diff --git a/pymc3/distributions/continuous.py b/pymc3/distributions/continuous.py index 8ec46c73d6..ab961f40bd 100644 --- a/pymc3/distributions/continuous.py +++ b/pymc3/distributions/continuous.py @@ -194,7 +194,8 @@ class Uniform(BoundedContinuous): import matplotlib.pyplot as plt import numpy as np - plt.style.use('seaborn-darkgrid') + import arviz as az + plt.style.use('arviz-darkgrid') x = np.linspace(-3, 3, 500) ls = [0., -2] us = [2., 1] @@ -445,7 +446,8 @@ class Normal(Continuous): import matplotlib.pyplot as plt import numpy as np import scipy.stats as st - plt.style.use('seaborn-darkgrid') + import arviz as az + plt.style.use('arviz-darkgrid') x = np.linspace(-5, 5, 1000) mus = [0., 0., 0., -2.] sigmas = [0.4, 1., 2., 0.4] @@ -591,7 +593,8 @@ class TruncatedNormal(BoundedContinuous): import matplotlib.pyplot as plt import numpy as np import scipy.stats as st - plt.style.use('seaborn-darkgrid') + import arviz as az + plt.style.use('arviz-darkgrid') x = np.linspace(-10, 10, 1000) mus = [0., 0., 0.] sigmas = [3.,5.,7.] @@ -809,7 +812,8 @@ class HalfNormal(PositiveContinuous): import matplotlib.pyplot as plt import numpy as np import scipy.stats as st - plt.style.use('seaborn-darkgrid') + import arviz as az + plt.style.use('arviz-darkgrid') x = np.linspace(0, 5, 200) for sigma in [0.4, 1., 2.]: pdf = st.halfnorm.pdf(x, scale=sigma) @@ -949,7 +953,8 @@ class Wald(PositiveContinuous): import matplotlib.pyplot as plt import numpy as np import scipy.stats as st - plt.style.use('seaborn-darkgrid') + import arviz as az + plt.style.use('arviz-darkgrid') x = np.linspace(0, 3, 500) mus = [1., 1., 1., 3.] lams = [1., .2, 3., 1.] @@ -1169,7 +1174,8 @@ class Beta(UnitContinuous): import matplotlib.pyplot as plt import numpy as np import scipy.stats as st - plt.style.use('seaborn-darkgrid') + import arviz as az + plt.style.use('arviz-darkgrid') x = np.linspace(0, 1, 200) alphas = [.5, 5., 1., 2., 2.] betas = [.5, 1., 3., 2., 5.] @@ -1347,7 +1353,8 @@ class Kumaraswamy(UnitContinuous): import matplotlib.pyplot as plt import numpy as np - plt.style.use('seaborn-darkgrid') + import arviz as az + plt.style.use('arviz-darkgrid') x = np.linspace(0, 1, 200) a_s = [.5, 5., 1., 2., 2.] b_s = [.5, 1., 3., 2., 5.] @@ -1453,7 +1460,8 @@ class Exponential(PositiveContinuous): import matplotlib.pyplot as plt import numpy as np import scipy.stats as st - plt.style.use('seaborn-darkgrid') + import arviz as az + plt.style.use('arviz-darkgrid') x = np.linspace(0, 3, 100) for lam in [0.5, 1., 2.]: pdf = st.expon.pdf(x, scale=1.0/lam) @@ -1566,7 +1574,8 @@ class Laplace(Continuous): import matplotlib.pyplot as plt import numpy as np import scipy.stats as st - plt.style.use('seaborn-darkgrid') + import arviz as az + plt.style.use('arviz-darkgrid') x = np.linspace(-10, 10, 1000) mus = [0., 0., 0., -5.] bs = [1., 2., 4., 4.] @@ -1794,7 +1803,8 @@ class Lognormal(PositiveContinuous): import matplotlib.pyplot as plt import numpy as np import scipy.stats as st - plt.style.use('seaborn-darkgrid') + import arviz as az + plt.style.use('arviz-darkgrid') x = np.linspace(0, 3, 100) mus = [0., 0., 