diff --git a/docs/_toc.yml b/docs/_toc.yml index fb8aeb9..6eb64e5 100644 --- a/docs/_toc.yml +++ b/docs/_toc.yml @@ -43,6 +43,7 @@ parts: - caption: Automatic Identification System chapters: - file: ais_intro + - file: ais_emissions - caption: Geospatial Poverty Map chapters: - file: poverty/vanuatu diff --git a/docs/ais_emissions.md b/docs/ais_emissions.md new file mode 100644 index 0000000..1fcaca1 --- /dev/null +++ b/docs/ais_emissions.md @@ -0,0 +1,15 @@ +# Emissions from AIS Data + +## Count of Vessels + + + + + +
+ + \ No newline at end of file diff --git a/docs/images/interactive/vessel_count/README.txt b/docs/images/interactive/vessel_count/README.txt new file mode 100644 index 0000000..46bba69 --- /dev/null +++ b/docs/images/interactive/vessel_count/README.txt @@ -0,0 +1 @@ +You can put static files in this directory. diff --git a/docs/images/interactive/vessel_count/choices/icons/cross-inverse.svg b/docs/images/interactive/vessel_count/choices/icons/cross-inverse.svg new file mode 100644 index 0000000..9d3271c --- /dev/null +++ b/docs/images/interactive/vessel_count/choices/icons/cross-inverse.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/docs/images/interactive/vessel_count/choices/icons/cross.svg b/docs/images/interactive/vessel_count/choices/icons/cross.svg new file mode 100644 index 0000000..d8a60eb --- /dev/null +++ b/docs/images/interactive/vessel_count/choices/icons/cross.svg @@ -0,0 +1 @@ + \ No newline at end of file diff --git a/docs/images/interactive/vessel_count/choices/styles/css/choices.min.css b/docs/images/interactive/vessel_count/choices/styles/css/choices.min.css new file mode 100644 index 0000000..c4cc17d --- /dev/null +++ b/docs/images/interactive/vessel_count/choices/styles/css/choices.min.css @@ -0,0 +1 @@ +.choices{position:relative;margin-bottom:24px;font-size:16px}.choices:focus{outline:none}.choices:last-child{margin-bottom:0}.choices.is-disabled .choices__inner,.choices.is-disabled .choices__input{background-color:#eaeaea;cursor:not-allowed;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none}.choices.is-disabled .choices__item{cursor:not-allowed}.choices[data-type*=select-one]{cursor:pointer}.choices[data-type*=select-one] .choices__inner{padding-bottom:7.5px}.choices[data-type*=select-one] .choices__input{display:block;width:100%;padding:10px;border-bottom:1px solid #ddd;background-color:#fff;margin:0}.choices[data-type*=select-one] .choices__button{background-image:url(../../icons/cross-inverse.svg);padding:0;background-size:8px;position:absolute;top:50%;right:0;margin-top:-10px;margin-right:25px;height:20px;width:20px;border-radius:10em;opacity:.5}.choices[data-type*=select-one] .choices__button:focus,.choices[data-type*=select-one] .choices__button:hover{opacity:1}.choices[data-type*=select-one] .choices__button:focus{box-shadow:0 0 0 2px #00bcd4}.choices[data-type*=select-one]:after{content:"";height:0;width:0;border-style:solid;border-color:#333 transparent transparent transparent;border-width:5px;position:absolute;right:11.5px;top:50%;margin-top:-2.5px;pointer-events:none}.choices[data-type*=select-one].is-open:after{border-color:transparent transparent #333 transparent;margin-top:-7.5px}.choices[data-type*=select-one][dir=rtl]:after{left:11.5px;right:auto}.choices[data-type*=select-one][dir=rtl] .choices__button{right:auto;left:0;margin-left:25px;margin-right:0}.choices[data-type*=select-multiple] .choices__inner,.choices[data-type*=text] .choices__inner{cursor:text}.choices[data-type*=select-multiple] .choices__button,.choices[data-type*=text] .choices__button{position:relative;display:inline-block;margin:0 -4px 0 8px;padding-left:16px;border-left:1px solid #008fa1;background-image:url(../../icons/cross.svg);background-size:8px;width:8px;line-height:1;opacity:.75}.choices[data-type*=select-multiple] .choices__button:focus,.choices[data-type*=select-multiple] .choices__button:hover,.choices[data-type*=text] .choices__button:focus,.choices[data-type*=text] .choices__button:hover{opacity:1}.choices__inner{display:inline-block;vertical-align:top;width:100%;background-color:#f9f9f9;padding:7.5px 7.5px 3.75px;;border-radius:2.5px;font-size:14px;min-height:44px;overflow:hidden}.is-open .choices__inner{border-radius:2.5px 2.5px 0 0}.is-flipped.is-open .choices__inner{border-radius:0 0 2.5px 2.5px}.choices__list{margin:0;padding-left:0;list-style:none}.choices__list--single{display:inline-block;padding:4px 16px 4px 4px;width:100%}[dir=rtl] .choices__list--single{padding-right:4px;padding-left:16px}.choices__list--single .choices__item{width:100%}.choices__list--multiple{display:inline}.choices__list--multiple .choices__item{display:inline-block;vertical-align:middle;border-radius:20px;padding:4px 10px;font-size:12px;font-weight:500;margin-right:3.75px;margin-bottom:3.75px;background-color:#00bcd4;border:1px solid #00a5bb;color:#fff;word-break:break-all}.choices__list--multiple .choices__item[data-deletable]{padding-right:5px}[dir=rtl] .choices__list--multiple .choices__item{margin-right:0;margin-left:3.75px}.choices__list--multiple .choices__item.is-highlighted{background-color:#00a5bb;border:1px solid #008fa1}.is-disabled .choices__list--multiple .choices__item{background-color:#aaa;border:1px solid #919191}.choices__list--dropdown{display:none;z-index:1;position:absolute;width:100%;top:100%;margin-top:-1px;border-bottom-left-radius:2.5px;border-bottom-right-radius:2.5px;overflow:hidden;word-break:break-all}.choices__list--dropdown.is-active{display:block}.is-flipped .choices__list--dropdown{top:auto;bottom:100%;margin-top:0;margin-bottom:-1px;border-radius:.25rem .25rem 0 0}.choices__list--dropdown .choices__list{position:relative;max-height:300px;overflow:auto;-webkit-overflow-scrolling:touch;will-change:scroll-position}.choices__list--dropdown .choices__item{position:relative;padding:10px;font-size:14px}[dir=rtl] .choices__list--dropdown .choices__item{text-align:right}@media (min-width:640px){.choices__list--dropdown .choices__item--selectable{padding-right:100px}.choices__list--dropdown .choices__item--selectable:after{content:attr(data-select-text);font-size:12px;opacity:0;position:absolute;right:10px;top:50%;-webkit-transform:translateY(-50%);transform:translateY(-50%)}[dir=rtl] .choices__list--dropdown .choices__item--selectable{text-align:right;padding-left:100px;padding-right:10px}[dir=rtl] .choices__list--dropdown .choices__item--selectable:after{right:auto;left:10px}}.choices__item{cursor:default}.choices__item--selectable{cursor:pointer}.choices__item--disabled{cursor:not-allowed;-webkit-user-select:none;-moz-user-select:none;-ms-user-select:none;user-select:none;opacity:.5}.choices__heading{font-weight:600;font-size:12px;padding:10px;border-bottom:1px solid #f7f7f7;color:gray}.choices__button{text-indent:-9999px;-webkit-appearance:none;-moz-appearance:none;appearance:none;border:0;background-color:transparent;background-repeat:no-repeat;background-position:center;cursor:pointer}.choices__button:focus{outline:none}.choices__input{display:inline-block;vertical-align:baseline;background-color:#f9f9f9;font-size:14px;margin-bottom:5px;border:0;border-radius:0;max-width:100%;padding:4px 0 4px 2px}.choices__input:focus{outline:0}[dir=rtl] .choices__input{padding-right:2px;padding-left:0}.choices__placeholder{opacity:.5} \ No newline at end of file diff --git a/docs/images/interactive/vessel_count/how_to_embed_me.txt b/docs/images/interactive/vessel_count/how_to_embed_me.txt new file mode 100644 index 0000000..f018e00 --- /dev/null +++ b/docs/images/interactive/vessel_count/how_to_embed_me.txt @@ -0,0 +1,32 @@ +Host the contents of this zipfile at a URL of your choice. Then on the page where you would like to embed the visualization or story, use one of the following embed codes, replacing {{URL}} with the real URL corresponding to the directory these unpacked files are in (see the notes below). + +Preferred method: flourish.embed.js +----------------------------------- + +Use the included flourish.embed.js file to embed (recommended; best for responsive). Target a container div where you want the visualization to appear using the data-src attribute, as follows: + +
