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author | Josh Blum | 2009-09-02 14:18:28 -0700 |
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committer | Josh Blum | 2009-09-02 14:18:28 -0700 |
commit | a70c291e3cd4fc3d51f2eebb6b39cdb9d46862da (patch) | |
tree | 4daa6284894acfe3ee4f0b3d6fe543d46a569760 | |
parent | eb1d1b5cfb474b087d41337356efc0cdb7342f28 (diff) | |
download | gnuradio-a70c291e3cd4fc3d51f2eebb6b39cdb9d46862da.tar.gz gnuradio-a70c291e3cd4fc3d51f2eebb6b39cdb9d46862da.tar.bz2 gnuradio-a70c291e3cd4fc3d51f2eebb6b39cdb9d46862da.zip |
waterfall and fft use a common autoscale function
-rw-r--r-- | gr-wxgui/src/python/common.py | 25 | ||||
-rw-r--r-- | gr-wxgui/src/python/fft_window.py | 12 | ||||
-rw-r--r-- | gr-wxgui/src/python/waterfall_window.py | 12 |
3 files changed, 26 insertions, 23 deletions
diff --git a/gr-wxgui/src/python/common.py b/gr-wxgui/src/python/common.py index d555a1f05..9c97ce1ec 100644 --- a/gr-wxgui/src/python/common.py +++ b/gr-wxgui/src/python/common.py @@ -137,6 +137,25 @@ def get_min_max(samples): scale_factor = 3 mean = numpy.average(samples) rms = numpy.max([scale_factor*((numpy.sum((samples-mean)**2)/len(samples))**.5), .1]) - min = mean - rms - max = mean + rms - return min, max + min_val = mean - rms + max_val = mean + rms + return min_val, max_val + +def get_min_max_fft(fft_samps): + """ + Get the minimum and maximum bounds for an array of fft samples. + @param samples the array of real values + @return a tuple of min, max + """ + #get the peak level (max of the samples) + peak_level = numpy.max(fft_samps) + #separate noise samples + noise_samps = numpy.sort(fft_samps)[:len(fft_samps)/2] + #get the noise floor + noise_floor = numpy.average(noise_samps) + #get the noise deviation + noise_dev = numpy.std(noise_samps) + #determine the maximum and minimum levels + max_level = peak_level + min_level = noise_floor - abs(2*noise_dev) + return min_level, max_level diff --git a/gr-wxgui/src/python/fft_window.py b/gr-wxgui/src/python/fft_window.py index 4c575f1a6..237c8940c 100644 --- a/gr-wxgui/src/python/fft_window.py +++ b/gr-wxgui/src/python/fft_window.py @@ -254,17 +254,7 @@ class fft_window(wx.Panel, pubsub.pubsub): Set the dynamic range and reference level. """ if not len(self.samples): return - #get the peak level (max of the samples) - peak_level = numpy.max(self.samples) - #separate noise samples - noise_samps = numpy.sort(self.samples)[:len(self.samples)/2] - #get the noise floor - noise_floor = numpy.average(noise_samps) - #get the noise deviation - noise_dev = numpy.std(noise_samps) - #determine the maximum and minimum levels - max_level = peak_level - min_level = noise_floor - abs(2*noise_dev) + min_level, max_level = common.get_min_max_fft(self.samples) #set the range to a clean number of the dynamic range self[Y_PER_DIV_KEY] = common.get_clean_num(1+(max_level - min_level)/self[Y_DIVS_KEY]) #set the reference level to a multiple of y per div diff --git a/gr-wxgui/src/python/waterfall_window.py b/gr-wxgui/src/python/waterfall_window.py index c00992e14..28e67a830 100644 --- a/gr-wxgui/src/python/waterfall_window.py +++ b/gr-wxgui/src/python/waterfall_window.py @@ -237,16 +237,10 @@ class waterfall_window(wx.Panel, pubsub.pubsub): Does not affect the current data in the waterfall. """ if not len(self.samples): return - #get the peak level (max of the samples) - peak_level = numpy.max(self.samples) - #get the noise floor (averge the smallest samples) - noise_floor = numpy.average(numpy.sort(self.samples)[:len(self.samples)/4]) - #padding - noise_floor -= abs(noise_floor)*.5 - peak_level += abs(peak_level)*.1 + min_level, max_level = common.get_min_max_fft(self.samples) #set the range and level - self[REF_LEVEL_KEY] = peak_level - self[DYNAMIC_RANGE_KEY] = peak_level - noise_floor + self[REF_LEVEL_KEY] = max_level + self[DYNAMIC_RANGE_KEY] = max_level - min_level def handle_msg(self, msg): """ |