ROLLOUT 4 · pyqtgraph/pyqtgraph
pyqtgraph__pyqtgraph-3278
Solved
390 stotal time
14tool calls
16model calls
5,234tokens generated
154,968prompt tokens read
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⚠ The request log has 15 model calls but the trace has 17 agent calls, so tokens are listed separately instead of beside each turn.
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System instructions 4,822 characters
You are a principal Python software engineer resolving a repository issue in `/workspace`. Your sole objective is to produce a minimal, root-cause source patch that passes every hidden fail-to-pass and pass-to-pass regression test on the first grading run. ## Critical Environment & Grader Rules 1. **Air-Gapped Sandbox & Available CLI Tools**: No network access. Never run `pip`, `uv`, `conda`, or `git fetch`. Note: `rg` (`ripgrep`) is NOT installed in the container — always use `grep -rn --include="*.py"` for fast exact searches so you never waste a tool call on `command not found`. 2. **Protected Grading Paths**: Before running official verification, the grading harness **strips and reverts all changes** to `tests/`, `test/`, `testing/`, `test_*.py`, `*_test.py`, `conftest.py`, `pyproject.toml`, `setup.cfg`, `pytest.ini`, and `tox.ini`. Every part of your fix MUST reside in non-test production source files. 3. **Untracked File Trap (`git add -N .`)**: `submit_patch` stages ALL untracked files inside `/workspace`! Never leave reproduction or scratch files inside `/workspace` — run reproductions inline via `python3 - <<'PY'` or inside `/tmp/repro.py` in a single `run_command`. 4. **Stdout Truncation Rule**: `run_command` truncates stdout after the first 5,000 characters from the TOP. Always run pytest quietly with short tracebacks: `pytest path/to/test_file.py -k pattern -q --tb=short --no-header` If a test file is large, redirect output to `/tmp/pytest.log` and inspect `tail -n 80 /tmp/pytest.log` so failure tracebacks at the bottom are never cut off. 5. **Strict Pacing Discipline**: `get_status` and `submit_patch` are free and do NOT count toward `max_tool_calls`. Target **16-22 counted tool calls total**, and **begin your first `edit_file` change no later than tool call 15**. Reserve at least 4 calls for verification, `git status --short && git diff`, and submission. ## Execution Protocol ### Phase 1 — Pinpoint Code & Tests Immediately (1-2 tool calls) - The user message already contains `Workspace Layout` — do NOT waste a turn running `ls` on top-level directories. - Run a single compound `run_command` with `grep -rn --include="*.py"` to locate: (a) the exact symbols, error strings, or functions from the problem description, (b) **all parallel or sibling implementations** (for example sync + async variants, `Client` + `AsyncClient`, serializer + deserializer, multiple backends/dialects, base + subclass methods, `__init__.py` / `__all__` exports), and (c) the closest existing unit test files under `tests/`. ### Phase 2 — Deep Read & Spec Anchor (2-5 tool calls) - Use `read_file` with generous `start_line` and `end_line` windows (180-300 lines per call so you read whole modules in fewer calls) on the target function/class, callers, type annotations, and neighboring tests. - Before editing, verify every explicit and implicit requirement from the issue: exact parameter names, defaults, boundary/domain edge cases, exception types/messages, `None` vs empty collection vs falsy (`0`, `False`, `""`) semantics, mutability/copying, and return types. ### Phase 3 — Reproduce Bug in One Call (1 tool call) - Run ONE concise command (`python3 - <<'PY' ... PY` or targeted `pytest ... -q --tb=short --no-header`) via `run_command` to observe the exact pre-fix failure. Do NOT spend multiple tool calls writing separate reproduction scripts. ### Phase 4 — Surgical Implementation Across All Parallel Paths (1-4 tool calls) - Begin editing by tool call 15! Use `edit_file` with 4-6 lines of exact surrounding context copied from `read_file`. - Update ALL parallel codepaths (sync/async, multiple classes/dialects, type hints) consistently so partial fixes do not fail sibling tests. - **2-Strike Edit Rule**: If `edit_file` fails twice on the same block due to whitespace or escaping, do not retry `edit_file` a third time — apply an atomic Python transformation via `run_command` and immediately verify `python3 -m py_compile path/to/file.py`. ### Phase 5 — Edge-Case Verification, Diff Hygiene & Clean Exit (2-4 tool calls) - Run a single compound `run_command` testing both your reproduction + boundary edge cases (`None`, empty input, leading/trailing delimiters, subdomain/prefix boundaries, subclass/async parity) AND the relevant existing `pytest` module(s) with `-q --tb=short --no-header`. - Run `git status --short && git diff` via `run_command`. Confirm: - ZERO untracked (`??`) files in `/workspace` (`rm -f` any untracked workspace files immediately), - ZERO modifications to `tests/` or protected config files (`git checkout -- tests/` if touched), - Minimal, clean diff with valid imports and indentation. - Call `submit_patch` once verification passes, then respond with a brief 2-sentence plain-text summary and make NO further tool calls.
Task prompt 9,569 characters
You are evaluating a software engineering task for repository pyqtgraph/pyqtgraph.
Problem Statement:
TypeError when using a record array to plot data
<!-- In the following, please describe your issue in detail! -->
<!-- If some sections do not apply, just remove them. -->
### Short description
<!-- This should summarize the issue. -->
Using a NumPy record array as data to plot results in the following:
`TypeError: Cannot compare structured or void to non-void arrays.`
### Code to reproduce
<!-- Please provide a minimal working example that reproduces the issue in the code block below.
Ideally, this should be a full example someone else could run without additional setup. -->
```python
import pyqtgraph as pg
import numpy as np
data_array = np.recarray((10,), dtype=[('x', float), ('y', float)])
pg.plot(data_array)
pg.exec()
```
### Expected behavior
<!-- What should happen? -->
According to the API reference, a PlotDataItem can be initialized with a NumPy recarray with `dtype=[('x', float), ('y', float), ...]`, so the above code should plot the (uninitialized) recarray data.
### Real behavior
<!-- What happens? -->
```
Traceback (most recent call last):
File "C:\Users\_\code\recarray_test.py", line 5, in <module>
pg.plot(data_array)
File "C:\Users\_\code\.venv\lib\site-packages\pyqtgraph\__init__.py", line 392, in plot
w.plot(*args, **dataArgs)
File "C:\Users\_\code\.venv\lib\site-packages\pyqtgraph\graphicsItems\PlotItem\PlotItem.py", line 630, in plot
item = PlotDataItem(*args, **kargs)
File "C:\Users\_\code\.venv\lib\site-packages\pyqtgraph\graphicsItems\PlotDataItem.py", line 375, in __init__
self.setData(*args, **kargs)
File "C:\Users\_\code\.venv\lib\site-packages\pyqtgraph\graphicsItems\PlotDataItem.py", line 719, in setData
if 'x' in data:
TypeError: Cannot compare structured or void to non-void arrays.
```
### Tested environment(s)
* PyQtGraph version: 0.13.7
* Qt Python binding: PyQt6 6.8.1 Qt 6.8.2
* Python version: 3.9.13
* NumPy version: 2.0.2
* Operating system: Windows 11
* Installation method: pip
## Task Budget (Session terminates when any budget is exhausted)
- Time allowance: 7.5 minutes
- Tool calls allowance: 30 calls
- Max loop iterations: 40 turns
## Execution Environment Rules
- Single command timeout: 90 seconds (commands exceeding this fail without ending the session)
- Command output limit: 5000 characters
- File view limit: 150 lines per read_file call
- File character limit: 10000 characters per read_file call
- Environment is offline (no network/PyPI access). All repository and test dependencies are ALREADY pre-installed. Do NOT attempt to run pip install or download packages.
## Instructions:
0. All source code is under `/workspace`. Do NOT search outside `/workspace` (e.g. `/usr/`, `/wheels/`, system site-packages). If imports fail, the issue is in the source code under `/workspace`, not in missing system packages.
1. Analyze the problem statement and any provided hints carefully to identify all requested script paths, CLI subcommands, or Python modules.
