Merge pull request #202 from jGaboardi/ruff_repo

Lint repo with `ruff`

.pre-commit-config.yaml

@@ -0,0 +1,15 @@
+files: "giddy\/"
+repos:
+  - repo: https://github.com/psf/black
+    rev: "23.9.1"
+    hooks:
+      - id: black
+        language_version: python3
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    rev: "v0.0.292"
+    hooks:
+      - id: ruff
+
+ci:
+  autofix_prs: false
+  autoupdate_schedule: quarterly

giddy/directional.py

@@ -6,7 +6,7 @@
 
 __all__ = ["Rose"]
 
-import warnings
+
 import numpy as np
 from libpysal import weights
 from libpysal.common import requires as _requires
@@ -27,7 +27,8 @@ class Rose(object):
     Parameters
     ----------
     Y : array (n,2)
-        Columns correspond to end-point time periods to calculate LISA vectors for n object.
+        Columns correspond to end-point time periods to
+        calculate LISA vectors for n object.
     w : PySAL W
         Spatial weights object.
     k : int
@@ -349,10 +350,8 @@ def plot_origin(self):  # TODO add attribute option to color vectors
         Plot vectors of positional transition of LISA values starting
         from the same origin.
         """
-        import matplotlib.cm as cm
         import matplotlib.pyplot as plt
 
-        ax = plt.subplot(111)
         xlim = [self._dx.min(), self._dx.max()]
         ylim = [self._dy.min(), self._dy.max()]
         for x, y in zip(self._dx, self._dy):

giddy/ergodic.py

@@ -201,17 +201,17 @@ def _mfpt_ergodic(P):
     k = P.shape[0]
     A = np.zeros_like(P)
     ss = _steady_state_ergodic(P)
-    for i in range(k):
-        A[:, i] = ss
+    for j in range(k):
+        A[:, j] = ss
     A = A.transpose()
-    I = np.identity(k)
-    Z = la.inv(I - P + A)
+    i = np.identity(k)
+    Z = la.inv(i - P + A)
     E = np.ones_like(Z)
     A_diag = np.diag(A)
     A_diag = A_diag + (A_diag == 0)
     D = np.diag(1.0 / A_diag)
     Zdg = np.diag(np.diag(Z))
-    M = (I - Z + E.dot(Zdg)).dot(D)
+    M = (i - Z + E.dot(Zdg)).dot(D)
     return M
 
 
@@ -353,7 +353,10 @@ 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",
+        (
+            "var_fmpt_ergodic is deprecated. It will be "
+            "replaced in giddy 2.5 with var_fmpt_ergodic"
+        ),
         DeprecationWarning,
         stacklevel=2,
     )
@@ -398,13 +401,13 @@ def var_mfpt_ergodic(p):
     k = P.shape[0]
     A = _steady_state_ergodic(P)
     A = np.tile(A, (k, 1))
-    I = np.identity(k)
-    Z = la.inv(I - P + A)
+    i = np.identity(k)
+    Z = la.inv(i - P + A)
     E = np.ones_like(Z)
     D = np.diag(1.0 / np.diag(A))
     Zdg = np.diag(np.diag(Z))
-    M = (I - Z + E.dot(Zdg)).dot(D)
+    M = (i - Z + E.dot(Zdg)).dot(D)
     ZM = Z.dot(M)
     ZMdg = np.diag(np.diag(ZM))
-    W = M.dot(2 * Zdg.dot(D) - I) + 2 * (ZM - E.dot(ZMdg))
+    W = M.dot(2 * Zdg.dot(D) - i) + 2 * (ZM - E.dot(ZMdg))
     return np.array(W - np.multiply(M, M))

giddy/markov.py

@@ -19,7 +19,6 @@
 from .ergodic import steady_state, mfpt
 from .util import fill_empty_diagonals
 from .components import Graph
-from warnings import warn
 from scipy import stats
 from scipy.stats import rankdata
 from operator import gt
@@ -282,13 +281,6 @@ def __init__(self, class_ids, classes=None, fill_empty_classes=False, summary=Tr
                     )
                 print(*self.astates_indices, sep=", ")
 
-    @property
-    def mfpt(self):
-        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
-
     @property
     def mfpt(self):
         if not hasattr(self, "_mfpt"):
@@ -708,7 +700,9 @@ class Spatial_Markov(object):
     We can easily adjust this assigning `fill_empty_classes = True` when initializing
     `Spatial_Markov`.
 
-    >>> sm = Spatial_Markov(rpci, w, cutoffs=cc, lag_cutoffs=cc, fill_empty_classes=True)
+    >>> sm = Spatial_Markov(
+    ...     rpci, w, cutoffs=cc, lag_cutoffs=cc, fill_empty_classes=True
+    ... )
     >>> for p in sm.P:
     ...     print(p)
     [[0.96703297 0.03296703 0.         0.         0.        ]
@@ -1075,7 +1069,7 @@ def _maybe_classify(self, y, k, cutoffs):
 
 
 def chi2(T1, T2):
-    """
+    r"""
     chi-squared test of difference between two transition matrices.
 
