Update more examples to use arviz 1.0

examples/case_studies/GEV.myst.md

@@ -5,7 +5,7 @@ jupytext:
     format_name: myst
     format_version: 0.13
 kernelspec:
-  display_name: default
+  display_name: eabm
   language: python
   name: python3
 ---
@@ -38,14 +38,13 @@ Note that this parametrization of the shape parameter $\xi$ is opposite in sign
 We will use the example of the Port Pirie annual maximum sea-level data used in {cite:t}`coles2001gev`, and compare with the frequentist results presented there.
 
 ```{code-cell} ipython3
-import arviz as az
+import arviz.preview as az
 import matplotlib.pyplot as plt
 import numpy as np
 import pymc as pm
 import pymc_extras.distributions as pmx
-import pytensor.tensor as pt
 
-from arviz.plots import plot_utils as azpu
+az.style.use("arviz-variat")
 ```
 
 ## Data
@@ -112,18 +111,13 @@ Let's get a feel for how well our selected priors cover the range of the data:
 
 ```{code-cell} ipython3
 idata = pm.sample_prior_predictive(samples=1000, model=model)
-az.plot_ppc(idata, group="prior", figsize=(12, 6))
-ax = plt.gca()
-ax.set_xlim([2, 6])
-ax.set_ylim([0, 2]);
+az.plot_ppc_dist(idata, group="prior_predictive", kind="ecdf")
 ```
 
 And we can look at the sampled values of the parameters, using the `plot_posterior` function, but passing in the `idata` object and specifying the `group` to be `"prior"`:
 
 ```{code-cell} ipython3
-az.plot_posterior(
-    idata, group="prior", var_names=["μ", "σ", "ξ"], hdi_prob="hide", point_estimate=None
-);
+az.plot_dist(idata, group="prior", var_names=["μ", "σ", "ξ"]);
 ```
 
 ## Inference
@@ -144,7 +138,7 @@ idata.extend(trace)
 ```
 
 ```{code-cell} ipython3
-az.plot_trace(idata, var_names=["μ", "σ", "ξ"], figsize=(12, 12));
+az.plot_trace_dist(idata, var_names=["μ", "σ", "ξ"]);
 ```
 
 ### Divergences
@@ -159,27 +153,32 @@ The trace exhibits divergences (usually). The HMC/NUTS sampler can have problems
 The 95% credible interval range of the parameter estimates is:
 
 ```{code-cell} ipython3
-az.hdi(idata, hdi_prob=0.95)
+az.hdi(idata, prob=0.95)
 ```
 
 And examine the prediction distribution, considering parameter variability (and without needing to assume normality):
 
 ```{code-cell} ipython3
-az.plot_posterior(idata, hdi_prob=0.95, var_names=["z_p"], round_to=4);
+az.plot_dist(
+    idata,
+    ci_prob=0.95,
+    var_names=["z_p"],
+    stats={"point_estimate": {"round_to": 2}},
+);
 ```
 
 And let's compare the prior and posterior predictions of $z_p$ to see how the data has influenced things:
 
 ```{code-cell} ipython3
-az.plot_dist_comparison(idata, var_names=["z_p"]);
+az.plot_prior_posterior(idata, var_names=["z_p"]);
 ```
 
 ## Comparison
 To compare with the results given in {cite:t}`coles2001gev`, we approximate the maximum likelihood estimates (MLE) using the mode of the posterior distributions (the *maximum a posteriori* or MAP estimate). These are close when the prior is reasonably flat around the posterior estimate.
 
 The MLE results given in {cite:t}`coles2001gev` are:
 
-$$\left(\hat{\mu}, \hat{\sigma}, \hat{\xi} \right) = \left( 3.87, 0.198, -0.050 \right) $$
+$$\left(\hat{\mu}, \hat{\sigma}, \hat{\xi} \right) = \left( 3.87, 0.198, -0.050 \right)$$
 
 
 And the variance-covariance matrix of the estimates is:
@@ -189,13 +188,8 @@ $$ V = \left[ \begin{array} 0.000780 & 0.000197 & -0.00107 \\
                             -0.00107 & -0.000778 & 0.00965 
               \end{array} \right] $$
 
-
-Note that extracting the MLE estimates from our inference involves accessing some of the Arviz back end functions to bash the xarray into something examinable:
-
 ```{code-cell} ipython3
-_, vals = az.sel_utils.xarray_to_ndarray(idata["posterior"], var_names=["μ", "σ", "ξ"])
-mle = [azpu.calculate_point_estimate("mode", val) for val in vals]
-mle
+az.mode(idata, var_names=["μ", "σ", "ξ"])
 ```
 
 ```{code-cell} ipython3
@@ -207,12 +201,17 @@ The results are a good match, but the benefit of doing this in a Bayesian settin
 Finally, we examine the pairs plots and see where any difficulties in inference lie using the divergences
 
 ```{code-cell} ipython3
-az.plot_pair(idata, var_names=["μ", "σ", "ξ"], kind="kde", marginals=True, divergences=True);
+az.plot_pair(
+    idata,
+    var_names=["μ", "σ", "ξ"],
+    visuals={"divergence": True},
+);
 ```
 
 ## Authors
 
 * Authored by [Colin Caprani](https://github.com/ccaprani), October 2021
+* Updated by Osvaldo Martin, January 2026
 
