minor plotting improvements and bug fixes

track_analyzer/plotting.py

@@ -998,7 +998,7 @@ def plot_all_traj(data_dir, df, image={'image_fn': None, 't_dim': None, 'z_dim':
     else:
         stack = []  # stack to store images
         groups = df.groupby('frame')
-        for frame in df['frame'].unique():
+        for frame in np.sort(df['frame'].unique()):
             frame = int(frame)
             fig = plot_traj(df, frame, data_dir, groups=groups, image=image, plot_dir=plot_dir,
                       traj_parameters=traj_parameters,
@@ -1047,7 +1047,7 @@ def plot_all_scalar_fields(data_dir, df, data, field, image={'image_fn': None, '
     plot_cmap(plot_dir, label, map_param_['cmap'], map_param_['vlim'][0], map_param_['vlim'][1],suffix='_'+field)
 
     stack = []  # stack to store images
-    for frame in df['frame'].unique():
+    for frame in np.sort(df['frame'].unique()):
         frame = int(frame)
         fig = plot_scalar_field(data_dir, df, data, field, frame, image=image,
                                     map_param=map_param_, plot_dir=plot_dir, plot_config=plot_config)
@@ -1100,7 +1100,7 @@ def plot_all_vector_fields(data_dir, df, data, field, plot_on_field=None, dim=3,
             plot_cmap(plot_dir, label, plot_on_field['cmap'], plot_on_field['vlim'][0], plot_on_field['vlim'][1],suffix='_vector_'+plot_on_field['plot_on'])
 
     stack = []  # stack to store images
-    for frame in df['frame'].unique():
+    for frame in np.sort(df['frame'].unique()):
         frame = int(frame)
         fig = plot_vector_field(data_dir, df, data, field, frame, plot_on_field=plot_on_field, dim=dim, image=image,
                           map_param=map_param, plot_dir=plot_dir, plot_config=plot_config)
@@ -1172,7 +1172,7 @@ def plot_all_Voronoi(data_dir, df, data, show_local_area=True, df_mean = None,
         plot_cmap(plot_dir, label, map_param['cmap'], map_param['vlim'][0], map_param['vlim'][1],suffix='_area')
 
     stack = []  # stack to store images
-    for frame in df['frame'].unique():
+    for frame in np.sort(df['frame'].unique()):
         frame = int(frame)
         fig = plot_Voronoi(data_dir, df, frame, data, show_local_area=show_local_area, image=image, map_param=map_param,
                      plot_dir=plot_dir, plot_config=plot_config, dont_print_count=dont_print_count)
@@ -1302,8 +1302,9 @@ def plot_all_MSD(data_dir, df=None, df_out=None, fit_model="biased_diff", msd_al
 
             lab = 'mean' if hue is None else hue_order[j]
             suffix = '' if hue is None else '_' + hue_order[j]
+            color_mean = 'k' if len(msd_all_list)==1 else color_list[j]
 
-            msd_mean.plot(x="tau", y="msd_mean", color=color_list[j], ax=ax, label=lab)
+            msd_mean.plot(x="tau", y="msd_mean", color=color_mean, ax=ax, label=lab)
             msd_mean.to_csv(osp.join(plot_dir, 'all_MSD_mean' + suffix + '.csv'))
             # calculate exponent
             msd_mean[['tau', 'msd_mean']] = msd_mean[['tau', 'msd_mean']].astype(np.float64)
@@ -1375,6 +1376,8 @@ def plot_total_traj(data_dir, df, dim=3, plot_dir=None, plot_fn=None, plot_confi
     cmap = specific_config['cmap']
     cmap_lim = specific_config['cmap_lim']
     subset_order = specific_config['subset_order']
+    transparency = specific_config['transparency']
+    show_legend = True
 
     # get info
     info = tpr.get_info(data_dir)
@@ -1399,7 +1402,7 @@ def plot_total_traj(data_dir, df, dim=3, plot_dir=None, plot_fn=None, plot_confi
             cmap_lim = [df['z_scaled'].min(), df['z_scaled'].max()]
 
         if len(cmap_lim) == 2:
-            plot_cmap(plot_dir, tpr.make_param_label('z', l_unit=info["length_unit"]), cmap, cmap_lim[0], cmap_lim[1],suffix='_total_traj')
+            plot_cmap(plot_dir, tpr.make_param_label('z_scaled', l_unit=info["length_unit"]), cmap, cmap_lim[0], cmap_lim[1],suffix='_total_traj')
 
     elif color_code == 't':
         if cmap_lim is None:
@@ -1424,6 +1427,7 @@ def plot_total_traj(data_dir, df, dim=3, plot_dir=None, plot_fn=None, plot_confi
     elif dim == 2 and color_code == 'z':
         color_code == 'random'
 