+ +You only need to include the script once in your HTML page, even if you have multiple visualizations displayed (though there is no harm in including it multiple times; it will only run once). + +In order to fully utilize the dynamic resizing features of Flourish, such as Flourish.setHeight, which adjusts the size of your visualization based on different devices and chart types, it's important to configure the domain where you're embedding the visualization. This setup is especially relevant when you are self-hosting the visualizations. To enable this feature, you need to add the domain of your embed site to a specific field in your company settings. Look for a setting titled 'Self-hosted embed domain' on your company's page in the Flourish platform. Simply enter the domain where you're embedding the visualization into this field. This step is crucial for the Flourish.setHeight function to operate correctly, ensuring that your visualizations and stories resize appropriately according to the context they are viewed in. + +If you simply want to make the embed fill a fixed-height container, add data-height="100%" to your embed code. See an example of this here: https://help.flourish.studio/article/502-how-to-set-custom-height-to-self-hosted-visualizations. + +Alternative method: iframe embed +-------------------------------- + +You can also embed via a simple iframe (this is best if your CMS blocks scripts): + + + +Be sure to specify scrolling="no" to avoid problems on iOS. + +Notes +----- + +1. Your {{URL}} must be a full path, not a local file URL - in other words it should begin http:// or https://, not file://. +2. If necessary, also update the height and width. +3. You may not remove the Flourish credit unless you are a premium customer or have permission from Flourish HQ. + + diff --git a/docs/images/interactive/vessel_count/index.html b/docs/images/interactive/vessel_count/index.html new file mode 100644 index 0000000..d087a70 --- /dev/null +++ b/docs/images/interactive/vessel_count/index.html @@ -0,0 +1,79 @@ + + + + + + + 0_vessel-count-by-country + + + + \ No newline at end of file diff --git a/notebooks/access/market-access-loop copy.ipynb b/notebooks/access/market-access-loop copy.ipynb deleted file mode 100644 index e024d00..0000000 --- a/notebooks/access/market-access-loop copy.ipynb +++ /dev/null @@ -1,1922 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Market Access - Pacific Observatory\n", - "This notebook is a template workflow to run a baseline accessibility analysis for the Pacific Observatory. It uses various tools developed by the World Bank's Geospatial Operations Support Team (GOST).\n", - "\n", - "This notebook focuses on a raster-based implementation of market access, using the motorized Global Friction Surface from the [Malaria Atlas Project](https://malariaatlas.org/project-resources/accessibility-to-healthcare/). Additionaly, it uses population data from [World Pop](https://hub.worldpop.org/project/categories?id=3) (Unconstrained UN-Adjusted 2020, 1km resolution).\n", - "\n", - "## Data Download Links\n", - "- [World Pop Raster](https://data.worldpop.org/GIS/Population/Global_2000_2020_1km/2020/PNG/png_ppp_2020_1km_Aggregated.tif)\n", - "- [Friction Surface](https://malariaatlas.org/geoserver/ows?service=CSW&version=2.0.1&request=DirectDownload&ResourceId=Explorer:2020_motorized_travel_time_to_healthcare)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 0. Setup\n", - "Import various packages required." - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "ERROR 1: PROJ: proj_create_from_database: Open of /home/jupyter-wb514197/.conda/envs/ox/share/proj failed\n" - ] - } - ], - "source": [ - "import sys, os\n", - "from os.path import join, expanduser, exists\n", - "import geopandas as gpd\n", - "import pandas as pd\n", - "from gadm import GADMDownloader\n", - "from tqdm import tqdm\n", - "import urllib.request" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": {}, - "outputs": [], - "source": [ - "## Visualization tools\n", - "# import folium as flm\n", - "import matplotlib.pyplot as plt\n", - "import matplotlib.colors as colors\n", - "from rasterio.plot import plotting_extent\n", - "from rasterio.plot import show\n", - "from mpl_toolkits.axes_grid1 import make_axes_locatable\n", - "import contextily as ctx" - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": {}, - "outputs": [], - "source": [ - "# Raster\n", - "import rasterio as rio\n", - "import numpy as np\n", - "from shapely.geometry import Polygon, box, Point\n", - "import skimage.graph as graph" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "The packages below were developed by GOST. They can be installed to the environment or referenced from a local folder." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": {}, - "outputs": [], - "source": [ - "sys.path.append(join(expanduser(\"~\"), 'Repos', 'gostrocks', 'src'))\n", - "sys.path.append(join(expanduser(\"~\"), 'Repos', 'GOSTNets_Raster', 'src'))\n", - "sys.path.append(join(expanduser(\"~\"), 'Repos', 'GOSTnets'))\n", - "sys.path.append(join(expanduser(\"~\"), 'Repos', 'GOST_Urban', 'src', 'GOST_Urban'))" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "metadata": {}, - "outputs": [], - "source": [ - "import GOSTRocks.rasterMisc as rMisc\n", - "import GOSTNetsRaster.market_access as ma\n", - "import UrbanRaster as urban" - ] - }, - { - "cell_type": "code", - "execution_count": 6, - "metadata": {}, - "outputs": [], - "source": [ - "# auto reload\n", - "%load_ext autoreload\n", - "%autoreload 2" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 1. Data Preparation" - ] - }, - { - "cell_type": "code", - "execution_count": 7, - "metadata": {}, - "outputs": [], - "source": [ - "# adm0 = gpd.read_file(join(expanduser(\"~\"), 'data', 'pacific', 'Adm0_Pacific_Edit.shp'))\n", - "adm1 = gpd.read_file(join(expanduser(\"~\"), 'data', 'pacific', 'PIC_adm1.json'))" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array(['Papua New Guinea', 'Kiribati', 'Kingdom of Tonga',\n", - " 'Federated States of Micronesia', 'Marshall Islands',\n", - " 'Solomon Islands', 'Vanuatu', 'Nauru', 'Palau', 'Tuvalu', 'Samoa',\n", - " 'Fiji'], dtype=object)" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "countries = adm1['ADM0_NAME'].unique()\n", - "countries" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "import country_converter as coco" - ] - }, - { - "cell_type": "code", - "execution_count": 22, - "metadata": {}, - "outputs": [], - "source": [ - "cc = coco.CountryConverter()\n", - "adm1.loc[:, 'iso'] = cc.pandas_convert(series=adm1.ADM0_PCODE, to='ISO3')" - ] - }, - { - "cell_type": "code", - "execution_count": 25, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "PNG Papua New Guinea\n", - "TON Kingdom of Tonga\n", - "FSM Federated States of Micronesia\n", - "MHL Marshall Islands\n", - "SLB Solomon Islands\n", - "VUT Vanuatu\n", - "NRU Nauru\n", - "PLW Palau\n", - "TUV Tuvalu\n", - "WSM Samoa\n" - ] - } - ], - "source": [ - "for country in countries:\n", - " if country in ['Fiji', 'Kiribati']:\n", - " continue\n", - " else:\n", - " adm1_sel = adm1.loc[adm1['ADM0_NAME'] == country].copy()\n", - " iso = adm1_sel['iso'].values[0]\n", - " print(iso, country)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 9, - "metadata": {}, - "outputs": [], - "source": [ - "# country = 'Tonga'\n", - "# iso = 'ton'\n", - "# downloader = GADMDownloader(version=\"4.0\")\n", - "# adm0 = downloader.get_shape_data_by_country_name(country_name=country, ad_level=0)" - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "# for idx, row in adm0.iterrows():\n", - "# country = row['WB_ADM0_NA']\n", - "# iso = row['ISO3'].lower()\n", - "# print(idx, country)" - ] - }, - { - "cell_type": "code", - "execution_count": 11, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'fji'" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# idx = 0\n", - "# row = adm0.iloc[idx]\n", - "# country = row['WB_ADM0_NA']\n", - "# iso = row['ISO3'].lower()\n", - "# iso" - ] - }, - { - "cell_type": "code", - "execution_count": 60, - "metadata": {}, - "outputs": [], - "source": [ - "# adm0 = adm0.loc[adm0['ISO3']=='FJI'].copy()" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [], - "source": [ - "scratch_dir = join(expanduser(\"~\"), 'data', 'market-access', iso)" - ] - }, - { - "cell_type": "code", - "execution_count": 13, - "metadata": {}, - "outputs": [], - "source": [ - "if not exists(scratch_dir):\n", - " os.mkdir(scratch_dir, mode=0o777) " - ] - }, - { - "cell_type": "code", - "execution_count": 14, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'https://data.worldpop.org/GIS/Population/Global_2000_2020_1km/2020/FJI/fji_ppp_2020_1km_Aggregated.tif'" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "wp_url = f'https://data.worldpop.org/GIS/Population/Global_2000_2020_1km/2020/{iso.upper()}/{iso}_ppp_2020_1km_Aggregated.tif'\n", - "wp_url" - ] - }, - { - "cell_type": "code", - "execution_count": 15, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'/home/jupyter-wb514197/data/worldpop/fji_ppp_2020_1km_Aggregated_UNadj.tif'" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "wp_path = join(expanduser(\"~\"), 'data', 'worldpop', f'{iso}_ppp_2020_1km_Aggregated_UNadj.tif') # Download from link above\n", - "wp_path" - ] - }, - { - "cell_type": "code", - "execution_count": 36, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "('/home/jupyter-wb514197/data/worldpop/fji_ppp_2020_1km_Aggregated_UNadj.tif',\n", - " )" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "urllib.request.urlretrieve(wp_url, wp_path)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Destinations (Cities)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "In this section, we create a geodataframe of cities based on the population data. We follow the [European Comission's Degree of Urbanization methodology](https://ghsl.jrc.ec.europa.eu/degurba.php), which defines urban areas as clusters of 5,000 people with a population density greater than 300 people per sq. km. \n", - "\n", - "The code below uses the World Pop raster to identify the clusters (contiguous cells that meet the thresholds)." - ] - }, - { - "cell_type": "code", - "execution_count": 16, - "metadata": {}, - "outputs": [], - "source": [ - "urban_calculator = urban.urbanGriddedPop(wp_path)" - ] - }, - { - "cell_type": "code", - "execution_count": 17, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "15:08:39\t: Read in urban data\n", - "15:08:39\t: Creating Shape 0\n" - ] - } - ], - "source": [ - "urban_extents = urban_calculator.calculateUrban(\n", - " densVal=190, totalPopThresh=5000, # changed the density value to capute more remote cities\n", - " smooth=True, queen=False, verbose=True\n", - " )" - ] - }, - { - "cell_type": "code", - "execution_count": 18, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "7" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(urban_extents)" - ] - }, - { - "cell_type": "code", - "execution_count": 20, - "metadata": {}, - "outputs": [], - "source": [ - "# urban_extents.explore()" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Convert the urban extents to points using the centroid." - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Ports" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [], - "source": [ - "# wpi = pd.read_csv(\"./data/UpdatedPub150.csv\")" - ] - }, - { - "cell_type": "code", - "execution_count": 32, - "metadata": {}, - "outputs": [], - "source": [ - "# wpi.loc[:, \"geometry\"] = wpi.apply(lambda x: Point(x['Longitude'], x['Latitude']), axis=1)" - ] - }, - { - "cell_type": "code", - "execution_count": 33, - "metadata": {}, - "outputs": [], - "source": [ - "# wpi = gpd.GeoDataFrame(wpi, geometry='geometry', crs='EPSG:4326')" - ] - }, - { - "cell_type": "code", - "execution_count": 34, - "metadata": {}, - "outputs": [], - "source": [ - "# wpi = wpi.loc[wpi['Country Code']==\"Tonga\"].copy()" - ] - }, - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "# m = urban_extents.explore()\n", - "# wpi.explore(m=m)\n", - "# m" - ] - }, - { - "cell_type": "code", - "execution_count": 21, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/tmp/ipykernel_1658446/1562541587.py:2: UserWarning: Geometry is in a geographic CRS. Results from 'centroid' are likely incorrect. Use 'GeoSeries.to_crs()' to re-project geometries to a projected CRS before this operation.\n", - "\n", - " dests.loc[:, 'geometry'] = dests.geometry.centroid\n" - ] - } - ], - "source": [ - "dests = urban_extents.copy()\n", - "dests.loc[:, 'geometry'] = dests.geometry.centroid" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Friction Surface" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Process the travel cost surface from the Malaria Atlas Project, clip the raster to our region of interest." - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [], - "source": [ - "gfs_path = join(expanduser(\"~\"), 'data', 'friction', '2020_motorized_friction_surface.geotiff') # Download from link above\n", - "gfs_rio = rio.open(gfs_path)\n", - "out_travel_surface = join(scratch_dir, f\"travel_surface_motorized_{iso}.tif\")\n", - "rMisc.clipRaster(gfs_rio, adm0, out_travel_surface, crop=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [], - "source": [ - "travel_surf = rio.open(out_travel_surface)\n", - "# pop_surf = rio.open(wp_path)" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [], - "source": [ - "from rasterio.warp import reproject, Resampling, calculate_default_transform" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'driver': 'GTiff',\n", - " 'dtype': 'float32',\n", - " 'nodata': -9999.0,\n", - " 'width': 43200,\n", - " 'height': 985,\n", - " 'count': 1,\n", - " 'crs': CRS.from_epsg(4326),\n", - " 'transform': Affine(0.008333333333333333, 0.0, -180.0,\n", - " 0.0, -0.008333333333333333, -12.474999999999994)}" - ] - }, - "execution_count": 66, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "travel_surf.meta" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'/home/jupyter-wb514197/data/market-access/fji/travel_surface_motorized_fji.tif'" - ] - }, - "execution_count": 67, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "dst_crs = 'EPSG:3460'\n", - "out_travel_surface" - ] - }, - { - "cell_type": "code", - "execution_count": 69, - "metadata": {}, - "outputs": [ - { - "ename": "RasterioIOError", - "evalue": "travel_surface_motorized_fji_proj.tif: Free disk space available is 1666425495552 bytes, whereas 4337125292500 are at least necessary. You can disable this check by defining the CHECK_DISK_FREE_SPACE configuration option to FALSE.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mCPLE_FileIOError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32mrasterio/_io.pyx:1486\u001b[0m, in \u001b[0;36mrasterio._io.DatasetWriterBase.__init__\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mrasterio/_err.pyx:221\u001b[0m, in \u001b[0;36mrasterio._err.exc_wrap_pointer\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mCPLE_FileIOError\u001b[0m: travel_surface_motorized_fji_proj.tif: Free disk space available is 1666425495552 bytes, whereas 4337125292500 are at least necessary. You can disable this check by defining the CHECK_DISK_FREE_SPACE configuration option to FALSE.", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mRasterioIOError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[69], line 12\u001b[0m\n\u001b[1;32m 4\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m src\u001b[38;5;241m.\u001b[39mmeta\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[1;32m 5\u001b[0m kwargs\u001b[38;5;241m.\u001b[39mupdate({\n\u001b[1;32m 6\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcrs\u001b[39m\u001b[38;5;124m'\u001b[39m: dst_crs,\n\u001b[1;32m 7\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtransform\u001b[39m\u001b[38;5;124m'\u001b[39m: transform,\n\u001b[1;32m 8\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwidth\u001b[39m\u001b[38;5;124m'\u001b[39m: width,\n\u001b[1;32m 9\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mheight\u001b[39m\u001b[38;5;124m'\u001b[39m: height\n\u001b[1;32m 10\u001b[0m })\n\u001b[0;32m---> 12\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mrio\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mjoin\u001b[49m\u001b[43m(\u001b[49m\u001b[43mscratch_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mtravel_surface_motorized_\u001b[39;49m\u001b[38;5;132;43;01m{\u001b[39;49;00m\u001b[43miso\u001b[49m\u001b[38;5;132;43;01m}\u001b[39;49;00m\u001b[38;5;124;43m_proj.tif\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mw\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m dst:\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m1\u001b[39m, src\u001b[38;5;241m.\u001b[39mcount \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m):\n\u001b[1;32m 14\u001b[0m reproject(\n\u001b[1;32m 15\u001b[0m source\u001b[38;5;241m=\u001b[39mrio\u001b[38;5;241m.\u001b[39mband(src, i),\n\u001b[1;32m 16\u001b[0m destination\u001b[38;5;241m=\u001b[39mrio\u001b[38;5;241m.\u001b[39mband(dst, i),\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 20\u001b[0m dst_crs\u001b[38;5;241m=\u001b[39mdst_crs,\n\u001b[1;32m 21\u001b[0m resampling\u001b[38;5;241m=\u001b[39mResampling\u001b[38;5;241m.\u001b[39mnearest)\n", - "File \u001b[0;32m~/.conda/envs/ox/lib/python3.12/site-packages/rasterio/env.py:451\u001b[0m, in \u001b[0;36mensure_env_with_credentials..wrapper\u001b[0;34m(*args, **kwds)\u001b[0m\n\u001b[1;32m 448\u001b[0m session \u001b[38;5;241m=\u001b[39m DummySession()\n\u001b[1;32m 450\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m env_ctor(session\u001b[38;5;241m=\u001b[39msession):\n\u001b[0;32m--> 451\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/.conda/envs/ox/lib/python3.12/site-packages/rasterio/__init__.py:314\u001b[0m, in \u001b[0;36mopen\u001b[0;34m(fp, mode, driver, width, height, count, crs, transform, dtype, nodata, sharing, **kwargs)\u001b[0m\n\u001b[1;32m 312\u001b[0m writer \u001b[38;5;241m=\u001b[39m get_writer_for_driver(driver)\n\u001b[1;32m 313\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m writer \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 314\u001b[0m dataset \u001b[38;5;241m=\u001b[39m \u001b[43mwriter\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 315\u001b[0m \u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 316\u001b[0m \u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 317\u001b[0m \u001b[43m \u001b[49m\u001b[43mdriver\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdriver\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 318\u001b[0m \u001b[43m \u001b[49m\u001b[43mwidth\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwidth\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 319\u001b[0m \u001b[43m \u001b[49m\u001b[43mheight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 320\u001b[0m \u001b[43m \u001b[49m\u001b[43mcount\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcount\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 321\u001b[0m \u001b[43m \u001b[49m\u001b[43mcrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 322\u001b[0m \u001b[43m \u001b[49m\u001b[43mtransform\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtransform\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 323\u001b[0m \u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 324\u001b[0m \u001b[43m \u001b[49m\u001b[43mnodata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnodata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 325\u001b[0m \u001b[43m \u001b[49m\u001b[43msharing\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msharing\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 326\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m 327\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 328\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 329\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m DriverCapabilityError(\n\u001b[1;32m 330\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWriter does not exist for driver: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m \u001b[38;5;28mstr\u001b[39m(driver)\n\u001b[1;32m 331\u001b[0m )\n", - "File \u001b[0;32mrasterio/_io.pyx:1491\u001b[0m, in \u001b[0;36mrasterio._io.DatasetWriterBase.