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[WIP] FEATURE: Convenience functions for volume rendering transfer functions #178

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[WIP] FEATURE: Convenience functions for volume rendering transfer functions #178

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c8bb77a
Add convenience functions for volume rendering transfer functions.
GenevieveBuckley Sep 23, 2018
47caedb
Fix typo
GenevieveBuckley Sep 23, 2018
8de9b84
Better matplotlib colormap namespace coverage with dir(matplotlib.cm)
GenevieveBuckley Sep 23, 2018
9bc0e9f
Travis CI, add matplotlib to test environment.
GenevieveBuckley Sep 24, 2018
68b330e
No single line if statements.
GenevieveBuckley Sep 24, 2018
89a6d79
Transfer functions - allow length of rgba array to be passed as kwarg
GenevieveBuckley Sep 24, 2018
a4d809c
More flexible color input for transfer functions with matplotlib.colo…
GenevieveBuckley Sep 24, 2018
2321bf7
Simplify predefined_transfer_functions() discovery of all the matplot…
GenevieveBuckley Sep 26, 2018
551ef9e
Merge master
GenevieveBuckley Mar 5, 2020
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4 changes: 2 additions & 2 deletions .travis.yml
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Original file line number Diff line number Diff line change
Expand Up @@ -28,7 +28,7 @@ before_install:
- conda config --set always_yes yes --set changeps1 no
- conda update -q conda
- conda info -a
- conda create -q -n test-environment python=$PYTHON_VERSION numpy scipy runipy
- conda create -q -n test-environment python=$PYTHON_VERSION numpy scipy runipy matplotlib
- source activate test-environment
- conda install -c conda-forge pytest pytest-cov bokeh
- pip install coveralls
Expand All @@ -42,4 +42,4 @@ script:
# - runipy examples/examples.ipynb

after_success:
coveralls
coveralls
139 changes: 139 additions & 0 deletions ipyvolume/transferfunction.py
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Expand Up @@ -4,6 +4,7 @@
from traittypes import Array
import traitlets
import numpy as np
import matplotlib.cm
from . import serialize
from .serialize import array_rgba_png_serialization, array_serialization
N = 1024
Expand Down Expand Up @@ -164,3 +165,141 @@ def control(self, max_opacity=0.2):
return ipywidgets.VBox(
[ipywidgets.HBox([ipywidgets.Label(value="levels:"), l1, l2, l3]), ipywidgets.HBox([ipywidgets.Label(value="opacities:"), o1, o2, o3])]
)


def linear_transfer_function(rgb_values,
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Instead of rgb_color, we can just name it color, and let matplotlib deal with it:
https://github.com/glue-viz/glue-jupyter/blob/7254451a2a671cdea07296861abd3f0cf50c6409/glue_jupyter/ipyvolume/volume.py#L43
It seems to accept named colors like 'red', hex values '#f0e6a4', and float tuples.

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That's much more elegant, thanks. Also means we can probably use this in conjunction with the color picker ipywidget, too! (Let me check that last part, but that would be great).

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It would be great if we do it on the frontend, and indeed use the color picker!

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What I've done in a previous project, was to put all the colormaps of matplotlib in 1 texture, of #texture x 1024 pixels, and then the index selected the row of the texture.

min_opacity=0,
max_opacity=0.05,
reverse_opacity=False):
"""Transfer function from a single RGB value with a linear opacity.

:param rgb_values: Tuple or list containing the RGB values
of red, green and blue, respectively.
:param min_opacity: Minimum opacity, default value is 0.0.
Lowest possible value is 0.0, optional.
:param max_opacity: Maximum opacity, default value is 0.05.
Highest possible value is 1.0, optional.
:param reverse_opacity: Linearly decrease opacity, optional.
:type rgb: listlike
:type min_opacity: float, int
:type max_opacity: float, int
:type reverse_opacity: bool
:return: transfer_function
:rtype: ipyvolume TransferFunction

:Example:
>>> import ipyvolume as ipv
>>> rgb = (0, 255, 0) # RGB value for green
>>> green_tf = ipv.transfer_function.linear_transfer_function(rgb)
>>> ds = ipv.datasets.aquariusA2.fetch()
>>> ipv.volshow(ds.data[::4,::4,::4], tf=green_tf)
>>> ipv.show()

.. seealso:: matplotlib_transfer_function()
"""
_num_elements = 256 # length of rgba transfer function array
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r, g, b = [value/255. for value in rgb_values] # rescales 0-255 to float
opacity = np.linspace(min_opacity, max_opacity, num=_num_elements)
if reverse_opacity: opacity = np.flip(opacity, axis=0)
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rgba = np.transpose(np.stack([[r] * _num_elements,
[g] * _num_elements,
[b] * _num_elements,
opacity]))
transfer_function = TransferFunction(rgba=rgba)
return transfer_function


def matplotlib_transfer_function(colormap_name,
min_opacity=0,
max_opacity=0.05,
reverse_colormap=False,
reverse_opacity=False):
"""Transfer function from matplotlib colormaps.

