Black coding style

deeplabcut/__init__.py

@@ -28,6 +28,7 @@
 
 try:
     import wx
+
     mpl.use("WxAgg")
     from deeplabcut import generate_training_dataset
     from deeplabcut import refine_training_dataset

deeplabcut/__main__.py

@@ -9,6 +9,7 @@
 """
 try:
     import wx
+
     lite = False
 except ModuleNotFoundError:
     lite = True
@@ -20,4 +21,6 @@
 
     deeplabcut.launch_dlc()
 else:
-    print("You installed DLC lite, thus GUI's cannot be used. If you need GUI support please: pip install deeplabcut[gui]")
+    print(
+        "You installed DLC lite, thus GUI's cannot be used. If you need GUI support please: pip install deeplabcut[gui]"
+    )

deeplabcut/generate_training_dataset/trainingsetmanipulation.py

@@ -397,7 +397,9 @@ def cropimagesandlabels(
                 # moving old entry to _original, dropping it from video_set and update crop parameters
                 video_orig = sep.join((vidpath, vidname + videotype))
                 if video_orig not in cfg["video_sets_original"]:
-                    cfg["video_sets_original"][video_orig] = cfg["video_sets"][video_orig]
+                    cfg["video_sets_original"][video_orig] = cfg["video_sets"][
+                        video_orig
+                    ]
                     cfg["video_sets"].pop(video_orig)
                     cfg["video_sets"][sep.join((vidpath, new_vidname + videotype))] = {
                         "crop": ", ".join(map(str, [0, temp_size[1], 0, temp_size[0]]))
@@ -461,7 +463,7 @@ def check_labels(
     for folder in folders:
         try:
             DataCombined = pd.read_hdf(
-                os.path.join(str(folder), "CollectedData_" + cfg["scorer"] + ".h5"),
+                os.path.join(str(folder), "CollectedData_" + cfg["scorer"] + ".h5")
             )
             if cfg.get("multianimalproject", False):
                 color_by = "individual" if visualizeindividuals else "bodypart"
@@ -887,7 +889,11 @@ def create_training_dataset(
         if net_type is None:  # loading & linking pretrained models
             net_type = cfg.get("default_net_type", "resnet_50")
         else:
-            if "resnet" in net_type or "mobilenet" in net_type or "efficientnet" in net_type:
+            if (
+                "resnet" in net_type
+                or "mobilenet" in net_type
+                or "efficientnet" in net_type
+            ):
                 pass
             else:
                 raise ValueError("Invalid network type:", net_type)

deeplabcut/gui/auxfun_drag.py

@@ -89,11 +89,7 @@ def on_press(self, event):
             message = f"Do you want to remove the label {self.bodyParts}?"
             if self.likelihood is not None:
                 message += " You cannot undo this step!"
-            msg = wx.MessageBox(
-                message,
-                "Remove!",
-                wx.YES_NO | wx.ICON_WARNING,
-            )
+            msg = wx.MessageBox(message, "Remove!", wx.YES_NO | wx.ICON_WARNING)
             if msg == 2:
                 self.delete_data()
 

deeplabcut/gui/create_training_dataset.py

@@ -146,7 +146,9 @@ def __init__(self, parent, gui_size, cfg):
             )
             self.cropandlabel.Bind(wx.EVT_RADIOBOX, self.input_crop_size)
             self.cropandlabel.SetSelection(0)
-            self.crop_text = wx.StaticBox(self, label="Crop settings (set to smaller than your input images)")
+            self.crop_text = wx.StaticBox(
+                self, label="Crop settings (set to smaller than your input images)"
+            )
             self.crop_sizer = wx.StaticBoxSizer(self.crop_text, wx.VERTICAL)
             self.crop_widgets = []
             for name, val in [
@@ -171,7 +173,9 @@ def __init__(self, parent, gui_size, cfg):
         self.hbox3.Add(self.userfeedback, 10, wx.EXPAND | wx.TOP | wx.BOTTOM, 5)
 
         if config_file.get("multianimalproject", False):
-            print("more networks are available soon for maDLC, but currenlty this uses DLC-ResNet50 only")
+            print(
+                "more networks are available soon for maDLC, but currenlty this uses DLC-ResNet50 only"
+            )
             self.model_comparison_choice = "No"
         else:
             self.model_comparison_choice = wx.RadioBox(

deeplabcut/gui/frame_extraction_toolbox.py

@@ -45,7 +45,7 @@ def getColorIndices(self, img, bodyparts):
 class MainFrame(BaseFrame):
     def __init__(self, parent, config, slider_width=25):
         super(MainFrame, self).__init__(
-            "DeepLabCut2.0 - Manual Frame Extraction", parent,
+            "DeepLabCut2.0 - Manual Frame Extraction", parent
         )
 