2. Inspect existing codebase conventions and test files before making edits.
3. Verify your implementation using targeted tests or inline assertions before submitting.
4. Call submit_patch only after your implementation is complete and verified.
5. As your final action, you must return a text-only response reporting your completion to terminate the session.
## Workspace Layout
The repository is located at `/workspace`. Here is the directory tree (up to 3 levels):
```
./CHANGELOG
./CODE_OF_CONDUCT.md
./CONTRIBUTING.md
./LICENSE.txt
./MANIFEST.in
./README.md
./benchmarks
./binder
./binder/apt.txt
./binder/requirements.txt
./binder/runtime.txt
./binder/start
./doc
./doc/Makefile
./doc/listmissing.py
./doc/make.bat
./doc/requirements.txt
./doc/source
./doc/source/_static
./doc/source/api_reference
./doc/source/conf.py
./doc/source/developer_guide
./doc/source/dictionaries
./doc/source/getting_started
./doc/source/images
./doc/source/index.rst
./doc/source/user_guide
./doc/uml_overview.txt
./pyproject.toml
./pyqtgraph
./pyqtgraph/GraphicsScene
./pyqtgraph/GraphicsScene/GraphicsScene.py
./pyqtgraph/GraphicsScene/__init__.py
./pyqtgraph/GraphicsScene/exportDialog.py
./pyqtgraph/GraphicsScene/exportDialogTemplate.ui
./pyqtgraph/GraphicsScene/exportDialogTemplate_generic.py
./pyqtgraph/GraphicsScene/mouseEvents.py
./pyqtgraph/PlotData.py
./pyqtgraph/Point.py
./pyqtgraph/Qt
./pyqtgraph/Qt/OpenGLConstants.py
./pyqtgraph/Qt/OpenGLHelpers.py
./pyqtgraph/Qt/QtCore
./pyqtgraph/Qt/QtGui
./pyqtgraph/Qt/QtSvg.pyi
./pyqtgraph/Qt/QtTest.pyi
./pyqtgraph/Qt/QtWidgets
./pyqtgraph/Qt/__init__.py
./pyqtgraph/Qt/__init__.pyi
./pyqtgraph/Qt/compat
./pyqtgraph/Qt/internals.py
./pyqtgraph/SRTTransform.py
./pyqtgraph/SRTTransform3D.py
./pyqtgraph/SignalProxy.py
./pyqtgraph/ThreadsafeTimer.py
./pyqtgraph/Transform3D.py
./pyqtgraph/Vector.py
./pyqtgraph/WidgetGroup.py
./pyqtgraph/__init__.py
./pyqtgraph/colormap.py
./pyqtgraph/colors
./pyqtgraph/colors/__init__.py
./pyqtgraph/colors/maps
./pyqtgraph/colors/palette.py
./pyqtgraph/configfile.py
./pyqtgraph/console
./pyqtgraph/console/CmdInput.py
./pyqtgraph/console/Console.py
./pyqtgraph/console/__init__.py
./pyqtgraph/console/exception_widget.py
./pyqtgraph/console/repl_widget.py
./pyqtgraph/console/stackwidget.py
./pyqtgraph/debug.py
./pyqtgraph/dockarea
./pyqtgraph/dockarea/Container.py
./pyqtgraph/dockarea/Dock.py
./pyqtgraph/dockarea/DockArea.py
./pyqtgraph/dockarea/DockDrop.py
./pyqtgraph/dockarea/__init__.py
./pyqtgraph/examples
./pyqtgraph/examples/Arrow.py
./pyqtgraph/examples/AxisItem_label_overlap.py
./pyqtgraph/examples/BarGraphItem.py
./pyqtgraph/examples/CLIexample.py
./pyqtgraph/examples/ColorBarItem.py
./pyqtgraph/examples/ColorButton.py
./pyqtgraph/examples/ColorGradientPlots.py
./pyqtgraph/examples/ConsoleWidget.py
./pyqtgraph/examples/CustomGraphItem.py
./pyqtgraph/examples/DataSlicing.py
./pyqtgraph/examples/DataTreeWidget.py
./pyqtgraph/examples/DateAxisItem.py
./pyqtgraph/examples/DateAxisItem_QtDesigner.py
./pyqtgraph/examples/DateAxisItem_QtDesigner.ui
./pyqtgraph/examples/DiffTreeWidget.py
./pyqtgraph/examples/Draw.py
./pyqtgraph/examples/ErrorBarItem.py
./pyqtgraph/examples/ExampleApp.py
./pyqtgraph/examples/FillBetweenItem.py
./pyqtgraph/examples/Flowchart.py
./pyqtgraph/examples/FlowchartCustomNode.py
./pyqtgraph/examples/GLBarGraphItem.py
./pyqtgraph/examples/GLGradientLegendItem.py
./pyqtgraph/examples/GLGraphItem.py
./pyqtgraph/examples/GLImageItem.py
./pyqtgraph/examples/GLIsosurface.py
./pyqtgraph/examples/GLLinePlotItem.py
./pyqtgraph/examples/GLMeshItem.py
./pyqtgraph/examples/GLPainterItem.py
./pyqtgraph/examples/GLScatterPlotItem.py
./pyqtgraph/examples/GLSurfacePlot.py
./pyqtgraph/examples/GLTextItem.py
./pyqtgraph/examples/GLViewWidget.py
./pyqtgraph/examples/GLVolumeItem.py
./pyqtgraph/examples/GLshaders.py
./pyqtgraph/examples/GradientEditor.py
./pyqtgraph/examples/GradientWidget.py
./pyqtgraph/examples/GraphItem.py
./pyqtgraph/examples/GraphicsLayout.py
./pyqtgraph/examples/GraphicsScene.py
./pyqtgraph/examples/HistogramLUT.py
./pyqtgraph/examples/ImageItem.py
./pyqtgraph/examples/ImageView.py
./pyqtgraph/examples/InfiniteLine.py
./pyqtgraph/examples/InteractiveParameter.py
./pyqtgraph/examples/JoystickButton.py
./pyqtgraph/examples/Legend.py
./pyqtgraph/examples/LogPlotTest.py
./pyqtgraph/examples/MatrixDisplayExample.py
./pyqtgraph/examples/MouseSelection.py
./pyqtgraph/examples/MultiDataPlot.py
./pyqtgraph/examples/MultiPlotSpeedTest.py
./pyqtgraph/examples/MultiplePlotAxes.py
./pyqtgraph/examples/NonUniformImage.py
./pyqtgraph/examples/PColorMeshItem.py
./pyqtgraph/examples/PanningPlot.py
./pyqtgraph/examples/PlotAutoRange.py
./pyqtgraph/examples/PlotSpeedTest.py
./pyqtgraph/examples/PlotWidget.py
./pyqtgraph/examples/Plotting.py
./pyqtgraph/examples/ProgressDialog.py
./pyqtgraph/examples/ROIExamples.py
./pyqtgraph/examples/ROItypes.py
./pyqtgraph/examples/RemoteGraphicsView.py
./pyqtgraph/examples/RemoteSpeedTest.py
./pyqtgraph/examples/RunExampleApp.py
./pyqtgraph/examples/ScaleBar.py
./pyqtgraph/examples/ScatterPlot.py
./pyqtgraph/examples/ScatterPlotSpeedTest.py
./pyqtgraph/examples/ScatterPlotWidget.py
./pyqtgraph/examples/SimplePlot.py
./pyqtgraph/examples/SpinBox.py
./pyqtgraph/examples/Symbols.py
./pyqtgraph/examples/TableWidget.py
./pyqtgraph/examples/TreeWidget.py
./pyqtgraph/examples/VideoSpeedTest.py
./pyqtgraph/examples/VideoTemplate.ui
./pyqtgraph/examples/VideoTemplate_generic.py
./pyqtgraph/examples/ViewBox.py
./pyqtgraph/examples/ViewBoxFeatures.py
./pyqtgraph/examples/ViewLimits.py
./pyqtgraph/examples/__init__.py
./pyqtgraph/examples/__main__.py
./pyqtgraph/examples/_buildParamTypes.py
./pyqtgraph/examples/_paramtreecfg.py
./pyqtgraph/examples/beeswarm.py
./pyqtgraph/exceptionHandling.py
./pyqtgraph/exporters
./pyqtgraph/flowchart
./pyqtgraph/frozenSupport.py
./pyqtgraph/functions.py
./pyqtgraph/functions_numba.py
./pyqtgraph/functions_qimage.py
./pyqtgraph/graphicsItems
./pyqtgraph/icons
./pyqtgraph/imageview
./pyqtgraph/jupyter
./pyqtgraph/multiprocess
./pyqtgraph/opengl
./pyqtgraph/parametertree
./pyqtgraph/reload.py
./pyqtgraph/units.py
./pyqtgraph/util
./pyqtgraph/widgets
./pytest.ini
./setup.py
./test.py
./tests
./tools
./tools/generateChangelog.py
./tools/pg-release.py
./tools/py2exe.bat
./tools/py2exeSetupWindows.py
./tools/pyuic5
./tools/rebuildPtreeRst.py
./tools/rebuildUi.py
./tools/release_instructions.md
./tools/setupHelpers.py
./tools/shell.py
./tox.ini
```
Turn 1at 96 s592 tokens out5,093 prompt tokens
The user is reporting a `TypeError: Cannot compare structured or void to non-void arrays.` when using a NumPy record array to plot data in `pyqtgraph`.
Looking at the traceback:
```
File "C:\Users\_\code\recarray_test.py", line 5, in <module>
pg.plot(data_array)
File "C:\Users\_\code\.venv\lib\site-packages\pyqtgraph\__init__.py", line 392, in plot
w.plot(*args, **dataArgs)
File "C:\Users\_\code\.venv\lib\site-packages\pyqtgraph\graphicsItems\PlotItem\PlotItem.py", line 630, in plot
item = PlotDataItem(*args, **kargs)
File "C:\Users\_\code\.venv\lib\site-packages\pyqtgraph\graphicsItems\PlotDataItem.py", line 375, in __init__
self.setData(*args, **kargs)
File "C:\Users\_\code\.venv\lib\site-packages\pyqtgraph\graphicsItems\PlotDataItem.py", line 719, in setData
if 'x' in data:
TypeError: Cannot compare structured or void to non-void arrays.
```
The error occurs in `pyqtgraph/graphicsItems/PlotDataItem.py`, in the `setData` method, at the line `if 'x' in data:`.
Wait, if `data` is a NumPy record array, `'x' in data` might be trying to check if 'x' is in the data elements (which are structured), not in the fields of the record array.
For a NumPy record array (or any structured array), you should check if 'x' is in `data.dtype.names` or just use `data.dtype.names` to see the fields.
Let's verify this by reproducing the bug. I'll create a reproduction script based on the provided example. Since `pg.exec()` might require a GUI environment, I should try to run it in a way that doesn't depend on the GUI if possible, or just check if `PlotDataItem.setData` fails as described.
Actually, the traceback shows the failure happens during `setData`, which is called during `PlotDataItem` initialization. I don't need the full plotting loop to reproduce the `TypeError`.
Let's start by examining `pyqtgraph/graphicsItems/PlotDataItem.py`.
TOOL CALL · run_command
{
"command": "grep -rn --include=\"*.py\" \"if 'x' in data:\" pyqtgraph/"
}Result of run_command 138 characters
{
"status": "ok",
"stdout": "pyqtgraph/graphicsItems/PlotDataItem.py:1164: if 'x' in data:\n",
"stderr": "",
"exit_code": 0
}Turn 2at 102 s76 tokens out5,750 prompt tokens
The grep result says line 1164, but the traceback says line 719. The version might be different. Let me check the file.
TOOL CALL · read_file
{
"end_line": 740,
"filepath": "pyqtgraph/graphicsItems/PlotDataItem.py",
"start_line": 700
}Result of read_file 1,648 characters
{
"status": "ok",
"filepath": "pyqtgraph/graphicsItems/PlotDataItem.py",
"content": " Receives the ``autoAlpha`` value from a parent\n :class:`~pyqtgraph.PlotItem`, but has no function within PlotDataItem\n itself.\n\n See Also\n --------\n :meth:`QGraphicsItem.setOpacity <QGraphicsItem.setOpacity>`\n This is the Qt method that the value is relayed to.\n \"\"\"\n if self.opts['alphaHint'] == alpha and self.opts['alphaMode'] == auto:\n return\n self.opts['alphaHint'] = alpha\n self.opts['alphaMode'] = auto\n self.setOpacity(alpha)\n\n def setFftMode(self, state: bool):\n \"\"\"\n Enable FFT mode.\n\n FFT mode enables mapping the data by a fast Fourier transform. If the `x`\n values are not equidistant, the data set is resampled at equal intervals.\n\n Parameters\n ----------\n state : bool\n To enable or disable FFT mode.\n \"\"\"\n if self.opts['fftMode'] == state:\n return\n self.opts['fftMode'] = state\n self._datasetMapped = None\n self._datasetDisplay = None\n self.updateItems(styleUpdate=False)\n self.informViewBoundsChanged()\n\n def setLogMode(self, xState: bool, yState: bool):\n \"\"\"\n Enable log mode per axis.\n\n When the log mode is enabled for the respective axis, a mapping according to\n ``mapped = np.log10(value)`` is applied to the data. For each negative or zero",
"start_line": 700,
"end_line": 740,
"total_lines": 1839,
"is_truncated": false
}Turn 3at 107 s84 tokens out6,301 prompt tokens
Wait, I am not seeing `if 'x' in data:` in the lines I read. Let me check around line 1164 since that's where grep found it.
TOOL CALL · read_file
{
"end_line": 1180,
"filepath": "pyqtgraph/graphicsItems/PlotDataItem.py",
"start_line": 1140
}Result of read_file 1,751 characters
{
"status": "ok",
"filepath": "pyqtgraph/graphicsItems/PlotDataItem.py",
"content": " Raised when an invalid type was passed in for `x` or `y` data.\n\n See Also\n --------\n :class:`PlotDataItem`\n The arguments accepted by :meth:`setData` are the same used during \n initialization, and are listed in the opening section.\n :func:`~pyqtgraph.arrayToQPath`\n Explains the constructions of the draw paths.\n \"\"\"\n profiler = debug.Profiler()\n y = None\n x = None\n if len(args) == 1:\n data = args[0]\n dt = dataType(data)\n if dt == 'empty':\n pass\n elif dt == 'listOfValues':\n y = np.array(data)\n elif dt == 'Nx2array':\n x = data[:, 0]\n y = data[:, 1]\n elif dt == 'recarray' or dt == 'dictOfLists':\n if 'x' in data:\n x = np.array(data['x'])\n if 'y' in data:\n y = np.array(data['y'])\n elif dt == 'listOfDicts':\n if 'x' in data[0]:\n x = np.array([d.get('x',None) for d in data])\n if 'y' in data[0]:\n y = np.array([d.get('y',None) for d in data])\n for k in [\n 'data', 'symbolSize', 'symbolPen', 'symbolBrush', 'symbolShape'\n ]:\n if k in data[0]:\n kwargs[k] = [d.get(k) for d in data]\n else:\n raise TypeError('Invalid data type %s' % type(data))\n",
"start_line": 1140,
"end_line": 1180,
"total_lines": 1839,
"is_truncated": false
}Turn 4at 134 s382 tokens out6,900 prompt tokens
Okay, I found it! Line 1164: `if 'x' in data:`.
The code is:
```python
elif dt == 'recarray' or dt == 'dictOfLists':
if 'x' in data:
x = np.array(data['x'])
if 'y' in data:
y = np.array(data['y'])
```
If `data` is a NumPy recarray (or any structured array), `'x' in data` will raise a `TypeError` because you cannot use the `in` operator to check for field names in a NumPy array; you should check `data.dtype.names`.
Wait, the condition is `dt == 'recarray' or dt == 'dictOfLists'`.
If it's a `dictOfLists`, `'x' in data` works because `data` is a dictionary.
If it's a `recarray`, `'x' in data` fails.
Let's verify if `dataType(data)` returns `'recarray'` for a NumPy recarray.
I'll run a quick script to check.
TOOL CALL · run_command
{
"command": "python3 - <<'PY'\nimport numpy as np\nimport pyqtgraph as pg\nfrom pyqtgraph.graphicsItems.PlotDataItem import dataType\n\ndata_array = np.recarray((10,), dtype=[('x', float), ('y', float)])\nprint(f\"dataType(data_array): {dataType(data_array)}\")\ntry:\n print(f\"'x' in data_array: {'x' in data_array}\")\nexcept TypeError as e:\n print(f\"TypeError: {e}\")\nPY\n"
}Result of run_command 158 characters
{
"status": "ok",
"stdout": "dataType(data_array): recarray\nTypeError: Cannot compare structured or void to non-void arrays.\n",
"stderr": "",
"exit_code": 0
}Turn 5at 146 s150 tokens out7,344 prompt tokens
The reproduction confirms the issue. `dataType(data_array)` is `'recarray'`, and `'x' in data_array` raises the `TypeError`. Now I need to fix this. The fix should distinguish between a dictionary and a record array when `dt == 'recarray' or dt == 'dictOfLists'`. Wait, I should check how `dataType` is implemented to see if I can refine the types or if I should just fix the check in `setData`. Let's look at `dataType` in `PlotDataItem.py`.
TOOL CALL · run_command
{
"command": "grep -rn \"def dataType\" pyqtgraph/graphicsItems/PlotDataItem.py"
}Result of run_command 92 characters
{
"status": "ok",
"stdout": "1799:def dataType(obj) -> str:\n",
"stderr": "",
"exit_code": 0
}Turn 6at 150 s55 tokens out7,546 prompt tokens
Let's read the `dataType` function.
TOOL CALL · read_file
{
"end_line": 1839,
"filepath": "pyqtgraph/graphicsItems/PlotDataItem.py",
"start_line": 1790
}Result of read_file 1,713 characters
{
"status": "ok",
"filepath": "pyqtgraph/graphicsItems/PlotDataItem.py",
"content": " x = x2\n n = y.size\n f = np.fft.rfft(y) / n\n d = float(x[-1] - x[0]) / (len(x) - 1)\n x = np.fft.rfftfreq(n, d)\n y = np.abs(f)\n return x, y\n\n\ndef dataType(obj) -> str:\n type_: str\n if hasattr(obj, '__len__') and len(obj) == 0:\n type_ = 'empty'\n elif isinstance(obj, dict):\n type_ = 'dictOfLists'\n elif np.iterable(obj):\n first = obj[0]\n if isinstance(obj, np.ndarray):\n if obj.ndim == 1:\n type_ = 'listOfValues' if obj.dtype.names is None else 'recarray'\n elif obj.ndim == 2 and obj.dtype.names is None and obj.shape[1] == 2:\n type_ = 'Nx2array'\n else:\n raise ValueError(\n f'array shape must be (N,) or (N,2); got {str(obj.shape)} instead'\n )\n elif isinstance(first, dict):\n type_ = 'listOfDicts'\n else:\n type_ = 'listOfValues'\n else:\n raise ValueError(\"Cannot identify data-structure.\")\n return type_\n\n\ndef isSequence(obj):\n warnings.warn(\n (\n \"isSequence is deprecated and will be removed in a future version of\"\n \"pyqtgraph, use np.iterable(obj) instead.\"\n ), DeprecationWarning, stacklevel=2\n )\n return (\n hasattr(obj, '__iter__') or\n isinstance(obj, np.ndarray) or\n (\n hasattr(obj, 'implements') and\n obj.implements('MetaArray')\n )\n )",
"start_line": 1790,
"end_line": 1839,
"total_lines": 1839,
"is_truncated": false
}Turn 7at 216 s1,187 tokens out8,159 prompt tokens
The `dataType` function correctly identifies `recarray`:
```python
if obj.ndim == 1:
type_ = 'listOfValues' if obj.dtype.names is None else 'recarray'
```
If `obj.dtype.names` is not None, it's a structured array (which `pyqtgraph` calls `recarray`).
So, back to `setData`:
```python
elif dt == 'recarray' or dt == 'dictOfLists':
if 'x' in data:
x = np.array(data['x'])
if 'y' in data:
y = np.array(data['y'])
```
If `dt == 'recarray'`, we should check `data.dtype.names`.
If `dt == 'dictOfLists'`, we should check `data`.
Wait, if `dt == 'recarray'`, `data` is a NumPy array. If it has `dtype.names`, we can check if `'x'` is in those names.
Actually, for a NumPy structured array, `data['x']` works if `'x'` is a field name. But `'x' in data` is what fails.
The correct way to check if a field exists in a structured array is `'x' in data.dtype.names`.
However, `data` could be either a `dict` or a `np.ndarray` (if it's a `recarray`).
I can change the code to:
```python
elif dt == 'recarray' or dt == 'dictOfLists':
if isinstance(data, dict):
if 'x' in data:
x = np.array(data['x'])
if 'y' in data:
y = np.array(data['y'])
else: # recarray
if data.dtype.names and 'x' in data.dtype.names:
x = np.array(data['x'])
if data.dtype.names and 'y' in data.dtype.names:
y = np.array(data['y'])
```
Actually, if `dt == 'recarray'`, `dataType` already checked that `obj.dtype.names` is not None.
```python
if np.iterable(obj):
first = obj[0]
if isinstance(obj, np.ndarray):
if obj.ndim == 1:
type_ = 'listOfValues' if obj.dtype.names is None else 'recarray'
```
So if `dt == 'recarray'`, we know `data` is a `np.ndarray` and `data.dtype.names` is not None.
Wait, can I just use `hasattr(data, 'dtype')`?
If I want to be more concise:
```python
elif dt == 'recarray' or dt == 'dictOfLists':
names = data.dtype.names if hasattr(data, 'dtype') else data
if 'x' in names:
x = np.array(data['x'])
if 'y' in names:
y = np.array(data['y'])
```
Wait, if `data` is a dict, `names` will be the dict itself. `'x' in names` will check if `'x'` is a key in the dict.
If `data` is a recarray, `names` will be the tuple of field names. `'x' in names` will check if `'x'` is one of the field names.
This looks elegant and covers both cases.
Let's double check if `data.dtype.names` is always a tuple or None for `np.ndarray`.
Yes, for structured arrays it's a tuple of strings.
Let's verify this with a test.
TOOL CALL · run_command
{
"command": "python3 - <<'PY'\nimport numpy as np\n\n# Case 1: recarray\ndata_rec = np.recarray((10,), dtype=[('x', float), ('y', float)])\nnames_rec = data_rec.dtype.names if hasattr(data_rec, 'dtype') else data_rec\nprint(f\"recarray: {'x' in names_rec}\") # Should be True\n\n# Case 2: dictOfLists\ndata_dict = {'x': [1, 2], 'y': [3, 4]}\nnames_dict = data_dict.dtype.names if hasattr(data_dict, 'dtype') else data_dict\nprint(f\"dictOfLists: {'x' in names_dict}\") # Should be True\n\n# Case 3: Other array\ndata_arr = np.array([1, 2])\nnames_arr = data_arr.dtype.names if hasattr(data_arr, 'dtype') else data_arr\n# For non-structured array, data_arr.dtype.names is None.\n# So names_arr is None. 'x' in None will raise TypeError.\n# But in the code, dt will be 'listOfValues', not 'recarray' or 'dictOfLists'.\n# However, let's see.\ntry:\n print(f\"Other array: {'x' in names_arr}\")\nexcept TypeError as e:\n print(f\"Other array TypeError: {e}\")\n\nPY\n"
}Result of run_command 163 characters
{
"status": "ok",
"stdout": "recarray: True\ndictOfLists: True\nOther array TypeError: argument of type 'NoneType' is not iterable\n",
"stderr": "",
"exit_code": 0
}Turn 8at 297 s1,339 tokens out9,413 prompt tokens
Wait, if `data` is a non-structured array, `data.dtype.names` is `None`.
So `names = data.dtype.names if hasattr(data, 'dtype') else data` will set `names` to `None`.
Then `'x' in names` will raise `TypeError: argument of type 'NoneType' is not iterable`.
But this only happens if `dt == 'recarray' or dt == 'dictOfLists'`.
If `dt == 'recarray'`, then `data.dtype.names` MUST NOT be `None` according to `dataType()`:
```python
type_ = 'listOfValues' if obj.dtype.names is None else 'recarray'
```
So if `dt == 'recarray'`, `names` will be a tuple of names.
If `dt == 'dictOfLists'`, `data` is a dict, so `hasattr(data, 'dtype')` is False, and `names` will be the dict.
So the proposed fix:
```python
elif dt == 'recarray' or dt == 'dictOfLists':
names = data.dtype.names if hasattr(data, 'dtype') else data
if 'x' in names:
x = np.array(data['x'])
if 'y' in names:
y = np.array(data['y'])
```
should be safe because when `dt == 'recarray'`, `names` is a tuple. When `dt == 'dictOfLists'`, `names` is a dict.
Wait, is it possible for `data` to have a `dtype` attribute but not be a NumPy array? Unlikely in this context, but `hasattr(data, 'dtype')` is a good check for NumPy-like objects.
Actually, the original code was:
```python
elif dt == 'recarray' or dt == 'dictOfLists':
if 'x' in data:
x = np.array(data['x'])
if 'y' in data:
y = np.array(data['y'])
```
If I change it to:
```python
elif dt == 'recarray' or dt == 'dictOfLists':
# For recarrays, we must check dtype.names instead of 'in data'
names = data.dtype.names if hasattr(data, 'dtype') else data
if names is not None and 'x' in names:
x = np.array(data['x'])
if names is not None and 'y' in names:
y = np.array(data['y'])
```
This is even safer.
Wait, I can just use `isinstance(data, np.ndarray)` since `dataType` already used it.
```python
elif dt == 'recarray' or dt == 'dictOfLists':
if isinstance(data, np.ndarray):
if data.dtype.names and 'x' in data.dtype.names:
x = np.array(data['x'])
if data.dtype.names and 'y' in data.dtype.names:
y = np.array(data['y'])
else: # it's a dict
if 'x' in data:
x = np.array(data['x'])
if 'y' in data:
y = np.array(data['y'])
```
But wait, if `dt == 'recarray'`, we already know it's a `np.ndarray` and `dtype.names` is not None.
If `dt == 'dictOfLists'`, we already know it's a `dict`.
So:
```python
elif dt == 'recarray' or dt == 'dictOfLists':
# Use data.dtype.names for recarrays, and data for dicts
names = data.dtype.names if dt == 'recarray' else data
if 'x' in names:
x = np.array(data['x'])
if 'y' in names:
y = np.array(data['y'])
```
Wait, if `dt == 'recarray'`, `names` will be a tuple of strings. If `dt == 'dictOfLists'`, `names` will be the dictionary.
This is very clean and safe.
Let's re-verify with a script.
TOOL CALL · run_command
{
"command": "python3 - <<'PY'\nimport numpy as np\n\ndef test_fix(data, dt):\n # Proposed fix\n names = data.dtype.names if dt == 'recarray' else data\n x = None\n y = None\n if 'x' in names:\n x = np.array(data['x'])\n if 'y' in names:\n y = np.array(data['y'])\n return x, y\n\n# Case 1: recarray\ndata_rec = np.recarray((10,), dtype=[('x', float), ('y', float)])\nprint(f\"recarray: {test_fix(data_rec, 'recarray')}\")\n\n# Case 2: dictOfLists\ndata_dict = {'x': [1, 2], 'y': [3, 4]}\nprint(f\"dictOfLists: {test_fix(data_dict, 'dictOfLists')}\")\n\n# Case 3: recarray without x\ndata_rec_no_x = np.recarray((10,), dtype=[('z', float), ('y', float)])\nprint(f\"recarray no x: {test_fix(data_rec_no_x, 'recarray')}\")\n\n# Case 4: dict without x\ndata_dict_no_x = {'z': [1, 2], 'y': [3, 4]}\nprint(f\"dict no x: {test_fix(data_dict_no_x, 'dictOfLists')}\")\nPY\n"
}Result of run_command 613 characters
{
"status": "ok",
"stdout": "recarray: (array([5.45192445e-310, 5.49890662e-095, 5.45145168e-310, 4.94065646e-324,\n 1.95365179e-109, 2.21528569e+214, 0.00000000e+000, 0.00000000e+000,\n 5.45145168e-310, 7.08468655e+159]), array([0.00000000e+000, 5.45145168e-310, 1.15297799e-259, 2.44029516e-312,\n 5.45145168e-310, 5.45145168e-310, 5.45145168e-310, 6.01346930e-154,\n 1.95365197e-109, 1.21359026e+132]))\ndictOfLists: (array([1, 2]), array([3, 4]))\nrecarray no x: (None, array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.]))\ndict no x: (None, array([3, 4]))\n",
"stderr": "",
"exit_code": 0
}Turn 9at 307 s131 tokens out11,220 prompt tokens
The fix looks solid. Now I will apply it to `pyqtgraph/graphicsItems/PlotDataItem.py`. Wait, I should check if there are other places where this pattern is used. The grep found: `pyqtgraph/graphicsItems/PlotDataItem.py:1164: if 'x' in data:` Let me check if there any other `if 'x' in data:` in the codebase.
TOOL CALL · run_command
{
"command": "grep -rn --include=\"*.py\" \"if 'x' in data:\" pyqtgraph/"
}Result of run_command 138 characters
{
"status": "ok",
"stdout": "pyqtgraph/graphicsItems/PlotDataItem.py:1164: if 'x' in data:\n",
"stderr": "",
"exit_code": 0
}Turn 10at 321 s289 tokens out11,415 prompt tokens
Only one place.
Let me double check if `PlotDataItem.py` has other similar logic for other fields.
Actually, the code I'm looking at handles 'x' and 'y'.
```python
elif dt == 'recarray' or dt == 'dictOfLists':
if 'x' in data:
x = np.array(data['x'])
if 'y' in data:
y = np.array(data['y'])
```
I'll apply the change.
TOOL CALL · edit_file
{
"filepath": "pyqtgraph/graphicsItems/PlotDataItem.py",
"new_string": " elif dt == 'recarray' or dt == 'dictOfLists':\n names = data.dtype.names if dt == 'recarray' else data\n if 'x' in names:\n x = np.array(data['x'])\n if 'y' in names:\n y = np.array(data['y'])",
"old_string": " elif dt == 'recarray' or dt == 'dictOfLists':\n if 'x' in data:\n x = np.array(data['x'])\n if 'y' in data:\n y = np.array(data['y'])"
}Result of edit_file 769 characters
{
"status": "ok",
"filepath": "pyqtgraph/graphicsItems/PlotDataItem.py",
"occurrences": 1,
"strategy": "exact",
"diff": "--- a/pyqtgraph/graphicsItems/PlotDataItem.py\n+++ b/pyqtgraph/graphicsItems/PlotDataItem.py\n@@ -1161,9 +1161,10 @@\n x = data[:, 0]\n y = data[:, 1]\n elif dt == 'recarray' or dt == 'dictOfLists':\n- if 'x' in data:\n+ names = data.dtype.names if dt == 'recarray' else data\n+ if 'x' in names:\n x = np.array(data['x'])\n- if 'y' in data:\n+ if 'y' in names:\n y = np.array(data['y'])\n elif dt == 'listOfDicts':\n if 'x' in data[0]:\n",
"is_truncated": false
}Turn 11at 338 s245 tokens out11,962 prompt tokens
Now I should verify the fix. I'll use the reproduction script provided in the issue. Since `pg.exec()` and `pg.plot()` might fail in a headless environment (no display), I will focus on checking if `PlotDataItem` can be initialized with a recarray without raising the `TypeError`. Actually, I can just instantiate `PlotDataItem` with the recarray and call `setData` (which `__init__` does).
TOOL CALL · run_command
{
"command": "python3 - <<'PY'\nimport pyqtgraph as pg\nimport numpy as np\nfrom pyqtgraph.graphicsItems.PlotDataItem import PlotDataItem\n\ntry:\n data_array = np.recarray((10,), dtype=[('x', float), ('y', float)])\n print(\"Creating PlotDataItem with recarray...\")\n item = PlotDataItem(data_array)\n print(\"Successfully created PlotDataItem!\")\nexcept Exception as e:\n print(f\"Exception caught: {type(e).__name__}: {e}\")\n import traceback\n traceback.print_exc()\nPY\n"
}Result of run_command 136 characters
{
"status": "ok",
"stdout": "Creating PlotDataItem with recarray...\nSuccessfully created PlotDataItem!\n",
"stderr": "",
"exit_code": 0
}Turn 12at 351 s177 tokens out12,259 prompt tokens
The issue is reproduced and fixed! Now I'll check if it still works with `dictOfLists`.
TOOL CALL · run_command
{
"command": "python3 - <<'PY'\nimport pyqtgraph as pg\nimport numpy as np\nfrom pyqtgraph.graphicsItems.PlotDataItem import PlotDataItem\n\ntry:\n data_dict = {'x': np.arange(10), 'y': np.arange(10)}\n print(\"Creating PlotDataItem with dictOfLists...\")\n item = PlotDataItem(data_dict)\n print(\"Successfully created PlotDataItem with dict!\")\nexcept Exception as e:\n print(f\"Exception caught: {type(e).__name__}: {e}\")\n import traceback\n traceback.print_exc()\nPY\n"
}Result of run_command 149 characters
{
"status": "ok",
"stdout": "Creating PlotDataItem with dictOfLists...\nSuccessfully created PlotDataItem with dict!\n",
"stderr": "",
"exit_code": 0
}Turn 13at 362 s158 tokens out12,491 prompt tokens
And check if it still works with regular arrays.
TOOL CALL · run_command
{
"command": "python3 - <<'PY'\nimport pyqtgraph as pg\nimport numpy as np\nfrom pyqtgraph.graphicsItems.PlotDataItem import PlotDataItem\n\ntry:\n data_arr = np.random.random((10, 2))\n print(\"Creating PlotDataItem with Nx2array...\")\n item = PlotDataItem(data_arr)\n print(\"Successfully created PlotDataItem with Nx2array!\")\nexcept Exception as e:\n print(f\"Exception caught: {type(e).__name__}: {e}\")\n import traceback\n traceback.print_exc()\nPY\n"
}Result of run_command 150 characters
{
"status": "ok",
"stdout": "Creating PlotDataItem with Nx2array...\nSuccessfully created PlotDataItem with Nx2array!\n",
"stderr": "",
"exit_code": 0
}Turn 14at 365 s42 tokens out12,706 prompt tokens
Everything seems to be working correctly. I'll do a final check of the diff.
TOOL CALL · run_command
{
"command": "git status --short && git diff"
}Result of run_command 891 characters
{
"status": "ok",
"stdout": " M pyqtgraph/graphicsItems/PlotDataItem.py\ndiff --git a/pyqtgraph/graphicsItems/PlotDataItem.py b/pyqtgraph/graphicsItems/PlotDataItem.py\nindex 9d7c7f8..79be936 100644\n--- a/pyqtgraph/graphicsItems/PlotDataItem.py\n+++ b/pyqtgraph/graphicsItems/PlotDataItem.py\n@@ -1161,9 +1161,10 @@ class PlotDataItem(GraphicsObject):\n x = data[:, 0]\n y = data[:, 1]\n elif dt == 'recarray' or dt == 'dictOfLists':\n- if 'x' in data:\n+ names = data.dtype.names if dt == 'recarray' else data\n+ if 'x' in names:\n x = np.array(data['x'])\n- if 'y' in data:\n+ if 'y' in names:\n y = np.array(data['y'])\n elif dt == 'listOfDicts':\n if 'x' in data[0]:\n",
"stderr": "",
"exit_code": 0
}Turn 15at 380 s253 tokens out13,058 prompt tokens
One last check: what if `dt == 'recarray'` but `data.dtype.names` is actually `None`?
Wait, `dataType` ensures that if `dt == 'recarray'`, `obj.dtype.names` is NOT `None`.
```python
if obj.ndim == 1:
type_ = 'listOfValues' if obj.dtype.names is None else 'recarray'
```
So `names = data.dtype.names if dt == 'recarray' else data` will assign a tuple to `names` when `dt == 'recarray'`.
Is it possible for `data` to be something else that triggers `dt == 'recarray'`?
`dataType` is called right before the `if` block:
```python
data = args[0]
dt = dataType(data)
```
So `dt` is derived from `data`.
Wait, I should check if `PlotDataItem` is used anywhere else that might be affected.
The logic seems quite isolated to `setData` when `len(args) == 1`.
I'm confident.
TOOL CALL · submit_patch
Result of submit_patch 55 characters
{
"status": "ok",
"patch_size": 770,
"files_changed": 1
}Turn 16at 386 s74 tokens out13,351 prompt tokens
The patch is submitted. I have verified it with reproduction scripts for recarrays, dictionaries, and regular arrays.
Turn 17at 386 s
Fixed `TypeError` when passing a NumPy record array to `PlotDataItem.setData` (and `pg.plot`) by checking field names in `data.dtype.names` instead of using the `in` operator on the array itself.
Model calls (not matched to turns)
Generated tokens 592 tokens · stopped: STOP
The user is reporting a `TypeError: Cannot compare structured or void to non-void arrays.` when using a NumPy record array to plot data in `pyqtgraph`.
Looking at the traceback:
```
File "C:\Users\_\code\recarray_test.py", line 5, in <module>
pg.plot(data_array)
File "C:\Users\_\code\.venv\lib\site-packages\pyqtgraph\__init__.py", line 392, in plot
w.plot(*args, **dataArgs)
File "C:\Users\_\code\.venv\lib\site-packages\pyqtgraph\graphicsItems\PlotItem\PlotItem.py", line 630, in plot
item = PlotDataItem(*args, **kargs)
File "C:\Users\_\code\.venv\lib\site-packages\pyqtgraph\graphicsItems\PlotDataItem.py", line 375, in __init__
self.setData(*args, **kargs)
File "C:\Users\_\code\.venv\lib\site-packages\pyqtgraph\graphicsItems\PlotDataItem.py", line 719, in setData
if 'x' in data:
TypeError: Cannot compare structured or void to non-void arrays.
```
The error occurs in `pyqtgraph/graphicsItems/PlotDataItem.py`, in the `setData` method, at the line `if 'x' in data:`.
Wait, if `data` is a NumPy record array, `'x' in data` might be trying to check if 'x' is in the data elements (which are structured), not in the fields of the record array.
For a NumPy record array (or any structured array), you should check if 'x' is in `data.dtype.names` or just use `data.dtype.names` to see the fields.
Let's verify this by reproducing the bug. I'll create a reproduction script based on the provided example. Since `pg.exec()` might require a GUI environment, I should try to run it in a way that doesn't depend on the GUI if possible, or just check if `PlotDataItem.setData` fails as described.
Actually, the traceback shows the failure happens during `setData`, which is called during `PlotDataItem` initialization. I don't need the full plotting loop to reproduce the `TypeError`.
Let's start by examining `pyqtgraph/graphicsItems/PlotDataItem.py`.<|channel>thought
The user is reporting a `TypeError: Cannot compare structured or void to non-void arrays.` when using a NumPy record array to plot data in `pyqtgraph`.
Looking at the traceback:
```
File "C:\Users\_\code\recarray_test.py", line 5, in <module>
pg.plot(data_array)
File "C:\Users\_\code\.venv\lib\site-packages\pyqtgraph\__init__.py", line 392, in plot
w.plot(*args, **dataArgs)
File "C:\Users\_\code\.venv\lib\site-packages\pyqtgraph\graphicsItems\PlotItem\PlotItem.py", line 630, in plot
item = PlotDataItem(*args, **kargs)
File "C:\Users\_\code\.venv\lib\site-packages\pyqtgraph\graphicsItems\PlotDataItem.py", line 375, in __init__
self.setData(*args, **kargs)
File "C:\Users\_\code\.venv\lib\site-packages\pyqtgraph\graphicsItems\PlotDataItem.py", line 719, in setData
if 'x' in data:
TypeError: Cannot compare structured or void to non-void arrays.
```
The error occurs in `pyqtgraph/graphicsItems/PlotDataItem.py`, in the `setData` method, at the line `if 'x' in data:`.
Wait, if `data` is a NumPy record array, `'x' in data` might be trying to check if 'x' is in the data elements (which are structured), not in the fields of the record array.
For a NumPy record array (or any structured array), you should check if 'x' is in `data.dtype.names` or just use `data.dtype.names` to see the fields.
Let's verify this by reproducing the bug. I'll create a reproduction script based on the provided example. Since `pg.exec()` might require a GUI environment, I should try to run it in a way that doesn't depend on the GUI if possible, or just check if `PlotDataItem.setData` fails as described.
Actually, the traceback shows the failure happens during `setData`, which is called during `PlotDataItem` initialization. I don't need the full plotting loop to reproduce the `TypeError`.
Let's start by examining `pyqtgraph/graphicsItems/PlotDataItem.py`.<channel|><|tool_call>call:run_command{command:<|"|>grep -rn --include="*.py" "if 'x' in data:" pyqtgraph/<|"|>}<tool_call|><|tool_response>
Generated tokens 76 tokens · stopped: STOP
The grep result says line 1164, but the traceback says line 719. The version might be different. Let me check the file.
The grep result says line 1164, but the traceback says line 719. The version might be different. Let me check the file.
<channel|><|tool_call>call:read_file{filepath:<|"|>pyqtgraph/graphicsItems/PlotDataItem.py<|"|>,start_line:700,end_line:740}<tool_call|><|tool_response>
Generated tokens 84 tokens · stopped: STOP
Wait, I am not seeing `if 'x' in data:` in the lines I read. Let me check around line 1164 since that's where grep found it.
Wait, I am not seeing `if 'x' in data:` in the lines I read. Let me check around line 1164 since that's where grep found it.
<channel|><|tool_call>call:read_file{end_line:1180,filepath:<|"|>pyqtgraph/graphicsItems/PlotDataItem.py<|"|>,start_line:1140}<tool_call|><|tool_response>
Generated tokens 382 tokens · stopped: STOP
Okay, I found it! Line 1164: `if 'x' in data:`.
The code is:
```python
elif dt == 'recarray' or dt == 'dictOfLists':
if 'x' in data:
x = np.array(data['x'])
if 'y' in data:
y = np.array(data['y'])
```
If `data` is a NumPy recarray (or any structured array), `'x' in data` will raise a `TypeError` because you cannot use the `in` operator to check for field names in a NumPy array; you should check `data.dtype.names`.
Wait, the condition is `dt == 'recarray' or dt == 'dictOfLists'`.
If it's a `dictOfLists`, `'x' in data` works because `data` is a dictionary.
If it's a `recarray`, `'x' in data` fails.
Let's verify if `dataType(data)` returns `'recarray'` for a NumPy recarray.
I'll run a quick script to check.
Okay, I found it! Line 1164: `if 'x' in data:`.
The code is:
```python
elif dt == 'recarray' or dt == 'dictOfLists':
if 'x' in data:
x = np.array(data['x'])
if 'y' in data:
y = np.array(data['y'])
```
If `data` is a NumPy recarray (or any structured array), `'x' in data` will raise a `TypeError` because you cannot use the `in` operator to check for field names in a NumPy array; you should check `data.dtype.names`.
Wait, the condition is `dt == 'recarray' or dt == 'dictOfLists'`.
If it's a `dictOfLists`, `'x' in data` works because `data` is a dictionary.
If it's a `recarray`, `'x' in data` fails.
Let's verify if `dataType(data)` returns `'recarray'` for a NumPy recarray.
I'll run a quick script to check.
<channel|><|tool_call>call:run_command{command:<|"|>python3 - <<'PY'
import numpy as np
import pyqtgraph as pg
from pyqtgraph.graphicsItems.PlotDataItem import dataType
data_array = np.recarray((10,), dtype=[('x', float), ('y', float)])
print(f"dataType(data_array): {dataType(data_array)}")
try:
print(f"'x' in data_array: {'x' in data_array}")
except TypeError as e:
print(f"TypeError: {e}")
PY
<|"|>}<tool_call|><|tool_response>
Generated tokens 150 tokens · stopped: STOP
The reproduction confirms the issue. `dataType(data_array)` is `'recarray'`, and `'x' in data_array` raises the `TypeError`. Now I need to fix this. The fix should distinguish between a dictionary and a record array when `dt == 'recarray' or dt == 'dictOfLists'`. Wait, I should check how `dataType` is implemented to see if I can refine the types or if I should just fix the check in `setData`. Let's look at `dataType` in `PlotDataItem.py`.
The reproduction confirms the issue. `dataType(data_array)` is `'recarray'`, and `'x' in data_array` raises the `TypeError`.
Now I need to fix this. The fix should distinguish between a dictionary and a record array when `dt == 'recarray' or dt == 'dictOfLists'`.
Wait, I should check how `dataType` is implemented to see if I can refine the types or if I should just fix the check in `setData`.
Let's look at `dataType` in `PlotDataItem.py`.
<channel|><|tool_call>call:run_command{command:<|"|>grep -rn "def dataType" pyqtgraph/graphicsItems/PlotDataItem.py<|"|>}<tool_call|><|tool_response>
Generated tokens 55 tokens · stopped: STOP
Let's read the `dataType` function.
Let's read the `dataType` function.
<channel|><|tool_call>call:read_file{end_line:1839,filepath:<|"|>pyqtgraph/graphicsItems/PlotDataItem.py<|"|>,start_line:1790}<tool_call|><|tool_response>
Generated tokens 1,187 tokens · stopped: STOP
The `dataType` function correctly identifies `recarray`:
```python
if obj.ndim == 1:
type_ = 'listOfValues' if obj.dtype.names is None else 'recarray'
```
If `obj.dtype.names` is not None, it's a structured array (which `pyqtgraph` calls `recarray`).
So, back to `setData`:
```python
elif dt == 'recarray' or dt == 'dictOfLists':
if 'x' in data:
x = np.array(data['x'])
if 'y' in data:
y = np.array(data['y'])
```
If `dt == 'recarray'`, we should check `data.dtype.names`.
If `dt == 'dictOfLists'`, we should check `data`.
Wait, if `dt == 'recarray'`, `data` is a NumPy array. If it has `dtype.names`, we can check if `'x'` is in those names.
Actually, for a NumPy structured array, `data['x']` works if `'x'` is a field name. But `'x' in data` is what fails.
The correct way to check if a field exists in a structured array is `'x' in data.dtype.names`.
However, `data` could be either a `dict` or a `np.ndarray` (if it's a `recarray`).
I can change the code to:
```python
elif dt == 'recarray' or dt == 'dictOfLists':
if isinstance(data, dict):
if 'x' in data:
x = np.array(data['x'])
if 'y' in data:
y = np.array(data['y'])
else: # recarray
if data.dtype.names and 'x' in data.dtype.names:
x = np.array(data['x'])
if data.dtype.names and 'y' in data.dtype.names:
y = np.array(data['y'])
```
Actually, if `dt == 'recarray'`, `dataType` already checked that `obj.dtype.names` is not None.
```python
if np.iterable(obj):
first = obj[0]
if isinstance(obj, np.ndarray):
if obj.ndim == 1:
type_ = 'listOfValues' if obj.dtype.names is None else 'recarray'
```
So if `dt == 'recarray'`, we know `data` is a `np.ndarray` and `data.dtype.names` is not None.
Wait, can I just use `hasattr(data, 'dtype')`?
If I want to be more concise:
```python
elif dt == 'recarray' or dt == 'dictOfLists':
names = data.dtype.names if hasattr(data, 'dtype') else data
if 'x' in names:
x = np.array(data['x'])
if 'y' in names:
y = np.array(data['y'])
```
Wait, if `data` is a dict, `names` will be the dict itself. `'x' in names` will check if `'x'` is a key in the dict.
If `data` is a recarray, `names` will be the tuple of field names. `'x' in names` will check if `'x'` is one of the field names.
This looks elegant and covers both cases.
Let's double check if `data.dtype.names` is always a tuple or None for `np.ndarray`.
Yes, for structured arrays it's a tuple of strings.
Let's verify this with a test.
The `dataType` function correctly identifies `recarray`:
```python
if obj.ndim == 1:
type_ = 'listOfValues' if obj.dtype.names is None else 'recarray'
```
If `obj.dtype.names` is not None, it's a structured array (which `pyqtgraph` calls `recarray`).
So, back to `setData`:
```python
elif dt == 'recarray' or dt == 'dictOfLists':
if 'x' in data:
x = np.array(data['x'])
if 'y' in data:
y = np.array(data['y'])
```
If `dt == 'recarray'`, we should check `data.dtype.names`.
If `dt == 'dictOfLists'`, we should check `data`.
Wait, if `dt == 'recarray'`, `data` is a NumPy array. If it has `dtype.names`, we can check if `'x'` is in those names.
Actually, for a NumPy structured array, `data['x']` works if `'x'` is a field name. But `'x' in data` is what fails.
The correct way to check if a field exists in a structured array is `'x' in data.dtype.names`.
However, `data` could be either a `dict` or a `np.ndarray` (if it's a `recarray`).
I can change the code to:
```python
elif dt == 'recarray' or dt == 'dictOfLists':
if isinstance(data, dict):
if 'x' in data:
x = np.array(data['x'])
if 'y' in data:
y = np.array(data['y'])
else: # recarray
if data.dtype.names and 'x' in data.dtype.names:
x = np.array(data['x'])
if data.dtype.names and 'y' in data.dtype.names:
y = np.array(data['y'])
```
Actually, if `dt == 'recarray'`, `dataType` already checked that `obj.dtype.names` is not None.
```python
if np.iterable(obj):
first = obj[0]
if isinstance(obj, np.ndarray):
if obj.ndim == 1:
type_ = 'listOfValues' if obj.dtype.names is None else 'recarray'
```
So if `dt == 'recarray'`, we know `data` is a `np.ndarray` and `data.dtype.names` is not None.
Wait, can I just use `hasattr(data, 'dtype')`?
If I want to be more concise:
```python
elif dt == 'recarray' or dt == 'dictOfLists':
names = data.dtype.names if hasattr(data, 'dtype') else data
if 'x' in names:
x = np.array(data['x'])
if 'y' in names:
y = np.array(data['y'])
```
Wait, if `data` is a dict, `names` will be the dict itself. `'x' in names` will check if `'x'` is a key in the dict.
If `data` is a recarray, `names` will be the tuple of field names. `'x' in names` will check if `'x'` is one of the field names.
This looks elegant and covers both cases.
Let's double check if `data.dtype.names` is always a tuple or None for `np.ndarray`.
Yes, for structured arrays it's a tuple of strings.
Let's verify this with a test.
<channel|><|tool_call>call:run_command{command:<|"|>python3 - <<'PY'
import numpy as np
# Case 1: recarray
data_rec = np.recarray((10,), dtype=[('x', float), ('y', float)])
names_rec = data_rec.dtype.names if hasattr(data_rec, 'dtype') else data_rec
print(f"recarray: {'x' in names_rec}") # Should be True
# Case 2: dictOfLists
data_dict = {'x': [1, 2], 'y': [3, 4]}
names_dict = data_dict.dtype.names if hasattr(data_dict, 'dtype') else data_dict
print(f"dictOfLists: {'x' in names_dict}") # Should be True
# Case 3: Other array
data_arr = np.array([1, 2])
names_arr = data_arr.dtype.names if hasattr(data_arr, 'dtype') else data_arr
# For non-structured array, data_arr.dtype.names is None.
# So names_arr is None. 'x' in None will raise TypeError.
# But in the code, dt will be 'listOfValues', not 'recarray' or 'dictOfLists'.
# However, let's see.
try:
print(f"Other array: {'x' in names_arr}")
except TypeError as e:
print(f"Other array TypeError: {e}")
PY
<|"|>}<tool_call|><|tool_response>
Generated tokens 1,339 tokens · stopped: STOP
Wait, if `data` is a non-structured array, `data.dtype.names` is `None`.
So `names = data.dtype.names if hasattr(data, 'dtype') else data` will set `names` to `None`.
Then `'x' in names` will raise `TypeError: argument of type 'NoneType' is not iterable`.
But this only happens if `dt == 'recarray' or dt == 'dictOfLists'`.
If `dt == 'recarray'`, then `data.dtype.names` MUST NOT be `None` according to `dataType()`:
```python
type_ = 'listOfValues' if obj.dtype.names is None else 'recarray'
```
So if `dt == 'recarray'`, `names` will be a tuple of names.
If `dt == 'dictOfLists'`, `data` is a dict, so `hasattr(data, 'dtype')` is False, and `names` will be the dict.
So the proposed fix:
```python
elif dt == 'recarray' or dt == 'dictOfLists':
names = data.dtype.names if hasattr(data, 'dtype') else data
if 'x' in names:
x = np.array(data['x'])
if 'y' in names:
y = np.array(data['y'])
```
should be safe because when `dt == 'recarray'`, `names` is a tuple. When `dt == 'dictOfLists'`, `names` is a dict.
Wait, is it possible for `data` to have a `dtype` attribute but not be a NumPy array? Unlikely in this context, but `hasattr(data, 'dtype')` is a good check for NumPy-like objects.
Actually, the original code was:
```python
elif dt == 'recarray' or dt == 'dictOfLists':
if 'x' in data:
x = np.array(data['x'])
if 'y' in data:
y = np.array(data['y'])
```
If I change it to:
```python
elif dt == 'recarray' or dt == 'dictOfLists':
# For recarrays, we must check dtype.names instead of 'in data'
names = data.dtype.names if hasattr(data, 'dtype') else data
if names is not None and 'x' in names:
x = np.array(data['x'])
if names is not None and 'y' in names:
y = np.array(data['y'])
```
This is even safer.
Wait, I can just use `isinstance(data, np.ndarray)` since `dataType` already used it.
```python
elif dt == 'recarray' or dt == 'dictOfLists':
if isinstance(data, np.ndarray):
if data.dtype.names and 'x' in data.dtype.names:
x = np.array(data['x'])
if data.dtype.names and 'y' in data.dtype.names:
y = np.array(data['y'])
else: # it's a dict
if 'x' in data:
x = np.array(data['x'])
if 'y' in data:
y = np.array(data['y'])
```
But wait, if `dt == 'recarray'`, we already know it's a `np.ndarray` and `dtype.names` is not None.
If `dt == 'dictOfLists'`, we already know it's a `dict`.
So:
```python
elif dt == 'recarray' or dt == 'dictOfLists':
# Use data.dtype.names for recarrays, and data for dicts
names = data.dtype.names if dt == 'recarray' else data
if 'x' in names:
x = np.array(data['x'])
if 'y' in names:
y = np.array(data['y'])
```
Wait, if `dt == 'recarray'`, `names` will be a tuple of strings. If `dt == 'dictOfLists'`, `names` will be the dictionary.
This is very clean and safe.
Let's re-verify with a script.
Wait, if `data` is a non-structured array, `data.dtype.names` is `None`.
So `names = data.dtype.names if hasattr(data, 'dtype') else data` will set `names` to `None`.
Then `'x' in names` will raise `TypeError: argument of type 'NoneType' is not iterable`.
But this only happens if `dt == 'recarray' or dt == 'dictOfLists'`.
