def exp_tracef(Injectcurr=150e-12):
global flnme
global exp_sampdur
global exp_samprate
global exp_samppoints
global exp_trace_injend
global exp_trace_injstart
stim1391 = ['Cell 3 of 181016.abf', 'cell 4 of 61016.abf', 'cell 4 of 111016.abf', 'cell 4 of 131016.abf', 'Cell 4 of 181016.abf', 'cell 5 of 61016.abf', 'Cell 5 of 181016.abf']
# flnme = 'Cell 3 of 10717.abf'
flnme = 'cell 4 of 61016.abf'
exp_tracefile = f'../../Raw_data/Deepanjali_data/WT step input cells/{flnme}'
reader = AxonIO(filename=exp_tracefile)
currno = int(Injectcurr*1e12/25+4)
seg = reader.read_block().segments[currno] # 10 means 150pA current
exp_trace = seg.analogsignals[0]
exp_samprate = float(exp_trace.sampling_rate)
exp_sampdur = float(exp_trace.t_stop) - float(exp_trace.t_start)
exp_samppoints = int(exp_samprate*exp_sampdur)
if flnme in stim1391:
exp_trace_injstart = 139.1e-3
exp_trace_injend = 639.1e-3
else:
exp_trace_injstart = 81.4e-3
exp_trace_injend = 581.4e-3
exp_trace = np.array(exp_trace).flatten()
return exp_trace
def convert_from_ABF_to_HDF5(experiment):
"""
Convierte los ficheros del experimento desde ABF y los graba en un fichero HDF5
:param experiment:
:return:
"""
# Asumimos que el fichero no existe todavia
f = experiment.open_experiment_data(mode='w')
nsig = experiment.abfsensors
datafiles = experiment.datafiles
for dataf in datafiles:
# Leemos los datos del fichero ABF
print('Reading: ', dataf, '...')
data = AxonIO(experiment.dpath + experiment.name + '/' + dataf + '.abf')
bl = data.read_block(lazy=False, cascade=True)
dim = bl.segments[0].analogsignals[0].shape[0]
matrix = np.zeros((len(nsig), dim))
for i, j in enumerate(nsig):
matrix[i][:] = bl.segments[0].analogsignals[j][:].magnitude
# Los guardamos en el almacenamiento del experimento
print('Saving: ', dataf, '...')
experiment.save_raw_data(f, dataf, matrix.T)
del matrix
del bl
datainfo.close_experiment_data(f)
def convert_from_ABF_to_HDF5(experiment):
"""
Convierte los ficheros del experimento desde ABF y los graba en un fichero HDF5
:param experiment:
:return:
"""
# Asumimos que el fichero no existe todavia
f = experiment.open_experiment_data(mode='w')
nsig = experiment.abfsensors
datafiles = experiment.datafiles
for dataf in datafiles:
# Leemos los datos del fichero ABF
print('Reading: ', dataf, '...')
data = AxonIO(experiment.dpath + experiment.name + '/' + dataf +
'.abf')
bl = data.read_block(lazy=False)
dim = bl.segments[0].analogsignals[0].shape[0]
matrix = np.zeros((len(nsig), dim))
for i, j in enumerate(nsig):
tmpmat = bl.segments[0].analogsignals[j][:].magnitude
matrix[i][:] = tmpmat.reshape(tmpmat.shape[0])
# Los guardamos en el almacenamiento del experimento
print('Saving: ', dataf, '...')
experiment.save_raw_data(f, dataf, matrix.T)
del matrix
del bl
datainfo.close_experiment_data(f)
def setup_class(cls):
TestBase.setup_class()
ctx = cls.local_stack.getAuthenticatedDataStoreCoordinator().getContext()
proj = ctx.insertProject('ABF import', 'ABF import', DateTime())
exp = proj.insertExperiment('ABF experiment', DateTime())
cls.device_info = {u'amplifier.mode': u'I-clamp',
u'amplifier.channels.0.gain': 2.5,
u'amplifier.channels.1.gain': 3.5}
# TODO when we move to beta3
#exp.setEquipmentSetupFromMap(to_map(cls.device_info))
cls.src = ctx.insertSource("recording source", "source-id")
abf_file = 'fixtures/example1.abf'
logging.info("Importing file...")
