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Fix typos again
Found via `codespell -L adin,visibles,recuse` and `typos --hidden --format brief` Co-authored-by: Kushal Kolar <[email protected]>
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CODE_OF_CONDUCT.md

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# Introduction
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This code of conduct applies to all spaces managed by the fastplotlib project,
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including all public and private mailing lists, issue trackers, wikis, blogs,
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Twitter, and any other communication channel used by our community, including
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any in-person events, whether directly organized by fastplotlib developers
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or associated people.
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This code of conduct applies to all spaces managed by the fastplotlib project,
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including all public and private mailing lists, issue trackers, wikis, blogs,
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Twitter, and any other communication channel used by our community, including
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any in-person events, whether directly organized by fastplotlib developers
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or associated people.
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This code of conduct should be honored by everyone who participates in
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the fastplotlib community formally or informally, or claims any affiliation with the
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report to the Code of Conduct committee directly, or ask the committee for
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advice, in confidence.
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You can report issues to the fastplotlib core team:
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You can report issues to the fastplotlib core team:
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[Kushal Kolar](https://github.com/kushalkolar)
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[Caitlin Lewis](https://github.com/clewis7)

CONTRIBUTING.md

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> **_NOTE:_** If your contributions modify how visualizations _look_, see the [Testing details](#testing-details) section at the very bottom.
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> **_NOTE:_** If your contributions modify the API, you must regenerate the API docs before making a PR, see
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> the [Documenation](#documentation) section below.
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> the [Documentation](#documentation) section below.
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### Contributing your changes back to `fastplotlib`
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GOVERNANCE.md

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1. Only individuals, not organizations, may be added to the leadership team. A candidate individual must be nominated by a current member of the leadership team.
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2. A candidate must:
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* Be committed to the fastplotlib mission.
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* Have demonstrated contibutions to `fastplotlib` through one of:
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* Have demonstrated contributions to `fastplotlib` through one of:
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* Significant contributions to the codebase.
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* Significant application of fastplotlib in a dependent package.
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* Significant technical guidance or feedback on the development of `fastplotlib`.

docs/source/api/graphics/LineCollection.rst

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LineCollection.rotate
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LineCollection.share_property
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LineCollection.unshare_property
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docs/source/api/graphics/LineStack.rst

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LineStack.rotate
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LineStack.share_property
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LineStack.unshare_property
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docs/source/user_guide/guide.rst

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Imgui
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-----
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Fastplotlib uses `imgui_bundle <https://github.com/pthom/imgui_bundle>`_ to provide within-canvas UI elemenents if you
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Fastplotlib uses `imgui_bundle <https://github.com/pthom/imgui_bundle>`_ to provide within-canvas UI elements if you
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installed ``fastplotlib`` using the ``imgui`` toggle, i.e. ``fastplotlib[imgui]``, or installed ``imgui_bundle`` afterwards.
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Fastplotlib comes built-in with imgui UIs for subplot toolbars and a standard right-click menu with a number of options.

examples/line/line_cmap_more.py

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# i.e., the color is a function of sine(x)
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line2 = figure[0, 0].add_line(sine, thickness=10, cmap="jet", cmap_transform=sine[:, 1], offset=(0, 4, 0))
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# make a line and change the cmap afterward, here we are using the cosine instead fot the transform
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# make a line and change the cmap afterward, here we are using the cosine instead for the transform
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line3 = figure[0, 0].add_line(sine, thickness=10, cmap="jet", cmap_transform=cosine[:, 1], offset=(0, 6, 0))
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# change the cmap
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line3.cmap = "bwr"

examples/line_collection/line_collection_colors.py

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Line Collection Colors
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======================
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Example showing one way ot setting colors for individual lines in a collection
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Example showing one way of setting colors for individual lines in a collection
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"""
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# test_example = true

examples/machine_learning/covariance.py

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X = X.T
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cov = X @ X.T / X.shape[1]
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# reshaped image for each sample wil be 64 x 64 pixels
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# reshaped image for each sample will be 64 x 64 pixels
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img = cov[0].reshape(64, 64)
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# figure kwargs for image widget

examples/misc/em_wave_animation.py

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start, stop = 0, 4 * np.pi
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# let's define the x, y and z axes for each with direction of wave propogation along the z-axis
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# let's define the x, y and z axes for each with direction of wave propagation along the z-axis
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# electric field in the xz plane travelling along
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zs = np.linspace(start, stop, 200)
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e_ys = np.zeros(200)

examples/notebooks/multiprocessing_zmq/multiprocessing_zmq_plot.ipynb

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"fig[0, 0].add_image(data, name=\"image\")\n",
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"\n",
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"def update_frame(subplot):\n",
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" # recieve bytes\n",
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" # receive bytes\n",
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" b = get_bytes()\n",
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" \n",
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" if b is not None:\n",

examples/notebooks/nb_test_utils.py

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def image_similarity(src, target, threshold=0.2):
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"""Compute normalized RMSE 0..1 and decide if similar based on threshold.
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For every pixel, the euclidian distance between RGB values is computed,
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For every pixel, the euclidean distance between RGB values is computed,
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and normalized by the maximum possible distance (between black and white).
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The RMSE is then computed from those errors.
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The normalized RMSE is used to compute the
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similarity metric, so larger errors (euclidian distance
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similarity metric, so larger errors (euclidean distance
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between two RGB colors) will have a disproportionately
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larger effect on the score than smaller errors.
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examples/notebooks/quickstart.ipynb

