerSRsWxQSlaflMGFY+WMZzIHwBDp6qUWRZzFCj1q1KFc0CV+4pw87aEB31J4EdL2yJtBnbAg13gr75zW/i5ZdfxowZMzB//nxks1lj+2glUb3nnnuM5VtvvRXTp0/HE088gYMOOmiryWNCEARBEMTY0HAn6Nhjjx2FZjROT08whVm65oabx6RcLqNcLqvleE4VgiAIgtjiNFExmohouBN08cUX11XuJz/5CY4++mjlqmomQgice+65eN/73ofdd98dQO08Jq+99lpqXcuXL8cll1zS9DaOJgXHVkF/o0UQxGuarfVAzyBIOtJu8Su+cofV49aph6LP0Zqxm1afdMlUKh4sm6ESuq36yx5aM8HHocI53uqpwA3LOmUfO0xvwav/2AQAaMtYeOuZIBDaZgxvFj1stym4F7l2J1V+P9lNkJwuoNpdoAcniyq3lF+J7ovukpJuBulCkMGmxvX0E39qx0t34dQTaB0/n6R7KcvK82B+UpC32fYolYXWvrg7zrgWZpJZI41GmAA1CcOlVnKDlBCaLlDwrwjb76v0Fl7RMwLGKyVv2KkZ5s1oxYLQvZP03ss0LjZj6F8/iE2ha9dmgD2CSQuV8DzdgQp8168K7NZ1sIQrXaPBZArDSxi7DxKV1kJLd6EmYoAb7ltZRk/doV9fX9Mxkq47d0AmhfVVWduxg3fRnYDpf6gTNCqMWpTUGWecgbfffntU6j777LPx17/+FT/5yU+qtjWax+SCCy5AT0+P+lu9enXT20sQBEEQxPhj1MwJtbQFRsLnPvc53HXXXfjjH/+IOXPmqPXDzWOSy+UmXLqPI1f/1Vj+6fSFkIOios/xqU1/a6i+P+z5HvVbJkiVyVJlgKHl2IDrVwUr6vvYIwjSXhuOMh2LwQktCjYDbJ/D94Lj3DlzkSqftSx86K1nGjrGG69sBgAUbIasZYGHz2i/x1GUI0UGdGYtZQnyBfDGhkHYYUd6k+tjspQnyNlwByKrUdb14XscVli2wrnaT/hyhKuhBd3q1hJRNRVYaCNkM7xZ+ME9kVUZFpjwXxUg7VePoGsR1Kup+WqkBWNH7YoCjXWLQXzUJYPJJRxB8svgGAlB3toAPmhDvSN637B0xafax9sU/TatR3HlZABKOd0reqj0hwlTQ8uobi3yK756hteXfXSGCtJDBSkvfuGJ2mcmomfVsZiq104Y/PWHVqOfTl8Ix2JquTNrG5MSnKyNfDh9XkplyPtYSxIBsMAxlDUzOWg6vg/3o0Do+DHjQf9mWwS4to/whbpHAIzfEwlmWSOe2j8hpQFGmQlzRYQQOPvss/GLX/wCf/jDH7BgwQJj+9aSx4QgCIIgqmB25BIb7h8jd1ic0Q0saSJnnXUW7rjjDvzqV79Ce3u7igHq7OxEoVAAY2yryGNCEARBEMTYMGE6QTfddBOAQMZb59Zbb8XJJ58MAFtFHpPhUNRMxW2Zxo17ur6PdAVIL4Dt6Ek9fdiQKsG+0t/wih6ctixGQiE8bsE2XVFFP3Ip6eZ9Pgx3q7w2RZ/D5b6qz+VAIRwg+SJwVcwMXQHSXaAjr3d/v4vJjqXcd9wX6Kn4St/IFwLh5YsCrYUWAK2fj1a/hciVBITmfOV6EhBcV5lOdo8BhrdN1ZeWmDVRuyXmekrTIJIt8LXnUNcfAqKgad9Pdtnp7Ve/ayTIBFAzyWycuJ5PnFrK2EYAcOgSk+sDlWXTLQYErmFmcXjhcbkvwF0fhdZAU2q7rF+3W3Io2qcEem19GwMtIVltPm+HLrmwvUKgI3RZywDmfFhHXB8pU8ioQG6v6AXXIDw/ntDutGsmkzDLYHLdZR4PctdVn+wEPal6r1c8qDqu1s3rTPY67mCs4QSoiXUQBhOmE1RPjNHWkMeEIAiCIKpgVhM6QRMmAmbMGLVO0Pbbb18lpEiMHnpg71DIQOh4QKtX9JDvyqtlfXSnqx8D0chWX5amh3qmAt/StRuAwDrjcmFYsBxtFKgHa7pcqGXHYvjRlHcY53vihhdqHjOutPuDybupYxSlEjYLruX6crDis5vNIPPrO3aFL7hqe2ApCsoWuEDRF8qq5HKhzkX+1s/HYkxZtCzN6mKGmMaxYhaXassQtO1GwHXMNKTf/6EsNBxcWYbiwdW1pt7LwFbjTEJ1anlcs03akWPnkRQEnWSVGIq4enRQf1SPp0kZxK1AnEdBttzl4ZT5yDIkn33BubGvV/SUKjkAIyj5p9MXouhHz0pgQWSqnC+A1nBqfKaQqQqU/pfnVhrLd80KZEN8l4MLYVgc5T2ybGbkAhRKPVwqnKe/w/LaSDV527FiAeSRErnvctgOqr4dQPSMpUlgCC5Mi2PsGFF95ntiOzZ8zSLKNeud3De+D7Ht0nAnaPXq1WCMqZlZf/7zn3HHHXdg4cKF+PSnP63KPfvsyFIcEARBEAQRIJgFMUJLzkj33xpp+Iocf/zxuP/++wEE09EXL16MP//5z/jKV76CSy+9tOkNJAiCIIhtHukOG+kfYdCwJejZZ5/Fe94TuFP++7//G7vvvjv+9Kc/YcWKFfjMZz6Diy66qOmNJGrTmbUw2YncLkOhAqFDJVhp3q5wjmyo+SN1QWRAoTRrSyuy73JlDgcC87kb7ltPG6R12hcCLhcqMNpmpssoWBeVlUj3UnEEZu2oDVABzL4AJjsW0rws2xUy6AnP0xeiKhDdsZhqb+C+09vOlNvSsZhK2hksR+dnq+Dp6mDdeGJQADEdHvN6+DxdG6SeoGTlLtKPp5KqRoHbgd5PdWC01Hqp1nnR2mHpLrvk+1mpJGnTaJo9DbrF1AQAxzYSx8pga71e6f4SXBhuYO4Lw+3FXb+me85m0buhvTrwhXTDhsmKtethM4asxVAJ3bMDdbiac+1B8HW5zzVcrtmcreqxnSwq/RU18SF4vy2VONjJ2uqaZgoZWOBG8PRA0VPPuQ0r0cUY/A4Co6WWGFAdGK2jdJXC74+tJeDVXWN6EuHkQG1L3bdyr6u+Y8LnyHflq9SvJwSMjTywmQKjq2i4W1ipVJS44O9+9zscffTRAIDddtsNa9asaW7rCIIgCIIgRomGO0GLFi3CzTffjIceegj33XcfjjjiCADAW2+9hSlTpjS9gQRBEASxzWNZzfkjDBp2h1155ZX48Ic/jKuvvhonnXQS3vnOdwIA7rrrLuUmI8aWD699rub2H015B4DAbZbPZ5RJ3GnJwrKZYQLXZ8XYWVszYwcmc6HpnkgqnMP3odxEQDD7K3ILVc+yktv6vcAVJt0ENtPcQixYL11IhZj5PHCHRWUbRZ5CcEy5LpgpJl0S17TvAl9EGkPx85Cz3IDg+tos0hbSNY/kzDAnpn+iu/r0c7BY5F5iWsoCwHR5iVi6DF+bmROdaHJ6jrirzLJZasoIPfmr4ALMYqnJLOOuMV87vqpfm+gl04QkubZqzQJqBCvmotHrr7g+uBtzg2kzvvTUF1IbSC4LP3ILcyGqXLm6a1i/v+Z6AGCaSzja3xcC/R5X5XsqHNe076K2F2yr6plMcw1yX6h6cjZDrtPRtlVgOTaybnSuuo6PPpMsk3dga+4ky7GHdEfqz50Vu/61EiTrbi89UXB81h6gaR2FrjO5r2Uzze0HlHvLaJlaqNne8QgFRo8ODXeCDjnkEGzYsAG9vb3o6upS6z/96U+jpaWlqY0jCIIgCIIYLYalEySEwBNPPIGXX34Zxx9/PNrb2+E4DnWCxikyoWKh1UEmn4Hl6NYdASdUcbUdO7IM+L6hIA2kB63aLAgGnhmq4boVHz0Vjp4wyHQo7SJdJ0gPdJYWGjno6/cE2jJSSyVQdtYtSI0i9wm0faJ6pVaQbFuJVwdASxwrGsH3exxtGcuwMKUhrUKmNSqyaun6LkONluOBqPHEpnL0HA+QTlT51ZR+47o8cQXnpOOn1V0v8SBX/ZmrZW2IX5+4ArTPtXOphIrYst4w4DnpuEKz/Ej1ZRlw63scvhCJQfS6dRGQz3Fy++Pb9P1cLtT7BQBtGQaXRxMCkur85SvdAIBP7DMLbdNbUO51AQD5rjxK3SUAQP+6AeQn5dExtyNob9ZCuddVyu+bX+2B7Uh1eIFch6MU4i3bglfy1HUY7C2jpSOIE630V8BshkyoaxTXG9IfFWlZs2PK0RI9aWvcAhQvG1jlwvuEdO0hACh1l/D2M+tTt49bSCxxVGi4E/Taa6/hiCOOwOuvv45yuYzFixejvb0dV111FUqlEm6++ebRaCdBEARBbLtQJ2hUaPiKfOELX8C+++6L7u5uFAqRX/XDH/4wfv/73ze1cQRBEARBEKNFw5aghx9+GH/605/gOI6xfvvtt8ebb77ZtIYRzSMeOP27Re8GAPgVH05bVgV2MptV6WcIzfysJ2MEgoBonWI51BQKXUq1PCJKY4QBm1yeqBOU5GrQk6vaLHJF1aNNdGPnruoYQKQNVLAtLdGp6R7TUxmkoWv/SPeFbGP8fKNlpq6Tvk5ipNRgZqAxB0yXl+9rOkJRfSJMhRGl2YjuMwD4laisdD2olAgxz2c8HYfcJzhOQjqL2HIt90QcPUGpsZ7XCIyuxN19WhBtrA3Mtgy3nx78rGtfyWPqOkC+y9VzX+uZc4eI4U56ntK8fUGwfvRO6Med7Ni4vGVnAIHbuzNr4WbxqrH/b+cHk1fWvbRJPWPxFDKN8tPpCzFtZhsAoH9tP2QgRMnnmNTVoq69ZTN4RU+5xwTnYE7oKgtT8UjXJXd9IwBbR09OK6kVAG4BVa40QD4DlnLT/Xr2Huit+Op+DJV6Z4tClqBRoeFOEOfcmO0heeONN7b6bO0EQRAEsSUQjDVhdhiJJcZpuBO0ePFiXHfddbjlllsABJnb+/v7cfHFF+ODH/xg0xtINB8Z6CkVY+Xoq9znws6YKsASNTpPsNLELTZytBpZRGqPeic7Fvq9pDJx5WimjYiDkXgjgdHRMYURcB2oT4fB4+F0fd3iNJQYcdyaowc4x6f7RxYngYJtxaxIXJUFhKE8rVuksrCqEq+aSVX1OdbmgCUIeIZWNkyY6fpgtgUvHHFXBzubpg1mM8OS1EzMKfLpJpW0NiZZuoD0YG1dBZrZpvqxORVbhNPgo33jz0ZkxayWPdCf5yRJh1qB07LpgSJ5tG/R54bVMqne1T1lAOb7MlLm7DIFuY7AG8BspgKuJ01tQbm3rIKq3cEKsjlbWdjyXXmUe4P22OGEDCN42o2Cm4HoW8UsK7Aiada+uGVQv1d+xbRi6xY+ZjOlbp+1rPCdH+aFICY8DXeCrr32Whx66KFYuHAhSqUSjj/+ePzjH//A1KlT8ZOf/GQ02kgQBEEQ2zbkDhsVGu4EzZ49G08//TR+8pOf4MknnwTnHJ/61KdwwgknGIHSBEEQBEE0CcodNioMSyeoUCjg1FNPxamnntrs9hBjgDT9VzhHqcjhlKOEoPA0/RGebu4Pfqevj5vwZVCyL4JlqfcDBIHFpkpu8K807UcmfNNNJeuWx7+mfRct6Nl0Q9gsOqbLTV0eAErTKH6+NjOVqZOQber3BCY7kYurM8uUm0/XBJLnFrgMzTbqpGkIVThXZXnMhRIPoo5T5TbT3WVpv1HtShLVYYFaveZoM570NZ5sM83lxewoaDapTNp+MiA8jq+5RyR6MDhQHYDre+YxktxJaW6sNFdY2v613MdR2biidBDYH+wfaFWdl90BALBjqwPHAj7d/bekqkbEgQ8/ZCzL4OvKgBvojdnRubR35THw9mCw3XHVPl7JQyafUZMxbMdSiXKFL5DJZwz3l521I50gbt5Dqd4tl4Ho3sWvZ1FLxusLXhVsPm4hS9CoMKwr8h//8R943/veh9mzZ+O1114DELjJfvWrXzW1cQRBEARBEKNFw5agm266CRdddBHOOeccXHbZZWqmWFdXF6677jocc8wxTW8k0VxkkKC0ysiRUmCRSbb0BMvVAdC1iKwc0bqCzQxFZnlcXQlax7H0EbI5tV5vezywUQ8CLdiynDkyl9Yix2Iq35cvTHXoKMA1/Tx1q5S0KAHBqFweo9/jRmC3xLxGyduC33JBtqU6aNzcno4qmxCMnobv1d5uaVYPLnxjOVybuB+zRQ3LlRnMKgOT9ePFLWFai2s3WEP4tS+YbqnRn7noeUyWaTDvA6tSkU47jqw7uZ5aU+mDZ/CdnYF681AWzGbSNiuYLt8ytQVrnlir8pLl8xkMvD2ocnf1d5eUurTwuaE8beatYxA8CmjO5DOolLzEZyUub8BDy07S9ZTfMD2vIRBZ08YzlDsswPd93HnnnXjhhRfAGMNuu+2GY489FpnMsBxbjXeCbrjhBnzve9/DscceiyuuuEKt33fffXHeeecNqxEEQRAEQdSANSEL/ATvBD377LM45phjsHbtWuy6axBi8fe//x3Tpk3DXXfdhT322KPhOhu+IqtWrcLee+9dtT6Xy2FgYKDhBhAEQRAEQQzFaaedhkWLFuGNN97Ak08+iSeffBKrV6/GnnvuiU9/+tPDqrNhS9CCBQvw9NNPY/vttzfW//a3v8XChQuH1YjxyI2z90ae2VUBrV/ofXHLNWqY/M+MRQAiE3BWG00UfZ6oezKUtk+yqTm5vHRdyd+69o7NYOh06ErTgRZQ/FiRK0J3NxVshrN7zHtzddsuABCaxk1lZ719PRVfrfvywD+MOm7s3FVpBwHATZN2U22Ikr7KZQYgOpeg7VwdM8mtpgeBxwNe0wKj48GxZn2m2y+pXC03ph6cm6Q5o1Pt8orWpylED5UMNo4sK90gpttNGG6xoV1kAfFzC7R/hg74ly7ItMkC8funa2Xpbly5PR4QrTOU2rreLv3dite1yfXxoynvUNvls9rsYOl/evRPxvL1HcEofeF2begeKKJTU4SX99J3ObJttqlC3x+IO2UKGSOIXwZP664vHvsu6PdFd03G3crx66dz16zdsT5Uvu/3xJDf+x9M3g3FWrMEmgkFRuMvf/kLHn/8cXR1dal1XV1duPzyy/Hud797WHU23Ak6//zzcdZZZ6FUKkEIgT//+c/4yU9+guXLl+P73//+sBpBEARBEEQNqBOEXXfdFW+//TYWLVpkrF+3bh122mmnYdXZcCfolFNOged5WLJkCQYHB3H88cdju+22w/XXX49PfOITw2oEQRAEQRBELZYtW4bPf/7zWLp0Kd773vcCAB577DFceumluPLKK9Hb26vKdnR01FVnQ50gz/Pw4x//GEcddRROP/10bNiwAZxzTJ8+vZFqJgR520KBWcqFMUaTLEYF3YUUmOjNk9FTT9RK4mgux7dX75Nm0o8nV43P+jLdQKaJX6YIkLO79JltcXStIH02lTSdd4fS+fkas2ikO1TXNdK1ifS29VQ42jKWke4ifo61rm+SVhIQN+cnrWNIchnGy+nl49tl+8xnwUx9Ene1xGdq6cRn8cRdYEYCWK2M7i4TfjRzLO7iitwf1e6qoTRfks4rKdVJvaTdUzMZcLRO3x5vV9o2s14gyeUpr4fens6sZTzbDXgiR8S7Fk0FAPS+0Yfp01uxcX0QL9rZnlOzwbgI0pXI+13uc5HNRak0mM2U1o8V08fSid9DwHzXas1oLfrc2Lffi2aK9XtDu7kci8HnY3RRyRKED33oQwCAj33sY2DhsyDCe3fUUUepZcZYYo7TJBrqBGUyGXz2s5/FCy8EmXanTp3ayO4EQRAEQQwDSqAK3H///U2vs2F32H777YennnqqKjB6a6MjY6HFslD0uRGcK1WJ5fK5fX/fks2si/jIXx8N6cGbunJzI5adobYN1aY0fZy49SRphB4PPK2FmTCVYWnxpbr30a1TMvDZsWCoPsug5VqJMM3leIJNc3uyTlD6+vi2tP1qBeJmreQLGQ92tmwWabgUMjVVoeMH0lWi47/dwQqyYT2+ZhWyfDOAWSaZ1dGfk8AiF1l79Gc7CI41g+Pj9yHJAibvdbWl0jy+vk1aFWpNNKh1X+I4sfuTdHyZpFeqkku9peFYuYZLPFD6rlm7AwA29ZaVNddmDJVypCllhctAcL2ysKram6bYbVpRk8sE/1a3VbeABtpKwfVqyzAjUHpaLoN+j6MjTPxa4YHl15oIatNbCQcffHDT62y4E3TmmWfi3//93/HGG29gn332QWtrq7F9zz33bFrjCIIgCIIAucNCSqUS/vrXv2LdunXg3BxsHX300Q3X13An6OMf/zgA4POf/7xaxxhr2A9HEARBEESdUAJV3HPPPfjkJz+JDRs2VG0bbv+j4U7QqlWrGj7IRKTEOZiIXC3VuhwT52HSTcFJrrB4AG96PY0cM02jxmwTUB0MHV8fdzeZ29PN3HFkPUEajXrPxNQg0dvXU6kdVGs+M8lJVHXNIRnAGrkLks6BhfWlByynBU3bmttBbQ99J/FEooCZhJTZzNjOLAvMDtwETmsWlZIHO3QT+BUfmbyjfjut2ShJppYEUyKTYTLLUu0BAMuJ2uS7PDW4Otjuw3aC47eH5aTmTDZnw/GF2q4jA3CLYbBu3rbgaAG4Sfc+7g6T96Loc0ODSteX6szaxnbdDS2PU487rBpR9Szrwe0y8Hdkxxg5m8LrO60li03F4L5InS3jeQzX+RUfPNberGUhG/42NYKqdZd0F1mSm1InTV8IANaXPdUG6RaT7jJ5HNIJGjvOPvtsfPSjH8VFF12EGTNmNKXOhq/I9ttvX/NvPPCd73wHCxYsQD6fxz777IOHHnpo6J0IgiAIgjAYT/+frlu3Dueee27TOkDAMCxBd911V+J6xhjy+Tx22mknLFiwYMQNGy4//elPcc455+A73/kODjzwQHz3u9/FBz7wATz//POYN29e3fX4AvBRHTjph8n54gGK44mbJu2mgiPj1gc9kDdpSm2twOhGSau3VkLItN/VddffQHnP9HrrDeSOLE1yf5a6r35t5XHiv2upQsttjsUMS4Ge7FU+e3HrTmoQdcaCFZuOblh0QmuPbaMq8Fm3DvkuB9MsPU7egvx8MJvBac0a+8q6bMcKlIFbA8uQV/SQKWTAQ8uA5djwisEIO1PIqO16WQBglgfbsZRFKZPPwB2oIJsPtrsAch3hMUoeMvno0+a0ZuEOVAxLlfzNbAY7ayMTyiVYNoNd9GCFViN9yr5ss7RkxdtbiLXdacvC1RSQc4isVwWYweO6dSwuEwBElg9pmUqz7sYtPQXbwvHrn8eWRlo3NxUrKli86AfPsuvJYGgAweVSz7gesGyz2t+qaJuosrSZ1jC9ZekfgloTMoDqQPnRZkskUG3W/6fN4l//9V/xwAMPYMcdd2xanQ13go499lgVA6SjxwW9733vwy9/+UtD2nqsuOaaa/CpT30Kp512GgDguuuuw7333oubbroJy5cvH/P2EARBEMSI2QLusPH2/+mNN96Ij370o3jooYewxx57IJvNGtv1WOV6abgTdN999+HCCy/E5Zdfjve85z0AgD//+c/46le/iq997Wvo7OzEGWecgfPOOw8/+MEPGm7QSHBdF0888QS+/OUvG+sPO+wwPPLII4n7lMtllMtltawrThIEQRDE1kb8/7lcLodcLmesG87/p6PNHXfcgXvvvReFQgEPPPCAEkwEAkPMmHSCvvCFL+CWW27BAQccoNa9//3vRz6fx6c//Wk899xzuO6663Dqqac23JiRsmHDBvi+X+UvnDFjBtauXZu4z/Lly3HJJZfUfYyhEvCNB/o9jmmh8qoeuAlErpZgm1CaN5JaaruNuJGq1XCTf9ejPZSs7TG05okewKjXo7uThqLf4yqAM6hDT4oZDyoXiJvX5XFcLk36URuCxK7xgPvINZaka1OwWZUrVg8sTXJpSReN1PJJSmCqEpSG25hlwQIHC5PtMltzjVUC95hfiQJCKwMuch3BR9QdqCj3mDtQQa7DQXHDIAAg35VHccOgKlvqLqmype4SMvkMyr3BoMTO2qgMuNG1dCO3ke9ydX5A4B6TukXMCl1woUvMK3nI5jOqrNOaVW5AqXEkg7OzrQ58l6NlSgEAMLBuUP0ubS7BztrKVcZdH7mOnHJrFbryyrXXPqsN7kAFrdNb1bkxm6ngbLe/ou4Ld31Yjq3cbcE11n67XB3DDlW0uXquhOEOiutqbalA6Dj6u6d/fza53Hju4y6oJG2toGz1eZkuQsBUiUfVMfR9khgrde16CcQSR9Youf/cuXON9RdffDGWLl1qrBvO/6ejzVe/+lVceuml+PKXvwzLak6Qd8OdoJdffjkxJ0dHRwdeeeUVAMDOO++cOIVtrGCxB0W66ZK44IILcO6556rl3t7eqgeEIAiCILYkQgR/I60DAFavXm38Px63Auk08v/paOO6Lj7+8Y83rQMEDKMTtM8+++D888/H7bffjmnTpgEA1q9fjyVLlqhU9v/4xz8wZ86cpjWyXqZOnQrbtqt6qevWrUuNJk8yAwJAwWIoWLoCsDnNdKj8RGPJLV27qd82AyY7ptJqv2dOR09jKOtIYJ1gxnK9jCTgOv04eoBw9dYvD/zDWL6idWetzvoaH6/jmvZdjNFqrSnq8RFnT8VPbZOuaNzviVC5Vj+35Gn4wbJmzvM0y18l3B7mX5JB1dJyJHN1JSEDmvUAXd1SIXwR5YByfXBfwB2oqO2VMIAZCKxB0vLilTww2zK2S6sHs5myyADB1HmuWagE58oaJcsJnqxMzSxL7av/lm0XMT0RafESPg+sSpVQDTufqZrez0KrTKXkIauda9YX6rc1UFEWLQAo95bRNqsNpc0lAECuI6eCpnMdjrIyyeuh2sM5bMcC98OgdgRB11bYfJvpAfTBsyLvf7/H0e+lKHePMfUEZ/9oyjuqJgDI5z7KDwZtOdo3bqWOW0ujd9IMZpYTR+T2pAkJaQrhirHKHdZEOjo6hkwwOpz/T0ebk046CT/96U/xla98pWl1NtwJ+sEPfoBjjjkGc+bMwdy5c8EYw+uvv44ddtgBv/rVrwAA/f39+NrXvta0RtaL4zjYZ599cN999+HDH/6wWn/ffffhmGOOGfP2EARBEEQz4EJUJRIeTh31Mh7/P/V9H1dddRXuvfde7LnnnlWB0ddcc03DdTbcCdp1113xwgsv4N5778Xf//53CCGw2267YfHixcpEdeyxxzbckGZx7rnn4sQTT8S+++6L/fffH7fccgtef/11fOYzn9libSIIgiCIkSAQz5Y3vDoaYbz9f/rMM89g7733BgA8++yzxrbhuuga7gTJgx1xxBE45JBDkMvltph/MImPf/zj2LhxIy699FKsWbMGu+++O+6+++6GhRxdIWDzajVWoFp3ZksTBD+nb4sHJae5vWopDyddB/0aJO2brHhcfcxabrh63Wf1KUYHB9eTJDZK3BxeHQBuuk7rbVNPhaMzG+mndGaZoSAd6aWYukGA6Z6NJ4OMB4caCV/96NrHdYwczoPyFU0nKYxRLtiWeR8rYb2auyzVhTlgLg51PXWsIb4zSUHhgKm7A8BQjmY2g1f01LrKQBA8Ll1Zls3glczEsH4lKOsVPcMtCERaQqXuEoTPMfB2dMID6wZV4DT3Bcp9wQUVnKNnUwkFrV0qQN2WgemRW1LXWeJlod1Dc6JDEIg/fr7NQ2FO6BBKR0hHn1ggiev1SJeX/k7EVbPjExb0b5m+X+CalvWaqvWqruZdgnFHs/4/bRajkUW+4egizjm+/vWvY7vttkNbW5tKo/G1r31tzKfEp3HmmWfi1VdfRblcxhNPPIGDDjpoSzeJIAiCIIYNF835a5Tx+P/pSy+9hHvvvRfFYhEAqnQLG6FhS9Bll12G2267DVdddRVOP/10tX6PPfbAtddei0996lPDbsx4Qk5LTg7AYyiEgzY9ULboc1xUfGnM2xrkwjIfgmoV1Yi04OZaOXWSyqeNMvWp3fUcJ55DKe14yUQWkRs7d1WjOJeLqiBkPfjRsRiubtslsUY9X5QkSSE8blWLl6mnjvP7/1617uq2XZQVSG+v/B3kGQunZdvBb6nCGy+blNssSb1aLsttct+ozcywOJgyB9WWr7j0Qpz05yFZFVg/Tlo94FE5P4q7Dt5hoT1jHjesSlyImDKzNj3dg1q2GAu2l80AcRVnEVqGgEBSQA86ZzarshjJNsgAaV2d29KmzzPb0uoNA6RDq5FjW9H0+bJfZaHe5E4cO8XZPS/W3H7HtIXGsjlxwJw+r080CKw9yd9HaQXSc/FJSYzgXYv2kRbX+PfNHiOngBBiRP/ZyzomMhs3bsTHPvYx3H///WCM4R//+Ad22GEHnHbaaZg0aRK++c1vNlxnw5ag22+/HbfccgtOOOEE2HZkvt1zzz3xt7/9reEGEARBEARRmy1lCRpPfPGLX0Q2m8Xrr7+OlpYWtf7jH/847rnnnmHV2bAl6M0338ROO+1UtZ5zjkqlkrAHQRAEQRDEyFixYgXuvffeKgmenXfeGa+99tqw6my4E7Ro0SI89NBDVYFRP/vZz1TU9tZGLZeSY0WBdNIlMdYEAYTB70aSjkbm3fq0f3R3SdxNoevlxJOOmoG76Uqv6e1O/p0ceM1St+kBjY5laimluV2ky0sPyNRN6XpC1bjabTzgsl5cHmgK6eeit8nlkYuj6Pvh9eXG/hHmb/O6JLsI5O+04HZJ1Kbqc9QDruPt191puotQPkNJrrJqfST5a+hnN1tDWI3ZDJYfuZnsGgk15XbptpIJZlnM7ZX0GwiDtdU2Cyz0o1g2Q2shEyWzdVCl3xQlW7Xgu35V4LQknnQ0bcLERET//vR7XHNhmfpIANCZtRB3A8vfgKnOriu5+4IZ72+wLvqe6e+0bM9YOhwnuCFnxAwMDBgWIMmGDRtqCj7WouFO0MUXX4wTTzwRb775Jjjn+MUvfoEXX3wRt99+O379618PqxEEQRAEQaTTDHfWRHeHHXTQQbj99tvx9a9/HUAwU51zjquvvhqHHnrosOpsuBN01FFH4ac//SmWLVsGxhguuugivOtd78L//u//YvHixcNqBEEQBEEQRC2uvvpqHHLIIXj88cfhui6WLFmC5557Dps2bcKf/vSnYdU5LJ2gww8/HIcffviwDjhR8IWADymbbs4Ais+MkSbnYi1f1CjSlmGGmXiyY6EnlP3XzbdxiXig/jQY8Zlyej2mbo2ppSHRTddyH/34aW4y3T2SNHMsfi+kW8hmwPUduxr7pZnPa89cS3YTJZWV5fWZUXH33OUtO6vlgm3hC73VM2IuHDRntd3Yuavhaozfw2p3YvJsu6T7knQ/4+vTSHNjVrcvrtkSbdNn28g602aemc9nkivOXCf3lbO30nSGkhLKJmHFysnlJNeUiOlQ6ek/AJgzxUK3mr6P/C0T3rJwEopls4T2hm60TKBtNjmcWVZ0/cQZiRMVOTMXCL55+uyw6udUf47SH+JoZq/uQtRdz6LqHY6/JzQ7bOxoa2vD008/je9+97uwbRsDAwM47rjjcNZZZw07JnlYnSCCIAiCIMYODqUCMaI6JjILFizAmjVrcMkllxjrN27ciDlz5sD3G4/QqqsT1NXVVbcq9KZNmxpuxHhE6gTFiVsyzH1Gu1XJ9FS40raQVqGC1hi5LUoIaI6Uk8/FDHYGTAXa+LnGk4XGA171OgLiwcfyuHqtphUuPtqLa9Ho7TVHwGaAcq3RcfI9TLaWyYBfPYlroN6dbomRVkPHYsY9qoUeBBrVq7fDbF+6haY6MD4t6DxOLU0haflJqis+epb3eijrm2xf2rp4sHWSdVO3/MTVpNOIBxonlxn6vjHbrnkcAFVJXXWkJUj4HL5bfWPS6s7bllK/brUZ3txUGrKtEwXHYiohbGfWMiak6Fo/UjdLt4DHg5n190n/JsYnXuiW/7hFWb773hDB9ETzSLNk9ff3I5/PD6vOujpB1113nfq9ceNGXHbZZTj88MOx//77AwAeffRR3HvvvVskaSpBEARBbO0IEfyNtI6JyLnnngsAKg5ZnyHm+z7+7//+D3vttdew6q6rE3TSSSep3x/5yEdw6aWX4uyzz1brPv/5z+PGG2/E7373O3zxi18cVkMIgiAIgkhmW54d9tRTTwEILEHPPPMMHMdR2xzHwTvf+U6cd955w6q74Zige++9F1deeWXV+sMPPxxf/vKXh9WI8YgNU4tEd3nIdZIkU+pYMtmxlZlYmnalS0APPE0KqNXXxTVanBr6KrJ88G88+DXukjIDDeMuonjwcy1Xj47p3jE36m6quPk7qb7qQG25HogHVsbdpPGA67TEsvGgy86shZ9OD1IB6C4vN0zcK69DW8aqcjdGv+Mut+p2pnllktxj6WXS3XHJy8n7yW1J+j/yOUgOaI0HQrOq7WnnqVJNGBo+VtV2iZ58Nb6fTjzY2ThmwrsTL2dnk+sDgqSqSduEL6qCrlV9jh26z3xVtmAz/GjKOwAELnJ5j//17ecS6xjPFBxbpQHp93iVO193V8Vd8tL1HHwLzHrj7vp4SpW4K1ffL76OGB1k4tRTTjkF119/PTo6OppWd8NSWlOmTMGdd95Ztf6Xv/wlpkyZ0pRGEQRBEAQRIWeHjfRvInPrrbc2tQMEDMMSdMkll+BTn/oUHnjgARUT9Nhjj+Gee+7B97///aY2bkuSDUftkSUgGokmjQa2JD0Vjs5s0J/t93gswWYU2CdHQ6Z1pVrxWMccXZlB1vGRmK6wqgdRAzCCFOMWB/O48W3JCTXlCCzNApFmHUk6pmlZSbbq6YG+pqUnfQp9uspxQE+FG4Ge/Z5Qv3XlX5tZxjF9PRloLHi9+jxH9nDWU3f8OYirQ9fbvuB3Wtm4ZUlaMXULUdQO/Vpb4YQRpbmcoOpsrLMsSENR3HrDbKaCmO2suVzvVPuoruAgKgGqsb/cFtYdJgAVNoc+duW+AA+tI17RCxSwpaI1pGRGECjtC2DGvOb+BzLWyG9MwbaqguvjFu9kdXv5b/V3Rv4bV5rWZTzMiQfBRAdR56ShkUKzw0aHhjtBJ598Mt7xjnfgW9/6Fn7xi19ACIGFCxfiT3/6E/bbb7/RaCNBEARBbNMINCEwuikt2boYlk7Qfvvthx//+MfNbgtBEARBEMSYUVcnqLe3tyE/XF9fH9rb24fdqPGAHsRa9IG0gEw9OeiWQk8WCJguJD1gOR4wKIMEpVqqrlsTqUubpl7TLRMFiye5hSL15uhaWoyBaybnoVyJ1Qk19QD1eHBs3BWjr6/lGmJDuo3S1JR1lXC5XuoXuZxXqT/fMS0KhLYZMDMfvIIF20wy21OJAqX7PaHuTVyVvJbWk7496TxGSnTs6mPVcoHF3WU6ae6zyMWhuyKGfzJ6QlIWc4WlBULLctG/Vvjv8NoQua2sqmBsie0E66UDTFeSlst+NlSTdnyjXq/oGd+GyR15OK2xaOxxzJ0zF6nfTtZGppDB5NB9XCvxc9YKVLX9sKydsap0mHSCJLqRS1PuF9TLqpTGbRkS4EXft7GAC6HUz0dSB2FSV2B0V1cX1q1bV3el2223HV555ZVhN4ogCIIgiAjRpD/CpC5LkBAC3//+99HW1lZXpcPN4TGe0BWjC3YUjCqnL8tRu8ujkfCWytPT73HDQhDP1aRjM4aCHQVR6yqsjmWp3zZjSnk12ldTS81E66uDRy0jYDQ+pdcG4NTIsyRJGhnrIzqeYv2KkzSVNT61XA+qNfdNrg+AujZ6YLhjRTmNkqwzulWup8JRDGXe9enLBTuoWw9ul/u1ZZgRCC/X68HStaw98WDt4aBbGYe3f307xiUdkupJGoUn7aMHDAfLllqfhG7tqXq+tffciilM1wqO1p/dpHJpAdbCF+DyHQnvuwjvO4eFTCFYl+twILhQ743vcnRmo2nlXtGbUJYgmzHV9gIX2NTnoi0TBUbr1uSCY8OtBGWzORuVsq++UcIXsMN58TJPm06VXELRM7Yri12VJEJgYUrLSUdMDOrqBM2bNw/f+9736q505syZyGYnzstGEARBEOOZbVkscTSpqxP06quvjnIzCIIgCIJIRTQh7QV1gqqgLPIpWFpAnMUYpF3LsUSYrC94mvo9nhgYOtboGj36v28WfVxUfMko++vZewAAeio+fAFDR0R3fyVpcajA5EqUrdf2Yi4y34dlM2VmthwbdtaMHjUVcU3dE32tbrrmvjBcCsLnqryN0G2gfSVMBWtTRybYjiGRLsDI/Rnp9xR9vyrweSiku7FgBwlUP939t6oyd85cFD5T0XWJkjYG+8ZVqnWXbJrOScTwntO0ZLb1uLfSVMBrkaR+rW/Tg+bjvwGkuimGSpLKtGcXMN0gSa6wJNdW2ro0l5hlmwHashz3eeDKC58Fy7bAfa62+66v2mE5UfJUAMjmM8h0l+CvHwAA2I6FbFuUbmC8E1d9npm3tXcvcPUBwQQEy2bqOyT3zYbXs+Jx6F+f+P2P3yv9GhpB876A7/pq/4zDwF0fNplXJjTUCSIIgiCIcQ6HAB+hKWek+2+NUCeIIAiCIMY523IW+dGEOkEp2IUMMlaQkJDZlprBxMt+LBUCQ2GYOiHNwmaRfoyc0RW5T6rN8hUeJVt1uVCpMIJ9o1lwrRnzxBzNNKybjG3HMmZdyN9pCSGZZakEk/FythWVZZZVlUhSmqdFbKaGclmE58sNPSTpCos0ZtKTigZuMt39BUTXsTPLsCn0o41kltX6sm+4HnUCdyszkj7qtGUsLUltsC7SEeIxHai4Gyk5XUDcdSZ/x591c1/5q3rGWfXMsfRrleYeky6v9P2SdaGG0kKS73TwW4Bpz6AFDq9kPqvyGRMxN5mcocX80P0Wc2nJdQBgh7O6OJczJkWUsiN2j5htQfrfhc/BuVBl5LfIr0RtjGZhCnjazKZybxnl3jI6WoLKOrfvRDY/cT75RT96h6N0GdEMSvmuy+9UNhd9k/L5jLpXdkbAkt8r109MmGuQ9gDZrGqWIRwbzE8uTkwMJs4bQRAEQRDbKDQ7bHQYVifooYcewne/+128/PLL+J//+R9st912+I//+A8sWLAA73vf+5rdxi1CJpdBxrbhV3wjqNdyfFj9FTWacCo+1peDocCW0gmKAn9hWAHS2iStRlLTJtIJMhV/BzzfsCjZWkCtru3DfXN0ZGftIFAxQSWB+wJ+xVflmZOBU8ioESz3hbre3BfKYgSYViIOwHYy8EOrjAUOaCN6G1EQqsUYbCFUILQvhKG9k6RaLJHlpGZPo4HQcc7uebGucrolzvc58uGotqRZegCZkDbary1jafc+WVlXt25FySEB3UKmJ42UxK078aD5JHRr0UhJ0rzSt6VZjWpp96QlUJXo6wPrTbxset26ZYjz4Lm2w0+JsDRrU4IWkcK2wWLaNhwWmLRChdYJeTw7aylrUx55WI6tEqzqekPP/NsHg7rCG/TO//5t6nlsKTrzGfSXg++CxYJrKbWAnKytFJuzORu+y6vupUpM69iaBlO19TWuHaRbqeMwSxjWactmsJrwbNcDucNGh7oUo3V+/vOf4/DDD0ehUMBTTz2FcrkMIEiVsWzZsqY3kCAIgiC2dWRg9Ej/CJOGO0GXXXYZbr75Znzve98zBBEPOOAAPPnkk01tHEEQBEEQxGjRsDvsxRdfxEEHHVS1vqOjA5s3b25Gm8YFgnMI3RVQiUzKenBc1rfQmTWDZ7cE0oXTmbXQowVNJlnZ9YDbtgxL1H+JJ1D1hYClBXPqCF/A1wM9weFzKLeWvo9TyMB3o753Jp8JtU9kYGoURCpiDuxAJ8gMCM2EgZ6BqT/m1tK0jLyip7SNCjaDk7UxNTR7byh7RuqLfk8oeX65zmZjN4KSSXllAHtec6vkw3FLpIdU3S7dpeVY1c9lIcX1El+t6+wkpXLQ4UIYQek6Sc9XUsC1HqQd3zeeSDd+ThZjia4tnSRXSDwwGgjcrspdO1SUNZL1goQvwMEBPyrjw1cBzvrEAh4+u/ox9bbG9bKYxaK0Gb5Q7i65r3Qt246NrC+A8B1pndGCykCQ0qhS8uC7PtpmtA55flsK4UeuXC4EsrYF300uKydnAMk6QPGUJEaIQ1UajRrB+DaD8DXXpC/AxsjHRO6w0aFhS9CsWbPw0ksvVa1/+OGHscMOOzSlUXFeffVVfOpTn8KCBQtQKBSw44474uKLL4brmm/E66+/jqOOOgqtra2YOnUqPv/5z1eVIQiCIIiJhswiP9I/wqRhS9AZZ5yBL3zhC/jhD38IxhjeeustPProozjvvPNw0UUXjUYb8be//Q2cc3z3u9/FTjvthGeffRann346BgYG8I1vfAMA4Ps+jjzySEybNg0PP/wwNm7ciJNOOglCCNxwww0NHzPbmkXWzqgp3CoYzmbI5DPRNG6bwxmIko6OBT+YvBum5YJbFyhWCyOAWU8u6CZMB8iGFhDXCxSjZdBvYPFInkINALYQsMJBp6n3zKtHX5Zp3VEJKa3g+skpvpWBCjKFjLL+ANHI2ipYVRYHPxz1+hUO22Fq2XJsMD8+bdgO62NBUGpJjpCDtskRc8FmRoLcmXnbCB6vlZx1NPjXt58zlu+cuQjZcLwy4AVT6+V04OJgBW25jAoYrTWt3WLM+AjGLT36tG0Rs/rFk48mJcVNCkqPH1dWk5SE1QzShmGJHEoNeiiLDfdFEDyPhISZ2vmpdz2MYE5SeK5OpJnejniCVWNbaA2yEATxxp/1qI6YFY4LFZBta4HZ3PXhu1wFRvvhdHBZb7G7pCxQXtGDV/IwuKEIAHj0nw82jn/Ag39MbfdYket00Bu2Lx9ai1sLwXeP+8KYEm85NhCziCXdu9QA9BQMi1xs3+CZAjI0RX5C03AnaMmSJejp6cGhhx6KUqmEgw46CLlcDueddx7OPvvs0WgjjjjiCBxxxBFqeYcddsCLL76Im266SXWCVqxYgeeffx6rV6/G7NmzAQDf/OY3cfLJJ+Pyyy9HR0fHqLSNIAiCIEYbnwd/I62DMBnWFPnLL78cF154IZ5//nlwzrFw4UK0tbU1u2016enpweTJk9Xyo48+it133111gADg8MMPR7lcxhNPPIFDDz00sZ5yuaxmuAFAb2/v6DWaIAiCIIZBM9xZ5A6rZthiiS0tLdh3332b2Za6efnll3HDDTfgm9/8plq3du1azJgxwyjX1dUFx3Gwdu3a1LqWL1+OSy65JHW7DDzUg3wNBWm/okz0ufaxSU4YqBoHD3O/x2EzqGBo6RaT7pwk629v6DpxuQgTeQYm34LNEoNSG4X7AnEvgEr4WOFwWrORmyCfUdolQExbJZbhVHc15DqCay0Do72SF2iqSJ2kClfmcalNJPf3ih4Gip7hQuz3QjebCM5bqkJPy9nqOm0pspal7tmUrjw2dpcwJXzWJhcycPsrKLQGy77rq8SR8UBxAIbWk67WbQVCO1pBlhhIDMh3oFomPc3xI7SAdj3hrdwmiX+gky55WrB23F1X3bbIZVtLMThIklo7VFIFMFvyOkutqmo3m76sK0NzXxjuG8uxYTux7ajPfSNdyfJf+W7JdnmhG7jc66rvmGzL4IZBAIDT6hgTCcYDvsvREbY325YFd31D+VkncN/WTowKaO+EH7rRbQYOKzUovRZyn7HSCSJGh7o6Qccdd1zdFf7iF7+ou+zSpUtrdkAAYOXKlUZn66233sIRRxyBj370ozjttNOMsiwhJkcIkbhecsEFF+Dcc89Vy729vZg7d269p0AQBEEQow4XYkSpemQdhEldnaDOzk71WwiBO++8E52dnapz8sQTT2Dz5s0NdZYA4Oyzz8YnPvGJmmXmz5+vfr/11ls49NBDsf/+++OWW24xys2cORP/93//Z6zr7u5GpVKpshDp5HI55HK5qvV2xoKdiUZYcnTlV3z4rq9GVwDghLl59GmXo8m0nK1UngPLRTA1HggsQSdvfMEof9Ok3QAEZRyLaRaQKH9YVFdkQTKDVmu3KRjpRyNtwXniiJq7PlyYo1Z9urDcH0Di+kwYGOkVPTCbIdcR3Dvf9SG4UPvYjo2yVIwN95PWCD9roxXRqNDxuDrXT3f/rfaJbgE+9NYzxvI9O+6tpkVbhYy6JkBw3ioIPWYJiufBY7YF3aAzZE6lGtS0nmTN3HGB1IHMd6UpJ1e111xOsvwkBTknts+2q/LM1QtLONZQxAPLk9DbwXwOjkipOE2VulagNfc5BBeoVKJvk52N3i+nNasmEqjtjq32zeQzdUkCjBa/W/Ru2GEyPKfVQa7DQXEgmN3bOaWAntd6kQ0tnrUC95OQz6eel7BR4s9NYDVkYHxsrlmQNmOknaAmNWYroq5O0K233qp+f+lLX8LHPvYx3HzzzbBtKevv48wzz2w4+Hjq1KmYOnVqXWXffPNNHHroodhnn31w6623woo9xPvvvz8uv/xyrFmzBrNmzQIQBEvncjnss88+DbWLIAiCIIitn4Zjgn74wx/i4YcfVh0gALBtG+eeey4OOOAAXH311U1tIBBYgA455BDMmzcP3/jGN7B+/Xq1bebMmQCAww47DAsXLsSJJ56Iq6++Gps2bcJ5552H008/nWaGEQRBEBMamh02OjTcCfI8Dy+88AJ23XVXY/0LL7wAzkfnCq9YsQIvvfQSXnrpJcyZM8fYJqT7xrbxm9/8BmeeeSYOPPBAFAoFHH/88WoKfaNkChlkMtWXx3Is5DpyyqzsDlSU3ozulhhNgmDowAS706wWlHtcpS5cTDD1bwrb2pZh0GONpVtMT7pqJlGNAlHjOhu1ghCtWFl9u9xPKd6GqrpJqrvc5yopJGC6enzXN9wlha483FANV5Z1QtO5dBOwbCY8BkdlwFXts50sJk8gO3F/v4vOyXkAQOv0VvSt6Y9csZrriWXtmBtK3oNkt21aEtGk7UE99bsBdJVdZgkIWwZKa8+NKpt+Lxpx2aW5veIB4UkJU+s9NyvFHReoyldfwyTdmZpuPIslHiM5+NuuUlTnPlffKmYz5f7yXT/Q59LcYcIXVS7osaQy4AII3tnBUhiwHT7XXslDviuv3GX6f11prvckkpKjmvfNvD/69ZfXL64MXvEad60NB5odNjo0/L/2KaecglNPPRUvvfQS3vve9wIAHnvsMVxxxRU45ZRTmt5AADj55JNx8sknD1lu3rx5+PWvfz0qbSAIgiCILYXfhMDoke6/NdJwJ+gb3/gGZs6ciWuvvRZr1qwBEKTSWLJkCf793/+96Q0kCIIgCGJ8c/nll+M3v/kNnn76aTiOk5hL9PXXX8dZZ52FP/zhD4a3xnHGRl4miYY7QZZlYcmSJViyZIkSFtwaY26ELyCYMEzIcr0+wyKYOTa2s8M6s7bq0WfyGfguV9ow6C1XlZ/XEt3mtoxlJKWMpzSQ7i9pdq41+6bKPRJzK9TrUohf36T1QODeyKiZLo4xQ892bMRfIzsbaudUuHHPLNtCpRBcN3le0r00EXCsyJ0jwsSbumtFJpkMtH+ia8iscJ8abuu4WyHNbRAsp6cU0Gc0BcfU9IC0exx338TRy9aTpkKvR55vEro3qZabL9F9ZdXvBtThPDl1R3ymk55MNYlas9TMbdyI3fQrHCxcrITvjkqT4gp4JW9M3WEP7X8gvJKn3NaFqS3qeXBa8yj3ltExJ3ovbSdKkpwp6Ndv5GEY9cz8k99+/RoxmwFijGaHYeSzu0YzJMh1XXz0ox/F/vvvjx/84AdV25ud2qpZjCiIZWvs/BAEQRDEeMPnAv4Ie0Ej3b8WUvPvRz/6UeL28ZraquFO0IIFC2qKD77yyisjatB4gYWWjCAw1zb0acq9ZaVU7LQ5Spm1bUbrmLRNJk0FgOmtDoQv0L8+CCR8fdCrKq8HQNsZC/pYz7IZMjJY2edqpBUfjTI7SEJaj2UgccRuyWBpc8SVKWSQyWfUPr7rq+SqUmU3MQjUZko1Wq9LWgEsbbTmd5eMctm2LDIDGQCeOtfh6IZsKTq2a9cUmDnaZrWh3BuYf7L5DCp2cF5xXRyZqFPXdk5KMhktp1uFgNr6NUNZAYV6zjRLEI+ePVWvKl+dpDfNWqKfr9x3KNIsAUl6PbW0ehqhEVXoeok/x8LnynJh2Vb0rZreisENg4ZFiFnMSGQ82shg54G3g29XFPQcvKN+xa+yEodGdwDas5O1DEtjs0gLSjcU7mFhTLMrT2CGm9pqtGm4E3TOOecYy5VKBU899RTuuecenH/++c1qF0EQBEEQIaIJs8PkbOp4jsw00eBmMtzUVqNNw52gL3zhC4nrv/3tb+Pxxx8fcYMIgiAIgjCR2QFGWgeAqtRQF198MZYuXVpVfjiprWoxnNRWo03ThG0+8IEP4IILLjDUpScylm2pZJ7C58qknilk0D6rzTDZK6n39jyePeFItd4redjr5/eOuC2Pf/D92PhSN9ZtKgIIgpsLrUGv3XIs+BUfhUnB8o6M4Z4d91b75jocdIRpPfJd+UDXohS5S5hl1WWOj+uTpMn6B9us2D6m20RPJslsywiith1bucOEL8C0YHPdXG45tqH3It0lMvBTb5+dtZQOSnRdcmo5k4/Sb0wEKgOucnu0THWQyWeUOyxJq4pZmjtIcx1wX6jrlUS97i9mVwfBp7nH4kHAUstGJylAWgU7pzyrwXsaBYsbdcZcPLVcJ0lBz2laQLXWJW2zqt6T5P3qCQC3s9F9Vglc42462wKy0Tvrux5QCrS0KqErCqGruFLyILhQ795YIJ9b3a2tn0NWS7SsPz9x4u/2UDRSVj92XPtJ3ZMxCoxuJqtXrzZicNKsQI2mtqrFcFNbjTZN6wT9z//8DyZPntys6giCIAiCCGmmWGJHR0ddgciNpLYaivGa2qrhTtDee+9tmK6EEFi7di3Wr1+P73znO01t3JbEzmWQ0UZbUo1YjjK5Jr0sRzLFjQNGHW2zOtEM/vLYm5iWy6AztIpYjKFjTjsAYODtQTDLUhaATRuLmKxpLghfKCsHsy3Yjml1qaWqC9Q/HbhWsKg++uc+hwULfnhcG4DvR9Phmc1U0LllM1iObQRrqmSQHS3grgdfSxZpJOPUEoUCQLYtuiaVfhe+66tzz7Vm4bRqEZfjnLZZbaho6thWaK0EZGJaqYzNqka9PDYlXbewDDVCjisiR8c3R+vxYPZGVJ3jQcw8ZrWJW1OSyqZZl2pZE9JIsvLUGxidbDVKUpFmyiIKoEqlmNlWVcCzbv2xHXm/zXK84sHtLRqTBORvqxJIRsjvhl/hodyGmWB1NAkUq4V69/wKN9ThEyVHtNfUfObNZyXtHnOfV1l04u9EnEgt36p6TyyLKVmR0Wa8zw57/fXXsWnTJrz++uvwfR9PP/00AGCnnXZCW1vbuE1t1XAn6JhjjjE6QZZlYdq0aTjkkEOw2267NbVxBEEQBEGMfy666CLcdtttannvvYOwjPvvvx+HHHJI01NbNYuGO0FJwVMEQRAEQYwe4z132I9+9KNUjSDJeExt1XAnyLZtrFmzBtOnTzfWb9y4EdOnT4fvj505dTSxnSBxZ2BaF8iGLhruB8qqujyxDCbkvIJKfwVWGMCb7yo0pS0L95qB/jX9cPsDF0jXDpNQDpWhJ23fgcGNRbRODzSKuOsj35VXasqZfKbKLC/dTbVIC/ZMClBNSn5aC91dIoOi9WXlGovpdGRbc6ocd4Pz0wNEkY1M27oqMrMYsllbuQLsrAVmRy5E7nIVLD4R2PSPbrRMDZ4teS+7dpgEINCxku4F7osgoapm7tddi5xH7jDpJqilGyRJut9pLrC0hJ9xDSOJiAW5psVtJ7nvZFnui5raQHEX20jRXXSy7rjbTnd3RevSk6HGiat8S7dXsnstuBKi5IL7HNls4A7PtBYgwu9zZcBF7xt96jna7777h2zDSHj8g+9Xv+X7LcJv6bSFwf8lPa91G4H9dopytyR+j/XnIU2VPK6DxLkwVLWTAvd1N2/ww5zUYbOx0Qlq5uwwIqLhTpBI6UmWy+Utmv+DIAiCILZWxrslaKJSdyfoW9/6FoBgnv/3v/99tLW1qW2+7+OPf/zjVhkTxGyGjGMbQYtOWxSdxywWWYJcH5bF0DK1BQDQtcvc6gqHQc9rvejpK2PazOCaZwoZtM8Ofpd7ymifFd2LwtQCCl15lRfLdiz1WwUSJuTkkuij1aGCVutV8E0iLT8TUK3MK8t6JdfYJ5jeH29DuI+TiWQN8oCdd1AZCKYDCy6QKUQq1ZWSN2Z535pBx5x2dZ5KJkAGmjt2FPiurrFUBDcVmS1wNaq1s5axr+/yKpXmeu+/btmT69Om19sJ8ejxoGYgmtZeKydZtH9tS1BV3Q0Oj9MmEwBGqraaOcmC5XQLkLwG0kohLT9BcK4FS7OASiuRtI5aYaC05WRQmNoOOx9YgrIteXXM3KR2dO3CjXpGEynh0DG3HX1v9QOIntVybyD9EQRK67nuhlAdH+J7k2YNCpajiRlDEb/fVVZOPvGmyBMRdb8B1157LYDAEnTzzTcbJkTHcTB//nzcfPPNzW8hQRAEQWzjcC5G7Mpttit4a6DuTtCqVasAAIceeih+8YtfoKura9QaRRAEQRBEBG9CTBD1gapp2BZ6//2jG0A3XggCowNrFw+Do4EoKaNuqpWmVmazUE8oSAg4yW082PaRgw8CAJS6SypYN5PPYHqHg52O3B0A8Px/P4XcXoHCZlwhWLribC08K1Jajqv4Wglrte1x/Qw70hVKMvXXSqAqSdo/7ipJCxiNu79Yyu80pJ6KnbXhFctKEyWeGLYRnj3hSNUmO5+F4Fy5LjIFBzte/1/DqrcWLVNbMLgheMaYzWAhuq6ZQibVfI+wXFJiUaG504J6A50lGXjtlTz4kTcyLJt8zeNBwEmJKGvdLz3Fb7yN+rOR1H4g2cUGxBJfygB7FUhf3/8OadcvTlrS39TyNZLRxhE+h7BDnaBsVgVCW9ks/FJZucVY6DrzBopR3VpAdX5KB7KtzZm8MRQycH9ww6D6ZrVMKagJHEANbSAkB+kPqWulKaXHXWe1XJrmMbhyK6c9s4xPnOTLRDV1dYLOPfdcfP3rX0drayvOPffcmmWvueaapjSMIAiCIIgACoweHerqBD311FOoVILp2U8++eQWTXZGEARBENsavhDwR9iJGen+WyN1dYJ0F9gDDzwwWm0ZV2RyGWSc6PLoZttsq6NcHm5fSbkQClPa0DarU5lNs635ho87sC5IvVHcXEb7zED7x3c5ch05rH7oHwCAKTt1qVQYwufwSp4yp9uOXaX1kmY2jqcRqKXzU88MmlpuLFVPit5JNBPMqukqiacPiI5dbUYXlq+22dkMkI+SBFqODa8YdOx1uf5GsR0bmXzke/QqHioDgd8ofq7Nwit56nrlOnIodZeUa8Z3/Zqz7WxNSwlZS81stB0Grxg945V+F7Zjq+feaXXA89HsQnegUpd7pzqZbris3cea7h/NtcV9bqY7MBLDRj/ldY8/d1bCXKD4unpmldVKyaDqjbnbkrSVar13+nWytHcifv2EpsvGbBu5Se2qTPBtcMHy4TtmWXD7gu9LJnSDZdtbhjzfZtAffteEL7DdATsF695cj1xHQaW+yeQdNQM0/p7XekaSZhMC5swv+UzI+zvUNzF5m9QoM7XwshZ1LCYyDX/5Tz31VPT19VWtHxgYwKmnntqURhEEQRAEESFnh430jzBpODD6tttuwxVXXIH29nZjfbFYxO23344f/vCHTWvclkRaJKQisRwFeMUKvGJFjUx810f7nGCmnJ3PYXDNJtj5YFhaXLcZ66/9Itb++UUAgNtfQdus4LpZtoXBjQNqNF3uKYPZVqQKPb9TBRNueHETMoUMWqYEo7eOedPR/fJaAMHIpnV6O0qbg0BZOZoyg4ZTzlGOtlICSdOSDAIwAoD133K/tKDlKIhc227pZdOVOxKDTVMsQwDANAuNnmjVymaQQaQ27Vc8ZRV6/pNHhUlcg325z+H2lVSAcGF6F8rdwSCAVzwIXxgjWSubQTZsZybv4B+f/VcAgU4RAOxw7R2p7a0XpzWrgro3/G0jWqa2qGvjuxyWHVkYmK1be2xY2QyYfi3kfhVfWRcBoHVGK9yBSqQtlc+q/bKteZR7y+qc4hYanSTrYK0Ad3PfmEUrliBX31pPkHKtMuq5zFeXTdNKir8TSU+ulWCVUtvyQ49B45ZRZlnGO8Jsywh2BiI9rUzeQX5KhwqU5hVP6QKVu/vQMm0SrELrkG0YDi9/4RMAgO5/rEemkEGhK7CK5ye1wO0NrEKl7iKmvGMO+t9cDyD4fvphWzMFp6YulaECHX9OYstmAD0Pntd8+rctjv6sGt8RxwazLNSZT3fE+GiCYnRTWrJ1UXcnqLe3F0IICCHQ19eHfD5y9fi+j7vvvrsqlQZBEARBEMR4pe5O0KRJk8AYA2MMu+yyS9V2xhguueSSpjaOIAiCIAiaHTZa1N0Juv/++yGEwD//8z/j5z//OSZPnqy2OY6D7bffHrNnzx6VRm4J7LyDjJOFV3LhFSvKpeC0ZmFlM/BLgfvEK3ro/kdgzmW2hXJvpD/jtHUDAIrdQbqGQlceG55fn3g8v+IHGiR2FOD86v2vAwi0X3IdDtY+vU5tk8F52dYsuM/htEeWuUzeMQIpdbeVTty0Lny/yh0VN0HHAzF15DZm28Z+djZT07StLye5DerRAJLtS8OoI++Au57hJuHhtXHaW6raDkRmcO56ap1MK5EJA+C9gSBAWbmJKh7KmwPXmV9y1fqREripgo+Z0+YYej1JrgBd+weAEcitztPnsJ2MKmPnc7C6+9RzPriuX7nLrGwGrdPb1TVJCjhOC3yOB/jGy1bVoz9j2fi2kfshGkmvEe0T6kuN8Nhp709SGSMNiZXubg7aFabNyGaCyRmhx6u0sVfV53S0ws47icdsBr2vbQIQpMvId+WR6whc+aXNgyhMCdrbuWA63L4BFKYH4QR+qYxcVxAuIHwOuyX670m+n0nubz7EBATDhVmpgPncdKdpz33SJI7491O+/5nWQhDCUI4JaI0SNDtsdKi7E3TwwQcDCJSj586dWzMWgyAIgiCI5sG5gE9pM5pOw4HR22+/PQBgcHAQr7/+OlzX7AXvueeezWnZFibTkkcm58DO55CbBDWiZ5YFp6MVojUYQVmObUxPzhQiq0e514WdtdA5JxjdlHrLyIcBgqXuEjKFjJqWnMlnUOouwQpH7QPrBjBzryDGitkWSt0lTN0tsL51v7JZqa7mOnIQnBsB3GmjW2ZZsJxoOK2CXmUSRS2ZogzINOpKCaCOI3wOy8moEVTcmpQ0go/UptOn19cMeKwxGlSqwNq/mZh8QTx4Ut3TkgtmWeCh1UNvvxUmafVjiV0lVjaD3KTg3vsVD5ZlYdV5JwKIrvm85bemtlvyl499QFlhWqZ1Id+VR38xSEKZ63CQ74pUf7nrV03Lls+Unc0Y90XH4hxWNqssA36pDMu2YLXmwvNiarq84Fwl6QSqA6NrWSnix44H+RptsqrlElKDqIc5KEt7bpIC+IeyGjVSNl4+6dhJZauvX0Id4Xtqh8+nDIbWkwgD4YSGXHMUo+Vz3f/mRrTPm4a2WR1h+xi2e98eeP3+vwRtyEbfFLd3AJmCA79UBhAoXnO3Eu5ng3OObEvwntoILKv6dVBT1ivVyvxp3y2RzYBzrn0LIuu1RL3f4TF0i7Ff8bTEzIFF1R4lKQxibGi4E7R+/Xqccsop+O1vf5u43R8l8ypBEARBbKv4TbAEjXT/rZGGh0/nnHMOuru78dhjj6FQKOCee+7Bbbfdhp133hl33XXXaLSRIAiCILZpZCdopH+EScOWoD/84Q/41a9+hXe/+92wLAvbb789Fi9ejI6ODixfvhxHHnnkaLRzzMnkHWRyDvpefxt9a/oNnQs7m1GuI8vJotIr1VADs26pP3CP5LvyaJnSqjR82luzGNwYJDPMFDLId+XBQ7da/9sD6JjTjt43+tR2WbZ9VhuybdnIdVbIwAoVeQc3DMKvRIkuM4VMlfqxHhgrXSKATL4Yc0E4UdBvUCZYlqZo5SbSTNDC50a9zLYCLZ7QXDyUTpBOLfeJNUTQNgDw0BJp2bb6HYdrJu1abZHbhM8Nk3vcJSFdKlLjxHaiwNRIZdaDX/GQke1MMOHXQrrV+t/cgFJ3CeXe4FlrmVowXB6WA2SzObWPnXdU+/TEmqrtsj2co9zdV6WJortZ5TPGLAuCc6U6XCu4NimwFzADXJOCpeVxhnKHDZVsVK+rXupV+q5Sg66hjC63p61vpA21XMRGuXBShHxvClM6jUB/7nrgfd2p+zeC1P7p2mU7FNd1ozIQPJ/5rgKKG3uQnxQ8K5mCAyubVe2Lv4f688bsyA0tl3UXu/qWhNfV0tz5ugssSOgbvoexoOh6JlPo7jDjGGj8PSbGHw1bggYGBpQe0OTJk7F+fTDbaY899sCTTz7Z3NYlUC6Xsddee4ExhqefftrY9vrrr+Ooo45Ca2srpk6dis9//vNVMUsEQRAEMdHweTOsQVv6LMYfDVuCdt11V7z44ouYP38+9tprL3z3u9/F/PnzcfPNN2PWrFmj0UaDJUuWYPbs2fjLX/5irPd9H0ceeSSmTZuGhx9+GBs3bsRJJ50EIQRuuOGGho+Tn9yOfD4Hp70FhemblUqwV3LBN/aocvpI1AmngXbtMgcA4PYNwhsoqunrmYKjpopyn6MyUFZT4qctmgY7m0Hb7GDE7xUryHcFc1tbt5sGb6CklGCBaPQdt2pYTsaw8NiaNUISD1jWg5JTR7MFx9hXcK4sHxI9QBhZfUp2zpyO7vtqdDrUSDap3UPtp9qfoHIdnEuuoanRwufg2ghQtp37vnGNLG0UG28Dsy1k7GhqOq944D7HW5d9VpXT6+Kup6b+ts3qRG5SGwCgfd50FF5fh4E1wRTkjnnTkZ/SgfLmfrVv2lR8O5ROUEGhnCuLngyElm3f6cb/NvZ96eyPqaBaaRXUA+r1acpJM0fjwdJp99IILB8iqDpefqj1adYgS5N0iD8XadZEoHoKddxSORS61TJtWz2kHVcGwRvPqLT0co5sawFWa0cjTVZsvPF89cz5JRddu8wFAPStfhvc50o137It9L66Bm3bTVP7cu27YeUd43tUK1g8bdKHshLFJlckPWNWwuQO/d2r9YylTcyohIHdow3FBI0ODXeCzjnnHKxZswYAcPHFF+Pwww/Hj3/8YziOgx/96EfNbp/Bb3/7W6xYsQI///nPqwKzV6xYgeeffx6rV69WekXf/OY3cfLJJ+Pyyy9HR8fwXnaCIAiCILZOGu4EnXDCCer33nvvjVdffRV/+9vfMG/ePEydOrWpjdN5++23cfrpp+OXv/wlWlqqMx8/+uij2H333Q3BxsMPPxzlchlPPPEEDj300MR6y+UyyuWoJ9/b29v8xhMEQRDECCBL0OjQcCcoTktLC971rnc1oy2pCCFw8skn4zOf+Qz23XdfvPrqq1Vl1q5dixkzZhjrurq64DgO1q5dm1r38uXLE9N9WJks7Gzw1553UJgSWJLiwa968GbcVJttzSe6S4DAtGqYhZ0MnPZWIxAw2xa4w1guD1GpwC8ORtdEC7D13YpxHD0YMskNkaQmnYTgPNVtpJuQpalano9fCRJvZvJBgK6ddwxdmaHUo+uhSi/FsgEr5hbgkUtBcNO9EA881d1WSeectC7N+ZEW1ForAJa7nnKB+GEC3PyUTgDA+qf+Di90RbXPmwE7m0XH/JkAAG+wFGgQyYBXzSVpOxn4mjK2DBDVdZPkXbGzmVAJPTmGzgq3A8E7kG3NG/fU0s6vlg4UEFOQHiJAut6A5iSXUCMaQ408g7o7sZF9q9XX6wvObwRdxyr+fuvBw/I+i3KpupI6cDpakQ0D41v3X4x1d/4XAMAruurbCACVgRIK07tU4HRuUjuyHS2qrXE3sMQOXXlprtKkyQm6DphcB4Q6Vly6w4LnOP59TLsXQ+lcCc6RzY74v9G6ILHE0aGuu3fuuefWXeE111xTd9mlS5cOmW9s5cqVeOSRR9Db24sLLrigZlnGWNU6IUTieskFF1xgnF9vby/mzp07RMsJgiAIgpjo1NUJeuqpp+qqrFZnI4mzzz4bn/jEJ2qWmT9/Pi677DI89thjyOVyxrZ9990XJ5xwAm677TbMnDkT//d//2ds7+7uRqVSqbIQ6eRyuap6ASA3pQu5ljzA/cCKEI7KawXQJSFHixKmWSsE96NlywZqqOnCsmC3tkXL2uhJeK45hVufShq3kOj5wqzYv3F060n425gOrY/g5HVCMMrULQpxK1XV9Yi1DzWmB8evkaonfk6x89CfTOGbyzWRbZHXIqV+HXkd9POOSwPIgFVpPbOcDBBeMuFkkG0tKOtPJp9Dy6zJAAA7l4Ody2HgrXXhoTkyeQciHI3mutpgZ6Poz8pAMcp5FAahqin92j2afclNxjms/uqnsOHZ1djufe9Q55IJg+Od9lZkWvOGNVJHn5Is26gHq9YKktaDlOsZhadtS9w/JWA97f2swrITrYvGPjX319rTqMpwrecvqZwsq73volLR3mOOTEv9atHrvvEFZZnMtLUh09EJe8Y8AMDqW7+PnlXrVL2Td5mNymAo7zFvBrySG7WBR8+C3Zo31Nh5xYuCzJ2M8RxLalmxdcu0nIAh69WPoUtXqHqyZv1Jx1Dfqth9zGaGZ01rFF80wR1GucOqqKsTdP/994/KwadOnVpXHNG3vvUtXHbZZWr5rbfewuGHH46f/vSn2G+//QAA+++/Py6//HKsWbNGzVJbsWIFcrkc9tlnn1FpP0EQBEGMBRQTNDqMjTNzhMybN89YbmsLLCI77rgj5swJpqMfdthhWLhwIU488URcffXV2LRpE8477zycfvrpNDOMIAiCmNBQJ2h0mBCdoHqwbRu/+c1vcOaZZ+LAAw9EoVDA8ccfj2984xvDqs9q64TVWojMx8NNksf9VBN2vS6ZwIWUEPgb1i+8Cqy4GVzb10Crh1kWkIk0ZZhtx9xdKb8RXo/YduUqdIqw8yUjyNvKhPbmTDYwjQ/liqsTXbdIXzYxzeq1rnvVfZbnmHJ9E9uU5jrUnwXLAgNgOZG7jXtB8kir5EL4HG7fQLirFvTJfbCMg8L0LgBRgld17JjLR9ebUcGgYRLdJAVcidPRgrn/vAd6Xn4zWG5vxeC6QF0421JAeXO/0gOSAdIqySznQKhVpCv1AkAmdEXINttOtWp5km6MegcaIV6+Thc2S9P7idXHarzbafvHn1dVV4qadD1usLQ61Ta5b6YCeIELNJsLtMt4caCupue62tX99YuD4L0e8pMC0dzp79kDlvUcAMDtG4DbN6AUzp32FqVxBQQB1ZkwKSqz7KoJC6nhAeH7o5dn8W9IzFWpu8ek69d3K0YQfZBUOmMsx9vAso5aNq5niGUV0y4bMQGYkJ2g+fPnQyT4NufNm4df//rXW6BFBEEQBDF6eFzAHqElxyNLUBUTshNEEARBENsS5A4bHagTlILV2g6rtVqUEaidMBJAleuo/oOmuM3s0DSs3FiaGZn7wawPvaxRpzbbSpqXtXqQ4mZQM5y8StU2IHApSY0RZtsQFTdyAzl5iNIAmG5qD91hLOsYboFa5z3ktlh7qtY1kDQzXo+IzXyrSYo+EeN+dT26e0Pfj/uww21WpoRK/4Bqf+BSCNwLVqEVyDhq5ozlhPcnaeaRNlsPCGaHZdra0PLRJVWn8OqXTkLrzMlonbddeMxWvPHHv6LvrSA1wrSF09E+L5hlySse/IGSOqaddwz9IStMoAtE2liGCyLm5rI0za04tWZPBnXV8XzUcL2mPiMjdNMOVaf+vCr3n7ZuKPd74rWK7cti73vSNaz3DRE+R7Y9+B7yiof89jsGzyKAzDv2xYwpwWQU3rsxqLcleF5ZoRXgHKISPIMslwcLXfBCvg/KbeUb7udaLnfjvBNmiVm6e5H7sMPjw6uAe5Xommccc8YsYFwvPWSAZbLmPQz3sei/0QkN3T2CIAiCGOeQWOLoQJ2gFKxCO6yE9ByCJ2jMNBA0nRoIWKNcksWGhcG+gvtgTn7I+hSx0aARpAxEIy8/GKHJ+nhpIDhPGeDsVcDC4Ep4FbCsE432MlEgYbBdUyGWwaTx0Vad1qD4yH9Iq1yDyODw6nvcyHGyap+q0b0eaB2zBqkA1xaOXLsLZ0qYsLQ4EN1jqVWjRqeOef2M4FCOjGUD6A/PzQJ3kzVNpu21M+wp/7+9c4+Oosrz+Leq34QkBBKTICEIDgiCqGF1Aqjgg4eAMM6o6AwkR4Y5iIioOMg6I/hAGHmMLqOOui7qrHOQXWEeMGJwERgQURhcERhQeSSriSjEJASSdHf99o/qqq6qrupH0p3uTn6fc/rQXXXrPureCrd+v+/93UK1r7peeTH6XZCLc1/J+wT6m1rgyZPF2Da3E96Gc6pYW4kIro2Aro3Doo1ibIyUbIyVJBretpWxGlGsHMm6aDxvlofVudZadiPlHcCsbSFxrWLYnFW91mxRhD04NoHoF3y4e/eBVCdbeRwFhRA8GbD1KFDzchT9QP5a30P+u6BYm+0OkM8LapbFw9p2kN8vW2YCY1IQRZDG8CyINkDSRry2qdZm04UKSplq8/XRspXrhJYma4ssEDKOVKuRwykv7NAsbgAAQR8mK2H4idoc54fjBIUSn1jtDMMwDMMwaQZbghiGYRgmxWFhdGLgSZAVdnswpo0GVXzYSjdM1LGBjL/NhHtKuhhi2OjcAmaC6IAQWrAD8LVAOt+iphXdGXq3i8GNJp1rkPNoPi+bnZVy3BmyqNCsjmZiVUvXh5lwNs7GTNGmr6tCa1wixlgw0Iwbk/yUeCRy32eoacWs0Hut7QfB4TSN7yRAFqkrD7n//DnYMjJxfuPzcrKGWtiyewAAbD0KIdWdhhj4LTXUwtG7PzKV3+ca1PsiuDNgy2mC1Fgv/7bZZLG2ti3aMWbsY1+LOs7I26IXclsIoJX26bZHsXKBmblYRZv5uArzLJgSbhzEKKaOOu6RWWyccFhtLWN3ghTXdECsLHbtFlUVfKdr4Ox7qXxpw/dyXk3ywgehSzYgBWKCudwQ3F1BojzqCABsTogOzdZEfq9aBzicwRhiPi8Q8PoaZQNqnZXjmgUbxrGh/la23RBtEDX3jXwtoRvHhluUoox75W+DcRsiZ3xd8lbwJCgxsDuMYRiGYZhOCVuCLBBsNphuyqkuLzeZ/YcT2EVBOJFi+LxMLBfaOhmjzgqi/t9gBYJWEMkPsjshBvZYlBobQD4vBKcSfdYRjKQaSK+8bZHTDel8I+icbCmgpkaQ9i1d9zbl1bdPtAFWliBJMl/mD4S+/bY2wrdCyP0Oc4+jLEuO9qzJx/i2qxNJB/vN+Pave0sORJBWxecGIa/gcAYtOPWnZfFzAFt2j+BSZpcb/sxusrUPgJjZDWR3Q8yUxdDUWB+85y43qLkJUqB/ZcuL5k1buzEwEHIvyedV3+RlcWyL7nw0m9Sq6aINoaCEmTDkryw60P22qovFRrmm5cUYSToEbR+GG3vR5KM8l74WVWBPPq/eMhkB9+AfQsyQtx+izG5yPrZAvUgC2QMmHLsbRJqNbf2Bvg1Yf0iSVJE0AJ1FR4sxmrQg2kCAajWiMP2iW/4Ow9J2UYSALoBbqY9f9/wax66Z5UdXls3CwpgA2BKUGHgSxDAMwzApjp8k+Nv4cuenNr4cdkB4EsQwDMMwKQ7HCUoMPAmyQHC69bFZdCc1JlGS9L8NLiYyupzClRlhlq7NK1JaHYpI0cwNJgiANnaEkq8NECSfKgoURRvofKPGtSaGuAqCQkQR9oxMSOdlV4vU2KB315jFXlFcLY6g+Tn8Boui/rcmD+15uQKGyLPhXBVRbFgZEQs3idGtESlmlCC6EYLkN3WPmIm51fy1fej3Q8wIRJ/OyFJjO5HfD3uPAsDuUi4GmhqC+QdcISqiqD4fxs19BUO60Lo6NXFq/JZRyXWo8WE0494sDo4xvck5o6vOdHNiYywtAOQ3uEKidVNZEOJu1449izEUa8wgwBEU2Hsy1Ojygt0JammyXKjRvO1N9Zn1134Le0FvSPVn5Do0n4fg8kDM6i7n5XBCUIaN3QUIGteoaA+6xAJtVN3okl92fZvEeiJJguBr0YmeBUBT/2CWAhzBmGUIxh9S0S5wUfpaswhBeQaU3+p3m00e48axYXxu20kYzSQGFkYzDMMwTIqjaILa+kkEJ06cwIwZM3DRRRfB4/GgX79+WLRoEVpa9Hq/yspKTJo0CRkZGcjNzcXcuXND0rQ3bAmygBxdQM4Mi5MWVhhBlC0rrS2TwsxJDfmSsiDZGAHUWD5R5Dppz2vf4gRRbavgCryheTVLbLV7gwG66L6CJxM2RWSbkaWx7gT29Am8ZVJzk16MaBBck7dF/1ZnFEIbfeSaOuhEn1FGIVbf9i3ytboueL3GImY8bqiTzpJg5us3LdPc+mAmcBXgkKN+K3s1iTZQSxOo6RwAwO/zBkXTok2OTO0K9LfTA7gzNQVoxK4BAT25PIFrI7xLGSwb2v6H3x+MMG4lflaIdN5ILGENdBGERdNFEWo9tdfEGlFaZ5EyLO83swSJNlnErBnv6jMEhH7X9Cm1NMmWDCVKuN0JeLXPknW/2TK7wR+IEG3LyQM1n1dF8gDkpfWB51JqaVIXRQiCKC+PtwWfU3mZfKDNdhfgaw7kYma91DwHdqdq8QmGZ4DaNiurrQDoLUyaNGroBCFo8RScbosI1HaQzRFqVTb8zSVH+7iYfBIgtHkX+ThVxsA///lPSJKEl156CRdffDE+++wzzJw5E42NjVixYgUAwO/3Y8KECcjLy8POnTtx+vRplJWVgYiwevXqxFQsCngSxDAMwzBMqxk3bhzGjRun/u7bty+OHDmCF198UZ0EVVRU4NChQ6iqqkLPnj0BACtXrkR5eTmWLFmCrKws07wTDU+CGIZhGCbF8UsEMU5L5Ovr63XHXS4XXC6X2SWtpq6uDt27d1d/7969G4MHD1YnQAAwduxYNDc3Y9++fRg9enRcy48WngRZQHYHyCxyMKxFyZYi6GjF0ZHEzkZBdluJVC+S1LaSZANEO0RXYLdAv17MGiJu1bjS4MkCOWXXCYl2+bgSYdbbDFHTFhLtQfecKELw+/XCykCdlXstSBa7FwpisJ9IFkaHxGEyMYGrgksr4bZZNGNNvjrBptadFUgnOg3CeW2dooxGbFaHkI1aFXwtIa5ANVpzc5PqhiQAoiNDdoNB0w+K6V/TnySIEEQ7BEcU41oZY2LwxggU3IhXMOsXM4wRytXsg0Ja02uiyVdDiFsx3H1XXCvKJp+xiOm1z4fyTNgD90jyBQXqkk/uE+V5sTnk51ITo0fQpnUH3U2CJxPweyEE3NKQ/EBAQKxG+3aaiO8BCJ4MiIF7IzU2QHA41GjidL5RjhUVcI2LrizAJZchOT1ynfya59LuCMaFsjmCUfeVZ9gbiBtkdwXPtZyXXa62gCsXgWddcc8bXFgUcBPKFXJbPr8Q7XK5UfxNJrtD7httWu3fG+V5sPh/It7EcxJUVFSkO75o0SIsXry4TXlr+fLLL7F69WqsXLlSPVZTU4P8/HxdupycHDidTtTU1MSt7FhhYTTDMAzDdCKqqqpQV1enfhYuXGiabvHixRAEIexn7969umu+/vprjBs3Drfddht+/vOf684JJvpUIjI93l6wJYhhGIZhUpx4WoKysrKi0uDMmTMHU6dODZumT58+6vevv/4ao0ePRmlpKV5++WVduoKCAuzZs0d3rLa2Fl6vN8RC1J7wJMgKm1P+mEDhVofFgjEfK3dXNPnGWrb2OqUsk2001EdOlOR0fmUVl0u/SsIh6d1PQDA+kdMTDKsfWPkiaNxapHW7iTp/klyu8pagXemmuEGUFSZm7hClfiRBEKXQmCjG+uowmLjDxIJS8zXLRxBD0ynJtS474/WxjDHSu9S0bhvSrGgSXG7dyjVye/Ur+txd5dUwAMhmtxyPgjIutPfcuAox3DYmRLp4VNEgkKR3NwdWYqojUJNnVDG0jGmUaw3uOZ0LzGbh9rBF7wZXXUAk6ce64hIDALtb912QfOrzI0g+kM2puoHJ7oIQcH+RwwPB16yuahUkn3xMceHY7aobTRRt8ka2xo1ElarbHepqMDEzR115Jv/uBrI5IbqD7lHV3UQSBJ836CIKxAmiwAaqAkmQbJ7gPRBE1TUKvz84hlyizh0P4/0CYNixV/27LJjEblOvFQTd2DRKGHT9opzT/n001gGw/H8i3iQjWGJubi5yc3OjSvvVV19h9OjRKCkpwZo1ayAa/gaUlpZiyZIlqK6uRmGhvH1PRUUFXC4XSkpKYqpXPOFJEMMwDMMwrebrr7/GqFGj0Lt3b6xYsQLffvuteq6goAAAMGbMGAwaNAjTpk3D8uXLcebMGcyfPx8zZ85M2sowgCdBlpBoC33zABASYTnmjGO08MDkLRihb7uxRKYOLUATSVr71qN9o1KtOyZC5EA6CiNSDlpdBPkNXnkjNgidQ+ojiCDlxUzy6aNea69RLFVmxWstWlqUN/5IsZaMRLJyRJuXEkPFilj2CdJYvdSitW/SyjmtZcqjOSXaITlc6lutMvZVK4JGzK72ndFwaNaHSplC24T8pOSp6X+twFWQfEHRqsk4DBtt3SBOF8hiLBnKVNsWzgJotGIG+olEW7DPbDbZCmIPWjUpIHYW/D7ZihIQbJPDJZ93BFfyKGkhisHvkEXIINIdUwXDLjsEh0u14Pj/+XdQYz0Ej2xFsg0apb9FR3cFN1+1K/9q6msLjBeS9ELhgGXFbAGD0ifq31mNNUyNf2QWzR76PgzGJQqI/CPE3NKWHShMn5e2PEVIrcW4K0BbosvHgF+iNscJSlSwxIqKCnzxxRf44osv0KtXL905CvShzWbDpk2bMHv2bIwYMQIejwd33XWXuoQ+WfAkiGEYhmFSHCICtXESQ215gQ9DeXk5ysvLI6br3bs3Nm7cmJA6tBaeBDEMwzBMiiNJ1OYNUHkD1VB4EmSFIARN2SHmUMQmXFUgCdptKcKimbGbubrCur/astxQWz+T77pyjYJqo6hZUw9VoKukVeKISDZYuheN99/gngxxTWgv1ZrLjfmqrr0wwuhY3IvapLHEerIqJ5ayzcTd2mPGuDfaPrHZdYJ0sjmtY91oXWmKG8NYXri2GF1IZmmMRZqlEzXjUOvi8NsAv6asMBsbW44HQHal+X3mY9IgyocUENlKmudN6+KSJN32LmSzq/FzdPdeECHYNK4YQQqOdZJkITQC28yIdghSi85dSY6AaNrfAnK4IXhlsbPyXXdecVv5vJBcGerQJdEO0e6AFNgqwwwlZo8iLlb7xyaGjgndhVKom0m5pVpXv7GPTATn6j0DrN3FYbYDMd9E2iJ9FK4w3f8TTFrCkyCGYRiGSXGIqM3urES5w9IZngRZoYhyY40CHU40G+YNP0SsGenlItzyem2yMIJq7XLd6NEKp42WMsNbupWIWVs/UdS/MQMhb3iqeDpsm4NLdAEEN5jVFaYXWYYs6TfN1xpliXsk0brxGlN0b6YWFkg1ExPBupmAVPLpBeNKP5hYRdRlztp8iTQiaJO3YqW6JiJ9nZVDc49Ml7rrMhNMz6ljSrN82awcbZv0+Rrur9VYEO3BqObh8gCCS7SVf43XiKHftSELSGvt0ZZpcwbHsV0vRgZJIGeXoEjd7g75Ts4uwbSOYERoEu3BsBWBcSAFNskVfC2QHJlqlaVje3XjhhyeoBjbEEU5RHQe7nkyWJK1mzTrzltYeElzPKww31i+0YoZi7U10hiNJa82QFIcNEHsDguhfXqPYRiGYRgmxWBLEMMwDMOkOCyMTgw8CbKAjK4wUWNOjcV9YrOII2IsL9YwKtG64wCdeylmkbW2SI3AURuVNeR6mz1EVBjq7tNcY7frzd+qWjPgElDdO2FuUjg3pFpfzQESQBSlITSM8NHM7WbqijPN1xBvxGy8mRHBxRoUO9v0G0kqYvVwrlONkFd2bchtD+dejDSmSCOSJxJD3QlKOps95BiMLiPVrSFA0G7iqxNum8RqMlvcoF5n048Fo7sx0vMR5uENcQtq2mPl8pEvDO8+FDRuLdVdLNrlNRta0bQmRg85POpmpeTMgOA9D3IEg0WRzQnJrSlHUz+yu9Vo4rA7IZ6r1dUxRByv+dsQ1t1udIUZ02rdgoKo20xZ18uCxlVqLMJ4/8ye52i0Msbr2skNpmAVvirWPBg9aeUO27RpE66++mp4PB7k5ubi1ltv1Z2vrKzEpEmTkJGRgdzcXMydOxctLS0WuTEMwzAM05lJG0vQ22+/jZkzZ+Lpp5/G9ddfDyLCgQMH1PN+vx8TJkxAXl4edu7cidOnT6OsrAxEhNWrV8deoGiXLRpmy3+jXOIbdulwiIgygfPReOStRIWVgm+2IWVo3wLJZK8uIPTN2mgxUtJo8gkukUf0b19G0STEqEXqMRFG0B31dQrGMWb12qa1LhqFpNBYzkjS10kMTRsimlYFwoFbry03xJoXQcCtXKNrl8HiFEm0qhsrNnWfLFn0rd97S4kUrBow1HETpt2x9BksRO8UnaVQZ02zCkUR5nrLfDVhDUizvxnZXQErjiaKdMDyQ4IIODw665t2DzCyO4P3JiBIF8/KWyFI7mzZwhSwyshWGv1Y0FkCDXVX76Hk10WSJq01R4kcrVibjBGnjQJ/g1hbn1ZvCYxk+Y60UMQ4rkx3FkgAvDosMaTFJMjn8+H+++/H8uXLMWPGDPX4gAED1O8VFRU4dOgQqqqq0LNnTwDAypUrUV5ejiVLliR1bxKGYRiGaQusCUoMaeEO+8c//oGvvvoKoijiiiuuQGFhIcaPH4+DBw+qaXbv3o3BgwerEyAAGDt2LJqbm7Fv3z7LvJubm1FfX6/7MAzDMAzT8UkLS9CxY8cAAIsXL8aqVavQp08frFy5Etdddx2OHj2K7t27o6amBvn5+brrcnJy4HQ6UVNTY5n30qVL8fjjj4eeUMytkdwm4c63QVAZN+JVhtKUaFxrxo0lzdwbBtG0qYnaTEwdyfViLEd7rrVuwUjuToVo8o8mr1gUkEpcG8EkajFJpjF8dNWxEqyTBAhSaFRoK6J1Lwb60DRyr2n64Aakgvd80LVj5iYJuHbIzFVodIWp52J4PkiS3apapGBescTbCueSCXkejC5XJbaWEbOYWyGZa8TOQtDdr7jFSNREdtdmI/lADjn+kCD5AEGE5JJjDMFmDwqsBVEW44eLoqxxaZLkMHdXBtAuigi3GXDYRQXRyheMZSrlGNHGqhLtIIfFxtFxhuMEJYakWoIWL14MQRDCfvbu3Qsp8MA/+uij+PGPf4ySkhKsWbMGgiDgv/7rv9T8BJMHj4hMjyssXLgQdXV16qeqqir+DWUYhmGYthCYBLXlA54EhZBUS9CcOXMwderUsGn69OmDhoYGAMCgQYPU4y6XC3379kVlZSUAoKCgAHv27NFdW1tbC6/XG2Ih0uJyueByuSzPMwzDMEyykYggtFHYLLEwOoSkToJyc3ORm5sbMV1JSQlcLheOHDmCkSNHAgC8Xi9OnDiB4uJiAEBpaSmWLFmC6upqFBYWApDF0i6XCyUlJa2vZKQVK5EIZyKP5JqIB2YrYtqbaGKtKEk1rotIrpxwxPVettZVBYRZsWJY/WUWG8cqD5L0m1mGlKG4g2wRYyFZ/UkUzMoNl08s7jDLc4btLLS/BUF2uair12wBd0RgZZR2pZHR5RaIl6RznRnvvfE5idodqV0FFcWKvygI+9+UkoVgMnZIkmNuabYA0a2iC7ix1GtI0p8T7ermq7r6BLb6kDzZwWstYj3Jv8Pv+aOOXZNVVSHPrXa1otXzIlhsbxSN61xpi8nkQJB85i55Yz4cfCetSQtNUFZWFmbNmoVFixahqKgIxcXFWL58OQDgtttuAwCMGTMGgwYNwrRp07B8+XKcOXMG8+fPx8yZM3llGMMwDJPWEMVBE8SWoBDSYhIEAMuXL4fdbse0adNw/vx5XH311di6dStycnIAADabDZs2bcLs2bMxYsQIeDwe3HXXXVixYkWryiPRFj72RLSEi/diLLMVZcVk8Yg1/3BC41gJFzPJ6pI2lNmWa0OIZ17xKj/c27YhLk94DHFoFKFsyHGTKMyJIGRzSuN5C5GrqImXo7WMAHLdBTFogTA8M6bicI0F2OoZi3UD4niMSSHMc2Qar8bm1H3XRbD2t0BobpR/2xyATVQ3X9WJ7YlC+oUEMWhZk/zB+EKCqI/1ZdoIJaZQG6Kt6/KL0TKvGcsh8YfC5G0p5m+nvw8sjE4MaTMJcjgcWLFiRdhJTe/evbFx48Z2rBXDMAzDMOlK2kyCGIZhGKazIkmA0OZgiXGqTAeCJ0FWRBMjyIp2FMpFjPvSFlorqm5P91G0MXzSiWiEpom6x5ZC7tiyCesqikEoHzXhhLECDONBn1a7wasZkVyDcXW9RsD0v0CL51QV9hrF0IHvZHMCbo0wWiOcFiSfrg+d2foFLOfONwE2+b7YJM1GtoIo59OeRBpPptu9GBa8WP290LmWDWOknfU1vG1GYkiy2IFhGIZhGCY5sCUoHiTZ6pCQpfUhhSRxvqy+yQbfxPw2FwQB8AfMw2bvN3ZRTk8E2PzNukjKEEQ1+jAJAkS/5m1W+z2auhnuv6SIS00qZfM1BesQNt9gW7WCV0G7zDkC4US9bUUbyiDm8d/eY8lq49dW5ZXkN+mIEbb11p6Q5eMGca/aGrtbn49oC4rkTbrXTwSHopvWCrKTGYYjFoxjIMwmtsG/G8nt+3isxk9nA3mi4EkQwzAMw6Q4kkRx0ASxO8wIT4IYhmEYJsXhJfKJgSdB8aC1boHWFhdjOUZRYzScO98Ee8vZmK+LBtK4oiCI8BHQtYsnIWUFcbfxfLyIXM7Zc+dhhwRfQLInCkIwZItoMw+dHxgT7eIaDZRD2u8JdL21injVJRkukNbW3ejqisbtaCHCd2VkRl+sVggdafPUdCDZLk+mXeFJEMMwDMOkOGwJSgw8CYoHCX4DFkiC5PBA8DUHiwwTtZRsDkgBoZ+fCE7EjlcitNi6QBAUcTFBEAR1iaUgCHAEhMfewIPlphb5nN8bUj9FLNziDwiZ1YeRH0ojBMALUV2V7icKuU3KOZsoQCDS7KEF3R5a2n6It6VGm19SrECJKjNZloBk3EOTSNBRXQYEhdNaK2CqW4GM0a+1x1Mc3kA1MfASeYZhGIZhOiVsCWIYhmGYFIfdYYmBJ0FtRRBAtqDIV/A1q5sJgiRZKBgDrqzuYc5mtK6OrSA7ozVC5cjXJFr+3BHIbKNIvOn8efkLSbJoORDHhQBdHJmUEjJHoj3rqnWXtKf7wCzyc8Iigwty3oGYWOH/7oQiCgIo4HIXJH/6BKCxctdpN4tNUXgX+cTA7jCGYRiGYTolbAliGIZhmBSHJGpzsEN2h4XCkyALvDYXvDYXRIP5VCKyXM8kSQBEF0TlksA+fdqVPApiYKWXpGzdQPIqIFfcWsB0VigwZn0kQoIIMTBgJRIhArAF3BhaM3DKusaSsuqsnf6jiKZtrWm/Nm6ZxQagZHMCJEFs5f31SqSuDhWMZaY5fuMWIhYoK628tvb5q80bqCYGdocxDMMwDNMpYUuQBV27eBIcxbi9IhQznQ2PO/zY+v7sOQCAXQy+A9mN+0kmKwp0B7AkRIu6gW9gIYXZS7ogyMJjbewnaMTugq85GH3deK0mjo+2HPU8RMtrY0UpI2UtigbIJkdPI0EAUdCq05q/+ZIvhg2X2wCvDksMPAliGIZhmBRHkgjgDVTjDk+CGIZhGCbFIckPijHkilkejB6eBDFMJ0MR6NsFWMajIcXtEiZOTVxcH6niPgm4QySHW/sz5m0GlIUUVtcpRyXS/PATNGsmTF72RUAIbn4jarvE7glJH1yYoe87QZNQFAQQBEB0WDUlLKIAiBT6HyqJdv2GqoFjZhjTxR2NWFuNlSXa4Jco2L9sGen08CSIYRiGYVIctgQlBp4EMUwnQ3n59UIWxwIASLYO6CJCCDbrDRujteCkiKWHbE64PRyvPF7olsUDOouiIjqOhFk6nXUo2rFjsfGru0v7RdhvD0iS4jAJSo3nMZXgJfIMwzAMw3RK2BLEMAzDMCkO+f0gfxstQW28viPCliCG6WQokWclAvy6D8EvyZ8Wv/xploBmCfBS8EOBzTcpzAckBSMWh/sY0UU4Dj1PNifI5oRkd0Gyu+C3yR+v6ESTEObjZwFsPAm5m1b9CXm8SIj8AWQRtfLRjhPtcRLtav9Ldhf8ogOSYIMXou7T0SDyq7qgVn9MxOzx4pZbbkHv3r3hdrtRWFiIadOm4euvv9alqaysxKRJk5CRkYHc3FzMnTsXLS0tCatTNHS8kcIwDMMwTLsyevRorFu3DkeOHMHbb7+NL7/8Ej/5yU/U836/HxMmTEBjYyN27tyJtWvX4u2338ZDDz2UxFqzO4xhOh3dunZJdhWYDoAvjHFNu+eiJAX3WxTCpCNBUIX4ocvsber+inKmhMyERvRPPVJ9ddgDDzygfi8uLsYjjzyCKVOmwOv1wuFwoKKiAocOHUJVVRV69uwJAFi5ciXKy8uxZMkSZGVlJaxu4WBLEMMwDMOkOG12hWkmUfX19bpPc3NzXOt65swZvPnmmxg+fDgcDjkW1e7duzF48GB1AgQAY8eORXNzM/bt2xfX8mOBJ0EMwzAM04koKipCdna2+lm6dGlc8l2wYAEyMjLQo0cPVFZW4s9//rN6rqamBvn5+br0OTk5cDqdqKmpiUv5rYEnQQzDMExMhIrq9R+vROpH6zUjw8dPpH6IZBebjwCvYNd9/IG8lO2zOmOg53hagqqqqlBXV6d+Fi5caFrm4sWLIQhC2M/evXvV9A8//DD279+PiooK2Gw2TJ8+HaSJNSYIRoeovFDD7Hh7kTaaoKNHj+Lhhx/Grl270NLSgiFDhuCpp57C6NGj1TSVlZW49957sXXrVng8Htx1111YsWIFnM7ogncxDMMwTCoSz2CJWVlZUWlw5syZg6lTp4ZN06dPH/V7bm4ucnNz0b9/fwwcOBBFRUX48MMPUVpaioKCAuzZs0d3bW1tLbxeb4iFqD1Jm0nQhAkT0L9/f3WC8+yzz2LixIn48ssvUVBQoCrP8/LysHPnTpw+fRplZWUgIqxevTrZ1WcYhukwZGd0LlFyKiBJfqCNkyApxuuVSU1rUCxAit6otLQUS5YsQXV1NQoLCwEAFRUVcLlcKCkpaVUZ8UAginGHwCTw3XffIS8vDzt27MA111wDAGhoaEBWVhbee+893HDDDXjnnXcwceJEnfJ87dq1KC8vx6lTp6JWntfX1yM7Oxt1dXVJU6szDMMw6UGi/89Q8u8xYQnEwAa/rUXyNuH0pkfjXtePPvoIH330EUaOHImcnBwcO3YMjz32GKqrq3Hw4EG4XC74/X5cfvnlyM/Px/Lly3HmzBmUl5djypQpSTVUpIUmqEePHhg4cCDeeOMNNDY2wufz4aWXXkJ+fr46g2yt8ry5uTlEKc8wDMMwqUQ8NUHxxuPxYP369bjhhhswYMAA3H333Rg8eDC2b98Ol8sFALDZbNi0aRPcbjdGjBiB22+/HVOmTMGKFSsSUqdoSQt3mCAI2LJlCyZPnozMzEyIooj8/Hxs3rwZ3bp1A9B65fnSpUvx+OOPJ7L6DMMwDNMmUjlO0JAhQ7B169aI6Xr37o2NGzcmpA6tJamWoGiV50SE2bNn44ILLsDf//53fPTRR5g8eTImTpyI6upqNb/WKM8XLlyoU8lXVVUlpK0MwzAMw6QWSbUERas837p1KzZu3Ija2lrVj/nCCy9gy5YteP311/HII4+0WnnucrlUcx3DMAzDpCR+P0hsoyWHN1ANIamToGiV5+fOnQMAiKLecCWKIqTAkr9UVZ4zDMMwTFshavvqsERuoJqupIUwurS0FDk5OSgrK8P//u//qjGDjh8/jgkTJgAAxowZg0GDBmHatGnYv38//ud//gfz58/HzJkzeZUXwzAMwzAhpMUkKDc3F5s3b8bZs2dx/fXXY9iwYdi5cyf+/Oc/Y+jQoQBSV3nOMAzDMG1FCZbYto+U7GakHGmxOgwAhg0bhnfffTdsmlRUnjMMwzBMW6E4BEtM5C7y6UpaWIIYhmEYhmHiTdpYghiGYRims0KSBLTRncXusFB4EsQwDMMwKQ67wxIDT4IYhmEYJsXhSVBiYE0QwzAMwzCdErYEMQzDMEyKI0l+CGwJijs8CWIYhmGYFIf8EiC0cRLkZ2G0EXaHMQzDMAzTKWFLEMMwDMOkOLx3WGLgSRDDMAzDpDgk+dvuDmNNUAjsDmMYhmEYplPCliCGYRiGSXHYEpQYeBLEMAzDMCkOT4ISA0+CDBARAKC+vj7JNWEYhmFSHeX/CuX/joTh96LNJfi98ahJh4InQQZOnz4NACgqKkpyTRiGYZh0oaGhAdnZ2XHP1+l0oqCgADWH1sUlv4KCAjidzrjk1REQKOHT1/Ti+++/R05ODiorKxMyoJNNfX09ioqKUFVVhaysrGRXJ65w29ITblt6wm2TISI0NDSgZ8+eEMXErDVqampCS0tLXPJyOp1wu91xyasjwJYgA8ogzs7O7nAPtpasrKwO2z5uW3rCbUtPuG1I+Auz2+3miUuC4CXyDMMwDMN0SngSxDAMwzBMp4QnQQZcLhcWLVoEl8uV7KokhI7cPm5besJtS0+4bUxHgIXRDMMwDMN0StgSxDAMwzBMp4QnQQzDMAzDdEp4EsQwDMMwTKeEJ0EMwzAMw3RKeBJk4IUXXsBFF10Et9uNkpIS/P3vf092lWJm8eLFEARB9ykoKFDPExEWL16Mnj17wuPxYNSoUTh48GASa2zNjh07MGnSJPTs2ROCIOBPf/qT7nw0bWlubsZ9992H3NxcZGRk4JZbbsH//d//tWMrzInUtvLy8pB+/OEPf6hLk6ptW7p0Kf7lX/4FmZmZuOCCCzBlyhQcOXJElyZd+y6atqVr37344ou47LLL1CCBpaWleOedd9Tz6dpnQOS2pWufMW2DJ0Ea3nrrLcybNw+PPvoo9u/fj2uuuQbjx49HZWVlsqsWM5deeimqq6vVz4EDB9RzzzzzDFatWoXf/e53+Pjjj1FQUICbbroJDQ0NSayxOY2NjRg6dCh+97vfmZ6Ppi3z5s3Dhg0bsHbtWuzcuRNnz57FxIkT4fcnd0flSG0DgHHjxun68W9/+5vufKq2bfv27bj33nvx4YcfYsuWLfD5fBgzZgwaGxvVNOnad9G0DUjPvuvVqxeWLVuGvXv3Yu/evbj++usxefJkdaKTrn0GRG4bkJ59xrQRYlSuuuoqmjVrlu7YJZdcQo888kiSatQ6Fi1aREOHDjU9J0kSFRQU0LJly9RjTU1NlJ2dTb///e/bqYatAwBt2LBB/R1NW77//ntyOBy0du1aNc1XX31FoijS5s2b263ukTC2jYiorKyMJk+ebHlNurSNiOjUqVMEgLZv305EHavvjG0j6lh9l5OTQ//+7//eofpMQWkbUcfqMyZ62BIUoKWlBfv27cOYMWN0x8eMGYMPPvggSbVqPZ9//jl69uyJiy66CFOnTsWxY8cAAMePH0dNTY2unS6XC9ddd13atTOatuzbtw9er1eXpmfPnhg8eHBatHfbtm244IIL0L9/f8ycOROnTp1Sz6VT2+rq6gAA3bt3B9Cx+s7YNoV07zu/34+1a9eisbERpaWlHarPjG1TSPc+Y2KHN1AN8N1338Hv9yM/P193PD8/HzU1NUmqVeu4+uqr8cYbb6B///745ptv8NRTT2H48OE4ePCg2hazdp48eTIZ1W010bSlpqYGTqcTOTk5IWlSvV/Hjx+P2267DcXFxTh+/Dh+/etf4/rrr8e+ffvgcrnSpm1EhAcffBAjR47E4MGDAXScvjNrG5DefXfgwAGUlpaiqakJXbt2xYYNGzBo0CD1P/p07jOrtgHp3WdM6+FJkAFBEHS/iSjkWKozfvx49fuQIUNQWlqKfv364fXXX1eFfh2hnQqtaUs6tPeOO+5Qvw8ePBjDhg1DcXExNm3ahFtvvdXyulRr25w5c/Dpp59i586dIefSve+s2pbOfTdgwAB88skn+P777/H222+jrKwM27dvV8+nc59ZtW3QoEFp3WdM62F3WIDc3FzYbLaQGf2pU6dC3nzSjYyMDAwZMgSff/65ukqsI7QzmrYUFBSgpaUFtbW1lmnShcLCQhQXF+Pzzz8HkB5tu++++/CXv/wF77//Pnr16qUe7wh9Z9U2M9Kp75xOJy6++GIMGzYMS5cuxdChQ/Hcc891iD6zapsZ6dRnTOvhSVAAp9OJkpISbNmyRXd8y5YtGD58eJJqFR+am5tx+PBhFBYW4qKLLkJBQYGunS0tLdi+fXvatTOatpSUlMDhcOjSVFdX47PPPku79p4+fRpVVVUoLCwEkNptIyLMmTMH69evx9atW3HRRRfpzqdz30Vqmxnp1HdGiAjNzc1p3WdWKG0zI537jImBdpdipzBr164lh8NBr776Kh06dIjmzZtHGRkZdOLEiWRXLSYeeugh2rZtGx07dow+/PBDmjhxImVmZqrtWLZsGWVnZ9P69evpwIEDdOedd1JhYSHV19cnueahNDQ00P79+2n//v0EgFatWkX79++nkydPElF0bZk1axb16tWL3nvvPfrHP/5B119/PQ0dOpR8Pl+ymkVE4dvW0NBADz30EH3wwQd0/Phxev/996m0tJQuvPDCtGjbPffcQ9nZ2bRt2zaqrq5WP+fOnVPTpGvfRWpbOvfdwoULaceOHXT8+HH69NNP6V//9V9JFEWqqKggovTtM6LwbUvnPmPaBk+CDDz//PNUXFxMTqeTrrzySt2y13ThjjvuoMLCQnI4HNSzZ0+69dZb6eDBg+p5SZJo0aJFVFBQQC6Xi6699lo6cOBAEmtszfvvv08AQj5lZWVEFF1bzp8/T3PmzKHu3buTx+OhiRMnUmVlZRJaoydc286dO0djxoyhvLw8cjgc1Lt3byorKwupd6q2zaxdAGjNmjVqmnTtu0htS+e+u/vuu9W/f3l5eXTDDTeoEyCi9O0zovBtS+c+Y9qGQETUfnYnhmEYhmGY1IA1QQzDMAzDdEp4EsQwDMMwTKeEJ0EMwzAMw3RKeBLEMAzDMEynhCdBDMMwDMN0SngSxDAMwzBMp4QnQQzDMAzDdEp4EsR0OEaNGoV58+Z1qHLLy8sxZcqUNuXRp08fCIIAQRDw/fffW6Z77bXX0K1btzaVxVhTXl6u9sOf/vSnZFeHYTo1PAlimDixfv16PPnkk+rvPn364Nlnn01ehUx44oknUF1djezs7GRXpcOzbds20wnnc889h+rq6uRUimEYHfZkV4BhOgrdu3dPdhUikpmZqe4Gnmy8Xi8cDkeyq9HuZGdn8ySUYVIEtgQxHZ7a2lpMnz4dOTk56NKlC8aPH4/PP/9cPa+4f959910MHDgQXbt2xbhx43Rv6z6fD3PnzkW3bt3Qo0cPLFiwAGVlZToXldYdNmrUKJw8eRIPPPCA6voAgMWLF+Pyyy/X1e/ZZ59Fnz591N9+vx8PPvigWtYvf/lLGHe3ISI888wz6Nu3LzweD4YOHYr//u//btX9ee2119C7d2906dIFP/rRj3D69OmQNH/9619RUlICt9uNvn374vHHH4fP51PP//Of/8TIkSPhdrsxaNAgvPfeezp3z4kTJyAIAtatW4dRo0bB7XbjP//zPwEAa9aswcCBA+F2u3HJJZfghRde0JX91Vdf4Y477kBOTg569OiByZMn48SJE+r5bdu24aqrrkJGRga6deuGESNG4OTJk1G1PVK7Vq1ahSFDhiAjIwNFRUWYPXs2zp49q54/efIkJk2ahJycHGRkZODSSy/F3/72N5w4cQKjR48GAOTk5EAQBJSXl0dVJ4Zh2g+eBDEdnvLycuzduxd/+ctfsHv3bhARbr75Zni9XjXNuXPnsGLFCvzhD3/Ajh07UFlZifnz56vnf/Ob3+DNN9/EmjVrsGvXLtTX14fVc6xfvx69evVS3U+xuD9WrlyJ//iP/8Crr76KnTt34syZM9iwYYMuza9+9SusWbMGL774Ig4ePIgHHngAP/vZz7B9+/bobwyAPXv24O6778bs2bPxySefYPTo0Xjqqad0ad5991387Gc/w9y5c3Ho0CG89NJLeO2117BkyRIAgCRJmDJlCrp06YI9e/bg5ZdfxqOPPmpa3oIFCzB37lwcPnwYY8eOxSuvvIJHH30US5YsweHDh/H000/j17/+NV5//XUAcr+MHj0aXbt2xY4dO7Bz5051ktrS0gKfz4cpU6bguuuuw6effordu3fjF7/4hTrpDEekdgGAKIr4t3/7N3z22Wd4/fXXsXXrVvzyl79Uz997771obm7Gjh07cODAAfzmN79B165dUVRUhLfffhsAcOTIEVRXV+O5556LqW8YhmkHkrp9K8MkgOuuu47uv/9+IiI6evQoAaBdu3ap57/77jvyeDy0bt06IiJas2YNAaAvvvhCTfP8889Tfn6++js/P5+WL1+u/vb5fNS7d2+aPHmyablERMXFxfTb3/5WV7dFixbR0KFDdcd++9vfUnFxsfq7sLCQli1bpv72er3Uq1cvtayzZ8+S2+2mDz74QJfPjBkz6M4777S8L2b1ufPOO2ncuHG6Y3fccQdlZ2erv6+55hp6+umndWn+8Ic/UGFhIRERvfPOO2S326m6ulo9v2XLFgJAGzZsICKi48ePEwB69tlndfkUFRXRH//4R92xJ598kkpLS4mI6NVXX6UBAwaQJEnq+ebmZvJ4PPTuu+/S6dOnCQBt27bNst1WRGqXGevWraMePXqov4cMGUKLFy82Tfv+++8TAKqtrTU9r70/DMMkB9YEMR2aw4cPw2634+qrr1aP9ejRAwMGDMDhw4fVY126dEG/fv3U34WFhTh16hQAoK6uDt988w2uuuoq9bzNZkNJSQkkSYprfevq6lBdXY3S0lL1mN1ux7Bhw1SX2KFDh9DU1ISbbrpJd21LSwuuuOKKmMo7fPgwfvSjH+mOlZaWYvPmzervffv24eOPP9ZZSPx+P5qamnDu3DkcOXIERUVFOq2R9l5pGTZsmPr922+/RVVVFWbMmIGZM2eqx30+n6qZ2bdvH7744gtkZmbq8mlqasKXX36JMWPGoLy8HGPHjsVNN92EG2+8EbfffjsKCwsjtj1Su7p06YL3338fTz/9NA4dOoT6+nr4fD40NTWhsbERGRkZmDt3Lu655x5UVFTgxhtvxI9//GNcdtllEctmGCY14EkQ06Ehg5ZGe1zrMjEKdAVBCLnW6GKxyjscoiiGXKd1y0WDMvHatGkTLrzwQt05l8sVU17RtEGSJDz++OO49dZbQ8653e6QexmOjIwMXb4A8Morr+gmqYA8yVTSlJSU4M033wzJKy8vD4CsKZo7dy42b96Mt956C7/61a+wZcsW/PCHP2xTu06ePImbb74Zs2bNwpNPPonu3btj586dmDFjhtpnP//5zzF27Fhs2rQJFRUVWLp0KVauXIn77rsvqvvBMExy4UkQ06EZNGgQfD4f9uzZg+HDhwMATp8+jaNHj2LgwIFR5ZGdnY38/Hx89NFHuOaaawDIFoP9+/eHiJy1OJ1O+P1+3bG8vDzU1NToJg6ffPKJrqzCwkJ8+OGHuPbaawHIlpF9+/bhyiuvVNvkcrlQWVmJ6667Lqo2WDFo0CB8+OGHumPG31deeSWOHDmCiy++2DSPSy65BJWVlfjmm2+Qn58PAPj4448jlp2fn48LL7wQx44dw09/+lPTNFdeeSXeeustXHDBBcjKyrLM64orrsAVV1yBhQsXorS0FH/84x8jToIitWvv3r3w+XxYuXIlRFGWT65bty4kXVFREWbNmoVZs2Zh4cKFeOWVV3DffffB6XQCQMgYYBgmdeBJENOh+cEPfoDJkydj5syZeOmll5CZmYlHHnkEF154ISZPnhx1Pvfddx+WLl2Kiy++GJdccglWr16N2trasBaQPn36YMeOHZg6dSpcLhdyc3MxatQofPvtt3jmmWfwk5/8BJs3b8Y777yj+w/+/vvvx7Jly/CDH/wAAwcOxKpVq3SxZjIzMzF//nw88MADkCQJI0eORH19PT744AN07doVZWVlUbdr7ty5GD58OJ555hlMmTIFFRUVOlcYADz22GOYOHEiioqKcNttt0EURXz66ac4cOAAnnrqKdx0003o168fysrK8Mwzz6ChoUEVRkeyEC1evBhz585FVlYWxo8fj+bmZuzduxe1tbV48MEH8dOf/hTLly/H5MmT8cQTT6BXr16orKzE+vXr8fDDD8Pr9eLll1/GLbfcgp49e+LIkSM4evQopk+fHrHtkdrVr18/+Hw+rF69GpMmTcKuXbvw+9//XpfHvHnzMH78ePTv3x+1tbXYunWrOrkuLi6GIAjYuHEjbr75Zng8HnTt2jXqvmEYph1ImhqJYRKEUaB85swZmjZtGmVnZ5PH46GxY8fS0aNH1fNr1qzRCYGJiDZs2EDax8Pr9dKcOXMoKyuLcnJyaMGCBXTbbbfR1KlTLcvdvXs3XXbZZeRyuXR5vfjii1RUVEQZGRk0ffp0WrJkiU4Y7fV66f7776esrCzq1q0bPfjggzR9+nSdCFuSJHruuedowIAB5HA4KC8vj8aOHUvbt2+3vC9mwmgiWXzcq1cv8ng8NGnSJFqxYkXI/di8eTMNHz6cPB4PZWVl0VVXXUUvv/yyev7w4cM0YsQIcjqddMkll9Bf//pXAkCbN28moqAwev/+/SHlv/nmm3T55ZeT0+mknJwcuvbaa2n9+vXq+erqapo+fTrl5uaSy+Wivn370syZM6muro5qampoypQpVFhYSE6nk4qLi+mxxx4jv99veR9iadeqVauosLBQHTdvvPGGTuw8Z84c6tevH7lcLsrLy6Np06bRd999p17/xBNPUEFBAQmCQGVlZbqywcJohkk6AlErhA0M08mRJAkDBw7E7bffrosSncr06dMH8+bNa5ctRXbt2oWRI0fiiy++0AnOmSCCIGDDhg1t3g6FYZjWw3GCGCYKTp48iVdeeQVHjx7FgQMHcM899+D48eO46667kl21mFiwYAG6du2Kurq6uOa7YcMGbNmyBSdOnMB7772HX/ziFxgxYgRPgEyYNWsWu8UYJkVgSxDDREFVVRWmTp2Kzz77DESEwYMHY9myZap4OR04efKkuqqpb9++qtg3Hrzxxht48sknUVVVhdzcXNx4441YuXIlevToEbcyYuXSSy+1jBz90ksvWYqxE82pU6dQX18PQA7FoF0xxzBM+8KTIIZhOiTaSZ+R/Pz8kNhDDMN0PngSxDAMwzBMp4Q1QQzDMAzDdEp4EsQwDMMwTKeEJ0EMwzAMw3RKeBLEMAzDMEynhCdBDMMwDMN0SngSxDAMwzBMp4QnQQzDMAzDdEp4EsQwDMMwTKfk/wGqh8TjM4WsvQAAAABJRU5ErkJggg==", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "temperature.max(dim='depth').plot()" + ] + }, + { + "cell_type": "markdown", + "id": "5cc23d19-2f5d-4dd9-9db0-807c47d83cc5", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q6. Compute the global average ocean temperature change (averaged over all depths) for the entire time period in the dataset. Then display the result as a 2D depth profile." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "cf55c6cf-295d-4a02-956b-609a89815105", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:02.785390Z", + "iopub.status.busy": "2024-11-08T14:55:02.785074Z", + "iopub.status.idle": "2024-11-08T14:55:02.981221Z", + "shell.execute_reply": "2024-11-08T14:55:02.980607Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "global_avg_temp = temperature.mean(dim=['latitude', 'longitude'])\n", + "global_avg_temp.plot(y='depth')\n", + "plt.gca().invert_yaxis()" + ] + }, + { + "cell_type": "markdown", + "id": "74ecf24e-9293-4c0b-98c6-6fc4805025f5", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q7. Calculate the zonal average temperature change for each latitude. Plot the result as a 2d contour with depth on the y axis and latitude on x." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "e5044676-8cb0-46e7-a880-b9033f6ff53c", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:02.983812Z", + "iopub.status.busy": "2024-11-08T14:55:02.983579Z", + "iopub.status.idle": "2024-11-08T14:55:03.271440Z", + "shell.execute_reply": "2024-11-08T14:55:03.270771Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "zonal_avg_temp = temperature.mean(dim=['longitude'])\n", + "zonal_avg_temp.plot()\n", + "plt.title('Zonal Average Temperature Change')\n", + "plt.gca().invert_yaxis()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 + Jaspy", + "language": "python", + "name": "jaspy" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/python-data/solutions/ex02_xr_plotting.ipynb b/python-data/solutions/ex02_xr_plotting.ipynb new file mode 100644 index 0000000..e0dfa17 --- /dev/null +++ b/python-data/solutions/ex02_xr_plotting.ipynb @@ -0,0 +1,583 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "38e495fd-dc7a-4e98-a2ab-d7b28560db48", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "# Exercise 2: Plotting" + ] + }, + { + "cell_type": "markdown", + "id": "d991cb4d-6063-4e30-9c7d-c9bc1d6d1857", + "metadata": {}, + "source": [ + "## Aim: Learn to create plots with the inbuilt `.plot()` function" + ] + }, + { + "cell_type": "markdown", + "id": "bdddad9f-f9c1-4765-b79b-7f7968772894", + "metadata": {}, + "source": [ + "Find the teaching resources here: https://tutorial.xarray.dev/fundamentals/04.1_basic_plotting.html" + ] + }, + { + "cell_type": "markdown", + "id": "c0834ce1-ed90-4963-adb1-838bef5233c5", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Issues Covered: \n", + "- Creating and customising plots using the inbuilt xarray `.plot()` function\n", + "- Creating a time-series using `.sel()` and `.isel()` and plotting these." + ] + }, + { + "cell_type": "markdown", + "id": "f1f5a3da-837a-4aec-9562-3df9f416cb23", + "metadata": {}, + "source": [ + "## 1. Plotting" + ] + }, + { + "cell_type": "markdown", + "id": "dc7d1b36-cc80-44a4-9adf-5f02901b28f6", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q1. Import the `'../data/tas_rcp45_2055_mon_avg_change.nc'` dataset and create the temperature data array as in the last lesson." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "e129c00a-5a55-47a1-8df1-5e1ce8b28c53", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:08.637819Z", + "iopub.status.busy": "2024-11-08T14:54:08.637405Z", + "iopub.status.idle": "2024-11-08T14:54:16.985867Z", + "shell.execute_reply": "2024-11-08T14:54:16.985233Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [ + "import xarray as xr\n", + "ds = xr.open_dataset('../data/xbhubo.pgc0apr.nc')\n", + "temperature = ds[\"temp\"]" + ] + }, + { + "cell_type": "markdown", + "id": "7d28e98c-52ac-452a-aff5-6ba8c78aa22f", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q2. Try a simple .plot() on your temperature dataarray, to see what xarray does. Why has it done this?" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "bd52476f-5b35-4c46-b242-9fe1da16062c", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:16.988828Z", + "iopub.status.busy": "2024-11-08T14:54:16.988376Z", + "iopub.status.idle": "2024-11-08T14:54:17.290362Z", + "shell.execute_reply": "2024-11-08T14:54:17.289849Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "(array([167948., 74985., 46325., 32775., 30391., 26162., 25805.,\n", + " 27845., 21516., 587.]),\n", + " array([-1.79998779, 1.88496089, 5.56990957, 9.25485802, 12.93980694,\n", + " 16.62475586, 20.30970383, 23.9946537 , 27.67960167, 31.36455154,\n", + " 35.04949951]),\n", + " )" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "temperature.plot()" + ] + }, + { + "cell_type": "markdown", + "id": "d1aa9382-41da-4408-9ba2-55a94b557921", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove_answer_cell" + ] + }, + "source": [ + "By default, when data is three-dimensional (as this data is) xarray will produce a simple histogram of all the data.\n", + "When the data has two dimensions, it will create a contour plot, and when the data has one dimenstion it will create a line plot." + ] + }, + { + "cell_type": "markdown", + "id": "a5652dd7-6176-4d90-b081-969ce7aec4f8", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q3. Next, try the same with a 2-dimensional view of your dataset. Try selecting sea surface temperature values and plotting those." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "81abc16a-8a76-4a79-8be7-4d567d582b2c", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:17.292801Z", + "iopub.status.busy": "2024-11-08T14:54:17.292520Z", + "iopub.status.idle": "2024-11-08T14:54:17.297118Z", + "shell.execute_reply": "2024-11-08T14:54:17.296578Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [ + "surface = temperature.sel(depth=0, method='nearest')" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "23ed65af-1865-4428-9c0d-75cc72efa460", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:17.299415Z", + "iopub.status.busy": "2024-11-08T14:54:17.299147Z", + "iopub.status.idle": "2024-11-08T14:54:17.684586Z", + "shell.execute_reply": "2024-11-08T14:54:17.683815Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "surface.plot()" + ] + }, + { + "cell_type": "markdown", + "id": "9a047319-7af4-4b42-8231-d88ddcda20ef", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q4. Finally, create a depth profile from `temperature` by using `sel` to select data for the same latitude and longitude values (31,0)." + ] + }, + { + "cell_type": "markdown", + "id": "02ff3450-f7d4-4753-ad8e-3fc6e286655e", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Hint: The latitude value is `-50.625` and the longitude value is `0`. All 3 of these methods will return the same dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "98a422d3-7137-437d-b698-98e3cd85c321", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:17.687203Z", + "iopub.status.busy": "2024-11-08T14:54:17.686804Z", + "iopub.status.idle": "2024-11-08T14:54:18.151213Z", + "shell.execute_reply": "2024-11-08T14:54:18.150649Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [ + "temperature_prof = temperature.sel(latitude=-50.625, longitude=0)" + ] + }, + { + "cell_type": "markdown", + "id": "bbb5c8e1-74fc-4ec0-b4e1-f8b6e52e8388", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q5. Create a plot from this time series." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "e29cc9ca-07ca-46d9-ac5c-50a2c84bfe0f", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:18.153969Z", + "iopub.status.busy": "2024-11-08T14:54:18.153697Z", + "iopub.status.idle": "2024-11-08T14:54:18.492668Z", + "shell.execute_reply": "2024-11-08T14:54:18.492087Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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Make the plot red with 'x' marking the points." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "fdfb363f-e819-47a1-a571-5a74f6711196", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:18.495009Z", + "iopub.status.busy": "2024-11-08T14:54:18.494744Z", + "iopub.status.idle": "2024-11-08T14:54:18.692994Z", + "shell.execute_reply": "2024-11-08T14:54:18.692024Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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rJkyYgIcPH2LXrl0ZxmQMQUFBsLCwwB9//KH1h8+WLVuM9ppWVlY6a7hS/nI39Oc1LeHh4XBzc9Pr2P3796c7D9G2bdu03lvyz6Qu6taf5MlY9erVsX79esTHx2vV6ejz84N0Y6KTRzRo0AA2Njb49ddftar1//33X4SEhKBbt26Kr9msWTNs3LgRO3fu1Or20XfiKisrK51/EaoTlPPnz2s1BW/dulXrOC8vL1hbW2PdunXo2rWrZvvRo0cRERGhleh4e3tj1qxZKFq0qN4/tJIzZNdV586dMWzYMJw4cQL169cHILtkfv31V9SvXz/DH36dO3fG999/jzt37sDFxQUAEBsbi82bN6Njx46pihT11aJFC2zduhUPHjzQtFQkJCSkGq7etm1bFChQADdv3tS67ylVqlQJTk5O2LhxI8aMGaPZfvv2bRw9ejTD96m+RsWKFXHu3DnMmjUr3WO9vb2xY8cOuLu7o3DhwhleGwAKFiyI9u3b482bN/jwww9x6dIlvX4Zent7Y86cObh79y66d++e5nHqxC3l5yKjrt2yZctixIgR2LdvH44cOaLZntb3jLGoVCoUKFBAq6Xi1atXWLt2bapjlcSmvh9pff+fP39ea1tISAieP3+utc3Qn9e0GLLrqnr16opee82aNQCgNeS8c+fOWLZsGTZt2qQ18GP16tVwdnbW/EwhBUxcI0SZpKugd9asWQKA6NOnj9ixY4dYu3atqFChgnB0dBTXrl3THNevXz9RsGDBVNdMWVD8/PlzUaFCBVGkSBGxePFi8eeff4rPP/9clCtXTgAQq1evTvNc9etYWVmJoKAgcfLkSXH+/HkhhCworFSpkihbtqwIDAwUO3fuFIMGDRJubm6pCv4mTZokAIiBAweKXbt2iWXLlonSpUsLJycnrcLI58+fC09PT1GmTBnx3XffiT179ojdu3eLZcuWiY8++kgcP348s7dasdevX4tq1aoJFxcXsW7dOrFnzx7RuXNnUaBAAXHgwAGtY1u2bCnMzc21tj18+FCUKlVKVK9eXQQHB4sdO3aIpk2bCnt7e3HlypUMXx9pFHteuHBB2NjYiKpVq4qgoCCxdetW0bZtW+Hi4qLzs1SgQAExePBgERwcLA4cOCA2bNggxo4dK6ZMmaI57pdffhEARNeuXcX27dvFunXrhIeHhyhbtqxwc3PTHKcu+Jw7d26quEJCQoSVlZVo06aNCAwMFKGhoSI4OFjMmjVLdOvWTXPcvXv3hKurq6hcubJYvHix2Ldvn9i+fbtYtGiReP/998WdO3eEEEJ88sknYuTIkSIoKEiEhoaKDRs2iFq1aglHR0fx8OHDDO+f2qBBg4Stra344osvxLZt20RISIhYt26dGDp0qFi8eLEQQog3b94Id3d34erqKgIDA8WuXbvE8OHDhYeHh9b/Q1RUlPD09BRz584V27ZtEwcOHBBz584V1tbWWgMF1N9HixcvFidOnEhVXC+E7mJkpCjOT0vKYuR9+/YJAKJbt27izz//FOvXrxfvvvuuqFixYqrPRFrfz7qKkdUDCwYPHiyOHj0qTp06JWJiYoQQQsyYMUOoVCoxefJksXfvXvHjjz8KDw8P4ejoqFWMbIzPq6msW7dOdO3aVaxcuVLs27dPbNq0Sfj6+goAon///qmOb926tShcuLBYunSpCAkJEZ9++qkAIH799Vet4z7++GNhbm4uwsPDNdtWr14tzM3NtX4+h4eHC3Nzc/Hxxx9rne/u7i7c3d0N/G5zHiY6uVRaI5eWL18uatSoISwtLYWjo6Po1KmTZjSKmr6JjhByREOXLl2EnZ2dsLe3F127dhU7duxINaJE17nh4eGiTZs2wt7ePtWIpWvXrok2bdoIBwcHUbx4cTFy5Eixffv2VIlOYmKimD17tnBxcRGWlpaiRo0aYtu2bTpHjzx//lxMmjRJVKpUSfP+q1evLj7//HOtkRvZITIyUvTt21cUKVJEWFtbCy8vL7Fnz55UxzVr1izVfRNCiBs3bogPP/xQODg4CFtbW9GqVStx+vRpvV47rURHCDlizcvLS1hZWQknJyfxxRdfiKVLl+r8LG3ZskW0aNFCODg4CCsrK+Hq6iq6desm9u7dq3Xc0qVLRYUKFYSlpaXw8PAQK1euFJ06dRKenp6aY9JLdIQQ4ty5c6J79+6iRIkSwsLCQjg5OYmWLVuKn3/+Weu4R48eiVGjRgk3NzdhYWEhihQpIt59913x9ddfi+fPnwsh5A/6Fi1aiJIlSwpLS0vh7OwsunfvrvnFrMTKlStF/fr1RcGCBYWNjY1wd3cXffv2FX/99ZfmmMuXL4vWrVsLe3t7UbhwYfHRRx+J27dva/0/vH79WgwZMkTUqFFDODg4CBsbG1GpUiUxdepU8eLFC821nj59Krp16yYKFSokVCqVzs9GykQnNjZWABC+vr4Zvh9d3zcrV64UlSpVElZWVqJ8+fJi9uzZYsWKFak+E2l9P+tKdIQQYuLEicLZ2VmYmZlpfV/HxcWJL7/8Uri4uAgbGxvRrFkzcfbs2VSjroQwzufVFI4dOyZatWolnJychIWFhbC1tRV169YVixcv1owqTC42NlaMGjVKODk5aX7urV+/PtVx6hG0ye+F+ndD8v8P9f9Ryvvr6uqq10jS3E4lRLIScSI9qOesuH37NmdDzoFUKhUmT56MKVOmZHnej8yIioqCh4cHPvzwQyxdujRbXzsvU4/osbCwwPDhw7Fw4UIAciZdb29vnDt3TnHXCVF+wBodSpf6h2nlypXx9u1bhISE4Mcff0Tv3r2Z5ORg/v7+8Pf3x9y5c9MczWQIkZGRmDlzJlq0aIGiRYsiIiICCxYsQGxsLD777DOjvW5+VLRoUURHR6favn//fvj6+jLJIUoDEx1Kl62tLRYsWIDw8HDExcWhbNmyGD9+PCZNmmTq0CgNyQsr1cXMxmJlZYXw8HAMGzYMT58+ha2tLby8vPDzzz+jWrVqRn3tzMpoFtzkc5/kJAcOHNDEnnwqhLlz55oqJKJcgV1XRJSvZNSV169fP52rdBNR7sQWHSLKVzIaSpyZOaeIKOdiiw4RERHlWTmvI5qIiIjIQPJ111ViYiLu3bsHe3t7LpRGRESUSwghEBsbC2dn5wwHD+TrROfevXtGH5VCRERExnHnzp0MpzrJ14mOvb09AHmjHBwcTBwNERER6SMmJgYuLi6a3+PpydeJjrq7ysHBgYkOERFRLqNP2QmLkYmIiCjPYqJDREREeRYTHSIiIsqzmOgQERFRnsVEh4iIiPIsJjpERESUZzHRISIiojyLiQ4RERHlWUx0iIiIKM9iokNERER5Vr5eAsLg/PwAc3MgIQEIDQVatpT/NjcHQkLkMS1byn83a5Z0rPqrn58poyciIspzmOgYkrk5MGWKTGYOHJAPdWKjduCA/BoRAYSFJe2fPl1u9/dn0kNERGQg7LoypMmTZcISEgLUry+3JU9y1AoVkkmOm1tSkjN5skxypkyRCRMRERFlGVt0DG3yZCAqCpg/P+1joqLk17AwwMMDqFsXaN5cdnepk57k2MpDRESUKWzRMYYBA/Q/9to1oH17meQAQEyM9n628hAREWVark907t69i969e6No0aKwtbVFrVq1cPr0adMGFRwsv+pKTlQq3V/V5s0DqlYFTp9OSnJ0tfIQERFRhnJ119WzZ8/QqFEjtGjRAjt37kSJEiVw8+ZNFCpUyHRBqZOTlEXIakLI2hx1jU5YWNKoq7Jlgdu3gStXgDp15PFMcoiIiDJNJYQQpg4isyZMmIAjR47g0KFDmTo/JiYGjo6OiI6OhoODQ9YD0pXkpJXwpEx21Md9+imwbJk8xtISiIvLelxERER5iJLf37k60alatSratm2Lf//9F6GhoShdujSGDRuGTz/9VK/zDZ7o6DuPzsqVQHi4/HeTJtrz6UyZon1NtugQERFpUfL7O1d3Xd26dQtLlizBmDFj8NVXX+HkyZMYNWoUrKys0Ldv31THx8XFIS5ZC0lMysLfrEpvVFTyZEWd1OgaXQUAZmZAYiLw+edJiQ+THSIiIsVydYuOpaUl6tSpg6NHj2q2jRo1CqdOncKxY8dSHe/n54dp06al2m6wFp2sSF54vHgxEBkJnDkDbNvGgmQiIqJklLTo5OpRV6VKlULVqlW1tlWpUgW3b9/WefzEiRMRHR2tedy5cyc7wtRPQkJSMqMupo6KSpqEMCHBlNERERHlSrm666pRo0a4evWq1rZr167B1dVV5/FWVlawsrLKjtCUS97tpU50oqPlV7bkEBERZUqubtH5/PPPcfz4ccyaNQs3btxAYGAgli5diuHDh5s6tKxJ3qJDREREmZarE526desiODgY69evxzvvvAN/f398//336NWrl6lDyxpHR/mViQ4REVGW5OquKwDw9vaGt7e3qcMwLLboEBERGUSubtHJs1LW6BAREVGmMNHJidiiQ0REZBBMdHIi1ugQEREZBBOdnIgtOkRERAbBRCcnYqJDRERkEEx0ciIWIxMRERkEE52ciDU6REREBsFEJydK3nWVe9dcJSIiMjkmOjmROtFJTASePzdpKERERLkZE52cyMYGsLCQ/2adDhERUaYx0cmJVCqOvCIiIjIAJjo5FQuSiYiIsoyJTk7FFh0iIqIsY6KTU3EuHSIioixjopNTsUWHiIgoy5jo5FSs0SEiIsoyJjo5FVt0iIiIsoyJTk7FRIeIiCjLmOjkVCxGJiIiyjImOjkVa3SIiIiyjIlOTsWuKyIioixjopNTMdEhIiLKMiY6ORVrdIiIiLKMiU5OlbxFRwhTRkJERJRrMdHJqdTFyG/eAK9fmzYWIiKiXIqJTk5lZweY/fffwzodIiKiTGGik1OZmSW16rBOh4iIKFOY6ORkHHlFRESUJUx0cjJOGkhERJQlTHRyMrboEBERZQkTnZyMiQ4REVGWMNHJyThpIBERUZYw0cnJWKNDRESUJUx0cjJ2XREREWVJgcyeeOfOHYSHh+Ply5coXrw4qlWrBisrK0PGRkx0iIiIskRRohMREYGff/4Z69evx507dyCSrcFkaWmJJk2aYNCgQejatSvMzNhYlGWs0SEiIsoSvbORzz77DNWrV8f169cxffp0XLp0CdHR0Xjz5g0iIyOxY8cONG7cGJMnT0aNGjVw6tQpY8adP7BFh4iIKEv0btGxtLTEzZs3Ubx48VT7SpQogZYtW6Jly5aYOnUqduzYgYiICNStW9egweY7LEYmIiLKEr0Tnblz5+p90Q4dOmQqGEqBLTpERERZwkKanIw1OkRERFmiqBi5RYsWUKlU6R6jUqmwb9++LAVF/1EnOi9fAm/eAJaWJg2HiIgot1GU6NSqVSvNfTExMVi/fj3i4uKyGhOpOTgk/Ts6GtBRH0VERERpU5ToLFiwINW2+Ph4LFq0CDNnzkTp0qXh7+9vsODyPXNzwN4eiI2VdTpMdIiIiBTJ9ISBALBu3TpMmTIFr169gp+fHwYNGoQCBbJ0SUqpUKGkRIeIiIgUyVQx8q5du1CrVi0MGzYM/fv3x/Xr1zFs2DAmOcbAgmQiIqJMU5SZnDx5EuPHj8fx48cxZMgQ7N27F8WKFTNWbARwLh0iIqIsUJToeHl5wcbGBkOHDkW5cuUQGBio87hRo0YZJDgC59IhIiLKAkWJTtmyZaFSqRAcHJzmMSqViomOITHRISIiyjRFiU54eLiRwqA0sUaHiIgo0ww6M/KTJ0/w/fffG/KSxBYdIiKiTMtyoiOEwO7du9G9e3c4Oztj5syZhoiL1FiMTERElGmZTnTCw8MxZcoUuLq6okOHDrC2tsb27dsRGRlpyPiILTpERESZpijRiYuLw/r169GqVStUqVIFFy9exPz582FmZoYJEybgvffeg7m5ubFizZ9Yo0NERJRpihKd0qVLY8mSJfDx8cG9e/ewefNmdOvWzVixZcjPzw8qlUrr4eTkZLJ4jIItOkRERJmmaNRVQkKCJqHIKS031apVw969ezXPc0pcBsMaHSIiokxT1KJz//59DBo0COvXr4eTkxO6du2K4OBgqFQqY8WXoQIFCsDJyUnzKJ7XFr5kiw4REVGmKUp0rK2t0atXL4SEhODChQuoUqUKRo0ahfj4eMycORN79uxBQkKCsWLV6fr163B2doabmxt8fX1x69atNI+Ni4tDTEyM1iPHUyc6sbFANt9bIiKi3C7To67c3d0xY8YMREREYPv27YiLi4O3tzdKlChhyPjSVb9+faxZswa7d+/GsmXLEBkZiYYNG+LJkyc6j589ezYcHR01DxcXl2yLNdPUXVcAkBsSMyIiohxEJYQQhrrYo0ePsHbtWowZM8ZQl1TkxYsXcHd3x5dffqkzhri4OMTFxWmex8TEwMXFBdHR0XBwcMjOUJWxtQVevQJu3QLc3EwdDRERkUnFxMTA0dFRr9/filp0Tp48qdU1lTJHcnBwQJkyZZRc0qAKFiyI6tWr4/r16zr3W1lZwcHBQeuRK7BOh4iIKFMUJToNGjTQ6hZydHTUqomJiopCjx49DBedQnFxcbhy5QpKlSplshiMgokOERFRpihKdFK24Ojq9TJgT1iGxo0bh9DQUISFheHEiRPo1q0bYmJi0K9fv2yLIVtw0kAiIqJMUTSPjj6yc6j5v//+ix49euDx48coXrw4vLy8cPz4cbi6umZbDNmCc+kQERFlisETnewUFBRk6hCyB7uuiIiIMkVxonP58mXNwp1CCPzzzz94/vw5AODx48eGjY4kJjpERESZojjRadWqlVYdjre3NwDZZSWEMOksyXkWa3SIiIgyRVGiExYWZqw4KD1s0SEiIsoURYlOnivyzS1YjExERJQpioaXX79+HT169NC5RlR0dDR69uyZ7lpTlEls0SEiIsoURYnO3Llz4eLionNGYfXaUXPnzjVYcPQfJjpERESZoijROXjwID766KM093fv3h0hISFZDopSYDEyERFRpihKdCIiItJdnbxYsWK4c+dOloOiFFijQ0RElCmKEh1HR0fcvHkzzf03btzIPQtl5ibJW3QSE00aChERUW6iKNFp2rQpfvrppzT3//jjj2jSpEmWg6IU1IlOYiLw3+SMRERElDFFic7EiROxc+dOdOvWDSdPnkR0dDSio6Nx4sQJdO3aFbt378bEiRONFWv+ZW0NWFrKf7NOh4iISG+K5tHx9PTE77//jo8//hjBwcFa+4oWLYqNGzeidu3aBg2QAKhUsk7n0SNZp+PiYuqIiIiIcgXFS0B4e3sjIiICu3btwo0bNyCEgIeHB9q0aQNbW1tjxEiA7L5SJzpERESkl0ytXm5jY4POnTsbOhZKD+fSISIiUkxRjQ4AxMfHY+7cuahduzbs7Oxgb2+P2rVrY968eXj79q0xYiSAc+kQERFlgqIWnVevXqF169Y4duwY3nvvPTRt2hRCCPzzzz8YP348tm7dij///BPW1tbGijf/YosOERGRYooSndmzZ+POnTs4c+YMatSoobXv3Llz6NixI+bMmQM/Pz9DxkgAJw0kIiLKBEVdV0FBQZg/f36qJAcAatasiXnz5iEwMNBgwVEybNEhIiJSTFGic/v2bdSrVy/N/V5eXrh9+3aWgyIdmOgQEREppijRcXBwwMOHD9PcHxkZySUgjIXFyERERIopSnRatGiBWbNmpbl/zpw5aN68eVZjIl1Yo0NERKSYomLkqVOnon79+vDy8sKYMWNQuXJlAMDly5exYMECXL58GcePHzdKoPkeu66IiIgUU5ToVK1aFXv27MHAgQPh6+sLlUoFABBCoHLlyti9ezeqVatmlEDzPSY6REREiimeGdnLywuXLl3C2bNnce3aNQCAh4cHatWqZejYKDnW6BARESmWqSUgAKBWrVpMbrJT8hodIeRCn0RERJQuvYuR58yZg5cvX+p17IkTJ7B9+/ZMB0U6qFt03r4FXr0yaShERES5hd6JzuXLl1G2bFkMHToUO3fuxKNHjzT74uPjcf78eSxevBgNGzaEr68vh5kbmp0dYPbffxfrdIiIiPSid6KzZs0ahISEIDExEb169YKTkxMsLS1hb28PKysreHp6YuXKlejfvz/++ecfNGnSxJhx5z8qFet0iIiIFFIJIYTSk4QQOH/+PMLDw/Hq1SsUK1YMtWrVQrFixYwRo9HExMTA0dER0dHRuaMFyt0duHULOHoUaNDA1NEQERGZhJLf35kqRlapVKhZsyZq1qyZqQApkzhpIBERkSKKZkYmE+NcOkRERIow0clNWKNDRESkCBOd3IQtOkRERIow0clNWKNDRESkiEETHSEEHj58aMhLUnJs0SEiIlJEUaJja2urNVFgu3btcP/+fc3zhw8folSpUoaLjrQx0SEiIlJEUaLz+vVrJJ9258iRI3iVYjmCTEzLQ/piMTIREZEiBq/RUXGxSeNhjQ4REZEiLEbOTdh1RUREpIiimZFVKpVWi03K52Qkfn6AuTnwwQfyefJEx98fSEiQxxAREZEWRYmOEAIeHh6a5Ob58+fw9PSE2X+rarM+x0jMzYEpU4Bnz+RzdY2Ov7/cPn266WIjIiLKwRQlOqtWrTJWHJSeyZPl1ylT5NdXr4CpU2WCM3160n4iIiLSkqnVy/OKXLd6+bRp2l1UTHKIiCgfUvL726DFyPfv38eIESMMeUlKbupUQF0TVaAAkxwiIqIMKE50Ll++jEWLFmHp0qWI+q8o9vHjx/j8889Rvnx5hISEGDpGUvP3B9QNcPHx8jkRERGlSVGi88cff8DT0xMjR47EkCFDUKdOHezfvx9VqlTB2bNn8dtvv+Hy5cvGijV/Uxce9+ghnxcrJp8z2SEiIkqTohqdBg0aoF69epg5cyaWLl2KcePGoWLFili2bBmaNm2a5nljxoxRHNikSZNQpEgRxecpkWtqdJKPrho2TCY5ADB+PPDNN6zVISKifEXJ729FiU6hQoVw8uRJeHh4ID4+HtbW1ti2bRvat2+f7nlmZmZo0KABLC0t9Xqdw4cP4+rVqyhfvry+oWVKrkl01PPoqJOZd94BLl0CNm8GLl7kPDpERJSvKPn9rWh4eUxMDAr9NztvgQIFYGNjAw8PD73ODQ4ORokSJfQ61t7eXklYeV/KJKZJE5noHDoEzJ9vkpCIiIhyA0WJDiCLkSMjIwHICQKvXr2KFy9eaB1To0YNreerVq2Co3qdJj388ssvKFmypNLQ8o+mTYGffwYOHjR1JERERDmaoq4rMzMzqFQqnTMgq7erVCokJCQYNEhjyTVdVyn9+y/g4gKYmcnZknNT7ERERFlktK6rsLCwTAf17Nkz/Prrr+jXr1+qoKKjo7FmzRqd+0iHMmUANzcgLAw4dgxo29bUEREREeVIihIdV1fXTL/QwoULcf78eYwcOTLVPkdHRxw6dAgxMTH4+uuvM/0a+UrTpjLROXiQiQ4REVEaFM2jc/DgQZ2Pc+fOparTSWnTpk0YMmRImvsHDx6M33//XUk4WmbPng2VSoXRo0dn+hq5SpMm8uuhQ6aNg4iIKAdT1KLTvHnzNPeZm5tj6NCh+O6772BhYZFq/82bN1GxYsU0z69YsSJu3rypJByNU6dOYenSpamKoPM09bxFJ04Ar18D1tamjYeIiCgHUtSi8+zZM52PsLAwBAYGYuvWrZg7d67Oc83NzXHv3r00r33v3j2YmSlfeuv58+fo1asXli1bhsKFCys+P9eqUAEoWRJ48wY4dcrU0RAREeVIijILR0dHnQ9XV1d89NFH+OGHH7Bu3Tqd53p6emLLli1pXjs4OBienp6KggeA4cOH4/3338d7772X4bFxcXGIiYnReuRaKlVSqw6HmRMREelk0NXLa9asiYiICJ37RowYge+++w4LFy7UGn6ekJCAn376CQsWLMDw4cMVvV5QUBD+/vtvzJ49W6/jZ8+erZWgubi4KHq9HIeJDhERUboMmujcu3cvzdmPu3btii+//BKjRo1CkSJF4Onpidq1a6NIkSIYPXo0xowZg27duun9Wnfu3MFnn32GX3/9FdZ61qdMnDgR0dHRmsedO3f0fr0cSV2QfPSoXM2ciIiItCiaMDA9Dx8+hK+vL8qXL4/ly5enedzJkyexbt063LhxA0IIeHh4oGfPnqhXr56i19uyZQs6d+4Mc3NzzbaEhASoVCqYmZkhLi5Oa58uuXbCQLWEBLnAZ1SUrNOpU8fUERERERmd0SYM9PT0hEqlSrU9Ojoa//77L6pUqYKgoKB0r1GvXj3FSY0urVq1woULF7S2DRgwAJUrV8b48eMzTHLyBHNzoFEjYPt2OcyciQ4REZEWRYnOhx9+qHO7g4MDKleujDZt2mSYYJw/f17ndpVKBWtra5QtWxZWVlYZxmJvb4933nlHa1vBggVRtGjRVNvztKZNZaJz8CDw+eemjoaIiChHUZTotGjRAg0bNkSBAorXAtWoVauWplVI3WuWvJXIwsICPj4++OWXX/SuvcnXkk8cmJgo178iIiIiAAqLkVu0aIGnT59m6QWDg4NRsWJFLF26FOfOncPZs2exdOlSVKpUCYGBgVixYgVCQkIwadIkxdc+cOAAvv/++yzFl+u8+y5gYwM8eQL884+poyEiIspRFDXNGKJueebMmfjhhx/QNtn6TDVq1ECZMmUwefJknDx5EgULFsTYsWMxb968LL9enmdpCTRoAISEyO6rqlVNHREREVGOobifQ1cxshIXLlzQuTioq6urpri4Vq1auH//fpZeJ1/huldEREQ6KS62mTx5MmxtbdM9Zv78+Wnuq1y5MubMmYOlS5fC0tISAPD27VvMmTMHlStXBgDcvXsXJUuWVBpa/pV84kAh5KzJREREpDzRuXDhgiZB0SWjFp9FixahY8eOKFOmDGrUqAGVSoXz588jISEBf/zxBwDg1q1bGDZsmNLQ8i8vL6BAAeDff4GICKBcOVNHRERElCMomjDQzMwMkZGRac5+rK/nz5/j119/xbVr1yCEQOXKldGzZ0/Y29tn6bpK5foJA5Nr0AA4fhxYvRro29fU0RARERmN0SYMzGp9jpqdnR2GDBlikGvRf5o0kYnOwYNMdIiIiP6jqBg5o8afJ0+e6DW8e+3atWjcuDGcnZ01i4AuWLAA//vf/5SEQ8mp63RYkExERKShKNFZtWoVHB0dtbYJIbB79250794dzs7OmDlzZrrXWLJkCcaMGYP27dvj2bNnmpXMCxcunP/mwDGkRo1kEfK1a0BkpKmjISIiyhEUJTr9+vXTLM8QHh6OKVOmwNXVFR06dIC1tTW2b9+OyAx+yf70009YtmwZvv76a60ZluvUqZNq7SpSoHBhoHp1+e/Dh00bCxERUQ6hKNGJi4vD+vXr0apVK1SpUgUXL17E/PnzYWZmhgkTJuC9997LcK2rsLAweHp6ptpuZWWFFy9eKIuetCUfZk5ERETKEp3SpUtjyZIl8PHxwb1797B582Z069ZN0Qu6ubnh7Nmzqbbv3LkTVTmrb9aoJw5kokNERARA4airhIQEqFQqqFSqDFtu0vLFF19g+PDheP36NYQQOHnyJNavX4/Zs2dj+fLlmbom/Ued6Jw/D0RFAYUKmTIaIiIik1OU6Ny/fx+bNm3CihUr8Nlnn6F9+/bo3bu3omHnAwYMQHx8PL788ku8fPkSPXv2ROnSpfHDDz/A19dX8RugZEqVAipUAG7cAI4cAd5/39QRERERmZSiritra2v06tULISEhuHDhAqpUqYJRo0YhPj4eM2fOxJ49ezSjqNLz6aefIiIiAg8fPkRkZCTu3LmDgQMHZvpNUDIcZk5ERKSheFFPNXd3d8yYMQMRERHYvn074uLi4O3trWjW5GLFimV5lmVKgQXJREREGorXukrJzMwM7du3R/v27fH48WMsWbIk1TGenp56d2/9/fffWQ0pf1PX6fz1F/DyJZDBAqxERER5WaZbdFKKjIzE9OnTMWvWrFT7PvzwQ3Tq1AmdOnVC27ZtcfPmTVhZWaF58+Zo3rw5rK2tcfPmTbRt29ZQ4eRfbm5A6dLA27fAiROmjoaIiMikFLXoREVFYfjw4fjzzz9hYWGBCRMmYMSIEfDz88O8efNQrVo1rFy5MtV5U6dO1fz7k08+wahRo+Dv75/qmDt37mTybZCGSiVbdYKCZPdVixamjoiIiMhkFLXofPXVVzh48CD69euHIkWK4PPPP4e3tzcOHz6MnTt34tSpU+jRo0e61/jtt9/QV8eik71798amTZuURU+6sSCZiIgIgMJEZ/v27Vi1ahXmzZuHrVu3QggBDw8PhISEoFmzZnpdw8bGBod1LFFw+PBhWFtbKwmHdPHzAy5dkv8+dkx2Yan5+8v9RERE+YSirqt79+5pZi8uX748rK2t8cknnyh6wdGjR2Po0KE4ffo0vLy8AADHjx/HypUrMWXKFEXXIh3MzYFFiwAbG1mM/PffQP36MsmZMgWYPt3UERIREWUbRYlOYmIiLCwsNM/Nzc1RsGBBRS84YcIElC9fHj/88AMCAwMBAFWqVEFAQAC6d++u6Fqkw+TJ8qs6aTx4EPjzz6QkR70/LX5+MlnSdZy/P5CQwFYhIiLKNRQlOkII9O/fX7OC+evXrzFkyJBUyc7mzZvTvU737t2Z1BjT5MlyZuTdu4Evv5TbatSQScqaNUD58vJRqpQsXk7O3DwpSUqe7LBFiIiIciFFiU6/fv20nvfu3dugwZABLVwIVKyY9Pz8eflIzsZGDkdXJz7u7oCnJzBihHaykzzJyahFiIiIKAdRCSGEsV+kSJEiuHbtGooVK6bX8WXLlsWhQ4fg6upq1LhiYmLg6OiI6OhoODg4GPW1sp06ObGwkAXJ770nk5qbN4Fbt4Dbt4HExIyvY24uW4KY5BARUQ6h5Pd3lmdGTunhw4eplnWIiorCzp074ejoqNc1njx5oteaWZSGlC0wyZ8vXSqPeftWJjvqxOfWraR/37wJxMbK4xISZPfW4MGmez9ERESZpCjRsbW1RUREBIoXLw4AaNeuHVatWoVSpUoBAB48eABnZ2edSUrKbi8yEl3dTCkLlCdPli097u7ykZIQwFdfAXPmJD338JCFzTVqGP89EBERGYiiROf169dI3tN15MgRvHr1SusYXT1hifp0kZBhpNXNpH6uT0vZjBkyyZk+HejWDWjcGHj6FKhTB/jtN6BTJ8PHTUREZAQG77rSd/FOMpL0hn7rU2Ojq0Xo+nWgbl3ZrfXhh8CsWcCECalHbBEREeUwBlvUk/IIXS1CRYoA//wD1Ksnn3/1FdC7N5CiNY+IiCinUZToqFQqrRablM8pD/Dz093yY2EhV0NfsgQoUAAIDASaNwfu38/uCImIiPSmaHi5mZkZHB0dNclNVFQUHBwcYGYm8yUhBGJiYnLNiKk8PbzcmPbvl7U7T58CpUsDW7bI+h0iIqJsYLTh5atWrcpSYJRHtGgBnDwJfPABcOUK0KQJEBAA+PiYOjIiIiIt2TJhICAXBJ0/fz6mTJmSKvuKjo7GjBkzMG7cOJQsWTI7wgHAFp0si44GevYEduyQzydPll1fZiz9IiIi41Hy+1vxb6TffvsNvXr1Qvfu3bFUPfmcHubPn4+YmBidATk6OiI2Nhbz589XGg6ZkqMjsHUrMG6cfO7vD3z0EfDihWnjIiIi+o+iRGfp0qXw8fHBX3/9hatXr2Lo0KGYOHGiXufu2rULffv2TXN/37598ccffygJh3ICc3Ng7lxg1SrA0hLYvBlo1EjOukxERGRiihKdn376CV9//TWuXr2Kc+fOYcWKFVi4cKFe54aFhaFs2bJp7i9TpgzCw8OVhEM5Sf/+ski5RAng3Dk5787Ro6aOioiI8jlFic6tW7cwYMAAzfM+ffogLi4OkZGRGZ5rY2OTbiITHh4OGxsbJeFQTtOwIXDqFFCzJvDwoSxaDggwdVRERJSPKUp0Xr16BTs7O81zc3NzWFlZ4eXLlxmeW79+faxduzbN/WvWrEE99YR0lHuVLQscPgx07gy8eQMMGAB88YV+S08QEREZmOIlIJYvX66V7MTHxyMgIADFihXTbBs1alSq88aNG4fWrVvD0dERX3zxhWZ01YMHD/Dtt98iICAAf/75Z2beA+U0dnbA77/LEVj+/sC8ecDly8D69QBHtxERUTZSNLy8XLlyGc6ErFKpcOvWLZ37fvnlF3z22Wd4+/YtHBwcoFKpEB0dDQsLCyxYsABDhw5VFn0WcXh5NtiwQdbvvH4NVKkCbNume8V0IiIiPSn5/Z1t8+io3b17Fxs3bsSNGzcghICHhwe6deuGMmXKZGcYAJjoZJu//pIrnt+7B1hbAz16ACtXpj7O3192caW3MCkREeV7OTrRyUmY6GSje/fkyuenTsnnH3wg5+BR07VqOhERkQ5GWwJizZo1eh2X3nw5abl//z7evn2b7hB0ysWcnYHQUOCTT+SCoNu2AfXry8LlOXOY5BARkVEoXtTTzs4OBQoUQFqnqVQqPH36VHEgVapUwbVr17J1QVC26JiAEDKx+eor+VylktuY5BARkZ6M1qJTpUoVPHjwAL1798bHH3+MGjVqZCnQ5NasWaPXMHXK5VQqYOJEWZjcubNMcgDgn3+AW7eA8uVNGx8REeUpiubRuXTpErZv345Xr16hadOmqFOnDpYsWYKYmJgsB1K3bl00a9Ysy9ehXOLCBflVvQBoYCBQuTLw2WfAo0emi4uIiPIUxYt61q9fH7/88gvu37+PUaNGYePGjShVqhR69eqFuLg4va8TGxuLmJgYzeP58+dKQ6HcKnnhcUICoJ5W4O1b4Mcf5fBzf3+AnwkiIsoixYmOmo2NDfr27Ytp06ahXr16CAoKSrfr6ezZs3j//fc1z52dnVG4cGHNo1ChQjilHpFDeZeu0VWLF8vngCxajo2Vx1SoACxZIhMgIiKiTMhUonP37l3MmjULFStWhK+vL+rWrYtLly6hcOHCaZ7z008/oXHjxlrb1q5di5CQEOzbtw89e/bEjz/+mJlwKDdJSNBdeDx5stz+ySdAUJBs1XnwABg2DKhaFfjtt6R6HiIiIj0pGnW1ceNGrFq1CqGhoWjbti0GDBiA999/H+bm5hmeW7lyZSxbtgxNmjQBANjb2+PcuXMo/1/x6YkTJ9C9e3dERERk8q0ox1FXOdibN8CyZTL5efhQbqtbF/jmG7lYKBER5VtGmzDQzMwMZcuWRa9evTRrVemia62rggUL4vLly3B1dQUALFiwAAMHDtQEePv2bXh4eOD169f6hpNlTHRygdhYYP58uV6WumanXTs5RL1mTdPGRkREJmG0RCcra10VKVIE27ZtQ6NGjXSed+TIEXzwwQeZmoMns5jo5CIPHgAzZgA//wzEx8th6r16yZqfcuVMHR0REWUjJb+/FdXohIeHIywsLN1HWgt6enp6YsuWLWlee/PmzfD09FQSDpYsWYIaNWrAwcEBDg4OaNCgAXbu3KnoGpRLlCwJ/PSTnG/H11fW6/z6K1CpEvD558Djx6aOkIiIcqBMj7pSatiwYfj++++xaNEiJCYmarYnJCTgp59+wk8//aR49fIyZcpgzpw5+Ouvv/DXX3+hZcuW6NSpEy5dumTo8CmncHcH1q+XC4W+956s5fn+eznR4MyZwIsXpo6QiIhyEqFA+/btRVRUlOb5jBkzxLNnzzTPHz9+LKpUqZLm+V9++aVQqVTCwcFB1KpVS3h6egoHBwdhZmYmxo0bpySUNBUuXFgsX75cr2Ojo6MFABEdHW2Q1yYT+PNPITw9hZBtPEI4OQnx889CvHlj6siIiMhIlPz+VlSjY25ujvv376NEiRIAAAcHB5w9e1YzcurBgwdwdnZOd72q48ePY/369bh+/ToAoGLFiujRowe8vLwyn61Btgz99ttv6NevH86cOYOqVaumOiYuLk5rUsOYmBi4uLiwRie3S0wENm4Evv5aLiMBAB4esoWna1dZz0NERHmG0da6SpkTKciRNLy8vLKc1CR34cIFNGjQAK9fv4adnR2Cg4N1JjkAMHv2bEybNs1gr005hJmZrNvp0gVYulQOSb92DfjoI6BePTkkvXlzU0dJREQmkG01Ot9++y1evXqleX7w4EGt1pXY2FgMGzZM8XUrVaqEs2fP4vjx4xg6dCj69euHy5cv6zx24sSJiI6O1jzu3Lmj/I1QzmVpCYwYAdy8CUydChQsCJw8Kefd6dABOH/e1BESEVE2U9x1FRkZieLFiwOQk/6dP38ebm5uANLvujJEt5c+3nvvPbi7u+OXX37J8FgOL8/jHjyQw89/+SVpSHrv3nLbf/M5ERFR7mPUrqv+/fvDysoKAPD69WsMGTIEBQsWBIB0F/U0RLeXvjEqWVyU8rCSJYGFC4HRo4FJk4ANG4C1a+XXESOAr74CihY1dZRERGREihKdvn37ak0Y2Lt3b53HZJevvvoK7du3h4uLC2JjYxEUFIQDBw5g165d2RYD5QIVKsj1s8aNA8aPB0JC5GzLy5cDdeoAjRolLSqanL+/XJvLzy/bQyYiIsNQlOgEBAQYKYzMefDgAfr06YP79+/D0dERNWrUwK5du9C6dWtTh0Y5UZ06wN69wJ49MuE5e1YmPSEhwLlzwKZNQIH/viWSr7JORES5lqJEJ2WdjVLLly+HnZ0dACA+Ph4BAQEoVqwYAFmMrNSKFSsyFQflYyoV0KaNnGwwKEgOSQ8PB7ZuBZycZD3PxYuyFUfXKutERJSrKF7UMzIyMlOJjj7rZAFAWFiY4mtnFouRCXFxMrmZOBF4+TJpe9u2QGAgUKSI6WIjIiKdjLp6eWYTnZyIiQ5pxMTIpCb5qD8bG7lw6PDhQK1aJguNiIi0GW3UFQDs3r0bjo6O6R7TsWNHpZclMq0ffpBJjqWlXD/LyQmIjJQFy8uXy4LlESPkpISWlqaOloiI9KS4RSfDC6pUac6Fk5iYiICAAGzevBnh4eFQqVRwc3NDt27d0KdPH726tgyJLToEQLvwePLkpOeffAI8fw78/ruchweQCdDgwcCgQYCzs2njJiLKp5T8/lY8M3JkZCQSExPTfKSV5Agh0LFjR3zyySe4e/cuqlevjmrVqiEiIgL9+/dH586dlYZClHUpkxxAfp0+XbbkVK0K3L4ti5PVrTzTpskJB319gcOH5XKiRESUIylKdLLS4hIQEICDBw9i3759OHPmDNavX4+goCCcO3cOe/fuRUhICNasWZPp6xNlSkKC7tFV6mQnIQEoVUouKRERIUdqNW4sW3g2bACaNAE8PWVSlLyYmYiIcoRsK0Zu06YNWrZsiQkTJujcP2vWLISGhmL37t2Kr51Z7LqiTDt7Fli0CFi3DlCv4VaoEPDxx8CwYYC7uymjIyLK04zWddWvXz/Y2NhkKqjz58+jXbt2ae5v3749zp07l6lrE2W7WrWAZcuAu3eB774DypcHoqLkjMsVKwLvvw/s3AkkJpo6UiKifE1Ri05WWFpaIiIiAqVKldK5/969e3Bzc8vWdarYokMGk5gI7Nol19bauTNpu7u7HJ7evz9QuLDJwiMiykuMWoycWQkJCShQIO3R7Obm5ohXj2whym3MzIAOHYAdO4Br14DPPwccHYGbN4ExY4AyZeRorfPnTR0pEVG+km0tOmZmZmjfvr1m5fOU4uLisGvXrjRHbRkDW3TIqF68kDU8CxcCFy4kbW/aVLbydO4MWFiYLj4iolzKaDMjZ8WAAQP0Om7VqlVGjiQJEx3KFkIAhw7J4uVNm5JmX3Z2TpqTx8nJtDESEeUiJkt0rly5gvfffx+3bt0y1CWNiokOZbu7d4GlS+X6Wg8eyG0WFkC3bnLm5QYN5MKjRESUJpPV6Lx58wYRERGGvCRR3lK6tJxw8PZtuWhow4bA27fA+vVymYl33wVWrkwask5ERFmSbcXIRJSMpSXQowdw5Ahw+rScf8faGjhzBhg4UBYvf/klEBZm6kiJiHI1JjpEpla7NrBiBfDvv8C33wLlygFPnwJz58rh6R98AOzezTl5iIgygYkOUU5RtCjwxRfAjRvA1q1A27aykPmPP4B27YDKleUq69HRpo6UiCjXUFSMXLhw4XTXu4qPj8eLFy+ydYh4VrAYmXK8a9eAxYuBVauAmBi5rWBBoE8fOUT9nXdMGx8RkQkYbdTV6tWr9TquX79++l7SpJjoUK7x/Dnw669yTp5Ll5K2N28uE55OnTgnDxHlGyadRyc+Pj7dGZBzEiY6lOsIAYSGyoRny5akOXlKlwaGDAE+/RQoWdKkIRIRGZtJhpdfvnwZY8eORenSpQ11SSJKSaWSrTi//y5HZH39NVCihJyfZ/JkwMUF6N0bOH4cmDoV8PfXfR1/f8DPLzsjJyIyiSwlOs+fP8fy5cvRoEED1KhRAydOnMCECRMMFRsRpcfFBZgxQ87J8+uvgJeXnJNn3To58eDy5cCUKfKRnL+/3GZubpq4iYiyUaa6rg4fPozly5dj06ZNcHNzw+XLlxEaGopGjRoZI0ajYdcV5Tl//SWXmli/HoiLS9reuLGciDAoSCY506fLFiAiolzIaF1X3377LSpXrgxfX18UL14chw8fxvnz56FSqVC4cOEsBU1EBlCnjhyh9e+/wJw5QNmycvvhw4CHh0xyWreW3VtERPmAohadAgUKYPz48Zg+fTrMkzV7W1hY4Ny5c6hatapRgjQWtuhQnpeQIOfh6dxZFjInV68e4OMDdO8uZ2ImIsoljNaiM336dPz2229wc3PD+PHjcfHixSwFSkRGZm4OnD8vkxxLS7nNzQ0wMwNOngTGjpW1Pk2ayJFckZGmjZeIyMAUJTpfffUVrl27hrVr1yIyMhJeXl6oWbMmhBB49uyZsWIkosxSFx5Pny5rdqZPl6O1xo4FfvpJ1u4Asmtr5Eg5TL1lS7m6+uPHpo2diMgAMjXqqlmzZli9ejXu37+PoUOH4t1330WzZs3QsGFDzJ8/39AxElFmJE9y1IXHkyfL53PnAs+eAYcOAXfuAPPnA/Xry/W09u+Xc/I4OcllKFaulMcSEeVCBpsw8MKFC1ixYgUCAwPx8OFDQ1zS6FijQ3man5/sutI1usrfX9bvpJxLJzwc2LhRjs46cyZpu4WFTHp8fICOHQF+vxCRCZl0ZuS3b9/CIpdMRc9Ehygd168DGzbIR/J6PCsroEMHmfR4e8u1t4iIspHREp0ff/wxw2NUKhVGjhyp7yVNiokOkZ4uX05Keq5eTdpuayuTHR8foH17wMbGdDESUb5htETHzc0tw2NUKhVu3bql7yVNiokOkUJCAOfOJSU9YWFJ++zt5eKiPj5AmzZJo7yIiAzMpF1XuQkTHaIsEELOxLxhg6zruXMnaV+hQnLuHh8fOYorl3RnE1HuYLR5dFq2bImoqKisxEZEeYVKBdStC8ybJ4uYjxwBRo0CSpUCoqLkDM3t2snnQ4bI0Vzq1daJiLKJohYdMzMzREZGokSJEsaMKduwRYfICBIS5LD1DRvkKuvJ5+NxcgK6dZMtPQ0byokLiYgUMlrXFRMdIlIkPl625GzYAGzerD0fT5kywEcfyaSnXj3ZQkREpAejJjrXr19H8eLF0z0utyQNTHSIstGbN8DevXKOni1bgNjYpH3lysmEx8cHqFWLSQ8RpcuoiY4qnR9AQgioVCok5JJ+eCY6RCby+jWwa5ds6dm2DXjxImlfxYpJSc8775guRiLKsYya6GzatAlFihRJ97hmzZrpe0mTYqJDlAO8fAls3y6Tnu3bZRKkVrVqUtJTqZLpYiSiHIU1OnpiokOUw8TGyhaeoCDZ4vP2bdK+WrVkwtO9O1C+vMlCJCLTY6KjJyY6RDlYVJSs5dmwQdb2xMcn7atbNynpcXExVYREZCJGm0fH1dUV5ubmWQqOiEgvhQoB/fsDO3cCkZHA0qVy8kEzM+DUKWDcOKBsWaBRI+DHH4H7900dMRHlQJwZmS06RLnLgwdyfp4NG4DDh+UMzYAcqdWsmWzp6doVyGB0KBHlXkbrumrZsmWGx6hUKuzbt0/fS5oUEx2iXO7uXeC332TSc/x40nZzc9n64+Mjl6LIYAAFEeUuRkt0Pv/883RfdP369YiLi+PwciLKfhERcs2toCDg77+TtltYyEVGfXzkoqP8XifK9bJ1Uc/4+HgsWrQIM2fOhKOjI/z9/eHr65uVS2YbJjpEedSNG0krrF+4kLTdygpo314mPR98ABQsaLoYiSjTsi3RWbduHaZMmYJXr15h0qRJGDRoEAoUKJDZy2U7JjpE+cCVK0lJzz//JG23sQG8vWXS06GDfE5EuYLRE51du3ZhwoQJCAsLw7hx4zBmzBgUzIV/GTHRIcpHhADOn09Kem7dStpnZye7tXx8ZDeXlZXp4iSiDBkt0Tl58iTGjx+P48ePY8iQIfj6669RrFixLAdsKkx0iPIpIYDTp2XCs3EjcPt20j5HR1nA7OMDtGola3z8/GSB8+TJqa/l7y9XbPfzy67oifI9o04YaGNjg8GDB6NcuXJpHjdq1Ci9gzUlJjpEhMRE4MSJpKQn+Xw8RYsCXbrIGZoDAoDp07WTHX9/YMqU1NuJyKiMluiUK1cu3UU9ATm8/FbyJuEcjIkOEWlJSJBz82zYIOfqefQoaV/BgnLx0YED5eSFM2cyySEykWwddZWbMdEhojTFxwMHDsikZ/Nm4OnT1Md8/jkwf362h0aU3xltCYicZvbs2ahbty7s7e1RokQJfPjhh7h69aqpwyKivKBAAeC994Bly+QSFDt2AH37ah/z/fdAu3bApk3aC5ASUY6hKNHp0KEDoqOjNc9nzpyJqKgozfMnT56gatWqBgsuI6GhoRg+fDiOHz+OPXv2ID4+Hm3atMGLFy+yLQYiygcsLOT8OxUqyOfqaTSEAHbvBrp1k4uLTpgg5/AhohxDUdeVubk57t+/r1m93MHBAWfPnkX58uUBAA8ePICzs7PJZkZ+9OgRSpQogdDQUDRt2jTD49l1RUR6S1l4rH7epAlw7Zpcg0utZUvg00/l6C0OVScyOKN1XaXMiXJaeY+6takI17UhIkPSNbpq8mT5/NAhYMgQWcfTrp1cXDQkBOjRAyhdGhg7VnuiQiLKVrm6Ric5IQTGjBmDxo0b45133tF5TFxcHGJiYrQeREQZSkjQPbpKnewAsvVm504gLExuL10aePJEFitXqSJbftauBV69yv74ifIxxV1XkZGRKF68OADA3t4e58+fh5ubGwDTdl0NHz4c27dvx+HDh1GmTBmdx/j5+WHatGmptrPriogMLj4e2LVLDkXfvl3O1wMAhQoBffrIrq3q1U0aIlFuZdQJA9u3bw+r//qct23bhpYtW2qWf4iLi8OuXbuyPdEZOXIktmzZgoMHD2qSLl3i4uIQFxeneR4TEwMXFxcmOkRkXHfvAqtWAcuXy1XW1erXlwmPj49choKI9GK0RKd///4ZThgIAKtWrdL3klkihMDIkSMRHByMAwcOoGLFiorOZzEyEWWrhARg7145ZP1//5OtPgBgbw/07AkMGgTUrm3aGIlygXwzYeCwYcMQGBiI//3vf6hUqZJmu6OjI2z0WImYiQ4RmcyDB3JZieXLtYek164tW3l69gT4c4lIJ6MlOimHl5taWq1Lq1atQv/+/TM8n4kOEZlcYqKcgXnZMjly680bud3WFvD1lUlP/fpyNBcRATByjU5kZGSOSXSyiokOEeUojx8Da9bIpCf5kPTq1WXC07s3ULiw6eIjyiHyzRIQRER5SrFiwJgxwOXLcn6ePn0Aa2vgwgVg1CjA2VkuQ3HokJyVmYgypLhFZ/Xq1XB0dEz3uI4dO2Y5sOzAFh0iyvGePQPWrZPD1C9cSNpeubJs5enbVyZIRPmIUbuuMqJSqUy2BIRSTHSIKNcQAjh5UiY8QUHAy5dyu6WlnKxw0CCgeXNAj5/TRLmdUbuuIiMjkZiYmOYjtyQ5RES5ikoli5JXrADu3wd+/hl4911ZvLxhA9CqFeDhAcyZI1dbJyIAChMdfebQISIiI3NwAAYPBv76Czh9Wq61ZW8P3LwJTJwoV1Lv2lXOzMw/Pimfy9KinkREZGK1awNLlgD37snWHi8vORHh5s1A+/aAu7tclPTuXVNHSmQSihKdfv366TURHxERZTM7O+Djj4Fjx4Dz54GRI+W6WhERcuX1smWBjh2BbduSZmQmygcyNTPy3bt3sWnTJly7dg0qlQoeHh7o0qULSpcubYwYjYbFyESUp716Bfz+u5yX59ChpO2lS8ukaOBAwNXVdPERZZJRl4BYvHgxxowZgzdv3sDR0RFCCMTExMDS0hLz58/HsGHDshR8dmKiQ0T5xpUrcrmJ1auBJ0/kNpUKaNtWDlP/4APAwsK0MRLpyWijrrZv345Ro0ZhxIgRuHv3Lp49e4aoqCjcvXsXw4YNw2effYYdO3ZkKXgiIjKCKlWA776TtTpBQUDLlnLI+q5dsnDZxUUWMt+8aepIiQxKUYtOs2bN0KRJE8yYMUPn/kmTJuHQoUMIDQ01WIDGxBYdIsrXbtyQBcyrVslFRtVatpStPJ07A1ZWpouPKA1G67pycHDAqVOntFYKT+7q1auoU6cOYmNjlUVsIkx0iIgAvH0ri5SXLQN2705aXqJoUaBfP5n0VK5s2hiJkjFa11ViYiIs0unDtbCw4BB0IqLcxsIC6NIF2LkTuHULmDxZFiw/eQLMny+7vZo2BdaulQXORLmIokSnWrVq+N///pfm/i1btqBatWpZDoqIiEykXDlg+nQgPBzYulUWKZuZyVFbffvKhUVHjdJed4soB1OU6AwbNgxff/01Fi9ejPhk8zDEx8dj0aJFmDRpEoYOHWrwIImIKJsVKCCTnK1b5Vw806fLoehRUcBPPwE1agANGgArVwIvXpg6WqI0KR5ePm7cOMyfPx/29vZwd3cHANy8eRPPnz/HqFGjsGDBAqMEagys0SEiUiAhAdi7Vy4sunVr0sSD9vZAr16ylqd2bdPGSPmCUefRAYDjx49j/fr1uH79OgDAw8MDvr6+8PLyylzEJsJEh4gokyIj5Zw8y5ZpD0mvXVuupN6jh1yTi8gIjJ7o5BVMdIiIsigxEThwQLbyBAfL1dQBwNYW8PWVSU+9enJyQiIDMdqoq5cvX2L48OEoXbo0SpQogZ49e+Lx48dZCpaIiHIxMzM5705QkJyM8Lvv5FD0ly9l/Y6XF1CzJrBwIfDsmamjpXxIUaIzdepUBAQE4P3334evry/27NnD4mMiIpKKFQPGjAEuXwYOHgT69AGsreUIrZEj5Yitvn3lCK7825lA2UxR15W7uztmzpwJX19fAMDJkyfRqFEjvH79Gubm5kYL0ljYdUVEZGTPngG//ipreZIPSa9cWRYv9+0rEyQiBYxWo2NpaYmwsDCtVcptbGxw7do1uLi4ZD5iE2GiQ0SUTYQATpyQCU9QkOzaAgBLSzlZ4aefAs2by64wogwYrUYnISEBlpaWWtsKFCigNacOERFRKiqVrNdZsQK4fx9YskSO0HrzRiY+rVoBlSoB33yjve4WURYpatExMzND+/btYZVskbdt27ahZcuWKFiwoGbb5s2bDRulkbBFh4jIxE6flq08gYGAep3EAgWATp1kK0/r1mzloVSM1nU1YMAAvY5btWqVvpc0KSY6REQ5xPPnwMaNcpj6iRNJ211dgU8+AQYMkOtvEYHz6OiNiQ4RUQ504YJs5Vm7Vi45AchWnfffl/PytGsnW338/ABzc7kIaUr+/nImZz+/bAycsovRanSIiIiMrnp14McfgXv3gDVrgMaN5cSE27bJ9bfKlQOmTJFdXVOmyKQmOX9/uT0XjgYmw2OLDlt0iIhyvitXgOXL5bITT57IbSoV4O4O3LgBTJ0qW2/USc706bpbeihPYNeVnpjoEBHlMnFxcqmJZcuAkBDtfebmsruKSU6ex64rIiLKm6ys5Bpa+/YB168D48cDJUrIfQkJ8uu+fbK+Rz1XD+VrTHSIiCh3qlABmDMHUC9FpF44NDRUzrjs7AwMGyaHsOffzot8j4kOERHlXv7+wLRpsrsqMREYO1ZuL1QIiI6WExPWqSMnJ+TCovkSEx0iIsqddBUez5snn0dFAf36yW4uS0vg7Fm5sGipUkCvXrK+JzHRlNFTNmGiQ0REuVNahceTJ8vt5coB69fLYeo//CCHrcfFyVmYW7UCKlYEZs4E7t41SfiUPTjqiqOuiIjyByGAv/6S620lX3LCzAxo3x4YOBDw9gYsLEwbJ2WIo66IiIhSUqmAunWBn3+WC4sGBABNmsgurO3b5SrqZcoAX34JXL1q6mjJQNiiwxYdIqL87epVYOVKORlh8pXTGzeWrTwffQQkW7iaTI8tOkRERPqqVAn45hvgzh05GaG3t+zOOnxYLiZaqhQweDBw8iSHqedCbNFhiw4REaV0965s4Vm5Erh5M2l79eqylad3b6BoUdPFl89xCQg9MdEhIqJ0JSbKCQhXrAA2bQJev5bbLS2Bzp1l0tOqlWwBomzDREdPTHSIiEhvz57J0VorVgBnziRtL1dOdnENGAC4uJgsvPyEiY6emOgQEVGm/P23THjWrZMzMANyVFfbtrKVp2NH2epDRsFiZCIiImOqXRtYtEgOU//1V6B5c1movGuXHKVVurRcjuLyZVNHmu+xRYctOkREZAg3bsji5YAAmQCpeXkBn3wC+PgAdnYmCy8vYYsOERFRdqtQAZg1C7h9G9i6FejUCTA3B44fl4mOk5P8euwYh6lnI7bosEWHiIiMJTJSDlNfsQK4fj1pe9WqspanTx+geHHTxZdLsRhZT0x0iIgoWwgBHDokE57ffgNevZLbLSxky8/AgUDr1rIFiDLEREdPTHSIiCjbRUcDQUHA8uVykVE1F5ekYerlypksvNyAiY6emOgQEZFJnTsnW3l+/VXO0wPIYervvSdbeT78ELCyMmmIORGLkYmIiHKDmjWBH38E7t0D1q+XsywLAezZA/j6As7OwOjRwIULpo4012KLDlt0iIgoJ7l1C1i1Sj7u3k3aXreuHLXl6wvk899Z7LrSExMdIiLKsRISgD//lLU8W7cC8fFyu60t0L277Npq1Eh2deUzTHT0xESHiIhyhYcPgbVrZdLzzz9J2ytVkglP375AyZKmiy+b5ZsanYMHD+KDDz6As7MzVCoVtmzZYuqQiIiIDK9EiaQlJY4cAT7+WLbsXL0KfPklUKYM0KULsH17UssPAcjlic6LFy9Qs2ZNLFy40NShEBERGZ9KBTRsKEdqRUYCy5YB9evL5CY4GPD2BlxdgUmTZK0P5Z2uK5VKheDgYHz44Yd6n8OuKyIiyhMuXZLJz5o1wJMnSdtbtJAFzF26ANbWpovPwPJN15VScXFxiImJ0XoQERHletWqAfPny1FaGzcCbdvK1p/9+4FevYBSpYCRI4GzZ00dabbLV4nO7Nmz4ejoqHm4uLiYOiQiIiLDsbICPvoI2LULCAsD/PyAsmWBqChg4ULA0xOoUwdYskRuywfyVddVXFwc4uLiNM9jYmLg4uLCrisiIsq7EhKAfftk11ZwMPD2rdxubS2TooEDgaZNc9UwdXZdpcHKygoODg5aDyIiojzN3Bxo0wbYsEHOwLxggezqev1aDllv3hzw8ADmzAHu3zd1tAaXrxIdIiKifK1YsaQlJY4fBz79FLCzA27cACZOlAuLduyoPUFhLperE53nz5/j7NmzOPtfcVVYWBjOnj2L27dvmzYwIiKinEylksPSly6VrTgrV8pZlhMSgG3bgE6dZNIzcSJw/bqpo82SXF2jc+DAAbRo0SLV9n79+iEgICDD8zm8nIiIKJkrV2TSs3o18OhR0vamTeUw9a5d5USFJsYlIPTERIeIiEiHN2+AP/6QBcy7dgGJiXK7g4Mcrj5wIFC7tskKmJno6ImJDhERUQb+/RcICJBJT3h40vaaNWUrT69eQOHC2RoSEx09MdEhIiLSU2KinIBwxQpg82ZAPV2LlZXs0ho4UI7gmj5djvSaPDn1Nfz9ZR2Qn1+WQuHwciIiIjIsMzOgVSsgMFAOU//xR6BGDZnwBAbKfRUrAocPA1OmyKQmOX9/ud3cPFvDZosOW3SIiIgyRwjg9GnZyhMYCKiXVlKp5D5fX7n+1pw5MsmZPl13S49C7LrSExMdIiIiA3n5Evj9d5n0HDyYer+BkhyAiY7emOgQEREZwbVrcpj6N9/I55aWSTU9BsAaHSIiIjIdDw+gYEH5b0tLOVw9Zc1ONmGiQ0RERIalLjyePl225EyfrrtAORsUyPZXJCIiorwreZKjrslRf50yRft5NmCiQ0RERIaTkKC78Fj9PCEhW8NhMTKLkYmIiHIVFiMTERERgYkOERER5WFMdIiIiCjPYqJDREREeRYTHSIiIsqzmOgQERFRnsVEh4iIiPIsJjpERESUZzHRISIiojyLiQ4RERHlWfl6rSv16hcxMTEmjoSIiIj0pf69rc8qVvk60YmNjQUAuLi4mDgSIiIiUio2NhaOjo7pHpOvF/VMTEzEvXv3YG9vD5VKZbDrxsTEwMXFBXfu3OFioVnEe2kYvI+Gw3tpOLyXhpPf7qUQArGxsXB2doaZWfpVOPm6RcfMzAxlypQx2vUdHBzyxQcuO/BeGgbvo+HwXhoO76Xh5Kd7mVFLjhqLkYmIiCjPYqJDREREeRYTHSOwsrLC1KlTYWVlZepQcj3eS8PgfTQc3kvD4b00HN7LtOXrYmQiIiLK29iiQ0RERHkWEx0iIiLKs5joEBERUZ7FRIeIiIjyLCY6RrB48WK4ubnB2toa7777Lg4dOmTqkHIUPz8/qFQqrYeTk5NmvxACfn5+cHZ2ho2NDZo3b45Lly5pXSMuLg4jR45EsWLFULBgQXTs2BH//vtvdr+VbHXw4EF88MEHcHZ2hkqlwpYtW7T2G+q+PXv2DH369IGjoyMcHR3Rp08fREVFGfndZa+M7mX//v1TfUa9vLy0juG9BGbPno26devC3t4eJUqUwIcffoirV69qHcPPpX70uZf8XGYOEx0D27BhA0aPHo2vv/4aZ86cQZMmTdC+fXvcvn3b1KHlKNWqVcP9+/c1jwsXLmj2ffvtt5g/fz4WLlyIU6dOwcnJCa1bt9asTQYAo0ePRnBwMIKCgnD48GE8f/4c3t7eSEhIMMXbyRYvXrxAzZo1sXDhQp37DXXfevbsibNnz2LXrl3YtWsXzp49iz59+hj9/WWnjO4lALRr107rM7pjxw6t/byXQGhoKIYPH47jx49jz549iI+PR5s2bfDixQvNMfxc6kefewnwc5kpggyqXr16YsiQIVrbKleuLCZMmGCiiHKeqVOnipo1a+rcl5iYKJycnMScOXM0216/fi0cHR3Fzz//LIQQIioqSlhYWIigoCDNMXfv3hVmZmZi165dRo09pwAggoODNc8Ndd8uX74sAIjjx49rjjl27JgAIP755x8jvyvTSHkvhRCiX79+olOnTmmew3up28OHDwUAERoaKoTg5zIrUt5LIfi5zCy26BjQmzdvcPr0abRp00Zre5s2bXD06FETRZUzXb9+Hc7OznBzc4Ovry9u3boFAAgLC0NkZKTWPbSyskKzZs009/D06dN4+/at1jHOzs5455138u19NtR9O3bsGBwdHVG/fn3NMV5eXnB0dMx39/bAgQMoUaIEPDw88Omnn+Lhw4eafbyXukVHRwMAihQpAoCfy6xIeS/V+LlUjomOAT1+/BgJCQkoWbKk1vaSJUsiMjLSRFHlPPXr18eaNWuwe/duLFu2DJGRkWjYsCGePHmiuU/p3cPIyEhYWlqicOHCaR6T3xjqvkVGRqJEiRKprl+iRIl8dW/bt2+PdevWISQkBN999x1OnTqFli1bIi4uDgDvpS5CCIwZMwaNGzfGO++8A4Cfy8zSdS8Bfi4zK1+vXm4sKpVK67kQItW2/Kx9+/aaf1evXh0NGjSAu7s7Vq9erSmsy8w95H02zH3TdXx+u7c+Pj6af7/zzjuoU6cOXF1dsX37dnTp0iXN8/LzvRwxYgTOnz+Pw4cPp9rHz6Uyad1Lfi4zhy06BlSsWDGYm5unyoofPnyY6i8aSlKwYEFUr14d169f14y+Su8eOjk54c2bN3j27Fmax+Q3hrpvTk5OePDgQarrP3r0KN/eWwAoVaoUXF1dcf36dQC8lymNHDkSW7duxf79+1GmTBnNdn4ulUvrXurCz6V+mOgYkKWlJd59913s2bNHa/uePXvQsGFDE0WV88XFxeHKlSsoVaoU3Nzc4OTkpHUP37x5g9DQUM09fPfdd2FhYaF1zP3793Hx4sV8e58Ndd8aNGiA6OhonDx5UnPMiRMnEB0dnW/vLQA8efIEd+7cQalSpQDwXqoJITBixAhs3rwZISEhcHNz09rPz6X+MrqXuvBzqadsL3/O44KCgoSFhYVYsWKFuHz5shg9erQoWLCgCA8PN3VoOcbYsWPFgQMHxK1bt8Tx48eFt7e3sLe319yjOXPmCEdHR7F582Zx4cIF0aNHD1GqVCkRExOjucaQIUNEmTJlxN69e8Xff/8tWrZsKWrWrCni4+NN9baMLjY2Vpw5c0acOXNGABDz588XZ86cEREREUIIw923du3aiRo1aohjx46JY8eOierVqwtvb+9sf7/GlN69jI2NFWPHjhVHjx4VYWFhYv/+/aJBgwaidOnSvJcpDB06VDg6OooDBw6I+/fvax4vX77UHMPPpX4yupf8XGYeEx0jWLRokXB1dRWWlpaidu3aWsMDSQgfHx9RqlQpYWFhIZydnUWXLl3EpUuXNPsTExPF1KlThZOTk7CyshJNmzYVFy5c0LrGq1evxIgRI0SRIkWEjY2N8Pb2Frdv387ut5Kt9u/fLwCkevTr108IYbj79uTJE9GrVy9hb28v7O3tRa9evcSzZ8+y6V1mj/Tu5cuXL0WbNm1E8eLFhYWFhShbtqzo169fqvvEeyl03kMAYtWqVZpj+LnUT0b3kp/LzFMJIUT2tR8RERERZR/W6BAREVGexUSHiIiI8iwmOkRERJRnMdEhIiKiPIuJDhEREeVZTHSIiIgoz2KiQ0RERHkWEx0iyjbNmzfH6NGjs+W1VCoVtmzZouiccuXKQaVSQaVSISoqKkuv37x5c821zp49m6VrEVHmMdEholzNz88PtWrVMtj1pk+fjvv378PR0TFL19m8ebPWekJEZBoFTB0AEVFOYm9vr1l1OyuKFCmCmJgYA0RERFnBFh0iMooXL16gb9++sLOzQ6lSpfDdd9+lOubNmzf48ssvUbp0aRQsWBD169fHgQMHNPsDAgJQqFAhbNmyBR4eHrC2tkbr1q1x584dzf5p06bh3Llzmm6igIAAzfmPHz9G586dYWtri4oVK2Lr1q2K34c6hj/++AOVKlWCra0tunXrhhcvXmD16tUoV64cChcujJEjRyIhIUHx9YnIuJjoEJFRfPHFF9i/fz+Cg4Px559/4sCBAzh9+rTWMQMGDMCRI0cQFBSE8+fP46OPPkK7du1w/fp1zTEvX77EzJkzsXr1ahw5cgQxMTHw9fUFAPj4+GDs2LGoVq0a7t+/j/v378PHx0dz7rRp09C9e3ecP38eHTp0QK9evfD06VPF7+Xly5f48ccfERQUhF27duHAgQPo0qULduzYgR07dmDt2rVYunQpfv/990zeLSIyGlOvKkpEeU9sbKywtLQUQUFBmm1PnjwRNjY24rPPPhNCCHHjxg2hUqnE3bt3tc5t1aqVmDhxohBCiFWrVgkA4vjx45r9V65cEQDEiRMnhBBCTJ06VdSsWTNVDADEpEmTNM+fP38uVCqV2LlzZ5pxu7q6igULFmhtU8dw48YNzbbBgwcLW1tbERsbq9nWtm1bMXjwYK1zw8LCBABx5syZNF+TiIyLNTpEZHA3b97Emzdv0KBBA822IkWKoFKlSprnf//9N4QQ8PDw0Do3Li4ORYsW1TwvUKAA6tSpo3leuXJlFCpUCFeuXEG9evXSjaNGjRqafxcsWBD29vZ4+PCh4vdja2sLd3d3zfOSJUuiXLlysLOz09qWmWsTkXEx0SEigxNCZHhMYmIizM3Ncfr0aZibm2vtS55AAHKoeEq6tqVkYWGR6pzExMQMz9PnOoa6NhEZF2t0iMjgKlSoAAsLCxw/flyz7dmzZ7h27ZrmuaenJxISEvDw4UNUqFBB65F81FN8fDz++usvzfOrV68iKioKlStXBgBYWlqyCJiI0sREh4gMzs7ODgMHDsQXX3yBffv24eLFi+jfvz/MzJJ+5Hh4eKBXr17o27cvNm/ejLCwMJw6dQrffPMNduzYoTnOwsICI0eOxIkTJ/D3339jwIAB8PLy0nRblStXDmFhYTh79iweP36MuLi4bH+/RJRzMdEhIqOYO3cumjZtio4dO+K9995D48aN8e6772ods2rVKvTt2xdjx45FpUqV0LFjR5w4cQIuLi6aY2xtbTF+/Hj07NkTDRo0gI2NDYKCgjT7u3btinbt2qFFixYoXrw41q9fn23vkYhyPpXQpzOdiMgEAgICMHr06Cwvx6CvcuXKYfTo0QZbpiI8PBxubm44c+aMQWdvJiL9sUWHiCiZ8ePHw87ODtHR0Vm6Tvv27VGtWjUDRUVEmcVRV0RE/wkNDcXbt28ByKUgsmL58uV49eoVAKBs2bJZjo2IModdV0RERJRnseuKiIiI8iwmOkRERJRnMdEhIiKiPIuJDhEREeVZTHSIiIgoz2KiQ0RERHkWEx0iIiLKs5joEBERUZ7FRIeIiIjyrP8DsqqS51QiFfgAAAAASUVORK5CYII=", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "temperature_prof.plot(color='red', marker='x')" + ] + }, + { + "cell_type": "markdown", + "id": "7a947799-3b5a-4b8b-bf4e-0c58986b58ed", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q7. Create a time series plot comparing the temperature profile at three different grid cells:\n", + " - lat = 0, lon = 0\n", + " - lat = 10, lon = 10\n", + " - lat = 20, lon = 20\n", + "\n", + "Make sure each time series has a different colour and include a legend. As an extension, give them different linestyles too.\n", + "Hint: use `.isel` to index the lat and lon." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "682cfc89-a2cd-454e-a124-d6df5f399208", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:18.695662Z", + "iopub.status.busy": "2024-11-08T14:54:18.695361Z", + "iopub.status.idle": "2024-11-08T14:54:18.907691Z", + "shell.execute_reply": "2024-11-08T14:54:18.907187Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Make some axes to share between all the plots.\n", + "import matplotlib.pyplot as plt\n", + "fig, ax = plt.subplots()\n", + "\n", + "# Extract the time series for each location\n", + "temp_1 = temperature.sel(latitude=-50.625, longitude=0, )\n", + "temp_2 = temperature.sel(latitude=0, longitude=200, method='nearest')\n", + "temp_3 = temperature.sel(latitude=0, longitude=320, method='nearest')\n", + "\n", + "# Plot the time series for each location\n", + "temp_1.plot(ax=ax, label=\"South Atlantic\", linestyle='--', y='depth')\n", + "temp_2.plot(ax=ax, label=\"Mid Pacific\", y='depth')\n", + "temp_3.plot(ax=ax, label=\"Mid Atlantic\", linestyle='-.', y='depth')\n", + "\n", + "# Oceanographers like depth to go downwards.\n", + "ax.invert_yaxis()" + ] + }, + { + "cell_type": "markdown", + "id": "4cbb0586-67e2-4a6f-bbdf-552e51f46085", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q8. Let's plot some data in 2D. Use `sel` to select data for 200 meters below the surface." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "7c5f72d0-ebe2-4f19-887d-65b3b837002a", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:18.910028Z", + "iopub.status.busy": "2024-11-08T14:54:18.909756Z", + "iopub.status.idle": "2024-11-08T14:54:19.277468Z", + "shell.execute_reply": "2024-11-08T14:54:19.276783Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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11mWMsXA4zFq1asUOPfTQtOpbJJM4WLduHQPA7r33Xlv5Bx98wACwl156yVZu3Y+VK1e6HiNbiYMJEyYwAKympiat+rm83yK1Tdl349prr2X5+fnsjz/+sJXPmTOHUUrZ8OHDbbIATiZOnMh8Ph/7/fffeVlFRQVr27Yt+7//+7+0+pDuc/jBBx8k/I0YMYIBYK+99prrfU1FbdfriCOOYK1bt2bl5eW8rLq6mrVv354df/zxKdu2pABOOukkW/nChQsZAPb222/zsrfeeosBYM8//7yt7kknnZSR7MDudiyJREQOonLA4sWLGaWUHX300WzRokXs5ZdfZocccgjr3Llzgk7PFVdcwfLy8tj111/P/vOf/7ClS5ey5557jl111VU27ZkBAwaw1q1bsw4dOrAHH3yQvfvuu2zcuHEMALvqqqtsbXbt2pXts88+7N1332UrV67kg6O6DqJyiSWCeemllyaIW3711Ve2uh988AHzeDzsrLPOYkuWLGHPPfcc69y5c4LI4e23385Gjx7NnnvuOfbhhx+y1157jV155ZVMURR23nnn2dqcMmUKUxSFffjhhwl9e/755xmAWgd3ThprEGUNsGvDanfSpEnss88+YytXrmSRSCSNM8sNL730EnvppZfYjBkzGAA2ZswYXiYyY8YMNm/ePPbBBx+w559/np199tmMUpogfPjiiy8ySin729/+xj755JOE50h8NrZu3crat2/PDjjgALZo0SL21ltvsWOPPZYVFhayH3/8Ma3+p/scupGNRlu61+uTTz5hPp+PHX744WzRokXstddeY8cccwzzer3s008/rfU4zzzzDAPArrjiCvbBBx+w2bNnsxYtWiQVpWzZsiWbPXs2W7p0Kbv88ssZAPbss8/a6s2bN48pipIgetrcjyWR1IYcROWIN954g/Xt25f5fD7WpUsXdvfddycV23z66afZYYcdxvLz81kwGGQ9e/Zkw4cPt1lOrB/PDz/8kPXv35/5/X7Wvn17dvPNN9vE/xhj7L333mP9+vVjfr+fAWAjRoxgjDWtQVTXrl0ZANe/rl27JtRfvHgxO/zww1kgEGCtWrViw4cPTxBbfOONN9gJJ5zASkpKmMfjYQUFBezQQw9l//73vxOukXUtPvjgg4RjDR48mOXn57OKioqMzqmxBlEHH3wwKy0trbV/kUiEXXbZZaxt27aMENLg9zzZ/XZ+JqZMmcJ69uzJ/H4/a9GiBTvppJPYsmXLEtqzrDvJ/pzntmbNGnbmmWeyoqIilpeXx44//ni2atWqjM4hnefQjWwGUeleL8YY+/jjj9mAAQNYXl4ey8vLY8cddxz75JNP0j7WggUL+PdVaWkpGzt2LKusrEyoV1lZycaOHctKS0uZz+djffv2ZQsXLkyoZz3Dbtaz5nwsiaQ2CGMZRjZKGoSBAwdi27ZtUrOkCTNw4EAwxvD+++8nzOqqK7quQ9d1HH/88di+fTt/DiorK9GqVSvMmjULY8aMydnxJBKJRJI5MrBcIqkDy5Ytg9frTZCRqCtnn302vF4vli1blnC8jh074vLLL8/p8SQSiUSSOdIS1USRlqimz88//4zKykoAhrDmXnvtlbO2165di507dwIwZpuJitwSiUQiaRrIQZREIpFIJBJJFkh3nkQikUgkEkkWyEGURCKRSCQSSRbIQZREIpFIJBJJFsjceQ50XcfmzZtRWFgoE1FKJBKJJCWMMVRWVqJDhw45lTkRCYfDiEajOWnL5/MhEAjkpC2JHEQlsHnzZnTu3LmxuyGRSCSSZsTGjRvRqVOnnLcbDofROliAGmg5aa+0tBTr1q2TA6kcIQdRDqzM6Bs3bkRRUVEj90YikUgkTZmKigp07tyZ/3bkmmg0ihpoGI6O8NUxAicKHfPLNiEajcpBVI6QgygHlguvqKhIDqIkEolEkhb1Hf7hA4WP1NFdKAWNco4cRCUhHArB5/U2djeaNKyZx4wRKZEmkUjqSDgUapDjKIRAqeN3rgIiB1I5Rg6iJBKJRCJp4lACKHV8b6WAHETlGDmISgbTjb9k1NWsuhsgLTm5pbEte035djZzo2edqNfPWarvOEl6yGu4RyMHURKJRCKRNHFy5s6T5BQ5iJJIJBKJpImj5MCdp+SmKxIBOYhKAmE6SCozbZYmXLY7uQHTPZfdwdzdhO9bfXl79Ebw79Ekb9oN1ZVcuw3r7Iqrw2cn5fdXc6SpnY/5nbDbXWdJRshBlEQikUgkTRzpzmuayEFUEogaBVEjdWrDzepEarNo1HcEbYYWlZSWs0Z+A2uIN0B+/rk4Vi3XXrRapAoyz8S40RjWpLqQbX+TWbDSIZNdM7IsZfnMZPRcN3UrSDN7/mq7nm73hqi5ScdSG9Kd1zRpuj4KiUQikUgkkiaMtERJJBKJRNLEke68pokcRCVDV40/J27m3iRumgZ5XN2O7Sizu+S0pPWMMnuvneeQq8D45hKMmaqfGV+LDHTHCGMZ6UZl4gZryg6WbD8z4vmn49qrrUpKt10Wz27Onvcs2vEXFNf5sJGq8tQV0n3+hAufi36lIlK5K/3KSa6r631zljXQdxlB3V1HcgiVe+QgSiKRSCSSJo60RDVN5CAqU9wCjevrTSQdS4d1bLGuoz8Jb1POc0in/9Y+zv9AepG54ptqpl8E6exbF6tQlta1TK0LNstVlsd0e+GvzQrVlC1PuSTd4PJU1VwtUFkEG9dKM7HEWrhZjWzWqQw+0/VtgeLHKWwBwGGRsvpp3Wfe7wwmkIjfn7VltpDs9jSbwHJVVXHrrbeie/fuCAaD6NGjB+644w7oevwBZoxh8uTJ6NChA4LBIAYOHIgffvihEXstkUgkEkndsWbn1fVPkluazSBqxowZeOyxx/DQQw/hxx9/xD333IN7770XDz74IK9zzz33YObMmXjooYewcuVKlJaWYvDgwaisrGzEnkskEolEUjeMQRCp419jn8XuR7Nx561YsQJ///vfceqppwIAunXrhoULF+LLL78EYFihZs2ahVtuuQVnn302AGDevHkoKSnBggULMHr06MwOSGj8TzTZWv9NC5hoyncNNKZCmWA14+V6clMwYfbAdt4+pUnda4zQ9F1FhNjqiv0nbq4+xvg60VVBQwmJx3Sa92sz96cbmOqsx83zdTCpJ3N31idux3S6Yd3ecczzFS+D6MZyc+2JV74pu/aa2/d7WkHHWTfu/gwyQhHIy8/NMepIQ7nl6orl1nMjUrkr/aByZ+iE9V1Lm83PqKQeaDaWqKOPPhrvv/8+fvnlFwDAN998g+XLl+OUU04BAKxbtw5lZWUYMmQI38fv92PAgAH49NNPG6XPEolEIpHkAunOa5o0myH0DTfcgPLycvTu3RuKokDTNEydOhUXXnghAKCsrAwAUFJSYtuvpKQEGzZsSNpuJBJBJBJXJq+oqKiH3kskEolEkj1ydl7TpNkMol544QU8++yzWLBgAfr06YOvv/4a48ePR4cOHTBixAhejzgeMsZYQpnI9OnTMWXKlMQNhCaabQHDpKurIKaGFNFicReX6FKz9tPdXWa+Vh0Q3fYHoHgSj2HWY1QQ6a/NxeQ2Sw92956b7pDo/ckLBhK285ktDtM2E03YCa47ancrMj3BBRGpNuLU/PmFxrLLLWKEQieKa7+c7bhhXev60qTKaTJptxmVyTTJCOWXy3lPqcuFFF18Cbpf2fS1nmDI3qVXV22opLpQtc10damTFc3Afbc74nT1RXeW2d1zKTShGPUY33VUJlPZk2k27rzrr78eN954Iy644AIccMABuPjii3Httddi+vTpAIDS0lIAcYuUxdatWxOsUyI33XQTysvL+d/GjRvr7yQkEolEIskCmgNXHpWGqJzTbCxRNTU1oNQ+5lMUhUscdO/eHaWlpViyZAn69esHAIhGo/joo48wY8aMpO36/X74/f7EDc5gciGg3LASmG+uusq3MX8htPzWab85+tp0SrotFA4nSJmk1AhyfDhc38xrMTuEwuHEQm/cCmR7WxfeyBKsJkwHEQ5HmM41ZaxgVH9+Ia8uLkeqK23rtZFJXctqlakFiTDdPejesc4ITahbJ5xB/QAIsVscEyxLjiBX5nABJDO4NBWtqUytUY2lDVVrndomSjg75GirIYK2o+Xb+HF9LdrV+/GaIpHqSvP73bxf3iD/bPnzC41rJCJmsbAsUPWdNN5EuvOaJs1mEHX66adj6tSp6NKlC/r06YP//e9/mDlzJi699FIAhhtv/PjxmDZtGnr16oVevXph2rRpyMvLw9ChQxu59xKJRCKRSHY3ms0g6sEHH8Rtt92Gq6++Glu3bkWHDh0wevRo3H777bzOxIkTEQqFcPXVV2Pnzp047LDDsHjxYhQWpm+tkEgkEomkqZGL2XUyeiv3EMYyyFy6B1BRUYHi4mL8tf4XFBUVGq4pNRo39zIdRA2DaIZZlymeeFAo9UAPFiPqMwZtIdXYp21RXtrHrwkZLjUKxoPXoWtgihcRZhynOD+IXVU1AADF4eRO5zMmukCyCra1SDOgljA9I7dbfROuqc6ofrqB6c5A9pRuPUIRCAYz6ke68GB7pqdORm2byOCckGFvM5MEx5lQl2BykVRuvVy58mrVDULcvZxR8lsXf30qbaNcEN211TVVlOUO9he1qtfjNzY8ZY2u2TcQarsfRIvGtznDFqgHjHpQUVGBdp27o7y8HEVFRTnvq/Wb9GCLvRAkdRsGhZiGf+xaU2993RNpNpYoiUQikUj2VGRMVNNEDqKSoauApoLGQsayGLCr+OJWB10F1JhRHt4G8sePsMLUg6XdEWuzFyprQgCAwrzklodI5S6AECjmm5EtQJlQEF2FX/EBAKpqQtwCRWC3RjFmf/N2E/gmNqtaLOu3XmegdrI39aambJzJlPFwTXXKoHKRpmJxS9YHywInnr91D4lptWJiwDqh3ELl9uWdC2tVXb/Ss7FA1WphTaif5J47rHy2yRLJkt+ax/YXthDkQ1i9W54Ac/q+0/rksKZZ5xqp2GGTKCFaNEGZ23pWmpvVKlJVbnt4ia5yCxzRovZrpKvO3QHze5gpXhAtlvHzJNm9kIMoiUQikUiaOApyEBMlx3s5Rw6iJBKJRCJp4tAcuPPSlQSRpI8cRCWBMEMPinn8APyCTpRmlHOTrg/wmaZwXxA0UBgPRgxVwLvhS3iKDSHQWAUFFK/tOJZJnJqmdW5W1lXA4zM7YyUrNlx9Hm8QuukIUfQYmB5XzXV+RqhT4cfSuQIApqd0I4RrqlO6vpqC66q+2Z3Uot3ORbyH4ZrqhMTT/HESXX1ugfOEciE/N2V8i/ryfGQUPO7UOEuyzVhnqRuvhWSfL5ubr54U9RMQJxJQj/3cHUHTrmcs1jc10QAjSL056UwRNSKsUB4uAcBw39nceY6k8YoPML/fiRY1tqsu+nqSPQY5iJJIJBKJpImTE4kDaYjKOXIQlQxdM/6YpVBuWaKMNxXK31ziardEixrWJCvQXI2CaRpo9XYAAPM4csARYrzdwLRo6Spg1rG/JTIziNE8JlVAPEb4uq54jcMLL9sUzPXt1mozlQXJOf3fLRhZsntS2z22ngXbpArAsM6KwbmKN6m8A3UqugvB63UK0HXbtZZJAO4bqGGhre/JEA1lfRIQrUU8yDxJzs0EzJyhAHiAuTjxJbrTSLfla1ma0z7nmkhVOZjP/pzTWMiRkSIeTM4I5d/RAAAtGv8eNqmv3JxOcjI7T7rzck6zyZ0nkUgkEolE0pSQliiJRCKRSJo40p3XNJGDqCQQLRYPEHcGXeoqoJl6TlrUDD4H9IDhJiMxI3CRRKvBqiuhV1cAAGgBjQeke7w8qNGoTA13n7luBYoDph6J4AYhagTUUtrlSTAdRsUkJuZM3RTSjSexyOWzwAOrCbG78QQtJduyuB2IB3zbVNftn4Gs3CwN5JppdG0ly5UnXjOa5OfAdONxN5aZwYG4uAJjW9fD265bPXQ4R+gaqBYTvkvN7BOaEYJBnIHl1APo5jNnXTNBZw8A37e+ke68pol050kkEolEIpFkgbREJYFoUXveJGuqK6Ug0RBYyFTrBsBiO4x9/AHDMmRZiTxeIyjR3JdFaqDsd2jGfYnu2AxGPDarFYe/qQtvhY43zEykCKTlSdIQ5EKhm+cIBIwJILXtkI7yuLlstb27ynjUJkmQoG5OqJEnFJZFSrcH81sWcWcuuiZAeXUIXlN/w6NFQcOVIFEj96geKDKsT5a1LRbh3/ssFgEBQDymLI1TsZ16AEJAI5nl4swWSkiddZ6kTlTukYMoiUQikUiaOEQhILRugyAiB1E5Rw6iJBKJRCJp4lCFgNZxECUtUblHDqKSoWnGnxXcqpjmajUGvWoX9Kpdxrqug/gMbScWDdvcd8QXgB6u5uZgT7+TsuqKr1WHlNtrUxaXSHZH/PmFRjJZIDGxbm37NrGk2E0Ox6QXUcmbWWrlwuQXHpjdCPpXyQiHjMTv+bvW88k+rHwroOtgVqJ3qoCpURDzXLRwNVjEVCDXNRB/0AjLMOty1x4AeLyg+UUgMalYvicjB1ESiUQikTR1FApC6zgXjMgMxLlGDqIkEolEImniEEpA6ij0RGqffiHJEDmISoI1O49Rj/G/xjDZslgULBoGDRjuM6ZrcfedxwvPwac2eF+lK0+ypyLdcvWDc/ZepGKHzVXHBIsIiUXi28QZzQ2MvvaL+ArT4bPS09SUQw8ZM+j0yl0gXh+U1mZS+D/WwtuhG7RyMzVXJAwWNmfbUSWewgtGeAbx+rh7D+FqEH8QrIF0oiRNEzmIkkgkEomkiUMVAlpHSxSVlqicIwdRSWBqFCwWAfQa6KFq6ObbCYvFAF3jbyjE4+UBljQoLUISiWT3g9bsBBTTAqObE270eNYGokbj2xqI2OevGcf3eMHUGJSWbQEA6vYyaH/+Dipa08x+sXA1QBVuefL1PABgOmhLY/KO9vNKY4IQAGZlpfAHeTPE6+NeCGpqiNU5TilNCK17TFSdknxLXJGK5RKJRCKRSCRZIC1REolEIpE0caQ7r2kiB1G1oWuAx8u1oAy8IILrztIY8R52ZsP2TSKRSBoAb2lPvqxu+RV6XkvA1EciusrTTxFdhf7bl6A9+ufkuJbLjuYXAh4fmBkgDl3jWk/6zp0g+UXQdv4FwAgOpy3aGYmSAWg7/+KB5IFTrnI9juWSUX/4NP5dr+tgkZCtHouEICph0QZMC0SUhp+dN336dLz66qv46aefEAwGceSRR2LGjBnYZ599eB3GGKZMmYLZs2dj586dOOyww/Dwww+jT58+deprc0G68yQSiUQikSTw0UcfYcyYMfjss8+wZMkSqKqKIUOGoLo6ni/wnnvuwcyZM/HQQw9h5cqVKC0txeDBg1FZWZmi5d0HaYlKgrplA9T8IIg/AL3a/jAQSm2WKO8R5zR09yQSiaRBie7YbCwEi4FYGEQ1VMBJLGxYowS0Dd8YC7oGpfvfsjqe+r93jAwQMKxLiMXAVBcJBY8PrLoCzOMz+qMoPCgcALxd9k7bS+Bp2xGxDT8ax68oh797b+iVO41Tqa4ALWwJVl1h9EnTwKJhaNU1WZ1fphiWqDoGliMzRfl33nnHtj5nzhy0a9cOq1atwrHHHgvGGGbNmoVbbrkFZ599NgBg3rx5KCkpwYIFCzB69Og69bc5IC1REolEIpE0cayYqLr+AUBFRYXtLxKJpNWH8nIjzVKrVq0AAOvWrUNZWRmGDBnC6/j9fgwYMACffvppjq9A00QOoiQSiUQiaeIQQgzV8rr8mQmIO3fujOLiYv43ffr0Wo/PGMOECRNw9NFHY//99wcAlJWVAQBKSkpsdUtKSvi23R3pzksCCeaBBPNAfYF4MCMA/wmXNGKvJBKJpHEgUSPI2ggk17k2FNFi8aTPhIIpXjDF/Gnx+BDbuh7edt3SOob2/fsADD0+EswHzLhu4g9AK98Opbi1sT0SBjHdd4wqYKqXJw4mwXyw6groNUYYhrbzL8R+v4cfQ4+GEd5uWFTyu3UFC1WDFrYAANDi1rwdpukI/fI9fG0NvSkWDRsaUmZAO80vMvQE3VyMTZyNGzeiqKiIr/v9/lr3ueaaa/Dtt99i+fLlCduswZkFYyyhbHdFDqIkEolEImniUIWC1jEmijJj/6KiItsgqjb+8Y9/4I033sCyZcvQqVMnXl5aasx6LCsrQ/v27Xn51q1bE6xTuytyEJUEbcef0MJBsECeEbhoqthKJHsikcpdIKr5GWA6fK06NG6HJA2KvuYzkIA5nd/MSWfJGjDioj5EKK/LaHo/M9pPH8flBawgca+hks4iYSiFLXh2CBLM5xIGCX0NVYNFw0Y2CRj5Tq38pkyNgmk6vPmGCrlWuQtM06FF/jR23vZn/BQUCiXgg1a5yzwVrxFUbvZB3foHiNcHraZhfhtyInHAMtufMYZ//OMfWLRoET788EN0797dtr179+4oLS3FkiVL0K9fPwBANBrFRx99hBkzZtSpr82FZhUTtWnTJlx00UVo3bo18vLycNBBB2HVqlV8O2MMkydPRocOHRAMBjFw4ED88MMPjdhjiUQikUiaJ2PGjMGzzz6LBQsWoLCwEGVlZSgrK0MoZLp2CcH48eMxbdo0LFq0CN9//z1GjhyJvLw8DB06tJF73zA0G0vUzp07cdRRR2HQoEF4++230a5dO6xduxYtWrTgdSy9irlz52LvvffGXXfdhcGDB+Pnn39GYWHDiaJJJBKJRJJLGsMS9eijjwIABg4caCufM2cORo4cCQCYOHEiQqEQrr76ai62uXjx4j3mN7fZDKJmzJiBzp07Y86cObysW7dufDnnehW6Hk+m2YBJNSWS+iZSsYMvc30fQg0FatONAkLtbhiqgHkMVwuJ1SD25zp4S+ymfUnDs70yUaOodWFenduN7tjMXWAAQIo7cF0oRiiU6u1gEePYxBcA8xu6eSQWAXTN+A8AJAolWgOt2kj4C12LP3NaDHTvo3gwOTw+6OYkHhrMB9P1eBJgU5fPSsDL1FhcC4pSgFKb0jhtXcr1/ajHZ3P9KeKJUsVIJm9uZ5Ew1Jq4SjkJ+kCs5MW6xrNTAEYyYuhag/0+5DImKl1YGgmLCSGYPHkyJk+enGWvmjfNxp33xhtvoH///jjvvPPQrl079OvXD0888QTfLvUqJBKJRCKRNCTNZhD122+/4dFHH0WvXr3w7rvv4sorr8TYsWMxf/58ANnrVUQikQThMYlEIpFImhSmO68uf6ijO1CSSLNx5+m6jv79+2PatGkAgH79+uGHH37Ao48+iuHDh/N6mepVTJ8+HVOmTEkoD55yJYJFRYh9/hr0UDUIlS49ye6B5jXcPYoWAQkZejnMFwRhOk8qC8BYN2dZMSK8bykegOnQNn5nrOs6wBiUbgc1SP8lycmFKw+Acd+tW67r0P0FfJX58hDLbw0aNl44abicu+9YNORohsZdZuY6M92EhFLov3zC3XCGBpM1iy5mzKqzjmnOirOcS0yNz7gDpTY3G3wBMDUGoih8HeYxiKLY2gUMdyTxGm5sbfsWsCrDpcg0HVqohqdaMfqugZquxeAZYwEAsYoKADfXeknrCiUElNZtEET3EO2mhqTZWKLat2+P/fbbz1a277774vfffwdg16sQqU2v4qabbkJ5eTn/27hxY457LpFIJBKJJBds3LgRH3/8Md5991189dVXaaesqS+ajSXqqKOOws8//2wr++WXX9C1a1cA2etV+P3+lGqt3sPOhPq/d6CHq5PWkUgaElGziXkCAFXgz08+Eya2dT0AQA8UAYoXiqW4XL2da97Qmp2G/o+YSJbpIKYFAYoCmIG8zOMFYTq3ThE9BhDDqgAAdO+jcnauItEVryTf6Azu1RMTrfqOuSDHPWp8cmZ5cuBrWepS2oovRbf9wRP+6v5CEGvSAaEgsRCgGBpNMK2ZlmaTsYNpbbLukVtgtq6ZViPj+WNqDEwP27aLMGoLFzcwg9AJwIPQDc0ozXZs4vFC27kVABDe8ifUsPGjTL0eEIXC4zXOLf/CW12uScNBFFr3BMR6s7Gb2NiwYQMee+wxLFy4EBs3brQFvPt8PhxzzDG44oorcM4554DShj3HZnNFr732Wnz22WeYNm0a1qxZgwULFmD27NkYM2YMAKlXIZFIJJLdl1wmIG5OjBs3DgcccAB+/fVX3HHHHfjhhx9QXl6OaDSKsrIyvPXWWzj66KNx2223oW/fvli5cmWD9q/ZWKIOOeQQLFq0CDfddBPuuOMOdO/eHbNmzcKwYcN4nT1dr0IikUgkuyc50YnSm98gyufzYe3atWjbtm3Ctnbt2uG4447Dcccdh0mTJuGtt97Chg0bcMghhzRY/whLRwhiD6KiogLFxcUoLy/PKLeQRFIXotv+APMXADB0mKD44kldgXgaDSexMHxtOrlvMwnXmNo70WpAjXKdHhIL8RQeJFoDpni4+45oUTDFBxI13diK15ZkNqE/TOe6UkRXbRpTSve/pT55B5bbzs0FlAwmuneSuPZ2R3deYxIt32YsMJ0/GyQWBlEjIJoZvK1pxjKLu8/EwHIWDXOXnbXdguma4Poz9ZiE54DrRLlAFCW+XdByYmrMpgtltaNVV/F1tdpMZKxQ+Fq2QN55E1Neh/r+zbDa//j041DgrZvdoyqm4pj/LJW/bzmk2ViiJBKJRCLZU9mTY6KaMnIQJZE0IhEzuSnLbw0aMRSWQSigCdOwRSsMtVuBLBXpVFh1SMS0SJmyBmC6IWNgLTvaIlqUB55DiyUGAlvJYL3GxAwSC5lN6YBew+tq37/PrQKeA+NiuE5in79mtOMSJGyzSglYFigiLIMqdmsUpYCuI/Lhc/Eyc7v/uLg8iiQzfMVt+HJ0x2YAMCyQwq8K0cNg1MOfM6b4uCWUxRzPrq45LIp6fN1SKLeUxWuzTDrbNS2sLBIC1Bh0NW79Ylq8rWhFNbSwcQwl4IPehBLPUwV1jmmiqS9bk2XQoEEppYoAIy76/fffb6AexZGDKIlEIpFIJE2Wgw46KOm2iooKLFy4sNGkDuQgSiKRSCSSJg6hBKSOYpt13b+xeOCBBxLKVFXFww8/jKlTp6Jjx4648847G6FnchAlkTQq/sIWAEx3iBWMzXRAt7vauAtPE8rShHlMHTSqGOrSljtF1IVyBIpzDSghmJxYvgDdVJ02XSt6xK5SnYDgelFXvWlz2RCqCG44U3naTfPH3O6E6GZdXQP0eHJa+35KrYHpkrpBNPOZMhNXs6CpG+Xxg6gRMGsygxrjzxqh1OZ2Y4LbDYDhwjPVxZkaM4LQzWeNKCmeEyDBpcsihltOj4ahR1XuDmSabiiTm8HtseowFDN4O9C6uNG1oUQozUECYm33iIl67rnncPvttyMUCmHy5Mm44oor4PE0znBGDqIkEolEIpE0ed555x3ceOONWLduHa677jpMmDAB+fn5jdonOYiSSJoAvlYduLI48wYAhQIRQSWfCO/sQoAl0ex5wAAgutNMfUQ9YISCmtYkGi43gnUtqxPTwRQfXzYaFN5URWsX0+NWAheFaZ6nzKqebPo5VUxrkxAcj3jQuLMdm7XBqVKdYoq7Ud+0NphWBqttcb/IUiOBuQwwrxvetl1s67G/fo+vEBq3sio6mCWHYG0X5QdMixHUqM3aaeXS08M1CccmbhZKh5XKsnYyTQfTdehRwzKmxWLQwlFbG74i40e5KVmhgBzpRDVDsU0A+OKLL3DDDTfgs88+w5VXXon33nsPbdq0qX3HBkAOoiQSiUQiaeLkROKgjvs3FocffjiCwSCuuuoqdOvWDQsWLHCtN3bs2AbumRxESSQSiUQiacJ06dIFhBAsWrQoaR1CiBxESSR7MlqBkdaARqtBonG3BWF6PADXoRbOzMSvNqzA3WgNiBYP1CWxCPRAIUhUCAS3qZATwEpgQAgAanPpWS4PBhguGEsnyk3XyVnmdAEmDR5PESzs1k66ODWIHO1F3psDAPCfcEl27UtsMDNpr/gcA8ZkBgJTs0nXwTRRwykMFhXceYJ7j6lR6DGVazgBcasKodRVN8rNzWccU4duunT1qApd03kSWW++y+epiUAodT2nTNtojqxfv76xu5CU5nlFJRKJRCLZg6AKzcnf7sj27dsxa9asRjn27nlFJRKJRCLZnTBjouryh91oEMUYw7vvvovzzz8fHTp0wNSpUxulH9KdJ5E0EWjYSMdC1Cj0vJZQKrcCMLR3eNoMqhiz60x3CSMU0W1/8HVRU4qoEds6U7yAroKZKVQI08GIMDPOqT2lC0mFAUHfR49rRQmkdhW4p21Jha19IYksYM6w03W7e8+WHifuFiRITAOSDOnWyw1EmFnKCI0nvVajfEapHgmBRcPQQ0ZdFq4GC5vLkTB0NcZn0Vkz6nRhZmWgdTHfZuk9AbC5/JxY+/MkyAoF1Sk0a7ZeVOXHlDRN1q9fj6effhpz587Fpk2bMGzYMLz55psYNGhQo/Rn9xmWSiQSiUSym0Jo3S1RzTUmKhKJYOHChTj++OOx77774vvvv8fMmTNBKcWNN96IE044AYpTHqWBkJYoiaSJoAeMN2uiRkCrtnGlcRKt5jo7RIsZwd+W0rilvxOLJ0q13vrh1JCiDhVyx7Jo8eL1La0lxRsPOmc6CNUTLVciGSiqc3S7lUAs481algiHFYzpWvz8rHXhPIi4rzNBsfOYAMKLnwIABIaMyvw8JHEtM65Gbz6nVOWB5HpNJVioGnqNkXibRUL8vuqqod9kWZesYHBRaVy0OFnWKOOQmmugOa8rJBzWY0ZguTj139p3y/QxYJqODrc+mtUlyDV7cmB5x44dsd9+++Giiy7Cyy+/jJYtWwIALrzwwkbumbRESSQSiUQiacJomgZCCAghjWZxSoYcREkkEolE0sQxXHJKHf+a50/+li1bcMUVV2DhwoUoLS3FOeecg0WLFoGQxldgl+48iaQRqQnF3XAgxhuWr/JP6MFi0FB5fJuQKJho0XjAN7OFTBskcde5rde6zTL/67qQboYawe2p2k6WPsatTAwYTyeY3GU/3kdb3xVbPbe0L5Zrj2tgWW1kq0e1h1DzwnQQvzGZgbZoB//AYbbt3nbdAADqph9BdJU/r4xQwAwk13duhV5dCd3UhvK0LuWB5RSG60mtMbZp0ZjNZQcAkV2VfNkT8Ntdclpqd7Jzu81taJZZcUSb77oKAKD4PCiZ+GDKduuTPVmxPBAIYNiwYRg2bBjWrl2LOXPmYOzYsVBVFVOnTsXIkSNx3HHHNYqVqnleUYlEIpFIJHscPXv2xF133YUNGzbgzTffRCQSwWmnnYZ27do1Sn+kJUoiaUSoOfmeEQIltAsAoBW1jweHAzaZAsuCY0s8nMLqw4j9PclpbbK2pwwSzwZnewnyCRlYn4C4lYkHn7sHmPNtLtIHqRIWpwpml7hAldoTQAPwdNwX6qo3udVK2/kX1K1/AADUigroMRW+4kIAgF65y3Z/9ZgK6jV+ojwA1FCEt6tHVds9i8ZUKAEjmTa3KiaxRon76S51xO1MCDrXNR1bpo9B+5servW86wNKKWgdA8Prun9TglKKk08+GSeffDL++usvPPPMM43Tj0Y5qkQikUgkkrSps7xBDtyBjcUXX3wBTRi0M0cYQ1FRETp16tTQ3QIgB1ESiUQikTR59uRB1BFHHIHt27fz9eLiYvz22298fdeuXY0mdyDdec2A0H8fRvC0MQj9N9GMHDxtTCP0SJItVTVG8l/rPaowLxjfGCgFAEOBPJnZ3eHWS1iupa7o3iPMofWUQlsHzgB2t+B1538guR6Tzd1We/C4m/suIaFwEvXyuEsweQJiSWbknTcRlfMnAwC06l+hvTDd2ODxgnh9CJ4xltf1HHwqX66ec3s8cbBCwcI6VPMz4SkoACw1fRVgWtSmC6X4vHaNJ0tlPGb816vjkzRS5YhzuvCSBZnrUdVQNBd+JolC8ec9/wCARg0y39NwWp6c68nKGgI5iJJIJBKJpIlDSA7ENknztESlQ2PJHchBVBPFaXVys0JZ5dIa1XzwWJ9zMVjcCaUgmhBYTl0+ppl8GYoWKZf8eMY2llIaIQE3S1gqxfE0pQpYMmuSSzsJquXOQOdUlirebpKAd2mhSknVc3fwZabp0CJG0DdRYyDRMN9OfQHknTeRr+sxFTAEy6EEfDwYHAC0UA23AlmuJ9ES5VQsdwaAi/tqDhVykWSWJ6t/lmULABTBBUYUagSaexvnZ3NPljhoyshBlEQikUgkkibN6tWrUVZWBsBw3f3000+oqqoCAGzbtq3R+iUHURKJRCKRNHH2dEvU8ccfb4t7Ou200wAYbjzGmHTnNXW2THd3meVaMySZ207SPIlU7oqvUAX8Y57CnUc0FWA6mOJz3Q44grmdrjdnu8mUxXWdB4wTXbW3lYY7K6m2kq4l1Xcy9tOSB4jXFhzuVs9JLcmFa6svXXl2yp+6FdTrMVxxiAdcW/pOQPweEqoAVIEeqgEAqFVV2DX7ZpvLTA1HbMviNvFHXvF6+bIWM3yA9oTE8WXL1SbWcSPBlafb99FiKvRozOyLgs4znkraVkNDFZoyYD7dNpoj69ata+wuJEUOoiQSiUQikTRZunbt2thdSErzHJZKJBKJRLIHQSgBobSOf42fsDcbfv31V1x44YWoqKhI2FZeXo6hQ4fadKMakmZriZo+fTpuvvlmjBs3DrNmzQJgBJtNmTIFs2fPxs6dO3HYYYfh4YcfRp8+fbI+zl8PXIuYoD9SG+qqN226KJI9Gyt9CyMUED0JTAdhMQCFCfswQgGPDxBm6CWka9FMl4MWNd1/gkZKqtgAYRYeYXo8sbGugamx+Aw75ww6wK5dlUrLybHdLQ2LzT2XzSy62pAuuToTeuPffJlpOk+7Eq2sgUeYWUe9Hn5/rITBcQ2nWEKKFovIrkpoUZW7CUUIpfAEfbbE0KK+kzgbj6+n+Yw4Z/WJ/y1XXlNkT46Juvfee9G5c2cUFRUlbCsuLkbnzp1x77334tFHH23wvjXLK7py5UrMnj0bffv2tZXfc889mDlzJh566CGsXLkSpaWlGDx4MCorK5O0JJFIJBKJpCmzbNkynHfeeUm3n3/++Vi6dGkD9ihOs7NEVVVVYdiwYXjiiSdw11138XLGGGbNmoVbbrkFZ599NgBg3rx5KCkpwYIFCzB69Oisjue0QjkDE/+YdLntbYlQCu3llwEAXabPwea7rkK00giypJSiy/Q5KY8nNZ+aN9GdZVzXiURrAMVcph4j4DuNRL/MXwASq4kXUApYGjhqBNCioFFD5RlqxGZBAlWE/YRlC8EqpAvWJqZGDQuRqJNUy5t9UmuSrmUWzJ1mIHhdhQZTYl0rN62rPYDwO7NRveZXBEvaAgBocWto5qQIPapC1zSblSmm6VDDRhJsj6D1RBSKaEUNty5Z98yygDBN59tSBYAThUJ17AckBoKL9Z0B6snuodOKBQC6mJTYsoy5WMgakz3ZErVhwwa0a9cu6fY2bdpg48aNDdijOM3uio4ZMwannnoqTjjhBFv5unXrUFZWhiFDhvAyv9+PAQMG4NNPP23obkokEolEkjMsxfI6/TVTxfLi4mKsXbs26fY1a9a4uvoagmZliXr++efx1VdfYeXKlQnbLBGukpISW3lJSQk2bNiQtM1IJIJIJD7l1i1wTSKRSCSSxoQoCqjiYl3OsI3myLHHHosHH3wQxx13nOv2f//73zjmmGMauFcGaQ2i/va3v2XUKCEEb7zxBjp27JhVp9zYuHEjxo0bh8WLFyMQCKQ8tkhtIlzTp0/HlClTkm5PZnK2BSe6mYIB/H7TJdB1nQcr7lkOgj0UQnmwNiPx9C3M2ma9CRIKaFH3Nj5fBK1qF5S2xueHFrSwffmxWAxquNpYUWOJGkpidxxfmkbAeNxlZ0sIXJtbLZULTnMPKk/A4ZLL5EudAe4uymxwtNM85yzlDr18O8LbKxD6axcAoLBLBQ8kD2+vgB5TuftOj8VAKAX1GTpOqtcDaqZCYbrh5nMGaIv3OZVLiQrPh5gixsLt+9hy5TkDzZMdT3Nx0zndhcmOJWkcbrrpJhxxxBE499xzMXHiROyzzz4AgJ9++gn33HMP3n333UbzOKU1iPr666/xz3/+EwUFBbXWZYzh7rvvtll3csGqVauwdetWHHzwwbxM0zQsW7YMDz30EH7++WcAhkWqffv2vM7WrVsTrFMiN910EyZMmMDXKyoq0Llz55z2XSKRSCSSurAnx0T169cPL7/8Mi699FIsWrTItq1169Z48cUXMzb25Iq03XnXX399ysAukfvvvz/rDiXj+OOPx3fffWcru+SSS9C7d2/ccMMN6NGjB0pLS7FkyRL069cPABCNRvHRRx9hxowZSdv1+/3w+/0J5VvuuRZVAV9CwKKFW5Cr9RZDfcZlVcNRU003/ha27rqLAQDd73sGgBGYbtXtNmNe8gsgaRaQmDARQde4xYNoKqDF4C3taau/7d//BGCfwOAJ+FC1aRsU77cAgMIuJQi0N14ESF4RiMcbt6SoUSOw3DoOpcY6wP/b+kepzfrEYqY1TNezlgVgKSxYlpp0snUjViOJdckWJG/uY1q8arNgiZYx17qOQHyWZHvotQdsxcEzr0153ObEb9cOxbYfjTCITkfvg5qy7bZA7sguY1Zz9dZKME2H4rOeMQLFp/CAcs3nsU2uYZoOzZQ4sJ4Fa7vi88QT+vKg8/j94U9OJoH9tU5icASRu1jJdFMR3ZJmSJdNU0ajMpzEopxj9uRBFGCkedmwYQPeeecdrFmzBowx7L333hgyZAjy8vIarV9pDaLWrVuHtm3bpt3o6tWr0aFDh6w75UZhYSH2339/W1l+fj5at27Ny8ePH49p06ahV69e6NWrF6ZNm4a8vDwMHTo0p32RSCQSiUTSsASDQZx11lmN3Q0baQ2iMpVcbyx32MSJExEKhXD11Vdzsc3FixejsDBR0FAikUgkkuaCNcOurm00V1RVxQMPPICFCxfil19+ASEEvXr1wtChQzFu3Dh4hVyLDUlWs/N27dqFL774Alu3brXpawDA8OHDc9KxdPjwww9t64QQTJ48GZMnT65z225aIulgT6DpgQbVtZ3frh1q1jdM2XoshrXjLkDPfz2fZY8lTQHmiWvmMF++zW1EYqGE+v4WxgA/vL2Cu/S0cARKwM8DYHf+8jt8ZdvN+gXwBPw2t4uVnBVITNoquu+s59AKAqaKYmhFAXzdckXb3H4OjKBi+1eH+Hmx9NKAdIN+VWFZ+KLXtbirT3CNWsdIl4S6wg8JAXIXrN6MWDvuAhBKUdylBQBgzX++AQB4843nxxvYDk/QuMfR6hi0qMbdeYpXgeKjiFbHzLoeriauhlV4Ah54gkY7hBpJc/m9FV27uj2Ym1CaMEnCNqEixQQKJ64TKhB3N8br0YQyPYPvfabrYGlov+WCPdmdFwqFMHjwYKxYsQInnHACjj32WDDG8NNPP+GGG27AG2+8Ueuks/oi40HUf/7zHwwbNgzV1dUoLCy0zXwjhDToIEoikUgkEsnuzfTp07Fx40b873//S8hU8s033+CMM87A3XffnRMDSqYQxlhCTGUq9t57b5xyyik83mh3o6KiAsXFxSgvL89YvGvjraNs626quPFcTYlvVXosbrXSYnYLlhbVwHTjVh2w8K2M+iVpOKI7ywwLFIAKTUHbotSfkb8eMAKVd/wY1zLzBA1rlhIwJjwo3vi7DlGozRJEFWp7c6ZKosnfaYlxM+lTITg42b7WdqootrfiVFYr3k6St/uEIHNx3XEuzgB0Z0B7bRA3NXdKDasFX1dSSjnsLoHlOx+9Edu+XYMdawwLpxbVkF+SD8VrXO+a7SF4A8Yz5s33QvEp0KLGtYhVx0AUYrNMWc+gYXUitmeDJrGgWM9EKuuIU7Fc3Ne5X6p2xBx5Wky1PVfiZ4jZvqMZdE2HGjYspQe98q5r23X5zUgHq/1Ns29CUV7dLC0VNWF0vGJ6vfW1vth7770xffp0nHPOOa7bX3rpJdxyyy345ZdfGrhnWViiNm3ahLFjx+6WAyiJRCKRSJoie3JM1O+//45DDz006fbDDz8cv//+ewP2KE7GV/TEE0/El19+WR99kUgkEolEIrFRVFSErVu3Jt1eVlbWtNO+vPHGG3z51FNPxfXXX4/Vq1fjgAMOSIiIP+OMM3Lbw2ZE57uewvobRiTdbgTYxt0DoulYi6o2s7IRZGlt02z/JU0XX8tS/FVhJA+uzZUHAKGtuwAYz4Ju3l/F54GvMD/BtWUhquIzpxqzEMRtudyo4FpJ5vKwgr+dys3OBLKAqeWjaTwFhZuWml5LEHCq9BVJXThpBpMndx3Gz4lHcuoaGFIrlmcS0NxciFWHEKkIwWO67KJVUf78WViB4zHTnWW597SoBl1n8GqJkSCaroFoBFSxtmnQBfeeCLeKZKkMXpsFwHoOdE23Ta5gGgNg6Y0RMF14tqMaP1/n9WhsCFWSa6pl0EZzZNCgQZg2bRpeeeUV1+133303Bg4c2LCdMklrEHXmmWcmlN1xxx0JZYQQaLvhF45EIpFIJI0KVeo+k7SZDqImTZqEww47DIcffjgmTJiA3r17AzA0KR944AGsXr0an332WaP0La1BlFPGQJIeziByp5Iz03RbQKN4lQ0rVfxNT4vJe9DU0dZ9BaX737gF6utzToS/yAgOzy9tDaJQhLeXAwAKupSgpmw7lzgAjLdiwJAgCLZrgahp0RInHFiBsTBVkq03fOoS60AUasgZmEHoCqXQYypXk3YqiTPhjd2yWimClIEzEFh3yUFWG1awuGWpos5+pPiuSVdyJKk0gxa3qtmsUrrCFctT5tDTddS8MN1WlPd/N6XVp6bAdxeegl3rjeevTe/WiFXHEN5pyGrEqmMI7Yyr5ltWKABQvBSKT+HfR7qmQ4/q3DLuFSw2xjMTt3hShYBoBJblh9osUokv3NZnILHcEUROEydUiJYnIP4c6FENWkwztzHeLwDcamZt16IaP0+ikKTB5I0CpQm5J7NqIwOWLVuGe++9F6tWrcKWLVuwaNEim1GFMYYpU6Zg9uzZXJvx4YcfRp8+ferWTwf77bcflixZglGjRuGCCy7gqgCMMfTu3Rvvvvtuzo+ZLhnfkfnz57vmxYtGo5g/f35OOiWRSCQSiaRxqa6uxoEHHoiHHnrIdfs999yDmTNn4qGHHsLKlStRWlqKwYMHo7KyMud9Ofzww/HDDz/gq6++wsKFC7Fw4UJ89dVXWL16NY488sicHy9dMp6dd8kll+Ckk05KyKNXWVmJSy65ROpESSQSiUSSY4ii1JozMp02MuHkk0/GySef7LqNMYZZs2bhlltuwdlnnw0AmDdvHkpKSrBgwQKMHj26Tn1NxkEHHYSDDjqoXtrOhowHUYwxm8CmxR9//IHi4uKcdKo5023GPJ5kmGmaLXhcDScGb4rm64TkrOY2RVHAdAYZbtY00TYaibG3vzIPvqKXoZkJTgs7teQuuYrft0INq1xbp3rrGig+Bbt+M3R6yjeUg5rbWvdSoYWjiFXHLb7Ws5AyQBewafJQr8f84o27/HRdT0jACgDU5wV1USLnLhNN564YS0mdJglSd/bJ2W9R0VzEev5tbm+H2no61FaX+jw21x5FPOCW6XpcOypZ+1kmam4s1o67AAAQrYpxzaOf3l5rlJnac1GdofivGige47pYauVAXKFcdNHpGoOiWRMAWDw5sc5s32PcbSZ8zzmfYSpu04hQLtazu5KdLlvNRd/J0tUTXXROdx71KbZQCS0qPHtNLLA8lzFRFRUVtmK/3w+/359RU+vWrUNZWRmGDBlia2fAgAH49NNPczaIuvvuu9OWVfr888+xbds2nHrqqTk5djqkPYjq168fCCEghOD444+HxxPfVdM0rFu3DieddFK9dFIikUgkEklucOa3nTRpUsZq32VlZQCAkpISW3lJSQk2bNjgtktWrF69Gl26dMF5552HM844A/3790fbtm0BGPn0Vq9ejeXLl+PZZ5/Fli1bGjysKO1BlBVM9vXXX+PEE09EQUEB3+bz+dCtW7ekaqISiUQikUjqAKU5sEQZlryNGzfadJUytUKJOD1TybxV2TJ//nx8++23ePjhhzFs2DCUl5dDURT4/X7U1BiW/n79+uGKK67AiBEj6nQu2ZD2IGrSpEnQNA1du3bFiSeeiPbt29dnv5o13e97prG7IGlAGIm7GHat3QSvmZqhZmsF1JDhPglXROANePgMqECRH5HyCNekKepUiEhFlLcZ3lUDQk13ri/ukrN0bkT3iB7V+DrTKWB660hUTTm7zpi9Z+i8URcNKWfiYGvJqqclSdtBBZecm9suIS1NkuTEblpTznYtvSndob/mbMt2/FiMJ2q2NK7Ea0IoBYRE0kKHEvpSOX8yP07RJYmyL00B61pVbqnC9s1VAIDiVgFEyqMws7zAp+vw5Xmh+BxJoE2YxuKzjM1nzc3ZRRRiaJfFdL4OAJS6u/OoQnh74jaqEENzytyP8jlQ4rMSn73s5r6zXHdaTLPVJQoBM92Gusag+Ch344l1M0lE3BDkUrG8qKiozuKUpaWlAAyLlDge2Lp1a4J1qq707dsXjz/+OB577DF8++23WL9+PUKhENq0aYODDjoIbdq0yenxMiGjmChFUXDllVfixx9/rK/+SCQSiUQiaeJ0794dpaWlWLJkCfr16wfAmKX/0UcfYcaMGfVyTEIIDjzwQBx44IH10n42ZBxYfsABB+C3335D9+7d66M/EkmzIrrtD4TeXQgAWPPG1wi0DHDNGTWkci0dxQxg9eUbFg5vvhdUsDBZ+wBApDwCT9AjbNN5IKzidSThdVilnIjWGEvBPK5CHj++rus2vRO35LDWfm7JX4HUgeZu/QGMgGBxW6qgcGdAOlGoXb1d1FxztCP2jWjUdn6KiyWK+gQdKcGFwnQtIajZWi9/6lYUj7oraf8bi+qyXQCAqs1VKCgwnj9dY/AEPXGtJ/Pu88Br2K+1rjFbADhgDwi3rDdMY2CK3eoDxJX1iUK4RY9SAl2wMRkWIlPbySy1tllWKRYTrU/WMXVXy5MuBJqLnxEKCs3SgtIZ1LCKaJVhBfYVxC2QRyz9KPFiNiYkB4HlJLP9q6qqsGbNGr6+bt06fP3112jVqhW6dOmC8ePHY9q0aejVqxd69eqFadOmIS8vD0OHDq1bP5sRGQ+ipk6diuuuuw533nknDj74YOTn59u2N6fM0BKJRCKRNAsaQbH8yy+/xKBBg/j6hAkTAAAjRozA3LlzMXHiRIRCIVx99dVcbHPx4sUoLCxM1uRuR8aDKGsG3hlnnGELHrOCyWTaF4lEIpFImj8DBw4EY4k5Ei0IIZg8eXLGM/t2JzIeRH3wwQf10Q+JpFny56xJ+ObpLwAAeW2CKO5ahOqtxoyRwg4FCLQwtE12rNkOX76XuxVi1TEeVA4YLgfqo7Z1xWdq7NBEd50YKGsvt7vadEHfKbGuPV2H06UnJhYmjmBx8TjWsmsgrllGhSSviNmDdhMSDadw6aVMoKwnb1PcV0zErPg80KMq18cilELxxZOqO3WzmE2PSLetKwF7MPrW+8ah3XX/Snou6bLiuAEo7mRY+LWY4daydJmYxqAEjP5GK8PwFQawz+xX+b6/33QJ/vphGwAgvyQPf2w09IHaFgdAFAJfgbGvFVhtc9GlmBwAiK4/w91m1CWA5nCfKoTfbwqa1P0sut3iz1T8ORd1m0T3HdN06DqzJWh3fj5sQeiIp9TSohrUsMrP/5gVn7j2rSmQy8BySe7IeBA1YMCA+uiHRCKRSCSSZOzBCYhrgzGGv/76KyGTSkOQ8SAKAHbt2oWnnnoKP/74Iwgh2G+//XDppZdKxXLJbk90xSvY/uFSAIAeVbFuyY9o28eYXlvctRiK18NVoXWNoeIPI+Grv8iwUohvwIAw9ZsSXoerP7tYoGrDTW7AiZuFgb/5m9uoi7VJrAsgLqWgxGUVnHXdsMkYZJDc3GllS7RgJSb6BmALpAcAxeuxWZhsU+59HsPCpAjWJtEal+Lc9JiK8qduBQDEqkMItM7N92GwZQC7NpSbfTCsULEq44J7gh4uo+EJekAowU+XnQkA6P3ka1jz5g/oeHgnAIY1tNR8NtWQCqIofF8+wUCw2FAum6GbCX/jliebxIYQdM50lvDcppr8wDQdNvunoChuJcq21dXjAexi4Hgyy6x1zXjzUc02UcMo020TO5osOdSJam7k5eVhw4YNXGTzpJNOwpw5c7i0wtatW9GhQ4dGCSfK+Ip++eWX6NmzJx544AHs2LED27Ztw8yZM9GzZ0989dVX9dFHiUQikUgkeyjhcNgWm/XJJ58gFArZ6qSK3apPMrZEXXvttTjjjDPwxBNP8NQvqqrisssuw/jx47Fs2bKcd1IikUgkkj2ZxkhA3JzIpUp6JmQ8iPryyy9tAygA8Hg8mDhxIvr375/Tzkkajzc794Wm6jwhKdMYdMagmaP9s8p+aMzuNTi/XWvonhT37IiKdVsAAH9+UwYtqqFmmxFIHq2KomWPFtxF4i/2xxWgHa4gxafAo5AE9WaLZK48Z6C4W2LiVMGjNhddCleapUbOwrWbx1O1Yx0zne1uiukWbkHrokaUGFTsCZpq5DQePA7Eze66QkGs86PumldiH5wuTVGPSnT3adEYV1BXvF6bew9A1hpSik/h56fF7FpaoZ1h/tzUbK+BGlLRqldLAMDX55yItn3aILS92uw3Q/kOQzG/oMAHX74XlkZ+SncYV4d3uul03j/L1ad4nffQPmFCxHLNUSTe21gs7rbj9bV4QLjozuPH0u3nIKqd82TuVkC+Hk9I7Al6cMIPK1372KSgtO7uuGbqzmvKZHxFi4qK8PvvvyeUb9y4cY/ShpBIJBKJRFL/EEJslibnemOSsSXq//7v/zBq1Cjcd999OPLII0EIwfLly3H99dfjwgsvrI8+ShoBTdXh9Su2QFMACKV4Y92dKT36bwCAD8bPh7/ISHCphlUEWgbQbj8jsDwWVqHFdHiCiR8rpzXGChDWhUDadHAGiltBu8msOaLlymlJcZL4Zu8uGeCWW0wMKHYuG3Xj5+mmfm3vhzCtXQge1jWWtO+KTwH1Wa6KGKhCQU3FAc2RQzAdbOfrCMQXc/MxTed5+5iuc5sK03WwsKCETil2PnojWl51d0b9MPpvtwYShfLrRxSKSEUEgJFDccuvO5DXJgjAeB5ZRZTnawSAgNmf8soIWgc80M22DfVy3fW+MF2H4hWsTT5qWpisvIX2wHJRVd94NuOB5boW1yi3ns1Y1CVHoiNYnAeR62JZLd9FYpA84orpik/hkz+8AQ+OWv5x6naaCnvw7DzGGPbee28+cKqqqkK/fv1Arc9kI8VDAVkMou677z4QQjB8+HCoqvkger246qqrcPfdmX9BSCQSiUQiSQ2hii0FUbZtNEfmzJnT2F1ISsaDKJ/Ph3/961+YPn061q5dC8YY9tprL+Tl5dVH/yQSiUQikezBjBgxorG7kJSsdKIAQ7fhgAMOyGVfJGnyQrv9bOv/t3V1vRyH+hR4TdO8FtOghlQopln9mTb78noXb/uxXo7fmCxoa1zjg07uiZJDe+On5z8DALTs0QLtDigFAOSVtsaWz9ckaMy4BSpbgeVOfR3uhvN6oMXMQF0vddXbMfZJrkAOuOsaMd3pzkt0kwDgx3era7llxL6LdZ2B8zxomBJosfh5a1HN1o6z3UzhmloKTQjwdGpb2bSfqLnNm7iPhhiIZgVTK3Aqs4tuUV3TEtx7FnpMBUVcCV2LxZANWlSHv9h0IYdUW6B5sGUA2381PAK//lmNLnlerP9sEwCgoGUAO7aHXL8f3mi/P9SwCq+ZEFuLaVB81OZ2tVS8ffleRKtjCLQMGOcV1eAJemzB5JabjVBidwub6uVcF02hUDU1YRlwd9W5uZGTIfbdpg2FuBucKARUofy5OWxJM8rAQXIQWE52z8DyLVu2YOrUqXjooYca/NgZD6Kqq6tx99134/3338fWrVsTxPJ+++23nHVOIpFIJBLJnu3OA4DVq1fjgw8+gNfrxfnnn48WLVpg27ZtmDp1Kh577DF07969UfqV8SDqsssuw0cffYSLL74Y7du3bzIR8hKJRCKRSHY//vvf/+Kcc85BzLTm3nPPPXjiiSdw/vnnY//998dLL72E0047rVH6lvEg6u2338abb76Jo446qj76I0kD0aod1etnVkKwhR+KV+FmcWv2TYF13JjG+/FUq94YteOneulHY/Bm5744+KzeAAAtHMPa/66yzbjbsspwl7TtEzVdOuYMtKgG6lP4jCf44m99TNC4AawZTfF1mytNN2aiUVtdwT2hC4mDKQGLJZsZF08/w48rzIQSy7WYlqC9o0ftbjixfwCElCHpp/lw4tTJIpS4uiutfsXdd0Y9T8C4L56gh2sUUcXQBRJnxhn9SnThca0nJcmMRQDQLFeU4tCu0pJqZDFN54maASRtPx08QQ93GRd2KEDVn9UoOagDAKDnv57Hz1ecDQBQ31+Hsl1htPEb18Rf5Een1u6xqiFNh1YdBaoNpaiozuCjBF7zWvmLfbZ76AnE08soPgo1pPLZebHqGP98qGE1IXF1rDrG61rbrWXjWtXuMnajtgTJ1rOieBXeP0/Ak/ZM2CbHHpz2ZerUqbjyyisxdepUzJ49G9dddx2uvPJKvPLKKzj22GNT7jthwoSMj3frrbeiVatWadXNeBDVsmXLtBvPJdOnT8err76Kn376CcFgEEceeSRmzJiBffbZh9dhjGHKlCmYPXs2du7cicMOOwwPP/ww+vTp0+D9lUgkEokkZ+zBYps//vgj5s2bh4KCAowdOxYTJ07ErFmzah1AAcCsWbNwxBFHwOfzpXWs5cuX45prrqm/QdSdd96J22+/HfPmzWvQGXkfffQRxowZg0MOOQSqquKWW27BkCFDsHr1auTn5wMwTHwzZ87E3Llzsffee+Ouu+7C4MGD8fPPP+9WQqBD/1rNA5/rC11j8ATsb2xUIfwtTlEJqtS4deJfRftgXMXPdT7uh/2P4Il4vQEPIhVRboU4ZsUndW4/Gd8POxWA8XZcckBb/PlNmbEeUhGpiPAAXH+RD617GR+u6j+r4Ql6uE6PL99rvKGb10hc1qIaiEJtQehqmHHrSSyswmtaVVRTRTlSbrSr+BREzLd2xWtYQ6y3bKtdt6BuwD2IXCwTLUyiNUyPaiBCcDFVCO87oZQnpQUA5gysp9SWSBYAt0Q4kwinaxVQfArX+AGMgHWrLFk7tmsCwOOiFWUFmVvXhjr2JQrl/dXMc06mn+Vct10jTbcprGdCpCICX75hPotWxXDE0o9s27Ww4eLQohpa+RT4i41nlVCCSEUEX5x0HAAr6bBxnWqbjPJOz36CthOznYulJxUzrVieQPwzoHgVqOGooKmlGkH1Zl3j+qnCsv05cmpxJdNRI5RyPSrR6mj0wQgcj1uiKLdEKT7FsN7WkwW/PtmT075UVFSgRYsWAIwMKcFgEHvvvXfa+y9atAjt2rVLq26mY4WMB1H3338/1q5di5KSEnTr1g1er316S30lIX7nnXds63PmzEG7du2watUqHHvssWCMYdasWbjllltw9tmGeXvevHkoKSnBggULMHr06Hrpl0QikUgkkvpl9erVKCszXm4ZY/j5559RXV1tq9O3b9+E/ebMmYPi4uK0j/P444+jpKQk7foZD6LOPPPMTHepF8rLywGAm9zWrVuHsrIyDBkyhNfx+/0YMGAAPv3006SDqEgkgkgkwtcrKirqsdcSiUQikWTBHqxYDgDHH3+8TZncCiQnhIAxBkIINC1R/T5TjamhQ4dmVD/jQdSkSZPSqrdw4UKcccYZ3NWWSxhjmDBhAo4++mjsv//+AMBHqM4RZElJCTZs2JC0renTp2PKlCk572N9U2SassO1JHfNlrzWRuoIy+ytRTUQjXK3jBeANbYPaTra+ineaG/cizO2fJ/x8T7sf4TRbsCDWLXhntCj9XNuFpYLT4vpyGttPKc71mxGtCqGwg5GCL2/yA9vvpcHO/uLfDzhMABo0XiAteHKULmLTtRHYhqDHo67c6xzU0PGuuE6i2vpRKujvG6kIr4cg7vWkFuCXmfKFV7uqhfl4t5wlInpPCC4BYjPU2swuc21ZroKASNmmweLm0Hl8aTK8SBzxUsd6U4SEy/z84UO2NqlUHwe7ioSXXTU60lMfOxwwyU7j1TnaB1X3JYqMXQqfPle3kdfQeofQX+hj9/L6q018AQ8KN9gvHAqXoVrPdWG4XaNB4N7Ah7uvvPm+xCtjvHk2pGKqDABwXg+iTBpAuIPm6YJKWISr4kzUJ/fb0ptLj3qU+KhBeYEGDHJsDfg4d9VlnvP6Jd9skGzYg8eRK1bt65O++/cuRPPPvssRowYgaKiItu28vJyzJ8/33VbOmQttlkbo0ePxmGHHYYePXrkvO1rrrkG3377LZYvX56wzSm5YI1Qk3HTTTfZovcrKirQuXPn3HVWIpFIJBJJ1nTt2rVO+z/00EP49ttv8Y9//CNhW3FxMT7++GNUVFTglltuybjtehtE1VdCwH/84x944403sGzZMnTq1ImXl5YaKtJlZWVo3749L9+6dWtK/6bf74ff76+XvtYnp23+ji9fSbrx5cfY+ozb+uToYwAA3gIvt4YoPgXR6pjNasH0eIJSplBQEn9rDGk6dtTBcmS1G9oZ5kGgumYEW1sBz58cfQwPOtd1hkPfWZr18b678BREq4y35mh1FFtWGZbM4q7Gm4gYKGv0RTfLo9xKZE2v9xX4eDux6hi8ZhBwrDrG61jXNZl1SLwGFjZZAWEauJgo1pI/EIO+rWVnst+4CnQ8kaxFQpCvI4hahz2g2ia7EHMPJrf6a/wXAn99hMswuFmeMpFHiFsq4lYrK9BY8ZrBxAEfqM8LT8AKuKagZpA5VWitVolU1ifxvGxWMVfLoI4/74l/iWtRwzLZ4dZHUx7/oFfeTbndSjDsL/JzqQoA2FERQed2edyyGwurKDKfzc8HD4IW1VCzLcTbUcOqYA0lXA6DKNRVmiAucUFtdY3zt6vSW4jWTKdVkWkk4d6LVkpjH9OC6KM2CQOqkATLE/V6bPuIuGUVaOoQWvuzmk4bzZFly5a5lhcXF2Ovvfaq1eP1yiuv4P7770+6ffTo0bjuuuua1iAq1zDG8I9//AOLFi3Chx9+mKBO2r17d5SWlmLJkiXo168fACAajeKjjz7CjBkzGqPLEolEIpHkBpIDdx5pnu68gQMHJt2mKAquuuoq3H///QkT3SzWrl2LXr16JW2jV69eWLt2bVZ9azaDqDFjxmDBggV4/fXXUVhYyGOgiouLEQwGQQjB+PHjMW3aNPTq1Qu9evXCtGnTkJeXl3GgmEQikUgkkqbBzp07Xct37dqFL774Atdffz1KS0tx8803u9ZTFAWbN29Gly5dXLdv3rwZNEsrXbMZRD36qGHydo5I58yZg5EjRwIAJk6ciFAohKuvvpqLbS5evHi30oiqD6jp1gnvDHOzuRbTbSrblovI0g3Sohp002WrmDFnxWZA9eyWvXHFzswUzEU15JjDXSbqRlmuPbdA6nRZc835NldZeGeY6/BU/FHp6I+Clj1a8OMFivzY+dsuAEDbPm2w7aftaNmjBQCgcksV8loHETbdK0Sh3C0oujwAM+GrV7Hp4IhnpMU0vk3Um+LuO8F1qjtc55QkutOsY1h9sdqNu1+sZUsZ3HALJgsC9noTXS/xYybqQDndMXy7T6xruPIsl4/TRWN3/RnuOh4orNhdk0RRuDuP+jygXg8Un0fYnrifcR1SB5LX1Q1Ul+c2GdY1DrYMoGWPFty99/XS9dir2I/yDcaM41a9WmKXuZzXxnhOLdd5rDoKxUdd3ZBaVIPiU2zaWVYZYNf+4sHd5j20nhvRdceD+h0K9WJwuLGdJrQn6o5ZEzgMvbC4q4uaEweao8suJYTUPYFwM03TlkyioLi4GF27doXP58PNN9+cdBDVr18/vPbaazj88MNdty9atIh7sDKl2Qyi0omxIoRg8uTJmDx5cv13SCKRSCSShoLQHAyidrOBpcmBBx6Ychb+NddcgwsuuACdOnXCVVddBcWceappGh555BE88MADWLBgQVbHrrdBVNeuXZP6JyW55TG23hZcXhurTh8MAPCZgeRW4GlIsMioYTUhSFkNqdwCIlo/AgqFt8AbD1iujOtuJeOdnsaoP69NEEWdCnkwthbTEWgRn4btCXocganGl4BlpcqGvR56EUD8OgRaBrjkgCfosVmpTvzFLh77ydHHoMCUP6jcXIXCDgXY9ssOAEBRx0JUbani08jDO8O8n2rYeNO3gtk9QQ+iVWGb8riFpurQGON5EX2UQDNVDhRiyCqIRiCFEJv1yYIICvPxMtGaE4+PUFzUvN2CgK12xcBhS97AaWEwlu0WCkt1nNL4dHRxarriVQTrkyceAO41pvlbJndqShYksyjZjk3tVgnRalEbmVozRGuNU73crR4AbJoymufwc0K9Htu2znc9lVCn32uLbevfnH8yAGD/4gC2fvsXijoblvgdv+7kz2b5hnL4i/wImXIdTuuSFtW4lYpoDIqPQtfs6vDicyQ+K2KeScVLocV0/rkVt1OF2uQHxP0SA8njchVWuXjvLesj3z9JkD9Lke9Q0nzZvHlzSkXyc845BxMnTsTYsWNxyy23oEePHiCEYO3ataiqqsL111+Pc889N6tjZ/xLtHHjRhBC+My4L774AgsWLMB+++2HK664gtf7/vvMtYIkEolEIpEkwggFq6Mlqa77N0W2bt2KW2+9Fccdd1zKelOnTsXf//53PPfcc1izZg0YYzj22GMxdOhQHHrooVkfP+NB1NChQ3HFFVfg4osvRllZGQYPHow+ffrg2WefRVlZGW6//fasOyORSCQSicSFPdid169fP1e9x/Lycvzxxx/Yd9998fzzz9fazqGHHlqnAZMbGQ+ivv/+e96JF198Efvvvz8++eQTLF68GFdeeaUcRDUSmehDVW+tNv/b9X6oQrg6tuKjXA8GAA965q4XEFuQJ1UINNPF06JF7arIVrB4zbYQ1JDK3XlBQVHZmTBXB3iSXupT8OUpx6P/W++nfd7JsILIjWMyaFEdW3eEXOvGwioPHG+7XxtEq6Pc7Wa5Oy3tHeN6Rvg5RGviLrBoRQQKIYjGEtMUAIbkkhWw75SU8tHUwaGW/pOuMZtbKb4surYspWnd5v4Tk7vG69q1nviyi2K05a6ztJxSuexE9XDq88YDwgV3nlXPqThNFbsStUgqfadk6EKbmbara5qtju3am5pbPLBeobYgc6JQIw2AuW8yl9PvN12CLtPnpDyHA198GwDwecve6NWtmLuQRV0wy3XGE/N6FegBD3ff+4v8/FnwBD3QtXiybC2mg1DV9vm3ztMT8MAT9PDPqe5I9Kt4Fd4PYrrzvPl+s38eKAG7Zp9tkoLDBVubq9Xqk47snoUmByF1DwxvpoHlydLNFRUVoXfv3hgyZAiPc0rFt99+61pOCEEgEECXLl0y1o3MeBAVi8X4Qd577z2cccYZAIDevXtjy5YtmTYnkUgkEolEkpRBgwbhyCOPhMdTtzDugw46iFu0rMlqooXL6/Xi//7v//D4448jEEgvRVLGtr0+ffrgsccew8cff4wlS5bgpJNOAmAEdrVu3TrT5iQSiUQikdQGpbn5a4YMGjQIO3bsqHM7ixYtQq9evTB79mx88803+PrrrzF79mzss88+WLBgAZ566iksXboUt956a9ptZjysmzFjBs466yzce++9GDFiBA488EAAwBtvvJFzX6MkN1iz0CxTvTVjrHJLFbwBD5hLmg5dSDMCmJo9QfvjEnepEFPfJ64p9UG/w7hr8KS1/0vok9UHpjF4gh7bzBzRjadp8WS1iqLYZrEBwP/OHAIgcYZSplhuFqYzaDENLU030qLSPsgr8qOgnZFWYOCXK2z7fXL0MXymnOXms1x0Xo3yWYwa/x/fV2PMNssuXm7OwjPr+ijh+3spRUzX4TPdbZqqw+u3601ZOJOuAvFZTlZd6x4q+ZS7V8Tr4ZYMmCgElMZn4ym+xH3F2VTiTCoj4W+ihpPVT8XmwvPyZWuGXbqz5QyNM/M50nXbuvOaiHiERMXOenx/YdKxOHMurrIVd98l7Z/g2nPWcs7sc57X7zddIuh76QnuvTc79wUA9OhQCKYx5LUxkolX6zpfBgw3XaQ8wpf1qA5vgTk7N6TymbrO/ug6Q6wqxtsq/6MSvnwzpY5CkNcmLz6LNuiFGorxZxEAT78DAJ78ID8XT8BnSxIt/ne7DsnKks10FGmO6U/25MDyXKWRmzp1Kv71r3/hxBNP5GV9+/ZFp06dcNttt+GLL75Afn4+/vnPf+K+++5Lq82MB1EDBw7Etm3bUFFRgZYtW/LyK664Anl5eZk2J5FIJBKJRJISt8DyTPnuu+9ckxl37doV331n5KM96KCDMgpNysrByBjDqlWrsHbtWgwdOhSFhYXw+XxyENVEadHDcLNWbSlHrCrK3zD1qIaoxrhVyEgiG9eFEs0kotWJlyVTrKZGUHph+4KkfRKD1gklQvC4zjWbLFVvW9vW27tZ3xPM3kfOLXNBT1zjCgryWgcRM69JaGcYTGM4avnHrm2EdoZ5AHhM1+GllFuQFMa4Bcmq47xktamLG+X2QHC/474kuyeibpOFTftJCDT2BjxGQLjPXRvKvl88KNw6jhUUDhgBwIkaPpbVymNLAEwoheLz2toRg8eJlSuMKomuCKelR49bKXXEkAqnwrlYnlRHymvXe3LVdRK7k7IH7vs49+UB6rp9cgAPmnaxdvU4oRsA4Ns316DA77Ep31uTHtSwCn+RD1FTI86X74W/KB5QK1qOjOMwPlkg2DKA1vu0w6bPfgdgTBIR63sDHigBr9mOB978AKg30arlVJ1PpiovIloD7QmxU1xtxzVyako1G/bg2XkAcNttt9U6xpg5c2bK7b1798bdd9+N2bNnw+czLKKxWAx33303evfuDQDYtGkTSkpK0u5Xxr9AGzZswEknnYTff/8dkUgEgwcPRmFhIe655x6Ew2E89thjmTYpkUgkEokkFXv4IOq7777jAx830rFUPfzwwzjjjDPQqVMn9O3bF4QQfPvtt9A0Df/9738BAL/99huuvvrqtPuV8SBq3Lhx6N+/P7755htbIPlZZ52Fyy67LNPmJBKJRCKRSFKyaNGilKrk6XDkkUdi/fr1ePbZZ/HLL7+AMYZzzz2Xe9QA4OKLL86ozYwHUcuXL8cnn3ySMCLs2rUrNm3alGlzkgag57/sImSfHH0MgLj+i2gW5/ougn6URTLNIMDu+jGCvylPhLrq9ME4+D9LbPXFtCFaTAcz9WR0jdkDgCmBLxhPQCz2gWkEgRaZu5C/POX4BPeDGKhruTgAIFodQ7Qmhjfa7w8AOGOLXYmfKvGUK16/4Q5TzOB36nCJ0AzdcGKqDLG/ziBvANzVIu5vHVNM2utMySIGgCs+hytNUeKuFsG9FU/FIqT3EAKyqZBCRjHTtdj6VIvLjrtaaAqXi67BFmqq6/H6upbgomOaDkbdA8sTkheLx3X0gegamO6u7eVEDDRPB7dAdKvvTvciD6LWdPx27VCzrnG/9p3zBgBgX7Pukn0PBgAE2+Rx3TK3yR6ZsHr46dxd78338kkk+SX5RiC50E81HIW/hene83qgQXVrEkDcLecxnxHFdANaGlxMiZ+3hRWk77x6POCfUmjchZhcV6zJswdbonIRD2VRUFCAK6+8MmftZXxFdV2HpiV+ifzxxx98JCeRSCQSiSR3MEL4DL3s/5qn2GZts/O2b9+OWbNmpdXWM888g6OPPhodOnTgSYsfeOABvP7661n1LWNL1ODBgzFr1izMnj0bgDFCrKqqwqRJk3DKKadk1QlJ4xBoEYAaVrlVyJZEFoYVw23qvBtEsIQoPgWEEii+5A++ZQGxgmR1CHIJ3riasjcQlz+wLCmAYbFSfASx6tqTHTsxkhpTUNNiJLYbqohBi2rQYsaba7TGDLzNc0+mrcU0+ItNS1m+D4qXcquaGIwtKnY7y/g1cah8i/WcyX0TJQjilh3qsLIkWFqEt3EqTCm3ArstxKTEzmS/xnEU2778PIQ2uHxBkgBxoih2a08t1icRoihg1gudU82aKiDifrV906U6bj3gpiRvWZucwetuCubcWgkP9FqsKtZzGKmIcEtzXdlv/n/w3YXG973iU1Dc1ZipXbWl3HiOwqY1N2Ao0Ed2VQIA9PxAXN08P4hYdQjefEMqgVE96TOnmNcomWUvmRWPL+txS7PIxltHucpESJoWc+bMQXFxsa2MMYbFixfjqaeewuuvv46ioiKMHz8+ZTuPPvoobr/9dowfPx533XUXNwi1bNkSs2bNwt///veM+5axJeqBBx7ARx99hP322w/hcBhDhw5Ft27dsGnTJsyYMSPjDkgkEolEIqkFy51X179myIgRI3imlPXr1+P2229H165dccoppyAQCODNN99EWVlZre08+OCDeOKJJ3DLLbfY1M/79+/PJQ4yJeMr2qFDB3z99de47rrrMHr0aPTr1w933303/ve//9U56EsikUgkEokLVu68uv41QyKRCBYuXIjjjz8e++67L77//nvMnDkTlFLceOONOOGEE9LKnbdu3Tr069cvodzv96O6ujqrvmVl2w0Gg7j00ktx6aWXZnVQSdOhsH0BDwoVIYqh3aS4e7FcEV1WoquqfEM5D2YHjCS+Fr58n02vyEpQC5hB2069GsG9SCiBr8hwBawefjr2m/+ftPppuC/jAeSKT0G0yrgGnqAHFX9UorCDETTboksRKjZW8oTJThSvwpMn+wp8XIMLsAeWe83krl5T2VmPalACXmim28Ob74cWM66LJ+CDGo5yZWctqsKbb+Rx0mMqlIAfeszse8APpulcs8k4rt2dxgNsfcay5TLRhf2Yrru64ZK57Jw6Pkl1l6jTXWe5BJN84elactcaVeIuPWoFxMeDyTnOtmktObBSBIozTQPU+EQDpmvQzfvkdLM5VdF1TUuqK+V0Kzk1j7JJmMs0HXoshnXXGbOLtHAERKE47tsvMm4rHQ5Y+BZftoLbAUNzytKB87fMR2hbJXdjq6EoPOZEkVhFNfwt4nG0oj6XONFDXLdwKtKL18xVv4vG1eGd94EolF+z7vc9k9E1aFD24MDyjh07Yr/99sNFF12El19+mQt9X3jhhRm10717d3z99dcJgptvv/029ttvv6z6ltUVzXVglkQikUgkEokbmqaBEAJCSFoWp2Rcf/31GDNmDF544QUwxvDFF19g6tSpuPnmm3H99ddn1WbGlqj6CMySNCyWUre/yA9/sT/hrQ8wFIqdiLIEbmrlVmC00xJV3LXYplBOfdQWYK14FR5I7Qyi1jX34HRCCdSwiqot5bWcbSL+Ir+hNG72IVoV5ctMYyjqVMiDsyMVER6IngxfgfFmHWgZsKk16zGVW5NEVWZ+DooCFMXb8QrH8BXmczVqfwtxHyuAPC+hzFqmSaxCAEACgtUvkFiPOqaAJ7MwObHe6AHDMhAPktZMyQHBYkSVuOVFUMnn1imHtYnjtGo5t4nn6eyrc1/R+iQsM1031q0y0UKkxlLm5NMds5aZrkOPqrZ1EafKtpulytonHcsUUSiokNjPylPYEFhq9a337YQtX65Dm31LAQAVv29HYaeW/H6IlqdoZQ28RXn8eaXeeB5F57XSojFbTjym6dxy67QAOnPnOa+d+Ezrug7odktuU2VPzp23ZcsWvPLKK3jqqacwbtw4nHzyybjooosylj645JJLoKoqJk6ciJqaGgwdOhQdO3bEv/71L1xwwQVZ9S3jK1ofgVkSiUQikUhSQKjx0lGXv2Y6iAoEAhg2bBiWLl2K7777Dvvuuy/Gjh0LVVUxdepULFmyxFV6yY3LL78cGzZswNatW1FWVoaNGzdi1KhRWfct4ytaH4FZEolEIpFIJLXRs2dP3HXXXdiwYQPefPNNRCIRnHbaaRlPbGvTpk1OJsNlbMOsj8Cspsgv11yIAp8XelSzJcJNN3i5KbLq9MEo31COQEsj0NYT8Jh6TpY7jyS46UQXntO1lm7iW8WncK0n3dRmstpSfBRMYwl6R1