minor improvements (#268)

* move azure cibuildwheel main yml to root

* print cmake status for version and whether tests are enabled

* increase verbosity for cibuildwheel on linux

* fix tests: compatibility to numpy 1.24, fixed some deprecation warnings regarding matplotlib and networkx

* make test_reversible_pi flaky

Author: clonker

CMakeLists.txt

@@ -13,6 +13,10 @@ endif()
 project(deeptime LANGUAGES C CXX VERSION ${DEEPTIME_VERSION})
 set(DEEPTIME_ROOT_DIRECTORY "${CMAKE_CURRENT_LIST_DIR}")
 
+message(STATUS "Got deeptime version ${DEEPTIME_VERSION}")
+message(STATUS "Got deeptime full version ${DEEPTIME_VERSION_INFO}")
+message(STATUS "Building tests: ${DEEPTIME_BUILD_CPP_TESTS}")
+
 include(GNUInstallDirs)
 
 if(SKBUILD)

azure-cibuildwheel.yml

@@ -0,0 +1,29 @@
+jobs:
+  - job: linux
+    pool: { vmImage: 'Ubuntu-22.04' }
+    variables:
+      CIBW_BUILD: cp3{8,9,10,11}-manylinux_x86_64
+      CIBW_BUILD_VERBOSITY: 2
+    steps:
+      - template: devtools/cibuildwheel.yml
+
+  - job: macos
+    pool: { vmImage: 'macOS-12' }
+    variables:
+      CIBW_BUILD: cp3{8,9,10,11}-macosx_*
+      CIBW_ARCHS_MACOS: $(arch)
+    strategy:
+      matrix:
+        x86_64:
+          arch: 'x86_64'
+        arm64:
+          arch: 'arm64'
+    steps:
+      - template: devtools/cibuildwheel.yml
+
+  - job: windows
+    pool: { vmImage: 'windows-2019' }
+    variables:
+      CIBW_BUILD: cp3{8,9,10,11}-win_amd64
+    steps:
+      - template: devtools/cibuildwheel.yml

deeptime/plots/chapman_kolmogorov.py

@@ -86,7 +86,7 @@ def legend(self, conf=None):
             handles.append(self._lpred_handles[ix])
             labels.append(predlabel)
             labels.append(estlabel)
-        self.figure.legend(handles, labels, 'upper center', ncol=2, frameon=False)
+        self.figure.legend(handles, labels, loc='upper center', ncol=2, frameon=False)
 
     @property
     def n_tests(self):

deeptime/plots/network.py

@@ -198,7 +198,7 @@ def _draw_arrow(ax, pos_1, pos_2, label="", width=1.0, arrow_curvature=1.0, colo
     @property
     def edge_base_scale(self):
         r""" Base scale for edges depending on the matplotlib default line width and the maximum off-diagonal
-        element in the adjacency matrix """
+        element in the adjacency matrix. Returns default line width if all off-diagonal elements are zero. """
         if issparse(self.adjacency_matrix):
             mat = self.adjacency_matrix.tocoo()
             max_off_diag = 0
@@ -209,7 +209,7 @@ def edge_base_scale(self):
             diag_mask = np.logical_not(np.eye(self.adjacency_matrix.shape[0], dtype=bool))
             max_off_diag = np.max(np.abs(self.adjacency_matrix[diag_mask]))
         default_lw = default_line_width()
-        return 2 * default_lw / max_off_diag
+        return 2 * default_lw / max_off_diag if max_off_diag > 0 else default_lw
 
     @property
     def edge_labels(self) -> Optional[np.ndarray]:
@@ -531,7 +531,7 @@ def plot_markov_model(msm: Union[MarkovStateModel, np.ndarray], pos=None, state_
             P[flux < minflux] = 0.0
     if pos is None:
         import networkx as nx
-        graph = nx.from_scipy_sparse_matrix(P) if msm.sparse else nx.from_numpy_matrix(P)
+        graph = nx.from_scipy_sparse_array(P) if msm.sparse else nx.from_numpy_array(P)
         pos = nx.spring_layout(graph)
     network = Network(P, pos=pos,  state_scale=state_scale, state_colors=state_colors, state_labels=state_labels,
                       state_sizes=state_sizes, edge_scale=edge_scale, edge_curvature=edge_curvature,

devtools/azure-pipelines-cibuildwheel.yml

@@ -0,0 +1,16 @@
+steps:
+  - template: checkout.yml
+  - task: UsePythonVersion@0
+    inputs:
+      versionSpec: '3.10'
+      architecture: 'x64'
+    displayName: Use Python 3.10
+  - bash: |
+      set -o errexit
+      python3 -m pip install --upgrade pip
+      pip3 install cibuildwheel
+    displayName: Install dependencies
+  - bash: cibuildwheel --output-dir wheelhouse .
+    displayName: Build wheels
+  - task: PublishBuildArtifacts@1
+    inputs: { pathtoPublish: 'wheelhouse' }

devtools/cibuildwheel.yml

@@ -56,7 +56,7 @@ def test_tram_different_input_data_types(dtrajs, ttrajs, bias_matrix_as_ndarray,
 
 
 def test_lagtime_too_long():
-    dtrajs = np.asarray([[0, 1, 0], [0, 1, 2, 1], [2, 3]])
+    dtrajs = np.asarray([[0, 1, 0], [0, 1, 2, 1], [2, 3]], dtype=object)
     bias_matrices = [np.random.rand(len(traj), 3) for traj in dtrajs]
     tram = TRAM(maxiter=100, lagtime=2)
     with np.testing.assert_raises(ValueError):
@@ -150,7 +150,7 @@ def to_numpy_arrays(dtrajs, bias_matrices, ttrajs):
         ttrajs = [np.asarray(traj) for traj in ttrajs]
 
     if not isinstance(bias_matrices, np.ndarray):
-        bias_matrices = [np.asarray(M) for M in bias_matrices]
+        bias_matrices = [np.asarray(M, dtype=object) for M in bias_matrices]
 
     return dtrajs, bias_matrices, ttrajs
 

tests/markov/msm/test_tram.py

@@ -3,7 +3,7 @@
 .. moduleauthor:: B.Trendelkamp-Schroer <benjamin DOT trendelkamp-schroer AT fu-berlin DOT de>
 
 """
-
+import flaky
 import numpy as np
 import pytest
 from deeptime.markov.tools.estimation import transition_matrix as tmatrix
@@ -60,7 +60,8 @@ def test_reversible(dtype):
 
 
 @pytest.mark.parametrize("dtype", (np.float32, np.float64, np.longdouble))
-def test_reversible_pi(fixed_seed, dtype):
+@flaky.flaky(3)
+def test_reversible_pi(dtype):
     C = np.array([[7048, 6, 0], [6, 2, 3], [0, 3, 2933]]).astype(dtype)
     pi = np.array([0.70532947, 0.00109989, 0.29357064])
     P_mle = tmatrix(C, reversible=True, mu=pi)

tests/markov/tools/estimation/impl/dense/tmatrix_sampler_test.py

@@ -198,7 +198,7 @@ def test_coarse_grain(sparse_mode):
     Csym = C + C.T
     P = Csym / np.sum(Csym, axis=1)[:, np.newaxis]
     if sparse_mode:
-        P = sparse.csr_matrix(P)
+        P = sparse.lil_matrix(P)
     msm = MarkovStateModel(P)
     tpt = msm.reactive_flux([0, 4], [11, 15])
     coarse_sets = [[2, 3, 6, 7], [10, 11, 14, 15], [0, 1, 4, 5], [8, 9, 12, 13], ]

tests/markov/tools/flux/test_reactive_flux.py

@@ -24,7 +24,7 @@ def test_network(labels, cmap):
 
     flux = MarkovStateModel(Psparse).reactive_flux([2], [3])
 
-    positions = nx.planar_layout(nx.from_scipy_sparse_matrix(flux.gross_flux))
+    positions = nx.planar_layout(nx.from_scipy_sparse_array(flux.gross_flux))
     pl = Network(flux.gross_flux, positions, edge_curvature=2., edge_labels=labels,
                  state_colors=np.linspace(0, 1, num=flux.n_states), cmap=cmap)
     ax = pl.plot()