def load(self, file_path=None, client=None, **kwargs):
"""
Loads spiketrains from a hdf5 file in the neo data format.
Parameters
----------
file_path : string
Path to file
client :
When file is loaded from a collab storage a appropriate client
must be provided.
Returns :
List of neo.SpikeTrains
"""
if file_path is None:
file_path = self.file_path
if file_path[-2:] != 'h5':
raise IOError('file must be in hdf5 file in Neo format')
if client is None:
data = NeoHdf5IO(file_path)
else:
store_path = './' + file_path.split('/')[-1]
client.download_file(file_path, store_path)
data = NeoHdf5IO(store_path)
spiketrains = data.read_block().list_children_by_class(SpikeTrain)
for i in xrange(len(spiketrains)):
spiketrains[i] = spiketrains[i].rescale('ms')
return spiketrains
def load(self, file_path, client=None, **kwargs):
"""
Loads spiketrains from a hdf5 file in the neo data format.
Parameters
----------
file_path : string
Path to file
client :
When file is loaded from a collab storage a appropriate client
must be provided.
Returns :
List of neo.SpikeTrains of length N
"""
# Load NEST or SpiNNaker data using NeoHdf5IO
if file_path[-2:] != 'h5':
raise IOError, 'file must be in hdf5 file in Neo format'
if client is None:
data = NeoHdf5IO(file_path)
else:
store_path = './' + file_path.split('/')[-1]
client.download_file(file_path, store_path)
data = NeoHdf5IO(store_path)
spiketrains = data.read_block().list_children_by_class(SpikeTrain)
return spiketrains
def load(self, file_path, client=None, **kwargs):
fnam = "spikes_L6I.h5"
if not os.path.isfile(fnam):
client.download_file(file_path + '/' + fnam, './' + fnam)
dataI = NeoHdf5IO('./' + fnam)
fnam = "spikes_L6E.h5"
if not os.path.isfile(fnam):
client.download_file(file_path + '/' + fnam, './' + fnam)
dataE = NeoHdf5IO('./' + fnam)
print file_path + " ... loaded"
sts_inh = dataI.read_block().list_children_by_class(SpikeTrain)
sts_exc = dataE.read_block().list_children_by_class(SpikeTrain)
for st in sts_inh:
st.annotations['neu_type'] = 'inh'
for st in sts_exc:
st.annotations['neu_type'] = 'exc'
self.spiketrains = sts_inh
self.spiketrains.extend(sts_exc)
return self.spiketrains
def _get_io(filename):
""" Return a Neo IO instance, guessing the type based on the filename\
suffix.
"""
logger.debug("Creating Neo IO for filename {}", filename)
directory = os.path.dirname(filename)
utility_calls.check_directory_exists_and_create_if_not(directory)
extension = os.path.splitext(filename)[1]
if extension in ('.txt', '.ras', '.v', '.gsyn'):
raise IOError(
"ASCII-based formats are not currently supported for output"
" data. Try using the file extension '.pkl' or '.h5'")
elif extension in ('.h5', ):
return NeoHdf5IO(filename=filename)
elif extension in ('.pkl', '.pickle'):
return PickleIO(filename=filename)
elif extension == '.mat':
return NeoMatlabIO(filename=filename)
else: # function to be improved later
raise Exception("file extension %s not supported" % extension)
# close(2)
#==============================================================================
alignments.append(good_align)
return alignments
################################################################################################################################
###############################################################################################################################
# Get the neural data
# if opening experiment file for the first time run CHRONICRHD2NEO
#==============================================================================
