Update example outputs and test that it works

README.rst

@@ -90,15 +90,15 @@ Imagine we conduct an experiment to predict the growth of a plant based on diffe
    }
    with pm.do(generative_model, fixed_parameters) as synthetic_model:
       idata = pm.sample_prior_predictive(random_seed=seed) # Sample from prior predictive distribution.
-      synthetic_y = idata.prior["plant growth (z-scored)"].sel(draw=0, chain=0)
+      synthetic_y = idata.prior["plant growth"].sel(draw=0, chain=0)
 
 
    # Infer parameters conditioned on observed data
-   with pm.observe(generative_model, {"plant growth (z-scored)": synthetic_y}) as inference_model:
-    idata = pm.sample(random_seed=seed)
+   with pm.observe(generative_model, {"plant growth": synthetic_y}) as inference_model:
+      idata = pm.sample(random_seed=seed)
 
-   summary = pm.stats.summary(idata, var_names=["betas", "sigma"]))
-   print(summary)
+      summary = pm.stats.summary(idata, var_names=["betas", "sigma"])
+      print(summary)
 
 
 From the summary, we can see that the mean of the inferred parameters are very close to the fixed parameters
@@ -116,14 +116,14 @@ sigma                   0.511  0.037     0.438      0.575        0.001      0
 
    # Simulate new data conditioned on inferred parameters
    new_x_data = pm.draw(
-    pm.Normal.dist(shape=(3, 3)),
-    random_seed=seed,
+      pm.Normal.dist(shape=(3, 3)),
+      random_seed=seed,
    )
    new_coords = coords | {"trial": [0, 1, 2]}
 
    with inference_model:
       pm.set_data({"x": new_x_data}, coords=new_coords)
-      idata = pm.sample_posterior_predictive(
+      pm.sample_posterior_predictive(
          idata,
          predictions=True,
          extend_inferencedata=True,
@@ -134,13 +134,13 @@ sigma                   0.511  0.037     0.438      0.575        0.001      0
 
 The new data conditioned on inferred parameters would look like:
 
-==========================  ======  =====  ========  =========
-Output                       mean    sd     hdi_3%    hdi_97%
-==========================  ======  =====  ========  =========
-plant growth (z-scored)[0]  14.21   0.509    13.232     15.144
-plant growth (z-scored)[1]  24.43   0.518    23.347     25.32
-plant growth (z-scored)[2]  -6.743  0.515    -7.778     -5.834
-==========================  ======  =====  ========  =========
+================ ======== ======= ======== =========
+Output            mean     sd      hdi_3%   hdi_97%
+================ ======== ======= ======== =========
+plant growth[0]   14.229   0.515   13.325   15.272
+plant growth[1]   24.418   0.511   23.428   25.326
+plant growth[2]   -6.747   0.511   -7.740   -5.797
+================ ======== ======= ======== =========
 
 .. code-block:: python
 
@@ -159,13 +159,13 @@ plant growth (z-scored)[2]  -6.743  0.515    -7.778     -5.834
 
 The new data, under the above scenario would look like:
 
-==========================  ======  =====  ========  =========
-Output                       mean    sd     hdi_3%    hdi_97%
-==========================  ======  =====  ========  =========
-plant growth (z-scored)[0]  14.153  0.509    13.181     15.096
-plant growth (z-scored)[1]  23.85   0.517    22.915     24.878
-plant growth (z-scored)[2]  -7.302  0.515    -8.315     -6.374
-==========================  ======  =====  ========  =========
+================ ======== ======= ======== =========
+Output            mean     sd      hdi_3%   hdi_97%
+================ ======== ======= ======== =========
+plant growth[0]   12.149   0.515   11.193   13.135
+plant growth[1]   29.809   0.508   28.832   30.717
+plant growth[2]   -0.131   0.507   -1.121    0.791
+================ ======== ======= ======== =========
 
 Getting started
 ===============