def __init__(self, content):

self.content = content

def is_date(self):

return "/" in self.content and ":" in self.content

def is_trial_number(self):

return self.content[0] == 't'

def is_empty(self):

return len(self.content) == 0

def is_not_a_star(self):

with open(file_path, 'rt') as file:

reader = csv.reader(file, delimiter='\t')

data = dict() # or {}

current_key = None

for row in reader:

try:

first_cell = Cell(row[0] if len(row) >= 1 else '')

second_cell = Cell(row[1] if len(row) >= 2 else '')

if not first_cell.is_empty() and not first_cell.is_date():

if first_cell.is_trial_number():

if current_key is None:

raise Exception("I did not succeed finding the key.")

# Add the content of the second cell to the appropriate entry

value = int(second_cell.content) if second_cell.is_not_a_star() else 0

data[current_key].append(value)

print("A value has been extracted from row '{}'.".format(row))

else:

# Create a new entry in the dictionary containing data

current_key = first_cell.content

data[current_key] = []

print("A key has been extracted from row '{}'.".format(row))

else:

print("Row '{}' will be ignored (either the first cell is empty or it is a date).".format(row))

except Exception as e:

print("Row '{}' will be ignored. It raised an exception ('{}')".format(row, e))

# Add a column for trial number

n_max = max([len(v) for v in data.values()])

data["trial"] = list(range(n_max))

# Remove keys without any value

data = {key: value for key, value in data.items() if len(value)}

# Complete missing values with zeros

for key in data.keys():

n_zeros_to_add = n_max - len(data[key])

data[key] += [0, ] * n_zeros_to_add

# Create a workbook and add a worksheet.

workbook = xlsxwriter.Workbook(file_path)

worksheet = workbook.add_worksheet()

# Sort column names by alphabetic order

column_names = sorted(list(data.keys()))

# Put trial as first column

if "trial" in column_names:

column_names.remove("trial")

column_names = ["trial", ] + column_names

# Start to fill beginning from the first column.

col = 0

# Write data column by column

for c_name in column_names:

row = 0

# Write the column_name

worksheet.write(row, col, c_name)

row += 1

# Fill the column with data

for v in data[c_name]:

worksheet.write(row, col, v)

row += 1

col += 1

workbook.close()

# Suppose there are two idx for rt

for rt_idx in [1, 2]:

# Convert your data in array for easier manipulation

rt_column_name = "RT {}".format(rt_idx)

rt = np.asarray(data[rt_column_name])

rt_mt_column_name = "RT-MT {}".format(rt_idx)

rt_mt = np.asarray(data[rt_mt_column_name])

# Look where 'rt' and 'rt_mt' are different to zero

cond0 = rt[:] != 0

cond1 = rt_mt[:] != 0

# Combine the two conditions

idx = cond0 * cond1

# Use the booleans as index and make a cut in your data

rt = rt[idx]

rt_mt = rt_mt[idx]

# Compute 'mt'

mt = rt_mt - rt

print("Short analysis.")

print("'mt {}' is: \n".format(rt_idx), mt)

# Save this in a new 'xlsx' file

new_data = dict()

new_data["RT{}".format(rt_idx)] = rt

new_data["MT{}".format(rt_idx)] = mt

write_a_new_file(file_path=analysis_file_path, data=new_data)

# Do some plots

plt.scatter(mt, rt)

plt.xlabel("mt")

plt.ylabel("rt")

plt.savefig("{}_scatter_rt{}.pdf".format(fig_root_name, rt_idx))

plt.close()

plt.hist(mt)

plt.xlabel("mt")

plt.savefig("{}_hist_mt{}.pdf".format(fig_root_name, rt_idx))

plt.close()

plt.hist(rt)

plt.xlabel("rt")

plt.savefig("{}_hist_rt{}.pdf".format(fig_root_name, rt_idx))

# Path of the folder where your raw data are

data_folder = "data"

# Paths of the folder where the outputs of this script will go

new_data_folder = "new_data"

figure_folder = "figures"

analysis_folder = "analysis_results"

# Create the 'outputs' folders

create_folder(figure_folder)

create_folder(new_data_folder)

create_folder(analysis_folder)

# List data files

data_files = [f for f in listdir(data_folder) if isfile(join(data_folder, f))]

for file_path in data_files:

extension = file_path.split('.')[-1]

if extension in ("xls", "csv"):

print("I will convert '{}'.\n".format(file_path))

complete_file_path = data_folder + "/" + file_path

file_path_without_extension = file_path.split(".")[0]

new_file_path = "{}/new_{}.xlsx".format(new_data_folder, file_path_without_extension)

analysis_file_path = "{}/analysis_{}.xlsx".format(analysis_folder, file_path_without_extension)

fig_root_name = "{}/fig_{}".format(figure_folder, file_path_without_extension)

data = extract_data(file_path=complete_file_path)

write_a_new_file(data=data, file_path=new_file_path)

short_analysis(data=data, analysis_file_path=analysis_file_path, fig_root_name=fig_root_name)

else: