bugfix after Pandas update: ensure df dtype is changed to numeric

track_analyzer/plotting.py

@@ -406,7 +406,7 @@ def plot_vector_field(data_dir, df, data, field, frame, plot_on_field=None, dim=
             dim = 2  # to ensure that arrows are plotted in black and the z data is not use
             fig = plot_scalar_field(data_dir, df, data, plot_on_field['plot_on'], frame, image=image,
                                         map_param=map_param_, plot_dir=plot_dir, plot_config=None,
-                                        dont_print_count=True, dont_save=True)
+                                        quiet=2, dont_save=True)
             ax = fig.gca()
             invert_yaxis = False  # to ensure it's not inverted a second time
         else:
@@ -455,7 +455,7 @@ def plot_vector_field(data_dir, df, data, field, frame, plot_on_field=None, dim=
 def plot_Voronoi(data_dir, df, frame, data, show_local_area=True,
                  image={'image_fn': None, 't_dim': None, 'z_dim': None},
                  map_param={'no_bkg': False, 'vlim': None, 'show_axis': False,'cmap':'plasma','size_factor': 1,'line_width':1.},
-                 plot_dir=None, plot_config=None, dont_print_count=False,quiet=0):
+                 plot_dir=None, plot_config=None,quiet=0):
     """
     Plot Voronoi tesselation and local area in 2D only.
     :param data_dir:
@@ -467,7 +467,6 @@ def plot_Voronoi(data_dir, df, frame, data, show_local_area=True,
     :param map_param:
     :param plot_dir:
     :param plot_config:
-    :param dont_print_count:
     :return:
     """
 
@@ -696,7 +695,8 @@ def plot_param_vs_param(data_dir, x_param, y_param, df=None, hue=None, hue_order
     if plot_config['despine']:
         sns.despine(fig)
 
-    ax.legend(frameon=False)
+    if hue is not None:
+        ax.legend(frameon=False)
 
     filename = osp.join(plot_dir, prefix + '{}_vs_{}{}{}'.format(y_param, x_param, suffix, plot_config['format']))
     fig.savefig(filename, dpi=plot_config['dpi'], bbox_inches='tight')
@@ -742,7 +742,8 @@ def plot_param_hist(data_dir, param, df=None, hue=None, hue_order=None, hist=Tru
 
     kind = "hist" if hist else "kde"
     g = sns.displot(data=df, x=param, hue=hue, kind=kind, kde=kde,facet_kws={'legend_out':False,'despine':plot_config['despine']})
-    sns.move_legend(g, "upper right", frameon=False, bbox_to_anchor=[0.95, 0.95],title=None)
+    if hue is not None:
+        sns.move_legend(g, "upper right", frameon=False, bbox_to_anchor=[0.95, 0.95],title=None)
     fig = g.figure
     ax = g.ax
     fig.set_size_inches(figsize[0], figsize[1])

track_analyzer/prepare.py

@@ -63,7 +63,7 @@ def get_cmap_color(value, colormap='plasma', vmin=None, vmax=None):
     """
     Get color corresponding to a value from a colormap. Optionally, give boundaries to colormap with vmin, vmax.
     :param value: value to be converted to color
-    :type value: float
+    :type value: float or list or numpy.array
     :param colormap: Matplotlib colormap name
     :type colormap: str
     :param vmin: if not None, minimum value of colormap
@@ -73,6 +73,7 @@ def get_cmap_color(value, colormap='plasma', vmin=None, vmax=None):
     :return: color
     :rtype: tuple
     """
+
     colormap = plt.get_cmap(colormap)
     norm = plt.Normalize(vmin, vmax)
     return colormap(norm(value))
@@ -580,7 +581,8 @@ def get_info(data_dir):
                 info[k] = float(info[k])
 
     else:
-        raise Exception("ERROR: info.txt doesn't exist or is not at the main data folder")
+        print("WARNING: info.txt doesn't exist or is not at the main data folder")
+        return None
 
     mandatory_info = ['timescale', 'lengthscale']
     for mand_info in mandatory_info:
@@ -639,6 +641,7 @@ def get_data(data_dir, df=None, refresh=False, split_traj=False, set_origin_=Fal
             df = pd.read_csv(data_file, sep=sep)  # columns must be ['x','y','z','frame','track']
 
         # check data type
+        df = df.apply(pd.to_numeric, errors='coerce')
         dimensions = ['x', 'y', 'z'] if 'z' in df.columns else ['x', 'y']
         dim = len(dimensions)
         df['frame'] = df['frame'].astype(np.int)
@@ -672,6 +675,11 @@ def get_data(data_dir, df=None, refresh=False, split_traj=False, set_origin_=Fal
     else:
         data = pickle.load(open(pickle_fn, "rb"))
 
+    # extra check that df is numerci (in case of old pickle)
+    df = data['df']
+    df = df.apply(pd.to_numeric, errors='coerce')
+    data['df'] = df
+
     return data
 
 
@@ -1176,12 +1184,17 @@ def get_image(data_dir, filename=None, verbose=False):
     else:
         # get image size
         info = get_info(data_dir)
-        if 'image_width' in info.keys() and 'image_height' in info.keys():
-            if info['image_width'] is not None and info['image_height'] is not None:
-                image_size = (info['image_height'],info['image_width'])
-        else: 
+
+        if info is None:
+            print("WARNING: no info.txt file so image size will be inferred")
             image_size = None
-            print("WARNING: no image in directory, and image size is not defined in info.txt")
+        else: 
+            if 'image_width' in info.keys() and 'image_height' in info.keys():
+                if info['image_width'] is not None and info['image_height'] is not None:
+                    image_size = (info['image_height'],info['image_width'])
+            else: 
+                image_size = None
+                print("WARNING: no image in directory, and image size is not defined in info.txt")
 
         image_dict = {'image_fn': None, 't_dim': None, 'z_dim': None, 'image_size': image_size}
 

track_analyzer/scripts/analyze_maps.py

@@ -201,10 +201,10 @@ def map_analysis(data_dir, data=None, image=None, refresh=False, parallelize=Fal
                                                          export_field=map_param['export_field'], outdir=sub_dir)
 
         # plot data
+        if quiet < 2:
+            print("Plotting scalar fields...")
         scalar_fields_ = {**scalar_fields, **vector_mean}  # merge scalar data in one single dict
         for field in scalar_fields_.keys():
-            if quiet < 2:
-                print("Plotting scalar fields...")
             if plot_config['save_as_stack']:
                 plot_dir = sub_dir
             else: # save series of images in a separate directory 
@@ -215,9 +215,10 @@ def map_analysis(data_dir, data=None, image=None, refresh=False, parallelize=Fal
             map_param_['cmap'] = scalar_fields_[field]['cmap'] if 'cmap' in scalar_fields_[field].keys() else 'plasma'
             tpl.plot_all_scalar_fields(data_dir, df, field_data, field, image=image, map_param=map_param_,
                                        plot_dir=plot_dir, plot_config=plot_config, quiet=quiet)
+        
+        if quiet < 2:
+            print("Plotting vector fields...")
         for field in vector_fields.keys():
-            if quiet < 2:
-                print("Plotting vector fields...")
             if plot_config['save_as_stack']:
                 plot_dir = sub_dir
             else: # save series of images in a separate directory