diff --git a/docs/_toc.yml b/docs/_toc.yml index 554ac4e..3ecfe7d 100644 --- a/docs/_toc.yml +++ b/docs/_toc.yml @@ -10,7 +10,10 @@ parts: - file: docs/foundational_datasets_and_data_products.md - caption: Data Products chapters: - - file: notebooks/nighttime-lights/nighttime-lights.ipynb + - file: notebooks/nighttime-lights/README + sections: + - file: notebooks/nighttime-lights/nighttime-lights.ipynb + - file: notebooks/nighttime-lights/nighttime-lights-in-jordan.ipynb - file: notebooks/conflict/acled.ipynb - caption: Acknowledgements chapters: diff --git a/notebooks/nighttime-lights/README.md b/notebooks/nighttime-lights/README.md new file mode 100644 index 0000000..3aa5b24 --- /dev/null +++ b/notebooks/nighttime-lights/README.md @@ -0,0 +1,46 @@ +# Nighttime Lights Trends + +Analyzing conflict dynamics through the lens of [NASA's Black Marble Nighttime Lights](https://blackmarble.gsfc.nasa.gov) dataset opens a unique window into the often-hidden facets of global unrest. In a world marked by diverse forms of conflict, from armed confrontations to civil unrest, the dataset offers an unconventional yet powerful tool for understanding the ripple effects of these conflicts on human settlements and infrastructure. By tracking nighttime light variations and disruptions, we can unearth vital insights into population displacement, economic destabilization, and the societal impacts of conflict. This analysis explores the potential of the Black Marble Nighttime Lights dataset to not only detect areas affected by conflict but also to quantify the extent of its influence on human lives and livelihoods, providing a valuable perspective on the multifaceted consequences of conflict worldwide. + +(nighttime-lights-limitations)= +## Limitations + +Using nighttime lights to estimate macroeconomic indicators during conflict may be a valuable approach, but it comes with several assumptions and limitations. Here's a list of some of the key assumptions and limitations: + +```{caution} +**Assumptions:** + +- **Luminosity Reflects Economic Activity:** The approach assumes that the level of nighttime lights is a reliable proxy for economic activity. It presupposes that areas with brighter lights correspond to higher economic productivity. + +- **Baseline Data Availability:** It assumes the availability of baseline nighttime lights data before the onset of the conflict. The accuracy of the estimates depends on the quality and relevance of this baseline data. + +- **Spatial Distribution:** The method assumes that nighttime lights are evenly distributed within a given geographic area and that changes in luminosity accurately reflect changes in economic activity across all locations. + + +**Limitations:** + +- **Confounding Factors and Data Interpretation:** The approach may require subjective interpretation, as it may not distinguish between reduced lighting due to conflict and reduced lighting due to other factors. Changes in nighttime lights can be influenced by factors other than economic activity, such as energy conservation measures, urban development, or seasonal variations. + +- **Generalization:** The approach might lead to overgeneralization, as a reduction in nighttime lights can be associated with various economic outcomes, from minor disruptions to severe economic downturns. + +- **Alternative Explanations:** Changes in nighttime lights can result from factors other than conflict, such as urban development, changes in economic activities, or natural disasters. Therefore, it may not always be clear whether a decline in nighttime lights is solely due to conflict. + +- **Geopolitical Factors:** The dataset may be subject to geopolitical biases, with some areas having less comprehensive coverage due to political reasons. + +- **Data Lag:** There can be a significant time lag between the occurrence of a conflict event and its reflection in the nighttime lights dataset. This lag may limit the dataset's utility for real-time conflict monitoring. + +- **Resolution and Urban Bias:** The dataset's spatial resolution may not be fine enough to capture small villages or isolated conflict events. It may also have an urban bias, making it less suitable for analyzing rural or remote conflicts. +``` + +To address these assumptions and limitations, it is crucial to complement nighttime lights data analysis with other sources of information and adopt a cautious and context-aware approach when interpreting the findings. + +## References + +{cite:empty}`ROMAN2018113` + +```{bibliography} +:filter: docname in docnames +:style: plain +``` + + diff --git a/notebooks/nighttime-lights/nighttime-lights-in-jordan.ipynb b/notebooks/nighttime-lights/nighttime-lights-in-jordan.ipynb new file mode 100644 index 0000000..d13bf76 --- /dev/null +++ b/notebooks/nighttime-lights/nighttime-lights-in-jordan.ipynb @@ -0,0 +1,1682 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "73b42bfe-7179-4172-b641-dfe5da287d55", + "metadata": {}, + "source": [ + "# Nighttime Lights Trends in Jordan\n", + "\n", + "The purpose of this notebook is to conduct an examination of the spatial and temporal distribution of artificial lights during the night across various regions in Jordan. The dataset utilized is derived from [NASA's Black Marble](https://blackmarble.gsfc.nasa.gov) and Visible Infrared Imaging Radiometer Suite (VIIRS) on the Suomi National Polar-orbiting Partnership (Suomi NPP) satellite. " + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "id": "ba22d742-7497-46df-ba8e-e0988f023ca3", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "import os\n", + "from datetime import datetime\n", + "\n", + "import colorcet as cc\n", + "import dask.dataframe as dd\n", + "import matplotlib as mpl\n", + "import matplotlib.pyplot as plt\n", + "import pandas as pd\n", + "import seaborn as sns\n", + "from blackmarble.bm_extract import bm_extract\n", + "from blackmarble.bm_raster import bm_raster\n", + "from bokeh.models import ColumnDataSource, HoverTool, Legend, Text, Title\n", + "from bokeh.plotting import figure, output_notebook, show\n", + "from gadm import GADMDownloader\n", + "from matplotlib.font_manager import get_font_names\n", + "\n", + "sns.set_theme()\n", + "sns.set_style(\"whitegrid\")\n", + "\n", + "mpl.rcParams[\"font.family\"] = \"Arial\"\n", + "\n", + "%load_ext autoreload\n", + "%autoreload 2" + ] + }, + { + "cell_type": "markdown", + "id": "cc878977-7626-4f07-b2a7-d8d727e05134", + "metadata": {}, + "source": [ + "## Data\n" + ] + }, + { + "cell_type": "markdown", + "id": "f1cce32c-f001-4324-9aeb-1246a3044457", + "metadata": {}, + "source": [ + "#### Define Region of Interest\n", + "\n", + "Define region of interest for where we want to download nighttime lights data" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "id": "5f3f2e01-0886-4ffb-8325-c1d003b071fd", + "metadata": { + "editable": true, + "scrolled": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
Make this Notebook Trusted to load map: File -> Trust Notebook
" + ], + "text/plain": [ + "" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "COUNTRY = GADMDownloader(version=\"4.0\").get_shape_data_by_country_name(\n", + " country_name=\"Jordan\", ad_level=2\n", + ")\n", + "COUNTRY.explore()" + ] + }, + { + "cell_type": "markdown", + "id": "d1cc7df3-6ef4-4a11-88ea-c518ba600d41", + "metadata": {}, + "source": [ + "### Black Marble \n", + "\n", + "[NASA's Black Marble](https://blackmarble.gsfc.nasa.gov) VIIRS (Visible Infrared Imaging Radiometer Suite) Nighttime Lights dataset represents a remarkable advancement in our ability to monitor and understand nocturnal light emissions on a global scale. By utilizing cutting-edge satellite technology and image processing techniques, the Black Marble VIIRS dataset offers a comprehensive and high-resolution view of the Earth's nighttime illumination patterns." + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "42d1f400-2063-4d39-8c3b-4ed4052281f4", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "outputs": [], + "source": [ + "dates = (\n", + " pd.date_range(\"2023-11-01\", \"2023-11-14\", freq=\"D\").strftime(\"%Y-%m-%d\").tolist()\n", + ")\n", + "\n", + "df = bm_extract(\n", + " roi_sf=COUNTRY,\n", + " product_id=\"VNP46A2\",\n", + " date=dates,\n", + " bearer=os.environ.get(\"BLACKMARBLE_TOKEN\"),\n", + " output_location_type=\"file\",\n", + " file_dir=\"data\",\n", + " file_prefix=\"jor_\",\n", + " aggregation_fun=[\"count\", \"mean\", \"min\", \"max\", \"median\", \"sum\"],\n", + " quiet=True,\n", + ")" + ] + }, + { + "cell_type": "markdown", + "id": "058fc345-a364-4719-94a9-30f225d469d7", + "metadata": {}, + "source": [ + "Now that the data was acquired, let's read the data files," + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "883bfe76-d1c0-423f-a1fb-6579b02a182f", + "metadata": { + "editable": true, + "scrolled": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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ID_0COUNTRYNAME_1NL_NAME_1ID_2NAME_2VARNAME_2NL_NAME_2TYPE_2ENGTYPE_2CC_2HASC_2ntl_minntl_maxntl_meanntl_countntl_sumntl_mediandate
0JORJordanAjlunJOR.1.1_1AjlounAjlunNahiaSub-ProvinceJO.AJ.AJ26.028.027.5000004110.028.02022-01-01
1JORJordanAjlunJOR.1.2_1KofranjahNahiaSub-ProvinceJO.AJ.KFNaNNaNNaN0NaNNaN2022-01-01
2JORJordanAmmanJOR.2.1_1AmmanNahiaSub-ProvinceJO.AM.AM21.01610.0305.159483928283188.0316.02022-01-01
3JORJordanAmmanJOR.2.2_1JizehAl-JizaNahiaSub-ProvinceJO.AM.JI0.01072.07.87366120532161662.02.02022-01-01
4JORJordanAmmanJOR.2.3_1MowaqqarAl-MwwqqarNahiaSub-ProvinceJO.AM.MO3.019.012.38775549607.012.02022-01-01
............................................................
47JORJordanTafilahJOR.11.2_1HesaAl-HasaNahiaSub-ProvinceJO.AT.HE1.0717.014.148782455764476.04.02023-11-05
48JORJordanTafilahJOR.11.3_1TafilehAl-TafilaNahiaSub-ProvinceJO.AT.AT1.01061.028.0652036104171310.05.02023-11-05
49JORJordanZarqaJOR.12.1_1AzraqAl-AzraqNahiaSub-ProvinceJO.AZ.ZQ0.01457.011.39288011179127361.09.02023-11-05
50JORJordanZarqaJOR.12.2_1BierainBirinNahiaSub-ProvinceJO.AZ.BR20.01115.0155.747934726113073.095.02023-11-05
51JORJordanZarqaJOR.12.3_1ZarqaAz-ZarqaNahiaSub-ProvinceJO.AZ.AZ4.02844.0197.9823623175628594.079.02023-11-05
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" + ], + "text/plain": [ + " ID_0 COUNTRY NAME_1 NL_NAME_1 ID_2 NAME_2 VARNAME_2 \\\n", + "0 JOR Jordan Ajlun JOR.1.1_1 Ajloun Ajlun \n", + "1 JOR Jordan Ajlun JOR.1.2_1 Kofranjah \n", + "2 JOR Jordan Amman JOR.2.1_1 Amman \n", + "3 JOR Jordan Amman JOR.2.2_1 Jizeh Al-Jiza \n", + "4 JOR Jordan Amman JOR.2.3_1 Mowaqqar Al-Mwwqqar \n", + ".. ... ... ... ... ... ... ... \n", + "47 JOR Jordan Tafilah JOR.11.2_1 Hesa Al-Hasa \n", + "48 JOR Jordan Tafilah JOR.11.3_1 Tafileh Al-Tafila \n", + "49 JOR Jordan Zarqa JOR.12.1_1 Azraq Al-Azraq \n", + "50 JOR Jordan Zarqa JOR.12.2_1 Bierain Birin \n", + "51 JOR Jordan Zarqa JOR.12.3_1 Zarqa Az-Zarqa \n", + "\n", + " NL_NAME_2 TYPE_2 ENGTYPE_2 CC_2 HASC_2 ntl_min ntl_max \\\n", + "0 Nahia Sub-Province JO.AJ.AJ 26.0 28.0 \n", + "1 Nahia Sub-Province JO.AJ.KF NaN NaN \n", + "2 Nahia Sub-Province JO.AM.AM 21.0 1610.0 \n", + "3 Nahia Sub-Province JO.AM.JI 0.0 1072.0 \n", + "4 Nahia Sub-Province JO.AM.MO 3.0 19.0 \n", + ".. ... ... ... ... ... ... ... \n", + "47 Nahia Sub-Province JO.AT.HE 1.0 717.0 \n", + "48 Nahia Sub-Province JO.AT.AT 1.0 1061.0 \n", + "49 Nahia Sub-Province JO.AZ.ZQ 0.0 1457.0 \n", + "50 Nahia Sub-Province JO.AZ.BR 20.0 1115.0 \n", + "51 Nahia Sub-Province JO.AZ.AZ 4.0 2844.0 \n", + "\n", + " ntl_mean ntl_count ntl_sum ntl_median date \n", + "0 27.500000 4 110.0 28.0 2022-01-01 \n", + "1 NaN 0 NaN NaN 2022-01-01 \n", + "2 305.159483 928 283188.0 316.0 2022-01-01 \n", + "3 7.873661 20532 161662.0 2.0 2022-01-01 \n", + "4 12.387755 49 607.0 12.0 2022-01-01 \n", + ".. ... ... ... ... ... \n", + "47 14.148782 4557 64476.0 4.0 2023-11-05 \n", + "48 28.065203 6104 171310.0 5.0 2023-11-05 \n", + "49 11.392880 11179 127361.0 9.0 2023-11-05 \n", + "50 155.747934 726 113073.0 95.0 2023-11-05 \n", + "51 197.982362 3175 628594.0 79.0 2023-11-05 \n", + "\n", + "[33956 rows x 19 columns]" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = dd.read_csv(\n", + " \"data/jor_VNP46A2*.csv\",\n", + " parse_dates=[\"date\"],\n", + ").compute()\n", + "df" + ] + }, + { + "cell_type": "markdown", + "id": "0f210e44-11c6-4b85-8f54-b12637d135c9", + "metadata": {}, + "source": [ + "```{important}\n", + "The [VNP46A2 Daily Moonlight-adjusted Nighttime Lights (NTL) Product](https://blackmarble.gsfc.nasa.gov/VNP46A2.html) is available daily. However, due data quality, cloud cover or other factors, the data may not be available always at a specific location.\n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "a9c10d77-c7e3-45b4-8555-1c2812864bdf", + "metadata": {}, + "source": [ + "## Methodology\n", + "\n", + "Creating a time series of weekly radiance using NASA's Black Marble data involves several steps, including data acquisition, pre-processing, zonal statistics calculation, and time series generation. Below is a general methodology for this process." + ] + }, + { + "cell_type": "markdown", + "id": "c60b94ee-6f68-4896-91f5-c6be73966df3", + "metadata": {}, + "source": [ + "### Time Series Generation\n", + "\n", + "Organize the zonal statistics results in a tabular format, where each columnn corresponds to a specific zone, and rows represent the daily radiance values. Next, we aggregate the data on a weekly basis, computing the desired statistical metric (e.g., mean radiance) for each zone for each week. Finally, we will visualize the time series data to observe trends, patterns, and anomalies over time." + ] + }, + { + "cell_type": "markdown", + "id": "292ee023-083e-4dd9-8532-435f85405844", + "metadata": {}, + "source": [ + "### Aggregate by `mean`" + ] + }, + { + "cell_type": "markdown", + "id": "fba71980-61f5-43fc-90c3-17c72583e7e2", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "parameters" + ] + }, + "source": [ + "In this step, we compute the weekly aggregation of the zonal statistics by for each second-level administrative division and for each week. In this case, we use the `mean` and the start of the week." + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "id": "929ff32a-8b04-4a69-b81b-303240691064", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "parameters", + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "# https://papermill.readthedocs.io/en/latest/usage-parameterize.html\n", + "VAR = \"ntl_mean\"" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "id": "8a0866cc-dc0c-419e-b5b7-1131d9a857fc", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "
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ntl_mean
NAME_1AjlunAmmanAqabaBalqaIrbidJarashKarakMa`anMadabaMafraqTafilahZarqa
date
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2022-01-0275.069969202.4764058.505361134.089928106.84187671.38409727.26128615.19356925.74989846.62293521.71045281.786189
2022-01-0973.258399146.1980917.302361126.774726109.84838070.58138022.45253314.71577426.12981241.54718210.76175968.849111
2022-01-1666.562622206.67654710.440221103.589737101.89219561.86266028.89743615.90135022.49652546.51710420.46586082.027899
2022-01-2350.736298131.44964010.701375110.01011492.38372458.47305522.60563413.27654719.78780032.54410715.55813551.250123
.......................................
