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

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

carbonplan_forest_risks/load/cmip.py

@@ -37,7 +37,6 @@ def cmip(
     historical=False,
     remove_nans=False,
 ):
-
     with warnings.catch_warnings():
         warnings.simplefilter('ignore', category=ResourceWarning)
         warnings.simplefilter('ignore', category=FutureWarning)
@@ -50,7 +49,6 @@ def cmip(
         if member is None:
             member = members[model]
 
-
         path = setup.loading(store)
 
         prefix = f'cmip6/{method}/conus/4000m/{sampling}/{model}.{scenario}.{member}.zarr'

carbonplan_forest_risks/load/terraclim.py

@@ -24,7 +24,6 @@ def terraclim(
     group_repeats=False,
     remove_nans=False,
 ):
-
     with warnings.catch_warnings():
         warnings.simplefilter('ignore', category=ResourceWarning)
         warnings.simplefilter('ignore', category=FutureWarning)

carbonplan_forest_risks/load/tiff.py

@@ -46,7 +46,7 @@ def impacts(
     if met_data == 'cmip':
         for scenario in scenarios:
             impact_ds = xr.Dataset()
-            for (gcm, ensemble_member) in gcms:
+            for gcm, ensemble_member in gcms:
                 year_coords, impact_ds_list = [], []
                 for start_year, end_year in zip(start_years, end_years):
                     if start_year < 2005:

carbonplan_forest_risks/plot/fire.py

@@ -300,7 +300,6 @@ def full_eval(
     comparison=True,
     clabel=None,
 ):
-
     a = data[data_var].mean("time").values.flatten()
     b = model[model_var].mean("time").values.flatten()
     inds = ~np.isnan(a) & ~np.isnan(b)

carbonplan_forest_risks/plot/paper.py

@@ -89,7 +89,6 @@ def ts_pretty(ax, impact, ylims):
 
 
 def multipanel_ts(results_dict, region_bboxes, fig_path):
-
     gcms = [
         ("MRI-ESM2-0", (0, 0)),
         ("MIROC-ES2L", (1, 0)),
@@ -149,7 +148,6 @@ def multipanel_ts(results_dict, region_bboxes, fig_path):
 
 
 def plot_future_ts_traces(ax, ds, scenario, gcms):
-
     scenario_colors = {
         "ssp245": "#59A82F",
         "ssp370": "#D8B525",
@@ -163,7 +161,7 @@ def plot_future_ts_traces(ax, ds, scenario, gcms):
 
     ssp_rename = {"ssp245": "SSP2-4.5", "ssp370": "SSP3-7.0", "ssp585": "SSP5-8.5"}
 
-    for (gcm, location) in gcms:
+    for gcm, location in gcms:
         ds.sel(gcm=gcm, scenario=scenario).plot(ax=ax, color=scenario_colors_light[scenario])
 
     ds.sel(scenario=scenario).mean(dim="gcm").plot(

carbonplan_forest_risks/prepare.py

@@ -206,7 +206,6 @@ def insects(df, eval_only=False, duration=10):
         return x, meta
 
     else:
-
         fit_vars = [
             'ppt_sum_min_1',
             'tavg_mean_max_1',

carbonplan_forest_risks/utils.py

@@ -128,7 +128,7 @@ def weighted_mean(ds, *args, **kwargs):
 
 
 def get_store(bucket, prefix, account_key=None):
-    ''' helper function to create a zarr store'''
+    '''helper function to create a zarr store'''
 
     if account_key is None:
         account_key = os.environ.get('BLOB_ACCOUNT_KEY', None)

notebooks/fire/fire_model_redux.ipynb

@@ -763,9 +763,8 @@
    "source": [
     "downscaling = \"quantile-mapping\"\n",
     "sampling = \"monthly\"\n",
-    "for (model, member) in cmip_models:\n",
+    "for model, member in cmip_models:\n",
     "    for scenario in [\"historical\", \"ssp245\", \"ssp370\", \"ssp585\"]:\n",
-    "\n",
     "        prefix = f\"cmip6/{downscaling}/conus/4000m/{sampling}/{model}.{scenario}.{member}.zarr\".format()\n",
     "\n",
     "        if store == \"az\":\n",

notebooks/paper/Figure-2/Figure-2.ipynb

@@ -282,7 +282,7 @@
     "## Fire\n",
     "\n",
     "for scenario in [\"ssp245\", \"ssp370\", \"ssp585\"]:\n",
-    "    for (gcm, location) in gcms:\n",
+    "    for gcm, location in gcms:\n",
     "        future_ts[\"fire\"].probability.sel(gcm=gcm, scenario=scenario).sel(\n",
     "            year=slice(\"1970\", \"2099\")\n",
     "        ).plot(ax=axarr[0], color=scenario_colors_light[scenario], alpha=1)\n",
@@ -303,7 +303,7 @@
     "    )\n",
     "\n",
     "    for scenario in [\"ssp245\", \"ssp370\", \"ssp585\"]:\n",
-    "        for (gcm, location) in gcms:\n",
+    "        for gcm, location in gcms:\n",
     "            future_ts[impact].probability.sel(scenario=scenario, gcm=gcm).sel(\n",
     "                year=slice(1970, 2099)\n",
     "            ).plot(\n",

scripts/article_prep.py

@@ -73,7 +73,7 @@ def build_climate_cube(
     all_scenarios = []
     for scenario in scenarios:
         all_gcms = []
-        for (gcm, ensemble_member) in gcms:
+        for gcm, ensemble_member in gcms:
             cmip = load.cmip(
                 store='az',
                 model=gcm,
@@ -106,7 +106,7 @@ def repackage_drought_insects(ds):
     scenarios = ['ssp245', 'ssp370', 'ssp585']
 
     all_gcms = []
-    for (gcm, ensemble_member) in gcms:
+    for gcm, ensemble_member in gcms:
         all_gcms.append(
             ds[['{}-{}'.format(gcm, scenario) for scenario in scenarios]]
             .to_array(dim='scenario', name='probability')
@@ -129,7 +129,7 @@ def timeseries_dict(ds, time_period='historical'):
 
     mean = []
     gcm_dict = {}
-    for (gcm, ensemble_member) in gcms:
+    for gcm, ensemble_member in gcms:
         gcm_dict[gcm] = []
     if time_period == 'historical':
         years = np.arange(1980, 2020, 10)
@@ -144,7 +144,7 @@ def timeseries_dict(ds, time_period='historical'):
             }
         )
         # then populate all the gcms
-        for (gcm, ensemble_member) in gcms:
+        for gcm, ensemble_member in gcms:
             gcm_dict[gcm].append({'y': year, 'r': ds.sel(gcm=gcm, year=year).values.item()})
     return mean, gcm_dict
 

scripts/fire.py

@@ -124,7 +124,7 @@
     ('MPI-ESM1-2-LR', 'r10i1p1f1'),
 ]
 scenarios = ['ssp245', 'ssp370', 'ssp585']
-for (cmip_model, member) in cmip_models:
+for cmip_model, member in cmip_models:
     for scenario in tqdm(scenarios):
         results = []
         climate = load.cmip(