Merge pull request #55 from sdfordham/future-py38

use future for py3.8

pysyncon/base.py

@@ -314,7 +314,7 @@ def att(
         if Z0 is not None and Z1 is not None:
             gaps = self._gaps(Z0=Z0.loc[time_period, :], Z1=Z1.loc[time_period])
         else:
-            raise ValueError("dataprep must be set or (Z0, Z1) must be set for plots.")
+            raise ValueError("dataprep must be set or (Z0, Z1) must be set for att.")
         att = np.mean(gaps)
         se = np.std(gaps, ddof=1) / np.sqrt(len(time_period))
 

pysyncon/generator.py

@@ -1,4 +1,5 @@
-from typing import Optional, Tuple
+from __future__ import annotations
+from typing import Optional
 
 import numpy as np
 import pandas as pd
@@ -9,12 +10,12 @@ class LinearFactorModel:
 
     def __init__(
         self,
-        observed_dist: Tuple[int] = (0, 1),
-        observed_params_dist: Tuple[int] = (0, 10),
-        unobserved_dist: Tuple[int] = (0, 1),
-        unobserved_params_dist: Tuple[int] = (0, 10),
-        effect_dist: Tuple[int] = (0, 20),
-        shocks_dist: Tuple[int] = (0, 1),
+        observed_dist: tuple[int] = (0, 1),
+        observed_params_dist: tuple[int] = (0, 10),
+        unobserved_dist: tuple[int] = (0, 1),
+        unobserved_params_dist: tuple[int] = (0, 10),
+        effect_dist: tuple[int] = (0, 20),
+        shocks_dist: tuple[int] = (0, 1),
         seed: Optional[int] = None,
         rng: Optional[np.random.Generator] = None,
     ) -> None:
@@ -60,7 +61,7 @@ def generate(
         n_unobservable: int,
         n_periods_pre: int,
         n_periods_post: int,
-    ) -> Tuple[pd.DataFrame, pd.Series, pd.DataFrame, pd.Series]:
+    ) -> tuple[pd.DataFrame, pd.Series, pd.DataFrame, pd.Series]:
         """Generate the matrices (:math:`X_0`, :math:`X_1`, :math:`Z_0`,
         :math:`Z_1`) that can be used as input to a synthetic control
         method (using the notation of Abadie & Gardeazabal :cite:`basque2003`).