Add section on variable naming

exercises/00_final/precipitation_climatology.py

@@ -2,7 +2,7 @@
 
 import json
 import numpy as np
-import matplotlib.pyplot as plt
+import matplotlib.pyplot as plot
 import matplotlib.ticker as mticker
 import xarray as xr
 import cartopy.crs as ccrs
@@ -12,34 +12,39 @@
 import regionmask
 
 
-def convert_pr_units(darray):
+def convert_precipitation_units(precipitation_in_kg_per_m_squared_s):
     """
     Convert precipitation units from [kg m-2 s-1] to [mm day-1].
 
     Parameters
     ----------
-    darray : xarray.DataArray
+    precipitation_in_kg_per_m_squared_s : xarray.DataArray
         xarray DataArray containing model precipitation data
 
     Returns
     -------
-    darray : xarray.DataArray
+    precipitation_in_mm_per_day : xarray.DataArray
         the input DataArray with precipitation units modified
     """
     # density 1000 kg m-3 => 1 kg m-2 == 1 mm
     # There are 60*60*24 = 86400 seconds per day
-    darray.data = darray.data * 86400
-    darray.attrs["units"] = "mm/day"
+    precipitation_in_mm_per_day = xr.DataArray(precipitation_in_kg_per_m_squared_s * 86400)
 
-    assert (
-        darray.data.min() >= 0.0
-    ), "There is at least one negative precipitation value"
-    assert darray.data.max() < 2000, "There is a precipitation value/s > 2000 mm/day"
+    precipitation_in_mm_per_day.attrs["units"] = "mm/day"
 
-    return darray
+    if precipitation_in_mm_per_day.data.min() < 0.0:
+        print("There is at least one negative precipitation value")
+    if precipitation_in_mm_per_day.data.max() > 2000:
+        print("There is a precipitation value/s > 2000 mm/day")
 
+    return precipitation_in_mm_per_day
 
-def plot_zonal(data):
+
+# I think it would be good to give more detail here about what the dimensions and
+# content of the input array needs to be, to save the reader having to go through
+# the code and infer it in order to use the method. Would it also be possible to give
+# the input variable a more specific name?
+def plot_zonally_averaged_precipitation(data):
     """
     Plot zonally-averaged precipitation data and save to file.
 
@@ -53,29 +58,30 @@ def plot_zonal(data):
     None
 
     """
-    zonal_pr = data["pr"].mean("lon", keep_attrs=True)
+    zonal_precipitation = data["precipitation"].mean("longitude", keep_attrs=True)
 
-    fig, ax = plt.subplots(nrows=4, ncols=1, figsize=(12, 8))
+    figure, axes = plot.subplots(nrows=4, ncols=1, figsize=(12, 8))
 
-    zonal_pr.sel(lat=[0]).plot.line(ax=ax[0], hue="lat")
-    zonal_pr.sel(lat=[-20, 20]).plot.line(ax=ax[1], hue="lat")
-    zonal_pr.sel(lat=[-45, 45]).plot.line(ax=ax[2], hue="lat")
-    zonal_pr.sel(lat=[-70, 70]).plot.line(ax=ax[3], hue="lat")
+    zonal_precipitation.sel(lat=[0]).plot.line(ax=axes[0], hue="latitude")
+    zonal_precipitation.sel(lat=[-20, 20]).plot.line(ax=axes[1], hue="latitude")
+    zonal_precipitation.sel(lat=[-45, 45]).plot.line(ax=axes[2], hue="latitude")
+    zonal_precipitation.sel(lat=[-70, 70]).plot.line(ax=axes[3], hue="latitude")
 
-    plt.tight_layout()
-    for axis in ax:
+    plot.tight_layout()
+    for axis in axes:
         axis.set_ylim(0.0, 1.0e-4)
         axis.grid()
-    plt.savefig("zonal.png", dpi=200)  # Save figure to file
-
-    fig, ax = plt.subplots(nrows=1, ncols=1, figsize=(12, 5))
+    plot.savefig("zonal.png", dpi=200)  # Save figure to file
 
-    zonal_pr.T.plot()
+    figure, axes = plot.subplots(nrows=1, ncols=1, figsize=(12, 5))
 
-    plt.savefig("zonal_map.png", dpi=200)  # Save figure to file
+    zonal_precipitation.T.plot()
 
+    plot.savefig("zonal_map.png", dpi=200)  # Save figure to file
 
-def get_country_ann_avg(data, countries):
+# could data be named more specifically and more detail be given in the docstring about
+# what the dimension and contents of the array are?
+def get_country_annual_average(data, countries):
     """
     Calculate annual precipitation averages for countries and save to file.
 
