Repository: arviz-devs/arviz

Issue:
CmdStanPy tests
Add tests for CmdStanPy. See if we can read cmdstan output csv to PosterierSample which then can be tested.


Discussion:
CmdStanPy is very beta, alas.  PR https://github.com/stan-dev/cmdstanpy/pull/43 was a refactor which eliminated object `PosteriorSample` and made some of its member functions into first-class functions.  Instead of a `PosteriorSample` object, function `sample` returns a `RunSet` object.    Need to update `from_cmdstanpy` accordingly.
Yes, I can make the changes.

Did cmdstanpy have functionality to read csv to Runset?
Yes, the RunSet object parses the csv and populates the following properties:

+ `draws` - number of draws per chain  (sampling iterations)
+ `columns` - total number of columns in the csv output - includes sampler state as well as model parameters, transformed parameter and generated quantities variables
+ `column_names` - csv column headers
+ `sample` - a 3-D numpy ndarray which contains all draws across all chain arranged as (draws, chains, columns) stored column major so that the values for each parameter are stored contiguously in memory, likewise all draws from a chain are contiguous.
+ `stepsize` - stepsize used by sampler for each chain.
+ `metric` - metric used by sampler for each chain.

If you look at the dimension of the metric, you'll get the number of parameters in the model - is that useful?  the csv columns are arranged:  sampler state, parameters, transformed parameters, generated quantities.  sampler state output names end in `_`.

The `sample`, `stepsize`, and `metric` properties are populated lazily - the first time that any of them are accessed, the RunSet class method `assemble_sample` is called.

hope this helps.
Reading through `arviz/data/converters.py` -
I think that you only need to change `PosteriorSample` to `RunSet` in the the following 2 lines:
```
70:        obj.__class__.__name__ in {"StanFit4Model", "PosteriorSample"}
77:        if obj.__class__.__name__ == "PosteriorSample":
```
tried this out locally and then ran the CmdStanPy tutorial notebook, adding the following - it just works!
<img width="1248" alt="arviz_from_cmdstanpy" src="https://user-images.githubusercontent.com/5466612/59967878-dbb22500-94fe-11e9-9c0d-e88fa817d297.png">

Yes, I tried it too, we only need to change `PosteriorSample` to `RunSet`.

Resolving patch:
diff --git a/arviz/data/io_cmdstanpy.py b/arviz/data/io_cmdstanpy.py
--- a/arviz/data/io_cmdstanpy.py
+++ b/arviz/data/io_cmdstanpy.py
@@ -114,6 +114,8 @@ def sample_stats_to_xarray(self):
                     coords[log_likelihood_dim_name] = coords.pop(default_dim_name)
 
         data = _unpack_frame(self.posterior.sample, columns, valid_cols)
+        if log_likelihood in data:
+            data["log_likelihood"] = data.pop(log_likelihood)
         for s_param in list(data.keys()):
             s_param_, *_ = s_param.split(".")
             name = re.sub("__$", "", s_param_)
@@ -193,7 +195,7 @@ def observed_data_to_xarray(self):
         observed_data = {}
         for key, vals in self.observed_data.items():
             vals = np.atleast_1d(vals)
-            val_dims = self.dims.get(key)
+            val_dims = self.dims.get(key) if self.dims is not None else None
             val_dims, coords = generate_dims_coords(
                 vals.shape, key, dims=val_dims, coords=self.coords
             )


Test patch:
diff --git a/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools-1.csv b/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools-1.csv
new file mode 100644
--- /dev/null
+++ b/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools-1.csv
@@ -0,0 +1,148 @@
+# stan_version_major = 2
+# stan_version_minor = 19
+# stan_version_patch = 1
+# model = schools_code_model
