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Merged
brews merged 1 commit into from
Aug 22, 2026
Merged

Auto format with ruff #24

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299 changes: 187 additions & 112 deletions analysis/a00_seasonal_forecast_analysis.ipynb
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525 changes: 341 additions & 184 deletions analysis/a01_august_seasonal_forecast_results.ipynb
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64 changes: 35 additions & 29 deletions analysis/a03_systems_comp.ipynb
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Original file line number Diff line number Diff line change
@@ -1,4 +1,4 @@
{

Check failure on line 1 in analysis/a03_systems_comp.ipynb

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ruff (F821) 

analysis/a03_systems_comp.ipynb:1:1: F821 Undefined name `da_land_only`
"cells": [
{
"cell_type": "code",
Expand All @@ -19,17 +19,19 @@
"metadata": {},
"outputs": [],
"source": [
"centres = {'ecmwf': ['51'], \n",
" \"meteo_france\": ['9'], \n",
" 'ukmo': ['610'], \n",
" 'dwd': ['22'], \n",
" 'cmcc': ['4'], \n",
" 'ncep': ['2'], \n",
" 'jma': ['4'], \n",
" 'eccc': ['4','5'],\n",
" 'bom': ['2']}\n",
"centres = {\n",
" \"ecmwf\": [\"51\"],\n",
" \"meteo_france\": [\"9\"],\n",
" \"ukmo\": [\"610\"],\n",
" \"dwd\": [\"22\"],\n",
" \"cmcc\": [\"4\"],\n",
" \"ncep\": [\"2\"],\n",
" \"jma\": [\"4\"],\n",
" \"eccc\": [\"4\", \"5\"],\n",
" \"bom\": [\"2\"],\n",
"}\n",
"\n",
"region_sel = 'SAS'"
"region_sel = \"SAS\""
]
},
{
Expand All @@ -50,6 +52,7 @@
" \"fcstdev\": \"monthly_standard_deviation\",\n",
"}\n",
"\n",
"\n",
"def zip_to_datatree(zip_path, extract_dir):\n",
" with zipfile.ZipFile(zip_path) as z:\n",
" names = z.namelist()\n",
Expand All @@ -64,6 +67,7 @@
"\n",
" return xr.DataTree.from_dict(tree_dict)\n",
"\n",
"\n",
"def land_only(da):\n",
" land_mask = regionmask.defined_regions.natural_earth_v5_0_0.land_110.mask(da)\n",
" da_land_only = da.where(land_mask.notnull())\n",
Expand All @@ -80,10 +84,10 @@
"trees = {}\n",
"for centre, systems in centres.items():\n",
" for system in systems:\n",
" path = f'/home/emily_zuetell/projects/poreallas/scripts//data/raw/2607_{centre}_s{system}.nc'\n",
" path = f\"/home/emily_zuetell/projects/poreallas/scripts//data/raw/2607_{centre}_s{system}.nc\"\n",
" extract_dir = path.replace(\".nc\", \"_extracted\")\n",
"\n",
" var = f'{centre}_s{system}'\n",
" var = f\"{centre}_s{system}\"\n",
" trees[var] = zip_to_datatree(path, extract_dir)"
]
},
Expand All @@ -102,9 +106,11 @@
"metadata": {},
"outputs": [],
"source": [
"#TODO switch to xesmf\n",
"# TODO switch to xesmf\n",
"def regrid_to_target(da, target):\n",
" return da.interp(latitude=target.latitude, longitude=target.longitude, method=\"linear\")\n"
" return da.interp(\n",
" latitude=target.latitude, longitude=target.longitude, method=\"linear\"\n",
" )"
]
},
{
Expand Down Expand Up @@ -148,7 +154,7 @@
"metadata": {},
"outputs": [],
"source": [
"diff = combined_anomaly.sel(centre_system = 'ecmwf_s51')-combined_anomaly"
