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scipy-datazhttps://github.com/scipy/)�path�base_urlr�urlsc�N�|�td���|�|��S)NzsMissing optional dependency 'pooch' required for scipy.datasets module. Please use pip or conda to install 'pooch'.)�ImportError�fetch)�dataset_name�data_fetchers  �:/usr/lib/python3/dist-packages/scipy/datasets/_fetchers.py�
fetch_datars5�����6�7�7�	7����l�+�+�+�c��ddl}td��}t|d��5}t|�|����}ddd��n#1swxYwY|S)a�
    Get an 8-bit grayscale bit-depth, 512 x 512 derived image for easy
    use in demos.

    The image is derived from accent-to-the-top.jpg at
    http://www.public-domain-image.com/people-public-domain-images-pictures/

    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()

    rNz
ascent.dat�rb)�pickler�openrr)r�fname�f�ascents    rrr"s���@�M�M�M�

�|�$�$�E�	
�e�T�	�	�'�a��v�{�{�1�~�~�&�&��'�'�'�'�'�'�'�'�'�'�'����'�'�'�'��Ms�#A�A�Ac���td��}t|��5}|d�t��}ddd��n#1swxYwY|dz
dz}|S)a{
    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()
    zecg.dat�ecgN�gi@)rr�astype�int)r�filers   r�electrocardiogramr!Ns���v
�y�!�!�E�	
�e���&���5�k� � ��%�%��&�&�&�&�&�&�&�&�&�&�&����&�&�&�&���:��
�C��Js�!A�A�AFc��ddl}td��}t|d��5}|���}ddd��n#1swxYwY|�|��}t|d���}d|_|durKd	|dd�dd�dfzd
|dd�dd�dfzzd|dd�dd�d
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    Get a 1024 x 768, color image of a raccoon face.

    raccoon-procyon-lotor.jpg at http://www.public-domain-image.com

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

    Returns
    -------
    face : ndarray
        image of a racoon 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()

    rNzface.datr�uint8)�dtype)ir�Tg�z�G��?g���Q��?rg�Q���?�)�bz2rr�read�
decompressr�shaper)�grayr'rr�rawdata�	face_data�faces       rr.r.�s'��B�J�J�J��z�"�"�E�	
�e�T�	�	��a��&�&�(�(�����������������������w�'�'�I��i�w�/�/�/�D��D�J��t�|�|��t�A�A�A�q�q�q�!�G�}�$�t�d�1�1�1�a�a�a��7�m�';�;��t�A�A�A�q�q�q�!�G�}�$�%�&,�f�W�o�o�	
��Ks�A�A	�A	)F)�numpyrrr�	_registryrr�pooch�create�os_cacherr
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