def test_draw_pdf_linear():
f = linear
draw_pdf(f, {'m': 1., 'c': 2.}, bound=(-10, 10))
from probfit.pdf import cauchy, rtv_breitwigner from probfit.plotting import draw_pdf from collections import OrderedDict import matplotlib.pyplot as plt bound = (5.24, 5.32) arg = OrderedDict(m=5.28, gamma=1) draw_pdf(cauchy, arg=arg, bound=bound, label='cauchy'+str(arg), density=True) arg = OrderedDict(m=-5.28, gamma=2) draw_pdf(cauchy, arg=arg, bound=bound, label='cauchy'+str(arg), density=True) arg = OrderedDict(m=5.28, gamma=1) draw_pdf(rtv_breitwigner, arg=arg, bound=bound, label='bw'+str(arg), density=True) arg = OrderedDict(m=-5.28, gamma=2) draw_pdf(rtv_breitwigner, arg=arg, bound=bound, label='bw'+str(arg), density=True) plt.grid(True) plt.legend().get_frame().set_alpha(0.5)
def test_draw_pdf():
f = gaussian
draw_pdf(f, {'mean': 1., 'sigma': 2.}, bound=(-10, 10))
def test_draw_pdf_linear():
f = linear
draw_pdf(f, {"m": 1.0, "c": 2.0}, bound=(-10, 10))
from probfit.pdf import exponential
from probfit.plotting import draw_pdf
import matplotlib.pyplot as plt
_arg = {"lambda": 0.5}
draw_pdf(exponential,
arg=_arg,
bound=(0, 5),
label=str(_arg),
density=False,
bins=100)
_arg = {"lambda": 1.0}
draw_pdf(exponential,
arg=_arg,
bound=(0, 5),
label=str(_arg),
density=False,
bins=100)
_arg = {"lambda": 1.5}
draw_pdf(exponential,
arg=_arg,
bound=(0, 5),
label=str(_arg),
density=False,
bins=100)
plt.grid(True)
plt.legend().get_frame().set_alpha(0.5)
# -*- coding: utf-8 -*-
from collections import OrderedDict
import matplotlib.pyplot as plt
from probfit.pdf import johnsonSU
from probfit.plotting import draw_pdf
bound = (-10, 10)
arg = OrderedDict(mean=2, sigma=1, nu=-4, tau=0.5)
draw_pdf(johnsonSU,
arg=arg,
bound=bound,
label=str(arg),
density=False,
bins=200)
arg = OrderedDict(mean=-3, sigma=2, nu=+4, tau=0.1)
draw_pdf(johnsonSU,
arg=arg,
bound=bound,
label=str(arg),
density=False,
bins=200)
arg = OrderedDict(mean=0, sigma=3, nu=+2, tau=0.9)
draw_pdf(johnsonSU,
arg=arg,
bound=bound,
label=str(arg),
def test_draw_pdf():
f = gaussian
draw_pdf(f, {"mean": 1.0, "sigma": 2.0}, bound=(-10, 10))
from probfit.pdf import Polynomial from probfit.plotting import draw_pdf import matplotlib.pyplot as plt bound = (-10, 10) p = Polynomial(3) arg = dict(c_0=0., c_1=1, c_2=2, c_3=3) draw_pdf(p, arg=arg, bound=bound, label=str(arg), density=False) p = Polynomial(2) arg = dict(c_0=0., c_1=1, c_2=2) draw_pdf(p, arg=arg, bound=bound, label=str(arg), density=False) plt.grid(True) plt.legend().get_frame().set_alpha(0.5)
# -*- coding: utf-8 -*- from collections import OrderedDict import matplotlib.pyplot as plt from probfit.pdf import ugaussian from probfit.plotting import draw_pdf bound = (-10, 10) arg = OrderedDict(mean=2, sigma=1) draw_pdf(ugaussian, arg=arg, bound=bound, label=str(arg), density=False) arg = OrderedDict(mean=-3, sigma=2) draw_pdf(ugaussian, arg=arg, bound=bound, label=str(arg), density=False) plt.grid(True) plt.legend().get_frame().set_alpha(0.5)
from probfit.pdf import johnsonSU
from probfit.plotting import draw_pdf
from collections import OrderedDict
import matplotlib.pyplot as plt
bound = (-10, 10)
