Enforce crs_matrix type when using tocsr()

climada/entity/exposures/litpop/nightlight.py

@@ -549,7 +549,7 @@ def unzip_tif_to_py(file_gz):
     nightlight = sparse.csc_matrix(plt.imread(file_name))
     # flip X axis
     nightlight.indices = -nightlight.indices + nightlight.shape[0] - 1
-    nightlight = nightlight.tocsr()
+    nightlight = sparse.csr_matrix(nightlight.tocsr())
     file_name.unlink()
     file_path = SYSTEM_DIR.joinpath(file_name.stem + ".p")
     save(file_path, nightlight)

climada/entity/measures/base.py

@@ -29,6 +29,7 @@
 import numpy as np
 import pandas as pd
 from geopandas import GeoDataFrame
+from scipy.sparse import csr_matrix
 
 import climada.util.checker as u_check
 from climada.entity.exposures.base import INDICATOR_CENTR, INDICATOR_IMPF, Exposures
@@ -565,6 +566,6 @@ def _filter_exposures(
             centr = np.delete(np.arange(hazard.intensity.shape[1]), np.unique(centr))
             new_haz_inten = new_haz.intensity.tolil()
             new_haz_inten[:, centr] = hazard.intensity[:, centr]
-            new_haz.intensity = new_haz_inten.tocsr()
+            new_haz.intensity = csr_matrix(new_haz_inten.tocsr())
 
         return new_exp, new_impfs, new_haz

climada/hazard/xarray.py

@@ -58,7 +58,9 @@ def _to_csr_matrix(array: xr.DataArray) -> sparse.csr_matrix:
         output_dtypes=[array.dtype],
     )
     sparse_coo = array.compute().data  # Load into memory
-    return sparse_coo.tocsr()  # Convert sparse.COO to scipy.sparse.csr_array
+    return sparse.csr_matrix(
+        sparse_coo.tocsr()
+    )  # Convert sparse.COO to scipy.sparse.csr_array
 
 
 # Define accessors for xarray DataArrays

climada/util/hdf5_handler.py

@@ -149,9 +149,11 @@ def get_sparse_csr_mat(mat_dict, shape):
     if ("data" not in mat_dict) or ("ir" not in mat_dict) or ("jc" not in mat_dict):
         raise ValueError("Input data is not a sparse matrix.")
 
-    return sparse.csc_matrix(
-        (mat_dict["data"], mat_dict["ir"], mat_dict["jc"]), shape
-    ).tocsr()
+    return sparse.csr_matrix(
+        sparse.csc_matrix(
+            (mat_dict["data"], mat_dict["ir"], mat_dict["jc"]), shape
+        ).tocsr()
+    )
 
 
 def to_string(str_or_bytes):