AIM-PIbd-32-Kurbanova-A-A/aimenv/Lib/site-packages/scipy/datasets/_fetchers.py

from numpy import array, frombuffer, load
from ._registry import registry, registry_urls

try:
    import pooch
except ImportError:
    pooch = None
    data_fetcher = None
else:
    data_fetcher = pooch.create(
        # Use the default cache folder for the operating system
        # Pooch uses appdirs (https://github.com/ActiveState/appdirs) to
        # select an appropriate directory for the cache on each platform.
        path=pooch.os_cache("scipy-data"),

        # The remote data is on Github
        # base_url is a required param, even though we override this
        # using individual urls in the registry.
        base_url="https://github.com/scipy/",
        registry=registry,
        urls=registry_urls
    )


def fetch_data(dataset_name, data_fetcher=data_fetcher):
    if data_fetcher is None:
        raise ImportError("Missing optional dependency 'pooch' required "
                          "for scipy.datasets module. Please use pip or "
                          "conda to install 'pooch'.")
    # The "fetch" method returns the full path to the downloaded data file.
    return data_fetcher.fetch(dataset_name)


def ascent():
    """
    Get an 8-bit grayscale bit-depth, 512 x 512 derived image for easy
    use in demos.

    The image is derived from
    https://pixnio.com/people/accent-to-the-top

    Parameters
    ----------
    None

    Returns
    -------
    ascent : ndarray
       convenient image to use for testing and demonstration

    Examples
    --------
    >>> import scipy.datasets
    >>> ascent = scipy.datasets.ascent()
    >>> ascent.shape
    (512, 512)
    >>> ascent.max()
    255

    >>> import matplotlib.pyplot as plt
    >>> plt.gray()
    >>> plt.imshow(ascent)
    >>> plt.show()

    """
    import pickle

    # The file will be downloaded automatically the first time this is run,
    # returning the path to the downloaded file. Afterwards, Pooch finds
    # it in the local cache and doesn't repeat the download.
    fname = fetch_data("ascent.dat")
    # Now we just need to load it with our standard Python tools.
    with open(fname, 'rb') as f:
        ascent = array(pickle.load(f))
    return ascent


def electrocardiogram():
    """
    Load an electrocardiogram as an example for a 1-D signal.

    The returned signal is a 5 minute long electrocardiogram (ECG), a medical
    recording of the heart's electrical activity, sampled at 360 Hz.

    Returns
    -------
    ecg : ndarray
        The electrocardiogram in millivolt (mV) sampled at 360 Hz.

    Notes
    -----
    The provided signal is an excerpt (19:35 to 24:35) from the `record 208`_
    (lead MLII) provided by the MIT-BIH Arrhythmia Database [1]_ on
    PhysioNet [2]_. The excerpt includes noise induced artifacts, typical
    heartbeats as well as pathological changes.

    .. _record 208: https://physionet.org/physiobank/database/html/mitdbdir/records.htm#208

    .. versionadded:: 1.1.0

    References
    ----------
    .. [1] Moody GB, Mark RG. The impact of the MIT-BIH Arrhythmia Database.
           IEEE Eng in Med and Biol 20(3):45-50 (May-June 2001).
           (PMID: 11446209); :doi:`10.13026/C2F305`
    .. [2] Goldberger AL, Amaral LAN, Glass L, Hausdorff JM, Ivanov PCh,
           Mark RG, Mietus JE, Moody GB, Peng C-K, Stanley HE. PhysioBank,
           PhysioToolkit, and PhysioNet: Components of a New Research Resource
           for Complex Physiologic Signals. Circulation 101(23):e215-e220;
           :doi:`10.1161/01.CIR.101.23.e215`

    Examples
    --------
    >>> from scipy.datasets import electrocardiogram
    >>> ecg = electrocardiogram()
    >>> ecg
    array([-0.245, -0.215, -0.185, ..., -0.405, -0.395, -0.385])
    >>> ecg.shape, ecg.mean(), ecg.std()
    ((108000,), -0.16510875, 0.5992473991177294)

    As stated the signal features several areas with a different morphology.
    E.g., the first few seconds show the electrical activity of a heart in
    normal sinus rhythm as seen below.

    >>> import numpy as np
    >>> import matplotlib.pyplot as plt
    >>> fs = 360
    >>> time = np.arange(ecg.size) / fs
    >>> plt.plot(time, ecg)
    >>> plt.xlabel("time in s")
    >>> plt.ylabel("ECG in mV")
    >>> plt.xlim(9, 10.2)
    >>> plt.ylim(-1, 1.5)
    >>> plt.show()

    After second 16, however, the first premature ventricular contractions,
    also called extrasystoles, appear. These have a different morphology
    compared to typical heartbeats. The difference can easily be observed
    in the following plot.

    >>> plt.plot(time, ecg)
    >>> plt.xlabel("time in s")
    >>> plt.ylabel("ECG in mV")
    >>> plt.xlim(46.5, 50)
    >>> plt.ylim(-2, 1.5)
    >>> plt.show()

    At several points large artifacts disturb the recording, e.g.:

    >>> plt.plot(time, ecg)
    >>> plt.xlabel("time in s")
    >>> plt.ylabel("ECG in mV")
    >>> plt.xlim(207, 215)
    >>> plt.ylim(-2, 3.5)
    >>> plt.show()

    Finally, examining the power spectrum reveals that most of the biosignal is
    made up of lower frequencies. At 60 Hz the noise induced by the mains
    electricity can be clearly observed.

    >>> from scipy.signal import welch
    >>> f, Pxx = welch(ecg, fs=fs, nperseg=2048, scaling="spectrum")
    >>> plt.semilogy(f, Pxx)
    >>> plt.xlabel("Frequency in Hz")
    >>> plt.ylabel("Power spectrum of the ECG in mV**2")
    >>> plt.xlim(f[[0, -1]])
    >>> plt.show()
    """
    fname = fetch_data("ecg.dat")
    with load(fname) as file:
        ecg = file["ecg"].astype(int)  # np.uint16 -> int
    # Convert raw output of ADC to mV: (ecg - adc_zero) / adc_gain
    ecg = (ecg - 1024) / 200.0
    return ecg


def face(gray=False):
    """
    Get a 1024 x 768, color image of a raccoon face.

    The image is derived from
    https://pixnio.com/fauna-animals/raccoons/raccoon-procyon-lotor

    Parameters
    ----------
    gray : bool, optional
        If True return 8-bit grey-scale image, otherwise return a color image

    Returns
    -------
    face : ndarray
        image of a raccoon face

    Examples
    --------
    >>> import scipy.datasets
    >>> face = scipy.datasets.face()
    >>> face.shape
    (768, 1024, 3)
    >>> face.max()
    255
    >>> face.dtype
    dtype('uint8')

    >>> import matplotlib.pyplot as plt
    >>> plt.gray()
    >>> plt.imshow(face)
    >>> plt.show()

    """
    import bz2
    fname = fetch_data("face.dat")
    with open(fname, 'rb') as f:
        rawdata = f.read()
    face_data = bz2.decompress(rawdata)
    face = frombuffer(face_data, dtype='uint8')
    face.shape = (768, 1024, 3)
    if gray is True:
        face = (0.21 * face[:, :, 0] + 0.71 * face[:, :, 1] +
                0.07 * face[:, :, 2]).astype('uint8')
    return face