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Morten Hjorth-Jensen
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Content in Jupyter Book
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=======================
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There are many ways to write content in Jupyter Book. This short section
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covers a few tips for how to do so.
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# Applied Data Analysis and Machine Learning, FYS-STK3155/4155 at the University of Oslo, Norway
|
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## Introduction
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|
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Probability theory and statistical methods play a central role in Science. Nowadays we are
|
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surrounded by huge amounts of data. For example, there are more than one trillion web pages; more than one
|
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hour of video is uploaded to YouTube every second, amounting to years of content every
|
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day; the genomes of 1000s of people, each of which has a length of more than a billion base pairs, have
|
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been sequenced by various labs and so on. This deluge of data calls for automated methods of data analysis,
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which is exactly what machine learning aims at providing.
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|
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## Learning outcomes
|
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|
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This course aims at giving you insights and knowledge about many of the central algorithms used in Data Analysis and Machine Learning. The course is project based and through various numerical projects and weekly exercises you will be exposed to fundamental research problems in these fields, with the aim to reproduce state of the art scientific results. Both supervised and unsupervised methods will be covered. The emphasis is on a frequentist approach with an emphasis on predictions and correaltions. However, we will try, where appropriate, to link our machine learning models with a Bayesian approach as well. You will learn to develop and structure large codes for studying different cases where Machine Learning is applied to, get acquainted with computing facilities and learn to handle large scientific projects. A good scientific and ethical conduct is emphasized throughout the course. More specifically, after this course you will
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|
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- Learn about basic data analysis, statistical analysis, Bayesian statistics, Monte Carlo sampling, data optimization and machine learning;
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- Be capable of extending the acquired knowledge to other systems and cases;
|
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- Have an understanding of central algorithms used in data analysis and machine learning;
|
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- Understand linear methods for regression and classification, from ordinary least squares, via Lasso and Ridge to Logistic regression and Kernel regression;
|
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- Learn about neural networks and deep learning methods for supervised and unsupervised learning. Emphasis on feed forward neural networks, convolutional and recurrent neural networks;
|
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- Learn about about decision trees, random forests, bagging and boosting methods;
|
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- Learn about support vector machines and kernel transformations;
|
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- Reduction of data sets and unsupervised learning, from PCA to clustering;
|
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- Autoencoders and Reinforcement Learning;
|
||||
- Work on numerical projects to illustrate the theory. The projects play a central role and you are expected to know modern programming languages like Python or C++ and/or Fortran (Fortran2003 or later).
|
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|
||||
## Prerequisites and background
|
||||
|
||||
Basic knowledge in programming and mathematics, with an emphasis on linear algebra. Knowledge of Python or/and C++ as programming languages is strongly recommended and experience with Jupyter notebooks is recommended. Required courses are the equivalents to the University of Oslo mathematics courses MAT1100, MAT1110, MAT1120 and at least one of the corresponding computing and programming courses INF1000/INF1110 or MAT-INF1100/MAT-INF1100L/BIOS1100/KJM-INF1100. Most universities offer nowadays a basic programming course (often compulsory) where Python is the recurring programming language.
|
||||
We recommend also refreshing your knowledge on Statistics and Probability theory. The lecture notes at https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html offer a review of Statistics and Probability theory.
|
||||
|
||||
## The course has two central parts
|
||||
|
||||
1. Statistical analysis and optimization of data
|
||||
2. Machine learning
|
||||
|
||||
|
||||
### Statistical analysis and optimization of data
|
||||
|
||||
The following topics will be covered
|
||||
- Basic concepts, expectation values, variance, covariance, correlation functions and errors;
|
||||
- Simpler models, binomial distribution, the Poisson distribution, simple and multivariate normal distributions;
|
||||
- Central elements of Bayesian statistics and modeling;
|
||||
- Gradient methods for data optimization,
|
||||
- Monte Carlo methods, Markov chains, Gibbs sampling and Metropolis-Hastings sampling;
|
||||
- Estimation of errors and resampling techniques such as the cross-validation, blocking, bootstrapping and jackknife methods;
|
||||
- Principal Component Analysis (PCA) and its mathematical foundation
|
||||
|
||||
### Machine learning
|
||||
|
||||
The following topics will be covered:
|
||||
- Linear Regression and Logistic Regression;
|
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- Neural networks and deep learning, including convolutional and recurrent neural networks
|
||||
- Decisions trees, Random Forests, Bagging and Boosting
|
||||
- Support vector machines
|
||||
- Bayesian linear and logistic regression
|
||||
- Boltzmann Machines
|
||||
- Unsupervised learning Dimensionality reduction, PCA, k-means and clustering
|
||||
- Autoenconders
|
||||
|
||||
Hands-on demonstrations, exercises and projects aim at deepening your understanding of these topics.
|
||||
|
||||
Computational aspects play a central role and you are
|
||||
expected to work on numerical examples and projects which illustrate
|
||||
the theory and various algorithms discussed during the lectures. We recommend strongly to form small project groups of 2-3 participants, if possible.
|
||||
|
||||
|
||||
|
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## Required Technologies
|
||||
|
||||
Course participants are expected to have their own laptops/PCs. We use _Git_ as version control software and the usage of providers like _GitHub_, _GitLab_ or similar are strongly recommended. If you are not familiar with Git as version control software, the following video may be of interest, see https://www.youtube.com/watch?v=RGOj5yH7evk&ab_channel=freeCodeCamp.org
|
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|
||||
We will make extensive use of Python as programming language and its
|
||||
myriad of available libraries. You will find
|
||||
Jupyter notebooks invaluable in your work. You can run _R_
|
||||
codes in the Jupyter/IPython notebooks, with the immediate benefit of
|
||||
visualizing your data. You can also use compiled languages like C++,
|
||||
Rust, Julia, Fortran etc if you prefer. The focus in these lectures will be
|
||||
on Python.
|
||||
|
||||
|
||||
If you have Python installed and you feel
|
||||
pretty familiar with installing different packages, we recommend that
|
||||
you install the following Python packages via _pip_ as
|
||||
|
||||
* pip install numpy scipy matplotlib ipython scikit-learn mglearn sympy pandas pillow
|
||||
|
||||
For OSX users we recommend, after having installed Xcode, to
|
||||
install _brew_. Brew allows for a seamless installation of additional
|
||||
software via for example
|
||||
|
||||
* brew install python3
|
||||
|
||||
For Linux users, with its variety of distributions like for example the widely popular Ubuntu distribution,
|
||||
you can use _pip_ as well and simply install Python as
|
||||
|
||||
* sudo apt-get install python3
|
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|
||||
### Python installers
|
||||
|
||||
If you don't want to perform these operations separately and venture
|
||||
into the hassle of exploring how to set up dependencies and paths, we
|
||||
recommend two widely used distrubutions which set up all relevant
|
||||
dependencies for Python, namely
|
||||
|
||||
* Anaconda:https://docs.anaconda.com/,
|
||||
|
||||
which is an open source
|
||||
distribution of the Python and R programming languages for large-scale
|
||||
data processing, predictive analytics, and scientific computing, that
|
||||
aims to simplify package management and deployment. Package versions
|
||||
are managed by the package management system _conda_.
|
||||
|
||||
* Enthought canopy:https://www.enthought.com/product/canopy/
|
||||
|
||||
is a Python
|
||||
distribution for scientific and analytic computing distribution and
|
||||
analysis environment, available for free and under a commercial
|
||||
license.
|
||||
|
||||
Furthermore, Google's Colab:https://colab.research.google.com/notebooks/welcome.ipynb is a free Jupyter notebook environment that requires
|
||||
no setup and runs entirely in the cloud. Try it out!
|
||||
|
||||
### Useful Python libraries
|
||||
Here we list several useful Python libraries we strongly recommend (if you use anaconda many of these are already there)
|
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|
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* _NumPy_:https://www.numpy.org/ is a highly popular library for large, multi-dimensional arrays and matrices, along with a large collection of high-level mathematical functions to operate on these arrays
|
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* _The pandas_:https://pandas.pydata.org/ library provides high-performance, easy-to-use data structures and data analysis tools
|
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* _Xarray_:http://xarray.pydata.org/en/stable/ is a Python package that makes working with labelled multi-dimensional arrays simple, efficient, and fun!
|
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* _Scipy_:https://www.scipy.org/ (pronounced “Sigh Pie”) is a Python-based ecosystem of open-source software for mathematics, science, and engineering.
|
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* _Matplotlib_:https://matplotlib.org/ is a Python 2D plotting library which produces publication quality figures in a variety of hardcopy formats and interactive environments across platforms.
|
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* _Autograd_:https://github.com/HIPS/autograd can automatically differentiate native Python and Numpy code. It can handle a large subset of Python's features, including loops, ifs, recursion and closures, and it can even take derivatives of derivatives of derivatives
|
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* _SymPy_:https://www.sympy.org/en/index.html is a Python library for symbolic mathematics.
|
||||
* _scikit-learn_:https://scikit-learn.org/stable/ has simple and efficient tools for machine learning, data mining and data analysis
|
||||
* _TensorFlow_:https://www.tensorflow.org/ is a Python library for fast numerical computing created and released by Google
|
||||
* _Keras_:https://keras.io/ is a high-level neural networks API, written in Python and capable of running on top of TensorFlow, CNTK, or Theano
|
||||
* And many more such as _pytorch_:https://pytorch.org/, _Theano_:https://pypi.org/project/Theano/ etc
|
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|
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|
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|
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# Teaching schedule with links to material
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This course will be delivered in a hybrid mode, with online lectures and on site or online laboratory sessions.
|
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1. Four lectures per week, Fall semester, 10 ECTS. The lectures are in person but will be recorded and linked to this site and the official University of Oslo website for the course;
|
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2. Two hours of laboratory sessions for work on computational projects and exercises for each group. There will also be fully digital laboratory sessions for those who cannot attend;
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3. Three projects which are graded and count 1/3 each of the final grade;
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4. A selected number of weekly assignments;
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5. The course is part of the CS Master of Science program, but is open to other bachelor and Master of Science students at the University of Oslo;
|
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6. The course is offered as a FYS-MAT4155 (Master of Science level) and a FYS-MAT3155 (senior undergraduate) course;
|
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7. Videos of teaching material are available via the links at https://compphysics.github.io/MachineLearning/doc/web/course.html;
|
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8. Weekly emails with summary of activities will be mailed to all participants;
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|
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## Weekly Schedule
|
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|
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For the reading assignments we use the following abbreviations:
|
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- GBC: Goodfellow, Bengio, and Courville, Deep Learning
|
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- CMB: Christopher M. Bishop, Pattern Recognition and Machine Learning
|
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- HTF: Hastie, Tibshirani, and Friedman, The Elements of Statistical Learning
|
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- AG: Aurelien Geron, Hands‑On Machine Learning with Scikit‑Learn and TensorFlow
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### Week 35 August 23-27
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- Lab Wednesday: Introduction to software and repetition of Python Programming
|
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- Lecture Thursday: Introduction to the course, what is Machine Learning and introduction to Linear Regression
|
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- Lecture Friday: Basics of Linear Regression
|
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- Reading recommendations: Refresh linear algebra, GBC chapters 1 and 2. CMB sections 1.1 and 3.1. HTF chapters 2 and 3. Install scikit-learn. See lecture notes for week 35 at https://compphysics.github.io/MachineLearning/doc/web/course.html
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### Week 36 August 30-September 3
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- Lab Wednesday:
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- Lecture Thursday: Linear Regression, from ordinary linear regression to Ridge and Lasso regression, linear algebra analysis, examples and discussions of codes
|
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- Lecture Friday: Linear Regression, Linear algebra and Ridge and Lasso Regression, linear algebra analysis, examples and discussions of codes
|
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- Reading recommendations: See lecture notes for week 36 at https://compphysics.github.io/MachineLearning/doc/web/course.html. HTF chapter 3. GBC chapters 1 and and sections 3.1-3.11 and 5.1 and CMB sections 1.1 and 3.1
|
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|
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### Week 37 September 6-10
|
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- Lab Wednesday:
|
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- Lecture Thursday: Statistical interpretation of Linear Regression
|
||||
- Lecture Friday: Bias-Variance tradeoff
|
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- Reading recommendations: See lecture notes for week 37 at https://compphysics.github.io/MachineLearning/doc/web/course.html. GBC sections 5.2-5.5, CMB section 3.2
|
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- Chapter
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### Week 38 September 13-17
|
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- Lab Wednesday:
|
||||
- Lecture Thursday: Resampling methods, cross-validation and Bootstrap
|
||||
- Lecture Friday: More on Resampling methods and summary of linear regression
|
||||
- Reading recommendations: See lecture notes for week 38 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
|
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- Chapter
|
||||
### Week 39 September 20-24
|
||||
- Lab Wednesday:
|
||||
- Lecture Thursday: Classification problems and Logistic Regression, from binary cases to several categories
|
||||
- Lecture Friday: Logistic Regression and gradient optimization
|
||||
- Reading recommendations: See lecture notes for week 39 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
|
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- Chapter
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### Week 40 September 27- October 1
|
||||
- Lab Wednesday:
|
||||
- Lecture Thursday: Gradient Optimization methods
|
||||
- Lecture Friday: Deep Learning and Neural Networks
|
||||
- Reading recommendations: See lecture notes for week 40 at https://compphysics.github.io/MachineLearning/doc/web/course.html.
|
||||
- Chapter
|
||||
### Week 41 October 4-8
|
||||
- Lab Wednesday:
|
||||
- Lecture Thursday: Writing a feed-forward Neural Network code for regression and classification
|
||||
- Lecture Friday: Deep Learning and TensorFlow and Keras
|
||||
- Reading recommendations:
|
||||
- Chapter
|
||||
### Week 42 October 11-15
|
||||
- Lab Wednesday:
|
||||
- Lecture Thursday: Deep learning and Neural Networks
|
||||
- Lecture Friday: Convolutional Neural Networks, basic elements
|
||||
- Reading recommendations:
|
||||
- GoodFellow et al, Chapter 9
|
||||
### Week 43 October 18-22
|
||||
- Lab Wednesday:
|
||||
- Lecture Thursday: Convolutional Neural Networks and classification problems
|
||||
- Lecture Friday: Convolutional Neural Networks and classification problems
|
||||
- Reading recommendations:
|
||||
- Chapter
|
||||
### Week 44 October 25-29
|
||||
- Lab Wednesday:
|
||||
- Lecture Thursday: Recurrent Neural Networks
|
||||
- Lecture Friday: Recurrent Neural Networks and time series
|
||||
- Reading recommendations:
|
||||
- Chapter
|
||||
### Week 45 November 1-5
|
||||
- Lab Wednesday:
|
||||
- Lecture Thursday: Decision trees, classification and regression
|
||||
- Lecture Friday: Decision trees, basic algorithms
|
||||
- Reading recommendations:
|
||||
- Chapter
|
||||
### Week 46 November 8-12
|
||||
- Lab Wednesday:
|
||||
- Lecture Thursday: Ensemble methods, bagging and random forests
|
||||
- Lecture Friday: Boosting and gradient boosting
|
||||
- Reading recommendations:
|
||||
- Chapter
|
||||
### Week 47 November 15-19
|
||||
- Lab Wednesday:
|
||||
- Lecture Thursday:
|
||||
- Lecture Friday: Unsupervised Learning, k-means
|
||||
- Reading recommendations:
|
||||
- Chapter
|
||||
### Week 48 November 22-26
|
||||
- Lab Wednesday:
|
||||
- Lecture Thursday: Unsupervised Learning, Principal Component Analysis (PCA)
|
||||
- Lecture Friday: Unsupervised Learning and PCA and Clustering
|
||||
- Reading recommendations:
|
||||
- Chapter
|
||||
### Week 49 November 29- December 2
|
||||
- Lab Wednesday:
|
||||
- Lecture Thursday: Unsupervised Learning and Clustering
|
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- Lecture Friday: Summary of course
|
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- Reading recommendations:
|
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- Chapter
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|
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|
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# Teachers and Grading
|
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## Instructor information
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* _Name_: Morten Hjorth-Jensen
|
||||
* _Email_: morten.hjorth-jensen@fys.uio.no
|
||||
* _Phone_: +47-48257387
|
||||
* _Office_: Department of Physics, University of Oslo, Eastern wing, room FØ470
|
||||
* _Office hours_: *Anytime*! Feel free to send an email for planning. Both in person meetings or digital meetings are possible.
