Remove attribute-style accesses in hazard module

climada/hazard/centroids/centr.py

@@ -138,14 +138,14 @@ def geometry(self):
     @property
     def on_land(self):
         """ Get the on_land property """
-        if self.gdf.on_land.isna().all():
+        if self.gdf['on_land'].isna().all():
             return None
         return self.gdf['on_land'].values
 
     @property
     def region_id(self):
         """ Get the assigned region_id """
-        if self.gdf.region_id.isna().all():
+        if self.gdf['region_id'].isna().all():
             return None
         return self.gdf['region_id'].values
 
@@ -479,7 +479,7 @@ def select_mask(self, sel_cen=None, reg_id=None, extent=None):
               (self.lat >= lat_min) & (self.lat <= lat_max)
             )
         return sel_cen
-    
+
     def plot(self, *, axis=None, figsize=(9, 13), **kwargs):
         """Plot centroids geodataframe using geopandas and cartopy plotting functions.
 
@@ -494,7 +494,7 @@ def plot(self, *, axis=None, figsize=(9, 13), **kwargs):
             positional arguments for geopandas.GeoDataFrame.plot
         kwargs : optional
             keyword arguments for geopandas.GeoDataFrame.plot
-            
+
         Returns
         -------
         ax : cartopy.mpl.geoaxes.GeoAxes instance

climada/hazard/isimip_data.py

@@ -56,6 +56,6 @@ def _read_one_nc(file_name, bbox=None, years=None):
     if not years:
         return data.sel(lat=slice(bbox[3], bbox[1]), lon=slice(bbox[0], bbox[2]))
 
-    time_id = years - int(data.time.units[12:16])
+    time_id = years - int(data['time'].units[12:16])
     return data.sel(lat=slice(bbox[3], bbox[1]), lon=slice(bbox[0], bbox[2]),
                     time=slice(time_id[0], time_id[1]))

climada/hazard/storm_europe.py

@@ -334,18 +334,18 @@ def from_cosmoe_file(cls, fp_file, run_datetime, event_date=None,
                           .stack(intensity=('y_1', 'x_1'))
             considered_dates = stacked['date'].values
         stacked = stacked.stack(date_ensemble=('date', 'epsd_1'))
-        stacked = stacked.where(stacked.VMAX_10M > intensity_thres)
+        stacked = stacked.where(stacked['VMAX_10M'] > intensity_thres)
         stacked = stacked.fillna(0)
 
         # fill in values from netCDF
-        intensity = sparse.csr_matrix(stacked.VMAX_10M.T)
-        event_id = np.arange(stacked.date_ensemble.size) + 1
+        intensity = sparse.csr_matrix(stacked['VMAX_10M'].T)
+        event_id = np.arange(stacked['date_ensemble'].size) + 1
         date = np.repeat(
             np.array(datetime64_to_ordinal(considered_dates)),
-            np.unique(ncdf.epsd_1).size
+            np.unique(ncdf['epsd_1']).size
         )
         orig = np.full_like(event_id, False)
-        orig[(stacked.epsd_1 == 0).values] = True
+        orig[(stacked['epsd_1'] == 0).values] = True
         if description is None:
             description = (model_name +
                            ' weather forecast windfield ' +
@@ -362,10 +362,10 @@ def from_cosmoe_file(cls, fp_file, run_datetime, event_date=None,
             date=date,
             event_name=[date_i + '_ens' + str(ens_i)
                         for date_i, ens_i
-                        in zip(date_to_str(date), stacked.epsd_1.values + 1)],
+                        in zip(date_to_str(date), stacked['epsd_1'].values + 1)],
             frequency=np.divide(
                 np.ones_like(event_id),
-                np.unique(ncdf.epsd_1).size),
+                np.unique(ncdf['epsd_1']).size),
         )
 
         # close netcdf file
@@ -477,30 +477,30 @@ def from_icon_grib(cls, run_datetime, event_date=None, model_name='icon-eu-eps',
         stacked = stacked.where(stacked > intensity_thres)
         stacked = stacked.fillna(0)
 
-        event_id = np.arange(stacked.date_ensemble.size) + 1
+        event_id = np.arange(stacked['date_ensemble'].size) + 1
         date = np.repeat(
             np.array(datetime64_to_ordinal(considered_dates)),
-            np.unique(stacked.number).size
+            np.unique(stacked['number']).size
         )
         orig = np.full_like(event_id, False)
-        orig[(stacked.number == 1).values] = True
+        orig[(stacked['number'] == 1).values] = True
         if description is None:
             description = ('icon weather forecast windfield for run started at ' +
                            run_datetime.strftime('%Y%m%d%H'))
 
