Merge pull request #14 from MStarmans91/development

Release 3.0.0

.eggs/PyWavelets-1.0.0-py3.7-linux-x86_64.egg/EGG-INFO/PKG-INFO

@@ -0,0 +1,44 @@
+Metadata-Version: 2.1
+Name: PyWavelets
+Version: 1.0.0
+Summary: PyWavelets, wavelet transform module
+Home-page: https://github.com/PyWavelets/pywt
+Maintainer: The PyWavelets Developers
+Maintainer-email: [email protected]
+License: MIT
+Download-URL: https://github.com/PyWavelets/pywt/releases
+Keywords: wavelets,wavelet transform,DWT,SWT,CWT,scientific
+Platform: Windows
+Platform: Linux
+Platform: Solaris
+Platform: Mac OS-X
+Platform: Unix
+Classifier: Development Status :: 5 - Production/Stable
+Classifier: Intended Audience :: Developers
+Classifier: Intended Audience :: Education
+Classifier: Intended Audience :: Science/Research
+Classifier: License :: OSI Approved :: MIT License
+Classifier: Operating System :: OS Independent
+Classifier: Programming Language :: C
+Classifier: Programming Language :: Python
+Classifier: Programming Language :: Python :: 3
+Classifier: Programming Language :: Python :: 2.7
+Classifier: Programming Language :: Python :: 3.5
+Classifier: Programming Language :: Python :: 3.6
+Classifier: Programming Language :: Python :: 3.7
+Classifier: Topic :: Software Development :: Libraries :: Python Modules
+Requires-Dist: numpy (>=1.9.1)
+
+PyWavelets is a Python wavelet transforms module that includes:
+
+* nD Forward and Inverse Discrete Wavelet Transform (DWT and IDWT)
+* 1D and 2D Forward and Inverse Stationary Wavelet Transform (Undecimated Wavelet Transform)
+* 1D and 2D Wavelet Packet decomposition and reconstruction
+* 1D Continuous Wavelet Tranfsorm
+* Computing Approximations of wavelet and scaling functions
+* Over 100 built-in wavelet filters and support for custom wavelets
+* Single and double precision calculations
+* Real and complex calculations
+* Results compatible with Matlab Wavelet Toolbox (TM)
+
+

.eggs/PyWavelets-1.0.0-py3.7-linux-x86_64.egg/EGG-INFO/RECORD

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.eggs/PyWavelets-1.0.0-py3.7-linux-x86_64.egg/EGG-INFO/WHEEL

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+Wheel-Version: 1.0
+Generator: bdist_wheel (0.31.1)
+Root-Is-Purelib: false
+Tag: cp37-cp37m-manylinux1_x86_64
+

.eggs/PyWavelets-1.0.0-py3.7-linux-x86_64.egg/EGG-INFO/requires.txt

@@ -0,0 +1 @@
+numpy>=1.9.1

.eggs/PyWavelets-1.0.0-py3.7-linux-x86_64.egg/EGG-INFO/top_level.txt

@@ -0,0 +1 @@
+pywt

.eggs/PyWavelets-1.0.0-py3.7-linux-x86_64.egg/pywt/__init__.py

@@ -0,0 +1,45 @@
+# flake8: noqa
+
+# Copyright (c) 2006-2012 Filip Wasilewski <http://en.ig.ma/>
+# Copyright (c) 2012-2016 The PyWavelets Developers
+#                         <https://github.com/PyWavelets/pywt>
+# See COPYING for license details.
+
+"""
+Discrete forward and inverse wavelet transform, stationary wavelet transform,
+wavelet packets signal decomposition and reconstruction module.
+"""
+
+from __future__ import division, print_function, absolute_import
+from distutils.version import LooseVersion
+
+from ._extensions._pywt import *
+from ._functions import *
+from ._multilevel import *
+from ._multidim import *
+from ._thresholding import *
+from ._wavelet_packets import *
+from ._dwt import *
+from ._swt import *
+from ._cwt import *
+
+from . import data
+
+__all__ = [s for s in dir() if not s.startswith('_')]
+try:
+    # In Python 2.x the name of the tempvar leaks out of the list
+    # comprehension.  Delete it to not make it show up in the main namespace.
+    del s
+except NameError:
+    pass
+
+from pywt.version import version as __version__
+
+import numpy as np
+if np.lib.NumpyVersion(np.__version__) >= '1.14.0':
+    from ._utils import is_nose_running
+    if is_nose_running():
+        np.set_printoptions(legacy='1.13')
+
+from numpy.testing import Tester
+test = Tester().test

.eggs/PyWavelets-1.0.0-py3.7-linux-x86_64.egg/pywt/_c99_config.py

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+# Autogenerated file containing compile-time definitions
+
+_have_c99_complex = 1

.eggs/PyWavelets-1.0.0-py3.7-linux-x86_64.egg/pywt/_cwt.py

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+import numpy as np
+
+from ._extensions._pywt import (DiscreteContinuousWavelet, ContinuousWavelet,
+                                Wavelet, _check_dtype)
+from ._functions import integrate_wavelet, scale2frequency
+
+__all__ = ["cwt"]
+
+
+def cwt(data, scales, wavelet, sampling_period=1.):
+    """
+    cwt(data, scales, wavelet)
+
+    One dimensional Continuous Wavelet Transform.
+
+    Parameters
+    ----------
+    data : array_like
+        Input signal
+    scales : array_like
+        The wavelet scales to use. One can use
+        ``f = scale2frequency(scale, wavelet)/sampling_period`` to determine
+        what physical frequency, ``f``. Here, ``f`` is in hertz when the
+        ``sampling_period`` is given in seconds.
+    wavelet : Wavelet object or name
+        Wavelet to use
+    sampling_period : float
+        Sampling period for the frequencies output (optional).
+        The values computed for ``coefs`` are independent of the choice of
+        ``sampling_period`` (i.e. ``scales`` is not scaled by the sampling
+        period).
+
+    Returns
+    -------
+    coefs : array_like
+        Continuous wavelet transform of the input signal for the given scales
+        and wavelet
+    frequencies : array_like
+        If the unit of sampling period are seconds and given, than frequencies
+        are in hertz. Otherwise, a sampling period of 1 is assumed.
+
+    Notes
+    -----
+    Size of coefficients arrays depends on the length of the input array and
+    the length of given scales.
+
+    Examples
+    --------
+    >>> import pywt
+    >>> import numpy as np
+    >>> import matplotlib.pyplot as plt
+    >>> x = np.arange(512)
+    >>> y = np.sin(2*np.pi*x/32)
+    >>> coef, freqs=pywt.cwt(y,np.arange(1,129),'gaus1')
+    >>> plt.matshow(coef) # doctest: +SKIP
+    >>> plt.show() # doctest: +SKIP
+    ----------
+    >>> import pywt
+    >>> import numpy as np
+    >>> import matplotlib.pyplot as plt
+    >>> t = np.linspace(-1, 1, 200, endpoint=False)
+    >>> sig  = np.cos(2 * np.pi * 7 * t) + np.real(np.exp(-7*(t-0.4)**2)*np.exp(1j*2*np.pi*2*(t-0.4)))
+    >>> widths = np.arange(1, 31)
+    >>> cwtmatr, freqs = pywt.cwt(sig, widths, 'mexh')
+    >>> plt.imshow(cwtmatr, extent=[-1, 1, 1, 31], cmap='PRGn', aspect='auto',
+    ...            vmax=abs(cwtmatr).max(), vmin=-abs(cwtmatr).max())  # doctest: +SKIP
+    >>> plt.show() # doctest: +SKIP
+    """
+
+    # accept array_like input; make a copy to ensure a contiguous array
+    dt = _check_dtype(data)
+    data = np.array(data, dtype=dt)
+    if not isinstance(wavelet, (ContinuousWavelet, Wavelet)):
+        wavelet = DiscreteContinuousWavelet(wavelet)
+    if np.isscalar(scales):
+        scales = np.array([scales])
+    if data.ndim == 1:
+        if wavelet.complex_cwt:
+            out = np.zeros((np.size(scales), data.size), dtype=complex)
+        else:
+            out = np.zeros((np.size(scales), data.size))
+        precision = 10
+        int_psi, x = integrate_wavelet(wavelet, precision=precision)
+        for i in np.arange(np.size(scales)):
+            step = x[1] - x[0]
+            j = np.floor(
+                np.arange(scales[i] * (x[-1] - x[0]) + 1) / (scales[i] * step))
+            if np.max(j) >= np.size(int_psi):
+                j = np.delete(j, np.where((j >= np.size(int_psi)))[0])
+            coef = - np.sqrt(scales[i]) * np.diff(
+                np.convolve(data, int_psi[j.astype(np.int)][::-1]))
+            d = (coef.size - data.size) / 2.
+            if d > 0:
+                out[i, :] = coef[int(np.floor(d)):int(-np.ceil(d))]
+            elif d == 0.:
+                out[i, :] = coef
+            else:
+                raise ValueError(
+                    "Selected scale of {} too small.".format(scales[i]))
+        frequencies = scale2frequency(wavelet, scales, precision)
+        if np.isscalar(frequencies):
+            frequencies = np.array([frequencies])
+        for i in np.arange(len(frequencies)):
+            frequencies[i] /= sampling_period
+        return out, frequencies
+    else:
+        raise ValueError("Only dim == 1 supportet")