fix codacy issues

docs/source/conf.py

@@ -10,11 +10,12 @@
 # add these directories to sys.path here. If the directory is relative to the
 # documentation root, use os.path.abspath to make it absolute, like shown here.
 #
+from nlmod import __version__
 import os
 import sys
 
 sys.path.insert(0, os.path.abspath('.'))
-from nlmod import __version__
+
 
 
 # -- Project information -----------------------------------------------------

nlmod/mdims/mbase.py

@@ -6,7 +6,8 @@
 
 from .. import util
 
-def get_empty_model_ds(model_name, model_ws, mfversion="mf6", 
+
+def get_empty_model_ds(model_name, model_ws, mfversion="mf6",
                        exe_name=None):
     """ get an empty model dataset
 
@@ -34,24 +35,22 @@ def get_empty_model_ds(model_name, model_ws, mfversion="mf6",
     model_ds.attrs['model_name'] = model_name
     model_ds.attrs['mfversion'] = mfversion
     model_ds.attrs['model_dataset_created_on'] = dt.datetime.now().strftime("%Y%m%d_%H:%M:%S")
-    
+
     if exe_name is None:
         exe_name = os.path.join(os.path.dirname(__file__),
                                 '..', '..', 'bin', model_ds.mfversion)
 
     # if working on Windows add .exe extension
     if sys.platform.startswith('win'):
         exe_name += ".exe"
-        
+
     model_ds.attrs["exe_name"] = exe_name
-    
-    # add some directories        
+
+    # add some directories
     if model_ws is not None:
         figdir, cachedir = util.get_model_dirs(model_ws)
         model_ds.attrs['model_ws'] = model_ws
         model_ds.attrs['figdir'] = figdir
         model_ds.attrs['cachedir'] = cachedir
 
-
-
     return model_ds

nlmod/mdims/mtime.py

@@ -14,7 +14,7 @@
 def set_model_ds_time(model_ds, start_time, steady_state,
                       steady_start=False, time_units='DAYS',
                       transient_timesteps=0,
-                      perlen=1.0, steady_perlen=3650, 
+                      perlen=1.0, steady_perlen=3650,
                       nstp=1, tsmult=1.0):
     """Set timing for a model dataset.
 

nlmod/mdims/resample.py

@@ -179,7 +179,7 @@ def resample_dataarray_to_structured_grid(da_in, extent=None, delr=None, delc=No
     Also flips the y-coordinates to make them descending instead of ascending.
     This makes it easier to export array to flopy. In other words, make sure
     that both lines of code create the same plot::
-    
+
         da_in['top'].sel(layer=b'Hlc').plot()
         plt.imshow(da_in['top'].sel(layer=b'Hlc').data)
 
@@ -635,7 +635,7 @@ def fillnan_dataarray_unstructured_grid(xar_in, gridprops=None,
 
 
 def resample_unstr_2d_da_to_struc_2d_da(da_in, model_ds, cellsize=25,
-                                        method ='nearest'):
+                                        method='nearest'):
     """resample a 2d dataarray (xarray) from an unstructured grid to a new 
     dataaraay from a structured grid.   
 
@@ -657,22 +657,22 @@ def resample_unstr_2d_da_to_struc_2d_da(da_in, model_ds, cellsize=25,
 
