Bump actions/checkout from 2 to 4

.github/workflows/main.yaml

@@ -14,7 +14,7 @@ jobs:
       matrix:
         python-version: [3.8]
     steps:
-      - uses: actions/checkout@v2
+      - uses: actions/checkout@v4
       - uses: conda-incubator/setup-miniconda@v2
         with:
           mamba-version: "*"

notebooks/Figure-1.ipynb

@@ -15,11 +15,12 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import json\n",
     "import os\n",
+    "\n",
     "import fsspec\n",
-    "import json\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt"
+    "import matplotlib.pyplot as plt\n",
+    "import numpy as np"
    ]
   },
   {

notebooks/Figure-3.ipynb

@@ -15,13 +15,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import json\n",
     "import os\n",
+    "\n",
     "import fsspec\n",
-    "import json\n",
-    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
     "import numpy as np\n",
-    "import statsmodels.formula.api as smf\n",
-    "import matplotlib.pyplot as plt"
+    "import pandas as pd\n",
+    "import statsmodels.formula.api as smf"
    ]
   },
   {

notebooks/Figure-5.ipynb

@@ -15,12 +15,13 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import json\n",
     "import os\n",
+    "\n",
     "import fsspec\n",
-    "import json\n",
-    "import pandas as pd\n",
-    "import numpy as np\n",
     "import matplotlib.pyplot as plt\n",
+    "import numpy as np\n",
+    "import pandas as pd\n",
     "from matplotlib import ticker"
    ]
   },
@@ -93,9 +94,15 @@
     "    np.percentile(data[key][\"delta_arbocs\"], 75) / get(key)[\"arbocs\"][\"issuance\"]\n",
     "    for key in data.keys()\n",
     "]\n",
-    "df[\"absolute_low\"] = [np.percentile(data[key][\"delta_arbocs\"], 5) for key in data.keys()]\n",
-    "df[\"absolute_med\"] = [np.percentile(data[key][\"delta_arbocs\"], 50) for key in data.keys()]\n",
-    "df[\"absolute_high\"] = [np.percentile(data[key][\"delta_arbocs\"], 95) for key in data.keys()]\n",
+    "df[\"absolute_low\"] = [\n",
+    "    np.percentile(data[key][\"delta_arbocs\"], 5) for key in data.keys()\n",
+    "]\n",
+    "df[\"absolute_med\"] = [\n",
+    "    np.percentile(data[key][\"delta_arbocs\"], 50) for key in data.keys()\n",
+    "]\n",