cls.epoch_group = import_file(abf_file,
exp,
"amplifier",
[cls.src])[0] #single block
reader = AxonIO(filename=abf_file)
cls.block = reader.read()[0] # single block
def test_read_protocol(self):
for f in self.files_to_test:
filename = self.get_filename_path(f)
reader = AxonIO(filename=filename)
bl = reader.read_block(lazy=True)
if bl.annotations['abf_version']>=2.:
reader.read_protocol()
def test_read_protocol(self):
for f in self.files_to_test:
filename = self.get_filename_path(f)
reader = AxonIO(filename=filename)
bl = reader.read_block(lazy=True)
if bl.annotations['abf_version'] >= 2.:
reader.read_protocol()
def read_neo(path):
# Read abf file with neo
reader = AxonIO(filename=path)
block = reader.read()
# Extract analog signals
if len(block[0].segments[0].analogsignals) == 1:
channel1 = np.empty(block[0].segments[0].analogsignals[0].T.shape)
channel1_units = block[0].segments[0].analogsignals[0].units
channel2 = None
channel2_units = None
t = block[0].segments[0].analogsignals[0].times.magnitude
for idx, seg in enumerate(block[0].segments):
channel1[idx] = seg.analogsignals[0].T
#
# return {'file_name': block.file_origin,
# 'recording_date': block.rec_datetime,
# 'time': t,
# 'channel1': channel1,
elif len(block[0].segments[0].analogsignals) == 2:
channel1 = np.empty((len(block[0].segments), block[0].segments[0].analogsignals[0].size))
channel1_units = block[0].segments[0].analogsignals[0].units
channel1_sampling = block[0].segments[0].analogsignals[0].sampling_rate
channel2 = np.empty((len(block[0].segments), block[0].segments[0].analogsignals[1].size))
channel2_units = block[0].segments[0].analogsignals[1].units
channel2_sampling = block[0].segments[0].analogsignals[1].sampling_rate
t = block[0].segments[0].analogsignals[0].times.magnitude
for idx, seg in enumerate(block[0].segments):
channel1[idx] = seg.analogsignals[0].T
channel2[idx] = seg.analogsignals[1].T
#channel1[idx] = seg.analogsignals[0]
#channel2[idx] = seg.analogsignals[1]
channel_1 = AnalogData(
signal=channel1,
time=t,
units=channel1_units,
sampling_rate=channel1_sampling
)
channel_2 = AnalogData(
signal=channel2,
time=t,
units=channel2_units,
sampling_rate=channel2_sampling)
else:
raise Exception('File {0} either has too many channels, or no channels.'.format(path))
return AbfFile(
[channel_1, channel_2],
file_name=block[0].file_origin,
date=block[0].rec_datetime)
def convert_from_ABF_to_HDF5(experiment):
"""
Convierte los ficheros del experimento desde ABF y los graba en un fichero HDF5
:param experiment:
:return:
"""
# Asumimos que el fichero no existe todavia
f = h5py.File(
experiment.dpath + experiment.name + '/' + experiment.name + '.hdf5',
'r+')
nsig = [s for s, _ in experiment.extrasensors]
datafiles = experiment.datafiles
for dataf in datafiles:
# Leemos los datos del fichero ABF
print('Reading: ', dataf, '...')
data = AxonIO(experiment.dpath + experiment.name + '/' + dataf +
'.abf')
bl = data.read_block(lazy=False, cascade=True)
dim = bl.segments[0].analogsignals[0].shape[0]
matrix = np.zeros((len(nsig), dim))
for i, j in enumerate(nsig):
matrix[i][:] = bl.segments[0].analogsignals[j][:].magnitude
# Los guardamos en una carpeta del fichero HDF5 para el fichero dentro de /Raw
print('Saving: ', dataf, '...')
dgroup = f[dataf]
if dataf + '/RawExtra' in f:
del f[dataf + '/RawExtra']
dgroup.create_dataset('RawExtra',
matrix.T.shape,
dtype='f',
data=matrix.T,
compression='gzip')
del matrix
del bl
f[dataf + '/RawExtra'].attrs['Sampling'] = experiment.sampling
f[dataf + '/RawExtra'].attrs['Sensors'] = [
s for _, s in experiment.extrasensors
]
f.flush()
f.close()
def load_file(filename, zoom=[0,np.inf]):
# loading the data file
try:
data = AxonIO(filename).read_block(lazy=False, cascade=True)
dt = float(data.segments[0].analogsignals[0].sampling_period)
if zoom[0]<data.segments[0].analogsignals[0].t_start:
zoom[0]=data.segments[0].analogsignals[0].t_start
if zoom[1]>data.segments[-1].analogsignals[0].t_stop:
zoom[1]=data.segments[-1].analogsignals[0].t_stop
###
ii = 0
while (ii<len(data.segments)) and (float(data.segments[min(ii,len(data.segments)-1)].analogsignals[0].t_start)<=zoom[0]):
ii+=1
tt = np.array(data.segments[ii-1].analogsignals[0].times)
cond = (tt>=zoom[0]) & (tt<=zoom[1])
DATA = {'t':tt[cond]}
for j in range(1, len(data.segments[ii-1].analogsignals)+1):
DATA['Ch'+str(j)] = np.array(data.segments[ii-1].analogsignals[j-1])[cond]
###
while (ii<len(data.segments)) and ((float(data.segments[min(ii,len(data.segments)-1)].analogsignals[0].t_start)<=zoom[1])):
tt = np.array(data.segments[ii].analogsignals[0].times)
cond = (tt>=zoom[0]) & (tt<=zoom[1])
DATA['t'] = np.concatenate([DATA['t'],\
np.array(data.segments[ii].analogsignals[0].times)[cond]])
for j in range(1, len(data.segments[ii].analogsignals)+1):
DATA['Ch'+str(j)] = np.concatenate([DATA['Ch'+str(j)],\
np.array(data.segments[ii].analogsignals[j-1])[cond]])
ii+=1
return DATA
except FileNotFoundError:
print('File not Found !')