     """
     points_unstr = np.array([model_ds.x.values, model_ds.y.values]).T
-    modelgrid_x = np.arange(model_ds.x.values.min(), 
-                            model_ds.x.values.max(), 
+    modelgrid_x = np.arange(model_ds.x.values.min(),
+                            model_ds.x.values.max(),
                             cellsize)
-    modelgrid_y = np.arange(model_ds.y.values.max(), 
-                            model_ds.y.values.min(), 
+    modelgrid_y = np.arange(model_ds.y.values.max(),
+                            model_ds.y.values.min(),
                             -cellsize)
     mg = np.meshgrid(modelgrid_x, modelgrid_y)
-    points = np.vstack((mg[0].ravel(), mg[1].ravel())).T  
-    
+    points = np.vstack((mg[0].ravel(), mg[1].ravel())).T
+
     arr_out_1d = griddata(points_unstr, da_in.values, points, method=method)
-    arr_out2d = arr_out_1d.reshape(len(modelgrid_y), 
+    arr_out2d = arr_out_1d.reshape(len(modelgrid_y),
                                    len(modelgrid_x))
-    
+
     da_out = xr.DataArray(arr_out2d,
-                      dims=('y','x'),
+                      dims=('y', 'x'),
                       coords={'y': modelgrid_y,
                               'x': modelgrid_x})
-    
+
     return da_out

nlmod/mfpackages/surface_water.py

@@ -287,7 +287,7 @@ def estimate_polygon_length(gdf):
 def distribute_cond_over_lays(cond, cellid, rivbot, laytop, laybot,
                               idomain=None, kh=None, stage=None):
 
-    if isinstance(rivbot, np.ndarray) or isinstance(rivbot, xr.DataArray):
+    if isinstance(rivbot, (np.ndarray, xr.DataArray)):
         rivbot = float(rivbot[cellid])
     if len(laybot.shape) == 3:
         # the grid is structured grid
@@ -435,5 +435,5 @@ def build_spd(celldata, pkg, model_ds):
                 spd.append([cid, stage, cond, rbot] + auxlist)
             elif pkg in ["DRN", "GHB"]:
                 spd.append([cid, stage, cond] + auxlist)
-            
+
     return spd

nlmod/read/geotop.py

@@ -71,7 +71,7 @@ def get_geotop_dataset(extent, delr, delc,
     geotop_ds = mdims.get_resampled_ml_layer_ds_struc(raw_ds=geotop_ds_raw,
                                                       extent=extent,
                                                       delr=delr, delc=delc)
-    
+
     for datavar in geotop_ds:
         geotop_ds[datavar].attrs['source'] = 'Geotop'
         geotop_ds[datavar].attrs['url'] = geotop_url
@@ -98,8 +98,6 @@ def get_geotop_raw_within_extent(extent, url):
         slices geotop netcdf.
     """
 
-
-
     geotop_ds_raw = xr.open_dataset(url)
 
     # set x and y dimensions to cell center

nlmod/read/jarkus.py

@@ -184,12 +184,11 @@ def bathymetry_to_model_dataset(model_ds,
 
     model_ds_out['bathymetry'] = xr.where(
         model_ds['northsea'], da_bathymetry, np.nan)
-    
+
     for datavar in model_ds_out:
         model_ds_out[datavar].attrs['source'] = 'Jarkus'
         model_ds_out[datavar].attrs['url'] = url
         model_ds_out[datavar].attrs['source'] = dt.datetime.now().strftime('%Y%m%d')
-
 
     return model_ds_out
 

nlmod/read/knmi.py

@@ -190,7 +190,7 @@ def add_knmi_to_model_dataset(model_ds,
 
             elif model_ds.gridtype == 'unstructured':
                 model_ds_out['recharge'].loc[loc_sel.index, :] = model_recharge.values
-                
+
     for datavar in model_ds_out:
         model_ds_out[datavar].attrs['source'] = 'KNMI'
         model_ds_out[datavar].attrs['date'] = dt.datetime.now().strftime('%Y%m%d')

nlmod/read/regis.py

@@ -80,8 +80,7 @@ def get_layer_models(extent, delr, delc,
 def get_combined_layer_models(extent, delr, delc,
                               regis_botm_layer=b'AKc',
                               use_regis=True, use_geotop=True,
-                              remove_nan_layers=True,
-                              cachedir=None, use_cache=False):
+                              remove_nan_layers=True):
     """combine layer models into a single layer model.
 
