/
mouse.py
478 lines (349 loc) · 20.8 KB
/
mouse.py
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#Animal class and subclasses
#Code: Cat
from analysis import *
import os
import glob
import numpy as np
import struct
import string, re
import scipy
import tifffile as tiff
import cPickle as pickle
import gc
#from skimage.measure import block_reduce
import shutil
from load_intan_rhd_format import *
from scipy.signal import butter, filtfilt
class Mouse(object):
def __init__(self, animal_name, home_dir):
self.name = animal_name
self.home_dir = home_dir
#self.move_files()
self.load_filenames()
self.probe = Probe() #Load intan probe map;
try:
self.img_rate = float(np.loadtxt(self.home_dir+self.name+'/img_rate.txt'))
except:
self.img_rate = 0
print "No img rate file..."
def move_files(self):
print "... making default directories and moving files..."
dirs = ['camera_files','movie_files','rhd_files','stm_files','tif_files','tsf_files']
for dir_ in dirs:
new_dir = self.home_dir+self.name+'/'+dir_
if not os.path.exists(new_dir):
print "...making dir: ", new_dir; os.makedirs(new_dir)
#Move .bin and .rhd files to correct foloders
temp_names = glob.glob(self.home_dir+self.name+'/*.rhd')
for r in range(len(temp_names)):
new_file = self.home_dir+self.name+'/rhd_files'+temp_names[r].replace(self.home_dir+self.name,'')
os.rename(temp_names[r], new_file)
#Move .bin and .rhd files to correct foloders
temp_names = glob.glob(self.home_dir+self.name+'/*.bin')
for r in range(len(temp_names)):
new_file = self.home_dir+self.name+'/tif_files'+temp_names[r].replace(self.home_dir+self.name,'')
os.rename(temp_names[r], new_file)
def load_filenames(self):
print "...loading filenames..."
self.filenames = glob.glob(self.home_dir+self.name+'/rhd_files/*.rhd') #use .tif extension otherwise will load .npy files
for f in range(len(self.filenames)):
self.filenames[f] = self.filenames[f][:-4]
#self.tsf_filenames = glob.glob( self.home_dir+self.name+'/tsf_files/*.tsf') #use .tif extension otherwise will load .npy files
def load_tsf_header(self, file_name):
self.tsf = Tsf_file(file_name)
def load_tsf(self, file_name):
self.tsf = Tsf_file(file_name)
self.tsf.read_ec_traces()
def load_channel(self, file_name, channel):
self.tsf = Tsf_file(file_name)
self.tsf.file_name = file_name
self.tsf.read_trace(channel)
def rhd_digital_save(self):
'''Read .rhd files, and save digital channels.
NB: there can be 2, 4 or 6 digital channels inside Intan file
chs 1 and 2 are laser meta data and laser pulse times (these are off for other experiments)
chs 3 and 4 are camera pulse times and on/off times from clampx computer (these are chs 1 and 2 usually as laser chs are off)
chs 5 and 6 are vis stim pulse times and meta data (these are usually 3 and 4 as not recorded w. laser on)
'''
print "...reading digital amp data..."
for file_name in self.filenames:
camera_frames_filename = file_name[0:file_name.find('rhd_files')]+'camera_files/'+file_name[file_name.find('rhd_files')+10:]+'_camera_pulses'
camera_onoff_filename = file_name[0:file_name.find('rhd_files')]+'camera_files/'+file_name[file_name.find('rhd_files')+10:]+'_camera_onoff'
if os.path.exists(camera_onoff_filename+'.npy')==True: continue
data = read_data(file_name+'.rhd')
SampleFrequency = data['frequency_parameters']['board_adc_sample_rate']
print "SampleFrequency: ", SampleFrequency
counter=0
#laser_pulses = data['board_dig_in_data'][counter]; np.save(laser_filename, laser_pulses); counter+=1
#meta_data = data['board_dig_in_data'][counter]; np.save(meta_filename, meta_data); counter+=1
camera_frames = data['board_dig_in_data'][counter]; np.save(camera_frames_filename, camera_frames); counter+=1
camera_onoff = data['board_dig_in_data'][counter]; np.save(camera_onoff_filename, camera_onoff); counter+=1
#Save stim info as well
if len(data['board_dig_in_data'])>2:
stim_pulses_filename = file_name[0:file_name.find('rhd_files')]+'stm_files/'+file_name[file_name.find('rhd_files')+10:]+'_stim_pulses'
stim_meta_filename = file_name[0:file_name.find('rhd_files')]+'stm_files/'+file_name[file_name.find('rhd_files')+10:]+'_stim_meta'
stim_pulses = data['board_dig_in_data'][counter]; np.save(stim_pulses_filename, stim_pulses); counter+=1
stim_meta = data['board_dig_in_data'][counter]; np.save(stim_meta_filename, stim_meta); counter+=1
def rhd_to_tsf(self):
'''Read .rhd files, convert to correct electrode mapping and save to .tsf file
NB: There are 2 possible mapping depending on the insertion of the AD converter
TODO: implement a wavelet high pass filter directly to avoid SpikeSorter Butterworth filter artifacts
'''
print "...reading amp data..."
