├── .gitignore
├── CODE_OF_CONDUCT.md
├── Dockerfile
├── LICENSE
├── README.rst
├── index.html
├── notebooks
    ├── gamut_mapping_compression_functions_01.ipynb
    ├── gamut_mapping_medicina_01.ipynb
    ├── gamut_mapping_ramblings_01.ipynb
    └── gamut_mapping_ramblings_02.ipynb
├── pyproject.toml
├── requirements.txt
└── resources
    ├── css
        └── bootstrap.min.css
    ├── images
        ├── 5DMarkII_Spac-o-ween_001.exr
        ├── 5DMarkII_Spac-o-ween_001_GamutMedicinaHSVControlWidget.png
        ├── 5DMarkII_Spac-o-ween_001_GamutMedicinaHSVControlWidget_OutOfGamut.png
        ├── A002_C029_06025K.exr
        ├── A009C002_190210_R0EI_Alexa_LogCWideGamut.exr
        ├── Carol Payne - J001_C001_08178N_001.exr
        ├── Collage_01.exr
        ├── Cornell_Box_Rigid_Spheres_190_Patch_Roughplastic_Point_Grey_Grasshopper_50S5C_RGB_W.exr
        ├── Cornell_Box_Rigid_Spheres_190_Patch_Roughplastic_Point_Grey_Grasshopper_50S5C_RGB_W_A.exr
        ├── Gamut_Medicina_01.png
        ├── Gamut_Medicina_01_Disabled.png
        ├── Gamut_Medicina_02.png
        ├── Gamut_Medicina_02_Disabled.png
        ├── Gamut_Medicina_03.png
        ├── Gamut_Medicina_04.png
        ├── Gamut_Medicina_05.png
        └── JupyterLab_Dark_Theme.png
    ├── js
        ├── bootstrap-material-design.min.js
        ├── jquery-3.3.1.slim.min.js
        └── popper.min.js
    ├── luts
        ├── ACES2065-1_display_P3_OT.csp
        └── ACES2065-1_sRGB_OT.csp
    └── py
        └── build_LUT.py


/.gitignore:
--------------------------------------------------------------------------------
 1 | *_GamutMedicina*.exr
 2 | *_GamutMedicina*.png
 3 | *.pyc
 4 | *.pyo
 5 | .DS_Store
 6 | .idea
 7 | .ipynb_checkpoints
 8 | __pycache__
 9 | poetry.lock
10 | resources/images/Colour_Wheel.exr
11 | 


--------------------------------------------------------------------------------
/CODE_OF_CONDUCT.md:
--------------------------------------------------------------------------------
 1 | # Code of Conduct
 2 | 
 3 | ## Our Pledge
 4 | 
 5 | In the interest of fostering an open and welcoming environment, we as contributors and maintainers pledge to making participation in our project and our community a harassment-free experience for everyone, regardless of age, body size, disability, ethnicity, sex characteristics, gender identity and expression, level of experience, education, socio-economic status, nationality, personal appearance, race, religion, or sexual identity and orientation.
 6 | 
 7 | ## Our Standards
 8 | 
 9 | Examples of behavior that contributes to creating a positive environment include:
10 | 
11 | * Using welcoming and inclusive language
12 | * Being respectful of differing viewpoints and experiences
13 | * Gracefully accepting constructive criticism
14 | * Focusing on what is best for the community
15 | * Showing empathy towards other community members
16 | 
17 | Examples of unacceptable behavior by participants include:
18 | 
19 | * The use of sexualized language or imagery and unwelcome sexual attention or advances
20 | * Trolling, insulting/derogatory comments, and personal or political attacks
21 | * Public or private harassment
22 | * Publishing others’ private information, such as a physical or electronic address, without explicit permission
23 | * Other conduct which could reasonably be considered inappropriate in a professional setting
24 | 
25 | ## Our Responsibilities
26 | 
27 | 
28 | Project maintainers are responsible for clarifying the standards of acceptable behavior and are expected to take appropriate and fair corrective action in response to any instances of unacceptable behavior.
29 | 
30 | Project maintainers have the right and responsibility to remove, edit, or reject comments, commits, code, wiki edits, issues, and other contributions that are not aligned to this Code of Conduct, or to ban temporarily or permanently any contributor for other behaviors that they deem inappropriate, threatening, offensive, or harmful.
31 | 
32 | ## Scope
33 | 
34 | This Code of Conduct applies within all project spaces, and it also applies when an individual is representing the project or its community in public spaces. Examples of representing a project or community include using an official project e-mail address, posting via an official social media account, or acting as an appointed representative at an online or offline event. Representation of a project may be further defined and clarified by project maintainers.
35 | 
36 | ## Enforcement
37 | 
38 | 
39 | Instances of abusive, harassing, or otherwise unacceptable behavior may be reported by contacting Thomas Mansencal and Michael Mauderer via email at thomas@colour-science.org and michael@colour-science.org respectively. All complaints will be reviewed and investigated and will result in a response that is deemed necessary and appropriate to the circumstances. The project team is obligated to maintain confidentiality with regard to the reporter of an incident. Further details of specific enforcement policies may be posted separately.
40 | 
41 | Project maintainers who do not follow or enforce the Code of Conduct in good faith may face temporary or permanent repercussions as determined by other members of the project’s leadership.
42 | 
43 | ## Attribution
44 | 
45 | This Code of Conduct is adapted from the Contributor Covenant, version 1.4, available at [https://www.contributor-covenant.org/version/1/4/code-of-conduct.html][homepage].
46 | 
47 | For answers to common questions about this code of conduct, see [https://www.contributor-covenant.org/faq][faq].
48 | 
49 | 
50 | [homepage]: https://www.contributor-covenant.org/version/1/4/code-of-conduct.html
51 | [faq]: https://www.contributor-covenant.org/faq


--------------------------------------------------------------------------------
/Dockerfile:
--------------------------------------------------------------------------------
 1 | FROM python:3.7
 2 | 
 3 | RUN curl -sL https://deb.nodesource.com/setup_13.x | bash - \
 4 |     && apt-get install -y nodejs
 5 | 
 6 | WORKDIR /tmp
 7 | COPY ./requirements.txt /tmp
 8 | RUN pip install -r requirements.txt \
 9 |     && rm /tmp/requirements.txt \
10 |     && python -c "import imageio;imageio.plugins.freeimage.download()" \
11 |     && jupyter labextension install @jupyter-widgets/jupyterlab-manager jupyter-matplotlib
12 | 
13 | RUN mkdir -p /home/colour-science/gamut-mapping-ramblings
14 | WORKDIR /home/colour-science/gamut-mapping-ramblings
15 | 
16 | CMD sh -c 'cd /home/colour-science/gamut-mapping-ramblings && jupyter lab --allow-root --ip=0.0.0.0 --port=8888'
17 | 


--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
 1 | Copyright (C) 2018, Colour Developers
 2 | All rights reserved.
 3 | 
 4 | Redistribution and use in source and binary forms, with or without
 5 | modification, are permitted provided that the following conditions are met:
 6 |     * Redistributions of source code must retain the above copyright
 7 |       notice, this list of conditions and the following disclaimer.
 8 |     * Redistributions in binary form must reproduce the above copyright
 9 |       notice, this list of conditions and the following disclaimer in the
10 |       documentation and/or other materials provided with the distribution.
11 |     * Neither the name of the Colour Developers nor the
12 |       names of its contributors may be used to endorse or promote products
13 |       derived from this software without specific prior written permission.
14 | 
15 | THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND
16 | ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED
17 | WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE
18 | DISCLAIMED. IN NO EVENT SHALL COLOUR DEVELOPERS BE LIABLE FOR ANY
19 | DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES
20 | (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES;
21 | LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND
22 | ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT
23 | (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS
24 | SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE.


--------------------------------------------------------------------------------
/README.rst:
--------------------------------------------------------------------------------
  1 | Gamut Mapping Ramblings
  2 | =======================
  3 | 
  4 | ..  image:: https://raw.githubusercontent.com/colour-science/gamut-mapping-ramblings/master/resources/images/Gamut_Medicina_01.png
  5 | 
  6 | Introduction
  7 | ------------
  8 | 
  9 | Ramblings in the world of Scene-Referred Gamut Mapping as part of the
 10 | `ACES VWG <https://community.acescentral.com/c/aces-development-acesnext/vwg-aces-gamut-mapping-working-group/80>`__.
 11 | 
 12 | The following study models are implemented in the `gamut_mapping_medicina_01 <https://github.com/colour-science/gamut-mapping-ramblings/blob/master/notebooks/gamut_mapping_medicina_01.ipynb>`__
 13 | notebook.
 14 | 
 15 | - `Mansencal and Scharfenberg (2020) <https://community.acescentral.com/t/gamut-mapping-in-cylindrical-and-conic-spaces/2870/4>`__
 16 | - `Smith (2020) <https://community.acescentral.com/t/rgb-saturation-gamut-mapping-approach-and-a-comp-vfx-perspective/>`__
 17 | 
 18 | The notebook is intended to be read with the **JupyterLab Dark Theme**:
 19 | 
 20 | ..  image:: https://raw.githubusercontent.com/colour-science/gamut-mapping-ramblings/master/resources/images/JupyterLab_Dark_Theme.png
 21 | 
 22 | Various compression functions are implemented in the `gamut_mapping_compression_functions_01 <https://github.com/colour-science/gamut-mapping-ramblings/blob/master/notebooks/gamut_mapping_compression_functions_01.ipynb>`__
 23 | notebook.
 24 | 
 25 | Images courtesy of:
 26 | 
 27 | - `Justin Holt <https://www.dropbox.com/sh/u6z2a0jboo4vno8/AAB-10qcflhpr0C5LWhs7Kq4a?dl=0>`__
 28 | - `Thomas Mansencal <https://community.acescentral.com/t/spectral-images-generation-and-processing/>`__
 29 | - `Fabian Matas <https://community.acescentral.com/t/spectral-images-generation-and-processing/>`__
 30 | - `Carol Payne <https://www.dropbox.com/sh/u6z2a0jboo4vno8/AAB-10qcflhpr0C5LWhs7Kq4a?dl=0>`__
 31 | - `Martin Smekal <https://community.acescentral.com/t/vfx-work-in-acescg-with-out-of-gamut-devices/2385>`__
 32 | 
 33 | Installation
 34 | ------------
 35 | 
 36 | Docker
 37 | ~~~~~~
 38 | 
 39 | .. code-block:: bash
 40 | 
 41 |     $ docker build -t colourscience/gamut-mapping-ramblings:latest "."
 42 | 
 43 | *Note*
 44 | 
 45 | Allocated memory will need to be increased in Docker preferences:
 46 | *Preferences --> Resources --> Advanced*, 8Go should be enough.
 47 | 
 48 | Pip
 49 | ~~~
 50 | 
 51 | `Node.js <https://nodejs.org/>`__ is required for the
 52 | `Jupyter Lab <https://jupyter.org/>`__ Matplotlib extension.
 53 | 
 54 | `Pip <https://pip.pypa.io/en/stable/installing/>`__ can be used to install the
 55 | main dependencies, assuming you have created and activated a
 56 | `Virtual Environment <https://docs.python.org/3/tutorial/venv.html>`__:
 57 | 
 58 | .. code-block:: bash
 59 | 
 60 |     $ pip install --user -r requirements.txt
 61 |     $ python -c "import imageio;imageio.plugins.freeimage.download()"
 62 |     $ jupyter labextension install @jupyter-widgets/jupyterlab-manager jupyter-matplotlib
 63 | 
 64 | Poetry
 65 | ~~~~~~
 66 | 
 67 | `Node.js <https://nodejs.org/>`__ is required for the
 68 | `Jupyter Lab <https://jupyter.org/>`__ Matplotlib extension.
 69 | 
 70 | `Poetry <https://python-poetry.org/>`__ can also be used to install the main
 71 | dependencies:
 72 | 
 73 | .. code-block:: bash
 74 | 
 75 |     $ poetry install
 76 |     $ poetry run python -c "import imageio;imageio.plugins.freeimage.download()"
 77 |     $ poetry run jupyter labextension install @jupyter-widgets/jupyterlab-manager jupyter-matplotlib
 78 | 
 79 | Usage
 80 | -----
 81 | 
 82 | Docker
 83 | ~~~~~~
 84 | 
 85 | .. code-block:: bash
 86 | 
 87 |     $ docker run -v $PWD:/home/colour-science/gamut-mapping-ramblings -p 8888:8888 colourscience/gamut-mapping-ramblings:latest
 88 | 
 89 | Pip
 90 | ~~~
 91 | 
 92 | .. code-block:: bash
 93 | 
 94 |     $ jupyter lab
 95 | 
 96 | Poetry
 97 | ~~~~~~
 98 | 
 99 | .. code-block:: bash
100 | 
101 |     $ poetry run jupyter lab
102 | 
103 | Code of Conduct
104 | ---------------
105 | 
106 | The *Code of Conduct*, adapted from the `Contributor Covenant 1.4 <https://www.contributor-covenant.org/version/1/4/code-of-conduct.html>`__,
107 | is available on the `Code of Conduct <https://www.colour-science.org/code-of-conduct/>`__ page.
108 | 
109 | Contact & Social
110 | ----------------
111 | 
112 | The *Colour Developers* can be reached via different means:
113 | 
114 | - `Email <mailto:colour-developers@colour-science.org>`__
115 | - `Facebook <https://www.facebook.com/python.colour.science>`__
116 | - `Gitter <https://gitter.im/colour-science/colour>`__
117 | - `X <https://x.com/colour_science>`__
118 | - `Bluesky <https://bsky.app/profile/colour-science.bsky.social>`__
119 | 
120 | About
121 | -----
122 | 
123 | | **Gamut Mapping Ramblings** by Colour Developers
124 | | Copyright 2019 – Colour Developers – `colour-developers@colour-science.org <colour-developers@colour-science.org>`__
125 | | This software is released under terms of New BSD License: https://opensource.org/licenses/BSD-3-Clause
126 | | `https://github.com/colour-science/gamut-mapping-ramblings <https://github.com/colour-science/gamut-mapping-ramblings>`__
127 | 


--------------------------------------------------------------------------------
/index.html:
--------------------------------------------------------------------------------
  1 | <!DOCTYPE html>
  2 | <html>
  3 | 
  4 | <head>
  5 |   <meta charset="utf-8" />
  6 |   <meta http-equiv="Content-Type" content="text/html; charset=UTF-8">
  7 |   <meta name="viewport"
  8 |     content="width=device-width, initial-scale=1, shrink-to-fit=no">
  9 |   <meta name="author" content="Colour Developers">
 10 |   <meta name="description"
 11 |     content="John Hunter - Excellence in Plotting - 2020 - colour-science">
 12 |   <meta name="keywords"
 13 |     content="color, color-science, color-space, color-spaces, colorspace, colorspaces, colour, colour-science, colour-space, colour-spaces, colourspace, colourspaces, data, dataset, datasets, display-referred, gamut-mapping, output-referred, scene-referred">
 14 | 
 15 |   <title>John Hunter - Excellence in Plotting - 2020 - colour-science</title>
 16 | 
 17 |   <link href="./resources/css/bootstrap.min.css" rel="stylesheet">
 18 |   <style>
 19 |     body {
 20 |       background-color: rgb(18, 18, 18);
 21 |       color: rgb(208, 208, 208);
 22 |       font-size: 1.15rem;
 23 |     }
 24 | 
 25 |     a,
 26 |     a:hover {
 27 |       color: #3498DB;
 28 |     }
 29 | 
 30 |     .navbar {
 31 |       background-color: #242424 !important;
 32 |     }
 33 | 
 34 |     .navbar-brand {
 35 |       color: #3498DB !important;
 36 |     }
 37 | 
 38 |     .hand-pointer-on-hover {
 39 |       cursor: pointer;
 40 |     }
 41 | 
 42 |     #content {
 43 |       margin-top: 5rem;
 44 |     }
 45 |   </style>
 46 | </head>
 47 | 
 48 | <body data-gr-c-s-loaded="true">
 49 |   <nav class="navbar navbar-expand-md navbar-dark bg-dark fixed-top"
 50 |     style="z-index: 1024">
 51 |     <a class="navbar-brand"
 52 |       href="https://acescentral.com/c/aces-development-acesnext/vwg-aces-gamut-mapping-working-group/80">Academy
 53 |       ACES -
 54 |       VWG</a>
 55 |     <button class="navbar-toggler" type="button" data-toggle="collapse"
 56 |       data-target="#navbarDefault" aria-controls="navbarDefault"
 57 |       aria-expanded="false" aria-label="Toggle navigation">
 58 |       <span class="navbar-toggler-icon"></span>
 59 |     </button>
 60 | 
 61 |     <div class="collapse navbar-collapse" id="navbarDefault">
 62 |       <ul class="navbar-nav mr-auto">
 63 |         <li>
 64 |           <a class="nav-link" href="https://jhepc.github.io/">John Hunter -
 65 |             Excellence in Plotting - 2020</a>
 66 |         </li>
 67 |       </ul>
 68 | 
 69 |       <ul class="nav navbar-nav navbar-right">
 70 |         <li><a class="nav-link"
 71 |             href="https://colour-science.org">colour-science.org</a></li>
 72 |       </ul>
 73 |     </div>
 74 |   </nav>
 75 | 
 76 |   <main role="main" class="container">
 77 |     <div id="content" class="container-fluid">
 78 |       <div class="row">
 79 |         <div class="col">
 80 |           <figure class="figure">
 81 |             <img src="./resources/images/Gamut_Medicina_01.png"
 82 |               alt="Mansencal and Scharfenberg (2020) - HSV Control Based Study Model"
 83 |               class="figure-img img-fluid hand-pointer-on-hover gamut-medicina-1">
 84 |             <figcaption class="figure-caption">
 85 |               <p>
 86 |                 <span class="font-weight-bold">Colour Developers Scientific
 87 |                   Plot Entry</span> for <a href="https://jhepc.github.io/">
 88 |                   <span class="font-weight-bold">John Hunter - Excellence in
 89 |                     Plotting - 2020</span></a>: An interactive <a
 90 |                   href="https://ipywidgets.readthedocs.io/en/latest/">Jupyter
 91 |                   Widget</a> driving <em>Mansencal and Scharfenberg (2020) HSV
 92 |                   Control Based Study Model</em>, and performing gamut mapping
 93 |                 on scene-referred synthetic imagery.
 94 |               </p>
 95 |               <p>The <a
 96 |                   href="https://github.com/colour-science/colour-mitsuba">Cornell
 97 |                   Box</a> is spectrally rendered with <a
 98 |                   href="https://github.com/mitsuba-renderer/mitsuba2">Mitsuba
 99 |                   2: A Retargetable Forward and Inverse (Open Source)
100 |                   Renderer</a>.
101 |               </p>
102 |               <div class="alert alert-dark" role="alert">
103 |                 <b>Note:</b>&nbsp;Click on the image to show the effect of the
104 |                 gamut mapping study model.
105 |               </div>
106 |             </figcaption>
107 |           </figure>
108 | 
109 |           <h2 id="Abstract">Gamut Mapping of Scene-Referred Light Values<a
110 |               class="anchor-link" href="#Abstract"></a>
111 |           </h2>
112 | 
113 |           <p>
114 |             The <a
115 |               href="https://www.oscars.org/science-technology/sci-tech-projects/aces">Academy
116 |               Color Encoding System
117 |               (ACES)</a> is a free, open, device-independent color management
118 |             and image interchange system used in the production of movies,
119 |             series and games. ACES adoption has increased during the last
120 |             decade and the system is becoming a successful standard in the
121 |             Media and Entertainment Industry. However, amid of the growing
122 |             success, ACES users are frequently reporting defects occurring on
123 |             their imagery while using the system.
124 |           </p>
125 | 
126 |           <div class="alert alert-dark" role="alert">
127 |             <b>Note:</b>&nbsp;It is assumed that the reader has basic knowledge
128 |             about image processing and colour management in the Media and
129 |             Entertainment Industry. An introductory publication on these topics
130 |             is <a href="https://cinematiccolor.org/">Cinematic Color: From Your
131 |               Monitor to the Big Screen</a> by Selan (2012).
132 |           </div>
133 | 
134 |           <p>The artefacts are often generated by digital still or
135 |             motion-picture cameras: their spectral sensitivities do not satisfy
136 |             the <a
137 |               href="https://en.wikipedia.org/wiki/Tristimulus_colorimeter">Maxwell-Ives
138 |               criterion (Luther condition)</a>. Put another way, they are not
139 |             colorimetric and capture the world differently than the <a
140 |               href="https://www.hunterlab.se/wp-content/uploads/2012/11/CIE-Standard-Observers.pdf">Standard
141 |               Observer</a>, i.e. a standardised average human viewer.
142 |             Ultimately, they produce non-physically realisable values outside
143 |             the <a href="https://en.wikipedia.org/wiki/Spectral_color">Spectral
144 |               Locus</a>, i.e. the volume bounding the extent of the
145 |             <em>Standard Observer</em> sensitivity to the electro-magnetic
146 |             spectrum. Colour grading tools used to infuse the creative vision
147 |             into the imagery are also source of defect generation.
148 |           </p>
149 |           <figure class="figure">
150 |             <img
151 |               src="./resources/images/5DMarkII_Spac-o-ween_001_GamutMedicinaHSVControlWidget.png"
152 |               alt="Show Out-of-Gamut Colours"
153 |               class="figure-img img-fluid hand-pointer-on-hover out-of-gamut-1">
154 |             <figcaption class="figure-caption">
155 |               <p>
156 |                 The <a href="https://en.wikipedia.org/wiki/Chromaticity">1976
157 |                   UCS Chromaticity Diagram</a> on the left highlights
158 |                 particularly well the non-colorimetric behaviour of the Canon
159 |                 EOS 5D Mark II. The <em>Spectral Locus</em> is represented by
160 |                 the largest colour-varying and rounded triangle.
161 |                 The red triangle defines the primaries of the standard ACES
162 |                 RGB working colourspace, i.e. ACES AP1, and the gray triangle
163 |                 that of the display colourspace, i.e. sRGB.
164 |               </p>
165 |               <p>
166 |                 Note how the astronaut suits, and the luminous tiles on the
167 |                 floor, whose colours are outside of the
168 |                 <em>Spectral Locus</em>, are looking posterised.
169 |               </p>
170 |               <div class="alert alert-dark" role="alert">
171 |                 <b>Note:</b>&nbsp;Click on the image to show the ACES AP1
172 |                 out-of-gamut colours.
173 |               </div>
174 |             </figcaption>
175 |           </figure>
176 |           <p>
177 |             In the context of this document, and the portion of interest of the
178 |             ACES pipeline, the imagery is said to be in a
179 |             <em>scene-referred</em> state. A scene-referred image encodes light
180 |             values as they were captured at the camera focal plane in a given
181 |             scene. The encoded values are intrinsically linear, and
182 |             proportional, relatively, to the incident light from the scene to
183 |             the camera.</p>
184 |           <p>
185 |             The <a href="https://www.oscars.org/">A.M.P.A.S</a> formed a new
186 |             <a
187 |               href="https://community.acescentral.com/t/new-aces-working-group-gamut-mapping">Virtual
188 |               Working Group</a> with the objective of finding strategies to
189 |             manage and correct the artefacts. The Working Group is researching
190 |             a source-agnostic model that performs the mapping of scene-referred
191 |             light values from any source, i.e. any light capturing device,
192 |             irrespective of its sensitivities, to a target RGB colourspace
193 |             primaries, e.g. ACES AP1.
194 |           </p>
195 |           <p>As a contribution to the initiative, the <a
196 |               href="https://colour-science.org/">Colour Developers</a> have
197 |             designed and built an <a
198 |               href="https://github.com/colour-science/gamut-mapping-ramblings/blob/master/notebooks/gamut_mapping_medicina_01.ipynb">interactive
199 |               Jupyter Widget</a>
200 |             to explore the problem space. The widget leverages <a
201 |               href="https://github.com/colour-science/colour">Colour</a>, an
202 |             open-source Python package providing a comprehensive number of
203 |             algorithms and datasets for colour science, and <a
204 |               href="https://matplotlib.org/">Matplotlib</a>.</p>
205 |           <p>
206 |             As of this writing, the widget implements two gamut mapping study
207 |             models via sub-classes:
208 |             <ul>
209 |               <li><em>Mansencal and Scharfenberg (2020) HSV Control Based Study
210 |                   Model</em> built on top of the <a
211 |                   href="https://en.wikipedia.org/wiki/HSL_and_HSV">HSV
212 |                   colourspace</a></li>
213 |               <li><em>Smith (2020) - RGB Saturation Study Model</em> built on
214 |                 top of the <a
215 |                   href="https://en.wikipedia.org/wiki/Color_spaces_with_RGB_primaries">RGB
216 |                   colourspace</a></li>
217 |             </ul>
218 | 
219 |             <figure class="figure">
220 |               <img src="./resources/images/Gamut_Medicina_03.png"
221 |                 alt="Mansencal and Scharfenberg (2020) - HSV Control Based & Smith (2020) RGB Saturation Study Models"
222 |                 class="figure-img img-fluid hand-pointer-on-hover gamut-medicina-3">
223 |               <figcaption class="figure-caption">
224 |                 <p>
225 |                   <em>Mansencal and Scharfenberg (2020) HSV Control Based Study
226 |                     Model</em> compared with <em>Smith (2020) RGB Saturation
227 |                     Study Model</em>. Out of the box, Smith (2020) model
228 |                   produces less hue clumping and more pleasing output. It is
229 |                   also simpler, faster and more elegant but lacks hue twists
230 |                   controls.
231 |                 </p>
232 |                 <div class="alert alert-dark" role="alert">
233 |                   <b>Note:</b>&nbsp;Click on the image to compare the effect of
234 |                   both gamut mapping study models.
235 |                 </div>
236 |               </figcaption>
237 |             </figure>
238 | 
239 |             Various gamut mapping controls are offered such as changing the
240 |             compression function and its effect, or, hue twist controls for the
241 |             <em>HSV Control Based Study Model</em>. The widget also implement
242 |             various viewing controls such as showing the out-of-gamut colours
243 |             or image exposure adjustments.
244 |           </p>
245 |           <figure class="figure">
246 |             <img src="./resources/images/Gamut_Medicina_02.png"
247 |               alt="Mansencal and Scharfenberg (2020) - HSV Control Based Study Model"
248 |               class="figure-img img-fluid hand-pointer-on-hover gamut-medicina-2">
249 |             <figcaption class="figure-caption">
250 |               <p>
251 |                 <em>Mansencal and Scharfenberg (2020) HSV Control Based Study
252 |                   Model</em> applied onto a photograph.
253 |               </p>
254 |               <div class="alert alert-dark" role="alert">
255 |                 <b>Note:</b>&nbsp;Click on the image to show the effect of the
256 |                 gamut mapping study model.
257 |               </div>
258 |             </figcaption>
259 |           </figure>
260 | 
261 |           <p>
262 |             While being order of magnitude slower than commercial applications
263 |             such as <a
264 |               href="https://www.blackmagicdesign.com/products/davinciresolve">Davinci
265 |               Resolve</a> or <a
266 |               href="https://www.foundry.com/products/nuke">The Foundry
267 |               Nuke</a>, the interactive widget is extremely useful as it allows
268 |             to tap directly into the entire Python scientific ecosystem while
269 |             promoting <em>Open Source Software</em> and reproducible research.
270 |           </p>
271 |           <p>
272 |             More information is available on the <a
273 |               href="https://github.com/colour-science/gamut-mapping-ramblings">colour-science's
274 |               Gamut Mapping Ramblings repository</a> and <a
275 |               href="https://community.acescentral.com/c/aces-development-acesnext/vwg-aces-gamut-mapping-working-group/80">ACESCentral</a>.
276 |           </p>
277 |           <p class="text-center font-weight-bolder">
278 |             Copyright © 2020 – Colour Developers – <a
279 |               href="mailto:colour-developers@colour-science.org">colour-developers@colour-science.org</a>
280 |           </p>
281 |         </div>
282 |       </div>
283 |     </div>
284 |   </main>
285 | 
286 |   <script src="./resources/js/jquery-3.3.1.slim.min.js"></script>
287 |   <script src="./resources/js/popper.min.js"></script>
288 |   <script src="./resources/js/bootstrap-material-design.min.js"></script>
289 | 
290 |   <script>
291 |     $(".gamut-medicina-1").click(function () {
292 |       var source;
293 |       if ($(this).attr("src") ==
294 |         "./resources/images/Gamut_Medicina_01.png") {
295 |         $(this).attr("src",
296 |           "./resources/images/Gamut_Medicina_01_Disabled.png");
297 |       } else {
298 |         $(this).attr("src", "./resources/images/Gamut_Medicina_01.png");
299 |       }
300 |     });
301 |     $(".out-of-gamut-1").click(function () {
302 |       var source;
303 |       if ($(this).attr("src") ==
304 |         "./resources/images/5DMarkII_Spac-o-ween_001_GamutMedicinaHSVControlWidget.png"
305 |       ) {
306 |         $(this).attr("src",
307 |           "./resources/images/5DMarkII_Spac-o-ween_001_GamutMedicinaHSVControlWidget_OutOfGamut.png"
308 |         );
309 |       } else {
310 |         $(this).attr("src",
311 |           "./resources/images/5DMarkII_Spac-o-ween_001_GamutMedicinaHSVControlWidget.png"
312 |         );
313 |       }
314 |     });
315 |     $(".gamut-medicina-2").click(function () {
316 |       var source;
317 |       if ($(this).attr("src") ==
318 |         "./resources/images/Gamut_Medicina_02.png") {
319 |         $(this).attr("src",
320 |           "./resources/images/Gamut_Medicina_02_Disabled.png");
321 |       } else {
322 |         $(this).attr("src", "./resources/images/Gamut_Medicina_02.png");
323 |       }
324 |     });
325 |     $(".gamut-medicina-3").click(function () {
326 |       var source;
327 |       if ($(this).attr("src") ==
328 |         "./resources/images/Gamut_Medicina_03.png") {
329 |         source = "./resources/images/Gamut_Medicina_04.png";
330 |         $(this).attr("src", source);
331 |       } else {
332 |         source = "./resources/images/Gamut_Medicina_03.png";
333 |         $(this).attr("src", source);
334 |       }
335 |     });
336 |   </script>
337 | </body>
338 | 
339 | </html>


--------------------------------------------------------------------------------
/notebooks/gamut_mapping_compression_functions_01.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {
  6 |     "colab_type": "text",
  7 |     "id": "pRS6nhpu1R6y"
  8 |    },
  9 |    "source": [
 10 |     "# Gamut Mapping - Compression Functions 01"
 11 |    ]
 12 |   },
 13 |   {
 14 |    "cell_type": "code",
 15 |    "execution_count": 1,
 16 |    "metadata": {},
 17 |    "outputs": [],
 18 |    "source": [
 19 |     "%matplotlib widget"
 20 |    ]
 21 |   },
 22 |   {
 23 |    "cell_type": "code",
 24 |    "execution_count": 2,
 25 |    "metadata": {
 26 |     "colab": {
 27 |      "base_uri": "https://localhost:8080/",
 28 |      "height": 340
 29 |     },
 30 |     "colab_type": "code",
 31 |     "id": "OydMmtZRxd3c",
 32 |     "outputId": "297a43b9-f566-405d-e8bc-cfae56b50959"
 33 |    },
 34 |    "outputs": [
 35 |     {
 36 |      "name": "stdout",
 37 |      "output_type": "stream",
 38 |      "text": [
 39 |       "===============================================================================\n",
 40 |       "*                                                                             *\n",
 41 |       "*   Interpreter :                                                             *\n",
 42 |       "*       python : 3.7.6 (default, Dec 30 2019, 19:38:26)                       *\n",
 43 |       "*                [Clang 11.0.0 (clang-1100.0.33.16)]                          *\n",
 44 |       "*                                                                             *\n",
 45 |       "*   colour-science.org :                                                      *\n",
 46 |       "*       colour : v0.3.15-141-g3bebd7e9                                        *\n",
 47 |       "*                                                                             *\n",
 48 |       "*   Runtime :                                                                 *\n",
 49 |       "*       imageio : 2.8.0                                                       *\n",
 50 |       "*       matplotlib : 3.0.3                                                    *\n",
 51 |       "*       numpy : 1.18.4                                                        *\n",
 52 |       "*       scipy : 1.4.1                                                         *\n",
 53 |       "*       six : 1.14.0                                                          *\n",
 54 |       "*                                                                             *\n",
 55 |       "===============================================================================\n"
 56 |      ]
 57 |     }
 58 |    ],
 59 |    "source": [
 60 |     "from __future__ import division, unicode_literals\n",
 61 |     "\n",
 62 |     "import colour\n",
 63 |     "import matplotlib.pyplot as plt\n",
 64 |     "import numpy as np\n",
 65 |     "import os\n",
 66 |     "import scipy\n",
 67 |     "\n",
 68 |     "COLOUR_STYLE = colour.plotting.colour_style()\n",
 69 |     "COLOUR_STYLE.update({\n",
 70 |     "    'figure.figsize': (11, 11),\n",
 71 |     "    'legend.framealpha':\n",
 72 |     "    colour.plotting.COLOUR_STYLE_CONSTANTS.opacity.low\n",
 73 |     "})\n",
 74 |     "\n",
 75 |     "plt.style.use(COLOUR_STYLE)\n",
 76 |     "\n",
 77 |     "plt.style.use('dark_background')\n",
 78 |     "\n",
 79 |     "colour.utilities.describe_environment()\n",
 80 |     "\n",
 81 |     "colour.utilities.filter_warnings(*[True] * 4);"
 82 |    ]
 83 |   },
 84 |   {
 85 |    "cell_type": "markdown",
 86 |    "metadata": {},
 87 |    "source": [
 88 |     "## Compression & Utility Functions"
 89 |    ]
 90 |   },
 91 |   {
 92 |    "cell_type": "code",
 93 |    "execution_count": 3,
 94 |    "metadata": {
 95 |     "colab": {},
 96 |     "colab_type": "code",
 97 |     "id": "RshonIbOI4Ij"
 98 |    },
 99 |    "outputs": [],
100 |    "source": [
101 |     "def tanh_compression_function(x, a=0.8, b=1 - 0.8):\n",
102 |     "    x = colour.utilities.as_float_array(x)\n",
103 |     "\n",
104 |     "    return np.where(x > a, a + b * np.tanh((x - a) / b), x)\n",
105 |     "\n",
106 |     "\n",
107 |     "def atan_compression_function(x, a=0.8, b=1 - 0.8):\n",
108 |     "    x = colour.utilities.as_float_array(x)\n",
109 |     "\n",
110 |     "    return np.where(x > a, a + b * 2 / np.pi * np.arctan(((np.pi / 2) * (x - a)) / b), x)\n",
111 |     "\n",
112 |     "\n",
113 |     "def power_compression_function(x, a=0.8, b=1 - 0.8, p=2.0):\n",
114 |     "    x = colour.utilities.as_float_array(x)\n",
115 |     "\n",
116 |     "    return np.where(x > a, a + (((x - a) / b) / np.power(1 + np.power((x - a) / b, p), 1.0/p)) * b, x)\n",
117 |     "\n",
118 |     "\n",
119 |     "def simple_compression_function(x, a=0.8, b=1 - 0.8):\n",
120 |     "    x = colour.utilities.as_float_array(x)\n",
121 |     "\n",
122 |     "    return np.where(x > a, a + (-1 / ((x - a) / b + 1) + 1) * b, x)\n",
123 |     "\n",
124 |     "\n",
125 |     "def derivative(x, func, d=1e-7):\n",
126 |     "    return (func(x + d) - func(x - d)) / (2 * d)\n"
127 |    ]
128 |   },
129 |   {
130 |    "cell_type": "code",
131 |    "execution_count": 4,
132 |    "metadata": {},
133 |    "outputs": [
134 |     {
135 |      "data": {
136 |       "application/vnd.jupyter.widget-view+json": {
137 |        "model_id": "ec62041dbc1b4fa1968fe8f0116534f8",
138 |        "version_major": 2,
139 |        "version_minor": 0
140 |       },
141 |       "text/plain": [
142 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
143 |       ]
144 |      },
145 |      "metadata": {},
146 |      "output_type": "display_data"
147 |     }
148 |    ],
149 |    "source": [
150 |     "colour.plotting.plot_multi_functions(\n",
151 |     "    {\n",
152 |     "        'tanh': tanh_compression_function,\n",
153 |     "        'd/dx(tanh)': lambda x: derivative(x, tanh_compression_function),\n",
154 |     "        'atan': atan_compression_function,\n",
155 |     "        'd/dx(atan)': lambda x: derivative(x, atan_compression_function),\n",
156 |     "        'power': power_compression_function,\n",
157 |     "        'd/dx(power)': lambda x: derivative(x, power_compression_function),\n",
158 |     "        'simple': simple_compression_function,\n",
159 |     "        'd/dx(simple)': lambda x: derivative(x, simple_compression_function),\n",
160 |     "    },\n",
161 |     "    **{\n",
162 |     "        'figure.figsize': (11, 11),\n",
163 |     "        'bounding_box': (0.5, 1.75, 0, 1.25),\n",
164 |     "        'samples':\n",
165 |     "        np.linspace(0, 2, 1000),\n",
166 |     "        'plot_kwargs': [\n",
167 |     "            {\n",
168 |     "                'c': 'r',\n",
169 |     "                'linestyle': 'dashdot'\n",
170 |     "            },\n",
171 |     "            {\n",
172 |     "                'c': 'r'\n",
173 |     "            },\n",
174 |     "            {\n",
175 |     "                'c': 'g',\n",
176 |     "                'linestyle': 'dashdot'\n",
177 |     "            },\n",
178 |     "            {\n",
179 |     "                'c': 'g'\n",
180 |     "            },\n",
181 |     "            {\n",
182 |     "                'c': 'b',\n",
183 |     "                'linestyle': 'dashdot'\n",
184 |     "            },\n",
185 |     "            {\n",
186 |     "                'c': 'b'\n",
187 |     "            },\n",
188 |     "            {\n",
189 |     "                'c': 'y',\n",
190 |     "                'linestyle': 'dashdot'\n",
191 |     "            },\n",
192 |     "            {\n",
193 |     "                'c': 'y'\n",
194 |     "            },\n",
195 |     "        ]\n",
196 |     "    },\n",
197 |     ");"
198 |    ]
199 |   },
200 |   {
201 |    "cell_type": "markdown",
202 |    "metadata": {},
203 |    "source": [
204 |     "## TANH - Power Fitting"
205 |    ]
206 |   },
207 |   {
208 |    "cell_type": "code",
209 |    "execution_count": 5,
210 |    "metadata": {},
211 |    "outputs": [
212 |     {
213 |      "name": "stdout",
214 |      "output_type": "stream",
215 |      "text": [
216 |       "[ 0.65953861  0.34051662  4.6993435 ]\n",
217 |       "7.53150995846e-08\n"
218 |      ]
219 |     },
220 |     {
221 |      "data": {
222 |       "application/vnd.jupyter.widget-view+json": {
223 |        "model_id": "906062bed9f244d1892820a200f3e4c2",
224 |        "version_major": 2,
225 |        "version_minor": 0
226 |       },
227 |       "text/plain": [
228 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
229 |       ]
230 |      },
231 |      "metadata": {},
232 |      "output_type": "display_data"
233 |     }
234 |    ],
235 |    "source": [
236 |     "SAMPLES = np.linspace(0, 8, 1000)\n",
237 |     "TANH_SAMPLES = tanh_compression_function(SAMPLES)\n",
238 |     "\n",
239 |     "POPT, PCOV = scipy.optimize.curve_fit(\n",
240 |     "    power_compression_function, SAMPLES, TANH_SAMPLES, maxfev=50000)\n",
241 |     "\n",
242 |     "print(POPT)\n",
243 |     "print(\n",
244 |     "    colour.utilities.metric_mse(TANH_SAMPLES,\n",
245 |     "                                power_compression_function(SAMPLES, *POPT)))\n",
246 |     "\n",
247 |     "colour.plotting.artist()\n",
248 |     "plt.plot(SAMPLES, TANH_SAMPLES, 'r', label='tanh')\n",
249 |     "plt.plot(SAMPLES, power_compression_function(SAMPLES, *POPT), 'g', label='power')\n",
250 |     "plt.legend()\n",
251 |     "plt.show()"
252 |    ]
253 |   },
254 |   {
255 |    "cell_type": "markdown",
256 |    "metadata": {},
257 |    "source": [
258 |     "## ATAN - Power Fitting"
259 |    ]
260 |   },
261 |   {
262 |    "cell_type": "code",
263 |    "execution_count": 6,
264 |    "metadata": {},
265 |    "outputs": [
266 |     {
267 |      "name": "stdout",
268 |      "output_type": "stream",
269 |      "text": [
270 |       "[ 0.83715264  0.16187046  1.18238345]\n",
271 |       "9.2282913999e-08\n"
272 |      ]
273 |     },
274 |     {
275 |      "data": {
276 |       "application/vnd.jupyter.widget-view+json": {
277 |        "model_id": "c323d4940f444fc392aa7e131e2d658a",
278 |        "version_major": 2,
279 |        "version_minor": 0
280 |       },
281 |       "text/plain": [
282 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
283 |       ]
284 |      },
285 |      "metadata": {},
286 |      "output_type": "display_data"
287 |     }
288 |    ],
289 |    "source": [
290 |     "ATAN_SAMPLES = atan_compression_function(SAMPLES)\n",
291 |     "\n",
292 |     "POPT, PCOV = scipy.optimize.curve_fit(\n",
293 |     "    power_compression_function, SAMPLES, ATAN_SAMPLES, maxfev=50000)\n",
294 |     "\n",
295 |     "print(POPT)\n",
296 |     "print(\n",
297 |     "    colour.utilities.metric_mse(ATAN_SAMPLES,\n",
298 |     "                                power_compression_function(SAMPLES, *POPT)))\n",
299 |     "\n",
300 |     "colour.plotting.artist()\n",
301 |     "plt.plot(SAMPLES, ATAN_SAMPLES, 'r', label='atan')\n",
302 |     "plt.plot(SAMPLES, power_compression_function(SAMPLES, *POPT), 'g', label='power')\n",
303 |     "plt.legend()\n",
304 |     "plt.show()"
305 |    ]
306 |   },
307 |   {
308 |    "cell_type": "markdown",
309 |    "metadata": {},
310 |    "source": [
311 |     "## Solving Intersection with Y = 1"
312 |    ]
313 |   },
314 |   {
315 |    "cell_type": "code",
316 |    "execution_count": 7,
317 |    "metadata": {},
318 |    "outputs": [
319 |     {
320 |      "data": {
321 |       "application/vnd.jupyter.widget-view+json": {
322 |        "model_id": "f1e84e0006c64a139d748773fd8be653",
323 |        "version_major": 2,
324 |        "version_minor": 0
325 |       },
326 |       "text/plain": [
327 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
328 |       ]
329 |      },
330 |      "metadata": {},
331 |      "output_type": "display_data"
332 |     }
333 |    ],
334 |    "source": [
335 |     "X, A, I = 1.2, 0.8, 1\n",
336 |     "\n",
337 |     "figure, axes = colour.plotting.plot_multi_functions(\n",
338 |     "    {\n",
339 |     "        'tanh': lambda x: tanh_compression_function(\n",
340 |     "            x, A, scipy.optimize.fsolve(\n",
341 |     "                lambda x: tanh_compression_function(X, A, x) - I, 0.5)),\n",
342 |     "        'simple': lambda x: simple_compression_function(\n",
343 |     "            x, A, scipy.optimize.fsolve(\n",
344 |     "                lambda x: simple_compression_function(X, A, x) - I, 0.5)),\n",
345 |     "    },\n",
346 |     "    **{\n",
347 |     "        'standalone': False,\n",
348 |     "        'bounding_box': [0, 2, 0, 2],\n",
349 |     "        'samples':\n",
350 |     "        np.linspace(0, 2, 1000),\n",
351 |     "        'plot_kwargs': [\n",
352 |     "            {\n",
353 |     "                'c': 'r',\n",
354 |     "            },\n",
355 |     "            {\n",
356 |     "                'c': 'g',\n",
357 |     "            },\n",
358 |     "        ]\n",
359 |     "    },\n",
360 |     ")\n",
361 |     "\n",
362 |     "axes.scatter(X, I, c='b', s=50, zorder=4);"
363 |    ]
364 |   },
365 |   {
366 |    "cell_type": "markdown",
367 |    "metadata": {},
368 |    "source": [
369 |     "## Maximum Representable Value"
370 |    ]
371 |   },
372 |   {
373 |    "cell_type": "code",
374 |    "execution_count": 8,
375 |    "metadata": {},
376 |    "outputs": [
377 |     {
378 |      "name": "stdout",
379 |      "output_type": "stream",
380 |      "text": [
381 |       "[1, 1.0009766, 1.0019531, 1.0029297, 1.0039062, 1.0048828, 1.0058594, 1.0068359, 1.0078125, 1.0087891]\n"
382 |      ]
383 |     }
384 |    ],
385 |    "source": [
386 |     "HFD = [1]\n",
387 |     "\n",
388 |     "while True:\n",
389 |     "    HFD.append(np.nextafter(HFD[-1], +np.inf, dtype=np.float16))\n",
390 |     "    if HFD[-1] > 65504:\n",
391 |     "        break\n",
392 |     "\n",
393 |     "print(HFD[:10])"
394 |    ]
395 |   },
396 |   {
397 |    "cell_type": "code",
398 |    "execution_count": 9,
399 |    "metadata": {},
400 |    "outputs": [
401 |     {
402 |      "name": "stdout",
403 |      "output_type": "stream",
404 |      "text": [
405 |       "[ Threshold 0.0 ]\n",
406 |       "tanh 6.58594\n",
407 |       "atan 7376.0\n",
408 |       "simple 23168.0\n",
409 |       "\n",
410 |       "\n",
411 |       "[ Threshold 0.1 ]\n",
412 |       "tanh 6.02734\n",
413 |       "atan 6636.0\n",
414 |       "simple 14744.0\n",
415 |       "\n",
416 |       "\n",
417 |       "[ Threshold 0.2 ]\n",
418 |       "tanh 5.46875\n",
419 |       "atan 5900.0\n",
420 |       "simple 13104.0\n",
421 |       "\n",
422 |       "\n",
423 |       "[ Threshold 0.3 ]\n",
424 |       "tanh 4.91016\n",
425 |       "atan 3650.0\n",
426 |       "simple 8108.0\n",
427 |       "\n",
428 |       "\n",
429 |       "[ Threshold 0.4 ]\n",
430 |       "tanh 4.35156\n",
431 |       "atan 3130.0\n",
432 |       "simple 6952.0\n",
433 |       "\n",
434 |       "\n",
435 |       "[ Threshold 0.5 ]\n",
436 |       "tanh 3.61914\n",
437 |       "atan 1844.0\n",
438 |       "simple 5792.0\n",
439 |       "\n",
440 |       "\n",
441 |       "[ Threshold 0.6 ]\n",
442 |       "tanh 3.0957\n",
443 |       "atan 1476.0\n",
444 |       "simple 3278.0\n",
445 |       "\n",
446 |       "\n",
447 |       "[ Threshold 0.7 ]\n",
448 |       "tanh 2.57227\n",
449 |       "atan 783.0\n",
450 |       "simple 1738.0\n",
451 |       "\n",
452 |       "\n",
453 |       "[ Threshold 0.8 ]\n",
454 |       "tanh 1.97852\n",
455 |       "atan 369.5\n",
456 |       "simple 820.0\n",
457 |       "\n",
458 |       "\n",
459 |       "[ Threshold 0.9 ]\n",
460 |       "tanh 1.48926\n",
461 |       "atan 93.0625\n",
462 |       "simple 205.625\n",
463 |       "\n",
464 |       "\n",
465 |       "[ Threshold 1.0 ]\n",
466 |       "tanh 1\n",
467 |       "atan 1\n",
468 |       "simple 1\n",
469 |       "\n",
470 |       "\n"
471 |      ]
472 |     }
473 |    ],
474 |    "source": [
475 |     "for t in np.linspace(0, 1, 11):\n",
476 |     "    print(f'[ Threshold {np.around(t, 1)} ]')\n",
477 |     "    for func in (tanh_compression_function, atan_compression_function, simple_compression_function):\n",
478 |     "        print(func.__name__.split('_')[0],\n",
479 |     "              HFD[np.argmax((func(HFD[:-1], t, 1 - t) - func(HFD[1:], t, 1 - t)).astype(np.float16))])\n",
480 |     "    print('\\n')"
481 |    ]
482 |   }
483 |  ],
484 |  "metadata": {
485 |   "colab": {
486 |    "collapsed_sections": [],
487 |    "name": "gamut_mapping_ramblings_01",
488 |    "provenance": []
489 |   },
490 |   "kernelspec": {
491 |    "display_name": "Python 3",
492 |    "language": "python",
493 |    "name": "python3"
494 |   },
495 |   "language_info": {
496 |    "codemirror_mode": {
497 |     "name": "ipython",
498 |     "version": 3
499 |    },
500 |    "file_extension": ".py",
501 |    "mimetype": "text/x-python",
502 |    "name": "python",
503 |    "nbconvert_exporter": "python",
504 |    "pygments_lexer": "ipython3",
505 |    "version": "3.7.6"
506 |   },
507 |   "widgets": {
508 |    "application/vnd.jupyter.widget-state+json": {
509 |     "state": {},
510 |     "version_major": 2,
511 |     "version_minor": 0
512 |    }
513 |   }
514 |  },
515 |  "nbformat": 4,
516 |  "nbformat_minor": 4
517 | }
518 | 


