working on keep list

src/spikeanalysis/spike_plotter.py

@@ -181,6 +181,7 @@ def _plot_scores(
         bar: Optional[list[int]] = None,
         indices: bool = False,
         show_stim: bool = True,
+
     ) -> Optional[np.array]:
         """
         Function to plot heatmaps of firing rate data
@@ -342,7 +343,7 @@ def _plot_scores(
         if indices:
             return sorted_cluster_ids
 
-    def plot_raster(self, window: Union[list, list[list]], show_stim: bool = True):
+    def plot_raster(self, window: Union[list, list[list]], show_stim: bool = True, include_ids: list | np.nadarry | None = None):
         """
         Function to plot rasters
 
@@ -353,6 +354,7 @@ def plot_raster(self, window: Union[list, list[list]], show_stim: bool = True):
             of [start, stop] format
         show_stim: bool, default True
             Show lines where stim onset and offset are
+        include_ids: list | np.ndarray | None, default: None
         """
         from .analysis_utils import histogram_functions as hf
 
@@ -372,6 +374,16 @@ def plot_raster(self, window: Union[list, list[list]], show_stim: bool = True):
 
         event_lengths = self._get_event_lengths()
 
+
+        if include_ids is not None:
+            cluster_indices = self.data.cluster_ids
+            keep_list = []
+            for cid in include_ids:
+                keep_list.append(np.where(cluster_indices==cid)[0][0])
+            keep_list = np.array(keep_list)
+        else:
+            keep_list = np.arange(0, len(cluster_indices), 1)
+
         for idx, stimulus in enumerate(psths.keys()):
             bins = psths[stimulus]["bins"]
             psth = psths[stimulus]["psth"]
@@ -384,8 +396,10 @@ def plot_raster(self, window: Union[list, list[list]], show_stim: bool = True):
             psth = psth[:, :, np.logical_and(bins > sub_window[0], bins < sub_window[1])]
             bins = bins[np.logical_and(bins >= sub_window[0], bins <= sub_window[1])]
 
-            for idx in range(np.shape(psth)[0]):
-                psth_sub = np.squeeze(psth[idx])
+            for idy in range(np.shape(psth)[0]):
+                if idy not in keep_list:
+                    pass
+                psth_sub = np.squeeze(psth[idy])
 
                 if np.sum(psth_sub) == 0:
                     continue
@@ -426,7 +440,7 @@ def plot_raster(self, window: Union[list, list[list]], show_stim: bool = True):
                     sns.despine()
                 else:
                     self._despine(ax)
-                plt.title(f"{self.data.cluster_ids[idx]} stim: {stimulus}", size=7)
+                plt.title(f"{self.data.cluster_ids[idy]} stim: {stimulus}", size=7)
                 plt.figure(dpi=self.dpi)
                 plt.show()
 
@@ -436,6 +450,7 @@ def plot_sm_fr(
         time_bin_ms: Union[float, list[float]],
         sm_time_ms: Union[float, list[float]],
         show_stim: bool = True,
+        include_ids: list | np.ndarray | None = None,
     ):
         """
         Function to plot smoothed firing rates
@@ -452,7 +467,9 @@ def plot_sm_fr(
             stimulus
         show_stim: bool, default True
             Show lines where stim onset and offset are
-
+        include_ids: list | np.ndarray | None
+            The ids to include for plotting
+            
         """
         import matplotlib as mpl
         from .analysis_utils import histogram_functions as hf
@@ -488,6 +505,15 @@ def plot_sm_fr(
                 number of bins is{len(time_bin_ms)} and should be {NUM_STIM}"
             time_bin_size = np.array(time_bin_ms) / 1000
 
+        if include_ids is not None:
+            cluster_indices = self.data.cluster_ids
+            keep_list = []
+            for cid in include_ids:
+                keep_list.append(np.where(cluster_indices==cid)[0][0])
+            keep_list = np.array(keep_list)
+        else:
+            keep_list = np.arange(0, len(cluster_indices), 1)
+
         stim_trial_groups = self._get_trial_groups()
         event_lengths = self._get_event_lengths_all()
         for idx, stimulus in enumerate(psths.keys()):
@@ -519,6 +545,8 @@ def plot_sm_fr(
             stderr = np.zeros((len(tg_set), len(bins)))
             event_len = np.zeros((len(tg_set)))
             for cluster_number in range(np.shape(psth)[0]):
+                if cluster_number not in keep_list:
+                    pass
                 smoothed_psth = gaussian_smoothing(psth[cluster_number], bin_size, sm_std)
 
                 for trial_number, trial in enumerate(tg_set):