Prerelease 2

 - Added `Wavelet` class
 - Faster wavelet recomputation
 - Deprecated `wfiltfn`
 - Added `setup.py` and requirements

README.md

@@ -13,6 +13,10 @@ Significant changes to some code structure are expected until v0.6.0, but whatev
   - More
 
 
+## Installation
+
+`pip install git+https://github.com/OverLordGoldDragon/ssqueezepy`, for now; PyPi-available after 0.5.0.
+
 ## Differences w.r.t. original
 
  - **Renamed variables/functions**; more Pythonic & readable
@@ -27,7 +31,3 @@ Significant changes to some code structure are expected until v0.6.0, but whatev
 
  **Other**:
   - Dense instead of sparse matrices for `stft_fwd` in [stft_transforms.py](https://github.com/OverLordGoldDragon/ssqueezepy/blob/master/synchrosqueezing/stft_transforms.py), as Numpy doesn't handle latter in ops involved
-
-
-
-

requirements-dev.txt

@@ -0,0 +1,6 @@
+setuptools>=41.0.0
+twine>=1.15.0
+coverage
+pytest
+pytest-cov
+pycode

requirements.txt

@@ -0,0 +1,5 @@
+numpy
+numba
+matplotlib
+quadpy
+termcolor

setup.py

@@ -0,0 +1,59 @@
+import os
+import re
+from setuptools import setup, find_packages
+
+current_path = os.path.abspath(os.path.dirname(__file__))
+
+
+def read_file(*parts):
+    with open(os.path.join(current_path, *parts), encoding='utf-8') as reader:
+        return reader.read()
+
+
+def get_requirements(*parts):
+    with open(os.path.join(current_path, *parts), encoding='utf-8') as reader:
+        return list(map(lambda x: x.strip(), reader.readlines()))
+
+
+def find_version(*file_paths):
+    version_file = read_file(*file_paths)
+    version_matched = re.search(r"^__version__ = ['\"]([^'\"]*)['\"]",
+                                version_file, re.M)
+    if version_matched:
+        return version_matched.group(1)
+    raise RuntimeError('Unable to find version')
+
+
+setup(
+    name="ssqueezepy",
+    version=find_version('ssqueezepy', '__init__.py'),
+    packages=find_packages(exclude=['tests', 'examples']),
+    url="https://github.com/OverLordGoldDragon/ssqueezepy",
+    license="TBD",
+    author="OverLordGoldDragon",
+    author_email="16495490+OverLordGoldDragon@users.noreply.github.com",
+    description=("Synchrosqueezing and wavelet transforms in Python"),
+    long_description=read_file('README.md'),
+    long_description_content_type="text/markdown",
+    keywords=(
+        "signal-processing python synchrosqueezing wavelet-transform"
+    ),
+    install_requires=get_requirements('requirements.txt'),
+    tests_require=["pytest>=4.0", "pytest-cov"],
+    include_package_data=True,
+    zip_safe=True,
+    classifiers=[
+        "Programming Language :: Python :: 3.6",
+        "Programming Language :: Python :: 3.7",
+        "Programming Language :: Python :: 3.8",
+        "Operating System :: OS Independent",
+        "Intended Audience :: Developers",
+        "Intended Audience :: Information Technology",
+        "Intended Audience :: Science/Research",
+        "Topic :: Utilities",
+        "Topic :: Scientific/Engineering",
+        "Topic :: Scientific/Engineering :: Information Analysis",
+        "Topic :: Software Development",
+        "Topic :: Software Development :: Libraries :: Python Modules",
+    ],
+)

ssqueezepy/__init__.py

@@ -1,17 +1,17 @@
 from . import ssqueezing
 from . import _ssq_cwt
 from . import _cwt
-from . import synsq_stft
 from . import utils
+from . import synsq_stft
 from . import stft_transforms
 from . import experimental
 
 from .ssqueezing import *
 from ._ssq_cwt import *
 from ._cwt import *
+from .utils import *
 from .synsq_stft import *
 from .stft_transforms import *
-from .utils import *
 
 
-__version__ = '0.5.0rc1'
+__version__ = '0.5.0rc2'

ssqueezepy/_cwt.py

@@ -1,8 +1,9 @@
 import numpy as np
 from numpy.fft import fft, ifft, ifftshift
-from .utils import pi, WARN, p2up, adm_cwt, adm_ssq, wfiltfn, wfilth
+from .utils import WARN, p2up, adm_cwt, adm_ssq, wfilth
 from .utils import padsignal, process_scales
 from .algos import replace_at_inf_or_nan
+from .wavelets import Wavelet
 
 
 def cwt(x, wavelet, scales='log', dt=1, nv=None, l1_norm=True,
@@ -70,23 +71,19 @@ def _process_args(x, scales, dt):
     xh = fft(x)
 
     pn = (-1) ** np.arange(N)
-    # TODO this can be trouble for very large N, fill xi directly instead
-    # and just move wavelet at scale computation to own function
-    xi = (2*pi / N) * np.hstack([np.arange(N // 2 + 1),
-                                 np.arange(-N // 2 + 1, 0)])
-    psihfn = wfiltfn(wavelet)  # TODO use wfiltfn instead?
+    psihfn = Wavelet(wavelet, N=N)
 
     # TODO vectorize? can FFT all at once if all `psih` are precomputed
     # but keep loop option in case of OOM
     for i, a in enumerate(scales):
         # sample FT of wavelet at scale `a`
         # `* pn` = freq-domain spectral reversal to center time-domain wavelet
-        psih = psihfn(a * xi) * pn
+        psih = psihfn(a) * pn
 
         xcpsi = ifftshift(ifft(xh * psih))
         Wx[i] = xcpsi
 
-        dpsih = (1j * xi / dt) * psih
+        dpsih = (1j * psihfn.xi / dt) * psih
         dxcpsi = ifftshift(ifft(dpsih * xh))
         dWx[i] = dxcpsi
 
@@ -102,7 +99,6 @@ def _process_args(x, scales, dt):
     return Wx, scales, dWx, x_mean
 
 
-# TODO add one-integral implementation
 def icwt(Wx, wavelet, scales='log', one_int=True, x_len=None, x_mean=0,
          padtype='symmetric', rpadded=False, l1_norm=True):
     """The inverse continuous wavelet transform of signal Wx via double integral.

ssqueezepy/experimental.py

@@ -1,6 +1,7 @@
 import numpy as np
 from numpy.fft import fft, ifft, ifftshift
-from .utils import pi, wfiltfn, adm_cwt
+from .utils import pi, adm_cwt
+from .wavelets import Wavelet
 from quadpy import quad as quadgk
 
 
@@ -14,7 +15,7 @@ def err_fix(x, wavelet, a0):  # primitive code, doesn't work
     N = len(x)
     xi = (2*pi/N) * np.arange(1, N//2 + 1)
 
-    psihfn = wfiltfn(wavelet)
+    psihfn = Wavelet(wavelet)
     # integrate from 0 to w, w spanning same spectrum as psih
     # this can be sped up by nature of brick-wall behavior, stopping computing
     # after first zero, also computing fewer in total and linearly interpolating