correct pylint

Author: gabrielaznar

climada/engine/cost_benefit.py

@@ -320,7 +320,7 @@ def plot_waterfall(self, hazard, entity, haz_future, ent_future,
         risk_curr = self._npv_unaverted_impact(risk_future, entity.disc_rates,
                                                time_dep)
         LOGGER.info('Risk function at {:d}: {:.3e}'.format(self.present_year,
-                    risk_future))
+                                                           risk_future))
 
         # changing future
         time_dep = self._time_dependency_array(imp_time_depen)
@@ -335,7 +335,7 @@ def plot_waterfall(self, hazard, entity, haz_future, ent_future,
         risk_tot = self._npv_unaverted_impact(risk_future, entity.disc_rates,
                                               time_dep, curr_risk)
         LOGGER.info('Risk function at {:d}: {:.3e}'.format(self.future_year,
-                    risk_future))
+                                                           risk_future))
 
         axis.bar(1, risk_curr/norm_fact)
         axis.text(1, risk_curr/norm_fact, str(int(round(risk_curr/norm_fact))), \
@@ -357,7 +357,7 @@ def plot_waterfall(self, hazard, entity, haz_future, ent_future,
             'Economic \ndevelopment', 'Climate \nchange', 'Risk ' + str(self.future_year)])
         axis.set_ylabel('Impact (' + self.unit + ' ' + norm_name + ')')
         axis.set_title('Total accumulated damage from {:d} to {:d}'.format(
-                self.present_year, self.future_year))
+            self.present_year, self.future_year))
 
         return fig, axis
 
@@ -515,7 +515,7 @@ def _print_results(self):
 
         table = []
         headers = ['Measure', 'Cost ' + norm_name, 'Benefit ' + norm_name, 'Benefit/Cost']
-        for meas_name in self.benefit.keys():
+        for meas_name in self.benefit:
             table.append([meas_name, \
             self.cost_ben_ratio[meas_name]*self.benefit[meas_name]/norm_fact, \
             self.benefit[meas_name]/norm_fact, 1/self.cost_ben_ratio[meas_name]])

climada/engine/test/test_cost_benefit.py

@@ -382,7 +382,7 @@ def test_impact(self):
         impact = Impact()
         ent.exposures.assign_centroids(hazard)
         impact.calc(ent.exposures, ent.impact_funcs, hazard)
-        return impact        
+        return impact
 
     def test_risk_aai_agg_pass(self):
         """Test risk_aai_agg"""

climada/entity/exposures/base.py

@@ -351,10 +351,10 @@ def plot_raster(self, res=None, raster_res=None, save_tiff=None,
 
     def plot_basemap(self, mask=None, ignore_zero=False, pop_name=True,
                      buffer=0.0, extend='neither', zoom=10,
-                     url='http://tile.stamen.com/terrain/tileZ/tileX/tileY.png', 
+                     url='http://tile.stamen.com/terrain/tileZ/tileX/tileY.png',
                      **kwargs):
-        """ Scatter points over satellite image using contextily 
-        
+        """ Scatter points over satellite image using contextily
+
          Parameters:
             mask (np.array, optional): mask to apply to eai_exp plotted.
             ignore_zero (bool, optional): flag to indicate if zero and negative
@@ -364,7 +364,7 @@ def plot_basemap(self, mask=None, ignore_zero=False, pop_name=True,
             extend (str, optional): extend border colorbar with arrows.
                 [ 'neither' | 'both' | 'min' | 'max' ]
             zoom (int, optional): zoom coefficient used in the satellite image
-            url (str, optional): satellite image source.
+            url (str, optional): image source, e.g. ctx.sources.OSM_C
             kwargs (optional): arguments for scatter matplotlib function, e.g.
                 cmap='Greys'. Default: 'Wistia'
          Returns:
@@ -374,12 +374,12 @@ def plot_basemap(self, mask=None, ignore_zero=False, pop_name=True,
             self.set_geometry_points()
         crs_ori = self.crs
         self.to_crs(epsg=3857, inplace=True)
-        fig, ax = self.plot_scatter(mask, ignore_zero, pop_name, buffer, extend,
-                                    shapes=False, **kwargs)
-        u_plot.add_basemap(ax[0, 0], zoom, url, flip=True)
-        ax[0, 0].set_axis_off()
+        fig, axis = self.plot_scatter(mask, ignore_zero, pop_name, buffer,
+                                      extend, shapes=False, **kwargs)
+        u_plot.add_basemap(axis[0, 0], zoom, url, flip=True)
+        axis[0, 0].set_axis_off()
         self.to_crs(crs_ori, inplace=True)
-        return fig, ax
+        return fig, axis
 
     def write_hdf5(self, file_name):
         """ Write data frame and metadata in hdf5 format """

climada/entity/exposures/test/test_base.py

@@ -22,7 +22,7 @@
 import unittest
 import numpy as np
 import pandas as pd
-import geopandas
+import geopandas as gpd
 import cartopy
 from sklearn.neighbors import DistanceMetric
 from climada.util.coordinates import coord_on_land
@@ -47,7 +47,7 @@ def good_exposures():
     data['region_id'] = np.array([1, 2, 3])
     data[INDICATOR_CENTR + 'TC'] = np.array([1, 2, 3])
 
-    expo = Exposures(geopandas.GeoDataFrame(data=data))
+    expo = Exposures(gpd.GeoDataFrame(data=data))
     return expo
 
 class TestFuncs(unittest.TestCase):
@@ -272,6 +272,15 @@ def test_to_crs_pass(self):
         self.assertEqual(exp_tr.tag.description, '')
         self.assertEqual(exp_tr.tag.file_name, '')
 
+    def test_constructoer_pass(self):
+        """ Test initialization with input GeiDataFrame """
+        in_gpd = gpd.GeoDataFrame()
+        in_gpd['value'] = np.zeros(10)
+        in_gpd.ref_year = 2015
+        in_exp = Exposures(in_gpd)
+        self.assertEqual(in_exp.ref_year, 2015)
+        self.assertTrue(np.array_equal(in_exp.value, np.zeros(10)))
+    
 # Execute Tests
 TESTS = unittest.TestLoader().loadTestsFromTestCase(TestChecker)
 TESTS.addTests(unittest.TestLoader().loadTestsFromTestCase(TestFuncs))

climada/entity/measures/measure_set.py

@@ -257,7 +257,7 @@ def size(self, haz_type=None, name=None):
             int
         """
         if (haz_type is not None) and (name is not None) and \
-        (isinstance(self.get_func(haz_type, name), Measure)):
+        (isinstance(self.get_measure(haz_type, name), Measure)):
             return 1
         if (haz_type is not None) or (name is not None):
             return len(self.get_measure(haz_type, name))
@@ -276,7 +276,7 @@ def check(self):
                     LOGGER.error("Wrong Measure.name: %s != %s.", name, \
                                  meas.name)
                     raise ValueError
-                if (key_haz != meas.haz_type):
+                if key_haz != meas.haz_type:
                     LOGGER.error("Wrong Measure.haz_type: %s != %s.",\
                                  key_haz, meas.haz_type)
                     raise ValueError
@@ -456,19 +456,19 @@ def write_meas(row_ini, imp_ws, xls_data):
                   var_names['col_name']['paa_b'], var_names['col_name']['fun_map'],
                   var_names['col_name']['exp_set'], var_names['col_name']['exp_reg'],
                   var_names['col_name']['risk_att'], var_names['col_name']['risk_cov'],
-                  var_names['col_name']['haz']]       
+                  var_names['col_name']['haz']]
         for icol, head_dat in enumerate(header):
             mead_ws.write(0, icol, head_dat)
         row_ini = 1
-        for haz_type, haz_dict in self._data.items():
+        for _, haz_dict in self._data.items():
             for meas_name, meas in haz_dict.items():
-             xls_data = [meas_name, ' '.join(list(map(str, meas.color_rgb))),
-                        meas.cost, meas.hazard_inten_imp[0],
-                        meas.hazard_inten_imp[1], meas.hazard_freq_cutoff,
-                        meas.hazard_set, meas.mdd_impact[0], meas.mdd_impact[1],
-                        meas.paa_impact[0], meas.paa_impact[1], meas.imp_fun_map,
-                        meas.exposures_set, meas.exp_region_id, meas.risk_transf_attach,
-                        meas.risk_transf_cover, meas.haz_type]
+                xls_data = [meas_name, ' '.join(list(map(str, meas.color_rgb))),
+                            meas.cost, meas.hazard_inten_imp[0],
+                            meas.hazard_inten_imp[1], meas.hazard_freq_cutoff,
+                            meas.hazard_set, meas.mdd_impact[0], meas.mdd_impact[1],
+                            meas.paa_impact[0], meas.paa_impact[1], meas.imp_fun_map,
+                            meas.exposures_set, meas.exp_region_id, meas.risk_transf_attach,
+                            meas.risk_transf_cover, meas.haz_type]
             write_meas(row_ini, mead_ws, xls_data)
             row_ini += 1
-        meas_wb.close()               
+        meas_wb.close()

climada/util/plot.py

@@ -34,9 +34,9 @@
 from mpl_toolkits.axes_grid1 import make_axes_locatable
 from shapely.geometry import box
 import cartopy.crs as ccrs
-import contextily as ctx
 from cartopy.io import shapereader
 from cartopy.mpl.gridliner import LONGITUDE_FORMATTER, LATITUDE_FORMATTER
+import contextily as ctx
 
 from climada.util.files_handler import to_list
 
@@ -509,11 +509,18 @@ def _get_borders(geo_coord, buffer=0, proj=ccrs.PlateCarree()):
     max_lat = min(np.max(geo_coord[:, 0])+buffer, proj.y_limits[1])
     return [min_lon, max_lon, min_lat, max_lat]
 
-def add_basemap(ax, zoom, url='http://tile.stamen.com/terrain/tileZ/tileX/tileY.png', 
+def add_basemap(axis, zoom, url='http://tile.stamen.com/terrain/tileZ/tileX/tileY.png',
                 flip=False):
-    xmin, xmax, ymin, ymax = ax.axis()
+    """ Add image to given axis. Coordinates need to be in epsg=3857.
+
+    Parameters:
+        (cartopy.mpl.geoaxes.GeoAxesSubplot): plot axis
+        zoom (int, optional): zoom coefficient used in the satellite image
+        url (str, optional): image source, e.g. ctx.sources.OSM_C
+    """
+    xmin, xmax, ymin, ymax = axis.axis()
     basemap, extent = ctx.bounds2img(xmin, ymin, xmax, ymax, zoom=zoom, url=url)
     if flip:
         basemap = np.flip(basemap, 0)
-    ax.imshow(basemap, extent=extent, interpolation='bilinear')
-    ax.axis((xmin, xmax, ymin, ymax))
+    axis.imshow(basemap, extent=extent, interpolation='bilinear')
+    axis.axis((xmin, xmax, ymin, ymax))