V2.x.unittest

.github/workflows/test_and_deploy.yml

@@ -2,76 +2,31 @@
 # For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions#publishing-to-package-registries
 
 # name: tests
+name: Run tests and upload coverage
 
-on:
-  push:
-    branches:
-      - main
-    tags:
-      - "v*" # Push events to matching v*, i.e. v1.0, v20.15.10
-  pull_request:
-    branches:
-      - main
-  workflow_dispatch:
+on: 
+  push
 
 jobs:
   test:
-    name: ${{ matrix.platform }} py${{ matrix.python-version }}
-    runs-on: ${{ matrix.platform }}
-    strategy:
-      matrix:
-        platform: [ubuntu-latest, windows-latest, macos-latest]
-        python-version: ['3.8', '3.9', '3.10']
-
+    name: Run tests and collect coverage
+    runs-on: ubuntu-latest
     steps:
-      - uses: actions/checkout@v3
+      - name: Checkout
+        uses: actions/checkout@v4
+        with:
+          fetch-depth: 2
 
-      - name: Set up Python ${{ matrix.python-version }}
+      - name: Set up Python
         uses: actions/setup-python@v4
-        with:
-          python-version: ${{ matrix.python-version }}
-      # note: if you need dependencies from conda, considering using
-      # setup-miniconda: https://github.com/conda-incubator/setup-miniconda
-      # and
-      # tox-conda: https://github.com/tox-dev/tox-conda
-      - name: Install dependencies
-        run: |
-          python -m pip install --upgrade pip
-          python -m pip install setuptools tox tox-gh-actions
 
-      # this runs the platform-specific tests declared in tox.ini
-      - name: Test with tox
-        uses: GabrielBB/xvfb-action@v1
-        with:
-          run: python -m tox
-        env:
-          PLATFORM: ${{ matrix.platform }}
+      - name: Install dependencies
+        run: pip install ".[test]"
 
-      - name: Coverage
-        uses: codecov/codecov-action@v2
+      - name: Run tests
+        run: pytest --cov --cov-branch --cov-report=xml
 
-  deploy:
-    # this will run when you have tagged a commit, starting with "v*"
-    # and requires that you have put your twine API key in your
-    # github secrets (see readme for details)
-    needs: [test]
-    runs-on: ubuntu-latest
-    if: contains(github.ref, 'tags')
-    steps:
-      - uses: actions/checkout@v2
-      - name: Set up Python
-        uses: actions/setup-python@v2
+      - name: Upload results to Codecov
+        uses: codecov/codecov-action@v5
         with:
-          python-version: "3.x"
-      - name: Install dependencies
-        run: |
-          python -m pip install --upgrade pip
-          pip install -U setuptools setuptools_scm wheel twine build
-      - name: Build and publish
-        env:
-          TWINE_USERNAME: __token__
-          TWINE_PASSWORD: ${{ secrets.TWINE_API_KEY }}
-        run: |
-          git tag
-          python -m build .
-          twine upload dist/*
+          token: ${{ secrets.CODECOV_TOKEN }}

.gitignore

@@ -85,12 +85,13 @@ venv/
 
 # specific ingores
 *.svg
-*.xml
+# *.xml
 *.png
 lineageTree2Treex.py
 test.tlp
-*.mastodon
+# *.mastodon
 bak_*
+# data/*
 
 .coverage
 

pyproject.toml

@@ -33,6 +33,7 @@ classifiers = [
     "Programming Language :: Python :: 3.12",
     "Programming Language :: Python :: 3.13",
 ]
+
 dependencies = [
     "scipy>=1.9",
     "numpy>=1.23",
@@ -41,6 +42,7 @@ dependencies = [
     "edist",
     "svgwrite",
 ]
+
 [project.optional-dependencies]
 dev = [
     "pytest",
@@ -55,6 +57,10 @@ doc = [
     "myst-parser",
     "sphinx_book_theme",
 ]
+test = [
+    "pytest",
+    "pytest-cov",
+]
 
 [project.urls]
 "Bug Tracker" = "https://github.com/GuignardLab/LineageTree/issues"

src/LineageTree/lineageTree.py

@@ -15,26 +15,17 @@
 from types import MappingProxyType
 from typing import Literal
 
+import matplotlib.pyplot as plt
+import numpy as np
 import svgwrite
+from edist import uted
 from matplotlib.collections import LineCollection
 from packaging.version import Version
+from scipy.interpolate import InterpolatedUnivariateSpline
 from scipy.sparse import dok_array
+from scipy.spatial import Delaunay, KDTree, distance
 
 from .tree_styles import tree_style
-
-try:
-    from edist import uted
-except ImportError:
-    warnings.warn(
-        "No edist installed therefore you will not be able to compute the tree edit distance.",
-        stacklevel=2,
-    )
-import matplotlib.pyplot as plt
-import numpy as np
-from scipy.interpolate import InterpolatedUnivariateSpline
-from scipy.spatial import Delaunay, distance
-from scipy.spatial import cKDTree as KDTree
-
 from .utils import (
     create_links_and_cycles,
     hierarchical_pos,
@@ -89,7 +80,7 @@ def raising_flag(self, *args, **kwargs):
 
         return raising_flag
 
-    def __check_cc_cycles(self, n) -> tuple[bool, set[int]]:
+    def __check_cc_cycles(self, n: int) -> tuple[bool, set[int]]:
         """Check if the connected component of a given node `n` has a cycle.
 
         Returns
@@ -146,7 +137,7 @@ def __eq__(self, other) -> bool:
             return False
 
     def get_next_id(self) -> int:
-        """Computes the next authorized id.
+        """Computes the next authorized id and assign it.
 
         Returns
         -------
@@ -198,10 +189,6 @@ def add_chain(
                 old_node = node
                 node = self._add_node(pred=[old_node])
                 self._time[node] = self._time[old_node] + 1
-                # if not pos:
-                #     self.pos[node] = self.pos[old_node]
-                # else:
-                #     self.pos[node] = pos
         else:
             if self._predecessor[node]:
                 raise Warning("The node already has a predecessor.")
@@ -213,10 +200,6 @@ def add_chain(
                 old_node = node
                 node = self._add_node(succ=[old_node])
                 self._time[node] = self._time[old_node] - 1
-                # if not pos:
-                #     self.pos[node] = self.pos[old_node]
-                # else:
-                #     self.pos[node] = pos
         return node
 
     @modifier
@@ -566,16 +549,16 @@ def node_color(x):
             from matplotlib import colormaps
 
             if node_color_map in colormaps:
-                node_color_map = colormaps[node_color_map]
+                cmap = colormaps[node_color_map]
             else:
-                node_color_map = colormaps["viridis"]
+                cmap = colormaps["viridis"]
             values = np.array(
                 [self._successor[node_color][c] for c in self.nodes]
             )
             normed_vals = normalize_values(values, self.nodes, 1, 0, 1)
 
             def node_color(x):
-                return [k * 255 for k in node_color_map(normed_vals(x))[:-1]]
+                return [k * 255 for k in cmap(normed_vals(x))[:-1]]
 
         coloring_edges = stroke_color is not None
         if not coloring_edges:
@@ -587,16 +570,16 @@ def stroke_color(x):
             from matplotlib import colormaps
 
             if node_color_map in colormaps:
-                node_color_map = colormaps[node_color_map]
+                cmap = colormaps[node_color_map]
             else:
-                node_color_map = colormaps["viridis"]
+                cmap = colormaps["viridis"]
             values = np.array(
                 [self._successor[stroke_color][c] for c in self.nodes]
             )
             normed_vals = normalize_values(values, self.nodes, 1, 0, 1)
 
             def stroke_color(x):
-                return [k * 255 for k in node_color_map(normed_vals(x))[:-1]]
+                return [k * 255 for k in cmap(normed_vals(x))[:-1]]
 
         prev_x = 0
         self.vert_space_factor = vert_space_factor
@@ -1484,6 +1467,8 @@ def compute_spatial_density(
         dict of int to float
             dictionary that maps a node id to its spatial density
         """
+        if not hasattr(self, "spatial_density"):
+            self.spatial_density = {}
         s_vol = 4 / 3.0 * np.pi * th**3
         if t_b is None:
             t_b = self.t_b
@@ -1518,14 +1503,22 @@ def compute_k_nearest_neighbours(self, k: int = 10) -> dict[int, set[int]]:
         self.kn_graph = {}
         for t in set(self._time.values()):
             nodes = self.nodes_at_t(t)
-            use_k = k if k < len(nodes) else len(nodes)
-            idx3d, nodes = self.get_idx3d(t)
-            pos = [self.pos[c] for c in nodes]
-            _, neighbs = idx3d.query(pos, use_k)
-            out = dict(
-                zip(nodes, [set(nodes[ni[1:]]) for ni in neighbs], strict=True)
-            )
-            self.kn_graph.update(out)
+            if 1 < len(nodes):
+                use_k = k if k < len(nodes) else len(nodes)
+                idx3d, nodes = self.get_idx3d(t)
+                pos = [self.pos[c] for c in nodes]
+                _, neighbs = idx3d.query(pos, use_k)
+                out = dict(
+                    zip(
+                        nodes,
+                        map(set, nodes[neighbs]),
+                        strict=True,
+                    )
+                )
+                self.kn_graph.update(out)
+            else:
+                n = nodes.pop
+                self.kn_graph.update({n: {n}})
         return self.kn_graph
 
     def compute_spatial_edges(self, th: int = 50) -> dict[int, set[int]]:
@@ -1575,7 +1568,7 @@ def main_axes(self, time: int | None = None) -> tuple[np.array, np.array]:
             sorted eigenvectors (3,)
         """
         time_nodes = {
-            t: len(self.nodes_at_t(t)) for t in range(self.t_b, self._te)
+            t: len(self.nodes_at_t(t)) for t in range(self.t_b, self.t_e)
         }
         if time is None:
             time = max(time_nodes, key=lambda x: len(time_nodes[x]))
@@ -1934,7 +1927,7 @@ def draw_tree_graph(
         if selected_edges is None:
             selected_edges = []
         if ax is None:
-            figure, ax = plt.subplots()
+            _, ax = plt.subplots()
         else:
             ax.clear()
         if not isinstance(selected_nodes, set):
@@ -3017,7 +3010,7 @@ def __init__(
         else:
             if starting_time is not None:
                 warnings.warn(
-                    f"Both `time` and `starting_time` were provided, `starting_time` was ignored.",
+                    "Both `time` and `starting_time` were provided, `starting_time` was ignored.",
                     stacklevel=2,
                 )
             self._time = {n: int(time[n]) for n in self.nodes}