--------------------------------------------------------------------------------
/notebooks/gamut_mapping_ramblings_02.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "markdown",
   5 |    "metadata": {
   6 |     "colab_type": "text",
   7 |     "id": "pRS6nhpu1R6y"
   8 |    },
   9 |    "source": [
  10 |     "# Gamut Mapping - RGB & HSV Gamut Mapping Study Models Fitting"
  11 |    ]
  12 |   },
  13 |   {
  14 |    "cell_type": "code",
  15 |    "execution_count": 1,
  16 |    "metadata": {},
  17 |    "outputs": [],
  18 |    "source": [
  19 |     "%matplotlib widget"
  20 |    ]
  21 |   },
  22 |   {
  23 |    "cell_type": "code",
  24 |    "execution_count": 2,
  25 |    "metadata": {
  26 |     "colab": {
  27 |      "base_uri": "https://localhost:8080/",
  28 |      "height": 340
  29 |     },
  30 |     "colab_type": "code",
  31 |     "id": "OydMmtZRxd3c",
  32 |     "outputId": "297a43b9-f566-405d-e8bc-cfae56b50959"
  33 |    },
  34 |    "outputs": [
  35 |     {
  36 |      "name": "stdout",
  37 |      "output_type": "stream",
  38 |      "text": [
  39 |       "===============================================================================\n",
  40 |       "*                                                                             *\n",
  41 |       "*   Interpreter :                                                             *\n",
  42 |       "*       python : 3.7.6 (default, Dec 30 2019, 19:38:26)                       *\n",
  43 |       "*                [Clang 11.0.0 (clang-1100.0.33.16)]                          *\n",
  44 |       "*                                                                             *\n",
  45 |       "*   colour-science.org :                                                      *\n",
  46 |       "*       colour : v0.3.15-141-g3bebd7e9                                        *\n",
  47 |       "*                                                                             *\n",
  48 |       "*   Runtime :                                                                 *\n",
  49 |       "*       imageio : 2.8.0                                                       *\n",
  50 |       "*       matplotlib : 3.0.3                                                    *\n",
  51 |       "*       numpy : 1.18.4                                                        *\n",
  52 |       "*       scipy : 1.4.1                                                         *\n",
  53 |       "*       six : 1.14.0                                                          *\n",
  54 |       "*                                                                             *\n",
  55 |       "===============================================================================\n"
  56 |      ]
  57 |     }
  58 |    ],
  59 |    "source": [
  60 |     "from __future__ import division, unicode_literals\n",
  61 |     "\n",
  62 |     "import colour\n",
  63 |     "import matplotlib.pyplot as plt\n",
  64 |     "import numpy as np\n",
  65 |     "import os\n",
  66 |     "import scipy.interpolate\n",
  67 |     "import scipy.optimize\n",
  68 |     "from functools import reduce\n",
  69 |     "\n",
  70 |     "COLOUR_STYLE = colour.plotting.colour_style()\n",
  71 |     "COLOUR_STYLE.update({\n",
  72 |     "    'figure.figsize': (10.24, 5.12),\n",
  73 |     "    'legend.framealpha':\n",
  74 |     "    colour.plotting.COLOUR_STYLE_CONSTANTS.opacity.low\n",
  75 |     "})\n",
  76 |     "\n",
  77 |     "plt.style.use(COLOUR_STYLE)\n",
  78 |     "\n",
  79 |     "plt.style.use('dark_background')\n",
  80 |     "\n",
  81 |     "colour.utilities.describe_environment()\n",
  82 |     "\n",
  83 |     "colour.utilities.filter_warnings(*[True] * 4);"
  84 |    ]
  85 |   },
  86 |   {
  87 |    "cell_type": "markdown",
  88 |    "metadata": {
  89 |     "colab_type": "text",
  90 |     "id": "5yCeFpnnp_wh"
  91 |    },
  92 |    "source": [
  93 |     "## Colour Wheel & Colour Stripe Generation"
  94 |    ]
  95 |   },
  96 |   {
  97 |    "cell_type": "code",
  98 |    "execution_count": 3,
  99 |    "metadata": {
 100 |     "colab": {},
 101 |     "colab_type": "code",
 102 |     "id": "RshonIbOI4Ij"
 103 |    },
 104 |    "outputs": [],
 105 |    "source": [
 106 |     "def colour_stripe(S=1, samples=360):\n",
 107 |     "    H = np.linspace(0, 1, samples)\n",
 108 |     "\n",
 109 |     "    HSV = colour.utilities.tstack([H, np.ones(samples) * S, np.ones(samples)])\n",
 110 |     "    RGB = colour.HSV_to_RGB(HSV)\n",
 111 |     " \n",
 112 |     "    return RGB[np.newaxis, ...]\n",
 113 |     "\n",
 114 |     "\n",
 115 |     "def colour_wheel(samples=1024, clip_circle=True, method='Colour'):\n",
 116 |     "    xx, yy = np.meshgrid(\n",
 117 |     "        np.linspace(-1, 1, samples), np.linspace(-1, 1, samples))\n",
 118 |     "\n",
 119 |     "    S = np.sqrt(xx ** 2 + yy ** 2)    \n",
 120 |     "    H = (np.arctan2(xx, yy) + np.pi) / (np.pi * 2)\n",
 121 |     "\n",
 122 |     "    HSV = colour.utilities.tstack([H, S, np.ones(H.shape)])\n",
 123 |     "    RGB = colour.HSV_to_RGB(HSV)\n",
 124 |     "\n",
 125 |     "    if clip_circle == True:\n",
 126 |     "        RGB[S > 1] = 0\n",
 127 |     "        A = np.where(S > 1, 0, 1)\n",
 128 |     "    else:\n",
 129 |     "        A = np.ones(S.shape)\n",
 130 |     "\n",
 131 |     "    if method.lower()== 'matplotlib':\n",
 132 |     "        RGB = colour.utilities.orient(RGB, '90 CW')\n",
 133 |     "    elif method.lower()== 'nuke':\n",
 134 |     "        RGB = colour.utilities.orient(RGB, 'Flip')\n",
 135 |     "        RGB = colour.utilities.orient(RGB, '90 CW')\n",
 136 |     "\n",
 137 |     "    R, G, B = colour.utilities.tsplit(RGB)\n",
 138 |     "    \n",
 139 |     "    return colour.utilities.tstack([R, G, B, A])"
 140 |    ]
 141 |   },
 142 |   {
 143 |    "cell_type": "code",
 144 |    "execution_count": 4,
 145 |    "metadata": {
 146 |     "colab": {
 147 |      "base_uri": "https://localhost:8080/",
 148 |      "height": 726
 149 |     },
 150 |     "colab_type": "code",
 151 |     "id": "154RGuYrmMNa",
 152 |     "outputId": "45ce8789-a351-4ca8-d6d0-88db89531df2"
 153 |    },
 154 |    "outputs": [
 155 |     {
 156 |      "data": {
 157 |       "application/vnd.jupyter.widget-view+json": {
 158 |        "model_id": "3d2ac7d114c14188825f2f93e68e2f0d",
 159 |        "version_major": 2,
 160 |        "version_minor": 0
 161 |       },
 162 |       "text/plain": [
 163 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
 164 |       ]
 165 |      },
 166 |      "metadata": {},
 167 |      "output_type": "display_data"
 168 |     },
 169 |     {
 170 |      "data": {
 171 |       "application/vnd.jupyter.widget-view+json": {
 172 |        "model_id": "2dc21106f85f49aca3199be32b460b33",
 173 |        "version_major": 2,
 174 |        "version_minor": 0
 175 |       },
 176 |       "text/plain": [
 177 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
 178 |       ]
 179 |      },
 180 |      "metadata": {},
 181 |      "output_type": "display_data"
 182 |     }
 183 |    ],
 184 |    "source": [
 185 |     "COLOUR_STRIPE = colour_stripe();\n",
 186 |     "\n",
 187 |     "COLOUR_WHEEL = colour_wheel(360, clip_circle=False)[..., 0:3]\n",
 188 |     "\n",
 189 |     "colour.plotting.plot_image(np.resize(COLOUR_STRIPE, [36, 360, 3]));\n",
 190 |     "\n",
 191 |     "with plt.style.context({'figure.figsize': (10.24, 10.24)}):\n",
 192 |     "\n",
 193 |     "    colour.plotting.plot_image(COLOUR_WHEEL);"
 194 |    ]
 195 |   },
 196 |   {
 197 |    "cell_type": "markdown",
 198 |    "metadata": {},
 199 |    "source": [
 200 |     "## Compression Function"
 201 |    ]
 202 |   },
 203 |   {
 204 |    "cell_type": "code",
 205 |    "execution_count": 5,
 206 |    "metadata": {},
 207 |    "outputs": [],
 208 |    "source": [
 209 |     "def tanh_compression_function(x, a=0.8, b=1 - 0.8):\n",
 210 |     "    x = colour.utilities.as_float_array(x)\n",
 211 |     "\n",
 212 |     "    return np.where(x > a, a + b * np.tanh((x - a) / b), x)"
 213 |    ]
 214 |   },
 215 |   {
 216 |    "cell_type": "markdown",
 217 |    "metadata": {},
 218 |    "source": [
 219 |     "## Gamut Mapping Study Models Difference"
 220 |    ]
 221 |   },
 222 |   {
 223 |    "cell_type": "code",
 224 |    "execution_count": 6,
 225 |    "metadata": {},
 226 |    "outputs": [],
 227 |    "source": [
 228 |     "SAMPLES = np.linspace(0, 1, 360)\n",
 229 |     "THRESHOLDS = np.linspace(0, 1, 21)\n",
 230 |     "\n",
 231 |     "\n",
 232 |     "def hue_offset(x, a, b, c):\n",
 233 |     "    return np.sin(x * a + b) * c\n",
 234 |     "\n",
 235 |     "\n",
 236 |     "def medicina_HSV_control(\n",
 237 |     "        a=(np.pi * 6, np.pi, np.pi / 180),\n",
 238 |     "        RGB=COLOUR_STRIPE,\n",
 239 |     "        S_c=0,\n",
 240 |     "        C_f=tanh_compression_function):\n",
 241 |     "    H, S, V = colour.utilities.tsplit(colour.RGB_to_HSV(RGB))\n",
 242 |     "\n",
 243 |     "    HSV = colour.utilities.tstack([(H + hue_offset(SAMPLES, *a)) % 1,\n",
 244 |     "                                   C_f(S, S_c, 1 - S_c), V])\n",
 245 |     "\n",
 246 |     "    return colour.HSV_to_RGB(HSV)\n",
 247 |     "\n",
 248 |     "\n",
 249 |     "def medicina_RGB_saturation(RGB=COLOUR_STRIPE,\n",
 250 |     "                            C_t=0,\n",
 251 |     "                            C_f=tanh_compression_function):\n",
 252 |     "    L = np.max(RGB, axis=-1)[..., np.newaxis]\n",
 253 |     "\n",
 254 |     "    D = np.abs(RGB - L) / L\n",
 255 |     "\n",
 256 |     "    D_c = C_f(D, C_t, 1 - C_t)\n",
 257 |     "\n",
 258 |     "    RGB_c = L - D_c * L\n",
 259 |     "\n",
 260 |     "    return RGB_c\n",
 261 |     "\n",
 262 |     "\n",
 263 |     "RGB_S = medicina_RGB_saturation()"
 264 |    ]
 265 |   },
 266 |   {
 267 |    "cell_type": "code",
 268 |    "execution_count": 7,
 269 |    "metadata": {},
 270 |    "outputs": [
 271 |     {
 272 |      "data": {
 273 |       "application/vnd.jupyter.widget-view+json": {
 274 |        "model_id": "48e1e74663564391a693e247156c4dcd",
 275 |        "version_major": 2,
 276 |        "version_minor": 0
 277 |       },
 278 |       "text/plain": [
 279 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
 280 |       ]
 281 |      },
 282 |      "metadata": {},
 283 |      "output_type": "display_data"
 284 |     }
 285 |    ],
 286 |    "source": [
 287 |     "colour.plotting.plot_image(np.resize(medicina_HSV_control(), [36, 360, 3]));"
 288 |    ]
 289 |   },
 290 |   {
 291 |    "cell_type": "code",
 292 |    "execution_count": 8,
 293 |    "metadata": {},
 294 |    "outputs": [
 295 |     {
 296 |      "data": {
 297 |       "application/vnd.jupyter.widget-view+json": {
 298 |        "model_id": "8b1a7b0f21974187ac4a06968a99a6a7",
 299 |        "version_major": 2,
 300 |        "version_minor": 0
 301 |       },
 302 |       "text/plain": [
 303 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
 304 |       ]
 305 |      },
 306 |      "metadata": {},
 307 |      "output_type": "display_data"
 308 |     }
 309 |    ],
 310 |    "source": [
 311 |     "colour.plotting.plot_image(np.resize(RGB_S, [36, 360, 3]));"
 312 |    ]
 313 |   },
 314 |   {
 315 |    "cell_type": "code",
 316 |    "execution_count": 9,
 317 |    "metadata": {},
 318 |    "outputs": [
 319 |     {
 320 |      "data": {
 321 |       "application/vnd.jupyter.widget-view+json": {
 322 |        "model_id": "24e8c686e20245bebb5150a559fb366e",
 323 |        "version_major": 2,
 324 |        "version_minor": 0
 325 |       },
 326 |       "text/plain": [
 327 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
 328 |       ]
 329 |      },
 330 |      "metadata": {},
 331 |      "output_type": "display_data"
 332 |     }
 333 |    ],
 334 |    "source": [
 335 |     "colour.plotting.plot_image(\n",
 336 |     "    np.resize(\n",
 337 |     "        np.abs(medicina_HSV_control([0, 0, 0]) - RGB_S) * 5, [36, 360, 3]));"
 338 |    ]
 339 |   },
 340 |   {
 341 |    "cell_type": "markdown",
 342 |    "metadata": {},
 343 |    "source": [
 344 |     "## Optimisation & Curve Fitting"
 345 |    ]
 346 |   },
 347 |   {
 348 |    "cell_type": "markdown",
 349 |    "metadata": {},
 350 |    "source": [
 351 |     "After some initial tests, it seemed like the closest function to minimize the error was a triangle or sine wave: https://community.acescentral.com/t/gamut-mapping-in-cylindrical-and-conic-spaces/2870/10?u=thomas_mansencal\n",
 352 |     "\n",
 353 |     "$\\sin\\bigg(x \\cdot \\pi \\cdot 6 + \\pi\\bigg) * \\cfrac{\\pi}{\\approx180}$\n",
 354 |     "\n",
 355 |     "is almost the best fitting function for the maximum saturation case, the rounded 180 value just happens to be convenient here but the actual value is slightly lower."
 356 |    ]
 357 |   },
 358 |   {
 359 |    "cell_type": "code",
 360 |    "execution_count": 10,
 361 |    "metadata": {},
 362 |    "outputs": [],
 363 |    "source": [
 364 |     "SATURATION_SAMPLES = np.linspace(0, 6, 1000)\n",
 365 |     "STRIPES = np.vstack([colour_stripe(a) for a in SATURATION_SAMPLES])\n",
 366 |     "\n",
 367 |     "PARAMETERS = []\n",
 368 |     "for threshold in THRESHOLDS:\n",
 369 |     "    i = 0\n",
 370 |     "\n",
 371 |     "    def objective_function(abc):\n",
 372 |     "        stripe = STRIPES[i]\n",
 373 |     "        return np.sum(\n",
 374 |     "            np.abs(\n",
 375 |     "                medicina_HSV_control(abc, stripe, S_c=threshold) -\n",
 376 |     "                medicina_RGB_saturation(stripe, C_t=threshold)))\n",
 377 |     "\n",
 378 |     "    parameters = []\n",
 379 |     "    for i in range(len(STRIPES)):\n",
 380 |     "        parameters.append(\n",
 381 |     "            scipy.optimize.minimize(objective_function,\n",
 382 |     "                                    (np.pi * 6, np.pi, np.pi / 180)))\n",
 383 |     "\n",
 384 |     "    PARAMETERS.append(parameters)"
 385 |    ]
 386 |   },
 387 |   {
 388 |    "cell_type": "code",
 389 |    "execution_count": 11,
 390 |    "metadata": {},
 391 |    "outputs": [
 392 |     {
 393 |      "data": {
 394 |       "application/vnd.jupyter.widget-view+json": {
 395 |        "model_id": "e1c922d1836342e2bf0450d8bab6af55",
 396 |        "version_major": 2,
 397 |        "version_minor": 0
 398 |       },
 399 |       "text/plain": [
 400 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
 401 |       ]
 402 |      },
 403 |      "metadata": {},
 404 |      "output_type": "display_data"
 405 |     },
 406 |     {
 407 |      "data": {
 408 |       "application/vnd.jupyter.widget-view+json": {
 409 |        "model_id": "0a359cb9ebf7438db4344e7709190afe",
 410 |        "version_major": 2,
 411 |        "version_minor": 0
 412 |       },
 413 |       "text/plain": [
 414 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
 415 |       ]
 416 |      },
 417 |      "metadata": {},
 418 |      "output_type": "display_data"
 419 |     },
 420 |     {
 421 |      "data": {
 422 |       "application/vnd.jupyter.widget-view+json": {
 423 |        "model_id": "8cd3ad519bb34974b180643eacf287c7",
 424 |        "version_major": 2,
 425 |        "version_minor": 0
 426 |       },
 427 |       "text/plain": [
 428 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
 429 |       ]
 430 |      },
 431 |      "metadata": {},
 432 |      "output_type": "display_data"
 433 |     },
 434 |     {
 435 |      "data": {
 436 |       "application/vnd.jupyter.widget-view+json": {
 437 |        "model_id": "ada5ad5ab0ba44d9a67c7f8fa2720794",
 438 |        "version_major": 2,
 439 |        "version_minor": 0
 440 |       },
 441 |       "text/plain": [
 442 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
 443 |       ]
 444 |      },
 445 |      "metadata": {},
 446 |      "output_type": "display_data"
 447 |     },
 448 |     {
 449 |      "data": {
 450 |       "application/vnd.jupyter.widget-view+json": {
 451 |        "model_id": "25460ec98b374ba8a44c0a092b816afd",
 452 |        "version_major": 2,
 453 |        "version_minor": 0
 454 |       },
 455 |       "text/plain": [
 456 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
 457 |       ]
 458 |      },
 459 |      "metadata": {},
 460 |      "output_type": "display_data"
 461 |     },
 462 |     {
 463 |      "data": {
 464 |       "application/vnd.jupyter.widget-view+json": {
 465 |        "model_id": "ac08e3b361634a54bcbeda720eb41dd9",
 466 |        "version_major": 2,
 467 |        "version_minor": 0
 468 |       },
 469 |       "text/plain": [
 470 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
 471 |       ]
 472 |      },
 473 |      "metadata": {},
 474 |      "output_type": "display_data"
 475 |     },
 476 |     {
 477 |      "data": {
 478 |       "application/vnd.jupyter.widget-view+json": {
 479 |        "model_id": "7b58f8d4e40246a681c14e25574aabbd",
 480 |        "version_major": 2,
 481 |        "version_minor": 0
 482 |       },
 483 |       "text/plain": [
 484 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
 485 |       ]
 486 |      },
 487 |      "metadata": {},
 488 |      "output_type": "display_data"
 489 |     },
 490 |     {
 491 |      "data": {
 492 |       "application/vnd.jupyter.widget-view+json": {
 493 |        "model_id": "34dd5fa8d2ae426f9db7b558d41ea34e",
 494 |        "version_major": 2,
 495 |        "version_minor": 0
 496 |       },
 497 |       "text/plain": [
 498 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
 499 |       ]
 500 |      },
 501 |      "metadata": {},
 502 |      "output_type": "display_data"
 503 |     },
 504 |     {
 505 |      "data": {
 506 |       "application/vnd.jupyter.widget-view+json": {
 507 |        "model_id": "b9d8a3b408c248bc9185545bf972fa4b",
 508 |        "version_major": 2,
 509 |        "version_minor": 0
 510 |       },
 511 |       "text/plain": [
 512 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
 513 |       ]
 514 |      },
 515 |      "metadata": {},
 516 |      "output_type": "display_data"
 517 |     },
 518 |     {
 519 |      "data": {
 520 |       "application/vnd.jupyter.widget-view+json": {
 521 |        "model_id": "18be74fc37de4e13b7010ee5c839355b",
 522 |        "version_major": 2,
 523 |        "version_minor": 0
 524 |       },
 525 |       "text/plain": [
 526 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
 527 |       ]
 528 |      },
 529 |      "metadata": {},
 530 |      "output_type": "display_data"
 531 |     },
 532 |     {
 533 |      "data": {
 534 |       "application/vnd.jupyter.widget-view+json": {
 535 |        "model_id": "51101f356a6049c189717e9d815eaf41",
 536 |        "version_major": 2,
 537 |        "version_minor": 0
 538 |       },
 539 |       "text/plain": [
 540 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
 541 |       ]
 542 |      },
 543 |      "metadata": {},
 544 |      "output_type": "display_data"
 545 |     },
 546 |     {
 547 |      "data": {
 548 |       "application/vnd.jupyter.widget-view+json": {
 549 |        "model_id": "a45cfb9982e44763bc9de50f5fbcd68c",
 550 |        "version_major": 2,
 551 |        "version_minor": 0
 552 |       },
 553 |       "text/plain": [
 554 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
 555 |       ]
 556 |      },
 557 |      "metadata": {},
 558 |      "output_type": "display_data"
 559 |     },
 560 |     {
 561 |      "data": {
 562 |       "application/vnd.jupyter.widget-view+json": {
 563 |        "model_id": "ab160808018648498690feed7e340656",
 564 |        "version_major": 2,
 565 |        "version_minor": 0
 566 |       },
 567 |       "text/plain": [
 568 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
 569 |       ]
 570 |      },
 571 |      "metadata": {},
 572 |      "output_type": "display_data"
 573 |     },
 574 |     {
 575 |      "data": {
 576 |       "application/vnd.jupyter.widget-view+json": {
 577 |        "model_id": "4c528fed07cc47b9b42d01dd2ae6a2d4",
 578 |        "version_major": 2,
 579 |        "version_minor": 0
 580 |       },
 581 |       "text/plain": [
 582 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
 583 |       ]
 584 |      },
 585 |      "metadata": {},
 586 |      "output_type": "display_data"
 587 |     },
 588 |     {
 589 |      "data": {
 590 |       "application/vnd.jupyter.widget-view+json": {
 591 |        "model_id": "89e7c5bc3b4d457ab62749218a74005c",
 592 |        "version_major": 2,
 593 |        "version_minor": 0
 594 |       },
 595 |       "text/plain": [
 596 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
 597 |       ]
 598 |      },
 599 |      "metadata": {},
 600 |      "output_type": "display_data"
 601 |     },
 602 |     {
 603 |      "data": {
 604 |       "application/vnd.jupyter.widget-view+json": {
 605 |        "model_id": "96134cb86d41487abf519ec28c2b20d1",
 606 |        "version_major": 2,
 607 |        "version_minor": 0
 608 |       },
 609 |       "text/plain": [
 610 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
 611 |       ]
 612 |      },
 613 |      "metadata": {},
 614 |      "output_type": "display_data"
 615 |     },
 616 |     {
 617 |      "data": {
 618 |       "application/vnd.jupyter.widget-view+json": {
 619 |        "model_id": "76ad4691df2642a7b0c3a44cfe1184b3",
 620 |        "version_major": 2,
 621 |        "version_minor": 0
 622 |       },
 623 |       "text/plain": [
 624 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
 625 |       ]
 626 |      },
 627 |      "metadata": {},
 628 |      "output_type": "display_data"
 629 |     },
 630 |     {
 631 |      "data": {
 632 |       "application/vnd.jupyter.widget-view+json": {
 633 |        "model_id": "1ab9aebe0d7c4b8c9b49d736c6fb0c04",
 634 |        "version_major": 2,
 635 |        "version_minor": 0
 636 |       },
 637 |       "text/plain": [
 638 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
 639 |       ]
 640 |      },
 641 |      "metadata": {},
 642 |      "output_type": "display_data"
 643 |     },
 644 |     {
 645 |      "data": {
 646 |       "application/vnd.jupyter.widget-view+json": {
 647 |        "model_id": "ef921f4330464cb698b5bcfa5674ad2e",
 648 |        "version_major": 2,
 649 |        "version_minor": 0
 650 |       },
 651 |       "text/plain": [
 652 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
 653 |       ]
 654 |      },
 655 |      "metadata": {},
 656 |      "output_type": "display_data"
 657 |     },
 658 |     {
 659 |      "data": {
 660 |       "application/vnd.jupyter.widget-view+json": {
 661 |        "model_id": "9d8c5931cb7f41509824f637c4389e3d",
 662 |        "version_major": 2,
 663 |        "version_minor": 0
 664 |       },
 665 |       "text/plain": [
 666 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
 667 |       ]
 668 |      },
 669 |      "metadata": {},
 670 |      "output_type": "display_data"
 671 |     },
 672 |     {
 673 |      "data": {
 674 |       "application/vnd.jupyter.widget-view+json": {
 675 |        "model_id": "12c54c13aefc42e8b95349d31b98559c",
 676 |        "version_major": 2,
 677 |        "version_minor": 0
 678 |       },
 679 |       "text/plain": [
 680 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
 681 |       ]
 682 |      },
 683 |      "metadata": {},
 684 |      "output_type": "display_data"
 685 |     }
 686 |    ],
 687 |    "source": [
 688 |     "for parameters in PARAMETERS:\n",
 689 |     "    with plt.style.context({'figure.figsize': (10.24, 10.24)}):\n",
 690 |     "        colour.plotting.artist()\n",
 691 |     "\n",
 692 |     "        for parameter in parameters[1::5]:\n",
 693 |     "            plt.plot(\n",
 694 |     "                SAMPLES,\n",
 695 |     "                np.sin(\n",
 696 |     "                    np.linspace(0, 1, len(SAMPLES)) * parameter.x[0] +\n",
 697 |     "                    parameter.x[1]) * parameter.x[2])\n",
 698 |     "\n",
 699 |     "        for i in np.arange(0, 360 + 60, 60) / 360:\n",
 700 |     "            plt.axvline(i, c='r')"
 701 |    ]
 702 |   },
 703 |   {
 704 |    "cell_type": "code",
 705 |    "execution_count": 12,
 706 |    "metadata": {},
 707 |    "outputs": [
 708 |     {
 709 |      "data": {
 710 |       "application/vnd.jupyter.widget-view+json": {
 711 |        "model_id": "bfb639ee195f40a28e41805dec17f976",
 712 |        "version_major": 2,
 713 |        "version_minor": 0
 714 |       },
 715 |       "text/plain": [
 716 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
 717 |       ]
 718 |      },
 719 |      "metadata": {},
 720 |      "output_type": "display_data"
 721 |     },
 722 |     {
 723 |      "data": {
 724 |       "application/vnd.jupyter.widget-view+json": {
 725 |        "model_id": "47ab801a219b41d893786213a4a30458",
 726 |        "version_major": 2,
 727 |        "version_minor": 0
 728 |       },
 729 |       "text/plain": [
 730 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
 731 |       ]
 732 |      },
 733 |      "metadata": {},
 734 |      "output_type": "display_data"
 735 |     },
 736 |     {
 737 |      "data": {
 738 |       "application/vnd.jupyter.widget-view+json": {
 739 |        "model_id": "a33c5dc8a0fd4cb19a461ea1054a8827",
 740 |        "version_major": 2,
 741 |        "version_minor": 0
 742 |       },
 743 |       "text/plain": [
 744 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
 745 |       ]
 746 |      },
 747 |      "metadata": {},
 748 |      "output_type": "display_data"
 749 |     }
 750 |    ],
 751 |    "source": [
 752 |     "with plt.style.context({'figure.figsize': (10.24, 10.24)}):\n",
 753 |     "    colour.plotting.artist()\n",
 754 |     "\n",
 755 |     "    Y = []\n",
 756 |     "    for parameters in PARAMETERS:\n",
 757 |     "        y = [parameter.x[0] for parameter in parameters]\n",
 758 |     "        Y.append(y)\n",
 759 |     "        plt.plot(SATURATION_SAMPLES, y)\n",
 760 |     "\n",
 761 |     "    plt.plot(SATURATION_SAMPLES, np.mean(Y, axis=0))\n",
 762 |     "\n",
 763 |     "with plt.style.context({'figure.figsize': (10.24, 10.24)}):\n",
 764 |     "    colour.plotting.artist()\n",
 765 |     "\n",
 766 |     "    Y = []\n",
 767 |     "    for parameters in PARAMETERS:\n",
 768 |     "        y = [parameter.x[1] for parameter in parameters]\n",
 769 |     "        Y.append(y)\n",
 770 |     "        plt.plot(SATURATION_SAMPLES, y)\n",
 771 |     "\n",
 772 |     "    plt.plot(SATURATION_SAMPLES, np.mean(Y, axis=0))\n",
 773 |     "\n",
 774 |     "with plt.style.context({'figure.figsize': (10.24, 10.24)}):\n",
 775 |     "    colour.plotting.artist()\n",
 776 |     "\n",
 777 |     "    Y = []\n",
 778 |     "    for parameters in PARAMETERS:\n",
 779 |     "        y = [parameter.x[2] for parameter in parameters]\n",
 780 |     "        Y.append(y)\n",
 781 |     "        plt.plot(SATURATION_SAMPLES, y)\n",
 782 |     "\n",
 783 |     "    plt.plot(SATURATION_SAMPLES, np.mean(Y, axis=0))"
 784 |    ]
 785 |   },
 786 |   {
 787 |    "cell_type": "code",
 788 |    "execution_count": 13,
 789 |    "metadata": {},
 790 |    "outputs": [],
 791 |    "source": [
 792 |     "def curve_segment(x, m_lnA, m_B, m_offsetX, m_offsetY, m_scaleX=1, m_scaleY=1):\n",
 793 |     "    x0 = (x - m_offsetX) * m_scaleX\n",
 794 |     "    y0 = np.exp(m_lnA + m_B * np.log(x0))\n",
 795 |     "\n",
 796 |     "    return np.where(x > 0, y0 * m_scaleY + m_offsetY, 0)\n",
 797 |     "\n",
 798 |     "\n",
 799 |     "def solve_A_B(x0, y0, m):\n",
 800 |     "    B = (m * x0) / y0\n",
 801 |     "    lnA = np.log(y0) - B * np.log(x0)\n",
 802 |     "\n",
 803 |     "    return lnA, B\n",
 804 |     "\n",
 805 |     "\n",
 806 |     "def as_slope_intercept(x0, x1, y0, y1):\n",
 807 |     "    dx = (x1 - x0)\n",
 808 |     "    dy = (y1 - y0)\n",
 809 |     "\n",
 810 |     "    m = 1 if dx == 0 else dy / dx\n",
 811 |     "    b = y0 - x0 * m\n",
 812 |     "\n",
 813 |     "    return m, b\n",
 814 |     "\n",
 815 |     "\n",
 816 |     "def eval_derivative_linear_gamma(m, b, g, x):\n",
 817 |     "    return g * m * np.power(m * x + b, g - 1.0)\n",
 818 |     "\n",
 819 |     "\n",
 820 |     "def curve(x, g, m_x0, m_y0, m_x1, m_y1, m_overshoot_x, m_overshoot_y):\n",
 821 |     "    m_x1 = max(m_x0, m_x1)\n",
 822 |     "    m_y1 = max(m_y0, m_y1)\n",
 823 |     "\n",
 824 |     "    m_overshoot_x = max(m_x1 - 1, m_overshoot_x)\n",
 825 |     "    m_overshoot_y = max(m_y1 - 1, m_overshoot_y)\n",
 826 |     "\n",
 827 |     "    m, b = as_slope_intercept(m_x0, m_x1, m_y0, m_y1)\n",
 828 |     "\n",
 829 |     "    linear_segment = curve_segment(x, g * np.log(m), g, -(b / m), 0)\n",
 830 |     "\n",
 831 |     "    toe_M = eval_derivative_linear_gamma(m, b, g, m_x0)\n",
 832 |     "    shoulder_M = eval_derivative_linear_gamma(m, b, g, m_x1)\n",
 833 |     "\n",
 834 |     "    m_y0 = np.power(m_y0, g)\n",
 835 |     "    m_y1 = np.power(m_y1, g)\n",
 836 |     "    m_overshoot_y = np.power(1 + m_overshoot_y, g) - 1\n",
 837 |     "\n",
 838 |     "    A_t, B_t = solve_A_B(m_x0, m_y0, toe_M)\n",
 839 |     "    toe_segment = curve_segment(x, A_t, B_t, 0, 0)\n",
 840 |     "\n",
 841 |     "    x0 = (1 + m_overshoot_x) - m_x1\n",
 842 |     "    y0 = (1 + m_overshoot_y) - m_y1\n",
 843 |     "    A_s, B_s = solve_A_B(x0, y0, shoulder_M)\n",
 844 |     "    shoulder_segment = curve_segment(x, A_s, B_s, 1 + m_overshoot_x,\n",
 845 |     "                                     1 + m_overshoot_y, -1, -1)\n",
 846 |     "\n",
 847 |     "    y = np.select(\n",
 848 |     "        [\n",
 849 |     "            x < m_x0,\n",
 850 |     "            np.logical_and(x > m_x0, x < m_x1),\n",
 851 |     "            np.logical_and(x > m_x1, x < m_overshoot_x + 1),\n",
 852 |     "            x > m_overshoot_x + 1,\n",
 853 |     "        ],\n",
 854 |     "        [\n",
 855 |     "            toe_segment, linear_segment, shoulder_segment,\n",
 856 |     "            np.nanmax(shoulder_segment, )\n",
 857 |     "        ],\n",
 858 |     "    )\n",
 859 |     "\n",
 860 |     "    return y\n",
 861 |     "\n",
 862 |     "\n",
 863 |     "partial_curve = lambda x, m_x0, m_y0, m_x1, m_y1, m_overshoot_x: curve(\n",
 864 |     "    x, 1.25, m_x0, m_y0, m_x1, m_y1, m_overshoot_x, -0.86)"
 865 |    ]
 866 |   },
 867 |   {
 868 |    "cell_type": "code",
 869 |    "execution_count": 14,
 870 |    "metadata": {},
 871 |    "outputs": [
 872 |     {
 873 |      "name": "stdout",
 874 |      "output_type": "stream",
 875 |      "text": [
 876 |       "[ 0.42061353  0.01        1.54999165  0.06898502  8.        ]\n",
 877 |       "1.42190622496e-07\n"
 878 |      ]
 879 |     },
 880 |     {
 881 |      "data": {
 882 |       "application/vnd.jupyter.widget-view+json": {
 883 |        "model_id": "1aeb7f48a1e04aa191f99b95373503f1",
 884 |        "version_major": 2,
 885 |        "version_minor": 0
 886 |       },
 887 |       "text/plain": [
 888 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
 889 |       ]
 890 |      },
 891 |      "metadata": {},
 892 |      "output_type": "display_data"
 893 |     },
 894 |     {
 895 |      "name": "stdout",
 896 |      "output_type": "stream",
 897 |      "text": [
 898 |       "[ 0.44945874  0.01        1.5458702   0.06998316  8.        ]\n",
 899 |       "1.09070593989e-07\n"
 900 |      ]
 901 |     },
 902 |     {
 903 |      "data": {
 904 |       "application/vnd.jupyter.widget-view+json": {
 905 |        "model_id": "37ad8ac5dbf247cf88488d4c88570262",
 906 |        "version_major": 2,
 907 |        "version_minor": 0
 908 |       },
 909 |       "text/plain": [
 910 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
 911 |       ]
 912 |      },
 913 |      "metadata": {},
 914 |      "output_type": "display_data"
 915 |     },
 916 |     {
 917 |      "name": "stdout",
 918 |      "output_type": "stream",
 919 |      "text": [
 920 |       "[ 0.48519176  0.01        1.52243267  0.07        8.        ]\n",
 921 |       "1.09009430496e-07\n"
 922 |      ]
 923 |     },
 924 |     {
 925 |      "data": {
 926 |       "application/vnd.jupyter.widget-view+json": {
 927 |        "model_id": "8cea3c0602a943b9b6ca13e3cd072bcc",
 928 |        "version_major": 2,
 929 |        "version_minor": 0
 930 |       },
 931 |       "text/plain": [
 932 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
 933 |       ]
 934 |      },
 935 |      "metadata": {},
 936 |      "output_type": "display_data"
 937 |     },
 938 |     {
 939 |      "name": "stdout",
 940 |      "output_type": "stream",
 941 |      "text": [
 942 |       "[ 0.52366926  0.01        1.50167967  0.07        8.        ]\n",
 943 |       "1.26675732511e-07\n"
 944 |      ]
 945 |     },
 946 |     {
 947 |      "data": {
 948 |       "application/vnd.jupyter.widget-view+json": {
 949 |        "model_id": "6767588783d44adc9767c0dc9f82f1ad",
 950 |        "version_major": 2,
 951 |        "version_minor": 0
 952 |       },
 953 |       "text/plain": [
 954 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
 955 |       ]
 956 |      },
 957 |      "metadata": {},
 958 |      "output_type": "display_data"
 959 |     },
 960 |     {
 961 |      "name": "stdout",
 962 |      "output_type": "stream",
 963 |      "text": [
 964 |       "[ 0.56434332  0.00999995  1.48373347  0.07        7.99999982]\n",
 965 |       "1.45074066025e-07\n"
 966 |      ]
 967 |     },
 968 |     {
 969 |      "data": {
 970 |       "application/vnd.jupyter.widget-view+json": {
 971 |        "model_id": "717190ebeb4d4e78849cfc8f06a7e245",
 972 |        "version_major": 2,
 973 |        "version_minor": 0
 974 |       },
 975 |       "text/plain": [
 976 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
 977 |       ]
 978 |      },
 979 |      "metadata": {},
 980 |      "output_type": "display_data"
 981 |     },
 982 |     {
 983 |      "name": "stdout",
 984 |      "output_type": "stream",
 985 |      "text": [
 986 |       "[ 0.60537668  0.01        1.46864029  0.07        7.99999998]\n",
 987 |       "1.7212552568e-07\n"
 988 |      ]
 989 |     },
 990 |     {
 991 |      "data": {
 992 |       "application/vnd.jupyter.widget-view+json": {
 993 |        "model_id": "6a14a4188dfb4443928c7e7ff499eea7",
 994 |        "version_major": 2,
 995 |        "version_minor": 0
 996 |       },
 997 |       "text/plain": [
 998 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
 999 |       ]
1000 |      },
1001 |      "metadata": {},
1002 |      "output_type": "display_data"
1003 |     },
1004 |     {
1005 |      "name": "stdout",
1006 |      "output_type": "stream",
1007 |      "text": [
1008 |       "[ 0.64671961  0.01        1.45617162  0.07        7.99999934]\n",
1009 |       "2.09304074727e-07\n"
1010 |      ]
1011 |     },
1012 |     {
1013 |      "data": {
1014 |       "application/vnd.jupyter.widget-view+json": {
1015 |        "model_id": "0f0572d959ab4c73b2f93965a2bd8989",
1016 |        "version_major": 2,
1017 |        "version_minor": 0
1018 |       },
1019 |       "text/plain": [
1020 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
1021 |       ]
1022 |      },
1023 |      "metadata": {},
1024 |      "output_type": "display_data"
1025 |     },
1026 |     {
1027 |      "name": "stdout",
1028 |      "output_type": "stream",
1029 |      "text": [
1030 |       "[ 0.68741166  0.01        1.44643813  0.07        7.82343878]\n",
1031 |       "2.46155681451e-07\n"
1032 |      ]
1033 |     },
1034 |     {
1035 |      "data": {
1036 |       "application/vnd.jupyter.widget-view+json": {
1037 |        "model_id": "ecd2143b06b341f38c0dcf80926fb691",
1038 |        "version_major": 2,
1039 |        "version_minor": 0
1040 |       },
1041 |       "text/plain": [
1042 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
1043 |       ]
1044 |      },
1045 |      "metadata": {},
1046 |      "output_type": "display_data"
1047 |     },
1048 |     {
1049 |      "name": "stdout",
1050 |      "output_type": "stream",
1051 |      "text": [
1052 |       "[ 0.72482095  0.01        1.43992726  0.07        7.1004392 ]\n",
1053 |       "2.88384615063e-07\n"
1054 |      ]
1055 |     },
1056 |     {
1057 |      "data": {
1058 |       "application/vnd.jupyter.widget-view+json": {
1059 |        "model_id": "9d53d8c58ca84062b1993cdc379c3378",
1060 |        "version_major": 2,
1061 |        "version_minor": 0
1062 |       },
1063 |       "text/plain": [
1064 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
1065 |       ]
1066 |      },
1067 |      "metadata": {},
1068 |      "output_type": "display_data"
1069 |     },
1070 |     {
1071 |      "name": "stdout",
1072 |      "output_type": "stream",
1073 |      "text": [
1074 |       "[ 0.76234831  0.01        1.43489649  0.07        6.68609959]\n",
1075 |       "3.36669927761e-07\n"
1076 |      ]
1077 |     },
1078 |     {
1079 |      "data": {
1080 |       "application/vnd.jupyter.widget-view+json": {
1081 |        "model_id": "30c8b411097740ab83578a101e2463cb",
1082 |        "version_major": 2,
1083 |        "version_minor": 0
1084 |       },
1085 |       "text/plain": [
1086 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
1087 |       ]
1088 |      },
1089 |      "metadata": {},
1090 |      "output_type": "display_data"
1091 |     },
1092 |     {
1093 |      "name": "stdout",
1094 |      "output_type": "stream",
1095 |      "text": [
1096 |       "[ 0.80276365  0.01        1.4049472   0.06781388  5.72450101]\n",
1097 |       "3.73510140288e-07\n"
1098 |      ]
1099 |     },
1100 |     {
1101 |      "data": {
1102 |       "application/vnd.jupyter.widget-view+json": {
1103 |        "model_id": "1753be4c4f9b49baa0c04e116169ec57",
1104 |        "version_major": 2,
1105 |        "version_minor": 0
1106 |       },
1107 |       "text/plain": [
1108 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
1109 |       ]
1110 |      },
1111 |      "metadata": {},
1112 |      "output_type": "display_data"
1113 |     },
1114 |     {
1115 |      "name": "stdout",
1116 |      "output_type": "stream",
1117 |      "text": [
1118 |       "[ 0.84116659  0.01        1.37700835  0.06534461  4.9520251 ]\n",
1119 |       "4.13324960727e-07\n"
1120 |      ]
1121 |     },
1122 |     {
1123 |      "data": {
1124 |       "application/vnd.jupyter.widget-view+json": {
1125 |        "model_id": "d7d80711a5054d1fa3c816e18a32d22a",
1126 |        "version_major": 2,
1127 |        "version_minor": 0
1128 |       },
1129 |       "text/plain": [
1130 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
1131 |       ]
1132 |      },
1133 |      "metadata": {},
1134 |      "output_type": "display_data"
1135 |     },
1136 |     {
1137 |      "name": "stdout",
1138 |      "output_type": "stream",
1139 |      "text": [
1140 |       "[ 0.87598059  0.01        1.35949114  0.06340935  4.47022722]\n",
1141 |       "4.61922699931e-07\n"
1142 |      ]
1143 |     },
1144 |     {
1145 |      "data": {
1146 |       "application/vnd.jupyter.widget-view+json": {
1147 |        "model_id": "f8d57bea2f7a409dbc229be198432f3c",
1148 |        "version_major": 2,
1149 |        "version_minor": 0
1150 |       },
1151 |       "text/plain": [
1152 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
1153 |       ]
1154 |      },
1155 |      "metadata": {},
1156 |      "output_type": "display_data"
1157 |     },
1158 |     {
1159 |      "name": "stdout",
1160 |      "output_type": "stream",
1161 |      "text": [
1162 |       "[ 0.90722179  0.01        1.34585395  0.06154288  4.09369171]\n",
1163 |       "5.40456895135e-07\n"
1164 |      ]
1165 |     },
1166 |     {
1167 |      "data": {
1168 |       "application/vnd.jupyter.widget-view+json": {
1169 |        "model_id": "8f8cdcf192ef4993a1721879d8e44be6",
1170 |        "version_major": 2,
1171 |        "version_minor": 0
1172 |       },
1173 |       "text/plain": [
1174 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
1175 |       ]
1176 |      },
1177 |      "metadata": {},
1178 |      "output_type": "display_data"
1179 |     },
1180 |     {
1181 |      "name": "stdout",
1182 |      "output_type": "stream",
1183 |      "text": [
1184 |       "[ 0.93505552  0.01        1.3349579   0.05973808  3.86429275]\n",
1185 |       "6.08983092254e-07\n"
1186 |      ]
1187 |     },
1188 |     {
1189 |      "data": {
1190 |       "application/vnd.jupyter.widget-view+json": {
1191 |        "model_id": "ec198632bba24e04acabf61fbd5a82d0",
1192 |        "version_major": 2,
1193 |        "version_minor": 0
1194 |       },
1195 |       "text/plain": [
1196 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
1197 |       ]
1198 |      },
1199 |      "metadata": {},
1200 |      "output_type": "display_data"
1201 |     },
1202 |     {
1203 |      "name": "stdout",
1204 |      "output_type": "stream",
1205 |      "text": [
1206 |       "[ 0.9597615   0.01        1.32039375  0.05735936  3.68399045]\n",
1207 |       "6.48792207631e-07\n"
1208 |      ]
1209 |     },
1210 |     {
1211 |      "data": {
1212 |       "application/vnd.jupyter.widget-view+json": {
1213 |        "model_id": "cf7540340461416e847340a9b163d019",
1214 |        "version_major": 2,
1215 |        "version_minor": 0
1216 |       },
1217 |       "text/plain": [
1218 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
1219 |       ]
1220 |      },
1221 |      "metadata": {},
1222 |      "output_type": "display_data"
1223 |     },
1224 |     {
1225 |      "name": "stdout",
1226 |      "output_type": "stream",
1227 |      "text": [
1228 |       "[ 0.98184951  0.01        1.29826872  0.05396549  3.54693685]\n",
1229 |       "6.59052628576e-07\n"
1230 |      ]
1231 |     },
1232 |     {
1233 |      "data": {
1234 |       "application/vnd.jupyter.widget-view+json": {
1235 |        "model_id": "f9b5d422e0964074809f933bc4ffa0cd",
1236 |        "version_major": 2,
1237 |        "version_minor": 0
1238 |       },
1239 |       "text/plain": [
1240 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
1241 |       ]
1242 |      },
1243 |      "metadata": {},
1244 |      "output_type": "display_data"
1245 |     },
1246 |     {
1247 |      "name": "stdout",
1248 |      "output_type": "stream",
1249 |      "text": [
1250 |       "[ 0.98974935  0.00842324  1.27120533  0.04986844  3.49201012]\n",
1251 |       "6.72251300356e-07\n"
1252 |      ]
1253 |     },
1254 |     {
1255 |      "data": {
1256 |       "application/vnd.jupyter.widget-view+json": {
1257 |        "model_id": "517c679d4c144e26aa2c42d87b03268e",
1258 |        "version_major": 2,
1259 |        "version_minor": 0
1260 |       },
1261 |       "text/plain": [
1262 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
1263 |       ]
1264 |      },
1265 |      "metadata": {},
1266 |      "output_type": "display_data"
1267 |     },
1268 |     {
1269 |      "name": "stdout",
1270 |      "output_type": "stream",
1271 |      "text": [
1272 |       "[ 0.98202054  0.005       1.25        0.04645707  3.53152081]\n",
1273 |       "7.13640898116e-07\n"
1274 |      ]
1275 |     },
1276 |     {
1277 |      "data": {
1278 |       "application/vnd.jupyter.widget-view+json": {
1279 |        "model_id": "f8179faea55742a7ba0df7b8268ea708",
1280 |        "version_major": 2,
1281 |        "version_minor": 0
1282 |       },
1283 |       "text/plain": [
1284 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
1285 |       ]
1286 |      },
1287 |      "metadata": {},
1288 |      "output_type": "display_data"
1289 |     },
1290 |     {
1291 |      "name": "stdout",
1292 |      "output_type": "stream",
1293 |      "text": [
1294 |       "[ 0.98948904  0.005       1.25        0.04603146  3.67331023]\n",
1295 |       "7.32129649302e-07\n"
1296 |      ]
1297 |     },
1298 |     {
1299 |      "data": {
1300 |       "application/vnd.jupyter.widget-view+json": {
1301 |        "model_id": "916849cbf88e4b6ca5998c4c43356596",
1302 |        "version_major": 2,
1303 |        "version_minor": 0
1304 |       },
1305 |       "text/plain": [
1306 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
1307 |       ]
1308 |      },
1309 |      "metadata": {},
1310 |      "output_type": "display_data"
1311 |     },
1312 |     {
1313 |      "name": "stdout",
1314 |      "output_type": "stream",
1315 |      "text": [
1316 |       "[ 0.99201562  0.005       1.25        0.04588997  3.72672641]\n",
1317 |       "7.46385075602e-07\n"
1318 |      ]
1319 |     },
1320 |     {
1321 |      "data": {
1322 |       "application/vnd.jupyter.widget-view+json": {
1323 |        "model_id": "b6d39bf014ac44298065f13edbaf5f60",
1324 |        "version_major": 2,
1325 |        "version_minor": 0
1326 |       },
1327 |       "text/plain": [
1328 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
1329 |       ]
1330 |      },
1331 |      "metadata": {},
1332 |      "output_type": "display_data"
1333 |     }
1334 |    ],
1335 |    "source": [
1336 |     "POPTS = []\n",
1337 |     "for parameters in PARAMETERS:\n",
1338 |     "    offset_r = [parameter.x[2] for parameter in parameters]\n",
1339 |     "\n",
1340 |     "    offset_r[0] = 0\n",
1341 |     "\n",
1342 |     "    popt, pcov = scipy.optimize.curve_fit(\n",
1343 |     "        partial_curve,\n",
1344 |     "        SATURATION_SAMPLES,\n",
1345 |     "        offset_r,\n",
1346 |     "        bounds=np.transpose([\n",
1347 |     "            (0.4, 1),\n",
1348 |     "            (0.005, 0.01),\n",
1349 |     "            (1.25, 1.55),\n",
1350 |     "            (0.04, 0.07),\n",
1351 |     "            (1, 8),\n",
1352 |     "        ]),\n",
1353 |     "        maxfev=50000)\n",
1354 |     "    \n",
1355 |     "    POPTS.append(popt)\n",
1356 |     "    print(popt)\n",
1357 |     "    print(\n",
1358 |     "        colour.utilities.metric_mse(offset_r, partial_curve(SATURATION_SAMPLES,\n",
1359 |     "                                                    *popt)))\n",
1360 |     "\n",
1361 |     "    with plt.style.context({'figure.figsize': (10.24, 10.24)}):\n",
1362 |     "        colour.plotting.artist()\n",
1363 |     "\n",
1364 |     "        plt.plot(SATURATION_SAMPLES, offset_r)\n",
1365 |     "        plt.plot(SATURATION_SAMPLES, partial_curve(SATURATION_SAMPLES, *popt), 'o')"
1366 |    ]
1367 |   },
1368 |   {
1369 |    "cell_type": "code",
1370 |    "execution_count": 15,
1371 |    "metadata": {},
1372 |    "outputs": [
1373 |     {
1374 |      "name": "stdout",
1375 |      "output_type": "stream",
1376 |      "text": [
1377 |       "[[  4.20613529e-01   4.49458739e-01   4.85191758e-01   5.23669259e-01\n",
1378 |       "    5.64343322e-01   6.05376678e-01   6.46719612e-01   6.87411661e-01\n",
1379 |       "    7.24820949e-01   7.62348310e-01   8.02763652e-01   8.41166592e-01\n",
1380 |       "    8.75980594e-01   9.07221789e-01   9.35055515e-01   9.59761498e-01\n",
1381 |       "    9.81849512e-01   9.89749348e-01   9.82020536e-01   9.89489039e-01\n",
1382 |       "    9.92015624e-01]\n",
1383 |       " [  9.99999990e-03   9.99999980e-03   1.00000000e-02   1.00000000e-02\n",
1384 |       "    9.99995100e-03   9.99999975e-03   9.99999976e-03   1.00000000e-02\n",
1385 |       "    1.00000000e-02   1.00000000e-02   1.00000000e-02   1.00000000e-02\n",
1386 |       "    1.00000000e-02   1.00000000e-02   1.00000000e-02   1.00000000e-02\n",
1387 |       "    1.00000000e-02   8.42324260e-03   5.00000000e-03   5.00000000e-03\n",
1388 |       "    5.00000000e-03]\n",
1389 |       " [  1.54999165e+00   1.54587020e+00   1.52243267e+00   1.50167967e+00\n",
1390 |       "    1.48373347e+00   1.46864029e+00   1.45617162e+00   1.44643813e+00\n",
1391 |       "    1.43992726e+00   1.43489649e+00   1.40494720e+00   1.37700835e+00\n",
1392 |       "    1.35949114e+00   1.34585395e+00   1.33495790e+00   1.32039375e+00\n",
1393 |       "    1.29826872e+00   1.27120533e+00   1.25000000e+00   1.25000000e+00\n",
1394 |       "    1.25000000e+00]\n",
1395 |       " [  6.89850195e-02   6.99831636e-02   6.99999997e-02   7.00000000e-02\n",
1396 |       "    6.99999981e-02   7.00000000e-02   7.00000000e-02   7.00000000e-02\n",
1397 |       "    7.00000000e-02   7.00000000e-02   6.78138820e-02   6.53446084e-02\n",
1398 |       "    6.34093458e-02   6.15428803e-02   5.97380787e-02   5.73593644e-02\n",
1399 |       "    5.39654921e-02   4.98684367e-02   4.64570696e-02   4.60314614e-02\n",
1400 |       "    4.58899651e-02]\n",
1401 |       " [  8.00000000e+00   8.00000000e+00   8.00000000e+00   8.00000000e+00\n",
1402 |       "    7.99999982e+00   7.99999998e+00   7.99999934e+00   7.82343878e+00\n",
1403 |       "    7.10043920e+00   6.68609959e+00   5.72450101e+00   4.95202510e+00\n",
1404 |       "    4.47022722e+00   4.09369171e+00   3.86429275e+00   3.68399045e+00\n",
1405 |       "    3.54693685e+00   3.49201012e+00   3.53152081e+00   3.67331023e+00\n",
1406 |       "    3.72672641e+00]]\n"
1407 |      ]
1408 |     },
1409 |     {
1410 |      "data": {
1411 |       "application/vnd.jupyter.widget-view+json": {
1412 |        "model_id": "b162f52e406546f28992f2654c80ddf3",
1413 |        "version_major": 2,
1414 |        "version_minor": 0
1415 |       },
1416 |       "text/plain": [
1417 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
1418 |       ]
1419 |      },
1420 |      "metadata": {},
1421 |      "output_type": "display_data"
1422 |     },
1423 |     {
1424 |      "data": {
1425 |       "application/vnd.jupyter.widget-view+json": {
1426 |        "model_id": "156fda7852ed4754abce32c6a1129549",
1427 |        "version_major": 2,
1428 |        "version_minor": 0
1429 |       },
1430 |       "text/plain": [
1431 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
1432 |       ]
1433 |      },
1434 |      "metadata": {},
1435 |      "output_type": "display_data"
1436 |     },
1437 |     {
1438 |      "data": {
1439 |       "application/vnd.jupyter.widget-view+json": {
1440 |        "model_id": "8a96440e5af34599b7fee0fa55ed91d0",
1441 |        "version_major": 2,
1442 |        "version_minor": 0
1443 |       },
1444 |       "text/plain": [
1445 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
1446 |       ]
1447 |      },
1448 |      "metadata": {},
1449 |      "output_type": "display_data"
1450 |     },
1451 |     {
1452 |      "data": {
1453 |       "application/vnd.jupyter.widget-view+json": {
1454 |        "model_id": "465a643e32934df4b59bbfbe74774500",
1455 |        "version_major": 2,
1456 |        "version_minor": 0
1457 |       },
1458 |       "text/plain": [
1459 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
1460 |       ]
1461 |      },
1462 |      "metadata": {},
1463 |      "output_type": "display_data"
1464 |     },
1465 |     {
1466 |      "data": {
1467 |       "application/vnd.jupyter.widget-view+json": {
1468 |        "model_id": "d72a4aed5580405db960bdd6d6e24647",
1469 |        "version_major": 2,
1470 |        "version_minor": 0
1471 |       },
1472 |       "text/plain": [
1473 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
1474 |       ]
1475 |      },
1476 |      "metadata": {},
1477 |      "output_type": "display_data"
1478 |     },
1479 |     {
1480 |      "data": {
1481 |       "application/vnd.jupyter.widget-view+json": {
1482 |        "model_id": "6d6bb6123f7944fb9a0789af8ed4e0fc",
1483 |        "version_major": 2,
1484 |        "version_minor": 0
1485 |       },
1486 |       "text/plain": [
1487 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
1488 |       ]
1489 |      },
1490 |      "metadata": {},
1491 |      "output_type": "display_data"
1492 |     }
1493 |    ],
1494 |    "source": [
1495 |     "print(np.transpose(POPTS))\n",
1496 |     "\n",
1497 |     "with plt.style.context({'figure.figsize': (10.24, 10.24)}):\n",
1498 |     "    colour.plotting.artist()\n",
1499 |     "    \n",
1500 |     "    for popt in POPTS:\n",
1501 |     "        plt.plot(SATURATION_SAMPLES, partial_curve(SATURATION_SAMPLES, *popt))\n",
1502 |     "        \n",
1503 |     "\n",
1504 |     "for popt in np.transpose(POPTS):\n",
1505 |     "    with plt.style.context({'figure.figsize': (10.24, 10.24)}):\n",
1506 |     "        colour.plotting.artist()\n",
1507 |     "        \n",
1508 |     "        plt.plot(popt)"
1509 |    ]
1510 |   },
1511 |   {
1512 |    "cell_type": "code",
1513 |    "execution_count": 16,
1514 |    "metadata": {},
1515 |    "outputs": [
1516 |     {
1517 |      "data": {
1518 |       "application/vnd.jupyter.widget-view+json": {
1519 |        "model_id": "67836d55a45d4805b87f495c86e2f69b",
1520 |        "version_major": 2,
1521 |        "version_minor": 0
1522 |       },
1523 |       "text/plain": [
1524 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
1525 |       ]
1526 |      },
1527 |      "metadata": {},
1528 |      "output_type": "display_data"
1529 |     }
1530 |    ],
1531 |    "source": [
1532 |     "with plt.style.context({'figure.figsize': (10.24, 10.24)}):\n",
1533 |     "    colour.plotting.artist()\n",
1534 |     "    \n",
1535 |     "    plt.plot(SATURATION_SAMPLES, partial_curve(SATURATION_SAMPLES, *POPTS[0]))\n",
1536 |     "    plt.plot(SATURATION_SAMPLES, partial_curve(SATURATION_SAMPLES, *POPTS[len(POPTS) // 2]))    \n",
1537 |     "    plt.plot(SATURATION_SAMPLES, partial_curve(SATURATION_SAMPLES, *POPTS[-1]))\n",
1538 |     "    plt.plot(SATURATION_SAMPLES, partial_curve(SATURATION_SAMPLES, *((POPTS[0] + POPTS[-1]) / 2)), '--')\n"
1539 |    ]
1540 |   },
1541 |   {
1542 |    "cell_type": "code",
1543 |    "execution_count": 17,
1544 |    "metadata": {},
1545 |    "outputs": [
1546 |     {
1547 |      "data": {
1548 |       "application/vnd.jupyter.widget-view+json": {
1549 |        "model_id": "a88fcab962144ec3ae88ec509424b779",
1550 |        "version_major": 2,
1551 |        "version_minor": 0
1552 |       },
1553 |       "text/plain": [
1554 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
1555 |       ]
1556 |      },
1557 |      "metadata": {},
1558 |      "output_type": "display_data"
1559 |     },
1560 |     {
1561 |      "data": {
1562 |       "application/vnd.jupyter.widget-view+json": {
1563 |        "model_id": "7147440f917e47bc9bd861e645e173d9",
1564 |        "version_major": 2,
1565 |        "version_minor": 0
1566 |       },
1567 |       "text/plain": [
1568 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
1569 |       ]
1570 |      },
1571 |      "metadata": {},
1572 |      "output_type": "display_data"
1573 |     }
1574 |    ],
1575 |    "source": [
1576 |     "def hue_offset_extended(x, S, m_x0, m_y0, m_x1, m_y1, m_overshoot_x):\n",
1577 |     "    return np.sin(x * np.pi * 6 + np.pi) * partial_curve(S, m_x0, m_y0, m_x1, m_y1, m_overshoot_x)\n",
1578 |     "\n",
1579 |     "\n",
1580 |     "def medicina_HSV_control_extended(a=POPTS[0],\n",
1581 |     "                                  RGB=COLOUR_STRIPE,\n",
1582 |     "                                  S_c=0,\n",
1583 |     "                                  C_f=tanh_compression_function):\n",
1584 |     "    H, S, V = colour.utilities.tsplit(colour.RGB_to_HSV(RGB))\n",
1585 |     "\n",
1586 |     "    HSV = colour.utilities.tstack(\n",
1587 |     "        [(H + hue_offset_extended(H, S, *a)) % 1,\n",
1588 |     "          C_f(S, S_c, 1 - S_c), V])\n",
1589 |     "\n",
1590 |     "    return colour.HSV_to_RGB(HSV)\n",
1591 |     "\n",
1592 |     "\n",
1593 |     "with plt.style.context({'figure.figsize': (10.24, 10.24)}):\n",
1594 |     "    # colour.plotting.plot_image(STRIPES);\n",
1595 |     "\n",
1596 |     "    colour.plotting.plot_image(\n",
1597 |     "            np.abs(\n",
1598 |     "                medicina_HSV_control([0, 0, 0], RGB=STRIPES) -\n",
1599 |     "                medicina_RGB_saturation(STRIPES)) * 5);\n",
1600 |     "\n",
1601 |     "    colour.plotting.plot_image(\n",
1602 |     "            np.abs(\n",
1603 |     "                medicina_HSV_control_extended(RGB=STRIPES) -\n",
1604 |     "                medicina_RGB_saturation(STRIPES)) * 5);"
1605 |    ]
1606 |   },
1607 |   {
1608 |    "cell_type": "code",
1609 |    "execution_count": 18,
1610 |    "metadata": {},
1611 |    "outputs": [
1612 |     {
1613 |      "data": {
1614 |       "application/vnd.jupyter.widget-view+json": {
1615 |        "model_id": "cfd2e8f07c2d444fabe28301c9a4a9a0",
1616 |        "version_major": 2,
1617 |        "version_minor": 0
1618 |       },
1619 |       "text/plain": [
1620 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
1621 |       ]
1622 |      },
1623 |      "metadata": {},
1624 |      "output_type": "display_data"
1625 |     },
1626 |     {
1627 |      "data": {
1628 |       "application/vnd.jupyter.widget-view+json": {
1629 |        "model_id": "de04514028834e56ae4a51da7d6242b9",
1630 |        "version_major": 2,
1631 |        "version_minor": 0
1632 |       },
1633 |       "text/plain": [
1634 |        "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
1635 |       ]
1636 |      },
1637 |      "metadata": {},
1638 |      "output_type": "display_data"
1639 |     }
1640 |    ],
1641 |    "source": [
1642 |     "with plt.style.context({'figure.figsize': (10.24, 10.24)}):\n",
1643 |     "    colour.plotting.plot_image(\n",
1644 |     "        np.abs(\n",
1645 |     "            medicina_HSV_control([0, 0, 0], RGB=COLOUR_WHEEL) -\n",
1646 |     "            medicina_RGB_saturation(COLOUR_WHEEL)) * 5)\n",
1647 |     "\n",
1648 |     "    colour.plotting.plot_image(\n",
1649 |     "        np.abs(\n",
1650 |     "            medicina_HSV_control_extended(RGB=COLOUR_WHEEL) -\n",
1651 |     "            medicina_RGB_saturation(COLOUR_WHEEL)) * 5)"
1652 |    ]
1653 |   }
1654 |  ],
1655 |  "metadata": {
1656 |   "colab": {
1657 |    "collapsed_sections": [],
1658 |    "name": "gamut_mapping_ramblings_01",
1659 |    "provenance": []
1660 |   },
1661 |   "kernelspec": {
1662 |    "display_name": "Python 3",
1663 |    "language": "python",
1664 |    "name": "python3"
1665 |   },
1666 |   "language_info": {
1667 |    "codemirror_mode": {
1668 |     "name": "ipython",
1669 |     "version": 3
1670 |    },
1671 |    "file_extension": ".py",
1672 |    "mimetype": "text/x-python",
1673 |    "name": "python",
1674 |    "nbconvert_exporter": "python",
1675 |    "pygments_lexer": "ipython3",
1676 |    "version": "3.7.6"
1677 |   },
1678 |   "widgets": {
1679 |    "application/vnd.jupyter.widget-state+json": {
1680 |     "state": {},
1681 |     "version_major": 2,
1682 |     "version_minor": 0
1683 |    }
1684 |   }
1685 |  },
1686 |  "nbformat": 4,
1687 |  "nbformat_minor": 4
1688 | }
1689 | 


--------------------------------------------------------------------------------
/pyproject.toml:
--------------------------------------------------------------------------------
 1 | [tool.poetry]
 2 | name = "gamut-mapping-ramblings"
 3 | version = "0.1.0"
 4 | description = "Gamut Mapping - Ramblings"
 5 | license = "BSD-3-Clause"
 6 | authors = [ "Colour Developers <colour-developers@colour-science.org>" ]
 7 | maintainers = [ "Colour Developers <colour-developers@colour-science.org>" ]
 8 | readme = 'README.rst'
 9 | repository = "https://github.com/colour-science/gamut-mapping-ramblings"
10 | homepage = "https://www.colour-science.org/"
11 | keywords = [
12 |     "color",
13 |     "color-science",
14 |     "color-space",
15 |     "color-spaces",
16 |     "colorspace",
17 |     "colorspaces",
18 |     "colour",
19 |     "colour-science",
20 |     "colour-space",
21 |     "colour-spaces",
22 |     "colourspace",
23 |     "colourspaces",
24 |     "python",
25 | ]
26 | classifiers = [
27 |     "Development Status :: 3 - Alpha",
28 |     "Environment :: Console",
29 |     "Intended Audience :: Developers",
30 |     "Intended Audience :: Science/Research",
31 |     "License :: OSI Approved",
32 |     "Natural Language :: English",
33 |     "Operating System :: OS Independent",
34 |     "Programming Language :: Python :: 3",
35 |     "Topic :: Scientific/Engineering",
36 |     "Topic :: Software Development"
37 | ]
38 | 
39 | [tool.poetry.dependencies]
40 | python = "^3.5"
41 | colour = { git = "https://github.com/colour-science/colour.git", branch = "develop" }
42 | 
43 | ipympl = { version = "*"}
44 | ipywidgets = { version = "*"}
45 | jupyter = { version = "*"}
46 | jupyterlab = { version = "*"}
47 | matplotlib = { version = "*"}
48 | ipynb = { version = "*"}
49 | 
50 | [build-system]
51 | requires = [ "poetry>=0.12" ]
52 | build-backend = "poetry.masonry.api"
53 | 


--------------------------------------------------------------------------------
/requirements.txt:
--------------------------------------------------------------------------------
 1 | appnope==0.1.0
 2 | attrs==19.3.0
 3 | backcall==0.1.0
 4 | bleach==3.1.5
 5 | certifi==2020.4.5.1
 6 | chardet==3.0.4
 7 | cycler==0.10.0
 8 | decorator==4.4.2
 9 | defusedxml==0.6.0
10 | entrypoints==0.3
11 | idna==2.9
12 | imageio==2.8.0
13 | importlib-metadata==1.6.0
14 | ipykernel==5.2.1
15 | ipympl==0.5.6
16 | ipynb==0.5.1
17 | ipython==7.9.0
18 | ipython-genutils==0.2.0
19 | ipywidgets==7.5.1
20 | jedi==0.17.0
21 | Jinja2==2.11.2
22 | json5==0.9.4
23 | jsonschema==3.2.0
24 | jupyter==1.0.0
25 | jupyter-client==6.1.3
26 | jupyter-console==6.1.0
27 | jupyter-core==4.6.3
28 | jupyterlab==2.1.2
29 | jupyterlab-server==1.1.3
30 | kiwisolver==1.1.0
31 | MarkupSafe==1.1.1
32 | matplotlib==3.0.3
33 | mistune==0.8.4
34 | nbconvert==5.6.1
35 | nbformat==5.0.6
36 | notebook==6.0.3
37 | numpy==1.18.4
38 | packaging==20.3
39 | pandocfilters==1.4.2
40 | parso==0.7.0
41 | pexpect==4.8.0
42 | pickleshare==0.7.5
43 | Pillow==7.1.2
44 | prometheus-client==0.7.1
45 | prompt-toolkit==2.0.10
46 | ptyprocess==0.6.0
47 | Pygments==2.6.1
48 | pyparsing==2.4.7
49 | pyrsistent==0.16.0
50 | python-dateutil==2.8.1
51 | pyzmq==19.0.1
52 | qtconsole==4.7.3
53 | QtPy==1.9.0
54 | requests==2.23.0
55 | scipy==1.4.1
56 | Send2Trash==1.5.0
57 | six==1.14.0
58 | terminado==0.8.3
59 | testpath==0.4.4
60 | tornado==6.0.4
61 | traitlets==4.3.3
62 | urllib3==1.25.9
63 | wcwidth==0.1.9
64 | webencodings==0.5.1
65 | widgetsnbextension==3.5.1
66 | zipp==1.2.0
67 | -e git+https://github.com/colour-science/colour.git@3bebd7e903cbcbee959f56786c020335a1b06cf7#egg=colour
68 | 


--------------------------------------------------------------------------------
/resources/images/5DMarkII_Spac-o-ween_001.exr:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/colour-science/gamut-mapping-ramblings/83f2e10690b1438790155b2e443b596249e8008e/resources/images/5DMarkII_Spac-o-ween_001.exr


--------------------------------------------------------------------------------
/resources/images/5DMarkII_Spac-o-ween_001_GamutMedicinaHSVControlWidget.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/colour-science/gamut-mapping-ramblings/83f2e10690b1438790155b2e443b596249e8008e/resources/images/5DMarkII_Spac-o-ween_001_GamutMedicinaHSVControlWidget.png


--------------------------------------------------------------------------------
/resources/images/5DMarkII_Spac-o-ween_001_GamutMedicinaHSVControlWidget_OutOfGamut.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/colour-science/gamut-mapping-ramblings/83f2e10690b1438790155b2e443b596249e8008e/resources/images/5DMarkII_Spac-o-ween_001_GamutMedicinaHSVControlWidget_OutOfGamut.png


--------------------------------------------------------------------------------
/resources/images/A002_C029_06025K.exr:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/colour-science/gamut-mapping-ramblings/83f2e10690b1438790155b2e443b596249e8008e/resources/images/A002_C029_06025K.exr


--------------------------------------------------------------------------------
/resources/images/A009C002_190210_R0EI_Alexa_LogCWideGamut.exr:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/colour-science/gamut-mapping-ramblings/83f2e10690b1438790155b2e443b596249e8008e/resources/images/A009C002_190210_R0EI_Alexa_LogCWideGamut.exr


--------------------------------------------------------------------------------
/resources/images/Carol Payne - J001_C001_08178N_001.exr:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/colour-science/gamut-mapping-ramblings/83f2e10690b1438790155b2e443b596249e8008e/resources/images/Carol Payne - J001_C001_08178N_001.exr


--------------------------------------------------------------------------------
/resources/images/Collage_01.exr:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/colour-science/gamut-mapping-ramblings/83f2e10690b1438790155b2e443b596249e8008e/resources/images/Collage_01.exr


--------------------------------------------------------------------------------
/resources/images/Cornell_Box_Rigid_Spheres_190_Patch_Roughplastic_Point_Grey_Grasshopper_50S5C_RGB_W.exr:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/colour-science/gamut-mapping-ramblings/83f2e10690b1438790155b2e443b596249e8008e/resources/images/Cornell_Box_Rigid_Spheres_190_Patch_Roughplastic_Point_Grey_Grasshopper_50S5C_RGB_W.exr


--------------------------------------------------------------------------------
/resources/images/Cornell_Box_Rigid_Spheres_190_Patch_Roughplastic_Point_Grey_Grasshopper_50S5C_RGB_W_A.exr:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/colour-science/gamut-mapping-ramblings/83f2e10690b1438790155b2e443b596249e8008e/resources/images/Cornell_Box_Rigid_Spheres_190_Patch_Roughplastic_Point_Grey_Grasshopper_50S5C_RGB_W_A.exr


--------------------------------------------------------------------------------
/resources/images/Gamut_Medicina_01.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/colour-science/gamut-mapping-ramblings/83f2e10690b1438790155b2e443b596249e8008e/resources/images/Gamut_Medicina_01.png


--------------------------------------------------------------------------------
/resources/images/Gamut_Medicina_01_Disabled.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/colour-science/gamut-mapping-ramblings/83f2e10690b1438790155b2e443b596249e8008e/resources/images/Gamut_Medicina_01_Disabled.png


--------------------------------------------------------------------------------
/resources/images/Gamut_Medicina_02.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/colour-science/gamut-mapping-ramblings/83f2e10690b1438790155b2e443b596249e8008e/resources/images/Gamut_Medicina_02.png


