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Jul 11, 2023
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removing outdated code block examples #281

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887d0f6
remove old code block examples now that we have nbs
clewis7 Jul 7, 2023
93c7189
remove old docs examples and regenerate mixin class
clewis7 Jul 11, 2023
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31 changes: 0 additions & 31 deletions fastplotlib/graphics/image.py
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Original file line number Diff line number Diff line change
Expand Up @@ -254,20 +254,6 @@ def __init__(
**present**: :class:`.PresentFeature`
Control the presence of the Graphic in the scene

Examples
--------
.. code-block:: python

from fastplotlib import Plot
# create a `Plot` instance
plot = Plot()
# make some random 2D image data
data = np.random.rand(512, 512)
# plot the image data
plot.add_image(data=data)
# show the plot
plot.show()

"""

super().__init__(*args, **kwargs)
Expand Down Expand Up @@ -424,23 +410,6 @@ def __init__(
**present**: :class:`.PresentFeature`
Control the presence of the Graphic in the scene

Examples
--------
.. code-block:: python

from fastplotlib import Plot
# create a `Plot` instance
plot = Plot()

# make some random 2D heatmap data
data = np.random.rand(10_000, 8_000)

# add a heatmap
plot.add_heatmap(data=data)

# show the plot
plot.show()

"""

super().__init__(*args, **kwargs)
Expand Down
107 changes: 0 additions & 107 deletions fastplotlib/graphics/line_collection.py
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Original file line number Diff line number Diff line change
Expand Up @@ -83,67 +83,8 @@ def __init__(

Collections support the same features as the underlying graphic. You just have to slice the selection.

.. code-block:: python

# slice only the collection
line_collection[10:20].colors = "blue"

# slice the collection and a feature
line_collection[20:30].colors[10:30] = "red"

# the data feature also works like this

See :class:`LineGraphic` details on the features.

Examples
--------
.. code-block:: python

from fastplotlib import Plot
from fastplotlib.graphics import LineCollection

# creating data for sine and cosine waves
xs = np.linspace(-10, 10, 100)
ys = np.sin(xs)

sine = np.dstack([xs, ys])[0]

ys = np.sin(xs) + 10
sine2 = np.dstack([xs, ys])[0]

ys = np.cos(xs) + 5
cosine = np.dstack([xs, ys])[0]

# creating plot
plot = Plot()

# creating a line collection using the sine and cosine wave data
line_collection = LineCollection(data=[sine, cosine, sine2], cmap=["Oranges", "Blues", "Reds"], thickness=20.0)

# add graphic to plot
plot.add_graphic(line_collection)

# show plot
plot.show()

# change the color of the sine wave to white
line_collection[0].colors = "w"

# change certain color indexes of the cosine data to red
line_collection[1].colors[0:15] = "r"

# toggle presence of sine2 and rescale graphics
line_collection[2].present = False

plot.autoscale()

line_collection[2].present = True

plot.autoscale()

# can also do slicing
line_collection[1:].colors[35:70] = "magenta"

"""

super(LineCollection, self).__init__(name)
Expand Down Expand Up @@ -596,56 +537,8 @@ def __init__(

Collections support the same features as the underlying graphic. You just have to slice the selection.

.. code-block:: python

# slice only the collection
line_collection[10:20].colors = "blue"

# slice the collection and a feature
line_collection[20:30].colors[10:30] = "red"

# the data feature also works like this

See :class:`LineGraphic` details on the features.


Examples
--------
.. code-block:: python

from fastplotlib import Plot
from fastplotlib.graphics import LineStack

# create plot
plot = Plot()

# create line data
xs = np.linspace(-10, 10, 100)
ys = np.sin(xs)

sine = np.dstack([xs, ys])[0]

ys = np.sin(xs)
cosine = np.dstack([xs, ys])[0]

# create line stack
line_stack = LineStack(data=[sine, cosine], cmap=["Oranges", "Blues"], thickness=20.0, separation=5.0)

# add graphic to plot
plot.add_graphic(line_stack)

# show plot
plot.show()

# change the color of the sine wave to white
line_stack[0].colors = "w"

# change certain color indexes of the cosine data to red
line_stack[1].colors[0:15] = "r"

# more slicing
line_stack[0].colors[35:70] = "magenta"

"""
super(LineStack, self).__init__(
data=data,
Expand Down
38 changes: 0 additions & 38 deletions fastplotlib/layouts/_plot.py
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Original file line number Diff line number Diff line change
Expand Up @@ -47,44 +47,6 @@ def __init__(
kwargs
passed to Subplot, for example ``name``

Examples
--------

Simple example

.. code-block:: python

from fastplotlib import Plot

# create a `Plot` instance
plot1 = Plot()

# make some random 2D image data
data = np.random.rand(512, 512)

# plot the image data
plot1.add_image(data=data)

# show the plot
plot1.show()

Sharing controllers, start from the previous example and create a new jupyter cell

