apply black

docs/conf.py

@@ -169,7 +169,7 @@ def setup(app):
 # The theme to use for HTML and HTML Help pages.  See the documentation for
 # a list of builtin themes.
 # html_theme = 'alabaster'
-html_theme = 'sphinx_rtd_theme'
+html_theme = "sphinx_rtd_theme"
 
 # Theme options are theme-specific and customize the look and feel of a theme
 # further.  For a list of options available for each theme, see the
@@ -301,4 +301,4 @@ def setup(app):
     "pandas": ("https://pandas.pydata.org/pandas-docs/stable", None),
     "scipy": ("https://docs.scipy.org/doc/scipy/reference", None),
     "pyscaffold": ("https://pyscaffold.org/en/stable", None),
-}
+}

experiments/corr_fn.py

@@ -10,7 +10,7 @@
     corr_bspline,
     corr_poly,
     create_2d_isotropic,
-    create_2d_sites
+    create_2d_sites,
 )
 from ellpy.oracles.lsq_corr_oracle import lsq_oracle
 from ellpy.oracles.mle_corr_oracle import mle_oracle
@@ -29,15 +29,15 @@ def lsq_corr_core2(Y: Arr, n: int, P: Callable):
     Returns:
         [type]: [description]
     """
-    normY = np.linalg.norm(Y, 'fro')
+    normY = np.linalg.norm(Y, "fro")
     normY2 = 32 * normY * normY
     val = 256 * np.ones(n + 1)
     val[-1] = normY2 * normY2
     x = np.zeros(n + 1)  # cannot all zeros
-    x[0] = 1.
+    x[0] = 1.0
     x[-1] = normY2 / 2
     E = ell(val, x)
-    xb, _, ell_info = cutting_plane_dc(P, E, float('inf'))
+    xb, _, ell_info = cutting_plane_dc(P, E, float("inf"))
     return xb[:-1], ell_info.num_iters, ell_info.feasible
 
 
@@ -67,13 +67,13 @@ def mle_corr_core(Y: Arr, n: int, P: Callable):
         [type]: [description]
     """
     x = np.zeros(n)
-    x[0] = 1.
-    E = ell(50., x)
+    x[0] = 1.0
+    E = ell(50.0, x)
     # E.use_parallel_cut = False
     # options = Options()
     # options.max_it = 2000
     # options.tol = 1e-8
-    xb, _, ell_info = cutting_plane_dc(P, E, float('inf'))
+    xb, _, ell_info = cutting_plane_dc(P, E, float("inf"))
     # print(num_iters, feasible, status)
     return xb, ell_info.num_iters, ell_info.feasible
 
@@ -124,10 +124,11 @@ def lsq_corr_bspline2(Y, s, m):
 
 if __name__ == "__main__":
     import matplotlib.pyplot as plt
+
     # import matplotlib.pylab as lab
     s = create_2d_sites(10, 8)
     Y = create_2d_isotropic(s, 1000)
-    print('start ell...')
+    print("start ell...")
     spl, num_iters, _ = lsq_corr_bspline2(Y, s, 5)
     pol, num_iters, _ = lsq_corr_poly2(Y, s, 5)
     # pol, num_iters, _ = mle_corr_poly(Y, s, 4)
@@ -141,9 +142,9 @@ def lsq_corr_bspline2(Y, s, m):
     # h = s[-1] - s[0]
     d = np.sqrt(10**2 + 8**2)
     xs = np.linspace(0, d, 100)
-    plt.plot(xs, spl(xs), 'g', label='BSpline')
+    plt.plot(xs, spl(xs), "g", label="BSpline")
     # plt.plot(xs, splcvx(xs), 'b', label='BSpline CVX')
-    plt.plot(xs, np.polyval(pol, xs), 'r', label='Polynomial')
+    plt.plot(xs, np.polyval(pol, xs), "r", label="Polynomial")
     # plt.plot(xs, np.polyval(polcvx, xs), 'r', label='Polynomial CVX')
-    plt.legend(loc='best')
+    plt.legend(loc="best")
     plt.show()

experiments/corr_fn_cvx.py

@@ -8,7 +8,7 @@
 from ellpy.oracles.corr_oracle import (
     construct_distance_matrix,
     create_2d_isotropic,
-    create_2d_sites
+    create_2d_sites,
 )
 
