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make w2w compatible with numpy 2.0 #130

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Sep 15, 2024
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make w2w compatible with numpy 2.0 #130

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23 changes: 14 additions & 9 deletions w2w/w2w.py
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Original file line number Diff line number Diff line change
Expand Up @@ -641,8 +641,10 @@ def _get_wrf_grid_info(info: Info) -> Dict[str, Any]:
# Grid parameters
# https://github.com/fmaussion/salem/blob/
# d3f2e5e340c2af36c84c82a9de6099c90fba12e8/salem/wrftools.py#L734
dx, dy = dst_data.DX, dst_data.DY
nx, ny = dst_data.dims['west_east'], dst_data.dims['south_north']
dx, dy = np.float64(dst_data.DX), np.float64(dst_data.DY)
nx, ny = np.float64(dst_data.sizes['west_east']), np.float64(
dst_data.sizes['south_north']
)

# Down left corner of the domain
x0 = -(nx - 1) / 2.0 * dx + e
Expand Down Expand Up @@ -676,7 +678,7 @@ def _get_lcz_arr(src_data: xr.DataArray, info: Info) -> NDArray[np.int_]:

# Get mask of selected built LCZs
lcz_urb_mask = xr.DataArray(
np.in1d(src_data, info.BUILT_LCZ).reshape(src_data.shape),
np.isin(src_data, info.BUILT_LCZ).reshape(src_data.shape),
dims=src_data.dims,
coords=src_data.coords,
)
Expand Down Expand Up @@ -846,7 +848,7 @@ def _hgt_resampler(

def _get_truncated_normal_sample(
lcz_i: int, ucp_table: pd.DataFrame, SAMPLE_SIZE: int = 100000
) -> NDArray[np.float_]:
) -> NDArray['np.floating[Any]']:
'''Helper function to return bounded normal distribution sample'''

# Create instance of a truncated normal distribution
Expand All @@ -867,7 +869,7 @@ def _get_truncated_normal_sample(

def _check_hi_values(
lcz_i: int,
hi_sample: NDArray[np.float_],
hi_sample: NDArray['np.floating[Any]'],
ucp_table: pd.DataFrame,
ERROR_MARGIN: float,
) -> None:
Expand Down Expand Up @@ -960,6 +962,7 @@ def _compute_hi_distribution(
df_hi.loc[i, :] = count_bins

# Set nans to zero
df_hi = df_hi.infer_objects()
df_hi = df_hi.fillna(0)

return df_hi
Expand All @@ -977,7 +980,7 @@ def _hi_resampler(
RESAMPLE_TYPE: str,
ucp_table: pd.DataFrame,
HI_THRES_MIN: int = 5,
) -> Tuple[NDArray[np.float_], float]:
) -> Tuple[NDArray['np.floating[Any]'], float]:
'''Helper function to resample ucp HI_URB2D_URB2D data to WRF grid'''

# Read gridded LCZ data
Expand Down Expand Up @@ -1048,7 +1051,7 @@ def _lcz_resampler(
info: Info,
frc_urb2d: xr.DataArray,
LCZ_NAT_MASK: bool,
) -> Tuple[NDArray[np.bool_], NDArray[np.float_]]:
) -> Tuple[NDArray[np.bool_], NDArray['np.floating[Any]']]:
'''Helper function to resample lcz classes to WRF grid using majority'''

# Read gridded LCZ data
Expand Down Expand Up @@ -1098,7 +1101,7 @@ def _adjust_greenfrac_landusef(
# GREENFRAC is set as average / month from GREENFRAC
# of original urban pixels
wrf_urb = xr.DataArray(
np.in1d(dst_data_orig['LU_INDEX'][0, :, :].values, [urban_cat]).reshape(
np.isin(dst_data_orig['LU_INDEX'][0, :, :].values, [urban_cat]).reshape(
dst_data_orig['LU_INDEX'][0, :, :].shape
),
dims=dst_data_orig['LU_INDEX'][0, :, :].dims,
Expand Down Expand Up @@ -1579,7 +1582,9 @@ def checks_and_cleaning(info: Info, ucp_table: pd.DataFrame, nbui_max: float) ->
'LF_URB2D': {'index': 96, 'range': [0, 5]},
}

def _check_range(darr: NDArray[np.float_], exp_range: List[int]) -> int:
def _check_range(
darr: NDArray['np.floating[Any]'], exp_range: List[int]
) -> int:
total_len = len(darr)
sel_len = ((darr >= exp_range[0]) & (darr <= exp_range[1])).sum(axis=0)

Expand Down
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