From 1fd4018ab494acf564168433987b1e2649bcfa0d Mon Sep 17 00:00:00 2001 From: nf679 Date: Wed, 6 Nov 2024 14:02:12 +0000 Subject: [PATCH 01/19] Edit README sections --- README.md | 17 ----------------- 1 file changed, 17 deletions(-) diff --git a/README.md b/README.md index 2122736..978dccd 100644 --- a/README.md +++ b/README.md @@ -1,23 +1,6 @@ # 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/README.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/README.md) - - [Exercises - Jupyter Notebooks](https://github.com/ncasuk/ncas-isc/tree/main/python-data/exercises) | [Solutions](https://github.com/ncasuk/ncas-isc/tree/main/python-data/solutions) - ## Index ### Overview Presentations * [Algorithmic thinking](https://github.com/ncasuk/ncas-isc/blob/main/working_practices/Algorithmic_thinking.pdf) From 4361a1c9550bc73faacbf7569093abe73a7cc628 Mon Sep 17 00:00:00 2001 From: nf679 Date: Wed, 6 Nov 2024 14:19:55 +0000 Subject: [PATCH 02/19] Rename exercises --- README.md | 1 + python-intro/exercises/ex05_coffee.ipynb | 63 ------------------- ...6_libraries.ipynb => ex05_libraries.ipynb} | 0 ...dataframes.ipynb => ex06_dataframes.ipynb} | 0 ...mes.ipynb => ex07_pandas_dataframes.ipynb} | 0 ...x09_plotting.ipynb => ex08_plotting.ipynb} | 0 .../{ex11_lists.ipynb => ex09_lists.ipynb} | 0 ...2_for_loops.ipynb => ex10_for_loops.ipynb} | 0 python-intro/exercises/ex10_lunch.ipynb | 63 ------------------- ...itionals.ipynb => ex11_conditionals.ipynb} | 0 ...ets.ipynb => ex12_looping_data_sets.ipynb} | 0 ...ons.ipynb => ex13_writing_functions.ipynb} | 0 ..._scope.ipynb => ex14_variable_scope.ipynb} | 0 python-intro/exercises/ex15_coffee.ipynb | 57 ----------------- ...yle.ipynb => ex15_programming_style.ipynb} | 0 ...{ex19_wrap_up.ipynb => ex16_wrap_up.ipynb} | 0 python-intro/exercises/ex20_feedback.ipynb | 41 ------------ 17 files changed, 1 insertion(+), 224 deletions(-) delete mode 100644 python-intro/exercises/ex05_coffee.ipynb rename python-intro/exercises/{ex06_libraries.ipynb => ex05_libraries.ipynb} (100%) rename python-intro/exercises/{ex07_dataframes.ipynb => ex06_dataframes.ipynb} (100%) rename python-intro/exercises/{ex08_pandas_dataframes.ipynb => ex07_pandas_dataframes.ipynb} (100%) rename python-intro/exercises/{ex09_plotting.ipynb => ex08_plotting.ipynb} (100%) rename python-intro/exercises/{ex11_lists.ipynb => ex09_lists.ipynb} (100%) rename python-intro/exercises/{ex12_for_loops.ipynb => ex10_for_loops.ipynb} (100%) delete mode 100644 python-intro/exercises/ex10_lunch.ipynb rename python-intro/exercises/{ex13_conditionals.ipynb => ex11_conditionals.ipynb} (100%) rename python-intro/exercises/{ex14_looping_data_sets.ipynb => ex12_looping_data_sets.ipynb} (100%) rename python-intro/exercises/{ex16_writing_functions.ipynb => ex13_writing_functions.ipynb} (100%) rename python-intro/exercises/{ex17_variable_scope.ipynb => ex14_variable_scope.ipynb} (100%) delete mode 100644 python-intro/exercises/ex15_coffee.ipynb rename python-intro/exercises/{ex18_programming_style.ipynb => ex15_programming_style.ipynb} (100%) rename python-intro/exercises/{ex19_wrap_up.ipynb => ex16_wrap_up.ipynb} (100%) delete mode 100644 python-intro/exercises/ex20_feedback.ipynb diff --git a/README.md b/README.md index 978dccd..ad299f8 100644 --- a/README.md +++ b/README.md @@ -35,6 +35,7 @@ This repository holds teaching materials for the NCAS Introduction to Scientific | [Writing functions](https://swcarpentry.github.io/python-novice-gapminder/16-writing-functions.html) | [Exercise 13](https://github.com/ncasuk/ncas-isc/blob/main/python-intro/exercises/ex16_writing_functions.ipynb) | [Solution 13](https://github.com/ncasuk/ncas-isc/blob/main/python-intro/exercises/ex16_writing_functions.ipynb) | | [Variable scope](https://swcarpentry.github.io/python-novice-gapminder/17-scope.html) | [Exercise 14](https://github.com/ncasuk/ncas-isc/blob/main/python-intro/exercises/ex17_variable_scope.ipynb) | [Solution 14](https://github.com/ncasuk/ncas-isc/blob/main/python-intro/exercises/ex17_variable_scope.ipynb) | | [Programming style](https://swcarpentry.github.io/python-novice-gapminder/18-style.html) | [Exercise 15](https://github.com/ncasuk/ncas-isc/blob/main/python-intro/exercises/ex18_programming_style.ipynb) | [Solution 15](https://github.com/ncasuk/ncas-isc/blob/main/python-intro/exercises/ex18_programming_style.ipynb) | +| [Wrap Up / Summary](/python-intro/exercises/ex16_wrap_up.ipynb) | ## Python - Working with Data 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 100% rename from python-intro/exercises/ex06_libraries.ipynb rename to python-intro/exercises/ex05_libraries.ipynb diff --git a/python-intro/exercises/ex07_dataframes.ipynb b/python-intro/exercises/ex06_dataframes.ipynb similarity index 100% rename from python-intro/exercises/ex07_dataframes.ipynb rename to python-intro/exercises/ex06_dataframes.ipynb diff --git a/python-intro/exercises/ex08_pandas_dataframes.ipynb b/python-intro/exercises/ex07_pandas_dataframes.ipynb similarity index 100% rename from python-intro/exercises/ex08_pandas_dataframes.ipynb rename to python-intro/exercises/ex07_pandas_dataframes.ipynb diff --git a/python-intro/exercises/ex09_plotting.ipynb b/python-intro/exercises/ex08_plotting.ipynb similarity index 100% rename from python-intro/exercises/ex09_plotting.ipynb rename to python-intro/exercises/ex08_plotting.ipynb diff --git a/python-intro/exercises/ex11_lists.ipynb b/python-intro/exercises/ex09_lists.ipynb similarity index 100% rename from python-intro/exercises/ex11_lists.ipynb rename to python-intro/exercises/ex09_lists.ipynb diff --git a/python-intro/exercises/ex12_for_loops.ipynb b/python-intro/exercises/ex10_for_loops.ipynb similarity index 100% rename from python-intro/exercises/ex12_for_loops.ipynb rename to python-intro/exercises/ex10_for_loops.ipynb 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 100% rename from python-intro/exercises/ex13_conditionals.ipynb rename to python-intro/exercises/ex11_conditionals.ipynb diff --git a/python-intro/exercises/ex14_looping_data_sets.ipynb b/python-intro/exercises/ex12_looping_data_sets.ipynb similarity index 100% rename from python-intro/exercises/ex14_looping_data_sets.ipynb rename to python-intro/exercises/ex12_looping_data_sets.ipynb diff --git a/python-intro/exercises/ex16_writing_functions.ipynb b/python-intro/exercises/ex13_writing_functions.ipynb similarity index 100% rename from python-intro/exercises/ex16_writing_functions.ipynb rename to python-intro/exercises/ex13_writing_functions.ipynb diff --git a/python-intro/exercises/ex17_variable_scope.ipynb b/python-intro/exercises/ex14_variable_scope.ipynb similarity index 100% rename from python-intro/exercises/ex17_variable_scope.ipynb rename to python-intro/exercises/ex14_variable_scope.ipynb 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 100% rename from python-intro/exercises/ex18_programming_style.ipynb rename to python-intro/exercises/ex15_programming_style.ipynb diff --git a/python-intro/exercises/ex19_wrap_up.ipynb b/python-intro/exercises/ex16_wrap_up.ipynb similarity index 100% rename from python-intro/exercises/ex19_wrap_up.ipynb rename to python-intro/exercises/ex16_wrap_up.ipynb diff --git a/python-intro/exercises/ex20_feedback.ipynb b/python-intro/exercises/ex20_feedback.ipynb deleted file mode 100644 index 15cf386..0000000 --- a/python-intro/exercises/ex20_feedback.ipynb +++ /dev/null @@ -1,41 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "e28978d6-c4de-4532-b36e-2136c3b8cd13", - "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!" - ] - } - ], - "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 -} From 3690ded28207f0ce11c5531305000e24bef8e970 Mon Sep 17 00:00:00 2001 From: nf679 Date: Wed, 6 Nov 2024 14:53:03 +0000 Subject: [PATCH 03/19] Update README links for basics --- README.md | 30 +++++++++++++++--------------- 1 file changed, 15 insertions(+), 15 deletions(-) diff --git a/README.md b/README.md index ad299f8..007d66c 100644 --- a/README.md +++ b/README.md @@ -20,21 +20,21 @@ This repository holds teaching materials for the NCAS Introduction to Scientific | Lesson | Exercise | Solution | | ------ | -------- | -------- | -| [Running and quitting](https://swcarpentry.github.io/python-novice-gapminder/01-run-quit.html) | [Exercise 01](https://github.com/ncasuk/ncas-isc/blob/main/python-intro/exercises/ex01_running_notebooks.ipynb) | [Solution 01](https://github.com/ncasuk/ncas-isc/blob/main/python-intro/exercises/ex01_running_notebooks.ipynb) | -| [Variables and assignment](https://swcarpentry.github.io/python-novice-gapminder/02-variables.html) | [Exercise 02](https://github.com/ncasuk/ncas-isc/blob/main/python-intro/exercises/ex02_variables_assignment.ipynb) | [Solution 02](https://github.com/ncasuk/ncas-isc/blob/main/python-intro/exercises/ex02_variables_assignment.ipynb) | -| [Data types and type conversion](https://swcarpentry.github.io/python-novice-gapminder/03-types-conversion.html) | [Exercise 03](https://github.com/ncasuk/ncas-isc/blob/main/python-intro/exercises/ex03_data_types.ipynb) | [Solution 03](https://github.com/ncasuk/ncas-isc/blob/main/python-intro/exercises/ex03_data_types.ipynb) | -| [Built-in functions and Help](https://swcarpentry.github.io/python-novice-gapminder/04-built-in.html) | [Exercise 04](https://github.com/ncasuk/ncas-isc/blob/main/python-intro/exercises/ex04_built_in_functions.ipynb) | [Solution 04](https://github.com/ncasuk/ncas-isc/blob/main/python-intro/exercises/ex04_built_in_functions.ipynb) | -| [Libraries](https://swcarpentry.github.io/python-novice-gapminder/06-libraries.html) | [Exercise 05](https://github.com/ncasuk/ncas-isc/blob/main/python-intro/exercises/ex06_libraries.ipynb) | [Solution 05](https://github.com/ncasuk/ncas-isc/blob/main/python-intro/exercises/ex06_libraries.ipynb) | -| [Reading tabular data into data frames](https://swcarpentry.github.io/python-novice-gapminder/07-reading-tabular.html) | [Exercise 06](https://github.com/ncasuk/ncas-isc/blob/main/python-intro/exercises/ex07_dataframes.ipynb) | [Solution 06](https://github.com/ncasuk/ncas-isc/blob/main/python-intro/exercises/ex07_dataframes.ipynb) | -| [Pandas data frames](https://swcarpentry.github.io/python-novice-gapminder/08-data-frames.html) | [Exercise 07](https://github.com/ncasuk/ncas-isc/blob/main/python-intro/exercises/ex08_pandas_dataframes.ipynb) | [Solution 07](https://github.com/ncasuk/ncas-isc/blob/main/python-intro/exercises/ex08_pandas_dataframes.ipynb) | -| [Plotting](https://swcarpentry.github.io/python-novice-gapminder/09-plotting.html) | [Exercise 08](https://github.com/ncasuk/ncas-isc/blob/main/python-intro/exercises/ex09_plotting.ipynb) | [Solution 08](https://github.com/ncasuk/ncas-isc/blob/main/python-intro/exercises/ex09_plotting.ipynb) | -| [Lists](https://swcarpentry.github.io/python-novice-gapminder/11-lists.html) | [Exercise 09](https://github.com/ncasuk/ncas-isc/blob/main/python-intro/exercises/ex11_lists.ipynb) | [Solution 09](https://github.com/ncasuk/ncas-isc/blob/main/python-intro/exercises/ex11_lists.ipynb) | -| [For loops](https://swcarpentry.github.io/python-novice-gapminder/12-for-loops.html) | [Exercise 10](https://github.com/ncasuk/ncas-isc/blob/main/python-intro/exercises/ex12_for_loops.ipynb) | [Solution 10](https://github.com/ncasuk/ncas-isc/blob/main/python-intro/exercises/ex12_for_loops.ipynb) | -| [Conditionals](https://swcarpentry.github.io/python-novice-gapminder/13-conditionals.html) | [Exercise 11](https://github.com/ncasuk/ncas-isc/blob/main/python-intro/exercises/ex13_conditionals.ipynb) | [Solution 11](https://github.com/ncasuk/ncas-isc/blob/main/python-intro/exercises/ex13_conditionals.ipynb) | -| [Looping over data sets](https://swcarpentry.github.io/python-novice-gapminder/14-looping-data-sets.html) | [Exercise 12](https://github.com/ncasuk/ncas-isc/blob/main/python-intro/exercises/ex14_looping_data_sets.ipynb) | [Solution 12](https://github.com/ncasuk/ncas-isc/blob/main/python-intro/exercises/ex14_looping_data_sets.ipynb) | -| [Writing functions](https://swcarpentry.github.io/python-novice-gapminder/16-writing-functions.html) | [Exercise 13](https://github.com/ncasuk/ncas-isc/blob/main/python-intro/exercises/ex16_writing_functions.ipynb) | [Solution 13](https://github.com/ncasuk/ncas-isc/blob/main/python-intro/exercises/ex16_writing_functions.ipynb) | -| [Variable scope](https://swcarpentry.github.io/python-novice-gapminder/17-scope.html) | [Exercise 14](https://github.com/ncasuk/ncas-isc/blob/main/python-intro/exercises/ex17_variable_scope.ipynb) | [Solution 14](https://github.com/ncasuk/ncas-isc/blob/main/python-intro/exercises/ex17_variable_scope.ipynb) | -| [Programming style](https://swcarpentry.github.io/python-novice-gapminder/18-style.html) | [Exercise 15](https://github.com/ncasuk/ncas-isc/blob/main/python-intro/exercises/ex18_programming_style.ipynb) | [Solution 15](https://github.com/ncasuk/ncas-isc/blob/main/python-intro/exercises/ex18_programming_style.ipynb) | +| [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/exercises/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/exercises/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/exercises/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/exercises/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/exercises/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/exercises/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/exercises/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/exercises/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/exercises/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/exercises/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/exercises/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/exercises/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/exercises/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/exercises/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 From 50c12ca5c89c4cccd49334d38dae6a1061127eb2 Mon Sep 17 00:00:00 2001 From: nf679 Date: Wed, 6 Nov 2024 14:59:21 +0000 Subject: [PATCH 04/19] Renumber basics --- python-intro/exercises/ex05_libraries.ipynb | 4 ++-- python-intro/exercises/ex06_dataframes.ipynb | 4 ++-- python-intro/exercises/ex07_pandas_dataframes.ipynb | 4 ++-- python-intro/exercises/ex08_plotting.ipynb | 2 +- python-intro/exercises/ex09_lists.ipynb | 4 ++-- python-intro/exercises/ex10_for_loops.ipynb | 4 ++-- python-intro/exercises/ex11_conditionals.ipynb | 4 ++-- python-intro/exercises/ex12_looping_data_sets.ipynb | 4 ++-- python-intro/exercises/ex13_writing_functions.ipynb | 4 ++-- python-intro/exercises/ex14_variable_scope.ipynb | 2 +- python-intro/exercises/ex15_programming_style.ipynb | 4 ++-- python-intro/exercises/ex16_wrap_up.ipynb | 4 ++-- 12 files changed, 22 insertions(+), 22 deletions(-) diff --git a/python-intro/exercises/ex05_libraries.ipynb b/python-intro/exercises/ex05_libraries.ipynb index 0ed2dfe..fadbae5 100644 --- a/python-intro/exercises/ex05_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/ex06_dataframes.ipynb b/python-intro/exercises/ex06_dataframes.ipynb index fbb6227..477eeb0 100644 --- a/python-intro/exercises/ex06_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/ex07_pandas_dataframes.ipynb b/python-intro/exercises/ex07_pandas_dataframes.ipynb index d30e3b3..cfad6a4 100644 --- a/python-intro/exercises/ex07_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/ex08_plotting.ipynb b/python-intro/exercises/ex08_plotting.ipynb index cab3ecc..f7a51ee 100644 --- a/python-intro/exercises/ex08_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`" ] }, { diff --git a/python-intro/exercises/ex09_lists.ipynb b/python-intro/exercises/ex09_lists.ipynb index a87acef..3f1cafc 100644 --- a/python-intro/exercises/ex09_lists.ipynb +++ b/python-intro/exercises/ex09_lists.ipynb @@ -11,7 +11,7 @@ "tags": [] }, "source": [ - "# Exercise 11: Lists" + "# Exercise 09: 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/ex10_for_loops.ipynb b/python-intro/exercises/ex10_for_loops.ipynb index 7e65da4..f3ec8ff 100644 --- a/python-intro/exercises/ex10_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/ex11_conditionals.ipynb b/python-intro/exercises/ex11_conditionals.ipynb index d42a399..84392bd 100644 --- a/python-intro/exercises/ex11_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/ex12_looping_data_sets.ipynb b/python-intro/exercises/ex12_looping_data_sets.ipynb index 2edda92..233ffb5 100644 --- a/python-intro/exercises/ex12_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/ex13_writing_functions.ipynb b/python-intro/exercises/ex13_writing_functions.ipynb index d89b4be..45bb886 100644 --- a/python-intro/exercises/ex13_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" ] }, { @@ -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/ex14_variable_scope.ipynb b/python-intro/exercises/ex14_variable_scope.ipynb index 755c39c..502115f 100644 --- a/python-intro/exercises/ex14_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" ] }, { diff --git a/python-intro/exercises/ex15_programming_style.ipynb b/python-intro/exercises/ex15_programming_style.ipynb index 0a4eea8..a364118 100644 --- a/python-intro/exercises/ex15_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/ex16_wrap_up.ipynb b/python-intro/exercises/ex16_wrap_up.ipynb index 72d5662..f736108 100644 --- a/python-intro/exercises/ex16_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, From 5cf1a769cd39ac1359824b195c3f3a9d3107936d Mon Sep 17 00:00:00 2001 From: nf679 Date: Wed, 6 Nov 2024 15:10:35 +0000 Subject: [PATCH 05/19] Renumber basics solutions --- README.md | 20 +++--- python-intro/solutions/ex05_coffee.ipynb | 63 ------------------- ...6_libraries.ipynb => ex05_libraries.ipynb} | 4 +- ...dataframes.ipynb => ex06_dataframes.ipynb} | 4 +- ...mes.ipynb => ex07_pandas_dataframes.ipynb} | 4 +- ...x09_plotting.ipynb => ex08_plotting.ipynb} | 2 +- .../{ex11_lists.ipynb => ex09_lists.ipynb} | 4 +- ...2_for_loops.ipynb => ex10_for_loops.ipynb} | 2 +- python-intro/solutions/ex10_lunch.ipynb | 63 ------------------- ...itionals.ipynb => ex11_conditionals.ipynb} | 2 +- ...ets.ipynb => ex12_looping_data_sets.ipynb} | 4 +- ...ons.ipynb => ex13_writing_functions.ipynb} | 2 +- ..._scope.ipynb => ex14_variable_scope.ipynb} | 2 +- python-intro/solutions/ex15_coffee.ipynb | 57 ----------------- ...yle.ipynb => ex15_programming_style.ipynb} | 2 +- ...{ex19_wrap_up.ipynb => ex16_wrap_up.ipynb} | 2 +- python-intro/solutions/ex20_feedback.ipynb | 41 ------------ 17 files changed, 27 insertions(+), 251 deletions(-) delete mode 100644 python-intro/solutions/ex05_coffee.ipynb rename python-intro/solutions/{ex06_libraries.ipynb => ex05_libraries.ipynb} (99%) rename python-intro/solutions/{ex07_dataframes.ipynb => ex06_dataframes.ipynb} (99%) rename python-intro/solutions/{ex08_pandas_dataframes.ipynb => ex07_pandas_dataframes.ipynb} (99%) rename python-intro/solutions/{ex09_plotting.ipynb => ex08_plotting.ipynb} (99%) rename python-intro/solutions/{ex11_lists.ipynb => ex09_lists.ipynb} (99%) rename python-intro/solutions/{ex12_for_loops.ipynb => ex10_for_loops.ipynb} (99%) delete mode 100644 python-intro/solutions/ex10_lunch.ipynb rename python-intro/solutions/{ex13_conditionals.ipynb => ex11_conditionals.ipynb} (99%) rename python-intro/solutions/{ex14_looping_data_sets.ipynb => ex12_looping_data_sets.ipynb} (99%) rename python-intro/solutions/{ex16_writing_functions.ipynb => ex13_writing_functions.ipynb} (99%) rename python-intro/solutions/{ex17_variable_scope.ipynb => ex14_variable_scope.ipynb} (99%) delete mode 100644 python-intro/solutions/ex15_coffee.ipynb rename python-intro/solutions/{ex18_programming_style.ipynb => ex15_programming_style.ipynb} (99%) rename python-intro/solutions/{ex19_wrap_up.ipynb => ex16_wrap_up.ipynb} (99%) delete mode 100644 python-intro/solutions/ex20_feedback.ipynb diff --git a/README.md b/README.md index 007d66c..6e509ff 100644 --- a/README.md +++ b/README.md @@ -24,16 +24,16 @@ This repository holds teaching materials for the NCAS Introduction to Scientific | [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/exercises/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/exercises/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/exercises/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/exercises/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/exercises/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/exercises/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/exercises/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/exercises/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/exercises/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/exercises/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/exercises/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/exercises/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/exercises/ex14_variable_scope.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) | 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 75d765e..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`" ] }, { 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 99% rename from python-intro/solutions/ex12_for_loops.ipynb rename to python-intro/solutions/ex10_for_loops.ipynb index 0a5b81f..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" ] }, { 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 99% rename from python-intro/solutions/ex16_writing_functions.ipynb rename to python-intro/solutions/ex13_writing_functions.ipynb index fd62c21..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" ] }, { diff --git a/python-intro/solutions/ex17_variable_scope.ipynb b/python-intro/solutions/ex14_variable_scope.ipynb similarity index 99% rename from python-intro/solutions/ex17_variable_scope.ipynb rename to python-intro/solutions/ex14_variable_scope.ipynb index f58ba45..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" ] }, { 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/solutions/ex20_feedback.ipynb b/python-intro/solutions/ex20_feedback.ipynb deleted file mode 100644 index 15cf386..0000000 --- a/python-intro/solutions/ex20_feedback.ipynb +++ /dev/null @@ -1,41 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "e28978d6-c4de-4532-b36e-2136c3b8cd13", - "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!" - ] - } - ], - "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 -} From c13ab4b1031ea22a45ab7468c02b9ee1560f5d60 Mon Sep 17 00:00:00 2001 From: nf679 Date: Wed, 6 Nov 2024 15:12:35 +0000 Subject: [PATCH 06/19] Fix link to solutions --- README.md | 8 ++++---- 1 file changed, 4 insertions(+), 4 deletions(-) diff --git a/README.md b/README.md index 6e509ff..bc4a776 100644 --- a/README.md +++ b/README.md @@ -20,10 +20,10 @@ This repository holds teaching materials for the NCAS Introduction to Scientific | 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/exercises/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/exercises/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/exercises/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/exercises/ex04_built_in_functions.ipynb) | +| [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) | From a2dafbf9fba63bdc3488f2279cb51bd8de4b81da Mon Sep 17 00:00:00 2001 From: nf679 Date: Wed, 6 Nov 2024 15:18:20 +0000 Subject: [PATCH 07/19] Edit header for consistency --- python-intro/exercises/ex09_lists.ipynb | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python-intro/exercises/ex09_lists.ipynb b/python-intro/exercises/ex09_lists.ipynb index 3f1cafc..c51bbe7 100644 --- a/python-intro/exercises/ex09_lists.ipynb +++ b/python-intro/exercises/ex09_lists.ipynb @@ -11,7 +11,7 @@ "tags": [] }, "source": [ - "# Exercise 09: Lists" + "# Exercise 9: Lists" ] }, { From 2e019db922ab03e297a33dddc1df5199bdccce1f Mon Sep 17 00:00:00 2001 From: nf679 Date: Wed, 6 Nov 2024 15:50:49 +0000 Subject: [PATCH 08/19] Add parallel exercises --- README.md | 18 +- python-data/solutions/ex08a_weather_api.ipynb | 947 +++++++++++++ .../solutions/ex08b_satellite_data.ipynb | 1202 +++++++++++++++++ 3 files changed, 2158 insertions(+), 9 deletions(-) create mode 100644 python-data/solutions/ex08a_weather_api.ipynb create mode 100644 python-data/solutions/ex08b_satellite_data.ipynb diff --git a/README.md b/README.md index bc4a776..00743aa 100644 --- a/README.md +++ b/README.md @@ -41,15 +41,15 @@ This repository holds teaching materials for the NCAS Introduction to Scientific | Lesson | Exercise | Solution | | ------ | -------- | -------- | -| __xarray:__ Introduction to [multidimensional arrays](https://geohackweek.github.io/nDarrays/01-introduction/), [xarray](https://geohackweek.github.io/nDarrays/02-xarray-architecture/) and [indexing](https://geohackweek.github.io/nDarrays/03-label-based-indexing/) | [Exercise 01]() | [Solution 01]() | -| __xarray:__ [Plotting]() and [Indexing]() | [Exercise 02]() | [Solution 02]() | -| __xarray:__ [GroupBy processing]() and [masking]() | [Exercise 03]() | [Solution 03]() | -| [cf-python]() | [Exercise 04]() | [Solution 04]() | -| [matplotlib]() | [Exercise 05]() | [Solution 05]() | -| [numpy]() | [Exercise 06]() | [Solution 06]() | -| [netCDF4]() | [Exercise 07]() | [Solution 07]() | -| [Weather Exercise]() | [Exercise 08]() | [Solution 08]() | -| [Sentinel Data Exercise]() | [Exercise 09]() | [Solution 09]() | +| __xarray:__ Introduction to [multidimensional arrays](https://geohackweek.github.io/nDarrays/01-introduction/), [xarray](https://geohackweek.github.io/nDarrays/02-xarray-architecture/) and [indexing](https://geohackweek.github.io/nDarrays/03-label-based-indexing/) | [Exercise 01](/python-data/exercises/ex01_xarray_intro.ipynb) | [Solution 01](/python-data/solutions/ex01_xarray_intro.ipynb) | +| __xarray:__ [Plotting](https://geohackweek.github.io/nDarrays/04-plotting/) and [Aggregation](https://geohackweek.github.io/nDarrays/05-aggregation/) | [Exercise 02](/python-data/exercises/ex02_plotting_and_aggregation.ipynb) | [Solution 02](/python-data/solutions/ex02_plotting_and_aggregation.ipynb) | +| __xarray:__ [GroupBy processing](https://geohackweek.github.io/nDarrays/07-groupby-processing/) and [masking](https://geohackweek.github.io/nDarrays/09-masking/) | [Exercise 03](/python-data/exercises/ex03_groupby_processing_and_masking.ipynb) | [Solution 03](/python-data/solutions/ex03_groupby_processing_and_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](https://unidata.github.io/netcdf4-python/#tutorial) | [Exercise 07a](/python-data/exercises/ex07a_netcdf4_basics.ipynb) [Exercise 07b](/python-data/exercises/ex07b_netcdf4_advanced.ipynb)| [Solution 07a](/python-data/exercises/ex07a_netcdf4_basics.ipynb) [Exercise 07b](/python-data/exercises/ex07b_netcdf4_advanced.ipynb)| +| [Weather Exercise] | [Exercise 08](/python-data/exercises/ex08a_weather_api.ipynb) | [Solution 08](/python-data/solutions/ex08a_weather_api.ipynb) | +| [Sentinel Data Exercise] | [Exercise 09](/python-data/exercises/ex08b_satellite_data.ipynb) | [Solution 09](ex08b_satellite_data.ipynb) | ## Useful materials and resources diff --git a/python-data/solutions/ex08a_weather_api.ipynb b/python-data/solutions/ex08a_weather_api.ipynb new file mode 100644 index 0000000..6e12317 --- /dev/null +++ b/python-data/solutions/ex08a_weather_api.ipynb @@ -0,0 +1,947 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# Exercise: 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": {}, + "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": "3.10.