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 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)