deploy: fd36d22

_downloads/9879d6db96fd29134fc802214163b95a/LICENSE

@@ -370,4 +370,4 @@ Exhibit B - "Incompatible With Secondary Licenses" Notice
 ---------------------------------------------------------
 
   This Source Code Form is "Incompatible With Secondary Licenses", as
-  defined by the Mozilla Public License, v. 2.0.
+  defined by the Mozilla Public License, v. 2.0.

_sources/docs/CONTRIBUTING.md

@@ -63,7 +63,7 @@ To run the notebooks locally, after (1) and (2) above, please follow these steps
   ```
 
   This command will create a new environment based on the specifications provided in the `environment.yml` file.
-  
+
 - [Activate the environment](https://conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#activating-an-environment), run [JupyterLab](https://jupyterlab.readthedocs.io) and execute `notebooks`:
 
   ```shell

_sources/notebooks/damage-assessment/README.md

@@ -33,12 +33,12 @@ We have employed all the Copernicus [Sentinel-1](https://sentinels.copernicus.eu
 ```{figure} ../../docs/images/damage-assessment-calendar.png
 ---
 ---
-Calendar with Copernicus Sentinel-1 acquisition dates after the war started on past 7th October 2023 until 9th January 2023. 
+Calendar with Copernicus Sentinel-1 acquisition dates after the war started on past 7th October 2023 until 9th January 2023.
 ```
 
 ### 2. Change detection based on time series statistics
 
-For the time series change detection, we have computed all the data pre-war September 2022 until end September 2023 to compute the statistics in non-war situation, and to use those statistics to classify the newer data acquired during the war period October 2023 until the present time with pixels for which had been detected a change (potentially attributable to war damage) using anomaly detection method with different thresholds (i.e. 3 sigma rule and 2.5 sigma rule).  
+For the time series change detection, we have computed all the data pre-war September 2022 until end September 2023 to compute the statistics in non-war situation, and to use those statistics to classify the newer data acquired during the war period October 2023 until the present time with pixels for which had been detected a change (potentially attributable to war damage) using anomaly detection method with different thresholds (i.e. 3 sigma rule and 2.5 sigma rule).
 
 The 3-sigma rule is more conservative and provides more conservative results with false alarms regarding the change detections. 3-sigma rule considers as anomaly values that are lower than the average minus 3 times their standard deviation which it means that are lower than the 99.6% of the value’s normal distribution (measured in non-war conditions) or are included in the 0.15% of possible values, and hence, detected as anomalous. Similarly happens for the 2.5 sigma rule, which pixels are considered as anomaly the ones being the 0.65%. This 2.5 sigma rule may increase some more false alarms, while the 3 sigma rule is considered more conservative anomaly detection rule. See example of this empirical rule below.
 
@@ -70,7 +70,7 @@ For the final assessment of infrastructural damage, in roads, points of interest
 ## Limitations
 
 ```{important}
-The following damage assessment maps are based on preliminary estimates and have not been verified through field survey or satellite imagery. They do not reflect the final estimations by the [World Bank](https://www.worldbank.org/en/country/westbankandgaza). The project team is currently working on procuring high resolution imagery to verify whether the buildings identified as damaged have collapsed or experienced impact. 
+The following damage assessment maps are based on preliminary estimates and have not been verified through field survey or satellite imagery. They do not reflect the final estimations by the [World Bank](https://www.worldbank.org/en/country/westbankandgaza). The project team is currently working on procuring high resolution imagery to verify whether the buildings identified as damaged have collapsed or experienced impact.
 ```
 
 ```{caution}
@@ -83,13 +83,13 @@ Using OpenStreetMap (OSM) and Interferometric Synthetic Aperture Radar (InSAR) f
 - **Temporal Limitations:**
     - **Data Timeliness:** OSM data might not be up to date due to infrequent updates or changes in the landscape that haven't been reflected yet.
     - **InSAR Timing:** InSAR data might not capture the most recent changes or damages, especially in rapidly evolving situations where damage continues to occur after the data collection period.
-    
+
 - **Contextual Understanding:**
     - **Local Knowledge:** OSM data might lack contextual information crucial for assessing damages accurately, such as the original state of buildings or variations in construction materials.
     - **Verification Challenges:** Verifying damages solely based on remote sensing data might lack the on-ground verification necessary for a comprehensive understanding of the situation.
 
 - **Conflict Data**
-ACLED is a crowdsourced dataset and is higly likely that the numbers are underreported. ACLED keep changing their data based on local validation. 
+ACLED is a crowdsourced dataset and is higly likely that the numbers are underreported. ACLED keep changing their data based on local validation.
 ```
 
