def get_scan(Model, filename=None):
if filename is None:
filename = str(Model['filename'])
zf = zipfile.ZipFile(filename, mode='r')
DATA = []
model_file = filename.replace('.zip', '_Model.npz')
data = zf.read(model_file)
with open(model_file, 'wb') as f:
f.write(data)
Model = dict(np.load(model_file).items())
F_aff = np.linspace(Model['faff0'], Model['faff1'], Model['ngrid'])
seeds = Model['SEEDS']
for i, j in product(range(len(F_aff)), range(len(seeds))):
fn = Model['FILENAMES'][i, j]
data = zf.read(fn)
with open(fn, 'wb') as f:
f.write(data)
with open(fn, 'rb') as f:
data = load_dict_from_hdf5(fn)
t = np.arange(int(data['tstop'] / data['dt'])) * data['dt']
data['t'] = t
Model['Faff0'] = Model['faff_bsl']
Model['Faff1'] = F_aff[i]
data['faff'] = waveform(t, Model)
DATA.append(data)
return F_aff, seeds, Model, DATA
def get_scan(args, filename=None):
if filename is None:
filename = str(args.filename)
zf = zipfile.ZipFile(filename, mode='r')
DATA = []
model_file = filename.replace('.zip', '_Model.npz')
data = zf.read(model_file)
with open(model_file, 'wb') as f:
f.write(data)
Model = dict(np.load(model_file).items())
PATTERNS = Model['PATTERNS']
seeds = Model['SEEDS']
for i, j in product(range(len(PATTERNS)), range(len(seeds))):
fn = Model['FILENAMES'][i, j]
data = zf.read(fn)
with open(fn, 'wb') as f:
f.write(data)
with open(fn, 'rb') as f:
data = load_dict_from_hdf5(fn)
t = np.arange(int(data['tstop'] / data['dt'])) * data['dt']
data['t'] = t
DATA.append(data)
return PATTERNS, seeds, Model, DATA
def load_continous_RTXI_recording(filename, with_metadata=False):
"""
....
"""
data = hdf5.load_dict_from_hdf5(filename)['Trial1']
formatted_data = {'filename': filename}
formatted_data['Downsampling Rate'] = data['Downsampling Rate']
formatted_data['Date'] = data['Date']
# time step
formatted_data['dt'] = 1e-9 * float(data['Period (ns)'])
# parameters
formatted_data['params'] = {}
for key, val in data['Parameters'].items():
formatted_data['params'][key] = float(val[0][1])
print(data['Synchronous Data'].keys())
# raw data
c = 0
for i, key in enumerate(data['Synchronous Data']):
if key != 'Channel Data':
formatted_data[key] = data['Synchronous Data']['Channel Data'][:,
c]
c += 1
if with_metadata:
find_metadata_file_and_add_parameters(formatted_data)
return formatted_data
def get_scan(Model, filename=None, filenames_only=False):
if filename is None:
filename = str(Model['zip_filename'])
zf = zipfile.ZipFile(filename, mode='r')
data = zf.read(filename.replace('.zip', '_Model.npz'))
with open(filename.replace('.zip', '_Model.npz'), 'wb') as f:
f.write(data)
Model = dict(np.load(filename.replace('.zip', '_Model.npz')).items())
if filenames_only:
print('/!\ datafiles have to be unziped before /!\ ')
return Model, dict(Model['PARAMS_SCAN'].all()), None
else:
DATA = []
for fn in (Model['PARAMS_SCAN'].all()['FILENAMES']):
print(fn)
data = zf.read(fn)
with open(fn, 'wb') as f:
f.write(data)
with open(fn, 'rb') as f:
data = load_dict_from_hdf5(fn)
DATA.append(data)
return Model, dict(Model['PARAMS_SCAN'].all()), DATA
def get_scan(Model, with_png_export=False,
filename=None):
if filename is None:
filename=str(Model['zip_filename'])
zf = zipfile.ZipFile(filename, mode='r')
data = zf.read(filename.replace('.zip', '_Model.npz'))
with open(filename.replace('.zip', '_Model.npz'), 'wb') as f: f.write(data)
Model = dict(np.load(filename.replace('.zip', '_Model.npz')).items())
seeds = Model['SEEDS']
DATA = []
for j in range(len(seeds)):
fn = Model['FILENAMES'][j]
data = zf.read(fn)
with open(fn, 'wb') as f: f.write(data)
with open(fn, 'rb') as f: data = load_dict_from_hdf5(fn)
DATA.append(data)
return Model, seeds, DATA
def get_scan(Model, filename=None):
if filename is None:
filename=str(Model['zip_filename'])
zf = zipfile.ZipFile(filename, mode='r')
data = zf.read(filename.replace('.zip', '_Model.npz'))
with open(filename.replace('.zip', '_Model.npz'), 'wb') as f: f.write(data)
Model = dict(np.load(filename.replace('.zip', '_Model.npz')).items())
F_aff, seeds = Model['F_AffExc_array'], Model['SEEDS']
DATA = []
for i, j in product(range(len(F_aff)), range(len(seeds))):
fn = Model['FILENAMES'][i,j]
data = zf.read(fn)
with open(fn, 'wb') as f: f.write(data)
with open(fn, 'rb') as f: data = load_dict_from_hdf5(fn)
data['faff'], data['seed'] = F_aff[i], seeds[j]
DATA.append(data)
return Model, F_aff, seeds, DATA