         ###################################################################################################################################################
@@ -158,8 +158,9 @@ def __init__(self, parent, config, slider_width=25):
         self.date = self.cfg["date"]
         self.trainFraction = self.cfg["TrainingFraction"]
         self.trainFraction = self.trainFraction[0]
-        self.videos = list(self.cfg.get("video_sets_original")
-                           or self.cfg["video_sets"])
+        self.videos = list(
+            self.cfg.get("video_sets_original") or self.cfg["video_sets"]
+        )
         self.bodyparts = self.cfg["bodyparts"]
         self.colormap = plt.get_cmap(self.cfg["colormap"])
         self.colormap = self.colormap.reversed()

deeplabcut/gui/label_frames.py

@@ -71,9 +71,7 @@ def label_frames(
     os.chdir(str(wd))
     cfg = auxiliaryfunctions.read_config(config)
     if cfg.get("multianimalproject", False) or multiple_individualsGUI:
-        from deeplabcut.gui import (
-            multiple_individuals_labeling_toolbox,
-        )
+        from deeplabcut.gui import multiple_individuals_labeling_toolbox
 
         multiple_individuals_labeling_toolbox.show(config, config3d, sourceCam)
     else:

deeplabcut/gui/labeling_toolbox.py

@@ -58,7 +58,9 @@ def retrieveData_and_computeEpLines(self, img, imNum):
                     if self.sourceCam is None:
                         sourceCam = [
                             otherCam for otherCam in cams if cam not in otherCam
-                        ][0] #WHY?
+                        ][
+                            0
+                        ]  # WHY?
                     else:
                         sourceCam = self.sourceCam
 
@@ -80,11 +82,13 @@ def retrieveData_and_computeEpLines(self, img, imNum):
 
             try:
                 dataFrame = pd.read_hdf(
-                    os.path.join(sourceCam_path, "CollectedData_" + scorer + ".h5"),
+                    os.path.join(sourceCam_path, "CollectedData_" + scorer + ".h5")
                 )
                 dataFrame.sort_index(inplace=True)
             except IOError:
-                print("source camera images have not yet been labeled, or you have opened this folder in the wrong mode!")
+                print(
+                    "source camera images have not yet been labeled, or you have opened this folder in the wrong mode!"
+                )
                 return None, None, None
 
             # Find offset terms for drawing epipolar Lines
@@ -241,7 +245,7 @@ def clearBoxer(self):
 class MainFrame(BaseFrame):
     def __init__(self, parent, config, imtypes, config3d, sourceCam):
         super(MainFrame, self).__init__(
-            "DeepLabCut2.0 - Labeling ToolBox", parent, imtypes,
+            "DeepLabCut2.0 - Labeling ToolBox", parent, imtypes
         )
 
         self.statusbar.SetStatusText(
@@ -370,9 +374,7 @@ def OnKeyPressed(self, event=None):
             inv = self.axes.transData.inverted()
             pos_rel = list(inv.transform(pos_abs))
             y1, y2 = self.axes.get_ylim()
-            pos_rel[1] = (
-                y1 - pos_rel[1] + y2
-            )  # Recall y-axis is inverted
+            pos_rel[1] = y1 - pos_rel[1] + y2  # Recall y-axis is inverted
             i = np.nanargmin(
                 [self.calc_distance(*dp.point.center, *pos_rel) for dp in self.drs]
             )
@@ -619,7 +621,7 @@ def browseDir(self, event):
         # Reading the existing dataset,if already present
         try:
             self.dataFrame = pd.read_hdf(
-                os.path.join(self.dir, "CollectedData_" + self.scorer + ".h5"),
+                os.path.join(self.dir, "CollectedData_" + self.scorer + ".h5")
             )
             self.dataFrame.sort_index(inplace=True)
             self.prev.Enable(True)
@@ -892,9 +894,7 @@ def plot(self, img):
                 )
             ]
             self.axes.add_patch(circle[0])
-            self.dr = auxfun_drag.DraggablePoint(
-                circle[0], self.bodyparts[bpindex]
-            )
+            self.dr = auxfun_drag.DraggablePoint(circle[0], self.bodyparts[bpindex])
             self.dr.connect()
             self.dr.coords = MainFrame.getLabels(self, self.iter)[bpindex]
             self.drs.append(self.dr)
@@ -952,7 +952,6 @@ def onChecked(self, event):
             self.slider.Enable(False)
 
 
-
 def show(config, config3d, sourceCam, imtypes=["*.png"]):
     app = wx.App()
     frame = MainFrame(None, config, imtypes, config3d, sourceCam).Show()

deeplabcut/gui/multiple_individuals_labeling_toolbox.py

@@ -86,11 +86,13 @@ def retrieveData_and_computeEpLines(self, img, imNum):
 
             try:
                 dataFrame = pd.read_hdf(
-                    os.path.join(sourceCam_path, "CollectedData_" + scorer + ".h5"),
+                    os.path.join(sourceCam_path, "CollectedData_" + scorer + ".h5")
                 )
                 dataFrame.sort_index(inplace=True)
             except IOError:
-                print("source camera images have not yet been labeled, or you have opened this folder in the wrong mode!")
+                print(
+                    "source camera images have not yet been labeled, or you have opened this folder in the wrong mode!"
+                )
                 return None, None, None
 