@@ -92,44 +98,30 @@ def get_country_ann_avg(data, countries):
     None
 
     """
-    data_avg = data["pr"].groupby("time.year").mean("time", keep_attrs=True)
-    data_avg = convert_pr_units(data_avg)
+    data_average = data["precipitation"].groupby("time.year").mean("time",
+                                                                   keep_attrs=True)
+    data_average = convert_precipitation_units(data_average)
 
-    land = regionmask.defined_regions.natural_earth_v5_0_0.countries_110.mask(data_avg)
+    # would it be possible to give land a more specific name?
+    land = (regionmask.
+            defined_regions.
+            natural_earth_v5_0_0
+            .countries_110.mask(data_average))
 
     with open("data.txt", "w", encoding="utf-8") as datafile:
+        # could k and v be named more specifically?
         for k, v in countries.items():
-            data_avg_mask = data_avg.where(land.cf == v)
-
-            # Debugging - plot countries to make sure mask works correctly
-            # fig, geo_axes = plt.subplots(nrows=1, ncols=1, figsize=(12,5),
-            #                 subplot_kw={'projection': ccrs.PlateCarree(central_longitude=180)})
-            # data_avg_mask.sel(year = 2010).plot.contourf(ax=geo_axes,
-            #                                       extend='max',
-            #                                       transform=ccrs.PlateCarree(),
-            #                                       cbar_kwargs={'label': data_avg_mask.units},
-            #                                       cmap=cmocean.cm.haline_r)
-            # geo_axes.add_feature(cfeature.COASTLINE, lw=0.5)
-            # gl = geo_axes.gridlines(crs=ccrs.PlateCarree(), draw_labels=True,
-            # linewidth=2, color='gray', alpha=0.5, linestyle='--')
-            # gl.top_labels = False
-            # gl.left_labels = True
-            # gl.xlocator = mticker.FixedLocator([-180, -90, 0, 90])
-            # gl.ylocator = mticker.FixedLocator([-66, -23, 0, 23, 66])
-            # gl.xformatter = LONGITUDE_FORMATTER
-            # gl.yformatter = LATITUDE_FORMATTER
-            # gl.xlabel_style = {'size': 15, 'color': 'gray'}
-            # gl.ylabel_style = {'size': 15, 'color': 'gray'}
-            # print("show %s" %k)
-            # plt.show()
-
-            for yr in data_avg_mask.year.values:
-                precip = data_avg_mask.sel(year=yr).mean().values
-                datafile.write(f"{k.ljust(25)} {yr} : {precip:2.3f} mm/day\n")
+            data_avg_mask = data_average.where(land.cf == v)
+
+            for year in data_avg_mask.year.values:
+                precipitation = data_avg_mask.sel(year=year).mean().values
+                datafile.write(f"{k.ljust(25)} {year} : {precipitation:2.3f} mm/day\n")
             datafile.write("\n")
 
 
-def plot_enso(data):
+# could data be named more specifically and more detail be given in the docstring about
+# what the dimension and contents of the array are?
+def plot_hovmoller_diagram(data):
     """
     Plot Hovmöller diagram of equatorial precipitation to visualise ENSO.
 
@@ -144,31 +136,32 @@ def plot_enso(data):
 
     """
     enso = (
-        data["pr"]
+        data["precipitation"]
         .sel(lat=slice(-1, 1))
         .sel(lon=slice(120, 280))
-        .mean(dim="lat", keep_attrs=True)
+        .mean(dim="latitude", keep_attrs=True)
     )
 
     enso.plot()
-    plt.savefig("enso.png", dpi=200)  # Save figure to file
+    plot.savefig("enso.png", dpi=200)  # Save figure to file
 
 
-def create_plot(clim, model, season, mask=None, gridlines=False, levels=None):
+def create_precipitation_climatology_plot(climatology_data, model_name, season, mask=None, plot_gridlines=False, levels=None):
     """
     Plot the precipitation climatology.
 
     Parameters
     ----------
-    clim : xarray.DataArray
+    climatology_data : xarray.DataArray
         Precipitation climatology data
-    model : str
+    model_name : str
         Name of the climate model
     season : str
         Climatological season (one of DJF, MAM, JJA, SON)
     mask : optional str
         mask to apply to plot (one of "land" or "ocean")
-    gridlines : bool
+    plot_gridlines : bool
+
         Select whether to plot gridlines
     levels : list
         Tick mark values for the colorbar
@@ -181,19 +174,19 @@ def create_plot(clim, model, season, mask=None, gridlines=False, levels=None):
     if not levels:
         levels = np.arange(0, 13.5, 1.5)
 
-    fig, geo_axes = plt.subplots(
+    fig, geo_axes = plot.subplots(
         nrows=1,
         ncols=1,
         figsize=(12, 5),
         subplot_kw={"projection": ccrs.PlateCarree(central_longitude=180)},
     )
 
-    clim.sel(season=season).plot.contourf(
+    climatology_data.sel(season=season).plot.contourf(
         ax=geo_axes,
         levels=levels,
         extend="max",
         transform=ccrs.PlateCarree(),
-        cbar_kwargs={"label": clim.units},
+        cbar_kwargs={"label": climatology_data.units},
         cmap=cmocean.cm.rain,
     )
 
@@ -211,52 +204,52 @@ def create_plot(clim, model, season, mask=None, gridlines=False, levels=None):
             alpha=0.75,
         )
 
