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Blacken notebooks #5890

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17 changes: 10 additions & 7 deletions doc/examples/ERA5-GRIB-example.ipynb
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Expand Up @@ -37,7 +37,7 @@
"metadata": {},
"outputs": [],
"source": [
"ds = xr.tutorial.load_dataset('era5-2mt-2019-03-uk.grib', engine='cfgrib')"
"ds = xr.tutorial.load_dataset(\"era5-2mt-2019-03-uk.grib\", engine=\"cfgrib\")"
]
},
{
Expand Down Expand Up @@ -72,11 +72,14 @@
"source": [
"import cartopy.crs as ccrs\n",
"import cartopy\n",
"fig = plt.figure(figsize=(10,10))\n",
"\n",
"fig = plt.figure(figsize=(10, 10))\n",
"ax = plt.axes(projection=ccrs.Robinson())\n",
"ax.coastlines(resolution='10m')\n",
"plot = ds.t2m[0].plot(cmap=plt.cm.coolwarm, transform=ccrs.PlateCarree(), cbar_kwargs={'shrink':0.6})\n",
"plt.title('ERA5 - 2m temperature British Isles March 2019')"
"ax.coastlines(resolution=\"10m\")\n",
"plot = ds.t2m[0].plot(\n",
" cmap=plt.cm.coolwarm, transform=ccrs.PlateCarree(), cbar_kwargs={\"shrink\": 0.6}\n",
")\n",
"plt.title(\"ERA5 - 2m temperature British Isles March 2019\")"
]
},
{
Expand All @@ -92,8 +95,8 @@
"metadata": {},
"outputs": [],
"source": [
"ds.t2m.sel(longitude=0,latitude=51.5).plot()\n",
"plt.title('ERA5 - London 2m temperature March 2019')"
"ds.t2m.sel(longitude=0, latitude=51.5).plot()\n",
"plt.title(\"ERA5 - London 2m temperature March 2019\")"
]
}
],
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23 changes: 13 additions & 10 deletions doc/examples/ROMS_ocean_model.ipynb
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Expand Up @@ -26,6 +26,7 @@
"import cartopy.crs as ccrs\n",
"import cartopy.feature as cfeature\n",
"import matplotlib.pyplot as plt\n",
"\n",
"%matplotlib inline\n",
"\n",
"import xarray as xr"
Expand Down Expand Up @@ -73,9 +74,9 @@
"outputs": [],
"source": [
"# load in the file\n",
"ds = xr.tutorial.open_dataset('ROMS_example.nc', chunks={'ocean_time': 1})\n",
"ds = xr.tutorial.open_dataset(\"ROMS_example.nc\", chunks={\"ocean_time\": 1})\n",
"\n",
"# This is a way to turn on chunking and lazy evaluation. Opening with mfdataset, or \n",
"# This is a way to turn on chunking and lazy evaluation. Opening with mfdataset, or\n",
"# setting the chunking in the open_dataset would also achive this.\n",
"ds"
]
Expand Down Expand Up @@ -105,12 +106,12 @@
"source": [
"if ds.Vtransform == 1:\n",
" Zo_rho = ds.hc * (ds.s_rho - ds.Cs_r) + ds.Cs_r * ds.h\n",
" z_rho = Zo_rho + ds.zeta * (1 + Zo_rho/ds.h)\n",
" z_rho = Zo_rho + ds.zeta * (1 + Zo_rho / ds.h)\n",
"elif ds.Vtransform == 2:\n",
" Zo_rho = (ds.hc * ds.s_rho + ds.Cs_r * ds.h) / (ds.hc + ds.h)\n",
" z_rho = ds.zeta + (ds.zeta + ds.h) * Zo_rho\n",
"\n",
"ds.coords['z_rho'] = z_rho.transpose() # needing transpose seems to be an xarray bug\n",
"ds.coords[\"z_rho\"] = z_rho.transpose() # needing transpose seems to be an xarray bug\n",
"ds.salt"
]