 ## References

_sources/notebooks/nighttime-lights/README.md

@@ -42,5 +42,3 @@ To address these assumptions and limitations, it is crucial to complement nightt
 :filter: docname in docnames
 :style: plain
 ```
-
-

_sources/notebooks/nighttime-lights/nighttime-lights-in-jordan.ipynb

@@ -40,11 +40,9 @@
     "import matplotlib.pyplot as plt\n",
     "import pandas as pd\n",
     "from blackmarble.bm_extract import bm_extract\n",
-    "from blackmarble.bm_raster import bm_raster\n",
-    "from bokeh.models import ColumnDataSource, HoverTool, Legend, Range1d, Span, Text, Title\n",
+    "from bokeh.models import HoverTool, Legend, Span, 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",
     "%load_ext autoreload\n",
     "%autoreload 2"
@@ -1131,7 +1129,9 @@
    ],
    "source": [
     "JO_2 = (\n",
-    "    VNP46A2.pivot_table(index=\"date\", columns=[\"NAME_2\"], values=[VAR]).resample(\"W\").mean()\n",
+    "    VNP46A2.pivot_table(index=\"date\", columns=[\"NAME_2\"], values=[VAR])\n",
+    "    .resample(\"W\")\n",
+    "    .mean()\n",
     ")\n",
     "\n",
     "JO_1 = (\n",
@@ -1560,7 +1560,7 @@
     ")\n",
     "p.add_layout(\n",
     "    Title(\n",
-    "        text=f\"Weekly Adm2 Radiance Average\",\n",
+    "        text=\"Weekly Adm2 Radiance Average\",\n",
     "        text_font_size=\"12pt\",\n",
     "        text_font_style=\"italic\",\n",
     "    ),\n",
@@ -2043,7 +2043,7 @@
     ")\n",
     "p.add_layout(\n",
     "    Title(\n",
-    "        text=f\"Monthly Adm2 Radiance (VNP46A2 and VNP46A3)\",\n",
+    "        text=\"Monthly Adm2 Radiance (VNP46A2 and VNP46A3)\",\n",
     "        text_font_size=\"12pt\",\n",
     "        text_font_style=\"italic\",\n",
     "    ),\n",
@@ -2095,7 +2095,7 @@
     "    )\n",
     "    r.visible = False\n",
     "    renderers.append(r)\n",
-    "    \n",
+    "\n",
     "renderers[-1].visible = True\n",
     "\n",
     "p.legend.location = \"bottom_left\"\n",
@@ -3027,7 +3027,7 @@
     "p.xaxis.major_label_orientation = math.pi / 4\n",
     "p.add_layout(\n",
     "    Title(\n",
-    "        text=f\"Percent change (compared to 2022) in NTL radiance for each second-level administrative division\",\n",
+    "        text=\"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",
@@ -3202,7 +3202,7 @@
     "p.xaxis.major_label_orientation = math.pi / 4\n",
     "p.add_layout(\n",
     "    Title(\n",
-    "        text=f\"Percent change week over week in NTL radiance for each second-level administrative division\",\n",
+    "        text=\"Percent change week over week in NTL radiance for each second-level administrative division\",\n",
     "        text_font_size=\"12pt\",\n",
     "        text_font_style=\"italic\",\n",
     "    ),\n",
@@ -3334,7 +3334,7 @@
     "sns.set_style(\"whitegrid\")\n",
     "\n",
     "fig, ax = plt.subplots(figsize=(10, 8))\n",
-    "fig.suptitle(f\"Jordan: Weekly Nighttime Lights (2022-2023)\", y=0.95)\n",
+    "fig.suptitle(\"Jordan: Weekly Nighttime Lights (2022-2023)\", y=0.95)\n",
     "\n",
     "JO_0.plot(ax=ax, marker=\"8\")\n",
     "ax.set_title(\"Weekly National Radiance Average\")\n",

_sources/notebooks/nighttime-lights/nighttime-lights.ipynb

@@ -39,19 +39,11 @@
     "import geopandas\n",
     "import pandas as pd\n",
     "from blackmarble.extract import bm_extract\n",
-    "from blackmarble.raster import bm_raster\n",
     "from bokeh.models import (\n",
-    "    BasicTicker,\n",
-    "    ColumnDataSource,\n",
-    "    Div,\n",
     "    HoverTool,\n",
     "    Legend,\n",
-    "    PrintfTickFormatter,\n",
     "    Range1d,\n",
     "    Span,\n",
-    "    TabPanel,\n",
-    "    Tabs,\n",
-    "    Text,\n",
     "    Title,\n",
