Fix linter

pySDC/helpers/blocks.py

@@ -102,7 +102,7 @@ def ranks(self):
     @property
     def localBounds(self):
         iLocList, nLocList = [], []
-        for rank, nPoints, nBlocks in zip(self.ranks, self.gridSizes, self.nBlocks):
+        for rank, nPoints, nBlocks in zip(self.ranks, self.gridSizes, self.nBlocks, strict=True):
             n0 = nPoints // nBlocks
             nRest = nPoints - nBlocks * n0
             nLoc = n0 + 1 * (rank < nRest)

pySDC/helpers/fieldsIO.py

@@ -714,7 +714,7 @@ def writeFields_MPI(fileName, dtypeIdx, algo, nSteps, nVar, gridSizes):
 
     iLoc, nLoc = blocks.localBounds
     Rectilinear.setupMPI(comm, iLoc, nLoc)
-    s = [slice(i, i + n) for i, n in zip(iLoc, nLoc)]
+    s = [slice(i, i + n) for i, n in zip(iLoc, nLoc, strict=True)]
     u0 = u0[(slice(None), *s)]
 
     f1 = Rectilinear(DTYPES[dtypeIdx], fileName)

pySDC/helpers/transfer_helper.py

@@ -27,7 +27,7 @@ def next_neighbors_periodic(p, ps, k):
 
     # zip it
     value_index = []
-    for d, i in zip(distance_to_p, range(distance_to_p.size)):
+    for d, i in zip(distance_to_p, range(distance_to_p.size), strict=True):
         value_index.append((d, i))
     # sort by distance
     value_index_sorted = sorted(value_index, key=lambda s: s[0])
@@ -53,7 +53,7 @@ def next_neighbors(p, ps, k):
     distance_to_p = np.abs(ps - p)
     # zip it
     value_index = []
-    for d, i in zip(distance_to_p, range(distance_to_p.size)):
+    for d, i in zip(distance_to_p, range(distance_to_p.size), strict=True):
         value_index.append((d, i))
     # sort by distance
     value_index_sorted = sorted(value_index, key=lambda s: s[0])
@@ -80,7 +80,7 @@ def continue_periodic_array(arr, nn):
     else:
         cont_arr = [arr[nn[0]]]
         shift = 0.0
-        for n, d in zip(nn[1:], d_nn):
+        for n, d in zip(nn[1:], d_nn, strict=True):
             if d != 1:
                 shift = -1
             cont_arr.append(arr[n] + shift)
@@ -107,7 +107,7 @@ def restriction_matrix_1d(fine_grid, coarse_grid, k=2, periodic=False, pad=1):
 
     if periodic:
         M = np.zeros((coarse_grid.size, fine_grid.size))
-        for i, p in zip(range(n_g), coarse_grid):
+        for i, p in zip(range(n_g), coarse_grid, strict=True):
             nn = next_neighbors_periodic(p, fine_grid, k)
             circulating_one = np.asarray([1.0] + [0.0] * (k - 1))
             cont_arr = continue_periodic_array(fine_grid, nn)
@@ -120,7 +120,7 @@ def restriction_matrix_1d(fine_grid, coarse_grid, k=2, periodic=False, pad=1):
                 M[i, nn] = np.asarray(list(map(lambda x: x(p), bary_pol)))
     else:
         M = np.zeros((coarse_grid.size, fine_grid.size + 2 * pad))
-        for i, p in zip(range(n_g), coarse_grid):
+        for i, p in zip(range(n_g), coarse_grid, strict=True):
             padded_f_grid = border_padding(fine_grid, pad, pad)
             nn = next_neighbors(p, padded_f_grid, k)
             # construct the lagrange polynomials for the k neighbors
@@ -158,7 +158,7 @@ def interpolation_matrix_1d(fine_grid, coarse_grid, k=2, periodic=False, pad=1,
         M = np.zeros((fine_grid.size, coarse_grid.size))
 
