Addressing #1181

deeptools · Jan 5, 2023 · 4c0c4fa · 4c0c4fa
1 parent 77dffdf
commit 4c0c4fa
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Showing 2 changed files with 4 additions and 4 deletions.
diff --git a/deeptools/heatmapper.py b/deeptools/heatmapper.py
@@ -777,7 +777,7 @@ def read_matrix_file(self, matrix_file):
             # split the line into bed interval and matrix values
             region = line.split('\t')
             chrom, start, end, name, score, strand = region[0:6]
-            matrix_row = np.ma.masked_invalid(np.fromiter(region[6:], np.float))
+            matrix_row = np.ma.masked_invalid(np.fromiter(region[6:], np.float64))
             matrix_rows.append(matrix_row)
             starts = start.split(",")
             ends = end.split(",")
@@ -852,7 +852,7 @@ def save_matrix(self, file_name):
             # join np_array values
             # keeping nans while converting them to strings
             if not np.ma.is_masked(score_list[idx]):
-                np.float(score_list[idx])
+                np.float64(score_list[idx])
             matrix_values = "\t".join(
                 np.char.mod('%f', self.matrix.matrix[idx, :]))
             starts = ["{0}".format(x[0]) for x in region[1]]
@@ -1345,7 +1345,7 @@ def removeempty(self):
         to_keep = []
         score_list = np.ma.masked_invalid(np.mean(self.matrix, axis=1))
         for idx, region in enumerate(self.regions):
-            if np.ma.is_masked(score_list[idx]) or np.float(score_list[idx]) == 0:
+            if np.ma.is_masked(score_list[idx]) or np.float64(score_list[idx]) == 0:
                 continue
             else:
                 to_keep.append(idx)

diff --git a/deeptools/sumCoveragePerBin.py b/deeptools/sumCoveragePerBin.py
@@ -90,7 +90,7 @@ def get_coverage_of_region(self, bamHandle, chrom, regions,
             except:
                 # bigWig input, as used by plotFingerprint
                 if bamHandle.chroms(chrom):
-                    _ = np.array(bamHandle.stats(chrom, regStart, regEnd, type="mean", nBins=nRegBins), dtype=np.float)
+                    _ = np.array(bamHandle.stats(chrom, regStart, regEnd, type="mean", nBins=nRegBins), dtype=np.float64)
                     _[np.isnan(_)] = 0.0
                     _ = _ * tileSize
                     coverages += _