--------------------------------------------------------------------------------
/resources/images/Gamut_Medicina_02_Disabled.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/colour-science/gamut-mapping-ramblings/83f2e10690b1438790155b2e443b596249e8008e/resources/images/Gamut_Medicina_02_Disabled.png


--------------------------------------------------------------------------------
/resources/images/Gamut_Medicina_03.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/colour-science/gamut-mapping-ramblings/83f2e10690b1438790155b2e443b596249e8008e/resources/images/Gamut_Medicina_03.png


--------------------------------------------------------------------------------
/resources/images/Gamut_Medicina_04.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/colour-science/gamut-mapping-ramblings/83f2e10690b1438790155b2e443b596249e8008e/resources/images/Gamut_Medicina_04.png


--------------------------------------------------------------------------------
/resources/images/Gamut_Medicina_05.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/colour-science/gamut-mapping-ramblings/83f2e10690b1438790155b2e443b596249e8008e/resources/images/Gamut_Medicina_05.png


--------------------------------------------------------------------------------
/resources/images/JupyterLab_Dark_Theme.png:
--------------------------------------------------------------------------------
https://raw.githubusercontent.com/colour-science/gamut-mapping-ramblings/83f2e10690b1438790155b2e443b596249e8008e/resources/images/JupyterLab_Dark_Theme.png


