Cross score sandbox fixes (#497)

* Make cross_score work via CLI and small style fixes (#474)

* Cross-score fix of edge case fit bin-wise annotation of cross-scores

* Black and parameter naming issue

---------

Co-authored-by: Aleksandr <r1kanat@yandex.ru>

cooltools/sandbox/cross_score.py

@@ -11,58 +11,58 @@
 
 import pandas as pd
 
-
 parser = argparse.ArgumentParser(
     description="""Calculate distance-dependent contact marginals of a Hi-C map.
-    
+
     These contact marginals are analogous to per-bin contact-frequency-vs-distance 
     curves, but over a small number of distance bins. Such marginals are useful to 
     reveal genomic sites participating in frequent long-distance contacts, e.g. 
     anchors of loops and stripes.
-    """)
-
-parser.add_argument('COOL_URI', metavar='COOL_URI', 
-                    type=str, 
-                    help='input cooler URI')
+    """
+)
 
-parser.add_argument('--distbins',
-                    default='1e3,3e4,1e6,3e7',
-                    help='a comma-separated list of distance bins')
+parser.add_argument("COOL_URI", metavar="COOL_URI", type=str, help="input cooler URI")
 
-parser.add_argument('--clr-weight-name', 
-                    type=str,
-                    default='weight',
-                    help='Name of the column to use for data balancing')
+parser.add_argument(
+    "--dist-bins",
+    default="1e3,3e4,1e6,3e7",
+    help="a comma-separated list of distance bins",
+)
 
-parser.add_argument('--ignore-diags', 
-                    type=int,
-                    default=2,
-                    help='How many diagonals to ignore')
+parser.add_argument(
+    "--clr-weight-name",
+    type=str,
+    default="weight",
+    help="Name of the column to use for data balancing",
+)
 
-parser.add_argument('--outfolder', 
-                    type=str,
-                    default='./',
-                    help='The output folder')
+parser.add_argument(
+    "--ignore-diags", type=int, default=2, help="How many diagonals to ignore"
+)
 
-parser.add_argument('--prefix', 
-                    type=str,
-                    default=None,
-                    help='The prefix for output files')
+parser.add_argument("--outfolder", type=str, default="./", help="The output folder")
 
-parser.add_argument('--format', 
-                    type=str,
-                    default='bigwig',
-                    help='Comma-separated list of the output formats. Possible values: bigwig, bedgraph.',)
+parser.add_argument(
+    "--prefix", type=str, default=None, help="The prefix for output files"
+)
 
-parser.add_argument('--nproc', 
-                    type=int,
-                    default=None,
-                    help='The number of processes. By default, use all available cores.')
+parser.add_argument(
+    "--format",
+    type=str,
+    default="bigwig",
+    help="Comma-separated list of the output formats. Possible values: bigwig, bedgraph.",
+)
 
-parser.add_argument('--chunksize',
-                    default=1e6,
-                    help='The number of pixels per processed chunk.')
+parser.add_argument(
+    "--nproc",
+    type=int,
+    default=None,
+    help="The number of processes. By default, use all available cores.",
+)
 
+parser.add_argument(
+    "--chunksize", default=1e6, help="The number of pixels per processed chunk."
+)
 
 
 def get_dist_margs(clr_path, lo, hi, dist_bins, weight_name, ignore_diags):
@@ -72,125 +72,127 @@ def get_dist_margs(clr_path, lo, hi, dist_bins, weight_name, ignore_diags):
     chunk = clr.pixels()[lo:hi]
     res = clr.binsize
 
-    chunk = chunk[chunk['bin2_id']-chunk['bin1_id']>=ignore_diags]
+    chunk = chunk[chunk["bin2_id"] - chunk["bin1_id"] >= ignore_diags]
 
     chunk = cooler.annotate(chunk, bins)
-    chunk['balanced'] = np.nan_to_num(chunk['count'] * chunk[f'{weight_name}1'] * chunk[f'{weight_name}2'])
+    chunk["balanced"] = np.nan_to_num(
+        chunk["count"] * chunk[f"{weight_name}1"] * chunk[f"{weight_name}2"]
+    )
     chunk = chunk[chunk.chrom1 == chunk.chrom2]
 
-    del(clr)
+    del clr
 
     return _dist_margs(chunk, dist_bins, res)
 
