def externDetailsPilote(self,
data_indir,
position_frames=0,
position_traitements=1):
"""
Extern some distinct information about pilots: Poids, Taille, Braquet,
Longueur Manivelle and the time of the best trial.
:param data_indir:Path to data indir.
:param position_frames: Position of the file frames in a trial by default 0.
:param position_traitements:Position of the file taitement in a trial by default 1.
:return:txt file.
"""
bestTrials = NavigateFiles().get_all_best_trials(data_indir)
masseRiders = []
BraquetRiders = []
longueurManivelleRiders = []
tailleRiders = []
f = open('Details_riders.txt', 'w')
print('-----------------------------', file=f)
for trial in bestTrials:
fileList = glob.glob((trial + '\\*.csv'))
traitement = pd.read_csv(
fileList[position_traitements]
) # 0 -> first file in list its CDonetraitement.csv
frames = pd.read_csv(fileList[position_frames]
) # 2 - > third file in list its frames
time = traitement['Time']
if 'Braquet' in frames.columns:
braquet = frames['Braquet'][0]
else:
braquet = 0
if 'MasseRider' in frames.columns:
masseRider = frames['MasseRider'][0]
else:
masseRider = 0
if 'longueurManivelle' in frames.columns:
longueurManivelle = frames['longueurManivelle'][0]
else:
longueurManivelle = 0
if 'TailleRider' in frames.columns:
tailleRider = frames['TailleRider'][0]
else:
tailleRider = 0
print('{} time: {:.3f}s'.format(
trial.split('\\')[-2], time.iloc[-2]),
file=f)
print('-----------------------------', file=f)
print('Poids: {} '.format(masseRider), file=f)
print('taille: {} '.format(tailleRider), file=f)
print('braquet: {}'.format(braquet), file=f)
print('Longueur Manivelle: {}'.format(longueurManivelle), file=f)
print('-----------------------------', file=f)
masseRiders.append(masseRider)
tailleRiders.append(tailleRider)
BraquetRiders.append(braquet * 10)
longueurManivelleRiders.append(longueurManivelle)
def get_tps_reaction_all_pilots(self):
files = NavigateFiles().get_all_files_by_name(data_indir='.\\data\\',
file_name=2)
for frames in files:
pilot_name = frames[0].split("\\")[-3]
f = open('tps_reaction\\{}.csv'.format(pilot_name), 'w')
print("TpsReaction,", file=f)
for frame in frames:
# pilot_name = frame.split("\\")[-3]
frames_df = pd.read_csv(frame)
tps_reaction = frames_df['TpsReaction'][0] - frames_df["Bip"][0]
print("{},".format(tps_reaction), file=f)
def plot_best_pilot_trial_single(self, data_indir, x_axis, y_axis):
"""
Plot pilots best trial in separated figure.
:param data_indir: Path to data.
:param x_axis: array of features to plot in x-axis.
:param y_axis: array of features to plot in the y-axis.
"""
best_trials = NavigateFiles().get_all_best_trials(data_indir)
legend = []
for xi in x_axis: # for all x axis variables
for trial in best_trials: # for the best trail of the pilot
fileList = glob.glob(
(trial + '\\*.csv')) # all files of a trial
traitement = pd.read_csv(
fileList[1]) # first file in the trail
frames = pd.read_csv(fileList[0])
pilot_name = trial.split('\\')[-2]
x = traitement[xi]
coups_de_pedal1 = frames['CoupsDePedal'][0] - frames["Bip"][0]
coups_de_pedal2 = frames['CoupsDePedal.1'][0] - frames["Bip"][0]
coups_de_pedal3 = frames['CoupsDePedal.2'][0] - frames["Bip"][0]
# plt.figure(num=None, figsize=(12.8, 7.2), dpi=300, facecolor='w', edgecolor='k')
for column in y_axis: # for all y axis variables
col = traitement[column]
if column == 'VitesseRider':
col = col * 100
legend.append('{}'.format(column + '* 100'))
else:
legend.append('{}'.format(column))
plt.plot(x, col)
str = ','.join(y_axis)
plt.xlabel(xi)
plt.ylabel(column)
plt.legend(legend)
plt.title('Pilote {}: {} en fonction de {}'.format(
pilot_name, str, xi))
plt.axvline(x[coups_de_pedal1], color='red', linestyle='--')
plt.axvline(x[frames['TpsReaction'][0] - frames["Bip"][0]],
color='red',
linestyle='--')
plt.axvline(x[coups_de_pedal2], color='red', linestyle='--')
plt.axvline(x[coups_de_pedal3], color='red', linestyle='--')
plt.grid()
plt.show()
def concat_all_traitement(self, data_indir, file_name='traitement'):
"""
Concatenat all csv traitement files onto 1 concatenated csv file
:param data_indir: Path to data file.
:param file_name: Prefix of file name.
