brain_region plot and cdf amplitude plot

Author: mayofaulkner

brainbox/ephys_plots.py

@@ -1,9 +1,10 @@
 import numpy as np
 from matplotlib import cm
-
+import matplotlib.pyplot as plt
 from brainbox.plot_base import (ImagePlot, ScatterPlot, ProbePlot, LinePlot, plot_line,
                                 plot_image, plot_probe, plot_scatter, arrange_channels2banks)
 from brainbox.processing import bincount2D, compute_cluster_average
+from ibllib.atlas.regions import BrainRegions
 
 
 def image_lfp_spectrum_plot(lfp_power, lfp_freq, chn_coords, chn_inds, freq_range=(0, 300),
@@ -372,3 +373,78 @@ def line_amp_plot(spike_amps, spike_depths, spike_times, chn_coords, d_bin=10, d
         fig, ax = plot_line(data.convert2dict())
         return data.convert2dict(), fig, ax
     return data
+
+
+def plot_brain_regions(channel_ids, channel_depths=None, brain_regions=None, display=True, ax=None):
+
+    if channel_depths is not None:
+        assert channel_ids.shape[0] == channel_depths.shape[0]
+
+    br = brain_regions or BrainRegions()
+
+    region_info = br.get(channel_ids)
+    boundaries = np.where(np.diff(region_info.id) != 0)[0]
+    boundaries = np.r_[0, boundaries, region_info.id.shape[0] - 1]
+
+    regions = np.c_[boundaries[0:-1], boundaries[1:]]
+    if channel_depths is not None:
+        regions = channel_depths[regions]
+    region_labels = np.c_[np.mean(regions, axis=1), region_info.acronym[boundaries[1:]]]
+    region_colours = region_info.rgb[boundaries[1:]]
+
+    if display:
+        if ax is None:
+            fig, ax = plt.subplots()
+
+        for reg, col in zip(regions, region_colours):
+            height = np.abs(reg[1] - reg[0])
+            color = col / 255
+            ax.bar(x=0.5, height=height, width=1, color=color, bottom=reg[0], edgecolor='w')
+        ax.set_yticks(region_labels[:, 0].astype(int))
+        ax.yaxis.set_tick_params(labelsize=8)
+        ax.get_xaxis().set_visible(False)
+        ax.set_yticklabels(region_labels[:, 1])
+        ax.spines['right'].set_visible(False)
+        ax.spines['top'].set_visible(False)
+        ax.spines['bottom'].set_visible(False)
+
+        return fig, ax
+    else:
+        return regions, region_labels, region_colours
+
+
+def plot_cdf(spike_amps, spike_depths, spike_times, n_amp_bins=10, d_bin=40, amp_range=None, d_range=None,
+                         display=False, cmap='hot'):
+
+    amp_range = amp_range or np.quantile(spike_amps, (0, 0.9))
+    amp_bins = np.linspace(amp_range[0], amp_range[1], n_amp_bins)
+    d_range = d_range or [0, 3840]
+    depth_bins = np.arange(d_range[0], d_range[1] + d_bin, d_bin)
+    t_bin = np.max(spike_times)
+
+    def histc(x, bins):
+        map_to_bins = np.digitize(x, bins)  # Get indices of the bins to which each value in input array belongs.
+        res = np.zeros(bins.shape)
+
+        for el in map_to_bins:
+            res[el - 1] += 1  # Increment appropriate bin.
+        return res
+
+    cdfs = np.empty((len(depth_bins) - 1, n_amp_bins))
+    for d in range(len(depth_bins) - 1):
+        spikes = np.bitwise_and(spike_depths > depth_bins[d], spike_depths <= depth_bins[d + 1])
+        h = histc(spike_amps[spikes], amp_bins) / t_bin
+        hcsum = np.cumsum(h[::-1])
+        cdfs[d, :] = hcsum[::-1]
+
+    cdfs[cdfs == 0] = np.nan
+
+    data = ImagePlot(cdfs.T, x=amp_bins * 1e6, y=depth_bins[:-1], cmap=cmap)
+    data.set_labels(title='Cumulative Amplitude', xlabel='Spike amplitude (uV)',
+                    ylabel='Distance from probe tip (um)', clabel='Firing Rate (Hz)')
+
+    if display:
+        fig, ax = plot_image(data.convert2dict(), fig_kwargs={'figsize': [3, 7]})
+        return data.convert2dict(), fig, ax
+
+    return data

brainbox/task/passive.py

@@ -206,8 +206,8 @@ def get_stim_aligned_activity(stim_events, spike_times, spike_depths, z_score_fl
         base_intervals = np.c_[stim_times - base_stim, stim_times - pre_stim]
         out_intervals = stim_intervals[:, 1] > times[-1]
 
-        idx_stim = np.searchsorted(times, stim_intervals)[np.invert(out_intervals)]
-        idx_base = np.searchsorted(times, base_intervals)[np.invert(out_intervals)]
+        idx_stim = np.searchsorted(times, stim_intervals, side='right')[np.invert(out_intervals)]
+        idx_base = np.searchsorted(times, base_intervals, side='right')[np.invert(out_intervals)]
 
         stim_trials = np.zeros((depths.shape[0], n_bins, idx_stim.shape[0]))
         noise_trials = np.zeros((depths.shape[0], n_bins_base, idx_stim.shape[0]))

ibllib/atlas/regions.py

@@ -149,6 +149,17 @@ def _mapping_from_regions_list(self, new_map, lateralize=False):
             mapind = mapind[iregion]
         return mapind
 
+    def remap(self, region_ids, source_map='Allen', target_map='Beryl'):
+        """
+        Remap atlas regions ids from source map to target map
+        :param region_ids:
+        :param source_map:
+        :param target_map:
+        :return:
+        """
+        _, inds = ismember(region_ids, self.id[self.mappings[source_map]])
+        return self.id[self.mappings[target_map][inds]]
+
 
 def regions_from_allen_csv():
     """