+    "df[\"absolute_high\"] = [\n",
+    "    np.percentile(data[key][\"delta_arbocs\"], 95) for key in data.keys()\n",
+    "]\n",
     "df[\"project_size\"] = [get(key)[\"arbocs\"][\"issuance\"] for key in data.keys()]\n",
     "df[\"cp_slag\"] = [get(key)[\"carbon\"][\"common_practice\"][\"value\"] for key in data.keys()]\n",
     "df[\"alternate_slag\"] = [np.percentile(data[key][\"alt_slag\"], 50) for key in data.keys()]\n",
@@ -117,8 +124,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "def format_si(num, precision=0, suffixes=[\"\", \"K\", \"M\", \"G\", \"T\", \"P\"], show_suffix=False):\n",
-    "    m = sum([abs(num / 1000.0 ** x) >= 1 for x in range(1, len(suffixes))])\n",
+    "def format_si(\n",
+    "    num, precision=0, suffixes=[\"\", \"K\", \"M\", \"G\", \"T\", \"P\"], show_suffix=False\n",
+    "):\n",
+    "    m = sum([abs(num / 1000.0**x) >= 1 for x in range(1, len(suffixes))])\n",
     "    if show_suffix:\n",
     "        return f\"{num/1000.0**m:.{precision}f}{suffixes[m]}\"\n",
     "    else:\n",
@@ -154,7 +163,10 @@
     "    total.append(np.nansum([data[key][\"delta_arbocs\"][i] for key in data.keys()]))\n",
     "    total_arbocs = np.percentile(total, [5, 50, 95])\n",
     "    total_percentage = np.percentile(total, [5, 50, 95]) / np.sum(\n",
-    "        [[x for x in db if x[\"id\"] == key][0][\"arbocs\"][\"issuance\"] for key in data.keys()]\n",
+    "        [\n",
+    "            [x for x in db if x[\"id\"] == key][0][\"arbocs\"][\"issuance\"]\n",
+    "            for key in data.keys()\n",
+    "        ]\n",
     "    )"
    ]
   },
@@ -284,7 +296,9 @@
     "axs[1].vlines(df[\"id\"], -1, 1.5, color=(0.95, 0.95, 0.95), linewidth=1)\n",
     "axs[1].hlines([0], [0], [len(df) - 1], color=(0.75, 0.75, 0.75), linewidth=2)\n",
     "axs[1].plot(df[\"id\"], df[\"percent_med\"], \".\", color=\"#7EB36A\", markersize=12)\n",
-    "axs[1].vlines(df[\"id\"], df[\"percent_low\"], df[\"percent_high\"], color=\"black\", linewidth=1.25)\n",
+    "axs[1].vlines(\n",
+    "    df[\"id\"], df[\"percent_low\"], df[\"percent_high\"], color=\"black\", linewidth=1.25\n",
+    ")\n",
     "axs[1].set_xticks([])\n",
     "axs[1].set_ylim([-1.1, 1.1])\n",
     "axs[1].set_ylabel(\"Crediting error (%)\")\n",

