yt-3.3.3/0000755000076500000240000000000013023400161013057 5ustar goldbaumstaff00000000000000yt-3.3.3/CITATION0000644000076500000240000000365513017603554014243 0ustar goldbaumstaff00000000000000To cite yt in publications, please use: Turk, M. J., Smith, B. D., Oishi, J. S., et al. 2011, ApJS, 192, 9 In the body of the text, please add a footnote to the yt webpage: http://yt-project.org/ For LaTex and BibTex users: \bibitem[Turk et al.(2011)]{2011ApJS..192....9T} Turk, M.~J., Smith, B.~D., Oishi, J.~S., et al.\ 2011, The Astrophysical Journal Supplement Series, 192, 9 @ARTICLE{2011ApJS..192....9T, author = {{Turk}, M.~J. and {Smith}, B.~D. and {Oishi}, J.~S. and {Skory}, S. and {Skillman}, S.~W. and {Abel}, T. and {Norman}, M.~L.}, title = "{yt: A Multi-code Analysis Toolkit for Astrophysical Simulation Data}", journal = {The Astrophysical Journal Supplement Series}, archivePrefix = "arXiv", eprint = {1011.3514}, primaryClass = "astro-ph.IM", keywords = {cosmology: theory, methods: data analysis, methods: numerical}, year = 2011, month = jan, volume = 192, eid = {9}, pages = {9}, doi = {10.1088/0067-0049/192/1/9}, adsurl = {http://adsabs.harvard.edu/abs/2011ApJS..192....9T}, adsnote = {Provided by the SAO/NASA Astrophysics Data System} } Using yt can also utilize other functionality. If you utilize ORIGAMI, we ask that you please cite the ORIGAMI paper: @ARTICLE{2012ApJ...754..126F, author = {{Falck}, B.~L. and {Neyrinck}, M.~C. and {Szalay}, A.~S.}, title = "{ORIGAMI: Delineating Halos Using Phase-space Folds}", journal = {\apj}, archivePrefix = "arXiv", eprint = {1201.2353}, primaryClass = "astro-ph.CO", keywords = {dark matter, galaxies: halos, large-scale structure of universe, methods: numerical}, year = 2012, month = aug, volume = 754, eid = {126}, pages = {126}, doi = {10.1088/0004-637X/754/2/126}, adsurl = {http://adsabs.harvard.edu/abs/2012ApJ...754..126F}, adsnote = {Provided by the SAO/NASA Astrophysics Data System} } The main homepage for ORIGAMI can be found here: http://icg.port.ac.uk/~falckb/origami.html yt-3.3.3/CONTRIBUTING.rst0000644000076500000240000011721013023400062015522 0ustar goldbaumstaff00000000000000.. This document is rendered in HTML with cross-reference links filled in at http://yt-project.org/doc/developing/ .. _getting-involved: Getting Involved ================ There are *lots* of ways to get involved with yt, as a community and as a technical system -- not all of them just contributing code, but also participating in the community, helping us with designing the websites, adding documentation, and sharing your scripts with others. Coding is only one way to be involved! Communication Channels ---------------------- There are five main communication channels for yt: * We have an IRC channel, on ``irc.freenode.net`` in ``#yt``. You can connect through our web gateway without any special client, at http://yt-project.org/irc.html . *IRC is the first stop for conversation!* * Many yt developers participate in the yt Slack community. Slack is a free chat service that many teams use to organize their work. You can get an invite to yt's Slack organization by clicking the "Join us @ Slack" button on this page: http://yt-project.org/community.html * `yt-users `_ is a relatively high-traffic mailing list where people are encouraged to ask questions about the code, figure things out and so on. * `yt-dev `_ is a much lower-traffic mailing list designed to focus on discussions of improvements to the code, ideas about planning, development issues, and so on. * `yt-svn `_ is the (now-inaccurately titled) mailing list where all pushes to the primary repository are sent. The easiest way to get involved with yt is to read the mailing lists, hang out in IRC or slack chat, and participate. If someone asks a question you know the answer to (or have your own question about!) write back and answer it. If you have an idea about something, suggest it! We not only welcome participation, we encourage it. Documentation ------------- The yt documentation is constantly being updated, and it is a task we would very much appreciate assistance with. Whether that is adding a section, updating an outdated section, contributing typo or grammatical fixes, adding a FAQ, or increasing coverage of functionality, it would be very helpful if you wanted to help out. The easiest way to help out is to fork the main yt repository (where the documentation lives in the ``doc`` directory in the root of the yt mercurial repository) and then make your changes in your own fork. When you are done, issue a pull request through the website for your new fork, and we can comment back and forth and eventually accept your changes. See :ref:`sharing-changes` for more information about contributing your changes to yt on bitbucket. Gallery Images and Videos ------------------------- If you have an image or video you'd like to display in the image or video galleries, getting it included it easy! You can either fork the `yt homepage repository `_ and add it there, or email it to us and we'll add it to the `Gallery `_. We're eager to show off the images and movies you make with yt, so please feel free to drop `us `_ a line and let us know if you've got something great! Technical Contributions ----------------------- Contributing code is another excellent way to participate -- whether it's bug fixes, new features, analysis modules, or a new code frontend. See :ref:`creating_frontend` for more details. The process is pretty simple: fork on BitBucket, make changes, issue a pull request. We can then go back and forth with comments in the pull request, but usually we end up accepting. For more information, see :ref:`contributing-code`, where we spell out how to get up and running with a development environment, how to commit, and how to use BitBucket. Online Presence --------------- Some of these fall under the other items, but if you'd like to help out with the website or any of the other ways yt is presented online, please feel free! Almost everything is kept in hg repositories on BitBucket, and it is very easy to fork and contribute back changes. Please feel free to dig in and contribute changes. Word of Mouth ------------- If you're using yt and it has increased your productivity, please feel encouraged to share that information. Cite our `paper `_, tell your colleagues, and just spread word of mouth. By telling people about your successes, you'll help bring more eyes and hands to the table -- in this manner, by increasing participation, collaboration, and simply spreading the limits of what the code is asked to do, we hope to help scale the utility and capability of yt with the community size. Feel free to `blog `_ about, `tweet `_ about and talk about what you are up to! Long-Term Projects ------------------ There are some wild-eyed, out-there ideas that have been bandied about for the future directions of yt -- some of them even written into the mission statement. The ultimate goal is to move past simple analysis and visualization of data and begin to approach it from the other side, of generating data, running solvers. We also hope to increase its ability to act as an in situ analysis code, by presenting a unified protocol. Other projects include interfacing with ParaView and VisIt, creating a web GUI for running simulations, creating a run-tracker that follows simulations in progress, a federated database for simulation outputs, and so on and so forth. yt is an ambitious project. Let's be ambitious together. yt Community Code of Conduct ---------------------------- The community of participants in open source Scientific projects is made up of members from around the globe with a diverse set of skills, personalities, and experiences. It is through these differences that our community experiences success and continued growth. We expect everyone in our community to follow these guidelines when interacting with others both inside and outside of our community. Our goal is to keep ours a positive, inclusive, successful, and growing community. As members of the community, - We pledge to treat all people with respect and provide a harassment- and bullying-free environment, regardless of sex, sexual orientation and/or gender identity, disability, physical appearance, body size, race, nationality, ethnicity, and religion. In particular, sexual language and imagery, sexist, racist, or otherwise exclusionary jokes are not appropriate. - We pledge to respect the work of others by recognizing acknowledgment/citation requests of original authors. As authors, we pledge to be explicit about how we want our own work to be cited or acknowledged. - We pledge to welcome those interested in joining the community, and realize that including people with a variety of opinions and backgrounds will only serve to enrich our community. In particular, discussions relating to pros/cons of various technologies, programming languages, and so on are welcome, but these should be done with respect, taking proactive measure to ensure that all participants are heard and feel confident that they can freely express their opinions. - We pledge to welcome questions and answer them respectfully, paying particular attention to those new to the community. We pledge to provide respectful criticisms and feedback in forums, especially in discussion threads resulting from code contributions. - We pledge to be conscientious of the perceptions of the wider community and to respond to criticism respectfully. We will strive to model behaviors that encourage productive debate and disagreement, both within our community and where we are criticized. We will treat those outside our community with the same respect as people within our community. - We pledge to help the entire community follow the code of conduct, and to not remain silent when we see violations of the code of conduct. We will take action when members of our community violate this code such as contacting confidential@yt-project.org (all emails sent to this address will be treated with the strictest confidence) or talking privately with the person. This code of conduct applies to all community situations online and offline, including mailing lists, forums, social media, conferences, meetings, associated social events, and one-to-one interactions. The yt Community Code of Conduct was adapted from the `Astropy Community Code of Conduct `_, which was partially inspired by the PSF code of conduct. .. _contributing-code: How to Develop yt ================= yt is a community project! We are very happy to accept patches, features, and bugfixes from any member of the community! yt is developed using mercurial, primarily because it enables very easy and straightforward submission of changesets. We're eager to hear from you, and if you are developing yt, we encourage you to subscribe to the `developer mailing list `_. Please feel free to hack around, commit changes, and send them upstream. .. note:: If you already know how to use the `mercurial version control system `_ and are comfortable with handling it yourself, the quickest way to contribute to yt is to `fork us on BitBucket `_, make your changes, push the changes to your fork and issue a `pull request `_. The rest of this document is just an explanation of how to do that. See :ref:`code-style-guide` for more information about coding style in yt and :ref:`docstrings` for an example docstring. Please read them before hacking on the codebase, and feel free to email any of the mailing lists for help with the codebase. Keep in touch, and happy hacking! .. _open-issues: Open Issues ----------- If you're interested in participating in yt development, take a look at the `issue tracker on bitbucket `_. Issues are marked with a milestone of "easy", "moderate", or "difficult" depending on the estimated level of difficulty for fixing the issue. While we try to triage the issue tracker regularly, it may be the case that issues marked "moderate" are actually easier than their milestone label indicates since that is the default value. Here are some predefined issue searches that might be useful: * Unresolved issues `marked "easy" `_. * Unresolved issues `marked "easy" or "moderate" `_ * `All unresolved issues `_ Submitting Changes ------------------ We provide a brief introduction to submitting changes here. yt thrives on the strength of its communities (http://arxiv.org/abs/1301.7064 has further discussion) and we encourage contributions from any user. While we do not discuss version control, mercurial or the advanced usage of BitBucket in detail here, we do provide an outline of how to submit changes and we are happy to provide further assistance or guidance. Licensing +++++++++ yt is `licensed `_ under the BSD 3-clause license. Versions previous to yt-2.6 were released under the GPLv3. All contributed code must be BSD-compatible. If you'd rather not license in this manner, but still want to contribute, please consider creating an external package, which we'll happily link to. How To Get The Source Code For Editing ++++++++++++++++++++++++++++++++++++++ yt is hosted on BitBucket, and you can see all of the yt repositories at http://bitbucket.org/yt_analysis/. With the yt installation script you should have a copy of Mercurial for checking out pieces of code. Make sure you have followed the steps above for bootstrapping your development (to assure you have a bitbucket account, etc.) In order to modify the source code for yt, we ask that you make a "fork" of the main yt repository on bitbucket. A fork is simply an exact copy of the main repository (along with its history) that you will now own and can make modifications as you please. You can create a personal fork by visiting the yt bitbucket webpage at https://bitbucket.org/yt_analysis/yt/ . After logging in, you should see an option near the top right labeled "fork". Click this option, and then click the fork repository button on the subsequent page. You now have a forked copy of the yt repository for your own personal modification. This forked copy exists on the bitbucket repository, so in order to access it locally, follow the instructions at the top of that webpage for that forked repository, namely run at a local command line: .. code-block:: bash $ hg clone http://bitbucket.org// This downloads that new forked repository to your local machine, so that you can access it, read it, make modifications, etc. It will put the repository in a local directory of the same name as the repository in the current working directory. You should also run the following command, to make sure you are at the "yt" branch, and not other ones like "stable" (this will be important later when you want to submit your pull requests): .. code-block:: bash $ hg update yt You can see any past state of the code by using the hg log command. For example, the following command would show you the last 5 changesets (modifications to the code) that were submitted to that repository. .. code-block:: bash $ cd $ hg log -l 5 Using the revision specifier (the number or hash identifier next to each changeset), you can update the local repository to any past state of the code (a previous changeset or version) by executing the command: .. code-block:: bash $ hg up revision_specifier Lastly, if you want to use this new downloaded version of your yt repository as the *active* version of yt on your computer (i.e. the one which is executed when you run yt from the command line or the one that is loaded when you do ``import yt``), then you must "activate" it using the following commands from within the repository directory. .. code-block:: bash $ cd $ python2.7 setup.py develop This will rebuild all C modules as well. .. _reading-source: How To Read The Source Code +++++++++++++++++++++++++++ If you just want to *look* at the source code, you may already have it on your computer. If you build yt using the install script, the source is available at ``$YT_DEST/src/yt-hg``. See :ref:`source-installation` for more details about to obtain the yt source code if you did not build yt using the install script. The root directory of the yt mercurial repository contains a number of subdirectories with different components of the code. Most of the yt source code is contained in the yt subdirectory. This directory its self contains the following subdirectories: ``frontends`` This is where interfaces to codes are created. Within each subdirectory of yt/frontends/ there must exist the following files, even if empty: * ``data_structures.py``, where subclasses of AMRGridPatch, Dataset and AMRHierarchy are defined. * ``io.py``, where a subclass of IOHandler is defined. * ``fields.py``, where fields we expect to find in datasets are defined * ``misc.py``, where any miscellaneous functions or classes are defined. * ``definitions.py``, where any definitions specific to the frontend are defined. (i.e., header formats, etc.) ``fields`` This is where all of the derived fields that ship with yt are defined. ``geometry`` This is where geometric helpler routines are defined. Handlers for grid and oct data, as well as helpers for coordinate transformations can be found here. ``visualization`` This is where all visualization modules are stored. This includes plot collections, the volume rendering interface, and pixelization frontends. ``data_objects`` All objects that handle data, processed or unprocessed, not explicitly defined as visualization are located in here. This includes the base classes for data regions, covering grids, time series, and so on. This also includes derived fields and derived quantities. ``analysis_modules`` This is where all mechanisms for processing data live. This includes things like clump finding, halo profiling, halo finding, and so on. This is something of a catchall, but it serves as a level of greater abstraction that simply data selection and modification. ``gui`` This is where all GUI components go. Typically this will be some small tool used for one or two things, which contains a launching mechanism on the command line. ``utilities`` All broadly useful code that doesn't clearly fit in one of the other categories goes here. ``extern`` Bundled external modules (i.e. code that was not written by one of the yt authors but that yt depends on) lives here. If you're looking for a specific file or function in the yt source code, use the unix find command: .. code-block:: bash $ find -name '' The above command will find the FILENAME in any subdirectory in the DIRECTORY_TREE_TO_SEARCH. Alternatively, if you're looking for a function call or a keyword in an unknown file in a directory tree, try: .. code-block:: bash $ grep -R This can be very useful for tracking down functions in the yt source. .. _building-yt: Building yt +++++++++++ If you have made changes to any C or Cython (``.pyx``) modules, you have to rebuild yt. If your changes have exclusively been to Python modules, you will not need to re-build, but (see below) you may need to re-install. If you are running from a clone that is executable in-place (i.e., has been installed via the installation script or you have run ``setup.py develop``) you can rebuild these modules by executing: .. code-block:: bash $ python2.7 setup.py develop If you have previously "installed" via ``setup.py install`` you have to re-install: .. code-block:: bash $ python2.7 setup.py install Only one of these two options is needed. .. _windows-developing: Developing yt on Windows ------------------------ If you plan to develop yt on Windows, it is necessary to use the `MinGW `_ gcc compiler that can be installed using the `Anaconda Python Distribution `_. The libpython package must be installed from Anaconda as well. These can both be installed with a single command: .. code-block:: bash $ conda install libpython mingw Additionally, the syntax for the setup command is slightly different; you must type: .. code-block:: bash $ python2.7 setup.py build --compiler=mingw32 develop or .. code-block:: bash $ python2.7 setup.py build --compiler=mingw32 install .. _requirements-for-code-submission: Requirements for Code Submission -------------------------------- Modifications to the code typically fall into one of three categories, each of which have different requirements for acceptance into the code base. These requirements are in place for a few reasons -- to make sure that the code is maintainable, testable, and that we can easily include information about changes in changelogs during the release procedure. (See `YTEP-0008 `_ for more detail.) * New Features * New unit tests (possibly new answer tests) (See :ref:`testing`) * Docstrings in the source code for the public API * Addition of new feature to the narrative documentation (See :ref:`writing_documentation`) * Addition of cookbook recipe (See :ref:`writing_documentation`) * Issue created on issue tracker, to ensure this is added to the changelog * Extension or Breakage of API in Existing Features * Update existing narrative docs and docstrings (See :ref:`writing_documentation`) * Update existing cookbook recipes (See :ref:`writing_documentation`) * Modify of create new unit tests (See :ref:`testing`) * Issue created on issue tracker, to ensure this is added to the changelog * Bug fixes * Unit test is encouraged, to ensure breakage does not happen again in the future. (See :ref:`testing`) * Issue created on issue tracker, to ensure this is added to the changelog When submitting, you will be asked to make sure that your changes meet all of these requirements. They are pretty easy to meet, and we're also happy to help out with them. In :ref:`code-style-guide` there is a list of handy tips for how to structure and write your code. .. _mercurial-with-yt: How to Use Mercurial with yt ---------------------------- If you're new to Mercurial, these three resources are pretty great for learning the ins and outs: * http://hginit.com/ * http://hgbook.red-bean.com/read/ * http://mercurial-scm.org/ * http://mercurial-scm.org/wiki The commands that are essential for using mercurial include: * ``hg help`` which provides help for any mercurial command. For example, you can learn more about the ``log`` command by doing ``hg help log``. Other useful topics to use with ``hg help`` are ``hg help glossary``, ``hg help config``, ``hg help extensions``, and ``hg help revsets``. * ``hg commit`` which commits changes in the working directory to the repository, creating a new "changeset object." * ``hg add`` which adds a new file to be tracked by mercurial. This does not change the working directory. * ``hg pull`` which pulls (from an optional path specifier) changeset objects from a remote source. The working directory is not modified. * ``hg push`` which sends (to an optional path specifier) changeset objects to a remote source. The working directory is not modified. * ``hg log`` which shows a log of all changeset objects in the current repository. Use ``-G`` to show a graph of changeset objects and their relationship. * ``hg update`` which (with an optional "revision" specifier) updates the state of the working directory to match a changeset object in the repository. * ``hg merge`` which combines two changesets to make a union of their lines of development. This updates the working directory. We are happy to asnswers questions about mercurial use on our IRC, slack chat or on the mailing list to walk you through any troubles you might have. Here are some general suggestions for using mercurial with yt: * Named branches are to be avoided. Try using bookmarks (``see hg help bookmark``) to track work. (`More info about bookmarks is available on the mercurial wiki `_) * Make sure you set a username in your ``~/.hgrc`` before you commit any changes! All of the tutorials above will describe how to do this as one of the very first steps. * When contributing changes, you might be asked to make a handful of modifications to your source code. We'll work through how to do this with you, and try to make it as painless as possible. * Your test may fail automated style checks. See :ref:`code-style-guide` for more information about automatically verifying your code style. * Please avoid deleting your yt forks, as that deletes the pull request discussion from process from BitBucket's website, even if your pull request is merged. * You should only need one fork. To keep it in sync, you can sync from the website. See Bitbucket's `Blog Post `_ about this. See :ref:`sharing-changes` for a description of the basic workflow and :ref:`multiple-PRs` for a discussion about what to do when you want to have multiple open pull requests at the same time. * If you run into any troubles, stop by IRC (see :ref:`irc`) or the mailing list. .. _sharing-changes: Making and Sharing Changes -------------------------- The simplest way to submit changes to yt is to do the following: * Build yt from the mercurial repository * Navigate to the root of the yt repository * Make some changes and commit them * Fork the `yt repository on BitBucket `_ * Push the changesets to your fork * Issue a pull request. Here's a more detailed flowchart of how to submit changes. #. If you have used the installation script, the source code for yt can be found in ``$YT_DEST/src/yt-hg``. Alternatively see :ref:`source-installation` for instructions on how to build yt from the mercurial repository. (Below, in :ref:`reading-source`, we describe how to find items of interest.) #. Edit the source file you are interested in and test your changes. (See :ref:`testing` for more information.) #. Fork yt on BitBucket. (This step only has to be done once.) You can do this at: https://bitbucket.org/yt_analysis/yt/fork. Call this repository yt. #. Create a bookmark to track your work. For example: ``hg bookmark my-first-pull-request`` #. Commit these changes, using ``hg commit``. This can take an argument which is a series of filenames, if you have some changes you do not want to commit. #. Remember that this is a large development effort and to keep the code accessible to everyone, good documentation is a must. Add in source code comments for what you are doing. Add in docstrings if you are adding a new function or class or keyword to a function. Add documentation to the appropriate section of the online docs so that people other than yourself know how to use your new code. #. If your changes include new functionality or cover an untested area of the code, add a test. (See :ref:`testing` for more information.) Commit these changes as well. #. Push your changes to your new fork using the command:: hg push -B my-first-pull-request https://bitbucket.org/YourUsername/yt/ Where you should substitute the name of the bookmark you are working on for ``my-first-pull-request``. If you end up doing considerable development, you can set an alias in the file ``.hg/hgrc`` to point to this path. .. note:: Note that the above approach uses HTTPS as the transfer protocol between your machine and BitBucket. If you prefer to use SSH - or perhaps you're behind a proxy that doesn't play well with SSL via HTTPS - you may want to set up an `SSH key`_ on BitBucket. Then, you use the syntax ``ssh://hg@bitbucket.org/YourUsername/yt``, or equivalent, in place of ``https://bitbucket.org/YourUsername/yt`` in Mercurial commands. For consistency, all commands we list in this document will use the HTTPS protocol. .. _SSH key: https://confluence.atlassian.com/display/BITBUCKET/Set+up+SSH+for+Mercurial #. Issue a pull request at https://bitbucket.org/YourUsername/yt/pull-request/new A pull request is essentially just asking people to review and accept the modifications you have made to your personal version of the code. During the course of your pull request you may be asked to make changes. These changes may be related to style issues, correctness issues, or even requesting tests. The process for responding to pull request code review is relatively straightforward. #. Make requested changes, or leave a comment indicating why you don't think they should be made. #. Commit those changes to your local repository. #. Push the changes to your fork: hg push https://bitbucket.org/YourUsername/yt/ #. Your pull request will be automatically updated. .. _multiple-PRs: Working with Multiple BitBucket Pull Requests --------------------------------------------- Once you become active developing for yt, you may be working on various aspects of the code or bugfixes at the same time. Currently, BitBucket's *modus operandi* for pull requests automatically updates your active pull request with every ``hg push`` of commits that are a descendant of the head of your pull request. In a normal workflow, this means that if you have an active pull request, make some changes locally for, say, an unrelated bugfix, then push those changes back to your fork in the hopes of creating a *new* pull request, you'll actually end up updating your current pull request! There are a few ways around this feature of BitBucket that will allow for multiple pull requests to coexist; we outline one such method below. We assume that you have a fork of yt at ``http://bitbucket.org/YourUsername/Your_yt`` (see :ref:`sharing-changes` for instructions on creating a fork) and that you have an active pull request to the main repository. The main issue with starting another pull request is to make sure that your push to BitBucket doesn't go to the same head as your existing pull request and trigger BitBucket's auto-update feature. Here's how to get your local repository away from your current pull request head using `revsets `_ and your ``hgrc`` file: #. Set up a Mercurial path for the main yt repository (note this is a convenience step and only needs to be done once). Add the following to your ``Your_yt/.hg/hgrc``:: [paths] upstream = https://bitbucket.org/yt_analysis/yt This will create a path called ``upstream`` that is aliased to the URL of the main yt repository. #. Now we'll use revsets_ to update your local repository to the tip of the ``upstream`` path: .. code-block:: bash $ hg pull upstream $ hg update -r "remote(yt, 'upstream')" After the above steps, your local repository should be at the current head of the ``yt`` branch in the main yt repository. If you find yourself doing this a lot, it may be worth aliasing this task in your ``hgrc`` file by adding something like:: [alias] ytupdate = update -r "remote(yt, 'upstream')" And then you can just issue ``hg ytupdate`` to get at the current head of the ``yt`` branch on main yt repository. Make sure you are on the branch you want to be on, and then you can make changes and ``hg commit`` them. If you prefer working with `bookmarks `_, you may want to make a bookmark before committing your changes, such as ``hg bookmark mybookmark``. To push your changes on a bookmark to bitbucket, you can issue the following command: .. code-block:: bash $ hg push -B myfeature https://bitbucket.org/YourUsername/Your_yt The ``-B`` means "publish my bookmark, the changeset the bookmark is pointing at, and any ancestors of that changeset that aren't already on the remote server". To push to your fork on BitBucket if you didn't use a bookmark, you issue the following: .. code-block:: bash $ hg push -r . -f https://bitbucket.org/YourUsername/Your_yt The ``-r .`` means "push only the commit I'm standing on and any ancestors." The ``-f`` is to force Mecurial to do the push since we are creating a new remote head without a bookmark. You can then go to the BitBucket interface and issue a new pull request based on your last changes, as usual. .. _code-style-guide: Coding Style Guide ================== Automatically checking code style --------------------------------- Below are a list of rules for coding style in yt. Some of these rules are suggestions are not explicitly enforced, while some are enforced via automated testing. The yt project uses a subset of the rules checked by ``flake8`` to verify our code. The ``flake8`` tool is a combination of the ``pyflakes`` and ``pep8`` tools. To check the coding style of your contributions locally you will need to install the ``flake8`` tool from ``pip``: .. code-block:: bash $ pip install flake8 And then navigate to the root of the yt repository and run ``flake8`` on the ``yt`` folder: .. code-block:: bash $ cd $YT_HG $ flake8 ./yt This will print out any ``flake8`` errors or warnings that your newly added code triggers. The errors will be in your newly added code because we have already cleaned up the rest of the yt codebase of the errors and warnings detected by the `flake8` tool. Note that this will only trigger a subset of the `full flake8 error and warning list `_, since we explicitly blacklist a large number of the full list of rules that are checked by ``flake8`` by default. Source code style guide ----------------------- * In general, follow PEP-8 guidelines. http://www.python.org/dev/peps/pep-0008/ * Classes are ``ConjoinedCapitals``, methods and functions are ``lowercase_with_underscores``. * Use 4 spaces, not tabs, to represent indentation. * Line widths should not be more than 80 characters. * Do not use nested classes unless you have a very good reason to, such as requiring a namespace or class-definition modification. Classes should live at the top level. ``__metaclass__`` is exempt from this. * Do not use unnecessary parenthesis in conditionals. ``if((something) and (something_else))`` should be rewritten as ``if something and something_else``. Python is more forgiving than C. * Avoid copying memory when possible. For example, don't do ``a = a.reshape(3,4)`` when ``a.shape = (3,4)`` will do, and ``a = a * 3`` should be ``np.multiply(a, 3, a)``. * In general, avoid all double-underscore method names: ``__something`` is usually unnecessary. * When writing a subclass, use the super built-in to access the super class, rather than explicitly. Ex: ``super(SpecialGridSubclass, self).__init__()`` rather than ``SpecialGrid.__init__()``. * Docstrings should describe input, output, behavior, and any state changes that occur on an object. See :ref:`docstrings` below for a fiducial example of a docstring. * Use only one top-level import per line. Unless there is a good reason not to, imports should happen at the top of the file, after the copyright blurb. * Never compare with ``True`` or ``False`` using ``==`` or ``!=``, always use ``is`` or ``is not``. * If you are comparing with a numpy boolean array, just refer to the array. Ex: do ``np.all(array)`` instead of ``np.all(array == True)``. * Never comapre with None using ``==`` or ``!=``, use ``is None`` or ``is not None``. * Use ``statement is not True`` instead of ``not statement is True`` * Only one statement per line, do not use semicolons to put two or more statements on a single line. * Only declare local variables if they will be used later. If you do not use the return value of a function, do not store it in a variable. * Add tests for new functionality. When fixing a bug, consider adding a test to prevent the bug from recurring. API Style Guide --------------- * Do not use ``from some_module import *`` * Internally, only import from source files directly -- instead of: ``from yt.visualization.api import ProjectionPlot`` do: ``from yt.visualization.plot_window import ProjectionPlot`` * Import symbols from the module where they are defined, avoid transitive imports. * Import standard library modules, functions, and classes from builtins, do not import them from other yt files. * Numpy is to be imported as ``np``. * Do not use too many keyword arguments. If you have a lot of keyword arguments, then you are doing too much in ``__init__`` and not enough via parameter setting. * In function arguments, place spaces before commas. ``def something(a,b,c)`` should be ``def something(a, b, c)``. * Don't create a new class to replicate the functionality of an old class -- replace the old class. Too many options makes for a confusing user experience. * Parameter files external to yt are a last resort. * The usage of the ``**kwargs`` construction should be avoided. If they cannot be avoided, they must be explained, even if they are only to be passed on to a nested function. .. _docstrings: Docstrings ---------- The following is an example docstring. You can use it as a template for docstrings in your code and as a guide for how we expect docstrings to look and the level of detail we are looking for. Note that we use NumPy style docstrings written in `Sphinx restructured text format `_. .. code-block:: rest r"""A one-line summary that does not use variable names or the function name. Several sentences providing an extended description. Refer to variables using back-ticks, e.g. ``var``. Parameters ---------- var1 : array_like Array_like means all those objects -- lists, nested lists, etc. -- that can be converted to an array. We can also refer to variables like ``var1``. var2 : int The type above can either refer to an actual Python type (e.g. ``int``), or describe the type of the variable in more detail, e.g. ``(N,) ndarray`` or ``array_like``. Long_variable_name : {'hi', 'ho'}, optional Choices in brackets, default first when optional. Returns ------- describe : type Explanation output : type Explanation tuple : type Explanation items : type even more explaining Other Parameters ---------------- only_seldom_used_keywords : type Explanation common_parameters_listed_above : type Explanation Raises ------ BadException Because you shouldn't have done that. See Also -------- otherfunc : relationship (optional) newfunc : Relationship (optional), which could be fairly long, in which case the line wraps here. thirdfunc, fourthfunc, fifthfunc Notes ----- Notes about the implementation algorithm (if needed). This can have multiple paragraphs. You may include some math: .. math:: X(e^{j\omega } ) = x(n)e^{ - j\omega n} And even use a greek symbol like :math:`omega` inline. References ---------- Cite the relevant literature, e.g. [1]_. You may also cite these references in the notes section above. .. [1] O. McNoleg, "The integration of GIS, remote sensing, expert systems and adaptive co-kriging for environmental habitat modelling of the Highland Haggis using object-oriented, fuzzy-logic and neural-network techniques," Computers & Geosciences, vol. 22, pp. 585-588, 1996. Examples -------- These are written in doctest format, and should illustrate how to use the function. Use the variables 'ds' for the dataset, 'pc' for a plot collection, 'c' for a center, and 'L' for a vector. >>> a=[1,2,3] >>> print [x + 3 for x in a] [4, 5, 6] >>> print "a\n\nb" a b """ Variable Names and Enzo-isms ---------------------------- Avoid Enzo-isms. This includes but is not limited to: * Hard-coding parameter names that are the same as those in Enzo. The following translation table should be of some help. Note that the parameters are now properties on a ``Dataset`` subclass: you access them like ds.refine_by . - ``RefineBy `` => `` refine_by`` - ``TopGridRank `` => `` dimensionality`` - ``TopGridDimensions `` => `` domain_dimensions`` - ``InitialTime `` => `` current_time`` - ``DomainLeftEdge `` => `` domain_left_edge`` - ``DomainRightEdge `` => `` domain_right_edge`` - ``CurrentTimeIdentifier `` => `` unique_identifier`` - ``CosmologyCurrentRedshift `` => `` current_redshift`` - ``ComovingCoordinates `` => `` cosmological_simulation`` - ``CosmologyOmegaMatterNow `` => `` omega_matter`` - ``CosmologyOmegaLambdaNow `` => `` omega_lambda`` - ``CosmologyHubbleConstantNow `` => `` hubble_constant`` * Do not assume that the domain runs from 0 .. 1. This is not true everywhere. * Variable names should be short but descriptive. * No globals! yt-3.3.3/COPYING.txt0000644000076500000240000000743713003701526014752 0ustar goldbaumstaff00000000000000=============================== The yt project licensing terms =============================== yt is licensed under the terms of the Modified BSD License (also known as New or Revised BSD), as follows: Copyright (c) 2013-, yt Development Team Copyright (c) 2006-2013, Matthew Turk All rights reserved. Redistribution and use in source and binary forms, with or without modification, are permitted provided that the following conditions are met: Redistributions of source code must retain the above copyright notice, this list of conditions and the following disclaimer. Redistributions in binary form must reproduce the above copyright notice, this list of conditions and the following disclaimer in the documentation and/or other materials provided with the distribution. Neither the name of the yt Development Team nor the names of its contributors may be used to endorse or promote products derived from this software without specific prior written permission. THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT OWNER OR CONTRIBUTORS BE LIABLE FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. About the yt Development Team ----------------------------- Matthew Turk began yt in 2006 and remains the project lead. Over time yt has grown to include contributions from a large number of individuals from many diverse institutions, scientific, and technical backgrounds. Until the fall of 2013, yt was licensed under the GPLv3. However, with consent from all developers and on a public mailing list, yt has been relicensed under the BSD 3-clause under a shared copyright model. For more information, see: http://lists.spacepope.org/pipermail/yt-dev-spacepope.org/2013-July/003239.html All versions of yt prior to this licensing change are available under the GPLv3; all subsequent versions are available under the BSD 3-clause license. The yt Development Team is the set of all contributors to the yt project. This includes all of the yt subprojects. The core team that coordinates development on BitBucket can be found here: http://bitbucket.org/yt_analysis/ Our Copyright Policy -------------------- yt uses a shared copyright model. Each contributor maintains copyright over their contributions to yt. But, it is important to note that these contributions are typically only changes to the repositories. Thus, the yt source code, in its entirety is not the copyright of any single person or institution. Instead, it is the collective copyright of the entire yt Development Team. If individual contributors want to maintain a record of what changes/contributions they have specific copyright on, they should indicate their copyright in the commit message of the change, when they commit the change to one of the yt repositories. With this in mind, the following banner should be used in any source code file to indicate the copyright and license terms: #----------------------------------------------------------------------------- # Copyright (c) 2013, yt Development Team. # # Distributed under the terms of the Modified BSD License. # # The full license is in the file COPYING.txt, distributed with this software. #----------------------------------------------------------------------------- yt-3.3.3/CREDITS0000644000076500000240000001260213023400062014100 0ustar goldbaumstaff00000000000000yt is a group effort. Contributors: Tom Abel (tabel@stanford.edu) Gabriel Altay (gabriel.altay@gmail.com) Kenza Arraki (karraki@gmail.com) Kirk Barrow (kssbarrow@gatech.edu) Ricarda Beckmann (Ricarda.Beckmann@astro.ox.ac.uk) Elliott Biondo (biondo@wisc.edu) Alex Bogert (fbogert@ucsc.edu) Robert Bradshaw (robertwb@gmail.com) André-Patrick Bubel (code@andre-bubel.de) Corentin Cadiou (corentin.cadiou@iap.fr) Pengfei Chen (madcpf@gmail.com) Yi-Hao Chen (yihaochentw@gmail.com) David Collins (dcollins4096@gmail.com) Brian Crosby (crosby.bd@gmail.com) Weiguang Cui (weiguang.cui@uwa.edu.au) Andrew Cunningham (ajcunn@gmail.com) Miguel de Val-Borro (miguel.deval@gmail.com) Bili Dong (qobilidop@gmail.com) Nicholas Earl (nchlsearl@gmail.com) Hilary Egan (hilaryye@gmail.com) Daniel Fenn (df11c@my.fsu.edu) John Forces (jforbes@ucolick.org) Adam Ginsburg (keflavich@gmail.com) Sam Geen (samgeen@gmail.com) Nathan Goldbaum (goldbaum@ucolick.org) William Gray (graywilliamj@gmail.com) Markus Haider (markus.haider@uibk.ac.at) Eric Hallman (hallman13@gmail.com) David Hannasch (David.A.Hannasch@gmail.com) Axel Huebl (a.huebl@hzdr.de) Cameron Hummels (chummels@gmail.com) Anni Järvenpää (anni.jarvenpaa@gmail.com) Allyson Julian (astrohckr@gmail.com) Christian Karch (chiffre@posteo.de) Maximilian Katz (maximilian.katz@stonybrook.edu) Ben W. Keller (kellerbw@mcmaster.ca) Chang-Goo Kim (changgoo@princeton.edu) Ji-hoon Kim (me@jihoonkim.org) Steffen Klemer (sklemer@phys.uni-goettingen.de) Fabian Holler (anokfireball@poseto.de) Kacper Kowalik (xarthisius.kk@gmail.com) Mark Krumholz (mkrumhol@ucsc.edu) Michael Kuhlen (mqk@astro.berkeley.edu) Meagan Lang (langmm.astro@gmail.com) Erwin Tin-Hay Lau (ethlau@gmail.com) Doris Lee (dorislee@berkeley.edu) Eve Lee (elee@cita.utoronto.ca) Sam Leitner (sam.leitner@gmail.com) Stuart Levy (salevy@illinois.edu) Yuan Li (yuan@astro.columbia.edu) Chris Malone (chris.m.malone@gmail.com) Josh Maloney (joshua.moloney@colorado.edu) Jonah Miller (jonah.maxwell.miller@gmail.com) Chris Moody (cemoody@ucsc.edu) Stuart Mumford (stuart@mumford.me.uk) Andrew Myers (atmyers@astro.berkeley.edu) Jill Naiman (jnaiman@ucolick.org) Desika Narayanan (dnarayan@haverford.edu) Kaylea Nelson (kaylea.nelson@yale.edu) Jeff Oishi (jsoishi@gmail.com) Brian O'Shea (bwoshea@gmail.com) Jean-Claude Passy (jcpassy@uvic.ca) John Regan (john.regan@helsinki.fi) Sherwood Richers (srichers@tapir.caltech.edu) Mark Richardson (Mark.L.Richardson@asu.edu) Thomas Robitaille (thomas.robitaille@gmail.com) Anna Rosen (rosen@ucolick.org) Chuck Rozhon (rozhon2@illinois.edu) Douglas Rudd (drudd@uchicago.edu) Rafael Ruggiero (rafael.ruggiero@usp.br) Hsi-Yu Schive (hyschive@gmail.com) Anthony Scopatz (scopatz@gmail.com) Noel Scudder (noel.scudder@stonybrook.edu) Pat Shriwise (shriwise@wisc.edu) Devin Silvia (devin.silvia@gmail.com) Sam Skillman (samskillman@gmail.com) Stephen Skory (s@skory.us) Aaron Smith (asmith@astro.as.utexas.edu) Britton Smith (brittonsmith@gmail.com) Geoffrey So (gsiisg@gmail.com) Casey Stark (caseywstark@gmail.com) Antoine Strugarek (strugarek@astro.umontreal.ca) Ji Suoqing (jisuoqing@gmail.com) Elizabeth Tasker (tasker@astro1.sci.hokudai.ac.jp) Benjamin Thompson (bthompson2090@gmail.com) Robert Thompson (rthompsonj@gmail.com) Joseph Tomlinson (jmtomlinson95@gmail.com) Stephanie Tonnesen (stonnes@gmail.com) Matthew Turk (matthewturk@gmail.com) Rich Wagner (rwagner@physics.ucsd.edu) Michael S. Warren (mswarren@gmail.com) Andrew Wetzel (andrew.wetzel@yale.edu) John Wise (jwise@physics.gatech.edu) Michael Zingale (michael.zingale@stonybrook.edu) John ZuHone (jzuhone@gmail.com) Several items included in the yt/extern directory were written by other individuals and may bear their own license, including the progressbar module by Nilton Volpato. The PasteBin interface code (as well as the PasteBin itself) was written by the Pocoo collective (pocoo.org). developed by Oliver Hahn. Thanks to everyone for all your contributions! yt-3.3.3/doc/0000755000076500000240000000000013023400160013623 5ustar goldbaumstaff00000000000000yt-3.3.3/doc/activate0000644000076500000240000000602212770647750015400 0ustar goldbaumstaff00000000000000### Adapted from virtualenv's activate script # This file must be used with "source bin/activate" *from bash* # you cannot run it directly deactivate () { # reset old environment variables if [ -n "$_OLD_VIRTUAL_PATH" ] ; then PATH="$_OLD_VIRTUAL_PATH" export PATH unset _OLD_VIRTUAL_PATH fi if [ -n "$_OLD_VIRTUAL_PYTHONHOME" ] ; then PYTHONHOME="$_OLD_VIRTUAL_PYTHONHOME" export PYTHONHOME unset _OLD_VIRTUAL_PYTHONHOME fi ### Begin extra yt vars if [ -n "$_OLD_VIRTUAL_YT_DEST" ] ; then YT_DEST="$_OLD_VIRTUAL_YT_DEST" export YT_DEST unset _OLD_VIRTUAL_PYTHONHOME fi if [ -n "$_OLD_VIRTUAL_PYTHONPATH" ] ; then PYTHONPATH="$_OLD_VIRTUAL_PYTHONPATH" export PYTHONPATH unset _OLD_VIRTUAL_PYTHONPATH fi if [ -n "$_OLD_VIRTUAL_LD_LIBRARY_PATH" ] ; then LD_LIBRARY_PATH="$_OLD_VIRTUAL_LD_LIBRARY_PATH" export LD_LIBRARY_PATH unset _OLD_VIRTUAL_LD_LIBRARY_PATH fi ### End extra yt vars # This should detect bash and zsh, which have a hash command that must # be called to get it to forget past commands. Without forgetting # past commands the $PATH changes we made may not be respected if [ -n "$BASH" -o -n "$ZSH_VERSION" ] ; then hash -r fi if [ -n "$_OLD_VIRTUAL_PS1" ] ; then PS1="$_OLD_VIRTUAL_PS1" export PS1 unset _OLD_VIRTUAL_PS1 fi unset VIRTUAL_ENV if [ ! "$1" = "nondestructive" ] ; then # Self destruct! unset -f deactivate fi } # unset irrelavent variables deactivate nondestructive VIRTUAL_ENV="__YT_DIR__" export VIRTUAL_ENV _OLD_VIRTUAL_PATH="$PATH" PATH="$VIRTUAL_ENV/bin:$PATH" export PATH ### Begin extra env vars for yt _OLD_VIRTUAL_YT_DEST="$YT_DEST" YT_DEST="$VIRTUAL_ENV" export YT_DEST _OLD_VIRTUAL_PYTHONPATH="$PYTHONPATH" PYTHONPATH="$VIRTUAL_ENV/lib/python2.7/site-packages" export PYTHONPATH _OLD_VIRTUAL_LD_LIBRARY_PATH="$LD_LIBRARY_PATH" LD_LIBRARY_PATH="$VIRTUAL_ENV/lib:$LD_LIBRARY_PATH" export LD_LIBRARY_PATH ### End extra env vars for yt # unset PYTHONHOME if set # this will fail if PYTHONHOME is set to the empty string (which is bad anyway) # could use `if (set -u; : $PYTHONHOME) ;` in bash if [ -n "$PYTHONHOME" ] ; then _OLD_VIRTUAL_PYTHONHOME="$PYTHONHOME" unset PYTHONHOME fi if [ -z "$VIRTUAL_ENV_DISABLE_PROMPT" ] ; then _OLD_VIRTUAL_PS1="$PS1" if [ "x" != x ] ; then PS1="$PS1" else if [ "`basename \"$VIRTUAL_ENV\"`" = "__" ] ; then # special case for Aspen magic directories # see http://www.zetadev.com/software/aspen/ PS1="[`basename \`dirname \"$VIRTUAL_ENV\"\``] $PS1" else PS1="(`basename \"$VIRTUAL_ENV\"`)$PS1" fi fi export PS1 fi # This should detect bash and zsh, which have a hash command that must # be called to get it to forget past commands. Without forgetting # past commands the $PATH changes we made may not be respected if [ -n "$BASH" -o -n "$ZSH_VERSION" ] ; then hash -r fi yt-3.3.3/doc/activate.csh0000644000076500000240000000365512770647750016165 0ustar goldbaumstaff00000000000000# This file must be used with "source bin/activate.csh" *from csh*. # You cannot run it directly. # Created by Davide Di Blasi . alias deactivate 'test $?_OLD_VIRTUAL_PATH != 0 && setenv PATH "$_OLD_VIRTUAL_PATH" && unset _OLD_VIRTUAL_PATH; test $?_OLD_VIRTUAL_YT_DEST != 0 && setenv YT_DEST "$_OLD_VIRTUAL_YT_DEST" && unset _OLD_VIRTUAL_YT_DEST; test $?_OLD_VIRTUAL_PYTHONPATH != 0 && setenv PYTHONPATH "$_OLD_VIRTUAL_PYTHONPATH" && unset _OLD_VIRTUAL_PYTHONPATH; test $?_OLD_VIRTUAL_LD_LIBRARY_PATH != 0 && setenv LD_LIBRARY_PATH "$_OLD_VIRTUAL_LD_LIBRARY_PATH" && unset _OLD_VIRTUAL_LD_LIBRARY_PATH; rehash; test $?_OLD_VIRTUAL_PROMPT != 0 && set prompt="$_OLD_VIRTUAL_PROMPT" && unset _OLD_VIRTUAL_PROMPT; unsetenv VIRTUAL_ENV; test "\!:*" != "nondestructive" && unalias deactivate' # Unset irrelavent variables. deactivate nondestructive setenv VIRTUAL_ENV "__YT_DIR__" if ($?PATH == 0) then setenv PATH endif set _OLD_VIRTUAL_PATH="$PATH" setenv PATH "${VIRTUAL_ENV}/bin:${PATH}" ### Begin extra yt vars if ($?YT_DEST == 0) then setenv YT_DEST endif set _OLD_VIRTUAL_YT_DEST="$YT_DEST" setenv YT_DEST "${VIRTUAL_ENV}" if ($?PYTHONPATH == 0) then setenv PYTHONPATH endif set _OLD_VIRTUAL_PYTHONPATH="$PYTHONPATH" setenv PYTHONPATH "${VIRTUAL_ENV}/lib/python2.7/site-packages:${PYTHONPATH}" if ($?LD_LIBRARY_PATH == 0) then setenv LD_LIBRARY_PATH endif set _OLD_VIRTUAL_LD_LIBRARY_PATH="$LD_LIBRARY_PATH" setenv LD_LIBRARY_PATH "${VIRTUAL_ENV}/lib:${LD_LIBRARY_PATH}" ### End extra yt vars set _OLD_VIRTUAL_PROMPT="$prompt" if ("" != "") then set env_name = "" else if (`basename "$VIRTUAL_ENV"` == "__") then # special case for Aspen magic directories # see http://www.zetadev.com/software/aspen/ set env_name = `basename \`dirname "$VIRTUAL_ENV"\`` else set env_name = `basename "$VIRTUAL_ENV"` endif endif set prompt = "[$env_name] $prompt" unset env_name rehash yt-3.3.3/doc/cheatsheet.tex0000644000076500000240000004722513017603554016513 0ustar goldbaumstaff00000000000000\documentclass[10pt,landscape]{article} \usepackage{multicol} \usepackage{calc} \usepackage{ifthen} \usepackage[landscape]{geometry} \usepackage[hyphens]{url} % To make this come out properly in landscape mode, do one of the following % 1. % pdflatex cheatsheet.tex % % 2. % latex cheatsheet.tex % dvips -P pdf -t landscape cheatsheet.dvi % ps2pdf cheatsheet.ps % If you're reading this, be prepared for confusion. Making this was % a learning experience for me, and it shows. Much of the placement % was hacked in; if you make it better, let me know... % 2008-04 % Changed page margin code to use the geometry package. Also added code for % conditional page margins, depending on paper size. Thanks to Uwe Ziegenhagen % for the suggestions. % 2006-08 % Made changes based on suggestions from Gene Cooperman. % 2012-11 - Stephen Skory % Converted the latex cheat sheet to a yt cheat sheet, taken from % http://www.stdout.org/~winston/latex/ % This sets page margins to .5 inch if using letter paper, and to 1cm % if using A4 paper. (This probably isn't strictly necessary.) % If using another size paper, use default 1cm margins. \ifthenelse{\lengthtest { \paperwidth = 11in}} { \geometry{top=.5in,left=.5in,right=.5in,bottom=0.85in} } {\ifthenelse{ \lengthtest{ \paperwidth = 297mm}} {\geometry{top=1cm,left=1cm,right=1cm,bottom=1cm} } {\geometry{top=1cm,left=1cm,right=1cm,bottom=1cm} } } % Turn off header and footer \pagestyle{empty} % Redefine section commands to use less space \makeatletter \renewcommand{\section}{\@startsection{section}{1}{0mm}% {-1ex plus -.5ex minus -.2ex}% {0.5ex plus .2ex}%x {\normalfont\large\bfseries}} \renewcommand{\subsection}{\@startsection{subsection}{2}{0mm}% {-1explus -.5ex minus -.2ex}% {0.5ex plus .2ex}% {\normalfont\normalsize\bfseries}} \renewcommand{\subsubsection}{\@startsection{subsubsection}{3}{0mm}% {-1ex plus -.5ex minus -.2ex}% {1ex plus .2ex}% {\normalfont\small\bfseries}} \makeatother % Define BibTeX command \def\BibTeX{{\rm B\kern-.05em{\sc i\kern-.025em b}\kern-.08em T\kern-.1667em\lower.7ex\hbox{E}\kern-.125emX}} % Don't print section numbers \setcounter{secnumdepth}{0} \setlength{\parindent}{0pt} \setlength{\parskip}{0pt plus 0.5ex} % ----------------------------------------------------------------------- \begin{document} \raggedright \fontsize{3mm}{3mm}\selectfont \begin{multicols}{3} % multicol parameters % These lengths are set only within the two main columns %\setlength{\columnseprule}{0.25pt} \setlength{\premulticols}{1pt} \setlength{\postmulticols}{1pt} \setlength{\multicolsep}{1pt} \setlength{\columnsep}{2pt} \begin{center} \Large{\textbf{yt Cheat Sheet}} \\ \end{center} \subsection{General Info} For everything yt please see \url{http://yt-project.org}. Documentation \url{http://yt-project.org/doc/index.html}. Need help? Start here \url{http://yt-project.org/doc/help/} and then try the IRC chat room \url{http://yt-project.org/irc.html}, or the mailing list \url{http://lists.spacepope.org/listinfo.cgi/yt-users-spacepope.org}. \\ \subsection{Installing yt} The easiest way to install yt is to use the installation script found on the yt homepage or the docs linked above. If you already have python set up with \texttt{numpy}, \texttt{scipy}, \texttt{matplotlib}, \texttt{h5py}, and \texttt{cython}, you can also use \texttt{pip install yt} \subsection{Command Line yt} yt, and its convenience functions, are launched from a command line prompt. Many commands have flags to control behavior. Commands can be followed by {\bf {-}{-}help} (e.g. {\bf yt render {-}{-}help}) for detailed help for that command including a list of the available flags. \texttt{iyt}\textemdash\ Load yt and IPython. \\ \texttt{yt load} \textit{dataset} \textemdash\ Load a single dataset. \\ \texttt{yt help} \textemdash\ Print yt help information. \\ \texttt{yt stats} \textit{dataset} \textemdash\ Print stats of a dataset. \\ \texttt{yt update} \textemdash\ Update yt to most recent version.\\ \texttt{yt update --all} \textemdash\ Update yt and dependencies to most recent version. \\ \texttt{yt version} \textemdash\ yt installation information. \\ \texttt{yt notebook} \textemdash\ Run the IPython notebook server. \\ \texttt{yt upload\_image} \textit{image.png} \textemdash\ Upload PNG image to imgur.com. \\ \texttt{yt upload\_notebook} \textit{notebook.nb} \textemdash\ Upload IPython notebook to hub.yt-project.org.\\ \texttt{yt plot} \textit{dataset} \textemdash\ Create a set of images.\\ \texttt{yt render} \textit{dataset} \textemdash\ Create a simple volume rendering. \\ \texttt{yt mapserver} \textit{dataset} \textemdash\ View a plot/projection in a Gmaps-like interface. \\ \texttt{yt pastebin} \textit{text.out} \textemdash\ Post text to the pastebin at paste.yt-project.org. \\ \texttt{yt pastebin\_grab} \textit{identifier} \textemdash\ Print content of pastebin to STDOUT. \\ \texttt{yt bugreport} \textemdash\ Report a yt bug. \\ \texttt{yt hop} \textit{dataset} \textemdash\ Run hop on a dataset. \\ \subsection{yt Imports} In order to use yt, Python must load the relevant yt modules into memory. The import commands are entered in the Python/IPython shell or used as part of a script. \newlength{\MyLen} \settowidth{\MyLen}{\texttt{letterpaper}/\texttt{a4paper} \ } \texttt{import yt} \textemdash\ Load yt. \\ \texttt{from yt.config import ytcfg} \textemdash\ Used to set yt configuration options. If used, must be called before importing any other module.\\ \texttt{from yt.analysis\_modules.\emph{halo\_finding}.api import \textasteriskcentered} \textemdash\ Load halo finding modules. Other modules are loaded in a similar way by swapping the \emph{emphasized} text. See the \textbf{Analysis Modules} section for a listing and short descriptions of each. \subsection{YTArray} Simulation data in yt is returned as a YTArray. YTArray is a numpy array that has unit data attached to it and can automatically handle unit conversions and detect unit errors. Just like a numpy array, YTArray provides a wealth of built-in functions to calculate properties of the data in the array. Here is a very brief list of some useful ones. \settowidth{\MyLen}{\texttt{multicol} }\\ \texttt{v = a.in\_cgs()} \textemdash\ Return the array in CGS units \\ \texttt{v = a.in\_units('Msun/pc**3')} \textemdash\ Return the array in solar masses per cubic parsec \\ \texttt{v = a.max(), a.min()} \textemdash\ Return maximum, minimum of \texttt{a}. \\ \texttt{index = a.argmax(), a.argmin()} \textemdash\ Return index of max, min value of \texttt{a}.\\ \texttt{v = a[}\textit{index}\texttt{]} \textemdash\ Select a single value from \texttt{a} at location \textit{index}.\\ \texttt{b = a[}\textit{i:j}\texttt{]} \textemdash\ Select the slice of values from \texttt{a} between locations \textit{i} to \textit{j-1} saved to a new Numpy array \texttt{b} with length \textit{j-i}. \\ \texttt{sel = (a > const)} \textemdash\ Create a new boolean Numpy array \texttt{sel}, of the same shape as \texttt{a}, that marks which values of \texttt{a > const}. Other operators (e.g. \textless, !=, \%) work as well.\\ \texttt{b = a[sel]} \textemdash\ Create a new Numpy array \texttt{b} made up of elements from \texttt{a} that correspond to elements of \texttt{sel} that are \textit{True}. In the above example \texttt{b} would be all elements of \texttt{a} that are greater than \texttt{const}.\\ \texttt{a.write\_hdf5(\textit{filename.h5})} \textemdash\ Save \texttt{a} to the hdf5 file \textit{filename.h5}.\\ \subsection{IPython Tips} \settowidth{\MyLen}{\texttt{multicol} } These tips work if IPython has been loaded, typically either by invoking \texttt{iyt} or \texttt{yt load} on the command line, or using the IPython notebook (\texttt{yt notebook}). \texttt{Tab complete} \textemdash\ IPython will attempt to auto-complete a variable or function name when the \texttt{Tab} key is pressed, e.g. \textit{HaloFi}\textendash\texttt{Tab} would auto-complete to \textit{HaloFinder}. This also works with imports, e.g. \textit{from numpy.random.}\textendash\texttt{Tab} would give you a list of random functions (note the trailing period before hitting \texttt{Tab}).\\ \texttt{?, ??} \textemdash\ Appending one or two question marks at the end of any object gives you detailed information about it, e.g. \textit{variable\_name}?.\\ Below a few IPython ``magics'' are listed, which are IPython-specific shortcut commands.\\ \texttt{\%paste} \textemdash\ Paste content from the system clipboard into the IPython shell.\\ \texttt{\%hist} \textemdash\ Print recent command history.\\ \texttt{\%quickref} \textemdash\ Print IPython quick reference.\\ \texttt{\%pdb} \textemdash\ Automatically enter the Python debugger at an exception.\\ \texttt{\%debug} \textemdash\ Drop into a debugger at the location of the last unhandled exception. \\ \texttt{\%time, \%timeit} \textemdash\ Find running time of expressions for benchmarking.\\ \texttt{\%lsmagic} \textemdash\ List all available IPython magics. Hint: \texttt{?} works with magics.\\ Please see \url{http://ipython.org/documentation.html} for the full IPython documentation. \subsection{Load and Access Data} The first step in using yt is to reference a simulation snapshot. After that, simulation data is generally accessed in yt using \textit{Data Containers} which are Python objects that define a region of simulation space from which data should be selected. \settowidth{\MyLen}{\texttt{multicol} } \texttt{ds = yt.load(}\textit{dataset}\texttt{)} \textemdash\ Reference a single snapshot.\\ \texttt{dd = ds.all\_data()} \textemdash\ Select the entire volume.\\ \texttt{a = dd[}\textit{field\_name}\texttt{]} \textemdash\ Copies the contents of \textit{field} into the YTArray \texttt{a}. Similarly for other data containers.\\ \texttt{ds.field\_list} \textemdash\ A list of available fields in the snapshot. \\ \texttt{ds.derived\_field\_list} \textemdash\ A list of available derived fields in the snapshot. \\ \texttt{val, loc = ds.find\_max("Density")} \textemdash\ Find the \texttt{val}ue of the maximum of the field \texttt{Density} and its \texttt{loc}ation. \\ \texttt{sp = ds.sphere(}\textit{cen}\texttt{,}\textit{radius}\texttt{)} \textemdash\ Create a spherical data container. \textit{cen} may be a coordinate, or ``max'' which centers on the max density point. \textit{radius} may be a float in code units or a tuple of (\textit{length, unit}).\\ \texttt{re = ds.region(\textit{cen}, \textit{left edge}, \textit{right edge})} \textemdash\ Create a rectilinear data container. \textit{cen} is required but not used. \textit{left} and \textit{right edge} are coordinate values that define the region. \texttt{di = ds.disk(\textit{cen}, \textit{normal}, \textit{radius}, \textit{height})} \textemdash\ Create a cylindrical data container centered at \textit{cen} along the direction set by \textit{normal},with total length 2$\times$\textit{height} and with radius \textit{radius}. \\ \texttt{ds.save\_object(sp, \textit{``sp\_for\_later''})} \textemdash\ Save an object (\texttt{sp}) for later use.\\ \texttt{sp = ds.load\_object(\textit{``sp\_for\_later''})} \textemdash\ Recover a saved object.\\ \subsection{Defining New Fields} \texttt{yt} expects on-disk fields, fields generated on-demand and in-memory. Field can either be created before a dataset is loaded using \texttt{add\_field}: \texttt{def \_metal\_mass(\textit{field},\textit{data})}\\ \texttt{\hspace{4 mm} return data["metallicity"]*data["cell\_mass"]}\\ \texttt{add\_field("metal\_mass", units='g', function=\_metal\_mass)}\\ Or added to an existing dataset using \texttt{ds.add\_field}: \texttt{ds.add\_field("metal\_mass", units='g', function=\_metal\_mass)}\\ \subsection{Slices and Projections} \settowidth{\MyLen}{\texttt{multicol} } \texttt{slc = yt.SlicePlot(ds, \textit{axis or normal vector}, \textit{field}, \textit{center=}, \textit{width=}, \textit{weight\_field=}, \textit{additional parameters})} \textemdash\ Make a slice plot perpendicular to \textit{axis} (specified via 'x', 'y', or 'z') or a normal vector for an off-axis slice of \textit{field} weighted by \textit{weight\_field} at (code-units) \textit{center} with \textit{width} in code units or a (value, unit) tuple. Hint: try \textit{yt.SlicePlot?} in IPython to see additional parameters.\\ \texttt{slc.save(\textit{file\_prefix})} \textemdash\ Save the slice to a png with name prefix \textit{file\_prefix}. \texttt{.save()} works similarly for the commands below.\\ \texttt{prj = yt.ProjectionPlot(ds, \textit{axis}, \textit{field}, \textit{addit. params})} \textemdash\ Make a projection. \\ \texttt{prj = yt.OffAxisProjectionPlot(ds, \textit{normal}, \textit{fields}, \textit{center=}, \textit{width=}, \textit{depth=},\textit{north\_vector=},\textit{weight\_field=})} \textemdash Make an off axis projection. Note this takes an array of fields. \\ \subsection{Plot Annotations} \settowidth{\MyLen}{\texttt{multicol} } Plot callbacks are functions itemized in a registry that is attached to every plot object. They can be accessed and then called like \texttt{ prj.annotate\_velocity(factor=16, normalize=False)}. Most callbacks also accept a \textit{plot\_args} dict that is fed to matplotlib annotator. \\ \texttt{velocity(\textit{factor=},\textit{scale=},\textit{scale\_units=}, \textit{normalize=})} \textemdash\ Uses field "x-velocity" to draw quivers\\ \texttt{magnetic\_field(\textit{factor=},\textit{scale=},\textit{scale\_units=}, \textit{normalize=})} \textemdash\ Uses field "Bx" to draw quivers\\ \texttt{quiver(\textit{field\_x},\textit{field\_y},\textit{factor=},\textit{scale=},\textit{scale\_units=}, \textit{normalize=})} \\ \texttt{contour(\textit{field=},\textit{ncont=},\textit{factor=},\textit{clim=},\textit{take\_log=}, \textit{additional parameters})} \textemdash Plots a number of contours \textit{ncont} to interpolate \textit{field} optionally using \textit{take\_log}, upper and lower \textit{c}ontour\textit{lim}its and \textit{factor} number of points in the interpolation.\\ \texttt{grids(\textit{alpha=}, \textit{draw\_ids=}, \textit{periodic=}, \textit{min\_level=}, \textit{max\_level=})} \textemdash Add grid boundaries. \\ \texttt{streamlines(\textit{field\_x},\textit{field\_y},\textit{factor=},\textit{density=})}\\ \texttt{clumps(\textit{clumplist})} \textemdash\ Generate \textit{clumplist} using the clump finder and plot. \\ \texttt{arrow(\textit{pos}, \textit{code\_size})} Add an arrow at a \textit{pos}ition. \\ \texttt{point(\textit{pos}, \textit{text})} \textemdash\ Add text at a \textit{pos}ition. \\ \texttt{marker(\textit{pos}, \textit{marker=})} \textemdash\ Add a matplotlib-defined marker at a \textit{pos}ition. \\ \texttt{sphere(\textit{center}, \textit{radius}, \textit{text=})} \textemdash\ Draw a circle and append \textit{text}.\\ \texttt{hop\_circles(\textit{hop\_output}, \textit{max\_number=}, \textit{annotate=}, \textit{min\_size=}, \textit{max\_size=}, \textit{font\_size=}, \textit{print\_halo\_size=}, \textit{fixed\_radius=}, \textit{min\_mass=}, \textit{print\_halo\_mass=}, \textit{width=})} \textemdash\ Draw a halo, printing it's ID, mass, clipping halos depending on number of particles (\textit{size}) and optionally fixing the drawn circle radius to be constant for all halos.\\ \texttt{hop\_particles(\textit{hop\_output},\textit{max\_number=},\textit{p\_size=},\\ \textit{min\_size},\textit{alpha=})} \textemdash\ Draw particle positions for member halos with a certain number of pixels per particle.\\ \texttt{particles(\textit{width},\textit{p\_size=},\textit{col=}, \textit{marker=}, \textit{stride=}, \textit{ptype=}, \textit{stars\_only=}, \textit{dm\_only=}, \textit{minimum\_mass=}, \textit{alpha=})} \textemdash\ Draw particles of \textit{p\_size} pixels in a slab of \textit{width} with \textit{col}or using a matplotlib \textit{marker} plotting only every \textit{stride} number of particles.\\ \texttt{title(\textit{text})}\\ \subsection{The $\sim$/.yt/ Directory} \settowidth{\MyLen}{\texttt{multicol} } yt will automatically check for configuration files in a special directory (\texttt{\$HOME/.yt/}) in the user's home directory. The \texttt{config} file \textemdash\ Settings that control runtime behavior. \\ The \texttt{my\_plugins.py} file \textemdash\ Add functions, derived fields, constants, or other commonly-used Python code to yt. \subsection{Analysis Modules} \settowidth{\MyLen}{\texttt{multicol}} The import name for each module is listed at the end of each description (see \textbf{yt Imports}). \texttt{Absorption Spectrum} \textemdash\ (\texttt{absorption\_spectrum}). \\ \texttt{Clump Finder} \textemdash\ Find clumps defined by density thresholds (\texttt{level\_sets}). \\ \texttt{Halo Finding} \textemdash\ Locate halos of dark matter particles (\texttt{halo\_finding}). \\ \texttt{Light Cone Generator} \textemdash\ Stitch datasets together to perform analysis over cosmological volumes. \\ \texttt{Light Ray Generator} \textemdash\ Analyze the path of light rays.\\ \texttt{Rockstar Halo Finding} \textemdash\ Locate halos of dark matter using the Rockstar halo finder (\texttt{halo\_finding.rockstar}). \\ \texttt{Star Particle Analysis} \textemdash\ Analyze star formation history and assemble spectra (\texttt{star\_analysis}). \\ \texttt{Sunrise Exporter} \textemdash\ Export data to the sunrise visualization format (\texttt{sunrise\_export}). \\ \subsection{Parallel Analysis} \settowidth{\MyLen}{\texttt{multicol}} Nearly all of yt is parallelized using MPI\@. The \textit{mpi4py} package must be installed for parallelism in yt. To install \textit{pip install mpi4py} on the command line usually works. Execute python in parallel similar to this:\\ \textit{mpirun -n 12 python script.py}\\ The file \texttt{script.py} must call the \texttt{yt.enable\_parallelism()} to turn on yt's parallelism. If this doesn't happen, all cores will execute the same serial yt script. This command may differ for each system on which you use yt; please consult the system documentation for details on how to run parallel applications. \texttt{parallel\_objects()} \textemdash\ A way to parallelize analysis over objects (such as halos or clumps).\\ \subsection{Mercurial} \settowidth{\MyLen}{\texttt{multicol}} Please see \url{http://mercurial.selenic.com/} for the full Mercurial documentation. \texttt{hg clone https://bitbucket.org/yt\_analysis/yt} \textemdash\ Clone a copy of yt. \\ \texttt{hg status} \textemdash\ Files changed in working directory.\\ \texttt{hg diff} \textemdash\ Print diff of all changed files in working directory. \\ \texttt{hg diff -r\textit{RevX} -r\textit{RevY}} \textemdash\ Print diff of all changes between revision \textit{RevX} and \textit{RevY}.\\ \texttt{hg log} \textemdash\ History of changes.\\ \texttt{hg cat -r\textit{RevX file}} \textemdash\ Print the contents of \textit{file} from revision \textit{RevX}.\\ \texttt{hg heads} \textemdash\ Print all the current heads. \\ \texttt{hg revert -r\textit{RevX file}} \textemdash\ Revert \textit{file} to revision \textit{RevX}. On-disk changed version is moved to \textit{file.orig}. \\ \texttt{hg commit} \textemdash\ Commit changes to repository. \\ \texttt{hg push} \textemdash\ Push changes to default remote repository. \\ \texttt{hg pull} \textemdash\ Pull changes from default remote repository. \\ \texttt{hg serve} \textemdash\ Launch a webserver on the local machine to examine the repository in a web browser. \\ \subsection{FAQ} \settowidth{\MyLen}{\texttt{multicol}} \texttt{slc.set\_log('field', False)} \textemdash\ When plotting \texttt{field}, use linear scaling instead of log scaling. %\rule{0.3\linewidth}{0.25pt} %\scriptsize % Can put some final stuff here like copyright etc... \end{multicols} \end{document} yt-3.3.3/doc/docstring_idioms.txt0000644000076500000240000000314612770647750017762 0ustar goldbaumstaff00000000000000Idioms for Docstrings in yt =========================== For a full list of recognized constructs for marking up docstrings, see the Sphinx documentation: http://sphinx.pocoo.org/ Specifically, this section: http://sphinx.pocoo.org/markup/index.html http://sphinx.pocoo.org/markup/inline.html#cross-referencing-python-objects Variables in Examples --------------------- In order to construct short, useful examples, some variables must be specified. However, because often examples require a bit of setup, here is a list of useful variable names that correspond to specific instances that the user is presupposed to have created. * `ds`: a dataset, loaded successfully * `sp`: a sphere * `c`: a 3-component "center" * `L`: a 3-component vector that corresponds to either angular momentum or a normal vector Cross-Referencing ----------------- To enable sufficient linkages between different sections of the documentation, good cross-referencing is key. To reference a section of the documentation, you can use this construction: For more information, see :ref:`image_writer`. This will insert a link to the section in the documentation which has been identified with `image_writer` as its name. Referencing Classes and Functions --------------------------------- To indicate the return type of a given object, you can reference it using this construction: This function returns a :class:`ProjectionPlot`. To reference a function, you can use: To write out this array, use :func:`save_image`. To reference a method, you can use: To add a projection, use :meth:`ProjectionPlot.set_width`. yt-3.3.3/doc/extensions/0000755000076500000240000000000013023400160016022 5ustar goldbaumstaff00000000000000yt-3.3.3/doc/extensions/pythonscript_sphinxext.py0000644000076500000240000000505113017603554023274 0ustar goldbaumstaff00000000000000import tempfile import os import glob import shutil import subprocess import uuid import errno from sphinx.util.compat import Directive from docutils import nodes class PythonScriptDirective(Directive): """Execute an inline python script and display images. This uses exec to execute an inline python script, copies any images produced by the script, and embeds them in the document along with the script. """ required_arguments = 0 optional_arguments = 0 has_content = True def run(self): cwd = os.getcwd() tmpdir = tempfile.mkdtemp() os.chdir(tmpdir) rst_file = self.state_machine.document.attributes['source'] rst_dir = os.path.abspath(os.path.dirname(rst_file)) image_dir, image_rel_dir = make_image_dir(setup, rst_dir) # Construct script from cell content content = "\n".join(self.content) with open("temp.py", "w") as f: f.write(content) # Use sphinx logger? print("") print(content) print("") subprocess.call(['python', 'temp.py']) text = '' for im in sorted(glob.glob("*.png")): text += get_image_tag(im, image_dir, image_rel_dir) code = content literal = nodes.literal_block(code, code) literal['language'] = 'python' attributes = {'format': 'html'} img_node = nodes.raw('', text, **attributes) # clean up os.chdir(cwd) shutil.rmtree(tmpdir, True) return [literal, img_node] def setup(app): app.add_directive('python-script', PythonScriptDirective) setup.app = app setup.config = app.config setup.confdir = app.confdir retdict = dict( version='0.1', parallel_read_safe=True, parallel_write_safe=True ) return retdict def get_image_tag(filename, image_dir, image_rel_dir): my_uuid = uuid.uuid4().hex shutil.move(filename, image_dir + os.path.sep + my_uuid + filename) relative_filename = image_rel_dir + os.path.sep + my_uuid + filename return '