         if equidist_nested:
-            for i, p in zip(range(n_f), fine_grid):
+            for i, p in zip(range(n_f), fine_grid, strict=True):
                 if i % 2 == 0:
                     M[i, int(i / 2)] = 1.0
                 else:
@@ -189,7 +189,7 @@ def interpolation_matrix_1d(fine_grid, coarse_grid, k=2, periodic=False, pad=1,
                         M[i, nn] = np.asarray(list(map(lambda x: x(p), bary_pol)))
 
         else:
-            for i, p in zip(range(n_f), fine_grid):
+            for i, p in zip(range(n_f), fine_grid, strict=True):
                 nn = next_neighbors_periodic(p, coarse_grid, k)
                 circulating_one = np.asarray([1.0] + [0.0] * (k - 1))
                 cont_arr = continue_periodic_array(coarse_grid, nn)
@@ -208,7 +208,7 @@ def interpolation_matrix_1d(fine_grid, coarse_grid, k=2, periodic=False, pad=1,
         padded_c_grid = border_padding(coarse_grid, pad, pad)
 
         if equidist_nested:
-            for i, p in zip(range(n_f), fine_grid):
+            for i, p in zip(range(n_f), fine_grid, strict=True):
                 if i % 2 != 0:
                     M[i, int((i - 1) / 2) + 1] = 1.0
                 else:
@@ -231,7 +231,7 @@ def interpolation_matrix_1d(fine_grid, coarse_grid, k=2, periodic=False, pad=1,
                         M[i, nn] = np.asarray(list(map(lambda x: x(p), bary_pol)))
 
         else:
-            for i, p in zip(range(n_f), fine_grid):
+            for i, p in zip(range(n_f), fine_grid, strict=True):
                 nn = next_neighbors(p, padded_c_grid, k)
                 # construct the lagrange polynomials for the k neighbors
                 circulating_one = np.asarray([1.0] + [0.0] * (k - 1))

pySDC/helpers/vtkIO.py

@@ -52,7 +52,7 @@ def field(u):
         return numpy_support.numpy_to_vtk(num_array=u.ravel(order='F'), deep=True, array_type=vtk.VTK_FLOAT)
 
     pointData = vtr.GetPointData()
-    for name, u in zip(varNames, data):
+    for name, u in zip(varNames, data, strict=True):
         uVTK = field(u)
         uVTK.SetName(name)
         pointData.AddArray(uVTK)

pySDC/implementations/problem_classes/AllenCahn_Temp_MPIFFT.py

@@ -134,7 +134,7 @@ def __init__(
         N = self.fft.global_shape()
         k = [np.fft.fftfreq(n, 1.0 / n).astype(int) for n in N[:-1]]
         k.append(np.fft.rfftfreq(N[-1], 1.0 / N[-1]).astype(int))
-        K = [ki[si] for ki, si in zip(k, s)]
+        K = [ki[si] for ki, si in zip(k, s, strict=True)]
         Ks = list(np.meshgrid(*K, indexing='ij', sparse=True))
         Lp = 2 * np.pi / L
         for i in range(ndim):

pySDC/implementations/problem_classes/Brusselator.py

@@ -178,7 +178,7 @@ def plot(self, u, t=None, fig=None):  # pragma: no cover
 
         vmin = u.min()
         vmax = u.max()
-        for i, label in zip([self.iU, self.iV], [r'$u$', r'$v$']):
+        for i, label in zip([self.iU, self.iV], [r'$u$', r'$v$'], strict=True):
             im = axs[i].pcolormesh(self.X[0], self.X[1], u[i], vmin=vmin, vmax=vmax)
             axs[i].set_aspect(1)
             axs[i].set_title(label)

pySDC/implementations/problem_classes/Burgers.py

@@ -324,7 +324,7 @@ def plot(self, u, t=None, fig=None, vmin=None, vmax=None):  # pragma: no cover
         imV = axs[1].pcolormesh(self.X, self.Z, u[iv].real, vmin=vmin, vmax=vmax)
         imVort = axs[2].pcolormesh(self.X, self.Z, self.compute_vorticity(u).real)
 
