Add new ipython and jupyter backend tests

ianthomas23 · ianthomas23 · commit acf737c0bd7c · 2024-04-10T10:15:32.000+01:00
diff --git a/lib/matplotlib/testing/__init__.py b/lib/matplotlib/testing/__init__.py
@@ -9,6 +9,8 @@
 import subprocess
 import sys
 
+import pytest
+
 import matplotlib as mpl
 from matplotlib import _api
 
@@ -177,3 +179,22 @@ def _has_tex_package(package):
         return True
     except FileNotFoundError:
         return False
+
+
+def ipython_in_subprocess(requested_backend_or_gui_framework, expected_backend):
+    pytest.importorskip("IPython")
+
+    code = ("import matplotlib as mpl, matplotlib.pyplot as plt;"
+            "fig, ax=plt.subplots(); ax.plot([1, 3, 2]); mpl.get_backend()")
+    proc = subprocess_run_for_testing(
+        [
+            "ipython",
+            "--no-simple-prompt",
+            f"--matplotlib={requested_backend_or_gui_framework}",
+            "-c", code,
+        ],
+        check=True,
+        capture_output=True,
+    )
+    
+    assert proc.stdout.strip() == f"Out[1]: '{expected_backend}'"
diff --git a/lib/matplotlib/testing/__init__.pyi b/lib/matplotlib/testing/__init__.pyi
@@ -47,3 +47,7 @@ def subprocess_run_helper(
 ) -> subprocess.CompletedProcess[str]: ...
 def _check_for_pgf(texsystem: str) -> bool: ...
 def _has_tex_package(package: str) -> bool: ...
+def ipython_in_subprocess(
+    requested_backend_or_gui_framework: str,
+    expected_backend: str,
+) -> None: ...
diff --git a/lib/matplotlib/tests/test_backend_inline.py b/lib/matplotlib/tests/test_backend_inline.py
@@ -0,0 +1,39 @@
+import os
+from pathlib import Path
+from tempfile import TemporaryDirectory
+
+import pytest
+
+from matplotlib.testing import subprocess_run_for_testing
+
+nbformat = pytest.importorskip('nbformat')
+pytest.importorskip('nbconvert')
+pytest.importorskip('ipykernel')
+pytest.importorskip('matplotlib_inline')
+
+
+def test_ipynb():
+    nb_path = Path(__file__).parent / 'test_inline_01.ipynb'
+
+    with TemporaryDirectory() as tmpdir:
+        out_path = Path(tmpdir, "out.ipynb")
+
+        subprocess_run_for_testing(
+            ["jupyter", "nbconvert", "--to", "notebook",
+             "--execute", "--ExecutePreprocessor.timeout=500",
+             "--output", str(out_path), str(nb_path)],
+            env={**os.environ, "IPYTHONDIR": tmpdir},
+            check=True)
+        with out_path.open() as out:
+            nb = nbformat.read(out, nbformat.current_nbformat)
+
+    errors = [output for cell in nb.cells for output in cell.get("outputs", [])
+              if output.output_type == "error"]
+    assert not errors
+    
+    backend_outputs = nb.cells[2]["outputs"]
+    assert backend_outputs[0]["data"]["text/plain"] == "'inline'"
+
+    image = nb.cells[1]["outputs"][1]["data"]
+    assert image["text/plain"] == "<Figure size 300x200 with 1 Axes>"
+    assert "image/png" in image
diff --git a/lib/matplotlib/tests/test_backend_macosx.py b/lib/matplotlib/tests/test_backend_macosx.py
@@ -44,3 +44,8 @@ def new_choose_save_file(title, directory, filename):
         # Check the savefig.directory rcParam got updated because
         # we added a subdirectory "test"
         assert mpl.rcParams["savefig.directory"] == f"{tmp_path}/test"
+
+
+def test_ipython():
+    from matplotlib.testing import ipython_in_subprocess
+    ipython_in_subprocess("osx", "macosx")
diff --git a/lib/matplotlib/tests/test_backend_nbagg.py b/lib/matplotlib/tests/test_backend_nbagg.py
@@ -30,3 +30,6 @@ def test_ipynb():
     errors = [output for cell in nb.cells for output in cell.get("outputs", [])
               if output.output_type == "error"]
     assert not errors
+    
+    backend_outputs = nb.cells[2]["outputs"]
+    assert backend_outputs[0]["data"]["text/plain"] == "'notebook'"
diff --git a/lib/matplotlib/tests/test_backend_qt.py b/lib/matplotlib/tests/test_backend_qt.py
@@ -14,7 +14,6 @@
 from matplotlib._pylab_helpers import Gcf
 from matplotlib import _c_internal_utils
 
-
 try:
     from matplotlib.backends.qt_compat import QtGui, QtWidgets  # type: ignore # noqa
     from matplotlib.backends.qt_editor import _formlayout
@@ -375,3 +374,8 @@ def custom_handler(signum, frame):
     finally:
         # Reset SIGINT handler to what it was before the test
         signal.signal(signal.SIGINT, original_handler)
+
+
+def test_ipython():
+    from matplotlib.testing import ipython_in_subprocess
+    ipython_in_subprocess("qt", "qtagg")
diff --git a/lib/matplotlib/tests/test_backend_tk.py b/lib/matplotlib/tests/test_backend_tk.py
@@ -261,3 +261,8 @@ def test_figure(master):
                        foreground="white")
     test_figure(root)
     print("success")
+
+
+def test_ipython():
+    from matplotlib.testing import ipython_in_subprocess
+    ipython_in_subprocess("tk", "tkagg")
diff --git a/lib/matplotlib/tests/test_inline_01.ipynb b/lib/matplotlib/tests/test_inline_01.ipynb
@@ -0,0 +1,112 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "%matplotlib inline\n",
+    "import matplotlib.pyplot as plt"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {
+    "collapsed": false,
+    "jupyter": {
+     "outputs_hidden": false
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "[<matplotlib.lines.Line2D at 0x11c2f29f0>]"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x200 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(figsize=(3, 2))\n",
+    "ax.plot([1, 3, 2])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'inline'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import matplotlib\n",
+    "matplotlib.get_backend()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.12.2"
+  },
+  "toc": {
+   "colors": {
+    "hover_highlight": "#DAA520",
+    "running_highlight": "#FF0000",
+    "selected_highlight": "#FFD700"
+   },
+   "moveMenuLeft": true,
+   "nav_menu": {
+    "height": "12px",
+    "width": "252px"
+   },
+   "navigate_menu": true,
+   "number_sections": true,
+   "sideBar": true,
+   "threshold": 4,
+   "toc_cell": false,
+   "toc_section_display": "block",
+   "toc_window_display": false,
+   "widenNotebook": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/lib/matplotlib/tests/test_nbagg_01.ipynb b/lib/matplotlib/tests/test_nbagg_01.ipynb
@@ -8,9 +8,8 @@
    },
    "outputs": [],
    "source": [
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "%matplotlib notebook\n"
+    "%matplotlib notebook\n",
+    "import matplotlib.pyplot as plt"
    ]
   },
   {
@@ -826,17 +825,31 @@
    ],
    "source": [
     "fig, ax = plt.subplots()\n",
-    "ax.plot(range(10))\n"
+    "ax.plot(range(10))"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 3,
    "metadata": {
     "collapsed": true
    },
-   "outputs": [],
-   "source": []
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "'notebook'"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import matplotlib\n",
+    "matplotlib.get_backend()"
+   ]
   }
  ],
  "metadata": {