 +++
 

examples/case_studies/factor_analysis.myst.md

@@ -6,7 +6,7 @@ jupytext:
     format_name: myst
     format_version: 0.13
 kernelspec:
-  display_name: Python 3 (ipykernel)
+  display_name: eabm
   language: python
   name: python3
 myst:
@@ -33,7 +33,7 @@ Factor analysis is a widely used probabilistic model for identifying low-rank st
 :::
 
 ```{code-cell} ipython3
-import arviz as az
+import arviz.preview as az
 import numpy as np
 import pymc as pm
 import pytensor.tensor as pt
@@ -42,7 +42,6 @@ import seaborn as sns
 import xarray as xr
 
 from matplotlib import pyplot as plt
-from matplotlib.lines import Line2D
 from numpy.random import default_rng
 from xarray_einstats import linalg
 from xarray_einstats.stats import XrContinuousRV
@@ -52,7 +51,7 @@ print(f"Running on PyMC v{pm.__version__}")
 
 ```{code-cell} ipython3
 %config InlineBackend.figure_format = 'retina'
-az.style.use("arviz-darkgrid")
+az.style.use("arviz-variat")
 
 np.set_printoptions(precision=3, suppress=True)
 RANDOM_SEED = 31415
@@ -128,11 +127,13 @@ with pm.Model(coords=coords) as PPCA:
 At this point, there are already several warnings regarding failed convergence checks. We can see further problems in the trace plot below. This plot shows the path taken by each sampler chain for a single entry in the matrix $W$ as well as the average evaluated over samples for each chain.
 
 ```{code-cell} ipython3
-for i in trace.posterior.chain.values:
-    samples = trace.posterior["W"].sel(chain=i, observed_columns=3, latent_columns=1)
-    plt.plot(samples, label=f"Chain {i + 1}")
-    plt.axhline(samples.mean(), color=f"C{i}")
-plt.legend(ncol=4, loc="upper center", fontsize=12, frameon=True), plt.xlabel("Sample");
+az.plot_trace(
+    trace,
+    var_names="W",
+    coords={"observed_columns": 3, "latent_columns": 1},
+    sample_dims=["draw"],
+    figure_kwargs={"sharey": True},
+);
 ```
 
 Each chain appears to have a different sample mean and we can also see that there is a great deal of autocorrelation across chains, manifest as long-range trends over sampling iterations.
@@ -194,13 +195,7 @@ with pm.Model(coords=coords) as PPCA_identified:
     F = pm.Normal("F", dims=("latent_columns", "rows"))
     sigma = pm.HalfNormal("sigma", 1.0)
     X = pm.Normal("X", mu=W @ F, sigma=sigma, observed=Y, dims=("observed_columns", "rows"))
-    trace = pm.sample(tune=2000, random_seed=rng)  # target_accept=0.9
-
-for i in range(4):
-    samples = trace.posterior["W"].sel(chain=i, observed_columns=3, latent_columns=1)
-    plt.plot(samples, label=f"Chain {i + 1}")
-
-plt.legend(ncol=4, loc="lower center", fontsize=8), plt.xlabel("Sample");
+    trace = pm.sample(tune=2000, random_seed=rng, target_accept=0.9)
 ```
 
 $W$ (and $F$!) now have entries with identical posterior distributions as compared between sampler chains, although it's apparent that some autocorrelation remains.
@@ -251,29 +246,28 @@ When we compare the posteriors calculated using MCMC and VI, we find that (for a
 ```{code-cell} ipython3
 col_selection = dict(observed_columns=3, latent_columns=1)
 
-ax = az.plot_kde(
-    trace.posterior["W"].sel(**col_selection).values,
-    label=f"MCMC posterior for the explicit model",
-    plot_kwargs={"color": f"C{1}"},
-)
-
-az.plot_kde(
-    trace_amortized.posterior["W"].sel(**col_selection).values,
-    label="MCMC posterior for amortized inference",
-    plot_kwargs={"color": f"C{2}", "linestyle": "--"},
+dt = az.from_dict(
+    {
+        "posterior": {
+            "MCMC_explicit": trace.posterior["W"].sel(**col_selection),
+            "MCMC_amortized": trace_amortized.posterior["W"].sel(**col_selection),
+            "FR-ADVI_amortized": trace_vi.posterior["W"].sel(**col_selection),
+        }
+    }
 )
 
-
-az.plot_kde(
-    trace_vi.posterior["W"].sel(**col_selection).squeeze().values,
-    label="FR-ADVI posterior for amortized inference",
-    plot_kwargs={"alpha": 0},
-    fill_kwargs={"alpha": 0.5, "color": f"C{0}"},
+pc = az.plot_dist(
+    dt,
+    cols=None,
+    aes={"color": ["__variable__"]},
+    visuals={
+        "title": False,
+        "point_estimate_text": False,
+        "point_estimate": False,
+        "credible_interval": False,
+    },
 )
-
-
-ax.set_title(rf"PDFs of $W$ estimate at {col_selection}")
-ax.legend(loc="upper left", fontsize=10);
+pc.add_legend("__variable__")
 ```
 
 ### Post-hoc identification of F
@@ -389,6 +383,7 @@ We find that our model does a decent job of capturing the variation in the origi
 * Updated by [Christopher Krapu](https://github.com/ckrapu) on April 4, 2021
 * Updated by Oriol Abril-Pla to use PyMC v4 and xarray-einstats on March, 2022
 * Updated by Erik Werner on Dec, 2023 ([pymc-examples#612](https://github.com/pymc-devs/pymc-examples/pull/612))
+* Updated by Osvaldo Martin on January, 2026
 
 +++