__init__\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mRasterioIOError\u001b[0m: travel_surface_motorized_fji_proj.tif: Free disk space available is 1666425495552 bytes, whereas 4337125292500 are at least necessary. You can disable this check by defining the CHECK_DISK_FREE_SPACE configuration option to FALSE." - ] - } - ], - "source": [ - "with rio.open(out_travel_surface) as src:\n", - " transform, width, height = calculate_default_transform(\n", - " src.crs, dst_crs, src.width, src.height, *src.bounds)\n", - " kwargs = src.meta.copy()\n", - " kwargs.update({\n", - " 'crs': dst_crs,\n", - " 'transform': transform,\n", - " 'width': width,\n", - " 'height': height\n", - " })\n", - "\n", - " with rio.open(join(scratch_dir, f\"travel_surface_motorized_{iso}_proj.tif\"), 'w', **kwargs) as dst:\n", - " for i in range(1, src.count + 1):\n", - " reproject(\n", - " source=rio.band(src, i),\n", - " destination=rio.band(dst, i),\n", - " src_transform=src.transform,\n", - " src_crs=src.crs,\n", - " dst_transform=transform,\n", - " dst_crs=dst_crs,\n", - " resampling=Resampling.nearest)" - ] - }, - { - "cell_type": "code", - "execution_count": 74, - "metadata": {}, - "outputs": [], - "source": [ - "out_pop_clip = join(scratch_dir, f\"WP_2020_1km_clip.tif\")" - ] - }, - { - "cell_type": "code", - "execution_count": 71, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'/home/jupyter-wb514197/data/market-access/fji/WP_2020_1km_STD_clip.tif'" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "out_pop_clip\n", - "pop_surf_clip" - ] - }, - { - "cell_type": "code", - "execution_count": 70, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'/home/jupyter-wb514197/data/market-access/fji/travel_surface_motorized_fji.tif'" - ] - }, - "execution_count": 70, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "out_travel_surface" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Try PoP" - ] - }, - { - "cell_type": "code", - "execution_count": 44, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'/home/jupyter-wb514197/data/worldpop/fji_ppp_2020_1km_Aggregated_UNadj.tif'" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "wp_path" - ] - }, - { - "cell_type": "code", - "execution_count": 45, - "metadata": {}, - "outputs": [ - { - "ename": "RasterioIOError", - "evalue": "/home/jupyter-wb514197/data/market-access/fji/WP_2020_1km_proj.tif: Free disk space available is 1664895000576 bytes, whereas 3874574475960 are at least necessary. You can disable this check by defining the CHECK_DISK_FREE_SPACE configuration option to FALSE.", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mCPLE_FileIOError\u001b[0m Traceback (most recent call last)", - "File \u001b[0;32mrasterio/_io.pyx:1486\u001b[0m, in \u001b[0;36mrasterio._io.DatasetWriterBase.__init__\u001b[0;34m()\u001b[0m\n", - "File \u001b[0;32mrasterio/_err.pyx:221\u001b[0m, in \u001b[0;36mrasterio._err.exc_wrap_pointer\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mCPLE_FileIOError\u001b[0m: /home/jupyter-wb514197/data/market-access/fji/WP_2020_1km_proj.tif: Free disk space available is 1664895000576 bytes, whereas 3874574475960 are at least necessary. You can disable this check by defining the CHECK_DISK_FREE_SPACE configuration option to FALSE.", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[0;31mRasterioIOError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[45], line 12\u001b[0m\n\u001b[1;32m 4\u001b[0m kwargs \u001b[38;5;241m=\u001b[39m src\u001b[38;5;241m.\u001b[39mmeta\u001b[38;5;241m.\u001b[39mcopy()\n\u001b[1;32m 5\u001b[0m kwargs\u001b[38;5;241m.\u001b[39mupdate({\n\u001b[1;32m 6\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mcrs\u001b[39m\u001b[38;5;124m'\u001b[39m: dst_crs,\n\u001b[1;32m 7\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mtransform\u001b[39m\u001b[38;5;124m'\u001b[39m: transform,\n\u001b[1;32m 8\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mwidth\u001b[39m\u001b[38;5;124m'\u001b[39m: width,\n\u001b[1;32m 9\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mheight\u001b[39m\u001b[38;5;124m'\u001b[39m: height\n\u001b[1;32m 10\u001b[0m })\n\u001b[0;32m---> 12\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[43mrio\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mopen\u001b[49m\u001b[43m(\u001b[49m\u001b[43mjoin\u001b[49m\u001b[43m(\u001b[49m\u001b[43mscratch_dir\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43mf\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mWP_2020_1km_proj.tif\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mw\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\u001b[43m)\u001b[49m \u001b[38;5;28;01mas\u001b[39;00m dst:\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[38;5;241m1\u001b[39m, src\u001b[38;5;241m.\u001b[39mcount \u001b[38;5;241m+\u001b[39m \u001b[38;5;241m1\u001b[39m):\n\u001b[1;32m 14\u001b[0m reproject(\n\u001b[1;32m 15\u001b[0m source\u001b[38;5;241m=\u001b[39mrio\u001b[38;5;241m.\u001b[39mband(src, i),\n\u001b[1;32m 16\u001b[0m destination\u001b[38;5;241m=\u001b[39mrio\u001b[38;5;241m.\u001b[39mband(dst, i),\n\u001b[0;32m (...)\u001b[0m\n\u001b[1;32m 20\u001b[0m dst_crs\u001b[38;5;241m=\u001b[39mdst_crs,\n\u001b[1;32m 21\u001b[0m resampling\u001b[38;5;241m=\u001b[39mResampling\u001b[38;5;241m.\u001b[39mnearest)\n", - "File \u001b[0;32m~/.conda/envs/ox/lib/python3.12/site-packages/rasterio/env.py:451\u001b[0m, in \u001b[0;36mensure_env_with_credentials..wrapper\u001b[0;34m(*args, **kwds)\u001b[0m\n\u001b[1;32m 448\u001b[0m session \u001b[38;5;241m=\u001b[39m DummySession()\n\u001b[1;32m 450\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m env_ctor(session\u001b[38;5;241m=\u001b[39msession):\n\u001b[0;32m--> 451\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwds\u001b[49m\u001b[43m)\u001b[49m\n", - "File \u001b[0;32m~/.conda/envs/ox/lib/python3.12/site-packages/rasterio/__init__.py:314\u001b[0m, in \u001b[0;36mopen\u001b[0;34m(fp, mode, driver, width, height, count, crs, transform, dtype, nodata, sharing, **kwargs)\u001b[0m\n\u001b[1;32m 312\u001b[0m writer \u001b[38;5;241m=\u001b[39m get_writer_for_driver(driver)\n\u001b[1;32m 313\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m writer \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m--> 314\u001b[0m dataset \u001b[38;5;241m=\u001b[39m \u001b[43mwriter\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m 315\u001b[0m \u001b[43m \u001b[49m\u001b[43mpath\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 316\u001b[0m \u001b[43m \u001b[49m\u001b[43mmode\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 317\u001b[0m \u001b[43m \u001b[49m\u001b[43mdriver\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdriver\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 318\u001b[0m \u001b[43m \u001b[49m\u001b[43mwidth\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mwidth\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 319\u001b[0m \u001b[43m \u001b[49m\u001b[43mheight\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mheight\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 320\u001b[0m \u001b[43m \u001b[49m\u001b[43mcount\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcount\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 321\u001b[0m \u001b[43m \u001b[49m\u001b[43mcrs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcrs\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 322\u001b[0m \u001b[43m \u001b[49m\u001b[43mtransform\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtransform\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 323\u001b[0m \u001b[43m \u001b[49m\u001b[43mdtype\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mdtype\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 324\u001b[0m \u001b[43m \u001b[49m\u001b[43mnodata\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mnodata\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 325\u001b[0m \u001b[43m \u001b[49m\u001b[43msharing\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43msharing\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 326\u001b[0m \u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mkwargs\u001b[49m\n\u001b[1;32m 327\u001b[0m \u001b[43m \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 328\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m 329\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m DriverCapabilityError(\n\u001b[1;32m 330\u001b[0m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mWriter does not exist for driver: \u001b[39m\u001b[38;5;132;01m%s\u001b[39;00m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m%\u001b[39m \u001b[38;5;28mstr\u001b[39m(driver)\n\u001b[1;32m 331\u001b[0m )\n", - "File \u001b[0;32mrasterio/_io.pyx:1491\u001b[0m, in \u001b[0;36mrasterio._io.DatasetWriterBase.