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""" Tools for generating simulated datasets
The intent of this module is to provide tools for generating simulated data that is usefulf for testing, debugging, and learning. This module is inspired by the :py:func:`sklearn.datasets.make_classification` and :py:func:`sklearn.datasets.make_regression`, among others. """ import numpy as np import pandas as pd
# Defaults _N_SAMPLES = 1000 _N_SERIES = 3 _N_SEGMENT = 2 _DATE_START = '2000-01-01' _DATE_END = '2010-01-01' _FREQ = '16D'
# ============================================================================= # Segments def make_segments(date_start=None, date_end=None, date_freq=None, n_series=3, n_segments=2, seg_sep=None, means=None, stds=None, trends=None, amplitudes=None, phases=None): """ Simulate data from multiple temporal segments
Parameters ---------- date_start : str, datetime, and more, optional Starting date (in a format known to Pandas) date_end : str, datetime, and more, optional Ending date (in a format known to Pandas) date_freq : str, optional Date frequency n_series : int Number of series/spectral bands to simulate n_segments : int Number of segments to simulate seg_sep : Sequence[float] Separability of segments (i.e., the size of the disturbance) means : Sequence[float] The mean value for each series/spectral band (passed to :py:func:`make_time_series_mean`) stds : Sequence[float] The standard deviation for each series/spectral band (passed to :py:func:`make_time_series_mean`) trends : Sequence[float] The time trend for each series/spectral band (passed to :py:func:`make_time_series_trend`) amplitudes : Sequence[float] The harmonic amplitude value for each series/spectral band (passed to :py:func:`make_time_series_harmonic`) phases : Sequence[float] The harmonic phase value for each series/spectral band (passed to :py:func:`make_time_series_harmonic`)
Returns ------- xr.DataArray Simulated data for ``n_segments`` across ``n_series`` series/spectral bands np.ndarray Array of ``datetime64`` indicating the dates of change (size=``n_segments - 1``) """ assert isinstance(n_series, int) assert isinstance(n_segments, int) and n_segments >= 1 # TODO pass
# ============================================================================= # Time series def make_dates(date_start=None, date_end=None, date_freq=None): """ Return ``datetime64`` dates
Parameters ---------- date_start : str, datetime, and more, optional Starting date (in a format known to Pandas) date_end : str, datetime, and more, optional Ending date (in a format known to Pandas) date_freq : str, optional Date frequency
Returns ------- np.ndarray Dates as ``np.datetime64``
See Also -------- pandas.date_range """ return pd.date_range(date_start, date_end, freq=date_freq).values
def make_time_series_mean(dates, mean=None, std=None): pass
def make_time_series_trend(dates, trend=None): pass
def make_time_series_harmonic(dates, amplitude=None, phase=None): pass
def make_time_series_noise(dates, mean=0., std=1.): """ Generate a time series of noise """ # TODO: implement Gaussian noise with mean/std # TODO: add kwarg to parametrize noise from clouds/shadows pass |