Fix bug in SyntheticControl.get_plot_data

Makefile

@@ -16,7 +16,7 @@ doctest:
 	pytest --doctest-modules --ignore=causalpy/tests/ causalpy/ --config-file=causalpy/tests/conftest.py
 
 test:
-	pytest
+	python -m pytest
 
 uml:
 	pyreverse -o png causalpy --output-directory docs/source/_static --ignore tests

causalpy/plot_utils.py

@@ -93,10 +93,17 @@ def get_hdi_to_df(
     :param hdi_prob:
         The size of the HDI, default is 0.94
     """
-    hdi = (
-        az.hdi(x, hdi_prob=hdi_prob)
-        .to_dataframe()
-        .unstack(level="hdi")
-        .droplevel(0, axis=1)
-    )
-    return hdi
+    hdi_result = az.hdi(x, hdi_prob=hdi_prob)
+
+    # Get the data variable name (typically 'mu' or 'x')
+    # We select only the data variable column to exclude coordinates like 'treated_units'
+    data_var = list(hdi_result.data_vars)[0]
+
+    # Convert to DataFrame, select only the data variable column, then unstack
+    # This prevents coordinate values (like 'treated_agg') from appearing as columns
+    hdi_df = hdi_result[data_var].to_dataframe()[[data_var]].unstack(level="hdi")
+
+    # Remove the top level of column MultiIndex to get just 'lower' and 'higher'
+    hdi_df.columns = hdi_df.columns.droplevel(0)
+
+    return hdi_df

causalpy/tests/test_plot_utils.py

@@ -0,0 +1,97 @@
+#   Copyright 2025 - 2025 The PyMC Labs Developers
+#
+#   Licensed under the Apache License, Version 2.0 (the "License");
+#   you may not use this file except in compliance with the License.
+#   You may obtain a copy of the License at
+#
+#       http://www.apache.org/licenses/LICENSE-2.0
+#
+#   Unless required by applicable law or agreed to in writing, software
+#   distributed under the License is distributed on an "AS IS" BASIS,
+#   WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
+#   See the License for the specific language governing permissions and
+#   limitations under the License.
+"""
+Tests for plot utility functions
+"""
+
+import numpy as np
+import pandas as pd
+import pytest
+import xarray as xr
+
+from causalpy.plot_utils import get_hdi_to_df
+
+
+@pytest.mark.integration
+def test_get_hdi_to_df_with_coordinate_dimensions():
+    """
+    Regression test for bug where get_hdi_to_df returned string coordinate values
+    instead of numeric HDI values when xarray had named coordinate dimensions.
+
+    This bug manifested in multi-cell synthetic control experiments where columns
+    like 'pred_hdi_upper_94' contained the string "treated_agg" instead of
+    numeric upper bound values.
+
+    See: https://github.com/pymc-labs/CausalPy/issues/532
+    """
+    # Create a mock xarray DataArray similar to what's produced in synthetic control
+    # with a coordinate dimension like 'treated_units'
+    np.random.seed(42)
+    n_chains = 2
+    n_draws = 100
+    n_obs = 10
+
+    # Simulate posterior samples with a named coordinate
+    data = np.random.normal(loc=5.0, scale=0.5, size=(n_chains, n_draws, n_obs))
+
+    xr_data = xr.DataArray(
+        data,
+        dims=["chain", "draw", "obs_ind"],
+        coords={
+            "chain": np.arange(n_chains),
+            "draw": np.arange(n_draws),
+            "obs_ind": np.arange(n_obs),
+            "treated_units": "treated_agg",  # This coordinate caused the bug
+        },
+    )
+
+    # Call get_hdi_to_df
+    result = get_hdi_to_df(xr_data, hdi_prob=0.94)
+
+    # Assertions to verify the bug is fixed
+    assert isinstance(result, pd.DataFrame), "Result should be a DataFrame"
+
+    # Check that we have exactly 2 columns (lower and higher)
+    assert result.shape[1] == 2, f"Expected 2 columns, got {result.shape[1]}"
+
+    # Check column names
+    assert "lower" in result.columns, "Should have 'lower' column"
+    assert "higher" in result.columns, "Should have 'higher' column"
+
+    # CRITICAL: Check that columns contain numeric data, not strings
+    assert result["lower"].dtype in [
+        np.float64,
+        np.float32,
+    ], f"'lower' column should be numeric, got {result['lower'].dtype}"
+    assert result["higher"].dtype in [
+        np.float64,
+        np.float32,
+    ], f"'higher' column should be numeric, got {result['higher'].dtype}"
+
+    # Check that no string values like 'treated_agg' appear in the data
+    assert not (result["lower"].astype(str).str.contains("treated_agg").any()), (
+        "'lower' column should not contain coordinate string values"
+    )
+    assert not (result["higher"].astype(str).str.contains("treated_agg").any()), (
+        "'higher' column should not contain coordinate string values"
+    )
+
+    # Verify HDI ordering
+    assert (result["lower"] <= result["higher"]).all(), (
+        "'lower' should be <= 'higher' for all rows"
+    )
+
+    # Verify reasonable HDI values (should be around the mean of 5.0)
+    assert result["lower"].min() > 3.0, "HDI lower bounds should be reasonable"
+    assert result["higher"].max() < 7.0, "HDI upper bounds should be reasonable"