__init__\u001b[0;34m()\u001b[0m\n", - "\u001b[0;31mRasterioIOError\u001b[0m: /home/jupyter-wb514197/data/market-access/fji/WP_2020_1km_proj.tif: Free disk space available is 1664895000576 bytes, whereas 3874574475960 are at least necessary. You can disable this check by defining the CHECK_DISK_FREE_SPACE configuration option to FALSE." - ] - } - ], - "source": [ - "with rio.open(wp_path) as src:\n", - " transform, width, height = calculate_default_transform(\n", - " src.crs, dst_crs, src.width, src.height, *src.bounds)\n", - " kwargs = src.meta.copy()\n", - " kwargs.update({\n", - " 'crs': dst_crs,\n", - " 'transform': transform,\n", - " 'width': width,\n", - " 'height': height\n", - " })\n", - " \n", - " with rio.open(join(scratch_dir, f\"WP_2020_1km_proj.tif\"), 'w', **kwargs) as dst:\n", - " for i in range(1, src.count + 1):\n", - " reproject(\n", - " source=rio.band(src, i),\n", - " destination=rio.band(dst, i),\n", - " src_transform=src.transform,\n", - " src_crs=src.crs,\n", - " dst_transform=transform,\n", - " dst_crs=dst_crs,\n", - " resampling=Resampling.nearest)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Clip" - ] - }, - { - "cell_type": "code", - "execution_count": 46, - "metadata": {}, - "outputs": [], - "source": [ - "wp_proj_path = join(scratch_dir, f\"{iso}_ppp_2020_1km_Aggregated_UNadj_proj.tif\")\n", - "pop_surf_proj = rio.open(wp_proj_path)" - ] - }, - { - "cell_type": "code", - "execution_count": 54, - "metadata": {}, - "outputs": [], - "source": [ - "adm0_fji = adm0.loc[adm0['ISO3']=='FJI'].copy()\n", - "adm0_fji = adm0_fji.to_crs(dst_crs)" - ] - }, - { - "cell_type": "code", - "execution_count": 55, - "metadata": {}, - "outputs": [], - "source": [ - "out_pop_clip = join(scratch_dir, f\"WP_2020_1km_clip.tif\")\n", - "rMisc.clipRaster(pop_surf_proj, adm0_fji, out_pop_clip, crop=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 56, - "metadata": {}, - "outputs": [], - "source": [ - "pop_surf_clip = rio.open(out_pop_clip)" - ] - }, - { - "cell_type": "code", - "execution_count": 58, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "{'driver': 'GTiff',\n", - " 'dtype': 'float32',\n", - " 'nodata': -99999.0,\n", - " 'width': 785,\n", - " 'height': 1378,\n", - " 'count': 1,\n", - " 'crs': CRS.from_epsg(3460),\n", - " 'transform': Affine(659.6981137784882, 0.0, 1800287.2032548701,\n", - " 0.0, -659.6981137784883, 4499755.301166313)}" - ] - }, - "execution_count": 58, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "pop_surf_clip.meta" - ] - }, - { - "cell_type": "code", - "execution_count": 102, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\u001b[0;31mSignature:\u001b[0m\n", - "\u001b[0mreproject\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0msource\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mdestination\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[0msrc_transform\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[0mgcps\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[0mrpcs\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[0msrc_crs\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[0msrc_nodata\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[0mdst_transform\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[0mdst_crs\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[0mdst_nodata\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[0mdst_resolution\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[0msrc_alpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mdst_alpha\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mresampling\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m<\u001b[0m\u001b[0mResampling\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnearest\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0;36m0\u001b[0m\u001b[0;34m>\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0mnum_threads\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0minit_dest_nodata\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[0mwarp_mem_limit\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\n", - "\u001b[0;34m\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\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\u001b[0m\u001b[0m\n", - "\u001b[0;31mDocstring:\u001b[0m\n", - "Reproject a source raster to a destination raster.\n", - "\n", - "If the source and destination are ndarrays, coordinate reference\n", - "system definitions and affine transformation parameters or ground\n", - "control points (gcps) are required for reprojection.\n", - "\n", - "If the source and destination are rasterio Bands, shorthand for\n", - "bands of datasets on disk, the coordinate reference systems and\n", - "transforms or GCPs will be read from the appropriate datasets.\n", - "\n", - "Parameters\n", - "------------\n", - "source: ndarray or Band\n", - " The source is a 2 or 3-D ndarray, or a single or a multiple\n", - " Rasterio Band object. The dimensionality of source\n", - " and destination must match, i.e., for multiband reprojection\n", - " the lengths of the first axes of the source and destination\n", - " must be the same.\n", - "destination: ndarray or Band, optional\n", - " The destination is a 2 or 3-D ndarray, or a single or a multiple\n", - " Rasterio Band object. The dimensionality of source\n", - " and destination must match, i.e., for multiband reprojection\n", - " the lengths of the first axes of the source and destination\n", - " must be the same.\n", - "src_transform: affine.Affine(), optional\n", - " Source affine transformation. Required if source and\n", - " destination are ndarrays. Will be derived from source if it is\n", - " a rasterio Band. An error will be raised if this parameter is\n", - " defined together with gcps.\n", - "gcps: sequence of GroundControlPoint, optional\n", - " Ground control points for the source. An error will be raised\n", - " if this parameter is defined together with src_transform or rpcs.\n", - "rpcs: RPC or dict, optional\n", - " Rational polynomial coefficients for the source. An error will\n", - " be raised if this parameter is defined together with src_transform\n", - " or gcps.\n", - "src_crs: CRS or dict, optional\n", - " Source coordinate reference system, in rasterio dict format.\n", - " Required if source and destination are ndarrays.\n", - " Will be derived from source if it is a rasterio Band.\n", - " Example: CRS({'init': 'EPSG:4326'})\n", - "src_nodata: int or float, optional\n", - " The source nodata value. Pixels with this value will not be\n", - " used for interpolation. If not set, it will default to the\n", - " nodata value of the source image if a masked ndarray or\n", - " rasterio band, if available.\n", - "dst_transform: affine.Affine(), optional\n", - " Target affine transformation. Required if source and\n", - " destination are ndarrays. Will be derived from target if it is\n", - " a rasterio Band.\n", - "dst_crs: CRS or dict, optional\n", - " Target coordinate reference system. Required if source and\n", - " destination are ndarrays. Will be derived from target if it\n", - " is a rasterio Band.\n", - "dst_nodata: int or float, optional\n", - " The nodata value used to initialize the destination; it will\n", - " remain in all areas not covered by the reprojected source.\n", - " Defaults to the nodata value of the destination image (if set),\n", - " the value of src_nodata, or 0 (GDAL default).\n", - "dst_resolution: tuple (x resolution, y resolution) or float, optional\n", - " Target resolution, in units of target coordinate reference\n", - " system.\n", - "src_alpha : int, optional\n", - " Index of a band to use as the alpha band when warping.\n", - "dst_alpha : int, optional\n", - " Index of a band to use as the alpha band when warping.\n", - "resampling: int, rasterio.enums.Resampling\n", - " Resampling method to use.\n", - " Default is :attr:`rasterio.enums.Resampling.nearest`.\n", - " An exception will be raised for a method not supported by the running\n", - " version of GDAL.\n", - "num_threads : int, optional\n", - " The number of warp worker threads. Default: 1.\n", - "init_dest_nodata: bool\n", - " Flag to specify initialization of nodata in destination;\n", - " prevents overwrite of previous warps. Defaults to True.\n", - "warp_mem_limit : int, optional\n", - " The warp operation memory limit in MB. Larger values allow the\n", - " warp operation to be carried out in fewer chunks. The amount of\n", - " memory required to warp a 3-band uint8 2000 row x 2000 col\n", - " raster to a destination of the same size is approximately\n", - " 56 MB. The default (0) means 64 MB with GDAL 2.2.\n", - "kwargs: dict, optional\n", - " Additional arguments passed to both the image to image\n", - " transformer :cpp:func:`GDALCreateGenImgProjTransformer2` (for example,\n", - " MAX_GCP_ORDER=2) and the :cpp:struct:`GDALWarpOptions` (for example,\n", - " INIT_DEST=NO_DATA).\n", - "\n", - "Returns\n", - "---------\n", - "destination: ndarray or Band\n", - " The transformed narray or Band.\n", - "dst_transform: Affine\n", - " THe affine transformation matrix of the destination.