This notebook shows how to deal with irregular functional data by analyzing the
dataset CD4 cell count.
"""
# Author: Steven Golovkine <*****@*****.**>
# License: MIT
# shinx_gallery_thumbnail_number = 2
from FDApy.misc.loader import read_csv
from FDApy.visualization.plot import plot
###############################################################################
# Load the data into Pandas dataframe.
cd4 = read_csv('./data/cd4.csv', index_col=0)
###############################################################################
# Print out an Irregular Functional data object.
# Print irregular functional data
print(cd4)
###############################################################################
# The sampling points of the data can easily be accessed.
# Accessing the argvals of the object
print(cd4.argvals['input_dim_0'].get(5))
###############################################################################
# The values associated to the sampling points are retrieved in a same way
This notebook shows how to deal with univariate and multivariate functional
data by analyzing the canadian weather dataset.
"""
# Author: Steven Golovkine <*****@*****.**>
# License: MIT
# shinx_gallery_thumbnail_number = 2
from FDApy.representation.functional_data import MultivariateFunctionalData
from FDApy.misc.loader import read_csv
from FDApy.visualization.plot import plot
###############################################################################
# Load the data as DenseFunctionalData.
precipitation = read_csv('./data/canadian_precipitation_monthly.csv',
index_col=0)
temperature = read_csv('./data/canadian_temperature_daily.csv', index_col=0)
# Create multivariate functional data for the Canadian weather data.
canadWeather = MultivariateFunctionalData([precipitation, temperature])
###############################################################################
# Print out an univariate functional data object.
# Print univariate functional data
print(temperature)
###############################################################################
# Print out a multivariate functional data object.
# Print multivariate functional data
def test_load_irregular(self):
data = read_csv(os.path.join(DATA, 'irregular.csv'), index_col=0)
self.assertEqual(data.n_dim, 1)
self.assertEqual(data.n_obs, 3)
self.assertTrue(np.allclose(data.argvals['input_dim_0'][0],
np.array([-2, 0, 3])))
"""
# Author: Steven Golovkine <*****@*****.**>
# License: MIT
# shinx_gallery_thumbnail_number = 2
import pandas as pd
from FDApy.preprocessing.dim_reduction.fpca import UFPCA
from FDApy.visualization.plot import plot
from FDApy.misc.loader import read_csv
###############################################################################
# Load the data into Pandas dataframe
temperature = read_csv('./data/canadian_temperature_daily.csv', index_col=0)
###############################################################################
# Perform a univariate functional PCA and explore the results.
# Perform a univariate FPCA on dailyTemp.
fpca = UFPCA(n_components=0.99)
fpca.fit(temperature)
# Plot the results of the FPCA (eigenfunctions)
_ = plot(fpca.eigenfunctions)
###############################################################################
# Compute the scores of the dailyTemp data into the eigenfunctions basis using
# numerical integration.
def test_load_dense(self):
data = read_csv(os.path.join(DATA, 'dense.csv'), index_col=0)
self.assertEqual(data.n_dim, 1)
self.assertEqual(data.n_obs, 3)
self.assertTrue(np.allclose(data.argvals['input_dim_0'],
np.array([0, 1, 2, 3])))