Merge pull request #36 from brews/flake8_cleanup

Bug fixes and cleanup to pass flake8 in CI

climate_toolbox/__init__.py

@@ -3,5 +3,5 @@
 """Top-level package for climate_toolbox."""
 
 __author__ = """Justin Simcock"""
-__email__ = '[email protected]'
-__version__ = '0.1.5'
+__email__ = "[email protected]"
+__version__ = "0.1.5"

climate_toolbox/aggregations/aggregations.py

@@ -19,29 +19,22 @@ def _reindex_spatial_data_to_regions(ds, df):
     """
 
     # use vectorized indexing in xarray >= 0.10
-    if LooseVersion(xr.__version__) > LooseVersion('0.9.999'):
+    if LooseVersion(xr.__version__) > LooseVersion("0.9.999"):
 
-        lon_indexer = xr.DataArray(df.lon.values, dims=('reshape_index', ))
-        lat_indexer = xr.DataArray(df.lat.values, dims=('reshape_index', ))
+        lon_indexer = xr.DataArray(df.lon.values, dims=("reshape_index",))
+        lat_indexer = xr.DataArray(df.lat.values, dims=("reshape_index",))
 
         return ds.sel(lon=lon_indexer, lat=lat_indexer)
 
     else:
-        res = ds.sel_points(
-            'reshape_index',
-            lat=df.lat.values,
-            lon=df.lon.values)
+        res = ds.sel_points("reshape_index", lat=df.lat.values, lon=df.lon.values)
 
         return res
 
 
 def _aggregate_reindexed_data_to_regions(
-        ds,
-        variable,
-        aggwt,
-        agglev,
-        weights,
-        backup_aggwt='areawt'):
+    ds, variable, aggwt, agglev, weights, backup_aggwt="areawt"
+):
     """
     Performs weighted avg for climate variable by region
 
@@ -69,39 +62,29 @@ def _aggregate_reindexed_data_to_regions(
     """
 
     ds.coords[agglev] = xr.DataArray(
-                weights[agglev].values,
-                dims={'reshape_index': weights.index.values})
+        weights[agglev].values, dims={"reshape_index": weights.index.values}
+    )
 
     # format weights
     ds[aggwt] = xr.DataArray(
-                weights[aggwt].values,
-                dims={'reshape_index': weights.index.values})
-
-    ds[aggwt] = (
-        ds[aggwt]
-        .where(ds[aggwt] > 0)
-        .fillna(weights[backup_aggwt].values))
-
-    weighted = xr.Dataset({
-        variable: (
-            (
-                (ds[variable]*ds[aggwt])
-                .groupby(agglev)
-                .sum(dim='reshape_index')) /
-            (
-                ds[aggwt]
-                .groupby(agglev)
-                .sum(dim='reshape_index')))})
+        weights[aggwt].values, dims={"reshape_index": weights.index.values}
+    )
+
+    ds[aggwt] = ds[aggwt].where(ds[aggwt] > 0).fillna(weights[backup_aggwt].values)
+
+    weighted = xr.Dataset(
+        {
+            variable: (
+                ((ds[variable] * ds[aggwt]).groupby(agglev).sum(dim="reshape_index"))
+                / (ds[aggwt].groupby(agglev).sum(dim="reshape_index"))
+            )
+        }
+    )
 
     return weighted
 
 
-def weighted_aggregate_grid_to_regions(
-        ds,
-        variable,
-        aggwt,
-        agglev,
-        weights=None):
+def weighted_aggregate_grid_to_regions(ds, variable, aggwt, agglev, weights=None):
     """
     Computes the weighted reshape of gridded data
 
@@ -136,12 +119,7 @@ def weighted_aggregate_grid_to_regions(
         weights = prepare_spatial_weights_data()
 
     ds = _reindex_spatial_data_to_regions(ds, weights)
-    ds = _aggregate_reindexed_data_to_regions(
-        ds,
-        variable,
-        aggwt,
-        agglev,
-        weights)
+    ds = _aggregate_reindexed_data_to_regions(ds, variable, aggwt, agglev, weights)
 
     return ds
 
@@ -163,14 +141,12 @@ def prepare_spatial_weights_data(weights_file):
     df = pd.read_csv(weights_file)
 
     # Re-label out-of-bounds pixel centers
-    df.set_value((df['pix_cent_x'] == 180.125), 'pix_cent_x', -179.875)
+    df.set_value((df["pix_cent_x"] == 180.125), "pix_cent_x", -179.875)
 
     # probably totally unnecessary
     df.drop_duplicates()
-    df.index.names = ['reshape_index']
+    df.index.names = ["reshape_index"]
 
-    df.rename(
-        columns={'pix_cent_x': 'lon', 'pix_cent_y': 'lat'},
-        inplace=True)
+    df.rename(columns={"pix_cent_x": "lon", "pix_cent_y": "lat"}, inplace=True)
 
     return df

climate_toolbox/climate_toolbox.py

@@ -1,4 +1,3 @@
 """
 This file describes the process for computing weighted climate data
 """
-

climate_toolbox/geo/distance.py

@@ -1,12 +1,14 @@
 import numpy as np
+
 #             model             major (km)   minor (km)     flattening
-ELLIPSOIDS = {'WGS-84':        (6378.137,    6356.7523142,  1 / 298.257223563),
-              'GRS-80':        (6378.137,    6356.7523141,  1 / 298.257222101),
-              'Airy (1830)':   (6377.563396, 6356.256909,   1 / 299.3249646),
-              'Intl 1924':     (6378.388,    6356.911946,   1 / 297.0),
-              'Clarke (1880)': (6378.249145, 6356.51486955, 1 / 293.465),
-              'GRS-67':        (6378.1600,   6356.774719,   1 / 298.25),
-              }
+ELLIPSOIDS = {
+    "WGS-84": (6378.137, 6356.7523142, 1 / 298.257223563),
+    "GRS-80": (6378.137, 6356.7523141, 1 / 298.257222101),
+    "Airy (1830)": (6377.563396, 6356.256909, 1 / 299.3249646),
+    "Intl 1924": (6378.388, 6356.911946, 1 / 297.0),
+    "Clarke (1880)": (6378.249145, 6356.51486955, 1 / 293.465),
+    "GRS-67": (6378.1600, 6356.774719, 1 / 298.25),
+}
 
 
 EARTH_RADIUS = 6371.009
@@ -57,9 +59,12 @@ def great_circle(ax, ay, bx, by, radius=EARTH_RADIUS):
     delta_lng = lng2 - lng1
     cos_delta_lng, sin_delta_lng = np.cos(delta_lng), np.sin(delta_lng)
 
-    d = np.arctan2(np.sqrt((cos_lat2 * sin_delta_lng) ** 2 +
-                   (cos_lat1 * sin_lat2 -
-                    sin_lat1 * cos_lat2 * cos_delta_lng) ** 2),
-              sin_lat1 * sin_lat2 + cos_lat1 * cos_lat2 * cos_delta_lng)
+    d = np.arctan2(
+        np.sqrt(
+            (cos_lat2 * sin_delta_lng) ** 2
+            + (cos_lat1 * sin_lat2 - sin_lat1 * cos_lat2 * cos_delta_lng) ** 2
+        ),
+        sin_lat1 * sin_lat2 + cos_lat1 * cos_lat2 * cos_delta_lng,
+    )
 
     return radius * d

climate_toolbox/io/io.py

@@ -1,6 +1,6 @@
 import xarray as xr
 
-from climate_toolbox.utils.utils import *
+from climate_toolbox.utils.utils import rename_coords_to_lon_and_lat, convert_lons_split
 
 
 def standardize_climate_data(ds):
@@ -19,12 +19,12 @@ def standardize_climate_data(ds):
     """
 
     ds = rename_coords_to_lon_and_lat(ds)
-    ds = convert_lons_split(ds, lon_name='lon')
+    ds = convert_lons_split(ds, lon_name="lon")
 
     return ds
 
 
-def load_bcsd(fp, varname, lon_name='lon', broadcast_dims=('time',)):
+def load_bcsd(fp, varname, lon_name="lon", broadcast_dims=("time",)):
     """
     Read and prepare climate data
 
@@ -48,11 +48,7 @@ def load_bcsd(fp, varname, lon_name='lon', broadcast_dims=('time',)):
     xr.Dataset
          xarray dataset loaded into memory
     """
-
-    if lon_name is not None:
-        lon_names = [lon_name]
-
-    if hasattr(fp, 'sel_points'):
+    if hasattr(fp, "sel_points"):
         ds = fp
 
     else:
@@ -62,10 +58,9 @@ def load_bcsd(fp, varname, lon_name='lon', broadcast_dims=('time',)):
     return standardize_climate_data(ds)
 
 
-def load_gmfd(fp, varname, lon_name='lon', broadcast_dims=('time',)):
+def load_gmfd(fp, varname, lon_name="lon", broadcast_dims=("time",)):
     pass
 
 
-def load_best(fp, varname, lon_name='lon', broadcast_dims=('time',)):
+def load_best(fp, varname, lon_name="lon", broadcast_dims=("time",)):
     pass
-

climate_toolbox/transformations/transformations.py

@@ -1,8 +1,7 @@
 import xarray as xr
 import numpy as np
 
-from climate_toolbox.utils.utils import \
-    remove_leap_days, convert_kelvin_to_celsius
+from climate_toolbox.utils.utils import remove_leap_days, convert_kelvin_to_celsius
 
 
 def snyder_edd(tasmin, tasmax, threshold):
@@ -67,9 +66,9 @@ def snyder_edd(tasmin, tasmax, threshold):
     assert not (tasmax < tasmin).any(), "values encountered where tasmin > tasmax"
 
