1 files changed, 164 insertions, 0 deletions
diff --git a/gr-qtgui/apps/plot_base.py b/gr-qtgui/apps/plot_base.py
new file mode 100644
index 0000000000..52c888cff3
--- /dev/null
+++ b/gr-qtgui/apps/plot_base.py
@@ -0,0 +1,164 @@
+#!/usr/bin/env python
+#
+# Copyright 2013 Free Software Foundation, Inc.
+#
+# This file is part of GNU Radio
+#
+# GNU Radio is free software; you can redistribute it and/or modify
+# it under the terms of the GNU General Public License as published by
+# the Free Software Foundation; either version 3, or (at your option)
+# any later version.
+#
+# GNU Radio is distributed in the hope that it will be useful,
+# but WITHOUT ANY WARRANTY; without even the implied warranty of
+# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.  See the
+# GNU General Public License for more details.
+#
+# You should have received a copy of the GNU General Public License
+# along with GNU Radio; see the file COPYING.  If not, write to
+# the Free Software Foundation, Inc., 51 Franklin Street,
+# Boston, MA 02110-1301, USA.
+#
+
+from gnuradio import gr, blocks
+from gnuradio.eng_option import eng_option
+from optparse import OptionParser
+import os, sys
+
+try:
+    from gnuradio import qtgui
+    from PyQt4 import QtGui, QtCore
+    import sip
+except ImportError:
+    print "Error: Program requires PyQt4 and gr-qtgui."
+    sys.exit(1)
+
+try:
+    import scipy
+except ImportError:
+    print "Error: Scipy required (www.scipy.org)."
+    sys.exit(1)
+
+try:
+    from gnuradio.qtgui.plot_constellation_form import *
+    from gnuradio.qtgui.plot_psd_form import *
+    from gnuradio.qtgui.plot_spectrogram_form import *
+    from gnuradio.qtgui.plot_time_form import *
+    from gnuradio.qtgui.plot_time_raster_form import *
+except ImportError:
+    from plot_constellation_form import *
+    from plot_psd_form import *
+    from plot_spectrogram_form import *
+    from plot_time_form import *
+    from plot_time_raster_form import *
+
+def read_samples(filename, start, in_size, min_size, dtype, dtype_size):
+    # Read in_size number of samples from file
+    fhandle = open(filename, 'r')
+    fhandle.seek(start*dtype_size, 0)
+    data = scipy.fromfile(fhandle, dtype=dtype, count=in_size)
+    data_min = 1.1*data.min()
+    data_max = 1.1*data.max()
+    data = data.tolist()
+    fhandle.close()
+
+    if(min_size > 0):
+        if(len(data) < in_size):
+            print "Warning: read in {0} samples but asked for {1} samples.".format(
+                len(data), in_size)
+    else:
+        # If we have to, append 0's to create min_size samples of data
+        if(len(data) < min_size):
+            data += (min_size - len(data)) * [dtype(0)]
+
+
+    return data, data_min, data_max
+
+def read_samples_f(filename, start, in_size, min_size=0):
+    return read_samples(filename, start, in_size, min_size,
+                        scipy.float32, gr.sizeof_float)
+
+def read_samples_i(filename, start, in_size, min_size=0):
+    return read_samples(filename, start, in_size, min_size,
+                        scipy.int32, gr.sizeof_int)
+
+def read_samples_s(filename, start, in_size, min_size=0):
+    return read_samples(filename, start, in_size, min_size,
+                        scipy.int16, gr.sizeof_short)
+
+def read_samples_b(filename, start, in_size, min_size=0):
+    d,mn,mx = read_samples(filename, start, in_size, min_size,
+                           scipy.int8, gr.sizeof_char)
+
+    # Bit of a hack since we want to read the data as signed ints, but
+    # the gr.vector_source_b will only accept unsigned. We read in as
+    # signed, do our min/max and things on that, then convert here.
+    d = scipy.array(d, dtype=scipy.uint8).tolist()
+    return d,mn,mx
+
+def read_samples_c(filename, start, in_size, min_size=0):
+    # Complex samples are handled differently
+    fhandle = open(filename, 'r')
+    fhandle.seek(start*gr.sizeof_gr_complex, 0)
+    data = scipy.fromfile(fhandle, dtype=scipy.complex64, count=in_size)
+    data_min = 1.1*float(min(data.real.min(), data.imag.min()))
+    data_max = 1.1*float(max(data.real.max(), data.imag.max()))
+    data = data.tolist()
+    fhandle.close()
+
+    if(min_size > 0):
+        if(len(data) < in_size):
+            print "Warning: read in {0} samples but asked for {1} samples.".format(
+                len(data), in_size)
+    else:
+        # If we have to, append 0's to create min_size samples of data
+        if(len(data) < min_size):
+            data += (min_size - len(data)) * [complex(0,0)]
+
+    return data, data_min, data_max
+
+class source_ints_to_float(gr.hier_block2):
+    def __init__(self, data):
+	gr.hier_block2.__init__(self, "ints_to_floats",
+				gr.io_signature(0, 0, 0),
+				gr.io_signature(1, 1, gr.sizeof_float))
+        self.src = gr.vector_source_i(data)
+        self.cvt = blocks.int_to_float()
+        self.connect(self.src, self.cvt, self)
+
+    def set_data(self, data):
+        self.src.set_data(data)
+
+class source_shorts_to_float(gr.hier_block2):
+    def __init__(self, data):
+	gr.hier_block2.__init__(self, "shorts_to_floats",
+				gr.io_signature(0, 0, 0),
+				gr.io_signature(1, 1, gr.sizeof_float))
+        self.src = gr.vector_source_s(data)
+        self.cvt = blocks.short_to_float()
+        self.connect(self.src, self.cvt, self)
+
+    def set_data(self, data):
+        self.src.set_data(data)
+
+class source_chars_to_float(gr.hier_block2):
+    def __init__(self, data):
+	gr.hier_block2.__init__(self, "chars_to_floats",
+				gr.io_signature(0, 0, 0),
+				gr.io_signature(1, 1, gr.sizeof_float))
+        self.src = gr.vector_source_b(data)
+        self.cvt = blocks.char_to_float()
+        self.connect(self.src, self.cvt, self)
+
+    def set_data(self, data):
+        self.src.set_data(data)
+
+def find_max_nsamples(filelist):
+    # Find the smallest number of samples in all files and use that as
+    # a maximum value possible.
+    filesizes = []
+    for f in filelist:
+        if(os.path.exists(f)):
+            filesizes.append(os.path.getsize(f) / gr.sizeof_gr_complex)
+    max_nsamples = min(filesizes)
+    return max_nsamples