Don't change matplotlib style on import of causalpy + misc dev fixes

Makefile

@@ -13,10 +13,10 @@ check_lint:
 	interrogate .
 
 doctest:
-	pytest --doctest-modules --ignore=causalpy/tests/ causalpy/ --config-file=causalpy/tests/conftest.py
+	python -m 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/__init__.py

@@ -11,7 +11,6 @@
 #   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.
-import arviz as az
 
 import causalpy.pymc_models as pymc_models
 import causalpy.skl_models as skl_models
@@ -28,8 +27,6 @@
 from .experiments.regression_kink import RegressionKink
 from .experiments.synthetic_control import SyntheticControl
 
-az.style.use("arviz-darkgrid")
-
 __all__ = [
     "__version__",
     "DifferenceInDifferences",

causalpy/experiments/base.py

@@ -17,6 +17,8 @@
 
 from abc import abstractmethod
 
+import arviz as az
+import matplotlib.pyplot as plt
 import pandas as pd
 from sklearn.base import RegressorMixin
 
@@ -63,12 +65,14 @@ def plot(self, *args, **kwargs) -> tuple:
         Internally, this function dispatches to either `_bayesian_plot` or `_ols_plot`
         depending on the model type.
         """
-        if isinstance(self.model, PyMCModel):
-            return self._bayesian_plot(*args, **kwargs)
-        elif isinstance(self.model, RegressorMixin):
-            return self._ols_plot(*args, **kwargs)
-        else:
-            raise ValueError("Unsupported model type")
+        # Apply arviz-darkgrid style only during plotting, then revert
+        with plt.style.context(az.style.library["arviz-darkgrid"]):
+            if isinstance(self.model, PyMCModel):
+                return self._bayesian_plot(*args, **kwargs)
+            elif isinstance(self.model, RegressorMixin):
+                return self._ols_plot(*args, **kwargs)
+            else:
+                raise ValueError("Unsupported model type")
 
     @abstractmethod
     def _bayesian_plot(self, *args, **kwargs):

environment.yml

@@ -16,3 +16,4 @@ dependencies:
   - statsmodels
   - xarray>=v2022.11.0
   - pymc-extras>=0.3.0
+  - python>=3.11