' % relative_filename def make_image_dir(setup, rst_dir): image_dir = setup.app.builder.outdir + os.path.sep + '_images' rel_dir = os.path.relpath(setup.confdir, rst_dir) image_rel_dir = rel_dir + os.path.sep + '_images' thread_safe_mkdir(image_dir) return image_dir, image_rel_dir def thread_safe_mkdir(dirname): try: os.makedirs(dirname) except OSError as e: if e.errno != errno.EEXIST: raise pass yt-3.3.3/doc/extensions/README0000644000076500000240000000023112770647750016730 0ustar goldbaumstaff00000000000000This includes a version of the Numpy Documentation extension that has been slightly modified to emit extra TOC tree items. -- Matt Turk, March 25, 2011 yt-3.3.3/doc/extensions/yt_colormaps.py0000644000076500000240000000420513017603554021127 0ustar goldbaumstaff00000000000000# This extension is quite simple: # 1. It accepts a script name # 2. This script is added to the document in a literalinclude # 3. Any _static images found will be added from sphinx.util.compat import Directive from docutils.parsers.rst import directives import os, glob, shutil # Some of this magic comes from the matplotlib plot_directive. def setup(app): app.add_directive('yt_colormaps', ColormapScript) setup.app = app setup.config = app.config setup.confdir = app.confdir retdict = dict( version='0.1', parallel_read_safe=True, parallel_write_safe=True ) return retdict class ColormapScript(Directive): required_arguments = 1 optional_arguments = 0 def run(self): rst_file = self.state_machine.document.attributes['source'] rst_dir = os.path.abspath(os.path.dirname(rst_file)) script_fn = directives.path(self.arguments[0]) script_bn = os.path.basename(script_fn) script_name = os.path.basename(self.arguments[0]).split(".")[0] # This magic is from matplotlib dest_dir = os.path.abspath(os.path.join(setup.app.builder.outdir, os.path.dirname(script_fn))) if not os.path.exists(dest_dir): os.makedirs(dest_dir) # no problem here for me, but just use built-ins rel_dir = os.path.relpath(rst_dir, setup.confdir) place = os.path.join(dest_dir, rel_dir) if not os.path.isdir(place): os.makedirs(place) shutil.copyfile(os.path.join(rst_dir, script_fn), os.path.join(place, script_bn)) im_path = os.path.join(rst_dir, "_static") images = sorted(glob.glob(os.path.join(im_path, "*.png"))) lines = [] for im in images: im_name = os.path.join("_static", os.path.basename(im)) lines.append(".. image:: %s" % im_name) lines.append(" :width: 400") lines.append(" :target: ../../_images/%s" % os.path.basename(im)) lines.append("\n") lines.append("\n") self.state_machine.insert_input(lines, rst_file) return [] yt-3.3.3/doc/extensions/yt_cookbook.py0000644000076500000240000000557013017603554020744 0ustar goldbaumstaff00000000000000# This extension is quite simple: # 1. It accepts a script name # 2. This script is added to the document in a literalinclude # 3. Any _static images found will be added from sphinx.util.compat import Directive from docutils.parsers.rst import directives import os, glob, shutil # Some of this magic comes from the matplotlib plot_directive. def setup(app): app.add_directive('yt_cookbook', CookbookScript) setup.app = app setup.config = app.config setup.confdir = app.confdir retdict = dict( version='0.1', parallel_read_safe=True, parallel_write_safe=True ) return retdict data_patterns = ["*.h5", "*.out", "*.dat"] class CookbookScript(Directive): required_arguments = 1 optional_arguments = 0 def run(self): rst_file = self.state_machine.document.attributes['source'] rst_dir = os.path.abspath(os.path.dirname(rst_file)) script_fn = directives.path(self.arguments[0]) script_bn = os.path.basename(script_fn) script_name = os.path.basename(self.arguments[0]).split(".")[0] # This magic is from matplotlib dest_dir = os.path.abspath(os.path.join(setup.app.builder.outdir, os.path.dirname(script_fn))) if not os.path.exists(dest_dir): os.makedirs(dest_dir) # no problem here for me, but just use built-ins rel_dir = os.path.relpath(rst_dir, setup.confdir) place = os.path.join(dest_dir, rel_dir) if not os.path.isdir(place): os.makedirs(place) shutil.copyfile(os.path.join(rst_dir, script_fn), os.path.join(place, script_bn)) im_path = os.path.join(rst_dir, "_static") images = sorted(glob.glob(os.path.join(im_path, "%s__*.png" % script_name))) lines = [] lines.append("(`%s <%s>`__)" % (script_bn, script_fn)) lines.append("\n") lines.append("\n") lines.append(".. literalinclude:: %s" % self.arguments[0]) lines.append("\n") lines.append("\n") for im in images: im_name = os.path.join("_static", os.path.basename(im)) lines.append(".. image:: %s" % im_name) lines.append(" :width: 400") lines.append(" :target: ../_images/%s" % os.path.basename(im)) lines.append("\n") lines.append("\n") for ext in data_patterns: data_files = sorted(glob.glob(os.path.join( im_path, "%s__*.%s" % (script_name, ext)))) for df in data_files: df_bn = os.path.basename(df) shutil.copyfile(os.path.join(rst_dir, df), os.path.join(dest_dir, rel_dir, df_bn)) lines.append(" * Data: `%s <%s>`__)" % (df_bn, df)) lines.append("\n") self.state_machine.insert_input(lines, rst_file) return [] yt-3.3.3/doc/get_yt.sh0000644000076500000240000000024313017603554015470 0ustar goldbaumstaff00000000000000echo "This script has been deprecated." echo "You can now create a conda-based build using install_script.sh" echo "Please download that script and run it" exit 0 yt-3.3.3/doc/helper_scripts/0000755000076500000240000000000013023400160016651 5ustar goldbaumstaff00000000000000yt-3.3.3/doc/helper_scripts/code_support.py0000644000076500000240000000675413017603554021764 0ustar goldbaumstaff00000000000000vals = ["FluidQuantities", "Particles", "Parameters", "Units", "ReadOnDemand", "LoadRawData", "LevelOfSupport", "ContactPerson"] class CodeSupport(object): def __init__(self, **kwargs): self.support = {} for v in vals: self.support[v] = "N" for k, v in kwargs.items(): if k in vals: self.support[k] = v Y = "Y" N = "N" code_names = ["Enzo", "Orion", "FLASH", "RAMSES", "Chombo", "Gadget", "ART", "ZEUS"] codes = dict( Enzo = CodeSupport( FluidQuantities = Y, Particles = Y, Parameters = Y, Units = Y, ReadOnDemand = Y, LoadRawData = Y, ContactPerson = "Matt Turk", LevelOfSupport = "Full"), Orion = CodeSupport( FluidQuantities = Y, Particles = N, Parameters = Y, Units = Y, ReadOnDemand = Y, LoadRawData = Y, ContactPerson = "Jeff Oishi", LevelOfSupport = "Full"), FLASH = CodeSupport( FluidQuantities = Y, Particles = N, Parameters = N, Units = Y, ReadOnDemand = Y, LoadRawData = Y, ContactPerson = "John !ZuHone", LevelOfSupport = "Partial"), RAMSES = CodeSupport( FluidQuantities = Y, Particles = N, Parameters = N, Units = N, ReadOnDemand = Y, LoadRawData = Y, ContactPerson = "Matt Turk", LevelOfSupport = "Partial"), Chombo = CodeSupport( FluidQuantities = Y, Particles = N, Parameters = N, Units = N, ReadOnDemand = Y, LoadRawData = Y, ContactPerson = "Jeff Oishi", LevelOfSupport = "Partial"), Gadget = CodeSupport( FluidQuantities = N, Particles = Y, Parameters = Y, Units = Y, ReadOnDemand = N, LoadRawData = N, ContactPerson = "Chris Moody", LevelOfSupport = "Partial"), ART = CodeSupport( FluidQuantities = N, Particles = N, Parameters = N, Units = N, ReadOnDemand = N, LoadRawData = N, ContactPerson = "Matt Turk", LevelOfSupport = "None"), ZEUS = CodeSupport( FluidQuantities = N, Particles = N, Parameters = N, Units = N, ReadOnDemand = N, LoadRawData = N, ContactPerson = "Matt Turk", LevelOfSupport = "None") ) print("|| . ||", end=' ') for c in code_names: print("%s || " % (c), end=' ') print() for vn in vals: print("|| !%s ||" % (vn), end=' ') for c in code_names: print("%s || " % (codes[c].support[vn]), end=' ') print() yt-3.3.3/doc/helper_scripts/generate_doap.py0000644000076500000240000001211413017603554022036 0ustar goldbaumstaff00000000000000import os import hglib import pkg_resources from email.utils import parseaddr templates = {"header": r""" The yt Project yt Multi-resolution volumetric analysis yt is a python package for analyzing and visualizing volumetric, multi-resolution data. Originally developed for astrophysical simulations, it is flexible enough to work with data from other domains such as weather, nuclear engineering, seismology and molecular dynamics. python cython """, "foaf": r""" %(realname)s """, "release": r""" %(name)s %(date)s %(revision)s """, "footer": r""" """ } known_releases = [ ("0.3" , "2007-12-17"), ("1.0.1", "2008-10-25"), ("1.5" , "2009-11-04"), ("1.6" , "2010-01-22"), ("1.6.1", "2010-02-11"), ("1.7" , "2010-06-27"), ] yt_provider = pkg_resources.get_provider("yt") yt_path = os.path.dirname(yt_provider.module_path) name_mappings = { # Sometimes things get filtered out by hgchurn pointing elsewhere. # So we can add them back in manually "andrew.wetzel@yale.edu" : "Andrew Wetzel", "df11c@my.fsu.edu": "Daniel Fenn", "dnarayan@haverford.edu": "Desika Narayanan", "jmtomlinson95@gmail.com": "Joseph Tomlinson", "kaylea.nelson@yale.edu": "Kaylea Nelson", "tabel@slac.stanford.edu": "Tom Abel", "pshriwise": "Patrick Shriwise", "jnaiman": "Jill Naiman", "gsiisg": "Geoffrey So", "dcollins4096@gmail.com": "David Collins", "bcrosby": "Brian Crosby", "astrugarek": "Antoine Strugarek", "AJ": "Allyson Julian", } name_ignores = ["convert-repo"] lastname_sort = lambda a: a.rsplit(None, 1)[-1] def get_release_tags(): with hglib.open(yt_path) as c: releases = {} for name, rev, node, islocal in c.tags(): if name.startswith("yt-"): releases[name] = node rr = [] for name, node in sorted(releases.items()): date = c.log(node)[-1][-1] rr.append((date, name[3:])) rr.sort() return [(_[1], _[0].strftime("%Y-%M-%d")) for _ in rr] def developer_names(): cmd = hglib.util.cmdbuilder("churn", "-c") with hglib.open(yt_path) as c: emails = set([]) for dev in c.rawcommand(cmd).split("\n"): if len(dev.strip()) == 0: continue emails.add(dev.rsplit(None, 2)[0]) print("Generating real names for {0} emails".format(len(emails))) names = set([]) for email in sorted(emails): if email in name_ignores: continue if email in name_mappings: names.add(name_mappings[email]) continue cset = c.log(revrange="last(author('%s'))" % email) if len(cset) == 0: print("Error finding {0}".format(email)) realname = email else: realname, addr = parseaddr(cset[0][4]) if realname == '': realname = email if realname in name_mappings: names.add(name_mappings[realname]) continue realname = realname.decode('utf-8') realname = realname.encode('ascii', 'xmlcharrefreplace') names.add(realname) #with open("devs.txt", "w") as f: # for name in sorted(names, key=lastname_sort): # f.write("%s\n" % name) devs = list(names) devs.sort(key=lastname_sort) return devs def generate_doap(): dev_names = developer_names() with open("doap.xml", "w") as f: f.write(templates["header"]) for dev_name in dev_names: f.write("\n") f.write(templates["foaf"] % {'realname': dev_name}) f.write("\n") for release in known_releases + get_release_tags(): f.write(templates["release"] % { 'name': "yt " + release[0], 'revision': release[0], 'date': release[1]} ) f.write(templates["footer"]) if __name__ == "__main__": generate_doap() yt-3.3.3/doc/helper_scripts/parse_cb_list.py0000644000076500000240000000245413017603554022060 0ustar goldbaumstaff00000000000000from yt.mods import * import inspect from textwrap import TextWrapper ds = load("RD0005-mine/RedshiftOutput0005") output = open("source/visualizing/_cb_docstrings.inc", "w") template = """ .. function:: %(clsname)s%(sig)s: (This is a proxy for :class:`~%(clsproxy)s`.) %(docstring)s """ tw = TextWrapper(initial_indent=' ', subsequent_indent=' ', width=60) def write_docstring(f, name, cls): if not hasattr(cls, '_type_name') or cls._type_name is None: return for clsi in inspect.getmro(cls): docstring = inspect.getdoc(clsi.__init__) if docstring is not None: break clsname = cls._type_name sig = inspect.formatargspec(*inspect.getargspec(cls.__init__)) sig = sig.replace("**kwargs", "**field_parameters") clsproxy = "yt.visualization.plot_modifications.%s" % (cls.__name__) #docstring = "\n".join([" %s" % line for line in docstring.split("\n")]) #print docstring f.write(template % dict(clsname = clsname, sig = sig, clsproxy=clsproxy, docstring = "\n".join(tw.wrap(docstring)))) #docstring = docstring)) for n,c in sorted(callback_registry.items()): write_docstring(output, n, c) print(".. autoclass:: yt.visualization.plot_modifications.%s" % n) print(" :members:") print() yt-3.3.3/doc/helper_scripts/parse_dq_list.py0000644000076500000240000000210113017603554022065 0ustar goldbaumstaff00000000000000from yt.mods import * import inspect from textwrap import TextWrapper ds = load("RD0005-mine/RedshiftOutput0005") output = open("source/analyzing/_dq_docstrings.inc", "w") template = """ .. function:: %(funcname)s%(sig)s: (This is a proxy for :func:`~%(funcproxy)s`.) %(docstring)s """ tw = TextWrapper(initial_indent=' ', subsequent_indent=' ', width=60) def write_docstring(f, name, func): docstring = inspect.getdoc(func) funcname = name sig = inspect.formatargspec(*inspect.getargspec(func)) sig = sig.replace("data, ", "") sig = sig.replace("(data)", "()") funcproxy = "yt.data_objects.derived_quantities.%s" % (func.__name__) docstring = "\n".join([" %s" % line for line in docstring.split("\n")]) f.write(template % dict(funcname = funcname, sig = sig, funcproxy=funcproxy, docstring = docstring)) #docstring = "\n".join(tw.wrap(docstring)))) dd = ds.all_data() for n,func in sorted(dd.quantities.functions.items()): print(n, func) write_docstring(output, n, func[1]) yt-3.3.3/doc/helper_scripts/parse_object_list.py0000644000076500000240000000203113017603554022731 0ustar goldbaumstaff00000000000000from yt.mods import * import inspect from textwrap import TextWrapper ds = load("RD0005-mine/RedshiftOutput0005") output = open("source/analyzing/_obj_docstrings.inc", "w") template = """ .. class:: %(clsname)s%(sig)s: For more information, see :ref:`%(docstring)s` (This is a proxy for :class:`~%(clsproxy)sBase`.) """ tw = TextWrapper(initial_indent=' ', subsequent_indent=' ', width=60) def write_docstring(f, name, cls): for clsi in inspect.getmro(cls): docstring = inspect.getdoc(clsi.__init__) if docstring is not None: break clsname = name sig = inspect.formatargspec(*inspect.getargspec(cls.__init__)) sig = sig.replace("**kwargs", "**field_parameters") clsproxy = "yt.data_objects.data_containers.%s" % (cls.__name__) f.write(template % dict(clsname = clsname, sig = sig, clsproxy=clsproxy, docstring = 'physical-object-api')) for n,c in sorted(ds.__dict__.items()): if hasattr(c, '_con_args'): print(n) write_docstring(output, n, c) yt-3.3.3/doc/helper_scripts/run_recipes.py0000644000076500000240000000516013017603554021562 0ustar goldbaumstaff00000000000000#!/usr/bin/python import os import glob import sys import shutil import tempfile import traceback import subprocess import matplotlib matplotlib.use('Agg') from multiprocessing import Pool from yt.config import ytcfg FPATTERNS = ['*.png', '*.txt', '*.h5', '*.dat'] DPATTERNS = ['LC*', 'LR', 'DD0046'] BADF = ['cloudy_emissivity.h5', 'apec_emissivity.h5', 'xray_emissivity.h5', 'AMRGridData_Slice_x_density.png'] CWD = os.getcwd() ytcfg["yt", "serialize"] = "False" PARALLEL_TEST = {"rockstar_nest": "3"} BLACKLIST = ["opengl_ipython", "opengl_vr"] def prep_dirs(): for directory in glob.glob('%s/*' % ytcfg.get("yt", "test_data_dir")): os.symlink(directory, os.path.basename(directory)) def run_recipe(payload): recipe, = payload module_name, ext = os.path.splitext(os.path.basename(recipe)) dest = os.path.join(os.path.dirname(recipe), '_static', module_name) if module_name in BLACKLIST: return 0 if not os.path.exists("%s/_temp/%s.done" % (CWD, module_name)): sys.stderr.write('Started %s\n' % module_name) tmpdir = tempfile.mkdtemp() os.chdir(tmpdir) prep_dirs() if module_name in PARALLEL_TEST: cmd = ["mpiexec", "-n", PARALLEL_TEST[module_name], "python2", recipe] else: cmd = ["python", recipe] try: subprocess.check_call(cmd) except: trace = "".join(traceback.format_exception(*sys.exc_info())) trace += " in module: %s\n" % module_name trace += " recipe: %s\n" % recipe raise Exception(trace) open("%s/_temp/%s.done" % (CWD, module_name), 'wb').close() for pattern in FPATTERNS: for fname in glob.glob(pattern): if fname not in BADF: shutil.move(fname, "%s__%s" % (dest, fname)) for pattern in DPATTERNS: for dname in glob.glob(pattern): shutil.move(dname, dest) os.chdir(CWD) shutil.rmtree(tmpdir, True) sys.stderr.write('Finished with %s\n' % module_name) return 0 for path in ['_temp', 'source/cookbook/_static', 'source/visualizing/colormaps/_static']: fpath = os.path.join(CWD, path) if os.path.exists(fpath): shutil.rmtree(fpath) os.makedirs(fpath) os.chdir('_temp') recipes = [] for rpath in ['source/cookbook', 'source/visualizing/colormaps']: fpath = os.path.join(CWD, rpath) sys.path.append(fpath) recipes += glob.glob('%s/*.py' % fpath) WPOOL = Pool(processes=6) RES = WPOOL.map_async(run_recipe, ((recipe,) for recipe in recipes)) RES.get() os.chdir(CWD) yt-3.3.3/doc/helper_scripts/show_fields.py0000644000076500000240000002307013022370065021544 0ustar goldbaumstaff00000000000000import inspect from yt.mods import * from yt.testing import * import numpy as np from yt.utilities.cosmology import \ Cosmology from yt.utilities.definitions import \ mpc_conversion, sec_conversion from yt.frontends.stream.fields import \ StreamFieldInfo from yt.frontends.api import _frontends from yt.fields.derived_field import NullFunc import yt.frontends as frontends_module from yt.units.yt_array import YTArray, Unit from yt.units import dimensions fields, units = [], [] for fname, (code_units, aliases, dn) in StreamFieldInfo.known_other_fields: fields.append(("gas", fname)) units.append(code_units) base_ds = fake_random_ds(4, fields = fields, units = units) base_ds.index base_ds.cosmological_simulation = 1 base_ds.cosmology = Cosmology() from yt.config import ytcfg ytcfg["yt","__withintesting"] = "True" np.seterr(all = 'ignore') def _strip_ftype(field): if not isinstance(field, tuple): return field elif field[0] == "all": return field return field[1] np.random.seed(int(0x4d3d3d3)) units = [base_ds._get_field_info(*f).units for f in fields] fields = [_strip_ftype(f) for f in fields] ds = fake_random_ds(16, fields=fields, units=units, particles=True) ds.parameters["HydroMethod"] = "streaming" ds.parameters["EOSType"] = 1.0 ds.parameters["EOSSoundSpeed"] = 1.0 ds.conversion_factors["Time"] = 1.0 ds.conversion_factors.update( dict((f, 1.0) for f in fields) ) ds.gamma = 5.0/3.0 ds.current_redshift = 0.0001 ds.cosmological_simulation = 1 ds.hubble_constant = 0.7 ds.omega_matter = 0.27 ds.omega_lambda = 0.73 ds.cosmology = Cosmology(hubble_constant=ds.hubble_constant, omega_matter=ds.omega_matter, omega_lambda=ds.omega_lambda, unit_registry=ds.unit_registry) for my_unit in ["m", "pc", "AU", "au"]: new_unit = "%scm" % my_unit ds.unit_registry.add(new_unit, base_ds.unit_registry.lut[my_unit][0], dimensions.length, "\\rm{%s}/(1+z)" % my_unit) header = r""" .. _field-list: Field List ========== This is a list of many of the fields available in yt. We have attempted to include most of the fields that are accessible through the plugin system, as well as the fields that are known by the frontends, however it is possible to generate many more permutations, particularly through vector operations. For more information about the fields framework, see :ref:`fields`. Some fields are recognized by specific frontends only. These are typically fields like density and temperature that have their own names and units in the different frontend datasets. Often, these fields are aliased to their yt-named counterpart fields (typically 'gas' fieldtypes). For example, in the ``FLASH`` frontend, the ``dens`` field (i.e. ``(flash, dens)``) is aliased to the gas field density (i.e. ``(gas, density)``), similarly ``(flash, velx)`` is aliased to ``(gas, velocity_x)``, and so on. In what follows, if a field is aliased it will be noted. Try using the ``ds.field_list`` and ``ds.derived_field_list`` to view the native and derived fields available for your dataset respectively. For example to display the native fields in alphabetical order: .. notebook-cell:: import yt ds = yt.load("Enzo_64/DD0043/data0043") for i in sorted(ds.field_list): print(i) To figure out out what all of the field types here mean, see :ref:`known-field-types`. .. contents:: Table of Contents :depth: 1 :local: :backlinks: none .. _yt-fields: Universal Fields ---------------- """ footer = """ Index of Fields --------------- .. contents:: :depth: 3 :backlinks: none """ print(header) seen = [] def fix_units(units, in_cgs=False): unit_object = Unit(units, registry=ds.unit_registry) if in_cgs: unit_object = unit_object.get_cgs_equivalent() latex = unit_object.latex_representation() return latex.replace('\ ', '~') def print_all_fields(fl): for fn in sorted(fl): df = fl[fn] f = df._function s = "%s" % (df.name,) print(s) print("^" * len(s)) print() if len(df.units) > 0: # Most universal fields are in CGS except for these special fields if df.name[1] in ['particle_position', 'particle_position_x', \ 'particle_position_y', 'particle_position_z', \ 'entropy', 'kT', 'metallicity', 'dx', 'dy', 'dz',\ 'cell_volume', 'x', 'y', 'z']: print(" * Units: :math:`%s`" % fix_units(df.units)) else: print(" * Units: :math:`%s`" % fix_units(df.units, in_cgs=True)) print(" * Particle Type: %s" % (df.particle_type)) print() print("**Field Source**") print() if f == NullFunc: print("No source available.") print() continue else: print(".. code-block:: python") print() for line in inspect.getsource(f).split("\n"): print(" " + line) print() ds.index print_all_fields(ds.field_info) class FieldInfo: """ a simple container to hold the information about fields """ def __init__(self, ftype, field, ptype): name = field[0] self.units = "" u = field[1][0] if len(u) > 0: self.units = ":math:`\mathrm{%s}`" % fix_units(u) a = ["``%s``" % f for f in field[1][1]] self.aliases = " ".join(a) self.dname = "" if field[1][2] is not None: self.dname = ":math:`{}`".format(field[1][2]) if ftype is not "particle_type": ftype = "'"+ftype+"'" self.name = "(%s, '%s')" % (ftype, name) self.ptype = ptype current_frontends = [f for f in _frontends if f not in ["stream"]] for frontend in current_frontends: this_f = getattr(frontends_module, frontend) field_info_names = [fi for fi in dir(this_f) if "FieldInfo" in fi] dataset_names = [dset for dset in dir(this_f) if "Dataset" in dset] if frontend == "gadget": # Drop duplicate entry for GadgetHDF5, add special case for FieldInfo # entry dataset_names = ['GadgetDataset'] field_info_names = ['SPHFieldInfo'] elif frontend == "boxlib": field_info_names = [] for d in dataset_names: if "Maestro" in d: field_info_names.append("MaestroFieldInfo") elif "Castro" in d: field_info_names.append("CastroFieldInfo") else: field_info_names.append("BoxlibFieldInfo") elif frontend == "chombo": # remove low dimensional field info containters for ChomboPIC field_info_names = [f for f in field_info_names if '1D' not in f and '2D' not in f] for dset_name, fi_name in zip(dataset_names, field_info_names): fi = getattr(this_f, fi_name) nfields = 0 if hasattr(fi, "known_other_fields"): known_other_fields = fi.known_other_fields nfields += len(known_other_fields) else: known_other_fields = [] if hasattr(fi, "known_particle_fields"): known_particle_fields = fi.known_particle_fields if 'Tipsy' in fi_name: known_particle_fields += tuple(fi.aux_particle_fields.values()) nfields += len(known_particle_fields) else: known_particle_fields = [] if nfields > 0: print(".. _%s_specific_fields:\n" % dset_name.replace("Dataset", "")) h = "%s-Specific Fields" % dset_name.replace("Dataset", "") print(h) print("-" * len(h) + "\n") field_stuff = [] for field in known_other_fields: field_stuff.append(FieldInfo(frontend, field, False)) for field in known_particle_fields: if frontend in ["sph", "halo_catalogs", "sdf"]: field_stuff.append(FieldInfo("particle_type", field, True)) else: field_stuff.append(FieldInfo("io", field, True)) # output len_name = 10 len_units = 5 len_aliases = 7 len_part = 9 len_disp = 12 for f in field_stuff: len_name = max(len_name, len(f.name)) len_aliases = max(len_aliases, len(f.aliases)) len_units = max(len_units, len(f.units)) len_disp = max(len_disp, len(f.dname)) fstr = "{nm:{nw}} {un:{uw}} {al:{aw}} {pt:{pw}} {dp:{dw}}" header = fstr.format(nm="field name", nw=len_name, un="units", uw=len_units, al="aliases", aw=len_aliases, pt="particle?", pw=len_part, dp="display name", dw=len_disp) div = fstr.format(nm="="*len_name, nw=len_name, un="="*len_units, uw=len_units, al="="*len_aliases, aw=len_aliases, pt="="*len_part, pw=len_part, dp="="*len_disp, dw=len_disp) print(div) print(header) print(div) for f in field_stuff: print(fstr.format(nm=f.name, nw=len_name, un=f.units, uw=len_units, al=f.aliases, aw=len_aliases, pt=f.ptype, pw=len_part, dp=f.dname, dw=len_disp)) print(div) print("") print(footer) yt-3.3.3/doc/helper_scripts/split_auto.py0000644000076500000240000000337713017603554021437 0ustar goldbaumstaff00000000000000import os, collections templates = dict( autoclass = r""" %(name)s %(header)s .. autoclass:: %(name)s :members: :inherited-members: :undoc-members: """, autofunction = r""" %(name)s %(header)s .. autofunction:: %(name)s """, index_file = r""" %(title)s %(header)s .. autosummary:: :toctree: generated/%(dn)s """ ) file_names = dict( ft = ("Field Types", "source/api/field_types/%s.rst"), pt = ("Plot Types", "source/api/plot_types/%s.rst"), cl = ("Callback List", "source/api/callback_list/%s.rst"), ee = ("Extension Types", "source/api/extension_types/%s.rst"), dd = ("Derived Datatypes", "source/api/derived_datatypes/%s.rst"), mt = ("Miscellaneous Types", "source/api/misc_types/%s.rst"), fl = ("Function List", "source/api/function_list/%s.rst"), ds = ("Data Sources", "source/api/data_sources/%s.rst"), dq = ("Derived Quantities", "source/api/derived_quantities/%s.rst"), ) to_include = collections.defaultdict(list) for line in open("auto_generated.txt"): ftype, name, file_name = [s.strip() for s in line.split("::")] cn = name.split(".")[-1] if cn[0] == "_": cn = cn[1:] # For leading _ fn = file_names[file_name][1] % cn #if not os.path.exists(os.path.dirname(fn)): # os.mkdir(os.path.dirname(fn)) header = "-" * len(name) dd = dict(header=header, name = name) #open(fn, "w").write(templates[ftype] % dd) to_include[file_name].append(name) for key, val in file_names.items(): title, file = val fn = file.rsplit("/", 1)[0] + ".rst" print(fn) f = open(fn, "w") dn = fn.split("/")[-1][:-4] dd = dict(header = "=" * len(title), title=title, dn = dn) f.write(templates["index_file"] % dd) for obj in sorted(to_include[key]): f.write(" %s\n" % obj) yt-3.3.3/doc/helper_scripts/table.py0000644000076500000240000000554013017603554020335 0ustar goldbaumstaff00000000000000contents = [ ("Getting Started", [ ("welcome/index.html", "Welcome to yt!", "What's yt all about?"), ("orientation/index.html", "yt Orientation", "Quickly get up and running with yt: zero to sixty."), ("help/index.html", "How to Ask for Help", "Some guidelines on how and where to ask for help with yt"), ("workshop.html", "Workshop Tutorials", "Videos, slides and scripts from the 2012 workshop covering many " + "aspects of yt, from beginning to advanced."), ]), ("Everyday yt", [ ("analyzing/index.html", "Analyzing Data", "An overview of different ways to handle and process data: loading " + "data, using and manipulating objects and fields, examining and " + "manipulating particles, derived fields, generating processed data, " + "time series analysis."), ("visualizing/index.html", "Visualizing Data", "An overview of different ways to visualize data: making projections, " + "slices, phase plots, streamlines, and volume rendering; modifying " + "plots; the fixed resolution buffer."), ("interacting/index.html", "Interacting with yt", "Different ways -- scripting, GUIs, prompts, explorers -- to explore " + "your data."), ("analysis_modules/index.html", "Analysis Modules", "Discussions of some provided procedures for astrophysical analysis " + "like halo finding and synthetic spectra. Halo finding, analyzing " + "cosmology simulations, halo mass functions, halo profiling, light " + "cone generator, making absorption spectrums, star particle " + "analysis, two-point functions, halo merger trees, clump finding, " + "radial column density, exporting to sunrise.") ]), ("Advanced Usage", [ ("advanced/index.html", "Advanced yt usage", "Advanced topics: parallelism, debugging, ways to drive yt, " + "developing"), ("getting_involved/index.html", "Getting Involved", "How to participate in the community, contribute code and share " + "scripts"), ]), ("Reference Materials", [ ("cookbook/index.html", "The Cookbook", "A bunch of illustrated examples of how to do things"), ("reference/index.html", "Reference Materials", "A list of all bundled fields, API documentation, the Change Log..."), ("faq/index.html", "FAQ", "Frequently Asked Questions: answered for you!") ]), ] heading_template = r"""

%s

%s
""" subheading_template = r"""

%s

%s

""" t = "" for heading, items in contents: s = "" for subheading in items: s += subheading_template % subheading t += heading_template % (heading, s) print(t) yt-3.3.3/doc/helper_scripts/update_recipes.py0000644000076500000240000000577213017603554022251 0ustar goldbaumstaff00000000000000header = """Cookbook ======== yt scripts can be a bit intimidating, and at times a bit obtuse. But there's a lot you can do, and this section of the manual will assist with figuring out how to do some fairly common tasks -- which can lead to combining these, with other Python code, into more complicated and advanced tasks. .. note:: All of these scripts are located in the mercurial repository at http://bitbucket.org/yt_analysis/cookbook/ """ footer = """ """ import cStringIO, sys, glob, os from mercurial import hg, ui, commands uii = ui.ui() if "--index" in sys.argv: recipes = open("source/cookbook/recipes.rst", "w") else: recipes = cStringIO.StringIO() recipes.write(header) url = "here: http://bitbucket.org/yt_analysis/cookbook/raw/tip/%s ." def cond_output(f, v): if not v: f.write(".. rubric:: Sample Output\n\n") return True repo = hg.repository(uii, "../cookbook/") commands.pull(uii, repo, "http://bitbucket.org/yt_analysis/cookbook/") ctx = repo["tip"] for file in ctx: if not file.startswith("recipes/"): continue print("Parsing %s" % (file)) lines = ctx[file].data().split("\n") fn = file[8:-3] title = fn.replace("_", " ").capitalize() title += "\n" + "-" * len(title) + "\n"*2 title = ".. _cookbook-%s:\n\n%s" % (fn, title) if lines[0] != '"""': print(" Bad docstring: breaking.") print(file) di = lines[1:].index('"""') docstring = lines[1:di+1] recipe = lines[di+2:] recipes.write(".. include:: %s.inc\n" % fn) output = open("source/cookbook/%s.inc" % fn, "w") output.write(title) output.write("\n".join(docstring)) output.write("\n") output.write("\nThe latest version of this recipe can be downloaded ") output.write(url % file) output.write("\n\n.. code-block:: python\n\n") for line in recipe: output.write(" " + line + "\n") if os.path.isdir("../cookbook/images/%s" % (fn)): output.write("\n") ndir = "source/cookbook/_%s/" % (fn) if not os.path.isdir(ndir): os.mkdir(ndir) written = False for ifn in sorted(glob.glob("../cookbook/images/%s/*.png" % (fn))): written = cond_output(output, written) ofn = "%s/%s_%s" % (ndir, fn, os.path.basename(ifn)) open(ofn, "wb").write(open(ifn, "rb").read()) output.write(".. image:: _%s/%s_%s\n" % (fn, fn, os.path.basename(ifn)) + " :width: 240\n" + " :target: ../_images/%s_%s\n" % (fn, os.path.basename(ifn)) ) for ifn in sorted(glob.glob("../cookbook/images/%s/*.txt" % (fn))): written = cond_output(output, written) open("%s/%s_%s" % (ndir, fn, os.path.basename(ifn)), "w").write(open(ifn, "r").read()) output.write("\n``%s``\n\n" % os.path.basename(ifn)) output.write(".. literalinclude:: _%s/%s_%s\n" % (fn, fn, os.path.basename(ifn))) output.write("\n\n") output.close() recipes.write(footer) recipes.close() yt-3.3.3/doc/install_script.sh0000755000076500000240000016031313023400062017221 0ustar goldbaumstaff00000000000000# # Hi there! Welcome to the yt installation script. # # First things first, if you experience problems, please visit the Help # section at http://yt-project.org. # # This script is designed to create a fully isolated Python installation # with the dependencies you need to run yt. # # If you would like to customize the yt installation, then please edit # the following options. # If you do not have a working compiler environment, use the following # configuration: INST_CONDA=1 # Should yt's dependencies be installed using miniconda? INST_YT_SOURCE=0 # Should yt itself be installed from source? # If you want to install yt's dependencies using conda but want to build yt # itself from source, use the following configuration: # INST_CONDA=1 # INST_YT_SOURCE=1 # If you would like to build yt and all dependencies from source, then # use the following configuration by uncommenting the lines below. # NOTE: Building yt's dependencies from source will cause the install script # to require substantially more time to finish. # INST_CONDA=0 # INST_YT_SOURCE=1 BRANCH="yt" # This is the branch to which we will forcibly update. # What follows are some other options that you may or may not need to change. # Here's where you put the HDF5 path if you like; otherwise it'll download it # and install it on its own #HDF5_DIR= # If you've got yt some other place, set this to point to it. The script will # already check the current directory and the one above it in the tree. YT_DIR="" # These options can be set to customize the installation. INST_PY3=0 # Install Python 3 along with Python 2. If this is turned # on, all Python packages (including yt) will be installed # in Python 3 (except Mercurial, which requires Python 2). INST_HG=1 # Install Mercurial or not? If hg is not already # installed, yt cannot be installed from source. INST_EMBREE=0 # Install dependencies needed for Embree-accelerated # ray tracing # These options control whether low-level system libraries are installed # they are necessary for building yt's dependencies from source and are # ignored when INST_CONDA=1 INST_ZLIB=1 # On some systems (Kraken) matplotlib has issues with # the system zlib, which is compiled statically. # If need be, you can turn this off. INST_BZLIB=1 # On some systems, libbzip2 is missing. This can # lead to broken mercurial installations. INST_PNG=1 # Install a local libpng? Same things apply as with zlib. INST_FTYPE=1 # Install FreeType2 locally? INST_SQLITE3=1 # Install a local version of SQLite3? INST_0MQ=1 # Install 0mq (for IPython) and affiliated bindings? # These variables control whether optional dependencies are installed INST_PYX=0 # Install PyX? Sometimes PyX can be problematic without a # working TeX installation. INST_ROCKSTAR=0 # Install the Rockstar halo finder? INST_SCIPY=0 # Install scipy? INST_H5PY=1 # Install h5py? INST_ASTROPY=0 # Install astropy? INST_NOSE=1 # Install nose? INST_NETCDF4=1 # Install netcdf4 and its python bindings? # These options allow you to customize the builds of yt dependencies. # They are only used if INST_CONDA=0. # If you need to pass anything to the matplotlib build, do so here. MPL_SUPP_LDFLAGS="" MPL_SUPP_CFLAGS="" MPL_SUPP_CXXFLAGS="" # If you need to supply arguments to the NumPy or SciPy build, supply them here # This one turns on gfortran manually: #NUMPY_ARGS="--fcompiler=gnu95" # If you absolutely can't get the fortran to work, try this: #NUMPY_ARGS="--fcompiler=fake" # If you want to spawn multiple Make jobs, here's the place to set the # arguments. For instance, "-j4" MAKE_PROCS="" # These variables control which miniconda version and yt recipe are used # when INST_CONDA=1. MINICONDA_URLBASE="http://repo.continuum.io/miniconda" MINICONDA_VERSION="latest" if [ ${REINST_YT} ] && [ ${REINST_YT} -eq 1 ] && [ -n ${YT_DEST} ] then DEST_DIR=${YT_DEST} INST_CONDA=0 fi if [ $INST_CONDA -ne 0 ] then if [ ! -z "${CONDA_DEFAULT_ENV}" ] then echo "Aborting the yt installation because you appear to already" echo "have a conda environment activated. Either deactivate it with:" echo echo " $ source deactivate" echo echo "or install yt into your current environment with:" echo echo " $ conda install -c conda-forge yt" echo exit 1 fi DEST_SUFFIX="yt-conda" if [ -n "${PYTHONPATH}" ] then echo "WARNING WARNING WARNING WARNING WARNING WARNING WARNING" echo "*******************************************************" echo echo "The PYTHONPATH environment variable is set to:" echo echo " $PYTHONPATH" echo echo "If dependencies of yt (numpy, scipy, matplotlib) are installed" echo "to this path, this may cause issues. Exit the install script" echo "with Ctrl-C and unset PYTHONPATH if you are unsure." echo "Hit enter to continue." echo echo "WARNING WARNING WARNING WARNING WARNING WARNING WARNING" echo "*******************************************************" read -p "[hit enter]" fi else if [ $INST_YT_SOURCE -eq 0 ] then echo "yt must be compiled from source if INST_CONDA is set to 0" echo "Please set INST_YT_SOURCE to 1 and re-run." exit 1 fi DEST_SUFFIX="yt-`uname -m`" fi if [ -z "${DEST_DIR}" ] then DEST_DIR="`pwd`/${DEST_SUFFIX/ /}" # Installation location fi # Make sure we are NOT being run as root if [[ $EUID -eq 0 ]] then echo "******************************************************" echo "* *" echo "* *" echo "* IT IS A BAD IDEA TO RUN THIS SCRIPT AS ROOT!!!! *" echo "* *" echo "* *" echo "******************************************************" echo echo "If you really want to do this, you must manually edit" echo "the script to re-enable root-level installation. Sorry!" exit 1 fi if [[ ${DEST_DIR%/} == /usr/local ]] then echo "******************************************************" echo "* *" echo "* *" echo "* THIS SCRIPT WILL NOT INSTALL TO /usr/local !!!! *" echo "* *" echo "* *" echo "******************************************************" echo echo "If you really want to do this, you must manually edit" echo "the script." echo "Sorry!" exit 1 fi #------------------------------------------------------------------------------# # # # Okay, the script starts here. Feel free to play with it, but hopefully # # it'll work as is. # # # #------------------------------------------------------------------------------# LOG_FILE="${DEST_DIR}/yt_install.log" function write_config { CONFIG_FILE=${DEST_DIR}/.yt_config echo INST_HG=${INST_HG} > ${CONFIG_FILE} echo INST_ZLIB=${INST_ZLIB} >> ${CONFIG_FILE} echo INST_BZLIB=${INST_BZLIB} >> ${CONFIG_FILE} echo INST_PNG=${INST_PNG} >> ${CONFIG_FILE} echo INST_FTYPE=${INST_FTYPE} >> ${CONFIG_FILE} echo INST_SQLITE3=${INST_SQLITE3} >> ${CONFIG_FILE} echo INST_PYX=${INST_PYX} >> ${CONFIG_FILE} echo INST_0MQ=${INST_0MQ} >> ${CONFIG_FILE} echo INST_PY3=${INST_PY3} >> ${CONFIG_FILE} echo INST_ROCKSTAR=${INST_ROCKSTAR} >> ${CONFIG_FILE} echo INST_SCIPY=${INST_SCIPY} >> ${CONFIG_FILE} echo YT_DIR=${YT_DIR} >> ${CONFIG_FILE} echo MPL_SUPP_LDFLAGS=${MPL_SUPP_LDFLAGS} >> ${CONFIG_FILE} echo MPL_SUPP_CFLAGS=${MPL_SUPP_CFLAGS} >> ${CONFIG_FILE} echo MPL_SUPP_CXXFLAGS=${MPL_SUPP_CXXFLAGS} >> ${CONFIG_FILE} echo MAKE_PROCS=${MAKE_PROCS} >> ${CONFIG_FILE} if [ ${HDF5_DIR} ] then echo ${HDF5_DIR} >> ${CONFIG_FILE} fi if [ ${NUMPY_ARGS} ] then echo ${NUMPY_ARGS} >> ${CONFIG_FILE} fi } function get_willwont { if [ $1 -eq 1 ] then echo -n "will " else echo -n "won't " fi } function host_specific { MYHOST=`hostname -s` # just give the short one, not FQDN MYHOSTLONG=`hostname` # FQDN, for Ranger MYOS=`uname -s` # A guess at the OS if [ "${MYHOST##kraken}" != "${MYHOST}" ] then echo "Looks like you're on Kraken." echo echo " ******************************************" echo " * It may be better to use the yt module! *" echo " * *" echo " * $ module load yt *" echo " * *" echo " ******************************************" echo echo "IF YOU CHOOSE TO PROCEED:" echo "YOU MUST BE IN THE GNU PROGRAMMING ENVIRONMENT" echo " $ module swap PrgEnv-pgi PrgEnv-gnu" echo return fi if [ "${MYHOST##nautilus}" != "${MYHOST}" ] then echo "Looks like you're on Nautilus." echo echo " ******************************************" echo " * It may be better to use the yt module! *" echo " * *" echo " * $ module load yt *" echo " * *" echo " ******************************************" echo echo "NOTE: YOU MUST BE IN THE GNU PROGRAMMING ENVIRONMENT" echo " $ module swap PE-intel PE-gnu" echo echo "Additionally, note that by default, yt will OVERWRITE" echo "any existing installations from Kraken! You might want" echo "to adjust the variable DEST_SUFFIX in the install script." echo return fi if [ "${MYHOST##verne}" != "${MYHOST}" ] then echo "Looks like you're on Verne." echo echo "NOTE: YOU MUST BE IN THE GNU PROGRAMMING ENVIRONMENT" echo "This command will take care of that for you:" echo echo " $ module swap PE-pgi PE-gnu" echo fi if [ "${MYHOST##steele}" != "${MYHOST}" ] then echo "Looks like you're on Steele." echo echo "NOTE: YOU MUST BE IN THE GNU PROGRAMMING ENVIRONMENT" echo "These commands should take care of that for you:" echo echo " $ module purge" echo " $ module load gcc" echo fi if [ "${MYHOST##midway}" != "${MYHOST}" ] then echo "Looks like you're on Midway." echo echo " ******************************************" echo " * It may be better to use the yt module! *" echo " * *" echo " * $ module load yt *" echo " * *" echo " ******************************************" echo return fi if [ "${MYOS##Darwin}" != "${MYOS}" ] then echo "Looks like you're running on Mac OSX." echo echo "NOTE: you must have the Xcode command line tools installed." echo echo "The instructions for obtaining these tools varies according" echo "to your exact OS version. On older versions of OS X, you" echo "must register for an account on the apple developer tools" echo "website: https://developer.apple.com/downloads to obtain the" echo "download link." echo echo "We have gathered some additional instructions for each" echo "version of OS X below. If you have trouble installing yt" echo "after following these instructions, don't hesitate to contact" echo "the yt user's e-mail list." echo echo "You can see which version of OSX you are running by clicking" echo "'About This Mac' in the apple menu on the left hand side of" echo "menu bar. We're assuming that you've installed all operating" echo "system updates; if you have an older version, we suggest" echo "running software update and installing all available updates." echo echo "OS X 10.5.8: search for and download Xcode 3.1.4 from the" echo "Apple developer tools website." echo echo "OS X 10.6.8: search for and download Xcode 3.2 from the Apple" echo "developer tools website. You can either download the" echo "Xcode 3.2.2 Developer Tools package (744 MB) and then use" echo "Software Update to update to XCode 3.2.6 or" echo "alternatively, you can download the Xcode 3.2.6/iOS SDK" echo "bundle (4.1 GB)." echo echo "OS X 10.7.5: download Xcode 4.2 from the mac app store" echo "(search for Xcode)." echo "Alternatively, download the Xcode command line tools from" echo "the Apple developer tools website." echo echo "OS X 10.8.4, 10.9, 10.10, and 10.11:" echo "download the appropriate version of Xcode from the" echo "mac app store (search for Xcode)." echo echo "Additionally, you will have to manually install the Xcode" echo "command line tools." echo echo "For OS X 10.8, see:" echo "http://stackoverflow.com/questions/9353444" echo echo "For OS X 10.9 and newer the command line tools can be installed" echo "with the following command:" echo " xcode-select --install" echo if [ $INST_CONDA -eq 0 ] then echo "For OS X 10.11, you will additionally need to install the" echo "OpenSSL library using a package manager like homebrew or" echo "macports." echo echo "If your install fails with a message like" echo " ImportError: cannot import HTTPSHandler" echo "then you do not have the OpenSSL headers available in a" echo "location visible to your C compiler. Consider setting" echo "INST_CONDA=1 instead, as conda's python bundles OpenSSL." fi OSX_VERSION=`sw_vers -productVersion` if [ "${OSX_VERSION##10.8}" != "${OSX_VERSION}" ] then MPL_SUPP_CFLAGS="${MPL_SUPP_CFLAGS} -mmacosx-version-min=10.7" MPL_SUPP_CXXFLAGS="${MPL_SUPP_CXXFLAGS} -mmacosx-version-min=10.7" fi fi if [ -f /etc/redhat-release ] then echo "Looks like you're on an Redhat-compatible machine." echo echo "You need to have these packages installed:" echo echo " * openssl-devel" echo " * uuid-devel" echo " * readline-devel" echo " * ncurses-devel" echo " * zip" echo " * gcc-{,c++,gfortran}" echo " * make" echo " * patch" echo echo "You can accomplish this by executing:" echo "$ sudo yum install gcc gcc-c++ gcc-gfortran make patch zip" echo "$ sudo yum install ncurses-devel uuid-devel openssl-devel readline-devel" fi if [ -f /etc/SuSE-release ] && [ `grep --count SUSE /etc/SuSE-release` -gt 0 ] then echo "Looks like you're on an OpenSUSE-compatible machine." echo echo "You need to have these packages installed:" echo echo " * devel_C_C++" echo " * libopenssl-devel" echo " * libuuid-devel" echo " * zip" echo " * gcc-c++" echo echo "You can accomplish this by executing:" echo echo "$ sudo zypper install -t pattern devel_C_C++" echo "$ sudo zypper install gcc-c++ libopenssl-devel libuuid-devel zip" echo echo "I am also setting special configure arguments to Python to" echo "specify control lib/lib64 issues." PYCONF_ARGS="--libdir=${DEST_DIR}/lib" fi if [ -f /etc/lsb-release ] && [ `grep --count buntu /etc/lsb-release` -gt 0 ] then echo "Looks like you're on an Ubuntu-compatible machine." echo echo "You need to have these packages installed:" echo echo " * libssl-dev" echo " * build-essential" echo " * libncurses5" echo " * libncurses5-dev" echo " * zip" echo " * uuid-dev" echo " * libfreetype6-dev" echo " * tk-dev" echo echo "You can accomplish this by executing:" echo echo "$ sudo apt-get install libssl-dev build-essential libncurses5 libncurses5-dev zip uuid-dev libfreetype6-dev tk-dev" echo echo echo " Additionally, if you want to put yt's lib dir in your LD_LIBRARY_PATH" echo " so you can use yt without the activate script, you might " echo " want to consider turning off LIBZ and FREETYPE in this" echo " install script by editing this file and setting" echo echo " INST_ZLIB=0" echo " INST_FTYPE=0" echo echo " to avoid conflicts with other command-line programs " echo " (like eog and evince, for example)." fi if [ $INST_SCIPY -eq 1 ] then echo echo "Looks like you've requested that the install script build SciPy." echo echo "If the SciPy build fails, please uncomment one of the the lines" echo "at the top of the install script that sets NUMPY_ARGS, delete" echo "any broken installation tree, and re-run the install script" echo "verbatim." echo echo "If that doesn't work, don't hesitate to ask for help on the yt" echo "user's mailing list." echo fi if [ ! -z "${CFLAGS}" ] then echo "******************************************" echo "******************************************" echo "** **" echo "** Your CFLAGS is not empty. **" echo "** This can break h5py compilation. **" echo "** **" echo "******************************************" echo "******************************************" fi } function log_cmd { echo "EXECUTING:" >> ${LOG_FILE} echo " $*" >> ${LOG_FILE} ( $* 2>&1 ) 1>> ${LOG_FILE} || do_exit } # set paths needed for Embree if [ $INST_EMBREE -ne 0 ] then if [ $INST_YT_SOURCE -eq 0 ] then echo "yt must be compiled from source to install Embree support." echo "Please set INST_YT_SOURCE to 1 and re-run the install script." exit 1 fi if [ $INST_CONDA -eq 0 ] then echo "Embree support has not yet been implemented for INST_CONDA=0." exit 1 fi if [ `uname` = "Darwin" ] then EMBREE="embree-2.8.0.x86_64.macosx" EMBREE_URL="https://github.com/embree/embree/releases/download/v2.8.0/$EMBREE.tar.gz" elif [ `uname` = "Linux" ] then EMBREE="embree-2.8.0.x86_64.linux" EMBREE_URL="https://github.com/embree/embree/releases/download/v2.8.0/$EMBREE.tar.gz" else echo "Embree is not supported on this platform." echo "Set INST_EMBREE=0 and re-run the install script." exit 1 fi PYEMBREE_URL="https://github.com/scopatz/pyembree/archive/master.zip" fi if [ $INST_ROCKSTAR -ne 0 ] then if [ $INST_YT_SOURCE -eq 0 ] then echo "yt must be compiled from source to install support for" echo "the rockstar halo finder. Please set INST_YT_SOURCE to 1" echo "and re-run the install script" exit 1 fi fi if [ $INST_NETCDF4 -ne 0 ] then if [ $INST_CONDA -eq 0 ] then echo "This script can only install netcdf4 through conda." echo "Please set INST_CONDA to 1 to install netcdf4" echo "Setting INST_NETCDF4=0" INST_NETCDF4=0 fi fi echo echo echo "========================================================================" echo echo "Hi there! This is the yt installation script. We're going to download" echo "some stuff and install it to create a self-contained, isolated" echo "environment for yt to run within." echo echo "Inside the installation script you can set a few variables. Here's what" echo "they're currently set to -- you can hit Ctrl-C and edit the values in " echo "the script if you aren't such a fan." echo printf "%-18s = %s so I " "INST_CONDA" "${INST_CONDA}" get_willwont ${INST_CONDA} echo "be installing a conda-based python environment" printf "%-18s = %s so I " "INST_YT_SOURCE" "${INST_YT_SOURCE}" get_willwont ${INST_YT_SOURCE} echo "be compiling yt from source" printf "%-18s = %s so I " "INST_PY3" "${INST_PY3}" get_willwont ${INST_PY3} echo "be installing Python 3" printf "%-18s = %s so I " "INST_HG" "${INST_HG}" get_willwont ${INST_HG} echo "be installing Mercurial" printf "%-18s = %s so I " "INST_EMBREE" "${INST_EMBREE}" get_willwont ${INST_EMBREE} echo "be installing Embree" if [ $INST_CONDA -eq 0 ] then printf "%-18s = %s so I " "INST_ZLIB" "${INST_ZLIB}" get_willwont ${INST_ZLIB} echo "be installing zlib" printf "%-18s = %s so I " "INST_BZLIB" "${INST_BZLIB}" get_willwont ${INST_BZLIB} echo "be installing bzlib" printf "%-18s = %s so I " "INST_PNG" "${INST_PNG}" get_willwont ${INST_PNG} echo "be installing libpng" printf "%-18s = %s so I " "INST_FTYPE" "${INST_FTYPE}" get_willwont ${INST_FTYPE} echo "be installing freetype2" printf "%-18s = %s so I " "INST_SQLITE3" "${INST_SQLITE3}" get_willwont ${INST_SQLITE3} echo "be installing SQLite3" fi printf "%-18s = %s so I " "INST_PYX" "${INST_PYX}" get_willwont ${INST_PYX} echo "be installing PyX" printf "%-18s = %s so I " "INST_ROCKSTAR" "${INST_ROCKSTAR}" get_willwont ${INST_ROCKSTAR} echo "be installing Rockstar" printf "%-18s = %s so I " "INST_H5PY" "${INST_H5PY}" get_willwont ${INST_H5PY} echo "be installing h5py" printf "%-18s = %s so I " "INST_ASTROPY" "${INST_ASTROPY}" get_willwont ${INST_ASTROPY} echo "be installing astropy" printf "%-18s = %s so I " "INST_NOSE" "${INST_NOSE}" get_willwont ${INST_NOSE} echo "be installing nose" echo if [ $INST_CONDA -eq 0 ] then if [ -z "$HDF5_DIR" ] then echo "HDF5_DIR is not set, so I will be installing HDF5" else echo "HDF5_DIR=${HDF5_DIR} , so I will not be installing HDF5" fi fi echo echo "Installation will be to" echo " ${DEST_DIR}" echo echo "and I'll be logging the installation in" echo " ${LOG_FILE}" echo echo "I think that about wraps it up. If you want to continue, hit enter. " echo "If you'd rather stop, maybe think things over, even grab a sandwich, " echo "hit Ctrl-C." echo if [ $INST_YT_SOURCE -ne 0 ] then host_specific fi if [ $INST_CONDA -eq 0 ] then if [ ${USED_CONFIG} ] then echo "Settings were loaded from ${CONFIG_FILE}." echo "Remove this file if you wish to return to the default settings." echo fi fi echo "========================================================================" echo if [[ $1 != "--yes" ]] then read -p "[hit enter] " fi echo echo "Awesome! Here we go." echo function do_exit { echo "********************************************" echo " FAILURE REPORT:" echo "********************************************" echo tail -n 10 ${LOG_FILE} echo echo "********************************************" echo "********************************************" echo "Failure. Check ${LOG_FILE}. The last 10 lines are above." exit 1 } if [ $INST_PY3 -eq 1 ] then PYTHON_EXEC='python3.5' else PYTHON_EXEC='python2.7' fi function do_setup_py { [ -e $1/done ] && return LIB=$1 shift if [ -z "$@" ] then echo "Installing $LIB" else echo "Installing $LIB (arguments: '$@')" fi [ ! -e $LIB/extracted ] && tar xfz $LIB.tar.gz touch $LIB/extracted BUILD_ARGS="" if [[ $LIB =~ .*mercurial.* ]] then PYEXE="python2.7" else PYEXE=${PYTHON_EXEC} fi case $LIB in *h5py*) pushd $LIB &> /dev/null ( ${DEST_DIR}/bin/${PYTHON_EXEC} setup.py configure --hdf5=${HDF5_DIR} 2>&1 ) 1>> ${LOG_FILE} || do_exit popd &> /dev/null ;; *numpy*) if [ -e ${DEST_DIR}/lib/${PYTHON_EXEC}/site-packages/numpy/__init__.py ] then VER=$(${DEST_DIR}/bin/${PYTHON_EXEC} -c 'from distutils.version import StrictVersion as SV; \ import numpy; print SV(numpy.__version__) < SV("1.8.0")') if [ $VER == "True" ] then echo "Removing previous NumPy instance (see issue #889)" rm -rf ${DEST_DIR}/lib/${PYTHON_EXEC}/site-packages/{numpy*,*.pth} fi fi ;; *) ;; esac cd $LIB ( ${DEST_DIR}/bin/${PYEXE} setup.py build ${BUILD_ARGS} $* 2>&1 ) 1>> ${LOG_FILE} || do_exit ( ${DEST_DIR}/bin/${PYEXE} setup.py install 2>&1 ) 1>> ${LOG_FILE} || do_exit touch done cd .. } if type -P curl &>/dev/null then echo "Using curl" export GETFILE="curl -sSOL" else echo "Using wget" export GETFILE="wget -nv" fi if type -P sha512sum &> /dev/null then echo "Using sha512sum" export SHASUM="sha512sum" elif type -P shasum &> /dev/null then echo "Using shasum -a 512" export SHASUM="shasum -a 512" else echo echo "I am unable to locate any shasum-like utility." echo "ALL FILE INTEGRITY IS NOT VERIFIABLE." echo "THIS IS PROBABLY A BIG DEAL." echo echo "(I'll hang out for a minute for you to consider this.)" sleep 60 fi function get_ytproject { [ -e $1 ] && return echo "Downloading $1 from yt-project.org" ${GETFILE} "http://yt-project.org/dependencies/$1" || do_exit ( ${SHASUM} -c $1.sha512 2>&1 ) 1>> ${LOG_FILE} || do_exit } function get_ytdata { echo "Downloading $1 from yt-project.org" [ -e $1 ] && return ${GETFILE} "http://yt-project.org/data/$1" || do_exit ( ${SHASUM} -c $1.sha512 2>&1 ) 1>> ${LOG_FILE} || do_exit } function test_install { echo "Testing that yt can be imported" ( ${DEST_DIR}/bin/${PYTHON_EXEC} -c "import yt" 2>&1 ) 1>> ${LOG_FILE} || do_exit } ORIG_PWD=`pwd` if [ -z "${DEST_DIR}" ] then echo "Edit this script, set the DEST_DIR parameter and re-run." exit 1 fi # Set paths to what they should be when yt is activated. if [ $INST_CONDA -eq 0 ] then export PATH=${DEST_DIR}/bin:$PATH export LD_LIBRARY_PATH=${DEST_DIR}/lib:$LD_LIBRARY_PATH export PYTHONPATH=${DEST_DIR}/lib/${PYTHON_EXEC}/site-packages # Write config settings to file. CONFIG_FILE=${DEST_DIR}/.yt_config mkdir -p ${DEST_DIR} if [ -z ${REINST_YT} ] || [ ${REINST_YT} -neq 1 ] then write_config elif [ ${REINST_YT} ] && [ ${REINST_YT} -eq 1 ] && [ -f ${CONFIG_FILE} ] then USED_CONFIG=1 source ${CONFIG_FILE} fi # Get supplemental data. mkdir -p ${DEST_DIR}/data cd ${DEST_DIR}/data echo 'de6d8c6ea849f0206d219303329a0276b3cce7c051eec34377d42aacbe0a4f47ac5145eb08966a338ecddd2b83c8f787ca9956508ad5c39ee2088ad875166410 cloudy_emissivity.h5' > cloudy_emissivity.h5.sha512 [ ! -e cloudy_emissivity.h5 ] && get_ytdata cloudy_emissivity.h5 echo '0f714ae2eace0141b1381abf1160dc8f8a521335e886f99919caf3beb31df1fe271d67c7b2a804b1467949eb16b0ef87a3d53abad0e8160fccac1e90d8d9e85f apec_emissivity.h5' > apec_emissivity.h5.sha512 [ ! -e apec_emissivity.h5 ] && get_ytdata apec_emissivity.h5 mkdir -p ${DEST_DIR}/src cd ${DEST_DIR}/src PYTHON2='Python-2.7.11' PYTHON3='Python-3.5.1' CYTHON='Cython-0.23.5' if [ $INST_PY3 -eq 0 ] then PYX='PyX-0.12.1' else PYX='PyX-0.14.1' fi BZLIB='bzip2-1.0.6' FREETYPE_VER='freetype-2.4.12' H5PY='h5py-2.5.0' HDF5='hdf5-1.8.14' LAPACK='lapack-3.4.2' PNG='libpng-1.6.3' MATPLOTLIB='matplotlib-1.5.1' MERCURIAL='mercurial-3.7.3' NOSE='nose-1.3.7' NUMPY='numpy-1.11.0' PYTHON_HGLIB='python-hglib-2.0' ROCKSTAR='rockstar-0.99.6' SCIPY='scipy-0.17.0' SQLITE='sqlite-autoconf-3071700' SYMPY='sympy-1.0' ZLIB='zlib-1.2.8' SETUPTOOLS='setuptools-20.6.7' ASTROPY='astropy-1.1.2' # Now we dump all our SHA512 files out. echo '9052d74bbd0c93757fd916939cc3c39eb1aba6c9692b48887ae577256bec64b39b1fd25b6c751e6c8fe723de4c0ddf9a1a207de39f75b0839500dfcdde69f925 Cython-0.23.5.tar.gz' > Cython-0.23.5.tar.gz.sha512 if [ $INST_PY3 -eq 0 ] then echo '4941f5aa21aff3743546495fb073c10d2657ff42b2aff401903498638093d0e31e344cce778980f28a7170c6d29eab72ac074277b9d4088376e8692dc71e55c1 PyX-0.12.1.tar.gz' > PyX-0.12.1.tar.gz.sha512 else echo '16265bbdcaf28ce194189a2987b32952f296c850b829454bcccce0abd23838bfca0276c3e9c8e96b8cbfaf1473bf14669f9b7f2032ee039b61ae59ea3aa45a20 PyX-0.14.1.tar.gz' > PyX-0.14.1.tar.gz.sha512 fi echo 'f21df53da87e9e3c14599a34388976e7dd09b951dff3c4b978fe224beeff07e749c0059ffd94f68ca9b75ecaef142b285d579b8dfaad4eab85aca33957114937 Python-2.7.11.tgz' > Python-2.7.11.tgz.sha512 echo '73f1477f3d3f5bd978c4ea1d1b679467b45e9fd2f443287b88c5c107a9ced580c56e0e8f33acea84e06b11a252e2a4e733120b721a9b6e1bb3d34493a3353bfb Python-3.5.1.tgz' > Python-3.5.1.tgz.sha512 echo 'b83c4a1415a3eb8c016507705d0d2f22971e4da937bb97953eec08f8f856933d8fa76ce8c536122235b19e7879b16add2e20fd2fee3e488f9b2b4bf1b9f4dbdb astropy-1.1.2.tar.gz' > astropy-1.1.2.tar.gz.sha512 echo '276bd9c061ec9a27d478b33078a86f93164ee2da72210e12e2c9da71dcffeb64767e4460b93f257302b09328eda8655e93c4b9ae85e74472869afbeae35ca71e blas.tar.gz' > blas.tar.gz.sha512 echo '00ace5438cfa0c577e5f578d8a808613187eff5217c35164ffe044fbafdfec9e98f4192c02a7d67e01e5a5ccced630583ad1003c37697219b0f147343a3fdd12 bzip2-1.0.6.tar.gz' > bzip2-1.0.6.tar.gz.sha512 echo '609a68a3675087e0cc95268574f31e104549daa48efe15a25a33b8e269a93b4bd160f4c3e8178dca9c950ef5ca514b039d6fd1b45db6af57f25342464d0429ce freetype-2.4.12.tar.gz' > freetype-2.4.12.tar.gz.sha512 echo '4a83f9ae1855a7fad90133b327d426201c8ccfd2e7fbe9f39b2d61a2eee2f3ebe2ea02cf80f3d4e1ad659f8e790c173df8cc99b87d0b7ce63d34aa88cfdc7939 h5py-2.5.0.tar.gz' > h5py-2.5.0.tar.gz.sha512 echo '4073fba510ccadaba41db0939f909613c9cb52ba8fb6c1062fc9118edc601394c75e102310be1af4077d07c9b327e6bbb1a6359939a7268dc140382d0c1e0199 hdf5-1.8.14.tar.gz' > hdf5-1.8.14.tar.gz.sha512 echo '8770214491e31f0a7a3efaade90eee7b0eb20a8a6ab635c5f854d78263f59a1849133c14ef5123d01023f0110cbb9fc6f818da053c01277914ae81473430a952 lapack-3.4.2.tar.gz' > lapack-3.4.2.tar.gz.sha512 echo '887582e5a22e4cde338aa8fec7a89f6dd31f2f02b8842735f00f970f64582333fa03401cea6d01704083403c7e8b7ebc26655468ce930165673b33efa4bcd586 libpng-1.6.3.tar.gz' > libpng-1.6.3.tar.gz.sha512 echo 'a0e78b5027a3a49cf8e77dc0d26f5f380dcd80f7b309b6121199acd5e1d94f48482864a9eee3bd397f7ac6f07fe1d3c21bf517217df3c72e8e3d105b7c2ae58e matplotlib-1.5.1.tar.gz' > matplotlib-1.5.1.tar.gz.sha512 echo '7f9f97229e40c7092c16ccf227b19a08a9839d8ce19a9d057341fff75876bff32241ee9aa10eab293f779ea3e8a1d97577597187bd96251fb499cbb1075a82cf mercurial-3.7.3.tar.gz' > mercurial-3.7.3.tar.gz.sha512 echo 'e65c914f621f8da06b9ab11a0ff2763d6e29b82ce2aaed56da0e3773dc899d9deb1f20015789d44c65a5dad7214520f5b659b3f8d7695fb207ad3f78e5cf1b62 nose-1.3.7.tar.gz' > nose-1.3.7.tar.gz.sha512 echo '92c1889397ad013e25da3a0657fc01e787d528fc19c29cc2acd286c3f07d41b984252583457b1b9259fc303afbe9694565cdcf5752eb4ecb950cc7a99ec1ad8b numpy-1.11.0.tar.gz' > numpy-1.11.0.tar.gz.sha512 echo '647cc82424783efc3d74540e34976af66acc35fc36d66afba169508946cc62027910c7e41dc9d11ec88c15d6b1e113ce22c2781711ea324de58db3b24d5079c4 python-hglib-2.0.tar.gz' > python-hglib-2.0.tar.gz.sha512 echo 'de6409d75a3ff3cf1e5391d3b09126f0bc7e1a40a15f9bee244195638fe2f8481fca032896d8534623e6122ff59aaf669664e27ff89cf1b094a5ce7312f220b7 scipy-0.17.0.tar.gz' > scipy-0.17.0.tar.gz.sha512 echo '96f3e51b46741450bc6b63779c10ebb4a7066860fe544385d64d1eda52592e376a589ef282ace2e1df73df61c10eab1a0d793abbdaf770e60289494d4bf3bcb4 sqlite-autoconf-3071700.tar.gz' > sqlite-autoconf-3071700.tar.gz.sha512 echo '977db6e9bc6a5918cceb255981a57e85e7060c0922aefd2968b004d25d704e25a5cb5bbe09eb387e8695581e23e2825d9c40310068fe25ece7e9c23037a21f39 sympy-1.0.tar.gz' > sympy-1.0.tar.gz.sha512 echo 'ece209d4c7ec0cb58ede791444dc754e0d10811cbbdebe3df61c0fd9f9f9867c1c3ccd5f1827f847c005e24eef34fb5bf87b5d3f894d75da04f1797538290e4a zlib-1.2.8.tar.gz' > zlib-1.2.8.tar.gz.sha512 echo '91a212b5007f9fdfacb4341e06dc0355c5c29897eb8ea407dd4864091f845ba1417bb0d33b5ed6897869d0233e2d0ec6548898d3dbe9eda23f751829bd51a104 setuptools-20.6.7.tar.gz' > setuptools-20.6.7.tar.gz.sha512 # Individual processes [ -z "$HDF5_DIR" ] && get_ytproject $HDF5.tar.gz [ $INST_ZLIB -eq 1 ] && get_ytproject $ZLIB.tar.gz [ $INST_BZLIB -eq 1 ] && get_ytproject $BZLIB.tar.gz [ $INST_PNG -eq 1 ] && get_ytproject $PNG.tar.gz [ $INST_FTYPE -eq 1 ] && get_ytproject $FREETYPE_VER.tar.gz [ $INST_SQLITE3 -eq 1 ] && get_ytproject $SQLITE.tar.gz [ $INST_PYX -eq 1 ] && get_ytproject $PYX.tar.gz [ $INST_SCIPY -eq 1 ] && get_ytproject $SCIPY.tar.gz [ $INST_SCIPY -eq 1 ] && get_ytproject blas.tar.gz [ $INST_SCIPY -eq 1 ] && get_ytproject $LAPACK.tar.gz [ $INST_HG -eq 1 ] && get_ytproject $MERCURIAL.tar.gz [ $INST_PY3 -eq 1 ] && get_ytproject $PYTHON3.tgz [ $INST_H5PY -eq 1 ] && get_ytproject $H5PY.tar.gz [ $INST_NOSE -eq 1 ] && get_ytproject $NOSE.tar.gz [ $INST_ASTROPY -eq 1 ] && get_ytproject $ASTROPY.tar.gz get_ytproject $PYTHON2.tgz get_ytproject $NUMPY.tar.gz get_ytproject $MATPLOTLIB.tar.gz get_ytproject $CYTHON.tar.gz get_ytproject $PYTHON_HGLIB.tar.gz get_ytproject $SYMPY.tar.gz get_ytproject $SETUPTOOLS.tar.gz if [ $INST_BZLIB -eq 1 ] then if [ ! -e $BZLIB/done ] then [ ! -e $BZLIB ] && tar xfz $BZLIB.tar.gz echo "Installing BZLIB" cd $BZLIB if [ `uname` = "Darwin" ] then if [ -z "${CC}" ] then sed -i.bak 's/soname/install_name/' Makefile-libbz2_so else sed -i.bak -e 's/soname/install_name/' -e "s|CC=gcc|CC=${CC}|" Makefile-libbz2_so fi fi ( make install CFLAGS=-fPIC LDFLAGS=-fPIC PREFIX=${DEST_DIR} 2>&1 ) 1>> ${LOG_FILE} || do_exit ( make -f Makefile-libbz2_so CFLAGS=-fPIC LDFLAGS=-fPIC PREFIX=${DEST_DIR} 2>&1 ) 1>> ${LOG_FILE} || do_exit ( cp -v libbz2.so.1.0.6 ${DEST_DIR}/lib 2>&1 ) 1>> ${LOG_FILE} || do_exit touch done cd .. fi BZLIB_DIR=${DEST_DIR} export LDFLAGS="${LDFLAGS} -L${BZLIB_DIR}/lib/ -L${BZLIB_DIR}/lib64/" LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:${BZLIB_DIR}/lib/" fi if [ $INST_ZLIB -eq 1 ] then if [ ! -e $ZLIB/done ] then [ ! -e $ZLIB ] && tar xfz $ZLIB.tar.gz echo "Installing ZLIB" cd $ZLIB ( ./configure --shared --prefix=${DEST_DIR}/ 2>&1 ) 1>> ${LOG_FILE} || do_exit ( make install 2>&1 ) 1>> ${LOG_FILE} || do_exit ( make clean 2>&1) 1>> ${LOG_FILE} || do_exit touch done cd .. fi ZLIB_DIR=${DEST_DIR} export LDFLAGS="${LDFLAGS} -L${ZLIB_DIR}/lib/ -L${ZLIB_DIR}/lib64/" LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:${ZLIB_DIR}/lib/" fi if [ $INST_PNG -eq 1 ] then if [ ! -e $PNG/done ] then [ ! -e $PNG ] && tar xfz $PNG.tar.gz echo "Installing PNG" cd $PNG ( ./configure CPPFLAGS=-I${DEST_DIR}/include CFLAGS=-I${DEST_DIR}/include --prefix=${DEST_DIR}/ 2>&1 ) 1>> ${LOG_FILE} || do_exit ( make install 2>&1 ) 1>> ${LOG_FILE} || do_exit ( make clean 2>&1) 1>> ${LOG_FILE} || do_exit touch done cd .. fi PNG_DIR=${DEST_DIR} export LDFLAGS="${LDFLAGS} -L${PNG_DIR}/lib/ -L${PNG_DIR}/lib64/" LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:${PNG_DIR}/lib/" fi if [ $INST_FTYPE -eq 1 ] then if [ ! -e $FREETYPE_VER/done ] then [ ! -e $FREETYPE_VER ] && tar xfz $FREETYPE_VER.tar.gz echo "Installing FreeType2" cd $FREETYPE_VER ( ./configure CFLAGS=-I${DEST_DIR}/include --prefix=${DEST_DIR}/ 2>&1 ) 1>> ${LOG_FILE} || do_exit ( make 2>&1 ) 1>> ${LOG_FILE} || do_exit ( make install 2>&1 ) 1>> ${LOG_FILE} || do_exit ( make clean 2>&1) 1>> ${LOG_FILE} || do_exit touch done cd .. fi FTYPE_DIR=${DEST_DIR} export LDFLAGS="${LDFLAGS} -L${FTYPE_DIR}/lib/ -L${FTYPE_DIR}/lib64/" LD_LIBRARY_PATH="${LD_LIBRARY_PATH}:${FTYPE_DIR}/lib/" fi if [ -z "$HDF5_DIR" ] then if [ ! -e $HDF5/done ] then [ ! -e $HDF5 ] && tar xfz $HDF5.tar.gz echo "Installing HDF5" cd $HDF5 ( ./configure --prefix=${DEST_DIR}/ --enable-shared 2>&1 ) 1>> ${LOG_FILE} || do_exit ( make ${MAKE_PROCS} install 2>&1 ) 1>> ${LOG_FILE} || do_exit ( make clean 2>&1) 1>> ${LOG_FILE} || do_exit touch done cd .. fi export HDF5_DIR=${DEST_DIR} else export HDF5_DIR=${HDF5_DIR} fi export HDF5_API=16 if [ $INST_SQLITE3 -eq 1 ] then if [ ! -e $SQLITE/done ] then [ ! -e $SQLITE ] && tar xfz $SQLITE.tar.gz echo "Installing SQLite3" cd $SQLITE ( ./configure --prefix=${DEST_DIR}/ 2>&1 ) 1>> ${LOG_FILE} || do_exit ( make ${MAKE_PROCS} install 2>&1 ) 1>> ${LOG_FILE} || do_exit ( make clean 2>&1) 1>> ${LOG_FILE} || do_exit touch done cd .. fi fi if [ ! -e $PYTHON2/done ] then echo "Installing Python 2. This may take a while, but don't worry. yt loves you." [ ! -e $PYTHON2 ] && tar xfz $PYTHON2.tgz cd $PYTHON2 ( ./configure --prefix=${DEST_DIR}/ ${PYCONF_ARGS} 2>&1 ) 1>> ${LOG_FILE} || do_exit ( make ${MAKE_PROCS} 2>&1 ) 1>> ${LOG_FILE} || do_exit ( make install 2>&1 ) 1>> ${LOG_FILE} || do_exit ( ln -sf ${DEST_DIR}/bin/python2.7 ${DEST_DIR}/bin/pyyt 2>&1 ) 1>> ${LOG_FILE} ( make clean 2>&1) 1>> ${LOG_FILE} || do_exit touch done cd .. fi ( ${DEST_DIR}/bin/python -c "import _ssl" 2>&1 ) 1>> ${LOG_FILE} RESULT=$? if [ $RESULT -ne 0 ] then echo "Unable to import the python SSL bindings." echo "This means that OpenSSL is not installed or your system's OpenSSL" echo "installation is out of date." echo "Please install OpenSSL or set INST_CONDA=1" do_exit fi if [ $INST_PY3 -eq 1 ] then if [ ! -e $PYTHON3/done ] then echo "Installing Python 3. Because two Pythons are better than one." [ ! -e $PYTHON3 ] && tar xfz $PYTHON3.tgz cd $PYTHON3 ( ./configure --prefix=${DEST_DIR}/ ${PYCONF_ARGS} 2>&1 ) 1>> ${LOG_FILE} || do_exit ( make ${MAKE_PROCS} 2>&1 ) 1>> ${LOG_FILE} || do_exit ( make install 2>&1 ) 1>> ${LOG_FILE} || do_exit ( ln -sf ${DEST_DIR}/bin/python3.5 ${DEST_DIR}/bin/pyyt 2>&1 ) 1>> ${LOG_FILE} ( ln -sf ${DEST_DIR}/bin/python3.5 ${DEST_DIR}/bin/python 2>&1 ) 1>> ${LOG_FILE} ( ln -sf ${DEST_DIR}/bin/python3-config ${DEST_DIR}/bin/python-config 2>&1 ) 1>> ${LOG_FILE} ( make clean 2>&1) 1>> ${LOG_FILE} || do_exit touch done cd .. fi fi export PYTHONPATH=${DEST_DIR}/lib/${PYTHON_EXEC}/site-packages/ # Install setuptools do_setup_py $SETUPTOOLS if [ $INST_HG -eq 1 ] then do_setup_py $MERCURIAL export HG_EXEC=${DEST_DIR}/bin/hg else # We assume that hg can be found in the path. if type -P hg &>/dev/null then export HG_EXEC=hg else echo "Cannot find mercurial. Please set INST_HG=1." do_exit fi fi if [ -z "$YT_DIR" ] then if [ -e $ORIG_PWD/yt/mods.py ] then YT_DIR="$ORIG_PWD" elif [ -e $ORIG_PWD/../yt/mods.py ] then YT_DIR=`dirname $ORIG_PWD` elif [ ! -e yt-hg ] then echo "Cloning yt" YT_DIR="$PWD/yt-hg/" ( ${HG_EXEC} --debug clone https://bitbucket.org/yt_analysis/yt-supplemental/ 2>&1 ) 1>> ${LOG_FILE} # Recently the hg server has had some issues with timeouts. In lieu of # a new webserver, we are now moving to a three-stage process. # First we clone the repo, but only up to r0. ( ${HG_EXEC} --debug clone https://bitbucket.org/yt_analysis/yt/ ./yt-hg 2>&1 ) 1>> ${LOG_FILE} # Now we update to the branch we're interested in. ( ${HG_EXEC} -R ${YT_DIR} up -C ${BRANCH} 2>&1 ) 1>> ${LOG_FILE} elif [ -e yt-hg ] then YT_DIR="$PWD/yt-hg/" fi echo Setting YT_DIR=${YT_DIR} fi # This fixes problems with gfortran linking. unset LDFLAGS echo "Installing pip" ( ${GETFILE} https://bootstrap.pypa.io/get-pip.py 2>&1 ) 1>> ${LOG_FILE} || do_exit ( ${DEST_DIR}/bin/${PYTHON_EXEC} get-pip.py 2>&1 ) 1>> ${LOG_FILE} || do_exit if [ $INST_SCIPY -eq 0 ] then do_setup_py $NUMPY ${NUMPY_ARGS} else if [ ! -e $SCIPY/done ] then if [ ! -e BLAS/done ] then tar xfz blas.tar.gz echo "Building BLAS" cd BLAS gfortran -O2 -fPIC -fno-second-underscore -c *.f ( ar r libfblas.a *.o 2>&1 ) 1>> ${LOG_FILE} ( ranlib libfblas.a 2>&1 ) 1>> ${LOG_FILE} rm -rf *.o touch done cd .. fi if [ ! -e $LAPACK/done ] then tar xfz $LAPACK.tar.gz echo "Building LAPACK" cd $LAPACK/ cp INSTALL/make.inc.gfortran make.inc ( make lapacklib OPTS="-fPIC -O2" NOOPT="-fPIC -O0" CFLAGS=-fPIC LDFLAGS=-fPIC 2>&1 ) 1>> ${LOG_FILE} || do_exit touch done cd .. fi fi export BLAS=$PWD/BLAS/libfblas.a export LAPACK=$PWD/$LAPACK/liblapack.a do_setup_py $NUMPY ${NUMPY_ARGS} do_setup_py $SCIPY ${NUMPY_ARGS} fi if [ -n "${MPL_SUPP_LDFLAGS}" ] then OLD_LDFLAGS=${LDFLAGS} export LDFLAGS="${MPL_SUPP_LDFLAGS}" echo "Setting LDFLAGS ${LDFLAGS}" fi if [ -n "${MPL_SUPP_CXXFLAGS}" ] then OLD_CXXFLAGS=${CXXFLAGS} export CXXFLAGS="${MPL_SUPP_CXXFLAGS}" echo "Setting CXXFLAGS ${CXXFLAGS}" fi if [ -n "${MPL_SUPP_CFLAGS}" ] then OLD_CFLAGS=${CFLAGS} export CFLAGS="${MPL_SUPP_CFLAGS}" echo "Setting CFLAGS ${CFLAGS}" fi # Now we set up the basedir for matplotlib: mkdir -p ${DEST_DIR}/src/$MATPLOTLIB echo "[directories]" >> ${DEST_DIR}/src/$MATPLOTLIB/setup.cfg echo "basedirlist = ${DEST_DIR}" >> ${DEST_DIR}/src/$MATPLOTLIB/setup.cfg if [ `uname` = "Darwin" ] then echo "[gui_support]" >> ${DEST_DIR}/src/$MATPLOTLIB/setup.cfg echo "macosx = False" >> ${DEST_DIR}/src/$MATPLOTLIB/setup.cfg fi _user_DISPLAY=$DISPLAY unset DISPLAY # see (yt-user link missing: "Installation failure" 01/29/15) do_setup_py $MATPLOTLIB export DISPLAY=${_user_DISPLAY} if [ -n "${OLD_LDFLAGS}" ] then export LDFLAG=${OLD_LDFLAGS} fi [ -n "${OLD_LDFLAGS}" ] && export LDFLAGS=${OLD_LDFLAGS} [ -n "${OLD_CXXFLAGS}" ] && export CXXFLAGS=${OLD_CXXFLAGS} [ -n "${OLD_CFLAGS}" ] && export CFLAGS=${OLD_CFLAGS} echo "Installing Jupyter" ( ${DEST_DIR}/bin/pip install "jupyter<2.0.0" 2>&1 ) 1>> ${LOG_FILE} do_setup_py $CYTHON if [ $INST_H5PY -eq 1 ] then do_setup_py $H5PY fi if [ $INST_NOSE -eq 1 ] then do_setup_py $NOSE fi if [ $INST_ASTROPY -eq 1 ] then do_setup_py $ASTROPY fi do_setup_py $PYTHON_HGLIB do_setup_py $SYMPY [ $INST_PYX -eq 1 ] && do_setup_py $PYX ( ${DEST_DIR}/bin/pip install jinja2 2>&1 ) 1>> ${LOG_FILE} # Now we build Rockstar and set its environment variable. if [ $INST_ROCKSTAR -eq 1 ] then if [ ! -e rockstar/done ] then echo "Building Rockstar" if [ ! -e rockstar ] then ( hg clone http://bitbucket.org/MatthewTurk/rockstar 2>&1 ) 1>> ${LOG_FILE} fi cd rockstar ( hg pull 2>&1 ) 1>> ${LOG_FILE} ( hg up -C tip 2>&1 ) 1>> ${LOG_FILE} ( make lib 2>&1 ) 1>> ${LOG_FILE} || do_exit cp librockstar.so ${DEST_DIR}/lib ROCKSTAR_DIR=${DEST_DIR}/src/rockstar echo $ROCKSTAR_DIR > ${YT_DIR}/rockstar.cfg touch done cd .. fi fi echo "Doing yt update, wiping local changes and updating to branch ${BRANCH}" MY_PWD=`pwd` cd $YT_DIR ( ${HG_EXEC} pull 2>1 && ${HG_EXEC} up -C 2>1 ${BRANCH} 2>&1 ) 1>> ${LOG_FILE} echo "Installing yt" [ $INST_PNG -eq 1 ] && echo $PNG_DIR > png.cfg ( export PATH=$DEST_DIR/bin:$PATH ; ${DEST_DIR}/bin/${PYTHON_EXEC} setup.py develop 2>&1 ) 1>> ${LOG_FILE} || do_exit touch done cd $MY_PWD if !( ( ${DEST_DIR}/bin/${PYTHON_EXEC} -c "import readline" 2>&1 )>> ${LOG_FILE}) || \ [[ "${MYOS##Darwin}" != "${MYOS}" && $INST_PY3 -eq 1 ]] then if !( ( ${DEST_DIR}/bin/${PYTHON_EXEC} -c "import gnureadline" 2>&1 )>> ${LOG_FILE}) then echo "Installing pure-python readline" ( ${DEST_DIR}/bin/pip install gnureadline 2>&1 ) 1>> ${LOG_FILE} fi fi if [ -e $HOME/.matplotlib/fontList.cache ] && \ ( grep -q python2.6 $HOME/.matplotlib/fontList.cache ) then echo "WARNING WARNING WARNING WARNING WARNING WARNING WARNING" echo "*******************************************************" echo echo " You likely need to remove your old fontList.cache!" echo " You can do this with this command:" echo "" echo " rm $HOME/.matplotlib/fontList.cache" echo echo "*******************************************************" fi # Add the environment scripts ( cp ${YT_DIR}/doc/activate ${DEST_DIR}/bin/activate 2>&1 ) 1>> ${LOG_FILE} sed -i.bak -e "s,__YT_DIR__,${DEST_DIR}," ${DEST_DIR}/bin/activate ( cp ${YT_DIR}/doc/activate.csh ${DEST_DIR}/bin/activate.csh 2>&1 ) 1>> ${LOG_FILE} sed -i.bak -e "s,__YT_DIR__,${DEST_DIR}," ${DEST_DIR}/bin/activate.csh test_install function print_afterword { echo echo echo "========================================================================" echo echo "yt is now installed in $DEST_DIR ." echo echo "To run from this new installation, use the activate script for this " echo "environment." echo echo " $ source $DEST_DIR/bin/activate" echo echo "This modifies the environment variables YT_DEST, PATH, PYTHONPATH, and" echo "LD_LIBRARY_PATH to match your new yt install. If you use csh, just" echo "append .csh to the above." echo echo "To get started with yt, check out the orientation:" echo echo " http://yt-project.org/doc/quickstart/" echo echo "The source for yt is located at:" echo " $YT_DIR" if [ $INST_HG -eq 1 ] then echo echo "Mercurial has also been installed:" echo echo "$DEST_DIR/bin/hg" echo fi echo echo "For support, see the website and join the mailing list:" echo echo " http://yt-project.org/" echo " http://yt-project.org/data/ (Sample data)" echo " http://yt-project.org/doc/ (Docs)" echo echo " http://lists.spacepope.org/listinfo.cgi/yt-users-spacepope.org" echo echo "========================================================================" echo echo "Oh, look at me, still talking when there's science to do!" echo "Good luck, and email the user list if you run into any problems." } print_afterword print_afterword >> ${LOG_FILE} echo "yt dependencies were last updated on" > ${DEST_DIR}/.yt_update date >> ${DEST_DIR}/.yt_update else # INST_CONDA -eq 1 MYARCH=`uname -m` # A guess at the OS MYOS=`uname -s` if [ $MYOS = "Darwin" ] then MINICONDA_OS="MacOSX" MINICONDA_ARCH="x86_64" elif [ $MYOS = "Linux" ] then MINICONDA_OS="Linux" if [ $MYARCH = "i386" ] then MINICONDA_ARCH="x86" elif [ $MYARCH = "i686" ] then MINICONDA_ARCH="x86" elif [ $MYARCH = "x86_64" ] then MINICONDA_ARCH="x86_64" else echo "Not sure which architecture you are running." echo "Going with x86_64 architecture." MINICONDA_OS="Linux-x86_64" fi fi if [ $INST_PY3 -eq 1 ] then PY_VERSION='3' else PY_VERSION='2' fi MINICONDA_PKG="Miniconda${PY_VERSION}-${MINICONDA_VERSION}-${MINICONDA_OS}-${MINICONDA_ARCH}.sh" echo echo "Downloading ${MINICONDA_URLBASE}/${MINICONDA_PKG}" echo if [ -f ${MINICONDA_PKG} ] then rm $MINICONDA_PKG fi echo "Installing the Miniconda python environment." if [ -e ${DEST_DIR} ] then rm -rf $DEST_DIR/* else mkdir $DEST_DIR fi log_cmd ${GETFILE} ${MINICONDA_URLBASE}/${MINICONDA_PKG} || do_exit log_cmd bash ./${MINICONDA_PKG} -b -p $DEST_DIR -f # Need to set PATH so we use miniconda's python environment export PATH=${DEST_DIR}/bin:$PATH echo "Installing the necessary packages for yt." echo "This may take a while, but don't worry. yt loves you." declare -a YT_DEPS YT_DEPS+=('python') YT_DEPS+=('setuptools') YT_DEPS+=('numpy') YT_DEPS+=('jupyter') YT_DEPS+=('ipython') YT_DEPS+=('sphinx') if [ $INST_H5PY -ne 0 ] then YT_DEPS+=('h5py') fi YT_DEPS+=('matplotlib') YT_DEPS+=('cython') if [ $INST_NOSE -ne 0 ] then YT_DEPS+=('nose') fi if [ $INST_SCIPY -ne 0 ] then YT_DEPS+=('scipy') fi if [ $INST_ASTROPY -ne 0 ] then YT_DEPS+=('astropy') fi YT_DEPS+=('conda-build') if [ $INST_PY3 -eq 0 ] then YT_DEPS+=('mercurial') fi YT_DEPS+=('sympy') if [ $INST_NETCDF4 -eq 1 ] then YT_DEPS+=('netcdf4') fi log_cmd ${DEST_DIR}/bin/conda update --yes conda log_cmd echo "DEPENDENCIES" ${YT_DEPS[@]} for YT_DEP in "${YT_DEPS[@]}"; do echo "Installing $YT_DEP" log_cmd ${DEST_DIR}/bin/conda install --yes ${YT_DEP} done if [ $INST_PY3 -eq 1 ] then echo "Installing mercurial" log_cmd ${DEST_DIR}/bin/conda create -y -n py27 python=2.7 mercurial log_cmd ln -s ${DEST_DIR}/envs/py27/bin/hg ${DEST_DIR}/bin fi log_cmd ${DEST_DIR}/bin/pip install python-hglib log_cmd ${DEST_DIR}/bin/hg clone https://bitbucket.org/yt_analysis/yt_conda ${DEST_DIR}/src/yt_conda if [ $INST_EMBREE -eq 1 ] then echo "Installing Embree" if [ ! -d ${DEST_DIR}/src ] then mkdir ${DEST_DIR}/src fi cd ${DEST_DIR}/src ( ${GETFILE} "$EMBREE_URL" 2>&1 ) 1>> ${LOG_FILE} || do_exit log_cmd tar xfz ${EMBREE}.tar.gz log_cmd mv ${DEST_DIR}/src/${EMBREE}/include/embree2 ${DEST_DIR}/include log_cmd mv ${DEST_DIR}/src/${EMBREE}/lib/lib*.* ${DEST_DIR}/lib if [ `uname` = "Darwin" ] then ln -s ${DEST_DIR}/lib/libembree.2.dylib ${DEST_DIR}/lib/libembree.dylib install_name_tool -id ${DEST_DIR}/lib/libembree.2.dylib ${DEST_DIR}/lib/libembree.2.dylib else ln -s ${DEST_DIR}/lib/libembree.so.2 ${DEST_DIR}/lib/libembree.so fi echo "Installing pyembree from source" ( ${GETFILE} "$PYEMBREE_URL" 2>&1 ) 1>> ${LOG_FILE} || do_exit log_cmd unzip ${DEST_DIR}/src/master.zip pushd ${DEST_DIR}/src/pyembree-master &> /dev/null log_cmd ${DEST_DIR}/bin/${PYTHON_EXEC} setup.py install build_ext -I${DEST_DIR}/include -L${DEST_DIR}/lib popd &> /dev/null fi if [ $INST_ROCKSTAR -eq 1 ] then echo "Building Rockstar" ( ${DEST_DIR}/bin/hg clone http://bitbucket.org/MatthewTurk/rockstar ${DEST_DIR}/src/rockstar/ 2>&1 ) 1>> ${LOG_FILE} ROCKSTAR_PACKAGE=$(${DEST_DIR}/bin/conda build ${DEST_DIR}/src/yt_conda/rockstar --output) log_cmd ${DEST_DIR}/bin/conda build ${DEST_DIR}/src/yt_conda/rockstar log_cmd ${DEST_DIR}/bin/conda install $ROCKSTAR_PACKAGE ROCKSTAR_DIR=${DEST_DIR}/src/rockstar fi # conda doesn't package pyx, so we install manually with pip if [ $INST_PYX -eq 1 ] then if [ $INST_PY3 -eq 1 ] then log_cmd ${DEST_DIR}/bin/pip install pyx else log_cmd ${DEST_DIR}/bin/pip install pyx==0.12.1 fi fi if [ $INST_YT_SOURCE -eq 0 ] then echo "Installing yt" log_cmd ${DEST_DIR}/bin/conda install -c conda-forge --yes yt else echo "Building yt from source" YT_DIR="${DEST_DIR}/src/yt-hg" log_cmd ${DEST_DIR}/bin/hg clone https://bitbucket.org/yt_analysis/yt ${YT_DIR} log_cmd ${DEST_DIR}/bin/hg -R ${YT_DIR} up -C ${BRANCH} if [ $INST_EMBREE -eq 1 ] then echo $DEST_DIR > ${YT_DIR}/embree.cfg fi if [ $INST_ROCKSTAR -eq 1 ] then echo $ROCKSTAR_DIR > ${YT_DIR}/rockstar.cfg ROCKSTAR_LIBRARY_PATH=${DEST_DIR}/lib fi pushd ${YT_DIR} &> /dev/null ( LIBRARY_PATH=$ROCKSTAR_LIBRARY_PATH ${DEST_DIR}/bin/${PYTHON_EXEC} setup.py develop 2>&1) 1>> ${LOG_FILE} || do_exit popd &> /dev/null fi test_install echo echo echo "========================================================================" echo echo "yt and the Conda system are now installed in $DEST_DIR" echo echo "To get started with yt, check out the orientation:" echo echo " http://yt-project.org/doc/orientation/" echo echo "For support, see the website and join the mailing list:" echo echo " http://yt-project.org/" echo " http://yt-project.org/data/ (Sample data)" echo " http://yt-project.org/doc/ (Docs)" echo echo " http://lists.spacepope.org/listinfo.cgi/yt-users-spacepope.org" echo echo "You must now prepend the following folder to your PATH environment variable:" echo echo " $DEST_DIR/bin" echo echo "On Bash-style shells you can copy/paste the following command to " echo "temporarily activate the yt installation:" echo echo " export PATH=$DEST_DIR/bin:\$PATH" echo echo "and on csh-style shells:" echo echo " setenv PATH $DEST_DIR/bin:\$PATH" echo echo "You can also update the init file appropriate for your shell" echo "(e.g. .bashrc, .bash_profile, .cshrc, or .zshrc) to include" echo "the same command." echo if [ $INST_ROCKSTAR -eq 1 ] then if [ $MYOS = "Darwin" ] then LD_NAME="DYLD_LIBRARY_PATH" else LD_NAME="LD_LIBRARY_PATH" fi echo echo "For rockstar to work, you must also set $LD_NAME:" echo echo " export $LD_NAME=$DEST_DIR/lib:\$$LD_NAME" echo echo "or whichever invocation is appropriate for your shell." fi echo "========================================================================" echo echo "Oh, look at me, still talking when there's science to do!" echo "Good luck, and email the mailing list if you run into any problems." fi yt-3.3.3/doc/Makefile0000644000076500000240000001152012770647750015314 0ustar goldbaumstaff00000000000000# Makefile for Sphinx documentation # # You can set these variables from the command line. 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{%- endblock %} {# Custom CSS overrides #} {% set bootswatch_css_custom = ['_static/custom.css'] %} yt-3.3.3/doc/source/about/0000755000076500000240000000000013023400160016235 5ustar goldbaumstaff00000000000000yt-3.3.3/doc/source/about/index.rst0000644000076500000240000000703513017603554020122 0ustar goldbaumstaff00000000000000.. _aboutyt: About yt ======== .. contents:: :depth: 1 :local: :backlinks: none What is yt? ----------- yt is a toolkit for analyzing and visualizing quantitative data. Originally written to analyze 3D grid-based astrophysical simulation data, it has grown to handle any kind of data represented in a 2D or 3D volume. yt is an Python-based open source project and is open for anyone to use or contribute code. The entire source code and history is available to all at https://bitbucket.org/yt_analysis/yt . .. _who-is-yt: Who is yt? ---------- As an open-source project, yt has a large number of user-developers. In September of 2014, the yt developer community collectively decided to endow the title of *member* on individuals who had contributed in a significant way to the project. For a list of those members and a description of their contributions to the code, see `our members website. `_ For an up-to-date list of everyone who has contributed to the yt codebase, see the current `CREDITS `_ file. For a more detailed breakup of contributions made by individual users, see out `Open HUB page `_. History of yt ------------- yt was originally begun by Matthew Turk in 2007 in the course of his graduate studies in computational astrophysics. The code was developed as a simple data-reader and exporter for grid-based hydrodynamical simulation data outputs from the *Enzo* code. Over the next few years, he invited collaborators and friends to contribute and use yt. As the community grew, so did the capabilities of yt. It is now a community-developed project with contributions from many people, the hospitality of several institutions, and benefiting from numerous grants. With this community-driven approach and contributions from a sizeable population of developers, it has evolved into a fully-featured toolkit for analysis and visualization of multidimensional data. It relies on no proprietary software -- although it can be and has been extended to interface with proprietary software and libraries -- and has been designed from the ground up to enable users to be as immersed in the data as they desire. How do I contact yt? -------------------- If you have any questions about the code, please contact the `yt users email list `_. If you're having other problems, please follow the steps in :ref:`asking-for-help`. How do I cite yt? ----------------- If you use yt in a publication, we'd very much appreciate a citation! You should feel free to cite the `ApJS paper `_ with the following BibTeX entry: :: @ARTICLE{2011ApJS..192....9T, author = {{Turk}, M.~J. and {Smith}, B.~D. and {Oishi}, J.~S. and {Skory}, S. and {Skillman}, S.~W. and {Abel}, T. and {Norman}, M.~L.}, title = "{yt: A Multi-code Analysis Toolkit for Astrophysical Simulation Data}", journal = {The Astrophysical Journal Supplement Series}, archivePrefix = "arXiv", eprint = {1011.3514}, primaryClass = "astro-ph.IM", keywords = {cosmology: theory, methods: data analysis, methods: numerical }, year = 2011, month = jan, volume = 192, eid = {9}, pages = {9}, doi = {10.1088/0067-0049/192/1/9}, adsurl = {http://adsabs.harvard.edu/abs/2011ApJS..192....9T}, adsnote = {Provided by the SAO/NASA Astrophysics Data System} } yt-3.3.3/doc/source/analyzing/0000755000076500000240000000000013023400160017117 5ustar goldbaumstaff00000000000000yt-3.3.3/doc/source/analyzing/_static/0000755000076500000240000000000013023400160020545 5ustar goldbaumstaff00000000000000yt-3.3.3/doc/source/analyzing/_static/axes.c0000644000076500000240000000102112770647750021675 0ustar goldbaumstaff00000000000000#include "axes.h" void calculate_axes(ParticleCollection *part, double *ax1, double *ax2, double *ax3) { int i; for (i = 0; i < part->npart; i++) { if (ax1[0] > part->xpos[i]) ax1[0] = part->xpos[i]; if (ax2[0] > part->ypos[i]) ax2[0] = part->ypos[i]; if (ax3[0] > part->zpos[i]) ax3[0] = part->zpos[i]; if (ax1[1] < part->xpos[i]) ax1[1] = part->xpos[i]; if (ax2[1] < part->ypos[i]) ax2[1] = part->ypos[i]; if (ax3[1] < part->zpos[i]) ax3[1] = part->zpos[i]; } } yt-3.3.3/doc/source/analyzing/_static/axes.h0000644000076500000240000000035312770647750021711 0ustar goldbaumstaff00000000000000typedef struct structParticleCollection { long npart; double *xpos; double *ypos; double *zpos; } ParticleCollection; void calculate_axes(ParticleCollection *part, double *ax1, double *ax2, double *ax3); yt-3.3.3/doc/source/analyzing/_static/axes_calculator.pyx0000644000076500000240000000214413017603554024500 0ustar goldbaumstaff00000000000000import numpy as np cimport numpy as np cimport cython from libc.stdlib cimport malloc, free cdef extern from "axes.h": ctypedef struct ParticleCollection: long npart double *xpos double *ypos double *zpos void calculate_axes(ParticleCollection *part, double *ax1, double *ax2, double *ax3) def examine_axes(np.ndarray[np.float64_t, ndim=1] xpos, np.ndarray[np.float64_t, ndim=1] ypos, np.ndarray[np.float64_t, ndim=1] zpos): cdef double ax1[3] cdef double ax2[3] cdef double ax3[3] cdef ParticleCollection particles cdef int i particles.npart = len(xpos) particles.xpos = xpos.data particles.ypos = ypos.data particles.zpos = zpos.data for i in range(particles.npart): particles.xpos[i] = xpos[i] particles.ypos[i] = ypos[i] particles.zpos[i] = zpos[i] calculate_axes(&particles, ax1, ax2, ax3) return ( (ax1[0], ax1[1], ax1[2]), (ax2[0], ax2[1], ax2[2]), (ax3[0], ax3[1], ax3[2]) ) yt-3.3.3/doc/source/analyzing/_static/axes_calculator_setup.txt0000644000076500000240000000113012770647750025724 0ustar goldbaumstaff00000000000000NAME = "axes_calculator" EXT_SOURCES = ["axes.c"] EXT_LIBRARIES = [] EXT_LIBRARY_DIRS = [] EXT_INCLUDE_DIRS = [] DEFINES = [] from distutils.core import setup from distutils.extension import Extension from Cython.Distutils import build_ext ext_modules = [Extension(NAME, [NAME+".pyx"] + EXT_SOURCES, libraries = EXT_LIBRARIES, library_dirs = EXT_LIBRARY_DIRS, include_dirs = EXT_INCLUDE_DIRS, define_macros = DEFINES) ] setup( name = NAME, cmdclass = {'build_ext': build_ext}, ext_modules = ext_modules ) yt-3.3.3/doc/source/analyzing/analysis_modules/0000755000076500000240000000000013023400160022472 5ustar goldbaumstaff00000000000000yt-3.3.3/doc/source/analyzing/analysis_modules/_images/0000755000076500000240000000000013023400160024076 5ustar goldbaumstaff00000000000000yt-3.3.3/doc/source/analyzing/analysis_modules/_images/2ptcorrelation.png0000644000076500000240000000670412770647750027614 0ustar goldbaumstaff00000000000000PNG  IHDRQ IDATx^OTw t*4;E@..]]zAźqY-Q APAkjDtEJFI[ֶڎr㊉`)0mNObe2'|wGg7#= zL4鵾=#&h"&h"&h"h"&h"&h"&h"hXy8@qȾ6@ h"1 Edi] +E DEF(! 0>Dx ^ G2 .]ک"tTL+lj\d ;ttFJM,uhECn ąts>.;Я<5~IM x{*Wnlj(8nh ue4>v f-70+or 7QJ|_siRW7ٝ8:k9槦.C955=}QQb+zr^#;^ S}0[~ τ! 9h=3gRo{0G&gǀyKƚʜsx,\`S6UA3 ,. 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These spectra can be created using the :class:`~yt.analysis_modules.absorption_spectrum.absorption_spectrum.AbsorptionSpectrum` and :class:`~yt.analysis_modules.cosmological_observation.light_ray.light_ray.LightRay` analysis modules. The :class:`~yt.analysis_modules.absorption_spectrum.absorption_spectrum.AbsorptionSpectrum` class and its workhorse method :meth:`~yt.analysis_modules.absorption_spectrum.absorption_spectrum.AbsorptionSpectrum.make_spectrum` return two arrays, one with wavelengths, the other with the normalized flux values at each of the wavelength values. It can also output a text file listing all important lines. For example, here is an absorption spectrum for the wavelength range from 900 to 1800 Angstroms made with a light ray extending from z = 0 to z = 0.4: .. image:: _images/spectrum_full.png :width: 500 And a zoom-in on the 1425-1450 Angstrom window: .. image:: _images/spectrum_zoom.png :width: 500 Method for Creating Absorption Spectra -------------------------------------- Once a :class:`~yt.analysis_modules.cosmological_observation.light_ray.light_ray.LightRay` has been created traversing a dataset using the :ref:`light-ray-generator`, a series of arrays store the various fields of the gas parcels (represented as cells) intersected along the ray. :class:`~yt.analysis_modules.absorption_spectrum.absorption_spectrum.AbsorptionSpectrum` steps through each element of the :class:`~yt.analysis_modules.cosmological_observation.light_ray.light_ray.LightRay`'s arrays and calculates the column density for desired ion by multiplying its number density with the path length through the cell. Using these column densities along with temperatures to calculate thermal broadening, voigt profiles are deposited on to a featureless background spectrum. By default, the peculiar velocity of the gas is included as a doppler redshift in addition to any cosmological redshift of the data dump itself. Subgrid Deposition ^^^^^^^^^^^^^^^^^^ For features not resolved (i.e. possessing narrower width than the spectral resolution), :class:`~yt.analysis_modules.absorption_spectrum.absorption_spectrum.AbsorptionSpectrum` performs subgrid deposition. The subgrid deposition algorithm creates a number of smaller virtual bins, by default the width of the virtual bins is 1/10th the width of the spectral feature. The Voigt profile is then deposited into these virtual bins where it is resolved, and then these virtual bins are numerically integrated back to the resolution of the original spectral bin size, yielding accurate equivalent widths values. :class:`~yt.analysis_modules.absorption_spectrum.absorption_spectrum.AbsorptionSpectrum` informs the user how many spectral features are deposited in this fashion. Tutorial on Creating an Absorption Spectrum ------------------------------------------- Initializing `AbsorptionSpectrum` Class ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ To instantiate an :class:`~yt.analysis_modules.absorption_spectrum.absorption_spectrum.AbsorptionSpectrum` object, the arguments required are the minimum and maximum wavelengths (assumed to be in Angstroms), and the number of wavelength bins to span this range (including the endpoints) .. code-block:: python from yt.analysis_modules.absorption_spectrum.api import AbsorptionSpectrum sp = AbsorptionSpectrum(900.0, 1800.0, 10001) Adding Features to the Spectrum ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Absorption lines and continuum features can then be added to the spectrum. To add a line, you must know some properties of the line: the rest wavelength, f-value, gamma value, and the atomic mass in amu of the atom. That line must be tied in some way to a field in the dataset you are loading, and this field must be added to the LightRay object when it is created. Below, we will add the H Lyman-alpha line, which is tied to the neutral hydrogen field ('H_number_density'). .. code-block:: python my_label = 'HI Lya' field = 'H_number_density' wavelength = 1215.6700 # Angstroms f_value = 4.164E-01 gamma = 6.265e+08 mass = 1.00794 sp.add_line(my_label, field, wavelength, f_value, gamma, mass, label_threshold=1.e10) In the above example, the *field* argument tells the spectrum generator which field from the ray data to use to calculate the column density. The ``label_threshold`` keyword tells the spectrum generator to add all lines above a column density of 10 :superscript:`10` cm :superscript:`-2` to the text line list output at the end. If None is provided, as is the default, no lines of this type will be added to the text list. Continuum features with optical depths that follow a power law can also be added. Like adding lines, you must specify details like the wavelength and the field in the dataset and LightRay that is tied to this feature. The wavelength refers to the location at which the continuum begins to be applied to the dataset, and as it moves to lower wavelength values, the optical depth value decreases according to the defined power law. The normalization value is the column density of the linked field which results in an optical depth of 1 at the defined wavelength. Below, we add the hydrogen Lyman continuum. .. code-block:: python my_label = 'HI Lya' field = 'H_number_density' wavelength = 912.323660 # Angstroms normalization = 1.6e17 index = 3.0 sp.add_continuum(my_label, field, wavelength, normalization, index) Making the Spectrum ^^^^^^^^^^^^^^^^^^^ Once all the lines and continuua are added, it is time to make a spectrum out of some light ray data. .. code-block:: python wavelength, flux = sp.make_spectrum('lightray.h5', output_file='spectrum.fits', line_list_file='lines.txt') A spectrum will be made using the specified ray data and the wavelength and flux arrays will also be returned. If you set the optional ``use_peculiar_velocity`` keyword to False, the lines will not incorporate doppler redshifts to shift the deposition of the line features. Three output file formats are supported for writing out the spectrum: fits, hdf5, and ascii. The file format used is based on the extension provided in the ``output_file`` keyword: ``.fits`` for a fits file, ``.h5`` for an hdf5 file, and anything else for an ascii file. .. note:: To write out a fits file, you must install the `astropy `_ python library in order to access the astropy.io.fits module. You can usually do this by simply running `pip install astropy` at the command line. Generating Spectra in Parallel ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ The `AbsorptionSpectrum` analysis module can be run in parallel simply by following the procedures laid out in :ref:`parallel-computation` for running yt scripts in parallel. Spectrum generation is parallelized using a multi-level strategy where each absorption line is deposited by a different processor. If the number of available processors is greater than the number of lines, then the deposition of individual lines will be divided over multiple processors. Fitting Absorption Spectra ========================== .. sectionauthor:: Hilary Egan This tool can be used to fit absorption spectra, particularly those generated using the (``AbsorptionSpectrum``) tool. For more details on its uses and implementation please see (`Egan et al. (2013) `_). If you find this tool useful we encourage you to cite accordingly. Loading an Absorption Spectrum ------------------------------ To load an absorption spectrum created by (:class:`~yt.analysis_modules.absorption_spectrum.absorption_spectrum.AbsorptionSpectrum``), we specify the output file name. It is advisable to use either an .h5 or .fits file, rather than an ascii file to save the spectrum as rounding errors produced in saving to a ascii file will negatively impact fit quality. .. code-block:: python f = h5py.File('spectrum.h5') wavelength = f["wavelength"][:] flux = f['flux'][:] f.close() Specifying Species Properties ----------------------------- Before fitting a spectrum, you must specify the properties of all the species included when generating the spectrum. The physical properties needed for each species are the rest wavelength, f-value, gamma value, and atomic mass. These will be the same values as used to generate the initial absorption spectrum. These values are given in list form as some species generate multiple lines (as in the OVI doublet). The number of lines is also specified on its own. To fine tune the fitting procedure and give results in a minimal number of optimizing steps, we specify expected maximum and minimum values for the column density, doppler parameter, and redshift. These values can be well outside the range of expected values for a typical line and are mostly to prevent the algorithm from fitting to negative values or becoming numerically unstable. Common initial guesses for doppler parameter and column density should also be given. These values will not affect the specific values generated by the fitting algorithm, provided they are in a reasonably appropriate range (ie: within the range given by the max and min values for the parameter). For a spectrum containing both the H Lyman-alpha line and the OVI doublet, we set up a fit as shown below. .. code-block:: python HI_parameters = {'name':'HI', 'f': [.4164], 'Gamma':[6.265E8], 'wavelength':[1215.67], 'numLines':1, 'maxN': 1E22, 'minN':1E11, 'maxb': 300, 'minb':1, 'maxz': 6, 'minz':0, 'init_b':30, 'init_N':1E14} OVI_parameters = {'name':'OVI', 'f':[.1325,.06580], 'Gamma':[4.148E8,4.076E8], 'wavelength':[1031.9261,1037.6167], 'numLines':2, 'maxN':1E17,'minN':1E11, 'maxb':300, 'minb':1, 'maxz':6, 'minz':0, 'init_b':20, 'init_N':1E12} speciesDicts = {'HI':HI_parameters,'OVI':OVI_parameters} Generating Fit of Spectrum -------------------------- After loading a spectrum and specifying the properties of the species used to generate the spectrum, an appropriate fit can be generated. .. code-block:: python orderFits = ['OVI','HI'] fitted_lines, fitted_flux = generate_total_fit(wavelength, flux, orderFits, speciesDicts) The orderFits variable is used to determine in what order the species should be fitted. This may affect the results of the resulting fit, as lines may be fit as an incorrect species. For best results, it is recommended to fit species the generate multiple lines first, as a fit will only be accepted if all of the lines are fit appropriately using a single set of parameters. At the moment no cross correlation between lines of different species is performed. The parameters of the lines that are needed to fit the spectrum are contained in the ``fitted_lines`` variable. Each species given in ``orderFits`` will be a key in the ``fitted_lines`` dictionary. The entry for each species key will be another dictionary containing entries for 'N','b','z', and 'group#' which are the column density, doppler parameter, redshift, and associate line complex respectively. The i :superscript:`th` line of a given species is then given by the parameters ``N[i]``, ``b[i]``, and ``z[i]`` and is part of the same complex (and was fitted at the same time) as all lines with the same group number as ``group#[i]``. The ``fitted_flux`` is an ndarray of the same size as ``flux`` and ``wavelength`` that contains the cumulative absorption spectrum generated by the lines contained in ``fitted_lines``. Saving a Spectrum Fit --------------------- Saving the results of a fitted spectrum for further analysis is accomplished automatically using the h5 file format. A group is made for each species that is fit, and each species group has a group for the corresponding N, b, z, and group# values. .. _fitting_procedure: Procedure for Generating Fits ----------------------------- .. sectionauthor:: Hilary Egan To generate a fit for a spectrum :func:`~yt.analysis_modules.absorption_spectrum.absorption_spectrum_fit.generate_total_fit` is called. This function controls the identification of line complexes, the fit of a series of absorption lines for each appropriate species, checks of those fits, and returns the results of the fits. Finding Line Complexes ---------------------- Line complexes are found using the :func:`~yt.analysis_modules.absorption_spectrum.absorption_spectrum_fit.find_complexes` function. The process by which line complexes are found involves walking through the array of flux in order from minimum to maximum wavelength, and finding series of spatially contiguous cells whose flux is less than some limit. These regions are then checked in terms of an additional flux limit and size. The bounds of all the passing regions are then listed and returned. Those bounds that cover an exceptionally large region of wavelength space will be broken up if a suitable cut point is found. This method is only appropriate for noiseless spectra. The optional parameter ``complexLim`` (default = 0.999), controls the limit that triggers the identification of a spatially contiguous region of flux that could be a line complex. This number should be very close to 1 but not exactly equal. It should also be at least an order of magnitude closer to 1 than the later discussed ``fitLim`` parameter, because a line complex where the flux of the trough is very close to the flux of the edge can be incredibly unstable when optimizing. The ``fitLim`` parameter controls what is the maximum flux that the trough of the region can have and still be considered a line complex. This effectively controls the sensitivity to very low column absorbers. Default value is ``fitLim`` = 0.99. If a region is identified where the flux of the trough is greater than this value, the region is simply ignored. The ``minLength`` parameter controls the minimum number of array elements that an identified region must have. This value must be greater than or equal to 3 as there are a minimum of 3 free parameters that must be fit. Default is ``minLength`` = 3. The ``maxLength`` parameter controls the maximum number of array elements that an identified region can have before it is split into separate regions. Default is ``maxLength`` = 1000. This should be adjusted based on the resolution of the spectrum to remain appropriate. The value correspond to a wavelength of roughly 50 angstroms. The ``splitLim`` parameter controls how exceptionally large regions are split. When such a region is identified by having more array elements than ``maxLength``, the point of maximum flux (or minimum absorption) in the middle two quartiles is identified. If that point has a flux greater than or equal to ``splitLim``, then two separate complexes are created: one from the lower wavelength edge to the minimum absorption point and the other from the minimum absorption point to the higher wavelength edge. The default value is ``splitLim`` =.99, but it should not drastically affect results, so long as the value is reasonably close to 1. Fitting a Line Complex ---------------------- After a complex is identified, it is fitted by iteratively adding and optimizing a set of Voigt Profiles for a particular species until the region is considered successfully fit. The optimizing is accomplished using scipy's least squares optimizer. This requires an initial estimate of the parameters to be fit (column density, b-value, redshift) for each line. Each time a line is added, the guess of the parameters is based on the difference between the line complex and the fit so far. For the first line this just means the initial guess is based solely on the flux of the line complex. The column density is given by the initial column density given in the species parameters dictionary. If the line is saturated (some portion of the flux with a value less than .1) than the larger initial column density guess is chosen. If the flux is relatively high (all values >.9) than the smaller initial guess is given. These values are chosen to make optimization faster and more stable by being closer to the actual value, but the final results of fitting should not depend on them as they merely provide a starting point. After the parameters for a line are optimized for the first time, the optimized parameters are then used for the initial guess on subsequent iterations with more lines. The complex is considered successfully fit when the sum of the squares of the difference between the flux generated from the fit and the desired flux profile is less than ``errBound``. ``errBound`` is related to the optional parameter to :meth:`~yt.analysis_modules.cosmological_observation.light_ray.light_ray.LightRay.generate_total_fit`, ``maxAvgError`` by the number of array elements in the region such that ``errBound`` = number of elements * ``maxAvgError``. There are several other conditions under which the cycle of adding and optimizing lines will halt. If the error of the optimized fit from adding a line is an order of magnitude worse than the error of the fit without that line, then it is assumed that the fitting has become unstable and the latest line is removed. Lines are also prevented from being added if the total number of lines is greater than the number of elements in the flux array being fit divided by 3. This is because there must not be more free parameters in a fit than the number of points to constrain them. Checking Fit Results -------------------- After an acceptable fit for a region is determined, there are several steps the algorithm must go through to validate the fits. First, the parameters must be in a reasonable range. This is a check to make sure that the optimization did not become unstable and generate a fit that diverges wildly outside the region where the fit was performed. This way, even if particular complex cannot be fit, the rest of the spectrum fitting still behaves as expected. The range of acceptability for each parameter is given in the species parameter dictionary. These are merely broad limits that will prevent numerical instability rather than physical limits. In cases where a single species generates multiple lines (as in the OVI doublet), the fits are then checked for higher wavelength lines. Originally the fits are generated only considering the lowest wavelength fit to a region. This is because we perform the fitting of complexes in order from the lowest wavelength to the highest, so any contribution to a complex being fit must come from the lower wavelength as the higher wavelength contributions would already have been subtracted out after fitting the lower wavelength. Saturated Lyman Alpha Fitting Tools ----------------------------------- In cases where a large or saturated line (there exists a point in the complex where the flux is less than .1) fails to be fit properly at first pass, a more robust set of fitting tools is used to try and remedy the situation. The basic approach is to simply try a much wider range of initial parameter guesses in order to find the true optimization minimum, rather than getting stuck in a local minimum. A set of hard coded initial parameter guesses for Lyman alpha lines is given by the function :func:`~yt.analysis_modules.absorption_spectrum.absorption_spectrum_fit.get_test_lines`. Also included in these parameter guesses is an an initial guess of a high column cool line overlapping a lower column warm line, indictive of a broad Lyman alpha (BLA) absorber. yt-3.3.3/doc/source/analyzing/analysis_modules/clump_finding.rst0000644000076500000240000001157013023400062026047 0ustar goldbaumstaff00000000000000.. _clump_finding: Clump Finding ============= The clump finder uses a contouring algorithm to identified topologically disconnected structures within a dataset. This works by first creating a single contour over the full range of the contouring field, then continually increasing the lower value of the contour until it reaches the maximum value of the field. As disconnected structures are identified as separate contours, the routine continues recursively through each object, creating a hierarchy of clumps. Individual clumps can be kept or removed from the hierarchy based on the result of user-specified functions, such as checking for gravitational boundedness. A sample recipe can be found in :ref:`cookbook-find_clumps`. The clump finder requires a data object (see :ref:`data-objects`) and a field over which the contouring is to be performed. .. code:: python import yt from yt.analysis_modules.level_sets.api import * ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030") data_source = ds.disk([0.5, 0.5, 0.5], [0., 0., 1.], (8, 'kpc'), (1, 'kpc')) master_clump = Clump(data_source, ("gas", "density")) At this point, every isolated contour will be considered a clump, whether this is physical or not. Validator functions can be added to determine if an individual contour should be considered a real clump. These functions are specified with the ``Clump.add_validator`` function. Current, two validators exist: a minimum number of cells and gravitational boundedness. .. code:: python master_clump.add_validator("min_cells", 20) master_clump.add_validator("gravitationally_bound", use_particles=False) As many validators as desired can be added, and a clump is only kept if all return True. If not, a clump is remerged into its parent. Custom validators can easily be added. A validator function must only accept a ``Clump`` object and either return True or False. .. code:: python def _minimum_gas_mass(clump, min_mass): return (clump["gas", "cell_mass"].sum() >= min_mass) add_validator("minimum_gas_mass", _minimum_gas_mass) The ``add_validator`` function adds the validator to a registry that can be accessed by the clump finder. Then, the validator can be added to the clump finding just like the others. .. code:: python master_clump.add_validator("minimum_gas_mass", ds.quan(1.0, "Msun")) The clump finding algorithm accepts the ``Clump`` object, the initial minimum and maximum of the contouring field, and the step size. The lower value of the contour finder will be continually multiplied by the step size. .. code:: python c_min = data_source["gas", "density"].min() c_max = data_source["gas", "density"].max() step = 2.0 find_clumps(master_clump, c_min, c_max, step) After the clump finding has finished, the master clump will represent the top of a hierarchy of clumps. The ``children`` attribute within a ``Clump`` object contains a list of all sub-clumps. Each sub-clump is also a ``Clump`` object with its own ``children`` attribute, and so on. A number of helper routines exist for examining the clump hierarchy. .. code:: python # Write a text file of the full hierarchy. write_clump_index(master_clump, 0, "%s_clump_hierarchy.txt" % ds) # Write a text file of only the leaf nodes. write_clumps(master_clump,0, "%s_clumps.txt" % ds) # Get a list of just the leaf nodes. leaf_clumps = get_lowest_clumps(master_clump) ``Clump`` objects can be used like all other data containers. .. code:: python print(leaf_clumps[0]["gas", "density"]) print(leaf_clumps[0].quantities.total_mass()) The writing functions will write out a series or properties about each clump by default. Additional properties can be appended with the ``Clump.add_info_item`` function. .. code:: python master_clump.add_info_item("total_cells") Just like the validators, custom info items can be added by defining functions that minimally accept a ``Clump`` object and return a string to be printed. .. code:: python def _mass_weighted_jeans_mass(clump): jeans_mass = clump.data.quantities.weighted_average_quantity( "jeans_mass", ("gas", "cell_mass")).in_units("Msun") return "Jeans Mass (mass-weighted): %.6e Msolar." % jeans_mass add_clump_info("mass_weighted_jeans_mass", _mass_weighted_jeans_mass) Then, add it to the list: .. code:: python master_clump.add_info_item("mass_weighted_jeans_mass") By default, the following info items are activated: **total_cells**, **cell_mass**, **mass_weighted_jeans_mass**, **volume_weighted_jeans_mass**, **max_grid_level**, **min_number_density**, **max_number_density**, and **distance_to_main_clump**. Clumps can be visualized using the ``annotate_clumps`` callback. .. code:: python prj = yt.ProjectionPlot(ds, 2, ("gas", "density"), center='c', width=(20,'kpc')) prj.annotate_clumps(leaf_clumps) prj.save('clumps') yt-3.3.3/doc/source/analyzing/analysis_modules/cosmology_calculator.rst0000644000076500000240000000612213017603554027470 0ustar goldbaumstaff00000000000000.. _cosmology-calculator: Cosmology Calculator ==================== The cosmology calculator can be used to calculate cosmological distances and times given a set of cosmological parameters. A cosmological dataset, `ds`, will automatically have a cosmology calculator configured with the correct parameters associated with it as `ds.cosmology`. A standalone :class:`~yt.utilities.cosmology.Cosmology` calculator object can be created in the following way: .. code-block:: python from yt.utilities.cosmology import Cosmology co = Cosmology(hubble_constant=0.7, omega_matter=0.3, omega_lambda=0.7, omega_curvature=0.0) Once created, various distance calculations as well as conversions between redshift and time are available: .. notebook-cell:: from yt.utilities.cosmology import Cosmology co = Cosmology(hubble_constant=0.7, omega_matter=0.3, omega_lambda=0.7, omega_curvature=0.0) # Hubble distance (c / h) print("hubble distance", co.hubble_distance()) # distance from z = 0 to 0.5 print("comoving radial distance", co.comoving_radial_distance(0, 0.5).in_units("Mpccm/h")) # transverse distance print("transverse distance", co.comoving_transverse_distance(0, 0.5).in_units("Mpccm/h")) # comoving volume print("comoving volume", co.comoving_volume(0, 0.5).in_units("Gpccm**3")) # angulare diameter distance print("angular diameter distance", co.angular_diameter_distance(0, 0.5).in_units("Mpc/h")) # angular scale print("angular scale", co.angular_scale(0, 0.5).in_units("Mpc/degree")) # luminosity distance print("luminosity distance", co.luminosity_distance(0, 0.5).in_units("Mpc/h")) # time between two redshifts print("lookback time", co.lookback_time(0, 0.5).in_units("Gyr")) # age of the Universe at a given redshift print("hubble time", co.hubble_time(0).in_units("Gyr")) # critical density print("critical density", co.critical_density(0)) # Hubble parameter at a given redshift print("hubble parameter", co.hubble_parameter(0).in_units("km/s/Mpc")) # convert time after Big Bang to redshift my_t = co.quan(8, "Gyr") print("z from t", co.z_from_t(my_t)) # convert redshift to time after Big Bang (same as Hubble time) print("t from z", co.t_from_z(0.5).in_units("Gyr")) .. warning:: Cosmological distance calculations return values that are either in the comoving or proper frame, depending on the specific quantity. For simplicity, the proper and comoving frames are set equal to each other within the cosmology calculator. This means that for some distance value, x, x.to("Mpc") and x.to("Mpccm") will be the same. The user should take care to understand which reference frame is correct for the given calculation. All of the above functions accept scalar values and arrays. The helper functions, `co.quan` and `co.arr` exist to create unitful `YTQuantities` and `YTArray` with the unit registry of the cosmology calculator. For more information on the usage and meaning of each calculation, consult the reference documentation at :ref:`cosmology-calculator-ref`. yt-3.3.3/doc/source/analyzing/analysis_modules/ellipsoid_analysis.rst0000644000076500000240000001375613017603554027146 0ustar goldbaumstaff00000000000000.. _ellipsoid_analysis: Halo Ellipsoid Analysis ======================= .. sectionauthor:: Geoffrey So .. warning:: This functionality is currently broken and needs to be updated to make use of the :ref:`halo_catalog` framework. Anyone interested in doing so should contact the yt-dev list. Purpose ------- The purpose of creating this feature in yt is to analyze field properties that surround dark matter haloes. Originally, this was usually done with the sphere 3D container, but since many halo particles are linked together in a more elongated shape, I thought it would be better to use an ellipsoid 3D container to wrap around the particles. This way, less of the empty-of-particle space around the halo would be included when doing the analysis of field properties where the particles are suppose to occupy. General Overview ---------------- In order to use the ellipsoid 3D container object, one must supply it with a center, the magnitude of the semi-principle axes, the direction of the first semi-principle axis, the tilt angle (rotation angle about the y axis that will align the first semi-principle axis with the x axis once it is aligned in the x-z plane.) Once those parameters are determined, the function "ellipsoid" will return the 3D object, and users will be able to get field attributes from the data object just as they would from spheres, cylinders etc. Example ------- To use the ellipsoid container to get field information, you will have to first determine the ellipsoid's parameters. This can be done with the haloes obtained from halo finding, but essentially it takes the information: #. Center position x,y,z #. List of particles position x,y,z And calculates the ellipsoid information needed for the 3D container. What I usually do is get this information from the halo finder output files in the .h5 HDF5 binary format. I load them into memory using the LoadHaloes() function instead of reading in the ASCII output. Halo Finding ~~~~~~~~~~~~ .. code-block:: python import yt from yt.analysis_modules.halo_finding.api import * ds = yt.load('Enzo_64/RD0006/RedshiftOutput0006') halo_list = HaloFinder(ds) halo_list.dump('MyHaloList') Ellipsoid Parameters ~~~~~~~~~~~~~~~~~~~~ .. code-block:: python import yt from yt.analysis_modules.halo_finding.api import * ds = yt.load('Enzo_64/RD0006/RedshiftOutput0006') haloes = LoadHaloes(ds, 'MyHaloList') Once the halo information is saved you can load it into the data object "haloes", you can get loop over the list of haloes and do .. code-block:: python ell_param = haloes[0].get_ellipsoid_parameters() This will return 6 items #. The center of mass as an array. #. A as a float. (Must have A>=B) #. B as a float. (Must have B>=C) #. C as a float. (Must have C > cell size) #. e0 vector as an array. (now normalized automatically in the code) #. tilt as a float. The center of mass would be the same one as returned by the halo finder. The A, B, C are the largest to smallest magnitude of the ellipsoid's semi-principle axes. "e0" is the largest semi-principle axis vector direction that would have magnitude A but normalized. The "tilt" is an angle measured in radians. It can be best described as after the rotation about the z-axis to align e0 to x in the x-y plane, and then rotating about the y-axis to align e0 completely to the x-axis, the angle remaining to rotate about the x-axis to align both e1 to the y-axis and e2 to the z-axis. Ellipsoid 3D Container ~~~~~~~~~~~~~~~~~~~~~~ Once the parameters are obtained from the get_ellipsoid_parameters() function, or picked at random by the user, it can be input into the ellipsoid container as: .. code-block:: python ell = ds.ellipsoid(ell_param[0], ell_param[1], ell_param[2], ell_param[3], ell_param[4], ell_param[5]) dens = ell.quantities['TotalQuantity']('density')[0] This way, "ell" will be the ellipsoid container, and "dens" will be the total density of the ellipsoid in an unigrid simulation. One can of course use this container object with parameters that they come up with, the ellipsoid parameters do not have to come from the Halo Finder. And of course, one can use the ellipsoid container with other derived fields or fields that they are interested in. Drawbacks --------- Since this is a first attempt, there are many drawbacks and corners cut. Many things listed here will be amended when I have time. * The ellipsoid 3D container like the boolean object, do not contain particle position and velocity information. * This currently assume periodic boundary condition, so if an ellipsoid center is at the edge, it will return part of the opposite edge field information. Will try to put in the option to turn off periodicity in the future. * This method gives a minimalistic ellipsoid centered around the center of mass that contains all the particles, but sometimes people prefer an inertial tensor triaxial ellipsoid described in `Dubinski, Carlberg 1991 `_. I have that method composed but it is not fully tested yet. * The method to obtain information from the halo still uses the center of mass as the center of the ellipsoid, so it is not making the smallest ellipsoid that contains the particles as possible. To start at the center of the particles based on position will require an O(:math:`N^2`) operation, right now I'm trying to limit everything to O(:math:`N`) operations. If particle count does not get too large, I may implement the O(:math:`N^2`) operation. * Currently the list of haloes can be analyzed using object parallelism (one halo per core), but I'm not sure if haloes will get big enough soon that other forms of parallelism will be needed to analyze them due to memory constraint. * This has only been tested on unigrid simulation data, not AMR. In unigrid simulations, I can take "dens" from the example and divide it by the total number of cells to get the average density, in AMR one would need to do an volume weighted average instead. yt-3.3.3/doc/source/analyzing/analysis_modules/exporting.rst0000644000076500000240000000024212770647750025273 0ustar goldbaumstaff00000000000000Exporting to External Radiation Transport Codes =============================================== .. toctree:: :maxdepth: 2 sunrise_export radmc3d_exportyt-3.3.3/doc/source/analyzing/analysis_modules/halo_analysis.rst0000644000076500000240000000061013017603554026066 0ustar goldbaumstaff00000000000000.. _halo-analysis: Halo Analysis ============= This section covers halo finding, performing extra analysis on halos, and the halo mass function calculator. If you already have halo catalogs and simply want to load them into yt, see :ref:`halo-catalog-data`. .. toctree:: :maxdepth: 2 halo_catalogs halo_mass_function halo_transition halo_merger_tree ellipsoid_analysis yt-3.3.3/doc/source/analyzing/analysis_modules/halo_catalogs.rst0000644000076500000240000004527613017603554026061 0ustar goldbaumstaff00000000000000.. _halo_catalog: Halo Finding and Analysis ========================= In yt-3.x, halo finding and analysis are combined into a single framework called the :class:`~yt.analysis_modules.halo_analysis.halo_catalog.HaloCatalog`. This framework is substantially different from the halo analysis machinery available in yt-2.x and is entirely backward incompatible. For a direct translation of various halo analysis tasks using yt-2.x to yt-3.x, see :ref:`halo-transition`. .. _halo_catalog_finding: Halo Finding ------------ If you already have a halo catalog, either produced by one of the methods below or in a format described in :ref:`halo-catalog-data`, and want to perform further analysis, skip to :ref:`halo_catalog_analysis`. Three halo finding methods exist within yt. These are: * :ref:`fof_finding`: a basic friend-of-friends algorithm (e.g. `Efstathiou et al. (1985) `_) * :ref:`hop_finding`: `Eisenstein and Hut (1998) `_. * :ref:`rockstar_finding`: a 6D phase-space halo finder developed by Peter Behroozi that scales well and does substructure finding (`Behroozi et al. 2011 `_) Halo finding is performed through the creation of a :class:`~yt.analysis_modules.halo_analysis.halo_catalog.HaloCatalog` object. The dataset on which halo finding is to be performed should be loaded and given to the :class:`~yt.analysis_modules.halo_analysis.halo_catalog.HaloCatalog` along with the ``finder_method`` keyword to specify the method to be used. .. code-block:: python import yt from yt.analysis_modules.halo_analysis.api import HaloCatalog data_ds = yt.load('Enzo_64/RD0006/RedshiftOutput0006') hc = HaloCatalog(data_ds=data_ds, finder_method='hop') hc.create() The ``finder_method`` options should be given as "fof", "hop", or "rockstar". Each of these methods has their own set of keyword arguments to control functionality. These can specified in the form of a dictinoary using the ``finder_kwargs`` keyword. .. code-block:: python import yt from yt.analysis_modules.halo_analysis.api import HaloCatalog data_ds = yt.load('Enzo_64/RD0006/RedshiftOutput0006') hc = HaloCatalog(data_ds=data_ds, finder_method='fof', finder_kwargs={"ptype": "stars", "padding": 0.02}) hc.create() For a full list of keywords for each halo finder, see :class:`~yt.analysis_modules.halo_finding.halo_objects.FOFHaloFinder`, :class:`~yt.analysis_modules.halo_finding.halo_objects.HOPHaloFinder`, and :class:`~yt.analysis_modules.halo_finding.rockstar.rockstar.RockstarHaloFinder`. .. _fof_finding: FOF ^^^ This is a basic friends-of-friends algorithm. See `Efstathiou et al. (1985) `_ for more details as well as :class:`~yt.analysis_modules.halo_finding.halo_objects.FOFHaloFinder`. .. _hop_finding: HOP ^^^ The version of HOP used in yt is an upgraded version of the `publicly available HOP code `_. Support for 64-bit floats and integers has been added, as well as parallel analysis through spatial decomposition. HOP builds groups in this fashion: #. Estimates the local density at each particle using a smoothing kernel. #. Builds chains of linked particles by 'hopping' from one particle to its densest neighbor. A particle which is its own densest neighbor is the end of the chain. #. All chains that share the same densest particle are grouped together. #. Groups are included, linked together, or discarded depending on the user-supplied over density threshold parameter. The default is 160.0. See the `HOP method paper `_ for full details as well as :class:`~yt.analysis_modules.halo_finding.halo_objects.HOPHaloFinder`. .. _rockstar_finding: Rockstar ^^^^^^^^ Rockstar uses an adaptive hierarchical refinement of friends-of-friends groups in six phase-space dimensions and one time dimension, which allows for robust (grid-independent, shape-independent, and noise- resilient) tracking of substructure. The code is prepackaged with yt, but also `separately available `_. The lead developer is Peter Behroozi, and the methods are described in `Behroozi et al. 2011 `_. In order to run the Rockstar halo finder in yt, make sure you've :ref:`installed it so that it can integrate with yt `. At the moment, Rockstar does not support multiple particle masses, instead using a fixed particle mass. This will not affect most dark matter simulations, but does make it less useful for finding halos from the stellar mass. In simulations where the highest-resolution particles all have the same mass (ie: zoom-in grid based simulations), one can set up a particle filter to select the lowest mass particles and perform the halo finding only on those. See the this cookbook recipe for an example: :ref:`cookbook-rockstar-nested-grid`. To run the Rockstar Halo finding, you must launch python with MPI and parallelization enabled. While Rockstar itself does not require MPI to run, the MPI libraries allow yt to distribute particle information across multiple nodes. .. warning:: At the moment, running Rockstar inside of yt on multiple compute nodes connected by an Infiniband network can be problematic. Therefore, for now we recommend forcing the use of the non-Infiniband network (e.g. Ethernet) using this flag: ``--mca btl ^openib``. For example, here is how Rockstar might be called using 24 cores: ``mpirun -n 24 --mca btl ^openib python ./run_rockstar.py --parallel``. The script above configures the Halo finder, launches a server process which disseminates run information and coordinates writer-reader processes. Afterwards, it launches reader and writer tasks, filling the available MPI slots, which alternately read particle information and analyze for halo content. The RockstarHaloFinder class has these options that can be supplied to the halo catalog through the ``finder_kwargs`` argument: * ``dm_type``, the index of the dark matter particle. Default is 1. * ``outbase``, This is where the out*list files that Rockstar makes should be placed. Default is 'rockstar_halos'. * ``num_readers``, the number of reader tasks (which are idle most of the time.) Default is 1. * ``num_writers``, the number of writer tasks (which are fed particles and do most of the analysis). Default is MPI_TASKS-num_readers-1. If left undefined, the above options are automatically configured from the number of available MPI tasks. * ``force_res``, the resolution that Rockstar uses for various calculations and smoothing lengths. This is in units of Mpc/h. If no value is provided, this parameter is automatically set to the width of the smallest grid element in the simulation from the last data snapshot (i.e. the one where time has evolved the longest) in the time series: ``ds_last.index.get_smallest_dx() * ds_last['Mpch']``. * ``total_particles``, if supplied, this is a pre-calculated total number of dark matter particles present in the simulation. For example, this is useful when analyzing a series of snapshots where the number of dark matter particles should not change and this will save some disk access time. If left unspecified, it will be calculated automatically. Default: ``None``. * ``dm_only``, if set to ``True``, it will be assumed that there are only dark matter particles present in the simulation. This option does not modify the halos found by Rockstar, however this option can save disk access time if there are no star particles (or other non-dark matter particles) in the simulation. Default: ``False``. Rockstar dumps halo information in a series of text (halo*list and out*list) and binary (halo*bin) files inside the ``outbase`` directory. We use the halo list classes to recover the information. Inside the ``outbase`` directory there is a text file named ``datasets.txt`` that records the connection between ds names and the Rockstar file names. .. _rockstar-installation: Installing Rockstar """"""""""""""""""" Because of changes in the Rockstar API over time, yt only currently works with a slightly older version of Rockstar. This version of Rockstar has been slightly patched and modified to run as a library inside of yt. By default it is not installed with yt, but installation is very easy. The :ref:`install-script` used to install yt from source has a line: ``INST_ROCKSTAR=0`` that must be changed to ``INST_ROCKSTAR=1``. You can rerun this installer script over the top of an existing installation, and it will only install components missing from the existing installation. You can do this as follows. Put your freshly modified install_script in the parent directory of the yt installation directory (e.g. the parent of ``$YT_DEST``, ``yt-x86_64``, ``yt-i386``, etc.), and rerun the installer: .. code-block:: bash cd $YT_DEST cd .. vi install_script.sh // or your favorite editor to change INST_ROCKSTAR=1 bash < install_script.sh This will download Rockstar and install it as a library in yt. .. _halo_catalog_analysis: Extra Halo Analysis ------------------- As a reminder, all halo catalogs created by the methods outlined in :ref:`halo_catalog_finding` as well as those in the formats discussed in :ref:`halo-catalog-data` can be loaded in to yt as first-class datasets. Once a halo catalog has been created, further analysis can be performed by providing both the halo catalog and the original simulation dataset to the :class:`~yt.analysis_modules.halo_analysis.halo_catalog.HaloCatalog`. .. code-block:: python halos_ds = yt.load('rockstar_halos/halos_0.0.bin') data_ds = yt.load('Enzo_64/RD0006/RedshiftOutput0006') hc = HaloCatalog(data_ds=data_ds, halos_ds=halos_ds) A data object can also be supplied via the keyword ``data_source``, associated with either dataset, to control the spatial region in which halo analysis will be performed. The :class:`~yt.analysis_modules.halo_analysis.halo_catalog.HaloCatalog` allows the user to create a pipeline of analysis actions that will be performed on all halos in the existing catalog. The analysis can be performed in parallel with separate processors or groups of processors being allocated to perform the entire pipeline on individual halos. The pipeline is setup by adding actions to the :class:`~yt.analysis_modules.halo_analysis.halo_catalog.HaloCatalog`. Each action is represented by a callback function that will be run on each halo. There are four types of actions: * :ref:`halo_catalog_filters` * :ref:`halo_catalog_quantities` * :ref:`halo_catalog_callbacks` * :ref:`halo_catalog_recipes` A list of all available filters, quantities, and callbacks can be found in :ref:`halo_analysis_ref`. All interaction with this analysis can be performed by importing from halo_analysis. .. _halo_catalog_filters: Filters ^^^^^^^ A filter is a function that returns True or False. If the return value is True, any further queued analysis will proceed and the halo in question will be added to the final catalog. If the return value False, further analysis will not be performed and the halo will not be included in the final catalog. An example of adding a filter: .. code-block:: python hc.add_filter('quantity_value', 'particle_mass', '>', 1E13, 'Msun') Currently quantity_value is the only available filter, but more can be added by the user by defining a function that accepts a halo object as the first argument and then adding it as an available filter. If you think that your filter may be of use to the general community, you can add it to ``yt/analysis_modules/halo_analysis/halo_filters.py`` and issue a pull request. An example of defining your own filter: .. code-block:: python def my_filter_function(halo): # Define condition for filter filter_value = True # Return a boolean value return filter_value # Add your filter to the filter registry add_filter("my_filter", my_filter_function) # ... Later on in your script hc.add_filter("my_filter") .. _halo_catalog_quantities: Quantities ^^^^^^^^^^ A quantity is a call back that returns a value or values. The return values are stored within the halo object in a dictionary called “quantities.” At the end of the analysis, all of these quantities will be written to disk as the final form of the generated halo catalog. Quantities may be available in the initial fields found in the halo catalog, or calculated from a function after supplying a definition. An example definition of center of mass is shown below. Currently available quantities are center_of_mass and bulk_velocity. Their definitions are available in ``yt/analysis_modules/halo_analysis/halo_quantities.py``. If you think that your quantity may be of use to the general community, add it to ``halo_quantities.py`` and issue a pull request. Default halo quantities are: * ``particle_identifier`` -- Halo ID (e.g. 0 to N) * ``particle_mass`` -- Mass of halo * ``particle_position_x`` -- Location of halo * ``particle_position_y`` -- Location of halo * ``particle_position_z`` -- Location of halo * ``virial_radius`` -- Virial radius of halo An example of adding a quantity: .. code-block:: python hc.add_quantity('center_of_mass') An example of defining your own quantity: .. code-block:: python def my_quantity_function(halo): # Define quantity to return quantity = 5 return quantity # Add your filter to the filter registry add_quantity('my_quantity', my_quantity_function) # ... Later on in your script hc.add_quantity("my_quantity") This quantity will then be accessible for functions called later via the *quantities* dictionary that is associated with the halo object. .. code-block:: python def my_new_function(halo): print(halo.quantities["my_quantity"]) add_callback("print_quantity", my_new_function) # ... Anywhere after "my_quantity" has been called hc.add_callback("print_quantity") .. _halo_catalog_callbacks: Callbacks ^^^^^^^^^ A callback is actually the super class for quantities and filters and is a general purpose function that does something, anything, to a Halo object. This can include hanging new attributes off the Halo object, performing analysis and writing to disk, etc. A callback does not return anything. An example of using a pre-defined callback where we create a sphere for each halo with a radius that is twice the saved ``radius``. .. code-block:: python hc.add_callback("sphere", factor=2.0) Currently available callbacks are located in ``yt/analysis_modules/halo_analysis/halo_callbacks.py``. New callbacks may be added by using the syntax shown below. If you think that your callback may be of use to the general community, add it to halo_callbacks.py and issue a pull request. An example of defining your own callback: .. code-block:: python def my_callback_function(halo): # Perform some callback actions here x = 2 halo.x_val = x # Add the callback to the callback registry add_callback('my_callback', my_callback_function) # ... Later on in your script hc.add_callback("my_callback") .. _halo_catalog_recipes: Recipes ^^^^^^^ Recipes allow you to create analysis tasks that consist of a series of callbacks, quantities, and filters that are run in succession. An example of this is :func:`~yt.analysis_modules.halo_analysis.halo_recipes.calculate_virial_quantities`, which calculates virial quantities by first creating a sphere container, performing 1D radial profiles, and then interpolating to get values at a specified threshold overdensity. All of these operations are separate callbacks, but the recipes allow you to add them to your analysis pipeline with one call. For example, .. code-block:: python hc.add_recipe("calculate_virial_quantities", ["radius", "matter_mass"]) The available recipes are located in ``yt/analysis_modules/halo_analysis/halo_recipes.py``. New recipes can be created in the following manner: .. code-block:: python def my_recipe(halo_catalog, fields, weight_field=None): # create a sphere halo_catalog.add_callback("sphere") # make profiles halo_catalog.add_callback("profile", ["radius"], fields, weight_field=weight_field) # save the profile data halo_catalog.add_callback("save_profiles", output_dir="profiles") # add recipe to the registry of recipes add_recipe("profile_and_save", my_recipe) # ... Later on in your script hc.add_recipe("profile_and_save", ["density", "temperature"], weight_field="cell_mass") Note, that unlike callback, filter, and quantity functions that take a ``Halo`` object as the first argument, recipe functions should take a ``HaloCatalog`` object as the first argument. Running the Pipeline -------------------- After all callbacks, quantities, and filters have been added, the analysis begins with a call to HaloCatalog.create. .. code-block:: python hc.create() The save_halos keyword determines whether the actual Halo objects are saved after analysis on them has completed or whether just the contents of their quantities dicts will be retained for creating the final catalog. The looping over halos uses a call to parallel_objects allowing the user to control how many processors work on each halo. The final catalog is written to disk in the output directory given when the :class:`~yt.analysis_modules.halo_analysis.halo_catalog.HaloCatalog` object was created. All callbacks, quantities, and filters are stored in an actions list, meaning that they are executed in the same order in which they were added. This enables the use of simple, reusable, single action callbacks that depend on each other. This also prevents unnecessary computation by allowing the user to add filters at multiple stages to skip remaining analysis if it is not warranted. Saving and Reloading Halo Catalogs ---------------------------------- A :class:`~yt.analysis_modules.halo_analysis.halo_catalog.HaloCatalog` saved to disk can be reloaded as a yt dataset with the standard call to ``yt.load``. Any side data, such as profiles, can be reloaded with a ``load_profiles`` callback and a call to :func:`~yt.analysis_modules.halo_analysis.halo_catalog.HaloCatalog.load`. .. code-block:: python hds = yt.load(path+"halo_catalogs/catalog_0046/catalog_0046.0.h5") hc = HaloCatalog(halos_ds=hds, output_dir="halo_catalogs/catalog_0046") hc.add_callback("load_profiles", output_dir="profiles", filename="virial_profiles") hc.load() Halo Catalog in Action ---------------------- For a full example of how to use these methods together see :ref:`halo-analysis-example`. yt-3.3.3/doc/source/analyzing/analysis_modules/halo_mass_function.rst0000644000076500000240000002050613017603554027121 0ustar goldbaumstaff00000000000000.. _halo_mass_function: Halo Mass Function ================== The Halo Mass Function extension is capable of outputting the halo mass function for a collection halos (input), and/or an analytical fit over a given mass range for a set of specified cosmological parameters. This extension is based on code generously provided by Brian O'Shea. General Overview ---------------- A halo mass function can be created for the halos identified in a cosmological simulation, as well as analytic fits using any arbitrary set of cosmological parameters. In order to create a mass function for simulated halos, they must first be identified (using HOP, FOF, or Rockstar, see :ref:`halo_catalog`) and loaded as a halo dataset object. The distribution of halo masses will then be found, and can be compared to the analytic prediction at the same redshift and using the same cosmological parameters as were used in the simulation. Care should be taken in this regard, as the analytic fit requires the specification of cosmological parameters that are not necessarily stored in the halo or simulation datasets, and must be specified by the user. Efforts have been made to set reasonable defaults for these parameters, but setting them to identically match those used in the simulation will produce a much better comparison. Analytic halo mass functions can also be created without a halo dataset by providing either a simulation dataset or specifying cosmological parameters by hand. yt includes 5 analytic fits for the halo mass function which can be selected. Analytical Fits --------------- There are five analytical fits to choose from. 1. `Press-Schechter (1974) `_ 2. `Jenkins (2001) `_ 3. `Sheth-Tormen (2002) `_ 4. `Warren (2006) `_ 5. `Tinker (2008) `_ We encourage reading each of the primary sources. In general, we recommend the Warren fitting function because it matches simulations over a wide range of masses very well. The Warren fitting function is the default (equivalent to not specifying ``fitting_function`` in ``HaloMassFcn()``, below). The Tinker fit is for the :math:`\Delta=300` fits given in the paper, which appears to fit HOP threshold=80.0 fairly well. Basic Halo Mass Function Creation --------------------------------- The simplest way to create a halo mass function object is to simply pass it no arguments and let it use the default cosmological parameters. .. code-block:: python from yt.analysis_modules.halo_mass_function.api import * hmf = HaloMassFcn() This will create a HaloMassFcn object off of which arrays holding the information about the analytic mass function hang. Creating the halo mass function for a set of simulated halos requires only the loaded halo dataset to be passed as an argument. This also creates the analytic mass function using all parameters that can be extracted from the halo dataset, at the same redshift, spanning a similar range of halo masses. .. code-block:: python from yt.mods import * from yt.analysis_modules.halo_mass_function.api import * my_halos = load("rockstar_halos/halos_0.0.bin") hmf = HaloMassFcn(halos_ds=my_halos) A simulation dataset can be passed along with additional cosmological parameters to create an analytic mass function. .. code-block:: python from yt.mods import * from yt.analysis_modules.halo_mass_function.api import * my_ds = load("RD0027/RedshiftOutput0027") hmf = HaloMassFcn(simulation_ds=my_ds, omega_baryon0=0.05, primordial_index=0.96, sigma8 = 0.8, log_mass_min=5, log_mass_max=9) The analytic mass function can be created for a set of arbitrary cosmological parameters without any dataset being passed as an argument. .. code-block:: python from yt.mods import * from yt.analysis_modules.halo_mass_function.api import * hmf = HaloMassFcn(omega_baryon0=0.05, omega_matter0=0.27, omega_lambda0=0.73, hubble0=0.7, this_redshift=10, log_mass_min=5, log_mass_max=9, fitting_function=5) Keyword Arguments ----------------- * **simulation_ds** (*Simulation dataset object*) The loaded simulation dataset, used to set cosmological parameters. Default : None. * **halos_ds** (*Halo dataset object*) The halos from a simulation to be used for creation of the halo mass function in the simulation. Default : None. * **make_analytic** (*bool*) Whether or not to calculate the analytic mass function to go with the simulated halo mass function. Automatically set to true if a simulation dataset is provided. Default : True. * **omega_matter0** (*float*) The fraction of the universe made up of matter (dark and baryonic). Default : 0.2726. * **omega_lambda0** (*float*) The fraction of the universe made up of dark energy. Default : 0.7274. * **omega_baryon0** (*float*) The fraction of the universe made up of baryonic matter. This is not always stored in the dataset and should be checked by hand. Default : 0.0456. * **hubble0** (*float*) The expansion rate of the universe in units of 100 km/s/Mpc. Default : 0.704. * **sigma8** (*float*) The amplitude of the linear power spectrum at z=0 as specified by the rms amplitude of mass-fluctuations in a top-hat sphere of radius 8 Mpc/h. This is not always stored in the dataset and should be checked by hand. Default : 0.86. * **primoridal_index** (*float*) This is the index of the mass power spectrum before modification by the transfer function. A value of 1 corresponds to the scale-free primordial spectrum. This is not always stored in the dataset and should be checked by hand. Default : 1.0. * **this_redshift** (*float*) The current redshift. Default : 0. * **log_mass_min** (*float*) The log10 of the mass of the minimum of the halo mass range. This is set automatically by the range of halo masses if a simulated halo dataset is provided. If a halo dataset if not provided and no value is specified, it will be set to 5. Units: M_solar Default : None. * **log_mass_max** (*float*) The log10 of the mass of the maximum of the halo mass range. This is set automatically by the range of halo masses if a simulated halo dataset is provided. If a halo dataset if not provided and no value is specified, it will be set to 16. Units: M_solar Default : None. * **num_sigma_bins** (*float*) The number of bins (points) to use for the calculation of the analytic mass function. Default : 360. * **fitting_function** (*int*) Which fitting function to use. 1 = Press-Schechter, 2 = Jenkins, 3 = Sheth-Tormen, 4 = Warren, 5 = Tinker Default : 4. Outputs ------- A HaloMassFnc object has several arrays hanging off of it containing the * **masses_sim**: Halo masses from simulated halos. Units: M_solar * **n_cumulative_sim**: Number density of halos with mass greater than the corresponding mass in masses_sim. Units: comoving Mpc^-3 * **masses_analytic**: Masses used for the generation of the analytic mass function. Units: M_solar * **n_cumulative_analytic**: Number density of halos with mass greater then the corresponding mass in masses_analytic. Units: comoving Mpc^-3 * **dndM_dM_analytic**: Differential number density of halos, (dn/dM)*dM. After the mass function has been created for both simulated halos and the corresponding analytic fits, they can be plotted though something along the lines of .. code-block:: python import yt from yt.analysis_modules.halo_mass_function.api import * import matplotlib.pyplot as plt my_halos = yt.load("rockstar_halos/halos_0.0.bin") hmf = HaloMassFcn(halos_ds=my_halos) plt.loglog(hmf.masses_sim, hmf.n_cumulative_sim) plt.loglog(hmf.masses_analytic, hmf.n_cumulative_analytic) Attached to ``hmf`` is the convenience function ``write_out``, which saves the halo mass function to a text file. (continued from above) .. code-block:: python hmf.write_out(prefix='hmf', analytic=True, simulated=True) This writes the files ``hmf-analytic.dat`` with columns: * mass [Msun] * cumulative number density of halos [comoving Mpc^-3] * (dn/dM)*dM (differential number density of halos) [comoving Mpc^-3] and the file ``hmf-simulated.dat`` with columns: * mass [Msun] * cumulative number density of halos [comoving Mpc^-3] yt-3.3.3/doc/source/analyzing/analysis_modules/halo_merger_tree.rst0000644000076500000240000000066613017603554026556 0ustar goldbaumstaff00000000000000.. _merger_tree: Halo Merger Tree ================ The ``yt`` merger tree was removed as of :code:`yt-3.0`. This functionality can still be found in :code:`yt-2.x`. However, the recommended option is to use the `ytree `_ package, which can be installed via pip: .. code-block:: bash pip install ytree For more information on ``ytree``, see the documentation `here `__. yt-3.3.3/doc/source/analyzing/analysis_modules/halo_transition.rst0000644000076500000240000000706613017603554026451 0ustar goldbaumstaff00000000000000.. _halo-transition: Transitioning From yt-2 to yt-3 =============================== If you're used to halo analysis in yt-2.x, heres a guide to how to update your analysis pipeline to take advantage of the new halo catalog infrastructure. If you're starting from scratch, see :ref:`halo_catalog`. Finding Halos ------------- Previously, halos were found using calls to ``HaloFinder``, ``FOFHaloFinder`` and ``RockstarHaloFinder``. Now it is encouraged that you find the halos upon creation of the halo catalog by supplying a value to the ``finder_method`` keyword when calling ``HaloCatalog``. Currently, only halos found using rockstar or a previous instance of a halo catalog are able to be loaded using the ``halos_ds`` keyword. To pass additional arguments to the halo finders themselves, supply a dictionary to ``finder_kwargs`` where each key in the dictionary is a keyword of the halo finder and the corresponding value is the value to be passed for that keyword. Getting Halo Information ------------------------ All quantities that used to be present in a ``halo_list`` are still able to be found but are not necessarily included by default. Every halo will by default have the following properties: * particle_position_i (where i can be x,y,z) * particle_mass * virial_radius * particle_identifier If other quantities are desired, they can be included by adding the corresponding quantity before the catalog is created. See the full halo catalog documentation for further information about how to add these quantities and what quantities are available. You no longer have to iterate over halos in the ``halo_list``. Now a halo dataset can be treated as a regular dataset and all quantities are available by accessing ``all_data``. Specifically, all quantities can be accessed as shown: .. code-block:: python import yt from yt.analysis_modules.halo_analysis.api import HaloCatalog data_ds = yt.load('Enzo_64/RD0006/RedshiftOutput0006') hc = HaloCatalog(data_ds=data_ds, finder_method='hop') hc.create() ad = hc.halos_ds.all_data() masses = ad['particle_mass'][:] Prefiltering Halos ------------------ Prefiltering halos before analysis takes place is now done by adding a filter before the call to create. An example is shown below .. code-block:: python import yt from yt.analysis_modules.halo_analysis.api import HaloCatalog data_ds = yt.load('Enzo_64/RD0006/RedshiftOutput0006') hc = HaloCatalog(data_ds=data_ds, finder_method='hop') hc.add_filter("quantity_value", "particle_mass", ">", 1e13, "Msun") hc.create() Profiling Halos --------------- The halo profiler available in yt-2.x has been removed, and profiling functionality is now completely contained within the halo catalog. A complete example of how to profile halos by radius using the new infrastructure is given in :ref:`halo-analysis-example`. Plotting Halos -------------- Annotating halo locations onto a slice or projection works in the same way as in yt-2.x, but now a halo catalog must be passed to the annotate halo call rather than a halo list. .. code-block:: python import yt from yt.analysis_modules.halo_analysis.api import HaloCatalog data_ds = yt.load('Enzo_64/RD0006/RedshiftOutput0006') hc = HaloCatalog(data_ds=data_ds, finder_method='hop') hc.create() prj = yt.ProjectionPlot(data_ds, 'z', 'density') prj.annotate_halos(hc) prj.save() Written Data ------------ Data is now written out in the form of h5 files rather than text files. The directory they are written out to is controlled by the keyword ``output_dir``. Each quantity is a field in the file. yt-3.3.3/doc/source/analyzing/analysis_modules/index.rst0000644000076500000240000000117613017603554024357 0ustar goldbaumstaff00000000000000.. _analysis-modules: Topic-Specific Analysis Modules =============================== These semi-autonomous analysis modules are unique to specific subject matter like tracking halos, generating synthetic observations, exporting output to external visualization routines, and more. Because they are somewhat specialized, they exist in their own corners of yt, and they do not get loaded by default when you "import yt". Read up on these advanced tools below. .. toctree:: :maxdepth: 2 cosmology_calculator halo_analysis synthetic_observation exporting two_point_functions clump_finding particle_trajectories yt-3.3.3/doc/source/analyzing/analysis_modules/light_cone_generator.rst0000644000076500000240000001315313017603554027427 0ustar goldbaumstaff00000000000000.. _light-cone-generator: Light Cone Generator ==================== Light cones are created by stacking multiple datasets together to continuously span a given redshift interval. To make a projection of a field through a light cone, the width of individual slices is adjusted such that each slice has the same angular size. Each slice is randomly shifted and projected along a random axis to ensure that the same structures are not sampled multiple times. A recipe for creating a simple light cone projection can be found in the cookbook under :ref:`cookbook-light_cone`. .. image:: _images/LightCone_full_small.png :width: 500 A light cone projection of the thermal Sunyaev-Zeldovich Y parameter from z = 0 to 0.4 with a 450x450 arcminute field of view using 9 individual slices. The panels shows the contributions from the 9 individual slices with the final light cone image shown in the bottom, right. Configuring the Light Cone Generator ------------------------------------ The required arguments to instantiate a :class:`~yt.analysis_modules.cosmological_observation.light_cone.light_cone.LightCone` object are the path to the simulation parameter file, the simulation type, the nearest redshift, and the furthest redshift of the light cone. .. code-block:: python from yt.analysis_modules.cosmological_observation.api import \ LightCone lc = LightCone('enzo_tiny_cosmology/32Mpc_32.enzo', 'Enzo', 0., 0.1) The additional keyword arguments are: * ``use_minimum_datasets`` (*bool*): If True, the minimum number of datasets is used to connect the initial and final redshift. If False, the light cone solution will contain as many entries as possible within the redshift interval. Default: True. * ``deltaz_min`` (*float*): Specifies the minimum Delta-z between consecutive datasets in the returned list. Default: 0.0. * ``minimum_coherent_box_fraction`` (*float*): Used with ``use_minimum_datasets`` set to False, this parameter specifies the fraction of the total box size to be traversed before rerandomizing the projection axis and center. This was invented to allow light cones with thin slices to sample coherent large scale structure, but in practice does not work so well. Try setting this parameter to 1 and see what happens. Default: 0.0. * ``time_data`` (*bool*): Whether or not to include time outputs when gathering datasets for time series. Default: True. * ``redshift_data`` (*bool*): Whether or not to include redshift outputs when gathering datasets for time series. Default: True. * ``set_parameters`` (*dict*): Dictionary of parameters to attach to ds.parameters. Default: None. * ``output_dir`` (*string*): The directory in which images and data files will be written. Default: 'LC'. * ``output_prefix`` (*string*): The prefix of all images and data files. Default: 'LightCone'. Creating Light Cone Solutions ----------------------------- A light cone solution consists of a list of datasets spanning a redshift interval with a random orientation for each dataset. A new solution is calculated with the :func:`~yt.analysis_modules.cosmological_observation.light_cone.light_cone.LightCone.calculate_light_cone_solution` function: .. code-block:: python lc.calculate_light_cone_solution(seed=123456789, filename='lightcone.dat') The keyword argument are: * ``seed`` (*int*): the seed for the random number generator. Any light cone solution can be reproduced by giving the same random seed. Default: None. * ``filename`` (*str*): if given, a text file detailing the solution will be written out. Default: None. Making a Light Cone Projection ------------------------------ With the light cone solution in place, projections with a given field of view and resolution can be made of any available field: .. code-block:: python field = 'density' field_of_view = (600.0, "arcmin") resolution = (60.0, "arcsec") lc.project_light_cone(field_of_vew, resolution, field , weight_field=None, save_stack=True, save_slice_images=True) The field of view and resolution can be specified either as a tuple of value and unit string or as a unitful ``YTQuantity``. Additional keyword arguments: * ``weight_field`` (*str*): the weight field of the projection. This has the same meaning as in standard projections. Default: None. * ``photon_field`` (*bool*): if True, the projection data for each slice is decremented by 4 pi R :superscript:`2` , where R is the luminosity distance between the observer and the slice redshift. Default: False. * ``save_stack`` (*bool*): if True, the unflatted light cone data including each individual slice is written to an hdf5 file. Default: True. * ``save_final_image`` (*bool*): if True, save an image of the final light cone projection. Default: True. * ``save_slice_images`` (*bool*): save images for each individual projection slice. Default: False. * ``cmap_name`` (*string*): color map for images. Default: "algae". * ``njobs`` (*int*): The number of parallel jobs over which the light cone projection will be split. Choose -1 for one processor per individual projection and 1 to have all processors work together on each projection. Default: 1. * ``dynamic`` (*bool*): If True, use dynamic load balancing to create the projections. Default: False. .. note:: As of :code:`yt-3.0`, the halo mask and unique light cone functionality no longer exist. These are still available in :code:`yt-2.x`. If you would like to use these features in :code:`yt-3.x`, help is needed to port them over. Contact the yt-users mailing list if you are interested in doing this. yt-3.3.3/doc/source/analyzing/analysis_modules/light_ray_generator.rst0000644000076500000240000002421313023400062027257 0ustar goldbaumstaff00000000000000.. _light-ray-generator: Light Ray Generator =================== Light rays are similar to light cones (:ref:`light-cone-generator`) in how they stack multiple datasets together to span a redshift interval. Unlike light cones, which stack randomly oriented projections from each dataset to create synthetic images, light rays use thin pencil beams to simulate QSO sight lines. A sample script can be found in the cookbook under :ref:`cookbook-light_ray`. .. image:: _images/lightray.png A ray segment records the information of all grid cells intersected by the ray as well as the path length, ``dl``, of the ray through the cell. Column densities can be calculated by multiplying physical densities by the path length. Configuring the Light Ray Generator ----------------------------------- Below follows the creation of a light ray from multiple datasets stacked together. However, a light ray can also be made from a single dataset. For an example of this, see :ref:`cookbook-single-dataset-light-ray`. The arguments required to instantiate a :class:`~yt.analysis_modules.cosmological_observation.light_ray.light_ray.LightRay` object are the same as those required for a :class:`~yt.analysis_modules.cosmological_observation.light_cone.light_cone.LightCone` object: the simulation parameter file, the simulation type, the nearest redshift, and the furthest redshift. .. code-block:: python from yt.analysis_modules.cosmological_observation.api import LightRay lr = LightRay("enzo_tiny_cosmology/32Mpc_32.enzo", simulation_type="Enzo", near_redshift=0.0, far_redshift=0.1) Additional keyword arguments are: * ``use_minimum_datasets`` (*bool*): If True, the minimum number of datasets is used to connect the initial and final redshift. If false, the light ray solution will contain as many entries as possible within the redshift interval. Default: True. * ``deltaz_min`` (*float*): Specifies the minimum Delta-z between consecutive datasets in the returned list. Default: 0.0. * ``max_box_fraction`` (*float*): In terms of the size of the domain, the maximum length a light ray segment can be in order to span the redshift interval from one dataset to another. If using a zoom-in simulation, this parameter can be set to the length of the high resolution region so as to limit ray segments to that size. If the high resolution region is not cubical, the smallest side should be used. Default: 1.0 (the size of the box) * ``minimum_coherent_box_fraction`` (*float*): Use to specify the minimum length of a ray, in terms of the size of the domain, before the trajectory is re-randomized. Set to 0 to have ray trajectory randomized for every dataset. Set to np.inf (infinity) to use a single trajectory for the entire ray. Default: 0.0. * ``time_data`` (*bool*): Whether or not to include time outputs when gathering datasets for time series. Default: True. * ``redshift_data`` (*bool*): Whether or not to include redshift outputs when gathering datasets for time series. Default: True. Making Light Ray Data --------------------- Once the LightRay object has been instantiated, the :func:`~yt.analysis_modules.cosmological_observation.light_ray.light_ray.LightRay.make_light_ray` function will trace out the rays in each dataset and collect information for all the fields requested. The output file will be an HDF5 file containing all the cell field values for all the cells that were intersected by the ray. A single LightRay object can be used over and over to make multiple randomizations, simply by changing the value of the random seed with the ``seed`` keyword. .. code-block:: python lr.make_light_ray(seed=8675309, fields=['temperature', 'density'], use_peculiar_velocity=True) The keyword arguments are: * ``seed`` (*int*): Seed for the random number generator. Default: None. * ``periodic`` (*bool*): If True, ray trajectories will make use of periodic boundaries. If False, ray trajectories will not be periodic. Default : True. * ``left_edge`` (iterable of *floats* or *YTArray*): The left corner of the region in which rays are to be generated. If None, the left edge will be that of the domain. Default: None. * ``right_edge`` (iterable of *floats* or *YTArray*): The right corner of the region in which rays are to be generated. If None, the right edge will be that of the domain. Default: None. * ``min_level`` (*int*): The minimum refinement level of the spatial region in which the ray passes. This can be used with zoom-in simulations where the high resolution region does not keep a constant geometry. Default: None. * ``start_position`` (*list* of floats): Used only if creating a light ray from a single dataset. The coordinates of the starting position of the ray. Default: None. * ``end_position`` (*list* of floats): Used only if creating a light ray from a single dataset. The coordinates of the ending position of the ray. Default: None. * ``trajectory`` (*list* of floats): Used only if creating a light ray from a single dataset. The (r, theta, phi) direction of the light ray. Use either ``end_position`` or ``trajectory``, not both. Default: None. * ``fields`` (*list*): A list of fields for which to get data. Default: None. * ``solution_filename`` (*string*): Path to a text file where the trajectories of each subray is written out. Default: None. * ``data_filename`` (*string*): Path to output file for ray data. Default: None. * ``use_peculiar_velocity`` (*bool*): If True, the doppler redshift from the peculiar velocity of gas along the ray is calculated and added to the cosmological redshift as the "effective" redshift. Default: True. * ``redshift`` (*float*): Used with light rays made from single datasets to specify a starting redshift for the ray. If not used, the starting redshift will be 0 for a non-cosmological dataset and the dataset redshift for a cosmological dataset. Default: None. * ``njobs`` (*int*): The number of parallel jobs over which the slices for the halo mask will be split. Choose -1 for one processor per individual slice and 1 to have all processors work together on each projection. Default: 1 Useful Tips for Making LightRays -------------------------------- Below are some tips that may come in handy for creating proper LightRays. How many snapshots do I need? ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ The number of snapshots required to traverse some redshift interval depends on the simulation box size and cosmological parameters. Before running an expensive simulation only to find out that you don't have enough outputs to span the redshift interval you want, have a look at :ref:`planning-cosmology-simulations`. The functionality described there will allow you to calculate the precise number of snapshots and specific redshifts at which they should be written. My snapshots are too far apart! ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ The ``max_box_fraction`` keyword, provided when creating the `Lightray`, allows the user to control how long a ray segment can be for an individual dataset. Be default, the `LightRay` generator will try to make segments no longer than the size of the box to avoid sampling the same structures more than once. However, this can be increased in the case that the redshift interval between datasets is longer than the box size. Increasing this value should be done with caution as longer ray segments run a greater risk of coming back to somewhere near their original position. What if I have a zoom-in simulation? ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ A zoom-in simulation has a high resolution region embedded within a larger, low resolution volume. In this type of simulation, it is likely that you will want the ray segments to stay within the high resolution region. To do this, you must first specify the size of the high resolution region when creating the `LightRay` using the ``max_box_fraction`` keyword. This will make sure that the calculation of the spacing of the segment datasets only takes into account the high resolution region and not the full box size. If your high resolution region is not a perfect cube, specify the smallest side. Then, in the call to :func:`~yt.analysis_modules.cosmological_observation.light_ray.light_ray.LightRay.make_light_ray`, use the ``left_edge`` and ``right_edge`` keyword arguments to specify the precise location of the high resolution region. Technically speaking, the ray segments should no longer be periodic since the high resolution region is only a sub-volume within the larger domain. To make the ray segments non-periodic, set the ``periodic`` keyword to False. The LightRay generator will continue to generate randomly oriented segments until it finds one that fits entirely within the high resolution region. If you have a high resolution region that can move and change shape slightly as structure forms, use the `min_level` keyword to mandate that the ray segment only pass through cells that are refined to at least some minimum level. If the size of the high resolution region is not large enough to span the required redshift interval, the `LightRay` generator can be configured to treat the high resolution region as if it were periodic simply by setting the ``periodic`` keyword to True. This option should be used with caution as it will lead to the creation of disconnected ray segments within a single dataset. I want a continous trajectory over the entire ray. ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Set the ``minimum_coherent_box_fraction`` keyword argument to a very large number, like infinity (`numpy.inf`). .. note:: As of :code:`yt-3.0`, the functionality for recording properties of the nearest halo to each element of the ray no longer exists. This is still available in :code:`yt-2.x`. If you would like to use this feature in :code:`yt-3.x`, help is needed to port it over. Contact the yt-users mailing list if you are interested in doing this. What Can I do with this? ------------------------ Once you have created a `LightRay`, you can use it to generate an :ref:`absorption_spectrum`. 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" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import yt\n", "import glob\n", "from yt.analysis_modules.particle_trajectories.api import ParticleTrajectories\n", "from yt.config import ytcfg\n", "path = ytcfg.get(\"yt\", \"test_data_dir\")\n", "import matplotlib.pyplot as plt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, let's start off with a FLASH dataset containing only two particles in a mutual circular orbit. We can get the list of filenames this way:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "my_fns = glob.glob(path+\"/Orbit/orbit_hdf5_chk_00[0-9][0-9]\")\n", "my_fns.sort()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And let's define a list of fields that we want to include in the trajectories. The position fields will be included by default, so let's just ask for the velocity fields:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fields = [\"particle_velocity_x\", \"particle_velocity_y\", \"particle_velocity_z\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are only two particles, but for consistency's sake let's grab their indices from the dataset itself:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ds = yt.load(my_fns[0])\n", "dd = ds.all_data()\n", "indices = dd[\"particle_index\"].astype(\"int\")\n", "print (indices)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "which is what we expected them to be. Now we're ready to create a `ParticleTrajectories` object:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "trajs = ParticleTrajectories(my_fns, indices, fields=fields)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `ParticleTrajectories` object `trajs` is essentially a dictionary-like container for the particle fields along the trajectory, and can be accessed as such:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print (trajs[\"particle_position_x\"])\n", "print (trajs[\"particle_position_x\"].shape)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that each field is a 2D NumPy array with the different particle indices along the first dimension and the times along the second dimension. As such, we can access them individually by indexing the field:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.plot(trajs[\"particle_position_x\"][0].ndarray_view(), trajs[\"particle_position_y\"][0].ndarray_view())\n", "plt.plot(trajs[\"particle_position_x\"][1].ndarray_view(), trajs[\"particle_position_y\"][1].ndarray_view())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And we can plot the velocity fields as well:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.plot(trajs[\"particle_velocity_x\"][0].ndarray_view(), trajs[\"particle_velocity_y\"][0].ndarray_view())\n", "plt.plot(trajs[\"particle_velocity_x\"][1].ndarray_view(), trajs[\"particle_velocity_y\"][1].ndarray_view())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we want to access the time along the trajectory, we use the key `\"particle_time\"`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.plot(trajs[\"particle_time\"].ndarray_view(), trajs[\"particle_velocity_x\"][1].ndarray_view())\n", "plt.plot(trajs[\"particle_time\"].ndarray_view(), trajs[\"particle_velocity_y\"][1].ndarray_view())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Alternatively, if we know the particle index we'd like to examine, we can get an individual trajectory corresponding to that index:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "particle1 = trajs.trajectory_from_index(1)\n", "plt.plot(particle1[\"particle_time\"].ndarray_view(), particle1[\"particle_position_x\"].ndarray_view())\n", "plt.plot(particle1[\"particle_time\"].ndarray_view(), particle1[\"particle_position_y\"].ndarray_view())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's look at a more complicated (and fun!) example. We'll use an Enzo cosmology dataset. First, we'll find the maximum density in the domain, and obtain the indices of the particles within some radius of the center. First, let's have a look at what we're getting:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ds = yt.load(\"enzo_tiny_cosmology/DD0046/DD0046\")\n", "slc = yt.SlicePlot(ds, \"x\", [\"density\",\"dark_matter_density\"], center=\"max\", width=(3.0, \"Mpc\"))\n", "slc.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "So far, so good--it looks like we've centered on a galaxy cluster. Let's grab all of the dark matter particles within a sphere of 0.5 Mpc (identified by `\"particle_type == 1\"`):" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sp = ds.sphere(\"max\", (0.5, \"Mpc\"))\n", "indices = sp[\"particle_index\"][sp[\"particle_type\"] == 1]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we'll get the list of datasets we want, and create trajectories for these particles:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "my_fns = glob.glob(path+\"/enzo_tiny_cosmology/DD*/*.hierarchy\")\n", "my_fns.sort()\n", "trajs = ParticleTrajectories(my_fns, indices)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Matplotlib can make 3D plots, so let's pick three particle trajectories at random and look at them in the volume:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import matplotlib.pyplot as plt\n", "from mpl_toolkits.mplot3d import Axes3D\n", "fig = plt.figure(figsize=(8.0, 8.0))\n", "ax = fig.add_subplot(111, projection='3d')\n", "ax.plot(trajs[\"particle_position_x\"][100].ndarray_view(), trajs[\"particle_position_z\"][100].ndarray_view(), \n", " trajs[\"particle_position_z\"][100].ndarray_view())\n", "ax.plot(trajs[\"particle_position_x\"][8].ndarray_view(), trajs[\"particle_position_z\"][8].ndarray_view(), \n", " trajs[\"particle_position_z\"][8].ndarray_view())\n", "ax.plot(trajs[\"particle_position_x\"][25].ndarray_view(), trajs[\"particle_position_z\"][25].ndarray_view(), \n", " trajs[\"particle_position_z\"][25].ndarray_view())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It looks like these three different particles fell into the cluster along different filaments. We can also look at their x-positions only as a function of time:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.plot(trajs[\"particle_time\"].ndarray_view(), trajs[\"particle_position_x\"][100].ndarray_view())\n", "plt.plot(trajs[\"particle_time\"].ndarray_view(), trajs[\"particle_position_x\"][8].ndarray_view())\n", "plt.plot(trajs[\"particle_time\"].ndarray_view(), trajs[\"particle_position_x\"][25].ndarray_view())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Suppose we wanted to know the gas density along the particle trajectory, but there wasn't a particle field corresponding to that in our dataset. Never fear! If the field exists as a grid field, yt will interpolate this field to the particle positions and add the interpolated field to the trajectory. To add such a field (or any field, including additional particle fields) we can call the `add_fields` method:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "trajs.add_fields([\"density\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We also could have included `\"density\"` in our original field list. Now, plot up the gas density for each particle as a function of time:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plt.plot(trajs[\"particle_time\"].ndarray_view(), trajs[\"density\"][100].ndarray_view())\n", "plt.plot(trajs[\"particle_time\"].ndarray_view(), trajs[\"density\"][8].ndarray_view())\n", "plt.plot(trajs[\"particle_time\"].ndarray_view(), trajs[\"density\"][25].ndarray_view())\n", "plt.yscale(\"log\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, the particle trajectories can be written to disk. Two options are provided: ASCII text files with a column for each field and the time, and HDF5 files:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "trajs.write_out(\"halo_trajectories\") # This will write a separate file for each trajectory\n", "trajs.write_out_h5(\"halo_trajectories.h5\") # This will write all trajectories to a single file" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 } yt-3.3.3/doc/source/analyzing/analysis_modules/particle_trajectories.rst0000644000076500000240000000016312770647750027637 0ustar goldbaumstaff00000000000000.. _particle-trajectories: Particle Trajectories --------------------- .. notebook:: Particle_Trajectories.ipynb yt-3.3.3/doc/source/analyzing/analysis_modules/photon_simulator.rst0000644000076500000240000006335513017603554026665 0ustar goldbaumstaff00000000000000.. _photon_simulator: Constructing Mock X-ray Observations ------------------------------------ .. note:: If you just want to create derived fields for X-ray emission, you should go `here `_ instead. The ``photon_simulator`` analysis module enables the creation of simulated X-ray photon lists of events from datasets that yt is able to read. The simulated events then can be exported to X-ray telescope simulators to produce realistic observations or can be analyzed in-line. For detailed information about the design of the algorithm in yt, check out `the SciPy 2014 Proceedings. `_. The algorithm is based off of that implemented in `PHOX `_ for SPH datasets by Veronica Biffi and Klaus Dolag. There are two relevant papers: `Biffi, V., Dolag, K., Bohringer, H., & Lemson, G. 2012, MNRAS, 420, 3545 `_ `Biffi, V., Dolag, K., Bohringer, H. 2013, MNRAS, 428, 1395 `_ The basic procedure is as follows: 1. Using a spectral model for the photon flux given the gas properties, and an algorithm for generating photons from the dataset loaded in yt, produce a large number of photons in three-dimensional space associated with the cells of the dataset. 2. Use this three-dimensional dataset as a sample from which to generate photon events that are projected along a line of sight, Doppler and cosmologically shifted, and absorbed by the Galactic foreground. 3. Optionally convolve these photons with instrument responses and produce images and spectra. We'll demonstrate the functionality on a realistic dataset of a galaxy cluster to get you started. .. note:: Currently, the ``photon_simulator`` analysis module only works with grid-based data. Creating an X-ray observation of a dataset on disk ++++++++++++++++++++++++++++++++++++++++++++++++++ .. code:: python import yt #yt.enable_parallelism() # If you want to run in parallel this should go here! from yt.analysis_modules.photon_simulator.api import * from yt.utilities.cosmology import Cosmology .. note:: For parallel runs using ``mpi4py``, the call to ``yt.enable_parallelism`` should go *before* the import of the ``photon_simulator`` module, as shown above. We're going to load up an Athena dataset of a galaxy cluster core: .. code:: python ds = yt.load("MHDSloshing/virgo_low_res.0054.vtk", units_override={"time_unit":(1.0,"Myr"), "length_unit":(1.0,"Mpc"), "mass_unit":(1.0e14,"Msun")}) First, to get a sense of what the resulting image will look like, let's make a new yt field called ``"density_squared"``, since the X-ray emission is proportional to :math:`\rho^2`, and a weak function of temperature and metallicity. .. code:: python def _density_squared(field, data): return data["density"]**2 ds.add_field("density_squared", function=_density_squared, units="g**2/cm**6") Then we'll project this field along the z-axis. .. code:: python prj = yt.ProjectionPlot(ds, "z", ["density_squared"], width=(500., "kpc")) prj.set_cmap("density_squared", "gray_r") prj.show() .. image:: _images/dsquared.png In this simulation the core gas is sloshing, producing spiral-shaped cold fronts. .. note:: To work out the following examples, you should install `AtomDB `_ and get the files from the `xray_data `_ auxiliary data package (see the :ref:`xray_data_README` for details on the latter). Make sure that in what follows you specify the full path to the locations of these files. To generate photons from this dataset, we have several different things we need to set up. The first is a standard yt data object. It could be all of the cells in the domain, a rectangular solid region, a cylindrical region, etc. Let's keep it simple and make a sphere at the center of the domain, with a radius of 250 kpc: .. code:: python sp = ds.sphere("c", (250., "kpc")) This will serve as our ``data_source`` that we will use later. Next, we need to create the ``SpectralModel`` instance that will determine how the data in the grid cells will generate photons. By default, two options are available. The first, ``XSpecThermalModel``, allows one to use any thermal model that is known to `XSPEC `_, such as ``"mekal"`` or ``"apec"``: .. code:: python mekal_model = XSpecThermalModel("mekal", 0.01, 10.0, 2000) This requires XSPEC and `PyXspec `_ to be installed. The second option, ``TableApecModel``, utilizes the data from the `AtomDB `_ tables. We'll use this one here: .. code:: python apec_model = TableApecModel("$SPECTRAL_DATA/spectral", 0.01, 20.0, 20000, thermal_broad=False, apec_vers="2.0.2") The first argument sets the location of the AtomDB files, and the next three arguments determine the minimum energy in keV, maximum energy in keV, and the number of linearly-spaced bins to bin the spectrum in. If the optional keyword ``thermal_broad`` is set to ``True``, the spectral lines will be thermally broadened. .. note:: ``SpectralModel`` objects based on XSPEC models (both the thermal emission and Galactic absorption models mentioned below) only work in Python 2.7, since currently PyXspec only works with Python 2.x. Now that we have our ``SpectralModel`` that gives us a spectrum, we need to connect this model to a ``PhotonModel`` that will connect the field data in the ``data_source`` to the spectral model to actually generate photons. For thermal spectra, we have a special ``PhotonModel`` called ``ThermalPhotonModel``: .. code:: python thermal_model = ThermalPhotonModel(apec_model, X_H=0.75, Zmet=0.3, photons_per_chunk=100000000, method="invert_cdf") Where we pass in the ``SpectralModel``, and can optionally set values for the hydrogen mass fraction ``X_H`` and metallicity ``Z_met``. If ``Z_met`` is a float, it will assume that value for the metallicity everywhere in terms of the solar metallicity. If it is a string, it will assume that is the name of the metallicity field (which may be spatially varying). The ``ThermalPhotonModel`` iterates over "chunks" of the supplied data source to generate the photons, to reduce memory usage and make parallelization more efficient. For each chunk, memory is set aside for the photon energies that will be generated. ``photons_per_chunk`` is an optional keyword argument which controls the size of this array. For large numbers of photons, you may find that this parameter needs to be set higher, or if you are looking to decrease memory usage, you might set this parameter lower. The ``method`` keyword argument is also optional, and determines how the individual photon energies are generated from the spectrum. It may be set to one of two values: * ``method="invert_cdf"``: Construct the cumulative distribution function of the spectrum and invert it, using uniformly drawn random numbers to determine the photon energies (fast, but relies on construction of the CDF and interpolation between the points, so for some spectra it may not be accurate enough). * ``method="accept_reject"``: Generate the photon energies from the spectrum using an acceptance-rejection technique (accurate, but likely to be slow). ``method="invert_cdf"`` (the default) should be sufficient for most cases. Next, we need to specify "fiducial" values for the telescope collecting area, exposure time, and cosmological redshift. Remember, the initial photon generation will act as a source for Monte-Carlo sampling for more realistic values of these parameters later, so choose generous values so that you have a large number of photons to sample from. We will also construct a ``Cosmology`` object: .. code:: python A = 3000. exp_time = 4.0e5 redshift = 0.05 cosmo = Cosmology() Now, we finally combine everything together and create a ``PhotonList`` instance: .. code:: python photons = PhotonList.from_scratch(sp, redshift, A, exp_time, thermal_model, center="c", cosmology=cosmo) By default, the angular diameter distance to the object is determined from the ``cosmology`` and the cosmological ``redshift``. If a ``Cosmology`` instance is not provided, one will be made from the default cosmological parameters. The ``center`` keyword argument specifies the center of the photon distribution, and the photon positions will be rescaled with this value as the origin. This argument accepts the following values: * A NumPy array or list corresponding to the coordinates of the center in units of code length. * A ``YTArray`` corresponding to the coordinates of the center in some length units. * ``"center"`` or ``"c"`` corresponds to the domain center. * ``"max"`` or ``"m"`` corresponds to the location of the maximum gas density. * A two-element tuple specifying the max or min of a specific field, e.g., ``("min","gravitational_potential")``, ``("max","dark_matter_density")`` If ``center`` is not specified, ``from_scratch`` will attempt to use the ``"center"`` field parameter of the ``data_source``. ``from_scratch`` takes a few other optional keyword arguments. If your source is local to the galaxy, you can set its distance directly, using a tuple, e.g. ``dist=(30, "kpc")``. In this case, the ``redshift`` and ``cosmology`` will be ignored. Finally, if the photon generating function accepts any parameters, they can be passed to ``from_scratch`` via a ``parameters`` dictionary. At this point, the ``photons`` are distributed in the three-dimensional space of the ``data_source``, with energies in the rest frame of the plasma. Doppler and/or cosmological shifting of the photons will be applied in the next step. The ``photons`` can be saved to disk in an HDF5 file: .. code:: python photons.write_h5_file("my_photons.h5") Which is most useful if it takes a long time to generate the photons, because a ``PhotonList`` can be created in-memory from the dataset stored on disk: .. code:: python photons = PhotonList.from_file("my_photons.h5") This enables one to make many simulated event sets, along different projections, at different redshifts, with different exposure times, and different instruments, with the same ``data_source``, without having to do the expensive step of generating the photons all over again! To get a set of photon events such as that observed by X-ray telescopes, we need to take the three-dimensional photon distribution and project it along a line of sight. Also, this is the step at which we put in the realistic values for the telescope collecting area, cosmological redshift and/or source distance, and exposure time. The order of operations goes like this: 1. From the adjusted exposure time, redshift and/or source distance, and telescope collecting area, determine the number of photons we will *actually* observe. 2. Determine the plane of projection from the supplied normal vector, and reproject the photon positions onto this plane. 3. Doppler-shift the photon energies according to the velocity along the line of sight, and apply cosmological redshift if the source is not local. 4. Optionally, alter the received distribution of photons via an energy-dependent galactic absorption model. 5. Optionally, alter the received distribution of photons using an effective area curve provided from an ancillary response file (ARF). 6. Optionally, scatter the photon energies into channels according to the information from a redistribution matrix file (RMF). First, if we want to apply galactic absorption, we need to set up a spectral model for the absorption coefficient, similar to the spectral model for the emitted photons we set up before. Here again, we have two options. The first, ``XSpecAbsorbModel``, allows one to use any absorption model that XSpec is aware of that takes only the Galactic column density :math:`N_H` as input: .. code:: python N_H = 0.1 abs_model = XSpecAbsorbModel("wabs", N_H) The second option, ``TableAbsorbModel``, takes as input an HDF5 file containing two datasets, ``"energy"`` (in keV), and ``"cross_section"`` (in :math:`cm^2`), and the Galactic column density :math:`N_H`: .. code:: python abs_model = TableAbsorbModel("tbabs_table.h5", 0.1) Now we're ready to project the photons. First, we choose a line-of-sight vector ``normal``. Second, we'll adjust the exposure time and the redshift. Third, we'll pass in the absorption ``SpectrumModel``. Fourth, we'll specify a ``sky_center`` in RA and DEC on the sky in degrees. Also, we're going to convolve the photons with instrument ``responses``. For this, you need a ARF/RMF pair with matching energy bins. This is of course far short of a full simulation of a telescope ray-trace, but it's a quick-and-dirty way to get something close to the real thing. We'll discuss how to get your simulated events into a format suitable for reading by telescope simulation codes later. If you just want to convolve the photons with an ARF, you may specify that as the only response, but some ARFs are unnormalized and still require the RMF for normalization. Check with the documentation associated with these files for details. If we are using the RMF to convolve energies, we must set ``convolve_energies=True``. .. code:: python ARF = "acisi_aimpt_cy17.arf" RMF = "acisi_aimpt_cy17.rmf" normal = [0.0,0.0,1.0] events = photons.project_photons(normal, exp_time_new=2.0e5, redshift_new=0.07, dist_new=None, absorb_model=abs_model, sky_center=(187.5,12.333), responses=[ARF,RMF], convolve_energies=True, no_shifting=False, north_vector=None, psf_sigma=None) In this case, we chose a three-vector ``normal`` to specify an arbitrary line-of-sight, but ``"x"``, ``"y"``, or ``"z"`` could also be chosen to project along one of those axes. ``project_photons`` takes several other optional keyword arguments. * ``no_shifting`` (default ``False``) controls whether or not Doppler shifting of photon energies is turned on. * ``dist_new`` is a (value, unit) tuple that is used to set a new angular diameter distance by hand instead of having it determined by the cosmology and the value of the redshift. Should only be used for simulations of nearby objects. * For off-axis ``normal`` vectors, the ``north_vector`` argument can be used to control what vector corresponds to the "up" direction in the resulting event list. * ``psf_sigma`` may be specified to provide a crude representation of a PSF, and corresponds to the standard deviation (in degrees) of a Gaussian PSF model. Let's just take a quick look at the raw events object: .. code:: python print(events) .. code:: python {'eobs': YTArray([ 0.32086522, 0.32271389, 0.32562708, ..., 8.90600621, 9.73534237, 10.21614256]) keV, 'xsky': YTArray([ 187.5177707 , 187.4887825 , 187.50733609, ..., 187.5059345 , 187.49897546, 187.47307048]) degree, 'ysky': YTArray([ 12.33519996, 12.3544496 , 12.32750903, ..., 12.34907707, 12.33327653, 12.32955225]) degree, 'ypix': array([ 133.85374195, 180.68583074, 115.14110561, ..., 167.61447493, 129.17278711, 120.11508562]), 'PI': array([ 27, 15, 25, ..., 609, 611, 672]), 'xpix': array([ 86.26331108, 155.15934197, 111.06337043, ..., 114.39586907, 130.93509652, 192.50639633])} We can bin up the events into an image and save it to a FITS file. The pixel size of the image is equivalent to the smallest cell size from the original dataset. We can specify limits for the photon energies to be placed in the image: .. code:: python events.write_fits_image("sloshing_image.fits", clobber=True, emin=0.5, emax=7.0) The resulting FITS image will have WCS coordinates in RA and Dec. It should be suitable for plotting in `ds9 `_, for example. There is also a great project for opening astronomical images in Python, called `APLpy `_: .. code:: python import aplpy fig = aplpy.FITSFigure("sloshing_image.fits", figsize=(10,10)) fig.show_colorscale(stretch="log", vmin=0.1, cmap="gray_r") fig.set_axis_labels_font(family="serif", size=16) fig.set_tick_labels_font(family="serif", size=16) .. image:: _images/Photon_Simulator_30_4.png Which is starting to look like a real observation! .. warning:: The binned images that result, even if you convolve with responses, are still of the same resolution as the finest cell size of the simulation dataset. If you want a more accurate simulation of a particular X-ray telescope, you should check out `Storing events for future use and for reading-in by telescope simulators`_. We can also bin up the spectrum into energy bins, and write it to a FITS table file. This is an example where we've binned up the spectrum according to the unconvolved photon energy: .. code:: python events.write_spectrum("virgo_spec.fits", bin_type="energy", emin=0.1, emax=10.0, nchan=2000, clobber=True) We can also set ``bin_type="channel"``. If we have convolved our events with response files, then any other keywords will be ignored and it will try to make a spectrum from the channel information that is contained within the RMF. Otherwise, the channels will be determined from the ``emin``, ``emax``, and ``nchan`` keywords, and will be numbered from 1 to ``nchan``. For now, we'll stick with the energy spectrum, and plot it up: .. code:: python import astropy.io.fits as pyfits f = pyfits.open("virgo_spec.fits") pylab.loglog(f["SPECTRUM"].data.field("ENERGY"), f["SPECTRUM"].data.field("COUNTS")) pylab.xlim(0.3, 10) pylab.xlabel("E (keV)") pylab.ylabel("counts/bin") .. image:: _images/Photon_Simulator_34_1.png We can also write the events to a FITS file that is of a format that can be manipulated by software packages like `CIAO `_ and read in by ds9 to do more standard X-ray analysis: .. code:: python events.write_fits_file("my_events.fits", clobber=True) .. warning:: We've done some very low-level testing of this feature, and it seems to work, but it may not be consistent with standard FITS events files in subtle ways that we haven't been able to identify. Please email jzuhone@gmail.com if you find any bugs! Two ``EventList`` instances can be added together, which is useful if they were created using different data sources: .. code:: python events3 = events1+events2 .. warning:: This only works if the two event lists were generated using the same parameters! Finally, a new ``EventList`` can be created from a subset of an existing ``EventList``, defined by a ds9 region (this functionality requires the `pyregion `_ package to be installed): .. code:: python circle_events = events.filter_events("circle.reg") Creating a X-ray observation from an in-memory dataset ++++++++++++++++++++++++++++++++++++++++++++++++++++++ It may be useful, especially for observational applications, to create datasets in-memory and then create simulated observations from them. Here is a relevant example of creating a toy cluster and evacuating two AGN-blown bubbles in it. First, we create the in-memory dataset (see :ref:`loading-numpy-array` for details on how to do this): .. code:: python import yt import numpy as np from yt.utilities.physical_ratios import cm_per_kpc, K_per_keV from yt.units import mp from yt.utilities.cosmology import Cosmology from yt.analysis_modules.photon_simulator.api import * import aplpy R = 1000. # in kpc r_c = 100. # in kpc rho_c = 1.673e-26 # in g/cm^3 beta = 1. T = 4. # in keV nx = 256 bub_rad = 30.0 bub_dist = 50.0 ddims = (nx,nx,nx) x, y, z = np.mgrid[-R:R:nx*1j, -R:R:nx*1j, -R:R:nx*1j] r = np.sqrt(x**2+y**2+z**2) dens = np.zeros(ddims) dens[r <= R] = rho_c*(1.+(r[r <= R]/r_c)**2)**(-1.5*beta) dens[r > R] = 0.0 temp = T*K_per_keV*np.ones(ddims) rbub1 = np.sqrt(x**2+(y-bub_rad)**2+z**2) rbub2 = np.sqrt(x**2+(y+bub_rad)**2+z**2) dens[rbub1 <= bub_rad] /= 100. dens[rbub2 <= bub_rad] /= 100. temp[rbub1 <= bub_rad] *= 100. temp[rbub2 <= bub_rad] *= 100. This created a cluster with a radius of 1 Mpc, a uniform temperature of 4 keV, and a density distribution from a :math:`\beta`-model. We then evacuated two "bubbles" of radius 30 kpc at a distance of 50 kpc from the center. Now, we create a yt Dataset object out of this dataset: .. code:: python data = {} data["density"] = (dens, "g/cm**3") data["temperature"] = (temp, "K") data["velocity_x"] = (np.zeros(ddims), "cm/s") data["velocity_y"] = (np.zeros(ddims), "cm/s") data["velocity_z"] = (np.zeros(ddims), "cm/s") bbox = np.array([[-0.5,0.5],[-0.5,0.5],[-0.5,0.5]]) ds = yt.load_uniform_grid(data, ddims, 2*R*cm_per_kpc, bbox=bbox) where for simplicity we have set the velocities to zero, though we could have created a realistic velocity field as well. Now, we generate the photon and event lists in the same way as the previous example: .. code:: python sphere = ds.sphere("c", (1.0,"Mpc")) A = 3000. exp_time = 2.0e5 redshift = 0.05 cosmo = Cosmology() apec_model = TableApecModel("/Users/jzuhone/Data/atomdb_v2.0.2", 0.01, 20.0, 20000) abs_model = TableAbsorbModel("tbabs_table.h5", 0.1) thermal_model = ThermalPhotonModel(apec_model, photons_per_chunk=40000000) photons = PhotonList.from_scratch(sphere, redshift, A, exp_time, thermal_model, center="c") events = photons.project_photons([0.0,0.0,1.0], responses=["acisi_aimpt_cy17.arf", "acisi_aimpt_cy17.rmf"], absorb_model=abs_model, north_vector=[0.0,1.0,0.0]) events.write_fits_image("img.fits", clobber=True) which yields the following image: .. code:: python fig = aplpy.FITSFigure("img.fits", figsize=(10,10)) fig.show_colorscale(stretch="log", vmin=0.1, vmax=600., cmap="jet") fig.set_axis_labels_font(family="serif", size=16) fig.set_tick_labels_font(family="serif", size=16) .. image:: _images/bubbles.png :width: 80 % Storing events for future use and for reading-in by telescope simulators ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ If you want a more accurate representation of an observation taken by a particular instrument, there are tools available for such purposes. For the *Chandra* telescope, there is the venerable `MARX `_. For a wide range of instruments, both existing and future, there is `SIMX `_. We'll discuss two ways to store your event files so that they can be input by these and other codes. The first option is the most general, and the simplest: simply dump the event data to an HDF5 file: .. code:: python events.write_h5_file("my_events.h5") This will dump the raw event data, as well as the associated parameters, into the file. If you want to read these events back in, it's just as simple: .. code:: python events = EventList.from_h5_file("my_events.h5") You can use event data written to HDF5 files to input events into MARX using `this code `_. The second option, for use with SIMX, is to dump the events into a SIMPUT file: .. code:: python events.write_simput_file("my_events", clobber=True, emin=0.1, emax=10.0) which will write two files, ``"my_events_phlist.fits"`` and ``"my_events_simput.fits"``, the former being a auxiliary file for the latter. .. note:: You can only write SIMPUT files if you didn't convolve the photons with responses, since the idea is to pass unconvolved photons to the telescope simulator. The following images were made from the same yt-generated events in both MARX and SIMX. They are 200 ks observations of the two example clusters from above (the Chandra images have been reblocked by a factor of 4): .. image:: _images/ds9_sloshing.png .. image:: _images/ds9_bubbles.png In November 2015, the structure of the photon and event HDF5 files changed. To convert an old-format file to the new format, use the ``convert_old_file`` utility: .. code:: python from yt.analysis_modules.photon_simulator.api import convert_old_file convert_old_file("old_photons.h5", "new_photons.h5", clobber=True) convert_old_file("old_events.h5", "new_events.h5", clobber=True) This utility will auto-detect the kind of file (photons or events) and will write the correct replacement for the new version. At times it may be convenient to write several ``EventLists`` to disk to be merged together later. This can be achieved with the ``merge_files`` utility. It takes a list of .. code:: python from yt.analysis_modules.photon_simulator.api import merge_files merge_files(["events_0.h5", "events_1.h5", "events_2.h5"], "merged_events.h5", add_exposure_times=True, clobber=False) At the current time this utility is very limited, as it only allows merging of ``EventLists`` which have the same parameters, with the exception of the exposure time. If the ``add_exposure_times`` argument to ``merge_files`` is set to ``True``, the lists will be merged together with the exposure times added. Otherwise, the exposure times of the different files must be equal. yt-3.3.3/doc/source/analyzing/analysis_modules/planning_cosmology_simulations.rst0000644000076500000240000000245413017603554031600 0ustar goldbaumstaff00000000000000.. _planning-cosmology-simulations: Planning Simulations to use LightCones or LightRays =================================================== If you want to run a cosmological simulation that will have just enough data outputs to create a light cone or light ray, the :meth:`~yt.analysis_modules.cosmological_observation.cosmology_splice.CosmologySplice.plan_cosmology_splice` function will calculate a list of redshifts outputs that will minimally connect a redshift interval. .. code-block:: python from yt.analysis_modules.cosmological_observation.api import CosmologySplice my_splice = CosmologySplice('enzo_tiny_cosmology/32Mpc_32.enzo', 'Enzo') my_splice.plan_cosmology_splice(0.0, 0.1, filename='redshifts.out') This will write out a file, formatted for simulation type, with a list of redshift dumps. The keyword arguments are: * ``decimals`` (*int*): The decimal place to which the output redshift will be rounded. If the decimal place in question is nonzero, the redshift will be rounded up to ensure continuity of the splice. Default: 3. * ``filename`` (*str*): If provided, a file will be written with the redshift outputs in the form in which they should be given in the enzo parameter file. Default: None. * ``start_index`` (*int*): The index of the first redshift output. Default: 0. yt-3.3.3/doc/source/analyzing/analysis_modules/ppv_cubes.rst0000644000076500000240000000017612770647750025250 0ustar goldbaumstaff00000000000000Creating Position-Position-Velocity FITS Cubes ------------------------------------------------- .. notebook:: PPVCube.ipynb yt-3.3.3/doc/source/analyzing/analysis_modules/PPVCube.ipynb0000644000076500000240000003224113017603554025022 0ustar goldbaumstaff00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Detailed spectra of astrophysical objects sometimes allow for determinations of how much of the gas is moving with a certain velocity along the line of sight, thanks to Doppler shifting of spectral lines. This enables \"data cubes\" to be created in RA, Dec, and line-of-sight velocity space. In yt, we can use the `PPVCube` analysis module to project fields along a given line of sight traveling at different line-of-sight velocities, to \"mock-up\" what would be seen in observations." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from yt.config import ytcfg\n", "\n", "import yt\n", "import numpy as np\n", "from yt.analysis_modules.ppv_cube.api import PPVCube\n", "import yt.units as u" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To demonstrate this functionality, we'll create a simple unigrid dataset from scratch of a rotating disk. We create a thin disk in the x-y midplane of the domain of three cells in height in either direction, and a radius of 10 kpc. The density and azimuthal velocity profiles of the disk as a function of radius will be given by the following functions:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Density: $\\rho(r) \\propto r^{\\alpha}$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Velocity: $v_{\\theta}(r) \\propto \\frac{r}{1+(r/r_0)^{\\beta}}$" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where for simplicity we won't worry about the normalizations of these profiles. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we'll set up the grid and the parameters of the profiles:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# increasing the resolution will make the images in this notebook more visually appealing\n", "nx,ny,nz = (64, 64, 64) # domain dimensions\n", "R = 10. # outer radius of disk, kpc\n", "r_0 = 3. # scale radius, kpc\n", "beta = 1.4 # for the tangential velocity profile\n", "alpha = -1. # for the radial density profile\n", "x, y = np.mgrid[-R:R:nx*1j,-R:R:ny*1j] # cartesian coordinates of x-y plane of disk\n", "r = np.sqrt(x*x+y*y) # polar coordinates\n", "theta = np.arctan2(y, x) # polar coordinates" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Second, we'll construct the data arrays for the density, temperature, and velocity of the disk. Since we have the tangential velocity profile, we have to use the polar coordinates we derived earlier to compute `velx` and `vely`. Everywhere outside the disk, all fields are set to zero. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dens = np.zeros((nx,ny,nz))\n", "dens[:,:,nz/2-3:nz/2+3] = (r**alpha).reshape(nx,ny,1) # the density profile of the disk\n", "temp = np.zeros((nx,ny,nz))\n", "temp[:,:,nz/2-3:nz/2+3] = 1.0e5 # Isothermal\n", "vel_theta = 100.*r/(1.+(r/r_0)**beta) # the azimuthal velocity profile of the disk\n", "velx = np.zeros((nx,ny,nz))\n", "vely = np.zeros((nx,ny,nz))\n", "velx[:,:,nz/2-3:nz/2+3] = (-vel_theta*np.sin(theta)).reshape(nx,ny,1) # convert polar to cartesian\n", "vely[:,:,nz/2-3:nz/2+3] = (vel_theta*np.cos(theta)).reshape(nx,ny,1) # convert polar to cartesian\n", "dens[r > R] = 0.0\n", "temp[r > R] = 0.0\n", "velx[r > R] = 0.0\n", "vely[r > R] = 0.0" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we'll package these data arrays up into a dictionary, which will then be shipped off to `load_uniform_grid`. We'll define the width of the grid to be `2*R` kpc, which will be equal to 1 `code_length`. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data = {}\n", "data[\"density\"] = (dens,\"g/cm**3\")\n", "data[\"temperature\"] = (temp, \"K\")\n", "data[\"velocity_x\"] = (velx, \"km/s\")\n", "data[\"velocity_y\"] = (vely, \"km/s\")\n", "data[\"velocity_z\"] = (np.zeros((nx,ny,nz)), \"km/s\") # zero velocity in the z-direction\n", "bbox = np.array([[-0.5,0.5],[-0.5,0.5],[-0.5,0.5]]) # bbox of width 1 on a side with center (0,0,0)\n", "ds = yt.load_uniform_grid(data, (nx,ny,nz), length_unit=(2*R,\"kpc\"), nprocs=1, bbox=bbox)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To get a sense of what the data looks like, we'll take a slice through the middle of the disk:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "slc = yt.SlicePlot(ds, \"z\", [\"density\",\"velocity_x\",\"velocity_y\",\"velocity_magnitude\"])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "slc.set_log(\"velocity_x\", False)\n", "slc.set_log(\"velocity_y\", False)\n", "slc.set_log(\"velocity_magnitude\", False)\n", "slc.set_unit(\"velocity_magnitude\", \"km/s\")\n", "slc.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Which shows a rotating disk with a specific density and velocity profile. Now, suppose we wanted to look at this disk galaxy from a certain orientation angle, and simulate a 3D FITS data cube where we can see the gas that is emitting at different velocities along the line of sight. We can do this using the `PPVCube` class. First, let's assume we rotate our viewing angle 60 degrees from face-on, from along the z-axis into the x-axis. We'll create a normal vector:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "i = 60.*np.pi/180.\n", "L = [np.sin(i),0.0,np.cos(i)]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we need to specify a field that will serve as the \"intensity\" of the emission that we see. For simplicity, we'll simply choose the gas density as this field, though it could be any field (including derived fields) in principle. We also need to choose the bounds in line-of-sight velocity that the data will be binned into, which is a 4-tuple in the shape of `(vmin, vmax, nbins, units)`, which specifies a linear range of `nbins` velocity bins from `vmin` to `vmax` in units of `units`. We may also optionally specify the dimensions of the data cube with the `dims` argument." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "scrolled": true }, "outputs": [], "source": [ "cube = PPVCube(ds, L, \"density\", (-150.,150.,50,\"km/s\"), dims=200, method=\"sum\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Following this, we can now write this cube to a FITS file. The x and y axes of the file can be in length units, which can be optionally specified by `length_unit`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cube.write_fits(\"cube.fits\", clobber=True, length_unit=\"kpc\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or one can use the `sky_scale` and `sky_center` keywords to set up the coordinates in RA and Dec:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sky_scale = (1.0, \"arcsec/kpc\")\n", "sky_center = (30., 45.) # RA, Dec in degrees\n", "cube.write_fits(\"cube_sky.fits\", clobber=True, sky_scale=sky_scale, sky_center=sky_center)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we'll look at the FITS dataset in yt and look at different slices along the velocity axis, which is the \"z\" axis:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ds_cube = yt.load(\"cube.fits\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Specifying no center gives us the center slice\n", "slc = yt.SlicePlot(ds_cube, \"z\", [\"density\"])\n", "slc.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Picking different velocities for the slices\n", "new_center = ds_cube.domain_center\n", "new_center[2] = ds_cube.spec2pixel(-100.*u.km/u.s)\n", "slc = yt.SlicePlot(ds_cube, \"z\", [\"density\"], center=new_center)\n", "slc.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "new_center[2] = ds_cube.spec2pixel(70.0*u.km/u.s)\n", "slc = yt.SlicePlot(ds_cube, \"z\", [\"density\"], center=new_center)\n", "slc.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "new_center[2] = ds_cube.spec2pixel(-30.0*u.km/u.s)\n", "slc = yt.SlicePlot(ds_cube, \"z\", [\"density\"], center=new_center)\n", "slc.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If we project all the emission at all the different velocities along the z-axis, we recover the entire disk:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "prj = yt.ProjectionPlot(ds_cube, \"z\", [\"density\"], method=\"sum\")\n", "prj.set_log(\"density\", True)\n", "prj.set_zlim(\"density\", 1.0e-3, 0.2)\n", "prj.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `thermal_broad` keyword allows one to simulate thermal line broadening based on the temperature, and the `atomic_weight` argument is used to specify the atomic weight of the particle that is doing the emitting." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cube2 = PPVCube(ds, L, \"density\", (-150.,150.,50,\"km/s\"), dims=200, thermal_broad=True, \n", " atomic_weight=12.0, method=\"sum\")\n", "cube2.write_fits(\"cube2.fits\", clobber=True, length_unit=\"kpc\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Taking a slice of this cube shows:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ds_cube2 = yt.load(\"cube2.fits\")\n", "new_center = ds_cube2.domain_center\n", "new_center[2] = ds_cube2.spec2pixel(70.0*u.km/u.s)\n", "slc = yt.SlicePlot(ds_cube2, \"z\", [\"density\"], center=new_center)\n", "slc.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "new_center[2] = ds_cube2.spec2pixel(-100.*u.km/u.s)\n", "slc = yt.SlicePlot(ds_cube2, \"z\", [\"density\"], center=new_center)\n", "slc.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where we can see the emission has been smeared into this velocity slice from neighboring slices due to the thermal broadening. \n", "\n", "Finally, the \"velocity\" or \"spectral\" axis of the cube can be changed to a different unit, such as wavelength, frequency, or energy: " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print (cube2.vbins[0], cube2.vbins[-1])\n", "cube2.transform_spectral_axis(400.0,\"nm\")\n", "print (cube2.vbins[0], cube2.vbins[-1])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If a FITS file is now written from the cube, the spectral axis will be in the new units. To reset the spectral axis back to the original velocity units:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "cube2.reset_spectral_axis()\n", "print (cube2.vbins[0], cube2.vbins[-1])" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 } yt-3.3.3/doc/source/analyzing/analysis_modules/radmc3d_export.rst0000644000076500000240000001605713017603554026172 0ustar goldbaumstaff00000000000000.. _radmc3d_export: Exporting to RADMC-3D ===================== .. sectionauthor:: Andrew Myers .. versionadded:: 2.6 .. figure:: _images/31micron.png Above: a sample image showing the continuum dust emission image around a massive protostar made using RADMC-3D and plotted with pyplot. `RADMC-3D `_ is a three-dimensional Monte-Carlo radiative transfer code that is capable of handling both line and continuum emission. yt comes equipped with a :class:`~yt.analysis_modules.radmc3d_export.RadMC3DInterface.RadMC3DWriter` class that exports AMR data to a format that RADMC-3D can read. Currently, only the ASCII-style data format is supported. In principle, this allows one to use RADMC-3D to make synthetic observations from any simulation data format that yt recognizes. Continuum Emission ------------------ To compute thermal emission intensities, RADMC-3D needs several inputs files that describe the spatial distribution of the dust and photon sources. To create these files, first import the RADMC-3D exporter, which is not loaded into your environment by default: .. code-block:: python import yt import numpy as np from yt.analysis_modules.radmc3d_export.api import RadMC3DWriter, RadMC3DSource Next, load up a dataset and instantiate the :class:`~yt.analysis_modules.radmc3d_export.RadMC3DInterface.RadMC3DWriter`. For this example, we'll use the "StarParticle" dataset, available `here `_. .. code-block:: python ds = yt.load("StarParticles/plrd01000/") writer = RadMC3DWriter(ds) The first data file to create is the "amr_grid.inp" file, which describes the structure of the AMR index. To create this file, simply call: .. code-block:: python writer.write_amr_grid() Next, we must give RADMC-3D information about the dust density. To do this, we define a field that calculates the dust density in each cell. We assume a constant dust-to-gas mass ratio of 0.01: .. code-block:: python dust_to_gas = 0.01 def _DustDensity(field, data): return dust_to_gas * data["density"] ds.add_field(("gas", "dust_density"), function=_DustDensity, units="g/cm**3") We save this information into a file called "dust_density.inp". .. code-block:: python writer.write_dust_file(("gas", "dust_density"), "dust_density.inp") Finally, we must give RADMC-3D information about any stellar sources that are present. To do this, we have provided the :class:`~yt.analysis_modules.radmc3d_export.RadMC3DInterface.RadMC3DSource` class. For this example, we place a single source with temperature 5780 K at the center of the domain: .. code-block:: python radius_cm = 6.96e10 mass_g = 1.989e33 position_cm = [0.0, 0.0, 0.0] temperature_K = 5780.0 star = RadMC3DSource(radius_cm, mass_g, position_cm, temperature_K) sources_list = [star] wavelengths_micron = np.logspace(-1.0, 4.0, 1000) writer.write_source_files(sources_list, wavelengths_micron) The last line creates the files "stars.inp" and "wavelength_micron.inp", which describe the locations and spectra of the stellar sources as well as the wavelengths RADMC-3D will use in it's calculations. If everything goes correctly, after executing the above code, you should have the files "amr_grid.inp", "dust_density.inp", "stars.inp", and "wavelength_micron.inp" sitting in your working directory. RADMC-3D needs a few more configuration files to compute the thermal dust emission. In particular, you need an opacity file, like the "dustkappa_silicate.inp" file included in RADMC-3D, a main "radmc3d.inp" file that sets some runtime parameters, and a "dustopac.inp" that describes the assumed composition of the dust. yt cannot make these files for you; in the example that follows, we used a "radmc3d.inp" file that looked like: :: nphot = 1000000 nphot_scat = 1000000 which basically tells RADMC-3D to use 1,000,000 photon packets instead of the default 100,000. The "dustopac.inp" file looked like: :: 2 1 ----------------------------- 1 0 silicate ----------------------------- To get RADMC-3D to compute the dust temperature, run the command: :: ./radmc3D mctherm in the directory that contains your "amr_grid.inp", "dust_density.inp", "stars.inp", "wavelength_micron.inp", "radmc3d.inp", "dustkappa_silicate.inp", and "dustopac.inp" files. If everything goes correctly, you should get a "dust_temperature.dat" file in your working directory. Once that file is generated, you can use RADMC-3D to generate SEDs, images, and so forth. For example, to create an image at 31 microns, do the command: :: ./radmc3d image lambda 31 sizeau 30000 npix 800 which should create a file called "image.out". You can view this image using pyplot or whatever other plotting package you want. To facilitate this, we provide helper functions that parse the image.out file, returning a header dictionary with some useful metadata and an np.array containing the image values. To plot this image in pyplot, you could do something like: .. code-block:: python import matplotlib.pyplot as plt import numpy as np from yt.analysis_modules.radmc3d_export.api import read_radmc3d_image header, image = read_radmc3d_image("image.out") Nx = header['Nx'] Ny = header['Ny'] x_hi = 0.5*header["pixel_size_cm_x"]*Nx x_lo = -x_hi y_hi = 0.5*header["pixel_size_cm_y"]*Ny y_lo = -y_hi X = np.linspace(x_lo, x_hi, Nx) Y = np.linspace(y_lo, y_hi, Ny) plt.pcolormesh(X, Y, np.log10(image), cmap='hot') cbar = plt.colorbar() plt.axis((x_lo, x_hi, y_lo, y_hi)) ax = plt.gca() ax.set_xlabel(r"$x$ (cm)") ax.set_ylabel(r"$y$ (cm)") cbar.set_label(r"Log Intensity (erg cm$^{-2}$ s$^{-1}$ Hz$^{-1}$ ster$^{-1}$)") plt.savefig('dust_continuum.png') The resulting image should look like: .. image:: _images/dust_continuum.png This barely scratches the surface of what you can do with RADMC-3D. Our goal here is just to describe how to use yt to export the data it knows about (densities, stellar sources, etc.) into a format that RADMC-3D can recognize. Line Emission ------------- The file format required for line emission is slightly different. The following script will generate two files, one called "numderdens_co.inp", which contains the number density of CO molecules for every cell in the index, and another called "gas-velocity.inp", which is useful if you want to include doppler broadening. .. code-block:: python import yt from yt.analysis_modules.radmc3d_export.api import RadMC3DWriter x_co = 1.0e-4 mu_h = yt.YTQuantity(2.34e-24, 'g') def _NumberDensityCO(field, data): return (x_co/mu_h)*data["density"] yt.add_field(("gas", "number_density_CO"), function=_NumberDensityCO, units="cm**-3") ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030") writer = RadMC3DWriter(ds) writer.write_amr_grid() writer.write_line_file(("gas", "number_density_CO"), "numberdens_co.inp") velocity_fields = ["velocity_x", "velocity_y", "velocity_z"] writer.write_line_file(velocity_fields, "gas_velocity.inp") yt-3.3.3/doc/source/analyzing/analysis_modules/star_analysis.rst0000644000076500000240000002520713017603554026125 0ustar goldbaumstaff00000000000000.. _star_analysis: Star Particle Analysis ====================== .. sectionauthor:: Stephen Skory .. versionadded:: 1.6 This document describes tools in yt for analyzing star particles. The Star Formation Rate tool bins stars by time to produce star formation statistics over several metrics. A synthetic flux spectrum and a spectral energy density plot can be calculated with the Spectrum tool. .. _star_formation_rate: Star Formation Rate ------------------- This tool can calculate various star formation statistics binned over time. As input it can accept either a yt ``data_source``, such as a region or sphere (see :ref:`available-objects`), or arrays containing the data for the stars you wish to analyze. This example will analyze all the stars in the volume: .. code-block:: python import yt from yt.analysis_modules.star_analysis.api import StarFormationRate ds = yt.load("Enzo_64/DD0030/data0030") ad = ds.all_data() sfr = StarFormationRate(ds, data_source=ad) or just a small part of the volume i.e. a small sphere at the center of the simulation volume with radius 10% the box size: .. code-block:: python import yt from yt.analysis_modules.star_analysis.api import StarFormationRate ds = yt.load("Enzo_64/DD0030/data0030") sp = ds.sphere([0.5, 0.5, 0.5], 0.1) sfr = StarFormationRate(ds, data_source=sp) If the stars to be analyzed cannot be defined by a ``data_source``, YTArrays can be passed. For backward compatibility it is also possible to pass generic numpy arrays. In this case, the units for the ``star_mass`` must be in :math:`(\mathrm{\rm{M}_\odot})`, the ``star_creation_time`` in code units, and the volume must be specified in :math:`(\mathrm{\rm{Mpc}})` as a float (but it doesn't have to be correct depending on which statistic is important). .. code-block:: python import yt from yt.analysis_modules.star_analysis.api import StarFormationRate from yt.data_objects.particle_filters import add_particle_filter def Stars(pfilter, data): return data[("all", "particle_type")] == 2 add_particle_filter("stars", function=Stars, filtered_type='all', requires=["particle_type"]) ds = yt.load("enzo_tiny_cosmology/RD0009/RD0009") ds.add_particle_filter('stars') v, center = ds.find_max("density") sp = ds.sphere(center, (50, "kpc")) # This puts the particle data for *all* the particles in the sphere sp # into the arrays sm and ct. mass = sp[("stars", "particle_mass")].in_units('Msun') age = sp[("stars", "age")].in_units('Myr') ct = sp[("stars", "creation_time")].in_units('Myr') # Pick out only old stars using Numpy array fancy indexing. threshold = ds.quan(100.0, "Myr") mass_old = mass[age > threshold] ct_old = ct[age > threshold] sfr = StarFormationRate(ds, star_mass=mass_old, star_creation_time=ct_old, volume=sp.volume()) To output the data to a text file, use the command ``.write_out``: .. code-block:: python sfr.write_out(name="StarFormationRate.out") In the file ``StarFormationRate.out``, there are seven columns of data: 1. Time (yr) 2. Look-back time (yr) 3. Redshift 4. Star formation rate in this bin per year :math:`(\mathrm{\rm{M}_\odot / \rm{yr}})` 5. Star formation rate in this bin per year per Mpc**3 :math:`(\mathrm{\rm{M}_\odot / \rm{h} / \rm{Mpc}^3})` 6. Stars formed in this time bin :math:`(\mathrm{\rm{M}_\odot})` 7. Cumulative stars formed up to this time bin :math:`(\mathrm{\rm{M}_\odot})` The output is easily plotted. This is a plot for some test data (that may or may not correspond to anything physical) using columns #2 and #4 for the x and y axes, respectively: .. image:: _images/SFR.png :width: 640 :height: 480 It is possible to access the output of the analysis without writing to disk. Attached to the ``sfr`` object are the following arrays which are identical to the ones that are saved to the text file as above: 1. ``sfr.time`` 2. ``sfr.lookback_time`` 3. ``sfr.redshift`` 4. ``sfr.Msol_yr`` 5. ``sfr.Msol_yr_vol`` 6. ``sfr.Msol`` 7. ``sfr.Msol_cumulative`` .. _synthetic_spectrum: Synthetic Spectrum Generator ---------------------------- Based on code generously provided by Kentaro Nagamine , this will generate a synthetic spectrum for the stars using the publicly-available tables of Bruzual & Charlot (hereafter B&C). Please see their `2003 paper `_ for more information and the `main data distribution page `_ for the original data. Based on the mass, age and metallicity of each star, a cumulative spectrum is generated and can be output in two ways, either raw, or as a spectral energy distribution. This analysis toolkit reads in the B&C data from HDF5 files that have been converted from the original ASCII files (available at the link above). The HDF5 files are one-quarter the size of the ASCII files, and greatly reduce the time required to read the data off disk. The HDF5 files are available from the main yt website `here `_. Both the Salpeter and Chabrier models have been converted, and it is simplest to download all the files to the same location. Please read the original B&C sources for information on the differences between the models. In order to analyze stars, first the Bruzual & Charlot data tables need to be read in from disk. This is accomplished by initializing ``SpectrumBuilder`` and specifying the location of the HDF5 files with the ``bcdir`` parameter. The models are chosen with the ``model`` parameter, which is either *"chabrier"* or *"salpeter"*. .. code-block:: python import yt from yt.analysis_modules.star_analysis.api import SpectrumBuilder ds = yt.load("enzo_tiny_cosmology/RD0009/RD0009") spec = SpectrumBuilder(ds, bcdir="bc", model="chabrier") In order to analyze a set of stars, use the ``calculate_spectrum`` command. It accepts either a ``data_source``, or a set of YTarrays with the star information. Continuing from the above example: .. code-block:: python v, center = ds.find_max("density") sp = ds.sphere(center, (50, "kpc")) spec.calculate_spectrum(data_source=sp) If a subset of stars are desired, call it like this: .. code-block:: python from yt.data_objects.particle_filters import add_particle_filter def Stars(pfilter, data): return data[("all", "particle_type")] == 2 add_particle_filter("stars", function=Stars, filtered_type='all', requires=["particle_type"]) # Pick out only old stars using Numpy array fancy indexing. threshold = ds.quan(100.0, "Myr") mass_old = sp[("stars", "age")][age > threshold] metal_old = sp[("stars", "metallicity_fraction")][age > threshold] ct_old = sp[("stars", "creation_time")][age > threshold] spec.calculate_spectrum(star_mass=mass_old, star_creation_time=ct_old, star_metallicity_fraction=metal_old) For backward compatibility numpy arrays can be used instead for ``star_mass`` (in units :math:`\mathrm{\rm{M}_\odot}`), ``star_creation_time`` and ``star_metallicity_fraction`` (in code units). Alternatively, when using either a ``data_source`` or individual arrays, the option ``star_metallicity_constant`` can be specified to force all the stars to have the same metallicity. If arrays are being used, the ``star_metallicity_fraction`` array need not be specified. .. code-block:: python # Make all the stars have solar metallicity. spec.calculate_spectrum(data_source=sp, star_metallicity_constant=0.02) Newly formed stars are often shrouded by thick gas. With the ``min_age`` option of ``calculate_spectrum``, young stars can be excluded from the spectrum. The units are in years. The default is zero, which is equivalent to including all stars. .. code-block:: python spec.calculate_spectrum(data_source=sp, star_metallicity_constant=0.02, min_age=ds.quan(1.0, "Myr")) There are two ways to write out the data once the spectrum has been calculated. The command ``write_out`` outputs two columns of data: 1. Wavelength :math:`(\text{\AA})` 2. Flux (Luminosity per unit wavelength :math:`(\mathrm{\rm{L}_\odot} / \text{\AA})` , where :math:`\mathrm{\rm{L}_\odot} = 3.826 \cdot 10^{33}\, \mathrm{ergs / s}` ). and can be called simply, specifying the output file: .. code-block:: python spec.write_out(name="spec.out") The other way is to output a spectral energy density plot. Along with the ``name`` parameter, this command can also take the ``flux_norm`` option, which is the wavelength in Angstroms of the flux to normalize the distribution to. The default is 5200 Angstroms. This command outputs the data in two columns: 1. Wavelength :math:`(\text{\AA})` 2. Relative flux normalized to the flux at *flux_norm*. .. code-block:: python spec.write_out_SED(name="SED.out", flux_norm=5200) Below is an example of an absurd SED for universe-old stars all with solar metallicity at a redshift of zero. Note that even in this example, a ``ds`` is required. .. code-block:: python import yt import numpy as np from yt.analysis_modules.star_analysis.api import SpectrumBuilder ds = yt.load("Enzo_64/DD0030/data0030") spec = SpectrumBuilder(ds, bcdir="bc", model="chabrier") sm = np.ones(100) ct = np.zeros(100) spec.calculate_spectrum(star_mass=sm, star_creation_time=ct, star_metallicity_constant=0.02) spec.write_out_SED('SED.out') And the plot: .. image:: _images/SED.png :width: 640 :height: 480 Iterate Over a Number of Halos ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ In this example below, the halos for a dataset are found, and the SED is calculated and written out for each. .. code-block:: python import yt from yt.analysis_modules.star_analysis.api import SpectrumBuilder from yt.data_objects.particle_filters import add_particle_filter from yt.analysis_modules.halo_finding.api import HaloFinder def Stars(pfilter, data): return data[("all", "particle_type")] == 2 add_particle_filter("stars", function=Stars, filtered_type='all', requires=["particle_type"]) ds = yt.load("enzo_tiny_cosmology/RD0009/RD0009") ds.add_particle_filter('stars') halos = HaloFinder(ds, dm_only=False) # Set up the spectrum builder. spec = SpectrumBuilder(ds, bcdir="bc", model="salpeter") # Iterate over the halos. for halo in halos: sp = halo.get_sphere() spec.calculate_spectrum( star_mass=sp[("stars", "particle_mass")], star_creation_time=sp[("stars", "creation_time")], star_metallicity_fraction=sp[("stars", "metallicity_fraction")]) # Write out the SED using the default flux normalization. spec.write_out_SED(name="halo%05d.out" % halo.id) yt-3.3.3/doc/source/analyzing/analysis_modules/sunrise_export.rst0000644000076500000240000002004613017603554026336 0ustar goldbaumstaff00000000000000.. _sunrise_export: Exporting to Sunrise ==================== .. sectionauthor:: Christopher Moody .. versionadded:: 1.8 .. note:: As of :code:`yt-3.0`, the sunrise exporter is not currently functional. This functionality is still available in :code:`yt-2.x`. If you would like to use these features in :code:`yt-3.x`, help is needed to port them over. Contact the yt-users mailing list if you are interested in doing this. The yt-Sunrise exporter essentially takes grid cell data and translates it into a binary octree format, attaches star particles, and saves the output to a FITS file Sunrise can read. For every cell, the gas mass, metals mass (a fraction of which is later assumed to be in the form of dust), and the temperature are saved. Star particles are defined entirely by their mass, position, metallicity, and a 'radius.' This guide outlines the steps to exporting the data, troubleshoots common problems, and reviews recommended sanity checks. Simple Export ------------- The code outlined here is a barebones Sunrise export: .. code-block:: python from yt.mods import * import numpy as na ds = ARTDataset(file_amr) potential_value,center=ds.find_min('Potential_New') root_cells = ds.domain_dimensions[0] le = np.floor(root_cells*center) #left edge re = np.ceil(root_cells*center) #right edge bounds = [(le[0], re[0]-le[0]), (le[1], re[1]-le[1]), (le[2], re[2]-le[2])] #bounds are left edge plus a span bounds = numpy.array(bounds,dtype='int') amods.sunrise_export.export_to_sunrise(ds, out_fits_file,subregion_bounds = bounds) To ensure that the camera is centered on the galaxy, we find the center by finding the minimum of the gravitational potential. The above code takes that center, and casts it in terms of which root cells should be extracted. At the moment, Sunrise accepts a strict octree, and you can only extract a 2x2x2 domain on the root grid, and not an arbitrary volume. See the optimization section later for workarounds. On my reasonably recent machine, the export process takes about 30 minutes. Some codes do not yet enjoy full yt support. As a result, export_to_sunrise() can manually include particles in the yt output fits file: .. code-block:: python import pyfits col_list = [] col_list.append(pyfits.Column("ID", format="I", array=np.arange(mass_current.size))) col_list.append(pyfits.Column("parent_ID", format="I", array=np.arange(mass_current.size))) col_list.append(pyfits.Column("position", format="3D", array=pos, unit="kpc")) col_list.append(pyfits.Column("velocity", format="3D", array=vel, unit="kpc/yr")) col_list.append(pyfits.Column("creation_mass", format="D", array=mass_initial, unit="Msun")) col_list.append(pyfits.Column("formation_time", format="D", array=formation_time, unit="yr")) col_list.append(pyfits.Column("radius", format="D", array=star_radius, unit="kpc")) col_list.append(pyfits.Column("mass", format="D", array=mass_current, unit="Msun")) col_list.append(pyfits.Column("age_m", format="D", array=age, unit="yr")) col_list.append(pyfits.Column("age_l", format="D", array=age, unit="yr")) col_list.append(pyfits.Column("metallicity", format="D",array=z)) col_list.append(pyfits.Column("L_bol", format="D",array=np.zeros(mass_current.size))) cols = pyfits.ColDefs(col_list) amods.sunrise_export.export_to_sunrise(ds, out_fits_file,write_particles=cols, subregion_bounds = bounds) This code snippet takes the stars in a region outlined by the ``bounds`` variable, organizes them into pyfits columns which are then passed to export_to_sunrise. Note that yt units are in CGS, and Sunrise accepts units in (physical) kpc, kelvin, solar masses, and years. Remember that in Sunrise, photons are not spawned at the exact point of the star particle, but stochastically in a radius around it. Default to setting this radius to the resolution (or smoothing kernel) of your simulation - and then test that Sunrise is not sensitive to a doubling or halving of this number. Sanity Check: Young Stars ------------------------- Young stars are treated in a special way in Sunrise. Stars under 10 Myr do not emit in the normal fashion; instead they are replaced with MAPPINGS III particles that emulate the emission characteristics of star forming clusters. Among other things this involves a calculation of the local pressure, P/k, which Sunrise reports for debugging purposes and is something you should also check. The code snippet below finds the location of every star under 10 Myr and looks up the cell containing it: .. code-block:: python for x,a in enumerate(zip(pos,age)): #loop over stars center = x*ds['kpc'] grid,idx = find_cell(ds.index.grids[0],center) pk[i] = grid['Pk'][idx] This code is how Sunrise calculates the pressure, so we can add our own derived field: .. code-block:: python def _Pk(field,data): #calculate pressure over Boltzmann's constant: P/k=(n/V)T #Local stellar ISM values are ~16500 Kcm^-3 vol = data['cell_volume'].astype('float64')*data.ds['cm']**3.0 #volume in cm m_g = data["cell_mass"]*1.988435e33 #mass of H in g n_g = m_g*5.97e23 #number of H atoms teff = data["temperature"] val = (n_g/vol)*teff #should be of order 1e2-1e5 return val add_field("Pk", function=_Pk,units=r"Kcm^{-3}") This snippet locates the cell containing a star and returns the grid and grid id. .. code-block:: python def find_cell(grid,position): x=grid #print(grid.LeftEdge) for child in grid.Children: if numpy.all(child.LeftEdge < position) and\ numpy.all(child.RightEdge > position): return find_cell(child,position) #if the point is not contained within any of the child grids #find it within the extent of the current grid le,re = x.LeftEdge,x.RightEdge ad = x.ActiveDimensions span = (re-le)/ad idx = (position-le)/span idx = numpy.floor(idx) idx = numpy.int64(idx) assert numpy.all(idx < ad) return grid,idx Sanity Check: Gas & Stars Line Up --------------------------------- If you add your star particles separately from the gas cell index, then it is worth checking that they still lined up once they've been loaded into Sunrise. This is fairly easy to do with a useful 'auxiliary' run. In Sunrise, set all of your rays to zero, (nrays_nonscatter, nrays_scatter,nrays_intensity,nrays_ir ) except for nrays_aux, and this will produce an mcrx FITS file with a gas map, a metals map, a temperature*gass_mass map and a stellar map for each camera. As long as you keep some cameras at theta,phi = 0,0 or 90,0, etc., then a standard yt projection down the code's xyz axes should look identical: .. code-block:: python pc.add_projection("density", 0, "density") Convergence: High Resolution ---------------------------- At the moment, yt exports are the only grid data format Sunrise accepts. Otherwise, Sunrise typically inputs SPH particles or AREPO Voronoi grids. Among the many convergence checks you should perform is a high resolution check, which subdivides all leaves in the octree and copies the parent data into them, effectively increasing the resolution but otherwise not adding more information. Sunrise should yield similar results, and it is worth checking that indeed it does. Do so by just passing export_to_sunrise(...,dummy_subdivide=True). The resulting file should be slightly less than 8 times larger because of newly added cells. Other checks: ------------- Check that the width of your extracted region is at least the size of your camera's field of view. It should probably be significantly larger than your FOV, and cutting that short could throw out otherwise interesting objects. A good idea is to leverage yt to find the inertia tensor of the stars, find the rotation matrix that diagonalizes it, and use that to define cameras for Sunrise. Unless your code grid is aligned with your galaxy, this is required for getting edge-on or face-on shots. The final product: ------------------ .. image:: _images/mw3_0420.jpg :width: 479 :height: 479 Above is a false color image where RGB are assigned to IR, optical and UV broadband filters, respectively. yt-3.3.3/doc/source/analyzing/analysis_modules/sunyaev_zeldovich.rst0000644000076500000240000000024013017603554027000 0ustar goldbaumstaff00000000000000.. _sunyaev-zeldovich: Mock Observations of the Sunyaev-Zeldovich Effect ------------------------------------------------- .. notebook:: SZ_projections.ipynb yt-3.3.3/doc/source/analyzing/analysis_modules/synthetic_observation.rst0000644000076500000240000000060513017603554027671 0ustar goldbaumstaff00000000000000.. _synthetic-observations: Synthetic Observation ===================== Methods for generating various types of synthetic observations from simulation data. .. toctree:: :maxdepth: 2 light_cone_generator light_ray_generator planning_cosmology_simulations absorption_spectrum star_analysis xray_emission_fields sunyaev_zeldovich photon_simulator ppv_cubes yt-3.3.3/doc/source/analyzing/analysis_modules/SZ_projections.ipynb0000644000076500000240000001762013017603554026535 0ustar goldbaumstaff00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "The change in the CMB intensity due to Compton scattering of CMB\n", "photons off of thermal electrons in galaxy clusters, otherwise known as the\n", "Sunyaev-Zeldovich (S-Z) effect, can to a reasonable approximation be represented by a\n", "projection of the pressure field of a cluster. However, the *full* S-Z signal is a combination of thermal and kinetic\n", "contributions, and for large frequencies and high temperatures\n", "relativistic effects are important. For computing the full S-Z signal\n", "incorporating all of these effects, there is a library:\n", "SZpack ([Chluba et al 2012](http://adsabs.harvard.edu/abs/2012MNRAS.426..510C)). \n", "\n", "The `sunyaev_zeldovich` analysis module in yt makes it possible\n", "to make projections of the full S-Z signal given the properties of the\n", "thermal gas in the simulation using SZpack. SZpack has several different options for computing the S-Z signal, from full\n", "integrations to very good approximations. Since a full or even a\n", "partial integration of the signal for each cell in the projection\n", "would be prohibitively expensive, we use the method outlined in\n", "[Chluba et al 2013](http://adsabs.harvard.edu/abs/2013MNRAS.430.3054C) to expand the\n", "total S-Z signal in terms of moments of the projected optical depth $\\tau$, projected electron temperature $T_e$, and\n", "velocities $\\beta_{c,\\parallel}$ and $\\beta_{c,\\perp}$ (their equation 18):" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "$$S(\\tau, T_{e},\\beta_{c,\\parallel},\\beta_{\\rm c,\\perp}) \\approx S_{\\rm iso}^{(0)} + S_{\\rm iso}^{(2)}\\omega^{(1)} + C_{\\rm iso}^{(1)}\\sigma^{(1)} + D_{\\rm iso}^{(2)}\\kappa^{(1)} + E_{\\rm iso}^{(2)}\\beta_{\\rm c,\\perp,SZ}^2 +~...