|
||||
|
||||
## Teaching Assistants Fall semester 2021
|
||||
* Øyvind Sigmundson Schøyen, oyvinssc@student.matnat.uio.no
|
||||
* Stian Bilek, stian.bilek@fys.uio.no
|
||||
* Linus Ekstrøm, linueks@gmail.com, linus.ekstrom@fys.uio.no
|
||||
* Nicholas Karlsen, nicholaskarlsen1102@gmail.com, nicholas.karlsen@fys.uio.no
|
||||
* Bendik Steinsvåg Dalen, b.s.dalen@fys.uio.no
|
||||
* Philip Karim Sørli Niane, p.k.s.niane@fys.uio.no
|
||||
|
||||
|
||||
|
||||
## Grading
|
||||
Grading scale: Grades are awarded on a scale from A to F, where A is the best grade and F is a fail. There are three projects which are graded and each project counts 1/3 of the final grade. The total score is thus the average from all three projects.
|
||||
|
||||
The final number of points is based on the average of all projects (including eventual additional points) and the grade follows the following table:
|
||||
|
||||
* 92-100 points: A
|
||||
* 77-91 points: B
|
||||
* 58-76 points: C
|
||||
* 46-57 points: D
|
||||
* 40-45 points: E
|
||||
* 0-39 points: F-failed
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -0,0 +1,44 @@
|
||||
|
||||
## Textbooks
|
||||
|
||||
_Recommended textbooks_:
|
||||
The lecture notes are collected as a jupyter-book at https://compphysics.github.io/MachineLearning/doc/LectureNotes/_build/html/intro.html. In addition to the electure notes, we recommend the books of Bishop and Goodfellow et al. We will follow these texts closely and the weekly reading assignments refer to these two texts.
|
||||
- Christopher M. Bishop, Pattern Recognition and Machine Learning, Springer, https://www.springer.com/gp/book/9780387310732. This is the main textbook and this course covers chapters 1-7, 11 and 12. You can download for free the textbook in PDF format at https://www.microsoft.com/en-us/research/uploads/prod/2006/01/Bishop-Pattern-Recognition-and-Machine-Learning-2006.pdf
|
||||
- Ian Goodfellow, Yoshua Bengio, and Aaron Courville. The different chapters are available for free at https://www.deeplearningbook.org/. Chapters 2-14 are highly recommended. The lectures follow to a larg extent this text.
|
||||
The weekly plans will include reading suggestions from these two textbooks.
|
||||
_Additional textbooks_:
|
||||
- Trevor Hastie, Robert Tibshirani, Jerome H. Friedman, The Elements of Statistical Learning, Springer, https://www.springer.com/gp/book/9780387848570. This is a well-known text and serves as additional literature.
|
||||
- Aurelien Geron, Hands‑On Machine Learning with Scikit‑Learn and TensorFlow, O'Reilly, https://www.oreilly.com/library/view/hands-on-machine-learning/9781492032632/. This text is very useful since it contains many code examples and hands-on applications of all algorithms discussed in this course.
|
||||
|
||||
|
||||
|
||||
|
||||
_General learning book on statistical analysis_:
|
||||
- Christian Robert and George Casella, Monte Carlo Statistical Methods, Springer
|
||||
- Peter Hoff, A first course in Bayesian statistical models, Springer
|
||||
|
||||
_General Machine Learning Books_:
|
||||
- Kevin Murphy, Machine Learning: A Probabilistic Perspective, MIT Press
|
||||
- David J.C. MacKay, Information Theory, Inference, and Learning Algorithms, Cambridge University Press
|
||||
- David Barber, Bayesian Reasoning and Machine Learning, Cambridge University Press
|
||||
|
||||
|
||||
|
||||
## Links to relevant courses at the University of Oslo
|
||||
The link here https://www.mn.uio.no/english/research/about/centre-focus/innovation/data-science/studies/ gives an excellent overview of courses on Machine learning at UiO.
|
||||
|
||||
- _STK2100 Machine learning and statistical methods for prediction and classification_ http://www.uio.no/studier/emner/matnat/math/STK2100/index-eng.html.
|
||||
- _IN3050 Introduction to Artificial Intelligence and Machine Learning_ https://www.uio.no/studier/emner/matnat/ifi/IN3050/index-eng.html. Introductory course in machine learning and AI with an algorithmic approach.
|
||||
- _STK-INF3000/4000 Selected Topics in Data Science_ http://www.uio.no/studier/emner/matnat/math/STK-INF3000/index-eng.html. The course provides insight into selected contemporary relevant topics within Data Science.
|
||||
- _IN4080 Natural Language Processing_ https://www.uio.no/studier/emner/matnat/ifi/IN4080/index.html. Probabilistic and machine learning techniques applied to natural language processing.
|
||||
- _STK-IN4300 Statistical learning methods in Data Science_ https://www.uio.no/studier/emner/matnat/math/STK-IN4300/index-eng.html. An advanced introduction to statistical and machine learning. For students with a good mathematics and statistics background.
|
||||
- _INF4490 Biologically Inspired Computing_ http://www.uio.no/studier/emner/matnat/ifi/INF4490/. An introduction to self-adapting methods also called artificial intelligence or machine learning.
|
||||
- _IN-STK5000 Adaptive Methods for Data-Based Decision Making_ https://www.uio.no/studier/emner/matnat/ifi/IN-STK5000/index-eng.html. Methods for adaptive collection and processing of data based on machine learning techniques.
|
||||
- _IN5400/INF5860 Machine Learning for Image Analysis_ https://www.uio.no/studier/emner/matnat/ifi/IN5400/. An introduction to deep learning with particular emphasis on applications within Image analysis, but useful for other application areas too.
|
||||
- _TEK5040 Deep learning for autonomous systems_ https://www.uio.no/studier/emner/matnat/its/TEK5040/. The course addresses advanced algorithms and architectures for deep learning with neural networks. The course provides an introduction to how deep-learning techniques can be used in the construction of key parts of advanced autonomous systems that exist in physical environments and cyber environments.
|
||||
- _STK4051 Computational Statistics_ https://www.uio.no/studier/emner/matnat/math/STK4051/index-eng.html
|
||||
- _STK4021 Applied Bayesian Analysis and Numerical Methods_ https://www.uio.no/studier/emner/matnat/math/STK4021/
|
||||
|
||||
|
||||
|
||||
|
||||
@@ -0,0 +1,856 @@
|
||||
/*
|
||||
* basic.css
|
||||
* ~~~~~~~~~
|
||||
*
|
||||
* Sphinx stylesheet -- basic theme.
|
||||
*
|
||||
* :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS.
|
||||
* :license: BSD, see LICENSE for details.
|
||||
*
|
||||
*/
|
||||
|
||||
/* -- main layout ----------------------------------------------------------- */
|
||||
|
||||
div.clearer {
|
||||
clear: both;
|
||||
}
|
||||
|
||||
div.section::after {
|
||||
display: block;
|
||||
content: '';
|
||||
clear: left;
|
||||
}
|
||||
|
||||
/* -- relbar ---------------------------------------------------------------- */
|
||||
|
||||
div.related {
|
||||
width: 100%;
|
||||
font-size: 90%;
|
||||
}
|
||||
|
||||
div.related h3 {
|
||||
display: none;
|
||||
}
|
||||
|
||||
div.related ul {
|
||||
margin: 0;
|
||||
padding: 0 0 0 10px;
|
||||
list-style: none;
|
||||
}
|
||||
|
||||
div.related li {
|
||||
display: inline;
|
||||
}
|
||||
|
||||
div.related li.right {
|
||||
float: right;
|
||||
margin-right: 5px;
|
||||
}
|
||||
|
||||
/* -- sidebar --------------------------------------------------------------- */
|
||||
|
||||
div.sphinxsidebarwrapper {
|
||||
padding: 10px 5px 0 10px;
|
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}
|
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|
||||
div.sphinxsidebar {
|
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float: left;
|
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width: 270px;
|
||||
margin-left: -100%;
|
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font-size: 90%;
|
||||
word-wrap: break-word;
|
||||
overflow-wrap : break-word;
|
||||
}
|
||||
|
||||
div.sphinxsidebar ul {
|
||||
list-style: none;
|
||||
}
|
||||
|
||||
div.sphinxsidebar ul ul,
|
||||
div.sphinxsidebar ul.want-points {
|
||||
margin-left: 20px;
|
||||
list-style: square;
|
||||
}
|
||||
|
||||
div.sphinxsidebar ul ul {
|
||||
margin-top: 0;
|
||||
margin-bottom: 0;
|
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}
|
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|
||||
div.sphinxsidebar form {
|
||||
margin-top: 10px;
|
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}
|
||||
|
||||
div.sphinxsidebar input {
|
||||
border: 1px solid #98dbcc;
|
||||
font-family: sans-serif;
|
||||
font-size: 1em;
|
||||
}
|
||||
|
||||
div.sphinxsidebar #searchbox form.search {
|
||||
overflow: hidden;
|
||||
}
|
||||
|
||||
div.sphinxsidebar #searchbox input[type="text"] {
|
||||
float: left;
|
||||
width: 80%;
|
||||
padding: 0.25em;
|
||||
box-sizing: border-box;
|
||||
}
|
||||
|
||||
div.sphinxsidebar #searchbox input[type="submit"] {
|
||||
float: left;
|
||||
width: 20%;
|
||||
border-left: none;
|
||||
padding: 0.25em;
|
||||
box-sizing: border-box;
|
||||
}
|
||||
|
||||
|
||||
img {
|
||||
border: 0;
|
||||
max-width: 100%;
|
||||
}
|
||||
|
||||
/* -- search page ----------------------------------------------------------- */
|
||||
|
||||
ul.search {
|
||||
margin: 10px 0 0 20px;
|
||||
padding: 0;
|
||||
}
|
||||
|
||||
ul.search li {
|
||||
padding: 5px 0 5px 20px;
|
||||
background-image: url(file.png);
|
||||
background-repeat: no-repeat;
|
||||
background-position: 0 7px;
|
||||
}
|
||||
|
||||
ul.search li a {
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
ul.search li div.context {
|
||||
color: #888;
|
||||
margin: 2px 0 0 30px;
|
||||
text-align: left;
|
||||
}
|
||||
|
||||
ul.keywordmatches li.goodmatch a {
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
/* -- index page ------------------------------------------------------------ */
|
||||
|
||||
table.contentstable {
|
||||
width: 90%;
|
||||
margin-left: auto;
|
||||
margin-right: auto;
|
||||
}
|
||||
|
||||
table.contentstable p.biglink {
|
||||
line-height: 150%;
|
||||
}
|
||||
|
||||
a.biglink {
|
||||
font-size: 1.3em;
|
||||
}
|
||||
|
||||
span.linkdescr {
|
||||
font-style: italic;
|
||||
padding-top: 5px;
|
||||
font-size: 90%;
|
||||
}
|
||||
|
||||
/* -- general index --------------------------------------------------------- */
|
||||
|
||||
table.indextable {
|
||||
width: 100%;
|
||||
}
|
||||
|
||||
table.indextable td {
|
||||
text-align: left;
|
||||
vertical-align: top;
|
||||
}
|
||||
|
||||
table.indextable ul {
|
||||
margin-top: 0;
|
||||
margin-bottom: 0;
|
||||
list-style-type: none;
|
||||
}
|
||||
|
||||
table.indextable > tbody > tr > td > ul {
|
||||
padding-left: 0em;
|
||||
}
|
||||
|
||||
table.indextable tr.pcap {
|
||||
height: 10px;
|
||||
}
|
||||
|
||||
table.indextable tr.cap {
|
||||
margin-top: 10px;
|
||||
background-color: #f2f2f2;
|
||||
}
|
||||
|
||||
img.toggler {
|
||||
margin-right: 3px;
|
||||
margin-top: 3px;
|
||||
cursor: pointer;
|
||||
}
|
||||
|
||||
div.modindex-jumpbox {
|
||||
border-top: 1px solid #ddd;
|
||||
border-bottom: 1px solid #ddd;
|
||||
margin: 1em 0 1em 0;
|
||||
padding: 0.4em;
|
||||
}
|
||||
|
||||
div.genindex-jumpbox {
|
||||
border-top: 1px solid #ddd;
|
||||
border-bottom: 1px solid #ddd;
|
||||
margin: 1em 0 1em 0;
|
||||
padding: 0.4em;
|
||||
}
|
||||
|
||||
/* -- domain module index --------------------------------------------------- */
|
||||
|
||||
table.modindextable td {
|
||||
padding: 2px;
|
||||
border-collapse: collapse;
|
||||
}
|
||||
|
||||
/* -- general body styles --------------------------------------------------- */
|
||||
|
||||
div.body {
|
||||
min-width: 450px;
|
||||
max-width: 800px;
|
||||
}
|
||||
|
||||
div.body p, div.body dd, div.body li, div.body blockquote {
|
||||
-moz-hyphens: auto;
|
||||
-ms-hyphens: auto;
|
||||
-webkit-hyphens: auto;
|
||||
hyphens: auto;
|
||||
}
|
||||
|
||||
a.headerlink {
|
||||
visibility: hidden;
|
||||
}
|
||||
|
||||
a.brackets:before,
|
||||
span.brackets > a:before{
|
||||
content: "[";
|
||||
}
|
||||
|
||||
a.brackets:after,
|
||||
span.brackets > a:after {
|
||||
content: "]";
|
||||
}
|
||||
|
||||
h1:hover > a.headerlink,
|
||||
h2:hover > a.headerlink,
|
||||
h3:hover > a.headerlink,
|
||||
h4:hover > a.headerlink,
|
||||
h5:hover > a.headerlink,
|
||||
h6:hover > a.headerlink,
|
||||
dt:hover > a.headerlink,
|
||||
caption:hover > a.headerlink,
|
||||
p.caption:hover > a.headerlink,
|
||||
div.code-block-caption:hover > a.headerlink {
|
||||
visibility: visible;
|
||||
}
|
||||
|
||||
div.body p.caption {
|
||||
text-align: inherit;
|
||||
}
|
||||
|
||||
div.body td {
|
||||
text-align: left;
|
||||
}
|
||||
|
||||
.first {
|
||||
margin-top: 0 !important;
|
||||
}
|
||||
|
||||
p.rubric {
|
||||
margin-top: 30px;
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
img.align-left, .figure.align-left, object.align-left {
|
||||
clear: left;
|
||||
float: left;
|
||||
margin-right: 1em;
|
||||
}
|
||||
|
||||
img.align-right, .figure.align-right, object.align-right {
|
||||
clear: right;
|
||||
float: right;
|
||||
margin-left: 1em;
|
||||
}
|
||||
|
||||
img.align-center, .figure.align-center, object.align-center {
|
||||
display: block;
|
||||
margin-left: auto;
|
||||
margin-right: auto;
|
||||
}
|
||||
|
||||
img.align-default, .figure.align-default {
|
||||
