def generate(mod, run, language, textgrid, overwrite=False):
name = os.path.basename(os.path.splitext(run)[0])
run_name = name.split('_')[-1] # extract the name of the run
save_all = None
# Defining paths
model_name = 'glove_model'
check_folder(
os.path.join(Paths().path2derivatives, 'fMRI/raw-features', language,
model_name))
path = os.path.join(
Paths().path2derivatives, 'fMRI/raw-features', language, model_name,
'raw-features_{}_{}_{}.csv'.format(language, model_name, run_name))
#### generating raw-features ####
if (os.path.exists(path)) & (not overwrite):
raw_features = pd.read_csv(path)
else:
raw_features = mod.generate(run, language, textgrid)
save_all = path
#### Retrieving data of interest ####
# columns2retrieve = [function.__name__ for function in mod.functions]
columns2retrieve = [
'embedding-{}'.format(index)
for index in range(mod.param['embedding-size'])
]
return raw_features[:textgrid.offsets.count()], columns2retrieve, save_all
def generate(mod, run, language, textgrid, overwrite=False):
from .WORDRATE import model
from .WORDRATE.utils import wordrate, function_words, content_words
name = os.path.basename(os.path.splitext(run)[0])
run_name = name.split('_')[-1] # extract the name of the run
save_all = None
mod = model.Wordrate([content_words, function_words, wordrate],
language) # all functions
model_name = 'wordrate_model'
check_folder(
os.path.join(Paths().path2derivatives, 'fMRI/raw-features', language,
model_name))
path = os.path.join(
Paths().path2derivatives, 'fMRI/raw-features', language, model_name,
'raw-features_{}_{}_{}.csv'.format(language, model_name, run_name))
#### parameters studied ####
parameters = sorted([wordrate])
#### generating raw-features ####
if (os.path.exists(path)) & (not overwrite):
raw_features = pd.read_csv(path)
else:
raw_features = mod.generate(run, language, textgrid)
save_all = path
#### Retrieving data of interest ####
columns2retrieve = [
function.__name__
for function in model.Wordrate(parameters, language).functions
]
return raw_features[:textgrid.offsets.count()], columns2retrieve, save_all
def generate(mod, run, language, textgrid, overwrite=False):
name = os.path.basename(os.path.splitext(run)[0])
run_name = name.split('_')[-1] # extract the name of the run
save_all = None
mod = model.EnergySpectrum([rms], language) # all functions for now
model_name = 'rms_model'
check_folder(
os.path.join(Paths().path2derivatives, 'fMRI/raw-features', language,
model_name))
path = os.path.join(
Paths().path2derivatives, 'fMRI/raw-features', language, model_name,
'raw-features_{}_{}_{}.csv'.format(language, model_name, run_name))
#### parameters studied ####
parameters = sorted([rms])
#### generating raw-features ####
if (os.path.exists(path)) & (not overwrite):
raw_features = pd.read_csv(path)
else:
raw_features = mod.generate(run,
language,
textgrid,
slice_period=10e-3)
save_all = path
#### Retrieving data of interest ####
columns2retrieve = [
function.__name__
for function in model.EnergySpectrum(parameters, language).functions
]
return raw_features[:textgrid.offsets.count()], columns2retrieve, save_all
def generate(mod, run, language, textgrid, overwrite=False):
name = os.path.basename(os.path.splitext(run)[0])
run_name = name.split('_')[-1] # extract the name of the run
save_all = None
model_name = 'bottomup_model'
check_folder(os.path.join(Paths().path2derivatives, 'fMRI/raw-features', language, model_name))
path = os.path.join(Paths().path2derivatives, 'fMRI/raw-features', language, model_name, 'raw-features_{}_{}_{}.csv'.format(language, model_name, run_name))
#### generating raw-features ####
if (os.path.exists(path)) & (not overwrite):
raw_features = pd.read_csv(path)
else:
raw_features = mod.generate(run, language, textgrid)
save_all = path
#### Retrieving data of interest ####
columns2retrieve = ['bottomup']
textgrid = pd.read_csv(os.path.join(paths.path2data, 'text', language, 'BOTTOMUP', 'onsets-offsets', '{}_{}_{}_onsets-offsets_{}'.format('text', language, 'BOTTOMUP', run_name)+'.csv')) # df with onsets-offsets-word
return raw_features[:textgrid.offsets.count()], columns2retrieve, save_all
def generate(mod, run, language, textgrid, overwrite=False):
name = os.path.basename(os.path.splitext(run)[0])
run_name = name.split('_')[-1] # extract the name of the run
save_all = None
model_name = 'mfcc_model'
check_folder(os.path.join(Paths().path2derivatives, 'fMRI/raw-features', language, model_name))
path = os.path.join(Paths().path2derivatives, 'fMRI/raw-features', language, model_name, 'raw-features_{}_{}_{}.csv'.format(language, model_name, run_name))
#### generating raw-features ####
if (os.path.exists(path)) & (not overwrite):
raw_features = pd.read_csv(path)
else:
raw_features = mod.generate(run, language)
save_all = path
#### Retrieving data of interest ####
columns2retrieve = ["mfcc #{}".format((i)//3) if i%3==0 else ("mfcc' #{}".format((i)//3) if i%3==1 else "mfcc'' #{}".format((i)//3)) for i in range(mod.num_cepstral*3)]
textgrid = pd.read_csv(os.path.join(paths.path2data, 'wave', language, 'MFCC', 'onsets-offsets', '{}_{}_{}_onsets-offsets_{}'.format('wave', language, 'MFCC', run_name)+'.csv')) # df with onsets-offsets-word
return raw_features[:textgrid.offsets.count()], columns2retrieve, save_all
def generate(mod, run, language, textgrid, overwrite=False):
from .OTHER import model
from .OTHER.utils import sentence_onset
name = os.path.basename(os.path.splitext(run)[0])
run_name = name.split('_')[-1] # extract the name of the run
save_all = None
mod = model.Other([sentence_onset], language) # all functions
model_name = 'other_sentence_onset'
check_folder(
os.path.join(Paths().path2derivatives, 'fMRI/raw-features', language,
model_name))
path = os.path.join(
Paths().path2derivatives, 'fMRI/raw-features', language, model_name,
'raw-features_{}_{}_{}.csv'.format(language, model_name, run_name))
#### generating raw-features ####
if (os.path.exists(path)) & (not overwrite):
raw_features = pd.read_csv(path)
else:
raw_features = mod.generate(run, language, textgrid)
save_all = path
#### Retrieving data of interest ####
columns2retrieve = [function.__name__ for function in mod.functions]
return raw_features[:textgrid.offsets.count()], columns2retrieve, save_all
################################################################
import sys
import os
root = os.path.dirname(
os.path.dirname(os.path.dirname(os.path.dirname(
os.path.abspath(__file__)))))
if root not in sys.path:
sys.path.append(root)
import pandas as pd
import numpy as np
from utilities.settings import Params, Paths
params = Params()
paths = Paths()
class Bottomup(object):
"""Container module with an encoder, a recurrent module, and a decoder."""
def __init__(self, language):
super(Bottomup, self).__init__()
self.language = language
def __name__(self):
return 'bottomup'
def generate(self, path, language, textgrid):
# create specific onsets-offsets
source = 'text'
model_category = 'BOTTOMUP'