notebooks/Figure-6.ipynb

@@ -15,9 +15,10 @@
    "metadata": {},
    "outputs": [],
    "source": [
+    "import json\n",
     "import os\n",
+    "\n",
     "import fsspec\n",
-    "import json\n",
     "import geopandas\n",
     "import matplotlib as mpl\n",
     "import matplotlib.pyplot as plt\n",
@@ -26,7 +27,6 @@
     "from mpl_toolkits.axes_grid1.inset_locator import inset_axes\n",
     "from shapely.geometry import Point\n",
     "\n",
-    "\n",
     "crs = \"+proj=aea +lat_0=23 +lon_0=-96 +lat_1=29.5 +lat_2=45.5 +x_0=0 +y_0=0 +datum=WGS84 +units=m +no_defs=True\""
    ]
   },
@@ -100,7 +100,9 @@
    "outputs": [],
    "source": [
     "proj_centroids = {\n",
-    "    project[\"opr_id\"]: Point(project[\"shape_centroid\"][0][0], project[\"shape_centroid\"][0][1])\n",
+    "    project[\"opr_id\"]: Point(\n",
+    "        project[\"shape_centroid\"][0][0], project[\"shape_centroid\"][0][1]\n",
+    "    )\n",
     "    for project in db\n",
     "    if (79 in project[\"supersection_ids\"])\n",
     "    and (\n",
@@ -119,7 +121,9 @@
    "outputs": [],
    "source": [
     "proj_points = geopandas.GeoDataFrame(\n",
-    "    data=list(proj_centroids.keys()), geometry=list(proj_centroids.values()), crs=\"epsg:4326\"\n",
+    "    data=list(proj_centroids.keys()),\n",
+    "    geometry=list(proj_centroids.values()),\n",
+    "    crs=\"epsg:4326\",\n",
     ")"
    ]
   },
@@ -214,9 +218,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "vmin, vmax = arbitrage_df.mean_local_slag.quantile(0.025), arbitrage_df.mean_local_slag.quantile(\n",
-    "    0.975\n",
-    ")\n",
+    "vmin, vmax = arbitrage_df.mean_local_slag.quantile(\n",
+    "    0.025\n",
+    "), arbitrage_df.mean_local_slag.quantile(0.975)\n",
     "\n",
     "norm = mpl.colors.Normalize(vmin, vmax)"
    ]
@@ -276,9 +280,13 @@
    "outputs": [],
    "source": [
     "supersection_sections = (\n",
-    "    ecomap_sections.loc[ecomap_sections[\"MAP_UNIT_S\"].isin(ecosections)].to_crs(crs).copy()\n",
+    "    ecomap_sections.loc[ecomap_sections[\"MAP_UNIT_S\"].isin(ecosections)]\n",
+    "    .to_crs(crs)\n",
+    "    .copy()\n",
+    ")\n",
+    "supersection_sections[\"slag\"] = supersection_sections[\"MAP_UNIT_S\"].map(\n",
+    "    slag_per_section\n",
     ")\n",
-    "supersection_sections[\"slag\"] = supersection_sections[\"MAP_UNIT_S\"].map(slag_per_section)\n",
     "supersection_outline = supersection_sections.dissolve(\"PROJECT\")"
    ]
   },
@@ -327,10 +335,14 @@
     "    vmax=55,\n",
     "    legend_kwds={\"label\": \"\", \"orientation\": \"vertical\"},\n",
     ")\n",
-    "cax.set_ylabel(\"$\\Delta$ Carbon\\n(tCO2e / acre)\", loc=\"center\", labelpad=-75, fontsize=12)\n",
+    "cax.set_ylabel(\n",
+    "    \"$\\\\Delta$ Carbon\\n(tCO2e / acre)\", loc=\"center\", labelpad=-75, fontsize=12\n",
+    ")\n",
     "cax.yaxis.set_ticks_position(\"none\")\n",
     "\n",
-    "proj_points.to_crs(crs).plot(ax=ax[0], marker=\"^\", color=\"k\", markersize=100, edgecolor=None)\n",
+    "proj_points.to_crs(crs).plot(\n",
+    "    ax=ax[0], marker=\"^\", color=\"k\", markersize=100, edgecolor=None\n",
+    ")\n",
     "supersection_outline.plot(ax=ax[0], edgecolor=\"k\", lw=0.2, color=\"None\")\n",
     "\n",
     "supersection_sections.plot(\n",
@@ -342,7 +354,9 @@
     "    vmin=-55,\n",
     "    vmax=55,\n",
     ")\n",
-    "proj_points.to_crs(crs).plot(ax=ax[1], marker=\"^\", color=\"k\", markersize=100, edgecolor=None)\n",
+    "proj_points.to_crs(crs).plot(\n",
+    "    ax=ax[1], marker=\"^\", color=\"k\", markersize=100, edgecolor=None\n",
+    ")\n",
     "\n",
     "\n",
     "xys = {\"M261D\": (0.7, 0.25), \"M261A\": (0.15, 0.8), \"M261B\": (0.4, 0.15)}\n",

