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test_sigmath.py
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test_sigmath.py
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import unittest
from sigmath import *
from qamwrapper import QAMWrapper, QAMLayer
from stackcall import StackCall
import os
import matplotlib
## Run this to show an example of all plot tools
class plot(unittest.TestCase):
def setUp(self):
pass
def getQam(self):
samples = (2 ** 9) * 4 + 2
msg_bits = np.random.randint(0, 2, samples)
custom = QAMWrapper(64) # This uses the default constructor
modrf = custom.mod(msg_bits)
return modrf
def getWave(self):
samples = int(1E3)
fsdown = int(12E3)
hz = 1E3
data_orig = tone_gen(samples, fsdown, hz)
return (data_orig, fsdown)
def getRfUpTuple(self):
data_orig, fsdown = self.getWave()
# modup = interp6(data_orig)
# fsup = fsdown*6
return (data_orig, fsdown)
def testNplotSpy(self):
x = 6
y = 45
H = np.zeros((x, y))
for i in range(x):
for j in range(y):
H[i,j] = np.random.random(1)>0.5
fig = nplotspy(H, s_("random", x, ",", y, "size"))
self.assertTrue(type(fig) is matplotlib.figure.Figure)
def testDots(self):
(rf, fs) = self.getWave()
fig = nplotdots(rf[0:800], "Dots real time domain")
self.assertTrue(type(fig) is matplotlib.figure.Figure)
def testQam(self):
(rf, fs) = self.getRfUpTuple()
qam = self.getQam()
fig = nplotqam(qam, "Qam")
self.assertTrue(type(fig) is matplotlib.figure.Figure)
# nplotqam(interp6(qam)[9:], "qam up by 6")
def tearDown(self):
pass
# print "teardown "
def testFFT(self):
(rf, fs) = self.getWave()
extend = np.concatenate((rf,np.zeros(10000)))
figold = nplotfftold(extend, "OLD style of fft pre, 2017")
fig = nplotfft(extend, fs, "new style (still not semilogy)")
self.assertTrue(type(fig) is matplotlib.figure.Figure)
def testHist(self):
bins = np.random.normal(0,1000,25000)
fig = nplothist(bins, "25k points of np normal in (0,1000) into 55 bins", 55)
self.assertTrue(type(fig) is matplotlib.figure.Figure)
def testBer(self):
bers = []
ebn0s = []
titles = []
ber = [0.158368318809598, 0.130644488522829, 0.103759095953406, 0.0786496035251426, 0.0562819519765415,
0.037506128358926, 0.0228784075610853, 0.0125008180407376, 0.00595386714777866, 0.00238829078093281,
0.000772674815378444, 0.000190907774075993, 3.36272284196175e-05, 3.87210821552204e-06]
ebn0 = [-3, -2, -1, 0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
title = "theoretical"
# first set
bers.append(ber)
ebn0s.append(ebn0)
titles.append(title)
# second set
bers.append([0.144, 0.116, 0.098, 0.076, 0.058, 0.035, 0.021, 0.009, 0.004, 0.002, 0.002, 0, 0, 0])
ebn0s.append(ebn0) # same as before
titles.append("j random sim run")
fig = nplotber(bers, ebn0s, titles, "Lookup table of theoretical BPSK Bit Error Rates")
self.assertTrue(type(fig) is matplotlib.figure.Figure)
def testRainbow(self):
sz = 314*2
arguments = range(0,sz)
arguments = np.array(arguments)/44.0
fs = int(2*np.pi*100)
sinrf = np.exp(1j*arguments)