     ")\n",
     "from bokeh.plotting import figure, output_notebook, show"
@@ -12059,7 +12051,7 @@
     ")\n",
     "p.add_layout(\n",
     "    Title(\n",
-    "        text=f\"Monthly NTL Radiance Average (VNP46A2 and VNP46A3) for each second-level administrative division\",\n",
+    "        text=\"Monthly NTL Radiance Average (VNP46A2 and VNP46A3) for each second-level administrative division\",\n",
     "        text_font_size=\"12pt\",\n",
     "        text_font_style=\"italic\",\n",
     "    ),\n",
@@ -12663,7 +12655,7 @@
     "\n",
     "    p.add_layout(\n",
     "        Title(\n",
-    "            text=f\"Weekly percent change (2022 baseline) in NTL radiance for each second-level administrative division\",\n",
+    "            text=\"Weekly percent change (2022 baseline) in NTL radiance for each second-level administrative division\",\n",
     "            text_font_size=\"12pt\",\n",
     "            text_font_style=\"italic\",\n",
     "        ),\n",
@@ -12875,7 +12867,7 @@
     "    )\n",
     "    p.add_layout(\n",
     "        Title(\n",
-    "            text=f\"Weekly percent change (2022 baseline) in NTL radiance\",\n",
+    "            text=\"Weekly percent change (2022 baseline) in NTL radiance\",\n",
     "            text_font_size=\"12pt\",\n",
     "            text_font_style=\"italic\",\n",
     "        ),\n",
@@ -13158,7 +13150,7 @@
     ")\n",
     "p.add_layout(\n",
     "    Title(\n",
-    "        text=f\"Weekly percent change week over week in NTL radiance for each second-level administrative division\",\n",
+    "        text=\"Weekly percent change week over week in NTL radiance for each second-level administrative division\",\n",
     "        text_font_size=\"12pt\",\n",
     "        text_font_style=\"italic\",\n",
     "    ),\n",

_sources/notebooks/sample-indicators/health.ipynb

@@ -19,11 +19,9 @@
    "source": [
     "import geopandas\n",
     "import matplotlib.pyplot as plt\n",
-    "import numpy as np\n",
     "import pandas as pd\n",
     "from matplotlib.ticker import FuncFormatter\n",
-    "from palettable.cartocolors.qualitative import Prism_10, Vivid_10\n",
-    "from palettable.tableau import ColorBlind_10, TableauMedium_10\n",
+    "from palettable.tableau import TableauMedium_10\n",
     "\n",
     "%config InlineBackend.figure_formats = ['svg']\n",
     "%matplotlib inline"
@@ -2033,10 +2031,12 @@
    ],
    "source": [
     "fig, ax = plt.subplots(figsize=(10, 6))\n",
-    "plt.suptitle(\"Gaza: Percentage of Damaged Healthcare Locations\", y=1.01, fontsize=20, x=0.47)\n",
+    "plt.suptitle(\n",
+    "    \"Gaza: Percentage of Damaged Healthcare Locations\", y=1.01, fontsize=20, x=0.47\n",
+    ")\n",
     "\n",
-    "df.pivot_table(\"perc\", \"ADM2_EN\", \"type\")[['Health']].plot(\n",
-    "    ax=ax, kind=\"barh\", color=TableauMedium_10.mpl_colors, legend = False\n",
+    "df.pivot_table(\"perc\", \"ADM2_EN\", \"type\")[[\"Health\"]].plot(\n",
+    "    ax=ax, kind=\"barh\", color=TableauMedium_10.mpl_colors, legend=False\n",
     ")\n",
     "\n",
     "# Add labels and customization\n",
@@ -2051,11 +2051,12 @@
     "ax.yaxis.set_ticks_position(\"left\")\n",
     "ax.xaxis.set_ticks_position(\"bottom\")\n",
     "ax.grid(which=\"both\", linestyle=\"--\", linewidth=0.25, color=\"gray\", alpha=0.5)\n",
-    "#ax.legend(bbox_to_anchor=(1.01, 0.9))\n",
+    "# ax.legend(bbox_to_anchor=(1.01, 0.9))\n",
     "\n",
     "ax.set_title(\n",
     "    \"Estimated percentage of damaged healthcare locations for each second-level administrative division\",\n",
-    "    fontsize=14,loc='left'\n",
+    "    fontsize=14,\n",
+    "    loc=\"left\",\n",
     ")\n",
     "ax.text(\n",
     "    0,\n",