0.] sigmas = [.25, .5, 1.] @@ -1951,7 +1961,8 @@ class StudentT(Continuous): import matplotlib.pyplot as plt import numpy as np import scipy.stats as st - plt.style.use('seaborn-darkgrid') + import arviz as az + plt.style.use('arviz-darkgrid') x = np.linspace(-8, 8, 200) mus = [0., 0., -2., -2.] sigmas = [1., 1., 1., 2.] @@ -2115,7 +2126,8 @@ class Pareto(Continuous): import matplotlib.pyplot as plt import numpy as np import scipy.stats as st - plt.style.use('seaborn-darkgrid') + import arviz as az + plt.style.use('arviz-darkgrid') x = np.linspace(0, 4, 1000) alphas = [1., 2., 5., 5.] ms = [1., 1., 1., 2.] @@ -2257,7 +2269,8 @@ class Cauchy(Continuous): import matplotlib.pyplot as plt import numpy as np import scipy.stats as st - plt.style.use('seaborn-darkgrid') + import arviz as az + plt.style.use('arviz-darkgrid') x = np.linspace(-5, 5, 500) alphas = [0., 0., 0., -2.] betas = [.5, 1., 2., 1.] @@ -2373,7 +2386,8 @@ class HalfCauchy(PositiveContinuous): import matplotlib.pyplot as plt import numpy as np import scipy.stats as st - plt.style.use('seaborn-darkgrid') + import arviz as az + plt.style.use('arviz-darkgrid') x = np.linspace(0, 5, 200) for b in [0.5, 1.0, 2.0]: pdf = st.cauchy.pdf(x, scale=b) @@ -2490,7 +2504,8 @@ class Gamma(PositiveContinuous): import matplotlib.pyplot as plt import numpy as np import scipy.stats as st - plt.style.use('seaborn-darkgrid') + import arviz as az + plt.style.use('arviz-darkgrid') x = np.linspace(0, 20, 200) alphas = [1., 2., 3., 7.5] betas = [.5, .5, 1., 1.] @@ -2654,7 +2669,8 @@ class InverseGamma(PositiveContinuous): import matplotlib.pyplot as plt import numpy as np import scipy.stats as st - plt.style.use('seaborn-darkgrid') + import arviz as az + plt.style.use('arviz-darkgrid') x = np.linspace(0, 3, 500) alphas = [1., 2., 3., 3.] betas = [1., 1., 1., .5] @@ -2823,7 +2839,8 @@ class ChiSquared(Gamma): import matplotlib.pyplot as plt import numpy as np import scipy.stats as st - plt.style.use('seaborn-darkgrid') + import arviz as az + plt.style.use('arviz-darkgrid') x = np.linspace(0, 15, 200) for df in [1, 2, 3, 6, 9]: pdf = st.chi2.pdf(x, df) @@ -2868,7 +2885,8 @@ class Weibull(PositiveContinuous): import matplotlib.pyplot as plt import numpy as np import scipy.stats as st - plt.style.use('seaborn-darkgrid') + import arviz as az + plt.style.use('arviz-darkgrid') x = np.linspace(0, 3, 200) alphas = [.5, 1., 1.5, 5., 5.] betas = [1., 1., 1., 1., 2] @@ -3003,7 +3021,8 @@ class HalfStudentT(PositiveContinuous): import matplotlib.pyplot as plt import numpy as np import scipy.stats as st - plt.style.use('seaborn-darkgrid') + import arviz as az + plt.style.use('arviz-darkgrid') x = np.linspace(0, 5, 200) sigmas = [1., 1., 2., 1.] nus = [.5, 1., 1., 30.] @@ -3138,7 +3157,8 @@ class ExGaussian(Continuous): import matplotlib.pyplot as plt import numpy as np import scipy.stats as st - plt.style.use('seaborn-darkgrid') + import arviz as az + plt.style.use('arviz-darkgrid') x = np.linspace(-6, 9, 200) mus = [0., -2., 0., -3.] sigmas = [1., 1., 3., 1.] @@ -3319,7 +3339,8 @@ class VonMises(Continuous): import matplotlib.pyplot as