h5_filename = os.path.join(
experiment_dir,
os.path.basename(experiment_file).replace(".rhd", "_neo.h5"))
h5_exporter = NeoHdf5IO(h5_filename)
block = h5_exporter.get("/Block_0")
# Get the sound database
if stims_given == 1:
stimulus_h5_filename = os.path.join(
experiment_dir,
os.path.basename(experiment_file).replace(".rhd", "_stimuli.h5"))
sm = sound_manager.SoundManager(
HDF5Store, stimulus_h5_filename) # make a sound manager object
# Get the number of trials and make a list of stimulus ids, 'sound_data' (because the sm object is a little hard to work with)
# TODO organizing stimulus times to validate against periods of vocalization
segment = block.segments[0] # The block only has one segment
# pull the microphone channel, filter it etc
mic = [
asig for asig in block.segments[0].analogsignalarrays
weight=0.005,
receptor_type='excitatory',
delay=20)
sim.run(tstop)
use_hdf5 = False
if use_hdf5:
from neo.io import NeoHdf5IO as NeoIO
suffix = 'h5'
results_file = "Results/NeuroMLTest_%s.%s" % (simulator_name, suffix)
if os.path.exists(results_file):
os.remove(results_file)
io = NeoIO(results_file)
pop_IF_curr_alpha.write_data(io)
pop_IF_curr_exp.write_data(io)
pop_IF_cond_alpha.write_data(io)
pop_IF_cond_exp.write_data(io)
pop_EIF_cond_exp_isfa_ista.write_data(io)
pop_HH_cond_exp.write_data(io)
pop_post1.write_data(io)
pop_post2.write_data(io)
else:
#from neo.io import AsciiSignalIO as NeoIO
#suffix = 'txt'
#results_file = "Results/NeuroMLTest_%s.%s" % (simulator_name, suffix)
for pop in [
# -*- coding: utf-8 -*-
"""
Created on Thu Mar 9 13:15:22 2017
@author: billewood
"""
from neo.core import EpochArray
from neo.io import RHDIO, NeoHdf5IO
from neosound import sound_manager
from neosound.sound_store import HDF5Store
filename = '/auto/tdrive/carson/processed_intan/LbY6074__161215_145633_neo.h5'
iom = NeoHdf5IO(filename)
block = iom.read_blcok("/Block_0")
mic = [asig for asig in block.segments[0].analogsignalarrays if asig.name == "Board ADC"][0] # This is the entire microphone recording
pupil_song_timings = [asig for asig in block.segments[0].epocharrays if asig.name == "student_birdsong"][0]
tutor_song_timings = [asig for asig in block.segments[0].epocharrays if asig.name == "tutor_birdsong"][0]
= np.int(mic.sampling_rate)
t_mic = np.asarray(mic.times)
too_long = too_long * fs_mic
mic = mic.squeeze()
connE = sim.connect(pop_EIF_cond_exp_isfa_ista, pop_post1, weight=0.01, receptor_type='excitatory',delay=10)
connE = sim.connect(pop_EIF_cond_exp_isfa_ista, pop_post2, weight=0.005, receptor_type='excitatory',delay=20)
sim.run(tstop)
use_hdf5 = False
if use_hdf5:
from neo.io import NeoHdf5IO as NeoIO
suffix = 'h5'
results_file = "Results/NeuroMLTest_%s.%s" % (simulator_name, suffix)
if os.path.exists(results_file):
os.remove(results_file)
io = NeoIO(results_file)
pop_IF_curr_alpha.write_data(io)
pop_IF_curr_exp.write_data(io)
pop_IF_cond_alpha.write_data(io)
pop_IF_cond_exp.write_data(io)
pop_EIF_cond_exp_isfa_ista.write_data(io)
pop_HH_cond_exp.write_data(io)
pop_post1.write_data(io)
pop_post2.write_data(io)
else:
#from neo.io import AsciiSignalIO as NeoIO
#suffix = 'txt'
#results_file = "Results/NeuroMLTest_%s.%s" % (simulator_name, suffix)
for pop in [pop_IF_curr_alpha, pop_IF_curr_exp, pop_IF_cond_exp, pop_IF_cond_alpha,pop_EIF_cond_exp_isfa_ista, pop_HH_cond_exp, pop_post1,pop_post2]:
