From 8cccaeca719c2ea7e48705cf6fa4a24b1b1f7e4d Mon Sep 17 00:00:00 2001 From: SahitiSarva <55742994+SahitiSarva@users.noreply.github.com> Date: Sun, 17 Mar 2024 18:08:32 -0400 Subject: [PATCH] adding toc, cleaning up (#22) --- .gitignore | 1 + Makefile | 8 + docs/_config.yml | 52 +++-- docs/_toc.yml | 8 - ...foundational_datasets_and_data_products.md | 19 ++ ...osal - Excess Heat in Filipino Cities.docx | Bin 0 -> 162 bytes docs/~WRL1187.tmp | Bin 0 -> 95370 bytes images/heatindex_pasay.png | Bin 0 -> 143562 bytes images/heatindex_tomorrowio.png | Bin 0 -> 84602 bytes notebooks/air-pollution/air-pollution.ipynb | 178 ++++++++++++++++++ notebooks/heat-and-air-pollution.ipynb | 50 ----- 11 files changed, 231 insertions(+), 85 deletions(-) create mode 100644 Makefile create mode 100644 docs/foundational_datasets_and_data_products.md create mode 100644 docs/~$DRR Grant proposal - Excess Heat in Filipino Cities.docx create mode 100644 docs/~WRL1187.tmp create mode 100644 images/heatindex_pasay.png create mode 100644 images/heatindex_tomorrowio.png create mode 100644 notebooks/air-pollution/air-pollution.ipynb delete mode 100644 notebooks/heat-and-air-pollution.ipynb diff --git a/.gitignore b/.gitignore index 01368d5..c908606 100644 --- a/.gitignore +++ b/.gitignore @@ -93,3 +93,4 @@ _build/ /data +/documents diff --git a/Makefile b/Makefile new file mode 100644 index 0000000..4939ec0 --- /dev/null +++ b/Makefile @@ -0,0 +1,8 @@ + +all: build serve + +build: + jupyter-book build . --config docs/_config.yml --toc docs/_toc.yml + +serve: + open _build/html/index.html diff --git a/docs/_config.yml b/docs/_config.yml index ba352d5..f452fbd 100644 --- a/docs/_config.yml +++ b/docs/_config.yml @@ -8,35 +8,33 @@ repository: url: https://github.com/datapartnership/heatwaves branch: main -####################################################################################### -# HTML-specific settings html: - home_page_in_navbar: false - extra_navbar: "" - use_edit_page_button: true - use_repository_button: true - use_issues_button: true - baseurl: https://datapartnership.github.io/datapartnership/heatwaves/philippines - extra_footer: | -
- All content (unless otherwise specified) is subject to the World Bank Master Community License Agreement. -
- google_analytics_id: G-ETJ4QT0E4L -======= -# home_page_in_navbar: true -# extra_navbar: "" -# use_edit_page_button: true -# use_repository_button: true -# use_issues_button: true -# baseurl: https://datapartnership.github.io/heatwaves/PH -# extra_footer: | -#
-# Country borders or names do not necessarily reflect the World Bank Group’s official position. All maps are for illustrative purposes and do not imply the expression of any opinion on the part of the World Bank, concerning the legal status of any country or territory or concerning the delimitation of frontiers or boundaries -#
-#
-# All content (unless otherwise specified) is subject to the Mozilla Public License. -#
+ # home_page_in_navbar: false + # extra_navbar: "" + # use_edit_page_button: true + # use_repository_button: true + # use_issues_button: true + # baseurl: https://datapartnership.github.io/datapartnership/heatwaves/philippines + # extra_footer: | + #
+ # All content (unless otherwise specified) is subject to the World Bank Master Community License Agreement. + #
+ # google_analytics_id: G-ETJ4QT0E4L + + home_page_in_navbar: true + extra_navbar: "" + use_edit_page_button: true + use_repository_button: true + use_issues_button: true + baseurl: https://datapartnership.github.io/heatwaves/PH + extra_footer: | +
+ Country borders or names do not necessarily reflect the World Bank Group’s official position. All maps are for illustrative purposes and do not imply the expression of any opinion on the part of the World Bank, concerning the legal status of any country or territory or concerning the delimitation of frontiers or boundaries +
+
+ All content (unless otherwise specified) is subject to the Mozilla Public License. +
####################################################################################### diff --git a/docs/_toc.yml b/docs/_toc.yml index 565578e..c7e215a 100644 --- a/docs/_toc.yml +++ b/docs/_toc.yml @@ -9,11 +9,3 @@ parts: - file: notebooks/measuring-heatwaves/heatwaves-in-philippines.ipynb - file: notebooks/socio-economic-indicators/census.ipynb - file: notebooks/air-pollution/air-pollution.ipynb - # - url: https://wbdatalab.org - # title: World Bank Data Lab - # - url: https://www.worldbank.org/en/about/unit/unit-dec - # title: World Bank DEC - # - url: https://www.worldbank.org/en/research/dime - # title: World Bank DIME -# ======= -# root: notebooks/measuring-heatwaves/heatwaves-in-philippines.ipynb diff --git a/docs/foundational_datasets_and_data_products.md b/docs/foundational_datasets_and_data_products.md new file mode 100644 index 0000000..da94005 --- /dev/null +++ b/docs/foundational_datasets_and_data_products.md @@ -0,0 +1,19 @@ +# Foundational Datasets, Data Products, & Sample Indicators + +## Foundational Datasets + +**Foundational Datasets** refer to **all** datasets used in the analytics prepared for a project. The Foundational Datasets table includes a description of the data and their update frequency, as well as access links and contact information for questions about use and access. Users should not require any datasets not included in this table to complete the analytical work for the Data Good. + +Following is list of all Foundational Datasets used in this Data Good: + +| ID | Indicator | Description | +| --- | ---------------------------------------- | ----------- | +| 1 | # of Heatwave days in the last 50 years | | +| 2 | CLUPs for all the cities in Manila | | +| 3 | # People who are vulnerable | | +| 4 | # NO2 levels in all the city | | +| 5 | # O3 levels in all the cities | | +| 6 | % Green space in each city within Manila | | +| 7 | Points of Interest from OSM | | + + diff --git a/docs/~$DRR Grant proposal - Excess Heat in Filipino Cities.docx b/docs/~$DRR Grant proposal - Excess Heat in Filipino Cities.docx new file mode 100644 index 0000000000000000000000000000000000000000..5560cb3cd44e18b664e512aff0907482627a2312 GIT binary patch literal 162 zcmd-HPRz(G$y5kVEGkQ6AQtd21T!QuWH4khlmM{;m{-J51{7mp_*;)KrHAM0T@Zf) 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bokeh.layouts import Row, column, gridplot\n", + "# from bokeh.models import Title, Legend, TapTool, Range1d, TabPanel, Tabs\n", + "# import matplotlib as mpl\n", + "\n", + "# from bokeh.core.validation.warnings import EMPTY_LAYOUT, MISSING_RENDERERS\n", + "# bokeh.core.validation.silence(EMPTY_LAYOUT, True)\n", + "# bokeh.core.validation.silence(MISSING_RENDERERS, True)\n", + "# from bokeh.plotting import figure, output_file, show, output_notebook\n", + "# from bokeh.models import Span\n" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "df = pd.read_csv('../../data/air_pollution/ozone/Manila_Barangay_Monthly_O3.csv')\n", + "df = df[['ADM0_EN', 'ADM0_PCODE', 'ADM1_EN', 'ADM1_PCODE',\n", + " 'ADM2_EN', 'ADM2_PCODE', 'O3_column_number_density', 'adm4_en','month', '.geo']]\n", + "import numpy as np\n", + "df['month']= df['month'].astype(np.int64)\n", + "\n", + "year = 2023\n", + "df['date'] = pd.to_datetime(df['month'].astype(str) + '/' + str(year), format='%m/%Y')\n", + "\n", + "\n", + "# Add a time component manually\n", + "time_component = '08:00:00' # Example time component\n", + "df['datetime'] = df['date'].dt.strftime('%Y-%m-%d') + ' ' + time_component\n", + "\n", + "air_pollution = pd.DataFrame()\n", + "air_pollution = pd.concat([air_pollution,df])\n", + "\n", + "df = pd.read_csv('../../data/air_pollution/ozone/Manila_Barangay_Monthly_O3_2022.csv')\n", + "df = df[['ADM0_EN', 'ADM0_PCODE', 'ADM1_EN', 'ADM1_PCODE',\n", + " 'ADM2_EN', 'ADM2_PCODE', 'O3_column_number_density', 'adm4_en','month', '.geo']]\n", + "\n", + "import numpy as np\n", + "df['month']= df['month'].astype(np.int64)\n", + "\n", + "year = 2022\n", + "df['date'] = pd.to_datetime(df['month'].astype(str) + '/' + str(year), format='%m/%Y')\n", + "\n", + "time_component = '08:00:00' # Example time component\n", + "df['datetime'] = df['date'].dt.strftime('%Y-%m-%d') + ' ' + time_component\n", + "\n", + "air_pollution = pd.concat([air_pollution,df])" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'air_pollution' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mNameError\u001b[0m Traceback (most recent call last)", + "Cell \u001b[1;32mIn[1], line 8\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m Polygon(geo_dict[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcoordinates\u001b[39m\u001b[38;5;124m'\u001b[39m][\u001b[38;5;241m0\u001b[39m])\n\u001b[0;32m 7\u001b[0m \u001b[38;5;66;03m# Apply the function to the 'geo' column to create a new 'geometry' column\u001b[39;00m\n\u001b[1;32m----> 8\u001b[0m air_pollution[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgeometry\u001b[39m\u001b[38;5;124m'\u001b[39m] \u001b[38;5;241m=\u001b[39m \u001b[43mair_pollution\u001b[49m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgeo\u001b[39m\u001b[38;5;124m'\u001b[39m]\u001b[38;5;241m.