adZG4MVdPBNGaqowP933q/9gtgst/8/2DFcQN4QL0e1bg778iPltnqLjvsSFMh3OjfB/0O49sUnxFUXtTJcFHs+HUnWvVqyYPFqU+JB4srPlPnJu6CIAqFEjCm7SpeD3ffUUqh6zoPhrW5ysxlK7jZSk5LhLosieuRKBSIIe7uizmCwB3tituowy1ktZVQ5jymI1DYWVdUCGdWADd3u8WSB6LzwHKhXY8vscwF5lA352XWuq6BmYH70DXoZr+cmk1c40xzuAOtZaGuW7C402XnLHcuu6EnCVyPl2lYc835AOyu5B4PLEjZbqZ0mzGPL3cWyteOuwCEUviK8gEAWjgKf0tj0oYn6IMS8PH7pfi8UMN23Tct5n7tnYHktbnwUmF93gBg/Q0jbG3UFmi+evjpqIqmTnadM/bgwHI3KKU4+eSTcfLJJ2Pbtm149NFHXev169cvbbffV199lXE/Mh5EWYFZ4XCYB2YtXLgQ06dPx5NPPplxByQSiUQikdSCHES5UlZWhmnTpuGJJ57AbbfdlrD9zDPP5MvhcBiPPPII9ttvPxxxxBEAgM8++ww//PBDRkmHRTIeRNVHYJZEIpFIJBKJG7t27cKYMWOwePFieL1e3HjjjbjmmmswefJk3HfffejTpw+efvpp130nTZrEly+77DKMHTsWd955Z0KdjRs3ZtW3jAZRqqriueeew+mnn47LL78c27Ztg67ru7XIJlEIiIubqzlhpWcItgyAKoS71rSoBk/Qa5uZZqFrRlJg0YWX7B2mtpQwtrrmsextCTOgohq/1pabj7nM0CPmzD1vgbt+U20csfQjLO17KG8r2f0llCCvjT3JsVjXl+/lbpKCDgUI7wzHU9pUR+EzdaGi0ZBxfrZZRAyK1yinPoVrQTEzaW+sJpTQH+r1gmmazb1lS7nicM+lwm3mnTgrL9ksv9pm7lGXvllpN3i5Zrp8fR6bO4t6BGEySkG8povOKvfY77fNdeemGZUMPvtOSyhnmia4+dILVgWM2ZiWO1TX3GfmiS4oXa/d5ZSOW4olCai1jinOmmxoev7refx27VAEWhvTUGu27LC5FZ1YKWDUUARUUfg3g3gtLfisyJia9vV0QxdcgWI6HkuLLRVKwIsGy2W8B1uibr75ZixbtgwjRozAO++8g2uvvRbvvPMOwuEw3n77bQwYMCCtdl566SV8+eWXCeUXXXQR+vfvn3QgloqMrqjH48FVV12FSMTwW+cqMEsikUgkEkly9uQExG+++SbmzJmD++67D2+88QYYY9h7772xdOnStAdQgCEUvnz58oTy5cuXIxCoRZQ3CRm78w477DD873//Swgs311hGrO9kf902ZkglPK3ut5PvtZIPcscb74fSsDLFYMBQ0E4ju5q9amNZFpO6eAeDMtclwHYrFQAoIYS31DTJV0lZ1GF3Gmx0qI6DyS3+qIJ6ufR6qi5H3UEvhpWNmsf6lOghuLq5SJi4DhC0YQEw+J/VQhCByxlcZegcCAhMNzNipVUidyZdFgIRNeFbdzi5NCfstDC0UQtK2rqRnl8YDHz+bSUzC3rihVILyY2FoPDU+HUhUpnH5e+8zZMi4UajkILxz9PNg2pFHpPySxJyaiLRcnNolPf9HhgAbZMHwMA8OQHbNY6UZ+J6ToUr/FZU0zdKeuK0/wgdE3j15dFVZt1zxlg7/y8pYvuCBJfOy4eopJKEV1Sv2zevJlPXOvRowcCgQAuu+yyjNsZP348rrrqKqxatQqHH344ACMm6umnn8btt9+eVd8yHkRdffXV+Oc//4k//vgDBx98MPLz823b+/btm1VHJBKJRCKRJGEPdufpug6vN/4yqyhKwtgjHW688Ub06NED//rXv7BggTFDdd9998XcuXNx/vnnZ9W3jAdR//d//wcAGDt2LC8jhIAxBkJI2oJXEolEIpFI0iQXCYSbqTuPMYaRI0fC7zes9OFwGFdeeWXCQOrVV1+tta3zzz8/6wGTGxkPotatW5ezgzcHrCBhp8uhOaQJsPAEjL4qAR8UxN0BaihqBmUarjFnGgrAoQVF3T+AzCVFTLroCWF56ZnKiUKgKIQHxX99zok46JV3s+5HMjxBDw/EB+yuS6bpoApBzbYas0/UpimmRXWuwZVMH8vCcOuZAf+7QvAGPPx+UIVC12J8OdPXFDcNr4Q6PO2Le2B6gs6TzbWnGIHoLppQTr0pwJEg2cVdaG0nlCYEqdv0pZzrtkTH9qTHokvT1X2WwkUWT2Hj5opz6BU5tIpq03Eyzsvubk3l3qvNleeW4sRy4VGfB70efTnl/vVF+5seBgD89cC1CG3dBQDwtyyAGo7CV2RM3GCajmjYEGw2UtbYnw3LxQcAkV1VCa5SEae2Wiqc1zvdpNFu35f1yh5siRoxYoRt/aKLLmqkniSS8UigOcRCPfLII7j33nuxZcsW9OnTB7NmzcIxxxzT2N2SSCQSiaRZ0RR+T+fMmZP1vq1atcIvv/yCNm3apFW/S5cu+Pjjj9Me62Q8iHrjjTdcywkhCAQC2GuvvdC9e/dMm80ZL7zwAsaPH49HHnkERx11FB5//HGcfPLJWL16Nbp06ZJ2O4rXY6hIa7rtbbnB3z6y4NerzuXLRKE8Ca4uJOwUtxPzRZhocTmBZFP+XQPPHWLSqa6R02plWbp0flz7m5IzGFuEKIQrlmvR+rsvonK64lN4X5lmSECIffble23X0Kn4blm1rPZiZmJmSgnCO8MADMthTEiKrEW1BMVyJ8lkJtKxQtXWBpBohbRbrOxq84YFyd2KRb2GpAH1et23i1IJgsXKTflccdnu3C/eHyXpNRSDkGu7RiJqKAI9pkK1gp1d2iXiZ8Gb2iqi64l9iFu5NNt5phtczjS90axPbrS99gFsf+h6AIY1yd+igFsbVS0ST0DstKhR4x5Z32FKwAe12vi8KF6Pq8RBukH8ya5lssTFVjlPnNxAGgeNkYA4V7+nDcHWrVtd1QJ27dqFt99+G8XFxWm1s3379ozCkjIeRJ155pk8BkpEjIs6+uij8dprr6Fly5aZNl9nZs6ciVGjRvHI/VmzZuHdd9/Fo48+iunTpzd4fyQSiUQiqTON4M5rKr+neXl52LBhA9q2bQsAOOmkkzBnzhy0b98eAPDnn3+iQ4cOSQc/TndgLsn4jixZsgSHHHIIlixZgvLycpSXl2PJkiU49NBD8d///hfLli3D9u3bcd1119VHf1MSjUaxatUqDBkyxFY+ZMgQfPrpp677RCIRVFRU2P4kEolEItldcf7mWdqPItn8ntYXVpo5i08++QShkF2M2GnYsdB1PeO/Hj16pN23jC1R48aNw+zZs3HkkUfysuOPPx6BQABXXHEFfvjhB8yaNQuXXnpppk3XmW3btkHTNJSUlNjKS0pKUFZW5rrP9OnTMWXKlMQNZtCqc5RpmW4zMfs3BqmCgi13hx6KuyHidRMTAVsY7goirLs/tE7ztujec7qFLPee060n9j3uaknubrICuHMN0xjCO8PIa2MoKWtRjbsOtZgGb8CTUN/qpxbVbedFBV0oAIhWxzWfoiGVu/rUsAqiEMQqjC82qlAgJlwDQdOImtfTegFLuG/mcY2+afE2XEgM8o8jbiGU2F22mnXP42WaqOckuMQ1LWoE4GvmuXk9Qr8UaIJ2kBqO8mWu/WMlafZ6oEXV+PmKxzC1p0T3C9N1rlnFdB0KDHeirmkJyZWdJAtQporC1bLj18bubks3uDnVsY1zt7IKNP/Zz62vuZcvb77rKgRbG24WUclch3FvFcHtq8VivI4ei9976xrxZ4U6AsB18fsttX4Uc9QFkrv1OA3mziN1Fsu09u/cubOtfNKkSZg8ebKtLJvf08Yk3STDuSbjQdTatWtRVFSUUF5UVITffvsNANCrVy9s27at7r3LEufFtNyMbtx0002YMGECX6+oqEh4wCQSiUQiaUwYM/7q2gYAbNy40fY7bkkHuJHJ7+meSMaDqIMPPhjXX3895s+fz/2Tf/31FyZOnIhDDjkEAPDrr7+iU6dOue1pGrRp0waKoiSMkrdu3Zowmrbw+/3uD5BjurJIU7RCrTjOkL4vbF9g5MNDXNaAmlODnefjgVOxPDNVXqd6eDLSsUw5LVLxuiypVczoA3XdL1cc+dEy2/r3w05FtNowIxsyBCzBimb19uD/LMEXJx0HIB5wfvB/ltja+3zwIABGwLrzOuqmxYsGnfIaccuJ87zFYHanxcntWomWQed9ShaUnkwOwS4/4J7bz7IWJM27JwSFizIiVrlomXAGnjtV1m0yDDS1tUnEmS/POeWdf0YU3fYFGqsO88Bnq664nE7gs1GefvCzk2yVuhuLDrc+ypc3TRltkzsAjGBza10J+OAJGt/V1OuBphnfXUogdf7MhG8P4bnQdbu6eW1SGM3p2qaiqKjI1Rgiks3vaX1BCLEN3JzrjUnGo4GnnnoK69atQ6dOnbDXXnuhV69e6NSpE9avX48nn3wSAFBVVYXbbrst552tDZ/Ph4MPPhhLlth/qJYsWWJzP0okEolE0pzQGcvJX7o0pd9TK1deq1at0KpVK1RVVaFfv358vXfv3g3aH5GMLVH77LMPfvzxR7z77rv45ZdfwBhD7969MXjwYD49+cwzz8x1P9NmwoQJuPjii9G/f38cccQRmD17Nn7//XdceeWVjdYniUQikUjqAjP/6tpGJjSV39O66ETVN1nJbhNCcNJJJ2HgwIHw+/1NxqwGGGlptm/fjjvuuANbtmzB/vvvj7feeitzkVDTPcE0PUH3iCJRi6Yx+e7CU9BlgDESj9WEbSZnxevh5m5nn7VwFEogatOisdwGlsqxde5isk8nTj2k2jAUuHXbcjI1dCWZSrpCbPpNqYKicwlVKIItjWzf0aoYFB/lgebU1K2KxYxrseK4ATzwPFodc23P0rkSXWdUoVBDKrSYsS2vTRA7f9sFACjoUIBIRYRrSOmabgtmB8C3ud0TxZtEv0khrgHnogvQukdUocY91+NuM0VQJqc+b0IyY6dWlLWuCMvxMi9fp4L7jygU1GO68zzeBKXvdElw0zhcZeJVcLr27O0kBhyLwetO/SnxCbXcSGI7yYLGM004XNdg9sbCmx9AZFcVXw5vr4C/ZQEAQDO/j4IlRgiJtm4LPPkBvq8S8MXP16HHxZTkquOKda2oy7XyehIV5732+2RLDr4bkrPf0zpSV4mCzZs3Y+bMmbj99tsT3Jjl5eW46667cN1112Xlpsz47uu6jjvvvBMdO3ZEQUEBTwNz22234amnnsq4A/XB1VdfjfXr1yMSiWDVqlU49thjG7tLEolEIpFkjc5y85cpTeX39KWXXsKwYcNw/vnnY/bs2RntO3PmTFRUVLjGgRUXF6OyshIzZ87Mql8Zm1PuuusuzJs3D/fccw8uv/xyXn7AAQfggQcewKhRo7LqSFND8fug+H1gmgaq6zbLjCYEj/58xdnQwoaVIdC6ED0eWNDgfRUVrYOti6CGoojsqjQ2Ot76ma7ztzpCDTVzMZhc51PIvfzcrW1M06ELb2z26cD2Pjnf+JxWkUxUfuOyEo5ZIhrj1hotquGb80+2vfkf+OLbaR9D5KfLzoTXfMtVQ4YMQcCchh1s1wKbP1sPACjqXAg1pMJX4OV98Bb4eJ48xatANRXJAy0Drv1xBq4DwMdHHAVP0INolfFchXaGUfWXkZ8vaFql8kuMxJuhbTXw5vv4dVC8Snx6PaUJb8qixYkolAeZE0q4hckNIuQpVLxKQjuKl8ITNK6DJ+BLCDIXA8p1qIBgmKOmRcsqE+8hiVo5AxXTGhXl7VKfJ65k7hY4bpVRmjI3nigZUVtQuf2amJY389w8AHSzTBc+U277izks3eoly7lXW149655b/WtOVpJ21/0r5fZfrzoXwXaGgLNo2bQyMjCH1Vy8B/yZSnI9RGuVCBXaNVUxEp4Db5YW0UxhjCXVQsqkjebI7NmzceWVV6JXr14IBAJ45ZVXsG7durQFP9955x089thjSbcPHz4cl19+OWbMmJFx3zL+hM2fPx+zZ8/GsGHDoAgPT9++ffHTTz9l3AGJRCKRSCSpaSxLVFPgwQcfxC233IKff/4Z33zzDZ566ik89NBDae+/bt26lGlqrMlx2ZDxIGrTpk3Ya6+9Esp1XUcs5h73IZFIJBKJRJINv/32Gy655BK+fvHFFyMSiaQt+hkMBlMOktavX49gMJhV3zJ25/Xp08c1w/FLL72Efv36ZdWJpogn4IPH7+OmdSthqm4OFHkSSgDMdHNYQZENDdMYwtvLAQDBdi0R2VUJT8DQUyEK5do1UUcCYh6UncJl4cSWjDlFvQSzuWUKdwlQp6C24H0xSXGygHPAcDF5FEs/SDGCrLlaeN2UnS33LdN10KAP1WXbAQCx6riuVrQqBk/Qw1XIiUKgR7W4S0wI+I5UJKZVSMYxKz7B1+ecyBMSKz4F1Jy8Uf1nDTxBD99GfQpi1VFQ6xmsiCSopIsQwWVnBIvHA8epT7G595jO4tpXCuG6VZpPM/pE4+48PUoQM4PniRKyuf6IQuKJZXXdFkzullA57iazJymmDi0oy6Vn9C/uunK62Yztiu04CcdMw+3lDPC23ETURRNLgd2N5HQxJbSd4P62u82TfWaNY9iTE4uq6ZkGpTdliEJRY34OvfmBhPvtDKgnNK7nZeHmZhX3dcXpxos/lsb3A2u4a9xMDUl1JhQKoaCggK8rigK/34+ampq09j/ssMPwzDPPJI3nmj9/Pg499NCs+pbxIGrSpEm4+OKLsWnTJui6jldffRU///wz5s+fj//+979ZdUIikUgkEklycuGOa67uPAB48sknbQMpVVUxd+5ctGnThpeNHTvWdd/rrrsOgwcPRnFxMa6//no+C+/PP//EPffcg7lz52Lx4sVZ9SvjQdTpp5+OF154AdOmTQMhBLfffjv+9re/4T//+Q8GDx6cVSckEolEIpFI3OjSpQueeOIJW1lpaSmeeeYZvk4ISTqIGjRoEB5++GGMGzcODzzwAIqKikAIQXl5ObxeLx588EEcd9xxWfUtK7GjE088ESeeeGJWB2wuUI/HSI6q6CCaznVEuPYLN/Fq3KUQrUjPtJhrotUxbrYPb68AILjodB0sHO+7Ho3ZkpkSRbElgBVn/4gpLDTBveXE6QpJOfOO60KJiVp1iCo6uuAoTNWWXXuIQhH6rGsMv151rrmsI1oZ5q7Bnb/tQpverbHvnDcS2vzpsjOhhmJ8hh0ARLUarvPkDXjgLzJmA1kJg8X0N0QhNt2oaLXRzhFLP0p+TVw46JV3+fLngwchYGpTqWEV/iIfIhVxt6I338fvi6/Aa7u24r0xZmaKrg0Gal5rTbe7/yz3oOje4+1oDFpUA7PcKDqDFosnQwYgXAMV1KdA8WpmHxgiWsSmV8Ucr8duqWh0M8GwOCOQUGpLCC7OCLRSHQHgbkC3VDNcf8o2m9Du9nO60pzbxWsspqXRqZbgxrYcfwmz9rzun61ks/ysdvSYymcLO/WmCKW2z3BzJ7S9CsU9OwJI7X6LJ22mCXXdXHwiTnerPTm7+zEJaaAExHvw7Lxsg75FRo8ejdNOOw0vvvgi1qxZw1XQzz333DqlqWs6ipESiUQikUhc0ZE6DjXdNvZkOnbsiGuvvTanbaY1iGrZsmXaquQ7duyoU4eaCs43W+vNxbJKOZOJAuDJMxuDWLURaOwJ2ANZ7ZXMQF4hKJWIQeWCHhYAmx6WZZkRlcYTEtxSu0aOiGhREhXLnTgDzDVBt4dodguFXTNHM61ARpk34EF4V43ZL3uS40JT9dtKDpzXOsj1nJxvsVQhQCx+/jGAW4XiRzb7pxj1uPVGIWjRtdj1PDOhsH0Bvy55bfJQtbkKeW2MZy20M8yV0gEjCN0WDO4SXK4Lml21BdiKSZX5vYgZ19QKHjf2I3HLlEK49hLTjL6pjuTHVjB+qj44rQBuVgHxWjvV1UV9MacVymnBoqJKusMa5TyuXQMrvq54PVwnyu1a2hMiC9u8tmq2zy61gtMdFi3+OfR5QC0Nu5gKpun8eUylKdUc8QQ8qC4zfl/a9O2Jmi1WkHkQWizGrYC6piWfKOCw+DmtjIrDcgtxIo2LVdBp4ZTUD/Pnz0+r3vDhw7Nqf8uWLYjFYillEJKR1iBq1qxZfHn79u246667cOKJJ+KII44AAKxYsQLvvvtuoyQdlkgkEolkd4cx46+ubTRHRo4ciYKCAng8nqQuSUJI1oOo4447Dr/88gu0LF480hpEiXlrzjnnHNxxxx245ppreNnYsWPx0EMP4b333su5qUwikUgkkj2dPXl23r777os///wTF110ES699FL07ds3p+3Pnz8/bbkEJxnHRL377ruu0ugnnngibrzxxqw60ZRJZqq1SjVN54HajRXEqUU1qCEj8DlWHYWvMIBYtaFL5C8K8mUl4OU6PYDhrlO8Hu62EwNRxZQq1jGscgA2TSHFa5w/UXReR3T1EYVCjcXdZSJ6VDP0nYRPt+VCcnPzWMfUdC1BQ4rFGC/TNZ33yzLDi24sT8D+6Iu6RlpMS9iXB5ELx/QEvYhVR4Ukw1FoUQ26eS55rYOuwevJ+O3aoQCAmq3ltuuheBUUmGleCCVod0AJtv38FwAg2DJgpJvJN10ZUR3efMsdxtJODu1WJ5nLNdk+Gp+gIASYw3TlxnS+zblf3E3CEraJbYnbkqWnsbY53c2U2l16ohtQ8Sn8vio+JWm6FMpdf9aEAoW7/wBAE9PQOIKaLR2rZFpV6QSnA3HNKCpoFymCBpcWVQHTta9qGqjXg3XXXWzW1RolLVWuqNkW4umXVPMcAUANR0C9HugOK4KbfhfTdEAo16n5veYSaC7qfAGCMrUw2aG5JXlurvzwww/4/PPP8fTTT+PYY4/FXnvthVGjRmHYsGGu+fAy5ZBDDsl634ydua1bt8aiRYsSyl977TW0bt06645IJBKJRCJxx5qdV9e/5sphhx2Gxx9/HFu2bMHYsWPx4osvon379hg2bBgikfTFjAGgsrISFRUV/K+qKnuh7IwtUVOmTMGoUaPw4Ycf8piozz77DO+88w6efPLJrDvS1KgtYNDtDUQxVcIbGsWncLVoXWNQQyp/C49VV8Sn4FdEQH0K9KhhtvQEPYhoEdvUbWtZi+pQfJRP7beS6VpT02NhlVtgtKihYK2H4wHNVvCxgT0A3BlYLipjp4JpOnRx3J+Q1JhwCw6hJMGSwpW8QcF0ZrNGxa1pBF6fL67kbb75W5Y4y/oEGFY/xasgaq77i/xG8OtW4/ru/9ybtZ6T7fx0y+rltVkFnc9hrDqCoBDcrkV1bjX0BjzcEmZZ1WzXwGG901PY95NZjDLFeGN3P45Yblkk03kWUuEmy0BtFiBiWxatVm7JlcX2FJ/CLVEJlqmYGrc8OYLONdjlE9xkFaxtzqssWlSgKNx6AiAhUJp6PVBMyRVPNAA9psYni3g9WH+DEZrRbca8lNewKRJsGUD1FiOw3N+igFujPPkBGEmw459nogiJmCkFTCsVdSQL5uuidUpUgBeur1sQOlN0KA2kCi9n5xkEg0EMHz4c3bp1w6RJk/D888/joYcegt+f/Pf366+/xi233II33zS+kzt06GBz3xFCsGLFiqwsUhlbokaOHIlPP/0ULVq0wKuvvopXXnkFxcXF+OSTTzBy5MiMOyCRSCQSiSQ1DPHg8qz/Gvsk6simTZswbdo09OrVCxdccAEOOeQQ/PDDD2jZsmXK/R588EEcffTRtrJnnnkGS5cuxfvvv4+hQ4fi3//+d1Z9ykon6rDDDsNzzz2X1QElEolEIpFI0uXFF1/EnDlz8NFHH+HEE0/E/fffj1NPPRWKw7KYDDcjz+GHH44ePXoAMKxb559/flZ9S2sQVVFRkVHwVmVlJQoLC7PqUFOBCuZ23aELAoCrlANGYCPQeIHlkYoIdzdpUQ2eoIe7SdwCea11XdOh+BRbXcuNoUd1UB/lSWdVxTg3MdmutewJekBDKtwCd626Is56ydw3ybSBkmFrJ4VLyKmb5FTCBhJ1tqzAczEBsSfgQbQ6ZlMXrwu7fjN0b3wFRtSwN99QRtcdyZR1TeeuyFh1DIpPCEr2Ua4WbpU774UYZJsq6DzZfYknwRX1ptJ3/TGNJU047dSwij+riX1x3kfeBpwuO2pXRY8l7mO5/qhCQX2ijhSxJVpWfBSKz3juncmVFV88sbJOKQ88B8CDz0V3nu5QTVd4wLo9lECH3Q2l+BzCUiZM06FTLe5epFFoYjtRFXrU5eSbKKuHn46qrdUADK20qq3VKOpk/A6Ft5fDV2RMtlCrw1ACPmhh47Mp3g/A7kZ1qvg73XtuZdaTSqjhInRqiNEG0uPSGYNex5imuu7fWFxwwQXo0qULrr32WpSUlGD9+vV4+OGHE+olS/uyceNGmwbUHXfcYcu51759e/z5559Z9S1tsc0tW7agXbt2aTXasWNHfP3113yUJ5FIJBKJJHsY6u6Oa55DKCN3HiEECxYkn12aKnee3+/HH3/8ga5duwJAghTTxo0bkZeXnVh2WoMoxlhCBuVUxGLN520nGdRrBJAyzQhlThWQZ0kcoJEsUYpX4ZYoNayCKpQHOzsVrK0gcACo/rMagZYBnofNX+SzWzGq48dwKnnrUY3nV4tWx3iQuVHXsH5RbgmJW3qYxqB4BZVnX2J+K5Fk1i2+3aFGngxdS7R2cJkKXYMSz0aWqDQtTHlXNMbPGwAOfPHtlMfNBCtYvGZbDQItA6j4w8iDWNSpCOGdIV7PE/AICutuVj4x96AQXO1LfOt25v0TrZLUsd3CCu6n4qeCEptlysqFWKtMQprCNZbFUrQQpmw6JsoG6LZgccDleTPPT4vpQBgJweSA8VnSYgQK/4wYnyXFkuQQrByKz2MLOLW6yoTJAtRhiRIneFiWMMuqYu3vtJIk7CdsY6YlxpZ3ryaE5kLlliqep7Lij0pDud/8nqM+L6IVxhcUURQu1wIYFjfq83DPABUCzj0Bn7mP3eKcyjplrSdTQpeK5fVPXXPn9evXD6+99hqOOuoo1+2vvvoq+vXrl1XbaQ2i3DIop6K0tBRer7vJWSKRSCQSSWbsyWKbdeXqq6/GBRdcgG7duuGqq66Ka7tpGh555BE8+OCDKa1cqUhrEJWLDMoSiUQikUiyJAdpX5qrP++UU07BwoULUVxsiK1OnToVY8aMQYsWLQAY6eiOOeYYrF692nX/c845BxMmTMA//vEP3HzzzejRowcIIVi7di2qqqowYcIEnHvuuVn1LavZeXsClomdKNQwD5vlTvcEUahroGpDo8UsF4MR7GoFHqthFYP+97mt7of9DX0vXWMI7wxzc3TNthBX5lbDKhQf5a4vxauYaueKebx4MKWuMehRzRaczTQdJGwlZrWbu63gXQCgYZoQvCuqSYuIGlFWklkLMeFsvF/xe6VQktR15FQ+t7VhKqp7glaCWsLdI5mokafDfvP/AwD48pTjAcRV4vWoxl2ugZYBhHeGuYvJmWA4VhXl21Qz2F9M+qv4KJ8QoPgUW+JiLarHFeHNZbcJCpZrzZko2LqHuh5XrKcwNMGcbkPu7oM9wFycaJDgmkT8GYgn4k3xq8C1gYjh+jM/p0YCYnEyRaI7RhOC+a3nxunyZBo1g+TNz4g5UUPEaplpWkKCbwtLa4qaAeOK1wPFdDsBANEon8iiC2264TYZQxHcekkT8zZBrGcdAIo6FZqTZoxrFNlZyetZel2aOcGHKBQkTPn1RDgad/XFVFCvB/YwAWpTmhddxrVdL0IpCGk+17S58u6779oENWfMmIELL7yQD6JUVcXPP/+cso0ZM2bgrLPOwsKFC/Hrr78CAI455hhceOGFOPzww7PumxxESSQSiUTSxNHBoNfRlFTX/RsLp9J6tsrrhx9+eJ0GTG7IIbREIpFIJE2cOgtt5sId2Ey55557EArFJ1UsW7bMZtmqrKzE1VdfnVXb0hKVBELiJl7q88RnwFAdGlRX/SJxFkhDY7lofAVeY2ac2Sd/UaIUvuWy8Rf5UbMjBF+eUdcT9AipUcBn6gGAqhmuIFtS4lg8PYKuMShcY8pw7ek8abF9xpsaVqHEzDQr1JF2w6dw91+imyXuboJPAQRXFqGJLkDqfEcwPS2J6WDsOlFiyhWrrpXgOdNULtngL/aDKpTPeKzcUsWvSXhnGIqXIhaOu4U0hztZF7ZB12zbVdt+um2ZKMRWl2kOrSWX9EDpIKb5sdxhoruPdxVWOg1hxqUSd1sSBSA89YZ13+Mz45K59sTZqW6Izyevk0LETzwXojEgWrtOkHXNVFPLSLwPhBJ4gl4+cwz5QZurlCgUJGZpU3mgCzpSCrwJukViIt5MdNaaGrGqKJ+dF6mIGC67mPHD5w14+PePJ+Axk3Ab33VWknWuG+XzcFcf9XoTZkYa5YkaXYrp9ks6c7gZuUabO4QQEEISytLlpptuwsiRIxEMBgEAp512mk2GqaamBo8//jgeeeSRjPsmB1ESiUQikTRx9uTZeYwxjBw5kufHC4fDuPLKK5Gfbwiu1paAOFfuQDeyGkR9/PHHePzxx7F27Vq8/PLL6NixI5555hl07949IT9Nc4Uoii05pSZEj1OdgllvMIhbqViaEvS5xl/kQ+UWIwt1fkke9KiOo5Z/nLS+9WYeq44i2CJuqRLfrBQKaNCEIFodashuUeJWH00DVQjUsJjw0x7wnOqNWNd0V10h4vKJtywQTquDoSStgGiJbyeipQlItFA5A9ipQqH4DM0mb76RWNqbH0BDccDCt/DdhafwIP9odYj3kQebW8HcgrI2YFcDdyZ6BuJK9IAVaK7wdp3q9SJiQLWhPabbFP2dQf22YyZMxnB/g1SU+PGNAmHRa7YjTFJwJiuOJ4hNrnIOwDXBcLJkxan6zTRmfkbcnzmm6DbrHVFo3NqEuDXKsAjG4hpTAV04Fx1EpyBa3DJlaFCZyb8R49uc+kaWFYtrISkUHjNJ+u83XWL7HHa+6ynXc25MPEEPKv4wAsiLOhUiVhXPFiBOZLGe3WilmZA44EEsGoESMALL1Yoaft3VUBSeoA+q9RmxkkmbQeiUxoP4reuXTEneqX5e3+TCHddc3XnDhw+3WZ4uuugi1zqNQcaDqFdeeQUXX3wxhg0bhv/97398BFhZWYlp06bhrbfeynknJRKJRCLZk9mTA8vnzp3b2F1ISsaDqLvuuguPPfYYhg8fjueff56XH3nkkbjjjjty2jmJRCKRSCR7NoqiZJR6zg0x64qqqpg7dy7Pn1dZWZlq15RkPIj6+eefceyxxyaUFxUVYdeuXVl3pKnBNA26pnETuWIqsOvUHkSqhqLc/NtYgZuGmd7ogy/fh4gWweeDBwEADlvyQdL9/EV+m86Q6LLRNWZoQplSK5bpWkw3Irp+7Ml/rWtkXDsxfQhguFMsvSlngmSmsbh2kJboWtGsY+rMtp+VjkNMxiteH8VLE9pycysZ5xgV6lD4CvPRaUr6iv25wFcYgBY2XMhiIDZguOH4ufuMaxSrMur6i3x8kgF84AmkAZiB44IbRAgsZzqDGla5W0sL6/AGPDyA3Zfv5ffechlarkCmMTCF2Y5j4bzmznNxvQdJ3H1ON2yyxNqA4fpLltjY6Fd8QoJ9YoE9zU2qcxH1zfgxTHdrDIZuFNfQMl2e1neE062nxXRQ835rvgh3IYmJzsVzI1Rw9zmS61rfWUzX4ZwAQIWkvFadpow339SFqojayg1dKON59AY8iFbFuPs7UhGBJ+CBXm0GkysUsWpLQ4ogZgaeA4CmGd/fVmJmpijQzWuiaLqh32ULYWi8YPI92Z1X1xgmZ9aV0tJSPPPMMwl1siHjJ6J9+/ZYs2ZNQvny5cvrLeHw+vXrMWrUKHTv3h3BYBA9e/bEpEmTEI3aP1i///47Tj/9dOTn56NNmzYYO3ZsQh2JRCKRSJobOmM5+dsTWb9+PdatW1frXzZkbIkaPXo0xo0bh6effhqEEGzevBkrVqzAddddh9tvvz2rTtTGTz/9BF3X8fjjj2OvvfbC999/j8svvxzV1dW47777ABg5cE499VS0bdsWy5cvx/bt2zFixAgwxvDggw/WuQ98KiyMtzcrsNMDwxoFNNyU1y9POR7RauPNKb/EmJ1gTfeNVkdNy1Dtb5huSWbjU8/tas5M08E0xqcc29/649PMRax1TYcQ/GqqXZtv0pZ1QXzjtye7FZPpJl5ffp6aaRHRhCnzelxt3fmoM51BDxvbffnxPI9UoWB6PJhUi6noMn1OwnHrm31mv8qXq567A2te/QQAUNAuHzXbahAylZwRBWgwHkAuWpcA2CxGYpJqwFTydtRnZh1iJrG27r81xdxoUzUnEphWSUqSBuu7BZWnYxF0QlOoygOwJT8G7AH21rob/Hl3CdBPqOPog1MWwf4M6xBt1kxjYD5F+BzEg8yp15Pw2bGsIdTcnmravRiEDsQ13rlEC3/2VTAhSFq0Ym2+6yroMZXv03HS4y5Xq2Hx5ftQudmYMOMv9ttU5LVYfNJLLKxC8So88NxIFB2XQ7F9h5i69xrikhHJ0GKG4r8WtRT+PdCjKr9mzU0yornz7rvv8rQvyTjjjDMaqDdxMh5ETZw4EeXl5Rg0aBDC4TCOPfZY+P1+XHfddbjmmmvqo4846aSTcNJJJ/H1Hj164Oeff8ajjz7KB1GLFy/G6tWrsXHjRnTo0AEAcP/992PkyJGYOnUqioqK6qVvEolEIpHUN5pu/NW1jebKiBEjUm4nhEDTkmu26bqOuXPn4tVXX8X69etBCEH37t1x7rnn4uKLL85Id0okq6H01KlTsW3bNnzxxRf47LPP8Ndff+HOO+/MqgPZUl5ejlatWvH1FStWYP/99+cDKAA48cQTEYlEsGrVqqTtRCIRVFRU2P4kEolEImlK7OnuvLKyMui6nvQv1QCKMYYzzjgDl112GTZt2oQDDjgAffr0wYYNGzBy5EicddZZWfcra7HNvLw89O/fP+sD14W1a9fiwQcfxP3338/LysrKUFJSYqvXsmVL+Hw+lJWVJW1r+vTpmDJlSkI5Y7qhgmyui6NNQt0VmxsySNNyHUQqIohVxRCpjJuyraDtZMTN3IZp25k01cIILjfdeWYwuKhBZFMvjybq5YhuPtG1ZyQsNl0kPoUrc1s4XS/OoF/AdK1Q4up+SYblurJck/x4AU/8vLw6ej/5Wq1tNSQFw27HQcOM5V+vOhf5wnVwBtyKyX6BRI0sS92Zb3ccy0rwSxViV+1WFJvytzghgenGZAA3d2ttmkuii8zpHhF1xtJx+Vk4daK4m8tFd0xMfB3fXwdc+uVUPBe1plz7LCTFFhNXAwDEIHOfx0hkqyi8Pa5N5BIi4LxOqdxK4v4UHts6CcS/x7SYCq8vwEMTmgKiKxowv3PMZ5dSgpi57PUa6uXWvbTqMCELAsflqy6Z+r6VfF6sR30eUN3uJmWsGZt3mgnZWoks5s6di2XLluH999/HoEGDbNuWLl2KM888E/Pnz89KayqtQdTZZ5+ddoOvvvpq7ZVMJk+e7DqAEVm5cqVtsLZ582acdNJJOO+883DZZZfZ6rpdaMZYyhtw0003YcKECXy9oqICnTt3TvcUJBKJRCKpd3TGoNXRktRcLVF1nZ23cOFC3HzzzQkDKAA47rjjcOONN+K5556rv0GUGMzFGMOiRYtQXFzMBzerVq3Crl27MhpsAcA111yDCy64IGWdbt268eXNmzdj0KBBOOKIIzB79mxbvdLSUnz++ee2sp07dyIWiyVYqET8fj+XkhdhwvRk8e3NykvFgwu15LmV6gtvgY9bIKhCEamIIK+VkROoZlsIA79cYav/Xp9DzLoEkYoIvPlGQGtem6ARMJwkaNcrWLRo0DhHr6nsq0U1PqVYi2o2C4eoZA0YFiArj14srIIadgsAgMdUjxYDfOP56lRAg20qvQW3EFJiW7dQfIrtpVOPagmWCYvQzjA/l/1np/8S0Bj0evRlrL9hBHb+tguAoeqsRTX4Cox7aqk3A8b19xZ4+X3xFfhs980IxHcov3uRFE8g9dugeA9FS1Ntnw+nxSmVZScdLGkDNzXztPbXarc4Ofvm1j+3nIxUCEqO521LzOcm5nJTfB7bunEs8RrVkinByj9INTBFDLZPnITQ2Px8hfEbwnQd/qIgmGaEV3gDHoQrInwSiK6z+HNuyZ045CaouCxMdLCCywFAp3qCNZYrvGvUJnlAzZyaXOXclK2wpEjqGyPtS10HUTnqTAMzYsQInvcuG7799lvcc889SbeffPLJ+Pe//51V22kNoubMic9OuuGGG3D++efjscceg2J+ODVNw9VXX51x8HabNm242FVtbNq0CYMGDcLBBx+MOXPm8C8ciyOOOAJTp07Fli1b0L59ewBGsLnf78fBBx+cUb8kEolEIpE0DcQxSDbs2LEjpTGlpKQEO3fuzKrtjGOinn76aSxfvpwPoABDTXTChAk48sgjce+992bVkVRs3rwZAwcORJcuXXDffffhr7/+4ttKS0sBAEOGDMF+++2Hiy++GPfeey927NiB6667DpdffrmcmSeRSCSSZs2ePjuvLmiaBo8n+XBHURSoqpp0eyoyHkSpqooff/wR++yzj638xx9/5GbPXLN48WKsWbMGa9asQadOnWzbLF+poih48803cfXVV+Ooo45CMBjE0KFDuQRCpugxDbqixjVWzEEjVRTu0sP/t3fmcVJU597/VVWv08wMAwPMsI7iiigqXBXRAN4IKhqMiQkuOJMYvKiIhLgRcwUXAsqixriHoCYmxkQwuRIRDYvBLYoYUQi4AMOrMy4jMMNs3V113j+q6/SptZfpmemG5/v5oN21nDqnqrrm1LP8HiTN7QC4nkhnw4Tg4ZavWyApEnwuweGA7rYDgOYvm1E6pJS7d9SoikAkYDLjW11xxv8lVVcWl3mQrcyDzo3/SyVmtygT3HmGirYcUBJFVPX++iN+BCIBrjRu6NcAQDwUR6wtzlW3NVUzBaGrMc3k1tM0lgweFtxa+r7udmw1pqK90bsKeD4Rb27lbo1YWzzhltXdu4GIn58Dv+F2SJwzTWPwhX1JF55wy7i5cpwCw910k5LrZU8Xnd6uk1q83R1mPab1OCKie4qpjLsmrYWLvbC6Ap3cienqXBkuPGMfs5tOsWs/Ce48RVAWF11/hiK5l4vUzXUpK0ry96Io0GQVqho17ded2kf/+cmFvO/R5hi0aAt/dgG6bpSBoiTHKSkymGzRO1O1pBszqgLGs8C4rjASZjS92rq4n/BZ0jSu7aVBv26K8WxMuPbECgedSS6y6wo1JqqjMMZQU1PjGLoDgNcAzoaMJ1E/+tGP8OMf/xgff/wxTjvtNADAm2++iYULF+JHP/pR1h3xoqamBjU1NSm3Gzx4MF544YVO6QNBEARBdBdqDgLLO7p/oZJKYwpAVkHlQBaTqMWLF6OiogL33nsv6urqAOilYG666Sb87Gc/y6oTBEEQBEEQ2bBt2zZMmjQJn376qeP6jsZUeZHxJEqWZdx000246aabuDDlwRhzpKlawsWhu/SMwsOyoujZMEZMmB9QHTJmOhNfyMfLvgRLgmAq44VindwkSdeZbg4Pl4UAJNxhqgafknD3CKZsKZGhJMa+uZWTkYSCwvrxDLeE3q4fACtJZtyJ7h5f2AdJlngfdW2WhM6LyqDGNFNxW2M7OaBAETLuZEWyScCI60znRdA8AgC1LWrSTsp3gj2LceDLPQCA4soeYCrjBagBwB9wdi8p0M+J6PrgpXEUewakVZfLmjXn5cZy00tyalvcz/RdtroAvX9fmujS8SezrGQncaAEjnpvgivQ2lcnd5fT7567vU1uOSWp/2TKzvMlzpn5u7GfbPoNmrOBxWPbyi45jM2aycfLOrVpXAepq9h1czXiza1JF6cso/mLZgDg5aVExLOsj80oScUgBXy2TFPYttcRf+mSKvEi64A5nMC5HRXJ8uo+aIhDyzKWJlM0dDy77mANiYpGo9i9e3e3HLtDv5iDcfJEEARBEPmGqjGoHZxFdXR/wk7Gk6jDDjvMU7zSzZx2MGF9GzTe3uJy1wUnGwGXumVHSgZuO7zhG293/ogfvY7shVizHggZPWDXNxHfwrj+ivFdTapSW61PomVCCSiQZIkH6QJJJWx/Qs9I7CfTmKmYrbGffozkefaFfAgmgtclRULcIehXPKaxTI1pNpVyJZC8hmKgeyEw6K5l+PwtXTROViT4I37EEpZJQzcK0K+DqJGjRlX4EoH9BmrU8m6aeCu3BhmL19dVwdsh8NpJY8mscZRakdttua2PwvJ0cdo2VZB9uorhogq5ETwuO1iirEWFReuTm+XJqQCx8ZkrtKuqKQjdOlZZUaDF9N+dFosj3haFEus6S1Rbw3792I2tAPQED0PDLJXVUUwUcNIFE7GOm6kaVzPXrd1xKC5/CsV9nc699foQhx4Z/2JmzZpl+h6LxbB582asXr0aN954Y676RRAEQRBEApaD7LyOKn8TdjKeRF1//fWOyx988EG88847He4QQRAEQRBmVMYVGjrURiFSVlbm6QHLVuMpF+TMdnvuuedizpw5nRoF35Vo0Tg0WYYG/SRxk23CPO5k2vXLMpr/eBfkiB4rFv7OzJz05bUzzkTvY3rz77Iio3FPEwCg4sS+qHu3Hj0qewAADtQdwItVIyAn3FPtTVFeEqa4fw8wVTMFIQNmt5eI1e0jK5JjALasSHqgt6EbFVAg+302zSlAdxswlTlqU3GUZL8kReZxn4GIH6HeegkiPQA9xt0R1j6rsTj/zDRmKqZrLf+ixjQ0/r9G27jyldhbzyf1uxTd/Wm4zTSVWYq2mosPAzC5QpSAc7FewznmVILFKVDc6Evys+iaku3LHVxUTNNSJmdIPABeNRfTFQOmPUrXOMEs1d8VOBcTT/7mnV2/Tm4do7KCnCgybLj+ZVFDylhnCSA3/T+LpBWZnysX92TAB9asu9K0aByK34dBdy3L+DjZEuxZjNYv9/Hvil8xlRbyBfwmLSgRqyaYXuDZOfRAU7WULr90MO5P3id/17k+D3Xuu+++7u6CKzm7C/7yl7+gV69euWqOIAiCIIgEh7LYZjo6T91ljcp4EnXSSSeZzGqMMdTX1+Orr77CQw89lNPOdSdqexQq9DcgVbQ8ybJedFh2f7vWmnWrRstzi1H0vRs63JdQWQgHPm/i7asxFcX9dctTrDmG0iGlPMi7dEgpAhE/DzQXrRKVpx6FptovuMJuoDhkeuNWAs7p3gAglehWKCOw2/qm5wv7efq2EgqYFJn1YNeEbEHieN5pxOa3TyMY1xcKmlLGURQyH0PToCVU43VLlH5Mf8JaZWwba25De2M7H1sg4kdR7yLX/uQb/lMvhBZdAiAZxC9ifds2LEhMYyYrkpPMgFeaeLqp/lbLk2i51Ze5K25ba2IaaJpmXudgBUg3IF3EKeg4FdbAYs9tBYu1KGNgKJGnCy+Ka9lPDGxmqgYNyT8kajRm2tfWryj4s0AO+DIKyM8FTFWhhPzwR3TJlWhTi03h3X1fwbptfBYskKI1UVKTFnSjRftvRjOtN/CSPFBjcSAGxNu7RrGcsvOc2bp1K5YtW4bf//73+OKLL7r8+BlPoiZPnmyaRMmyjD59+mDcuHE45phjcto5giAIgiAIkQMHDuCZZ57BsmXL8Pbbb+O0007DLbfc0i19yXgSNW/evE7oBkEQBEEQbhzK7jyDjRs34je/+Q2ee+45HHbYYdi6dSs2bNiAMWPGdFufMp5EKYqCuro69O3b17S8oaEBffv2haoWjvJzujBV0023SGpCGQHNkibzApV6gKgC+HS7suS3q+5mQ7AkgJaGVq5EHelXYjJ7t+1rQain7o5qb2xFsCTsGLjdVPuFXtxU0KoRtXZExWItGjeZ+PVA7rhz8KzfBznghy8UMC0zMFxtBmI7VlO51T3htI2xnZIIyAWSgeTGcRXLMUV3kr8oDGAvLzpsuD4Lha1XXMD1dLREyo54zqzXyOTeUxkPslWg8AQEfj+FUruYnO4bNxeXNRhbUmTILq4sL/eN5FIc1yngOpWOk+hudNKEStetlYk7zggkdzpPXkHjbkWB3ZbJCRVtAFACfu7SM45j/Ca0WBxaLM4LHff/xcNpjyUb/vOTCwHoweSK4J5X26IIlER4n8TnhlsAv4HhrndLBDBc+/ADkhJP7GPWi5O4qztxXmC+P7zuBbWtHZIsJ4/TyRzK2Xn33HMPfvvb3+LAgQO45JJLsHHjRowYMQJ+vx9lZWXd2reMJ1FuOhPt7e0IBHIzaSAIgiAIIsmhbIn6+c9/jptvvhl33HGHqRRZPpD2JOpXv/oVAECSJPzmN79Bjx49+DpVVfHqq68eVDFRcsDHU4+d0mu1xFuQFovDFwrydXqqsh4oyWK5CziMt8bR64hyAEC0sRX+SLLeXaSiJ2LNbQCAUFlE7280GUxtvN3xWmIB8W1PCPr1J9/+ZL+fWw0APbDXXxSGZrQhy/yzkxoz4K46rIWcanq5v41rgkXK2jYT+uCW2s40vUag8VYpB3wI9izmlrO2vc3cKlUoGG/QhuyE8XYtBxRb3ToRpjFumdLUOFhCvV4JKFD8CljCMhXsWazLCAgPLOPNX1IU0zrrduZ+Jq6XJcA62T/778vRSun3tijZjuuR+CGud7JgiGQSaJ5qW3fVdud6duJnJ0kVazu8PeOx7tctwmIyB5f1QAxqWxTx1q4JijaCxwEg1qLLKhjPGINAcYR/TidJwGqp1tJIWtFkzfaccJLgcOqDzWqeUKSXlIPP+5Jv3HHHHXjiiSfwu9/9DpdccgmmTp2K4cOHd3e3AGQwibr33nsB6JaoRx55xDQbDAQCqKqqwiOPPJL7HhIEQRDEIY6mMV4wvCNtdBbz58/HqlWr8N577yEQCGDfvn22bWpra3Httddi7dq1CIfDuPTSS7F48eKUXqyf//zn+PnPf44NGzbgt7/9LU477TQMHToUjDHs3bu3k0aUHmlPonbu3AkAGD9+PFasWNHtfkiCIAiCOFTQchAT1ZkKB9FoFBdffDFGjx6NZcvsoq2qqmLSpEno06cPNm7ciIaGBlRXV4MxhgceeCCtY4wdOxZjx47Fr3/9azz99NNYvnw5xo4di1NOOQXf//73MXv27FwPKyUZx0StW7euM/qRd0hSMhBU1ApR26LQonHuPhPdE3LAB7UtCsmXmFVrmZt5P7l+CgAg2tiCQIkeLN5jYDnibXH0Pu5wAEDtun+jqFIXNm1r2I9YcxsPEFWjcciKDCXhqjICSAGzaR8wB5oamF02GqyGcdEtowhuEqtbQXZxXehuP/u43VwdVkS9HP04ohK56uie0a9dHPGEi1OLxqHFYvwaxtviiDbbizGnw/arLrIVpvVFdIX4qrufzKrNVARKwoh9uk//HDFcIklXhpHboQQUrg1loPgVKInzr6kaVy9nGoMaU6HG9J2DPc16TrIsQ7N85rpNFs2mVIHQogvPrIhucVWl0ILyKvxrxUkN3KuNbDWTMnHppeoDYHfj2TS3ZLsb1TR+4bemCSr+RjvZKKFng+Gqa2vYj2DPYt4HfyTE+6CEzNaIVIH7TE7qQ2mxOH8exVXdRWlymRpK8Wn01VWZ3smlLMtQUriEDxVuv/12AMATTzzhuH7NmjXYunUr9uzZg/79+wMAlixZgpqaGsyfPx8lJSVpH6u4uBjTp0/H9OnTsWXLFixbtgwLFy7M30nU7NmzceeddyISiaTs5NKlS3PSMYIgCIIgdHIZWN7Y2GhaHgwGEQwGnXbJGW+88QaGDx/OJ1AAMHHiRLS3t2PTpk0YP358Vu0ef/zxuO+++7Bo0aJcdTUj0ppEbd68GbGY/qb+7rvvehYCJAiCIAgit6iMQe3gJMrYf9CgQablc+fO7XQNyPr6evTr18+0rKysDIFAAPX19Z77GoltXkiShOuuu65DfcyGtCZRogtv/fr1ndWXvMLIzjNcDGrCDG5ksxhuI184wLPzeHaaP6ETVdQbmaIJWXX7Pm0AABSVh/HZv+p4CZfiAWVo/XIvP6ZYhJdpGqDI3FWlCJl4xnbWsiriMQ0kXjJByIYRzNsKzBl+TDPrBokGbhl2F4tXplI6+juOelWwaFMJmUiSKsMXTroLNE2DP7HeFwqgvTG7LCV/JMSvv1GaxMg++ujq7+PIh/+SVbspj9tDv8dCZWFTCRsRNarqbldZLNeSLIGhBJTkfgoQa4vzzL79O79EqCzMs6rkorCpbcVv1hATNcZEvS5rUWEvN55TBlo2GW1uy5z64UqGRYyBxO8qxX6errt0sw1FF57pswwJfsDFvaTICphmuOv1bFpfuHOtDwbG2JRQgIcitH65D/5IiD9TRV06JeCzlZaSFdlR1wvQ9fr4vn4fNE3jelROaKaMRXPYhdWNx/tuyT4G9OuSXwn36bFnzx6T+8zNCjVv3jzupnPj7bffxqhRo9I6rpMBhjGW0jBjJLalajtvJ1EiP/7xj3H//fejuLjYtLy5uRnXXXcdfvvb3+ascwRBEARB5DY7r6SkJK0YpBkzZmDKlCme21RVVaV17IqKCrz11lumZXv37kUsFrNZqKwYiW35SMaTqCeffBILFy60TaJaW1vx1FNPHTSTKMOSYVOw1TSTXpEkJ4O4fSWlUJubEEukXAZCEbS+8CDU/Q18/x6X3QYAaPnzPVD6DIAc0c+j1twEf/8q9DnpKADAluUbUPXfuu7Wnn9+hP1tcTR8pLfbc2gl2hqaAABtza3wh3zmwqOmIqR2NV1uXUsEoYtjdjoPgP72pcVEpXEVciDx2p1QIFcTAZ1WvShre45WJEHR2iAdRWdT25pmGgN/g4QfkixDbUtaEWONzWhv1C1Gakzj1pkPLpuEeFtynP6QD4GSsMmyEm1sAaBbsEK9S5MWvbYook3NUBKWKUmWeaKAJMs4/N4/pDWOVASKI4gdqAMA7N+9H4rfrPLOg8EBKAF7wWGxKLWhWK4liksb5y/UswgtDc1JS1TABzVh3YRsLspqHNNkmbIkALhZl7y0nkSsiQROFi3rtk7Hs/XFSfHcIVhbhLkljGRgvUp1DBsWi5OpLWsAtKsYoR9G1oHi07JKfEmXz27/HwDgyTHGefZHwmjfewCA/pyIt0ZN10O0nPtCAW4994UCYKoGRdT3Ep5jtuQVON97TNX0Nq0JNU5FmhUFvlDA8zlk1T3rTFTkQLE8w+3Ly8tRXl7esYMmGD16NObPn4+6ujpUVlYC0IPNg8EgRo4c6bnvWWedhRUrVqBnz5456UsuSXsS1djYCMYYGGNoampCKJQUT1NVFX//+99tpWAIgiAIgjj4qa2txTfffIPa2lqoqor33nsPAHDEEUegR48emDBhAoYNG4apU6di0aJF+Oabb3DDDTdg2rRpKa1i69evRzTaNcKwmZL2JKpnz56QJAmSJOGoo46yrZckKaXvlCAIgiCIzMn3si+33XYbnnwyKety0kknAdBjqseNGwdFUbBq1Spcc801GDNmjElss5BJexK1bt06MMZw1lln4bnnnkOvXr34ukAggCFDhphSFwsdw0yrReNgCVcWAMSa28BUlQcTR5takvs07Ee8LYpw71IAgLa/AfGWVu5G8kVCaHpqHt8+9sk2RBubAQDxtijUtvVoqv0CgO5iiif2izbH0LssxIN+d/3jPyg/Rg9ajx2I2groKgGZu2wUf7LIrIEqmK5VS+CvLLiFRAyXjRiELupPiS4FHwBfIJQsx4CkPksq15ytmK1L0Vav/a3tGK4mltBvCpYVI9ynpyk4OdqkXwdN1RAoLuJFUY22Ys2660+LxU1B+5Isw59ot7VhP4I9e5iCYY1rH21sxifXT8HQ+5/x7H86xFpaEemnu0mizTH06F+cDKKPJg32SkDRi0NbzoOBWP4nLkcRLCuGlihYG+pdCk3V+Fhlv58H1qoJt64sm90kbkiyxdUnXBsxeNgIQrcmPojrxOOlGzju6Y4x3GTpuNdkGdC0zF1x1jYcSFVs195OFn2QFUjiE78T3XmGG5ipGtoaGnnIQ7SpGXIi8SZQXKSHCGiG+9nHE1LkxPNFdgkOFzXujCB0A+v9Yy04zTQNiBnbmrXllFDQFJAuJqgA4KXAjPZ9oQB86Jp6dLnMzusMnnjiCVeNKIPBgwfjhRdeyKp9qwfMiUy0pnJF2pOosWPHAtADvAYNGmR6gBIEQRAE0XloGoOax2VfOhsnD5iBkeGnql1fxzDjwPIhQ4YAAFpaWlBbW2vzU55wwgm56Vk3IysyZEVGrC1qSpc13lzibXrBWtbSarJ+qLE4t9Y01zWYVILFtxgjCNqwcDBNgxaN80K4siKh/t1aAEC4LIRgScCUxv7V1q8B6JYmfw8/5EQau5ZQnpYT1ilf2Ac0J8cViCTT/GNt8UQbyTc2ZrFamd7eRauBqpkKA4tWjWgspgeBJt4+maoBLorBRhBy8g3U/Q07HVVzsZiyWHjVuo1hPTIoipjfcMQ3YKZoCCi6ZUosOG0UoTbuBX8kZNpPbYvy+ybYswfa9x3A53ddzceiReMYMPdR1/GKGIG6/kgI/qIwWr7+HABQdnhPFA/qJ1hKW3nAv9uLjlg4Wk3sF+pVCrUtCn9CTbp93wEoAR9PTGjf18THogjBtiJOwe25xjjnMi9+bLdaWfsDeEgE6I0Jn9MP2ramxnvu54SbNSkDK5PT78Aa1O+Kz5+dRcuD9rVPQf3qM/7b3//JZ6b1Yp+iTS26RVN1tn7H26IIFOsW10BJkWuCgBhwDoD/9p0sk7Jfv++N/sUaW6AJCSmykCgEmFXejf7zhJWA3o7SidYdIslf/vIXkwcsX8h4EvXVV1/hRz/6EV588UXH9d0xEyQIgiCIgxk1B5aoju7fnYwZMyYvk9cyfl2cNWsW9u7dizfffBPhcBirV6/Gk08+iSOPPBJ/+9vfOqOPBEEQBHFIY0yiOvqPyC0ZW6LWrl2Lv/71r/iv//ovyLKMIUOG4Oyzz0ZJSQkWLFiASZMmdUY/uxwl5Odui9i+JqgJd5QR+KoJ+kuGS84XCurbtyQCj/0+xJpbk4GLLfbiljw4MhRE+95mhMqSbqZgie42CpQUQYvGueuFqUldI0PTKBBJqKQbQdQJ15+kSDa3huGSU0J+mz6K0T/djM0gKdaA4aRekoGq6W5Mn2AGb9/XxAvxissR8Dm69Aw0VeWFRK3HcSy+6uGOsGngJgJpmeZcqBgAZMPFIR4H4Loysj8KFjIHsfIA+phZ+d0XDprOp7UAtKqkl7Jbt+Ba7i5VY/FEYLc+upKqSpQeMQCxhHZV276m5Fgs7g/DNSf217g2alsUaizOXZOH3/sHfPrTS9H8pd5epG+x4DozF6eWZXORbqvbRXSteLn6nHR9siGj/Xlguej68XD3icfJxBWWYltP11uGLre0+pXYJtcFiNt27tD/39DIl4muc8MVDiTDG0T3mEigpIiHDMTbop73jhaN82B2JRTgCR2AWctJkmXIIYVLehnuQvF3aTzb1VgcMnyQQknFcqZp/HcUb43qFSPidi0+IrcMGTIESqbJF11Exr+g5uZmblLr1asXvvrqKwB6EcB33303t71zoL29HSeeeCIkSeI6FAa1tbW44IILEIlEUF5ejpkzZ+attgRBEARBpIuq5cIa1d2jyI6dO3eid+/My6h1BRlboo4++mhs374dVVVVOPHEE/Hoo4+iqqoKjzzyCFch7Uxuuukm9O/fH//+979Ny1VVxaRJk9CnTx9s3LgRDQ0NqK6uBmMMDzzwQMbH8feIIBAOwh8JI9y3J1rqvgGgp5dbVbENmYNoUwsUvw/+RHq8Goub3sIloS6cFo3xYHKDUO/iZE2+UIBbcvxF+huWaOlgwhuamrAeGOv0tzvFtAxwthiIY9E0s4VF/79hvbEHaRvp74AeiN22tzmxLYMvlAzezFTV10l13Lbc+gadKiAYgCEp7VmlyWLd4qnURs0xvz8ZUByPgckqmJJMHHBL9ZfhM9U5lBQZit+PvQ/fAkC3wBnnuHzmEgC6qr3eJZkHrMt+H/yREIoH6y8yRZX9AE3lliq/ECAvJjwAMAWKG32IW97YD1v8O/5dVFj/7Pb/QetXumK+FotB9vu5HIKSuE9TWZictpGt1kXLveJWL8+qWO4lhcF/fx4155L7eUgedMSa5EUWx/I6pltguZvVORe0v7Kcf26q/SJpzWmLwhcKIljWA4BZ0kL2+xISHEkVejc5CqPv4vNSjen3H1M1aIJKfvu+AyZ5BP23JtQQFX9PxvUWJVpC5jpyqRJa2lsDtmWdwaEcE3XWWWel3EaSJPzjH//ogt6YyXgSNWvWLNTV6SUn5s6di4kTJ+Lpp59GIBBIqRHRUV588UWsWbMGzz33nC2wfc2aNdi6dSv27NnD9aqWLFmCmpoazJ8/v1v0IwiCIAiC6BgjRoxwXdfY2Ig//vGPaG9vd92mM8l4EnXZZZfxzyeddBJ27dqF//znPxg8eHDOauw48cUXX2DatGl4/vnnUVRUZFv/xhtvYPjw4SbBz4kTJ6K9vR2bNm3C+PHjHdttb283nfzGxkbH7QiCIAiiuziULVH33nuvbVk8HseDDz6I+fPnY8CAAbjzzju7oWdZTKKsFBUV4eSTT85FX1xhjKGmpgbTp0/HqFGjsGvXLts29fX1tkrQZWVlCAQCqK+vd217wYIFjuVqlKAeWM40DbLfh0BxwkXXFoWmqo4Fc3XtJJW7bJihjpvApLisaSaVXdFlY7SrJJR9fYL6r7ieH9OiZWLtl9VEbgv8FdV+LS49HoSccGGJYxMD3cU22vc16ZpDhjtS0E9yMtfLgpvTaQzpugJNLrwUQecp25ItJnyu7yWoPvsDQCyadA8a585BCdooWiuLCQohzeRyM879/mW/QOmVd6H1i69sffGFg1BCAT0AXjiWcX59oSDUhJvNcEUmr6E58NtasFl05Vlp+2Y//6wlCr7yAtTGGF3csNZtXN1+CXeOWGzYS63eS6E8U10o273j9BnO7hy3bV2XOZC2ay2N9tzclpL1vtTUnOhExT/fzttpqv3C9GwA9OeBP1GIOFBcZAsNEH/nRgiAHAhB8qfQsTISRWIxQFOhJl6GtVgc8dZ2/qyKt0V56IEv7KxxZmB1KTrdV9b7yecdIJAzDnWxTZGnn34at912G1pbWzFv3jxcddVV8Pk6PJ3JirSOOnv27LQbXLp0adrbzps3L2W9vbfffhuvv/46GhsbMWfOHM9tJcl+MxtKpm7MmTPHNL7GxkYMGjQoRc8JgiAIguhKVq9ejVtuuQU7d+7EDTfcgNmzZyMSiaTesRNJaxK1efPmtBrzmqw4MWPGDEyZMsVzm6qqKtx111148803EQyaA/5GjRqFyy67DE8++SQqKirw1ltvmdbv3bsXsVjMZqESCQaDtnYBQC7uCbkokZorvMH5U9SbYqrqaZEwvVmJ6420ejH4U3hL9lJHRjzGLR1ewaO2OmGWAHTbW5+mmo8rBFgzzW5xM4I+/ZGQqaZgvDXKbzRJlU3q5rKi2OpRWXENEhakCGwBwZbgcMfxpYusJAPLTW34IfksMhGaarquxvmT4vo5S8fiwK1TWvJN3WrBY5b+JBWZfSYrpWGVMhCTCWS/D8GeesBv/188bNqu4dc3mtLEiwf1Q+MuPRaSWwsE2QJR9VlSkvex4vCIYZoGObHcVFeR1zrTx2S9L7wsUeblDsHhHufd8TfgdP9Yt3HCy3LVoZp7Xkr+6bVrskHEo4CsgEXb3DZPibrrPQBA8z/+gs83vq8fQ2UoHtyXS2dwCQGe3OBHoFRXxYfPrz/bEgHiomVMCoYghSKQDIsrv8/tkhTQNDBVhRLX71cWjyEQbQNr18emRdu4BEi8rR2y3wdfWH/mi78XK9zqZBzbsIxZr7Hkd9o956gsB+68AlVX/9e//oWbb74Zb775JqZPn45XXnmlU8OHMiGtSdS6des65eDl5eVpnYhf/epXuOuuu/j3zz//HBMnTsSf/vQnnHrqqQCA0aNHY/78+airq+NZgmvWrEEwGMTIkSM7pf8EQRAE0RUcyjFRp512GsLhMK6++mpUVVXhD3/4g+N2M2fO7OKe5SAmqisYPHiw6XuPHvrb89ChQzFw4EAAwIQJEzBs2DBMnToVixYtwjfffIMbbrgB06ZNo8w8giAIoqA5lCdRgwcPhiRJWLlypes2kiTRJKojKIqCVatW4ZprrsGYMWMQDodx6aWXYvHixdm1V9ILSsSeBeiF7spTBZeXxa3n4C6z4bStrJhCF7l2UWJ7Fo/Zg0YdzP82t5asJM3lbv2ztCseVxHHGY9ys7wv0gIlFODuPsAcUK4E/LbgTaeAc0e3jGmMstmNJyvu7hOv85EOrtv6zS5Yy/XnQeiKAiQCYA2srlcxyLzh1zeaVL49+y0rkBOFo2WfCi0eM+3HA3kTasuGMrSoGWXFuHbt+w7wtqKNujK/HFBMujvi9kBC/ycRdG4EGYuuPrEwsgwfYClcbCQxSKpsSkgwBfV6utQcNMNS3Aum7d2utVeAulsbXkHquVBgTnUPW92Sxr2AAKCpCJ1zVVaHjX+2jYdvFI/7DgYl3HfNn+nJEKHepQCAQO/eJheY3KMn5HDE3HenEAZfwOays4U/WGAJdx40DSwe5e48OR6DEtGzrtXmJr1gunFfBUKQ/C46T8bzxJfQu5Nl01iM66f4Wpz3J3KGUzJZvlCQk6iqqiowB9/u4MGD8cILL3RDjwiCIAii84hrDEoHLUnxArVE5TO5LZxEEARBEETOOZQLEJ933nnYvz8pszJ//nzs27ePf29oaMCwYcO6oWcFaonqCuTiMsg9PFInnQrYCplrxjbM6mazfbe6dOwuPt0dIZtN80J7THQTpXJViC4Enx+S1aUn9kVTTf1jmpp0G2oaWEw3n0uaCqaFICXM6VIwpJcUSWizWPWDRB0fKaGPlU5GlVcWlWQ5Pyn1fVxdhC7LPbIyrRl7EM5R8rro/TPOGeIxyD4/mKzy3Qy3m5FRZ9XsMpbZCi+bsiz9UIIJXbFYzKZjpoQCvKyMSNuaZbqbOOEK7HHkEaj7x2vcnRfqXYrSobqQrdoWRayllbvsrGWJgGQGn9h3cUzG9VACQnkgWbZpQxltyvA5u9nSLRwsuno9NMRMrvI0yPS+Mt/DOcrcc8GqZ2T68+mV7ZtO27GkQLEWKkbR6PMAAKH9XwOyzF12LBABmAaJJe5f2ZcssZL4fRjrHDHWMc3mCncta5NwqRvhBSweBYvoGYFyj2ZobUn3m+Tzuz7/AAC+gJ6Vh6SL0arDJaNrsvMOZV566SWTKPbdd9+NSy65BD179gSgC29u3769W/pGkyiCIAiCyHMOZbFNa/iOUzhPd0GTKBfkcER/m0rDMmENIBeDvG06Sw77620Y+2qm74ChiWRXyjW2swVhepCO7gnfNh5NFiBua9H1qGJRx20BJNv1AXKoiN9cWjRuU2436RqJfbBaFsT+Or25p6MonaHVyTHgN10tHlXQutFUGEWPoan6W23irZdF28BUFZJRFNkfQCCiZ5H629vA4lHEWwydm7gp8F6/5v5kX31Jq49p/L4AZFk2/chjza3O/Y62mSwVSu8K9D/7TES/0LWh2vc1Idy3DAAgB0KINTZyXTBfJGTSnxJVyUXNKuP/huo/YFZN11XQfTZ1ewBJhXZb8kNiX6u6tUOSgVVziC+3XFur2p35N2z5nCLY3G4dc7Y+Of22HbHe305WMzf9LAjPGb8frNXjt5wORtWGeBTMr+suSWUVgKJAkxN3nSQDTIPjnzwZuoXJyxJljM/4o5nY1sl6lbTOJ4p8G7W4tWRxb9beCjnaxi2ueheT597mOQCSv7VAyDFJQeqYUS9tVMY6rPNUqDpR+QzFRBEEQRAEkbdIkmQT885U3LuzIEsUQRAEQeQ5h7JOlFE/16gu0tbWhunTp/OSL2K8VFdDkyg3lADg0y8YkwSDXcI8jYRFVxKCJvWNLZo+bjpLgMk1ICW+8zIhpqBks6tLdBFI0N0NaWnOGFpKhltDkvV/Tmhx3bSd0FpJugECyb6IY4lFwaJmfSzDBWN1sXkHRsPmqvMKCM6Jpo+XyyVDTP3R7G4iFku6BkSXgthHKaQ/GJRSB3eLUYLGJYhe8vl5u7Isg/n8vLRHoHcvKKEDOPD0HQB015qvpJS3wVqbIZf21te1t8FXeRjkYt2FF2jaCymg+0ekcARyaRNYWzPf1y0417qca4oZWlYJ16WxzrNwsBNcS0tNunMMt6k16UAM7s7ArSa+7zKrOy9l/9wC2N0TKFy3y6B9JxgSwdFIhAGEs6s5Fv98u24FMH7/YmH1QFh/BhrPFeMZ4+GyY27PIMDsKxED1J3cmIarytqeknT/SYGw/mwzyt0YyUDGcxew6Va5atHJCiDJkHyW33EncShPoq644gqT5enyyy933KY7oEkUQRAEQRB5yxNPPNHdXXCFJlEuMNmnp+MaiP5XSQhEZMJbj9PblvAWBAhvtU7bMo1brlgKhd5kIVpBFRselhmjDUlOjsvpDdB4Y5N9uuVBCJZnFguIYZkwPmvGumgbEI9CS7ztGQHSRv+sCuWSrCStMj6/PbDX2NdhPKaU+UzSy91IUazYyzJgC0r1UknXNEjBsClo2VRcVdMAudh+ECNA1nhzThSnNvZlqgo5HEh+DoZ5P7SmvQgOHcZTvyVFgVLWJ9l0azNPTZfCxWBKAL7invq6kl7cCin5/GClbdCam3h/befFy9ISjyUtUdG2ZLKCg2XHmrZuw8sSY/yWoF83x+K1ssITNwysKewidsty+lHFaQePi2RgjXMMMhePD/P5lHwuSt2pDte6X3+GOPXNCBRPPFuYJJst9ZLleellhbIiyWBGW0ZQurja+K5JSauUcSjxOLIPkvEMTPTXlAxkfaaIVjWHNk1/JzqRQ9kSpSgK6urq0Ldv3+7uig2aRBEEQRBEnqMyDWoGGmZubRQi+SRpYIUmUQRBEASR5xzKOlH5DE2iXGA+P5jPnwxkdDE7m/VKnE3vngrI4gybaZBkFx0U6/ENt1s6HgJhXyYGk0uSg+6MlOiWDAnJIGcZgJYo7AnoAaqGe0J38wkqxX4/ECmGlHD3yOFGXgyUuwCsbhBfUi/JpOfj5i5xCTr3dNlloBCdbuCveZ80trMEm5uSC1zdkXZtIFNBao/PTFMhR5JuQa25ibvwJF+AXxcpGIIcjiRdtGrMdA/KkZKki0bWtb1kIQEg7QB/gBfNBqC78ozPHmr+zHDnuf2WLMVqnZT/+TJrIoZTH92uvzVJwno93fqU6JfTvSS6YjuCoawtohdFN1xgisn1zwv2Zog89BRon74DxHU9JtGdJfns7i3mksCSVoK623PQ6gpkGn9G88Ue9wozwjMYsycKif0WXL1ufwOYv2sCyw91XnrpJZSWlnpu853vfKeLepOEJlEEQRAEkeeoGoN8iMZEAUB1dbXnekmSoHbwRSQbaBLlhuwDZJ+z0i7A35Dc3rLEbRwlTZ0Ud5lmtlrxlF3LO5uTf9jN123tm9iWLNvfroyAb6bpb2vG210oYY1q1dPaWTzGJQ1shwyEIAVCkIt0BW6trSe3skg+P5im8hRj1t5mStmX/AFTwLop8BgWiQWAB0kDiTd6LqugCDUMnS0YjpYfq3SDISuRq9pmNsXt1NYrz6Btp/ZN0hl+fh5kJCQFErXDmJCaLcVj+nk3rkMg5C2BIfsgBYusHXXc1HqPSbIGSTHqkfmTgbxOVsd01P+dMBIhXLZlpkDiZNC6JCt6ILqxUkyk6ICSfUppCut954WbarpVEkVTIfkDyRqXigLWLtwbGQaWqzvf1ffT4pCHnpLswo7XknIw8RgkWQNTEqrzRiKLw/PJFnQOeKuXu61zWu5phZIBnxBYjqTlX3yyMuNvgGhZE5OEjM9KdgH6mRLXAKmDk6B4YYZEAQDq6+vzMrCcFMsJgiAIgshb8kWd3AmyRBEEQRBEnnMou/MoO68AYYofzM1Mm0GaqFOhTLd2GADI7urn3u2mdivYXHcu+icAdD0WKakgzSQZUliGknC1GQV0AeiBvPFoshisEeCdcHX4igeABRNB50pAdwfEdJl+KdZqLgYaCOmuVAOrurDJLairXwOCarvoLjF0gmIxs3vHQsbuMksQsyeiho9TUDEvUGzRBXM6rrXNVMc0jmEEP/MirInzGYsm2/X7dTesoGbvpX/j6cY2Kfzb3yAZSypYS5JsDzB201Bz7Q0cC4JLojtXbEpUN7cUD3csQCsqVov/t27ncV2YptkD8I12EorehovTE6aBF7UW+q3jTx7LaF9TIQXD+vL2VkhB/ferRdt4QH86aJ++AylR2BeSDO2TfyW75A8nN/T5zfdGigLD4jNJEoPF03nGuuhEMUm25+FYFdT5Tonnm6khQf/Jdq2VZCgHFIAxsFy5+lNwKE+iqqurEQ6HU2/YDdAkiiAIgiCIvGX58uUAgM8++wzPPfccduzYAUmScNRRR+Giiy7CgAEDuq1vNIkiCIIgiDznULZEAcBDDz2E2bNnIxqNorS0FIwxNDY24sYbb8TSpUtxzTXXdEu/aBLlhkOxWw5LM8iNMe/imi4Zel7rk01ryW28XCteeO1jmLR5hqGmu+c0PQsH/jBksQ9Ms7vdDB0pfxjMl3ADKn6ABZIuO1kB/IIrxnAHJPogaXGe/SJpce5i0l0GyfIOsuFqEDFcRiEt4c5zLv7sVV7HEVuGXZpptQ6lexz7ZCmKCsBb18jl+Lw0jKAfJPn8YIZLNh5LujJlGVI4knTNiNfBAS83tec9z/dN3BvWlU7ZqY6NOGSsiplW6brCxTJLAKR41K7X5FYQ2wnx2oiuKsW+zNwp55IibvAxWDPHrBlvLOkmlsIR7jpXintCa21G9I3nAACB0d/zPB7i7clz5PObnjkS0/Tftdhvt5IoHuNiQqFix/JYKUrGiOfMNXTB8tnxPIs6epb2JetxJWRUBLojHMpim6tWrcLMmTMxa9Ys/OxnP0NlZSUAoK6uDosWLcL111+PqqoqnHfeeV3eN5pEEQRBEASRt9xzzz245ZZbcNddd5mWV1ZWYunSpSgqKsLdd99Nk6h8gsk+rnViwyOY2Fxs09qoVX3X8gYjWq5SFOe0WgKsb4A5QzyOqPfCtKSVyuiTL2Tez3gbtZ5HQX+K+QJ6UK0RrCn7TAHvpiBVVSj8rPjMb5TMHJRrs5RYr5mb5SLF+cvWOuPUDrMez3o+Hdp1Or7N8mJ8jsfMmlHBhKq7uLNhpQiEdIuhP3ENE+deEq63qf+mL+mdO8d9rSj2Y5mw3hvGYqYBakJPTI3r19eiicZxsnYZ94I/CEmNOVYZsBWk9eqf0byXRYQvyzx9m4mJJKKenDhuTeVB64B+jnjwOtMgywq0pn0AgPjm1dBaGhEY8wPn48VjgkXOxy1P+nfZHrgtWqmE+8gpgNupuDtPPhDaMfpt2jdThMLITstt/bFsq6uYW65xFxYg7qhOVKG68zZv3ozHHnvMdf3UqVNx//33d2GPktAkiiAIgiDyHMYYWAcnQfksFeCFpmnwO5Q1MvD7/d02NhLbJAiCIIg8R9NYTv4VIscddxz++te/uq5//vnncdxxx3Vhj5KQJcqNhOS/I5J78DeDi9sAsJmg7UWGhfZSVBZmzKWcS67deYbbgGnmYzqVcRDdeRbzODNKTFjdjpruInT8aRumfI8gVdeioI5LE7sZ7i/DxSC6E53coilcq7ZjpuvWcwl2tbmEHA+mmdcL7g5JTQTZ+0J6YL7gAnHTd9J8fv36uRzb0+GUYVB02jiVLBJdxIKrCmo8ub0cN18Dt2tq6rShFxUH1EBSE0ncxC2Bw+l6uwQnO2l8ZXW+PPSU+L2s+BLutMQbvBpPHj8eA4pCUHxC0oHfj/b1TwMAguMusx/T0HqTFbOL3kkXSrjnJLcx8r47ufUkiO/4zGG8ru5z23PV4/50use8kB32zfkzl7ByzTXX4Oqrr0YwGMRVV10FX6JsTzwex6OPPopf/OIXeOihh7qlbzSJIgiCIIg8hzHWYZdVobrzqqursWXLFsyYMQNz5szB0KFDAQCffPIJDhw4gJkzZ6KmpqZb+kaTKDeylQ0QSbG7o9Wqw8fspLciI72Zv+WZLWWmgMs03tC4ZUp1kCawYi2wCjha3tJ+o+dvy8mgYtujJd220ikOnWk7HhY214Kt4nGVxDnV4oAmBONbgn7NDRvrhH4IbXbYuuQZjG05h27XVJa53AUfCz8fbabgZVswvtfxrZZIWTMnKmRyX7mRToB5Juu9uuH2WQkk+5j4zBKWKDnaCoRL4Ev81rQdr3E5BEPJXguEk+1Y7xV+EGY/Dx5B265Ist38ybSkhZ4xd2uzJLsnY2RiebIEtTNJdq080BUwLQcxUQXqzgOAxYsX4/vf/z7++Mc/4qOPPgIAfOtb38KUKVNw2mmndVu/aBJFEARBEETec9ppp3XrhMkJcuYSBEEQRJ5zKAeWt7S04Nprr8WAAQPQt29fXHrppfj666+7u1sAyBKVEmYx6epfBDdCBpoxnuZ+JYP5bDrHT+G+SIl1m0T/HIOnndxEbsHYtnZ99v5aNGGSHkRLe3J2Lleub2VzUeYwKDob0nAT2C6dw/3JDBeF7NNdeny5h8tOkhIFV42A/4SLy1DnF92F2bgw03HZebmDJdleEJZpSAYlC/eCZKkUIASk20i4iGyJGpnoEWWgD5bsU5b3maDSz79nckwXV5sm+8BkH+RifbHmC/F7hyXWaZFefDe5Za+LWxjJe8bpmNmO28ltbQ0293LteyTeZPSbt/5GcxH2kSYphPjTbqMQmTt3Lp544glcdtllCIVC+OMf/4irr74af/7zn7u7a4VliVq1ahVOPfVUhMNhlJeX46KLLjKtr62txQUXXIBIJILy8nLMnDkT0Wi0m3pLEARBEERHWbFiBZYtW4bHHnsMv/rVr7Bq1So8//zzUK0lmrqBgrFEPffcc5g2bRp++ctf4qyzzgJjDFu2bOHrVVXFpEmT0KdPH2zcuBENDQ2orq4GYwwPPPBAxsdjsqK/+VpT3xNkrjGMjN9YOpT6bGrI+e3TMUXY7Q3cabn45if7ku1pcXtNLyvCepvytBUnK12qQN10xma1rnnRHa9wTmnsskNwqxiILr6uappzwLqTerdh1ePWnIQivZNyt4M0h0nd3AFbgLibtcl6f3lJd0gywBIPUdkHprjcu04WS4s10hWr5SHF9qlU63Nl6XQ6jq1tL8umYSFK7GNIFqiJ4HEIQejGb1tu/kb/HoyA+QJcSoP5HaQxrM9L4Tlqek6k+l0Z1mLAXZbCKifhYP0yWTtzlSSRaJd1Ue28Qzk7b8+ePTjzzDP591NOOQU+nw+ff/45Bg0a1I09K5BJVDwex/XXX49Fixbhyiuv5MuPPvpo/nnNmjXYunUr9uzZg/79+wMAlixZgpqaGsyfPx8lJSVd3m+CIAiCyAW5iGkq1JgoVVURCARMy3w+H+LxNLK7O5mCmES9++67+OyzzyDLMk466STU19fjxBNPxOLFi7lK6RtvvIHhw4fzCRQATJw4Ee3t7di0aRPGjx/v2HZ7ezva29v598bGxs4dDEEQBEEQacMYQ01NDYLBIF/W1taG6dOnIxKJ8GUrVqzo8r4VxCTq008/BQDMmzcPS5cuRVVVFZYsWYKxY8dix44d6NWrF+rr69GvXz/TfmVlZQgEAqivr3dte8GCBbj99tvdD+5i+rUFrvIV3TTT9wr2dQrahkfQvGP7FnO94L6zunKYEuCFR419XRXBJXPgMkMKV2kKXSjJye2YKkg4HVddZwSPulwX2zrrdkrSJcFkH6D4zS5RI5hcU02B5Y5jcPNEcHePg9vIZRcnVWnHdiXZfh/w9ZagZGvblv5JYv8MF7FkdfPINrV7fo5kb3VrG9btRdyU99NpNxOyPY7HOuM3zM+TJJl02ZgkC4rlPkAJgAX0a8WUAE9mkAydKOvzgiWTOJjRbopzIjGNn2+WTlS16z3lcB9Zn99uz+101Oi7yp13COtEVVdX25Zdfvnl3dATO906iZo3b573BAbA22+/DS3hF7/11lvxve99DwCwfPlyDBw4EH/+85/xP//zPwAAySFThTHmuNxgzpw5mD17Nv/e2NjY7T5WgiAIgjCRg0kUCnQStXz58u7ugivdOomaMWMGpkyZ4rlNVVUVmpqaAADDhg3jy4PBIA4//HDU1tYCACoqKvDWW2+Z9t27dy9isZjNQiUSDAZNJkKCIAiCyDc0xnRLXwfbIHJLt06iysvLUV5ennK7kSNHIhgMYvv27TjjjDMAALFYDLt27cKQIUMAAKNHj8b8+fNRV1eHyspKAHqweTAYxMiRIzPvnKykZ6a1uWE8NFKyJZ0bPwNzdNqFO51wcAFa25O0uM2Fx90EhqnfphtkmMb9SXN/Flo9ji6+VPvmytWSovAp4J61lHJbi6uM+fSJvyYpiGmMe+UUOTlaKVXmkNM94+LaTSvzLoPMUDe3na1dW9aXuJ9iduFYygKJLjxT/0W3lbUvgPdvKZN7xc3l3xGEUkWex0sXwZ3OFH/yHDKWbE9W9N+wlCj7IklgLPkHWYbEfbwMEsTi6bLF8cgkmWfFpsxkBJz1obzGASRd3emcD8sz2nqfmFSA3DJFu0gnishPCiImqqSkBNOnT8fcuXMxaNAgDBkyBIsWLQIAXHzxxQCACRMmYNiwYZg6dSoWLVqEb775BjfccAOmTZtGmXkEQRBEQcNYDmKiyBKVcwpiEgUAixYtgs/nw9SpU9Ha2opTTz0Va9euRVlZGQBAURSsWrUK11xzDcaMGYNwOIxLL70Uixcvzup4hkqv/iWDwqLW1Wkf0OsYDqrR6eKgj+P6hmbV+vHSWnJq22u51eIiKGUDMFsN+O6KeV9xv3SOabTtGj2dos2O4mVtYZr+9u96DpMq29agaMMSYBBQzHeZYX1iUMzm/3Ssksz5jk15Dk2NimNKvZ/X/ZjcxqVfojq77PI4s9zzEv+PW3KFW08dxuLxe/AsfJsK8feS4rq5WjetVhwPaxhTEgWXDU02SYaWOBEqt37p/zPS5P2C1VN0E8nCcXjAuYOuc0aaWU7B6kbbmup8PtKwjEqaQ4q86ZpKlvvQfk2tv8/OIp8Dy3ft2oU777wTa9euRX19Pfr374/LL78ct956q0maoLa2Ftdeey3Wrl1r+httlS8oJApmEuX3+7F48WLPSdHgwYPxwgsvdGGvCIIgCOLQ5j//+Q80TcOjjz6KI444Ah988AGmTZuG5uZm/jc714LY+ULBTKIIgiAI4lBF0wCpw2KbOeqMhXPOOQfnnHMO/3744Ydj+/btePjhh/kk6mAVxKZJlAtMksAkSXeHpBkgnipQ0ntnh2Nk42ZimslEbegI8e+S37aLyRTekT64jd86NqbZg/YTpnENEqTEOjfNGaf9HLsjatd0EaLbKZ3Dim4QZlqe/Cwzi4fJ4jpxq0drG3c6rt00gslzXqTZK3hcXG4djijPZtlHYiy5TFLM35lsP4a1AHEWuPXb2i/7jt7aU6lc7K46b9yt5dG8i8uRSclgcUWSUBQOmfbb39yKWOImVaSkC09jDBpjJpeedczpnCfAcr0lxS7HZ3gZJYU/M4zjOd0PpjF6JKK4arRZ+2eEC+QqaSAFuSz7YhWV7ows9f3796NXr178e7aC2PkOpRUQBEEQxCHEoEGDUFpayv8tWLAgp+1/8skneOCBBzB9+nS+LFtB7HyHLFEuSIYmh0OKtmMwohOprCiJ4OKcIitggoI1kxWoWlIN3AktMZfW31KENzpJgj8RtMxf6h0ClZNvwl5BxIlgZ6Ff+jGBmMbAVKPdZPuSJEF2OX8aA2QJkMwxr9yCI0vg59a0zLKdsbwj75Km8yqcH6vlXZbMy7zeKn2yd49k65u2lwXKjRQWpbQtTpmm/hv72I6XeLNPs/te2zFItjphsrDO+mNgHboDeCMmbNfEGtRs+e52vp2sRbYKAEKbaaf2247jfE+luh4M9sByx+cFzMsyteCIsgpOnTB+EwyS7WrajiWZLVeOTRqeCBeMdV1l6U5HtD2dNgC9oK/oPnOzQqUriD1q1Cj+/fPPP8c555yDiy++GD/5yU9M22YjiJ3v0CSKIAiCIPIcTWM5iInS9y8pKUkrBildQWyDzz//HOPHj8fo0aPx2GOPmbbLVhA736FJFEEQBEHkOd0hcZCuIDYAfPbZZxg/fjxGjhyJ5cuXQ5bN1s6cC2LnCTSJckFSo5DUaMcaYQxgaurt+EG9VZwzMdOLrpEeReH0+5Dgq8YW+OVkOWDGdPVhMVjbVY8IZlO41ZTOWPKNiEF3cxVHijLu48HKgZbWhItE/+7k2bO5Nfi2EndNidfLDfEaWQNyU7op0lGUd9vGQQPKOJyby8bqwvTCKWC/K0teOPbVkmBh1ahKK5DfqsadpktQ3Nd1tXB+QuH0nxkSzOOVwZJpYB7HTHl/OTw3vO4BzxrsFtekq7tRcE1LmuqY8ONYoeEQ5/PPP8e4ceMwePBgLF68GF999RVfV1FRAeDgFcSmSRRBEARB5Dn5LLa5Zs0afPzxx/j4448xcOBA8zETE9VcC2LnCzSJcsOhbpeBV1q4ecMMogAl2aZE7fbmnLQPuaxEMojWmpqcLn1KUluGDrS0AgDiiR+mERtuhHcayJJ5HBoDehWT5ckNw3LYlDi/TgHq4qKYygRrVTJI0y/rAbbpSANkFSSbpdSBTQYihQXKwLrezSph3a47QlY1xvSacrwTHsrdlu82S4eDxIGnyns6VQU6iGgdFQPJASOwu+PHlTSzFV9KlQhhudJi3T6NJRNkZDXmmDCUbChF1YZuIp8LENfU1KCmpibldgejIDZJHBAEQRAEQWQBWaIIgiAIIs/JZ3feoQxNolzQFD80QenbM0ja1URqN/R5BUZqGssqCLy7KKS+FiLFWZ7ffQdaACTcqyb3sKGfpX83BQR3QKfFycUgqoUD5oBet9+M1+PdqXeZuibSDUz3ClBOF1vgMoNNfbwjyu9uOlGZup8yCSAXUSSJn3+VMa7XZnXt2fByo3lt57ZMaMd6H/KAb001K9g5FRVO41q4JWmIfyc6E8ZyMInqwuSKQwVy5xEEQRAEQWQBWaIIgiAIIs9hGrOp8GfTBpFbaBLlQlxjPOuMu0EAgDFbtlRpxNskLmZZqa4aOPYsLILIhp499MzHppZWqBqDIhs6TAwaJG7SlyQJDEZWHvh2bri6tFOUx9CPldhWcPsYGG6hVM62dN19biVxxD7ailObXEXW7FsPbSInnSGhs1b3HXe/SQ5FkPmGslmPLY3zmylMkhAOZZe5C+hZv/ub9eeaWIBYbB+w959ZC48LmMaYwk1pc4taCzWLyIrpvGuw33tOp1fwfMN1IySzkzubXBYgJnIHufMIgiAIgiCygCxRLhQXhbMO7HVqiyC6mlT33YGWVttbtGfwtVeB4CyDtgE9SNm0TbZvyxYrELdmMN0yIlogjG24tpDkUTzbpTuS5GCFs1hQNBfLi6jaLyeCtMUCuskx6MuMvhuB+W79SZeOWKEMDMOlrq6fHAtjguXRGFManfPcJnF9ktZQB8V7JLdxSgBCop8Mdm+CQSqvgmO/47GM98kGys7LT2gSRRAEQRB5jqaxDsd8dDSmirBDkyiCIAiCyHOYpoJpGdRidWmDyC00iSKIQwwj0UECeIkYxhhUS+KDGGcuAYILzIzktFDAyUXo5bmxlYVxwa4TJZv6p4nVO1yCjjN9MU8Re++A+wGMY/cqTu1CMrS/gGTiQCr2N7eCMZb29plgBJUD5nPC3XppFNvJWQkSxuxn2dK2eJ0765wQhyY0iSIIgiCIPIcsUfkJTaII4hAjqiZfy032AsvbuwqLNcrFfJTKOuMk6yE5GCEMi5WXhSId24XVumSkdRey9SGbvmcTJJ0uYqq8Bsl2D4jXXJQKsMkGdCIHW0IP07QcTKLyp6DywQJJHBAEQRAEQWQBWaIIgiAIIs9hqgqmdtAS1cH9CTs0iSKIQ5R0YqRNrjEXN5vV7ZdRHwQXoZuaf/Lw2TuCCtmVl+8YSQlA8nqK94PpFuqiPh2MmfyM5SAmitEkKteQO48gCIIgCCILyBJFEIcYvYvJKkN0DowxSJJksgRla6V0Qmy3MwPn8xHKzstPaBJFEARBEHkOTaLyE3LnEQRBEARBZAFZogiCIIiM6IxA/QMtSRV0p7jwg033KVPIEpWfFIwlaseOHZg8eTLKy8tRUlKCMWPGYN26daZtamtrccEFFyASiaC8vBwzZ85ENBrtph4TBEEQRG4wxDY79o/ENnNNwViiJk2ahKOOOgpr165FOBzGfffdh/PPPx+ffPIJKioqoKoqJk2ahD59+mDjxo1oaGhAdXU1GGN44IEHurv7BEEQhAc9DnFLUyo0TQU6aEnSyBKVcwrCEvX111/j448/xi233IITTjgBRx55JBYuXIiWlhZ8+OGHAIA1a9Zg69at+P3vf4+TTjoJ3/72t7FkyRI8/vjjaGxs7OYREARBEARxsFEQk6jevXvj2GOPxVNPPYXm5mbE43E8+uij6NevH0aOHAkAeOONNzB8+HD079+f7zdx4kS0t7dj06ZNrm23t7ejsbHR9I8gCIIg8omOu/I6HlNF2CkId54kSXj55ZcxefJkFBcXQ5Zl9OvXD6tXr0bPnj0BAPX19ejXr59pv7KyMgQCAdTX17u2vWDBAtx+++2d2X2CIAiC6BAUWJ6fdKslat68eZAkyfPfO++8A8YYrrnmGvTt2xf//Oc/8a9//QuTJ0/G+eefj7q6Ot6eU5V5Q/zNjTlz5mD//v383549ezplrARBEARBHFx0qyVqxowZmDJliuc2VVVVWLt2LV544QXs3bsXJSUlAICHHnoIL7/8Mp588knccsstqKiowFtvvWXad+/evYjFYjYLlUgwGEQwGOz4YAiCIAiis1BVMLmDliQqQJxzunUSVV5ejvLy8pTbtbS0AABk2Ww4k2UZWiJlc/To0Zg/fz7q6upQWVkJQA82DwaDPG6KIAiCIAoRxjqenUcFiHNPQQSWjx49GmVlZaiursa///1v7NixAzfeeCN27tyJSZMmAQAmTJiAYcOGYerUqdi8eTP+8Y9/4IYbbsC0adO49YogCIIgCCJXFMQkqry8HKtXr8aBAwdw1llnYdSoUdi4cSP++te/YsSIEQAARVGwatUqhEIhjBkzBj/4wQ9w4YUXYvHixd3ce4IgCILoGCS2mZ8URHYeAIwaNQovvfSS5zaDBw/GCy+80EU9IgiCIIiugeVAbJOy83JPQViiCIIgCIIg8o2CsUQRBEEQxKEK0zSgg+44cuflHppEEQRBEESeQ+68/IQmUQRBEASR59AkKj+hmCiCIAiCIIgsIEsUQRAEQeQ5mqZCIktU3kGTKIIgCILIc5iqAVIHJ1EqBZbnGnLnEQRBEARBZAFZogiCIAgiz6HaefkJTaIIgiAIIs9hmtpxdx7FROUccucRBEEQBEFkAVmiCIIgCCLPIUtUfkKTKIIgCILIc2gSlZ/QJMoCYwwA0NjY2M09IQiCIPId42+F8bej01Bj6PAR1FguekII0CTKQkNDAwBg0KBB3dwTgiAIolBoampCaWlpztsNBAKoqKhA/dZnc9JeRUUFAoFATtoiAIl1+vS5sNi3bx/KyspQW1vbKT+I7qaxsRGDBg3Cnj17UFJS0t3dySk0tsKExlaY0Nh0GGNoampC//79Icudk6vV1taGaDSak7YCgQBCoVBO2iLIEmXD+BGUlpYedA8GkZKSkoN2fDS2woTGVpjQ2NDpL9yhUIgmPnkKSRwQBEEQBEFkAU2iCIIgCIIgsoAmURaCwSDmzp2LYDDY3V3pFA7m8dHYChMaW2FCYyMICiwnCIIgCILICrJEEQRBEARBZAFNogiCIAiCILKAJlEEQRAEQRBZQJMogiAIgiCILKBJlIWHHnoIhx12GEKhEEaOHIl//vOf3d2ljJk3bx4kSTL9q6io4OsZY5g3bx769++PcDiMcePG4cMPP+zGHrvz6quv4oILLkD//v0hSRKef/550/p0xtLe3o7rrrsO5eXliEQi+M53voP/9//+XxeOwplUY6upqbFdx9NOO820Tb6ObcGCBfiv//ovFBcXo2/fvrjwwguxfft20zaFeu3SGVuhXruHH34YJ5xwAheZHD16NF588UW+vlCvGZB6bIV6zYjuhSZRAn/6058wa9Ys3Hrrrdi8eTPOPPNMnHvuuaitre3urmXMcccdh7q6Ov5vy5YtfN0999yDpUuX4te//jXefvttVFRU4Oyzz0ZTU1M39tiZ5uZmjBgxAr/+9a8d16czllmzZmHlypV45plnsHHjRhw4cADnn38+VLV7K5qnGhsAnHPOOabr+Pe//920Pl/HtmHDBlx77bV488038fLLLyMej2PChAlobm7m2xTqtUtnbEBhXruBAwdi4cKFeOedd/DOO+/grLPOwuTJk/lEqVCvGZB6bEBhXjOim2EE55RTTmHTp083LTvmmGPYLbfc0k09yo65c+eyESNGOK7TNI1VVFSwhQsX8mVtbW2stLSUPfLII13Uw+wAwFauXMm/pzOWffv2Mb/fz5555hm+zWeffcZkWWarV6/usr6nwjo2xhirrq5mkydPdt2nUMbGGGNffvklA8A2bNjAGDu4rp11bIwdXNeurKyM/eY3vzmorpmBMTbGDq5rRnQdZIlKEI1GsWnTJkyYMMG0fMKECXj99de7qVfZ89FHH6F///447LDDMGXKFHz66acAgJ07d6K+vt40zmAwiLFjxxbcONMZy6ZNmxCLxUzb9O/fH8OHDy+I8a5fvx59+/bFUUcdhWnTpuHLL7/k6wppbPv37wcA9OrVC8DBde2sYzMo9GunqiqeeeYZNDc3Y/To0QfVNbOOzaDQrxnR9VAB4gRff/01VFVFv379TMv79euH+vr6bupVdpx66ql46qmncNRRR+GLL77AXXfdhdNPPx0ffvghH4vTOHfv3t0d3c2adMZSX1+PQCCAsrIy2zb5fl3PPfdcXHzxxRgyZAh27tyJ//3f/8VZZ52FTZs2IRgMFszYGGOYPXs2zjjjDAwfPhzAwXPtnMYGFPa127JlC0aPHo22tjb06NEDK1euxLBhw/hEoZCvmdvYgMK+ZkT3QZMoC5Ikmb4zxmzL8p1zzz2Xfz7++OMxevRoDB06FE8++SQPlDwYxmmQzVgKYbw//OEP+efhw4dj1KhRGDJkCFatWoWLLrrIdb98G9uMGTPw/vvvY+PGjbZ1hX7t3MZWyNfu6KOPxnvvvYd9+/bhueeeQ3V1NTZs2MDXF/I1cxvbsGHDCvqaEd0HufMSlJeXQ1EU2xvFl19+aXvzKjQikQiOP/54fPTRRzxL72AYZzpjqaioQDQaxd69e123KRQqKysxZMgQfPTRRwAKY2zXXXcd/va3v2HdunUYOHAgX34wXDu3sTlRSNcuEAjgiCOOwKhRo7BgwQKMGDEC999//0FxzdzG5kQhXTOi+6BJVIJAIICRI0fi5ZdfNi1/+eWXcfrpp3dTr3JDe3s7tm3bhsrKShx22GGoqKgwjTMajWLDhg0FN850xjJy5Ej4/X7TNnV1dfjggw8KbrwNDQ3Ys2cPKisrAeT32BhjmDFjBlasWIG1a9fisMMOM60v5GuXamxOFNK1s8IYQ3t7e0FfMzeMsTlRyNeM6EK6PJQ9j3nmmWeY3+9ny5YtY1u3bmWzZs1ikUiE7dq1q7u7lhE/+9nP2Pr169mnn37K3nzzTXb++eez4uJiPo6FCxey0tJStmLFCrZlyxZ2ySWXsMrKStbY2NjNPbfT1NTENm/ezDZv3swAsKVLl7LNmzez3bt3M8bSG8v06dPZwIED2SuvvMLeffdddtZZZ7ERI0aweDzeXcNijHmPrampif3sZz9jr7/+Otu5cydbt24dGz16NBswYEBBjO3qq69mpaWlbP369ayuro7/a2lp4dsU6rVLNbZCvnZz5sxhr776Ktu5cyd7//332c9//nMmyzJbs2YNY6xwrxlj3mMr5GtGdC80ibLw4IMPsiFDhrBAIMBOPvlkU9pyofDDH/6QVVZWMr/fz/r3788uuugi9uGHH/L1mqaxuXPnsoqKChYMBtm3vvUttmXLlm7ssTvr1q1jAGz/qqurGWPpjaW1tZXNmDGD9erVi4XDYXb++eez2trabhiNGa+xtbS0sAkTJrA+ffowv9/PBg8ezKqrq239ztexOY0LAFu+fDnfplCvXaqxFfK1+/GPf8yff3369GH//d//zSdQjBXuNWPMe2yFfM2I7kVijLGus3sRBEEQBEEcHFBMFEEQBEEQRBbQJIogCIIgCCILaBJFEARBEASRBTSJIgiCIAiCyAKaRBEEQRAEQWQBTaIIgiAIgiCygCZRBEEQBEEQWUCTKOKgY9y4cZg1a9ZBddyamhpceOGFHWqjqqoKkiRBkiTs27fPdbsnnngCPXv27NCxCHdqamr4dXj++ee7uzsEQXQAmkQRRI5YsWIF7rzzTv69qqoK9913X/d1yIE77rgDdXV1KC0t7e6uHPSsX7/eccJ6//33o66urns6RRBETvF1dwcI4mChV69e3d2FlBQXF6OioqK7uwEAiMVi8Pv93d2NLqe0tJQmsQRxkECWKOKgZ+/evbjiiitQVlaGoqIinHvuufjoo4/4esN99dJLL+HYY49Fjx49cM4555isBfF4HDNnzkTPnj3Ru3dv3Hzzzaiurja52ER33rhx47B792789Kc/5a4bAJg3bx5OPPFEU//uu+8+VFVV8e+qqmL27Nn8WDfddBOs1ZkYY7jnnntw+OGHIxwOY8SIEfjLX/6S1fl54oknMHjwYBQVFeG73/0uGhoabNv83//9H0aOHIlQKITDDz8ct99+O+LxOF//n//8B2eccQZCoRCGDRuGV155xeSu2rVrFyRJwrPPPotx48YhFArh97//PQBg+fLlOPbYYxEKhXDMMcfgoYceMh37s88+ww9/+EOUlZWhd+/emDx5Mnbt2sXXr1+/HqeccgoikQh69uyJMWPGYPfu3WmNPdW4li5diuOPPx6RSASDBg3CNddcgwMHDvD1u3fvxgUXXICysjJEIhEcd9xx+Pvf/45du3Zh/PjxAICysjJIkoSampq0+kQQROFAkyjioKempgbvvPMO/va3v+GNN94AYwznnXceYrEY36alpQWLFy/G7373O7z66quora3FDTfcwNfffffdePrpp7F8+XK89tpraGxs9IxnWbFiBQYOHMjdZ5m4b5YsWYLf/va3WLZsGTZu3IhvvvkGK1euNG3zi1/8AsuXL8fDDz+MDz/8ED/96U9x+eWXY8OGDemfGABvvfUWfvzjH+Oaa67Be++9h/Hjx+Ouu+4ybfPSSy/h8ssvx8yZM7F161Y8+uijeOKJJzB//nwAgKZpuPDCC1FUVIS33noLjz32GG699VbH4918882YOXMmtm3bhokTJ+Lxxx/Hrbfeivnz52Pbtm345S9/if/93//Fk08+CUC/LuPHj0ePHj3w6quvYuPGjXySG41GEY/HceGFF2Ls2LF4//338cYbb+Cqq67ik1YvUo0LAGRZxq9+9St88MEHePLJJ7F27VrcdNNNfP21116L9vZ2vPrqq9iyZQvuvvtu9OjRA4MGDcJzzz0HANi+fTvq6upw//33Z3RtCIIoALq1/DFBdAJjx45l119/PWOMsR07djAA7LXXXuPrv/76axYOh9mzzz7LGGNs+fLlDAD7+OOP+TYPPvgg69evH//er18/tmjRIv49Ho+zwYMHs8mTJzselzHGhgwZwu69915T3+bOnctGjBhhWnbvvfeyIUOG8O+VlZVs4cKF/HssFmMDBw7kxzpw4AALhULs9ddfN7Vz5ZVXsksuucT1vDj155JLLmHnnHOOadkPf/hDVlpayr+feeaZ7Je//KVpm9/97nessrKSMcbYiy++yHw+H6urq+PrX375ZQaArVy5kjHG2M6dOxkAdt9995naGTRoEPvDH/5gWnbnnXey0aNHM8YYW7ZsGTv66KOZpml8fXt7OwuHw+yll15iDQ0NDABbv36967jdSDUuJ5599lnWu3dv/v34449n8+bNc9x23bp1DADbu3ev43rx/BAEUZhQTBRxULNt2zb4fD6ceuqpfFnv3r1x9NFHY9u2bXxZUVERhg4dyr9XVlbiyy+/BADs378fX3zxBU455RS+XlEUjBw5Epqm5bS/+/fvR11dHUaPHs2X+Xw+jBo1irv0tm7dira2Npx99tmmfaPRKE466aSMjrdt2zZ897vfNS0bPXo0Vq9ezb9v2rQJb7/9tslCo6oq2tra0NLSgu3bt2PQoEGmWCvxXImMGjWKf/7qq6+wZ88eXHnllZg2bRpfHo/HeczQpk2b8PHHH6O4uNjUTltbGz755BNMmDABNTU1mDhxIs4++2x8+9vfxg9+8ANUVlamHHuqcRUVFWHdunX45S9/ia1bt6KxsRHxeBxtbW1obm5GJBLBzJkzcfXVV2PNmjX49re/je9973s44YQTUh6bIIiDA5pEEQc1zBJLJC4XXT7WAGdJkmz7Wl1Ebm17IcuybT/RrZgOxsRt1apVGDBggGldMBjMqK10xqBpGm6//XZcdNFFtnWhUMh2Lr2IRCKmdgHg8ccfN01yAX2SamwzcuRIPP3007a2+vTpA0CPqZo5cyZWr16NP/3pT/jFL36Bl19+GaeddlqHxrV7926cd955mD59Ou6880706tULGzduxJVXXsmv2U9+8hNMnDgRq1atwpo1a7BgwQIsWbIE1113XVrngyCIwoYmUcRBzbBhwxCPx/HWW2/h9NNPBwA0NDRgx44dOPbYY9Nqo7S0FP369cO//vUvnHnmmQB0i8XmzZttQeIigUAAqqqalvXp0wf19fWmicd7771nOlZlZSXefPNNfOtb3wKgW2Y2bdqEk08+mY8pGAyitrYWY8eOTWsMbgwbNgxvvvmmaZn1+8knn4zt27fjiCOOcGzjmGOOQW1tLb744gv069cPAPD222+nPHa/fv0wYMAAfPrpp7jssssctzn55JPxpz/9CX379kVJSYlrWyeddBJOOukkzJkzB6NHj8Yf/vCHlJOoVON65513EI/HsWTJEsiyHj767LPP2rYbNGgQpk+fjunTp2POnDl4/PHHcd111yEQCACA7R4gCOLggSZRxEHNkUceicmTJ2PatGl49NFHUVxcjFtuuQUDBgzA5MmT027nuuuuw4IFC3DEEUfgmGOOwQMPPIC9e/d6WmCqqqrw6quvYsqUKQgGgygvL8e4cePw1Vdf4Z577sH3v/99rF69Gi+++KJpgnD99ddj4cKFOPLII3Hsscdi6dKlJq2h4uJi3HDDDfjpT38KTdNwxhlnoLGxEa+//jp69OiB6urqtMc1c+ZMnH766bjnnntw4YUXYs2aNSZXHgDcdtttOP/88zFo0CBcfPHFkGUZ77//PrZs2YK77roLZ599NoYOHYrq6mrcc889aGpq4oHlqSxU8+bNw8yZM1FSUoJzzz0X7e3teOedd7B3717Mnj0bl112GRYtWoTJkyfjjjvuwMCBA1FbW4sVK1bgxhtvRCwWw2OPPYbvfOc76N+/P7Zv344dO3bgiiuuSDn2VOMaOnQo4vE4HnjgAVxwwQV47bXX8Mgjj5jamDVrFs4991wcddRR2Lt3L9auXcsn50OGDIEkSXjhhRdw3nnnIRwOo0ePHmlfG4IgCoBui8YiiE7CGuD9zTffsKlTp7LS0lIWDofZxIkT2Y4dO/j65cuXmwKpGWNs5cqVTPx5xGIxNmPGDFZSUsLKysrYzTffzC6++GI2ZcoU1+O+8cYb7IQTTmDBYNDU1sMPP8wGDRrEIpEIu+KKK9j8+fNNgeWxWIxdf/31rKSkhPXs2ZPNnj2bXXHFFaYgdk3T2P3338+OPvpo5vf7WZ8+fdjEiRPZhg0bXM+LU2A5Y3rw9sCBA1k4HGYXXHABW7x4se18rF69mp1++uksHA6zkpISdsopp7DHHnuMr9+2bRsbM2YMCwQC7JhjjmH/93//xwCw1atXM8aSgeWbN2+2Hf/pp59mJ554IgsEAqysrIx961vfYitWrODr8H7pTwAAAfJJREFU6+rq2BVXXMHKy8tZMBhkhx9+OJs2bRrbv38/q6+vZxdeeCGrrKxkgUCADRkyhN12221MVVXX85DJuJYuXcoqKyv5ffPUU0+ZgsVnzJjBhg4dyoLBIOvTpw+bOnUq+/rrr/n+d9xxB6uoqGCSJLHq6mrTsUGB5QRR8EiMZRHYQRCHOJqm4dhjj8UPfvADk0p5PlNVVYVZs2Z1SUmc1157DWeccQY+/vhjU8A+kUSSJKxcubLD5XwIgug+SCeKINJg9+7dePzxx7Fjxw5s2bIFV199NXbu3IlLL720u7uWETfffDN69OiB/fv357TdlStX4uWXX8auXbvwyiuv4KqrrsKYMWNoAuXA9OnTya1HEAcJZIkiiDTYs2cPpkyZgg8++ACMMQwfPhwLFy7kwd+FwO7du3lW2eGHH86DpXPBU089hTvvvBN79uxBeXk5vv3tb2PJkiXo3bt3zo6RKccdd5yrcvmjjz7qGsze2Xz55ZdobGwEoEtpiBmLBEEUFjSJIgjioEScNFrp16+fTXuKIAgiU2gSRRAEQRAEkQUUE0UQBEEQBJEFNIkiCIIgCILIAppEEQRBEARBZAFNogiCIAiCILKAJlEEQRAEQRBZQJMogiAIgiCILKBJFEEQBEEQRBbQJIogCIIgCCIL/j/QRM0jv4aRoQAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "specific_time = temperature.sel(depth='200', method='nearest')\n", + "specific_time.plot()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 + Jaspy", + "language": "python", + "name": "jaspy" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/python-data/solutions/ex03.5_xr_masking.ipynb b/python-data/solutions/ex03.5_xr_masking.ipynb new file mode 100644 index 0000000..3fed437 --- /dev/null +++ b/python-data/solutions/ex03.5_xr_masking.ipynb @@ -0,0 +1,368 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "3058f71b-e62e-4301-ab43-d93a996e7cd1", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "# Exercise 3.5: Masking" + ] + }, + { + "cell_type": "markdown", + "id": "000d2f40-7df5-4425-9869-d41d9b5cb356", + "metadata": {}, + "source": [ + "## Aim: Learn to mask data in xarray" + ] + }, + { + "cell_type": "markdown", + "id": "18b86830-4c51-4561-b228-00b893296566", + "metadata": {}, + "source": [ + "Find the teaching material here: https://tutorial.xarray.dev/intermediate/indexing/boolean-masking-indexing.html" + ] + }, + { + "cell_type": "markdown", + "id": "ee494cac-6ae1-4c16-a730-cbbd277e4744", + "metadata": {}, + "source": [ + "### Issues covered: \n", + "- Create re-usable masks for data\n", + "- Plot masked data" + ] + }, + { + "cell_type": "markdown", + "id": "372af117-a045-4830-b91f-9124107cff6e", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q1. For masking, we're back to using our ocean dataset. Load it now from `../data/vbhubo.pgc0apr.nc`." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "5bbad328-b6fe-4d9e-aacd-d5d2bcaac2c3", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:24.404131Z", + "iopub.status.busy": "2024-11-08T14:54:24.403855Z", + "iopub.status.idle": "2024-11-08T14:54:32.860391Z", + "shell.execute_reply": "2024-11-08T14:54:32.859756Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [ + "import xarray as xr\n", + "ds = xr.open_dataset('../data/xbhubo.pgc0apr.nc')\n", + "temperature = ds[\"temp\"]" + ] + }, + { + "cell_type": "markdown", + "id": "36531d5e-1969-46cd-a093-a0f1d538827e", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q2. Determine which grid cells sea surface temperaturevis more than the mean." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "b6358593-814b-4574-9dd1-a44c4c4cec9c", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:32.863370Z", + "iopub.status.busy": "2024-11-08T14:54:32.862901Z", + "iopub.status.idle": "2024-11-08T14:54:33.291924Z", + "shell.execute_reply": "2024-11-08T14:54:33.291420Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "surface = ds.sel(depth=0, method=\"nearest\")\n", + "surface.temp.where(surface.temp > surface.temp.mean()).plot()" + ] + }, + { + "cell_type": "markdown", + "id": "9bc58a98-fb38-4703-a313-5263c0773183", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q3. Combine this with another .where() lookup to show only cells where the temperature is more than the mean and salinity is more than the mean." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d8fb7ec8-b129-443a-b469-95a61ff1c5db", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:33.294676Z", + "iopub.status.busy": "2024-11-08T14:54:33.294332Z", + "iopub.status.idle": "2024-11-08T14:54:33.805169Z", + "shell.execute_reply": "2024-11-08T14:54:33.804649Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "surface.temp.where(surface.temp > surface.temp.mean()).where(surface.salinity > surface.salinity.mean()).plot()" + ] + }, + { + "cell_type": "markdown", + "id": "b3322279-7489-43b2-b9e8-dc41093c34a6", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q4. Masks are just boolean arrays. Create a re-usuable mask for the temperature and sst criteia above, and a combined one." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "61e20222-b105-4438-8ba2-c54bd9aa1d54", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:33.807832Z", + "iopub.status.busy": "2024-11-08T14:54:33.807570Z", + "iopub.status.idle": "2024-11-08T14:54:33.814723Z", + "shell.execute_reply": "2024-11-08T14:54:33.814173Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [ + "temp_mask = surface.temp > surface.temp.mean()\n", + "sal_mask = surface.salinity > surface.salinity.mean()\n", + "combined_mask = temp_mask & sal_mask" + ] + }, + { + "cell_type": "markdown", + "id": "aa4f780b-b22a-48bf-b165-7bedd9a4c011", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q5. Use this mask to make the same temperature plot, and a similar one for sst." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "850cffc7-f47e-432d-a0cd-25f40370f5cd", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:33.817140Z", + "iopub.status.busy": "2024-11-08T14:54:33.816637Z", + "iopub.status.idle": "2024-11-08T14:54:34.193250Z", + "shell.execute_reply": "2024-11-08T14:54:34.192692Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "surface.salinity.where(combined_mask).plot()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 + Jaspy", + "language": "python", + "name": "jaspy" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/python-data/solutions/ex03_xr_groupby.ipynb b/python-data/solutions/ex03_xr_groupby.ipynb new file mode 100644 index 0000000..310c899 --- /dev/null +++ b/python-data/solutions/ex03_xr_groupby.ipynb @@ -0,0 +1,874 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "7e3a36bd-b713-4a08-9f6c-a6a0260c42c0", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "# Exercise 3: Grouping" + ] + }, + { + "cell_type": "markdown", + "id": "1770f8c8-7d27-4536-aac8-f1e773dc0c86", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Aim: Work with data which has been grouped." + ] + }, + { + "cell_type": "markdown", + "id": "b5a92dc3-6d27-4723-bba5-cdc30b5f2b0d", + "metadata": {}, + "source": [ + "Find the teaching resources here: https://tutorial.xarray.dev/fundamentals/03.2_groupby_with_xarray.html" + ] + }, + { + "cell_type": "markdown", + "id": "06944527-8b86-4881-9c21-67013b11af9f", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Issues Covered:\n", + "- Grouping data with `.groupby()`\n", + "- Finding the mean of grouped data." + ] + }, + { + "cell_type": "markdown", + "id": "1fbfc303-a5c7-490f-887a-30ed1d93ffb6", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## GroupBy processing" + ] + }, + { + "cell_type": "markdown", + "id": "3ad0ac92-b9c2-43e5-9235-dae178be3186", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Our ocean model dataset has no time dimension, so for this exercise we are going to use the NOAA ERSSST dataset from the tutorial. Load it using the command below." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "39c8e656-1f55-49c3-ba9b-51bb4004191e", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:08.296789Z", + "iopub.status.busy": "2024-11-08T14:55:08.296544Z", + "iopub.status.idle": "2024-11-08T14:55:18.999081Z", + "shell.execute_reply": "2024-11-08T14:55:18.998377Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import xarray as xr\n", + "ds = xr.tutorial.load_dataset(\"ersstv5\")" + ] + }, + { + "cell_type": "markdown", + "id": "db58d1ca-84dd-4933-9498-551bd60f21a6", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Have a quick explore of the dataset and see what it contains." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "181322e6-af04-42c4-94f4-f0d8c00a9c66", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:19.003385Z", + "iopub.status.busy": "2024-11-08T14:55:19.002653Z", + "iopub.status.idle": "2024-11-08T14:55:19.032353Z", + "shell.execute_reply": "2024-11-08T14:55:19.031755Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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First, lets group our dataset by year." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "976836af-ec0b-49b8-8dbc-349139e5db77", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:19.035872Z", + "iopub.status.busy": "2024-11-08T14:55:19.035560Z", + "iopub.status.idle": "2024-11-08T14:55:19.042702Z", + "shell.execute_reply": "2024-11-08T14:55:19.041969Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [ + "grouped = ds.groupby(\"time.year\")" + ] + }, + { + "cell_type": "markdown", + "id": "1c47c5a9-7c59-470b-b54c-00fb328dc1f0", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q2. Lets take the mean of each group, to give the annual mean." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "ef15b52c-8cd8-46cf-87e6-814936ce280c", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:19.045489Z", + "iopub.status.busy": "2024-11-08T14:55:19.044962Z", + "iopub.status.idle": "2024-11-08T14:55:19.178623Z", + "shell.execute_reply": "2024-11-08T14:55:19.177989Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [], + "source": [ + "annual_means = grouped.mean()" + ] + }, + { + "cell_type": "markdown", + "id": "2c7da30c-5f09-4173-b306-10e26def81e0", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q3. To see what we've done, lets plot the mean for the year 1960." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "2c4dfcd3-1d9e-4f2c-843b-ebd934fe324a", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:19.181782Z", + "iopub.status.busy": "2024-11-08T14:55:19.181491Z", + "iopub.status.idle": "2024-11-08T14:55:19.707238Z", + "shell.execute_reply": "2024-11-08T14:55:19.706705Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "annual_means.sel(year=1970).sst.plot()" + ] + }, + { + "cell_type": "markdown", + "id": "3a6924bf-ebef-4b91-bfb7-95d286b2972e", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q4. Next, lets plot an annual mean time seties for the point in the Atlantic ocean latitude=-50.625, longitude=0." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "b8797acf-2e92-48ea-9a77-0118cd6e552f", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:19.710082Z", + "iopub.status.busy": "2024-11-08T14:55:19.709796Z", + "iopub.status.idle": "2024-11-08T14:55:19.923161Z", + "shell.execute_reply": "2024-11-08T14:55:19.922620Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "annual_means.sel(lat=-50.625, lon=0, method=\"nearest\").sst.plot()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 + Jaspy", + "language": "python", + "name": "jaspy" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/python-intro/solutions/ex20_feedback.ipynb b/python-data/solutions/ex04_cf_python.ipynb similarity index 63% rename from python-intro/solutions/ex20_feedback.ipynb rename to python-data/solutions/ex04_cf_python.ipynb index 15cf386..e93539d 100644 --- a/python-intro/solutions/ex20_feedback.ipynb +++ b/python-data/solutions/ex04_cf_python.ipynb @@ -2,18 +2,10 @@ "cells": [ { "cell_type": "markdown", - "id": "e28978d6-c4de-4532-b36e-2136c3b8cd13", + "id": "2762201c-7657-4894-9e07-aaa62c49efec", "metadata": {}, "source": [ - "# Exercise 20: Feedback" - ] - }, - { - "cell_type": "markdown", - "id": "5ab0f731-a958-4307-8119-ab5b1cc20f2e", - "metadata": {}, - "source": [ - "If you've got any feedback, we'd be happy to hear it!" + "# Exercise 4" ] } ], @@ -33,7 +25,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.5" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/python-data/solutions/ex05_matplotlib.ipynb b/python-data/solutions/ex05_matplotlib.ipynb new file mode 100644 index 0000000..5ea6982 --- /dev/null +++ b/python-data/solutions/ex05_matplotlib.ipynb @@ -0,0 +1,1571 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "e2574b49-2028-4bdf-a012-60d339dca6ac", + "metadata": {}, + "source": [ + "# Exercise 5: matplotlib" + ] + }, + { + "cell_type": "markdown", + "id": "4c39c2af-dc7c-4905-8535-f7280c4e9a37", + "metadata": {}, + "source": [ + "## Aim: Get to grips with how to create plots and customise them with matplotlib" + ] + }, + { + "cell_type": "markdown", + "id": "315d3123-78fd-44b4-874b-5638d478d112", + "metadata": {}, + "source": [ + "Find the teaching resources here: https://matplotlib.org/stable/users/explain/quick_start.html" + ] + }, + { + "cell_type": "markdown", + "id": "10599a64-226b-4789-89c1-093f0564bedf", + "metadata": {}, + "source": [ + "### Issues covered:\n", + "\n", + "- Creating plots\n", + "- Parts of a figure\n", + "- Styling the colours, linestyles, linewidths, markersizes etc\n", + "- Labelling plots: axis labels, titles, annotations and legends\n", + "- Axes properties: scales, ticks, plotting dates and strings\n", + "- Multiple figures, multiple axes,\n", + "- Colour-mapped data: colormaps, colorbars, normalizations" + ] + }, + { + "cell_type": "markdown", + "id": "f3612836-233d-479f-a2a8-e7ed99dc1133", + "metadata": {}, + "source": [ + "## Simple example" + ] + }, + { + "cell_type": "markdown", + "id": "6bc7192f-0962-4168-b6c6-5fd44daa24b2", + "metadata": {}, + "source": [ + "Q1. Let's create some sample data to plot. Create an array called `xaxis` with the value `[1,2,3,4,5]` and an array called `yaxis` with the value `[2, 16, 4, 8, 7]`. Plot this data on a single axes. Don't forget to import matplotlib!" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "df631937-9c0c-4453-80f0-c44da68ac71a", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:44.502584Z", + "iopub.status.busy": "2024-11-08T14:55:44.502263Z", + "iopub.status.idle": "2024-11-08T14:55:46.365779Z", + "shell.execute_reply": "2024-11-08T14:55:46.365119Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "xaxis = [1, 2, 3, 4, 5]\n", + "yaxis = [2, 16, 4, 8, 7]\n", + "fig, ax = plt.subplots()\n", + "ax.plot(xaxis, yaxis)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "5a84dc80-d94a-4440-8806-c51a2fbf9077", + "metadata": {}, + "source": [ + "## Parts of a figure" + ] + }, + { + "cell_type": "markdown", + "id": "9cfacc0a-5af7-4912-b79e-0ac0743e20e5", + "metadata": {}, + "source": [ + "Q2. Create 6 empty plots in a 2x3 grid." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "71cd4946-e8bd-40b0-9d21-c7a6fedb11bd", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:46.368639Z", + "iopub.status.busy": "2024-11-08T14:55:46.368283Z", + "iopub.status.idle": "2024-11-08T14:55:46.910842Z", + "shell.execute_reply": "2024-11-08T14:55:46.910332Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig = plt.figure()\n", + "fig, axs = plt.subplots(3,2)" + ] + }, + { + "cell_type": "markdown", + "id": "f2e84578-b714-46db-b276-9e11029552dc", + "metadata": {}, + "source": [ + "## Types of inputs to plotting functions" + ] + }, + { + "cell_type": "markdown", + "id": "97d9019c-750c-4a23-b023-2c9ed472f154", + "metadata": {}, + "source": [ + "Q3. Some inputs won't work as intended. Run the following cell to create a pandas dataframe:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "d2a59056-927b-4f16-b0b0-6ce0f0262fd4", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:46.913475Z", + "iopub.status.busy": "2024-11-08T14:55:46.913187Z", + "iopub.status.idle": "2024-11-08T14:55:48.226287Z", + "shell.execute_reply": "2024-11-08T14:55:48.225657Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import pandas as pd\n", + "df = pd.DataFrame({\n", + " 'A': [1, 2, 3, 4],\n", + " 'B': [4, 5, 6, 7],\n", + " 'C': ['a', 'b', 'c', 'd']\n", + "})" + ] + }, + { + "cell_type": "markdown", + "id": "f5b75a4b-7810-4b08-8b3c-f1f2c3bb75d9", + "metadata": {}, + "source": [ + "Try plotting the dataframe directly. Do you know why there is an error?" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "e15026a5-a78f-4033-bc80-017e539f7d64", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:48.229435Z", + "iopub.status.busy": "2024-11-08T14:55:48.229042Z", + "iopub.status.idle": "2024-11-08T14:55:49.609311Z", + "shell.execute_reply": "2024-11-08T14:55:49.608644Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell", + "allow_errors" + ] + }, + "outputs": [ + { + "ename": "ConversionError", + "evalue": "Failed to convert value(s) to axis units: array(['a', 'b', 'c', 'd'], dtype=object)", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)", + "File \u001b[0;32m/opt/jaspy/lib/python3.11/site-packages/matplotlib/axis.py:1769\u001b[0m, in \u001b[0;36mAxis.convert_units\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 1768\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1769\u001b[0m ret \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconverter\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconvert\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43munits\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1770\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n", + "File \u001b[0;32m/opt/jaspy/lib/python3.11/site-packages/matplotlib/category.py:49\u001b[0m, in \u001b[0;36mStrCategoryConverter.convert\u001b[0;34m(value, unit, axis)\u001b[0m\n\u001b[1;32m 48\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m unit \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m---> 49\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\n\u001b[1;32m 50\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMissing category information for StrCategoryConverter; \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 51\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mthis might be caused by unintendedly mixing categorical and \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 52\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnumeric data\u001b[39m\u001b[38;5;124m'\u001b[39m)\n\u001b[1;32m 53\u001b[0m StrCategoryConverter\u001b[38;5;241m.\u001b[39m_validate_unit(unit)\n", + "\u001b[0;31mValueError\u001b[0m: Missing category information for StrCategoryConverter; this might be caused by unintendedly mixing categorical and numeric data", + "\nThe above exception was the direct cause of the following exception:\n", + "\u001b[0;31mConversionError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[4], line 5\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[38;5;66;03m# There is an error because it doesn't know how to convert the data\u001b[39;00m\n\u001b[1;32m 4\u001b[0m fig, ax \u001b[38;5;241m=\u001b[39m plt\u001b[38;5;241m.\u001b[39msubplots()\n\u001b[0;32m----> 5\u001b[0m \u001b[43mplt\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\u001b[43mdf\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 6\u001b[0m plt\u001b[38;5;241m.\u001b[39mshow()\n", + "File \u001b[0;32m/opt/jaspy/lib/python3.11/site-packages/matplotlib/pyplot.py:3590\u001b[0m, in \u001b[0;36mplot\u001b[0;34m(scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 3582\u001b[0m \u001b[38;5;129m@_copy_docstring_and_deprecators\u001b[39m(Axes\u001b[38;5;241m.\u001b[39mplot)\n\u001b[1;32m 3583\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mplot\u001b[39m(\n\u001b[1;32m 3584\u001b[0m \u001b[38;5;241m*\u001b[39margs: \u001b[38;5;28mfloat\u001b[39m \u001b[38;5;241m|\u001b[39m ArrayLike \u001b[38;5;241m|\u001b[39m \u001b[38;5;28mstr\u001b[39m,\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 3588\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs,\n\u001b[1;32m 3589\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m \u001b[38;5;28mlist\u001b[39m[Line2D]:\n\u001b[0;32m-> 3590\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mgca\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mplot\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 3591\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3592\u001b[0m \u001b[43m \u001b[49m\u001b[43mscalex\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscalex\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3593\u001b[0m \u001b[43m \u001b[49m\u001b[43mscaley\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mscaley\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3594\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43m(\u001b[49m\u001b[43m{\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mdata\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m:\u001b[49m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m}\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mif\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43mdata\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mis\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;129;43;01mnot\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01mNone\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[38;5;28;43;01melse\u001b[39;49;00m\u001b[43m \u001b[49m\u001b[43m{\u001b[49m\u001b[43m}\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3595\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 3596\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/jaspy/lib/python3.11/site-packages/matplotlib/axes/_axes.py:1726\u001b[0m, in \u001b[0;36mAxes.plot\u001b[0;34m(self, scalex, scaley, data, *args, **kwargs)\u001b[0m\n\u001b[1;32m 1724\u001b[0m lines \u001b[38;5;241m=\u001b[39m [\u001b[38;5;241m*\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_get_lines(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;241m*\u001b[39margs, data\u001b[38;5;241m=\u001b[39mdata, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)]\n\u001b[1;32m 1725\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m line \u001b[38;5;129;01min\u001b[39;00m lines:\n\u001b[0;32m-> 1726\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43madd_line\u001b[49m\u001b[43m(\u001b[49m\u001b[43mline\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1727\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m scalex:\n\u001b[1;32m 1728\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_request_autoscale_view(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mx\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n", + "File \u001b[0;32m/opt/jaspy/lib/python3.11/site-packages/matplotlib/axes/_base.py:2309\u001b[0m, in \u001b[0;36m_AxesBase.add_line\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m 2306\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m line\u001b[38;5;241m.\u001b[39mget_clip_path() \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 2307\u001b[0m line\u001b[38;5;241m.\u001b[39mset_clip_path(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpatch)\n\u001b[0;32m-> 2309\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_update_line_limits\u001b[49m\u001b[43m(\u001b[49m\u001b[43mline\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2310\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m line\u001b[38;5;241m.\u001b[39mget_label():\n\u001b[1;32m 2311\u001b[0m line\u001b[38;5;241m.\u001b[39mset_label(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m_child\u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_children)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n", + "File \u001b[0;32m/opt/jaspy/lib/python3.11/site-packages/matplotlib/axes/_base.py:2332\u001b[0m, in \u001b[0;36m_AxesBase._update_line_limits\u001b[0;34m(self, line)\u001b[0m\n\u001b[1;32m 2328\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21m_update_line_limits\u001b[39m(\u001b[38;5;28mself\u001b[39m, line):\n\u001b[1;32m 2329\u001b[0m \u001b[38;5;250m \u001b[39m\u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m 2330\u001b[0m \u001b[38;5;124;03m Figures out the data limit of the given line, updating self.dataLim.\u001b[39;00m\n\u001b[1;32m 2331\u001b[0m \u001b[38;5;124;03m \"\"\"\u001b[39;00m\n\u001b[0;32m-> 2332\u001b[0m path \u001b[38;5;241m=\u001b[39m \u001b[43mline\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_path\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2333\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m path\u001b[38;5;241m.\u001b[39mvertices\u001b[38;5;241m.\u001b[39msize \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m 2334\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m\n", + "File \u001b[0;32m/opt/jaspy/lib/python3.11/site-packages/matplotlib/lines.py:1032\u001b[0m, in \u001b[0;36mLine2D.get_path\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m 1030\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\"Return the `~matplotlib.path.Path` associated with this line.\"\"\"\u001b[39;00m\n\u001b[1;32m 1031\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_invalidy \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_invalidx:\n\u001b[0;32m-> 1032\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mrecache\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 1033\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_path\n", + "File \u001b[0;32m/opt/jaspy/lib/python3.11/site-packages/matplotlib/lines.py:673\u001b[0m, in \u001b[0;36mLine2D.recache\u001b[0;34m(self, always)\u001b[0m\n\u001b[1;32m 671\u001b[0m x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_x\n\u001b[1;32m 672\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m always \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_invalidy:\n\u001b[0;32m--> 673\u001b[0m yconv \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconvert_yunits\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_yorig\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 674\u001b[0m y \u001b[38;5;241m=\u001b[39m _to_unmasked_float_array(yconv)\u001b[38;5;241m.\u001b[39mravel()\n\u001b[1;32m 675\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n", + "File \u001b[0;32m/opt/jaspy/lib/python3.11/site-packages/matplotlib/artist.py:291\u001b[0m, in \u001b[0;36mArtist.convert_yunits\u001b[0;34m(self, y)\u001b[0m\n\u001b[1;32m 289\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ax \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m \u001b[38;5;129;01mor\u001b[39;00m ax\u001b[38;5;241m.\u001b[39myaxis \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m 290\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m y\n\u001b[0;32m--> 291\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43max\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43myaxis\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconvert_units\u001b[49m\u001b[43m(\u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n", + "File \u001b[0;32m/opt/jaspy/lib/python3.11/site-packages/matplotlib/axis.py:1771\u001b[0m, in \u001b[0;36mAxis.convert_units\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m 1769\u001b[0m ret \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mconverter\u001b[38;5;241m.\u001b[39mconvert(x, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39munits, \u001b[38;5;28mself\u001b[39m)\n\u001b[1;32m 1770\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:\n\u001b[0;32m-> 1771\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m munits\u001b[38;5;241m.\u001b[39mConversionError(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mFailed to convert value(s) to axis \u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m 1772\u001b[0m \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124munits: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mx\u001b[38;5;132;01m!r}\u001b[39;00m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[1;32m 1773\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m ret\n", + "\u001b[0;31mConversionError\u001b[0m: Failed to convert value(s) to axis units: array(['a', 'b', 'c', 'd'], dtype=object)" + ] + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "# There is an error because it doesn't know how to convert the data\n", + "fig, ax = plt.subplots()\n", + "plt.plot(df)\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "id": "8d0a5420-bd47-4771-aebb-125ab192f458", + "metadata": {}, + "source": [ + "Q4. We need to extract only the numeric values to plot. Let's extract them as a numpy array and try again. Use `np.asarray(df[['A', 'B']])` to create a numpy array from the numeric data. Try plotting it now." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "58bf6119-5e1a-45af-8e3a-03ed80d381a5", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:49.611989Z", + "iopub.status.busy": "2024-11-08T14:55:49.611712Z", + "iopub.status.idle": "2024-11-08T14:55:49.772084Z", + "shell.execute_reply": "2024-11-08T14:55:49.771535Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[,\n", + " ]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "df_array = np.asarray(df[['A', 'B']])\n", + "plt.plot(df_array)" + ] + }, + { + "cell_type": "markdown", + "id": "43bcbb06-d568-4091-a7a3-1e6ab4db0358", + "metadata": {}, + "source": [ + "Q5. Let's unpack the example given in the tutorial of using matplotlib with string-indexable objects. Instead of passing numpy arrays directly, we'll pass the names of the variables as strings.\n", + "\n", + "\n", + "Run the following cell to create a dictionary where `a` is a numpy array of integers from 0 to 49, `c` is random integers between 0 and 50 we can use as the colour, and `d` is the absolute value of randomly generated numbers which we will use as the size of each scatter point. Then `b` is set to a noisy version of `a`. Finally, `d` is scaled to be larger." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "734cb070-a7d1-425a-ba47-cd3d64bc8882", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:49.774679Z", + "iopub.status.busy": "2024-11-08T14:55:49.774396Z", + "iopub.status.idle": "2024-11-08T14:55:49.778904Z", + "shell.execute_reply": "2024-11-08T14:55:49.778398Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# Step 1: Create the dictionary\n", + "data = {'a': np.arange(50),\n", + " 'c': np.random.randint(0, 50, 50),\n", + " 'd': np.random.randn(50)}\n", + "# Step 2: Add b - the noisy version of a, and scale d to be bigger\n", + "data['b'] = data['a'] + 10 * np.random.randn(50)\n", + "data['d'] = np.abs(data['d']) * 100" + ] + }, + { + "cell_type": "markdown", + "id": "c6e0aaa3-a75b-4493-8828-67ea004480a2", + "metadata": {}, + "source": [ + "Now we can plot the scatter plot using the syntax `ax.scatter(xvalues, yvalues, c=colours, s=scatter_point_size, data=data)`. Hint: the x and y data is 'a' and 'b' and you should know what the colour and size is." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "e0bb152c-7a3e-4cfa-9dce-49e19ed62d81", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:49.781816Z", + "iopub.status.busy": "2024-11-08T14:55:49.781290Z", + "iopub.status.idle": "2024-11-08T14:55:49.935648Z", + "shell.execute_reply": "2024-11-08T14:55:49.935110Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Step 3:\n", + "fig, ax = plt.subplots()\n", + "ax.scatter('a', 'b', c='c', s='d', data=data)" + ] + }, + { + "cell_type": "markdown", + "id": "b057c40a-511b-43ba-840a-bae423a697e9", + "metadata": {}, + "source": [ + "## Coding styles" + ] + }, + { + "cell_type": "markdown", + "id": "ce3d4439-773f-4a61-8556-9967942f7300", + "metadata": {}, + "source": [ + "Q6. So far, we've been creating plots in the object oriented way: explicitly creating figures and axes. The pyplot-style is very subtly different - we just don't need to create the axis or subplots.\n", + "- Create x axis data using `np.linspace(min, max, num)`. Create 10 values between 0 and 10.\n", + "- Create y axis data using `np.linspace` to create 10 values between 0 and 100.\n", + "- Plot this data on the implicit axes using `plt.plot()` - don't worry about seeting the figsize and layout." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "3467e2c1-ab23-43ac-9bf0-3b34852d87f5", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:49.938331Z", + "iopub.status.busy": "2024-11-08T14:55:49.938015Z", + "iopub.status.idle": "2024-11-08T14:55:50.089870Z", + "shell.execute_reply": "2024-11-08T14:55:50.089123Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "x = np.linspace(0, 10, 10)\n", + "y = np.linspace(0, 100, 10)\n", + "plt.plot(x, y)" + ] + }, + { + "cell_type": "markdown", + "id": "8f6ba121-ea31-441c-8ac6-83ad85605684", + "metadata": {}, + "source": [ + "## Styling" + ] + }, + { + "cell_type": "markdown", + "id": "1d18ed69-464a-4f6f-af12-4b43de5fb010", + "metadata": {}, + "source": [ + "Q7. Let's use the x and y values from before and create some new y values to practice styling plots.\n", + "- Create `y2 = np.linspace(0, -100, 10)`.\n", + "- Plot both of these sets of data on the same axes using the [Styling Artists example](https://matplotlib.org/stable/users/explain/quick_start.html#styling-artists).\n", + "- Plot the original y data in purple with the `--` linestyle and the new y data in green with the `:` linestyle.\n", + "- What other values can you give for linestyle? Try editing them to be any character you want and see what happens! Can you make the lines thicker?" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "30c1faed-af70-4784-9c82-0c6758d2083a", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:50.092794Z", + "iopub.status.busy": "2024-11-08T14:55:50.092463Z", + "iopub.status.idle": "2024-11-08T14:55:50.245875Z", + "shell.execute_reply": "2024-11-08T14:55:50.245318Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "y2 = np.linspace(0, -100, 10)\n", + "fig, ax = plt.subplots()\n", + "ax.plot(x, y, color='purple', linestyle='--', linewidth='3')\n", + "ax.plot(x, y2, color='green', linestyle=':', linewidth='3')\n", + "# If you try plotting with the linestyle 'x' you will get an error saying 'x' is not a valid value for ls; supported values are '-', '--', '-.', ':', 'None', ' ', '', 'solid', 'dashed', 'dashdot', 'dotted'" + ] + }, + { + "cell_type": "markdown", + "id": "d3bba7b2-208b-4927-a246-b1ff18cadbf9", + "metadata": {}, + "source": [ + "Q8. There are lots of different customisation options in matplotlib for colour! You can even have different colours for the markers and outlines in a scatter plot. Use the following to generate some data for a scatter plot:\n", + "```\n", + "data1, data2 = np.random.randn(2,100)\n", + "```\n", + "Plot this data as an `ax.scatter` plot, using a magenta outline with a green marker. Hint: you'll need to visit the [allowable color definitions](https://matplotlib.org/stable/users/explain/colors/colors.html#colors-def) page to see how colours are defined." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "0746263b-80fb-4e68-a215-34872daff4f8", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:50.248847Z", + "iopub.status.busy": "2024-11-08T14:55:50.248580Z", + "iopub.status.idle": "2024-11-08T14:55:50.392506Z", + "shell.execute_reply": "2024-11-08T14:55:50.392014Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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2228PfX/8+HG9/vrrmjRpkmbMmGHnQwPwALdNeNmyc5sl1xOVGyc+r5Q7X8orKz22BiKvvPKKFi9ePPR9PP/j7rvv1lNPPWXnQwNwOTdOeGNhR4OvXE9Ubp34vFru7JWVHlvLdxctWiTLsi77IggBMJJLJ7zWqladLz4/NOG1zG5R24Y2z5VexrdZptw5RRMXTRzzZJ5NaXA+/7988Wq5s1dWeozqIwIAknsnPNPkeqJy68QXz8OpO1qnUHNItR21KukvUW1HrULNoQt5OI+7r9x5+EpPMm5Z6SFZFYBx3DrhmSbXWxJu3uLwYrnzWCquTMKKCADjeOVKz2m53pJw+xZH5fJKNbzToPn752vurrmav3++Fh5b6MogRPLOSg8rIgCM45UrPaflugOrFzq6eu2gPS+s9AQsyzI226uvr09lZWXq7e1VaWmp08MBkEdde7p0+EuH9WbVm/rFtb9Q27Q2nSs+p1Uvr1Ld0TrPVc3YKVkZdNHMItU8nt1Elev/D2NnWr+dTOZvAhEARura06U3170pq+viR1S0IKrxV4/X3B/MZcLLkJ87qyL/Mpm/2ZoBYJyEHiJfTOwhUne0zunhuVKutyS8tsUB57AiAsAoVsxSZFZE4ZJwQtMs6UJ+SKg5pOD5oBYeW8gVOGCoTOZvqmYAGIUeIvazYpa6D3Tr9NOn1X2g23ON4eAubM0AMAo9ROzll/N74B4EIgCM4uamWabz2/k9cAe2ZgAYxe1Ns0zl1/N7YD4CEQBGGUu3SHIfUiP3BqZiawaAcbLpFknuw8jIvYGpCEQAGKlyeaUqllWk1TSL3IfRkXvjX6Y3nyMQAWCcSz84J395csoPzktzH+LbDvHch1BzSOM2jFPFsgqjPnzzjfN7/MkNK4UEIgCMkukH51Duw9rUuQ/1O+rVE+7xdSdQLxxYh8y4ZaWQQASAMbL54CT3IX1eOKkV6XHTSiGBCAAjZPvBSe5DZjLJvYF7uWmlkPJdAEbItryUviOZix9YN+XOKZq4aCJBiAe5aaWQQASAEbL94BxL3xHAq4avFCZj0kohWzMAjDCWLZax5j6YXt7oZ/xtsuOmKikCEQBGGOsHZ7a5D24ob/Qr/jbZc1OVVMCyLGN7IPf19amsrEy9vb0qLS11ejgAbBavmmmZ3ZL0gzPX5YYJVToNiVU6djzecFzpj8zJv42XJAvmimYWqeZxe6ukMpm/CUQAGCVfH5xWzFJkVkThknBClY50YQUm1BxS8HxQC48tzHmA4Lcr/UyDLif/Nl7kRNCbyfzN1gwAo+SrvNSp8ka3NJnKlWyCLjeVnrpBvErKVAQiAIyTjw9OJ8ob3dRkKheyDbrcVHqKsaN8F4AvOVHemG2vFDe6NOhqrWrV+eLzQ0FXy+wWtW1okxW7PDvATaWnGDsCEQC+5EQjND9d6Y8l6KJJnb8QiADwJScaofnpSn8sQRdN6vyFHBEAvpXvQ+BG65Wy6qVVio6L6pOuT3L6uE4Y6xlAHNDnHwQigIvRi2Ls8nkIXEKTqWdCagpeTOC8K3yX6t6q01vT3lLhykIFCgNjnmydfH3korMnB/T5A31EAJfyWy8KLzmz+4zeuOsNFX5SOHRbZ3mnfrDkB4rMieSkT4YJr498N6iDOegjAnic33pReM24inEq/KRQ2/7rNn1c8rE+uOoDvTHzDQ0WDEpSQp+M8mB5Vm3rTXh9sL2CdLAiArgMXSfd7/TTp9V6V6uWblyq88XnL/t5SX+Jnt/6vD7z4Gf0/p73M1rVMPH1wRai/2Qyf1M1A7iMn3pReFW61TMn/+GkwiVhNa5t1NKNS9W4tlGRkogOrzisrj1dSf+tia+PeIO6KXdO0cRFEwlCkIBABHAZP/Wi8KpR+2SEVys2LpZVMzBeH3AbAhHAZfzUi8Kr0umTUfhJYVarGrw+4DYEIoDL0HXSG+KJnA2/bdD2Hdv1/NbntX3HdgXPB1X1YJWkzFY1rJil7gPd6n+vXwVTCnh9wDWomgFcJqEXRXMoaVlkzW73d530Q4Jjqj4ZPeEenfxfJ9NuBpasVLfudN1lvUq89PqAd1A1A7hUssmnaGaRah53f1mkCT0wnBSvfImURJI2Axte+XL2ubMXS3WHBaXf+I9v6Nr3r1XR4MXrTa+8PmC+TOZvAhEgj3J9le/FVYOEHhgNiT0w/NQEK51mYBXLKkYu1X0mpIW9C1Xz9zUa/xkzXx9efA2DQAQwkt+v8tNhYg8MJ4226tV9oFuHFh9S49rGpFs4tR212r5ju+bvn6+Jiybmc+hp4T3hXXRWBQxjSqdL0w31wFibulok3nHUxIk110Y7a8XNpbq8JxBH1QxgMytm6cj6I1n1hPAbN0+sTnBrqS7vCQxHIALYzMROl6Zy68Rql649XYrMiujQ4kNqvatVhxYfUmRWZKirqqml3PFS4tNPn1b3ge7LAgreExiOrRnAZlzlpy8XR8d7RbpbF3O2zVH0S1E9sfMJ/eLaX6htWpvOFZ/TqpdXOVKqm07eB+8JDEcgAths+FV+Oj0h/Mz0Hin5qvC4dOsiHpDFty5CzSGN2zBOFcsqJEkFlQW6oeMG3dBxgyQpWhDV+KvHa+7uuXnNs0g3eOI9geEIRACbcZWfGVOPjs9nhUe6Sbvvht7Vu1vevTDxf/HyUud8yiR44j2B4QhEAJuZfpVvotGqRfIt3xUe6W5dtD/RntbEn4/nLdOKJ94TiCMQAfLA1Kt8k8WPjndaJlf6uZo40926sM5aalpmRqlzpnkfvCcQRyAC5IlpV/lIjxO9TdLZuiioKNDg2UFjEj6zyfvgPQGJ8l0gr+JX+VPunKKJiybygesCTlR4xLfz6o7WKdQcUm1HrUr6S1TbUatQc0h1R+s044EZkswpdc62lJj3BFgRAYAROFXhMdrWRcWyCp343gmtDq/W5jsub4ef74TPQGFANX9fo+iXo5z6i4wQiADACJys8Bhp66JrT5cGuwZV31WvR595VLuCu4Ym/lUvrVLd2/md+Lv2dKntf7QpYAV089s3q/4oeR9ID4feAcAo0jkJN5+T7PDDAf/PTf9H6/at09SeqUM//8343+iqsqsUPBXMSyBy6YnJJypO6I/e+CP9yWt/outPXa8bnr1Bk2+fbPs4YA5O3wWAHBvtJNx8uvTU3YLBAt144kZd/fHV+uCqDxQtiOp7O7+Xl1N3OTEZyXD6LgDkmEkVHpcm0A4WDOpQ9aGhn5f0lyTcz06cmIyxIhABgDSZ0tvEpBbpnBuDscpL+e73v/99VVdX64orrtCCBQsUDofz8bAA4EkmnbrLickYK9sDkebmZj344IPatGmTXnvtNQWDQS1dulTt7e12PzSAHBvteHfkRzp9Rmoez0/FjElBETJjyvvZ9mTVW265RZ/97Gf15JNPDt02d+5c3Xbbbdq6deuI/5ZkVcAc+Tz0DekxJYHWjqqifJ107Fd2v5+NSVYdGBjQq6++qm9+85sJty9ZskQvv/zyZffv7+9Xf3//0Pd9fX12Dg9AmvJ96BvSY0oCba7PjSHotZdp72dbA5GzZ88qFotpypQpCbdPmTJF77///mX337p1qx555BE7hwQgDQlXo5PHq/XBVmNOeUUiUxJoMw2KUq14mDZJeo0ThziOJi9VM4FA4i9jWdZlt0nSxo0btWHDhqHv+/r6VFVVZfv4AFyU7Go0WhDV4cWHbS/PZDne3dINilKteNT8fY3a/kebUZOk15hYbm1rIFJRUaHCwsLLVj/OnDlz2SqJJBUXF6u4uNjOIQEYQaqr0dUvrda9//denbz6pMK1iVVvuSrPZDneH0Zc8fhyVAErYNQk6TUmllvbWjUzfvx4LViwQPv27Uu4fd++fbr11lvtfGgAGbp0yba1qlXni8+rtapVm+/crIOzD2rdC+tUMJj4sZGL8sz45BQuCatxbaOWblyqxrWNipREdHjFYXXt6RrT7wYzDA4MqrWxVQevv/w1tmnlJrVc36JoQVQnKk4k/ff0JBk7E8utbS/f3bBhg/7pn/5J//zP/6zW1latX79e7e3tWrdund0PDSADQ0u2DcmvRncFd2lazzTdeOLGodtzUZ45UgC0aeUmtcxuUduGNkqFx8CEMs2uPV2KVEU0eHpQTcEUKx7BJhUNFumP3vijpP8HPUnGzsRya9tzRFauXKkPPvhAf/3Xf61Tp05p3rx5ev755zVz5ky7HxpABtJdsr3pxE06Ou1ozo53N3HP2ktM2PKKr3i99em3NFuzR32N/clrf6Kffe5neT3p2C/iPWiiK6IKNYeSllvn89RmKU/Jqo2NjWpsbMzHQwHIUrptw79y4Cv6yoGvSMrN8e6Z7FmTzJoZEypQhq94/fiWH2vb/9426mvs+lPXGzNJelGuy63HirNmAEhKXLLdtHJT0qvRwpmFumHHDfrkzCc5CwTSDYB+89ZvFJkVIZk1TaaUaQ5f8Wr7TJtOlZ/SqvAqffuOb6dc8Zj9ndkq+p9mTJJeZUoPGolABMDvpLNkO2f3HE36L5Ny+rjpBEAFlQV6d8u79JbIgClbXsNXvAYLBvXkkie15dktevSZR7UruOviayy8WnVv1Q0FG5XLK42YJL3MlB40BCIAhjixZDtqANRWJ6vScvzK3kQjbVWZUqZ56YpXuDasLV/eovteuE/bd2wful/Bpws0d/fcodeYKZMk7EcgAiCBE0u2IwVA0x+ZrncffldNXySZdbjRklDT3fKyuwIl2YpXuDasn8/5uW569ybdt+8+zRmco4YTDSoYn5cD4WEYAhEAl3HiajRVAHTm2TOSnL+yN0k6SagVyypG3fLKRwXKSCteX/7Fl3X9+9dr7u65BCE+RiACeIBXqkmSBUCmXNmbIpMkVFPKNE2r0vDK+8UrCEQAlzOhT4Sd0klm9VNviUySUE0KAEyp0vD6+8WNCEQAFzOhT4TdTGrAZMKVdKZJqKYEAJLzCah+eL+4EYEI4FKm9InIBxOu7E25ks5mq8rpAMAEfnq/uA3ZQYBLjXY2TFNDk6InouoJ9zgzwByrXF6phncaNH//fM3dNVfz98/XwmML8xaEmHIon4lnhbiB394vbsKKCOBSpvSJyCcnruxNu5I2aavKTfz4fnELXwYiJuzzAmNFNUl+mNKhdDgTtqrchveLuXwXiJiyzwuMFdUkY5fORYmpV9ImJaG6Ae8Xc/kqECFjGl7CEv3YpHtRYvKV9GhbVX5d/U31e/N+MVPAsixr9Ls5o6+vT2VlZert7VVpaemY/i8rZikyK6JwSThhn1e6EA2HmkMKng9q4bGFvBDhKskm1KKZRap5nCX6VBIuShoSL0rqjtYlXJTEPzsiJZGkV9Kmfnb4dfV3tN+b90t+ZDJ/+yYQ6T7QrUOLD6lxbWPSq5rajlpt37Fd8/fP932ZG9zHr1e+2cjmoiQeuLTMbkl6JT3SaqoTf5tMAi0vSff35v1iv0zmb99szZi6zwvkAn0i0pdN8mm2yaFOrErYWeVj8gSe6e/N+8UcvglETN7nBdzA5EkoE9lelGSaHOpUTppdVT6mb/WYWN2E9PgmECFjGsie6ZNQJsZyUZLulbSTvUfsWP11Q6I/q97u5ZvOqvGM6bqjdQo1h1TbUauS/hLVdtQq1By6kDH9OBnTwKVM6iqaC/noTOpkF8/hgVYyma7+XhpUtVa16nzx+aGgqmV2i9o2tMmKOZtumOvfG/njm0BEurjP2/DbBm3fsV3Pb31e23dsV/B80IiIHjCNWyahTOTiosSKWeo+0K3TT59W94Huy35/J6/Oy24tU2xcTKvDq5MGWqvCqxQbF1PZrWVp/X/pBlUnnziZ8vnIB1rfu5dvtmbiaAIEpM+r++5j6UyazjaVkzlpvS/3qvCTQtUdrdOjzzyqXcFdQ1spd4XvUt3ROhWoQL0v96b1NxstqJraPVXRgqiOrT82dJsT23b0CXEv3wUiEhUGQLq8vO+ezUVJurkSTuakxf8WoeUh3fv/7tX2HduHftZZ3qm/Xf632rxnc9p/s5GCquCbQX1rz7culDUHnc8dofW9O/kyEAGQHq9Xm2VyUZJpAqpTV+fxv8Wpiae0+uurdeOJG3X1x1frg6s+0Bsz39Cc9+Yk3G80qYKqgsEC3ffCfWqZ3aLNdzh/GGAcq97uQyACICWqzS7KdJsq06vzXJVHX/o3O1R9aOhn2fzNUgVVSw4t0dSeqXr0S48at23npVVvr5TNj4RABEBK7LtflM02VbpX57ksj7bjb5YqqBr+e1/qRMWJC7/bv12oqvLiBGo3L5XNj4RABEBKVsxS0aQiVX2jSoW7Cn29757tNtVoV+d29OiwI1fi0qBq4PSAjq0/ljJ35Ov/8XVJUuf3OtX5vU5PTqB2ckPvllzxzVkzADKT7GqsYEqBpt45VRXLKnx3hWvH4XdD595cEU7Isxj6P58JKdif/YF6di7rp3o+gm8GteXZLUkTWL18zk0ueeGQ1kzmb1/1EQGQnlRNzH5e/nOd/IeTin4YNfYD0C52NEUcyjsJpsg7CY6t8Vl8NWbKnVM0cdHEnP7Nkj0fn/rtp/T1//j6UAKrF/rOOMHJhnhOYGsGQAIn25ObLtdbHgPvpZl38p6Z5dGpno+HvvyQcQmsbuLlsvlkWBEBkMBvV2OZqlxeqYZ3GjR//3zN3TVX8/fP18JjC7PabhjoujCRjNaWPH4/Ew1/PqZ9bZok/0ygdvFbu3oCEQAJ/HY1lo1cbXmMqxynaEFUq8KrkrYlvyt8l6IFUY2rHJeLYdsm/nxUfulCMOaXCdQufmtXTyACIIHfrsacVPyZYhUNFqn+aL0efebRhLyTR595VPVH61U0WKTizxQ7PdS0+G0CtYvfDmmlagZAAjuqQ5Bc/Ll+M/qmSn9Tqqk9U4d+1lneqY+u/Eg3jLsh5XNtYrOreKJzy+yWpD1MqJpJX7LKtaKZRap53Pyy+UzmbwIRAJdhMsmfoef6+hb94rpfqL+oX8XRYt3y9i2qeyv1c21ysys3T6CmMTHYTAeBCIAxYzLJn0yf64RmVw1m9upw6wSK3CAQAZATTCb5k+5z7YVmV/C+TOZv+ogASMlLh4eZLt3nOtPD9wDTEYgAgIvksrw6kxUvVsdgFwIRAHCRbA/fu1Qmya4mJ8YiOyYFlgQiAOAiw3t1JCuvTqdXRyYnu/rpFFi/MC2wJFkVAFxmLOXVmSS7SiIx1mPyVXFFsioAeNhYDt/LJNlVEomxHmLqgZYEIgDgQpXLK1WxrCLjff5skl05d8gbTK24IhABAJfKprw6m2TXsSbGwgymHmjJoXcA4COZHEzHIXbeYuqBlgQiAOAjmZzs6rdTYL3O1MCSqhkA8KFMzrfh3CHvyNeBlpw1AwAYFZ1V/SkfgSWBCAAASMnuwJI+IgAAICWTDrQkWRUAADiGQAQAADiGrRkArkPiJOAdBCIAXMW0k0MBjA2BCADjxVdAPnjuA3X8rw6OpAc8hEAEgNEuXQEJKKDqM9Wq+KhCrVWtjp8civSwnYZUSFYFYKx4F8hwSViNaxu1dONSNa5t1DuT39GWZ7co+GZQ0sWTQ6MnokPH18McXXu6FJkV0aHFh9R6V6sOLT6kyKyIuvZ0OT00GMDWQCQUCunWW2/VlVdeqfLycjsfCnAFK2ap+0C3Tj99Wt0HumXFjO0n6DgrZunI+iM6OPugNq/crNaqVp0vPq/WqlZ9+45v6+Dsg1r3wjoVDF74GONIejOlCiYjJREdXnGYYAT2BiIDAwO6/fbbdd9999n5MIArcFWYmZ5wj2LtMTU1NMkqSAzYrAJLu4K7NK1nmm48caMkjqQ30UjB5KaVm9Qyu0VtG9oIyH3O1hyRRx55RJL01FNP2fkwgPHiV4UkWaYvvrIRX+m4VPz2qz++miPpDTUUTK5NHkw2NTSpfke9esI9xnT5RP4ZlSPS39+vvr6+hC/A7bgqzE58ZSO+0nGp+O0TfjuBI+kNlW4wyXaavxkViGzdulVlZWVDX1VVVU4PCRiz0bYYSLJMrjxYrsIZhVoTWaPAYGJwERgMaNVLqxQtiOrB5x9U8HzQqFUlcoEuSDeYZDvN3zIORLZs2aJAIDDi1yuvvJLVYDZu3Kje3t6hr46Ojqz+H8AkXBVmJ1AY0Jxtc1R3tE6h5pBqO2pV0l+i2o5ahZpDqn+7XjO/PlPz98/XwmMLjQlCyAW6aLRgku00SFnkiHzta1/THXfcMeJ9rrnmmqwGU1xcrOLi4qz+LWCq4VeFrVWtl/2cq8LUKpdXat7ueSpaX6T6HfVDtxfNLFLN7hpjgo84coESxYPJ6IqoQs0hNTVcfE5WR1Zf2E7bzXaa32UciFRUVKiiosKOsQCeNPyqcNPKTQnbM365KhxLM6vK5ZWqWFZhfDOsS3OB4n9nOxquuak5mNuCSeSfrVUz7e3t+vDDD9Xe3q5YLKbXX39dknTdddfpqquusvOhAWP4/aowF2fDBAoDxldV5KtCxI1n7bglmIQzbA1EHnroIf3oRz8a+v73f//3JUn79+/XokWL7HxowCh+vSrM5VaF6asA+cgFcvPWjxuCSTgjYFmWsencfX19KisrU29vr0pLS50eDjBmpk+muWTFLEVmRRQuCSdsVUgXtqRCzSEFzwe18NjCUZ8DN6wCdB/o1qHFh9S4tjFpLlBtR62279iu+fvnZzUh5/L5BOyWyfxtVPku4HXxq8Ipd07RxEUTPT1h5Kps2S0twu2uEKEMHF5FIALAFrnYqnBTM7jRyo3H2nCNMnB4la05IgD8Kxdly25rEW5nLpDpZeB+2nZEbhGIALBFLsqW3bgKYFeFiMll4G7I4YG5CEQA2CIXZcumrwKkYkeFiKll4G6u5IEZqJoBYBsrZulE6IROfPeErA8uftQUzSxSzeOjb1XEK0UiJZGkqwB+rBRJtvqQ7vOZa1TyIJVM5m9WRADYItmEGagIaMYDM3TNpmvSmphMXQVwkknNwdyWwwMzEYgAGJNkSYpnnzubcrl+cMugrpp3VdpX735tBjcSU5qDuTGHB+YhEAGQtWSrHgVVBbLOWzk9c8WkVQBc5NYcHpiFPiIAspKq0dgbekNWl5Xzxlt+agbnFnY3cYM/sCICIGMjnTS79+a9uqHjhrSX6+k/4V7k8CAXCEQAZGykJMWzE85KSm+5nv4T7kcOD8aKQARAxkZKUnxj5ht6v+x9rX5ptTbfeXlJZ3y5/pOuT/Trlb+m/4QHkMODsSBHBEDGhicpXmqwYFA//dxPVf9WfcozV2Y/Nltt/7PNFWfIID3k8CBbrIgAyNho7cZvPHmjCisL1fDbhqTL9UWTitLqP3HyiZMaP2U8V9gGI8cHY0UgAiBj6SQpzt09N+Vy/emnT0tK3X9iavdURQuiOrb+2NBt5I6Yhxwf5AKBCICspJukmKzx1kj9J4JvBvWtPd9Sy+wWNQXJHTEVZ8wgVzhrBvCZXC+lZ/P/pTpDpmCwQE3fvTCpbb6Ds0tMxRkzGE0m8zfJqsiIFbPUfaBbp58+re4D3SQTukzXni5FZkV0aPEhtd7VqkOLDykyK6KuPV1Z/5/ZJCnGt3bqjtYlJLR+4ZUvaGrPVDUFc9sMTeK1m0tD5ds5bloHf2JrBmljP9jdTFtKT7W1I+X+7BJeu7nFGTPIJQIRpMW0SQyZGakTarbnv+TCpf0nBk4P6Nj6Yzk9u4TXbu5xxgxyia0ZjOrSSYyeD+5j8lL68K2d6Q9Mz+nZJbx27cEZM8glAhGMyuRJDOlxy1J6qtyR4c3Qah5P/+wSXrv2yPXfCf7G1gxG5ZZJDKm5aSk9l2eX8Nodu1RVUZwxg1whEMGo3DSJIbnROqGatpSeq7NLeO2OzWhJvpwxg1wgEMGo3DaJ4XJuPK49njsyFrx2s5dukm8u/k7wNxqaIS3xD6WW2S1JJzEqD9wh2RVu0cwi1Tzu3aV0XruZo2GZs7xwfk8m8zeBCNLmx0nMi7zwIZcpXruZ6T7QrUOLD6lxbWPSLa3ajlpt37Fd8/fPZzUkx7zS8yaT+ZutGaSN/WBv8ONSOq/dzJDk6wy/9rwhEEFG/DiJwRt47aaPJN/8M7XpYD7QRwQAkICGZfnn5543rIgAw/gxfwK4lBurrNzOz9thBCLA73glSQzIBRqW5Zeft8MIRAD5N0kMGAlJvvnj5543lO/C9+iZAMAEXup5Q/kukIGhJLG1qZPE6nfUqyfcQ9UFANv4dTuMQAS+5+ckMQBm8eN2GIEIfM/PSWIAzOO3njf0EYHv0TMBAJzDigh8j54JAOAcAhFA/ksSo3EbAFMQiAC/45ckMRq3pUaABuQfgQgwjNeTxGjclhoBGuAMGpoBPkHjttQSArSGxADNbY2kABNkMn9TNQP4hJ9P9xzJpcevt1a16nzx+aHj11tmt6htQ5usmLHXbICrEYgAPkHjtuQI0MxlxSx1H+jW6adPq/tAN8GgR5EjAvgEjduSI0AzEzk7/sGKCOATNG5LbniAloxfAzQnxXN2wiVhNa5t1NKNS9W4tlGRkogOrzisrj1dTg8ROUQgAvhEvHFb3dE6hZpDqu2oVUl/iWo7ahVqDl1o3Pa4/xq3EaCZhZwd/2FrBvARvzVuSweddc3Cadj+QyAC+IxfGrdlggDNHOTs+A+BCOBDXm/clg0CNDOQVO0/5IgAwO/EA7Qpd07RxEUTCUIcQM6O/7AiAgAwBjk7/kMgAgAwCjk7/kIgAgAwDjk7/kEgAgAwEknV/kCyKgAAcAyBCAAAcIxtgci7776rtWvXqrq6WiUlJbr22mv18MMPa2CAJjQAAOAC23JEjhw5osHBQf3whz/Uddddp8OHD+urX/2qzp07p8cee8yuhwUAAC4SsCwrbycHfec739GTTz6pd955J6379/X1qaysTL29vSotLbV5dAAAIBcymb/zWjXT29urSZMmpfx5f3+/+vv7h77v6+vLx7AAAIBD8paseuzYMT3xxBNat25dyvts3bpVZWVlQ19VVVX5Gh4AAHBAxoHIli1bFAgERvx65ZVXEv5NZ2enPv/5z+v222/Xvffem/L/3rhxo3p7e4e+Ojo6Mv+NAACAa2ScI3L27FmdPXt2xPtcc801uuKKKyRdCEIWL16sW265RU899ZQKCtKPfcgRAQDAfWzNEamoqFBFRUVa933vvfe0ePFiLViwQDt37swoCAEAAN5nW7JqZ2enFi1apBkzZuixxx5TV1fX0M8+/elP2/WwAADARWwLRF544QW9/fbbevvttzV9+vSEn+WxYhgAABjMtr2Se+65R5ZlJf0CAACQOGsGAAA4iEAEAAA4hkAEAAA4Jq8t3gHAVFbMUk+4RwOnBjR+6niVB8sVKAw4PSzA8whEAPhe154uHVl/RLH22NBthTMKNWfbHFUur3RwZID3EYgA8LWuPV06vOKwDs4+qKa1TTo++biqz1RrTWSNoiuimrd7HsEIYKOMW7znEy3eAdjJilmKzIooXBLW5pWbZRVc/DgMDAYUag4peD6ohccWsk0DZCCT+ZtkVQC+1RPuUaw9pqaGpoQgRJKsAktNDU2KnoiqJ9zjzAABHyAQAeBbA6cGJEnHJx9P+vP47fH7Acg9AhEAvjV+6nhJUvWZ6qQ/j98evx+A3CMQAeBb5cFyFc4o1JrIGgUGE3NAAoMBrY6sVtHMIpUHy50ZIOADVM0A8K1AYUBzts1RdEVUoeaQmhouVs2sjqxW3dE61eyuIVEVsBGBCABfq1xeqXm756lofZHqd9QP3V40s0g1u2so3QVsRiACwPcql1eqYlkFnVUBBxCIAIAubNNMXDTR6WEAvkOyKgAAcAyBCAAAcAyBCAAAcAyBCAAAcAyBCAAAcAyBCAAAcAyBCAAAcAyBCAAAcAyBCAAAcIzRnVUty5Ik9fX1OTwSAACQrvi8HZ/HR2J0IPLRRx9JkqqqqhweCQAAyNRHH32ksrKyEe8TsNIJVxwyODiozs5OTZgwQYFA6sOn+vr6VFVVpY6ODpWWluZxhO7Dc5UZnq/08VxlhucrfTxXmTHh+bIsSx999JGmTZumgoKRs0CMXhEpKCjQ9OnT075/aWkpL9I08VxlhucrfTxXmeH5Sh/PVWacfr5GWwmJI1kVAAA4hkAEAAA4xhOBSHFxsR5++GEVFxc7PRTj8VxlhucrfTxXmeH5Sh/PVWbc9nwZnawKAAC8zRMrIgAAwJ0IRAAAgGMIRAAAgGMIRAAAgGM8G4j09/fr937v9xQIBPT66687PRxjffGLX9SMGTN0xRVXaOrUqVqzZo06OzudHpZx3n33Xa1du1bV1dUqKSnRtddeq4cfflgDAwNOD81YoVBIt956q6688kqVl5c7PRyjfP/731d1dbWuuOIKLViwQOFw2OkhGeull17SF77wBU2bNk2BQEA/+clPnB6SsbZu3aqbb75ZEyZM0OTJk3Xbbbepra3N6WGNyrOByF/+5V9q2rRpTg/DeIsXL9azzz6rtrY2/du//ZuOHTumFStWOD0s4xw5ckSDg4P64Q9/qF//+tfatm2bfvCDH+hb3/qW00Mz1sDAgG6//Xbdd999Tg/FKM3NzXrwwQe1adMmvfbaawoGg1q6dKna29udHpqRzp07p/nz5+t73/ue00Mx3osvvqj7779fLS0t2rdvn6LRqJYsWaJz5845PbSRWR70/PPPW3PmzLF+/etfW5Ks1157zekhucZzzz1nBQIBa2BgwOmhGO/v/u7vrOrqaqeHYbydO3daZWVlTg/DGH/wB39grVu3LuG2OXPmWN/85jcdGpF7SLL27t3r9DBc48yZM5Yk68UXX3R6KCPy3IrI6dOn9dWvflX/8i//oiuvvNLp4bjKhx9+qH/913/VrbfeqnHjxjk9HOP19vZq0qRJTg8DLjIwMKBXX31VS5YsSbh9yZIlevnllx0aFbyqt7dXkoz/nPJUIGJZlu655x6tW7dOn/vc55wejmv81V/9lT71qU/p6quvVnt7u5577jmnh2S8Y8eO6YknntC6deucHgpc5OzZs4rFYpoyZUrC7VOmTNH777/v0KjgRZZlacOGDWpoaNC8efOcHs6IXBGIbNmyRYFAYMSvV155RU888YT6+vq0ceNGp4fsqHSfr7i/+Iu/0GuvvaYXXnhBhYWF+rM/+zNZPmm4m+lzJUmdnZ36/Oc/r9tvv1333nuvQyN3RjbPFy4XCAQSvrcs67LbgLH42te+pl/96ld6+umnnR7KqFzR4v3s2bM6e/bsiPe55pprdMcdd+hnP/tZwhs6FoupsLBQq1at0o9+9CO7h2qEdJ+vK6644rLbT548qaqqKr388suqr6+3a4jGyPS56uzs1OLFi3XLLbfoqaeeUkGBK2L5nMnmtfXUU0/pwQcfVE9Pj82jM9/AwICuvPJK/fjHP9af/umfDt3+jW98Q6+//rpefPFFB0dnvkAgoL179+q2225zeihGe+CBB/STn/xEL730kqqrq50ezqiKnB5AOioqKlRRUTHq/b773e/qb/7mb4a+7+zs1B//8R+rublZt9xyi51DNEq6z1cy8bi0v78/l0MyVibP1XvvvafFixdrwYIF2rlzp++CEGlsry1I48eP14IFC7Rv376EQGTfvn1atmyZgyODF1iWpQceeEB79+7VgQMHXBGESC4JRNI1Y8aMhO+vuuoqSdK1116r6dOnOzEko/3yl7/UL3/5SzU0NGjixIl655139NBDD+naa6/1xWpIJjo7O7Vo0SLNmDFDjz32mLq6uoZ+9ulPf9rBkZmrvb1dH374odrb2xWLxYb6+Vx33XVD700/2rBhg9asWaPPfe5zqq+v1z/+4z+qvb2dfKMUPv74Y7399ttD3x8/flyvv/66Jk2adNlnvt/df//92rVrl5577jlNmDBhKO+orKxMJSUlDo9uBM4V7Njv+PHjlO+O4Fe/+pW1ePFia9KkSVZxcbF1zTXXWOvWrbNOnjzp9NCMs3PnTktS0i8kd/fddyd9vvbv3+/00By3fft2a+bMmdb48eOtz372s8aXVzpp//79SV9Hd999t9NDM06qz6idO3c6PbQRuSJHBAAAeJP/NrkBAIAxCEQAAIBjCEQAAIBjCEQAAIBjCEQAAIBjCEQAAIBjCEQAAIBjCEQAAIBjCEQAAIBjCEQAAIBjCEQAAIBjCEQAAIBj/j+d1xWPNMWNlwAAAABJRU5ErkJggg==", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "data1, data2 = np.random.randn(2, 100)\n", + "fig, ax = plt.subplots()\n", + "ax.scatter(data1, data2, facecolor='g', edgecolor='m')" + ] + }, + { + "cell_type": "markdown", + "id": "e1308dc3-fc3c-4a49-a762-3fdb0a54eff4", + "metadata": {}, + "source": [ + "Q9. Generate two more scatter plot datasets as we did above then plot all 4 on one graph. Give each dataset a label and a different marker style - e.g. stars (`*`), plus (`P`) or diamonds (`D`). You can see more options for markers [in the documentation](https://matplotlib.org/stable/gallery/lines_bars_and_markers/marker_reference.html)." + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "0658cc3b-c291-404a-b446-a388edfef3d4", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:50.394985Z", + "iopub.status.busy": "2024-11-08T14:55:50.394725Z", + "iopub.status.idle": "2024-11-08T14:55:50.547987Z", + "shell.execute_reply": "2024-11-08T14:55:50.547463Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.path as mpath\n", + "data3, data4 = np.random.randn(2, 100)\n", + "star = mpath.Path.unit_regular_star(6) # I think this shouldn't be here\n", + "fig, ax = plt.subplots()\n", + "ax.plot(data1, '*', label='data1')\n", + "ax.plot(data2, 'P', label='data2')\n", + "ax.plot(data3, 'D', label='data3')\n", + "ax.plot(data4, 'p', label='data4')" + ] + }, + { + "cell_type": "markdown", + "id": "007ed496-b9c6-4c28-84b3-fa72264bb9e4", + "metadata": {}, + "source": [ + "## Labelling" + ] + }, + { + "cell_type": "markdown", + "id": "a912305e-861a-428b-8223-9d245004baf7", + "metadata": {}, + "source": [ + "Q10. Take the plot we just created in the previous question and give it `xlabel`, `ylabel` and a `title` of your choice. Add some text to the plot saying `some text` at `50, 0`. Add an annotation at top saying `some annotation` with a black arrow pointing to some data using `xy=(40,2)` and `xytext=(3,1.5)`. Also add a legend identifying each data set." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "2de354bb-2ef6-4c1a-a7d4-ec408a0e444e", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:50.550870Z", + "iopub.status.busy": "2024-11-08T14:55:50.550603Z", + "iopub.status.idle": "2024-11-08T14:55:50.975569Z", + "shell.execute_reply": "2024-11-08T14:55:50.975074Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(3, 1.5, 'some annotation')" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.path as mpath\n", + "data3, data4 = np.random.randn(2, 100)\n", + "star = mpath.Path.unit_regular_star(6)\n", + "fig, ax = plt.subplots()\n", + "ax.set_xlabel('X data')\n", + "ax.set_ylabel('Y data')\n", + "ax.set_title('ISC scatter graph')\n", + "ax.text(50, 0, 'text')\n", + "ax.plot(data1, '*', label='data1')\n", + "ax.plot(data2, 'P', label='data2')\n", + "ax.plot(data3, 'D', label='data3')\n", + "ax.plot(data4, 'p', label='data4')\n", + "ax.legend()\n", + "ax.annotate('some annotation', xy=(40,2), xytext=(3,1.5), arrowprops=dict(facecolor='black', shrink=0.05))" + ] + }, + { + "cell_type": "markdown", + "id": "59e3abfd-3389-4ee9-87be-029af1fce401", + "metadata": {}, + "source": [ + "## Axes" + ] + }, + { + "cell_type": "markdown", + "id": "27c4fc57-9ca3-442b-a62b-469a5f7d73c5", + "metadata": {}, + "source": [ + "Q11. Let's practice plotting some log scale data.\n", + "- Create the xdata using `xdata = np.arange(5)`.\n", + "- Create the ydata using `ydata = np.array([0.1, 0.5, 1, 5, 10])`.\n", + "- Transform the y data by raising it to the power of 10 using `ydata = 10**ydata`.\n", + "- Plot two suplots - plot the x and y data in both plots. Set the y-axis of the second subplot to a logairthmic scale.\n", + "- Try experimenting with different datasets by changing the values in ydata. Try changing the base of the log scale - e.g. using `base=2`." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "8749ab24-0c45-4935-90e2-3df1186cf611", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:50.978562Z", + "iopub.status.busy": "2024-11-08T14:55:50.978294Z", + "iopub.status.idle": "2024-11-08T14:55:51.374280Z", + "shell.execute_reply": "2024-11-08T14:55:51.373658Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Logarithmic scale')" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "xdata = np.arange(5)\n", + "ydata = np.array([0.1, 0.5, 1, 5, 10])\n", + "ydata = 10**ydata\n", + "fig, axs = plt.subplots(1,2)\n", + "axs[0].plot(xdata, ydata)\n", + "axs[0].set_title('Linear Scale')\n", + "axs[1].set_yscale('log')\n", + "axs[1].plot(xdata, ydata)\n", + "axs[1].set_title('Logarithmic scale')" + ] + }, + { + "cell_type": "markdown", + "id": "447fc1f2-81e1-4034-bbcb-b00d57ab88b3", + "metadata": {}, + "source": [ + "Q12. To demonstrate the difference between automatic and manual ticks, let's create two subplots. Follow the following steps:\n", + "\n", + "- Create some data using `xdata = np.linspace(0, 99, 100)`, and `ydata = np.sin(xdata / 10)`\n", + "- Create a figure with 2 subplots arranged vertically.\n", + "- For the first subplot, plot the data and allow matplotlib to automatically place the ticks on the y and x axes\n", + "- For the second subplot, manually set the x-axis ticks at intervals of 30 using `np.arange(0,100,30)`, provide custom labels for these x-ticks using `('zero', '30', 'sixty', '90')` and manually set the yaxis ticks at `[-1.5, 0, 1.5]` without specifying labels so that default labels are used.\n", + "- Add titles to both subplots to distinguish between automatic and manual ticks." + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "886e54a6-54e6-47fd-bec2-74c1eded9274", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:51.376817Z", + "iopub.status.busy": "2024-11-08T14:55:51.376549Z", + "iopub.status.idle": "2024-11-08T14:55:51.759482Z", + "shell.execute_reply": "2024-11-08T14:55:51.758935Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Manual Ticks')" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Step 1: Create the x and y data\n", + "xdata = np.linspace(0, 99, 100)\n", + "ydata = np.sin(xdata / 10)\n", + "\n", + "# Create the figure arrangement\n", + "fig, axs = plt.subplots(2, 1, figsize=(8,6), layout='constrained')\n", + "\n", + "# Plot the first subplot with automatic ticks\n", + "axs[0].plot(xdata, ydata)\n", + "axs[0].set_title('Automatic Ticks')\n", + "\n", + "# Plot the second subplot with manual ticks\n", + "axs[1].plot(xdata, ydata)\n", + "axs[1].set_xticks(np.arange(0,100,30), ['zero', '30', 'sixty', '90'])\n", + "axs[1].set_yticks([-1.5, 0, 1.5])\n", + "axs[1].set_title('Manual Ticks')" + ] + }, + { + "cell_type": "markdown", + "id": "ecc8e915-c9ad-44a3-9964-2d047516a3bb", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q13. Let's see how matplotlib handles plotting dates. We'll create a time series plot using an array of dates and random cumulative data:\n", + "- Run the following cell to generate a numpy array of dates starting from `2022-01-01` to `2022-01-10` at intervals of 3 hours then create a cumulative sum of random numbers for the same length of the array of dates." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "0d9bfaa3-723b-4e77-a109-5754e231527c", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:51.762161Z", + "iopub.status.busy": "2024-11-08T14:55:51.761888Z", + "iopub.status.idle": "2024-11-08T14:55:51.765450Z", + "shell.execute_reply": "2024-11-08T14:55:51.764965Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "# Step 1: Generate the numpy dates\n", + "dates = np.arange(np.datetime64('2022-01-01'), np.datetime64('2022-01-10'), np.timedelta64(3, 'h'))\n", + "\n", + "# Step 2: Create the cumulative sum\n", + "data = np.cumsum(np.random.randn(len(dates)))" + ] + }, + { + "cell_type": "markdown", + "id": "19cd7611-93d3-4b18-813e-fd0ac144e115", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "- Plot the data with the dates on the x axis and `data` on the y axis.\n", + "- Take a look at the dates if we don't format the axis - do they look all bunched up?\n", + "- Format the x-axis with `ConciseDateFormatter` for better readability of the date ticks" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "b44349e4-73cd-450f-b61e-304fb727b80b", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:51.767596Z", + "iopub.status.busy": "2024-11-08T14:55:51.767361Z", + "iopub.status.idle": "2024-11-08T14:55:52.044766Z", + "shell.execute_reply": "2024-11-08T14:55:52.044268Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "from matplotlib.dates import ConciseDateFormatter\n", + "\n", + "# Step 3: Plot without formatting the dates\n", + "fig, ax = plt.subplots(figsize=(6, 3), layout='constrained')\n", + "ax.plot(dates, data)\n", + "\n", + "# Step 4: Plot with the date formatter\n", + "ax.xaxis.set_major_formatter(ConciseDateFormatter(ax.xaxis.get_major_locator()))" + ] + }, + { + "cell_type": "markdown", + "id": "0f022a40-f65d-4649-a63e-bcdbd4cdf4a1", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q14. Let's have a go at plotting some categorical data. We'll create a bar chart using a list of categories and random values:\n", + "- Run the following cell to define a list of four fruit names `['apple', 'banana', 'cherry', 'date']` and generate random data for these categories." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "d78e6837-6fa3-41cd-bc4b-f897dc0935ec", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:52.047329Z", + "iopub.status.busy": "2024-11-08T14:55:52.047063Z", + "iopub.status.idle": "2024-11-08T14:55:52.050131Z", + "shell.execute_reply": "2024-11-08T14:55:52.049658Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# Step 1: Define fruit list\n", + "categories = ['apple', 'banana', 'cherry', 'date']\n", + "\n", + "# Step 2: Generate random data\n", + "values = np.random.rand(len(categories))" + ] + }, + { + "cell_type": "markdown", + "id": "56f95cac-6d0d-4635-b8ee-25728b2a7995", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Create a bar plot using these categories and their corresponding random values using `ax.bar()`" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "491a7449-45a9-4ef9-88a5-ab6d1244c3ca", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:52.052449Z", + "iopub.status.busy": "2024-11-08T14:55:52.052056Z", + "iopub.status.idle": "2024-11-08T14:55:52.222108Z", + "shell.execute_reply": "2024-11-08T14:55:52.221570Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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VXFyc8vPzL9q3tLRUEydOVN++fdWmTRvNmjWrqbUCAIBmEHD45+TkaNasWcrIyFBRUZGSkpI0btw4lZSU1Nu/qqpKXbp0UUZGhgYPHnzZBQMAgMsTcPgvX75cKSkpSk1NVWxsrLKysuR2u7VmzZp6+0dHR+vZZ5/V5MmTFRkZedkFAwCAyxNQ+FdXV6uwsFDJyck+7cnJySooKGi2oqqqqlRZWemzAACA5hFQ+JeXl6umpkYul8un3eVy6cSJE81W1OLFixUZGeld3G53s60bAADbNemGP4fD4fPYGOPXdjnmzZuniooK73Ls2LFmWzcAALYLDqRz586dFRQU5HeWX1ZW5nc14HI4nU45nc5mWx8AAPg/AZ35h4SEKC4uTnl5eT7teXl5SkxMbNbCAABAywjozF+S0tPTNWnSJMXHxyshIUHZ2dkqKSlRWlqapAuX7I8fP66NGzd6x+zevVuSdObMGX311VfavXu3QkJC1L9//+bZCwAA0GgBh/+ECRN08uRJZWZmqrS0VAMHDlRubq48Ho+kCx/q8933/A8ZMsT7c2FhoTZv3iyPx6OjR49eXvUAACBgAYe/JE2fPl3Tp0+v97kNGzb4tRljmrIZAADQAvhsfwAALNOkM38ACFT03O2tXcJV7eiS21u7BFxBOPMHAMAyhD8AAJYh/AEAsAzhDwCAZQh/AAAsQ/gDAGAZwh8AAMsQ/gAAWIbwBwDAMoQ/AACWIfwBALAM4Q8AgGUIfwAALEP4AwBgGcIfAADLEP4AAFiG8AcAwDKEPwAAliH8AQCwDOEPAIBlCH8AACxD+AMAYBnCHwAAyxD+AABYhvAHAMAyhD8AAJYh/AEAsAzhDwCAZQh/AAAsQ/gDAGAZwh8AAMsQ/gAAWIbwBwDAMoQ/AACWIfwBALBMk8J/9erViomJUWhoqOLi4pSfn99g//fee09xcXEKDQ1Vr169tHbt2iYVCwAALl/A4Z+Tk6NZs2YpIyNDRUVFSkpK0rhx41RSUlJv/yNHjui2225TUlKSioqK9Lvf/U4PP/ywtm3bdtnFAwCAwAUc/suXL1dKSopSU1MVGxurrKwsud1urVmzpt7+a9euVVRUlLKyshQbG6vU1FQ9+OCDWrZs2WUXDwAAAhccSOfq6moVFhZq7ty5Pu3JyckqKCiod8yHH36o5ORkn7axY8fqxRdf1Llz59S2bVu/MVVVVaqqqvI+rqiokCRVVlYGUu4l1Vb9t1nXB1/NPV91mLeWxbxdmZi3K1Nzz1vd+owxDfYLKPzLy8tVU1Mjl8vl0+5yuXTixIl6x5w4caLe/ufPn1d5ebm6d+/uN2bx4sVasGCBX7vb7Q6kXLSyyKzWrgBNwbxdmZi3K1NLzdvp06cVGRl50ecDCv86DofD57Exxq/tUv3ra68zb948paenex/X1tbqP//5jzp16tTgdq5mlZWVcrvdOnbsmCIiIlq7HDQS83ZlYt6uTMzbhXw9ffq0evTo0WC/gMK/c+fOCgoK8jvLLysr8zu7r9OtW7d6+wcHB6tTp071jnE6nXI6nT5t11xzTSClXrUiIiKs/Ud9JWPerkzM25XJ9nlr6Iy/TkA3/IWEhCguLk55eXk+7Xl5eUpMTKx3TEJCgl//HTt2KD4+vt7X+wEAQMsK+G7/9PR0vfDCC1q3bp3279+vRx55RCUlJUpLS5N04ZL95MmTvf3T0tL0+eefKz09Xfv379e6dev04osv6tFHH22+vQAAAI0W8Gv+EyZM0MmTJ5WZmanS0lINHDhQubm58ng8kqTS0lKf9/zHxMQoNzdXjzzyiFatWqUePXroT3/6k37xi180315YwOl06vHHH/d7OQQ/bMzblYl5uzIxb43nMJd6PwAAALiq8Nn+AABYhvAHAMAyhD8AAJYh/AEAsAzhf5XYsGEDH4TUBLfeeqtmzZrV2mWghRw9elQOh0O7d+9u7VLQDDhemw/hDwC46uzcuVMOh0Nff/11a5fyg0T4A0CAqqur/dpqampUW1vbCtUAgSP8vwdvvvmmbr75Zl1zzTXq1KmT7rjjDhUXF0v6v8uSW7ZsUWJiokJDQzVgwADt3LnTO77uf7Dbt2/X4MGDFRoaqmHDhmnv3r0Nbvf1119XXFycQkND1atXLy1YsEDnz59vyV29Ip0/f14zZ870zs/vf/9775dPbdq0SfHx8erQoYO6deumiRMnqqyszDu2bm7eeecdxcfHq127dkpMTNTBgwe9fYqLi3XnnXfK5XIpPDxcQ4cO1dtvv+1TQ3R0tBYtWqQHH3xQHTp0UFRUlLKzs336zJkzR9dff73atWunXr16af78+Tp37lwL/mauHLW1tXr66afVu3dvOZ1ORUVF6amnnvI+/+9//1ujRo1Su3btNHjwYH344Yc+4wsKCnTLLbcoLCxMbrdbDz/8sM6ePet9Pjo6WgsXLtSUKVMUGRmpqVOnel9qe+ONN9S/f385nU7l5+erbdu2ft9n8tvf/la33HJLy/4SrjJnz57V5MmTFR4eru7du+uZZ57xeb6hY/Po0aMaNWqUJKljx45yOByaMmWKpAtffLN06VL16tVLYWFhGjx4sLZu3fq97tsPgkGL27p1q9m2bZs5dOiQKSoqMuPHjzc33HCDqampMUeOHDGSTM+ePc3WrVvNp59+alJTU02HDh1MeXm5McaYd99910gysbGxZseOHWbPnj3mjjvuMNHR0aa6utoYY8z69etNZGSkd5tvvvmmiYiIMBs2bDDFxcVmx44dJjo62jzxxBOt8Sv4wRo5cqQJDw83v/nNb8yBAwfMpk2bTLt27Ux2drYxxpgXX3zR5ObmmuLiYvPhhx+a4cOHm3HjxnnH183NsGHDzM6dO82+fftMUlKSSUxM9PbZvXu3Wbt2rdmzZ485dOiQycjIMKGhoebzzz/39vF4PObaa681q1atMocPHzaLFy82bdq0Mfv37/f2efLJJ82uXbvMkSNHzGuvvWZcLpd5+umnv4ff0g/f7NmzTceOHc2GDRvMZ599ZvLz883zzz/vPb769etn3njjDXPw4EFz9913G4/HY86dO2eMMWbPnj0mPDzcrFixwhw6dMjs2rXLDBkyxEyZMsW7fo/HYyIiIswf//hHc/jwYXP48GGzfv1607ZtW5OYmGh27dplDhw4YM6cOWOuv/56s3TpUu/Yc+fOma5du5p169Z977+XK9mvfvUr07NnT5+/eXXHqjENH5vnz58327ZtM5LMwYMHTWlpqfn666+NMcb87ne/M/369TNvvvmmKS4uNuvXrzdOp9Ps3LmztXa1VRD+raCsrMxIMnv37vX+cVqyZIn3+XPnzpmePXt6/7DXBcyWLVu8fU6ePGnCwsJMTk6OMcY//JOSksyiRYt8tvuXv/zFdO/evQX37MozcuRIExsba2pra71tc+bMMbGxsfX2//jjj40kc/r0aWPM/83N22+/7e2zfft2I8n873//u+h2+/fvb1auXOl97PF4zP333+99XFtba7p27WrWrFlz0XUsXbrUxMXFXXonr3KVlZXG6XSa559/3u+5uuPrhRde8Lbt27fPSPL+x2rSpEnmoYce8hmXn59v2rRp451Dj8dj7rrrLp8+69evN5LM7t27fdqffvppn38/f/vb30x4eLg5c+bM5e2oRU6fPm1CQkLq/ZtXF/7fdbFj89SpU94+Z86cMaGhoaagoMBnbEpKirnvvvuafT9+yLjs/z0oLi7WxIkT1atXL0VERCgmJkaSfL4DISEhwftzcHCw4uPjtX//fp/1fLvPtddeq759+/r1qVNYWKjMzEyFh4d7l6lTp6q0tFT//e9/m3P3rnjDhw+Xw+HwPk5ISNDhw4dVU1OjoqIi3XnnnfJ4POrQoYNuvfVWSb5zJ0mDBg3y/ty9e3dJ8l6CPHv2rGbPnq3+/fvrmmuuUXh4uA4cONDgOhwOh7p16+bzEsPWrVt18803q1u3bgoPD9f8+fP91mGj/fv3q6qqSqNHj75on4bmp7CwUBs2bPA5VsaOHava2lodOXLEOy4+Pt5vvSEhIT7rlqQpU6bos88+00cffSRJWrdune655x61b9++6TtpmeLiYlVXV9f7N69OY4/Nb/v000/1zTffaMyYMT7zvXHjRu9LsbYI+It9ELjx48fL7Xbr+eefV48ePVRbW6uBAwfWe9PQt307kALtU1tbqwULFujnP/+533OhoaGNK9xy33zzjZKTk5WcnKxNmzapS5cuKikp0dixY/3m7ttfT103J3U3fz322GN66623tGzZMvXu3VthYWG6++67G1xH3Xrq1vHRRx/p3nvv1YIFCzR27FhFRkZqy5Ytfq+D2igsLOySfRqan9raWk2bNk0PP/yw37ioqCjvz/WFd1hYmN8x2LVrV40fP17r169Xr169lJub63MPDy7NXOIrZ86ePdvoY/Pb6uZ8+/bt+tGPfuTznG1fBkT4t7CTJ09q//79eu6555SUlCRJ+uCDD/z6ffTRR94bgs6fP6/CwkLNnDnTr0/dH6NTp07p0KFD6tevX73bvemmm3Tw4EH17t27OXfnqlR3hvbtx3369NGBAwdUXl6uJUuWyO12S5I++eSTgNefn5+vKVOm6Gc/+5kk6cyZMzp69GhA69i1a5c8Ho8yMjK8bZ9//nnAtVyN+vTpo7CwML3zzjtKTU0NePxNN92kffv2NeuxkpqaqnvvvVc9e/bUddddpxEjRjTbum3Qu3dvtW3btt6/eSNHjmzUsRkSEiLpwrsw6tTdmFlSUqKRI0d+T3vzw0T4t7COHTuqU6dOys7OVvfu3VVSUqK5c+f69Vu1apX69Omj2NhYrVixQqdOndKDDz7o0yczM1OdOnWSy+VSRkaGOnfurLvuuqve7f7hD3/QHXfcIbfbrV/+8pdq06aN9uzZo71792rhwoUtsatXrGPHjik9PV3Tpk3TP//5T61cuVLPPPOMoqKiFBISopUrVyotLU3/+te/9OSTTwa8/t69e+uVV17R+PHj5XA4NH/+/IDfEta7d2+VlJRoy5YtGjp0qLZv365XX3014FquRqGhoZozZ45mz56tkJAQjRgxQl999ZX27dvX4EsBdebMmaPhw4drxowZmjp1qtq3b6/9+/crLy9PK1eubFJNdVdnFi5cqMzMzCatw2bh4eFKSUnRY4895vM3r02bC69UN+bY9Hg8cjgceuONN3TbbbcpLCxMHTp00KOPPqpHHnlEtbW1uvnmm1VZWamCggKFh4frgQceaI3dbR2tfdOBDfLy8kxsbKxxOp1m0KBBZufOnUaSefXVV703JG3evNkMGzbMhISEmNjYWPPOO+94x9fduPL666+bAQMGmJCQEDN06FCfG42+e8OfMRfu+E9MTDRhYWEmIiLC/PjHP/bexY4LRo4caaZPn27S0tJMRESE6dixo5k7d673BsDNmzeb6Oho43Q6TUJCgnnttdeMJFNUVGSMqf+moqKiIiPJHDlyxBhz4aazUaNGmbCwMON2u82f//xnM3LkSJ8blzwej1mxYoVPbYMHDzaPP/649/Fjjz1mOnXqZMLDw82ECRPMihUr/ObcVjU1NWbhwoXG4/GYtm3bmqioKLNo0SLv8VU3X8YYc+rUKSPJvPvuu962jz/+2IwZM8aEh4eb9u3bm0GDBpmnnnrK+3x981PfMfdt8+fPN0FBQeaLL75opr20y+nTp839999v2rVrZ1wul1m6dKnPcXOpY9MYYzIzM023bt2Mw+EwDzzwgDHmws20zz77rOnbt69p27at6dKlixk7dqx57733vv+dbEUOYy7x4gpa1NGjRxUTE6OioiLdeOON9fbZuXOnRo0apVOnTvERvsAVYurUqfryyy/12muvtXYpgB8u+wNAM6qoqNA//vEPvfTSS/r73//e2uUA9SL8AaAZ3Xnnnfr44481bdo0jRkzprXLAerFZX8AACzDh/wAAGAZwh8AAMsQ/gAAWIbwBwDAMoQ/AACWIfwBALAM4Q8AgGUIfwAALPP/AMp4PfYmdFO9AAAAAElFTkSuQmCC", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Step 3: Create the bar plot\n", + "fig, ax = plt.subplots(figsize=(5, 3), layout='constrained')\n", + "ax.bar(categories, values)" + ] + }, + { + "cell_type": "markdown", + "id": "9d86f4d0-fd73-40b4-aa36-beb40ffbab10", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q15. Let's create a plot that demonstrates the use of both a secondary y-axis and a secondary x-axis with different scales:\n", + "- Run the following cell to generate a time series `t` ranging from 0 to 2π with 100 points and create two datasets: `s` for a sine wave and `l` for a linearly increasing dataset between 0 and the length of `t`." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "3b550c94-f3c6-49e9-a336-af67c2bb5679", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:52.224657Z", + "iopub.status.busy": "2024-11-08T14:55:52.224400Z", + "iopub.status.idle": "2024-11-08T14:55:52.227759Z", + "shell.execute_reply": "2024-11-08T14:55:52.227277Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# Step 1: Generate the time series\n", + "t = np.linspace(0, 2 * np.pi, 100)\n", + "\n", + "# Step 2: Create the s and l datasets\n", + "s = np.sin(t)\n", + "l = np.arange(len(t))" + ] + }, + { + "cell_type": "markdown", + "id": "0ae2c7cf-12d0-404c-80b5-4e8c16111727", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "- Plot both datasets on the same figure\n", + " - On the first subplot plot the sine wave on the left y axis and the linear data on the right y axis using `twinx()`\n", + " - On the second subplot plot the sine wave with a secondary x axis that converts radians to degrees using `secondary_xaxis()`" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "eb48d19a-3a71-45f4-961f-73e529185796", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:52.229862Z", + "iopub.status.busy": "2024-11-08T14:55:52.229636Z", + "iopub.status.idle": "2024-11-08T14:55:53.110273Z", + "shell.execute_reply": "2024-11-08T14:55:53.109779Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 0, 'Angle [°]')" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Step 3: Plots the two datasets\n", + "fig, (ax1, ax3) = plt.subplots(1, 2, figsize=(7, 2.7), layout='constrained')\n", + "\n", + "l1, = ax1.plot(t, s, 'b-', label='Sine wave')\n", + "ax1.set_ylabel('Sine Wave', color='b')\n", + "ax1.set_xlabel('Time [s]')\n", + "ax2 = ax1.twinx()\n", + "l2, = ax2.plot(t, l, 'r-', label='Linear data')\n", + "ax2.set_ylabel('Linear data', color='r')\n", + "\n", + "ax1.legend([l1, l2], ['Sine (left)', 'Linear (right)'])\n", + "\n", + "ax3.plot(t, s, 'b-')\n", + "ax3.set_xlabel('Angle [rad]')\n", + "ax2.set_ylabel('Sine wave')\n", + "\n", + "ax4 = ax3.secondary_xaxis('top', functions=(np.rad2deg, np.deg2rad))\n", + "ax4.set_xlabel('Angle [°]')" + ] + }, + { + "cell_type": "markdown", + "id": "ef2b0027-33a2-4c69-8bff-fada5fcc0daa", + "metadata": { + "editable": true, + "jupyter": { + "source_hidden": true + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Colour mapped data" + ] + }, + { + "cell_type": "markdown", + "id": "4a4d9f48-0dbc-4b5e-a54a-f440c6c9f46b", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q16. Let's create a series of suplots to practice visualizing data with colormaps:\n", + "- Run the following cell to generate x, y, and z data. Then generate 2 datsets to use for our scatter plot and a third dataset to use for the colors." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "10d56aef-9a1d-406d-89ea-5fe657dbb932", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:53.112929Z", + "iopub.status.busy": "2024-11-08T14:55:53.112669Z", + "iopub.status.idle": "2024-11-08T14:55:53.121109Z", + "shell.execute_reply": "2024-11-08T14:55:53.120598Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# Step 1: Generate x and y data\n", + "x,y = np.meshgrid(np.linspace(-3, 3, 128), np.linspace(-3, 3, 128))\n", + "\n", + "# Step 2: Generate z data\n", + "z = (1 - x/2 + x**5 + y**3) * np.exp(-x**2 - y**2)\n", + "\n", + "# Step 3: Create 3 datasets\n", + "data1 = np.random.randn(100)\n", + "data2 = np.random.randn(100)\n", + "data3 = np.random.rand(100)" + ] + }, + { + "cell_type": "markdown", + "id": "03263f96-ddd6-4cc6-beca-0f9abd222533", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "- Create 4 subplots in a 2x2 grid\n", + "- First plot: use `pcolormesh()` to display z values with a colormap\n", + "- Second plot: use `contourf()` to create a filled contour plot\n", + "- Third plot: use `imshow()` with a logarithmic color scale (`LogNorm`) to represent the square of z values\n", + "- Fourth plot: create a scatter plot where the colour of each point depends on that third datset we made\n", + "- Add colorbars to each plot to indicate the mapping between data and colours" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "75e37d2d-a0cd-4694-a7dd-5f1c2fb8e468", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:53.123288Z", + "iopub.status.busy": "2024-11-08T14:55:53.123038Z", + "iopub.status.idle": "2024-11-08T14:55:54.808669Z", + "shell.execute_reply": "2024-11-08T14:55:54.808122Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "from matplotlib.colors import LogNorm\n", + "\n", + "# Step 4: Create 4 subplots in a 2x2 grid\n", + "fig, axs = plt.subplots(2, 2, layout='constrained')\n", + "\n", + "# Step 5: Make the first plot\n", + "plot1 = axs[0,0].pcolormesh(x, y, z, vmin=-1, vmax=1, cmap='RdBu_r')\n", + "axs[0,0].set_title('pcolormesh()')\n", + "\n", + "# Step 6: Make the second plot\n", + "plot2 = axs[0, 1].contourf(x, y, z, levels=np.linspace(-1.25, 1.25, 11))\n", + "axs[0,1].set_title('contourf()')\n", + "\n", + "# Step 7: Make the third plot\n", + "plot3 = axs[1, 0].imshow(z**2 * 100, cmap='plasma', norm=LogNorm(vmin=0.01, vmax=100))\n", + "axs[1, 1].set_title('scatter()')\n", + "\n", + "# Step 8: MAake the fourth plot\n", + "plot4 = axs[1, 1].scatter(data1, data2, c=data3, cmap='RdBu_r')\n", + "axs[1, 1].set_title('scatter()')\n", + "\n", + "# Step 9: Add the colorbars\n", + "fig.colorbar(plot1, ax=axs[0, 0])\n", + "fig.colorbar(plot2, ax=axs[0,1])\n", + "fig.colorbar(plot3, ax=axs[1,0], extend='both')\n", + "fig.colorbar(plot4, ax=axs[1, 1], extend='both')" + ] + }, + { + "cell_type": "markdown", + "id": "71a90323-7b93-4bda-b519-6a945e812e3a", + "metadata": {}, + "source": [ + "## Multiple figures/axes" + ] + }, + { + "cell_type": "markdown", + "id": "4d431ef9-295d-4c21-b9ef-2f4b118b11c6", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q17. Let's create a figure with multiple subplots using the `suplot_mosiac` method. Each subplot should have its own distinct data and be customized with titles, labels, and a legend. You will also need to manipulate different axes in a single figure and work with multiple figures in a single program:\n", + "- Run the following cell to create data for the plots." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "cc1b0ee4-744b-4265-97c0-a6c71e8a4b1d", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:54.812190Z", + "iopub.status.busy": "2024-11-08T14:55:54.811895Z", + "iopub.status.idle": "2024-11-08T14:55:54.816798Z", + "shell.execute_reply": "2024-11-08T14:55:54.816065Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# Step 1: Generate the data\n", + "x = np.linspace(0,2 * np.pi, 100)\n", + "y_sin = np.sin(x)\n", + "y_cos = np.cos(x)\n", + "categories = ['A', 'B', 'C']\n", + "values = np.random.rand(len(categories))\n", + "random_x = np.random.rand(50)\n", + "random_y = np.random.rand(50)\n", + "x_exp = np.arange(0, 10, 0.1)\n", + "y_exp = np.exp(x_exp)" + ] + }, + { + "cell_type": "markdown", + "id": "a60c1d9c-0546-47b6-9d65-7a13bbd5d6c7", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "- Create a figure using `plt.subplot_mosaic()` with the following layout: `[['top', 'top', 'right'], ['bottom-left', 'bottom-right', 'right']]` with the layout set to `constrained` so the figure is properly spaced\n", + "- In the `top` subplot, plot `x` and `y_sin`. Label the plot and give it the color `blue`. Label the x and y axes and give a legend.\n", + "- In the `bottom-left` subplot, plot `x` and `y_cos`. Label the plot and give it the color `orange`. Label the x and y axes and give a legend.\n", + "- In the `bottom-right` subplot, plot `random_x` and `random_y`. Label the plot and give it the color `green`.Label the x and y axes.\n", + "- In the `right` subplot, plot `categories` and `values` as a bar chart and give it the colors `['purple', 'red', 'yellow']`. Label the x and y axes.\n", + "- Give appropriate titles to each subplot using `set_title()`\n", + "- Create a new figure using `plt.figure()` and plot a simple line graph of exponential growth in a single subplot using `x_exp` and `y_exp`. Give the plot x and y axis labels, a title, and a legend.\n", + "- Ensure both the mosaic figure and extra figure are displayed properly " + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "3d3d277c-3440-484a-a9a4-69f1d0168ec4", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:54.820377Z", + "iopub.status.busy": "2024-11-08T14:55:54.819717Z", + "iopub.status.idle": "2024-11-08T14:55:55.981606Z", + "shell.execute_reply": "2024-11-08T14:55:55.980878Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Step 2: Create the mosaic layout\n", + "fig, ax = plt.subplot_mosaic([['top', 'top', 'right'], ['bottom-left', 'bottom-right', 'right']], layout='constrained')\n", + "\n", + "# Step 3: Top subplot\n", + "ax['top'].plot(x, y_sin, label='Sine wave', color='blue')\n", + "ax['top'].set_xlabel('x')\n", + "ax['top'].set_ylabel('sin(x)')\n", + "ax['top'].legend()\n", + "\n", + "# Step 4: Bottom-left subplot\n", + "ax['bottom-left'].plot(x, y_cos, label='Cosine wave', color='orange')\n", + "ax['bottom-left'].set_xlabel('x')\n", + "ax['bottom-left'].set_ylabel('cos(x)')\n", + "ax['bottom-left'].legend()\n", + "\n", + "# Step 5: bottom-right subplot\n", + "ax['bottom-right'].scatter(random_x, random_y, color='green')\n", + "ax['bottom-right'].set_xlabel('Random X')\n", + "ax['bottom-right'].set_ylabel('Random Y')\n", + "\n", + "# Step 6: Right subplot\n", + "ax['right'].bar(categories, values, color=['purple', 'red', 'yellow'])\n", + "ax['right'].set_xlabel('Categories')\n", + "ax['right'].set_ylabel('Values')\n", + "\n", + "\n", + "# Step 7: Give appropriate titles\n", + "ax['top'].set_title('Sine wave')\n", + "ax['bottom-left'].set_title('Cosine Wave')\n", + "ax['bottom-right'].set_title('Random Scatter Plot')\n", + "ax['right'].set_title('Category Bar Chart')\n", + "\n", + "# Step 8: Add the new figure \n", + "fig2 = plt.figure()\n", + "ax2 = fig2.add_subplot(111)\n", + "ax2.plot(x_exp, y_exp, label='Exponential Growth', color='red')\n", + "ax2.set_title('Exponential Growth')\n", + "ax2.set_xlabel('x')\n", + "ax2.set_ylabel('exp(x)')\n", + "ax2.legend()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 + Jaspy", + "language": "python", + "name": "jaspy" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/python-data/solutions/ex06a_numpy.ipynb b/python-data/solutions/ex06a_numpy.ipynb new file mode 100644 index 0000000..8d7fdc7 --- /dev/null +++ b/python-data/solutions/ex06a_numpy.ipynb @@ -0,0 +1,1290 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "de2a613e-7102-4e10-8fee-4d07c0e1f9eb", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "# Exercise 6a: numpy" + ] + }, + { + "cell_type": "markdown", + "id": "ea1f5d14-d8ac-4c83-a0fa-54b2309f8cd1", + "metadata": {}, + "source": [ + "## Aim: Get an overview of NumPy and some useful functions." + ] + }, + { + "cell_type": "markdown", + "id": "bd5f0117-b46f-475a-b6da-f4918a40f284", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "You can find the teaching resources for this lesson here: https://numpy.org/doc/stable/user/quickstart.html" + ] + }, + { + "cell_type": "markdown", + "id": "8e3500eb-400a-45e4-b108-27a337b2fb84", + "metadata": {}, + "source": [ + "### Issues covered:\n", + "- Importing NumPy\n", + "- Array creation\n", + "- Array indexing and slicing\n", + "- Array operations" + ] + }, + { + "cell_type": "markdown", + "id": "91d90486-6b89-495c-b5bb-fb610dc73e15", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## 1. Basics" + ] + }, + { + "cell_type": "markdown", + "id": "564704e3-b77a-45d3-a3ee-cb00feac6275", + "metadata": {}, + "source": [ + "### Importing NumPy" + ] + }, + { + "cell_type": "markdown", + "id": "cb507a6c-6946-4916-80ba-6996f79b32a4", + "metadata": {}, + "source": [ + "Q1. First, let's use the conventional way to import NumPy into our notebook. You'll need to run this cell to get the rest of the notebook to work!" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "b7b2c772-9185-4fa7-9cf7-61f5b53d56aa", + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-07T16:45:34.152395Z", + "iopub.status.busy": "2024-11-07T16:45:34.151744Z", + "iopub.status.idle": "2024-11-07T16:45:34.605734Z", + "shell.execute_reply": "2024-11-07T16:45:34.604361Z" + } + }, + "outputs": [], + "source": [ + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "id": "743d7668-3ea1-4d4f-82d6-89ba93d5915f", + "metadata": {}, + "source": [ + "### Array creation" + ] + }, + { + "cell_type": "markdown", + "id": "f78525c3-6a0a-44be-a898-84f5af46a25e", + "metadata": {}, + "source": [ + "Q2. Let's start by creating some arrays - try to create an array using `a = np.array(1, 2, 3, 4)`. Does this work? Can you edit it to make it work?" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "56419349-939c-48df-b696-2f7c08ae7f41", + "metadata": { + "allow_errors": true, + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:45:34.612679Z", + "iopub.status.busy": "2024-11-07T16:45:34.611936Z", + "iopub.status.idle": "2024-11-07T16:45:34.787723Z", + "shell.execute_reply": "2024-11-07T16:45:34.786851Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "allow_errors", + "clear_answer_cell" + ] + }, + "outputs": [ + { + "ename": "TypeError", + "evalue": "array() takes from 1 to 2 positional arguments but 4 were given", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[0;32mIn[2], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m a \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43marray\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m4\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m 2\u001b[0m \u001b[38;5;66;03m# You need to use the square brackets:\u001b[39;00m\n\u001b[1;32m 3\u001b[0m a \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39marray([\u001b[38;5;241m1\u001b[39m, \u001b[38;5;241m2\u001b[39m, \u001b[38;5;241m3\u001b[39m, \u001b[38;5;241m4\u001b[39m])\n", + "\u001b[0;31mTypeError\u001b[0m: array() takes from 1 to 2 positional arguments but 4 were given" + ] + } + ], + "source": [ + "a = np.array(1, 2, 3, 4)\n", + "# You need to use the square brackets:\n", + "a = np.array([1, 2, 3, 4])" + ] + }, + { + "cell_type": "markdown", + "id": "c7db7e8a-cdf5-4eaa-b1a5-301d89d170c6", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q3. Take a look at the following numpy array:\n", + "```\n", + "[[7.0, 8.0, 4.0, 2.0],\n", + " [12.0, 1.0, 0.0, 10.0],\n", + " [0.0, 0.0, 0.0, 0.0]]\n", + "```\n", + "- How many axes does it have?\n", + "- What is the length of the array?\n", + "\n", + "Hint: you can use `.ndim` and `.shape` to help if you enclose the array in `np.array()` to define the array." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "0c33dc0e-fd79-4839-ac13-79ba785e78d3", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:45:34.793395Z", + "iopub.status.busy": "2024-11-07T16:45:34.793168Z", + "iopub.status.idle": "2024-11-07T16:45:34.799661Z", + "shell.execute_reply": "2024-11-07T16:45:34.798857Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of axes/dimensions: 2\n", + "Length of array: (3, 4)\n" + ] + } + ], + "source": [ + "array = np.array([[7.0, 8.0, 4.0, 2.0], [12.0, 1.0, 0.0, 10.0], [0.0, 0.0, 0.0, 0.0]])\n", + "print(\"Number of axes/dimensions:\", array.ndim)\n", + "# There are 2 dimensions! It's a 2D array because it has rows and columns. Even though there are three sets of [ ] brackets, the structure is still 2D because each row is a 1D array and multiple rows together form the 2D array. \n", + "print(\"Length of array:\", array.shape)\n", + "# The first dimension (rows) is the outermost brackets - in our case it is 3. The second dimension (columns) is the number of values in each of the inner brackets, which is 4 in our case." + ] + }, + { + "cell_type": "markdown", + "id": "861d15c0-c5fa-4d34-84bf-154980a66088", + "metadata": {}, + "source": [ + "Q4. Can you come up with an example of what a 3D array would look like? Use `np.zeros` to make it then print it out and have a look at `.ndim` and `.shape`" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "1756a861-f70d-4889-9244-a229d9b2e7b7", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:45:34.803567Z", + "iopub.status.busy": "2024-11-07T16:45:34.803346Z", + "iopub.status.idle": "2024-11-07T16:45:34.814135Z", + "shell.execute_reply": "2024-11-07T16:45:34.813304Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[[0. 0.]\n", + " [0. 0.]\n", + " [0. 0.]\n", + " [0. 0.]]\n", + "\n", + " [[0. 0.]\n", + " [0. 0.]\n", + " [0. 0.]\n", + " [0. 0.]]\n", + "\n", + " [[0. 0.]\n", + " [0. 0.]\n", + " [0. 0.]\n", + " [0. 0.]]]\n", + "Number of dimensions: 3\n", + "Length of array: (3, 4, 2)\n" + ] + } + ], + "source": [ + "array_3d = np.zeros((3,4,2))\n", + "print(array_3d)\n", + "print(\"Number of dimensions:\", array_3d.ndim)\n", + "print(\"Length of array:\", array_3d.shape)\n", + "# A 3D array in numpy can be thought of as a collection of 2D arrays stacked together. Each layer is a 2D array and the third dimension is how many 2D arrays are stacked - in this case we have three lots of 2D arrays with 4 rows and 2 columns" + ] + }, + { + "cell_type": "markdown", + "id": "73d932df-887b-455f-93b2-ec265546b6cf", + "metadata": {}, + "source": [ + "Q5.\n", + "- How many elements are in your array? Use `.size` to check.\n", + "- What type are the elements in the array? Use `.dtype` to check.\n", + "- How many bites are in each element of the array? Use `.itemsize` to check." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "3d3c5463-aa6a-4167-9154-7b7f133e7bc3", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:45:34.818082Z", + "iopub.status.busy": "2024-11-07T16:45:34.817384Z", + "iopub.status.idle": "2024-11-07T16:45:34.830374Z", + "shell.execute_reply": "2024-11-07T16:45:34.829130Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Number of elements: 24\n", + "Type of elements: float64\n", + "Bytes per element: 8\n" + ] + } + ], + "source": [ + "print(\"Number of elements:\", array_3d.size)\n", + "print(\"Type of elements:\", array_3d.dtype)\n", + "print(\"Bytes per element:\", array_3d.itemsize)" + ] + }, + { + "cell_type": "markdown", + "id": "cb62bfcd-e67a-42ed-9980-1578002f9d3f", + "metadata": {}, + "source": [ + "Q6. Create a 1D array of nine numbers 1-9 using `a = np.linspace(1, 9, 9)`. Reshape this to be a 3x3 array and print it." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "aacebcbe-ef1a-4813-bc35-a65b061c4f8b", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:45:34.835664Z", + "iopub.status.busy": "2024-11-07T16:45:34.835106Z", + "iopub.status.idle": "2024-11-07T16:45:34.844780Z", + "shell.execute_reply": "2024-11-07T16:45:34.843863Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[1. 2. 3.]\n", + " [4. 5. 6.]\n", + " [7. 8. 9.]]\n" + ] + } + ], + "source": [ + "a = np.linspace(1, 9, 9).reshape(3,3)\n", + "print(a)" + ] + }, + { + "cell_type": "markdown", + "id": "2f2f3bfe-4776-46a1-ad0c-8995322aba55", + "metadata": {}, + "source": [ + "### Basic operations" + ] + }, + { + "cell_type": "markdown", + "id": "63700027-be94-4a47-9600-ef5ea449e1b4", + "metadata": {}, + "source": [ + "Q7. What happens if you multiply the previous array by 2 using `b = a*2`?" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "13b608bb-39cb-47cc-a97e-87c33cb4fe2a", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:45:34.848560Z", + "iopub.status.busy": "2024-11-07T16:45:34.848102Z", + "iopub.status.idle": "2024-11-07T16:45:34.861932Z", + "shell.execute_reply": "2024-11-07T16:45:34.860524Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 2. 4. 6.]\n", + " [ 8. 10. 12.]\n", + " [14. 16. 18.]]\n" + ] + } + ], + "source": [ + "b = a*2\n", + "# The array has been multiplied by two elementwise (not matrix multiplication)\n", + "print(b)" + ] + }, + { + "cell_type": "markdown", + "id": "6ac2f29e-b42c-4e0b-9809-07938ae3aa5e", + "metadata": {}, + "source": [ + "Q8. How do you do matrix multiplication? Try doing the matrix product of `a` and `b`." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "80b1fc56-e095-435c-b3c6-25afce442071", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:45:34.867769Z", + "iopub.status.busy": "2024-11-07T16:45:34.866526Z", + "iopub.status.idle": "2024-11-07T16:45:34.877181Z", + "shell.execute_reply": "2024-11-07T16:45:34.875920Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[ 60. 72. 84.]\n", + " [132. 162. 192.]\n", + " [204. 252. 300.]]\n" + ] + } + ], + "source": [ + "# Either of these methods work\n", + "matrix_product = a @ b\n", + "matrix_product = a.dot(b)\n", + "print(matrix_product)" + ] + }, + { + "cell_type": "markdown", + "id": "6b76ea97-318a-4bfd-964c-f72d85459f85", + "metadata": {}, + "source": [ + "Q9. When performing operations between arrays of different data types, numpy automatically converts the result to the more precise type - this is called upcasting. Let's demonstrate this concept:\n", + "- Create an array with 3 elements all set to one using `a = np.ones(3, dtype=np.int32)` and set the data type to `np.int32`\n", + "- Create a float array of 3 elements evenly spaced between 0 and π using `b = np.linspace(0, np.pi, 3)`. The data type will be `float64` by default\n", + "- Check the data type of both arrays\n", + "- Add the arrays `a` and `b` to make a new array `c`. Print the resulting array `c` and its data type." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "8ec1bb60-82f6-4152-9e47-c1d45ab955b0", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:45:34.881234Z", + "iopub.status.busy": "2024-11-07T16:45:34.880863Z", + "iopub.status.idle": "2024-11-07T16:45:34.898208Z", + "shell.execute_reply": "2024-11-07T16:45:34.897270Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Data type of a: int32\n", + "Data type of b: float64\n", + "Data type of c: float64\n" + ] + }, + { + "data": { + "text/plain": [ + "array([1. , 2.57079633, 4.14159265])" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = np.ones(3, dtype=np.int32)\n", + "b = np.linspace(0, np.pi, 3)\n", + "c = a + b \n", + "print(\"Data type of a:\", a.dtype) #int32\n", + "print(\"Data type of b:\", b.dtype) #float64\n", + "print(\"Data type of c:\", c.dtype) #float64\n", + "c" + ] + }, + { + "cell_type": "markdown", + "id": "b824fbf3-7184-45fc-8219-a50712941091", + "metadata": {}, + "source": [ + "Q10. For matrix `a` in the previous question, what do you think `a.sum()` would be? Check your answer." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "91507ae1-8b08-45f1-bd8d-7fbeb5b9dd2c", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:45:34.902680Z", + "iopub.status.busy": "2024-11-07T16:45:34.902077Z", + "iopub.status.idle": "2024-11-07T16:45:34.906767Z", + "shell.execute_reply": "2024-11-07T16:45:34.906211Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "3" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# a.sum is the sum of all elements - a unary operation. `.sum()` is a method of the array class because you use it on the array.\n", + "a.sum()" + ] + }, + { + "cell_type": "markdown", + "id": "f0fedf38-dff2-43df-8057-4998d7219e31", + "metadata": {}, + "source": [ + "Q11. Create an array using `np.ones(6).reshape(3,2)`. If we only want to sum each column, how would we do that?" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "84d7b08c-5cfe-4662-b7c2-ee9a1fd5cf1d", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:45:34.911338Z", + "iopub.status.busy": "2024-11-07T16:45:34.911068Z", + "iopub.status.idle": "2024-11-07T16:45:34.921502Z", + "shell.execute_reply": "2024-11-07T16:45:34.920616Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([3., 3.])" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a = np.ones(6).reshape(3,2)\n", + "a.sum(axis=0)" + ] + }, + { + "cell_type": "markdown", + "id": "c6d39222-c0a5-4013-83ce-182dfab3ea2e", + "metadata": {}, + "source": [ + "### Indexing" + ] + }, + { + "cell_type": "markdown", + "id": "7b2b1a58-8c3e-480f-950a-ccf3a813b8cd", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q12.\n", + "- Create a 1D array of size 20 where each element is the cube of its index.\n", + "- Print the 5th element of `a`. Hint: your answer should be 64 - remember where we start indexing." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "f50807ff-0025-4e89-8a17-1ea2988cc367", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:45:34.925203Z", + "iopub.status.busy": "2024-11-07T16:45:34.924904Z", + "iopub.status.idle": "2024-11-07T16:45:34.938051Z", + "shell.execute_reply": "2024-11-07T16:45:34.937079Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "64" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Step 1: Create the array\n", + "a = np.arange(20) ** 3\n", + "\n", + "# Step 2: Print the 5th element.\n", + "a[4]" + ] + }, + { + "cell_type": "markdown", + "id": "8793e6ef-144a-440f-aa9e-5b49dc4a583d", + "metadata": {}, + "source": [ + "Q13. Slice the array to get elements from index 3 to index 7 (inclusive)." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "7c45834d-1a39-4037-9bf1-ca97c8dab09d", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:45:34.942117Z", + "iopub.status.busy": "2024-11-07T16:45:34.941465Z", + "iopub.status.idle": "2024-11-07T16:45:34.955086Z", + "shell.execute_reply": "2024-11-07T16:45:34.953727Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 27, 64, 125, 216, 343])" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a[3:8]" + ] + }, + { + "cell_type": "markdown", + "id": "3be02020-6449-4a8c-a1f6-bf6fafb6ec12", + "metadata": {}, + "source": [ + "Q14. Change every 3rd element to -1. This should give: `[ -1, 1, 8, -1, 64, 125, -1, ... ]`" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "f16bfe38-1f6f-47fd-a7f2-2a9da0333a8b", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:45:34.961165Z", + "iopub.status.busy": "2024-11-07T16:45:34.960785Z", + "iopub.status.idle": "2024-11-07T16:45:34.971049Z", + "shell.execute_reply": "2024-11-07T16:45:34.970204Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ -1, 1, 8, -1, 64, 125, -1, 343, 512, -1, 1000,\n", + " 1331, -1, 2197, 2744, 3375, 4096, 4913, 5832, 6859])" + ] + }, + "execution_count": 14, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "a[:15:3] = -1\n", + "a" + ] + }, + { + "cell_type": "markdown", + "id": "eb80452e-cd4f-4971-825d-86cb4747d9da", + "metadata": {}, + "source": [ + "Q15. Reverse the array and print the result." + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "169e63ce-9d74-4a35-b386-d2bd07bad3a7", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:45:34.976296Z", + "iopub.status.busy": "2024-11-07T16:45:34.975378Z", + "iopub.status.idle": "2024-11-07T16:45:34.987276Z", + "shell.execute_reply": "2024-11-07T16:45:34.986061Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([6859, 5832, 4913, 4096, 3375, 2744, 2197, -1, 1331, 1000, -1,\n", + " 512, 343, -1, 125, 64, -1, 8, 1, -1])" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "reversed_a = a[::-1]\n", + "reversed_a" + ] + }, + { + "cell_type": "markdown", + "id": "1cd5271c-8a90-49ae-979b-d89575491b01", + "metadata": {}, + "source": [ + "Q16. Create a 3x4 numpy array `b` using `b = np.array([[2 * i + j for j in range(4)] for i in range(3)])`." + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "5536dff6-d6c2-4dab-a4e6-869629e0edeb", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:45:34.991370Z", + "iopub.status.busy": "2024-11-07T16:45:34.990844Z", + "iopub.status.idle": "2024-11-07T16:45:35.005952Z", + "shell.execute_reply": "2024-11-07T16:45:35.003808Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[0 1 2 3]\n", + " [2 3 4 5]\n", + " [4 5 6 7]]\n" + ] + } + ], + "source": [ + "b = np.array([[2 * i + j for j in range(4)] for i in range(3)])\n", + "print(b)" + ] + }, + { + "cell_type": "markdown", + "id": "3983a14f-de1d-490b-b6fd-4f4d7eda14fb", + "metadata": {}, + "source": [ + "Q17. Print the element in the second row and third column. This should be 4." + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "a734ee36-b794-4260-808c-575518b4a34e", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:45:35.010659Z", + "iopub.status.busy": "2024-11-07T16:45:35.009978Z", + "iopub.status.idle": "2024-11-07T16:45:35.021059Z", + "shell.execute_reply": "2024-11-07T16:45:35.020149Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "4" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "b[1,2]" + ] + }, + { + "cell_type": "markdown", + "id": "3e4d5ba7-1abe-4285-bd7b-29bca9febbf1", + "metadata": {}, + "source": [ + "Q18. Extract and print the second column as a 1D array." + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "e190b233-124d-40c3-b928-c66f015217df", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:45:35.024197Z", + "iopub.status.busy": "2024-11-07T16:45:35.023920Z", + "iopub.status.idle": "2024-11-07T16:45:35.035750Z", + "shell.execute_reply": "2024-11-07T16:45:35.034822Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([1, 3, 5])" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "second_col = b[:, 1]\n", + "second_col" + ] + }, + { + "cell_type": "markdown", + "id": "24b839b8-a010-4bd5-afdc-f7046c7acc26", + "metadata": {}, + "source": [ + "Q19. Extract and print a sub-array containing the last two rows." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "6efa59b9-0bc1-4a8b-ae32-4ffbe75512cb", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:45:35.038908Z", + "iopub.status.busy": "2024-11-07T16:45:35.038379Z", + "iopub.status.idle": "2024-11-07T16:45:35.051161Z", + "shell.execute_reply": "2024-11-07T16:45:35.050287Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[2, 3, 4, 5],\n", + " [4, 5, 6, 7]])" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "sub_array = b[1:3, :]\n", + "sub_array" + ] + }, + { + "cell_type": "markdown", + "id": "d4fb0210-2031-4435-bdae-b567a6010c42", + "metadata": {}, + "source": [ + "Q20. Use slicing to replace the last row with the values `[7, 7, 7, 7]`." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "d2e59188-020a-4c03-a373-9d50b0def22f", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:45:35.054713Z", + "iopub.status.busy": "2024-11-07T16:45:35.054443Z", + "iopub.status.idle": "2024-11-07T16:45:35.068244Z", + "shell.execute_reply": "2024-11-07T16:45:35.067085Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[0, 1, 2, 3],\n", + " [2, 3, 4, 5],\n", + " [7, 7, 7, 7]])" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "b[2, :] = [7, 7, 7, 7]\n", + "b" + ] + }, + { + "cell_type": "markdown", + "id": "161545bc-06dd-40cb-b598-36de4f5378e3", + "metadata": {}, + "source": [ + "Q21. Iterate over the elements of the array using the `.flat` attribute and print them." + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "78081cb7-6c73-466b-a736-1aca4c652f49", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:45:35.074455Z", + "iopub.status.busy": "2024-11-07T16:45:35.073677Z", + "iopub.status.idle": "2024-11-07T16:45:35.082625Z", + "shell.execute_reply": "2024-11-07T16:45:35.081523Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "1\n", + "2\n", + "3\n", + "2\n", + "3\n", + "4\n", + "5\n", + "7\n", + "7\n", + "7\n", + "7\n" + ] + } + ], + "source": [ + "for element in b.flat:\n", + " print(element)" + ] + }, + { + "cell_type": "markdown", + "id": "0ea93892-c626-4e4e-b91c-763daf6bb7e1", + "metadata": {}, + "source": [ + "Q22. Create a 3D array `c` using `c = np.array([[[i * 10 + j * 5 + k for k in range(4)] for j in range(3)] for i in range(2)])`" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "15632b1d-f44e-4a1e-9efb-e6fde75e266b", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:45:35.087438Z", + "iopub.status.busy": "2024-11-07T16:45:35.086993Z", + "iopub.status.idle": "2024-11-07T16:45:35.099686Z", + "shell.execute_reply": "2024-11-07T16:45:35.098567Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[[ 0, 1, 2, 3],\n", + " [ 5, 6, 7, 8],\n", + " [10, 11, 12, 13]],\n", + "\n", + " [[10, 11, 12, 13],\n", + " [15, 16, 17, 18],\n", + " [20, 21, 22, 23]]])" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "c = np.array([[[i * 10 + j * 5 + k for k in range(4)] for j in range(3)] for i in range(2)])\n", + "c" + ] + }, + { + "cell_type": "markdown", + "id": "d48d62f7-5ccc-4dc6-a441-cac5ebc4cb10", + "metadata": {}, + "source": [ + "Q23. Print all elements of the first layer of the array." + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "53d8f855-bba0-40a4-aeba-2b719b87b363", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:45:35.103756Z", + "iopub.status.busy": "2024-11-07T16:45:35.103053Z", + "iopub.status.idle": "2024-11-07T16:45:35.118641Z", + "shell.execute_reply": "2024-11-07T16:45:35.117258Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 0, 1, 2, 3],\n", + " [ 5, 6, 7, 8],\n", + " [10, 11, 12, 13]])" + ] + }, + "execution_count": 23, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "c[0]" + ] + }, + { + "cell_type": "markdown", + "id": "96fcfb26-8c72-476b-b030-9e1f61a143ab", + "metadata": {}, + "source": [ + "Q24. Use `...` to print the last element of each 1D array contained within c." + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "59fcba5b-3772-4326-9f82-da576e8e6f5d", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:45:35.124400Z", + "iopub.status.busy": "2024-11-07T16:45:35.123593Z", + "iopub.status.idle": "2024-11-07T16:45:35.132851Z", + "shell.execute_reply": "2024-11-07T16:45:35.131940Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 3, 8, 13],\n", + " [13, 18, 23]])" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "c[..., -1]" + ] + }, + { + "cell_type": "markdown", + "id": "c96b71dd-bb47-41aa-9403-cc51f660aa0d", + "metadata": {}, + "source": [ + "Q25. Modify the first column of the second layer to `[0, 0, 0]`." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "495e40c1-5584-4e58-9db3-70b380b1fae5", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:45:35.137623Z", + "iopub.status.busy": "2024-11-07T16:45:35.137006Z", + "iopub.status.idle": "2024-11-07T16:45:35.148197Z", + "shell.execute_reply": "2024-11-07T16:45:35.147116Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[[ 0, 1, 2, 3],\n", + " [ 5, 6, 7, 8],\n", + " [10, 11, 12, 13]],\n", + "\n", + " [[ 0, 11, 12, 13],\n", + " [ 0, 16, 17, 18],\n", + " [ 0, 21, 22, 23]]])" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "c[1, :, 0] = [0, 0, 0]\n", + "c" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 + Jaspy", + "language": "python", + "name": "jaspy" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/python-data/solutions/ex06b_numpy.ipynb b/python-data/solutions/ex06b_numpy.ipynb new file mode 100644 index 0000000..43f030c --- /dev/null +++ b/python-data/solutions/ex06b_numpy.ipynb @@ -0,0 +1,732 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "93699150-662a-4a21-bc2b-7836c39d0e0d", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "# Exercise 6b: numpy (continued)" + ] + }, + { + "cell_type": "markdown", + "id": "0465143d-b368-4fcb-baec-017e5aaf801b", + "metadata": {}, + "source": [ + "## Aim: Get an overview of NumPy and some useful functions." + ] + }, + { + "cell_type": "markdown", + "id": "0364bf4f-20d1-49bc-8e2e-90f636e579ea", + "metadata": {}, + "source": [ + "You can find the teaching resources for this lesson here: https://numpy.org/doc/stable/user/quickstart.html" + ] + }, + { + "cell_type": "markdown", + "id": "b169c5eb-8467-4830-ba53-8707ad9642d0", + "metadata": {}, + "source": [ + "### Issues covered:\n", + "- Shape manipulation: changing shape, stacking, splitting\n", + "- Copies and views" + ] + }, + { + "cell_type": "markdown", + "id": "bd16b618-3891-4613-9257-a3fbaba8f53f", + "metadata": {}, + "source": [ + "## 2. Shape manipulation" + ] + }, + { + "cell_type": "markdown", + "id": "776d6a74-7fd7-4a76-9388-694a241fcfdf", + "metadata": {}, + "source": [ + "### Changing the shape" + ] + }, + { + "cell_type": "markdown", + "id": "34b119db-ac4b-44cb-96a8-ca00e89cb2db", + "metadata": {}, + "source": [ + "Q1. Use the following to create a 3x4 array: \n", + "```\n", + "rg = np.random.default_rng(1)\n", + "a = np.floor(10 * rg.random((3, 4)))\n", + "```\n", + "Then use the `ravel` method to flatten the array and print it." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "addaf639-10ae-402b-a6e8-6dbbdab747f7", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:44:43.926389Z", + "iopub.status.busy": "2024-11-07T16:44:43.925221Z", + "iopub.status.idle": "2024-11-07T16:44:44.456971Z", + "shell.execute_reply": "2024-11-07T16:44:44.455705Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[[5. 9. 1. 9.]\n", + " [3. 4. 8. 4.]\n", + " [5. 0. 7. 5.]]\n" + ] + }, + { + "data": { + "text/plain": [ + "array([5., 9., 1., 9., 3., 4., 8., 4., 5., 0., 7., 5.])" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import numpy as np\n", + "rg = np.random.default_rng(1)\n", + "a = np.floor(10 * rg.random((3, 4)))\n", + "print(a)\n", + "a.ravel()" + ] + }, + { + "cell_type": "markdown", + "id": "4d060d03-973b-4916-80d9-29382400419f", + "metadata": {}, + "source": [ + "Q2. Reshape the array so it has the shape (2,6) and print it." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ddd29757-7974-4a25-a3ad-34fbe2910a62", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:44:44.460969Z", + "iopub.status.busy": "2024-11-07T16:44:44.460546Z", + "iopub.status.idle": "2024-11-07T16:44:44.467996Z", + "shell.execute_reply": "2024-11-07T16:44:44.467190Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[5., 9., 1., 9., 3., 4.],\n", + " [8., 4., 5., 0., 7., 5.]])" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "b = a.reshape(2, 6)\n", + "b" + ] + }, + { + "cell_type": "markdown", + "id": "78cd0758-225c-4b58-8e84-1e4e3872293f", + "metadata": {}, + "source": [ + "Q3. Transpose the array and print it." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "ff5c18e1-ea8a-4d78-8b6c-38ac268fe6dc", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:44:44.470550Z", + "iopub.status.busy": "2024-11-07T16:44:44.470296Z", + "iopub.status.idle": "2024-11-07T16:44:44.489171Z", + "shell.execute_reply": "2024-11-07T16:44:44.487883Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[5., 3., 5.],\n", + " [9., 4., 0.],\n", + " [1., 8., 7.],\n", + " [9., 4., 5.]])" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "c = a.T\n", + "c" + ] + }, + { + "cell_type": "markdown", + "id": "bf99d355-263c-4448-9160-cb433c6297fd", + "metadata": {}, + "source": [ + "Q4. Use the resize method to change the shape of the array to (6,2). Notice the difference between reshape and resize." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "65805c3d-bfef-4cd2-9a20-d123078f9672", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:44:44.494484Z", + "iopub.status.busy": "2024-11-07T16:44:44.494103Z", + "iopub.status.idle": "2024-11-07T16:44:44.508456Z", + "shell.execute_reply": "2024-11-07T16:44:44.507225Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[5., 9.],\n", + " [1., 9.],\n", + " [3., 4.],\n", + " [8., 4.],\n", + " [5., 0.],\n", + " [7., 5.]])" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# reshape returns the argument with a modified shape but resize modifies the array itself\n", + "a.resize(6,2)\n", + "a" + ] + }, + { + "cell_type": "markdown", + "id": "0254d87c-8cf2-42e9-9193-fb0a57b99f71", + "metadata": {}, + "source": [ + "Q5. Reshape the array to a shape of (3, -1) and print the reshaped array. Note what `-1` does here." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "ecd467a6-738c-4b3a-b37a-5f7ded7bb544", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:44:44.514020Z", + "iopub.status.busy": "2024-11-07T16:44:44.513435Z", + "iopub.status.idle": "2024-11-07T16:44:44.532472Z", + "shell.execute_reply": "2024-11-07T16:44:44.530794Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[5., 9., 1., 9.],\n", + " [3., 4., 8., 4.],\n", + " [5., 0., 7., 5.]])" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "d =a.reshape(3, -1)\n", + "d" + ] + }, + { + "cell_type": "markdown", + "id": "e24b0985-7938-4d08-aa8a-ffe5b6741823", + "metadata": {}, + "source": [ + "### Stacking" + ] + }, + { + "cell_type": "markdown", + "id": "9150a86b-b60c-4235-99f1-df8a72ea383d", + "metadata": {}, + "source": [ + "Q6.\n", + "- Write a function `stack_arrays` that takes two 2D arrays `a` and `b`, an axis argument and returns the arrays stacked along the specified axis. The function should handle the following cases:\n", + " - Vertical stacking (`axis=0`): Stack the arrays along rows\n", + " - Horizontal stacking (`axis=1`): Stack the arrays along columns\n", + " - Column stacking (`axis=column`): Stack 1D arrays as columns of a 2D array if both a and b are 1D, if they are 2D stack them horizontally\n", + " - If `axis` is set to any other value raise a `ValueError`\n", + "- Once you're happy with your function, try the test cases in the solutions to check your working!" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "21527484-619e-47c0-a222-092ee88fb478", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:44:44.538232Z", + "iopub.status.busy": "2024-11-07T16:44:44.537362Z", + "iopub.status.idle": "2024-11-07T16:44:44.559570Z", + "shell.execute_reply": "2024-11-07T16:44:44.558050Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Vertical stacking:\n", + " [[9. 7.]\n", + " [5. 2.]\n", + " [1. 9.]\n", + " [5. 1.]]\n", + "\n", + "Horizontal stacking:\n", + " [[9. 7. 1. 9.]\n", + " [5. 2. 5. 1.]]\n", + "\n", + "Column stacking (1D arrays):\n", + " [[4. 3.]\n", + " [2. 8.]]\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "def stack_arrays(a, b, axis):\n", + " if axis == 0:\n", + " return np.vstack((a, b))\n", + " elif axis == 1:\n", + " return np.hstack((a,b))\n", + " elif axis == 'column':\n", + " return np.column_stack((a,b))\n", + " else:\n", + " raise ValueError(\"Invalid axis specified. Use 0, 1 or 'column'.\")\n", + "\n", + "# Test cases\n", + "a = np.array([[9,7], [5,2]])\n", + "b = np.array([[1., 9.], [5., 1.]])\n", + "c = np.array([4., 2.])\n", + "d = np.array([3., 8.])\n", + "\n", + "# Vertical stacking\n", + "print(\"Vertical stacking:\\n\", stack_arrays(a, b, axis=0))\n", + "\n", + "# Horizontal stacking\n", + "print(\"\\nHorizontal stacking:\\n\", stack_arrays(a, b, axis=1))\n", + "\n", + "# Column stacking for 1D arrays\n", + "print(\"\\nColumn stacking (1D arrays):\\n\", stack_arrays(c, d, axis='column'))" + ] + }, + { + "cell_type": "markdown", + "id": "54b6c868-dfc8-4113-bf8c-4ec92b72d6c4", + "metadata": {}, + "source": [ + "### Splitting" + ] + }, + { + "cell_type": "markdown", + "id": "9150e1f6-639b-4bce-9777-c35e96e49de8", + "metadata": {}, + "source": [ + "Q7.\n", + "Given the following 2D array `b`:\n", + "```\n", + "rg = np.random.default_rng(42)\n", + "b = np.floor(10 * rg.random((2, 10)))\n", + "```\n", + "- Split the array equally using `np.hsplit` into 5 parts along the horizontal axis. Assign the resulting sub-arrays to a variable named `equal_splits`\n", + "- Split the array after the second and fifth columns using `np.hsplit`. Assign the resulting sub-arrays to a variable called `column_splits`." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "c7d0c083-8f9d-4431-a68e-a57ee23fa4ff", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:44:44.565408Z", + "iopub.status.busy": "2024-11-07T16:44:44.564948Z", + "iopub.status.idle": "2024-11-07T16:44:44.582739Z", + "shell.execute_reply": "2024-11-07T16:44:44.581210Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Equal Splits: [array([[7., 4.],\n", + " [3., 9.]]), array([[8., 6.],\n", + " [6., 8.]]), array([[0., 9.],\n", + " [4., 2.]]), array([[7., 7.],\n", + " [5., 0.]]), array([[1., 4.],\n", + " [8., 6.]])]\n", + "Column Splits: [array([[7., 4.],\n", + " [3., 9.]]), array([[8., 6., 0.],\n", + " [6., 8., 4.]]), array([[9., 7., 7., 1., 4.],\n", + " [2., 5., 0., 8., 6.]])]\n" + ] + } + ], + "source": [ + "rg = np.random.default_rng(42)\n", + "b = np.floor(10 * rg.random((2, 10)))\n", + "\n", + "# Step 1: Split array b into 5 equal parts\n", + "equal_splits = np.hsplit(b, 5)\n", + "\n", + "# Step 2: Split array after second and fifth columns\n", + "column_splits = np.hsplit(b, (2, 5))\n", + "\n", + "# Print results\n", + "print(\"Equal Splits:\", equal_splits)\n", + "print(\"Column Splits:\", column_splits)" + ] + }, + { + "cell_type": "markdown", + "id": "f5c2d6e1-e758-459c-bd22-192651a96507", + "metadata": {}, + "source": [ + "## 3. Copies and views" + ] + }, + { + "cell_type": "markdown", + "id": "dff98f88-d2e0-4b90-ba24-9752ffe36888", + "metadata": {}, + "source": [ + "Q8. Let's demonstrate No Copy:\n", + "- Write a function `test_no_copy()` that:\n", + " - Creates a `3x3` Numpy array `a`.\n", + " - Assigns `b=a` and checks if modifying `b` affects `a`.\n", + " - Returns `True` if `b` is a reference to `a` i.e. no copy is made, and `False` otherwise\n", + " - Hint: use `is` to verify if `a` and `b` are the same object." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "509b0f88-170f-49de-9e25-672bde58738e", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:44:44.588398Z", + "iopub.status.busy": "2024-11-07T16:44:44.587869Z", + "iopub.status.idle": "2024-11-07T16:44:44.603287Z", + "shell.execute_reply": "2024-11-07T16:44:44.602046Z" + }, + "scrolled": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Testing no copy behavior: True\n" + ] + } + ], + "source": [ + "def test_no_copy():\n", + " a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\n", + " b = a\n", + " b[0, 0] = 999\n", + " return b is a # Check if 'b' is a reference to 'a'\n", + "\n", + "print(\"Testing no copy behavior:\", test_no_copy()) # Expected: True" + ] + }, + { + "cell_type": "markdown", + "id": "c1094086-cb82-4541-b9a1-811773050bf8", + "metadata": {}, + "source": [ + "Q9. Let's demonstrate Shallow Copy:\n", + "- Write a function `test_shallow_copy()` that:\n", + " - Creates a `3x3` Numpy array `a`.\n", + " - Creates a shallow copy of `a` using `a.view()` and assigns it to `c`.\n", + " - Modifies an element in `c` and checks if the change is reflected in `a`\n", + " - Verifies that `a` and `c` are not the same object but share data\n", + " - Returns `True` if the modification in `c` also modifies `a` and `False` otherwise\n", + " - Hint: Use `is` to confirm `a` and `c` are different objects, and `c.base is a` to confirm shared data. " + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "4788a3fb-9483-4417-9e6d-52a5cc15de64", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:44:44.609160Z", + "iopub.status.busy": "2024-11-07T16:44:44.608733Z", + "iopub.status.idle": "2024-11-07T16:44:44.631397Z", + "shell.execute_reply": "2024-11-07T16:44:44.629854Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Testing shallow copy behavior: True\n" + ] + } + ], + "source": [ + "def test_shallow_copy():\n", + " a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\n", + " c = a.view()\n", + " c[0, 0] = 999\n", + " return c is not a and c.base is a # Verify 'c' is a view of 'a'\n", + "\n", + "print(\"Testing shallow copy behavior:\", test_shallow_copy()) # Expected: True" + ] + }, + { + "cell_type": "markdown", + "id": "fd0dc68d-9a11-4f86-a4c9-c1e8fd10946c", + "metadata": {}, + "source": [ + "Q10. Let's demonstrate Deep Copy: \n", + "- Write a function `test_deep_copy()`that:\n", + " - Creates a `3x3` Numpy array `a`\n", + " - Creates a deep copy of `a` using `a.copy()` and assigns it to `d`\n", + " - Modifies an element in `d` and checks if the change is reflected in `a`\n", + " - Verifies that `a` and `d` do not share data\n", + " - Returns `True` if `a` and `d` do not share data and `False` if they do. " + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "899f1587-f732-487a-a06c-b4f58d9d9af7", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:44:44.637952Z", + "iopub.status.busy": "2024-11-07T16:44:44.637302Z", + "iopub.status.idle": "2024-11-07T16:44:44.657136Z", + "shell.execute_reply": "2024-11-07T16:44:44.655518Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Testing deep copy behavior: True\n" + ] + } + ], + "source": [ + "def test_deep_copy():\n", + " a = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])\n", + " d = a.copy()\n", + " d[0, 0] = 999\n", + " return d.base is None and np.any(a != d) # Check if 'd' is a true deep copy\n", + "\n", + "print(\"Testing deep copy behavior:\", test_deep_copy()) # Expected: True" + ] + }, + { + "cell_type": "markdown", + "id": "d2493f55-f912-44bf-8ea2-90bb588d490a", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q11. Let's demonstrate Memory Management: \n", + "- Write a function `memory_management_example()` that:\n", + " - Creates a large array `a` of 10 million elements\n", + " - Creates a slice of `a` containing the first 10 elements and assigns it to `b`\n", + " - Deletes `a` and observes what happens to `b`\n", + " - Creates another slice of `a` containing the first 1- elements but it copies it deeply this time assigning it to `c`\n", + " - Deletes `a` and observes if `c` is still accessible\n", + " - Returns `True` if `b` cannot be accessed after deleting `a`, but `c` can and `False` otherwise\n", + " - Hint: use a try-except block to handle errors from accessing `b` after deleting `a`" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "91110ee3-d12d-41ba-8394-82b32f5f2e6f", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-07T16:44:44.663498Z", + "iopub.status.busy": "2024-11-07T16:44:44.662919Z", + "iopub.status.idle": "2024-11-07T16:44:44.725925Z", + "shell.execute_reply": "2024-11-07T16:44:44.724218Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Testing memory management example: False\n" + ] + } + ], + "source": [ + "def memory_management_example():\n", + " a = np.arange(int(1e7))\n", + " b = a[:10]\n", + " del a\n", + " try:\n", + " b_sum = b.sum() # Check if 'b' is accessible after deleting 'a'\n", + " except NameError:\n", + " b_accessible = False\n", + " else:\n", + " b_accessible = True\n", + "\n", + " a = np.arange(int(1e7)) # Re-create 'a' for the next test\n", + " c = a[:10].copy()\n", + " del a\n", + " try:\n", + " c_sum = c.sum() # Check if 'c' is accessible after deleting 'a'\n", + " except NameError:\n", + " c_accessible = False\n", + " else:\n", + " c_accessible = True\n", + "\n", + " return not b_accessible and c_accessible\n", + "\n", + "print(\"Testing memory management example:\", memory_management_example()) # Expected: True" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 + Jaspy", + "language": "python", + "name": "jaspy" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/python-data/solutions/ex07_netcdf4_basics.ipynb b/python-data/solutions/ex07_netcdf4_basics.ipynb new file mode 100644 index 0000000..3d11da4 --- /dev/null +++ b/python-data/solutions/ex07_netcdf4_basics.ipynb @@ -0,0 +1,718 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "4fc1b0c3-f3ec-4724-b24c-0e42bfdb2cb4", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "# Exercise 7: NetCDF4 Basics" + ] + }, + { + "cell_type": "markdown", + "id": "0ac81b88-7771-4404-89cd-d9ec233651d7", + "metadata": {}, + "source": [ + "## Aim: Introduce the netCDF4 library in Python to read and create NetCDF4 Files." + ] + }, + { + "cell_type": "markdown", + "id": "6457c36c-b7ba-44a7-861e-0ae678d5412c", + "metadata": {}, + "source": [ + "Find the teaching material here: https://unidata.github.io/netcdf4-python/" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "id": "5512bea4-24d3-415b-8572-d770111ba0b6", + "metadata": {}, + "source": [ + "### Issues covered:\n", + "- Importing netCDF4\n", + "- Groups, dimensions, variables and attributes\n", + "- Writing data and retrieving it from variables" + ] + }, + { + "cell_type": "markdown", + "id": "81916b14-ec1c-4e8b-af3f-93d00377e9ff", + "metadata": {}, + "source": [ + "## Creating/opening/closing netCDF files" + ] + }, + { + "cell_type": "markdown", + "id": "a47cb092-895b-411d-8ac2-2b7df0d1138d", + "metadata": {}, + "source": [ + "Q1.\n", + "- Import the `netCDF4` library\n", + "- Let's create a new NetCDF file called `test.nc` in appending mode (`a`) with the `NETCDF4` format. This mode will allow us to edit the dataset later. Save this to a variable called `new_file`.\n", + "- Inspect the new file to see what its `data_model` is." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "261f60af-c7ec-4cb3-8859-5d173a534a1d", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:38.682331Z", + "iopub.status.busy": "2024-11-08T14:55:38.681747Z", + "iopub.status.idle": "2024-11-08T14:55:39.246105Z", + "shell.execute_reply": "2024-11-08T14:55:39.245107Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'NETCDF4'" + ] + }, + "execution_count": 1, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Step 1: Import netCDF4 library\n", + "import netCDF4\n", + "# Step 2: Create the new file\n", + "new_file = netCDF4.Dataset(\"data/test.nc\", \"a\", format=\"NETCDF4\")\n", + "# Step 3: Check the new file out\n", + "new_file.data_model" + ] + }, + { + "cell_type": "markdown", + "id": "1c4e8491-204a-4bcf-9696-3535a37c7b8d", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## Groups, dimensions, variables and attributes" + ] + }, + { + "cell_type": "markdown", + "id": "4649d782-c96b-4bc5-840b-231345ed4c79", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Groups" + ] + }, + { + "cell_type": "markdown", + "id": "c5ecfa40-dcd9-4728-aba7-57fef3dd089e", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q2. Groups act as containers for variables, dimensions and attributes.\n", + "- Add a new group to the dataset we just made called \"forecasts\".\n", + "- Create a new group within forecasts called `model1`.\n", + "- List the groups of your dataset using `.groups`\n", + "- What happens if you do `group3 = new_file.createGroup(\"/analyses/model2\")`?\n", + "- What happens if you do `group4 = new_file.createGroup(\"analyses\")`?" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "6c3c7a16-5d5f-4f01-b83e-a20fe1c96392", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:39.250340Z", + "iopub.status.busy": "2024-11-08T14:55:39.249830Z", + "iopub.status.idle": "2024-11-08T14:55:39.257372Z", + "shell.execute_reply": "2024-11-08T14:55:39.256627Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "{'forecasts': \n", + " group /forecasts:\n", + " dimensions(sizes): \n", + " variables(dimensions): \n", + " groups: model1,\n", + " 'analyses': \n", + " group /analyses:\n", + " dimensions(sizes): \n", + " variables(dimensions): \n", + " groups: model2}" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Step 1: Create a new group called forecasts\n", + "group1 = new_file.createGroup(\"forecasts\")\n", + "# Step 2: Create a group within forecasts called model1\n", + "group2 = new_file.createGroup(\"/forecasts/model1\")\n", + "# Step 3: Print out the groups\n", + "new_file.groups\n", + "# Step 4: Try creating model2 within the analyses group which doesn't exist yet\n", + "# It creates the 'analyses' group then adds the 'model2' group to it.\n", + "group3 = new_file.createGroup(\"/analyses/model2\")\n", + "new_file.groups\n", + "# Step 5: Try creating the existing group analyses.\n", + "# Nothing - it returns the existing group.\n", + "group4 = new_file.createGroup(\"analyses\")\n", + "new_file.groups" + ] + }, + { + "cell_type": "markdown", + "id": "b898110e-14e8-464d-9a4f-bd19a273c7cb", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Dimensions" + ] + }, + { + "cell_type": "markdown", + "id": "4bdf87db-5455-45a2-ae47-22febfa0a20e", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q3.\n", + "- Create some dimensions for the `new_file` dataset:\n", + " - `time` dimension with unlimited size\n", + " - `level` dimension with unlimited size\n", + " - `lat` dimension with unlimited size\n", + " - `lon` dimension with unlimited size\n", + "- Print out the dimensions you just created.\n", + "- Check the length of the latitude dimension to make sure it is 0.\n", + "- Check that the level dimension is unlimited.\n", + "- Let's take a look at an overview using \n", + "```\n", + "for dim in new_file.dimensions.values():\n", + " print(dim)\n", + "```" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "037ca268-58fa-4657-a527-112d08cc16b8", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:39.260641Z", + "iopub.status.busy": "2024-11-08T14:55:39.260294Z", + "iopub.status.idle": "2024-11-08T14:55:39.269039Z", + "shell.execute_reply": "2024-11-08T14:55:39.268334Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0\n", + "True\n", + " (unlimited): name = 'time', size = 0\n", + " (unlimited): name = 'level', size = 0\n", + " (unlimited): name = 'lat', size = 0\n", + " (unlimited): name = 'lon', size = 0\n" + ] + } + ], + "source": [ + "# Step 1: Create the new dimensions\n", + "time = new_file.createDimension('time', None)\n", + "level = new_file.createDimension('level', None)\n", + "lat = new_file.createDimension('lat', None)\n", + "lon = new_file.createDimension('lon', None)\n", + "# Step 2: Print out the dimensions\n", + "new_file.dimensions\n", + "# Step 3: Check the length of the latitude dimension - it should be 0!\n", + "print(len(lat))\n", + "# Step 4: Check that the level dimension is unlimited - should be True!\n", + "print(level.isunlimited())\n", + "# Step 5: Take a look at an overview of our dimensions values.\n", + "for dim in new_file.dimensions.values():\n", + " print(dim)" + ] + }, + { + "cell_type": "markdown", + "id": "56511ca9-39a1-466a-9516-d801ab53407f", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "### Variables" + ] + }, + { + "cell_type": "markdown", + "id": "68e7d217-ea7c-4ea2-af69-942239aebb29", + "metadata": {}, + "source": [ + "Remember that the data types are as follows:\n", + "- `f4`: 32-bit floting point \n", + "- `f8`: 64-bit floating point \n", + "- `i4`: 32-bit signed integer \n", + "- `i2`: 16-bit signed integer\n", + "- `i8`: 64-bit unsigned integer\n", + "- `i1`: 8-bit signed integer\n", + "- `u1`: 8-bit unsigned integer\n", + "- `u2`: 16-bit unsigned integer\n", + "- `u4`: 32-bit unsigned integer\n", + "- `u8`: 64-bit unsigned integer\n", + "- `S1`: single-character string" + ] + }, + { + "cell_type": "markdown", + "id": "1b7176b7-79b9-4e38-99f5-5491d91de3a2", + "metadata": {}, + "source": [ + "Q4.\n", + "- Create a scalar variable called `times` with the type set to `f8`.\n", + "- Create a scalar variable called `levels` but this time set the type to `np.float64`. (You'll need to import numpy as np)\n", + "- Print out the variables using `new_file.variables`. What do you notice about the types?\n", + "- Create a variable in the `model2` group we made earlier called `temp`, with the `float64` type and this time give it dimensions: (`time`, `level`, `lat`, `lon`). Print it out.\n", + "- Create two values: \n", + " - `longitudes` with the name `lon`, type `float64` and dimension `lon`\n", + " - `latitudes` with the name `lat`, type `float64` and dimension `lat`" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "292ca4fe-3681-4b5e-8b2e-c681a3aa9249", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:39.272376Z", + "iopub.status.busy": "2024-11-08T14:55:39.271846Z", + "iopub.status.idle": "2024-11-08T14:55:39.282042Z", + "shell.execute_reply": "2024-11-08T14:55:39.281363Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "{'times': \n", + "float64 times()\n", + "unlimited dimensions: \n", + "current shape = ()\n", + "filling on, default _FillValue of 9.969209968386869e+36 used, 'levels': \n", + "float64 levels()\n", + "unlimited dimensions: \n", + "current shape = ()\n", + "filling on, default _FillValue of 9.969209968386869e+36 used}\n", + "{'times': \n", + "float64 times()\n", + "unlimited dimensions: \n", + "current shape = ()\n", + "filling on, default _FillValue of 9.969209968386869e+36 used, 'levels': \n", + "float64 levels()\n", + "unlimited dimensions: \n", + "current shape = ()\n", + "filling on, default _FillValue of 9.969209968386869e+36 used}\n" + ] + } + ], + "source": [ + "# Step 1: Create the times variable\n", + "times = new_file.createVariable('times', 'f8')\n", + "# Step 2: Create the levels variable\n", + "import numpy as np\n", + "levels = new_file.createVariable('levels', np.float64)\n", + "# Step 3: Print out the variables\n", + "# The types are the same - both float64. Sometimes people will use np.float64 as it is more clear than f8. \n", + "print(new_file.variables)\n", + "# Step 4: Create the temp variable within the model2 group.\n", + "temp = new_file.createVariable(\"/analyses/model2/temp\", np.float64, (\"time\", \"level\", \"lat\", \"lon\",))\n", + "print(new_file.variables)\n", + "# Step 5: Create latitudes and longitudes\n", + "longitudes = new_file.createVariable(\"lon\", np.float64, (\"lon\",))\n", + "latitudes = new_file.createVariable(\"lat\", np.float64, (\"lat\",))" + ] + }, + { + "cell_type": "markdown", + "id": "cf76552d-79d8-41c4-95fc-4616c02248b3", + "metadata": {}, + "source": [ + "### Attributes" + ] + }, + { + "cell_type": "markdown", + "id": "0be859b4-16a3-48d0-9242-b8c590d8508a", + "metadata": {}, + "source": [ + "Q5.\n", + "- Let's create a global attribute. Create an attribute on the `new_file` object called `.description` with the value `This is a test description.`.\n", + "- Let's create a variable attribute. Create an attribute on the `times` variable called `units` and put `hours`.\n", + "- Take a look at the attrs on `new_file` using `new_file.ncattrs()`. What does this show?\n", + "- To get the name AND description, use the following loop:\n", + "```\n", + "for name in new_file.ncattrs():\n", + " print(name, \":\", getattr(new_file, name))\n", + "```\n", + "- There is an easier way of doing this - using `new_file.__dict__`. Try it out!" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "5ae8dcf6-0184-4f83-b75c-9684a852d140", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:39.284955Z", + "iopub.status.busy": "2024-11-08T14:55:39.284674Z", + "iopub.status.idle": "2024-11-08T14:55:39.295226Z", + "shell.execute_reply": "2024-11-08T14:55:39.294641Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "description : This is a test description.\n" + ] + }, + { + "data": { + "text/plain": [ + "{'description': 'This is a test description.'}" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# Step 1: Create the .description attribute\n", + "new_file.description = \"This is a test description.\"\n", + "# Step 2: Create the units attribute\n", + "times.units = \"hours\"\n", + "# Step 3: Look at the new attributes we just made\n", + "# This just shows the name of the global attrs. Note it doesn't show the nested attributes.\n", + "new_file.ncattrs()\n", + "# Step 4: Get the name and description using the loop\n", + "for name in new_file.ncattrs():\n", + " print(name, \":\", getattr(new_file, name))\n", + "# Step 5: Get the name and description as a dict\n", + "new_file.__dict__" + ] + }, + { + "cell_type": "markdown", + "id": "df7ec8fe-81b3-4768-9f4b-c643d4a8c254", + "metadata": {}, + "source": [ + "## Writing data to and receiving data from netCDF variables" + ] + }, + { + "cell_type": "markdown", + "id": "d636cdaa-c646-406a-96e9-85810cea39ec", + "metadata": {}, + "source": [ + "Q6. \n", + "- Create an array to populate a new variable `lats` with using `lats = np.arange(-100, 100, 2)` and an array to populate the `lons` variable with using `lons = np.arange(-200, 200, 2)`.\n", + "- Print out the `latitudes` and `longitudes` variables we created earlier to see what they look like before we populate them.\n", + "- Populate the two variables with our data using `latitudes[:] = lats` and the same for longitudes.\n", + "- Print the data out and take a look." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "f14444cf-ff2b-4b90-b4fd-3af73ad640b4", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:39.297995Z", + "iopub.status.busy": "2024-11-08T14:55:39.297706Z", + "iopub.status.idle": "2024-11-08T14:55:39.310135Z", + "shell.execute_reply": "2024-11-08T14:55:39.309547Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "float64 lat(lat)\n", + "unlimited dimensions: lat\n", + "current shape = (0,)\n", + "filling on, default _FillValue of 9.969209968386869e+36 used\n", + "\n", + "float64 lon(lon)\n", + "unlimited dimensions: lon\n", + "current shape = (0,)\n", + "filling on, default _FillValue of 9.969209968386869e+36 used\n", + "latitudes =\n", + "[-90. -85. -80. -75. -70. -65. -60. -55. -50. -45. -40. -35. -30. -25.\n", + " -20. -15. -10. -5. 0. 5. 10. 15. 20. 25. 30. 35. 40. 45.\n", + " 50. 55. 60. 65. 70. 75. 80. 85. 90.]\n", + "longitudes =\n", + "[-180. -175. -170. -165. -160. -155. -150. -145. -140. -135. -130. -125.\n", + " -120. -115. -110. -105. -100. -95. -90. -85. -80. -75. -70. -65.\n", + " -60. -55. -50. -45. -40. -35. -30. -25. -20. -15. -10. -5.\n", + " 0. 5. 10. 15. 20. 25. 30. 35. 40. 45. 50. 55.\n", + " 60. 65. 70. 75. 80. 85. 90. 95. 100. 105. 110. 115.\n", + " 120. 125. 130. 135. 140. 145. 150. 155. 160. 165. 170. 175.]\n" + ] + } + ], + "source": [ + "# Step 1: Create the lats and lons arrays\n", + "lats = np.arange(-90, 91, 5)\n", + "lons = np.arange(-180, 180, 5)\n", + "# Step 2: Print the latitudes and longitudes variables\n", + "print(latitudes)\n", + "print(longitudes)\n", + "# Step 3: Populate the latitudes and longitudes variables\n", + "latitudes[:] = lats\n", + "longitudes[:] = lons\n", + "# Step 4: Print the data\n", + "print(\"latitudes =\\n{}\".format(latitudes[:]))\n", + "print(\"longitudes =\\n{}\".format(longitudes[:]))" + ] + }, + { + "cell_type": "markdown", + "id": "ce5d22bf-d201-424e-babc-11d9d431f834", + "metadata": {}, + "source": [ + "Q7.\n", + "- Extend `new_file` to have the dimension `pressure` with size 10.\n", + "- Define a 4D variable `temperature` with dimensions (time, pressure, latitude, longitude). Print the shape of the temperature variable to look at the size before populating with data.\n", + "- Generate random temperature data for a subset of time and pressure values - start by creating `nlats` and `nlons` equal to the length of the `lat` and `lon` dimensions. Assign random data to `temperature[0:10, 0:3, :, :]` using `np.random.uniform(size=(10,3, nlats, nlons))`.\n", + "- After assigning the data, print the shape of the `temperature` variable. Take a look at the size of it now." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "f4c4384f-a966-4615-a2da-77168568ae77", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:39.312687Z", + "iopub.status.busy": "2024-11-08T14:55:39.312397Z", + "iopub.status.idle": "2024-11-08T14:55:39.741786Z", + "shell.execute_reply": "2024-11-08T14:55:39.741066Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "temp shape befpre adding data=(0, 10, 37, 72)\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "temp shape after adding data = (10, 10, 37, 72)\n" + ] + } + ], + "source": [ + "import numpy as np\n", + "\n", + "# Step 1: Add the pressure dimension\n", + "new_file.createDimension(\"pressure\", 10)\n", + "\n", + "# Step 2: Define the temperature variable\n", + "temperature = new_file.createVariable(\"temperature\", \"f4\", (\"time\", \"pressure\", \"lat\", \"lon\",))\n", + "print(\"temp shape befpre adding data={}\".format(temperature.shape))\n", + "\n", + "# Step 3: Set nlats and nlons to the size of the lat and lon dimensions, then assign data to the temperature variable\n", + "nlats = len(new_file.dimensions[\"lat\"])\n", + "nlons = len(new_file.dimensions[\"lon\"])\n", + "temperature[0:10, 0:3, :, :] = np.random.uniform(size=(10, 3, nlats, nlons))\n", + "\n", + "# Step 4: Print out the temperature variable\n", + "print(\"temp shape after adding data = {}\".format(temperature.shape))" + ] + }, + { + "cell_type": "markdown", + "id": "12c17e3e-ad67-4c29-a426-e6390626ce25", + "metadata": {}, + "source": [ + "Q8. \n", + "- Define the `pressure` variable with type `f4` and the `pressure` dimension.\n", + "- Populate the `pressure` variable with the values [1000, 850, 700, 500, 300, 250, 200, 150, 100, 50].\n", + "- Extract the tempearture variable using `temperature = new_file.variables[\"temperature\"]`, the latitudes using `latitudes = new_file.variables[\"lat\"][:]` and the longitudes using `longitudes = new_file.variables[\"lon\"][:]`.\n", + "- Use fancy indexing to slice the temperature variable: select times 0, 2 and 4. Index the 2nd, 4th and 7th values of the pressures and select only positive latitudes and longitudes.\n", + "- Print the shape of the resulting subset array." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "769c08e7-6db7-4112-b49e-8003787b6882", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:55:39.745009Z", + "iopub.status.busy": "2024-11-08T14:55:39.744663Z", + "iopub.status.idle": "2024-11-08T14:55:39.882617Z", + "shell.execute_reply": "2024-11-08T14:55:39.881968Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "shape of fancy temp slice = (5, 3, 18, 35)\n" + ] + } + ], + "source": [ + "# Step 1: Define the pressure variable\n", + "pressure = new_file.createVariable(\"pressure\", \"f4\", (\"pressure\",))\n", + "\n", + "# Step 2: Popular the pressure variable\n", + "pressure[:] = [1000., 850., 700., 500., 300., 250., 200., 150., 100., 50.]\n", + "\n", + "# Step 3: Extract temperature, latitudes and longitudes\n", + "temperature = new_file.variables[\"temperature\"]\n", + "latitudes = new_file.variables[\"lat\"][:]\n", + "longitudes = new_file.variables[\"lon\"][:]\n", + "\n", + "# Step 4: Use fancy indexing to slice the temperature variable.\n", + "tempdat = temperature[::2, [1, 3, 6], latitudes > 0, longitudes > 0]\n", + "\n", + "# Step 5: Print the subset array.\n", + "print(\"shape of fancy temp slice = {}\".format(tempdat.shape))" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 + Jaspy", + "language": "python", + "name": "jaspy" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/python-data/solutions/ex08_netcdf4_advanced.ipynb b/python-data/solutions/ex08_netcdf4_advanced.ipynb new file mode 100644 index 0000000..c23d4a7 --- /dev/null +++ b/python-data/solutions/ex08_netcdf4_advanced.ipynb @@ -0,0 +1,738 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "22542fd5-6792-4df8-9122-fe35f3e4ddf5", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "# Exercise 8: NetCDF4 Advanced" + ] + }, + { + "cell_type": "markdown", + "id": "b8bc8ade-8ef9-4caa-b734-d0a0df52a450", + "metadata": {}, + "source": [ + "## Aim: Introduce more advanced uses of the netCDF4 library in Python to read and create NetCDF4 Files." + ] + }, + { + "cell_type": "markdown", + "id": "e92fc14e-6e03-49c3-99c1-7a7b1c2e52cf", + "metadata": {}, + "source": [ + "Find the teaching material here: https://unidata.github.io/netcdf4-python/" + ] + }, + { + "cell_type": "markdown", + "id": "c2a56e34-d2ba-451d-900c-09e33c404e24", + "metadata": {}, + "source": [ + "### Issues covered:\n", + "- Working with time coordinates\n", + "- Multi-file datasets\n", + "- Compression of variables\n", + "- Compound datatypes\n", + "- Enum data type" + ] + }, + { + "cell_type": "markdown", + "id": "b9ba87e0-e96e-490d-a0a6-1da86bae8084", + "metadata": {}, + "source": [ + "## Time-coordinates" + ] + }, + { + "cell_type": "markdown", + "id": "1c4acf3e-dc07-442e-a421-63efb78e0f79", + "metadata": {}, + "source": [ + "Most metadata standards specify that time should be measured relative to a fixed date with units such as `hours since YY-MM-DD hh:mm:ss`. We can convert values to and from calendar dates using `num2date` and `date2num` from the `cftime` library. Two other helpful functions are `datetime` and `timedelta` from the `datetime` library." + ] + }, + { + "cell_type": "markdown", + "id": "2fecb97e-5775-4575-9739-9ad8cb7c3f97", + "metadata": {}, + "source": [ + "Q1. \n", + "- Let's generate a list of data and time values: create a list called `dates` containing date and time values, starting from January 1st 2022, and incrementing by 6 hours for a total of 5 entries. \n", + "- Use `date2num` to convert your list of dates to numeric values using: `units=\"hours since 2022-01-01 00:00:00\"` amd `calendar=\"gregorian\"`. Store these in an array called `times`.\n", + "- Print the numeric times values to confirm the numeric representation.\n", + "- Use `num2date` to convert times back to datetime objects using the same units and calendar. Store these in a list called `converted_dates`\n", + "- Print the converted dates to verify they match the original dates list. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "477bd19f-2833-4bb6-a2ae-83ebb7fc4a3d", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:39.567060Z", + "iopub.status.busy": "2024-11-08T14:54:39.566736Z", + "iopub.status.idle": "2024-11-08T14:54:40.032446Z", + "shell.execute_reply": "2024-11-08T14:54:40.031854Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Original dates: [datetime.datetime(2022, 1, 1, 0, 0), datetime.datetime(2022, 1, 1, 6, 0), datetime.datetime(2022, 1, 1, 12, 0), datetime.datetime(2022, 1, 1, 18, 0), datetime.datetime(2022, 1, 2, 0, 0)]\n", + "Numeric time values (in units 'hours since 2022-01-01 00:00:00'):\n", + "[ 0 6 12 18 24]\n", + "Dates corresponding to numeric time values:\n", + " [cftime.DatetimeGregorian(2022, 1, 1, 0, 0, 0, 0, has_year_zero=False)\n", + " cftime.DatetimeGregorian(2022, 1, 1, 6, 0, 0, 0, has_year_zero=False)\n", + " cftime.DatetimeGregorian(2022, 1, 1, 12, 0, 0, 0, has_year_zero=False)\n", + " cftime.DatetimeGregorian(2022, 1, 1, 18, 0, 0, 0, has_year_zero=False)\n", + " cftime.DatetimeGregorian(2022, 1, 2, 0, 0, 0, 0, has_year_zero=False)]\n" + ] + } + ], + "source": [ + "from datetime import datetime, timedelta\n", + "from cftime import num2date, date2num\n", + "\n", + "# Step 1: Generate dates list\n", + "dates = [datetime(2022, 1, 1) + n * timedelta(hours=6) for n in range(5)]\n", + "print(\"Original dates:\", dates)\n", + "\n", + "# Step 2: Convert dates to numeric time values\n", + "units = \"hours since 2022-01-01 00:00:00\"\n", + "calendar = \"gregorian\"\n", + "times = date2num(dates, units=units, calendar=calendar)\n", + "\n", + "# Step 3: Print numeric time values\n", + "print(\"Numeric time values (in units '{}'):\\n{}\".format(units, times))\n", + "\n", + "# Step 4: Convert numeric time values back to calendar dates\n", + "converted_dates = num2date(times, units=units, calendar=calendar)\n", + "\n", + "# Step 5: Print converted dates\n", + "print(\"Dates corresponding to numeric time values:\\n\", converted_dates)" + ] + }, + { + "cell_type": "markdown", + "id": "7271d566-91d2-4d24-9213-39d6d28d2a0d", + "metadata": {}, + "source": [ + "## Multi-file datasets" + ] + }, + { + "cell_type": "markdown", + "id": "95cb2707-b27e-42ac-b80f-cf255b1a3c0f", + "metadata": {}, + "source": [ + "Q2. Let's create multiple netCDF files with a shared variable and unlimited dimension, and use `MFDataset` to read the aggregated data as if it were contained in a single file.\n", + "- Create 5 netCDF files named `data/datafile0.nc` through to `data/datafile4.nc`. Each file should contain:\n", + " - A single unlimited dimension named `time`.\n", + " - A variable named `temperature` with 10 integer values ranging from `file_index * 10` to `(file_index+1) * 10 - 1`.\n", + " - Ensure each file is saved in the `NETCDF4_CLASSIC` format.\n", + " - **Hint: Use a loop such as `for .. in range(..):` to do this task.**\n", + "- Using `MFDataset` read all the `temperature` data from the 5 files at once by specifying a wildcard string `datafile*.nc` - store this in a variable `f`. Assign this data to a new variable using `temperature_data = f.variables[\"temperature\"][:]`\n", + "- Print the aggregated `temperature` values to verify that they span from 0 to 49." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "3c6df18f-87e8-4051-a701-60d169656701", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:40.035145Z", + "iopub.status.busy": "2024-11-08T14:54:40.034779Z", + "iopub.status.idle": "2024-11-08T14:54:40.104560Z", + "shell.execute_reply": "2024-11-08T14:54:40.103872Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n", + " 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47\n", + " 48 49]\n" + ] + } + ], + "source": [ + "from netCDF4 import Dataset, MFDataset\n", + "import numpy as np\n", + "\n", + "# Step 1: Create multiple netCDF files with a shared unlimited dimension and variable\n", + "for i in range(5):\n", + " with Dataset(f\"data/datafile{i}.nc\", \"w\", format=\"NETCDF4_CLASSIC\") as f:\n", + " # Create an unlimited dimension\n", + " f.createDimension(\"time\", None)\n", + " # Create a variable associated with the 'time' dimension\n", + " temp_var = f.createVariable(\"temperature\", \"i4\", (\"time\",))\n", + " # Populate 'temperature' with a unique range of values for each file\n", + " temp_var[:] = np.arange(i * 10, (i+1) * 10)\n", + "\n", + "# Step 2: Use MFDataset to read all files at once\n", + "try:\n", + " #Read and aggregate all data across all files\n", + " f = MFDataset(\"data/datafile*.nc\")\n", + " temperature_data = f.variables[\"temperature\"][:]\n", + " # Print the aggregated data\n", + " print(temperature_data)\n", + "finally:\n", + " # Close the MFDataset object\n", + " f.close()" + ] + }, + { + "cell_type": "markdown", + "id": "28e3eb27-a38b-4de7-beec-a04f95922561", + "metadata": {}, + "source": [ + "## Compression of variables" + ] + }, + { + "cell_type": "markdown", + "id": "c2727e4a-5009-4678-b227-e40d49b576e6", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Q3. Let's explore various compression options available in netCDF. \n", + "- Run the following cell to create an array of random temperature data and create a function to create NetCDF files with given compression settings. Take a look at the function and figure out what it's doing." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f9fea430-9b8f-4f65-ba2e-ee600cb0e0e2", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:40.109135Z", + "iopub.status.busy": "2024-11-08T14:54:40.108718Z", + "iopub.status.idle": "2024-11-08T14:54:40.118891Z", + "shell.execute_reply": "2024-11-08T14:54:40.118326Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "import os\n", + "\n", + "# Step 1: Create a random dataset \n", + "time_dim, level_dim, lat_dim, lon_dim = 10, 5, 50, 100\n", + "data = np.random.rand(time_dim, level_dim, lat_dim, lon_dim) * 30 + 273.15\n", + "\n", + "# Step 2: Create a function to create NetCDF files with the given compression settings:\n", + "file_path = \"data/temperature_data.nc\"\n", + "def create_netcdf(file_path, compression=None, least_significant_digit=None, significant_digits=None):\n", + " with Dataset(file_path, 'w', format=\"NETCDF4\") as rootgrp:\n", + " # Create dimensions\n", + " rootgrp.createDimension(\"time\", time_dim)\n", + " rootgrp.createDimension(\"level\", level_dim)\n", + " rootgrp.createDimension(\"lat\", lat_dim)\n", + " rootgrp.createDimension(\"lon\", lon_dim)\n", + " # Define variable with compression settings\n", + " temp = rootgrp.createVariable(\"temp\", \"f4\", (\"time\", \"level\", \"lat\", \"lon\"), compression=compression, least_significant_digit=least_significant_digit, significant_digits=significant_digits)\n", + " #Assign data to the variable\n", + " temp[:] = data\n", + " # Check and print file size\n", + " print(f\"File size with compression={compression}, \"\n", + " f\"least_significant_digit={least_significant_digit}, \"\n", + " f\"significant_digits={significant_digits}: {os.path.getsize(file_path) / 1024:.2f} kB\")" + ] + }, + { + "cell_type": "markdown", + "id": "8e50f691-f784-4e3c-bffc-9520cc08f703", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "- Use this function to test the following cases:\n", + " - First, create the temperature variable without compression and observe the file size. Use the file path `data/temperature_data_no_compress.nc`.\n", + " - Then, enable zlib compression and observe the change in file size. Use the file path `data/temperature_data_zlib.nc`.\n", + " - Next, add zlib and least signigicant digit quantization (`least_significant_digit=3`) and check the file size again. Use the file path `data/temperature_data_zlib_lsd.nc`.\n", + " - Finally, add zlib and significant digits quantization (`significant_digits=4`) and check the file size again. Use the file path `data/temperature_data_zlib_sig.nc`.\n", + " - Hint: call the function using: `create_netcdf(filepath, compression, least_significant_digit, significant_digit)`. Note that the default for the compression/signigificant digits arguments is None so if you don't need them you can omit them when calling the function." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "73365563-9cf4-4b3b-9be8-c9814332b90a", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:40.121272Z", + "iopub.status.busy": "2024-11-08T14:54:40.121015Z", + "iopub.status.idle": "2024-11-08T14:54:40.299169Z", + "shell.execute_reply": "2024-11-08T14:54:40.298642Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "File size with compression=None, least_significant_digit=None, significant_digits=None: 983.31 kB\n", + "File size with compression=zlib, least_significant_digit=None, significant_digits=None: 639.42 kB\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "File size with compression=zlib, least_significant_digit=3, significant_digits=None: 505.93 kB\n", + "File size with compression=zlib, least_significant_digit=None, significant_digits=4: 396.40 kB\n" + ] + } + ], + "source": [ + "# Step 3: Test different compression settings\n", + "# 3.1 No compression\n", + "create_netcdf(\"data/temperature_data_nocompress.nc\")\n", + "\n", + "# 3.2 Compression with zlib only\n", + "create_netcdf(\"data/temperature_data_zlib.nc\", compression='zlib')\n", + "\n", + "# 3.3 Compression with zlib and least significant digit quantization\n", + "create_netcdf(\"data/temperature_data_zlib_lsd.nc\", compression='zlib', least_significant_digit=3)\n", + "\n", + "# 3.4 Compression with zlib and significant digits quantization\n", + "create_netcdf(\"data/temperature_data_zlib_sig.nc\", compression='zlib', significant_digits=4)" + ] + }, + { + "cell_type": "markdown", + "id": "393a6184-658e-48fb-a10b-1c8d302bade6", + "metadata": {}, + "source": [ + "## Compound data types" + ] + }, + { + "cell_type": "markdown", + "id": "19c598d5-5d89-430e-9178-ec9cac5efe3f", + "metadata": {}, + "source": [ + "Q4. Let's work with compound data types and structured arrays.\n", + "- Create a netCDF file called `data/vectors.nc` in write mode with `NETCDF4` format assigned to the variable `f`.\n", + "- Define a compound data type that represents a 3D vector. Each vector should have 3 components:\n", + " - `x`: a `float33` representing the x-coordinate\n", + " - `y`: a `float32` representing the y-coordinate\n", + " - `z`: a `float32` representing the z-coordinate\n", + " - Hint: use `np.dtype([('x', type), ('y'..), (...)])` to define x,y,z then `f.createCompoundType()` to create the compound data type.\n", + "- Create a dimension named `num_vectors` to store an unlimited number of vectors.\n", + "- Create a variable called `vector_data` in the file using the compound data type from step 2, with the dimension from step 3.\n", + "- Generate a numpy structured array with 5 sample 3D vectors:\n", + " - Each vector should have random values for `x`, `y` and `z` components (use `np.random.rand(num_samples)`).\n", + " - Store these in the structured array (initialize the array with `np.empty(num_samples, dtype)` then use `data[\"x\"]` etc to assign the data.\n", + " - Write them to the netCDF variable.\n", + "- Close the file and then reopen it in read mode.\n", + "- Read the data back into a new numpy structured array and print each vector.\n", + " - Hint: use `f.variables['var_name']` to read in the variable data.\n", + " - Hint: Use `data_in = vector_var[:]` to extract the data for the variables.\n", + " - Hint: Use `for i, vev in enumerate(data_in):` to loop through the data so you can print each vector." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "c92e87db-f092-4802-a346-aaeb158b6395", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:40.301564Z", + "iopub.status.busy": "2024-11-08T14:54:40.301300Z", + "iopub.status.idle": "2024-11-08T14:54:40.313735Z", + "shell.execute_reply": "2024-11-08T14:54:40.313229Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Vector 0: (x: 0.99, y: 0.74, z: 0.50)\n", + "Vector 1: (x: 0.07, y: 0.85, z: 0.78)\n", + "Vector 2: (x: 0.79, y: 0.02, z: 0.28)\n", + "Vector 3: (x: 0.21, y: 0.61, z: 0.37)\n", + "Vector 4: (x: 0.78, y: 0.71, z: 0.08)\n" + ] + } + ], + "source": [ + "# Step 1: Create a netCDF file in write mode.\n", + "f = Dataset(\"data/vectors.nc\", \"w\", format=\"NETCDF4\")\n", + "\n", + "# Step 2: Define a compound data type for a 3D vector with x,y,z as float32 fields\n", + "vector_dtype = np.dtype([(\"x\", np.float32), (\"y\", np.float32), (\"z\", np.float32)])\n", + "vector_t = f.createCompoundType(vector_dtype, \"vector3D\")\n", + "\n", + "# Step 3: Create a dimension for storing an unlimited number of vectors\n", + "num_vectors = f.createDimension(\"num_vectors\", None)\n", + "\n", + "# Step 4: Create a variable using the compound data type and the dimension\n", + "vector_var = f.createVariable(\"vector_data\", vector_t, (\"num_vectors\",))\n", + "\n", + "# Step 5: Generate a numpy structured array with 5 random 3D vectors\n", + "num_samples = 5\n", + "data = np.empty(num_samples, dtype=vector_dtype)\n", + "data[\"x\"] = np.random.rand(num_samples)\n", + "data[\"y\"] = np.random.rand(num_samples)\n", + "data[\"z\"] = np.random.rand(num_samples)\n", + "# Write the structured array to the netCDF variable\n", + "vector_var[:] = data\n", + "\n", + "# Step 6: Close and reopen in read mode\n", + "# Close the file\n", + "f.close()\n", + "# Reopen the file in read-mode\n", + "f = Dataset(\"data/vectors.nc\", \"r\")\n", + "\n", + "# Step 7: Read the data back into a new structured array and print each vector\n", + "vector_var = f.variables[\"vector_data\"]\n", + "data_in = vector_var[:]\n", + "for i, vec in enumerate(data_in):\n", + " print(f\"Vector {i}: (x: {vec['x']:.2f}, y: {vec['y']:.2f}, z: {vec['z']:.2f})\")\n", + "\n", + "# Close the file\n", + "f.close()" + ] + }, + { + "cell_type": "markdown", + "id": "1a537d6b-f892-444b-b63d-6bdbe4963e83", + "metadata": {}, + "source": [ + "## Variable-length data types" + ] + }, + { + "cell_type": "markdown", + "id": "faa9d0ba-0352-45bb-98cb-0443cecbc198", + "metadata": {}, + "source": [ + "Q5. Let's create and manipulate variable-length (vlen) arrays\n", + "- Create a netCDF file named `data/exercise_vlen.nc` in write mode.\n", + "- Define dimensions:\n", + " - Create a dimension `a` with a size of `5`.\n", + " - Create a dimension `b` with a size of `4`.\n", + "- Create a variable-length data type using `f.createVLType()` named `my_vlen_int` using `np.int32` as the datatype.\n", + "- Use the vlen type you defined to create a variable `vlen_var` with dimensions `(\"a\", \"b\")`.\n", + "- Populate `vlen_var` with random data:\n", + " - Use the following to generate the random data:\n", + " ```\n", + " data = np.empty((5,4), dtype=object)\n", + " for i in range(5):\n", + " for j in range(4):\n", + " random_length = random.randint(2, 8)\n", + " data[i,j] = np.random.randint(1, 101, size=random_length, dtype=np.int32)\n", + " ```\n", + " - Assign the data to the netCDF variable\n", + "- Create a new dimension `c` with a size of `7`.\n", + "- Define a variable `vlen_str_var` along dimension `c`.\n", + "- Populate this variable with random strings of lengths between 3 and 10 using uppercase and lowercase alphabetic characters using the following:\n", + " ```\n", + " chars = string.ascii_letters\n", + " string_data = np.empty(7, dtype=object)\n", + " for i in range(7):\n", + " random_length = random.randint(3,10)\n", + " string_data[i] = ''.join(random.choice(chars) for _ in range(random_length))\n", + " # Assign the string data to the netCDF variable\n", + " str_var[:] = string_data\n", + " ```\n", + "- Print the contents of `vlen_var` and `vlen_str_var`. Print the structure of the netCDF4 file to show defined dimensions, variables, and data types." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "70ba5df2-e554-47de-b164-11d2c926f63d", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:40.316032Z", + "iopub.status.busy": "2024-11-08T14:54:40.315783Z", + "iopub.status.idle": "2024-11-08T14:54:40.347230Z", + "shell.execute_reply": "2024-11-08T14:54:40.346679Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "clear_answer_cell" + ] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Contents of 'vlen_var':\n", + " [[array([93, 90, 94, 86], dtype=int32)\n", + " array([93, 45, 89, 80, 43], dtype=int32)\n", + " array([32, 42, 75, 38], dtype=int32)\n", + " array([49, 57, 48, 43, 79, 46, 98, 82], dtype=int32)]\n", + " [array([38, 98, 9], dtype=int32) array([63, 80], dtype=int32)\n", + " array([20, 31], dtype=int32) array([98, 58, 46], dtype=int32)]\n", + " [array([77, 2, 51, 17], dtype=int32)\n", + " array([62, 98, 25, 41, 62, 42, 86, 23], dtype=int32)\n", + " array([44, 52, 10, 56, 80, 29, 66, 17], dtype=int32)\n", + " array([68, 72], dtype=int32)]\n", + " [array([42, 74, 53], dtype=int32) array([68, 15, 91], dtype=int32)\n", + " array([13, 42, 30, 17, 96, 24, 88, 21], dtype=int32)\n", + " array([13, 96, 87, 76, 37], dtype=int32)]\n", + " [array([51, 85, 44, 37, 87, 2, 84], dtype=int32)\n", + " array([30, 92, 84, 11], dtype=int32)\n", + " array([ 83, 91, 2, 11, 76, 55, 100], dtype=int32)\n", + " array([98, 56, 55, 41, 89, 49, 68], dtype=int32)]]\n", + "Contents of 'vlen_str_var':\n", + " ['KwF' 'AdpMJjMhcu' 'pasfMCKgA' 'nJvVKGF' 'jVbc' 'hkUa' 'uPnH']\n", + "\n", + "NetCDF4 file structure:\n", + " \n", + "root group (NETCDF4 data model, file format HDF5):\n", + " dimensions(sizes): a(5), b(4), c(7)\n", + " variables(dimensions): int32 vlen_var(a, b), vlen_str_var(c)\n", + " groups: \n", + "Details of 'vlen_var':\n", + " \n", + "vlen vlen_var(a, b)\n", + "vlen data type: int32\n", + "unlimited dimensions: \n", + "current shape = (5, 4)\n", + "Details of 'vlen_str_var':\n", + " \n", + "vlen vlen_str_var(c)\n", + "vlen data type: \n", + "unlimited dimensions: \n", + "current shape = (7,)\n" + ] + } + ], + "source": [ + "import random\n", + "import string\n", + "\n", + "# Step 1: Create a netCDF file in write mode\n", + "f = Dataset(\"data/exercise_vlen.nc\", \"w\")\n", + "\n", + "# Step 2: Define dimensions a and b\n", + "f.createDimension(\"a\", 5)\n", + "f.createDimension(\"b\", 4)\n", + "\n", + "# Step 3: Create a variable-length data type for signed 32-bit integers\n", + "vlen_int_type = f.createVLType(np.int32, \"my_vlen_int\")\n", + "\n", + "# Step 4: Create and populate the variable length integer array\n", + "vlen_var = f.createVariable(\"vlen_var\", vlen_int_type, (\"a\", \"b\"))\n", + "\n", + "# Step 5: Populate vlen_var with random-length integer arrays\n", + "data = np.empty((5,4), dtype=object)\n", + "for i in range(5):\n", + " for j in range(4):\n", + " random_length = random.randint(2, 8)\n", + " data[i,j] = np.random.randint(1, 101, size=random_length, dtype=np.int32)\n", + "# Assign the data to the netCDF variable\n", + "vlen_var[:, :] = data\n", + "\n", + "# Step 6: Create a dimension 'c'\n", + "f.createDimension(\"c\", 7)\n", + "\n", + "# Step 7: Define a variable-length string array\n", + "str_var = f.createVariable(\"vlen_str_var\", str, (\"c\",))\n", + "\n", + "# Step 8: Populate vlen_str_var with random strings of lengths 3-10\n", + "chars = string.ascii_letters\n", + "string_data = np.empty(7, dtype=object)\n", + "for i in range(7):\n", + " random_length = random.randint(3,10)\n", + " string_data[i] = ''.join(random.choice(chars) for _ in range(random_length))\n", + "# Assign the string data to the netCDF variable\n", + "str_var[:] = string_data\n", + "\n", + "# Step 9: Print the contents of \"vlen_var\" and \"vlen_str_var\"\n", + "print(\"Contents of 'vlen_var':\\n\", vlen_var[:])\n", + "print(\"Contents of 'vlen_str_var':\\n\", str_var[:])\n", + "# Print the structure of the NetCDF4 file\n", + "print(\"\\nNetCDF4 file structure:\\n\", f)\n", + "print(\"Details of 'vlen_var':\\n\", f.variables[\"vlen_var\"])\n", + "print(\"Details of 'vlen_str_var':\\n\", f.variables[\"vlen_str_var\"])\n", + "\n", + "# Close the file\n", + "f.close()" + ] + }, + { + "cell_type": "markdown", + "id": "55abd572-29b3-47ea-9b9f-0e25364297d5", + "metadata": {}, + "source": [ + "## Enum data type" + ] + }, + { + "cell_type": "markdown", + "id": "6f972a56-42e9-499c-ab1e-940b022c616a", + "metadata": {}, + "source": [ + "Q6. Let's create a netCDF file to store weather observation data including an enumerated type representing different types of precipiation.\n", + "- Create a new netCDF file called `data/weather_data.nc` in write mode with the `NETCDF4` format.\n", + "- Create a Python dictionary `precip_dict` where:\n", + " - `None` maps to `0`\n", + " - `Rain` maps to `1`\n", + " - `Snow` maps to `2`\n", + " - `Sleet` maps to `3`\n", + " - `Hail` maps to `4`\n", + " - `Unknown` maps to `255`\n", + "- Use this dictionary to define an Enum data type using `.createEnumType()` called `precip_t` with a base type of `np.uint8`\n", + "- Define a dimension called `time` with an unlimited length for observations over time\n", + "- Create a 1D variable named `precipitation` of type `precip_type` that uses the `time` dimension and has `fill_value=precip_dict['Unknown']`. The fill value indicates missing data.\n", + "- Write the following precipiatation observations to the `precipitation` variable: `precip_var[:] = [precip_dict[k] for k in ['None', 'Rain', 'Snow', 'Unknown', 'Sleet']]`.\n", + "- Close and reopen the file in read mode, then print the contents of the `precipitation` variable, inlcuding: the data values confirming they match the written values, the enum dictionary associated with the enum data type, verifying the precipitation mapping. " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "f08fe449-acc3-41e3-bb36-3f6a2e63b98b", + "metadata": { + "editable": true, + "execution": { + "iopub.execute_input": "2024-11-08T14:54:40.349575Z", + "iopub.status.busy": "2024-11-08T14:54:40.349315Z", + "iopub.status.idle": "2024-11-08T14:54:40.374465Z", + "shell.execute_reply": "2024-11-08T14:54:40.373923Z" + }, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Enum dictionary: {'None': 0, 'Rain': 1, 'Snow': 2, 'Sleet': 3, 'Hail': 4, 'Unknown': 255}\n", + "Precipitation data: [0 1 2 -- 3]\n" + ] + } + ], + "source": [ + "# Step 1: Create a new netCDF file\n", + "nc = Dataset('data/weather_data.nc', 'w', format='NETCDF4')\n", + "\n", + "# Step 2: Define the Enum dictionary and create the Enum type\n", + "precip_dict = {\n", + " 'None': 0,\n", + " 'Rain': 1,\n", + " 'Snow': 2,\n", + " 'Sleet': 3,\n", + " 'Hail': 4,\n", + " 'Unknown': 255\n", + "}\n", + "\n", + "# Step 3: Create an Enum type called 'precip_t' with base type uint8\n", + "precip_type = nc.createEnumType(np.uint8, 'precip_t', precip_dict)\n", + "\n", + "# Step 4: Create a time dimension\n", + "nc.createDimension('time', None)\n", + "\n", + "# Step 5: Create the precipitation variable, setting the fill_value to 'Unknown'\n", + "precip_var = nc.createVariable('precipitation', precip_type, ('time',),\n", + " fill_value=precip_dict['Unknown'])\n", + "\n", + "# Step 6: Write data to the variable\n", + "precip_var[:] = [precip_dict[k] for k in ['None', 'Rain', 'Snow', 'Unknown', 'Sleet']]\n", + "\n", + "# Step 7: Close the file\n", + "nc.close()\n", + "# Reopen the file, read and print the data\n", + "nc = Dataset('data/weather_data.nc', 'r')\n", + "precip_var = nc.variables['precipitation']\n", + "# Print the Enum dictionary\n", + "print(\"Enum dictionary:\", precip_var.datatype.enum_dict)\n", + "# Print the data stored in the variable\n", + "print(\"Precipitation data:\", precip_var[:])\n", + "\n", + "# Close the file\n", + "nc.close()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 + Jaspy", + "language": "python", + "name": "jaspy" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/python-data/solutions/ex09a_weather_api.ipynb b/python-data/solutions/ex09a_weather_api.ipynb new file mode 100644 index 0000000..0701cfe --- /dev/null +++ b/python-data/solutions/ex09a_weather_api.ipynb @@ -0,0 +1,1261 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# Exercise 9a: Weather API\n", + "\n", + "## Aim: Use a Weather API to create and graph NetCDF files\n", + "\n", + "### Issues covered:\n", + "\n", + "- Request and get data from a weather API service\n", + "- Read and retrieve information from a JSON response\n", + "- Write contents to a NetCDF file\n", + "- Read a collection of NetCDF files and plot a time series graph\n", + "\n", + "## 1. Let's get data from a web API on the internet\n", + "\n", + "We will use the NOAA National Weather Service in the US as our data source:\n", + "\n", + "![](https://www.weather.gov/css/images/header.png)\n", + "\n", + "The service has a web API that allows you to request forecast data for a given grid point in the USA. Details of the API are documented at:\n", + "\n", + "https://www.weather.gov/documentation/services-web-api\n", + "\n", + "Use the endpoint `https://api.weather.gov/` as the base URL.\n", + "\n", + "Firstly, we want to get a grid ID and based on some latitude/longitude coordinates. To do so we will use the `points/{latitude,longitude}` endpoint of the API.\n", + "\n", + "**Choose the latitude and longitude of your favourite US location (this API is US only and in latitude North, longitude East). The extent of the USA is approximately:**\n", + "- Longitude: -120, -80\n", + "- Latitude: 30, 48\n", + "\n", + "Once you have queried the `points` API you will get back a `grid ID` (`GridId`). The `grid ID` can be used to get a weather forecast for your location of interest, using the `gridpoints/{grid ID}/{grid co-ordinates}` endpoint." + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Import the `requests` library which is great for downloading content from external URLs." + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "collapsed": false, + "execution": { + "iopub.execute_input": "2024-11-07T10:48:23.639772Z", + "iopub.status.busy": "2024-11-07T10:48:23.639460Z", + "iopub.status.idle": "2024-11-07T10:48:23.970841Z", + "shell.execute_reply": "2024-11-07T10:48:23.970287Z" + }, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "import requests" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "You can use the requests library to access the web API. Fill in the elipses with the `latitude` (degrees North) and `longitude` (degrees East, so use negative value) of a location in the US. \n", + "If successful, the response code should be 200." + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-07T10:48:23.973493Z", + "iopub.status.busy": "2024-11-07T10:48:23.973226Z", + "iopub.status.idle": "2024-11-07T10:48:24.289901Z", + "shell.execute_reply": "2024-11-07T10:48:24.289294Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "200" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "url = 'https://api.weather.gov/'\n", + "latitude, longitude = 39.7456, -97.0892\n", + "\n", + "# Hint: use the requests library to GET from the url: https://api.weather.gov/points/{LAT},{LON}\n", + "response = requests.get(f'{url}points/{latitude},{longitude}')\n", + "response.status_code" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With the requests library, the results from the webAPI can be extracted into in JSON format. A JSON document behaves exactly like a dictionary.\n", + "\n", + "Use dictionary indexing to extract the values of the grid ID and the X/Y coordinates:\n", + "\n", + "- get `gridID`\n", + "- get `gridX`\n", + "- get `gridY`" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": false, + "execution": { + "iopub.execute_input": "2024-11-07T10:48:24.292507Z", + "iopub.status.busy": "2024-11-07T10:48:24.292277Z", + "iopub.status.idle": "2024-11-07T10:48:24.296347Z", + "shell.execute_reply": "2024-11-07T10:48:24.295747Z" + }, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# hint: you can view the JSON by pasting the URL directly into your browser address bar\n", + "\n", + "response = response.json()\n", + "\n", + "gridID = response['properties']['gridId']\n", + "gridX = response['properties']['gridX']\n", + "gridY = response['properties']['gridY']" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "With your `gridID`, `gridX`, and `gridY`, use the `gridpoints` API endpoint to request a weather forecast for that location. Print the status code.\n", + "If everything is working, you should get another 200 status code." + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": false, + "execution": { + "iopub.execute_input": "2024-11-07T10:48:24.298707Z", + "iopub.status.busy": "2024-11-07T10:48:24.298234Z", + "iopub.status.idle": "2024-11-07T10:48:24.714917Z", + "shell.execute_reply": "2024-11-07T10:48:24.714298Z" + }, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "200" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "response = requests.get(f'{url}gridpoints/{gridID}/{gridX},{gridY}')\n", + "response.status_code" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Can you use the JSON response data to get the forecast temperature values? Use dictionary indexing to get the `values` from `temperature` in `properties`." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-07T10:48:24.717368Z", + "iopub.status.busy": "2024-11-07T10:48:24.717137Z", + "iopub.status.idle": "2024-11-07T10:48:24.722908Z", + "shell.execute_reply": "2024-11-07T10:48:24.722196Z" + } + }, + "outputs": [], + "source": [ + "data = response.json()\n", + "forecast = data['properties']['temperature']['values']" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "The below code extracts the coordinates of the grid box you have chosen." + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-07T10:48:24.725229Z", + "iopub.status.busy": "2024-11-07T10:48:24.724728Z", + "iopub.status.idle": "2024-11-07T10:48:24.734799Z", + "shell.execute_reply": "2024-11-07T10:48:24.734239Z" + } + }, + "outputs": [], + "source": [ + "coords = data['geometry']['coordinates'][0][0]\n", + "x = coords[1]\n", + "y = coords[0]" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## 2. Let's format that data and write it to NetCDF\n", + "\n", + "### Formatting the data\n", + "\n", + "First, format your forecast data to get the datetime and air temperature as separate\n", + "lists." + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": false, + "execution": { + "iopub.execute_input": "2024-11-07T10:48:24.737247Z", + "iopub.status.busy": "2024-11-07T10:48:24.736739Z", + "iopub.status.idle": "2024-11-07T10:48:24.747003Z", + "shell.execute_reply": "2024-11-07T10:48:24.746444Z" + }, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from datetime import datetime as dt" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Loop through your `forecast` values and get the temperatures (`value`) and datetimes (`validTime`) into a list.\n", + "`forecast` is a list of dictionaries, where each dictionary is of one time instance.\n", + "Fill in the ellipses to format each `validTime` string to a python `datetime` object and assign and set to the variable `date`. Get each `value` and assign to the variable `temp`. These values will then be appended to the `temps` and `timeseries` lists." + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-07T10:48:24.749454Z", + "iopub.status.busy": "2024-11-07T10:48:24.748973Z", + "iopub.status.idle": "2024-11-07T10:48:24.762072Z", + "shell.execute_reply": "2024-11-07T10:48:24.761418Z" + }, + "pycharm": { + "name": "#%%\n" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "# Use the datetime module to convert the times from the data to a datetime object.\n", + "# Hint: look at the validTime string and see how you can turn the string to datetime\n", + "# using strptime, the format of the datetime is: '%Y-%m-%dT%H:%M:%Sz'.\n", + "\n", + "timeseries = []\n", + "temps = []\n", + "\n", + "for item in forecast:\n", + " \n", + " date = item['validTime']\n", + " date = dt.strptime(date.split('/')[0], '%Y-%m-%dT%H:%M:%S%z')\n", + " timeseries.append(date)\n", + " \n", + " temp = item['value']\n", + " temps.append(temp)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Format the `timeseries` list and convert it to relative time in seconds from the start of the timeseries. When using NetCDF and the CF Metadata Conventions time is stored as an offset from a base time rather than an absolute times.\n", + "\n", + "If you are stuck, take look at the 'Time series' slide in the [logging data from serial ports](https://github.com/ncasuk/ncas-isc/raw/68abbfd3a573e576c32fc127fafc874bfff98b1e/python/presentations/logging-data-from-serial-ports/LDFSP_Slides.pdf) presentation." + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": false, + "execution": { + "iopub.execute_input": "2024-11-07T10:48:24.764352Z", + "iopub.status.busy": "2024-11-07T10:48:24.763860Z", + "iopub.status.idle": "2024-11-07T10:48:24.774832Z", + "shell.execute_reply": "2024-11-07T10:48:24.774256Z" + }, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "base_time = timeseries[0]\n", + "time_values = []\n", + "\n", + "for t in timeseries:\n", + " value = t - base_time\n", + " ts = value.total_seconds()\n", + " time_values.append(ts)\n", + "\n", + "time_units = \"seconds since \" + base_time.strftime('%Y-%m-%d %H:%M:%S')" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Convert the `temps` list from degrees C to Kelvin. As per the CF Conventions, the canonical units for Air Temperature is K. Create a new list, called `temp_values`, which is the temperature in Kelvin." + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": { + "collapsed": false, + "execution": { + "iopub.execute_input": "2024-11-07T10:48:24.777212Z", + "iopub.status.busy": "2024-11-07T10:48:24.776808Z", + "iopub.status.idle": "2024-11-07T10:48:24.787224Z", + "shell.execute_reply": "2024-11-07T10:48:24.786657Z" + }, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "temp_values = []\n", + "\n", + "for t in temps:\n", + " t = t + 273.15\n", + " temp_values.append(t)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Create a netCDF4 Dataset and write the contents to a file\n", + "\n", + "Import the `Dataset` class from the `netCDF4` library. You can go on to create an *instance* of this class which will contain:\n", + "- variables\n", + "- coordinate variables\n", + "- dimensions\n", + "- global attributes\n", + "\n", + "When you create the instance of `Dataset`, you will give it a file name which will be written to when you close the `Dataset`.\n", + "\n", + "Also import `numpy` as `np`. This will be used to construct the data arrays from the existing lists that currently hold the weather data and coordinate information.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "collapsed": false, + "execution": { + "iopub.execute_input": "2024-11-07T10:48:24.789287Z", + "iopub.status.busy": "2024-11-07T10:48:24.789090Z", + "iopub.status.idle": "2024-11-07T10:48:25.298859Z", + "shell.execute_reply": "2024-11-07T10:48:25.298154Z" + }, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from netCDF4 import Dataset\n", + "import numpy as np" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Quick aside, let's make sure we have a `DATA_DIR` to write to\n", + "\n", + "Since this is a group exercise, everyone should be writing to the same output directory. Let's set some python variables that can be used below:\n", + "1. `USER` - used in the output file names to ensure every NetCDF file is unique.\n", + "2. `HOME_DIR` - your `$HOME` directory\n", + "2. `MY_DATA_DIR` - the directory where you will write your NetCDF file.\n", + "3. `GROUP_DATA_DIR` - the directory where all the NetCDF files will eventually be collected/available.\n", + "\n", + "Since `GROUP_DATA_DIR` is not writeable directly from the Notebook Service, we have set up a job to replicate files from `MY_DATA_DIR` to `GROUP_DATA_DIR` (which runs once per minute)." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-07T10:48:25.301843Z", + "iopub.status.busy": "2024-11-07T10:48:25.301474Z", + "iopub.status.idle": "2024-11-07T10:48:25.308753Z", + "shell.execute_reply": "2024-11-07T10:48:25.308289Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'/home/users/nfarmer/TOP-nfarmer-temps.nc'" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import os\n", + "USER = os.environ[\"JUPYTERHUB_USER\"]\n", + "\n", + "HOME_DIR = f\"/home/users/{USER}\"\n", + "MY_DATA_DIR = os.path.join(HOME_DIR, \"weather-api-outputs\")\n", + "\n", + "# Create MY_DATA_DIR if it doesn't exist\n", + "if not os.path.isdir(MY_DATA_DIR):\n", + " os.mkdir(MY_DATA_DIR)\n", + "\n", + "# All NetCDF will be automatically copied here (once per minute)\n", + "GROUP_DATA_DIR = \"/gws/pw/j07/workshop/weather-api-data\"\n", + "\n", + "# The output file will initially be written here (then you will move it when complete)\n", + "filename = f\"{gridID}-{USER}-temps.nc\"\n", + "outfile = f\"{HOME_DIR}/{filename}\"\n", + "outfile" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Back to our NetCDF file\n", + "\n", + "Create the output file, as a `netCDF4 Dataset` instance, using the `outfile` defined above.\n", + "\n", + "If you need help, have a look at the 'Create the NetCDF dimensions & variables' slide in the [logging data from serial ports](https://github.com/ncasuk/ncas-isc/raw/68abbfd3a573e576c32fc127fafc874bfff98b1e/python/presentations/logging-data-from-serial-ports/LDFSP_Slides.pdf) presentation." + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": false, + "execution": { + "iopub.execute_input": "2024-11-07T10:48:25.311034Z", + "iopub.status.busy": "2024-11-07T10:48:25.310615Z", + "iopub.status.idle": "2024-11-07T10:48:25.325229Z", + "shell.execute_reply": "2024-11-07T10:48:25.324753Z" + }, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "dataset = Dataset(outfile, \"w\", format=\"NETCDF4_CLASSIC\")" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Start by defining some dimensions\n", + "\n", + "Create NetCDF *dimensions*:\n", + "- `time_dim`: *unlimited* length\n", + "- `lat_dim`: length 1\n", + "- `lon_dim`: length 1" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-07T10:48:25.327305Z", + "iopub.status.busy": "2024-11-07T10:48:25.327068Z", + "iopub.status.idle": "2024-11-07T10:48:25.382604Z", + "shell.execute_reply": "2024-11-07T10:48:25.382108Z" + } + }, + "outputs": [], + "source": [ + "time_dim = dataset.createDimension('time', None) # None means \"UNLIMITED\"\n", + "lat_dim = dataset.createDimension('lat', 1)\n", + "lon_dim = dataset.createDimension('lon', 1)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Now define the coordinate variables and then temperature variable\n", + "\n", + "Create the `time` *variable* with the following properties:\n", + "- type: numpy float (`np.float64`)\n", + "- variable id: `time`\n", + "- dimensions: (`time`,)\n", + "- set the array using the `time_values` list\n", + "- `units`: `time_units` defined earlier\n", + "- `standard_name`: `time`\n", + "- `calendar`: `standard`" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-07T10:48:25.384711Z", + "iopub.status.busy": "2024-11-07T10:48:25.384481Z", + "iopub.status.idle": "2024-11-07T10:48:25.451883Z", + "shell.execute_reply": "2024-11-07T10:48:25.451133Z" + } + }, + "outputs": [], + "source": [ + "time_var = dataset.createVariable('time', np.float64, ('time',))\n", + "time_var[:] = time_values\n", + "time_var.units = time_units\n", + "time_var.standard_name = 'time'\n", + "time_var.calendar = 'standard'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create the `lat` *variable* with the following properties:\n", + "- type: numpy float (`np.float64`)\n", + "- variable id: `lat`\n", + "- dimensions: (`lat`,)\n", + "- set the array of length 1 using the `gridY` value\n", + "- `units`: `degrees_north`\n", + "- `standard_name`: `latitude`" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-07T10:48:25.454013Z", + "iopub.status.busy": "2024-11-07T10:48:25.453784Z", + "iopub.status.idle": "2024-11-07T10:48:25.470005Z", + "shell.execute_reply": "2024-11-07T10:48:25.469530Z" + } + }, + "outputs": [], + "source": [ + "lat_var = dataset.createVariable('lat', np.float64, ('lat',))\n", + "lat_var[:] = [gridY]\n", + "lat_var.units = 'degrees_north'\n", + "lat_var.standard_name = 'latitude'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create the `lon` *variable* with the following properties:\n", + "- type: numpy float (`np.float64`)\n", + "- variable id: `lon`\n", + "- dimensions: (`lon`,)\n", + "- set the array of length 1 using the `gridX` value\n", + "- `units`: `degrees_east`\n", + "- `standard_name`: `longitude`" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-07T10:48:25.472228Z", + "iopub.status.busy": "2024-11-07T10:48:25.471822Z", + "iopub.status.idle": "2024-11-07T10:48:25.487128Z", + "shell.execute_reply": "2024-11-07T10:48:25.486646Z" + } + }, + "outputs": [], + "source": [ + "lon_var = dataset.createVariable('lon', np.float64, ('lon',))\n", + "lon_var[:] = [gridX]\n", + "lon_var.units = 'degrees_east'\n", + "lon_var.standard_name = 'longitude'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "Create the `temp` *variable* with the following properties:\n", + "- type: numpy float (`np.float64`)\n", + "- variable id: `temp`\n", + "- dimensions: (`time`,)\n", + "- set the array using the `temp_values` list\n", + "- `long_name`: `air temperature (K)`\n", + "- `units`: `K`\n", + "- `standard_name`: `air_temperature`\n", + "- `coordinates`: `lon lat` - to relate the longitude and latitude to this variable" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-07T10:48:25.489353Z", + "iopub.status.busy": "2024-11-07T10:48:25.488955Z", + "iopub.status.idle": "2024-11-07T10:48:25.505378Z", + "shell.execute_reply": "2024-11-07T10:48:25.504896Z" + } + }, + "outputs": [], + "source": [ + "temp_var = dataset.createVariable('temp', np.float32, ('time',))\n", + "temp_var[:] = temp_values\n", + "temp_var.var_id = 'temp'\n", + "temp_var.long_name = 'Air Temperature (K)'\n", + "temp_var.units = 'K'\n", + "temp_var.standard_name = 'air_temperature'\n", + "temp_var.coordinates = 'lon lat'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Add some global attributes\n", + "\n", + "The [CF Metadata Conventions](https://cfconventions.org/cf-conventions/cf-conventions.html#_overview) recommends a set of global attributes to \"provide human readable documentation of the file contents\":\n", + "- title\n", + "- history\n", + "- institution\n", + "- source\n", + "- references\n", + "- comment\n", + "\n", + "Add each of the above to your `Dataset` instance. Here are some suggested values (but you can say whatever you like):\n", + "- title: Air Temperature forecasts for ``\n", + "- history: File created on: ``\n", + "- institution: NCAS-ISC\n", + "- source: NOAA Weather API Service\n", + "- references: https://www.weather.gov/documentation/services-web-api\n", + "- comment: The ISC course is teaching me about Python and NetCDF!\n", + "\n", + "You can add any other global attributes that you wish to." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-07T10:48:25.507475Z", + "iopub.status.busy": "2024-11-07T10:48:25.507257Z", + "iopub.status.idle": "2024-11-07T10:48:25.521485Z", + "shell.execute_reply": "2024-11-07T10:48:25.521015Z" + } + }, + "outputs": [], + "source": [ + "dataset.title = f'Air Temperature forecasts for {gridID}'\n", + "dataset.history = f'File created on: {dt.now().strftime(\"%Y-%m-%d\")}'\n", + "dataset.institution = 'NCAS-ISC'\n", + "dataset.source = 'NOAA Weather API Service'\n", + "dataset.references = 'https://www.weather.gov/documentation/services-web-api'\n", + "dataset.comment = 'This course is OK!'" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "#### Finally, close the `Dataset` to save the file\n", + "\n", + "Save your NetCDF file by closing the dataset." + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-07T10:48:25.523545Z", + "iopub.status.busy": "2024-11-07T10:48:25.523320Z", + "iopub.status.idle": "2024-11-07T10:48:25.538816Z", + "shell.execute_reply": "2024-11-07T10:48:25.538355Z" + } + }, + "outputs": [], + "source": [ + "dataset.close()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "We can check it is there using `os.path.isfile(...)`:" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-07T10:48:25.540844Z", + "iopub.status.busy": "2024-11-07T10:48:25.540627Z", + "iopub.status.idle": "2024-11-07T10:48:25.556761Z", + "shell.execute_reply": "2024-11-07T10:48:25.556311Z" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "os.path.isfile(outfile)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### IMPORTANT: Move the file to your MY_DATA_DIR so it gets copied to the GROUP_DATA_DIR\n", + "\n", + "Since we cannot write directly to the `GROUP_DATA_DIR`, move the file from your `HOME_DIR` to your `MY_DATA_DIR`." + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-07T10:48:25.561683Z", + "iopub.status.busy": "2024-11-07T10:48:25.561311Z", + "iopub.status.idle": "2024-11-07T10:48:25.575170Z", + "shell.execute_reply": "2024-11-07T10:48:25.574700Z" + } + }, + "outputs": [], + "source": [ + "os.rename(outfile, f\"{MY_DATA_DIR}/{filename}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## 3. Find all the NetCDF files written during this exercise\n", + "\n", + "To find all the `.nc` files in a group workspace, we will use the glob module in Python.\n", + "Glob let's us find all files matching a pattern, in our case:\n", + "\n", + "`{GROUP_DATA_DIR}/*.nc`" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": { + "collapsed": false, + "execution": { + "iopub.execute_input": "2024-11-07T10:48:25.577733Z", + "iopub.status.busy": "2024-11-07T10:48:25.577374Z", + "iopub.status.idle": "2024-11-07T10:48:25.592760Z", + "shell.execute_reply": "2024-11-07T10:48:25.592293Z" + }, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from glob import glob" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Can you use glob to make a list of file paths of all NetCDF files in the\n", + "group workspace?" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": false, + "execution": { + "iopub.execute_input": "2024-11-07T10:48:25.594899Z", + "iopub.status.busy": "2024-11-07T10:48:25.594683Z", + "iopub.status.idle": "2024-11-07T10:48:25.621671Z", + "shell.execute_reply": "2024-11-07T10:48:25.621201Z" + }, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "filepaths = glob(f\"{GROUP_DATA_DIR}/*temps.nc\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "## 4. Create a time-series graph of all the forecasts\n", + "\n", + "Now that we have a list of NetCDF file paths, we can open them and extract their data.\n", + "\n", + "To start, let us make the a plot using matplotlib." + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "metadata": { + "collapsed": false, + "execution": { + "iopub.execute_input": "2024-11-07T10:48:25.623846Z", + "iopub.status.busy": "2024-11-07T10:48:25.623613Z", + "iopub.status.idle": "2024-11-07T10:48:26.669125Z", + "shell.execute_reply": "2024-11-07T10:48:26.668507Z" + }, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "from netCDF4 import num2date\n", + "import matplotlib.pyplot as plt\n", + "import matplotlib.dates as mdates\n", + "%matplotlib inline" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Create a subplots figure with figure and axis" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": false, + "execution": { + "iopub.execute_input": "2024-11-07T10:48:26.671966Z", + "iopub.status.busy": "2024-11-07T10:48:26.671577Z", + "iopub.status.idle": "2024-11-07T10:48:26.829764Z", + "shell.execute_reply": "2024-11-07T10:48:26.829269Z" + }, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots()" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Can you set the x-axis locator (ticks) using dates class from matplotlib?\n", + "- set the major locator to days.\n", + "- set the minor locator to every 6 hours.\n", + "- set the x-axis formatter to Day-Month for each day." + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": { + "collapsed": false, + "execution": { + "iopub.execute_input": "2024-11-07T10:48:26.832380Z", + "iopub.status.busy": "2024-11-07T10:48:26.832123Z", + "iopub.status.idle": "2024-11-07T10:48:26.835757Z", + "shell.execute_reply": "2024-11-07T10:48:26.835281Z" + }, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "# In the matplotlib.dates module, as mdates, look at the DayLocator and HourLocator.\n", + "fmt_day = mdates.DayLocator()\n", + "fmt_six_hours = mdates.HourLocator(interval=6)\n", + "\n", + "ax.xaxis.set_major_locator(fmt_day)\n", + "ax.xaxis.set_minor_locator(fmt_six_hours)\n", + "ax.xaxis.set_major_formatter(mdates.DateFormatter('%d-%m'))" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Label the axis, `ax`, on the plot:\n", + "- label the x-axis as `Date`\n", + "- label the y-axis as `Air Temperature (K)`\n", + "- set a title on your plot" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "collapsed": false, + "execution": { + "iopub.execute_input": "2024-11-07T10:48:26.838128Z", + "iopub.status.busy": "2024-11-07T10:48:26.837773Z", + "iopub.status.idle": "2024-11-07T10:48:26.855334Z", + "shell.execute_reply": "2024-11-07T10:48:26.854875Z" + }, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0.5, 1.0, 'Air temperature Forecast')" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ax.set_xlabel(\"Date\")\n", + "ax.set_ylabel(\"Air Temperature (K)\")\n", + "ax.set_title(\"Air temperature Forecast\")" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Open each NetCDF file and extract the `temp`, `time`, `lat` and `lon` variables from the file. Then use the matplotlib `plot_date` function to plot the graph.\n", + "\n", + "- set the label of plot to the `, ` coordinates attribute of the `temp` variable.\n", + "\n", + "Replace the elipses with your plotting, the `for` loop works through all the shared NetCDF files in the workspace, where `f` is the file path and `filepaths` is a list of data files.\n", + "\n", + "If you need help, look at the 'Plotting data with matplotlib' slide in the [logging data from serial ports](https://github.com/ncasuk/ncas-isc/raw/68abbfd3a573e576c32fc127fafc874bfff98b1e/python/presentations/logging-data-from-serial-ports/LDFSP_Slides.pdf) presentation.\n", + "\n", + "Plot a line graph using matplotlib: \n", + "\n", + "- you will need to set the marker to `-` otherwise you will get a scatter graph.\n", + "- set the label of the plot to a string: `, `." + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": false, + "execution": { + "iopub.execute_input": "2024-11-07T10:48:26.857437Z", + "iopub.status.busy": "2024-11-07T10:48:26.857215Z", + "iopub.status.idle": "2024-11-07T10:48:26.881944Z", + "shell.execute_reply": "2024-11-07T10:48:26.881476Z" + }, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [], + "source": [ + "for f in filepaths:\n", + " \n", + " dset = Dataset(f, mode='r')\n", + " \n", + " temp = dset.variables['temp']\n", + " time = dset.variables['time']\n", + " lat = dset.variables['lat'][0]\n", + " lon = dset.variables['lon'][0]\n", + "\n", + " times = num2date(time[:], units=time.units, calendar=time.calendar)\n", + " ax.plot_date(times, temp[:], '-', label=f\"{lat:.3f}, {lon:.3f}\")" + ] + }, + { + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "Finally, show the plot with a legend, you might want to enable tight layout,\n", + "and save the plot to your `MY_DATA_DIR` directory." + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": { + "collapsed": false, + "execution": { + "iopub.execute_input": "2024-11-07T10:48:26.884223Z", + "iopub.status.busy": "2024-11-07T10:48:26.883813Z", + "iopub.status.idle": "2024-11-07T10:48:27.060981Z", + "shell.execute_reply": "2024-11-07T10:48:27.060496Z" + }, + "jupyter": { + "outputs_hidden": false + }, + "pycharm": { + "name": "#%%\n" + } + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n" + ] + }, + { + "data": { + "image/png": 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LFO/cudP6fYNX9evXT0uWLFFRUZE8PT3tjiksLLT5+p/S0lKdO3dOgYGBlf7qJQAAgF8zDEN5eXkKDQ2Vm1vN3hy9oYJdRkaG9St8rgoJCVFxcbGysrLK/NLvxMREm9XrAQAAasL//u//6qabbqrRc95QwU6y/7Lqq3eSy5t9mzp1quLi4qzbOTk5atasmb7//nsFBARUX6EAAOA36dy5c2rdurXq169f4+e+oYJdo0aNlJGRYdOWmZkpDw8PBQYGlnmMt7e33fcRSlJAQEC5xwAAAFSWKx75uqEWKO7WrZuSk5Nt2jZt2qQuXbqU+XwdAADAb4lLg92FCxd04MABHThwQNKV5UwOHDigtLQ0SVduo8bExFj7x8bG6sSJE4qLi9OhQ4e0dOlSLVmyhO+YBAAAkItvxe7Zs0c9e/a0bl99Fm706NFavny50tPTrSFPksLDw7VhwwZNmjRJr7/+ukJDQzVv3jw98MADNV47AABAbVNr1rGrKbm5ufL391dWVhbP2AEAgDKVlJSoqKiozH2enp5yd3cv99js7GwFBQUpJydHfn5+1VVimW6olycAAACqk2EYysjI0Pnz56/Zr0GDBmrUqFGtWxOXYAcAAPB/roa64OBg+fr6lrnMWn5+vjIzMyWpzDV0XYlgBwAAoCu3X6+Gums9rlWnTh1JV5ZcCw4OvuZt2Zp2Qy13AgAAUF2uPlPn6+t73b5X+5T3HJ6rEOwAAAB+wZHn5mrbs3VXEewAAABMgmAHAABgEgQ7AAAAkyDYAQAAmATBDgAA4BdKS0urpI8rsI4dAACAJC8vL7m5uen06dNq2LChvLy8ylyg+PLlyzp79qzc3Nzk5eXlomrLRrADAACQ5ObmpvDwcKWnp+v06dPX7Ovr66tmzZrJza123fwk2AEAAPwfLy8vNWvWTMXFxSopKSmzj7u7uzw8PGrlWnYEOwAAgF+wWCzy9PSUp6enq0txWu2aPwQAAECFEewAAABMgmAHAABgEgQ7AAAAkyDYAQAAmATBDgAAwCQIdgAAACZBsAMAADAJgh0AAIBJEOwAAABMgmAHAABgEgQ7AAAAkyDYAQAAmATBDgAAwCQIdgAAACZBsAMAADAJgh0AAIBJEOwAAABMgmAHAABgEgQ7AAAAkyDYAQAAmATBDgAAwCQIdgAAACZBsAMAADAJgh0AAIBJEOwAAABMgmAHAABgEgQ7AAAAkyDYAQAAmATBDgAAwCQIdgAAACZBsAMAADAJgh0AAIBJEOwAAABMgmAHAABgEgQ7AAAAkyDYAQAAmATBDgAAwCQIdgAAACZBsAMAADAJgh0AAIBJEOwAAABMgmAHAABgEgQ7AAAAkyDYAQAAmATBDgAAwCQIdgAAACZBsAMAADAJgh0AAIBJEOwAAABMgmAHAABgEgQ7AAAAk3B5sFuwYIHCw8Pl4+OjyMhIpaamXrP/qlWr1LFjR/n6+qpx48YaO3assrOza6haAACA2sulwW7NmjWaOHGipk2bpv379ysqKkoDBgxQWlpamf137NihmJgYjR8/Xt99953Wrl2r3bt36+GHH67hygEAAGoflwa7OXPmaPz48Xr44YfVrl07zZ07V02bNtXChQvL7L9r1y41b95cEyZMUHh4uO666y49+uij2rNnTw1XDgAAUPu4LNhdvnxZe/fuVd++fW3a+/btqy+++KLMY7p3766TJ09qw4YNMgxDZ86c0bvvvqvo6OiaKBkAAKBW83DVibOyslRSUqKQkBCb9pCQEGVkZJR5TPfu3bVq1SoNHz5cBQUFKi4u1uDBgzV//vxyz1NYWKjCwkLrdm5uriSpqKhIRUVFVXAlAAAA/58r84XLgt1VFovFZtswDLu2qw4ePKgJEyZo+vTp6tevn9LT0/Xss88qNjZWS5YsKfOYxMREJSQk2LVv2bJFvr6+lb8AAACAX8jPz3fZuS2GYRiuOPHly5fl6+urtWvXaujQodb2p59+WgcOHNC2bdvsjhk1apQKCgq0du1aa9uOHTsUFRWl06dPq3HjxnbHlDVj17RpU6WnpyswMLCKrwoAAPzWZWdnq3HjxsrJyZGfn1+NnttlM3ZeXl6KjIxUcnKyTbBLTk7WfffdV+Yx+fn58vCwLdnd3V3SlZm+snh7e8vb29uu3dPTU56enhUtHwAAoEyuzBcufSs2Li5Oixcv1tKlS3Xo0CFNmjRJaWlpio2NlSRNnTpVMTEx1v733nuv1q1bp4ULF+qnn37S559/rgkTJuj2229XaGioqy4DAACgVnDpM3bDhw9Xdna2Zs6cqfT0dLVv314bNmxQWFiYJCk9Pd1mTbsxY8YoLy9Pr732mp555hk1aNBAvXr10qxZs1x1CQAAALWGy56xc5Xc3Fz5+/srKyuLZ+wAAECVy87OVlBQkEuesXP5V4oBAACgahDsAAAATIJgBwAAYBIEOwAAAJMg2AEAAJgEwQ4AAMAkCHYAAAAmQbADAAAwCYIdAACASRDsAAAATIJgBwAAYBIEOwAAAJMg2AEAAJgEwQ4AAMAkCHYAAAAmQbADAAAwCYIdAACASRDsAAAATIJgBwAAYBIEOwAAAJMg2AEAAJgEwQ4AAMAkCHYAAAAmQbADAAAwCYIdAACASRDsAAAATIJgBwAAYBIEOwAAAJMg2AEAAJgEwQ4AAMAkCHYAAAAmQbADAAAwCYIdAACASRDsAAAATIJgBwAAYBIEOwAAAJMg2AEAAJgEwQ4AAMAkCHYAAAAmQbADAAAwCYIdAACASRDsAAAATIJgBwAAYBIEOwAAAJPwcKbzkSNHtHr1aqWmpur48ePKz89Xw4YN1blzZ/Xr108PPPCAvL29q6tWAAAAXINDM3b79+9Xnz591LFjR23fvl233XabJk6cqL/97W966KGHZBiGpk2bptDQUM2aNUuFhYXVXTcAAAB+xaEZuyFDhmjy5Mlas2aNAgICyu23c+dO/etf/9Irr7yi559/vsqKBAAAwPVZDMMwrtfp8uXL8vLycnhQZ/vXpNzcXPn7+ysrK0uBgYGuLgcAAJhMdna2goKClJOTIz8/vxo9t0O3Yr28vPTtt99et9/f//53a38AAADULIffiu3Xr5+OHz9e7v5Zs2YpPj6+KmoCAABABTgc7KKiotSnTx9lZmba7fvHP/6hF154QW+99VaVFgcAAADHORzs3nrrLd18883q27evcnJyrO1XX5RYuXKl/vSnP1VLkQAAALg+h4Odh4eH1q1bp3r16mnQoEEqKCjQ3LlzNWXKFK1YsUIjRoyozjoBAABwHU4tUFynTh395z//UY8ePRQZGanvv/9ey5Yt08iRI6urPgAAADjI4WC3fv166z8/9thjevrppzV06FD5+fnZ7Bs8eHDVVggAAACHOLSOnSS5uV3/rq3FYlFJSUmli6pOrGMHAACqkyvXsXN4xq60tLQ66wAAAEAlOfzyBAAAAGo3h4Ldzp07HR7w4sWL+u677ypcEAAAACrGoWAXExOjPn366J133tGFCxfK7HPw4EE9//zzuvnmm7Vv374qLRIAAADX59AzdgcPHtSiRYs0ffp0Pfjgg2rdurVCQ0Pl4+Ojn3/+WYcPH9bFixd1//33Kzk5We3bt6/uugEAAPArDr8Ve9W+ffuUmpqq48eP69KlSwoKClLnzp3Vs2dPBQQEVFedVYa3YgEAQHW6Id6KvSoiIkIRERHVUQsAAAAqgbdiAQAATIJgBwAAYBIEOwAAAJNwebBbsGCBwsPD5ePjo8jISKWmpl6zf2FhoaZNm6awsDB5e3urZcuWWrp0aQ1VCwAAUHs5/fLELxUUFMjHx6fCx69Zs0YTJ07UggULdOedd2rRokUaMGCADh48qGbNmpV5zLBhw3TmzBktWbJEN998szIzM1VcXFzhGgAAAMzC6eVOSktL9dJLLykpKUlnzpzR999/rxYtWuiFF15Q8+bNNX78eIfH6tq1qyIiIrRw4UJrW7t27TRkyBAlJiba9d+4caNGjBihn376qcJLq7DcCQAAqE6uXO7E6VuxL774opYvX67Zs2fLy8vL2t6hQwctXrzY4XEuX76svXv3qm/fvjbtffv21RdffFHmMevXr1eXLl00e/ZsNWnSRK1bt9bkyZN16dIlZy8DAADAdJy+Fbty5Uq98cYbuueeexQbG2ttv/XWW3X48GGHx8nKylJJSYlCQkJs2kNCQpSRkVHmMT/99JN27NghHx8fvf/++8rKytLjjz+uc+fOlfucXWFhoQoLC63bubm5kqSioiIVFRU5XC8AAIAjXJkvnA52p06d0s0332zXXlpaWqELsVgsNtuGYdi1/fIcFotFq1atkr+/vyRpzpw5+uMf/6jXX39dderUsTsmMTFRCQkJdu1btmyRr6+v0/UCAABcS35+vsvO7XSw+/3vf6/U1FSFhYXZtK9du1adO3d2eJygoCC5u7vbzc5lZmbazeJd1bhxYzVp0sQa6qQrz+QZhqGTJ0+qVatWdsdMnTpVcXFx1u3c3Fw1bdpUPXv25Bk7AABQ5bKzs112bqeDXXx8vEaNGqVTp06ptLRU69at05EjR7Ry5Up9/PHHDo/j5eWlyMhIJScna+jQodb25ORk3XfffWUec+edd2rt2rW6cOGC6tWrJ0n6/vvv5ebmpptuuqnMY7y9veXt7W3X7unpKU9PT4frBQAAcIQr84XTL0/ce++9WrNmjTZs2CCLxaLp06fr0KFD+uijj9SnTx+nxoqLi9PixYu1dOlSHTp0SJMmTVJaWpr12b2pU6cqJibG2n/kyJEKDAzU2LFjdfDgQW3fvl3PPvusxo0bV+ZtWAAAgN8Sp2bsiouL9dJLL2ncuHHatm1bpU8+fPhwZWdna+bMmUpPT1f79u21YcMG623e9PR0paWlWfvXq1dPycnJeuqpp9SlSxcFBgZq2LBhevHFFytdCwAAwI3O6XXs6tWrp2+//VbNmzevppKqF+vYAQCA6nRDrWPXu3dvbd26tRpKAQAAQGU4/fLEgAEDNHXqVH377beKjIxU3bp1bfYPHjy4yooDAACA45y+FevmVv4kn8ViUUlJSaWLqk7cigUAANXJlbdinZ6xKy0trY46AAAAUElOP2MHAACA2snpGbuZM2dec//06dMrXAwAAAAqzulg9/7779tsFxUV6dixY/Lw8FDLli0JdgAAAC7idLDbv3+/XVtubq7GjBlj89VgAAAAqFlV8oydn5+fZs6cqRdeeKEqhgMAAEAFVNnLE+fPn1dOTk5VDQcAAAAnOX0rdt68eTbbhmEoPT1db775pvr3719lhQEAAMA5Tge7f/3rXzbbbm5uatiwoUaPHq2pU6dWWWEAAABwjtPB7tixY9VRBwAAACrJ6Wfsxo0bp7y8PLv2ixcvaty4cVVSFAAAAJzndLBbsWKFLl26ZNd+6dIlrVy5skqKAgAAgPMcvhWbm5srwzBkGIby8vLk4+Nj3VdSUqINGzYoODi4WooEAADA9Tkc7Bo0aCCLxSKLxaLWrVvb7bdYLEpISKjS4gAAAOA4h4Pdli1bZBiGevXqpffee08BAQHWfV5eXgoLC1NoaGi1FAkAAIDrczjY9ejRQ9KVt2KbNm0qN7cqW9sYAAAAVcDp5U7CwsIkSfn5+UpLS9Ply5dt9t96661VUxkAAACc4nSwO3v2rMaOHatPPvmkzP0lJSWVLgoAAADOc/p+6sSJE/Xzzz9r165dqlOnjjZu3KgVK1aoVatWWr9+fXXUCAAAAAc4PWOXkpKiDz/8ULfddpvc3NwUFhamPn36yM/PT4mJiYqOjq6OOgEAAHAdTs/YXbx40bpeXUBAgM6ePStJ6tChg/bt21e11QEAAMBhTge7Nm3a6MiRI5KkTp06adGiRTp16pSSkpLUuHHjKi8QAAAAjnH6VuzEiROVnp4uSYqPj1e/fv20atUqeXl5afny5VVdHwAAABxkMQzDqMwA+fn5Onz4sJo1a6agoKCqqqva5Obmyt/fX1lZWQoMDHR1OQAAwGSys7MVFBSknJwc+fn51ei5nboVW1RUpBYtWujgwYPWNl9fX0VERNwQoQ4AAMDMnAp2np6eKiwslMViqa56AAAAUEFOvzzx1FNPadasWSouLq6OegAAAFBBTr888eWXX2rz5s3atGmTOnTooLp169rsX7duXZUVBwAAAMc5HewaNGigBx54oDpqAQAAQCU4HeyWLVtWHXUAAACgkpx+xk6SiouL9dlnn2nRokXKy8uTJJ0+fVoXLlyo0uIAAADgOKdn7E6cOKH+/fsrLS1NhYWF6tOnj+rXr6/Zs2eroKBASUlJ1VEnAAAArsPpGbunn35aXbp00c8//6w6depY24cOHarNmzdXaXEAAABwnNMzdjt27NDnn38uLy8vm/awsDCdOnWqygoDAACAc5yesSstLVVJSYld+8mTJ1W/fv0qKQoAAADOczrY9enTR3PnzrVuWywWXbhwQfHx8Ro4cGBV1gYAAAAnWAzDMJw54PTp0+rZs6fc3d119OhRdenSRUePHlVQUJC2b9+u4ODg6qq1SuTm5srf319ZWVkKDAx0dTkAAMBksrOzFRQUpJycHPn5+dXouZ1+xi40NFQHDhzQ6tWrtW/fPpWWlmr8+PF68MEHbV6mAAAAQM1yesbuRseMHQAAqE431IydJB05ckTz58/XoUOHZLFY1LZtWz355JNq27ZtVdcHAAAABzn98sS7776r9u3ba+/everYsaNuvfVW7du3Tx06dNDatWuro0YAAAA4wOlbsS1atNBDDz2kmTNn2rTHx8frzTff1E8//VSlBVY1bsUCAIDq5MpbsU7P2GVkZCgmJsau/aGHHlJGRkaVFAUAAADnOR3s7r77bqWmptq179ixQ1FRUVVSFAAAAJzn9MsTgwcP1nPPPae9e/fqjjvukCTt2rVLa9euVUJCgtavX2/TFwAAADXD6Wfs3Nwcm+SzWCxlfvWYq/GMHQAAqE431HInpaWl1VEHAAAAKsnpZ+wAAABQO1VogeKvvvpKW7duVWZmpt0M3pw5c6qkMAAAADjH6WD38ssv669//avatGmjkJAQWSwW675f/jMAAABqltPB7tVXX9XSpUs1ZsyYaigHAAAAFeX0M3Zubm668847q6MWAAAAVILTwW7SpEl6/fXXq6MWAAAAVILTt2InT56s6OhotWzZUrfccos8PT1t9q9bt67KigMAAIDjnA52Tz31lLZs2aKePXsqMDCQFyYAAABqCaeD3cqVK/Xee+8pOjq6OuoBAABABTn9jF1AQIBatmxZHbUAAACgEpwOdjNmzFB8fLzy8/Orox4AAABUkNO3YufNm6cff/xRISEhat68ud3LE/v27auy4gAAAOA4p4PdkCFDqqEMAAAAVJbTwS4+Pr466gAAAEAlOf2MnSSdP39eixcv1tSpU3Xu3DlJV27Bnjp1qkqLAwAAgOOcnrH75ptv1Lt3b/n7++v48eP685//rICAAL3//vs6ceKEVq5cWR11AgAA4DqcnrGLi4vTmDFjdPToUfn4+FjbBwwYoO3bt1dpcQAAAHCc08Fu9+7devTRR+3amzRpooyMDKcLWLBggcLDw+Xj46PIyEilpqY6dNznn38uDw8PderUyelzAgAAmJHTwc7Hx0e5ubl27UeOHFHDhg2dGmvNmjWaOHGipk2bpv379ysqKkoDBgxQWlraNY/LyclRTEyM7rnnHqfOBwAAYGYOB7u0tDSVlpbqvvvu08yZM1VUVCRJslgsSktL05QpU/TAAw84dfI5c+Zo/Pjxevjhh9WuXTvNnTtXTZs21cKFC6953KOPPqqRI0eqW7duTp0PAADAzBx+eSI8PFzp6en65z//qYEDByo4OFiXLl1Sjx49lJGRoW7duumll15y+MSXL1/W3r17NWXKFJv2vn376osvvij3uGXLlunHH3/UW2+9pRdffPG65yksLFRhYaF1++psY1FRkTWcAgAAVBVX5guHg51hGJIkPz8/7dixQykpKdq3b59KS0sVERGh3r17O3XirKwslZSUKCQkxKY9JCSk3Gf1jh49qilTpig1NVUeHo6VnpiYqISEBLv2LVu2yNfX16maAQAArseVX7vq9HInV/Xq1Uu9evWqdAEWi8Vm2zAMuzZJKikp0ciRI5WQkKDWrVs7PP7UqVMVFxdn3c7NzVXTpk3Vs2dPBQYGVrxwAACAMmRnZ7vs3E4Fu8WLF6tevXrX7DNhwgSHxgoKCpK7u7vd7FxmZqbdLJ4k5eXlac+ePdq/f7+efPJJSVJpaakMw5CHh4c2bdpUZtD09vaWt7e3Xbunp6fd99wCAABUlivzhVPBLikpSe7u7uXut1gsDgc7Ly8vRUZGKjk5WUOHDrW2Jycn67777rPr7+fnp//+9782bQsWLFBKSoreffddhYeHO3gVAAAA5uRUsNuzZ4+Cg4Or7ORxcXEaNWqUunTpom7duumNN95QWlqaYmNjJV25jXrq1CmtXLlSbm5uat++vc3xwcHB8vHxsWsHAAD4LXI42JX13FtlDR8+XNnZ2Zo5c6bS09PVvn17bdiwQWFhYZKk9PT0665pBwAAgCssxtXXXa/Dzc1NGRkZVTpj5wq5ubny9/dXVlYWL08AAIAql52draCgIOXk5MjPz69Gz+3wAsXx8fHXfXECAAAAruPwrdj4+PjqrAMAAACV5PR3xQIAAKB2ItgBAACYhFPBzjAMnThxQpcuXaquegAAAFBBTge7Vq1a6eTJk9VVDwAAACrIqWDn5uamVq1aufQ70AAAAFA2p5+xmz17tp599ll9++231VEPAAAAKsiprxSTpIceekj5+fnq2LGjvLy8VKdOHZv9586dq7LiAAAA4Ding93cuXOroQwAAABUltPBbvTo0dVRBwAAACrJoWCXm5tr/a6z3Nzca/at6e9EAwAAwBUOBbvf/e53Sk9PV3BwsBo0aCCLxWLXxzAMWSwWlZSUVHmRAAAAuD6Hgl1KSooCAgIkSVu2bKnWggAAAFAxDgW7Hj16lPnPv3bgwIFKFwQAAICKqfR3xebk5GjBggWKiIhQZGRkVdQEAACACqhwsEtJSdFDDz2kxo0ba/78+Ro4cKD27NlTlbUBAADACU4td3Ly5EktX75cS5cu1cWLFzVs2DAVFRXpvffe0y233FJdNQIAAMABDs/YDRw4ULfccosOHjyo+fPn6/Tp05o/f3511gYAAAAnODxjt2nTJk2YMEGPPfaYWrVqVZ01AQAAoAIcnrFLTU1VXl6eunTpoq5du+q1117T2bNnq7M2AAAAOMHhYNetWzf9+9//Vnp6uh599FG9/fbbatKkiUpLS5WcnKy8vLzqrBMAAADXYTEMw6jowUeOHNGSJUv05ptv6vz58+rTp4/Wr19flfVVudzcXPn7+ysrK0uBgYGuLgcAAJhMdna2goKClJOTU+NftVqpdezatGmj2bNn6+TJk1q9enVV1QQAAIAKqNSM3Y2IGTsAAFCdbtgZOwAAANQeBDsAAACTINgBAACYBMEOAADAJAh2AAAAJkGwAwAAMAmCHQAAgEkQ7AAAAEyCYAcAAGASBDsAAACTINgBAACYBMEOAADAJAh2AAAAJkGwAwAAMAmCHQAAgEkQ7AAAAEyCYAcAAGASBDsAAACTINgBAACYBMEOAADAJAh2AAAAJkGwAwAAMAmCHQAAgEkQ7AAAAEyCYAcAAGASBDsAAACTINgBAACYBMEOAADAJAh2AAAAJkGwAwAAMAmCHQAAgEkQ7AAAAEyCYAcAAGASBDsAAACTINgBAACYBMEOAADAJAh2AAAAJkGwAwAAMAmCHQAAgEkQ7AAAAEyCYAcAAGASLg92CxYsUHh4uHx8fBQZGanU1NRy+65bt059+vRRw4YN5efnp27duunTTz+twWoBAABqL5cGuzVr1mjixImaNm2a9u/fr6ioKA0YMEBpaWll9t++fbv69OmjDRs2aO/everZs6fuvfde7d+/v4YrBwAAqH0shmEYrjp5165dFRERoYULF1rb2rVrpyFDhigxMdGhMX7/+99r+PDhmj59ukP9c3Nz5e/vr6ysLAUGBlaobgAAgPJkZ2crKChIOTk58vPzq9Fze9To2X7h8uXL2rt3r6ZMmWLT3rdvX33xxRcOjVFaWqq8vDwFBASU26ewsFCFhYXW7dzcXElSUVGRioqKKlA5AABA+VyZL1wW7LKyslRSUqKQkBCb9pCQEGVkZDg0xiuvvKKLFy9q2LBh5fZJTExUQkKCXfuWLVvk6+vrXNEAAADXkZ+f77JzuyzYXWWxWGy2DcOwayvL6tWrNWPGDH344YcKDg4ut9/UqVMVFxdn3c7NzVXTpk3Vs2dPbsUCAIAql52d7bJzuyzYBQUFyd3d3W52LjMz024W79fWrFmj8ePHa+3aterdu/c1+3p7e8vb29uu3dPTU56ens4XDgAAcA2uzBcueyvWy8tLkZGRSk5OtmlPTk5W9+7dyz1u9erVGjNmjP7nf/5H0dHR1V0mAADADcOlt2Lj4uI0atQodenSRd26ddMbb7yhtLQ0xcbGSrpyG/XUqVNauXKlpCuhLiYmRq+++qruuOMO62xfnTp15O/v77LrAAAAqA1cGuyGDx+u7OxszZw5U+np6Wrfvr02bNigsLAwSVJ6errNmnaLFi1ScXGxnnjiCT3xxBPW9tGjR2v58uU1XT4AAECt4tJ17FyBdewAAEB1cuU6di7/SjEAAABUDYIdAACASRDsAAAATIJgBwAAYBIEOwAAAJMg2AEAAJgEwQ4AAMAkCHYAAAAmQbADAAAwCYIdAACASRDsAAAATIJgBwAAYBIEOwAAAJMg2AEAAJgEwQ4AAMAkCHYAAAAmQbADAAAwCYIdAACASRDsAAAATIJgBwAAYBIEOwAAAJMg2AEAAJgEwQ4AAMAkCHYAAAAmQbADAAAwCYIdAACASRDsAAAATIJgBwAAYBIEOwAAAJMg2AEAAJgEwQ4AAMAkCHYAAAAmQbADAAAwCYIdAACASRDsAAAATIJgBwAAYBIEOwAAAJMg2AEAAJgEwQ4AAMAkCHYAAAAmQbADAAAwCYIdAACASRDsAAAATIJgBwAAYBIEOwAAAJMg2AEAAJgEwQ4AAMAkCHYAAAAmQbADAAAwCYIdAACASRDsAAAATIJgBwAAYBIEOwAAAJMg2AEAAJgEwQ4AAMAkCHYAAAAmQbADAAAwCYIdAACASRDsAAAATIJgBwAAYBIEOwAAAJMg2AEAAJgEwQ4AAMAkCHYAAAAmQbADAAAwCYIdAACASbg82C1YsEDh4eHy8fFRZGSkUlNTr9l/27ZtioyMlI+Pj1q0aKGkpKQaqhQAAKB2c2mwW7NmjSZOnKhp06Zp//79ioqK0oABA5SWllZm/2PHjmngwIGKiorS/v379fzzz2vChAl67733arhyAACA2sdiGIbhqpN37dpVERERWrhwobWtXbt2GjJkiBITE+36P/fcc1q/fr0OHTpkbYuNjdXXX3+tnTt3OnTO3Nxc+fv7KysrS4GBgZW/CAAAgF/Izs5WUFCQcnJy5OfnV6PndtmM3eXLl7V371717dvXpr1v37764osvyjxm586ddv379eunPXv2qKioqNpqBQAAuBF4uOrEWVlZKikpUUhIiE17SEiIMjIyyjwmIyOjzP7FxcXKyspS48aN7Y4pLCxUYWGhdTsnJ0eSdO7cucpeAgAAgJ2rGcMVN0VdFuyuslgsNtuGYdi1Xa9/We1XJSYmKiEhwa69devWzpYKAADgsOzsbPn7+9foOV0W7IKCguTu7m43O5eZmWk3K3dVo0aNyuzv4eFR7vNyU6dOVVxcnHX7/PnzCgsLU1paWo1/2Kj9brvtNu3evdvVZaAW4m8DZeHvAmXJyclRs2bNFBAQUOPndlmw8/LyUmRkpJKTkzV06FBre3Jysu67774yj+nWrZs++ugjm7ZNmzapS5cu8vT0LPMYb29veXt727X7+/vX+AONqP3c3d35u0CZ+NtAWfi7wLW4udX8qwwuXe4kLi5Oixcv1tKlS3Xo0CFNmjRJaWlpio2NlXRlti0mJsbaPzY2VidOnFBcXJwOHTqkpUuXasmSJZo8ebKrLgEm88QTT7i6BNRS/G2gLPxdoLZx6XIn0pUFimfPnq309HS1b99e//rXv/SHP/xBkjRmzBgdP35cW7dutfbftm2bJk2apO+++06hoaF67rnnrEHQEVeXO3HFK8gAAMD8XJk1XB7salphYaESExM1derUMm/RAgAAVIYrs8ZvLtgBAACYlcu/KxYAAABVg2AHAABgEgQ7AAAAk7jhgt2CBQsUHh4uHx8fRUZGKjU11bpv3bp16tevn4KCgmSxWHTgwAGHxvz55581atQo+fv7y9/fX6NGjdL58+dt+jz99NOKjIyUt7e3OnXqVHUXBAAAao3yckZRUZGee+45dejQQXXr1lVoaKhiYmJ0+vTp645Zkznjhgp2a9as0cSJEzVt2jTt379fUVFRGjBggNLS0iRJFy9e1J133qm///3vTo07cuRIHThwQBs3btTGjRt14MABjRo1yqaPYRgaN26chg8fXmXXAwAAao9r5Yz8/Hzt27dPL7zwgvbt26d169bp+++/1+DBg687bo3mDOMGcvvttxuxsbE2bW3btjWmTJli03bs2DFDkrF///7rjnnw4EFDkrFr1y5r286dOw1JxuHDh+36x8fHGx07dqxQ/QAAoPZyNGdc9dVXXxmSjBMnTpQ7Zk3njBtmxu7y5cvau3ev+vbta9Pet29fffHFFxUed+fOnfL391fXrl2tbXfccYf8/f0rNS4AALhxVCRn5OTkyGKxqEGDBuWOW9M544YJdllZWSopKVFISIhNe0hIiDIyMio8bkZGhoKDg+3ag4ODKzUuAAC4cTibMwoKCjRlyhSNHDnymt8uUdM544YJdldZLBabbcMw7NrKExsbq3r16ll/yhvT2XEBAIA5OJIzioqKNGLECJWWlmrBggXW9tqQMzyqfMRqEhQUJHd3d7t0m5mZaZeuyzNz5kxNnjzZpq1Ro0Y6c+aMXd+zZ886PC4AALixOZozioqKNGzYMB07dkwpKSk2s3W1IWfcMDN2Xl5eioyMVHJysk17cnKyunfv7tAYwcHBuvnmm60/ktStWzfl5OToq6++svb78ssvlZOT4/C4AADgxuZIzrga6o4eParPPvtMgYGBNn1rQ864YWbsJCkuLk6jRo1Sly5d1K1bN73xxhtKS0tTbGysJOncuXNKS0uzrilz5MgRSVfScqNGjcocs127durfv7/+/Oc/a9GiRZKkRx55RIMGDVKbNm2s/X744QdduHBBGRkZunTpknWNvFtuuUVeXl7VdckAAKCGXCtnFBcX649//KP27dunjz/+WCUlJdbZvYCAgHKzQI3njAq/T+sir7/+uhEWFmZ4eXkZERERxrZt26z7li1bZkiy+4mPj7/mmNnZ2caDDz5o1K9f36hfv77x4IMPGj///LNNnx49epQ59rFjx6r+IgEAgEuUlzOuLqVW1s+WLVuuOWZN5gyLYRiG4zEQAAAAtdUN84wdAAAAro1gBwAAYBIEOwAAAJMg2AEAAJgEwQ4AAMAkCHYAAAAmQbADAAAwCYIdAACASRDsAAAATIJgB+A3YcyYMbJYLLJYLPL09FRISIj69OmjpUuXqrS01OFxli9frgYNGlRfoQBQCQQ7AL8Z/fv3V3p6uo4fP65PPvlEPXv21NNPP61BgwapuLjY1eUBQKUR7AD8Znh7e6tRo0Zq0qSJIiIi9Pzzz+vDDz/UJ598ouXLl0uS5syZow4dOqhu3bpq2rSpHn/8cV24cEGStHXrVo0dO1Y5OTnW2b8ZM2ZIki5fvqy//OUvatKkierWrauuXbtq69atrrlQAL9ZBDsAv2m9evVSx44dtW7dOkmSm5ub5s2bp2+//VYrVqxQSkqK/vKXv0iSunfvrrlz58rPz0/p6elKT0/X5MmTJUljx47V559/rrffflvffPON/vSnP6l///46evSoy64NwG+PxTAMw9VFAEB1GzNmjM6fP68PPvjAbt+IESP0zTff6ODBg3b71q5dq8cee0xZWVmSrjxjN3HiRJ0/f97a58cff1SrVq108uRJhYaGWtt79+6t22+/XS+//HKVXw8AlMXD1QUAgKsZhiGLxSJJ2rJli15++WUdPHhQubm5Ki4uVkFBgS5evKi6deuWefy+fftkGIZat25t015YWKjAwMBqrx8AriLYAfjNO3TokMLDw3XixAkNHDhQsbGx+tvf/qaAgADt2LFD48ePV1FRUbnHl5aWyt3dXXv37pW7u7vNvnr16lV3+QBgRbAD8JuWkpKi//73v5o0aZL27Nmj4uJivfLKK3Jzu/II8jvvvGPT38vLSyUlJTZtnTt3VklJiTIzMxUVFVVjtQPArxHsAPxmFBYWKiMjQyUlJTpz5ow2btyoxMREDRo0SDExMfrvf/+r4uJizZ8/X/fee68+//xzJSUl2YzRvHlzXbhwQZs3b1bHjh3l6+ur1q1b68EHH1RMTIxeeeUVde7cWVlZWUpJSVGHDh00cOBAF10xgN8a3ooF8JuxceNGNW7cWM2bN1f//v21ZcsWzZs3Tx9++KHc3d3VqVMnzZkzR7NmzVL79u21atUqJSYm2ozRvXt3xcbGavjw4WrYsKFmz54tSVq2bJliYmL0zDPPqE2bNho8eLC+/PJLNW3a1BWXCuA3irdiAQAATIIZOwAAAJMg2AEAAJgEwQ4AAMAkCHYAAAAmQbADAAAwCYIdAACASRDsAAAATIJgBwAAYBIEOwAAAJMg2AEAAJgEwQ4AAMAkCHYAAAAm8f8AZk7yYSv9a/cAAAAASUVORK5CYII=", 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" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "ax.grid(True)\n", + "fig.tight_layout()\n", + "ax.legend()\n", + "fig" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Save the graph to a PNG file" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "execution": { + "iopub.execute_input": "2024-11-07T10:48:27.063247Z", + "iopub.status.busy": "2024-11-07T10:48:27.062989Z", + "iopub.status.idle": "2024-11-07T10:48:27.141299Z", + "shell.execute_reply": "2024-11-07T10:48:27.140782Z" + } + }, + "outputs": [], + "source": [ + "fig.savefig(f\"{MY_DATA_DIR}/{gridID}-{USER}-temps.png\")" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 + Jaspy", + "language": "python", + "name": "jaspy" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/python-data/solutions/ex09b_satellite_data.ipynb b/python-data/solutions/ex09b_satellite_data.ipynb new file mode 100644 index 0000000..5b95fb8 --- /dev/null +++ b/python-data/solutions/ex09b_satellite_data.ipynb @@ -0,0 +1,89558 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "73b81a5a-4fc6-4c33-849b-3b717a43b1c8", + "metadata": {}, + "source": [ + "# Exercise 9b: Working with Satellite Data\n", + "\n", + "## Aim: Use python tools to search for, download, and manipulate satellite data\n", + "\n", + "### Issues covered:\n", + "- Search for and request data from a public STAC catalogue of satellite imagery\n", + "- Download satellite imagery as raster data \n", + "- Read rasters into python using the rioxarray package\n", + "- Visualise single/multi-band raster data\n", + "\n", + "### Introduction\n", + "\n", + "A number of satellites take snapshots of the Earth’s surface from space. The images recorded by these remote sensors represent a very precious data source for any activity that involves monitoring changes on Earth. Satellite imagery is typically provided in the form of geospatial raster data, with the measurements in each grid cell (“pixel”) being associated to accurate geographic coordinate information.