add raw data plots to alignment gui

atlaselectrophysiology/alignment_with_easyqc.py

@@ -1,7 +1,7 @@
 from easyqc.gui import viewseis
 from ibllib.dsp import voltage
 from ibllib.ephys import neuropixel
-from viewspikes.data import stream, get_ks2
+from viewspikes.data import stream
 from viewspikes.plots import overlay_spikes
 import scipy
 from PyQt5 import QtCore, QtGui
@@ -140,12 +140,11 @@ def stream_ap(self, t):
         sos = scipy.signal.butter(3, 300 / self.ap.fs / 2, btype='highpass', output='sos')
         butt = scipy.signal.sosfiltfilt(sos, raw)
         destripe = voltage.destripe(raw, fs=self.ap.fs)
-        ks2 = get_ks2(raw, dsets, self.loaddata.one)
+
         self.eqc['butterworth'] = viewseis(butt.T, si=1 / self.ap.fs, h=h, t0=t0, title='butt',
                                            taxis=0)
         self.eqc['destripe'] = viewseis(destripe.T, si=1 / self.ap.fs, h=h, t0=t0, title='destr',
                                         taxis=0)
-        self.eqc['ks2'] = viewseis(ks2.T, si=1 / self.ap.fs, h=h, t0=t0, title='ks2', taxis=0)
 
         overlay_spikes(self.eqc['butterworth'], self.plotdata.spikes, self.plotdata.clusters,
                        self.plotdata.channels)

atlaselectrophysiology/ephys_atlas_gui.py

@@ -1273,6 +1273,11 @@ def data_button_pressed(self):
             self.probe_rfmap, self.rfmap_boundaries = self.plotdata.get_rfmap_data()
             self.img_stim_data = self.plotdata.get_passive_events()
 
+            if not self.offline:
+                self.img_raw_data = self.plotdata.get_raw_data_image(self.loaddata.probe_id, one=self.loaddata.one)
+            else:
+                self.img_raw_data = {}
+
             if self.histology_exists:
                 self.slice_data = self.loaddata.get_slice_images(self.ephysalign.xyz_samples)
             else:
@@ -2113,7 +2118,7 @@ def viewer(probe_id, one=None, histology=False, spike_collection=None, title=Non
 
     parser = argparse.ArgumentParser(description='Offline vs online mode')
     parser.add_argument('-o', '--offline', default=False, required=False, help='Offline mode')
-    parser.add_argument('-r', '--remote', default=False, required=False,  action='store_true', help='Remote mode')
+    parser.add_argument('-r', '--remote', default=False, required=False, action='store_true', help='Remote mode')
     parser.add_argument('-i', '--insertion', default=None, required=False, help='Insertion mode')
     args = parser.parse_args()
 

atlaselectrophysiology/ephys_gui_setup.py

@@ -105,6 +105,14 @@ def init_menubar(self):
         img_options.addAction(scatter_amp)
         self.img_options_group.addAction(scatter_amp)
 
+        raw_type = list(self.img_raw_data.keys())
+        for raw in raw_type:
+            img = QtWidgets.QAction(raw, self, checkable=True, checked=False)
+            img.triggered.connect(lambda checked, item=raw: self.plot_image(
+                                  self.img_raw_data[item]))
+            img_options.addAction(img)
+            self.img_options_group.addAction(img)
+
         stim_type = list(self.img_stim_data.keys())
         for stim in stim_type:
             img = QtWidgets.QAction(stim, self, checkable=True, checked=False)

atlaselectrophysiology/example_code.py

@@ -6,6 +6,3 @@
 
 
 av = viewer(probe_id, one=one)
-
-
-# To add trials to the window

atlaselectrophysiology/plot_data.py

@@ -3,8 +3,10 @@
 import numpy as np
 import one.alf as alf
 from brainbox.processing import bincount2D
+from brainbox.io.spikeglx import stream
 from brainbox.population.decode import xcorr
 from brainbox.task import passive
+from ibllib.dsp import voltage
 import scipy
 from PyQt5 import QtGui
 
@@ -472,6 +474,38 @@ def median_subtract(a):
 
         return data_img, data_probe
 
+    # only for IBL sorry
+    def get_raw_data_image(self, pid, t0=(1000, 2000, 3000), one=None):
+
+        def gain2level(gain):
+            return 10 ** (gain / 20) * 4 * np.array([-1, 1])
+        data_img = dict()
+        for t in t0:
+
+            sr, t = stream(pid, t, one=one)
+            raw = sr[:, :-sr.nsync].T
+            channel_labels, channel_features = voltage.detect_bad_channels(raw, sr.fs)
+            raw = voltage.destripe(raw, fs=sr.fs, channel_labels=channel_labels)
+            raw_image = raw[:, int((450 / 1e3) * sr.fs):int((500 / 1e3) * sr.fs)].T
+            x_range = np.array([0, raw_image.shape[0] - 1]) / sr.fs * 1e3
+            levels = gain2level(-90)
+            xscale = (x_range[1] - x_range[0]) / raw_image.shape[0]
+            yscale = (self.chn_max - self.chn_min) / raw_image.shape[1]
+
+            data_raw = {
+                'img': raw_image,
+                'scale': np.array([xscale, yscale]),
+                'levels': levels,
+                'offset': np.array([0, 0]),
+                'cmap': 'bone',
+                'xrange': x_range,
+                'xaxis': 'Time (ms)',
+                'title': 'Power (uV)'
+            }
+            data_img[f'Raw data t={t}'] = data_raw
+
+        return data_img
+
     def get_lfp_spectrum_data(self):
         freq_bands = np.vstack(([0, 4], [4, 10], [10, 30], [30, 80], [80, 200]))
         data_probe = {}