.. code-block:: python

# use the controller from the previous plot
# this will sync the pan & zoom controller
plot2 = Plot(controller=plot1.controller)

# make some random 2D image data
data = np.random.rand(512, 512)

# plot the image data
plot2.add_image(data=data)

# show the plot
plot2.show()

"""
super(Plot, self).__init__(
parent=None,
Expand Down
138 changes: 0 additions & 138 deletions fastplotlib/layouts/graphic_methods_mixin.py
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Original file line number Diff line number Diff line change
Expand Up @@ -78,23 +78,6 @@ def add_heatmap(self, data: Any, vmin: int = None, vmax: int = None, cmap: str =
**present**: :class:`.PresentFeature`
Control the presence of the Graphic in the scene

Examples
--------
.. code-block:: python

from fastplotlib import Plot
# create a `Plot` instance
plot = Plot()

# make some random 2D heatmap data
data = np.random.rand(10_000, 8_000)

# add a heatmap
plot.add_heatmap(data=data)

# show the plot
plot.show()


"""
return self._create_graphic(HeatmapGraphic, data, vmin, vmax, cmap, filter, chunk_size, isolated_buffer, *args, **kwargs)
Expand Down Expand Up @@ -146,20 +129,6 @@ def add_image(self, data: Any, vmin: int = None, vmax: int = None, cmap: str = '
**present**: :class:`.PresentFeature`
Control the presence of the Graphic in the scene

Examples
--------
.. code-block:: python

from fastplotlib import Plot
# create a `Plot` instance
plot = Plot()
# make some random 2D image data
data = np.random.rand(512, 512)
# plot the image data
plot.add_image(data=data)
# show the plot
plot.show()


"""
return self._create_graphic(ImageGraphic, data, vmin, vmax, cmap, filter, isolated_buffer, *args, **kwargs)
Expand Down Expand Up @@ -218,67 +187,8 @@ def add_line_collection(self, data: List[numpy.ndarray], z_position: Union[List[

Collections support the same features as the underlying graphic. You just have to slice the selection.

.. code-block:: python

# slice only the collection
line_collection[10:20].colors = "blue"

# slice the collection and a feature
line_collection[20:30].colors[10:30] = "red"

# the data feature also works like this

See :class:`LineGraphic` details on the features.

Examples
--------
.. code-block:: python

from fastplotlib import Plot
from fastplotlib.graphics import LineCollection

# creating data for sine and cosine waves
xs = np.linspace(-10, 10, 100)
ys = np.sin(xs)

sine = np.dstack([xs, ys])[0]

ys = np.sin(xs) + 10
sine2 = np.dstack([xs, ys])[0]

ys = np.cos(xs) + 5
cosine = np.dstack([xs, ys])[0]

# creating plot
plot = Plot()

# creating a line collection using the sine and cosine wave data
line_collection = LineCollection(data=[sine, cosine, sine2], cmap=["Oranges", "Blues", "Reds"], thickness=20.0)

# add graphic to plot
plot.add_graphic(line_collection)

# show plot
plot.show()

# change the color of the sine wave to white
line_collection[0].colors = "w"

# change certain color indexes of the cosine data to red
line_collection[1].colors[0:15] = "r"

# toggle presence of sine2 and rescale graphics
line_collection[2].present = False

plot.autoscale()

line_collection[2].present = True

plot.autoscale()

# can also do slicing
line_collection[1:].colors[35:70] = "magenta"


"""
return self._create_graphic(LineCollection, data, z_position, thickness, colors, alpha, cmap, cmap_values, name, metadata, *args, **kwargs)
Expand Down Expand Up @@ -397,56 +307,8 @@ def add_line_stack(self, data: List[numpy.ndarray], z_position: Union[List[float

Collections support the same features as the underlying graphic. You just have to slice the selection.

.. code-block:: python

# slice only the collection
line_collection[10:20].colors = "blue"

# slice the collection and a feature
line_collection[20:30].colors[10:30] = "red"

# the data feature also works like this

See :class:`LineGraphic` details on the features.


Examples
--------
.. code-block:: python

from fastplotlib import Plot
from fastplotlib.graphics import LineStack

# create plot
plot = Plot()

# create line data
xs = np.linspace(-10, 10, 100)
ys = np.sin(xs)

sine = np.dstack([xs, ys])[0]

ys = np.sin(xs)
cosine = np.dstack([xs, ys])[0]

# create line stack
line_stack = LineStack(data=[sine, cosine], cmap=["Oranges", "Blues"], thickness=20.0, separation=5.0)

# add graphic to plot
plot.add_graphic(line_stack)

# show plot
plot.show()

# change the color of the sine wave to white
line_stack[0].colors = "w"

# change certain color indexes of the cosine data to red
line_stack[1].colors[0:15] = "r"

# more slicing
line_stack[0].colors[35:70] = "magenta"


"""
return self._create_graphic(LineStack, data, z_position, thickness, colors, cmap, separation, separation_axis, name, *args, **kwargs)
Expand Down
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