$$\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "yt makes projections of the various moments needed for the\n", "calculation, and then the resulting projected fields are used to\n", "compute the S-Z signal. In our implementation, the expansion is carried out to first-order\n", "terms in $T_e$ and zeroth-order terms in $\\beta_{c,\\parallel}$ by default, but terms up to second-order in can be optionally\n", "included. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Installing SZpack" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "SZpack can be downloaded [here](http://www.cita.utoronto.ca/~jchluba/Science_Jens/SZpack/SZpack.html). Make\n", "sure you install a version later than v1.1.1. For computing the S-Z\n", "integrals, SZpack requires the [GNU Scientific Library](http://www.gnu.org/software/gsl/). For compiling\n", "the Python module, you need to have a recent version of [swig](http://www.swig.org>) installed. After running `make` in the top-level SZpack directory, you'll need to run it in the `python` subdirectory, which is the\n", "location of the `SZpack` module. You may have to include this location in the `PYTHONPATH` environment variable.\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "**NOTE**: Currently, use of the SZpack library to create S-Z projections in yt is limited to Python 2.x." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Creating S-Z Projections" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once you have SZpack installed, making S-Z projections from yt\n", "datasets is fairly straightforward:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import yt\n", "from yt.analysis_modules.sunyaev_zeldovich.api import SZProjection\n", "\n", "ds = yt.load(\"enzo_tiny_cosmology/DD0046/DD0046\")\n", "\n", "freqs = [90.,180.,240.]\n", "szprj = SZProjection(ds, freqs)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`freqs` is a list or array of frequencies in GHz at which the signal\n", "is to be computed. The `SZProjection` constructor also accepts the\n", "optional keywords, `mue` (mean molecular weight for computing the\n", "electron number density, 1.143 is the default) and `high_order` (set\n", "to True to compute terms in the S-Z signal expansion up to\n", "second-order in $T_{e,SZ}$ and $\\beta$). " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Once you have created the `SZProjection` object, you can use it to\n", "make on-axis and off-axis projections:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# An on-axis projection along the z-axis with width 10 Mpc, centered on the gas density maximum\n", "szprj.on_axis(\"z\", center=\"max\", width=(10.0, \"Mpc\"), nx=400)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To make an off-axis projection, `szprj.off_axis` is called in the same way, except that the first argument is a three-component normal vector. \n", "\n", "Currently, only one projection can be in memory at once. These methods\n", "create images of the projected S-Z signal at each requested frequency,\n", "which can be accessed dict-like from the projection object (e.g.,\n", "`szprj[\"90_GHz\"]`). Projections of other quantities may also be\n", "accessed; to see what fields are available call `szprj.keys()`. The methods also accept standard yt\n", "keywords for projections such as `center`, `width`, and `source`. The image buffer size can be controlled by setting `nx`. \n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Writing out the S-Z Projections" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You may want to output the S-Z images to figures suitable for\n", "inclusion in a paper, or save them to disk for later use. There are a\n", "few methods included for this purpose. For PNG figures with a colorbar\n", "and axes, use `write_png`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "szprj.write_png(\"SZ_example\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For simple output of the image data to disk, call `write_hdf5`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "szprj.write_hdf5(\"SZ_example.h5\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, for output to FITS files which can be opened or analyzed\n", "using other programs (such as ds9), call `export_fits`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "szprj.write_fits(\"SZ_example.fits\", clobber=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "which would write all of the projections to a single FITS file,\n", "including coordinate information in kpc. The optional keyword\n", "`clobber` allows a previous file to be overwritten. \n" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 } yt-3.3.3/doc/source/analyzing/analysis_modules/two_point_functions.rst0000644000076500000240000011442413017603554027363 0ustar goldbaumstaff00000000000000.. _two_point_functions: Two Point Functions =================== .. sectionauthor:: Stephen Skory .. versionadded:: 1.7 .. note:: As of :code:`yt-3.0`, the two point function analysis module is not currently functional. This functionality is still available in :code:`yt-2.x`. If you would like to use these features in :code:`yt-3.x`, help is needed to port them over. Contact the yt-users mailing list if you are interested in doing this. The Two Point Functions framework (TPF) is capable of running several multi-dimensional two point functions simultaneously on a dataset using memory and workload parallelism. Examples of two point functions are structure functions and two-point correlation functions. It can analyze the entire simulation, or a small rectangular subvolume. The results can be output in convenient text format and in efficient HDF5 files. Requirements ------------ The TPF relies on the Fortran kD-tree that is used by the parallel HOP halo finder. The kD-tree is not built by default with yt so it must be built by hand. Quick Example ------------- It is very simple to setup and run a structure point function on a dataset. The script below will output the RMS velocity difference over the entire volume for a range of distances. There are some brief comments given below for each step. .. code-block:: python from yt.mods import * from yt.analysis_modules.two_point_functions.api import * ds = load("data0005") # Calculate the S in RMS velocity difference between the two points. # All functions have five inputs. The first two are containers # for field values, and the second two are the raw point coordinates # for the point pair. The fifth is the normal vector between the two points # in r1 and r2. Not all the inputs must be used. # The name of the function is used to name output files. def rms_vel(a, b, r1, r2, vec): vdiff = a - b np.power(vdiff, 2.0, vdiff) vdiff = np.sum(vdiff, axis=1) return vdiff # Initialize a function generator object. # Set the input fields for the function(s), # the number of pairs of points to calculate, how big a data queue to # use, the range of pair separations and how many lengths to use, # and how to divide that range (linear or log). tpf = TwoPointFunctions(ds, ["velocity_x", "velocity_y", "velocity_z"], total_values=1e5, comm_size=10000, length_number=10, length_range=[1./128, .5], length_type="log") # Adds the function to the generator. An output label is given, # and whether or not to square-root the results in the text output is given. # Note that the items below are being added as lists. f1 = tpf.add_function(function=rms_vel, out_labels=['RMSvdiff'], sqrt=[True]) # Define the bins used to store the results of the function. f1.set_pdf_params(bin_type='log', bin_range=[5e4, 5.5e13], bin_number=1000) # Runs the functions. tpf.run_generator() # This calculates the M in RMS and writes out a text file with # the RMS values and the lengths. The R happens because sqrt=True in # add_function, above. # If one is doing turbulence, the contents of this text file are what # is wanted for plotting. # The file is named 'rms_vel.txt'. tpf.write_out_means() # Writes out the raw PDF bins and bin edges to a HDF5 file. # The file is named 'rms_vel.h5'. tpf.write_out_arrays() As an aside, note that any analysis function in yt can be accessed directly and imported automatically using the ``amods`` construct. Here is an abbreviated example: .. code-block:: python from yt.mods import * ... tpf = amods.two_point_functions.TwoPointFunctions(ds, ...) Probability Distribution Function --------------------------------- For a given length of separation between points, the TPF stores the Probability Distribution Function (PDF) of the output values. The PDF allows more varied analysis of the TPF output than storing the function itself. The image below assists in how to think about this. If the function is measuring the absolute difference in temperature between two points, for each point separation length L, the measured differences are binned by temperature difference (delta T). Therefore in the figure below, for a length L, the x-axis is temperature difference (delta T), and the y-axis is the probability of finding that temperature difference. To find the mean temperature difference for the length L, one just needs to multiply the value of the temperature difference bin by its probability, and add up over all the bins. .. image:: _images/PDF.png :width: 538 :height: 494 How It Works ------------ In order to use the TPF, one must understand how it works. When run in parallel the defined analysis volume, whether it is the full volume or a small region, is subdivided evenly and each task is assigned a different subvolume. The total number of point pairs to be created per pair separation length is ``total_values``, and each task is given an equal share of that total. Each task will create its share of ``total_values`` by first making a randomly placed point in its local volume. The second point will be placed a distance away with location set by random values of (phi, theta) in spherical coordinates and length by the length ranges. If that second point is inside the tasks subvolume, the functions are evaluated and their results binned. However, if the second point lies outside the subvolume (as in a different tasks subvolume), the point pair is stored in a point data queue, as well as the field values for the first point in a companion data queue. When a task makes its share of ``total_values``, or it fills up its data queue with points it can't fully process, it passes its queues to its neighbor on the right. It then receives the data queues from its neighbor on the left, and processes the queues. If it can evaluate a point in the received data queues, meaning it can find the field values for the second point, it computes the functions for that point pair, and removes that entry from the queue. If it still needs to fulfill ``total_values``, it can put its own point pair into that entry in the queues. Once the queues are full of points that a task cannot process, it passes them on. The data communication cycle ends when all tasks have made their share of ``total_values``, and all the data queues are cleared. When all the cycles have run, the bins are added up globally to find the global PDF. Below is a two-dimensional representation of how the full simulation is subdivided into 16 smaller subvolumes. Each subvolume is assigned to one of 16 tasks labelled with an integer [0-15]. Each task is responsible for only the field values inside its subvolume - it is completely ignorant about all the other subvolumes. When point separation rulers are laid down, some like the ruler labelled A, have both points completely inside a single subvolume. In this case, task 5 can evaluate the function(s) on its own. In situations like B or C, the points lie in different subvolumes, and no one task can evaluate the functions independently. .. image:: _images/struct_fcn_subvolumes0.png :width: 403 :height: 403 This next figure shows how the data queues are passed from task to task. Once task 0 is done with its points, or its queue is full, it passes the queue to task 1. Likewise, 1 passes to 2, and 15 passes back around to 0, completing the circle. If a point pair lies in the subvolumes of 0 and 15, it can take up to 15 communication cycles for that pair to be evaluated. .. image:: _images/struct_fcn_subvolumes1.png :width: 526 :height: 403 Sometimes the sizes of the data fields being computed on are not very large, and the memory-parallelism of the TPF isn't crucial. However, if one still wants to run with lots of processors to make large amounts of random pairs, subdividing the volumes as above is not as efficient as it could be due to communication overhead. By using the ``vol_ratio`` setting of TPF (see :ref:`Create the Function Generator Object `), the full volume can be subdivided into larger subvolumes than above, and tasks will own non-unique copies of the fields data. In the figure below, the two-dimensional volume has been subdivided into four subvolumes, and four tasks each own a copy of the data in each subvolume. As shown, the queues are handed off in the same order as before. But in this simple example, the maximum number of communication cycles for any point to be evaluated is three. This means that the communication overhead will be lower and runtimes somewhat faster. .. image:: _images/struct_fcn_subvolumes2.png :width: 526 :height: 403 A Step By Step Overview ----------------------- In order to run the TPF, these steps must be taken: #. Load yt (of course), and any other Python modules that are needed. #. Define any non-default fields in the standard yt manner. #. :ref:`tpf_fcns`. #. :ref:`tpf_tpf`. #. :ref:`tpf_add_fcns`. #. :ref:`tpf_pdf`. #. :ref:`tpf_run`. #. :ref:`tpf_output`. .. _tpf_fcns: Define Functions ^^^^^^^^^^^^^^^^ All functions must adhere to these specifications: * There must be five input variables. The first two are arrays for the fields needed by the function, and the next two are the raw coordinate values for the points. The fifth input is an array with the normal vector between each of the points in r1 and r2. * The output must be in array format. * The names of the functions need to be unique. The first two variables of a function are arrays that contain the field values. The order of the field values in the lists is set by the call to ``TwoPointFunctions`` (that comes later). In the example above, ``a`` and ``b`` contain the field velocities for the two points, respectively, in an N by M array, where N is equal to ``comm_size`` (set in ``TwoPointFunctions``), and M is the total number of input fields used by functions. ``a[:,0]`` and ``b[:,0]`` are the ``x-velocity`` field values because that field is the first field given in the ``TwoPointFunctions``. The second two variables ``r1`` and ``r2`` are the raw point coordinates for the two points. The fifth input is an array containing the normal vector between each pair of points. These arrays are all N by 3 arrays. Note that they are not used in the example above because they are not needed. Functions need to output in array format, with dimensionality N by R, where R is the dimensionality of the function. Multi-dimensional functions can be written that output several values simultaneously. The names of the functions must be unique because they are used to name output files, and name collisions will result in over-written output. .. _tpf_tpf: Create the Two Point Function Generator Object ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Before any functions can be added, the ``TwoPointFunctions`` object needs to be created. It has these inputs: * ``ds`` (the only required input and is always the first term). * Field list, required, an ordered list of field names used by the functions. The order in this list will need to be referenced when writing functions. Derived fields may be used here if they are defined first. * ``left_edge``, ``right_edge``, three-element lists of floats: Used to define a sub-region of the full volume in which to run the TDS. Default=None, which is equivalent to running on the full volume. Both must be set to have any effect. * ``total_values``, integer: The number of random points to generate globally per point separation length. If run in parallel, each task generates its fair share of this number. Default=1000000. * ``comm_size``, integer: How many pairs of points that are stored in the data queue objects on each task. Too large wastes memory, and too small will result in longer run times due to extra communication cycles. Each unit of ``comm_size`` costs (6 + number_of_fields)*8 bytes, where number_of_fields is the size of the set of unique data fields used by all the functions added to the TPF. In the RMS velocity example above, number_of_fields=3, and a ``comm_size`` of 10,000 means each queue costs 10,000*8*(6+3) = 720 KB per task. Default=10000. * ``length_type``, string ("lin" or "log"): Sets how to evenly space the point separation lengths, either linearly or logarithmic (log10). Default="lin". * ``length_number``, integer: How many point separations to run. Default=10. * ``length_range``, two-element list of floats: Two values that define the minimum and maximum point separations to run over. The lengths that will be used are divided into ``length_number`` pieces evenly separated according to ``length_type``. Default=None, which is equivalent to [sqrt(3)*dx, min_simulation_edge/2.], where min_simulation_edge is the length of the smallest edge (1D) of the simulation, and dx is the smallest cell size in the dataset. The sqrt(3) is there because that is the distance between opposite corners of a unit cube, and that guarantees that the point pairs will be in different cells for the most refined regions. If the first term of the list is -1, the minimum length will be automatically set to sqrt(3)*dx, ex: ``length_range = [-1, 10/ds['kpc']]``. * ``vol_ratio``, integer: How to multiply-assign subvolumes to the parallel tasks. This number must be an integer factor of the total number of tasks or very bad things will happen. The default value of 1 will assign one task to each subvolume, and there will be an equal number of subvolumes as tasks. A value of 2 will assign two tasks to each subvolume and there will be one-half as many subvolumes as tasks. A value equal to the number of parallel tasks will result in each task owning a complete copy of all the fields data, meaning each task will be operating on the identical full volume. Setting this to -1 automatically adjusts ``vol_ratio`` such that all tasks are given the full volume. * ``salt``, integer: A number that will be added to the random number generator seed. Use this if a different random series of numbers is desired when keeping everything else constant from this set: (MPI task count, number of ruler lengths, ruler min/max, number of functions, number of point pairs per ruler length). Default: 0. * ``theta``, float: For random pairs of points, the second point is found by traversing a distance along a ray set by the angle (phi, theta) from the first point. To keep this angle constant, set ``theta`` to a value in the range [0, pi]. Default = None, which will randomize theta for every pair of points. * ``phi``, float: Similar to theta above, but the range of values is [0, 2*pi). Default = None, which will randomize phi for every pair of points. .. _tpf_add_fcns: Add Functions ^^^^^^^^^^^^^ Each function is added to the TPF using the ``add_function`` command. Each call must have the following inputs: #. The function name as previously defined. #. A list with label(s) for the output(s) of the function. Even if the function outputs only one value, this must be a list. These labels are used for output. #. A list with bools of whether or not to sqrt the output, in the same order as the output label list. E.g. ``[True, False]``. The call to ``add_function`` returns a ``FcnSet`` object. For convenience, it is best to store the output in a variable (as in the example above) so it can be referenced later. The functions can also be referenced through the ``TwoPointFunctions`` object in the order in which they were added. So would ``tpf[0]`` refer to the same thing as ``f1`` in the quick example, above. .. _tpf_pdf: Set PDF Parameters ^^^^^^^^^^^^^^^^^^ Once the function is added to the TPF, the probability distribution bins need to be defined for each using the command ``set_pdf_params``. It has these inputs: * ``bin_type``, string or list of strings ("lin" or "log"): How to evenly subdivide the bins over the given range. If the function has multiple outputs, the input needs to be a list with equal elements. * ``bin_range``, list or list of lists: Define the min/max values for the bins for the output(s) of the function. If there are multiple outputs, there must be an equal number of lists. * ``bin_number``, integer or list of integers: How many bins to create over the min/max range defined by ``bin_range`` evenly spaced by the ``bin_type`` parameter. If there are multiple outputs, there must be an equal number of integers. The memory costs associated with the PDF bins must be considered when writing an analysis script. There is one set of PDF bins created per function, per point separation length. Each PDF bin costs product(bin_number)*8 bytes, where product(bin_number) is the product of the entries in the bin_number list, and this is duplicated on every task. For multidimensional PDFs, the memory costs can grow very quickly. For example, for 3 functions, each with two outputs, with 1000 point separation lengths set for the TPF, and with 5000 PDF bins per output dimension, the PDF bins will cost: 3*1000*(5000)^2*8=600 GB of memory *per task*! Note: ``bin_number`` actually specifies the number of *bin edges* to make, rather than the number of bins to make. The number of bins will actually be ``bin_number``-1 because values are dropped into bins between the two closest bin edge values, and values outside the min/max bin edges are thrown away. If precisely ``bin_number`` bins are wanted, add 1 when setting the PDF parameters. .. _tpf_run: Run the TPF ^^^^^^^^^^^ The command ``run_generator()`` pulls the trigger and runs the TPF. There are no inputs. After the generator runs, it will print messages like this, one per function:: yt INFO 2010-03-13 12:46:54,541 Function rms_vel had 1 values too high and 4960 too low that were not binned. Consider changing the range of the PDF bins to reduce or eliminate un-binned values. .. _tpf_output: Output the Results ^^^^^^^^^^^^^^^^^^ There are two ways to output data from the TPF for structure functions. #. The command ``write_out_means`` writes out a text file per function that contains the means for each dimension of the function output for each point separation length. The file is named "function_name.txt", so in the example the file is named "rms_vel.txt". In the example above, the ``sqrt=True`` option is turned on, which square-roots the mean values. Here is some example output for the RMS velocity example:: # length count RMSvdiff 7.81250e-03 95040 8.00152e+04 1.24016e-02 100000 1.07115e+05 1.96863e-02 100000 1.53741e+05 3.12500e-02 100000 2.15070e+05 4.96063e-02 100000 2.97069e+05 7.87451e-02 99999 4.02917e+05 1.25000e-01 100000 5.54454e+05 1.98425e-01 100000 7.53650e+05 3.14980e-01 100000 9.57470e+05 5.00000e-01 100000 1.12415e+06 The ``count`` column lists the number of pair points successfully binned at that point separation length. If the output is multidimensional, pass a list of bools to control the sqrt column by column (``sqrt=[False, True]``) to ``add_function``. For multidimensional functions, the means are calculated by first collapsing the values in the PDF matrix in the other dimensions, before multiplying the result by the bin edges for that output dimension. So in the extremely simple fabricated case of: .. code-block:: python # Temperature difference bin edges # dimension 0 Tdiff_bins = [10, 100, 1000] # Density difference bin edges # dimension 1 Ddiff_bins = [50,500,5000] # 2-D PDF for a point pair length of 0.05 PDF = [ [ 0.3, 0.1], [ 0.4, 0.2] ] What the PDF is recording is that there is a 30% probability of getting a temperature difference between [10, 100), at the same time of getting a density difference between [50, 500). There is a 40% probability for Tdiff in [10, 100) and Ddiff in [500, 5000). The text output of this PDF is calculated like this: .. code-block:: python # Temperature T_PDF = PDF.sum(axis=0) # ... which gets ... T_PDF = [0.7, 0.3] # Then to get the mean, multiply by the centers of the temperature bins. means = [0.7, 0.3] * [55, 550] # ... which gets ... means = [38.5, 165] mean = sum(means) # ... which gets ... mean = 203.5 # Density D_PDF = PDF.sum(axis=1) # ... which gets ... D_PDF = [0.4, 0.6] # As above... means = [0.4, 0.6] * [275, 2750] mean = sum(means) # ... which gets ... mean = 1760 The text file would look something like this:: # length count Tdiff Ddiff 0.05 980242 2.03500e+02 1.76000e+3 #. The command ``write_out_arrays()`` writes the raw PDF bins, as well as the bin edges for each output dimension to a HDF5 file named ``function_name.h5``. Here is example content for the RMS velocity script above:: $ h5ls rms_vel.h5 bin_edges_00_RMSvdiff Dataset {1000} bin_edges_names Dataset {1} counts Dataset {10} lengths Dataset {10} prob_bins_00000 Dataset {999} prob_bins_00001 Dataset {999} prob_bins_00002 Dataset {999} prob_bins_00003 Dataset {999} prob_bins_00004 Dataset {999} prob_bins_00005 Dataset {999} prob_bins_00006 Dataset {999} prob_bins_00007 Dataset {999} prob_bins_00008 Dataset {999} prob_bins_00009 Dataset {999} Every HDF5 file produced will have the datasets ``lengths``, ``bin_edges_names``, and ``counts``. ``lengths`` contains the list of the pair separation lengths used for the TPF, and is identical to the first column in the text output file. ``bin_edges_names`` lists the name(s) of the dataset(s) that contain the bin edge values. ``counts`` contains the number of successfully binned point pairs for each point separation length, and is equivalent to the second column in the text output file. In the HDF5 file above, the ``lengths`` dataset looks like this:: $ h5dump -d lengths rms_vel.h5 HDF5 "rms_vel.h5" { DATASET "lengths" { DATATYPE H5T_IEEE_F64LE DATASPACE SIMPLE { ( 10 ) / ( 10 ) } DATA { (0): 0.0078125, 0.0124016, 0.0196863, 0.03125, 0.0496063, 0.0787451, (6): 0.125, 0.198425, 0.31498, 0.5 } } } There are ten length values. ``prob_bins_00000`` is the PDF for pairs of points separated by the first length value given, which is 0.0078125. Points separated by 0.0124016 are recorded in ``prob_bins_00001``, and so on. The entries in the ``prob_bins`` datasets are the raw PDF for that function for that point separation length. If the function has multiple outputs, the arrays stored in the datasets are multidimensional. ``bin_edges_names`` looks like this:: $ h5dump -d bin_edges_names rms_vel.h5 HDF5 "rms_vel.h5" { DATASET "bin_edges_names" { DATATYPE H5T_STRING { STRSIZE 22; STRPAD H5T_STR_NULLPAD; CSET H5T_CSET_ASCII; CTYPE H5T_C_S1; } DATASPACE SIMPLE { ( 1 ) / ( 1 ) } DATA { (0): "/bin_edges_00_RMSvdiff" } } } This gives the names of the datasets that contain the bin edges, in the same order as the function output the data. If the function outputs several items, there will be more than one dataset listed in ``bin_edges-names``. ``bin_edges_00_RMSvdiff`` therefore contains the (dimension 0) bin edges as specified when the PDF parameters were set. If there were other output fields, they would be named ``bin_edges_01_outfield1``, ``bin_edges_02_outfield2`` respectively. .. _tpf_strategies: Strategies for Computational Efficiency --------------------------------------- Here are a few recommendations that will make the function generator run as quickly as possible, in particular when running in parallel. * Calculate how much memory the data fields and PDFs will require, and figure out what fraction can fit on a single compute node. For example (ignoring the PDF memory costs), if four data fields are required, and each takes up 8GB of memory (as in each field has 1e9 doubles), 32GB total is needed. If the analysis is being run on a machine with 4GB per node, at least eight nodes must be used (but in practice it is often just under 4GB available to applications, so more than eight nodes are needed). The number of nodes gives the minimal number of MPI tasks to use, which corresponds to the minimal volume decomposition required. Benchmark tests show that the function generator runs the quickest when each MPI task owns as much of the full volume as possible. If this number of MPI tasks calculated above is fewer than desired due to the number of pairs to be generated, instead of further subdividing the volume, use the ``vol_ratio`` parameter to multiply-assign tasks to the same subvolume. The total number of compute nodes will have to be increased because field data is being duplicated in memory, but tests have shown that things run faster in this mode. The bottom line: pick a vol_ratio that is as large as possible. * The ideal ``comm_size`` appears to be around 1e5 or 1e6 in size. * If possible, write the functions using only Numpy functions and methods. The input and output must be in array format, but the logic inside the function need not be. However, it will run much slower if optimized methods are not used. * Run a few test runs before doing a large run so that the PDF parameters can be correctly set. Advanced Two Point Function Techniques -------------------------------------- Density Threshold ^^^^^^^^^^^^^^^^^ If points are to only be compared if they both are above some density threshold, simply pass the density field to the function, and return a value that lies outside the PDF min/max if the density is too low. Here are the modifications to the RMS velocity example to do this that requires a gas density of at least 1e-26 g cm^-3 at each point: .. code-block:: python def rms_vel(a, b, r1, r2, vec): # Pick out points with only good densities a_good = a[:,3] >= 1.e-26 b_good = b[:,3] >= 1.e-26 # Pick out the pairs with both good densities both_good = np.bitwise_and(a_good, b_good) # Operate only on the velocity columns vdiff = a[:,0:3] - b[:,0:3] np.power(vdiff, 2.0, vdiff) vdiff = np.sum(vdiff, axis=1) # Multiplying by a boolean array has the effect of multiplying by 1 for # True, and 0 for False. This operation below will force pairs of not # good points to zero, outside the PDF (see below), and leave good # pairs unchanged. vdiff *= both_good return vdiff ... tpf = TwoPointFunctions(ds, ["velocity_x", "velocity_y", "velocity_z", "density"], total_values=1e5, comm_size=10000, length_number=10, length_range=[1./128, .5], length_type="log") tpf.add_function(rms_vel, ['RMSvdiff'], [False]) tpf[0].set_pdf_params(bin_type='log', bin_range=[5e4, 5.5e13], bin_number=1000) Because 0 is outside of the ``bin_range``, a pair of points that don't satisfy the density requirements do not contribute to the PDF. If density cutoffs are to be done in this fashion, the fractional volume that is above the density threshold should be calculated first, and ``total_values`` multiplied by the square of the inverse of this (which should be a multiplicative factor greater than one, meaning more point pairs will be generated to compensate for trashed points). Multidimensional PDFs ^^^^^^^^^^^^^^^^^^^^^ It is easy to modify the example above to output in multiple dimensions. In this example, the ratio of the densities of the two points is recorded at the same time as the velocity differences. .. code-block:: python from yt.mods import * from yt.analysis_modules.two_point_functions.api import * ds = load("data0005") # Calculate the S in RMS velocity difference between the two points. # Also store the ratio of densities (keeping them >= 1). # All functions have four inputs. The first two are containers # for field values, and the second two are the raw point coordinates # for the point pair. The name of the function is used to name # output files. def rms_vel_D(a, b, r1, r2, vec): # Operate only on the velocity columns vdiff = a[:,0:3] - b[:,0:3] np.power(vdiff, 2.0, vdiff) vdiff = np.sum(vdiff, axis=1) # Density ratio Dratio = np.max(a[:,3]/b[:,3], b[:,3]/a[:,3]) return [vdiff, Dratio] # Initialize a function generator object. # Set the number of pairs of points to calculate, how big a data queue to # use, the range of pair separations and how many lengths to use, # and how to divide that range (linear or log). tpf = TwoPointFunctions(ds, ["velocity_x", "velocity_y", "velocity_z", "density"], total_values=1e5, comm_size=10000, length_number=10, length_range=[1./128, .5], length_type="log") # Adds the function to the generator. f1 = tpf.add_function(rms_vel, ['RMSvdiff', 'Dratio'], [True, False]) # Define the bins used to store the results of the function. # Note that the bin edges can have different division, "lin" and "log". # In particular, a bin edge of 0 doesn't play well with "log". f1.set_pdf_params(bin_type=['log', 'lin'], bin_range=[[5e4, 5.5e13], [1., 10000.]], bin_number=[1000, 1000]) # Runs the functions. tpf.run_generator() # This calculates the M in RMS and writes out a text file with # the RMS values and the lengths. The R happens because sqrt=[True, False] # in add_function. # The file is named 'rms_vel_D.txt'. It will sqrt only the MS velocity column. tpf.write_out_means() # Writes out the raw PDF bins and bin edges to a HDF5 file. # The file is named 'rms_vel_D.h5'. tpf.write_out_arrays() Two-Point Correlation Functions ------------------------------- In a Gaussian random field of galaxies, the probability of finding a pair of galaxies within the volumes :math:`dV_1` and :math:`dV_2` is .. math:: dP = n^2 dV_1 dV_2 where n is the average number density of galaxies. Real galaxies are not distributed randomly, rather they tend to be clustered on a characteristic length scale. Therefore, the probability of two galaxies being paired is a function of radius .. math:: dP = n^2 (1 + \xi(\mathbf{r}_{12})) dV_1 dV_2 where :math:`\xi(\mathbf{r}_{12})` gives the excess probability as a function of :math:`\mathbf{r}_{12}`, and is the two-point correlation function. Values of :math:`\xi` greater than one mean galaxies are super-gaussian, and visa-versa. In order to use the TPF to calculate two point correlation functions, the number of pairs of galaxies between the two dV volumes is measured. A PDF is built that gives the probabilities of finding the number of pairs. To find the excess probability, a function `write_out_correlation` does something similar to `write_out_means` (above), but also normalizes by the number density of galaxies and the dV volumes. As an aside, a good rule of thumb is that for galaxies, :math:`\xi(r) = (r_0/r)^{1.8}` where :math:`r_0=5` Mpc/h. .. image:: _images/2ptcorrelation.png :width: 275 :height: 192 It is possible to calculate the correlation function for galaxies using the TPF using a script based on the example below. Unlike the figure above, the volumes are spherical. This script can be run in parallel. .. code-block:: python from yt.mods import * from yt.utilities.kdtree import * from yt.analysis_modules.two_point_functions.api import * # Specify the dataset on which we want to base our work. ds = load('data0005') # Read in the halo centers of masses. CoM = [] data = file('HopAnalysis.out', 'r') for line in data: if '#' in line: continue line = line.split() xp = float(line[7]) yp = float(line[8]) zp = float(line[9]) CoM.append(np.array([xp, yp, zp])) data.close() # This is the same dV as in the formulation of the two-point correlation. dV = 0.05 radius = (3./4. * dV / np.pi)**(2./3.) # Instantiate our TPF object. # For technical reasons (hopefully to be fixed someday) `vol_ratio` # needs to be equal to the number of tasks used if this is run # in parallel. A value of -1 automatically does this. tpf = TwoPointFunctions(ds, ['x'], total_values=1e7, comm_size=10000, length_number=11, length_range=[2*radius, .5], length_type="lin", vol_ratio=-1) # Build the kD tree of halos. This will be built on all # tasks so it shouldn't be too large. # All of these need to be set even if they're not used. # Convert the data to fortran major/minor ordering add_tree(1) fKD.t1.pos = np.array(CoM).T fKD.t1.nfound_many = np.empty(tpf.comm_size, dtype='int64') fKD.t1.radius = radius # These must be set because the function find_many_r_nearest # does more than how we are using it, and it needs these. fKD.t1.radius_n = 1 fKD.t1.nn_dist = np.empty((fKD.t1.radius_n, tpf.comm_size), dtype='float64') fKD.t1.nn_tags = np.empty((fKD.t1.radius_n, tpf.comm_size), dtype='int64') # Makes the kD tree. create_tree(1) # Remembering that two of the arguments for a function are the raw # coordinates, we define a two-point correlation function as follows. def tpcorr(a, b, r1, r2, vec): # First, we will find out how many halos are within fKD.t1.radius of our # first set of points, r1, which will be stored in fKD.t1.nfound_many. fKD.t1.qv_many = r1.T find_many_r_nearest(1) nfirst = fKD.t1.nfound_many.copy() # Second. fKD.t1.qv_many = r2.T find_many_r_nearest(1) nsecond = fKD.t1.nfound_many.copy() # Now we simply multiply these two arrays together. The rest comes later. nn = nfirst * nsecond return nn # Now we add the function to the TPF. # ``corr_norm`` is used to normalize the correlation function. tpf.add_function(function=tpcorr, out_labels=['tpcorr'], sqrt=[False], corr_norm=dV**2 * len(CoM)**2) # And define how we want to bin things. # It has to be linear bin_type because we want 0 to be in the range. # The big end of bin_range should correspond to the square of the maximum # number of halos expected inside dV in the volume. tpf[0].set_pdf_params(bin_type='lin', bin_range=[0, 2500000], bin_number=1000) # Runs the functions. tpf.run_generator() # Write out the data to "tpcorr_correlation.txt" # The file has two columns, the first is radius, and the second is # the value of \xi. tpf.write_out_correlation() # Empty the kdtree del fKD.t1.pos, fKD.t1.nfound_many, fKD.t1.nn_dist, fKD.t1.nn_tags free_tree(1) If one wishes to operate on field values, rather than discrete objects like halos, the situation is a bit simpler, but still a bit confusing. In the example below, we find the two-point correlation of cells above a particular density threshold. Instead of constant-size spherical dVs, the dVs here are the sizes of the grid cells at each end of the rulers. Because there can be cells of different volumes when using AMR, the number of pairs counted is actually the number of most-refined-cells contained within the volume of the cell. For one level of refinement, this means that a root-grid cell has the equivalent of 8 refined grid cells in it. Therefore, when the number of pairs are counted, it has to be normalized by the volume of the cells. .. code-block:: python from yt.mods import * from yt.utilities.kdtree import * from yt.analysis_modules.two_point_functions.api import * # Specify the dataset on which we want to base our work. ds = load('data0005') # We work in simulation's units, these are for conversion. vol_conv = ds['cm'] ** 3 sm = ds.index.get_smallest_dx()**3 # Our density limit, in gm/cm**3 dens = 2e-31 # We need to find out how many cells (equivalent to the most refined level) # are denser than our limit overall. def _NumDens(data): select = data["density"] >= dens cv = data["cell_volume"][select] / vol_conv / sm return (cv.sum(),) def _combNumDens(data, d): return d.sum() add_quantity("TotalNumDens", function=_NumDens, combine_function=_combNumDens, n_ret=1) all = ds.all_data() n = all.quantities["TotalNumDens"]() print(n,'n') # Instantiate our TPF object. tpf = TwoPointFunctions(ds, ['density', 'cell_volume'], total_values=1e5, comm_size=10000, length_number=11, length_range=[-1, .5], length_type="lin", vol_ratio=1) # Define the density threshold two point correlation function. def dens_tpcorr(a, b, r1, r2, vec): # We want to find out which pairs of Densities from a and b are both # dense enough. The first column is density. abig = (a[:,0] >= dens) bbig = (b[:,0] >= dens) both = np.bitwise_and(abig, bbig) # We normalize by the volume of the most refined cells. both = both.astype('float') both *= a[:,1] * b[:,1] / vol_conv**2 / sm**2 return both # Now we add the function to the TPF. # ``corr_norm`` is used to normalize the correlation function. tpf.add_function(function=dens_tpcorr, out_labels=['tpcorr'], sqrt=[False], corr_norm=n**2 * sm**2) # And define how we want to bin things. # It has to be linear bin_type because we want 0 to be in the range. # The top end of bin_range should be 2^(2l)+1, where l is the number of # levels, and bin_number=2^(2l)+2 tpf[0].set_pdf_params(bin_type='lin', bin_range=[0, 2], bin_number=3) # Runs the functions. tpf.run_generator() # Write out the data to "dens_tpcorr_correlation.txt" # The file has two columns, the first is radius, and the second is # the value of \xi. tpf.write_out_correlation() yt-3.3.3/doc/source/analyzing/analysis_modules/xray_data_README.rst0000644000076500000240000000422713017603554026241 0ustar goldbaumstaff00000000000000.. _xray_data_README: Auxiliary Data Files for use with yt's Photon Simulator ======================================================= Included in the `xray_data `_ package are a number of files that you may find useful when working with yt's X-ray `photon_simulator `_ analysis module. They have been tested to give spectral fitting results consistent with input parameters. Spectral Model Tables --------------------- * tbabs_table.h5: Tabulated values of the galactic absorption cross-section in HDF5 format, generated from the routines at http://pulsar.sternwarte.uni-erlangen.de/wilms/research/tbabs/ ARFs and RMFs ------------- We have tested the following ARFs and RMFs with the photon simulator. These can be used to generate a very simplified representation of an X-ray observation, using a uniform, on-axis response. For more accurate models of X-ray observations we suggest using MARX or SIMX (detailed below_). * Chandra: chandra_ACIS-S3_onaxis_arf.fits, chandra_ACIS-S3_onaxis_rmf.fits Generated from the CIAO tools, on-axis on the ACIS-S3 chip. * XMM-Newton: pn-med.arf, pn-med.rmf EPIC pn CCDs (medium filter), taken from SIMX * Astro-H: sxt-s_100208_ts02um_intall.arf, ah_sxs_7ev_basefilt_20090216.rmf SXT-S+SXS responses taken from http://astro-h.isas.jaxa.jp/researchers/sim/response.html * NuSTAR: nustarA.arf, nustarA.rmf Averaged responses for NuSTAR telescope A generated by Dan Wik (NASA/GSFC) .. _below: Other Useful Things Not Included Here ------------------------------------- * AtomDB: http://www.atomdb.org FITS table data for emission lines and continuum emission. Must have it installed to use the TableApecModel spectral model. * PyXspec: http://heasarc.gsfc.nasa.gov/xanadu/xspec/python/html/ Python interface to the XSPEC spectral-fitting program. Two of the spectral models for the photon simulator use it. * MARX: http://space.mit.edu/ASC/MARX/ Detailed ray-trace simulations of Chandra. * SIMX: http://hea-www.harvard.edu/simx/ Simulates a photon-counting detector's response to an input source, including a simplified models of telescopes. yt-3.3.3/doc/source/analyzing/analysis_modules/xray_emission_fields.rst0000644000076500000240000000636113017603554027470 0ustar goldbaumstaff00000000000000.. _xray_emission_fields: X-ray Emission Fields ===================== .. sectionauthor:: Britton Smith , John ZuHone .. note:: If you came here trying to figure out how to create simulated X-ray photons and observations, you should go `here `_ instead. This functionality provides the ability to create metallicity-dependent X-ray luminosity, emissivity, and photon emissivity fields for a given photon energy range. This works by interpolating from emission tables created from the photoionization code `Cloudy `_ or the collisional ionization database `AtomDB `_. If you installed yt with the install script, these data files should be located in the *data* directory inside the installation directory, or can be downloaded from ``_. Emission fields can be made for any interval between 0.1 keV and 100 keV. Adding Emission Fields ---------------------- Fields will be created for luminosity :math:`{\rm (erg~s^{-1})}`, emissivity :math:`{\rm (erg~s^{-1}~cm^{-3})}`, and photon emissivity :math:`{\rm (photons~s^{-1}~cm^{-3})}`. The only required arguments are the dataset object, and the minimum and maximum energies of the energy band. .. code-block:: python import yt from yt.analysis_modules.spectral_integrator.api import \ add_xray_emissivity_field xray_fields = add_xray_emissivity_field(ds, 0.5, 7.0) Additional keyword arguments are: * **filename** (*string*): Path to data file containing emissivity values. If None, a file called "cloudy_emissivity.h5" is used, for photoionized plasmas. A second option, for collisionally ionized plasmas, is in the file "apec_emissivity.h5", available at http://yt-project.org/data. These files contain emissivity tables for primordial elements and for metals at solar metallicity for the energy range 0.1 to 100 keV. Default: None. * **with_metals** (*bool*): If True, use the metallicity field to add the contribution from metals. If False, only the emission from H/He is considered. Default: True. * **constant_metallicity** (*float*): If specified, assume a constant metallicity for the emission from metals. The *with_metals* keyword must be set to False to use this. It should be given in unit of solar metallicity. Default: None. The resulting fields can be used like all normal fields. The function will return the names of the created fields in a Python list. .. code-block:: python import yt from yt.analysis_modules.spectral_integrator.api import \ add_xray_emissivity_field xray_fields = add_xray_emissivity_field(ds, 0.5, 7.0, filename="apec_emissivity.h5") ds = yt.load("enzo_tiny_cosmology/DD0046/DD0046") plot = yt.SlicePlot(ds, 'x', 'xray_luminosity_0.5_7.0_keV') plot.save() plot = yt.ProjectionPlot(ds, 'x', 'xray_emissivity_0.5_7.0_keV') plot.save() plot = yt.ProjectionPlot(ds, 'x', 'xray_photon_emissivity_0.5_7.0_keV') plot.save() .. warning:: The X-ray fields depend on the number density of hydrogen atoms, in the yt field ``H_number_density``. If this field is not defined (either in the dataset or by the user), the primordial hydrogen mass fraction (X = 0.76) will be used to construct it. yt-3.3.3/doc/source/analyzing/fields.rst0000644000076500000240000006127713023400062021135 0ustar goldbaumstaff00000000000000.. _fields: Fields in yt ============ Fields are spatially-dependent quantities associated with a parent dataset. Examples of fields are gas density, gas temperature, particle mass, etc. The fundamental way to query data in yt is to access a field, either in its raw form (by examining a data container) or a processed form (derived quantities, projections, and so on). "Field" is something of a loaded word, as it can refer to quantities that are defined everywhere, which we refer to as "mesh" or "fluid" fields, or discrete points that populate the domain, traditionally thought of as "particle" fields. The word "particle" here is gradually falling out of favor, as these discrete fields can be any type of sparsely populated data. What are fields? ---------------- Fields in yt are denoted by a two-element tuple, of the form ``(field_type, field_name)``. The first element, the "field type" is a category for a field. Possible field types used in yt include *gas* (for fluid mesh fields defined on a mesh) or *io* (for fields defined at particle locations). Field types can also correspond to distinct particle of fluid types in a single simulation. For example, a plasma physics simulation using the Particle in Cell method might have particle types corresponding to *electrons* and *ions*. See :ref:`known-field-types` below for more info about field types in yt. The second element of field tuples, the "field name", denotes the specific field to select, given the field type. Possible field names include *density*, *velocity_x* or *pressure* --- these three fields are examples of field names that might be used for a fluid defined on a mesh. Examples of particle fields include *particle_mass*, *particle_position*, or *particle_velocity_x*. In general, particle field names are prefixed by "particle\_", which makes it easy to distinguish between a particle field or a mesh field when no field type is provided. What fields are available? -------------------------- We provide a full list of fields that yt recognizes by default at :ref:`field-list`. If you want to create additional custom derived fields, see :ref:`creating-derived-fields`. Every dataset has an attribute, ``ds.fields``. This attribute possesses attributes itself, each of which is a "field type," and each field type has as its attributes the fields themselves. When one of these is printed, it returns information about the field and things like units and so on. You can use this for tab-completing as well as easier access to information. As an example, you might browse the available fields like so: .. code-block:: python print(dir(ds.fields)) print(dir(ds.fields.gas)) print(ds.fields.gas.density) On an Enzo dataset, the result from the final command would look something like this::: Alias Field for "('enzo', 'Density')" (gas, density): (units: g/cm**3) You can use this to easily explore available fields, particularly through tab-completion in Jupyter/IPython. For a more programmatic method of accessing fields, you can utilize the ``ds.field_list``, ``ds.derived_field_list`` and some accessor methods to gain information about fields. The full list of fields available for a dataset can be found as the attribute ``field_list`` for native, on-disk fields and ``derived_field_list`` for derived fields (``derived_field_list`` is a superset of ``field_list``). You can view these lists by examining a dataset like this: .. code-block:: python ds = yt.load("my_data") print(ds.field_list) print(ds.derived_field_list) By using the ``field_info()`` class, one can access information about a given field, like its default units or the source code for it. .. code-block:: python ds = yt.load("my_data") ds.index print(ds.field_info["gas", "pressure"].get_units()) print(ds.field_info["gas", "pressure"].get_source()) Using fields to access data --------------------------- The primary *use* of fields in yt is to access data from a dataset. For example, if I want to use a data object (see :ref:`Data-objects` for more detail about data objects) to access the ``('gas', 'density')`` field, one can do any of the following: .. code-block:: python ad = ds.all_data() # just a field name density = ad['density'] # field tuple with no parentheses density = ad['gas', 'density'] # full field tuple density = ad[('gas', 'density')] # through the ds.fields object density = ad[ds.fields.gas.density] The first data access example is the simplest. In that example, the field type is inferred from the name of the field. The next two examples use the field type explicitly, this might be necessary if there is more than one field type with a "density" field defined in the same dataset. The third example is slightly more verbose but is syntactically identical to the second example due to the way indexing works in the Python language. The final example uses the ``ds.fields`` object described above. This way of accessing fields lends itself to interactive use, especially if you make heavy use of IPython's tab completion features. Any of these ways of denoting the ``('gas', 'density')`` field can be used when supplying a field name to a yt data object, analysis routines, or plotting and visualization function. Accessing Fields without a Field Type ------------------------------------- In previous versions of yt, there was a single mechanism of accessing fields on a data container -- by their name, which was mandated to be a single string, and which often varied between different code frontends. yt 3.0 allows for datasets containing multiple different types of fluid fields, mesh fields, particles (with overlapping or disjoint lists of fields). However, to preserve backward compatibility and make interactive use simpler, yt will still accept field names given as a string and will try to infer the field type given a field name. As an example, we may be in a situation where have multiple types of particles which possess the ``particle_position`` field. In the case where a data container, here called ``ad`` (short for "all data") contains a field, we can specify which particular particle type we want to query: .. code-block:: python print(ad["dark_matter", "particle_position"]) print(ad["stars", "particle_position"]) print(ad["black_holes", "particle_position"]) Each of these three fields may have different sizes. In order to enable falling back on asking only for a field by the name, yt will use the most recently requested field type for subsequent queries. (By default, if no field has been queried, it will look for the special field ``all``, which concatenates all particle types.) For example, if I were to then query for the velocity: .. code-block:: python print(ad["particle_velocity"]) it would select ``black_holes`` as the field type, since the last field accessed used that field type. The same operations work for fluid and mesh fields. As an example, in some cosmology simulations, we may want to examine the mass of particles in a region versus the mass of gas. We can do so by examining the special "deposit" field types (described below) versus the gas fields: .. code-block:: python print(ad["deposit", "dark_matter_density"] / ad["gas", "density"]) The ``deposit`` field type is a mesh field, so it will have the same shape as the gas density. If we weren't using ``deposit``, and instead directly querying a particle field, this *wouldn't* work, as they are different shapes. This is the primary difference, in practice, between mesh and particle fields -- they will be different shapes and so cannot be directly compared without translating one to the other, typically through a "deposition" or "smoothing" step. How are fields implemented? --------------------------- There are two classes of fields in yt. The first are those fields that exist external to yt, which are immutable and can be queried -- most commonly, these are fields that exist on disk. These will often be returned in units that are not in a known, external unit system (except possibly by design, on the part of the code that wrote the data), and yt will take every effort possible to use the names by which they are referred to by the data producer. The default field type for mesh fields that are "on-disk" is the name of the code frontend. (For example, ``art``, ``enzo``, ``pyne``, and so on.) The default name for particle fields, if they do not have a particle type affiliated with them, is ``io``. The second class of field is the "derived field." These are fields that are functionally defined, either *ab initio* or as a transformation or combination of other fields. For example, when dealing with simulation codes, often the fields that are evolved and output to disk are not the fields that are the most relevant to researchers. Rather than examining the internal gas energy, it is more convenient to think of the temperature. By applying one or multiple functions to on-disk quantities, yt can construct new derived fields from them. Derived fields do not always have to relate to the data found on disk; special fields such as ``x``, ``y``, ``phi`` and ``dz`` all relate exclusively to the geometry of the mesh, and provide information about the mesh that can be used elsewhere for further transformations. For more information, see :ref:`creating-derived-fields`. There is a third, borderline class of field in yt, as well. This is the "alias" type, where a field on disk (for example, (frontend, ``Density``)) is aliased into an internal yt-name (for example, (``gas``, ``density``)). The aliasing process allows universally-defined derived fields to take advantage of internal names, and it also provides an easy way to address what units something should be returned in. If an aliased field is requested (and aliased fields will always be lowercase, with underscores separating words) it will be returned in the units specified by the unit system of the database (see :ref:`unit_systems` for a guide to using the different unit systems in yt), whereas if the frontend-specific field is requested, it will not undergo any unit conversions from its natural units. (This rule is occasionally violated for fields which are mesh-dependent, specifically particle masses in some cosmology codes.) .. _known-field-types: Field types known to yt ----------------------- Recall that fields are formally accessed in two parts: ('*field type*', '*field name*'). Here we describe the different field types you will encounter: * frontend-name -- Mesh or fluid fields that exist on-disk default to having the name of the frontend as their type name (e.g., ``enzo``, ``flash``, ``pyne`` and so on). The units of these types are whatever units are designated by the source frontend when it writes the data. * ``index`` -- This field type refers to characteristics of the mesh, whether that mesh is defined by the simulation or internally by an octree indexing of particle data. A few handy fields are ``x``, ``y``, ``z``, ``theta``, ``phi``, ``radius``, ``dx``, ``dy``, ``dz`` and so on. Default units are in CGS. * ``gas`` -- This is the usual default for simulation frontends for fluid types. These fields are typically aliased to the frontend-specific mesh fields for grid-based codes or to the deposit fields for particle-based codes. Default units are in the unit system of the dataset (see :ref:`unit_systems` for more information). * particle type -- These are particle fields that exist on-disk as written by individual frontends. If the frontend designates names for these particles (i.e. particle type) those names are the field types. Additionally, any particle unions or filters will be accessible as field types. Examples of particle types are ``Stars``, ``DM``, ``io``, etc. Like the front-end specific mesh or fluid fields, the units of these fields are whatever was designated by the source frontend when written to disk. * ``io`` -- If a data frontend does not have a set of multiple particle types, this is the default for all particles. * ``all`` -- This is a special particle field type that represents a concatenation of all particle field types using :ref:`particle-unions`. * ``deposit`` -- This field type refers to the deposition of particles (discrete data) onto a mesh, typically to compute smoothing kernels, local density estimates, counts, and the like. See :ref:`deposited-particle-fields` for more information. While it is best to be explicit access fields by their full names (i.e. ('*field type*', '*field name*')), yt provides an abbreviated interface for accessing common fields (i.e. '*field name*'). In the abbreviated case, yt will assume you want the last *field type* accessed. If you haven't previously accessed a *field type*, it will default to *field type* = ``'all'`` in the case of particle fields and *field type* = ``'gas'`` in the case of mesh fields. Field Plugins ------------- Derived fields are organized via plugins. Inside yt are a number of field plugins, which take information about fields in a dataset and then construct derived fields on top of them. This allows them to take into account variations in naming system, units, data representations, and most importantly, allows only the fields that are relevant to be added. This system will be expanded in future versions to enable much deeper semantic awareness of the data types being analyzed by yt. The field plugin system works in this order: * Available, inherent fields are identified by yt * The list of enabled field plugins is iterated over. Each is called, and new derived fields are added as relevant. * Any fields which are not available, or which throw errors, are discarded. * Remaining fields are added to the list of derived fields available for a dataset * Dependencies for every derived field are identified, to enable data preloading Field plugins can be loaded dynamically, although at present this is not particularly useful. Plans for extending field plugins to dynamically load, to enable simple definition of common types (divergence, curl, etc), and to more verbosely describe available fields, have been put in place for future versions. The field plugins currently available include: * Angular momentum fields for particles and fluids * Astrophysical fields, such as those related to cosmology * Vector fields for fluid fields, such as gradients and divergences * Particle vector fields * Magnetic field-related fields * Species fields, such as for chemistry species (yt can recognize the entire periodic table in field names and construct ionization fields as need be) .. _bfields: Magnetic Fields --------------- Magnetic fields require special handling, because their dimensions are different in different systems of units, in particular between the CGS and MKS (SI) systems of units. Superficially, it would appear that they are in the same dimensions, since the units of the magnetic field in the CGS and MKS system are gauss (:math:`\rm{G}`) and tesla (:math:`\rm{T}`), respectively, and numerically :math:`1~\rm{G} = 10^{-4}~\rm{T}`. However, if we examine the base units, we find that they do indeed have different dimensions: .. math:: \rm{1~G = 1~\frac{\sqrt{g}}{\sqrt{cm}\cdot{s}}} \\ \rm{1~T = 1~\frac{kg}{A\cdot{s^2}}} It is easier to see the difference between the dimensionality of the magnetic field in the two systems in terms of the definition of the magnetic pressure: .. math:: p_B = \frac{B^2}{8\pi}~\rm{(cgs)} \\ p_B = \frac{B^2}{2\mu_0}~\rm{(MKS)} where :math:`\mu_0 = 4\pi \times 10^{-7}~\rm{N/A^2}` is the vacuum permeability. yt automatically detects on a per-frontend basis what units the magnetic should be in, and allows conversion between different magnetic field units in the different :ref:`unit systems ` as well. To determine how to set up special magnetic field handling when designing a new frontend, check out :ref:`bfields-frontend`. Particle Fields --------------- Naturally, particle fields contain properties of particles rather than grid cells. By examining the particle field in detail, you can see that each element of the field array represents a single particle, whereas in mesh fields each element represents a single mesh cell. This means that for the most part, operations cannot operate on both particle fields and mesh fields simultaneously in the same way, like filters (see :ref:`filtering-data`). However, many of the particle fields have corresponding mesh fields that can be populated by "depositing" the particle values onto a yt grid as described below. .. _field_parameters: Field Parameters ---------------- Certain fields require external information in order to be calculated. For example, the radius field has to be defined based on some point of reference and the radial velocity field needs to know the bulk velocity of the data object so that it can be subtracted. This information is passed into a field function by setting field parameters, which are user-specified data that can be associated with a data object. The :meth:`~yt.data_objects.data_containers.YTDataContainer.set_field_parameter` and :meth:`~yt.data_objects.data_containers.YTDataContainer.get_field_parameter` functions are used to set and retrieve field parameter values for a given data object. In the cases above, the field parameters are ``center`` and ``bulk_velocity`` respectively -- the two most commonly used field parameters. .. code-block:: python ds = yt.load("my_data") ad = ds.all_data() ad.set_field_parameter("wickets", 13) print(ad.get_field_parameter("wickets")) If a field parameter is not set, ``get_field_parameter`` will return None. Within a field function, these can then be retrieved and used in the same way. .. code-block:: python def _wicket_density(field, data): n_wickets = data.get_field_parameter("wickets") if n_wickets is None: # use a default if unset n_wickets = 88 return data["gas", "density"] * n_wickets For a practical application of this, see :ref:`cookbook-radial-velocity`. Gradient Fields --------------- yt provides a way to compute gradients of spatial fields using the :meth:`~yt.frontends.flash.data_structures.FLASHDataset.add_gradient_fields` method. If you have a spatially-based field such as density or temperature, and want to calculate the gradient of that field, you can do it like so: .. code-block:: python ds = yt.load("GasSloshing/sloshing_nomag2_hdf5_plt_cnt_0150") grad_fields = ds.add_gradient_fields(("gas","temperature")) where the ``grad_fields`` list will now have a list of new field names that can be used in calculations, representing the 3 different components of the field and the magnitude of the gradient, e.g., ``"temperature_gradient_x"``, ``"temperature_gradient_y"``, ``"temperature_gradient_z"``, and ``"temperature_gradient_magnitude"``. To see an example of how to create and use these fields, see :ref:`cookbook-complicated-derived-fields`. .. note:: ``add_gradient_fields`` currently only supports Cartesian geometries! General Particle Fields ----------------------- Every particle will contain both a ``particle_position`` and ``particle_velocity`` that tracks the position and velocity (respectively) in code units. .. _deposited-particle-fields: Deposited Particle Fields ------------------------- In order to turn particle (discrete) fields into fields that are deposited in some regular, space-filling way (even if that space is empty, it is defined everywhere) yt provides mechanisms for depositing particles onto a mesh. These are in the special field-type space ``deposit``, and are typically of the form ``("deposit", "particletype_depositiontype")`` where ``depositiontype`` is the mechanism by which the field is deposited, and ``particletype`` is the particle type of the particles being deposited. If you are attempting to examine the cloud-in-cell (``cic``) deposition of the ``all`` particle type, you would access the field ``("deposit", "all_cic")``. yt defines a few particular types of deposition internally, and creating new ones can be done by modifying the files ``yt/geometry/particle_deposit.pyx`` and ``yt/fields/particle_fields.py``, although that is an advanced topic somewhat outside the scope of this section. The default deposition types available are: * ``count`` - this field counts the total number of particles of a given type in a given mesh zone. Note that because, in general, the mesh for particle datasets is defined by the number of particles in a region, this may not be the most useful metric. This may be made more useful by depositing particle data onto an :ref:`arbitrary-grid`. * ``density`` - this field takes the total sum of ``particle_mass`` in a given mesh field and divides by the volume. * ``mass`` - this field takes the total sum of ``particle_mass`` in each mesh zone. * ``cic`` - this field performs cloud-in-cell interpolation (see `Section 2.2 `_ for more information) of the density of particles in a given mesh zone. * ``smoothed`` - this is a special deposition type. See discussion below for more information, in :ref:`sph-fields`. .. _sph-fields: SPH Fields ---------- For gas particles from SPH simulations, each particle will typically carry a field for the smoothing length ``h``, which is roughly equivalent to ``(m/\rho)^{1/3}``, where ``m`` and ``rho`` are the particle mass and density respectively. This can be useful for doing neighbour finding. As a note, SPH fields are special cases of the "deposited" particle fields. They contain an additional piece of information about what is being examined, and any fields that are recognized as being identical to intrinsic yt fields will be aliased. For example, in a Gadget dataset, the smoothed density of ``Gas`` particles will be aliased to the mesh field ``("gas", "density")`` so that operations conducted on the mesh field ``density`` (which are frequent occurrences) will operate on the smoothed gas density from the SPH particles. The special deposition types based on smoothing (``smoothed``) are defined in the file ``yt/geometry/particle_smooth.pyx``, and they require non-local operations defined on a variable number of neighbors. The default smoothing type utilizes a cubic spline kernel and uses 64 nearest neighbors, providing a volume-normalized smoothing. Other types are possible, and yt provides functionality for many different types of non-local correlation between particles. (For instance, a friends-of-friends grouper has been built on this same infrastructure.) Every particle field on a smoothed particle type is the source for a smoothed field; this is not always useful, but it errs on the side of extra fields, rather than too few fields. (For instance, it may be unlikely that the smoothed angular momentum field will be useful.) The naming scheme is an extension of the scheme described in :ref:`deposited-particle-fields`, and is defined as such: ``("deposit", "particletype_smoothed_fieldname")``, where ``fieldname`` is the name of the field being smoothed. For example, smoothed ``Temperature`` of the ``Gas`` particle type would be ``("deposit", "Gas_smoothed_Temperature")``, which in most cases would be aliased to the field ``("gas", "temperature")`` for convenience. Other smoothing kernels besides the cubic spline one are available through a keyword argument ``kernel_name`` of the method ``add_smoothed_particle_field``. Current available kernel names include: * ``cubic``, ``quartic``, and ``quintic`` - spline kernels. * ``wendland2``, ``wendland4`` and ``wendland6`` - Wendland kernels. The added smoothed particle field can be accessed by ``("deposit", "particletype_kernelname_smoothed_fieldname")`` (except for the cubic spline kernel, which obeys the naming scheme given above). Computing the Nth Nearest Neighbor ---------------------------------- One particularly useful field that can be created is that of the distance to the Nth-nearest neighbor. This field can then be used as input to smoothing operations, in the case when a particular particle type does not have an associated smoothing length or other length estimate. yt defines this field as a plugin, and it can be added like so: .. code-block:: python import yt from yt.fields.particle_fields import \ add_nearest_neighbor_field ds = yt.load("snapshot_033/snap_033.0.hdf5") fn, = add_nearest_neighbor_field("all", "particle_position", ds) dd = ds.all_data() print(dd[fn]) Note that ``fn`` here is the "field name" that yt adds. It will be of the form ``(ptype, nearest_neighbor_distance_NN)`` where ``NN`` is the integer. By default this is 64, but it can be supplied as the final argument to ``add_nearest_neighbor_field``. For the example above, it would be ``nearest_neighbor_64``. This can then be used as input to the function ``add_volume_weighted_smoothed_field``, which can enable smoothing particle types that would normally not be smoothed. yt-3.3.3/doc/source/analyzing/filtering.rst0000644000076500000240000002145213017603554021657 0ustar goldbaumstaff00000000000000.. _filtering-data: Filtering your Dataset ====================== Large datasets are oftentimes too overwhelming to deal with in their entirety, and it can be useful and faster to analyze subsets of these datasets. Furthermore, filtering the dataset based on some field condition can reveal subtle information not easily accessible by looking at the whole dataset. Filters can be generated based on spatial position, say in a sphere in the center of your dataset space, or more generally they can be defined by the properties of any field in the simulation. Because `mesh fields` are internally different from `particle fields`, there are different ways of filtering each type as indicated below; however, filtering fields by spatial location (i.e. geometric objects) will apply to both types equally. .. _filtering-mesh: Filtering Mesh Fields ---------------------- Mesh fields can be filtered by two methods: cut region objects (:class:`~yt.data_objects.selection_data_containers.YTCutRegion`) and NumPy boolean masks. Boolean masks are simpler, but they only work for examining datasets, whereas cut regions objects create wholly new data objects suitable for full analysis (data examination, image generation, etc.) Boolean Masks ^^^^^^^^^^^^^ NumPy boolean masks can be used with any NumPy array simply by passing the array a conditional. As a general example of this: .. notebook-cell:: import numpy as np a = np.arange(5) bigger_than_two = (a > 2) print("Original Array: a = \n%s" % a) print("Boolean Mask: bigger_than_two = \n%s" % bigger_than_two) print("Masked Array: a[bigger_than_two] = \n%s" % a[bigger_than_two]) Similarly, if you've created a yt data object (e.g. a region, a sphere), you can examine its field values as a NumPy array by simply indexing it with the field name. Thus, it too can be masked using a NumPy boolean mask. Let's set a simple mask based on the contents of one of our fields. .. notebook-cell:: import yt ds = yt.load('Enzo_64/DD0042/data0042') ad = ds.all_data() hot = ad["temperature"].in_units('K') > 1e6 print('Temperature of all data: ad["temperature"] = \n%s' % ad["temperature"]) print("Boolean Mask: hot = \n%s" % hot) print('Temperature of "hot" data: ad["temperature"][hot] = \n%s' % ad['temperature'][hot]) This was a simple example, but one can make the conditionals that define a boolean mask have multiple parts, and one can stack masks together to make very complex cuts on one's data. Once the data is filtered, it can be used if you simply need to access the NumPy arrays: .. notebook-cell:: import yt ds = yt.load('Enzo_64/DD0042/data0042') ad = ds.all_data() overpressure_and_fast = (ad["pressure"] > 1e-14) & (ad["velocity_magnitude"].in_units('km/s') > 1e2) print('Density of all data: ad["density"] = \n%s' % ad['density']) print('Density of "overpressure and fast" data: ad["density"][overpressure_and_fast] = \n%s' % ad['density'][overpressure_and_fast]) .. _cut-regions: Cut Regions ^^^^^^^^^^^ Cut regions are a more general solution to filtering mesh fields. The output of a cut region is an entirely new data object, which can be treated like any other data object to generate images, examine its values, etc. .. notebook:: mesh_filter.ipynb Cut regions can also operator on particle fields, but a single cut region object cannot operate on both particle fields and mesh fields at the same time. .. _filtering-particles: Filtering Particle Fields ------------------------- Particle filters create new particle fields based on the manipulation and cuts on existing particle fields. You can apply cuts to them to effectively mask out everything except the particles with which you are concerned. Creating a particle filter takes a few steps. You must first define a function which accepts a data object (e.g. all_data, sphere, etc.) as its argument. It uses the fields and information in this geometric object in order to produce some sort of conditional mask that is then returned to create a new particle type. Here is a particle filter to create a new ``star`` particle type. For Enzo simulations, stars have ``particle_type`` set to 2, so our filter will select only the particles with ``particle_type`` (i.e. field = ``('all', 'particle_type')`` equal to 2. .. code-block:: python @yt.particle_filter(requires=["particle_type"], filtered_type='all') def stars(pfilter, data): filter = data[(pfilter.filtered_type, "particle_type")] == 2 return filter The :func:`~yt.data_objects.particle_filters.particle_filter` decorator takes a few options. You must specify the names of the particle fields that are required in order to define the filter --- in this case the ``particle_type`` field. Additionally, you must specify the particle type to be filtered --- in this case we filter all the particle in dataset by specifying the ``all`` particle type. In addition, you may specify a name for the newly defined particle type. If no name is specified, the name for the particle type will be inferred from the name of the filter definition --- in this case the inferred name will be ``stars``. Lastly, the filter must be applied to our dataset of choice. Note that this filter can be added to as many datasets as we wish. It will only actually create new filtered fields if the dataset has the required fields, though. .. code-block:: python import yt ds = yt.load('IsolatedGalaxy/galaxy0030/galaxy0030') ds.add_particle_filter('stars') And that's it! We can now access all of the ('stars', field) fields from our dataset ``ds`` and treat them as any other particle field. In addition, it created some ``deposit`` fields, where the particles were deposited on to the grid as mesh fields. As an alternative syntax, you can also define a new particle filter via the :func:`~yt.data_objects.particle_filter.add_particle_filter` function. .. code-block:: python def Stars(pfilter, data): filter = data[(pfilter.filtered_type, "particle_type")] == 2 return filter add_particle_filter("stars", function=Stars, filtered_type='all', requires=["particle_type"]) This is equivalent to our use of the ``particle_filter`` decorator above. The choice to use either the ``particle_filter`` decorator or the ``add_particle_fitler`` function is a purely stylistic choice. .. notebook:: particle_filter.ipynb .. _particle-unions: Particle Unions --------------- Multiple types of particles can be combined into a single, conceptual type. As an example, the NMSU-ART code has multiple "species" of dark matter, which we union into a single ``darkmatter`` field. The ``all`` particle type is a special case of this. To create a particle union, you need to import the ``ParticleUnion`` class from ``yt.data_objects.particle_unions``, which you then create and pass into ``add_particle_union`` on a dataset object. Here is an example, where we union the ``halo`` and ``disk`` particle types into a single type, ``star``. yt will then determine which fields are accessible to this new particle type and it will add them. .. code-block:: python from yt.data_objects.particle_unions import \ ParticleUnion u = ParticleUnion("star", ["halo", "disk"]) ds.add_particle_union(u) .. _filtering-by-location: Filtering Fields by Spatial Location: Geometric Objects ------------------------------------------------------- Creating geometric objects for a dataset provides a means for filtering a field based on spatial location. The most commonly used of these are spheres, regions (3D prisms), ellipsoids, disks, and rays. The ``all_data`` object which gets used throughout this documentation section is an example of a geometric object, but it defaults to including all the data in the dataset volume. To see all of the geometric objects available, see :ref:`available-objects`. Consult the object documentation section for all of the different objects one can use, but here is a simple example using a sphere object to filter a dataset. Let's filter out everything not within 10 Mpc of some random location, say [0.2, 0.5, 0.1], in the simulation volume. The resulting object will only contain grid cells with centers falling inside of our defined sphere, which may look offset based on the presence of different resolution elements distributed throughout the dataset. .. notebook-cell:: import yt ds = yt.load('Enzo_64/DD0042/data0042') center = [0.20, 0.50, 0.10] sp = ds.sphere(center, (10, 'Mpc')) prj = yt.ProjectionPlot(ds, "x", "density", center=center, width=(50, "Mpc"), data_source=sp) # Mark the center with a big X prj.annotate_marker(center, 'x', plot_args={'s':100}) prj.show() slc = yt.SlicePlot(ds, "x", "density", center=center, width=(50, "Mpc"), data_source=sp) slc.show() yt-3.3.3/doc/source/analyzing/generating_processed_data.rst0000644000076500000240000002207113023400062025037 0ustar goldbaumstaff00000000000000.. _generating-processed-data: Generating Processed Data ========================= Although yt provides a number of built-in visualization methods that can process data and construct from that plots, it is often useful to generate the data by hand and construct plots which can then be combined with other plots, modified in some way, or even (gasp) created and modified in some other tool or program. .. _generating-2d-image-arrays: 2D Image Arrays --------------- When making a slice, a projection or an oblique slice in yt, the resultant :class:`~yt.data_objects.data_containers.YTSelectionContainer2D` object is created and contains flattened arrays of the finest available data. This means a set of arrays for the x, y, (possibly z), dx, dy, (possibly dz) and data values, for every point that constitutes the object. This presents something of a challenge for visualization, as it will require the transformation of a variable mesh of points consisting of positions and sizes into a fixed-size array that appears like an image. This process is that of pixelization, which yt handles transparently internally. You can access this functionality by constructing a :class:`~yt.visualization.fixed_resolution.FixedResolutionBuffer` (or :class:`~yt.visualization.fixed_resolution.ObliqueFixedResolutionBuffer`) and supplying to it your :class:`~yt.data_objects.data_containers.YTSelectionContainer2D` object, as well as some information about how you want the final image to look. You can specify both the bounds of the image (in the appropriate x-y plane) and the resolution of the output image. You can then have yt pixelize any field you like. To create :class:`~yt.data_objects.data_containers.YTSelectionContainer2D` objects, you can access them as described in :ref:`data-objects`, specifically the section :ref:`available-objects`. Here is an example of how to window into a slice of resolution(512, 512) with bounds of (0.3, 0.5) and (0.6, 0.8). The next step is to generate the actual 2D image array, which is accomplished by accessing the desired field. .. code-block:: python sl = ds.slice(0, 0.5) frb = FixedResolutionBuffer(sl, (0.3, 0.5, 0.6, 0.8), (512, 512)) my_image = frb["density"] This image may then be used in a hand-constructed Matplotlib image, for instance using :func:`~matplotlib.pyplot.imshow`. The buffer arrays can be saved out to disk in either HDF5 or FITS format: .. code-block:: python frb.save_as_dataset("my_images.h5", fields=["density","temperature"]) frb.export_fits("my_images.fits", fields=["density","temperature"], clobber=True, units="kpc") In the HDF5 case, the created file can be reloaded just like a regular dataset with ``yt.load`` and will, itself, be a first-class dataset. For more information on this, see :ref:`saving-grid-data-containers`. In the FITS case, there is an option for setting the ``units`` of the coordinate system in the file. If you want to overwrite a file with the same name, set ``clobber=True``. The :class:`~yt.visualization.fixed_resolution.FixedResolutionBuffer` can even be exported as a 2D dataset itself, which may be operated on in the same way as any other dataset in yt: .. code-block:: python ds_frb = frb.export_dataset(fields=["density","temperature"], nprocs=8) sp = ds_frb.sphere("c", (100.,"kpc")) where the ``nprocs`` parameter can be used to decompose the image into ``nprocs`` number of grids. .. _generating-profiles-and-histograms: Profiles and Histograms ----------------------- Profiles and histograms can also be generated using the :class:`~yt.visualization.profile_plotter.ProfilePlot` and :class:`~yt.visualization.profile_plotter.PhasePlot` functions (described in :ref:`how-to-make-1d-profiles` and :ref:`how-to-make-2d-profiles`). These generate profiles transparently, but the objects they handle and create can be handled manually, as well, for more control and access. The :func:`~yt.data_objects.profiles.create_profile` function can be used to generate 1, 2, and 3D profiles. Profile objects can be created from any data object (see :ref:`data-objects`, specifically the section :ref:`available-objects` for more information) and are best thought of as distribution calculations. They can either sum up or average one quantity with respect to one or more other quantities, and they do this over all the data contained in their source object. When calculating average values, the variance will also be calculated. To generate a profile, one need only specify the binning fields and the field to be profiled. The binning fields are given together in a list. The :func:`~yt.data_objects.profiles.create_profile` function will guess the dimensionality of the profile based on the number of fields given. For example, a one-dimensional profile of the mass-weighted average temperature as a function of density within a sphere can be created in the following way: .. code-block:: python import yt ds = yt.load("galaxy0030/galaxy0030") source = ds.sphere( "c", (10, "kpc")) profile = yt.create_profile(source, [("gas", "density")], # the bin field [("gas", "temperature"), # profile field ("gas", "radial_velocity")], # profile field weight_field=("gas", "cell_mass")) The binning, weight, and profile data can now be access as: .. code-block:: python print(profile.x) # bin field print(profile.weight) # weight field print(profile["gas", "temperature"]) # profile field print(profile["gas", "radial_velocity"]) # profile field The ``profile.used`` attribute gives a boolean array of the bins which actually have data. .. code-block:: python print(profile.used) If a weight field was given, the profile data will represent the weighted mean of a field. In this case, the weighted variance will be calculated automatically and can be access via the ``profile.variance`` attribute. .. code-block:: python print(profile.variance["gas", "temperature"]) A two-dimensional profile of the total gas mass in bins of density and temperature can be created as follows: .. code-block:: python profile2d = yt.create_profile(source, [("gas", "density"), # the x bin field ("gas", "temperature")], # the y bin field [("gas", "cell_mass")], # the profile field weight_field=None) Accessing the x, y, and profile fields work just as with one-dimensional profiles: .. code-block:: python print(profile2d.x) print(profile2d.y) print(profile2d["gas", "cell_mass"]) One of the more interesting things that is enabled with this approach is the generation of 1D profiles that correspond to 2D profiles. For instance, a phase plot that shows the distribution of mass in the density-temperature plane, with the average temperature overplotted. The :func:`~matplotlib.pyplot.pcolormesh` function can be used to manually plot the 2D profile. Three-dimensional profiles can be generated and accessed following the same procedures. Additional keyword arguments are available to control the following for each of the bin fields: the number of bins, min and max, units, whether to use a log or linear scale, and whether or not to do accumulation to create a cumulative distribution function. For more information, see the API documentation on the :func:`~yt.data_objects.profiles.create_profile` function. .. _generating-line-queries: Line Queries and Planar Integrals --------------------------------- To calculate the values along a line connecting two points in a simulation, you can use the object :class:`~yt.data_objects.selection_data_containers.YTRay`, accessible as the ``ray`` property on a index. (See :ref:`data-objects` for more information on this.) To do so, you can supply two points and access fields within the returned object. For instance, this code will generate a ray between the points (0.3, 0.5, 0.9) and (0.1, 0.8, 0.5) and examine the density along that ray: .. code-block:: python ray = ds.ray((0.3, 0.5, 0.9), (0.1, 0.8, 0.5)) print(ray["density"]) The points are ordered, but the ray is also traversing cells of varying length, as well as taking a varying distance to cross each cell. To determine the distance traveled by the ray within each cell (for instance, for integration) the field ``dt`` is available; this field will sum to 1.0, as the ray's path will be normalized to 1.0, independent of how far it travels through the domain. To determine the value of ``t`` at which the ray enters each cell, the field ``t`` is available. For instance: .. code-block:: python print(ray['dts'].sum()) print(ray['t']) These can be used as inputs to, for instance, the Matplotlib function :func:`~matplotlib.pyplot.plot`, or they can be saved to disk. The volume rendering functionality in yt can also be used to calculate off-axis plane integrals, using the :class:`~yt.visualization.volume_rendering.transfer_functions.ProjectionTransferFunction` in a manner similar to that described in :ref:`volume_rendering`. yt-3.3.3/doc/source/analyzing/index.rst0000644000076500000240000000140513017603554020777 0ustar goldbaumstaff00000000000000.. _analyzing: General Data Analysis ===================== This documentation describes much of the yt infrastructure for manipulating one's data to extract the relevant information. Fields, data objects, and units are at the heart of how yt represents data. Beyond this, we provide a full description for how to filter your datasets based on specific criteria, how to analyze chronological datasets from the same underlying simulation or source (i.e. time series analysis), and how to run yt in parallel on multiple processors to accomplish tasks faster. .. toctree:: :maxdepth: 2 fields ../developing/creating_derived_fields objects units/index filtering generating_processed_data saving_data time_series_analysis parallel_computation yt-3.3.3/doc/source/analyzing/ionization_cube.py0000644000076500000240000000262213017603554022673 0ustar goldbaumstaff00000000000000import yt from yt.utilities.parallel_tools.parallel_analysis_interface \ import communication_system import h5py import time import numpy as np @yt.derived_field(name="IonizedHydrogen", units="", display_name=r"\frac{\rho_{HII}}{\rho_H}") def IonizedHydrogen(field, data): return data["HII_Density"]/(data["HI_Density"]+data["HII_Density"]) ts = yt.DatasetSeries("SED800/DD*/*.index", parallel=8) ionized_z = np.zeros(ts[0].domain_dimensions, dtype="float32") t1 = time.time() for ds in ts.piter(): z = ds.current_redshift for g in parallel_objects(ds.index.grids, njobs = 16): i1, j1, k1 = g.get_global_startindex() # Index into our domain i2, j2, k2 = g.get_global_startindex() + g.ActiveDimensions # Look for the newly ionized gas newly_ion = ((g["IonizedHydrogen"] > 0.999) & (ionized_z[i1:i2,j1:j2,k1:k2] < z)) ionized_z[i1:i2,j1:j2,k1:k2][newly_ion] = z g.clear_data() print("Iteration completed %0.3e" % (time.time()-t1)) comm = communication_system.communicators[-1] for i in range(ionized_z.shape[0]): ionized_z[i,:,:] = comm.mpi_allreduce(ionized_z[i,:,:], op="max") print("Slab % 3i has minimum z of %0.3e" % (i, ionized_z[i,:,:].max())) t2 = time.time() print("Completed. %0.3e" % (t2-t1)) if comm.rank == 0: f = h5py.File("IonizationCube.h5", "w") f.create_dataset("/z", data=ionized_z) yt-3.3.3/doc/source/analyzing/mesh_filter.ipynb0000644000076500000240000001221413017603554022502 0ustar goldbaumstaff00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Let us demonstrate this with an example using the same dataset as we used with the boolean masks." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import yt\n", "ds = yt.load(\"Enzo_64/DD0042/data0042\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The only argument to a cut region is a conditional on field output from a data object. The only catch is that you *must* denote the data object in the conditional as \"obj\" regardless of the actual object's name. \n", "\n", "Here we create three new data objects which are copies of the all_data object (a region object covering the entire spatial domain of the simulation), but we've filtered on just \"hot\" material, the \"dense\" material, and the \"overpressure and fast\" material." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ad = ds.all_data()\n", "hot_ad = ad.cut_region([\"obj['temperature'] > 1e6\"])\n", "dense_ad = ad.cut_region(['obj[\"density\"] > 5e-30'])\n", "\n", "# you can chain cut regions in two ways:\n", "dense_and_cool_ad = dense_ad.cut_region([\"obj['temperature'] < 1e5\"])\n", "overpressure_and_fast_ad = ad.cut_region(['(obj[\"pressure\"] > 1e-14) & (obj[\"velocity_magnitude\"].in_units(\"km/s\") > 1e2)'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Upon inspection of our \"hot_ad\" object, we can still get the same results as we got with the boolean masks example above:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print (\"Temperature of all cells:\\n ad['temperature'] = \\n%s\\n\" % ad[\"temperature\"])\n", "print (\"Temperatures of all \\\"hot\\\" cells:\\n hot_ad['temperature'] = \\n%s\" % hot_ad['temperature'])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print (\"Density of dense, cool material:\\n dense_and_cool_ad['density'] = \\n%s\\n\" % dense_and_cool_ad['density'])\n", "print (\"Temperature of dense, cool material:\\n dense_and_cool_ad['temperature'] = \\n%s\" % dense_and_cool_ad['temperature'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we've constructed a `cut_region`, we can use it as a data source for further analysis. To create a plot based on a `cut_region`, use the `data_source` keyword argument provided by yt's plotting objects.\n", "\n", "Here's an example using projections:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "proj1 = yt.ProjectionPlot(ds, 'x', \"density\", weight_field=\"density\")\n", "proj1.annotate_title('No Cuts')\n", "proj1.set_figure_size(5)\n", "proj1.show()\n", "\n", "proj2 = yt.ProjectionPlot(ds, 'x', \"density\", weight_field=\"density\", data_source=hot_ad)\n", "proj2.annotate_title('Hot Gas')\n", "proj2.set_zlim(\"density\", 3e-31, 3e-27)\n", "proj2.set_figure_size(5)\n", "proj2.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `data_source` keyword argument is also accepted by `SlicePlot`, `ProfilePlot` and `PhasePlot`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "slc1 = yt.SlicePlot(ds, 'x', \"density\", center='m')\n", "slc1.set_zlim('density', 3e-31, 3e-27)\n", "slc1.annotate_title('No Cuts')\n", "slc1.set_figure_size(5)\n", "slc1.show()\n", "\n", "slc2 = yt.SlicePlot(ds, 'x', \"density\", center='m', data_source=dense_ad)\n", "slc2.set_zlim('density', 3e-31, 3e-27)\n", "slc2.annotate_title('Dense Gas')\n", "slc2.set_figure_size(5)\n", "slc2.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ph1 = yt.PhasePlot(ad, 'density', 'temperature', 'cell_mass', weight_field=None)\n", "ph1.set_xlim(3e-31, 3e-27)\n", "ph1.annotate_title('No Cuts')\n", "ph1.set_figure_size(5)\n", "ph1.show()\n", "\n", "ph1 = yt.PhasePlot(dense_ad, 'density', 'temperature', 'cell_mass', weight_field=None)\n", "ph1.set_xlim(3e-31, 3e-27)\n", "ph1.annotate_title('Dense Gas')\n", "ph1.set_figure_size(5)\n", "ph1.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 } yt-3.3.3/doc/source/analyzing/objects.rst0000644000076500000240000007055713023400062021321 0ustar goldbaumstaff00000000000000.. _Data-objects: Data Objects ============ What are Data Objects in yt? ---------------------------- Data objects (also called *Data Containers*) are used in yt as convenience structures for grouping data in logical ways that make sense in the context of the dataset as a whole. Some of the data objects are geometrical groupings of data (e.g. sphere, box, cylinder, etc.). Others represent data products derived from your dataset (e.g. slices, streamlines, surfaces). Still other data objects group multiple objects together or filter them (e.g. data collection, cut region). To generate standard plots, objects rarely need to be directly constructed. However, for detailed data inspection as well as hand-crafted derived data, objects can be exceptionally useful and even necessary. How to Create and Use an Object ------------------------------- To create an object, you usually only need a loaded dataset, the name of the object type, and the relevant parameters for your object. Here is a common example for creating a ``Region`` object that covers all of your data volume. .. code-block:: python import yt ds = yt.load("RedshiftOutput0005") ad = ds.all_data() Alternatively, we could create a sphere object of radius 1 kpc on location [0.5, 0.5, 0.5]: .. code-block:: python import yt ds = yt.load("RedshiftOutput0005") sp = ds.sphere([0.5, 0.5, 0.5], (1, 'kpc')) After an object has been created, it can be used as a data_source to certain tasks like ``ProjectionPlot`` (see :class:`~yt.visualization.plot_window.ProjectionPlot`), one can compute the bulk quantities associated with that object (see :ref:`derived-quantities`), or the data can be examined directly. For example, if you want to figure out the temperature at all indexed locations in the central sphere of your dataset you could: .. code-block:: python import yt ds = yt.load("RedshiftOutput0005") sp = ds.sphere([0.5, 0.5, 0.5], (1, 'kpc')) # Show all temperature values print(sp["temperature"]) # Print things in a more human-friendly manner: one temperature at a time print("(x, y, z) Temperature") print("-----------------------") for i in range(sp["temperature"].size): print("(%f, %f, %f) %f" % (sp["x"][i], sp["y"][i], sp["z"][i], sp["temperature"][i])) .. _quickly-selecting-data: Slicing Syntax for Selecting Data --------------------------------- yt provides a mechanism for easily selecting data while doing interactive work on the command line. This allows for region selection based on the full domain of the object. Selecting in this manner is exposed through a slice-like syntax. All of these attributes are exposed through the ``RegionExpression`` object, which is an attribute of a ``DataSet`` object, called ``r``. Getting All The Data ^^^^^^^^^^^^^^^^^^^^ The ``.r`` attribute serves as a persistent means of accessing the full data from a dataset. You can access this shorthand operation by querying any field on the ``.r`` object, like so: .. code-block:: python ds = yt.load("RedshiftOutput0005") rho = ds.r["density"] This will return a *flattened* array of data. The region expression object (``r``) doesn't have any derived quantities on it. This is completely equivalent to this set of statements: .. code-block:: python ds = yt.load("RedshiftOutput0005") dd = ds.all_data() rho = dd["density"] .. warning:: One thing to keep in mind with accessing data in this way is that it is *persistent*. It is loaded into memory, and then retained until the dataset is deleted or garbage collected. Selecting Multiresolution Regions ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ To select rectilinear regions, where the data is selected the same way that it is selected in a :ref:`region-reference`, you can utilize slice-like syntax, supplying start and stop, but not supplying a step argument. This requires that three components of the slice must be specified. These take a start and a stop, and are for the three axes in simulation order (if your data is ordered z, y, x for instance, this would be in z, y, x order). The slices can have both position and, optionally, unit values. These define the value with respect to the ``domain_left_edge`` of the dataset. So for instance, you could specify it like so::: ds.r[(100, 'kpc'):(200,'kpc'),:,:] This would return a region that included everything between 100 kpc from the left edge of the dataset to 200 kpc from the left edge of the dataset in the first dimension, and which spans the entire dataset in the second and third dimensions. By default, if the units are unspecified, they are in the "native" code units of the dataset. This works in all types of datasets, as well. For instance, if you have a geographic dataset (which is usually ordered latitude, longitude, altitude) you can easily select, for instance, one hemisphere with a region selection::: ds.r[:,-180:0,:] If you specify a single slice, it will be repeated along all three dimensions. For instance, this will give all data::: ds.r[:] And this will select a box running from 0.4 to 0.6 along all three dimensions::: ds.r[0.4:0.6] Selecting Fixed Resolution Regions ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ yt also provides functionality for selecting regions that have been turned into voxels. This returns an :ref:`arbitrary-grid` object. It can be created by specifying a complex slice "step", where the start and stop follow the same rules as above. This is similar to how the numpy ``mgrid`` operation works. For instance, this code block will generate a grid covering the full domain, but converted to being 21x35x100 dimensions::: region = ds.r[::21j, ::35j, ::100j] The left and right edges, as above, can be specified to provide bounds as well. For instance, to select a 10 meter cube, with 24 cells in each dimension, we could supply::: region = ds.r[(20,'m'):(30,'m'):24j, (30,'m'):(40,'m'):24j, (7,'m'):(17,'m'):24j] This can select both particles and mesh fields. Mesh fields will be 3D arrays, and generated through volume-weighted overlap calculations. Selecting Slices ^^^^^^^^^^^^^^^^ If one dimension is specified as a single value, that will be the dimension along which a slice is made. This provides a simple means of generating a slice from a subset of the data. For instance, to create a slice of a dataset, you can very simply specify the full domain along two axes::: sl = ds.r[:,:,0.25] This can also be very easily plotted::: sl = ds.r[:,:,0.25] sl.plot() This accepts arguments the same way::: sl = ds.r[(20.1, 'km'):(31.0, 'km'), (504.143,'m'):(1000.0,'m'), (900.1, 'm')] sl.plot() .. _available-objects: Available Objects ----------------- As noted above, there are numerous types of objects. Here we group them into: * *Geometric Objects* Data is selected based on spatial shapes in the dataset * *Filtering Objects* Data is selected based on other field criteria * *Collection Objects* Multiple objects grouped together * *Construction Objects* Objects represent some sort of data product constructed by additional analysis If you want to create your own custom data object type, see :ref:`creating-objects`. .. _geometric-objects: Geometric Objects ^^^^^^^^^^^^^^^^^ For 0D, 1D, and 2D geometric objects, if the extent of the object intersects a grid cell, then the cell is included in the object; however, for 3D objects the *center* of the cell must be within the object in order for the grid cell to be incorporated. 0D Objects """""""""" **Point** | Class :class:`~yt.data_objects.selection_data_containers.YTPoint` | Usage: ``point(coord, ds=None, field_parameters=None, data_source=None)`` | A point defined by a single cell at specified coordinates. 1D Objects """""""""" **Ray (Axis-Aligned)** | Class :class:`~yt.data_objects.selection_data_containers.YTOrthoRay` | Usage: ``ortho_ray(axis, coord, ds=None, field_parameters=None, data_source=None)`` | A line (of data cells) stretching through the full domain aligned with one of the x,y,z axes. Defined by an axis and a point to be intersected. Please see this :ref:`note about ray data value ordering `. **Ray (Arbitrarily-Aligned)** | Class :class:`~yt.data_objects.selection_data_containers.YTRay` | Usage: ``ray(start_coord, end_coord, ds=None, field_parameters=None, data_source=None)`` | A line (of data cells) defined by arbitrary start and end coordinates. Please see this :ref:`note about ray data value ordering `. 2D Objects """""""""" **Slice (Axis-Aligned)** | Class :class:`~yt.data_objects.selection_data_containers.YTSlice` | Usage: ``slice(axis, coord, center=None, ds=None, field_parameters=None, data_source=None)`` | A plane normal to one of the axes and intersecting a particular coordinate. **Slice (Arbitrarily-Aligned)** | Class :class:`~yt.data_objects.selection_data_containers.YTCuttingPlane` | Usage: ``cutting(normal, coord, north_vector=None, ds=None, field_parameters=None, data_source=None)`` | A plane normal to a specified vector and intersecting a particular coordinate. .. _region-reference: 3D Objects """""""""" **All Data** | Function :meth:`~yt.data_objects.static_output.Dataset.all_data` | Usage: ``all_data(find_max=False)`` | ``all_data()`` is a wrapper on the Box Region class which defaults to creating a Region covering the entire dataset domain. It is effectively ``ds.region(ds.domain_center, ds.domain_left_edge, ds.domain_right_edge)``. **Box Region** | Class :class:`~yt.data_objects.selection_data_containers.YTRegion` | Usage: ``region(center, left_edge, right_edge, fields=None, ds=None, field_parameters=None, data_source=None)`` | Alternatively: ``box(left_edge, right_edge, fields=None, ds=None, field_parameters=None, data_source=None)`` | A box-like region aligned with the grid axis orientation. It is defined by a left_edge, a right_edge, and a center. The left_edge and right_edge are the minimum and maximum bounds in the three axes respectively. The center is arbitrary and must only be contained within the left_edge and right_edge. By using the ``box`` wrapper, the center is assumed to be the midpoint between the left and right edges. **Disk/Cylinder** | Class: :class:`~yt.data_objects.selection_data_containers.YTDisk` | Usage: ``disk(center, normal, radius, height, fields=None, ds=None, field_parameters=None, data_source=None)`` | A cylinder defined by a point at the center of one of the circular bases, a normal vector to it defining the orientation of the length of the cylinder, and radius and height values for the cylinder's dimensions. **Ellipsoid** | Class :class:`~yt.data_objects.selection_data_containers.YTEllipsoid` | Usage: ``ellipsoid(center, semi_major_axis_length, semi_medium_axis_length, semi_minor_axis_length, semi_major_vector, tilt, fields=None, ds=None, field_parameters=None, data_source=None)`` | An ellipsoid with axis magnitudes set by semi_major_axis_length, semi_medium_axis_length, and semi_minor_axis_length. semi_major_vector sets the direction of the semi_major_axis. tilt defines the orientation of the semi-medium and semi_minor axes. **Sphere** | Class :class:`~yt.data_objects.selection_data_containers.YTSphere` | Usage: ``sphere(center, radius, ds=None, field_parameters=None, data_source=None)`` | A sphere defined by a central coordinate and a radius. .. _collection-objects: Filtering and Collection Objects ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ See also the section on :ref:`filtering-data`. **Intersecting Regions** | Most Region objects provide a data_source parameter, which allows you to subselect | one region from another (in the coordinate system of the DataSet). Note, this can | easily lead to empty data for non-intersecting regions. | Usage: ``slice(axis, coord, ds, data_source=sph)`` **Boolean Regions** | **Note: not yet implemented in yt 3.0** | Usage: ``boolean()`` | See :ref:`boolean_data_objects`. **Filter** | Class :class:`~yt.data_objects.selection_data_containers.YTCutRegion` | Usage: ``cut_region(base_object, conditionals, ds=None, field_parameters=None)`` | A ``cut_region`` is a filter which can be applied to any other data object. The filter is defined by the conditionals present, which apply cuts to the data in the object. A ``cut_region`` will work for either particle fields or mesh fields, but not on both simultaneously. For more detailed information and examples, see :ref:`cut-regions`. **Collection of Data Objects** | Class :class:`~yt.data_objects.selection_data_containers.YTDataCollection` | Usage: ``data_collection(center, obj_list, ds=None, field_parameters=None)`` | A ``data_collection`` is a list of data objects that can be sampled and processed as a whole in a single data object. .. _construction-objects: Construction Objects ^^^^^^^^^^^^^^^^^^^^ **Fixed-Resolution Region** | Class :class:`~yt.data_objects.construction_data_containers.YTCoveringGrid` | Usage: ``covering_grid(level, left_edge, dimensions, fields=None, ds=None, num_ghost_zones=0, use_pbar=True, field_parameters=None)`` | A 3D region with all data extracted to a single, specified resolution. See :ref:`examining-grid-data-in-a-fixed-resolution-array`. **Fixed-Resolution Region with Smoothing** | Class :class:`~yt.data_objects.construction_data_containers.YTSmoothedCoveringGrid` | Usage: ``smoothed_covering_grid(level, left_edge, dimensions, fields=None, ds=None, num_ghost_zones=0, use_pbar=True, field_parameters=None)`` | A 3D region with all data extracted and interpolated to a single, specified resolution. Identical to covering_grid, except that it interpolates as necessary from coarse regions to fine. See :ref:`examining-grid-data-in-a-fixed-resolution-array`. **Fixed-Resolution Region** | Class :class:`~yt.data_objects.construction_data_containers.YTArbitraryGrid` | Usage: ``arbitrary_grid(left_edge, right_edge, dimensions, ds=None, field_parameters=None)`` | When particles are deposited on to mesh fields, they use the existing mesh structure, but this may have too much or too little resolution relative to the particle locations (or it may not exist at all!). An `arbitrary_grid` provides a means for generating a new independent mesh structure for particle deposition and simple mesh field interpolation. See :ref:`arbitrary-grid` for more information. **Projection** | Class :class:`~yt.data_objects.construction_data_containers.YTQuadTreeProj` | Usage: ``proj(field, axis, weight_field=None, center=None, ds=None, data_source=None, method="integrate", field_parameters=None)`` | A 2D projection of a 3D volume along one of the axis directions. By default, this is a line integral through the entire simulation volume (although it can be a subset of that volume specified by a data object with the ``data_source`` keyword). Alternatively, one can specify a weight_field and different ``method`` values to change the nature of the projection outcome. See :ref:`projection-types` for more information. **Streamline** | Class :class:`~yt.data_objects.construction_data_containers.YTStreamline` | Usage: ``streamline(coord_list, length, fields=None, ds=None, field_parameters=None)`` | A ``streamline`` can be traced out by identifying a starting coordinate (or list of coordinates) and allowing it to trace a vector field, like gas velocity. See :ref:`streamlines` for more information. **Surface** | Class :class:`~yt.data_objects.construction_data_containers.YTSurface` | Usage: ``surface(data_source, field, field_value)`` | The surface defined by all an isocontour in any mesh field. An existing data object must be provided as the source, as well as a mesh field and the value of the field which you desire the isocontour. See :ref:`extracting-isocontour-information`. .. _derived-quantities: Processing Objects: Derived Quantities -------------------------------------- Derived quantities are a way of calculating some bulk quantities associated with all of the grid cells contained in a data object. Derived quantities can be accessed via the ``quantities`` interface. Here is an example of how to get the angular momentum vector calculated from all the cells contained in a sphere at the center of our dataset. .. code-block:: python ds = load("my_data") sp = ds.sphere('c', (10, 'kpc')) print(sp.quantities.angular_momentum_vector()) Quickly Processing Data ^^^^^^^^^^^^^^^^^^^^^^^ Most data objects now have multiple numpy-like methods that allow you to quickly process data. More of these methods will be added over time and added to this list. Most, if not all, of these map to other yt operations and are designed as syntactic sugar to slightly simplify otherwise somewhat obtuse pipelines. These operations are parallelized. You can compute the extrema of a field by using the ``max`` or ``min`` functions. This will cache the extrema in between, so calling ``min`` right after ``max`` will be considerably faster. Here is an example::: ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030") reg = ds.r[0.3:0.6, 0.2:0.4, 0.9:0.95] min_rho = reg.min("density") max_rho = reg.max("density") This is equivalent to::: min_rho, max_rho = reg.quantities.extrema("density") The ``max`` operation can also compute the maximum intensity projection::: proj = reg.max("density", axis="x") proj.plot() This is equivalent to::: proj = ds.proj("density", "x", data_source=reg, method="mip") proj.plot() The ``min`` operator does not do this, however, as a minimum intensity projection is not currently implemented. You can also compute the ``mean`` value, which accepts a field, axis and wight function. If the axis is not specified, it will return the average value of the specified field, weighted by the weight argument. The weight argument defaults to ``ones``, which performs an arithmetic average. For instance::: mean_rho = reg.mean("density") rho_by_vol = reg.mean("density", weight="cell_volume") This is equivalent to::: mean_rho = reg.quantities.weighted_average("density", weight_field="ones") rho_by_vol = reg.quantities.weighted_average("density", weight_field="cell_volume") If an axis is provided, it will project along that axis and return it to you::: rho_proj = reg.mean("temperature", axis="y", weight="density") rho_proj.plot() The ``sum`` function will add all the values in the data object. It accepts a field and, optionally, an axis. If the axis is left unspecified, it will sum the values in the object::: vol = reg.sum("cell_volume") If the axis is specified, it will compute a projection using the method ``sum`` (which does *not* take into account varying path length!) and return that to you.:: cell_count = reg.sum("ones", axis="z") cell_count.plot() To compute a projection where the path length *is* taken into account, you can use the ``integrate`` function::: proj = reg.integrate("density", "x") All of these projections supply the data object as their base input. Often, it can be useful to sample a field at the minimum and maximum of a different field. You can use the ``argmax`` and ``argmin`` operations to do this.:: reg.argmin("density", axis="temperature") This will return the temperature at the minimum density. If you don't specify an ``axis``, it will return the spatial position of the maximum value of the queried field. Here is an example::: x, y, z = reg.argmin("density") Available Derived Quantities ^^^^^^^^^^^^^^^^^^^^^^^^^^^^ **Angular Momentum Vector** | Class :class:`~yt.data_objects.derived_quantities.AngularMomentumVector` | Usage: ``angular_momentum_vector(use_gas=True, use_particles=True)`` | The mass-weighted average angular momentum vector of the particles, gas, or both. **Bulk Velocity** | Class :class:`~yt.data_objects.derived_quantities.BulkVelocity` | Usage: ``bulk_velocity(use_gas=True, use_particles=True)`` | The mass-weighted average velocity of the particles, gas, or both. **Center of Mass** | Class :class:`~yt.data_objects.derived_quantities.CenterOfMass` | Usage: ``center_of_mass(use_cells=True, use_particles=False)`` | The location of the center of mass. By default, it computes of the *non-particle* data in the object, but it can be used on particles, gas, or both. **Extrema** | Class :class:`~yt.data_objects.derived_quantities.Extrema` | Usage: ``extrema(fields, non_zero=False)`` | The extrema of a field or list of fields. **Maximum Location Sampling** | Class :class:`~yt.data_objects.derived_quantities.SampleAtMaxFieldValues` | Usage: ``sample_at_max_field_values(fields, sample_fields)`` | The value of sample_fields at the maximum value in fields. **Minimum Location Sampling** | Class :class:`~yt.data_objects.derived_quantities.SampleAtMinFieldValues` | Usage: ``sample_at_min_field_values(fields, sample_fields)`` | The value of sample_fields at the minimum value in fields. **Minimum Location** | Class :class:`~yt.data_objects.derived_quantities.MinLocation` | Usage: ``min_location(fields)`` | The minimum of a field or list of fields as well as the x,y,z location of that minimum. **Maximum Location** | Class :class:`~yt.data_objects.derived_quantities.MaxLocation` | Usage: ``max_location(fields)`` | The maximum of a field or list of fields as well as the x,y,z location of that maximum. **Spin Parameter** | Class :class:`~yt.data_objects.derived_quantities.SpinParameter` | Usage: ``spin_parameter(use_gas=True, use_particles=True)`` | The spin parameter for the baryons using the particles, gas, or both. **Total Mass** | Class :class:`~yt.data_objects.derived_quantities.TotalMass` | Usage: ``total_mass()`` | The total mass of the object as a tuple of (total gas, total particle) mass. **Total of a Field** | Class :class:`~yt.data_objects.derived_quantities.TotalQuantity` | Usage: ``total_quantity(fields)`` | The sum of a given field (or list of fields) over the entire object. **Weighted Average of a Field** | Class :class:`~yt.data_objects.derived_quantities.WeightedAverageQuantity` | Usage: ``weighted_average_quantity(fields, weight)`` | The weighted average of a field (or list of fields) over an entire data object. If you want an unweighted average, then set your weight to be the field: ``ones``. **Weighted Variance of a Field** | Class :class:`~yt.data_objects.derived_quantities.WeightedVariance` | Usage: ``weighted_variance(fields, weight)`` | The weighted variance of a field (or list of fields) over an entire data object and the weighted mean. If you want an unweighted variance, then set your weight to be the field: ``ones``. .. _arbitrary-grid: Arbitrary Grids Objects ----------------------- The covering grid and smoothed covering grid objects mandate that they be exactly aligned with the mesh. This is a holdover from the time when yt was used exclusively for data that came in regularly structured grid patches, and does not necessarily work as well for data that is composed of discrete objects like particles. To augment this, the :class:`~yt.data_objects.construction_data_containers.YTArbitraryGrid` object was created, which enables construction of meshes (onto which particles can be deposited or smoothed) in arbitrary regions. This eliminates any assumptions on yt's part about how the data is organized, and will allow for more fine-grained control over visualizations. An example of creating an arbitrary grid would be to construct one, then query the deposited particle density, like so: .. code-block:: python import yt ds = yt.load("snapshot_010.hdf5") obj = ds.arbitrary_grid([0.0, 0.0, 0.0], [0.99, 0.99, 0.99], dims=[128, 128, 128]) print(obj["deposit", "all_density"]) While these cannot yet be used as input to projections or slices, slices and projections can be taken of the data in them and visualized by hand. These objects, as of yt 3.3, are now also able to "voxelize" mesh fields. This means that you can query the "density" field and it will return the density field as deposited, identically to how it would be deposited in a fixed resolution buffer. Note that this means that contributions from misaligned or partially-overlapping cells are added in a volume-weighted way, which makes it inappropriate for some types of analysis. .. _boolean_data_objects: Combining Objects: Boolean Data Objects --------------------------------------- .. note:: Boolean Data Objects have not yet been ported to yt 3.0 from yt 2.x. If you are interested in aiding in this port, please contact the yt-dev mailing list. Until it is ported, this functionality below will not work. A special type of data object is the *boolean* data object. It works only on three-dimensional objects. It is built by relating already existing data objects with boolean operators. The boolean logic may be nested using parentheses, and it supports the standard "AND", "OR", and "NOT" operators: * **"AND"** When two data objects are related with an "AND", the combined data object is the volume of the simulation covered by both objects, and not by just a single object. * **"OR"** When two data objects are related with an "OR", the combined data object is the volume(s) of the simulation covered by either of the objects. For example, this may be used to combine disjoint objects into one. * **"NOT"** When two data objects are related with a "NOT", the combined data object is the volume of the first object that the second does not cover. For example, this may be used to cut out part(s) of the first data object utilizing the second data object. * **"(" or ")"** Nested logic is surrounded by parentheses. The order of operations is such that the boolean logic is evaluated inside the inner-most parentheses, first, then goes upwards. The logic is read left-to-right at all levels (crucial for the "NOT" operator). Please see the :ref:`cookbook` for some examples of how to use the boolean data object. .. _extracting-connected-sets: Connected Sets and Clump Finding -------------------------------- The underlying machinery used in :ref:`clump_finding` is accessible from any data object. This includes the ability to obtain and examine topologically connected sets. These sets are identified by examining cells between two threshold values and connecting them. What is returned to the user is a list of the intervals of values found, and extracted regions that contain only those cells that are connected. To use this, call :meth:`~yt.data_objects.data_containers.YTSelectionContainer3D.extract_connected_sets` on any 3D data object. This requests a field, the number of levels of levels sets to extract, the min and the max value between which sets will be identified, and whether or not to conduct it in log space. .. code-block:: python sp = ds.sphere("max", (1.0, 'pc')) contour_values, connected_sets = sp.extract_connected_sets( "density", 3, 1e-30, 1e-20) The first item, ``contour_values``, will be an array of the min value for each set of level sets. The second (``connected_sets``) will be a dict of dicts. The key for the first (outer) dict is the level of the contour, corresponding to ``contour_values``. The inner dict returned is keyed by the contour ID. It contains :class:`~yt.data_objects.selection_data_containers.YTCutRegion` objects. These can be queried just as any other data object. The clump finder (:ref:`clump_finding`) differs from the above method in that the contour identification is performed recursively within each individual structure, and structures can be kept or remerged later based on additional criteria, such as gravitational boundedness. .. _object-serialization: Storing and Loading Objects --------------------------- Often, when operating interactively or via the scripting interface, it is convenient to save an object to disk and then restart the calculation later or transfer the data from a container to another filesystem. This can be particularly useful when working with extremely large datasets. Field data can be saved to disk in a format that allows for it to be reloaded just like a regular dataset. For information on how to do this, see :ref:`saving-data-containers`. yt-3.3.3/doc/source/analyzing/parallel_computation.rst0000644000076500000240000006223013017603554024111 0ustar goldbaumstaff00000000000000.. _parallel-computation: Parallel Computation With yt ============================ yt has been instrumented with the ability to compute many -- most, even -- quantities in parallel. This utilizes the package `mpi4py `_ to parallelize using the Message Passing Interface, typically installed on clusters. .. _capabilities: Capabilities ------------ Currently, yt is able to perform the following actions in parallel: * Projections (:ref:`projection-plots`) * Slices (:ref:`slice-plots`) * Cutting planes (oblique slices) (:ref:`off-axis-slices`) * Derived Quantities (total mass, angular momentum, etc) (:ref:`creating_derived_quantities`, :ref:`derived-quantities`) * 1-, 2-, and 3-D profiles (:ref:`generating-profiles-and-histograms`) * Halo analysis (:ref:`halo-analysis`) * Volume rendering (:ref:`volume_rendering`) * Isocontours & flux calculations (:ref:`extracting-isocontour-information`) This list covers just about every action yt can take! Additionally, almost all scripts will benefit from parallelization with minimal modification. The goal of Parallel-yt has been to retain API compatibility and abstract all parallelism. Setting Up Parallel yt -------------------------- To run scripts in parallel, you must first install `mpi4py `_ as well as an MPI library, if one is not already available on your system. Instructions for doing so are provided on the mpi4py website, but you may have luck by just running: .. code-block:: bash $ pip install mpi4py If you have an Anaconda installation of yt and there is no MPI library on the system you are using try: .. code-block:: bash $ conda install mpi4py This will install `MPICH2 `_ and will interfere with other MPI libraries that are already installed. Therefore, it is preferable to use the ``pip`` installation method. Once mpi4py has been installed, you're all done! You just need to launch your scripts with ``mpirun`` (or equivalent) and signal to yt that you want to run them in parallel by invoking the ``yt.enable_parallelism()`` function in your script. In general, that's all it takes to get a speed benefit on a multi-core machine. Here is an example on an 8-core desktop: .. code-block:: bash $ mpirun -np 8 python script.py Throughout its normal operation, yt keeps you aware of what is happening with regular messages to the stderr usually prefaced with: .. code-block:: bash yt : [INFO ] YYY-MM-DD HH:MM:SS However, when operating in parallel mode, yt outputs information from each of your processors to this log mode, as in: .. code-block:: bash P000 yt : [INFO ] YYY-MM-DD HH:MM:SS P001 yt : [INFO ] YYY-MM-DD HH:MM:SS in the case of two cores being used. It's important to note that all of the processes listed in :ref:`capabilities` work in parallel -- and no additional work is necessary to parallelize those processes. Running a yt Script in Parallel ------------------------------- Many basic yt operations will run in parallel if yt's parallelism is enabled at startup. For example, the following script finds the maximum density location in the simulation and then makes a plot of the projected density: .. code-block:: python import yt yt.enable_parallelism() ds = yt.load("RD0035/RedshiftOutput0035") v, c = ds.find_max("density") print(v, c) p = yt.ProjectionPlot(ds, "x", "density") p.save() If this script is run in parallel, two of the most expensive operations - finding of the maximum density and the projection will be calculated in parallel. If we save the script as ``my_script.py``, we would run it on 16 MPI processes using the following Bash command: .. code-block:: bash $ mpirun -np 16 python2.7 my_script.py .. note:: If you run into problems, the you can use :ref:`remote-debugging` to examine what went wrong. How do I run my yt job on a subset of available processes +++++++++++++++++++++++++++++++++++++++++++++++++++++++++ You can set the ``communicator`` keyword in the :func:`~yt.utilities.parallel_tools.parallel_analysis_interface.enable_parallelism` call to a specific MPI communicator to specify a subset of available MPI processes. If none is specified, it defaults to ``COMM_WORLD``. Creating Parallel and Serial Sections in a Script +++++++++++++++++++++++++++++++++++++++++++++++++ Many yt operations will automatically run in parallel (see the next section for a full enumeration), however some operations, particularly ones that print output or save data to the filesystem, will be run by all processors in a parallel script. For example, in the script above the lines ``print(v, c)`` and ``p.save()`` will be run on all 16 processors. This means that your terminal output will contain 16 repetitions of the output of the print statement and the plot will be saved to disk 16 times (overwritten each time). yt provides two convenience functions that make it easier to run most of a script in parallel but run some subset of the script on only one processor. The first, :func:`~yt.funcs.is_root`, returns ``True`` if run on the 'root' processor (the processor with MPI rank 0) and ``False`` otherwise. One could rewrite the above script to take advantage of :func:`~yt.funcs.is_root` like so: .. code-block:: python import yt yt.enable_parallelism() ds = yt.load("RD0035/RedshiftOutput0035") v, c = ds.find_max("density") p = yt.ProjectionPlot(ds, "x", "density") if yt.is_root(): print(v, c) p.save() The second function, :func:`~yt.funcs.only_on_root` accepts the name of a function as well as a set of parameters and keyword arguments to pass to the function. This is useful when the serial component of your parallel script would clutter the script or if you like writing your scripts as a series of isolated function calls. I can rewrite the example from the beginning of this section once more using :func:`~yt.funcs.only_on_root` to give you the flavor of how to use it: .. code-block:: python import yt yt.enable_parallelism() def print_and_save_plot(v, c, plot, verbose=True): if verbose: print(v, c) plot.save() ds = yt.load("RD0035/RedshiftOutput0035") v, c = ds.find_max("density") p = yt.ProjectionPlot(ds, "x", "density") yt.only_on_root(print_and_save_plot, v, c, plot, verbose=True) Types of Parallelism -------------------- In order to divide up the work, yt will attempt to send different tasks to different processors. However, to minimize inter-process communication, yt will decompose the information in different ways based on the task. Spatial Decomposition +++++++++++++++++++++ During this process, the index will be decomposed along either all three axes or along an image plane, if the process is that of projection. This type of parallelism is overall less efficient than grid-based parallelism, but it has been shown to obtain good results overall. The following operations use spatial decomposition: * :ref:`halo-analysis` * :ref:`volume_rendering` Grid Decomposition ++++++++++++++++++ The alternative to spatial decomposition is a simple round-robin of data chunks, which could be grids, octs, or whatever chunking mechanism is used by the code frontend begin used. This process allows yt to pool data access to a given data file, which ultimately results in faster read times and better parallelism. The following operations use chunk decomposition: * Projections (see :ref:`available-objects`) * Slices (see :ref:`available-objects`) * Cutting planes (see :ref:`available-objects`) * Derived Quantities (see :ref:`derived-quantities`) * 1-, 2-, and 3-D profiles (see :ref:`generating-profiles-and-histograms`) * Isocontours & flux calculations (see :ref:`surfaces`) Parallelization over Multiple Objects and Datasets ++++++++++++++++++++++++++++++++++++++++++++++++++ If you have a set of computational steps that need to apply identically and independently to several different objects or datasets, a so-called `embarrassingly parallel `_ task, yt can do that easily. See the sections below on :ref:`parallelizing-your-analysis` and :ref:`parallel-time-series-analysis`. Use of ``piter()`` ^^^^^^^^^^^^^^^^^^ If you use parallelism over objects or datasets, you will encounter the ``piter()`` function. ``piter`` is a parallel iterator, which effectively doles out each item of a DatasetSeries object to a different processor. In serial processing, you might iterate over a DatasetSeries by: .. code-block:: python for dataset in dataset_series: But in parallel, you can use ``piter()`` to force each dataset to go to a different processor: .. code-block:: python yt.enable_parallelism() for dataset in dataset_series.piter(): In order to store information from the parallel processing step to a data structure that exists on all of the processors operating in parallel we offer the ``storage`` keyword in the ``piter`` function. You may define an empty dictionary and include it as the keyword argument ``storage`` to ``piter()``. Then, during the processing step, you can access this dictionary as the ``sto`` object. After the loop is finished, the dictionary is re-aggregated from all of the processors, and you can access the contents: .. code-block:: python yt.enable_parallelism() my_dictionary = {} for sto, dataset in dataset_series.piter(storage=my_dictionary): sto.result = sto.result_id = print(my_dictionary) .. _parallelizing-your-analysis: Parallelizing over Multiple Objects ----------------------------------- It is easy within yt to parallelize a list of tasks, as long as those tasks are independent of one another. Using object-based parallelism, the function :func:`~yt.utilities.parallel_tools.parallel_analysis_interface.parallel_objects` will automatically split up a list of tasks over the specified number of processors (or cores). Please see this heavily-commented example: .. code-block:: python # As always... import yt yt.enable_parallelism() import glob # The number 4, below, is the number of processes to parallelize over, which # is generally equal to the number of MPI tasks the job is launched with. # If num_procs is set to zero or a negative number, the for loop below # will be run such that each iteration of the loop is done by a single MPI # task. Put another way, setting it to zero means that no matter how many # MPI tasks the job is run with, num_procs will default to the number of # MPI tasks automatically. num_procs = 4 # fns is a list of all the simulation data files in the current directory. fns = glob.glob("./plot*") fns.sort() # This dict will store information collected in the loop, below. # Inside the loop each task will have a local copy of the dict, but # the dict will be combined once the loop finishes. my_storage = {} # In this example, because the storage option is used in the # parallel_objects function, the loop yields a tuple, which gets used # as (sto, fn) inside the loop. # In the loop, sto is essentially my_storage, but a local copy of it. # If data does not need to be combined after the loop is done, the line # would look like: # for fn in parallel_objects(fns, num_procs): for sto, fn in yt.parallel_objects(fns, num_procs, storage = my_storage): # Open a data file, remembering that fn is different on each task. ds = yt.load(fn) dd = ds.all_data() # This copies fn and the min/max of density to the local copy of # my_storage sto.result_id = fn sto.result = dd.quantities.extrema("density") # Makes and saves a plot of the gas density. p = yt.ProjectionPlot(ds, "x", "density") p.save() # At this point, as the loop exits, the local copies of my_storage are # combined such that all tasks now have an identical and full version of # my_storage. Until this point, each task is unaware of what the other # tasks have produced. # Below, the values in my_storage are printed by only one task. The other # tasks do nothing. if yt.is_root() for fn, vals in sorted(my_storage.items()): print(fn, vals) This example above can be modified to loop over anything that can be saved to a Python list: halos, data files, arrays, and more. .. _parallel-time-series-analysis: Parallelization over Multiple Datasets (including Time Series) -------------------------------------------------------------- The same ``parallel_objects`` machinery discussed above is turned on by default when using a :class:`~yt.data_objects.time_series.DatasetSeries` object (see :ref:`time-series-analysis`) to iterate over simulation outputs. The syntax for this is very simple. As an example, we can use the following script to find the angular momentum vector in a 1 pc sphere centered on the maximum density cell in a large number of simulation outputs: .. code-block:: python import yt yt.enable_parallelism() # Load all of the DD*/output_* files into a DatasetSeries object # in this case it is a Time Series ts = yt.load("DD*/output_*") # Define an empty storage dictionary for collecting information # in parallel through processing storage = {} # Use piter() to iterate over the time series, one proc per dataset # and store the resulting information from each dataset in # the storage dictionary for sto, ds in ts.piter(storage=storage): sphere = ds.sphere("max", (1.0, "pc")) sto.result = sphere.quantities.angular_momentum_vector() sto.result_id = str(ds) # Print out the angular momentum vector for all of the datasets for L in sorted(storage.items()): print(L) Note that this script can be run in serial or parallel with an arbitrary number of processors. When running in parallel, each output is given to a different processor. You can also request a fixed number of processors to calculate each angular momentum vector. For example, the following script will calculate each angular momentum vector using 4 workgroups, splitting up the pool available processors. Note that parallel=1 implies that the analysis will be run using 1 workgroup, whereas parallel=True will run with Nprocs workgroups. .. code-block:: python import yt yt.enable_parallelism() ts = yt.DatasetSeries("DD*/output_*", parallel = 4) for ds in ts.piter(): sphere = ds.sphere("max", (1.0, "pc)) L_vecs = sphere.quantities.angular_momentum_vector() If you do not want to use ``parallel_objects`` parallelism when using a DatasetSeries object, set ``parallel = False``. When running python in parallel, this will use all of the available processors to evaluate the requested operation on each simulation output. Some care and possibly trial and error might be necessary to estimate the correct settings for your simulation outputs. Parallel Performance, Resources, and Tuning ------------------------------------------- Optimizing parallel jobs in yt is difficult; there are many parameters that affect how well and quickly the job runs. In many cases, the only way to find out what the minimum (or optimal) number of processors is, or amount of memory needed, is through trial and error. However, this section will attempt to provide some insight into what are good starting values for a given parallel task. Chunk Decomposition +++++++++++++++++++ In general, these types of parallel calculations scale very well with number of processors. They are also fairly memory-conservative. The two limiting factors is therefore the number of chunks in the dataset, and the speed of the disk the data is stored on. There is no point in running a parallel job of this kind with more processors than chunks, because the extra processors will do absolutely nothing, and will in fact probably just serve to slow down the whole calculation due to the extra overhead. The speed of the disk is also a consideration - if it is not a high-end parallel file system, adding more tasks will not speed up the calculation if the disk is already swamped with activity. The best advice for these sort of calculations is to run with just a few processors and go from there, seeing if it the runtime improves noticeably. **Projections, Slices, and Cutting Planes** Projections, slices and cutting planes are the most common methods of creating two-dimensional representations of data. All three have been parallelized in a chunk-based fashion. * **Projections**: projections are parallelized utilizing a quad-tree approach. Data is loaded for each processor, typically by a process that consolidates open/close/read operations, and each grid is then iterated over and cells are deposited into a data structure that stores values corresponding to positions in the two-dimensional plane. This provides excellent load balancing, and in serial is quite fast. However, the operation by which quadtrees are joined across processors scales poorly; while memory consumption scales well, the time to completion does not. As such, projections can often be done very fast when operating only on a single processor! The quadtree algorithm can be used inline (and, indeed, it is for this reason that it is slow.) It is recommended that you attempt to project in serial before projecting in parallel; even for the very largest datasets (Enzo 1024^3 root grid with 7 levels of refinement) in the absence of IO the quadtree algorithm takes only three minutes or so on a decent processor. * **Slices**: to generate a slice, chunks that intersect a given slice are iterated over and their finest-resolution cells are deposited. The chunks are decomposed via standard load balancing. While this operation is parallel, **it is almost never necessary to slice a dataset in parallel**, as all data is loaded on demand anyway. The slice operation has been parallelized so as to enable slicing when running *in situ*. * **Cutting planes**: cutting planes are parallelized exactly as slices are. However, in contrast to slices, because the data-selection operation can be much more time consuming, cutting planes often benefit from parallelism. Object-Based ++++++++++++ Like chunk decomposition, it does not help to run with more processors than the number of objects to be iterated over. There is also the matter of the kind of work being done on each object, and whether it is disk-intensive, cpu-intensive, or memory-intensive. It is up to the user to figure out what limits the performance of their script, and use the correct amount of resources, accordingly. Disk-intensive jobs are limited by the speed of the file system, as above, and extra processors beyond its capability are likely counter-productive. It may require some testing or research (e.g. supercomputer documentation) to find out what the file system is capable of. If it is cpu-intensive, it's best to use as many processors as possible and practical. For a memory-intensive job, each processor needs to be able to allocate enough memory, which may mean using fewer than the maximum number of tasks per compute node, and increasing the number of nodes. The memory used per processor should be calculated, compared to the memory on each compute node, which dictates how many tasks per node. After that, the number of processors used overall is dictated by the disk system or CPU-intensity of the job. Domain Decomposition ++++++++++++++++++++ The various types of analysis that utilize domain decomposition use them in different enough ways that they are be discussed separately. **Halo-Finding** Halo finding, along with the merger tree that uses halo finding, operates on the particles in the volume, and is therefore mostly chunk-agnostic. Generally, the biggest concern for halo finding is the amount of memory needed. There is subtle art in estimating the amount of memory needed for halo finding, but a rule of thumb is that the HOP halo finder is the most memory intensive (:func:`HaloFinder`), and Friends of Friends (:func:`FOFHaloFinder`) being the most memory-conservative. For more information, see :ref:`halo-analysis`. **Volume Rendering** The simplest way to think about volume rendering, is that it load-balances over the i/o chunks in the dataset. Each processor is given roughly the same sized volume to operate on. In practice, there are just a few things to keep in mind when doing volume rendering. First, it only uses a power of two number of processors. If the job is run with 100 processors, only 64 of them will actually do anything. Second, the absolute maximum number of processors is the number of chunks. In order to keep work distributed evenly, typically the number of processors should be no greater than one-eighth or one-quarter the number of processors that were used to produce the dataset. For more information, see :ref:`volume_rendering`. Additional Tips --------------- * Don't be afraid to change how a parallel job is run. Change the number of processors, or memory allocated, and see if things work better or worse. After all, it's just a computer, it doesn't pass moral judgment! * Similarly, human time is more valuable than computer time. Try increasing the number of processors, and see if the runtime drops significantly. There will be a sweet spot between speed of run and the waiting time in the job scheduler queue; it may be worth trying to find it. * If you are using object-based parallelism but doing CPU-intensive computations on each object, you may find that setting ``num_procs`` equal to the number of processors per compute node can lead to significant speedups. By default, most mpi implementations will assign tasks to processors on a 'by-slot' basis, so this setting will tell yt to do computations on a single object using only the processors on a single compute node. A nice application for this type of parallelism is calculating a list of derived quantities for a large number of simulation outputs. * It is impossible to tune a parallel operation without understanding what's going on. Read the documentation, look at the underlying code, or talk to other yt users. Get informed! * Sometimes it is difficult to know if a job is cpu, memory, or disk intensive, especially if the parallel job utilizes several of the kinds of parallelism discussed above. In this case, it may be worthwhile to put some simple timers in your script (as below) around different parts. .. code-block:: python import yt import time yt.enable_parallelism() ds = yt.load("DD0152") t0 = time.time() bigstuff, hugestuff = StuffFinder(ds) BigHugeStuffParallelFunction(ds, bigstuff, hugestuff) t1 = time.time() for i in range(1000000): tinystuff, ministuff = GetTinyMiniStuffOffDisk("in%06d.txt" % i) array = TinyTeensyParallelFunction(ds, tinystuff, ministuff) SaveTinyMiniStuffToDisk("out%06d.txt" % i, array) t2 = time.time() if yt.is_root() print("BigStuff took %.5e sec, TinyStuff took %.5e sec" % (t1 - t0, t2 - t1)) * Remember that if the script handles disk IO explicitly, and does not use a built-in yt function to write data to disk, care must be taken to avoid `race-conditions `_. Be explicit about which MPI task writes to disk using a construction something like this: .. code-block:: python if yt.is_root() file = open("out.txt", "w") file.write(stuff) file.close() * Many supercomputers allow users to ssh into the nodes that their job is running on. Many job schedulers send the names of the nodes that are used in the notification emails, or a command like ``qstat -f NNNN``, where ``NNNN`` is the job ID, will also show this information. By ssh-ing into nodes, the memory usage of each task can be viewed in real-time as the job runs (using ``top``, for example), and can give valuable feedback about the resources the task requires. An Advanced Worked Example -------------------------- Below is a script used to calculate the redshift of first 99.9% ionization in a simulation. This script was designed to analyze a set of 100 outputs on Gordon, running on 128 processors. This script goes through three phases: #. Define a new derived field, which calculates the fraction of ionized hydrogen as a function only of the total hydrogen density. #. Load a time series up, specifying ``parallel = 8``. This means that it will decompose into 8 jobs. So if we ran on 128 processors, we would have 16 processors assigned to each output in the time series. #. Creating a big cube that will hold our results for this set of processors. Note that this will be only for each output considered by this processor, and this cube will not necessarily be filled in in every cell. #. For each output, distribute the grids to each of the sixteen processors working on that output. Each of these takes the max of the ionized redshift in their zone versus the accumulation cube. #. Iterate over slabs and find the maximum redshift in each slab of our accumulation cube. At the end, the root processor (of the global calculation) writes out an ionization cube that contains the redshift of first reionization for each zone across all outputs. .. literalinclude:: ionization_cube.py yt-3.3.3/doc/source/analyzing/particle_filter.ipynb0000644000076500000240000001243213017603554023353 0ustar goldbaumstaff00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Let us go through a full worked example. Here we have a Tipsy SPH dataset. By general\n", "inspection, we see that there are stars present in the dataset, since\n", "there are fields with field type: `Stars` in the `ds.field_list`. Let's look \n", "at the `derived_field_list` for all of the `Stars` fields. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import yt\n", "import numpy as np\n", "\n", "ds = yt.load(\"TipsyGalaxy/galaxy.00300\")\n", "for field in ds.derived_field_list:\n", " if field[0] == 'Stars':\n", " print (field)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will filter these into young stars and old stars by masking on the ('Stars', 'creation_time') field. \n", "\n", "In order to do this, we first make a function which applies our desired cut. This function must accept two arguments: `pfilter` and `data`. The first argument is a `ParticleFilter` object that contains metadata about the filter its self. The second argument is a yt data container.