refactor: fixes for numpy-2.0 deprecation warnings, require numpy>=1.…

…20.3 (#2237)

This PR resolves several deprecation warnings from NumPy 2.0 (xref #2153):

* np.in1d() → np.isin()
* "a16" → "S16" to describe 16-length string of bytes
* The numpy.core.records module is private, so use numpy.rec instead, which seems to work with older versions

This also bumps the minimum version of numpy from 1.15.0 to 1.20.3, since this is the minimum for pandas 2.0.0, thus the minimum versions were incompatible.

.docs/Notebooks/zonebudget_example.py

@@ -111,7 +111,7 @@
 inyrbud = inyr.get_budget()
 
 names = ["FROM_RECHARGE"]
-rowidx = np.in1d(cmdbud["name"], names)
+rowidx = np.isin(cmdbud["name"], names)
 colidx = "ZONE_1"
 
 print(f"{cmdbud[rowidx][colidx][0]:,.1f} cubic meters/day")

README.md

@@ -35,7 +35,7 @@ Installation
 FloPy requires **Python** 3.8+ with: 
 
 ```
-numpy >=1.15.0,<2.0.0
+numpy >=1.20.3,<2.0.0
 matplotlib >=1.4.0
 pandas >=2.0.0
 ```

autotest/test_mf6.py

@@ -102,7 +102,7 @@ def write_head(
             ("kper", "i4"),
             ("pertim", "f8"),
             ("totim", "f8"),
-            ("text", "a16"),
+            ("text", "S16"),
             ("ncol", "i4"),
             ("nrow", "i4"),
             ("ilay", "i4"),

autotest/test_mp6.py

@@ -175,7 +175,7 @@ def test_get_destination_data(function_tmpdir, mp6_test_path):
         np.array(well_pthld)[["k", "i", "j"]].tolist(),
         dtype=starting_locs.dtype,
     )
-    assert np.all(np.in1d(starting_locs, pathline_locs))
+    assert np.all(np.isin(starting_locs, pathline_locs))
 
     # test writing a shapefile of endpoints
     epd.write_shapefile(

autotest/test_particledata.py

@@ -83,7 +83,7 @@ def test_particledata_structured_ctor_with_partlocs_as_list_of_tuples():
     assert isinstance(data.particledata, pd.DataFrame)
     assert np.array_equal(
         data.particledata.to_records(index=False),
-        np.core.records.fromrecords(
+        np.rec.fromrecords(
             [
                 (0, 1, 1, 0.5, 0.5, 0.5, 0.0, 0),
                 (0, 1, 2, 0.5, 0.5, 0.5, 0.0, 0),
@@ -102,7 +102,7 @@ def test_particledata_structured_ctor_with_partlocs_as_ndarray():
     assert isinstance(data.particledata, pd.DataFrame)
     assert np.array_equal(
         data.particledata.to_records(index=False),
-        np.core.records.fromrecords(
+        np.rec.fromrecords(
             [
                 (0, 1, 1, 0.5, 0.5, 0.5, 0.0, 0),
                 (0, 1, 2, 0.5, 0.5, 0.5, 0.0, 0),
@@ -121,7 +121,7 @@ def test_particledata_unstructured_ctor_with_partlocs_as_ndarray():
     assert isinstance(data.particledata, pd.DataFrame)
     assert np.array_equal(
         data.particledata.to_records(index=False),
-        np.core.records.fromrecords(
+        np.rec.fromrecords(
             [
                 (0, 0.5, 0.5, 0.5, 0.0, 0),
                 (1, 0.5, 0.5, 0.5, 0.0, 0),
@@ -141,7 +141,7 @@ def test_particledata_unstructured_ctor_with_partlocs_as_list():
     assert isinstance(data.particledata, pd.DataFrame)
     assert np.array_equal(
         data.particledata.to_records(index=False),
-        np.core.records.fromrecords(
+        np.rec.fromrecords(
             [
                 (0, 0.5, 0.5, 0.5, 0.0, 0),
                 (1, 0.5, 0.5, 0.5, 0.0, 0),
@@ -161,7 +161,7 @@ def test_particledata_unstructured_ctor_with_partlocs_as_ndarray():
     assert isinstance(data.particledata, pd.DataFrame)
     assert np.array_equal(
         data.particledata.to_records(index=False),
-        np.core.records.fromrecords(
+        np.rec.fromrecords(
             [
                 (0, 0.5, 0.5, 0.5, 0.0, 0),
                 (1, 0.5, 0.5, 0.5, 0.0, 0),
@@ -181,7 +181,7 @@ def test_particledata_structured_ctor_with_partlocs_as_list_of_lists():
     assert isinstance(data.particledata, pd.DataFrame)
     assert np.array_equal(
         data.particledata.to_records(index=False),
-        np.core.records.fromrecords(
+        np.rec.fromrecords(
             [
                 (0, 1, 1, 0.5, 0.5, 0.5, 0.0, 0),
                 (0, 1, 2, 0.5, 0.5, 0.5, 0.0, 0),
@@ -212,7 +212,7 @@ def test_particledata_to_prp_dis_1():
     )  # each coord should be a tuple (irpt, k, i, j, x, y, z)
 