for file_name in self.filenames:
ec_traces = 0.; ec_traces_hp = 0.; data=0. #Delete previous large arrays;
file_out = file_name[:file_name.find('rhd_files')]+'tsf_files/'+ file_name[file_name.find('rhd_files')+10:]+'_hp.tsf'
if os.path.exists(file_out)==True: continue
print "Processing: \n", file_name+'.rhd'
data = read_data(file_name+'.rhd')
ec_traces = data['amplifier_data'] #*10 #Multiply by 10 to increase resolution for int16 conversion
ec_traces*=10.
SampleFrequency = int(data['frequency_parameters']['board_adc_sample_rate']); print "SampleFrequency: ", SampleFrequency
header = 'Test spike file '
iformat = 1002
n_vd_samples = len(ec_traces[0]); print "Number of samples: ", n_vd_samples
vscale_HP = 0.1 #voltage scale factor
n_cell_spikes = 0
print "Converting data to int16..."
ec_traces = np.array(ec_traces, dtype=np.int16)
#SAVE RAW DATA - ******NB: SHOULD CLEAN THIS UP: the write function should be shared by all, just data is changing so no need to repeat;
if True:
print "Writing raw data ..."
#print "CHANGE THIS TO WORK THROUGH FUNCTION WITHOUT REPEATING"
file_out = file_name[:file_name.find('rhd_files')]+'tsf_files/'+ file_name[file_name.find('rhd_files')+10:]+'_raw.tsf'
fout = open(file_out, 'wb')
fout.write(header)
fout.write(struct.pack('i', 1002))
fout.write(struct.pack('i', SampleFrequency))
fout.write(struct.pack('i', self.probe.n_electrodes))
fout.write(struct.pack('i', n_vd_samples))
fout.write(struct.pack('f', vscale_HP))
for i in range (self.probe.n_electrodes):
fout.write(struct.pack('h', self.probe.Siteloc[i][0]))
fout.write(struct.pack('h', self.probe.Siteloc[i][1]))
fout.write(struct.pack('i', i+1))
for i in range(self.probe.n_electrodes):
print i,
ec_traces[self.probe.layout[i]].tofile(fout) #Frontside
fout.write(struct.pack('i', n_cell_spikes))
fout.close()
#SAVE HIGH PASS WAVELET FILTERED DATA
if True:
print "Writing hp data ..."
file_out = file_name[:file_name.find('rhd_files')]+'tsf_files/'+ file_name[file_name.find('rhd_files')+10:]+'_hp.tsf'
fout = open(file_out, 'wb')
fout.write(header)
fout.write(struct.pack('i', 1002))
fout.write(struct.pack('i', SampleFrequency))
fout.write(struct.pack('i', self.probe.n_electrodes))
fout.write(struct.pack('i', n_vd_samples))
fout.write(struct.pack('f', vscale_HP))
for i in range (self.probe.n_electrodes):
fout.write(struct.pack('h', self.probe.Siteloc[i][0]))
fout.write(struct.pack('h', self.probe.Siteloc[i][1]))
fout.write(struct.pack('i', i+1))
print "Wavelet filtering..."