 Arr = Union[np.ndarray]
@@ -41,11 +41,11 @@ def lsq_corr_poly(Y: Arr, s: Arr, n: int):
         D = np.multiply(D, D1)
         Sig += D * a[n - 2 - i]
     constraints = [Sig >> 0]
-    prob = cvx.Problem(cvx.Minimize(cvx.norm(Sig - Y, 'fro')), constraints)
+    prob = cvx.Problem(cvx.Minimize(cvx.norm(Sig - Y, "fro")), constraints)
     prob.solve(solver=cvx.CVXOPT)
     # prob.solve()
     if prob.status != cvx.OPTIMAL:
-        raise Exception('CVXPY Error')
+        raise Exception("CVXPY Error")
     return np.poly1d(np.array(a.value).flatten())
 
 
@@ -90,18 +90,19 @@ def lsq_corr_bspline(Y: Arr, s: Arr, n: int):
     constraints = [Sig >> 0]
     for i in range(n - 1):
         constraints += [c[i] >= c[i + 1]]
-    constraints += [c[-1] >= 0.]
+    constraints += [c[-1] >= 0.0]
 
-    prob = cvx.Problem(cvx.Minimize(cvx.norm(Sig - Y, 'fro')), constraints)
+    prob = cvx.Problem(cvx.Minimize(cvx.norm(Sig - Y, "fro")), constraints)
     prob.solve(solver=cvx.CVXOPT)
     # prob.solve()
     if prob.status != cvx.OPTIMAL:
-        raise Exception('CVXPY Error')
+        raise Exception("CVXPY Error")
     return BSpline(t, np.array(c.value).flatten(), k)
 
 
 if __name__ == "__main__":
     import matplotlib.pyplot as plt
+
     # import matplotlib.pylab as lab
     s = create_2d_sites(10, 8)
     Y = create_2d_isotropic(s, 1000)
@@ -111,16 +112,16 @@ def lsq_corr_bspline(Y: Arr, s: Arr, n: int):
     # # pol, num_iters, _ = mle_corr_poly(Y, s, 4)
     # print(pol)
     # print(num_iters)
-    print('start cvx...')
+    print("start cvx...")
     splcvx = lsq_corr_bspline(Y, s, 5)
     polcvx = lsq_corr_poly(Y, s, 5)
 
     # h = s[-1] - s[0]
     d = np.sqrt(10**2 + 8**2)
     xs = np.linspace(0, d, 100)
     # plt.plot(xs, spl(xs), 'g', label='BSpline')
-    plt.plot(xs, splcvx(xs), 'b', label='BSpline CVX')
+    plt.plot(xs, splcvx(xs), "b", label="BSpline CVX")
     # plt.plot(xs, np.polyval(pol, xs), 'r', label='Polynomial')
-    plt.plot(xs, np.polyval(polcvx, xs), 'r', label='Polynomial CVX')
-    plt.legend(loc='best')
+    plt.plot(xs, np.polyval(polcvx, xs), "r", label="Polynomial CVX")
+    plt.legend(loc="best")
     plt.show()

experiments/exp2d.py

@@ -2,6 +2,7 @@
 from __future__ import print_function
 
 import matplotlib.pylab as lab
+
 # from pprint import pprint
 import matplotlib.pyplot as plt
 import numpy as np
@@ -11,13 +12,13 @@
 # from corr_fn import lsq_corr_poly, lsq_corr_bspline
 
 # a fake dataset to make the bumps with
-nx = 80   # number of points
+nx = 80  # number of points
 ny = 64
-n = nx*ny
-s_end = [10., 8.]
+n = nx * ny
+s_end = [10.0, 8.0]
 sdkern = 0.25  # width of kernel
-var = 2.     # standard derivation
-tau = 0.00001    # standard derivation of white noise
+var = 2.0  # standard derivation
+tau = 0.00001  # standard derivation of white noise
 N = 4  # number of samples
 
 # create sites s
@@ -39,19 +40,19 @@
 # ym = np.random.randn(n)
 for k in range(N):
     x = var * np.random.randn(n)
-    y = A @ x + tau*np.random.randn(n)
+    y = A @ x + tau * np.random.randn(n)
     Ys[:, k] = y
 
 Y = np.cov(Ys, bias=True)
 
 plt.subplot(2, 2, 1)
-lab.contourf(xx, yy, np.reshape(Ys[:, 0], (ny, nx)), cmap='Greens')
+lab.contourf(xx, yy, np.reshape(Ys[:, 0], (ny, nx)), cmap="Greens")
 plt.subplot(2, 2, 2)
-lab.contourf(xx, yy, np.reshape(Ys[:, 1], (ny, nx)), cmap='Greens')
+lab.contourf(xx, yy, np.reshape(Ys[:, 1], (ny, nx)), cmap="Greens")
 plt.subplot(2, 2, 3)
-lab.contourf(xx, yy, np.reshape(Ys[:, 2], (ny, nx)), cmap='Greens')
+lab.contourf(xx, yy, np.reshape(Ys[:, 2], (ny, nx)), cmap="Greens")
 plt.subplot(2, 2, 4)
-lab.contourf(xx, yy, np.reshape(Ys[:, 3], (ny, nx)), cmap='Greens')
+lab.contourf(xx, yy, np.reshape(Ys[:, 3], (ny, nx)), cmap="Greens")
 plt.show()
 
 

src/ellpy/cutting_plane.py

@@ -29,8 +29,7 @@ def __init__(self, feasible: bool, num_iters: int, status: CUTStatus):
         self.status: CUTStatus = status
 
 
-def cutting_plane_feas(Omega: Callable[[Any], Any], S,
-                       options=Options()) -> CInfo:
+def cutting_plane_feas(Omega: Callable[[Any], Any], S, options=Options()) -> CInfo:
     """Find a point in a convex set (defined through a cutting-plane oracle).
 
     Description:
@@ -75,8 +74,9 @@ def cutting_plane_feas(Omega: Callable[[Any], Any], S,
     return CInfo(feasible, niter + 1, status)
 
 
-def cutting_plane_dc(Omega: Callable[[Any, Any], Any], S, t,
-                     options=Options()) -> Tuple[Any, Any, CInfo]:
+def cutting_plane_dc(
+    Omega: Callable[[Any, Any], Any], S, t, options=Options()
+) -> Tuple[Any, Any, CInfo]:
     """Cutting-plane method for solving convex optimization problem
 
     Arguments:
@@ -134,7 +134,7 @@ def cutting_plane_q(Omega, S, t, options=Options()):
     status = CUTStatus.nosoln
 
     for niter in range(options.max_it):
-        retry = (status == CUTStatus.noeffect)
+        retry = status == CUTStatus.noeffect
         cut, x0, t1, more_alt = Omega(S.xc, t, retry)
         if t1 is not None:  # better t obtained
             t = t1
@@ -153,8 +153,9 @@ def cutting_plane_q(Omega, S, t, options=Options()):
     return x_best, t, ret
 
 
-def bsearch(Omega: Callable[[Any], bool], Interval: Tuple,
-            options=Options()) -> Tuple[Any, CInfo]:
+def bsearch(
+    Omega: Callable[[Any], bool], Interval: Tuple, options=Options()
+) -> Tuple[Any, CInfo]:
     """[summary]
 
     Arguments:

src/ellpy/oracles/chol_ext.py

@@ -10,15 +10,16 @@
 class chol_ext:
     """LDLT factorization (mainly for LMI oracles)
 
-       - LDL^T square-root-free version
-       - Option allow semidefinite
-       - Cholesky–Banachiewicz style, row-based
-       - Lazy evaluation
-       - A matrix A in R^{m x m} is positive definite
-                            iff v' A v > 0 for all v in R^n.
-       - O(p^3) per iteration, independent of N
+    - LDL^T square-root-free version
+    - Option allow semidefinite
+    - Cholesky–Banachiewicz style, row-based
+    - Lazy evaluation
+    - A matrix A in R^{m x m} is positive definite
+                         iff v' A v > 0 for all v in R^n.
+    - O(p^3) per iteration, independent of N
     """
-    __slots__ = ('p', 'v', '_n', '_T', 'allow_semidefinite')
+
+    __slots__ = ("p", "v", "_n", "_T", "allow_semidefinite")
 