+# method = sample (Default)
+#   sample
+#     num_samples = 100
+#     num_warmup = 1000 (Default)
+#     save_warmup = 0 (Default)
+#     thin = 1 (Default)
+#     adapt
+#       engaged = 1 (Default)
+#       gamma = 0.050000000000000003 (Default)
+#       delta = 0.80000000000000004 (Default)
+#       kappa = 0.75 (Default)
+#       t0 = 10 (Default)
+#       init_buffer = 75 (Default)
+#       term_buffer = 50 (Default)
+#       window = 25 (Default)
+#     algorithm = hmc (Default)
+#       hmc
+#         engine = nuts (Default)
+#           nuts
+#             max_depth = 10 (Default)
+#         metric = diag_e (Default)
+#         metric_file =  (Default)
+#         stepsize = 1 (Default)
+#         stepsize_jitter = 0 (Default)
+# id = 1
+# data
+#   file = C:\Users\user\AppData\Local\Temp\tmpz11s67y0\tmp6jbl10z0.json
+# init = 2 (Default)
+# random
+#   seed = 49006
+# output
+#   file = C:\Users\user\AppData\Local\Temp\tmpz11s67y0\stan-schools_code-draws-1-ruhdz7a3.csv
+#   diagnostic_file =  (Default)
+#   refresh = 100 (Default)
+lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,mu,tau,eta.1,eta.2,eta.3,eta.4,eta.5,eta.6,eta.7,eta.8,theta.1,theta.2,theta.3,theta.4,theta.5,theta.6,theta.7,theta.8,log_lik.1,log_lik.2,log_lik.3,log_lik.4,log_lik.5,log_lik.6,log_lik.7,log_lik.8,y_hat.1,y_hat.2,y_hat.3,y_hat.4,y_hat.5,y_hat.6,y_hat.7,y_hat.8
+# Adaptation terminated
+# Step size = 0.444467
+# Diagonal elements of inverse mass matrix:
+# 11.0093, 1.27339, 0.934374, 0.903688, 0.942144, 0.840391, 0.802124, 0.995019, 0.80084, 0.971203
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+-7.85333,1,0.444467,3,7,0,12.5093,2.46123,0.813081,0.337276,-1.41003,-1.0043,0.36634,0.373567,-0.246375,-1.28202,-1.75622,2.73547,1.31477,1.64465,2.7591,2.76497,2.26091,1.41884,1.03328,-5.04543,-3.44499,-3.73366,-3.39115,-3.20366,-3.3234,-4.5962,-3.99491,-13.3021,0.0502464,-4.54774,8.52617,9.88216,-1.47624,20.7528,-9.41028
+-8.00181,0.942318,0.444467,3,15,0,11.7018,6.34482,0.774735,-0.367689,0.43327,1.80309,-1.63041,-0.729354,0.857202,0.147996,1.18727,6.05995,6.68049,7.74174,5.08168,5.77976,7.00892,6.45947,7.26463,-4.69669,-3.23023,-3.91689,-3.33204,-3.3999,-3.46604,-3.88744,-3.84391,28.6113,14.4921,4.991,7.87221,4.13305,1.88146,2.89457,32.5311
+-9.35715,0.89283,0.444467,3,15,0,15.7673,8.015,0.564617,0.374518,-0.786905,1.08851,-0.212104,-0.931013,1.7963,2.05337,0.396671,8.22646,7.5707,8.62959,7.89524,7.48933,9.02922,9.17437,8.23897,-4.49586,-3.22245,-3.95568,-3.32015,-3.56103,-3.58323,-3.61098,-3.83114,32.6065,7.56105,18.8794,14.3608,8.60324,-14.0247,-0.963251,-18.1021
+#
+#  Elapsed Time: 0.035 seconds (Warm-up)
+#                0.008 seconds (Sampling)
+#                0.043 seconds (Total)
+#
diff --git a/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools-2.csv b/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools-2.csv
new file mode 100644
--- /dev/null
+++ b/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools-2.csv
@@ -0,0 +1,148 @@
+# stan_version_major = 2
+# stan_version_minor = 19
+# stan_version_patch = 1
+# model = schools_code_model
+# method = sample (Default)
+#   sample
+#     num_samples = 100
+#     num_warmup = 1000 (Default)
+#     save_warmup = 0 (Default)
+#     thin = 1 (Default)
+#     adapt
+#       engaged = 1 (Default)
+#       gamma = 0.050000000000000003 (Default)
+#       delta = 0.80000000000000004 (Default)
+#       kappa = 0.75 (Default)
+#       t0 = 10 (Default)
+#       init_buffer = 75 (Default)
+#       term_buffer = 50 (Default)
+#       window = 25 (Default)
+#     algorithm = hmc (Default)
+#       hmc
+#         engine = nuts (Default)
+#           nuts
+#             max_depth = 10 (Default)
+#         metric = diag_e (Default)
+#         metric_file =  (Default)