--------------------------------------------------------------------------------
/resources/js/jquery-3.3.1.slim.min.js:
--------------------------------------------------------------------------------
1 | /*! jQuery v3.3.1 -ajax,-ajax/jsonp,-ajax/load,-ajax/parseXML,-ajax/script,-ajax/var/location,-ajax/var/nonce,-ajax/var/rquery,-ajax/xhr,-manipulation/_evalUrl,-event/ajax,-effects,-effects/Tween,-effects/animatedSelector | (c) JS Foundation and other contributors | jquery.org/license */
2 | !function(e,t){"use strict";"object"==typeof module&&"object"==typeof module.exports?module.exports=e.document?t(e,!0):function(e){if(!e.document)throw new Error("jQuery requires a window with a document");return t(e)}:t(e)}("undefined"!=typeof window?window:this,function(e,t){"use strict";var n=[],r=e.document,i=Object.getPrototypeOf,o=n.slice,a=n.concat,u=n.push,s=n.indexOf,l={},c=l.toString,f=l.hasOwnProperty,d=f.toString,p=d.call(Object),h={},g=function e(t){return"function"==typeof t&&"number"!=typeof t.nodeType},v=function e(t){return null!=t&&t===t.window},y={type:!0,src:!0,noModule:!0};function m(e,t,n){var i,o=(t=t||r).createElement("script");if(o.text=e,n)for(i in y)n[i]&&(o[i]=n[i]);t.head.appendChild(o).parentNode.removeChild(o)}function b(e){return null==e?e+"":"object"==typeof e||"function"==typeof e?l[c.call(e)]||"object":typeof e}var x="3.3.1 -ajax,-ajax/jsonp,-ajax/load,-ajax/parseXML,-ajax/script,-ajax/var/location,-ajax/var/nonce,-ajax/var/rquery,-ajax/xhr,-manipulation/_evalUrl,-event/ajax,-effects,-effects/Tween,-effects/animatedSelector",w=function(e,t){return new w.fn.init(e,t)},C=/^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g;w.fn=w.prototype={jquery:x,constructor:w,length:0,toArray:function(){return o.call(this)},get:function(e){return null==e?o.call(this):e<0?this[e+this.length]:this[e]},pushStack:function(e){var t=w.merge(this.constructor(),e);return t.prevObject=this,t},each:function(e){return w.each(this,e)},map:function(e){return this.pushStack(w.map(this,function(t,n){return e.call(t,n,t)}))},slice:function(){return this.pushStack(o.apply(this,arguments))},first:function(){return this.eq(0)},last:function(){return this.eq(-1)},eq:function(e){var t=this.length,n=+e+(e<0?t:0);return this.pushStack(n>=0&&n<t?[this[n]]:[])},end:function(){return this.prevObject||this.constructor()},push:u,sort:n.sort,splice:n.splice},w.extend=w.fn.extend=function(){var e,t,n,r,i,o,a=arguments[0]||{},u=1,s=arguments.length,l=!1;for("boolean"==typeof a&&(l=a,a=arguments[u]||{},u++),"object"==typeof a||g(a)||(a={}),u===s&&(a=this,u--);u<s;u++)if(null!=(e=arguments[u]))for(t in e)n=a[t],a!==(r=e[t])&&(l&&r&&(w.isPlainObject(r)||(i=Array.isArray(r)))?(i?(i=!1,o=n&&Array.isArray(n)?n:[]):o=n&&w.isPlainObject(n)?n:{},a[t]=w.extend(l,o,r)):void 0!==r&&(a[t]=r));return a},w.extend({expando:"jQuery"+(x+Math.random()).replace(/\D/g,""),isReady:!0,error:function(e){throw new Error(e)},noop:function(){},isPlainObject:function(e){var t,n;return!(!e||"[object Object]"!==c.call(e))&&(!(t=i(e))||"function"==typeof(n=f.call(t,"constructor")&&t.constructor)&&d.call(n)===p)},isEmptyObject:function(e){var t;for(t in e)return!1;return!0},globalEval:function(e){m(e)},each:function(e,t){var n,r=0;if(T(e)){for(n=e.length;r<n;r++)if(!1===t.call(e[r],r,e[r]))break}else for(r in e)if(!1===t.call(e[r],r,e[r]))break;return e},trim:function(e){return null==e?"":(e+"").replace(C,"")},makeArray:function(e,t){var n=t||[];return null!=e&&(T(Object(e))?w.merge(n,"string"==typeof e?[e]:e):u.call(n,e)),n},inArray:function(e,t,n){return null==t?-1:s.call(t,e,n)},merge:function(e,t){for(var n=+t.length,r=0,i=e.length;r<n;r++)e[i++]=t[r];return e.length=i,e},grep:function(e,t,n){for(var r,i=[],o=0,a=e.length,u=!n;o<a;o++)(r=!t(e[o],o))!==u&&i.push(e[o]);return i},map:function(e,t,n){var r,i,o=0,u=[];if(T(e))for(r=e.length;o<r;o++)null!=(i=t(e[o],o,n))&&u.push(i);else for(o in e)null!=(i=t(e[o],o,n))&&u.push(i);return a.apply([],u)},guid:1,support:h}),"function"==typeof Symbol&&(w.fn[Symbol.iterator]=n[Symbol.iterator]),w.each("Boolean Number String Function Array Date RegExp Object Error Symbol".split(" "),function(e,t){l["[object "+t+"]"]=t.toLowerCase()});function T(e){var t=!!e&&"length"in e&&e.length,n=b(e);return!g(e)&&!v(e)&&("array"===n||0===t||"number"==typeof t&&t>0&&t-1 in e)}var E=function(e){var t,n,r,i,o,a,u,s,l,c,f,d,p,h,g,v,y,m,b,x="sizzle"+1*new Date,w=e.document,C=0,T=0,E=ae(),N=ae(),k=ae(),A=function(e,t){return e===t&&(f=!0),0},D={}.hasOwnProperty,S=[],L=S.pop,j=S.push,q=S.push,O=S.slice,P=function(e,t){for(var n=0,r=e.length;n<r;n++)if(e[n]===t)return n;return-1},H="checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|ismap|loop|multiple|open|readonly|required|scoped",I="[\\x20\\t\\r\\n\\f]",R="(?:\\\\.|[\\w-]|[^\0-\\xa0])+",B="\\["+I+"*("+R+")(?:"+I+"*([*^$|!~]?=)"+I+"*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|("+R+"))|)"+I+"*\\]",M=":("+R+")(?:\\((('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|((?:\\\\.|[^\\\\()[\\]]|"+B+")*)|.*)\\)|)",W=new RegExp(I+"+","g"),$=new RegExp("^"+I+"+|((?:^|[^\\\\])(?:\\\\.)*)"+I+"+$","g"),F=new RegExp("^"+I+"*,"+I+"*"),z=new RegExp("^"+I+"*([>+~]|"+I+")"+I+"*"),_=new RegExp("="+I+"*([^\\]'\"]*?)"+I+"*\\]","g"),U=new RegExp(M),V=new RegExp("^"+R+"$"),X={ID:new RegExp("^#("+R+")"),CLASS:new RegExp("^\\.("+R+")"),TAG:new RegExp("^("+R+"|[*])"),ATTR:new RegExp("^"+B),PSEUDO:new RegExp("^"+M),CHILD:new RegExp("^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\("+I+"*(even|odd|(([+-]|)(\\d*)n|)"+I+"*(?:([+-]|)"+I+"*(\\d+)|))"+I+"*\\)|)","i"),bool:new RegExp("^(?:"+H+")$","i"),needsContext:new RegExp("^"+I+"*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\("+I+"*((?:-\\d)?\\d*)"+I+"*\\)|)(?=[^-]|$)","i")},Q=/^(?:input|select|textarea|button)$/i,Y=/^h\d$/i,G=/^[^{]+\{\s*\[native \w/,K=/^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/,J=/[+~]/,Z=new RegExp("\\\\([\\da-f]{1,6}"+I+"?|("+I+")|.)","ig"),ee=function(e,t,n){var r="0x"+t-65536;return r!==r||n?t:r<0?String.fromCharCode(r+65536):String.fromCharCode(r>>10|55296,1023&r|56320)},te=/([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g,ne=function(e,t){return t?"\0"===e?"\ufffd":e.slice(0,-1)+"\\"+e.charCodeAt(e.length-1).toString(16)+" ":"\\"+e},re=function(){d()},ie=me(function(e){return!0===e.disabled&&("form"in e||"label"in e)},{dir:"parentNode",next:"legend"});try{q.apply(S=O.call(w.childNodes),w.childNodes),S[w.childNodes.length].nodeType}catch(e){q={apply:S.length?function(e,t){j.apply(e,O.call(t))}:function(e,t){var n=e.length,r=0;while(e[n++]=t[r++]);e.length=n-1}}}function oe(e,t,r,i){var o,u,l,c,f,h,y,m=t&&t.ownerDocument,C=t?t.nodeType:9;if(r=r||[],"string"!=typeof e||!e||1!==C&&9!==C&&11!==C)return r;if(!i&&((t?t.ownerDocument||t:w)!==p&&d(t),t=t||p,g)){if(11!==C&&(f=K.exec(e)))if(o=f[1]){if(9===C){if(!(l=t.getElementById(o)))return r;if(l.id===o)return r.push(l),r}else if(m&&(l=m.getElementById(o))&&b(t,l)&&l.id===o)return r.push(l),r}else{if(f[2])return q.apply(r,t.getElementsByTagName(e)),r;if((o=f[3])&&n.getElementsByClassName&&t.getElementsByClassName)return q.apply(r,t.getElementsByClassName(o)),r}if(n.qsa&&!k[e+" "]&&(!v||!v.test(e))){if(1!==C)m=t,y=e;else if("object"!==t.nodeName.toLowerCase()){(c=t.getAttribute("id"))?c=c.replace(te,ne):t.setAttribute("id",c=x),u=(h=a(e)).length;while(u--)h[u]="#"+c+" "+ye(h[u]);y=h.join(","),m=J.test(e)&&ge(t.parentNode)||t}if(y)try{return q.apply(r,m.querySelectorAll(y)),r}catch(e){}finally{c===x&&t.removeAttribute("id")}}}return s(e.replace($,"$1"),t,r,i)}function ae(){var e=[];function t(n,i){return e.push(n+" ")>r.cacheLength&&delete t[e.shift()],t[n+" "]=i}return t}function ue(e){return e[x]=!0,e}function se(e){var t=p.createElement("fieldset");try{return!!e(t)}catch(e){return!1}finally{t.parentNode&&t.parentNode.removeChild(t),t=null}}function le(e,t){var n=e.split("|"),i=n.length;while(i--)r.attrHandle[n[i]]=t}function ce(e,t){var n=t&&e,r=n&&1===e.nodeType&&1===t.nodeType&&e.sourceIndex-t.sourceIndex;if(r)return r;if(n)while(n=n.nextSibling)if(n===t)return-1;return e?1:-1}function fe(e){return function(t){return"input"===t.nodeName.toLowerCase()&&t.type===e}}function de(e){return function(t){var n=t.nodeName.toLowerCase();return("input"===n||"button"===n)&&t.type===e}}function pe(e){return function(t){return"form"in t?t.parentNode&&!1===t.disabled?"label"in t?"label"in t.parentNode?t.parentNode.disabled===e:t.disabled===e:t.isDisabled===e||t.isDisabled!==!e&&ie(t)===e:t.disabled===e:"label"in t&&t.disabled===e}}function he(e){return ue(function(t){return t=+t,ue(function(n,r){var i,o=e([],n.length,t),a=o.length;while(a--)n[i=o[a]]&&(n[i]=!(r[i]=n[i]))})})}function ge(e){return e&&"undefined"!=typeof e.getElementsByTagName&&e}n=oe.support={},o=oe.isXML=function(e){var t=e&&(e.ownerDocument||e).documentElement;return!!t&&"HTML"!==t.nodeName},d=oe.setDocument=function(e){var t,i,a=e?e.ownerDocument||e:w;return a!==p&&9===a.nodeType&&a.documentElement?(p=a,h=p.documentElement,g=!o(p),w!==p&&(i=p.defaultView)&&i.top!==i&&(i.addEventListener?i.addEventListener("unload",re,!1):i.attachEvent&&i.attachEvent("onunload",re)),n.attributes=se(function(e){return e.className="i",!e.getAttribute("className")}),n.getElementsByTagName=se(function(e){return e.appendChild(p.createComment("")),!e.getElementsByTagName("*").length}),n.getElementsByClassName=G.test(p.getElementsByClassName),n.getById=se(function(e){return h.appendChild(e).id=x,!p.getElementsByName||!p.getElementsByName(x).length}),n.getById?(r.filter.ID=function(e){var t=e.replace(Z,ee);return function(e){return e.getAttribute("id")===t}},r.find.ID=function(e,t){if("undefined"!=typeof t.getElementById&&g){var n=t.getElementById(e);return n?[n]:[]}}):(r.filter.ID=function(e){var t=e.replace(Z,ee);return function(e){var n="undefined"!=typeof e.getAttributeNode&&e.getAttributeNode("id");return n&&n.value===t}},r.find.ID=function(e,t){if("undefined"!=typeof t.getElementById&&g){var n,r,i,o=t.getElementById(e);if(o){if((n=o.getAttributeNode("id"))&&n.value===e)return[o];i=t.getElementsByName(e),r=0;while(o=i[r++])if((n=o.getAttributeNode("id"))&&n.value===e)return[o]}return[]}}),r.find.TAG=n.getElementsByTagName?function(e,t){return"undefined"!=typeof t.getElementsByTagName?t.getElementsByTagName(e):n.qsa?t.querySelectorAll(e):void 0}:function(e,t){var n,r=[],i=0,o=t.getElementsByTagName(e);if("*"===e){while(n=o[i++])1===n.nodeType&&r.push(n);return r}return o},r.find.CLASS=n.getElementsByClassName&&function(e,t){if("undefined"!=typeof t.getElementsByClassName&&g)return t.getElementsByClassName(e)},y=[],v=[],(n.qsa=G.test(p.querySelectorAll))&&(se(function(e){h.appendChild(e).innerHTML="<a id='"+x+"'></a><select id='"+x+"-\r\\' msallowcapture=''><option selected=''></option></select>",e.querySelectorAll("[msallowcapture^='']").length&&v.push("[*^$]="+I+"*(?:''|\"\")"),e.querySelectorAll("[selected]").length||v.push("\\["+I+"*(?:value|"+H+")"),e.querySelectorAll("[id~="+x+"-]").length||v.push("~="),e.querySelectorAll(":checked").length||v.push(":checked"),e.querySelectorAll("a#"+x+"+*").length||v.push(".#.+[+~]")}),se(function(e){e.innerHTML="<a href='' disabled='disabled'></a><select disabled='disabled'><option/></select>";var t=p.createElement("input");t.setAttribute("type","hidden"),e.appendChild(t).setAttribute("name","D"),e.querySelectorAll("[name=d]").length&&v.push("name"+I+"*[*^$|!~]?="),2!==e.querySelectorAll(":enabled").length&&v.push(":enabled",":disabled"),h.appendChild(e).disabled=!0,2!==e.querySelectorAll(":disabled").length&&v.push(":enabled",":disabled"),e.querySelectorAll("*,:x"),v.push(",.*:")})),(n.matchesSelector=G.test(m=h.matches||h.webkitMatchesSelector||h.mozMatchesSelector||h.oMatchesSelector||h.msMatchesSelector))&&se(function(e){n.disconnectedMatch=m.call(e,"*"),m.call(e,"[s!='']:x"),y.push("!=",M)}),v=v.length&&new RegExp(v.join("|")),y=y.length&&new RegExp(y.join("|")),t=G.test(h.compareDocumentPosition),b=t||G.test(h.contains)?function(e,t){var n=9===e.nodeType?e.documentElement:e,r=t&&t.parentNode;return e===r||!(!r||1!==r.nodeType||!(n.contains?n.contains(r):e.compareDocumentPosition&&16&e.compareDocumentPosition(r)))}:function(e,t){if(t)while(t=t.parentNode)if(t===e)return!0;return!1},A=t?function(e,t){if(e===t)return f=!0,0;var r=!e.compareDocumentPosition-!t.compareDocumentPosition;return r||(1&(r=(e.ownerDocument||e)===(t.ownerDocument||t)?e.compareDocumentPosition(t):1)||!n.sortDetached&&t.compareDocumentPosition(e)===r?e===p||e.ownerDocument===w&&b(w,e)?-1:t===p||t.ownerDocument===w&&b(w,t)?1:c?P(c,e)-P(c,t):0:4&r?-1:1)}:function(e,t){if(e===t)return f=!0,0;var n,r=0,i=e.parentNode,o=t.parentNode,a=[e],u=[t];if(!i||!o)return e===p?-1:t===p?1:i?-1:o?1:c?P(c,e)-P(c,t):0;if(i===o)return ce(e,t);n=e;while(n=n.parentNode)a.unshift(n);n=t;while(n=n.parentNode)u.unshift(n);while(a[r]===u[r])r++;return r?ce(a[r],u[r]):a[r]===w?-1:u[r]===w?1:0},p):p},oe.matches=function(e,t){return oe(e,null,null,t)},oe.matchesSelector=function(e,t){if((e.ownerDocument||e)!==p&&d(e),t=t.replace(_,"='$1']"),n.matchesSelector&&g&&!k[t+" "]&&(!y||!y.test(t))&&(!v||!v.test(t)))try{var r=m.call(e,t);if(r||n.disconnectedMatch||e.document&&11!==e.document.nodeType)return r}catch(e){}return oe(t,p,null,[e]).length>0},oe.contains=function(e,t){return(e.ownerDocument||e)!==p&&d(e),b(e,t)},oe.attr=function(e,t){(e.ownerDocument||e)!==p&&d(e);var i=r.attrHandle[t.toLowerCase()],o=i&&D.call(r.attrHandle,t.toLowerCase())?i(e,t,!g):void 0;return void 0!==o?o:n.attributes||!g?e.getAttribute(t):(o=e.getAttributeNode(t))&&o.specified?o.value:null},oe.escape=function(e){return(e+"").replace(te,ne)},oe.error=function(e){throw new Error("Syntax error, unrecognized expression: "+e)},oe.uniqueSort=function(e){var t,r=[],i=0,o=0;if(f=!n.detectDuplicates,c=!n.sortStable&&e.slice(0),e.sort(A),f){while(t=e[o++])t===e[o]&&(i=r.push(o));while(i--)e.splice(r[i],1)}return c=null,e},i=oe.getText=function(e){var t,n="",r=0,o=e.nodeType;if(o){if(1===o||9===o||11===o){if("string"==typeof e.textContent)return e.textContent;for(e=e.firstChild;e;e=e.nextSibling)n+=i(e)}else if(3===o||4===o)return e.nodeValue}else while(t=e[r++])n+=i(t);return n},(r=oe.selectors={cacheLength:50,createPseudo:ue,match:X,attrHandle:{},find:{},relative:{">":{dir:"parentNode",first:!0}," ":{dir:"parentNode"},"+":{dir:"previousSibling",first:!0},"~":{dir:"previousSibling"}},preFilter:{ATTR:function(e){return e[1]=e[1].replace(Z,ee),e[3]=(e[3]||e[4]||e[5]||"").replace(Z,ee),"~="===e[2]&&(e[3]=" "+e[3]+" "),e.slice(0,4)},CHILD:function(e){return e[1]=e[1].toLowerCase(),"nth"===e[1].slice(0,3)?(e[3]||oe.error(e[0]),e[4]=+(e[4]?e[5]+(e[6]||1):2*("even"===e[3]||"odd"===e[3])),e[5]=+(e[7]+e[8]||"odd"===e[3])):e[3]&&oe.error(e[0]),e},PSEUDO:function(e){var t,n=!e[6]&&e[2];return X.CHILD.test(e[0])?null:(e[3]?e[2]=e[4]||e[5]||"":n&&U.test(n)&&(t=a(n,!0))&&(t=n.indexOf(")",n.length-t)-n.length)&&(e[0]=e[0].slice(0,t),e[2]=n.slice(0,t)),e.slice(0,3))}},filter:{TAG:function(e){var t=e.replace(Z,ee).toLowerCase();return"*"===e?function(){return!0}:function(e){return e.nodeName&&e.nodeName.toLowerCase()===t}},CLASS:function(e){var t=E[e+" "];return t||(t=new RegExp("(^|"+I+")"+e+"("+I+"|$)"))&&E(e,function(e){return t.test("string"==typeof e.className&&e.className||"undefined"!=typeof e.getAttribute&&e.getAttribute("class")||"")})},ATTR:function(e,t,n){return function(r){var i=oe.attr(r,e);return null==i?"!="===t:!t||(i+="","="===t?i===n:"!="===t?i!==n:"^="===t?n&&0===i.indexOf(n):"*="===t?n&&i.indexOf(n)>-1:"$="===t?n&&i.slice(-n.length)===n:"~="===t?(" "+i.replace(W," ")+" ").indexOf(n)>-1:"|="===t&&(i===n||i.slice(0,n.length+1)===n+"-"))}},CHILD:function(e,t,n,r,i){var o="nth"!==e.slice(0,3),a="last"!==e.slice(-4),u="of-type"===t;return 1===r&&0===i?function(e){return!!e.parentNode}:function(t,n,s){var l,c,f,d,p,h,g=o!==a?"nextSibling":"previousSibling",v=t.parentNode,y=u&&t.nodeName.toLowerCase(),m=!s&&!u,b=!1;if(v){if(o){while(g){d=t;while(d=d[g])if(u?d.nodeName.toLowerCase()===y:1===d.nodeType)return!1;h=g="only"===e&&!h&&"nextSibling"}return!0}if(h=[a?v.firstChild:v.lastChild],a&&m){b=(p=(l=(c=(f=(d=v)[x]||(d[x]={}))[d.uniqueID]||(f[d.uniqueID]={}))[e]||[])[0]===C&&l[1])&&l[2],d=p&&v.childNodes[p];while(d=++p&&d&&d[g]||(b=p=0)||h.pop())if(1===d.nodeType&&++b&&d===t){c[e]=[C,p,b];break}}else if(m&&(b=p=(l=(c=(f=(d=t)[x]||(d[x]={}))[d.uniqueID]||(f[d.uniqueID]={}))[e]||[])[0]===C&&l[1]),!1===b)while(d=++p&&d&&d[g]||(b=p=0)||h.pop())if((u?d.nodeName.toLowerCase()===y:1===d.nodeType)&&++b&&(m&&((c=(f=d[x]||(d[x]={}))[d.uniqueID]||(f[d.uniqueID]={}))[e]=[C,b]),d===t))break;return(b-=i)===r||b%r==0&&b/r>=0}}},PSEUDO:function(e,t){var n,i=r.pseudos[e]||r.setFilters[e.toLowerCase()]||oe.error("unsupported pseudo: "+e);return i[x]?i(t):i.length>1?(n=[e,e,"",t],r.setFilters.hasOwnProperty(e.toLowerCase())?ue(function(e,n){var r,o=i(e,t),a=o.length;while(a--)e[r=P(e,o[a])]=!(n[r]=o[a])}):function(e){return i(e,0,n)}):i}},pseudos:{not:ue(function(e){var t=[],n=[],r=u(e.replace($,"$1"));return r[x]?ue(function(e,t,n,i){var o,a=r(e,null,i,[]),u=e.length;while(u--)(o=a[u])&&(e[u]=!(t[u]=o))}):function(e,i,o){return t[0]=e,r(t,null,o,n),t[0]=null,!n.pop()}}),has:ue(function(e){return function(t){return oe(e,t).length>0}}),contains:ue(function(e){return e=e.replace(Z,ee),function(t){return(t.textContent||t.innerText||i(t)).indexOf(e)>-1}}),lang:ue(function(e){return V.test(e||"")||oe.error("unsupported lang: "+e),e=e.replace(Z,ee).toLowerCase(),function(t){var n;do{if(n=g?t.lang:t.getAttribute("xml:lang")||t.getAttribute("lang"))return(n=n.toLowerCase())===e||0===n.indexOf(e+"-")}while((t=t.parentNode)&&1===t.nodeType);return!1}}),target:function(t){var n=e.location&&e.location.hash;return n&&n.slice(1)===t.id},root:function(e){return e===h},focus:function(e){return e===p.activeElement&&(!p.hasFocus||p.hasFocus())&&!!(e.type||e.href||~e.tabIndex)},enabled:pe(!1),disabled:pe(!0),checked:function(e){var t=e.nodeName.toLowerCase();return"input"===t&&!!e.checked||"option"===t&&!!e.selected},selected:function(e){return e.parentNode&&e.parentNode.selectedIndex,!0===e.selected},empty:function(e){for(e=e.firstChild;e;e=e.nextSibling)if(e.nodeType<6)return!1;return!0},parent:function(e){return!r.pseudos.empty(e)},header:function(e){return Y.test(e.nodeName)},input:function(e){return Q.test(e.nodeName)},button:function(e){var t=e.nodeName.toLowerCase();return"input"===t&&"button"===e.type||"button"===t},text:function(e){var t;return"input"===e.nodeName.toLowerCase()&&"text"===e.type&&(null==(t=e.getAttribute("type"))||"text"===t.toLowerCase())},first:he(function(){return[0]}),last:he(function(e,t){return[t-1]}),eq:he(function(e,t,n){return[n<0?n+t:n]}),even:he(function(e,t){for(var n=0;n<t;n+=2)e.push(n);return e}),odd:he(function(e,t){for(var n=1;n<t;n+=2)e.push(n);return e}),lt:he(function(e,t,n){for(var r=n<0?n+t:n;--r>=0;)e.push(r);return e}),gt:he(function(e,t,n){for(var r=n<0?n+t:n;++r<t;)e.push(r);return e})}}).pseudos.nth=r.pseudos.eq;for(t in{radio:!0,checkbox:!0,file:!0,password:!0,image:!0})r.pseudos[t]=fe(t);for(t in{submit:!0,reset:!0})r.pseudos[t]=de(t);function ve(){}ve.prototype=r.filters=r.pseudos,r.setFilters=new ve,a=oe.tokenize=function(e,t){var n,i,o,a,u,s,l,c=N[e+" "];if(c)return t?0:c.slice(0);u=e,s=[],l=r.preFilter;while(u){n&&!(i=F.exec(u))||(i&&(u=u.slice(i[0].length)||u),s.push(o=[])),n=!1,(i=z.exec(u))&&(n=i.shift(),o.push({value:n,type:i[0].replace($," ")}),u=u.slice(n.length));for(a in r.filter)!(i=X[a].exec(u))||l[a]&&!(i=l[a](i))||(n=i.shift(),o.push({value:n,type:a,matches:i}),u=u.slice(n.length));if(!n)break}return t?u.length:u?oe.error(e):N(e,s).slice(0)};function ye(e){for(var t=0,n=e.length,r="";t<n;t++)r+=e[t].value;return r}function me(e,t,n){var r=t.dir,i=t.next,o=i||r,a=n&&"parentNode"===o,u=T++;return t.first?function(t,n,i){while(t=t[r])if(1===t.nodeType||a)return e(t,n,i);return!1}:function(t,n,s){var l,c,f,d=[C,u];if(s){while(t=t[r])if((1===t.nodeType||a)&&e(t,n,s))return!0}else while(t=t[r])if(1===t.nodeType||a)if(f=t[x]||(t[x]={}),c=f[t.uniqueID]||(f[t.uniqueID]={}),i&&i===t.nodeName.toLowerCase())t=t[r]||t;else{if((l=c[o])&&l[0]===C&&l[1]===u)return d[2]=l[2];if(c[o]=d,d[2]=e(t,n,s))return!0}return!1}}function be(e){return e.length>1?function(t,n,r){var i=e.length;while(i--)if(!e[i](t,n,r))return!1;return!0}:e[0]}function xe(e,t,n){for(var r=0,i=t.length;r<i;r++)oe(e,t[r],n);return n}function we(e,t,n,r,i){for(var o,a=[],u=0,s=e.length,l=null!=t;u<s;u++)(o=e[u])&&(n&&!n(o,r,i)||(a.push(o),l&&t.push(u)));return a}function Ce(e,t,n,r,i,o){return r&&!r[x]&&(r=Ce(r)),i&&!i[x]&&(i=Ce(i,o)),ue(function(o,a,u,s){var l,c,f,d=[],p=[],h=a.length,g=o||xe(t||"*",u.nodeType?[u]:u,[]),v=!e||!o&&t?g:we(g,d,e,u,s),y=n?i||(o?e:h||r)?[]:a:v;if(n&&n(v,y,u,s),r){l=we(y,p),r(l,[],u,s),c=l.length;while(c--)(f=l[c])&&(y[p[c]]=!(v[p[c]]=f))}if(o){if(i||e){if(i){l=[],c=y.length;while(c--)(f=y[c])&&l.push(v[c]=f);i(null,y=[],l,s)}c=y.length;while(c--)(f=y[c])&&(l=i?P(o,f):d[c])>-1&&(o[l]=!(a[l]=f))}}else y=we(y===a?y.splice(h,y.length):y),i?i(null,a,y,s):q.apply(a,y)})}function Te(e){for(var t,n,i,o=e.length,a=r.relative[e[0].type],u=a||r.relative[" "],s=a?1:0,c=me(function(e){return e===t},u,!0),f=me(function(e){return P(t,e)>-1},u,!0),d=[function(e,n,r){var i=!a&&(r||n!==l)||((t=n).nodeType?c(e,n,r):f(e,n,r));return t=null,i}];s<o;s++)if(n=r.relative[e[s].type])d=[me(be(d),n)];else{if((n=r.filter[e[s].type].apply(null,e[s].matches))[x]){for(i=++s;i<o;i++)if(r.relative[e[i].type])break;return Ce(s>1&&be(d),s>1&&ye(e.slice(0,s-1).concat({value:" "===e[s-2].type?"*":""})).replace($,"$1"),n,s<i&&Te(e.slice(s,i)),i<o&&Te(e=e.slice(i)),i<o&&ye(e))}d.push(n)}return be(d)}function Ee(e,t){var n=t.length>0,i=e.length>0,o=function(o,a,u,s,c){var f,h,v,y=0,m="0",b=o&&[],x=[],w=l,T=o||i&&r.find.TAG("*",c),E=C+=null==w?1:Math.random()||.1,N=T.length;for(c&&(l=a===p||a||c);m!==N&&null!=(f=T[m]);m++){if(i&&f){h=0,a||f.ownerDocument===p||(d(f),u=!g);while(v=e[h++])if(v(f,a||p,u)){s.push(f);break}c&&(C=E)}n&&((f=!v&&f)&&y--,o&&b.push(f))}if(y+=m,n&&m!==y){h=0;while(v=t[h++])v(b,x,a,u);if(o){if(y>0)while(m--)b[m]||x[m]||(x[m]=L.call(s));x=we(x)}q.apply(s,x),c&&!o&&x.length>0&&y+t.length>1&&oe.uniqueSort(s)}return c&&(C=E,l=w),b};return n?ue(o):o}return u=oe.compile=function(e,t){var n,r=[],i=[],o=k[e+" "];if(!o){t||(t=a(e)),n=t.length;while(n--)(o=Te(t[n]))[x]?r.push(o):i.push(o);(o=k(e,Ee(i,r))).selector=e}return o},s=oe.select=function(e,t,n,i){var o,s,l,c,f,d="function"==typeof e&&e,p=!i&&a(e=d.selector||e);if(n=n||[],1===p.length){if((s=p[0]=p[0].slice(0)).length>2&&"ID"===(l=s[0]).type&&9===t.nodeType&&g&&r.relative[s[1].type]){if(!(t=(r.find.ID(l.matches[0].replace(Z,ee),t)||[])[0]))return n;d&&(t=t.parentNode),e=e.slice(s.shift().value.length)}o=X.needsContext.test(e)?0:s.length;while(o--){if(l=s[o],r.relative[c=l.type])break;if((f=r.find[c])&&(i=f(l.matches[0].replace(Z,ee),J.test(s[0].type)&&ge(t.parentNode)||t))){if(s.splice(o,1),!(e=i.length&&ye(s)))return q.apply(n,i),n;break}}}return(d||u(e,p))(i,t,!g,n,!t||J.test(e)&&ge(t.parentNode)||t),n},n.sortStable=x.split("").sort(A).join("")===x,n.detectDuplicates=!!f,d(),n.sortDetached=se(function(e){return 1&e.compareDocumentPosition(p.createElement("fieldset"))}),se(function(e){return e.innerHTML="<a href='#'></a>","#"===e.firstChild.getAttribute("href")})||le("type|href|height|width",function(e,t,n){if(!n)return e.getAttribute(t,"type"===t.toLowerCase()?1:2)}),n.attributes&&se(function(e){return e.innerHTML="<input/>",e.firstChild.setAttribute("value",""),""===e.firstChild.getAttribute("value")})||le("value",function(e,t,n){if(!n&&"input"===e.nodeName.toLowerCase())return e.defaultValue}),se(function(e){return null==e.getAttribute("disabled")})||le(H,function(e,t,n){var r;if(!n)return!0===e[t]?t.toLowerCase():(r=e.getAttributeNode(t))&&r.specified?r.value:null}),oe}(e);w.find=E,w.expr=E.selectors,w.expr[":"]=w.expr.pseudos,w.uniqueSort=w.unique=E.uniqueSort,w.text=E.getText,w.isXMLDoc=E.isXML,w.contains=E.contains,w.escapeSelector=E.escape;var N=function(e,t,n){var r=[],i=void 0!==n;while((e=e[t])&&9!==e.nodeType)if(1===e.nodeType){if(i&&w(e).is(n))break;r.push(e)}return r},k=function(e,t){for(var n=[];e;e=e.nextSibling)1===e.nodeType&&e!==t&&n.push(e);return n},A=w.expr.match.needsContext;function D(e,t){return e.nodeName&&e.nodeName.toLowerCase()===t.toLowerCase()}var S=/^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i;function L(e,t,n){return g(t)?w.grep(e,function(e,r){return!!t.call(e,r,e)!==n}):t.nodeType?w.grep(e,function(e){return e===t!==n}):"string"!=typeof t?w.grep(e,function(e){return s.call(t,e)>-1!==n}):w.filter(t,e,n)}w.filter=function(e,t,n){var r=t[0];return n&&(e=":not("+e+")"),1===t.length&&1===r.nodeType?w.find.matchesSelector(r,e)?[r]:[]:w.find.matches(e,w.grep(t,function(e){return 1===e.nodeType}))},w.fn.extend({find:function(e){var t,n,r=this.length,i=this;if("string"!=typeof e)return this.pushStack(w(e).filter(function(){for(t=0;t<r;t++)if(w.contains(i[t],this))return!0}));for(n=this.pushStack([]),t=0;t<r;t++)w.find(e,i[t],n);return r>1?w.uniqueSort(n):n},filter:function(e){return this.pushStack(L(this,e||[],!1))},not:function(e){return this.pushStack(L(this,e||[],!0))},is:function(e){return!!L(this,"string"==typeof e&&A.test(e)?w(e):e||[],!1).length}});var j,q=/^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/;(w.fn.init=function(e,t,n){var i,o;if(!e)return this;if(n=n||j,"string"==typeof e){if(!(i="<"===e[0]&&">"===e[e.length-1]&&e.length>=3?[null,e,null]:q.exec(e))||!i[1]&&t)return!t||t.jquery?(t||n).find(e):this.constructor(t).find(e);if(i[1]){if(t=t instanceof w?t[0]:t,w.merge(this,w.parseHTML(i[1],t&&t.nodeType?t.ownerDocument||t:r,!0)),S.test(i[1])&&w.isPlainObject(t))for(i in t)g(this[i])?this[i](t[i]):this.attr(i,t[i]);return this}return(o=r.getElementById(i[2]))&&(this[0]=o,this.length=1),this}return e.nodeType?(this[0]=e,this.length=1,this):g(e)?void 0!==n.ready?n.ready(e):e(w):w.makeArray(e,this)}).prototype=w.fn,j=w(r);var O=/^(?:parents|prev(?:Until|All))/,P={children:!0,contents:!0,next:!0,prev:!0};w.fn.extend({has:function(e){var t=w(e,this),n=t.length;return this.filter(function(){for(var e=0;e<n;e++)if(w.contains(this,t[e]))return!0})},closest:function(e,t){var n,r=0,i=this.length,o=[],a="string"!=typeof e&&w(e);if(!A.test(e))for(;r<i;r++)for(n=this[r];n&&n!==t;n=n.parentNode)if(n.nodeType<11&&(a?a.index(n)>-1:1===n.nodeType&&w.find.matchesSelector(n,e))){o.push(n);break}return this.pushStack(o.length>1?w.uniqueSort(o):o)},index:function(e){return e?"string"==typeof e?s.call(w(e),this[0]):s.call(this,e.jquery?e[0]:e):this[0]&&this[0].parentNode?this.first().prevAll().length:-1},add:function(e,t){return this.pushStack(w.uniqueSort(w.merge(this.get(),w(e,t))))},addBack:function(e){return this.add(null==e?this.prevObject:this.prevObject.filter(e))}});function H(e,t){while((e=e[t])&&1!==e.nodeType);return e}w.each({parent:function(e){var t=e.parentNode;return t&&11!==t.nodeType?t:null},parents:function(e){return N(e,"parentNode")},parentsUntil:function(e,t,n){return N(e,"parentNode",n)},next:function(e){return H(e,"nextSibling")},prev:function(e){return H(e,"previousSibling")},nextAll:function(e){return N(e,"nextSibling")},prevAll:function(e){return N(e,"previousSibling")},nextUntil:function(e,t,n){return N(e,"nextSibling",n)},prevUntil:function(e,t,n){return N(e,"previousSibling",n)},siblings:function(e){return k((e.parentNode||{}).firstChild,e)},children:function(e){return k(e.firstChild)},contents:function(e){return D(e,"iframe")?e.contentDocument:(D(e,"template")&&(e=e.content||e),w.merge([],e.childNodes))}},function(e,t){w.fn[e]=function(n,r){var i=w.map(this,t,n);return"Until"!==e.slice(-5)&&(r=n),r&&"string"==typeof r&&(i=w.filter(r,i)),this.length>1&&(P[e]||w.uniqueSort(i),O.test(e)&&i.reverse()),this.pushStack(i)}});var I=/[^\x20\t\r\n\f]+/g;function R(e){var t={};return w.each(e.match(I)||[],function(e,n){t[n]=!0}),t}w.Callbacks=function(e){e="string"==typeof e?R(e):w.extend({},e);var t,n,r,i,o=[],a=[],u=-1,s=function(){for(i=i||e.once,r=t=!0;a.length;u=-1){n=a.shift();while(++u<o.length)!1===o[u].apply(n[0],n[1])&&e.stopOnFalse&&(u=o.length,n=!1)}e.memory||(n=!1),t=!1,i&&(o=n?[]:"")},l={add:function(){return o&&(n&&!t&&(u=o.length-1,a.push(n)),function t(n){w.each(n,function(n,r){g(r)?e.unique&&l.has(r)||o.push(r):r&&r.length&&"string"!==b(r)&&t(r)})}(arguments),n&&!t&&s()),this},remove:function(){return w.each(arguments,function(e,t){var n;while((n=w.inArray(t,o,n))>-1)o.splice(n,1),n<=u&&u--}),this},has:function(e){return e?w.inArray(e,o)>-1:o.length>0},empty:function(){return o&&(o=[]),this},disable:function(){return i=a=[],o=n="",this},disabled:function(){return!o},lock:function(){return i=a=[],n||t||(o=n=""),this},locked:function(){return!!i},fireWith:function(e,n){return i||(n=[e,(n=n||[]).slice?n.slice():n],a.push(n),t||s()),this},fire:function(){return l.fireWith(this,arguments),this},fired:function(){return!!r}};return l};function B(e){return e}function M(e){throw e}function W(e,t,n,r){var i;try{e&&g(i=e.promise)?i.call(e).done(t).fail(n):e&&g(i=e.then)?i.call(e,t,n):t.apply(void 0,[e].slice(r))}catch(e){n.apply(void 0,[e])}}w.extend({Deferred:function(t){var n=[["notify","progress",w.Callbacks("memory"),w.Callbacks("memory"),2],["resolve","done",w.Callbacks("once memory"),w.Callbacks("once memory"),0,"resolved"],["reject","fail",w.Callbacks("once memory"),w.Callbacks("once memory"),1,"rejected"]],r="pending",i={state:function(){return r},always:function(){return o.done(arguments).fail(arguments),this},"catch":function(e){return i.then(null,e)},pipe:function(){var e=arguments;return w.Deferred(function(t){w.each(n,function(n,r){var i=g(e[r[4]])&&e[r[4]];o[r[1]](function(){var e=i&&i.apply(this,arguments);e&&g(e.promise)?e.promise().progress(t.notify).done(t.resolve).fail(t.reject):t[r[0]+"With"](this,i?[e]:arguments)})}),e=null}).promise()},then:function(t,r,i){var o=0;function a(t,n,r,i){return function(){var u=this,s=arguments,l=function(){var e,l;if(!(t<o)){if((e=r.apply(u,s))===n.promise())throw new TypeError("Thenable self-resolution");l=e&&("object"==typeof e||"function"==typeof e)&&e.then,g(l)?i?l.call(e,a(o,n,B,i),a(o,n,M,i)):(o++,l.call(e,a(o,n,B,i),a(o,n,M,i),a(o,n,B,n.notifyWith))):(r!==B&&(u=void 0,s=[e]),(i||n.resolveWith)(u,s))}},c=i?l:function(){try{l()}catch(e){w.Deferred.exceptionHook&&w.Deferred.exceptionHook(e,c.stackTrace),t+1>=o&&(r!==M&&(u=void 0,s=[e]),n.rejectWith(u,s))}};t?c():(w.Deferred.getStackHook&&(c.stackTrace=w.Deferred.getStackHook()),e.setTimeout(c))}}return w.Deferred(function(e){n[0][3].add(a(0,e,g(i)?i:B,e.notifyWith)),n[1][3].add(a(0,e,g(t)?t:B)),n[2][3].add(a(0,e,g(r)?r:M))}).promise()},promise:function(e){return null!=e?w.extend(e,i):i}},o={};return w.each(n,function(e,t){var a=t[2],u=t[5];i[t[1]]=a.add,u&&a.add(function(){r=u},n[3-e][2].disable,n[3-e][3].disable,n[0][2].lock,n[0][3].lock),a.add(t[3].fire),o[t[0]]=function(){return o[t[0]+"With"](this===o?void 0:this,arguments),this},o[t[0]+"With"]=a.fireWith}),i.promise(o),t&&t.call(o,o),o},when:function(e){var t=arguments.length,n=t,r=Array(n),i=o.call(arguments),a=w.Deferred(),u=function(e){return function(n){r[e]=this,i[e]=arguments.length>1?o.call(arguments):n,--t||a.resolveWith(r,i)}};if(t<=1&&(W(e,a.done(u(n)).resolve,a.reject,!t),"pending"===a.state()||g(i[n]&&i[n].then)))return a.then();while(n--)W(i[n],u(n),a.reject);return a.promise()}});var $=/^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/;w.Deferred.exceptionHook=function(t,n){e.console&&e.console.warn&&t&&$.test(t.name)&&e.console.warn("jQuery.Deferred exception: "+t.message,t.stack,n)},w.readyException=function(t){e.setTimeout(function(){throw t})};var F=w.Deferred();w.fn.ready=function(e){return F.then(e)["catch"](function(e){w.readyException(e)}),this},w.extend({isReady:!1,readyWait:1,ready:function(e){(!0===e?--w.readyWait:w.isReady)||(w.isReady=!0,!0!==e&&--w.readyWait>0||F.resolveWith(r,[w]))}}),w.ready.then=F.then;function z(){r.removeEventListener("DOMContentLoaded",z),e.removeEventListener("load",z),w.ready()}"complete"===r.readyState||"loading"!==r.readyState&&!r.documentElement.doScroll?e.setTimeout(w.ready):(r.addEventListener("DOMContentLoaded",z),e.addEventListener("load",z));var _=function(e,t,n,r,i,o,a){var u=0,s=e.length,l=null==n;if("object"===b(n)){i=!0;for(u in n)_(e,t,u,n[u],!0,o,a)}else if(void 0!==r&&(i=!0,g(r)||(a=!0),l&&(a?(t.call(e,r),t=null):(l=t,t=function(e,t,n){return l.call(w(e),n)})),t))for(;u<s;u++)t(e[u],n,a?r:r.call(e[u],u,t(e[u],n)));return i?e:l?t.call(e):s?t(e[0],n):o},U=/^-ms-/,V=/-([a-z])/g;function X(e,t){return t.toUpperCase()}function Q(e){return e.replace(U,"ms-").replace(V,X)}var Y=function(e){return 1===e.nodeType||9===e.nodeType||!+e.nodeType};function G(){this.expando=w.expando+G.uid++}G.uid=1,G.prototype={cache:function(e){var t=e[this.expando];return t||(t={},Y(e)&&(e.nodeType?e[this.expando]=t:Object.defineProperty(e,this.expando,{value:t,configurable:!0}))),t},set:function(e,t,n){var r,i=this.cache(e);if("string"==typeof t)i[Q(t)]=n;else for(r in t)i[Q(r)]=t[r];return i},get:function(e,t){return void 0===t?this.cache(e):e[this.expando]&&e[this.expando][Q(t)]},access:function(e,t,n){return void 0===t||t&&"string"==typeof t&&void 0===n?this.get(e,t):(this.set(e,t,n),void 0!==n?n:t)},remove:function(e,t){var n,r=e[this.expando];if(void 0!==r){if(void 0!==t){n=(t=Array.isArray(t)?t.map(Q):(t=Q(t))in r?[t]:t.match(I)||[]).length;while(n--)delete r[t[n]]}(void 0===t||w.isEmptyObject(r))&&(e.nodeType?e[this.expando]=void 0:delete e[this.expando])}},hasData:function(e){var t=e[this.expando];return void 0!==t&&!w.isEmptyObject(t)}};var K=new G,J=new G,Z=/^(?:\{[\w\W]*\}|\[[\w\W]*\])$/,ee=/[A-Z]/g;function te(e){return"true"===e||"false"!==e&&("null"===e?null:e===+e+""?+e:Z.test(e)?JSON.parse(e):e)}function ne(e,t,n){var r;if(void 0===n&&1===e.nodeType)if(r="data-"+t.replace(ee,"-$&").toLowerCase(),"string"==typeof(n=e.getAttribute(r))){try{n=te(n)}catch(e){}J.set(e,t,n)}else n=void 0;return n}w.extend({hasData:function(e){return J.hasData(e)||K.hasData(e)},data:function(e,t,n){return J.access(e,t,n)},removeData:function(e,t){J.remove(e,t)},_data:function(e,t,n){return K.access(e,t,n)},_removeData:function(e,t){K.remove(e,t)}}),w.fn.extend({data:function(e,t){var n,r,i,o=this[0],a=o&&o.attributes;if(void 0===e){if(this.length&&(i=J.get(o),1===o.nodeType&&!K.get(o,"hasDataAttrs"))){n=a.length;while(n--)a[n]&&0===(r=a[n].name).indexOf("data-")&&(r=Q(r.slice(5)),ne(o,r,i[r]));K.set(o,"hasDataAttrs",!0)}return i}return"object"==typeof e?this.each(function(){J.set(this,e)}):_(this,function(t){var n;if(o&&void 0===t){if(void 0!==(n=J.get(o,e)))return n;if(void 0!==(n=ne(o,e)))return n}else this.each(function(){J.set(this,e,t)})},null,t,arguments.length>1,null,!0)},removeData:function(e){return this.each(function(){J.remove(this,e)})}}),w.extend({queue:function(e,t,n){var r;if(e)return t=(t||"fx")+"queue",r=K.get(e,t),n&&(!r||Array.isArray(n)?r=K.access(e,t,w.makeArray(n)):r.push(n)),r||[]},dequeue:function(e,t){t=t||"fx";var n=w.queue(e,t),r=n.length,i=n.shift(),o=w._queueHooks(e,t),a=function(){w.dequeue(e,t)};"inprogress"===i&&(i=n.shift(),r--),i&&("fx"===t&&n.unshift("inprogress"),delete o.stop,i.call(e,a,o)),!r&&o&&o.empty.fire()},_queueHooks:function(e,t){var n=t+"queueHooks";return K.get(e,n)||K.access(e,n,{empty:w.Callbacks("once memory").add(function(){K.remove(e,[t+"queue",n])})})}}),w.fn.extend({queue:function(e,t){var n=2;return"string"!=typeof e&&(t=e,e="fx",n--),arguments.length<n?w.queue(this[0],e):void 0===t?this:this.each(function(){var n=w.queue(this,e,t);w._queueHooks(this,e),"fx"===e&&"inprogress"!==n[0]&&w.dequeue(this,e)})},dequeue:function(e){return this.each(function(){w.dequeue(this,e)})},clearQueue:function(e){return this.queue(e||"fx",[])},promise:function(e,t){var n,r=1,i=w.Deferred(),o=this,a=this.length,u=function(){--r||i.resolveWith(o,[o])};"string"!=typeof e&&(t=e,e=void 0),e=e||"fx";while(a--)(n=K.get(o[a],e+"queueHooks"))&&n.empty&&(r++,n.empty.add(u));return u(),i.promise(t)}});var re=/[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/.source,ie=new RegExp("^(?:([+-])=|)("+re+")([a-z%]*)$","i"),oe=["Top","Right","Bottom","Left"],ae=function(e,t){return"none"===(e=t||e).style.display||""===e.style.display&&w.contains(e.ownerDocument,e)&&"none"===w.css(e,"display")},ue=function(e,t,n,r){var i,o,a={};for(o in t)a[o]=e.style[o],e.style[o]=t[o];i=n.apply(e,r||[]);for(o in t)e.style[o]=a[o];return i};function se(e,t,n,r){var i,o,a=20,u=r?function(){return r.cur()}:function(){return w.css(e,t,"")},s=u(),l=n&&n[3]||(w.cssNumber[t]?"":"px"),c=(w.cssNumber[t]||"px"!==l&&+s)&&ie.exec(w.css(e,t));if(c&&c[3]!==l){s/=2,l=l||c[3],c=+s||1;while(a--)w.style(e,t,c+l),(1-o)*(1-(o=u()/s||.5))<=0&&(a=0),c/=o;c*=2,w.style(e,t,c+l),n=n||[]}return n&&(c=+c||+s||0,i=n[1]?c+(n[1]+1)*n[2]:+n[2],r&&(r.unit=l,r.start=c,r.end=i)),i}var le={};function ce(e){var t,n=e.ownerDocument,r=e.nodeName,i=le[r];return i||(t=n.body.appendChild(n.createElement(r)),i=w.css(t,"display"),t.parentNode.removeChild(t),"none"===i&&(i="block"),le[r]=i,i)}function fe(e,t){for(var n,r,i=[],o=0,a=e.length;o<a;o++)(r=e[o]).style&&(n=r.style.display,t?("none"===n&&(i[o]=K.get(r,"display")||null,i[o]||(r.style.display="")),""===r.style.display&&ae(r)&&(i[o]=ce(r))):"none"!==n&&(i[o]="none",K.set(r,"display",n)));for(o=0;o<a;o++)null!=i[o]&&(e[o].style.display=i[o]);return e}w.fn.extend({show:function(){return fe(this,!0)},hide:function(){return fe(this)},toggle:function(e){return"boolean"==typeof e?e?this.show():this.hide():this.each(function(){ae(this)?w(this).show():w(this).hide()})}});var de=/^(?:checkbox|radio)$/i,pe=/<([a-z][^\/\0>\x20\t\r\n\f]+)/i,he=/^$|^module$|\/(?:java|ecma)script/i,ge={option:[1,"<select multiple='multiple'>","</select>"],thead:[1,"<table>","</table>"],col:[2,"<table><colgroup>","</colgroup></table>"],tr:[2,"<table><tbody>","</tbody></table>"],td:[3,"<table><tbody><tr>","</tr></tbody></table>"],_default:[0,"",""]};ge.optgroup=ge.option,ge.tbody=ge.tfoot=ge.colgroup=ge.caption=ge.thead,ge.th=ge.td;function ve(e,t){var n;return n="undefined"!=typeof e.getElementsByTagName?e.getElementsByTagName(t||"*"):"undefined"!=typeof e.querySelectorAll?e.querySelectorAll(t||"*"):[],void 0===t||t&&D(e,t)?w.merge([e],n):n}function ye(e,t){for(var n=0,r=e.length;n<r;n++)K.set(e[n],"globalEval",!t||K.get(t[n],"globalEval"))}var me=/<|&#?\w+;/;function be(e,t,n,r,i){for(var o,a,u,s,l,c,f=t.createDocumentFragment(),d=[],p=0,h=e.length;p<h;p++)if((o=e[p])||0===o)if("object"===b(o))w.merge(d,o.nodeType?[o]:o);else if(me.test(o)){a=a||f.appendChild(t.createElement("div")),u=(pe.exec(o)||["",""])[1].toLowerCase(),s=ge[u]||ge._default,a.innerHTML=s[1]+w.htmlPrefilter(o)+s[2],c=s[0];while(c--)a=a.lastChild;w.merge(d,a.childNodes),(a=f.firstChild).textContent=""}else d.push(t.createTextNode(o));f.textContent="",p=0;while(o=d[p++])if(r&&w.inArray(o,r)>-1)i&&i.push(o);else if(l=w.contains(o.ownerDocument,o),a=ve(f.appendChild(o),"script"),l&&ye(a),n){c=0;while(o=a[c++])he.test(o.type||"")&&n.push(o)}return f}!function(){var e=r.createDocumentFragment().appendChild(r.createElement("div")),t=r.createElement("input");t.setAttribute("type","radio"),t.setAttribute("checked","checked"),t.setAttribute("name","t"),e.appendChild(t),h.checkClone=e.cloneNode(!0).cloneNode(!0).lastChild.checked,e.innerHTML="<textarea>x</textarea>",h.noCloneChecked=!!e.cloneNode(!0).lastChild.defaultValue}();var xe=r.documentElement,we=/^key/,Ce=/^(?:mouse|pointer|contextmenu|drag|drop)|click/,Te=/^([^.]*)(?:\.(.+)|)/;function Ee(){return!0}function Ne(){return!1}function ke(){try{return r.activeElement}catch(e){}}function Ae(e,t,n,r,i,o){var a,u;if("object"==typeof t){"string"!=typeof n&&(r=r||n,n=void 0);for(u in t)Ae(e,u,n,r,t[u],o);return e}if(null==r&&null==i?(i=n,r=n=void 0):null==i&&("string"==typeof n?(i=r,r=void 0):(i=r,r=n,n=void 0)),!1===i)i=Ne;else if(!i)return e;return 1===o&&(a=i,(i=function(e){return w().off(e),a.apply(this,arguments)}).guid=a.guid||(a.guid=w.guid++)),e.each(function(){w.event.add(this,t,i,r,n)})}w.event={global:{},add:function(e,t,n,r,i){var o,a,u,s,l,c,f,d,p,h,g,v=K.get(e);if(v){n.handler&&(n=(o=n).handler,i=o.selector),i&&w.find.matchesSelector(xe,i),n.guid||(n.guid=w.guid++),(s=v.events)||(s=v.events={}),(a=v.handle)||(a=v.handle=function(t){return"undefined"!=typeof w&&w.event.triggered!==t.type?w.event.dispatch.apply(e,arguments):void 0}),l=(t=(t||"").match(I)||[""]).length;while(l--)p=g=(u=Te.exec(t[l])||[])[1],h=(u[2]||"").split(".").sort(),p&&(f=w.event.special[p]||{},p=(i?f.delegateType:f.bindType)||p,f=w.event.special[p]||{},c=w.extend({type:p,origType:g,data:r,handler:n,guid:n.guid,selector:i,needsContext:i&&w.expr.match.needsContext.test(i),namespace:h.join(".")},o),(d=s[p])||((d=s[p]=[]).delegateCount=0,f.setup&&!1!==f.setup.call(e,r,h,a)||e.addEventListener&&e.addEventListener(p,a)),f.add&&(f.add.call(e,c),c.handler.guid||(c.handler.guid=n.guid)),i?d.splice(d.delegateCount++,0,c):d.push(c),w.event.global[p]=!0)}},remove:function(e,t,n,r,i){var o,a,u,s,l,c,f,d,p,h,g,v=K.hasData(e)&&K.get(e);if(v&&(s=v.events)){l=(t=(t||"").match(I)||[""]).length;while(l--)if(u=Te.exec(t[l])||[],p=g=u[1],h=(u[2]||"").split(".").sort(),p){f=w.event.special[p]||{},d=s[p=(r?f.delegateType:f.bindType)||p]||[],u=u[2]&&new RegExp("(^|\\.)"+h.join("\\.(?:.*\\.|)")+"(\\.|$)"),a=o=d.length;while(o--)c=d[o],!i&&g!==c.origType||n&&n.guid!==c.guid||u&&!u.test(c.namespace)||r&&r!==c.selector&&("**"!==r||!c.selector)||(d.splice(o,1),c.selector&&d.delegateCount--,f.remove&&f.remove.call(e,c));a&&!d.length&&(f.teardown&&!1!==f.teardown.call(e,h,v.handle)||w.removeEvent(e,p,v.handle),delete s[p])}else for(p in s)w.event.remove(e,p+t[l],n,r,!0);w.isEmptyObject(s)&&K.remove(e,"handle events")}},dispatch:function(e){var t=w.event.fix(e),n,r,i,o,a,u,s=new Array(arguments.length),l=(K.get(this,"events")||{})[t.type]||[],c=w.event.special[t.type]||{};for(s[0]=t,n=1;n<arguments.length;n++)s[n]=arguments[n];if(t.delegateTarget=this,!c.preDispatch||!1!==c.preDispatch.call(this,t)){u=w.event.handlers.call(this,t,l),n=0;while((o=u[n++])&&!t.isPropagationStopped()){t.currentTarget=o.elem,r=0;while((a=o.handlers[r++])&&!t.isImmediatePropagationStopped())t.rnamespace&&!t.rnamespace.test(a.namespace)||(t.handleObj=a,t.data=a.data,void 0!==(i=((w.event.special[a.origType]||{}).handle||a.handler).apply(o.elem,s))&&!1===(t.result=i)&&(t.preventDefault(),t.stopPropagation()))}return c.postDispatch&&c.postDispatch.call(this,t),t.result}},handlers:function(e,t){var n,r,i,o,a,u=[],s=t.delegateCount,l=e.target;if(s&&l.nodeType&&!("click"===e.type&&e.button>=1))for(;l!==this;l=l.parentNode||this)if(1===l.nodeType&&("click"!==e.type||!0!==l.disabled)){for(o=[],a={},n=0;n<s;n++)void 0===a[i=(r=t[n]).selector+" "]&&(a[i]=r.needsContext?w(i,this).index(l)>-1:w.find(i,this,null,[l]).length),a[i]&&o.push(r);o.length&&u.push({elem:l,handlers:o})}return l=this,s<t.length&&u.push({elem:l,handlers:t.slice(s)}),u},addProp:function(e,t){Object.defineProperty(w.Event.prototype,e,{enumerable:!0,configurable:!0,get:g(t)?function(){if(this.originalEvent)return t(this.originalEvent)}:function(){if(this.originalEvent)return this.originalEvent[e]},set:function(t){Object.defineProperty(this,e,{enumerable:!0,configurable:!0,writable:!0,value:t})}})},fix:function(e){return e[w.expando]?e:new w.Event(e)},special:{load:{noBubble:!0},focus:{trigger:function(){if(this!==ke()&&this.focus)return this.focus(),!1},delegateType:"focusin"},blur:{trigger:function(){if(this===ke()&&this.blur)return this.blur(),!1},delegateType:"focusout"},click:{trigger:function(){if("checkbox"===this.type&&this.click&&D(this,"input"))return this.click(),!1},_default:function(e){return D(e.target,"a")}},beforeunload:{postDispatch:function(e){void 0!==e.result&&e.originalEvent&&(e.originalEvent.returnValue=e.result)}}}},w.removeEvent=function(e,t,n){e.removeEventListener&&e.removeEventListener(t,n)},w.Event=function(e,t){if(!(this instanceof w.Event))return new w.Event(e,t);e&&e.type?(this.originalEvent=e,this.type=e.type,this.isDefaultPrevented=e.defaultPrevented||void 0===e.defaultPrevented&&!1===e.returnValue?Ee:Ne,this.target=e.target&&3===e.target.nodeType?e.target.parentNode:e.target,this.currentTarget=e.currentTarget,this.relatedTarget=e.relatedTarget):this.type=e,t&&w.extend(this,t),this.timeStamp=e&&e.timeStamp||Date.now(),this[w.expando]=!0},w.Event.prototype={constructor:w.Event,isDefaultPrevented:Ne,isPropagationStopped:Ne,isImmediatePropagationStopped:Ne,isSimulated:!1,preventDefault:function(){var e=this.originalEvent;this.isDefaultPrevented=Ee,e&&!this.isSimulated&&e.preventDefault()},stopPropagation:function(){var e=this.originalEvent;this.isPropagationStopped=Ee,e&&!this.isSimulated&&e.stopPropagation()},stopImmediatePropagation:function(){var e=this.originalEvent;this.isImmediatePropagationStopped=Ee,e&&!this.isSimulated&&e.stopImmediatePropagation(),this.stopPropagation()}},w.each({altKey:!0,bubbles:!0,cancelable:!0,changedTouches:!0,ctrlKey:!0,detail:!0,eventPhase:!0,metaKey:!0,pageX:!0,pageY:!0,shiftKey:!0,view:!0,"char":!0,charCode:!0,key:!0,keyCode:!0,button:!0,buttons:!0,clientX:!0,clientY:!0,offsetX:!0,offsetY:!0,pointerId:!0,pointerType:!0,screenX:!0,screenY:!0,targetTouches:!0,toElement:!0,touches:!0,which:function(e){var t=e.button;return null==e.which&&we.test(e.type)?null!=e.charCode?e.charCode:e.keyCode:!e.which&&void 0!==t&&Ce.test(e.type)?1&t?1:2&t?3:4&t?2:0:e.which}},w.event.addProp),w.each({mouseenter:"mouseover",mouseleave:"mouseout",pointerenter:"pointerover",pointerleave:"pointerout"},function(e,t){w.event.special[e]={delegateType:t,bindType:t,handle:function(e){var n,r=this,i=e.relatedTarget,o=e.handleObj;return i&&(i===r||w.contains(r,i))||(e.type=o.origType,n=o.handler.apply(this,arguments),e.type=t),n}}}),w.fn.extend({on:function(e,t,n,r){return Ae(this,e,t,n,r)},one:function(e,t,n,r){return Ae(this,e,t,n,r,1)},off:function(e,t,n){var r,i;if(e&&e.preventDefault&&e.handleObj)return r=e.handleObj,w(e.delegateTarget).off(r.namespace?r.origType+"."+r.namespace:r.origType,r.selector,r.handler),this;if("object"==typeof e){for(i in e)this.off(i,t,e[i]);return this}return!1!==t&&"function"!=typeof t||(n=t,t=void 0),!1===n&&(n=Ne),this.each(function(){w.event.remove(this,e,n,t)})}});var De=/<(?!area|br|col|embed|hr|img|input|link|meta|param)(([a-z][^\/\0>\x20\t\r\n\f]*)[^>]*)\/>/gi,Se=/<script|<style|<link/i,Le=/checked\s*(?:[^=]|=\s*.checked.)/i,je=/^\s*<!(?:\[CDATA\[|--)|(?:\]\]|--)>\s*$/g;function qe(e,t){return D(e,"table")&&D(11!==t.nodeType?t:t.firstChild,"tr")?w(e).children("tbody")[0]||e:e}function Oe(e){return e.type=(null!==e.getAttribute("type"))+"/"+e.type,e}function Pe(e){return"true/"===(e.type||"").slice(0,5)?e.type=e.type.slice(5):e.removeAttribute("type"),e}function He(e,t){var n,r,i,o,a,u,s,l;if(1===t.nodeType){if(K.hasData(e)&&(o=K.access(e),a=K.set(t,o),l=o.events)){delete a.handle,a.events={};for(i in l)for(n=0,r=l[i].length;n<r;n++)w.event.add(t,i,l[i][n])}J.hasData(e)&&(u=J.access(e),s=w.extend({},u),J.set(t,s))}}function Ie(e,t){var n=t.nodeName.toLowerCase();"input"===n&&de.test(e.type)?t.checked=e.checked:"input"!==n&&"textarea"!==n||(t.defaultValue=e.defaultValue)}function Re(e,t,n,r){t=a.apply([],t);var i,o,u,s,l,c,f=0,d=e.length,p=d-1,v=t[0],y=g(v);if(y||d>1&&"string"==typeof v&&!h.checkClone&&Le.test(v))return e.each(function(i){var o=e.eq(i);y&&(t[0]=v.call(this,i,o.html())),Re(o,t,n,r)});if(d&&(i=be(t,e[0].ownerDocument,!1,e,r),o=i.firstChild,1===i.childNodes.length&&(i=o),o||r)){for(s=(u=w.map(ve(i,"script"),Oe)).length;f<d;f++)l=i,f!==p&&(l=w.clone(l,!0,!0),s&&w.merge(u,ve(l,"script"))),n.call(e[f],l,f);if(s)for(c=u[u.length-1].ownerDocument,w.map(u,Pe),f=0;f<s;f++)l=u[f],he.test(l.type||"")&&!K.access(l,"globalEval")&&w.contains(c,l)&&(l.src&&"module"!==(l.type||"").toLowerCase()?w._evalUrl&&w._evalUrl(l.src):m(l.textContent.replace(je,""),c,l))}return e}function Be(e,t,n){for(var r,i=t?w.filter(t,e):e,o=0;null!=(r=i[o]);o++)n||1!==r.nodeType||w.cleanData(ve(r)),r.parentNode&&(n&&w.contains(r.ownerDocument,r)&&ye(ve(r,"script")),r.parentNode.removeChild(r));return e}w.extend({htmlPrefilter:function(e){return e.replace(De,"<$1></$2>")},clone:function(e,t,n){var r,i,o,a,u=e.cloneNode(!0),s=w.contains(e.ownerDocument,e);if(!(h.noCloneChecked||1!==e.nodeType&&11!==e.nodeType||w.isXMLDoc(e)))for(a=ve(u),r=0,i=(o=ve(e)).length;r<i;r++)Ie(o[r],a[r]);if(t)if(n)for(o=o||ve(e),a=a||ve(u),r=0,i=o.length;r<i;r++)He(o[r],a[r]);else He(e,u);return(a=ve(u,"script")).length>0&&ye(a,!s&&ve(e,"script")),u},cleanData:function(e){for(var t,n,r,i=w.event.special,o=0;void 0!==(n=e[o]);o++)if(Y(n)){if(t=n[K.expando]){if(t.events)for(r in t.events)i[r]?w.event.remove(n,r):w.removeEvent(n,r,t.handle);n[K.expando]=void 0}n[J.expando]&&(n[J.expando]=void 0)}}}),w.fn.extend({detach:function(e){return Be(this,e,!0)},remove:function(e){return Be(this,e)},text:function(e){return _(this,function(e){return void 0===e?w.text(this):this.empty().each(function(){1!==this.nodeType&&11!==this.nodeType&&9!==this.nodeType||(this.textContent=e)})},null,e,arguments.length)},append:function(){return Re(this,arguments,function(e){1!==this.nodeType&&11!==this.nodeType&&9!==this.nodeType||qe(this,e).appendChild(e)})},prepend:function(){return Re(this,arguments,function(e){if(1===this.nodeType||11===this.nodeType||9===this.nodeType){var t=qe(this,e);t.insertBefore(e,t.firstChild)}})},before:function(){return Re(this,arguments,function(e){this.parentNode&&this.parentNode.insertBefore(e,this)})},after:function(){return Re(this,arguments,function(e){this.parentNode&&this.parentNode.insertBefore(e,this.nextSibling)})},empty:function(){for(var e,t=0;null!=(e=this[t]);t++)1===e.nodeType&&(w.cleanData(ve(e,!1)),e.textContent="");return this},clone:function(e,t){return e=null!=e&&e,t=null==t?e:t,this.map(function(){return w.clone(this,e,t)})},html:function(e){return _(this,function(e){var t=this[0]||{},n=0,r=this.length;if(void 0===e&&1===t.nodeType)return t.innerHTML;if("string"==typeof e&&!Se.test(e)&&!ge[(pe.exec(e)||["",""])[1].toLowerCase()]){e=w.htmlPrefilter(e);try{for(;n<r;n++)1===(t=this[n]||{}).nodeType&&(w.cleanData(ve(t,!1)),t.innerHTML=e);t=0}catch(e){}}t&&this.empty().append(e)},null,e,arguments.length)},replaceWith:function(){var e=[];return Re(this,arguments,function(t){var n=this.parentNode;w.inArray(this,e)<0&&(w.cleanData(ve(this)),n&&n.replaceChild(t,this))},e)}}),w.each({appendTo:"append",prependTo:"prepend",insertBefore:"before",insertAfter:"after",replaceAll:"replaceWith"},function(e,t){w.fn[e]=function(e){for(var n,r=[],i=w(e),o=i.length-1,a=0;a<=o;a++)n=a===o?this:this.clone(!0),w(i[a])[t](n),u.apply(r,n.get());return this.pushStack(r)}});var Me=new RegExp("^("+re+")(?!px)[a-z%]+$","i"),We=function(t){var n=t.ownerDocument.defaultView;return n&&n.opener||(n=e),n.getComputedStyle(t)},$e=new RegExp(oe.join("|"),"i");!function(){function t(){if(c){l.style.cssText="position:absolute;left:-11111px;width:60px;margin-top:1px;padding:0;border:0",c.style.cssText="position:relative;display:block;box-sizing:border-box;overflow:scroll;margin:auto;border:1px;padding:1px;width:60%;top:1%",xe.appendChild(l).appendChild(c);var t=e.getComputedStyle(c);i="1%"!==t.top,s=12===n(t.marginLeft),c.style.right="60%",u=36===n(t.right),o=36===n(t.width),c.style.position="absolute",a=36===c.offsetWidth||"absolute",xe.removeChild(l),c=null}}function n(e){return Math.round(parseFloat(e))}var i,o,a,u,s,l=r.createElement("div"),c=r.createElement("div");c.style&&(c.style.backgroundClip="content-box",c.cloneNode(!0).style.backgroundClip="",h.clearCloneStyle="content-box"===c.style.backgroundClip,w.extend(h,{boxSizingReliable:function(){return t(),o},pixelBoxStyles:function(){return t(),u},pixelPosition:function(){return t(),i},reliableMarginLeft:function(){return t(),s},scrollboxSize:function(){return t(),a}}))}();function Fe(e,t,n){var r,i,o,a,u=e.style;return(n=n||We(e))&&(""!==(a=n.getPropertyValue(t)||n[t])||w.contains(e.ownerDocument,e)||(a=w.style(e,t)),!h.pixelBoxStyles()&&Me.test(a)&&$e.test(t)&&(r=u.width,i=u.minWidth,o=u.maxWidth,u.minWidth=u.maxWidth=u.width=a,a=n.width,u.width=r,u.minWidth=i,u.maxWidth=o)),void 0!==a?a+"":a}function ze(e,t){return{get:function(){if(!e())return(this.get=t).apply(this,arguments);delete this.get}}}var _e=/^(none|table(?!-c[ea]).+)/,Ue=/^--/,Ve={position:"absolute",visibility:"hidden",display:"block"},Xe={letterSpacing:"0",fontWeight:"400"},Qe=["Webkit","Moz","ms"],Ye=r.createElement("div").style;function Ge(e){if(e in Ye)return e;var t=e[0].toUpperCase()+e.slice(1),n=Qe.length;while(n--)if((e=Qe[n]+t)in Ye)return e}function Ke(e){var t=w.cssProps[e];return t||(t=w.cssProps[e]=Ge(e)||e),t}function Je(e,t,n){var r=ie.exec(t);return r?Math.max(0,r[2]-(n||0))+(r[3]||"px"):t}function Ze(e,t,n,r,i,o){var a="width"===t?1:0,u=0,s=0;if(n===(r?"border":"content"))return 0;for(;a<4;a+=2)"margin"===n&&(s+=w.css(e,n+oe[a],!0,i)),r?("content"===n&&(s-=w.css(e,"padding"+oe[a],!0,i)),"margin"!==n&&(s-=w.css(e,"border"+oe[a]+"Width",!0,i))):(s+=w.css(e,"padding"+oe[a],!0,i),"padding"!==n?s+=w.css(e,"border"+oe[a]+"Width",!0,i):u+=w.css(e,"border"+oe[a]+"Width",!0,i));return!r&&o>=0&&(s+=Math.max(0,Math.ceil(e["offset"+t[0].toUpperCase()+t.slice(1)]-o-s-u-.5))),s}function et(e,t,n){var r=We(e),i=Fe(e,t,r),o="border-box"===w.css(e,"boxSizing",!1,r),a=o;if(Me.test(i)){if(!n)return i;i="auto"}return a=a&&(h.boxSizingReliable()||i===e.style[t]),("auto"===i||!parseFloat(i)&&"inline"===w.css(e,"display",!1,r))&&(i=e["offset"+t[0].toUpperCase()+t.slice(1)],a=!0),(i=parseFloat(i)||0)+Ze(e,t,n||(o?"border":"content"),a,r,i)+"px"}w.extend({cssHooks:{opacity:{get:function(e,t){if(t){var n=Fe(e,"opacity");return""===n?"1":n}}}},cssNumber:{animationIterationCount:!0,columnCount:!0,fillOpacity:!0,flexGrow:!0,flexShrink:!0,fontWeight:!0,lineHeight:!0,opacity:!0,order:!0,orphans:!0,widows:!0,zIndex:!0,zoom:!0},cssProps:{},style:function(e,t,n,r){if(e&&3!==e.nodeType&&8!==e.nodeType&&e.style){var i,o,a,u=Q(t),s=Ue.test(t),l=e.style;if(s||(t=Ke(u)),a=w.cssHooks[t]||w.cssHooks[u],void 0===n)return a&&"get"in a&&void 0!==(i=a.get(e,!1,r))?i:l[t];"string"==(o=typeof n)&&(i=ie.exec(n))&&i[1]&&(n=se(e,t,i),o="number"),null!=n&&n===n&&("number"===o&&(n+=i&&i[3]||(w.cssNumber[u]?"":"px")),h.clearCloneStyle||""!==n||0!==t.indexOf("background")||(l[t]="inherit"),a&&"set"in a&&void 0===(n=a.set(e,n,r))||(s?l.setProperty(t,n):l[t]=n))}},css:function(e,t,n,r){var i,o,a,u=Q(t);return Ue.test(t)||(t=Ke(u)),(a=w.cssHooks[t]||w.cssHooks[u])&&"get"in a&&(i=a.get(e,!0,n)),void 0===i&&(i=Fe(e,t,r)),"normal"===i&&t in Xe&&(i=Xe[t]),""===n||n?(o=parseFloat(i),!0===n||isFinite(o)?o||0:i):i}}),w.each(["height","width"],function(e,t){w.cssHooks[t]={get:function(e,n,r){if(n)return!_e.test(w.css(e,"display"))||e.getClientRects().length&&e.getBoundingClientRect().width?et(e,t,r):ue(e,Ve,function(){return et(e,t,r)})},set:function(e,n,r){var i,o=We(e),a="border-box"===w.css(e,"boxSizing",!1,o),u=r&&Ze(e,t,r,a,o);return a&&h.scrollboxSize()===o.position&&(u-=Math.ceil(e["offset"+t[0].toUpperCase()+t.slice(1)]-parseFloat(o[t])-Ze(e,t,"border",!1,o)-.5)),u&&(i=ie.exec(n))&&"px"!==(i[3]||"px")&&(e.style[t]=n,n=w.css(e,t)),Je(e,n,u)}}}),w.cssHooks.marginLeft=ze(h.reliableMarginLeft,function(e,t){if(t)return(parseFloat(Fe(e,"marginLeft"))||e.getBoundingClientRect().left-ue(e,{marginLeft:0},function(){return e.getBoundingClientRect().left}))+"px"}),w.each({margin:"",padding:"",border:"Width"},function(e,t){w.cssHooks[e+t]={expand:function(n){for(var r=0,i={},o="string"==typeof n?n.split(" "):[n];r<4;r++)i[e+oe[r]+t]=o[r]||o[r-2]||o[0];return i}},"margin"!==e&&(w.cssHooks[e+t].set=Je)}),w.fn.extend({css:function(e,t){return _(this,function(e,t,n){var r,i,o={},a=0;if(Array.isArray(t)){for(r=We(e),i=t.length;a<i;a++)o[t[a]]=w.css(e,t[a],!1,r);return o}return void 0!==n?w.style(e,t,n):w.css(e,t)},e,t,arguments.length>1)}}),w.fn.delay=function(t,n){return t=w.fx?w.fx.speeds[t]||t:t,n=n||"fx",this.queue(n,function(n,r){var i=e.setTimeout(n,t);r.stop=function(){e.clearTimeout(i)}})},function(){var e=r.createElement("input"),t=r.createElement("select").appendChild(r.createElement("option"));e.type="checkbox",h.checkOn=""!==e.value,h.optSelected=t.selected,(e=r.createElement("input")).value="t",e.type="radio",h.radioValue="t"===e.value}();var tt,nt=w.expr.attrHandle;w.fn.extend({attr:function(e,t){return _(this,w.attr,e,t,arguments.length>1)},removeAttr:function(e){return this.each(function(){w.removeAttr(this,e)})}}),w.extend({attr:function(e,t,n){var r,i,o=e.nodeType;if(3!==o&&8!==o&&2!==o)return"undefined"==typeof e.getAttribute?w.prop(e,t,n):(1===o&&w.isXMLDoc(e)||(i=w.attrHooks[t.toLowerCase()]||(w.expr.match.bool.test(t)?tt:void 0)),void 0!==n?null===n?void w.removeAttr(e,t):i&&"set"in i&&void 0!==(r=i.set(e,n,t))?r:(e.setAttribute(t,n+""),n):i&&"get"in i&&null!==(r=i.get(e,t))?r:null==(r=w.find.attr(e,t))?void 0:r)},attrHooks:{type:{set:function(e,t){if(!h.radioValue&&"radio"===t&&D(e,"input")){var n=e.value;return e.setAttribute("type",t),n&&(e.value=n),t}}}},removeAttr:function(e,t){var n,r=0,i=t&&t.match(I);if(i&&1===e.nodeType)while(n=i[r++])e.removeAttribute(n)}}),tt={set:function(e,t,n){return!1===t?w.removeAttr(e,n):e.setAttribute(n,n),n}},w.each(w.expr.match.bool.source.match(/\w+/g),function(e,t){var n=nt[t]||w.find.attr;nt[t]=function(e,t,r){var i,o,a=t.toLowerCase();return r||(o=nt[a],nt[a]=i,i=null!=n(e,t,r)?a:null,nt[a]=o),i}});var rt=/^(?:input|select|textarea|button)$/i,it=/^(?:a|area)$/i;w.fn.extend({prop:function(e,t){return _(this,w.prop,e,t,arguments.length>1)},removeProp:function(e){return this.each(function(){delete this[w.propFix[e]||e]})}}),w.extend({prop:function(e,t,n){var r,i,o=e.nodeType;if(3!==o&&8!==o&&2!==o)return 1===o&&w.isXMLDoc(e)||(t=w.propFix[t]||t,i=w.propHooks[t]),void 0!==n?i&&"set"in i&&void 0!==(r=i.set(e,n,t))?r:e[t]=n:i&&"get"in i&&null!==(r=i.get(e,t))?r:e[t]},propHooks:{tabIndex:{get:function(e){var t=w.find.attr(e,"tabindex");return t?parseInt(t,10):rt.test(e.nodeName)||it.test(e.nodeName)&&e.href?0:-1}}},propFix:{"for":"htmlFor","class":"className"}}),h.optSelected||(w.propHooks.selected={get:function(e){var t=e.parentNode;return t&&t.parentNode&&t.parentNode.selectedIndex,null},set:function(e){var t=e.parentNode;t&&(t.selectedIndex,t.parentNode&&t.parentNode.selectedIndex)}}),w.each(["tabIndex","readOnly","maxLength","cellSpacing","cellPadding","rowSpan","colSpan","useMap","frameBorder","contentEditable"],function(){w.propFix[this.toLowerCase()]=this});function ot(e){return(e.match(I)||[]).join(" ")}function at(e){return e.getAttribute&&e.getAttribute("class")||""}function ut(e){return Array.isArray(e)?e:"string"==typeof e?e.match(I)||[]:[]}w.fn.extend({addClass:function(e){var t,n,r,i,o,a,u,s=0;if(g(e))return this.each(function(t){w(this).addClass(e.call(this,t,at(this)))});if((t=ut(e)).length)while(n=this[s++])if(i=at(n),r=1===n.nodeType&&" "+ot(i)+" "){a=0;while(o=t[a++])r.indexOf(" "+o+" ")<0&&(r+=o+" ");i!==(u=ot(r))&&n.setAttribute("class",u)}return this},removeClass:function(e){var t,n,r,i,o,a,u,s=0;if(g(e))return this.each(function(t){w(this).removeClass(e.call(this,t,at(this)))});if(!arguments.length)return this.attr("class","");if((t=ut(e)).length)while(n=this[s++])if(i=at(n),r=1===n.nodeType&&" "+ot(i)+" "){a=0;while(o=t[a++])while(r.indexOf(" "+o+" ")>-1)r=r.replace(" "+o+" "," ");i!==(u=ot(r))&&n.setAttribute("class",u)}return this},toggleClass:function(e,t){var n=typeof e,r="string"===n||Array.isArray(e);return"boolean"==typeof t&&r?t?this.addClass(e):this.removeClass(e):g(e)?this.each(function(n){w(this).toggleClass(e.call(this,n,at(this),t),t)}):this.each(function(){var t,i,o,a;if(r){i=0,o=w(this),a=ut(e);while(t=a[i++])o.hasClass(t)?o.removeClass(t):o.addClass(t)}else void 0!==e&&"boolean"!==n||((t=at(this))&&K.set(this,"__className__",t),this.setAttribute&&this.setAttribute("class",t||!1===e?"":K.get(this,"__className__")||""))})},hasClass:function(e){var t,n,r=0;t=" "+e+" ";while(n=this[r++])if(1===n.nodeType&&(" "+ot(at(n))+" ").indexOf(t)>-1)return!0;return!1}});var st=/\r/g;w.fn.extend({val:function(e){var t,n,r,i=this[0];{if(arguments.length)return r=g(e),this.each(function(n){var i;1===this.nodeType&&(null==(i=r?e.call(this,n,w(this).val()):e)?i="":"number"==typeof i?i+="":Array.isArray(i)&&(i=w.map(i,function(e){return null==e?"":e+""})),(t=w.valHooks[this.type]||w.valHooks[this.nodeName.toLowerCase()])&&"set"in t&&void 0!==t.set(this,i,"value")||(this.value=i))});if(i)return(t=w.valHooks[i.type]||w.valHooks[i.nodeName.toLowerCase()])&&"get"in t&&void 0!==(n=t.get(i,"value"))?n:"string"==typeof(n=i.value)?n.replace(st,""):null==n?"":n}}}),w.extend({valHooks:{option:{get:function(e){var t=w.find.attr(e,"value");return null!=t?t:ot(w.text(e))}},select:{get:function(e){var t,n,r,i=e.options,o=e.selectedIndex,a="select-one"===e.type,u=a?null:[],s=a?o+1:i.length;for(r=o<0?s:a?o:0;r<s;r++)if(((n=i[r]).selected||r===o)&&!n.disabled&&(!n.parentNode.disabled||!D(n.parentNode,"optgroup"))){if(t=w(n).val(),a)return t;u.push(t)}return u},set:function(e,t){var n,r,i=e.options,o=w.makeArray(t),a=i.length;while(a--)((r=i[a]).selected=w.inArray(w.valHooks.option.get(r),o)>-1)&&(n=!0);return n||(e.selectedIndex=-1),o}}}}),w.each(["radio","checkbox"],function(){w.valHooks[this]={set:function(e,t){if(Array.isArray(t))return e.checked=w.inArray(w(e).val(),t)>-1}},h.checkOn||(w.valHooks[this].get=function(e){return null===e.getAttribute("value")?"on":e.value})}),h.focusin="onfocusin"in e;var lt=/^(?:focusinfocus|focusoutblur)$/,ct=function(e){e.stopPropagation()};w.extend(w.event,{trigger:function(t,n,i,o){var a,u,s,l,c,d,p,h,y=[i||r],m=f.call(t,"type")?t.type:t,b=f.call(t,"namespace")?t.namespace.split("."):[];if(u=h=s=i=i||r,3!==i.nodeType&&8!==i.nodeType&&!lt.test(m+w.event.triggered)&&(m.indexOf(".")>-1&&(m=(b=m.split(".")).shift(),b.sort()),c=m.indexOf(":")<0&&"on"+m,t=t[w.expando]?t:new w.Event(m,"object"==typeof t&&t),t.isTrigger=o?2:3,t.namespace=b.join("."),t.rnamespace=t.namespace?new RegExp("(^|\\.)"+b.join("\\.(?:.*\\.|)")+"(\\.|$)"):null,t.result=void 0,t.target||(t.target=i),n=null==n?[t]:w.makeArray(n,[t]),p=w.event.special[m]||{},o||!p.trigger||!1!==p.trigger.apply(i,n))){if(!o&&!p.noBubble&&!v(i)){for(l=p.delegateType||m,lt.test(l+m)||(u=u.parentNode);u;u=u.parentNode)y.push(u),s=u;s===(i.ownerDocument||r)&&y.push(s.defaultView||s.parentWindow||e)}a=0;while((u=y[a++])&&!t.isPropagationStopped())h=u,t.type=a>1?l:p.bindType||m,(d=(K.get(u,"events")||{})[t.type]&&K.get(u,"handle"))&&d.apply(u,n),(d=c&&u[c])&&d.apply&&Y(u)&&(t.result=d.apply(u,n),!1===t.result&&t.preventDefault());return t.type=m,o||t.isDefaultPrevented()||p._default&&!1!==p._default.apply(y.pop(),n)||!Y(i)||c&&g(i[m])&&!v(i)&&((s=i[c])&&(i[c]=null),w.event.triggered=m,t.isPropagationStopped()&&h.addEventListener(m,ct),i[m](),t.isPropagationStopped()&&h.removeEventListener(m,ct),w.event.triggered=void 0,s&&(i[c]=s)),t.result}},simulate:function(e,t,n){var r=w.extend(new w.Event,n,{type:e,isSimulated:!0});w.event.trigger(r,null,t)}}),w.fn.extend({trigger:function(e,t){return this.each(function(){w.event.trigger(e,t,this)})},triggerHandler:function(e,t){var n=this[0];if(n)return w.event.trigger(e,t,n,!0)}}),h.focusin||w.each({focus:"focusin",blur:"focusout"},function(e,t){var n=function(e){w.event.simulate(t,e.target,w.event.fix(e))};w.event.special[t]={setup:function(){var r=this.ownerDocument||this,i=K.access(r,t);i||r.addEventListener(e,n,!0),K.access(r,t,(i||0)+1)},teardown:function(){var r=this.ownerDocument||this,i=K.access(r,t)-1;i?K.access(r,t,i):(r.removeEventListener(e,n,!0),K.remove(r,t))}}});var ft=/\[\]$/,dt=/\r?\n/g,pt=/^(?:submit|button|image|reset|file)$/i,ht=/^(?:input|select|textarea|keygen)/i;function gt(e,t,n,r){var i;if(Array.isArray(t))w.each(t,function(t,i){n||ft.test(e)?r(e,i):gt(e+"["+("object"==typeof i&&null!=i?t:"")+"]",i,n,r)});else if(n||"object"!==b(t))r(e,t);else for(i in t)gt(e+"["+i+"]",t[i],n,r)}w.param=function(e,t){var n,r=[],i=function(e,t){var n=g(t)?t():t;r[r.length]=encodeURIComponent(e)+"="+encodeURIComponent(null==n?"":n)};if(Array.isArray(e)||e.jquery&&!w.isPlainObject(e))w.each(e,function(){i(this.name,this.value)});else for(n in e)gt(n,e[n],t,i);return r.join("&")},w.fn.extend({serialize:function(){return w.param(this.serializeArray())},serializeArray:function(){return this.map(function(){var e=w.prop(this,"elements");return e?w.makeArray(e):this}).filter(function(){var e=this.type;return this.name&&!w(this).is(":disabled")&&ht.test(this.nodeName)&&!pt.test(e)&&(this.checked||!de.test(e))}).map(function(e,t){var n=w(this).val();return null==n?null:Array.isArray(n)?w.map(n,function(e){return{name:t.name,value:e.replace(dt,"\r\n")}}):{name:t.name,value:n.replace(dt,"\r\n")}}).get()}}),w.fn.extend({wrapAll:function(e){var t;return this[0]&&(g(e)&&(e=e.call(this[0])),t=w(e,this[0].ownerDocument).eq(0).clone(!0),this[0].parentNode&&t.insertBefore(this[0]),t.map(function(){var e=this;while(e.firstElementChild)e=e.firstElementChild;return e}).append(this)),this},wrapInner:function(e){return g(e)?this.each(function(t){w(this).wrapInner(e.call(this,t))}):this.each(function(){var t=w(this),n=t.contents();n.length?n.wrapAll(e):t.append(e)})},wrap:function(e){var t=g(e);return this.each(function(n){w(this).wrapAll(t?e.call(this,n):e)})},unwrap:function(e){return this.parent(e).not("body").each(function(){w(this).replaceWith(this.childNodes)}),this}}),w.expr.pseudos.hidden=function(e){return!w.expr.pseudos.visible(e)},w.expr.pseudos.visible=function(e){return!!(e.offsetWidth||e.offsetHeight||e.getClientRects().length)},h.createHTMLDocument=function(){var e=r.implementation.createHTMLDocument("").body;return e.innerHTML="<form></form><form></form>",2===e.childNodes.length}(),w.parseHTML=function(e,t,n){if("string"!=typeof e)return[];"boolean"==typeof t&&(n=t,t=!1);var i,o,a;return t||(h.createHTMLDocument?((i=(t=r.implementation.createHTMLDocument("")).createElement("base")).href=r.location.href,t.head.appendChild(i)):t=r),o=S.exec(e),a=!n&&[],o?[t.createElement(o[1])]:(o=be([e],t,a),a&&a.length&&w(a).remove(),w.merge([],o.childNodes))},w.offset={setOffset:function(e,t,n){var r,i,o,a,u,s,l,c=w.css(e,"position"),f=w(e),d={};"static"===c&&(e.style.position="relative"),u=f.offset(),o=w.css(e,"top"),s=w.css(e,"left"),(l=("absolute"===c||"fixed"===c)&&(o+s).indexOf("auto")>-1)?(a=(r=f.position()).top,i=r.left):(a=parseFloat(o)||0,i=parseFloat(s)||0),g(t)&&(t=t.call(e,n,w.extend({},u))),null!=t.top&&(d.top=t.top-u.top+a),null!=t.left&&(d.left=t.left-u.left+i),"using"in t?t.using.call(e,d):f.css(d)}},w.fn.extend({offset:function(e){if(arguments.length)return void 0===e?this:this.each(function(t){w.offset.setOffset(this,e,t)});var t,n,r=this[0];if(r)return r.getClientRects().length?(t=r.getBoundingClientRect(),n=r.ownerDocument.defaultView,{top:t.top+n.pageYOffset,left:t.left+n.pageXOffset}):{top:0,left:0}},position:function(){if(this[0]){var e,t,n,r=this[0],i={top:0,left:0};if("fixed"===w.css(r,"position"))t=r.getBoundingClientRect();else{t=this.offset(),n=r.ownerDocument,e=r.offsetParent||n.documentElement;while(e&&(e===n.body||e===n.documentElement)&&"static"===w.css(e,"position"))e=e.parentNode;e&&e!==r&&1===e.nodeType&&((i=w(e).offset()).top+=w.css(e,"borderTopWidth",!0),i.left+=w.css(e,"borderLeftWidth",!0))}return{top:t.top-i.top-w.css(r,"marginTop",!0),left:t.left-i.left-w.css(r,"marginLeft",!0)}}},offsetParent:function(){return this.map(function(){var e=this.offsetParent;while(e&&"static"===w.css(e,"position"))e=e.offsetParent;return e||xe})}}),w.each({scrollLeft:"pageXOffset",scrollTop:"pageYOffset"},function(e,t){var n="pageYOffset"===t;w.fn[e]=function(r){return _(this,function(e,r,i){var o;if(v(e)?o=e:9===e.nodeType&&(o=e.defaultView),void 0===i)return o?o[t]:e[r];o?o.scrollTo(n?o.pageXOffset:i,n?i:o.pageYOffset):e[r]=i},e,r,arguments.length)}}),w.each(["top","left"],function(e,t){w.cssHooks[t]=ze(h.pixelPosition,function(e,n){if(n)return n=Fe(e,t),Me.test(n)?w(e).position()[t]+"px":n})}),w.each({Height:"height",Width:"width"},function(e,t){w.each({padding:"inner"+e,content:t,"":"outer"+e},function(n,r){w.fn[r]=function(i,o){var a=arguments.length&&(n||"boolean"!=typeof i),u=n||(!0===i||!0===o?"margin":"border");return _(this,function(t,n,i){var o;return v(t)?0===r.indexOf("outer")?t["inner"+e]:t.document.documentElement["client"+e]:9===t.nodeType?(o=t.documentElement,Math.max(t.body["scroll"+e],o["scroll"+e],t.body["offset"+e],o["offset"+e],o["client"+e])):void 0===i?w.css(t,n,u):w.style(t,n,i,u)},t,a?i:void 0,a)}})}),w.each("blur focus focusin focusout resize scroll click dblclick mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave change select submit keydown keypress keyup contextmenu".split(" "),function(e,t){w.fn[t]=function(e,n){return arguments.length>0?this.on(t,null,e,n):this.trigger(t)}}),w.fn.extend({hover:function(e,t){return this.mouseenter(e).mouseleave(t||e)}}),w.fn.extend({bind:function(e,t,n){return this.on(e,null,t,n)},unbind:function(e,t){return this.off(e,null,t)},delegate:function(e,t,n,r){return this.on(t,e,n,r)},undelegate:function(e,t,n){return 1===arguments.length?this.off(e,"**"):this.off(t,e||"**",n)}}),w.proxy=function(e,t){var n,r,i;if("string"==typeof t&&(n=e[t],t=e,e=n),g(e))return r=o.call(arguments,2),i=function(){return e.apply(t||this,r.concat(o.call(arguments)))},i.guid=e.guid=e.guid||w.guid++,i},w.holdReady=function(e){e?w.readyWait++:w.ready(!0)},w.isArray=Array.isArray,w.parseJSON=JSON.parse,w.nodeName=D,w.isFunction=g,w.isWindow=v,w.camelCase=Q,w.type=b,w.now=Date.now,w.isNumeric=function(e){var t=w.type(e);return("number"===t||"string"===t)&&!isNaN(e-parseFloat(e))},"function"==typeof define&&define.amd&&define("jquery",[],function(){return w});var vt=e.jQuery,yt=e.$;return w.noConflict=function(t){return e.$===w&&(e.$=yt),t&&e.jQuery===w&&(e.jQuery=vt),w},t||(e.jQuery=e.$=w),w});
3 | 


