def test_mean(self):
h = Histogram(10,[0,10])
h.fill([3,3,3])
assert_almost_equal(h.mean()[0],3.5)
h.fill([1,5])
assert_almost_equal(h.mean()[0],3.5)
def test_hist_1d_line(self):
rand.seed(1)
h = Histogram(40, [0, 1], 'χ (cm)', 'counts', 'Some Data')
h.fill(rand.normal(0.5, 0.1, 300))
fig, ax = plt.subplots()
pt = ax.plothist(h, style='line')
self.assertTrue(
conf.comparator.compare_images(fig.savefig, 'hist_1d_line.png'))
def test_hist_2d(self):
rand.seed(1)
h = Histogram(40, [0, 1], 'χ (cm)', 30, [-5, 5], 'ψ', 'counts',
'Some Data')
h.fill(rand.normal(0.5, 0.1, 1000), rand.normal(0, 1, 1000))
fig, ax = plt.subplots()
pt = ax.plothist(h)
self.assertTrue(
conf.comparator.compare_images(fig.savefig, 'hist_2d.png'))
def test_plot_hist2d():
npoints = 100000
h2 = Histogram((100, (0, 10), 'x'), (100, (0, 10), 'y'), 'z', 'title')
h2.fill(rand.normal(5, 2, npoints), rand.uniform(0, 10, npoints))
fig, ax = pyplot.subplots(1, 2)
ax[0].plothist(h2)
ax[1].plothist(h2)
ax[1].plothist(h2.smooth(1), style='contour', overlay=True)
pyplot.savefig('test_images/test_plotting_fig_hist2d.png')
def test_plot_hist2d():
npoints = 100000
h2 = Histogram((100,(0,10),'x'),(100,(0,10),'y'),'z','title')
h2.fill(rand.normal(5,2,npoints),
rand.uniform(0,10,npoints))
fig,ax = pyplot.subplots(1,2)
ax[0].plothist(h2)
ax[1].plothist(h2)
ax[1].plothist(h2.smooth(1), style='contour', overlay=True)
pyplot.savefig('test_images/test_plotting_fig_hist2d.png')
def test_plot_hist1d():
npoints = 100000
h1 = Histogram(100,(0,10),'x','y','title')
h1.fill(rand.normal(5,2,npoints))
fig,ax = pyplot.subplots(2,2)
ax[0,0].plothist(h1, style='polygon' , baseline='bottom')
ax[0,1].plothist(h1, style='errorbar', baseline='bottom')
ax[1,0].plothist(h1, style='polygon' )#, baseline='left')
ax[1,1].plothist(h1, style='errorbar')#, baseline='left')
pyplot.savefig('test_images/test_plotting_fig_hist1d.png')
def test_plot_hist1d():
npoints = 100000
h1 = Histogram(100, (0, 10), 'x', 'y', 'title')
h1.fill(rand.normal(5, 2, npoints))
fig, ax = pyplot.subplots(2, 2)
ax[0, 0].plothist(h1, style='polygon', baseline='bottom')
ax[0, 1].plothist(h1, style='errorbar', baseline='bottom')
ax[1, 0].plothist(h1, style='polygon') #, baseline='left')
ax[1, 1].plothist(h1, style='errorbar') #, baseline='left')
pyplot.savefig('test_images/test_plotting_fig_hist1d.png')
def setUp(self):
rc.overwrite.overwrite = 'always'
np.random.seed(1)
h = Histogram(100,[0,10],'Δx', 'y', 'title')
h.fill(np.random.normal(5,2,10000))
h.uncert = np.sqrt(h.data)
if sys.version_info < (3,0):
def _to_unicode(s):
if not isinstance(s,unicode):
return unicode(s,'utf-8')
else:
return s
h.title = _to_unicode(h.title)
h.label = _to_unicode(h.label)
for ax in h.axes:
ax.label = _to_unicode(ax.label)
self.h = h
''' Simple 2D histogram example. ''' import numpy as np from matplotlib import pyplot, cm from histogram import Histogram, plothist np.random.seed(1) # 2D histogram with 30x40 bins h = Histogram(30,[0,10],40,[-5,5]) # filling the histogram with some random data npoints = 100000 datax = np.random.normal(5,1,npoints) datay = np.random.uniform(-5,5,npoints) h.fill(datax,datay) # filling with even more data datax = np.random.uniform(0,10,npoints) datay = np.random.normal(0,1,npoints) h.fill(datax,datay) # using the plothist() convenience method fig,ax,pt = plothist(h, cmap=cm.Blues) # display figure to screen pyplot.show()
''' Simple 2D histogram example. ''' import numpy as np from matplotlib import pyplot, cm from histogram import Histogram, plothist np.random.seed(1) # 2D histogram with 30x40 bins h = Histogram(30, [0, 10], 40, [-5, 5]) # filling the histogram with some random data npoints = 100000 datax = np.random.normal(5, 1, npoints) datay = np.random.uniform(-5, 5, npoints) h.fill(datax, datay) # filling with even more data datax = np.random.uniform(0, 10, npoints) datay = np.random.normal(0, 1, npoints) h.fill(datax, datay) # using the plothist() convenience method fig, ax, pt = plothist(h, cmap=cm.Blues) # display figure to screen pyplot.show()
def test_occupancy(self):
h = Histogram(10,[0,10])
h.fill([1,1,1,2,2,2,3])
hocc = h.occupancy(4,[-0.5,3.5])
assert_array_almost_equal(hocc.data, [7,1,0,2])
from numpy import random as rand from matplotlib import pyplot, cm from histogram import Histogram npoints = 1000000 datax = rand.normal(100,40,npoints) datay = rand.normal(100,60,npoints) dataz = rand.normal(50,20,npoints) d0 = (10,[0,100],'x') d1 = (9, [0,100],'y') d2 = (100,[0,100],'z') h3 = Histogram(d0,d1,d2,'counts','Random Data') h3.fill(datax,datay,dataz) fig = pyplot.figure(figsize=(8,8)) axs,axtot,axins = fig.plothist_grid(h3, ymin=0, cmap=cm.viridis) #copper_r) pyplot.show()
from numpy import random as rand from matplotlib import pyplot, cm from histogram import Histogram npoints = 1000000 datax = rand.normal(100,40,npoints) datay = rand.normal(100,60,npoints) dataz = rand.normal(50,20,npoints) d0 = (10,[0,100],'x') d1 = (9, [0,100],'y') d2 = (100,[0,100],'z') h3 = Histogram(d0,d1,d2,'counts','Random Data') h3.fill(datax,datay,dataz) fig = pyplot.figure(figsize=(12,12)) axs,axtot,axins = fig.plothist_grid(h3, cmap=cm.copper_r) pyplot.show()
import numpy as np
from histogram import Histogram
import matplotlib.pyplot as plt
from matplotlib.style import use
use('ggplot')
hist1 = Histogram(100, [0, 10])
hist2 = Histogram(100, [0, 10])
for i in range(1000):
hist1.fill(np.random.normal(5, 1, 10000))
hist2.fill(np.random.exponential(2, 10000))
print(hist2.n_entries)
print(hist2.n_underflow)
print(hist2.n_overflow)
hist1.plot()
hist2.plot(kind='bar', alpha=0.3)
plt.xlim(-0.1, 10.1)
plt.show()
# -*- coding: utf-8 -*- import numpy as np from histogram import Histogram np.random.seed(1) h = Histogram(100,[0,10],'Δx', 'y', 'title') h.fill(np.random.normal(5,2,10000)) h.uncert = np.sqrt(h.data)
import matplotlib
matplotlib.use('GTK3Agg')
from numpy import random as rand
from matplotlib import pyplot
from histogram import Histogram
rand.seed(1)
npoints = 10000
xdata = rand.normal(100, 50, npoints)
ydata = rand.normal(50, 10, npoints)
d0 = (10, [0, 100], '$x$')
d1 = (10, [-0.5, 100.5], '$y$')
h2 = Histogram(d0, d1, '$z$', 'Random Data')
h2.fill(xdata, ydata)
h2slices = list(h2.slices())
axslices = h2.axes[0]
fig = pyplot.figure(figsize=(12, 8))
axs, saxs = fig.plothist_strip(h2slices, axslices)
pyplot.show()
from numpy import random as rand from matplotlib import pyplot from histogram import Histogram rand.seed(1) npoints = 10000 xdata = rand.normal(100,50,npoints) ydata = rand.normal(50,10,npoints) d0 = (10, [0,100],'$x$') d1 = (10,[-0.5,100.5],'$y$') h2 = Histogram(d0,d1,'$z$','Random Data') h2.fill(xdata,ydata) h2slices = list(h2.slices()) axslices = h2.axes[0] fig = pyplot.figure(figsize=(12,12)) axs,saxs = fig.plothist_strip(h2slices,axslices) pyplot.show()
''' About as basic an example as one can get. ''' import numpy as np from matplotlib import pyplot from histogram import Histogram np.random.seed(1) h = Histogram(30,[0,10]) h.fill(np.random.normal(5,1,1000)) fig,ax = pyplot.subplots() pt = ax.plothist(h, alpha=0.3) pyplot.show()
else:
if baseline_image.shape != test_image.shape:
good_match = False
else:
rms = ImageComparator._rms(baseline_image, test_image)
good_match = rms <= tol
diff_fpath = path.join(self.diff_directory, fname)
if good_match:
if path.exists(diff_fpath):
os.remove(diff_fpath)
else:
diff_image = ImageComparator._diff(baseline_fpath, test_fpath)
with Image.fromarray(diff_image, 'L') as img:
img.save(diff_fpath)
return good_match
if __name__ == '__main__':
rand.seed(1)
h = Histogram(40, [0, 1], 'χ (cm)', 'counts', 'Some Data')
h.fill(rand.normal(0.5, 0.1, 2000))
fig, ax = plt.subplots()
pt = ax.plothist(h)
comparator = ImageComparator()
assert comparator.compare_images(fig.savefig, 'hist_1d.png')