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/python-data/solutions/ex08b_satellite_data.ipynb b/python-data/solutions/ex08b_satellite_data.ipynb new file mode 100644 index 0000000..087fc79 --- /dev/null +++ b/python-data/solutions/ex08b_satellite_data.ipynb @@ -0,0 +1,1202 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "73b81a5a-4fc6-4c33-849b-3b717a43b1c8", + "metadata": {}, + "source": [ + "# Exercise: 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": "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" + }, + "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 +} From be86d389bf2205e5d7d36024505c615127d52bc0 Mon Sep 17 00:00:00 2001 From: nf679 Date: Wed, 6 Nov 2024 15:52:24 +0000 Subject: [PATCH 09/19] Remove typo --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index 00743aa..29e1827 100644 --- a/README.md +++ b/README.md @@ -48,8 +48,8 @@ This repository holds teaching materials for the NCAS Introduction to Scientific | [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](https://unidata.github.io/netcdf4-python/#tutorial) | [Exercise 07a](/python-data/exercises/ex07a_netcdf4_basics.ipynb) [Exercise 07b](/python-data/exercises/ex07b_netcdf4_advanced.ipynb)| [Solution 07a](/python-data/exercises/ex07a_netcdf4_basics.ipynb) [Exercise 07b](/python-data/exercises/ex07b_netcdf4_advanced.ipynb)| -| [Weather Exercise] | [Exercise 08](/python-data/exercises/ex08a_weather_api.ipynb) | [Solution 08](/python-data/solutions/ex08a_weather_api.ipynb) | -| [Sentinel Data Exercise] | [Exercise 09](/python-data/exercises/ex08b_satellite_data.ipynb) | [Solution 09](ex08b_satellite_data.ipynb) | +| Weather Exercise | [Exercise 08](/python-data/exercises/ex08a_weather_api.ipynb) | [Solution 08](/python-data/solutions/ex08a_weather_api.ipynb) | +| Sentinel Data Exercise | [Exercise 09](/python-data/exercises/ex08b_satellite_data.ipynb) | [Solution 09](ex08b_satellite_data.ipynb) | ## Useful materials and resources From c45998e187126774269ae27bcb918558f7cc937b Mon Sep 17 00:00:00 2001 From: nf679 Date: Wed, 6 Nov 2024 15:55:44 +0000 Subject: [PATCH 10/19] Edit python-data/README.md --- python-data/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python-data/README.md b/python-data/README.md index 7b57a9a..686a52b 100644 --- a/python-data/README.md +++ b/python-data/README.md @@ -17,4 +17,4 @@ Presentation material is used from the links listed below: 6. [numpy](https://numpy.org/doc/stable/user/quickstart.html) 7. [NetCDF4](https://unidata.github.io/netcdf4-python/#tutorial) -Each of these has an equivalent notebook in the [exercises](/python-data/exercises) folder with the solutions in the [solutions](python-data/solutions) folder. \ No newline at end of file +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 From a9235e99a2b54a116d8a4eeb4302ba7de1d307ef Mon Sep 17 00:00:00 2001 From: nf679 Date: Wed, 6 Nov 2024 15:56:30 +0000 Subject: [PATCH 11/19] Edit python-data/README.md --- python-data/README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python-data/README.md b/python-data/README.md index 686a52b..ecea2c7 100644 --- a/python-data/README.md +++ b/python-data/README.md @@ -17,4 +17,4 @@ Presentation material is used from the links listed below: 6. [numpy](https://numpy.org/doc/stable/user/quickstart.html) 7. [NetCDF4](https://unidata.github.io/netcdf4-python/#tutorial) -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 +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 From 8add12e9a515ceef10a9522dc9fe900a26589232 Mon Sep 17 00:00:00 2001 From: nf679 Date: Wed, 6 Nov 2024 16:07:47 +0000 Subject: [PATCH 12/19] Edit READMEs and add parallel notebooks --- README.md | 4 +- ...pynb => ex04a_weather_api_solutions.ipynb} | 0 python-data/README.md | 1 + python-data/exercises/ex08a_weather_api.ipynb | 947 + .../exercises/ex08b_satellite_data.ipynb | 1202 + python-data/solutions/ex08a_weather_api.ipynb | 287 +- .../solutions/ex08b_satellite_data.ipynb | 88477 +++++++++++++++- python-intro/images/coffee.png | Bin 202512 -> 0 bytes python-intro/images/lunch.png | Bin 39370 -> 0 bytes 9 files changed, 90725 insertions(+), 193 deletions(-) rename old_material/data_old_materials/solutions/{ex04_weather_api_solutions.ipynb => ex04a_weather_api_solutions.ipynb} (100%) create mode 100644 python-data/exercises/ex08a_weather_api.ipynb create mode 100644 python-data/exercises/ex08b_satellite_data.ipynb delete mode 100644 python-intro/images/coffee.png delete mode 100644 python-intro/images/lunch.png diff --git a/README.md b/README.md index 29e1827..939e43f 100644 --- a/README.md +++ b/README.md @@ -48,8 +48,8 @@ This repository holds teaching materials for the NCAS Introduction to Scientific | [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](https://unidata.github.io/netcdf4-python/#tutorial) | [Exercise 07a](/python-data/exercises/ex07a_netcdf4_basics.ipynb) [Exercise 07b](/python-data/exercises/ex07b_netcdf4_advanced.ipynb)| [Solution 07a](/python-data/exercises/ex07a_netcdf4_basics.ipynb) [Exercise 07b](/python-data/exercises/ex07b_netcdf4_advanced.ipynb)| -| Weather Exercise | [Exercise 08](/python-data/exercises/ex08a_weather_api.ipynb) | [Solution 08](/python-data/solutions/ex08a_weather_api.ipynb) | -| Sentinel Data Exercise | [Exercise 09](/python-data/exercises/ex08b_satellite_data.ipynb) | [Solution 09](ex08b_satellite_data.ipynb) | +| Weather Exercise | [Exercise 08a](/python-data/exercises/ex08a_weather_api.ipynb) | [Solution 08](/python-data/solutions/ex08a_weather_api.ipynb) | +| Sentinel Data Exercise | [Exercise 08b](/python-data/exercises/ex08b_satellite_data.ipynb) | [Solution 09](ex08b_satellite_data.ipynb) | ## Useful materials and resources diff --git a/old_material/data_old_materials/solutions/ex04_weather_api_solutions.ipynb b/old_material/data_old_materials/solutions/ex04a_weather_api_solutions.ipynb similarity index 100% rename from old_material/data_old_materials/solutions/ex04_weather_api_solutions.ipynb rename to old_material/data_old_materials/solutions/ex04a_weather_api_solutions.ipynb diff --git a/python-data/README.md b/python-data/README.md index ecea2c7..7b7981d 100644 --- a/python-data/README.md +++ b/python-data/README.md @@ -16,5 +16,6 @@ Presentation material is used from the links listed below: 5. [matplotlib](https://matplotlib.org/stable/users/explain/quick_start.html) 6. [numpy](https://numpy.org/doc/stable/user/quickstart.html) 7. [NetCDF4](https://unidata.github.io/netcdf4-python/#tutorial) +8. [Weather Exercise](./exercises/ex08a_weather_api.ipynb) and [Satellite Exercise](./exercises/ex08b_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/exercises/ex08a_weather_api.ipynb b/python-data/exercises/ex08a_weather_api.ipynb new file mode 100644 index 0000000..6e12317 --- /dev/null +++ b/python-data/exercises/ex08a_weather_api.ipynb @@ -0,0 +1,947 @@ +{ + "cells": [ + { + "attachments": {}, + "cell_type": "markdown", + "metadata": { + "pycharm": { + "name": "#%% md\n" + } + }, + "source": [ + "# Exercise: 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": {}, + "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": "3.10.5" + } + }, + "nbformat": 4, + "nbformat_minor": 4 +} diff --git a/python-data/exercises/ex08b_satellite_data.ipynb b/python-data/exercises/ex08b_satellite_data.ipynb new file mode 100644 index 0000000..087fc79 --- /dev/null +++ b/python-data/exercises/ex08b_satellite_data.ipynb @@ -0,0 +1,1202 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "73b81a5a-4fc6-4c33-849b-3b717a43b1c8", + "metadata": {}, + "source": [ + "# Exercise: 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": "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" + }, + "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-data/solutions/ex08a_weather_api.ipynb b/python-data/solutions/ex08a_weather_api.ipynb index 6e12317..137bc4d 100644 --- a/python-data/solutions/ex08a_weather_api.ipynb +++ b/python-data/solutions/ex08a_weather_api.ipynb @@ -38,7 +38,7 @@ "- 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." + "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." ] }, { @@ -79,13 +79,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "200" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "url = 'https://api.weather.gov/'\n", - "latitude = ...\n", - "longitude = ...\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", @@ -108,7 +118,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": { "collapsed": false, "jupyter": { @@ -124,9 +134,9 @@ "\n", "response = response.json()\n", "\n", - "gridID = ...\n", - "gridX = ...\n", - "gridY = ..." + "gridID = response['properties']['gridId']\n", + "gridX = response['properties']['gridX']\n", + "gridY = response['properties']['gridY']" ] }, { @@ -139,7 +149,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": { "collapsed": false, "jupyter": { @@ -149,7 +159,18 @@ "name": "#%%\n" } }, - "outputs": [], + "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" @@ -164,12 +185,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ "data = response.json()\n", - "forecast = ..." + "forecast = data['properties']['temperature']['values']" ] }, { @@ -182,7 +203,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -205,7 +226,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": { "collapsed": false, "jupyter": { @@ -231,7 +252,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "metadata": { "pycharm": { "name": "#%%\n" @@ -248,8 +269,12 @@ "temps = []\n", "\n", "for item in forecast:\n", - " ...\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)" ] }, @@ -265,7 +290,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": { "collapsed": false, "jupyter": { @@ -281,9 +306,11 @@ "time_values = []\n", "\n", "for t in timeseries:\n", - " ...\n", + " value = t - base_time\n", + " ts = value.total_seconds()\n", + " time_values.append(ts)\n", "\n", - "time_units = ..." + "time_units = \"seconds since \" + base_time.strftime('%Y-%m-%d %H:%M:%S')" ] }, { @@ -299,7 +326,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": { "collapsed": false, "jupyter": { @@ -313,7 +340,9 @@ "source": [ "temp_values = []\n", "\n", - "..." + "for t in temps:\n", + " t = t + 273.15\n", + " temp_values.append(t)" ] }, { @@ -335,7 +364,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 11, "metadata": { "collapsed": false, "jupyter": { @@ -368,9 +397,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "'/home/users/train041/TOP-train041-temps.nc'" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import os\n", "USER = os.environ[\"JUPYTERHUB_USER\"]\n", @@ -385,10 +425,10 @@ "# 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", + "# 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}\"" + "outfile = f\"{HOME_DIR}/{filename}\"\n", + "outfile" ] }, { @@ -405,7 +445,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": { "collapsed": false, "jupyter": { @@ -417,7 +457,7 @@ }, "outputs": [], "source": [ - "dataset = ..." + "dataset = Dataset(outfile, \"w\", format=\"NETCDF4_CLASSIC\")" ] }, { @@ -434,13 +474,13 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ - "time_dim = ...\n", - "lat_dim = ...\n", - "lon_dim = ..." + "time_dim = dataset.createDimension('time', None) # None means \"UNLIMITED\"\n", + "lat_dim = dataset.createDimension('lat', 1)\n", + "lon_dim = dataset.createDimension('lon', 1)" ] }, { @@ -461,15 +501,15 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ - "time_var = ...\n", - "time_var[:] = ...\n", - "time_var.units = ...\n", - "time_var.standard_name = ...\n", - "time_var.calendar = ..." + "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'" ] }, { @@ -487,14 +527,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ - "lat_var = ...\n", - "lat_var[:] = ...\n", - "lat_var.units = ...\n", - "lat_var.standard_name = ..." + "lat_var = dataset.createVariable('lat', np.float64, ('lat',))\n", + "lat_var[:] = [gridY]\n", + "lat_var.units = 'degrees_north'\n", + "lat_var.standard_name = 'latitude'" ] }, { @@ -512,14 +552,14 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ - "lon_var = ...\n", - "lon_var[:] = ...\n", - "lon_var.units = ...\n", - "lon_var.standard_name = ..." + "lon_var = dataset.createVariable('lon', np.float64, ('lon',))\n", + "lon_var[:] = [gridX]\n", + "lon_var.units = 'degrees_east'\n", + "lon_var.standard_name = 'longitude'" ] }, { @@ -539,17 +579,17 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "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 = ..." + "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'" ] }, { @@ -579,16 +619,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ - "dataset.title = ...\n", - "dataset.history = ...\n", - "dataset.institution = ...\n", - "dataset.source = ...\n", - "dataset.references = ...\n", - "dataset.comment = ..." + "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!'" ] }, { @@ -602,7 +642,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -618,9 +658,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "True" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "os.path.isfile(outfile)" ] @@ -636,7 +687,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -661,7 +712,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": { "collapsed": false, "jupyter": { @@ -690,7 +741,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": { "collapsed": false, "jupyter": { @@ -702,7 +753,7 @@ }, "outputs": [], "source": [ - "filepaths = glob(f\"{...}*temps.nc\")" + "filepaths = glob(f\"{GROUP_DATA_DIR}/*temps.nc\")" ] }, { @@ -722,7 +773,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": { "collapsed": false, "jupyter": { @@ -753,7 +804,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": { "collapsed": false, "jupyter": { @@ -763,9 +814,22 @@ "name": "#%%\n" } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "fig, ax = ..." + "fig, ax = plt.subplots()" ] }, { @@ -784,7 +848,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": { "collapsed": false, "jupyter": { @@ -797,8 +861,8 @@ "outputs": [], "source": [ "# In the matplotlib.dates module, as mdates, look at the DayLocator and HourLocator.\n", - "fmt_day = ...\n", - "fmt_six_hours = ...\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", @@ -806,6 +870,7 @@ ] }, { + "attachments": {}, "cell_type": "markdown", "metadata": { "pycharm": { @@ -815,13 +880,13 @@ "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", + "- label the y-axis as `Air Temperature (K)`\n", "- set a title on your plot" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": { "collapsed": false, "jupyter": { @@ -831,9 +896,22 @@ "name": "#%%\n" } }, - "outputs": [], + "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\")" ] }, { @@ -861,7 +939,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": { "collapsed": false, "jupyter": { @@ -874,7 +952,16 @@ "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}\")" ] }, { @@ -891,7 +978,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": { "collapsed": false, "jupyter": { @@ -901,9 +988,24 @@ "name": "#%%\n" } }, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "..." + "ax.grid(True)\n", + "fig.tight_layout()\n", + "ax.legend()\n", + "fig" ] }, { @@ -915,12 +1017,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ "fig.savefig(f\"{MY_DATA_DIR}/{gridID}-{USER}-temps.png\")" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/python-data/solutions/ex08b_satellite_data.ipynb b/python-data/solutions/ex08b_satellite_data.ipynb index 087fc79..c919210 100644 --- a/python-data/solutions/ex08b_satellite_data.ipynb +++ b/python-data/solutions/ex08b_satellite_data.ipynb @@ -63,7 +63,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "id": "3c869fc6-9581-4ad2-9a37-72d1d61b84d3", "metadata": {}, "outputs": [], @@ -112,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "id": "ca27bf8b-05c4-4d6b-b8ee-d152487e6f06", "metadata": {}, "outputs": [], @@ -130,7 +130,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "id": "cac729fb-8cda-484b-9fd4-da68a2c8267c", "metadata": {}, "outputs": [], @@ -150,7 +150,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "id": "4e7e407d-721e-4187-8a47-e8e9634262c9", "metadata": {}, "outputs": [], @@ -182,7 +182,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 5, "id": "b58e48e4-2609-4e86-b434-ed664dafa6f6", "metadata": {}, "outputs": [], @@ -201,7 +201,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "id": "08d64392-75f9-442c-bcc5-e173e1448683", "metadata": {}, "outputs": [], @@ -224,12 +224,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "id": "2fd8d411-a898-452a-be69-f19a8cdb920c", "metadata": { "tags": [] }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1102\n" + ] + } + ], "source": [ "print(search.matched())" ] @@ -244,7 +252,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 8, "id": "2790d193-02a1-42e2-a51c-42adfc17041c", "metadata": { "tags": [] @@ -264,10 +272,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "id": "ebb1f4c0-104a-4c38-953d-f5b1f2117643", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "10\n" + ] + } + ], "source": [ "print(len(items))" ] @@ -282,10 +298,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "id": "9c94ff82-849e-4164-89b2-8fb6bec6d622", "metadata": {}, - "outputs": [], + "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)" @@ -303,10 +336,20 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "id": "f32b169d-31df-4081-ba4d-9219f4efeb15", "metadata": {}, - "outputs": [], + "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", @@ -330,13 +373,83 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "id": "b7b4d17e-2746-4546-9c83-a7c43342ce9f", "metadata": {}, "outputs": [], "source": [ - "# Try something in here:\n", - "\n" + "# 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\")" ] }, { @@ -351,17 +464,23 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "id": "18778b46-71fd-4f21-872f-4112a5656439", "metadata": { "tags": [] }, - "outputs": [], + "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", - "\n", - "# Have a look at the keys\n", - "print(...)\n" + "print(assets.keys())\n" ] }, { @@ -376,10 +495,52 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "id": "5d4f2cb8-c392-4d43-b298-824529df3131", "metadata": {}, - "outputs": [], + "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}\")" @@ -395,10 +556,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "id": "20a37bef-2d6e-41d8-acbf-3eb25fa88581", "metadata": {}, - "outputs": [], + "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)" ] @@ -423,10 +592,30 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "id": "a78e28a3-2c80-41b8-be30-47941de2186b", "metadata": {}, - "outputs": [], + "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", @@ -444,7 +633,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "id": "45965a50-8df3-40f1-b3d4-b23906c84fd6", "metadata": {}, "outputs": [], @@ -466,7 +655,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "id": "aa06ed25-cdcc-4efe-bee0-cbe6f37af9b6", "metadata": {}, "outputs": [], @@ -496,7 +685,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "id": "87816ef7-b896-47ea-a254-9b066434cf4d", "metadata": {}, "outputs": [], @@ -504,6 +693,117 @@ "# 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", @@ -527,7 +827,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "id": "20eb8446-450a-4769-9e94-df0ff0f20373", "metadata": {}, "outputs": [], @@ -597,12 +897,73843 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "id": "727637ca-374f-47cf-884d-d2b2766742c1", "metadata": { "tags": [] }, - "outputs": [], + "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/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/S2A_31UFU_20200328_1_L2A/TCI.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/S2A_31UFU_20200328_0_L2A/TCI.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/S2A_31UFU_20200328_0_L2A/WVP.tif\"\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", + " href\n", + " \"s3://sentinel-s2-l2a/tiles/31/U/FU/2020/3/28/0/B01.jp2\"\n", + "
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            • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
          • \n", + " title\n", + " \"Coastal aerosol (band 1) - 60m\"\n", + "
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            • \n", + " 0\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", + " \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_20200326_0_L2A/TCI.tif\"\n", + "
            • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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              • \n", + " center_wavelength\n", + " 0.665\n", + "
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              • \n", + " full_width_half_max\n", + " 0.038\n", + "
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              • \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", + "
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              • \n", + " full_width_half_max\n", + " 0.045\n", + "
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              • \n", + " common_name\n", + " \"blue\"\n", + "
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              • \n", + " description\n", + " \"Blue (band 2)\"\n", + "
                • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
              • \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", + " 0\n", + " 10\n", + "
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            • \n", + " 2\n", + " 600000\n", + "
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            • \n", + " 3\n", + " 0\n", + "
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              \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/S2B_31UFU_20200326_0_L2A/WVP.tif\"\n", + "
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          • \n", + " title\n", + " \"Water vapour (WVP)\"\n", + "
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            \n", + " \n", + " \n", + " \n", + "
          • \n", + " href\n", + " \"s3://sentinel-s2-l2a/tiles/31/U/FU/2020/3/26/0/AOT.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_20200323_1_L2A/thumbnail.jpg\"\n", + "
            • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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            • \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", + " 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/S2B_31UFU_20200323_1_L2A/tileinfo_metadata.json\"\n", + "
            • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
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              \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/S2B_31UFU_20200323_1_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", + " \n", + " \n", + " \n", + " \n", + " \n", + "
              • \n", + " description\n", + " \"Red (band 4)\"\n", + "
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              • \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", + " 0\n", + " \"visual\"\n", + "
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        • \n", + " wvp\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/WVP.tif\"\n", + "
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            \n", + " \n", + " \n", + " \n", + "
          • \n", + " href\n", + " \"s3://sentinel-s2-l2a/tiles/31/U/FU/2020/3/23/1/AOT.jp2\"\n", + "
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          • \n", + " href\n", + " \"s3://sentinel-s2-l2a/tiles/31/U/FU/2020/3/23/1/B02.jp2\"\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", + " gsd\n", + " 10\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/tileinfo_metadata.json\"\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/S2B_31UFU_20200323_0_L2A/TCI.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/S2A_31UFU_20200321_0_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/S2A_31UFU_20200321_0_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/S2A_31UFU_20200321_0_L2A/TCI.tif\"\n", + "
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            • \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
          • \n", + " title\n", + " \"True color image\"\n", + "
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" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "import pystac\n", "items = pystac.ItemCollection.from_file(\"search.json\")\n", @@ -625,13 +74756,12401 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "id": "720cdf4b-6472-483c-9e48-2e7e6817392f", "metadata": {}, "outputs": [], "source": [ - "# Try something in here\n", - "\n" + "# 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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" + ], + "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)" ] }, { @@ -644,12 +87163,401 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "id": "eedd0705-06ee-4490-9aa0-9677e495a390", "metadata": { "tags": [] }, - "outputs": [], + "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",
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+       "    scale_factor:        1.0\n",
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" + ], + "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" ] @@ -668,10 +87576,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "id": "28143c46-f7dd-464b-94b5-431cf7cc81d3", "metadata": {}, - "outputs": [], + "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", @@ -702,10 +87622,27 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "id": "7a8ac587-b7ab-4e59-9262-702790888e43", "metadata": {}, - "outputs": [], + "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" ] @@ -722,12 +87659,41 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "id": "959a6f86-4db2-4d14-bd10-f870d9796160", - "metadata": {}, - "outputs": [], + "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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\n", 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ - "raster_ams_b9 ..." + "raster_ams_b9.plot(vmin=100, vmax=7000)" ] }, { @@ -793,10 +87811,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "id": "a2024c3e-ceb2-4b7b-9420-25e727f2047e", "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "EPSG:32631\n" + ] + } + ], "source": [ "print(raster_ams_b9.rio.crs)\n" ] @@ -811,10 +87837,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "id": "86c0e9eb-5587-4e00-8ec2-09eb09d3248d", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "32631" + ] + }, + "execution_count": 39, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "raster_ams_b9.rio.crs.to_epsg()" ] @@ -829,10 +87866,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "id": "5803b0ea-3ae6-41dc-95a4-da0d3231d604", "metadata": {}, - "outputs": [], + "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", @@ -853,10 +87914,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "id": "a5caeb4d-cfde-4726-b265-f316cef2896a", "metadata": {}, - "outputs": [], + "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" ] @@ -872,13 +87944,35 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "id": "5d1f760a-6fd9-496e-bfdc-531e7f6a1f95", "metadata": {}, "outputs": [], "source": [ - "# Try something in here\n", - "\n" + "# 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`." ] }, { @@ -891,10 +87985,34 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 43, "id": "108a05cf-5faa-41fd-8451-03922edfe641", "metadata": {}, - "outputs": [], + "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" ] @@ -923,15 +88041,44 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 44, "id": "c46bd232-2850-4cca-963d-5cf571d109d1", - "metadata": {}, - "outputs": [], + "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...)\n", - "print(raster_ams_b9...)\n", - "print(raster_ams_b9...)\n", - "print(raster_ams_b9...)" + "print(raster_ams_b9.min())\n", + "print(raster_ams_b9.max())\n", + "print(raster_ams_b9.mean())\n", + "print(raster_ams_b9.std())" ] }, { @@ -944,10 +88091,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 45, "id": "3bd4db6c-374b-48d1-a1c8-db4f7472fb33", "metadata": {}, - "outputs": [], + "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]))" ] @@ -963,10 +88121,19 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "id": "fe927ff4-6467-4488-a671-05e59a884d12", "metadata": {}, - "outputs": [], + "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", @@ -983,12 +88150,128 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 47, "id": "25334bd8-d927-4df3-987a-b2f0fc2fd443", "metadata": { "tags": [] }, - "outputs": [], + "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" ] @@ -1012,7 +88295,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 48, "id": "3aefc091-a57f-48c3-aba0-aa415a516495", "metadata": {}, "outputs": [], @@ -1030,10 +88313,418 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 49, "id": "9704ddf0-8692-496d-82f5-d0dd53f0b069", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/html": [ + "
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+ "execution_count": 50, "id": "08403853-b38c-49e8-b5f1-06ff6d885cdf", "metadata": {}, - "outputs": [], + "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...)\n", - "print(raster_ams_b9...)\n", - "print(raster_ams_b9...)\n", - "print(raster_ams_b9...)" + "print(raster_ams_b9.min())\n", + "print(raster_ams_b9.max())\n", + "print(raster_ams_b9.mean())\n", + "print(raster_ams_b9.std())" ] }, { @@ -1073,12 +88787,41 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 51, "id": "a827b027-9b97-4c4b-808d-0e87a9fa6b18", - "metadata": {}, - "outputs": [], + "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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evvb3/OryR1g2+kVqT/kz3kE7Nc+W0eRP4sH+yZSZEpuJpuU0cnVSE++PeonpSXU8UvjxQvr9sGBAt4OE768+mVeDiQH2tqyN3JG98hPP767uw3ncl0JLewrnH7qco5K3svK42zklcz2TzGbOqD8CgCkWE7qURGSM3pCdNcHSfW3kqi4ev+QOdgRzuX3s0yzYfQzmtBAI6K1OwzBLorrKsrUjuTJlPX8bLADgl90z6DeCHLZjEesiMR4azKRV91GkfSwANkcTvn6vD47l9qvOJPpiBr966hR0QyG1oodwSRSrGiPPOsCuS+5loNPF9etP5oWOCcyzxRnx3vkk7ZVU3dyCWO/mte1jWNKZiJ+3/302RKMs2H3MV/ZefsSl57jbb/h2CiaT4Dc3pPHjX/d+NLE96Pk6VwKZwAtDz0EDnpBSviGEuBxASnkfUAX8TQihAztJBAZDShkXQlxFYgunCjw07M04jH23mV3FWUzffC3p75vA1EdgShFKHNzFA/y1cQZmNY7iibJsyXjCpVGULAOttZfGy6uIW6H03jriRZmo/gjSrOGbmIswJP3lGsklvZQm9VLs6GFtTyHz83bzcu0YtvVlo5p1kgt9hGMmFjh3cOLWC9BfTCf/lHa2DOQy27WbmFT5dtHb9OhB/tS+kHpvKgOrMjBMoEShoNlH9h8byRcGVzYej1mJM6PgVZb4i0nyBBjItRLsSeaM8vXc3TudumAajxe9z3mNs3mkcBmt0WSggV/2VDLB3sDPq4/lD1VPUXJYAz8seJ1Z1o+f1dKQE6uI0RhLY1sgjw+ensTa7onU/eo+AK5qm8rxjgCnO/sB2LCsgnHdmdw19u+c9+Y11C26n4crHqcmlrSvzV4jQL4GlbY2rCJOzZ4c3Ls1TCaIJkHqxC6mpDdx1ZiHeMlfysmuWhKLetgbM/H+qJcoe/8SHp7+ECmKmc3RMI/2zeC2rI2MNyc6v+KCSZhsMdI2hYlbXZhuTyWWqZFsF0ybVsflnlY6dD83zlrChe7Oj9+NTTbCSRAtzSRYoKN1mgjlmpi++WTKkrv5ac6rnPfD79FfqbDjkrvZF5/kK0AIUTXnEBv3/DahKqsqN1NZZuKqizwG/wVqof8pj+Fhm8D/LrOP+z09FwZwP+Mi+Z1ajPxM6o93E3cZKGEFkx/UMIQmhlAabIx4qIM9l2dS8GaMlsPMGGaJYZY4GxSy/7gGxW4nNLuK9kM0lEof8bjCYSV7GeNswZAKrdFkltSOIhbWMAbN5JZ2c1hmDWkmP39YMY/k7EGCW1KIZMQpLu5kalojFiXOoc5qaqOZfNBXwbp1ZaRsEUhFkLIrSON3DCoyunip7E02R8P8tWcWAzEbipDU+1Jo25CDZ0wPUzKa+E7GO5SZPqnXn7jhNLx1SfzxmEfojHv4e+sUIrrGjaWv8KG/gpvSPzlPqvzwHIxaJzmT20i3BdiwpYSrDnuL7ybXAzBq1VnkJQ8Q01XeHfUype9cQO2Rf2XappM5sXAzP0jZ+w9/h/u8udz59HFYJ/ShvpqCxWvQfrhBVkEfoZjGKxMeJEOx0WWEiElYsOpKMpMHWTb6xX1t7I4FPmFbeGgwk7/++HhiDkH4pAG83U5EXIAA1R3FtsGOrzxO/cKEPaPXCPDrrlnclpVQiW2Ohrnm29cQzFA5+orl/CJjO1UPLCaSplP4ikHXxSFuGP06y70V3Je3istbDuGBKX/bIKWc/FW8m8cd5ZQ/uCqZQybb9pV1dMVZeFYbm7ZHLEM7HA9avm7D8DDDfGkOO/p3dF0SJlbnBgHxEbm0HG7H2guDGQakRwj3WvAUDyBrkrF2QzTHQ/FLYbrH23C2gLdCQkoUvzAjxlVCTRNqUEe3qkS9VggrrLIUscmci8McJRA1E69zougCzJJur5O39Eo6W5IRUUFwSwqxgjDF2b1MTWsk2RRggXM7l+88m7FpbazZNIKkPQqBHJi1aDNvfziO9JfgJ794BTAx3mwlyRTk1d2jOKpyJy5ThMOO2MLKliJag0kMyoSW9I2QmaNsiTEk/n4KaUd0k6H6+Hn1sfQ2JTN30nasIs5N6TvYFg3tMx6fVDsPZYeT0sMbeLX8dQBezbJyjD2hdvlZ9yh2HPL4J57zLdOeZ3kYLihZxW2vHssPzvykEBi16ixSnEHkSD9j09vZLlLomAFYDC4tXs56fxG5akL1kqZYsQgT1bP+xvTNJ3NK3Vz+UrSECc99m+cW3cmgEWJlxMNb3tHsOtzJmLc3c0/uGmZsOYmKyno2NuVz2djlLHRt5VdF3+LRwg94NWjl+w9fwPfOfn6fALjPm8uf7z6OwcMNCke2kmEeBGDHJXczb+cifn70EqwixhSLibNcqwAotH11/qDvPZ8nTZr4hAAAyMrQWHS0g/NPd0c4yFcDw0JgmIOe3ioTV418g7u2HosSMxgssRFJk8RcAmuLRrhUx9mkMOB2Uvx6DMvqaoxQGDXZQ2xmOWKaF6PDidpuIXU79I53k7I1jLZ8K3n2CWh+nZ4xJkK9HmQIesuiWJrM5K2LY/JGiSab8RY5iYeduF0Qc0C4NILJouMwRTkreTUuJU5YKhR7+njvg7E4ugVxO1im91Fo62Xv6ffB6fCMP43TXjsNzadi64IkH6xJLiQUMTEnrYbuNCdPlC7BJhKG5Bd7JzEx+y2mv/xdRqwNUVOZxPX3XUG4SoPKKJvvHYuyWNKVvJ27G47DZY6QYgky0tXOc5e9xVVtUzn0mstoO0Li2qNS/p3fE0UhzfRx6oCIjDH671djmCULZ2zkhJT11Jx5377z0zadTIFnAMu7bkSbg+ywwYr5I0k7rpsflyznCPte3gqUf8IQbRkKCFv8yiVcf+jrLHLtwq24GDmukTva57P9kVGY/ZK4RWB7pp0Vf8+myjMRz5RuDATSgEPsNVSaHJTYe7jPm8vlnlaWn7SSP953Ir8/1MdZ5et55dbDMCuSPxzzKNcuPZuJpQ00xAOc+cPriXgEt5x29D4hCHBR80z+kr9iXwq+L4MQQpkxxcpDf8j8zPPfvTyZQ49t5tqfCI+U8tNJfA4ahoXAMAc9t199P5e9fAlFKyKY1+4hNLsKc79GJNXAXasAFiyH96C8lUbPGMh+N4jUdbBYUGIgV3ko2hIDDCw9ISJpNpSRIxCRGPbVtdRfWYEwIGOTDhLSNwnsDd0gJbKzh/ZLR2KYIFRukFveRY/PgUM1SHUGqH+jGP1ygY7ALhL753WnQeUhdWz5sIxLi9fz3ZQaPtqDcYrTyz2vGNjr+9h1VQpqWGG8p59bCl5kYziXdqcHm7AQkhFGvXQ1jmw/c5oup+6E+2Eoo/HUVZejzBgg7WUPjvY4S9eO4Z3UctS9dqj08/Nxr9ASTaE27mfFw5PwLoxywthNTJlbT4fuZJYVRiU1AVCy9ELs1RbeWvw7ftZ6DHfkrEFB4cWAg+MdAQAeGf0Iq0PF/CanjPSNYfhVLxPMEdbvKkIvVijSXFziaSckI9zWO47FKRuoi5n5XevRfPfQN2mPJuEZEgpLyt9g5MqzSe026JgmcLRC/OVsdCcIA76Vt4Ntg7ncOu0ZZloV3giZuSl9ByfVzuNyTyt9MQdKDEyrXQRHmLnyx8/y+/tP4TtrTiVvRBcXPX4l8ZIQFZc2EtXVTwiA0+qOZH1dAZVNJcBPv/R7+fi9Wfqq9SHKSsyfed5hV7jywiTqG2MDHMSrgWGbwDAHPdduOoM3XplKwesBDLNC7SkWRFIUiy1GJGRC9JqxtyhkfxhA2bgLaUiUyhJCeW50m4IW0LG2+4mmOWieayGWEyU9w0vvnjQczQJHhyTptV0Y5fmobX3o7R0oHjfGiDxiHgv1Jys46jT0KT5ykwcoc3dzafoHjDdbeTVo5dX+cZiEQZ0/lZrudErTe8i1e3FpYZxqBFUYnOTZwCiTnfJHFpO0R+LoiNN/uR9NNehr8fD8UX/aZxwF6NL9fKd5IQ8VvrVvVg3wXMDF9W+cyaPH3MslG88l2uxEywtAtZOKWXX0R2y8M+pZVkc0Zllh3NozeGvSA3QbCqNM9s98vqXPXIYSExRNaOGIjD38KPUf/Zym/ehyLF6DgREqoYkhtBobsREhfjv1Od4ZGMk9uWt4NWil0txDqZawZfQbQZKVxD2/1zGR27I2cmd/EdcmNzD554vpH2WQtFtBDUsu+f5L/Ll2Fn3NSVg7VMI5cb4z602uHhJWMKT6qTkU58MeeseoqCE4+ozVtIY8rFtWRSw1RnlpO29WvvqZ33PkyrPJ/aOG5o2wdPPNX8omIISwjB9tCb/5ZC5pqern1tN1ycyFzazbHCmQUjZ/bsVvkOGVwDAHNWW33EH2lDwKXg+gDQSJpzjw7FHwF1iIZIH0mtACCsEJIWqz7dgPnYwpAGlbgthafcQ9VpSYQc+UZFzNMaQmSf3QzGB+Op4OsAxKkt+pJTquBKkJfIfmY2/LQNlYg7/QQes8AzWoECiJU5o8wKX5y6kOZ1NlUlkRNritfj6/L3uWhlgqS7adyoQRTbhNYRxqhHNSVhIwzMy0KrTqOs8FXBS+Gca8p53g2DyUN5MJHuHDnBLm6uoz+PmIlznSlsiN80qglMeL3gdMlLxxEU8dfh9TLCZOcvhYc+hazl2yGE+1QuqiDlTF4P0LH6X4tYt5+sh7uc9byn27ZhFqd6DEBed5TuWXxS9+5vM9q+EwZFKMxZPfw5AK96w7jKdSJrJ5ypNAwjHtuiXnkGZA99lBYi0O9h72MJtnhLEKnYt2ncOKsc8Tk3GmW3u5sf1IRti7eOIPRxFJgnCm5JZFj+/T4d/53nwe3q0SKAaSo/SPNLPkuDtY9MJ3MLTEhDSabFB3bMII/FzAxbNdU9jdm054Uwo3nP4MO27MZZ57O0fadBriPo547bukj+lmZGon6RY/74RUplkC+3wSAObtWki00YlhiWH9U28ig8WXY+q0idZ/KgAAVFVw/uluzjmFJg7S1cCwEBjmoCZ5t8Rxmw+SFQhH8E1Mw1ci0fICpC5xkPpWPb5phVjehFCGxBSIY6/uhkAQvacXVVXpOXcyPVPj9BwK5laBsy1Gxnvd6CkuDKvK4MxiQqkq1gGDqFPBP9FGcOEYpAlETKBmh5hVWM+2nmyS1AAV1nb6jQilJri0cDmTzGZubpyBZ5OZzYMlpJf1snzck5jEx4PQX/qn8re35+AeKcjqS6J5gUZaRTfPjH6YO7qO4P3nJ3HkmI+To+2//fH8ySs5Z/2FbJrxIKNevJqZE6tJ2qWgxCH0XBbJpzUnZvOAjmCbP4+jincxfUwteyOZ/Ch1Dzf3jGNS2i4AnvQnU2bu5I/tc9namYPaaeaudYdTN/8h7NMijLM2ccj3r6DzEANsOsl7BIu+/w4T7A3UjcwAYLzZyqO+NN4e83f8hmTs21fw7hF3clzKJo6yRfnuTffyvY6JpJn8+5zYyh67nJy1kkA2/OzEp/jt/adx0YWvc9qGi5lzyHbagh6uK3hznyAEuL95NtHbcshu9BLJjvILyynsOffefeeLNBdav0a3SGbn02ms+c19NMV9jH/6eq5Y8Oa+nVCGFNg6BTk37+X4tI1fhdORYrWCgfGFFa02uOja7muuvuHL3/TrYFgIDHNQ43l0DXGAvn600iKSXt2J54UoAyeOJ+W9BtB1XCtqweVEW92GNCRxXQeZ+OcUFgvuxijhFDNxBxgmsG9rI97ajhbPwTcjj2CGQjgVfMUKSgxsE/tIUXSEgK4OD0aTnU3WXDZPeZJ5uxbS2p9EWXo3YV0jzRpgQK8hxRJkW4WO5lPpHXCysHoRdR3puD60EcqAtG0GaQ7oGScZmOTk+SP/wLpQMWGpJAyqV63l1aCVJCXIEu8EbsncAiRmwj9N28VP03bxnfYZYNep/VMVuGGgUqInxxnYmkdyWR8Pjv4bzfEU7s9LOIqti8Q4xZmwR/40bRcTfrOYwSlhauf+lecCqXxYUwoCssZ2sWLs8wBckdQCKNjOa8P+Xi6ONgXfQh8PfHAYKUUDPDX2IWIyIdzOcfUAFjbHwmRkDDJ32dUsGrmVo2yJWf9tWRu5uaeKyr8sJlYYpvTlEF2T7biadG75y2lcefHLXO5pZeS4FubZ4viNMBF0Epk8EyEm/FELzt4I3lHJhM4e4C+jnuKcxjk8WvgBFX9dTM6HMV7/8+8p1ZxM2ryYVt3HB6Fi1p16O68F8oBEyAzfE7nk7ArQsLeCn44cCWz80u+mRKLLLxYCxkGuch8WAsMctMxTTkFLT0Pv60fqOr2HZOLPzcLihazn94KqEu/uRXU6CE0uxlzfBNJAqCpSB8ViRUlJxvzhDvJWCJTkJIyePuLRKEwbA11ezF6dvioFoYMyykdlRhebqwsoKe5ElwrjRjSzJVjE5ilPsjwMDZ1p6IMmGpeXkLojSrNT5fcnFeNyh0CCpcLLIbkNzPLs4TfvnUJsnpeNU//KjFu+zcC4GMWFXQxGrJz4wRXMLK9llv3jbZhH2LzYhIWplg2AxhWt05iftJ3HfSnkmvp5afs4RhW3Ul9YjBIDIyNCUXYvbf0e7OYoJmFwrD24r70plk+6RMVcQL+Z4lcv5t4j/sbOI+/fZ28oe/98ag57mIiM8aw/kxNyN3Ovlkv3nBjzC2rRinQWJG1ndaiAa9qn8Wr56/t0/uPNVlaPf5Zb+0q5/9V53HbOxwPso9un4WkBr8VCzw/6cZkHaW1PwdKocuvrx3KnT2DtSfh4LP/5H0gVCQHwuC+Fh646AacvhogbBNMVvL0Jo/aswg8oefFSZGYMb4mJ+S9+DySYs+D0nefS0efmiEPv4ixXH/cM5LFk3QQyYhDItdE3UiF1xxcP3AeCgST+idTWn1fvq7nf18WwEBjmoKXpphk42iDz/S7wBXC2RBksshDMhMjIfNpnWMh7J4OoQ0OJSwbOnYbZL5EC7J0RInYN67q9KHY78b5+jODH4acMs0LbwhyUaCKSqG6GZFuYbJuXYGk7J2dvJGhYqAulszM9C4Clg6NxrbAhdHC1xAmlaxgmQfHDAgw77h910borkysmvMt4s5WBM17lja5RTLvj2+Se2MgRng5e3TuKI4r3cM/ENdzYNXqf05TfCBOUcWyqBZNI/Fv2Rpz8as+3MF5JwzJgMKI5TMPMYsIZElSJ0x2mpTeJY8u3M8NVQ3M8iZMeuYhRh+/lVwUvfsIhq/KhxZh0OHPOCt7vLCNJCe4TAA1xH05niBu6xvLrjK2oQvL0T45Cnwx1C/7CiCcvR8sPMHf8Dv7cOPsTjl/7c11KLdedU8vMrSeSZguwbXshZY+GqT1Fcu68D1jTV8Tu5iww4PBvbWRzTx6DIQtvT32A8/eeRJ8RJVdNbI194HsnYfFFqLnEzI6j7uYnnYcwy1VNU9xHgeai8GUD7foOBpfnIc0GdUOB8cbetpjLLljKzmgy17QdzrYPR1D6Zpi6E82QFOWW6c9z41NnwrNf/v2UUqIfwCzfOMCFgBDiOyTC50gSATUvIOFy/RRQBDQAp0op+4fq/4hElAUduEZK+eZQ+STgYRI5pF8DrpX/ZAfQsBAY5qBFDSUG22BJMlJLQcQkZl8ijLClzUvathT6R9qRiiCYmQggZ+8SxGwQzLCR/VYn+qA/sTpQBKAiRlegDPhoG2cjnAIxl8TkF2jRRPwbMpopdvbx6w+PQQmqqCGFixa+xbjfLybv9W5s4xOzUn+Ohm6FTT+8l/JHL8faJfC3p5A6oo8TX7+GRxfcx9VJTfh1K39NLeKw9D2kqAFuOzQxS37cl8JNGVtZEnRyrD2IU7GyPizIUBP/q6NWnUW01kXGRokUBh0LYljr7GSvjFJ/jkSzxPE3uTl0yi5eWDWZDwpHsH7i0+y++F62RUOfEABTf3I5kUNiRPMM1vYV8sPS1+mIe7i8eyz35a2iSHMxNr2dN++byfNHjkPd7MRjMTD3J1RKRnIMTdW5vX4ey0a/yHMBFyc5fCQrdmIyvk9ofURbUyptpDLimRj+IhulzwR5JH06TncYI6xiazaxJScX+Xg6nrO7mLnyckozeshVXfywcxxv3zsDJV0S8dh5a96t2IST27I28pveckaZOxl1z2Jc6RLHLZn4DhHUHfcA8864AMOsEFog2eLL495352LKClLyop+BCgfSHcOz3spPLItQqnx8FUgSq4EvrvfFdYQQucA1wEgpZUgI8TSJmGkjgXeklLcMZVj8IfADIcTIofOjgBzgbSFEuZRSB+4lEV5/NQkhcBTw+j/cdIhhITDMQck85RTSj5qCfUsz0h9AH1WCWL8TS+Zk0pbWI1M9DBZpDIyJYe4yoYYhkm6g6Ar5b3hRWrvB+OQyXFgshHMc6CUuzD6JbhaoEUEwX8ecHsIYtPD6ljEoFp2bZ7/Icm85e24eRcVJ7UgVeqek4c8XxG0QTdapGt3MopoF3HzCUzzaNp26D4owVluwZsCqYBkX/n0+h8/bzJ7z7v1EP5YE7dz2x9Mo+N69ZKleGIpkc5hV8qQ/mee6JuF82YV1wEAYksF8jdVH/pHXppfwx56TsFdDME/D2qtwdOo2VqYUc8fIpwEYv+70fTt7eo0Ah3y4mPSgRIRVMAS3ljy7z6v4eMcqftg5jmUdI8h3DWBokPyiAzViIC7p4vjMGgLSzJiSFhZmbOUSTzvAPkPvgt3HcGfp0zzrHc1rraOYl7Obp16aTfkbQVRvEBGOEUrPpOZ8C1lvqDjawJav0TNB4vm+mbqTBc4XMtn9s4+fz+uPzCCpU6f5OIP8lwTz3vo2miPKntl/4+3OClb3FWNvB3+uQI1qhLN1Zn7nMkKjFAwz/PzYpyg3d7LCNYJYux1fMXiP8+P5wIV5UDKtuIap7jou+QreUR1J7ABUPfEDEAJDaIBNCBEjsQJoA34EHDZ0/hHgfeAHJJJsPSmljJDIob4XmDqUzdEtpVwFIIT4G3A8w0JgmP82Gp4ah6wzUfJ2L0IRaDUtSLcTJQ5N55Zg6YPBUgNnWpDxo1tZsacUS4OFzHVRDIuGUZqD1tSN4nSgewdBGqgOO7pFwbWhFTE+h4hHw1+ig03HtNaJYgeTF5QjfJSZOyhL72D6/Su5qHkmaQtaaV+Zixzppyy9m709afSH7JxRuI4/1R3O4HuZOAYg4oGs1TEejs4nVhal3p/C8jDMssIFTbP4a8Fy5lh72fjjjwa+hACoXHEOi0cu59rkBm5cn0deZ5y4XSGUoRE+wsdRmy8iuCmF6CidaeP3oik6a98diVVEqZnzyL7ndlHZSt4ImbnirfPYcexdGHGFzumgpkY4Z/QaxphttOo+clUXj/rSeLl2DGGfhe4d6Yh8ifnoHjpak7kydyvXpdRyVdtUzs1ZuW/gh0TIiZvSd1Dm7ua4J66j4O0IrkCcFbZpFIYDxJ0mtK44hBNJZFy7NbonSNSImf5KQd67cfpuiWP6AP76o9tJaC1g3O8Wo8ZgoERlVGkTS+5LhH+uun8xi7IX0P98HuY1g6RYgiRd0ELtQCqXFG7mR4v2cM9AHhd4aoc8rU2cM3E1r949GzB4Zep99E2ycPlvr+G+vEToiK9CCEg4IHXQ0EpghhDivP2K798/yZaUslUIcSvQBISApVLKpUKITCll+1Cd9qHcLJBIvLV6v/ZahspiQ8efLv9cvun0ksMM85nMKKxPZJBSElurde8ghneQ5Nd3U/BgNVpEkveOAas8rFlWhXtTQpdsbfaiNXUTdZsw+vpB1xFqYi+3zEojkqTQeHYBbbNV9MO8zJ+yDUIqgRKdcFGUjE1homtSqIlmMd2icZ83l/VPjqV9ZS6OVlC2OWlcUozFFGdezm7+uOVwpBQcctIWwimQd3QjbRdFmHTcDuaP3slDZU9yQ80JPO5L4a8Fy9kWTcTWv6J1GiH5caat3TMf5ZWO0Yy5czFGj4XOqSZajjYYdeEOfjv+eTKdPuIOyZxJu1hTU4Q/ZuHO0//C2kAi1HPF8nMZs+ZMgkYi1tDzR/2JS5vmk53uxdKtMLWogamOWgDu75sOwC3bF5CdlNg9pAUEml/Qvz0N926NV9pHc1HzTIqsvfsEwIT1pzFy5dmsuWgCs66+jA1/mEjB0ghCl6i+EKbBCF1THDh/0sKSZc/R8McU2o6J46uMowUF/vMGqL7gXn545yOEl6az/po/7luV3OfNZdqZWxgYrRNOgxOyNlH8SmKoXnfxHYz1tJKxwY+/0EHDMXZybAOsn/g0P0rdQ8nSi7h901x+2H4oAD/sHMdN6TvonRKje7xCTAq+V3MKG376yRXZlyWhDjqwD7BSSjl5v88nsiwKIZJJzO6LSah3HEKIs//J7T8v8dYBJ+T6iGEhMMxBSdfJLoqf9yENidR1pK6j2GwEZ5QRGV9E6qouLP0x3E0G+W/HUKKQuT4OXh9y0If13S3ISAQjFAZpoNhs9E5KJuYQhLIMTEV+Jmc3s6K1mDvnP4aWHMbUZqZjupWC36/nd385hZLXL+b2l47DV6ZjbwdHRxzTxAH8YyIEghZuSt9BzZxHCETMfPjqOMwz+vhW5nZGZXeQYg5wZca7qAiWj3mBs1x9ADTEUwG4J3cNH4YdnFI3l6Z4YpCtbc7gpxc9gXTGyZrVyi9nP88jhcs43NpH3cpCDLNkY0ceE0qbybT6OMoW5dcZW/lex0ReO+Qetk17Yl/kz+cGJuOPWTApOsYEH6u2lXHdlpMBuCl9BztiQUpSe2npTYawSs7yKLnLwigxgaVf4n0pB6ca5bqUhOAoeetCvN0unK85CebZiboEvQuD+ArN1J5m4oinNtJ2o8Er1/8OuxbjkBuu4MTSLZw6bj1XHPou6Zt1DsvdyzmNc7hi+Tk8cO0f9xmmV0fiPNE8hV39mbiqVWRpgAvdndQvfICQjHDcrlNZf8EYOqc5Kf/BDh4784+8tH0cg0aIkqUXUvHHEIeU1PN2YznAvu219cc8iBqB1/yjGQxZaRh6zpe2zPhK3lGJRD+AzwHuDZoL1Espu6WUMeB5YAbQKYTIBhj62TVUv4XPTrzVMnT86fLPZVgdNMxBx3zT6SAUaO9IxAACEAqhQ6voq9RIqhVYolG0zXtJqraC2UxWmwMUBeJxjFAYpbIE0daFDIWR0ShGKISjPU5/mQnpjjO7oJZlTaVkeHz8sfEILBscFLzQgdHWQcMNkym+bRtISfWvRoNNJ/PDAfwjkgiFTdirLaghGLVjMSk7dSwehZlXbKQ95GG6rZYrSxuIyBg28XFkyTv7i5hhryFHGwDM+8I2k/0+BomwCk/MuZ9XveMxt5lpCGVxU+9Cns3pYPvaElytUDirnjJXN282VnLbxOc5YsdZLC78YMgb9+Ow05ujYY5072CrN5eYoZL8vJOOmQYTslsB2BYN0aW7qFlWjKsFkmoj1J2kMeKJOGlbDM686VWuSGrhN73ljFx5NsnOIPZqC7oFLF6DmEMh4/wGwgPJaKd3MdPTyxP3zGf1j/+ATbj4e/G78Ot3qXpgMbnLIrRPt1DQNMjShkom5rQwrayeDeEiplha2RCNEpYW+t7PJmtNBEuBxJmUSKhzQ9dYntwyGcd2C/pxoIzzckfu20xddSmuzRbmv/MdcgOScLZKw62VFO7sY17GBXROsSJnetk27QkKX/OTdFpwyE6SiHD6bk35V/KeGhJiB6Du1w/MJNAETBdC2Emog44E1gMB4DwSqXjPA14aqv8y8IQQ4nYSK4cyYK2UUhdC+IQQ04E1wLnAn/7ZjYeFwDAHHdKQwMeD/0e7ezqnmBASXNu6kb396H4/+Hx0XzGDxVe/yC1Lj6Py1310XjGF7L/tRIbCKB4XMhhC9/sxTAIlDvg03tw6CosnQluvB9lkp+TDAPHaBoQiKPlTdWJdnZJE7nsSR3MYEYnTX67iWOnAOyHK+ZNWEjNUrk5dzX39U/DGbZyWtY4zVlzC3iMe3hcF9COuTW7gUV8257h6uLzlkIQAAObZ4nw0OC31jeGpN2ZR+HaYjqlWYv0Omt8tJeukDrKm+6h7rIwtY4rIXKlwxMC1XDH5g33OYJAwOL/eP4Y3d43E1GglmqzjqVZJ6YqS/6bCoyd8AMCi16/F3K1iCkDmsm4Gxqci4jBQbsOfJ3iyZTJnuPfQFXVht0bp6PJglCQM8EhJz8Iw/d3p/HzcK/yuej7zU3Yw/9odjHp7MddNWco8x27KTE7y3g2jW1VcTRLDolH40yDV06pY+8uPI5Q+0DWH5jOzyHd7ibsTz6yz183PukfxzM6JKKpBoDSO4opRkdLP1FWXom1yEp3pw1jnIjw+ROkfDLrmOXHVqESSTWStCeFrd3PYQ5fg6OzknttO5DeHh6g57GGm/uRyjFmxr+Q9NUgEDvziel+MlHKNEOJZEl5scRL51u8nId2fFkJcREJQnDJUf8fQDqKdQ/WvHNoZBLCYj7eIvs4/MQrDsBAY5iDkIzuA0EzIeAwxeiTCMHC0Qe94g/ozsyi8pQXFbqf+kRFcOvINnp9URIV5N7idZL/Tg7DbEquAUBgZi9N/4SEMFgsqZtchB5PIvl4nluZAGwhBcwP6oA+tuIBwaRrKUChqpSAbR6OPhuOTiLlcSNWgcGQ7Uzw9vHzXYXjLJS+UjMVs0olENUIFJoQCJUsvwuqMcOeEJ4cG+QQ/+/B4ni9rYvvaEt46YR3zbHFu7qnijdaRqIpBR5+bwjcTdgJ3k0H7UTHsU/sYDFkpcvdRfk4181J38Pq4Mbijln2qmtv7i3m2aSIdtamkr1PI8UuEoaPEJI6aLoIlybx4z53UxAxu65wHQqLbJGavYM9Fafzo2BdojKSx8vmpVFxSR1/EzvQVlxMLmDA7o6S/bSGcLPA06LQfopKbNkBrTxI3PXE6Rx27FgWDn65ZxPIj7qQh5qDM5KT4tYspj8Uw9fjpq0oj99I2wufZqbpk577nMX3zyUReTycjPYDWF6D0vnpuzXmP9VEbFy25FFedwmC5jq1NJWdFnKiRSZ6qYJii+Dpd9EyPkf6WjdbDBOF0g3OffZPTnf2MvX0x+U/UEx6Zy5gXGtEGI6wvexOA/pGQ+Y6JryLLvOTAfAAO1GFYSvkz4GefKo6QWBV8Vv1fAb/6jPL1wOgDu+uwEBjmYEQoCEWgJLmJVuXR8C0LZu+QYJCJVI2Bb01AtwqoVrhPn4X7LAeKLjH7JJ5tvRg9vUhdx4gmErLoZkE0J4YvaiHX5WX3zzIZcUsUWduEHKqz66dpjCluIX5pJorFRLDARdOxoDhCXD3hfTJNXn7xyOmINekkx8PoZiv+HDORMJgtMY5O3sY1M9+lOe75xOAPcMSO43BtN9GwpZQl37uVSpODKZtOobfTjbXejGGG4qUh9p5q4qjpm9nck4fntQxyywa5ofQ1vv34Ray/8A52xVQuLOmkIe6jIZ6InfPd5Hoeu+coso/tpF2k4d6joptBC4MW8lB2004OW38RW6b+naWbRoMAZ5Mg68MBGo9LYknXOHw/y8Ofb+bclB389IMTOHHyBp7fOBE9rtI9SaLEIHhIGKPVTuuWbLSAIFIU5Y7sDQDc/bqZufXfZ9clCePryF8n4jcB5L4cpb2zhJUf/hlIbF09+qffw3p6B3oQ1OomGi+von5JBgtnZ+C4SqPS6MY7Ph1DU8l/rpVIaRrm3iCRTCe9o83ctPhv3HHdWYRSIGt+MzFD5XRnP1e1TSVzbYj6i4operGP5Z2l+0JilH1wHo5mgRr5qjyGIXoAZtX4wRk3bh/DQmCYg4p56mkIRWDEJEZnF6ZwhLTckXTOjWJqNyNNEtusHppHeCh93KB0XR81F2eTtDdCzK3hXF2P3vPJzFFaeirJ1REGR5hp8mXzgwUvU90xD1HbtE9IaNmZpKw0E/5zJsFRVgJZKgOjdMZVNbBtQwmvdY5iVlotqXPa6SCb2JgAJtMgPx71Fue7u7i5p4pr3z6LuuMeoNL0sQC4tm0Kyx+azGCZxKWDr8Tg+vqTqO1JI+y1otriRCtCpKX4aSizo3TAm6vGY1gN8hZ2sPmtClIX+nn4nD/hVKxMsSQSoySZQry4fRwWe4xIq4Oy9X7ESgtuw0fjcUk4WySDpYLvXPsEx9jD3Grv5IausWAxcOwyY/tWF53zNWLVks57iznktnVs7c/Bp1upX/gAM7achOaIYdtgJ1BgoEQFI77fS9d8B4FsOP6kFbSGkih54VImjK1n5W1/5ta+UgaNEEdtO5uOn3pw7MxBC0LMCfdeei8xGeeQTWfi35iKXiVJfSyLtKc2Epw3FtP0PtZOehyT0Jg643J6JhlkrYC4A1qOzwUBgXwzFRMbib1bxAPHHo3D5EUYbvqezWfDjQnhs6U3F3tc4prezWuLn+UBb/a+v8WIrG6qR+TjbP1q3lUpBYb84gFeDguBYYY5cIQiEBYLMpRIgyiLcjEFDVJXmNFCkmCmSq/dherVMK/bBnYbjnbw55kJpQvUcCG2zRDv6tnXppGbzmCxGUuvIFgW47btcym9sAYjGhuyP4BMcTNQIRG6g4FKSdLIHirsAbbU55G1VlJjyaWGXLKXCeJjgQYH9516777k7se5N/FY0pRPfJcXAw5ef2cSSWFJ6haYftV6TkhZT0M0nZu3H4+WFsZsjmN+24PWbcGYLkjeKeidEidtjYZ/axYf/vBWHvGOxiriTFh/FicUbWUwZuO99aOYMq6WPU+Xk9Rt0HKkE3uHJHWDF8MMd//0T5z9xDWs8JeToW5ggq2By166BFufIOoBhzmC8pc0zHboOyHICysnU3diYtfio740+nx29IhK2rYYnjoF985ujFQ3pqDk3UtvZfZj1zNqZi0bFv1hX86AhHrKhvnuFDJSVAbKIDw+xPY5D2ARJm7omphYhampFL+UCCbXcMNEkFCZ1M/T/gyW9o0mbcMAqVsUeiZ4KH6oEe8h+fScEuS7o9/jiqQWfugZx1N5U6m4P8S5v3+Zv7dOYebWE1kx9nnspiiDJTZiRpheI8Bv1hzNr+MKph4Ttk6Qo2Lo1q9m2EtYrg7EJjAsBIYZ5oCRuo4MBlFsNvTJlbRNsuErNUjfIAknC7KX+bD2OTH7JOTn0DY3jZw3u5AmFRSFUI4TGQwNNWYkVEs1TaT3JiHdDqKZTizbOzDi8X3B5pAGIhzDUyOw9en0Kwq93S4GulLJ2A7W3hgpm8z0TY7TV6miWyVJ5X287RvFLOsOamJ+xprtHFu+nVnbTqBvWRahsgiWRgu5q2K0Hq5hbxe8vreKPZnpNPamkFToZaDZgx4XJHcaqBGD/LcktlYf/aOTWXTte6zpK2Luxgs5oWgr481WNk1+iu91TMSpRTClhSl3dtK7o5C+SguOVonvWD9rf/l3xqw5kzOfu5qaCxKz48N2nEzPezkY+TqWaV5CbW5q9+RQ2h0l5tYwr3ViHtJb39pXyhP1kyn8g4LW66X+9AyKnu2hc1Y6ji6dQJYgS3Vy2tHLeb+zbJ8AAGjVfcx55noqNjZg6x8gacwIAvkORnVczd7T7+PdtnLsdyaR4TTor7QTTgNzPwQKDF4YsZReI8CNS0+h3Bakc4oTT2Oc7nkF2PoMzGuc2McmVm3PLJtO/Sl/hoUJQbsgcxevtI9m+uaT8a5N58Gb76bMFCRVcVI3/yEAFtUsYO/SEnLy+ugr++x0kP8qOgox+c/zCQDo8uDeiT8sBIY5aJinnAKAYrcTnDsaRYeUmjgCjXASpO6KwebdJG8GNTcLvbWDzJp68LgRDhvR4gz8eSbM/sC+NoWqJraItnUiujVMe3QMEjuQFJcLo6IA3aIymGvB1RKnZa6CtUsQjZsxlw3imexl77Y8wMC9U2NwfJQ/zXqc7z59AYePTBg5y0xOWnUft2VtZPwj0xA2cOywEBgZZaDHjK0zEaCOegeTKzdS6e7k1aVTEXlhZFCja5KGrVPBXyQxtCSsPYKHVs0iu7CX6yre2udjcEXrNJYum8CcQ7ch9jh4zDcdy+EmhA5VZ+1mQ0s+N/dUERi0UvCBzvSRJ7N6/LM0VWciqsLIiMpgTTIVj3qRJhUllIhOoAU1BqZEOXLnsfT4naT82QEyStOiDMbM28P6lBE4msB8Yi8z01oYe9tiShfV8uqoJ5iw/nykFLwx8UHOuuK7lC/bSXR8Kb6CIjIvrqe+Q2HvrL/xw85xJP/YhBLsx5xkp32WHcMiKf1rF6HSVKoGFqNEwTLBR+c0F95RcbyjBCImsbWpuJsk9/zuRO50CsS0EItqFvDrohfI1WIc7wjwt7/OJ3VHjJS2fq7pvBLLcV2sHPcc26Ihrq45HYsaJ1QWxRbX8NR+NaGdD3wlcHAzLASGOajQKssw7GbSrm+gyNHLQMzGe7sqsbtDDIY8ZGZloLd3oLe0JQZ4Q6IPDCD7+jH1e0lZ9rEAQCRmYPt8DXQdhJLYcWQ2I+w2+kY6SHtpN+5wHql/aKFpWxmlCxoA8EatLMjcSceSAkyBhG57VHErSUqQ6vMTs+zvd06gMZDC1rfLsU3qY7BCx57lR1UNZmW0cfTMbQDsDWfyWuso7GqUV5tHEU/SKXpMxdbYz+4bXMQrIxhtTgoqOmmqy2BURTNLyt/AwOCM+rmsXV+Ge6+CkgsfvjOW0id6MNxW+kZp9Ew0WP9+FaNn7cWvWyGg0XS8we1lr1PywqVoAQV7fgjfoA2kBkLgK3Fg8hs0z1PwlPRBk4dJqS10O518cHwlR02sxh1xoSk60iQJjA+T9Ssn29PHEZwtqe7KYELjFWS+ZqZrquCwbdeTZjUw/H5Mm2tJ6csmeGM2adkWyhsux7NHkKr62H1FGppfQc0OoHst7P21G6PJhJ4RQe02k/GMA4TBEedv4IV1k3j82HuYbtEofuUShFlH0Qw0k85LZW8SkioT/nIN0YIoRduiNBynIs0uFFsYky+xQslUdc7KW8vvlizCGhD06W4qtg9+Je+qRBzQLH9YHTTMMAeI0Ez0TE8j5bwmksxBXlo2BREVuDoE1j4TgSwIV2Zj6ugCaWDE4vsEAYDuD+xLJpNoUNmnEoLE7F8oQ78bknhhBoPFAmVhJZ69IVbuGEH9UFrD6ZtPpqsxhQc/PAq7HxCgH+rlp4VLOG3ZZZhtMXYf+iidYRfrN4ygfE4jIz0dvNAymSMLargzZx1/GijAoUR4smsqO3syWZC/m0erp5LkCGHpUmk/REUemk7+03GErtJ4HMTvz4K5Bj8vehkwc0bdPNbXFWDL9+E1O0nZpDIwO0TLMWmkb4kSP6Ef69oUjjtxJb/O3MSEtWdRWtHGW1WvcEHTLDAEntG99NalgDNOxcP9VF+cxIIZm3l/yURGj6/jpbI3KR24gO6Ikw+2VGJPD1DrS+OIjGpeah5HyhaV33z/IX7yzsWkre7BUZiOtseFkQuHfn81rz01HYsXOo6PYMydRP6r0HVuiLFZzShRK91dqVjXWHHc0Ult6VsAlC87F8UWR9nloODdEJovQjhHJeJW8Ozxs/OSKrLKFa5edzXy+F5M/Solz4SIpFuZ8uuER/DlTfPIfzdM62wrbbPB0gM5H0aRikLDcTZKll6Eqc1MLDWOsEpiCgi/ioh+cufWv4tEYBzA7iB5kAdmGBYCwxw0KFWldM3Qcf8sgzpXLr+540keap1JdUM2ed9vIDUURqQmYygCqe/nT6CIfYJgqGDf4f6OZ0IRGFEdrbSYlmOzyfv7Xopu6kXNzEDv7GJkaw7zHr+AuE0lqTdMqrcXw2Ehmmqjab6JU0q288PaE/n+1De53JPYYpJkCqFkhFGEZHNfLkdN3kKSKciUTacwNq2NhzoPweezMWdEDU9vnILFE0a7P5W8vjCRFBO6WdAxXSNznQ72OB/e+RCXtszgns7DqXJ0EDVUzPVWCv/ug2gnqCrJezzUXhChbFEz75S+xWzL8UOhEhQCtR56GpK41jOFjU+NJikKYmMqOX6Je+cgustG/tsGly76gN2zMnlpaP+8okrWtBQiFUm02kN/jZs3W7OJFJsIZcFrA+N465d3sCVq47zXLiV1k0rZofU8u3ESsiROtE9DtFuxBATeEtg14zGm/uRylBgo5YK4TbKrMxNKE7kThABrtZXsFRHMXX6kWcOXq6Eu6qbrlXT6R+moIYGzog9vUxJKbpja05yoYcF7d02nsnA6KVO6iJdYUOKJ8OL2Tqg/Cx6d8wBnv3spKavNAHjtCiavgrtO4s9XaFmQmojW/yXRpSB6ADaB+LBNYJhhvpjZi35P47UulICCuaad8IwidBTihoJrqxkjEMQIhRGh0CeuE0MCAfjErP/zEKpK3/Qssu9aBx43amE+0mZGFmbgzbPRV6mghsHZphF1u7B1G7QtjFE3/z4W7D6Gn5csYabVoEsP8puuOUQMjdTXbdQdl4qy2YW+JYvU7zcQXJXGe8UeLM0mNA3qnhhJRopK6toQEGLXd1JI2qYSyoDUbQb9ZSqpy1Sem+Hi/ryVjF93Ou92jsSRFkAKiGa5MDSF9hlmdAsoaog9r4xgXGQEyce0MWrVWYh1biwqeCdEeX1vFSINYkk6Jc/H0bzRRFgNoPv8IDsiOdhMMW7sGs3jm6ehWWOEu+1krVAIpUL6m41gMWNtMtM/MZUXN03gvfQyNk95EmEIeifrnJ2+myXlb1Cy9ELyJnXS0ptE0bm7qP3FJEa8dz7GrBjnTFzNUy/NxuLViTY7ubRlBsveHIerBZKrw2BI/OXJRC7vx6Z2MPB2Fmn1MXxFGsfMXccHD00lLSixDFpQozpNC2HdRQ/wuC+FG1ecgDwkjqXThL0NXE2JNKIzFyjUf+tBbj+kmHs2zcFUayNrbRz79nbEnDxsvV+cDexASKwEDmSL6MHNsBAY5qCg49ww5al9ZNl8xJ9TaGj388uHTsdTb5DVHAQ9EToikTry439iIxr97IH/cwSCsFjwPLUeqevEqvJpn2nH0gcDowzGTaqjvyuDQLcd0+xBril7j23BPHZ6s5iy6RTiusJDXbPIz3mNc3efQ65jkF3PVOD26RT+DtTBbhp+ZWFwIAX7IT2IiInkvBCh57KIuBV8hYLuyWlYuxUUV4ioS+PY41azdkYB2WqckZ4OHmo9lOt35KOmRBAxgel9D8IKtaeaOHzSDt7JX8HIlWczOaeF2qWV+HMFbetziKXF0FISw43Sb0JpMxPNi4Iu6C+34GzT8GerXH7ti+wOZdMeS6bS3cnjm6bh2mrGfVQfvTsc2Dui2LoFRkYShs2M6o+QdHETqypeAxJxh6Qnxjnj13CuZyfTN58LAY3e13JRkqH7vMnkTGyjeWcW2RXd3JS+gw/WzcCfq6GE4f13x2H1gq3HwFzXhXTZ6ZySxuEZjWzqyyUwIs627zy47+9VMmYiFQ8EUQYCdM7NJmUj3DhtNGv7Cqn6QQMyGmP37eVEs0ELm7jl0r8yb9dC6trSOW7kVsx7bGhB0E2CYFUWShz6yr+qLaIC/QBUPQeiMvomGRYCwxwUpDxvJ3ahl3VvjOI7p79I7X2VpL7TgJGZjKhtwYgn4r3sH1BO6jpCMyWihNrtCZvAR+xvD5DGvlzFis2GHo2hmDSUuk5iC4rRp/g4dcRWnlp2CIZNR3HEeX/Cw7gVG7i7IAuO2XM0R2Xs4K5th3Fa7wV0d3voqc/GFoaIRyGY5sLRZSfUBaacQQYHHSChZ6uHovVegnkOHK2Chy+6iykWE1e0TmO1u4hnt06gbt5DvB8WtMaSiRgau7Q8XMvtuJvjtBwhSdkKcrqfv+SvACDS6qD39/nYkuMs+N4qbkrfwYh3z8fUYiLmlBwyYxcbXhtJ+V0RfCMcBLKgY6qKUuHjEk87N8eSeK+nnIa3i8isl/SNBN7IIjIhTGfMihaC0JwkslbHmXb3VpK1AOPXnc6MnHreXDUeLSPEQMzOpHeuQkZUPHtUnK0GwlDI+LAHudZOuTJI/5gMRi9bDBd4KU3tpbcmH9WnEcqAvNf7iOemUnuVgh6KcWfOOmb35VI3ZJOBhJe1NS1EKNuOzTCI2cA0txe7GqV+XT7GDQnvccIGRS/pxO0Gt/zgPHQzyPlxXntzKjlrYxgWBSVqEHOpWHvjJH/Qyvav4J01EMTkFw+hw1tEhxnmC5h2zu10T4OkZ3OJHRbktmePJ2NQh1iMUI4DR4sZ/J+6SBqJwb+yDNHeQ7yv/x/aFaoKQkEdUUr/hFRcT66FtGRUhw3pcbLrKjdKyMAEHOnaQc90J7fmvMP8reckBMAQx+45itquNF6Mj8NmjaJLhYo7g0CQUJ6TmEOhdxz0z45x4bhVPPHc4TgHIOe9fuJuCbqkd7SGEoFBw0rlg5dy/LErqUrtpM9p59a+UjZ4C1m7oQypSWyZAQaqnAxMNVAtUZRNNkzvezj0icuwLW7Fs0ehaX4iaujb7RU88+xs0usl4ZREeIwVmyvwTOkjttqJu8aPrdtC8xEWrh/9Fjd2jeax9dMRcYHJDoPFAvMAJNXp6BYrhS92E8l1453v44PznwESEU5j65JZPU2ihgTZf7eyp2EEfBdSNmmY/BL3rj5CaWl0zUpDC0lO+MHbPPjGXPTkGPa1HrblOTH5VeIunao/9yE1hUn3bubNjK0AzFl8KVN+tn5fwptFNQto3JlN0ZI4ajhK7WlJFE9twqrGWdIyBiOh7iepGiwDYNvbQ8eCHCLzvMR1FbOEshmtdO4twtEWR5oEMbtACwk6jiuCe778e2tIgX4AHsMH++6gg1tEDfP/Au8Jfo6YsY1ANugRleLnB3A0JUZ9e4MXGY19rNoRyr5VgDF7AihKQgDstytIqCrq6ES4YBmPEd9dg+vvq0EahAuSaD61gD0XJFP5oxrK/9xHtmeQv3XNRJeCiU99h9Xjn2XDUDiJRTUL2NWShaJI7FqMwQE7mmIQzHfScHwSHVM02o7UsRUNsv7wu3lo+WxiFSGEDiIQxtQToP6kJBwzuyk+ro5f1C3E7IX2sIe1DUWM9HTwTncFhfZeTFlBXDUqQkgwGeS+rFJ4v0rKxj5iTvjl7+5nb10W7sY49g7Imt1KeVI3ucvDeIsFUkDGBh13ziBZNyr0jDbTOc1NwwUG1RfcS5m5k8eXz0S1xbG2aSgxCOXoBIp1mk+Mk74tTjTDSdSlcUvl84ndRcCLH05BN8NAmxsMgaN2gOrLXBT+XcHeqdMzHoIlSfgLoG9ajBt/9jAjrW1sPeNOrA0mDAtU3dnDiMf7qPptK4bNRPvhqViVGNuiIUY8eTnWzjCvvzSNXNXFVW1TqXutBC2g0Hykmf5KO3GPzhEZ1WTbBkm1BTB5BZZeQdQtUKOShjNzWHDZCn4waim7Zz4KQMvzxQgDIskqJl8cd2OEUJrKd7/91Ffy3n6kDvqiz8EuBIZXAsN841jfc7E5OJbYOIPye6IofT6M9k50XUd296KYzfvZAfR9ah5t9Q70WPxjASANtLJS4jW16Nt27xMY+wsIa003eW0WumamUXNXEd8q34FbC/Fq8yieGvsXzj9hBYNGiLs65vPBupE4GxTmnLqV9zaMotXiwbrXQpfhwXxOmLL0bpIsIca5WniifjKTll6NNElkVyL2fufcbELpoI32smbCs5Q/shhnC+hJsKK2BIstxovLp+AsHKT9xUKiE6KYTBCJmKi6tQdptxAodtOwMAWyQlz43kVkfaDhyxP0j9GJB+3EpYJMMYEC0STQuxWM5cl0zEpEIs36Ti3PlLwNwI/3Ho8WUIhjwdDA0g/WbpVIKqRthdZDVeJuwW/nPcWRNp3742aqHliM4pDEisJobVZSd0g6Z6XirAVUibdExbMX2g5ViWdFKHhG5XcvnUP7IRqiJEBqraRrqsRwWghnOmi+MBktN8Ap5cuZ79rGGfd9l+RuMN/Sxc7yRDrJ19aMxyFBjUDRC/1UX+Thujmv4VGDPPvneXjqIuhHSRx1AkeHjrU7TCTNjiEF99XPZltmPcoOJ7Zug65pYLjjVP2yD8wm/DOtVFo6vpL3dlgddAAMJT32kdijF5dSTv7UeQ/wGFAw1JdbpZR/HTr3HeBiEsb1bcAFUsrw19nfYf7zzFNOIdNmQ0lPJf2VMLic6K0d/2j8hY8H/9RUZDyO7v04lj5CIbRoCrYX1wAJr2Ohqp+0EwBYTPRPSMVXBKLRRl+xnUvTP6AumEamqrI8lMnvmiagX5NE7giJyR9lZ+cYrCWC9MesdHx/ADYm4akz0S0c7JwXYUdyFoWefhQhEUKS8T1J36Q0LOe3c9eI5zh75cWcUjcXqUrMx/QQXZ6O2mgjlKVBUozBbgcuDfKfV+g4K4Ba42Dn980UvAKDF3m5ruxDyswdXL3pDDpn2FFCCkpSlHDURKA6iRGNPronuPHslXTMj2GtMxMYGybYYiXsTebmnioee/UwLP1gj0IgN5FXIWVXDFMgjqm2g1hpFgVLFX704CNc9uIlHH/6Xex6sZxIkY61S8XUasXZZqBGJf5cFUebZKBYI24HNQxFS0I0HGNHC0QI5JiZO3cj9+SuYbLjVCyrUtl9hYNLpy/DJHQOd+7i9Geu4fmBWaCBtwyOTUkEd/7TQAHSJAlnSEw+wd6zkpAmg9s2zCMzbZD+MQaBXAt578bomG4i890edn07jXMO/ZCYVPlu6Vtcv/xUyIkTnOjDviwZqZkxkhygSwwTXHzbtcB3v/S7e8DqoAOo803ynxBRh0spx39aAAxxJbBTSjkOOAy4TQhhFkLkAtcAk6WUowEVOP0/0Ndh/sOoHg/CbKbl5AJilfkYQ45g+xjS/e8/24/39v6DABCqiu3FNQhVRWgmjGAQ3ef7RFtadhahwiRMAQOzF9K3GKxpLOQnjcezvSubmesuwqMGqV5VTDTdjqvGSyDHhBqWFL40gBKO4eu3E0k1sPbG6Zqpk5XhpSy5hy31eQxUp2B+NAV/RQq6GVo6k7l407nML99FpnUQ8kP07EpDt0LG5A6cO01kZXhRgiqhLEk4WcW91IFeGkRIuO72xwlFzPylbgY37D6BcLcdzauCKjEiKnqjA1uXoHuim5jbQBjg2GlGiUHJX8BTI1k34Rn6Y3YMqyTqARFP5BNO2Wlg39ONtrWOeHsnprpOGi4yONKmYynycW7DfGzdkow1Cp5aiVQglKrQX5bYFx91Jwa21B0GnoY4Ssyg8M0wvaMshE4dIMkUomrl2QQ2pOJol7h3mNgbTOe538zjxoVno0YEahiy1kSx9sAkez1zLr+EB+8/BtWn4qroJ3tFlORdgIC8Z010dHpI2qWAgIYzDUxeMJwW1KBCUyiFl5+eyU+eOBv3FjOOvRqOJzw42wzcjQYibtD/mxhxu8Rb9dVsEWXIWeyLPgcSRVQIUSGE2LzfZ1AI8W0hRIoQ4i0hRM3Qz+T9rvmREGKvEKJaCLFgv/JJQohtQ+f+KIT4px34ptcpEnANddIJ9JHIkgOJlYFNCKEBdr4gT+Yw/53U/GQkqAq5D+9CWbUdIxT+pOPXF6BWlYM0kB/tHjLkvuOPEJoJtaqcnvnFaCEdz7VNnHbWe1zy8xdQVMm29SWkOgJsm/YEJzi8aGHw5ZsZrEyCU3oIpSs0HZtE9xQPh4/cjbNBQbuug2eOups0W4Ct75RjtsUw+QW2zhgtRwqu/uGzGHEFhyXK0j1VvP36JPSQRt77OmoE3NcoJNXq9HgdpG0SlD7jJ2W7D1PQwPWhHW1A5cHWWaS6AvgCVvSX00ndoFL2YDsVv21A7TOhBgVI0C1g7VLw1AQpeLSOgpd7MLcOELcn/veX7BqDGhLY28HaLyl5sBFbVwSiMURyEuFjJ7PrR/lY7VFKXroU9wsu+r+TgzAgdWktalSS/1wrmSsGKHi2jUB5jIEpEZJqYygxAzVssOdCK/WXSJjfR2RzMr/O2Eq83omjBUJpgrw3umm/MIeeY8Ls+rYbc39CfdV2qBljhpcf3XsBfVUaig7WHkHyH51YG/qw9eiU/S1MX/nHTlm6BSp/58MUgLjHyj0nPkjNrSPRLWDyQSgD7F2SqEtBC0k8W/qIptuJ6ipaUJC68YsdvA4EYyhsxBd9DsirWMrqocnyeGASEAReAH4IvCOlLAPeGfodIcRIEhPjUcBRwD1CiI++2L3ApSRSTpYNnf9cvm4hIIGlQogNQohLP+P8XUAViQF+G3CtlNKQUrYCt5JIp9YOeKWUSz/rBkKIS4UQ64UQ67u7u7+ebzHM14bQBXt+WoEMhBIqoE+vAv4JxpyJ6Lv2fNzWUETQT7SvmRg4YzLRHBdKHEy/6GRu+m6sSoynT5hD8bk1uOoFjR2pLAnaWXDmRRT9aRdJj6zB7I3j3ZiOrcdg6sLtrP/ZvfwlfwVqFOK3Z/PLpoVEDZVIXox4mx2jMkDDxQaWnADz7A2krLDQO+DEtsVGzG1gaTZja/GTti2O4bDgy1fJeM7GYLHAsGgEChz0jVIYPCREPEnHF7UgpSDe6sDWrZPxegOEwsi0JMz9Ai2c0Ps7OgwKXvdiWFQilbn4y5LZ9aNU5l68mplbT8SIaBgmiW6BlA+a0HNTUVduJ97Sit7ShhqRzJxUzWMTH8LepNI9EZqOcpH6biMyPRnnCxvQ09ygCqqvyka1xVF7zChxScd0jeZ5JiaNqicnfYCXxj+IedwAY+5cjMkv0C3gqTfonJNO06I00pN9mHpMGGaw9IGjFeI7Pfgq4uhWCGaDVKF1lploXhKOnV3UH29n/Ik7kTEFe5eObjcwHBYyVvfhLbFwT+sRpF7dgHtyN8EJITLX6wyUJwSkL1+l/vQ0ekZb2DjpKSLpOkl7vxqtsiEFMal+4edAVEaf4kigVkrZCCwCHhkqfwQ4fuh4EfCklDIipawH9gJTh5LRu6WUq6SUEvjbftd8Jl+3YXimlLJNCJEBvCWE2C2lXLbf+QXAZuAIoHSoznIS6p9FQDEwADwjhDhbSvnYp28gpbyfRC5OJk+efLA75w2zH0fnXk36nCLcL29N6P33d/D6rBhAHx2T8BRWPtj4ifb2tyMIzYRisxKaWUnqyg5k/wCm4ly43MGb5umIQJhQZRLxkSnkvNVNJCWdey4ahRLbjDSb4ZCxmN/ZRK6cgKEJGn3JzN5+PFIKfDNDDExRme1p5dFN01EscRypAQJ1HhwlXhTF4NifX49/gR8jaCKYZ1DybISecTba5iSh26D4e31UmjrY1ZNBpM/B3lMtCE8UqyMAhoLRYqOhKQNThwnpNOiapBLMKMLsk/hPHsRp7sbyYApKXGIajCeigsYM+ittFF9Yw7KSd6h8cDHWcf1o3SYsvYnBtfOYQtL+si6xWhIKamEeA6UmmpdXcWLGCKoeqSc6IovaUyzond3I1jYUsxnDpNBX5cQwSRxbbATydeqP00gq6iOwNYUN20pw7VH5lv8yLqlYiVJucM8zx2Bo4N7ehyPJRs1ZVqwvZjBiRS+G1UzHTBdmv8RdB0ajhmFO9DHigbjbwNQbpOV2O061n3XNBaSuNdF8TIyq27wMjE8hlK7w5vW/5zddcyixdfNkaDJI6KtUsfZAxvvtDI7PxNkqiboVDr/kEkwzVGpPBd778u/vv+gsNkMIcd5+xfcPjV2fxenA34eOM6WU7QBSyvahsRQgF1i93zUtQ2WxoeNPl38uX+tKQErZNvSzi8TSZuqnqlwAPC8T7AXqgUpgLlAvpeyWUsaA54EZX2dfh/nPo3f34nxqLUYw+M9DPuwnENRRZQn1j/7P9boyHkO4XdhafXTOzabtr1kE8h1Es1xY7uqj8ZRsrNe14lq6A31vA/k3r94niITVgtbrRz90HIOFZrrHmUi3JQzM9p+5cDjC/OKQF1jZU0xGhhej30JwjwdHiyAUNuFtd9M7M0rUbwYBSlhQd7IFNZpwdvLM6aR6dTHVT1RgeT4Jxathygjh2GplRFoP0YAZ8wBoXSbSNxvYOhSM4hC2HoNAtuDool301qQSTlYQBpj6Q6jtffSOsWOogvagi5t7qoi7JLG1yThaE2k58/6wnswPuvd5Xi/c0cvuazJZ8qPfkb4ZKi7bTrwwg9qzTFT9sg5IrK6MSVUYFhXvgiCZqwXRiQHKHw1R+ed+kv7kRKqgeVUGR8XQt3l4pWM0dy49msx1cXLuWQ/NbRgWlRFPRshY1oNhNzNY7kSJgb0zStqaXjJW96GbwF8gUaOQ876k+pIkbOYYA30OjFong6VQ+KJg9/fdRF0KA6PjtOka30rawh2r5tO7LhNFM8jYGCOYLZF2C/4chYERKuEkQdsMDb0whLnnq1EHJXIMiy/8DM1MV0opJ+/3+UwBIIQwA8cBz3zB7T9reSH/Sfnn8rWtBIQQDkCRUvqGjucDN3+qWhOJpc9yIUQmUAHUkfgi04UQdiA0VGf919XXYb4ZPhH3Bz5b/SMUtLQU4t09aCVFxLdXH3D78dY2NCGI21JIvcuB6d31IBTC81QKPTXwEEhVRZhNyODHdgR90IcIBDFHcgjMyWXn5fdyQ9dYdr1YjpoaZ7DJxp9ts/G+no1vXJS0jYJgliA0NYCy14EZiBWEMdnixNrt6DZJ0m6FqAu+O+J9fv/oSUw5Zhdr1lQiFYm0Gmh7HOiHDOKLWijO6yKcZaK9LZmOGSZsbZD1rJnWw8HaA68/MZ2KR2roOrYM3SyIZDiw6DqGCqmnNvPrkhf47nVXkZQq8NRHMX+wNZGsBxJ+ABUTMF/Txu0rJ2LvUjj1uutwtoYQIwromuIgfaVk77UjGPG3bmJpDuIOE10TTNhXQMecOOlv2qm+JI492cD8gZmcD+PEnAoRlwlne5xGax4Wn2CwQMFusRCeUUncpmDrijA4OoXu8QqxtDjmLo3MVSG8Y1Jw1QUITgui7rUTt0PbHAVLr6DLnIyrWkOq4KuI0XpWjKwlNvKv2IM96OLpgak8uWI6lh4Vz16JL+wgmCEpv7uF1hPy2fz9eyl5/WIIqqBIUt+zkbIzQM2//dZ+jIFygFtE/yWhczSwUUrZOfR7pxAie2gVkA10DZW3APn7XZdHQq3eMnT86fLP5etUB2UCLwwZpjXgCSnlG0KIywGklPcBvwAeFkJsIzHw/0BK2QP0CCGeBTaSMBRvYkjlM8z/BvOUUxK5A6prAVAsVozIZ+hqpYHe1w9CIV7X8I/nP0d9pLpdCKuFWFE6WXetQ5hNdF88jcxnqzGCQULjC1HDOt5SKymPrt93nZadiTHgRUlJZvd3s8lYIyl57jKUmMAYHSGYY8bSJxICYHSMql/1E8t00TNHBa8FVw/4xkbRWq1EXTqKAa56BU9djJwb93LH7iMJFsdYs6YSEQNnhZfoxmSKD2+g2NnLyvZiYh+mYJnVi2qLI70mcj4MoETiaH4P4XQdbAbVPxhB7jKdqEtBNwu0oJ3wET7mZu7m6p9djasnQijVStcEM2LsZAwL2DsklgGDnnEqKyueJrnKzmGXXYL19U1IXafxx9PRLSQ8awNQf0o6lv5Enl+zD5ytOmlb47QcriF0gcUcJ+nYVrSbU7B2xEnyh+m/Q+KMa2Q6fcR/nglSYtveyq5fZ1N5VS2uDTGirgnEOjXUCAyM9NA9GQqubkPfW4TVD3EbWHoFodIoiklHiWt4x0ax7zVj7TVh64qyYW0Z2aM6WdpSCTLh8+DZG8QymMj3GazKIuqG0+qOxFFtQokn/i4Z73Uibeav5B02EF9HUpkz+FgVBPAycB5wy9DPl/Yrf0IIcTuQQ8IAvFZKqQshfEKI6cAa4FzgT//shl+bEJBS1gHjPqP8vv2O20isED7r+p8BP/u6+jfMN4taVY6vIhnbkBD4TAFAYgvpJ7aDfgGKzYawWRGahl6UhbJqG4rdjhEKJzKHnVOBtV/SOx6Sd5hJX9OHNGn71Et6Zxf1v5jGiAfbKL9+M/6F48l9T9JXpaDnxVCcUcJeK5FCCUjMf/HT9kwmBHQ8+V7YloJi0YmlxUjNHsRzpwvDrNB4jEJTbTFCSDR7nDhg7jIx2OdAjAizqzGbJncy4Ro3JgsENqSiaGAeACUYQ1o1hAE4dZQ+E45SL22GB6lCxW1N7Pp1NkqTjZeeOxJTVBLItWAZNAjkKuhmcDVBfyWUPdKP89Umnjq1jF2BHILpKtYhAVp0bzXBqSV0TDNRsDRI81w7yXuiNB6jUfJ8FARoviha0Irs1hh02hgctCGOs+JoFQjDhbcthvCrDCQ54HQN87yxpOyUKP2SxZs2csuPz6FnsjGkSxEUvxynbHw77UEXYsCEEoO8NWEaF1jJXaLScrQgNMuPudpJKMsgkiJQIya0vABHZO3hxUdmo6WDFoC4w4ShCazdEXSryp/Pv5frdp1McHQYkzWOc42TYHk69t1dn/3y/ItICcYBOILJA3QWG9J8zAMu26/4FuBpIcRFJDQnpyTalDuEEE8DO0lMlK+Uct+6ejHwMGADXh/6fC7DHsPDfCN0z0ijd7xB+SsqMv7Zc6XeS2aQ/reNn3luH59SIRmhEIRCiRVCd2/Cr2BcKVIRaL4Yue8MgmHQMzGFpL0Rdl/nwNQygZJfbgIhEFYLxT9dh2E2EfjWOEKpCv58MMoCqLucqH6Q5THMzhjqDgct6R4Gp4a5bMJy/rz8CNQZIXS/CXujhvNlD1ogSt0CG+YBQdSpY7JHiXbbsPSpRHNiEFTRvGZkSYBAhxNNQt67QbTeIM3HplHwQgd6qpPWw5xUX3Av74RU/tgyl+quDERc4NkL0dJMCp5V8WcLXA1BAGpPsWPpUyhc4qN5gYu0LUGylnSDqlD9+/HcvbsU53Nu0rb0YwiFgfMmIwX0VyVm1X0/DqK9Z6fjEDMp26BrghWzH3yFVgwNzF6Id1jRQgKTLxHPXzuqhyWjH+G4ZVdCTEGJCNy1kidv+T3z11zBTb87j745OknbVcKpoMShaZ4Jl99FOGLC3K/gG6Gjm63kLoshdInWbyJui2Ok6tgyA6Q+5iDiUUha4uC5jbPBAtmrdXx5Kv0VZlK3hzF1B6i/LIULXrwcwyRxNihEp/kThmeT4Iq3l/JG6Zd/h+VXvBKQUgaB1E+V9ZJQh39W/V8Bv/qM8vXA6AO87bAQGOabIePtZqSSj1JZgr6jJqH37+lL7ESJRtFSkkl9YOWXy88qDRSLFblq274Q1Lohic+dSOXvmuhcWETuSzqOl9dhxGNohfkYHd1IXSdy+AScb+1APbSKQK4JpcaBoSWSoluTw8TrnZS8NEBsuYvecwQPbpsJ9jguVwjpDJH0vAtfvonGRZLkzH58fisETcTa7ZgCCiW37qDh26MI5cWQioq620HZ/fU0nVVMf6Udb6mdeFYUw21j7xl29p56L0fsOI6IrtHemIoIKyQ1Qt/EOBwbxKLFybws4Vkdb2rFtGA6oUyDhkUuSv/cmEitabHgH5vJabNXUWVr46bGkwgnpSCPTMHdoDMwQkULQdQNnr+lID2JlJrBrIRgsHfFkUJjYE4Ix1g/weZk7LWJqKAAyeYIo0x2nK4w9mc8xJyCwWK4su4UYiGNvjEGCBIJ5r0QyJNkrJd0qsmYvYJIbhz3roQXctv5UWJhDc0SxOi0Ie060VoX9rYA4WQHg0WJ7adqFDqmqZj7QYtA1KMRyEmh8NU49ScpmHo1/CNjpLzlJGN1H0jJD7edyFeRVcaQCjHjK7cJ/McZFgLD/Mc5yn0Bwu0ibZMPfcjQa3gHE/v8R5XCxh3Ee3v/9Yb330o6tNvIiEaJz52I5osh1m1PxBd6az1xoZD5fJSeb5XhNJsSl/QNoBTkInr7sDUl8tBa+sKYvSYME+S+46N/tJPu6TawG1Rfa4eIiqldJZquYOrT8A6YkE4dWWEiZVcUR5dK26xklKhAFoQwNIMRv2un7rujyNgQp9OUUIEUvOEHh52CR2roPmYEpnIftuVuqi9VOGRsNV26H5spRvfbuajjQuhmlVC6GSwG/o2p9HsMkmglVpCGaGym5K/NxHNSqDnfQvtxhcSccMrZ7/PTtJcpWXoh2a+byIlKfHmJWbxUwDCBow18BeBoixBOsYGAokebE3+jFBftM5KQUtBdlwquOGoUUnYnnnnH8lzGvbqYmBv6F/kxbXCSXC2pUYqR6XG0oEALKpgGwTsqjqVDI24DNSwIF0XJeM/EwLF+nO840VY5sAHWPjN9IwXSaqB7dBq/5cTZDLoVpCqJJBmY+lSkCWQUApkaqdsDNB3lQPNKdLvElhQilGGib3wy7oYw4Rrrl3uBh5AcWITQ4UTzwwzzKURuFtFsN5EkE/ZNiRm6EY0iDx2P+HDzv9+wND6Rc/ijMu2txK4gJdmDDIRgVCkt8zzIaV7OKX+bJ5Lnkf2nNcQmjGDhve/zxpRs5M4aonMn0ldlQrcmdPP1J7qIpsWp+tMggRIPHVPNxJ1GIoWiNY6jTcNbISGskLY1QsylEnUl1CJKDEzbbZh9sOuneQh7mJYkM6YcP8omJ/2VDuI2J0o8k9StPqTqBgm5xT00+5OY/fD1RNPjaCkSo9sCJkl4RATPegsD42IkbTUhXYnk6urYKmRTO53H51P162Z65+QRThM88t5slo8uxdJgwdYVwVdoRguBZcDAvaUL114rDYuSKVoSIJxhwVthYJglvbPziNsFMUciqJsw60QtBsVPQCTZoH22RAkruOsgdLiPzIdtOJ4Ksuu7VqITIxiNdiwdGtEkAzUkcLUa+MbrxB0qMbvA0gexXPCOEMSbHWghSf/IxN8w6lKwdYGpTsNbJok7JL5CgRIFe6egf7xB3CmQioKrUdI3UhDKdBAvDxIPmEjeaMLzpo3uieA/YZCJ+bXs3XbAmpJ//rodaKL5/88B5IYZ5rMwmlpRqvdi/2jAFgqqw47+ZQTAENKQn73VVBpEJpYSTtXwvLGLXE1BLvDy2KPzyP3TGqQhMTd08+L35mKJbExsqVQg6kmEIshcNQBC4Ct10X5YCim7orjrVOIOhZgdrHusMLePAlsY7ZZUlJhOz1gLobw4qes0Uh5aPbQlVid9qEv+06bTPd5J2rY4TQsBk0HKKjO1JzshL4TssqBuyMIwSxQ1MVu39Aj8FXEwBEIXaCHIXKaRuqaL3mnp9FcIstaY8M1KZrBSJ8fjZLBYkFxt0F+h0NidQmqNxLKnHfO6QXpPGoMvX8FVZ6fxWA8iDjXnWVGCCq46QdbyAbyVboIZgnBRDFOniViPFWeOH2+pB4CyqiYUIWkoTCHSY6N5nsKIXgfJm7TEzp4RiRSa/jwFJQZts8G215IIt22Av8jAs8FM1A2OZkHEDeYBQfpmnZ6x4Cs2qLqjg4GqbHSHjlRUTD5BIBe0AY2s1ZJQKnRPkdhaBZ56g/Z0Kym7FQK5EDw8QrxJI+0FN29MH0dqUT+NX/pNS+QYjh2AqudAHMq+SYaFwDD/cYyhPMFS11EmjcbYsB3d/+msMf8m+zuWpSQjwxEUj5vYiGysWxuxZKQgrBb2nG+n4pi95ITbUBx2jFCIeFML5qaWfZ41UbeKEoGYAzoOTSJjQ5CYQ2DyS1ouiaFpYZzWCL5+J5ZdNgY6XaQ9ZqdpvgXb6H7GpzcycLyGzEyl4cbpaAHIf6mDUEkKTUebcDYKyh7qxHDbEHYzFluM3ikKZ0xdwzNvzUArChDtsaG4otBow9Sv4i+LY2kzJQyzTkjd6kPtD9I+L5NwWiLZur01iOPdBpKnVyJau8jY4KLtUI14ZhTLbgcIyc6bcnHWFCFVSK5O5EfIntVC66o8LF0qee+GCWaZ6R/twVcgCI+IYG62oMTAtVUlttdDOAUs0/uoacoEKZBxgTaoYi0bZO/pLsz9kPd+GFPAjC9PIeYCV7MkeYeCZVASdQhSdwRR4nZ0c6Lv/nxQYonIpIFMFWeTJHO9QcuiHIpeidA5yUKgNE7EAWlrVIIZgp6xAt0Mlh6FSCp0ZAtsHQJ3Q4yBGToiomIrGaRXd4NNR3k69bPfnX/1VUMcUIRQeZDHMTi4RdQw/3PMU07ZdyymjcXY8FUk+tsPoaBlZ6Glp2F4B5GRCE3nlGBoCp0nlDEwJpndN5RQec8ARjiSuOZTeYs/wvXcBtQwFLzpI6kuzt7TrHiPCRDIEbidIabmNjEYSuiXnS2S7Pc0mq+Mo4YhFDZT700lPLaQ9sNSiKQa5PxhDdVXZtK4UCNvdDvWXsmem11UX+jAao8S7rWRuVxl3bcnYW8XRHtsiKjA8JqJZcXQrRLFFifuNghPCeBoBcOs4hudxmC5QdasVmKHeam+wIFv/ihCGSaqbyinZ4yGnhsmab2ZSKqBL09QcX+YpBodkx/iVkFfpUr7sjyUKLgaJI1HWXHvHqTnqDCxMQlv6WhOFGOUn94pMVJ2xf6PvfeOs/O66/zf56m3l5k7faSZkTRqlmxJbrKcxHEcx+kF0gihBNhACJAsLAvssg2WHyxkCQvZACEQAtmQXklxEnfHRc2S1etoer9ze3nKOb8/zkgey5I9tuXEIfN5vZ7Xvfc8/d7nnu853/L5ILeXqe9vwT3nQsMkfsamfb+i4yMRYmMGHfsCah0OXsKg1q1oOS5ppgX1Npi+URHNSxptLtG5kMJ1HtnjNdr3hzRbFNWBALOpaPvGaaLDRZKjkukdLskxhTNrgWdQWiNobK3Tc28TI4TUkCL3uEKZChHA7L+rYY1ESKbqSCnofEQSPeNQ7r9S1M7LZRF9cXezL+6rW8G/TSwWeKlHH7/yx1aSYGoGYlGE42D2drP670/gzJSZv84nOhuw8c/HUWdHL2yvFmcmF67t/PUFPr3fmuHsTyaYusEivaZA9qtx6us8Qmkw9D830vN/9Qh+9hUe6eMlkt9JsPa2IVbn8kyOZbHveQxlwroP7sZIJOi+X7Lr+uOMHu3E9BRv3HCIDZvHqBeiiIbB7PWKZtam+68fw6gZRGYM2gfymvehxSNyPKLVvR6Mkz1Rx2iEjL0SsoN5oraPY4dgKea3an9799YpUi+bIXeXS3IsJH3CoNmiOPmeGOO36g45e2Cejt1NGh0hmV0zBD+xQHIYzr0lQ2x/lJ5/dun9skX8uEPrF2MISzJ3tY1Xs2n2NQkj0PmAgZdVSEswfa1LsxXmtlhEZ3xicyG5gxBEBYkJSfdDTVoeNyj1GZR7TWrtJokjDvV2F3e+Sfte6LzPJL8FSCaYuqWV4hoD6WrjEbqKvm8sGu1Zl/FbtGtJmVDqE7QcMqiultSKUfxOn8rxLPW5GLPbTYTUmU5XAqES+NJ4xuU5EMj9QLFiBFbwA4XV0f6M7KDPF4ZtEYyMozwPghBvSx8Iwcb3H8S6az/B8CjGqu4L13FBtOZ8dtGS65Mj42ROQLNFUjqZRb1rDpoG4hstVDssJn+tSe//tlj/UZ8zb08zf23I0HcGGJrMsfn3hxn69Ba67itgre1HJOKs+u2TTP92P4P/XGXyNQFfO7EV470RRMVk4/8aZt0HdjO3xeTEX2xl9Z0+kXkwP9lK7kGb3PdcVn+riDIVQRxOv93l7H8waOvPk4tVGbqrn8beFiKTFs0WyfW/vJ+xyRbylRjz10BhrUn7virr/vtBlC1pGVig9+4m6uwolV6HzBGT/L52/AdaaNtbJjWkSI5JDE/ilAO6Hq5jNhWrvmzpTrds0/KQi9nUHEADX6lhBIr2xzwGfvchYtMKL2OBguhcQGpIFwQW+13smiR3yCMxEVJZBfEpxfwWk4UNMaSpq5Yj8wK/O8Nv/cZnWfO6szg35ImNGYhAMLvNRgkdJ2l/LCRzUuEWJekhSb0djJYmGIrEMRujrwaWInPtLLXekOToldET0NxBxjKWF7cRWIkJrOAHhldf818Ip69MteblICyb+qu2ERsqII+fAaVottg4o02kVJjZNGpVF8Hjx5+680XUE0Y0wuxPb6OR04VN7oJgZiaF09IgshCjmTHo+qjLxEtcqn0hiJCzb/oYP3nmdiwh2fuh1eABRsj8TR3M3Ch5qTjO2AdDvJEEVqRG0LA48SvtGB4Ek9NYfauITYERWJx7p4coS3rvVjgViTtbp7ApRfqkoLAlpOWASaUnTrURZ3bQI13QvvTWw1VO/qLDw5/cAVsC5ISL6KvT9R80h5AUBht/9eAF4zf73l3krw6JjZm0HFVM3hpwdodB25cVdk0iTYGhQJmCyFwTZQj8VBRn3qSwQZEcXMD5vKL8v31qX48jLZvKb++i3qVYfaek1m4Rm6wTxGwieZ/YZEgj51Lt1Km5a/9pmlO/0EGQDbBLFuUNEqtksO6f5jn31hz/7RtvR4QQ5nzoD8keMmnkIHNKUekxEDJg+iWS+JBJI6cY/Oc8szMt+Ald55D8XhwvBXOFHKkRQa3tqT/9c8Hyi8Ve3EZgZSawgh8YwkOX6HivMIxEnPjDZ2h2pTAzGYLJaaJf2UMwNKKzc6p11OETTz8bEQbWwGryb7+G9kfyBDFoPaALnLq+ZRN4JlZdEp8KmdzpUuuVbLpqFKNusOGBn+X4TDulV5TY+AdF3KjP1M0p5q5V9H1DMnNLndXvPM6aL9VJJRpEEh4iBLskmPn1nQz9TA+VPmi2KtxRh2R/EWWAWQsxpwvM3NGkmQWjYVC7vYyflogAImcdyv2Kegcoy0D4BunhgPXv34ef8wmLDuHNV2N1dQI6KG+1tyFv2UHuQBkMUBZEZ3zcKZvB35ol8/1RrFpII2fRzFoEUZMwajG/NUrfN8p07gnIHYTiSJqR17cyPtFCcXOI4SuChFYwm9xlIy3wMi5hxKDW4WBWPSJzTSILIeXVgvqaFroeCen5jsD0IDZisvZzZYJsjEZ7iFUTJIYFHd+zad1vYlcVyWHNNpo5I6l2WCRPmCAgfVowfnsLhfUKswnSgvx1AZkzIW37IT4tCaJX5lnTtBHLZhF90WJlJrCCH10sum8Mx8FIp8B1kfN5wnod91EPZS8+3kvcPuIyQeClsHq6aPa1kv3n3YRhyMDJCJO/soOO3VVO/UwEPJOJl5lIR5cLtT4mOFPoR/b4XNU5zXAhi1jbz7mfzNH66ZDEtw/QnU4RTM0gleTsp7cTFh16PhFh4U0esiVAhDaRPOQOhbh5D/P7j1+odzAiLrJeZ+LXdmE6VZwi1Nd51PNRrKZAGeCnFclzgiAGox8IaLkzivv1h1BA4pRN9lTI5Eui5FKrmbt6DUhoOR7ymj+8lwdf2UeyO0clEWGuFiF3UFK/qpv5zQ6ZoZDIQogIFDPbbbInQ5oZOPf6JJlTipY988zsynA+U3LN5wP8pEnnbkm13aSyWhC6AiPQbiWrHlJbnSQy00AZeoZV7bLwYwJpQ6MV2veHVPri+AkDZ0EQugq7itY37hVUB0IyhywieUm1Q2cdWVVNkBebDpjdYRO2+dTqDqkhhThrUlwjCKLglAWV6+uX+eWfHSQGwTJSRFfqBFawAp6cFXTFcL5z9wPk7NwiUZz+g4fl8lM2tzraCZbhjgpW5XAPDxMsGgsVhlg1mLsmjllThJGQ0FVgKFK9JeasFO6sSSTd4PR31nDzmw7ywB+uYU3uHKONfib+ZiPu6QgDn7Q5/ps9MAvxCYPYRBWEiVEzUbYitAXRrzwKwnjS6NFoyWKYrRSv9aDoUrwqQJRs3AUDZUJ0FlIjEB+rc/bfCSIHkuQ+sUcfQxj4KchvMFHXlhhrT5A8o90k5V6Tjz92M33bTPz9Nuk5SJ/1iR2f4dj/yGFYdRJTLs2kiZ/QrqbZ7SZeRrL2802G3hxByFaiowJlQe5+G/Oehzn7f29EORJ3ShCfgPh0yLnXOQx8tUkzY2PVQpo5V1f3Hg3w4wZmUweVQ0eQ3D2CSic5/qstJIcEQUxgBIr8ZoPWwxI/YeEWFI2sgTL0/Ve7QdqC4qCNVQV3xKG5ysObd7ArimYG1IYqq/5MEpuJM/Q8Hz3QM4EVofkVrOCHDWHomYCrUzWFZV9203BuCRWFMBA3bH2KkI28ZQfGwVMEc/kLbZU3X0tiMqDcD0E6AFNBMiA2blKaSBEbNTG3FsnE6/T/8wj33n0NjhVS9R26XjuCmHVptkpO/Pse0msKtO0TeGkwD59l8OcPInzB4Cfm6fqrRzGiUfK/sBNhPNFxhFPTnPjgKqwpB9wQAsHAV32aqz38vgahC42sACGIHYrQejik9LZrL+yfGNFU0MHxFMIXOCXFqm8XKFwdID2T4oBN9qSk45Ei0dEiRBwIBa2ZCgvrDZoZsKuQmJRYVUifMBh9VRSzIZh+lYcyIPd4SGxOG83+r0nSR2zi45Df4TP1jgZd31fM7IhgV3WlMEB8JiR+Mo/ZVAipC8dqVzdobuhm4o4cypW68za1W6f1UMjUjQI/Jan0CISC8oYAEehK5mp/iBKa/sJPS1oedShuCMlv1Z/tx+IMvyFJvfXKdHu6TuCZA8PLEZr/YWJlJrCCH20sisw/k4sHniw/iZKoPUcufDQTCejrRnz/ECIeg1rtwrr0wVkmXt2B3+pjRELavuuSHGkyuw3MikEzq4g+lGa2XTH1hx7UQsS9GUa2RcnsdrG6NNPl9HUWC1NJunzFwH/ZDevXcvJXWxj89YcJ0SpeRixG6yf36GtdNFBjv30jsqWJrFts+MsG89tSzG11oSKxygZBFNJDIeU+l3q3xPBMuv7qUT0TUJLkqM/CehunBL3fLSECSfZvpkh/dhOlzQF2VeHFDQqbU9TeWqRWd9jQOcl0JUnHvoDp6ywqKW1Imi2KxASkRiVjtwmsKYdGZ0hl3qSwNWT9t2wWNtgoA8p9ED9rYzZtyr26oy4OOHT86xD5W/tJna2Rv76N0IFmRtD7L2eJ5FcxdqtN61FJZN6kmYXkqO7c85t04NpLGcSmJfWcQe+dgqmdivi4IH7OBKWL6Iy6Qf5GD2fcAQVGKDQVN7DrPfs4+JHn/+hJJQiW4epZSRFdwQpeYDyjAXg62UolMWIxwmoNMTWPsWGAsPRkV1J9TQvFaz3WfSqk9W6X1vvGGLs1gnhFHuko7IrAT4DqbuDEfJxcnWCRoyz6xmnSZxTDrzZotIds/k9DJD+zGyUV4fHTrP/gHkC7qk59+Hpm3rzhAgcSgLFjM41OiTnjkH3cZPh1aeLTIbmDTVJnDJJDUFuluX8K6wXpE8aTBAaH/3AXhUGbWrfC8OD0u1Icf1+KR/ZsoLxWkjxuUe3UO0y/wmd1ZoH+9nlOT7VRHMogPjhNYkThFGB2u0C6itZHZ8lvMImNGUgL0idMmreVIBow9AfXE5+QlDYGBNkALwWVPknzpWXqrygTOhBMTJI+VeHsT8QwfEXL4xVCBxpbepnaaeMPNDAbCi8lsMuKcq9JYa2Jl1Xkt0Bxc0B5lUGtQ3MLZY9qXWKA6Jy+1u6HArK7tbxnchgis9B7V4k1n5rm2w/seDaP19NiWYHhF7kRWJkJrOAFxwsSD3g2eIZMINVsYqZTEHFRp0curDJ2XEW1L8H8VSaRsxBGfHJ78hz9/U7WrRnlzGg7mZPawETfMM3MQhKvakPTRGbAnHMwvt7GzGsCzLLF4O/sJ/CDiy5N6VhGbzvKlfhJg+H/eiPSgiAhSZ02cGfBSytiM5JGi8HIOwLMSRenqGmZE+cMTr6vkyDtkxyyIcoFTiYvI/FWBRh2SDln0XGPhfHuOSrf6aDZomMD7QcChK9wvmNx/E0dOE5AWLdIDBvMTPYQEwppg3IUHY8Kjv1ajp57JVM7BelTmljOfiBFtK7dNgsbDIw6RCcNzAbUegTNqRgYkFlQTP37XWTOBKRPCdwFHwzIHQmYvcYhiCoiJyJ4SaXjFn2QPgPF9ZLkGQMjBD9pUdtexz0WRRkQmw2RtoWfgNyXjxFu6mPm+jjVLu1iMj1F/mpFpJBk5rU2MvCuzGOlxPJEZZbpDhJCZICPo7UAFPALwAngs0A/cA54u1JqYXH73wN+EQiB31BK3bnYfi1PiMp8E/iAUpcnr1iZCazgxxLnYwhGxMVIpwgLBYLxCa1wtmg0jMl5SqtN6l0hzVbJ8LtDjv1GmmxnmbEHVuGecwkdKFznMTmRJVxwSWbq2HMWQVyy/iMTlHsN+r4iWPcfdiMvMgDnzz/2/m2c+PkEomFQb4PYJChLgUAXPvkQnRZM3ALNnMI+FwGl+XJiEyBNCDIBVjxg/iUelX7JyY9di3/7Djb8hwMYCzaJRIPYGRvj3TOU7u7A8CB7XNH1iEe5x2L8FpsgIgg988LI1UtrF04QEWRPhtgFg+yeGeIjBlM3CWKTgtIanYpp+JC/OqT1iC4IG/iKj3Sg0Q52RWDlGihLMf0SSbMV4ufKSBuG3y05+T6X+Y0WjZymlQ6iUO0S9P7lY6z5jw9T2KiIThs6eyinuY6yd0dp3+dryov1Fi1Ha3Tflad6ywZO/6pJ42VlXSw2BsnhBrFxg5nXNehuL2Dkr4y85HnuoOUsy8T/Ab6tlNqIVmU8BvwucJdSahC4a/EzQojNwDuBq4BXAx8VQpxPVfpr4L1oycnBxfWXxcpMYAU/fhAGRipB5WXbSPzrAa1hfBGsNf2EI+P0fNGk8ct9SAvoCHnJujPcf3g9os+j7wsGkz/fgOkoQgrW/30JY67A3CvTzN/e5Nh/7KT3OyHx43MwuIZgUUrzPAzb4sRHtoDwEBWLyKxBGIFmBqSrEK1NvLhF9KCNl4TMMS1W3/O/H32SC0yYJoWfvoGZWyB2xiYyB6FjMHc1dJU3IKMh6p4s1c0B4p4OuKlI5mMxUNpH7yUh6G4y2ynIPuyysMUifdyksM1HWTZqTZXCQoR4Z5HZ4XZaTgQsSIvygCR9ykAobTCISOa2REiMK0Qo6bm/gTIFYy93CaejRBYMpAOsr3D8AwnSB8CwJe7xCNW1AUbNxKoKpIBaj2Tm57bTzILwFYavDaBVFQipMD3F/FU2dhWCmFY8yz5WZew2wdu27OcLR7ez+u6A6LFJCrt6MQJQUjB+rAO3cGXcM5LlxQSWUywmhEgBLwN+HkAp5QGeEOJNwMsXN/skcC/wO8CbgM8opZrAkBDiNHCDEOIckFJKPbx43H8C3szTSEyuGIEVvKD4obuCLgUlCReKJL6+H7ljE8ZjJ/QMQBg6K0cYBEPaLRSMjRPEVpMcFlQHYPoDq2nZYVPtheHXhNgnE5AJUWmfoZ/MsPavZsj806Mo80aSoz7OfY+jEnHCBa2TLEyT0d+5nugceElIHAM/CdFp6PrKEONvG8BLQdteQaU7indtFXfBovNvH9MBcKkupH6ehxGN0vqNkxTWb9BKYOubiKKNXTIIEjaJ0xbl9SHtD5kUBqHliynGboOu70sieUliUhKfdggdgeEr2vYYlAbArJjEJqC6Blr3mszfEMeOCnb8t/3cObSJ6GNJpAFeC6TPKqLTNq0HyyjLwBqapLajj+IabZS8ngBzytGkd+MJYmhXlHMkRteDdWZ3RCkPSJo5ReqUgVUzKK5XhMkA4RsEdRMR6poIN+8z866AZtFFhIKe7xgkj+c5/r5W7rjpAJ+/fydOT5Vyb5RaxyqKawV+WkLeoX23YHbHlSnfOl8s9kxY3GaXEOLnljR/TCn1sSWf1wCzwCeEENcA+4APAB1KqUl9PjUphFjUcaMHeGTJ/mOLbf7i+4vbL4sVd9AKfuxguBHMlgxzP3Md1vA0KvD1CiW1jkAYYkTcJ9xCvqC4o4lzMM7MdUkKmyQ993koV9K6Ywa7aBI/4hIkFGffv57K228g97lDRE5OM/H+6zj5+xsxrxqEG7cy/pvXAxCdDYnOKfwkmHVo++hDyK5Wmi06C6e8WlfPJr8XJ/2pR1HXDCIsG6slC/BECqmShJUKsq8TrzXEbGqufqO9gRHA6C/6JCYUG/6+SmG93qfaKWjbpwOubiFAWrpztepqsbALBj70OLn9OuCd+U6MMAIb/6JA2/4qX9m/nXo+qkfhCV1JPXdHg/kbAsShU/DIQcL+TqxaSHxKogSs+4eA7ElJ6EK5X6EMqHdJvLTizLtsqjfWMBsCZer4Q+iCXRZER2z6viYJYopwdZ1mq+TMewyEUCRO2qz5TIhVDZm6JYdK+3zr4FZEaxNzfxK3JNn0q0fwU4re70mi0wYLGwUyd4ViAiw3MAzAQ0qp65YsH7vocBawA/hrpdR2oMqi6+cyuJT1UU/TflmsGIEV/FhBmCZGa5bpNw3iFjXj6JOyixarkM/TTFtrB1j73/YTP+rS9piPEcLma4YprNN+5fo324mPAwra90LXQx6Jzz6CrNcJxybo/uh+Wg/C/HUt5K+KY9U1b35ptcnsrR4oyJ6SjP3nXUzvTKFMKF/bIIhBMwttn9yLEY1QXRXXGsmFAsAT6mnCYOiPdzH2yjTxsybS1HnyYcMidMA8Hid77zka7VGiM2DVQAmYvC3ET4Kz0MBsSFJDTQrrBalhn+isT1ip0PrwDD3/6xGyx+vEJ0NEEOJlHdoettj4gUNkj/s01jfwU6DmXIyKyel/3EjlHTsZfWWCofcqKl0GQRzO/mSUuasNvDREZoXmY2oKrKrus8IFV7vcTKh1Kzr2BlgVHWuw6iEdjyqcE1FSpw2UZxC7K0liQnH2rRZCKgpbQs6+6h947433k7szQm3QQ0jY94UtKFMx8VKTRpvC6/EQxcvXkjwbnE8RfaZFLq+bHQPGlFKPLn7+AtooTAshugAWX2eWbL9qyf69wMRie+8l2i+LFXfQCn5scD4YTBjS+vHFmfSlMofOs4kKg/DcCNU3X4/hw/BbBLhNKjNteFeFtO62aT1U5dRPRzECQb1Hsf4PzkAmA4agdvMgC4NaOL3RJrHLgo7dAbGxKrPXpnFGXKKzYPiS1iOK+atMYpNQFRG8TAi2QkSjiGyGxDcPgiFQS7NhlcSIRjF8qHcorLrALkK9R9J+t015tR6lq5YU4y+zCdp9lARnxkYEglpPyPFfjrHp/xQ49sE0wm5yLmex8W+KhEBwajGG8dABYkBomhTu6KDzY3uRfoDz7T3Ert6lye9iAqcoqMUdvKSmckh9P0qjTVcaiwBdD5ALIR6SesxBSC0W7x6zEEqneVoTJl4a4kdniJ8wGX5bB2d/CRL7DBoDHi3ftLCr+jstrhXER0ym3lvDDg3WfPGXUZGQVJsgetqhtArSQ5JKn1Yqa7YooukG7Z+Lcu6KPFHLC/rKZXiflFJTQohRIcQGpdQJ4Dbg6OLyc8CfLL5+dXGXrwGfFkL8OdCNDgDvVkqFQoiyEGIn8Cjws8BfPd25V4zACl4wvOjiAUpipFPIUvkpnb+ZySA6cgQnzz5ZnSzXSrXDIPe6MX65+zHuyW9k35nVtD5msnCVQqg4uccUxXWC7gcCTvz3DaROG6Cg4+Ey/o4kdhFSJYPyGsnoqwUd30/pzrGpg8DlAZMgFeJOgx/T9MiJIRNpQ/2m9UQfPEbpjdeQ/MKTi8gQBlP/bjtGAJgKJQTNFrAqBnN31DFGo5qbZ00aswkqb+HOC2LTigXb1JW4UnH2nTk6emap3d2mA7yjU0/SarbW9DHyE1107m7Q8dePIpdIeHb/2SN4r72OBSwMD5oZk9IAtO+TTL4ErKrWCK53QveDIRPv8FDTEboeKDJ9UxqnCIVNiuSQID2kaHlgDNmS5MT7u8hunKd+WpLNVpG31XCbNumDC4y9rgMBpG6cxRCKqeEWco+a1NsEQczALoNTguJaKG+S2HMW6roSwXwMziYZeY16mjDps3iclhkTeBZ1Ar8O/D8hhAOcBd6D9tZ8Tgjxi8AI8DZ9THVECPE5tJEIgPcrdWGI8D6eSBH9Fs9wtytGYAU/HhAG0vOQS+gglkLVG4QXGQAAuVCgdmuFiO/w8Y+8AV6Th1Awvz2EREg955AchXX/MMXZn+lk44dGCCcW4wyWjffua1HCIEgoohMG8UnFzA0KGQ1xZyyChEQmQ0TNJDEGrQerIGDojXGCLo/KrAMv2UTm7tPIbZsYfmOKIK4QvhZob7TpatnQNfDaAqLjFs21DaKHowilFc+8pEn6rKKwToAB0hIkz+mMnuisoLIKZiYzpALoftBHOPYT5HW2BUFA7//Ro3+1OEO6YIiUZGGdhZfSHw1fYDZg+gZBbj845RDTV5hNk8KAiXUqqllFW6JE3zDN3L4OzLqg7UAde6ZMdWsXUzdaJEYheXUTHjco+C2EEUn6pAlmA/8lZdSRJI1qFH8iBpbWEkiNBFQ7HeptgmZWp8DOvlzq7/hsErehNZm91BXKDlKCQC5HaH5551NKHQCuu8Sq2y6z/R8Bf3SJ9r3oWoNlYSUmsIIfH1xCNOY8nlQf4Dic+tsbGP/iJurf6CYYjzE1naHj4QKtfxalpb1MvKeCM+JgBJA+VWHu5g6C9TXO/uJqhPOEz9kqG/hJRfYIuEWYf22d9j0Cs2Li5QLMmgENg9QZg8REwJl3xKh1R+n/Zp2+zwgSEz6jt1uM//R6pnemaPY3iU4KnDIYIbQc1fTIdgXseYtGZ4g9EqHWF9JoVRQGBZkvHaCeE1g1nYXkpbVucnWNTyOruXmEHeIUFe69hwhmn+BYMtpyyJm5CzUO5yuZl6L1qIcyIXNa0flowOpvlckcFxQHBdIRFNZaBBGBl9VB8PgEhBGDybM5nCKEEcWpn7U49uutjLxacPwX/xr79jlGDndTWQXpUwLDM0DC8V9t5fb+E6TPKCwzBIEWmdlhUupzWNggqPRL1nxinGZWsOorJhs+licxrI1TbdBDrroyLKIKXQ38jMsKd9AKfhzxQ3UFLfHpX+jwL+74l4xkz9NLz/zaLso762QyNaIPWzCcZjKehqhC+YJGZ5yRV5ukZJ3qQpQNf3IAsXY1SMnsTVFEYOAugPJ8hGly+kPXooTEbAjmtylUIiB+IEZ5NVirKliPJZAmpM6YRPMh8X2jDJS7OPsWh1Xfswkdg3KvweCOYU44q7BLBm13O5TW6ICpHn0LlNCdTHwC5KxJdFZRXGfS/7USxvQCxGL0fmEEValSum0Ddl3QTAsyj2uuf7MBq/9OUW9XF4RmzmcfhVPTT/ra1CUc3MUBh2ZbSEGaSNsCkiBh4EtFRu9Ia0K3hMJsCtoO+MTO5PF60hgNLcbT/zVJM2uy8d8fZqya4X/l1xGxAoQPbQdC3IJPy1FFI+cQRE2+vm8b8Q6B+900jfWK1GlBrRNKawQiBGkrmgM57DKMviUgdjKHVdPVw53fsZi+6cp1e8upAVjRE1jBCn5QuJgj6OnoIpasU2GIesk2sqd8yjvBv7uVZq9i7RfqDL0pilUTyKiBtBUyKmmJ1ajOtCBrNTh0nPK7biJ5XBDELSqrtHoZqSTZwwbz10hEKLBLgmY8xL+2QvZrcabXuggXWg9J8psNhDSY/GA/1poK2TsjTP1MjfBcnDAqac63EJs0aLQpYnMhtU6L6pqA1t0WYQRqnRC6ilV/cQBZr3PqH64Dz+DEzyfY8FsnkZ6H1TbI/K2rMEIQoY4fmB64BUX6L3RCStx4QgBFSQU3bkXsOXzJjv88wtuupdYFyZMmucMeEy91aK72SO93GL0jjVWDMALZE4r0kTwIwdjrO2jkoHXDLLG/yhCZKDP87hiTe65CpHxOneoGz6DtKPhxg+h0iHVmAnvIofILSX55YB+fO3A7tdvKyLKLn3AIkpIwESKaBkbTQNoC01dQs/CSOisqPSSJTvu48+5zfcKe/AhxxWMCPxSsGIEV/GjjUuRw59060ahO9byEMVgqLiMsm1PvsRF2iDERoXZdndy3I5x+ZwSzDl6vhwoE8bMlhJdh9tu9rPv4UUKAm66h3qoFUVLDipl2xbE/WIudN7GrYHgCaSn6v93g9M9YSCmYfqXHqi+ZzG6D/GYDt6BdO2HOh3MJCpsUFpA5ISjc1kCgffvSEoz+lE/sgIU9b7FwlSIyo/3cdkUw/Z7thBHIPaCot+nMGyObQU7PcOrn2ojO6MI0tyAQARTWKzof1h28uGELavfhJ39Jjx66wER6/ntSYYgwBKO/eyOxKc3jb3g6OX1qp0OzPaT3ayZeQuGldf3B6s+MMvHGVZT6WvGTMPjSIb6+/ttc/efvo3VsHhlz6OucZ+5AD7UdHnZLg+xX4sxfDT33h0jXJH/7OoxQYX9R8PcbbqdnyMf9UoJquyB3xGPqRodVn6piTs4z+aZ+grjJ3HYY+FLI0JshesSg0m0wt9UljFyZsblUgvAKxgR+WFgxAiv40cRS//5S988iDMdB+cFlU0CXjm4nPng9gx+vcurf2bgFgTkZpfW+Ueqtq0i/ZpLGv3RS7hOcfUcraz/f4NzrDcJCAWvtAGdeEycxCoWtAcq0sEsCuwT1a+rYh6O0HIa5l3tUe1yiYwZOwaayWjH6aolV1CRxjXbF+n+q4RZjVDsFHXdNcfKXO5i/PoCCQxi6mJ2C2BTY5Sjla3RFcHzEIDGpKPcIrAZUeyF5DuZuDEEJuu8WF0R0hILUiMQuB0ztdPBaJOnjBtVugXr7DSQ+t/vC91H42RsxQij3CrwMROag3q7ofjCk2mkRnw7p2Osz8VIbd07TShfWGsQmoeWYIPFdbUxaO9rA9znxm73YRTC2lLi59xwPDK1lzdH3supUyNgdrVS3NegMLYwAUg9FCW2YuUHi5g2ELymsixCfDnjNH9/D333vNuLnNHVE9v4RslJCLEpvNYU5Ok31hn7iUyGl1Sar7vQZvd1GhIriWoFVAdZVcY7Er8wzuEyG0B/rmcAij0UZzXIXKKWuu2h9GvgUsHrxWj6klPrE4roMFzHqnefDWMGLG0+KByz1y19JLD3mJfz+0g8wHAdhiAt+7kvuC3R96CHYtQ2jZLHqzhJGqc6ZX1qF39+AWpR4Q+EPNrCGoozfomcH8+/dxfx1ASIIaW6tkPx+GrcAc+t8vFUS1bAwG9DMCtZ/1GfsFS7Jc4papyBzUuC/pkwt69D3KQcloLg+wdw2CHMepYFOwljI2sFJhvesIoxKTA8qr6zQ8pU40bsdmmlBEIFyjyA+rWUXg7jELQFSkNtroMwnDJ2zABMvg+77LVoPh9TaTertYJcBARO/vZPEhKLSo6uEpQ1+q4+1YJEaVnQ91GDoTVHMBlR6TaRjsup7HmOvcGjmNKU0ApJjISIaYeQX1rPqX+cYfWsXiSEoX9fgjf3H+eqeHRAN6RyYJzYWodSX4n3b72fOT/C57Vna7rEpXqtQpiI6A5HxEoaf5NybbP7l47ejBiTt++vkr4oSxHqJ/et+wpdsxZksUbmxn2qXhdnQ+sOjd1iYNYE7J+i5r87o7VFC30AmrhBtBMt1B12R071g+EHMBG5VSs1dZt37gaNKqTcIIdqAE0KI/7dInnSeUe+ti3mzsR/Ata7gSuDpArI/KCj51M7/aWAePUfyzGaG3pzCa41zzZYzHB7vonE0Q/X1ddq+GcH+uUmK3+yitMXHazWITFo020KqxQj29VUaCuKPx/HjkBzRNAzp03XCiEXbYz7l1Ta1LQ1avmBj/F2CZNRg+PUKEfdZ/WlFbMImsdcitBUzN8HZiTbSo9DMGFS7FeF8lJnrwS5qF5BbgPbPH2P67ZvoubdGELPw0hYdDxmUVwv6/vYkgTDw7riW3u8VGH9FBj+uydiciiL3t49SfesNzG43sKqaqiIyp4Vj2vcqnKLC9JosDEaACEZToAREZyA5HlJc6yBCaHYEZB636P78acimGfqV9XhZRXlDlu77FovphOIr+3bQfY9BccAi9dEYxfUx6i+t8LffvJ3YhKBjTlHtEAgf1n2mwfC/lxQXWpjeCTIqseoGmeMG1R6XSF5iNiSlt19H5pvHOP0fNyMHargHLU0/YUHL41Bvg9WfGWH6jtWkhhSxnQss7O++Mo+YEsuTl1zJDnpaKCAphBBAAsgDweUY9X5YF7mCZ4fK228g8dlHnnnDHwSWaYTCQoH2jzxC4eduZK7f59hUB2HdQvXVydwfZWEDqL1d+Ft9ImM6BTQ2BZtfMcSZL6wjPm2TOFfHmpng2H/NUVrrE05HkVaU0BVkTnvEZkOs77pEv7GXM398HWu/UMUqxen5qokRKqQF+Y0GyoLYiImXMSmul7g9VdSYdmFkDwsiCyHVLpNaJ5Rv3UAjB0NvimL4Ojum2RmQPGFpnYSrBonM1jn7tgyJUU3RXN3oY+RtzOYN5N9Rxa+4ZO+yERKyhwoImcWuBNTbbWrtAregKPZrBlCzCd3fnGDupV2Yb5zFG2rBnbLo/uJZJt62jmYLNAY8kIJKt83cNXFaD0J03mFmu0nmwXNIu4/Tv26ilMeZm/+Z66Jvp+K14qcFflIRnRbkN8WIfB9m3lRDBQaRUxHa/9/jzLzrahKjDYrrotQ6bLo+cQgRcem9x2PCi+Fvr2I+HKfWpUiMK6p9Er+vjUhBMnOdYO5UB1x95biDVtxBzwwFfEcIoYC/vQRp0kfQ5c8TQBJ4h1JKCiEuyainlKq+wNe7gueJDX/wYTK2eOHcQMvB09QDPBNav3ac7KcqTH5hUD+RJxKU1iiCuMTIetjnoigBbh7qOTg9nyOaV6ROVWj+SZnZL/dALSRA0XuvIjLX5PS7HBauNkl2lUn9c4ra63fQc1/I2K0JgnSAdA2KvSbhDWXW/H6DMBVh5vokQRx67guZ2plE5kI6HzDI3nOW+dsHSI4GlNaZlPoM7ArEx6HcD31fLzO9K4kfh9qtmzEbEhT42ZBi1CC3H9wFBz8Bky9VtHw7idEGrQ+Mo+Iuk6/I4RYU5dU28cmARotFca3uxOyypnMef2M3pfUhzv42knno/fI4Z351DdICdwGchEc4HKc0KLG7asy3R1n9TcXaT06ClPgxgZiMkBwWDE7/CiIQKEeBErRdNYvYk6MwaND1cBMRxKj26HMH29bRaIH8phj5awPwDboGehl6U5YwCl6bT9dXY+Q3Ax0NvGQUZwFe9n8fodtZ4B9HbqL+uS4iC4LhK/CYqWVqBbzIvUEveLHYzUqpHcBrgPcLIV520fo7gANo7ottwEcWZwHLZtQTQrxXCLFXCLF3dnb2hbmLFSwbVlUHFOd+ZecP90IuJyn5DDivLdD7Ox7Noku4pk6QlFhlE/t0FD8dYjWgMiBRFpQLUWZe4SNqHvPf6mHTu48hkj6rvmyBAntohvg5EztvYtyVoZExMAJF6AhyhwMGviAxAkVpnSI8kaSyPku9M4pVU7CxQhAz8VNK8/sPCuZeNUC1UzD2SoO1v/konX/+EB1/8RB+QrDmb85SGYhT7Yb4hMKPGdgLTdzpCtmDJgNfbtC6Z47siQbSBHfOIL9VErlZF4dN3pojsqCYvU7hvmWasbcGVHbqwqrIvCa+Q0JpWxMRCNx56P3WLM2BHF5GgqGw6iCOJrD6Kzg9Vby6zepvKuK7z1G5qg3ZnWPVu88QmRH4KbAqgiARIjubeD0etTvbSR/O03NfHfM/T1NeqwiyAdlTAV5Wk/ZVe8AqmsQmDcKES2Oth12C9gctQlvHP6KPR5E2BAN1huqtDDXamN7TRTMDhbVXrttTannLixkvqBFQSk0svs4AXwZuuGiT9wBfUhqngSFgI5dn1LvUOT52np61ra3thbiNFTwLWHWdhrhwowc7r/nBX8DzmAVYuRbMlqymfFgosv6X9mCdihKZNonOQrM9RKR9gqjmxGl0BZhzWsf2xH9K4CfgsTs30fEtlyAiSB6Y4uQHV1Pd1OTn33gXjVvKFDYpigMWpqeY3WZRHHCY36QNRveDPqN3CEbeoMhfowhG44zfLrFLWm3LaEK1R9D9Z48w+Jv7nqCTBto/8gjjf5Nl4mVaUzd0BamvP45ZriNdG6esCJI2LFb+Vjd52GXIHjUoHW7l2O91ECkoEqNNEsMGswfbsUYjWKeidD/gE5/Sco/KUrQ87LL6TknXQ2X8tgSNnE2sp4zhCaJzkmafhziUJHJ/EsLFazQMYt87zIn3xHn8XA9hDDInJc22EBEKTCckekbPUKZflsPwQoqfWIVZF/R+WzDyOsHImxVOWc8KnKLArkCtK4Id9UkNS6L5kNmbQpSlq6JLN9Wx3YAD/3g19/zPm4nOgF2BZssVedI0d5A0nnFRyxCe+WHiBXMHCSHigKGUKi++fxXwBxdtNoLmxXhACNEBbADOKqXmLsOot4IXOawGdOwJMR8QjN4Rp/9wgrBSeeFP/BxH/kuhwhBV0R7HYEbPKlf/t4cAWHjPTXgzJg1b4qclq78V4qVNEudqmCdHkMUSwnVBKYzuTo5/oIPpm7qQWQ97zOXv5EuJpJq071FY9RCn4LH66xWCTIQz7xGYMw71NovYqKDWC5kjhmY6vYwxE04EguBCrYMRcbG+maVFaS5+Y3H0Of7qdiJ5hVsMcRY8ahvbGH2lScsjArumsGqS7HGf4hoXLykY/bWAZGyO+qEc2ROK+ath5KdD+v9JYTYtvKRBYaOi3GcRm0pSXB+SPmFg3Z8mMy6RtsAed7DLmpoiccQhdCTHf7ef/3LHl/njL21j9Rd8vFRA/EwBq5lh6gaTtR+pcOy3XDb8dYXbP/0IxSDGp49cj2FKJmJRMCTWrE1ho0RITRURmRNIC/yZKPNbDboelsSHLKwamLsW2JSb4egXNlDt1sF1twCZ0x5uyebMJb/VZ/m8LJNF9MU+E3ghYwIdwJd1zBcL+LRS6ttCiF8BUEr9DfCHwD8KIQ6h601+Z0km0aUY9VbwIsb17/lz8q9s4pyJkBgzUQYMf/Bq+v7PIcJy+Yd9ec8MP2Di166j88NP7Xyzn3iY7EWbO+gCqnBRlEbVagDIM0Os+40hEAZmIs7xD20CU+F7FjPXCsJ2iTkTpXVLlcZ3MriJMpH9LrO3N4geieDOm7ra9XKzGWGgms0n1TrIRpPcx5fITgoDo28VN/z0QUZfrgiv3YBR9Yg0fFbdqRPt6u0WtXaTSq9JbFripQTXdE1wcLIbp6DFZ8w6tH/DYfo6gyCmKSbCeIhdtPBuKfGqVWe5p7QNPxtS2hbyhi2Pc+c3r6PrwTITL0tS3uSz6hsFEqdtel6fJ3UGCusihC4U1uaIzimyxxWjb+xg8B+rCKX4zIfuoLJaEHYFhIBTNmjZLSiugaAtxJ606dgdMLvdInQMMEI6Hw1ppkzMBrhFRfDNLHvXZogJSJ9W1NoF0VlJrcMmMd58ng/KIpbp6vmxNQJKqbNoseSL2/9myfsJ9AzhUvsf4NKMeit4kWJ+u0LWLRAw/5ImkSGX2CT429dh3P/YczvocgPMVyAIHZbLdP75Q89qn/OqZFZXJ8Hk1BMrFq87LJcZ/OXd2iDEYxz/4y1s+HCNE7+aYC6fgNUKxuIYKTAnXBpb6kSORGndM094mXMCT+rsURIzHiOs1i6sN+Mx5m7phreGyPoE4oHHwHGYf/e1xKdD6i0mKF3xG66rUwoECDj+xfU0N/vEC5qOIjEG9VYDuwq1/oB1n/Kx8lWUZXEym+JosoPEKLR/tsy5n8jw9fAaLAfOvC1B+iR03WWihKC+KsGnZnZx2689xNe+sov0GcXM9YowatD3qRFamh7zd6wliMRZuEpyw3UnGf+zdRTWmjRysLBBaNeRE5I5aTF2m0HrAc0eavomI2/wSR8yiSwoAlfgFiV2VYvYOCVBfEoRWfBpZmyCyFNJ8J4LlBLIZVQM/9i6g1bw44dV35WUuy1K6xSZ3S71dihsVJh+hPT9z/GgP6gMo+eTzSSMJxuAyyCsVBj89UeQwKb/0cWx/7aK+EAJ72ia/s/PMHtzG3MJh94/fZQwfBoTsFTv4KpBWCSQ4/BJQNNljL/3arq+X+HEB1aROrsat6AIXaj0CFLDIUZgUM8ZrP1/+rxKaJqJ6i1VOr8eY+Z6yfp/LDH+igxOGWotgCM59bMWXXe34FQk/V8PGErkSMRBjE5i1TJ4dohTsmlsbmAciyAk1PvS5Dda2LUURz9+FQP3TVHdlMNomiCheGMv5R6DyoAkuqrE7b1D3HvnNqyNYHpaG6C2waP3KybxkSbTN0WQ8YBGi43VECy8pIEpFHbVZvZaReKcoPiKBp2fdymuMYnNhgipmLzJpev7Tc7+PFdGT4DlZf68yCcCK1TSK7gyuOmn/jez11hUe6D1EDTadKZQ+pRALO1br4Dv/gXB8zE2l9r34rYlnw03AkIw+Mu76Xnrcfp//xEoVWj55G7Wf2D/k0b53muuv+x3ZjgOIl+CkUkI9PGt/j6M7k5CB0ZvT5A5rvP8kyMNTfewx+fsuwxSZ2s4JcXY69ppPVhmfkdIEANnb5xKj8CsGZx7c4bEhMSqK4KEovM7FmbVpLza4N6//TuG3gXUTCqDAeM/v4nojCL9aIRGm5aCtOqK+S0Gg39wlPJGn8o/9BKbC2kMtDCzzSIxojOkpAXVfsmZt/0tSgn2/e02/LQkezJEWiAtiJ52sCshKEUzA93fNUkNS+ptBtn7I8QORMlvlcQmtKBPOBdhfqt2SVa6TaxaSGpIUVzjEj98ZQjkgGVRSS8XQohzQohDQogDQoi9i20tQojvCiFOLb5ml2z/e0KI00KIE0KIO5a0X7t4nNNCiL9crMO6LF6k/8gV/KhhfquBNMHL6jxwswbShUYL1HIv7mKZFxrCerKmrWw2CKd14FmFoTYQQYCS6oJ7SZgmKInzrT2XN1Cb1nHyg/1cf/88J34vgbWqG5mO01zdQmpEgdJ005H5gPktUUbeERAdK7H2kwGltXFKAzrDJkg6tO3RgVOrAUEUEBCZhfiER9svnmPw76bJPjBK9rBBZZ3Pmm//Im6qSefDgrX/EuClwEsL6m0QmRGEUeBn5/DTku8e3IxRM8nun2fiHR4z2xzcBai3a5bQ+a0G7RvmuOqj76M5ousjEqtLzFxrYviQHFHEJxReymT0jgxOEeavMigOGARRaD1cJTatyB7Wn/0UIBTNFkmtR1JZBfObXAqDgvw1kkbbFaKNUAIll7c8C9yqlNq2hGLnd4G7lFKDwF2LnxFCbAbeCVwFvBr4qBDivJ/rr4H3oiUnBxfXXxYrRmAFVwSJEfBTisxxAz8GRqD/vEKBvVTD4yKit39rEKb5lPszezqx+vswk8kLbec7e9AzgzC/8BR662fE8bMkxgSfPbGDDX9cQaUTNDvjnPlpg+kbFblDAav+dYbYoXEWtoaomsXx92eY3BVFGVp/ILe/zMy2CHZFIW3InPLx1jZInYXovGTsFRGKf7EamY4x/pN92D85g5tu4o44BEMJQgcmb4pg1bTBb98f0MgpRADTJ3MIX9Cy38ZZMDj7PyIwEcH0obgloOshn8nX+Jqg7pvt1LtD7JLAumOO2sk0QUJSu6HG7K0eQURQ6jMZePUQ5XUSLyux6tD5SIP8VXHsqsSPa/eRdMDwDeySQe4xgV2G4gZJGIHWA4LOR6+cg2ZZdQLP7xRvAj65+P6TwJuXtH9GKdVUSg0Bp4EbFsXoU0qph5VSCvinJftcEv92/40r+IGimdZi3o0WTT7mFhULGwWRl82xsPnyrpFnxFIpw6VtVwJX2BgZsRhTv3ajlmRc0oZlEZwbRnS0Pemc8uW69MXo7Vpep7+I84ZGRFza/+oR1v7XOjLpMnFbC3/1sb+kvatA+x7BzA4LCiXwPKyyQW63ScsBk+4HayysFzgVhfBDWk4FVLsMssebBHGTNR/X6Z12VZI+rUg+Pk0zFyX1ukkagUUwEUM6EKRDyn2C7ImQ5Kik5bhi4iUmYTIkdU7R8ajA8ATtDxU0Id1kjPa9itRwyMAXJdO/1KDtXodKn6Tao+/NurpIqaKlMclqegflGygDymskY18cwOyqQyIgNic58w6bcj80WkxMXxe1NTt9eu8OyZzS9Nstx0NSpw2sGkQWJPNbrtDvrpbpDtJWYNf5otbF5b2XPiLfEULsW7K+Qyk1CbD42r7Y3gOMLtl3bLGtZ/H9xe2XxYoRWMHzxuav/HfYVaTeE9J7Tw2rDvGJAGcB+HKO7ucSFL5Ix/ZC29LPzwKG4zy18QoEnQ03cqEoTtZqdPyfh55EXKeaTcJh/Z+UI2NP3vfe/bp98snqXU/axnEu3LfhOFhtOZqv2sHQn9wIYYgwTRZ25JjemeSqtx/jDV//INXvt+FHBemzivlXrUV1tZHeMk8zI6h2QRC36b2nSX6T1hyutpskR0MarTaVHoPJXTqF0wghOhegXIdzbzAYn85SOJeBUBDbkidzyEJaEMn7xKZ8mimBkAKzarKwCZQBgx+fpLI2ibQVsXGD6RsF0akGlR6b1FcTeAlB58PQsTuk5aCJ2pPGOBan5/4QY8rFORIjMm5T7wDlSsr9CvtgHGFJyj3GE8VoCorrJcVBRdfdFoW1FoavMHzF6GsUdgVajklGX6NIDV2pmYAAtcwFHjpf1Lq4XEyhA8/MsHDRyZ8C9TTtl8WKEVjB80bHR6P4h9Okj5qc/XUIYjB9g40RQGw2JHX4ciSyzxLPo9NeDqPopfRznwnBzs1Yo5egKxEG1bftxGxtwYhGMBxH6/Qu3oPZkr0wE5C12lP3X3rd50VdXBcVhkRHigQxxcn/sZWTf7mDuasFnQ8U2Pe9TbjzJu37fOyaIjncJDoX8L4vfY3fGvwePd+ao+1AiLQFw+8N6X7QZ35bmuSYj+krvKQAqWUYO/Z5REfLRGbqVAczOO11EskGqTMGkVmB+fUWmlmwS+AnLcZuc6m3gYxIREeDtZ+tYNUVZ97Txcw7GoQx7Z9vOQSx/zVFaAtCR5AeDkCBdDT5nV3SnbWXNIjMC+LjiuSIIjkMq74pWPuFmnY3zbpUV0tWf0fiZUOkrUXu06cE+c0Cp6iw6orWxwoM/rNPcVAbq+67DeZ2XKmYwBPF6U+7LPN0l2FYmF508bD4OrO4+RiwasnuvWgOtrHF9xe3XxYrRmAFzxtB3CQypwOKsUdjIKD7gSaNHIzdJlATlx/pApd3yzxH+ofnimfjkjkP4779BONP/Y+ZqSTxL+y+UHl83gBY3V2A5igy7jvwlP0uZ4isrk5EWyuqWkdUm6z9fBPRXeembSeJTsGJD2oXSushiV32EVIhHYNzPwkf+NbP8MWZa5m+JcfCBpP4qTyRAzGcQpO2r53E3XeG6WstovMhbknRvs+jnrOQjsWJ98aodlkE43H8/RkS4xIvq2i0Qu5QSJCAiV0WzU4f6YLRWSdccGi2Rai1G4Rra2QSNUQg6L5PaQP02bUoC5yKxEsYROd8YpMeqWGPzGmfyHzA5O0hbgHiUwGpIY/MyTpBVDC/JU7r4+DmBXbZQFoCs24QnZf0f71JMwPpU1rVLDrdIL8tw/gtUWREMrfVxV0Iad99hRIVlu0OeubzCSHiQojk+ffo+qnDaILNn1vc7OeAry6+/xrwTiGEK4QYQAeAdy+6jMpCiJ2LWUE/u2SfS2KlTmAFzwu3G29j9r/swvB1LCB7OmRui4mXsUgNQbVLV7dewKXy8Zd+fh4unx8GlspULkVYLC6ug7BSvXA/qlTWMo2Bf8l7vEAD4UY0jcViADmYmuHc/9xJ6+FOYjM+oWPwlo37uHN4I7E5RTXvkD2hSJ4pUe1PMr0TEsMObqqCF0R4bM861CZJfNSAIKTWIzn9UzHWfbob6ZgEUQhdg5Z984iZPBEhoCXDms84zG636HpQYTZD5q62CDqaDP5DkeLWHIkRMH3FvGURmwRjOE5xUDH8euhfP478eg+iEGVVXgvZZI83KAxGMD1FEBHEpgOaaQsjBKfoIwKBnzQZ+Iyk0SoxPYkyBaFr6UyhUZ9yn43hQ3xc0cgYJIcE09dDkLFwZ8A9ISmtMRl7RRwvrYgMFlEjSbInfJQAp/zsjf1lceVizJdjWNgDfE4I8Ytomp23ASiljgghPoem0wmA9yulzt/Y+4B/BKLoioinrYpYMQIreF7I/9IuTE+LibceCql0mUgb5jfr9D7TWxwFn8fFHd/SWcAzjPytDetQs/MXmD5/6BDGpWcPwsBMJQmLRf158Z7ky3dgTlcQZ849bd9huBGM/l6UYcDYJGG5jGFb9P/+I9Tecj3umTnG3tzDV+7ciVOESrcml5u81SdzMKDWbhIbg2YGxLEEtOqsm4HPzHPmZ3Ic+612rKJB37cbvP2fvsv+Sh/h721l6M0mdjlNZGiMwk9cQ2QuYO4am9WfOMW59w6SPQ5dDzUI9xqc/al2mm0hRlPz/4MWq/Hj0HoY6jmTsUo3ZitIQ9BMWeQON2hmbZKjPsoSlLstvLSJUwzxkyalfpfksIfhKcZudUiMgLQcrLrCSwjiUwG1dovonMSqCUoD2oVU7wqxiybpoxbZ4z6z2228rMSsGWSPQ/YrNkFc02Jn7jlD6aVrr8jPr91BV4Y76GkYFubR3GmX2uePgD+6RPtetCLjsrDiDlrB80L+lgaGr/n1q50mickQpwSRvPYtdz7SePIOF7t+ljpPL7V+SYA4OHEakc1w7v/bpbNufsgw45e+BmEIlO9jRKNPtJkm5gMHEYXSkzh/LgXpeQQnzxIeO/lEvOCqdaAksS89yrmf7kEoHUwVoU6JbDsg6f+KYGFbK+Hr8mTOSqSriMwBQqfvnv7ZHIYPfV+X9NznY5Ua/OVf/wQpq0EzYxGZNvGTJrPv3oZ61xzX//E+IvOw8Mk0bQcDTF+xsD6Cl7KIj4NI+SSGBalhSXxc0MhBeVuTaqegvD4gdxCauZDy+pDQhfnNEaqdJkHMIHAFRqCwqxIjVPhxQXReGwOrHmKXwC1KIgshc1cbtO6eR4SK6FygXV22prYOIxAfMfHaAkQI535C0HtXmc7vQ+5xhRKCc6+LMXmTQ3y8QX17P+XeK9XtiWexvHixYgRW8Lxgn4sQndN/SiMEL2FolkcJucc9FtZHntj4fAd/uY7+UrGBi2YHwZkh+v/TQ08bTP1B4XLsqCoMkbUasr5YICGMC0FdVa48qUbg0gd44p7PzzTkY5pEV5gmHXt9EuOaTdOuQtsBPdOKHZthYYPAf6QFIRVmQ+AWFalTJr13hyhTYZdh+jqb/CabiVsz2BX48lduZuZaA8OHua0G9TZYeKwNXxp4SZjJJ8lvsFACInmJXZfEZkNWf9okMSFx8zq4mxqSpPe7GD60P2TiJQXxcyarv6Vo5MDLAAKK/aZmG60rlAH5jQ7KAD9mUOmxWNgYwSmDU5ZY1UAbMlMQOTqOEWjm08KgoNYOmZOKeqdi3acDOh8sIJyQRkeUzL1nCW2orAbD15KYw6+NMXuNjXklNQrVMpcXMVaMwAqeM3a++89BQKNFaFH1FNTbBM2Mfj97jUO1l0vHAC5nEH6AsQCrLYcwzWeXFfRcaguURNb1jOiytNrCuHQa60UY/60bscs+qZNFEqcWaOTAbEoaGYP6uhxuXschy70mflLRunsOPwEz27Tn1ylDdBbK60PKV/ksbJWYzfPVwYr+b1T4xXd9G7/T56uHrqHWpRDjETr2NpjdbhKd8xCBws17NFotRKhQtoESUGs3cEqK8haf6GxAfDLACGD6Ok0q6C6AU9IymlZdIgJdMewWFU5ZEZv2iCxIonMSt6CwywHS1uylw29uBSFopvU52/dJwijkNwtaH4ehN7jUViXY+KEK09dalG8aIIwsaibnQpLjAWs/dIzORxvEp65QTEABUjzz8iKXl1wxAit4zkiMNui9q0loQ+aMj+lBZVsDd0GPvhB6pAY8ufN8po506azgBawuDubyOkj7DO6Z8zCi0QsuoEu6oy5zrVZvD1ZXh64puAzMeExfhzAwE4mnHto0mX/vLsIoBDELpKQ2kCE2AbUOm8wZjzCi2T6VCS3HPOyyoL46jZDQ6AoxfEGlF2pdYFYNEkdt4ud0EVXHnjqJcZ+x2xJ85IFXYrgB6b0unY8qOh+RjL08QnQxyWvyJpvp66NEZ3zsqsSqBFgNyJwJUAISR23qOQuEoDIgCSOK6JROHKi1CUwP5q62SJ0o4BZ0hxxEBNIyqLcamE2JXZM0cg5eyqLr+zWsKhz7z6uITzSwqj5hRNB6WIGhiCxInILAKfgcf3+G9FlFqc+kuE6RPekTGzeZ32jhbV9Dca2L4V+hofkyVcVe7FTSK0ZgBc8Z81uiTO10MUJdsRnJK+IHIzglwID4lKLaeb6YZ8kI/3L+/8utX/p6JY2CkshmY9mzD9loXhjJL3VHXejcL3EcIxrFW9cBgLAvn4cRVioXMobCSuXS9ylg9bcrRI6O81NfvJtauw7CT98skZbuBP24pm6wagFrPjVNcY2eXbgzJnYVWo9IzAas/+sponMKuwaJCYkzWWJmh4MIIHHKwj0WJT4lKQ5oOUwhIT0cMH1dRNNNHPaZ3e6wMGgzsyNC9kSTuS0W8cmAykBItVMgLUFk2gAl8DJ6FhCbU5geGB5U1qWZudamkRU4Fc31nxoJMJva1XV+AB3EbXq/OEL7IwZDb4gx/Jo4tTZBtUMgQkEzZdB6JKQwGEXZOo6QPeETZAMmd9mIEMIYTO9w8eOCUt8VzIf5N+AOWskOWsFzwo73fZggquX6TA+shmLirU1avxOh8NoaHf8SQUiFVbso+2dpCuilXEGXiwssfX0mLCPN1Ew8s+KZ4ThPKjKz2lqRxbI2HEsgm41Lp74KA6OrA+f0NMHqdqyzAVx8zstRWCupZymLBHNTv34jhgeEipnXDPAXHx4gMRcQnW4Qm41h+NotE5+WxHcPMf3mQWqdMTof9QmmDZxCwPjLXeyqRJkGw2/rQigtB2rVJdOv6NDHN/QsLnMmZGGD1hwo92q1roVBi8i8InusxthtcRJjuhir2p9kcpdL7nBIfpNN4hy0HfTIb3AwfO1qsmt6tB8pSOrtAj+hmOgyaHlcUbijhtWI4xYlzZSJSJpIC0r9gpZjkpkdNqsfKhJEV+sMpIyk7SBM3AJGQzBzo8RsGFgVMIsWI68LEbGQzjtdTE8iQkVxwMRPQmJS4UevVJ3Ai9/VsxyszARW8JzgRyGaV5hN/b7WZtL1JZdqj6D9sxGUKSgOWDilAHHDVqb+/ZKMnsuN6C+XObSc0f+S2cJ5Bs7L7ScsG7V2Ndbg2qcwfC7FxVXGsqw78EvGEC7TkatEFJTC2H+MYHb+SavNTAYzEcfq6b7ktZ6fGVi9PXQ8UiU95FPti1PtgdhbpiiusZi6KYG7EFDrsBG+JIgK/A291Lqg9bDm0S8OmIy+0iU2BfMbLdJnFV5W4RRBGjD2SoNKD5Q2B8SmFdF5RXm1SbUvxClDuV/hJ7VsZWWVYPwVccII1DoF46/IMr/Zwq5CaAtis3rbyZscjBCarTr4G8lLsieaGD7EJjTPVPtuPUS2jsapdcL8FoPorI/hK2ptAqumEwxiU4rTv78FP6pZatd8oUml2yA2rquX2/YaZI9CozvAqgniZyxihyIU1wgWNhg6AymA3CFJcUCQHHuGwPxyobiQBPG0y4t8JrBiBFbwnGB6ELgCZegEOKcsmdopaN/r4SUNKt0GucNNoidn8bIRvAyIyLPkcb84NvBMcYLFdmEIHfA1Lj1KM+IxKmuT1Na3Yq7te0Yjcz7VU9bryGbjSbUBWhvAeML4XHQ94ePHCMYnnqCMXoKwUCCsVHXF8WXqJ4RpEoxPMvy6OGZDkvvAEI01Hurj7QQxiM4p5rY6zG8VTN4cpdotmLwpSrNV4qUMDF8SunrE78chiIMXFxi+IIxAkAAnr2MJRt1gfqt2s1S7wZ01aabBqgmS5zQdZs/9DZQAdx6UAS3HfU3aloVSn+byt8s6LiEtiMwKar2Scq9BI2cjpCI+E5I9oa9v7hYPL63w40qneL7JxGpIlAm5Qz5BRBCfDBCBdilFpw2mr48SOpAYVzRbYf4aTYG96SNlEiPQ/WCV2iqJsqDr+03CiEFsRheetT8W4KWujLIYwLPgDnrRYsUdtIJnjduNt+H8zE0UBgVWA0QA+c0GvXf7+AmTSEGSPtPET9gE3VmMQNL9gEf9hnU4396jO8wlHfQlA7NP5/p5JreQaSLOH3fpMExJzKs3Ue9JkNo/hZqbJ6jWLnu889XAsl6/rItpqWtIXZx0cn42ouTlKSkudy8XpYi27w9xR/IMf2YtfUM+lR4DuwLpU1Uq3QnceejY26S4RnP4KFNR7TYo90VoOxDSTBksbBQkz0EYhcQoBBHt+jEbWncgcc4gfS7E8BV2zcKq647ZqUj8uEFiXFHrcEgPKZopTT4nFJiezsdPTOpRf31HnbZ/jdBMC5q3lMnclaS0VhHJC6RjUOuA5LDOCOr8joPhKyZeG2ANO8TGTGa26a/AS5lUegXStuh8NKDeajJ3nSQybSJCKPUJgkxA7lGT/EZBvSVLab0kNWyz/u9LeO0xxm51yZ7UM4vI0ZBql3XlU0SvxDY/RKzMBFbwrFH+qZ1E5wLSZxVuXs8K3HkIIwaFQRMvaTC7PYZT8jn1XouRVzmU+hyqXdaFNEgl1YXOXxjiScsFPF39wMXb6IMCOoAr/eCSo291/AyxI5Oo+TxqCTnbJQ9t2ReObaZTz5pg7kLK57MNZl+0vbBsYqM1jv1mO+nhAGUKRKiorFYMvSmB6UH7AY9qt4NdV4RRWPcvTXruqZIYUeTXmyTGPGJT0GhFGwmhO3CjqX+/toMByoBau8nMtRbR2ZDs5w+gTF0EWF4lCCKCZtqg1C+QFohQZwq5RUlkHirdgshCSOqBKI2MIHPKI3pXkloHJIYFRgDSBLOuBWh0jYCg3GuQe8DGa5EEUZ3GGkZh+kaBuwD5zTD6KsHcSwLaH9FTz+SootEhISIprRFE5rWa3cCXdcC6si7F6CttEqPgJQXSAS9pYgTa+F0RKEAuY3mRG4GVmcAKnjViMz7VLofCekHuoCT93RNM/vQmJl5ikD2Grv6ck4zeHqPtXsXMrpB6m0X2pGTql6+j46OPAk/MAJRUT3XdCIO5X9mJ2VAs3N5g9SdMxm9x8JOS3H6BXVXEv/AIZiJOWC4DOm3zkkVkS+IDYrCf4OjpZ5xNWL094HmIpkdYLBIWCsv7cs4LvyeTqMHVsP/I8vZ7GqjAZ2Frgpu2H+fo8U14KcgdCpiLCVoOG0hTUO20CSI6yyYyJ5h4SYyOfR5td55j9tX9zOxwic4pgpggMaKPK03t0smc0uml5/XQ7aIOFtdedTXNlNDi7VHdkUbykmbauKAXkRqC8mqdpWR6MHu1hQih0aYobrCwqk/MOoTURqjrEY/5qxzqrYLorBa7z54KKRUszCa69qEBZlNgeorEqCAxISistcn8yx4ySjL1azfizgqaWUn3gz5GIFFCECRMzCZEpxtkTkQxfEViwqPY71LtNuj8+IEnc1k9rx9mma6eF7kRWJkJrOBZo7DWxaor/G6PSpfB8Q+tpd6h/7QLVynsqqKRNbCLYDYVRsMgPaTT/to/8giTv3EjZiZzoeM//7p0dmD1rcIt6IBe+r4oVjWg89GA3GOCao/A9CTy5TsovGELlXfuhF3bUJ6PvGXHpS96sdMPj5y8vAFYOvOoN5CFImGpvOzvxUwmLwjKyFoN+VwMwEXZQkY0CsKgsBEK74yD0P59oWDtZ31S55rYNe1DTw172DUd2LUaUFrtcOK3+imuE0gbyn0Cs6mpFpSlXUHpc5LCOodSn0X2VEijRVd+25WAaqdJvRMWNgoUevtqp4HVgFq7IDYFxXW6XTq6gzc8bQzssiB12sBPSgobJb2fHaKZFsQnodplYzYgfS4kMSFp3yeptZnI9VX8lHZBNTpDGqt9yqt1fGLiZeClYfy3rkds2UBiQpIcVaz5ez0DrbfZhFGD0Bb4cTj1sw7SErp4MW0RzYekzwaIZRTkLfunQv8Oz7hcsTO+MFgxAit4Vlj3px+m3g4zb6+TOORgv3IeN9Wk7TFJyxGFdCXVTkGkoLNEpCVY9V1Jrc2gmTaY+fWddP/fvTR2DGCkUwjHuWRMYPStPdg1PXJtpmB+awy76JM5WcfNQyNrErpaSLzUZzC/Jcbo71zP1I0Rzv7pTUx/cNdzu8HFjKRgfv6ZNQguclXJekPfz0u3P4k36FmffwlkvY4Zj9H1kKR0fS/NrHbn5NdbzOyIMH1dhOhcQGFQoAyBXQlwiyGGD6U1ujMWgR5Z99zfQJqLRVsd2hAoQ3euhqddQZE5sGpops+Gwp3XvFBWQweW/YTePz6tf7PoDBeOr908+pgihNiMpOVxg64HYeTdA3gprUBnNhXNFshvMCmv0s9FNC+JPxhHBIIgphj85wapx22ko40KAvxMSHV1yOzODKnjBaQlKPW5VNtNGlmDwlqLWodJo13izFmUBrTBi000mN9sokzB7Fs3Y3Z1Prff5im/1bNYXsR4RneQEOLXgP+nlHqRUDeu4IeJMCpxigZ+3cZfG7L2Q3Gqt0YYvzUkedqgbbfB3HaJkAb+5hpePkZpjUX7/oCROwRG1iM+sYP4eJ3KLeuxKyHuw8cJr16HdAwqPS6J0QZuAeotBqGjR6Z+DGZ2RIksKLInmozf4iKUxfxr6mS/G8UtSGoduiDK8ASV1YqOZ3tzSj6lNmC5tQlmMqndDH3d2EdHdMD5OeJ8QNq8ehPyyEkW3rxVV9ZagkZnSOawSXReUlplYISanqP7AR+rFlygcEiOhZgNk8oqyJ5UFNcKZrdFUBaYVYhPQq0dam0GIoTWIzWarQ5Owac0ECE+4VPpsTGb4FQ0wVu9AzInFI2soLBOM3hKF8prQxJDJlYDzKY2GOb5eLkB5R7jQgW54Wmj3bHbY26Lg5/QbYW1JqlhSaVPEGQDht4YJzqjDZJZAy8LrftN2h6a48T7WshvTbPquyHS1NTQtQ4bUQTDU6TOhTjFBma5gYw5lAdi2FWodJnEZkLkRam6zwcv9vTP5WA5M4FOYI8Q4nNCiFcvChWs4McUVtWgNujR8zWT1v0mp99loUyITJsX5r12d43y2hDjbAyUzu2udFqYNYPOr7jMbzEorI9hNiRCKpo3bcRL2dTbHP25xSFzsk5qxCN3qIFTguSYRC6m9M9d7ZIYhcJ6QfyRGHPbFWOvlTT6fUIXmrsqyHTw1IDsMiqOl6NA9iScdzNVqhjdXahT5y4ZkH5WhwxDnUE1V8Ds6iT1qYdZGLSwywF9/6q0KLwlaDkZ0nIioNKvYy75q6IU1rr4cYPCGpP02SbxSaj0CJyCPrbRBLekQEL7gYDWIw2yp0Jmt8WITtSorIpQzwnOvUGPD/0kZI+UCSK66Cu/FUwf3AIECUUQU8RGTWLTCj+uZwHlfoWyoJ4ziE8GNHM62IuEWrcuMOSiwHT5mib1nEH3AyFmxcQt6BTT2KTC9HUaaxCFsdfmSJwzMBsG5R7NSCoXA+V2JaTeZmA2JNZMCbFQxhydxfAUkbwePEzdZLDwju3P+bd58g/Fv4kU0Wc0Akqp30er1vw98PPAKSHE/yeEuDKk3Cv4kULLUQVKUFxj4pQl7oxmlzQ9zRI5e70k9a04xEPCqKL1cB1laG576Srymw2SI4owImhmLaSlR67FNTZWQxI6gkaLwdzVUYoDLsU1Ll4Cyr0G3Q9WCaKC7Amf5KhHYhji05KeeyF9yCb3fQtlQNc/uGT22VhdT8wFLhSQXUksBpyFaWJuXEc4NoH0vOUHkZ/h2MHEJMHYOKB5eYprHCKTVZpZQT0nKKw1WRi0SAyZ2DVF9niTepse3cenFXa+jr9Yn+eltSun3qGzeKQNdjkAAc6CR3xaIl2LWrsgeypg1fckxXWC9FnJ/NVJonOKege48zozSAmITQiSZwVWHZoZgZfVbLKRGQFKF5cFUQMR6A7dqYBT1Ncwe41D5kyAUwYjUDgjLgubJfObLboe0DOJ6pqQcr8gdMApaYOUGpGIEKyKIDEZEp/08BMmsRmfxOPT5P7mEcx7HyMcGiacmsFb10mjxcSPCYZf6xCZERTXXsFO+Qq6g4QQphDiMSHEvy5+bhFCfFcIcWrxNbtk298TQpwWQpwQQtyxpP1aIcShxXV/uZxB+7JiAkopBUwtLgGQBb4ghPjT5d3eCv4t4Krf+TCz1yky+23q7VoZquWowilA7aoGuYMhREOCiB7mdTyqOPfGCN33KtLnJNFJg8ZAE6uuMBsKpxyysMFm/OUOtS4orbaorBJ4KUGlT9FoAZQeeXY9VGHm+jhBFGqdlh5FBoqF9QZTN+rq0mZKYPhQGLSRNgQTkxeu/ULc4QUwBCoMCY+ffmaK6GeLJbOW2e0W2VNN/HSEyipF62GPlhMBQUxTNAQRwdRNmrqh9UiTyJyP3xIlcybAS2kfvV2G+AS4Rd2hAohQEUYtEucqNFsd0mc1CVwtZ5I+rUjvm8IIoJkVJEY1dXVpnSSI6wC1l9Z+92YWhC+ITyoiC4r0OV19HLqCZn8Tu6awGlq6MjalpUiLAxbNDHgpQfeDPgNf8Rd1KBTt++us/RePIKqorlbagHVJau0G0obUkCI61WBmu0voCvIbXYLh0Sf/NGGIvf8UIlR03DdH6+M6a8mqXqGf58pXDH8AOLbk8+8CdymlBoG7Fj8jhNgMvBO4Cng18FEhxPn85b8G3oseuA8urn9aPKMREEL8hhBiH/CnwPeBrUqp9wHXAj+5rFtbwb8JVPtCIlMGhes9gkxI6OgRaRCHZLrO+OtD+j4vWNjuY047LGwwiE4JmhmD6HST5Kii7V6HWrvB/NU6aBxEwapCfFz7sdsOBMRmFIYnaLbA/DZNXja3LUFhS0BqWAc9F9brLI/IPLQe0sHK6Lyi9ahH24E66XMhC79w0xMXr6SeDbwArKRGLHbZ6uRni/O1COcrns8HmJWhCdBmr3F15xMqCBUduz3tCqlJnKJut2o6PjC/1aWwziI2pYPAyTEJCuKTIVYVgrgFSmmVr4EE7ryHXQ0JYgbR+ZDkmIeKuTgViVNSRPKSeg4yxwwypyWVVVo8KDIHKGjfLzFCyJxqYBd9mhlBEBWk97v4MYEfE+Q3OVoHoaxnh+fJ5CZeYlPvsEkN+8TvPIQSMHVTlNiUQNoSP6VIDBl4KT3DkJZA7DlK798doeU7p2j76CNPfIdLfouwWqP1sweRp4YQCjInBZnTV1he8grMBIQQvcDrgI8vaX4T8MnF958E3ryk/TNKqaZSagg4DdywKESfUko9vDhw/6cl+1wWy6kTyAE/oZQaXtqolJJCiNcvY/8V/BuBVTH1KHLCwSnoP3GtVxIbM2j6Fpm9DhgBq/9VkN8o8FLQsS9g+loLJaK4RYlTURQGTTInBXNbTOp9PmbFxLmxQPnOFvw41DsVkVloPRKgDIERBoy+0qD9+ybjrwpJHdcjf7ekR5pBTOAnIDajxdUNX2KXAlDmEymXwrhQj/CUyt7niSspcHO+QvjC66IwTethiekpnIKP8UiI0fQpbU+TGvIWc+T1/mZD4ie1gXSKOogbRCB0oLzKQKG1BhptUK2Z1LfEiE8qojOau7/S7ZAaqlPrimA2JNMvbUUZ+qustwmii2pelW5tTJXQVBSJcUWt3SAxHjJ3dRQjALeg9CAhBsR1KmkY0TEB6QCGNm7JsQChLGpthq5S7uvFeOAgrenrmLjZIj6i402RBS1hKRT4UYGwLWSlcsFgXvh9L8o2q71yC37CZH4rdD4SEkSv0EDg2WX+7BJC/NySzx9TSn1syee/AP4jkFzS1rEoHI9SalII0b7Y3gM8smS7scU2f/H9xe1Pi2c0Akqp//o0645dbt0K/m3h2n/3YfydPpHHTIo3N7GmomRO+jTHLWrtEPlWkvltkkqfTtvMnNA+6Oh9R3EHriaIg7QNqt2gLIWXFPgpRfK4TXRWERxvobJaZ5d0369dNkYIlTaTxIRP9oiBU5G07rWYf4lHeq9Dqc8gOSop9xjkjgR4CQNp6d6wmbUQEiZ/ayeJcUnqc3t0HcLl6Bte5HAXAoKYwemfMVnzGQPTXgy6tuhYSiNrYlcVxTU648ZLQd+3KtS6o1S6dCcqTc0VlD0uCaI6oOoWIDobgCmYeGmE7gcbVHsi1HMGVl1zATUz0HJcU0c0cnr0r4SewUlHu3bqi4RvRcfU7g+l26SlXSJBTLukOh/1yG/S7KLVNQFrPh8yv8klc8an1G8z+qaQ7J4cueOnsWoharVPpdVCOBIvrbmn3Dx0f/hRXbuwcS3y+JknFRyen02pMKTxxusxPEnqs7tJfVZXX18xt50SiGVoDC9a6IeUUj91qdWLg+kZpdQ+IcTLl3HmS51UPU3702KlTmAFy0Lx9irRMZv8JoP4/igoaLRa+FFBaV1I+OY8RghhRJHelCeSD0FA+VVXIW094nPKivRZRcsR3Um171W4CxCbCwlikDqrt6t2mgRRzRM/f23I+MtsjEAxv8UgiEDfZxb1bAc0373VgJE7DKJz/oXccbcYYFdDuu8tkxyqX5Lc7UcGwmDuagenFDD4yYBGzsLKV5naaePmfc0Meq5JctQjOeLTsafO6jurjL8swcJ6c5HfR3+3yoDSagO7pjmBRACz22wqnRadj3hYCw3EItWBnzBIjmvpysmbdaFZfAJS5wKEhLYDPk5Js5G27w+JzOtRemRekRrVkpNOSY/8g5giNSKRjiZzM3xIH7UorHNJjmu+ouisxB11sCsKa90AZ95pkPtmhPbvW6AgSEhCV2n68vY2gCcZADOTofaG62i8ZvuFDK3I1/bgfHsfxuZBvb3nLVtEaFm4Mu6gm4E3CiHOAZ8BXiGE+BQwvejiYfF1ZnH7MWDVkv17gYnF9t5LtD8tXtB/ghDi3GKk+oAQYu8l1qeFEF8XQhwUQhwRQrznovVPipav4IcH80QcuwTNrgCkDgpWenTueG6fQX1/C6rVo+Vxg+KxFqbe1kQNVhEKqqsU1W6FlxKax8WE7gc9nIokfbapWSkXFjNL5sDQ/Q6VHgMRCBJjkD1ao+eeOrnH6zgln957G6z5kk/n7gbROR10nt7hYjalzmlvswkjBl6Li7H/2FNTP3+AMpbPG0qnx3opC2UIYhNN5m5opeshn/GXu2TOhsxu1aPkcp/NxM1RwoiFsTjp8WN61G41tf89Oq+w6oriWoN6u87Fr/bojJ3y+hQAxQ2S2e2CSreFU9TZOEEMiut1emrogrvQREgdFA5dgVuUSAtqHYKFddaFIq/kaEDXQxI/KpjbbCFNgTLBLitCFwprTOa2ODqI3BnQ+s2T5G9sp+tuk+nbPapdAnvcpfUxgcz61Nth8ifWUPypGzC7OrHWDSBcFxIxrLok8q3HNG/TEqLC8PBJzI72CwyzV+Z3WWbF8DMYAaXU7ymlepVS/eiA791KqXcDXwPOu5B+Dvjq4vuvAe8UQrhCiAF0AHj3ouuoLITYuZgV9LNL9rksfhDDoVuVUtuUUtddYt37gaNKqWuAlwP/WwixtK774mj5Cn5IaLaGlNdIRMOgvC5k/vrgQoAPAdbVRaJHI3rEXxRE98UIJ6NMvs0jMSII0yH1HKA0D/3Ia2wKay0qvQ5eyiSal+S3B0QWFMlRH7cQ0r6vSf/XA1LDASKQFNdGCOIWQcxC2gZexkKZAukIhNSdm5v3SEyGKAHxfaM4dx984iZ+lDr+ixCbVpgNSWGdZj9rOVTBS5mIACJzHtKBiZtdGq3Qsc/Dy1gEsfO6vlDrBCSkzunCscJ63RGbDe2rbzsYXsjtr3YaqERI8pzAbOpsn0heVwdHZgT5zSbKhLNvTlBrF3Ts8VjYYOAu6NG/n9QFY9LWLqD8JovZbSamD22Pa0lKp6yQtsCPQWxWZwA1soLu7xk0t/UTnfGxGoqBT2l+IacExVfXsKYdWo5KnY0koXDzKtTsPEauhZlX6sFx8/ZtiPX9WD1dmC1ZjO2bMOMxZKGId9t2am+4DLXIc8ELWy38J8DtQohTwO2Ln1FKHQE+BxwFvg28X6kLka73oYPLp4EzwLee6SQ/bAI5BSQXrVYCyKNTUJdGy/8I+M0f2hWugM3/+cN0jBo0Uzrzx5208TNaptBLA0KQ+myS0FHMXK/ofAhm39gg+lgM50SU6q0VYo8lMDwdGIxPKOyqoLqrSvYfbCo9NtIy6LxP0xJbVZ9TP+uw5nOC/AaHRg7ik0kaLRBZMJC2AqFz0UWgiMwH1Ftsyn0CpxwhOuORHJ5HtmUZf/cA0TlFdF67Bn4UDYHhONTa9YzIaiqCmEWzxaHWpitxx14RJXNSEpvyMesBte4IVlPRdiCg0mNR7QG7pOMthfUCvyUkfcQkiOpRaq0LzKaJ0dREb3YVOr9nUhoALyGITykaOT2LyJ6QlFcZF+gcDB/mr3IIomD4mrMojEBpQJEYESxsVvTcJzE8iVUNCGIWbt5DmS7lVYLIgq4hWP+PBWZuylLsN4jOKhY2u7q+pMUkiCuyR8B4JI6fhLlrDJLnwI/ppADnpkFK/TbJ0YAgqmkoIMnMm1ro2OPTzJrIzVvJ/usxgriJm78yMYHzKaLPuN2zeOSUUvcC9y6+nwduu8x2f4TuGy9u3wtsWf4ZX/iZgAK+I4TYJ4R47yXWfwTYhPZbHQI+oNSFf+lfoKPlT/sVCiHeK4TYK4TYOzs7e+WufAUX4BS1QHhkQZJ53CZ9VpE4axDEoNkmSY6G2BVJpUfQ8aggdAXpu2M4JUhMhHR9MkKzVRGfVlQ2+eSv0pkr7r44595i4ic1DUGjVeAWAryMQ+q4hZ8wKQ1KOvcE1DoABdM3mISugVPwMRuScp/L3Bab2JwkPgHJrx3AuG8/aqGAPHiU7j99iJbDFWLfegyUxNqwjvwv7XqC5vnFDmEgPQ8vDUHMwCmF1Dosxu7Qkp5mAxr9HkFU4KUt8lfFcRcC7JKPUBCfCkkNKaQLsdmQ+Bi40yZeWhdvReZ1XEYZ0P1ACdODRgtUuwyi05CYUOSvElhV8LKKeqtB6xEfqwZWBdyi7vjTp+Dc6x1N8+GDXdGC8olhwejtaAMQtxBS4adtSqsNMmcCUkMe8YkAry1GrUMHrofe5FK8KqC8RtLo8bWIfFlhV9EV6N26E6/noN4O1U6L5GiAnzCQtsAIIXLfEdoOBJRX2Xhxg/S/7EaWy8S+vh+rcgUFBX7EeYPghZ8J3KyUmlhMbfquEOK4Uur+JevvAA4ArwDWLm7zAPAylhktX0yz+hjAdddd9yPwlf/oodyvyB4DtyRxKpJ6i6nlBXsksUmD6XdW8fMRomMQLlIKS1PPEqrtJl5K0wOIUJLZb1O43iP