+#         stepsize = 1 (Default)
+#         stepsize_jitter = 0 (Default)
+# id = 2
+# data
+#   file = C:\Users\user\AppData\Local\Temp\tmpz11s67y0\tmp6jbl10z0.json
+# init = 2 (Default)
+# random
+#   seed = 49006
+# output
+#   file = C:\Users\user\AppData\Local\Temp\tmpz11s67y0\stan-schools_code-draws-2-3tn89rz6.csv
+#   diagnostic_file =  (Default)
+#   refresh = 100 (Default)
+lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,mu,tau,eta.1,eta.2,eta.3,eta.4,eta.5,eta.6,eta.7,eta.8,theta.1,theta.2,theta.3,theta.4,theta.5,theta.6,theta.7,theta.8,log_lik.1,log_lik.2,log_lik.3,log_lik.4,log_lik.5,log_lik.6,log_lik.7,log_lik.8,y_hat.1,y_hat.2,y_hat.3,y_hat.4,y_hat.5,y_hat.6,y_hat.7,y_hat.8
+# Adaptation terminated
+# Step size = 0.409697
+# Diagonal elements of inverse mass matrix:
+# 9.74347, 1.05193, 1.04414, 0.836565, 0.854341, 0.814735, 0.879733, 0.956685, 0.813884, 0.940565
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+-5.7912,0.952955,0.409697,3,7,0,9.10887,1.59249,2.3283,1.54898,0.890208,-0.344264,-0.0739024,-0.248601,-0.1416,2.21929,0.0417257,5.19898,3.66517,0.790944,1.42043,1.01368,1.26281,6.75967,1.68964,-4.78229,-3.31548,-3.7196,-3.44548,-3.14119,-3.31712,-3.85325,-3.97336,-5.57789,4.42058,-27.3458,3.74741,6.06627,-12.3081,3.00594,0.113982
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+-6.30802,1,0.409697,3,7,0,8.9076,0.717837,2.21031,0.741613,-0.53056,-1.85381,-0.290449,0.627704,0.869901,1.17384,0.545461,2.35703,-0.454866,-3.37966,0.075854,2.10526,2.64059,3.3124,1.92347,-5.08824,-3.57895,-3.69181,-3.51495,-3.17569,-3.32796,-4.30015,-3.966,-1.13364,-16.8611,-30.7261,17.2151,-7.81655,9.31199,9.2024,-7.87928
+-2.45569,0.972373,0.409697,3,7,0,8.48276,2.5368,14.7295,1.54725,0.508608,0.495479,0.368409,-0.116724,0.0936454,0.493936,-0.301789,25.327,10.0283,9.83496,7.96327,0.817508,3.91615,9.81223,-1.9084,-3.64287,-3.24209,-4.01328,-3.32067,-3.13655,-3.35197,-3.55672,-4.10783,27.4997,13.3834,12.1634,13.5757,1.12536,8.40295,8.44051,5.52376
+-3.80908,0.860463,0.409697,3,7,0,5.60619,0.926953,6.563,-0.191822,-0.206029,-0.313273,-0.121586,-0.118293,-0.287493,0.810418,1.51082,-0.331971,-0.425213,-1.12906,0.128986,0.1506,-0.959865,6.24573,10.8425,-5.41077,-3.57644,-3.69836,-3.51192,-3.12434,-3.33271,-3.91234,-3.81138,-8.69953,-7.1054,-2.44254,21.214,5.86237,-8.12406,10.2281,26.8332
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+-5.46291,0.729488,0.409697,3,7,0,7.75447,-3.76137,9.84595,0.403996,0.199454,0.158597,-0.0781651,0.123398,0.254422,1.31476,1.83428,0.216348,-1.79755,-2.19983,-4.53098,-2.5464,-1.25635,9.18368,14.2988,-5.34239,-3.70148,-3.69278,-3.86627,-3.13092,-3.33787,-3.61016,-3.81747,-10.5269,-15.48,-3.51332,-7.473,-8.78123,-7.12667,22.4213,51.5507
+-6.05664,0.95591,0.409697,3,15,0,11.531,3.13373,4.68862,1.45131,0.0281425,0.644908,1.11254,-0.726769,1.48774,1.093,1.47731,9.93836,3.26568,6.15746,8.35003,-0.273813,10.1092,8.2584,10.0603,-4.35193,-3.33359,-3.85531,-3.32437,-3.11942,-3.65972,-3.69602,-3.81512,13.5056,-7.70527,-9.36714,-3.51189,-10.7248,19.5355,5.02659,25.2535
+-6.68105,0.961881,0.409697,4,31,0,9.75449,5.24321,0.345284,-1.31964,-1.07084,-1.07983,0.244689,-0.278339,-0.350764,0.216083,-0.799599,4.78756,4.87347,4.87036,5.3277,5.14711,5.1221,5.31782,4.96713,-4.82436,-3.2704,-3.81251,-3.32839,-3.34942,-3.38705,-4.02571,-3.88564,3.23482,-1.49646,-12.8238,13.6673,0.797981,-8.81763,1.83009,-6.57216
+-10.6449,0.929646,0.409697,3,7,0,13.4462,5.2235,3.30745,-1.85569,-1.92767,-0.285037,1.25067,-0.38056,1.42781,-0.250798,-1.65166,-0.914104,-1.15216,4.28076,9.36002,3.96482,9.9459,4.394,-0.23926,-5.48482,-3.64033,-3.79506,-3.33985,-3.26832,-3.64753,-4.14714,-4.04048,19.9155,12.056,25.0783,8.1351,2.6721,-11.8973,6.14123,5.6903
+-13.5646,0.769925,0.409697,4,31,0,22.7864,1.9839,5.84419,-1.67705,-2.30763,-0.953687,0.873028,-0.129743,0.359037,-0.324826,-2.08413,-7.8171,-11.5023,-3.58963,7.08605,1.22566,4.08218,0.085552,-10.1962,-6.4778,-5.12323,-3.69221,-3.31686,-3.14674,-3.35609,-4.82616,-4.5696,-26.1873,-11.0954,-42.3526,16.0217,-10.7739,-8.94758,-13.4545,-16.1403
+-7.08368,1,0.409697,3,7,0,16.0128,4.53181,0.75846,1.27321,0.0310346,-0.422244,-1.15438,0.707287,-0.0328225,2.02969,0.438761,5.49749,4.55535,4.21156,3.65626,5.06826,4.50692,6.07125,4.8646,-4.75224,-3.28085,-3.7931,-3.36303,-3.34347,-3.36765,-3.933,-3.88788,17.9296,-6.3114,5.59902,1.07536,12.4862,9.77758,8.51335,22.2351
+-7.88388,0.764587,0.409697,3,7,0,10.5926,4.82487,16.8919,0.120031,0.866962,0.0995478,0.72756,-1.57606,0.17791,0.236126,-0.273855,6.85242,19.4695,6.50643,17.1148,-21.7977,7.83012,8.81349,0.198931,-4.62081,-3.87927,-3.86804,-3.73959,-5.7862,-3.5096,-3.64348,-4.02423,39.6833,11.0881,32.6517,25.5543,-10.2699,2.00795,-2.70484,24.4587
+-3.41361,0.812729,0.409697,3,15,0,12.0538,2.50913,10.2821,1.41502,0.733247,-0.488676,-0.613169,0.629428,0.102489,0.685886,0.565623,17.0585,10.0485,-2.5155,-3.79556,8.98099,3.56294,9.5615,8.32494,-3.89302,-3.2425,-3.69199,-3.79842,-3.7311,-3.34398,-3.57756,-3.83015,23.5459,15.2699,-7.21964,-3.66415,9.31192,4.75249,16.657,-17.4747
+-5.34894,0.953481,0.409697,3,7,0,7.09785,-0.677862,5.1703,0.559835,0.12686,0.630455,1.69346,-0.968578,-0.0755078,0.844688,-0.62685,2.21665,-0.0219577,2.58178,8.07784,-5.6857,-1.06826,3.68943,-3.91886,-5.10428,-3.54328,-3.75238,-3.32163,-3.25169,-3.33451,-4.24549,-4.20038,6.56111,-13.9214,-7.76674,-10.2469,-7.59843,1.94847,16.5901,1.20555
+-4.82499,0.987928,0.409697,4,23,0,8.25464,4.0583,5.74103,1.71691,0.459366,-0.224646,-0.694227,-0.0572689,0.518682,-0.795432,0.867964,13.9151,6.69553,2.7686,0.0727195,3.72951,7.03606,-0.508303,9.0413,-4.06784,-3.23003,-3.75652,-3.51513,-3.25424,-3.46739,-4.93431,-3.82282,35.1523,7.24045,10.578,-14.0721,8.15821,2.0814,10.8456,7.24741
+#
+#  Elapsed Time: 0.038 seconds (Warm-up)
+#                0.011 seconds (Sampling)
+#                0.049 seconds (Total)
+#
diff --git a/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools-3.csv b/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools-3.csv
new file mode 100644
--- /dev/null
+++ b/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools-3.csv
@@ -0,0 +1,148 @@
+# stan_version_major = 2
+# stan_version_minor = 19
+# stan_version_patch = 1
+# model = schools_code_model
+# method = sample (Default)
+#   sample
+#     num_samples = 100
+#     num_warmup = 1000 (Default)
+#     save_warmup = 0 (Default)
+#     thin = 1 (Default)
+#     adapt
+#       engaged = 1 (Default)
+#       gamma = 0.050000000000000003 (Default)
+#       delta = 0.80000000000000004 (Default)
+#       kappa = 0.75 (Default)
+#       t0 = 10 (Default)
+#       init_buffer = 75 (Default)
+#       term_buffer = 50 (Default)
+#       window = 25 (Default)
+#     algorithm = hmc (Default)
+#       hmc
+#         engine = nuts (Default)
+#           nuts
+#             max_depth = 10 (Default)
+#         metric = diag_e (Default)
+#         metric_file =  (Default)
+#         stepsize = 1 (Default)
+#         stepsize_jitter = 0 (Default)
+# id = 3
+# data
+#   file = C:\Users\user\AppData\Local\Temp\tmpz11s67y0\tmp6jbl10z0.json
+# init = 2 (Default)
+# random
+#   seed = 49006
+# output
+#   file = C:\Users\user\AppData\Local\Temp\tmpz11s67y0\stan-schools_code-draws-3-k2vbz5q0.csv
+#   diagnostic_file =  (Default)
+#   refresh = 100 (Default)
+lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,mu,tau,eta.1,eta.2,eta.3,eta.4,eta.5,eta.6,eta.7,eta.8,theta.1,theta.2,theta.3,theta.4,theta.5,theta.6,theta.7,theta.8,log_lik.1,log_lik.2,log_lik.3,log_lik.4,log_lik.5,log_lik.6,log_lik.7,log_lik.8,y_hat.1,y_hat.2,y_hat.3,y_hat.4,y_hat.5,y_hat.6,y_hat.7,y_hat.8
+# Adaptation terminated
+# Step size = 0.431414
+# Diagonal elements of inverse mass matrix:
+# 10.322, 1.49738, 1.20159, 0.929056, 0.95979, 0.88158, 0.862366, 0.949804, 0.922541, 1.13512
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+-7.41682,0.985446,0.431414,3,7,0,12.5106,2.47388,9.56998,1.18057,0.379331,-1.99424,1.32798,-0.479911,1.37353,0.159114,-0.361393,13.7719,6.10406,-16.611,15.1826,-2.11886,15.6186,3.99659,-0.984645,-4.07685,-3.2395,-4.05336,-3.59351,-3.12389,-4.1999,-4.202,-4.0695,21.4864,-0.909156,-22.4851,20.918,-0.106546,12.8148,-4.89908,31.3807
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+-5.23761,0.993303,0.431414,3,7,0,9.86521,2.96195,4.74175,0.333288,0.69416,-0.48927,0.509545,1.46244,0.0733792,0.0275208,1.55784,4.54232,6.25349,0.641954,5.37809,9.89647,3.3099,3.09245,10.3488,-4.84979,-3.23678,-3.71743,-3.3277,-3.84908,-3.33888,-4.3327,-3.81352,-9.0983,-1.07011,-12.0048,-12.6702,2.0896,17.9074,14.9806,24.6713
+-6.51648,0.785447,0.431414,3,15,0,13.0343,6.2336,8.71692,-0.129102,-0.365414,-0.00166026,-0.64202,-1.87678,-0.209708,0.573915,-1.59462,5.10823,3.04831,6.21913,0.637163,-10.1262,4.40559,11.2364,-7.66657,-4.79151,-3.34412,-3.85753,-3.48413,-3.63028,-3.36476,-3.45026,-4.40618,-29.739,-18.2129,17.6336,-0.631714,-12.0578,3.4701,9.49405,-7.39318
+-5.95352,0.484003,0.431414,3,7,0,12.9372,6.32809,1.81955,0.887256,-0.845198,-0.4081,0.840306,0.355822,0.562838,1.03421,-1.69343,7.94249,4.7902,5.58553,7.85706,6.97552,7.3522,8.20989,3.2468,-4.521,-3.27304,-3.83549,-3.31987,-3.50881,-3.48357,-3.70076,-3.92755,-15.0749,15.9865,2.95116,13.143,8.70309,0.25273,-4.18249,2.21766
+-7.29735,0.882098,0.431414,3,7,0,10.2959,-0.453297,3.84868,0.32646,1.34267,0.371403,0.11615,-0.485606,-0.786492,-0.120527,2.40018,0.803144,4.71421,0.976115,-0.00627253,-2.32224,-3.48025,-0.917167,8.78421,-5.2707,-3.27551,-3.72241,-3.51968,-3.12696,-3.39978,-5.01082,-3.82527,5.25793,19.7194,0.32681,11.2347,19.1521,7.5954,15.1904,20.7868
+-5.40235,0.985015,0.431414,3,7,0,11.4838,6.41434,3.12953,0.988554,-0.575646,-0.316931,-1.60978,0.30545,-0.0642897,1.47686,-0.747816,9.50805,4.61283,5.42249,1.37647,7.37025,6.21314,11.0362,4.07402,-4.38688,-3.27889,-3.83008,-3.44751,-3.54864,-3.42913,-3.464,-3.90626,29.9288,4.97092,-8.48871,2.94491,7.92417,7.13224,15.4767,19.367
+-5.18648,0.977373,0.431414,3,7,0,7.68727,6.52189,0.922877,0.843611,-0.291696,0.0609931,-1.22378,-0.019977,0.613203,1.08864,0.296479,7.30043,6.25269,6.57817,5.39249,6.50345,7.0878,7.52656,6.7955,-4.57915,-3.23679,-3.87071,-3.32751,-3.4637,-3.46998,-3.76999,-3.85111,16.874,0.222372,11.5445,13.0421,3.23466,14.7917,28.8211,-24.2687
+-7.34498,0.852646,0.431414,3,7,0,12.1268,6.13502,0.711094,0.508043,-1.28034,0.689718,-1.24355,-0.413932,0.737188,1.74875,0.429822,6.49628,5.22458,6.62547,5.25074,5.84067,6.65923,7.37854,6.44066,-4.65457,-3.26004,-3.87248,-3.32948,-3.40502,-3.44918,-3.7856,-3.85701,5.79848,5.83836,7.68863,2.90773,20.0899,-3.77031,13.3891,15.2259
+-5.18544,0.997905,0.431414,3,7,0,8.94292,3.55664,7.99393,-0.540466,-0.418828,-0.574113,1.1418,0.070488,-1.41037,0.201582,0.147055,-0.763812,0.208558,-1.03278,12.6841,4.12011,-7.71779,5.16806,4.73218,-5.46556,-3.52506,-3.69909,-3.45034,-3.27799,-3.63088,-4.04482,-3.89082,-7.17588,-9.04514,-0.527605,20.6524,-3.48988,-19.018,-14.6546,1.74405
+-9.34673,0.921739,0.431414,3,7,0,11.9516,7.12406,1.16998,0.412814,-1.38768,-1.44589,-1.30564,-0.835795,1.95044,1.21511,-0.764346,7.60705,5.50051,5.4324,5.59649,6.1462,9.40604,8.54572,6.22979,-4.55115,-3.25276,-3.83041,-3.32497,-3.4314,-3.60882,-3.66844,-3.86069,-3.52069,18.1792,0.0937964,1.36053,17.3753,2.65421,22.8436,-9.03928
+-11.9209,0.925457,0.431414,3,15,0,19.8528,8.72387,4.45108,0.963502,1.53254,1.15599,1.24614,-1.79629,1.60051,2.17914,0.966211,13.0125,15.5454,13.8693,14.2706,0.72843,15.8479,18.4234,13.0246,-4.12616,-3.50619,-4.24733,-3.53527,-3.1346,-4.22782,-3.22242,-3.81093,26.8017,3.43093,22.2471,10.4958,0.758822,-8.60659,14.9105,36.3717
+-10.7436,0.935107,0.431414,3,7,0,21.6225,1.70798,4.92271,2.076,1.27808,-1.94029,-0.219039,0.494156,-2.09948,1.93131,1.37815,11.9275,7.99962,-7.8435,0.629713,4.14056,-8.62714,11.2153,8.4922,-4.20104,-3.22152,-3.73735,-3.48452,-3.27928,-3.69982,-3.45169,-3.8283,10.0475,7.44073,-32.8863,5.42262,-5.70123,-16.0225,6.90047,14.5725
+-5.05291,0.998723,0.431414,3,7,0,12.5812,5.30778,5.42588,0.688093,0.711884,-0.559656,-0.250597,0.745573,-1.74029,1.00338,0.994419,9.04128,9.17037,2.27115,3.94807,9.35316,-4.13482,10.752,10.7034,-4.42573,-3.22837,-3.74579,-3.35532,-3.77782,-3.42579,-3.48419,-3.8119,-2.38813,4.26704,3.09808,14.4359,25.4813,-1.27407,7.02996,-4.87006
+-4.3046,0.861714,0.431414,3,7,0,9.71542,3.85196,3.79434,0.131104,-0.74965,-0.299733,-0.913898,0.0320292,1.02314,1.0279,-0.769315,4.34942,1.00753,2.71467,0.384319,3.97349,7.73412,7.75218,0.932918,-4.86999,-3.466,-3.75531,-3.49769,-3.26885,-3.50422,-3.74661,-3.99832,18.7433,2.11369,-20.8029,8.78019,19.817,18.3862,2.5122,11.1758
+#
+#  Elapsed Time: 0.048 seconds (Warm-up)
+#                0.01 seconds (Sampling)
+#                0.058 seconds (Total)
+#
diff --git a/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools-4.csv b/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools-4.csv
new file mode 100644
--- /dev/null
+++ b/arviz/tests/saved_models/cmdstanpy/cmdstanpy_eight_schools-4.csv
@@ -0,0 +1,148 @@
+# stan_version_major = 2
+# stan_version_minor = 19
+# stan_version_patch = 1
+# model = schools_code_model
+# method = sample (Default)
+#   sample
+#     num_samples = 100
+#     num_warmup = 1000 (Default)
+#     save_warmup = 0 (Default)
+#     thin = 1 (Default)
+#     adapt
+#       engaged = 1 (Default)
+#       gamma = 0.050000000000000003 (Default)
+#       delta = 0.80000000000000004 (Default)
+#       kappa = 0.75 (Default)
+#       t0 = 10 (Default)
+#       init_buffer = 75 (Default)
+#       term_buffer = 50 (Default)
+#       window = 25 (Default)
+#     algorithm = hmc (Default)
+#       hmc
+#         engine = nuts (Default)
+#           nuts
+#             max_depth = 10 (Default)
+#         metric = diag_e (Default)
+#         metric_file =  (Default)
+#         stepsize = 1 (Default)
+#         stepsize_jitter = 0 (Default)
+# id = 4
+# data
+#   file = C:\Users\user\AppData\Local\Temp\tmpz11s67y0\tmp6jbl10z0.json
+# init = 2 (Default)
+# random
+#   seed = 49006
+# output
+#   file = C:\Users\user\AppData\Local\Temp\tmpz11s67y0\stan-schools_code-draws-4-y82c1kj3.csv
+#   diagnostic_file =  (Default)
+#   refresh = 100 (Default)
+lp__,accept_stat__,stepsize__,treedepth__,n_leapfrog__,divergent__,energy__,mu,tau,eta.1,eta.2,eta.3,eta.4,eta.5,eta.6,eta.7,eta.8,theta.1,theta.2,theta.3,theta.4,theta.5,theta.6,theta.7,theta.8,log_lik.1,log_lik.2,log_lik.3,log_lik.4,log_lik.5,log_lik.6,log_lik.7,log_lik.8,y_hat.1,y_hat.2,y_hat.3,y_hat.4,y_hat.5,y_hat.6,y_hat.7,y_hat.8
+# Adaptation terminated
+# Step size = 0.411307
+# Diagonal elements of inverse mass matrix:
+# 10.3584, 1.2492, 0.804788, 0.870948, 0.800705, 0.782754, 0.807559, 0.811814, 0.919356, 0.930502
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+-4.14778,0.956302,0.411307,3,7,0,8.55244,7.45149,11.6211,0.345534,0.341511,1.11625,0.33137,-0.62536,-0.343514,0.846692,-0.589634,11.467,11.4202,20.4235,11.3024,0.184135,3.45949,17.291,0.599306,-4.23441,-3.28001,-4.76313,-3.39332,-3.12482,-3.34183,-3.22404,-4.00989,15.9606,14.1607,33.4962,7.9611,-2.53748,17.5185,13.659,13.8385
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+-4.23921,0.869004,0.411307,3,7,0,9.77403,2.71016,2.72708,0.342112,0.624396,-0.36191,0.259521,-0.459546,-1.68363,0.0806198,0.599227,3.64313,4.41294,1.7232,3.4179,1.45694,-1.88124,2.93002,4.3443,-4.94534,-3.28586,-3.7351,-3.36986,-3.15343,-3.35114,-4.35705,-3.89976,-3.89187,-4.52627,8.38855,21.0244,10.5724,6.15545,-8.4633,6.24161
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+-10.1341,0.839977,0.411307,3,7,0,13.127,1.69379,2.42002,1.34823,-0.666872,-0.29505,0.731147,-2.29243,-1.42026,1.74498,1.84886,4.95655,0.0799494,0.979768,3.46319,-3.85394,-1.74328,5.91668,6.16808,-4.80699,-3.53516,-3.72246,-3.36852,-3.16644,-3.34793,-3.95156,-3.8618,-20.7999,17.5478,2.12017,11.8441,-5.74157,4.35072,-8.16512,43.3967
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+-4.73315,0.992924,0.411307,3,7,0,10.6571,6.75561,4.38557,-0.0633051,-0.513556,-0.555905,0.0685072,-2.05299,-0.528125,0.467172,0.897106,6.47798,4.50338,4.31765,7.05606,-2.24793,4.43948,8.80443,10.6899,-4.65632,-3.28266,-3.79611,-3.31685,-3.12578,-3.36572,-3.64432,-3.81196,-8.67662,-0.642039,-12.2142,3.16648,-5.63154,26.1027,-4.79442,2.87946
+-4.10922,0.908481,0.411307,4,31,0,7.4068,5.47564,1.45909,-0.880537,-0.2972,-0.247508,0.261554,-0.278227,0.0328608,-0.867884,-0.474156,4.19086,5.042,5.1145,5.85727,5.06968,5.52359,4.20932,4.7838,-4.88671,-3.26527,-3.82013,-3.32223,-3.34358,-3.40139,-4.17244,-3.88967,-7.43026,14.6039,-18.9291,1.50422,12.4809,15.9337,26.2597,30.789
+-5.60628,0.93877,0.411307,3,7,0,10.424,7.58397,3.34122,0.272664,-0.483492,-1.88895,0.937029,-1.13383,-0.303623,-0.334102,-0.648876,8.495,5.96852,1.27257,10.7148,3.79561,6.5695,6.46766,5.41593,-4.47242,-3.24216,-3.72718,-3.37386,-3.25813,-3.44501,-3.8865,-3.87621,-2.40412,19.8786,24.2241,17.6846,-2.09855,10.7918,15.3394,-1.46301
+-6.34678,0.983395,0.411307,4,15,0,9.15259,3.87606,4.98044,0.170736,-0.126673,-2.37458,1.53622,0.259982,0.00374382,-0.0830168,-0.132344,4.7264,3.24517,-7.95038,11.5271,5.17089,3.89471,3.4626,3.21693,-4.83068,-3.33457,-3.73939,-3.40152,-3.35122,-3.35146,-4.2782,-3.92836,19.6571,-8.35679,-26.4887,13.6977,1.40544,9.22058,-10.0329,-4.13948
+#
+#  Elapsed Time: 0.046 seconds (Warm-up)
+#                0.008 seconds (Sampling)
+#                0.054 seconds (Total)
+#
diff --git a/arviz/tests/test_data_cmdstanpy.py b/arviz/tests/test_data_cmdstanpy.py
new file mode 100644
--- /dev/null
+++ b/arviz/tests/test_data_cmdstanpy.py
@@ -0,0 +1,154 @@
+# pylint: disable=redefined-outer-name
+from glob import glob
+import os
+import numpy as np
+import pytest
+
+from arviz import from_cmdstanpy
+from .helpers import (  # pylint: disable=unused-import
+    chains,
+    check_multiple_attrs,
+    draws,
+    eight_schools_params,
+    load_cached_models,
+    pystan_version,
+)
+
+
+class TestDataCmdStanPy:
+    @pytest.fixture(scope="session")
+    def data_directory(self):
+        here = os.path.dirname(os.path.abspath(__file__))
+        data_directory = os.path.join(here, "saved_models")
+        return data_directory
+
+    @pytest.fixture(scope="class")
+    def filepaths(self, data_directory):
+        files = glob(os.path.join(data_directory, "cmdstanpy", "cmdstanpy_eight_schools-[1-4].csv"))
+        return files
+
+    @pytest.fixture(scope="class")
+    def data(self, filepaths):
+        from cmdstanpy import StanFit
+        from cmdstanpy.model import CmdStanArgs, SamplerArgs
+
+        class Data:
+            args = CmdStanArgs(
+                "dummy.stan", "dummy.exe", list(range(1, 5)), method_args=SamplerArgs()
+            )
+            obj = StanFit(args)
+            obj.csv_files = filepaths
+            obj._validate_csv_files()  # pylint: disable=protected-access
+            obj._assemble_sample()  # pylint: disable=protected-access
+
+        return Data
+
+    def get_inference_data(self, data, eight_schools_params):
+        """vars as str."""
