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')