def select_directory(self):
"""Open a directory selection dialog box"""
value = self.item.from_string(to_text_string(self.edit.text()))
parent = self.parent_layout.parent
child_title = _get_child_title_func(parent)
dname = getexistingdirectory(parent, child_title(self.item), value)
if dname:
self.edit.setText(dname)
def saveCalibrationPickle(stereo_var,calib_var):
print '\n# SAVE CALIBRATION'
# save data to file
app = guidata.qapplication()
# get selected directory
myDir = getexistingdirectory()
with open(myDir+'\calibration.pickle', 'w') as f:
pickle.dump([stereo_var,calib_var], f)
def loadCalibrationPickle():
print '\n# LOAD CALIBRATION'
# save data to file
app = guidata.qapplication()
# get selected directory
myDir = getexistingdirectory()
with open(myDir+'\calibration.pickle') as f:
stereo_var,calib_var = pickle.load(f)
return stereo_var,calib_var
def getFiles():
app = guidata.qapplication()
# get selected directory
myDir = getexistingdirectory()
# change to selected directory
os.chdir(myDir)
files = glob.glob("*.tif")
print "\n# DATA PATH"
print myDir
return files
def getFiles():
app = guidata.qapplication()
# get selected directory
myDir = getexistingdirectory()
# change to selected directory
os.chdir(myDir)
files_name = glob.glob("*.tif")
# print calibration path
print "\n# CALIBRATION PATH"
print myDir
return files_name
def _open(self):
saved_in, saved_out, saved_err = sys.stdin, sys.stdout, sys.stderr
sys.stdout = None
dirname = getexistingdirectory(self,
_("Open"),
"",
options=QFileDialog.ShowDirsOnly)
sys.stdin, sys.stdout, sys.stderr = saved_in, saved_out, saved_err
if dirname:
self.data = dwdata.DWObs()
self.data.open_dir(dirname)
cfeeds.set(self.data.list2choices(self.data.sections.keys()))
csections.set(
self.data.list2choices(self.data.sections.values()[0]))
cpolars.set(self.data.list2choices(self.data.polars))
self.setup_central_widget(MainWidget, self.toolbar)
#try:
# self.data.dw_io.new_file(dirname, self.data.flist, 4, 8192)
#except:
# print "file exists"
self.mainwidget.open_setup(self.data)
self.setWindowTitle(APP_NAME + " - " + dirname)
import os
import glob
from shutil import copyfile
from guidata.qt.compat import getexistingdirectory
import guidata
_app = guidata.qapplication()
image_path = getexistingdirectory(None, 'path to images')
#image_path = 'G:/Pictures/04092018-meg/E3-10fps/Separated_tif'
num_convert = 2000
fps = 10
FilesNames = glob.glob(image_path + "\*.png")
temp_name = FilesNames[0].split('t=')
name_root = temp_name[0] + 't='
temp_name = temp_name[1].split('.png')
#time_start = float(temp_name[0])
time_start = -3.0
if os.path.exists(image_path + "/temp"):
print('temp folder already exists')
else:
os.mkdir(image_path + '/temp')
i = 0
while i <= num_convert:
time = time_start + i / fps
try:
src = name_root + str(round(time, 1)) + '.png'
print(src)
dst = image_path + '/temp/pic' + '%04d' % i + '.png'
copyfile(src, dst)
except FileNotFoundError:
@author: kiraz """ #importing libraries import matplotlib.pyplot as plt import numpy as np import pandas as pd from functions import * from guidata.qt.compat import getexistingdirectory """ Read data """ #Select data directory directory = getexistingdirectory() #directory = 'C:/Users/kiraz/Documents/McGill/Data/Espontanea-RD1/20191219/ND4LB/Data0125/Data0125.GUI' #create a pandas data frame with the information coming from the cluster_group file df = pd.read_csv(directory + "/cluster_group.tsv", sep="\t") #Select unique values from data frame labels = df.iloc[:, 0].unique() # Create a column of Boolean values. All the "good" values will have the label True. df['label'] = df['group'] == 'good' #Take the cluster id corresponding to the templates marked as good neurons = df[df['label']].cluster_id.values #load spike data/todos los spike times combinados de todos las neuronas detectadas neuralData = np.load(directory + "/spike_times.npy") #load clusters/son los clusters id ordenados segun la ocurrencia de los spike times, se repiten por cada spike time klusters = np.load(directory + "/spike_clusters.npy") #stack arrays coming from the spikes and klusters/une clusters y spike times en un arreglo np