notebooks/Inline-Statistics.ipynb

@@ -16,20 +16,18 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "from collections import Counter\n",
-    "import fsspec\n",
     "import json\n",
-    "import os\n",
     "\n",
-    "import matplotlib.pyplot as plt\n",
+    "import fsspec\n",
     "import numpy as np\n",
     "import pandas as pd\n",
     "import seaborn as sns\n",
-    "from sklearn.metrics import mean_squared_error\n",
-    "\n",
-    "from carbonplan_forest_offsets.analysis.project_crediting_error import get_slag_to_total_scalar\n",
+    "from carbonplan_forest_offsets.analysis.project_crediting_error import (\n",
+    "    get_slag_to_total_scalar,\n",
+    ")\n",
     "from carbonplan_forest_offsets.data import cat\n",
-    "from carbonplan_forest_offsets.load.issuance import load_issuance_table, ifm_opr_ids"
+    "from carbonplan_forest_offsets.load.issuance import ifm_opr_ids, load_issuance_table\n",
+    "from sklearn.metrics import mean_squared_error"
    ]
   },
   {
@@ -133,7 +131,8 @@
     "subsets = {\n",
     "    \"all\": np.tile(True, len(df)),\n",
     "    \"all_forest\": df[\"project_type\"] == \"forest\",\n",
-    "    \"compliance_ifm\": (df[\"opr_id\"].isin(ifm_opr_ids)) & (df[\"Early Action/ Compliance\"] == \"COP\"),\n",
+    "    \"compliance_ifm\": (df[\"opr_id\"].isin(ifm_opr_ids))\n",
+    "    & (df[\"Early Action/ Compliance\"] == \"COP\"),\n",
     "    \"non_graduated_compliance_ifms\": (df[\"opr_id\"].isin(compliance_opr_ids))\n",
     "    & (df[\"Early Action/ Compliance\"] == \"COP\"),\n",
     "    \"upfront_ifm\": (df[\"opr_id\"].isin(upfront_opr_ids)) & (df[\"arb_rp_id\"].isin([\"A\"])),\n",
@@ -202,7 +201,9 @@
    "source": [
     "sc_data = cat.rfia_all(assessment_area_id=297).read()\n",
     "\n",
-    "sc_data = sc_data[sc_data[\"YEAR\"] == 2010].copy()  # use 2010 because comporable to CP data\n",
+    "sc_data = sc_data[\n",
+    "    sc_data[\"YEAR\"] == 2010\n",
+    "].copy()  # use 2010 because comporable to CP data\n",
     "\n",
     "\n",
     "sc_data[\"CARB_ACRE\"] = sc_data[\"CARB_ACRE\"] * 44 / 12 * 0.907185"
@@ -237,7 +238,9 @@
    "source": [
     "standing_carbon = {}\n",
     "for k, v in fortyps_of_interest.items():\n",
-    "    standing_carbon[k] = round(sc_data.loc[sc_data[\"FORTYPCD\"] == v, \"CARB_ACRE\"].item(), 1)\n",
+    "    standing_carbon[k] = round(\n",
+    "        sc_data.loc[sc_data[\"FORTYPCD\"] == v, \"CARB_ACRE\"].item(), 1\n",
+    "    )\n",
     "display(standing_carbon)"
    ]
   },
@@ -258,7 +261,9 @@
    "source": [
     "# ak has three assessment areas but lets summarize across all to report inline value\n",
     "ak_assessment_areas = [285, 286, 287]\n",
-    "ak_all = pd.concat([cat.rfia_all(assessment_area_id=aa_id).read() for aa_id in ak_assessment_areas])\n",
+    "ak_all = pd.concat(\n",
+    "    [cat.rfia_all(assessment_area_id=aa_id).read() for aa_id in ak_assessment_areas]\n",
+    ")\n",
     "\n",
     "ak_all = ak_all[ak_all[\"YEAR\"] == 2013].copy()  # 2013 to match what used in CP\n",
     "\n",
@@ -340,7 +345,9 @@
     "    ).round(1)\n",
     "\n",
     "    as_frac = crediting_error / project[\"arbocs\"][\"calculated\"]\n",
-    "    print(f\"{project['opr_id']} has a {crediting_error[1]} crediting error ({as_frac[1].round(3)})\")\n",
+    "    print(\n",
+    "        f\"{project['opr_id']} has a {crediting_error[1]} crediting error ({as_frac[1].round(3)})\"\n",
+    "    )\n",
