pep8 + added option for plotting confidence interval in plotesf

wafo/misc.py

@@ -1094,14 +1094,14 @@ def findrfc_astm(tp):
         sig_rfc[:,2] Cycle type, half (=0.5) or full (=1.0)
     """
     return numba_misc.findrfc_astm(tp)
-    y1 = atleast_1d(tp).ravel()
+#     y1 = atleast_1d(tp).ravel()
-    sig_rfc, cnr = clib.findrfc3_astm(y1)
+#     sig_rfc, cnr = clib.findrfc3_astm(y1)
-    # the sig_rfc was constructed too big in rainflow.rf3, so
+#     # the sig_rfc was constructed too big in rainflow.rf3, so
-    # reduce the sig_rfc array as done originally by a matlab mex c function
+#     # reduce the sig_rfc array as done originally by a matlab mex c function
-    n = len(sig_rfc)
+#     n = len(sig_rfc)
-    # sig_rfc = sig_rfc.__getslice__(0, n - cnr[0])
+#     # sig_rfc = sig_rfc.__getslice__(0, n - cnr[0])
-    # sig_rfc holds the actual rainflow counted cycles, not the indices
+#     # sig_rfc holds the actual rainflow counted cycles, not the indices
-    return sig_rfc[:n - cnr[0]]
+#     return sig_rfc[:n - cnr[0]]
 
 
 def findrfc(tp, h=0.0, method='clib'):
@@ -1404,18 +1404,20 @@ def mctp2tc(f_Mm, utc, param, f_mM=None):
                     if dim_m == 1:
                         tempm[0, 0] = (Bm / (1 - Am * Bm) * em)
                     else:
-                        tempm = np.dot(Bm,
+                        rh = Im - np.dot(Am, Bm)
-                                       linalg.lstsq(Im-np.dot(Am, Bm), em)[0])
+                        tempm = np.dot(Bm, linalg.lstsq(rh, em)[0])
                     # end
                 # end
             # end
 
             if (j > ntc) and (i < ntc):
-                a = np.dot(np.dot(tempp.T, f_mM[i:ntc-1, n-ntc:-1:n-j+1]),
+                a = np.dot(np.dot(tempp.T,
+                                  f_mM[i:ntc - 1, n - ntc:-1:n - j + 1]),
                            tempm)
                 b = np.dot(np.dot(tempp.T, f_mM[i:ntc - 1, n - j:-1:1]),
                            ones((n - j, 1)))
-                c = np.dot(np.dot(ones((1, i-1)), f_mM[1:i-1, n-ntc:-1:n-j+1]),
+                c = np.dot(np.dot(ones((1, i - 1)),
+                                  f_mM[1:i - 1, n - ntc:-1:n - j + 1]),
                            tempm)
                 F[i, n - j + 1] = F[i, n - j + 1] + a + b + c
             # end
@@ -1428,7 +1430,8 @@ def mctp2tc(f_Mm, utc, param, f_mM=None):
                 # end
             # end
             if (j > ntc) and (i == ntc):
-                c = np.dot(np.dot(ones((1, i-1)), f_mM[1:i-1, n-ntc:-1:n-j+1]),
+                c = np.dot(np.dot(ones((1, i - 1)),
+                                  f_mM[1:i - 1, n - ntc:-1:n - j + 1]),
                            tempm)
                 F[i, n - j + 1] = F[i, n - j + 1] + c
                 for k in range(ntc, n):
@@ -3109,7 +3112,8 @@ def test_docstrings():
     # np.set_printoptions(precision=6)
     import doctest
     print('Testing docstrings in %s' % __file__)
-    doctest.testmod(optionflags=doctest.NORMALIZE_WHITESPACE|doctest.ELLIPSIS)
+    doctest.testmod(optionflags=doctest.NORMALIZE_WHITESPACE |
+                    doctest.ELLIPSIS)
 
 
 if __name__ == "__main__":

wafo/stats/estimation.py

@@ -1407,7 +1407,7 @@ class FitDistribution(rv_frozen):
         except Exception:
             pass
 
-    def plotesf(self, symb1='r-', symb2='b.', axis=None):
+    def plotesf(self, symb1='r-', symb2='b.', axis=None, plot_ci=False):
         '''  Plot Empirical and fitted Survival Function
 
         The purpose of the plot is to graphically assess whether
@@ -1422,7 +1422,7 @@ class FitDistribution(rv_frozen):
         axis.semilogy(self.data, sf, symb2,
                       self.data, self.sf(self.data), symb1)
 
-        if True:
+        if plot_ci:
             low = int(np.log10(1.0/n)-0.7) - 1
             sf1 = np.logspace(low, -0.5, 7)[::-1]
             ci1 = self.ci_sf(sf, alpha=0.05, i=2)