Formatting (#55)

notebooks/mobility/README.md

@@ -1,20 +1,20 @@
 # Gaza Mobility Analysis
 
-This notebook aims to analyze mobility data from Mapbox in Gaza after the attacks on October 7th. The analysis is exploratory in nature and has not been validated with other data sources primarily due to the lack of availability of similar datasets for the time period in consideration. 
+This notebook aims to analyze mobility data from Mapbox in Gaza after the attacks on October 7th. The analysis is exploratory in nature and has not been validated with other data sources primarily due to the lack of availability of similar datasets for the time period in consideration.
 
-Mobility data has often been used as a proxy for economic activity. In this case, it could shed light on 
+Mobility data has often been used as a proxy for economic activity. In this case, it could shed light on
 * The regions that saw activity in peacetime (Jan-June 2023) but no activity after October 7th, 2023.
 * The regions that saw activity in the time of conflict. This could potentially indicate the congregation of people
 
-The analysis is conducted at two types of aggregation - H3 grids at 0.73 sq. km and admin 2 regions at a weekly level. The temporal and spatial aggregation allows for comparison with Night Time Lights and damage indicators in the other notebooks. 
+The analysis is conducted at two types of aggregation - H3 grids at 0.73 sq. km and admin 2 regions at a weekly level. The temporal and spatial aggregation allows for comparison with Night Time Lights and damage indicators in the other notebooks.
 
 ## Data Description
 
 ### Mapbox Movement
 
 Mapbox Movement is a global dataset of de-identified and aggregated mobile device activity. The dataset captures significant driving and non-driving mobile device activity aggregated into geographic tiles of 100-meter resolution (approximately one city block) and select Mapbox boundaries. Mapbox Movement typically relies on location data such as GPS coordinates, timestamps, and possibly other metadata attributes like speed, heading, and altitude. This data is typically collected from devices or sources that track location information over time.
 
-The Mapbox Movement data used in this analysis is aggregated at a daily level and is represented in the form of an activity index. 
+The Mapbox Movement data used in this analysis is aggregated at a daily level and is represented in the form of an activity index.
 
 
 #### Activity Levels
@@ -33,6 +33,6 @@ Because the raw activity counts in a later time span may be greater than activit
 
 The Mapbox Movement data set is based on de-identified underlying mobile device activity that grows and changes every day. Any mobility data set is inherently skewed between highly populated regions (urban and metro areas) and sparsely populated regions (rural areas). So instead of providing "raw counts" of measurements, a custom normalization process is applied. This process measures and smooths out the impact of otherwise unpredictable changes in mobile device usage and calculates an activity index, which is more appropriate for comparison across time spans.
 
-#### Baselining 
+#### Baselining
 
-When activity levels are calculated in the context of Mapbox Movement, the baseline typically refers to the normal or expected levels of activity for a given area, time of day, day of the week, or other relevant factors. This baseline serves as a reference point for detecting deviations and anomalies in movement patterns or activity levels.
+When activity levels are calculated in the context of Mapbox Movement, the baseline typically refers to the normal or expected levels of activity for a given area, time of day, day of the week, or other relevant factors. This baseline serves as a reference point for detecting deviations and anomalies in movement patterns or activity levels.

