def test_activity_power_profile_max_duration_too_large():
# test that there is no segmentation fault when max_duration is set too
# large and that we fall back to the largest possible interval.
activity = bikeread(load_fit()[0])
power_profile = activity_power_profile(activity, max_duration=1000000)
assert power_profile.shape == (13536,)
assert power_profile.iloc[-1] == pytest.approx(8.2117765957446736)
def test_activity_power_profile_max_duration_too_large():
# test that there is no segmentation fault when max_duration is set too
# large and that we fall back to the largest possible interval.
activity = bikeread(load_fit()[0])
power_profile = activity_power_profile(activity, max_duration=1000000)
assert power_profile.shape == (13536, )
assert power_profile.iloc[-1] == pytest.approx(8.2117765957446736)
cyclist during a cycling ride.
"""
print(__doc__)
# Authors: Guillaume Lemaitre <*****@*****.**>
# License: BSD 3 clause
###############################################################################
# We can first read a toy ride from the library
from skcycling.datasets import load_fit
from skcycling.io import bikeread
ride = bikeread(load_fit()[0])
###############################################################################
# Different scores are available in ``scikit-cycling``. We can first compute
# the normalized power® which corresponds to an average power during the ride
# with an additional smoothing. To compute this score, we need to know the
# maximum power aerobic (or the functional threshold power).
from skcycling.metrics import normalized_power_score
mpa = 400
np_score = normalized_power_score(ride['power'], mpa)
print('Normalized power {:.2f}'.format(np_score))
###############################################################################
# The intensity factor® normalize the normalized power using the functional
cyclist during a cycling ride.
"""
print(__doc__)
# Authors: Guillaume Lemaitre <*****@*****.**>
# License: BSD 3 clause
###############################################################################
# We can first read a toy ride from the library
from skcycling.datasets import load_fit
from skcycling.io import bikeread
ride = bikeread(load_fit()[0])
###############################################################################
# Different scores are available in ``scikit-cycling``. We can first compute
# the normalized power® which corresponds to an average power during the ride
# with an additional smoothing. To compute this score, we need to know the
# maximum power aerobic (or the functional threshold power).
from skcycling.metrics import normalized_power_score
mpa = 400
np_score = normalized_power_score(ride['power'], mpa)
print('Normalized power {:.2f}'.format(np_score))
###############################################################################
# The intensity factor® normalize the normalized power using the functional
'2018-05-18-11-46-19',
'2018-05-18-13-55-23',
'2018-05-18-14-37-07',
'2018-05-18-15-00-26',
'2018-05-18-15-06-46']
exten = '.fit'
f_crop = 'check_std_cedric.csv'
df = pd.read_csv(f_path_data + f_path_user + f_crop)
for f in range(len(f_name)):
filename_fit = f_path_data + f_path_user + f_name[f] + exten
print('Nom du fichier traité : ', filename_fit)
ride = bikeread(filename_fit, drop_nan='columns')
ride['speed'] = ride['speed'] * 3.6
# print('The ride is the following:\n {}'.format(ride.head()))
# print('The available data are {}'.format(ride.columns))
all_time = pd.to_datetime(df[f_name[f]]).tolist()
all_time_15s = []
for t in all_time:
t_15s = t + datetime.timedelta(seconds=15)
all_time_15s.append(t_15s)
for i in range(len(all_time)):
print('Index du crop : ', i)
debut = str(all_time[i])
fin = str(all_time_15s[i])
to a cyclist to infer power produced by a cyclist. """ print(__doc__) # Authors: Guillaume Lemaitre <*****@*****.**> # License: BSD 3 clause ############################################################################### # We can first grab a file and read all data available from skcycling.datasets import load_fit from skcycling.io import bikeread ride = bikeread(load_fit()[0], drop_nan='columns') ############################################################################### # We can use a physical model to infer the power. from skcycling.model import strava_power_model power = strava_power_model(ride, cyclist_weight=72) ############################################################################### # We can plot the measured and estimated power to observe the difference. We # can also compute the median absolute error to asses the quality of the # estimation. To ease the interpretation, we will first resample the data. import matplotlib.pyplot as plt from sklearn.metrics import median_absolute_error
def test_activity_power_profile(max_duration, power_profile_shape,
first_element):
activity = bikeread(load_fit()[0])
power_profile = activity_power_profile(activity, max_duration=max_duration)
assert power_profile.shape == power_profile_shape
assert power_profile.iloc[-1] == pytest.approx(first_element)
def test_activity_power_profile(max_duration, power_profile_shape,
first_element):
activity = bikeread(load_fit()[0])
power_profile = activity_power_profile(activity, max_duration=max_duration)
assert power_profile.shape == power_profile_shape
assert power_profile.iloc[-1] == pytest.approx(first_element)
"""
# Authors: Guillaume Lemaitre <*****@*****.**>
# License: BSD 3 clause
print(__doc__)
###############################################################################
# First, we will load an activity from the toy data set available in
# scikit-cycling.
from skcycling.datasets import load_fit
from skcycling.io import bikeread
ride = bikeread(load_fit()[0], drop_nan='columns')
###############################################################################
# We will only select some of interesting information
columns_selected = ['power', 'speed', 'cadence']
ride = ride[columns_selected]
###############################################################################
# The power-profile is extracted from the ride. By default, the maximum
# duration corresponds to the duration of the ride. However, to limit the
# processing, we limit the extraction to 8 minutes.
from skcycling.extraction import activity_power_profile
power_profile = activity_power_profile(ride, '00:08:00')
print('The power-profile is:\n {}'.format(power_profile))
# `scikit-cycling` has couple of `fit` files stored which can be used as toy
# data.
from skcycling.datasets import load_fit
filename_fit = load_fit()[0] # catch the first toy file
print('The fit file which will be used is stored at: \n {}'
.format(filename_fit))
###############################################################################
# The function :func:`skcycling.io.bikeread` allows to read the file without
# any extra information regarding the format.
from skcycling.io import bikeread
ride = bikeread(filename_fit)
print('The ride is the following:\n {}'.format(ride.head()))
###############################################################################
# :func:`skcycling.io.bikeread` returns a pandas DataFrame. Thus, this is
# possible to export it in different format. We will use CSV format in this
# case.
filename_export = 'ride_exported.csv'
ride.to_csv(filename_export)
###############################################################################
# Then, it is always possible to read the file exported using pandas
import pandas as pd
# `scikit-cycling` has couple of `fit` files stored which can be used as toy
# data.
from skcycling.datasets import load_fit
filename_fit = load_fit()[0] # catch the first toy file
print('The fit file which will be used is stored at: \n {}'
.format(filename_fit))
###############################################################################
# The function :func:`skcycling.io.bikeread` allows to read the file without
# any extra information regarding the format.
from skcycling.io import bikeread
ride = bikeread(filename_fit, drop_nan='columns')
print('The ride is the following:\n {}'.format(ride.head()))
###############################################################################
# First, we can list the type of data available in the DataFrame
print('The available data are {}'.format(ride.columns))
###############################################################################
# Plotting a specific column (e.g. power) is easy using the pandas ``plot``
# function.
import matplotlib.pyplot as plt
ride['power'].plot(legend=True)
plt.xlabel('Time')