Make black workflow pass

Author: StefRe

probscale/algo.py

@@ -127,7 +127,7 @@ def _estimate_from_fit(xhat, slope, intercept, xlog=False, ylog=False):
     xhat = numpy.asarray(xhat)
     if ylog:
         if xlog:
-            yhat = numpy.exp(intercept) * xhat ** slope
+            yhat = numpy.exp(intercept) * xhat**slope
         else:
             yhat = numpy.exp(intercept) * numpy.exp(slope) ** xhat
 

probscale/formatters.py

@@ -45,7 +45,6 @@ def _sig_figs(cls, x, n, expthresh=5, forceint=False):
 
         # check on the number provided
         elif x is not None and numpy.isfinite(x):
-
             # check on the _sig_figs
             if n < 1:
                 raise ValueError("number of sig figs (n) must be greater " "than zero")

probscale/probscale.py

@@ -29,7 +29,7 @@ def _approx_erf(cls, x):
 
         """
 
-        guts = -(x ** 2) * (4.0 / numpy.pi + cls._A * x ** 2) / (1.0 + cls._A * x ** 2)
+        guts = -(x**2) * (4.0 / numpy.pi + cls._A * x**2) / (1.0 + cls._A * x**2)
         with warnings.catch_warnings():
             warnings.filterwarnings("ignore", "invalid value encountered in sign")
             return numpy.sign(x) * numpy.sqrt(1.0 - numpy.exp(guts))
@@ -42,11 +42,11 @@ def _approx_inv_erf(cls, z):
 
         """
 
-        _b = (2 / numpy.pi / cls._A) + (0.5 * numpy.log(1 - z ** 2))
-        _c = numpy.log(1 - z ** 2) / cls._A
+        _b = (2 / numpy.pi / cls._A) + (0.5 * numpy.log(1 - z**2))
+        _c = numpy.log(1 - z**2) / cls._A
         with warnings.catch_warnings():
             warnings.filterwarnings("ignore", "invalid value encountered in sign")
-            return numpy.sign(z) * numpy.sqrt(numpy.sqrt(_b ** 2 - _c) - _b)
+            return numpy.sign(z) * numpy.sqrt(numpy.sqrt(_b**2 - _c) - _b)
 
     @classmethod
     def ppf(cls, q):