notebooks/mobility/mapbox.ipynb

@@ -16,8 +16,8 @@
     "import geopandas\n",
     "import pandas as pd\n",
     "\n",
-    "from gaza_israel_conflict_analysis.movement_analysis import h3_functions\n",
-    "from gaza_israel_conflict_analysis.movement_analysis import visuals"
+    "from gaza_israel_conflict_impact_analysis.movement_analysis import h3_functions\n",
+    "from gaza_israel_conflict_impact_analysis.movement_analysis import visuals"
    ]
   },
   {
@@ -670,22 +670,20 @@
     "        legend_bool = False\n",
     "    gaza_h3.merge(\n",
     "        movement_day[movement_day[\"agg_day_period\"] == date], on=\"h3_index\"\n",
-    "    ).plot(\n",
-    "        column=\"activity_index_total\", legend=False, ax=ax[idx], vmin=0, vmax=1.5\n",
-    "    )\n",
+    "    ).plot(column=\"activity_index_total\", legend=False, ax=ax[idx], vmin=0, vmax=1.5)\n",
     "    ax[idx].axis(\"off\")\n",
     "    ax[idx].set_title(date)\n",
-    "    #ax[0].text(x=0, y=0, s='Source: Mapbox Movement')\n",
+    "    # ax[0].text(x=0, y=0, s='Source: Mapbox Movement')\n",
     "\n",
     "norm = mcolors.Normalize(vmin=0, vmax=1.5)\n",
-    "sm = plt.cm.ScalarMappable(cmap='viridis', norm=norm)\n",
+    "sm = plt.cm.ScalarMappable(cmap=\"viridis\", norm=norm)\n",
     "sm.set_array([])  # You have to set an empty array to avoid an error\n",
     "\n",
     "# Create a colorbar as a continuous legend\n",
-    "cbar = plt.colorbar(sm, ax=ax, orientation='vertical', fraction=0.046, pad=0.04)\n",
+    "cbar = plt.colorbar(sm, ax=ax, orientation=\"vertical\", fraction=0.046, pad=0.04)\n",
     "\n",
-    "fig.text(s='Source: Mapbox Movement', x=0.1, y=0, fontsize=11)\n",
-    "plt.suptitle('Comparing Daily Activity in Gaza', fontsize=16, x=0.3, y=0.99);"
+    "fig.text(s=\"Source: Mapbox Movement\", x=0.1, y=0, fontsize=11)\n",
+    "plt.suptitle(\"Comparing Daily Activity in Gaza\", fontsize=16, x=0.3, y=0.99);"
    ]
   },
   {
@@ -734,14 +732,14 @@
     "    ax[idx].set_title(date)\n",
     "\n",
     "norm = mcolors.Normalize(vmin=0, vmax=1.5)\n",
-    "sm = plt.cm.ScalarMappable(cmap='viridis', norm=norm)\n",
+    "sm = plt.cm.ScalarMappable(cmap=\"viridis\", norm=norm)\n",
     "sm.set_array([])  # You have to set an empty array to avoid an error\n",
     "\n",
     "# Create a colorbar as a continuous legend\n",
-    "cbar = plt.colorbar(sm, ax=ax[3], orientation='vertical', fraction=0.046, pad=0.04)\n",
+    "cbar = plt.colorbar(sm, ax=ax[3], orientation=\"vertical\", fraction=0.046, pad=0.04)\n",
     "\n",
-    "fig.text(s='Source: Mapbox Movement', x=0.2, y=0, fontsize=11)\n",
-    "plt.suptitle('Comparing Weekly Activity in Gaza', fontsize=16, x=0.3, y=0.95);"
+    "fig.text(s=\"Source: Mapbox Movement\", x=0.2, y=0, fontsize=11)\n",
+    "plt.suptitle(\"Comparing Weekly Activity in Gaza\", fontsize=16, x=0.3, y=0.95);"
    ]
   },
   {

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"
@@ -2032,10 +2030,12 @@
    ],
    "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",
@@ -2050,11 +2050,12 @@
     "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",

notebooks/sample-indicators/sample-indicators.md

@@ -1 +1 @@
-# Combining insights from multiple data sources for Gaza
+# Combining insights from multiple data sources for Gaza

pyproject.toml

@@ -1,47 +1,60 @@
 [build-system]
-requires = ["setuptools>=61.0.0"]
-build-backend = "setuptools.build_meta"
+requires = ["hatchling>=1.21.0", "hatch-vcs>=0.3.0"]
+build-backend = "hatchling.build"
 
