diff --git a/metaloci/VERSION b/metaloci/VERSION
index 28af839..08456a4 100644
--- a/metaloci/VERSION
+++ b/metaloci/VERSION
@@ -1 +1 @@
-0.2.5
\ No newline at end of file
+0.2.8
\ No newline at end of file
diff --git a/metaloci/plot/plot.py b/metaloci/plot/plot.py
index 4653609..bab99ef 100644
--- a/metaloci/plot/plot.py
+++ b/metaloci/plot/plot.py
@@ -14,13 +14,12 @@
 from scipy.ndimage import rotate
 from scipy.stats import linregress, zscore
 from shapely.geometry import Point
-from shapely.geometry.multipolygon import MultiPolygon
 
 from metaloci import mlo
 from metaloci.misc import misc
 
 
-def get_kk_plot(mlobject: mlo.MetalociObject, restraints: bool = True):
+def get_kk_plot(mlobject: mlo.MetalociObject, restraints: bool = True, neighbourhood: float = None):
     """
     Generate Kamada-Kawai plot from pre-calculated restraints.
 
@@ -36,7 +35,6 @@ def get_kk_plot(mlobject: mlo.MetalociObject, restraints: bool = True):
     matplotlib.pyplot.figure.Figure
         Kamada-Kawai layout plot object.
     """
-
     PLOTSIZE = 10
     POINTSIZE = PLOTSIZE * 5
 
@@ -48,7 +46,7 @@ def get_kk_plot(mlobject: mlo.MetalociObject, restraints: bool = True):
     options = {"node_size": 50, "edge_color": "black", "linewidths": 0.1, "width": 0.05}
 
     if restraints == True:
-
+        
         nx.draw(
             mlobject.kk_graph,
             mlobject.kk_nodes,
@@ -69,8 +67,15 @@ def get_kk_plot(mlobject: mlo.MetalociObject, restraints: bool = True):
     g.annotate(f"      {mlobject.chrom}:{mlobject.start}", (xs[0], ys[0]), size=8)
     g.annotate(f"      {mlobject.chrom}:{mlobject.end}", (xs[len(xs) - 1], ys[len(ys) - 1]), size=8)
 
+    poi_x, poi_y = xs[mlobject.poi], ys[mlobject.poi]
+
+    if neighbourhood:
+
+        circle = plt.Circle((poi_x, poi_y), neighbourhood, color="red", fill=False, lw=1, zorder=3)
+        plt.gca().add_patch(circle)
+
     sns.scatterplot(
-        x=[xs[mlobject.poi]], y=[ys[mlobject.poi]], s=POINTSIZE * 1.5, ec="lime", fc="none", zorder=3
+        x=[poi_x], y=[poi_y], s=POINTSIZE * 1.5, ec="lime", fc="none", zorder=4
     )
 
     return kk_plt
@@ -207,8 +212,6 @@ def get_gaudi_signal_plot(mlobject: mlo.MetalociObject, lmi_geometry: pd.DataFra
         matplotlib figure containing the Gaudi signal plot.
     """
 
-    poi = lmi_geometry.loc[lmi_geometry["moran_index"] == mlobject.poi, "bin_index"].iloc[0]
-
     cmap = "PuOr_r"
     min_value = lmi_geometry.signal.min()
     max_value = lmi_geometry.signal.max()
@@ -216,8 +219,7 @@ def get_gaudi_signal_plot(mlobject: mlo.MetalociObject, lmi_geometry: pd.DataFra
     gsp, ax = plt.subplots(figsize=(12, 10), subplot_kw={"aspect": "equal"})
     lmi_geometry.plot(column="signal", cmap=cmap, linewidth=2, edgecolor="white", ax=ax)
 
-    sns.scatterplot(x=[lmi_geometry.X[poi]], y=[lmi_geometry.Y[poi]], s=50, ec="none", fc="lime")
-
+    sns.scatterplot(x=[lmi_geometry.X[mlobject.poi]], y=[lmi_geometry.Y[mlobject.poi]], s=50, ec="none", fc="lime")
     sm = plt.cm.ScalarMappable(cmap=cmap, norm=plt.Normalize(vmin=min_value, vmax=max_value))
     sm.set_array([1, 2, 3, 4])
 