6gEGtwyI2ahKZV9hlnfs9ucsBqTuSuWtMOh6R1HImHbt9pCMorrEorjFoZCIg9DV1/fmjoCQJnhgxhMUi7NqGuVBDHThxISYQnDhNy4nTSHQWyfT7bqTrk4cu0FG/aHA+tXVxTOQUQBkCqxbilAVrvhBQ63AQoaIsBeXVglq7SXxSIUKdMiotwezVJm0HdeHW5C4bp6BjAFZN59uHjlgsBoPJl6SwaloPmMVivCAiLmgEowRWQzF1o+YUMjyQtuYQ8lLg5sUitaamlQBoPeohlEMY0cbbCBRe0iRzJiBwDRIzDfykQ63dpvVwSGrvOI31nZiNgNPvdhYJ5aD402XUo2ki82BXbMp92kXktYYYJwxCR+AUQ2rtFrHpAP+mTZRWW8RmJYavLlR2z/78DtJnrpARWIa///x2L2a8oEZAKTWx+DojhPgycAOw1Ai8B/iTxcKG00KIIWAjT0TLXwtEgJQQ4lOLwZIV/ACx5bc/TMIXVHpBWhapYY/MySamH0Mog2aLwjgZh7aA5KjSaYhRQXQuJLIAUzcZ9NwfsjBosbDRIHtCkdntMHmTpqAwAsXkyyXulIXZgPZ9AZUuSweJC4qpXUDKQxiKlvsiVLsVylF0PdRk/qoo9VaDxOVcPA8d4OkSQlUY0v6RhwgBq6Odys4BIl999AX4Fp8lLgjZpLUxQ4usOMWAarfWcei5N8BdCJi51mbNpyVK6NTLzMka+avimu3T1EVitTaLeoc2JH5KuydM7wl+/2ZHQOqYhVXXOf/xKYlV02mkCxs0xUT2VEBiQlDqM2k5rr/v4oBO+ZUW9N7jgYDR23QxmJ+A7AnF6G0Oq+7yKPfaSEtg1xRORZLfaBGbVozfkqD1aIARaCNXuq4Hqy6Rls2az4dUuxyKA1AtRMlNK+Ze6hM77RCf0G6nju8bVDsFmTOgLF1trkxr8fmTKEMbw+I7dUgye6Jxya/8OeNF3sEvBy+YERBCxAFDKVVefP8q4A8u2mwE7fN6QAjRAWwAziqlfg/4vcXjvBz4DysG4IeD6jUNzIkIiVGIT+tgcDPnIk2d+heb0n/GyEEDPwHNtMCuQTNtkD1SIdGdZOoGizCqWPPFGjPXx1ECOh8JKK6xEFIgmrqDSo7pzqGZhfRpLVXY8agAtJuh2gWDnypQHUhRXBshOR5Q6Vx8hC/i4X+2CKZniHx1hrlf2UXubx66It/dsnEp6UolLxgAAD+h8NIW0Vkfp2JiF+oEvQl676oyfktck/jNQ70jQrVLd/B2BXrvaTBye+RCiqizoAO35+mesycUHDdQptI++jw0k3pkrQw9oo9NSyo9Fl4CMCA25TH+0ghGoIPFjRaY2ulcoJR2CnqE7CU11cTMNgerruk//IQAYeAWNF2En4TCWi1UD7rmIHtSMvFSm66HA2LTPpE5QX3YZm6HIn7c0S6hlKa/aGQEThHG3hCSPOzouJMjqHZYZM42Kfa7CKVIDXuEtsHUDRGCOHDPFfjZljkTeLEzjb6QMYEO4EEhxEFgN/ANpdS3hRC/IoT4lcVt/hBdSXcIzY3xO0qpuRfwmlbwbFFwkK7CbML4LSbDr7eY2WZdEBJf2Cg0fcC8T+ZEjfK6kOyJBm5JUtyYoOWER+aUwmwISmtiROZ1KujoawTlAUlltSJ9Uuf6F9YbxGaU5qhXUBgUTN0SEJkLmH95E39rlWZ7nOl318ndM0a9xaTtk3uf6ESvQO5/2989itXT/dz1AJ4tll7z01x/37c8YmM1lKldQkM/maHYb6Esg469HpG8du+UV+mRemReZ92MvCpC9wM+8XHN999ywtcZXYtBTS8p8OMGtTZdONbM6OCzHxO0HCpT69AZQ6mRgJaTISiY3BlB2tCx16Pert0yKD1bMRt6BqeErlIGnSmEWNQjiOuALixqHAeL4jZnmihTz0xmr7Hxk5KZbRbFNQ5TN9lUegXrfmsP3X/2CD1//iimpzURilsDqr1g5m1dO7JOUlyvKA4qau26viRzqoESgomX2XQ9XNf3f6VwhWgjfph4wWYCSqmzwDWXaP+bJe8n0DOEpzvOvSxGy1fwg8XtxtsQf7ETuyjI39IgfiCC1RBUXlolnIrTetSj5ZhWdrJqPl7aoeNhA2kbROY8GtkI85sd4tMSNy9wyiHlXgu7Aps2jTL+lX6aGSju1MeWFgQRQWlHk9R+lyCqiI7aTN8Ind80sWqSobcINv1eBX9VKwubBLl0CjmXv2JZPyoMCcYnEJaNuWk94bGTV+S4T8FFPv8nzWQuvhdhYN6zDyOTIaJ6ka5FbMKhfXcZo+lTuC6r/fMmdOwuE8Qd8ptcqr2a4G9ho+4gRQjT19k4Zf1ehFrgJbT1qF8sxmJCW5Aa9vEzLslzOr3UKZn4CUFqWOIWQs69RTC31SEyB9VeRWxCF5MBNFqE1hMQet/0aUW9TRDJ696wnhNYjSeopCMFxfwWl8xpn9C2yQwF5AMtNZk52aSZcun+0COwqEFtxGLkDuqTDb3ZppkLsQsmXrtPZMLGqmmiuGbaoP0f9lJ7zXbyG01W3dWk3Be5IKP5vPEj0MEvBz9CZZMr+EFD3rKD5FmDZp+HNRKhcX2V9j1l3P1x7LrC8CWNVj2OCGI2/z97/x1tWXaWd6O/OecKO+99cq6cuqpTdVd3VXdLKAeMQAIjwocBAzZJxtjX9gXse21/fOa7+LMNA8OwMQYbkQxCBiSBIgpIrc6hqqu7unI4dXLcOay15pz3j7nr9KnUdbpVAiGdd4w99j5rr73W2uHMd87nfd7nmfxmn+xchPEFOlSkVg2FS445JAxMvU1Svjth9S7N9J9uQwfw8De/QPZoirgArXHHMy89FZK/rOl/AUpnDANHnVhdeadH7rzCZkJm/l8xOmNIbpQANuAbcKuQqRB98izL//B1OpTdKq695ldLYlc8C8plzLMvUtmVJjunaU5kqO8qUrzQodMDhcmEhUN5ZGLIzLribxI67SCh3Sw9LtgunRMyiwbjCaznzGGSlFM4sJ7D16tbAnKzCZk5aIw6m8egolm4z2fnHyZr2H96VriGsZQb2L0WdHpxK8QlB/sI4zD/qOASTmbeEBUhP+UkRFQEUUGRmzUs3uPR+3JM4VLC1FtCxn75aWQqXPs4TLNJeGYOmRj2/L+Pkj+nSC1Dz/M+ScbSHHWfV++JFu133ItqaYafaHPp3QGLB52Exe2KDamIfo2zkjeTwGbcNM7/A6jsNmSKLbY9NIlJJI3xDNaDlf2CxmhIWDWEqzE6lEx8KqE56FMf9bFKEOUk2csNyjs90kuWzJR0jWV9baq7DPU9MV/+1N007mnT6deMf8pp0+SmNY1hRWNIUB+TXH6fpjYhaGyxTHx0ntW7SxTSHfb8i6M3vvD1M+zXEUIpdL2ONzHK4GNLLPzjr1IiuOWF3Pjfs/g7j5P506cJqgleQ6NDRf/xmMawR8/piPn70xQvRI79E7pBOSgDAjLTTv9faAfLCGPJX04Iyq4o3H88ovdEi/qw6ziWsSW94jyMvaaTCmkPWuKcx8DRNjrlDGOCCl0pEWdGn5lzCWDg+RY6dJBRuwfnLRA4d7FwGbxml8mkYPkuyfJ+Sf8LCeWdPssHPLb/u2ewWmO77K4rPtTJ9Czi0WN03nQnQRVquzWrB2PMcAdhISlozn1vQPrRU6TPr4C1DD9uMFlN9pZCChv8em5Dt/DXQmwmgc24aZiWR88JiT5epPlrY2z9oKK6VRFnXYHxinJlc8jN0kwgKL1YJjcd49disnMxUW/Kec0+MU962TL4ZYUQzpRepRNXXH4xhQ0NjWH3c1w+oOj0uKJ08bxGLTqv2bHPa5p7+lh4Z4fkjwcc7fOr0Pwl02nUHXsgThCrNUb/fIbq9z301dEbutExb1QoXr+66UJH/peOs3ync2OzAurjoNOSzKJFRprxz7Vp97ki6hXJZoRLADrlZuhhxZKkJK0h5+J1JawHlZ0CnZIs3utsPlUbMvMxvS9AuNxh7nCK/mOa/FRMz5kEnYLBZyIaw5L6uEsaS/ekaQ9YWv2sQUCFC5bMvAUBS3crZ1I/0LWSbENQiRn69acZ+49PYrW+ocyDN9DHmf95HzP/MGL5oRgM7Pgji636WAm5Mx7FlxRn/j/7OftDwzTGQsLVhNSsR338usO9/tisCWzG12vc/w9/mT0vR5z+IYuqeyzcrwgqiuybF7GfG0B13CxTxIbVez2GntHknp2icXAcr6WJij5BOSLqDfHaluWHhijvFgQ1KOWa1Ldr9EsFgirYb1nBq6UZ/lyFUz/eS98xgRWC1qhPeZcifxHKhyLsUUXqk8+z75kSenkVe+0gCVdj7Ov/fg2h63U4eRZvyxi2mMOeuUjvE4KFHzlC72/+NTCHutLXN4x1tQMTRQw93XJF2IJP3wnDyl6Hpbd70qTKlm0fWcUqwaVvLSGMG/xllx7aGtX0nHL+AV5D0ikJ2r1Bl47q9pl6q1styAiSRLBwMEDFMPdQlk6vZebNgi0fB4QTlFORIT9lUJETivMamuaIz/hf1ph7Q96tKFqWsJLgdTyasSTJwvbfOI9eXL7x99XddkX4rfp9h1l8d5vSYwE937HAhdogInEzkq0fM9THfFKrmjgnqYbuNdnZiNpESHredUjfnu9pgx3Dm0lgM/5WhnDSz9nzvnOmsl264K+U6Lu0SOXOPmrjEq/PY/RLTvO+/MYtrNwhKZ1WhBVNczRNuBIjO4JwJaa6I0Xvy5r57AA6a7Alg16WVOby5E77LB9OEZQFrT4Y/VKD+pYsjS2aO35thaEnAjh5gcW/f4j85Zjgk10SWXewvE5H/itdIVhDcunyK6Yuq2UG/qTMyvc/RPF3H//Kjn3NeTa0fZ0vwvpEIB49xtlffYDspMPF+15OWDjoLD8zi5bz7+/pNuJ1O3lrMPhsxOI9AVs/Zpj+poCw3HUbS79iFK/D7v4VSbjitPyr2x3ttHBRM3dEgRWk5wSNESceh4W5wylkBL0nNY0RRRK6zuP69iyd0itaUwsHAyZ+4wT5bpOeXnvbr0hCr3+fV7ZP//PDzr3sxTRxFqaeGqf3IlR2W5buChj79BJBOU19a5rc5YjluwOCssB78mX6jmeov2EXq9fRVV7vd8fGZvmbSWAz/jZGfQLCSsDACwlxRnZ1aixxTiEmihRfXCZJ91P6xEnsjjGslBTOxFiZp/ChZ1DFAqRCop1DyATO/xgUHoOFg4q4R6MaErWliT2fxV/xGHm8yaVvzhCXNGZfm9N7A9KlGoEVLD7UT/+fvozI55yg2WePvaIlss4wZkMD/436CV6lx8B0TV3o3hd/70nUgT3ol75KrKFrr2n9iubax93ax56ffobZnzpMY9xh9vlLlvxkh+lvcgNy0KVEJlmQDVi4LyDJwtwRn+wsdIqOqqkzEPuQmekyhRJobk3oP+6+//SCk4kAnNVk3ZKbjun0eLT6FWHFMPxERHPIB1wXcv/xBK+pmXpTSG4K+v/H09d1d1/9tl0fgcxmmPzAAbyGS0b9LzQxUlA8Z1i5QxIXLF7T9QisHI4pvOBTvJCgixlMIMnOdIgLPl5DkJmD6nvvIb0Y49cTROTfvu/pa3yA30hsJoHNuC7eId+P9y8epj4mqW8VmGJM6kLAwDFN9mKN1miOyW8bYPzXjqJbbXiujAxTyO0TlD56EQPoShW7olFz87S+5X5SL3rUt1oKZwWrRYGMBHHbI96lUS3J5DszZOahsbNN3PbY++NHX5kFao1RClvW9PzeMmb9gP9aYZ9XSwAbaTiz5itLABs5x436Hq5dCazbbo1l5Neepvme+9ChYPmAROqQJOOa+dp9rg5gFWTnLOWdziGsdDZi6a6AsAK1rc7i0/juFtQs7V5B4YTH9Dtj+p6RpJcMrV5JfVTR2KYpvaRo93vUxh2kk5821CYC4m9bxf+dIukF8Joa7wtH2f6oDwd2IosFTK22ZvBzZdZ/ZaYv79hFa2uRdo+iuSVh5POS+cOC1lAG47uVSmYGVoYMuUlF+001tvx2mkvvjfEbbnCfO+IgsaFnErZ9tMrFby2QWpHE6YA4JxDJ7Rm5vxoCcn8TsZkENuO6OPefHmLgedul8lmW7wzoe8nZ8lX2F8l/+FlGPxljhET1FLHtDvr+vdjHXrgKkrkC0WQfPUPmkw1ELoterRD/0yO0BkCUfWQssCNt/GxEK1akH8sx+l+eYenvP0Df8Tr2yRfWBjpvZJhk6xA8efz2FWlfSwL4SuNm57g2EV17XTfbbz0UpjWZP3+O5I13kx4MWbwP+o9acpNtpt+cZvCZGJlYZt4Q4NfcwDTzDyK8owGtDPQdd9r+SdrpAxmvS/nUUHjJJ85AvqmJJyTCws4PxazuVaTnY4KKu45L7xXkzsDETzfRl0+T37cTk0sRv/Ug8z/RonU5T/9zBYSB7KwTgfO/cAxx9x6igQyX/o5CdgS5fasE/7sHkUmoTaQwoabjC/yqpL01QrUCgrIizkOnFjL5rZC94LN8KKG64mF8SzSYcHFMIOIsNogJyi5BqDbs/dUZLtyur3RzJbAZX4+RuyzAul93akUz8rhh+YAzFxn4zaevYmzo1Yoz6nj0GGqwH6wlWVx2BxIS0Ih0GlOtY1YrYA3Dv/QY8//kYaq7LLoUQ6wwzxZJ1WHoV57AAL2/9YTzy1MKaywyCGgfGMf//PNuBb4eErkd8TqO4w30kyzeosH91ZrA1p/7tey37u8rnw+4FZNOSYaerKE6eXKTLabemnaSEb5ANRKuCLwZD7Kfzzk9/xVLu8/5NEdFS2rR8frDVcjOa4LViGC2Cp0O6ScbYCwkCQNPu/MjJRjDnk83id96kMnvHkNGY7T7nRtY/rykOavIzkhW7rIw2mYhlpSeCql86/2k5yTGc8Y3VkL1Qgn7TQn5oyka97ego+h/1CezkHDpvYLGVg1ZjZn3yR8L6PRCc1eEP+u6260CEUtUXTL+uYSVO3ykgdUD0HPCkgyXuC1ZYLMmsBlfr1G9N2Lgf2isklx4n09mRhIVYOITq5hrBighBcLzWfzh+xj8rWcwcbK2/YrFXzIzB4AqFtCVKjKdIjNvGPv9c+iVVdcRu3Wc5Pylq46rdmxDX5iEhw5gnzxO6uQsSXfAE56b2VnNxgfwm824X2Od4Eoki0t4W8ZJJqduvMNGoZ9r+xo2usq5Zj8hBcEnnwUp6H/WQSxbv/xKwVxIwdbHXmHZ3Pyw15u263WwDYAq5LGtFng+slSAXBYVxyxtd4Xm8je1ENMpMjOOXlo4rRAa6Ad1Nk26CuUHO4iyj2oBaYg9S2ZOUN2v2fpngqm3WsITaVoTCUsHLaklj+JLTp02XHFy2I0trls5qfiY7S3U2TRxySDbEp01XPy74K1azFCHHb9pERasfxupvrcpCQghUjhxzRA3Ln/YWvtvhBC9wB8B24CLwHdZa1e7r/k54EdwdfV/bK39VHf7/cBvA2ng4ziJ/ptexWafwGZcFXf8619m+FMeC4fSTL85ZOhJQWNHwuBzhsa2PMBVOC44qmL/rz/xSgJYx+sWSnHhFw8j0ylqb9vn9m+1KfzxsyBcEdBqvZYAhFJ4o8OuIejsBWwSI4+eBiHRw71rx7Vav2IYvtFB82ZdujeiZG4wsSSTU3hbriGe36iYe23cSjPoSmJYf7v2OdznIA/ewfI/OOJWTL53zWHsDR9fdaxuyIN34O3aftX+oivVsD68kSHah51nrxwZwg70oC9O0rpjBGG6TKTLaXTekL9sqe+JSS9brOf8BZzlqCG8GBKUXT3Ba4EejNABFF/0mfw7Ap1LMD7ItnTHPVAnzroVSn5K03tCE6wK6js0JqMxiyE6Y7HKggV/VZGa9p1kyYUUs4+kqU+kWLrrNulCbbBZbANW9AAd4K1dl8V7gXcLIY4APwt81lq7G6ev9rMAQoj9OCvKA8C7gf8ihLjyj/dfgR/F2U7u7j5/09hMAptxVbQHDFYK9BsqWOWW5/mXPZbullz+O1xVtLwyOEjfWxt8hBSo3tLaY4Ad//IphJQkqe6/w0N3u4F/cRmZzSBT4SsDzbqVA9ZQ/54jYF0R0T770isXejPmzM3iVoP8V6JAOjl1/fGvoXJu6Jpu9fyVGoBSJO84xOrfP8ziBx6m05+m51SL5G33uc+p21V77eB95e+rtnePufLDR7AvnEZfmKTxvgeuet0a3NS9T2bnCT53DP2Ge7DVKmJ+GZnLUR/1qOx0InQIS3paMff2GATMvz2iMWap7bC0RxJEX4fOgKazrUNrRFPdrVELAVZBdV+CyMVgBTptKZyWFE9Lcp/N0em1VA4kLN2pWLhfkVrB2Wae8LCexa8Kel9QmKym55SlM6CJCtAejUktQc/nLzD0ZPXVP/cNhmBjSWAjKwHrot790+/eLPBe4IPd7R8E3td9/F7gD621HWvtBZyd5INdQ/qCtfbx7uz/d9a95oaxmQQ246oonJXoEJqVNPHWNssHBPkpw8hjMV5FIZRCDQ9dxeW2Wl/F9DBlJ4FstWN/y1wO3WhS+IMn3czyyeMIKfB2bsM0msjR4bXVw9ps1RpmfuZhCn/yHKbduXo2vP7+Vjj6lbgRs+Z2xrUz9RvFlfNfg+uvPXfl7/UQVdcMZfrnHmblh49w/t8fpvG+B1i5I6DVL+iUYO6hgPZASJKRzP2jw4gHDnQPdfXocyU5XLtdSOc33HnXQc7+h0N4bbOW1G+UTOTBO1BDA/jHzqNXK9iRAUyjibCgs4bOftfAhoXcSwGipaDplGfDZYHKxOQLLWQkyB913ebFlxXWh06/RXQkVgt2/X6CFVDb7lRs4wyklgX5kx75SYswTnq6eFqRZKDnRUXpjGF1v6F0zGPlAORPK9KLUHjZZ+VQwtK7ttMauU3iQa+tW/jhKw6I3dt1TotCCCWEOAosAJ+x1j4JDHUN5OneD3Z3HwMur3v5VHfbWPfxtdtvGptJYDOuCh1AbYsgfS4gdSpF0puQv1CnNuGTvyCQQ/3ouXn0YTfQXDvgCaXWBn+hFHLPzqt08d1L3EC0+sCgK2ZenLyqliCkQKbTjHy56ba/mkDcRgb1G9AqbxaqWESVSu5xLnf9cV7tNOvlDTaK8d/ouoSEw3ex9OMPU/6Bw1z4+cM0JzRJGtLzrvGuU3L2kIPPxagWBNWE8g4PGUNnIM3lf32EyX97hEs/f4SFDxxh4aeOIH3v6gF9HaTU9xuPYSXs+hfPgLHIYqF7Ka/sb950L0IKzPMvg++jK1W8ndsQi6tgnQaQ0ALmUuiCJslAY4th8ClB7rzCBIb2oMF0PGrnSxjfUru3A75l5PdO4FcF6VmBakoyp0OsJ9CDMTv+tEV5r6UwaWgNGbJzTvgO4XoRkm4XdBLC/DdHZCedBpVfE2SWLH4dwrJFNhVB1TD1tutlKF5XdCmiGxSQe8xae2jd7TeuO5y12lp7LzCOm9W/mlfwjVAm+yrbbxqbheHNWItv+rb/QDDi0XvKuXshIDPnsfBgASsgP63R07MOe37shevgDje4J1cVFO25iyAkydvvw//c81fNQgsfehq6zUGgu8dSqIlxkkuXEV9+YWN4/avBLjebdd/omELC6BAmH8LTVZKDu/GOX0BXqtfj8jc4v9XaFYov30Ch7Nrmr2tj3ax/+YcOI4x1Zu5ViHs1O/8wZu5BRX7KoFOS/uOa6hZFdauPTsPy/oCBYx2sJ2j1e/g1V0BNL0JtpyW1IDj384e62jyuF8B60O6z7P2Vy+jpWcI/fwYLBJ94mmTtwhz7yNu7E7507JW6QxzT/I4HaQ5I2n3Qd2IrQc0iI4FqCXqfkhT+7DmQgjP/7l5QhnBZEfUY/FyEzUJwIot3QdHYYjj1qzvY8d9bzB1JM/isoT4mWbwnRK5YFu8N2PKpiOk3BfQds9THHJNJaIi2dSidDlg+aNnxvzvEhTTNcYspJIiGwipJkrGEK84nWackQ0/Y20YR/Wowf6y1ZSHEF3BY/rwQYsRaO9uFeha6u00BE+teNo7zap/qPr52+01jcyWwGWsx84iHii21cQ+pLTqA5fs1VkDhckJqsYN94M6rZofXinupXHatXnCFvqiKBfzPH1vbf62wfAW/7rKIePheAPTUzE2PfxWss55Zs37bzVYJNxv81z0WUYyaczNb9cxJRBiw/A+OXP9h3WQwvy4BbHAFcuUzW/6hw9iuRaeVMPdGw+jnBNVtITJxxjvNIY/amCJJO1nk1KITX1u4L2TpQIBMoOd0wtDTCaoNVlqiAox9KXE+Al36Z1CB8c9rmgdGmPupw0z+2yNM/+wRWu87jDfQj8q/QgTQZ86vJSmUwiwukf2zpxn+6AW2/skS5R2KJCPIXhbEu1os3ym5+HP3Uf22exh91LD7Z4463f+RJnHLR0qLuatObV+MjATBuRQ6pcBCZYckO2e6LCBBZa9h+s3OoWbpfkN2zvU0ZGbBdhRL91n6nhfMHU6TXoT8BUE45aMaDtqUkUAH4Fdc0bpTuj3D3kZrAhtyHxNiQAhR6j5OA28HTgIfBX6wu9sPAh/pPv4o8D1CiFAIsR1XAH6qCxnVhBBHhBAC+IF1r7lhbK4ENmMtdMbQ6pe0BywyFmChdFwRViyLd3tk5tzqoPfJLk5sjaNodkOmU5hm85UNV3TwK64Qd+U1V2I9x12VStjnT7lu4HXOwNcxWq4tBt9sUL8Z4+ZGiWF9IqjWsFHsGD9xgq3VGfrCPCZMXa9aepNjeWOjVxW3bwlZdfdZ/IeHueLAdUX5E2GJ8rIr+wwjj0U0RnxSq5bekzGdksfMOwxDX1J0egRDz7Sp7AhZPOjR96IlO6+JMwppoDbuOZP5FgQNiyxbrHLJxooudJFAeZeiU9pNWDHUxhTCQFB3/tHCQlgxtHsk2dmE8DNHEUvLjP+3GU7+/+7EphP8qTTRYELmgocOBaVHJ7G7trDnfzaZeVOefAWWjghyZ3xMj+sPuAJiSO0gnuaAJFx1WkWpZSdGV90q6XtRsHhQUDjvoMv0ZY8kZ5EaopLzMchfdNpHcW9C9qLzq87OwOoBS5wXRD23q7eE20YRBUaAD3YZPhL4kLX2z4UQjwMfEkL8CM6O9/0A1tqXhBAfAk4ACfABa9f+G3+CVyiin+jebhqbSWAzANj23/8DXlvRHLVs/UTM0l0BXtPJB4AzI2Fe4DcM8u592JfOXDcg2ii+6m+suWFx8lqKKYAY6MOcv3jji7vZ4H2jeC3Q0A2OkSwugZCo8SFMOkQcL0OgXqGj3uzc6+CetQTwatfZDXnffuT8KtHOIVYf6pA7HpJasQgtKF7QtGY9gqohvWxo9isW7g1AOvmEyo4AqWHgcUFzQFA6ZzCBJDeTYJVPZbvAespBQ1nIl60zlskKjAetXolMYPWAof85S2MCCouCqAjNIQG47uCoAFHR+RBYAeklS5ISJBmF/eaDZD5/gs5D+9j7z4+y+t0HaYwAQtG+s0V6Mc3Md2wjP6Xxmoa+l2LSU1UGPjyPvmMr1Z0ZFt7VYdsHJVYJBp9tk6QVwsLF77aElwPCVWgMS/yG8yHofcmydC+YtCEzpSicFazsd0w2rJPACJdBdTzaA5bSScHyQU1qQbHtl49z6hcOvOp3suHYoGzEBtlBLwAHb7B9GefDfqPX/ALwCzfY/gzwavWEq2IzCWyGi9BQOqWwQlDZ5qQFTAAysc59SjkmRpKW9EzNo29EsdxAkfY6tc9u6PMXr99+s8auG8VrvZ5Xe66rDyQO3YXIpkEp1NhItx7yKtj+um03e5/Xhqy2aBwcY/LbDTQUrftaNLTATyXYyQxWQm2La9bzmg7GEQZaA5BZcCJucVaQnTP4dU1twsd4gvSywW/gVhFZpwDbHOhSerWzeMxNG1THYnxFpwRDTxpW9os18/g4IzA+FC8Yqlsk7UfqZD+XIyoowqpFRc4xLnpgL8sHAkb+yrDwgCUzLQhWBa1RQWU3FM9CeraN9SVx1qM9mifZVQKg5/llOoV+VvZBz5kYE7jPUKck+RcUrWEH4YSr7n02dia0hhXpWYFMFK1BaI5qRDHGVn3CJUWn1+C1JDplyV4SVLeDV1fICM7+q7uQ0W0E8r/Gu4E3Eps1gc1wISwrB1izCVSRpTWIEyS7U1B6WSA0ZBYMp39mzw2NPm5Ek7yWm36zgfFazaF1f9z48Y3Ou74W8FokJW5CHbXPHMc2WtizrpGt8d5DNz/3NaHWNV3dLMo/eJgL3zNEdYsH0pI7p8g9lsZqSe+fZmn1Sqpva1DbmzjfBQntflfQVRHkphPSSwmdHshdbmMCQf5yjIqsG7R7JO1ep8IZ1CwqdisI1ZWKBmj1K7yWUxhVkUUkUDwX039c0+6D0tmEJC3o9EHq0RyqbRHG4jeME3nrV9THA3LThgv/30Ps/WfP0em3RCVQ0ym8FlR2gU57iMSAEtTHfPy6RqcEKMXQbx8lqFla/R7lnQGzb/CZfI9Bpx3uH1SgstvVBtKXPWx/B+uD3wSkJX9WEZ5JUTylyMyBajmIK+7R1LcbkpGOk8VuOmXU1OIG27duERutB3yt6wttJoHN4B3y/YwNr2J6Y+I8VO7vsHy3w1y9piW96FYErSHoFCRIWP3u+4Hu4H2liSkIrjv2zSSer+WeX/cat9PGBvLXShm90arhJrN702k7mKvdIfMnT96YmXRNeFsnSE6ff9VLmPpXR1h8c0RY6coz1x2PPrVq2f3fYsq7Bdn5hInf9Mme9yidTfC65u1J2nn7xjnJ6m6f7Kxl5Y4MqYU2cVYR5QRJFnKzmuIFgwmgPiYIVy1ei1eYNXlJekljlfNyFtp19da2+LR7JDoF5Z0eqVVD/qK77sySBgtJSpBa1QR19111ChKvAbM/eYhd/+r5rjS1cb7FnmX2kZDlO7NulbJikJFG+4LWRB7u2EH/o3MEdUPxQkzvSwav7LD8+lZLfYu73nBJIjuQez6FSKA56GigOu1WrZ0iNIdAJgIZO3exXX/UZt8HTiGv0J0sTPz3l2/9G9lIWJcQb3m7uWLD10RsJoHN4PK/eZjqp4ahpeg7bvDmQgaeh/7jFq9tyM4aoqKgeNaiIovOaXo+fBQAb7DfHWQD0Mv6RrDrGpZutLK41XGvxM3omxspDt/sdev2sUlMsrD46ucHZBCANSQXL910VyEFM//8CAPHErb+kcRImD9iSC0oGls0Cw9azn9HhqACmUt1VNuNXlHeMWeGnoownvPvNZ5AdRxO33uyRW1bhk5JkmQgN2VRbUN9VJKbMaQXoNXnZsgmcLCS6riic3Y2obJdEuUVY1/oONOeumXgqKHTC6ptaPdDULXEGdf57bUs7ZIiqCRUdwhycwk9p7WDqt5xNzt+8QWCVaffk5Sc9WRUhKDs1Eytkiy8JaY5oNBpDzxFZrJGuNikMSTJzDpKpxhuEw04VlNra0ynF1rDlsYdEeEqdPqcsXzhvCUuWPKXrDPBWXF9FOLJ45z513eRpN315y9C9W17b/5dvtZ4bQ1jX5OxmQQ2g9JpR8cT+ZjWgARh6RQEnaKgPuZR3Sbpe6kDQGY+oe8Zj8UfOIi49w700iuWgDZOrh481wmXXecYdU1caTC7adxKfuHaVcNGisPrX/tq+96IfnqD/UynffX5b9BI1/zWQxQmHZTSGPHoOxHR/6ykeM6SnlXkLkiKZwWFS5qpd5WIc3535i9oDcLygYC+F2NU5LDyJAsrd0Kc84hykspOweBzEZVdgvIun7Bsycw5k/bMkkUmlpFHW2SWNK0+4Vg1eUXPGYPxIc4rrBK0+iSdgqT3ZUNjxCnIGk9Qm5C0+wTTbxUsvDUmzisGn0swCuKsJDelqW71OP8zd5NaAq8m8Rd9vLqraZz7Xud6lmQVpWcCCufbRKWAyl39NLbmmX+oSGsI6tsM7QGwCyH+skeSgXDOJylokpwhmApIf8sCCEu44t639S35yzGDz3fwWo7NxMH9bP1EhE4b+j92mlTZUNn+10sR/VpPApuF4c2gvEuiOuBfSOE3LP1HLY0h5wdc3wL9xzSVbSGls4654TctjWHBwgMF+tJ3IR8//qrHv5H0wA12ujF751ZyD+tft1FO/o2OcaPXXXsN669l3XnXd0nLICB+411MvzGk/wWN8QWVnRKvCcULGmEsOpSu+W7SuXIZX+C1DWFZUN1hSS8IWgOO1bN0t096EYKqoXhOYHxBY8RbSwylMwajJMv7fWTiHL9kYshdwnkF1y318ZDWEKimwG/B5benyV+CnrMJxhcO2lns0OpL0+5V6FAQZ139IKw6HL3dD30vGvJTmrkjPpkpSZwPaPWB3zDowFFYjS/ILBhy0xYdCprDEgTkZtxvKnfGo7LNFaf7TrRZvC+N14DmMOQvW/peauPXQ0pn2nT6Apb3eSQ58PZXaEznHHUZ1wS3cKof41mMD+EKtAL3+QSrMYULhtrWFNaX6IxCtQX1R3ayeK8k/SqLutcUG2QHbdYENuNrOnb+p1+i74ShNWzwWrB0D13c1hJWDdkpVxzOTcfUtoRIbfGahtSKJcrDyh2Z1zTgrheKu+aJG/99M0jnZq/baFx7jFdbSWwkyVwphO/Zjg4k2RnXlGSlg3Ccz6/Ab2hSqxqvY4mKHun5DnEWKjslcQ7CVff5DD0d0xpyg4xqQ6coifKu6UlYyM4a/Dq0BiRWuQE7qFmycwmV7aGTTBbQ7pVEOUHvyxoTOP2dwec1cd4xvbyGpt0jqW5PkVnQtEsCGVmCKjRHoTEkaQwJMrNQG5PUtvjkL1qioluJNL+5yvKdkvIeQZQXXbaRQGpL4aPHGH5KkxQ1xdNNmuOG2h2u7rR6QLN0d4r8pCY/FTN4NKFwoUOS9RAWvLkywWrEwAsxxoPoRJHsJQVaMPQ4RHkwpRihBfkLkFlwI+3yAZ/lu7NgLa1+wcL9WTCWwacttQmP0hlL8XzCbYmvAygINpPAN3yolmD2DQI50KZwya4xJ+KsQCZ2bUnb7vdIrWp0KEktR/gNi99wA8XSj1ytOnlDuOda4bL1tM/XGhsd+Deq4HmjBrNXW32sCxkEV/UQTL+jj3afR3YuITcVU90maQ75hBVXeK1uCfDqmsxMB6+paQ2F+HXo9No1Q3jVgcawg2B6T3VrAgXXOGUlNAcF9TGJjN2g7rWcOXx1h2Bln2vou2IUrwNXSE4tRfSe0ngtaA4oooLrSo4KHvlpTW4mJlyJCCsWv2lBQuE8qNh5FMsEpAHtQ1QQ5C+C9S3j/1Gx5TNtSqctq/fFJBmJMJYkJbFxQmWbonBSYQKJzWlyZ3xaIwasoLbVrRZqW3xEYokLHvP3eTSGBcuPjOCtNjG+QGcsOmOJC2BCw/JdkolP1dj7Ky22/XlCesWQuxxhJbSGXCH5/LenGTjaZvCZBqptKL64TH5Kk17WeI1bQI+vIb4e4KDNJPANHn4d8ucEmaczNAYF8cEGSdrxw5fuVIRlg9eypJYTVvZ6rOzzaPcFeG23PTML5X0WWci9Mpi+yuD7qh3A1+989TE3qhh67bFfLdZDPeu3bTBMFF11vtK5hPqIcKupU/P0v5jQ7nWQ0OpeRXbWQTA67SEMFE5V8NqWwjnH7c9NQVCxVHYL/DrMPegh/DmOkwAAgxJJREFUE+uguYlXkkScc4lBGCcdEedcg1RYdtuK52NSq4bqbkNUgqjkUx9VBHWHm488FtHuEQQ1zewRhZWCdn+AikAHgqBmiXOQWjGkVg1xzhVWk4wTYwsa7prbAyH18RCvY9n+x+A1DaptifJuMpGkYezDF5l9JE3+uI+MYN+vzFM8I0kvCBojksYoqMiQPbHItl9+gS1/cInqNsHKfX2OnbQgCMqCzkiMMIId/+ElLr87z7nvLrHwky1afZLw1AymJyY9J0gylu0faWOFoLorQ5JVmGxI7sUFZGSobb2exfa6YqMrga/xJLBZE/gGjnfI95P5i90sn+zHZAxeWaFOZp1a5bJFJAK/ZYhykvaokyrwG5b6qCK9bEivGC6/A4QWiEIBuvIQrxZXNVHdCM+/EQ7/yotvXju49vH6uFXj2EbrDzcJb3QEs1rm0j8/SFCD/hMJ7R7FyhvGERZKZ2NXB1CC1qBHWNbIyGI9QVxKYXzoO95i7qE0mTkngZCddrTI7Ay0+wR+wzF6vLaTbYgakiTlZuqi6gThkrSr4RTPQn3UR4cw+JSl3QtJSmIUNPsFnaJ7bvzDk1QeHGfLpzs0RgPirGD1TkPhtCRVtoRliw4EzSFBatk9lrG7nsy80yOqhB5B1VLdJmnvjdjyh4LauEd6UaOKBcb+qgG+z8iXW/iLDUQn4tJ3j7pre14zf0ix89cvMvXd2xB3jRBnR/Aa7v3UxwRhGar3RNhEMvoZRenZBWpv3eckI+Yhc7xA7+fOQybN2F94VLc6pVVhYeZhB4u1+jzSM5b2jj5Sp+aQnYHX9P3eLJzR/K1H+E2K6GZ8zUbt/3iIpbN9DD4Lvc8pvN01RLcI2CkKTOC45M1B13RU3SZY3Ssp36kJy9qZxEiwxYQL3z92Q17/ml7Qes2g9SyhV5t1Xzs4X3nN1Se48eOb7XPluOvvb5RcNhBX3kfznnEWf+AgVrGGNy8esoQVjfYhySi8lmPfaF9gpaAx6pGkJYsH03gtR5nMzFlq2wTtkpuJBzWD37BYCVEOCpccjJGknBn8laJkknargrBswbjvLmgYogLkpjpOxgEoXUjoOZcw9GiZ0tmE5TdPUDi5is4orHLQ0sgXITejaQ6662gOCrJzFoQ7R3rZkp2zxDmBajvdn/Jel6BKj6W4/DZnI6liC3knxT317WNc+I6Ql/9ZkVMfGMGvQWd7h8vv1fg1mPy+bWu9C0ENOn0w/vkOwsDwZ+cJJwN2/X5C6fPnuPB9w8wdVuS6ivmZhZhk6xBnf2SEwpfOuc7iPMy8Ic3YXzXpOWXoeykiyYfolKR6aAyvsU4C5CuMzWaxzfhbHXNvjxl5FGrjwnUJP5knOwP9LxiscvhvuySp7NUULlp6T1r6X9AMPOmalKpbJalZRepcgF+Hi//mldrAVW5U62bX612vxJqM9NqLrr7AWzWAbRS2ebXjbqTJ7FXOI32PzLMXGXiqTFCFqKDwajG9xwSVbR5B3TUMRXlF/kITK93scemRBKsgN20cuyZ0RWS/ArWHWo5ymZHEWcHIow1ys5aluxw8p31BWNZYz2H/pXMxfsOtCpIdLRCwdLekdM7Q6g8YeUIjtKU+4iEjy/J9RackWtbU9pSwgrUaj/ssHBQlLKRWLO0eJybopMVjopzTHpIx5OYS8hdcQpARGN926ZkCPTWDfPZlkixgBKrmYXpj2v2AhYEv+mv1C+M5KYw45+Cs+nhA+2CT8n0DpFagtiXk3E/tpj2SkOQ1K0ci6gfbXHyPx7l/JNnyyRZmfJDcdMzEp1u09reJcz6p5QidluiUIs5Ix4bK+hv73Wwk/pZDQbCZBL6xQ1k6BUlqBfKXdZe1YZGxRWjIT1uKF2JKJxSVXYKVfYIkLahuF8R5p9rYHtUMPR3T6XOzQpm+tX/rTSUkbjYob5Tzv9H9NvKaDbzeGouJE/TyCuffX6JwyeHhJnTYv3KtFWTP11CxpbI7S99LTTpFxc4PJljpiu8zb1DEeUV9wtFyBz6ewkrIzcToABYOZWn1CbIzTkgtfzmmU1KEZUun5IrIXsvh8Jnn06gODD2lafdKKjucAmlzULlj3ecT1C1eS9MpKhCOVlkbl86gJS0o75CkVg3ZeffZhBVLkhb0nE4woSS9bKg+3KJ8uIOMLKlV45LBVMzwY44yany3AhR7truEEQlGv2jw5gLSixBeClnd5/yr2+MJUY8hvQStYUO0vU15j8A/kXGrihaUdwuikkE2FdlLCm8+gNUAG1hYSNHpC2gPZUgyimCmwtbfU2ROzHLpXSmMJ4jyiqDiVrDt/ttTExDGrV5udeN1cB/+OuOrmgSEEBeFEMeFEEeFEM/c4PmiEOJjQohjQoiXhBA/1N0+IYT4vBDi5e72n/5qXuc3Yuz8o1+g74shK3cbmsPg1zWjX47Z/mdt0gsd6uNQ3imIc64vYOLTDYSFuTc6GYnmnggZQ//TkkvfaxwTJQ3TP3r3Lc99I4vDV42NwjSvpzfg9VJM1x3HGsu2jzUIV2KCmmZ5f4CwULwYkVqOKN9ZxK8nZOZjGqMpjAft/oCluwWtPkVqSaBDQeGixWu7W6dHYD1Bdt6Qm9Fk5wxxHkrnEqznoJpWn6B43qJTrnNYxq5LFgvLBxSpZUNh0tAclPhNS25Wk5t2SX51T0iSEgjtuoCz84bB5yIWHtEULxo3OVhK0IEgCR31s7LDQyQu2RS+nCa8EHLxew0IaPYrmkMefsOwcEi5hiwhOfmBEsJCegFmH5Ekee2KzBXHOMqf9EhPuiJ5EoLNJdiqT1CBqM9Q3i1ZvdMQlwxIiwkMOnR9AsGqJH9agYDamOLyOz2m3m05+VODVLf6JKN96IwT1kvSgiTjVrB+4/aNyptw0MbiLdbae621N1Df4gPACWvtPcCbgf8khAhw+tj/zFp7B3AE+IAQYv9fw7V+w4SuBiRZ2PIJTVR8RVe+0+v8akceT9j652XaPU5uuNMfEpSdZ2ucg97HA6wAowQ9T4REPQ5Cys5ZZv/x4Vs3h90qXg919KtxDLjqvVxn1i4kqlggLgRYT2ACQelcQpKWyNigQ0VQ02hfokNHnyxcjIhzkp6TllTZkJtxo4TXsoRVZ+LjNWFlj7c2YMnE0u5zxj9+PaHTBwNHO1gBmXlNnBFk5xJk5KQ9UstgvW79oOlUQ1u9ijgjWNnvPAKquyxT74CgpknPx1S3BhBLdCDILGnmHwjwm04zytGEYekuH+0LihdiZAKFoyHVCYnXsVR2uEnD0NNOs6jzjnsJFxRx3l17ek4QlBUYV8fQgaUx4fSKwMlZiJqHarreB68uae6NsMUEGQlkJB0JwUB1f+xqEm3AuPdqpWuOEImgthWinoCJvzSEz57DbxhW7lBOqG7gNvFhrN3Y7Ws8/qbhIAvkuw44OWAFSKy1s9ba5wCstTXgZW5hlrwZry1G/1KSXjQE5Yj8BUGSlkQFhd/QTk/mTJn2cNbpyNQMQTnGSshPGZqjBhVZxr4UMfjYEr0vt9n2kZihpxJkbBn9Qpmz//HB15cIbtcM/XYdg6thqyuPZbGAkIKVHz5C7a17kZGhU/ToFBVBJQYBIjGkZuvIyPVb6NBdj051G7+6ncKq42bnsuvqVtkuHc3zgiZJSarbFK0+Rf4SDBx1OH96HibfFdLqF6z+vTqtd1dJ0pLmsGDpsEZYSEJB/lKE8SE/abrfX4JqunsZuwGzusVj+gMR5X2W4cedbLjXdD0F7jHUxwU6ZdEp8FuWpbt8RALVXRqE0yES2q0oVdtQeHmVlX0+qg1+1dUY0kuW1KLT/omKluyM8xQ2nhvw9b110vPOw0DGDl7MvRhAU+FXBKlF5z/sNUFVPdqDluYQqLagui9xfgIGJj6n0RlLklF0igpbb5Be6NAeNFR2ijX66lcaotsxvEGP4Vc/1k3QDyFErxDiM0KIM937nnWv+TkhxFkhxCkhxLvWbb+/i8CcFUL85+74etP4aicBC3xaCPGsEOJHb/D8rwF34DwwjwM/be3V0zchxDac2cKTNzqBEOJHhRDPCCGeWVy8Xf3gX9/xDvl+ZOy4/zJKXAFwqUNmLiLKK9KLCdU7e/EaCaqV0OxXyEhTuGSY+/YOE58xrLyrxYXvlJz9gT4WDqawys3QrASdC5n4VILavvW6c980MdymWftt7SG44ctcQVv09SL37ya1qpGJJSq5TtfUqkbEhqCcEBcDKncUMYFAdTT5kyukVmJU25Cd1WQXNHMPOvXQyk5JZYcivWjIzruGreagYxU17m9Rn3ADcnWLw/mTtOsLCKqQ+VgB/4sFwA3YE3/hOmrjrKC8OyQ367qCw5pBRob0iqXZr8hOQ99RQeGyZuQ3QvIXBUkIpRN1VvYFFC7pLp6Pk5g+I+g5bVi837GBCpcMpA2q4+CnoecSjCdoDHvYM5fo9LrBPLUE7UFLdZugMWYJqgK/LsjMO4XT9CLEfQmpL+doDblkpQNojmtEAjYwtEc12RmL13Ry55lp4VYWVdj5v5bZ85stxj5v6TmuuPR+jTfeYPpbEuKMYOYnD1Hfkmbf/zMJAlYO3qaOYW4rFHQz9ONngc9aa3cDn+3+Tfe57wEO4LyI/0vXlQzgvwI/irOc3N19/qbx1U4Cj1hr7wO+Gfemvuma598FHAVGgXuBXxNCFK48KYTIAf8b+CfW2huS0K21v2GtPWStPTQwcHv4v1/vcfZXjiCMxfoSE3oUz0VYTxIVPfJnq8jEkF6MMIFzeGoNwdK9OVRsGf3jgNq4h/IMpRc84r6EzKJl+UCAjAzZ6TZxziPJSJL+/HXnvooZtD5u1C/weuJ10jxfyz7zHzhM9e4BklKa/MurJBlJUE0IKq4/oD2UIs4rROLona1+RZJWLDzSjw4Vqp1glUB2DKrt5CRKp51sx+w3uUTaKQiaIzD1domcSpG7DOklBx2VzmpU1KVTlqB0pkV2zn1ehUnN8n6P3pOuk7Y57PDyK52rnV4P1XEjU5wBrwN+PXFyIC2L14Gzfy9Lds5ghcPt/abFhFDb4nSmUgtutdApSUrP+cgYek9orHSaQEHNUYK95pVzgs4lTr207NRBwxWX+BBQ224JljyaIxaT0ejehKgI6RlFc9zS87yP9Q21LYKoZIkGtGuAKzpYaflQL6d/JINMLM1hoOHR+ycZAGeu03SfabR7mJHHYsK529gedZsgoVdBP94LfLC72weB93Ufvxf4Q2ttx1p7ATgLPNg1oy9Yax+31lrgd9a95obxVU0C1tqZ7v0C8KfAg9fs8kPAn1gXZ4ELwD4AIYSPSwC/b639k6/mdX6jRXpOUt3mEWckUTEgKnh4tYigmhANZIgKHqoZozqalX1pdOgohOBkhXMzCf0fyhCWLSKWpFY1fS9F+LWYpbszyNiSmWkjnjlx9YmvqI1upDD8enwBXkfcSNzu2r+vSlwH9zP4q0+Q+d9PIDsJ9X09VLdIyjtCKjsDtA/1UcX8gx6NEY/luyRh2Tlw1bY6emhrOEVQdSuIntMGHUBYcXLJ/c+JNY2g4jlLz4uS9Lxg+aGYTkF0vwtD/rKjmHptqOxM0+6RdIqSxpAiyUJzUNHslxTPWzp9Xf+BjCvyWgmdkiC97JQ2jeeuXxhnTrP7f1bxm4baFoUOBMv3WnKXHZRjhWPrFC7FxBmn31+YTJh+j0a1XOLw65rmNx+kua9DpxeioiV92Se1AJ0e199QuSuhPWiQHZcgCuctPSdANhV9jzvRvNZEglcXlA9FjH1SERcsfl2w9c8tQQX6jxuSDBQudtj5RzEz3+T2GXpM0hqQTHxEkVrskJtJ0AFgIc4pRh67PX0Cr9FU5uEriEX3diNkxB33avRjqGseT/d+sLvbGHB53cumutvGuo+v3X7T+KolASFEVgiRv/IYeCfw4jW7TdL1zxRCDAF7gfNdDOu3gJettb/01brGb9TIX7b0nIpJL0R4bU16ya0ERGIQsWNTdAbSNIdD6lshzlsWHrQsHFTMPuIz/SZFWHaD0NCXJam5JqqjSXI+A8830GmJ9cT18tCvV9nzZvFVwPxv9Hftew+jtm1BptOIE+de2e+p4xjlmDm52YT8VELxXJvWAGAhOxMTVKDdo+j0KPpfMMw8EuDXNJ0e5QqU1pKfjEmdmCE/GdPuFaSXDH0vxyShQMaW7Kyh/1FvDVtuDXhY6RrKrIJOj3ssjJN16DllWXpQ4zcszX6BX3N8//K7mvhNS6foVEzjjKAx7OiTSQaq76lT3Z5i8j0l2j2K1LKjf/a+4GikVxzNZAKL9/j0H4/IzsDyfo9UwfFhs596Af8zzzH1NkF4MSS1BOGKoP+4pjXk6gOtAZdMchckKob8SZ/KLkc9HnjWMZbq2wzBgpux9z0asPCAYMunYvwa1MY9wjLUxiWpZWgOBZz7AcnIo4aeE4Kg7lYh5Z2KTl/gaiGXY5KMYy1d+tbbNOxtsCbQ7RV47Api0b39xo0OuRH048quN76im26/aXw1VwJDwKNCiGPAU8BfWGs/KYT4cSHEj3f3+b9wGfI4Du/6GWvtEvAI8P3AW7v00qNCiL/zVbzWb5jY/y9/GdW2BMtt1/LeHfB0ykOnPZJsd6msLVFOMPRUQnpW4FclQcXxnrPTgpW9AcvvbjP/joi5hwqs3JGmujVAGMvUWxXy8eM3hn3gug7iG8a1sNDtqhmsHf5qxs+NrlMGASqXpeexGUwujdm/HSEl5k33Uv2+h5j/Jw8jrNNfStISvxLRHA0pnTHkLoNfi+k5rQkahtylFknKcf2XDwRoXxDUDDolqewIWP2mLSzd7VPf6mSZOyVHzY2KTtvfSsHim2O8lvPaTTLu88nMG/qPd8hNdwiqFr/uagHb/tQS1CydR+oUz2tkAgN/lkYmlsKlGL/lYBqrYOWAJL1sSX0hT2PYSTXMvyWhNSCYfUTgNyyVnQ6Pl90kEFZg5g0B5YfbtA824YU8U28NMO2OkwZRdq37vDVsWLxXYZUlMwfJSIfUrIfXcjWDqARBGXJTsLpXsHKPRcSC3DTEBUtll5OCuPx2n4GjETKyGA9Gv1Cj3e8G2p6nAuYfVGTnEhpDivSSxq/DzBsk029R6JSbmAwci9n2kdskIGe7/z+3um2wLnAT9GO+C/HQvV/obp8CJta9fBxXW53qPr52+03jq6YdZK09D9xzg+2/vu7xDG6FcO0+j3LjjLYZX2EUJi21CUlQDUnNNWiN5UgttKjuyhGWnUoo0rK8z/00yns9sjPQyhmiiRiqPl5DkqpZxv7AZ2WfR+WAhsAg6or+3z/Drmeiq86pcllMs3m1bSRsTKrhtdhGvoZYb3iz/u8r2+Z/4jBDT9UQrRgjpZPH8BQohX/0PO279uPXAesYM6L8yrGNL1i5V+M3MrQGBEkaqluyZOYtmUXHvsrMx+iUxG8YrJQgBHEBhp4AGVvq45Li+YTVvR6FixYVQep8QKvfaRF1ejy8piUqCLyWoj7q4bXdqqKy03dCaeM+PR/J4jdipr8jxrucIjcFxXMOv1ctaA/A+OdjFu/2yc5bKvsNmcuKkU+7QdtvCMBSPOMkQuIATOIw9nAVel8OsEqw/J4GO77vRaw1VL/rCCIfEZxWqCWnW1TfZlAtycobOmAEcc52TegFqWVIUq5juHjOsnTIkr0kGfzyMumlHuIMBHVDz2knv218Qf8LTeKekNEvxk6CIwWDzzhWVGbBDfLZee3ot8LVXYKy67HoFG9nTeD27PMq6MdHgR8EfrF7/5F12/9ACPFLuJrqbuApa60WQtSEEEdwcNIPAL/6aufeFJD7BouF+wFl6PQGpOcD6g+38E8WSC3DwiFBsKNGfDbP0JOaxrDjk7cGYPjLsHB/iNxRp1PPkVkEqwRRCVKzijgvkZFYmwkCXNHk0fXG2mz7hnLSrzVeh84PsGb+cq3T2fpr8rZvwfTkGH6sgogS7JlLIAX2wC6ENiAEIvAZeL7J5LszbiptHZul0xfitSwr+yRb/8KCSQgrTlE0zkqycxHlHSGZJY3qGJKsQiaWRWfXzMCzlsaIJL1oCVct1a0uAfd9/ySXPr8Vr+lON/+gT+mM21e1XfF49Atu4Cvv8hEGVvaH+A3w64Yor5j4kGT+kDOnl7Gl53SHucMhpVOWcKGB1y6BhfHPQJSzNIYlQc1S3WEZ+bIhXE0o7w5o91lCJWju7UDVJypJ8pcs3ktZrLF427ZS2SlJnU4hNKw+EJG6GDDwrKA+Dn3HA7JzEUla0+pT1CecFWY0ccUqU1B8WWElzL+hD+O5elRtXFHZY0gtur6VqODjtTXCCLLzjjlkfYFqa9CuH8MErjdDJm42Hhc8OkW5IcrmhsJyOwXkrqAfx4UQR7vb/iVu8P+QEOJHcPD5+wGstS8JIT4EnMAxiz5grb2yxPkJ4LeBNPCJ7u2msZkEvoHincH/gf6v9zL+CUl5J9SOtMg8myYuOOOS4ccN4rEszQHB7HdEhC+nSbIGm09YlAHGt4RP52gPWhqjguaAIjMDCLDSYcZCqbUuWiHXicYFATaKrk8EV+K1DOwbEXy75vn17l/uEOKqgq9QCu7YRVwI8Rdq2NAHKRFB9/7kBawxICWEAUJbslNOKM0qQbtHEmchvaQZOKrXFDeTlKQ2Luk5l9AcCvBbltqEc++yUhBlBQjD8KOw+r11+n43iw4FjWFJ78mY1b0+Z+cGSLWhOWKZ+MuE1b0+tXGBihxnf/yzluoWhelqCYUVyMxprBI0RhStQYj3ttj6W5raeEB1q8fwF8vkLwXUxwQr9xTJX9Z4LUN1m091O6QWYeUu5zexcL9H4YKlMQbDTxim3yIoPRVSe0MTsZChOSQYeMF9tu3dA8iOkxypjQl6nwyo7LYsHDFY3xCUfVb3ucZDoWHw2ZjZh3yifk3+tMIEbpVRO9TGVnyEFshIEC5D6WW5xnKSiSVJuW5ho8AGbrCP8h5WXdE2sgRVRwdtjPjURwUmhDhn4Xc39lN71Z8hG6OAbmSfW6Afb7vJa34B+IUbbH8GuPPWZ3XxN90sthl/jXHu/z5E6ZjP0t1uhj/2IZ/4oRrt0ZjVuwwzbxaUdyp6TrfRTTej7DsmsMbN0sJVQXvIIuNXBMRaQ67ztOeUITcJsz/1AAh5PeNGqdcuF/FqsVEYqRvXFqnXX8fCjx1GBAHi8uxaAhBRgkm7BAAg8jmE75Pcuws9MYRVTskzzgpUZCleSlxhNi26bl6Sdq