[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Author: pre-commit-ci[bot]

docs/_toc.yml

@@ -46,7 +46,7 @@ parts:
     chapters:
       - file: notebooks/conflict/acled-update-october24.ipynb
       - file: notebooks/aviation/aviation_update.ipynb
-      - file: notebooks/vegetation-conditions/ChangeMaps.ipynb  
+      - file: notebooks/vegetation-conditions/ChangeMaps.ipynb
       - file: notebooks/syria-forest-cover/2023-summer-tree-cover-loss.md
         sections:
         - file: notebooks/syria-forest-cover/syria_forest.ipynb

node_modules/dotenv/README-es.md

@@ -38,11 +38,7 @@
     "import pandas as pd\n",
     "import folium\n",
     "import h3.api.numpy_int as h3int\n",
-    "from shapely.geometry import mapping, Polygon\n",
-    "\n",
-    "import os\n",
-    "import subprocess\n",
-    "import sys"
+    "from shapely.geometry import mapping, Polygon"
    ]
   },
   {
@@ -249,43 +245,25 @@
    "outputs": [],
    "source": [
     "tartus_geojson = {\n",
-    "  \"type\": \"FeatureCollection\",\n",
-    "  \"features\": [\n",
-    "    {\n",
-    "      \"type\": \"Feature\",\n",
-    "      \"properties\": {\n",
-    "        \"Port_name\": \"Tartus\",\n",
-    "        \"Country\": \"Syria\"\n",
-    "      },\n",
-    "      \"geometry\": {\n",
-    "        \"coordinates\": [\n",
-    "          [\n",
-    "            [\n",
-    "              35.85288336140917,\n",
-    "              34.91038874399062\n",
-    "            ],\n",
-    "            [\n",
-    "              35.85682749616299,\n",
-    "              34.899312694758734\n",
-    "            ],\n",
-    "            [\n",
-    "              35.87511294210401,\n",
-    "              34.89617739672141\n",
-    "            ],\n",
-    "            [\n",
-    "              35.876428488429326,\n",
-    "              34.91891484062019\n",
-    "            ],\n",
-    "            [\n",
-    "              35.85288336140917,\n",
-    "              34.91038874399062\n",
-    "            ]\n",
-    "          ]\n",
-    "        ],\n",
-    "        \"type\": \"Polygon\"\n",
-    "      }\n",
-    "    }\n",
-    "  ]\n",
+    "    \"type\": \"FeatureCollection\",\n",
+    "    \"features\": [\n",
+    "        {\n",
+    "            \"type\": \"Feature\",\n",
+    "            \"properties\": {\"Port_name\": \"Tartus\", \"Country\": \"Syria\"},\n",
+    "            \"geometry\": {\n",
+    "                \"coordinates\": [\n",
+    "                    [\n",
+    "                        [35.85288336140917, 34.91038874399062],\n",
+    "                        [35.85682749616299, 34.899312694758734],\n",
+    "                        [35.87511294210401, 34.89617739672141],\n",
+    "                        [35.876428488429326, 34.91891484062019],\n",
+    "                        [35.85288336140917, 34.91038874399062],\n",
+    "                    ]\n",
+    "                ],\n",
+    "                \"type\": \"Polygon\",\n",
+    "            },\n",
+    "        }\n",
+    "    ],\n",
     "}"
    ]
   },
@@ -297,7 +275,7 @@
    "outputs": [],
    "source": [
     "tartus_df = (\n",
-    "    gpd.GeoDataFrame.from_features(tartus_geojson) # json.loads(chokepoints_str)\n",
+    "    gpd.GeoDataFrame.from_features(tartus_geojson)  # json.loads(chokepoints_str)\n",
     "    .rename(columns={\"geometry\": \"port_boundary\"})\n",
     "    .set_geometry(\"port_boundary\")\n",
     "    .set_crs(crs=\"epsg:4326\")\n",
@@ -613,7 +591,7 @@
     "# [\n",
     "#     (port_df.loc[i][\"Port\"], mapping(port_df.loc[i].port_boundary))\n",
     "#     for i in range(port_df.shape[0])\n",
-    "# ] + \n",
+    "# ] +\n",
     "\n",
     "\n",
     "port_df_hex = af.polygon_to_hex_df(input_polygons, h3_resolution)\n",

node_modules/dotenv/README.md

@@ -74,10 +74,7 @@
    },
    "outputs": [],
    "source": [
-    "import pandas as pd\n",
-    "import geopandas as gpd\n",
-    "\n",
-    "import json"
+    "import pandas as pd"
    ]
   },
   {
@@ -295,10 +292,12 @@
    "source": [
     "s3path = \"s3a://ungp-ais-data-historical-backup/user_temp/\"\n",
     "wb_path = f\"{s3path}worldbank/\"\n",
-    "imf = pd.read_pickle(wb_path + \"imf_port_boundary.pkl\") \\\n",
-    "            .set_crs(crs=\"epsg:4326\") \\\n",
-    "            .rename(columns={'geometry':'port_boundary'}) \\\n",
-    "            .set_geometry('port_boundary')"
+    "imf = (\n",
+    "    pd.read_pickle(wb_path + \"imf_port_boundary.pkl\")\n",
+    "    .set_crs(crs=\"epsg:4326\")\n",
+    "    .rename(columns={\"geometry\": \"port_boundary\"})\n",
+    "    .set_geometry(\"port_boundary\")\n",
+    ")"
    ]
   },
   {
@@ -2604,7 +2603,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "next_df = pd.read_pickle(portcall_path_next+\"2019_2024.pkl\")"
+    "next_df = pd.read_pickle(portcall_path_next + \"2019_2024.pkl\")"
    ]
   },
   {
@@ -3176,8 +3175,7 @@
     }
    ],
    "source": [
-    "port_calls_v2 = port_calls.merge(next_df,\n",
-    "                how='left')\n",
+    "port_calls_v2 = port_calls.merge(next_df, how=\"left\")\n",
     "port_calls_v2.info()"
    ]
   },

notebooks/ais-analysis/fall-2024/01-data-extraction.ipynb

@@ -146,9 +146,7 @@
    "id": "84792c7f",
    "metadata": {},
    "outputs": [],
-   "source": [
-    "from os.path import join, expanduser"
-   ]
+   "source": []
   },
   {
    "cell_type": "code",
@@ -1990,8 +1988,8 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "group_cols = [\"Country\", \"Port\", \"year\", \"month\", \"ym\", \"Type\"] # \"ShipTypeLevel2\"\n",
-    "data_cols = ['trade_flow_final_abs']"
+    "group_cols = [\"Country\", \"Port\", \"year\", \"month\", \"ym\", \"Type\"]  # \"ShipTypeLevel2\"\n",
+    "data_cols = [\"trade_flow_final_abs\"]"
    ]
   },
   {
@@ -2011,7 +2009,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df_all = df.groupby([\"Country\", \"year\", \"month\", \"ym\", \"Type\"])[data_cols].sum().reset_index().assign(Port=\"Syria\")"
+    "df_all = (\n",
+    "    df.groupby([\"Country\", \"year\", \"month\", \"ym\", \"Type\"])[data_cols]\n",
+    "    .sum()\n",
+    "    .reset_index()\n",
+    "    .assign(Port=\"Syria\")\n",
+    ")"
    ]
   },
   {
@@ -2031,7 +2034,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df_all = df_all.pivot_table(index=[\"Country\", \"Port\", \"year\", \"month\", \"ym\"], columns='Type', values='trade_flow_final_abs').reset_index().fillna(0)\n",
+    "df_all = (\n",
+    "    df_all.pivot_table(\n",
+    "        index=[\"Country\", \"Port\", \"year\", \"month\", \"ym\"],\n",
+    "        columns=\"Type\",\n",
+    "        values=\"trade_flow_final_abs\",\n",
+    "    )\n",
+    "    .reset_index()\n",
+    "    .fillna(0)\n",
+    ")\n",
     "# .to_csv(\"./Trade_Estimation_Syria_Monthly.csv\", index=False)"
    ]
   },
@@ -2073,7 +2084,18 @@
    "outputs": [],
    "source": [
     "# count unique mmsi per year and ship type\n",
-    "df_count = df.groupby(['year', 'ShipTypeLevel2', 'ShiptypeLevel5', ])[['mmsi']].nunique().reset_index().rename(columns={'mmsi':'unique_mmsi'})"
+    "df_count = (\n",
+    "    df.groupby(\n",
+    "        [\n",
+    "            \"year\",\n",
+    "            \"ShipTypeLevel2\",\n",
+    "            \"ShiptypeLevel5\",\n",
+    "        ]\n",
+    "    )[[\"mmsi\"]]\n",
+    "    .nunique()\n",
+    "    .reset_index()\n",
+    "    .rename(columns={\"mmsi\": \"unique_mmsi\"})\n",
+    ")"
    ]
   },
   {
@@ -2083,12 +2105,15 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df_count.rename(columns={\n",
-    "    'year':'Year',\n",
-    "    'ShipTypeLevel2':'Ship Type Level 2',\n",
-    "    'ShiptypeLevel5':'Ship Type Level 5',\n",
-    "    'unique_mmsi':'No. of vessels'\n",
-    "}, inplace=True)"
+    "df_count.rename(\n",
+    "    columns={\n",
+    "        \"year\": \"Year\",\n",
+    "        \"ShipTypeLevel2\": \"Ship Type Level 2\",\n",
+    "        \"ShiptypeLevel5\": \"Ship Type Level 5\",\n",
+    "        \"unique_mmsi\": \"No. of vessels\",\n",
+    "    },\n",
+    "    inplace=True,\n",
+    ")"
    ]
   },
   {
@@ -2098,7 +2123,16 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "table = df_count.pivot(index=['Ship Type Level 2', 'Ship Type Level 5'], columns='Year', values='No. of vessels').fillna(0).reset_index().to_markdown(tablefmt=\"github\", index=False)"
+    "table = (\n",
+    "    df_count.pivot(\n",
+    "        index=[\"Ship Type Level 2\", \"Ship Type Level 5\"],\n",
+    "        columns=\"Year\",\n",
+    "        values=\"No. of vessels\",\n",
+    "    )\n",
+    "    .fillna(0)\n",
+    "    .reset_index()\n",
+    "    .to_markdown(tablefmt=\"github\", index=False)\n",
+    ")"
    ]
   },
   {

notebooks/ais-analysis/fall-2024/03-next-draft.ipynb

@@ -92,4 +92,4 @@ The vessel-level data can be accessed via this [link](https://worldbankgroup.sha
 ## References
 
 ```{bibliography}
-```
+```