\n", "\n", "Let's call \"young\" stars only those stars with ages less 5 million years. Since Tipsy assigns a very large `creation_time` for stars in the initial conditions, we need to also exclude stars with negative ages. \n", "\n", "Conversely, let's define \"old\" stars as those stars formed dynamically in the simulation with ages greater than 5 Myr. We also include stars with negative ages, since these stars were included in the simulation initial conditions.\n", "\n", "We make use of `pfilter.filtered_type` so that the filter definition will use the same particle type as the one specified in the call to `add_particle_filter` below. This makes the filter definition usable for arbitrary particle types. Since we're only filtering the `\"Stars\"` particle type in this example, we could have also replaced `pfilter.filtered_type` with `\"Stars\"` and gotten the same result." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def young_stars(pfilter, data):\n", " age = data.ds.current_time - data[pfilter.filtered_type, \"creation_time\"]\n", " filter = np.logical_and(age.in_units('Myr') <= 5, age >= 0)\n", " return filter\n", "\n", "def old_stars(pfilter, data):\n", " age = data.ds.current_time - data[pfilter.filtered_type, \"creation_time\"]\n", " filter = np.logical_or(age.in_units('Myr') >= 5, age < 0)\n", " return filter" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we define these as particle filters within the yt universe with the\n", "`add_particle_filter()` function." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "yt.add_particle_filter(\"young_stars\", function=young_stars, filtered_type='Stars', requires=[\"creation_time\"])\n", "\n", "yt.add_particle_filter(\"old_stars\", function=old_stars, filtered_type='Stars', requires=[\"creation_time\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let us now apply these filters specifically to our dataset.\n", "\n", "Let's double check that it worked by looking at the derived_field_list for any new fields created by our filter." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ds.add_particle_filter('young_stars')\n", "ds.add_particle_filter('old_stars')\n", "\n", "for field in ds.derived_field_list:\n", " if \"young_stars\" in field or \"young_stars\" in field[1]:\n", " print (field)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see all of the new `young_stars` fields as well as the 4 deposit fields. These deposit fields are `mesh` fields generated by depositing particle fields on the grid. Let's generate a couple of projections of where the young and old stars reside in this simulation by accessing some of these new fields." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "p = yt.ProjectionPlot(ds, 'z', [('deposit', 'young_stars_cic'), ('deposit', 'old_stars_cic')], width=(40, 'kpc'), center='m')\n", "p.set_figure_size(5)\n", "p.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We see that young stars are concentrated in regions of active star formation, while old stars are more spatially extended." ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 } yt-3.3.3/doc/source/analyzing/saving_data.rst0000644000076500000240000001667113017603554022163 0ustar goldbaumstaff00000000000000.. _saving_data: Saving Reloadable Data ====================== Most of the data loaded into or generated with yt can be saved to a format that can be reloaded as a first-class dataset. This includes the following: * geometric data containers (regions, spheres, disks, rays, etc.) * grid data containers (covering grids, arbitrary grids, fixed resolution buffers) * spatial plots (projections, slices, cutting planes) * profiles * generic array data In the case of projections, slices, and profiles, reloaded data can be used to remake plots. For information on this, see :ref:`remaking-plots`. .. _saving-data-containers: Geometric Data Containers ------------------------- Data from geometric data containers can be saved with the :func:`~yt.data_objects.data_containers.save_as_dataset`` function. .. notebook-cell:: import yt ds = yt.load("enzo_tiny_cosmology/DD0046/DD0046") sphere = ds.sphere([0.5]*3, (10, "Mpc")) fn = sphere.save_as_dataset(fields=["density", "particle_mass"]) print (fn) This function will return the name of the file to which the dataset was saved. The filename will be a combination of the name of the original dataset and the type of data container. Optionally, a specific filename can be given with the ``filename`` keyword. If no fields are given, the fields that have previously been queried will be saved. The newly created dataset can be loaded like all other supported data through ``yt.load``. Once loaded, field data can be accessed through the traditional data containers or through the ``data`` attribute, which will be a data container configured like the original data container used to make the dataset. Grid data is accessed by the ``grid`` data type and particle data is accessed with the original particle type. As with the original dataset, grid positions and cell sizes are accessible with, for example, ("grid", "x") and ("grid", "dx"). Particle positions are accessible as (, "particle_position_x"). All original simulation parameters are accessible in the ``parameters`` dictionary, normally associated with all datasets. .. code-block:: python sphere_ds = yt.load("DD0046_sphere.h5") # use the original data container print (sphere_ds.data["grid", "density"]) # create a new data container ad = sphere_ds.all_data() # grid data print (ad["grid", "density"]) print (ad["grid", "x"]) print (ad["grid", "dx"]) # particle data print (ad["all", "particle_mass"]) print (ad["all", "particle_position_x"]) Note that because field data queried from geometric containers is returned as unordered 1D arrays, data container datasets are treated, effectively, as particle data. Thus, 3D indexing of grid data from these datasets is not possible. .. _saving-grid-data-containers: Grid Data Containers -------------------- Data containers that return field data as multidimensional arrays can be saved so as to preserve this type of access. This includes covering grids, arbitrary grids, and fixed resolution buffers. Saving data from these containers works just as with geometric data containers. Field data can be accessed through geometric data containers. .. code-block:: python cg = ds.covering_grid(level=0, left_edge=[0.25]*3, dims=[16]*3) fn = cg.save_as_dataset(fields=["density", "particle_mass"]) cg_ds = yt.load(fn) ad = cg_ds.all_data() print (ad["grid", "density"]) Multidimensional indexing of field data is also available through the ``data`` attribute. .. code-block:: python print (cg_ds.data["grid", "density"]) Fixed resolution buffers work just the same. .. code-block:: python my_proj = ds.proj("density", "x", weight_field="density") frb = my_proj.to_frb(1.0, (800, 800)) fn = frb.save_as_dataset(fields=["density"]) frb_ds = yt.load(fn) print (frb_ds.data["density"]) .. _saving-spatial-plots: Spatial Plots ------------- Spatial plots, such as projections, slices, and off-axis slices (cutting planes) can also be saved and reloaded. .. code-block:: python proj = ds.proj("density", "x", weight_field="density") proj.save_as_dataset() Once reloaded, they can be handed to their associated plotting functions to make images. .. code-block:: python proj_ds = yt.load("DD0046_proj.h5") p = yt.ProjectionPlot(proj_ds, "x", "density", weight_field="density") p.save() .. _saving-profile-data: Profiles -------- Profiles created with :func:`~yt.data_objects.profiles.create_profile`, :class:`~yt.visualization.profile_plotter.ProfilePlot`, and :class:`~yt.visualization.profile_plotter.PhasePlot` can be saved with the :func:`~yt.data_objects.profiles.save_as_dataset` function, which works just as above. Profile datasets are a type of non-spatial grid datasets. Geometric selection is not possible, but data can be accessed through the ``.data`` attribute. .. notebook-cell:: import yt ds = yt.load("enzo_tiny_cosmology/DD0046/DD0046") ad = ds.all_data() profile_2d = yt.create_profile(ad, ["density", "temperature"], "cell_mass", weight_field=None, n_bins=(128, 128)) profile_2d.save_as_dataset() prof_2d_ds = yt.load("DD0046_Profile2D.h5") print (prof_2d_ds.data["cell_mass"]) The x, y (if at least 2D), and z (if 3D) bin fields can be accessed as 1D arrays with "x", "y", and "z". .. code-block:: python print (prof_2d_ds.data["x"]) The bin fields can also be returned with the same shape as the profile data by accessing them with their original names. This allows for boolean masking of profile data using the bin fields. .. code-block:: python # density is the x bin field print (prof_2d_ds.data["density"]) For 1, 2, and 3D profile datasets, a fake profile object will be constructed by accessing the ".profile" attribute. This is used primarily in the case of 1 and 2D profiles to create figures using :class:`~yt.visualization.profile_plotter.ProfilePlot` and :class:`~yt.visualization.profile_plotter.PhasePlot`. .. code-block:: python p = yt.PhasePlot(prof_2d_ds.data, "density", "temperature", "cell_mass", weight_field=None) p.save() .. _saving-array-data: Generic Array Data ------------------ Generic arrays can be saved and reloaded as non-spatial data using the :func:`~yt.frontends.ytdata.utilities.save_as_dataset` function, also available as ``yt.save_as_dataset``. As with profiles, geometric selection is not possible, but the data can be accessed through the ``.data`` attribute. .. notebook-cell:: import yt ds = yt.load("enzo_tiny_cosmology/DD0046/DD0046") region = ds.box([0.25]*3, [0.75]*3) sphere = ds.sphere(ds.domain_center, (10, "Mpc")) my_data = {} my_data["region_density"] = region["density"] my_data["sphere_density"] = sphere["density"] yt.save_as_dataset(ds, "test_data.h5", my_data) array_ds = yt.load("test_data.h5") print (array_ds.data["region_density"]) print (array_ds.data["sphere_density"]) Array data can be saved with or without a dataset loaded. If no dataset has been loaded, as fake dataset can be provided as a dictionary. .. notebook-cell:: import numpy as np import yt my_data = {"density": yt.YTArray(np.random.random(10), "g/cm**3"), "temperature": yt.YTArray(np.random.random(10), "K")} fake_ds = {"current_time": yt.YTQuantity(10, "Myr")} yt.save_as_dataset(fake_ds, "random_data.h5", my_data) new_ds = yt.load("random_data.h5") print (new_ds.data["density"]) yt-3.3.3/doc/source/analyzing/time_series_analysis.rst0000644000076500000240000001532213017603554024106 0ustar goldbaumstaff00000000000000.. _time-series-analysis: Time Series Analysis ==================== Often, one wants to analyze a continuous set of outputs from a simulation in a uniform manner. A simple example would be to calculate the peak density in a set of outputs that were written out. The problem with time series analysis in yt is general an issue of verbosity and clunkiness. Typically, one sets up a loop: .. code-block:: python for dsi in range(30): fn = "DD%04i/DD%04i" % (dsi, dsi) ds = load(fn) process_output(ds) But this is not really very nice. This ends up requiring a lot of maintenance. The :class:`~yt.data_objects.time_series.DatasetSeries` object has been designed to remove some of this clunkiness and present an easier, more unified approach to analyzing sets of data. Even better, :class:`~yt.data_objects.time_series.DatasetSeries` works in parallel by default (see :ref:`parallel-computation`), so you can use a ``DatasetSeries`` object to quickly and easily parallelize your analysis. Since doing the same analysis task on many simulation outputs is 'embarrassingly' parallel, this naturally allows for almost arbitrary speedup - limited only by the number of available processors and the number of simulation outputs. The idea behind the current implementation of time series analysis is that the underlying data and the operators that act on that data can and should be distinct. There are several operators provided, as well as facilities for creating your own, and these operators can be applied either to datasets on the whole or to subregions of individual datasets. The simplest mechanism for creating a ``DatasetSeries`` object is to pass a glob pattern to the ``yt.load`` function. .. code-block:: python import yt ts = yt.load("DD????/DD????") This will create a new time series, populated with all datasets that match the pattern "DD" followed by four digits. This object, here called ``ts``, can now be analyzed in bulk. Alternately, you can specify an already formatted list of filenames directly to the :class:`~yt.data_objects.time_series.DatasetSeries` initializer: .. code-block:: python import yt ts = yt.DatasetSeries(["DD0030/DD0030", "DD0040/DD0040"]) Analyzing Each Dataset In Sequence ---------------------------------- The :class:`~yt.data_objects.time_series.DatasetSeries` object has two primary methods of iteration. The first is a very simple iteration, where each object is returned for iteration: .. code-block:: python import yt ts = yt.load("*/*.index") for ds in ts: print(ds.current_time) This can also operate in parallel, using :meth:`~yt.data_objects.time_series.DatasetSeries.piter`. For more examples, see: * :ref:`parallel-time-series-analysis` * The cookbook recipe for :ref:`cookbook-time-series-analysis` * :class:`~yt.data_objects.time_series.DatasetSeries` .. _analyzing-an-entire-simulation: Analyzing an Entire Simulation ------------------------------ .. note:: Implemented for the Enzo, Gadget, OWLS, and Exodus II frontends. The parameter file used to run a simulation contains all the information necessary to know what datasets should be available. The ``simulation`` convenience function allows one to create a ``DatasetSeries`` object of all or a subset of all data created by a single simulation. To instantiate, give the parameter file and the simulation type. .. code-block:: python import yt my_sim = yt.simulation('enzo_tiny_cosmology/32Mpc_32.enzo', 'Enzo') Then, create a ``DatasetSeries`` object with the :meth:`frontends.enzo.simulation_handling.EnzoSimulation.get_time_series` function. With no additional keywords, the time series will include every dataset. If the ``find_outputs`` keyword is set to ``True``, a search of the simulation directory will be performed looking for potential datasets. These datasets will be temporarily loaded in order to figure out the time and redshift associated with them. This can be used when simulation data was created in a non-standard way, making it difficult to guess the corresponding time and redshift information .. code-block:: python my_sim.get_time_series() After this, time series analysis can be done normally. .. code-block:: python for ds in my_sim.piter() all_data = ds.all_data() print(all_data.quantities.extrema('density')) Additional keywords can be given to :meth:`frontends.enzo.simulation_handling.EnzoSimulation.get_time_series` to select a subset of the total data: * ``time_data`` (*bool*): Whether or not to include time outputs when gathering datasets for time series. Default: True. (Enzo only) * ``redshift_data`` (*bool*): Whether or not to include redshift outputs when gathering datasets for time series. Default: True. (Enzo only) * ``initial_time`` (*float*): The earliest time for outputs to be included. If None, the initial time of the simulation is used. This can be used in combination with either ``final_time`` or ``final_redshift``. Default: None. * ``final_time`` (*float*): The latest time for outputs to be included. If None, the final time of the simulation is used. This can be used in combination with either ``initial_time`` or ``initial_redshift``. Default: None. * ``times`` (*list*): A list of times for which outputs will be found. Default: None. * ``initial_redshift`` (*float*): The earliest redshift for outputs to be included. If None, the initial redshift of the simulation is used. This can be used in combination with either ``final_time`` or ``final_redshift``. Default: None. * ``final_redshift`` (*float*): The latest redshift for outputs to be included. If None, the final redshift of the simulation is used. This can be used in combination with either ``initial_time`` or ``initial_redshift``. Default: None. * ``redshifts`` (*list*): A list of redshifts for which outputs will be found. Default: None. * ``initial_cycle`` (*float*): The earliest cycle for outputs to be included. If None, the initial cycle of the simulation is used. This can only be used with final_cycle. Default: None. (Enzo only) * ``final_cycle`` (*float*): The latest cycle for outputs to be included. If None, the final cycle of the simulation is used. This can only be used in combination with initial_cycle. Default: None. (Enzo only) * ``tolerance`` (*float*): Used in combination with ``times`` or ``redshifts`` keywords, this is the tolerance within which outputs are accepted given the requested times or redshifts. If None, the nearest output is always taken. Default: None. * ``parallel`` (*bool*/*int*): If True, the generated ``DatasetSeries`` will divide the work such that a single processor works on each dataset. If an integer is supplied, the work will be divided into that number of jobs. Default: True. yt-3.3.3/doc/source/analyzing/units/0000755000076500000240000000000013023400160020261 5ustar goldbaumstaff00000000000000yt-3.3.3/doc/source/analyzing/units/1)_Symbolic_Units.ipynb0000644000076500000240000004233513017603554024606 0ustar goldbaumstaff00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "### Dimensional analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The fastest way to get into the unit system is to explore the quantities that live in the `yt.units` namespace:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from yt.units import meter, gram, kilogram, second, joule\n", "print (kilogram*meter**2/second**2 == joule)\n", "print (kilogram*meter**2/second**2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from yt.units import m, kg, s, W\n", "kg*m**2/s**3 == W" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from yt.units import kilometer\n", "three_kilometers = 3*kilometer\n", "print (three_kilometers)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from yt.units import gram, kilogram\n", "print (gram+kilogram)\n", "\n", "print (kilogram+gram)\n", "\n", "print (kilogram/gram)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These unit symbols are all instances of a new class we've added to yt 3.0, `YTQuantity`. `YTQuantity` is useful for storing a single data point." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "type(kilogram)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We also provide `YTArray`, which can store arrays of quantities:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "arr = [3,4,5]*kilogram\n", "\n", "print (arr)\n", "\n", "print (type(arr))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Creating arrays and quantities" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Most people will interact with the new unit system using `YTArray` and `YTQuantity`. These are both subclasses of numpy's fast array type, `ndarray`, and can be used interchangably with other NumPy arrays. These new classes make use of the unit system to append unit metadata to the underlying `ndarray`. `YTArray` is intended to store array data, while `YTQuantitity` is intended to store scalars in a particular unit system.\n", "\n", "There are two ways to create arrays and quantities. The first is to explicitly create it by calling the class constructor and supplying a unit string:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from yt.units.yt_array import YTArray\n", "\n", "sample_array = YTArray([1,2,3], 'g/cm**3')\n", "\n", "print (sample_array)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The unit string can be an arbitrary combination of metric unit names. Just a few examples:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from yt.units.yt_array import YTQuantity\n", "from yt.units import kboltz\n", "from numpy.random import random\n", "import numpy as np\n", "\n", "print (\"Length:\")\n", "print (YTQuantity(random(), 'm'))\n", "print (YTQuantity(random(), 'cm'))\n", "print (YTQuantity(random(), 'Mpc'))\n", "print (YTQuantity(random(), 'AU'))\n", "print ('')\n", "\n", "print (\"Time:\")\n", "print (YTQuantity(random(), 's'))\n", "print (YTQuantity(random(), 'min'))\n", "print (YTQuantity(random(), 'hr'))\n", "print (YTQuantity(random(), 'day'))\n", "print (YTQuantity(random(), 'yr'))\n", "print ('')\n", "\n", "print (\"Mass:\")\n", "print (YTQuantity(random(), 'g'))\n", "print (YTQuantity(random(), 'kg'))\n", "print (YTQuantity(random(), 'Msun'))\n", "print ('')\n", "\n", "print (\"Energy:\")\n", "print (YTQuantity(random(), 'erg'))\n", "print (YTQuantity(random(), 'g*cm**2/s**2'))\n", "print (YTQuantity(random(), 'eV'))\n", "print (YTQuantity(random(), 'J'))\n", "print ('')\n", "\n", "print (\"Temperature:\")\n", "print (YTQuantity(random(), 'K'))\n", "print ((YTQuantity(random(), 'eV')/kboltz).in_cgs())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Dimensional arrays and quantities can also be created by multiplication with another array or quantity:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from yt.units import kilometer\n", "print (kilometer)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "three_kilometers = 3*kilometer\n", "print (three_kilometers)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When working with a YTArray with complicated units, you can use `unit_array` and `unit_quantity` to conveniently apply units to data:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "test_array = YTArray(np.random.random(20), 'erg/s')\n", "\n", "print (test_array)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`unit_quantity` returns a `YTQuantity` with a value of 1.0 and the same units as the array it is a attached to." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print (test_array.unit_quantity)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`unit_array` returns a `YTArray` with the same units and shape as the array it is a attached to and with all values set to 1.0." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print (test_array.unit_array)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These are useful when doing arithmetic:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print (test_array + 1.0*test_array.unit_quantity)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print (test_array + np.arange(20)*test_array.unit_array)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For convenience, `unit_quantity` is also available via `uq` and `unit_array` is available via `ua`. You can use these arrays to create dummy arrays with the same units as another array - this is sometimes easier than manually creating a new array or quantity." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print (test_array.uq)\n", "\n", "print (test_array.unit_quantity == test_array.uq)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from numpy import array_equal\n", "\n", "print (test_array.ua)\n", "\n", "print (array_equal(test_array.ua, test_array.unit_array))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Unit metadata is encoded in the `units` attribute that hangs off of `YTArray` or `YTQuantity` instances:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from yt.units import kilometer, erg\n", "\n", "print (\"kilometer's units:\", kilometer.units)\n", "print (\"kilometer's dimensions:\", kilometer.units.dimensions)\n", "\n", "print ('')\n", "\n", "print (\"erg's units:\", erg.units)\n", "print (\"erg's dimensions: \", erg.units.dimensions)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Arithmetic with `YTQuantity` and `YTArray`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Of course it wouldn't be very useful if all we could do is create data with units. The real power of the new unit system is that we can add, subtract, mutliply, and divide using quantities and dimensional arrays:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "a = YTQuantity(3, 'cm')\n", "b = YTQuantity(3, 'm')\n", "\n", "print (a+b)\n", "print (b+a)\n", "print ('')\n", "\n", "print ((a+b).in_units('ft'))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "a = YTQuantity(42, 'mm')\n", "b = YTQuantity(1, 's')\n", "\n", "print (a/b)\n", "print ((a/b).in_cgs())\n", "print ((a/b).in_mks())\n", "print ((a/b).in_units('km/s'))\n", "print ('')\n", "\n", "print (a*b)\n", "print ((a*b).in_cgs())\n", "print ((a*b).in_mks())" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "m = YTQuantity(35, 'g')\n", "a = YTQuantity(9.8, 'm/s**2')\n", "\n", "print (m*a)\n", "print ((m*a).in_units('dyne'))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from yt.units import G, kboltz\n", "\n", "print (\"Newton's constant: \", G)\n", "print (\"Newton's constant in MKS: \", G.in_mks(), \"\\n\")\n", "\n", "print (\"Boltzmann constant: \", kboltz)\n", "print (\"Boltzmann constant in MKS: \", kboltz.in_mks())" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "rho = YTQuantity(1, 'g/cm**3')\n", "t_ff = (G*rho)**(-0.5)\n", "\n", "print (t_ff)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "An exception is raised if we try to do a unit operation that doesn't make any sense:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from yt.utilities.exceptions import YTUnitOperationError\n", "\n", "a = YTQuantity(3, 'm')\n", "b = YTQuantity(5, 'erg')\n", "\n", "try:\n", " print (a+b)\n", "except YTUnitOperationError as e:\n", " print (e)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "A plain `ndarray` or a `YTArray` created with empty units is treated as a dimensionless quantity and can be used in situations where unit consistency allows it to be used: " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "a = YTArray([1.,2.,3.], 'm')\n", "b = np.array([2.,2.,2.])\n", "\n", "print (\"a: \", a)\n", "print (\"b: \", b)\n", "print (\"a*b: \", a*b)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "c = YTArray([2,2,2])\n", "\n", "print (\"c: \", c)\n", "print (\"a*c: \", a*c)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Saving and Loading `YTArray`s to/from disk" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`YTArray`s can be written to disk, to be loaded again to be used in yt or in a different context later. There are two formats that can be written to/read from: HDF5 and ASCII. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### HDF5" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To write to HDF5, use `write_hdf5`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "my_dens = YTArray(np.random.random(10), 'Msun/kpc**3')\n", "my_temp = YTArray(np.random.random(10), 'K')\n", "my_dens.write_hdf5(\"my_data.h5\", dataset_name=\"density\")\n", "my_temp.write_hdf5(\"my_data.h5\", dataset_name=\"temperature\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Where we used the `dataset_name` keyword argument to create a separate dataset for each array in the same file.\n", "\n", "We can use the `from_hdf5` classmethod to read the data back in:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "read_dens = YTArray.from_hdf5(\"my_data.h5\", dataset_name=\"density\")\n", "print (read_dens)\n", "print (my_dens)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can use the `info` keyword argument to `write_hdf5` to write some additional data to the file, which will be stored as attributes of the dataset:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "my_vels = YTArray(np.random.normal(10), 'km/s')\n", "info = {\"source\":\"galaxy cluster\",\"user\":\"jzuhone\"}\n", "my_vels.write_hdf5(\"my_data.h5\", dataset_name=\"velocity\", info=info)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you want to read/write a dataset from/to a specific group within the HDF5 file, use the `group_name` keyword:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "my_vels.write_hdf5(\"data_in_group.h5\", dataset_name=\"velocity\", info=info, group_name=\"/data/fields\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where we have used the standard HDF5 slash notation for writing a group hierarchy (e.g., group within a group):" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "#### ASCII" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To write one or more `YTArray`s to an ASCII text file, use `yt.savetxt`, which works a lot like NumPy's `savetxt`, except with units:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import yt\n", "a = YTArray(np.random.random(size=10), \"cm\")\n", "b = YTArray(np.random.random(size=10), \"g\")\n", "c = YTArray(np.random.random(size=10), \"s\")\n", "yt.savetxt(\"my_data.dat\", [a,b,c], header='My cool data', footer='Data is over', delimiter=\"\\t\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The file we wrote can then be easily used in other contexts, such as plotting in Gnuplot, or loading into a spreadsheet, or just for causal examination. We can quickly check it here:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%%bash \n", "more my_data.dat" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can see that the header comes first, and then right before the data we have a subheader marking the units of each column. The footer comes after the data. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`yt.loadtxt` can be used to read the same data with units back in, or read data that has been generated from some other source. Just make sure it's in the format above. `loadtxt` can also selectively read from particular columns in the file with the `usecols` keyword argument:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "bb, cc = yt.loadtxt(\"my_data.dat\", usecols=(1,2), delimiter=\"\\t\")\n", "print (bb)\n", "print (b)\n", "print ('')\n", "print (cc)\n", "print (c)" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 } yt-3.3.3/doc/source/analyzing/units/2)_Fields_and_unit_conversion.ipynb0000644000076500000240000003523613017603554027202 0ustar goldbaumstaff00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "In the past, querying a data object with a field name returned a NumPy `ndarray` . In the new unit system, data object queries will return a `YTArray`, a subclass of `ndarray` that preserves all of the nice properties of `ndarray`, including broadcasting, deep and shallow copies, and views. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Selecting data from an object" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`YTArray` is 'unit-aware'. Let's show how this works in practice using a sample Enzo dataset:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import yt\n", "ds = yt.load('IsolatedGalaxy/galaxy0030/galaxy0030')\n", "\n", "dd = ds.all_data()\n", "maxval, maxloc = ds.find_max('density')\n", "\n", "dens = dd['density']" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print (maxval)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print (dens)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "mass = dd['cell_mass']\n", "\n", "print (\"Cell Masses in CGS: \\n\", mass, \"\\n\")\n", "print (\"Cell Masses in MKS: \\n\", mass.in_mks(), \"\\n\")\n", "print (\"Cell Masses in Solar Masses: \\n\", mass.in_units('Msun'), \"\\n\")\n", "print (\"Cell Masses in code units: \\n\", mass.in_units('code_mass'), \"\\n\")" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dx = dd['dx']\n", "print (\"Cell dx in code units: \\n\", dx, \"\\n\")\n", "print (\"Cell dx in centimeters: \\n\", dx.in_cgs(), \"\\n\")\n", "print (\"Cell dx in meters: \\n\", dx.in_units('m'), \"\\n\")\n", "print (\"Cell dx in megaparsecs: \\n\", dx.in_units('Mpc'), \"\\n\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Unit conversions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "YTArray defines several user-visible member functions that allow data to be converted from one unit system to another:\n", "\n", "* `in_units`\n", "* `in_cgs`\n", "* `in_mks`\n", "* `in_base`\n", "* `convert_to_units`\n", "* `convert_to_cgs`\n", "* `convert_to_mks`\n", "* `convert_to_base`" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first method, `in_units`, returns a copy of the array in the units denoted by a string argument:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print (dd['density'].in_units('Msun/pc**3'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`in_cgs` and `in_mks` return a copy of the array converted to CGS and MKS units, respectively:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print (dd['pressure'])\n", "print (dd['pressure'].in_cgs())\n", "print (dd['pressure'].in_mks())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`in_cgs` and `in_mks` are just special cases of the more general `in_base`, which can convert a `YTArray` to a number of different unit systems:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print (dd['pressure'].in_base('imperial')) # Imperial/English base units\n", "print (dd['pressure'].in_base('galactic')) # Base units of kpc, Msun, Myr\n", "print (dd['pressure'].in_base('planck')) # Base units in the Planck system\n", "print (dd['pressure'].in_base()) # defaults to cgs if no argument given" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`in_base` can even take a dataset as the argument to convert the `YTArray` into the base units of the dataset:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print (dd['pressure'].in_base(ds)) # The IsolatedGalaxy dataset from above" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "yt defines a number of unit systems, and new unit systems may be added by the user, which can also be passed to `in_base`. To learn more about the unit systems, how to use them with datasets and other objects, and how to add new ones, see [Unit Systems](unit_systems.html)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The rest of the methods do in-place conversions:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "dens = dd['density']\n", "print (dens)\n", "\n", "dens.convert_to_units('Msun/pc**3')\n", "print (dens)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "One possibly confusing wrinkle when using in-place conversions is if you try to query `dd['density']` again, you'll find that it has been converted to solar masses per cubic parsec:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print (dd['density'])\n", "\n", "dens.convert_to_units('g/cm**3')\n", "\n", "print (dens)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since the unit metadata is preserved and the array values are still correct in the new unit system, all numerical operations will still be correct.\n", "\n", "One of the nicest aspects of this new unit system is that the symbolic algebra for mathematical operations on data with units is performed automatically by sympy. This example shows how we can construct a field with density units from two other fields that have units of mass and volume:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print (dd['cell_mass'])\n", "print (dd['cell_volume'].in_units('cm**3'))\n", "\n", "print ((dd['cell_mass']/dd['cell_volume']).in_cgs())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Electrostatic/Electromagnetic Units" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Electromagnetic units can be a bit tricky, because the units for such quantities in different unit systems can have entirely different dimensions, even if they are meant to represent the same physical quantities. For example, in the SI system of units, current in Amperes is a fundamental unit of measure, so the unit of charge \"coulomb\" is equal to one ampere-second. On the other hand, in the Gaussian/CGS system, there is no equivalent base electromagnetic unit, and the electrostatic charge unit \"esu\" is equal to one $\\mathrm{cm^{3/2}g^{-1/2}s^{-1}}$ (which does not have any apparent physical significance). `yt` recognizes this difference:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "q1 = yt.YTArray(1.0,\"C\") # coulombs\n", "q2 = yt.YTArray(1.0,\"esu\") # electrostatic units / statcoulomb\n", "\n", "print (\"units =\", q1.in_mks().units, \", dims =\", q1.units.dimensions)\n", "print (\"units =\", q2.in_cgs().units, \", dims =\", q2.units.dimensions)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "B1 = yt.YTArray(1.0,\"T\") # tesla\n", "B2 = yt.YTArray(1.0,\"gauss\") # gauss\n", "\n", "print (\"units =\", B1.in_mks().units, \", dims =\", B1.units.dimensions)\n", "print (\"units =\", B2.in_cgs().units, \", dims =\", B2.units.dimensions)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To convert between these two systems, use [Unit Equivalencies](unit_equivalencies.html)." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Working with views and converting to ndarray" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "There are two ways to convert the data into a numpy array. The most straightforward and safe way to do this is to create a copy of the array data. The following cell demonstrates four equivalent ways of doing this, in increasing degree of terseness." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import numpy as np\n", "\n", "dens = dd['cell_mass']\n", "\n", "print (dens.to_ndarray())\n", "print (np.array(dens))\n", "print (dens.value)\n", "print (dens.v)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since we have a copy of the data, we can mess with it however we wish without disturbing the original data returned by the yt data object." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Another way to touch the raw array data is to get a _view_. A numpy view is a lightweight array interface to a memory buffer. There are four ways to create views of YTArray instances:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print (dd['cell_mass'].ndarray_view())\n", "print (dd['cell_mass'].view(np.ndarray))\n", "print (dd['cell_mass'].ndview)\n", "print (dd['cell_mass'].d)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When working with views, rememeber that you are touching the raw array data and no longer have any of the unit checking provided by the unit system. This can be useful where it might be more straightforward to treat the array as if it didn't have units but without copying the data." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "density_values = dd['density'].d\n", "density_values[0:10] = 0\n", "\n", "# The original array was updated\n", "print (dd['density'])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Round-Trip Conversions to and from Other Unit Systems" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, a `YTArray` or `YTQuantity` may be converted to an [AstroPy quantity](http://astropy.readthedocs.org/en/latest/units/), which is a NumPy array or a scalar associated with units from AstroPy's units system. You may use this facility if you have AstroPy installed. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some examples of converting from AstroPy units to yt:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from astropy import units as u\n", "\n", "x = 42.0 * u.meter\n", "y = yt.YTQuantity.from_astropy(x)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print (x, type(x))\n", "print (y, type(y))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "a = np.random.random(size=10) * u.km/u.s\n", "b = yt.YTArray.from_astropy(a)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print (a, type(a))\n", "print (b, type(b))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It also works the other way around, converting a `YTArray` or `YTQuantity` to an AstroPy quantity via the method `to_astropy`. For arrays:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "temp = dd[\"temperature\"]\n", "atemp = temp.to_astropy()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print (temp, type(temp))\n", "print (atemp, type(atemp))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and quantities:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from yt.units import kboltz\n", "kb = kboltz.to_astropy()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print (kboltz, type(kboltz))\n", "print (kb, type(kb))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As a sanity check, you can show that it works round-trip:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "k1 = kboltz.to_astropy()\n", "k2 = yt.YTQuantity.from_astropy(kb)\n", "print (k1 == k2)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "c = yt.YTArray.from_astropy(a)\n", "d = c.to_astropy()\n", "print (a == d)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also do the same thing with unitful quantities from the [Pint package](http://pint.readthedocs.org), using essentially the same procedure:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "from pint import UnitRegistry\n", "ureg = UnitRegistry()\n", "v = 1000.*ureg.km/ureg.s\n", "w = yt.YTQuantity.from_pint(v)" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print (v, type(v))\n", "print (w, type(w))" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "ptemp = temp.to_pint()" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print (temp, type(temp))\n", "print (ptemp, type(ptemp))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3.0 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }yt-3.3.3/doc/source/analyzing/units/3)_Comoving_units_and_code_units.ipynb0000644000076500000240000002736513017603554027714 0ustar goldbaumstaff00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "In yt 3.0, we want to make it easier to access \"raw\" simulation data that a code writes directly to disk. The new unit system makes it much simpler to convert back and forth between physical coordinates and the unscaled \"raw\" coordinate system used internally in the simulation code. In some cases, this conversion involves transforming to comoving coordinates, so that is also covered here." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Code units" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's take a look at a cosmological enzo dataset to play with converting between physical units and code units:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import yt\n", "ds = yt.load('Enzo_64/DD0043/data0043')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The conversion factors between Enzo's internal unit system and the physical CGS system are stored in the dataset's `unit_registry` object. Code units have names like `code_length` and `code_time`. Let's take a look at the names of all of the code units, along with their CGS conversion factors for this cosmological enzo dataset:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "reg = ds.unit_registry\n", "\n", "for un in reg.keys():\n", " if un.startswith('code_'):\n", " fmt_tup = (un, reg.lut[un][0], str(reg.lut[un][1]))\n", " print (\"Unit name: {:<15}\\nCGS conversion: {:<15}\\nDimensions: {:<15}\\n\".format(*fmt_tup))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fmt_tup" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Most of the time you will not have to deal with the unit registry. For example, the conversion factors to code units are stored as attributes of the dataset object:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print (\"Length unit: \", ds.length_unit)\n", "print (\"Time unit: \", ds.time_unit)\n", "print (\"Mass unit: \", ds.mass_unit)\n", "print (\"Velocity unit: \", ds.velocity_unit)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Conversion factors will be supplied in CGS by default. We can also ask what the conversion factors are in code units." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print (\"Length unit: \", ds.length_unit.in_units('code_length'))\n", "print (\"Time unit: \", ds.time_unit.in_units('code_time'))\n", "print (\"Mass unit: \", ds.mass_unit.in_units('code_mass'))\n", "print (\"Velocity unit: \", ds.velocity_unit.in_units('code_velocity'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "as expected, all the conversion factors are unity in code units." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also play with unit conversions on `ds.domain_width`. First, we see for enzo how code length units are defined relative to the domain width:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ds.domain_width" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ds.domain_width.in_cgs()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ds.domain_width.in_units('Mpccm/h')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Comoving units" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This last example uses a cosmological unit. In english, I asked for the domain width in comoving megaparsecs, scaled as if the hubble constant were 100 km/s/Mpc. Although $h$ isn't really a unit, yt treats it as one for the purposes of the unit system. \n", "\n", "As an aside, Darren Croton's [research note](http://arxiv.org/abs/1308.4150) on the history, use, and interpretation of $h$ as it appears in the astronomical literature is pretty much required reading for anyone who has to deal with factors of $h$ every now and then.\n", "\n", "In yt, comoving length unit symbols are named following the pattern “(length symbol)cm”, i.e. `pccm` for comoving parsec or `mcm` for a comoving meter. A comoving length unit is different from the normal length unit by a factor of $(1+z)$:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "z = ds.current_redshift\n", " \n", "print (ds.quan(1, 'Mpc')/ds.quan(1, 'Mpccm'))\n", "print (1+z)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As we saw before, $h$ is treated like any other unit symbol. It has `dimensionless` units, just like a scalar:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print (ds.quan(1, 'Mpc')/ds.quan(1, 'Mpc/h'))\n", "print (ds.hubble_constant)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These units can be used in readily used in plots and anywhere a length unit is appropriate in yt." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "slc = yt.SlicePlot(ds, 0, 'density', width=(128, 'Mpccm/h'))\n", "slc.set_figure_size(6)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### The unit registry" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When you create a `YTArray` without referring to a unit registry, yt uses the default unit registry, which does not include code units or comoving units." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from yt import YTQuantity\n", "\n", "a = YTQuantity(3, 'cm')\n", "\n", "print (a.units.registry.keys())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When a dataset is loaded, yt infers conversion factors from the internal simulation unit system to the CGS unit system. These conversion factors are stored in a `unit_registry` along with conversion factors to the other known unit symbols. For the cosmological Enzo dataset we loaded earlier, we can see there are a number of additional unit symbols not defined in the default unit lookup table:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print (sorted([k for k in ds.unit_registry.keys() if k not in a.units.registry.keys()]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since code units do not appear in the default unit symbol lookup table, one must explicitly refer to a unit registry when creating a `YTArray` to be able to convert to the unit system of a simulation." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To make this as clean as possible, there are array and quantity-creating convenience functions attached to the `Dataset` object:\n", "\n", "* `ds.arr()`\n", "* `ds.quan()`\n", "\n", "These functions make it straightforward to create arrays and quantities that can be converted to code units or comoving units. For example:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "a = ds.quan(3, 'code_length')\n", "\n", "print (a)\n", "print (a.in_cgs())\n", "print (a.in_units('Mpccm/h'))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "b = ds.arr([3, 4, 5], 'Mpccm/h')\n", "print (b)\n", "print (b.in_cgs())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Overriding Code Unit Definitions" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "On occasion, you might have a dataset for a supported frontend that does not have the conversions to code units accessible (for example, Athena data) or you may want to change them outright. `yt` provides a mechanism so that one may provide their own code unit definitions to `load`, which override the default rules for a given frontend for defining code units. This is provided through the `units_override` dictionary. We'll use an example of an Athena dataset. First, a call to `load` without `units_override`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ds1 = yt.load(\"MHDSloshing/virgo_low_res.0054.vtk\")\n", "print (ds1.length_unit)\n", "print (ds1.mass_unit)\n", "print (ds1.time_unit)\n", "sp1 = ds1.sphere(\"c\",(0.1,\"unitary\"))\n", "print (sp1[\"density\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This is a galaxy cluster dataset, so it is not likely that the units of density are correct. We happen to know that the unit definitions are different, so we can override the units:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "units_override = {\"length_unit\":(1.0,\"Mpc\"),\n", " \"time_unit\":(1.0,\"Myr\"),\n", " \"mass_unit\":(1.0e14,\"Msun\")}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`units_override` can take the following keys:\n", "\n", "* `length_unit`\n", "* `time_unit`\n", "* `mass_unit`\n", "* `magnetic_unit`\n", "* `temperature_unit`\n", "\n", "and the associated values can be (value, unit) tuples, `YTQuantities`, or floats (in the latter case they are assumed to have the corresponding cgs unit). " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ds2 = yt.load(\"MHDSloshing/virgo_low_res.0054.vtk\", units_override=units_override)\n", "print (ds2.length_unit)\n", "print (ds2.mass_unit)\n", "print (ds2.time_unit)\n", "sp2 = ds2.sphere(\"c\",(0.1,\"unitary\"))\n", "print (sp2[\"density\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This option should be used very carefully, and *only* if you know that the dataset does not provide units or that the unit definitions generated are incorrect for some reason. " ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 } yt-3.3.3/doc/source/analyzing/units/4)_Comparing_units_from_different_datasets.ipynb0000644000076500000240000000760313017603554031747 0ustar goldbaumstaff00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Units that refer to the internal simulation coordinate system will have different CGS conversion factors in different datasets. Depending on how a unit system is implemented, this could add an element of uncertainty when we compare dimensional array instances produced by different unit systems. Fortunately, this is not a problem for `YTArray` since all `YTArray` unit systems are defined in terms of physical CGS units.\n", "\n", "As an example, let's load up two enzo datasets from different redshifts in the same cosmology simulation." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# A high redshift output from z ~ 8\n", "import yt\n", "\n", "ds1 = yt.load('Enzo_64/DD0002/data0002')\n", "print (\"z = %s\" % ds1.current_redshift)\n", "print (\"Internal length units = %s\" % ds1.length_unit)\n", "print (\"Internal length units in cgs = %s\" % ds1.length_unit.in_cgs())" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "# A low redshift output from z ~ 0\n", "ds2 = yt.load('Enzo_64/DD0043/data0043')\n", "print (\"z = %s\" % ds2.current_redshift)\n", "print (\"Internal length units = %s\" % ds2.length_unit)\n", "print (\"Internal length units in cgs = %s\" % ds2.length_unit.in_cgs())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Given that these are from the same simulation in comoving units, the CGS length units are different by a factor of $(1+z_1)/(1+z_2)$:" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print (ds2.length_unit.in_cgs()/ds1.length_unit.in_cgs() == (1+ds1.current_redshift)/(1+ds2.current_redshift))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "It's not necessary to convert to CGS units either. yt will automatically account for the fact that a comoving megapaersec in the first output is physically different compared to a comoving megaparsec in the second output." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "print (ds2.length_unit/ds1.length_unit)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Time series analysis is also straightforward. Since dimensional arrays and quantities carry around the conversion factors to CGS with them, we can safely pickle them, share them with other processors, or combine them without worrying about differences in unit definitions.\n", "\n", "The following snippet, which iterates over a time series and saves the `length_unit` quantity to a storage dictionary. This should work correctly on one core or in a script run in parallel." ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [ "import yt\n", "yt.enable_parallelism()\n", "\n", "ts = yt.load(\"Enzo_64/DD????/data????\")\n", "\n", "storage = {}\n", "\n", "for sto, ds in ts.piter(storage=storage):\n", " sto.result_id = float(ds.current_time.in_units('Gyr'))\n", " sto.result = ds.length_unit\n", "\n", "if yt.is_root():\n", " for t in sorted(storage.keys()):\n", " print (t, storage[t].in_units('Mpc'))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3.0 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 }yt-3.3.3/doc/source/analyzing/units/5)_Units_and_plotting.ipynb0000644000076500000240000000775713017603554025524 0ustar goldbaumstaff00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "It's now easy to adjust the units of a field you are plotting.\n", "\n", "> Note: the following examples use `SlicePlot`, but the same thing should work for `ProjectionPlot`, `OffAxisSlicePlot`, and `OffAxisProjectionPlot`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, let's create a new `SlicePlot`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import yt\n", "ds = yt.load('IsolatedGalaxy/galaxy0030/galaxy0030')\n", "slc = yt.SlicePlot(ds, 2, 'density', center=[0.5, 0.5, 0.5], width=(15, 'kpc'))\n", "slc.set_figure_size(6)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The units used to scale the colorbar can be adjusted by calling the `set_unit` function that is attached to the plot object. This example creates a plot of density in code units:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "slc.set_unit('density', 'code_mass/code_length**3')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This example creates a plot of gas density in solar masses per cubic parsec:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "slc.set_unit('density', 'Msun/pc**3')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `set_unit` function will accept any unit string that is dimensionally equivalent to the plotted field. If it is supplied a unit that is not dimensionally equivalent, it will raise an error:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from yt.utilities.exceptions import YTUnitConversionError\n", "\n", "try:\n", " slc.set_unit('density', 'Msun')\n", "except YTUnitConversionError as e:\n", " print (e)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Similarly, set_unit is defined for `ProfilePlot` and `PhasePlot` instances as well.\n", "\n", "To illustrate this point, let's first create a new `ProfilePlot`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dd = ds.all_data()\n", "plot = yt.ProfilePlot(dd, 'density', 'temperature', weight_field='cell_mass')\n", "plot.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And adjust the unit of the y-axis:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plot.set_unit('density', 'Msun/pc**3')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Similarly for PhasePlot:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plot = yt.PhasePlot(dd, 'density', 'temperature', 'cell_mass')\n", "plot.set_figure_size(6)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plot.set_unit('cell_mass', 'Msun')\n", "plot.set_unit('density', 'Msun/pc**3')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 } yt-3.3.3/doc/source/analyzing/units/6)_Unit_Equivalencies.ipynb0000644000076500000240000002253313017603554025442 0ustar goldbaumstaff00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Some physical quantities are directly related to other unitful quantities by a constant, but otherwise do not have the same units. To facilitate conversions between these quantities, `yt` implements a system of unit equivalencies (inspired by the [AstroPy implementation](http://docs.astropy.org/en/latest/units/equivalencies.html)). The possible unit equivalencies are:\n", "\n", "* `\"thermal\"`: conversions between temperature and energy ($E = k_BT$)\n", "* `\"spectral\"`: conversions between wavelength, frequency, and energy for photons ($E = h\\nu = hc/\\lambda, c = \\lambda\\nu$)\n", "* `\"mass_energy\"`: conversions between mass and energy ($E = mc^2$)\n", "* `\"lorentz\"`: conversions between velocity and Lorentz factor ($\\gamma = 1/\\sqrt{1-(v/c)^2}$)\n", "* `\"schwarzschild\"`: conversions between mass and Schwarzschild radius ($R_S = 2GM/c^2$)\n", "* `\"compton\"`: conversions between mass and Compton wavelength ($\\lambda = h/mc$)\n", "\n", "The following unit equivalencies only apply under conditions applicable for an ideal gas with a constant mean molecular weight $\\mu$ and ratio of specific heats $\\gamma$:\n", "\n", "* `\"number_density\"`: conversions between density and number density ($n = \\rho/\\mu{m_p}$)\n", "* `\"sound_speed\"`: conversions between temperature and sound speed for an ideal gas ($c_s^2 = \\gamma{k_BT}/\\mu{m_p}$)\n", "\n", "A `YTArray` or `YTQuantity` can be converted to an equivalent using `to_equivalent`, where the unit and the equivalence name are provided as arguments:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import yt\n", "from yt import YTQuantity\n", "import numpy as np\n", "\n", "ds = yt.load('IsolatedGalaxy/galaxy0030/galaxy0030')\n", "\n", "dd = ds.all_data()\n", "\n", "print (dd[\"temperature\"].to_equivalent(\"erg\", \"thermal\"))\n", "print (dd[\"temperature\"].to_equivalent(\"eV\", \"thermal\"))\n", "\n", "# Rest energy of the proton\n", "from yt.units import mp\n", "E_p = mp.to_equivalent(\"GeV\", \"mass_energy\")\n", "print (E_p)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Most equivalencies can go in both directions, without any information required other than the unit you want to convert to (this is not the case for the electromagnetic equivalencies, which we'll discuss later):" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from yt.units import clight\n", "v = 0.1*clight\n", "g = v.to_equivalent(\"dimensionless\", \"lorentz\")\n", "print (g)\n", "print (g.to_equivalent(\"c\", \"lorentz\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Special Equivalencies" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Some equivalencies can take supplemental information. The `\"number_density\"` equivalence can take a custom mean molecular weight (default is $\\mu = 0.6$):" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print (dd[\"density\"].max())\n", "print (dd[\"density\"].to_equivalent(\"cm**-3\", \"number_density\").max())\n", "print (dd[\"density\"].to_equivalent(\"cm**-3\", \"number_density\", mu=0.75).max())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `\"sound_speed\"` equivalence optionally takes the ratio of specific heats $\\gamma$ and the mean molecular weight $\\mu$ (defaults are $\\gamma$ = 5/3, $\\mu = 0.6$):" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print (dd[\"temperature\"].to_equivalent(\"km/s\", \"sound_speed\").mean())\n", "print (dd[\"temperature\"].to_equivalent(\"km/s\", \"sound_speed\", gamma=4./3., mu=0.5).mean())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These options must be used with caution, and only if you know the underlying data adheres to these assumptions!" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Electromagnetic Equivalencies" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Special, one-way equivalencies exist for converting between electromagnetic units in the cgs and SI unit systems. These exist since in the cgs system, electromagnetic units are comprised of the base units of seconds, grams and centimeters, whereas in the SI system Ampere is a base unit. For example, the dimensions of charge are completely different in the two systems:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "Q1 = YTQuantity(1.0,\"C\")\n", "Q2 = YTQuantity(1.0,\"esu\")\n", "print (\"Q1 dims =\", Q1.units.dimensions)\n", "print (\"Q2 dims =\", Q2.units.dimensions)\n", "print (\"Q1 base units =\", Q1.in_mks())\n", "print (\"Q2 base units =\", Q2.in_cgs())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To convert from a cgs unit to an SI unit, use the \"SI\" equivalency:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from yt.units import qp # the elementary charge in esu\n", "qp_SI = qp.to_equivalent(\"C\",\"SI\") # convert to Coulombs\n", "print (qp)\n", "print (qp_SI)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To convert from an SI unit to a cgs unit, use the \"CGS\" equivalency:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "B = YTQuantity(1.0,\"T\") # magnetic field in Tesla\n", "print (B, B.to_equivalent(\"gauss\",\"CGS\")) # convert to Gauss" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Equivalencies exist between the SI and cgs dimensions of charge, current, magnetic field, electric potential, and resistance. As a neat example, we can convert current in Amperes and resistance in Ohms to their cgs equivalents, and then use them to calculate the \"Joule heating\" of a conductor with resistance $R$ and current $I$:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "I = YTQuantity(1.0,\"A\")\n", "I_cgs = I.to_equivalent(\"statA\",\"CGS\")\n", "R = YTQuantity(1.0,\"ohm\")\n", "R_cgs = R.to_equivalent(\"statohm\",\"CGS\")\n", "P = I**2*R\n", "P_cgs = I_cgs**2*R_cgs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The dimensions of current and resistance in the two systems are completely different, but the formula gives us the power dissipated dimensions of energy per time, so the dimensions and the result should be the same, which we can check:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print (P_cgs.units.dimensions == P.units.dimensions)\n", "print (P.in_units(\"W\"), P_cgs.in_units(\"W\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Determining Valid Equivalencies" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If a certain equivalence does not exist for a particular unit, then an error will be thrown:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from yt.utilities.exceptions import YTInvalidUnitEquivalence\n", "\n", "try:\n", " x = v.to_equivalent(\"angstrom\", \"spectral\")\n", "except YTInvalidUnitEquivalence as e:\n", " print (e)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You can check if a `YTArray` has a given equivalence with `has_equivalent`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print (mp.has_equivalent(\"compton\"))\n", "print (mp.has_equivalent(\"thermal\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To list the equivalencies available for a given `YTArray` or `YTQuantity`, use the `list_equivalencies` method:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "E_p.list_equivalencies()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 } yt-3.3.3/doc/source/analyzing/units/7)_Unit_Systems.ipynb0000644000076500000240000003465513023400062024307 0ustar goldbaumstaff00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "By default, the results of most calculations in yt are expressed in a \"centimeters-grams-seconds\" (CGS) set of units. This includes the values of derived fields and aliased fields.\n", "\n", "However, this system of units may not be the most natural for a given dataset or an entire class of datasets. For this reason, yt provides the ability to define new unit systems and use them in a way that is highly configurable by the end-user. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Unit Systems Available in yt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Several unit systems are already supplied for use within yt. They are:\n", "\n", "* `\"cgs\"`: Centimeters-grams-seconds unit system, with base of `(cm, g, s, K, radian)`. Uses the Gaussian normalization for electromagnetic units. \n", "* `\"mks\"`: Meters-kilograms-seconds unit system, with base of `(m, kg, s, K, radian, A)`.\n", "* `\"imperial\"`: Imperial unit system, with base of `(mile, lbm, s, R, radian)`.\n", "* `\"galactic\"`: \"Galactic\" unit system, with base of `(kpc, Msun, Myr, K, radian)`.\n", "* `\"solar\"`: \"Solar\" unit system, with base of `(AU, Mearth, yr, K, radian)`. \n", "* `\"planck\"`: Planck natural units $(\\hbar = c = G = k_B = 1)$, with base of `(l_pl, m_pl, t_pl, T_pl, radian)`. \n", "* `\"geometrized\"`: Geometrized natural units $(c = G = 1)$, with base of `(l_geom, m_geom, t_geom, K, radian)`. \n", "\n", "We can examine these unit systems by querying them from the `unit_system_registry`. For example, we can look at the default CGS system:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import yt\n", "yt.unit_system_registry[\"cgs\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see that we have two sets of units that this system defines: \"base\" and \"other\" units. The \"base\" units are the set of units from which all other units in the system are composed of, such as centimeters, grams, and seconds. The \"other\" units are compound units which fields with specific dimensionalities are converted to, such as ergs, dynes, gauss, and electrostatic units (esu). \n", "\n", "We see a similar setup for the MKS system, except that in this case, there is a base unit of current, the Ampere:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "yt.unit_system_registry[\"mks\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also look at the imperial system:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "yt.unit_system_registry[\"imperial\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "and the \"galactic\" system as well:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "yt.unit_system_registry[\"galactic\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Converting `YTArrays` to the Different Unit Systems" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Choosing a Unit System When Loading a Dataset" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "When a dataset is `load`ed, a unit system may be specified. When this happens, all aliased and derived fields will be converted to the units of the given system. The default is `\"cgs\"`.\n", "\n", "For example, we can specify that the fields from a FLASH dataset can be expressed in MKS units:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ds_flash = yt.load(\"GasSloshing/sloshing_nomag2_hdf5_plt_cnt_0100\", unit_system=\"mks\")\n", "sp = ds_flash.sphere(\"c\", (100.,\"kpc\"))\n", "print (sp[\"density\"]) # This is an alias for (\"flash\",\"dens\")\n", "print (sp[\"pressure\"]) # This is an alias for (\"flash\",\"pres\")\n", "print (sp[\"angular_momentum_x\"]) # This is a derived field\n", "print (sp[\"kinetic_energy\"]) # This is also a derived field" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Aliased fields are converted to the requested unit system, but the on-disk fields that they correspond to remain in their original (code) units:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print (sp[\"flash\",\"dens\"]) # This is aliased to (\"gas\", \"density\")\n", "print (sp[\"flash\",\"pres\"]) # This is aliased to (\"gas\", \"pressure\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can take an `Enzo` dataset and express it in `\"galactic\"` units:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ds_enzo = yt.load(\"IsolatedGalaxy/galaxy0030/galaxy0030\", unit_system=\"galactic\")\n", "sp = ds_enzo.sphere(\"c\", (20.,\"kpc\"))\n", "print (sp[\"density\"])\n", "print (sp[\"pressure\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also express all of the fields associated with a dataset in that dataset's system of \"code\" units. Though the on-disk fields are already in these units, this means that we can express even derived fields in code units as well:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ds_chombo = yt.load(\"KelvinHelmholtz/data.0004.hdf5\", unit_system=\"code\")\n", "dd = ds_chombo.all_data()\n", "print (dd[\"density\"])\n", "print (dd[\"kinetic_energy\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Defining Fields So That They Can Use the Different Unit Systems" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you define a new derived field for use in yt and wish to make the different unit systems available to it, you will need to specify this when calling `add_field`. Suppose I defined a new field called `\"momentum_x\"` and wanted it to have general units. I would have to set it up in this fashion, using the `unit_system` attribute of the dataset and querying it for the appropriate dimensions:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mom_units = ds_flash.unit_system[\"velocity\"]*ds_flash.unit_system[\"density\"]\n", "def _momentum_x(field, data):\n", " return data[\"density\"]*data[\"velocity_x\"]\n", "ds_flash.add_field((\"gas\",\"momentum_x\"), function=_momentum_x, units=mom_units)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, the field will automatically be expressed in whatever units the dataset was called with. In this case, it was MKS:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "slc = yt.SlicePlot(ds_flash, \"z\", [\"momentum_x\"], width=(300.,\"kpc\"))\n", "slc.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the momentum density has been plotted with the correct MKS units of $\\mathrm{kg/(m^2\\cdot{s})}$." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "If you don't create a derived field from a dataset but instead use `yt.add_field`, and still want to use the unit system of that dataset for the units, the only option at present is to set `units=\"auto\"` in the call to `yt.add_field` and the `dimensions` keyword to the correct dimensions for the field:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from yt.units import clight\n", "\n", "def _rest_energy(field, data):\n", " return data[\"cell_mass\"]*clight*clight\n", "yt.add_field((\"gas\",\"rest_energy\"), function=_rest_energy, units=\"auto\", dimensions=\"energy\")\n", "\n", "ds_flash2 = yt.load(\"GasSloshing/sloshing_nomag2_hdf5_plt_cnt_0150\", unit_system=\"galactic\")\n", "\n", "sp = ds_flash2.sphere(\"c\", (100.,\"kpc\"))\n", "sp[\"rest_energy\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Obtaining Physical Constants in a Specific Unit System" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Each unit system provides the ability to obtain any physical constant in yt's physical constants database in the base units of that system via the `constants` attribute of the unit system. For example, to obtain the value of Newton's universal constant of gravitation in different base units:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "for name in [\"cgs\", \"mks\", \"imperial\", \"planck\", \"geometrized\"]:\n", " unit_system = yt.unit_system_registry[name]\n", " print (name, unit_system.constants.G)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Equivalently, one could import a physical constant from the main database and convert it using `in_base`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from yt.units import G\n", "print (G.in_base(\"mks\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Defining Your Own Unit System" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "You are not limited to using the unit systems already defined by yt. A new unit system can be defined by creating a new `UnitSystem` instance. For example, to create a unit system where the default units are in millimeters, centigrams, and microseconds:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "small_unit_system = yt.UnitSystem(\"small\", \"mm\", \"cg\", \"us\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "where the required arguments are a `name` for the unit system, and the `length_unit`, `mass_unit`, and `time_unit` for the unit system, which serve as the \"base\" units to convert everything else to. Once a unit system instance is created, it is automatically added to the `unit_system_registry` so that it may be used throughout yt:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "yt.unit_system_registry[\"small\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the base units for the dimensions of angle and temperature have been automatically set to radians and Kelvin, respectively. If desired, these can be specified using optional arguments when creating the `UnitSystem` object:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "wacky_unit_system = yt.UnitSystem(\"wacky\", \"mile\", \"kg\", \"day\", temperature_unit=\"R\", angle_unit=\"deg\")\n", "wacky_unit_system" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Though it will rarely be necessary, an MKS-style system of units where a unit of current can be specified as a base unit can also be created using the `current_mks` optional argument:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "mksish_unit_system = yt.UnitSystem(\"mksish\", \"dm\", \"ug\", \"ks\", current_mks_unit=\"mA\")\n", "mksish_unit_system" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Initializing a `UnitSystem` object only sets up the base units. In this case, all fields will be converted to combinations of these base units based on their dimensionality. However, you may want to specify that fields of a given dimensionality use a compound unit by default instead. For example, you might prefer that in the `\"small\"` unit system that pressures be represented in microdynes per millimeter squared. To do this, set these to be the units of the `\"pressure\"` dimension explicitly:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "small_unit_system[\"pressure\"] = \"udyne/mm**2\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can now look at the `small_unit_system` object and see that these units are now defined for pressure in the \"Other Units\" category:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "small_unit_system" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can do the same for a few other dimensionalities:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "small_unit_system[\"magnetic_field_cgs\"] = \"mG\"\n", "small_unit_system[\"specific_energy\"] = \"cerg/ug\"\n", "small_unit_system[\"velocity\"] = \"cm/s\"\n", "small_unit_system" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 } yt-3.3.3/doc/source/analyzing/units/comoving_units_and_code_units.rst0000644000076500000240000000237113017603554027136 0ustar goldbaumstaff00000000000000.. _comoving_units_and_code_units: Comoving units and code units ============================= .. notebook:: 3)_Comoving_units_and_code_units.ipynb .. _cosmological-units: Units for Cosmological Datasets ------------------------------- yt has additional capabilities to handle the comoving coordinate system used internally in cosmological simulations. Simulations that use comoving coordinates, all length units have three other counterparts corresponding to comoving units, scaled comoving units, and scaled proper units. In all cases 'scaled' units refer to scaling by the reduced Hubble parameter - i.e. the length unit is what it would be in a universe where Hubble's parameter is 100 km/s/Mpc. To access these different units, yt has a common naming system. Scaled units are denoted by dividing by the scaled Hubble parameter ``h`` (which is in itself a unit). Comoving units are denoted by appending ``cm`` to the end of the unit name. Using the parsec as an example, ``pc`` Proper parsecs, :math:`\rm{pc}`. ``pccm`` Comoving parsecs, :math:`\rm{pc}/(1+z)`. ``pccm/h`` Comoving parsecs normalized by the scaled hubble constant, :math:`\rm{pc}/h/(1+z)`. ``pc/h`` Proper parsecs, normalized by the scaled hubble constant, :math:`\rm{pc}/h`. yt-3.3.3/doc/source/analyzing/units/comparing_units_from_different_datasets.rst0000644000076500000240000000027612770647750031214 0ustar goldbaumstaff00000000000000.. _comparing_units_from_different_datasets: Comparing units from different datasets ======================================= .. notebook:: 4)_Comparing_units_from_different_datasets.ipynb yt-3.3.3/doc/source/analyzing/units/fields_and_unit_conversion.rst0000644000076500000240000000400413017603554026424 0ustar goldbaumstaff00000000000000.. _data_selection_and_fields: Fields and Unit Conversion ========================== .. notebook:: 2)_Fields_and_unit_conversion.ipynb Derived Fields -------------- .. This needs to be added outside the notebook since user-defined derived fields require a 'fresh' kernel. The following example creates a derived field for the square root of the cell volume. .. notebook-cell:: import yt import numpy as np # Function defining the derived field def root_cell_volume(field, data): return np.sqrt(data['cell_volume']) # Load the dataset ds = yt.load('HiresIsolatedGalaxy/DD0044/DD0044') # Add the field to the dataset, linking to the derived field function and # units of the field ds.add_field(("gas", "root_cell_volume"), units="cm**(3/2)", function=root_cell_volume) # Access the derived field like any other field ad = ds.all_data() ad['root_cell_volume'] No special unit logic needs to happen inside of the function - `np.sqrt` will convert the units of the `density` field appropriately: .. notebook-cell:: :skip_exceptions: import yt import numpy as np ds = yt.load('HiresIsolatedGalaxy/DD0044/DD0044') ad = ds.all_data() print(ad['cell_volume'].in_cgs()) print(np.sqrt(ad['cell_volume'].in_cgs())) That said, it is necessary to specify the units in the call to the :code:`add_field` function. Not only does this ensure the returned units will be exactly what you expect, it also allows an in-place conversion of units, just in case the function returns a field with dimensionally equivalent units. For example, let's redo the above example but ask for units of :code:`Mpc**(3/2)`: .. notebook-cell:: import yt import numpy as np def root_cell_volume(field, data): return np.sqrt(data['cell_volume']) ds = yt.load('HiresIsolatedGalaxy/DD0044/DD0044') # Here we set the default units to Mpc^(3/2) ds.add_field(("gas", "root_cell_volume"), units="Mpc**(3/2)", function=root_cell_volume) ad = ds.all_data() ad['root_cell_volume'] yt-3.3.3/doc/source/analyzing/units/index.rst0000644000076500000240000000265513017603554022151 0ustar goldbaumstaff00000000000000.. _units: Symbolic Units ============== This section describes yt's symbolic unit capabilities. This is provided as quick introduction for those who are already familiar with yt but want to learn more about the unit system. Please see :ref:`analyzing` and :ref:`visualizing` for more detail about querying, analyzing, and visualizing data in yt. Each subsection is a notebook. To open these notebooks in a "live" IPython session and execute the documentation interactively, you need to download the repository and start the IPython notebook. You will then need to navigate to :code:`$YT_HG/doc/source/units` (where $YT_HG is the location of a clone of the yt mercurial repository), and then start an IPython notebook server: .. code:: bash $ ipython notebook .. warning:: The pre-filled out notebooks are *far* less fun than running them yourself! Here are the notebooks, which have been filled in for inspection: .. toctree:: :maxdepth: 1 symbolic_units fields_and_unit_conversion comoving_units_and_code_units comparing_units_from_different_datasets units_and_plotting unit_equivalencies unit_systems .. note:: The notebooks use sample datasets that are available for download at http://yt-project.org/data. See :ref:`quickstart-introduction` for more details. Let us know if you would like to contribute other example notebooks, or have any suggestions for how these can be improved. yt-3.3.3/doc/source/analyzing/units/symbolic_units.rst0000644000076500000240000000022312770647750024105 0ustar goldbaumstaff00000000000000.. _symbolic_units: Symbolic units: :code:`yt.units` ================================ .. notebook:: 1)_Symbolic_Units.ipynb :skip_exceptions: yt-3.3.3/doc/source/analyzing/units/unit_equivalencies.rst0000644000076500000240000000017713017603554024733 0ustar goldbaumstaff00000000000000.. _unit_equivalencies: Unit Equivalencies ================== .. notebook:: 6)_Unit_Equivalencies.ipynb :skip_exceptions: yt-3.3.3/doc/source/analyzing/units/unit_systems.rst0000644000076500000240000000014413017603554023577 0ustar goldbaumstaff00000000000000.. _unit_systems: Unit Systems ============ .. notebook:: 7)_Unit_Systems.ipynb :skip_exceptions: yt-3.3.3/doc/source/analyzing/units/units_and_plotting.rst0000644000076500000240000000017712770647750024756 0ustar goldbaumstaff00000000000000.. _units_and_plotting: Units and Plotting ================== .. notebook:: 5)_Units_and_plotting.ipynb :skip_exceptions: yt-3.3.3/doc/source/conf.py0000644000076500000240000002077413023400062016435 0ustar goldbaumstaff00000000000000# -*- coding: utf-8 -*- # # yt documentation build configuration file, created by # sphinx-quickstart on Tue Jan 11 09:46:53 2011. # # This file is execfile()d with the current directory set to its containing dir. # # Note that not all possible configuration values are present in this # autogenerated file. # # All configuration values have a default; values that are commented out # serve to show the default. import sys, os, glob, re from sphinx.search import WordCollector from docutils.nodes import comment, title, Text, SkipNode on_rtd = os.environ.get("READTHEDOCS", None) == "True" # If extensions (or modules to document with autodoc) are in another directory, # add these directories to sys.path here. If the directory is relative to the # documentation root, use os.path.abspath to make it absolute, like shown here. sys.path.insert(0, os.path.abspath('../extensions/')) # -- General configuration ----------------------------------------------------- # If your documentation needs a minimal Sphinx version, state it here. #needs_sphinx = '1.0' # Add any Sphinx extension module names here, as strings. They can be extensions # coming with Sphinx (named 'sphinx.ext.