ec_traces_hp = wavelet(ec_traces, wname="db4", maxlevel=6)
print ec_traces_hp.shape
for i in range(self.probe.n_electrodes):
print i,
ec_traces_hp[self.probe.layout[i]].tofile(fout) #Frontside
fout.write(struct.pack('i', n_cell_spikes))
fout.close()
#OPTIONAL: SAVE LOW PASS DATA
if False:
file_out = file_name[:file_name.find('rhd_files')]+'tsf_files/'+ file_name[file_name.find('rhd_files')+10:]+'_lp.tsf'
fout = open(file_out, 'wb')
fout.write(header)
fout.write(struct.pack('i', 1002))
fout.write(struct.pack('i', SampleFrequency))
fout.write(struct.pack('i', self.probe.n_electrodes))
fout.write(struct.pack('i', n_vd_samples))
fout.write(struct.pack('f', vscale_HP))
for i in range (self.probe.n_electrodes):
fout.write(struct.pack('h', self.probe.Siteloc[i][0]))
fout.write(struct.pack('h', self.probe.Siteloc[i][1]))
fout.write(struct.pack('i', i+1))
for i in range(self.probe.n_electrodes):
(ec_traces[self.probe.layout[i]]-ec_traces_hp[self.probe.layout[i]]).tofile(fout) #Frontside
fout.write(struct.pack('i', n_cell_spikes))
fout.close()
def tsf_to_lfp(self):
'''Read .tsf files - subsample to 1Khz, save as *_lp.tsf
'''
print "...making low-pass tsf files (1Khz sample rates)..."
for file_name in self.filenames:
file_out = file_name[:file_name.find('rhd_files')]+'tsf_files/'+ file_name[file_name.find('rhd_files')+10:]+'_lp.tsf'
if os.path.exists(file_out)==True: continue
file_in = file_name[:file_name.find('rhd_files')]+'tsf_files/'+ file_name[file_name.find('rhd_files')+10:]+'_raw.tsf'
print "Processing: \n", file_in
self.load_tsf(file_in)
#self.load_channel(file_in, 0)
print self.tsf.Siteloc.shape
n_vd_samples = len(self.tsf.ec_traces[0]); print "Number of samples: ", n_vd_samples
print "...converting raw to .lfp (1Khz) sample rate tsf files ..."
temp_array=[]
lowcut = 0.1; highcut=110; fs=1000
#import matplotlib.pyplot as plt
#lowcut = 5; highcut=240; fs=1000 #Use 5Hz low cutoff to reduce slower oscillations for spike sorting;
for k in range(len(self.tsf.ec_traces)): #Subsample to 1Khz and notch filter
print "ch: ", k
temp = np.array(butter_bandpass_filter(self.tsf.ec_traces[k][::int(self.tsf.SampleFrequency/1000)], lowcut, highcut, fs, order = 2), dtype=np.int16)
#Home made notch filter; filter.notch doesn't seem to work...
notch = np.array(butter_bandpass_filter(temp, 59.9, 60.1, fs, order = 2), dtype=np.int16)
temp = temp-notch
temp_array.append(temp)
ec_traces = np.int16(temp_array)
print ec_traces.shape
#SAVE RAW DATA ******NB: SHOULD CLEAN THIS UP: the write function should be shared by all, just data is changing so no need to repeat;
print "Writing raw data ..."
fout = open(file_out, 'wb')
fout.write(self.tsf.header)
fout.write(struct.pack('i', 1002))
fout.write(struct.pack('i', 1000))
fout.write(struct.pack('i', self.tsf.n_electrodes))
fout.write(struct.pack('i', len(ec_traces[0])))
fout.write(struct.pack('f', self.tsf.vscale_HP))
for i in range (self.tsf.n_electrodes):
fout.write(struct.pack('h', self.tsf.Siteloc[i*2]))
fout.write(struct.pack('h', self.tsf.Siteloc[i*2+1]))
fout.write(struct.pack('i', i+1))
for i in range(self.tsf.n_electrodes):
print i,
ec_traces[i].tofile(fout) #Frontside
fout.write(struct.pack('i', self.tsf.n_cell_spikes))
fout.close()
#quit()
def bin_to_npy(self):
print "... converting .bin to .npy ..."