},
Expand Down Expand Up @@ -148,7 +149,7 @@
"outputs": [],
"source": [
"section = ds.salt.isel(xi_rho=50, eta_rho=slice(0, 167), ocean_time=0)\n",
"section.plot(x='lon_rho', y='z_rho', figsize=(15, 6), clim=(25, 35))\n",
"section.plot(x=\"lon_rho\", y=\"z_rho\", figsize=(15, 6), clim=(25, 35))\n",
"plt.ylim([-100, 1]);"
]
},
Expand All @@ -167,7 +168,7 @@
"metadata": {},
"outputs": [],
"source": [
"ds.salt.isel(s_rho=-1, ocean_time=0).plot(x='lon_rho', y='lat_rho')"
"ds.salt.isel(s_rho=-1, ocean_time=0).plot(x=\"lon_rho\", y=\"lat_rho\")"
]
},
{
Expand All @@ -186,11 +187,13 @@
"proj = ccrs.LambertConformal(central_longitude=-92, central_latitude=29)\n",
"fig = plt.figure(figsize=(15, 5))\n",
"ax = plt.axes(projection=proj)\n",
"ds.salt.isel(s_rho=-1, ocean_time=0).plot(x='lon_rho', y='lat_rho', \n",
" transform=ccrs.PlateCarree())\n",
"ds.salt.isel(s_rho=-1, ocean_time=0).plot(\n",
" x=\"lon_rho\", y=\"lat_rho\", transform=ccrs.PlateCarree()\n",
")\n",
"\n",
"coast_10m = cfeature.NaturalEarthFeature('physical', 'land', '10m',\n",
" edgecolor='k', facecolor='0.8')\n",
"coast_10m = cfeature.NaturalEarthFeature(\n",
" \"physical\", \"land\", \"10m\", edgecolor=\"k\", facecolor=\"0.8\"\n",
")\n",
"ax.add_feature(coast_10m)"
]
},
Expand Down
4 changes: 3 additions & 1 deletion doc/examples/apply_ufunc_vectorize_1d.ipynb
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Expand Up @@ -674,7 +674,9 @@
" exclude_dims=set((dim,)), # dimensions allowed to change size. Must be a set!\n",
" # vectorize=True, # not needed since numba takes care of vectorizing\n",
" dask=\"parallelized\",\n",
" output_dtypes=[data.dtype], # one per output; could also be float or np.dtype(\"float64\")\n",
" output_dtypes=[\n",
" data.dtype\n",
" ], # one per output; could also be float or np.dtype(\"float64\")\n",
" ).rename({\"__newdim__\": dim})\n",
" interped[dim] = newdim # need to add this manually\n",
"\n",
Expand Down
89 changes: 54 additions & 35 deletions doc/examples/monthly-means.ipynb
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Expand Up @@ -29,7 +29,7 @@
"import numpy as np\n",
"import pandas as pd\n",
"import xarray as xr\n",
"import matplotlib.pyplot as plt "
"import matplotlib.pyplot as plt"
]
},
{
Expand All @@ -50,7 +50,7 @@
},
"outputs": [],
"source": [
"ds = xr.tutorial.open_dataset('rasm').load()\n",
"ds = xr.tutorial.open_dataset(\"rasm\").load()\n",
"ds"
]
},
Expand Down Expand Up @@ -88,13 +88,15 @@
"outputs": [],
"source": [
"# Calculate the weights by grouping by 'time.season'.\n",
"weights = month_length.groupby('time.season') / month_length.groupby('time.season').sum()\n",
"weights = (\n",
" month_length.groupby(\"time.season\") / month_length.groupby(\"time.season\").sum()\n",
")\n",
"\n",
"# Test that the sum of the weights for each season is 1.0\n",
"np.testing.assert_allclose(weights.groupby('time.season').sum().values, np.ones(4))\n",
"np.testing.assert_allclose(weights.groupby(\"time.season\").sum().values, np.ones(4))\n",
