# -*- coding: utf-8 -*-
"""
Created on Mon Sep 17 12:43:05 2018
@author: svc_ccg
"""
import fileIO
import pandas as pd
import cv2
import os
import numpy as np
imageDictPickleFile = fileIO.getFile()
imageDict = pd.read_pickle(imageDictPickleFile)
saveDir = fileIO.getDir()
downSampleFactor = 9
for image in imageDict:
im = imageDict[image][image]
im_thumb = cv2.resize(im,
tuple(np.array(im.shape)[::-1] / downSampleFactor),
interpolation=cv2.INTER_AREA)
cv2.imwrite(os.path.join(saveDir, image + ".jpg"), im)
cv2.imwrite(os.path.join(saveDir, image + "_thumbnail.jpg"), im_thumb)
# -*- coding: utf-8 -*- """ Created on Mon Aug 12 12:36:41 2019 @author: svc_ccg """ """ Script that plots the performance of individual mice over time. For the position task, Produces 2 separate plots: 1 includes percent correct by side and no-gos. The other has no response trials and the direction turned for no-go trials. For the orientation task, returns a single plot with all information (no no-go trials) """ import h5py import fileIO import numpy as np from performanceData import performance_data f = fileIO.getFile(rootDir=r'\\allen\programs\braintv\workgroups\nc-ophys\corbettb\Masking') d = h5py.File(f) performance_data(mouse='477210', ignoreRepeats=True)
#ymin = rawData[channelsToPlot,samplesToPlot].min()
#ymax = rawData[channelsToPlot,samplesToPlot].max()
#for i,ch in enumerate(channelsToPlot):
# ax = fig.add_subplot(len(channelsToPlot),1,i+1)
# ax.plot(rawData[ch,samplesToPlot],'k')
# for side in ('right','top'):
# ax.spines[side].set_visible(False)
# ax.set_ylim([ymin,ymax])
# ax.set_ylabel('uV')
# if i==len(channelsToPlot)-1:
# ax.set_xlabel('Sample')
# ax.set_title('ch '+str(ch))
#plt.tight_layout()
# sync data
syncFile = fileIO.getFile('select sync file')
syncDataset = sync.Dataset(syncFile)
probeEventsDir = os.path.join(
os.path.join(probeDataDir, 'events',
'Neuropix-PXI-' + pxiDict[probeLabel]), 'TTL_1')
# get barcodes from sync file
bRising, bFalling = get_sync_line_data(syncDataset, 'barcode')
bs_t, bs = ecephys.extract_barcodes_from_times(bRising, bFalling)
# get barcodes from ephys data
channel_states = np.load(os.path.join(probeEventsDir, 'channel_states.npy'))
event_times = np.load(os.path.join(probeEventsDir, 'event_timestamps.npy'))
beRising = event_times[channel_states > 0] / 30000.