-        for i, label in zip([0, 1, 2], [r'$u$', '$v$', 'vorticity']):
+        for i, label in zip([0, 1, 2], [r'$u$', '$v$', 'vorticity'], strict=True):
             axs[i].set_aspect(1)
             axs[i].set_title(label)
 

pySDC/implementations/problem_classes/GrayScott_MPIFFT.py

@@ -343,7 +343,7 @@ def plot(self, u, t=None, fig=None):  # pragma: no cover
 
         vmin = u.min()
         vmax = u.max()
-        for i, label in zip([self.iU, self.iV], [r'$u$', r'$v$']):
+        for i, label in zip([self.iU, self.iV], [r'$u$', r'$v$'], strict=True):
             im = axs[i].pcolormesh(self.X[0], self.X[1], u[i], vmin=vmin, vmax=vmax)
             axs[i].set_aspect(1)
             axs[i].set_title(label)

pySDC/implementations/problem_classes/RayleighBenard.py

@@ -363,7 +363,7 @@ def plot(self, u, t=None, fig=None, quantity='T'):  # pragma: no cover
 
         imT = axs[0].pcolormesh(self.X, self.Z, u[self.index(quantity)].real)
 
-        for i, label in zip([0, 1], [rf'${quantity}$', 'vorticity']):
+        for i, label in zip([0, 1], [rf'${quantity}$', 'vorticity'], strict=True):
             axs[i].set_aspect(1)
             axs[i].set_title(label)
 
@@ -630,6 +630,7 @@ def post_step(self, step, level_number):
         for key, value in zip(
             ['Nusselt', 'buoyancy_production', 'viscous_dissipation'],
             [Nusselt, buoyancy_production, viscous_dissipation],
+            strict=True,
         ):
             self.add_to_stats(
                 process=step.status.slot,

pySDC/implementations/problem_classes/RayleighBenard3D.py

@@ -255,7 +255,7 @@ def eval_f(self, u, *args, **kwargs):
 
         fexpl_pad = self.xp.zeros_like(u_pad)
         for i in derivative_indices:
-            for i_vel, iD in zip([iu, iv, iw], range(self.ndim)):
+            for i_vel, iD in zip([iu, iv, iw], range(self.ndim), strict=True):
                 fexpl_pad[i] -= u_pad[i_vel] * derivatives[iD][i]
 
         if self.spectral_space:
@@ -398,7 +398,7 @@ def get_frequency_spectrum(self, u):
 
         # compute local spectrum
         local_spectrum = self.xp.empty(shape=(2, energy.shape[3], n_k))
-        for i, k in zip(range(n_k), unique_k):
+        for i, k in zip(range(n_k), unique_k, strict=True):
             mask = xp.logical_or(abs_kx == k, abs_ky == k)
             local_spectrum[..., i] = xp.sum(energy[indices, mask, :], axis=1)
 
@@ -411,7 +411,7 @@ def get_frequency_spectrum(self, u):
 
         spectra = self.comm.allgather(local_spectrum)
         spectrum = self.xp.zeros(shape=(2, self.axes[2].N, n_k_all))
-        for ks, _spectrum in zip(k_all, spectra):
+        for ks, _spectrum in zip(k_all, spectra, strict=True):
             ks = list(ks)
             unique_k_all = list(unique_k_all)
             for k in ks:

pySDC/implementations/problem_classes/generic_MPIFFT_Laplacian.py

@@ -120,7 +120,7 @@ def getLaplacian(self):
         s = self.fft.local_slice()
         N = self.fft.global_shape()
         k = [self.xp.fft.fftfreq(n, 1.0 / n).astype(int) for n in N]
-        K = [ki[si] for ki, si in zip(k, s)]
+        K = [ki[si] for ki, si in zip(k, s, strict=True)]
         Ks = list(self.xp.meshgrid(*K, indexing='ij', sparse=True))
         Lp = 2 * np.pi / self.L
         for i in range(self.ndim):

