Histogram

[1]:

import smpl
from smpl import plot
from smpl import stat
from smpl import functions as f
import numpy as np
np.random.seed(1337)
print(smpl.__version__)

1.4.0

Data will be binned for histogram like treatment if bins is set.

[2]:

x= np.random.randn(1000000)
# Default uncertainty of bins is poisson distributed in y direction and none for x
plot.fit(x,f.gauss,bins=20,label="data",binunc=stat.no_dist,init=True)
plot.fit(x,f.gauss,bins=20,label="data",init=True)
plot.fit(x,f.gauss,bins=20,label="data",fmt="hist",init=True)
plot.fit(x,f.gauss,bins=20,label="data",sigmas=1,fmt="step",init=True)

[2]:

[0.0010842265649107883+/-0.0012645726779068741,
 192350.94141512472+/-294.09436483928886,
 1.0105224821893826+/-0.0008924698440256419,
 -0.7360648753124597+/-1.826650379669566]

../../../_images/example_plot_histogram_plot_histogram_3_1.png

../../../_images/example_plot_histogram_plot_histogram_3_2.png

../../../_images/example_plot_histogram_plot_histogram_3_3.png

../../../_images/example_plot_histogram_plot_histogram_3_4.png

[3]:

x= np.random.randn(100000)
plot.fit(stat.normalize(x),stat.normalize(x**3),f.gauss,bins=50,lpos=-1,binunc=stat.no_dist,init=False)

[3]:

[[0.52103884804064+/-0.00039671571197933753,
  6910.278158279774+/-22.25321038191503,
  0.11555535800606304+/-0.0004925003769908676,
  -1.5523556305041395+/-12.263909940094713],
 [0.5664108968256599+/-0.00023031532616933382,
  72621.89660637952+/-666.1540111843453,
  -0.009812650364528625+/-0.0001250903903132005,
  200.50920072112027+/-83.20183658852706]]

../../../_images/example_plot_histogram_plot_histogram_4_1.png

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