\u001b[39mapply(create_polygon)\n\u001b[0;32m 10\u001b[0m \u001b[38;5;66;03m# Convert the DataFrame to a GeoDataFrame\u001b[39;00m\n\u001b[0;32m 11\u001b[0m gdf \u001b[38;5;241m=\u001b[39m gpd\u001b[38;5;241m.\u001b[39mGeoDataFrame(air_pollution, geometry\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mgeometry\u001b[39m\u001b[38;5;124m'\u001b[39m)\n", + "\u001b[1;31mNameError\u001b[0m: name 'air_pollution' is not defined" + ] + } + ], + "source": [ + "# Function to convert JSON strings to Polygon objects\n", + "def create_polygon(geo_json):\n", + " geo_dict = json.loads(geo_json)\n", + " if geo_dict['type'] == 'Polygon':\n", + " return Polygon(geo_dict['coordinates'][0])\n", + "\n", + "# Apply the function to the 'geo' column to create a new 'geometry' column\n", + "air_pollution['geometry'] = air_pollution['geo'].apply(create_polygon)\n", + "\n", + "# Convert the DataFrame to a GeoDataFrame\n", + "gdf = gpd.GeoDataFrame(air_pollution, geometry='geometry')" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "air_pollution[['adm4_en', 'O3_column_number_density', 'date']].set_index(['date']).plot()" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [], + "source": [ + "air_pollution.to_csv('../../data/air_pollution/monthly_o3_2022-2023.csv')" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "heatwaves", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.11" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/notebooks/heat-and-air-pollution.ipynb b/notebooks/heat-and-air-pollution.ipynb deleted file mode 100644 index 9a3c44b..0000000 --- a/notebooks/heat-and-air-pollution.ipynb +++ /dev/null @@ -1,50 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [ - { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'fcntl'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[1;32mIn[5], line 1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mee\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\sahit\\anaconda3\\envs\\heatwaves\\lib\\site-packages\\ee\\__init__.py:1\u001b[0m\n\u001b[1;32m----> 1\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39m.\u001b[39;00m\u001b[39mmain\u001b[39;00m \u001b[39mimport\u001b[39;00m main\n", - "File \u001b[1;32mc:\\Users\\sahit\\anaconda3\\envs\\heatwaves\\lib\\site-packages\\ee\\main.py:5\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39msubprocess\u001b[39;00m\n\u001b[0;32m 4\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39msys\u001b[39;00m\n\u001b[1;32m----> 5\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mblessings\u001b[39;00m\n\u001b[0;32m 6\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mre\u001b[39;00m\n\u001b[0;32m 7\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mos\u001b[39;00m\n", - "File \u001b[1;32mc:\\Users\\sahit\\anaconda3\\envs\\heatwaves\\lib\\site-packages\\blessings\\__init__.py:7\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[39mimport\u001b[39;00m \u001b[39mcurses\u001b[39;00m\n\u001b[0;32m 6\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mcurses\u001b[39;00m \u001b[39mimport\u001b[39;00m setupterm, tigetnum, tigetstr, tparm\n\u001b[1;32m----> 7\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39mfcntl\u001b[39;00m \u001b[39mimport\u001b[39;00m ioctl\n\u001b[0;32m 8\u001b[0m \u001b[39mfrom\u001b[39;00m \u001b[39msix\u001b[39;00m \u001b[39mimport\u001b[39;00m text_type, string_types\n\u001b[0;32m 10\u001b[0m \u001b[39mtry\u001b[39;00m:\n", - "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'fcntl'" - ] - } - ], - "source": [ - "import ee" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "heatwaves", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.11" - }, - "orig_nbformat": 4 - }, - "nbformat": 4, - "nbformat_minor": 2 -}