Merge branch 'other-stim' of https://github.com/zm711/spikeanalysis i…

…nto other-stim

.github/workflows/python-package-conda.yml

@@ -35,7 +35,7 @@ jobs:
     - name: Set up Python 3.10
       uses: actions/setup-python@v4
       with:
-          python-version: '3.10'
+          python-version: '3.11'
     - uses: conda-incubator/[email protected]
       with:
         miniforge-variant: Mambaforge

docs/source/submodules/stimulus_data.rst

@@ -117,7 +117,7 @@ do the following:
 
 .. code-block:: python
 
-    stim.delete_events(del_index=24, digital=True, digital_channel="DIGITAL-IN-01") #python is 0 based so 25th event is 24 index
+    stim.delete_events(del_index=24, digital=True, channel_name="DIGITAL-IN-01") #python is 0 based so 25th event is 24th index
 
 Saving files for easy loading
 -----------------------------
@@ -159,4 +159,4 @@ And remember to :code:`save_events`.
     stim.run_all(stim_length_seconds=10, stim_name=['ana1'])
     stim.set_trial_groups(trial_dictionary=my_dictionary)
     stim.set_stimulus_name(stim_names=my_name_dictionary)
-    stim.save_events()
+    stim.save_events()

environment.yml

@@ -3,7 +3,7 @@ channels:
   - conda-forge
   - defaults
 dependencies:
-  - python
+  - python<3.12
   - numpy
   - scipy
   - matplotlib

environment_dev.yml

@@ -3,7 +3,7 @@ channels:
   - conda-forge
   - defaults
 dependencies:
-  - python
+  - python<3.12
   - numpy
   - scipy
   - matplotlib
@@ -17,4 +17,4 @@ dependencies:
   - pytest-cov
   - pip
   - pip:
-    - git+https://github.com/NeuralEnsemble/python-neo.git
+    - git+https://github.com/NeuralEnsemble/python-neo.git

src/spikeanalysis/intrinsic_plotter.py

@@ -133,15 +133,22 @@ def plot_waveforms(self, sp: SpikeData):
             A SpikeData object which has raw waveform values loaded"""
 
         waveforms = sp.waveforms
-
+        sp.reload_data()
         if len(sp._cids) != np.shape(waveforms)[0]:  # if not same need to run set_qc
             sp.set_qc()
         if len(sp._cids) != np.shape(waveforms)[0]:  # still not same need to index waveforms
             waveforms = waveforms[sp._qc_threshold, ...]
 
+        try:
+            noise = sp.noise
+        except AttributeError:
+            noise = np.array([])
+
         mean_waveforms = np.nanmean(waveforms, axis=1)
 
         for cluster in range(np.shape(waveforms)[0]):
+            if self._cids[cluster] in noise:
+                pass
             max_val = np.argwhere(mean_waveforms[cluster] == np.min(mean_waveforms[cluster]))[0]
             max_channel = max_val[0]
 

src/spikeanalysis/spike_data.py

@@ -187,7 +187,7 @@ def denoise_data(self):
 
         # if len(cids) > len(self._cids):
         #    cids = self._cids
-        # self._cids = self._cids[np.isin(cids, noise_clusters, invert=True)]
+        self._cids = self._cids[np.isin(self._cids, noise_clusters, invert=True)]
 
         self._return_to_dir(current_dir)
 

src/spikeanalysis/spike_plotter.py

@@ -80,7 +80,7 @@ def set_analysis(self, analysis: SpikeAnalysis):
     def plot_zscores(
         self,
         figsize: Optional[tuple] = (24, 10),
-        sorting_index: Optional[int] = None,
+        sorting_index: Optional[int] | list[int] = None,
         z_bar: Optional[list[int]] = None,
         indices: bool = False,
         show_stim: bool = True,
@@ -95,7 +95,7 @@ def plot_zscores(
         ----------
         figsize : Optional[tuple], optional
             Matplotlib figsize tuple. For multiple trial groups bigger is better. The default is (24, 10).
-        sorting_index : Optional[int], optional
+        sorting_index : Optional[int] | list[int], optional
             The trial group to sort all values on. The default is None (which uses the largest trial group).
         z_bar: list[int]
             If given a list with min z score for the cbar at index 0 and the max at index 1. Overrides cbar generation
@@ -110,7 +110,9 @@ def plot_zscores(
             if indices is True, the function will return the cluster ids as displayed in the z bar graph
 