pySDC/implementations/sweeper_classes/Runge_Kutta.py

@@ -279,16 +279,16 @@ def compute_end_point(self):
             lvl.uend = lvl.prob.dtype_u(lvl.u[-1])
             if type(self.coll) == ButcherTableauEmbedded:
                 self.u_secondary = lvl.prob.dtype_u(lvl.u[0])
-                for w2, k in zip(self.coll.weights[1], lvl.f[1:]):
+                for w2, k in zip(self.coll.weights[1], lvl.f[1:], strict=True):
                     self.u_secondary += lvl.dt * w2 * k
         else:
             lvl.uend = lvl.prob.dtype_u(lvl.u[0])
             if type(self.coll) == ButcherTableau:
-                for w, k in zip(self.coll.weights, lvl.f[1:]):
+                for w, k in zip(self.coll.weights, lvl.f[1:], strict=True):
                     lvl.uend += lvl.dt * w * k
             elif type(self.coll) == ButcherTableauEmbedded:
                 self.u_secondary = lvl.prob.dtype_u(lvl.u[0])
-                for w1, w2, k in zip(self.coll.weights[0], self.coll.weights[1], lvl.f[1:]):
+                for w1, w2, k in zip(self.coll.weights[0], self.coll.weights[1], lvl.f[1:], strict=True):
                     lvl.uend += lvl.dt * w1 * k
                     self.u_secondary += lvl.dt * w2 * k
 
@@ -459,12 +459,12 @@ def compute_end_point(self):
             lvl.uend = lvl.u[-1]
             if type(self.coll) == ButcherTableauEmbedded:
                 self.u_secondary = lvl.prob.dtype_u(lvl.u[0])
-                for w2, w2E, k in zip(self.coll.weights[1], self.coll_explicit.weights[1], lvl.f[1:]):
+                for w2, w2E, k in zip(self.coll.weights[1], self.coll_explicit.weights[1], lvl.f[1:], strict=True):
                     self.u_secondary += lvl.dt * (w2 * k.impl + w2E * k.expl)
         else:
             lvl.uend = lvl.prob.dtype_u(lvl.u[0])
             if type(self.coll) == ButcherTableau:
-                for w, wE, k in zip(self.coll.weights, self.coll_explicit.weights, lvl.f[1:]):
+                for w, wE, k in zip(self.coll.weights, self.coll_explicit.weights, lvl.f[1:], strict=True):
                     lvl.uend += lvl.dt * (w * k.impl + wE * k.expl)
             elif type(self.coll) == ButcherTableauEmbedded:
                 self.u_secondary = lvl.u[0].copy()
@@ -474,6 +474,7 @@ def compute_end_point(self):
                     self.coll_explicit.weights[0],
                     self.coll_explicit.weights[1],
                     lvl.f[1:],
+                    strict=True,
                 ):
                     lvl.uend += lvl.dt * (w1 * k.impl + w1E * k.expl)
                     self.u_secondary += lvl.dt * (w2 * k.impl + w2E * k.expl)

pySDC/implementations/transfer_classes/TransferMesh_MPIFFT.py

@@ -29,7 +29,7 @@ def __init__(self, fine_prob, coarse_prob, params):
 
         Nf = list(self.fine_prob.fft.global_shape())
         Nc = list(self.coarse_prob.fft.global_shape())
-        self.ratio = [int(nf / nc) for nf, nc in zip(Nf, Nc)]
+        self.ratio = [int(nf / nc) for nf, nc in zip(Nf, Nc, strict=True)]
         axes = tuple(range(len(Nf)))
 
         fft_args = {}

pySDC/projects/AllenCahn_Bayreuth/run_simple_forcing_verification.py

@@ -124,7 +124,7 @@ def run_simulation(name='', spectral=None, nprocs_space=None):
         # print and store radii and error over time
         err_test = 0.0
         results = dict()
-        for cr, er, cv, ev in zip(computed_radii, exact_radii, computed_vol, exact_vol):
+        for cr, er, cv, ev in zip(computed_radii, exact_radii, computed_vol, exact_vol, strict=True):
             if name == 'AC-test-noforce':
                 exrad = er[1]
                 exvol = ev[1]

pySDC/projects/AllenCahn_Bayreuth/visualize.py

@@ -21,7 +21,7 @@ def plot_data(name=''):
     json_files = sorted(glob.glob(f'./data/{name}_*.json'))
     data_files = sorted(glob.glob(f'./data/{name}_*.dat'))
 
-    for json_file, data_file in zip(json_files, data_files):
+    for json_file, data_file in zip(json_files, data_files, strict=True):
         with open(json_file, 'r') as fp:
             obj = json.load(fp)
 

pySDC/projects/AllenCahn_Bayreuth/visualize_temp.py

@@ -27,7 +27,7 @@ def plot_data(path='./data', name='', output='.'):
     json_files = sorted(glob.glob(f'{path}/{name}_*.json'))
     data_files = sorted(glob.glob(f'{path}/{name}_*.dat'))
 
-    for json_file, data_file in zip(json_files, data_files):
+    for json_file, data_file in zip(json_files, data_files, strict=True):
         with open(json_file, 'r') as fp:
             obj = json.load(fp)
 
@@ -74,7 +74,7 @@ def make_movie(path='./data', name='', output='.'):
     data_files = sorted(glob.glob(f'{path}/{name}_*.dat'))
 
     img_list = []
-    for json_file, data_file in zip(json_files, data_files):
+    for json_file, data_file in zip(json_files, data_files, strict=True):
         with open(json_file, 'r') as fp:
             obj = json.load(fp)
 

pySDC/projects/AsympConv/conv_test_to0.py

@@ -33,7 +33,7 @@ def compute_and_plot_specrad(Nnodes, lam):
     color_list = ['red', 'blue']
     marker_list = ['s', 'o']
 
-    setup_list = zip(Nsteps_list, color_list, marker_list)
+    setup_list = zip(Nsteps_list, color_list, marker_list, strict=True)
 
     xlist = [0.1**i for i in range(11)]
 

pySDC/projects/AsympConv/conv_test_toinf.py

@@ -35,7 +35,7 @@ def compute_and_plot_specrad(Nnodes, lam):
     color_list = ['red', 'blue', 'green']
     marker_list = ['s', 'o', 'd']
 
-    setup_list = zip(Nsweep_list, color_list, marker_list)
+    setup_list = zip(Nsweep_list, color_list, marker_list, strict=True)
 
     xlist = [10**i for i in range(11)]
 

pySDC/projects/DAE/plotting/loglog_plot.py

@@ -68,7 +68,7 @@ def plot_convergence():  # pragma: no cover
         ylim = (sys.float_info.max, sys.float_info.min)
 
         for num_nodes, color, shape, style, order in zip(
-            num_nodes_list, color_list, shape_list, style_list, order_list
+            num_nodes_list, color_list, shape_list, style_list, order_list, strict=True
         ):
             # Plot convergence data
             ax.loglog(
@@ -99,6 +99,7 @@ def plot_convergence():  # pragma: no cover
                 data[qd_type][num_nodes]['dt'],
                 data[qd_type][num_nodes]['position'],
                 data[qd_type][num_nodes]['offset'],
+                strict=True,
             ):
                 ax.annotate(
                     niter,

pySDC/projects/DAE/plotting/semilogy_plot.py

@@ -29,7 +29,7 @@ def plot_convergence():  # pragma: no cover
         lns1 = list()
         lns2 = list()
 
-        for num_nodes, color, shape in zip(num_nodes_list, color_list, shape_list):
+        for num_nodes, color, shape in zip(num_nodes_list, color_list, shape_list, strict=True):
             # Plot convergence data
             lns1.append(
                 ax1.semilogy(

pySDC/projects/DAE/problems/wscc9BusSystem.py

@@ -866,7 +866,7 @@ def __init__(self, newton_tol=1e-10):
         self.IC6 = [row[3] for row in self.bus]  # Column 4 in MATLAB is indexed as 3 in Python
         self.IC6 = [val / self.baseMVA for val in self.IC6]
 