return {}
def list_keys(args):
""" list the keys of the """
Block = AxonIO(args.filename).read_block(lazy=False, cascade=True)
KEYS = []
for i in range(len(Block.segments[0].analogsignals)):
print('key', i, '-->', Block.segments[0].analogsignals[i].name)
KEYS.append(Block.segments[0].analogsignals[i].name)
return KEYS
def translate(args, ikey, keys):
data = {} # dictionary for hdf5 export
Block = AxonIO(args.filename).read_block(lazy=False, cascade=True)
RT = Block.rec_datetime #
params = {} # parameters stored here
params['day'] = ("%04d" % RT.year) + '_' + ("%02d" % RT.month) + '_' + (
"%02d" % RT.day)
params['time'] = ("%02d" % RT.hour) + '_' + ("%02d" % RT.minute) + '_' + (
"%02d" % RT.second)
Block = AxonIO(args.filename).read_block(lazy=False, cascade=True)
params['dt'] = np.array(
[Block.segments[0].analogsignals[0].sampling_period])
new_filename = args.filename.replace('.abf', '.dat')
if len(Block.segments) == 1:
# if only one episode, we swith to continuous
args.force_continuous = True
t = np.arange(len(Block.segments[0].analogsignals[0])) * params['dt']
cond = (t >= args.tstart) & (t <= args.tend)
# ARRAY = []
# for s in range(len(Block.segments)):
# ARRAY.append(Block.segments[s].analogsignals[ikey][cond])
X = Block.segments[0].analogsignals[ikey][cond] / quantities.mV
# 'short int' (int16) has values from −32,768 to 32,767
# SWITCH TO int16 !!!
# X -= X.mean()
# scale = 30000/np.abs(X).max()
# X2 = np.reshape(np.array(scale*X), (len(X), 1)).astype('int16')
# X2.tofile(new_filename.replace('abf', 'dat'))
# float32 by waiting
scale = 1.
X2 = np.reshape(np.array(X), (len(X), 1)).astype('float32')
X2.tofile(new_filename.replace('abf', 'dat'))
print('file exported as: ', new_filename)
return {'facq': 1. / params['dt'], 'gain': scale}
def convert_from_ABF_to_HDF5(experiment):
"""
Convierte los ficheros del experimento desde ABF y los graba en un fichero HDF5
:param experiment:
:return:
"""
# Asumimos que el fichero no existe todavia
f = h5py.File(experiment.dpath + experiment.name + "/" + experiment.name + ".hdf5", "r+")
nsig = [s for s, _ in experiment.extrasensors]
datafiles = experiment.datafiles
for dataf in datafiles:
# Leemos los datos del fichero ABF
print("Reading: ", dataf, "...")
data = AxonIO(experiment.dpath + experiment.name + "/" + dataf + ".abf")
bl = data.read_block(lazy=False, cascade=True)
dim = bl.segments[0].analogsignals[0].shape[0]
matrix = np.zeros((len(nsig), dim))
for i, j in enumerate(nsig):
matrix[i][:] = bl.segments[0].analogsignals[j][:].magnitude
# Los guardamos en una carpeta del fichero HDF5 para el fichero dentro de /Raw
print("Saving: ", dataf, "...")
