Esempio n. 1
0
from matplotlib.mathtext import MathTextParser
mathtext_parser = MathTextParser("Bitmap")
def mathtext_to_wxbitmap(s):
    ftimage, depth = mathtext_parser.parse(s, 150)
    return wx.BitmapFromBufferRGBA(
        ftimage.get_width(), ftimage.get_height(),
        ftimage.as_rgba_str())
############################################################

from pytfd import windows
from pytfd.distributions import *
from signals_test import signals

# Windows
T = 64
w = windows.hanning(T) # Rectangular window
P = windows.hanning(2 + 1)


def _abs(X):
    return abs(X)[X.shape[0]//2:]

distributions = [
    (r'STFT'                 , lambda x: _abs(stft(x, w))),
    (r'S-method'             , lambda x: _abs(sm(x, w, P))),
    (r'PWD'                 , lambda x: _abs(pwd(x, w))),
    (r'WD'                 , lambda x: _abs(wd(x))),
]

class CanvasFrame(wx.Frame):
    def __init__(self, parent, title):
Esempio n. 2
0
from __future__ import division
import pytfd.plot
from test_data1 import *
from pytfd.sm import sm
from pytfd import windows

#from enthought.mayavi import mlab as M


for i, T in enumerate([32, 64]):
    w = windows.rectangular(T) # Rectangular window
    P = windows.hanning(2 + 1)
    #P = array([0, 1, 0])
    delta1 = zeros(N)
    delta1[N//4] = 5
    delta2 = zeros(N)
    delta2[3*N//4] = 5
    y = sin(2*pi*10*t) + sin(2*pi*30*t) + delta1 + delta2
    Y_sm = sm(y, w, P)
    #pytfd.plot.contour(abs(Y_sm)[N//2:])
    #x, y = meshgrid(t, f)
    #x, y = mg rid[0:256, 0:256]
    #M.surf(x, y, abs(Y_sm))
    #M.surf(x, y, lambda x,y:sin(x**2 + y**2))
    #M.axes()
    #M.title('Demoing mlab.surf')
    figure()
    imshow(abs(Y_sm)[N//2:])
#     contour(t, f[:N//2], abs(Y_sm)[N//2:])
#     xlabel("Time")
#     ylabel("Frequency")
Esempio n. 3
0
def mathtext_to_wxbitmap(s):
    ftimage, depth = mathtext_parser.parse(s, 150)
    return wx.BitmapFromBufferRGBA(ftimage.get_width(), ftimage.get_height(),
                                   ftimage.as_rgba_str())


############################################################

from pytfd import windows
from pytfd.distributions import *
from signals_test import signals

# Windows
T = 64
w = windows.hanning(T)  # Rectangular window
P = windows.hanning(2 + 1)


def _abs(X):
    return abs(X)[X.shape[0] // 2:]


distributions = [
    (r'STFT', lambda x: _abs(stft(x, w))),
    (r'S-method', lambda x: _abs(sm(x, w, P))),
    (r'PWD', lambda x: _abs(pwd(x, w))),
    (r'WD', lambda x: _abs(wd(x))),
]