update(Raster, ModelTime): Deprecation warnings maintenance:

flopy/discretization/modeltime.py

@@ -1,6 +1,6 @@
 import calendar
 from dataclasses import dataclass, field
-from datetime import datetime, timedelta
+from datetime import datetime, timedelta, timezone
 from difflib import SequenceMatcher
 
 import numpy as np
@@ -479,7 +479,8 @@ def parse_datetime(
         elif isinstance(datetime_obj, np.datetime64):
             unix_time_0 = datetime(1970, 1, 1)
             ts = (datetime_obj - np.datetime64(unix_time_0)) / np.timedelta64(1, "s")
-            datetime_obj = datetime.utcfromtimestamp(ts)
+            datetime_obj = datetime.fromtimestamp(ts, tz=timezone.utc)
+            datetime_obj = datetime_obj.replace(tzinfo=None)
         elif isinstance(datetime_obj, pd.Timestamp):
             datetime_obj = datetime_obj.to_pydatetime()
         elif isinstance(datetime_obj, datetime):

flopy/discretization/structuredgrid.py

@@ -2057,8 +2057,8 @@ def from_binary_grid_file(cls, file_path, verbose=False):
         nlay, nrow, ncol = (grb_obj.nlay, grb_obj.nrow, grb_obj.ncol)
         delr, delc = grb_obj.delr, grb_obj.delc
         top, botm = grb_obj.top, grb_obj.bot
-        top.shape = (nrow, ncol)
-        botm.shape = (nlay, nrow, ncol)
+        top = top.reshape((nrow, ncol))
+        botm = botm.reshape((nlay, nrow, ncol))
         return cls(
             delc,
             delr,

flopy/discretization/vertexgrid.py

@@ -847,7 +847,7 @@ def from_binary_grid_file(cls, file_path, verbose=False):
         nlay, ncpl = grb_obj.nlay, grb_obj.ncpl
         top = np.ravel(grb_obj.top)
         botm = grb_obj.bot
-        botm.shape = (nlay, ncpl)
+        botm = botm.reshape((nlay, ncpl))
         vertices, cell2d = grb_obj.cell2d
 
         return cls(

flopy/mf6/utils/binaryfile_utils.py

@@ -375,9 +375,9 @@ def _reshape_binary_data(data, dtype=None):
         return data
     elif dtype == "V":
         nodes = len(data[0][0][0])
-        data.shape = (time, -1, nodes)
+        data = data.reshape((time, -1, nodes))
     elif dtype == "U":
-        data.shape = (time, -1)
+        data = data.reshape((time, -1))
     else:
         err = "Invalid dtype flag supplied, valid are dtype='U', dtype='V'"
         raise Exception(err)

flopy/mf6/utils/binarygrid_util.py

@@ -238,7 +238,7 @@ def _set_modelgrid(self):
                 nlay, ncpl = self.nlay, self.ncpl
                 vertices, cell2d = self.cell2d
                 top = np.ravel(top)
-                botm.shape = (nlay, ncpl)
+                botm = botm.reshape((nlay, ncpl))
                 modelgrid = VertexGrid(
                     vertices,
                     cell2d,
@@ -258,8 +258,8 @@ def _set_modelgrid(self):
                 )
                 delr, delc = self.delr, self.delc
 
-                top.shape = (nrow, ncol)
-                botm.shape = (nlay, nrow, ncol)
+                top.reshape((nrow, ncol))
+                botm = botm.reshape((nlay, nrow, ncol))
                 modelgrid = StructuredGrid(
                     delc,
                     delr,

flopy/mf6/utils/model_splitter.py

@@ -833,7 +833,7 @@ def reconstruct_array(self, arrays):
 
             new_array[new_nodes] = array[old_nodes]
 
-        new_array.shape = shape
+        new_array = new_array.reshape(shape)
         return new_array
 
     def reconstruct_recarray(self, recarrays):

flopy/plot/plotutil.py

@@ -1482,11 +1482,11 @@ def saturated_thickness(head, top, botm, laytyp, mask_values=None):
             head = np.copy(head)
             nlay, nrow, ncol = head.shape
             ncpl = nrow * ncol
-            head.shape = (nlay, ncpl)
-            top.shape = (ncpl,)
-            botm.shape = (nlay, ncpl)
+            head = head.reshape((nlay, ncpl))
+            top = top.reshape((ncpl,))
+            botm = botm.reshape((nlay, ncpl))
             if laytyp.ndim == 3:
-                laytyp.shape = (nlay, ncpl)
+                laytyp = laytyp.reshape((nlay, ncpl))
 
         else:
             nrow, ncol = None, None
@@ -1531,7 +1531,7 @@ def saturated_thickness(head, top, botm, laytyp, mask_values=None):
         sat_thk = np.where(laytyp != 0, sat_thk_unconf, sat_thk_conf)
 
         if nrow is not None and ncol is not None:
-            sat_thk.shape = (nlay, nrow, ncol)
+            sat_thk = sat_thk.reshape((nlay, nrow, ncol))
 
         return sat_thk
 

flopy/utils/postprocessing.py

@@ -801,7 +801,7 @@ def get_specific_discharge(
     modelgrid = model.modelgrid
 
     if head is not None:
-        head.shape = modelgrid.shape
+        head = head.reshape(modelgrid.shape)
 
     if isinstance(vectors, (list, tuple)):
         classical_budget = True
@@ -857,7 +857,7 @@ def get_specific_discharge(
                 head, mask=[model.hdry, model.hnoflo]
             )
 
-        saturated_thickness.shape = modelgrid.shape
+        saturated_thickness = saturated_thickness.reshape(modelgrid.shape)
 
         # inform modelgrid of no-flow and dry cells
         modelgrid = model.modelgrid
@@ -927,9 +927,9 @@ def get_specific_discharge(
         qx[idx] = spdis["qx"]
         qy[idx] = spdis["qy"]
         qz[idx] = spdis["qz"]
-        qx.shape = modelgrid.shape
-        qy.shape = modelgrid.shape
-        qz.shape = modelgrid.shape
+        qx = qx.reshape(modelgrid.shape)
+        qy = qy.reshape(modelgrid.shape)
+        qz = qz.reshape(modelgrid.shape)
 
     # set no-flow and dry cells to NaN
     if head is not None and position == "centers":

flopy/utils/rasters.py

@@ -588,7 +588,7 @@ def resample_to_grid(
             data = np.where(np.isnan(data), extrapolate, data)
 
         # step 4: return grid to user in shape provided
-        data.shape = data_shape
+        data = data.reshape(data_shape)
 
         # step 5: re-apply nodata values
         data[np.isnan(data)] = self.nodatavals[0]

flopy/utils/zonbud.py

@@ -2677,7 +2677,7 @@ def _read_zb_csv2(fname, add_prefix=True, aliases=None):
         array = np.genfromtxt(foo, delimiter=",").T
         if len(array) != len(dtype):
             array = array[:-1]
-        array.shape = (len(dtype), -1)
+        array = array.reshape((len(dtype), -1))
         data = {name[0]: list(array[ix]) for ix, name in enumerate(dtype)}
         data["KPER"] = list(np.array(data["KPER"]) - 1)
         data["KSTP"] = list(np.array(data["KSTP"]) - 1)