no longer need to cast int() on outputs from math.floor and math.ceil

src/westpa/mclib/_mclib.pyx

@@ -145,8 +145,8 @@ cpdef mcbs_ci(dataset, estimator, alpha, dlen, n_sets=None, args=None, kwargs=No
         del indices
 
     f_synth_sorted = sort(f_synth)
-    lbi = int(math.floor(n_sets*alpha/2.0))
-    ubi = int(math.ceil(n_sets*(1-alpha/2.0)))
+    lbi = math.floor(n_sets*alpha/2.0)
+    ubi = math.ceil(n_sets*(1-alpha/2.0))
     lb = f_synth_sorted[lbi]
     ub = f_synth_sorted[ubi]
     sterr = numpy.std(f_synth_sorted)
@@ -194,8 +194,8 @@ cpdef mcbs_correltime_small(dataset, alpha, n_sets):
         raise ValueError('alpha ({}) > 0.5'.format(alpha))
 
     dlen = len(dataset)
-    lbi = int(math.floor(n_sets*alpha/2.0))
-    ubi = int(math.ceil(n_sets*(1-alpha/2.0)))
+    lbi = math.floor(n_sets*alpha/2.0)
+    ubi = math.ceil(n_sets*(1-alpha/2.0))
 
     synth_sets = numpy.empty((n_sets,)+dataset.shape, dtype=dataset.dtype)
     synth_acf_elems = numpy.empty((n_sets,), numpy.float64)

src/westpa/oldtools/aframe/mcbs.py

@@ -50,7 +50,7 @@ def process_args(self, args, upcall=True):
         self.mcbs_alpha = 1 - args.mcbs_confidence
         self.mcbs_nsets = args.mcbs_size if args.mcbs_nsets else min(1000, calc_mcbs_nsets(self.mcbs_alpha))
         self.mcbs_display_confidence = '{:.{cp}f}'.format(
-            100 * args.mcbs_confidence, cp=-int(math.floor(math.log10(self.mcbs_alpha))) - 2
+            100 * args.mcbs_confidence, cp=-math.floor(math.log10(self.mcbs_alpha)) - 2
         )
         westpa.rc.pstatus(
             'Using bootstrap of {:d} sets to calculate {:s}% confidence interval (alpha={:g}).'.format(
@@ -85,7 +85,7 @@ def calc_mcbs_nsets(alpha):
 
 
 def calc_ci_bound_indices(n_sets, alpha):
-    return (int(math.floor(n_sets * alpha / 2)), int(math.ceil(n_sets * (1 - alpha / 2))))
+    return (math.floor(n_sets * alpha / 2), math.ceil(n_sets * (1 - alpha / 2)))
 
 
 def bootstrap_ci_ll(estimator, data, alpha, n_sets, storage, sort, eargs=(), ekwargs={}, fhat=None):
@@ -105,8 +105,8 @@ def bootstrap_ci_ll(estimator, data, alpha, n_sets, storage, sort, eargs=(), ekw
         storage[iset] = estimator(data[indices], *eargs, **ekwargs)
 
     synth_sorted = sort(storage)
-    lbi = int(math.floor(n_sets * alpha / 2))
-    ubi = int(math.ceil(n_sets * (1 - alpha / 2)))
+    lbi = math.floor(n_sets * alpha / 2)
+    ubi = math.ceil(n_sets * (1 - alpha / 2))
 
     lb = synth_sorted[lbi]
     ub = synth_sorted[ubi]

src/westpa/oldtools/stats/mcbs.py

@@ -74,8 +74,8 @@ def bootstrap_ci(estimator, data, alpha, n_sets=None, args=(), kwargs={}, sort=n
         f_synth[i] = estimator(data[indices], *args, **kwargs)
 
     f_synth_sorted = sort(f_synth)
-    lbi = int(math.floor(n_sets * alpha / 2))
-    ubi = int(math.ceil(n_sets * (1 - alpha / 2)))
+    lbi = math.floor(n_sets * alpha / 2)
+    ubi = math.ceil(n_sets * (1 - alpha / 2))
     lb = f_synth_sorted[lbi]
     ub = f_synth_sorted[ubi]
 

src/westpa/tools/binning.py

@@ -144,7 +144,7 @@ def write_bin_info(mapper, assignments, weights, n_target_states, outfile=sys.st
     min_seg_weight = weights.min()
     max_seg_weight = weights.max()
 
-    ndec = int(math.ceil(-math.log10(1 / n_active)))
+    ndec = math.ceil(-math.log10(1 / n_active))
 
     outfile.write('{:d} segments\n'.format(len(weights)))
     outfile.write(