     # expected
-    exp = np.core.records.fromrecords(
+    exp = np.rec.fromrecords(
         [
             (0, 1, 1, 0.5, 0.5, 0.5, 0.0, 0),
             (0, 1, 2, 0.5, 0.5, 0.5, 0.0, 0),

etc/environment.yml

@@ -6,8 +6,9 @@ dependencies:
 
   # required
   - python>=3.8
-  - numpy>=1.15.0,<2.0.0
+  - numpy>=1.20.3,<2.0.0
   - matplotlib>=1.4.0
+  - pandas>=2.0.0
 
   # lint
   - cffconvert
@@ -33,7 +34,6 @@ dependencies:
   # optional
   - affine
   - scipy
-  - pandas
   - netcdf4
   - pyshp
   - rasterio

flopy/modflow/mffhb.py

@@ -210,7 +210,7 @@ def __init__(
                 ds5 = ds5.to_records(index=False)
             # convert numpy array to a recarray
             if ds5.dtype != dtype:
-                ds5 = np.core.records.fromarrays(ds5.transpose(), dtype=dtype)
+                ds5 = np.rec.fromarrays(ds5.transpose(), dtype=dtype)
 
         # assign dataset 5
         self.ds5 = ds5
@@ -229,7 +229,7 @@ def __init__(
                 ds7 = ds7.to_records(index=False)
             # convert numpy array to a recarray
             if ds7.dtype != dtype:
-                ds7 = np.core.records.fromarrays(ds7.transpose(), dtype=dtype)
+                ds7 = np.rec.fromarrays(ds7.transpose(), dtype=dtype)
 
         # assign dataset 7
         self.ds7 = ds7

flopy/modflow/mfgage.py

@@ -131,7 +131,7 @@ def __init__(
             # convert gage_data to a recarray, if necessary
             if isinstance(gage_data, np.ndarray):
                 if not gage_data.dtype == dtype:
-                    gage_data = np.core.records.fromarrays(
+                    gage_data = np.rec.fromarrays(
                         gage_data.transpose(), dtype=dtype
                     )
             elif isinstance(gage_data, pd.DataFrame):

flopy/modflow/mfsfr2.py

@@ -1372,9 +1372,7 @@ def get_variable_by_stress_period(self, varname):
             all_data[inds, per] = self.segment_data[per][varname]
             dtype.append((f"{varname}{per}", float))
         isvar = all_data.sum(axis=1) != 0
-        ra = np.core.records.fromarrays(
-            all_data[isvar].transpose().copy(), dtype=dtype
-        )
+        ra = np.rec.fromarrays(all_data[isvar].transpose().copy(), dtype=dtype)
         segs = self.segment_data[0].nseg[isvar]
         isseg = np.array(
             [True if s in segs else False for s in self.reach_data.iseg]
@@ -1387,7 +1385,7 @@ def get_variable_by_stress_period(self, varname):
         return ra.view(np.recarray)
 