:param colormap_name: name of matplotlib colormap
:param min_opacity: Minimum opacity, default value is 0.
Lowest possible value is 0, optional.
:param max_opacity: Maximum opacity, default value is 0.05.
Highest possible value is 1.0, optional.
:param reverse_colormap: reversed matplotlib colormap, optional.
:param reverse_opacity: Linearly decrease opacity, optional.
:type colormap_name: str
:type min_opacity: float, int
:type max_opacity: float, int
:type reverse_colormap: bool
:type reverse_opacity: bool
:return: transfer_function
:rtype: ipyvolume TransferFunction

:Example:
>>> import ipyvolume as ipv
>>> rgb = (0, 255, 0) # RGB value for green
>>> green_tf = ipv.transfer_function.matplotlib_transfer_function('viridis')
>>> ds = ipv.datasets.aquariusA2.fetch()
>>> ipv.volshow(ds.data[::4,::4,::4], tf=green_tf)
>>> ipv.show()

.. seealso:: linear_transfer_function()
"""
_num_elements = 256 # length of rgba transfer function array
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cmap = matplotlib.cm.get_cmap(name=colormap_name)
rgba = np.array([cmap(i) for i in np.linspace(0, 1, _num_elements)])
if reverse_colormap: rgba = np.flip(rgba, axis=0)
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# Create opacity values to overwrite default matplotlib opacity=1.0
opacity = np.linspace(min_opacity, max_opacity, num=_num_elements)
if reverse_opacity: opacity = np.flip(opacity, axis=0)
rgba[:,-1] = opacity # replace opacity=1 with actual opacity
transfer_function = TransferFunction(rgba=rgba)
return transfer_function


def load_transfer_functions(include_rgb_linear=True,
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I would not call it load (get?), and I'd make it 'singleton' like, so it will always return the same widgets, what do you think? (it will basically cache the widgets).
My guess is this will be used as part of a gui to select a particular colormap?

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Let's call it 'predefined_transfer_functions'.

I'm not sure I understand correctly: do you want this to stay a function but always return the same dictionary, or have me make a singleton class for it?

I have no major plans for a colormap gui. But I do want to set future efforts at a GUI up for success as much as possible, so if you have advice on things I'm doing (or not doing here I'm happy to hear it.

What I want in the short term is a bunch of transfer function widgets so I can easily switch between them programmatically for the multivolume rendering I'm working on now.

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What I would like to see, is when a transfer function is created with the same set of parameters (same key for the dictionary), that it returns the same transfer function. That will limit the number of widgets created, what do you think?

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I'm not sure how best to implement the idea, but I think it's a good strategy/goal.

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@maartenbreddels maartenbreddels Oct 1, 2018 •
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sth like this:

tf_cache = {}
def get_transfer_functions(...)
  tf = tf_cache.get(key)
  if not tf:
    tf = ....
    tf_cache[key] = tf
  ...

include_matplotlib=True,
include_matplotlib_reversed=True):
"""Load predefined transfer functions into a dictionary.

:param include_rgb_linear: load transfer functions from individual
RGB values & linear opacity, optional.
:param include_matplotlib: load transfer functions from matplotlib
colormaps & linear opacity, optional.
:param include_matplotlib_reversed: load transfer functions from
REVERSED matplotlib colormaps, optional.
:type include_rgb_linear: bool
:type include_matplotlib: bool
:type include_matplotlib_reversed: bool
:return: dictionary of predefined transfer functions.
:rtype: dict of ipyvolume TransferFunction instances
"""
transfer_functions = {}
# RGB primary and secondary colors
if include_rgb_linear:
colors = {'grey': (0, 0, 0),
'red': (255, 0, 0),
'green': (0, 255, 0),
'blue': (0, 0, 255),
'yellow': (255, 255, 0),
'magenta': (255, 0, 255),
'cyan': (0, 255, 255)}
for color_key in colors:
rgb = colors[color_key]
tf = linear_transfer_function(rgb)
transfer_functions[color_key] = tf
tf_reversed = linear_transfer_function(rgb, reverse_opacity=True)
transfer_functions[color_key + '_r'] = tf_reversed
# matplotlib colormaps
if include_matplotlib:
matplotlib_colormaps = [m for m in dir(matplotlib.cm) if not m.endswith("_r")]
for colormap in matplotlib_colormaps:
try:
transfer_functions[colormap] = matplotlib_transfer_function(colormap)
except ValueError:
continue
# reversed matplotlib colormaps
if include_matplotlib_reversed:
reversed_matplotlib_colormaps = [m for m in dir(matplotlib.cm) if m.endswith("_r")]
for colormap in reversed_matplotlib_colormaps:
try:
transfer_functions[colormap] = matplotlib_transfer_function(colormap)
except ValueError:
continue
return transfer_functions