for file_name in self.filenames:
file_out = file_name[:file_name.find('rhd_files')]+'tif_files/'+ file_name[file_name.find('rhd_files')+10:]+'.npy'
if os.path.exists(file_out)==True: continue
print "...reading raw bin: ", file_out
#file_name = '/media/cat/12TB/in_vivo/tim/cat/2016_06_09_test/test_laser_imaging'
#IS THIS DOUBLING DATA SIZE UNECESSARILY? ORIGINAL DATA MAY BE UINT8 SO NO NEED TO MAKE IT INT16
data = np.fromfile(file_out[:-4]+'.bin', dtype=np.int16)
print "...reshaping array..."
data = data.reshape((-1, 128, 128))
print "...saving .npy array..."
np.save(file_out, data)
#import matplotlib.pyplot as plt
#print data[0]
#plt.imshow(data[0], cmap=plt.get_cmap('gray'))
#plt.show()
#def save_dig_input(self):
##Read and save digital input data
#laser_filename = file_name[0:file_name.find('rhd_files')]+'laser_files/'+file_name[file_name.find('rhd_files')+10:]+'_laser_times'
#meta_filename = file_name[0:file_name.find('rhd_files')]+'laser_files/'+file_name[file_name.find('rhd_files')+10:]+'_meta_data'
#camera_pulses_filename = file_name[0:file_name.find('rhd_files')]+'camera_files/'+file_name[file_name.find('rhd_files')+10:]+'_camera_pulses'
#camera_onoff_filename = file_name[0:file_name.find('rhd_files')]+'camera_files/'+file_name[file_name.find('rhd_files')+10:]+'_camera_onoff'
#SampleFrequency = data['frequency_parameters']['board_adc_sample_rate']
#print "SampleFrequency: ", SampleFrequency
#counter=0
##laser_pulses = data['board_dig_in_data'][counter]; np.save(laser_filename, laser_pulses); counter+=1
##meta_data = data['board_dig_in_data'][counter]; np.save(meta_filename, meta_data); counter+=1
##camera_frames = data['board_dig_in_data'][counter]; np.save(camera_frames_filename, camera_frames); counter+=1
#camera_onoff = data['board_dig_in_data'][counter]; np.save(camera_onoff_filename, camera_onoff); counter+=1
##plt.plot(camera_onoff)
##plt.show()
#class Tsf_file(object):
#def __init__(self, file_name):
#self.read_header(file_name)
#def read_header(self, file_name):
#self.fin = open(file_name, "rb")
#self.header = self.fin.read(16)
#self.iformat = struct.unpack('i',self.fin.read(4))[0]
#self.SampleFrequency = struct.unpack('i',self.fin.read(4))[0]
#self.n_electrodes = struct.unpack('i',self.fin.read(4))[0]
#self.n_vd_samples = struct.unpack('i',self.fin.read(4))[0]
#self.vscale_HP = struct.unpack('f',self.fin.read(4))[0]
#if self.iformat==1001:
#self.Siteloc = np.zeros((2*self.n_electrodes), dtype=np.int16)
#self.Siteloc = struct.unpack(str(2*self.n_electrodes)+'h', self.fin.read(2*self.n_electrodes*2))
#if self.iformat==1002:
#self.Siteloc = np.zeros((2*self.n_electrodes), dtype=np.int16)
#self.Readloc = np.zeros((self.n_electrodes), dtype=np.int32)
#for i in range(self.n_electrodes):
#self.Siteloc[i*2] = struct.unpack('h', self.fin.read(2))[0]
#self.Siteloc[i*2+1] = struct.unpack('h', self.fin.read(2))[0]
#self.Readloc[i] = struct.unpack('i', self.fin.read(4))[0]
#def read_ec_traces(self):
#print " ... reading data.....chs: ", self.n_electrodes, " ... nsamples: ", self.n_vd_samples
#self.ec_traces = np.fromfile(self.fin, dtype=np.int16, count=self.n_electrodes*self.n_vd_samples)
#self.ec_traces.shape = self.n_electrodes, self.n_vd_samples
#self.n_cell_spikes = struct.unpack('i',self.fin.read(4))[0]
#print "No. ground truth cell spikes: ", self.n_cell_spikes
#if (self.n_cell_spikes>0):
#if (self.iformat==1001):
#self.vertical_site_spacing = struct.unpack('i',self.fin.read(4))[0]
#self.n_cell_spikes = struct.unpack('i',self.fin.read(4))[0]
#self.fake_spike_times = np.fromfile(self.fin, dtype=np.int32, count=self.n_cell_spikes)
#self.fake_spike_assignment = np.fromfile(self.fin, dtype=np.int32, count=self.n_cell_spikes)
#self.fake_spike_channels = np.fromfile(self.fin, dtype=np.int32, count=self.n_cell_spikes)
#self.fin.close()
#def read_trace(self, channel):
##Load single channel
#indent = 16+20+self.n_electrodes*8
#self.fin.seek(indent+channel*2*self.n_vd_samples, os.SEEK_SET) #Not 100% sure this indent is correct.