"\n",
"# Calculate the weighted average\n",
"ds_weighted = (ds * weights).groupby('time.season').sum(dim='time')"
"ds_weighted = (ds * weights).groupby(\"time.season\").sum(dim=\"time\")"
]
},
{
Expand Down Expand Up @@ -123,7 +125,7 @@
"outputs": [],
"source": [
"# only used for comparisons\n",
"ds_unweighted = ds.groupby('time.season').mean('time')\n",
"ds_unweighted = ds.groupby(\"time.season\").mean(\"time\")\n",
"ds_diff = ds_weighted - ds_unweighted"
]
},
Expand All @@ -139,39 +141,54 @@
"outputs": [],
"source": [
"# Quick plot to show the results\n",
"notnull = pd.notnull(ds_unweighted['Tair'][0])\n",
"\n",
"fig, axes = plt.subplots(nrows=4, ncols=3, figsize=(14,12))\n",
"for i, season in enumerate(('DJF', 'MAM', 'JJA', 'SON')):\n",
" ds_weighted['Tair'].sel(season=season).where(notnull).plot.pcolormesh(\n",
" ax=axes[i, 0], vmin=-30, vmax=30, cmap='Spectral_r', \n",
" add_colorbar=True, extend='both')\n",
" \n",
" ds_unweighted['Tair'].sel(season=season).where(notnull).plot.pcolormesh(\n",
" ax=axes[i, 1], vmin=-30, vmax=30, cmap='Spectral_r', \n",
" add_colorbar=True, extend='both')\n",
"\n",
" ds_diff['Tair'].sel(season=season).where(notnull).plot.pcolormesh(\n",
" ax=axes[i, 2], vmin=-0.1, vmax=.1, cmap='RdBu_r',\n",
" add_colorbar=True, extend='both')\n",
"notnull = pd.notnull(ds_unweighted[\"Tair\"][0])\n",
"\n",
"fig, axes = plt.subplots(nrows=4, ncols=3, figsize=(14, 12))\n",
"for i, season in enumerate((\"DJF\", \"MAM\", \"JJA\", \"SON\")):\n",
" ds_weighted[\"Tair\"].sel(season=season).where(notnull).plot.pcolormesh(\n",
" ax=axes[i, 0],\n",
" vmin=-30,\n",
" vmax=30,\n",
" cmap=\"Spectral_r\",\n",
" add_colorbar=True,\n",
" extend=\"both\",\n",
" )\n",
"\n",
" ds_unweighted[\"Tair\"].sel(season=season).where(notnull).plot.pcolormesh(\n",
" ax=axes[i, 1],\n",
" vmin=-30,\n",
" vmax=30,\n",
" cmap=\"Spectral_r\",\n",
" add_colorbar=True,\n",
" extend=\"both\",\n",
" )\n",
"\n",
" ds_diff[\"Tair\"].sel(season=season).where(notnull).plot.pcolormesh(\n",
" ax=axes[i, 2],\n",
" vmin=-0.1,\n",
" vmax=0.1,\n",
" cmap=\"RdBu_r\",\n",
" add_colorbar=True,\n",
" extend=\"both\",\n",
" )\n",
"\n",
" axes[i, 0].set_ylabel(season)\n",
" axes[i, 1].set_ylabel('')\n",
" axes[i, 2].set_ylabel('')\n",
" axes[i, 1].set_ylabel(\"\")\n",
" axes[i, 2].set_ylabel(\"\")\n",
"\n",
"for ax in axes.flat:\n",
" ax.axes.get_xaxis().set_ticklabels([])\n",
" ax.axes.get_yaxis().set_ticklabels([])\n",
" ax.axes.axis('tight')\n",
" ax.set_xlabel('')\n",
" \n",
"axes[0, 0].set_title('Weighted by DPM')\n",
"axes[0, 1].set_title('Equal Weighting')\n",
"axes[0, 2].set_title('Difference')\n",
" \n",
" ax.axes.axis(\"tight\")\n",
" ax.set_xlabel(\"\")\n",
"\n",
"axes[0, 0].set_title(\"Weighted by DPM\")\n",
"axes[0, 1].set_title(\"Equal Weighting\")\n",
"axes[0, 2].set_title(\"Difference\")\n",
"\n",
"plt.tight_layout()\n",
"\n",
"fig.suptitle('Seasonal Surface Air Temperature', fontsize=16, y=1.02)"