\n", - "\u001b[0;31mFile:\u001b[0m ~/.conda/envs/ox/lib/python3.12/site-packages/rasterio/warp.py\n", - "\u001b[0;31mType:\u001b[0m function" - ] - } - ], - "source": [ - "reproject?" - ] - }, - { - "cell_type": "code", - "execution_count": 76, - "metadata": {}, - "outputs": [], - "source": [ - "with rio.open(out_travel_surface) as src:\n", - "\n", - " with rio.open(out_pop_clip) as template:\n", - " # transform, width, height = calculate_default_transform(\n", - " # src.crs, dst_crs, src.width, src.height, *src.bounds)\n", - " kwargs = template.meta.copy()\n", - " transform = template.transform\n", - " # kwargs.update({\n", - " # 'crs': dst_crs,\n", - " # 'transform': transform,\n", - " # 'width': width,\n", - " # 'height': height\n", - " # })\n", - "\n", - " out_travel_surface_std = join(scratch_dir, f\"travel_surface_motorized_{iso}_STD.tif\")\n", - " with rio.open(out_travel_surface_std, 'w', **kwargs) as dst:\n", - " for i in range(1, src.count + 1):\n", - " reproject(\n", - " source=rio.band(src, i),\n", - " destination=rio.band(dst, i),\n", - " src_transform=src.transform,\n", - " src_crs=src.crs,\n", - " dst_transform=transform,\n", - " dst_crs=dst_crs,\n", - " resampling=Resampling.nearest)" - ] - }, - { - "cell_type": "code", - "execution_count": 68, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[array([[[0.00316, 0. , 0. , ..., 0. , 0. , 0. ],\n", - " [0.00316, 0. , 0. , ..., 0. , 0. , 0. ],\n", - " [0.00316, 0. , 0. , ..., 0. , 0. , 0. ],\n", - " ...,\n", - " [0.00316, 0.00316, 0.00316, ..., 0.00316, 0.00316, 0.00316],\n", - " [0.00316, 0.00316, 0.00316, ..., 0.00316, 0.00316, 0.00316],\n", - " [0. , 0. , 0. , ..., 0.00316, 0.00316, 0.00316]]],\n", - " dtype=float32),\n", - " {'driver': 'GTiff',\n", - " 'dtype': 'float32',\n", - " 'nodata': -9999.0,\n", - " 'width': 785,\n", - " 'height': 1378,\n", - " 'count': 1,\n", - " 'crs': CRS.from_epsg(3460),\n", - " 'transform': Affine(659.6981137784882, 0.0, 1800287.2032548701,\n", - " 0.0, -659.6981137784883, 4499755.301166313)}]" - ] - }, - "execution_count": 68, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "# out_pop_surface_std = join(expanduser(\"~\"), 'data', 'worldpop', \"WP_2020_1km_STD.tif\")\n", - "# out_pop_surface_std = join(scratch_dir, \"WP_2020_1km_STD.tif\")\n", - "out_travel_surface_std = join(scratch_dir, f\"travel_surface_motorized_{iso}_STD.tif\")\n", - "# rMisc.standardizeInputRasters(travel_surf, pop_surf_clip, out_travel_surface_std, resampling_type=\"nearest\")\n", - "\n", - "# rMisc.standardizeInputRasters(travel_surf, pop_surf_proj, out_travel_surface_std, resampling_type=\"nearest\")\n" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Align the population raster to the friction surface, ensuring that they have the same extent and resolution." - ] - }, - { - "cell_type": "code", - "execution_count": 77, - "metadata": {}, - "outputs": [], - "source": [ - "# out_pop_surface_std = join(expanduser(\"~\"), 'data', 'worldpop', \"WP_2020_1km_STD.tif\")\n", - "# out_pop_surface_std = join(scratch_dir, \"WP_2020_1km_STD.tif\")\n", - "# rMisc.standardizeInputRasters(pop_surf, travel_surf, out_pop_surface_std, resampling_type=\"nearest\")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "### Origins" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "We prepare a standard grid using each cell from the 1km World Pop raster." - ] - }, - { - "cell_type": "code", - "execution_count": 132, - "metadata": {}, - "outputs": [], - "source": [ - "pop_surf = rio.open(out_pop_clip)\n", - "pop = pop_surf.read(1, masked=False)" - ] - }, - { - "cell_type": "code", - "execution_count": 133, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "pop_copy = pop.copy()\n", - "pop_copy[pop_copy==0] = np.nan\n", - "pop_copy[pop_copy==-99999] = np.nan\n", - "\n", - "fig, ax = plt.subplots(figsize=(6,8))\n", - "im = ax.imshow(pop_copy, norm=colors.PowerNorm(gamma=0.5), cmap='viridis')" - ] - }, - { - "cell_type": "code", - "execution_count": 88, - "metadata": {}, - "outputs": [], - "source": [ - "indices = list(np.ndindex(pop.shape))\n", - "xys = [Point(pop_surf.xy(ind[0], ind[1])) for ind in indices]\n", - "res_df = pd.DataFrame({\n", - " 'spatial_index': indices, \n", - " 'xy': xys, \n", - " 'pop': pop.flatten()\n", - "})\n", - "res_df['pointid'] = res_df.index" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Create an MCP graph object from the friction surface." - ] - }, - { - "cell_type": "code", - "execution_count": 114, - "metadata": {}, - "outputs": [], - "source": [ - "travel_surf_std = rio.open(out_travel_surface_std)" - ] - }, - { - "cell_type": "code", - "execution_count": 115, - "metadata": {}, - "outputs": [], - "source": [ - "# convert friction surface to traversal time (lazily). Original data are\n", - "# are minutes to travel 1 m, so we will convert to \n", - "# minutes to cross the cell. This could be revised\n", - "inG_data = travel_surf_std.read(1) * 1000 \n", - "\n", - "# Correct no data values. Not needed but good to check\n", - "# inG_data[inG_data < 0] = 99999999\n", - "# inG_data[inG_data < 0] = np.nan\n", - "inG_data[inG_data < 0] = inG_data.max()\n", - "mcp = graph.MCP_Geometric(inG_data)" - ] - }, - { - "cell_type": "code", - "execution_count": 116, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Mean: 4.363716125488281\n", - "Max: 150.3118133544922\n", - "Min: 0.9230769276618958\n", - "Std: 8.672630310058594\n" - ] - } - ], - "source": [ - "# descriptive stats for numpy array\n", - "print(f\"Mean: {np.mean(inG_data)}\")\n", - "print(f\"Max: {np.max(inG_data)}\")\n", - "print(f\"Min: {np.min(inG_data)}\")\n", - "print(f\"Std: {np.std(inG_data)}\")" - ] - }, - { - "cell_type": "code", - "execution_count": 117, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 117, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "show(inG_data, norm=colors.PowerNorm(gamma=0.5), cmap='magma')" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## 2. Data Analysis" - ] - }, - { - "cell_type": "code", - "execution_count": 147, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(1081730, 7)" - ] - }, - "execution_count": 147, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(res_df), len(dests)" - ] - }, - { - "cell_type": "code", - "execution_count": 148, - "metadata": {}, - "outputs": [], - "source": [ - "dests = dests.to_crs(dst_crs)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Calculate the travel time from each grid cell to the nearest destination." - ] - }, - { - "cell_type": "code", - "execution_count": 149, - "metadata": {}, - "outputs": [], - "source": [ - "res = ma.calculate_travel_time(travel_surf_std, mcp, dests)[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 150, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([[2590.59121746, 2589.28230256, 2587.97338767, ..., 2560.12322802,\n", - " 2561.43214291, 2562.7410578 ],\n", - " [2587.43121737, 2586.12230248, 2584.81338759, ..., 2483.3873213 ,\n", - " 2484.69623619, 2486.00515108],\n", - " [2584.27121728, 2582.96230239, 2581.6533875 , ..., 2480.22732121,\n", - " 2481.53623611, 2482.845151 ],\n", - " ...,\n", - " [1459.25547082, 1457.94655593, 1456.63764104, ..., 2171.71698477,\n", - " 2174.87698486, 2178.03698495],\n", - " [1462.41547091, 1461.10655602, 1459.79764113, ..., 2173.02589967,\n", - " 2176.18589975, 2179.34589984],\n", - " [1539.15137763, 1537.84246274, 1536.53354785, ..., 2174.33481456,\n", - " 2177.49481465, 2180.65481473]])" - ] - }, - "execution_count": 150, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "res" - ] - }, - { - "cell_type": "code", - "execution_count": 151, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(1081730, 1081730)" - ] - }, - "execution_count": 151, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "len(res.flatten()), len(res_df)" - ] - }, - { - "cell_type": "code", - "execution_count": 152, - "metadata": {}, - "outputs": [], - "source": [ - "res_df.loc[:, 'tt_city_min'] = res.flatten()" - ] - }, - { - "cell_type": "code", - "execution_count": 134, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
spatial_indexxypoppointidtt_city_min
0(0, 0)POINT (1800617.0523117594 4499425.4521094235)-99999.002590.591217
1(0, 1)POINT (1801276.7504255378 4499425.4521094235)-99999.012589.282303
2(0, 2)POINT (1801936.4485393164 4499425.4521094235)-99999.022587.973388
\n", - "
" - ], - "text/plain": [ - " spatial_index xy pop \\\n", - "0 (0, 0) POINT (1800617.0523117594 4499425.4521094235) -99999.0 \n", - "1 (0, 1) POINT (1801276.7504255378 4499425.4521094235) -99999.0 \n", - "2 (0, 2) POINT (1801936.4485393164 4499425.4521094235) -99999.0 \n", - "\n", - " pointid tt_city_min \n", - "0 0 2590.591217 \n", - "1 1 2589.282303 \n", - "2 2 2587.973388 " - ] - }, - "execution_count": 134, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "res_df.head(3)" - ] - }, - { - "cell_type": "code", - "execution_count": 135, - "metadata": {}, - "outputs": [], - "source": [ - "# remove values where pop is 0 or nan\n", - "res_df = res_df.loc[res_df['pop']!=0].copy()\n", - "res_df = res_df.loc[~(res_df['pop'].isna())].copy()" - ] - }, - { - "cell_type": "code", - "execution_count": 136, - "metadata": {}, - "outputs": [], - "source": [ - "res_df.loc[:,'xy'] = res_df.loc[:,'xy'].apply(lambda x: Point(x))" - ] - }, - { - "cell_type": "code", - "execution_count": 146, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/jupyter-wb514197/Repos/gostrocks/src/GOSTRocks/rasterMisc.py:52: SyntaxWarning: invalid escape sequence '\\W'\n", - " ''' Clip input raster\n", - "/home/jupyter-wb514197/Repos/gostrocks/src/GOSTRocks/rasterMisc.py:88: SyntaxWarning: invalid escape sequence '\\T'\n", - " ''' Convert input geopandas dataframe into a raster file\n", - "/home/jupyter-wb514197/Repos/gostrocks/src/GOSTRocks/rasterMisc.py:523: SyntaxWarning: invalid escape sequence '\\G'\n", - " exampleText = '''\n" - ] - }, - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
spatial_indexxypoppointidtt_city_min
0(0, 0)POINT (1800617.0523117594 4499425.4521094235)-99999.002590.591217
1(0, 1)POINT (1801276.7504255378 4499425.4521094235)-99999.012589.282303
2(0, 2)POINT (1801936.4485393164 4499425.4521094235)-99999.022587.973388
3(0, 3)POINT (1802596.1466530948 4499425.4521094235)-99999.032586.664473
4(0, 4)POINT (1803255.8447668734 4499425.4521094235)-99999.042585.355558
..................