--------------------------------------------------------------------------------
/resources/js/popper.min.js:
--------------------------------------------------------------------------------
1 | /*
2 |  Copyright (C) Federico Zivolo 2017
3 |  Distributed under the MIT License (license terms are at http://opensource.org/licenses/MIT).
4 |  */(function(e,t){'object'==typeof exports&&'undefined'!=typeof module?module.exports=t():'function'==typeof define&&define.amd?define(t):e.Popper=t()})(this,function(){'use strict';function e(e){return e&&'[object Function]'==={}.toString.call(e)}function t(e,t){if(1!==e.nodeType)return[];var o=window.getComputedStyle(e,null);return t?o[t]:o}function o(e){return'HTML'===e.nodeName?e:e.parentNode||e.host}function n(e){if(!e||-1!==['HTML','BODY','#document'].indexOf(e.nodeName))return window.document.body;var i=t(e),r=i.overflow,p=i.overflowX,s=i.overflowY;return /(auto|scroll)/.test(r+s+p)?e:n(o(e))}function r(e){var o=e&&e.offsetParent,i=o&&o.nodeName;return i&&'BODY'!==i&&'HTML'!==i?-1!==['TD','TABLE'].indexOf(o.nodeName)&&'static'===t(o,'position')?r(o):o:window.document.documentElement}function p(e){var t=e.nodeName;return'BODY'!==t&&('HTML'===t||r(e.firstElementChild)===e)}function s(e){return null===e.parentNode?e:s(e.parentNode)}function d(e,t){if(!e||!e.nodeType||!t||!t.nodeType)return window.document.documentElement;var o=e.compareDocumentPosition(t)&Node.DOCUMENT_POSITION_FOLLOWING,i=o?e:t,n=o?t:e,a=document.createRange();a.setStart(i,0),a.setEnd(n,0);var f=a.commonAncestorContainer;if(e!==f&&t!==f||i.contains(n))return p(f)?f:r(f);var l=s(e);return l.host?d(l.host,t):d(e,s(t).host)}function a(e){var t=1<arguments.length&&void 0!==arguments[1]?arguments[1]:'top',o='top'===t?'scrollTop':'scrollLeft',i=e.nodeName;if('BODY'===i||'HTML'===i){var n=window.document.documentElement,r=window.document.scrollingElement||n;return r[o]}return e[o]}function f(e,t){var o=2<arguments.length&&void 0!==arguments[2]&&arguments[2],i=a(t,'top'),n=a(t,'left'),r=o?-1:1;return e.top+=i*r,e.bottom+=i*r,e.left+=n*r,e.right+=n*r,e}function l(e,t){var o='x'===t?'Left':'Top',i='Left'==o?'Right':'Bottom';return+e['border'+o+'Width'].split('px')[0]+ +e['border'+i+'Width'].split('px')[0]}function m(e,t,o,i){return _(t['offset'+e],o['client'+e],o['offset'+e],ie()?o['offset'+e]+i['margin'+('Height'===e?'Top':'Left')]+i['margin'+('Height'===e?'Bottom':'Right')]:0)}function h(){var e=window.document.body,t=window.document.documentElement,o=ie()&&window.getComputedStyle(t);return{height:m('Height',e,t,o),width:m('Width',e,t,o)}}function c(e){return se({},e,{right:e.left+e.width,bottom:e.top+e.height})}function g(e){var o={};if(ie())try{o=e.getBoundingClientRect();var i=a(e,'top'),n=a(e,'left');o.top+=i,o.left+=n,o.bottom+=i,o.right+=n}catch(e){}else o=e.getBoundingClientRect();var r={left:o.left,top:o.top,width:o.right-o.left,height:o.bottom-o.top},p='HTML'===e.nodeName?h():{},s=p.width||e.clientWidth||r.right-r.left,d=p.height||e.clientHeight||r.bottom-r.top,f=e.offsetWidth-s,m=e.offsetHeight-d;if(f||m){var g=t(e);f-=l(g,'x'),m-=l(g,'y'),r.width-=f,r.height-=m}return c(r)}function u(e,o){var i=ie(),r='HTML'===o.nodeName,p=g(e),s=g(o),d=n(e),a=t(o),l=+a.borderTopWidth.split('px')[0],m=+a.borderLeftWidth.split('px')[0],h=c({top:p.top-s.top-l,left:p.left-s.left-m,width:p.width,height:p.height});if(h.marginTop=0,h.marginLeft=0,!i&&r){var u=+a.marginTop.split('px')[0],b=+a.marginLeft.split('px')[0];h.top-=l-u,h.bottom-=l-u,h.left-=m-b,h.right-=m-b,h.marginTop=u,h.marginLeft=b}return(i?o.contains(d):o===d&&'BODY'!==d.nodeName)&&(h=f(h,o)),h}function b(e){var t=window.document.documentElement,o=u(e,t),i=_(t.clientWidth,window.innerWidth||0),n=_(t.clientHeight,window.innerHeight||0),r=a(t),p=a(t,'left'),s={top:r-o.top+o.marginTop,left:p-o.left+o.marginLeft,width:i,height:n};return c(s)}function y(e){var i=e.nodeName;return'BODY'===i||'HTML'===i?!1:'fixed'===t(e,'position')||y(o(e))}function w(e,t,i,r){var p={top:0,left:0},s=d(e,t);if('viewport'===r)p=b(s);else{var a;'scrollParent'===r?(a=n(o(e)),'BODY'===a.nodeName&&(a=window.document.documentElement)):'window'===r?a=window.document.documentElement:a=r;var f=u(a,s);if('HTML'===a.nodeName&&!y(s)){var l=h(),m=l.height,c=l.width;p.top+=f.top-f.marginTop,p.bottom=m+f.top,p.left+=f.left-f.marginLeft,p.right=c+f.left}else p=f}return p.left+=i,p.top+=i,p.right-=i,p.bottom-=i,p}function v(e){var t=e.width,o=e.height;return t*o}function E(e,t,o,i,n){var r=5<arguments.length&&void 0!==arguments[5]?arguments[5]:0;if(-1===e.indexOf('auto'))return e;var p=w(o,i,r,n),s={top:{width:p.width,height:t.top-p.top},right:{width:p.right-t.right,height:p.height},bottom:{width:p.width,height:p.bottom-t.bottom},left:{width:t.left-p.left,height:p.height}},d=Object.keys(s).map(function(e){return se({key:e},s[e],{area:v(s[e])})}).sort(function(e,t){return t.area-e.area}),a=d.filter(function(e){var t=e.width,i=e.height;return t>=o.clientWidth&&i>=o.clientHeight}),f=0<a.length?a[0].key:d[0].key,l=e.split('-')[1];return f+(l?'-'+l:'')}function x(e,t,o){var i=d(t,o);return u(o,i)}function O(e){var t=window.getComputedStyle(e),o=parseFloat(t.marginTop)+parseFloat(t.marginBottom),i=parseFloat(t.marginLeft)+parseFloat(t.marginRight),n={width:e.offsetWidth+i,height:e.offsetHeight+o};return n}function L(e){var t={left:'right',right:'left',bottom:'top',top:'bottom'};return e.replace(/left|right|bottom|top/g,function(e){return t[e]})}function S(e,t,o){o=o.split('-')[0];var i=O(e),n={width:i.width,height:i.height},r=-1!==['right','left'].indexOf(o),p=r?'top':'left',s=r?'left':'top',d=r?'height':'width',a=r?'width':'height';return n[p]=t[p]+t[d]/2-i[d]/2,n[s]=o===s?t[s]-i[a]:t[L(s)],n}function T(e,t){return Array.prototype.find?e.find(t):e.filter(t)[0]}function C(e,t,o){if(Array.prototype.findIndex)return e.findIndex(function(e){return e[t]===o});var i=T(e,function(e){return e[t]===o});return e.indexOf(i)}function N(t,o,i){var n=void 0===i?t:t.slice(0,C(t,'name',i));return n.forEach(function(t){t.function&&console.warn('`modifier.function` is deprecated, use `modifier.fn`!');var i=t.function||t.fn;t.enabled&&e(i)&&(o.offsets.popper=c(o.offsets.popper),o.offsets.reference=c(o.offsets.reference),o=i(o,t))}),o}function k(){if(!this.state.isDestroyed){var e={instance:this,styles:{},attributes:{},flipped:!1,offsets:{}};e.offsets.reference=x(this.state,this.popper,this.reference),e.placement=E(this.options.placement,e.offsets.reference,this.popper,this.reference,this.options.modifiers.flip.boundariesElement,this.options.modifiers.flip.padding),e.originalPlacement=e.placement,e.offsets.popper=S(this.popper,e.offsets.reference,e.placement),e.offsets.popper.position='absolute',e=N(this.modifiers,e),this.state.isCreated?this.options.onUpdate(e):(this.state.isCreated=!0,this.options.onCreate(e))}}function W(e,t){return e.some(function(e){var o=e.name,i=e.enabled;return i&&o===t})}function B(e){for(var t=[!1,'ms','Webkit','Moz','O'],o=e.charAt(0).toUpperCase()+e.slice(1),n=0;n<t.length-1;n++){var i=t[n],r=i?''+i+o:e;if('undefined'!=typeof window.document.body.style[r])return r}return null}function D(){return this.state.isDestroyed=!0,W(this.modifiers,'applyStyle')&&(this.popper.removeAttribute('x-placement'),this.popper.style.left='',this.popper.style.position='',this.popper.style.top='',this.popper.style[B('transform')]=''),this.disableEventListeners(),this.options.removeOnDestroy&&this.popper.parentNode.removeChild(this.popper),this}function H(e,t,o,i){var r='BODY'===e.nodeName,p=r?window:e;p.addEventListener(t,o,{passive:!0}),r||H(n(p.parentNode),t,o,i),i.push(p)}function P(e,t,o,i){o.updateBound=i,window.addEventListener('resize',o.updateBound,{passive:!0});var r=n(e);return H(r,'scroll',o.updateBound,o.scrollParents),o.scrollElement=r,o.eventsEnabled=!0,o}function A(){this.state.eventsEnabled||(this.state=P(this.reference,this.options,this.state,this.scheduleUpdate))}function M(e,t){return window.removeEventListener('resize',t.updateBound),t.scrollParents.forEach(function(e){e.removeEventListener('scroll',t.updateBound)}),t.updateBound=null,t.scrollParents=[],t.scrollElement=null,t.eventsEnabled=!1,t}function I(){this.state.eventsEnabled&&(window.cancelAnimationFrame(this.scheduleUpdate),this.state=M(this.reference,this.state))}function R(e){return''!==e&&!isNaN(parseFloat(e))&&isFinite(e)}function U(e,t){Object.keys(t).forEach(function(o){var i='';-1!==['width','height','top','right','bottom','left'].indexOf(o)&&R(t[o])&&(i='px'),e.style[o]=t[o]+i})}function Y(e,t){Object.keys(t).forEach(function(o){var i=t[o];!1===i?e.removeAttribute(o):e.setAttribute(o,t[o])})}function F(e,t,o){var i=T(e,function(e){var o=e.name;return o===t}),n=!!i&&e.some(function(e){return e.name===o&&e.enabled&&e.order<i.order});if(!n){var r='`'+t+'`';console.warn('`'+o+'`'+' modifier is required by '+r+' modifier in order to work, be sure to include it before '+r+'!')}return n}function j(e){return'end'===e?'start':'start'===e?'end':e}function K(e){var t=1<arguments.length&&void 0!==arguments[1]&&arguments[1],o=ae.indexOf(e),i=ae.slice(o+1).concat(ae.slice(0,o));return t?i.reverse():i}function q(e,t,o,i){var n=e.match(/((?:\-|\+)?\d*\.?\d*)(.*)/),r=+n[1],p=n[2];if(!r)return e;if(0===p.indexOf('%')){var s;switch(p){case'%p':s=o;break;case'%':case'%r':default:s=i;}var d=c(s);return d[t]/100*r}if('vh'===p||'vw'===p){var a;return a='vh'===p?_(document.documentElement.clientHeight,window.innerHeight||0):_(document.documentElement.clientWidth,window.innerWidth||0),a/100*r}return r}function G(e,t,o,i){var n=[0,0],r=-1!==['right','left'].indexOf(i),p=e.split(/(\+|\-)/).map(function(e){return e.trim()}),s=p.indexOf(T(p,function(e){return-1!==e.search(/,|\s/)}));p[s]&&-1===p[s].indexOf(',')&&console.warn('Offsets separated by white space(s) are deprecated, use a comma (,) instead.');var d=/\s*,\s*|\s+/,a=-1===s?[p]:[p.slice(0,s).concat([p[s].split(d)[0]]),[p[s].split(d)[1]].concat(p.slice(s+1))];return a=a.map(function(e,i){var n=(1===i?!r:r)?'height':'width',p=!1;return e.reduce(function(e,t){return''===e[e.length-1]&&-1!==['+','-'].indexOf(t)?(e[e.length-1]=t,p=!0,e):p?(e[e.length-1]+=t,p=!1,e):e.concat(t)},[]).map(function(e){return q(e,n,t,o)})}),a.forEach(function(e,t){e.forEach(function(o,i){R(o)&&(n[t]+=o*('-'===e[i-1]?-1:1))})}),n}for(var z=Math.min,V=Math.floor,_=Math.max,X=['native code','[object MutationObserverConstructor]'],Q=function(e){return X.some(function(t){return-1<(e||'').toString().indexOf(t)})},J='undefined'!=typeof window,Z=['Edge','Trident','Firefox'],$=0,ee=0;ee<Z.length;ee+=1)if(J&&0<=navigator.userAgent.indexOf(Z[ee])){$=1;break}var i,te=J&&Q(window.MutationObserver),oe=te?function(e){var t=!1,o=0,i=document.createElement('span'),n=new MutationObserver(function(){e(),t=!1});return n.observe(i,{attributes:!0}),function(){t||(t=!0,i.setAttribute('x-index',o),++o)}}:function(e){var t=!1;return function(){t||(t=!0,setTimeout(function(){t=!1,e()},$))}},ie=function(){return void 0==i&&(i=-1!==navigator.appVersion.indexOf('MSIE 10')),i},ne=function(e,t){if(!(e instanceof t))throw new TypeError('Cannot call a class as a function')},re=function(){function e(e,t){for(var o,n=0;n<t.length;n++)o=t[n],o.enumerable=o.enumerable||!1,o.configurable=!0,'value'in o&&(o.writable=!0),Object.defineProperty(e,o.key,o)}return function(t,o,i){return o&&e(t.prototype,o),i&&e(t,i),t}}(),pe=function(e,t,o){return t in e?Object.defineProperty(e,t,{value:o,enumerable:!0,configurable:!0,writable:!0}):e[t]=o,e},se=Object.assign||function(e){for(var t,o=1;o<arguments.length;o++)for(var i in t=arguments[o],t)Object.prototype.hasOwnProperty.call(t,i)&&(e[i]=t[i]);return e},de=['auto-start','auto','auto-end','top-start','top','top-end','right-start','right','right-end','bottom-end','bottom','bottom-start','left-end','left','left-start'],ae=de.slice(3),fe={FLIP:'flip',CLOCKWISE:'clockwise',COUNTERCLOCKWISE:'counterclockwise'},le=function(){function t(o,i){var n=this,r=2<arguments.length&&void 0!==arguments[2]?arguments[2]:{};ne(this,t),this.scheduleUpdate=function(){return requestAnimationFrame(n.update)},this.update=oe(this.update.bind(this)),this.options=se({},t.Defaults,r),this.state={isDestroyed:!1,isCreated:!1,scrollParents:[]},this.reference=o.jquery?o[0]:o,this.popper=i.jquery?i[0]:i,this.options.modifiers={},Object.keys(se({},t.Defaults.modifiers,r.modifiers)).forEach(function(e){n.options.modifiers[e]=se({},t.Defaults.modifiers[e]||{},r.modifiers?r.modifiers[e]:{})}),this.modifiers=Object.keys(this.options.modifiers).map(function(e){return se({name:e},n.options.modifiers[e])}).sort(function(e,t){return e.order-t.order}),this.modifiers.forEach(function(t){t.enabled&&e(t.onLoad)&&t.onLoad(n.reference,n.popper,n.options,t,n.state)}),this.update();var p=this.options.eventsEnabled;p&&this.enableEventListeners(),this.state.eventsEnabled=p}return re(t,[{key:'update',value:function(){return k.call(this)}},{key:'destroy',value:function(){return D.call(this)}},{key:'enableEventListeners',value:function(){return A.call(this)}},{key:'disableEventListeners',value:function(){return I.call(this)}}]),t}();return le.Utils=('undefined'==typeof window?global:window).PopperUtils,le.placements=de,le.Defaults={placement:'bottom',eventsEnabled:!0,removeOnDestroy:!1,onCreate:function(){},onUpdate:function(){},modifiers:{shift:{order:100,enabled:!0,fn:function(e){var t=e.placement,o=t.split('-')[0],i=t.split('-')[1];if(i){var n=e.offsets,r=n.reference,p=n.popper,s=-1!==['bottom','top'].indexOf(o),d=s?'left':'top',a=s?'width':'height',f={start:pe({},d,r[d]),end:pe({},d,r[d]+r[a]-p[a])};e.offsets.popper=se({},p,f[i])}return e}},offset:{order:200,enabled:!0,fn:function(e,t){var o,i=t.offset,n=e.placement,r=e.offsets,p=r.popper,s=r.reference,d=n.split('-')[0];return o=R(+i)?[+i,0]:G(i,p,s,d),'left'===d?(p.top+=o[0],p.left-=o[1]):'right'===d?(p.top+=o[0],p.left+=o[1]):'top'===d?(p.left+=o[0],p.top-=o[1]):'bottom'===d&&(p.left+=o[0],p.top+=o[1]),e.popper=p,e},offset:0},preventOverflow:{order:300,enabled:!0,fn:function(e,t){var o=t.boundariesElement||r(e.instance.popper);e.instance.reference===o&&(o=r(o));var i=w(e.instance.popper,e.instance.reference,t.padding,o);t.boundaries=i;var n=t.priority,p=e.offsets.popper,s={primary:function(e){var o=p[e];return p[e]<i[e]&&!t.escapeWithReference&&(o=_(p[e],i[e])),pe({},e,o)},secondary:function(e){var o='right'===e?'left':'top',n=p[o];return p[e]>i[e]&&!t.escapeWithReference&&(n=z(p[o],i[e]-('right'===e?p.width:p.height))),pe({},o,n)}};return n.forEach(function(e){var t=-1===['left','top'].indexOf(e)?'secondary':'primary';p=se({},p,s[t](e))}),e.offsets.popper=p,e},priority:['left','right','top','bottom'],padding:5,boundariesElement:'scrollParent'},keepTogether:{order:400,enabled:!0,fn:function(e){var t=e.offsets,o=t.popper,i=t.reference,n=e.placement.split('-')[0],r=V,p=-1!==['top','bottom'].indexOf(n),s=p?'right':'bottom',d=p?'left':'top',a=p?'width':'height';return o[s]<r(i[d])&&(e.offsets.popper[d]=r(i[d])-o[a]),o[d]>r(i[s])&&(e.offsets.popper[d]=r(i[s])),e}},arrow:{order:500,enabled:!0,fn:function(e,t){if(!F(e.instance.modifiers,'arrow','keepTogether'))return e;var o=t.element;if('string'==typeof o){if(o=e.instance.popper.querySelector(o),!o)return e;}else if(!e.instance.popper.contains(o))return console.warn('WARNING: `arrow.element` must be child of its popper element!'),e;var i=e.placement.split('-')[0],n=e.offsets,r=n.popper,p=n.reference,s=-1!==['left','right'].indexOf(i),d=s?'height':'width',a=s?'top':'left',f=s?'left':'top',l=s?'bottom':'right',m=O(o)[d];p[l]-m<r[a]&&(e.offsets.popper[a]-=r[a]-(p[l]-m)),p[a]+m>r[l]&&(e.offsets.popper[a]+=p[a]+m-r[l]);var h=p[a]+p[d]/2-m/2,g=h-c(e.offsets.popper)[a];return g=_(z(r[d]-m,g),0),e.arrowElement=o,e.offsets.arrow={},e.offsets.arrow[a]=Math.round(g),e.offsets.arrow[f]='',e},element:'[x-arrow]'},flip:{order:600,enabled:!0,fn:function(e,t){if(W(e.instance.modifiers,'inner'))return e;if(e.flipped&&e.placement===e.originalPlacement)return e;var o=w(e.instance.popper,e.instance.reference,t.padding,t.boundariesElement),i=e.placement.split('-')[0],n=L(i),r=e.placement.split('-')[1]||'',p=[];switch(t.behavior){case fe.FLIP:p=[i,n];break;case fe.CLOCKWISE:p=K(i);break;case fe.COUNTERCLOCKWISE:p=K(i,!0);break;default:p=t.behavior;}return p.forEach(function(s,d){if(i!==s||p.length===d+1)return e;i=e.placement.split('-')[0],n=L(i);var a=e.offsets.popper,f=e.offsets.reference,l=V,m='left'===i&&l(a.right)>l(f.left)||'right'===i&&l(a.left)<l(f.right)||'top'===i&&l(a.bottom)>l(f.top)||'bottom'===i&&l(a.top)<l(f.bottom),h=l(a.left)<l(o.left),c=l(a.right)>l(o.right),g=l(a.top)<l(o.top),u=l(a.bottom)>l(o.bottom),b='left'===i&&h||'right'===i&&c||'top'===i&&g||'bottom'===i&&u,y=-1!==['top','bottom'].indexOf(i),w=!!t.flipVariations&&(y&&'start'===r&&h||y&&'end'===r&&c||!y&&'start'===r&&g||!y&&'end'===r&&u);(m||b||w)&&(e.flipped=!0,(m||b)&&(i=p[d+1]),w&&(r=j(r)),e.placement=i+(r?'-'+r:''),e.offsets.popper=se({},e.offsets.popper,S(e.instance.popper,e.offsets.reference,e.placement)),e=N(e.instance.modifiers,e,'flip'))}),e},behavior:'flip',padding:5,boundariesElement:'viewport'},inner:{order:700,enabled:!1,fn:function(e){var t=e.placement,o=t.split('-')[0],i=e.offsets,n=i.popper,r=i.reference,p=-1!==['left','right'].indexOf(o),s=-1===['top','left'].indexOf(o);return n[p?'left':'top']=r[t]-(s?n[p?'width':'height']:0),e.placement=L(t),e.offsets.popper=c(n),e}},hide:{order:800,enabled:!0,fn:function(e){if(!F(e.instance.modifiers,'hide','preventOverflow'))return e;var t=e.offsets.reference,o=T(e.instance.modifiers,function(e){return'preventOverflow'===e.name}).boundaries;if(t.bottom<o.top||t.left>o.right||t.top>o.bottom||t.right<o.left){if(!0===e.hide)return e;e.hide=!0,e.attributes['x-out-of-boundaries']=''}else{if(!1===e.hide)return e;e.hide=!1,e.attributes['x-out-of-boundaries']=!1}return e}},computeStyle:{order:850,enabled:!0,fn:function(e,t){var o=t.x,i=t.y,n=e.offsets.popper,p=T(e.instance.modifiers,function(e){return'applyStyle'===e.name}).gpuAcceleration;void 0!==p&&console.warn('WARNING: `gpuAcceleration` option moved to `computeStyle` modifier and will not be supported in future versions of Popper.js!');var s,d,a=void 0===p?t.gpuAcceleration:p,f=r(e.instance.popper),l=g(f),m={position:n.position},h={left:V(n.left),top:V(n.top),bottom:V(n.bottom),right:V(n.right)},c='bottom'===o?'top':'bottom',u='right'===i?'left':'right',b=B('transform');if(d='bottom'==c?-l.height+h.bottom:h.top,s='right'==u?-l.width+h.right:h.left,a&&b)m[b]='translate3d('+s+'px, '+d+'px, 0)',m[c]=0,m[u]=0,m.willChange='transform';else{var y='bottom'==c?-1:1,w='right'==u?-1:1;m[c]=d*y,m[u]=s*w,m.willChange=c+', '+u}var v={"x-placement":e.placement};return e.attributes=se({},v,e.attributes),e.styles=se({},m,e.styles),e},gpuAcceleration:!0,x:'bottom',y:'right'},applyStyle:{order:900,enabled:!0,fn:function(e){return U(e.instance.popper,e.styles),Y(e.instance.popper,e.attributes),e.offsets.arrow&&U(e.arrowElement,e.offsets.arrow),e},onLoad:function(e,t,o,i,n){var r=x(n,t,e),p=E(o.placement,r,t,e,o.modifiers.flip.boundariesElement,o.modifiers.flip.padding);return t.setAttribute('x-placement',p),U(t,{position:'absolute'}),o},gpuAcceleration:void 0}}},le});
5 | 


--------------------------------------------------------------------------------
/resources/py/build_LUT.py:
--------------------------------------------------------------------------------
 1 | #!/usr/bin/env python
 2 | # -*- coding: utf-8 -*-
 3 | 
 4 | import numpy as np
 5 | import colour
 6 | from colour import read_LUT, write_LUT, LUT3D, LUT3x1D, LUTSequence
 7 | from colour.models import RGB_to_RGB, BT2020_COLOURSPACE, P3_D65_COLOURSPACE, eotf_inverse_sRGB
 8 | from colour.utilities import tstack
 9 | 
10 | Log2_48_nits_Shaper_to_linear = read_LUT(
11 |     '/Library/Application Support/OpenColorIO/aces_1.1/luts/Log2_48_nits_Shaper_to_linear.spi1d'
12 | )
13 | bt2020_cube = read_LUT(
14 |     '/Library/Application Support/OpenColorIO/aces_1.1/luts/Log2_48_nits_Shaper.RRT.Rec.2020__P3D65_Limited_.spi3d'
15 | )
16 | 
17 | table = bt2020_cube.table**2.4
18 | table = RGB_to_RGB(table, BT2020_COLOURSPACE, P3_D65_COLOURSPACE)
19 | table = eotf_inverse_sRGB(table)
20 | table = np.clip(table, 0, 1)
21 | 
22 | display_p3_cube = LUT3D(table=table)
23 | 
24 | samples = np.linspace(0, 1, 4096)
25 | 
26 | domain = tstack(
27 |     (Log2_48_nits_Shaper_to_linear.table, Log2_48_nits_Shaper_to_linear.table,
28 |      Log2_48_nits_Shaper_to_linear.table))
29 | 
30 | shaper = LUT3x1D(domain=domain, table=tstack([samples, samples, samples]))
31 | 
32 | LUT = LUTSequence(shaper, display_p3_cube)
33 | 
34 | write_LUT(LUT, '../luts/ACES2065-1_display_P3_OT.csp')
35 | 


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