         # Create Hazard
         haz = cls(
-            intensity=sparse.csr_matrix(stacked.gust.T),
+            intensity=sparse.csr_matrix(stacked['gust'].T),
             centroids=cls._centroids_from_nc(nc_centroids_file),
             event_id=event_id,
             date=date,
             orig=orig,
             event_name=[date_i + '_ens' + str(ens_i)
                         for date_i, ens_i
-                        in zip(date_to_str(date), stacked.number.values)],
+                        in zip(date_to_str(date), stacked['number'].values)],
             frequency=np.divide(
                 np.ones_like(event_id),
-                np.unique(stacked.number).size),
+                np.unique(stacked['number']).size),
         )
         haz.check()
 
@@ -527,25 +527,25 @@ def _centroids_from_nc(file_name):
         ncdf = xr.open_dataset(file_name)
         create_meshgrid = True
         if hasattr(ncdf, 'latitude'):
-            lats = ncdf.latitude.data
-            lons = ncdf.longitude.data
+            lats = ncdf['latitude'].data
+            lons = ncdf['longitude'].data
         elif hasattr(ncdf, 'lat'):
-            lats = ncdf.lat.data
-            lons = ncdf.lon.data
+            lats = ncdf['lat'].data
+            lons = ncdf['lon'].data
         elif hasattr(ncdf, 'lat_1'):
-            if len(ncdf.lon_1.shape)>1 & \
-                (ncdf.lon_1.shape == ncdf.lat_1.shape) \
+            if len(ncdf['lon_1'].shape)>1 & \
+                (ncdf['lon_1'].shape == ncdf['lat_1'].shape) \
                 :
-                lats = ncdf.lat_1.data.flatten()
-                lons = ncdf.lon_1.data.flatten()
+                lats = ncdf['lat_1'].data.flatten()
+                lons = ncdf['lon_1'].data.flatten()
                 create_meshgrid = False
             else:
-                lats = ncdf.lat_1.data
-                lons = ncdf.lon_1.data
+                lats = ncdf['lat_1'].data
+                lons = ncdf['lon_1'].data
         elif hasattr(ncdf, 'clat'):
-            lats = ncdf.clat.data
-            lons = ncdf.clon.data
-            if ncdf.clat.attrs['units']=='radian':
+            lats = ncdf['clat'].data
+            lons = ncdf['clon'].data
+            if ncdf['clat'].attrs['units']=='radian':
                 lats = np.rad2deg(lats)
                 lons = np.rad2deg(lons)
             create_meshgrid = False
@@ -713,16 +713,16 @@ def plot_ssi(self, full_area=False):
             'orig': self.orig,
         })
         ssi_freq = ssi_freq.sort_values('ssi', ascending=False)
-        ssi_freq['freq_cum'] = np.cumsum(ssi_freq.freq)
+        ssi_freq['freq_cum'] = np.cumsum(ssi_freq['freq'])
 
-        ssi_hist = ssi_freq.loc[ssi_freq.orig].copy()
-        ssi_hist.freq = ssi_hist.freq * self.orig.size / self.orig.sum()
-        ssi_hist['freq_cum'] = np.cumsum(ssi_hist.freq)
+        ssi_hist = ssi_freq.loc[ssi_freq['orig']].copy()
+        ssi_hist['freq'] = ssi_hist['freq'] * self.orig.size / self.orig.sum()
+        ssi_hist['freq_cum'] = np.cumsum(ssi_hist['freq'])
 
         # plotting
         fig, axs = plt.subplots()
-        axs.plot(ssi_freq.freq_cum, ssi_freq.ssi, label='All Events')
-        axs.scatter(ssi_hist.freq_cum, ssi_hist.ssi,
+        axs.plot(ssi_freq['freq_cum'], ssi_freq['ssi'], label='All Events')
+        axs.scatter(ssi_hist['freq_cum'], ssi_hist['ssi'],
                     color='red', label='Historic Events')
         axs.legend()
         axs.set_xlabel('Exceedance Frequency [1/a]')

climada/hazard/tc_clim_change.py

@@ -159,7 +159,7 @@ def calc_scale_knutson(ref_year=2050, rcp_scenario=45):
     rad_force = pd.read_excel(TOT_RADIATIVE_FORCE)
     years = np.array([year for year in rad_force.columns if isinstance(year, int)])
     rad_rcp = np.array([int(float(sce[sce.index('.') - 1:sce.index('.') + 2]) * 10)
-                        for sce in rad_force.Scenario if isinstance(sce, str)])
+                        for sce in rad_force['Scenario'] if isinstance(sce, str)])
 
     # mean values for Knutson values
     rf_vals = np.argwhere(rad_rcp == rcp_knu).reshape(-1)[0]