1081725(1377, 780)POINT (2315181.58105898 3591021.149436445)-99999.010817252168.014814
1081726(1377, 781)POINT (2315841.2791727586 3591021.149436445)-99999.010817262171.174814
1081727(1377, 782)POINT (2316500.977286537 3591021.149436445)-99999.010817272174.334815
1081728(1377, 783)POINT (2317160.675400316 3591021.149436445)-99999.010817282177.494815
1081729(1377, 784)POINT (2317820.373514094 3591021.149436445)-99999.010817292180.654815
\n", - "

1081730 rows × 5 columns

\n", - "
" - ], - "text/plain": [ - " spatial_index xy pop \\\n", - "0 (0, 0) POINT (1800617.0523117594 4499425.4521094235) -99999.0 \n", - "1 (0, 1) POINT (1801276.7504255378 4499425.4521094235) -99999.0 \n", - "2 (0, 2) POINT (1801936.4485393164 4499425.4521094235) -99999.0 \n", - "3 (0, 3) POINT (1802596.1466530948 4499425.4521094235) -99999.0 \n", - "4 (0, 4) POINT (1803255.8447668734 4499425.4521094235) -99999.0 \n", - "... ... ... ... \n", - "1081725 (1377, 780) POINT (2315181.58105898 3591021.149436445) -99999.0 \n", - "1081726 (1377, 781) POINT (2315841.2791727586 3591021.149436445) -99999.0 \n", - "1081727 (1377, 782) POINT (2316500.977286537 3591021.149436445) -99999.0 \n", - "1081728 (1377, 783) POINT (2317160.675400316 3591021.149436445) -99999.0 \n", - "1081729 (1377, 784) POINT (2317820.373514094 3591021.149436445) -99999.0 \n", - "\n", - " pointid tt_city_min \n", - "0 0 2590.591217 \n", - "1 1 2589.282303 \n", - "2 2 2587.973388 \n", - "3 3 2586.664473 \n", - "4 4 2585.355558 \n", - "... ... ... \n", - "1081725 1081725 2168.014814 \n", - "1081726 1081726 2171.174814 \n", - "1081727 1081727 2174.334815 \n", - "1081728 1081728 2177.494815 \n", - "1081729 1081729 2180.654815 \n", - "\n", - "[1081730 rows x 5 columns]" - ] - }, - "execution_count": 146, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "res_df" - ] - }, - { - "cell_type": "code", - "execution_count": 139, - "metadata": {}, - "outputs": [], - "source": [ - "origins = gpd.GeoDataFrame(res_df, geometry='xy', crs=dst_crs) #'EPSG:4326'" - ] - }, - { - "cell_type": "code", - "execution_count": 140, - "metadata": {}, - "outputs": [], - "source": [ - "origins.rename(columns={'xy':'geometry'}, inplace=True)\n", - "origins.set_geometry('geometry', inplace=True)" - ] - }, - { - "cell_type": "code", - "execution_count": 141, - "metadata": {}, - "outputs": [], - "source": [ - "# convert travel time to hours\n", - "origins.loc[:, \"tt_city_min_hrs\"] = origins.loc[:, \"tt_city_min\"] / 60" - ] - }, - { - "cell_type": "code", - "execution_count": 142, - "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
spatial_indexgeometrypoppointidtt_city_mintt_city_min_hrs
0(0, 0)POINT (1800617.052 4499425.452)-99999.002590.59121743.176520
1(0, 1)POINT (1801276.750 4499425.452)-99999.012589.28230343.154705
\n", - "
" - ], - "text/plain": [ - " spatial_index geometry pop pointid \\\n", - "0 (0, 0) POINT (1800617.052 4499425.452) -99999.0 0 \n", - "1 (0, 1) POINT (1801276.750 4499425.452) -99999.0 1 \n", - "\n", - " tt_city_min tt_city_min_hrs \n", - "0 2590.591217 43.176520 \n", - "1 2589.282303 43.154705 " - ] - }, - "execution_count": 142, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "origins.head(2)" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "Save results as a raster" - ] - }, - { - "cell_type": "code", - "execution_count": 143, - "metadata": {}, - "outputs": [], - "source": [ - "tt_raster = join(scratch_dir, f\"tt_city_min_motorized_friction.tif\")" - ] - }, - { - "cell_type": "code", - "execution_count": 145, - "metadata": {}, - "outputs": [], - "source": [ - "rMisc.rasterizeDataFrame(\n", - " inD = origins,\n", - " outFile = tt_raster,\n", - " idField = 'tt_city_min_hrs',\n", - " templateRaster = out_travel_surface_std\n", - ")" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## Map Results" - ] - }, - { - "cell_type": "code", - "execution_count": 62, - "metadata": {}, - "outputs": [], - "source": [ - "tt_rio = rio.open(tt_raster)" - ] - }, - { - "cell_type": "code", - "execution_count": 63, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'/home/jupyter-wb514197/Repos/pacific-access'" - ] - }, - "execution_count": 63, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "os.getcwd()" - ] - }, - { - "cell_type": "code", - "execution_count": 64, - "metadata": {}, - "outputs": [], - "source": [ - "# ext_custom = tuple(np.add(ext, (-1, +1, -1, +1)))" - ] - }, - { - "cell_type": "code", - "execution_count": 65, - "metadata": {}, - "outputs": [], - "source": [ - "# ctx.providers" - ] - }, - { - "cell_type": "code", - "execution_count": 66, - "metadata": {}, - "outputs": [], - "source": [ - "# ctx.providers.Esri" - ] - }, - { - "cell_type": "code", - "execution_count": 67, - "metadata": {}, - "outputs": [], - "source": [ - "plt.rcParams[\"font.family\"] = \"Ubuntu Condensed\"" - ] - }, - { - "cell_type": "code", - "execution_count": 75, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": "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", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "figsize = (12, 12)\n", - "fig, ax = plt.subplots(1, 1, figsize = figsize)\n", - "\n", - "ax.set_title(\"Travel time to nearest city\", fontsize=16, horizontalalignment='right')\n", - "plt.axis('off')\n", - "ext = plotting_extent(tt_rio)\n", - "im = ax.imshow(tt_rio.read(1), vmin=0, vmax=8, cmap='magma', extent=ext)\n", - "dests.plot(ax=ax, facecolor='teal', edgecolor='white', markersize=100, alpha=0.75)\n", - "# wpi.plot(ax=ax, facecolor='red', edgecolor='black', markersize=25, alpha=0.75)\n", - "divider = make_axes_locatable(ax)\n", - "cax = divider.append_axes('right', size=\"4%\", pad=0.1)\n", - "cb = fig.colorbar(im, cax=cax, orientation='vertical')\n", - "# ctx.add_basemap(ax, source=ctx.providers.CartoDB.Positron, crs='EPSG:4326', zorder=-1)\n", - "ctx.add_basemap(ax, source=ctx.providers.Esri.WorldTerrain, crs='EPSG:4326', zorder=-1)\n", - "txt=\"Cities are defined as clusters of at least 5,000 people with a density of at least 190 ppl. per sq. km.\"\n", - "txt2 = \"Travel time is estimated using the motorized global friction surface (2020)\"\n", - "\n", - "plt.figtext(0.3, 0.08, txt, wrap=True, horizontalalignment='left', fontsize=10)\n", - "plt.figtext(0.3, 0.06, txt2, wrap=True, horizontalalignment='left', fontsize=10)\n", - "plt.figtext(0.63, 0.89, \"Travel Time (hrs.)\", wrap=False, fontsize=10)\n", - "plt.savefig(f\"travel-time-friction-{iso}.png\", dpi=300, bbox_inches='tight', facecolor='white')" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 125, - "metadata": {}, - "outputs": [], - "source": [ - "# import matplotlib.font_manager\n", - "# flist = matplotlib.font_manager.get_font_names()\n", - "# print(flist)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "network", - "language": "python", - "name": "ox" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.12.0" - } - }, - "nbformat": 4, - "nbformat_minor": 4 -} diff --git a/notebooks/emissions.ipynb b/notebooks/emissions.ipynb index c618905..25b2eb6 100644 --- a/notebooks/emissions.ipynb +++ b/notebooks/emissions.ipynb @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 4, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -22,13 +22,55 @@ "input_dir = join(emissions_dir, '20240614')\n", "output_dir = join(emissions_dir, 'for_charts')\n", "df = pd.read_csv(join(input_dir, 'Emissions Pacific 201901_202405.csv'))\n", - "df.loc[:, 'ym'] = pd.to_datetime(df[['year','month']].assign(day=1)).dt.to_period('M')\n", + "df.loc[:, 'ymd'] = pd.to_datetime(df[['year','month']].assign(day=1)) \n", + "df.loc[:, 'ym'] = df['ymd'].dt.to_period('M')\n", "df.columns = df.columns.str.lower()" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "kir_islands = ['Phoenix Group', 'Gilbert Islands', 'Line Group']\n", + "df.loc[df['country'].isin(kir_islands), 'country'] = 'Kiribati'" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": {}, + "outputs": [], + "source": [ + "# df.to_csv(join(output_dir, 'emissions.csv'))" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": {}, + "outputs": [], + "source": [ + "group_cols = ['country', 'ymd', 'ym'] # '_vessel_group_ais' '_w_fishing'\n", + "data_cols = ['count_vessel', 'count_vessel_day', '_missing_hours', '_total_hours']\n", + "# data_cols = ['_ch4_e', '_co_e', '_n2o_e', '_nmvoc_e', '_pm10_e', '_pm25_e', '_nox_e', '_bc_e', '_co2_f', '_sox_f', '_bc_f']" + ] + }, + { + "cell_type": "code", + "execution_count": 47, + "metadata": {}, + "outputs": [], + "source": [ + "df_re = df.groupby(group_cols)[data_cols].sum()\n", + "df_re.reset_index(inplace=True)\n", + "# df_re.to_csv(join(output_dir, 'emissions_clean_by_vessel_group.csv'))" + ] + }, + { + "cell_type": "code", + "execution_count": 48, "metadata": {}, "outputs": [ { @@ -53,259 +95,98 @@ " \n", " \n", " country\n", - " year\n", - " month\n", - " type\n", - " _vessel_group_ais\n", - " _vessel_class_no\n", - " _vessel_class\n", - " _w_fishing\n", - " _op_phase\n", - " count_vessel\n", - " ...