plt import numpy as np import scipy.stats as st - plt.style.use('seaborn-darkgrid') + import arviz as az + plt.style.use('arviz-darkgrid') x = np.linspace(-np.pi, np.pi, 200) mus = [0., 0., 0., -2.5] kappas = [.01, 0.5, 4., 2.] @@ -3419,7 +3440,8 @@ class SkewNormal(Continuous): import matplotlib.pyplot as plt import numpy as np import scipy.stats as st - plt.style.use('seaborn-darkgrid') + import arviz as az + plt.style.use('arviz-darkgrid') x = np.linspace(-4, 4, 200) for alpha in [-6, 0, 6]: pdf = st.skewnorm.pdf(x, alpha, loc=0, scale=1) @@ -3554,7 +3576,8 @@ class Triangular(BoundedContinuous): import matplotlib.pyplot as plt import numpy as np import scipy.stats as st - plt.style.use('seaborn-darkgrid') + import arviz as az + plt.style.use('arviz-darkgrid') x = np.linspace(-2, 10, 500) lowers = [0., -1, 2] cs = [2., 0., 6.5] @@ -3709,7 +3732,8 @@ class Gumbel(Continuous): import matplotlib.pyplot as plt import numpy as np import scipy.stats as st - plt.style.use('seaborn-darkgrid') + import arviz as az + plt.style.use('arviz-darkgrid') x = np.linspace(-10, 20, 200) mus = [0., 4., -1.] betas = [2., 2., 4.] @@ -3832,7 +3856,8 @@ class Rice(PositiveContinuous): import matplotlib.pyplot as plt import numpy as np import scipy.stats as st - plt.style.use('seaborn-darkgrid') + import arviz as az + plt.style.use('arviz-darkgrid') x = np.linspace(0, 8, 500) nus = [0., 0., 4., 4.] sigmas = [1., 2., 1., 2.] @@ -3994,7 +4019,8 @@ class Logistic(Continuous): import matplotlib.pyplot as plt import numpy as np import scipy.stats as st - plt.style.use('seaborn-darkgrid') + import arviz as az + plt.style.use('arviz-darkgrid') x = np.linspace(-5, 5, 200) mus = [0., 0., 0., -2.] ss = [.4, 1., 2., .4] @@ -4116,7 +4142,8 @@ class LogitNormal(UnitContinuous): import numpy as np import scipy.stats as st from scipy.special import logit - plt.style.use('seaborn-darkgrid') + import arviz as az + plt.style.use('arviz-darkgrid') x = np.linspace(0.0001, 0.9999, 500) mus = [0., 0., 0., 1.] sigmas = [0.3, 1., 2., 1.] @@ -4350,7 +4377,8 @@ class Moyal(Continuous): import matplotlib.pyplot as plt import numpy as np import scipy.stats as st - plt.style.use('seaborn-darkgrid') + import arviz as az + plt.style.use('arviz-darkgrid') x = np.linspace(-10, 20, 200) mus = [-1., 0., 4.] sigmas = [2., 2., 4.] diff --git a/pymc3/distributions/discrete.py b/pymc3/distributions/discrete.py index ab1aa2a198..254e81080e 100644 --- a/pymc3/distributions/discrete.py +++ b/pymc3/distributions/discrete.py @@ -72,7 +72,8 @@ class Binomial(Discrete): import matplotlib.pyplot as plt import numpy as np import scipy.stats as st - plt.style.use('seaborn-darkgrid') + import arviz as az + plt.style.use('arviz-darkgrid') x = np.arange(0, 22) ns = [10, 17] ps = [0.5, 0.7] @@ -206,7 +207,8 @@ class BetaBinomial(Discrete): import numpy as np import scipy.stats as st from scipy import special - plt.style.use('seaborn-darkgrid') + import arviz as az + plt.style.use('arviz-darkgrid') def BetaBinom(a, b, n, x): pmf = special.binom(n, x) * (special.beta(x+a, n-x+b) / special.beta(a, b)) @@ -369,7 +371,8 @@ class