dgroup = f[dataf]
if dataf + "/RawExtra" in f:
del f[dataf + "/RawExtra"]
dgroup.create_dataset("RawExtra", matrix.T.shape, dtype="f", data=matrix.T, compression="gzip")
del matrix
del bl
f[dataf + "/RawExtra"].attrs["Sampling"] = experiment.sampling
f[dataf + "/RawExtra"].attrs["Sensors"] = [s for _, s in experiment.extrasensors]
f.flush()
f.close()
def show(args):
keys = list_keys(args)
Block = AxonIO(args.filename).read_block(lazy=False, cascade=True)
fig, AX = plt.subplots(len(keys), figsize=(5, 3 * len(keys)))
for i in range(len(keys)):
if len(keys) > 1:
ax = AX[i]
else:
ax = AX
ax.set_title(keys[i])
ax.plot(Block.segments[0].analogsignals[i][:10000])
plt.show()
def exp_tracef(currno=10):
global flnme
global exp_trace
global exp_sampdur
global exp_samprate
global exp_samppoints
global exp_trace_injend
global exp_trace_injstart
stim1391 = ['Cell 3 of 181016.abf', 'cell 4 of 61016.abf', 'cell 4 of 111016.abf', 'cell 4 of 131016.abf', 'Cell 4 of 181016.abf', 'cell 5 of 61016.abf', 'Cell 5 of 181016.abf']
exp_tracefile = Exp_tracedir+flnme
reader = AxonIO(filename=exp_tracefile)
seg = reader.read_block().segments[currno] # 10 means 15pA current
exp_trace = seg.analogsignals[0]
exp_samprate = float(exp_trace.sampling_rate)
exp_sampdur = float(exp_trace.t_stop) - float(exp_trace.t_start)
exp_samppoints = int(exp_samprate*exp_sampdur)
if flnme in stim1391:
exp_trace_injstart = 139.1e-3
exp_trace_injend = 639.1e-3
else:
exp_trace_injstart = 81.4e-3
exp_trace_injend = 581.4e-3
exp_trace = np.array(exp_trace).flatten()
def translate(args):
keys = list_keys(args)
data = {} # dictionary for hdf5 export
Block = AxonIO(args.filename).read_block(lazy=False, cascade=True)
RT = Block.rec_datetime #
params = {} # parameters stored here
params['day'] = ("%04d" % RT.year) + '_' + ("%02d" % RT.month) + '_' + (
"%02d" % RT.day)
params['time'] = ("%02d" % RT.hour) + '_' + ("%02d" % RT.minute) + '_' + (
"%02d" % RT.second)
params['dt'] = np.array(
[Block.segments[0].analogsignals[0].sampling_period])
if not os.path.exists(params['day']):
os.makedirs(params['day'])
protocol = args.protocol
args.filename = params['day'] + os.path.sep + params[
'time'] + '_' + protocol + '.h5'
if args.new_keys is not None:
if len(args.new_keys) == len(keys):
keys = args.new_keys
else:
print('need to give a new to all former keys (in the order)')
if len(Block.segments) == 1:
# if only one episode, we swith to continuous
args.force_continuous = True
t = np.arange(len(Block.segments[0].analogsignals[0])) * params['dt']
cond = (t >= args.tstart) & (t <= args.tend)
for i in range(len(keys)):
data[keys[i]] = []
for s in range(len(Block.segments)):
data[keys[i]].append(Block.segments[s].analogsignals[i][cond])
if args.force_continuous:
data[keys[i]] = np.array(data[keys[i]]).flatten()
params[keys[i] + '_unit'] = Block.segments[s].analogsignals[i][
0].dimensionality.string
data['params'] = params
data['t'] = t
save_dict_to_hdf5(data, args.filename)
def get_metadata(filename):
params = {'main_protocol':'undefined', 'protocol':'undefined'}
# loading metadata
data = AxonIO(filename).read_block(lazy=False, cascade=True)
params['dt'] = float(data.segments[0].analogsignals[0].sampling_period)
params['tstart'] = float(data.segments[0].analogsignals[0].times[0])
params['tstop'] = float(data.segments[0].analogsignals[0].times[-1])
params['Nepisodes'] = len(data.segments)
params['Nchannels'] = len(data.segments[0].analogsignals)
# prtocol name in case
protocol = get_protocol_name(filename)
if protocol!='':
params['protocol'] = protocol
params['main_protocol'] = 'classic_electrophy'
return params
def load_file(filename, zoom=[0, np.inf]):
# loading the data file
try:
data = AxonIO(filename).read_block(lazy=False)
dt = float(data.segments[0].analogsignals[0].sampling_period)
if zoom[0] < data.segments[0].analogsignals[0].t_start:
zoom[0] = data.segments[0].analogsignals[0].t_start
if zoom[1] > data.segments[-1].analogsignals[0].t_stop:
zoom[1] = data.segments[-1].analogsignals[0].t_stop
###
ii = 0
while (ii < len(data.segments)) and (float(data.segments[min(
ii,
len(data.segments) - 1)].analogsignals[0].t_start) <= zoom[0]):
ii += 1
tt = np.array(data.segments[ii - 1].analogsignals[0].times)
cond = (tt >= zoom[0]) & (tt <= zoom[1])
VEC = [tt[cond]]
for j in range(1, len(data.segments[ii - 1].analogsignals) + 1):
VEC.append(
np.array(data.segments[ii -
1].analogsignals[j -
1])[cond].flatten())
###
while (ii < len(data.segments)) and ((float(data.segments[min(
ii,
len(data.segments) - 1)].analogsignals[0].t_start) <=
zoom[1])):
tt = np.array(data.segments[ii].analogsignals[0].times)
cond = (tt >= zoom[0]) & (tt <= zoom[1])
VEC[0] = np.concatenate([VEC[0],\
np.array(data.segments[ii].analogsignals[0].times)[cond]])
for j in range(1, len(data.segments[ii].analogsignals) + 1):
VEC[j] = np.concatenate([VEC[j],\
np.array(data.segments[ii].analogsignals[j-1])[cond].flatten()])
ii += 1
return VEC[0], VEC[1:]
except FileNotFoundError:
print('File not Found !')
return [[], []]
'''
max_values = []
for s_n in range(len(list_of_segments)):
max_y = -100000
for t_p in range(len(list_of_segments[s_n].analogsignals[0])):
if list_of_segments[s_n].analogsignals[0][t_p] > max_y:
max_y = list_of_segments[s_n].analogsignals[0][t_p]
t_p = t_p + 1
max_values.append(max_y)
#print('The length of the seg is ' + str(len(segs[s_n].analogsignals[0])) + ' for current signal')
#print('The max current is ' + str(max_y))
return max_values
abf_file_name = './data/input/18227014.abf'
abf = AxonIO(abf_file_name)
#print file info
print(abf.read_protocol)
#count segments
n_seg = abf.segment_count(block_index=0)
print('Found ' + str(n_seg) + ' segments.')
# Generate an array containing segments
segs = get_segments(abf)
print(segs[15])
# Visually check the values of the analog signal
# in seg[15].
print(segs[15].analogsignals[0])
a_seg_1 = np.array(segs[15].analogsignals[0])
from config.paths import cinvesdata, cinvesdatanew
datafiles = [(['15514028', '15514029', '15514030',
'15514031', '15514032', '15514033', '15514034', '15514035', '15514036', '15514037', '15514038'], 12, 10000.0)
]
# datafiles = [(['15514027'],
# 12, 10000.0)]
for dataf, nsig, _ in datafiles:
for files in dataf:
print 'Reading: ', files, '...'
data = AxonIO(cinvesdatanew + 'e150514/' + files + '.abf')
bl = data.read_block(lazy=False, cascade=True)
dim = bl.segments[0].analogsignals[0].shape[0]
print dim
matrix = np.zeros((nsig, dim))
# for sig in bl.segments[0].analogsignals:
# i += 1
# #print sig.duration, sig.signal, len(sig.times), i
# print sig.name, sig.units, sig.sampling_rate, sig.dtype, sig.duration, sig.shape
for j in range(nsig):
matrix[j][:] = bl.segments[0].analogsignals[j][:].magnitude
def exp_tracef(Injectcurr=150e-12):
global flnme
global exp_sampdur
global exp_samprate
global exp_samppoints
global exp_trace_injend
global exp_trace_injstart
global Vrest
global sm_diam
global sm_len
global Gin
stim1391 = [
'Cell 3 of 181016.abf', 'cell 4 of 61016.abf', 'cell 4 of 111016.abf',
'cell 4 of 131016.abf', 'Cell 4 of 181016.abf', 'cell 5 of 61016.abf',
'Cell 5 of 181016.abf'
]
# flnme = 'Cell 3 of 10717.abf'
flnme = 'cell 4 of 61016.abf'
exp_tracefile = f'../../Raw_data/Deepanjali_data/WT step input cells/{flnme}'
reader = AxonIO(filename=exp_tracefile)
currno = int(Injectcurr * 1e12 / 25 + 4)
seg = reader.read_block().segments[currno] # 10 means 150pA current
exp_trace = seg.analogsignals[0]
exp_samprate = float(exp_trace.sampling_rate)
exp_sampdur = float(exp_trace.t_stop) - float(exp_trace.t_start)
exp_samppoints = int(exp_samprate * exp_sampdur)
if flnme in stim1391:
exp_trace_injstart = 139.1e-3
exp_trace_injend = 639.1e-3
else:
exp_trace_injstart = 81.4e-3
exp_trace_injend = 581.4e-3
exp_trace = np.array(exp_trace).flatten() * 1e-3
Vrest = np.mean(
np.array(
reader.read_block().segments[4].analogsignals[0]).flatten()) * 1e-3
Rinp25 = np.abs(
np.max(
np.array(reader.read_block().segments[5].analogsignals[0]).flatten(
)) * 1e-3 - Vrest) / 25e-12
Rinn25 = np.abs(
np.min(
np.array(reader.read_block().segments[3].analogsignals[0]).flatten(
)) * 1e-3 - Vrest) / 25e-12
Gin = 2 / (Rinp25 + Rinn25)
print(Gin)
t = np.linspace(0, exp_sampdur, int(exp_sampdur * exp_samprate))
Vtracep25 = np.array(
reader.read_block().segments[5].analogsignals[0]).flatten() * 1e-3
Vtracen25 = np.array(
reader.read_block().segments[3].analogsignals[0]).flatten() * 1e-3
