Hello, I'm new to numpy, and I'm a stuck on my first real project with it. I am trying to take the rfft of a numpy array, like this:
my_rfft = numpy.fft.rfft(my_numpy_array)
and replace the amplitudes that can be obtained with:
my_amplitudes = numpy.abs(my_rfft)
with amplitudes from an arbitrary numpy array's rFFT, which is to then be converted back using numpy.fft.irfft . Alternately, some future plans will involve having to modify individual array element amplitudes directly based on other parameters. I would think that modifying and re-synthesizing signals using FFT is a fairly common use-case, but my attempts at Googling example code have been fruitless. I'm not sure if my rudimentary knowledge of FFT is failing me, or if I'm just not understanding how numpy represents and exposes the data, but I would really appreciate any help I can get :) /carl/
I am trying to take the rfft of a numpy array, like this:
my_rfft = numpy.fft.rfft(my_numpy_array)
and replace the amplitudes that can be obtained with:
my_amplitudes = numpy.abs(my_rfft)
with amplitudes from an arbitrary numpy array's rFFT, which is to then be converted back using numpy.fft.irfft . Alternately, some future plans will involve having to modify individual array element amplitudes directly based on other parameters. I would think that modifying and re-synthesizing signals using FFT is a fairly common use-case, but my attempts at Googling example code have been fruitless.
I have FFT transform filter in my tidal analysis package. See http://sourceforge.net/apps/mediawiki/tappy/index.php?title=CompareTidalFilt... for a comparison and short description. See my function below. My earlier self made some poor variable name choices. The 'low_bound' variable is actually where frequencies greater are set to zero ('factor[freq > low_bound] = 0.0'), then factor is ramped from 0 at 'low_bound' to 1 at 'high_bound'. To filter out tidal signals if your water elevations are hourly then 'low_bound' = 1/30.0 and 'high_bound' = 1/40.0. Having this gradual change in the frequency domain rather than an abrupt change makes a better filter. def fft_lowpass(nelevation, low_bound, high_bound): """ Performs a low pass filter on the nelevation series. low_bound and high_bound specifies the boundary of the filter. """ import numpy.fft as F if len(nelevation) % 2: result = F.rfft(nelevation, len(nelevation)) else: result = F.rfft(nelevation) freq = F.fftfreq(len(nelevation))[:len(nelevation)/2] factor = np.ones_like(result) factor[freq > low_bound] = 0.0 sl = np.logical_and(high_bound < freq, freq < low_bound) a = factor[sl] # Create float array of required length and reverse a = np.arange(len(a) + 2).astype(float)[::-1] # Ramp from 1 to 0 exclusive a = (a/a[0])[1:-1] # Insert ramp into factor factor[sl] = a result = result * factor print 'result=', len(result) relevation = F.irfft(result, len(nelevation)) print 'result=', len(relevation) return relevation Kindest regards, Tim
Hi Tim, Brilliant! Many thanks... I think this is exactly what I need, I owe you a beer (or other beverage of your choice). I'm now going to lock myself in the basement until I can work out an implementation of this for my use-case :) /Carl On Tue, Sep 3, 2013 at 9:05 PM, Cera, Tim <tim@cerazone.net> wrote:
I am trying to take the rfft of a numpy array, like this:
my_rfft = numpy.fft.rfft(my_numpy_array)
and replace the amplitudes that can be obtained with:
my_amplitudes = numpy.abs(my_rfft)
with amplitudes from an arbitrary numpy array's rFFT, which is to then be converted back using numpy.fft.irfft . Alternately, some future plans will involve having to modify individual array element amplitudes directly based on other parameters. I would think that modifying and re-synthesizing signals using FFT is a fairly common use-case, but my attempts at Googling example code have been fruitless.
I have FFT transform filter in my tidal analysis package. See
http://sourceforge.net/apps/mediawiki/tappy/index.php?title=CompareTidalFilt... for a comparison and short description.
See my function below. My earlier self made some poor variable name choices. The 'low_bound' variable is actually where frequencies greater are set to zero ('factor[freq > low_bound] = 0.0'), then factor is ramped from 0 at 'low_bound' to 1 at 'high_bound'. To filter out tidal signals if your water elevations are hourly then 'low_bound' = 1/30.0 and 'high_bound' = 1/40.0. Having this gradual change in the frequency domain rather than an abrupt change makes a better filter.
def fft_lowpass(nelevation, low_bound, high_bound): """ Performs a low pass filter on the nelevation series. low_bound and high_bound specifies the boundary of the filter. """ import numpy.fft as F if len(nelevation) % 2: result = F.rfft(nelevation, len(nelevation)) else: result = F.rfft(nelevation) freq = F.fftfreq(len(nelevation))[:len(nelevation)/2] factor = np.ones_like(result) factor[freq > low_bound] = 0.0
sl = np.logical_and(high_bound < freq, freq < low_bound)
a = factor[sl] # Create float array of required length and reverse a = np.arange(len(a) + 2).astype(float)[::-1]
# Ramp from 1 to 0 exclusive a = (a/a[0])[1:-1]
# Insert ramp into factor factor[sl] = a
result = result * factor print 'result=', len(result) relevation = F.irfft(result, len(nelevation)) print 'result=', len(relevation) return relevation
Kindest regards, Tim _______________________________________________ NumPy-Discussion mailing list NumPy-Discussion@scipy.org http://mail.scipy.org/mailman/listinfo/numpy-discussion
Hi experts! I wanna use networkx.has_path(). This is applies, necesary, to a networkx graph. I have a adjacency matrix of a undirected graph (M, wich is a numpy matrix (array of N x N elements)). How can I do for use M in networkx.has_path()? If I must transform M into a networkx graph: how can I do that? Waiting for your answers. Thanks a lot!! José Luis
On Fri, Sep 6, 2013 at 3:45 PM, Josè Luis Mietta < joseluismietta@yahoo.com.ar> wrote:
Hi experts!
I wanna use networkx.has_path(). This is applies, necesary, to a networkx
graph. I have a adjacency matrix of a undirected graph (M, wich is a numpy matrix (array of N x N elements)).
How can I do for use M in networkx.has_path()?
If I must transform M into a networkx graph: how can I do that?
networkx has some nice, searchable documentation. http://networkx.github.io/documentation/latest/reference/generated/networkx.... And its own mailing list. https://groups.google.com/forum/#!forum/networkx-discuss -- Robert Kern
participants (4)
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Carl Canuck -
Cera, Tim -
Josè Luis Mietta -
Robert Kern