Merge pull request #110 from jaidevd/jd-minor-fixes

Minor cleanup

tftb/processing/ambiguity.py

@@ -12,10 +12,10 @@
 
 import numpy as np
 from scipy.signal import hilbert
-from tftb.utils import init_default_args, nextpow2
+from tftb.utils import nextpow2
 
 
-def wide_band(signal, fmin, fmax, N=None):
+def wide_band(signal, fmin=None, fmax=None, N=None):
     if 1 in signal.shape:
         signal = signal.ravel()
     elif signal.ndim != 1:
@@ -28,12 +28,26 @@ def wide_band(signal, fmin, fmax, N=None):
     tmax = nx - 1
     T = tmax - tmin
 
+    # determine default values for fmin, fmax
+    if (fmin is None) or (fmax is None):
+        from matplotlib.mlab import find
+        STF = np.fft.fftshift(s_ana)
+        sp = np.abs(STF[:m]) ** 2
+        maxsp = np.amax(sp)
+        f = np.linspace(0, 0.5, m + 1)
+        f = f[:m]
+        indmin = find(sp > maxsp / 100.0).min()
+        indmax = find(sp > maxsp / 100.0).max()
+        if fmin is None:
+            fmin = max([0.01, 0.05 * np.fix(f[indmin] / 0.05)])
+        if fmax is None:
+            fmax = 0.05 * np.ceil(f[indmax] / 0.05)
     B = fmax - fmin
     R = B / ((fmin + fmax) / 2.0)
     nq = np.ceil((B * T * (1 + 2.0 / R) * np.log((1 + R / 2.0) / (1 - R / 2.0))) / 2.0)
     nmin = nq - (nq % 2)
     if N is None:
-        N = 2 ** (nextpow2(nmin))
+        N = int(2 ** (nextpow2(nmin)))
 
     # geometric sampling for the analyzed spectrum
     k = np.arange(1, N + 1)
@@ -101,7 +115,8 @@ def narrow_band(signal, lag=None, n_fbins=None):
         lag = np.arange(tau_start, tau_end)
     taucol = lag.shape[0]
 
-    n_fbins = init_default_args(signal, n_fbins=n_fbins)[0]
+    if n_fbins is None:
+        n_fbins = signal.shape[0]
 
     naf = np.zeros((n_fbins, taucol), dtype=complex)
     for icol in range(taucol):
@@ -122,7 +137,7 @@ def narrow_band(signal, lag=None, n_fbins=None):
 if __name__ == '__main__':
     from tftb.generators.misc import altes
     sig = altes(128, 0.1, 0.45)
-    waf, tau, theta = wide_band(sig, 0.1, 0.35, 64)
+    waf, tau, theta = wide_band(sig)
     from matplotlib.pyplot import contour, show
     contour(tau, theta, np.abs(waf) ** 2)
     show()

tftb/processing/cohen.py

@@ -11,7 +11,6 @@
 """
 
 import numpy as np
-from tftb.utils import init_default_args
 from tftb.processing.linear import ShortTimeFourierTransform
 from tftb.processing.base import BaseTFRepresentation
 
@@ -208,8 +207,10 @@ def smoothed_pseudo_wigner_ville(signal, timestamps=None, freq_bins=None,
     :return: Smoothed pseudo Wigner Ville distribution
     :rtype: array-like
     """
-    timestamps, freq_bins = init_default_args(signal, timestamps=timestamps,
-                                              n_fbins=freq_bins)
+    if timestamps is None:
+        timestamps = np.arange(signal.shape[0])
+    if freq_bins is None:
+        freq_bins = signal.shape[0]
 
     if fwindow is None:
         winlength = np.floor(freq_bins / 4.0)

tftb/processing/postprocessing.py

@@ -11,7 +11,6 @@
 """
 
 import numpy as np
-from tftb.utils import init_default_args
 from tftb.processing.utils import integrate_2d
 
 
@@ -114,8 +113,10 @@ def ideal_tfr(iflaws, timestamps=None, n_fbins=None):
 :rtype:
     """
     ifrow, ifcol = iflaws.shape
-    timestamps, n_fbins = init_default_args(iflaws[0, :],
-            timestamps=timestamps, n_fbins=n_fbins)
+    if timestamps is None:
+        timestamps = np.arange(iflaws[0, :].shape[0])
+    if n_fbins is None:
+        n_fbins = iflaws[0, :].shape[0]
 
     tcol = timestamps.shape[0]
 

tftb/processing/reassigned.py

@@ -11,7 +11,6 @@
 import numpy as np
 import scipy.signal as ssig
 from tftb.processing.utils import derive_window
-from tftb.utils import init_default_args
 
 
 def pseudo_wigner_ville(signal, timestamps=None, n_fbins=None, fwindow=None):
@@ -29,8 +28,10 @@ def pseudo_wigner_ville(signal, timestamps=None, n_fbins=None, fwindow=None):
 :rtype:
     """
     xrow = signal.shape[0]
-    timestamps, n_fbins = init_default_args(signal, timestamps=timestamps,
-                                            n_fbins=n_fbins)
+    if timestamps is None:
+        timestamps = np.arange(signal.shape[0])
+    if n_fbins is None:
+        n_fbins = signal.shape[0]
     tcol = timestamps.shape[0]
 
     if fwindow is None:
@@ -107,8 +108,10 @@ def pseudo_margenau_hill(signal, timestamps=None, n_fbins=None, fwindow=None):
 :rtype:
     """
     xrow = signal.shape[0]
-    timestamps, n_fbins = init_default_args(signal, timestamps=timestamps,
-                                            n_fbins=n_fbins)
+    if timestamps is None:
+        timestamps = np.arange(signal.shape[0])
+    if n_fbins is None:
+        n_fbins = signal.shape[0]
     tcol = timestamps.shape[0]
 
     if fwindow is None:
@@ -179,8 +182,10 @@ def pseudo_page(signal, timestamps=None, n_fbins=None, fwindow=None):
 :return:
 :rtype:
     """
-    timestamps, n_fbins = init_default_args(signal, timestamps=timestamps,
-                                            n_fbins=n_fbins)
+    if timestamps is None:
+        timestamps = np.arange(signal.shape[0])
+    if n_fbins is None:
+        n_fbins = signal.shape[0]
     tcol = timestamps.shape[0]
 
     if fwindow is None:
@@ -252,8 +257,10 @@ def morlet_scalogram(signal, timestamps=None, n_fbins=None, tbp=0.25):
 :rtype:
     """
     xrow = signal.shape[0]
-    timestamps, n_fbins = init_default_args(signal, timestamps=timestamps,
-                                            n_fbins=n_fbins)
+    if timestamps is None:
+        timestamps = np.arange(signal.shape[0])
+    if n_fbins is None:
+        n_fbins = signal.shape[0]
     k = 0.001
     tcol = timestamps.shape[0]
     deltat = timestamps[1:] - timestamps[:-1]
@@ -371,8 +378,10 @@ def smoothed_pseudo_wigner_ville(signal, timestamps=None, n_fbins=None,
     """
     xrow = signal.shape[0]
 
-    timestamps, n_fbins = init_default_args(signal, timestamps=timestamps,
-                                            n_fbins=n_fbins)
+    if timestamps is None:
+        timestamps = np.arange(signal.shape[0])
+    if n_fbins is None:
+        n_fbins = signal.shape[0]
     if fwindow is None:
         hlength = np.floor(n_fbins / 4.0)
         hlength += 1 - (hlength % 2)
@@ -479,7 +488,8 @@ def spectrogram(signal, time_samples=None, n_fbins=None, window=None):
         time_samples = np.arange(signal.shape[0])
     elif np.unique(np.diff(time_samples)).shape[0] > 1:
         raise ValueError('Time instants must be regularly sampled.')
-    n_fbins = init_default_args(signal, n_fbins=n_fbins)[0]
+    if n_fbins is None:
+        n_fbins = signal.shape[0]
     if window is None:
         wlength = int(np.floor(signal.shape[0] / 4.0))
         wlength += 1 - np.remainder(wlength, 2)

tftb/utils.py

@@ -9,7 +9,6 @@
 """Miscellaneous utilities."""
 
 import numpy as np
-import warnings
 
 
 TYPE1 = ['pmh', 'rpmh', 'sp', 'rsp', 'ppage', 'rppag', 'mhs', 'rgab', 'mh',
@@ -38,31 +37,6 @@ def is_linear(x, decimals=5):
     return np.unique(derivative).shape[0] == 1
 
 
-def init_default_args(signal, **kwargs):
-    """Initialize default arguments for common time frequency representations.
-
-    :param x: signal, based on which default arguments are initialized.
-    :type x: numpy.ndarray
-    :return: tuple of default values
-    :rtype: tuple
-    """
-    # FIXME: Remove tftb.utils.init_default_args
-    # This function was used like __init__, no need for it now since most
-    # distributions have their own classes.
-    for varname, value in kwargs.items():
-        if "time" in varname:
-            if value is None:
-                kwargs[varname] = np.arange(signal.shape[0])
-        elif ("n_fbins" in varname) or ("freq_bins" in varname):
-            if value is None:
-                kwargs[varname] = signal.shape[0]
-            elif 2 ** nextpow2(value) != value:
-                msg = "For faster computations, n_fbins should be a power of 2."
-                warnings.warn(msg, UserWarning)
-
-    return list(kwargs.values())
-
-
 def izak(x):
     """Inverse Zak transform."""
     if x.ndim == 2:
@@ -128,7 +102,3 @@ def modulo(x, N):
     else:
         y = np.mod(np.real(x), N) + 1j * np.mod(np.imag(x), N)
     return y
-
-
-if __name__ == '__main__':
-    print(init_default_args(np.arange(10), timestamps=list(range(10)), n_fbins=3))