     Parameters
@@ -1849,14 +1843,14 @@ def summary(self, file_name=None, title="Markov Homogeneity Test"):
         lead = "-" * width
         head = title.center(width)
         contents = [lead, head, lead]
-        l = "Number of regimes: %d" % int(self.m)
+        L = "Number of regimes: %d" % int(self.m)
         k = "Number of classes: %d" % int(self.k)
         r = "Regime names: "
         r += ", ".join(regime_names)
         t = "Number of transitions: %d" % int(self.t_total)
         contents.append(k)
         contents.append(t)
-        contents.append(l)
+        contents.append(L)
         contents.append(r)
         contents.append(lead)
         h = "%7s %20s %20s" % ("Test", "LR", "Chi-2")
@@ -2091,7 +2085,7 @@ def sojourn_time(p, summary=True):
     if (p.sum(axis=1) == 0).sum() > 0:
         p = fill_empty_diagonals(p)
 
-    markovchain = qe.MarkovChain(p)
+    # markovchain = qe.MarkovChain(p)
     pii = p.diagonal()
 
     if not (1 - pii).all():
@@ -2213,7 +2207,8 @@ class GeoRank_Markov(Markov):
 
     def __init__(self, y, fill_empty_classes=False, summary=True):
         y = np.asarray(y)
-        n = y.shape[0]
+        # n = y.shape[0]
+
         # resolve ties: All values are given a distinct rank, corresponding
         # to the order that the values occur in each cross section.
         ranks = np.array([rankdata(col, method="ordinal") for col in y.T]).T

giddy/rank.py

@@ -16,7 +16,6 @@
 from scipy.stats.mstats import rankdata
 from scipy.special import erfc
 import numpy as np
-import scipy as sp
 from libpysal import weights
 
 
@@ -184,7 +183,6 @@ def _calc(self, x, y):
         n = len(y)
         perm = list(range(n))
         perm.sort(key=lambda a: (x[a], y[a]))
-        vals = y[perm]
         ExtraY = 0
         ExtraX = 0
         ACount = 0
@@ -315,8 +313,9 @@ class SpatialTau(object):
     taus               : float
                          spatial tau values for permuted samples (if permutations>0).
     tau_spatial_psim   : float
-                         one-sided pseudo p-value for observed tau_spatial under the null
-                         of spatial randomness of rank exchanges (if permutations>0).
+                         one-sided pseudo p-value for observed tau_spatial
+                         under the null of spatial randomness of rank exchanges
+                         (if permutations>0).
 
     Notes
     -----

giddy/sequence.py

@@ -84,7 +84,14 @@ class Sequence(object):
 
     1.2 User-defined substitution cost matrix and indel cost
 
-    >>> subs_mat = np.array([[0, 0.76, 0.29, 0.05],[0.30, 0, 0.40, 0.60],[0.16, 0.61, 0, 0.26],[0.38, 0.20, 0.12, 0]])
+    >>> subs_mat = np.array(
+    ...     [
+    ...         [0, 0.76, 0.29, 0.05],
+    ...         [0.30, 0, 0.40, 0.60],
+    ...         [0.16, 0.61, 0, 0.26],
+    ...         [0.38, 0.20, 0.12, 0]
+    ...     ]
+    ... )
     >>> indel = subs_mat.max()
     >>> seqAna = Sequence([seq1,seq2,seq3], subs_mat=subs_mat, indel=indel)
     >>> seqAna.seq_dis_mat
@@ -117,7 +124,14 @@ class Sequence(object):
     between different types is always 1 and indel cost is 2) - give the same
     sequence distance matrix as "hamming" distance
 
-    >>> subs_mat = np.array([[0., 1., 1., 1.],[1., 0., 1., 1.],[1., 1., 0., 1.],[1., 1., 1., 0.]])
+    >>> subs_mat = np.array(
+    ...     [
+    ...         [0., 1., 1., 1.],
+    ...         [1., 0., 1., 1.],
+    ...         [1., 1., 0., 1.],
+    ...         [1., 1., 1., 0.]
+    ...     ]
+    ... )
     >>> indel = 2
     >>> seqAna = Sequence([seq1,seq2,seq3], subs_mat=subs_mat, indel=indel)
     >>> seqAna.seq_dis_mat
@@ -130,7 +144,14 @@ class Sequence(object):
     slightly different sequence distance matrix from "hamming" distance since
     insertion and deletion is happening
 
-    >>> subs_mat = np.array([[0., 1., 1., 1.],[1., 0., 1., 1.],[1., 1., 0.,1.],[1., 1., 1., 0.]])
+    >>> subs_mat = np.array(
+    ...     [
+    ...         [0., 1., 1., 1.],
+    ...         [1., 0., 1., 1.],
+    ...         [1., 1., 0., 1.],
+    ...         [1., 1., 1., 0.]
+    ...     ]
+    ... )
     >>> indel = 1
     >>> seqAna = Sequence([seq1,seq2,seq3], subs_mat=subs_mat, indel=indel)
     >>> seqAna.seq_dis_mat

giddy/tests/test_sequence.py

@@ -1,4 +1,3 @@
-import unittest
 import numpy as np
 import pytest
 from ..sequence import Sequence