If `dt == 'recarray'`, then `data.dtype.names` MUST NOT be `None` according to `dataType()`:
```python
type_ = 'listOfValues' if obj.dtype.names is None else 'recarray'
```
So if `dt == 'recarray'`, `names` will be a tuple of names.
If `dt == 'dictOfLists'`, `data` is a dict, so `hasattr(data, 'dtype')` is False, and `names` will be the dict.
So the proposed fix:
```python
elif dt == 'recarray' or dt == 'dictOfLists':
names = data.dtype.names if hasattr(data, 'dtype') else data
if 'x' in names:
x = np.array(data['x'])
if 'y' in names:
y = np.array(data['y'])
```
should be safe because when `dt == 'recarray'`, `names` is a tuple. When `dt == 'dictOfLists'`, `names` is a dict.
Wait, is it possible for `data` to have a `dtype` attribute but not be a NumPy array? Unlikely in this context, but `hasattr(data, 'dtype')` is a good check for NumPy-like objects.
Actually, the original code was:
```python
elif dt == 'recarray' or dt == 'dictOfLists':
if 'x' in data:
x = np.array(data['x'])
if 'y' in data:
y = np.array(data['y'])
```
If I change it to:
```python
elif dt == 'recarray' or dt == 'dictOfLists':
# For recarrays, we must check dtype.names instead of 'in data'
names = data.dtype.names if hasattr(data, 'dtype') else data
if names is not None and 'x' in names:
x = np.array(data['x'])
if names is not None and 'y' in names:
y = np.array(data['y'])
```
This is even safer.
Wait, I can just use `isinstance(data, np.ndarray)` since `dataType` already used it.
```python
elif dt == 'recarray' or dt == 'dictOfLists':
if isinstance(data, np.ndarray):
if data.dtype.names and 'x' in data.dtype.names:
x = np.array(data['x'])
if data.dtype.names and 'y' in data.dtype.names:
y = np.array(data['y'])
else: # it's a dict
if 'x' in data:
x = np.array(data['x'])
if 'y' in data:
y = np.array(data['y'])
```
But wait, if `dt == 'recarray'`, we already know it's a `np.ndarray` and `dtype.names` is not None.
If `dt == 'dictOfLists'`, we already know it's a `dict`.
So:
```python
elif dt == 'recarray' or dt == 'dictOfLists':
# Use data.dtype.names for recarrays, and data for dicts
names = data.dtype.names if dt == 'recarray' else data
if 'x' in names:
x = np.array(data['x'])
if 'y' in names:
y = np.array(data['y'])
```
Wait, if `dt == 'recarray'`, `names` will be a tuple of strings. If `dt == 'dictOfLists'`, `names` will be the dictionary.
This is very clean and safe.
Let's re-verify with a script.
<channel|><|tool_call>call:run_command{command:<|"|>python3 - <<'PY'
import numpy as np
def test_fix(data, dt):
# Proposed fix
names = data.dtype.names if dt == 'recarray' else data
x = None
y = None
if 'x' in names:
x = np.array(data['x'])
if 'y' in names:
y = np.array(data['y'])
return x, y
# Case 1: recarray
data_rec = np.recarray((10,), dtype=[('x', float), ('y', float)])
print(f"recarray: {test_fix(data_rec, 'recarray')}")
# Case 2: dictOfLists
data_dict = {'x': [1, 2], 'y': [3, 4]}
print(f"dictOfLists: {test_fix(data_dict, 'dictOfLists')}")
# Case 3: recarray without x
data_rec_no_x = np.recarray((10,), dtype=[('z', float), ('y', float)])
print(f"recarray no x: {test_fix(data_rec_no_x, 'recarray')}")
# Case 4: dict without x
data_dict_no_x = {'z': [1, 2], 'y': [3, 4]}
print(f"dict no x: {test_fix(data_dict_no_x, 'dictOfLists')}")
PY
<|"|>}<tool_call|><|tool_response>
Generated tokens 131 tokens · stopped: STOP
The fix looks solid. Now I will apply it to `pyqtgraph/graphicsItems/PlotDataItem.py`. Wait, I should check if there are other places where this pattern is used. The grep found: `pyqtgraph/graphicsItems/PlotDataItem.py:1164: if 'x' in data:` Let me check if there any other `if 'x' in data:` in the codebase.
The fix looks solid.
Now I will apply it to `pyqtgraph/graphicsItems/PlotDataItem.py`.
Wait, I should check if there are other places where this pattern is used.
The grep found:
`pyqtgraph/graphicsItems/PlotDataItem.py:1164: if 'x' in data:`
Let me check if there any other `if 'x' in data:` in the codebase.
<channel|><|tool_call>call:run_command{command:<|"|>grep -rn --include="*.py" "if 'x' in data:" pyqtgraph/<|"|>}<tool_call|><|tool_response>
Generated tokens 289 tokens · stopped: STOP
Only one place.
Let me double check if `PlotDataItem.py` has other similar logic for other fields.
Actually, the code I'm looking at handles 'x' and 'y'.
```python
elif dt == 'recarray' or dt == 'dictOfLists':
if 'x' in data:
x = np.array(data['x'])
if 'y' in data:
y = np.array(data['y'])
```
I'll apply the change.
Only one place.
Let me double check if `PlotDataItem.py` has other similar logic for other fields.
Actually, the code I'm looking at handles 'x' and 'y'.
```python
elif dt == 'recarray' or dt == 'dictOfLists':
if 'x' in data:
x = np.array(data['x'])
if 'y' in data:
y = np.array(data['y'])
```
I'll apply the change.
<channel|><|tool_call>call:edit_file{filepath:<|"|>pyqtgraph/graphicsItems/PlotDataItem.py<|"|>,new_string:<|"|> elif dt == 'recarray' or dt == 'dictOfLists':
names = data.dtype.names if dt == 'recarray' else data
if 'x' in names:
x = np.array(data['x'])
if 'y' in names:
y = np.array(data['y'])<|"|>,old_string:<|"|> elif dt == 'recarray' or dt == 'dictOfLists':
if 'x' in data:
x = np.array(data['x'])
if 'y' in data:
y = np.array(data['y'])<|"|>}<tool_call|><|tool_response>
Generated tokens 245 tokens · stopped: STOP
Now I should verify the fix. I'll use the reproduction script provided in the issue. Since `pg.exec()` and `pg.plot()` might fail in a headless environment (no display), I will focus on checking if `PlotDataItem` can be initialized with a recarray without raising the `TypeError`. Actually, I can just instantiate `PlotDataItem` with the recarray and call `setData` (which `__init__` does).
Now I should verify the fix.
I'll use the reproduction script provided in the issue. Since `pg.exec()` and `pg.plot()` might fail in a headless environment (no display), I will focus on checking if `PlotDataItem` can be initialized with a recarray without raising the `TypeError`.
Actually, I can just instantiate `PlotDataItem` with the recarray and call `setData` (which `__init__` does).
<channel|><|tool_call>call:run_command{command:<|"|>python3 - <<'PY'
import pyqtgraph as pg
import numpy as np
from pyqtgraph.graphicsItems.PlotDataItem import PlotDataItem
try:
data_array = np.recarray((10,), dtype=[('x', float), ('y', float)])
print("Creating PlotDataItem with recarray...")
item = PlotDataItem(data_array)
print("Successfully created PlotDataItem!")
except Exception as e:
print(f"Exception caught: {type(e).__name__}: {e}")
import traceback
traceback.print_exc()
PY
<|"|>}<tool_call|><|tool_response>
Generated tokens 177 tokens · stopped: STOP
The issue is reproduced and fixed! Now I'll check if it still works with `dictOfLists`.
The issue is reproduced and fixed!
Now I'll check if it still works with `dictOfLists`.
<channel|><|tool_call>call:run_command{command:<|"|>python3 - <<'PY'
import pyqtgraph as pg
import numpy as np
from pyqtgraph.graphicsItems.PlotDataItem import PlotDataItem
try:
data_dict = {'x': np.arange(10), 'y': np.arange(10)}
print("Creating PlotDataItem with dictOfLists...")
item = PlotDataItem(data_dict)
print("Successfully created PlotDataItem with dict!")
except Exception as e:
print(f"Exception caught: {type(e).__name__}: {e}")
import traceback
traceback.print_exc()
PY
<|"|>}<tool_call|><|tool_response>
Generated tokens 158 tokens · stopped: STOP
And check if it still works with regular arrays.
And check if it still works with regular arrays.
<channel|><|tool_call>call:run_command{command:<|"|>python3 - <<'PY'
import pyqtgraph as pg
import numpy as np
from pyqtgraph.graphicsItems.PlotDataItem import PlotDataItem
try:
data_arr = np.random.random((10, 2))
print("Creating PlotDataItem with Nx2array...")
item = PlotDataItem(data_arr)
print("Successfully created PlotDataItem with Nx2array!")
except Exception as e:
print(f"Exception caught: {type(e).__name__}: {e}")
import traceback
traceback.print_exc()
PY
<|"|>}<tool_call|><|tool_response>
Generated tokens 42 tokens · stopped: STOP
Everything seems to be working correctly. I'll do a final check of the diff.
Everything seems to be working correctly.
I'll do a final check of the diff.
<channel|><|tool_call>call:run_command{command:<|"|>git status --short && git diff<|"|>}<tool_call|><|tool_response>
Generated tokens 253 tokens · stopped: STOP
One last check: what if `dt == 'recarray'` but `data.dtype.names` is actually `None`?
Wait, `dataType` ensures that if `dt == 'recarray'`, `obj.dtype.names` is NOT `None`.
```python
if obj.ndim == 1:
type_ = 'listOfValues' if obj.dtype.names is None else 'recarray'
```
So `names = data.dtype.names if dt == 'recarray' else data` will assign a tuple to `names` when `dt == 'recarray'`.
Is it possible for `data` to be something else that triggers `dt == 'recarray'`?
`dataType` is called right before the `if` block:
```python
data = args[0]
dt = dataType(data)
```
So `dt` is derived from `data`.
Wait, I should check if `PlotDataItem` is used anywhere else that might be affected.
The logic seems quite isolated to `setData` when `len(args) == 1`.
I'm confident.One last check: what if `dt == 'recarray'` but `data.dtype.names` is actually `None`?
Wait, `dataType` ensures that if `dt == 'recarray'`, `obj.dtype.names` is NOT `None`.
```python
if obj.ndim == 1:
type_ = 'listOfValues' if obj.dtype.names is None else 'recarray'
```
So `names = data.dtype.names if dt == 'recarray' else data` will assign a tuple to `names` when `dt == 'recarray'`.