@@ -262,7 +264,6 @@ def get_gaudi_type_plot(
     gtp : matplotlib.pyplot.figure.Figure
         matplotlib figure containing the Gaudi type plot.
     """
-    poi = lmi_geometry.loc[lmi_geometry["moran_index"] == mlobject.poi, "bin_index"].iloc[0]
 
     legend_elements = [
         Line2D([0], [0], marker="o", color="w", markerfacecolor=colors[1], label="HH", markersize=20),
@@ -272,15 +273,15 @@ def get_gaudi_type_plot(
     ]
 
     cmap = LinearSegmentedColormap.from_list("Custom cmap", [colors[nu] for nu in colors], len(colors))
-    alpha = [1.0 if quad <= signipval else 0.3 for quad in lmi_geometry.LMI_pvalue]
+    alpha = [1.0 if pval <= signipval else 0.3 for pval in lmi_geometry.LMI_pvalue]
 
     gtp, ax = plt.subplots(figsize=(12, 10), subplot_kw={"aspect": "equal"})
     lmi_geometry.plot(column="moran_quadrant", cmap=cmap, alpha=alpha, linewidth=2, edgecolor="white", ax=ax)
     plt.axis("off")
 
     sns.scatterplot(
-        x=[lmi_geometry.X[poi]],
-        y=[lmi_geometry.Y[poi]],
+        x=[lmi_geometry.X[mlobject.poi]],
+        y=[lmi_geometry.Y[mlobject.poi]],
         s=50,
         ec="none",
         fc="lime",
@@ -368,6 +369,94 @@ def get_lmi_scatterplot(
 
     return scatter, r_value_scat, p_value_scat
 
+# def signal_bed(
+#     mlobject: mlo.MetalociObject,
+#     lmi_geometry: pd.DataFrame,
+#     neighbourhood: float,
+#     quadrants: list = [1, 3],
+#     signipval: float = 0.05,
+# ):
+#     """
+#     signal_bed _summary_
+
+#     Parameters
+#     ----------
+#     mlobject : mlo.MetalociObject
+#         _description_
+#     lmi_geometry : pd.DataFrame
+#         _description_
+#     neighbourhood : float
+#         _description_
+#     quadrants : list, optional
+#         _description_, by default [1, 3]
+#     signipval : float, optional
+#         _description_, by default 0.05
+
+#     Returns
+#     -------
+#     _type_
+#         _description_
+#     """
+
+#     poi_distance = mlobject.kk_distances[mlobject.poi]
+
+#     # Select the polygons that are in the quadrant of interest and are significative.
+#     ml_indexes = lmi_geometry[
+#         (lmi_geometry.moran_quadrant.isin(quadrants)) & (lmi_geometry.LMI_pvalue <= signipval)
+#     ].bin_index.values.tolist()
+
+#     # Make a big polygon from the small poligons that are significant.
+#     metalocis = lmi_geometry[lmi_geometry.bin_index.isin(ml_indexes)].unary_union
+
+#     if metalocis and metalocis.geom_type == "Polygon":
+
+#         metalocis = MultiPolygon([metalocis])  # Need a multipolygon in order for the code to work.
+
+#     poi_point = Point(
+#         (lmi_geometry[lmi_geometry.moran_index == mlobject.poi].X, lmi_geometry[lmi_geometry.moran_index == mlobject.poi].Y)
+#     )
+
+#     metalocis_bed = []
+
+#     bed_data = defaultdict(list)
+
+#     try:
+
+#         for metaloci in metalocis.geoms:
+
+#             metaloci = gpd.GeoSeries(metaloci)
+
+#             if poi_point.within(metaloci[0]):
+
+#                 for _, row_ml in lmi_geometry.iterrows():
+
+#                     adjacent_point = Point((row_ml.X, row_ml.Y))
+
+#                     if adjacent_point.within(metaloci[0]):
+
+#                         metalocis_bed.append(row_ml.bin_index)
+
+#                 # Add close particles
+#                 metalocis_bed.sort()
+#                 close_bins = [i for i, distance in enumerate(poi_distance) if distance <= neighbourhood / 2]
+#                 metalocis_bed = np.sort(list(set(close_bins + metalocis_bed)))
+
+#                 for point in metalocis_bed:
+
+#                     bed_data["chr"].append(lmi_geometry.bin_chr[point])
+#                     bed_data["start"].append(lmi_geometry.bin_start[point])
+#                     bed_data["end"].append(lmi_geometry.bin_end[point])
+#                     bed_data["bin"].append(point)
+
+#     except:
+
+#         pass
+
+#     bed_data = pd.DataFrame(bed_data)
+
+#     return bed_data, metalocis_bed
+
+
 
 def signal_bed(
     mlobject: mlo.MetalociObject,
@@ -375,6 +464,7 @@ def signal_bed(
     neighbourhood: float,
     quadrants: list = [1, 3],
     signipval: float = 0.05,
+    metaloci_only: bool = False,
 ):
     """
     signal_bed _summary_
@@ -391,6 +481,8 @@ def signal_bed(
         _description_, by default [1, 3]
     signipval : float, optional
         _description_, by default 0.05
+    metaloci_only : bool, optional
+        _description_, by default False
 
     Returns
     -------
@@ -398,65 +490,57 @@ def signal_bed(
         _description_
     """
 
-    poi_distance = mlobject.kk_distances[mlobject.poi]
+    metalocis_bed = []
 
-    # Select the polygons that are in the quadrant of interest and are significative.
-    ml_indexes = lmi_geometry[
-        (lmi_geometry.moran_quadrant.isin(quadrants)) & (lmi_geometry.LMI_pvalue <= signipval)
-    ].bin_index.values.tolist()
+    bed_data = defaultdict(list)
 
-    # Make a big polygon from the small poligons that are significant.
-    metalocis = lmi_geometry[lmi_geometry.bin_index.isin(ml_indexes)].unary_union
+    if metaloci_only == False:
 
-    if metalocis and metalocis.geom_type == "Polygon":
+        for _, row_ml in lmi_geometry.iterrows():
 
-        metalocis = MultiPolygon([metalocis])  # Need a multipolygon in order for the code to work.
+            if row_ml.LMI_pvalue <= signipval and row_ml.moran_quadrant in quadrants:
 
-    poi_point = Point(
-        (lmi_geometry[lmi_geometry.bin_index == mlobject.poi].X, lmi_geometry[lmi_geometry.bin_index == mlobject.poi].Y)
-    )
+                metalocis_bed.append(row_ml.bin_index)
 
-    metalocis_bed = []
+    else:
 
-    bed_data = defaultdict(list)
+        poi_row = lmi_geometry[lmi_geometry.bin_index == mlobject.poi].squeeze()
 
-    try:
+        # print(lmi_geometry[lmi_geometry.bin_index == mlobject.poi])
 
-        for metaloci in metalocis.geoms:
+        if poi_row.LMI_pvalue <= signipval and poi_row.moran_quadrant in quadrants:
 
-            metaloci = gpd.GeoSeries(metaloci)
+            poi_point = Point(
+            (lmi_geometry[lmi_geometry.bin_index == mlobject.poi].X, lmi_geometry[lmi_geometry.bin_index == mlobject.poi].Y)
+            )
 
-            if poi_point.within(metaloci[0]):
+            poi_point = Point(lmi_geometry.loc[lmi_geometry["bin_index"] == mlobject.poi, ["X", "Y"]].iloc[0])
 
-                for _, row_ml in lmi_geometry.iterrows():
+            for _, row_ml in lmi_geometry.iterrows():
 
-                    adjacent_point = Point((row_ml.X, row_ml.Y))
+                adjacent_point = Point((row_ml.X, row_ml.Y))
 
-                    if adjacent_point.within(metaloci[0]):
+                if adjacent_point.distance(poi_point) <= neighbourhood:
 
-                        metalocis_bed.append(row_ml.bin_index)
+                    metalocis_bed.append(row_ml.bin_index)
 
-                # Add close particles
-                metalocis_bed.sort()
-                close_bins = [i for i, distance in enumerate(poi_distance) if distance <= neighbourhood / 2]
-                metalocis_bed = np.sort(list(set(close_bins + metalocis_bed)))
+        metalocis_bed.sort()
 
-                for point in metalocis_bed:
+        # print(metalocis_bed)
 
-                    bed_data["chr"].append(lmi_geometry.bin_chr[point])
-                    bed_data["start"].append(lmi_geometry.bin_start[point])
-                    bed_data["end"].append(lmi_geometry.bin_end[point])
-                    bed_data["bin"].append(point)
+    for point in metalocis_bed:
 
-    except:
+        bed_data["chr"].append(lmi_geometry.bin_chr[point])
+        bed_data["start"].append(lmi_geometry.bin_start[point])
+        bed_data["end"].append(lmi_geometry.bin_end[point])
+        bed_data["bin"].append(point)
+        bed_data["quadrant"].append(lmi_geometry.moran_quadrant[point])
 
-        pass
 
     bed_data = pd.DataFrame(bed_data)
 
     return bed_data, metalocis_bed
 
-
 def signal_plot(mlobject: mlo.MetalociObject, lmi_geometry: pd.DataFrame, metalocis, bins_sig, coords_sig):
 
     sig_plt = plt.figure(figsize=(10, 1.5))
diff --git a/metaloci/spatial_stats/lmi.py b/metaloci/spatial_stats/lmi.py
index 68f8dae..670b0cd 100644
--- a/metaloci/spatial_stats/lmi.py
+++ b/metaloci/spatial_stats/lmi.py
@@ -18,7 +18,6 @@
 from metaloci import mlo
 from metaloci.misc import misc
 
-
 warnings.filterwarnings("ignore", category=ShapelyDeprecationWarning)
 
 
@@ -85,8 +84,8 @@ def construct_voronoi(mlobject: mlo.MetalociObject, buffer: float):
 
         df_geometry["bin_index"].append(x)
         df_geometry["moran_index"].append(i)
-        df_geometry["X"].append(mlobject.kk_coords[i][0])
-        df_geometry["Y"].append(mlobject.kk_coords[i][1])
+        df_geometry["X"].append(mlobject.kk_coords[x][0])
+        df_geometry["Y"].append(mlobject.kk_coords[x][1])
         df_geometry["geometry"].append(geometry_data.loc[i, "geometry"])
 
     df_geometry = pd.DataFrame(df_geometry)
@@ -197,9 +196,16 @@ def load_region_signals(mlobject: mlo.MetalociObject, signal_data: dict, signal_
     """
 
     # Read signal file. Will only process the signals present in this list.
-    with open(signal_file) as signals_handler:
 
-        signal_types = [line.rstrip() for line in signals_handler]
+    if os.path.isfile(signal_file):
+
+        with open(signal_file) as signals_handler:
+
+            signal_types = [line.rstrip() for line in signals_handler]
+
+    else:
+
+        signal_types = [signal_file]
 
     # region_signal = signal_data[mlobject.chrom][
     #     (signal_data[mlobject.chrom]["start"] >= int(mlobject.start / mlobject.resolution) * mlobject.resolution)
diff --git a/metaloci/tools/figure.py b/metaloci/tools/figure.py
index 2a61c07..a8a2fd1 100644
--- a/metaloci/tools/figure.py
+++ b/metaloci/tools/figure.py
@@ -5,7 +5,7 @@
 import pathlib
 import pickle
 import re
-from argparse import (ArgumentParser, HelpFormatter, RawTextHelpFormatter)
+from argparse import HelpFormatter
 from datetime import timedelta
 from time import time
 
@@ -75,11 +75,13 @@ def populate_args(parser):
     )
 
     optional_arg.add_argument(
-        "-l",
-        "--log",
-        dest="log",
+        "-m",
+        "--metalocis",
+        dest="metalocis",
         action="store_true",
-        help="Flag to write bed with metaloci, for each region.",
+        help="Flag to select highlightning of the signal plots. If True, only the neighbouring bins from the point of interest will be "
+        "highlighted (independently of the quadrant and significance of those bins, but only if the point of interest is significant). "
+        "If False, all significant regions that correspond to the quadrant selected with -q will be highlighted (default: False).",
     )
 
     optional_arg.add_argument(
@@ -125,7 +127,7 @@ def run(opts):
     work_dir = opts.work_dir
     regions = opts.regions
     signals = opts.signals
-    metaloci_bed = opts.log
+    metaloci_bed = opts.metalocis
     quadrants = opts.quart
     signipval = opts.signipval
     rmtypes = opts.rm_types
@@ -270,6 +272,7 @@ def run(opts):
                 buffer * BFACT,
                 quadrants,
                 signipval,
+                metaloci_bed
             )
 
             # selmetaloci = []
@@ -314,7 +317,7 @@ def run(opts):
 
             yticks_signal = ax.get_yticks()[1:-1]
             max_chr_yax = max(len(str(max(yticks_signal))), len(str(min(yticks_signal))))
-            signal_left = {3 : 45, 4 : 30, 5 : 20, 6 : 9}
+            signal_left = {3 : 41, 4 : 30, 5 : 20, 6 : 9}
             
             if max_chr_yax not in signal_left.keys():
 
@@ -338,14 +341,14 @@ def run(opts):
             plt.close()
             print(f"\t\tFinal composite figure for region '{region}' and signal '{signal}' -> done.")
 
-            if metaloci_bed and len(bed_data) > 0:
+            if len(bed_data) > 0:
 
                 metaloci_bed_path = f"{work_dir}{mlobject.chrom}/metalocis_log/{signal}"
                 pathlib.Path(metaloci_bed_path).mkdir(parents=True, exist_ok=True) 
 
-                bed_data.to_csv(f"{metaloci_bed_path}/{mlobject.region}_{mlobject.poi}_{signal}_metalocis.txt", sep="\t", index=False)
+                bed_data.to_csv(f"{metaloci_bed_path}/{mlobject.region}_{mlobject.poi}_{signal}_metalocis.bed", sep="\t", index=False)
                 print(f"\t\tBed file with metalocis location saved to: "
-                      f"{metaloci_bed_path}/{mlobject.region}_{mlobject.poi}_{signal}.txt")
+                      f"{metaloci_bed_path}/{mlobject.region}_{mlobject.poi}_{signal}.bed")
 
             # Log
             for signal_key, df in mlobject.lmi_info.items():
@@ -392,6 +395,6 @@ def run(opts):
                     os.remove(f"{plot_filename}_gsp.{ext}")
                     os.remove(f"{plot_filename}_gtp.{ext}")
 
-    print(f"\nInformation saved to {os.path.join(work_dir, 'moran_info.txt')}")
+    print(f"\nInformation saved to: '{os.path.join(work_dir, 'moran_info.txt')}'")
     print(f"\nTotal time spent: {timedelta(seconds=round(time() - start_timer))}")
     print("\nall done.")
diff --git a/metaloci/tools/layout.py b/metaloci/tools/layout.py
index 4724b5a..66c5a14 100644
--- a/metaloci/tools/layout.py
+++ b/metaloci/tools/layout.py
@@ -237,16 +237,28 @@ def get_region_layout(row, opts, progress=None, silent: bool = True):
                 if silent == False: print(f"\tPlotting Kamada-Kawai...")
 
                 pathlib.Path(os.path.join(work_dir, region_chrom), "plots", "KK").mkdir(parents=True, exist_ok=True)
+                pathlib.Path(os.path.join(work_dir, region_chrom), "plots", "mixed_matrices").mkdir(
+                    parents=True, exist_ok=True
+                )
 
-                kk_plt = plot.get_kk_plot(mlobject)
-
-                fig_save_path = os.path.join(
+                plot.get_kk_plot(mlobject).savefig(os.path.join(
                     work_dir,
                     region_chrom,
                     f"plots/KK/{region_coords}_" f"{mlobject.kk_cutoff}_KK.pdf",
+                ),
+                dpi=300)
+
+                plt.close()
+
+                plot.mixed_matrices_plot(mlobject).savefig(
+                    os.path.join(
+                        work_dir,
+                        region_chrom,
+                        f"plots/mixed_matrices/{region_coords}_" f"{mlobject.kk_cutoff}_mixed-matrices.pdf",
+                    ),
+                    dpi=300,
                 )
 
-                kk_plt.savefig(fig_save_path, dpi=300)
                 plt.close()
 
                 if progress is not None: progress["plots"] = True
@@ -306,24 +318,6 @@ def get_region_layout(row, opts, progress=None, silent: bool = True):
             # Get submatrix of restraints
             restraints_matrix, mlobject = kk.get_restraints_matrix(mlobject)
 
-            if save_plots:
-
-                mixed_matrices_plot = plot.mixed_matrices_plot(mlobject)
-
-                pathlib.Path(os.path.join(work_dir, region_chrom), "plots", "mixed_matrices").mkdir(
-                    parents=True, exist_ok=True
-                )
-
-                mixed_matrices_plot.savefig(
-                    os.path.join(
-                        work_dir,
-                        region_chrom,
-                        f"plots/mixed_matrices/{region_coords}_" f"{mlobject.kk_cutoff}_mixed-matrices.pdf",
-                    ),
-                    dpi=300,
-                )
-
-                plt.close()
 
             if silent == False:
 
@@ -336,17 +330,31 @@ def get_region_layout(row, opts, progress=None, silent: bool = True):
 
             if len(cutoffs) > 1 or save_plots:
 
-                pathlib.Path(os.path.join(work_dir, region_chrom), "plots", "KK").mkdir(parents=True, exist_ok=True)
+                if silent == False: print(f"\tPlotting Kamada-Kawai...")
 
-                kk_plt = plot.get_kk_plot(mlobject)
+                pathlib.Path(os.path.join(work_dir, region_chrom), "plots", "KK").mkdir(parents=True, exist_ok=True)
+                pathlib.Path(os.path.join(work_dir, region_chrom), "plots", "mixed_matrices").mkdir(
+                    parents=True, exist_ok=True
+                )
 
-                fig_save_path = os.path.join(
+                plot.get_kk_plot(mlobject).savefig(os.path.join(
                     work_dir,
                     region_chrom,
                     f"plots/KK/{region_coords}_" f"{mlobject.kk_cutoff}_KK.pdf",
+                ),
+                dpi=300)
+
+                plt.close()
+
+                plot.mixed_matrices_plot(mlobject).savefig(
+                    os.path.join(
+                        work_dir,
+                        region_chrom,
+                        f"plots/mixed_matrices/{region_coords}_" f"{mlobject.kk_cutoff}_mixed-matrices.pdf",
+                    ),
+                    dpi=300,
                 )
 
-                kk_plt.savefig(fig_save_path, dpi=300)
                 plt.close()
 
                 if progress is not None: progress["plots"] = True
@@ -359,8 +367,8 @@ def get_region_layout(row, opts, progress=None, silent: bool = True):
 
                 if silent == False:
                     print(
-                        f"\tKamada-Kawai layout of region {mlobject.region} "
-                        f"at {int(cutoff * 100)} % cutoff saved to file: {mlobject.save_path}"
+                        f"\tKamada-Kawai layout of region '{mlobject.region}' "
+                        f"at {int(cutoff * 100)} % cutoff saved to file: '{mlobject.save_path}'"
                     )
 
                 # Write to file a list of bad regions, according to the filters defined in clean_matrix().
diff --git a/metaloci/tools/ml.py b/metaloci/tools/ml.py
index 5b070a5..d76acf3 100644
--- a/metaloci/tools/ml.py
+++ b/metaloci/tools/ml.py
@@ -3,13 +3,13 @@
 """
 import multiprocessing as mp
 import os
+import pathlib
 import pickle
 import re
 import subprocess as sp
 from argparse import HelpFormatter
 from datetime import timedelta
 from time import time
-import pathlib
 
 import pandas as pd
 
@@ -80,11 +80,11 @@ def populate_args(parser):
     )
 
     optional_arg.add_argument(
-        "-l",
-        "--log",
-        dest="log",
+        "-i",
+        "--info",
+        dest="info",
         action="store_true",
-        help="Flag to unpickle LMI log.",
+        help="Flag to unpickle LMI info.",
     )
 
     optional_arg.add_argument(
@@ -114,7 +114,7 @@ def get_lmi(region_iter, opts, signal_data, progress=None, i=None, silent: bool
     regions = opts.region_file
     n_permutations = opts.perms
     signipval = opts.signipval
-    moran_log = opts.log
+    moran_info = opts.info
     force = opts.force
 
     if not work_dir.endswith("/"):
@@ -184,7 +184,7 @@ def get_lmi(region_iter, opts, signal_data, progress=None, i=None, silent: bool
 
             if progress is not None: progress["done"] = True
 
-            if moran_log:
+            if moran_info:
 
                 for signal, df in mlobject.lmi_info.items():
 
@@ -229,7 +229,7 @@ def get_lmi(region_iter, opts, signal_data, progress=None, i=None, silent: bool
 
         mlobject.save(hamlo_namendle)
 
-    if moran_log:
+    if moran_info:
 
         for signal, df in mlobject.lmi_info.items():
 
@@ -260,7 +260,7 @@ def run(opts):
     regions = opts.region_file
     multiprocess = opts.multiprocess
     cores = opts.threads
-    moran_log = opts.log
+    moran_info = opts.info
 
     if opts.threads is None:
 
@@ -305,15 +305,15 @@ def run(opts):
 
                         print("\tSome regions had already been computed and have been skipped.", end="")
                     
-                        if moran_log:
+                        if moran_info:
 
-                            print(f"\n\tLog saved to: {work_dir}chrN/moran_log", end="")
+                            print(f"\n\tLog saved to: '{work_dir}chr/moran_log/'", end="")
 
                 except Exception:
 
                     if progress["missing_signal"] is not None:
 
-                        print(f"\tSignal {progress['missing_signal']} is in the signal list but has not been processed with prep.\n"
+                        print(f"\tSignal '{progress['missing_signal']}' is in the signal list but has not been processed with prep.\n"
                               "\tprocess that signal or remove it from the signal list.\nExiting...")
                 
                     pool.close()
diff --git a/metaloci/tools/prep.py b/metaloci/tools/prep.py
index e379296..11623b5 100644
--- a/metaloci/tools/prep.py
+++ b/metaloci/tools/prep.py
@@ -7,14 +7,15 @@
 import subprocess as sp
 import warnings
 from argparse import HelpFormatter
-from pybedtools import BedTool
-import pandas as pd
 
-from metaloci.misc import misc
-from numba.core.errors import NumbaDeprecationWarning, NumbaPendingDeprecationWarning
-import warnings
 import h5py
 import hicstraw
+import pandas as pd
+from numba.core.errors import (NumbaDeprecationWarning,
+                               NumbaPendingDeprecationWarning)
+from pybedtools import BedTool
+
+from metaloci.misc import misc
 
 warnings.simplefilter('ignore', category=NumbaDeprecationWarning)
 warnings.simplefilter('ignore', category=NumbaPendingDeprecationWarning)
diff --git a/setup.py b/setup.py
index 34b08f2..db5cf98 100644
--- a/setup.py
+++ b/setup.py
@@ -36,6 +36,10 @@ def read_requirements(path):
     long_description_content_type="text/markdown",
     author="Leo Zuber, Iago Maceda, Juan Antonio Rodríguez and Marc Martí-Renom",
     author_email="martirenom@cnag.eu",
+    classifiers=["Programming Language :: Python :: 3.9",
+                 "License :: OSI Approved :: GNU General Public License v3 (GPLv3)", 
+                 "Topic :: Scientific/Engineering :: Bio-Informatics",
+                 "Operating System :: POSIX :: Linux",],
     packages=find_packages(exclude=["tests", ".github"]),
     install_requires=read_requirements("requirements.txt"),
     entry_points={