str_ind = (np.abs(t - exp_trace_injend + 100e-3)).argmin()
stp_ind = (np.abs(t - exp_trace_injend)).argmin()
Vtracep25_choppped = Vtracep25[:stp_ind]
Vtracen25_choppped = Vtracen25[:stp_ind]
vp63 = np.abs(
np.max(
np.array(reader.read_block().segments[5].analogsignals[0]).flatten(
)) * 1e-3 - Vrest) * 0.63 + Vrest
vn63 = -np.abs(
np.min(
np.array(reader.read_block().segments[3].analogsignals[0]).flatten(
)) * 1e-3 - Vrest) * 0.63 + Vrest
tau = (t[(np.abs(Vtracep25_choppped - vp63)).argmin()] - exp_trace_injstart
+ t[(np.abs(Vtracen25_choppped - vn63)).argmin()] -
exp_trace_injstart) / 2
tauinv = (t[len(Vtracep25_choppped) -
(np.abs(Vtracep25_choppped[stp_ind::-1] - vp63)).argmin()] -
exp_trace_injstart +
t[len(Vtracep25_choppped) -
(np.abs(Vtracep25_choppped[::-1] - vp63)).argmin()] -
exp_trace_injstart) / 2
Cm = (tau + tauinv) * Gin / 2
print(Cm)
sm_len = np.sqrt(Cm / CM / np.pi)
sm_diam = sm_len
return exp_trace
import matplotlib.pyplot as plt
from neo.io import AxonIO
import os
from allensdk.ephys.ephys_extractor import EphysSweepFeatureExtractor
from allensdk.core.cell_types_cache import CellTypesCache
filename = 'Cell 6 of 171117.abf'
seg_no = 17 #0 is -100pA, 4 is 0pA, 20 is 400pA. Now extrapolate
stim_start = 81.4e-3 #in s
stim_stop = 581.4e-3 #in s
Actualstim_start = seg_no * 2 + stim_start
Actualstim_stop = seg_no * 2 + stim_stop
Inputcurr = seg_no * 25 - 100 #in pA
reader = AxonIO(filename='Deepanjali data/WT step input cells/' + filename)
Vtrace = reader.read_block().segments[seg_no].analogsignals[0]
i = np.zeros(int((Vtrace.t_stop - Vtrace.t_start) * Vtrace.sampling_rate))
i[int(stim_start * Vtrace.sampling_rate):int(stim_stop *
Vtrace.sampling_rate)] = Inputcurr
i = np.array(i)
v = np.array([float(V) for V in Vtrace])
t = np.linspace(0, float(Vtrace.t_stop - Vtrace.t_start),
int((Vtrace.t_stop - Vtrace.t_start) * Vtrace.sampling_rate))
t = np.array(t)
sweep_ext = EphysSweepFeatureExtractor(t=t, v=v, i=i, filter=5)
sweep_ext.process_spikes()
sweep_ext.spike_feature_keys() #Lists all the features that can be extracted
import numpy as np
import matplotlib.pyplot as plt
import glob
#change directory as needed
flydir = 'C:\\Users\\rancher\\Desktop\\S328_.5stim\\'
from neo.io import AxonIO
abf_name_list = glob.glob(flydir + '*.abf')
for i in abf_name_list:
abf_name = i.replace('.abf','')
abf_name = abf_name.replace(flydir,'')
localfile = flydir + abf_name + '.abf' #change file name as needed
r = AxonIO(localfile)
bl = r.read_block(lazy=False, cascade=True)
# Get info from channels:
l_amp = np.asarray(bl.segments[0].analogsignals[3])*60/np.pi #Left wing from Kinefly
r_amp = np.asarray(bl.segments[0].analogsignals[4])*60/np.pi #Right wing from Kinefly
wbf = np.asarray(bl.segments[0].analogsignals[0])*100. #wing beat frequency from wing beat analyzer
# l_plus_r = np.asarray(bl.segments[0].analogsignals[1])*60/np.pi #from wing beat analyzer
pattern = np.asarray(bl.segments[0].analogsignals[7])
#frames = np.asarray(bl.segments[0].analogsignals[3])
#amp_sum = l_amp + r_amp #use this line if we use Channel 1 l_plus_r from wing beat analyzer again
l_plus_r = l_amp + r_amp
amp_diff = l_amp - r_amp # Note: amp_diff is L-R
def binary_to_csv(filename=None):
'''
Writes data for given abf file into a csv format. Two .csv files will be created into csv_data folder
One will be meta_data, one will be time series data.