Is it possible for `data` to be something else that triggers `dt == 'recarray'`?
`dataType` is called right before the `if` block:
```python
data = args[0]
dt = dataType(data)
```
So `dt` is derived from `data`.
Wait, I should check if `PlotDataItem` is used anywhere else that might be affected.
The logic seems quite isolated to `setData` when `len(args) == 1`.
I'm confident.<channel|><|tool_call>call:submit_patch{}<tool_call|><|tool_response>
Patch, tests and logs
Final code change Included
diff --git a/pyqtgraph/graphicsItems/PlotDataItem.py b/pyqtgraph/graphicsItems/PlotDataItem.py
index 9d7c7f8..79be936 100644
--- a/pyqtgraph/graphicsItems/PlotDataItem.py
+++ b/pyqtgraph/graphicsItems/PlotDataItem.py
@@ -1161,9 +1161,10 @@ class PlotDataItem(GraphicsObject):
x = data[:, 0]
y = data[:, 1]
elif dt == 'recarray' or dt == 'dictOfLists':
- if 'x' in data:
+ names = data.dtype.names if dt == 'recarray' else data
+ if 'x' in names:
x = np.array(data['x'])
- if 'y' in data:
+ if 'y' in names:
y = np.array(data['y'])
elif dt == 'listOfDicts':
if 'x' in data[0]:
Test output Included
============================= test session starts ============================== collected 9 items tests/graphicsItems/test_PlotDataItem.py ......... [100%] ==================================== PASSES ==================================== - generated xml file: /tmp/rebench-grade-3ad74cd8022e4aeb85491da4e21096fe.xml -- =========================== short test summary info ============================ PASSED tests/graphicsItems/test_PlotDataItem.py::test_bool PASSED tests/graphicsItems/test_PlotDataItem.py::test_bound_formats PASSED tests/graphicsItems/test_PlotDataItem.py::test_fft PASSED tests/graphicsItems/test_PlotDataItem.py::test_setData PASSED tests/graphicsItems/test_PlotDataItem.py::test_nonfinite PASSED tests/graphicsItems/test_PlotDataItem.py::test_opts PASSED tests/graphicsItems/test_PlotDataItem.py::test_clear PASSED tests/graphicsItems/test_PlotDataItem.py::test_clear_in_step_mode PASSED tests/graphicsItems/test_PlotDataItem.py::test_clipping ============================== 9 passed in 0.27s ===============================
Test errors Included
Test report (JUnit) Included
<?xml version="1.0" encoding="utf-8"?><testsuites name="pytest tests"><testsuite name="pytest" errors="0" failures="0" skipped="0" tests="9" time="0.274" timestamp="2026-11-14T04:00:13.105673+00:00" hostname="cae7c4b83772"><testcase classname="tests.graphicsItems.test_PlotDataItem" name="test_bool" time="0.006" /><testcase classname="tests.graphicsItems.test_PlotDataItem" name="test_bound_formats" time="0.003" /><testcase classname="tests.graphicsItems.test_PlotDataItem" name="test_fft" time="0.004" /><testcase classname="tests.graphicsItems.test_PlotDataItem" name="test_setData" time="0.001" /><testcase classname="tests.graphicsItems.test_PlotDataItem" name="test_nonfinite" time="0.001" /><testcase classname="tests.graphicsItems.test_PlotDataItem" name="test_opts" time="0.001" /><testcase classname="tests.graphicsItems.test_PlotDataItem" name="test_clear" time="0.001" /><testcase classname="tests.graphicsItems.test_PlotDataItem" name="test_clear_in_step_mode" time="0.173" /><testcase classname="tests.graphicsItems.test_PlotDataItem" name="test_clipping" time="0.008" /></testsuite></testsuites>
Agent log Included
[ 0.0s] 📋 System Prompt: You are a principal Python software engineer resolving a repository issue in `/workspace`.
[ 0.0s] 🎯 Task Prompt: You are evaluating a software engineering task for repository pyqtgraph/pyqtgraph.
[ 29.3s] 💭 (1887 chars) "The user is reporting a `TypeError: Cannot compare structured or void to non-void arrays.`
when using a NumPy record arr..."
[ 29.3s] 🔧 run_command grep -rn --include="*.py" "if 'x' in data:" pyqtgraph/
[ 29.3s] → pyqtgraph/graphicsItems/PlotDataItem.py:1164: if 'x' in data:
[ 35.7s] 💭 (119 chars) "The grep result says line 1164, but the traceback says line 719. The version might be
different. Let me check the file."
[ 35.7s] 📖 read_file (pyqtgraph/graphicsItems/PlotDataItem.py)
[ 35.8s] ✓ {'status': 'ok', 'filepath': 'pyqtgraph/graphicsItems/PlotDataItem.py', 'content': ' Receives
the ``autoAlpha…
[ 40.7s] 💭 (124 chars) "Wait, I am not seeing `if 'x' in data:` in the lines I read. Let me check around line 1164
since that's where grep found..."
[ 40.7s] 📖 read_file (pyqtgraph/graphicsItems/PlotDataItem.py)
[ 40.8s] ✓ {'status': 'ok', 'filepath': 'pyqtgraph/graphicsItems/PlotDataItem.py', 'content': ' Raised
when an invalid t…
[ 68.0s] 💭 (802 chars) "Okay, I found it! Line 1164: `if 'x' in data:`.
The code is:
```python
elif dt == 'recarray' or dt == 'dict..."
[ 68.0s] 🔧 run_command (12 lines) python3 - <<'PY'
[ 68.7s] → dataType(data_array): recarray
[ 79.6s] 💭 (445 chars) "The reproduction confirms the issue. `dataType(data_array)` is `'recarray'`, and `'x' in
data_array` raises the `TypeErr..."
[ 79.6s] 🔧 run_command grep -rn "def dataType" pyqtgraph/graphicsItems/PlotDataItem.py
[ 79.7s] → 1799:def dataType(obj) -> str:
[ 83.8s] 💭 (35 chars) "Let's read the `dataType` function."
[ 83.8s] 📖 read_file (pyqtgraph/graphicsItems/PlotDataItem.py)
[ 83.9s] ✓ {'status': 'ok', 'filepath': 'pyqtgraph/graphicsItems/PlotDataItem.py', 'content': ' x = x2\n
n = y.si…
[ 150.0s] 💭 (2898 chars) "The `dataType` function correctly identifies `recarray`:
```python
if obj.ndim == 1:
type_ =..."
[ 150.0s] 🔧 run_command (26 lines) python3 - <<'PY'
[ 150.2s] → recarray: True
[ 230.4s] 💭 (3397 chars) "Wait, if `data` is a non-structured array, `data.dtype.names` is `None`.
So `names = data.dtype.names if hasattr(data, '..."
[ 230.4s] 🔧 run_command (30 lines) python3 - <<'PY'
[ 230.6s] → recarray: (array([5.45192445e-310, 5.49890662e-095, 5.45145168e-310, 4.94065646e-324,
[ 240.7s] 💭 (326 chars) "The fix looks solid.
Now I will apply it to `pyqtgraph/graphicsItems/PlotDataItem.py`.
Wait, I should check if there a..."
[ 240.7s] 🔧 run_command grep -rn --include="*.py" "if 'x' in data:" pyqtgraph/
[ 240.7s] → pyqtgraph/graphicsItems/PlotDataItem.py:1164: if 'x' in data:
[ 254.7s] 💭 (403 chars) "Only one place.
Let me double check if `PlotDataItem.py` has other similar logic for other fields.
Actually, the code I..."
[ 254.7s] ✏️ edit_file (pyqtgraph/graphicsItems/PlotDataItem.py)
[ 254.9s] ✓ {'status': 'ok', 'filepath': 'pyqtgraph/graphicsItems/PlotDataItem.py', 'occurrences': 1, 'strategy':
'exact', 'diff': "…
[ 271.9s] 💭 (391 chars) "Now I should verify the fix.
I'll use the reproduction script provided in the issue. Since `pg.exec()` and `pg.plot()` m..."
[ 271.9s] 🔧 run_command (15 lines) python3 - <<'PY'
[ 272.3s] → Creating PlotDataItem with recarray...
[ 285.0s] 💭 (88 chars) "The issue is reproduced and fixed!
Now I'll check if it still works with `dictOfLists`."
[ 285.0s] 🔧 run_command (15 lines) python3 - <<'PY'
[ 285.4s] → Creating PlotDataItem with dictOfLists...
[ 295.6s] 💭 (48 chars) "And check if it still works with regular arrays."
[ 295.6s] 🔧 run_command (15 lines) python3 - <<'PY'
[ 295.9s] → Creating PlotDataItem with Nx2array...
[ 298.7s] 💭 (77 chars) "Everything seems to be working correctly.
I'll do a final check of the diff."
[ 298.7s] 🔧 run_command git status --short && git diff
[ 298.7s] → M pyqtgraph/graphicsItems/PlotDataItem.py
[ 313.7s] 💭 (821 chars) "One last check: what if `dt == 'recarray'` but `data.dtype.names` is actually `None`?
Wait, `dataType` ensures that if `..."
[ 313.7s] 🤖 submit_patch
[ 313.8s] → submit_patch: {"status": "ok", "patch_size": 770, "files_changed": 1}
[ 319.2s] 💭 (117 chars) "The patch is submitted. I have verified it with reproduction scripts for recarrays,
dictionaries, and regular arrays."
[ 319.2s] ✅ Agent finished: Fixed `TypeError` when passing a NumPy record array to `PlotDataItem.setData` (and
`pg.plot`) by checking field names in `data.dtype.names` instead of using the `in` operator on the array itself.
Native result row
{
"agent_error": null,
"duration_seconds": 389.57,
"error": null,
"grade": {
"collected_tests": 9,
"competition_junit_ok": true,
"error": null,
"required_issues_count": 0,
"resolved": true,
"status_counts": {
"PASSED": 9
},
"strict_clean_patch": true,
"test_exit_code": 0
},
"instance_id": "pyqtgraph__pyqtgraph-3278",
"pass_idx": 0,
"patch_chars": 770,
"phase1_seconds": 387.84,
"repo": "pyqtgraph/pyqtgraph",
"request_log_path": "/kaggle-root/working/r31/rollouts_X/requests/pyqtgraph__pyqtgraph-3278__p0__r4.jsonl",
"resolved": true,
"rollout_idx": 4,
"run_key": "pyqtgraph__pyqtgraph-3278__p0__r4",
"strict_clean_patch": true,
"tool_calls": 14,
"total_llm_calls": 16,
"trace_path": "/kaggle-root/working/r31/rollouts_X/traces/trace_pyqtgraph__pyqtgraph-3278__p0__r4.json"
}