def test_histogram(self):
# in this special case, cf.histogram and np.histogram must yield equal results
# check hist and edges
cfh, cfe = cf.histogram(self.data, bins=20, range=(0, 1), shape=0)
nph, npe = np.histogram(self.data, bins=20, range=(0, 1))
self.assertListEqual(list(nph), list(cfh))
self.assertListEqual(list(npe), list(cfe))
def test_histogram(self):
# in this special case, cf.histogram and np.histogram must yield equal results
# check hist and edges
cfh, cfe = cf.histogram(self.data, bins=20, range=(0,1), shape=0)
nph, npe = np.histogram(self.data, bins=20, range=(0,1))
self.assertListEqual(list(nph), list(cfh))
self.assertListEqual(list(npe), list(cfe))
def test_histogramo2(self):
cfh, cfe = cf.histogram(self.data, bins=100, range=(0.0, 1.0), shape=2)
nph, npe = np.histogram(self.data, bins=100, range=(0.0, 1.0))
# just check that no mass is lost (cfh.base includes ghost cells)
totalmass = len(self.data)
self.assertAlmostEqual(np.sum(cfh.base) - totalmass, 0)
self.assertAlmostEqual(np.sum(nph) - totalmass, 0)
self.assertListEqual(list(npe), list(cfe))
def test_histogramo2w(self):
cfh, cfe = cf.histogram(self.data, bins=100, range=(0.0,1.0), shape=2, weights=self.weights)
nph, npe = np.histogram(self.data, bins=100, range=(0.0,1.0), weights=self.weights)
# just check that no mass is lost (cfh.base includes ghost cells)
totalmass = np.sum(self.weights)
self.assertAlmostEqual(np.sum(cfh.base) - totalmass, 0)
self.assertAlmostEqual(np.sum(nph) - totalmass, 0)
self.assertListEqual(list(npe), list(cfe))
def test_histogramw(self):
# in this special case, cf.histogram and np.histogram must yield equal results
# check hist and edges
cfh, cfe = cf.histogram(self.data, bins=100, range=(0,1), shape=0, weights=self.weights)
nph, npe = np.histogram(self.data, bins=100, range=(0,1), weights=self.weights)
diff = np.abs(cfh - nph)
self.assertAlmostEqual(np.sum(diff), 0)
self.assertListEqual(list(npe), list(cfe))
def time1D(data, bins, weights, shape, tn):
t = timeit.Timer(lambda: cf.histogram(data,
range=(0.001, 0.999),
bins=bins,
weights=weights,
shape=shape))
tc = t.timeit(number=5) / 5.0
ws = ' ' if weights is None else 'weights'
print('1D, {:d} shape, {:s}: {:0.2e} sec -> factor {:5.2f} faster'.
format(shape, ws, tc, tn / tc))
def test_histogramw(self):
# in this special case, cf.histogram and np.histogram must yield equal results
# check hist and edges
cfh, cfe = cf.histogram(self.data,
bins=100,
range=(0, 1),
shape=0,
weights=self.weights)
nph, npe = np.histogram(self.data,
bins=100,
range=(0, 1),
weights=self.weights)
diff = np.abs(cfh - nph)
self.assertAlmostEqual(np.sum(diff), 0)
self.assertListEqual(list(npe), list(cfe))
def particleshapedemo(shape):
import postpic.cythonfunctions as cf
import matplotlib.pyplot as plt
ptclpos = np.array([4.5, 9.75, 15.0, 20.25])
y, edges = cf.histogram(ptclpos, bins=25, range=(0,25), shape=shape)
x = np.convolve(edges, [0.5, 0.5], mode='valid')
fig = plt.figure()
fig.suptitle('ParticleShape: {:s}'.format(str(shape)))
ax = fig.add_subplot(111)
ax.plot(x,y)
ax.set_ylim((0,1))
ax.set_xticks(x, minor=True)
ax.grid(which='minor')
for ix in ptclpos:
ax.axvline(x=ix, color='y')
fig.savefig(savedir + 'particleshapedemo{:s}.png'.format(str(shape)), dpi=160)
plt.close(fig)
def particleshapedemo(shape):
import postpic.cythonfunctions as cf
import matplotlib.pyplot as plt
ptclpos = np.array([4.5, 9.75, 15.0, 20.25])
y, edges = cf.histogram(ptclpos, bins=25, range=(0, 25), shape=shape)
x = np.convolve(edges, [0.5, 0.5], mode='valid')
fig = plt.figure()
fig.suptitle('ParticleShape: {:s}'.format(str(shape)))
ax = fig.add_subplot(111)
ax.plot(x, y)
ax.set_ylim((0, 1))
ax.set_xticks(x, minor=True)
ax.grid(which='minor')
for ix in ptclpos:
ax.axvline(x=ix, color='y')
fig.savefig(savedir + 'particleshapedemo{:s}.png'.format(str(shape)),
dpi=160)
plt.close(fig)
def time1D(data, bins, weights, shape, tn):
t = timeit.Timer(lambda: cf.histogram(data, range=(0.001,0.999), bins=bins, weights=weights, shape=shape))
tc = t.timeit(number=5)/5.0
ws = ' ' if weights is None else 'weights'
print('1D, {:d} shape, {:s}: {:0.2e} sec -> factor {:5.2f} faster'.format(shape, ws, tc, tn/tc))