Source code for pmdarima.datasets.heartrate

# -*- coding: utf-8 -*-

import numpy as np
import pandas as pd

from ..compat import DTYPE

__all__ = [
    'load_heartrate'
]


[docs]def load_heartrate(as_series=False, dtype=DTYPE): """Uniform heart-rate data. A sample of heartrate data borrowed from an `MIT database <http://ecg.mit.edu/time-series/>`_. The sample consists of 150 evenly spaced (0.5 seconds) heartrate measurements. Parameters ---------- as_series : bool, optional (default=False) Whether to return a Pandas series. If False, will return a 1d numpy array. dtype : type, optional (default=np.float64) The type to return for the array. Default is np.float64, which is used throughout the package as the default type. Returns ------- rslt : array-like, shape=(n_samples,) The heartrate vector. Examples -------- >>> from pmdarima.datasets import load_heartrate >>> load_heartrate() array([84.2697, 84.2697, 84.0619, 85.6542, 87.2093, 87.1246, 86.8726, 86.7052, 87.5899, 89.1475, 89.8204, 89.8204, 90.4375, 91.7605, 93.1081, 94.3291, 95.8003, 97.5119, 98.7457, 98.904 , 98.3437, 98.3075, 98.8313, 99.0789, 98.8157, 98.2998, 97.7311, 97.6471, 97.7922, 97.2974, 96.2042, 95.2318, 94.9367, 95.0867, 95.389 , 95.5414, 95.2439, 94.9415, 95.3557, 96.3423, 97.1563, 97.4026, 96.7028, 96.5516, 97.9837, 98.9879, 97.6312, 95.4064, 93.8603, 93.0552, 94.6012, 95.8476, 95.7692, 95.9236, 95.7692, 95.9211, 95.8501, 94.6703, 93.0993, 91.972 , 91.7821, 91.7911, 90.807 , 89.3196, 88.1511, 88.7762, 90.2265, 90.8066, 91.2284, 92.4238, 93.243 , 92.8472, 92.5926, 91.7778, 91.2974, 91.6364, 91.2952, 91.771 , 93.2285, 93.3199, 91.8799, 91.2239, 92.4055, 93.8716, 94.5825, 94.5594, 94.9453, 96.2412, 96.6879, 95.8295, 94.7819, 93.4731, 92.7997, 92.963 , 92.6996, 91.9648, 91.2417, 91.9312, 93.9548, 95.3044, 95.2511, 94.5358, 93.8093, 93.2287, 92.2065, 92.1588, 93.6376, 94.899 , 95.1592, 95.2415, 95.5414, 95.0971, 94.528 , 95.5887, 96.4715, 96.6158, 97.0769, 96.8531, 96.3947, 97.4291, 98.1767, 97.0148, 96.044 , 95.9581, 96.4814, 96.5211, 95.3629, 93.5741, 92.077 , 90.4094, 90.1751, 91.3312, 91.2883, 89.0592, 87.052 , 86.6226, 85.7889, 85.6348, 85.3911, 83.8064, 82.8729, 82.6266, 82.645 , 82.645 , 82.645 , 82.645 , 82.645 , 82.645 , 82.645 , 82.645 ]) >>> load_heartrate(True).head() 0 84.2697 1 84.2697 2 84.0619 3 85.6542 4 87.2093 dtype: float64 References ---------- .. [1] Goldberger AL, Rigney DR. Nonlinear dynamics at the bedside. In: Glass L, Hunter P, McCulloch A, eds. Theory of Heart: Biomechanics, Biophysics, and Nonlinear Dynamics of Cardiac Function. New York: Springer-Verlag, 1991, pp. 583-605. """ rslt = np.array([ 84.2697, 84.2697, 84.0619, 85.6542, 87.2093, 87.1246, 86.8726, 86.7052, 87.5899, 89.1475, 89.8204, 89.8204, 90.4375, 91.7605, 93.1081, 94.3291, 95.8003, 97.5119, 98.7457, 98.904, 98.3437, 98.3075, 98.8313, 99.0789, 98.8157, 98.2998, 97.7311, 97.6471, 97.7922, 97.2974, 96.2042, 95.2318, 94.9367, 95.0867, 95.389, 95.5414, 95.2439, 94.9415, 95.3557, 96.3423, 97.1563, 97.4026, 96.7028, 96.5516, 97.9837, 98.9879, 97.6312, 95.4064, 93.8603, 93.0552, 94.6012, 95.8476, 95.7692, 95.9236, 95.7692, 95.9211, 95.8501, 94.6703, 93.0993, 91.972, 91.7821, 91.7911, 90.807, 89.3196, 88.1511, 88.7762, 90.2265, 90.8066, 91.2284, 92.4238, 93.243, 92.8472, 92.5926, 91.7778, 91.2974, 91.6364, 91.2952, 91.771, 93.2285, 93.3199, 91.8799, 91.2239, 92.4055, 93.8716, 94.5825, 94.5594, 94.9453, 96.2412, 96.6879, 95.8295, 94.7819, 93.4731, 92.7997, 92.963, 92.6996, 91.9648, 91.2417, 91.9312, 93.9548, 95.3044, 95.2511, 94.5358, 93.8093, 93.2287, 92.2065, 92.1588, 93.6376, 94.899, 95.1592, 95.2415, 95.5414, 95.0971, 94.528, 95.5887, 96.4715, 96.6158, 97.0769, 96.8531, 96.3947, 97.4291, 98.1767, 97.0148, 96.044, 95.9581, 96.4814, 96.5211, 95.3629, 93.5741, 92.077, 90.4094, 90.1751, 91.3312, 91.2883, 89.0592, 87.052, 86.6226, 85.7889, 85.6348, 85.3911, 83.8064, 82.8729, 82.6266, 82.645, 82.645, 82.645, 82.645, 82.645, 82.645, 82.645, 82.645]).astype(dtype) if as_series: return pd.Series(rslt) return rslt