         """
+        reset = False
         if self.cmap is None:
+            reset = True
             self.cmap = "vlag"
 
         sorted_cluster_ids = self._plot_scores(
@@ -121,16 +123,16 @@ def plot_zscores(
             indices=indices,
             show_stim=show_stim,
         )
-
-        self.cmap = None
+        if reset:
+            self.cmap = None
 
         if indices:
             return sorted_cluster_ids
 
     def plot_raw_firing(
         self,
         figsize: Optional[tuple] = (24, 10),
-        sorting_index: Optional[int] = None,
+        sorting_index: Optional[int] | list[int] = None,
         bar: Optional[list[int]] = None,
         indices: bool = False,
         show_stim: bool = True,
@@ -146,7 +148,7 @@ def plot_raw_firing(
         ----------
         figsize : Optional[tuple], optional
             Matplotlib figsize tuple. For multiple trial groups bigger is better. The default is (24, 10).
-        sorting_index : Optional[int], optional
+        sorting_index : Optional[int] | list[int], optional
             The trial group to sort all values on. The default is None (which uses the largest trial group).
         bar: list[int]
             If given a list with min firing rate for the cbar at index 0 and the max at index 1. Overrides cbar generation
@@ -161,14 +163,17 @@ def plot_raw_firing(
             if indices is True, the function will return the cluster ids as displayed in the z bar graph
 
         """
+        reset = False
         if self.cmap is None:
+            reset = True
             self.cmap = "viridis"
 
         sorted_cluster_ids = self._plot_scores(
             data="raw-data", figsize=figsize, sorting_index=sorting_index, bar=bar, indices=indices, show_stim=show_stim
         )
 
-        self.cmap = None
+        if reset:
+            self.cmap = None
 
         if indices:
             return sorted_cluster_ids
@@ -177,7 +182,7 @@ def _plot_scores(
         self,
         data: str = "zscore",
         figsize: Optional[tuple] = (24, 10),
-        sorting_index: Optional[int] = None,
+        sorting_index: Optional[int] | list[int] = None,
         bar: Optional[list[int]] = None,
         indices: bool = False,
         show_stim: bool = True,
@@ -207,7 +212,7 @@ def _plot_scores(
             if indices is True, the function will return the cluster ids as displayed in the z bar graph
 
         """
-
+        
         if data == "zscore":
             z_scores = self.data.z_scores
         elif data == "raw-data":
@@ -230,7 +235,7 @@ def _plot_scores(
 
         stim_lengths = self._get_event_lengths()
         sorted_cluster_ids = {}
-        for stimulus in z_scores.keys():
+        for stim_idx, stimulus in enumerate(z_scores.keys()):
             if len(np.shape(z_scores)) < 3:
                 sub_zscores = np.expand_dims(z_scores[stimulus], axis=1)
             sub_zscores = z_scores[stimulus]
@@ -250,14 +255,19 @@ def _plot_scores(
             bins = bins[np.logical_and(bins >= z_window[0], bins <= z_window[1])]
 
             if sorting_index is None:
-                sorting_index = np.shape(sub_zscores)[1] - 1
+                current_sorting_index = np.shape(sub_zscores)[1] - 1
                 RESET_INDEX = True
 
             else:
                 RESET_INDEX = False
+                assert isinstance(sorting_index, (list,int)), "sorting_index must be list or int"
+                if isinstance(sorting_index, list):
+                    current_sorting_index = sorting_index[stim_idx]
+                else:
+                    current_sorting_index = sorting_index
             event_window = np.logical_and(bins >= 0, bins <= length)
 
-            z_score_sorting_index = np.argsort(-np.sum(sub_zscores[:, sorting_index, event_window], axis=1))
+            z_score_sorting_index = np.argsort(-np.sum(sub_zscores[:, current_sorting_index, event_window], axis=1))
             sorted_cluster_ids[stimulus] = self.data.cluster_ids[z_score_sorting_index]
             sorted_z_scores = sub_zscores[z_score_sorting_index, :, :]