Small bug fix

CHANGELOG.md

@@ -23,6 +23,7 @@ not work well for very large datasets with millions of variants and it causes ov
 - Fixed bugs and re-wrote how the `block` LD estimator is computed using both the `plink` and `xarray` backends.
 - Updated `from_plink_table` method in `LDMatrix` to handle cases where boundaries are different from what 
 `plink` computes.
+- Fixed bug in `symmetrize_ut_csr_matrix` utility functions.
 
 ### Added
 

magenpy/GWADataLoader.py

@@ -660,6 +660,9 @@ def compute_ld(self,
         the implementations of `WindowedLD`, `ShrinkageLD`, and `BlockLD` for details.
         """
 
+        if self.genotype is None:
+            raise ValueError("Cannot compute LD without genotype data.")
+
         if self.verbose and len(self.genotype) < 2:
             print("> Computing LD matrix...")
 

magenpy/stats/ld/c_utils.pyx

@@ -279,8 +279,8 @@ cpdef symmetrize_ut_csr_matrix_with_mask(indptr_type[::1] indptr,
         int64_t[::1] new_indptr = new_idx[0]
         int32_t[::1] leftmost_col = new_idx[1]
         int32_t[::1] cum_skipped_rows = np.cumsum(~mask, dtype=np.int32)
-        int32_t curr_row, curr_col, curr_row_size, filt_curr_row, filt_curr_col
-        int32_t curr_data_idx, new_idx_1, new_idx_2, curr_shape=indptr.shape[0]-1
+        int32_t curr_row, curr_col, curr_row_size, filt_curr_row, filt_curr_col, curr_shape=indptr.shape[0]-1
+        int64_t curr_data_idx, new_idx_1, new_idx_2
         noncomplex_numeric[::1] new_data = np.empty_like(data, shape=(new_idx[0][new_indptr.shape[0] - 1], ))
 
 
@@ -343,8 +343,8 @@ cpdef symmetrize_ut_csr_matrix(indptr_type[::1] indptr,
     cdef:
         int64_t[::1] new_indptr = new_idx[0]
         int32_t[::1] leftmost_col = new_idx[1]
-        int32_t curr_row, curr_col, curr_row_size
-        int32_t curr_data_idx, new_idx_1, new_idx_2, curr_shape=indptr.shape[0] - 1
+        int32_t curr_row, curr_col, curr_row_size, curr_shape=indptr.shape[0] - 1
+        int64_t curr_data_idx, new_idx_1, new_idx_2
         noncomplex_numeric[::1] new_data = np.empty_like(data, shape=(new_idx[0][new_indptr.shape[0] - 1], ))
 
     with nogil: