fixing lint errors

Signed-off-by: Nathaniel <[email protected]>

Author: NathanielF

causalpy/data/__init__.py

@@ -1,4 +1,5 @@
 """Code for loading datasets."""
+
 from .datasets import load_data
 
 __all__ = ["load_data"]

causalpy/data/datasets.py

@@ -1,6 +1,7 @@
 """
 Functions to load example datasets
 """
+
 import pathlib
 
 import pandas as pd

causalpy/data/simulate_data.py

@@ -1,6 +1,7 @@
 """
 Functions that generate data sets used in examples
 """
+
 import numpy as np
 import pandas as pd
 from scipy.stats import dirichlet, gamma, norm, uniform

causalpy/pymc_experiments.py

@@ -1496,7 +1496,7 @@ class InversePropensityWeighting(ExperimentalDesign, PropensityDataValidator):
         A string denoting which weighting scheme to use among: 'raw', 'robust',
         'doubly robust' or 'overlap'. See Aronow and Miller "Foundations
         of Agnostic Statistics" for discussion and computation of these
-        weighting schemes. 
+        weighting schemes.
     :param model:
         A PyMC model
 
@@ -1550,9 +1550,9 @@ def __init__(
         self.model.fit(X=self.X, t=self.t, coords=COORDS)
 
     def make_robust_adjustments(self, ps):
-        """ This estimator is discussed in Aronow
-          and Miller's book as being related to the
-          Horvitz Thompson method """
+        """This estimator is discussed in Aronow
+        and Miller's book as being related to the
+        Horvitz Thompson method"""
         X = pd.DataFrame(self.X, columns=self.labels)
         X["ps"] = ps
         X[self.outcome_variable] = self.y
@@ -1570,8 +1570,8 @@ def make_robust_adjustments(self, ps):
         return weighted_outcome0, weighted_outcome1, n_ntrt, n_trt
 
     def make_raw_adjustments(self, ps):
-        """ This estimator is discussed in Aronow and
-        Miller as the simplest of base form of 
+        """This estimator is discussed in Aronow and
+        Miller as the simplest of base form of
         inverse propensity weighting schemes"""
         X = pd.DataFrame(self.X, columns=self.labels)
         X["ps"] = ps
@@ -1589,10 +1589,10 @@ def make_raw_adjustments(self, ps):
         return weighted_outcome0, weighted_outcome1, n_ntrt, n_trt
 
     def make_overlap_adjustments(self, ps):
-        """This weighting scheme was adapted from 
-         Lucy D’Agostino McGowan's blog on 
-         Propensity Score Weights referenced in 
-         the primary CausalPy explainer notebook"""
+        """This weighting scheme was adapted from
+        Lucy D’Agostino McGowan's blog on
+        Propensity Score Weights referenced in
+        the primary CausalPy explainer notebook"""
         X = pd.DataFrame(self.X, columns=self.labels)
         X["ps"] = ps
         X[self.outcome_variable] = self.y
@@ -1609,7 +1609,7 @@ def make_overlap_adjustments(self, ps):
         return weighted_outcome0, weighted_outcome1, n_ntrt, n_trt
 
     def make_doubly_robust_adjustment(self, ps):
-        """ The doubly robust weighting scheme is also
+        """The doubly robust weighting scheme is also
         discussed in Aronow and Miller, but a bit more generally
         than our implementation here. Here we have specified
         the outcome model to be a simple OLS model.
@@ -1740,7 +1740,8 @@ def make_hists(idata, i, axs, method=method):
             0.9, linestyle="--", label="Hi Extreme Propensity Scores", color="black"
         )
         axs[0].set_title(
-            "Weighted and Unweighted Draws from the Posterior \n  Propensity Scores Distribution", fontsize=20
+            "Weighted and Unweighted Draws from the Posterior \n  Propensity Scores Distribution",
+            fontsize=20,
         )
         axs[0].set_ylabel("Counts of Observations")
         axs[0].set_xlabel("Propensity Scores")

causalpy/skl_experiments.py

@@ -8,6 +8,7 @@
 - DifferenceInDifferences
 - RegressionDiscontinuity
 """
+
 import warnings
 from typing import Optional
 

causalpy/skl_models.py

@@ -4,6 +4,7 @@
 - Weighted Proportion
 
 """
+
 from functools import partial
 
 import numpy as np

causalpy/tests/conftest.py

@@ -4,6 +4,7 @@
 Functions:
 * rng: random number generator with session level scope
 """
+
 import numpy as np
 import pytest
 

causalpy/tests/test_data_loading.py

@@ -1,6 +1,7 @@
 """
 Tests that example data can be loaded into data frames.
 """
+
 import pandas as pd
 import pytest
 

causalpy/tests/test_pymc_experiments.py

@@ -1,6 +1,7 @@
 """
 Unit tests for pymc_experiments.py
 """
+
 import arviz as az
 import matplotlib as mpl
 import pandas as pd

causalpy/utils.py

@@ -1,6 +1,7 @@
 """
 Utility functions
 """
+
 import numpy as np
 import pandas as pd