codacy suggestions

nlmod/dims/base.py

@@ -207,7 +207,7 @@ def extrapolate_ds(ds, mask=None):
         # all of the model cells are is inside the known area
         return ds
     if mask.all():
-        raise (Exception("The model only contains NaNs"))
+        raise (ValueError("The model only contains NaNs"))
     if "gridtype" in ds.attrs and ds.gridtype == "vertex":
         x = ds.x.data
         y = ds.y.data
@@ -452,10 +452,10 @@ def _get_vertex_grid_ds(
     coords = {"layer": layers, "y": y, "x": x}
     dims = ("layer", "icell2d")
     ds = xr.Dataset(
-        data_vars=dict(
-            top=(dims[1:], top),
-            botm=(dims, botm),
-        ),
+        data_vars={
+            "top": (dims[1:], top),
+            "botm": (dims, botm),
+        },
         coords=coords,
         attrs=attrs,
     )
@@ -605,7 +605,7 @@ def get_ds(
 
     resample._set_angrot_attributes(extent, xorigin, yorigin, angrot, attrs)
     x, y = resample.get_xy_mid_structured(attrs["extent"], delr, delc)
-    coords = dict(x=x, y=y, layer=layer)
+    coords = {"x": x, "y": y, "layer": layer}
     if angrot != 0.0:
         affine = resample.get_affine_mod_to_world(attrs)
         xc, yc = affine * np.meshgrid(x, y)

nlmod/dims/grid.py

@@ -384,7 +384,9 @@ def refine(
     ds_has_rotation = "angrot" in ds.attrs and ds.attrs["angrot"] != 0.0
     if model_coordinates:
         if not ds_has_rotation:
-            raise (Exception("The supplied shapes need to be in realworld coordinates"))
+            raise (
+                ValueError("The supplied shapes need to be in realworld coordinates")
+            )
     elif ds_has_rotation:
         affine_matrix = get_affine_world_to_mod(ds).to_shapely()
 
@@ -395,7 +397,9 @@ def refine(
                 fname, geom_type, level = refinement_feature
                 if not model_coordinates and ds_has_rotation:
                     raise (
-                        Exception("Converting files to model coordinates not supported")
+                        NotImplementedError(
+                            "Converting files to model coordinates not supported"
+                        )
                     )
                 g.add_refinement_features(fname, geom_type, level, layers=[0])
             elif len(refinement_feature) == 2:
@@ -1419,7 +1423,7 @@ def gdf_to_grid(
         The GeoDataFrame with the geometries per grid-cell.
     """
     if ml is None and ix is None:
-        raise (Exception("Either specify ml or ix"))
+        raise (ValueError("Either specify ml or ix"))
 
     if ml is not None:
         if isinstance(ml, xr.Dataset):

nlmod/dims/layers.py

@@ -40,7 +40,7 @@ def calculate_thickness(ds, top="top", bot="botm"):
         if ds[top].shape[-1] == ds[bot].shape[-1]:
             # top is only top of first layer
             thickness = xr.zeros_like(ds[bot])
-            for lay in range(len(thickness)):
+            for lay, _ in enumerate(thickness):
                 if lay == 0:
                     thickness[lay] = ds[top] - ds[bot][lay]
                 else:
@@ -242,7 +242,7 @@ def split_layers_ds(
     layers_org = layers.copy()
     # add extra layers (keep the original ones for now, as we will copy data first)
     for lay0 in split_dict:
-        for i in range(len(split_dict[lay0])):
+        for i, _ in enumerate(split_dict[lay0]):
             index = layers.index(lay0)
             layers.insert(index, lay0 + "_" + str(i + 1))
             layers_org.insert(index, lay0)

nlmod/dims/resample.py

@@ -208,13 +208,13 @@ def _set_angrot_attributes(extent, xorigin, yorigin, angrot, attrs):
             extent[0] = 0.0
             extent[1] = extent[1] - xorigin
         elif extent[0] != 0.0:
-            raise (Exception("Either extent[0] or xorigin needs to be 0.0"))
+            raise (ValueError("Either extent[0] or xorigin needs to be 0.0"))
         if yorigin == 0.0:
             yorigin = extent[2]
             extent[2] = 0.0
             extent[3] = extent[3] - yorigin
         elif extent[2] != 0.0:
-            raise (Exception("Either extent[2] or yorigin needs to be 0.0"))
+            raise (ValueError("Either extent[2] or yorigin needs to be 0.0"))
         attrs["extent"] = extent
         attrs["xorigin"] = xorigin
         attrs["yorigin"] = yorigin
@@ -441,7 +441,7 @@ def dim_to_regular_dim(da, dims, z):
         # just use griddata
         z = griddata(points, da.data, xi, method=method)
         dims = ["y", "x"]
-        coords = dict(x=ds.x, y=ds.y)
+        coords = {"x": ds.x, "y": ds.y}
     return xr.DataArray(z, dims=dims, coords=coords)
 
 
@@ -486,7 +486,7 @@ def structured_da_to_ds(da, ds, method="average", nodata=np.NaN):
         if hasattr(rasterio.enums.Resampling, method):
             resampling = getattr(rasterio.enums.Resampling, method)
         else:
-            raise (Exception(f"Unknown resample method: {method}"))
+            raise ValueError(f"Unknown resample method: {method}")
     # fill crs if it is None for da or ds
     if ds.rio.crs is None and da.rio.crs is None:
         logger.info("No crs in da and ds. Assuming ds and da are both in EPSG:28992")
@@ -533,11 +533,11 @@ def structured_da_to_ds(da, ds, method="average", nodata=np.NaN):
                 da_temp = da_temp.assign_coords(x=x, y=y)
 
             mask = ds["area"] == area
-            da_out.loc[dict(icell2d=mask)] = da_temp.sel(
+            da_out.loc[{"icell2d": mask}] = da_temp.sel(
                 y=ds["y"][mask], x=ds["x"][mask]
             )
     else:
-        raise (Exception(f"Gridtype {ds.gridtype} not supported"))
+        raise (NotImplementedError(f"Gridtype {ds.gridtype} not supported"))
 
     # some stuff is added by the reproject_match function that should not be there
     added_coords = set(da_out.coords) - set(ds.coords)

nlmod/gwf/output.py

@@ -305,7 +305,11 @@ def get_head_at_point(head, x, y, ds=None, gi=None, drop_nan_layers=True):
     if "icell2d" in head.dims:
         if gi is None:
             if ds is None:
-                raise (Exception("Please supply either gi or ds for a vertex grid"))
+                raise (
+                    ValueError(
+                        "Please supply either gi (GridIntersect) or ds for a vertex grid"
+                    )
+                )
             gi = flopy.utils.GridIntersect(modelgrid_from_ds(ds), method="vertex")
         icelld2 = gi.intersect(Point(x, y))["cellids"][0]
         head_point = head[:, :, icelld2]

nlmod/gwf/recharge.py

@@ -119,7 +119,7 @@ def ds_to_evt(gwf, ds, pname="evt", nseg=1, surface=None, depth=None, **kwargs):
     """
     assert nseg == 1, "More than one evaporation segment not yet supported"
     if "surf_rate_specified" in kwargs:
-        raise (Exception("surf_rate_specified not yet supported"))
+        raise (NotImplementedError("surf_rate_specified not yet supported"))
     if surface is None:
         logger.info("Setting evaporation surface to 1 meter below top")
         surface = ds["top"] - 1.0

nlmod/gwf/surface_water.py

@@ -1,5 +1,6 @@
 import logging
 import warnings
+from functools import partial
 
 import flopy
 import numpy as np
@@ -461,12 +462,12 @@ def build_spd(
                 mask = (stage > botm_cell) & (idomain_cell > 0)
                 if not mask.any():
                     raise (
-                        Exception("rbot and stage are below the bottom of the model")
+                        ValueError("rbot and stage are below the bottom of the model")
                     )
             lays = [np.where(mask)[0][0]]
             conds = [cond]
         else:
-            raise (Exception(f"Method {layer_method} unknown"))
+            raise (ValueError(f"Method {layer_method} unknown"))
 
         auxlist = []
         if "aux" in row:
@@ -519,7 +520,7 @@ def add_info_to_gdf(
             measure = overlap.length
         else:
             msg = f"Unsupported geometry type: {geom_type}"
-            raise (Exception(msg))
+            raise TypeError(msg)
 
         if np.any(measure.sum() > min_total_overlap * measure_org):
             # take the largest
@@ -806,7 +807,7 @@ def get_gdf(ds=None, extent=None, fname_ahn=None, ahn=None, buffer=0.0):
     """
     if extent is None:
         if ds is None:
-            raise (Exception("Please supply either ds or extent to get_gdf"))
+            raise (ValueError("Please supply either ds or extent to get_gdf"))
         extent = get_extent_polygon(ds)
     gdf = bgt.get_bgt(extent)
     if fname_ahn is not None:
@@ -851,7 +852,6 @@ def add_min_ahn_to_gdf(gdf, ahn, buffer=0.0, column="ahn_min"):
         A GeoDataFrame with surface water features, with an added column containing the
         minimum surface level height near the features.
     """
-    from functools import partial
 
