Merge pull request #201 from jGaboardi/format_repo

black format repo

docs/conf.py

@@ -90,7 +90,7 @@
 #
 # This is also used if you do content translation via gettext catalogs.
 # Usually you set "language" from the command line for these cases.
-language = 'en'
+language = "en"
 
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.

giddy/ergodic.py

@@ -148,8 +148,11 @@ def steady_state(P, fill_empty_classes=False):
 
 
 def _fmpt_ergodic(P):
-    warn('_fmpt_ergodic is deprecated. It will be replaced in giddy 2.5 with _mfpt_',
-         DeprecationWarning, stacklevel=2)
+    warn(
+        "_fmpt_ergodic is deprecated. It will be replaced in giddy 2.5 with _mfpt_",
+        DeprecationWarning,
+        stacklevel=2,
+    )
     return _mfpt_ergodic(P)
 
 
@@ -213,8 +216,11 @@ def _mfpt_ergodic(P):
 
 
 def fmpt(P, fill_empty_classes=False):
-    warn('fmpt is deprecated. It will be replaced in giddy 2.5 with mfpt',
-         DeprecationWarning, stacklevel=2)
+    warn(
+        "fmpt is deprecated. It will be replaced in giddy 2.5 with mfpt",
+        DeprecationWarning,
+        stacklevel=2,
+    )
     return mfpt(P, fill_empty_classes)
 
 
@@ -346,8 +352,11 @@ def mfpt(P, fill_empty_classes=False):
 
 
 def var_fmpt_ergodic(p):
-    warn('var_fmpt_ergodic is deprecated. It will be replaced in giddy 2.5 with var_fmpt_ergodic',
-         DeprecationWarning, stacklevel=2)
+    warn(
+        "var_fmpt_ergodic is deprecated. It will be replaced in giddy 2.5 with var_fmpt_ergodic",
+        DeprecationWarning,
+        stacklevel=2,
+    )
     return var_mfpt_ergodic(p)
 
 

giddy/markov.py

@@ -284,7 +284,7 @@ def __init__(self, class_ids, classes=None, fill_empty_classes=False, summary=Tr
 
     @property
     def mfpt(self):
-        warn('self._mfpt is deprecated. Please use self._mfpt')
+        warn("self._mfpt is deprecated. Please use self._mfpt")
         if not hasattr(self, "_mfpt"):
             self._mfpt = mfpt(self.p, fill_empty_classes=True)
         return self._mfpt
@@ -800,7 +800,6 @@ def __init__(
         variable_name=None,
         fill_empty_classes=False,
     ):
-
         y = np.asarray(y)
         self.fixed = fixed
         self.discrete = discrete
@@ -2038,7 +2037,6 @@ class FullRank_Markov(Markov):
     """
 
     def __init__(self, y, fill_empty_classes=False, summary=True):
-
         y = np.asarray(y)
         # resolve ties: All values are given a distinct rank, corresponding
         # to the order that the values occur in each cross section.

giddy/rank.py

@@ -351,7 +351,6 @@ class SpatialTau(object):
     """
 
     def __init__(self, x, y, w, permutations=0):
-
         w.transform = "b"
         self.n = len(x)
         res = Tau(x, y)
@@ -478,7 +477,6 @@ class Tau_Local:
     """
 
     def __init__(self, x, y):
-
         self.n = len(x)
         x = np.asarray(x)
         y = np.asarray(y)
@@ -596,7 +594,6 @@ class Tau_Local_Neighbor:
     """
 
     def __init__(self, x, y, w, permutations=0):
-
         x = np.asarray(x)
         y = np.asarray(y)
         self.n = len(x)
@@ -751,7 +748,6 @@ class Tau_Local_Neighborhood:
     """
 
     def __init__(self, x, y, w, permutations=0):
-
         x = np.asarray(x)
         y = np.asarray(y)
         res = Tau_Local(x, y)
@@ -885,7 +881,6 @@ class Tau_Regional:
     """
 
     def __init__(self, x, y, regime, permutations=0):
-
         x = np.asarray(x)
         y = np.asarray(y)
         res = Tau_Local(x, y)
@@ -926,7 +921,6 @@ def __init__(self, x, y, regime, permutations=0):
             self.tau_reg_pvalues = pvalues
 
     def _calc(self, W, WH, P, S):
-
         nomi = np.dot(P, np.dot(S, P.T))
         denomi = np.dot(P, np.dot(W, P.T)) + np.dot(P, np.dot(WH, P.T))
         T = nomi / denomi

giddy/sequence.py

@@ -11,6 +11,7 @@
 import scipy.spatial.distance as d
 from .markov import Markov
 
+
 class Sequence(object):
     """
     Pairwise sequence analysis.
@@ -153,7 +154,6 @@ class Sequence(object):
     """
 
     def __init__(self, y, subs_mat=None, dist_type=None, indel=None, cluster_type=None):
-
         y = np.asarray(y)
         merged = list(itertools.chain.from_iterable(y))
         self.classes = np.unique(merged)
@@ -192,7 +192,9 @@ def __init__(self, y, subs_mat=None, dist_type=None, indel=None, cluster_type=No
                             "sequences of unequal lengths!"
                         )
 
-                    hamming_dist = d.pdist(y_int.astype(int), metric="hamming") * y_int.shape[1]
+                    hamming_dist = (
+                        d.pdist(y_int.astype(int), metric="hamming") * y_int.shape[1]
+                    )
                     self.seq_dis_mat = d.squareform(hamming_dist)
 
                 elif dist_type.lower() == "arbitrary":

notebooks/RankBasedMethods.ipynb

@@ -968,11 +968,15 @@
     "from libpysal.weights import block_weights\n",
     "\n",
     "with warnings.catch_warnings():\n",
-    "    warnings.filterwarnings(\"ignore\", message=\"The weights matrix is not fully connected\")\n",
+    "    warnings.filterwarnings(\n",
+    "        \"ignore\", message=\"The weights matrix is not fully connected\"\n",
+    "    )\n",
     "\n",
     "    w = block_weights(complete_table[\"BEA region\"])\n",
     "    np.random.seed(12345)\n",
-    "    tau_w = giddy.rank.SpatialTau(complete_table[\"1929\"], complete_table[\"2009\"], w, 999)"
+    "    tau_w = giddy.rank.SpatialTau(\n",
+    "        complete_table[\"1929\"], complete_table[\"2009\"], w, 999\n",
+    "    )"
    ]
   },
   {
@@ -1244,11 +1248,16 @@
    "outputs": [],
    "source": [
     "with warnings.catch_warnings():\n",
-    "    warnings.filterwarnings(\"ignore\", message=\"The weights matrix is not fully connected\")\n",
+    "    warnings.filterwarnings(\n",
+    "        \"ignore\", message=\"The weights matrix is not fully connected\"\n",
+    "    )\n",
     "    np.random.seed(12345)\n",
-    "    \n",
+    "\n",
     "    tau_w = giddy.rank.Tau_Regional(\n",
-    "        complete_table[\"1929\"], complete_table[\"2009\"], complete_table[\"BEA region\"], 999\n",
+    "        complete_table[\"1929\"],\n",
+    "        complete_table[\"2009\"],\n",
+    "        complete_table[\"BEA region\"],\n",
+    "        999,\n",
     "    )"
    ]
   },

requirements_docs.txt

@@ -1,5 +1,4 @@
 nbsphinx
-nbsphinx-link
 numpydoc
 sphinx>=1.4.3
 sphinxcontrib-bibtex