Merge pull request #41 from zm711/updates

Working on some minor updates

docs/source/submodules/stimulus_data.rst

@@ -92,9 +92,9 @@ trial groups can be set with a utility function. :code:`set_trial_groups`, which
 a key of the Intan channel and a value of the desired trial groups as an :code:`np.array`. Since the channel names are not always easy to know
 they can be returned using :code:`get_stimulus_channels`. Finally stimulus' should be named with :code:`set_stimulus_name`.
 
-.. code:-block:: python
+.. code-block:: python
 
-    stim_dict = stim.get_stimluus_channels()
+    stim_dict = stim.get_stimulus_channels()
     stim.set_trial_groups(trial_dictionary=trial_dictionary) # dict as explained above
     sitm.set_stimulus_names(stim_names = name_dictionary) # same keys with str values
 
@@ -108,6 +108,17 @@ in the case of optogenetic trains rather than looking at :code:`events`, :code:`
 :code:`stim_time_secs` (length of the train) must be given.
 
 
+Deleting Accidental events
+--------------------------
+
+In the case of an accidental event (for example turning on a TTL signal for just a moment accidental) one can use the :code:`delete_events()`
+function. Either one :code:`del_index` is given or a list of indices to delete. This means that if event "25" was a mistaken signal one could
+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
+
 Saving files for easy loading
 -----------------------------
 
@@ -148,5 +159,5 @@ And remember to :code:`save_events`.
     stim = StimulusData(file_path='home/myawesomedata')
     stim.run_all(stim_length_seconds=10, stim_name=['ana1'])
     stim.set_trial_groups(trial_dictionary=my_dictionary)
-    stim.set_stimulus_names(stim_names=my_name_dictionary)
+    stim.set_stimulus_name(stim_names=my_name_dictionary)
     stim.save_events()

src/spikeanalysis/spike_data.py

@@ -95,6 +95,8 @@ def run_all(
         idthres: float,
         rpv: float,
         sil: float,
+        amp_std: float,
+        amp_cutoff: float,
         recurated: bool = False,
         set_caching: bool = True,
         depth: float = 0,
@@ -113,6 +115,11 @@ def run_all(
             the refractory period violation fraction allowed: 0.02 would be 2% violations
         sil: float
             the silhouette score allowed -1 (bad) to 1 (perfect)
+        amp_std: float
+            the std to use for calculating the number of spikes which past that range
+        amp_cutoff: float
+            the cutoff value to use for qc for the amplitudes values, example .98 means 98% fall within
+            amp_std stds of the mean amplitude
         recurated: bool
             If more Phy curation has occurred such that any cached or currently loaded values
             need to be overwritten (True), Default False
@@ -140,10 +147,12 @@ def run_all(
             except AttributeError:
                 self.generate_pcs()
                 self.generate_qcmetrics()
-            self.qc_preprocessing(idthres=idthres, rpv=rpv, sil=sil, recurated=recurated)
-            self.set_qc()
+
             self.get_waveforms()
             self.get_waveform_values(depth=depth)
+            self.get_amplitudes(std=amp_std)
+            self.qc_preprocessing(idthres=idthres, rpv=rpv, sil=sil, amp_cutoff=amp_cutoff, recurated=recurated)
+            self.set_qc()
             self._return_to_dir(current_dir)
 
     def denoise_data(self):

src/spikeanalysis/spike_plotter.py

@@ -654,9 +654,7 @@ def _get_event_lengths(self) -> dict:
         stim_dict = self.data._get_key_for_stim()
 
         for key, value in stim_dict.items():
-            stim_lengths[key] = np.mean(
-                np.array(self.data.events[value]["lengths"]) / self.data._sampling_rate
-            )
+            stim_lengths[key] = np.mean(np.array(self.data.events[value]["lengths"]) / self.data._sampling_rate)
         return stim_lengths
 
     def _get_event_lengths_all(self) -> dict:
@@ -672,7 +670,7 @@ def _get_event_lengths_all(self) -> dict:
 
         for key, value in stim_dict.items():
             stim_lengths[key] = np.array(self.data.events[value]["lengths"]) / self.data._sampling_rate
-            
+
         return stim_lengths
 
     def _get_trial_groups(self) -> dict: