Please take a look at: Cutting of unused frequencies in specgram matplotlib
Here's a version of the above with different arguments illustrate their effects:
from pylab import *
from matplotlib import *
# 100, 200, and 400 Hz sine 'wave'
# Using more sample points
dt = 0.00005
t = arange(0.0, 20.000, dt)
s1 = sin(2*pi*100*t)
s2 = 2*sin(2*pi*400*t)
s3 = 2*sin(2*pi*200*t)
# create a transient "chirp"
mask = where(logical_and(t>10, t<12), 1.0, 0.0)
s2 = s2 * mask
# add some noise into the mix
nse = 0.01*randn(len(t))
x = s1 + s2 + +s3 + nse # the signal
#x = s1 + s2 + nse # the signal
# Longer window
NFFT = 2048 # the length of the windowing segments
Fs = int(1.0/dt) # the sampling frequency
# modified specgram()
def my_specgram(x, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none,
window=mlab.window_hanning, noverlap=128,
cmap=None, xextent=None, pad_to=None, sides='default',
scale_by_freq=None, minfreq = None, maxfreq = None, **kwargs):
"""
call signature::
specgram(x, NFFT=256, Fs=2, Fc=0, detrend=mlab.detrend_none,
window=mlab.window_hanning, noverlap=128,
cmap=None, xextent=None, pad_to=None, sides='default',
scale_by_freq=None, minfreq = None, maxfreq = None, **kwargs)
Compute a spectrogram of data in *x*. Data are split into
*NFFT* length segments and the PSD of each section is
computed. The windowing function *window* is applied to each
segment, and the amount of overlap of each segment is
specified with *noverlap*.
%(PSD)s
*Fc*: integer
The center frequency of *x* (defaults to 0), which offsets
the y extents of the plot to reflect the frequency range used
when a signal is acquired and then filtered and downsampled to
baseband.
*cmap*:
A :class:`matplotlib.cm.Colormap` instance; if *None* use
default determined by rc
*xextent*:
The image extent along the x-axis. xextent = (xmin,xmax)
The default is (0,max(bins)), where bins is the return
value from :func:`mlab.specgram`
*minfreq, maxfreq*
Limits y-axis. Both required
*kwargs*:
Additional kwargs are passed on to imshow which makes the
specgram image
Return value is (*Pxx*, *freqs*, *bins*, *im*):
- *bins* are the time points the spectrogram is calculated over
- *freqs* is an array of frequencies
- *Pxx* is a len(times) x len(freqs) array of power
- *im* is a :class:`matplotlib.image.AxesImage` instance
Note: If *x* is real (i.e. non-complex), only the positive
spectrum is shown. If *x* is complex, both positive and
negative parts of the spectrum are shown. This can be
overridden using the *sides* keyword argument.
**Example:**
.. plot:: mpl_examples/pylab_examples/specgram_demo.py
"""
#####################################
# modified axes.specgram() to limit
# the frequencies plotted
#####################################
# this will fail if there isn't a current axis in the global scope
ax = gca()
Pxx, freqs, bins = mlab.specgram(x, NFFT, Fs, detrend,
window, noverlap, pad_to, sides, scale_by_freq)
# modified here
#####################################
if minfreq is not None and maxfreq is not None:
Pxx = Pxx[(freqs >= minfreq) & (freqs <= maxfreq)]
freqs = freqs[(freqs >= minfreq) & (freqs <= maxfreq)]
#####################################
Z = 10. * np.log10(Pxx)
Z = np.flipud(Z)
if xextent is None: xextent = 0, np.amax(bins)
xmin, xmax = xextent
freqs += Fc
extent = xmin, xmax, freqs[0], freqs[-1]
im = ax.imshow(Z, cmap, extent=extent, **kwargs)
ax.axis('auto')
return Pxx, freqs, bins, im
# plot
ax1 = subplot(211)
plot(t, x)
subplot(212, sharex=ax1)
# Windowing+greater overlap + limiting bandwidth to plot:
# the minfreq and maxfreq args will limit the frequencies
Pxx, freqs, bins, im = my_specgram(x, NFFT=NFFT, Fs=Fs, noverlap=2000, window=numpy.kaiser(NFFT,1.0), cmap=cm.gist_heat, minfreq = 0, maxfreq = 1000)
show()
close()