8bpGTiOquFtqSWEooXHH9daAcPFU5L/JbBe7SA19TMvSei52yC6mj6j7UZ+4OAoAql04LKDp+eUzG5GUumq+1TG1dkFg1BlTWcvT6qENqSXjIMPRUz/rsejeGA1oAgP6Wp7yqQWXANgJn5hNXditU9PjqAgecNjS0G2RaMfjGmeM46ZdNVWLpH0fOSpHApwc6lEQYmPl7GCqh9z4PoUNLYplm8FxrbNEnaFYH9VUXPUZ/cjDOjN6Gzt1y438eEltx5RemCJkm5pjI5FxIuuc8v6Unw2m5FkJvRZOc1nZLH0j0+OiXXmraSUKI6BhUZ/KYhSUuStCTOud9738uazKyb7NyWsBu8fY3H5krgGyj8hiCzZEgyktSK87ot/VmOyg7H+wbHvb7w3hCRGKKSswpUFY9On8HmNIXjPiP/7XlkJkP7vu1c+F4IJwMKH3qKlR98ENXpwj7X1N50vY59w72IL79w44u7zZj/RsLr7cFaw/DvvwRCgDZEQ3m8RoxoRWAtdtsYeJKoEOI/cQL5xEtw3z7nq7zzQdKLBh06DSAVuVlfZlEz84hH4Rw0RgTFc8bVARYS2n0Ov3YNVpL8JefO1hz26H3Z0OnxGPi0IElZamMpVGRZOmjZ8skYpGDxLo+5Iz5CQ2ZekFoxlM66btnUUkKcU6zsd6sCmShSZUNj2COsuuZAr626Xb2a6TelGTiW0Bj2MCGkpwyLBwW1HYKBZyCsai5+Jwx9EYoXNXHOdTobD5bu9Og7Zil98AmMNWRfDhD7dnL5XT1kLjtzm8JFg9eKqW8Nyc641eb8g4rsFJTvjzEnHJSUXkqYfcjn8rsANKAonQKvbUkvai6+R9F7IsKqLnznucRbOssa1COMk8sAUC0HtYWVK7UPg2oZpLYgFDNvEq78+hWH3RjU8zUuHbG5EvgGivxFi1839L+UsHIXVLZLlg8Ihp6KGftihzhviQsGv+KW4HqkQ3tHh+yUID0vSU36RAU4/Yv3cOEn9wKw75+eYcv/+QQAPf/zSQb+6xOY+PqOTOH5zD6SwbzpXoQUSN+j+n2HaX77A1+5ztDrCHnffmynA1eutTvjTzIe9tgpdDGD0BaUwHoSmRj0oX2Yh+5EHDsDQqIii9SuISy16vx5hYbamIeVFhN0O2aXYzILCVFB0eoVpFYN9e+sIrUzgVEtJ9+9utetILyWRcaG7LymskPQ85JkZa/Pyh6P7Kxl5LEY1QHVtsy+0cEnScYxYJKMpPdljdCQm3WaQoWLEbkLNTq9Hl5D4zUS6hMpvDpEOUl1p0AkOBaSDwPPOM2h+oii/wmf9GJC/rlpMvMxhYsxQc3Sc8ZQH3vle5Nbxjnz/SUmPrVKc0LT92IT6wkqO0PGPu98FRpjEKzC6r0JI59x9ZDMQsL0m3xSy1A8oSieVOgU9D9fJc46Eb2tnzSYUK7BQABzD3h0Cq7eIrRFdkxX/dZiQolqG1R3m9TW0Za7tNrtH71NfQIGhLa3vn0jq4huxtdOPPxd/wkroTHk0epTjH6xKxO8Ahe/Ey5+q48JDCarMT70viCQcyGZkyHNMUt+0tIeTShccF2d7e0RUcGD0SG8iVGWfvgwQqnr6JZXYBfVU6TntOby21LM/JPDLH3/IUp/cozsR575yiGi10Af1W8+iMxkEKcuXv2EMSR3bSdzdtlZZUrcSqCbHOKCj3j0GI3RkJmfvI/2Nx9c0/zJzMd0igqv5czYq7sswgiqD7dck1bBo7zLx2u75iZhwD5ZpHDeUSbTS84QPj/pDNyTjNNyagwpek5byndYwoql52xC7VvrNIc8VAfaPYKeF7s9GdJpE1kBK3udSYxqaef6tT2gM5BBBxDnFdXtaRqDju3jtSyDz2oyC5b0smHiL50WUG42YfUeTXpJ0+71aNw9SmPYxypBUHcw2LZfP7VWezn5r0t4TUFnMMvgE5LmcAoE9D1fcbpJGWgPGAafjxj/lFxTMJ18l4fXhCQL+WknU5GbstS255x3QiAQiXW00EBgBQTlmPHPtajuFG6WHxmsFKi2IckojOcotFY46q7x3H5YS3WLO95ti004aDP+toRMLCoShJWEOOu6SwuThvqoxF/ySYoaNGQu+LR2R0R3NuBUifagRbUF8484fGflgMBf8TBjbecQNV5Ap0oMfPAZ7A0G4ysF2Nnv2oNft0x8to38q6PABmXWb+Qudv1JbnkYoRRqoA/19CkH/cAr90qCMcjHj2MzGZpvvoNwsYMQFnH8DFhDOpfFSEH+w0/T+eHDdIqKJOXon6oN7T5X6ExyMPolQ3m7olYM6D+uufztCVv/2LJw0CN/ySIjgzCKJC0IK90+DR8quwUygtaAoP849Jxu0+4LCJckzWGobvforKTp67qNYUGHgvqoR1g2a0qiKwd8el+yjslkLEILVvf4lM7E1Lb4FM9HtHsC4jxIbSnvdEmlMSIIqpLyXQlbPwojf+Wgn+xMRGVHiA5BRZKgbsifrpIsLwOw8I+OYCuG9kjC0l0BScb5B/hNSXlnifo2Q3YSZCyY/GFNz1/6LN1nKL3skz8PrWFXN+kUJH4N8pfaLunqrrqtJzAKgmpCuy/ASoHX0mz9iwZxIVj7fUelVxrqVNtglfu9W+GSZJxTDByLWdmfgi9s5Md3i7BsDOr5GoeDNpPAN0hkLzvbqIUHCm7g6lL5rYBtf95k6i0Zek4brDS0hzxa0z0oC0OPJmT+8kXO/Lt73Eoh5aQBskfTtPstmfnIyQWsK/peC+94/b2M/PEZTKWG6bRfG/6/vk/gdVJD53/6CCO/cRRbq4NSYBzVky4/X4QhNk5QWyeo3T1EUEnwFiroS64rX+bzmEqV6nc94DR/uppAScrZLcYZQe9JR6ntOeOkoIO6s+xcuhtEVYDRTHymTlQKqWz3KV5w0tvzhwUDz0JtmyC8s0xtNk/xZQXW0in5xBlBesnpBqUXOlglWN2dojAZYwLhVnaDkFqGVNkw96DPyOMJ2nffQackafc4Km/PKUt6SdMc8kmyULhkafUp/JpLBrlpV3wNy4r5Q4Kg7GQp5h4KUB0YeTzBryb4R8+hqzX33Y4MU3+4iTeZJrzskV6yzL9RI3xD5nQAAjKXJToFPScE7bk0iw/HlI76xFnoPZmgOh6VPRa/KqjttKwc8tn5vxKignNC89qaqOC5QvpsGwCdVlgtkIkb9Y0n8BoaE0qn2eQ5N7Qo7+G1HE1WJi6pVLffrkHZbnoMb8bfjniHfD/Ld+dpjmXILBoGXugw/Hsv0ftijfy0oT0Qkl6ExohTqAwXJTpriEZi2j2KU//+bnQhQcYSU0ywxYR2n7OnbA65mZgaH70KCrqSFKyx6JVVksVlTBStDeKvqw5wqwRwzUqk8XcfRGUzjP7PlxwFVKm158TQAGiNGOrH1huIwX462/sIVyJkrEmGS4ggcEwnpah874MIC1FekJ1NaA4K0iuW2rig3ef0csKKYekuxfTfjQnLhnYftMYSBp8UTL3FozGeodOjUJHFb7prHX7MHSOogH6yxOjnBI2HG9QmJNWtik6PoD4BQSVhZV+adl9AelljPeEkqo1l+MkO1oNWr6R4wbJ0wPkX65TD0b0W+DWo7AjwOq7BLL3o8Op2ryC9rDGeo7RGOZfkBp/XIJ1ip9cSjH8+YulOD/XoMXSlAsDyjz3My/92C+JSBhPgmDwZQf60h7fg09gRk3KLBbLTDv8vndV4Kx7ZeUN70DL1dokJYPRRQ8/piO1/1mHLn4MOFTJ2jV6NkcDpWpX8rly0G1SjngAMa/COCSQ6cPDYlW1eFwbyG5rmkGLuQY/C+b9eKemvdUhoMwl8A8TSjz+Mip3CozCWxXtCpn/4ALUdOYSGdo8kysPYhy9ROmfQafDLEuFrlt/RxqtLggWf1KIAYUmdC0hKmu1/ACv7BAsHA/TUK53pV/HvpUB413u63jaq6PpYlyS8rRPkP/4C+u5dDu6RV1YUAjE6RDKQJ75vN/FgHrN/O8lgAa8e4680MaFyjWH7d7L8gw8w9/69zv+3YRAaqtt8Smc1q3sFhUlDbsqxqFr9ktFHO6RPpFjZL+k9qfFyMdVtAm9Hnfn3t5xkR80VMhvDktRSTPGCoXg+YehptzzLfyFL654WnTfWKN8dk5uETsmjcMk9LxPnBnbhO13heeFgSHNAolNOfG7wuYiVAw5zb44I4pxrMgtqbuYvtIOckpQgN20Qxq0QrqiLqsgVjP0a9J1wReZ2n8fYLz7hqLZCcuEXD9PuAxKBzhhsf4eeU5rGaNfhrCKY+Lgks6DRKVg+aIkKLgEOPe0SUFAWBKuSzIJBJq65K8l5YFmDb6BrJt+VM2kNpWiNpLDCDfRJVmECidR2rTCsu01iIrHoUKwdJ85A7jLkp2+TlPQVOOiWt9tzuq9WbMJB3wCRndes3KHoebbM9Df3E2ehcNHS7nHL9OKFBBkrzv/DrQw+p/FrTu/Fn3RWiEPPGBa/u4kXxoz8QZH5I5b8KcWF74Bw0c00F37sAYZ//wS1t+1zWvCDkv4XOsR5ReZTN6GF3iheJ+RzJRY+cISRLyzTHs7jjZSwEuz2MWSlie7L0ekJCcoRIjYIT9IaDEktR3iVNtZXmEyAarn6R22Hk89O0s4ApjmgCOqW7HSHc98Z0HcUSp89y/z79hBWBbkZQ2vIJyw7dc2p9yVs/V2PJK2Z3h3Q+3hIY9gZsDSGA4Yfq9EedJ7MScZx2is7nOH8wCdSVLYLCjWnAFq8kHD57T5WwfBjbrDPn/RJ0s7NTQeO0WN8aA755CahOaAwPuTmnOyEiiwr+93qILUMS/dZtn0sWetcttJJLzsqpiA7n9AY8dj2fz/nVnEAQnL2d+5FLFtSy4Lmbs3O33LeCfURp+KZvwhxXjD1VkFQkfQfM3TmJfmLTVbuyJBe1ngdy+CzjvbqNRKStEJqS7vPI6y6Am9Q1ahWQtTjmvOsABUZjHKCdkK7xGGl+1vGFtU2jtHl5ivI2JKkJdUhH7/p3kJ1y+1zFtuEgzbjb0WoyLL1ty+gT56l78WI4nnH7oizEFQsi3d7rBxwGHB5p0KHEJTdTHDgeUe1G/lgikYzZHWvJH9eoFOw97/XnHVgv4MNKu/eR2PYURzHPnyR+kRA6mPPvDKA3CzWwzgbNY+/JuZ/+gjtbz2E1LD0YC/VrQG17U5T3noSk08hYk36UgWd9WhOZPDPzZGkHcYe96YdtKAt5T1pVu/IEuUE2ZmY7Lxx3dDG+S6v7klxx8+fRaegdd92hIE4I/Hajs0Slo3jyT8fsrLPZ/pbDLkXQ/y6ZfjzC4QVQ1C1rNyZI8rLbn1BsHKHpHDREPU4bf/elw39L0UMPtdmdXeACS12pI1f11R3CBr3tLurAoEVgtysxm84K0kTQGtQ0B7StPoErX5BlBMkKWgPaYKaZfgxiIqe0xmacB4QUoPXNKTKmuo2j/7ffHLt+5v/x0e49PNHMG1FuCQp77OIqsPqF+/2WD6o8RqCdq8gO2Ppf14QFQ3VLZLcVIzQltLZDunFyCXnrqdCklYYX6DTChm7VYDqGFQroTUUEmccFVRqwEK4GlPd2m0UM272rzrOjEbFBoxFJG51Vt7pE1Q0ft2twEzgmhtvW2yuBDbjaz3e+O3/EQm0D4xh7xpHdTRe2836Kruhvt0V5ArnofKWJpknM0S9hi2fMlz6Dktr0MNreiAgddSjvjOheF4x+vE5Tn1gGL/mNO6thMKfHqX5o/exsl+xdPc2Jn7+sdc3q1+/GrjF673RYZr3jmOBTlGRWjFUtzr9nVafIH/e0hxNk395haUj/VgBhcnIado/shUduKJobSLA7/Fp90r8hsX4rti4cH9AetHSKUJjq6HnuCKoG1bfvgsZO0nl7Jwld6lFfWuaoOboivnLhtqEJLNoSS741A9E9J6UzL5tkPSy8xeIc65nwCjBwre0GftjN5tOLeCkuVPOp7g5FNDphZ7jktyMR33cY+ipBH0soF0SxBmnxCm18+ZNLzkYJLVsSS0ropxjLq3cCdY3pObdzF/GFr9hCD57lP6uphJA/NaDhH95nMGPR2vj1/TPHKE1Yhh6UlCOfIwHJjTkzium35R1RveliEgGRKOa/Kc82j2SgWdh+S7L4HOGqBiAErQz/lpNxHiO/aM6boBWMfjVhCSjaA2GeG1DuByRZD1UW9McClEdSfG8S0zWl6+sCnyBTLoe0MbSGPBILxu8ZgLCY/YtCf6Kh1+9TX0plo1R3L7Gk8DmSuDrPax1dMaMWsNLp99qWTxkESNtvJp0ZiZHa1gtiN9QIzXvmpZoS0xPTN+LhuwU6BTIlhPgqhwcIqgIOls7ZOYdjrz6XQdd92YCxfPX/PKvyEdfM7MXUlw/0G8gcag79wBQfuMWRGIZ/4t5gpohyrkEsPSgIaxYyvsydIqSxYf76f/SHH7L0hwKaPVKN4OWwrln1V3BVMa2S4V0q5vSGc3yvZZOn5PLCGqW5pCkstN9brlpS3WrYOabsqjIQQ+pxQ4z742o3hs53fzLluFPe0z+XUN23s2MvbZ1tNCtiuaQZPDjKVZ3KWrbnRz09PdFLN4naQ4FhGXN4LMxmcUE4ztaaZyV+HVNnIfUqkXGsHoHVLfD0kHn7qUDV8fQGWi9sU7xrMB6boWxuleQ/cgz+J9+dk1SQ6bTWGMJZ6qOxQWoQp4z//lBOv2WzLRk7p2RczTbFlF6SaE6To7Ba4FdCuk9qggnA+aPCPJTCWHZMP55TZJT0O349ZuG+ojX/V2635MJ3HcRZyWqFePXE7y2S5ZRwccoQVT08RvuWq0SroGs25DltTR+Q7N8IKA14NHu9/FblnavxKtHhBWD6EiCsoPMbkeIbsfwrW6bFNHN+BsNr2kIqo4rneQU1QmP1LyrCSzdm8LsaCJPZZh+Sx55GcyuOtGopjXsTL1lRRGWY6KcRDUh05a0S6C7+PPenzjB2f/zXoSFxUMW1RLs+FdPXl/4XT+wr5vp36xAfDOhOfPGeyjvSeM3LfHhIwz+1TzLRwZp9w7gtSyFyQgdSoa+rGgOCIoXHWVTh4LZd41gPRh4vkVzOCTOdmeNmq4PsMORU8tOSqHTA/Pf1cY/naV01mKUIHe5TWvAaeZ0eoBVQWbOSR13CtIVXMMUg59yx1o+4LyXs9OC4rMBi/dCz8krna2uMcpvutcGdcjOg0wMqY+mSNIWr2Vo9XuoyLJwnyS94HyFG2PQ2qIRbY1fV+g0ZGYEnR7oOw6z79SMfkKhYkvxnEG9nEYYDVaR/fATZD98/QRVNxxobk6eAyHxdm5j7u3DZC9Da8TitSCYDGkNgj8T0BjrmtCPKKpvbBG+nCZOu/c99gXTZc84eRKv4ZzUrgz6udmri7NW4KC0lYT2UBqvrrvicIKgWzPwGk7q3Hjd7y12Hd0yNpR3pcgsavpejGiM+LR7BJkFy/AXlmjuKDH5rYbMBedYlmQ39K9z67DW0Y03st/XcGwmga/jeId8P/UffIj8ZMTCfSFDz7bJLEpURzrGSgPiRNJzyrBywP1zxjMZel+UGN/5t9a3a0wg3D9+Ayq7Dfv+zSlEGDL9XTu4/E8PYgPD+Kc0mZdm0TPzdN55H8Ennr7pwH/d9mu3sS45dF8X/Z1DToisluA3DNmZCASU7x9AJs6Y3WtqZEfj1WI6xQypVUttTLmZcmJp90HPaUcZbAy79+jXnAmL17JdvR0nxzz0m8+x+vMHGf6jFHHGojqW+SMCmaTw665BTKcgyTi9JWEcQ6XTA1Y4/HrwuTare1P0vWiojUuCqtPL7xSdumd60VLeI8hNKpojkJl1b9l4gsaQs3/slBRhxVDe2WW4FADrtIeEgIFnJFZaBp5zvQ/Fi66QnX/JR1hDZZuTYZj49ZfQ1Rqpaz7vmyXbc793D6kX06SWoDlu8KuSqOD6BtqDhsxlVz/wa25QtbMpRynthZEnEy69RzD0ZYVfN3gNN6C3+xxnXySWTq+HX3dKpzpwbmCxL9BpiQ4cx98KwFqStHpFEhrXCKZDuaa5GWcV6RXDpfdZRKSwnmbbR9znGPdlsQJKx3x6T0bMPBLQGb49shFfL3DQZhL4eo6H76V0uknUGxIXIM57BJWEcBWChkdqOWFKpli616LaEPVrRCKov6OOPZMjHokoPRNQ2Qa9JzVLdyl2fajD5I/fgdAQZx3tNDPlBojgsqOJBp98duMQz822dwd/1dtD+9BO0pNVkmKKqCegeKKKOXaC6N0PICM3u1RtTeqFSTr7J4h6PKyCwoUOS3elUB1L7nKToBoSLrbp9KfILBoyc64TNs4KgrrrVF2+M42MYO7H7qN42tFng6rD2HtOQFRwGLzfddnKzWjMnGDhPknhvCC1COkVi9BQHwsJV2Hmba4Jb+Tzzvs2N22oTkhqWwVe3dE3S2cNlW0Sr+2YLaVzGuO7Yq7QltSKJVx1omqL9wnGvqgpXFR4LY3xnTlNkpE0BxStfsjOOl+DiX//NFZr9LrPdP3ne20CaH/bA8w/oFCXHCupOaYhbbA1V2/wqyASJxue6iYxK8DkNNmXPVKrhnZJMfYZg99I3HOBG63DsntPnR7lZCB8p/qpA8e+8huGxqAiN5s4qYeOcawgKQhXXF0AKdApiYoM2pcYRVdSArLnfIaejpxqaGJQsSEq+UR5ReW+iDgfYHwY+qLH5Ov7j7rmN7ox5s8mO2gz/sbC+JKle7J4tZgtn2hQ2eahIuOaekJBOFNl6yeamNAS5yxjn4XBpwWFT2RJcgZvIaA15OCSmTdKtvzCkyzek0bEbpWgUw4/n/jzRXIfeuq2XrtMpzFvupfam/cQLrWo7yriz1YQsaW1JYd+y/0ElYiV/Yr6mMBrxKy+bSfNkYDL71KkFxN0KEmtWlKrMe3BFO0+j7gU4jfcasL4kv5nygw83yR3uU15d5qoCPUJqG2zBA2D17KoyLhEUbW0BgHpdPt1CI0RxeJBSX7SzYhVBOU9gtV9gnaP8/ctHfcQnqHdI6nudB7OqVVLaglyM24FUt0qURHkpg29J2OEhZV9ksaomylnZ2KWjmgQgvS8cGyklkG1DanFDjIxBOWE4rkOQ89EGE8w/guPYZP4utWWt2XslQ5srjwlWP37h9G+IBqJHTyTNeTPK0RdMfC8Icm6lc7AczBwNCY7GzP0tGbwOcPwFyTZucQVnBPrfBY8gQnEmpZPp0eRpJzshNeVfNah+1t0xeEKk06HSeruwCncMTq9rinRrQ7caqu8x8drGqwniAqKvpcSFu4LnFeDAK+lkbFl+W4IpgL3nWUsS/fcph/pFTjoVreNJAoh/ocQYkEI8eK6bb1CiM8IIc5073vWPfdzQoizQohTQoh3rdt+vxDiePe5/9x1LHvV2FwJfB1HMFfF2xYCMHcky8ijNaq7s6SXEoK6BaUwvmLLJzS1cZ/ZN8o15kS47OSlrXSUOtkRNN73gKMQdtxMOEkJRn7pCZJX4/bfYPZ5q16A8g8cRhhILyYUji1AkpA7P01SLhOcu3DVvlueDbj4rw4xdyRDatlSuBix48Oa1mDoVCrnY6K8x+JBRf8xzeqegIHnGqi2w9rjbIFO0eHMYcVQ2y4ZeE6jQ9dBqyI30BbPuxlm/wtObymzYBy0YSC1Kmn1SlLLtqssKjGe++zCqkEtGlIrHqv7YOBZx2lvdr18Fw9B3zFLUHMUVOO7GoKV0PuywfhQ3aLoe9nQ95TH3COa9KxCBwLjK0ILjVGf3HRMeU+ADmD4yzV6P/vcTT/jZHJ67TvwhgdZfsd2Wr2C1jCY8TaZE2mCMiAkSQoQDtv3q5LhJyMW7g/IzrgCbWoxYfahDL2nnZy1X00QWpGkHbxohTPekS1XIDXdbt6oqFAdl2AxFisEUV6i2pbUiksmOiWdc7oBcHWUJC0RviAR0HMqRqclle0e4apl6m2S7KRLqKNfarFyIEtrAHTaUDgvaIyCaoq1LubbErdPO+i3gV8Dfmfdtp8FPmut/UUhxM92//4ZIcR+4HuAAzhryb8UQuzpOov9V+BHgSeAjwPvZtNZ7Bsz3pX6PkQQ0P/xMvg+w/EgK3fnKFyICBbryDhLUggJlhqYtE8xMpTOWprDAc0hSe+JiNlHAvyqE0XzWjD7BkHxNPQfbeE/fxaUcjDDq7F51j93bQK49vGV3SyEFU1zyMNvlBDaMvcdEwz/8mMAyPsOMPX2IkHNySmPPhox9eYAoZ0y5sKhDL0nIpb3Bww920LGhr7jAqsEfS+2mD+cpfdk7GagsUUPKfy6pbxLMviMRrUN5d0+QRWCmiAZ8ugUHQsnNxWTXvIwnjODVx0HVyRppxeUpASdAgQ1yE85OQbjS9KLCTL2mHm7gViSPwf1cTCFhMUjgsykR+m0odUvAQ/tAwKWDlqyl6G808drO/OV1IolqBt3bE9QmxC0ewKkhtHffRm9snr9539tv0W3znLpsEduGrILFp0SxCbt1Dcz0Bqy9JyAzoMtlg9kGftiG+NLiudcQ1mcVfg1Te+pBJFY/Lam1R/gt5yEM4DqNqEJ64q8UV6iYuuotMIlOxO6VY3UbgKiQwmYLmQk8OuuMGyVQFhXEzC+U1oFyE9qqlsVqilQMQw82VwTlht+qsPcg872068JoiKkF28TPHMbawLW2i8KIbZds/m9wJu7jz+Ik737me72P7TWdoALQoizwINCiItAwVr7OIAQ4neA97GZBL4xQx8+gH96mmRhCfHgnWBh4AuzRBO9NHaWSC20aYynyWqLCZTDLQUEFWeNeOG9HqplCVcF/c9DdRvkLrmCnfzSMfS6Afw1WUZeA0Os7Qdc/IWHSXKG/HmB15KklzU6pfA/9zwjT8LMP38YAL8OA8diVEc72muoGP98RJJVVLaHDBxtY5Wg90xCpzdAGEtqOebitwVs/4ig53SMaiVUt6TplCCsOiplfsrp8XeKkv4XYqrbfLTv/INzs+79TX6zz8Sn3Ux15Y4Ar+GKmlGPoO+lhCQjSa90G4QELO9XFC9YrHB1itG/lDQHJbU3NtFNn8EveDRGBK0Ry/J9Dfr+MMvsG2HgGYEOBb3HBbV31sl8LMvCI5rUtEctL1h8xDD6GYXXNOQvS/q/OEtyYZJrvHyu+nw773mQKC+ZPwLWs5DRiKohWZZUdkFu0llTVh5p4Z91DKj6hGDkd0NSczV0xiNYaeFXJXExdHTazBX3NDebD2oamRisULT6FEHN4HUsSeg0iVKrjq2FcA1hJpBUxhXF8wbZcT0WQnQH+sBJP8R5D9kxRAWPsOxosn5Do32J9Z2GUqdHMfpYgpWCi9+SITsD2VlDbSIgycLCQ4bScUXprF4rMH/lsTFTme4+DwshfnDd5t+w1v7GLV46ZK2dBbDWzgohBrvbx3Az/Ssx1d0Wdx9fu/1VYzMJfJ2GakTY/hJi6xD2yRfwdu8kmuilNRiQnWoR5/01V6Yko0hP1WiN50G6QW3wKUmnKGgNQXNIEFQd/3/4I+cxQYAc6ifpFoKvTQBrSWH9zH89Ln3N9pmfeRi/DvlJaA5L1zXahaJW9gWIPYcdH/2leG1WZT1BFHrknryIXFpm+p8dZuBoRFBziao2HhBWDViYfYNi4HnJ4FOW2kRIqqyJSj6psuPtL9/pkZ1z8FOnpPCMm43mppMuZm2pbpV4Teh9wUES7R6FarGmKVQ4J4i68tLtXkFQc4XzsAzphRiEw62jvKDnTERmMcXsG6Gyy7G0spcFUTXPzFs0xVOKhYc0E5+Cy9+uGf3jDNPvSggWfLb+p6PU330XC4FPeTds+ViV1T0lkguT67+AVz7v7n35Bx+i8p4G+mIWk3HHisFBPRryF13NIzcVsXK/x/YvOBlrV2uIiHtCJ0qXdh3COuV6SWrjkuy8pTTbojOUdrN+7fotMksa7buahtRgu81oqm2IcwoZudVMYTIB4bj/Qltk5MTghLHYQFLd4tFzOsJvOKewqKAI6k5eenWPR2rZ0hlJaA4oZAI6Y6hvEVT2WnpelKg2qJokO+fIDWNfbN+efzJjQW9gKeD+Px6z1n7v7TnxDb2I7atsf9XYLAx/HcY75PuxnkR0EtRyHYRk+cggMtLkLjQQsSE1U8Ova2bemCU102Dmrb14jQTZcQNnUDOoCIIKpJbc/eCzDdp3jmOTGD3t+Iw3UgO1xl7fGHZl8Hc7ABC9637KP/gQQRVq251UgtcAv+l0YowvaYw6GCaoOQhAp5xnrPEEQTmmc+cEjfceoue0RmqHyRtfOiw+slS3KjKzriO1cK5BkhFEOUmUV9THnA9watlp2c8+5LqI0vMxUV4SFRz2Xh9zejvZOU1+Kqa8U5FadY9NAPVRj06PAAGdkiAsu+toDblVxPL+gMbwlcFTUN0aEGcFpROSTr+meWeb2t0d4qKhdEJR3WnIXFbMvEESTAYkKUHP8z7b//XT2E6H7EeeYef/WnFNe/sLDP7aE9d+AVd97mf/8xEWHzToS1mEFgQLPv0vGPyKQiSCxrgTV0sygvPv98hc8Fm4N4UOoFOURD0B/mpnDePXKYnpeiznZixhWdMay2DUKwM81mK6oq22+xO5UjRuDTqCgnMLswjblZCIu1pAfpdxFEhE7NRFlw8EmMD9flIrSdeTwSWwVNnQc9Rj6aBl4bAhXJIUzgkyM4ryw21XEO5LqG5TZGdgdW/qK/wPW/9Zb0A24vXHvBBiBKB7v9DdPgVMrNtvHJjpbh+/wfZXjc2VwNdhqLvvwMYak01h0j5ee5jSHzzlpJ2B5O33ET5xEe+lBmOfExitGXpBonJZbJIQbpugfG8v5X2W0S8aVu5QDD/eYO6hLOllS4/nr+nJXAcDXZnh36hOsP45a2iMeNS3CFQTklJCZYdP30uOiROuaqbf4jH2hRi/FhMXA5KUJEkLsrMRqqWJCj4zb/TZ8pk2jZEAr+W6aMu7A/yG4/YXL7jZPRaW78o7ffqW6w7OzBs3c++BvllNXJIEVU2nxyOoGae137LkplyBdmW/onhOULhsiAqSqCDJzBvmHxTovEFoN+qV9wiGntak5xV+wzWSqa5UR/F8jGq7lcjKPsXWjxkW702RWbDIBBbe2iF1PiS9AGHZmaH0fe4SttXC3LcPEyjqEyn8piW1CD1PL2B8DxNFqFwOkc/R2T+GFYLUVIVz3zdA6YTraaju0ci2xK8JFg4JNwAb1xvR2B8RPh4w9GVJZSdkFt3gjhTIyNAadUJ3JuX4+qrjjNwz87GTrO4Wbo0n8JqaJKu6kI5z9YoKitSKprItoDAZu8J75FYUItbYQBLn1Rrbx53M0hrwqU0I/AZgHTTULknSKxrVMuSnHIS0epcmXHQdzO1+x0zqDCXkn0sR9UA45dMctfScdv7PtyU2Osi//kTwUeAHgV/s3n9k3fY/EEL8Eq4wvBt4ylqrhRA1IcQR4EngB4BfvdVJNpPA12NYJ6BFYlBaQzqF2jpBMlzCW6yh5urou3aiTl5CpFLQidDlMqKnyPy3bGHwqSr5P3wKHR4m87FnSP+Zkw8ujD2IDsVaArhhLWCjRWKgcDEiveIx+a2GwgkfGTufWxPA4mFJ/owr5rb7Q4cBF52Buk4p/HKHcEkz+iUwnkSHgoX7fTKzltSqIXexRXM0hdROQK856F7rBlvH41cdZzKSnXMJY/ufdDj/d0Pnp7zkFDXjjEBZN1CGq+41UUGSXtKUd7p/n6GnnTSHTCzm/cuM/LcSUd41qSVpQWW7JMk6OE2HiuJZH69jKVwy1CZ8ek9qkrSD4fq+FJJkHMw0/PlFzPlL6K6sg077BOfmyZsBALJfugTpNFZrvF07iMZL+CstVMMpb64c6kdG7ljNYRh4SrL0tg7hZIr+Y4bpd8DEJ5zEc73iOPT1UcHAMYfbCwNePSZJK8QVxmZXc8gK8DrOz1es/wl0Z/VXYEav4bwPwtWEi+/xGH7coENX4C3v8imddY1bQlvo9hBUJyRey9lNBjVNz2lBeacizkpqE5Ke004iojbhs3oHFM4JrJegWmACV+8YekYz+5BH38sxsw/59Jy2rNwhWN3lEeytvM5/rGvC8lrgoFcNIcT/whWB+4UQU8C/wQ3+HxJC/AgwCbwfwFr7khDiQ8AJIAE+0GUGAfwEjmmUxhWEX7UoDJtJ4OszEoPJhojA8ZhFYrDCw5tZJdraB0BrMCDtbcNrxIhz3VpSnFC4ENMeztD4ocP0/s4zr2D7QPbPnr5q0P+KPAGERH3heS79P4fxVrosj8u6W5gVjH0GchfKtMZyyMTi1WJybU19IuV8Z09fwtRqhEpRe/8D5C9FWBFQPN8hySjigg9CoH2njV88Hzll0QlJZlGgA6ev3+x3eHWccQNF4Wx3AJqMqI/7qAiStKBTEKSXDO0ehde21MZdoVfGTu7AKli815B+vB/R78TrvJabgadWIO5A4ZJBxga/pvEaEZU9OVYejsj9b3dOKwXLDyRkz3uM/PZxknqj+1F1sfRHj5EYi5idc2Y91oCo0HrvA3RKTo20f6WF9QQrd/ikVix+AwqXE/ymIjubsDIfojqwdLfCX4GZb7JkZgTtAUujqNny5+A1NSKxTL85xdAzgiQUqNiifYHXcrN7kViSlFt5WeEYPum5NlEpdKQZzxVvhQXZcdj/+OdcAl7Z7Qbn4sXEwUahRPuC8m5F/4sJ1pO0BqE5qiiesSwdMqiWpXgRek5rxy5aapL1sjQHfUwA/qqi02uxPqRnJcazpBegNuEjLCw8aEnPCurbDaVPF1//7/aqsNeTHG4Yt97nVeoFb7vJ/r8A/MINtj8D3LmBi1qLzZrA11m869C/RSQaEWtko4OsdYtgEmoHh8Fa/NUW2ek2/moLrMVuH6Xz7vtJtg+TOb1I+Mln6X2pAdagetw/jFjnyrWR8Hp7br0TsPPnniF/0XnZrtyh6BQEXtMNFrIZERUU4UpEeyhFYywkvRijWgbdHSDVxDj5P37aGaOsGHRKoSKD19JkLzVI0g5rXtkX4LVd0gorGr/hZsDFi5rsnCsQr+wP6D0ZEdQMlZ0BUUGQWkpYuhvaA47DryIH66w+1CEz72Al1bFgYOhxideA2lanW6M6TtEzs6BJL1oaQ64fISp6tIfSZKcjMqcDGkMeUV7S7FeoqmLLb51ae3/r40rSvSL4diVUx+C1LZlFTXNrHuMr0kuWlf3u/OUdHosPGjo9itSCS2Y9py09pyxjf+UonoPPWrb+mTt+lPeIih6FC3atBqBaxiWCUK7p+Rsflvf70C3odnpDkowkKiqCmoOSrIRO0RnFeE0NxtJzNuk2WrkagGoZ/IbBr7v303dCM/SsZuyvYhpjAtVyYn0r+yRBxdUDdDagPuoz8YkyAMOPG/qPQXpG0Bp2dS0r3Wpz9NEOOmOI86Da4vZJSW9IRtpuiCL6NxmbSeDrLGQzwmRChDHOYNv3QBus55GdbCATS2cwi0hc96n1JCYd0C4pePwF9OQ0zW87hCo34dABGBpA3rcfrPMRlr7H/D8+wuJPHsEbHb7+AroF4Ssm5LcMa0iV3Yx18LkYr23JziUgBOV7+8lOd4iKPuFK5DRlgE6vx9KPH2H5Rx9m5j1jLPzYYeQXj6LahuUDPtUtAbUtKSp7c9QmJLnpmLBiCWoaFUGrT7Fwvxu8a+OKcDVxMMPJiOqWwKmBhnQHdJ/BZ113r+6qT0YFQc8TTuc+teLqBV7Hdf4GNVAtZ9qeXXAwz+oehdeyWA8q2xXNIYWILbUtAT2nDZ2SW03oEHZ+qE6ynucPV/k3A1exf4QUhCsdCmfqyMjiVxxGn5mLKZwXXS1+2PIJS2Wb7EJfBtXpKskaQFuCiubid8DMw56DtboevVZCUO3aT0YuOSOcXEhQM/S+3IVzur6+Mnaid50eBcYSZxVBNcEEgiSjaIx4rraTVS65tJ0PgPEF+SlNlJdUtitneBS6xrmhJ5wwoVUw88bQkQPyPoXJiOX7ipTvipl6Oyze5/oRVFPSHFSoNlz4dsX59/mousJrgk5bvPrtkpLusoNudftquOjdxthMAl9nMfPOAVoTOTCGxUf6sWk3cokoQcQaEWlSUxU6vSl0LsRKifGdGFf8zvtZ/XuHyD92gcVHBqjsyiIWlhGxhvv3420Zw8QJxoPWIFz6/m3XX8CVwu8t5KDXexE3hkXXYtBQuNCh1d+dGQ9I5zgVW0RiKFxo4692UB1LasUNgr0nI+Is1L/rQTolV4htjnQlixuGoAb1MZ/0imb6LYr8lMZrWXpOOuZL8b0znP8egdd0GHh2PiFJCTKLlvxUTGMUJ1R3p8brOKXU1iCkVpzUQVA31MckjSHHDvLaltSKk5hevEdhFOQvWzpFSXPIdVovH4rp9HosflNCfUySmzFk5xO8lkWeunhriGHd8/qN92CloL41S5yVmEDh12KCSkTvyTYqshQvuuLpwLGY3OWIoJLgNTVbPu4ol1JbopJCKMvIE5qpt7kaQGpVd6mdEOcdJo8Ar+Hgov9/e28eJdl113l+7n1r7JGR+1arpJK1S1VajW3ZWLKxwWwNhp4zMDCcPkwDM01PM8AwC4ee6T5ANzM99LA1MAwNbrDd9rTbGO82li2rSvta+5pZWblnxh7xlnvnj9/LVEmWbIFLqEoV33PyZOSLlxnvRsa7v3t/v+/v+8VYcKRhbdtvN6sZBBvSw+E3hLuvU2EHFRYSvK4hzksASHMa42u8RozTM1mfing5X7jXJb9oaU/KDiuuWJwurNzi0h9yWTogRvaFUx5Dzzv4mxK4inOwcaPB6cPQcw5eQzP5VUNnbywiiaVL2Cz2WncDlzEGQeBNhAf0DzH5xTVy51tY12Xk0U1M4JJWxGGLOKUzm8c6CreXkgYO7dkcSc4hvxhjNeQXExa//xqqx7oM/dVh1t5/HfMPDpHmPcxIBXXnjTgxYGH2tx7D3b3jW17Xq5nKb+e6+9CrCh3Q6SUsvS2lXxH++NqNHibQRFWf5s6QjRuL9KqacC3Bb0qOPWhIMXLxPinclk9nq0s3M21xxRrS+IbUU/Rqmt6QrJCTP5hg+FEPrPj4oqBwIcI6ZKwXkeOuPuewsU+x47OGmS/22Lhe09wJzRkXvw7tvQntSSXG8zUpRrttobcmoUgn7PhMF6th9lMixzD8dY84L3pDOrZUTvZY+eGbv2lD3cXHm//wXinAFj1yKxFeK2vU0or+SEhUdvE6li0bROMpznzAp1/zQIse/xbt02sZdnxcBNnGDmXFXy3P6cjitdLtuoD19LaGj4rNtvUjwNLdHmc/IPUJlLyGddV2d7AyFqcntQLrKlScMY0KLlixl5x8uENzlyWpZg5qM5bemCVYU8QlKf5u7FPkliG/IgqluVXD7OfbxCXojUDlqGbjZkNzBxjP0ppxKB72UJHKZCguBV5rALiKg4BS6kwmZvSUUuqxV3i+opT6z0qpp5VSzyulfuKi596biSOdyHQzBvhWuO82ovEiKjEoY4hHcjitPtbTKGuJRwuUXlgjHi3SHfGxjiK3IgXQzoS37c9aPRGho5T41j0YF2Y/ep6N68SeUaWWyT99gdphQ/fB20jPnX+JqfzL4daGXpLKcGenXnKe0op+dWs16rF0d5Ghp11UKk5hEwc7RCVNkncINkXTJ6jLrqFwvk99b0CcA7+ZMvzUlmmJlRWsK1IFbt+ycreh9ozUC5yMmeO1Lf2yTGgy6SuWb3OJKh655YTVmx0mvh6z/haXcN0QrsH6dS6tmYD8BSiclwmy+94GU58XRlF+2TL6VJzJI4hMtd8U+ufmtTncjvgSGFd49uOPRbidFKef0p4JKS7EaM/95p3WSmPecQfhaiypmUgsGlGK1NckeZfekEPqi9VnVNKcf4fD3IOK4tlsEjRWuPaxkc9HJtiWhpo4Lz8rA0lOb6uAWi01kdSTXc+WSYxKLE5saMy6jD2eMPMZCcrGk0lepcLCUrGVawwlBacj6REwvpbzfQnuC9+RZ/aLKTv+yjLxSMLUQynDz8Lo0wnWQZRXlXgqJKHYoHodQxq6xCVLflGeCxc11rWkJUMSgnER8b/8JYoClksmIPdG4u9jJ/BOa+1t1toDr/DczwAvWGtvRehR/1op5SulHOD/Br4LuAH40Uw0aYBvAt2NcNoJaTHABB5xwSEtBOh+gimIoJr1hDNfPNMmKju0pwL8lqF8qovTTzNp3pS4ErDwtpA4rzj7D6cZ/5tl7JGTcPhUFhyEPx7ff+tLrmFrgt/6/vL8djK3sJ3jtsaiSyVGnksJN6TJyG9YVAJB3RCVFes35GlP6O2VZulcTFBPaE0HWEcTrhuqpxLclvjmRkU5sbFLeg2E024pnnTwm5b2pKyOja/oV4SWWZpPqF9n6Q0pqifFg6A34jJxMCYpOHgtaE9ogg2RdAZZ1XodKcZWP1ykNaMJNw251ZTGbo9g0+A1LbnVlDgviqLGB68rrx1umG3pCeNrGrsCmdQRyY+twrpz8z4ZeBYAFn7hPhZ//h7SQNMZ91i9KSQNHeH7R+Ip0Bl3CTZl5a5SS24tZfKrhh2fsRQuZPIM7osTO0YmaR1ZmZwTS3fEzdJbEjR7w674+VqE9ZMFkiQvuf1+1aOwKP4AOrXbJu9bk7zuG3Rq0Yn4CaDFVlJ2AwYdGdZucFm51UMZOPsPpA9h/jsdLtzrEq6npL6mckICajQdE9UM69drRp/qYlzF3AMBpbOK0rmYpGjIrcLQC1A8IWnCyUd6OD2FSi5hTeC1BIHLvCbwRlNELVDK5E6LwDrCe70bOGGtPQWglPoLRDTphTfqQq8EdHaUxJ+16JA/0yA/b0hKASaUVaHxNbl+gtvs05ktEq4LB9ztpjiNLslQnmA9EpNu32HssVi6Pxfh1I+OUT0+Sm5VmB21Q8uYYoB56gi97z5A+KnH6T9wB8ZXxEWHoS+d5vD/vBNvw8FvyEq7M5Ny7c88+pJrTusNwk88yvLP3UN+yeC1oT8tXrFSdFWM/snjmPhFJ6rmj9xFv6KonIhZeHvA6JMJ/ZpHc1YTF4E5mP5Sm/Z0jiSUfLRfB6ylcjJm7UYfHQv3v7FL4zU1wTrk1izLd1lIYfyQZe0GD6cP5bMJ3REp7hoP8ssxvSFRZ+0OixTE+CNtTvxjh4lP+bLq9ERtNQ0UK3cZJr4qqqRS8JQCst80FC4Ylu9wmPxaRG/YlT6ESkD5YFPen2cOA8LOmv/Fu3E60BsD62lyqwmluZS1G8LtwrfKehqUBRUJSwYFTubB67eMNHJFElScviEpir7/FvJLL77X1lWZoqqwXFJP0a94BPVUUkHGinZPzcU4ivySyGBv9Rkoa0m0rDXTINthpMKmMl5mHg80dvtYDaPPJly424WeQxooJr9qWbpTdh6tac3YoTatXTnaBxJMxyGqWpb256ieTqm9YFm611Cc11CLaO4K0bHKpD0gCR2CNSicv0Rr34zh9JrOu4zxegcBC3xWKWWB338FwaR/i3S/LQAl4IPWWqOUmgbmLjpvHgkM3wCl1D9CpFPZseNb56ffrHj32/93fK3wNnu4bZfOzjJOz+A1+lit6Q95FI9sEI8XcboJKrXyPZFtdP2mIVJfUTnRxXoatyMNR50xTWunZewxQ7gas3ldwPgnz9C5ZRrja1r33EPhQkr07jtQFtyuobHTJbxlFn+ki1op0h23lE4rKCQc/+072fc/PIvpvlS/Zey3HyF6z376NZf8sqX8l4e200iiJPyiGF3pLw5R8X1sEjN7SM7Rnkvrpw9QOSX+u6vvKlA7nIrEQSrsHbdr2bzGR6UQ1G0mM6GIi5l+z3yPoXyOuAAoSSlVT0Sc+RGY/qQ0rvWrGvAklVRVtHZYvDrMf2eB8CgsvCth9lPSnOb2hPU09KyDTqRJyjriQ9DJKdZu1Ux/OcVraqxWtMc1w4djIu0w988OMP0vMzkIpWl88C7cDlROJTSMS7fmUD3aYnNfEb8pktlpTuPV422NHpA0m/H1i5OVseLdm0hQsI5w/rdE3XQi6Sp53eyPKIXKCsSyC7CQmb24XVEDdTsGr51iHCVZkkwSAqQ+YnxJQ7ldmfWTUKQ/lvc7TBxKKJ+JST0PUsvoU6n0H2jpSQhXNeff7qAj2HhLnuYuBSsBw89qnMjSnkBkPoqK6gsOy/vB1iEpWNJiQvmwR2cClPWIy9C7pw2/cynuOot9DX0C9ioPAm+11i5k6nefU0odsdZ+5aLn3wM8BbwL2Jud8xB/CyGkLLD8AcCBAwcu73f7dURrR47cSkxvooDbTcmfa9IfK9AbyxGVHayC1r4hcks9dDdGJQFWKzoTIX4jQcfgbyYYXxMX5WPRmnLpjsPYY5YkzHK264al9+8iv5yyeK/GOpb2tMNYVvEpHTxL1Z1FpZaZ3/VY/Lk6/c0c9f0WWi5OTzP/s7cx9VCbqBYQfu4pNj64n9qT6+QeOY5fbwCv8M9+WY58q2t5K01iooix334kO1UxdM1ult82RrBpqO/RpKEi2BAZ5m5NVock0uxlNTSvSXGiHI09lolHLNaBxl5D+ayD8mL8Jizf7lM9kdKadqicTmj5LsYTamT1qBRf/YbL3Ptjao870qk7qinNJ2xc51JcMCijCDdkAja+YumAS3FO2DeVMym9YTcrxsrY3J2zNG+fYvGBGNVwqb1gKJ1Lqe9ycHYXqBzvSJHXlTGd/EmHHR+VWkfQMKShMGYU0Blzt719RehN0kBbwm2Q5ee3YkBqRfwtkRQMmT/JlnewTi3G1VmxVwrwWwqd/kqfqOzhxAbj6u0V/9aiYyu15LagPe5IWu1EjE6lCJ3mHBbeJjn9YBVMIFLQcVEKwlMPJSze7TP9N12GH21z+odHicsWM96HdZ9g1SEp2iwAyDW1p4SG6z5/iUyGtyiir+W8yxiva03AWruQfV8GPg7c9bJTfgL4mBWcAE4D1/PqAkkDvArCtWT75rVakRZ8nH6K1xJKZ3On0Pjisk9cyxFeaBOXfdpT0vYfbMZiBJ5zyJ9tEBekQWfnXzVpT8qKqzPusn6DfGSso8DICjGqpaze4tCccTjxc3toTXn46116Ix4jf1zACVP0pof1LEklkZTPcEBz2iV6+y0kOYV1HNIsAACvWmj+BryCb7E1luTYKWp/9Ailjz7Kzg/NUTtsKJ43qFQcwJQR9lBrRvyTa085VM4khCuKzpgmCRSTDwmvv3wwJCo5+E2hneZWzXZKo3BOUz1mMxorFBYNtSc8cqvCUCpeEGnu8pmUYD3B7Vlyawa3Yxl+IaV8xhI0zPYq2ziAkmI8wMq7Zli6y2HyMy4jTyo6E56c58HazZqlewpEFY9oSPTzpz7p4vSNsIKMyIfozMGreCEhCTVkRd+tSKvjjA2U7QpEyVNSSWmgRDG1LwVOq6V2IIVeuW6VTfxRWcxk0kCTFFyspzCuCPHhKJSxJHlZYBhfNIiGTqTkl8XDQRlJOSkDXith6ispTlsTF6B0Uv6/zb1GKMHTHsPPpyzdmWfjjhHyS8ICKjwdUjyriSvyWetMWIJN8ZmwSgxmvObf5Q57BbzWwvBrMaN/A/G6BQGlVEEpVdp6DDwIPPey086RtUUrpcaBfcAp4FHgWqXUbqWUj7jofOL1utYrHQ/oHxIHp0g02KOqR1L00FHK4t0h3RHFzj+fY+Nah9WbPKKKR/PaMt0RkeHVsfi1bl7j0xl3Sao5dGypvdCmO5Fj+Pk+9b2axm6F7ks6Y+Naze5PtBl71DL+sKY/bGjutbC3TWcS5t5bYfEeRb/ikHsix9SXLUNPu+z6mOTLxTjGsHy7T2ccdKcvrBheRZPoNcIdHX7Jz9ZY0vkFSh95lPJfPwcKxh6PSXJK9HoaUDlhZZLQYqTenmZ75ZqEQuF0YktpLhEJ5FhWyYVl4dkvPSiNbGs3i4xCYVGKsiMfeY40Mz/RadZ5G8nq2uuIb7HXFsVRvyVplfxyQq+qSM6cBeQ98ppsN085We6+djQhKViGn4+2V/JJXhqz0lC/OKFmK/wtg3evmYCjttM/wHZaSMcy+cdFh5XbfVbu8AjXkkyRNUv3eGIVqVJLksmRe40+OIrcarxdoN5akKQXSUtYLfRS48mT69dJY9pWF7KykkaKyg6b1wS0x93tLuLWbplIvbqmda10EucvdJn+wgZWQa8G/obGa0PjLTFeXaE7DmnB0Lk2IqkkoivkWJo3XSqj+Su/RwBe33TQOPDxzOLSBT5krf20UuqnAay1vwf8c+BPlFLPIh/JX7TWrgIopX4W+AzgAH9srX3+dbzWKxrd77tbtuhtcXiyWopw/R15SnOWzqji3A/PMvupVU7/0AhzD8gKtnZETFB0P8WUXcYO1Vm5s4JxM6enKMXfjHF6CRMH4cz7fHQCtaOZr20vpnjO0J7Js+djMae/LyBu+ejbmvQvFEBZopJDe9bgNx2Cuqwgy2ek2aixQ9OZMVjfcvwnx9n7kRDn5Dxpo/nKgeBlEslOuUR86x6so1m/IaByJsE9vIxa38Sm6Ut2EtZYTLdL5T8cQrke6ftupTXlMPx8j7UbQjoTAC4WCFegPa5o3RyTL3fxP1Ji6YBL/gIUFxJa0y7Gc7FaUTpvKSz6LN9tcdqKjX0e+WWRUTYP3CAd2lVNblWK3E7f0q2J2FzhQpyl37KawC6X6olYTGkAfesNbF7jkFuRJjQdW1qTms6Ij9exTH/ZSA5ey2rezYq9Khb7Rr8uHrtx2cXtpBhfk3qa1Zsdxh+N6Yy55JfFMyEuQmtC0x+BZFeX8IUcxoPNa3ysA6V5WL9eWDbBhuX8/QpTSNnxCY1vDKSWNDOBASlc68hilX0x2Pgq6xCWoDV8JBGqqpbfSXJ6u65UPG9Zvt0lKUCwBu29ht64ZehZh6mvWtb3wfl3FLEulM5awjXwW8IOCxY9kqKVHUEReX89KJ+G7ohDd/IS3XjWYl/DKv/b0tj6e8DrFgQyZs+tr3D89y56vIDsEF7p9z+FeGQO8C1QON0kGsvTr/kEa326YwGFcx364znaY9L9WZ4z9GYq9HdE+Od9akcSNq+RnUB7Jkf5hXUab6kxdKRHazYgqKes3lameD6msTsEBfv+3Spz7x8lLmhKj69iK0X6YwXyi32ceod9/+cKyw/MsvmuhGBdM3TUsnKbxemKHn9nROSbw79+kvQ7bqW9w2Hya9AvOwwd7bF8V4VqLUd/yKX4sUe3i8FKK+ydN3Hsp3zCeY/Rp9LtIqbTNaDAb1jyJzeIp4eIrxulM+Ey9FwLnjoC1khAUFp0d5KY0ldPUioV2bxzgnDT4nZh/WaY/VxCe9KlsUtRftKn8RZNNYbyKcvQkTbHf9Zl9sMQ5zXhesLCd3i4bRh/RDT5e0OAlb/Xr2hK8zGFcy3aO4psXOcQbIjvcGOXJtwUY5VgPaE76jF0LELHhsrRLgZo7y7RnZD6Qe2I2F96za18vmjx9Gou4XqC205I8q7s6sIsACSW3rAnO4humnHqE8Yfy96zlqR+LrwNas8oCssWZRV2NUd7h0xc448JC6ixUxhVjb2G2c8bpr+kWLnNY/FumG3mSHOyy1HJFt00q9WEmiTQ+A2pRSgjTDHrqu2UlDIiIgcZowlJN448m9Iddsitpwwdl0gSrHdJ8i7luZT1fU7GQrK0pxQsKOrXihue1xQ2Vnc2JjzvoRNYvz1Btx35PF4KvOaawOWdDnqjKaIDfJt4QP8Q9jtuIy46BBvCDOnVHNqTJYaORjixQ2sIjKOJc4rwtKK3J2JuGvb9m03iWg63GRGNlaRYlxpyyzFOZBjaiHAePUIuDEgbTdi3l2ATwtWE/rXjLB0IcHpQmtMUUoMGNm6wjP+ngNS3dEcU1jc4TcXGjYbdn4hxuinKcTCepnxSS2NbCp1Jn7GDm2zeWKEzolDfdyfdmqa1U2oOM5+FXR+3pF7K8h1i+J4GitaEy9DRLvllhSkEpKGssovnY6ynSd9xi3QiP/zcdjBY/am70ZGl9qeHKJ2dY+Uf3U3thR5OFHDhPuGpDx0Vxs3okynGd1i91WftlgK1v1G0JmXCt1rkpY0rnco6tvgtwEr6RlmIyi7GL4hx/JGEJKfx2obieUW/rIkLDuWzMV7HEJUcjO9SPngYlObCW8VlLRqSCbk4L8XUjX2K3v0tSr/r4zcyu0YjVofWzXwCrNQ8/EYitaCiSGknedH5Bwki1tPs+k8x1lF0xj264zD7hT7Dh6VJLM5ptK8oLCWUFqB2RGixxfMxuRVH7DcDnXVIJyShg0otcVHqA7mVmKgo9GSVyjWKwFy2e8i6stsTDkkehp9LReZjLaU37GTXKc2BazcGWO0TlTVrN4Pbkf9TklOUzlpaM4qprxrOv1Ph1TXtPQnVpz16o9AfsgRLLmkA0w8lnLokd599bRP8ZZ4SGgSBKxzpd+7HbUaSU838dstnIpyerAyVkckqDcU5qnABgrpP/Y6I0z9YZfaLPXoTOYLVPrrk0JkISQNFbiUmGfJIfuA24oJMXDqGuAhnv1ujUkfs/xyL8RwK85bGDTXGHoPF+zLWjWswpZTEKApzkorojnrYnXdQ/vCjTHwJOh84QH8IWjs0+YWA4rke1ef76LUG4a5R4mIOjENcsKxcp6get4w/JhMbfbJuX42/0SMuB/jr8h0Nui+yxHHRxdx/K+GpVZbfOQVA7U8PbXfhjv67R9H7dtO7NcQ6MPy8wWsZkrxm/nvkFrHFmMnPObQmFTqB/Krk8fU5CDdi2hO+THgVF7+eoFI3m4hh/VoXJ4bi+VTE6XIap2/pjGk290eU52Sn5HUMvdDFdLss/7f34ddlfMXzlsasJtywrN8IE4+kqCfzpH7W9GVh5Y4ClbNCDtCxTMJeMyHNiebPFp8fxbbQm/HUtvCbML