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"id": "c29f81f9-601b-49f4-b20c-575c56e58026",
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"metadata": {},
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"source": [
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"## Graphic _data_ is also slicable and settable"
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"## Graphic _data_ is also sliceable and settable"
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]
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},
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{
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"metadata": {},
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"outputs": [],
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"source": [
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"# change the FOV of the persepctive camera\n",
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"# change the FOV of the perspective camera\n",
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"fig_l3d[0, 0].camera.fov = 70"
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]
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},
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"\n",
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"start, stop = 0, 4 * np.pi\n",
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"\n",
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"# let's define the x, y and z axes for each with direction of wave propogation along the z-axis\n",
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"# let's define the x, y and z axes for each with direction of wave propagation along the z-axis\n",
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"# electric field in the xz plane travelling along\n",
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"zs = np.linspace(start, stop, 200)\n",
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"e_ys = np.zeros(200)\n",

examples/selection_tools/unit_circle.py

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import fastplotlib as fpl
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# helper function to make a cirlce
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# helper function to make a circle
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def make_circle(center, radius: float, n_points: int) -> np.ndarray:
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xs = radius * np.cos(theta)
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)
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# line to show the circle radius
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# use it to indicate the current position of the sine and cosine selctors (below)
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# use it to indicate the current position of the sine and cosine selectors (below)
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radius_data = np.array([[0, 0, 0], [*circle_data[0], 0]])
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circle_radius = figure["unit circle"].add_line(
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radius_data, thickness=6, colors="magenta"

examples/tests/testutils.py

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def image_similarity(src, target, threshold=0.2):
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For every pixel, the euclidean distance between RGB values is computed,
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and normalized by the maximum possible distance (between black and white).
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The RMSE is then computed from those errors.
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The normalized RMSE is used to compute the
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similarity metric, so larger errors (euclidian distance
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similarity metric, so larger errors (euclidean distance
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between two RGB colors) will have a disproportionately
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any pixels with a euclidian color distance < fuzz will be ignored
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any pixels with a euclidean color distance < fuzz will be ignored
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# compute euclidean distance between pixels
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error = np.linalg.norm(np.abs(target - src), axis=-1) / max_dist

fastplotlib/graphics/_axes.py

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(xscreen_10, yscreen_10)
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)
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# force origin since None is not supported for Persepctive projections
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# force origin since None is not supported for Perspective projections
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fastplotlib/graphics/_collection_base.py

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def visibles(self) -> np.ndarray[bool]:
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def visible(self) -> np.ndarray[bool]:
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@visibles.setter
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def visibles(self, values: np.ndarray[bool] | list[bool]):
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@visible.setter
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def visible(self, values: np.ndarray[bool] | list[bool]):
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fastplotlib/graphics/_features/_base.py

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| target | WorldObject | pygfx rendering engine object for the graphic |
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| time_stamp | float | time when the event occured, in ms |
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| time_stamp | float | time when the event occurred, in ms |
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+------------+-------------+-----------------------------------------------+
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"""

fastplotlib/graphics/line_collection.py

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names of the individual lines in the collection, ``len(names)`` must equal ``len(data)``
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metadata: Any
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meatadata associated with the collection as a whole
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metadata associated with the collection as a whole
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metadatas: Iterable or array
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metadata for each individual line associated with this collection, this is for the user to manage.

fastplotlib/graphics/selectors/_rectangle.py

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Returns
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data indicies of the selection
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data indices of the selection
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| tuple of [row_indices, col_indices] if the graphic is an image
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| list of indices along the x-dimension for each line if graphic is a line collection
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| array of indices along the x-dimension if graphic is a line

fastplotlib/layouts/_graphic_methods_mixin.py

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names of the individual lines in the collection, ``len(names)`` must equal ``len(data)``
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metadata associated with the collection as a whole
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metadata for each individual line associated with this collection, this is for the user to manage.

fastplotlib/tools/_histogram_lut.py

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----------
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data
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image_graphic
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nbins: int, defaut 100.
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nbins: int, default 100.
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Total number of bins used in the histogram
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flank_divisor: float, default 5.0.
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Fraction of empty histogram bins on the tails of the distribution set `np.inf` for no flanks

fastplotlib/ui/_base.py

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additional args for the GUI
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additional kwargs for the GUI
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fastplotlib/utils/_plot_helpers.py

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in order of closest to furthest. Uses the distance between ``pos`` and the center of the
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fastplotlib/utils/gui.py

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charactor = chr(0x2705) # green checkmark
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character = chr(0x2705) # green checkmark
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tooltip = "This adapter can be used with fastplotlib"
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charactor = chr(0x0000274C) # red x
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character = chr(0x0000274C) # red x
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tooltip = "This adapter cannot be used with fastplotlib"
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elif device.startswith("llvmpipe") or atype == "CPU":
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charactor = f"{chr(0x00002757)} limited" # red !
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character = f"{chr(0x00002757)} limited" # red !
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tooltip = "CPU rendering support is limited and mainly for testing purposes"
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charactor = f"{chr(0x00002757)} unknown" # red !
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character = f"{chr(0x00002757)} unknown" # red !
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tooltip = "Unknown adapter type and backend"
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for s in [f"{character}{default}", device, atype, backend, driver]:
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table_str += f"<td>{s}</td>"
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table_str += "</tr>"
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