arg = OrderedDict(mean=2, sigma=1, nu=-4, tau=0.5)
draw_pdf(johnsonSU, arg=arg, bound=bound, label=str(arg), density=False,
bins=200)
arg = OrderedDict(mean=-3, sigma=2, nu=+4, tau=0.1)
draw_pdf(johnsonSU, arg=arg, bound=bound, label=str(arg), density=False,
bins=200)
arg = OrderedDict(mean=0, sigma=3, nu=+2, tau=0.9)
draw_pdf(johnsonSU, arg=arg, bound=bound, label=str(arg), density=False,
bins=200)
plt.grid(True)
plt.legend().get_frame().set_alpha(0.5)
from probfit.pdf import cauchy, rtv_breitwigner
from probfit.plotting import draw_pdf
import matplotlib.pyplot as plt
bound = (1, 7.0)
arg = dict(m=5.28, gamma=0.5)
draw_pdf(cauchy, arg=arg, bound=bound, label='cauchy' + str(arg), density=True)
arg = dict(m=5.28, gamma=1.0)
draw_pdf(cauchy, arg=arg, bound=bound, label='cauchy' + str(arg), density=True)
arg = dict(m=5.28, gamma=1.0)
draw_pdf(rtv_breitwigner,
arg=arg,
bound=bound,
label='bw' + str(arg),
density=True)
arg = dict(m=5.28, gamma=2.0)
draw_pdf(rtv_breitwigner,
arg=arg,
bound=bound,
label='bw' + str(arg),
density=True)
plt.grid(True)
plt.legend().get_frame().set_alpha(0.5)
# -*- coding: utf-8 -*- import matplotlib.pyplot as plt from probfit.pdf import cauchy, rtv_breitwigner from probfit.plotting import draw_pdf bound = (1, 7.0) arg = dict(m=5.28, gamma=0.5) draw_pdf(cauchy, arg=arg, bound=bound, label="cauchy" + str(arg), density=True) arg = dict(m=5.28, gamma=1.0) draw_pdf(cauchy, arg=arg, bound=bound, label="cauchy" + str(arg), density=True) arg = dict(m=5.28, gamma=1.0) draw_pdf(rtv_breitwigner, arg=arg, bound=bound, label="bw" + str(arg), density=True) arg = dict(m=5.28, gamma=2.0) draw_pdf(rtv_breitwigner, arg=arg, bound=bound, label="bw" + str(arg), density=True) plt.grid(True) plt.legend().get_frame().set_alpha(0.5)
# -*- coding: utf-8 -*- import matplotlib.pyplot as plt from probfit.pdf import gaussian from probfit.plotting import draw_pdf bound = (-10, 10) arg = dict(mean=2, sigma=1) draw_pdf(gaussian, arg=arg, bound=bound, label=str(arg), density=True) arg = dict(mean=-3, sigma=2) draw_pdf(gaussian, arg=arg, bound=bound, label=str(arg), density=True) plt.grid(True) plt.legend().get_frame().set_alpha(0.5)
def test_draw_pdf_linear():
plt.figure()
f = linear
draw_pdf(f, {'m':1., 'c':2.}, bound=(-10, 10))
from probfit.pdf import exponential
from probfit.plotting import draw_pdf
import matplotlib.pyplot as plt
_arg = {"lambda": 0.5}
draw_pdf(exponential, arg=_arg, bound=(0, 5), label=str(_arg), density=False,
bins=100)
_arg = {"lambda": 1.0}
draw_pdf(exponential, arg=_arg, bound=(0, 5), label=str(_arg), density=False,
bins=100)
_arg = {"lambda": 1.5}
draw_pdf(exponential, arg=_arg, bound=(0, 5), label=str(_arg), density=False,
bins=100)
plt.grid(True)
plt.legend().get_frame().set_alpha(0.5)
from probfit.pdf import gaussian from probfit.plotting import draw_pdf import matplotlib.pyplot as plt bound = (-10, 10) arg = dict(mean=2, sigma=1) draw_pdf(gaussian, arg=arg, bound=bound, label=str(arg), density=True) arg = dict(mean=-3, sigma=2) draw_pdf(gaussian, arg=arg, bound=bound, label=str(arg), density=True) plt.grid(True) plt.legend().get_frame().set_alpha(0.5)