8ScsseSwcC8UsONWs3aoafM5hQETQMrSlRBG3OelgNQyflf2Gz2sOWTaSOLfmGMLjyyzGpl3UeayX6QKHG7Vic2LJxrcvIs32Mo4hLLuGmmMznVlPaEy5JTtPY5TH6ZE+KzjlNYU7RHxF9o/Nvl2CRlFLm9qTQc4jLFt126NegXzNUjmvq1xrCFc3SHS588tu/96x9jameyzwIDLSDrnA4X3wS6zqEa7GkIVqiIhlVfaKKy8Z1ispJy+ynNilcSCmfjRh5psfolzz6UzEb+0LioqY7EaK22CA1RXfUyxgbUJqLpUlMQXvaMvs5CNY0XkORn5O869wDJdZu0nSHFUMvaK75UIPiaU0w71E9rBg+nNAdc1l/i8aJIH7n7SjHIf+Jxxh9KqJ82lK/JmRjX4604NPfO45b71OaM0w/FBGuJ4w8LbIVnVFpTNKRJVxLSAoO/eEQtx0RlwN0nIKBeChk8a4cTi+b0L93iv4QjPzhQXQ+v51u6r/ndsyRk0z8/hMYz1LfIyqU9d0uQ89pTD5l5pOaxftEOK++P8qKqHabErl8l6Wx20fHlrkHRMO+MavxOoahk4nUBFxFbklsGoUzbxl+2GfhrVIgjYsO5Q8L19av2+0mJ6sVbl/SVbXnISpruiMOxlO0x1yiisPQ8UhYP1kx2OlnAnf1mNxKLMykftbwFYhFp9dItjt+2+MOi/d4hGuWmc9uSsrvWIOpr0XU92iW7oXl2116NQg3DLm1lKGjUfYeSPczxooXgZFAEJVd+hUXqxRO3+BvxtuWk1vGNCCMKuNq+kMuXkvor43dnjCyWpYkrymeNzR2B9T3iMR3moPSaWhOu2AVTk8x/LhD/qiP09aYXIqOFP0Rg9NVGC16Qv1hQ1S9RJOysdg0/dZfl3lNYBAErnBozyXJu8Rll85EiAmkCUfHljivmH6oLzaBjhiDqMTSHfFp7lDc8IunGPvzZ/DahmAjpjHrUjnaYuhYTHG+L93CSgp2Z7/LY+0mTVo0zL1b0dvTx2uIPo/bFcZPYQFZeVXh1A9WmHyoLv69VWhNSOv/zJd6+I0E9wtPEL3jFpK334bVUFiIMwmIPv2az+LdgWgfZV4Am3s9zr9zSxvIYB3xG7aerAyTvMPazSVAdgb9moeKDWNP9UnzDvPfqTMv26xI3OnIG2gN/qdk4jVRxN5fOkRuGbqjansHpTxDfbdD5aimdtii6i6dUc369R7GU/RqHrs+mbC5zzL/IAw/C2s3ajrT4jgWF8RHIC4qGntyYC1RSRhC7WkYedqwdKdLY4fGJsJcWXlnjPHAa8HmPouOIVhT9KuK/GJCYTGhO+yQX03xG+l2bn3bzzcymUaR3OI6tiSBMHu2egT6Qx5uJ6Uz6rD6joj+aEpnTBFXxTC+s7MoCp8tmPwK5FaguGC3u411JhzndCQQpzlXNIwArCVYi2Rxku0O4rKH2zEkeWd7R2I1+HVJafktkYrIrVpyq4b6NSLtceGdCe1xWTy0Z0AlMP5oRGE5YeMmQzwSUzgPGzcKyystpeiug5ntMvqYwmsroiGo35yw49MpOJdmUraIbPq3+nrR9OvyxCAddAXjAf1D9D5wF34jIVxJWLozT1DX5Jb7NHeGuD1La8pn7WbQSYXiHFROWbyuwbp6W9fHOGJ00h2D7nSezohDZ9ITO8QQor09bKyJHBfyKc6KR+o75NbsthZ8XITmDojGY4rHPYxnOfe+CvFNbZKeR3fdy1Z6LsPPdYjesx+3l+IePAzGcvpX7mDy4RirxEB++st94oqP10ioHje4nYT8SpCxliw2oxkaV+HXZUWb5CHNO3SHHZKcIs6H5FYT5u93GH4awvWUzriDc/M+zPPHtt9H5TgvFo+B2h89gjNUYeX730K4aWjP+PTvaZEs5AmXNZMPWc4/mDD0lEtzRlNYlIlt5ktys3eHYeTZlNakw/oN4DUV/WHL8NOizX/+XRp3uk2/HlA4KbuFiUNGPB0A+7bbsbEiKVjW70gZespl7faU0Uf1tnibjq0I5fUN/ZqL30yZe7dmx2ezwqoWXr5ODKmvsb5QNf1Gus3WSUPh9A8/XqdXq1I+Y/CbMUnR2WYjYSXQL75VUTkGSaDI9yWVlGYS5FHFlzqDtSjUNgsoKWTpSFcRlUTMbosyajxJX+o48y5AUl9uZl5jHBg/mIoRzoI4vG1eqyielQXH/Dt9cstglSF3zsPtWUw+pXZYYbVLVLakGyLtUTpj6Ywr/KMuK7dCsHpJbr+nNlghtQmOevVp1FrLIucAvnxJXvV1wGAncAUjfvAAxacv0Bn36Y4HFM8bvEZEmnNYfDChsUtjXNCxIh5K2bg14dx7Xc49qOnXDNF79nP6X9xDklOs3iyCaW47RaeW6nFDZ1dCfyTFOxui6x7jBxU7/1Jhxvs45YiVOw2b+6A9BdVjlmADSoc9CguW6379GN1rItLlHM6yh6nG9O9sU1hMae7MkT+2wsa1Icmd15PcewPFOfCyVJbkrxOUsSwdkBs5rnjbaYYtWK1E8C4Vzn1pXhgwazdBfa+kapzIMPok9CuKuKiJ83Dm+4dwqtUX/85FW/at7+lGneE/OUh7XFM5pvAeL5Kf03Sv75EGiuFHXcINSZctvt0SrPWBbMezYbbF09wO5JcsaS1m4y2KYCMGqxj/9yHheY+R5xI2rnHJPzVP+UMHUY7DqR8IqTzv4bYVTssht2YYe0TTGRPFVSxsXOfhdcS8xWsbVGzZ+akUt51I30DGubeOwomkuc0EwtHfkpXY2gWc/d6qqKq2JIipOGv+qjgYX1HflzL5kEFlkkLGV7Qn3O1AjCIr+pI1mr342CqRm86tyGcrCUVOYku2QscS4LcE7bbqFHFBYz1FdySrSbgQrspOzniQFA1B3TL7eelmXr7bgFHMf09Kf8Qw8gyEy5p+VbG63+J2wOlJkb2z89vvE7DW1qfYxTmOf9PzVrlAjgLW2me/7Rd9nTDYCVzhSCeGCNfFck/3U6xSYEDXXSqnDKs3a0pnoKEdRp82NGYVcUla7xu7NG4HFt8bQ8dlvevR2mm49r9/jOYP7mfHJ2HuQcXk12LOvVcYHWc/aHBcS9p1mfyKYvNa0cMZfmiB+k9M059IaN1ggesY/TJUj3Rwzy4B0L9+mvaUdIra5VVQU5x7T45oPGb2kynRkJ+tID2cboK/2qF8Tjpge0UPpy+Ta2fcJagbOT7u4vYdSRFphVWWyYct+fkO8+8u0dgdYBUEm9CrKsJ1MVlZ+d7rGHqhQ2cqlC7gE6ukZ8695L21xjL6Ow+z8jP3UT5rqO/WFJ8I6YyKiFtxThPULVNfUHTHAvpVTWEpQafQq0pT2cgzCY2dLqXnpKP3wn05yieETbTz1w5ijWXCGrYk2zrfczfDTwvVNM1ZSqcU9T2KJA9JzrLn4x3mHigw+XAkzWd9i78ZE5fcF7n6qTQAijYGGFcmf7eTUt8dUJwXnSG3a2hPi7je5jWasccTCRxaAsXyfsW1f7IJDHHhbRpTiRj9ikdjh0tuNWPFaIXTyfSAtorRWgrdTmQwnqSfrFbEBQevnSmNJhZSURHd6iLe8jZYu8GjNG/ojDoitZFaCospSU4TF4T6OfEw9IYUC2/VgKV8zKG12+Av+aQ+tKbEp0LHkD+vadzVQ637uE2NMpdGRfQYTwclqv1puxtfhd/wvLGGUxymycbMJXnB1wmDIHAFwwSa5q48jV1ys489EUnjl69J84YkdIimY+p5l/x5RVTUdGZFDkBZTVQ1uC2Nu+iTTETERYc9H4/ovu923J7wvccfUZz5AXCLPebeHaLqGoNMgOF6xPBzDq0phxO/XmXmDyP8rzy3resTvWe/qFAur4h09E0z1L50htM/tRvj3UxQN8Rrmp2fTkhyDnHRQSUyWcXlQMxvujLZ+M10mxOeX0qwniIqO+SXkhc7cT3p9I1Kms5sHpVmlpAKmrssI09BYSGisdvHuIqlewqkATg9h/SWSZx4MpMWEEqh21Hs/q3nGf2dR3DHRlg+sFfe58csbk/om+Uzfep7Ary2yFxHZYdwtY/VPjrRxAVN9YQ0hSU5RfEvDwFZCur2G3AvrGEbTdCa9C27mH8ASifEpD53QZHkoHDBUj7VZeP6PMbT1A4blu8QIbzhI5kWkKOkRrAkO6gk59AbdiksROg0FTVNV1Gai3F6KYkSSfGJR2KUsaQ5BxNoklBTefgczbtmmXrI0J2tMHZQ7Cr9esT6DR6jT3aIyh6N3R5Dx/rSRVwQivKWDwEK4rK7vauwiCyGVSIZkXhSt0p9Tb8iUtjBeoL1Ff0hAE1+2dIbkkAX1FOsA7nVlPPvgk0No4csac4y8oTCbxrcjqY9I0GtvStl/Gsav5myNOXinwpJClm39eMuZy/B/WetjW5R93KKw1zP7d/w/AJnGGGchl0/fwle7nXDIAhcobj7v/wtymsRC9+RY/gZS3tSsbnXJ2hYlg/A5JcsS/dYlJfi1T0Kiwa3a/E25YZLCpbcoqZfk7w+LZfuTMLCW0PGH4tRqTBq3I7BabmodRflgXUtKlXMfvgcC9+3g+Yui3UM6nwef7m+HQCUVoRffJroHbdg7r8NtykS1Yf/11nGv2IJ10WCobXTEGyElM9FpIGiM+ZROS0sJZSSnPeQS7CR0BsWq0y3J+mPYDMlKjnbdoUqtSzf7lCch5U7ZIU480WhGTqRFJS741LMxUh6gKxYXFywxAVFY4+sIMMVRVKAY796A9f98yMky6vs+YVV5v/He8gtRYSrksqwrqJ6rEsaurSnPPplhdUhSU5Rv0ax89cOkr0hAJz413djHUthLusBqJRE1jqC9g7D0LPiWJbUEsovuPgNS7iesnprHq8tInVex1A5DV4zxe2mGE92V4VFoYhuNV7lVhPpUs6qxUleVurbKqNKpKSNp7ZlJnJLfcxIFZ3IZD73oIsJDdOfd1i7qUj1RMKxn/CY+LIrMhoF6Ttw2yntKWkyBMQ9LAsGKusHsEaayKTWIOqmqa/wWsIu0qnsDiYPJvjrfVo7czgR217D3REJdCOPWzbeIqt8p6soXEhoznoYH6rHLZvXKkYPapqzisIFhyRv8Rsy5tGnDOfvv3T34bM8oqsMm7ZtUhCVHHmvbcw8J2lRr1y6V3t9MKgJXKFYuwXaUyFuW5QRlQG/aVm5QwqRS/fAjs8a8s+H9EfFGMTtGoZfMPgNcDuK3o1dvJbCBAZVilGRorNTGpo2rvNJA83C/Qo92SEZSTDVGKer0H3F5r0zbN6UYEf72GLKjk/HrN9WIXrP/u3uXL1jhuDQcbyNHurp43THPQgMfjPrZq1p/A1N8/0tejUPv2UoXkhFdtjChXsz+uKEQ1KQJjCvnW53o8YFTbgR4zXibdrl+OMJwaZh70e77Pi0rMxbsxDnZQJxeiL0pjN5jbgo7KbVW6G5SyYJpwvls4ZgXd7r0//dW3B378C5eR8z/+IRVm4P0FFKd8zH6ab0a7KzSALF+CNNCv/xEJV//3V2/OojWGNp/cCdrP7Xd7L+YwcI1hTFs5qoCp1Jmch6YxaVQn5e09wFU19LGH7UpT0jgm3r+1zcTiatUPBY2i+9CF47QaXSSLXVeKWMxW0Jx1+lYuWIFfVOrx6TBll/x1YxNuP0Q6b542l6UwU294qaafGsonTMwe1bai/EpL7m+t/rkl8WO1K3Y+iOZcE527U5vZQk59CvOKJg6yi8pkiF6EycTme+An4zFTprJmdtMw2nqOrjdgy55UQa/zRS82pB8XzMxEFLv6K49s82Oft+h9JCQvVEzOL9CV4TcXUrwfpN4PQUTk8K3L0hjY4ukakMYK21m6y97QQvTfmf5RjT7MFa23iVX71sMNgJXKEIVhX13UKx27xWkVRSChcUY4/CxvWKylHN6k2Sj/XXNb0adMYc2lOQlA2lU5q4HFKYh+I5zep+D3ekh0k0vVpIXITzt6eoWFN+qCAB5oAlmYjwzwYs71fkz7kkOZdkR4+Ft4dM/02E+/knUL7PuV+4g86umJGvT2AdGG9MkYSK0S97LB1QGA9mvhyzeb3L1J/lcXop1lPbzU4o2PmpLs1dIaV5KXZu69V7Ws6PZGWZhiKcFhcc2mMuxfMxSd7FeprWpMLJRO+SHOhE05kAv65wYiidk2Aw86WE+m6P8vEW9V1l6rs1aQ7KJxTDz/VIxiokJY9wbYKJ/+MRnEIev1oBR5M7KG5pw9aw9lP3kZ86QOprmjvERN6vp6zdpAk2FPklqO+FtGAIlzTGh9IpxdCxmMW7PNKcOGwNHU8JDik29yo6O1Kc2KGx17B2i0vuAqze4hOuWSqn+kQVmYSdbgpK8vnhWkSv5hGuG6Kyi07JzIIUVsmOIMlJYG1P+3gtg3IURinSQDN0XHR9Rp6RdE/u1Drnv3uC7iigiqJV1DUv9ggATlcep76W5zuGOOsW3tqFqER2M2kgncUmk4/QiSEquRIcsoCu48zzoZfibnTo7K7Qr+rMgS6hMB9hQpfCnCZYabN8Zwl/Seo/SQgjzxqWDyhK5yBcNwQbmv7rsC631n51TE2zaVepqhH6tssqF2iy6V/6V7v0UJe74cHfBgcOHLCPPfYNVsZvOjzgfJDu996ZrbBEV10ngJWVZXuH4frfXubwPx3FegZ3U7o80eDVxeawcsJSPS62fCd/1Mdpady2mKtUT6YsfF+MsyBBwu1CYSnh3HcpyKfYWKNSRfU5h+4Y7PhrcdWa+Y8ey3fI9jvNG8JFR/K4K9DYa9nxmQS3kzB/fw6dQO+mLlMf9TO2i8PIM7FoyiTC7OmOefSGFIULKUE9IfWl4JjkNSiF25bA4TVkQlMWesMuxoHyadGYWXibT5K31J4X5kkaSCNWc4fCa4HfkBW5SmTl6NehPSudpcaD2S/0LnLDsizeV6BwwVD6i0Mv+Z+s/+Q9FJYT8dQ1lvakh07BONCelpWocaSPIs0byic1vWGIixYTGMYOSc0g9WQVu35XTP64x8yXWpx7sIiOISnKa8Ulw+jjiv4/2CD/oSpOX1bRbt/idNKsI9hsT/JWZ+5gFzW3WUdJIHUVVim6Y6IY2q866ESYQ1t+A+0JF69rt2mnWIiKmtxaittJSfKOrPAzC0lA/lcZeysNNG4vs7X0tOw4tHgQo8hqVGz7HfeqDsX5PklRit29EReVSvqvOesxdKSLdTSrtwQUlkR7ae0mH+OIx3N3VNHak6JiRfmEpN36NanzpEVD8YTD8//qnz7+Kpa3fycopfZVGT6yn/s5zBOMMMHT9uFLt+V4HTHYCVyBiN6zn35FExcVXskjXIPNm1NUMZa87gshh/+nGsFZB6xDuA7Nu7uM/+eAqKiIyiJRnAYO/mqbmc/4LN6j2PPH52junybOa8Y/HdCcBrQUY89+r9yp7ppHWkwJFx06E5CGlpM/nIcNi1+P6I9b/GUXp+8w9lRCe9zFiaTZqZ/x4OOymLbs+mNNd1T0+60mMxmRVX/qa7mpl0QjKA0doqIYkRtPEa4nON2E+jU52uMu1eM9jKdZu1Ex+fUE62iRJuiCMlJg7UyIOXxrVlGct/gty+a1mtJZ0TnSMSKsVpcs6dBRi0oMxnPoTnmUznSIKpBfUmz8V3cTVRReE/y2oXwuIlhqEw/l2LwmJNw0dIeFopsGQmssn7LM/vU6R36mSnsKRp80tKY0WIf2uIj8tSY1SQHyx8UFqzseYnzIrYLxYezJhOXbXDpj4H66hnEsyhGpa6eTkgaa+h6PwpLB7YmpjO4b/EZKVHa3efrdUZeo5OH0LLl1kX6Oi5K6sdlED1LEbe5S6EhRPSkNaGlAZjSTrdT7LzaPqcRu1wH6ZYfOuDR/qY70JeiUbRVRxwiLaYvZZAJRIQ03U6KKTE1u31Lfq5j9bAcTOBQX1LYu0ujTPdnlxBkFNJLPVbhuxUBoI6VfEXE5HUFuMfOt+EYiz7cNa+3RWXUNp3iBLi2e4etXTKp9EASuQChjKZ/t05nwWbldsfcvNmnsrZB6Drk5KZC5F3zS0OL0FP23NzGrOVZv0dResIQblmAzxnv8OGp0mMKnTnHNl/OkzSa5uQVKIzXq77gGlKJxjWHm84adn1Cc/YClckzRH3Lpjdpty8FgXbHz3zzH+vffhO5ZTGDxNxTLt7sUFqCxW+FvQlRSFM91SXOa2c/Bme/xUJGC2Q7TH/KlyHuRtk1hMd6ejJKCKGPW9/j4TSONTqFPbi1FR5Y0dFi70cPpwdwDGhu4VJ91tyfg1oys+rujYAKIC9KxG65Ca0ahEgjX5VyrAA3dYUVxTnPuvT7jBw0nPijy2J1xRRrC8HPCzEnykvJYPVCVQu6moTWtaU+C9SxeQzH59Qi3nXD8x2qUjwgDZvG7I4pPhiR56E5Kyic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+       "  * 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",
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" + ], + "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" @@ -1118,10 +89252,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 53, "id": "e13291ec-3701-4bbd-96cf-b33c8b76f165", "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "(3, 687, 687)" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ "raster_ams_overview.shape" ] @@ -1136,10 +89281,33 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 54, "id": "91c685c2-4375-4822-9829-bc6ae6a01cf1", "metadata": {}, - "outputs": [], + "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()" ] @@ -1157,13 +89325,118 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 55, "id": "26d2ac75-1bd5-4594-8b97-538742660295", "metadata": {}, "outputs": [], "source": [ - "# Try something in here\n", - "\n" + "# 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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ztMc~t)eUTy5!GHL297JXkD_;M6YKr?<$n!P<>f}N(v%i+55Ni(k_C>Ws+as;xA z;`3jsU%l}54v0I`COu;ne)=Q#)aJ#u*AxY=~W^80_4R? z1Y~Eb3;zP3GOq=vlt#U%BZmJ_&nfR;heiZB+Z1b@6L)xiP)|PWPv2T%rn8s;j$rq6 z)LtXp%Ar@91%))F}17VYWSg?_)Kr|Hl)=~_tYh{v^Z@D!=o#Ntce2h~z209j$n4FJ80ooZ^5Ex6B& wg2>1LrwVM50 Date: Wed, 6 Nov 2024 16:10:52 +0000 Subject: [PATCH 13/19] Edit README --- README.md | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/README.md b/README.md index 939e43f..3d8fd1f 100644 --- a/README.md +++ b/README.md @@ -47,9 +47,10 @@ This repository holds teaching materials for the NCAS Introduction to Scientific | [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](https://unidata.github.io/netcdf4-python/#tutorial) | [Exercise 07a](/python-data/exercises/ex07a_netcdf4_basics.ipynb) [Exercise 07b](/python-data/exercises/ex07b_netcdf4_advanced.ipynb)| [Solution 07a](/python-data/exercises/ex07a_netcdf4_basics.ipynb) [Exercise 07b](/python-data/exercises/ex07b_netcdf4_advanced.ipynb)| -| Weather Exercise | [Exercise 08a](/python-data/exercises/ex08a_weather_api.ipynb) | [Solution 08](/python-data/solutions/ex08a_weather_api.ipynb) | -| Sentinel Data Exercise | [Exercise 08b](/python-data/exercises/ex08b_satellite_data.ipynb) | [Solution 09](ex08b_satellite_data.ipynb) | +| [netCDF4 basics](https://unidata.github.io/netcdf4-python/#tutorial) | [Exercise 07a](/python-data/exercises/ex07a_netcdf4_basics.ipynb) | [Solution 07a](/python-data/exercises/ex07a_netcdf4_basics.ipynb) | +| [netCDF advanced](https://unidata.github.io/netcdf4-python/#tutorial) | [Exercise 07b](/python-data/exercises/ex07b_netcdf4_advanced.ipynb) | [Exercise 07b](/python-data/exercises/ex07b_netcdf4_advanced.ipynb) | +| Weather Exercise | [Exercise 08a](/python-data/exercises/ex08a_weather_api.ipynb) | [Solution 08b](/python-data/solutions/ex08a_weather_api.ipynb) | +| Sentinel Data Exercise | [Exercise 08b](/python-data/exercises/ex08b_satellite_data.ipynb) | [Solution 08b](/python-data/ex08b_satellite_data.ipynb) | ## Useful materials and resources From ecda85293be29525328948a76fb145e102bb221b Mon Sep 17 00:00:00 2001 From: nf679 Date: Wed, 6 Nov 2024 16:19:27 +0000 Subject: [PATCH 14/19] Renumber exercises --- README.md | 8 ++++---- python-data/README.md | 2 +- ...etcdf4_basics.ipynb => ex07_netcdf4_basics.ipynb} | 10 ++++++++-- ...f4_advanced.ipynb => ex08_netcdf4_advanced.ipynb} | 10 ++++++++-- ...08a_weather_api.ipynb => ex09a_weather_api.ipynb} | 12 +++++++++--- ...tellite_data.ipynb => ex09b_satellite_data.ipynb} | 4 ++-- ...etcdf4_basics.ipynb => ex07_netcdf4_basics.ipynb} | 2 +- ...f4_advanced.ipynb => ex08_netcdf4_advanced.ipynb} | 2 +- ...08a_weather_api.ipynb => ex09a_weather_api.ipynb} | 4 ++-- ...tellite_data.ipynb => ex09b_satellite_data.ipynb} | 4 ++-- 10 files changed, 38 insertions(+), 20 deletions(-) rename python-data/exercises/{ex07a_netcdf4_basics.ipynb => ex07_netcdf4_basics.ipynb} (98%) rename python-data/exercises/{ex07b_netcdf4_advanced.ipynb => ex08_netcdf4_advanced.ipynb} (99%) rename python-data/exercises/{ex08a_weather_api.ipynb => ex09a_weather_api.ipynb} (99%) rename python-data/exercises/{ex08b_satellite_data.ipynb => ex09b_satellite_data.ipynb} (99%) rename python-data/solutions/{ex07a_netcdf4_basics.ipynb => ex07_netcdf4_basics.ipynb} (99%) rename python-data/solutions/{ex07b_netcdf4_advanced.ipynb => ex08_netcdf4_advanced.ipynb} (99%) rename python-data/solutions/{ex08a_weather_api.ipynb => ex09a_weather_api.ipynb} (99%) rename python-data/solutions/{ex08b_satellite_data.ipynb => ex09b_satellite_data.ipynb} (99%) diff --git a/README.md b/README.md index 3d8fd1f..a6bd99d 100644 --- a/README.md +++ b/README.md @@ -47,10 +47,10 @@ This repository holds teaching materials for the NCAS Introduction to Scientific | [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 07a](/python-data/exercises/ex07a_netcdf4_basics.ipynb) | [Solution 07a](/python-data/exercises/ex07a_netcdf4_basics.ipynb) | -| [netCDF advanced](https://unidata.github.io/netcdf4-python/#tutorial) | [Exercise 07b](/python-data/exercises/ex07b_netcdf4_advanced.ipynb) | [Exercise 07b](/python-data/exercises/ex07b_netcdf4_advanced.ipynb) | -| Weather Exercise | [Exercise 08a](/python-data/exercises/ex08a_weather_api.ipynb) | [Solution 08b](/python-data/solutions/ex08a_weather_api.ipynb) | -| Sentinel Data Exercise | [Exercise 08b](/python-data/exercises/ex08b_satellite_data.ipynb) | [Solution 08b](/python-data/ex08b_satellite_data.ipynb) | +| [netCDF4 basics](https://unidata.github.io/netcdf4-python/#tutorial) | [Exercise 07](/python-data/exercises/ex07_netcdf4_basics.ipynb) | [Solution 07](/python-data/exercises/ex07_netcdf4_basics.ipynb) | +| [netCDF advanced](https://unidata.github.io/netcdf4-python/#tutorial) | [Exercise 08](/python-data/exercises/ex08_netcdf4_advanced.ipynb) | [Exercise 08](/python-data/exercises/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/ex09b_satellite_data.ipynb) | ## Useful materials and resources diff --git a/python-data/README.md b/python-data/README.md index 7b7981d..61d4d3c 100644 --- a/python-data/README.md +++ b/python-data/README.md @@ -16,6 +16,6 @@ Presentation material is used from the links listed below: 5. [matplotlib](https://matplotlib.org/stable/users/explain/quick_start.html) 6. [numpy](https://numpy.org/doc/stable/user/quickstart.html) 7. [NetCDF4](https://unidata.github.io/netcdf4-python/#tutorial) -8. [Weather Exercise](./exercises/ex08a_weather_api.ipynb) and [Satellite Exercise](./exercises/ex08b_satellite_data.ipynb) +8. [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/exercises/ex07a_netcdf4_basics.ipynb b/python-data/exercises/ex07_netcdf4_basics.ipynb similarity index 98% rename from python-data/exercises/ex07a_netcdf4_basics.ipynb rename to python-data/exercises/ex07_netcdf4_basics.ipynb index fe77044..38ce1e6 100644 --- a/python-data/exercises/ex07a_netcdf4_basics.ipynb +++ b/python-data/exercises/ex07_netcdf4_basics.ipynb @@ -11,13 +11,19 @@ "tags": [] }, "source": [ - "# Exercise 7a: NetCDF4 Basics" + "# Exercise 7: NetCDF4 Basics" ] }, { "cell_type": "markdown", "id": "0ac81b88-7771-4404-89cd-d9ec233651d7", - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "source": [ "## Aim: Introduce the netCDF4 library in Python to read and create NetCDF4 Files." ] diff --git a/python-data/exercises/ex07b_netcdf4_advanced.ipynb b/python-data/exercises/ex08_netcdf4_advanced.ipynb similarity index 99% rename from python-data/exercises/ex07b_netcdf4_advanced.ipynb rename to python-data/exercises/ex08_netcdf4_advanced.ipynb index 41db953..4764078 100644 --- a/python-data/exercises/ex07b_netcdf4_advanced.ipynb +++ b/python-data/exercises/ex08_netcdf4_advanced.ipynb @@ -5,13 +5,19 @@ "id": "22542fd5-6792-4df8-9122-fe35f3e4ddf5", "metadata": {}, "source": [ - "# Exercise 7b: NetCDF4 Advanced" + "# Exercise 8: NetCDF4 Advanced" ] }, { "cell_type": "markdown", "id": "b8bc8ade-8ef9-4caa-b734-d0a0df52a450", - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "source": [ "## Aim: Introduce more advanced uses of the netCDF4 library in Python to read and create NetCDF4 Files." ] diff --git a/python-data/exercises/ex08a_weather_api.ipynb b/python-data/exercises/ex09a_weather_api.ipynb similarity index 99% rename from python-data/exercises/ex08a_weather_api.ipynb rename to python-data/exercises/ex09a_weather_api.ipynb index 6e12317..fe7f97d 100644 --- a/python-data/exercises/ex08a_weather_api.ipynb +++ b/python-data/exercises/ex09a_weather_api.ipynb @@ -9,7 +9,7 @@ } }, "source": [ - "# Exercise: Weather API\n", + "# Exercise 9a: Weather API\n", "\n", "## Aim: Use a Weather API to create and graph NetCDF files\n", "\n", @@ -43,7 +43,13 @@ }, { "cell_type": "markdown", - "metadata": {}, + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, "source": [ "Import the `requests` library which is great for downloading content from external URLs." ] @@ -939,7 +945,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.5" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/python-data/exercises/ex08b_satellite_data.ipynb b/python-data/exercises/ex09b_satellite_data.ipynb similarity index 99% rename from python-data/exercises/ex08b_satellite_data.ipynb rename to python-data/exercises/ex09b_satellite_data.ipynb index 087fc79..8565654 100644 --- a/python-data/exercises/ex08b_satellite_data.ipynb +++ b/python-data/exercises/ex09b_satellite_data.ipynb @@ -5,7 +5,7 @@ "id": "73b81a5a-4fc6-4c33-849b-3b717a43b1c8", "metadata": {}, "source": [ - "# Exercise: Working with Satellite Data\n", + "# Exercise 9b: Working with Satellite Data\n", "\n", "## Aim: Use python tools to search for, download, and manipulate satellite data\n", "\n", @@ -1194,7 +1194,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.5" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/python-data/solutions/ex07a_netcdf4_basics.ipynb b/python-data/solutions/ex07_netcdf4_basics.ipynb similarity index 99% rename from python-data/solutions/ex07a_netcdf4_basics.ipynb rename to python-data/solutions/ex07_netcdf4_basics.ipynb index b90a852..dbbf761 100644 --- a/python-data/solutions/ex07a_netcdf4_basics.ipynb +++ b/python-data/solutions/ex07_netcdf4_basics.ipynb @@ -11,7 +11,7 @@ "tags": [] }, "source": [ - "# Exercise 7a: NetCDF4 Basics" + "# Exercise 7: NetCDF4 Basics" ] }, { diff --git a/python-data/solutions/ex07b_netcdf4_advanced.ipynb b/python-data/solutions/ex08_netcdf4_advanced.ipynb similarity index 99% rename from python-data/solutions/ex07b_netcdf4_advanced.ipynb rename to python-data/solutions/ex08_netcdf4_advanced.ipynb index bd46a09..849d9a3 100644 --- a/python-data/solutions/ex07b_netcdf4_advanced.ipynb +++ b/python-data/solutions/ex08_netcdf4_advanced.ipynb @@ -5,7 +5,7 @@ "id": "22542fd5-6792-4df8-9122-fe35f3e4ddf5", "metadata": {}, "source": [ - "# Exercise 7b: NetCDF4 Advanced" + "# Exercise 8: NetCDF4 Advanced" ] }, { diff --git a/python-data/solutions/ex08a_weather_api.ipynb b/python-data/solutions/ex09a_weather_api.ipynb similarity index 99% rename from python-data/solutions/ex08a_weather_api.ipynb rename to python-data/solutions/ex09a_weather_api.ipynb index 137bc4d..29178d4 100644 --- a/python-data/solutions/ex08a_weather_api.ipynb +++ b/python-data/solutions/ex09a_weather_api.ipynb @@ -9,7 +9,7 @@ } }, "source": [ - "# Exercise: Weather API\n", + "# Exercise 9a: Weather API\n", "\n", "## Aim: Use a Weather API to create and graph NetCDF files\n", "\n", @@ -1048,7 +1048,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.5" + "version": "3.11.9" } }, "nbformat": 4, diff --git a/python-data/solutions/ex08b_satellite_data.ipynb b/python-data/solutions/ex09b_satellite_data.ipynb similarity index 99% rename from python-data/solutions/ex08b_satellite_data.ipynb rename to python-data/solutions/ex09b_satellite_data.ipynb index c919210..8bde0fa 100644 --- a/python-data/solutions/ex08b_satellite_data.ipynb +++ b/python-data/solutions/ex09b_satellite_data.ipynb @@ -5,7 +5,7 @@ "id": "73b81a5a-4fc6-4c33-849b-3b717a43b1c8", "metadata": {}, "source": [ - "# Exercise: Working with Satellite Data\n", + "# Exercise 9b: Working with Satellite Data\n", "\n", "## Aim: Use python tools to search for, download, and manipulate satellite data\n", "\n", @@ -89467,7 +89467,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.5" + "version": "3.11.9" } }, "nbformat": 4, From e0ad0599c969b4ac4c183f0b32c4db56a358c9d3 Mon Sep 17 00:00:00 2001 From: nf679 Date: Wed, 6 Nov 2024 16:20:45 +0000 Subject: [PATCH 15/19] Edit README --- README.md | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/README.md b/README.md index a6bd99d..b59d7d4 100644 --- a/README.md +++ b/README.md @@ -47,8 +47,8 @@ This repository holds teaching materials for the NCAS Introduction to Scientific | [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/exercises/ex07_netcdf4_basics.ipynb) | -| [netCDF advanced](https://unidata.github.io/netcdf4-python/#tutorial) | [Exercise 08](/python-data/exercises/ex08_netcdf4_advanced.ipynb) | [Exercise 08](/python-data/exercises/ex08_netcdf4_advanced.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/#tutorial) | [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/ex09b_satellite_data.ipynb) | From 5400c24d24a204fc66c73dd0fc9faa4bbf96f649 Mon Sep 17 00:00:00 2001 From: nf679 Date: Wed, 6 Nov 2024 16:22:10 +0000 Subject: [PATCH 16/19] Edit README --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index b59d7d4..a6f6b7d 100644 --- a/README.md +++ b/README.md @@ -50,7 +50,7 @@ This repository holds teaching materials for the NCAS Introduction to Scientific | [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/#tutorial) | [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/ex09b_satellite_data.ipynb) | +| Sentinel Data Exercise | [Exercise 09b](/python-data/exercises/ex09b_satellite_data.ipynb) | [Solution 09b](/python-data/solutions/ex09b_satellite_data.ipynb) | ## Useful materials and resources From 441b11e7d0c8cd334db06690b9c5c30543cfa6d4 Mon Sep 17 00:00:00 2001 From: nf679 Date: Wed, 6 Nov 2024 16:24:45 +0000 Subject: [PATCH 17/19] Edit README --- python-data/README.md | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/python-data/README.md b/python-data/README.md index 61d4d3c..4090ba4 100644 --- a/python-data/README.md +++ b/python-data/README.md @@ -15,7 +15,8 @@ Presentation material is used from the links listed below: 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](https://unidata.github.io/netcdf4-python/#tutorial) -8. [Weather Exercise](./exercises/ex09a_weather_api.ipynb) and [Satellite Exercise](./exercises/ex09b_satellite_data.ipynb) +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 From c3c4b7460fd5607eaddd00eae103adb1a4607418 Mon Sep 17 00:00:00 2001 From: nf679 Date: Wed, 6 Nov 2024 16:25:27 +0000 Subject: [PATCH 18/19] Edit README --- README.md | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/README.md b/README.md index a6f6b7d..5c843b2 100644 --- a/README.md +++ b/README.md @@ -48,7 +48,7 @@ This repository holds teaching materials for the NCAS Introduction to Scientific | [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/#tutorial) | [Exercise 08](/python-data/exercises/ex08_netcdf4_advanced.ipynb) | [Exercise 08](/python-data/solutions/ex08_netcdf4_advanced.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) | From 5b1e6323309b0395ab095790a5c97f9033c5e6f1 Mon Sep 17 00:00:00 2001 From: nf679 Date: Wed, 6 Nov 2024 16:32:42 +0000 Subject: [PATCH 19/19] Edit satellite data notebook --- .../exercises/ex09b_satellite_data.ipynb | 30 +++++++ .../solutions/ex09b_satellite_data.ipynb | 85 ++++++++++++++++++- 2 files changed, 114 insertions(+), 1 deletion(-) diff --git a/python-data/exercises/ex09b_satellite_data.ipynb b/python-data/exercises/ex09b_satellite_data.ipynb index 8565654..17b8518 100644 --- a/python-data/exercises/ex09b_satellite_data.ipynb +++ b/python-data/exercises/ex09b_satellite_data.ipynb @@ -46,6 +46,36 @@ "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", diff --git a/python-data/solutions/ex09b_satellite_data.ipynb b/python-data/solutions/ex09b_satellite_data.ipynb index 8bde0fa..5b95fb8 100644 --- a/python-data/solutions/ex09b_satellite_data.ipynb +++ b/python-data/solutions/ex09b_satellite_data.ipynb @@ -48,8 +48,91 @@ }, { "cell_type": "markdown", - "id": "517be10e-1c03-433c-b6b9-3722cc0d15b9", + "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",