\n", - " _n2o_e\n", - " _nmvoc_e\n", - " _pm10_e\n", - " _pm25_e\n", - " _nox_e\n", - " _bc_e\n", - " _co2_f\n", - " _sox_f\n", - " _bc_f\n", + " ymd\n", " ym\n", + " count_vessel\n", + " count_vessel_day\n", + " _missing_hours\n", + " _total_hours\n", " \n", " \n", " \n", " \n", " 0\n", " Cook Islands\n", - " 2019\n", - " 1\n", - " Type 1 & 2\n", - " Cargo\n", - " 1.0\n", - " Bulk carrier\n", - " False\n", - " Anchored\n", - " 9\n", - " ...\n", - " 1.133754e+02\n", - " 1.074684e+04\n", - " 4.767601e+03\n", - " 4.386193e+03\n", - " 6.319236e+04\n", - " 0.000000\n", - " 2.790828e+05\n", - " 4.555624e+03\n", - " 8.539840e+01\n", + " 2019-01-01\n", " 2019-01\n", + " 1046\n", + " 2229\n", + " 17801.0\n", + " 30445.0\n", " \n", " \n", " 1\n", " Cook Islands\n", - " 2019\n", - " 1\n", - " Type 1 & 2\n", - " Cargo\n", - " 1.0\n", - " Bulk carrier\n", - " False\n", - " Sea\n", - " 186\n", - " ...\n", - " 8.208455e+05\n", - " 1.679013e+07\n", - " 2.203410e+07\n", - " 2.027137e+07\n", - " 4.082239e+08\n", - " 0.000000\n", - " 7.895228e+09\n", - " 1.288782e+08\n", - " 5.099240e+05\n", - " 2019-01\n", - " \n", - " \n", - " 2\n", - " Cook Islands\n", - " 2019\n", - " 1\n", - " Type 1 & 2\n", - " Cargo\n", - " 3.0\n", - " Container\n", - " False\n", - " Anchored\n", - " 3\n", - " ...\n", - " 3.230226e+02\n", - " 3.061929e+04\n", - " 1.358467e+04\n", - " 1.249790e+04\n", - " 1.525090e+05\n", - " 0.000000\n", - " 7.972757e+05\n", - " 1.301438e+04\n", - " 4.362581e+02\n", - " 2019-01\n", - " \n", - " \n", - " 3\n", - " Cook Islands\n", - " 2019\n", - " 1\n", - " Type 1 & 2\n", - " Cargo\n", - " 3.0\n", - " Container\n", - " False\n", - " Sea\n", - " 106\n", - " ...\n", - " 2.169845e+06\n", - " 4.333122e+07\n", - " 5.637624e+07\n", - " 5.186614e+07\n", - " 1.110225e+09\n", - " 1884.648168\n", - " 2.131204e+10\n", - " 3.419855e+08\n", - " 1.373343e+06\n", - " 2019-01\n", - " \n", - " \n", - " 4\n", - " Cook Islands\n", - " 2019\n", - " 1\n", - " Type 1 & 2\n", - " Cargo\n", - " 4.0\n", - " General cargo\n", - " False\n", - " Anchored\n", - " 8\n", - " ...\n", - " 2.150720e+02\n", - " 1.933015e+04\n", - " 8.882461e+03\n", - " 8.171864e+03\n", - " 9.266611e+04\n", - " 0.000000\n", - " 5.440458e+05\n", - " 8.880582e+03\n", - " 1.675230e+04\n", - " 2019-01\n", + " 2019-02-01\n", + " 2019-02\n", + " 997\n", + " 1825\n", + " 18252.0\n", + " 26144.0\n", " \n", " \n", "\n", - "

5 rows × 69 columns

\n", "" ], "text/plain": [ - " country year month type _vessel_group_ais _vessel_class_no \\\n", - "0 Cook Islands 2019 1 Type 1 & 2 Cargo 1.0 \n", - "1 Cook Islands 2019 1 Type 1 & 2 Cargo 1.0 \n", - "2 Cook Islands 2019 1 Type 1 & 2 Cargo 3.0 \n", - "3 Cook Islands 2019 1 Type 1 & 2 Cargo 3.0 \n", - "4 Cook Islands 2019 1 Type 1 & 2 Cargo 4.0 \n", - "\n", - " _vessel_class _w_fishing _op_phase count_vessel ... _n2o_e \\\n", - "0 Bulk carrier False Anchored 9 ... 1.133754e+02 \n", - "1 Bulk carrier False Sea 186 ... 8.208455e+05 \n", - "2 Container False Anchored 3 ... 3.230226e+02 \n", - "3 Container False Sea 106 ... 2.169845e+06 \n", - "4 General cargo False Anchored 8 ... 2.150720e+02 \n", - "\n", - " _nmvoc_e _pm10_e _pm25_e _nox_e _bc_e \\\n", - "0 1.074684e+04 4.767601e+03 4.386193e+03 6.319236e+04 0.000000 \n", - "1 1.679013e+07 2.203410e+07 2.027137e+07 4.082239e+08 0.000000 \n", - "2 3.061929e+04 1.358467e+04 1.249790e+04 1.525090e+05 0.000000 \n", - "3 4.333122e+07 5.637624e+07 5.186614e+07 1.110225e+09 1884.648168 \n", - "4 1.933015e+04 8.882461e+03 8.171864e+03 9.266611e+04 0.000000 \n", + " country ymd ym count_vessel count_vessel_day \\\n", + "0 Cook Islands 2019-01-01 2019-01 1046 2229 \n", + "1 Cook Islands 2019-02-01 2019-02 997 1825 \n", "\n", - " _co2_f _sox_f _bc_f ym \n", - "0 2.790828e+05 4.555624e+03 8.539840e+01 2019-01 \n", - "1 7.895228e+09 1.288782e+08 5.099240e+05 2019-01 \n", - "2 7.972757e+05 1.301438e+04 4.362581e+02 2019-01 \n", - "3 2.131204e+10 3.419855e+08 1.373343e+06 2019-01 \n", - "4 5.440458e+05 8.880582e+03 1.675230e+04 2019-01 \n", - "\n", - "[5 rows x 69 columns]" + " _missing_hours _total_hours \n", + "0 17801.0 30445.0 \n", + "1 18252.0 26144.0 " ] }, - "execution_count": 18, + "execution_count": 48, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df.head()" + "df_re.head(2)" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 51, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Index(['country', 'year', 'month', 'type', '_vessel_group_ais',\n", - " '_vessel_class_no', '_vessel_class', '_w_fishing', '_op_phase',\n", - " 'count_vessel', 'count_vessel_day', '_missing_hours', '_total_hours',\n", - " '_ch4_energy', '_co_energy', '_n2o_energy', '_nmvoc_energy',\n", - " '_pm10_energy', '_pm25_energy', '_nox_energy', '_bc_energy',\n", - " '_ch4_energy_pilot', '_co_energy_pilot', '_n2o_energy_pilot',\n", - " '_nmvoc_energy_pilot', '_pm10_energy_pilot', '_pm25_energy_pilot',\n", - " '_nox_energy_pilot', '_bc_energy_pilot', '_ab_ch4_energy',\n", - " '_ab_co_energy', '_ab_n2o_energy', '_ab_nmvoc_energy',\n", - " '_ab_pm10_energy', '_ab_pm25_energy', '_ab_nox_energy', '_ab_bc_energy',\n", - " '_ae_ch4_energy', '_ae_co_energy', '_ae_n2o_energy', '_ae_nmvoc_energy',\n", - " '_ae_pm10_energy', '_ae_pm25_energy', '_ae_nox_energy', '_ae_bc_energy',\n", - " '_co2_fuel', '_sox_fuel', '_bc_fuel', '_co2_fuel_pilot',\n", - " '_sox_fuel_pilot', '_bc_fuel_pilot', '_ab_co2_fuel', '_ab_sox_fuel',\n", - " '_ab_bc_fuel', '_ae_co2_fuel', '_ae_sox_fuel', '_ae_bc_fuel', '_ch4_e',\n", - " '_co_e', '_n2o_e', '_nmvoc_e', '_pm10_e', '_pm25_e', '_nox_e', '_bc_e',\n", - " '_co2_f', '_sox_f', '_bc_f', 'ym'],\n", - " dtype='object')" - ] - }, - "execution_count": 19, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "df.columns" + "# df_piv = df_re[group_cols+['_co2_f']].pivot(columns='_vessel_group_ais', values='_co2_f', index=['country', 'ymd', 'ym']).reset_index()\n", + "# df_piv = df_re[group_cols+['count_vessel']].pivot(columns=['_vessel_group_ais'], values='count_vessel', index=['country', 'ymd', 'ym']).reset_index()\n", + "df_piv = df_re[group_cols+['count_vessel']].pivot(columns=['country'], values='count_vessel', index=['ymd', 'ym']).reset_index()" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 68, "metadata": {}, "outputs": [], "source": [ - "group_cols = ['country', 'ym']" + "order = list(df_re.groupby('country').mean().sort_values('count_vessel', ascending=False).index)" ] }, { "cell_type": "code", - "execution_count": 56, + "execution_count": 73, "metadata": {}, "outputs": [], "source": [ - "df_re = df.groupby(group_cols)[['_co2_f']].sum()\n", - "df_re.reset_index(inplace=True)\n", - "df_piv = df_re.pivot(columns='country', values='_co2_f', index='ym')" + "# df_piv.to_csv(join(output_dir, 'co2_emissions_by_vessel_group.csv'))\n", + "df_piv[['ym', 'ymd']+order].to_csv(join(output_dir, 'vessel_count_by_country.csv'), index=False)" ] }, { "cell_type": "code", - "execution_count": 58, + "execution_count": 56, "metadata": {}, "outputs": [], "source": [ - "df_piv.to_csv(join(output_dir, 'co2_emissions.csv'))" + "# group_cols = ['country', 'ymd', 'ym']\n", + "# df_re = df.groupby(group_cols)[['_co2_f']].sum()\n", + "# df_re.reset_index(inplace=True)\n", + "# df_piv = df_re.pivot(columns='country', values='_co2_f', index='ym')" ] }, {