     # compute useful quantities for use in the transformation
-    snyder_mean = ((tasmax + tasmin)/2)
-    snyder_width = ((tasmax - tasmin)/2)
-    snyder_theta = xr.ufuncs.arcsin((threshold - snyder_mean)/snyder_width)
+    snyder_mean = (tasmax + tasmin) / 2
+    snyder_width = (tasmax - tasmin) / 2
+    snyder_theta = xr.ufuncs.arcsin((threshold - snyder_mean) / snyder_width)
 
     # the trasnformation is computed using numpy arrays, taking advantage of
     # numpy's second where clause. Note that in the current dev build of
@@ -79,13 +78,17 @@ def snyder_edd(tasmin, tasmax, threshold):
         tasmin < threshold,
         xr.where(
             tasmax > threshold,
-            ((snyder_mean - threshold) * (np.pi/2 - snyder_theta)
-                + (snyder_width * np.cos(snyder_theta))) / np.pi,
-            0),
-        snyder_mean - threshold)
-
-    res.attrs['units'] = (
-        'degreedays_{}{}'.format(threshold, tasmax.attrs['units']))
+            (
+                (snyder_mean - threshold) * (np.pi / 2 - snyder_theta)
+                + (snyder_width * np.cos(snyder_theta))
+            )
+            / np.pi,
+            0,
+        ),
+        snyder_mean - threshold,
+    )
+
+    res.attrs["units"] = "degreedays_{}{}".format(threshold, tasmax.attrs["units"])
 
     return res
 
@@ -133,19 +136,19 @@ def snyder_gdd(tasmin, tasmax, threshold_low, threshold_high):
     # Check for unit agreement
     assert tasmin.units == tasmax.units
 
-    res = (
-        snyder_edd(tasmin, tasmax, threshold_low)
-        - snyder_edd(tasmin, tasmax, threshold_high))
-
-    res.attrs['units'] = (
-        'degreedays_{}-{}{}'.format(threshold_low, threshold_high, tasmax.attrs['units']))
+    res = snyder_edd(tasmin, tasmax, threshold_low) - snyder_edd(
+        tasmin, tasmax, threshold_high
+    )
 
+    res.attrs["units"] = "degreedays_{}-{}{}".format(
+        threshold_low, threshold_high, tasmax.attrs["units"]
+    )
 
     return res
 
 
 def validate_edd_snyder_agriculture(ds, thresholds):
-    msg_null = 'hierid dims do not match 24378'
+    msg_null = "hierid dims do not match 24378"
 
     assert ds.hierid.shape == (24378,), msg_null
 
@@ -164,48 +167,48 @@ def tas_poly(ds, power, varname):
 
     powername = ordinal(power)
 
-    description = ('''
+    description = (
+        """
             Daily average temperature (degrees C){raised}
 
             Leap years are removed before counting days (uses a 365 day
             calendar).
-            '''.format(
-                raised='' if power == 1 else (
-                    ' raised to the {powername} power'
-                    .format(powername=powername)))).strip()
+            """.format(
+            raised=""
+            if power == 1
+            else (" raised to the {powername} power".format(powername=powername))
+        )
+    ).strip()
 
     ds1 = xr.Dataset()
 
     # remove leap years
     ds = remove_leap_days(ds)
 
     # do transformation
-    ds1[varname] = (ds.tas - 273.15)**power
+    ds1[varname] = (ds.tas - 273.15) ** power
 
     # Replace datetime64[ns] 'time' with YYYYDDD int 'day'
-    if ds.dims['time'] > 365:
+    if ds.dims["time"] > 365:
         raise ValueError
 
-    ds1.coords['day'] = ds['time.year']*1000 + np.arange(1, len(ds.time)+1)
-    ds1 = ds1.swap_dims({'time': 'day'})
-    ds1 = ds1.drop('time')
+    ds1.coords["day"] = ds["time.year"] * 1000 + np.arange(1, len(ds.time) + 1)
+    ds1 = ds1.swap_dims({"time": "day"})
+    ds1 = ds1.drop("time")
 
-    ds1 = ds1.rename({'day': 'time'})
+    ds1 = ds1.rename({"day": "time"})
 
     # document variable
-    ds1[varname].attrs['units'] = (
-        'C^{}'.format(power) if power > 1 else 'C')
+    ds1[varname].attrs["units"] = "C^{}".format(power) if power > 1 else "C"
 
-    ds1[varname].attrs['long_title'] = description.splitlines()[0]
-    ds1[varname].attrs['description'] = description
-    ds1[varname].attrs['variable'] = varname
+    ds1[varname].attrs["long_title"] = description.splitlines()[0]
+    ds1[varname].attrs["description"] = description
+    ds1[varname].attrs["variable"] = varname
 
     return ds1
 
 
 def ordinal(n):
-    """ Converts numbers into ordinal strings """
+    """Converts numbers into ordinal strings"""
 
-    return (
-        "%d%s" %
-        (n, "tsnrhtdd"[(n // 10 % 10 != 1) * (n % 10 < 4) * n % 10::4]))
+    return "%d%s" % (n, "tsnrhtdd"[(n // 10 % 10 != 1) * (n % 10 < 4) * n % 10 :: 4])