"diff = combined_anomaly.sel(centre_system=\"ecmwf_s51\") - combined_anomaly"
]
},
{
Expand All @@ -170,9 +176,8 @@
],
"source": [
"import cartopy.crs as ccrs\n",
"import matplotlib.pyplot as plt\n",
"\n",
"p = diff.sel(forecastMonth = 2).plot(\n",
"p = diff.sel(forecastMonth=2).plot(\n",
" col=\"centre_system\",\n",
" col_wrap=3,\n",
" vmin=-3,\n",
Expand All @@ -182,7 +187,7 @@
" subplot_kws={\"projection\": ccrs.PlateCarree()},\n",
" transform=ccrs.PlateCarree(),\n",
")\n",
"p.fig.suptitle('ForecastMonth = 2')\n",
"p.fig.suptitle(\"ForecastMonth = 2\")\n",
"for ax in p.axs.flat:\n",
" ax.coastlines()"
]
Expand All @@ -202,7 +207,7 @@
"metadata": {},
"outputs": [],
"source": [
"system_std = combined_anomaly.std('centre_system')"
"system_std = combined_anomaly.std(\"centre_system\")"
]
},
{
Expand Down Expand Up @@ -293,24 +298,25 @@
"outputs": [],
"source": [
"import pandas as pd\n",
"import regionmask\n",
"\n",
"# e.g. AR6 land regions\n",
"ar6_regions = regionmask.defined_regions.ar6.land\n",
"\n",
"\n",
"def select_region(da):\n",
" mask = ar6_regions.mask(da)\n",
" region_da = da.where(mask == ar6_regions.map_keys(region_sel))\n",
" return region_da\n",
"\n",
"\n",
"records = []\n",
"for centre_system, tree in trees.items(): # e.g. key = \"ecmwf_s51\"\n",
" da = tree[\"monthly_mean\"][\"t2m\"] \n",
" da = tree[\"monthly_mean\"][\"t2m\"]\n",
" da_hc = tree[\"hindcast_climate_mean\"][\"t2m\"]\n",
" #select region\n",
" # select region\n",
" da = select_region(da)\n",
" da_hc = select_region(da_hc)\n",
" df = (da-da_hc).to_dataframe().reset_index()\n",
" df = (da - da_hc).to_dataframe().reset_index()\n",
" df[\"centre_system\"] = centre_system\n",
" records.append(df[[\"centre_system\", \"forecastMonth\", \"t2m\"]])\n",
"\n",
Expand All @@ -328,11 +334,11 @@
"for centre_system, tree in trees.items():\n",
" da = tree[\"ensemble_mean\"][\"t2m\"]\n",
" da_hc = tree[\"hindcast_climate_mean\"][\"t2m\"]\n",
" #select region\n",
" # select region\n",
" da = select_region(da)\n",
" da_hc = select_region(da_hc)\n",
"\n",
" df = (da-da_hc).to_dataframe().reset_index()\n",
" df = (da - da_hc).to_dataframe().reset_index()\n",
" df[\"centre_system\"] = centre_system\n",
" records_em.append(df[[\"centre_system\", \"forecastMonth\", \"t2m\"]])\n",
"\n",
Expand Down Expand Up @@ -360,11 +366,11 @@
")\n",
"for ax, month in zip(g.axes.flat, g.col_names):\n",
" vals = em_df.loc[em_df[\"forecastMonth\"] == month, \"t2m\"]\n",
" #q1, med, q3 = vals.quantile([0.25, 0.5, 0.75])\n",
" #ax.axvspan(q1, q3, color=\"black\", alpha=0.15, zorder=10)\n",
" # q1, med, q3 = vals.quantile([0.25, 0.5, 0.75])\n",
" # ax.axvspan(q1, q3, color=\"black\", alpha=0.15, zorder=10)\n",
" ax.axvline(vals.mean(), color=\"black\", linewidth=1.5, zorder=11)\n",
" ax.set_xlabel('t2m anomaly')\n",
"g.fig.suptitle(f'T2M Anomaly: AR6 Region = {region_sel}', y=1.02)\n",
" ax.set_xlabel(\"t2m anomaly\")\n",
"g.fig.suptitle(f\"T2M Anomaly: AR6 Region = {region_sel}\", y=1.02)\n",
"g.fig"
]
},
Expand Down
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