#self.ec_traces = np.fromfile(self.fin, dtype=np.int16, count=self.n_vd_samples)
#self.fin.close()
class Probe(object):
def __init__(self):
print "...loading probe..."
self.name = "NeuroNexus 64Ch probe" #Hardwired, but should add options here...
self.load_layout()
def load_layout(self):
''' Load intan probe map layout
'''
self.n_electrodes = 64
#Fixed location array for NeurNexus probe layotus
self.Siteloc = np.zeros((self.n_electrodes,2), dtype=np.int16) #Read as 1D array
for i in range (self.n_electrodes):
self.Siteloc[i][0]=30*(i%2)
self.Siteloc[i][1]=i*23
#A64 Omnetics adaptor
adaptor_map = []
adaptor_map.append([34,35,62,33,60,54,57,55,10,8,11,5,32,3,30,31])
adaptor_map.append([64,58,63,56,61,59,52,50,15,13,6,4,9,2,7,1])
adaptor_map.append([53,51,49,47,45,36,37,38,27,28,29,20,18,16,14,12])
adaptor_map.append([48,46,44,42,40,39,43,41,24,22,26,25,23,21,19,17])
adaptor_layout1=[] #Concatenated rows
for maps in adaptor_map:
adaptor_layout1.extend(maps)
#Intan adapter - if inserted right-side up
intan_map = []
intan_map.append(list(reversed([46,44,42,40,38,36,34,32,30,28,26,24,22,20,18,16]))) #NB: need to reverse these arrays: list(reversed(...))
intan_map.append(list(reversed([47,45,43,41,39,37,35,33,31,29,27,25,23,21,19,17])))
intan_map.append(list(reversed([49,51,53,55,57,59,61,63,1,3,5,7,9,11,13,15])))
intan_map.append(list(reversed([48,50,52,54,56,58,60,62,0,2,4,6,8,10,12,14])))
intan_layout1=[]
for maps in intan_map:
intan_layout1.extend(maps)
#Intan adapter - if inserted upside-down; no need to reverse
intan_map = []
intan_map.append([48,50,52,54,56,58,60,62,0,2,4,6,8,10,12,14])
intan_map.append([49,51,53,55,57,59,61,63,1,3,5,7,9,11,13,15])
intan_map.append([47,45,43,41,39,37,35,33,31,29,27,25,23,21,19,17])
intan_map.append([46,44,42,40,38,36,34,32,30,28,26,24,22,20,18,16])
intan_layout2=[]
for maps in intan_map:
intan_layout2.extend(maps)
#A1x64 probe layout
a = [27,26,25,24,23,22,21,20,19,18,17,16,15,14,13,12,11,10,9,8,7,6,5,4,3,2,1,28,29,30,31,32]
b = [37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,36,35,34,33]
probe_map = a+b
probe_map[::2] = a
probe_map[1::2] = b
self.layout = []
for i in range(len(probe_map)):
self.layout.append(intan_layout1[adaptor_layout1.index(probe_map[i])])
def convert_tif_DUPLICATE(self):
if (os.path.exists(self.tif_file[:-4] +'.npy')==False):
print "...read: ", self.tif_file
images_raw = tiff.imread(self.tif_file)
print "... saving .npy"
np.save(self.tif_file[:-4], images_raw)
def find_previous(self, array, value):
temp = (np.abs(array-value)).argmin()
if array[temp]>value: return temp-1
else: return temp