These sheets are also returned as pandas data frames by this function
'''
if filename is None:
sys.exit('must specify filename and path')
# Set up path to save csv output to
pathtocsv = str.split(filename, '/')
pathtocsv = "/".join(pathtocsv[:-2]) + "/csv_data/"
#Use neo function "AxonIO" to read in the axonb binary file
r = AxonIO(filename=filename)
r = r.read_block()
# for name purposes later on, remove ".abf" extension from filename
filename = splitext(basename(filename))[0]
# Define number of sweeps in this protocol (.abf file)
n_sweeps = len(r.segments)
n_channels = len(r.segments[0].analogsignals)
# Name sweeps
sweep_ids = []
for i in range(0, n_sweeps):
sweep_ids.append('sweep' + str(i + 1))
# List the traits to hold in meta data
meta_traits = ['fs', 'celltype', 'date']
for i in range(0, n_channels):
meta_traits.append('ch' + str(i + 1) + '_units')
# create empty data frame for meta data
meta_data = pd.DataFrame(columns=meta_traits, index=sweep_ids)
# fill meta_data
for i, seg in enumerate(r.segments):
for ch in range(0, n_channels):
meta_data['ch' + str(ch + 1) + '_units'][sweep_ids[i]] = str(
seg.analogsignals[ch].units)[4:]
meta_data['fs'][sweep_ids[i]] = float(
seg.analogsignals[0].sampling_rate)
meta_data['date'][sweep_ids[i]] = filename[-10:]
meta_data['celltype'][sweep_ids[i]] = str.split(
filename,
'_')[0] # example of using string split to get information
# create empty data frame to hold data
data_cols = []
for i in range(0, n_sweeps):
for ch in range(0, n_channels):
data_cols.append('ch' + str(ch + 1) + '_sweep' + str(i + 1))
# Warning about how we are handling time series
import warnings
#warnings.warn('If fs is same for all sweeps, hard coding time series to be the same for all sweeps within an .abf file')
# make sure all sampling rates are the same across segments
if len(meta_data['fs'].unique()) == 1:
time = np.linspace(
0,
len(r.segments[0].analogsignals[0]) /
float(meta_data['fs'].unique()),
len(r.segments[0].analogsignals[0]))
else:
sys.exit(
'sampling rates are different - need to figure out what to do in this case'
)
# initiate the data frame for data
data = pd.DataFrame(index=time, columns=data_cols)
# fill the data frame for data
for s_index, seg in enumerate(r.segments):
for ch_index, ch in enumerate(seg.analogsignals):
data['ch' + str(ch_index + 1) + '_sweep' + str(s_index + 1)] = ch
# Convert the data frames to .csv files
meta_data.to_csv(pathtocsv + 'meta_data_' + str(filename) + '.csv')
data.to_csv(pathtocsv + 'data_' + str(filename) + '.csv')
print('saving data in csv format. Stored in csv_data folder')
# return data frames for further processing
return meta_data, data
from neo.io import AxonIO
import os
from tkinter.filedialog import askdirectory
Direc = '../../Raw_data/Deepanjali_data/WT step input cells/' #Directory where all the files are stored
CurrAmp = 5 #0 is for -100pA, 1 for -75pA, and so on till 20 which is 400pA. 10 for 150pA
# flist = os.listdir(Direc)
flist = ['cell 4 of 61016.abf']
i = 0
print(os.listdir(Direc))
for filename in flist:
i = i + 1
print(i, end='\r')
try: #Try is used to skip unsupported files that may be there in the folder
reader = AxonIO(filename=Direc + filename)
Samprate = reader.get_signal_sampling_rate()
seg = reader.read_block().segments[CurrAmp]
Tdur = np.array(seg.t_stop - seg.t_start)
plt.plot(np.linspace(0, Tdur, Samprate * Tdur),
seg.analogsignals[0],
label=f'{filename}')
# plt.title('WT 150pA current injection for 0.5s')
# plt.xlim([0,1])
plt.ylim([-75, 50])
plt.xlabel('Time (s)')
plt.ylabel('Membrane potential (mV)')
plt.legend()
mng = plt.get_current_fig_manager()
mng.resize(*mng.window.maxsize())
plt.show()
params = {'main_protocol':'undefined', 'protocol':'undefined'}
# loading metadata
data = AxonIO(filename).read_block(lazy=False, cascade=True)
params['dt'] = float(data.segments[0].analogsignals[0].sampling_period)
params['tstart'] = float(data.segments[0].analogsignals[0].times[0])
params['tstop'] = float(data.segments[0].analogsignals[0].times[-1])
params['Nepisodes'] = len(data.segments)
params['Nchannels'] = len(data.segments[0].analogsignals)
# prtocol name in case
protocol = get_protocol_name(filename)
if protocol!='':
params['protocol'] = protocol
params['main_protocol'] = 'classic_electrophy'
return params
if __name__ == '__main__':