     from geocube.api.core import make_geocube
     from geocube.rasterize import rasterize_image
@@ -1001,7 +1001,7 @@ def gdf_to_seasonal_pkg(
     elif pkg == "GHB":
         cl = flopy.mf6.ModflowGwfghb
     else:
-        raise (Exception(f"Unknown package: {pkg}"))
+        raise (ValueError(f"Unknown package: {pkg}"))
     package = cl(
         gwf,
         stress_period_data={0: spd},

nlmod/plot/dcs.py

@@ -6,8 +6,8 @@
 import xarray as xr
 from matplotlib.collections import LineCollection, PatchCollection
 from matplotlib.patches import Rectangle
-from shapely.geometry import LineString, MultiLineString, Point, Polygon
 from shapely.affinity import affine_transform
+from shapely.geometry import LineString, MultiLineString, Point, Polygon
 
 from ..dims.grid import modelgrid_from_ds
 from ..dims.resample import get_affine_world_to_mod

nlmod/plot/flopy.py

@@ -1,4 +1,3 @@
-import os
 from functools import partial
 
 import flopy

nlmod/plot/plot.py

@@ -178,7 +178,7 @@ def data_array(da, ds=None, ax=None, rotated=False, edgecolor=None, **kwargs):
         ax = plt.gca()
     if "icell2d" in da.dims:
         if ds is None:
-            raise (Exception("Supply model dataset (ds) for grid information"))
+            raise (ValueError("Supply model dataset (ds) for grid information"))
         if isinstance(ds, list):
             patches = ds
         else:

nlmod/plot/plotutil.py

@@ -214,7 +214,7 @@ def colorbar_inside(
         cax.yaxis.tick_left()
         cax.yaxis.set_label_position("left")
     if isinstance(bbox_labels, bool) and bbox_labels is True:
-        bbox_labels = dict(facecolor="w", alpha=0.5)
+        bbox_labels = {"facecolor": "w", "alpha": 0.5}
     if isinstance(bbox_labels, dict):
         for label in cb.ax.yaxis.get_ticklabels():
             label.set_bbox(bbox_labels)
@@ -237,7 +237,7 @@ def title_inside(
         ax = plt.gca()
     if isinstance(bbox, bool):
         if bbox:
-            bbox = dict(facecolor="w", alpha=0.5)
+            bbox = {"facecolor": "w", "alpha": 0.5}
         else:
             bbox = None
     return ax.text(

nlmod/read/__init__.py

@@ -1,6 +1,7 @@
 from . import (
     ahn,
     bgt,
+    boundaries,
     brp,
     geotop,
     jarkus,
@@ -11,7 +12,6 @@
     rws,
     waterboard,
     webservices,
-    boundaries,
 )
 from .geotop import get_geotop
 from .regis import get_regis

nlmod/read/ahn.py

@@ -54,7 +54,7 @@ def get_ahn(ds=None, identifier="AHN4_DTM_5m", method="average", extent=None):
     elif version == 4:
         ahn_ds_raw = get_ahn4(extent, identifier=identifier)
     else:
-        raise (Exception(f"Unknown ahn-version: {version}"))
+        raise (ValueError(f"Unknown ahn-version: {version}"))
 
     ahn_ds_raw = ahn_ds_raw.drop_vars("band")
 
@@ -98,7 +98,7 @@ def _infer_url(identifier=None):
     if "ahn3" in identifier:
         url = "https://service.pdok.nl/rws/ahn3/wcs/v1_0?service=wcs"
     else:
-        ValueError(f"unknown identifier -> {identifier}")
+        raise ValueError(f"unknown identifier -> {identifier}")
 
     return url
 

nlmod/read/bgt.py

@@ -1,7 +1,7 @@
 import json
 import time
-import xml.etree.ElementTree as ET
 from io import BytesIO
+from xml.etree import ElementTree
 from zipfile import ZipFile
 
 import geopandas as gpd
@@ -246,7 +246,7 @@ def read_label(child, d):
         d["label_plaatsingspunt"] = Point(xy)
         d["label_hoek"] = float(positie.find(f"{ns}hoek").text)
 