 
-def _dist_margs(chunk, dist_bins, res): 
-    min_bin_id = chunk['bin1_id'].values[0]
+def _dist_margs(chunk, dist_bins, res):
+    min_bin_id = chunk["bin1_id"].values[0]
 
-    dists = (chunk['bin2_id'] - chunk['bin1_id']) * res
-    dist_bin_id = np.searchsorted(dist_bins, dists, 'right') 
+    dists = (chunk["bin2_id"] - chunk["bin1_id"]) * res
+    dist_bin_id = np.searchsorted(dist_bins, dists, "right")
     n_dist_bins = len(dist_bins)
     margs_down_loc = np.bincount(
-        (chunk['bin1_id'].values - min_bin_id) * n_dist_bins + dist_bin_id,
-        weights=np.nan_to_num(chunk['balanced'].values)
-        )
+        (chunk["bin1_id"].values - min_bin_id) * n_dist_bins + dist_bin_id,
+        weights=np.nan_to_num(chunk["balanced"].values),
+    )
 
     margs_up_loc = np.bincount(
-        (chunk['bin2_id'].values - min_bin_id) * n_dist_bins + dist_bin_id,
-        weights=np.nan_to_num(chunk['balanced'].values)
-            )
+        (chunk["bin2_id"].values - min_bin_id) * n_dist_bins + dist_bin_id,
+        weights=np.nan_to_num(chunk["balanced"].values),
+    )
 
     return min_bin_id, margs_down_loc, margs_up_loc
 
 
-
 def drop_resolution(clrname):
-    name_parts = clrname.split('.')
-    if name_parts[-1] in ['cool', 'mcool']:
+    name_parts = clrname.split(".")
+    if name_parts[-1] in ["cool", "mcool"]:
         name_parts = name_parts[:-1]
     if name_parts[-1].isnumeric():
         name_parts = name_parts[:-1]
-    return '.'.join(name_parts)
+    return ".".join(name_parts)
 
 
 args = parser.parse_args()
 
 logging.basicConfig(level=logging.INFO)
+if __name__ == "__main__":
+    mp.freeze_support()
+    chunksize = int(float(args.chunksize))
+    clr = cooler.Cooler(args.COOL_URI)
+    bins = clr.bins()[:]
+    n_pixels = clr.pixels().shape[0]
+    dist_bins = np.array([int(float(i)) for i in args.dist_bins.split(",")])
+    weight_name = args.clr_weight_name
+    ignore_diags = args.ignore_diags
+    formats = args.format.split(",")
 
-chunksize = int(float(args.chunksize))
-clr = cooler.Cooler(args.COOL_URI)
-bins = clr.bins()[:]
-n_pixels = clr.pixels().shape[0]
-dist_bins = np.array([int(float(i)) for i in args.distbins.split(',')])
-weight_name = args.clr_weight_name
-ignore_diags = args.ignore_diags
-formats = args.format.split(',')
+    n_dist_bins = len(dist_bins)
 
-n_dist_bins = len(dist_bins)
+    chunk_spans = np.r_[np.arange(0, n_pixels, chunksize), n_pixels]
 
-chunk_spans = np.r_[np.arange(0, n_pixels, chunksize), n_pixels]
+    nproc = mp.cpu_count() if args.nproc is None else args.nproc
 
-nproc = mp.cpu_count() if args.nproc is None else args.nproc
+    logging.info(f"Calculating marginals for {args.COOL_URI} using {nproc} processes")
+    with mp.Pool(nproc) as pool:
+        out = pool.starmap(
+            get_dist_margs,
+            [
+                (args.COOL_URI, lo, hi, dist_bins, weight_name, ignore_diags)
+                for lo, hi in zip(chunk_spans[:-1], chunk_spans[1:])
+            ],
+        )
 
-logging.info(f'Calculating marginals for {args.COOL_URI} using {nproc} processes')
-with mp.Pool(nproc) as pool:
-    out = pool.starmap(get_dist_margs,
-        [
-            (args.COOL_URI, lo, hi, dist_bins, weight_name, ignore_diags)
-            for lo, hi in zip(chunk_spans[:-1], chunk_spans[1:])
-        ]
-    )
+    margs_up = np.zeros(len(bins) * n_dist_bins + 1)
+    margs_down = np.zeros(len(bins) * n_dist_bins + 1)
+
+    for min_bin_id, margs_down_loc, margs_up_loc in out:
+        margs_down[
+            min_bin_id * n_dist_bins : min_bin_id * n_dist_bins + len(margs_down_loc)
+        ] += margs_down_loc
+
+        margs_up[
+            min_bin_id * n_dist_bins : min_bin_id * n_dist_bins + len(margs_up_loc)
+        ] += margs_up_loc
+
+    margs_down = margs_down[:-1].reshape((len(bins), n_dist_bins)).T
+    margs_up = margs_up[:-1].reshape((len(bins), n_dist_bins)).T
+
+    out_folder = pathlib.Path(args.outfolder)
+    clr_name = pathlib.Path(args.COOL_URI.split(":")[0]).name
+    clr_name = drop_resolution(clr_name)
+
+    prefix = clr_name if args.prefix is None else args.prefix
+    res = clr.binsize
 
-margs_up = np.zeros(len(bins) * n_dist_bins)
-margs_down = np.zeros(len(bins) * n_dist_bins)
-
-for min_bin_id, margs_down_loc, margs_up_loc in out:
-    margs_down[
-        min_bin_id * n_dist_bins:
-        min_bin_id * n_dist_bins + len(margs_down_loc)
-    ] += margs_down_loc
-
-    margs_up[
-        min_bin_id * n_dist_bins:
-        min_bin_id * n_dist_bins + len(margs_up_loc)
-    ] += margs_up_loc
-
-
-margs_down = margs_down.reshape((len(bins), n_dist_bins)).T
-margs_up = margs_up.reshape((len(bins), n_dist_bins)).T
-
-
-out_folder = pathlib.Path(args.outfolder)
-clr_name = pathlib.Path(args.COOL_URI.split(':')[0]).name
-clr_name = drop_resolution(clr_name)
-
-prefix = clr_name if args.prefix is None else args.prefix
-res = clr.binsize
-
-for dist_bin_id in range(n_dist_bins):
-    lo = np.r_[0, dist_bins][dist_bin_id]
-    hi = np.r_[0, dist_bins][dist_bin_id+1]
-
-    for dir_str, margs in [('up', margs_up), ('down', margs_down), ('both', margs_up+margs_down)]:
-        out_df = bins[['chrom', 'start', 'end']].copy()
-        out_df['marg'] = margs[dist_bin_id]
-        out_df['marg'] = out_df['marg'].mask(bins[weight_name].isnull(), np.nan)
-
-        if 'bigwig' in formats:
-            file_name = f'{prefix}.{res}.cross.{dir_str}.{lo}-{hi}.bw'
-            logging.info(f'Write output into {file_name}')
-            bioframe.io.to_bigwig(
-                out_df,
-                chromsizes=clr.chromsizes,
-                outpath=str(out_folder / file_name))
-
-        if 'bedgraph' in formats:
-            file_name = f'{prefix}.{res}.cross.{dir_str}.{lo}-{hi}.bg.gz'
-            logging.info(f'Write output into {file_name}')
-            out_df.to_csv(
-                str(out_folder / file_name),
-                sep='\t',
-                index=False,
-                header=False,
-            )
-
-
+    for dist_bin_id in range(n_dist_bins):
+        lo = np.r_[0, dist_bins][dist_bin_id]
+        hi = np.r_[0, dist_bins][dist_bin_id + 1]
+
+        for dir_str, margs in [
+            ("up", margs_up),
+            ("down", margs_down),
+            ("both", margs_up + margs_down),
+        ]:
+            out_df = bins[["chrom", "start", "end"]].copy()
+            out_df["marg"] = margs[dist_bin_id]
+            out_df["marg"] = out_df["marg"].mask(bins[weight_name].isnull(), np.nan)
+
+            if "bigwig" in formats:
+                file_name = f"{prefix}.{res}.cross.{dir_str}.{lo}-{hi}.bw"
+                logging.info(f"Write output into {file_name}")
+                bioframe.io.to_bigwig(
+                    out_df,
+                    chromsizes=clr.chromsizes,
+                    outpath=str(out_folder / file_name),
+                )
+
+            if "bedgraph" in formats:
+                file_name = f"{prefix}.{res}.cross.{dir_str}.{lo}-{hi}.bg.gz"
+                logging.info(f"Write output into {file_name}")
+                out_df.to_csv(
+                    str(out_folder / file_name),
+                    sep="\t",
+                    index=False,
+                    header=False,
+                )