fix format

Author: SvenKlaassen

doubleml/_utils_plots.py

@@ -36,15 +36,15 @@ def _sensitivity_contour_plot(x,
 
     if include_scenario:
         fig.add_trace(go.Scatter(x=[scenario_x],
-                                    y=[scenario_y],
-                                    mode="markers+text",
-                                    marker=dict(size=10, color='red', line=dict(width=2, color=text_col)),
-                                    hovertemplate=hov_temp + f': {round(scenario_value, 3)}' + '</b>',
-                                    name='Scenario',
-                                    textfont=dict(color=text_col, size=14),
-                                    text=['<b>Scenario</b>'],
-                                    textposition="top right",
-                                    showlegend=False))
+                                 y=[scenario_y],
+                                 mode="markers+text",
+                                 marker=dict(size=10, color='red', line=dict(width=2, color=text_col)),
+                                 hovertemplate=hov_temp + f': {round(scenario_value, 3)}' + '</b>',
+                                 name='Scenario',
+                                 textfont=dict(color=text_col, size=14),
+                                 text=['<b>Scenario</b>'],
+                                 textposition="top right",
+                                 showlegend=False))
 
     # add unadjusted
     fig.add_trace(go.Scatter(x=[0],

doubleml/datasets.py

@@ -957,7 +957,8 @@ def make_confounded_irm_data(n_obs=500, theta=5.0, cf_y=0.04, cf_d=0.04):
 
     The observed data is given as :math:`W = (Y, D, X)`.
     Further, orcale values of the confounder :math:`A`, the transformed covariated :math:`Z`,
-    the potential outcomes of :math:`Y`, the coefficients :math:`\\gamma_a`, :math:`\\beta_a`, the long and short forms of the main regression and the propensity score
+    the potential outcomes of :math:`Y`, the coefficients :math:`\\gamma_a`, :math:`\\beta_a`, the
+    long and short forms of the main regression and the propensity score
     are returned in a dictionary.
 
     Parameters

doubleml/double_ml.py

@@ -1657,7 +1657,8 @@ def sensitivity_analysis(self, cf_y=0.03, cf_d=0.03, rho=1.0, level=0.95, null_h
 
         null_hypothesis : float or numpy.ndarray
             Null hypothesis for the effect. Determines the robustness values.
-            If it is a single float uses the same null hypothesis for all estimated parameters. Else the array has to be of shape (n_coefs,). 
+            If it is a single float uses the same null hypothesis for all estimated parameters.
+            Else the array has to be of shape (n_coefs,).
             Default is ``0.0``.
 
         Returns
@@ -1673,7 +1674,8 @@ def sensitivity_analysis(self, cf_y=0.03, cf_d=0.03, rho=1.0, level=0.95, null_h
             if null_hypothesis.shape == (self._dml_data.n_treat,):
                 null_hypothesis_vec = null_hypothesis
             else:
-                raise ValueError(f"null_hypothesis is numpy.ndarray but does not have the required shape ({self._dml_data.n_treat},). "
+                raise ValueError("null_hypothesis is numpy.ndarray but does not have the required "
+                                 f"shape ({self._dml_data.n_treat},). "
                                  f'Array of shape {str(null_hypothesis.shape)} was passed.')
         else:
             raise TypeError("null_hypothesis has to be of type float or np.ndarry. "
@@ -1684,7 +1686,8 @@ def sensitivity_analysis(self, cf_y=0.03, cf_d=0.03, rho=1.0, level=0.95, null_h
         rva = np.full(shape=self._dml_data.n_treat, fill_value=np.nan)
 
         for i_treat in range(self._dml_data.n_treat):
-            rv[i_treat], rva[i_treat] = self._calc_robustness_value(null_hypothesis=null_hypothesis_vec[i_treat], level=level, rho=rho, idx_treatment=i_treat)
+            rv[i_treat], rva[i_treat] = self._calc_robustness_value(null_hypothesis=null_hypothesis_vec[i_treat],
+                                                                    level=level, rho=rho, idx_treatment=i_treat)
 
         sensitivity_dict['rv'] = rv
         sensitivity_dict['rva'] = rva