diff options
| author | Marcus Müller <marcus@hostalia.de> | 2018-08-24 23:00:55 +0200 |
|---|---|---|
| committer | Marcus Müller <marcus@hostalia.de> | 2018-11-02 22:15:53 +0100 |
| commit | 797994a11ac5ec6bee9ea01c092947d0c34115f1 (patch) | |
| tree | 7381f53008ba56e6b93398fa92be482d12da4f43 /gr-qtgui/apps/plot_base.py | |
| parent | e07751acc8424f4dd987f79c32dd247ed347902c (diff) | |
Replace scipy/pylab where numpy/pyplot is sufficient
This should reduce the number of times users are prompted to install
pylab || scipy when they'd actually get away with functionality fully
contained in numpy and matplotlib.
This only solves the obvious cases. There's some usage of `pylab.mlab`
that would need more than 20s of consideration.
Diffstat (limited to 'gr-qtgui/apps/plot_base.py')
| -rw-r--r-- | gr-qtgui/apps/plot_base.py | 20 |
1 files changed, 8 insertions, 12 deletions
diff --git a/gr-qtgui/apps/plot_base.py b/gr-qtgui/apps/plot_base.py index ce409000bd..46f85ab8f5 100644 --- a/gr-qtgui/apps/plot_base.py +++ b/gr-qtgui/apps/plot_base.py @@ -37,11 +37,7 @@ 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) +import numpy try: from gnuradio.qtgui.plot_constellation_form import * @@ -60,7 +56,7 @@ 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 = numpy.fromfile(fhandle, dtype=dtype, count=in_size) data_min = 1.1*data.min() data_max = 1.1*data.max() data = data.tolist() @@ -80,31 +76,31 @@ def read_samples(filename, start, in_size, min_size, dtype, dtype_size): 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) + numpy.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) + numpy.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) + numpy.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) + numpy.int8, gr.sizeof_char) # Bit of a hack since we want to read the data as signed ints, but # the blocks.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() + d = numpy.array(d, dtype=numpy.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 = numpy.fromfile(fhandle, dtype=numpy.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() |