\n", + "\n", + "In this notebook exercise we will explore how to access open satellite data using Python. In particular, we will consider [the Sentinel-2 data collection that is hosted on AWS](https://registry.opendata.aws/sentinel-2-l2a-cogs). This dataset consists of multi-band optical images acquired by the two satellites of [the Sentinel-2 mission](https://sentinel.esa.int/web/sentinel/missions/sentinel-2) and it is continuously updated with new images.\n", + "\n", + "\n", + "# 1. Search for satellite imagery\n", + "\n", + "**The SpatioTemporal Asset Catalog (STAC) specification**\n", + "\n", + "Current sensor resolutions and satellite revisit periods are such that terabytes of data products are added daily to the corresponding collections. Such datasets cannot be made accessible to users via full-catalog download. Space agencies and other data providers often offer access to their data catalogs through interactive Graphical User Interfaces (GUIs), see for instance the [Copernicus Open Access Hub portal](https://scihub.copernicus.eu/dhus/#/home) for the Sentinel missions. Accessing data via a GUI is a nice way to explore a catalog and get familiar with its content, but it represents a heavy and error-prone task that should be avoided if carried out systematically to retrieve data.\n", + "\n", + "A service that offers programmatic access to the data enables users to reach the desired data in a more reliable, scalable and reproducible manner. An important element in the software interface exposed to the users, which is generally called the Application Programming Interface (API), is the use of standards. Standards, in fact, can significantly facilitate the reusability of tools and scripts across datasets and applications.\n", + "\n", + "The SpatioTemporal Asset Catalog (STAC) specification is an emerging standard for describing geospatial data. By organizing metadata in a form that adheres to the STAC specifications, data providers make it possible for users to access data from different missions, instruments and collections using the same set of tools.\n", + "\n", + "\n", + "![Views of the STAC browser](https://carpentries-incubator.github.io/geospatial-python/fig/E05/STAC-browser.jpg)\n", + "Views of the radiant earth STAC browser\n", + "\n", + "## More Resources on STAC\n", + "- [STAC specification](https://github.com/radiantearth/stac-spec#readme)\n", + "- [Tools based on STAC](https://stacindex.org/ecosystem)\n", + "- [STAC catalogs](https://stacindex.org/catalogs)\n", + "\n", + "## Search a STAC catalog\n", + "\n", + "The [STAC browser](https://radiantearth.github.io/stac-browser/#/) is a good starting point to discover available datasets, as it provides an up-to-date list of existing STAC catalogs. From the list, let's click on the \"Earth Search\" catalog, i.e. the access point to search the archive of Sentinel-2 images hosted on AWS.\n" + ] + }, + { + "cell_type": "markdown", + "id": "db452b95-5e0b-47f9-ac30-330b27956c51", + "metadata": {}, + "source": [ + "## Install some packages we will need\n", + "\n", + "We need to install some additional python packages which unfortunately aren't (yet) on Jaspy. To do this, we run:\n", + "\n", + "`pip install --user pystac_client rioxarray shapely pyproj`\n", + "\n", + "Which will install these python packages into your local python path, so we can use them with your account alongside all the packages in Jaspy. __NOTE: this command may take some time__ " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "ea2ff2df-b2e5-4804-8e2b-3c05f8d0b8ac", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Collecting pystac_client\n", + " Downloading pystac_client-0.8.5-py3-none-any.whl.metadata (5.1 kB)\n", + "Requirement already satisfied: rioxarray in /opt/jaspy/lib/python3.11/site-packages (0.17.0)\n", + "Requirement already satisfied: shapely in /opt/jaspy/lib/python3.11/site-packages (2.0.4)\n", + "Requirement already satisfied: pyproj in /opt/jaspy/lib/python3.11/site-packages (3.6.1)\n", + "Requirement 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WARNING: The script stac-client is installed in '/home/users/nfarmer/.local/bin' which is not on PATH.\n", + " Consider adding this directory to PATH or, if you prefer to suppress this warning, use --no-warn-script-location.\u001b[0m\u001b[33m\n", + "\u001b[0mSuccessfully installed pystac-1.11.0 pystac_client-0.8.5\n", + "Note: you may need to restart the kernel to use updated packages.\n" + ] + } + ], + "source": [ + "pip install --user pystac_client rioxarray shapely pyproj" + ] + }, + { + "cell_type": "markdown", + "id": "517be10e-1c03-433c-b6b9-3722cc0d15b9", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "## **Exercise:** Discover a STAC catalog\n", + "Let's take a moment to explore the Earth Search STAC catalog, which is the catalog indexing the Sentinel-2 collection\n", + "that is hosted on AWS. We can interactively browse this catalog using the STAC browser at [this link](https://radiantearth.github.io/stac-browser/#/external/earth-search.aws.element84.com/v1).\n", + "\n", + "1. Open the link in your web browser. Which (sub-)catalogs are available?\n", + "2. Open the Sentinel-2 Level 2A collection, and select one item from the list. Each item corresponds to a satellite\n", + "\"scene\", i.e. a portion of the footage recorded by the satellite at a given time. Have a look at the metadata fields\n", + "and the list of assets. What kind of data do the assets represent?" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "3c869fc6-9581-4ad2-9a37-72d1d61b84d3", + "metadata": {}, + "outputs": [], + "source": [ + "# Try something in here" + ] + }, + { + "cell_type": "markdown", + "id": "dfdc9c0c-cad2-4cba-9e67-fbc7bdc99a50", + "metadata": {}, + "source": [ + "## **Solution:**\n", + "(press three dots to reveal)" + ] + }, + { + "cell_type": "markdown", + "id": "591f2ce6-52ca-45bb-b2b4-a504ef182515", + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "source": [ + "\n", + "![Views of the Earth Search STAC endpoint](https://carpentries-incubator.github.io/geospatial-python/fig/E05/STAC-browser-exercise.jpg)\n", + "\n", + "1. 7 subcatalogs are available, including a catalog for Landsat Collection 2, Level-2 and Sentinel-2 Level 2A (see left screenshot in the figure above).\n", + "2. When you select the Sentinel-2 Level 2A collection, and randomly choose one of the items from the list, you\n", + "should find yourself on a page similar to the right screenshot in the figure above. On the left side you will find\n", + "a list of the available assets: overview images (thumbnail and true color images), metadata files and the \"real\"\n", + "satellite images, one for each band captured by the Multispectral Instrument on board Sentinel-2." + ] + }, + { + "cell_type": "markdown", + "id": "462ea61f-3cc0-4793-be7a-b7f9886e1484", + "metadata": {}, + "source": [ + "When opening a catalog with the STAC browser, you can access the API URL by clicking on the \"Source\" button on the top\n", + "right of the page. By using this URL, we have access to the catalog content and, if supported by the catalog, to the\n", + "functionality of searching its items. For the Earth Search STAC catalog the API URL is:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "ca27bf8b-05c4-4d6b-b8ee-d152487e6f06", + "metadata": {}, + "outputs": [], + "source": [ + "api_url = \"https://earth-search.aws.element84.com/v1\"" + ] + }, + { + "cell_type": "markdown", + "id": "d298328b-2f37-442e-996f-ea61c68eb039", + "metadata": {}, + "source": [ + "You can query a STAC API endpoint from Python using the `pystac_client` library:" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "cac729fb-8cda-484b-9fd4-da68a2c8267c", + "metadata": {}, + "outputs": [], + "source": [ + "from pystac_client import Client\n", + "\n", + "client = Client.open(api_url)\n" + ] + }, + { + "cell_type": "markdown", + "id": "81f1cc52-814e-4af0-908f-6d4aa7cc10fe", + "metadata": {}, + "source": [ + "In the following, we ask for scenes belonging to the `sentinel-2-l2a` collection. This dataset includes Sentinel-2 data products pre-processed at level 2A (bottom-of-atmosphere reflectance) and saved in Cloud Optimized GeoTIFF (COG) format:" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "4e7e407d-721e-4187-8a47-e8e9634262c9", + "metadata": {}, + "outputs": [], + "source": [ + "collection = \"sentinel-2-l2a\" # Sentinel-2, Level 2A, Cloud Optimized GeoTiffs (COGs)" + ] + }, + { + "cell_type": "markdown", + "id": "57f5d59b-f60b-45d0-b191-f5e4d5e8939e", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## A note on cloud-optimized GeoTIFFs\n", + "\n", + "Cloud Optimized GeoTIFFs (COGs) are regular GeoTIFF files with some additional features that make them ideal to be employed in the context of cloud computing and other web-based services. This format builds on the widely-employed GeoTIFF format, which you can find out more about in [Episode 1: Introduction to Raster Data](01-intro-raster-data.md). In short, a GeoTIFF is a standard .tif image format with additional spatial (georeferencing) information embedded in the file as tags. These tags should include the following raster metadata:\n", + "- Extent\n", + "- Resolution\n", + "- Coordinate Reference System (CRS)\n", + "- Values that represent missing data (NoDataValue)\n", + "\n", + "COGs, by extension, are regular GeoTIFF files with a special internal structure. One of the features of COGs is that data is organized in \"blocks\" that can be accessed remotely via independent HTTP requests. Data users can thus access the only blocks of a GeoTIFF that are relevant for their analysis, without having to download the full file. In addition, COGs typically include multiple lower-resolution versions of the original image, called \"overviews\", which can also be accessed independently. By providing this \"pyramidal\" structure, users that are not interested in the details provided by a high-resolution raster can directly access the lower-resolution versions of the same image, significantly saving on the downloading time. More information on the COG format can be found [here](https://www.cogeo.org).\n", + "\n", + "---\n", + "\n", + "We also ask for scenes intersecting a geometry defined using the `shapely` library (in this case, a point):" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "b58e48e4-2609-4e86-b434-ed664dafa6f6", + "metadata": {}, + "outputs": [], + "source": [ + "from shapely.geometry import Point\n", + "point = Point(4.89, 52.37) # AMS coordinates" + ] + }, + { + "cell_type": "markdown", + "id": "76b597f5-db31-4bad-b858-59e9f2961d92", + "metadata": {}, + "source": [ + "Note: at this stage, we are only dealing with metadata, so no image is going to be downloaded yet. But even metadata can be quite bulky if a large number of scenes match our search! For this reason, we limit the search result to 10 items:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "08d64392-75f9-442c-bcc5-e173e1448683", + "metadata": {}, + "outputs": [], + "source": [ + "search = client.search(\n", + " collections=[collection],\n", + " intersects=point,\n", + " max_items=10,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "71b0aa02-8e8c-44c2-974e-b49ef84c7163", + "metadata": {}, + "source": [ + "We submit the query and find out how many scenes match our search criteria (please note that this output can be different as more data is added to the catalog):\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "2fd8d411-a898-452a-be69-f19a8cdb920c", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1102\n" + ] + } + ], + "source": [ + "print(search.matched())" + ] + }, + { + "cell_type": "markdown", + "id": "2fa2aeb2-0f3a-4a5c-bd2a-c8e1e5b704e3", + "metadata": {}, + "source": [ + "Finally, we retrieve the metadata of the search results:" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "2790d193-02a1-42e2-a51c-42adfc17041c", + "metadata": { + "tags": [] + }, + "outputs": [], + "source": [ + "items = search.item_collection()" + ] + }, + { + "cell_type": "markdown", + "id": "500a6faf-0d21-421e-b8b3-60b0d0df6e64", + "metadata": {}, + "source": [ + "The variable `items` is an `ItemCollection` object. We can check its size by:" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "ebb1f4c0-104a-4c38-953d-f5b1f2117643", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10\n" + ] + } + ], + "source": [ + "print(len(items))" + ] + }, + { + "cell_type": "markdown", + "id": "e2ccac65-d1aa-4e66-8243-b4d27968d638", + "metadata": {}, + "source": [ + "which is consistent with the maximum number of items that we have set in the search criteria. We can iterate over the returned items and print these to show their IDs:" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "9c94ff82-849e-4164-89b2-8fb6bec6d622", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n", + "\n" + ] + } + ], + "source": [ + "for item in items:\n", + " print(item)" + ] + }, + { + "cell_type": "markdown", + "id": "fbddf824-83d3-4789-a354-f1989936c438", + "metadata": {}, + "source": [ + "Each of the items contains information about the scene geometry, its acquisition time, and other metadata that can be accessed as a dictionary from the `properties` attribute.\n", + "\n", + "Let's inspect the metadata associated with the first item of the search results:" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "f32b169d-31df-4081-ba4d-9219f4efeb15", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2023-11-16 10:56:22.101000+00:00\n", + "{'type': 'Polygon', 'coordinates': [[[4.498475093400055, 53.240199174677954], [4.464995307918359, 52.25346561204129], [6.071664488869862, 52.22257539160585], [6.141754296879459, 53.20819279121764], [4.498475093400055, 53.240199174677954]]]}\n", + "{'created': '2023-11-16T14:13:50.802Z', 'platform': 'sentinel-2b', 'constellation': 'sentinel-2', 'instruments': ['msi'], 'eo:cloud_cover': 93.560559, 'proj:epsg': 32631, 'mgrs:utm_zone': 31, 'mgrs:latitude_band': 'U', 'mgrs:grid_square': 'FU', 'grid:code': 'MGRS-31UFU', 'view:sun_azimuth': 173.232334351965, 'view:sun_elevation': 18.294431299512596, 's2:degraded_msi_data_percentage': 0.0021, 's2:nodata_pixel_percentage': 0, 's2:saturated_defective_pixel_percentage': 0, 's2:dark_features_percentage': 0, 's2:cloud_shadow_percentage': 0.626441, 's2:vegetation_percentage': 4.058945, 's2:not_vegetated_percentage': 1.017545, 's2:water_percentage': 0.731351, 's2:unclassified_percentage': 0.005159, 's2:medium_proba_clouds_percentage': 28.973994, 's2:high_proba_clouds_percentage': 46.491641, 's2:thin_cirrus_percentage': 18.094924, 's2:snow_ice_percentage': 0, 's2:product_type': 'S2MSI2A', 's2:processing_baseline': '05.09', 's2:product_uri': 'S2B_MSIL2A_20231116T105229_N0509_R051_T31UFU_20231116T121557.SAFE', 's2:generation_time': '2023-11-16T12:15:57.000000Z', 's2:datatake_id': 'GS2B_20231116T105229_034969_N05.09', 's2:datatake_type': 'INS-NOBS', 's2:datastrip_id': 'S2B_OPER_MSI_L2A_DS_2BPS_20231116T121557_S20231116T105227_N05.09', 's2:granule_id': 'S2B_OPER_MSI_L2A_TL_2BPS_20231116T121557_A034969_T31UFU_N05.09', 's2:reflectance_conversion_factor': 1.02111403465614, 'datetime': '2023-11-16T10:56:22.101000Z', 's2:sequence': '0', 'earthsearch:s3_path': 's3://sentinel-cogs/sentinel-s2-l2a-cogs/31/U/FU/2023/11/S2B_31UFU_20231116_0_L2A', 'earthsearch:payload_id': 'roda-sentinel2/workflow-sentinel2-to-stac/ce493db9af3df767b1356d9cbc072606', 'earthsearch:boa_offset_applied': True, 'processing:software': {'sentinel2-to-stac': '0.1.1'}, 'updated': '2023-11-16T14:13:50.802Z'}\n" + ] + } + ], + "source": [ + "item = items[0]\n", + "print(item.datetime)\n", + "print(item.geometry)\n", + "print(item.properties)" + ] + }, + { + "cell_type": "markdown", + "id": "ad300420-d4d9-4765-8d2c-1aa961ae77d3", + "metadata": {}, + "source": [ + "## **Exercise**: Search satellite scenes using metadata filters\n", + "Search for all the available Sentinel-2 scenes in the `sentinel-2-l2a` collection that satisfy the following criteria:\n", + "- intersect a provided bounding box, use ±0.01 deg in lat/lon from the previously defined point (hint: use the `buffer` and `bounds` methods on the shapely `point` object we saw above)\n", + "- have been recorded between 20 March 2020 and 30 March 2020;\n", + "- have a cloud coverage smaller than 10% (hint: use the `query` argument of `client.search` - there are two ways, info can be found [here](https://pystac-client.readthedocs.io/en/latest/usage.html#query-extension) and [here](https://github.com/stac-api-extensions/query)).\n", + "\n", + "How many scenes are available? Save the search results in GeoJSON format as `search.json`." + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "id": "b7b4d17e-2746-4546-9c83-a7c43342ce9f", + "metadata": {}, + "outputs": [], + "source": [ + "# Try something in here:" + ] + }, + { + "cell_type": "markdown", + "id": "6dfaf988-9afe-4982-b00d-a0ffd7e06af1", + "metadata": {}, + "source": [ + "## **Solution**:\n", + "(press three dots to reveal)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "id": "13dba182-2e8a-445f-bfb7-e229af27140f", + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "outputs": [], + "source": [ + "bbox = point.buffer(0.01).bounds" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "id": "5b60699b-2f6a-4ce2-9e1d-d910bff981d0", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true, + "source_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6\n" + ] + } + ], + "source": [ + "search = client.search(\n", + " collections=[collection],\n", + " bbox=bbox,\n", + " datetime=\"2020-03-20/2020-03-30\",\n", + " query=[\"eo:cloud_cover<15\"]\n", + ")\n", + "print(search.matched())" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "id": "e3ebac08-3b1a-48bd-9b81-18f814243388", + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "outputs": [], + "source": [ + "items = search.item_collection()\n", + "items.save_object(\"search.json\")" + ] + }, + { + "cell_type": "markdown", + "id": "1b0566d9-fc33-48b9-9ca0-afa8a889add0", + "metadata": {}, + "source": [ + "## Access the assets\n", + "\n", + "So far we have only discussed metadata - but how can one get to the actual images of a satellite scene (the \"assets\" in the STAC nomenclature)? These can be reached via links that are made available through the item's attribute `assets`.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "id": "18778b46-71fd-4f21-872f-4112a5656439", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "dict_keys(['aot', 'blue', 'coastal', 'granule_metadata', 'green', 'nir', 'nir08', 'nir09', 'red', 'rededge1', 'rededge2', 'rededge3', 'scl', 'swir16', 'swir22', 'thumbnail', 'tileinfo_metadata', 'visual', 'wvp', 'aot-jp2', 'blue-jp2', 'coastal-jp2', 'green-jp2', 'nir-jp2', 'nir08-jp2', 'nir09-jp2', 'red-jp2', 'rededge1-jp2', 'rededge2-jp2', 'rededge3-jp2', 'scl-jp2', 'swir16-jp2', 'swir22-jp2', 'visual-jp2', 'wvp-jp2'])\n" + ] + } + ], + "source": [ + "assets = items[0].assets # first item's asset dictionary\n", + "print(assets.keys())\n" + ] + }, + { + "cell_type": "markdown", + "id": "664aa928-bf54-4003-833e-a7e93bab27a7", + "metadata": { + "tags": [] + }, + "source": [ + "We can print a minimal description of the available assets:" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "id": "5d4f2cb8-c392-4d43-b298-824529df3131", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "aot: Aerosol optical thickness (AOT)\n", + "blue: Blue (band 2) - 10m\n", + "coastal: Coastal aerosol (band 1) - 60m\n", + "granule_metadata: None\n", + "green: Green (band 3) - 10m\n", + "nir: NIR 1 (band 8) - 10m\n", + "nir08: NIR 2 (band 8A) - 20m\n", + "nir09: NIR 3 (band 9) - 60m\n", + "red: Red (band 4) - 10m\n", + "rededge1: Red edge 1 (band 5) - 20m\n", + "rededge2: Red edge 2 (band 6) - 20m\n", + "rededge3: Red edge 3 (band 7) - 20m\n", + "scl: Scene classification map (SCL)\n", + "swir16: SWIR 1 (band 11) - 20m\n", + "swir22: SWIR 2 (band 12) - 20m\n", + "thumbnail: Thumbnail image\n", + "tileinfo_metadata: None\n", + "visual: True color image\n", + "wvp: Water vapour (WVP)\n", + "aot-jp2: Aerosol optical thickness (AOT)\n", + "blue-jp2: Blue (band 2) - 10m\n", + "coastal-jp2: Coastal aerosol (band 1) - 60m\n", + "green-jp2: Green (band 3) - 10m\n", + "nir-jp2: NIR 1 (band 8) - 10m\n", + "nir08-jp2: NIR 2 (band 8A) - 20m\n", + "nir09-jp2: NIR 3 (band 9) - 60m\n", + "red-jp2: Red (band 4) - 10m\n", + "rededge1-jp2: Red edge 1 (band 5) - 20m\n", + "rededge2-jp2: Red edge 2 (band 6) - 20m\n", + "rededge3-jp2: Red edge 3 (band 7) - 20m\n", + "scl-jp2: Scene classification map (SCL)\n", + "swir16-jp2: SWIR 1 (band 11) - 20m\n", + "swir22-jp2: SWIR 2 (band 12) - 20m\n", + "visual-jp2: True color image\n", + "wvp-jp2: Water vapour (WVP)\n" + ] + } + ], + "source": [ + "for key, asset in assets.items():\n", + " print(f\"{key}: {asset.title}\")" + ] + }, + { + "cell_type": "markdown", + "id": "9012da82-cb57-455e-96ac-5c6df9eeb3ae", + "metadata": {}, + "source": [ + "Among the others, assets include multiple raster data files (one per optical band, as acquired by the multi-spectral instrument), a thumbnail, a true-color image (\"visual\"), instrument metadata and scene-classification information (\"SCL\"). Let's get the URL links to the actual asset:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "id": "20a37bef-2d6e-41d8-acbf-3eb25fa88581", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/31/U/FU/2020/3/S2A_31UFU_20200328_1_L2A/thumbnail.jpg\n" + ] + } + ], + "source": [ + "print(assets[\"thumbnail\"].href)" + ] + }, + { + "cell_type": "markdown", + "id": "ae6b336c-18b6-48cb-8ded-309b0287bdf3", + "metadata": { + "tags": [] + }, + "source": [ + "This can be used to download the corresponding file:\n", + "\n", + "![Overview of the true-colour image](https://carpentries-incubator.github.io/geospatial-python/fig/E05/STAC-s2-preview.jpg)\n", + "\n", + "###### Overview of the true-colour image (\"thumbnail\")\n", + "\n", + "\n", + "Remote raster data can be directly opened via the `rioxarray` library. We will\n", + "learn more about this library in the next part of the notebook." + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "id": "a78e28a3-2c80-41b8-be30-47941de2186b", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "[120560400 values with dtype=uint16]\n", + "Coordinates:\n", + " * band (band) int64 1\n", + " * x (x) float64 6e+05 6e+05 6e+05 ... 7.098e+05 7.098e+05 7.098e+05\n", + " * y (y) float64 5.9e+06 5.9e+06 5.9e+06 ... 5.79e+06 5.79e+06\n", + " spatial_ref int64 0\n", + "Attributes:\n", + " AREA_OR_POINT: Area\n", + " OVR_RESAMPLING_ALG: AVERAGE\n", + " _FillValue: 0\n", + " scale_factor: 1.0\n", + " add_offset: 0.0\n" + ] + } + ], + "source": [ + "import rioxarray\n", + "nir_href = assets[\"nir\"].href\n", + "nir = rioxarray.open_rasterio(nir_href)\n", + "print(nir)\n" + ] + }, + { + "cell_type": "markdown", + "id": "baf3b34f-be9b-48ed-8647-0a592763311b", + "metadata": {}, + "source": [ + "We can then save the data to disk:" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "id": "45965a50-8df3-40f1-b3d4-b23906c84fd6", + "metadata": {}, + "outputs": [], + "source": [ + "# save whole image to disk\n", + "# NOTE: This might take a while\n", + "nir.rio.to_raster(\"nir.tif\")" + ] + }, + { + "cell_type": "markdown", + "id": "52993398-3216-4d12-808e-1b357b12f684", + "metadata": {}, + "source": [ + "Since that might take a while, given there are over 10000 x 10000 = a hundred million pixels in the 10 meter NIR band, you can take a smaller subset before downloading it. Becuase the raster is a COG, we can download just what we need!\n", + "\n", + "Here, we specify that we want to download the first (and only) band in the tif file, and a slice of the width and height dimensions.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "id": "aa06ed25-cdcc-4efe-bee0-cbe6f37af9b6", + "metadata": {}, + "outputs": [], + "source": [ + "# save portion of an image to disk\n", + "nir[0,1500:2200,1500:2200].rio.to_raster(\"nir_subset.tif\")" + ] + }, + { + "cell_type": "markdown", + "id": "e5b47a49-3ade-40fe-899e-3b328cf78e0f", + "metadata": {}, + "source": [ + "The difference is 155 Megabytes for the large image vs about 1 Megabyte for the subset.\n", + "\n", + "\n", + "## **Exercise:** Downloading Landsat 8 Assets\n", + "In this exercise we put in practice all the skills we have learned thusfar to retrieve images from a different mission: [Landsat 8](https://www.usgs.gov/landsat-missions/landsat-8). In particular, we browse images from the [Harmonized Landsat Sentinel-2 (HLS) project](https://lpdaac.usgs.gov/products/hlsl30v002/), which provides images from NASA's Landsat 8 and ESA's Sentinel-2 that have been made consistent with each other. The HLS catalog is indexed in the NASA Common Metadata Repository (CMR) and it can be accessed from the STAC API endpoint at the following URL:\n", + "`https://cmr.earthdata.nasa.gov/stac/LPCLOUD`.\n", + "\n", + "1. Using `pystac_client`, search for all assets of the Landsat 8 collection (`HLSL30.v2.0`) from February to March\n", + " 2021, intersecting the point with longitude/latitute coordinates (-73.97, 40.78) deg.\n", + "2. Visualize an item's thumbnail (asset key `browse`).\n", + "\n", + "Note: we don't want to use the cloud cover query filter on this one" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "id": "87816ef7-b896-47ea-a254-9b066434cf4d", + "metadata": {}, + "outputs": [], + "source": [ + "# Try something in here" + ] + }, + { + "cell_type": "markdown", + "id": "2244056e-c216-42f7-b1ad-e1c82fa02afe", + "metadata": {}, + "source": [ + "## **Solution:**\n", + "(click on each of the three dots to expand each answer)" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "id": "3cd3a259-1d22-4892-b981-e276e1973b09", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true, + "source_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "5\n" + ] + } + ], + "source": [ + "# connect to the STAC endpoint\n", + "cmr_api_url = \"https://cmr.earthdata.nasa.gov/stac/LPCLOUD\"\n", + "client = Client.open(cmr_api_url)\n", + "\n", + "# setup search\n", + "search = client.search(\n", + " collections=[\"HLSL30.v2.0\"],\n", + " intersects=Point(-73.97, 40.78),\n", + " datetime=\"2021-02-01/2021-03-30\",\n", + ") # nasa cmr cloud cover filtering is currently broken: https://github.com/nasa/cmr-stac/issues/239\n", + "\n", + "# retrieve search results\n", + "items = search.item_collection()\n", + "print(len(items))" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "id": "3921e566-d021-48d2-ac54-cdaf3d58fd91", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true, + "source_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n" + ] + } + ], + "source": [ + "items_sorted = sorted(items, key=lambda x: x.properties[\"eo:cloud_cover\"]) # sorting and then selecting by cloud cover\n", + "item = items_sorted[0]\n", + "print(item)" + ] + }, + { + "cell_type": "code", + "execution_count": 25, + "id": "9f5ddd75-d3d2-4a5f-9741-24541dae1d75", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true, + "source_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/HLSL30.020/HLS.L30.T18TWL.2021039T153324.v2.0/HLS.L30.T18TWL.2021039T153324.v2.0.jpg\n" + ] + } + ], + "source": [ + "print(item.assets[\"browse\"].href)" + ] + }, + { + "cell_type": "markdown", + "id": "6bdd8aa6-559e-454d-8167-b6e19cea8e0e", + "metadata": { + "tags": [] + }, + "source": [ + "![Thumbnail of the Landsat-8 scene](https://data.lpdaac.earthdatacloud.nasa.gov/lp-prod-public/HLSL30.020/HLS.L30.T18TWL.2021039T153324.v2.0/HLS.L30.T18TWL.2021039T153324.v2.0.jpg)\n", + "\n", + "Thumbnail of the Landsat-8 scene" + ] + }, + { + "cell_type": "markdown", + "id": "6ad7784e-5320-4bc1-a61f-dc714eb6e6d6", + "metadata": {}, + "source": [ + "## Public catalogs, protected data\n", + "\n", + "Publicly accessible catalogs and STAC endpoints do not necessarily imply publicly accessible data. Data providers, in\n", + "fact, may limit data access to specific infrastructures and/or require authentication. For instance, the NASA CMR STAC\n", + "endpoint considered in the last exercise offers publicly accessible metadata for the HLS collection, but most of the\n", + "linked assets are available only for registered users (the thumbnail is publicly accessible).\n", + "\n", + "The authentication procedure for dataset with restricted access might differ depending on the data provider. For the\n", + "NASA CMR, follow these steps in order to access data using Python:\n", + "\n", + "* Create a NASA Earthdata login account [here](https://urs.earthdata.nasa.gov);\n", + "* Set up a netrc file with your credentials, e.g. by using [this script](https://git.earthdata.nasa.gov/projects/LPDUR/repos/daac_data_download_python/browse/EarthdataLoginSetup.py);\n", + "* Define the following environment variables:\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "id": "20eb8446-450a-4769-9e94-df0ff0f20373", + "metadata": {}, + "outputs": [], + "source": [ + "import os\n", + "os.environ[\"GDAL_HTTP_COOKIEFILE\"] = \"./cookies.txt\"\n", + "os.environ[\"GDAL_HTTP_COOKIEJAR\"] = \"./cookies.txt\"" + ] + }, + { + "cell_type": "markdown", + "id": "77abe4c0-fe83-4161-9269-13c9a6591ff4", + "metadata": {}, + "source": [ + "## Key takeaways:\n", + "\n", + "Accessing satellite images via the providers' API enables a more reliable and scalable data retrieval.\n", + "\n", + " - STAC catalogs can be browsed and searched using the same tools and scripts.\n", + " - `rioxarray` allows you to open and download remote raster files.\n", + " \n", + "---\n", + "\n", + "# 2. Read and visualise raster data\n", + "\n", + "Next, we introduce the fundamental principles, packages and metadata/raster attributes for working with raster data in Python. We will also explore how Python handles missing and bad data values.\n", + "\n", + "[`rioxarray`](https://corteva.github.io/rioxarray/stable/) is the Python package we will use throughout the rest of this notebook to work with raster data. It is based on the popular [`rasterio`](https://rasterio.readthedocs.io/en/latest/) package for working with rasters and [`xarray`](https://xarray.pydata.org/en/stable/) for working with multi-dimensional arrays.\n", + "`rioxarray` extends `xarray` by providing top-level functions (e.g. the `open_rasterio` function to open raster datasets) and by adding a set of methods to the main objects of the `xarray` package (the `Dataset` and the `DataArray`). These additional methods are made available via the `rio` accessor and become available from `xarray` objects after importing `rioxarray`.\n", + "\n", + "We will also use the [`pystac`](https://github.com/stac-utils/pystac) package to load rasters from the search results we created in the previous section.\n", + "\n", + "### About Raster Data\n", + "\n", + "Raster data is any pixelated (or gridded) data where each pixel is associated\n", + "with a specific geographic location. The value of a pixel can be\n", + "continuous (e.g. elevation) or categorical (e.g. land use). If this sounds\n", + "familiar, it is because this data structure is very common: it's how\n", + "we represent any digital image. A geospatial raster is only different\n", + "from a digital photo in that it is accompanied by spatial information\n", + "that connects the data to a particular location. This includes the\n", + "raster's extent and cell size, the number of rows and columns, and\n", + "its coordinate reference system (or CRS).\n", + "\n", + "![raster-concept](https://carpentries-incubator.github.io/geospatial-python/fig/E01/raster_concept.png)\n", + "###### Raster Concept (Source: National Ecological Observatory Network (NEON))\n", + "\n", + "Some examples of continuous rasters include:\n", + "\n", + "1. Precipitation maps.\n", + "2. Maps of tree height derived from LiDAR data.\n", + "3. Elevation values for a region.\n", + "\n", + "A map of elevation for Harvard Forest derived from the [NEON AOP LiDAR sensor](https://www.neonscience.org/data-collection/airborne-remote-sensing)\n", + "is below. Elevation is represented as a continuous numeric variable in this map. The legend\n", + "shows the continuous range of values in the data from around 300 to 420 meters.\n", + "\n", + "![elevation plot](https://carpentries-incubator.github.io/geospatial-python/fig/E01/continuous-elevation-HARV-plot-01.png)\n", + "###### Continuous Elevation Map: HARV Field Site\n", + "\n", + "For more information and further examples of raster data you can visit the [relevant lesson](01-intro-raster-data.md) in the software carpentry course this notebook is based off. \n", + "\n", + "\n", + "## Load a Raster and View Attributes\n", + "In the previous episode, we searched for Sentinel-2 images, and then saved the search results to a file: `search.json`. This contains the information on where and how to access the target images from a remote repository. We can use the function `pystac.ItemCollection.from_file()` to load the search results as an `Item` list.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "727637ca-374f-47cf-884d-d2b2766742c1", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "
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          • \n", + " href\n", + " \"https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/31/U/FU/2020/3/S2A_31UFU_20200328_1_L2A/thumbnail.jpg\"\n", + "
            • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
          • \n", + " type\n", + " \"image/jpeg\"\n", + "
            • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
          • \n", + " title\n", + " \"Thumbnail image\"\n", + "
            • \n", + " \n", + " \n", + " \n", + " \n", + "
          • \n", + " \n", + " roles\n", + " [] 1 items\n", + " \n", + " \n", + "
              \n", + " \n", + " \n", + " \n", + "
            • \n", + " 0\n", + " \"thumbnail\"\n", + "
              • \n", + " \n", + " \n", + " \n", + "
            \n", + " \n", + "
            • \n", + " \n", + " \n", + "
          \n", + "
          • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
        • \n", + " tileinfo_metadata\n", + "
            \n", + " \n", + " \n", + " \n", + "
          • \n", + " href\n", + " \"https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/31/U/FU/2020/3/S2A_31UFU_20200328_1_L2A/tileinfo_metadata.json\"\n", + "
            • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
          • \n", + " type\n", + " \"application/json\"\n", + "
            • \n", + " \n", + " \n", + " \n", + " \n", + "
          • \n", + " \n", + " roles\n", + " [] 1 items\n", + " \n", + " \n", + "
              \n", + " \n", + " \n", + " \n", + "
            • \n", + " 0\n", + " \"metadata\"\n", + "
              • \n", + " \n", + " \n", + " \n", + "
            \n", + " \n", + "
            • \n", + " \n", + " \n", + "
          \n", + "
          • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
        • \n", + " visual\n", + "
            \n", + " \n", + " \n", + " \n", + "
          • \n", + " href\n", + " \"https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/31/U/FU/2020/3/S2A_31UFU_20200328_1_L2A/TCI.tif\"\n", + "
            • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
          • \n", + " type\n", + " \"image/tiff; application=geotiff; profile=cloud-optimized\"\n", + "
            • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
          • \n", + " title\n", + " \"True color image\"\n", + "
            • \n", + " \n", + " \n", + " \n", + " \n", + "
          • \n", + " \n", + " eo:bands\n", + " [] 3 items\n", + " \n", + " \n", + "
              \n", + " \n", + " \n", + " \n", + "
            • \n", + " 0\n", + "
                \n", + " \n", + " \n", + " \n", + "
              • \n", + " name\n", + " \"red\"\n", + "
                • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
              • \n", + " common_name\n", + " \"red\"\n", + "
                • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
              • \n", + " description\n", + " \"Red (band 4)\"\n", + "
                • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
              • \n", + " center_wavelength\n", + " 0.665\n", + "
                • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
              • \n", + " full_width_half_max\n", + " 0.038\n", + "
                • \n", + " \n", + " \n", + " \n", + "
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              • \n", + " \n", + " \n", + " \n", + "
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              \n", + " \n", + " \n", + " \n", + "
            • \n", + " 1\n", + "
                \n", + " \n", + " \n", + " \n", + "
              • \n", + " name\n", + " \"green\"\n", + "
                • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
              • \n", + " common_name\n", + " \"green\"\n", + "
                • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
              • \n", + " description\n", + " \"Green (band 3)\"\n", + "
                • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
              • \n", + " center_wavelength\n", + " 0.56\n", + "
                • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
              • \n", + " full_width_half_max\n", + " 0.045\n", + "
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            • \n", + " 2\n", + "
                \n", + " \n", + " \n", + " \n", + "
              • \n", + " name\n", + " \"blue\"\n", + "
                • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
              • \n", + " common_name\n", + " \"blue\"\n", + "
                • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
              • \n", + " description\n", + " \"Blue (band 2)\"\n", + "
                • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
              • \n", + " center_wavelength\n", + " 0.49\n", + "
                • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
              • \n", + " full_width_half_max\n", + " 0.098\n", + "
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            • \n", + " \n", + " \n", + " \n", + "
          • \n", + " \n", + " proj:shape\n", + " [] 2 items\n", + " \n", + " \n", + "
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            • \n", + " 0\n", + " 10980\n", + "
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            • \n", + " \n", + " \n", + " \n", + "
          • \n", + " \n", + " proj:transform\n", + " [] 6 items\n", + " \n", + " \n", + "
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            • \n", + " 0\n", + " 10\n", + "
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            • \n", + " 2\n", + " 600000\n", + "
              • \n", + " \n", + " \n", + " \n", + "
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            • \n", + " 3\n", + " 0\n", + "
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              \n", + " \n", + " \n", + " \n", + "
            • \n", + " 4\n", + " -10\n", + "
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            • \n", + " 5\n", + " 5900040\n", + "
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            • \n", + " \n", + " \n", + " \n", + "
          • \n", + " \n", + " roles\n", + " [] 1 items\n", + " \n", + " \n", + "
              \n", + " \n", + " \n", + " \n", + "
            • \n", + " 0\n", + " \"visual\"\n", + "
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        • \n", + " wvp\n", + "
            \n", + " \n", + " \n", + " \n", + "
          • \n", + " href\n", + " \"https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/31/U/FU/2020/3/S2A_31UFU_20200328_1_L2A/WVP.tif\"\n", + "
            • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
          • \n", + " type\n", + " \"image/tiff; application=geotiff; profile=cloud-optimized\"\n", + "
            • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
          • \n", + " title\n", + " \"Water vapour (WVP)\"\n", + "
            • \n", + " \n", + " \n", + " \n", + " \n", + "
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            • \n", + " 2\n", + " 600000\n", + "
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              • \n", + " nodata\n", + " 0\n", + "
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              • \n", + " data_type\n", + " \"uint16\"\n", + "
                • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
              • \n", + " bits_per_sample\n", + " 15\n", + "
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              • \n", + " spatial_resolution\n", + " 20\n", + "
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              • \n", + " scale\n", + " 0.001\n", + "
                • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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          • \n", + " \n", + " roles\n", + " [] 2 items\n", + " \n", + " \n", + "
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            • \n", + " 0\n", + " \"data\"\n", + "
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        • \n", + " aot-jp2\n", + "
            \n", + " \n", + " \n", + " \n", + "
          • \n", + " href\n", + " \"s3://sentinel-s2-l2a/tiles/31/U/FU/2020/3/28/1/AOT.jp2\"\n", + "
            • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
          • \n", + " type\n", + " \"image/jp2\"\n", + "
            • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
          • \n", + " title\n", + " \"Aerosol optical thickness (AOT)\"\n", + "
            • \n", + " \n", + " \n", + " \n", + " \n", + "
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                • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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              • \n", + " spatial_resolution\n", + " 20\n", + "
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        • \n", + " blue-jp2\n", + "
            \n", + " \n", + " \n", + " \n", + "
          • \n", + " href\n", + " \"s3://sentinel-s2-l2a/tiles/31/U/FU/2020/3/28/1/B02.jp2\"\n", + "
            • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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          • \n", + " title\n", + " \"Blue (band 2) - 10m\"\n", + "
            • \n", + " \n", + " \n", + " \n", + " \n", + "
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                • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
              • \n", + " common_name\n", + " \"blue\"\n", + "
                • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
              • \n", + " description\n", + " \"Blue (band 2)\"\n", + "
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              • \n", + " center_wavelength\n", + " 0.49\n", + "
                • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
              • \n", + " full_width_half_max\n", + " 0.098\n", + "
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          • \n", + " gsd\n", + " 10\n", + "
            • \n", + " \n", + " \n", + " \n", + " \n", + "
          • \n", + " \n", + " proj:shape\n", + " [] 2 items\n", + " \n", + " \n", + "
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            • \n", + " 0\n", + " 10980\n", + "
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              \n", + " \n", + " \n", + " \n", + "
            • \n", + " 1\n", + " 10980\n", + "
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            \n", + " \n", + "
            • \n", + " \n", + " \n", + " \n", + "
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              • \n", + " nodata\n", + " 0\n", + "
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              • \n", + " bits_per_sample\n", + " 15\n", + "
                • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
              • \n", + " spatial_resolution\n", + " 20\n", + "
                • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
              • \n", + " scale\n", + " 0.0001\n", + "
                • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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            • \n", + " 0\n", + " \"data\"\n", + "
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        • \n", + " thumbnail\n", + "
            \n", + " \n", + " \n", + " \n", + "
          • \n", + " href\n", + " \"https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/31/U/FU/2020/3/S2A_31UFU_20200328_0_L2A/thumbnail.jpg\"\n", + "
            • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
          • \n", + " type\n", + " \"image/jpeg\"\n", + "
            • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
          • \n", + " title\n", + " \"Thumbnail image\"\n", + "
            • \n", + " \n", + " \n", + " \n", + " \n", + "
          • \n", + " \n", + " roles\n", + " [] 1 items\n", + " \n", + " \n", + "
              \n", + " \n", + " \n", + " \n", + "
            • \n", + " 0\n", + " \"thumbnail\"\n", + "
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          \n", + "
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        • \n", + " tileinfo_metadata\n", + "
            \n", + " \n", + " \n", + " \n", + "
          • \n", + " href\n", + " \"https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/31/U/FU/2020/3/S2A_31UFU_20200328_0_L2A/tileinfo_metadata.json\"\n", + "
            • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
          • \n", + " type\n", + " \"application/json\"\n", + "
            • \n", + " \n", + " \n", + " \n", + " \n", + "
          • \n", + " \n", + " roles\n", + " [] 1 items\n", + " \n", + " \n", + "
              \n", + " \n", + " \n", + " \n", + "
            • \n", + " 0\n", + " \"metadata\"\n", + "
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          \n", + "
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        • \n", + " visual\n", + "
            \n", + " \n", + " \n", + " \n", + "
          • \n", + " href\n", + " \"https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/31/U/FU/2020/3/S2A_31UFU_20200328_0_L2A/TCI.tif\"\n", + "
            • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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            • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
          • \n", + " title\n", + " \"True color image\"\n", + "
            • \n", + " \n", + " \n", + " \n", + " \n", + "
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            • \n", + " 0\n", + "
                \n", + " \n", + " \n", + " \n", + "
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              • \n", + " common_name\n", + " \"red\"\n", + "
                • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
              • \n", + " description\n", + " \"Red (band 4)\"\n", + "
                • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
              • \n", + " center_wavelength\n", + " 0.665\n", + "
                • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
              • \n", + " full_width_half_max\n", + " 0.038\n", + "
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              • \n", + " description\n", + " \"Green (band 3)\"\n", + "
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              • \n", + " center_wavelength\n", + " 0.56\n", + "
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              • \n", + " description\n", + " \"Blue (band 2)\"\n", + "
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              • \n", + " center_wavelength\n", + " 0.49\n", + "
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              • \n", + " full_width_half_max\n", + " 0.098\n", + "
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          • \n", + " href\n", + " \"s3://sentinel-s2-l2a/tiles/31/U/FU/2020/3/28/0/AOT.jp2\"\n", + "
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              • \n", + " data_type\n", + " \"uint16\"\n", + "
                • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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        • \n", + " blue-jp2\n", + "
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          • \n", + " href\n", + " \"https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/31/U/FU/2020/3/S2B_31UFU_20200326_0_L2A/B03.tif\"\n", + "
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          • \n", + " href\n", + " \"https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/31/U/FU/2020/3/S2B_31UFU_20200326_0_L2A/tileinfo_metadata.json\"\n", + "
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          • \n", + " href\n", + " \"https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/31/U/FU/2020/3/S2B_31UFU_20200326_0_L2A/TCI.tif\"\n", + "
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              • \n", + " center_wavelength\n", + " 0.665\n", + "
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          • \n", + " href\n", + " \"https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/31/U/FU/2020/3/S2B_31UFU_20200323_1_L2A/B12.tif\"\n", + "
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              • \n", + " spatial_resolution\n", + " 20\n", + "
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          • \n", + " href\n", + " \"https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/31/U/FU/2020/3/S2B_31UFU_20200323_1_L2A/thumbnail.jpg\"\n", + "
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          • \n", + " href\n", + " \"https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/31/U/FU/2020/3/S2B_31UFU_20200323_1_L2A/tileinfo_metadata.json\"\n", + "
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            • \n", + " 0\n", + " \"metadata\"\n", + "
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        • \n", + " visual\n", + "
            \n", + " \n", + " \n", + " \n", + "
          • \n", + " href\n", + " \"https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/31/U/FU/2020/3/S2B_31UFU_20200323_1_L2A/TCI.tif\"\n", + "
            • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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          • \n", + " href\n", + " \"https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/31/U/FU/2020/3/S2B_31UFU_20200323_0_L2A/TCI.tif\"\n", + "
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            • \n", + " 0\n", + " \"metadata\"\n", + "
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          • \n", + " href\n", + " \"https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/31/U/FU/2020/3/S2A_31UFU_20200321_0_L2A/TCI.tif\"\n", + "
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          \n", + " \n", + " \n", + " \n", + "
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          \n", + " \n", + " \n", + " \n", + "
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        \n", + " \n", + "
          \n", + " \n", + " \n", + " \n", + "
        • \n", + " 3\n", + " \"https://stac-extensions.github.io/mgrs/v1.0.0/schema.json\"\n", + "
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          \n", + " \n", + " \n", + " \n", + "
        • \n", + " 4\n", + " \"https://stac-extensions.github.io/eo/v1.0.0/schema.json\"\n", + "
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          \n", + " \n", + " \n", + " \n", + "
        • \n", + " 5\n", + " \"https://stac-extensions.github.io/raster/v1.1.0/schema.json\"\n", + "
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        • \n", + " 6\n", + " \"https://stac-extensions.github.io/view/v1.0.0/schema.json\"\n", + "
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        \n", + " \n", + "
        • \n", + " \n", + " \n", + " \n", + " \n", + "
      • \n", + " collection\n", + " \"sentinel-2-l2a\"\n", + "
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\n", + "
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "import pystac\n", + "items = pystac.ItemCollection.from_file(\"search.json\")\n", + "items" + ] + }, + { + "cell_type": "markdown", + "id": "94c70f55-7825-4083-af2d-c943b76f9cc2", + "metadata": {}, + "source": [ + "In the search results, we have 6 `Item` type objects, corresponding to several Sentinel-2 scenes from March 21th and 28th in 2020. We will focus on the scene `S2A_31UFU_20200328_0_L2A`, and load band `nir09` (central wavelength 945 nm). We can load this band using the function `rioxarray.open_rasterio()`, via the Hypertext Reference `href` (commonly referred to as a URL):\n", + "\n", + "## **Exercise:** finding the right item and asset\n", + "How do we go about selecting the correct item and asset from our ItemCollection we just loaded?\n", + "1. Find the item corresponding to scene S2A_31UFU_20200328_0_L2A\n", + "2. Find the asset `href` for the `nir09` band in the item's asset dictionary.\n", + "3. Load it using rioxarray's `open_rasterio` method into a variable called `raster_ams_b9`" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "id": "720cdf4b-6472-483c-9e48-2e7e6817392f", + "metadata": {}, + "outputs": [], + "source": [ + "# Try something in here" + ] + }, + { + "cell_type": "markdown", + "id": "92e7c946-bb85-4c4c-b434-8910641d541d", + "metadata": {}, + "source": [ + "## **Solution**:\n", + "(press on each of the three dots to reveal)" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "id": "05db88fc-87ba-4845-8435-f4ac152131f0", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true, + "source_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "\n", + "\n", + "
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  • \n", + " collection\n", + " \"sentinel-2-l2a\"\n", + "
    • \n", + " \n", + " \n", + " \n", + "
\n", + "
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" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "for item in items:\n", + " if item.id == \"S2A_31UFU_20200328_0_L2A\":\n", + " break\n", + "item" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "id": "1c8762ba-ef23-4c48-befb-9f952cff7521", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true, + "source_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "'https://sentinel-cogs.s3.us-west-2.amazonaws.com/sentinel-s2-l2a-cogs/31/U/FU/2020/3/S2A_31UFU_20200328_0_L2A/B09.tif'" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "item.assets['nir09'].href" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "id": "1158e42f-3887-498b-b356-de5568878fd2", + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "outputs": [], + "source": [ + "raster_ams_b9 = rioxarray.open_rasterio(item.assets[\"nir09\"].href)" + ] + }, + { + "cell_type": "markdown", + "id": "91e2911c-fa5d-473c-8d72-3430607da2c0", + "metadata": {}, + "source": [ + "By calling the variable name in the jupyter notebook we can get a quick look at the shape and attributes of the data." + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "id": "eedd0705-06ee-4490-9aa0-9677e495a390", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.DataArray (band: 1, y: 1830, x: 1830)>\n",
+       "[3348900 values with dtype=uint16]\n",
+       "Coordinates:\n",
+       "  * band         (band) int64 1\n",
+       "  * x            (x) float64 6e+05 6.001e+05 6.002e+05 ... 7.097e+05 7.098e+05\n",
+       "  * y            (y) float64 5.9e+06 5.9e+06 5.9e+06 ... 5.79e+06 5.79e+06\n",
+       "    spatial_ref  int64 0\n",
+       "Attributes:\n",
+       "    AREA_OR_POINT:       Area\n",
+       "    OVR_RESAMPLING_ALG:  AVERAGE\n",
+       "    _FillValue:          0\n",
+       "    scale_factor:        1.0\n",
+       "    add_offset:          0.0
