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authorjblum2008-10-10 21:13:33 +0000
committerjblum2008-10-10 21:13:33 +0000
commit3fc664ececbafa04ab5a117b418cb7b4fc0652c6 (patch)
treee083ddbe197729a6f3131fa1341afa952b38a3d9 /gr-wxgui/src
parent4bea843cfe50369eed9ac13d7c60e28214804845 (diff)
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check for samples before autoscale, avoids potential error condition
git-svn-id: http://gnuradio.org/svn/gnuradio/trunk@9767 221aa14e-8319-0410-a670-987f0aec2ac5
Diffstat (limited to 'gr-wxgui/src')
-rw-r--r--gr-wxgui/src/python/fft_window.py2
-rw-r--r--gr-wxgui/src/python/waterfall_window.py2
2 files changed, 4 insertions, 0 deletions
diff --git a/gr-wxgui/src/python/fft_window.py b/gr-wxgui/src/python/fft_window.py
index e7affb1b0..6e54aec87 100644
--- a/gr-wxgui/src/python/fft_window.py
+++ b/gr-wxgui/src/python/fft_window.py
@@ -149,6 +149,7 @@ class fft_window(wx.Panel, pubsub.pubsub, common.prop_setter):
#ensure y_per_div
if y_per_div not in DIV_LEVELS: y_per_div = DIV_LEVELS[0]
#setup
+ self.samples = list()
self.ext_controller = controller
self.real = real
self.fft_size = fft_size
@@ -197,6 +198,7 @@ class fft_window(wx.Panel, pubsub.pubsub, common.prop_setter):
Autoscale the fft plot to the last frame.
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)
#get the noise floor (averge the smallest samples)
diff --git a/gr-wxgui/src/python/waterfall_window.py b/gr-wxgui/src/python/waterfall_window.py
index 702f97099..f24b142a7 100644
--- a/gr-wxgui/src/python/waterfall_window.py
+++ b/gr-wxgui/src/python/waterfall_window.py
@@ -168,6 +168,7 @@ class waterfall_window(wx.Panel, pubsub.pubsub, common.prop_setter):
):
pubsub.pubsub.__init__(self)
#setup
+ self.samples = list()
self.ext_controller = controller
self.real = real
self.fft_size = fft_size
@@ -220,6 +221,7 @@ class waterfall_window(wx.Panel, pubsub.pubsub, common.prop_setter):
Set the dynamic range and reference level.
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)