display: block;
|
||||
margin-left: auto;
|
||||
margin-right: auto;
|
||||
}
|
||||
|
||||
.align-left {
|
||||
text-align: left;
|
||||
}
|
||||
|
||||
.align-center {
|
||||
text-align: center;
|
||||
}
|
||||
|
||||
.align-default {
|
||||
text-align: center;
|
||||
}
|
||||
|
||||
.align-right {
|
||||
text-align: right;
|
||||
}
|
||||
|
||||
/* -- sidebars -------------------------------------------------------------- */
|
||||
|
||||
div.sidebar {
|
||||
margin: 0 0 0.5em 1em;
|
||||
border: 1px solid #ddb;
|
||||
padding: 7px;
|
||||
background-color: #ffe;
|
||||
width: 40%;
|
||||
float: right;
|
||||
clear: right;
|
||||
overflow-x: auto;
|
||||
}
|
||||
|
||||
p.sidebar-title {
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
div.admonition, div.topic, blockquote {
|
||||
clear: left;
|
||||
}
|
||||
|
||||
/* -- topics ---------------------------------------------------------------- */
|
||||
|
||||
div.topic {
|
||||
border: 1px solid #ccc;
|
||||
padding: 7px;
|
||||
margin: 10px 0 10px 0;
|
||||
}
|
||||
|
||||
p.topic-title {
|
||||
font-size: 1.1em;
|
||||
font-weight: bold;
|
||||
margin-top: 10px;
|
||||
}
|
||||
|
||||
/* -- admonitions ----------------------------------------------------------- */
|
||||
|
||||
div.admonition {
|
||||
margin-top: 10px;
|
||||
margin-bottom: 10px;
|
||||
padding: 7px;
|
||||
}
|
||||
|
||||
div.admonition dt {
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
p.admonition-title {
|
||||
margin: 0px 10px 5px 0px;
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
div.body p.centered {
|
||||
text-align: center;
|
||||
margin-top: 25px;
|
||||
}
|
||||
|
||||
/* -- content of sidebars/topics/admonitions -------------------------------- */
|
||||
|
||||
div.sidebar > :last-child,
|
||||
div.topic > :last-child,
|
||||
div.admonition > :last-child {
|
||||
margin-bottom: 0;
|
||||
}
|
||||
|
||||
div.sidebar::after,
|
||||
div.topic::after,
|
||||
div.admonition::after,
|
||||
blockquote::after {
|
||||
display: block;
|
||||
content: '';
|
||||
clear: both;
|
||||
}
|
||||
|
||||
/* -- tables ---------------------------------------------------------------- */
|
||||
|
||||
table.docutils {
|
||||
margin-top: 10px;
|
||||
margin-bottom: 10px;
|
||||
border: 0;
|
||||
border-collapse: collapse;
|
||||
}
|
||||
|
||||
table.align-center {
|
||||
margin-left: auto;
|
||||
margin-right: auto;
|
||||
}
|
||||
|
||||
table.align-default {
|
||||
margin-left: auto;
|
||||
margin-right: auto;
|
||||
}
|
||||
|
||||
table caption span.caption-number {
|
||||
font-style: italic;
|
||||
}
|
||||
|
||||
table caption span.caption-text {
|
||||
}
|
||||
|
||||
table.docutils td, table.docutils th {
|
||||
padding: 1px 8px 1px 5px;
|
||||
border-top: 0;
|
||||
border-left: 0;
|
||||
border-right: 0;
|
||||
border-bottom: 1px solid #aaa;
|
||||
}
|
||||
|
||||
table.footnote td, table.footnote th {
|
||||
border: 0 !important;
|
||||
}
|
||||
|
||||
th {
|
||||
text-align: left;
|
||||
padding-right: 5px;
|
||||
}
|
||||
|
||||
table.citation {
|
||||
border-left: solid 1px gray;
|
||||
margin-left: 1px;
|
||||
}
|
||||
|
||||
table.citation td {
|
||||
border-bottom: none;
|
||||
}
|
||||
|
||||
th > :first-child,
|
||||
td > :first-child {
|
||||
margin-top: 0px;
|
||||
}
|
||||
|
||||
th > :last-child,
|
||||
td > :last-child {
|
||||
margin-bottom: 0px;
|
||||
}
|
||||
|
||||
/* -- figures --------------------------------------------------------------- */
|
||||
|
||||
div.figure {
|
||||
margin: 0.5em;
|
||||
padding: 0.5em;
|
||||
}
|
||||
|
||||
div.figure p.caption {
|
||||
padding: 0.3em;
|
||||
}
|
||||
|
||||
div.figure p.caption span.caption-number {
|
||||
font-style: italic;
|
||||
}
|
||||
|
||||
div.figure p.caption span.caption-text {
|
||||
}
|
||||
|
||||
/* -- field list styles ----------------------------------------------------- */
|
||||
|
||||
table.field-list td, table.field-list th {
|
||||
border: 0 !important;
|
||||
}
|
||||
|
||||
.field-list ul {
|
||||
margin: 0;
|
||||
padding-left: 1em;
|
||||
}
|
||||
|
||||
.field-list p {
|
||||
margin: 0;
|
||||
}
|
||||
|
||||
.field-name {
|
||||
-moz-hyphens: manual;
|
||||
-ms-hyphens: manual;
|
||||
-webkit-hyphens: manual;
|
||||
hyphens: manual;
|
||||
}
|
||||
|
||||
/* -- hlist styles ---------------------------------------------------------- */
|
||||
|
||||
table.hlist {
|
||||
margin: 1em 0;
|
||||
}
|
||||
|
||||
table.hlist td {
|
||||
vertical-align: top;
|
||||
}
|
||||
|
||||
|
||||
/* -- other body styles ----------------------------------------------------- */
|
||||
|
||||
ol.arabic {
|
||||
list-style: decimal;
|
||||
}
|
||||
|
||||
ol.loweralpha {
|
||||
list-style: lower-alpha;
|
||||
}
|
||||
|
||||
ol.upperalpha {
|
||||
list-style: upper-alpha;
|
||||
}
|
||||
|
||||
ol.lowerroman {
|
||||
list-style: lower-roman;
|
||||
}
|
||||
|
||||
ol.upperroman {
|
||||
list-style: upper-roman;
|
||||
}
|
||||
|
||||
:not(li) > ol > li:first-child > :first-child,
|
||||
:not(li) > ul > li:first-child > :first-child {
|
||||
margin-top: 0px;
|
||||
}
|
||||
|
||||
:not(li) > ol > li:last-child > :last-child,
|
||||
:not(li) > ul > li:last-child > :last-child {
|
||||
margin-bottom: 0px;
|
||||
}
|
||||
|
||||
ol.simple ol p,
|
||||
ol.simple ul p,
|
||||
ul.simple ol p,
|
||||
ul.simple ul p {
|
||||
margin-top: 0;
|
||||
}
|
||||
|
||||
ol.simple > li:not(:first-child) > p,
|
||||
ul.simple > li:not(:first-child) > p {
|
||||
margin-top: 0;
|
||||
}
|
||||
|
||||
ol.simple p,
|
||||
ul.simple p {
|
||||
margin-bottom: 0;
|
||||
}
|
||||
|
||||
dl.footnote > dt,
|
||||
dl.citation > dt {
|
||||
float: left;
|
||||
margin-right: 0.5em;
|
||||
}
|
||||
|
||||
dl.footnote > dd,
|
||||
dl.citation > dd {
|
||||
margin-bottom: 0em;
|
||||
}
|
||||
|
||||
dl.footnote > dd:after,
|
||||
dl.citation > dd:after {
|
||||
content: "";
|
||||
clear: both;
|
||||
}
|
||||
|
||||
dl.field-list {
|
||||
display: grid;
|
||||
grid-template-columns: fit-content(30%) auto;
|
||||
}
|
||||
|
||||
dl.field-list > dt {
|
||||
font-weight: bold;
|
||||
word-break: break-word;
|
||||
padding-left: 0.5em;
|
||||
padding-right: 5px;
|
||||
}
|
||||
|
||||
dl.field-list > dt:after {
|
||||
content: ":";
|
||||
}
|
||||
|
||||
dl.field-list > dd {
|
||||
padding-left: 0.5em;
|
||||
margin-top: 0em;
|
||||
margin-left: 0em;
|
||||
margin-bottom: 0em;
|
||||
}
|
||||
|
||||
dl {
|
||||
margin-bottom: 15px;
|
||||
}
|
||||
|
||||
dd > :first-child {
|
||||
margin-top: 0px;
|
||||
}
|
||||
|
||||
dd ul, dd table {
|
||||
margin-bottom: 10px;
|
||||
}
|
||||
|
||||
dd {
|
||||
margin-top: 3px;
|
||||
margin-bottom: 10px;
|
||||
margin-left: 30px;
|
||||
}
|
||||
|
||||
dl > dd:last-child,
|
||||
dl > dd:last-child > :last-child {
|
||||
margin-bottom: 0;
|
||||
}
|
||||
|
||||
dt:target, span.highlighted {
|
||||
background-color: #fbe54e;
|
||||
}
|
||||
|
||||
rect.highlighted {
|
||||
fill: #fbe54e;
|
||||
}
|
||||
|
||||
dl.glossary dt {
|
||||
font-weight: bold;
|
||||
font-size: 1.1em;
|
||||
}
|
||||
|
||||
.optional {
|
||||
font-size: 1.3em;
|
||||
}
|
||||
|
||||
.sig-paren {
|
||||
font-size: larger;
|
||||
}
|
||||
|
||||
.versionmodified {
|
||||
font-style: italic;
|
||||
}
|
||||
|
||||
.system-message {
|
||||
background-color: #fda;
|
||||
padding: 5px;
|
||||
border: 3px solid red;
|
||||
}
|
||||
|
||||
.footnote:target {
|
||||
background-color: #ffa;
|
||||
}
|
||||
|
||||
.line-block {
|
||||
display: block;
|
||||
margin-top: 1em;
|
||||
margin-bottom: 1em;
|
||||
}
|
||||
|
||||
.line-block .line-block {
|
||||
margin-top: 0;
|
||||
margin-bottom: 0;
|
||||
margin-left: 1.5em;
|
||||
}
|
||||
|
||||
.guilabel, .menuselection {
|
||||
font-family: sans-serif;
|
||||
}
|
||||
|
||||
.accelerator {
|
||||
text-decoration: underline;
|
||||
}
|
||||
|
||||
.classifier {
|
||||
font-style: oblique;
|
||||
}
|
||||
|
||||
.classifier:before {
|
||||
font-style: normal;
|
||||
margin: 0.5em;
|
||||
content: ":";
|
||||
}
|
||||
|
||||
abbr, acronym {
|
||||
border-bottom: dotted 1px;
|
||||
cursor: help;
|
||||
}
|
||||
|
||||
/* -- code displays --------------------------------------------------------- */
|
||||
|
||||
pre {
|
||||
overflow: auto;
|
||||
overflow-y: hidden; /* fixes display issues on Chrome browsers */
|
||||
}
|
||||
|
||||
pre, div[class*="highlight-"] {
|
||||
clear: both;
|
||||
}
|
||||
|
||||
span.pre {
|
||||
-moz-hyphens: none;
|
||||
-ms-hyphens: none;
|
||||
-webkit-hyphens: none;
|
||||
hyphens: none;
|
||||
}
|
||||
|
||||
div[class*="highlight-"] {
|
||||
margin: 1em 0;
|
||||
}
|
||||
|
||||
td.linenos pre {
|
||||
border: 0;
|
||||
background-color: transparent;
|
||||
color: #aaa;
|
||||
}
|
||||
|
||||
table.highlighttable {
|
||||
display: block;
|
||||
}
|
||||
|
||||
table.highlighttable tbody {
|
||||
display: block;
|
||||
}
|
||||
|
||||
table.highlighttable tr {
|
||||
display: flex;
|
||||
}
|
||||
|
||||
table.highlighttable td {
|
||||
margin: 0;
|
||||
padding: 0;
|
||||
}
|
||||
|
||||
table.highlighttable td.linenos {
|
||||
padding-right: 0.5em;
|
||||
}
|
||||
|
||||
table.highlighttable td.code {
|
||||
flex: 1;
|
||||
overflow: hidden;
|
||||
}
|
||||
|
||||
.highlight .hll {
|
||||
display: block;
|
||||
}
|
||||
|
||||
div.highlight pre,
|
||||
table.highlighttable pre {
|
||||
margin: 0;
|
||||
}
|
||||
|
||||
div.code-block-caption + div {
|
||||
margin-top: 0;
|
||||
}
|
||||
|
||||
div.code-block-caption {
|
||||
margin-top: 1em;
|
||||
padding: 2px 5px;
|
||||
font-size: small;
|
||||
}
|
||||
|
||||
div.code-block-caption code {
|
||||
background-color: transparent;
|
||||
}
|
||||
|
||||
table.highlighttable td.linenos,
|
||||
span.linenos,
|
||||
div.doctest > div.highlight span.gp { /* gp: Generic.Prompt */
|
||||
user-select: none;
|
||||
}
|
||||
|
||||
div.code-block-caption span.caption-number {
|
||||
padding: 0.1em 0.3em;
|
||||
font-style: italic;
|
||||
}
|
||||
|
||||
div.code-block-caption span.caption-text {
|
||||
}
|
||||
|
||||
div.literal-block-wrapper {
|
||||
margin: 1em 0;
|
||||
}
|
||||
|
||||
code.descname {
|
||||
background-color: transparent;
|
||||
font-weight: bold;
|
||||
font-size: 1.2em;
|
||||
}
|
||||
|
||||
code.descclassname {
|
||||
background-color: transparent;
|
||||
}
|
||||
|
||||
code.xref, a code {
|
||||
background-color: transparent;
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
h1 code, h2 code, h3 code, h4 code, h5 code, h6 code {
|
||||
background-color: transparent;
|
||||
}
|
||||
|
||||
.viewcode-link {
|
||||
float: right;
|
||||
}
|
||||
|
||||
.viewcode-back {
|
||||
float: right;
|
||||
font-family: sans-serif;
|
||||
}
|
||||
|
||||
div.viewcode-block:target {
|
||||
margin: -1px -10px;
|
||||
padding: 0 10px;
|
||||
}
|
||||
|
||||
/* -- math display ---------------------------------------------------------- */
|
||||
|
||||
img.math {
|
||||
vertical-align: middle;
|
||||
}
|
||||
|
||||
div.body div.math p {
|
||||
text-align: center;
|
||||
}
|
||||
|
||||
span.eqno {
|
||||
float: right;
|
||||
}
|
||||
|
||||
span.eqno a.headerlink {
|
||||
position: absolute;
|
||||
z-index: 1;
|
||||
}
|
||||
|
||||
div.math:hover a.headerlink {
|
||||
visibility: visible;
|
||||
}
|
||||
|
||||
/* -- printout stylesheet --------------------------------------------------- */
|
||||
|
||||
@media print {
|
||||
div.document,
|
||||
div.documentwrapper,
|
||||
div.bodywrapper {
|
||||
margin: 0 !important;
|
||||
width: 100%;
|
||||
}
|
||||
|
||||
div.sphinxsidebar,
|
||||
div.related,
|
||||
div.footer,
|
||||
#top-link {
|
||||
display: none;
|
||||
}
|
||||
}
|
||||
File diff suppressed because one or more lines are too long
@@ -0,0 +1,5 @@
|
||||
<svg xmlns="http://www.w3.org/2000/svg" width="24" height="24" viewBox="0 0 24 24" stroke-width="1.5" stroke="#607D8B" fill="none" stroke-linecap="round" stroke-linejoin="round">
|
||||
<path stroke="none" d="M0 0h24v24H0z" fill="none"/>
|
||||
<rect x="8" y="8" width="12" height="12" rx="2" />
|
||||
<path d="M16 8v-2a2 2 0 0 0 -2 -2h-8a2 2 0 0 0 -2 2v8a2 2 0 0 0 2 2h2" />
|
||||
</svg>
|
||||
|
After Width: | Height: | Size: 369 B |
@@ -0,0 +1,67 @@
|
||||
/* Copy buttons */
|
||||
a.copybtn {
|
||||
position: absolute;
|
||||
top: .2em;
|
||||
right: .2em;
|
||||
width: 1em;
|
||||
height: 1em;
|
||||
opacity: .3;
|
||||
transition: opacity 0.5s;
|
||||
border: none;
|
||||
user-select: none;
|
||||
}
|
||||
|
||||
div.highlight {
|
||||
position: relative;
|
||||
}
|
||||
|
||||
a.copybtn > img {
|
||||
vertical-align: top;
|
||||
margin: 0;
|
||||
top: 0;
|
||||
left: 0;
|
||||
position: absolute;
|
||||
}
|
||||
|
||||
.highlight:hover .copybtn {
|
||||
opacity: 1;
|
||||
}
|
||||
|
||||
/**
|
||||
* A minimal CSS-only tooltip copied from:
|
||||
* https://codepen.io/mildrenben/pen/rVBrpK
|
||||
*
|
||||
* To use, write HTML like the following:
|
||||
*
|
||||
* <p class="o-tooltip--left" data-tooltip="Hey">Short</p>
|
||||
*/
|
||||
.o-tooltip--left {
|
||||
position: relative;
|
||||
}
|
||||
|
||||
.o-tooltip--left:after {
|
||||
opacity: 0;
|
||||
visibility: hidden;
|
||||
position: absolute;
|
||||
content: attr(data-tooltip);