-        self.IC = list(zip(self.IC1, self.IC2, self.IC3, self.IC4, self.IC5, self.IC6))
+        self.IC = list(zip(self.IC1, self.IC2, self.IC3, self.IC4, self.IC5, self.IC6, strict=True))
 
         self.PL = [row[4] for row in self.IC]  # Column 5 in MATLAB is indexed as 4 in Python
         self.QL = [row[5] for row in self.IC]  # Column 6 in MATLAB is indexed as 5 in Python

pySDC/projects/DAE/run/synchronous_machine_playground.py

@@ -98,7 +98,7 @@ def main():
     sol_data = np.array(
         [
             [(sol[j][1].diff[id], sol[j][1].alg[ia]) for j in range(len(sol))]
-            for id, ia in zip(range(len(uend.diff)), range(len(uend.alg)))
+            for id, ia in zip(range(len(uend.diff)), range(len(uend.alg)), strict=True)
         ]
     )
     niter = filter_stats(stats, type='niter')

pySDC/projects/GPU/analysis_scripts/plot_RBC_matrix.py

@@ -15,7 +15,7 @@ def plot_preconditioners():  # pragma: no cover
 
     fig, axs = plt.subplots(1, 4, figsize=figsize_by_journal('TUHH_thesis', 1, 0.4), sharex=True, sharey=True)
 
-    for M, ax in zip([A, A_b, A_r, A_l], axs):
+    for M, ax in zip([A, A_b, A_r, A_l], axs, strict=True):
         ax.imshow((M / abs(M)).real + (M / abs(M)).imag, rasterized=False, cmap='Spectral')
 
     for ax in axs:

pySDC/projects/GPU/analysis_scripts/plot_large_simulations.py

@@ -65,7 +65,7 @@ def prob(self):
 
     def plot_work(self):  # pragma: no cover
         fig, ax = self.get_fig()
-        for key, label in zip(['factorizations', 'rhs'], ['LU decompositions', 'rhs evaluations']):
+        for key, label in zip(['factorizations', 'rhs'], ['LU decompositions', 'rhs evaluations'], strict=True):
             work = get_sorted(self.stats, type=f'work_{key}')
             ax.plot([me[0] for me in work], np.cumsum([4 * me[1] for me in work]), label=fr'\#{label}')
         ax.set_yscale('log')
@@ -191,7 +191,7 @@ def get_CFL_limit(self, recompute=False):
 
     def plot_work(self):  # pragma: no cover
         fig, ax = self.get_fig()
-        for key, label in zip(['factorizations', 'rhs'], ['LU decompositions', 'rhs evaluations']):
+        for key, label in zip(['factorizations', 'rhs'], ['LU decompositions', 'rhs evaluations'], strict=True):
             work = get_sorted(self.stats, type=f'work_{key}')
             ax.plot([me[0] for me in work], np.cumsum([4 * me[1] for me in work]), label=fr'\#{label}')
         ax.set_yscale('log')
@@ -251,7 +251,7 @@ def plot_single(idx, ax):  # pragma: no cover
                 im = ax.pcolormesh(X[r], Z[r], data['u'][2], vmin=0, vmax=2, cmap='plasma', rasterized=True), data['t']
             return im
 
-        for i, ax in zip(indices, axs):
+        for i, ax in zip(indices, axs, strict=True):
             im, t = plot_single(i, ax)
             fig.colorbar(im, caxs[ax], label=f'$T(t={{{t:.1f}}})$')
 

pySDC/projects/GPU/run_experiment.py

@@ -124,7 +124,7 @@ def plot_series(args, config):  # pragma: no cover
 
     idxs = np.linspace(0, config.num_frames * 0.9, 9, dtype=int)
 
-    for idx, ax in zip(idxs, axs.flatten()):
+    for idx, ax in zip(idxs, axs.flatten(), strict=True):
         try:
             _fig = config.plot(P=P, idx=idx, n_procs_list=args['procs'], ax=ax)
         except FileNotFoundError: