Vehicle-Anti-Theft-Face-Recognition-System/venv/Lib/site-packages/pywt/data/_readers.py

import os

import numpy as np


def ascent():
    """
    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 pywt.data
    >>> ascent = pywt.data.ascent()
    >>> ascent.shape == (512, 512)
    True
    >>> ascent.max()
    255

    >>> import matplotlib.pyplot as plt
    >>> plt.gray()
    >>> plt.imshow(ascent) # doctest: +ELLIPSIS
    <matplotlib.image.AxesImage object at ...>
    >>> plt.show() # doctest: +SKIP

    """
    fname = os.path.join(os.path.dirname(__file__), 'ascent.npz')
    ascent = np.load(fname)['data']
    return ascent


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

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

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

    Examples
    --------
    >>> import pywt.data
    >>> aero = pywt.data.ascent()
    >>> aero.shape == (512, 512)
    True
    >>> aero.max()
    255

    >>> import matplotlib.pyplot as plt
    >>> plt.gray()
    >>> plt.imshow(aero) # doctest: +ELLIPSIS
    <matplotlib.image.AxesImage object at ...>
    >>> plt.show() # doctest: +SKIP

    """
    fname = os.path.join(os.path.dirname(__file__), 'aero.npz')
    aero = np.load(fname)['data']
    return aero


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

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

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

    Examples
    --------
    >>> import pywt.data
    >>> camera = pywt.data.ascent()
    >>> camera.shape == (512, 512)
    True

    >>> import matplotlib.pyplot as plt
    >>> plt.gray()
    >>> plt.imshow(camera) # doctest: +ELLIPSIS
    <matplotlib.image.AxesImage object at ...>
    >>> plt.show() # doctest: +SKIP

    """
    fname = os.path.join(os.path.dirname(__file__), 'camera.npz')
    camera = np.load(fname)['data']
    return camera


def ecg():
    """
    Get 1024 points of an ECG timeseries.

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

    Returns
    -------
    ecg : ndarray
       convenient timeseries to use for testing and demonstration

    Examples
    --------
    >>> import pywt.data
    >>> ecg = pywt.data.ecg()
    >>> ecg.shape == (1024,)
    True

    >>> import matplotlib.pyplot as plt
    >>> plt.plot(ecg) # doctest: +ELLIPSIS
    [<matplotlib.lines.Line2D object at ...>]
    >>> plt.show() # doctest: +SKIP
    """
    fname = os.path.join(os.path.dirname(__file__), 'ecg.npy')
    ecg = np.load(fname)
    return ecg


def nino():
    """
    This data contains the averaged monthly sea surface temperature in degrees
    Celcius of the Pacific Ocean, between 0-10 degrees South and 90-80 degrees West, from 1950 to 2016.
    This dataset is in the public domain and was obtained from NOAA.
    National Oceanic and Atmospheric Administration's National Weather Service
    ERSSTv4 dataset, nino 3, http://www.cpc.ncep.noaa.gov/data/indices/

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

    Returns
    -------
    time : ndarray
       convenient timeseries to use for testing and demonstration
    sst : ndarray
       convenient timeseries to use for testing and demonstration

    Examples
    --------
    >>> import pywt.data
    >>> time, sst = pywt.data.nino()
    >>> sst.shape == (264,)
    True

    >>> import matplotlib.pyplot as plt
    >>> plt.plot(time,sst) # doctest: +ELLIPSIS
    [<matplotlib.lines.Line2D object at ...>]
    >>> plt.show() # doctest: +SKIP
    """
    fname = os.path.join(os.path.dirname(__file__), 'sst_nino3.npz')
    sst_csv = np.load(fname)['sst_csv']
    # sst_csv = pd.read_csv("http://www.cpc.ncep.noaa.gov/data/indices/ersst4.nino.mth.81-10.ascii", sep=' ', skipinitialspace=True)
    # take only full years
    n = int(np.floor(sst_csv.shape[0]/12.)*12.)
    # Building the mean of three mounth
    # the 4. column is nino 3
    sst = np.mean(np.reshape(np.array(sst_csv)[:n, 4], (n//3, -1)), axis=1)
    sst = (sst - np.mean(sst)) / np.std(sst, ddof=1)

    dt = 0.25
    time = np.arange(len(sst)) * dt + 1950.0  # construct time array
    return time, sst