     "    print(f\"CI: {crediting_error[0]}, {crediting_error[2]}\")\n",
     "    print(f\"% CI: {as_frac[0].round(3)}, {as_frac[2].round(3)}\")"
    ]
@@ -886,7 +893,8 @@
     "projects = [\n",
     "    x\n",
     "    for x in db\n",
-    "    if x[\"carbon\"][\"initial_carbon_stock\"][\"value\"] > x[\"carbon\"][\"common_practice\"][\"value\"]\n",
+    "    if x[\"carbon\"][\"initial_carbon_stock\"][\"value\"]\n",
+    "    > x[\"carbon\"][\"common_practice\"][\"value\"]\n",
     "]"
    ]
   },
@@ -937,7 +945,7 @@
     }
    ],
    "source": [
-    "sum((cp_df[\"baseline\"] <= cp_df[\"cp\"] * 1.05)) / len(cp_df.dropna())"
+    "sum(cp_df[\"baseline\"] <= cp_df[\"cp\"] * 1.05) / len(cp_df.dropna())"
    ]
   },
   {
@@ -955,7 +963,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "fn = f\"https://carbonplan.blob.core.windows.net/carbonplan-forests/offsets/archive/results/common-practice-verification.json\"\n",
+    "fn = \"https://carbonplan.blob.core.windows.net/carbonplan-forests/offsets/archive/results/common-practice-verification.json\"\n",
     "with fsspec.open(fn, mode=\"r\") as f:\n",
     "    cp_verification = json.load(f)"
    ]
@@ -1000,7 +1008,9 @@
     }
    ],
    "source": [
-    "data = pd.DataFrame(cp_verification[\"projects\"])[[\"opr_id\", \"recalculated\", \"project_reported\"]]\n",
+    "data = pd.DataFrame(cp_verification[\"projects\"])[\n",
+    "    [\"opr_id\", \"recalculated\", \"project_reported\"]\n",
+    "]\n",
     "mean_squared_error(data[\"recalculated\"], data[\"project_reported\"]) ** 0.5"
    ]
   },
@@ -1011,7 +1021,9 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "data[\"diff\"] = (data[\"recalculated\"] - data[\"project_reported\"]) / (data[\"project_reported\"])\n",
+    "data[\"diff\"] = (data[\"recalculated\"] - data[\"project_reported\"]) / (\n",
+    "    data[\"project_reported\"]\n",
+    ")\n",
     "data = data[np.isfinite(data[\"diff\"])]  # CAR1186 = infite bc original CP = 0."
    ]
   },
@@ -1188,7 +1200,9 @@
     "\n",
     "crediting_df = pd.DataFrame({k: v[\"delta_arbocs\"] for k, v in crediting_error.items()})\n",
     "\n",
-    "median_crediting_error = {k: np.median(v[\"delta_arbocs\"]) for k, v in crediting_error.items()}"
+    "median_crediting_error = {\n",
+    "    k: np.median(v[\"delta_arbocs\"]) for k, v in crediting_error.items()\n",
+    "}"
    ]
   },
   {
@@ -1199,7 +1213,10 @@
    "outputs": [],
    "source": [
     "tp = pd.concat(\n",
-    "    [pd.Series(median_crediting_error).rename(\"crediting_error\"), error_cp0.rename(\"cp\")],\n",
+    "    [\n",
+    "        pd.Series(median_crediting_error).rename(\"crediting_error\"),\n",
+    "        error_cp0.rename(\"cp\"),\n",
+    "    ],\n",
     "    axis=1,\n",
     ")"
    ]
@@ -1332,7 +1349,9 @@
    "outputs": [],
    "source": [
     "ak_assessment_areas = [285, 286, 287]\n",
-    "ak_all = pd.concat([cat.rfia_all(assessment_area_id=aa_id).read() for aa_id in ak_assessment_areas])"
+    "ak_all = pd.concat(\n",
+    "    [cat.rfia_all(assessment_area_id=aa_id).read() for aa_id in ak_assessment_areas]\n",
+    ")"
    ]
   },
   {
@@ -1388,7 +1407,13 @@
     }
    ],
    "source": [
-    "sum([project[\"arbocs\"][\"issuance\"] for project in db if 287 in project[\"supersection_ids\"]])"
+    "sum(\n",
+    "    [\n",
+    "        project[\"arbocs\"][\"issuance\"]\n",
+    "        for project in db\n",
+    "        if 287 in project[\"supersection_ids\"]\n",
+    "    ]\n",
+    ")"
    ]
   },
   {