Merge commit '2fc5fc49'

* commit '2fc5fc49':
  Update to python 3.8

distributions.py

@@ -35,10 +35,10 @@ def cdf_matrix(pdfmat, var_span, verbose=False, pbar=False):
     """
     nt = pdfmat.shape[0]
     bj = 0.5 * np.r_[
-                     var_span[1] - var_span[0],
-                     var_span[2:] - var_span[:-2],
-                     var_span[-1] - var_span[-2]
-                    ]                               # Riemann sum width
+        var_span[1] - var_span[0],
+        var_span[2:] - var_span[:-2],
+        var_span[-1] - var_span[-2]
+    ]  # Riemann sum width
     _printmsg("Estimating CDFs...", verbose)
     cdfmat = np.zeros(pdfmat.shape)
     prog_bar = _progressbar_start(nt, pbar)

events.py

@@ -71,7 +71,7 @@ def __divide_at_midpoint(G):
     return G1, G2
 
 
-def community_strength_data(G, time, wsize, wstep, 
+def community_strength_data(G, time, wsize, wstep,
                             verbose=False, pbar=False):
     """
     Saves intra-community link fraction for specified data set.
@@ -111,7 +111,7 @@ def community_strength_random_model(G, time, wsize, wstep, nsurr,
     tmid = []
     Qsurr, LDsurr = [np.zeros((int(nwind), int(nsurr)), "float") for i in range(2)]
     _printmsg("Estimating intra-community link fraction...", verbose)
-    prog_bar = _progressbar_start(nwind*nsurr, pbar)
+    prog_bar = _progressbar_start(nwind * nsurr, pbar)
     count = 0
     for i in range(int(nwind)):
         k = i * wstep
@@ -165,7 +165,7 @@ def holm(pvals, alpha=0.05, corr_type="dunn"):
     p_ = pvals[sortidx]
     j = np.arange(1, n + 1)
     if corr_type == "bonf":
-            corr_factor = alpha / (n - j + 1)
+        corr_factor = alpha / (n - j + 1)
     elif corr_type == "dunn":
         corr_factor = 1. - (1. - alpha) ** (1. / (n - j + 1))
     try:
@@ -174,4 +174,3 @@ def holm(pvals, alpha=0.05, corr_type="dunn"):
     except IndexError:
         idx = []
     return idx
-

networks.py

@@ -132,7 +132,7 @@ def _precnet_check_limits(dist_list, var_span, ld, iqr,
     if not cond:
         str0 = "Target link density could not be bracketed!"
         str1 = "Increase initial THR bracket or change target LD."
-        str2 = "LD = %.3f for THR = %.2E and %.3f for THR = %.2f"\
+        str2 = "LD = %.3f for THR = %.2E and %.3f for THR = %.2f" \
                % (ld_lims[0], thr_lims[0], ld_lims[1], thr_lims[1])
         print(str0 + "\n" + str1 + "\n" + str2)
     return cond, ld_lims
@@ -175,7 +175,7 @@ def _precnet_igraph(dist_list, var_span, e, verbose=False, pbar=False):
     Returns weighted igraph object by estimating prob. of rec. mat.
     """
     P = prob_recurrence_matrix(dist_list, var_span, e, verbose, pbar)
-    np.fill_diagonal(P, 0.)     # remove self-loops
+    np.fill_diagonal(P, 0.)  # remove self-loops
     G = _precmat_to_igraph(P)
     return G
 
@@ -297,11 +297,11 @@ def prob_recurrence_matrix(dist_list, var_span, e=0.1,
     _printmsg("\tPairwise recurrence probability bounds...", verbose)
     f1, f2 = np.zeros((n, len(u))), np.zeros((n, len(u)))
     for j in range(n):
-        f1[j, :] = np.interp(u - e, u, dist_list[j])    # for z = e
-        f2[j, :] = np.interp(u + e, u, dist_list[j])    # for z = -e
+        f1[j, :] = np.interp(u - e, u, dist_list[j])  # for z = e
+        f2[j, :] = np.interp(u + e, u, dist_list[j])  # for z = -e
         prog_bar = _progressbar_start(n, pbar)
     for i in range(n):
-        fi = np.interp(u, u, dist_list[i])              # Xi.cdf(u)
+        fi = np.interp(u, u, dist_list[i])  # Xi.cdf(u)
         plus_lo, plus_hi = _bounds_williamson(fi, f1)
         mnus_lo, mnus_hi = _bounds_williamson(fi, f2)
         diff = plus_lo - mnus_hi