"fig.suptitle(\"Seasonal Surface Air Temperature\", fontsize=16, y=1.02)"
]
},
{
Expand All @@ -186,18 +203,20 @@
"outputs": [],
"source": [
"# Wrap it into a simple function\n",
"def season_mean(ds, calendar='standard'):\n",
"def season_mean(ds, calendar=\"standard\"):\n",
" # Make a DataArray with the number of days in each month, size = len(time)\n",
" month_length = ds.time.dt.days_in_month\n",
"\n",
" # Calculate the weights by grouping by 'time.season'\n",
" weights = month_length.groupby('time.season') / month_length.groupby('time.season').sum()\n",
" weights = (\n",
" month_length.groupby(\"time.season\") / month_length.groupby(\"time.season\").sum()\n",
" )\n",
"\n",
" # Test that the sum of the weights for each season is 1.0\n",
" np.testing.assert_allclose(weights.groupby('time.season').sum().values, np.ones(4))\n",
" np.testing.assert_allclose(weights.groupby(\"time.season\").sum().values, np.ones(4))\n",
"\n",
" # Calculate the weighted average\n",
" return (ds * weights).groupby('time.season').sum(dim='time')"
" return (ds * weights).groupby(\"time.season\").sum(dim=\"time\")"
]
}
],
Expand Down
20 changes: 12 additions & 8 deletions doc/examples/multidimensional-coords.ipynb
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Original file line number Diff line number Diff line change
Expand Up @@ -48,7 +48,7 @@
},
"outputs": [],
"source": [
"ds = xr.tutorial.open_dataset('rasm').load()\n",
"ds = xr.tutorial.open_dataset(\"rasm\").load()\n",
"ds"
]
},
Expand Down Expand Up @@ -94,7 +94,7 @@
},
"outputs": [],
"source": [
"fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(14,4))\n",
"fig, (ax1, ax2) = plt.subplots(ncols=2, figsize=(14, 4))\n",
"ds.xc.plot(ax=ax1)\n",
"ds.yc.plot(ax=ax2)"
]
Expand Down Expand Up @@ -140,12 +140,14 @@
},
"outputs": [],
"source": [
"plt.figure(figsize=(14,6))\n",
"plt.figure(figsize=(14, 6))\n",
"ax = plt.axes(projection=ccrs.PlateCarree())\n",
"ax.set_global()\n",
"ds.Tair[0].plot.pcolormesh(ax=ax, transform=ccrs.PlateCarree(), x='xc', y='yc', add_colorbar=False)\n",
"ds.Tair[0].plot.pcolormesh(\n",
" ax=ax, transform=ccrs.PlateCarree(), x=\"xc\", y=\"yc\", add_colorbar=False\n",
")\n",
"ax.coastlines()\n",
"ax.set_ylim([0,90]);"
"ax.set_ylim([0, 90]);"
]
},
{
Expand All @@ -169,11 +171,13 @@
"outputs": [],
"source": [
"# define two-degree wide latitude bins\n",
"lat_bins = np.arange(0,91,2)\n",
"lat_bins = np.arange(0, 91, 2)\n",
"# define a label for each bin corresponding to the central latitude\n",
"lat_center = np.arange(1,90,2)\n",
"lat_center = np.arange(1, 90, 2)\n",
"# group according to those bins and take the mean\n",
"Tair_lat_mean = ds.Tair.groupby_bins('xc', lat_bins, labels=lat_center).mean(dim=xr.ALL_DIMS)\n",
"Tair_lat_mean = ds.Tair.groupby_bins(\"xc\", lat_bins, labels=lat_center).mean(\n",
" dim=xr.ALL_DIMS\n",
")\n",
"# plot the result\n",
"Tair_lat_mean.plot()"
]
Expand Down
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