From a70c291e3cd4fc3d51f2eebb6b39cdb9d46862da Mon Sep 17 00:00:00 2001
From: Josh Blum
Date: Wed, 2 Sep 2009 14:18:28 -0700
Subject: waterfall and fft use a common autoscale function

---
 gr-wxgui/src/python/common.py           | 25 ++++++++++++++++++++++---
 gr-wxgui/src/python/fft_window.py       | 12 +-----------
 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):
 		"""
-- 
cgit