add sorted

src/spikeanalysis/spike_plotter.py

@@ -343,7 +343,7 @@ def _plot_scores(
         if indices:
             return sorted_cluster_ids
 
-    def plot_raster(self, window: Union[list, list[list]], show_stim: bool = True, include_ids: list | np.nadarry | None = None):
+    def plot_raster(self, window: Union[list, list[list]], show_stim: bool = True, include_ids: list | np.nadarry | None = None, sorted: bool = False):
         """
         Function to plot rasters
 
@@ -355,6 +355,8 @@ def plot_raster(self, window: Union[list, list[list]], show_stim: bool = True, i
         show_stim: bool, default True
             Show lines where stim onset and offset are
         include_ids: list | np.ndarray | None, default: None
+           sub ids to include
+        sorted: bool, default = False
         """
         from .analysis_utils import histogram_functions as hf
 
@@ -384,6 +386,12 @@ def plot_raster(self, window: Union[list, list[list]], show_stim: bool = True, i
         else:
             keep_list = np.arange(0, len(cluster_indices), 1)
 
+        if sorted:
+            sorted_indices = []
+            for value_id in include_ids:
+                sorted_indices.append(np.nonzero(self.cluster_ids==value_id)[0][0])
+            sorted_indices = np.array(sorted_indices)
+
         for idx, stimulus in enumerate(psths.keys()):
             bins = psths[stimulus]["bins"]
             psth = psths[stimulus]["psth"]
@@ -394,6 +402,10 @@ def plot_raster(self, window: Union[list, list[list]], show_stim: bool = True, i
             tg_set = np.unique(trial_groups)
 
             psth = psth[:, :, np.logical_and(bins > sub_window[0], bins < sub_window[1])]
+
+            if sorted:
+                psth = psth[sorted_indices,...]
+
             bins = bins[np.logical_and(bins >= sub_window[0], bins <= sub_window[1])]
 
             for idy in range(np.shape(psth)[0]):
@@ -440,7 +452,10 @@ def plot_raster(self, window: Union[list, list[list]], show_stim: bool = True, i
                     sns.despine()
                 else:
                     self._despine(ax)
-                plt.title(f"{self.data.cluster_ids[idy]} stim: {stimulus}", size=7)
+                if sorted:
+                    plt.title(f"{stimulus}: {self.data.cluster_ids[sorted_indices][idy]}", fontsize=8)
+                else:
+                    plt.title(f"{stimulus}: {self.data.cluster_ids[idy]}", fontsize=8)
                 plt.figure(dpi=self.dpi)
                 plt.show()
 
@@ -451,6 +466,7 @@ def plot_sm_fr(
         sm_time_ms: Union[float, list[float]],
         show_stim: bool = True,
         include_ids: list | np.ndarray | None = None,
+        sorted: bool = False,
     ):
         """
         Function to plot smoothed firing rates
@@ -469,6 +485,7 @@ def plot_sm_fr(
             Show lines where stim onset and offset are
         include_ids: list | np.ndarray | None
             The ids to include for plotting
+        sorted: bool = False,
             
         """
         import matplotlib as mpl
@@ -514,6 +531,12 @@ def plot_sm_fr(
         else:
             keep_list = np.arange(0, len(cluster_indices), 1)
 
+        if sorted:
+            sorted_indices = []
+            for value_id in include_ids:
+                sorted_indices.append(np.nonzero(self.cluster_ids==value_id)[0][0])
+            sorted_indices = np.array(sorted_indices)
+
         stim_trial_groups = self._get_trial_groups()
         event_lengths = self._get_event_lengths_all()
         for idx, stimulus in enumerate(psths.keys()):
@@ -531,6 +554,8 @@ def plot_sm_fr(
             trial_groups = stim_trial_groups[stimulus]
             sub_window = windows[idx]
             psth = psth[:, :, np.logical_and(bins > sub_window[0], bins < sub_window[1])]
+            if sorted:
+                psth = psth[sorted_indices, ...]
             bins = bins[np.logical_and(bins > sub_window[0], bins < sub_window[1])]
             events = event_lengths[stimulus]
             tg_set = np.unique(trial_groups)
@@ -590,8 +615,10 @@ def plot_sm_fr(
                         sns.despine()
                     else:
                         self._despine(ax)
-
-                plt.title(f"{stimulus}: {self.data.cluster_ids[cluster_number]}", fontsize=8)
+                if sorted:
+                    plt.title(f"{stimulus}: {self.data.cluster_ids[sorted_indices][cluster_number]}", fontsize=8)
+                else:
+                    plt.title(f"{stimulus}: {self.data.cluster_ids[cluster_number]}", fontsize=8)
                 plt.figure(dpi=self.dpi)
                 plt.show()