def save_notes(notes, simID, path):
"""
Save the notes about the current simulation
Parameters
----------
notes : string
text specifing what is special about the simulation
simID : string
simulation ID
path : string
directory to save
"""
from utils.utils import check_directory
import csv
folder = path + '/Log/'
check_directory(path + '/Log/')
fields = [simID, notes]
try:
with open(folder + 'notes.csv', 'a') as file:
writer = csv.writer(file)
writer.writerow(fields)
except IOError:
with open(folder + 'notes.csv', 'w') as file:
writer = csv.writer(file)
writer.writerow(fields)
def save_data(data, filename, file_format, folder='/Data'):
"""
Saves the data for later use
Parameters
----------
data: array_like
data to be stored
filename: stringor
filename to store to
file_format: sting
file format
folder:
folder to store to
"""
from utils.utils import check_directory
check_directory(folder)
if file_format == 'pkl' and type(data) == pd.core.frame.DataFrame:
data.to_pickle(folder + '/' + filename + '.pkl')
elif file_format == 'pkl' and isinstance(data, object):
with open(folder + '/' + filename + '.pkl', 'wb') as output:
pickle.dump(data, output, pickle.HIGHEST_PROTOCOL)
def __init__(self, params, nn=2, cond='30'):
self.cond = cond
self.input_dir = f'{params.path}/Data/{params.simID}/{cond}/'
self.params = params
self.output_dir = params.path + '/Figures/'
check_directory(self.output_dir)
self.fig_format = 'pdf'
self.save = params.save_figs
self.nn = nn
self.o = -1
self.fit = 'Rn:0'
self.title = f'o={params.offset[self.o]}, {omega}={params.noise[self.nn]}, cond={cond}'
def __init__(self, params, data):
self.params = params
self.N = params.list_length
self.input_dir = params.data_dir
self.output_dir = params.path + '/Figures/'
check_directory(self.output_dir)
self.data = data
self.fig_format = '.pdf'
filenames = sorted([
item for item in os.listdir(self.input_dir) if item[-3:] == 'pkl'
])
self.data_observed = [
pd.read_pickle(self.input_dir + file) for file in filenames
]
def __init__(self, params):
self.params = params
self.N = params.list_length
self.input_dir = params.data_dir
self.output_dir = params.path + '/Figures'
self.fig_format = 'pdf'
check_directory(self.output_dir)
filenames = sorted([
item for item in os.listdir(self.input_dir)
if item[-3:] == 'pkl' and 'std' not in item
])
self.data = [
pd.read_pickle(self.input_dir + file) for file in filenames
]
self.save = params.save_figs
def prepare_data(self):
"""
Select the positive responses for targets and lures
and save them in csv-files sorted by condition
"""
params = self.params
from utils.data import select_data
input_dir = params.data_dir
filenames = sorted([
item for item in os.listdir(input_dir)
if item[-3:] == 'pkl' and 'std' not in item
])
data = [pd.read_pickle(input_dir + file) for file in filenames]
print(filenames)
self.target, self.lure = np.ones((len(filenames))).tolist(), np.ones(
(len(filenames))).tolist()
for i, dat in enumerate(data):
self.target[i] = [
select_data(dat, ['target'], o, params.noise)['target']
for o in params.offset
]
self.lure[i] = [
select_data(dat, ['lure'], o, params.noise)['lure']
for o in params.offset
]
self.filenames = [item.strip('.pkl') for item in filenames]
path = params.data_dir + '/Matlab/Model_input/'
path1 = params.data_dir + '/Matlab/Model_output/' # needed for later
check_directory(path)
check_directory(path1)
for i in range(len(self.target)):
for ii in range(len(self.target[i])):
name = path + self.filenames[i] + "_" + str(
params.offset[ii]) + ".csv"
self.save_csv(self.target[i][ii], self.lure[i][ii], name,
ii + 1)
def __init__(self,simID):
self.simID=simID
self.params=load_params(simID,os.path.abspath('..'))
self.data=pd.read_pickle(self.params.data_dir+'data.pkl')
self.output_dir=self.params.path+'/Figures/'
check_directory(self.output_dir)