Added python 3.10 and 3.11 support

.github/workflows/test.yml

@@ -15,7 +15,7 @@ jobs:
     timeout-minutes: 20
     strategy:
       matrix:
-        python-version: [3.8, 3.9]
+        python-version: ["3.8", "3.9", "3.10", "3.11"]
 
     steps:
     - uses: actions/checkout@v2
@@ -34,7 +34,7 @@ jobs:
       run: |
         pip install pytest-cov
         pip install codecov
-        pip install ".[spatial]"
+        pip install .
     - name: Test with pytest
       run: |
         pytest -vv test/ --cov-report=xml --cov=cassiopeia

.gitignore

@@ -167,3 +167,4 @@ cassiopeia/tools/branch_length_estimator/_iid_exponential_bayesian.cpp
 docs/api/reference/**
 .vscode
 cassiopeia/config.ini
+environment.yml

cassiopeia/plotting/utilities.py

@@ -395,9 +395,9 @@ def get_random_indel_colors(
         if "none" in indel.lower():
             indel2color[indel] = rgb_to_hsv((0.75, 0.75, 0.75))
         elif indel == "NC":
-            indel2color[indel] = rgb_to_hsv((0, 0, 0))
+            indel2color[indel] = rgb_to_hsv((0.0, 0.0, 0.0))
         elif indel == "missing":
-            indel2color[indel] = rgb_to_hsv((1, 1, 1))
+            indel2color[indel] = rgb_to_hsv((1.0, 1.0, 1.0))
         else:
             # randomly pick a color family and then draw random colors
             # from that family

cassiopeia/solver/HybridSolver.py

@@ -13,7 +13,6 @@
 import multiprocessing
 import networkx as nx
 import numpy as np
-from numpy.lib.arraysetops import unique
 import pandas as pd
 from tqdm.auto import tqdm
 

conftest.py

@@ -3,19 +3,27 @@
 
 def pytest_addoption(parser):
     parser.addoption(
-        "--runslow", action="store_true", default=False, help="run slow tests"
+        "--runslow", action="store_true", default=False, help="run slow tests",
+    )
+    parser.addoption(
+        "--runspatial", action="store_true", default=False, help="run spatial tests",
     )
 
 
 def pytest_configure(config):
     config.addinivalue_line("markers", "slow: mark test as slow to run")
+    config.addinivalue_line("markers", "spatial: mark test as spatial to run")
+
 
 
 def pytest_collection_modifyitems(config, items):
-    if config.getoption("--runslow"):
-        # --runslow given in cli: do not skip slow tests
-        return
+    run_slow = config.getoption("--runslow")
+    run_spatial = config.getoption("--runspatial")
     skip_slow = pytest.mark.skip(reason="need --runslow option to run")
+    skip_spatial = pytest.mark.skip(reason="need --runspatial option to run")
+
     for item in items:
-        if "slow" in item.keywords:
+        if "slow" in item.keywords and not run_slow:
             item.add_marker(skip_slow)
+        if "spatial" in item.keywords and not run_spatial:
+            item.add_marker(skip_spatial)

pyproject.toml

@@ -4,9 +4,10 @@ classifiers = [
   "Development Status :: 4 - Beta",
   "Intended Audience :: Science/Research",
   "Natural Language :: English",
-  "Programming Language :: Python :: 3.7",
   "Programming Language :: Python :: 3.8",
   "Programming Language :: Python :: 3.9",
+  "Programming Language :: Python :: 3.10",
+  "Programming Language :: Python :: 3.11",
   "Operating System :: MacOS :: MacOS X",
   "Operating System :: Microsoft :: Windows",
   "Operating System :: POSIX :: Linux",
@@ -20,7 +21,7 @@ license = "MIT"
 name = "cassiopeia-lineage"
 readme = 'README.md'
 repository = "https://github.com/YosefLab/Cassiopeia"
-version = "2.0.0"
+version = "2.1.0"
 
