Skip to content

Commit

Permalink
Finish noise2d, noise3d icons, docs
Browse files Browse the repository at this point in the history
  • Loading branch information
luisbarrancos committed Jan 27, 2018
1 parent ee51e30 commit 7960e54
Show file tree
Hide file tree
Showing 12 changed files with 3,278 additions and 2,689 deletions.
1 change: 1 addition & 0 deletions docs/source/_images/icons/asNoise2D.png
1 change: 1 addition & 0 deletions docs/source/_images/icons/asNoise3D.png
2 changes: 2 additions & 0 deletions docs/source/shaders/texture/as_noise2d.rst
Original file line number Diff line number Diff line change
Expand Up @@ -15,6 +15,8 @@ asNoise2D

A fractal noise node, with recursion, and an ample choice of noise primitives.

|
Parameters
----------

Expand Down
4 changes: 3 additions & 1 deletion docs/source/shaders/texture/as_noise3d.rst
Original file line number Diff line number Diff line change
Expand Up @@ -4,7 +4,7 @@

|
.. image:: /_images/icons/asnoise3d.png
.. image:: /_images/icons/asNoise3D.png
:width: 128px
:align: left
:height: 128px
Expand All @@ -15,6 +15,8 @@ asNoise3D

A fractal solid noise node, with recursion, and an ample choice of noise primitives. The 3D counterpart of :ref:`asNoise2D <label_as_noise2d>` using the surface point P and a placement matrix, instead of UV coordinates.

|
Parameters
----------

Expand Down
2 changes: 2 additions & 0 deletions docs/source/shaders/texture/as_texture.rst
Original file line number Diff line number Diff line change
Expand Up @@ -15,6 +15,8 @@ asTexture

A texture lookup node with full control over OSL's texture() call.

|
Parameters
----------

Expand Down
26 changes: 21 additions & 5 deletions docs/source/shaders/texture/as_voronoi2d.rst
Original file line number Diff line number Diff line change
Expand Up @@ -15,6 +15,8 @@ asVoronoi2D

A procedural 2D Worley :cite:`Worley:1996:CTB:237170.237267` like noise shader, that outputs not only the resulting color, but the four nearest features to the evaluated point, their respective positions, and their cell color IDs. See also :cite:`Ebert:2002:TMP:572337`.

|
Parameters
----------

Expand Down Expand Up @@ -65,15 +67,15 @@ Cell Parameters
*Metric*
Which metric to choose to calculate the distance from cell to feature points. There are several to choose from, resulting in different types of patterns.

* Euclidian distance
* Euclidian distance [#]_
* Sum of square difference
* Tchebychev distance
* Tchebychev distance [#]_
* Sum of absolute difference
* Akritean distance
* Minkowski metric
* Karlsruhe metric
* Minkowski metric [#]_
* Karlsruhe metric [#]_

The sum of the square difference is also known as the Manhattan metric.
The sum of the square difference is also known as the Manhattan metric [#]_.

The Minkowski metric is a generalized metric whose P parameter allows you to go from the Euclidian distance when P has a value of 2, to the Manhattan distance when P has a value of 1, and as P reaches infinity, it represents the Tchebychev metric.

Expand Down Expand Up @@ -219,6 +221,20 @@ Some examples of feature output modes and metrics.

-----

.. rubric:: Footnotes

.. [#] The Euclidian distance or Euclidian metric, also known as :math:`L_2` norm, is the straight line distance between two points in Euclidian space.
.. [#] The Chebyshev (or Tchebychev) distance, also known as Chessboard distance or :math:`L_\infty` norm, is a metric on a vector space where the distance between two vectors is the greatest of their differences along any coordinate dimension.
.. [#] The Minkowski distance, also known as :math:`L_P` norm, is a metric which is a generalization of both the Euclidian distance and the Manhattan distance, being equal to the Manhattan distance when its *p* parameter is equal to 1, or equal to the Euclidian distance when its *p* parameter is equal to 2. On the limit as *p* approaches infinity, it is equal to the Chebyshev distance.
.. [#] In metric geometry, the Karlsruhe metric is a measure of distance that assumes travel is only possible along rays through the origin and circular arcs centered at the origin.
.. [#] The Manhattan distance, also known as *taxicab* metric or :math:`L_1` norm, is a metric in which the distance between two points is the sum of the absolute differences of their Cartesian coordinates.
-----

.. rubric:: References

.. bibliography:: /bibtex/references.bib
Expand Down
29 changes: 23 additions & 6 deletions docs/source/shaders/texture/as_voronoi3d.rst
Original file line number Diff line number Diff line change
Expand Up @@ -15,6 +15,8 @@ asVoronoi3D

A procedural 3D Worley :cite:`Worley:1996:CTB:237170.237267` like noise shader, that outputs not only the resulting color, but the four nearest features to the evaluated point, their respective positions, and their cell color IDs. See also :cite:`Ebert:2002:TMP:572337`.

|
Parameters
----------

Expand Down Expand Up @@ -65,15 +67,15 @@ Cell Parameters
*Metric*
Which metric to choose to calculate the distance from cell to feature points. There are several to choose from, resulting in different types of patterns.

* Euclidian distance
* Euclidian distance [#]_
* Sum of square difference
* Tchebychev distance
* Tchebychev distance [#]_
* Sum of absolute difference
* Akritean distance
* Minkowski metric
* Karlsruhe metric
* Minkowski metric [#]_
* Karlsruhe metric [#]_

The sum of the square difference is also known as the Manhattan metric.
The sum of the square difference is also known as the Manhattan metric [#]_.

The Minkowski metric is a generalized metric whose P parameter allows you to go from the Euclidian distance when P has a value of 2, to the Manhattan distance when P has a value of 1, and as P reaches infinity, it represents the Tchebychev metric.

Expand Down Expand Up @@ -218,7 +220,22 @@ Some examples of different metrics and feature output combinations used.

Tchebychev metric, with 4 octaves, and the output set to the product of the two nearest features to the cell.

-----------
-----

.. rubric:: Footnotes


.. [#] The Euclidian distance or Euclidian metric, also known as :math:`L_2` norm, is the straight line distance between two points in Euclidian space.
.. [#] The Chebyshev (or Tchebychev) distance, also known as Chessboard distance or :math:`L_\infty` norm, is a metric on a vector space where the distance between two vectors is the greatest of their differences along any coordinate dimension.
.. [#] The Minkowski distance, also known as :math:`L_P` norm, is a metric which is a generalization of both the Euclidian distance and the Manhattan distance, being equal to the Manhattan distance when its *p* parameter is equal to 1, or equal to the Euclidian distance when its *p* parameter is equal to 2. On the limit as *p* approaches infinity, it is equal to the Chebyshev distance.
.. [#] In metric geometry, the Karlsruhe metric is a measure of distance that assumes travel is only possible along rays through the origin and circular arcs centered at the origin.
.. [#] The Manhattan distance, also known as *taxicab* metric or :math:`L_1` norm, is a metric in which the distance between two points is the sum of the absolute differences of their Cartesian coordinates.
-----

.. rubric:: References

Expand Down
Binary file added icons/asNoise2D.png
Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
Binary file added icons/asNoise3D.png
Loading
Sorry, something went wrong. Reload?
Sorry, we cannot display this file.
Sorry, this file is invalid so it cannot be displayed.
Loading

0 comments on commit 7960e54

Please sign in to comment.