     def repair_outsegs(self):
-        isasegment = np.in1d(
+        isasegment = np.isin(
             self.segment_data[0].outseg, self.segment_data[0].nseg
         )
         isasegment = isasegment | (self.segment_data[0].outseg < 0)

flopy/modflow/mfstr.py

@@ -371,9 +371,7 @@ def __init__(
                     )
                     assert d.dtype == self.dtype, e
                 elif isinstance(d, np.ndarray):
-                    d = np.core.records.fromarrays(
-                        d.transpose(), dtype=self.dtype
-                    )
+                    d = np.rec.fromarrays(d.transpose(), dtype=self.dtype)
                 elif isinstance(d, int):
                     if model.verbose:
                         if d < 0:
@@ -404,9 +402,7 @@ def __init__(
                     )
                     assert d.dtype == self.dtype2, e
                 elif isinstance(d, np.ndarray):
-                    d = np.core.records.fromarrays(
-                        d.transpose(), dtype=self.dtype2
-                    )
+                    d = np.rec.fromarrays(d.transpose(), dtype=self.dtype2)
                 elif isinstance(d, int):
                     if model.verbose:
                         if d < 0:

flopy/plot/plotutil.py

@@ -2732,7 +2732,7 @@ def to_mp7_pathlines(
     data = data.to_records(index=False)
 
     # build mp7 format recarray
-    ret = np.core.records.fromarrays(
+    ret = np.rec.fromarrays(
         [
             data[seqn_key],
             data["iprp"],
@@ -2841,7 +2841,7 @@ def to_mp7_endpoints(
     endpts = endpts.to_records(index=False)
 
     # build mp7 format recarray
-    ret = np.core.records.fromarrays(
+    ret = np.rec.fromarrays(
         [
             endpts["sequencenumber"],
             endpts["iprp"],
@@ -2928,7 +2928,7 @@ def to_prt_pathlines(
     data = data.to_records(index=False)
 
     # build prt format recarray
-    ret = np.core.records.fromarrays(
+    ret = np.rec.fromarrays(
         [
             data["stressperiod"],
             data["timestep"],

flopy/utils/binaryfile.py

@@ -299,7 +299,7 @@ def binaryread_struct(file, vartype, shape=(1,), charlen=16):
         # find the number of bytes for one value
         numbytes = vartype(1).nbytes
         # find the number of values
-        nval = np.core.fromnumeric.prod(shape)
+        nval = np.prod(shape)
         fmt = str(nval) + fmt
         s = file.read(numbytes * nval)
         result = struct.unpack(fmt, s)
@@ -1138,7 +1138,7 @@ def _set_precision(self, precision="single"):
         h1dt = [
             ("kstp", "i4"),
             ("kper", "i4"),
-            ("text", "a16"),
+            ("text", "S16"),
             ("ncol", "i4"),
             ("nrow", "i4"),
             ("nlay", "i4"),
@@ -1161,10 +1161,10 @@ def _set_precision(self, precision="single"):
             ("delt", ffmt),
             ("pertim", ffmt),
             ("totim", ffmt),
-            ("modelnam", "a16"),
-            ("paknam", "a16"),
-            ("modelnam2", "a16"),
-            ("paknam2", "a16"),
+            ("modelnam", "S16"),
+            ("paknam", "S16"),
+            ("modelnam2", "S16"),
+            ("paknam2", "S16"),
         ]
         self.header1_dtype = np.dtype(h1dt)
         self.header2_dtype0 = np.dtype(h2dt0)

flopy/utils/datafile.py

@@ -43,7 +43,7 @@ def __init__(self, filetype=None, precision="single"):
                         ("kper", "i4"),
                         ("pertim", floattype),
                         ("totim", floattype),
-                        ("text", "a16"),
+                        ("text", "S16"),
                         ("ncol", "i4"),
                         ("nrow", "i4"),
                         ("ilay", "i4"),
@@ -56,7 +56,7 @@ def __init__(self, filetype=None, precision="single"):
                         ("kper", "i4"),
                         ("pertim", floattype),
                         ("totim", floattype),
-                        ("text", "a16"),
+                        ("text", "S16"),
                         ("ncol", "i4"),
                         ("nrow", "i4"),
                         ("ilay", "i4"),
@@ -69,7 +69,7 @@ def __init__(self, filetype=None, precision="single"):
                         ("kstp", "i4"),
                         ("kper", "i4"),
                         ("totim", floattype),
-                        ("text", "a16"),
+                        ("text", "S16"),
                         ("ncol", "i4"),
                         ("nrow", "i4"),
                         ("ilay", "i4"),
@@ -82,7 +82,7 @@ def __init__(self, filetype=None, precision="single"):
                         ("kper", "i4"),
                         ("pertim", floattype),
                         ("totim", floattype),
-                        ("text", "a16"),
+                        ("text", "S16"),
                         ("m1", "i4"),
                         ("m2", "i4"),
                         ("m3", "i4"),
@@ -95,7 +95,7 @@ def __init__(self, filetype=None, precision="single"):
                         ("kper", "i4"),
                         ("pertim", floattype),
                         ("totim", floattype),
-                        ("text", "a16"),
+                        ("text", "S16"),
                         ("m1", "i4"),
                         ("m2", "i4"),
                         ("m3", "i4"),
@@ -108,7 +108,7 @@ def __init__(self, filetype=None, precision="single"):
                         ("kper", "i4"),
                         ("pertim", floattype),
                         ("totim", floattype),
-                        ("text", "a16"),
+                        ("text", "S16"),
                         ("m1", "i4"),
                         ("m2", "i4"),
                         ("m3", "i4"),

flopy/utils/modpathfile.py

@@ -124,12 +124,12 @@ def intersect(
                         cells = t
 
         cells = np.array(cells, dtype=raslice.dtype)
-        inds = np.in1d(raslice, cells)
+        inds = np.isin(raslice, cells)
         epdest = self._data[inds].copy().view(np.recarray)
 
         if to_recarray:
             # use particle ids to get the rest of the paths
-            inds = np.in1d(self._data["particleid"], epdest.particleid)
+            inds = np.isin(self._data["particleid"], epdest.particleid)
             series = self._data[inds].copy()
             series.sort(order=["particleid", "time"])
             series = series.view(np.recarray)
@@ -693,7 +693,7 @@ def get_destination_endpoint_data(self, dest_cells, source=False):
         dtype = np.dtype(dtype)
         dest_cells = np.array(dest_cells, dtype=dtype)
 
-        inds = np.in1d(raslice, dest_cells)
+        inds = np.isin(raslice, dest_cells)
         return data[inds].copy().view(np.recarray)
 
     def write_shapefile(

flopy/utils/recarray_utils.py

@@ -62,7 +62,7 @@ def ra_slice(ra, cols):
     --------
     >>> import numpy as np
     >>> from flopy.utils import ra_slice
-    >>> a = np.core.records.fromrecords([("a", 1, 1.1), ("b", 2, 2.1)])
+    >>> a = np.rec.fromrecords([("a", 1, 1.1), ("b", 2, 2.1)])
     >>> ra_slice(a, ['f0', 'f1'])
     rec.array([('a', 1), ('b', 2)],
               dtype=[('f0', '<U1'), ('f1', '<i4')])
@@ -75,7 +75,7 @@ def recarray(array, dtype):
     Convert a list of lists or tuples to a recarray.
 
     .. deprecated:: 3.5
-        Use numpy.core.records.fromrecords instead
+        Use numpy.rec.fromrecords instead
 
     Parameters
     ----------

flopy/utils/util_list.py

@@ -419,7 +419,7 @@ def __cast_ndarray(self, kper, d):
                 f"dtype len: {len(self.dtype)}"
             )
         try:
-            self.__data[kper] = np.core.records.fromarrays(
+            self.__data[kper] = np.rec.fromarrays(
                 d.transpose(), dtype=self.dtype
             )
         except Exception as e: