fix deprecation warnings and adapt code to matplotlib 3.5.0

Author: cmdoret

hicstuff/digest.py

@@ -401,7 +401,7 @@ def frag_len(
         ax.set_xlabel("Fragment length [bp]")
         ax.set_ylabel("Log10 number of fragments")
         ax.set_title("Distribution of restriction fragment length")
-        ax.set_yscale("log", basey=10)
+        ax.set_yscale("log", base=10)
         ax.annotate(
             "Total fragments: {}".format(nfrags),
             xy=(0.95, 0.95),

hicstuff/distance_law.py

@@ -255,7 +255,7 @@ def logbins_xs(fragments, chr_segment_length, base=1.1, circular=False):
         # For each chromosome/arm compute a logspace to have the logbin
         # equivalent to the size of the arms and increasing size of bins
         xs[i] = np.unique(
-            np.logspace(0, n_bins, num=n_bins + 1, base=base, dtype=np.int)
+            np.logspace(0, n_bins, num=n_bins + 1, base=base, dtype=int)
         )
     return xs
 
@@ -787,7 +787,7 @@ def plot_ps_slope(xs, ps, labels, fig_path=None, inf=3000, sup=None):
     for i in range(len(slope)):
         xs2[i] = xs[i][:-1]
         col = next(cols)
-        ax2.semilogx(xs2[i], slope[i], label=labels[i], subsx=[2, 3, 4, 5, 6, 7, 8, 9])
+        ax2.semilogx(xs2[i], slope[i], label=labels[i], subs=[2, 3, 4, 5, 6, 7, 8, 9])
     ax2.legend(loc="upper left", bbox_to_anchor=(1.02, 1.00), ncol=1, fontsize="large")
     # Save the figure in svg
     if fig_path is not None:

hicstuff/hicstuff.py

@@ -89,7 +89,7 @@ def distance_law_from_mat(matrix, indices=None, log_bins=True, base=1.1):
     else:
         n_bins = int(np.log(n) / np.log(base) + 1)
         logbin = np.unique(
-            np.logspace(0, n_bins - 1, num=n_bins, base=base, dtype=np.int)
+            np.logspace(0, n_bins - 1, num=n_bins, base=base, dtype=int)
         )
         logbin = np.insert(logbin, 0, 0)
         logbin[-1] = min(n, logbin[-1])
@@ -512,7 +512,7 @@ def mad(M, axis=None):
     else:
         if axis < 0:
             axis += 2
-        dist = np.array(M.sum(axis=axis, dtype=np.float)).flatten()
+        dist = np.array(M.sum(axis=axis, dtype=float)).flatten()
 
     return np.median(np.absolute(dist - np.median(dist)))
 

hicstuff/io.py

@@ -910,7 +910,7 @@ def flexible_hic_loader(
             # Guess if bin size is fixed based on MAD
             bg2 = pd.read_csv(mat, sep="\t")
             sizes = np.array(bg2.iloc[:, 2] - bg2.iloc[:, 1])
-            size_mad = ss.median_absolute_deviation(sizes)
+            size_mad = ss.median_abs_deviation(sizes, scale='normal')
             # Use only the bg2
             if size_mad > 0:
                 mat, frags, chroms = load_bedgraph2d(mat)

tests/test_distance_law.py

@@ -81,17 +81,17 @@ def test_logbins_xs():
     xs = hcdl.logbins_xs(fragments, [60000, 20000])
     assert len(xs) == 2
     assert np.all(
-        xs[0] == np.unique(np.logspace(0, 115, num=116, base=1.1, dtype=np.int))
+        xs[0] == np.unique(np.logspace(0, 115, num=116, base=1.1, dtype=int))
     )
     # Test changing base.
     xs = hcdl.logbins_xs(fragments, [60000, 20000], base=1.5)
     assert np.all(
-        xs[0] == np.unique(np.logspace(0, 27, num=28, base=1.5, dtype=np.int))
+        xs[0] == np.unique(np.logspace(0, 27, num=28, base=1.5, dtype=int))
     )
     # Test with the circular option.
     xs = hcdl.logbins_xs(fragments, [60000, 20000], circular=True)
     assert np.all(
-        xs[0] == np.unique(np.logspace(0, 108, num=109, base=1.1, dtype=np.int))
+        xs[0] == np.unique(np.logspace(0, 108, num=109, base=1.1, dtype=int))
     )
 
 

tests/test_pipeline.py

@@ -123,6 +123,7 @@ def test_full_pipeline_bin(mapping, aligner):
             distance_law=True,
             start_stage=stage,
             mat_fmt="cooler",
+            no_cleanup=True,
             force=True,
         )
     shutil.rmtree("test_out2/")