             # Find offset terms for drawing epipolar Lines
@@ -283,7 +285,7 @@ def clearBoxer(self):
 class MainFrame(BaseFrame):
     def __init__(self, parent, config, config3d, sourceCam):
         super(MainFrame, self).__init__(
-            "DeepLabCut2.0 - Multiple Individuals Labeling ToolBox", parent,
+            "DeepLabCut2.0 - Multiple Individuals Labeling ToolBox", parent
         )
 
         self.statusbar.SetStatusText(
@@ -415,9 +417,7 @@ def OnKeyPressed(self, event=None):
             inv = self.axes.transData.inverted()
             pos_rel = list(inv.transform(pos_abs))
             y1, y2 = self.axes.get_ylim()
-            pos_rel[1] = (
-                y1 - pos_rel[1] + y2
-            )  # Recall y-axis is inverted
+            pos_rel[1] = y1 - pos_rel[1] + y2  # Recall y-axis is inverted
             i = np.nanargmin(
                 [self.calc_distance(*dp.point.center, *pos_rel) for dp in self.drs]
             )
@@ -789,7 +789,7 @@ def browseDir(self, event):
         # Reading the existing dataset,if already present
         try:
             self.dataFrame = pd.read_hdf(
-                os.path.join(self.dir, "CollectedData_" + self.scorer + ".h5"),
+                os.path.join(self.dir, "CollectedData_" + self.scorer + ".h5")
             )
             # Handle data previously labeled on a different platform
             sep = "/" if "/" in self.dataFrame.index[0] else "\\"
@@ -1205,9 +1205,7 @@ def plot(self, img):
                     )
                     self.axes.add_patch(circle)
                     self.dr = auxfun_drag.DraggablePoint(
-                        circle,
-                        self.uniquebodyparts[c],
-                        individual_names=ind,
+                        circle, self.uniquebodyparts[c], individual_names=ind
                     )
                     self.dr.connect()
                     self.dr.coords = image_points
@@ -1243,9 +1241,7 @@ def plot(self, img):
                     )
                     self.axes.add_patch(circle)
                     self.dr = auxfun_drag.DraggablePoint(
-                        circle,
-                        self.multibodyparts[c],
-                        individual_names=ind,
+                        circle, self.multibodyparts[c], individual_names=ind
                     )
                     self.dr.connect()
                     self.dr.coords = image_points

deeplabcut/gui/multiple_individuals_refinement_toolbox.py

@@ -75,7 +75,7 @@ def drawplot(
             self.axes.set_xlim(xlim)
             self.axes.set_ylim(ylim)
         self.figure.canvas.draw()
-        if not hasattr(self, 'toolbar'):
+        if not hasattr(self, "toolbar"):
             self.toolbar = NavigationToolbar(self.canvas)
         return (self.figure, self.axes, self.canvas, self.toolbar, self.ax)
 
@@ -137,9 +137,7 @@ def clearBoxer(self):
 
 class MainFrame(BaseFrame):
     def __init__(self, parent, config):
-        super(MainFrame, self).__init__(
-            "DeepLabCut - Refinement ToolBox", parent,
-        )
+        super(MainFrame, self).__init__("DeepLabCut - Refinement ToolBox", parent)
         self.Bind(wx.EVT_CHAR_HOOK, self.OnKeyPressed)
 
         ###################################################################################################################################################
@@ -281,9 +279,7 @@ def OnKeyPressed(self, event=None):
             inv = self.axes.transData.inverted()
             pos_rel = list(inv.transform(pos_abs))
             y1, y2 = self.axes.get_ylim()
-            pos_rel[1] = (
-                y1 - pos_rel[1] + y2
-            )  # Recall y-axis is inverted
+            pos_rel[1] = y1 - pos_rel[1] + y2  # Recall y-axis is inverted
             i = np.nanargmin(
                 [self.calc_distance(*dp.point.center, *pos_rel) for dp in self.drs]
             )
@@ -810,7 +806,7 @@ def saveDataSet(self, event):
                 "A training dataset file is already found for this video. The refined machine labels are merged to this data!"
             )
             DataU1 = pd.read_hdf(
-                os.path.join(self.dir, "CollectedData_" + self.humanscorer + ".h5"),
+                os.path.join(self.dir, "CollectedData_" + self.humanscorer + ".h5")
             )
             # combine datasets Original Col. + corrected machinefiles:
             DataCombined = pd.concat([self.Dataframe, DataU1])

deeplabcut/gui/outlier_frame_extraction_toolbox.py

@@ -80,7 +80,7 @@ def __init__(
         self, parent, config, video, shuffle, Dataframe, savelabeled, multianimal
     ):
         super(MainFrame, self).__init__(
-            "DeepLabCut2.0 - Manual Outlier Frame Extraction", parent,
+            "DeepLabCut2.0 - Manual Outlier Frame Extraction", parent
         )
 
         ###################################################################################################################################################

deeplabcut/gui/refine_labels.py

@@ -54,9 +54,7 @@ def refine_labels(config, multianimal=False):
 
         refinement.show(config)
     else:  # loading multianimal labeling GUI
-        from deeplabcut.gui import (
-            multiple_individuals_refinement_toolbox,
-        )
+        from deeplabcut.gui import multiple_individuals_refinement_toolbox
 
         multiple_individuals_refinement_toolbox.show(config)
 

deeplabcut/gui/refine_tracklets.py

@@ -185,7 +185,6 @@ def __init__(self, parent, gui_size, cfg):
             hbox2, pos=(7, 0), flag=wx.EXPAND | wx.TOP | wx.LEFT | wx.RIGHT, border=10
         )
 
-
         self.inf_cfg_text = wx.Button(self, label="Edit inference_config.yaml")
         sizer.Add(self.inf_cfg_text, pos=(10, 1), flag=wx.BOTTOM | wx.RIGHT, border=10)
         self.inf_cfg_text.Bind(wx.EVT_BUTTON, self.edit_inf_config)
@@ -202,7 +201,9 @@ def __init__(self, parent, gui_size, cfg):
         sizer.Add(self.reset, pos=(8, 1), flag=wx.BOTTOM | wx.RIGHT, border=10)
         self.reset.Bind(wx.EVT_BUTTON, self.reset_refine_tracklets)
 
-        self.filter = wx.Button(self, label=" Step2: Filter Tracks (then you also get a CSV file!)")
+        self.filter = wx.Button(
+            self, label=" Step2: Filter Tracks (then you also get a CSV file!)"
+        )
         sizer.Add(self.filter, pos=(8, 3), flag=wx.BOTTOM | wx.RIGHT, border=10)
         self.filter.Bind(wx.EVT_BUTTON, self.filter_after_refinement)
 

deeplabcut/gui/refinement.py

@@ -93,7 +93,7 @@ def drawplot(
             self.axes.set_xlim(xlim)
             self.axes.set_ylim(ylim)
         self.figure.canvas.draw()
-        if not hasattr(self, 'toolbar'):
+        if not hasattr(self, "toolbar"):
             self.toolbar = NavigationToolbar(self.canvas)
         return (self.figure, self.axes, self.canvas, self.toolbar)
 
@@ -145,9 +145,7 @@ def clearBoxer(self):
 
 class MainFrame(BaseFrame):
     def __init__(self, parent, config):
-        super(MainFrame, self).__init__(
-            "DeepLabCut2.0 - Refinement ToolBox", parent,
-        )
+        super(MainFrame, self).__init__("DeepLabCut2.0 - Refinement ToolBox", parent)
         self.Bind(wx.EVT_CHAR_HOOK, self.OnKeyPressed)
 
         ###################################################################################################################################################
@@ -279,9 +277,7 @@ def OnKeyPressed(self, event=None):
             inv = self.axes.transData.inverted()
             pos_rel = list(inv.transform(pos_abs))
             y1, y2 = self.axes.get_ylim()
-            pos_rel[1] = (
-                y1 - pos_rel[1] + y2
-            )  # Recall y-axis is inverted
+            pos_rel[1] = y1 - pos_rel[1] + y2  # Recall y-axis is inverted
             i = np.nanargmin(
                 [self.calc_distance(*dp.point.center, *pos_rel) for dp in self.drs]
             )
@@ -739,7 +735,7 @@ def saveDataSet(self, event):
                 "A training dataset file is already found for this video. The refined machine labels are merged to this data!"
             )
             DataU1 = pd.read_hdf(
-                os.path.join(self.dir, "CollectedData_" + self.humanscorer + ".h5"),
+                os.path.join(self.dir, "CollectedData_" + self.humanscorer + ".h5")
             )
             # combine datasets Original Col. + corrected machinefiles:
             DataCombined = pd.concat([self.Dataframe, DataU1])
@@ -886,9 +882,7 @@ def plot(self, im):
 
             self.axes.add_patch(circle[0])
             self.dr = auxfun_drag.DraggablePoint(
-                circle[0],
-                bp,
-                likelihood=self.likelihood
+                circle[0], bp, likelihood=self.likelihood
             )
             self.dr.connect()
             self.dr.coords = MainFrame.getLabels(self, self.iter)[bpindex]