Blackened.

bebi103/__init__.py

@@ -47,4 +47,4 @@
 
 __author__ = """Justin Bois"""
 __email__ = "[email protected]"
-__version__ = "0.1.3"
+__version__ = "0.1.4"

bebi103/gp.py

@@ -445,7 +445,7 @@ def _d1_se_kernel(x1, x2, alpha, rho):
     x_diff = x1 - x2
     rho2 = rho ** 2
 
-    return -alpha ** 2 * x_diff * np.exp(-x_diff ** 2 / 2.0 / rho2) / rho2
+    return -(alpha ** 2) * x_diff * np.exp(-(x_diff ** 2) / 2.0 / rho2) / rho2
 
 
 @numba.njit
@@ -457,10 +457,10 @@ def _d2_se_kernel(x1, x2, alpha, rho):
 @numba.njit
 def _d1_d2_se_kernel(x1, x2, alpha, rho):
     """Derivative of first variable of squared exponential kernel."""
-    x_diff2 = (x1 - x2)**2
-    rho2 = rho**2
+    x_diff2 = (x1 - x2) ** 2
+    rho2 = rho ** 2
 
-    return (alpha / rho)**2 * np.exp(-x_diff2 / 2.0 / rho2) * (1 - x_diff2 / rho2)
+    return (alpha / rho) ** 2 * np.exp(-x_diff2 / 2.0 / rho2) * (1 - x_diff2 / rho2)
 
 
 def _matern_kernel(x1, x2, alpha, rho, nu):
@@ -951,4 +951,3 @@ def _solve_mean_cov(y, Ky, Kstar, Kstarstar, delta):
     )
 
     return mstar, Sigmastar
-

bebi103/image.py

@@ -21,6 +21,7 @@
 from . import viz
 from . import utils
 
+
 def imshow(
     im,
     cmap=None,

bebi103/stan.py

@@ -53,6 +53,7 @@
 
 from . import viz
 
+
 def StanModel(
     file=None,
     charset="utf-8",
@@ -447,7 +448,7 @@ def df_to_datadict_hier(
     return data, new_df
 
 
-def arviz_to_dataframe(data, var_names=None, diagnostics=('diverging',)):
+def arviz_to_dataframe(data, var_names=None, diagnostics=("diverging",)):
     """Convert ArviZ InferenceData to a Pandas data frame.
 
     Any multi-dimensional parameters are converted to one-dimensional
@@ -502,12 +503,14 @@ def arviz_to_dataframe(data, var_names=None, diagnostics=('diverging',)):
     diag_dict = {}
     if diagnostics is not None and len(diagnostics) > 0:
         if not hasattr(data, "sample_stats"):
-            raise RuntimeError("Asking for diagnostics, but input has not attribute sample_stats")
+            raise RuntimeError(
+                "Asking for diagnostics, but input has not attribute sample_stats"
+            )
         for diag in diagnostics:
             if hasattr(data.sample_stats, diag):
                 diag_dict[diag + "__"] = np.ravel(data.sample_stats[diag])
             else:
-                raise RuntimeError(f'{diag} not in data.sample_stats.')
+                raise RuntimeError(f"{diag} not in data.sample_stats.")
 
     cols, data_as_ndarray = _xarray_to_ndarray(data.posterior, var_names=var_names)
 
@@ -1715,7 +1718,9 @@ def _xarray_to_ndarray(ds, var_names=None, omit_dunders=True):
 
     """
 
-    names, vals = arviz.plots.plot_utils.xarray_to_ndarray(ds, var_names=var_names, combined=True)
+    names, vals = arviz.plots.plot_utils.xarray_to_ndarray(
+        ds, var_names=var_names, combined=True
+    )
 
     names = [
         name.replace("\n", "[").replace(", ", ",") + "]" if "\n" in name else name