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Replace matplotlib seaborn style plots with Arviz Plots in Documentation #4563

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Mar 24, 2021
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Replace matplotlib seaborn style plots with Arviz Plots in Documentation #4563

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84 changes: 56 additions & 28 deletions pymc3/distributions/continuous.py
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Original file line number Diff line number Diff line change
Expand Up @@ -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]
Expand Down Expand Up @@ -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]
Expand Down Expand Up @@ -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.]
Expand Down Expand Up @@ -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)
Expand Down Expand Up @@ -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.]
Expand Down Expand Up @@ -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.]
Expand Down Expand Up @@ -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.]
Expand Down Expand Up @@ -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)
Expand Down Expand Up @@ -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.]
Expand Down Expand Up @@ -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.]
Expand Down Expand Up @@ -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.]
Expand Down Expand Up @@ -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.]
Expand Down Expand Up @@ -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.]
Expand Down Expand Up @@ -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)
Expand Down Expand Up @@ -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.]
Expand Down Expand Up @@ -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]
Expand Down Expand Up @@ -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)
Expand Down Expand Up @@ -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]
Expand Down Expand Up @@ -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.]
Expand Down Expand Up @@ -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.]
Expand Down Expand Up @@ -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.]
Expand Down Expand Up @@ -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)
Expand Down Expand Up @@ -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]
Expand Down Expand Up @@ -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.]
Expand Down Expand Up @@ -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.]
Expand Down Expand Up @@ -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]
Expand Down Expand Up @@ -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.]
Expand Down Expand Up @@ -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.]
Expand Down
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