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97 rows × 12 columns

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" + ], + "text/plain": [ + " ntl_mean \\\n", + "NAME_1 Ajlun Amman Aqaba Balqa Irbid \n", + "date \n", + "2021-12-26 24.449405 100.560509 7.354693 69.469869 34.211491 \n", + "2022-01-02 75.069969 202.476405 8.505361 134.089928 106.841876 \n", + "2022-01-09 73.258399 146.198091 7.302361 126.774726 109.848380 \n", + "2022-01-16 66.562622 206.676547 10.440221 103.589737 101.892195 \n", + "2022-01-23 50.736298 131.449640 10.701375 110.010114 92.383724 \n", + "... ... ... ... ... ... \n", + "2023-10-01 82.834816 166.543513 10.767144 139.648121 135.710503 \n", + "2023-10-08 74.725720 268.089576 15.548408 118.383982 171.830205 \n", + "2023-10-15 77.643016 219.769259 15.844977 131.277761 182.848500 \n", + "2023-10-22 97.636910 246.901782 14.079673 149.398513 215.814159 \n", + "2023-10-29 86.800853 217.892463 14.177184 149.693422 193.366805 \n", + "\n", + " \\\n", + "NAME_1 Jarash Karak Ma`an Madaba Mafraq Tafilah \n", + "date \n", + "2021-12-26 26.072344 32.249152 14.246122 35.418149 21.112632 17.448854 \n", + "2022-01-02 71.384097 27.261286 15.193569 25.749898 46.622935 21.710452 \n", + "2022-01-09 70.581380 22.452533 14.715774 26.129812 41.547182 10.761759 \n", + "2022-01-16 61.862660 28.897436 15.901350 22.496525 46.517104 20.465860 \n", + "2022-01-23 58.473055 22.605634 13.276547 19.787800 32.544107 15.558135 \n", + "... ... ... ... ... ... ... \n", + "2023-10-01 72.164824 32.195193 14.636889 42.565155 46.562834 23.539344 \n", + "2023-10-08 79.998565 46.072971 22.191168 37.634376 59.277453 28.815367 \n", + "2023-10-15 88.222131 42.819114 23.521066 38.187475 60.871674 31.162284 \n", + "2023-10-22 93.072254 43.123620 24.703762 47.732346 55.785789 31.345070 \n", + "2023-10-29 93.819495 48.715216 22.026993 57.222549 67.613157 28.489876 \n", + "\n", + " \n", + "NAME_1 Zarqa \n", + "date \n", + "2021-12-26 27.433938 \n", + "2022-01-02 81.786189 \n", + "2022-01-09 68.849111 \n", + "2022-01-16 82.027899 \n", + "2022-01-23 51.250123 \n", + "... ... \n", + "2023-10-01 78.440840 \n", + "2023-10-08 79.310402 \n", + "2023-10-15 105.373608 \n", + "2023-10-22 107.245041 \n", + "2023-10-29 109.128964 \n", + "\n", + "[97 rows x 12 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "JO_2 = (\n", + " df.pivot_table(index=\"date\", columns=[\"NAME_2\"], values=[VAR]).resample(\"W\").mean()\n", + ")\n", + "\n", + "JO_1 = (\n", + " df.pivot_table(index=\"date\", columns=[\"NAME_1\"], values=[VAR], aggfunc=\"mean\")\n", + " .resample(\"W\", label=\"left\")\n", + " .mean()\n", + ")\n", + "JO_1" + ] + }, + { + "cell_type": "markdown", + "id": "f9183aba-d35b-4b8e-9f36-cf677d79ae2c", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [] + }, + "source": [ + "Now, we visualize, " + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "id": "7f14a501-f0c0-4a98-8123-32e2dad20f94", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "data = JO_1" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "id": "3a0390f3-2d52-4a37-9df6-cf05fdaa9517", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hide-input" + ] + }, + "outputs": [ + { + "data": { + "text/html": [ + "\n", + "
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\\n\"+\n", + " \"

\\n\"+\n", + " \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n", + " \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n", + " \"

\\n\"+\n", + " \"\\n\"+\n", + " \"\\n\"+\n", + " \"from bokeh.resources import INLINE\\n\"+\n", + " \"output_notebook(resources=INLINE)\\n\"+\n", + " \"\\n\"+\n", + " \"
\"}};\n", + "\n", + " function display_loaded() {\n", + " const el = document.getElementById(\"e9120fc6-5ce1-4a0b-81f6-258f204381a4\");\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS is loading...\";\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " if (el != null) {\n", + " el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n", + " }\n", + " } else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(display_loaded, 100)\n", + " }\n", + " }\n", + "\n", + " function run_callbacks() {\n", + " try {\n", + " root._bokeh_onload_callbacks.forEach(function(callback) {\n", + " if (callback != null)\n", + " callback();\n", + " });\n", + " } finally {\n", + " delete root._bokeh_onload_callbacks\n", + " }\n", + " console.debug(\"Bokeh: all callbacks have finished\");\n", + " }\n", + "\n", + " function load_libs(css_urls, js_urls, callback) {\n", + " if (css_urls == null) css_urls = [];\n", + " if (js_urls == null) js_urls = [];\n", + "\n", + " root._bokeh_onload_callbacks.push(callback);\n", + " if (root._bokeh_is_loading > 0) {\n", + " console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n", + " return null;\n", + " }\n", + " if (js_urls == null || js_urls.length === 0) {\n", + " run_callbacks();\n", + " return null;\n", + " }\n", + " console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n", + " root._bokeh_is_loading = css_urls.length + js_urls.length;\n", + "\n", + " function on_load() {\n", + " root._bokeh_is_loading--;\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n", + " run_callbacks()\n", + " }\n", + " }\n", + "\n", + " function on_error(url) {\n", + " console.error(\"failed to load \" + url);\n", + " }\n", + "\n", + " for (let i = 0; i < css_urls.length; i++) {\n", + " const url = css_urls[i];\n", + " const element = document.createElement(\"link\");\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\n", + " element.rel = \"stylesheet\";\n", + " element.type = \"text/css\";\n", + " element.href = url;\n", + " console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " for (let i = 0; i < js_urls.length; i++) {\n", + " const url = js_urls[i];\n", + " const element = document.createElement('script');\n", + " element.onload = on_load;\n", + " element.onerror = on_error.bind(null, url);\n", + " element.async = false;\n", + " element.src = url;\n", + " console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n", + " document.head.appendChild(element);\n", + " }\n", + " };\n", + "\n", + " function inject_raw_css(css) {\n", + " const element = document.createElement(\"style\");\n", + " element.appendChild(document.createTextNode(css));\n", + " document.body.appendChild(element);\n", + " }\n", + "\n", + " const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.0.min.js\"];\n", + " const css_urls = [];\n", + "\n", + " const inline_js = [ function(Bokeh) {\n", + " Bokeh.set_log_level(\"info\");\n", + " },\n", + "function(Bokeh) {\n", + " }\n", + " ];\n", + "\n", + " function run_inline_js() {\n", + " if (root.Bokeh !== undefined || force === true) {\n", + " for (let i = 0; i < inline_js.length; i++) {\n", + " inline_js[i].call(root, root.Bokeh);\n", + " }\n", + "if (force === true) {\n", + " display_loaded();\n", + " }} else if (Date.now() < root._bokeh_timeout) {\n", + " setTimeout(run_inline_js, 100);\n", + " } else if (!root._bokeh_failed_load) {\n", + " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", + " root._bokeh_failed_load = true;\n", + " } else if (force !== true) {\n", + " const cell = $(document.getElementById(\"e9120fc6-5ce1-4a0b-81f6-258f204381a4\")).parents('.cell').data().cell;\n", + " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", + " }\n", + " }\n", + "\n", + " if (root._bokeh_is_loading === 0) {\n", + " console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n", + " run_inline_js();\n", + " } else {\n", + " load_libs(css_urls, js_urls, function() {\n", + " console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n", + " run_inline_js();\n", + " });\n", + " }\n", + "}(window));" + ], + "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
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Feedback: datalab@worldbank.org.\",\"text_font_size\":\"10pt\",\"text_font_style\":\"italic\"}}],\"center\":[{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1029\",\"attributes\":{\"axis\":{\"id\":\"p1013\"}}},{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p1034\",\"attributes\":{\"dimension\":1,\"axis\":{\"id\":\"p1030\"}}}]}}]}};\n", + " const render_items = [{\"docid\":\"0ec129ab-1d49-4b5a-ab29-e21327d7c58e\",\"roots\":{\"p1001\":\"ed1d040b-e1a4-4616-a7b9-6157689ea8b3\"},\"root_ids\":[\"p1001\"]}];\n", + " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", + " }\n", + " if (root.Bokeh !== undefined) {\n", + " embed_document(root);\n", + " } else {\n", + " let attempts = 0;\n", + " const timer = setInterval(function(root) {\n", + " if (root.Bokeh !== undefined) {\n", + " clearInterval(timer);\n", + " embed_document(root);\n", + " } else {\n", + " attempts++;\n", + " if (attempts > 100) {\n", + " clearInterval(timer);\n", + " console.log(\"Bokeh: ERROR: Unable to run BokehJS code because BokehJS library is missing\");\n", + " }\n", + " }\n", + " }, 10, root)\n", + " }\n", + "})(window);" + ], + "application/vnd.bokehjs_exec.v0+json": "" + }, + "metadata": { + "application/vnd.bokehjs_exec.v0+json": { + "id": "p1001" + } + }, + "output_type": "display_data" + } + ], + "source": [ + "p = figure(\n", + " title=\"Jordan: Weekly Nighttime Lights (2022-2023)\",\n", + " width=800,\n", + " height=700,\n", + " x_axis_label=\"Date\",\n", + " x_axis_type=\"datetime\",\n", + " y_axis_label=r\"Radiance [nW $$cm^{-2}$$ $$sr^{-1}$$]\",\n", + " tools=\"pan,wheel_zoom,box_zoom,reset,save,box_select\",\n", + ")\n", + "p.add_layout(\n", + " Title(\n", + " text=f\"Weekly Adm2 Radiance\",\n", + " text_font_size=\"12pt\",\n", + " text_font_style=\"italic\",\n", + " ),\n", + " \"above\",\n", + ")\n", + "p.add_layout(\n", + " Title(\n", + " text=f\"Data Source: NASA Black Marble. Creation date: {datetime.today().strftime('%d %B %Y')}. Feedback: datalab@worldbank.org.\",\n", + " text_font_size=\"10pt\",\n", + " text_font_style=\"italic\",\n", + " ),\n", + " \"below\",\n", + ")\n", + "p.add_layout(Legend(), \"right\")\n", + "\n", + "p.add_tools(\n", + " HoverTool(\n", + " tooltips=[\n", + " (\"Week\", \"@x{%F} (@x{%W of %Y})\"),\n", + " (\"Radiance\", \"@y{00.0}\"),\n", + " ],\n", + " formatters={\"@x\": \"datetime\"},\n", + " )\n", + ")\n", + "\n", + "for column, color in zip(data.columns, cc.b_glasbey_category10):\n", + " try:\n", + " r = p.line(\n", + " data.index,\n", + " data[column],\n", + " legend_label=column[1],\n", + " line_color=color,\n", + " line_width=2,\n", + " )\n", + " renderers.append(r)\n", + " except:\n", + " pass\n", + "\n", + "p.legend.location = \"bottom_left\"\n", + "p.legend.click_policy = \"hide\"\n", + "p.title.text_font_size = \"16pt\"\n", + "p.sizing_mode = \"scale_both\"\n", + "\n", + "output_notebook()\n", + "show(p)" + ] + }, + { + "cell_type": "markdown", + "id": "8868d736-1b5e-4c7e-ac6d-1911b07f3bba", + "metadata": {}, + "source": [ + "```{figure} ../../docs/images/logo.png\n", + "---\n", + "height: 0px\n", + "---\n", + "The indicator above consists of the weekly average of the daily zonal statistics (i.e., `mean`) derived from NASA Black Marble for each second-level administrative division. \n", + "```" + ] + }, + { + "cell_type": "markdown", + "id": "62013173-a8e8-475d-9ec8-0f3d5fe63b1e", + "metadata": {}, + "source": [ + "## Findings" + ] + }, + { + "cell_type": "markdown", + "id": "8846e1c2-4299-4778-9bf4-e522b0b6af1b", + "metadata": {}, + "source": [ + "### National Average Weekly Radiance" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "38eb48fc-6b58-4d5c-b5cd-ed29f41fe7d5", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "JO_0 = JO_1.mean(axis=\"columns\").to_frame(VAR)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "id": "8de84d23-42b4-44bb-b478-d3ed311599d3", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "hide-input", + "remove-input" + ] + }, + "outputs": [ + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "fig, ax = plt.subplots(figsize=(10, 8))\n", + "fig.suptitle(f\"Jordan: Weekly Nighttime Lights (2022-2023)\", y=0.95)\n", + "\n", + "JO_0.plot(ax=ax, marker=\"8\")\n", + "ax.set_title(\"Weekly Adm2 Radiance Average\")\n", + "ax.set_xlabel(\"Date\")\n", + "ax.set_ylabel(r\"Radiance [nW $cm^{-2}$ $sr^{-1}$]\")\n", + "ax.ticklabel_format(style=\"plain\", axis=\"y\")\n", + "\n", + "ax.annotate(\n", + " \"Data Source: NASA Black Marble VNP46A2 Gap Filled BRDF corrected DNB NTL\",\n", + " xy=(1.0, -0.2),\n", + " xycoords=\"axes fraction\",\n", + " ha=\"right\",\n", + " va=\"center\",\n", + " fontsize=10,\n", + ")\n", + "\n", + "sns.set_context(\"notebook\", font_scale=1.25, rc={\"lines.linewidth\": 2.5})\n", + "fig.autofmt_xdate()" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "id": "e75cc298-16ec-48b8-a3b6-6a10864bd5b9", + "metadata": { + "editable": true, + "slideshow": { + "slide_type": "" + }, + "tags": [ + "remove-cell" + ] + }, + "outputs": [], + "source": [ + "fig.tight_layout()\n", + "fig.savefig(\"jor_mean_weekly_radiance.png\", dpi=300)" + ] + }, + { + "cell_type": "markdown", + "id": "0a68040a-db5a-4e1f-b264-002a419e36b0", + "metadata": {}, + "source": [ + "## Limitations\n", + "\n", + "```{seealso}\n", + "{ref}`nighttime-lights-limitations`\n", + "```" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "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": "3.11.6" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/nighttime-lights/nighttime-lights.ipynb b/notebooks/nighttime-lights/nighttime-lights.ipynb index 82bbded..7fe3602 100644 --- a/notebooks/nighttime-lights/nighttime-lights.ipynb +++ b/notebooks/nighttime-lights/nighttime-lights.ipynb @@ -5,7 +5,7 @@ "id": "7776d467-20ad-4044-b10e-37b67bf0d914", "metadata": {}, "source": [ - "# Nighttime Lights Trends\n", + "# Nighttime Lights Trends in Gaza\n", "\n", "Analyzing conflict dynamics through the lens of [NASA's Black Marble Nighttime Lights](https://blackmarble.gsfc.nasa.gov) dataset opens a unique window into the often-hidden facets of global unrest. In a world marked by diverse forms of conflict, from armed confrontations to civil unrest, the dataset offers an unconventional yet powerful tool for understanding the ripple effects of these conflicts on human settlements and infrastructure. By tracking nighttime light variations and disruptions, we can unearth vital insights into population displacement, economic destabilization, and the societal impacts of conflict. This analysis explores the potential of the Black Marble Nighttime Lights dataset to not only detect areas affected by conflict but also to quantify the extent of its influence on human lives and livelihoods, providing a valuable perspective on the multifaceted consequences of conflict worldwide." ] @@ -74,7 +74,7 @@ "outputs": [], "source": [ "group = lambda flat, size: [flat[i:i+size] for i in range(0,len(flat), size)]\n", - "COLORS = cc.b_glasbey_hv" + "COLORS = cc.b_glasbey_bw_minc_20" ] }, { @@ -127,15 +127,14 @@ "id": "86165371-b848-4235-b4f3-5c354c8b7ac0", "metadata": {}, "source": [ - "### Percent Change in Average NTL Luminosity \n", + "### Percent Change in NTL Radiance Average \n", "\n", - "n this exploratory analysis, we conducted analysis of luminosity trends, comparing the observed luminosity levels to a baseline established in the year 2022." + "n this exploratory analysis, we conducted analysis of NTL radiance trends, comparing the observed radiance levels to a baseline established in the year 2022." ] }, { - "cell_type": "code", - "execution_count": 4, - "id": "b7286949-7488-4460-a433-18f3a5aa5934", + "cell_type": "markdown", + "id": "ef560485-2c03-454c-9a91-d8b79fbc932e", "metadata": { "editable": true, "slideshow": { @@ -145,23 +144,22 @@ "remove-cell" ] }, - "outputs": [], "source": [ "def create_plot(data):\n", " p = figure(\n", - " title=\"Palestine: Percent Change in Nighttime Lights Average Luminosity\",\n", + " title=\"Palestine: Percent Change in Nighttime Lights Radiance Average\",\n", " width=800,\n", " height=800,\n", " x_axis_label=\"Date\",\n", " x_axis_type=\"datetime\",\n", - " y_axis_label=\"NTL Luminosity Percent change (%)\",\n", + " y_axis_label=\"NTL Radiance Percent change (%)\",\n", " tools=\"pan,wheel_zoom,box_zoom,reset,save,box_select\",\n", " )\n", " p.y_range = Range1d(-100, 100, bounds=(-100, 100))\n", " p.xaxis.major_label_orientation = math.pi/4\n", " p.add_layout(\n", " Title(\n", - " text=f\"Percent change (compared to 2022) in NTL luminosity for each second-level administrative division\",\n", + " text=f\"Percent change (compared to 2022) in NTL radiance for each second-level administrative division\",\n", " text_font_size=\"12pt\",\n", " text_font_style=\"italic\",\n", " ),\n", @@ -189,7 +187,7 @@ " )\n", " p.add_tools(\n", " HoverTool(\n", - " tooltips=\"date: @x{%F}, percent change: @y\",\n", + " tooltips=\"Date: @x{%F}, Percent Change: @y\",\n", " formatters={\"@x\": \"datetime\"},\n", " )\n", " )\n", @@ -238,7 +236,7 @@ " \n", "
\n", " \n", - " Loading BokehJS ...\n", + " Loading BokehJS ...\n", "
\n" ] }, @@ -410,7 +408,7 @@ " \"\"}};\n", "\n", " function display_loaded() {\n", - " const el = document.getElementById(\"b2e4a99b-7b90-4a33-b6ee-2d00d5b5b519\");\n", + " const el = document.getElementById(\"e9b4bc23-298c-4817-82f3-c7fea1586a4d\");\n", " if (el != null) {\n", " el.textContent = \"BokehJS is loading...\";\n", " }\n", @@ -516,7 +514,7 @@ " console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n", " root._bokeh_failed_load = true;\n", " } else if (force !== true) {\n", - " const cell = $(document.getElementById(\"b2e4a99b-7b90-4a33-b6ee-2d00d5b5b519\")).parents('.cell').data().cell;\n", + " const cell = $(document.getElementById(\"e9b4bc23-298c-4817-82f3-c7fea1586a4d\")).parents('.cell').data().cell;\n", " cell.output_area.append_execute_result(NB_LOAD_WARNING)\n", " }\n", " }\n", @@ -532,7 +530,7 @@ " }\n", "}(window));" ], - "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"b2e4a99b-7b90-4a33-b6ee-2d00d5b5b519\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.0.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"b2e4a99b-7b90-4a33-b6ee-2d00d5b5b519\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" + "application/vnd.bokehjs_load.v0+json": "(function(root) {\n function now() {\n return new Date();\n }\n\n const force = true;\n\n if (typeof root._bokeh_onload_callbacks === \"undefined\" || force === true) {\n root._bokeh_onload_callbacks = [];\n root._bokeh_is_loading = undefined;\n }\n\n\n if (typeof (root._bokeh_timeout) === \"undefined\" || force === true) {\n root._bokeh_timeout = Date.now() + 5000;\n root._bokeh_failed_load = false;\n }\n\n const NB_LOAD_WARNING = {'data': {'text/html':\n \"
\\n\"+\n \"

\\n\"+\n \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n \"

\\n\"+\n \"
    \\n\"+\n \"
  • re-rerun `output_notebook()` to attempt to load from CDN again, or
    • \\n\"+\n \"
  • use INLINE resources instead, as so:
    • \\n\"+\n \"
\\n\"+\n \"\\n\"+\n \"from bokeh.resources import INLINE\\n\"+\n \"output_notebook(resources=INLINE)\\n\"+\n \"\\n\"+\n \"
\"}};\n\n function display_loaded() {\n const el = document.getElementById(\"e9b4bc23-298c-4817-82f3-c7fea1586a4d\");\n if (el != null) {\n el.textContent = \"BokehJS is loading...\";\n }\n if (root.Bokeh !== undefined) {\n if (el != null) {\n el.textContent = \"BokehJS \" + root.Bokeh.version + \" successfully loaded.\";\n }\n } else if (Date.now() < root._bokeh_timeout) {\n setTimeout(display_loaded, 100)\n }\n }\n\n function run_callbacks() {\n try {\n root._bokeh_onload_callbacks.forEach(function(callback) {\n if (callback != null)\n callback();\n });\n } finally {\n delete root._bokeh_onload_callbacks\n }\n console.debug(\"Bokeh: all callbacks have finished\");\n }\n\n function load_libs(css_urls, js_urls, callback) {\n if (css_urls == null) css_urls = [];\n if (js_urls == null) js_urls = [];\n\n root._bokeh_onload_callbacks.push(callback);\n if (root._bokeh_is_loading > 0) {\n console.debug(\"Bokeh: BokehJS is being loaded, scheduling callback at\", now());\n return null;\n }\n if (js_urls == null || js_urls.length === 0) {\n run_callbacks();\n return null;\n }\n console.debug(\"Bokeh: BokehJS not loaded, scheduling load and callback at\", now());\n root._bokeh_is_loading = css_urls.length + js_urls.length;\n\n function on_load() {\n root._bokeh_is_loading--;\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: all BokehJS libraries/stylesheets loaded\");\n run_callbacks()\n }\n }\n\n function on_error(url) {\n console.error(\"failed to load \" + url);\n }\n\n for (let i = 0; i < css_urls.length; i++) {\n const url = css_urls[i];\n const element = document.createElement(\"link\");\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.rel = \"stylesheet\";\n element.type = \"text/css\";\n element.href = url;\n console.debug(\"Bokeh: injecting link tag for BokehJS stylesheet: \", url);\n document.body.appendChild(element);\n }\n\n for (let i = 0; i < js_urls.length; i++) {\n const url = js_urls[i];\n const element = document.createElement('script');\n element.onload = on_load;\n element.onerror = on_error.bind(null, url);\n element.async = false;\n element.src = url;\n console.debug(\"Bokeh: injecting script tag for BokehJS library: \", url);\n document.head.appendChild(element);\n }\n };\n\n function inject_raw_css(css) {\n const element = document.createElement(\"style\");\n element.appendChild(document.createTextNode(css));\n document.body.appendChild(element);\n }\n\n const js_urls = [\"https://cdn.bokeh.org/bokeh/release/bokeh-3.3.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-gl-3.3.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-widgets-3.3.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-tables-3.3.0.min.js\", \"https://cdn.bokeh.org/bokeh/release/bokeh-mathjax-3.3.0.min.js\"];\n const css_urls = [];\n\n const inline_js = [ function(Bokeh) {\n Bokeh.set_log_level(\"info\");\n },\nfunction(Bokeh) {\n }\n ];\n\n function run_inline_js() {\n if (root.Bokeh !== undefined || force === true) {\n for (let i = 0; i < inline_js.length; i++) {\n inline_js[i].call(root, root.Bokeh);\n }\nif (force === true) {\n display_loaded();\n }} else if (Date.now() < root._bokeh_timeout) {\n setTimeout(run_inline_js, 100);\n } else if (!root._bokeh_failed_load) {\n console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n root._bokeh_failed_load = true;\n } else if (force !== true) {\n const cell = $(document.getElementById(\"e9b4bc23-298c-4817-82f3-c7fea1586a4d\")).parents('.cell').data().cell;\n cell.output_area.append_execute_result(NB_LOAD_WARNING)\n }\n }\n\n if (root._bokeh_is_loading === 0) {\n console.debug(\"Bokeh: BokehJS loaded, going straight to plotting\");\n run_inline_js();\n } else {\n load_libs(css_urls, js_urls, function() {\n console.debug(\"Bokeh: BokehJS plotting callback run at\", now());\n run_inline_js();\n });\n }\n}(window));" }, "metadata": {}, "output_type": "display_data" @@ -541,7 +539,7 @@ "data": { "text/html": [ "\n", - "
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+ " const render_items = [{\"docid\":\"f2052921-e67a-46b9-8a4e-c320d2f4f403\",\"roots\":{\"p2129\":\"c2d04aef-d5ec-4509-8eae-3a278c17cb67\"},\"root_ids\":[\"p2129\"]}];\n", " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", " }\n", " if (root.Bokeh !== undefined) {\n", @@ -791,10 +789,6 @@ }, "outputs": [], "source": [ - "# this is the colormap from the original NYTimes plot\n", - "#colors = [\"#75968f\", \"#a5bab7\", \"#c9d9d3\", \"#e2e2e2\", \"#dfccce\", \"#ddb7b1\", \"#cc7878\", \"#933b41\", \"#550b1d\"]\n", - "#colors = colors[::-1]\n", - "\n", "from palettable.colorbrewer.diverging import RdBu_7 as palette\n", "colors = palette.hex_colors\n", "\n", @@ -803,7 +797,7 @@ "p = figure(title=f\"Palestine: Weekly Percent Change (compared to 2022 average) in Nighttime Lights Luminosity\",\n", " x_range=df[\"date\"].unique(), y_range=df[\"NAME_2\"].unique(),\n", " x_axis_location=\"above\", width=1800, height=800,\n", - " tools=TOOLS, toolbar_location='below',\n", + " tools=\"hover,save,pan,box_zoom,reset,wheel_zoom\", toolbar_location='below',\n", " tooltips=[('date', '@date @NAME_2'), ('rate', '@ntl_mean%')])\n", "\n", "p.title.text_font_size = \"18pt\"\n", @@ -855,7 +849,7 @@ "data": { "text/html": [ "\n", - "
\n" + "
\n" ] }, "metadata": {}, @@ -866,8 +860,8 @@ "application/javascript": [ "(function(root) {\n", " function embed_document(root) {\n", - " const docs_json = {\"2fb8813f-2ce2-415e-b6c6-4411405bafcc\":{\"version\":\"3.3.0\",\"title\":\"Bokeh 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\"},\"shape\":[1504],\"dtype\":\"float64\",\"order\":\"little\"}]]}}},\"view\":{\"type\":\"object\",\"name\":\"CDSView\",\"id\":\"p2205\",\"attributes\":{\"filter\":{\"type\":\"object\",\"name\":\"AllIndices\",\"id\":\"p2206\"}}},\"glyph\":{\"type\":\"object\",\"name\":\"Rect\",\"id\":\"p2201\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"date\"},\"y\":{\"type\":\"field\",\"field\":\"NAME_2\"},\"width\":{\"type\":\"value\",\"value\":1},\"height\":{\"type\":\"value\",\"value\":1},\"line_color\":{\"type\":\"value\",\"value\":null},\"fill_color\":{\"type\":\"field\",\"field\":\"ntl_mean\",\"transform\":{\"type\":\"object\",\"name\":\"LinearColorMapper\",\"id\":\"p2194\",\"attributes\":{\"palette\":[\"#B2182B\",\"#EF8A62\",\"#FDDBC7\",\"#F7F7F7\",\"#D1E5F0\",\"#67A9CF\",\"#2166AC\"],\"low\":-75,\"high\":75}}}}},\"nonselection_glyph\":{\"type\":\"object\",\"name\":\"Rect\",\"id\":\"p2202\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"date\"},\"y\":{\"type\":\"field\",\"field\":\"NAME_2\"},\"width\":{\"type\":\"value\",\"value\":1},\"height\":{\"type\":\"value\",\"value\":1},\"line_color\":{\"type\":\"value\",\"value\":null},\"line_alpha\":{\"type\":\"value\",\"value\":0.1},\"fill_color\":{\"type\":\"field\",\"field\":\"ntl_mean\",\"transform\":{\"id\":\"p2194\"}},\"fill_alpha\":{\"type\":\"value\",\"value\":0.1},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.1}}},\"muted_glyph\":{\"type\":\"object\",\"name\":\"Rect\",\"id\":\"p2203\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"date\"},\"y\":{\"type\":\"field\",\"field\":\"NAME_2\"},\"width\":{\"type\":\"value\",\"value\":1},\"height\":{\"type\":\"value\",\"value\":1},\"line_color\":{\"type\":\"value\",\"value\":null},\"line_alpha\":{\"type\":\"value\",\"value\":0.2},\"fill_color\":{\"type\":\"field\",\"field\":\"ntl_mean\",\"transform\":{\"id\":\"p2194\"}},\"fill_alpha\":{\"type\":\"value\",\"value\":0.2},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.2}}}}}],\"toolbar\":{\"type\":\"object\",\"name\":\"Toolbar\",\"id\":\"p2172\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"HoverTool\",\"id\":\"p2187\",\"attributes\":{\"renderers\":\"auto\",\"tooltips\":[[\"date\",\"@date @NAME_2\"],[\"rate\",\"@ntl_mean%\"]]}},{\"type\":\"object\",\"name\":\"SaveTool\",\"id\":\"p2188\"},{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p2189\"},{\"type\":\"object\",\"name\":\"BoxZoomTool\",\"id\":\"p2190\",\"attributes\":{\"overlay\":{\"type\":\"object\",\"name\":\"BoxAnnotation\",\"id\":\"p2191\",\"attributes\":{\"syncable\":false,\"level\":\"overlay\",\"visible\":false,\"left_units\":\"canvas\",\"right_units\":\"canvas\",\"top_units\":\"canvas\",\"bottom_units\":\"canvas\",\"line_color\":\"black\",\"line_alpha\":1.0,\"line_width\":2,\"line_dash\":[4,4],\"fill_color\":\"lightgrey\",\"fill_alpha\":0.5}}}},{\"type\":\"object\",\"name\":\"ResetTool\",\"id\":\"p2192\"},{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p2193\",\"attributes\":{\"renderers\":\"auto\"}}]}},\"toolbar_location\":\"below\",\"left\":[{\"type\":\"object\",\"name\":\"CategoricalAxis\",\"id\":\"p2182\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"CategoricalTicker\",\"id\":\"p2183\"},\"formatter\":{\"type\":\"object\",\"name\":\"CategoricalTickFormatter\",\"id\":\"p2184\"},\"major_label_standoff\":0,\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p2185\"},\"major_label_text_font_size\":\"16px\",\"axis_line_color\":null,\"major_tick_line_color\":null}}],\"right\":[{\"type\":\"object\",\"name\":\"ColorBar\",\"id\":\"p2210\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p2208\",\"attributes\":{\"desired_num_ticks\":7,\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"PrintfTickFormatter\",\"id\":\"p2209\",\"attributes\":{\"format\":\"%d%%\"}},\"major_label_policy\":{\"type\":\"object\",\"name\":\"NoOverlap\",\"id\":\"p2211\"},\"padding\":50,\"major_label_text_font_size\":\"16px\",\"label_standoff\":6,\"color_mapper\":{\"id\":\"p2194\"}}}],\"above\":[{\"type\":\"object\",\"name\":\"CategoricalAxis\",\"id\":\"p2177\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"CategoricalTicker\",\"id\":\"p2178\"},\"formatter\":{\"type\":\"object\",\"name\":\"CategoricalTickFormatter\",\"id\":\"p2179\"},\"major_label_standoff\":0,\"major_label_orientation\":1.0471975511965976,\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p2180\"},\"major_label_text_font_size\":\"16px\",\"axis_line_color\":null,\"major_tick_line_color\":null}}],\"below\":[{\"type\":\"object\",\"name\":\"Title\",\"id\":\"p2207\",\"attributes\":{\"text\":\"Source: NASA Black Marble. Creation date: 31 October 2023. Feedback: datalab@worldbank.org.\",\"text_font_size\":\"10pt\",\"text_font_style\":\"italic\"}}],\"center\":[{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p2181\",\"attributes\":{\"axis\":{\"id\":\"p2177\"},\"grid_line_color\":null}},{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p2186\",\"attributes\":{\"dimension\":1,\"axis\":{\"id\":\"p2182\"},\"grid_line_color\":null}}]}}]}};\n", - " const render_items = [{\"docid\":\"2fb8813f-2ce2-415e-b6c6-4411405bafcc\",\"roots\":{\"p2163\":\"ed41429e-dcbb-461b-a035-632d18bf12f7\"},\"root_ids\":[\"p2163\"]}];\n", + " const docs_json = {\"f4b85f18-74ff-4a1c-82aa-05f605caef5c\":{\"version\":\"3.3.0\",\"title\":\"Bokeh 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\"},\"shape\":[1504],\"dtype\":\"float64\",\"order\":\"little\"}]]}}},\"view\":{\"type\":\"object\",\"name\":\"CDSView\",\"id\":\"p2205\",\"attributes\":{\"filter\":{\"type\":\"object\",\"name\":\"AllIndices\",\"id\":\"p2206\"}}},\"glyph\":{\"type\":\"object\",\"name\":\"Rect\",\"id\":\"p2201\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"date\"},\"y\":{\"type\":\"field\",\"field\":\"NAME_2\"},\"width\":{\"type\":\"value\",\"value\":1},\"height\":{\"type\":\"value\",\"value\":1},\"line_color\":{\"type\":\"value\",\"value\":null},\"fill_color\":{\"type\":\"field\",\"field\":\"ntl_mean\",\"transform\":{\"type\":\"object\",\"name\":\"LinearColorMapper\",\"id\":\"p2194\",\"attributes\":{\"palette\":[\"#B2182B\",\"#EF8A62\",\"#FDDBC7\",\"#F7F7F7\",\"#D1E5F0\",\"#67A9CF\",\"#2166AC\"],\"low\":-75,\"high\":75}}}}},\"nonselection_glyph\":{\"type\":\"object\",\"name\":\"Rect\",\"id\":\"p2202\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"date\"},\"y\":{\"type\":\"field\",\"field\":\"NAME_2\"},\"width\":{\"type\":\"value\",\"value\":1},\"height\":{\"type\":\"value\",\"value\":1},\"line_color\":{\"type\":\"value\",\"value\":null},\"line_alpha\":{\"type\":\"value\",\"value\":0.1},\"fill_color\":{\"type\":\"field\",\"field\":\"ntl_mean\",\"transform\":{\"id\":\"p2194\"}},\"fill_alpha\":{\"type\":\"value\",\"value\":0.1},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.1}}},\"muted_glyph\":{\"type\":\"object\",\"name\":\"Rect\",\"id\":\"p2203\",\"attributes\":{\"x\":{\"type\":\"field\",\"field\":\"date\"},\"y\":{\"type\":\"field\",\"field\":\"NAME_2\"},\"width\":{\"type\":\"value\",\"value\":1},\"height\":{\"type\":\"value\",\"value\":1},\"line_color\":{\"type\":\"value\",\"value\":null},\"line_alpha\":{\"type\":\"value\",\"value\":0.2},\"fill_color\":{\"type\":\"field\",\"field\":\"ntl_mean\",\"transform\":{\"id\":\"p2194\"}},\"fill_alpha\":{\"type\":\"value\",\"value\":0.2},\"hatch_alpha\":{\"type\":\"value\",\"value\":0.2}}}}}],\"toolbar\":{\"type\":\"object\",\"name\":\"Toolbar\",\"id\":\"p2172\",\"attributes\":{\"tools\":[{\"type\":\"object\",\"name\":\"HoverTool\",\"id\":\"p2187\",\"attributes\":{\"renderers\":\"auto\",\"tooltips\":[[\"date\",\"@date @NAME_2\"],[\"rate\",\"@ntl_mean%\"]]}},{\"type\":\"object\",\"name\":\"SaveTool\",\"id\":\"p2188\"},{\"type\":\"object\",\"name\":\"PanTool\",\"id\":\"p2189\"},{\"type\":\"object\",\"name\":\"BoxZoomTool\",\"id\":\"p2190\",\"attributes\":{\"overlay\":{\"type\":\"object\",\"name\":\"BoxAnnotation\",\"id\":\"p2191\",\"attributes\":{\"syncable\":false,\"level\":\"overlay\",\"visible\":false,\"left_units\":\"canvas\",\"right_units\":\"canvas\",\"top_units\":\"canvas\",\"bottom_units\":\"canvas\",\"line_color\":\"black\",\"line_alpha\":1.0,\"line_width\":2,\"line_dash\":[4,4],\"fill_color\":\"lightgrey\",\"fill_alpha\":0.5}}}},{\"type\":\"object\",\"name\":\"ResetTool\",\"id\":\"p2192\"},{\"type\":\"object\",\"name\":\"WheelZoomTool\",\"id\":\"p2193\",\"attributes\":{\"renderers\":\"auto\"}}]}},\"toolbar_location\":\"below\",\"left\":[{\"type\":\"object\",\"name\":\"CategoricalAxis\",\"id\":\"p2182\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"CategoricalTicker\",\"id\":\"p2183\"},\"formatter\":{\"type\":\"object\",\"name\":\"CategoricalTickFormatter\",\"id\":\"p2184\"},\"major_label_standoff\":0,\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p2185\"},\"major_label_text_font_size\":\"16px\",\"axis_line_color\":null,\"major_tick_line_color\":null}}],\"right\":[{\"type\":\"object\",\"name\":\"ColorBar\",\"id\":\"p2210\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"BasicTicker\",\"id\":\"p2208\",\"attributes\":{\"desired_num_ticks\":7,\"mantissas\":[1,2,5]}},\"formatter\":{\"type\":\"object\",\"name\":\"PrintfTickFormatter\",\"id\":\"p2209\",\"attributes\":{\"format\":\"%d%%\"}},\"major_label_policy\":{\"type\":\"object\",\"name\":\"NoOverlap\",\"id\":\"p2211\"},\"padding\":50,\"major_label_text_font_size\":\"16px\",\"label_standoff\":6,\"color_mapper\":{\"id\":\"p2194\"}}}],\"above\":[{\"type\":\"object\",\"name\":\"CategoricalAxis\",\"id\":\"p2177\",\"attributes\":{\"ticker\":{\"type\":\"object\",\"name\":\"CategoricalTicker\",\"id\":\"p2178\"},\"formatter\":{\"type\":\"object\",\"name\":\"CategoricalTickFormatter\",\"id\":\"p2179\"},\"major_label_standoff\":0,\"major_label_orientation\":1.0471975511965976,\"major_label_policy\":{\"type\":\"object\",\"name\":\"AllLabels\",\"id\":\"p2180\"},\"major_label_text_font_size\":\"16px\",\"axis_line_color\":null,\"major_tick_line_color\":null}}],\"below\":[{\"type\":\"object\",\"name\":\"Title\",\"id\":\"p2207\",\"attributes\":{\"text\":\"Source: NASA Black Marble. Creation date: 13 November 2023. Feedback: datalab@worldbank.org.\",\"text_font_size\":\"10pt\",\"text_font_style\":\"italic\"}}],\"center\":[{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p2181\",\"attributes\":{\"axis\":{\"id\":\"p2177\"},\"grid_line_color\":null}},{\"type\":\"object\",\"name\":\"Grid\",\"id\":\"p2186\",\"attributes\":{\"dimension\":1,\"axis\":{\"id\":\"p2182\"},\"grid_line_color\":null}}]}}]}};\n", + " const render_items = [{\"docid\":\"f4b85f18-74ff-4a1c-82aa-05f605caef5c\",\"roots\":{\"p2163\":\"f44a17b5-ae31-4c43-a9c8-0b407591bca7\"},\"root_ids\":[\"p2163\"]}];\n", " root.Bokeh.embed.embed_items_notebook(docs_json, render_items);\n", " }\n", " if (root.Bokeh !== undefined) {\n", @@ -931,8 +925,9 @@ ] }, { - "cell_type": "markdown", - "id": "5c7979d4-72ea-4ee1-b268-d53ee9f2f99e", + "cell_type": "code", + "execution_count": 12, + "id": "62f8e6b9-f8b2-483d-a3e0-9e239666c4d3", "metadata": { "editable": true, "slideshow": { @@ -942,6 +937,7 @@ "remove-cell" ] }, + "outputs": [], "source": [ "PSE = geopandas.read_file(\"../../data/boundaries/gadm41_PSE_shp/gadm41_PSE_2.shp\")" ] @@ -998,7 +994,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_be706a19a147631b65eae058ac753101 {\n", + " #map_a07a5ad24fd48a16a26ba267e6c97b53 {\n", " position: relative;\n", " width: 100.0%;\n", " height: 100.0%;\n", @@ -1029,14 +1025,14 @@ "<body>\n", " \n", " \n", - " <div class="folium-map" id="map_be706a19a147631b65eae058ac753101" ></div>\n", + " <div class="folium-map" id="map_a07a5ad24fd48a16a26ba267e6c97b53" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_be706a19a147631b65eae058ac753101 = L.map(\n", - " "map_be706a19a147631b65eae058ac753101",\n", + " var map_a07a5ad24fd48a16a26ba267e6c97b53 = L.map(\n", + " "map_a07a5ad24fd48a16a26ba267e6c97b53",\n", " {\n", " center: [31.887586594000084, 34.902240753000115],\n", " crs: L.CRS.EPSG3857,\n", @@ -1045,25 +1041,25 @@ " preferCanvas: false,\n", " }\n", " );\n", - " L.control.scale().addTo(map_be706a19a147631b65eae058ac753101);\n", + " L.control.scale().addTo(map_a07a5ad24fd48a16a26ba267e6c97b53);\n", "\n", " \n", "\n", " \n", " \n", - " var tile_layer_06455d52817615ec003a3356ce8720d1 = L.tileLayer(\n", + " var tile_layer_3e616b89f3b4d0c3f6aba5948d7a3ca9 = L.tileLayer(\n", " "https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",\n", " {"attribution": "Data by \\u0026copy; \\u003ca target=\\"_blank\\" href=\\"http://openstreetmap.org\\"\\u003eOpenStreetMap\\u003c/a\\u003e, under \\u003ca target=\\"_blank\\" href=\\"http://www.openstreetmap.org/copyright\\"\\u003eODbL\\u003c/a\\u003e.", "detectRetina": false, "maxNativeZoom": 18, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}\n", - " ).addTo(map_be706a19a147631b65eae058ac753101);\n", + " ).addTo(map_a07a5ad24fd48a16a26ba267e6c97b53);\n", " \n", " \n", - " map_be706a19a147631b65eae058ac753101.fitBounds(\n", + " map_a07a5ad24fd48a16a26ba267e6c97b53.fitBounds(\n", " [[31.223606110000105, 34.22902679400016], [32.55156707800006, 35.57545471200007]],\n", " {}\n", " );\n", " \n", " \n", - " function geo_json_dd733d6aeacdc0b60613927b7166ec61_styler(feature) {\n", + " function geo_json_24dcd4ba4fffaa11c5bf67136b97b90a_styler(feature) {\n", " switch(feature.id) {\n", " case "0": \n", " return {"color": "#ff8585", "fillColor": "#ff8585", "fillOpacity": 0.5, "weight": 2};\n", @@ -1095,59 +1091,59 @@ " return {"color": "#ffb9b9", "fillColor": "#ffb9b9", "fillOpacity": 0.5, "weight": 2};\n", " }\n", " }\n", - " function geo_json_dd733d6aeacdc0b60613927b7166ec61_highlighter(feature) {\n", + " function geo_json_24dcd4ba4fffaa11c5bf67136b97b90a_highlighter(feature) {\n", " switch(feature.id) {\n", " default:\n", " return {"fillOpacity": 0.75};\n", " }\n", " }\n", - " function geo_json_dd733d6aeacdc0b60613927b7166ec61_pointToLayer(feature, latlng) {\n", + " function geo_json_24dcd4ba4fffaa11c5bf67136b97b90a_pointToLayer(feature, latlng) {\n", " var opts = {"bubblingMouseEvents": true, "color": "#3388ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3388ff", "fillOpacity": 0.2, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 2, "stroke": true, "weight": 3};\n", " \n", - " let style = geo_json_dd733d6aeacdc0b60613927b7166ec61_styler(feature)\n", + " let style = geo_json_24dcd4ba4fffaa11c5bf67136b97b90a_styler(feature)\n", " Object.assign(opts, style)\n", " \n", " return new L.CircleMarker(latlng, opts)\n", " }\n", "\n", - " function geo_json_dd733d6aeacdc0b60613927b7166ec61_onEachFeature(feature, layer) {\n", + " function geo_json_24dcd4ba4fffaa11c5bf67136b97b90a_onEachFeature(feature, layer) {\n", " layer.on({\n", " mouseout: function(e) {\n", " if(typeof e.target.setStyle === "function"){\n", - " geo_json_dd733d6aeacdc0b60613927b7166ec61.resetStyle(e.target);\n", + " geo_json_24dcd4ba4fffaa11c5bf67136b97b90a.resetStyle(e.target);\n", " }\n", " },\n", " mouseover: function(e) {\n", " if(typeof e.target.setStyle === "function"){\n", - " const highlightStyle = geo_json_dd733d6aeacdc0b60613927b7166ec61_highlighter(e.target.feature)\n", + " const highlightStyle = geo_json_24dcd4ba4fffaa11c5bf67136b97b90a_highlighter(e.target.feature)\n", " e.target.setStyle(highlightStyle);\n", " }\n", " },\n", " });\n", " };\n", - " var geo_json_dd733d6aeacdc0b60613927b7166ec61 = L.geoJson(null, {\n", - " onEachFeature: geo_json_dd733d6aeacdc0b60613927b7166ec61_onEachFeature,\n", + " var geo_json_24dcd4ba4fffaa11c5bf67136b97b90a = L.geoJson(null, {\n", + " onEachFeature: geo_json_24dcd4ba4fffaa11c5bf67136b97b90a_onEachFeature,\n", " \n", - " style: geo_json_dd733d6aeacdc0b60613927b7166ec61_styler,\n", - " pointToLayer: geo_json_dd733d6aeacdc0b60613927b7166ec61_pointToLayer\n", + " style: geo_json_24dcd4ba4fffaa11c5bf67136b97b90a_styler,\n", + " pointToLayer: geo_json_24dcd4ba4fffaa11c5bf67136b97b90a_pointToLayer\n", " });\n", "\n", - " function geo_json_dd733d6aeacdc0b60613927b7166ec61_add (data) {\n", - " geo_json_dd733d6aeacdc0b60613927b7166ec61\n", + " function geo_json_24dcd4ba4fffaa11c5bf67136b97b90a_add (data) {\n", + " geo_json_24dcd4ba4fffaa11c5bf67136b97b90a\n", " .addData(data)\n", - " .addTo(map_be706a19a147631b65eae058ac753101);\n", + " .addTo(map_a07a5ad24fd48a16a26ba267e6c97b53);\n", " }\n", - " geo_json_dd733d6aeacdc0b60613927b7166ec61_add({"bbox": [34.22902679400016, 31.223606110000105, 35.57545471200007, 32.55156707800006], "features": [{"bbox": [34.30597305300017, 31.372600555000133, 34.415882110000155, 31.462638856000126], "geometry": {"coordinates": [[[34.415882110000155, 31.42720031800019], [34.40790176400009, 31.420608522000123], [34.37456893900003, 31.38384056100017], [34.370647431000066, 31.372600555000133], [34.364295960000106, 31.373163224000052], [34.36157989600008, 31.37386703400017], [34.35890960800015, 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[35.04635620100015, 32.22626876900017], [35.04191207800005, 32.2294998160001], [35.03383255000017, 32.24000549300007], [35.01443862800011, 32.26182556099997], [35.00716781500006, 32.269756316999974], [35.00926208500016, 32.271026612000014], [35.012577056000055, 32.27845764200009], [35.00787735100016, 32.307075501000156], [35.00900268600009, 32.33402252200017], [35.0195922850001, 32.35098266600011], [35.026256562000185, 32.35810089200004], [35.052234649000184, 32.36799240100004], [35.05580902200006, 32.37092971800013], [35.059238435000054, 32.37374878000014], [35.06133270300012, 32.38663864200015]]], "type": "Polygon"}, "id": "15", "properties": {"CC_2": "NA", "COUNTRY": "Palestine", "ENGTYPE_2": "Governorate", "GID_0": "PSE", "GID_1": "PSE.2_1", "GID_2": "PSE.2.11_1", "HASC_2": "PS.WE.TL", "NAME_1": "West Bank", "NAME_2": "Tulkarm", "NL_NAME_1": "NA", "NL_NAME_2": "NA", "TYPE_2": "Governorate", "VARNAME_2": "NA", "__folium_color": "#b9b9ff", "level_0": "ntl_mean", "radiance": 13.85624590082748}, "type": "Feature"}], "type": "FeatureCollection"});\n", "\n", " \n", " \n", - " geo_json_dd733d6aeacdc0b60613927b7166ec61.bindTooltip(\n", + " geo_json_24dcd4ba4fffaa11c5bf67136b97b90a.bindTooltip(\n", " function(layer){\n", " let div = L.DomUtil.create('div');\n", " \n", " let handleObject = feature=>typeof(feature)=='object' ? JSON.stringify(feature) : feature;\n", - " let fields = ["GID_2", "GID_0", "COUNTRY", "GID_1", "NAME_1", "NL_NAME_1", "NAME_2", "VARNAME_2", "NL_NAME_2", "TYPE_2", "ENGTYPE_2", "CC_2", "HASC_2", "level_0", "luminosity"];\n", - " let aliases = ["GID_2", "GID_0", "COUNTRY", "GID_1", "NAME_1", "NL_NAME_1", "NAME_2", "VARNAME_2", "NL_NAME_2", "TYPE_2", "ENGTYPE_2", "CC_2", "HASC_2", "level_0", "luminosity"];\n", + " let fields = ["GID_2", "GID_0", "COUNTRY", "GID_1", "NAME_1", "NL_NAME_1", "NAME_2", "VARNAME_2", "NL_NAME_2", "TYPE_2", "ENGTYPE_2", "CC_2", "HASC_2", "level_0", "radiance"];\n", + " let aliases = ["GID_2", "GID_0", "COUNTRY", "GID_1", "NAME_1", "NL_NAME_1", "NAME_2", "VARNAME_2", "NL_NAME_2", "TYPE_2", "ENGTYPE_2", "CC_2", "HASC_2", "level_0", "radiance"];\n", " let table = '<table>' +\n", " String(\n", " fields.map(\n", @@ -1165,42 +1161,42 @@ " ,{"className": "foliumtooltip", "sticky": true});\n", " \n", " \n", - " var color_map_31a2b5b5db19437d874807ebf6868043 = {};\n", + 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'#ffbdbdff', '#ffbfbfff', '#ffc1c1ff', '#ffc3c3ff', '#ffc5c5ff', '#ffc7c7ff', '#ffc9c9ff', '#ffcbcbff', '#ffcdcdff', '#ffcfcfff', '#ffd1d1ff', '#ffd3d3ff', '#ffd5d5ff', '#ffd7d7ff', '#ffdadaff', '#ffdcdcff', '#ffdedeff', '#ffe0e0ff', '#ffe2e2ff', '#ffe4e4ff', '#ffe6e6ff', '#ffe8e8ff', '#ffeaeaff', '#ffececff', '#ffeeeeff', '#fff0f0ff', '#fff2f2ff', '#fff4f4ff', '#fff6f6ff', '#fff8f8ff', '#fffafaff', '#fffcfcff', '#fffdfeff', '#fefdffff', '#fcfcffff', '#fafaffff', '#f8f8ffff', '#f6f6ffff', '#f4f4ffff', '#f2f2ffff', '#f0f0ffff', '#eeeeffff', '#ececffff', '#eaeaffff', '#e8e8ffff', '#e6e6ffff', '#e4e4ffff', '#e2e2ffff', '#e0e0ffff', '#dedeffff', '#dcdcffff', '#dadaffff', '#d7d7ffff', '#d5d5ffff', '#d3d3ffff', '#d1d1ffff', '#cfcfffff', '#cdcdffff', '#cbcbffff', '#c9c9ffff', '#c7c7ffff', '#c5c5ffff', '#c3c3ffff', '#c1c1ffff', '#bfbfffff', '#bdbdffff', '#bbbbffff', '#b9b9ffff', '#b7b7ffff', '#b5b5ffff', '#b3b3ffff', '#b0b0ffff', '#aeaeffff', '#acacffff', '#aaaaffff', '#a8a8ffff', '#a6a6ffff', '#a4a4ffff', '#a2a2ffff', '#a0a0ffff', '#9e9effff', '#9c9cffff', '#9a9affff', '#9898ffff', '#9696ffff', '#9494ffff', '#9292ffff', '#9090ffff', '#8e8effff', '#8c8cffff', '#8a8affff', '#8787ffff', '#8585ffff', '#8383ffff', '#8181ffff', '#7f7fffff', '#7d7dffff', '#7b7bffff', '#7979ffff', '#7777ffff', '#7575ffff', '#7373ffff', '#7171ffff', '#6f6fffff', '#6d6dffff', '#6b6bffff', '#6969ffff', '#6767ffff', '#6565ffff', '#6363ffff', '#6060ffff', '#5e5effff', '#5c5cffff', '#5a5affff', '#5858ffff', '#5656ffff', '#5454ffff', '#5252ffff', '#5050ffff', '#4e4effff', '#4c4cffff', '#4a4affff', '#4848ffff', '#4646ffff', '#4444ffff', '#4242ffff', '#4040ffff', '#3e3effff', '#3c3cffff', '#3939ffff', '#3737ffff', '#3535ffff', '#3333ffff', '#3131ffff', '#2f2fffff', '#2d2dffff', '#2b2bffff', '#2929ffff', '#2727ffff', '#2525ffff', '#2323ffff', '#2121ffff', '#1f1fffff', '#1d1dffff', '#1b1bffff', '#1919ffff', '#1717ffff', '#1515ffff', '#1212ffff', '#1010ffff', '#0e0effff', '#0c0cffff', '#0a0affff', '#0808ffff', '#0606ffff', '#0404ffff', '#0202ffff', '#0000ffff', '#0000feff', '#0000fdff', '#0000fcff', '#0000faff', '#0000f8ff', '#0000f7ff', '#0000f5ff', '#0000f4ff', '#0000f3ff', '#0000f2ff', '#0000f0ff', '#0000efff', '#0000edff', '#0000ecff', '#0000eaff', '#0000e9ff', '#0000e7ff', '#0000e6ff', '#0000e5ff', '#0000e3ff', '#0000e2ff', '#0000e0ff', '#0000dfff', '#0000ddff', '#0000dcff', '#0000daff', '#0000d9ff', '#0000d7ff', '#0000d6ff', '#0000d5ff', '#0000d3ff', '#0000d2ff', '#0000d0ff', '#0000cfff', '#0000cdff', '#0000ccff', '#0000cbff', '#0000caff', '#0000c8ff', '#0000c7ff', '#0000c5ff', '#0000c4ff', '#0000c2ff', '#0000c0ff', '#0000bfff', '#0000bdff', '#0000bcff', '#0000bbff', '#0000baff', '#0000b8ff', '#0000b7ff', '#0000b5ff', '#0000b4ff', '#0000b2ff', '#0000b1ff', '#0000afff', '#0000aeff', '#0000adff', '#0000abff', '#0000aaff', '#0000a8ff', '#0000a7ff', '#0000a5ff', '#0000a4ff', '#0000a2ff', '#0000a1ff', '#00009fff', '#00009eff', '#00009dff', '#00009bff', '#00009aff', '#000098ff', '#000097ff', '#000095ff', '#000094ff', '#000093ff', '#000092ff', '#000090ff', '#00008fff', '#00008dff', '#00008cff', '#00008aff', '#000088ff', '#000087ff', '#000085ff', '#000084ff', '#000083ff', '#000082ff', '#000080ff', '#00007fff', '#00007dff', '#00007cff', '#00007aff', '#000079ff', '#000078ff', '#000076ff', '#000075ff', '#000073ff', '#000072ff', '#000070ff', '#00006fff', '#00006dff', '#00006cff', '#00006aff', '#000069ff', '#000067ff', '#000066ff', '#000065ff', '#000063ff', '#000062ff', '#000060ff', '#00005fff', '#00005dff', '#00005cff', '#00005bff', '#00005aff', '#000058ff', '#000057ff', '#000055ff', '#000053ff', '#000052ff', '#000050ff', '#00004fff', '#00004dff', '#00004cff']);\n", " \n", "\n", - " color_map_31a2b5b5db19437d874807ebf6868043.x = d3.scale.linear()\n", + " color_map_00e1424a0ef296b6f08abb8393c2c7b7.x = d3.scale.linear()\n", " .domain([-100.0, 100.0])\n", " .range([0, 450 - 50]);\n", "\n", - " color_map_31a2b5b5db19437d874807ebf6868043.legend = L.control({position: 'topright'});\n", - " color_map_31a2b5b5db19437d874807ebf6868043.legend.onAdd = function (map) {var div = L.DomUtil.create('div', 'legend'); return div};\n", - " color_map_31a2b5b5db19437d874807ebf6868043.legend.addTo(map_be706a19a147631b65eae058ac753101);\n", + " color_map_00e1424a0ef296b6f08abb8393c2c7b7.legend = L.control({position: 'topright'});\n", + " color_map_00e1424a0ef296b6f08abb8393c2c7b7.legend.onAdd = function (map) {var div = L.DomUtil.create('div', 'legend'); return div};\n", + " color_map_00e1424a0ef296b6f08abb8393c2c7b7.legend.addTo(map_a07a5ad24fd48a16a26ba267e6c97b53);\n", "\n", - " color_map_31a2b5b5db19437d874807ebf6868043.xAxis = d3.svg.axis()\n", - " .scale(color_map_31a2b5b5db19437d874807ebf6868043.x)\n", + " color_map_00e1424a0ef296b6f08abb8393c2c7b7.xAxis = d3.svg.axis()\n", + " .scale(color_map_00e1424a0ef296b6f08abb8393c2c7b7.x)\n", " .orient("top")\n", " .tickSize(1)\n", " .tickValues([-100.0, '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', 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83.52941176470588, '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '']);\n", "\n", - " color_map_31a2b5b5db19437d874807ebf6868043.svg = d3.select(".legend.leaflet-control").append("svg")\n", + " color_map_00e1424a0ef296b6f08abb8393c2c7b7.svg = d3.select(".legend.leaflet-control").append("svg")\n", " .attr("id", 'legend')\n", " .attr("width", 450)\n", " .attr("height", 40);\n", "\n", - " color_map_31a2b5b5db19437d874807ebf6868043.g = color_map_31a2b5b5db19437d874807ebf6868043.svg.append("g")\n", + " color_map_00e1424a0ef296b6f08abb8393c2c7b7.g = color_map_00e1424a0ef296b6f08abb8393c2c7b7.svg.append("g")\n", " .attr("class", "key")\n", " .attr("transform", "translate(25,16)");\n", "\n", - " color_map_31a2b5b5db19437d874807ebf6868043.g.selectAll("rect")\n", - " .data(color_map_31a2b5b5db19437d874807ebf6868043.color.range().map(function(d, i) {\n", + " color_map_00e1424a0ef296b6f08abb8393c2c7b7.g.selectAll("rect")\n", + " .data(color_map_00e1424a0ef296b6f08abb8393c2c7b7.color.range().map(function(d, i) {\n", " return {\n", - " x0: i ? color_map_31a2b5b5db19437d874807ebf6868043.x(color_map_31a2b5b5db19437d874807ebf6868043.color.domain()[i - 1]) : color_map_31a2b5b5db19437d874807ebf6868043.x.range()[0],\n", - " x1: i < color_map_31a2b5b5db19437d874807ebf6868043.color.domain().length ? color_map_31a2b5b5db19437d874807ebf6868043.x(color_map_31a2b5b5db19437d874807ebf6868043.color.domain()[i]) : color_map_31a2b5b5db19437d874807ebf6868043.x.range()[1],\n", + " x0: i ? color_map_00e1424a0ef296b6f08abb8393c2c7b7.x(color_map_00e1424a0ef296b6f08abb8393c2c7b7.color.domain()[i - 1]) : color_map_00e1424a0ef296b6f08abb8393c2c7b7.x.range()[0],\n", + " x1: i < color_map_00e1424a0ef296b6f08abb8393c2c7b7.color.domain().length ? color_map_00e1424a0ef296b6f08abb8393c2c7b7.x(color_map_00e1424a0ef296b6f08abb8393c2c7b7.color.domain()[i]) : color_map_00e1424a0ef296b6f08abb8393c2c7b7.x.range()[1],\n", " z: d\n", " };\n", " }))\n", @@ -1210,15 +1206,15 @@ " .attr("width", function(d) { return d.x1 - d.x0; })\n", " .style("fill", function(d) { return d.z; });\n", "\n", - " color_map_31a2b5b5db19437d874807ebf6868043.g.call(color_map_31a2b5b5db19437d874807ebf6868043.xAxis).append("text")\n", + " color_map_00e1424a0ef296b6f08abb8393c2c7b7.g.call(color_map_00e1424a0ef296b6f08abb8393c2c7b7.xAxis).append("text")\n", " .attr("class", "caption")\n", " .attr("y", 21)\n", - " .text("luminosity");\n", + " .text("radiance");\n", "</script>\n", "</html>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen>" ], "text/plain": [ - "" + "" ] }, "execution_count": 13, @@ -1227,7 +1223,7 @@ } ], "source": [ - "PSE.merge(data.iloc[-3].to_frame(\"luminosity\").reset_index(), on=\"NAME_2\").explore(column=\"luminosity\", cmap=\"seismic_r\", vmin=-100, vmax=100)" + "PSE.merge(data.iloc[-3].to_frame(\"radiance\").reset_index(), on=\"NAME_2\").explore(column=\"radiance\", cmap=\"seismic_r\", vmin=-100, vmax=100)" ] }, { @@ -1282,7 +1278,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_9ee68f41dc3150d15490ad1f5ae271bd {\n", + " #map_6b8fb61bd47002f39851b8f8d69f6e4a {\n", " position: relative;\n", " width: 100.0%;\n", " height: 100.0%;\n", @@ -1313,14 +1309,14 @@ "<body>\n", " \n", " \n", - " <div class="folium-map" id="map_9ee68f41dc3150d15490ad1f5ae271bd" ></div>\n", + " <div class="folium-map" id="map_6b8fb61bd47002f39851b8f8d69f6e4a" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_9ee68f41dc3150d15490ad1f5ae271bd = L.map(\n", - " "map_9ee68f41dc3150d15490ad1f5ae271bd",\n", + " var map_6b8fb61bd47002f39851b8f8d69f6e4a = L.map(\n", + " "map_6b8fb61bd47002f39851b8f8d69f6e4a",\n", " {\n", " center: [31.887586594000084, 34.902240753000115],\n", " crs: L.CRS.EPSG3857,\n", @@ -1329,25 +1325,25 @@ " preferCanvas: false,\n", " }\n", " );\n", - " L.control.scale().addTo(map_9ee68f41dc3150d15490ad1f5ae271bd);\n", + " L.control.scale().addTo(map_6b8fb61bd47002f39851b8f8d69f6e4a);\n", "\n", " \n", "\n", " \n", " \n", - " var tile_layer_a0d4de82a01654df7cc62e4feac013bb = L.tileLayer(\n", + " var tile_layer_31539b284c80b3aebdcbb3709876ea93 = L.tileLayer(\n", " "https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",\n", " {"attribution": "Data by \\u0026copy; \\u003ca target=\\"_blank\\" href=\\"http://openstreetmap.org\\"\\u003eOpenStreetMap\\u003c/a\\u003e, under \\u003ca target=\\"_blank\\" href=\\"http://www.openstreetmap.org/copyright\\"\\u003eODbL\\u003c/a\\u003e.", "detectRetina": false, "maxNativeZoom": 18, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}\n", - " ).addTo(map_9ee68f41dc3150d15490ad1f5ae271bd);\n", + " ).addTo(map_6b8fb61bd47002f39851b8f8d69f6e4a);\n", " \n", " \n", - " map_9ee68f41dc3150d15490ad1f5ae271bd.fitBounds(\n", + " map_6b8fb61bd47002f39851b8f8d69f6e4a.fitBounds(\n", " [[31.223606110000105, 34.22902679400016], [32.55156707800006, 35.57545471200007]],\n", " {}\n", " );\n", " \n", " \n", - " function geo_json_e7b459b5f7aec7d15068fc1310bc731f_styler(feature) {\n", + " function geo_json_82ab940b595240c8e35b54c9defaa110_styler(feature) {\n", " switch(feature.id) {\n", " case "0": case "2": \n", " return {"color": "#a30000", "fillColor": "#a30000", "fillOpacity": 0.5, "weight": 2};\n", @@ -1379,59 +1375,59 @@ " return {"color": "#e1e1ff", "fillColor": "#e1e1ff", "fillOpacity": 0.5, "weight": 2};\n", " }\n", " }\n", - " function geo_json_e7b459b5f7aec7d15068fc1310bc731f_highlighter(feature) {\n", + " function geo_json_82ab940b595240c8e35b54c9defaa110_highlighter(feature) {\n", " switch(feature.id) {\n", " default:\n", " return {"fillOpacity": 0.75};\n", " }\n", " }\n", - " function geo_json_e7b459b5f7aec7d15068fc1310bc731f_pointToLayer(feature, latlng) {\n", + " function geo_json_82ab940b595240c8e35b54c9defaa110_pointToLayer(feature, latlng) {\n", " var opts = {"bubblingMouseEvents": true, "color": "#3388ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3388ff", "fillOpacity": 0.2, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 2, "stroke": true, "weight": 3};\n", " \n", - " let style = geo_json_e7b459b5f7aec7d15068fc1310bc731f_styler(feature)\n", + " let style = geo_json_82ab940b595240c8e35b54c9defaa110_styler(feature)\n", " Object.assign(opts, style)\n", " \n", " return new L.CircleMarker(latlng, opts)\n", " }\n", "\n", - " function geo_json_e7b459b5f7aec7d15068fc1310bc731f_onEachFeature(feature, layer) {\n", + " function geo_json_82ab940b595240c8e35b54c9defaa110_onEachFeature(feature, layer) {\n", " layer.on({\n", " mouseout: function(e) {\n", " if(typeof e.target.setStyle === "function"){\n", - " geo_json_e7b459b5f7aec7d15068fc1310bc731f.resetStyle(e.target);\n", + " geo_json_82ab940b595240c8e35b54c9defaa110.resetStyle(e.target);\n", " }\n", " },\n", " mouseover: function(e) {\n", " if(typeof e.target.setStyle === "function"){\n", - " const highlightStyle = geo_json_e7b459b5f7aec7d15068fc1310bc731f_highlighter(e.target.feature)\n", + " const highlightStyle = geo_json_82ab940b595240c8e35b54c9defaa110_highlighter(e.target.feature)\n", " e.target.setStyle(highlightStyle);\n", " }\n", " },\n", " });\n", " };\n", - " var geo_json_e7b459b5f7aec7d15068fc1310bc731f = L.geoJson(null, {\n", - " onEachFeature: geo_json_e7b459b5f7aec7d15068fc1310bc731f_onEachFeature,\n", + " var geo_json_82ab940b595240c8e35b54c9defaa110 = L.geoJson(null, {\n", + " onEachFeature: geo_json_82ab940b595240c8e35b54c9defaa110_onEachFeature,\n", " \n", - " style: geo_json_e7b459b5f7aec7d15068fc1310bc731f_styler,\n", - " pointToLayer: geo_json_e7b459b5f7aec7d15068fc1310bc731f_pointToLayer\n", + " style: geo_json_82ab940b595240c8e35b54c9defaa110_styler,\n", + " pointToLayer: geo_json_82ab940b595240c8e35b54c9defaa110_pointToLayer\n", " });\n", "\n", - " function geo_json_e7b459b5f7aec7d15068fc1310bc731f_add (data) {\n", - " geo_json_e7b459b5f7aec7d15068fc1310bc731f\n", + " function geo_json_82ab940b595240c8e35b54c9defaa110_add (data) {\n", + " geo_json_82ab940b595240c8e35b54c9defaa110\n", " .addData(data)\n", - " .addTo(map_9ee68f41dc3150d15490ad1f5ae271bd);\n", + " .addTo(map_6b8fb61bd47002f39851b8f8d69f6e4a);\n", " }\n", - " geo_json_e7b459b5f7aec7d15068fc1310bc731f_add({"bbox": [34.22902679400016, 31.223606110000105, 35.57545471200007, 32.55156707800006], "features": [{"bbox": [34.30597305300017, 31.372600555000133, 34.415882110000155, 31.462638856000126], "geometry": {"coordinates": [[[34.415882110000155, 31.42720031800019], [34.40790176400009, 31.420608522000123], [34.37456893900003, 31.38384056100017], [34.370647431000066, 31.372600555000133], [34.364295960000106, 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"COUNTRY": "Palestine", "ENGTYPE_2": "Governorate", "GID_0": "PSE", "GID_1": "PSE.2_1", "GID_2": "PSE.2.11_1", "HASC_2": "PS.WE.TL", "NAME_1": "West Bank", "NAME_2": "Tulkarm", "NL_NAME_1": "NA", "NL_NAME_2": "NA", "TYPE_2": "Governorate", "VARNAME_2": "NA", "__folium_color": "#ffd9d9", "level_0": "ntl_mean", "radiance": -7.583985765906876}, "type": "Feature"}], "type": "FeatureCollection"});\n", "\n", " \n", " \n", - " geo_json_e7b459b5f7aec7d15068fc1310bc731f.bindTooltip(\n", + " geo_json_82ab940b595240c8e35b54c9defaa110.bindTooltip(\n", " function(layer){\n", " let div = L.DomUtil.create('div');\n", " \n", " let handleObject = feature=>typeof(feature)=='object' ? JSON.stringify(feature) : feature;\n", - " let fields = ["GID_2", "GID_0", "COUNTRY", "GID_1", "NAME_1", "NL_NAME_1", "NAME_2", "VARNAME_2", "NL_NAME_2", "TYPE_2", "ENGTYPE_2", "CC_2", "HASC_2", "level_0", "luminosity"];\n", - " let aliases = ["GID_2", "GID_0", "COUNTRY", "GID_1", "NAME_1", "NL_NAME_1", "NAME_2", "VARNAME_2", "NL_NAME_2", "TYPE_2", "ENGTYPE_2", "CC_2", "HASC_2", "level_0", "luminosity"];\n", + " let fields = ["GID_2", "GID_0", "COUNTRY", "GID_1", "NAME_1", "NL_NAME_1", "NAME_2", "VARNAME_2", "NL_NAME_2", "TYPE_2", "ENGTYPE_2", "CC_2", "HASC_2", "level_0", "radiance"];\n", + " let aliases = ["GID_2", "GID_0", "COUNTRY", "GID_1", "NAME_1", "NL_NAME_1", "NAME_2", "VARNAME_2", "NL_NAME_2", "TYPE_2", "ENGTYPE_2", "CC_2", "HASC_2", "level_0", "radiance"];\n", " let table = '<table>' +\n", " String(\n", " fields.map(\n", @@ -1449,42 +1445,42 @@ " ,{"className": "foliumtooltip", "sticky": true});\n", " \n", " \n", - " var color_map_423bd39b53afa1428b2a572f3b58e752 = {};\n", + 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'#0606ffff', '#0404ffff', '#0202ffff', '#0000ffff', '#0000feff', '#0000fdff', '#0000fcff', '#0000faff', '#0000f8ff', '#0000f7ff', '#0000f5ff', '#0000f4ff', '#0000f3ff', '#0000f2ff', '#0000f0ff', '#0000efff', '#0000edff', '#0000ecff', '#0000eaff', '#0000e9ff', '#0000e7ff', '#0000e6ff', '#0000e5ff', '#0000e3ff', '#0000e2ff', '#0000e0ff', '#0000dfff', '#0000ddff', '#0000dcff', '#0000daff', '#0000d9ff', '#0000d7ff', '#0000d6ff', '#0000d5ff', '#0000d3ff', '#0000d2ff', '#0000d0ff', '#0000cfff', '#0000cdff', '#0000ccff', '#0000cbff', '#0000caff', '#0000c8ff', '#0000c7ff', '#0000c5ff', '#0000c4ff', '#0000c2ff', '#0000c0ff', '#0000bfff', '#0000bdff', '#0000bcff', '#0000bbff', '#0000baff', '#0000b8ff', '#0000b7ff', '#0000b5ff', '#0000b4ff', '#0000b2ff', '#0000b1ff', '#0000afff', '#0000aeff', '#0000adff', '#0000abff', '#0000aaff', '#0000a8ff', '#0000a7ff', '#0000a5ff', '#0000a4ff', '#0000a2ff', '#0000a1ff', '#00009fff', '#00009eff', '#00009dff', '#00009bff', '#00009aff', '#000098ff', '#000097ff', '#000095ff', '#000094ff', '#000093ff', '#000092ff', '#000090ff', '#00008fff', '#00008dff', '#00008cff', '#00008aff', '#000088ff', '#000087ff', '#000085ff', '#000084ff', '#000083ff', '#000082ff', '#000080ff', '#00007fff', '#00007dff', '#00007cff', '#00007aff', '#000079ff', '#000078ff', '#000076ff', '#000075ff', '#000073ff', '#000072ff', '#000070ff', '#00006fff', '#00006dff', '#00006cff', '#00006aff', '#000069ff', '#000067ff', '#000066ff', '#000065ff', '#000063ff', '#000062ff', '#000060ff', '#00005fff', '#00005dff', '#00005cff', '#00005bff', '#00005aff', '#000058ff', '#000057ff', '#000055ff', '#000053ff', '#000052ff', '#000050ff', '#00004fff', '#00004dff', '#00004cff']);\n", " \n", "\n", - " color_map_423bd39b53afa1428b2a572f3b58e752.x = d3.scale.linear()\n", + " color_map_0e0f78e55adc0213c56338b6c70a3e71.x = d3.scale.linear()\n", " .domain([-100.0, 100.0])\n", " .range([0, 450 - 50]);\n", "\n", - " color_map_423bd39b53afa1428b2a572f3b58e752.legend = L.control({position: 'topright'});\n", - " color_map_423bd39b53afa1428b2a572f3b58e752.legend.onAdd = function (map) {var div = L.DomUtil.create('div', 'legend'); return div};\n", - " color_map_423bd39b53afa1428b2a572f3b58e752.legend.addTo(map_9ee68f41dc3150d15490ad1f5ae271bd);\n", + " color_map_0e0f78e55adc0213c56338b6c70a3e71.legend = L.control({position: 'topright'});\n", + " color_map_0e0f78e55adc0213c56338b6c70a3e71.legend.onAdd = function (map) {var div = L.DomUtil.create('div', 'legend'); return div};\n", + " color_map_0e0f78e55adc0213c56338b6c70a3e71.legend.addTo(map_6b8fb61bd47002f39851b8f8d69f6e4a);\n", "\n", - " color_map_423bd39b53afa1428b2a572f3b58e752.xAxis = d3.svg.axis()\n", - " .scale(color_map_423bd39b53afa1428b2a572f3b58e752.x)\n", + " color_map_0e0f78e55adc0213c56338b6c70a3e71.xAxis = d3.svg.axis()\n", + " .scale(color_map_0e0f78e55adc0213c56338b6c70a3e71.x)\n", " .orient("top")\n", " .tickSize(1)\n", " .tickValues([-100.0, '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', 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83.52941176470588, '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '']);\n", "\n", - " color_map_423bd39b53afa1428b2a572f3b58e752.svg = d3.select(".legend.leaflet-control").append("svg")\n", + " color_map_0e0f78e55adc0213c56338b6c70a3e71.svg = d3.select(".legend.leaflet-control").append("svg")\n", " .attr("id", 'legend')\n", " .attr("width", 450)\n", " .attr("height", 40);\n", "\n", - " color_map_423bd39b53afa1428b2a572f3b58e752.g = color_map_423bd39b53afa1428b2a572f3b58e752.svg.append("g")\n", + " color_map_0e0f78e55adc0213c56338b6c70a3e71.g = color_map_0e0f78e55adc0213c56338b6c70a3e71.svg.append("g")\n", " .attr("class", "key")\n", " .attr("transform", "translate(25,16)");\n", "\n", - " color_map_423bd39b53afa1428b2a572f3b58e752.g.selectAll("rect")\n", - " .data(color_map_423bd39b53afa1428b2a572f3b58e752.color.range().map(function(d, i) {\n", + " color_map_0e0f78e55adc0213c56338b6c70a3e71.g.selectAll("rect")\n", + " .data(color_map_0e0f78e55adc0213c56338b6c70a3e71.color.range().map(function(d, i) {\n", " return {\n", - " x0: i ? color_map_423bd39b53afa1428b2a572f3b58e752.x(color_map_423bd39b53afa1428b2a572f3b58e752.color.domain()[i - 1]) : color_map_423bd39b53afa1428b2a572f3b58e752.x.range()[0],\n", - " x1: i < color_map_423bd39b53afa1428b2a572f3b58e752.color.domain().length ? color_map_423bd39b53afa1428b2a572f3b58e752.x(color_map_423bd39b53afa1428b2a572f3b58e752.color.domain()[i]) : color_map_423bd39b53afa1428b2a572f3b58e752.x.range()[1],\n", + " x0: i ? color_map_0e0f78e55adc0213c56338b6c70a3e71.x(color_map_0e0f78e55adc0213c56338b6c70a3e71.color.domain()[i - 1]) : color_map_0e0f78e55adc0213c56338b6c70a3e71.x.range()[0],\n", + " x1: i < color_map_0e0f78e55adc0213c56338b6c70a3e71.color.domain().length ? color_map_0e0f78e55adc0213c56338b6c70a3e71.x(color_map_0e0f78e55adc0213c56338b6c70a3e71.color.domain()[i]) : color_map_0e0f78e55adc0213c56338b6c70a3e71.x.range()[1],\n", " z: d\n", " };\n", " }))\n", @@ -1494,15 +1490,15 @@ " .attr("width", function(d) { return d.x1 - d.x0; })\n", " .style("fill", function(d) { return d.z; });\n", "\n", - " color_map_423bd39b53afa1428b2a572f3b58e752.g.call(color_map_423bd39b53afa1428b2a572f3b58e752.xAxis).append("text")\n", + " color_map_0e0f78e55adc0213c56338b6c70a3e71.g.call(color_map_0e0f78e55adc0213c56338b6c70a3e71.xAxis).append("text")\n", " .attr("class", "caption")\n", " .attr("y", 21)\n", - " .text("luminosity");\n", + " .text("radiance");\n", "</script>\n", "</html>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen>" ], "text/plain": [ - "" + "" ] }, "execution_count": 14, @@ -1511,7 +1507,7 @@ } ], "source": [ - "PSE.merge(data.iloc[-2].to_frame(\"luminosity\").reset_index(), on=\"NAME_2\").explore(column=\"luminosity\", cmap=\"seismic_r\", vmin=-100, vmax=100)" + "PSE.merge(data.iloc[-2].to_frame(\"radiance\").reset_index(), on=\"NAME_2\").explore(column=\"radiance\", cmap=\"seismic_r\", vmin=-100, vmax=100)" ] }, { @@ -1566,7 +1562,7 @@ " <meta name="viewport" content="width=device-width,\n", " initial-scale=1.0, maximum-scale=1.0, user-scalable=no" />\n", " <style>\n", - " #map_d7f5043427f8fef4e1b0aedd736d0fc1 {\n", + " #map_49d5861321dc49e7e0b4305c114fc6f6 {\n", " position: relative;\n", " width: 100.0%;\n", " height: 100.0%;\n", @@ -1597,14 +1593,14 @@ "<body>\n", " \n", " \n", - " <div class="folium-map" id="map_d7f5043427f8fef4e1b0aedd736d0fc1" ></div>\n", + " <div class="folium-map" id="map_49d5861321dc49e7e0b4305c114fc6f6" ></div>\n", " \n", "</body>\n", "<script>\n", " \n", " \n", - " var map_d7f5043427f8fef4e1b0aedd736d0fc1 = L.map(\n", - " "map_d7f5043427f8fef4e1b0aedd736d0fc1",\n", + " var map_49d5861321dc49e7e0b4305c114fc6f6 = L.map(\n", + " "map_49d5861321dc49e7e0b4305c114fc6f6",\n", " {\n", " center: [31.887586594000084, 34.902240753000115],\n", " crs: L.CRS.EPSG3857,\n", @@ -1613,25 +1609,25 @@ " preferCanvas: false,\n", " }\n", " );\n", - " L.control.scale().addTo(map_d7f5043427f8fef4e1b0aedd736d0fc1);\n", + " L.control.scale().addTo(map_49d5861321dc49e7e0b4305c114fc6f6);\n", "\n", " \n", "\n", " \n", " \n", - " var tile_layer_db5f3a242bd1a2d3dc97fc5b04e74aa6 = L.tileLayer(\n", + " var tile_layer_74e70f7c695db1c43fd3ee76ce4612ee = L.tileLayer(\n", " "https://{s}.tile.openstreetmap.org/{z}/{x}/{y}.png",\n", " {"attribution": "Data by \\u0026copy; \\u003ca target=\\"_blank\\" href=\\"http://openstreetmap.org\\"\\u003eOpenStreetMap\\u003c/a\\u003e, under \\u003ca target=\\"_blank\\" href=\\"http://www.openstreetmap.org/copyright\\"\\u003eODbL\\u003c/a\\u003e.", "detectRetina": false, "maxNativeZoom": 18, "maxZoom": 18, "minZoom": 0, "noWrap": false, "opacity": 1, "subdomains": "abc", "tms": false}\n", - " ).addTo(map_d7f5043427f8fef4e1b0aedd736d0fc1);\n", + " ).addTo(map_49d5861321dc49e7e0b4305c114fc6f6);\n", " \n", " \n", - " map_d7f5043427f8fef4e1b0aedd736d0fc1.fitBounds(\n", + " map_49d5861321dc49e7e0b4305c114fc6f6.fitBounds(\n", " [[31.223606110000105, 34.22902679400016], [32.55156707800006, 35.57545471200007]],\n", " {}\n", " );\n", " \n", " \n", - " function geo_json_cf6e82337a77a65b97120bdfe7ac0ecd_styler(feature) {\n", + " function geo_json_ed5caf2cd0133ea2134b1afcfdf88fa0_styler(feature) {\n", " switch(feature.id) {\n", " case "0": \n", " return {"color": "#a00000", "fillColor": "#a00000", "fillOpacity": 0.5, "weight": 2};\n", @@ -1661,59 +1657,59 @@ " return {"color": "#fffdfd", "fillColor": "#fffdfd", "fillOpacity": 0.5, "weight": 2};\n", " }\n", " }\n", - " function geo_json_cf6e82337a77a65b97120bdfe7ac0ecd_highlighter(feature) {\n", + " function geo_json_ed5caf2cd0133ea2134b1afcfdf88fa0_highlighter(feature) {\n", " switch(feature.id) {\n", " default:\n", " return {"fillOpacity": 0.75};\n", " }\n", " }\n", - " function geo_json_cf6e82337a77a65b97120bdfe7ac0ecd_pointToLayer(feature, latlng) {\n", + " function geo_json_ed5caf2cd0133ea2134b1afcfdf88fa0_pointToLayer(feature, latlng) {\n", " var opts = {"bubblingMouseEvents": true, "color": "#3388ff", "dashArray": null, "dashOffset": null, "fill": true, "fillColor": "#3388ff", "fillOpacity": 0.2, "fillRule": "evenodd", "lineCap": "round", "lineJoin": "round", "opacity": 1.0, "radius": 2, "stroke": true, "weight": 3};\n", " \n", - " let style = geo_json_cf6e82337a77a65b97120bdfe7ac0ecd_styler(feature)\n", + " let style = geo_json_ed5caf2cd0133ea2134b1afcfdf88fa0_styler(feature)\n", " Object.assign(opts, style)\n", " \n", " return new L.CircleMarker(latlng, opts)\n", " }\n", "\n", - " function geo_json_cf6e82337a77a65b97120bdfe7ac0ecd_onEachFeature(feature, layer) {\n", + " function geo_json_ed5caf2cd0133ea2134b1afcfdf88fa0_onEachFeature(feature, layer) {\n", " layer.on({\n", " mouseout: function(e) {\n", " if(typeof e.target.setStyle === "function"){\n", - " geo_json_cf6e82337a77a65b97120bdfe7ac0ecd.resetStyle(e.target);\n", + " geo_json_ed5caf2cd0133ea2134b1afcfdf88fa0.resetStyle(e.target);\n", " }\n", " },\n", " mouseover: function(e) {\n", " if(typeof e.target.setStyle === "function"){\n", - " const highlightStyle = geo_json_cf6e82337a77a65b97120bdfe7ac0ecd_highlighter(e.target.feature)\n", + " const highlightStyle = geo_json_ed5caf2cd0133ea2134b1afcfdf88fa0_highlighter(e.target.feature)\n", " e.target.setStyle(highlightStyle);\n", " }\n", " },\n", " });\n", " };\n", - " var geo_json_cf6e82337a77a65b97120bdfe7ac0ecd = L.geoJson(null, {\n", - " onEachFeature: geo_json_cf6e82337a77a65b97120bdfe7ac0ecd_onEachFeature,\n", + " var geo_json_ed5caf2cd0133ea2134b1afcfdf88fa0 = L.geoJson(null, {\n", + " onEachFeature: geo_json_ed5caf2cd0133ea2134b1afcfdf88fa0_onEachFeature,\n", " \n", - " style: geo_json_cf6e82337a77a65b97120bdfe7ac0ecd_styler,\n", - " pointToLayer: geo_json_cf6e82337a77a65b97120bdfe7ac0ecd_pointToLayer\n", + " style: geo_json_ed5caf2cd0133ea2134b1afcfdf88fa0_styler,\n", + " pointToLayer: geo_json_ed5caf2cd0133ea2134b1afcfdf88fa0_pointToLayer\n", " });\n", "\n", - " function geo_json_cf6e82337a77a65b97120bdfe7ac0ecd_add (data) {\n", - " geo_json_cf6e82337a77a65b97120bdfe7ac0ecd\n", + " function geo_json_ed5caf2cd0133ea2134b1afcfdf88fa0_add (data) {\n", + " geo_json_ed5caf2cd0133ea2134b1afcfdf88fa0\n", " .addData(data)\n", - " .addTo(map_d7f5043427f8fef4e1b0aedd736d0fc1);\n", + " .addTo(map_49d5861321dc49e7e0b4305c114fc6f6);\n", " }\n", - " geo_json_cf6e82337a77a65b97120bdfe7ac0ecd_add({"bbox": [34.22902679400016, 31.223606110000105, 35.57545471200007, 32.55156707800006], "features": [{"bbox": [34.30597305300017, 31.372600555000133, 34.415882110000155, 31.462638856000126], "geometry": {"coordinates": [[[34.415882110000155, 31.42720031800019], [34.40790176400009, 31.420608522000123], [34.37456893900003, 31.38384056100017], [34.370647431000066, 31.372600555000133], [34.364295960000106, 31.373163224000052], [34.36157989600008, 31.37386703400017], [34.35890960800015, 31.375257492000117], [34.356483459, 31.377065659000095], [34.35039901700003, 31.38258552500008], [34.34825134300013, 31.38376236000005], [34.34557342600016, 31.38476562500017], [34.33489227300004, 31.38765144300004], [34.32719421400003, 31.39049911600017], [34.31578445500003, 31.39388465999997], [34.311809541000116, 31.395610810000164], [34.309684754000045, 31.396917344000087], [34.30693054200009, 31.399486542000034], [34.30597305300017, 31.400417328000117], [34.30597305300017, 31.400695800999983], [34.306526184000006, 31.400695800999983], [34.306526184000006, 31.400972367000122], [34.30680465700004, 31.400972367000122], [34.30680465700004, 31.40125083999999], [34.30708313000008, 31.40125083999999], [34.30708313000008, 31.40152740500008], [34.307640075999984, 31.40152740500008], [34.307640075999984, 31.40180587800012], [34.30791854900002, 31.40180587800012], [34.30791854900002, 31.402082444000087], [34.308193207000045, 31.402082444000087], [34.308193207000045, 31.402360917000124], 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6.513281698116646}, "type": "Feature"}, {"bbox": [35.00716781500006, 32.20525741500006, 35.181259155000134, 32.464771271000075], "geometry": {"coordinates": [[[35.06133270300012, 32.38663864200015], [35.06336212100018, 32.39910888600002], [35.07206344600007, 32.419593811000084], [35.07672500700005, 32.44520568899998], [35.08116150000001, 32.45911789000013], [35.0844802850001, 32.464408874000185], [35.084854125000106, 32.46454239000008], [35.087406158000135, 32.464771271000075], [35.09077072200017, 32.46457672100013], [35.09384155400005, 32.46416473500017], [35.096752168000194, 32.46345138600003], [35.099613189000024, 32.462070464000135], [35.10198211800002, 32.46014404400012], [35.10396194500015, 32.45791626000016], [35.10530471800013, 32.455303192000144], [35.10591506999998, 32.452713013000164], [35.10610961900005, 32.45001220700016], [35.105960847000176, 32.44730758700018], [35.105426789000035, 32.44469070500003], [35.104244232000156, 32.442001344000175], [35.102485657000045, 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2.747108669715548}, "type": "Feature"}], "type": "FeatureCollection"});\n", "\n", " \n", " \n", - " geo_json_cf6e82337a77a65b97120bdfe7ac0ecd.bindTooltip(\n", + " geo_json_ed5caf2cd0133ea2134b1afcfdf88fa0.bindTooltip(\n", " function(layer){\n", " let div = L.DomUtil.create('div');\n", " \n", " let handleObject = feature=>typeof(feature)=='object' ? JSON.stringify(feature) : feature;\n", - " let fields = ["GID_2", "GID_0", "COUNTRY", "GID_1", "NAME_1", "NL_NAME_1", "NAME_2", "VARNAME_2", "NL_NAME_2", "TYPE_2", "ENGTYPE_2", "CC_2", "HASC_2", "level_0", "luminosity"];\n", - " let aliases = ["GID_2", "GID_0", "COUNTRY", "GID_1", "NAME_1", "NL_NAME_1", "NAME_2", "VARNAME_2", "NL_NAME_2", "TYPE_2", "ENGTYPE_2", "CC_2", "HASC_2", "level_0", "luminosity"];\n", + " let fields = ["GID_2", "GID_0", "COUNTRY", "GID_1", "NAME_1", "NL_NAME_1", "NAME_2", "VARNAME_2", "NL_NAME_2", "TYPE_2", "ENGTYPE_2", "CC_2", "HASC_2", "level_0", "radiance"];\n", + " let aliases = ["GID_2", "GID_0", "COUNTRY", "GID_1", "NAME_1", "NL_NAME_1", "NAME_2", "VARNAME_2", "NL_NAME_2", "TYPE_2", "ENGTYPE_2", "CC_2", "HASC_2", "level_0", "radiance"];\n", " let table = '<table>' +\n", " String(\n", " fields.map(\n", @@ -1731,42 +1727,42 @@ " ,{"className": "foliumtooltip", "sticky": true});\n", " \n", " \n", - " var color_map_24de3e2b7a0bf663f2dd8c5087388836 = {};\n", + " var color_map_22b1321d19dcdd02a68569fc538e0441 = {};\n", "\n", " \n", - " color_map_24de3e2b7a0bf663f2dd8c5087388836.color = d3.scale.threshold()\n", + " color_map_22b1321d19dcdd02a68569fc538e0441.color = d3.scale.threshold()\n", " .domain([-100.0, -99.59919839679358, -99.19839679358718, -98.79759519038076, -98.39679358717434, -97.99599198396794, -97.59519038076152, -97.1943887775551, -96.7935871743487, -96.39278557114228, -95.99198396793587, -95.59118236472946, -95.19038076152304, -94.78957915831663, -94.38877755511022, -93.9879759519038, -93.58717434869739, -93.18637274549098, -92.78557114228457, -92.38476953907815, -91.98396793587175, -91.58316633266533, -91.18236472945893, -90.78156312625251, -90.38076152304609, -89.97995991983967, -89.57915831663327, -89.17835671342685, -88.77755511022045, -88.37675350701403, -87.97595190380761, -87.5751503006012, -87.17434869739479, -86.77354709418837, -86.37274549098197, -85.97194388777555, -85.57114228456913, -85.17034068136273, 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'#000095ff', '#000094ff', '#000093ff', '#000092ff', '#000090ff', '#00008fff', '#00008dff', '#00008cff', '#00008aff', '#000088ff', '#000087ff', '#000085ff', '#000084ff', '#000083ff', '#000082ff', '#000080ff', '#00007fff', '#00007dff', '#00007cff', '#00007aff', '#000079ff', '#000078ff', '#000076ff', '#000075ff', '#000073ff', '#000072ff', '#000070ff', '#00006fff', '#00006dff', '#00006cff', '#00006aff', '#000069ff', '#000067ff', '#000066ff', '#000065ff', '#000063ff', '#000062ff', '#000060ff', '#00005fff', '#00005dff', '#00005cff', '#00005bff', '#00005aff', '#000058ff', '#000057ff', '#000055ff', '#000053ff', '#000052ff', '#000050ff', '#00004fff', '#00004dff', '#00004cff']);\n", " \n", "\n", - " color_map_24de3e2b7a0bf663f2dd8c5087388836.x = d3.scale.linear()\n", + " color_map_22b1321d19dcdd02a68569fc538e0441.x = d3.scale.linear()\n", " .domain([-100.0, 100.0])\n", " .range([0, 450 - 50]);\n", "\n", - " color_map_24de3e2b7a0bf663f2dd8c5087388836.legend = L.control({position: 'topright'});\n", - " color_map_24de3e2b7a0bf663f2dd8c5087388836.legend.onAdd = function (map) {var div = L.DomUtil.create('div', 'legend'); return div};\n", - " color_map_24de3e2b7a0bf663f2dd8c5087388836.legend.addTo(map_d7f5043427f8fef4e1b0aedd736d0fc1);\n", + " color_map_22b1321d19dcdd02a68569fc538e0441.legend = L.control({position: 'topright'});\n", + " color_map_22b1321d19dcdd02a68569fc538e0441.legend.onAdd = function (map) {var div = L.DomUtil.create('div', 'legend'); return div};\n", + " color_map_22b1321d19dcdd02a68569fc538e0441.legend.addTo(map_49d5861321dc49e7e0b4305c114fc6f6);\n", "\n", - " color_map_24de3e2b7a0bf663f2dd8c5087388836.xAxis = d3.svg.axis()\n", - " .scale(color_map_24de3e2b7a0bf663f2dd8c5087388836.x)\n", + " color_map_22b1321d19dcdd02a68569fc538e0441.xAxis = d3.svg.axis()\n", + " .scale(color_map_22b1321d19dcdd02a68569fc538e0441.x)\n", " .orient("top")\n", " .tickSize(1)\n", " .tickValues([-100.0, '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', 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83.52941176470588, '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '', '']);\n", "\n", - " color_map_24de3e2b7a0bf663f2dd8c5087388836.svg = d3.select(".legend.leaflet-control").append("svg")\n", + " color_map_22b1321d19dcdd02a68569fc538e0441.svg = d3.select(".legend.leaflet-control").append("svg")\n", " .attr("id", 'legend')\n", " .attr("width", 450)\n", " .attr("height", 40);\n", "\n", - " color_map_24de3e2b7a0bf663f2dd8c5087388836.g = color_map_24de3e2b7a0bf663f2dd8c5087388836.svg.append("g")\n", + " color_map_22b1321d19dcdd02a68569fc538e0441.g = color_map_22b1321d19dcdd02a68569fc538e0441.svg.append("g")\n", " .attr("class", "key")\n", " .attr("transform", "translate(25,16)");\n", "\n", - " color_map_24de3e2b7a0bf663f2dd8c5087388836.g.selectAll("rect")\n", - " .data(color_map_24de3e2b7a0bf663f2dd8c5087388836.color.range().map(function(d, i) {\n", + " color_map_22b1321d19dcdd02a68569fc538e0441.g.selectAll("rect")\n", + " .data(color_map_22b1321d19dcdd02a68569fc538e0441.color.range().map(function(d, i) {\n", " return {\n", - " x0: i ? color_map_24de3e2b7a0bf663f2dd8c5087388836.x(color_map_24de3e2b7a0bf663f2dd8c5087388836.color.domain()[i - 1]) : color_map_24de3e2b7a0bf663f2dd8c5087388836.x.range()[0],\n", - " x1: i < color_map_24de3e2b7a0bf663f2dd8c5087388836.color.domain().length ? color_map_24de3e2b7a0bf663f2dd8c5087388836.x(color_map_24de3e2b7a0bf663f2dd8c5087388836.color.domain()[i]) : color_map_24de3e2b7a0bf663f2dd8c5087388836.x.range()[1],\n", + " x0: i ? color_map_22b1321d19dcdd02a68569fc538e0441.x(color_map_22b1321d19dcdd02a68569fc538e0441.color.domain()[i - 1]) : color_map_22b1321d19dcdd02a68569fc538e0441.x.range()[0],\n", + " x1: i < color_map_22b1321d19dcdd02a68569fc538e0441.color.domain().length ? color_map_22b1321d19dcdd02a68569fc538e0441.x(color_map_22b1321d19dcdd02a68569fc538e0441.color.domain()[i]) : color_map_22b1321d19dcdd02a68569fc538e0441.x.range()[1],\n", " z: d\n", " };\n", " }))\n", @@ -1776,15 +1772,15 @@ " .attr("width", function(d) { return d.x1 - d.x0; })\n", " .style("fill", function(d) { return d.z; });\n", "\n", - " color_map_24de3e2b7a0bf663f2dd8c5087388836.g.call(color_map_24de3e2b7a0bf663f2dd8c5087388836.xAxis).append("text")\n", + " color_map_22b1321d19dcdd02a68569fc538e0441.g.call(color_map_22b1321d19dcdd02a68569fc538e0441.xAxis).append("text")\n", " .attr("class", "caption")\n", " .attr("y", 21)\n", - " .text("luminosity");\n", + " .text("radiance");\n", "</script>\n", "</html>\" style=\"position:absolute;width:100%;height:100%;left:0;top:0;border:none !important;\" allowfullscreen webkitallowfullscreen mozallowfullscreen>" ], "text/plain": [ - "" + "" ] }, "execution_count": 15, @@ -1793,7 +1789,7 @@ } ], "source": [ - "PSE.merge(data.iloc[-1].to_frame(\"luminosity\").reset_index(), on=\"NAME_2\").explore(column=\"luminosity\", cmap=\"seismic_r\", vmin=-100, vmax=100)" + "PSE.merge(data.iloc[-1].to_frame(\"radiance\").reset_index(), on=\"NAME_2\").explore(column=\"radiance\", cmap=\"seismic_r\", vmin=-100, vmax=100)" ] }, {