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Update from_cmdstanpy converter to follow schema convention #1550

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Feb 15, 2021
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Update from_cmdstanpy converter to follow schema convention #1550

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4 changes: 2 additions & 2 deletions CHANGELOG.md
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Original file line number Diff line number Diff line change
Expand Up @@ -2,11 +2,11 @@

## v0.x.x Unreleased
### New features
* Added `to_zarr` and `from_zarr` methods to InferenceData ([1518](https://github.com/arviz-devs/arviz/pull/1535))
* Added `to_zarr` and `from_zarr` methods to InferenceData ([1518](https://github.com/arviz-devs/arviz/pull/1518))
* Added confidence interval band to auto-correlation plot ([1535](https://github.com/arviz-devs/arviz/pull/1535))

### Maintenance and fixes
* Updated `from_cmdstan`, `from_numpyro` and `from_pymc3` converters to follow schema convention ([1541](https://github.com/arviz-devs/arviz/pull/1541), [1525](https://github.com/arviz-devs/arviz/pull/1525) and [1555](https://github.com/arviz-devs/arviz/pull/1555))
* Updated `from_cmdstanpy`, `from_cmdstan`, `from_numpyro` and `from_pymc3` converters to follow schema convention ([1550](https://github.com/arviz-devs/arviz/pull/1550), [1541](https://github.com/arviz-devs/arviz/pull/1541), [1525](https://github.com/arviz-devs/arviz/pull/1525) and [1555](https://github.com/arviz-devs/arviz/pull/1555))
* Fix calculation of mode as point estimate ([1552](https://github.com/arviz-devs/arviz/pull/1552))
* Remove variable name from legend in posterior predictive plot ([1559](https://github.com/arviz-devs/arviz/pull/1559))

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9 changes: 8 additions & 1 deletion arviz/data/io_cmdstanpy.py
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Expand Up @@ -113,6 +113,13 @@ def stats_to_xarray(self, fit):

dtypes = {"divergent__": bool, "n_leapfrog__": np.int64, "treedepth__": np.int64}
items = list(self.posterior.sampler_vars_cols.keys())
rename_dict = {
"divergent": "diverging",
"n_leapfrog": "n_steps",
"treedepth": "tree_depth",
"stepsize": "step_size",
"accept_stat": "acceptance_rate",
}

data, data_warmup = _unpack_fit(
fit,
Expand All @@ -121,7 +128,7 @@ def stats_to_xarray(self, fit):
)
for item in items:
name = re.sub("__$", "", item)
name = "diverging" if name == "divergent" else name
name = rename_dict.get(name, name)
data[name] = data.pop(item).astype(dtypes.get(item, float))
if data_warmup:
data_warmup[name] = data_warmup.pop(item).astype(dtypes.get(item, float))
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