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plot_mountainsort_clusters.py~
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import numpy as np
import matplotlib.pylab as plt
from scipy import stats,signal
import seaborn as sns
import sys,os
sns.set_style('ticks')
import pandas as pd
import json
sys.path.append("/Users/guitchounts/Documents/GitHub/mountainlab/old/WIP/python/mda/")
sys.path.append('/Users/guitchounts/Dropbox (coxlab)/Scripts/Repositories/continuous-ephys/utils')
import read_titan_rhd
from mdaio import readmda
def filter(ephys,freq_range,filt_order = 4,filt_type='bandpass',fs=10.):
# design Elliptic filter:
[b,a] = signal.butter(filt_order,[freq/(fs/2) for freq in freq_range],btype=filt_type)
filtered_trace = signal.filtfilt(b,a,ephys,axis=0)
return filtered_trace
if __name__ == "__main__":
#tet4_firings = readmda('/Volumes/coxfs01/guitchounts/MountainSortCluster-master/TetrodeData/Tetrode4/data/firings2-4.mda')
### starting in a dir like GRat54/636xxxxx/0/
#tetrode_num = 1
for tetrode_num in range(1,8):
tet_raw = readmda('./TetrodeData/Tetrode%d/data/rawT%d.mda' %(tetrode_num,tetrode_num))
## ^^ can also load from original RHD file.
#### this stuff will actually be on /n/regal (or wherever you put it)
clust_dir = './Tetrode%d/mountainlab/prvbucket/_mountainprocess/' % tetrode_num
firings_files = [out_file for out_file in os.listdir(clust_dir) if out_file.startswith('output_firings_out')]
output_firings_out = firings_files[np.argmax([os.path.getsize(clust_dir + file) for file in firings_files])]
cluster_times_ids = readmda(clust_dir + output_firings_out)
metrics_files = [out_file for out_file in os.listdir(clust_dir) if out_file.startswith('output_metrics_out')]
output_metrics_out = metrics_files[np.argmax([os.path.getsize(clust_dir + file) for file in metrics_files])]
metrics_path = (clust_dir + output_metrics_out)
with open(metrics_path) as json_data_file:
metrics = json.load(json_data_file)
# rhd_path = '/Volumes/coxfs01/guitchounts/ephys/GRat27/636520606779599145.rhd'
# rhd = read_titan_rhd.ReadTitanRHD(rhd_path)
# length = 1e6
# ephys_timestamps,acc,ephys = rhd.read_file(length)
fs = 3e4
tet_filt = filter(tet_raw.T,[800,8e3],fs=fs).T
### Gather the avg waveforms from each cluster:
## for plotting purposes, want a mat with the extracted waveforms:
## e.g. 4x 32 x 100000 waveforms. use cluster_times_ids mat to separate cluster IDs.
## in tet_filt, find the indexes of spiketimes and extract -16 until +16 timepoints for each.
spike_width = 32 # number of samples to take, centered at spike peak. #(16,16)
waveforms = [] ## list of arrays. length = number of clusters
isis = []
for clu in np.unique(cluster_times_ids[2,:]): #[9.,11.]: #
tmp_waveforms = np.empty([4,np.where(cluster_times_ids[2,:] == clu)[0].shape[0],spike_width]) ### 4xnum_spikesx32
peaks = cluster_times_ids[1,np.where(cluster_times_ids[2,:]==clu)[0]].astype('int') ## index of tet4 where peak happens
clu_times = cluster_times_ids[1,np.where(cluster_times_ids[2,:]==clu)[0]]
#clu_times = xx[1,np.where(xx[2,:]==clu)[0]]
isis.append(np.diff(clu_times)/fs*1e3) ## in milliseconds
tmp_waveforms = tet_filt[:,[np.arange(peak-spike_width/2,peak+spike_width/2) for peak in peaks]]
waveforms.append(tmp_waveforms)
#### Make the figure
clusts = np.unique(cluster_times_ids[2,:]).astype('int')
f,axarr = plt.subplots(len(clusts),3,dpi=600,sharey='col',gridspec_kw={'hspace':2,'wspace':0})
#'width_ratios':[1,2]}) # , figsize=(2,6)
#print('clusts = ',clusts)
for clu_idx,clu in enumerate(clusts):
for ch in range(4):
#clust_times = np.where(xx[2,:].astype('int') == 9)[0]
#axarr[clu_idx].plot(range(0+32*ch,32+32*ch), np.mean(waveforms[clu_idx][ch,:,:],axis=0))
#print('clu,waveforms[clu_idx].shape = ',clu,waveforms[clu_idx].shape)
y = np.mean(waveforms[clu_idx][ch,:,:],axis=0)
err = np.std(waveforms[clu_idx][ch,:,:],axis=0)
x = range(0+spike_width*ch,spike_width+spike_width*ch)
axarr[clu_idx,0].plot(x,y,c='k',lw=.5)
axarr[clu_idx,0].fill_between(x, y-err, y+err,alpha=.25,color='k',linewidth=0)
axarr[clu_idx,0].set_title('%d Spikes in Cluster %d' % (waveforms[clu_idx].shape[1],clu),fontdict={'fontsize' : 6})
#axarr[clu_idx,1].hist(isis[clu_idx],bins=200)
#clu_isi = np.diff(clu_times) ## convert from seconds to ms
axarr[clu_idx,1].hist(isis[clu_idx],bins=np.logspace(-1,2,100),range=[0.1,100],histtype='stepfilled')
#n, bins, patches =
#plt.setp(patches, 'facecolor', 'magenta', 'alpha', 0.5)
axarr[clu_idx,1].set_xscale('log')
axarr[clu_idx,1].set_xlim([0.1, 100])
if len(isis[clu_idx]) > 1:
violations = np.float(len(np.where(isis[clu_idx]<=1.0)[0])) / np.float(len(isis[clu_idx])) * 100
else:
violations = 0
axarr[clu_idx,1].set_title('Violations: %.2f%% <1ms' % (violations),fontdict={'fontsize' : 4})
axarr[clu_idx,0].axes.xaxis.set_ticklabels([])
axarr[clu_idx,0].axes.xaxis.set_ticks([])
axarr[clu_idx,1].axes.xaxis.set_ticklabels([])
axarr[clu_idx,1].axes.xaxis.set_ticks([])
axarr[clu_idx,1].axes.yaxis.set_ticklabels([])
axarr[clu_idx,1].axes.yaxis.set_ticks([])
axarr[clu_idx,2].text(0,0,metrics['clusters'][clu_idx],wrap=True,fontdict={'fontsize' : 6})
axarr[clu_idx,2].axes.yaxis.set_ticklabels([])
axarr[clu_idx,2].axes.yaxis.set_ticks([])
axarr[clu_idx,2].axes.xaxis.set_ticklabels([])
axarr[clu_idx,2].axes.xaxis.set_ticks([])
axarr[-1,1].set_xlabel('ISI (ms)')
axarr[-1,1].axes.xaxis.set_ticklabels([0.1,1,10,100])
axarr[-1,1].axes.xaxis.set_ticks([0.1,1,10,100])
#axarr[i].plot(np.mean(tet4_filt[i,[np.arange(j-32,j+32) for j in clust_times]],axis=0))
#axarr[i].plot(16,peaks[i,clust_times],'x')
#plt.tight_layout()
sns.despine(left=True,bottom=True)
save_path = './Tetrode%d' % tetrode_num
f.savefig(save_path + '/clusters.pdf')
# save cluster results with spike times:
d = dict(times = cluster_times_ids[1,:]/3e4,clusters=cluster_times_ids[2,:]) ### save times in seconds and cluster assignments
cluster_assignments = pd.DataFrame.from_dict(d)
cluster_assignments.to_csv(save_path + '/cluster_assignments.csv')