|
||||
padding: 2px;
|
||||
top: 0;
|
||||
left: -.2em;
|
||||
background: grey;
|
||||
font-size: 1rem;
|
||||
color: white;
|
||||
white-space: nowrap;
|
||||
z-index: 2;
|
||||
border-radius: 2px;
|
||||
transform: translateX(-102%) translateY(0);
|
||||
transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1);
|
||||
}
|
||||
|
||||
.o-tooltip--left:hover:after {
|
||||
display: block;
|
||||
opacity: 1;
|
||||
visibility: visible;
|
||||
transform: translateX(-100%) translateY(0);
|
||||
transition: opacity 0.2s cubic-bezier(0.64, 0.09, 0.08, 1), transform 0.2s cubic-bezier(0.64, 0.09, 0.08, 1);
|
||||
transition-delay: .5s;
|
||||
}
|
||||
@@ -0,0 +1,153 @@
|
||||
// Localization support
|
||||
const messages = {
|
||||
'en': {
|
||||
'copy': 'Copy',
|
||||
'copy_to_clipboard': 'Copy to clipboard',
|
||||
'copy_success': 'Copied!',
|
||||
'copy_failure': 'Failed to copy',
|
||||
},
|
||||
'es' : {
|
||||
'copy': 'Copiar',
|
||||
'copy_to_clipboard': 'Copiar al portapapeles',
|
||||
'copy_success': '¡Copiado!',
|
||||
'copy_failure': 'Error al copiar',
|
||||
},
|
||||
'de' : {
|
||||
'copy': 'Kopieren',
|
||||
'copy_to_clipboard': 'In die Zwischenablage kopieren',
|
||||
'copy_success': 'Kopiert!',
|
||||
'copy_failure': 'Fehler beim Kopieren',
|
||||
}
|
||||
}
|
||||
|
||||
let locale = 'en'
|
||||
if( document.documentElement.lang !== undefined
|
||||
&& messages[document.documentElement.lang] !== undefined ) {
|
||||
locale = document.documentElement.lang
|
||||
}
|
||||
|
||||
/**
|
||||
* Set up copy/paste for code blocks
|
||||
*/
|
||||
|
||||
const runWhenDOMLoaded = cb => {
|
||||
if (document.readyState != 'loading') {
|
||||
cb()
|
||||
} else if (document.addEventListener) {
|
||||
document.addEventListener('DOMContentLoaded', cb)
|
||||
} else {
|
||||
document.attachEvent('onreadystatechange', function() {
|
||||
if (document.readyState == 'complete') cb()
|
||||
})
|
||||
}
|
||||
}
|
||||
|
||||
const codeCellId = index => `codecell${index}`
|
||||
|
||||
// Clears selected text since ClipboardJS will select the text when copying
|
||||
const clearSelection = () => {
|
||||
if (window.getSelection) {
|
||||
window.getSelection().removeAllRanges()
|
||||
} else if (document.selection) {
|
||||
document.selection.empty()
|
||||
}
|
||||
}
|
||||
|
||||
// Changes tooltip text for two seconds, then changes it back
|
||||
const temporarilyChangeTooltip = (el, newText) => {
|
||||
const oldText = el.getAttribute('data-tooltip')
|
||||
el.setAttribute('data-tooltip', newText)
|
||||
setTimeout(() => el.setAttribute('data-tooltip', oldText), 2000)
|
||||
}
|
||||
|
||||
const addCopyButtonToCodeCells = () => {
|
||||
// If ClipboardJS hasn't loaded, wait a bit and try again. This
|
||||
// happens because we load ClipboardJS asynchronously.
|
||||
if (window.ClipboardJS === undefined) {
|
||||
setTimeout(addCopyButtonToCodeCells, 250)
|
||||
return
|
||||
}
|
||||
|
||||
// Add copybuttons to all of our code cells
|
||||
const codeCells = document.querySelectorAll('div.highlight pre')
|
||||
codeCells.forEach((codeCell, index) => {
|
||||
const id = codeCellId(index)
|
||||
codeCell.setAttribute('id', id)
|
||||
const pre_bg = getComputedStyle(codeCell).backgroundColor;
|
||||
|
||||
const clipboardButton = id =>
|
||||
`<a class="copybtn o-tooltip--left" style="background-color: ${pre_bg}" data-tooltip="${messages[locale]['copy']}" data-clipboard-target="#${id}">
|
||||
<img src="${DOCUMENTATION_OPTIONS.URL_ROOT}_static/copy-button.svg" alt="${messages[locale]['copy_to_clipboard']}">
|
||||
</a>`
|
||||
codeCell.insertAdjacentHTML('afterend', clipboardButton(id))
|
||||
})
|
||||
|
||||
function escapeRegExp(string) {
|
||||
return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
|
||||
}
|
||||
|
||||
// Callback when a copy button is clicked. Will be passed the node that was clicked
|
||||
// should then grab the text and replace pieces of text that shouldn't be used in output
|
||||
function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true) {
|
||||
|
||||
var regexp;
|
||||
var match;
|
||||
|
||||
// create regexp to capture prompt and remaining line
|
||||
if (isRegexp) {
|
||||
regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)')
|
||||
} else {
|
||||
regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)')
|
||||
}
|
||||
|
||||
const outputLines = [];
|
||||
var promptFound = false;
|
||||
for (const line of textContent.split('\n')) {
|
||||
match = line.match(regexp)
|
||||
if (match) {
|
||||
promptFound = true
|
||||
if (removePrompts) {
|
||||
outputLines.push(match[2])
|
||||
} else {
|
||||
outputLines.push(line)
|
||||
}
|
||||
} else {
|
||||
if (!onlyCopyPromptLines) {
|
||||
outputLines.push(line)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// If no lines with the prompt were found then just use original lines
|
||||
if (promptFound) {
|
||||
textContent = outputLines.join('\n');
|
||||
}
|
||||
|
||||
// Remove a trailing newline to avoid auto-running when pasting
|
||||
if (textContent.endsWith("\n")) {
|
||||
textContent = textContent.slice(0, -1)
|
||||
}
|
||||
return textContent
|
||||
}
|
||||
|
||||
|
||||
var copyTargetText = (trigger) => {
|
||||
var target = document.querySelector(trigger.attributes['data-clipboard-target'].value);
|
||||
return formatCopyText(target.innerText, '', false, true, true)
|
||||
}
|
||||
|
||||
// Initialize with a callback so we can modify the text before copy
|
||||
const clipboard = new ClipboardJS('.copybtn', {text: copyTargetText})
|
||||
|
||||
// Update UI with error/success messages
|
||||
clipboard.on('success', event => {
|
||||
clearSelection()
|
||||
temporarilyChangeTooltip(event.trigger, messages[locale]['copy_success'])
|
||||
})
|
||||
|
||||
clipboard.on('error', event => {
|
||||
temporarilyChangeTooltip(event.trigger, messages[locale]['copy_failure'])
|
||||
})
|
||||
}
|
||||
|
||||
runWhenDOMLoaded(addCopyButtonToCodeCells)
|
||||
@@ -0,0 +1,47 @@
|
||||
function escapeRegExp(string) {
|
||||
return string.replace(/[.*+?^${}()|[\]\\]/g, '\\$&'); // $& means the whole matched string
|
||||
}
|
||||
|
||||
// Callback when a copy button is clicked. Will be passed the node that was clicked
|
||||
// should then grab the text and replace pieces of text that shouldn't be used in output
|
||||
export function formatCopyText(textContent, copybuttonPromptText, isRegexp = false, onlyCopyPromptLines = true, removePrompts = true) {
|
||||
|
||||
var regexp;
|
||||
var match;
|
||||
|
||||
// create regexp to capture prompt and remaining line
|
||||
if (isRegexp) {
|
||||
regexp = new RegExp('^(' + copybuttonPromptText + ')(.*)')
|
||||
} else {
|
||||
regexp = new RegExp('^(' + escapeRegExp(copybuttonPromptText) + ')(.*)')
|
||||
}
|
||||
|
||||
const outputLines = [];
|
||||
var promptFound = false;
|
||||
for (const line of textContent.split('\n')) {
|
||||
match = line.match(regexp)
|
||||
if (match) {
|
||||
promptFound = true
|
||||
if (removePrompts) {
|
||||
outputLines.push(match[2])
|
||||
} else {
|
||||
outputLines.push(line)
|
||||
}
|
||||
} else {
|
||||
if (!onlyCopyPromptLines) {
|
||||
outputLines.push(line)
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// If no lines with the prompt were found then just use original lines
|
||||
if (promptFound) {
|
||||
textContent = outputLines.join('\n');
|
||||
}
|
||||
|
||||
// Remove a trailing newline to avoid auto-running when pasting
|
||||
if (textContent.endsWith("\n")) {
|
||||
textContent = textContent.slice(0, -1)
|
||||
}
|
||||
return textContent
|
||||
}
|
||||
File diff suppressed because one or more lines are too long
@@ -0,0 +1,117 @@
|
||||
:root {
|
||||
/*****************************************************************************
|
||||
* Theme config
|
||||
**/
|
||||
--pst-header-height: 60px;
|
||||
|
||||
/*****************************************************************************
|
||||
* Font size
|
||||
**/
|
||||
--pst-font-size-base: 15px; /* base font size - applied at body / html level */
|
||||
|
||||
/* heading font sizes */
|
||||
--pst-font-size-h1: 36px;
|
||||
--pst-font-size-h2: 32px;
|
||||
--pst-font-size-h3: 26px;
|
||||
--pst-font-size-h4: 21px;
|
||||
--pst-font-size-h5: 18px;
|
||||
--pst-font-size-h6: 16px;
|
||||
|
||||
/* smaller then heading font sizes*/
|
||||
--pst-font-size-milli: 12px;
|
||||
|
||||
--pst-sidebar-font-size: .9em;
|
||||
--pst-sidebar-caption-font-size: .9em;
|
||||
|
||||
/*****************************************************************************
|
||||
* Font family
|
||||
**/
|
||||
/* These are adapted from https://systemfontstack.com/ */
|
||||
--pst-font-family-base-system: -apple-system, BlinkMacSystemFont, Segoe UI, "Helvetica Neue",
|
||||
Arial, sans-serif, Apple Color Emoji, Segoe UI Emoji, Segoe UI Symbol;
|
||||
--pst-font-family-monospace-system: "SFMono-Regular", Menlo, Consolas, Monaco,
|
||||
Liberation Mono, Lucida Console, monospace;
|
||||
|
||||
--pst-font-family-base: var(--pst-font-family-base-system);
|
||||
--pst-font-family-heading: var(--pst-font-family-base);
|
||||
--pst-font-family-monospace: var(--pst-font-family-monospace-system);
|
||||
|
||||
/*****************************************************************************
|
||||
* Color
|
||||
*
|
||||
* Colors are defined in rgb string way, "red, green, blue"
|
||||
**/
|
||||
--pst-color-primary: 19, 6, 84;
|
||||
--pst-color-success: 40, 167, 69;
|
||||
--pst-color-info: 0, 123, 255; /*23, 162, 184;*/
|
||||
--pst-color-warning: 255, 193, 7;
|
||||
--pst-color-danger: 220, 53, 69;
|
||||
--pst-color-text-base: 51, 51, 51;
|
||||
|
||||
--pst-color-h1: var(--pst-color-primary);
|
||||
--pst-color-h2: var(--pst-color-primary);
|
||||
--pst-color-h3: var(--pst-color-text-base);
|
||||
--pst-color-h4: var(--pst-color-text-base);
|
||||
--pst-color-h5: var(--pst-color-text-base);
|
||||
--pst-color-h6: var(--pst-color-text-base);
|
||||
--pst-color-paragraph: var(--pst-color-text-base);
|
||||
--pst-color-link: 0, 91, 129;
|
||||
--pst-color-link-hover: 227, 46, 0;
|
||||
--pst-color-headerlink: 198, 15, 15;
|
||||
--pst-color-headerlink-hover: 255, 255, 255;
|
||||
--pst-color-preformatted-text: 34, 34, 34;
|
||||
--pst-color-preformatted-background: 250, 250, 250;
|
||||
--pst-color-inline-code: 232, 62, 140;
|
||||
|
||||
--pst-color-active-navigation: 19, 6, 84;
|
||||
--pst-color-navbar-link: 77, 77, 77;
|
||||
--pst-color-navbar-link-hover: var(--pst-color-active-navigation);
|
||||
--pst-color-navbar-link-active: var(--pst-color-active-navigation);
|
||||
--pst-color-sidebar-link: 77, 77, 77;
|
||||
--pst-color-sidebar-link-hover: var(--pst-color-active-navigation);
|
||||
--pst-color-sidebar-link-active: var(--pst-color-active-navigation);
|
||||
--pst-color-sidebar-expander-background-hover: 244, 244, 244;
|
||||
--pst-color-sidebar-caption: 77, 77, 77;
|
||||
--pst-color-toc-link: 119, 117, 122;
|
||||
--pst-color-toc-link-hover: var(--pst-color-active-navigation);
|
||||
--pst-color-toc-link-active: var(--pst-color-active-navigation);
|
||||
|
||||
/*****************************************************************************
|
||||
* Icon
|
||||
**/
|
||||
|
||||
/* font awesome icons*/
|
||||
--pst-icon-check-circle: '\f058';
|
||||
--pst-icon-info-circle: '\f05a';
|
||||
--pst-icon-exclamation-triangle: '\f071';
|
||||
--pst-icon-exclamation-circle: '\f06a';
|
||||
--pst-icon-times-circle: '\f057';
|
||||
--pst-icon-lightbulb: '\f0eb';
|
||||
|
||||
/*****************************************************************************
|
||||
* Admonitions
|
||||
**/
|
||||
|
||||
--pst-color-admonition-default: var(--pst-color-info);
|
||||
--pst-color-admonition-note: var(--pst-color-info);
|
||||
--pst-color-admonition-attention: var(--pst-color-warning);
|
||||
--pst-color-admonition-caution: var(--pst-color-warning);
|
||||
--pst-color-admonition-warning: var(--pst-color-warning);
|
||||
--pst-color-admonition-danger: var(--pst-color-danger);
|
||||
--pst-color-admonition-error: var(--pst-color-danger);
|
||||
--pst-color-admonition-hint: var(--pst-color-success);
|
||||
--pst-color-admonition-tip: var(--pst-color-success);
|
||||
--pst-color-admonition-important: var(--pst-color-success);
|
||||
|
||||
--pst-icon-admonition-default: var(--pst-icon-info-circle);
|
||||
--pst-icon-admonition-note: var(--pst-icon-info-circle);
|
||||
--pst-icon-admonition-attention: var(--pst-icon-exclamation-circle);
|
||||
--pst-icon-admonition-caution: var(--pst-icon-exclamation-triangle);
|
||||
--pst-icon-admonition-warning: var(--pst-icon-exclamation-triangle);
|
||||
--pst-icon-admonition-danger: var(--pst-icon-exclamation-triangle);
|
||||
--pst-icon-admonition-error: var(--pst-icon-times-circle);
|
||||
--pst-icon-admonition-hint: var(--pst-icon-lightbulb);
|
||||
--pst-icon-admonition-tip: var(--pst-icon-lightbulb);
|
||||
--pst-icon-admonition-important: var(--pst-icon-exclamation-circle);
|
||||
|
||||
}
|
||||
@@ -0,0 +1,316 @@
|
||||
/*
|
||||
* doctools.js
|
||||
* ~~~~~~~~~~~
|
||||
*
|
||||
* Sphinx JavaScript utilities for all documentation.
