v0.5.0 Release

See [Releases](https://github.com/OverLordGoldDragon/ssqueezepy/releases).

README.md

@@ -24,7 +24,9 @@ Synchrosqueezing is a powerful _reassignment method_ that focuses time-frequency
   - Generalized Morse Wavelets
 
 ## Installation
-`pip install git+https://github.com/OverLordGoldDragon/ssqueezepy` or clone repository; PyPi-available after 0.5.0.
+`pip install ssqueezepy`. Or, for latest version (most likely stable): 
+
+`pip install git+https://github.com/OverLordGoldDragon/ssqueezepy`
 
 ## Examples
 

examples/ridge_chirp.py

@@ -33,10 +33,10 @@ def echirp(N):
 pkw = dict(abs=1, w=.86, h=.9, aspect='auto', cmap='bone')
 _Tx = np.pad(Tx, [[4, 4]])  # improve display of top- & bottom-most freqs
 imshow(Wx, **pkw)
-imshow(np.flipud(_Tx), norm=(0, 2e-1), **pkw)
+imshow(np.flipud(_Tx), norm=(0, 4e-1), **pkw)
 #%%# Estimate inversion ridge ###############################################
 bw, slope, offset = .035, .45, .45
-Cs, freqband = lin_band(Tx, slope, offset, bw, norm=(0, 2e-1))
+Cs, freqband = lin_band(Tx, slope, offset, bw, norm=(0, 4e-1))
 #%%###########################################################################
 xrec = issq_cwt(Tx, kw['wavelet'], Cs, freqband)[0]
 plot(xo)

examples/se_ans0.py

@@ -18,14 +18,14 @@ def cos_f(freqs, N=128, phi=0):
 f, N = 12, 512
 
 x = cos_f([f], N=N)
-Wx, scales, *_ = cwt(x, wavelet, dt=1/N)
+Wx, scales, *_ = cwt(x, wavelet, fs=N)
 
 #%%# Show, print max row
 imshow(Wx, abs=1, yticks=scales, title="f=%d, N=%d" % (f, N), show=1)
 mxidx = np.where(np.abs(Wx) == np.abs(Wx).max())[0][0]
 print("Max row idx:", mxidx, flush=True)
 
-#%%# Plot around max row
+#%%# Plot aroundI max row
 idxs = slice(mxidx - 30, mxidx + 20)
 Wxz = Wx[idxs]
 imshow(Wxz, abs=1, title="abs(CWT), zoomed", show=0)

ssqueezepy/wavelets.py

@@ -12,7 +12,7 @@
 
 class Wavelet():
     """Central wavelet class. `__call__` computes Fourier frequency-domain
-    wavelet,  `psih`, `.psifn` computes time-domain wavelet, `psi`.
+    wavelet, `psih`, `.psifn` computes time-domain wavelet, `psi`.
 
     `Wavelet.SUPPORTED` for names of built-in wavelets passable to `__init__()`;
     `Wavelet.VISUALS`   for names of visualizations    passable to `viz()`.
@@ -359,17 +359,18 @@ def _hhhat(_w):
 
 
 #### Wavelet properties ######################################################
-def center_frequency(wavelet, scale=10, N=1024, kind='energy', force_int=True,
+def center_frequency(wavelet, scale=10, N=1024, kind='energy', force_int=None,
                      viz=False):
     """Energy center frequency (radian); Eq 4.52 of [1]:
         wc_1     = int w |wavelet(w)|^2 dw  0..inf
         wc_scale = int (scale*w) |wavelet(scale*w)|^2 dw 0..inf = wc_1 / scale
 
-    `force_int` (relevant only if kind='energy') can be set to False to compute
-    via formula - i.e. first integrate at a "well-behaved" scale, then rescale.
-    For intermediate scales, not much difference either way. Fro extremes, it
-    matches the continuous-time result closer - but this isn't recommended, as it
-    overlooks limitations imposed by discretization (trimmed/few-sample bell).
+    `force_int` (relevant only if kind='energy', defaults to True) can be set to
+    False to compute via formula - i.e. first integrate at a "well-behaved" scale,
+    then rescale. For intermediate scales, not much difference either way. For
+    extremes, it matches the continuous-time result closer - but this isn't
+    recommended, as it overlooks limitations imposed by discretization
+    (trimmed/few-sample bell).
 
     For very high scales, 'energy' w/ `force_int=True` will match 'peak'; for
     very low scales, 'energy' will always be less than 'peak'.
@@ -428,6 +429,7 @@ def _peak_wc(wavelet, scale, N):
 
     wavelet = Wavelet._init_if_not_isinstance(wavelet)
     if kind == 'energy':
+        force_int = force_int or True
         wc, params = _energy_wc(wavelet, scale, N, force_int)
     elif kind == 'peak':
         wc, params = _peak_wc(wavelet, scale, N)
@@ -468,6 +470,7 @@ def _viz():
 
     wavelet = Wavelet._init_if_not_isinstance(wavelet)
 
+    # formula criterion not optimal; thresholds will vary by wavelet config
     use_formula = ((scale < 4 or scale > N / 5) and not force_int)
     if use_formula:
         scale_orig = scale
@@ -572,6 +575,7 @@ def _make_integration_t(wavelet, scale, N, min_decay, max_mult, min_mult):
 
     wavelet = Wavelet._init_if_not_isinstance(wavelet)
 
+    # formula criterion not optimal; thresholds will vary by wavelet config
     use_formula = ((scale < 4 or scale > N / 5) and not force_int)
     if use_formula:
         scale_orig = scale