"""
files = NavigateFiles().get_all_files_by_name(data_indir, file_name)
appended_data = []
for pilote in files:
for file in pilote: # the file in th trial
traitemnt = pd.read_csv(file)
appended_data.append(traitemnt)
appended_data = pd.concat(appended_data, axis=0)
current_directory = os.path.dirname(os.path.realpath(__file__))
appended_data.to_csv(
"{}\\AlltraitementConcatenated.csv".format(current_directory),
index=0)
def plot_by_pilotes_names_trials_nums_dates(self, data_indir,
pilotes_names, trials_nums,
dates_trial, x_axis, y_axis):
"""
:param data_indir:
:param pilotes_names:
:param trials_nums:
:param x_axis:
:param y_axis:
:return:
"""
trial_files = NavigateFiles(
).get_files_by_pilotes_names_trials_nums_dates(data_indir,
pilotes_names,
trials_nums,
dates_trial)
legend = []
for xi in x_axis:
for column in y_axis:
# plt.figure(num=None, figsize=(12.8, 7.2), dpi=300, facecolor='w', edgecolor='k')
for trial in trial_files:
traitement = pd.read_csv(trial[1]) # first file in the
x = traitement[xi]
col = traitement[column]
pilot_name = trial[0].split('\\')[-3]
trial_and_number = trial[0].split('\\')[-2]
plt.plot(x, col)
legend.append('{} {} '.format(pilot_name,
trial_and_number))
plt.xlabel(xi)
plt.ylabel(column)
plt.legend(legend)
plt.title('{} en fonction de {}'.format(column, xi))
plt.grid()
# txt = "{}".format()
# plt.figtext(0.5, 0.01, txt, wrap=True, horizontalalignment='center', fontsize=12)
# plt.savefig('figures/{}_{}.png'.format(column, xi))
plt.show()
def plot_best_pilots_trial_together(self, data_indir, x_axis, y_axis):
"""
Plot the best trial of each pilot in one figure.
:param data_indir: Path to data.
:param x_axis: array of features to plot in xaxis.
:param y_axis: array of features to plot in the y-axis.
"""
bestTrials = NavigateFiles().get_all_best_trials(data_indir)
legend = []
for xi in x_axis:
for column in y_axis:
# plt.figure(num=None, figsize=(12.8, 7.2), dpi=300, facecolor='w', edgecolor='k')
for trial in bestTrials:
fileList = glob.glob(
(trial + '\\*.csv')) # all files of a trial
traitement = pd.read_csv(fileList[1]) # first file in the
x = traitement[xi]
time = traitement['Time']
pilot_name = trial.split('\\')[-2]
trial_and_number = trial.split('\\')[-1]
col = traitement[column]
plt.plot(x, col)
legend.append('{} {} time:{:.3f}s'.format(
pilot_name, trial_and_number, time.iloc[-2]))
print('{} time: {}'.format(
trial.split('\\')[-2], time.iloc[-2]))
print()
print()
plt.xlabel(xi)
plt.ylabel(column)
plt.legend(legend)
plt.title('{} en fonction de {}'.format(column, xi))
plt.grid()
# txt = "{}".format()
# plt.figtext(0.5, 0.01, txt, wrap=True, horizontalalignment='center', fontsize=12)
# plt.savefig('figures/{}_{}.png'.format(column, xi))
plt.show()
def concat_all_frames(self, data_indir, file_name='frames'):
"""
Concatenat all csv frame files onto 1 concatenated csv file
:param data_indir: Path to data.
:param file_name: Prefix of file name.
"""
frames = NavigateFiles().get_all_files_by_name(data_indir, file_name)
appended_data = []
for pilote in frames:
for f in pilote:
trials = "\\".join(f.split('\\')[0:-1])
traitements = glob.glob((trials + '\\traitement_*.csv'))
traits = pd.read_csv(traitements[0])
time = traits['Time'][len(traits) - 2]
frames = pd.read_csv(f)
coups_de_pedal0 = frames["TpsReaction"][0] - frames["Bip"][0]
coups_de_pedal1 = frames['CoupsDePedal'][0] - frames["Bip"][0]
coups_de_pedal2 = frames['CoupsDePedal.1'][0] - frames["Bip"][0]
coups_de_pedal3 = frames['CoupsDePedal.2'][0] - frames["Bip"][0]
frame = frames[['TailleRider', 'longueurManivelle']]
if 'Braquet' in frames.columns:
frame['Braquet'] = frames['Braquet']
if 'MasseRider' in frames.columns:
frame['MasseRider'] = frames['MasseRider']
if 'MasseBike' in frames.columns:
frame['MasseBike'] = frames['MasseBike']
frame['TimeEnd'] = time
partie_coups_de_pedal1 = traits[int(coups_de_pedal0
):int(coups_de_pedal1)]
frame['moyennePuissance1'] = partie_coups_de_pedal1[
'Puissance'].mean()
partie_coups_de_pedal1 = traits[int(coups_de_pedal1
):int(coups_de_pedal2)]
frame['moyennePuissance2'] = partie_coups_de_pedal1[
'Puissance'].mean()
partie_coups_de_pedal1 = traits[int(coups_de_pedal2
):int(coups_de_pedal3)]
frame['moyennePuissance3'] = partie_coups_de_pedal1[
'Puissance'].mean()
partie_coups_de_pedal1 = traits[int(coups_de_pedal3):]
frame['moyennePuissance4'] = partie_coups_de_pedal1[
'Puissance'].mean()
frame['ThetaManivelleDepart'] = frames['ThetaManivelleDepart'][
0]
frame['DistanceRecul'] = frames['DistanceRecul'][0]
frame['AlphaGaitDmin'] = frames['AlphaGaitDmin'][0]
frame['TpsReaction'] = traits['Time'][
int(frames['TpsReaction'][0]) - int(frames['Bip'][0])]
frame['DEpauleMin'] = frames['DEpauleMin'][0]
frame['DAlignementMin'] = frames['DAlignementMin'][0]
frame['Dmin'] = traits['Time'][int(frames['Dmin'][0]) -
int(frames['Bip'][0])]
appended_data.append(frame.iloc[0:1])
appended_data = pd.concat(appended_data, axis=0)
current_directory = os.path.dirname(os.path.realpath(__file__))
print(current_directory)
appended_data.to_csv(
"{}\\AllframesConcatenated.csv".format(current_directory), index=0)