Fixes following numpy 2 (#365)

pysteps/blending/steps.py

@@ -1098,7 +1098,7 @@ def worker(j):
  neginf=zerovalue,
  )
  # Put back the mask
- R_f_ip_recomp[domain_mask] = np.NaN
+ R_f_ip_recomp[domain_mask] = np.nan
  extrap_kwargs["displacement_prev"] = D[j]
  R_f_ep_recomp_, D[j] = extrapolator(
  R_f_ip_recomp,
@@ -1131,7 +1131,7 @@ def worker(j):
  neginf=np.nanmin(R_f_ip),
  )
  for i in range(n_cascade_levels):
- R_f_ep[i][temp_mask] = np.NaN
+ R_f_ep[i][temp_mask] = np.nan
  # B. Noise
  Yn_ip_recomp = blending.utils.recompose_cascade(
  combined_cascade=Yn_ip,
@@ -1182,7 +1182,7 @@ def worker(j):
  extrap_kwargs_pb["displacement_prev"] = D_pb[j]
  # Apply the domain mask to the extrapolation component
  R_ = precip.copy()
- R_[domain_mask] = np.NaN
+ R_[domain_mask] = np.nan
  R_pm_ep_, D_pb[j] = extrapolator(
  R_,
  velocity_blended,

pysteps/feature/shitomasi.py

@@ -149,7 +149,7 @@ def detection(
 
  # convert to 8-bit
  input_image = np.ndarray.astype(input_image, "uint8")
- mask = (-1 * mask + 1).astype("uint8")
+ mask = ~mask & 1
 
  params = dict(
  maxCorners=max_num_features if max_num_features is not None else max_corners,

pysteps/io/importers.py

@@ -810,15 +810,15 @@ def import_knmi_hdf5(filename, qty="ACRR", accutime=5.0, pixelsize=1000.0, **kwa
  # the no data value. The precision of the data is two decimals (0.01 mm).
  if qty == "ACRR":
  precip = np.where(
- precip_intermediate == 65535, np.NaN, precip_intermediate / 100.0
+ precip_intermediate == 65535, np.nan, precip_intermediate / 100.0
  )
 
  # In case reflectivities are imported, the no data value is 255. Values are
  # saved as integers. The reflectivities are not directly saved in dBZ, but
  # as: dBZ = 0.5 * pixel_value - 32.0 (this used to be 31.5).
  if qty == "DBZH":
  precip = np.where(
- precip_intermediate == 255, np.NaN, precip_intermediate * 0.5 - 32.0
+ precip_intermediate == 255, np.nan, precip_intermediate * 0.5 - 32.0
  )
 
  if precip is None:
@@ -1366,9 +1366,9 @@ def import_odim_hdf5(filename, qty="RATE", **kwargs):
  mask = np.logical_and(~mask_u, ~mask_n)
  quality = np.empty(arr.shape) # , dtype=float)
  quality[mask] = arr[mask] * gain + offset
- quality[~mask] = (
- np.nan
- ) # a qui -----------------------------
+ quality[
+ ~mask
+ ] = np.nan # a qui -----------------------------
 
  if precip is None:
  raise IOError("requested quantity %s not found" % qty)

pysteps/tests/test_io_nowcast_importers.py

@@ -18,6 +18,9 @@
  [(precip, metadata), (np.zeros_like(precip), metadata)],
 )
 def test_import_netcdf(precip, metadata, tmp_path):
+
+ pytest.importorskip("pyproj")
+
  field_shape = (precip.shape[1], precip.shape[2])
  startdate = metadata["timestamps"][-1]
  timestep = metadata["accutime"]

pysteps/tests/test_nowcasts_linda.py

@@ -144,6 +144,9 @@ def test_linda_wrong_inputs():
 
 def test_linda_callback(tmp_path):
  """Test LINDA callback functionality to export the output as a netcdf."""
+
+ pytest.importorskip("skimage")
+
  n_ens_members = 2
  n_timesteps = 3