nplot(np.imag(sinrf), "Hold On")
plt.hold(True)
for x in drange(-0.9, 1.9, 0.05):
shift = tone_gen(sz, fs, x)
shifted = shift * sinrf
nplot(np.imag(shifted), newfig=False)
def testMulti(self):
liny = [1,2,3,4,5,6]
blueline = [1E1, 1E2, 1E3, 1E4, 1E5, 1E6]
greenline = [1,2,3,4,5,6]
xvals = [blueline,greenline]
yvals = [liny,liny]
leg = ["Exponential (blue)", "Linear (green)"]
xlabel = "text for normies"
ylabel = "text the funny way"
title = "semilogy view of exponential vs linear values"
semilog = True
fig1 = nplotmulti(xvals,yvals,leg,xlabel,ylabel,title,semilog)
self.assertTrue(type(fig1) is matplotlib.figure.Figure)
fig2 = nplotmulti(xvals, yvals, leg, xlabel, ylabel, "Linear view of exponential vs linear values", False)
self.assertTrue(type(fig2) is matplotlib.figure.Figure)
def testAngle(self):
chunks = 9
target = np.pi
for i in range(chunks):
rad = i*(target/(chunks-1))
nplotangle(rad, s_("Theta [0,", target, "] in ", chunks, " lines "), False)
fig = nplotangle(3*np.pi/4, "3pi/4")
self.assertTrue(type(fig) is matplotlib.figure.Figure)
def testResponse(self):
fs = int(6E9)
cutoff = 6.654321E7
brx, arx = butter_lowpass(cutoff, fs, order=10)
self.assertEqual(len(brx),len(arx))
taps = len(brx)
title = s_('Butterworth lowpass 10th order\ncutoff:',cutoff,'sample rate:', fs, 'taps:',taps)
fig = nplotresponse(brx, arx, 'frequency', title, fs, cutoff)
self.assertTrue(type(fig) is matplotlib.figure.Figure)
def testFFTPeaks(self):
(rf, fs) = self.getWave()
extend = np.concatenate((rf, np.zeros(10000)))
fig = nplotfft(extend, fs, "FFT with hz annotations at peaks", peaks=1, peaksHzSeparation=12)
self.assertTrue(type(fig) is matplotlib.figure.Figure)
def testPlotText(self):
str = "A quick brown\nfox jumps\nover the lazy dog"
names = ["ha", "sd", "ss", "xx"]
for i in range(3):
plt.subplot(2, 2, i + 1)
nplotangle(1.1 * i, names[i], False)
nplottext(str, plt.subplot(2,2,4))
nplottext("free standing\ntext")
@classmethod
def tearDownClass(cls):
print "Showing all figures"
nplotshow()
class working(unittest.TestCase):
def testTypicalXcr(self):
a1 = np.exp(1j*3.0)
a2 = np.exp(1j*2.8)
s1 = [a1]
packet = np.array([0, a2, 0])
# hard way
xcr = np.correlate(packet, s1, 'full')
absxcr = abs(xcr)
xcrmax, xcridx = sig_max(absxcr)
peaksample = xcr[xcridx]
packetangle = np.angle(peaksample)
expected = np.angle(a2)-np.angle(a1)
# sanity check
self.assertAlmostEqual(packetangle, expected, 7, "Sanity check failed, one of broke: math, sig_max(), np.correlate()")
# easy way
idx, xcrpk, ang = typical_xcorr(packet, s1)
self.assertAlmostEqual(ang, expected, 7, "typical_xcorr failed to get angle correct")
self.assertAlmostEqual(idx, 1, 7, "typical_xcorr failed to get idx correct")
def testComplexRawMultiFile(self):
samples = tone_gen(100, 100, 14.5)
f1 = '/tmp/tcrm1.raw'
f2 = '/tmp/tcrm2.raw'
f3 = '/tmp/tcrm3.raw'
save_rf_grc(f1, samples)
dumpfile = open(f2, 'w')
for s in samples:
dumpfile.write(complex_to_raw(s))
dumpfile.close()
dumpfile3 = open(f3, 'w')
ms = complex_to_raw_multi(samples)
dumpfile3.write(ms)
dumpfile3.close()
def testComplexRawMultiSimple(self):
samples = tone_gen(2, 100, 14.5)
o1 = ''
for s in samples:
o1 += complex_to_raw(s)
# print_rose(o1)
o2 = complex_to_raw_multi(samples)
# print_rose(o2)
self.assertEqual(o1, o2, "complex_to_raw_multi is wrong")
pass
def testSaveGrc(self):
samples = np.arange(0, 1, 0.1, dtype=np.complex128)
fname = '/tmp/sigmathtest1.raw'
save_rf_grc(fname, samples)
readback = read_rf_grc(fname)
self.assertTrue(np.allclose(readback, samples))
readback2 = read_rf_grc(fname, 4)
self.assertTrue(np.allclose(readback2, samples[0:4]))
readback3 = read_rf_grc(fname, 40) # too long
self.assertTrue(np.allclose(readback3, samples))
## \test Test if unique_matrix_pair works and if it respects upper triangular flag
def testUniquePair(self):
pairs = unique_matrix_pair(3)
expected = [(0,1),(0,2),(1,2)]
for e in expected:
self.assertTrue(e in pairs, "unique_matrix_pair(3) not correct")
# print pairs
# for x in pairs:
# print x
pairslt = unique_matrix_pair(3, True)
self.assertNotEqual(pairs, pairslt, "unique_matrix_pair() not respecting lower triangular flag")
for x in pairslt:
# print x
p1 = x[0]
p2 = x[1]
self.assertTrue((p2,p1) in pairs, "lt not found in ut version of unique_matrix_pair()")
## \test
# Test if sigfdelay of 0.0 and 1.0 are valid, does not test center
def testSigfdelay(self):
signal = np.array([0.0, 1.0, 0.75, 0.5, 0.25, 0.0])
res1 = sigfdelay(signal, 0.0)
self.assertEqual(list(signal), list(res1), "fdelay of 0.0 is not idential to input")
res2 = sigfdelay(signal, 1.0)
res2_expected = np.concatenate(([0],res2[1:]))
self.assertEqual(list(res2), list(res2_expected), "fdelay of 1.0 is not identical to input shifted by 1 sample")
# indices = range(len(signal))
# nplotmulti([signal, res2], [indices, indices], ['Orig', 'resampled'], 'samples in', 'samples out', title='resampled', newfig=True)
# nplotshow()
## \test tone_gen() should work for fractional hz or something
def testToneGen(self):
count = int(12E3)
fs = int(26E6)
res = tone_gen(count, fs, 06.0E6 + 1 * 5000)
# seems like a trivial test, but previous tone_gen fails this
self.assertEqual(len(res), count)
## \test Test if sig_lin_interp() works
def test_lin_interp_baked(self):
self.assertEquals(sig_lin_interp(1, 2, 1.0), 2.0)
self.assertEquals(sig_lin_interp(0, 100, 0.5), 50.)
## \test Test, circle_shift_peak() should always circle shifts correctly
def test_phase_recovery(self):
# V is right most sample
t1 = [3972.0134951819764, 5743.9865218806526, 6641.4896699904775, 6527.8862210980451, 5420.4712704794019,
3487.8387056246884, 1024.2143151752171, 1595.3374202337948]
# V is center
t2 = [6452.3443262904584, 5242.6348373616111, 3234.7817190476057, 734.46240737790356, 1877.6721478967816,
4203.9481397945883, 5890.2111362934529, 6679.7428821885542]
# V is right of center
t3 = [6419.8612069240753, 6691.1477874040193, 5943.7677726574584, 4291.5029949128266, 1985.8957887642405,
622.04604903354812, 3135.2870147274548, 5171.2089538766695]
# V is flat, and max is split across RL boundary
t4 = [5546.2553321930354, 3672.018754587069, 1238.7506085281454, 1383.106088376979, 3794.3974212146222,
5628.0261429706143, 6604.8389026433606, 6576.1248124024023]
t1ideal = np.roll(t1,1)
expectedmax = [3,3,4,3]
for i in range(4):
series = [t1, t2, t3, t4][i]
(rolled, shift) = circle_shift_peak(series)
(maxval,maxidx) = sig_max(rolled)
assert maxidx == expectedmax[i]
for i in range(8):
t1shift = np.roll(t1, i)
(rolled, shift) = circle_shift_peak(t1shift)
self.assertItemsEqual(t1ideal, rolled)
## \test Test polyfit_peak_climb(), bakes in some values
def test_interp_peak_climb(self):
t1 = [3972.0134951819764, 5743.9865218806526, 6641.4896699904775, 6527.8862210980451, 5420.4712704794019,
3487.8387056246884, 1024.2143151752171, 1595.3374202337948]
t2 = [6452.3443262904584, 5242.6348373616111, 3234.7817190476057, 734.46240737790356, 1877.6721478967816,
4203.9481397945883, 5890.2111362934529, 6679.7428821885542]
t3 = [6419.8612069240753, 6691.1477874040193, 5943.7677726574584, 4291.5029949128266, 1985.8957887642405,
622.04604903354812, 3135.2870147274548, 5171.2089538766695]
t4 = [5546.2553321930354, 3672.018754587069, 1238.7506085281454, 1383.106088376979, 3794.3974212146222,
5628.0261429706143, 6604.8389026433606, 6576.1248124024023]
showplot = False
order = 4
expected = [3.39004538, 3.2796452, 3.76305884, 3.47221838]
for i in range(4):
series = [t1, t2, t3, t4][i]
(rolled, shift) = circle_shift_peak(series)
# nplotfigure()
max_x = polyfit_peak_climb(rolled, order, showplot)
# self.assertAlmostEqual(max_x, expected[i], 4)
# print "max_x was", max_x
if showplot:
nplotshow()
## \test Test tone_gen against Matlab example
def test_tone_gen(self):
# matlab output for: freq_shift(ones(1,20),10,1)
expected = [ 1.0000 + 0.0000j,
0.8090 + 0.5878j,
0.3090 + 0.9511j,
-0.3090 + 0.9511j,
-0.8090 + 0.5878j,
-1.0000 + 0.0000j,
-0.8090 - 0.5878j,
-0.3090 - 0.9511j,
0.3090 - 0.9511j,
0.8090 - 0.5878j,
1.0000 - 0.0000j,
0.8090 + 0.5878j,
0.3090 + 0.9511j,
-0.3090 + 0.9511j,
-0.8090 + 0.5878j,
-1.0000 + 0.0000j,
-0.8090 - 0.5878j,
-0.3090 - 0.9511j,
0.3090 - 0.9511j,
0.8090 - 0.5878j]
generated = tone_gen(20,10,1).tolist()
self.assertEqual(len(generated), len(expected))
places = 3
for i in range(len(generated)):
self.assertAlmostEqual(np.real(expected[i]), np.real(generated[i]), places)
self.assertAlmostEqual(np.imag(expected[i]), np.imag(generated[i]), places)
## \test Test that sig_fft works
def testSigFft(self):
arguments = np.array(range(0,314*2))*2.0
fs = 2*np.pi*100 # aka 628
swave = np.exp(1j*arguments)
(fdata, freqs) = sig_fft(swave, fs)
absdata = abs(fdata)
m,idx = sig_max(absdata)
hz = freqs[idx]
self.assertAlmostEqual(200, hz, 0)
## \test Test that sif_fft with zero padding works
def testSigFftStretch(self):
arguments = np.array(range(0,314*2))*2.0
fs = 2*np.pi*100 # aka 628
swave = np.exp(1j*arguments)
swave = np.append(swave, [0]*10000)
(fdata, freqs) = sig_fft(swave, fs)
absdata = abs(fdata)
m,idx = sig_max(absdata)
hz = freqs[idx]
self.assertAlmostEqual(200, hz, 3)
## \test Test that complex_to_raw() and raw_to_complex() convert correctly
def testConversionsSingles(self):
cnt = 1000
rnd = np.random.random(cnt) + np.random.random(cnt)*1j
bytes = ""
for i in range(cnt):
bytes = bytes + complex_to_raw(rnd[i])
# print "made bytes:", len(bytes)
self.assertEquals(len(bytes) % 8, 0)
rndout = []
for i in range(0, len(bytes), 8):
gonuse = bytes[i:i+8]
# print len(gonuse)
# print repr(gonuse)
rndout.append(raw_to_complex(gonuse))
self.assertEquals(len(rnd), len(rndout))
for i in range(len(rnd)):
self.assertAlmostEqual(rnd[i], rndout[i], 6)
## \test Test that raw_to_complex_multi() works
def testConversionsMulti(self):
cnt = 1000
rnd = np.random.random(cnt) + np.random.random(cnt) * 1j
bytes = ""
for i in range(cnt):
bytes = bytes + complex_to_raw(rnd[i])
# print "made bytes:", len(bytes)
self.assertEquals(len(bytes) % 8, 0)
rndout = raw_to_complex_multi(bytes)
self.assertEquals(len(rnd), len(rndout))
for i in range(len(rnd)):
self.assertAlmostEqual(rnd[i], rndout[i], 6)
# ## \test Test sig_sha256 wrapper and sig_sha256_matrix() (that marshal matrices into a standard format) work
# def testSigSha(self):
# empty = sig_sha256("")
# # test basics, https://en.wikipedia.org/wiki/SHA-2
# self.assertEqual(empty, "\xe3\xb0\xc4\x42\x98\xfc\x1c\x14\x9a\xfb\xf4\xc8\x99\x6f\xb9\x24\x27\xae\x41\xe4\x64\x9b\x93\x4c\xa4\x95\x99\x1b\x78\x52\xb8\x55", "empty string not correct")
# H1 = np.eye(42, dtype=np.int16)
# sha1 = sig_sha256(np.int16(H1).tolist())
# sha2 = sig_sha256_matrix(H1)
# self.assertEqual(sha1, sha2, "sig_sha256_matrix not converting right")
# sha3 = sig_sha256_matrix(np.eye(42, dtype=np.float64))
# self.assertEqual(sha1, sha3, "not correct when eye() generated with float64")
# H2 = np.double(H1)
# sha4 = sig_sha256_matrix(H2)
# self.assertEqual(sha4, sha1, "failed after explicit cast to double")
# H1sparse = scipy.sparse.eye(42)
# sha5 = sig_sha256_sparse_matrix(H1sparse)
# self.assertEqual(sha5, sha1, "failed after eye was generated with sparse")
# H1sparseconv = scipy.sparse.bsr_matrix(H1)
# sha6 = sig_sha256_sparse_matrix(H1sparseconv)
# self.assertEqual(sha6, sha1, "failed after dense eye was made sparse")
## \test Test that get_rose() and reverse_rose() work
def testRose(self):
data = rand_string_ascii(15)
rose = get_rose(data)
reloaded = reverse_rose(rose)
self.assertEqual(reloaded, data)
class PrintSyntaxSugar(unittest.TestCase):
def testPathloss(self):
fc = 2000000000
distance = 2355.55
ch_Type = 'a'
htx = 10
hrx = 2
corr_fact = 'atnt'
mod = 'mod'
res = o_PL_IEEE80216d(fc,distance,ch_Type,htx,hrx,corr_fact,mod)
# print "res",res
def testSimpleSnr(self):
fc = 915E6
fc = 2E9
distance = 1500
distance = 184
bw = 20E6
mw = 1000
snr = simple_snr(bw,mw,fc,distance)
# print "snr", snr
def test_basic(self):
capture = StringIO.StringIO()
print >>capture, 'Second line.'
out = s_('Second line.')
self.assertEqual(capture.getvalue()[:-1], out)
def test_none(self):
capture = StringIO.StringIO()
print >>capture, None
out = s_(None)
self.assertEqual(capture.getvalue()[:-1], out)
def test_afew(self):
capture = StringIO.StringIO()
print >>capture, 1, '2', 3
out = s_(1, '2', 3)
self.assertEqual(capture.getvalue()[:-1], out)
def test_list(self):
capture = StringIO.StringIO()
print >>capture, [3,4,'a'], 'a', 3.14, capture
out = s_([3,4,'a'], 'a', 3.14, capture)
self.assertEqual(capture.getvalue()[:-1], out)
def testDrange(self):
d1 = []
for x in drange(-0.2,0.2,0.1):
d1.append(x)
d2 = []
for x in drange_DO_NOT_USE(-0.2,0.2,0.1):
d2.append(x)
d3 = []
for x in drange_DO_NOT_USE2(-0.2,0.2,0.1):
d3.append(x)
builtin = []
for x in [i/10. for i in range(-2,2)]:
builtin.append(x)
# print [i/10. for i in range(-2,2)]
# print drange(-0.2,0.2,0.1)
# print d3
# print frange3(-0.2,0.2,0.1)
# print d1
# print d2
# print d3
# print builtin
self.assertListEqual(builtin, d1, "drange does not match built in range")
self.assertEqual(d1, builtin)
# self.assertNotEqual(builtin, d2, "one I thought was bad actually was good")
self.assertNotEqual(builtin, d3, "one I thought was bad actually was good")
def tesxJustPrint(self):
ranges = [[-0.2,0.2,0.1],[0, 0.1, 0.01]]
for rr in ranges:
# rr[0], rr[1], rr[2]
print "starting on ", rr[0], rr[1], rr[2]
print "#" * 15
d1 = []
for x in drange(rr[0], rr[1], rr[2]):
d1.append(x)
d2 = []
for x in drange_DO_NOT_USE(rr[0], rr[1], rr[2]):
d2.append(x)
d3 = []
for x in drange_DO_NOT_USE2(rr[0], rr[1], rr[2]):
d3.append(x)
d4 = []
for x in np.arange(rr[0],rr[1],rr[2]):
d4.append(x)
print d1
print d2
print d3
print d4
print ""
print ""
#
# self.assertListEqual(builtin, d1, "drange does not match built in range")
# self.assertEqual(d1, builtin)
#
# # self.assertNotEqual(builtin, d2, "one I thought was bad actually was good")
# self.assertNotEqual(builtin, d3, "one I thought was bad actually was good")
def tesxInterpfft(self):
# force octave server
# sigmath_octave_use_client()
arguments = range(0,314*2)
arguments = np.array(arguments)*10.0
sinrf = np.exp(1j*arguments*-1)
nplotfft(sinrf)
modup = interpn(sinrf, 3, 1, 0.333)
nplotfft(modup)
# nplot(np.imag(sinrf))
# nplot(np.imag(sinrf))
nplotshow()
def tesxSigDiff(self):
s = "hello"
for i in range(120, 130):
s += chr(i)
sig_diff(s, s[0:8])
s1 = ""
for i in range(0, 256):
s1 += chr(i)
sig_diff(s1, s1[0:78])
def tesxSigMax(self):
count = 10000000
big = abs(np.random.rand(count)*1j)
big[700000] = 99
biglist = list(big)
a = time.time()
mx, idx = sig_max(big)
b = time.time()
print "found max in", b-a
self.assertEqual(idx, 700000)
self.assertEqual(mx, 99)
a = time.time()
mx, idx = sig_max(biglist)
b = time.time()
print "found max in", b-a
self.assertEqual(idx, 700000)
self.assertEqual(mx, 99)
# -----------------
a = time.time()
mx, idx = sig_max2(big)
b = time.time()
print "found max in", b-a
self.assertEqual(idx, 700000)
self.assertEqual(mx, 99)
a = time.time()
mx, idx = sig_max2(biglist)
b = time.time()
print "found max in", b-a
self.assertEqual(idx, 700000)
self.assertEqual(mx, 99)
# -----------------
# a = time.time()
# mx, idx = sig_max3(big)
# b = time.time()
# print "found max in", b-a
# self.assertEqual(idx, 700000)
# self.assertEqual(mx, 99)
a = time.time()
mx, idx = sig_max3(biglist)
b = time.time()
print "found max in", b-a
self.assertEqual(idx, 700000)
self.assertEqual(mx, 99)
# -----------------
# a = time.time()
# mx, idx = sig_max4(big)
# b = time.time()
# print "found max in", b-a
# self.assertEqual(idx, 700000)
# self.assertEqual(mx, 99)
a = time.time()
mx, idx = sig_max4(biglist)
b = time.time()
print "found max in", b-a
self.assertEqual(idx, 700000)
self.assertEqual(mx, 99)
def tesxSigChannel(self):
mod = QAMWrapper(64)
ideal_bits = str_to_bits(rand_string_ascii(400))
signal = mod.mod(ideal_bits)
# snr = 6
nplotqam(signal)
for snr in range(15, 40, 5):
noisy = sig_awgn(signal, snr)
nplotqam(noisy,str(snr))
nplotshow()
pass
# requries 'octave_server.py' to run in a separate process
def testOctaveClient(self):
c = get_octave_via_server()
# push and pull an eye matrix
c.eval('a = eye(3)')
aout = c.pull('a')
self.assertEqual(aout.tolist(), np.eye(3).tolist())
def testSaveLoad(self):
path = 'tmp/test_sigmath_t1.npz'
try:
os.remove(path)
except OSError:
pass
H = np.eye(4200, dtype=np.int16)
sha1 = sig_sha256_matrix(H)
Hsparse = scipy.sparse.bsr_matrix(H, dtype=np.int16)
save_sparse_csr(path, Hsparse)
time.sleep(0.001)
Hloaded = load_sparse_csr(path)
self.assertEqual(sig_sha256_sparse_matrix(Hsparse), sig_sha256_sparse_matrix(Hloaded))
## some junk for sig_peaks()
class proto(unittest.TestCase):
## \test Test str_to_bits() and str_to_bits_cython() identical
def test_str_to_bits_cython(self):
s = rand_string_ascii(1000)
# a = time.time()
resold = str_to_bits(s)
# print "ran in ", time.time()-a
# b = time.time()
res = str_to_bits_cython(s)
# print "ran in ", time.time()-b
self.assertEqual(list(res), list(resold))
def testSave(self):
data = [1+0j, 0.5+0.5j, 0.1, + 0.9j]
nplotqam(data)
plt.ylim([-1,1])
plt.xlim([-1,1])
plt.savefig('other.png')
# if you generate a bunch of png's this way with a predictable filename
# such as:
# filename = "filename%02d.png" % (i,)
# you can run
# ffmpeg -i filename%02d.png -qmin 0 -qmax 1 -sws_dither bayer output.gif
# nplotshow()
def tesxSubplot(self):
names = ["ha","sd","ss","xx"]
for i in range(4):
plt.subplot(2, 2, i+1)
nplotangle(1.1*i, names[i], False)
# names[i]
nplotshow()
def tesx_sigpeaks(self):
fs = int(1E4)
sz = 2*fs # should produce half hz bins
tone = 3042.24
t1 = tone_gen(sz, fs, tone)
fig = nplotfft(t1, fs, peaks=2)
bins,hz = sig_fft(t1,fs)
# res = sig_peaks(bins, hz, 1, 1)
# self.assertEqual(1,len(res))
# self.assertAlmostEqual(hz[res[0]], tone, 0)
res2 = sig_peaks(bins, hz, 2, 200)
print res2
#
# print res
#
nplotshow()
def tesx_sigpeaks(self):
fs = int(1E4)
sz = int(0.3*fs) # should produce half hz bins
tonehz = 3042.24
tone2hz = 1000
d1 = tone_gen(sz, fs, tonehz) + tone_gen(sz, fs, tone2hz)
d2 = np.concatenate((d1,np.zeros(1)))
fig = nplotfft(d2, fs, peaks=2, peaksHzSeparation=2040)
nplotshow()
if __name__ == '__main__':
unittest.main()