Quickstart

In [1]:

import pandas as pd

from lactopy import OBLA

import pandas as pd from lactopy import OBLA

In [2]:

df = pd.read_csv('../tests/test_data/lactate_test_data copy.csv')

df = pd.read_csv('../tests/test_data/lactate_test_data copy.csv')

In [3]:

# fit model returns a scikit learn like model object
obla = OBLA()
obla.fit(df['watt'], df['lactate'], method='spline')

# fit model returns a scikit learn like model object obla = OBLA() obla.fit(df['watt'], df['lactate'], method='spline')

Out[3]:

OBLA()

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In [4]:

# plot fit of data
obla.plot.plot_fit()

# plot fit of data obla.plot.plot_fit()

Out[4]:

<Axes: title={'center': 'Lactate Model Plot'}, xlabel='Intensity', ylabel='Lactate'>

In [5]:

# predict intensity at 4 mmol
obla.predict(4)

# predict intensity at 4 mmol obla.predict(4)

Out[5]:

np.float64(147.14747308909196)

In [6]:

# Plot the prediction at 4 mmol
obla.plot.plot_predictions(4)

# Plot the prediction at 4 mmol obla.plot.plot_predictions(4)

Out[6]:

<Axes: title={'center': 'Lactate Model Plot'}, xlabel='Intensity', ylabel='Lactate'>

In [7]:

from lactopy.lactate_models import Dmax

from lactopy.lactate_models import Dmax

In [8]:

model = Dmax()
model.fit(df['watt'], df['lactate'], impl='modified')
# plot fit of data
model.plot.plot_fit()

model = Dmax() model.fit(df['watt'], df['lactate'], impl='modified') # plot fit of data model.plot.plot_fit()

Out[8]:

<Axes: title={'center': 'Dmax Model Fit'}, xlabel='Intensity', ylabel='Lactate'>

In [9]:

model.plot.plot_predictions()

model.plot.plot_predictions()

Out[9]:

<Axes: title={'center': 'Dmax Model Fit'}, xlabel='Intensity', ylabel='Lactate'>