|
||||
*
|
||||
* :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS.
|
||||
* :license: BSD, see LICENSE for details.
|
||||
*
|
||||
*/
|
||||
|
||||
/**
|
||||
* select a different prefix for underscore
|
||||
*/
|
||||
$u = _.noConflict();
|
||||
|
||||
/**
|
||||
* make the code below compatible with browsers without
|
||||
* an installed firebug like debugger
|
||||
if (!window.console || !console.firebug) {
|
||||
var names = ["log", "debug", "info", "warn", "error", "assert", "dir",
|
||||
"dirxml", "group", "groupEnd", "time", "timeEnd", "count", "trace",
|
||||
"profile", "profileEnd"];
|
||||
window.console = {};
|
||||
for (var i = 0; i < names.length; ++i)
|
||||
window.console[names[i]] = function() {};
|
||||
}
|
||||
*/
|
||||
|
||||
/**
|
||||
* small helper function to urldecode strings
|
||||
*/
|
||||
jQuery.urldecode = function(x) {
|
||||
return decodeURIComponent(x).replace(/\+/g, ' ');
|
||||
};
|
||||
|
||||
/**
|
||||
* small helper function to urlencode strings
|
||||
*/
|
||||
jQuery.urlencode = encodeURIComponent;
|
||||
|
||||
/**
|
||||
* This function returns the parsed url parameters of the
|
||||
* current request. Multiple values per key are supported,
|
||||
* it will always return arrays of strings for the value parts.
|
||||
*/
|
||||
jQuery.getQueryParameters = function(s) {
|
||||
if (typeof s === 'undefined')
|
||||
s = document.location.search;
|
||||
var parts = s.substr(s.indexOf('?') + 1).split('&');
|
||||
var result = {};
|
||||
for (var i = 0; i < parts.length; i++) {
|
||||
var tmp = parts[i].split('=', 2);
|
||||
var key = jQuery.urldecode(tmp[0]);
|
||||
var value = jQuery.urldecode(tmp[1]);
|
||||
if (key in result)
|
||||
result[key].push(value);
|
||||
else
|
||||
result[key] = [value];
|
||||
}
|
||||
return result;
|
||||
};
|
||||
|
||||
/**
|
||||
* highlight a given string on a jquery object by wrapping it in
|
||||
* span elements with the given class name.
|
||||
*/
|
||||
jQuery.fn.highlightText = function(text, className) {
|
||||
function highlight(node, addItems) {
|
||||
if (node.nodeType === 3) {
|
||||
var val = node.nodeValue;
|
||||
var pos = val.toLowerCase().indexOf(text);
|
||||
if (pos >= 0 &&
|
||||
!jQuery(node.parentNode).hasClass(className) &&
|
||||
!jQuery(node.parentNode).hasClass("nohighlight")) {
|
||||
var span;
|
||||
var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg");
|
||||
if (isInSVG) {
|
||||
span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
|
||||
} else {
|
||||
span = document.createElement("span");
|
||||
span.className = className;
|
||||
}
|
||||
span.appendChild(document.createTextNode(val.substr(pos, text.length)));
|
||||
node.parentNode.insertBefore(span, node.parentNode.insertBefore(
|
||||
document.createTextNode(val.substr(pos + text.length)),
|
||||
node.nextSibling));
|
||||
node.nodeValue = val.substr(0, pos);
|
||||
if (isInSVG) {
|
||||
var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect");
|
||||
var bbox = node.parentElement.getBBox();
|
||||
rect.x.baseVal.value = bbox.x;
|
||||
rect.y.baseVal.value = bbox.y;
|
||||
rect.width.baseVal.value = bbox.width;
|
||||
rect.height.baseVal.value = bbox.height;
|
||||
rect.setAttribute('class', className);
|
||||
addItems.push({
|
||||
"parent": node.parentNode,
|
||||
"target": rect});
|
||||
}
|
||||
}
|
||||
}
|
||||
else if (!jQuery(node).is("button, select, textarea")) {
|
||||
jQuery.each(node.childNodes, function() {
|
||||
highlight(this, addItems);
|
||||
});
|
||||
}
|
||||
}
|
||||
var addItems = [];
|
||||
var result = this.each(function() {
|
||||
highlight(this, addItems);
|
||||
});
|
||||
for (var i = 0; i < addItems.length; ++i) {
|
||||
jQuery(addItems[i].parent).before(addItems[i].target);
|
||||
}
|
||||
return result;
|
||||
};
|
||||
|
||||
/*
|
||||
* backward compatibility for jQuery.browser
|
||||
* This will be supported until firefox bug is fixed.
|
||||
*/
|
||||
if (!jQuery.browser) {
|
||||
jQuery.uaMatch = function(ua) {
|
||||
ua = ua.toLowerCase();
|
||||
|
||||
var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
|
||||
/(webkit)[ \/]([\w.]+)/.exec(ua) ||
|
||||
/(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) ||
|
||||
/(msie) ([\w.]+)/.exec(ua) ||
|
||||
ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) ||
|
||||
[];
|
||||
|
||||
return {
|
||||
browser: match[ 1 ] || "",
|
||||
version: match[ 2 ] || "0"
|
||||
};
|
||||
};
|
||||
jQuery.browser = {};
|
||||
jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;
|
||||
}
|
||||
|
||||
/**
|
||||
* Small JavaScript module for the documentation.
|
||||
*/
|
||||
var Documentation = {
|
||||
|
||||
init : function() {
|
||||
this.fixFirefoxAnchorBug();
|
||||
this.highlightSearchWords();
|
||||
this.initIndexTable();
|
||||
if (DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) {
|
||||
this.initOnKeyListeners();
|
||||
}
|
||||
},
|
||||
|
||||
/**
|
||||
* i18n support
|
||||
*/
|
||||
TRANSLATIONS : {},
|
||||
PLURAL_EXPR : function(n) { return n === 1 ? 0 : 1; },
|
||||
LOCALE : 'unknown',
|
||||
|
||||
// gettext and ngettext don't access this so that the functions
|
||||
// can safely bound to a different name (_ = Documentation.gettext)
|
||||
gettext : function(string) {
|
||||
var translated = Documentation.TRANSLATIONS[string];
|
||||
if (typeof translated === 'undefined')
|
||||
return string;
|
||||
return (typeof translated === 'string') ? translated : translated[0];
|
||||
},
|
||||
|
||||
ngettext : function(singular, plural, n) {
|
||||
var translated = Documentation.TRANSLATIONS[singular];
|
||||
if (typeof translated === 'undefined')
|
||||
return (n == 1) ? singular : plural;
|
||||
return translated[Documentation.PLURALEXPR(n)];
|
||||
},
|
||||
|
||||
addTranslations : function(catalog) {
|
||||
for (var key in catalog.messages)
|
||||
this.TRANSLATIONS[key] = catalog.messages[key];
|
||||
this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')');
|
||||
this.LOCALE = catalog.locale;
|
||||
},
|
||||
|
||||
/**
|
||||
* add context elements like header anchor links
|
||||
*/
|
||||
addContextElements : function() {
|
||||
$('div[id] > :header:first').each(function() {
|
||||
$('<a class="headerlink">\u00B6</a>').
|
||||
attr('href', '#' + this.id).
|
||||
attr('title', _('Permalink to this headline')).
|
||||
appendTo(this);
|
||||
});
|
||||
$('dt[id]').each(function() {
|
||||
$('<a class="headerlink">\u00B6</a>').
|
||||
attr('href', '#' + this.id).
|
||||
attr('title', _('Permalink to this definition')).
|
||||
appendTo(this);
|
||||
});
|
||||
},
|
||||
|
||||
/**
|
||||
* workaround a firefox stupidity
|
||||
* see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075
|
||||
*/
|
||||
fixFirefoxAnchorBug : function() {
|
||||
if (document.location.hash && $.browser.mozilla)
|
||||
window.setTimeout(function() {
|
||||
document.location.href += '';
|
||||
}, 10);
|
||||
},
|
||||
|
||||
/**
|
||||
* highlight the search words provided in the url in the text
|
||||
*/
|
||||
highlightSearchWords : function() {
|
||||
var params = $.getQueryParameters();
|
||||
var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : [];
|
||||
if (terms.length) {
|
||||
var body = $('div.body');
|
||||
if (!body.length) {
|
||||
body = $('body');
|
||||
}
|
||||
window.setTimeout(function() {
|
||||
$.each(terms, function() {
|
||||
body.highlightText(this.toLowerCase(), 'highlighted');
|
||||
});
|
||||
}, 10);
|
||||
$('<p class="highlight-link"><a href="javascript:Documentation.' +
|
||||
'hideSearchWords()">' + _('Hide Search Matches') + '</a></p>')
|
||||
.appendTo($('#searchbox'));
|
||||
}
|
||||
},
|
||||
|
||||
/**
|
||||
* init the domain index toggle buttons
|
||||
*/
|
||||
initIndexTable : function() {
|
||||
var togglers = $('img.toggler').click(function() {
|
||||
var src = $(this).attr('src');
|
||||
var idnum = $(this).attr('id').substr(7);
|
||||
$('tr.cg-' + idnum).toggle();
|
||||
if (src.substr(-9) === 'minus.png')
|
||||
$(this).attr('src', src.substr(0, src.length-9) + 'plus.png');
|
||||
else
|
||||
$(this).attr('src', src.substr(0, src.length-8) + 'minus.png');
|
||||
}).css('display', '');
|
||||
if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) {
|
||||
togglers.click();
|
||||
}
|
||||
},
|
||||
|
||||
/**
|
||||
* helper function to hide the search marks again
|
||||
*/
|
||||
hideSearchWords : function() {
|
||||
$('#searchbox .highlight-link').fadeOut(300);
|
||||
$('span.highlighted').removeClass('highlighted');
|
||||
},
|
||||
|
||||
/**
|
||||
* make the url absolute
|
||||
*/
|
||||
makeURL : function(relativeURL) {
|
||||
return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL;
|
||||
},
|
||||
|
||||
/**
|
||||
* get the current relative url
|
||||
*/
|
||||
getCurrentURL : function() {
|
||||
var path = document.location.pathname;
|
||||
var parts = path.split(/\//);
|
||||
$.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() {
|
||||
if (this === '..')
|
||||
parts.pop();
|
||||
});
|
||||
var url = parts.join('/');
|
||||
return path.substring(url.lastIndexOf('/') + 1, path.length - 1);
|
||||
},
|
||||
|
||||
initOnKeyListeners: function() {
|
||||
$(document).keydown(function(event) {
|
||||
var activeElementType = document.activeElement.tagName;
|
||||
// don't navigate when in search box, textarea, dropdown or button
|
||||
if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT'
|
||||
&& activeElementType !== 'BUTTON' && !event.altKey && !event.ctrlKey && !event.metaKey
|
||||
&& !event.shiftKey) {
|
||||
switch (event.keyCode) {
|
||||
case 37: // left
|
||||
var prevHref = $('link[rel="prev"]').prop('href');
|
||||
if (prevHref) {
|
||||
window.location.href = prevHref;
|
||||
return false;
|
||||
}
|
||||
case 39: // right
|
||||
var nextHref = $('link[rel="next"]').prop('href');
|
||||
if (nextHref) {
|
||||
window.location.href = nextHref;
|
||||
return false;
|
||||
}
|
||||
}
|
||||
}
|
||||
});
|
||||
}
|
||||
};
|
||||
|
||||
// quick alias for translations
|
||||
_ = Documentation.gettext;
|
||||
|
||||
$(document).ready(function() {
|
||||
Documentation.init();
|
||||
});
|
||||
@@ -0,0 +1,12 @@
|
||||
var DOCUMENTATION_OPTIONS = {
|
||||
URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
|
||||
VERSION: '',
|
||||
LANGUAGE: 'None',
|
||||
COLLAPSE_INDEX: false,
|
||||
BUILDER: 'html',
|
||||
FILE_SUFFIX: '.html',
|
||||
LINK_SUFFIX: '.html',
|
||||
HAS_SOURCE: true,
|
||||
SOURCELINK_SUFFIX: '',
|
||||
NAVIGATION_WITH_KEYS: true
|
||||
};
|
||||
Binary file not shown.
|
After Width: | Height: | Size: 286 B |
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|
||||
<?xml version="1.0" encoding="utf-8"?>
|
||||
<!-- Generator: Adobe Illustrator 23.0.1, SVG Export Plug-In . SVG Version: 6.00 Build 0) -->
|
||||
<svg version="1.1" id="Layer_1" xmlns="http://www.w3.org/2000/svg" xmlns:xlink="http://www.w3.org/1999/xlink" x="0px" y="0px"
|
||||
viewBox="0 0 44.4 44.4" style="enable-background:new 0 0 44.4 44.4;" xml:space="preserve">
|
||||
<style type="text/css">
|
||||
.st0{fill:none;stroke:#F5A252;stroke-width:5;stroke-miterlimit:10;}
|
||||
.st1{fill:none;stroke:#579ACA;stroke-width:5;stroke-miterlimit:10;}
|
||||
.st2{fill:none;stroke:#E66581;stroke-width:5;stroke-miterlimit:10;}
|
||||
</style>
|
||||
<title>logo</title>
|
||||
<g>
|
||||
<path class="st0" d="M33.9,6.4c3.6,3.9,3.4,9.9-0.5,13.5s-9.9,3.4-13.5-0.5s-3.4-9.9,0.5-13.5l0,0C24.2,2.4,30.2,2.6,33.9,6.4z"/>
|
||||
<path class="st1" d="M35.1,27.3c2.6,4.6,1.1,10.4-3.5,13c-4.6,2.6-10.4,1.1-13-3.5s-1.1-10.4,3.5-13l0,0
|
||||
C26.6,21.2,32.4,22.7,35.1,27.3z"/>
|
||||
<path class="st2" d="M25.9,17.8c2.6,4.6,1.1,10.4-3.5,13s-10.4,1.1-13-3.5s-1.1-10.4,3.5-13l0,0C17.5,11.7,23.3,13.2,25.9,17.8z"/>
|
||||
<path class="st1" d="M19.2,26.4c3.1-4.3,9.1-5.2,13.3-2.1c1.1,0.8,2,1.8,2.7,3"/>
|
||||
<path class="st0" d="M19.9,19.4c-3.6-3.9-3.4-9.9,0.5-13.5s9.9-3.4,13.5,0.5"/>
|
||||
</g>
|
||||
</svg>
|
||||
|
After Width: | Height: | Size: 1.2 KiB |
Binary file not shown.
|
After Width: | Height: | Size: 7.4 KiB |
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|
||||
<svg id="Layer_1" data-name="Layer 1" xmlns="http://www.w3.org/2000/svg" width="38.73" height="50" viewBox="0 0 38.73 50"><defs><style>.cls-1{fill:#767677;}.cls-2{fill:#f37726;}.cls-3{fill:#9e9e9e;}.cls-4{fill:#616262;}.cls-5{font-size:17.07px;fill:#fff;font-family:Roboto-Regular, Roboto;}</style></defs><title>logo_jupyterhub</title><g id="Canvas"><path id="path7_fill" data-name="path7 fill" class="cls-1" d="M39.51,3.53a3,3,0,0,1-1.7,2.9A3,3,0,0,1,34.48,6a3,3,0,0,1-.82-3.26,3,3,0,0,1,1.05-1.41A3,3,0,0,1,37.52.86a2.88,2.88,0,0,1,1,.6,3,3,0,0,1,.7.93,3.18,3.18,0,0,1,.28,1.14Z" transform="translate(-1.87 -0.69)"/><path id="path8_fill" data-name="path8 fill" class="cls-2" d="M21.91,38.39c-8,0-15.06-2.87-18.7-7.12a19.93,19.93,0,0,0,37.39,0C37,35.52,30,38.39,21.91,38.39Z" transform="translate(-1.87 -0.69)"/><path id="path9_fill" data-name="path9 fill" class="cls-2" d="M21.91,10.78c8,0,15.05,2.87,18.69,7.12a19.93,19.93,0,0,0-37.39,0C6.85,13.64,13.86,10.78,21.91,10.78Z" transform="translate(-1.87 -0.69)"/><path id="path10_fill" data-name="path10 fill" class="cls-3" d="M10.88,46.66a3.86,3.86,0,0,1-.52,2.15,3.81,3.81,0,0,1-1.62,1.51,3.93,3.93,0,0,1-2.19.34,3.79,3.79,0,0,1-2-.94,3.73,3.73,0,0,1-1.14-1.9,3.79,3.79,0,0,1,.1-2.21,3.86,3.86,0,0,1,1.33-1.78,3.92,3.92,0,0,1,3.54-.53,3.85,3.85,0,0,1,2.14,1.93,3.74,3.74,0,0,1,.37,1.43Z" transform="translate(-1.87 -0.69)"/><path id="path11_fill" data-name="path11 fill" class="cls-4" d="M4.12,9.81A2.18,2.18,0,0,1,2.9,9.48a2.23,2.23,0,0,1-.84-1A2.26,2.26,0,0,1,1.9,7.26a2.13,2.13,0,0,1,.56-1.13,2.18,2.18,0,0,1,2.36-.56,2.13,2.13,0,0,1,1,.76,2.18,2.18,0,0,1,.42,1.2A2.22,2.22,0,0,1,4.12,9.81Z" transform="translate(-1.87 -0.69)"/></g><text class="cls-5" transform="translate(5.24 30.01)">Hub</text></svg>
|
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|
After Width: | Height: | Size: 1.7 KiB |
+10872
File diff suppressed because it is too large
Load Diff
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
@@ -0,0 +1,297 @@
|
||||
/*
|
||||
* language_data.js
|
||||
* ~~~~~~~~~~~~~~~~
|
||||
*
|
||||
* This script contains the language-specific data used by searchtools.js,
|
||||
* namely the list of stopwords, stemmer, scorer and splitter.
|
||||
*
|
||||
* :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS.
|
||||
* :license: BSD, see LICENSE for details.
|
||||
*
|
||||
*/
|
||||
|
||||
var stopwords = ["a","and","are","as","at","be","but","by","for","if","in","into","is","it","near","no","not","of","on","or","such","that","the","their","then","there","these","they","this","to","was","will","with"];
|
||||
|
||||
|
||||
/* Non-minified version JS is _stemmer.js if file is provided */
|
||||
/**
|
||||
* Porter Stemmer
|
||||
*/
|
||||
var Stemmer = function() {
|
||||
|
||||
var step2list = {
|
||||
ational: 'ate',
|
||||
tional: 'tion',
|
||||
enci: 'ence',
|
||||
anci: 'ance',
|
||||
izer: 'ize',
|
||||
bli: 'ble',
|
||||
alli: 'al',
|
||||
entli: 'ent',
|
||||
eli: 'e',
|
||||
ousli: 'ous',
|
||||
ization: 'ize',
|
||||
ation: 'ate',
|
||||
ator: 'ate',
|
||||
alism: 'al',
|
||||
iveness: 'ive',
|
||||
fulness: 'ful',
|
||||
ousness: 'ous',
|
||||
aliti: 'al',
|
||||
iviti: 'ive',
|
||||
biliti: 'ble',
|
||||
logi: 'log'
|
||||
};
|
||||
|
||||
var step3list = {
|
||||
icate: 'ic',
|
||||
ative: '',
|
||||
alize: 'al',
|
||||
iciti: 'ic',
|
||||
ical: 'ic',
|
||||
ful: '',
|
||||
ness: ''
|
||||
};
|
||||
|
||||
var c = "[^aeiou]"; // consonant
|
||||
var v = "[aeiouy]"; // vowel
|
||||
var C = c + "[^aeiouy]*"; // consonant sequence
|
||||
var V = v + "[aeiou]*"; // vowel sequence
|
||||
|
||||
var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0
|
||||
var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1
|
||||
var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1
|
||||
var s_v = "^(" + C + ")?" + v; // vowel in stem
|
||||
|
||||
this.stemWord = function (w) {
|
||||
var stem;
|
||||
var suffix;
|
||||
var firstch;
|
||||
var origword = w;
|
||||
|
||||
if (w.length < 3)
|
||||
return w;
|
||||
|
||||
var re;
|
||||
var re2;
|
||||
var re3;
|
||||
var re4;
|
||||
|
||||
firstch = w.substr(0,1);
|
||||
if (firstch == "y")
|
||||
w = firstch.toUpperCase() + w.substr(1);
|
||||
|
||||
// Step 1a
|
||||
re = /^(.+?)(ss|i)es$/;
|
||||
re2 = /^(.+?)([^s])s$/;
|
||||
|
||||
if (re.test(w))
|
||||
w = w.replace(re,"$1$2");
|
||||
else if (re2.test(w))
|
||||
w = w.replace(re2,"$1$2");
|
||||
|
||||
// Step 1b
|
||||
re = /^(.+?)eed$/;
|
||||
re2 = /^(.+?)(ed|ing)$/;
|
||||
if (re.test(w)) {
|
||||
var fp = re.exec(w);
|
||||
re = new RegExp(mgr0);
|
||||
if (re.test(fp[1])) {
|
||||
re = /.$/;
|
||||
w = w.replace(re,"");
|
||||
}
|
||||
}
|
||||
else if (re2.test(w)) {
|
||||
var fp = re2.exec(w);
|
||||
stem = fp[1];
|
||||
re2 = new RegExp(s_v);
|
||||
if (re2.test(stem)) {
|
||||
w = stem;
|
||||
re2 = /(at|bl|iz)$/;
|
||||
re3 = new RegExp("([^aeiouylsz])\\1$");
|
||||
re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
|
||||
if (re2.test(w))
|
||||
w = w + "e";
|
||||
else if (re3.test(w)) {
|
||||
re = /.$/;
|
||||
w = w.replace(re,"");
|
||||
}
|
||||
else if (re4.test(w))
|
||||
w = w + "e";
|
||||
}
|
||||
}
|
||||
|
||||
// Step 1c
|
||||
re = /^(.+?)y$/;
|
||||
if (re.test(w)) {
|
||||
var fp = re.exec(w);
|
||||
stem = fp[1];
|
||||
re = new RegExp(s_v);
|
||||
if (re.test(stem))
|
||||
w = stem + "i";
|
||||
}
|
||||
|
||||
// Step 2
|
||||
re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
|
||||
if (re.test(w)) {
|
||||
var fp = re.exec(w);
|
||||
stem = fp[1];
|
||||
suffix = fp[2];
|
||||
re = new RegExp(mgr0);
|
||||
if (re.test(stem))
|
||||
w = stem + step2list[suffix];
|
||||
}
|
||||
|
||||
// Step 3
|
||||
re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
|
||||
if (re.test(w)) {
|
||||
var fp = re.exec(w);
|
||||
stem = fp[1];
|
||||
suffix = fp[2];
|
||||
re = new RegExp(mgr0);
|
||||
if (re.test(stem))
|
||||
w = stem + step3list[suffix];
|
||||
}
|
||||
|
||||
// Step 4
|
||||
re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
|
||||
re2 = /^(.+?)(s|t)(ion)$/;
|
||||
if (re.test(w)) {
|
||||
var fp = re.exec(w);
|
||||
stem = fp[1];
|
||||
re = new RegExp(mgr1);
|
||||
if (re.test(stem))
|
||||
w = stem;
|
||||
}
|
||||
else if (re2.test(w)) {
|
||||
var fp = re2.exec(w);
|
||||
stem = fp[1] + fp[2];
|
||||
re2 = new RegExp(mgr1);
|
||||
if (re2.test(stem))
|
||||
w = stem;
|
||||
}
|
||||
|
||||
// Step 5
|
||||
re = /^(.+?)e$/;
|
||||
if (re.test(w)) {
|
||||
var fp = re.exec(w);
|
||||
stem = fp[1];
|
||||
re = new RegExp(mgr1);
|
||||
re2 = new RegExp(meq1);
|
||||
re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
|
||||
if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
|
||||
w = stem;
|
||||
}
|
||||
re = /ll$/;
|
||||
re2 = new RegExp(mgr1);
|
||||
if (re.test(w) && re2.test(w)) {
|
||||
re = /.$/;
|
||||
w = w.replace(re,"");
|
||||
}
|
||||
|
||||
// and turn initial Y back to y
|
||||
if (firstch == "y")
|
||||
w = firstch.toLowerCase() + w.substr(1);
|
||||
return w;
|
||||
}
|
||||
}
|
||||
|
||||
|
||||
|
||||
|
||||
|
||||
var splitChars = (function() {
|
||||
var result = {};
|
||||
var singles = [96, 180, 187, 191, 215, 247, 749, 885, 903, 907, 909, 930, 1014, 1648,
|
||||
1748, 1809, 2416, 2473, 2481, 2526, 2601, 2609, 2612, 2615, 2653, 2702,
|
||||
2706, 2729, 2737, 2740, 2857, 2865, 2868, 2910, 2928, 2948, 2961, 2971,
|
||||
2973, 3085, 3089, 3113, 3124, 3213, 3217, 3241, 3252, 3295, 3341, 3345,
|
||||
3369, 3506, 3516, 3633, 3715, 3721, 3736, 3744, 3748, 3750, 3756, 3761,
|
||||
3781, 3912, 4239, 4347, 4681, 4695, 4697, 4745, 4785, 4799, 4801, 4823,
|
||||
4881, 5760, 5901, 5997, 6313, 7405, 8024, 8026, 8028, 8030, 8117, 8125,
|
||||
8133, 8181, 8468, 8485, 8487, 8489, 8494, 8527, 11311, 11359, 11687, 11695,
|
||||
11703, 11711, 11719, 11727, 11735, 12448, 12539, 43010, 43014, 43019, 43587,
|
||||
43696, 43713, 64286, 64297, 64311, 64317, 64319, 64322, 64325, 65141];
|
||||
var i, j, start, end;
|
||||
for (i = 0; i < singles.length; i++) {
|
||||
result[singles[i]] = true;
|
||||
}
|
||||
var ranges = [[0, 47], [58, 64], [91, 94], [123, 169], [171, 177], [182, 184], [706, 709],
|
||||
[722, 735], [741, 747], [751, 879], [888, 889], [894, 901], [1154, 1161],
|
||||
[1318, 1328], [1367, 1368], [1370, 1376], [1416, 1487], [1515, 1519], [1523, 1568],
|
||||
[1611, 1631], [1642, 1645], [1750, 1764], [1767, 1773], [1789, 1790], [1792, 1807],
|
||||
[1840, 1868], [1958, 1968], [1970, 1983], [2027, 2035], [2038, 2041], [2043, 2047],
|
||||
[2070, 2073], [2075, 2083], [2085, 2087], [2089, 2307], [2362, 2364], [2366, 2383],
|
||||
[2385, 2391], [2402, 2405], [2419, 2424], [2432, 2436], [2445, 2446], [2449, 2450],
|
||||
[2483, 2485], [2490, 2492], [2494, 2509], [2511, 2523], [2530, 2533], [2546, 2547],
|
||||
[2554, 2564], [2571, 2574], [2577, 2578], [2618, 2648], [2655, 2661], [2672, 2673],
|
||||
[2677, 2692], [2746, 2748], [2750, 2767], [2769, 2783], [2786, 2789], [2800, 2820],
|
||||
[2829, 2830], [2833, 2834], [2874, 2876], [2878, 2907], [2914, 2917], [2930, 2946],
|
||||
[2955, 2957], [2966, 2968], [2976, 2978], [2981, 2983], [2987, 2989], [3002, 3023],
|
||||
[3025, 3045], [3059, 3076], [3130, 3132], [3134, 3159], [3162, 3167], [3170, 3173],
|
||||
[3184, 3191], [3199, 3204], [3258, 3260], [3262, 3293], [3298, 3301], [3312, 3332],
|
||||
[3386, 3388], [3390, 3423], [3426, 3429], [3446, 3449], [3456, 3460], [3479, 3481],
|
||||
[3518, 3519], [3527, 3584], [3636, 3647], [3655, 3663], [3674, 3712], [3717, 3718],
|
||||
[3723, 3724], [3726, 3731], [3752, 3753], [3764, 3772], [3774, 3775], [3783, 3791],
|
||||
[3802, 3803], [3806, 3839], [3841, 3871], [3892, 3903], [3949, 3975], [3980, 4095],
|
||||
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@@ -0,0 +1,514 @@
|
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/*
|
||||
* searchtools.js
|
||||
* ~~~~~~~~~~~~~~~~
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||||
*
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* Sphinx JavaScript utilities for the full-text search.
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*
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||||
* :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS.
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* :license: BSD, see LICENSE for details.
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*
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||||
*/
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||||
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||||
if (!Scorer) {
|
||||
/**
|
||||
* Simple result scoring code.
|
||||
*/
|
||||
var Scorer = {
|
||||
// Implement the following function to further tweak the score for each result
|
||||
// The function takes a result array [filename, title, anchor, descr, score]
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||||
// and returns the new score.
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||||
/*
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||||
score: function(result) {
|
||||
return result[4];
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||||
},
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||||
*/
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||||
|
||||
// query matches the full name of an object
|
||||
objNameMatch: 11,
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||||
// or matches in the last dotted part of the object name
|
||||
objPartialMatch: 6,
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||||
// Additive scores depending on the priority of the object
|
||||
objPrio: {0: 15, // used to be importantResults
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||||
1: 5, // used to be objectResults
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||||
2: -5}, // used to be unimportantResults
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||||
// Used when the priority is not in the mapping.
|
||||
objPrioDefault: 0,
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||||
|
||||
// query found in title
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||||
title: 15,
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||||
partialTitle: 7,
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||||
// query found in terms
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||||
term: 5,
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||||
partialTerm: 2
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||||
};
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||||
}
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||||
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||||
if (!splitQuery) {
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||||
function splitQuery(query) {
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||||
return query.split(/\s+/);
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||||
}
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||||
}
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||||
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||||
/**
|
||||
* Search Module
|
||||
*/
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||||
var Search = {
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||||
|
||||
_index : null,
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||||
_queued_query : null,
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||||
_pulse_status : -1,
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||||
|
||||
htmlToText : function(htmlString) {
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||||
var virtualDocument = document.implementation.createHTMLDocument('virtual');
|
||||
var htmlElement = $(htmlString, virtualDocument);
|
||||
htmlElement.find('.headerlink').remove();
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||||
docContent = htmlElement.find('[role=main]')[0];
|
||||
if(docContent === undefined) {
|
||||
console.warn("Content block not found. Sphinx search tries to obtain it " +
|
||||
"via '[role=main]'. Could you check your theme or template.");
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||||
return "";
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||||
}
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||||
return docContent.textContent || docContent.innerText;
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||||
},
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||||
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||||
init : function() {
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||||
var params = $.getQueryParameters();
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||||
if (params.q) {
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||||
var query = params.q[0];
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||||
$('input[name="q"]')[0].value = query;
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this.performSearch(query);
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}
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},
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loadIndex : function(url) {
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||||
$.ajax({type: "GET", url: url, data: null,
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||||
dataType: "script", cache: true,
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complete: function(jqxhr, textstatus) {
|
||||
if (textstatus != "success") {
|
||||
document.getElementById("searchindexloader").src = url;
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||||
}
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}});
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},
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||||
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setIndex : function(index) {
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||||
var q;
|
||||
this._index = index;
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||||
if ((q = this._queued_query) !== null) {
|
||||
this._queued_query = null;
|
||||
Search.query(q);
|
||||
}
|
||||
},
|
||||
|
||||
hasIndex : function() {
|
||||
return this._index !== null;
|
||||
},
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||||
|
||||
deferQuery : function(query) {
|
||||
this._queued_query = query;
|
||||
},
|
||||
|
||||
stopPulse : function() {
|
||||
this._pulse_status = 0;
|
||||
},
|
||||
|
||||
startPulse : function() {
|
||||
if (this._pulse_status >= 0)
|
||||
return;
|
||||
function pulse() {
|
||||
var i;
|
||||
Search._pulse_status = (Search._pulse_status + 1) % 4;
|
||||
var dotString = '';
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||||
for (i = 0; i < Search._pulse_status; i++)
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||||
dotString += '.';
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||||
Search.dots.text(dotString);
|
||||
if (Search._pulse_status > -1)
|
||||
window.setTimeout(pulse, 500);
|
||||
}
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||||
pulse();
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||||
},
|
||||
|
||||
/**
|
||||
* perform a search for something (or wait until index is loaded)
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||||
*/
|
||||
performSearch : function(query) {
|
||||
// create the required interface elements
|
||||
this.out = $('#search-results');
|
||||
this.title = $('<h2>' + _('Searching') + '</h2>').appendTo(this.out);
|
||||
this.dots = $('<span></span>').appendTo(this.title);
|
||||
this.status = $('<p class="search-summary"> </p>').appendTo(this.out);
|
||||
this.output = $('<ul class="search"/>').appendTo(this.out);
|
||||
|
||||
$('#search-progress').text(_('Preparing search...'));
|
||||
this.startPulse();
|
||||
|
||||
// index already loaded, the browser was quick!
|
||||
if (this.hasIndex())
|
||||
this.query(query);
|
||||
else
|
||||
this.deferQuery(query);
|
||||
},
|
||||
|
||||
/**
|
||||
* execute search (requires search index to be loaded)
|
||||
*/
|
||||
query : function(query) {
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||||
var i;
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|
||||
// stem the searchterms and add them to the correct list
|
||||
var stemmer = new Stemmer();
|
||||
var searchterms = [];
|
||||
var excluded = [];
|
||||
var hlterms = [];
|
||||
var tmp = splitQuery(query);
|
||||
var objectterms = [];
|
||||
for (i = 0; i < tmp.length; i++) {
|
||||
if (tmp[i] !== "") {
|
||||
objectterms.push(tmp[i].toLowerCase());
|
||||
}
|
||||
|
||||
if ($u.indexOf(stopwords, tmp[i].toLowerCase()) != -1 || tmp[i] === "") {
|
||||
// skip this "word"
|
||||
continue;
|
||||
}
|
||||
// stem the word
|
||||
var word = stemmer.stemWord(tmp[i].toLowerCase());
|
||||
// prevent stemmer from cutting word smaller than two chars
|
||||
if(word.length < 3 && tmp[i].length >= 3) {
|
||||
word = tmp[i];
|
||||
}
|
||||
var toAppend;
|
||||
// select the correct list
|
||||
if (word[0] == '-') {
|
||||
toAppend = excluded;
|
||||
word = word.substr(1);
|
||||
}
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||||
else {
|
||||
toAppend = searchterms;
|
||||
hlterms.push(tmp[i].toLowerCase());
|
||||
}
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// only add if not already in the list
|
||||
if (!$u.contains(toAppend, word))
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toAppend.push(word);
|
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}
|
||||
var highlightstring = '?highlight=' + $.urlencode(hlterms.join(" "));
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||||
|
||||
// console.debug('SEARCH: searching for:');
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// console.info('required: ', searchterms);
|
||||
// console.info('excluded: ', excluded);
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||||
|
||||
// prepare search
|
||||
var terms = this._index.terms;
|
||||
var titleterms = this._index.titleterms;
|
||||
|
||||
// array of [filename, title, anchor, descr, score]
|
||||
var results = [];
|
||||
$('#search-progress').empty();
|
||||
|
||||
// lookup as object
|
||||
for (i = 0; i < objectterms.length; i++) {
|
||||
var others = [].concat(objectterms.slice(0, i),
|
||||
objectterms.slice(i+1, objectterms.length));
|
||||
results = results.concat(this.performObjectSearch(objectterms[i], others));
|
||||
}
|
||||
|
||||
// lookup as search terms in fulltext
|
||||
results = results.concat(this.performTermsSearch(searchterms, excluded, terms, titleterms));
|
||||
|
||||
// let the scorer override scores with a custom scoring function
|
||||
if (Scorer.score) {
|
||||
for (i = 0; i < results.length; i++)
|
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results[i][4] = Scorer.score(results[i]);
|
||||
}
|
||||
|
||||
// now sort the results by score (in opposite order of appearance, since the
|
||||
// display function below uses pop() to retrieve items) and then
|
||||
// alphabetically
|
||||
results.sort(function(a, b) {
|
||||
var left = a[4];
|
||||
var right = b[4];
|
||||
if (left > right) {
|
||||
return 1;
|
||||
} else if (left < right) {
|
||||
return -1;
|
||||
} else {
|
||||
// same score: sort alphabetically
|
||||
left = a[1].toLowerCase();
|
||||
right = b[1].toLowerCase();
|
||||
return (left > right) ? -1 : ((left < right) ? 1 : 0);
|
||||
}
|
||||
});
|
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|
||||
// for debugging
|
||||
//Search.lastresults = results.slice(); // a copy
|
||||
//console.info('search results:', Search.lastresults);
|
||||
|
||||
// print the results
|
||||
var resultCount = results.length;
|
||||
function displayNextItem() {
|
||||
// results left, load the summary and display it
|
||||
if (results.length) {
|
||||
var item = results.pop();
|
||||
var listItem = $('<li style="display:none"></li>');
|
||||
var requestUrl = "";
|
||||
var linkUrl = "";
|
||||
if (DOCUMENTATION_OPTIONS.BUILDER === 'dirhtml') {
|
||||
// dirhtml builder
|
||||
var dirname = item[0] + '/';
|
||||
if (dirname.match(/\/index\/$/)) {
|
||||
dirname = dirname.substring(0, dirname.length-6);
|
||||
} else if (dirname == 'index/') {
|
||||
dirname = '';
|
||||
}
|
||||
requestUrl = DOCUMENTATION_OPTIONS.URL_ROOT + dirname;
|
||||
linkUrl = requestUrl;
|
||||
|
||||
} else {
|
||||
// normal html builders
|
||||
requestUrl = DOCUMENTATION_OPTIONS.URL_ROOT + item[0] + DOCUMENTATION_OPTIONS.FILE_SUFFIX;
|
||||
linkUrl = item[0] + DOCUMENTATION_OPTIONS.LINK_SUFFIX;
|
||||
}
|
||||
listItem.append($('<a/>').attr('href',
|
||||
linkUrl +
|
||||
highlightstring + item[2]).html(item[1]));
|
||||
if (item[3]) {
|
||||
listItem.append($('<span> (' + item[3] + ')</span>'));
|
||||
Search.output.append(listItem);
|
||||
listItem.slideDown(5, function() {
|
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displayNextItem();
|
||||
});
|
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} else if (DOCUMENTATION_OPTIONS.HAS_SOURCE) {
|
||||
$.ajax({url: requestUrl,
|
||||
dataType: "text",
|
||||
complete: function(jqxhr, textstatus) {
|
||||
var data = jqxhr.responseText;
|
||||
if (data !== '' && data !== undefined) {
|
||||
listItem.append(Search.makeSearchSummary(data, searchterms, hlterms));
|
||||
}
|
||||
Search.output.append(listItem);
|
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listItem.slideDown(5, function() {
|
||||
displayNextItem();
|
||||
});
|
||||
}});
|
||||
} else {
|
||||
// no source available, just display title
|
||||
Search.output.append(listItem);
|
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listItem.slideDown(5, function() {
|
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displayNextItem();
|
||||
});
|
||||
}
|
||||
}
|
||||
// search finished, update title and status message
|
||||
else {
|
||||
Search.stopPulse();
|
||||
Search.title.text(_('Search Results'));
|
||||
if (!resultCount)
|
||||
Search.status.text(_('Your search did not match any documents. Please make sure that all words are spelled correctly and that you\'ve selected enough categories.'));
|
||||
else
|
||||
Search.status.text(_('Search finished, found %s page(s) matching the search query.').replace('%s', resultCount));
|
||||
Search.status.fadeIn(500);
|
||||
}
|
||||
}
|
||||
displayNextItem();
|
||||
},
|
||||
|
||||
/**
|
||||
* search for object names
|
||||
*/
|
||||
performObjectSearch : function(object, otherterms) {
|
||||
var filenames = this._index.filenames;
|
||||
var docnames = this._index.docnames;
|
||||
var objects = this._index.objects;
|
||||
var objnames = this._index.objnames;
|
||||
var titles = this._index.titles;
|
||||
|
||||
var i;
|
||||
var results = [];
|
||||
|
||||
for (var prefix in objects) {
|
||||
for (var name in objects[prefix]) {
|
||||
var fullname = (prefix ? prefix + '.' : '') + name;
|
||||
var fullnameLower = fullname.toLowerCase()
|
||||
if (fullnameLower.indexOf(object) > -1) {
|
||||
var score = 0;
|
||||
var parts = fullnameLower.split('.');
|
||||
// check for different match types: exact matches of full name or
|
||||
// "last name" (i.e. last dotted part)
|
||||
if (fullnameLower == object || parts[parts.length - 1] == object) {
|
||||
score += Scorer.objNameMatch;
|
||||
// matches in last name
|
||||
} else if (parts[parts.length - 1].indexOf(object) > -1) {
|
||||
score += Scorer.objPartialMatch;
|
||||
}
|
||||
var match = objects[prefix][name];
|
||||
var objname = objnames[match[1]][2];
|
||||
var title = titles[match[0]];
|
||||
// If more than one term searched for, we require other words to be
|
||||
// found in the name/title/description
|
||||
if (otherterms.length > 0) {
|
||||
var haystack = (prefix + ' ' + name + ' ' +
|
||||
objname + ' ' + title).toLowerCase();
|
||||
var allfound = true;
|
||||
for (i = 0; i < otherterms.length; i++) {
|
||||
if (haystack.indexOf(otherterms[i]) == -1) {
|
||||
allfound = false;
|
||||
break;
|
||||
}
|
||||
}
|
||||
if (!allfound) {
|
||||
continue;
|
||||
}
|
||||
}
|
||||
var descr = objname + _(', in ') + title;
|
||||
|
||||
var anchor = match[3];
|
||||
if (anchor === '')
|
||||
anchor = fullname;
|
||||
else if (anchor == '-')
|
||||
anchor = objnames[match[1]][1] + '-' + fullname;
|
||||
// add custom score for some objects according to scorer
|
||||
if (Scorer.objPrio.hasOwnProperty(match[2])) {
|
||||
score += Scorer.objPrio[match[2]];
|
||||
} else {
|
||||
score += Scorer.objPrioDefault;
|
||||
}
|
||||
results.push([docnames[match[0]], fullname, '#'+anchor, descr, score, filenames[match[0]]]);
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
return results;
|
||||
},
|
||||
|
||||
/**
|
||||
* search for full-text terms in the index
|
||||
*/
|
||||
performTermsSearch : function(searchterms, excluded, terms, titleterms) {
|
||||
var docnames = this._index.docnames;
|
||||
var filenames = this._index.filenames;
|
||||
var titles = this._index.titles;
|
||||
|
||||
var i, j, file;
|
||||
var fileMap = {};
|
||||
var scoreMap = {};
|
||||
var results = [];
|
||||
|
||||
// perform the search on the required terms
|
||||
for (i = 0; i < searchterms.length; i++) {
|
||||
var word = searchterms[i];
|
||||
var files = [];
|
||||
var _o = [
|
||||
{files: terms[word], score: Scorer.term},
|
||||
{files: titleterms[word], score: Scorer.title}
|
||||
];
|
||||
// add support for partial matches
|
||||
if (word.length > 2) {
|
||||
for (var w in terms) {
|
||||
if (w.match(word) && !terms[word]) {
|
||||
_o.push({files: terms[w], score: Scorer.partialTerm})
|
||||
}
|
||||
}
|
||||
for (var w in titleterms) {
|
||||
if (w.match(word) && !titleterms[word]) {
|
||||
_o.push({files: titleterms[w], score: Scorer.partialTitle})
|
||||
}
|
||||
}
|
||||
}
|
||||
|
||||
// no match but word was a required one
|
||||
if ($u.every(_o, function(o){return o.files === undefined;})) {
|
||||
break;
|
||||
}
|
||||
// found search word in contents
|
||||
$u.each(_o, function(o) {
|
||||
var _files = o.files;
|
||||
if (_files === undefined)
|
||||
return
|
||||
|
||||
if (_files.length === undefined)
|
||||
_files = [_files];
|
||||
files = files.concat(_files);
|
||||
|
||||
// set score for the word in each file to Scorer.term
|
||||
for (j = 0; j < _files.length; j++) {
|
||||
file = _files[j];
|
||||
if (!(file in scoreMap))
|
||||
scoreMap[file] = {};
|
||||
scoreMap[file][word] = o.score;
|
||||
}
|
||||
});
|
||||
|
||||
// create the mapping
|
||||
for (j = 0; j < files.length; j++) {
|
||||
file = files[j];
|
||||
if (file in fileMap && fileMap[file].indexOf(word) === -1)
|
||||
fileMap[file].push(word);
|
||||
else
|
||||
fileMap[file] = [word];
|
||||
}
|
||||
}
|
||||
|
||||
// now check if the files don't contain excluded terms
|
||||
for (file in fileMap) {
|
||||
var valid = true;
|
||||
|
||||
// check if all requirements are matched
|
||||
var filteredTermCount = // as search terms with length < 3 are discarded: ignore
|
||||
searchterms.filter(function(term){return term.length > 2}).length
|
||||
if (
|
||||
fileMap[file].length != searchterms.length &&
|
||||
fileMap[file].length != filteredTermCount
|
||||
) continue;
|
||||
|
||||
// ensure that none of the excluded terms is in the search result
|
||||
for (i = 0; i < excluded.length; i++) {
|
||||
if (terms[excluded[i]] == file ||
|
||||
titleterms[excluded[i]] == file ||
|
||||
$u.contains(terms[excluded[i]] || [], file) ||
|
||||
$u.contains(titleterms[excluded[i]] || [], file)) {
|
||||
valid = false;
|
||||
break;
|
||||
}
|
||||
}
|
||||
|
||||
// if we have still a valid result we can add it to the result list
|
||||
if (valid) {
|
||||
// select one (max) score for the file.
|
||||
// for better ranking, we should calculate ranking by using words statistics like basic tf-idf...
|
||||
var score = $u.max($u.map(fileMap[file], function(w){return scoreMap[file][w]}));
|
||||
results.push([docnames[file], titles[file], '', null, score, filenames[file]]);
|
||||
}
|
||||
}
|
||||
return results;
|
||||
},
|
||||
|
||||
/**
|
||||
* helper function to return a node containing the
|
||||
* search summary for a given text. keywords is a list
|
||||
* of stemmed words, hlwords is the list of normal, unstemmed
|
||||
* words. the first one is used to find the occurrence, the
|
||||
* latter for highlighting it.
|
||||
*/
|
||||
makeSearchSummary : function(htmlText, keywords, hlwords) {
|
||||
var text = Search.htmlToText(htmlText);
|
||||
var textLower = text.toLowerCase();
|
||||
var start = 0;
|
||||
$.each(keywords, function() {
|
||||
var i = textLower.indexOf(this.toLowerCase());
|
||||
if (i > -1)
|
||||
start = i;
|
||||
});
|
||||
start = Math.max(start - 120, 0);
|
||||
var excerpt = ((start > 0) ? '...' : '') +
|
||||
$.trim(text.substr(start, 240)) +
|
||||
((start + 240 - text.length) ? '...' : '');
|
||||
var rv = $('<div class="context"></div>').text(excerpt);
|
||||
$.each(hlwords, function() {
|
||||
rv = rv.highlightText(this, 'highlighted');
|
||||
});
|
||||
return rv;
|
||||
}
|
||||
};
|
||||
|
||||
$(document).ready(function() {
|
||||
Search.init();
|
||||
});
|
||||
+18
@@ -0,0 +1,18 @@
|
||||
var initTriggerNavBar=()=>{if($(window).width()<768){$("#navbar-toggler").trigger("click")}}
|
||||
var scrollToActive=()=>{var navbar=document.getElementById('site-navigation')
|
||||
var active_pages=navbar.querySelectorAll(".active")
|
||||
var active_page=active_pages[active_pages.length-1]
|
||||
if(active_page!==undefined&&active_page.offsetTop>($(window).height()*.5)){navbar.scrollTop=active_page.offsetTop-($(window).height()*.2)}}
|
||||
var sbRunWhenDOMLoaded=cb=>{if(document.readyState!='loading'){cb()}else if(document.addEventListener){document.addEventListener('DOMContentLoaded',cb)}else{document.attachEvent('onreadystatechange',function(){if(document.readyState=='complete')cb()})}}
|
||||
function toggleFullScreen(){var navToggler=$("#navbar-toggler");if(!document.fullscreenElement){document.documentElement.requestFullscreen();if(!navToggler.hasClass("collapsed")){navToggler.click();}}else{if(document.exitFullscreen){document.exitFullscreen();if(navToggler.hasClass("collapsed")){navToggler.click();}}}}
|
||||
var initTooltips=()=>{$(document).ready(function(){$('[data-toggle="tooltip"]').tooltip();});}
|
||||
var initTocHide=()=>{var scrollTimeout;var throttle=200;var tocHeight=$("#bd-toc-nav").outerHeight(true)+$(".bd-toc").outerHeight(true);var hideTocAfter=tocHeight+200;var checkTocScroll=function(){var margin_content=$(".margin, .tag_margin, .full-width, .full_width, .tag_full-width, .tag_full_width, .sidebar, .tag_sidebar, .popout, .tag_popout");margin_content.each((index,item)=>{var topOffset=$(item).offset().top-$(window).scrollTop();var bottomOffset=topOffset+$(item).outerHeight(true);var topOverlaps=((topOffset>=0)&&(topOffset<hideTocAfter));var bottomOverlaps=((bottomOffset>=0)&&(bottomOffset<hideTocAfter));var removeToc=(topOverlaps||bottomOverlaps);if(removeToc&&window.pageYOffset>20){$("div.bd-toc").removeClass("show")
|
||||
return false}else{$("div.bd-toc").addClass("show")};})};var manageScrolledClassOnBody=function(){if(window.scrollY>0){document.body.classList.add("scrolled");}else{document.body.classList.remove("scrolled");}}
|
||||
$(window).on('scroll',function(){if(!scrollTimeout){scrollTimeout=setTimeout(function(){checkTocScroll();manageScrolledClassOnBody();scrollTimeout=null;},throttle);}});}
|
||||
var initThebeSBT=()=>{var title=$("div.section h1")[0]
|
||||
if(!$(title).next().hasClass("thebe-launch-button")){$("<button class='thebe-launch-button'></button>").insertAfter($(title))}
|
||||
initThebe();}
|
||||
sbRunWhenDOMLoaded(initTooltips)
|
||||
sbRunWhenDOMLoaded(initTriggerNavBar)
|
||||
sbRunWhenDOMLoaded(scrollToActive)
|
||||
sbRunWhenDOMLoaded(initTocHide)
|
||||
+5
File diff suppressed because one or more lines are too long
File diff suppressed because one or more lines are too long
@@ -0,0 +1,120 @@
|
||||
/* Thebelab Buttons */
|
||||
.thebelab-button {
|
||||
z-index: 999;
|
||||
display: inline-block;
|
||||
padding: 0.35em 1.2em;
|
||||
margin: 0px 1px;
|
||||
border-radius: 0.12em;
|
||||
box-sizing: border-box;
|
||||
text-decoration: none;
|
||||
font-family: 'Roboto', sans-serif;
|
||||
font-weight: 300;
|
||||
text-align: center;
|
||||
transition: all 0.2s;
|
||||
background-color: #dddddd;
|
||||
border: 0.05em solid white;
|
||||
color: #000000;
|
||||
}
|
||||
|
||||
.thebelab-button:hover{
|
||||
border: 0.05em solid black;
|
||||
background-color: #fcfcfc;
|
||||
}
|
||||
|
||||
.thebe-launch-button {
|
||||
height: 2.2em;
|
||||
font-size: .8em;
|
||||
border: 1px black solid;
|
||||
}
|
||||
|
||||
/* Thebelab Cell */
|
||||
.thebelab-cell pre {
|
||||
background: none;
|
||||
}
|
||||
|
||||
.thebelab-cell .thebelab-input {
|
||||
padding-left: 1em;
|
||||
margin-bottom: .5em;
|
||||
margin-top: .5em;
|
||||
}
|
||||
|
||||
.thebelab-cell .jp-OutputArea {
|
||||
margin-top: .5em;
|
||||
margin-left: 1em;
|
||||
}
|
||||
|
||||
button.thebelab-button.thebelab-run-button {
|
||||
margin-left: 1.5em;
|
||||
margin-bottom: .5em;
|
||||
}
|
||||
|
||||
/* Loading button */
|
||||
button.thebe-launch-button div.spinner {
|
||||
float: left;
|
||||
margin-right: 1em;
|
||||
}
|
||||
|
||||
/* Remove the spinner when thebelab is ready */
|
||||
.thebe-launch-button.thebe-status-ready .spinner {
|
||||
display: none;
|
||||
}
|
||||
|
||||
.thebe-launch-button span.status {
|
||||
font-family: monospace;
|
||||
font-weight: bold;
|
||||
}
|
||||
|
||||
.thebe-launch-button.thebe-status-ready span.status {
|
||||
color: green;
|
||||
}
|
||||
|
||||
.spinner {
|
||||
height: 2em;
|
||||
text-align: center;
|
||||
font-size: 0.7em;
|
||||
}
|
||||
|
||||
.spinner > div {
|
||||
background-color: #F37726;
|
||||
height: 100%;
|
||||
width: 6px;
|
||||
display: inline-block;
|
||||
|
||||
-webkit-animation: sk-stretchdelay 1.2s infinite ease-in-out;
|
||||
animation: sk-stretchdelay 1.2s infinite ease-in-out;
|
||||
}
|
||||
|
||||
.spinner .rect2 {
|
||||
-webkit-animation-delay: -1.1s;
|
||||
animation-delay: -1.1s;
|
||||
}
|
||||
|
||||
.spinner .rect3 {
|
||||
-webkit-animation-delay: -1.0s;
|
||||
animation-delay: -1.0s;
|
||||
}
|
||||
|
||||
.spinner .rect4 {
|
||||
-webkit-animation-delay: -0.9s;
|
||||
animation-delay: -0.9s;
|
||||
}
|
||||
|
||||
.spinner .rect5 {
|
||||
-webkit-animation-delay: -0.8s;
|
||||
animation-delay: -0.8s;
|
||||
}
|
||||
|
||||
@-webkit-keyframes sk-stretchdelay {
|
||||
0%, 40%, 100% { -webkit-transform: scaleY(0.4) }
|
||||
20% { -webkit-transform: scaleY(1.0) }
|
||||
}
|
||||
|
||||
@keyframes sk-stretchdelay {
|
||||
0%, 40%, 100% {
|
||||
transform: scaleY(0.4);
|
||||
-webkit-transform: scaleY(0.4);
|
||||
} 20% {
|
||||
transform: scaleY(1.0);
|
||||
-webkit-transform: scaleY(1.0);
|
||||
}
|
||||
}
|
||||
@@ -0,0 +1,96 @@
|
||||
/**
|
||||
* Add attributes to Thebe blocks to initialize thebe properly
|
||||
*/
|
||||
|
||||
var initThebe = () => {
|
||||
// If Thebelab hasn't loaded, wait a bit and try again. This
|
||||
// happens because we load ClipboardJS asynchronously.
|
||||
if (window.thebelab === undefined) {
|
||||
console.log("thebe not loaded, retrying...");
|
||||
setTimeout(initThebe, 500)
|
||||
return
|
||||
}
|
||||
|
||||
console.log("Adding thebe to code cells...");
|
||||
|
||||
// Load thebe config in case we want to update it as some point
|
||||
thebe_config = $('script[type="text/x-thebe-config"]')[0]
|
||||
|
||||
|
||||
// If we already detect a Thebe cell, don't re-run
|
||||
if (document.querySelectorAll('div.thebe-cell').length > 0) {
|
||||
return;
|
||||
}
|
||||
|
||||
// Update thebe buttons with loading message
|
||||
$(".thebe-launch-button").each((ii, button) => {
|
||||
button.innerHTML = `
|
||||
<div class="spinner">
|
||||
<div class="rect1"></div>
|
||||
<div class="rect2"></div>
|
||||
<div class="rect3"></div>
|
||||
<div class="rect4"></div>
|
||||
</div>
|
||||
<span class="loading-text"></span>`;
|
||||
})
|
||||
|
||||
// Set thebe event hooks
|
||||
var thebeStatus;
|
||||
thebelab.on("status", function (evt, data) {
|
||||
console.log("Status changed:", data.status, data.message);
|
||||
|
||||
$(".thebe-launch-button ")
|
||||
.removeClass("thebe-status-" + thebeStatus)
|
||||
.addClass("thebe-status-" + data.status)
|
||||
.find(".loading-text").html("<span class='launch_msg'>Launching from mybinder.org: </span><span class='status'>" + data.status + "</span>");
|
||||
|
||||
// Now update our thebe status
|
||||
thebeStatus = data.status;
|
||||
|
||||
// Find any cells with an initialization tag and ask thebe to run them when ready
|
||||
if (data.status === "ready") {
|
||||
var thebeInitCells = document.querySelectorAll('.thebe-init, .tag_thebe-init');
|
||||
thebeInitCells.forEach((cell) => {
|
||||
console.log("Initializing Thebe with cell: " + cell.id);
|
||||
cell.querySelector('.thebelab-run-button').click();
|
||||
});
|
||||
}
|
||||
});
|
||||
|
||||
|
||||
// Find all code cells, replace with Thebe interactive code cells
|
||||
const codeCells = document.querySelectorAll(thebe_selector)
|
||||
codeCells.forEach((codeCell, index) => {
|
||||
const codeCellId = index => `codecell${index}`;
|
||||
codeCell.id = codeCellId(index);
|
||||
codeCellText = codeCell.querySelector(thebe_selector_input);
|
||||
codeCellOutput = codeCell.querySelector(thebe_selector_output);
|
||||
|
||||
// Clean up the language to make it work w/ CodeMirror and add it to the cell
|
||||
dataLanguage = detectLanguage(kernelName);
|
||||
|
||||
if (codeCellText) {
|
||||
codeCellText.setAttribute('data-language', dataLanguage);
|
||||
codeCellText.setAttribute('data-executable', 'true');
|
||||
|
||||
// If we had an output, insert it just after the `pre` cell
|
||||
if (codeCellOutput) {
|
||||
$(codeCellOutput).attr("data-output", "");
|
||||
$(codeCellOutput).insertAfter(codeCellText);
|
||||
}
|
||||
}
|
||||
});
|
||||
|
||||
// Init thebe
|
||||
thebelab.bootstrap();
|
||||
}
|
||||
|
||||
// Helper function to munge the language name
|
||||
var detectLanguage = (language) => {
|
||||
if (language.indexOf('python') > -1) {
|
||||
language = "python";
|
||||
} else if (language === 'ir') {
|
||||
language = "r"
|
||||
}
|
||||
return language;
|
||||
}
|
||||
Some files were not shown because too many files have changed in this diff Show More
Reference in New Issue
Block a user