     def __init__(self, N: int):
         """Construct a new chol ext object
@@ -65,9 +66,9 @@ def factor(self, getA: Callable[[int, int], float]) -> bool:
                 s = j + 1
                 d = getA(i, s) - (self._T[i, start:s] @ self._T[start:s, s])
             self._T[i, i] = d
-            if d > 0.:
+            if d > 0.0:
                 continue
-            if d < 0. or not self.allow_semidefinite:
+            if d < 0.0 or not self.allow_semidefinite:
                 self.p = start, i + 1
                 break
             start = i + 1  # T[i, i] == 0 (very unlikely), restart at i+1
@@ -98,7 +99,7 @@ def witness(self):
             raise AssertionError()
         start, n = self.p
         m = n - 1
-        self.v[m] = 1.
+        self.v[m] = 1.0
         for i in range(m, start, -1):
             self.v[i - 1] = -(self._T[i:n, i - 1] @ self.v[i:n])
         return -self._T[m, m]

src/ellpy/oracles/corr_bspline_oracle.py

@@ -25,8 +25,8 @@ def mono_oracle(x):
     for i in range(n - 1):
         fj = x[i + 1] - x[i]
         if fj > 0:
-            g[i] = -1.
-            g[i + 1] = 1.
+            g[i] = -1.0
+            g[i + 1] = 1.0
             return g, fj
 
 
@@ -36,6 +36,7 @@ class mono_decreasing_oracle2:
     Returns:
         [type]: [description]
     """
+
     def __init__(self, basis):
         """[summary]
 
@@ -61,7 +62,7 @@ def __call__(self, x: Arr, t: float) -> Tuple[Cut, Optional[float]]:
         if cut:
             g1, fj = cut
             g[:-1] = g1
-            g[-1] = 0.
+            g[-1] = 0.0
             return (g, fj), None
         return self.basis(x, t)
 

src/ellpy/oracles/corr_oracle.py

@@ -19,7 +19,7 @@ def create_2d_sites(nx=10, ny=8) -> Arr:
         Arr: location of sites
     """
     n = nx * ny
-    s_end = np.array([10., 8.])
+    s_end = np.array([10.0, 8.0])
     hgen = halton([2, 3])
     s = s_end * np.array([hgen() for _ in range(n)])
     return s
@@ -39,7 +39,7 @@ def create_2d_isotropic(s: Arr, N=3000) -> Arr:
     """
     n = s.shape[0]
     sdkern = 0.12  # width of kernel
-    var = 2.  # standard derivation
+    var = 2.0  # standard derivation
     tau = 0.00001  # standard derivation of white noise
     np.random.seed(5)
 

src/ellpy/oracles/csdlowpass_oracle.py

@@ -18,6 +18,7 @@ class csdlowpass_oracle:
     Returns:
         [type]: [description]
     """
+
     rcsd = None
 
     def __init__(self, nnz, lowpass):

src/ellpy/oracles/cycle_ratio_oracle.py

@@ -12,16 +12,17 @@
 class cycle_ratio_oracle:
     """Oracle for minimum ratio cycle problem.
 
-        This example solves the following convex problem:
+    This example solves the following convex problem:
 
-            max     t
-            s.t.    u[j] - u[i] ≤ mij - sij * x,
-                    t ≤ x
+        max     t
+        s.t.    u[j] - u[i] ≤ mij - sij * x,
+                t ≤ x
 
-        where sij is not necessarily positive.
-        The problem could be unbounded???
+    where sij is not necessarily positive.
+    The problem could be unbounded???
 
     """
+
     class ratio:
         def __init__(self, G):
             """[summary]
@@ -42,8 +43,8 @@ def eval(self, e, x):
                 Any: function evaluation
             """
             u, v = e
-            cost = self.G[u][v]['cost']
-            time = self.G[u][v]['time']
+            cost = self.G[u][v]["cost"]
+            time = self.G[u][v]["time"]
             return cost - time * x
 
         def grad(self, e, x):
@@ -57,7 +58,7 @@ def grad(self, e, x):
                 [type]: [description]
             """
             u, v = e
-            time = self.G[u][v]['time']
+            time = self.G[u][v]["time"]
             return -time
 
     def __init__(self, G, u):
@@ -89,4 +90,4 @@ def __call__(self, x, t) -> Tuple[Cut, Any]:
         if cut:
             return cut, None
 
-        return (-1, 0.), x
+        return (-1, 0.0), x