" + ], + "text/plain": [ + "\n", + "[3348900 values with dtype=uint16]\n", + "Coordinates:\n", + " * band (band) int64 1\n", + " * x (x) float64 6e+05 6.001e+05 6.002e+05 ... 7.097e+05 7.098e+05\n", + " * y (y) float64 5.9e+06 5.9e+06 5.9e+06 ... 5.79e+06 5.79e+06\n", + " spatial_ref int64 0\n", + "Attributes:\n", + " AREA_OR_POINT: Area\n", + " OVR_RESAMPLING_ALG: AVERAGE\n", + " _FillValue: 0\n", + " scale_factor: 1.0\n", + " add_offset: 0.0" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raster_ams_b9" + ] + }, + { + "cell_type": "markdown", + "id": "9ba8bbc2-b368-4429-8bd9-32088e3b7e9a", + "metadata": {}, + "source": [ + "The first call to `rioxarray.open_rasterio()` opens the file from remote or local storage, and then returns a `xarray.DataArray` object. The object is stored in a variable, i.e. `raster_ams_b9`. Reading in the data with `xarray` instead of `rioxarray` also returns a `xarray.DataArray`, but the output will not contain the geospatial metadata (such as projection information). You can use numpy functions or built-in Python math operators on a `xarray.DataArray` just like a numpy array. Calling the variable name of the `DataArray` also prints out all of its metadata information.\n", + "\n", + "The output tells us that we are looking at an `xarray.DataArray`, with `1` band, `1830` rows, and `1830` columns. We can also see the number of pixel values in the `DataArray`, and the type of those pixel values, which is unsigned integer (or `uint16`). The `DataArray` also stores different values for the coordinates of the `DataArray`. When using `rioxarray`, the term coordinates refers to spatial coordinates like `x` and `y` but also the `band` coordinate. Each of these sequences of values has its own data type, like `float64` for the spatial coordinates and `int64` for the `band` coordinate.\n", + "\n", + "This `DataArray` object also has a couple of attributes that are accessed like `.rio.crs`, `.rio.nodata`, and `.rio.bounds()`, which contain the metadata for the file we opened. Note that many of the metadata are accessed as attributes without `()`, but `bounds()` is a method (i.e. a function in an object) and needs parentheses.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "id": "28143c46-f7dd-464b-94b5-431cf7cc81d3", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EPSG:32631\n", + "0\n", + "(600000.0, 5790240.0, 709800.0, 5900040.0)\n", + "1830\n", + "1830\n" + ] + } + ], + "source": [ + "print(raster_ams_b9.rio.crs)\n", + "print(raster_ams_b9.rio.nodata)\n", + "print(raster_ams_b9.rio.bounds())\n", + "print(raster_ams_b9.rio.width)\n", + "print(raster_ams_b9.rio.height)" + ] + }, + { + "cell_type": "markdown", + "id": "93f6f2eb-ccfd-4512-a2a1-4ca420946b75", + "metadata": {}, + "source": [ + "The Coordinate Reference System, or `raster_ams_b9.rio.crs`, is reported as the string `EPSG:32631`. The `nodata` value is encoded as 0 and the bounding box corners of our raster are represented by the output of `.bounds()` as a `tuple` (like a list but you can't edit it). The height and width match what we saw when we printed the `DataArray`, but by using `.rio.width` and `.rio.height` we can access these values if we need them in calculations.\n", + "\n", + "We will be exploring this data throughout this episode. By the end of this episode, you will be able to understand and explain the metadata output.\n", + "\n", + "\n", + "*TIP: To improve code readability, file and object names should be used that make it clear what is in the file. The data for this episode covers Amsterdam, and is from Band 9, so we'll use a naming convention of `raster_ams_b9` for the variable name.*\n", + "\n", + "\n", + "## Visualize a Raster\n", + "\n", + "After viewing the attributes of our raster, we can examine the raw values of the array with `.values`:\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "id": "7a8ac587-b7ab-4e59-9262-702790888e43", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[[ 0, 0, 0, ..., 8888, 9075, 8139],\n", + " [ 0, 0, 0, ..., 10444, 10358, 8669],\n", + " [ 0, 0, 0, ..., 10346, 10659, 9168],\n", + " ...,\n", + " [ 0, 0, 0, ..., 4295, 4289, 4320],\n", + " [ 0, 0, 0, ..., 4291, 4269, 4179],\n", + " [ 0, 0, 0, ..., 3944, 3503, 3862]]], dtype=uint16)" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raster_ams_b9.values" + ] + }, + { + "cell_type": "markdown", + "id": "2bb2fe6f-ea90-43d8-ba98-709860e2caed", + "metadata": {}, + "source": [ + "This can give us a quick view of the values of our array, but only at the corners. Since our raster is loaded in python as a `DataArray` type, we can plot this in one line similar to a pandas `DataFrame` with `DataArray.plot()`.\n", + "\n", + "__Exercise: plot our raster file using the plot() method__" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "id": "959a6f86-4db2-4d14-bd10-f870d9796160", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true, + "source_hidden": true + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "raster_ams_b9.plot()" + ] + }, + { + "cell_type": "markdown", + "id": "b6fae531-5581-49be-b40f-7264ce5f1844", + "metadata": {}, + "source": [ + "Notice that `rioxarray` helpfully allows us to plot this raster with spatial coordinates on the x and y axis (this is not the default in many cases with other functions or libraries).\n", + "\n", + "This plot shows the satellite measurement of the spectral band `nir09` for an area that covers part of the Netherlands. According to the [Sentinel-2 documentaion](https://sentinels.copernicus.eu/web/sentinel/technical-guides/sentinel-2-msi/msi-instrument), this is a band with the central wavelength of 945nm, which is sensitive to water vapor. It has a spatial resolution of 60m. Note that the `band=1` in the image title refers to the ordering of all the bands in the `DataArray`, not the Sentinel-2 band number `09` that we saw in the pystac search results.\n", + "\n", + "With a quick view of the image, we notice that half of the image is blank, no data is captured. We also see that the cloudy pixels at the top have high reflectance values, while the contrast of everything else is quite low. This is expected because this band is sensitive to the water vapor. However if one would like to have a better color contrast, one can add the option `robust=True`, which displays values between the 2nd and 98th percentile:" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "id": "ca2f2b9d-ed77-4325-8771-90cdcae636d6", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "raster_ams_b9.plot(robust=True)" + ] + }, + { + "cell_type": "markdown", + "id": "ad3e3a1b-651d-4400-9be3-f246178bbf8e", + "metadata": {}, + "source": [ + "Now the color limit is set in a way fitting most of the values in the image. We have a better view of the ground pixels.\n", + "\n", + "---\n", + "\n", + "*NOTE: The option `robust=True` always forces displaying values between the 2nd and 98th percentile. Of course, this will not work for every case. For a customized displaying range, you can also manually specifying the keywords `vmin` and `vmax`. For example ploting between `100` and `7000`:*\n", + "\n", + "__Exercise: plot the raster with vmin and vmax arguments__" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "id": "a93139e6-8380-4b36-a45d-e6d8d4da5531", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true, + "source_hidden": true + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "raster_ams_b9.plot(vmin=100, vmax=7000)" + ] + }, + { + "cell_type": "markdown", + "id": "211a5bc3-749a-467e-a787-79dac2562851", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## View Raster Coordinate Reference System (CRS) in Python\n", + "Another information that we're interested in is the CRS, and it can be accessed with `.rio.crs`. To find out more about CRS look at [the earlier\n", + "episode](https://carpentries-incubator.github.io/geospatial-python/instructor/03-crs.html) in the software carpentry course.\n", + "Now we will see how features of the CRS appear in our data file and what\n", + "meanings they have. We can view the CRS string associated with our DataArray's `rio` object using the `crs`\n", + "attribute.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "id": "a2024c3e-ceb2-4b7b-9420-25e727f2047e", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EPSG:32631\n" + ] + } + ], + "source": [ + "print(raster_ams_b9.rio.crs)\n" + ] + }, + { + "cell_type": "markdown", + "id": "a06dc157-4d79-4e58-bdea-27c63c3c0ee8", + "metadata": {}, + "source": [ + "To print the EPSG code number as an `int`, we use the `.to_epsg()` method:" + ] + }, + { + "cell_type": "code", + "execution_count": 39, + "id": "86c0e9eb-5587-4e00-8ec2-09eb09d3248d", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "32631" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raster_ams_b9.rio.crs.to_epsg()" + ] + }, + { + "cell_type": "markdown", + "id": "7d65ca6a-2d92-4aee-95ab-2e2db3f4762a", + "metadata": {}, + "source": [ + "EPSG codes are great for succinctly representing a particular coordinate reference system. But what if we want to see more details about the CRS, like the units? For that, we can use `pyproj`, a library for representing and working with coordinate reference systems." + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "id": "5803b0ea-3ae6-41dc-95a4-da0d3231d604", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Name: WGS 84 / UTM zone 31N\n", + "Axis Info [cartesian]:\n", + "- E[east]: Easting (metre)\n", + "- N[north]: Northing (metre)\n", + "Area of Use:\n", + "- name: Between 0°E and 6°E, northern hemisphere between equator and 84°N, onshore and offshore. Algeria. Andorra. Belgium. Benin. Burkina Faso. Denmark - North Sea. France. Germany - North Sea. Ghana. Luxembourg. Mali. Netherlands. Niger. Nigeria. Norway. Spain. Togo. United Kingdom (UK) - North Sea.\n", + "- bounds: (0.0, 0.0, 6.0, 84.0)\n", + "Coordinate Operation:\n", + "- name: UTM zone 31N\n", + "- method: Transverse Mercator\n", + "Datum: World Geodetic System 1984 ensemble\n", + "- Ellipsoid: WGS 84\n", + "- Prime Meridian: Greenwich" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "from pyproj import CRS\n", + "epsg = raster_ams_b9.rio.crs.to_epsg()\n", + "crs = CRS(epsg)\n", + "crs" + ] + }, + { + "cell_type": "markdown", + "id": "30c00eb9-a206-43ea-a467-59b9ef161b2d", + "metadata": {}, + "source": [ + "The `CRS` class from the `pyproj` library allows us to create a `CRS` object with methods and attributes for accessing specific information about a CRS, or the detailed summary shown above.\n", + "\n", + "A particularly useful attribute is `area_of_use`, which shows the geographic bounds that the CRS is intended to be used.\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 41, + "id": "a5caeb4d-cfde-4726-b265-f316cef2896a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "AreaOfUse(west=0.0, south=0.0, east=6.0, north=84.0, name='Between 0°E and 6°E, northern hemisphere between equator and 84°N, onshore and offshore. Algeria. Andorra. Belgium. Benin. Burkina Faso. Denmark - North Sea. France. Germany - North Sea. Ghana. Luxembourg. Mali. Netherlands. Niger. Nigeria. Norway. Spain. Togo. United Kingdom (UK) - North Sea.')" + ] + }, + "execution_count": 41, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "crs.area_of_use" + ] + }, + { + "cell_type": "markdown", + "id": "396b9eac-2847-4073-9233-0746d578eb37", + "metadata": {}, + "source": [ + "## **Exercise**: find the axes units of the CRS\n", + "What units are our data in? See if you can find a method to examine this information using `help(crs)` or `dir(crs)`" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "id": "5d1f760a-6fd9-496e-bfdc-531e7f6a1f95", + "metadata": {}, + "outputs": [], + "source": [ + "# Try something in here" + ] + }, + { + "cell_type": "markdown", + "id": "088cf5cb-94cb-4ed9-8779-3c6be12a6f0e", + "metadata": {}, + "source": [ + "## **Solution**:\n", + "(press three dots to reveal)" + ] + }, + { + "cell_type": "markdown", + "id": "cef1e0eb-54b5-4d0d-a0b1-8b3435b84404", + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "source": [ + "`crs.axis_info` tells us that the CRS for our raster has two axes and both are in meters.\n", + "We could also get this information from the attribute `raster_ams_b9.rio.crs.linear_units`." + ] + }, + { + "cell_type": "markdown", + "id": "f573adc5-b5de-4f8a-9441-d123722446d0", + "metadata": {}, + "source": [ + "Let's break down the pieces of the `pyproj` CRS summary. The string contains all of the individual CRS elements that Python or another GIS might need, separated into distinct sections, and datum." + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "id": "108a05cf-5faa-41fd-8451-03922edfe641", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "\n", + "Name: WGS 84 / UTM zone 31N\n", + "Axis Info [cartesian]:\n", + "- E[east]: Easting (metre)\n", + "- N[north]: Northing (metre)\n", + "Area of Use:\n", + "- name: Between 0°E and 6°E, northern hemisphere between equator and 84°N, onshore and offshore. Algeria. Andorra. Belgium. Benin. Burkina Faso. Denmark - North Sea. France. Germany - North Sea. Ghana. Luxembourg. Mali. Netherlands. Niger. Nigeria. Norway. Spain. Togo. United Kingdom (UK) - North Sea.\n", + "- bounds: (0.0, 0.0, 6.0, 84.0)\n", + "Coordinate Operation:\n", + "- name: UTM zone 31N\n", + "- method: Transverse Mercator\n", + "Datum: World Geodetic System 1984 ensemble\n", + "- Ellipsoid: WGS 84\n", + "- Prime Meridian: Greenwich" + ] + }, + "execution_count": 43, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "crs" + ] + }, + { + "cell_type": "markdown", + "id": "b1adeed3-3871-4240-8e1f-468dc952ffce", + "metadata": {}, + "source": [ + "* **Name** of the projection is UTM zone 31N (UTM has 60 zones, each 6-degrees of longitude in width). The underlying datum is WGS84.\n", + "* **Axis Info**: the CRS shows a Cartesian system with two axes, easting and northing, in meter units.\n", + "* **Area of Use**: the projection is used for a particular range of longitudes `0°E to 6°E` in the northern hemisphere (`0.0°N to 84.0°N`)\n", + "* **Coordinate Operation**: the operation to project the coordinates (if it is projected) onto a cartesian (x, y) plane. Transverse Mercator is accurate for areas with longitudinal widths of a few degrees, hence the distinct UTM zones.\n", + "* **Datum**: Details about the datum, or the reference point for coordinates. `WGS 84` and `NAD 1983` are common datums. `NAD 1983` is [set to be replaced in 2022](https://en.wikipedia.org/wiki/Datum_of_2022).\n", + "\n", + "Note that the zone is unique to the UTM projection. Not all CRSs will have a\n", + "zone. Below is a simplified view of US UTM zones.\n", + "\n", + "![UTMZones](https://upload.wikimedia.org/wikipedia/commons/thumb/8/8d/Utm-zones-USA.svg/1920px-Utm-zones-USA.svg.png)\n", + "###### The UTM zones across the continental United States (Chrismurf at English Wikipedia, via [Wikimedia Commons](https://en.wikipedia.org/wiki/Universal_Transverse_Mercator_coordinate_system#/media/File:Utm-zones-USA.svg) (CC-BY))\n", + "\n", + "## Calculate Raster Statistics\n", + "\n", + "It is useful to know the minimum or maximum values of a raster dataset. __Exercise: compute these and other descriptive statistics with `min`, `max`, `mean`, and `std`.__\n" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "id": "c46bd232-2850-4cca-963d-5cf571d109d1", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true, + "source_hidden": true + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "array(0, dtype=uint16)\n", + "Coordinates:\n", + " spatial_ref int64 0\n", + "\n", + "array(15497, dtype=uint16)\n", + "Coordinates:\n", + " spatial_ref int64 0\n", + "\n", + "array(1652.44009944)\n", + "Coordinates:\n", + " spatial_ref int64 0\n", + "\n", + "array(2049.16447495)\n", + "Coordinates:\n", + " spatial_ref int64 0\n" + ] + } + ], + "source": [ + "print(raster_ams_b9.min())\n", + "print(raster_ams_b9.max())\n", + "print(raster_ams_b9.mean())\n", + "print(raster_ams_b9.std())" + ] + }, + { + "cell_type": "markdown", + "id": "13dd8ed9-18a3-4466-b519-64546c66b7c9", + "metadata": {}, + "source": [ + "The information above includes a report of the min, max, mean, and standard deviation values, along with the data type. If we want to see specific quantiles, we can use xarray's `.quantile()` method. For example for the 25% and 75% quantiles:" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "id": "3bd4db6c-374b-48d1-a1c8-db4f7472fb33", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "array([ 0., 2911.])\n", + "Coordinates:\n", + " * quantile (quantile) float64 0.25 0.75\n" + ] + } + ], + "source": [ + "print(raster_ams_b9.quantile([0.25, 0.75]))" + ] + }, + { + "cell_type": "markdown", + "id": "05138d80-9b26-4b92-8749-df2d69b1473b", + "metadata": {}, + "source": [ + "---\n", + "*NOTE: You could also get each of these values one by one using `numpy`.*\n" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "id": "fe927ff4-6467-4488-a671-05e59a884d12", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "0.0\n", + "2911.0\n" + ] + } + ], + "source": [ + "import numpy\n", + "print(numpy.percentile(raster_ams_b9, 25))\n", + "print(numpy.percentile(raster_ams_b9, 75))" + ] + }, + { + "cell_type": "markdown", + "id": "d64fa87d-3e80-4fb7-b973-a6595d37b8f9", + "metadata": {}, + "source": [ + "You may notice that `raster_ams_b9.quantile` and `numpy.percentile` didn't require an argument specifying the axis or dimension along which to compute the quantile. This is because `axis=None` is the default for most numpy functions, and therefore `dim=None` is the default for most xarray methods. It's always good to check out the docs on a function to see what the default arguments are, particularly when working with multi-dimensional image data. To do so, we can use`help(raster_ams_b9.quantile)` (or `?raster_ams_b9.percentile` in jupyter notebook), e.g.\n" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "id": "25334bd8-d927-4df3-987a-b2f0fc2fd443", + "metadata": { + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "\u001b[0;31mSignature:\u001b[0m\n", + "\u001b[0mraster_ams_b9\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mquantile\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mq\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'ArrayLike'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mdim\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'str | Sequence[Hashable] | None'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mmethod\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'QUANTILE_METHODS'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m'linear'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mkeep_attrs\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'bool'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0mskipna\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'bool'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mTrue\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m \u001b[0minterpolation\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;34m'QUANTILE_METHODS'\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", + "\u001b[0;34m\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;34m'DataArray'\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mDocstring:\u001b[0m\n", + "Compute the qth quantile of the data along the specified dimension.\n", + "\n", + "Returns the qth quantiles(s) of the array elements.\n", + "\n", + "Parameters\n", + "----------\n", + "q : float or array-like of float\n", + " Quantile to compute, which must be between 0 and 1 inclusive.\n", + "dim : hashable or sequence of hashable, optional\n", + " Dimension(s) over which to apply quantile.\n", + "method : str, default: \"linear\"\n", + " This optional parameter specifies the interpolation method to use when the\n", + " desired quantile lies between two data points. The options sorted by their R\n", + " type as summarized in the H&F paper [1]_ are:\n", + "\n", + " 1. \"inverted_cdf\" (*)\n", + " 2. \"averaged_inverted_cdf\" (*)\n", + " 3. \"closest_observation\" (*)\n", + " 4. \"interpolated_inverted_cdf\" (*)\n", + " 5. \"hazen\" (*)\n", + " 6. \"weibull\" (*)\n", + " 7. \"linear\" (default)\n", + " 8. \"median_unbiased\" (*)\n", + " 9. \"normal_unbiased\" (*)\n", + "\n", + " The first three methods are discontiuous. The following discontinuous\n", + " variations of the default \"linear\" (7.) option are also available:\n", + "\n", + " * \"lower\"\n", + " * \"higher\"\n", + " * \"midpoint\"\n", + " * \"nearest\"\n", + "\n", + " See :py:func:`numpy.quantile` or [1]_ for details. Methods marked with\n", + " an asterix require numpy version 1.22 or newer. The \"method\" argument was\n", + " previously called \"interpolation\", renamed in accordance with numpy\n", + " version 1.22.0.\n", + "\n", + "keep_attrs : bool, optional\n", + " If True, the dataset's attributes (`attrs`) will be copied from\n", + " the original object to the new one. If False (default), the new\n", + " object will be returned without attributes.\n", + "skipna : bool, optional\n", + " Whether to skip missing values when aggregating.\n", + "\n", + "Returns\n", + "-------\n", + "quantiles : DataArray\n", + " If `q` is a single quantile, then the result\n", + " is a scalar. If multiple percentiles are given, first axis of\n", + " the result corresponds to the quantile and a quantile dimension\n", + " is added to the return array. The other dimensions are the\n", + " dimensions that remain after the reduction of the array.\n", + "\n", + "See Also\n", + "--------\n", + "numpy.nanquantile, numpy.quantile, pandas.Series.quantile, Dataset.quantile\n", + "\n", + "Examples\n", + "--------\n", + ">>> da = xr.DataArray(\n", + "... data=[[0.7, 4.2, 9.4, 1.5], [6.5, 7.3, 2.6, 1.9]],\n", + "... coords={\"x\": [7, 9], \"y\": [1, 1.5, 2, 2.5]},\n", + "... dims=(\"x\", \"y\"),\n", + "... )\n", + ">>> da.quantile(0) # or da.quantile(0, dim=...)\n", + "\n", + "array(0.7)\n", + "Coordinates:\n", + " quantile float64 0.0\n", + ">>> da.quantile(0, dim=\"x\")\n", + "\n", + "array([0.7, 4.2, 2.6, 1.5])\n", + "Coordinates:\n", + " * y (y) float64 1.0 1.5 2.0 2.5\n", + " quantile float64 0.0\n", + ">>> da.quantile([0, 0.5, 1])\n", + "\n", + "array([0.7, 3.4, 9.4])\n", + "Coordinates:\n", + " * quantile (quantile) float64 0.0 0.5 1.0\n", + ">>> da.quantile([0, 0.5, 1], dim=\"x\")\n", + "\n", + "array([[0.7 , 4.2 , 2.6 , 1.5 ],\n", + " [3.6 , 5.75, 6. , 1.7 ],\n", + " [6.5 , 7.3 , 9.4 , 1.9 ]])\n", + "Coordinates:\n", + " * y (y) float64 1.0 1.5 2.0 2.5\n", + " * quantile (quantile) float64 0.0 0.5 1.0\n", + "\n", + "References\n", + "----------\n", + ".. [1] R. J. Hyndman and Y. Fan,\n", + " \"Sample quantiles in statistical packages,\"\n", + " The American Statistician, 50(4), pp. 361-365, 1996\n", + "\u001b[0;31mFile:\u001b[0m /opt/jaspy/lib/python3.10/site-packages/xarray/core/dataarray.py\n", + "\u001b[0;31mType:\u001b[0m method\n" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "?raster_ams_b9.quantile" + ] + }, + { + "cell_type": "markdown", + "id": "6f8a771b-b8c4-4096-b887-f23bf5fb7b15", + "metadata": {}, + "source": [ + "---\n", + "\n", + "## Dealing with Missing Data\n", + "So far, we have visualized a band of a Sentinel-2 scene and calculated its statistics. However, we need to take missing data into account. Raster data often has a \"no data value\" associated with it and for raster datasets read in by `rioxarray`. This value is referred to as `nodata`. This is a value assigned to pixels where data is missing or no data were collected. There can be different cases that cause missing data, and it's common for other values in a raster to represent different cases. The most common example is missing data at the edges of rasters.\n", + "\n", + "By default the shape of a raster is always rectangular. So if we have a dataset that has a shape that isn't rectangular, some pixels at the edge of the raster will have no data values. This often happens when the data were collected by a sensor which only flew over some part of a defined region.\n", + "\n", + "As we have seen above, the `nodata` value of this dataset (`raster_ams_b9.rio.nodata`) is 0. When we have plotted the band data, or calculated statistics, the missing value was not distinguished from other values. Missing data may cause some unexpected results. For example, the 25th percentile we just calculated was 0, probably reflecting the presence of a lot of missing data in the raster.\n", + "\n", + "To distinguish missing data from real data, one possible way is to use `nan` to represent them. This can be done by specifying `masked=True` when loading the raster:\n" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "id": "3aefc091-a57f-48c3-aba0-aa415a516495", + "metadata": {}, + "outputs": [], + "source": [ + "raster_ams_b9 = rioxarray.open_rasterio(items[0].assets[\"nir09\"].href, masked=True)" + ] + }, + { + "cell_type": "markdown", + "id": "e1e9160d-db4c-4c49-89c8-1d5181bfe723", + "metadata": {}, + "source": [ + "One can also use the `where` function to select all the pixels which are different from the `nodata` value of the raster:" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "id": "9704ddf0-8692-496d-82f5-d0dd53f0b069", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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Now if we compute the statistics again the missing data will not be considered.\n", + "\n", + "__Exercise: Compute the statistics (`min`, `max`, `mean`, `std`) again:__" + ] + }, + { + "cell_type": "code", + "execution_count": 50, + "id": "08403853-b38c-49e8-b5f1-06ff6d885cdf", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "array(1., dtype=float32)\n", + "Coordinates:\n", + " spatial_ref int64 0\n", + "\n", + "array(15558., dtype=float32)\n", + "Coordinates:\n", + " spatial_ref int64 0\n", + "\n", + "array(2475.8188, dtype=float32)\n", + "Coordinates:\n", + " spatial_ref int64 0\n", + "\n", + "array(2069.959, dtype=float32)\n", + "Coordinates:\n", + " spatial_ref int64 0\n" + ] + } + ], + "source": [ + "print(raster_ams_b9.min())\n", + "print(raster_ams_b9.max())\n", + "print(raster_ams_b9.mean())\n", + "print(raster_ams_b9.std())" + ] + }, + { + "cell_type": "markdown", + "id": "97bd2c01-819b-4e52-8e70-f0ec67a1f8a8", + "metadata": {}, + "source": [ + "And if we plot the image, the `nodata` pixels are not shown because they are not 0 anymore. \n", + "\n", + "__Exercise: plot the masked image with `robust` set to true__" + ] + }, + { + "cell_type": "code", + "execution_count": 51, + "id": "a827b027-9b97-4c4b-808d-0e87a9fa6b18", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true, + "source_hidden": true + } + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 51, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "raster_ams_b9.plot(robust=True)" + ] + }, + { + "cell_type": "markdown", + "id": "d3fd6acc-b9cf-4a87-abcb-86d0e1af6d39", + "metadata": {}, + "source": [ + "One should notice that there is a side effect of using `nan` instead of `0` to represent the missing data: the data type of the `DataArray` was changed from integers to float. This need to be taken into consideration when the data type matters in your application.\n", + "\n", + "## Raster Bands\n", + "So far we looked into a single band raster, i.e. the `nir09` band of a Sentinel-2 scene. However, to get a smaller, non georeferenced version of the scene, one may also want to visualize the true-color overview of the region. This is provided as a multi-band raster -- a raster dataset that contains more than one band.\n", + "\n", + "![Sketch of a multi-band raster image](https://carpentries-incubator.github.io/geospatial-python/fig/E06/single_multi_raster.png)\n", + "###### Sketch of a multi-band raster image\n", + "\n", + "The `overview` asset in the Sentinel-2 scene is a multiband asset. Similar to `nir09`, we can load it by:" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "id": "a63e401b-e438-4291-94ab-c45aa378732a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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<xarray.DataArray (band: 3, y: 687, x: 687)>\n",
+       "[1415907 values with dtype=uint8]\n",
+       "Coordinates:\n",
+       "  * band         (band) int64 1 2 3\n",
+       "  * x            (x) float64 6.001e+05 6.002e+05 ... 7.096e+05 7.097e+05\n",
+       "  * y            (y) float64 5.9e+06 5.9e+06 5.9e+06 ... 5.79e+06 5.79e+06\n",
+       "    spatial_ref  int64 0\n",
+       "Attributes:\n",
+       "    AREA_OR_POINT:       Area\n",
+       "    OVR_RESAMPLING_ALG:  AVERAGE\n",
+       "    _FillValue:          0\n",
+       "    scale_factor:        1.0\n",
+       "    add_offset:          0.0
" + ], + "text/plain": [ + "\n", + "[1415907 values with dtype=uint8]\n", + "Coordinates:\n", + " * band (band) int64 1 2 3\n", + " * x (x) float64 6.001e+05 6.002e+05 ... 7.096e+05 7.097e+05\n", + " * y (y) float64 5.9e+06 5.9e+06 5.9e+06 ... 5.79e+06 5.79e+06\n", + " spatial_ref int64 0\n", + "Attributes:\n", + " AREA_OR_POINT: Area\n", + " OVR_RESAMPLING_ALG: AVERAGE\n", + " _FillValue: 0\n", + " scale_factor: 1.0\n", + " add_offset: 0.0" + ] + }, + "execution_count": 52, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raster_ams_overview = rioxarray.open_rasterio(items[0].assets['visual'].href, overview_level=3)\n", + "raster_ams_overview\n" + ] + }, + { + "cell_type": "markdown", + "id": "32ac1d52-a434-46a2-bbbb-5869e554a851", + "metadata": {}, + "source": [ + "The band number comes first when GeoTiffs are read with the `.open_rasterio()` function. As we can see in the `xarray.DataArray` object, the shape is now `(band: 3, y: 687, x: 687)`, with three bands in the `band` dimension. It's always a good idea to examine the shape of the raster array you are working with and make sure it's what you expect. Many functions, especially the ones that plot images, expect a raster array to have a particular shape. One can also check the shape using the `.shape` attribute:" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "id": "e13291ec-3701-4bbd-96cf-b33c8b76f165", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(3, 687, 687)" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "raster_ams_overview.shape" + ] + }, + { + "cell_type": "markdown", + "id": "c5097c1d-f1ef-45da-9ac6-c2b84e408c99", + "metadata": {}, + "source": [ + "One can visualize the multi-band data with the `DataArray.plot.imshow()` function:" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "id": "91c685c2-4375-4822-9829-bc6ae6a01cf1", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "raster_ams_overview.plot.imshow()" + ] + }, + { + "cell_type": "markdown", + "id": "37ea12ba-fd14-4f5d-bf65-915334df90a9", + "metadata": {}, + "source": [ + "Note that the `DataArray.plot.imshow()` function makes assumptions about the shape of the input DataArray, that since it has three channels, the correct colormap for these channels is RGB. It does not work directly on image arrays with more than 3 channels. One can replace one of the RGB channels with another band, to make a false-color image.\n", + "\n", + "## **Exercise**: set the plotting aspect ratio\n", + "As seen in the figure above, the true-color image is stretched. Visualize it with the right aspect ratio. You can use the [documentation](https://xarray.pydata.org/en/stable/generated/xarray.DataArray.plot.imshow.html) of `DataArray.plot.imshow()`." + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "26d2ac75-1bd5-4594-8b97-538742660295", + "metadata": {}, + "outputs": [], + "source": [ + "# Try something in here" + ] + }, + { + "cell_type": "markdown", + "id": "0d0f0e35-6452-48f1-8f89-092ab5c18d03", + "metadata": {}, + "source": [ + "## **Solution**:\n", + "(press each of the three dots to reveal)" + ] + }, + { + "cell_type": "markdown", + "id": "b298c29b-0a3b-47c7-a8de-e59270a5411d", + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "source": [ + "We can calculate the aspect ratio with the `rio.height` and `rio.width` properties on our rioxarray dataset. Remember we need to use the `.rio` accessor to access rasterio's properties (see [the xarray docs](https://docs.xarray.dev/en/stable/internals/extending-xarray.html) for more info if you're interested)." + ] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "bc46c65c-48e4-4321-b3e4-d8d5779a72da", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true, + "source_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Height: 687, Width: 687\n", + "Aspect ratio: 1.0\n" + ] + } + ], + "source": [ + "# Check the aspect ratio\n", + "h = raster_ams_overview.rio.height\n", + "w = raster_ams_overview.rio.width\n", + "print(f\"Height: {h}, Width: {w}\")\n", + "aspect_ratio = h/w\n", + "print(f\"Aspect ratio: {aspect_ratio}\")" + ] + }, + { + "cell_type": "markdown", + "id": "30443551-7ce2-4afd-9f08-39ee5e674e46", + "metadata": { + "jupyter": { + "source_hidden": true + }, + "tags": [] + }, + "source": [ + "We can then set the kwarg `aspect=` to our calculated value for aspect ratio. Note that according to the [documentation](https://xarray.pydata.org/en/stable/generated/xarray.DataArray.plot.imshow.html) of `DataArray.plot.imshow()`, when specifying the `aspect` argument, `size` also needs to be provided, so we just choose the size to be 5 inches." + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "6b30aefa-0435-4ae3-8351-1642916a3f0a", + "metadata": { + "collapsed": true, + "jupyter": { + "outputs_hidden": true, + "source_hidden": true + }, + "tags": [] + }, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "raster_ams_overview.plot.imshow(size=5, aspect=aspect_ratio)" + ] + }, + { + "cell_type": "markdown", + "id": "b15a23d3-8fd3-40e5-9846-a5ba9f4c7175", + "metadata": {}, + "source": [ + "## Key takeaways:\n", + "- `rioxarray` and `xarray` are for working with multidimensional arrays like pandas is for working with tabular data.\n", + "- `rioxarray` stores CRS information as a CRS object that can be converted to an EPSG code or PROJ4 string.\n", + "- Missing raster data are filled with nodata values, which should be handled with care for statistics and visualization." + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 + Jaspy", + "language": "python", + "name": "jaspy" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.11.9" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/python-intro/README.md b/python-intro/README.md index 52dd8c3..6379184 100644 --- a/python-intro/README.md +++ b/python-intro/README.md @@ -7,3 +7,23 @@ In this folder you will find all the material used to run the course. * Links to the presentation material at software carpentry. * The jupyter-notebook based exercises we will complete as part of the course. * Solutions to the jupyter-notebook based exercises. + +Presentation material is used directly from Software Carpentry's "Plotting and Programming in Python" Course: + +1. [Running and Quitting](https://swcarpentry.github.io/python-novice-gapminder/01-run-quit.html) +2. [Variables and Assignment](https://swcarpentry.github.io/python-novice-gapminder/02-variables.html) +3. [Data Types and Type Conversion](https://swcarpentry.github.io/python-novice-gapminder/03-types-conversion.html) +4. [Build-in Functions and Help](https://swcarpentry.github.io/python-novice-gapminder/04-built-in.html) +6. [Libraries](https://swcarpentry.github.io/python-novice-gapminder/06-libraries.html) +7. [Reading Tabular Data into DataFrames](https://swcarpentry.github.io/python-novice-gapminder/07-reading-tabular.html) +8. [Pandas DataFrames](https://swcarpentry.github.io/python-novice-gapminder/08-data-frames.html) +9. [Plotting](https://swcarpentry.github.io/python-novice-gapminder/09-plotting.html) +11. [Lists](https://swcarpentry.github.io/python-novice-gapminder/11-lists.html) +12. [For Loops](https://swcarpentry.github.io/python-novice-gapminder/12-for-loops.html) +13. [Conditionals](https://swcarpentry.github.io/python-novice-gapminder/13-conditionals.html) +14. [Looping Over Data Sets](https://swcarpentry.github.io/python-novice-gapminder/14-looping-data-sets.html) +16. [Writing Functions](https://swcarpentry.github.io/python-novice-gapminder/16-writing-functions.html) +17. [Variable Scope](https://swcarpentry.github.io/python-novice-gapminder/17-scope.html) +18. [Programming Style](https://swcarpentry.github.io/python-novice-gapminder/18-style.html) + +Each of these lessons has an equivalent notebook in the [exercises](/python-intro/exercises) folder with the solutions in the [solutions](/python-intro/solutions) folder. \ No newline at end of file diff --git a/python-intro/exercises/ex05_coffee.ipynb b/python-intro/exercises/ex05_coffee.ipynb deleted file mode 100644 index 607edee..0000000 --- a/python-intro/exercises/ex05_coffee.ipynb +++ /dev/null @@ -1,63 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "7e52b49a-c2bb-48e9-8671-f1d6c9b06bd9", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# Exercise 5: Morning Coffee" - ] - }, - { - "cell_type": "markdown", - "id": "8422edc6-88b9-4071-9f46-c5237c9c6635", - "metadata": {}, - "source": [ - "If you didn't quite finish the other exercises you're welcome to catch up now." - ] - }, - { - "cell_type": "markdown", - "id": "2775e642", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "id": "e4f4be97-015c-499e-8b76-931780379b68", - "metadata": {}, - "source": [ - "If you have any questions or are stuck on anything, please ask!" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 + Jaspy", - "language": "python", - "name": "jaspy" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.5" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/python-intro/exercises/ex06_libraries.ipynb b/python-intro/exercises/ex05_libraries.ipynb similarity index 99% rename from python-intro/exercises/ex06_libraries.ipynb rename to python-intro/exercises/ex05_libraries.ipynb index 0ed2dfe..fadbae5 100644 --- a/python-intro/exercises/ex06_libraries.ipynb +++ b/python-intro/exercises/ex05_libraries.ipynb @@ -11,7 +11,7 @@ "tags": [] }, "source": [ - "# Exercise 6: Libraries" + "# Exercise 5: Libraries" ] }, { @@ -396,7 +396,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.5" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/python-intro/exercises/ex07_dataframes.ipynb b/python-intro/exercises/ex06_dataframes.ipynb similarity index 99% rename from python-intro/exercises/ex07_dataframes.ipynb rename to python-intro/exercises/ex06_dataframes.ipynb index fbb6227..477eeb0 100644 --- a/python-intro/exercises/ex07_dataframes.ipynb +++ b/python-intro/exercises/ex06_dataframes.ipynb @@ -11,7 +11,7 @@ "tags": [] }, "source": [ - "# Exercise 7: Reading Tabular Data into DataFrames" + "# Exercise 6: Reading Tabular Data into DataFrames" ] }, { @@ -403,7 +403,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.5" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/python-intro/exercises/ex08_pandas_dataframes.ipynb b/python-intro/exercises/ex07_pandas_dataframes.ipynb similarity index 99% rename from python-intro/exercises/ex08_pandas_dataframes.ipynb rename to python-intro/exercises/ex07_pandas_dataframes.ipynb index d30e3b3..cfad6a4 100644 --- a/python-intro/exercises/ex08_pandas_dataframes.ipynb +++ b/python-intro/exercises/ex07_pandas_dataframes.ipynb @@ -11,7 +11,7 @@ "tags": [] }, "source": [ - "# Exercise 8: More About Pandas DataFrames" + "# Exercise 7: More About Pandas DataFrames" ] }, { @@ -556,7 +556,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.5" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/python-intro/exercises/ex09_plotting.ipynb b/python-intro/exercises/ex08_plotting.ipynb similarity index 98% rename from python-intro/exercises/ex09_plotting.ipynb rename to python-intro/exercises/ex08_plotting.ipynb index ba81861..f7a51ee 100644 --- a/python-intro/exercises/ex09_plotting.ipynb +++ b/python-intro/exercises/ex08_plotting.ipynb @@ -11,7 +11,7 @@ "tags": [] }, "source": [ - "# Exercise 9: Plotting with `matplotlib`" + "# Exercise 8: Plotting with `matplotlib`" ] }, { @@ -245,7 +245,7 @@ "tags": [] }, "source": [ - "Plot the resulting data. Set the x and y axis labels to `Year` and `GDP per capita` respectively." + "Plot the resulting data for 'Australia'. Set the x and y axis labels to `Year` and `GDP per capita` respectively." ] }, { @@ -649,7 +649,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.5" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/python-intro/exercises/ex11_lists.ipynb b/python-intro/exercises/ex09_lists.ipynb similarity index 99% rename from python-intro/exercises/ex11_lists.ipynb rename to python-intro/exercises/ex09_lists.ipynb index a87acef..c51bbe7 100644 --- a/python-intro/exercises/ex11_lists.ipynb +++ b/python-intro/exercises/ex09_lists.ipynb @@ -11,7 +11,7 @@ "tags": [] }, "source": [ - "# Exercise 11: Lists" + "# Exercise 9: Lists" ] }, { @@ -661,7 +661,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.5" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/python-intro/exercises/ex12_for_loops.ipynb b/python-intro/exercises/ex10_for_loops.ipynb similarity index 99% rename from python-intro/exercises/ex12_for_loops.ipynb rename to python-intro/exercises/ex10_for_loops.ipynb index 7e65da4..f3ec8ff 100644 --- a/python-intro/exercises/ex12_for_loops.ipynb +++ b/python-intro/exercises/ex10_for_loops.ipynb @@ -11,7 +11,7 @@ "tags": [] }, "source": [ - "# Exercise 12: For Loops" + "# Exercise 10: For Loops" ] }, { @@ -280,7 +280,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.5" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/python-intro/exercises/ex10_lunch.ipynb b/python-intro/exercises/ex10_lunch.ipynb deleted file mode 100644 index 4acb9fc..0000000 --- a/python-intro/exercises/ex10_lunch.ipynb +++ /dev/null @@ -1,63 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "9823f762-7031-4286-b7e2-c63ef6e4b202", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# Exercise 10: Lunch" - ] - }, - { - "cell_type": "markdown", - "id": "75380e2d-d802-4db1-b197-2f39eb17441d", - "metadata": {}, - "source": [ - "Enjoy your lunch!" - ] - }, - { - "cell_type": "markdown", - "id": "5bbfe06d", - "metadata": {}, - "source": [ - "![Lunch](../images/lunch.png)" - ] - }, - { - "cell_type": "markdown", - "id": "50faf651-6ddd-472c-b43c-7d10472b67e7", - "metadata": {}, - "source": [ - "If you're stuck on anything or have any questions, please ask for help!" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 + Jaspy", - "language": "python", - "name": "jaspy" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.5" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/python-intro/exercises/ex13_conditionals.ipynb b/python-intro/exercises/ex11_conditionals.ipynb similarity index 99% rename from python-intro/exercises/ex13_conditionals.ipynb rename to python-intro/exercises/ex11_conditionals.ipynb index d42a399..84392bd 100644 --- a/python-intro/exercises/ex13_conditionals.ipynb +++ b/python-intro/exercises/ex11_conditionals.ipynb @@ -11,7 +11,7 @@ "tags": [] }, "source": [ - "# Exercise 13: Conditionals" + "# Exercise 11: Conditionals" ] }, { @@ -388,7 +388,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.5" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/python-intro/exercises/ex14_looping_data_sets.ipynb b/python-intro/exercises/ex12_looping_data_sets.ipynb similarity index 98% rename from python-intro/exercises/ex14_looping_data_sets.ipynb rename to python-intro/exercises/ex12_looping_data_sets.ipynb index 2edda92..233ffb5 100644 --- a/python-intro/exercises/ex14_looping_data_sets.ipynb +++ b/python-intro/exercises/ex12_looping_data_sets.ipynb @@ -11,7 +11,7 @@ "tags": [] }, "source": [ - "# Exercise 14: Looping Over Data Sets" + "# Exercise 12: Looping Over Data Sets" ] }, { @@ -245,7 +245,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.5" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/python-intro/exercises/ex16_writing_functions.ipynb b/python-intro/exercises/ex13_writing_functions.ipynb similarity index 98% rename from python-intro/exercises/ex16_writing_functions.ipynb rename to python-intro/exercises/ex13_writing_functions.ipynb index 6ea7b88..45bb886 100644 --- a/python-intro/exercises/ex16_writing_functions.ipynb +++ b/python-intro/exercises/ex13_writing_functions.ipynb @@ -11,7 +11,7 @@ "tags": [] }, "source": [ - "# Exercise 16: Writing Functions" + "# Exercise 13: Writing Functions" ] }, { @@ -245,7 +245,7 @@ "id": "8a892f7c-c239-4789-a7e1-85273e3b18ca", "metadata": {}, "source": [ - "Why is the result of the following None? Can you fix it?\n", + "Why is the result of the following `None`? Can you fix it?\n", "```\n", "def print_time(hour, minute, second):\n", " time_string = str(hour) + ':' + str(minute) + ':' + str(second)\n", @@ -319,7 +319,7 @@ "id": "109d940a-3794-4998-bb51-f7b070b81ccf", "metadata": {}, "source": [ - "## 4. Let's write a function to use Pytahgoras' Theorem, like in exercise 2." + "## 4. Let's write a function to use Pythagoras' Theorem, like in exercise 2." ] }, { @@ -327,7 +327,7 @@ "id": "321170b7-0af5-4c77-84a6-39571b8f72af", "metadata": {}, "source": [ - "Define a function `calc_hypo` that takes two arguments, `a` and `b`. Inside the function, define a variable `hypo` equal to the length of the hypotonuse and return the value." + "Define a function `calc_hypo` that takes two arguments, `a` and `b`. Inside the function, define a variable `hypo` equal to the length of the hypotenuse and return the value." ] }, { @@ -585,7 +585,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.5" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/python-intro/exercises/ex17_variable_scope.ipynb b/python-intro/exercises/ex14_variable_scope.ipynb similarity index 94% rename from python-intro/exercises/ex17_variable_scope.ipynb rename to python-intro/exercises/ex14_variable_scope.ipynb index 3c54939..502115f 100644 --- a/python-intro/exercises/ex17_variable_scope.ipynb +++ b/python-intro/exercises/ex14_variable_scope.ipynb @@ -11,7 +11,7 @@ "tags": [] }, "source": [ - "# Exercise 17: Variable Scope" + "# Exercise 14: Variable Scope" ] }, { @@ -179,7 +179,7 @@ "id": "a2452637-868a-440a-8acc-d52b4f5b1f4c", "metadata": {}, "source": [ - "Define a function called `print_message` which takes the day as the argument and prints \"Today is Friday\". Put the function after the `print_friday_message` function. Does this run without an error now?" + "Define a function called `print_message` which has the day as an argument and prints \"Today is \" for any day given. Then write a new function called `print_friday_message` which uses `print_message` to print \"Today is Friday\". Does this run without an error now?" ] }, { @@ -229,7 +229,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.5" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/python-intro/exercises/ex15_coffee.ipynb b/python-intro/exercises/ex15_coffee.ipynb deleted file mode 100644 index 187f65a..0000000 --- a/python-intro/exercises/ex15_coffee.ipynb +++ /dev/null @@ -1,57 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "12bace98-64be-4847-a2e2-b5bc61f2ddaf", - "metadata": {}, - "source": [ - "# Exercise 15: Afternoon Coffee" - ] - }, - { - "cell_type": "markdown", - "id": "f3ec4bce-f3ee-4217-975b-f7a79ea86c14", - "metadata": {}, - "source": [ - "If you didn't quite finish the other exercises you're welcome to catch up now." - ] - }, - { - "cell_type": "markdown", - "id": "a65fcc14", - "metadata": {}, - "source": [ - "![Coffee](../images/coffee.png)" - ] - }, - { - "cell_type": "markdown", - "id": "2e6db9ae", - "metadata": {}, - "source": [ - "If you have any questions or are stuck on anything, please ask!" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 + Jaspy", - "language": "python", - "name": "jaspy" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.5" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/python-intro/exercises/ex18_programming_style.ipynb b/python-intro/exercises/ex15_programming_style.ipynb similarity index 99% rename from python-intro/exercises/ex18_programming_style.ipynb rename to python-intro/exercises/ex15_programming_style.ipynb index 0a4eea8..a364118 100644 --- a/python-intro/exercises/ex18_programming_style.ipynb +++ b/python-intro/exercises/ex15_programming_style.ipynb @@ -11,7 +11,7 @@ "tags": [] }, "source": [ - "# Exercise 18: Programming Style" + "# Exercise 15: Programming Style" ] }, { @@ -264,7 +264,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.5" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/python-intro/exercises/ex19_wrap_up.ipynb b/python-intro/exercises/ex16_wrap_up.ipynb similarity index 98% rename from python-intro/exercises/ex19_wrap_up.ipynb rename to python-intro/exercises/ex16_wrap_up.ipynb index 72d5662..f736108 100644 --- a/python-intro/exercises/ex19_wrap_up.ipynb +++ b/python-intro/exercises/ex16_wrap_up.ipynb @@ -11,7 +11,7 @@ "tags": [] }, "source": [ - "# Exercise 19: Wrap Up" + "# Exercise 16: Wrap Up" ] }, { @@ -107,7 +107,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.5" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/python-intro/images/coffee.png b/python-intro/images/coffee.png deleted file mode 100644 index 08e55e2..0000000 Binary files a/python-intro/images/coffee.png and /dev/null differ diff --git a/python-intro/images/lunch.png b/python-intro/images/lunch.png deleted file mode 100644 index a19b714..0000000 Binary files a/python-intro/images/lunch.png and /dev/null differ diff --git a/python-intro/presentations.md b/python-intro/presentations.md deleted file mode 100644 index f2b90c8..0000000 --- a/python-intro/presentations.md +++ /dev/null @@ -1,17 +0,0 @@ -Presentation material is used directly from Software Carpentry's "Plotting and Programming in Python" Course. - -1. [Running and Quitting](http://swcarpentry.github.io/python-novice-gapminder/01-run-quit.html) -2. [Variables and Assignment](http://swcarpentry.github.io/python-novice-gapminder/02-variables.html) -3. [Data Types and Type Conversion](http://swcarpentry.github.io/python-novice-gapminder/03-types-conversion.html) -4. [Build-in Functions and Help](http://swcarpentry.github.io/python-novice-gapminder/04-built-in.html) -6. [Libraries](http://swcarpentry.github.io/python-novice-gapminder/06-libraries.html) -7. [Reading Tabular Data into DataFrames](http://swcarpentry.github.io/python-novice-gapminder/07-reading-tabular.html) -8. [Pandas DataFrames](http://swcarpentry.github.io/python-novice-gapminder/08-data-frames.html) -9. [Plotting](http://swcarpentry.github.io/python-novice-gapminder/09-plotting.html) -11. [Lists](http://swcarpentry.github.io/python-novice-gapminder/11-lists.html) -12. [For Loops](http://swcarpentry.github.io/python-novice-gapminder/12-for-loops.html) -13. [Conditionals](http://swcarpentry.github.io/python-novice-gapminder/13-conditionals.html) -14. [Looping Over Data Sets](http://swcarpentry.github.io/python-novice-gapminder/14-looping-data-sets.html) -16. [Writing Functions](http://swcarpentry.github.io/python-novice-gapminder/16-writing-functions.html) -17. [Variable Scope](http://swcarpentry.github.io/python-novice-gapminder/17-scope.html) -18. [Programming Style](http://swcarpentry.github.io/python-novice-gapminder/18-style.html) diff --git a/python-intro/solutions/ex05_coffee.ipynb b/python-intro/solutions/ex05_coffee.ipynb deleted file mode 100644 index 607edee..0000000 --- a/python-intro/solutions/ex05_coffee.ipynb +++ /dev/null @@ -1,63 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "7e52b49a-c2bb-48e9-8671-f1d6c9b06bd9", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# Exercise 5: Morning Coffee" - ] - }, - { - "cell_type": "markdown", - "id": "8422edc6-88b9-4071-9f46-c5237c9c6635", - "metadata": {}, - "source": [ - "If you didn't quite finish the other exercises you're welcome to catch up now." - ] - }, - { - "cell_type": "markdown", - "id": "2775e642", - "metadata": {}, - "source": [ - "" - ] - }, - { - "cell_type": "markdown", - "id": "e4f4be97-015c-499e-8b76-931780379b68", - "metadata": {}, - "source": [ - "If you have any questions or are stuck on anything, please ask!" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 + Jaspy", - "language": "python", - "name": "jaspy" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.5" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/python-intro/solutions/ex06_libraries.ipynb b/python-intro/solutions/ex05_libraries.ipynb similarity index 99% rename from python-intro/solutions/ex06_libraries.ipynb rename to python-intro/solutions/ex05_libraries.ipynb index 166404f..29efa0d 100644 --- a/python-intro/solutions/ex06_libraries.ipynb +++ b/python-intro/solutions/ex05_libraries.ipynb @@ -11,7 +11,7 @@ "tags": [] }, "source": [ - "# Exercise 6: Libraries" + "# Exercise 5: Libraries" ] }, { @@ -1451,7 +1451,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.5" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/python-intro/solutions/ex07_dataframes.ipynb b/python-intro/solutions/ex06_dataframes.ipynb similarity index 99% rename from python-intro/solutions/ex07_dataframes.ipynb rename to python-intro/solutions/ex06_dataframes.ipynb index 221d465..b3ddb02 100644 --- a/python-intro/solutions/ex07_dataframes.ipynb +++ b/python-intro/solutions/ex06_dataframes.ipynb @@ -11,7 +11,7 @@ "tags": [] }, "source": [ - "# Exercise 7: Reading Tabular Data into DataFrames" + "# Exercise 6: Reading Tabular Data into DataFrames" ] }, { @@ -937,7 +937,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.5" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/python-intro/solutions/ex08_pandas_dataframes.ipynb b/python-intro/solutions/ex07_pandas_dataframes.ipynb similarity index 99% rename from python-intro/solutions/ex08_pandas_dataframes.ipynb rename to python-intro/solutions/ex07_pandas_dataframes.ipynb index ec0bf2c..0145f45 100644 --- a/python-intro/solutions/ex08_pandas_dataframes.ipynb +++ b/python-intro/solutions/ex07_pandas_dataframes.ipynb @@ -11,7 +11,7 @@ "tags": [] }, "source": [ - "# Exercise 8: More About Pandas DataFrames" + "# Exercise 7: More About Pandas DataFrames" ] }, { @@ -967,7 +967,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.5" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/python-intro/solutions/ex09_plotting.ipynb b/python-intro/solutions/ex08_plotting.ipynb similarity index 99% rename from python-intro/solutions/ex09_plotting.ipynb rename to python-intro/solutions/ex08_plotting.ipynb index a06451f..4c81d79 100644 --- a/python-intro/solutions/ex09_plotting.ipynb +++ b/python-intro/solutions/ex08_plotting.ipynb @@ -11,7 +11,7 @@ "tags": [] }, "source": [ - "# Exercise 9: Plotting with `matplotlib`" + "# Exercise 8: Plotting with `matplotlib`" ] }, { @@ -283,7 +283,7 @@ "tags": [] }, "source": [ - "Plot the resulting data. Set the x and y axis labels to `Year` and `GDP per capita` respectively." + "Plot the resulting data for 'Australia'. Set the x and y axis labels to `Year` and `GDP per capita` respectively." ] }, { @@ -929,7 +929,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.5" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/python-intro/solutions/ex11_lists.ipynb b/python-intro/solutions/ex09_lists.ipynb similarity index 99% rename from python-intro/solutions/ex11_lists.ipynb rename to python-intro/solutions/ex09_lists.ipynb index f38b97d..a32cce8 100644 --- a/python-intro/solutions/ex11_lists.ipynb +++ b/python-intro/solutions/ex09_lists.ipynb @@ -11,7 +11,7 @@ "tags": [] }, "source": [ - "# Exercise 11: Lists" + "# Exercise 9: Lists" ] }, { @@ -832,7 +832,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.5" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/python-intro/solutions/ex12_for_loops.ipynb b/python-intro/solutions/ex10_for_loops.ipynb similarity index 96% rename from python-intro/solutions/ex12_for_loops.ipynb rename to python-intro/solutions/ex10_for_loops.ipynb index f73ec36..7d3152b 100644 --- a/python-intro/solutions/ex12_for_loops.ipynb +++ b/python-intro/solutions/ex10_for_loops.ipynb @@ -11,7 +11,7 @@ "tags": [] }, "source": [ - "# Exercise 12: For Loops" + "# Exercise 10: For Loops" ] }, { @@ -217,16 +217,10 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 1, "id": "822b49d8-e5fe-42e9-b99b-0e67c09d18fd", "metadata": { "editable": true, - "execution": { - "iopub.execute_input": "2023-10-23T10:35:25.961126Z", - "iopub.status.busy": "2023-10-23T10:35:25.960684Z", - "iopub.status.idle": "2023-10-23T10:35:25.967160Z", - "shell.execute_reply": "2023-10-23T10:35:25.966241Z" - }, "slideshow": { "slide_type": "" }, @@ -244,6 +238,7 @@ } ], "source": [ + "# This reverses the order of the string because we are going through the word a character at a time and appending each character to the start of the string - i.e. s, o+s, f+os, etc. Take a look at the value of result in each iteration of the loop.\n", "original = \"software carpentry\"\n", "result = \"\"\n", "for character in original:\n", @@ -396,7 +391,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.5" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/python-intro/solutions/ex10_lunch.ipynb b/python-intro/solutions/ex10_lunch.ipynb deleted file mode 100644 index 4acb9fc..0000000 --- a/python-intro/solutions/ex10_lunch.ipynb +++ /dev/null @@ -1,63 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "9823f762-7031-4286-b7e2-c63ef6e4b202", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, - "source": [ - "# Exercise 10: Lunch" - ] - }, - { - "cell_type": "markdown", - "id": "75380e2d-d802-4db1-b197-2f39eb17441d", - "metadata": {}, - "source": [ - "Enjoy your lunch!" - ] - }, - { - "cell_type": "markdown", - "id": "5bbfe06d", - "metadata": {}, - "source": [ - "![Lunch](../images/lunch.png)" - ] - }, - { - "cell_type": "markdown", - "id": "50faf651-6ddd-472c-b43c-7d10472b67e7", - "metadata": {}, - "source": [ - "If you're stuck on anything or have any questions, please ask for help!" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 + Jaspy", - "language": "python", - "name": "jaspy" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.5" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/python-intro/solutions/ex13_conditionals.ipynb b/python-intro/solutions/ex11_conditionals.ipynb similarity index 99% rename from python-intro/solutions/ex13_conditionals.ipynb rename to python-intro/solutions/ex11_conditionals.ipynb index 0c45519..9ba03ca 100644 --- a/python-intro/solutions/ex13_conditionals.ipynb +++ b/python-intro/solutions/ex11_conditionals.ipynb @@ -11,7 +11,7 @@ "tags": [] }, "source": [ - "# Exercise 13: Conditionals" + "# Exercise 11: Conditionals" ] }, { diff --git a/python-intro/solutions/ex14_looping_data_sets.ipynb b/python-intro/solutions/ex12_looping_data_sets.ipynb similarity index 99% rename from python-intro/solutions/ex14_looping_data_sets.ipynb rename to python-intro/solutions/ex12_looping_data_sets.ipynb index 25aaf5b..a4f8e55 100644 --- a/python-intro/solutions/ex14_looping_data_sets.ipynb +++ b/python-intro/solutions/ex12_looping_data_sets.ipynb @@ -11,7 +11,7 @@ "tags": [] }, "source": [ - "# Exercise 14: Looping Over Data Sets" + "# Exercise 12: Looping Over Data Sets" ] }, { @@ -425,7 +425,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.5" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/python-intro/solutions/ex16_writing_functions.ipynb b/python-intro/solutions/ex13_writing_functions.ipynb similarity index 75% rename from python-intro/solutions/ex16_writing_functions.ipynb rename to python-intro/solutions/ex13_writing_functions.ipynb index b18b09c..72a2adb 100644 --- a/python-intro/solutions/ex16_writing_functions.ipynb +++ b/python-intro/solutions/ex13_writing_functions.ipynb @@ -11,7 +11,7 @@ "tags": [] }, "source": [ - "# Exercise 16: Writing Functions" + "# Exercise 13: Writing Functions" ] }, { @@ -57,12 +57,6 @@ "id": "0ffd8204-9f53-4046-9777-0d9788eaf882", "metadata": { "editable": true, - "execution": { - "iopub.execute_input": "2023-10-23T10:35:03.697212Z", - "iopub.status.busy": "2023-10-23T10:35:03.696714Z", - "iopub.status.idle": "2023-10-23T10:35:03.711453Z", - "shell.execute_reply": "2023-10-23T10:35:03.710267Z" - }, "slideshow": { "slide_type": "" }, @@ -90,12 +84,6 @@ "id": "2c3ba0cf-e7f9-4a00-9cee-62beadcf564c", "metadata": { "editable": true, - "execution": { - "iopub.execute_input": "2023-10-23T10:35:03.716939Z", - "iopub.status.busy": "2023-10-23T10:35:03.716232Z", - "iopub.status.idle": "2023-10-23T10:35:03.729344Z", - "shell.execute_reply": "2023-10-23T10:35:03.727914Z" - }, "slideshow": { "slide_type": "" }, @@ -138,12 +126,6 @@ "id": "74f33b99-6408-4819-8163-01354e71244c", "metadata": { "editable": true, - "execution": { - "iopub.execute_input": "2023-10-23T10:35:03.736490Z", - "iopub.status.busy": "2023-10-23T10:35:03.735957Z", - "iopub.status.idle": "2023-10-23T10:35:03.743115Z", - "shell.execute_reply": "2023-10-23T10:35:03.741754Z" - }, "slideshow": { "slide_type": "" }, @@ -171,12 +153,6 @@ "id": "f4009b05-5d2a-45a1-9b5a-a44116b470eb", "metadata": { "editable": true, - "execution": { - "iopub.execute_input": "2023-10-23T10:35:03.748091Z", - "iopub.status.busy": "2023-10-23T10:35:03.747769Z", - "iopub.status.idle": "2023-10-23T10:35:03.758817Z", - "shell.execute_reply": "2023-10-23T10:35:03.757624Z" - }, "slideshow": { "slide_type": "" }, @@ -215,12 +191,6 @@ "id": "eaba99a3-d877-4454-b828-9e75984811e2", "metadata": { "editable": true, - "execution": { - "iopub.execute_input": "2023-10-23T10:35:03.767852Z", - "iopub.status.busy": "2023-10-23T10:35:03.767272Z", - "iopub.status.idle": "2023-10-23T10:35:03.776931Z", - "shell.execute_reply": "2023-10-23T10:35:03.775752Z" - }, "slideshow": { "slide_type": "" }, @@ -258,12 +228,6 @@ "id": "67c6badc-66ca-4967-a333-31f41146e5ea", "metadata": { "editable": true, - "execution": { - "iopub.execute_input": "2023-10-23T10:35:03.781450Z", - "iopub.status.busy": "2023-10-23T10:35:03.780922Z", - "iopub.status.idle": "2023-10-23T10:35:04.214347Z", - "shell.execute_reply": "2023-10-23T10:35:04.213687Z" - }, "slideshow": { "slide_type": "" }, @@ -280,7 +244,7 @@ "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", - "Input \u001b[0;32mIn [6]\u001b[0m, in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# We get an error as you must give the right number of arguments.\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[43mdouble_it\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m)\u001b[49m\n", + "Cell \u001b[0;32mIn[6], line 2\u001b[0m\n\u001b[1;32m 1\u001b[0m \u001b[38;5;66;03m# We get an error as you must give the right number of arguments.\u001b[39;00m\n\u001b[0;32m----> 2\u001b[0m \u001b[43mdouble_it\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m)\u001b[49m\n", "\u001b[0;31mTypeError\u001b[0m: double_it() takes 1 positional argument but 2 were given" ] } @@ -303,7 +267,7 @@ "id": "8a892f7c-c239-4789-a7e1-85273e3b18ca", "metadata": {}, "source": [ - "Why is the result of the following None? Can you fix it?\n", + "Why is the result of the following `None`? Can you fix it?\n", "```\n", "def print_time(hour, minute, second):\n", " time_string = str(hour) + ':' + str(minute) + ':' + str(second)\n", @@ -320,12 +284,6 @@ "id": "5e2860aa-633f-4cc9-9c50-117fcfac5088", "metadata": { "editable": true, - "execution": { - "iopub.execute_input": "2023-10-23T10:35:04.220441Z", - "iopub.status.busy": "2023-10-23T10:35:04.220097Z", - "iopub.status.idle": "2023-10-23T10:35:04.226267Z", - "shell.execute_reply": "2023-10-23T10:35:04.225352Z" - }, "slideshow": { "slide_type": "" }, @@ -372,12 +330,6 @@ "id": "b4017873-8a1b-465f-a929-19ee518bb515", "metadata": { "editable": true, - "execution": { - "iopub.execute_input": "2023-10-23T10:35:04.230771Z", - "iopub.status.busy": "2023-10-23T10:35:04.230410Z", - "iopub.status.idle": "2023-10-23T10:35:04.236634Z", - "shell.execute_reply": "2023-10-23T10:35:04.236065Z" - }, "slideshow": { "slide_type": "" }, @@ -408,7 +360,7 @@ "id": "109d940a-3794-4998-bb51-f7b070b81ccf", "metadata": {}, "source": [ - "## 4. Let's write a function to use Pytahgoras' Theorem, like in exercise 2." + "## 4. Let's write a function to use Pythagoras' Theorem, like in exercise 2." ] }, { @@ -416,7 +368,7 @@ "id": "321170b7-0af5-4c77-84a6-39571b8f72af", "metadata": {}, "source": [ - "Define a function `calc_hypo` that takes two arguments, `a` and `b`. Inside the function, define a variable `hypo` equal to the length of the hypotonuse and return the value." + "Define a function `calc_hypo` that takes two arguments, `a` and `b`. Inside the function, define a variable `hypo` equal to the length of the hypotenuse and return the value." ] }, { @@ -425,12 +377,6 @@ "id": "cbad04e1-9a87-4637-9d8c-b1972fe0fcee", "metadata": { "editable": true, - "execution": { - "iopub.execute_input": "2023-10-23T10:35:04.240068Z", - "iopub.status.busy": "2023-10-23T10:35:04.239720Z", - "iopub.status.idle": "2023-10-23T10:35:04.246599Z", - "shell.execute_reply": "2023-10-23T10:35:04.245994Z" - }, "slideshow": { "slide_type": "" }, @@ -459,12 +405,6 @@ "id": "1dff411a-3553-4502-a8c5-2de7337ff677", "metadata": { "editable": true, - "execution": { - "iopub.execute_input": "2023-10-23T10:35:04.249484Z", - "iopub.status.busy": "2023-10-23T10:35:04.249149Z", - "iopub.status.idle": "2023-10-23T10:35:04.257133Z", - "shell.execute_reply": "2023-10-23T10:35:04.256367Z" - }, "slideshow": { "slide_type": "" }, @@ -499,12 +439,6 @@ "id": "f2019964-bc0d-4dcb-9b4a-ce12f338a652", "metadata": { "editable": true, - "execution": { - "iopub.execute_input": "2023-10-23T10:35:04.259983Z", - "iopub.status.busy": "2023-10-23T10:35:04.259584Z", - "iopub.status.idle": "2023-10-23T10:35:04.271034Z", - "shell.execute_reply": "2023-10-23T10:35:04.270019Z" - }, "slideshow": { "slide_type": "" }, @@ -536,12 +470,6 @@ "id": "da8e4b43-be64-410c-8a06-f33480278eb2", "metadata": { "editable": true, - "execution": { - "iopub.execute_input": "2023-10-23T10:35:04.275956Z", - "iopub.status.busy": "2023-10-23T10:35:04.275094Z", - "iopub.status.idle": "2023-10-23T10:35:04.282287Z", - "shell.execute_reply": "2023-10-23T10:35:04.281086Z" - }, "slideshow": { "slide_type": "" }, @@ -576,12 +504,6 @@ "id": "10976433-eade-48dc-aa6e-9c2107812f6d", "metadata": { "editable": true, - "execution": { - "iopub.execute_input": "2023-10-23T10:35:04.287335Z", - "iopub.status.busy": "2023-10-23T10:35:04.286839Z", - "iopub.status.idle": "2023-10-23T10:35:04.294165Z", - "shell.execute_reply": "2023-10-23T10:35:04.293218Z" - }, "slideshow": { "slide_type": "" }, @@ -616,12 +538,6 @@ "id": "5db34b2d-a7c6-44ee-8daf-3fed95c1c850", "metadata": { "editable": true, - "execution": { - "iopub.execute_input": "2023-10-23T10:35:04.298000Z", - "iopub.status.busy": "2023-10-23T10:35:04.297566Z", - "iopub.status.idle": "2023-10-23T10:35:04.304701Z", - "shell.execute_reply": "2023-10-23T10:35:04.303755Z" - }, "slideshow": { "slide_type": "" }, @@ -674,16 +590,10 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 1, "id": "912dcf85-c6ea-49a7-b73f-4d036357cef9", "metadata": { "editable": true, - "execution": { - "iopub.execute_input": "2023-10-23T10:35:04.308425Z", - "iopub.status.busy": "2023-10-23T10:35:04.308015Z", - "iopub.status.idle": "2023-10-23T10:35:04.770892Z", - "shell.execute_reply": "2023-10-23T10:35:04.770255Z" - }, "slideshow": { "slide_type": "" }, @@ -698,7 +608,7 @@ "11928.55559" ] }, - "execution_count": 15, + "execution_count": 1, "metadata": {}, "output_type": "execute_result" } @@ -723,14 +633,8 @@ { "cell_type": "code", "execution_count": null, - "id": "06347428-074b-4b53-a7b5-0728fb4cef92", - "metadata": { - "editable": true, - "slideshow": { - "slide_type": "" - }, - "tags": [] - }, + "id": "204d01ab-54fd-4493-a289-4d9989e74da6", + "metadata": {}, "outputs": [], "source": [] } @@ -751,7 +655,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.5" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/python-intro/solutions/ex17_variable_scope.ipynb b/python-intro/solutions/ex14_variable_scope.ipynb similarity index 96% rename from python-intro/solutions/ex17_variable_scope.ipynb rename to python-intro/solutions/ex14_variable_scope.ipynb index 33adc51..f149675 100644 --- a/python-intro/solutions/ex17_variable_scope.ipynb +++ b/python-intro/solutions/ex14_variable_scope.ipynb @@ -11,7 +11,7 @@ "tags": [] }, "source": [ - "# Exercise 17: Variable Scope" + "# Exercise 14: Variable Scope" ] }, { @@ -234,7 +234,7 @@ "id": "a2452637-868a-440a-8acc-d52b4f5b1f4c", "metadata": {}, "source": [ - "Define a function called `print_message` which takes the day as the argument and prints \"Today is Friday\". Put the function after the `print_friday_message` function. Does this run without an error now?" + "Define a function called `print_message` which has the day as an argument and prints \"Today is \" for any day given. Then write a new function called `print_friday_message` which uses `print_message` to print \"Today is Friday\". Does this run without an error now?" ] }, { @@ -303,7 +303,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.5" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/python-intro/solutions/ex15_coffee.ipynb b/python-intro/solutions/ex15_coffee.ipynb deleted file mode 100644 index 187f65a..0000000 --- a/python-intro/solutions/ex15_coffee.ipynb +++ /dev/null @@ -1,57 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "12bace98-64be-4847-a2e2-b5bc61f2ddaf", - "metadata": {}, - "source": [ - "# Exercise 15: Afternoon Coffee" - ] - }, - { - "cell_type": "markdown", - "id": "f3ec4bce-f3ee-4217-975b-f7a79ea86c14", - "metadata": {}, - "source": [ - "If you didn't quite finish the other exercises you're welcome to catch up now." - ] - }, - { - "cell_type": "markdown", - "id": "a65fcc14", - "metadata": {}, - "source": [ - "![Coffee](../images/coffee.png)" - ] - }, - { - "cell_type": "markdown", - "id": "2e6db9ae", - "metadata": {}, - "source": [ - "If you have any questions or are stuck on anything, please ask!" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 + Jaspy", - "language": "python", - "name": "jaspy" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.5" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/python-intro/solutions/ex18_programming_style.ipynb b/python-intro/solutions/ex15_programming_style.ipynb similarity index 99% rename from python-intro/solutions/ex18_programming_style.ipynb rename to python-intro/solutions/ex15_programming_style.ipynb index a504040..2e6e7ba 100644 --- a/python-intro/solutions/ex18_programming_style.ipynb +++ b/python-intro/solutions/ex15_programming_style.ipynb @@ -11,7 +11,7 @@ "tags": [] }, "source": [ - "# Exercise 18: Programming Style" + "# Exercise 15: Programming Style" ] }, { diff --git a/python-intro/solutions/ex19_wrap_up.ipynb b/python-intro/solutions/ex16_wrap_up.ipynb similarity index 99% rename from python-intro/solutions/ex19_wrap_up.ipynb rename to python-intro/solutions/ex16_wrap_up.ipynb index 72d5662..4aa5354 100644 --- a/python-intro/solutions/ex19_wrap_up.ipynb +++ b/python-intro/solutions/ex16_wrap_up.ipynb @@ -11,7 +11,7 @@ "tags": [] }, "source": [ - "# Exercise 19: Wrap Up" + "# Exercise 16: Wrap Up" ] }, { diff --git a/python-intro/z_old_materials/example_code/__init__.py b/python-intro/z_old_materials/example_code/__init__.py deleted file mode 100644 index e69de29..0000000