     Possibilities so far include:
@@ -110,12 +109,6 @@ def get_combined_layer_models(extent, delr, delc,
         if True regis and geotop layers with only nans are removed from the
         model. if False nan layers are kept which might be usefull if you want
         to keep some layers that exist in other models. The default is True.
-    cachedir : str
-        directory to store cached values, if None a temporary directory is
-        used. default is None
-    use_cache : bool, optional
-        if True the cached resampled regis dataset is used.
-        The default is False.
 
     Raises
     ------
@@ -216,7 +209,7 @@ def get_regis_dataset(extent, delr, delc, botm_layer=b'AKc'):
     regis_ds = mdims.get_resampled_ml_layer_ds_struc(raw_ds=regis_ds_raw2,
                                                      extent=extent,
                                                      delr=delr, delc=delc)
-    
+
     for datavar in regis_ds:
         regis_ds[datavar].attrs['source'] = 'REGIS'
         regis_ds[datavar].attrs['url'] = regis_url
@@ -332,8 +325,6 @@ def add_geotop_to_regis_hlc(regis_ds, geotop_ds,
         regis_geotop_ds[key].attrs['regis_url'] = regis_ds[key].url
         regis_geotop_ds[key].attrs['geotop_url'] = geotop_ds[key].url
         regis_geotop_ds[key].attrs['date'] = dt.datetime.now().strftime('%Y%m%d')
-
-
 
     return regis_geotop_ds
 

nlmod/read/rws.py

@@ -94,7 +94,7 @@ def surface_water_to_model_dataset(model_ds, modelgrid, da_name):
     area = xr.zeros_like(model_ds['top'])
     cond = xr.zeros_like(model_ds['top'])
     peil = xr.zeros_like(model_ds['top'])
-    for i, row in gdf.iterrows():
+    for _, row in gdf.iterrows():
         area_pol = mdims.polygon_to_area(modelgrid, row['geometry'],
                                          xr.ones_like(model_ds['top']),
                                          model_ds.gridtype)
@@ -106,7 +106,7 @@ def surface_water_to_model_dataset(model_ds, modelgrid, da_name):
     model_ds_out[f'{da_name}_area'] = area
     model_ds_out[f'{da_name}_cond'] = cond
     model_ds_out[f'{da_name}_peil'] = peil
-    
+
     for datavar in model_ds_out:
         model_ds_out[datavar].attrs['source'] = 'RWS'
         model_ds_out[datavar].attrs['date'] = dt.datetime.now().strftime('%Y%m%d')

nlmod/util.py

@@ -19,20 +19,16 @@
 from shapely.geometry import box
 from shutil import copyfile
 
-import __main__
-
-
 logger = logging.getLogger(__name__)
 
 
-
 def write_and_run_model(gwf, model_ds, write_model_ds=True,
                         nb_path=None):
     """ write modflow files and run the model. 2 extra options:
         1. write the model dataset to cache
         2. copy the modelscript (typically a Jupyter Notebook) to the model
         workspace with a timestamp.
-    
+
 
     Parameters
     ----------
@@ -49,29 +45,26 @@ def write_and_run_model(gwf, model_ds, write_model_ds=True,
         of a Jupyter Notebook from within the notebook itself.
 
     """
-    
-    if not nb_path is None:
+
+    if nb_path is not None:
         new_nb_fname = f'{dt.datetime.now().strftime("%Y%m%d")}' + os.path.split(nb_path)[-1]
-        dst = os.path.join(model_ds.model_ws,  new_nb_fname)
+        dst = os.path.join(model_ds.model_ws, new_nb_fname)
         logger.info(f'write script {new_nb_fname} to model workspace')
         copyfile(nb_path, dst)
-
-
+
     if write_model_ds:
         logger.info('write model dataset to cache')
-        model_ds.to_netcdf(os.path.join(model_ds.attrs['cachedir'], 
+        model_ds.to_netcdf(os.path.join(model_ds.attrs['cachedir'],
                                         'full_model_ds.nc'))
         model_ds.attrs['model_dataset_written_to_disk_on'] = dt.datetime.now().strftime("%Y%m%d_%H:%M:%S")
-    
+
     logger.info('write modflow files to model workspace')
     gwf.simulation.write_simulation()
     model_ds.attrs['model_data_written_to_disk_on'] = dt.datetime.now().strftime("%Y%m%d_%H:%M:%S")
-    
+
     logger.info('run model')
     assert gwf.simulation.run_simulation()[0]
     model_ds.attrs['model_ran_on'] = dt.datetime.now().strftime("%Y%m%d_%H:%M:%S")
-
-
 