 [project]
-name = "gaza_israel_conflict_analysis"
-dynamic = ["version"]
-description = "An Python package to support the Gaza Israel Conflict Impact Analysis"
+name = "gaza-israel-conflict-impact-analysis"
+description = "A Python package to support the Gaza Israel Conflict Impact Analysis"
 readme = { file = "README.md", content-type = "text/markdown" }
 license = { file = "LICENSE" }
 keywords = [
-	"world bank",
-	"reproducible research",
-	"conflict monitor",
 	"gaza",
+	"reproducible research",
+	"world bank",
 ]
 authors = [
-	{ name = "World Bank Development Data Group", email = "datalab@worldbank.org" },
+	{ name = "World Bank Data Lab", email = "datalab@worldbank.org" },
 ]
 maintainers = [
 	{ name = "Gabriel Stefanini Vicente", email = "gvicente@worldbank.org" },
 	{ name = "Sahiti Sarva", email = "ssarva@worldbank.org" },
 ]
 classifiers = [
 	"Programming Language :: Python :: 3",
-	"Programming Language :: Python :: 3.7",
-	"Programming Language :: Python :: 3.8",
-	"Programming Language :: Python :: 3.9",
 	"Programming Language :: Python :: 3.10",
 	"Programming Language :: Python :: 3.11",
 	"Programming Language :: Python :: 3 :: Only",
-	"Development Status :: 3 - Alpha",
-	"Intended Audience :: Developers",
+	"License :: OSI Approved :: Mozilla Public License 2.0 (MPL 2.0)",
 	"Operating System :: OS Independent",
+	"Intended Audience :: Developers",
+	"Topic :: Scientific/Engineering",
 ]
-requires-python = ">=3.7"
+dynamic = ["version"]
+
+requires-python = ">=3.10"
 dependencies = ["pandas>=2"]
 [project.optional-dependencies]
 notebook = ["notebook>=6.5.2"]
+docs = [
+	"docutils==0.17.1",    # https://jupyterbook.org/en/stable/content/citations.html?highlight=docutils#citations-and-bibliographies
+	"jupyter-book>=1,<2",
+]
 
 [project.urls]
 "Homepage" = "https://github.com/datapartnership/gaza-israel-conflict-impact-analysis"
 "Bug Reports" = "https://github.com/datapartnership/gaza-israel-conflict-impact-analysis/issues"
 "Source" = "https://github.com/datapartnership/gaza-israel-conflict-impact-analysis"
 
-[tool.setuptools.dynamic]
-version = { attr = "gaza_israel_conflict_analysis.__version__" }
+[tool.codespell]
+skip = 'docs/_build,docs/references.bib,*.png,*.gz,*.whl'
+ignore-regex = '^\s*"image\/png":\s.*'
+ignore-words-list = "gost,"
+
+[tool.hatch.build.targets.wheel]
+packages = ["src/gaza_israel_conflict_impact_analysis"]
+
+[tool.hatch.version]
+source = "vcs"
+
+[tool.ruff.lint.pydocstyle]
+convention = "numpy"

reports/20240317/README.md

@@ -38,4 +38,4 @@ The following is a comparative visualization that contrasts the overall estimate
 |- | -|
 |![buildings-20231025_dark](../20240202/buildings-20231025_dark.png) | ![buildings-20240317](buildings-20230317_dark.png)|
 
-> Estimated damage inflicted to buildings in the Gaza Strip utilizing SAR imagery to evaluate the extent and geographical distribution of estimated destruction. Each bar represents a region, with height indicating the number of affected buildings and color denoting the intensity of estimated damage on scale from 0 to 1, from yellow to red, where 1 signifies total damage.
+> Estimated damage inflicted to buildings in the Gaza Strip utilizing SAR imagery to evaluate the extent and geographical distribution of estimated destruction. Each bar represents a region, with height indicating the number of affected buildings and color denoting the intensity of estimated damage on scale from 0 to 1, from yellow to red, where 1 signifies total damage.

src/gaza_israel_conflict_impact_analysis/__init__.py

@@ -0,0 +1,7 @@
+from importlib.metadata import PackageNotFoundError, version
+
+try:
+    __version__ = version("gaza-israel-conflict-impact-analysis")
+except PackageNotFoundError:
+    # package is not installed
+    pass