+        return from_cmdstanpy(
+            posterior=data.obj,
+            posterior_predictive="y_hat",
+            prior=data.obj,
+            prior_predictive="y_hat",
+            observed_data={"y": eight_schools_params["y"]},
+            log_likelihood="log_lik",
+            coords={"school": np.arange(eight_schools_params["J"])},
+            dims={
+                "theta": ["school"],
+                "y": ["school"],
+                "log_lik": ["school"],
+                "y_hat": ["school"],
+                "eta": ["school"],
+            },
+        )
+
+    def get_inference_data2(self, data, eight_schools_params):
+        """vars as lists."""
+        return from_cmdstanpy(
+            posterior=data.obj,
+            posterior_predictive=["y_hat"],
+            prior=data.obj,
+            prior_predictive=["y_hat"],
+            observed_data={"y": eight_schools_params["y"]},
+            log_likelihood="log_lik",
+            coords={
+                "school": np.arange(eight_schools_params["J"]),
+                "log_likelihood_dim": np.arange(eight_schools_params["J"]),
+            },
+            dims={
+                "theta": ["school"],
+                "y": ["school"],
+                "y_hat": ["school"],
+                "eta": ["school"],
+                "log_lik": ["log_likelihood_dim"],
+            },
+        )
+
+    def get_inference_data3(self, data, eight_schools_params):
+        """multiple vars as lists."""
+        return from_cmdstanpy(
+            posterior=data.obj,
+            posterior_predictive=["y_hat", "log_lik"],
+            prior=data.obj,
+            prior_predictive=["y_hat", "log_lik"],
+            observed_data={"y": eight_schools_params["y"]},
+            coords={"school": np.arange(eight_schools_params["J"])},
+            dims={"theta": ["school"], "y": ["school"], "y_hat": ["school"], "eta": ["school"]},
+        )
+
+    def get_inference_data4(self, data, eight_schools_params):
+        """multiple vars as lists."""
+        return from_cmdstanpy(
+            posterior=data.obj,
+            posterior_predictive=None,
+            prior=data.obj,
+            prior_predictive=None,
+            observed_data={"y": eight_schools_params["y"]},
+            coords=None,
+            dims=None,
+        )
+
+    def test_sampler_stats(self, data, eight_schools_params):
+        inference_data = self.get_inference_data(data, eight_schools_params)
+        test_dict = {"sample_stats": ["lp", "diverging"]}
+        fails = check_multiple_attrs(test_dict, inference_data)
+        assert not fails
+
+    def test_inference_data(self, data, eight_schools_params):
+        inference_data1 = self.get_inference_data(data, eight_schools_params)
+        inference_data2 = self.get_inference_data2(data, eight_schools_params)
+        inference_data3 = self.get_inference_data3(data, eight_schools_params)
+        inference_data4 = self.get_inference_data4(data, eight_schools_params)
+        # inference_data 1
+        test_dict = {
+            "posterior": ["theta"],
+            "observed_data": ["y"],
+            "sample_stats": ["log_likelihood"],
+            "prior": ["theta"],
+        }
+        fails = check_multiple_attrs(test_dict, inference_data1)
+        assert not fails
+        # inference_data 2
+        test_dict = {
+            "posterior_predictive": ["y_hat"],
+            "observed_data": ["y"],
+            "sample_stats_prior": ["lp"],
+            "sample_stats": ["lp"],
+            "prior_predictive": ["y_hat"],
+        }
+        fails = check_multiple_attrs(test_dict, inference_data2)
+        assert not fails
+        # inference_data 3
+        test_dict = {
+            "posterior_predictive": ["y_hat"],
+            "observed_data": ["y"],
+            "sample_stats_prior": ["lp"],
+            "sample_stats": ["lp"],
+            "prior_predictive": ["y_hat"],
+        }
+        fails = check_multiple_attrs(test_dict, inference_data3)
+        assert not fails
+        # inference_data 4
+        test_dict = {"posterior": ["theta"], "prior": ["theta"]}
+        fails = check_multiple_attrs(test_dict, inference_data4)
+        assert not fails