pmdarima.datasets.load_heartrate

pmdarima.datasets.load_heartrate(as_series=False, dtype=<class 'numpy.float64'>)[source][source]

Uniform heart-rate data.

A sample of heartrate data borrowed from an MIT database. 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.

References

[R74]

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.

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

Examples using pmdarima.datasets.load_heartrate

Dataset loading

Dataset loading