 include = [
   {path = "cassiopeia/preprocess/*.so", format = "wheel"},
@@ -46,20 +47,19 @@ codecov = {version = ">=2.0.8", optional = true}
 cvxpy = "*"
 ete3 = ">=3.1.1"
 hits = "*"
-importlib-metadata = {version = "^1.0", python = "<3.8"}
-ipython = {version = ">=7.20", optional = true, python = ">=3.7"}
+ipython = {version = ">=7.20", optional = true}
 isort = {version = ">=5.7", optional = true}
 itolapi = "*"
 jupyter = {version = ">=1.0", optional = true}
 matplotlib = ">=2.2.2"
-nbconvert = ">=5.4.0"
-nbformat = ">=4.4.0"
+nbconvert = {version = ">=5.4.0", optional = true}
+nbformat = {version = ">=4.4.0", optional = true}
 nbsphinx = {version = "*", optional = true}
 nbsphinx-link = {version = "*", optional = true}
-networkx = "==3.1"
+networkx = ">=3.1"
 ngs-tools = ">=1.5.6"
-numba = ">=0.51.0,<0.59.0"
-numpy = ">=1.22"
+numba = ">=0.51.0"
+numpy = ">=1.22, <3.0"
 opencv-python = {version = ">=4.5.4.60", optional = true}
 pandas = ">=1.1.4"
 parameterized = "*"
@@ -75,7 +75,7 @@ pyvista = {version = "=0.41.0", optional = true}
 scanpydoc = {version = ">=0.5", optional = true}
 scikit-image = {version = ">=0.19.1", optional = true}
 scikit-learn = {version = ">=1.0.2", optional = true}
-scipy = ">=1.2.0,<=1.11.4"
+scipy = ">=1.2.0"
 sphinx = {version = ">=3.4", optional = true}
 sphinx-autodoc-typehints = {version = "*", optional = true}
 sphinx-gallery = {version = ">0.6", optional = true}
@@ -93,7 +93,7 @@ script = "build.py"
 
 [build-system]
 build-backend = "poetry.core.masonry.api"
-requires = ["poetry-core>=1.0.7", "Cython", "numpy>=1.19.5,<1.22", "setuptools", "pip>=22.0.0"]
+requires = ["poetry-core>=1.0.7", "Cython", "numpy>=1.19.5", "setuptools", "pip>=22.0.0"]
 
 [tool.poetry.scripts]
 cassiopeia-preprocess = 'cassiopeia.preprocess.cassiopeia_preprocess:main'
@@ -103,6 +103,8 @@ dev = ["black", "pytest", "flake8", "codecov", "jupyter", "pre-commit", "isort"]
 docs = [
   "sphinx",
   "scanpydoc",
+  "nbconvert",
+  "nbformat",
   "nbsphinx",
   "nbsphinx-link",
   "ipython",

test/plotting_tests/local_3d_test.py

@@ -1,9 +1,7 @@
+import pytest
 import unittest
-from unittest import mock
 
-import networkx as nx
 import numpy as np
-import pandas as pd
 
 import cassiopeia as cas
 from cassiopeia.plotting import local_3d
@@ -20,6 +18,7 @@ def setUp(self):
 
         self.labels = local_3d.labels_from_coordinates(self.tree)
 
+    @pytest.mark.spatial
     def test_interpolate_branch(self):
         parent = (0, 0, 0)
         child = (1, 1, 1)
@@ -28,6 +27,7 @@ def test_interpolate_branch(self):
             local_3d.interpolate_branch(parent, child),
         )
 
+    @pytest.mark.spatial
     def test_polyline_from_points(self):
         points = np.array(
             [
@@ -39,6 +39,7 @@ def test_polyline_from_points(self):
         poly = local_3d.polyline_from_points(points)
         np.testing.assert_array_equal(points, poly.points)
 
+    @pytest.mark.spatial
     def test_average_mixing(self):
         c1 = (0, 0, 0)
         c2 = (0.1, 0.2, 0.3)
@@ -47,14 +48,17 @@ def test_average_mixing(self):
             (0.2, 0.3, 0.1), local_3d.average_mixing(c1, c2, c3)
         )
 
+    @pytest.mark.spatial
     def test_highlight(self):
         c = (0.8, 0.2, 0.0)
         np.testing.assert_allclose((1.0, 0.25, 0.0), local_3d.highlight(c))
 
+    @pytest.mark.spatial
     def test_lowlight(self):
         c = (0.8, 0.2, 0.0)
         np.testing.assert_allclose((0.3, 0.075, 0.0), local_3d.lowlight(c))
 
+    @pytest.mark.spatial
     def test_labels_from_coordinates(self):
         # invalid shape
         with self.assertRaises(ValueError):
@@ -82,6 +86,7 @@ def test_labels_from_coordinates(self):
         spatial_simulator.overlay_data(dense_tree)
         labels = local_3d.labels_from_coordinates(dense_tree, shape=(100, 100))
 
+    @pytest.mark.spatial
     def test_Tree3D(self):
         # There isn't a good way to test this, other than making sure there
         # are no errors on initialization.

test/simulator_tests/clonal_spatial_simulator_test.py

@@ -1,3 +1,4 @@
+import pytest
 import unittest
 
 import networkx as nx
@@ -53,6 +54,7 @@ def setUp(self):
             tree=topology, cell_meta=self.cell_meta
         )
 
+    @pytest.mark.spatial
     def test_init(self):
         with self.assertRaises(DataSimulatorError):
             ClonalSpatialDataSimulator()
@@ -69,6 +71,7 @@ def test_init(self):
         self.assertEqual(simulator.dim, 3)
         np.testing.assert_array_equal(np.ones((10, 10, 10), dtype=bool), simulator.space)
 
+    @pytest.mark.spatial
     def test_overlay_data(self):
         simulator = ClonalSpatialDataSimulator((100, 100))
         simulator.overlay_data(self.basic_tree)
@@ -107,6 +110,7 @@ def test_overlay_data(self):
             self.basic_tree.cell_meta, expected_cell_meta
         )
 
+    @pytest.mark.spatial
     def test_overlay_data_with_space(self):
         simulator = ClonalSpatialDataSimulator(space=np.ones((100, 100), dtype=bool))
         simulator.overlay_data(self.basic_tree)
@@ -144,6 +148,7 @@ def test_overlay_data_with_space(self):
             self.basic_tree.cell_meta, expected_cell_meta
         )
 
+    @pytest.mark.spatial
     def test_overlay_data_with_existing_cell_meta(self):
         simulator = ClonalSpatialDataSimulator((100, 100))
         simulator.overlay_data(self.tree_with_cell_meta)

test/solver_tests/sharedmutationjoiner_test.py

@@ -348,79 +348,6 @@ def test_basic_solver(self):
                     nx.shortest_path_length(expected_tree, sample1, sample2),
                 )
 
-    def test_solver_no_numba(self):
-        self.smj_solver_no_numba.solve(self.basic_tree)
-
-        # test that the dissimilarity map and character matrix were not altered
-        cm = pd.DataFrame.from_dict(
-            {
-                "a": [0, 1, 2],
-                "b": [1, 1, 2],
-                "c": [2, 2, 2],
-                "d": [1, 1, 1],
-                "e": [0, 0, 0],
-            },
-            orient="index",
-            columns=["x1", "x2", "x3"],
-        )
-        for i in self.basic_similarity_map.index:
-            for j in self.basic_similarity_map.columns:
-                self.assertEqual(
-                    self.basic_similarity_map.loc[i, j],
-                    self.basic_tree.get_dissimilarity_map().loc[i, j],
-                )
-        for i in self.basic_tree.character_matrix.index:
-            for j in self.basic_tree.character_matrix.columns:
-                self.assertEqual(
-                    cm.loc[i, j], self.basic_tree.character_matrix.loc[i, j]
-                )
-
-        # test leaves exist in tree
-        _leaves = self.basic_tree.leaves
-
-        self.assertEqual(len(_leaves), self.basic_similarity_map.shape[0])
-        for _leaf in _leaves:
-            self.assertIn(_leaf, self.basic_similarity_map.index.values)
-
-        # test for expected number of edges
-        edges = list(self.basic_tree.edges)
-        self.assertEqual(len(edges), 8)
-
-        # test relationships between samples
-        expected_tree = nx.DiGraph()
-        expected_tree.add_edges_from(
-            [
-                ("5", "a"),
-                ("5", "b"),
-                ("6", "5"),
-                ("6", "c"),
-                ("7", "d"),
-                ("7", "e"),
-                ("8", "6"),
-                ("8", "7"),
-            ]
-        )
-
-        observed_tree = self.basic_tree.get_tree_topology()
-        triplets = itertools.combinations(["a", "b", "c", "d", "e"], 3)
-        for triplet in triplets:
-
-            expected_triplet = find_triplet_structure(triplet, expected_tree)
-            observed_triplet = find_triplet_structure(triplet, observed_tree)
-            self.assertEqual(expected_triplet, observed_triplet)
-
-        # compare tree distances
-        observed_tree = observed_tree.to_undirected()
-        expected_tree = expected_tree.to_undirected()
-        for i in range(len(_leaves)):
-            sample1 = _leaves[i]
-            for j in range(i + 1, len(_leaves)):
-                sample2 = _leaves[j]
-                self.assertEqual(
-                    nx.shortest_path_length(observed_tree, sample1, sample2),
-                    nx.shortest_path_length(expected_tree, sample1, sample2),
-                )
-
     def test_smj_solver_weights(self):
         self.smj_solver_modified_pp.solve(self.pp_tree_priors)
         observed_tree = self.pp_tree_priors.get_tree_topology()