From 797994a11ac5ec6bee9ea01c092947d0c34115f1 Mon Sep 17 00:00:00 2001
From: Marcus Müller <marcus@hostalia.de>
Date: Fri, 24 Aug 2018 23:00:55 +0200
Subject: 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.
---
gr-filter/examples/interpolate.py | 36 +++++++++++++++---------------------
1 file changed, 15 insertions(+), 21 deletions(-)
(limited to 'gr-filter/examples/interpolate.py')
diff --git a/gr-filter/examples/interpolate.py b/gr-filter/examples/interpolate.py
index 1f1357211b..94c90c2b03 100644
--- a/gr-filter/examples/interpolate.py
+++ b/gr-filter/examples/interpolate.py
@@ -27,6 +27,7 @@ from gnuradio import gr
from gnuradio import blocks
from gnuradio import filter
import sys, time
+import numpy
try:
from gnuradio import analog
@@ -34,13 +35,6 @@ except ImportError:
sys.stderr.write("Error: Program requires gr-analog.\n")
sys.exit(1)
-try:
- import scipy
- from scipy import fftpack
-except ImportError:
- sys.stderr.write("Error: Program requires scipy (see: www.scipy.org).\n")
- sys.exit(1)
-
try:
import pylab
from pylab import mlab
@@ -82,7 +76,7 @@ class pfb_top_block(gr.top_block):
window=filter.firdes.WIN_BLACKMAN_hARRIS)
# Calculate the number of taps per channel for our own information
- tpc = scipy.ceil(float(len(self._taps)) / float(self._interp))
+ tpc = numpy.ceil(float(len(self._taps)) / float(self._interp))
print("Number of taps: ", len(self._taps))
print("Number of filters: ", self._interp)
print("Taps per channel: ", tpc)
@@ -136,7 +130,7 @@ def main():
Ne = 10000
fftlen = 8192
- winfunc = scipy.blackman
+ winfunc = numpy.blackman
# Plot input signal
fs = tb._fs
@@ -147,8 +141,8 @@ def main():
X,freq = mlab.psd(d, NFFT=fftlen, noverlap=fftlen / 4, Fs=fs,
window = lambda d: d*winfunc(fftlen),
scale_by_freq=True)
- X_in = 10.0*scipy.log10(abs(fftpack.fftshift(X)))
- f_in = scipy.arange(-fs / 2.0, fs / 2.0, fs / float(X_in.size))
+ X_in = 10.0*numpy.log10(abs(numpy.fft.fftshift(X)))
+ f_in = numpy.arange(-fs / 2.0, fs / 2.0, fs / float(X_in.size))
p1_f = sp1_f.plot(f_in, X_in, "b")
sp1_f.set_xlim([min(f_in), max(f_in)+1])
sp1_f.set_ylim([-200.0, 50.0])
@@ -161,8 +155,8 @@ def main():
Ts = 1.0 / fs
Tmax = len(d)*Ts
- t_in = scipy.arange(0, Tmax, Ts)
- x_in = scipy.array(d)
+ t_in = numpy.arange(0, Tmax, Ts)
+ x_in = numpy.array(d)
sp1_t = fig1.add_subplot(2, 1, 2)
p1_t = sp1_t.plot(t_in, x_in.real, "b-o")
#p1_t = sp1_t.plot(t_in, x_in.imag, "r-o")
@@ -181,8 +175,8 @@ def main():
X,freq = mlab.psd(d, NFFT=fftlen, noverlap=fftlen / 4, Fs=fs,
window = lambda d: d*winfunc(fftlen),
scale_by_freq=True)
- X_o = 10.0*scipy.log10(abs(fftpack.fftshift(X)))
- f_o = scipy.arange(-fs_int / 2.0, fs_int / 2.0, fs_int / float(X_o.size))
+ X_o = 10.0*numpy.log10(abs(numpy.fft.fftshift(X)))
+ f_o = numpy.arange(-fs_int / 2.0, fs_int / 2.0, fs_int / float(X_o.size))
p2_f = sp2_f.plot(f_o, X_o, "b")
sp2_f.set_xlim([min(f_o), max(f_o)+1])
sp2_f.set_ylim([-200.0, 50.0])
@@ -194,8 +188,8 @@ def main():
Ts_int = 1.0 / fs_int
Tmax = len(d)*Ts_int
- t_o = scipy.arange(0, Tmax, Ts_int)
- x_o1 = scipy.array(d)
+ t_o = numpy.arange(0, Tmax, Ts_int)
+ x_o1 = numpy.array(d)
sp2_t = fig2.add_subplot(2, 1, 2)
p2_t = sp2_t.plot(t_o, x_o1.real, "b-o")
#p2_t = sp2_t.plot(t_o, x_o.imag, "r-o")
@@ -214,8 +208,8 @@ def main():
X,freq = mlab.psd(d, NFFT=fftlen, noverlap=fftlen / 4, Fs=fs,
window = lambda d: d*winfunc(fftlen),
scale_by_freq=True)
- X_o = 10.0*scipy.log10(abs(fftpack.fftshift(X)))
- f_o = scipy.arange(-fs_aint / 2.0, fs_aint / 2.0, fs_aint / float(X_o.size))
+ X_o = 10.0*numpy.log10(abs(numpy.fft.fftshift(X)))
+ f_o = numpy.arange(-fs_aint / 2.0, fs_aint / 2.0, fs_aint / float(X_o.size))
p3_f = sp3_f.plot(f_o, X_o, "b")
sp3_f.set_xlim([min(f_o), max(f_o)+1])
sp3_f.set_ylim([-200.0, 50.0])
@@ -227,8 +221,8 @@ def main():
Ts_aint = 1.0 / fs_aint
Tmax = len(d)*Ts_aint
- t_o = scipy.arange(0, Tmax, Ts_aint)
- x_o2 = scipy.array(d)
+ t_o = numpy.arange(0, Tmax, Ts_aint)
+ x_o2 = numpy.array(d)
sp3_f = fig3.add_subplot(2, 1, 2)
p3_f = sp3_f.plot(t_o, x_o2.real, "b-o")
p3_f = sp3_f.plot(t_o, x_o1.real, "m-o")
--
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