 
 def get_model_dirs(model_ws):
@@ -105,7 +98,6 @@ def get_model_dirs(model_ws):
         os.mkdir(cachedir)
 
     return figdir, cachedir
-
 
 
 def get_model_ds_empty(model_ds):
@@ -160,7 +152,7 @@ def check_delr_delc_extent(dic, model_ds):
     if 'extent' in dic.keys():
         key_check = (np.array(dic['extent']) == np.array(
             model_ds.attrs['extent'])).all()
-        
+
         if key_check:
             logger.info('extent of current grid is the same as cached grid')
         else:
@@ -698,6 +690,7 @@ def getmfexes(pth='.', version='', pltfrm=None):
 
     return
 
+
 def add_heads_to_model_ds(model_ds, fname_hds=None):
     """reads the heads from a modflow .hds file and returns an xarray
     DataArray.
@@ -717,18 +710,16 @@ def add_heads_to_model_ds(model_ds, fname_hds=None):
 
     if fname_hds is None:
         fname_hds = os.path.join(model_ds.model_ws, model_ds.model_name + '.hds')
-    
+
     head_filled = get_heads_array(fname_hds, gridtype=model_ds.gridtype)
-
-
 
     if model_ds.gridtype == 'unstructured':
         head_ar = xr.DataArray(data=head_filled[:, :, :],
                                dims=('time', 'layer', 'cid'),
                                coords={'cid': model_ds.cid,
                                        'layer': model_ds.layer,
                                        'time': model_ds.time})
-    elif model_ds.gridtype =='structured':
+    elif model_ds.gridtype == 'structured':
         head_ar = xr.DataArray(data=head_filled,
                            dims=('time', 'layer', 'y', 'x'),
                            coords={'x': model_ds.x,
@@ -738,6 +729,7 @@ def add_heads_to_model_ds(model_ds, fname_hds=None):
 
     return head_ar
 
+
 def get_heads_array(fname_hds, gridtype='structured',
                     fill_nans=True):
     """reads the heads from a modflow .hds file and returns a numpy array.
@@ -767,7 +759,7 @@ def get_heads_array(fname_hds, gridtype='structured',
 
     if gridtype == 'unstructured':
         head_filled = np.ones((head.shape[0], head.shape[1], head.shape[3])) * np.nan
-        
+
         for t in range(head.shape[0]):
             for lay in range(head.shape[1] - 1, -1, -1):
                 head_filled[t][lay] = head[t][lay][0]
@@ -777,7 +769,7 @@ def get_heads_array(fname_hds, gridtype='structured',
                                                        head_filled[t][lay + 1],
                                                        head_filled[t][lay])
 
-    elif gridtype =='structured':
+    elif gridtype == 'structured':
         head_filled = np.zeros_like(head)
         for t in range(head.shape[0]):
             for lay in range(head.shape[1] - 1, -1, -1):
@@ -789,7 +781,7 @@ def get_heads_array(fname_hds, gridtype='structured',
                                                        head_filled[t][lay])
     else:
         raise ValueError('wrong gridtype')
-        
+
     return head_filled
 
 
@@ -811,5 +803,3 @@ def download_mfbinaries(binpath=None, version='6.0'):
     pltfrm = get_platform(None)
     # Download and unpack mf6 exes
     getmfexes(pth=binpath, version=version, pltfrm=pltfrm)
-
-