import sys
import matplotlib.pylab as plt
filename = sys.argv[-1]
data = AxonIO(filename).read_block(lazy=False, cascade=True)
print(get_metadata(filename))
# data = load_file(filename, zoom=[-5.,np.inf])
# # for i in range(10):
# # plt.plot(t, data[0][i])
# plt.plot(data['t'], data['Ch1'])
# plt.show()
def _read_data_from_file(self, file_name):
return AxonIO(file_name).read()[0].segments
def test_annotations(self):
reader = AxonIO(filename=self.get_filename_path('File_axon_2.abf'))
bl = reader.read_block()
ev = bl.segments[0].events[0]
assert 'comments' in ev.annotations
def test_annotations(self):
reader = AxonIO(filename=self.get_filename_path('File_axon_2.abf'))
bl = reader.read_block()
ev = bl.segments[0].events[0]
assert 'comments' in ev.annotations
Sno = 0
features_pd = pd.DataFrame()
for filename in os.listdir(foldername):
stim1391 = ['Cell 3 of 181016.abf', 'cell 4 of 61016.abf', 'cell 4 of 111016.abf', 'cell 4 of 131016.abf', 'Cell 4 of 181016.abf', 'cell 5 of 61016.abf', 'Cell 5 of 181016.abf', 'Cell 2 of 19_10_2016', 'Cell 1 of 27_10_2016.abf', 'Cell 1 of 14_10_2016.abf', 'Cell 4 of 7_10_2016.abf', 'Cell 6 of 12_10_2016.abf', 'Cell 7 of 12_10_2016.abf']
if filename in stim1391:
stim_start = 139.1e-3
stim_end = 639.1e-3
else:
stim_start = 81.4e-3 #in s
stim_end = 581.4e-3 #in s
if filename in fskip:
print(f'{filename} skipped')
continue
if filename[-4:] == '.abf':
reader = AxonIO(filename='Deepanjali data/WT step input cells/'+filename)
else:
continue
features = singlefeature(seg_nol,reader,filename,stim_start,stim_end)
features_pd = features_pd.append(pd.DataFrame(features,index = [Sno]))
Sno = Sno +1
print(Sno)
print(features['Cm'])
print(features['Rinput'])
# features_pd.to_csv('Deepanjali data/WT step input cells/features.csv', sep='\t')
""" from neo.io import AxonIO import pandas as pd import matplotlib.pyplot as plt from scipy import signal import pylab as pl import numpy as np ''' Preliminary steps to read in the data. ''' # I commonly use the variable 'fh' as shorthand for 'file handle'. In # programming parlance, 'handle' is an abstract reference to a resource (in this # case, the AxonIO instance that allows you to read from the file). fh = AxonIO(filename='PVcell3.abf') # The block is a container for the voltage and current data stored in the file. block = fh.read_block() # Some information regarding the experiment has been saved in the file. We need # to parse this information out for the analysis. A block consists of multiple # sweeps (i.e., ,trials), known as `segments`. Each segment can contain multiple # signals, known as `analogsignals`. For this particular data file, the number # of channels, number of timepoints, sampling rate and sampling period are # identical across all sweeps. n_channels = len(block.segments[0].analogsignals) n_sweeps = len(block.segments) n_timepoints = len(block.segments[0].analogsignals[0].times) sampling_rate = int(block.segments[0].analogsignals[0].sampling_rate) sampling_period = 1.0/sampling_rate
"""
Crash course on using NeoIO to display info about ABF files
"""
from imports import *
from neo.io import AxonIO
import pprint
pp = pprint.PrettyPrinter(indent=4)
if __name__ == "__main__":
fname = PATH_DATA + "/18702001-biphasicTrain.abf"
reader = AxonIO(fname)
headerText = pprint.pformat(reader._axon_info)
out = "# AxonIO()._axon_info\n"
out += f"\n```\n{headerText}\n```\n"
with open(__file__ + ".md", 'w') as f:
f.write(out)
print("DONE")
from bokeh.io import output_file, show
from bokeh.layouts import widgetbox, row
from bokeh.models import CustomJS, ColumnDataSource, Span
from bokeh.models.widgets import Button, RadioButtonGroup, Select, Slider, RangeSlider
from bokeh.plotting import figure
from bokeh.layouts import layout
import numpy as np
from neo.io import AxonIO
# Set output
output_file("dashboard.html")
# Read data
reader = AxonIO('/home/matt/Downloads/2017_02_23_0110.abf')
block = reader.read()
ch1_data = []
ch2_data = []
for idx, seg in enumerate(block[0].segments):
ch1_data.append(seg.analogsignals[0])
ch2_data.append(seg.analogsignals[1])
ch1_data = np.array(ch1_data).reshape(10,100000)
ch2_data = np.array(ch2_data).reshape(10,100000)
# Plot data
source = ColumnDataSource(data=dict(
x=np.arange(0, len(ch2_data[0])),
y=ch2_data[0],
main=ch2_data[0]
)
)
