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

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

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",
+ ")"
  ]
  },
  {