Bernoulli(Discrete): import matplotlib.pyplot as plt import numpy as np import scipy.stats as st - plt.style.use('seaborn-darkgrid') + import arviz as az + plt.style.use('arviz-darkgrid') x = [0, 1] for p in [0, 0.5, 0.8]: pmf = st.bernoulli.pmf(x, p) @@ -504,7 +507,8 @@ class DiscreteWeibull(Discrete): import numpy as np import scipy.stats as st from scipy import special - plt.style.use('seaborn-darkgrid') + import arviz as az + plt.style.use('arviz-darkgrid') def DiscreteWeibull(q, b, x): return q**(x**b) - q**((x + 1)**b) @@ -640,7 +644,8 @@ class Poisson(Discrete): import matplotlib.pyplot as plt import numpy as np import scipy.stats as st - plt.style.use('seaborn-darkgrid') + import arviz as az + plt.style.use('arviz-darkgrid') x = np.arange(0, 15) for m in [0.5, 3, 8]: pmf = st.poisson.pmf(x, m) @@ -761,7 +766,8 @@ class NegativeBinomial(Discrete): import numpy as np import scipy.stats as st from scipy import special - plt.style.use('seaborn-darkgrid') + import arviz as az + plt.style.use('arviz-darkgrid') def NegBinom(a, m, x): pmf = special.binom(x + a - 1, x) * (a / (m + a))**a * (m / (m + a))**x @@ -947,7 +953,8 @@ class Geometric(Discrete): import matplotlib.pyplot as plt import numpy as np import scipy.stats as st - plt.style.use('seaborn-darkgrid') + import arviz as az + plt.style.use('arviz-darkgrid') x = np.arange(1, 11) for p in [0.1, 0.25, 0.75]: pmf = st.geom.pmf(x, p) @@ -1052,7 +1059,8 @@ class HyperGeometric(Discrete): import matplotlib.pyplot as plt import numpy as np import scipy.stats as st - plt.style.use('seaborn-darkgrid') + import arviz as az + plt.style.use('arviz-darkgrid') x = np.arange(1, 15) N = 50 k = 10 @@ -1203,7 +1211,8 @@ class DiscreteUniform(Discrete): import matplotlib.pyplot as plt import numpy as np import scipy.stats as st - plt.style.use('seaborn-darkgrid') + import arviz as az + plt.style.use('arviz-darkgrid') ls = [1, -2] us = [6, 2] for l, u in zip(ls, us): @@ -1327,7 +1336,8 @@ class Categorical(Discrete): import matplotlib.pyplot as plt import numpy as np import scipy.stats as st - plt.style.use('seaborn-darkgrid') + import arviz as az + plt.style.use('arviz-darkgrid') ps = [[0.1, 0.6, 0.3], [0.3, 0.1, 0.1, 0.5]] for p in ps: x = range(len(p)) @@ -1518,7 +1528,8 @@ class ZeroInflatedPoisson(Discrete): import matplotlib.pyplot as plt import numpy as np import scipy.stats as st - plt.style.use('seaborn-darkgrid') + import arviz as az + plt.style.use('arviz-darkgrid') x = np.arange(0, 22) psis = [0.7, 0.4] thetas = [8, 4] @@ -1645,7 +1656,8 @@ class ZeroInflatedBinomial(Discrete): import matplotlib.pyplot as plt import numpy as np import scipy.stats as st - plt.style.use('seaborn-darkgrid') + import arviz as az + plt.style.use('arviz-darkgrid') x = np.arange(0, 25) ns = [10, 20] ps = [0.5, 0.7] @@ -1794,7 +1806,8 @@ class ZeroInflatedNegativeBinomial(Discrete): import numpy as np import scipy.stats as st from scipy import special - plt.style.use('seaborn-darkgrid') + import arviz as az + plt.style.use('arviz-darkgrid') def ZeroInfNegBinom(a, m, psi, x): pmf = special.binom(x + a - 1, x) * (a / (m + a))**a * (m / (m + a))**x