-    if gridlines:
-        gl = geo_axes.gridlines(
+    if plot_gridlines:
+        gridlines = geo_axes.gridlines(
             crs=ccrs.PlateCarree(),
             draw_labels=True,
             linewidth=2,
             color="gray",
             alpha=0.5,
             linestyle="--",
         )
-        gl.top_labels = False
-        gl.left_labels = True
+        gridlines.top_labels = False
+        gridlines.left_labels = True
         # gl.xlines = False
-        gl.xlocator = mticker.FixedLocator([-180, -90, 0, 90, 180])
-        gl.ylocator = mticker.FixedLocator(
+        gridlines.xlocator = mticker.FixedLocator([-180, -90, 0, 90, 180])
+        gridlines.ylocator = mticker.FixedLocator(
             [-66, -23, 0, 23, 66]
         )  # Tropics & Polar Circles
-        gl.xformatter = LONGITUDE_FORMATTER
-        gl.yformatter = LATITUDE_FORMATTER
-        gl.xlabel_style = {"size": 15, "color": "gray"}
-        gl.ylabel_style = {"size": 15, "color": "gray"}
+        gridlines.xformatter = LONGITUDE_FORMATTER
+        gridlines.yformatter = LATITUDE_FORMATTER
+        gridlines.xlabel_style = {"size": 15, "color": "gray"}
+        gridlines.ylabel_style = {"size": 15, "color": "gray"}
 
-    title = f"{model} precipitation climatology ({season})"
-    plt.title(title)
+    title = f"{model_name} precipitation climatology ({season})"
+    plot.title(title)
 
 
 def main(
-    pr_file,
-    season="DJF",
-    output_file="output.png",
-    gridlines=False,
-    mask=None,
-    cbar_levels=None,
-    countries=None,
+        precipitation_netcdf_file,
+        season="DJF",
+        output_file="output.png",
+        plot_gridlines=False,
+        mask=None,
+        cbar_levels=None,
+        countries=None,
 ):
     """
     Run the program for producing precipitation plots.
 
     Parameters
     ----------
-    pr_file : str
+    precipitation_netcdf_file : str
         netCDF filename to read precipitation data from
     season : optional str
         Climatological season (one of DJF, MAM, JJA, SON)
     output_file : optional str
         filename to save main image to
-    gridlines : optional bool
+    plot_gridlines : optional bool
         Select whether to plot gridlines
     mask : optional str
         mask to apply to plot (one of "land" or "ocean")
@@ -274,38 +267,38 @@ def main(
     if countries is None:
         countries = {"United Kingdom": "GB"}
 
-    dset = xr.open_dataset(pr_file)
+    input_data = xr.open_dataset(precipitation_netcdf_file)
 
-    plot_zonal(dset)
-    plot_enso(dset)
-    get_country_ann_avg(dset, countries)
+    plot_zonally_averaged_precipitation(input_data)
+    plot_hovmoller_diagram(input_data)
+    get_country_annual_average(input_data, countries)
 
-    clim = dset["pr"].groupby("time.season").mean("time", keep_attrs=True)
+    climatology = input_data["precipitation"].groupby("time.season").mean("time", keep_attrs=True)
 
     try:
-        input_units = clim.attrs["units"]
+        input_units = climatology.attrs["units"]
     except KeyError as exc:
         raise KeyError(
             "Precipitation variable in {pr_file} must have a units attribute"
         ) from exc
 
     if input_units == "kg m-2 s-1":
-        clim = convert_pr_units(clim)
+        climatology = convert_precipitation_units(climatology)
     elif input_units == "mm/day":
         pass
     else:
         raise ValueError("""Input units are not 'kg m-2 s-1' or 'mm/day'""")
 
-    create_plot(
-        clim,
-        dset.attrs["source_id"],
+    create_precipitation_climatology_plot(
+        climatology,
+        input_data.attrs["source_id"],
         season,
         mask=mask,
-        gridlines=gridlines,
+        plot_gridlines=plot_gridlines,
         levels=cbar_levels,
     )
 
-    plt.savefig(output_file, dpi=200)
+    plot.savefig(output_file, dpi=200)
 
 
 if __name__ == "__main__":
@@ -316,8 +309,8 @@ def main(
         config_name = "default_config"
     else:
         print(f"Using configuration in '{config_name}.json'.")
-    configfile = config_name + ".json"
-    with open(configfile, encoding="utf-8") as json_file:
+    config_file = config_name + ".json"
+    with open(config_file, encoding="utf-8") as json_file:
         config = json.load(json_file)
 
     output_filename = f"{config_name}_output.png"
@@ -329,6 +322,6 @@ def main(
         season=config["season_to_plot"],
         output_file=output_filename,
         mask=config["mask_id"],
-        gridlines=config["gridlines_on"],
+        plot_gridlines=config["gridlines_on"],
         countries=config["countries_to_record"],
     )

exercises/05_better_code/README.md

@@ -18,6 +18,13 @@ Is the intent clearer?\
 Is the layout of the data written to file easier to understand?
 
 
+## Naming
+
+Look through the code for any names of methods or variables that could be improved or 
+clarified and update them. Note if you are using an IDE you can use automatic renaming.
+Does this make the code easier to follow?
+
+
 ## Configuration settings
 
 The original author of the code has helpfully put a list of the configurable