-    tree = ET.parse(fname)
+    tree = ElementTree.parse(fname)
     ns = "{http://www.opengis.net/citygml/2.0}"
     data = []
     for com in tree.findall(f".//{ns}cityObjectMember"):
@@ -286,7 +286,7 @@ def read_label(child, d):
                     elif child[0].tag == f"{ns}Point":
                         d[key] = Point(read_point(child[0]))
                     else:
-                        raise (Exception((f"Unsupported tag: {child[0].tag}")))
+                        raise (ValueError((f"Unsupported tag: {child[0].tag}")))
                 elif key == "nummeraanduidingreeks":
                     ns = "{http://www.geostandaarden.nl/imgeo/2.1}"
                     nar = child.find(f"{ns}Nummeraanduidingreeks").find(
@@ -301,11 +301,11 @@ def read_label(child, d):
                     elif child[0].tag == f"{ns}Curve":
                         d[key] = LineString(read_curve(child[0]))
                     else:
-                        raise (Exception((f"Unsupported tag: {child[0].tag}")))
+                        raise (ValueError((f"Unsupported tag: {child[0].tag}")))
                 elif key == "openbareRuimteNaam":
                     read_label(child, d)
                 else:
-                    raise (Exception((f"Unknown key: {key}")))
+                    raise (KeyError((f"Unknown key: {key}")))
         data.append(d)
     if len(data) > 0:
         if geometry is None:

nlmod/read/boundaries.py

@@ -1,5 +1,4 @@
-from . import webservices
-from . import waterboard
+from . import waterboard, webservices
 
 
 def get_municipalities(source="cbs", drop_water=True, **kwargs):
@@ -43,7 +42,7 @@ def get_provinces(source="cbs", **kwargs):
         gdf = webservices.wfs(url, layer, **kwargs)
         gdf = gdf.set_index("statnaam")
     else:
-        raise (Exception(f"Unknown source: {source}"))
+        raise (ValueError(f"Unknown source: {source}"))
     return gdf
 
 

nlmod/read/brp.py

@@ -10,7 +10,7 @@ def get_percelen(extent, year=None):
         gdf = gdf.set_index("fuuid")
     else:
         if year < 2009 or year > 2021:
-            raise (Exception("Only data available from 2009 up to and including 2021"))
+            raise (ValueError("Only data available from 2009 up to and including 2021"))
         url = f"https://services.arcgis.com/nSZVuSZjHpEZZbRo/ArcGIS/rest/services/BRP_{year}/FeatureServer"
         gdf = webservices.arcrest(url, 0, extent=extent)
     return gdf

nlmod/read/geotop.py

@@ -36,7 +36,7 @@ def get_lithok_colors():
         8: (216, 163, 32),
         9: (95, 95, 255),
     }
-    colors = {key: tuple([x / 255 for x in colors[key]]) for key in colors}
+    colors = {key: tuple([x / 255 for x in color]) for key, color in colors.items()}
     return colors
 
 

nlmod/read/jarkus.py

@@ -18,7 +18,7 @@
 import xarray as xr
 
 from .. import cache
-from ..dims.resample import fillnan_da, structured_da_to_ds, get_extent
+from ..dims.resample import fillnan_da, get_extent, structured_da_to_ds
 from ..util import get_da_from_da_ds, get_ds_empty
 
 logger = logging.getLogger(__name__)
@@ -190,7 +190,7 @@ def get_jarkus_tilenames(extent, kind="jarkus"):
     elif kind == "vaklodingen":
         url = "http://opendap.deltares.nl/thredds/dodsC/opendap/rijkswaterstaat/vaklodingen/catalog.nc"
     else:
-        raise (Exception(f"Unsupported kind: {kind}"))
+        raise (ValueError(f"Unsupported kind: {kind}"))
 
     ds_jarkus_catalog = xr.open_dataset(url)
     ew_x = ds_jarkus_catalog["projectionCoverage_x"].values

nlmod/read/knmi.py

@@ -116,7 +116,7 @@ def get_recharge(ds, method="linear", most_common_station=False):
             loc_sel = locations.loc[(locations["stn_ev24"] == stn)]
             _add_ts_to_ds(ts, loc_sel, "evaporation", ds_out)
     else:
-        raise (Exception(f"Unknown method: {method}"))
+        raise (ValueError(f"Unknown method: {method}"))
     for datavar in ds_out:
         ds_out[datavar].attrs["source"] = "KNMI"
         ds_out[datavar].attrs["date"] = dt.datetime.now().strftime("%Y%m%d")
@@ -145,7 +145,7 @@ def _add_ts_to_ds(timeseries, loc_sel, variable, ds):
         # there will be NaN's, which we fill by backfill
         model_recharge = model_recharge.fillna(method="bfill")
         if model_recharge.isna().any():
-            raise (Exception("There are NaN-values in {variable}"))
+            raise (ValueError(f"There are NaN-values in {variable}."))
 
     # add data to ds
     values = np.repeat(model_recharge.values[:, np.newaxis], loc_sel.shape[0], 1)