+    label_list = []  # to store label that have already been used 
     fig, ax = plt.subplots(1, 1, figsize=plot_config['figsize'])
     for i, track in enumerate(df['track'].unique()):
         traj = tpr.get_traj(track_groups, track)
@@ -1440,6 +1444,7 @@ def plot_total_traj(data_dir, df, dim=3, plot_dir=None, plot_fn=None, plot_confi
             traj['y_scaled'] -= y0
 
         # color code
+        label = None
         if color_code == "z":
             colors = tpr.get_cmap_color(z, cmap, vmin=cmap_lim[0], vmax=cmap_lim[1])
         elif color_code == "t":
@@ -1451,6 +1456,10 @@ def plot_total_traj(data_dir, df, dim=3, plot_dir=None, plot_fn=None, plot_confi
         elif color_code == "group":
             if 'subset_order' in df.columns:
                 colors = color_list[traj['subset_order'].values[0] % len(color_list)]
+                subset = df[df['track']==track]['subset'].values[0]
+                if subset not in label_list:
+                    label = subset
+                    label_list.append(subset)
             else:
                 colors = color_list[0]  # if color_coded by subset but there's none, use only one color
         else:
@@ -1459,11 +1468,11 @@ def plot_total_traj(data_dir, df, dim=3, plot_dir=None, plot_fn=None, plot_confi
         if traj_length > 1:  # dont plot single points
             if color_code == 'z' or color_code == "t":
                 for j in range(1, traj_length):
-                    ax.plot([x[j - 1], x[j]], [y[j - 1], y[j]], ls='-', color=colors[j])
-                ax.plot(x[-1], y[-1], marker='.', color=colors[-1])
+                    ax.plot([x[j - 1], x[j]], [y[j - 1], y[j]], ls='-', color=colors[j],alpha=transparency)
+                ax.plot(x[-1], y[-1], marker='.', color=colors[-1],alpha=transparency)
             else:
-                ax.plot(x, y, ls='-', color=colors)
-                ax.plot(x[-1], y[-1], marker='.', color=colors)
+                ax.plot(x, y, ls='-', color=colors,alpha=transparency,label=label)
+                ax.plot(x[-1], y[-1], marker='.', color=colors,alpha=transparency)
 
         if hide_labels is False:
             s = '{}'.format(int(track))
@@ -1471,8 +1480,8 @@ def plot_total_traj(data_dir, df, dim=3, plot_dir=None, plot_fn=None, plot_confi
         if set_axis_lim is not None:
             ax.set_xlim(set_axis_lim[0], set_axis_lim[1])
             ax.set_ylim(set_axis_lim[2], set_axis_lim[3])
-        ax.set_xlabel(r'x ($\mu m$)')
-        ax.set_ylabel(r'y ($\mu m$)')
+        ax.set_xlabel(tpr.make_param_label('x_scaled', l_unit=info["length_unit"]))
+        ax.set_ylabel(tpr.make_param_label('y_scaled', l_unit=info["length_unit"]))
 
     if equal_axis:
         ax.set_aspect('equal')
@@ -1481,6 +1490,10 @@ def plot_total_traj(data_dir, df, dim=3, plot_dir=None, plot_fn=None, plot_confi
         ylim = ax.get_ylim()
         ax.set_ylim(ylim[1], ylim[0])
 
+    if show_legend: 
+        if len(label_list) > 0:
+            ax.legend(frameon=False)
+
     fig.tight_layout()
 
     filename = osp.join(plot_dir,'total_traj'+plot_config['format']) if plot_fn is None else plot_fn

track_analyzer/prepare.py

@@ -957,7 +957,7 @@ def select_traj_by_xyzt(df, xlim=None, ylim=None, zlim=None, frame_lim=None):
     :rtype: list
     """
     # filter by frame subset
-    if type(frame_lim) is list:
+    if type(frame_lim) is list or type(frame_lim) is tuple:
         df = filter_by_frame_subset(df, frame_subset=frame_lim)
     elif type(frame_lim) is int or type(frame_lim) is float:
         df = df[df['frame'] == frame_lim]
@@ -1337,6 +1337,7 @@ def make_traj_config(data_dir=None, export_config=True):
                          'cmap': 'plasma',  # colormap to be used if color_code is 'z'
                          'cmap_lim': None,  # pass custom colormap limits
                          'subset_order': None,  # if color_code is group, order of group in color cycle
+                         'transparency': 1,  #plot transparency
                          }
 
     # config of voronoi plot