filepaths = [".\S10_CF_004.glm", ".\S10_CF_005.glm", ".\S10_CF_006.glm"]
# Initialisation des structures servant à stocker les vakeurs de TF/NF et NF
# aux moments des glissements
all_mu_points_thumb = []
all_NF_thumb = []
all_mu_points_index = []
all_NF_index = []
#%% Filtrage des donnees
freqAcq = 800 # Frequence d'acquisition des donnees
freqFiltForces = 20
# Frequence de coupure du filtrage des forces (peut etre modifié)
for filepath in filepaths:
glm_df = glm.import_data(filepath)
for i in range(21, 43):
glm_df.iloc[:, i] = glm.filter_signal(glm_df.iloc[:, i], freqAcq,
freqFiltForces)
#%% Extraction des signaux et mise a zero des forces en soustrayant la valeur
# moyenne des 500 premieres ms
baseline = range(0, 400)
# Normal Force exerted by the thumb
NF_thumb = glm_df.loc[:, 'Fygl'] - np.nanmean(glm_df.loc[baseline, 'Fygl'])
# Vertical Tangential Force exerted by the thumb
TFx_thumb = glm_df.loc[:, 'Fxgl'] - np.nanmean(glm_df.loc[baseline,
'Fxgl'])
#Horizontal Tangential Force exerted by the thumb
TFz_thumb = glm_df.loc[:, 'Fzgl'] - np.nanmean(glm_df.loc[baseline,
'Fzgl'])
"SEFRD": np.zeros((80, 4800)),
"SEFRDcount": 0
}
ntrials = 2 #Number of trials for each subject
# Double for-loop that runs thrgough all subjects and trials
subject_number = 0
for s in subjects:
for exp in expe:
for trial in range(1, ntrials + 1):
# Set data path
glm_path = "DataGroupe4/%s%s_00%d.glm" % (s, exp, trial)
# Import data
glm_df = glm.import_data(glm_path)
baseline = range(0, 400)
# Normal Force exerted by the thumb
NF_thumb = glm_df.loc[:, 'Fygl'] - np.nanmean(glm_df.loc[baseline,
'Fygl'])
# Vertical Tangential Force exerted by the thumb
TFx_thumb = glm_df.loc[:, 'Fxgl'] - np.nanmean(glm_df.loc[baseline,
'Fxgl'])
#Horizontal Tangential Force exerted by the thumb
TFz_thumb = glm_df.loc[:, 'Fzgl'] - np.nanmean(glm_df.loc[baseline,
'Fzgl'])
# Normal Force exerted by the index
NF_index = -(glm_df.loc[:, 'Fygr'] -
np.nanmean(glm_df.loc[baseline, 'Fygr']))
import glm_data_processing as glm
import get_mu_points as gmp
import get_mu_fit as gmf
# Fermeture des figures ouvertes
plt.close('all')
# Chemins d'acces aux fichiers (A MODIFIER)
filepath_strong = '.\S10_CF_004.glm'
filepath_medium = '.\S10_CF_005.glm'
filepath_weak = '.\S10_CF_006.glm'
# Importation des donnes
glm_strong_df = glm.import_data(filepath_strong)
glm_medium_df = glm.import_data(filepath_medium)
glm_weak_df = glm.import_data(filepath_weak)
#%% Filtrage des donnees
freqAcq=800 # Frequence d'acquisition des donnees
freqFiltForces=20; # Frequence de coupure du filtrage des forces (peut etre modifié)
for i in range(21,43):
glm_strong_df.iloc[:,i]=glm.filter_signal(glm_strong_df.iloc[:,i],freqAcq,freqFiltForces)
glm_medium_df.iloc[:,i]=glm.filter_signal(glm_medium_df.iloc[:,i],freqAcq,freqFiltForces)
glm_weak_df.iloc[:,i]=glm.filter_signal(glm_weak_df.iloc[:,i],freqAcq,freqFiltForces)
# Frequence de coupure du filtrage du COP (peut etre modifié)
# COP = Center Of Pressure. Il s'agit de la résultante des forces appliquées
# par le doigt sur le capteur. Cela donne donc une idee de la position du doigt