*') or your custom ones. extensions = ['sphinx.ext.autodoc', 'sphinx.ext.intersphinx', 'sphinx.ext.pngmath', 'sphinx.ext.viewcode', 'sphinx.ext.napoleon', 'yt_cookbook', 'yt_colormaps'] if not on_rtd: extensions.append('sphinx.ext.autosummary') extensions.append('pythonscript_sphinxext') try: import RunNotebook import nbconvert if not on_rtd: extensions.append('RunNotebook.notebook_sphinxext') extensions.append('RunNotebook.notebookcell_sphinxext') except ImportError: pass # Add any paths that contain templates here, relative to this directory. templates_path = ['_templates'] # The suffix of source filenames. source_suffix = '.rst' # The encoding of source files. #source_encoding = 'utf-8-sig' # The master toctree document. master_doc = 'index' # General information about the project. project = u'The yt Project' copyright = u'2013-2016, the yt Project' # The version info for the project you're documenting, acts as replacement for # |version| and |release|, also used in various other places throughout the # built documents. # # The short X.Y version. version = '3.3.3' # The full version, including alpha/beta/rc tags. release = '3.3.3' # The language for content autogenerated by Sphinx. Refer to documentation # for a list of supported languages. #language = None # There are two options for replacing |today|: either, you set today to some # non-false value, then it is used: #today = '' # Else, today_fmt is used as the format for a strftime call. #today_fmt = '%B %d, %Y' # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. exclude_patterns = [] # The reST default role (used for this markup: `text`) to use for all documents. #default_role = None # If true, '()' will be appended to :func: etc. cross-reference text. #add_function_parentheses = True # If true, the current module name will be prepended to all description # unit titles (such as .. function::). #add_module_names = True # If true, sectionauthor and moduleauthor directives will be shown in the # output. They are ignored by default. show_authors = False # The name of the Pygments (syntax highlighting) style to use. pygments_style = 'sphinx' # A list of ignored prefixes for module index sorting. #modindex_common_prefix = [] # -- Options for HTML output --------------------------------------------------- # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. import sphinx_bootstrap_theme html_theme = 'bootstrap' html_theme_path = sphinx_bootstrap_theme.get_html_theme_path() # Theme options are theme-specific and customize the look and feel of a theme # further. For a list of options available for each theme, see the # documentation. html_theme_options = dict( bootstrap_version = "3", bootswatch_theme = "readable", navbar_links = [ ("How to get help", "help/index"), ("Quickstart notebooks", "quickstart/index"), ("Cookbook", "cookbook/index"), ], navbar_sidebarrel = False, globaltoc_depth = 2, ) # Add any paths that contain custom themes here, relative to this directory. #html_theme_path = [] # The name for this set of Sphinx documents. If None, it defaults to # " v documentation". #html_title = None # A shorter title for the navigation bar. Default is the same as html_title. #html_short_title = None # The name of an image file (relative to this directory) to place at the top # of the sidebar. html_logo = "_static/yt_icon.png" # The name of an image file (within the static path) to use as favicon of the # docs. This file should be a Windows icon file (.ico) being 16x16 or 32x32 # pixels large. #html_favicon = None # Add any paths that contain custom static files (such as style sheets) here, # relative to this directory. They are copied after the builtin static files, # so a file named "default.css" will overwrite the builtin "default.css". html_static_path = ['_static', 'analyzing/_static'] # If not '', a 'Last updated on:' timestamp is inserted at every page bottom, # using the given strftime format. #html_last_updated_fmt = '%b %d, %Y' # If true, SmartyPants will be used to convert quotes and dashes to # typographically correct entities. #html_use_smartypants = True # Custom sidebar templates, maps document names to template names. #html_sidebars = {} # Additional templates that should be rendered to pages, maps page names to # template names. #html_additional_pages = {} # If false, no module index is generated. html_domain_indices = False # If false, no index is generated. html_use_index = True # If true, the index is split into individual pages for each letter. #html_split_index = False # If true, links to the reST sources are added to the pages. html_show_sourcelink = False # If true, "Created using Sphinx" is shown in the HTML footer. Default is True. #html_show_sphinx = True # If true, "(C) Copyright ..." is shown in the HTML footer. Default is True. #html_show_copyright = True # If true, an OpenSearch description file will be output, and all pages will # contain a tag referring to it. The value of this option must be the # base URL from which the finished HTML is served. #html_use_opensearch = '' # This is the file name suffix for HTML files (e.g. ".xhtml"). #html_file_suffix = None # Output file base name for HTML help builder. htmlhelp_basename = 'ytdoc' # -- Options for LaTeX output -------------------------------------------------- # The paper size ('letter' or 'a4'). #latex_paper_size = 'letter' # The font size ('10pt', '11pt' or '12pt'). #latex_font_size = '10pt' # Grouping the document tree into LaTeX files. List of tuples # (source start file, target name, title, author, documentclass [howto/manual]). latex_documents = [ ('index', 'yt.tex', u'yt Documentation', u'The yt Project', 'manual'), ] # The name of an image file (relative to this directory) to place at the top of # the title page. #latex_logo = None # For "manual" documents, if this is true, then toplevel headings are parts, # not chapters. #latex_use_parts = False # If true, show page references after internal links. #latex_show_pagerefs = False # If true, show URL addresses after external links. #latex_show_urls = False # Additional stuff for the LaTeX preamble. latex_preamble = r""" \renewcommand{\AA}{\text{\r{A}}} % Allow \AA in math mode \usepackage[utf8]{inputenc} % Allow unicode symbols in text \DeclareUnicodeCharacter {212B} {\AA} % Angstrom """ # Documents to append as an appendix to all manuals. #latex_appendices = [] # If false, no module index is generated. #latex_domain_indices = True # -- Options for manual page output -------------------------------------------- # One entry per manual page. List of tuples # (source start file, name, description, authors, manual section). man_pages = [ ('index', 'yt', u'yt Documentation', [u'The yt Project'], 1) ] # Example configuration for intersphinx: refer to the Python standard library. intersphinx_mapping = {'http://docs.python.org/': None, 'http://ipython.org/ipython-doc/stable/': None, 'http://docs.scipy.org/doc/numpy/': None, 'http://matplotlib.sourceforge.net/': None, } if not on_rtd: autosummary_generate = glob.glob("reference/api/api.rst") yt-3.3.3/doc/source/cookbook/0000755000076500000240000000000013023400160016731 5ustar goldbaumstaff00000000000000yt-3.3.3/doc/source/cookbook/amrkdtree_downsampling.py0000644000076500000240000000470313023400062024050 0ustar goldbaumstaff00000000000000# Using AMRKDTree Homogenized Volumes to examine large datasets # at lower resolution. # In this example we will show how to use the AMRKDTree to take a simulation # with 8 levels of refinement and only use levels 0-3 to render the dataset. # Currently this cookbook is flawed in that the data that is covered by the # higher resolution data gets masked during the rendering. This should be # fixed by changing either the data source or the code in # yt/utilities/amr_kdtree.py where data is being masked for the partitioned # grid. Right now the quick fix is to create a data_collection, but this # will only work for patch based simulations that have ds.index.grids. # We begin by loading up yt, and importing the AMRKDTree import numpy as np import yt from yt.utilities.amr_kdtree.api import AMRKDTree # Load up a dataset and define the kdtree ds = yt.load('IsolatedGalaxy/galaxy0030/galaxy0030') im, sc = yt.volume_render(ds, 'density', fname='v0.png') sc.camera.set_width(ds.arr(100, 'kpc')) render_source = sc.get_source(0) kd=render_source.volume # Print out specifics of KD Tree print("Total volume of all bricks = %i" % kd.count_volume()) print("Total number of cells = %i" % kd.count_cells()) new_source = ds.all_data() new_source.max_level=3 kd_low_res = AMRKDTree(ds, data_source=new_source) print(kd_low_res.count_volume()) print(kd_low_res.count_cells()) # Now we pass this in as the volume to our camera, and render the snapshot # again. render_source.set_volume(kd_low_res) render_source.set_field('density') sc.render() sc.save("v1.png", sigma_clip=6.0) # This operation was substantiall faster. Now lets modify the low resolution # rendering until we find something we like. tf = render_source.transfer_function tf.clear() tf.add_layers(4, 0.01, col_bounds=[-27.5, -25.5], alpha=np.ones(4, dtype='float64'), colormap='RdBu_r') sc.render() sc.save("v2.png", sigma_clip=6.0) # This looks better. Now let's try turning on opacity. tf.grey_opacity = True sc.render() sc.save("v3.png", sigma_clip=6.0) # ## That seemed to pick out som interesting structures. Now let's bump up the ## opacity. # tf.clear() tf.add_layers(4, 0.01, col_bounds=[-27.5, -25.5], alpha=10.0 * np.ones(4, dtype='float64'), colormap='RdBu_r') sc.render() sc.save("v4.png", sigma_clip=6.0) # ## This looks pretty good, now lets go back to the full resolution AMRKDTree # render_source.set_volume(kd) sc.render() sc.save("v5.png", sigma_clip=6.0) # This looks great! yt-3.3.3/doc/source/cookbook/annotate_timestamp_and_scale.py0000644000076500000240000000040313017603554025204 0ustar goldbaumstaff00000000000000import yt ts = yt.load("enzo_tiny_cosmology/DD000?/DD000?") for ds in ts: p = yt.ProjectionPlot(ds, "z", "density") p.annotate_timestamp(corner='upper_left', redshift=True, draw_inset_box=True) p.annotate_scale(corner='upper_right') p.save() yt-3.3.3/doc/source/cookbook/annotations.py0000644000076500000240000000131013017603554021652 0ustar goldbaumstaff00000000000000import yt ds = yt.load("enzo_tiny_cosmology/DD0046/DD0046") p = yt.ProjectionPlot(ds, "z", "density") p.annotate_sphere([0.54,0.72], radius=(1, 'Mpc'), coord_system='axis', text='Halo #7') p.annotate_sphere([0.65,0.38,0.3], radius=(1.5, 'Mpc'), coord_system='data', circle_args={'color':'green', 'linewidth':4, 'linestyle':'dashed'}) p.annotate_arrow([0.87,0.59,0.2], coord_system='data', plot_args={'color':'red'}) p.annotate_text([10,20], 'Some halos', coord_system='plot') p.annotate_marker([0.45,0.1,0.4], coord_system='data', plot_args={'color':'yellow', 's':500}) p.annotate_line([0.2,0.4], [0.3,0.9], coord_system='axis') p.annotate_timestamp(redshift=True) p.annotate_scale() p.save() yt-3.3.3/doc/source/cookbook/average_value.py0000644000076500000240000000104113017603554022124 0ustar goldbaumstaff00000000000000import yt ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030") # load data field = "temperature" # The field to average weight = "cell_mass" # The weight for the average ad = ds.all_data() # This is a region describing the entire box, # but note it doesn't read anything in yet! # We now use our 'quantities' call to get the average quantity average_value = ad.quantities.weighted_average_quantity(field, weight) print("Average %s (weighted by %s) is %0.3e %s" % (field, weight, average_value, average_value.units)) yt-3.3.3/doc/source/cookbook/calculating_information.rst0000644000076500000240000001000413017603554024370 0ustar goldbaumstaff00000000000000Calculating Dataset Information ------------------------------- These recipes demonstrate methods of calculating quantities in a simulation, either for later visualization or for understanding properties of fluids and particles in the simulation. Average Field Value ~~~~~~~~~~~~~~~~~~~ This recipe is a very simple method of calculating the global average of a given field, as weighted by another field. See :ref:`derived-quantities` for more information. .. yt_cookbook:: average_value.py Mass Enclosed in a Sphere ~~~~~~~~~~~~~~~~~~~~~~~~~ This recipe constructs a sphere and then sums the total mass in particles and fluids in the sphere. See :ref:`available-objects` and :ref:`derived-quantities` for more information. .. yt_cookbook:: sum_mass_in_sphere.py Global Phase Plot ~~~~~~~~~~~~~~~~~ This is a simple recipe to show how to open a dataset and then plot a couple global phase diagrams, save them, and quit. See :ref:`how-to-make-2d-profiles` for more information. .. yt_cookbook:: global_phase_plots.py .. _cookbook-radial-velocity: Radial Velocity Profile ~~~~~~~~~~~~~~~~~~~~~~~ This recipe demonstrates how to subtract off a bulk velocity on a sphere before calculating the radial velocity within that sphere. See :ref:`how-to-make-1d-profiles` for more information on creating profiles and :ref:`field_parameters` for an explanation of how the bulk velocity is provided to the radial velocity field function. .. yt_cookbook:: rad_velocity.py Simulation Analysis ~~~~~~~~~~~~~~~~~~~ This uses :class:`~yt.data_objects.time_series.DatasetSeries` to calculate the extrema of a series of outputs, whose names it guesses in advance. This will run in parallel and take advantage of multiple MPI tasks. See :ref:`parallel-computation` and :ref:`time-series-analysis` for more information. .. yt_cookbook:: simulation_analysis.py Smoothed Fields ~~~~~~~~~~~~~~~ This recipe demonstrates how to create a smoothed field, corresponding to a user-created derived field, using the :meth:`~yt.fields.particle_fields.add_volume_weighted_smoothed_field` method. See :ref:`gadget-notebook` for how to work with Gadget data. .. yt_cookbook:: smoothed_field.py .. _cookbook-time-series-analysis: Time Series Analysis ~~~~~~~~~~~~~~~~~~~~ This recipe shows how to calculate a number of quantities on a set of parameter files. Note that it is parallel aware, and that if you only wanted to run in serial the operation ``for pf in ts:`` would also have worked identically. See :ref:`parallel-computation` and :ref:`time-series-analysis` for more information. .. yt_cookbook:: time_series.py .. _cookbook-simple-derived-fields: Simple Derived Fields ~~~~~~~~~~~~~~~~~~~~~ This recipe demonstrates how to create a simple derived field, ``thermal_energy_density``, and then generate a projection from it. See :ref:`creating-derived-fields` and :ref:`projection-plots` for more information. .. yt_cookbook:: derived_field.py .. _cookbook-complicated-derived-fields: Complicated Derived Fields ~~~~~~~~~~~~~~~~~~~~~~~~~~ This recipe demonstrates how to use the :meth:`~yt.frontends.flash.data_structures.FLASHDataset.add_gradient_fields` method to generate gradient fields and use them in a more complex derived field. .. yt_cookbook:: hse_field.py Using Particle Filters to Calculate Star Formation Rates ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ This recipe demonstrates how to use a particle filter to calculate the star formation rate in a galaxy evolution simulation. See :ref:`filtering-particles` for more information. .. yt_cookbook:: particle_filter_sfr.py Making a Turbulent Kinetic Energy Power Spectrum ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ This recipe shows how to use `yt` to read data and put it on a uniform grid to interface with the NumPy FFT routines and create a turbulent kinetic energy power spectrum. (Note: the dataset used here is of low resolution, so the turbulence is not very well-developed. The spike at high wavenumbers is due to non-periodicity in the z-direction). .. yt_cookbook:: power_spectrum_example.py yt-3.3.3/doc/source/cookbook/camera_movement.py0000644000076500000240000000135713017603554022472 0ustar goldbaumstaff00000000000000import yt import numpy as np ds = yt.load("MOOSE_sample_data/out.e-s010") sc = yt.create_scene(ds) cam = sc.camera # save an image at the starting position frame = 0 sc.save('camera_movement_%04i.png' % frame) frame += 1 # Zoom out by a factor of 2 over 5 frames for _ in cam.iter_zoom(0.5, 5): sc.render() sc.save('camera_movement_%04i.png' % frame) frame += 1 # Move to the position [-10.0, 10.0, -10.0] over 5 frames pos = ds.arr([-10.0, 10.0, -10.0], 'code_length') for _ in cam.iter_move(pos, 5): sc.render() sc.save('camera_movement_%04i.png' % frame) frame += 1 # Rotate by 180 degrees over 5 frames for _ in cam.iter_rotate(np.pi, 5): sc.render() sc.save('camera_movement_%04i.png' % frame) frame += 1 yt-3.3.3/doc/source/cookbook/colormaps.py0000644000076500000240000000107613017603554021325 0ustar goldbaumstaff00000000000000import yt # Load the dataset ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030") # Create a projection and save it with the default colormap ('algae') p = yt.ProjectionPlot(ds, "z", "density", width=(100, 'kpc')) p.save() # Change the colormap to 'dusk' and save again. We must specify # a different filename here or it will save it over the top of # our first projection. p.set_cmap(field="density", cmap='dusk') p.save('proj_with_dusk_cmap.png') # Change the colormap to 'hot' and save again. p.set_cmap(field="density", cmap='hot') p.save('proj_with_hot_cmap.png') yt-3.3.3/doc/source/cookbook/complex_plots.rst0000644000076500000240000003063713017603554022403 0ustar goldbaumstaff00000000000000A Few Complex Plots ------------------- The built-in plotting functionality covers the very simple use cases that are most common. These scripts will demonstrate how to construct more complex plots or publication-quality plots. In many cases these show how to make multi-panel plots. Multi-Width Image ~~~~~~~~~~~~~~~~~ This is a simple recipe to show how to open a dataset and then plot slices through it at varying widths. See :ref:`slice-plots` for more information. .. yt_cookbook:: multi_width_image.py Multipanel with Axes Labels ~~~~~~~~~~~~~~~~~~~~~~~~~~~ This illustrates how to use a SlicePlot to control a multipanel plot. This plot uses axes labels to illustrate the length scales in the plot. See :ref:`slice-plots` and the `Matplotlib AxesGrid Object `_ for more information. .. yt_cookbook:: multiplot_2x2.py Time Series Multipanel ~~~~~~~~~~~~~~~~~~~~~~ This illustrates how to create a multipanel plot of a time series dataset. See :ref:`projection-plots`, :ref:`time-series-analysis`, and the `Matplotlib AxesGrid Object `_ for more information. .. yt_cookbook:: multiplot_2x2_time_series.py Mutiple Slice Multipanel ~~~~~~~~~~~~~~~~~~~~~~~~ This illustrates how to create a multipanel plot of slices along the coordinate axes. To focus on what's happening in the x-y plane, we make an additional Temperature slice for the bottom-right subpanel. See :ref:`slice-plots` and the `Matplotlib AxesGrid Object `_ for more information. .. yt_cookbook:: multiplot_2x2_coordaxes_slice.py Multi-Plot Slice and Projections ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ This shows how to combine multiple slices and projections into a single image, with detailed control over colorbars, titles and color limits. See :ref:`slice-plots` and :ref:`projection-plots` for more information. .. yt_cookbook:: multi_plot_slice_and_proj.py .. _advanced-multi-panel: Advanced Multi-Plot Multi-Panel ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ This produces a series of slices of multiple fields with different color maps and zlimits, and makes use of the FixedResolutionBuffer. While this is more complex than the equivalent plot collection-based solution, it allows for a *lot* more flexibility. Every part of the script uses matplotlib commands, allowing its full power to be exercised. See :ref:`slice-plots` and :ref:`projection-plots` for more information. .. yt_cookbook:: multi_plot_3x2_FRB.py .. _cookbook-offaxis_projection: Off-Axis Projection (an alternate method) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ This recipe demonstrates how to take an image-plane line integral along an arbitrary axis in a simulation. This uses alternate machinery than the standard :ref:`PlotWindow interface ` to create an off-axis projection as demonstrated in this :ref:`recipe `. .. yt_cookbook:: offaxis_projection.py Off-Axis Projection with a Colorbar (an alternate method) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ This recipe shows how to generate a colorbar with a projection of a dataset from an arbitrary projection angle (so you are not confined to the x, y, and z axes). This uses alternate machinery than the standard :ref:`PlotWindow interface ` to create an off-axis projection as demonstrated in this :ref:`recipe `. .. yt_cookbook:: offaxis_projection_colorbar.py .. _thin-slice-projections: Thin-Slice Projections ~~~~~~~~~~~~~~~~~~~~~~ This recipe is an example of how to project through only a given data object, in this case a thin region, and then display the result. See :ref:`projection-plots` and :ref:`available-objects` for more information. .. yt_cookbook:: thin_slice_projection.py Plotting Particles Over Fluids ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ This recipe demonstrates how to overplot particles on top of a fluid image. See :ref:`annotate-particles` for more information. .. yt_cookbook:: overplot_particles.py Plotting Grid Edges Over Fluids ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ This recipe demonstrates how to overplot grid boxes on top of a fluid image. Each level is represented with a different color from white (low refinement) to black (high refinement). One can change the colormap used for the grids colors by using the cmap keyword (or set it to None to get all grid edges as black). See :ref:`annotate-grids` for more information. .. yt_cookbook:: overplot_grids.py Overplotting Velocity Vectors ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ This recipe demonstrates how to plot velocity vectors on top of a slice. See :ref:`annotate-velocity` for more information. .. yt_cookbook:: velocity_vectors_on_slice.py Overplotting Contours ~~~~~~~~~~~~~~~~~~~~~ This is a simple recipe to show how to open a dataset, plot a slice through it, and add contours of another quantity on top. See :ref:`annotate-contours` for more information. .. yt_cookbook:: contours_on_slice.py Simple Contours in a Slice ~~~~~~~~~~~~~~~~~~~~~~~~~~ Sometimes it is useful to plot just a few contours of a quantity in a dataset. This shows how one does this by first making a slice, adding contours, and then hiding the colormap plot of the slice to leave the plot containing only the contours that one has added. See :ref:`annotate-contours` for more information. .. yt_cookbook:: simple_contour_in_slice.py Styling Radial Profile Plots ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ This recipe demonstrates a method of calculating radial profiles for several quantities, styling them and saving out the resultant plot. See :ref:`how-to-make-1d-profiles` for more information. .. yt_cookbook:: radial_profile_styles.py Customized Profile Plot ~~~~~~~~~~~~~~~~~~~~~~~ This recipe demonstrates how to create a fully customized 1D profile object using the :func:`~yt.data_objects.profiles.create_profile` function and then create a :class:`~yt.visualization.profile_plotter.ProfilePlot` using the customized profile. This illustrates how a ``ProfilePlot`` created this way inherits the properties of the profile it is constructed from. See :ref:`how-to-make-1d-profiles` for more information. .. yt_cookbook:: customized_profile_plot.py Customized Phase Plot ~~~~~~~~~~~~~~~~~~~~~ Similar to the recipe above, this demonstrates how to create a fully customized 2D profile object using the :func:`~yt.data_objects.profiles.create_profile` function and then create a :class:`~yt.visualization.profile_plotter.PhasePlot` using the customized profile object. This illustrates how a ``PhasePlot`` created this way inherits the properties of the profile object from which it is constructed. See :ref:`how-to-make-2d-profiles` for more information. .. yt_cookbook:: customized_phase_plot.py .. _cookbook-camera_movement: Moving a Volume Rendering Camera ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ In this recipe, we move a camera through a domain and take multiple volume rendering snapshots. This recipe uses an unstructured mesh dataset (see :ref:`unstructured_mesh_rendering`), which makes it easier to visualize what the Camera is doing, but you can manipulate the Camera for other dataset types in exactly the same manner. See :ref:`camera_movement` for more information. .. yt_cookbook:: camera_movement.py Volume Rendering with Custom Camera ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ In this recipe we modify the :ref:`cookbook-simple_volume_rendering` recipe to use customized camera properties. See :ref:`volume_rendering` for more information. .. yt_cookbook:: custom_camera_volume_rendering.py .. _cookbook-custom-transfer-function: Volume Rendering with a Custom Transfer Function ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ In this recipe we modify the :ref:`cookbook-simple_volume_rendering` recipe to use customized camera properties. See :ref:`volume_rendering` for more information. .. yt_cookbook:: custom_transfer_function_volume_rendering.py .. _cookbook-sigma_clip: Volume Rendering with Sigma Clipping ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ In this recipe we output several images with different values of sigma_clip set in order to change the contrast of the resulting image. See :ref:`sigma_clip` for more information. .. yt_cookbook:: sigma_clip.py Zooming into an Image ~~~~~~~~~~~~~~~~~~~~~ This is a recipe that takes a slice through the most dense point, then creates a bunch of frames as it zooms in. It's important to note that this particular recipe is provided to show how to be more flexible and add annotations and the like -- the base system, of a zoomin, is provided by the "yt zoomin" command on the command line. See :ref:`slice-plots` and :ref:`callbacks` for more information. .. yt_cookbook:: zoomin_frames.py .. _cookbook-various_lens: Various Lens Types for Volume Rendering ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ This example illustrates the usage and feature of different lenses for volume rendering. .. yt_cookbook:: various_lens.py .. _cookbook-opaque_rendering: Opaque Volume Rendering ~~~~~~~~~~~~~~~~~~~~~~~ This recipe demonstrates how to make semi-opaque volume renderings, but also how to step through and try different things to identify the type of volume rendering you want. See :ref:`opaque_rendering` for more information. .. yt_cookbook:: opaque_rendering.py .. _cookbook-amrkdtree_downsampling: Downsampling Data for Volume Rendering ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ This recipe demonstrates how to downsample data in a simulation to speed up volume rendering. See :ref:`volume_rendering` for more information. .. yt_cookbook:: amrkdtree_downsampling.py .. _cookbook-volume_rendering_annotations: Volume Rendering with Bounding Box and Overlaid Grids ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ This recipe demonstrates how to overplot a bounding box on a volume rendering as well as overplotting grids representing the level of refinement achieved in different regions of the code. See :ref:`volume_rendering_annotations` for more information. .. yt_cookbook:: rendering_with_box_and_grids.py Volume Rendering with Annotation ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ This recipe demonstrates how to write the simulation time, show an axis triad indicating the direction of the coordinate system, and show the transfer function on a volume rendering. Please note that this recipe relies on the old volume rendering interface. While one can continue to use this interface, it may be incompatible with some of the new developments and the infrastructure described in :ref:`volume_rendering`. .. yt_cookbook:: vol-annotated.py .. _cookbook-vol-points: Volume Rendering with Points ~~~~~~~~~~~~~~~~~~~~~~~~~~~~ This recipe demonstrates how to make a volume rendering composited with point sources. This could represent star or dark matter particles, for example. .. yt_cookbook:: vol-points.py .. _cookbook-vol-lines: Volume Rendering with Lines ~~~~~~~~~~~~~~~~~~~~~~~~~~~ This recipe demonstrates how to make a volume rendering composited with line sources. .. yt_cookbook:: vol-lines.py .. _cookbook-opengl_vr: Advanced Interactive Data Visualization ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ This recipe demonstrates how to manually create all components required to start the Interactive Data Visualization. For more information see :ref:`interactive_data_visualization`. .. yt_cookbook:: opengl_vr.py Embedding Interactive Data Visualization ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ This recipe demonstrates how to embed the Interactive Data Visualization inside the Jupyter notebook. For more information see :ref:`interactive_data_visualization`. .. yt_cookbook:: opengl_ipython.py Plotting Streamlines ~~~~~~~~~~~~~~~~~~~~ This recipe demonstrates how to display streamlines in a simulation. (Note: streamlines can also be queried for values!) See :ref:`streamlines` for more information. .. yt_cookbook:: streamlines.py Plotting Isocontours ~~~~~~~~~~~~~~~~~~~~ This recipe demonstrates how to extract an isocontour and then plot it in matplotlib, coloring the surface by a second quantity. See :ref:`surfaces` for more information. .. yt_cookbook:: surface_plot.py Plotting Isocontours and Streamlines ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ This recipe plots both isocontours and streamlines simultaneously. Note that this will not include any blending, so streamlines that are occluded by the surface will still be visible. See :ref:`streamlines` and :ref:`surfaces` for more information. .. yt_cookbook:: streamlines_isocontour.py yt-3.3.3/doc/source/cookbook/constructing_data_objects.rst0000644000076500000240000000223712770647750024745 0ustar goldbaumstaff00000000000000Constructing Data Objects ------------------------- These recipes demonstrate a few uncommon methods of constructing data objects from a simulation. Creating Particle Filters ~~~~~~~~~~~~~~~~~~~~~~~~~ Create particle filters based on the age of star particles in an isolated disk galaxy simulation. Determine the total mass of each stellar age bin in the simulation. Generate projections for each of the stellar age bins. .. yt_cookbook:: particle_filter.py .. _cookbook-find_clumps: Identifying Clumps ~~~~~~~~~~~~~~~~~~ This is a recipe to show how to find topologically connected sets of cells inside a dataset. It returns these clumps and they can be inspected or visualized as would any other data object. More detail on this method can be found in `astro-ph/0806.1653`. .. yt_cookbook:: find_clumps.py .. _extract_frb: Extracting Fixed Resolution Data ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ This is a recipe to show how to open a dataset and extract it to a file at a fixed resolution with no interpolation or smoothing. Additionally, this recipe shows how to insert a dataset into an external HDF5 file using h5py. .. yt_cookbook:: extract_fixed_resolution_data.py yt-3.3.3/doc/source/cookbook/contours_on_slice.py0000644000076500000240000000062213003701576023047 0ustar goldbaumstaff00000000000000import yt # first add density contours on a density slice ds = yt.load("GasSloshing/sloshing_nomag2_hdf5_plt_cnt_0150") # add density contours on the density slice. p = yt.SlicePlot(ds, "x", "density") p.annotate_contour("density") p.save() # then add temperature contours on the same density slice p = yt.SlicePlot(ds, "x", "density") p.annotate_contour("temperature") p.save(str(ds)+'_T_contour') yt-3.3.3/doc/source/cookbook/cosmological_analysis.rst0000644000076500000240000000531313017603554024062 0ustar goldbaumstaff00000000000000Cosmological Analysis --------------------- These scripts demonstrate some basic and more advanced analysis that can be performed on cosmological simulation datasets. Most of the following recipes are derived from functionality in yt's :ref:`analysis-modules`. Plotting Halos ~~~~~~~~~~~~~~ This is a mechanism for plotting circles representing identified particle halos on an image. See :ref:`halo-analysis` and :ref:`annotate-halos` for more information. .. yt_cookbook:: halo_plotting.py .. _cookbook-rockstar-nested-grid: Running Rockstar to Find Halos on Multi-Resolution-Particle Datasets ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ The version of Rockstar installed with yt does not have the capability to work on datasets with particles of different masses. Unfortunately, many simulations possess particles of different masses, notably cosmological zoom datasets. This recipe uses Rockstar in two different ways to generate a HaloCatalog from the highest resolution dark matter particles (the ones inside the zoom region). It then overlays some of those halos on a projection as a demonstration. See :ref:`rockstar` and :ref:`annotate-halos` for more information. .. yt_cookbook:: rockstar_nest.py .. _cookbook-halo_finding: Halo Profiling and Custom Analysis ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ This script demonstrates the use of the halo catalog to create radial profiles for each halo in a cosmological dataset. See :ref:`halo_catalog` for more information. .. yt_cookbook:: halo_profiler.py .. _cookbook-light_cone: Light Cone Projection ~~~~~~~~~~~~~~~~~~~~~ This script creates a light cone projection, a synthetic observation that stacks together projections from multiple datasets to extend over a given redshift interval. See :ref:`light-cone-generator` for more information. .. yt_cookbook:: light_cone_projection.py .. _cookbook-light_ray: Light Ray ~~~~~~~~~ This script demonstrates how to make a synthetic quasar sight line that extends over multiple datasets and can be used to generate a synthetic absorption spectrum. See :ref:`light-ray-generator` and :ref:`absorption_spectrum` for more information. .. yt_cookbook:: light_ray.py .. _cookbook-single-dataset-light-ray: Single Dataset Light Ray ~~~~~~~~~~~~~~~~~~~~~~~~ This script demonstrates how to make a light ray from a single dataset. .. yt_cookbook:: single_dataset_light_ray.py Creating and Fitting Absorption Spectra ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ This script demonstrates how to use light rays to create corresponding absorption spectra and then fit the spectra to find absorbing structures. See :ref:`light-ray-generator` and :ref:`absorption_spectrum` for more information. .. yt_cookbook:: fit_spectrum.py yt-3.3.3/doc/source/cookbook/count.sh0000644000076500000240000000017512770647750020452 0ustar goldbaumstaff00000000000000for fn in *.py do COUNT=`cat *.rst | grep --count ${fn}` if [ $COUNT -lt 1 ] then echo ${fn} fi done yt-3.3.3/doc/source/cookbook/custom_camera_volume_rendering.py0000644000076500000240000000121613017603554025570 0ustar goldbaumstaff00000000000000import yt # Load the dataset ds = yt.load("Enzo_64/DD0043/data0043") # Create a volume rendering sc = yt.create_scene(ds, field=('gas', 'density')) # Now increase the resolution sc.camera.resolution = (1024, 1024) # Set the camera focus to a position that is offset from the center of # the domain sc.camera.focus = ds.arr([0.3, 0.3, 0.3], 'unitary') # Move the camera position to the other side of the dataset sc.camera.position = ds.arr([0, 0, 0], 'unitary') # save to disk with a custom filename and apply sigma clipping to eliminate # very bright pixels, producing an image with better contrast. sc.render() sc.save('custom.png', sigma_clip=4) yt-3.3.3/doc/source/cookbook/custom_colorbar_tickmarks.ipynb0000644000076500000240000000515613017603554025267 0ustar goldbaumstaff00000000000000{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import yt" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ds = yt.load('IsolatedGalaxy/galaxy0030/galaxy0030')\n", "slc = yt.SlicePlot(ds, 'x', 'density')\n", "slc" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`PlotWindow` plots are containers for plots, keyed to field names. Below, we get a copy of the plot for the `Density` field." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "plot = slc.plots['density']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The plot has a few attributes that point to underlying `matplotlib` plot primites. For example, the `colorbar` object corresponds to the `cb` attribute of the plot." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "colorbar = plot.cb" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, we call `_setup_plots()` to ensure the plot is properly initialized. Without this, the custom tickmarks we are adding will be ignored." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "slc._setup_plots()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To set custom tickmarks, simply call the `matplotlib` [`set_ticks`](http://matplotlib.org/api/colorbar_api.html#matplotlib.colorbar.ColorbarBase.set_ticks) and [`set_ticklabels`](http://matplotlib.org/api/colorbar_api.html#matplotlib.colorbar.ColorbarBase.set_ticklabels) functions." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "colorbar.set_ticks([1e-28])\n", "colorbar.set_ticklabels(['$10^{-28}$'])\n", "slc" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 } yt-3.3.3/doc/source/cookbook/custom_colorbar_tickmarks.rst0000644000076500000240000000014312770647750024760 0ustar goldbaumstaff00000000000000Custom Colorbar Tickmarks ------------------------- .. notebook:: custom_colorbar_tickmarks.ipynb yt-3.3.3/doc/source/cookbook/custom_transfer_function_volume_rendering.py0000644000076500000240000000122613023400062030054 0ustar goldbaumstaff00000000000000import yt import numpy as np # Load the dataset ds = yt.load("Enzo_64/DD0043/data0043") # Create a volume rendering sc = yt.create_scene(ds, field=('gas', 'density')) # Modify the transfer function # First get the render source, in this case the entire domain, with field ('gas','density') render_source = sc.get_source(0) # Clear the transfer function render_source.transfer_function.clear() # Map a range of density values (in log space) to the Reds_r colormap render_source.transfer_function.map_to_colormap( np.log10(ds.quan(5.0e-31, 'g/cm**3')), np.log10(ds.quan(1.0e-29, 'g/cm**3')), scale=30.0, colormap='RdBu_r') sc.save('new_tf.png') yt-3.3.3/doc/source/cookbook/customized_phase_plot.py0000644000076500000240000000142113003701576023722 0ustar goldbaumstaff00000000000000import yt import yt.units as u ds = yt.load('HiresIsolatedGalaxy/DD0044/DD0044') center = [0.53, 0.53, 0.53] normal = [0,0,1] radius = 40*u.kpc height = 2*u.kpc disk = ds.disk(center, [0,0,1], radius, height) profile = yt.create_profile( data_source=disk, bin_fields=["radius", "cylindrical_tangential_velocity"], fields=["cell_mass"], n_bins=256, units=dict(radius="kpc", cylindrical_tangential_velocity="km/s", cell_mass="Msun"), logs=dict(radius=False, cylindrical_tangential_velocity=False), weight_field=None, extrema=dict(radius=(0,40), cylindrical_tangential_velocity=(-250, 250)), ) plot = yt.PhasePlot.from_profile(profile) plot.set_cmap("cell_mass", "YlOrRd") plot.save() yt-3.3.3/doc/source/cookbook/customized_profile_plot.py0000644000076500000240000000135313003701576024266 0ustar goldbaumstaff00000000000000import yt import yt.units as u ds = yt.load('HiresIsolatedGalaxy/DD0044/DD0044') center = [0.53, 0.53, 0.53] normal = [0,0,1] radius = 40*u.kpc height = 5*u.kpc disk = ds.disk(center, [0,0,1], radius, height) profile = yt.create_profile( data_source=disk, bin_fields=["radius"], fields=["cylindrical_tangential_velocity_absolute"], n_bins=256, units=dict(radius="kpc", cylindrical_tangential_velocity_absolute="km/s"), logs=dict(radius=False), weight_field='cell_mass', extrema=dict(radius=(0,40)), ) plot = yt.ProfilePlot.from_profiles(profile) plot.set_log('cylindrical_tangential_velocity_absolute', False) plot.set_ylim('cylindrical_tangential_velocity_absolute', 60, 160) plot.save() yt-3.3.3/doc/source/cookbook/derived_field.py0000644000076500000240000000151713017603554022113 0ustar goldbaumstaff00000000000000import yt # Load the dataset. ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030") # You can create a derived field by manipulating any existing derived fields # in any way you choose. In this case, let's just make a simple one: # thermal_energy_density = 3/2 nkT # First create a function which yields your new derived field def thermal_energy_dens(field, data): return (3/2)*data['gas', 'number_density'] * data['gas', 'kT'] # Then add it to your dataset and define the units ds.add_field(("gas", "thermal_energy_density"), units="erg/cm**3", function=thermal_energy_dens) # It will now show up in your derived_field_list for i in sorted(ds.derived_field_list): print(i) # Let's use it to make a projection ad = ds.all_data() yt.ProjectionPlot(ds, "x", "thermal_energy_density", weight_field="density", width=(200, 'kpc')).save() yt-3.3.3/doc/source/cookbook/extract_fixed_resolution_data.py0000644000076500000240000000213513017603554025430 0ustar goldbaumstaff00000000000000import yt # For this example we will use h5py to write to our output file. import h5py ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030") level = 2 dims = ds.domain_dimensions * ds.refine_by**level # We construct an object that describes the data region and structure we want # In this case, we want all data up to the maximum "level" of refinement # across the entire simulation volume. Higher levels than this will not # contribute to our covering grid. cube = ds.covering_grid(level, left_edge=[0.0, 0.0, 0.0], dims=dims, # And any fields to preload (this is optional!) fields=["density"]) # Now we open our output file using h5py # Note that we open with 'w' (write), which will overwrite existing files! f = h5py.File("my_data.h5", "w") # We create a dataset at the root, calling it "density" f.create_dataset("/density", data=cube["density"]) # We close our file f.close() # If we want to then access this datacube in the h5 file, we can now... f = h5py.File("my_data.h5", "r") print(f["density"].value) yt-3.3.3/doc/source/cookbook/find_clumps.py0000644000076500000240000000333713023400062021615 0ustar goldbaumstaff00000000000000import numpy as np import yt from yt.analysis_modules.level_sets.api import * ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030") data_source = ds.disk([0.5, 0.5, 0.5], [0., 0., 1.], (8, 'kpc'), (1, 'kpc')) # the field to be used for contouring field = ("gas", "density") # This is the multiplicative interval between contours. step = 2.0 # Now we set some sane min/max values between which we want to find contours. # This is how we tell the clump finder what to look for -- it won't look for # contours connected below or above these threshold values. c_min = 10**np.floor(np.log10(data_source[field]).min() ) c_max = 10**np.floor(np.log10(data_source[field]).max()+1) # Now find get our 'base' clump -- this one just covers the whole domain. master_clump = Clump(data_source, field) # Add a "validator" to weed out clumps with less than 20 cells. # As many validators can be added as you want. master_clump.add_validator("min_cells", 20) # Begin clump finding. find_clumps(master_clump, c_min, c_max, step) # Write out the full clump hierarchy. write_clump_index(master_clump, 0, "%s_clump_hierarchy.txt" % ds) # Write out only the leaf nodes of the hierarchy. write_clumps(master_clump,0, "%s_clumps.txt" % ds) # We can traverse the clump hierarchy to get a list of all of the 'leaf' clumps leaf_clumps = get_lowest_clumps(master_clump) # If you'd like to visualize these clumps, a list of clumps can be supplied to # the "clumps" callback on a plot. First, we create a projection plot: prj = yt.ProjectionPlot(ds, 2, field, center='c', width=(20,'kpc')) # Next we annotate the plot with contours on the borders of the clumps prj.annotate_clumps(leaf_clumps) # Lastly, we write the plot to disk. prj.save('clumps') yt-3.3.3/doc/source/cookbook/fit_spectrum.py0000644000076500000240000000755513017603554022042 0ustar goldbaumstaff00000000000000import yt from yt.analysis_modules.cosmological_observation.light_ray.api import LightRay from yt.analysis_modules.absorption_spectrum.api import AbsorptionSpectrum from yt.analysis_modules.absorption_spectrum.api import generate_total_fit # Define a field to simulate OVI based on a constant relationship to HI # Do *NOT* use this for science, because this is not how OVI actually behaves; # it is just an example. def _OVI_number_density(field, data): return data['H_number_density']*2.0 # Define a function that will accept a ds and add the new field # defined above. This will be given to the LightRay below. def setup_ds(ds): ds.add_field(("gas","O_p5_number_density"), function=_OVI_number_density, units="cm**-3") # Define species and associated parameters to add to continuum # Parameters used for both adding the transition to the spectrum # and for fitting # Note that for single species that produce multiple lines # (as in the OVI doublet), 'numLines' will be equal to the number # of lines, and f,gamma, and wavelength will have multiple values. HI_parameters = {'name': 'HI', 'field': 'H_number_density', 'f': [.4164], 'Gamma': [6.265E8], 'wavelength': [1215.67], 'mass': 1.00794, 'numLines': 1, 'maxN': 1E22, 'minN': 1E11, 'maxb': 300, 'minb': 1, 'maxz': 6, 'minz': 0, 'init_b': 30, 'init_N': 1E14} OVI_parameters = {'name': 'OVI', 'field': 'O_p5_number_density', 'f': [.1325, .06580], 'Gamma': [4.148E8, 4.076E8], 'wavelength': [1031.9261, 1037.6167], 'mass': 15.9994, 'numLines': 2, 'maxN': 1E17, 'minN': 1E11, 'maxb': 300, 'minb': 1, 'maxz': 6, 'minz': 0, 'init_b': 20, 'init_N': 1E12} species_dicts = {'HI': HI_parameters, 'OVI': OVI_parameters} # Create a LightRay object extending from z = 0 to z = 0.1 # and use only the redshift dumps. lr = LightRay('enzo_cosmology_plus/AMRCosmology.enzo', 'Enzo', 0.0, 0.1, use_minimum_datasets=True, time_data=False ) # Get all fields that need to be added to the light ray fields = ['temperature'] for s, params in species_dicts.items(): fields.append(params['field']) # Make a light ray, and set njobs to -1 to use one core # per dataset. lr.make_light_ray(seed=123456780, solution_filename='lightraysolution.txt', data_filename='lightray.h5', fields=fields, setup_function=setup_ds, njobs=-1) # Create an AbsorptionSpectrum object extending from # lambda = 900 to lambda = 1800, with 10000 pixels sp = AbsorptionSpectrum(900.0, 1400.0, 50000) # Iterate over species for s, params in species_dicts.items(): # Iterate over transitions for a single species for i in range(params['numLines']): # Add the lines to the spectrum sp.add_line(s, params['field'], params['wavelength'][i], params['f'][i], params['Gamma'][i], params['mass'], label_threshold=1.e10) # Make and save spectrum wavelength, flux = sp.make_spectrum('lightray.h5', output_file='spectrum.h5', line_list_file='lines.txt', use_peculiar_velocity=True) # Define order to fit species in order_fits = ['OVI', 'HI'] # Fit spectrum and save fit fitted_lines, fitted_flux = generate_total_fit(wavelength, flux, order_fits, species_dicts, output_file='spectrum_fit.h5') yt-3.3.3/doc/source/cookbook/fits_radio_cubes.ipynb0000644000076500000240000002765613017603554023337 0ustar goldbaumstaff00000000000000{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import yt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook demonstrates some of the capabilties of yt on some FITS \"position-position-spectrum\" cubes of radio data.\n", "\n", "Note that it depends on some external dependencies, including `astropy`, `wcsaxes`, and `pyregion`." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## M33 VLA Image" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The dataset `\"m33_hi.fits\"` has `NaN`s in it, so we'll mask them out by setting `nan_mask` = 0:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ds = yt.load(\"radio_fits/m33_hi.fits\", nan_mask=0.0, z_axis_decomp=True)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First, we'll take a slice of the data along the z-axis, which is the velocity axis of the FITS cube:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "slc = yt.SlicePlot(ds, \"z\", [\"intensity\"], origin=\"native\")\n", "slc.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The x and y axes are in units of the image pixel. When making plots of FITS data, to see the image coordinates as they are in the file, it is helpful to set the keyword `origin = \"native\"`. If you want to see the celestial coordinates along the axes, you can import the `PlotWindowWCS` class and feed it the `SlicePlot`. For this to work, the [WCSAxes](http://wcsaxes.readthedocs.org/en/latest/) package needs to be installed." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from yt.frontends.fits.misc import PlotWindowWCS\n", "PlotWindowWCS(slc)\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Generally, it is best to get the plot in the shape you want it before feeding it to `PlotWindowWCS`. Once it looks the way you want, you can save it just like a normal `PlotWindow` plot:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "slc.save()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also take slices of this dataset at a few different values along the \"z\" axis (corresponding to the velocity), so let's try a few. To pick specific velocity values for slices, we will need to use the dataset's `spec2pixel` method to determine which pixels to slice on:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import yt.units as u\n", "new_center = ds.domain_center\n", "new_center[2] = ds.spec2pixel(-250000.*u.m/u.s)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we can use this new center to create a new slice:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "slc = yt.SlicePlot(ds, \"z\", [\"intensity\"], center=new_center, origin=\"native\")\n", "slc.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can do this a few more times for different values of the velocity:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "new_center[2] = ds.spec2pixel(-100000.*u.m/u.s)\n", "slc = yt.SlicePlot(ds, \"z\", [\"intensity\"], center=new_center, origin=\"native\")\n", "slc.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "new_center[2] = ds.spec2pixel(-150000.*u.m/u.s)\n", "slc = yt.SlicePlot(ds, \"z\", [\"intensity\"], center=new_center, origin=\"native\")\n", "slc.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "These slices demonstrate the intensity of the radio emission at different line-of-sight velocities. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also make a projection of all the emission along the line of sight. Since we're not doing an integration along a path length, we needed to specify `method = \"sum\"`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "prj = yt.ProjectionPlot(ds, \"z\", [\"intensity\"], origin=\"native\")\n", "prj.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also look at the slices perpendicular to the other axes, which will show us the structure along the velocity axis:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "slc = yt.SlicePlot(ds, \"x\", [\"intensity\"], origin=\"native\", window_size=(8,8))\n", "slc.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "slc = yt.SlicePlot(ds, \"y\", [\"intensity\"], origin=\"native\", window_size=(8,8))\n", "slc.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In these cases, we needed to explicitly declare a square `window_size` to get a figure that looks good. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## $^{13}$CO GRS Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This next example uses one of the cubes from the [Boston University Galactic Ring Survey](http://www.bu.edu/galacticring/new_index.htm). " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ds = yt.load(\"radio_fits/grs-50-cube.fits\", nan_mask=0.0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can use the `quantities` methods to determine derived quantities of the dataset. For example, we could find the maximum and minimum temperature:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dd = ds.all_data() # A region containing the entire dataset\n", "extrema = dd.quantities.extrema(\"temperature\")\n", "print (extrema)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can compute the average temperature along the \"velocity\" axis for all positions by making a `ProjectionPlot`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "prj = yt.ProjectionPlot(ds, \"z\", [\"temperature\"], origin=\"native\", \n", " weight_field=\"ones\") # \"ones\" weights each cell by 1\n", "prj.set_log(\"temperature\", True)\n", "prj.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also make a histogram of the temperature field of this region:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pplot = yt.ProfilePlot(dd, \"temperature\", [\"ones\"], weight_field=None, n_bins=128)\n", "pplot.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can see from this histogram and our calculation of the dataset's extrema that there is a lot of noise. Suppose we wanted to make a projection, but instead make it only of the cells which had a positive temperature value. We can do this by doing a \"field cut\" on the data:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fc = dd.cut_region([\"obj['temperature'] > 0\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's check the extents of this region:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print (fc.quantities.extrema(\"temperature\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Looks like we were successful in filtering out the negative temperatures. To compute the average temperature of this new region:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fc.quantities.weighted_average_quantity(\"temperature\", \"ones\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, let's make a projection of the dataset, using the field cut `fc` as a `data_source`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "prj = yt.ProjectionPlot(ds, \"z\", [\"temperature\"], data_source=fc, origin=\"native\", \n", " weight_field=\"ones\") # \"ones\" weights each cell by 1\n", "prj.set_log(\"temperature\", True)\n", "prj.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we can also take an existing [ds9](http://ds9.si.edu/site/Home.html) region and use it to create a \"cut region\" as well, using `ds9_region` (the [pyregion](http://leejjoon.github.io/pyregion/) package needs to be installed for this):" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from yt.frontends.fits.misc import ds9_region" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "For this example we'll create a ds9 region from scratch and load it up:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "region = 'galactic;box(+49:26:35.150,-0:30:04.410,1926.1927\",1483.3701\",0.0)'\n", "box_reg = ds9_region(ds, region)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This region may now be used to compute derived quantities:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print (box_reg.quantities.extrema(\"temperature\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or in projections:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "prj = yt.ProjectionPlot(ds, \"z\", [\"temperature\"], origin=\"native\", \n", " data_source=box_reg, weight_field=\"ones\") # \"ones\" weights each cell by 1\n", "prj.set_zlim(\"temperature\", 1.0e-2, 1.5)\n", "prj.set_log(\"temperature\", True)\n", "prj.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 } yt-3.3.3/doc/source/cookbook/fits_radio_cubes.rst0000644000076500000240000000014512770647750023021 0ustar goldbaumstaff00000000000000.. _radio_cubes: FITS Radio Cubes in yt ---------------------- .. notebook:: fits_radio_cubes.ipynbyt-3.3.3/doc/source/cookbook/fits_xray_images.ipynb0000644000076500000240000003047313017603554023357 0ustar goldbaumstaff00000000000000{ "cells": [ { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import yt\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This notebook shows how to use yt to make plots and examine FITS X-ray images and events files. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Sloshing, Shocks, and Bubbles in Abell 2052" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This example uses data provided by [Scott Randall](http://hea-www.cfa.harvard.edu/~srandall/), presented originally in [Blanton, E.L., Randall, S.W., Clarke, T.E., et al. 2011, ApJ, 737, 99](http://adsabs.harvard.edu/cgi-bin/bib_query?2011ApJ...737...99B). They consist of two files, a \"flux map\" in counts/s/pixel between 0.3 and 2 keV, and a spectroscopic temperature map in keV. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ds = yt.load(\"xray_fits/A2052_merged_0.3-2_match-core_tmap_bgecorr.fits\", \n", " auxiliary_files=[\"xray_fits/A2052_core_tmap_b1_m2000_.fits\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Since the flux and projected temperature images are in two different files, we had to use one of them (in this case the \"flux\" file) as a master file, and pass in the \"temperature\" file with the `auxiliary_files` keyword to `load`. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next, let's derive some new fields for the number of counts, the \"pseudo-pressure\", and the \"pseudo-entropy\":" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def _counts(field, data):\n", " exposure_time = data.get_field_parameter(\"exposure_time\")\n", " return data[\"flux\"]*data[\"pixel\"]*exposure_time\n", "ds.add_field((\"gas\",\"counts\"), function=_counts, units=\"counts\", take_log=False)\n", "\n", "def _pp(field, data):\n", " return np.sqrt(data[\"counts\"])*data[\"projected_temperature\"]\n", "ds.add_field((\"gas\",\"pseudo_pressure\"), function=_pp, units=\"sqrt(counts)*keV\", take_log=False)\n", "\n", "def _pe(field, data):\n", " return data[\"projected_temperature\"]*data[\"counts\"]**(-1./3.)\n", "ds.add_field((\"gas\",\"pseudo_entropy\"), function=_pe, units=\"keV*(counts)**(-1/3)\", take_log=False)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here, we're deriving a \"counts\" field from the \"flux\" field by passing it a `field_parameter` for the exposure time of the time and multiplying by the pixel scale. Second, we use the fact that the surface brightness is strongly dependent on density ($S_X \\propto \\rho^2$) to use the counts in each pixel as a \"stand-in\". Next, we'll grab the exposure time from the primary FITS header of the flux file and create a `YTQuantity` from it, to be used as a `field_parameter`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "exposure_time = ds.quan(ds.primary_header[\"exposure\"], \"s\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we can make the `SlicePlot` object of the fields we want, passing in the `exposure_time` as a `field_parameter`. We'll also set the width of the image to 250 pixels." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "slc = yt.SlicePlot(ds, \"z\", \n", " [\"flux\",\"projected_temperature\",\"pseudo_pressure\",\"pseudo_entropy\"], \n", " origin=\"native\", field_parameters={\"exposure_time\":exposure_time})\n", "slc.set_log(\"flux\",True)\n", "slc.set_log(\"pseudo_pressure\",False)\n", "slc.set_log(\"pseudo_entropy\",False)\n", "slc.set_width(250.)\n", "slc.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To add the celestial coordinates to the image, we can use `PlotWindowWCS`, if you have the [WCSAxes](http://wcsaxes.readthedocs.org/en/latest/) package installed:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from yt.frontends.fits.misc import PlotWindowWCS\n", "wcs_slc = PlotWindowWCS(slc)\n", "wcs_slc.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can make use of yt's facilities for profile plotting as well." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "v, c = ds.find_max(\"flux\") # Find the maxmimum flux and its center\n", "my_sphere = ds.sphere(c, (100.,\"code_length\")) # Radius of 150 pixels\n", "my_sphere.set_field_parameter(\"exposure_time\", exposure_time)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Such as a radial profile plot:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "radial_profile = yt.ProfilePlot(my_sphere, \"radius\", \n", " [\"counts\",\"pseudo_pressure\",\"pseudo_entropy\"], \n", " n_bins=30, weight_field=\"ones\")\n", "radial_profile.set_log(\"counts\", True)\n", "radial_profile.set_log(\"pseudo_pressure\", True)\n", "radial_profile.set_log(\"pseudo_entropy\", True)\n", "radial_profile.set_xlim(3,100.)\n", "radial_profile.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or a phase plot:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "phase_plot = yt.PhasePlot(my_sphere, \"pseudo_pressure\", \"pseudo_entropy\", [\"counts\"], weight_field=None)\n", "phase_plot.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally, we can also take an existing [ds9](http://ds9.si.edu/site/Home.html) region and use it to create a \"cut region\", using `ds9_region` (the [pyregion](http://leejjoon.github.io/pyregion/) package needs to be installed for this):" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from yt.frontends.fits.misc import ds9_region\n", "reg_file = [\"# Region file format: DS9 version 4.1\\n\",\n", " \"global color=green dashlist=8 3 width=3 include=1 source=1 fk5\\n\",\n", " \"circle(15:16:44.817,+7:01:19.62,34.6256\\\")\"]\n", "f = open(\"circle.reg\",\"w\")\n", "f.writelines(reg_file)\n", "f.close()\n", "circle_reg = ds9_region(ds, \"circle.reg\", field_parameters={\"exposure_time\":exposure_time})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This region may now be used to compute derived quantities:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print (circle_reg.quantities.weighted_average_quantity(\"projected_temperature\", \"counts\"))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Or used in projections:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "prj = yt.ProjectionPlot(ds, \"z\", \n", " [\"flux\",\"projected_temperature\",\"pseudo_pressure\",\"pseudo_entropy\"], \n", " origin=\"native\", field_parameters={\"exposure_time\":exposure_time},\n", " data_source=circle_reg,\n", " method=\"sum\")\n", "prj.set_log(\"flux\",True)\n", "prj.set_log(\"pseudo_pressure\",False)\n", "prj.set_log(\"pseudo_entropy\",False)\n", "prj.set_width(250.)\n", "prj.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## The Bullet Cluster" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This example uses an events table file from a ~100 ks exposure of the \"Bullet Cluster\" from the [Chandra Data Archive](http://cxc.harvard.edu/cda/). In this case, the individual photon events are treated as particle fields in yt. However, you can make images of the object in different energy bands using the `setup_counts_fields` function. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "from yt.frontends.fits.api import setup_counts_fields" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`load` will handle the events file as FITS image files, and will set up a grid using the WCS information in the file. Optionally, the events may be reblocked to a new resolution. by setting the `\"reblock\"` parameter in the `parameters` dictionary in `load`. `\"reblock\"` must be a power of 2. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ds2 = yt.load(\"xray_fits/acisf05356N003_evt2.fits.gz\", parameters={\"reblock\":2})" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`setup_counts_fields` will take a list of energy bounds (emin, emax) in keV and create a new field from each where the photons in that energy range will be deposited onto the image grid. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ebounds = [(0.1,2.0),(2.0,5.0)]\n", "setup_counts_fields(ds2, ebounds)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The \"x\", \"y\", \"energy\", and \"time\" fields in the events table are loaded as particle fields. Each one has a name given by \"event\\_\" plus the name of the field:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dd = ds2.all_data()\n", "print (dd[\"event_x\"])\n", "print (dd[\"event_y\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we'll make a plot of the two counts fields we made, and pan and zoom to the bullet:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "slc = yt.SlicePlot(ds2, \"z\", [\"counts_0.1-2.0\",\"counts_2.0-5.0\"], origin=\"native\")\n", "slc.pan((100.,100.))\n", "slc.set_width(500.)\n", "slc.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The counts fields can take the field parameter `\"sigma\"` and use [AstroPy's convolution routines](http://astropy.readthedocs.org/en/latest/convolution/) to smooth the data with a Gaussian:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "slc = yt.SlicePlot(ds2, \"z\", [\"counts_0.1-2.0\",\"counts_2.0-5.0\"], origin=\"native\",\n", " field_parameters={\"sigma\":2.}) # This value is in pixel scale\n", "slc.pan((100.,100.))\n", "slc.set_width(500.)\n", "slc.set_zlim(\"counts_0.1-2.0\", 0.01, 100.)\n", "slc.set_zlim(\"counts_2.0-5.0\", 0.01, 50.)\n", "slc.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 } yt-3.3.3/doc/source/cookbook/fits_xray_images.rst0000644000076500000240000000014612770647750023053 0ustar goldbaumstaff00000000000000.. _xray_fits: FITS X-ray Images in yt ----------------------- .. notebook:: fits_xray_images.ipynb yt-3.3.3/doc/source/cookbook/gadget_notebook.rst0000644000076500000240000000024412770647750022650 0ustar goldbaumstaff00000000000000.. _gadget-notebook: Using yt to view and analyze Gadget outputs ++++++++++++++++++++++++++++++++++++++++++++++++++++++++ .. notebook:: yt_gadget_analysis.ipynb yt-3.3.3/doc/source/cookbook/global_phase_plots.py0000644000076500000240000000056113003701576023163 0ustar goldbaumstaff00000000000000import yt # load the dataset ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030") # This is an object that describes the entire box ad = ds.all_data() # We plot the average velocity magnitude (mass-weighted) in our object # as a function of density and temperature plot = yt.PhasePlot(ad, "density", "temperature", "velocity_magnitude") # save the plot plot.save() yt-3.3.3/doc/source/cookbook/Halo_Analysis.ipynb0000644000076500000240000003326613017603554022553 0ustar goldbaumstaff00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Full Halo Analysis" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Creating a Catalog" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we put everything together to perform some realistic analysis. First we load a full simulation dataset." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import yt\n", "from yt.analysis_modules.halo_analysis.api import *\n", "import tempfile\n", "import shutil\n", "import os\n", "\n", "# Create temporary directory for storing files\n", "tmpdir = tempfile.mkdtemp()\n", "\n", "# Load the data set with the full simulation information\n", "data_ds = yt.load('Enzo_64/RD0006/RedshiftOutput0006')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we load a rockstar halos binary file. This is the output from running the rockstar halo finder on the dataset loaded above. It is also possible to require the HaloCatalog to find the halos in the full simulation dataset at runtime by specifying a `finder_method` keyword." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Load the rockstar data files\n", "halos_ds = yt.load('rockstar_halos/halos_0.0.bin')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "From these two loaded datasets we create a halo catalog object. No analysis is done at this point, we are simply defining an object we can add analysis tasks to. These analysis tasks will be run in the order they are added to the halo catalog object." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Instantiate a catalog using those two paramter files\n", "hc = HaloCatalog(data_ds=data_ds, halos_ds=halos_ds, \n", " output_dir=os.path.join(tmpdir, 'halo_catalog'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first analysis task we add is a filter for the most massive halos; those with masses great than $10^{14}~M_\\odot$. Note that all following analysis will only be performed on these massive halos and we will not waste computational time calculating quantities for halos we are not interested in. This is a result of adding this filter first. If we had called `add_filter` after some other `add_quantity` or `add_callback` to the halo catalog, the quantity and callback calculations would have been performed for all halos, not just those which pass the filter." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Filter out less massive halos\n", "hc.add_filter(\"quantity_value\", \"particle_mass\", \">\", 1e14, \"Msun\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Finding Radial Profiles" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Our first analysis goal is going to be constructing radial profiles for our halos. We would like these profiles to be in terms of the virial radius. Unfortunately we have no guarantee that values of center and virial radius recorded by the halo finder are actually physical. Therefore we should recalculate these quantities ourselves using the values recorded by the halo finder as a starting point." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first step is going to be creating a sphere object that we will create radial profiles along. This attaches a sphere data object to every halo left in the catalog." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# attach a sphere object to each halo whose radius extends to twice the radius of the halo\n", "hc.add_callback(\"sphere\", factor=2.0)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Next we find the radial profile of the gas overdensity along the sphere object in order to find the virial radius. `radius` is the axis along which we make bins for the radial profiles. `[(\"gas\",\"overdensity\")]` is the quantity that we are profiling. This is a list so we can profile as many quantities as we want. The `weight_field` indicates how the cells should be weighted, but note that this is not a list, so all quantities will be weighted in the same way. The `accumulation` keyword indicates if the profile should be cummulative; this is useful for calculating profiles such as enclosed mass. The `storage` keyword indicates the name of the attribute of a halo where these profiles will be stored. Setting the storage keyword to \"virial_quantities_profiles\" means that the profiles will be stored in a dictionary that can be accessed by `halo.virial_quantities_profiles`." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# use the sphere to calculate radial profiles of gas density weighted by cell volume in terms of the virial radius\n", "hc.add_callback(\"profile\", [\"radius\"],\n", " [(\"gas\", \"overdensity\")],\n", " weight_field=\"cell_volume\", \n", " accumulation=True,\n", " storage=\"virial_quantities_profiles\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we calculate the virial radius of halo using the sphere object. As this is a callback, not a quantity, the virial radius will not be written out with the rest of the halo properties in the final halo catalog. This also has a `profile_storage` keyword to specify where the radial profiles are stored that will allow the callback to calculate the relevant virial quantities. We supply this keyword with the same string we gave to `storage` in the last `profile` callback." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Define a virial radius for the halo.\n", "hc.add_callback(\"virial_quantities\", [\"radius\"], \n", " profile_storage = \"virial_quantities_profiles\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have calculated the virial radius, we delete the profiles we used to find it." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "hc.add_callback('delete_attribute','virial_quantities_profiles')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now that we have calculated virial quantities we can add a new sphere that is aware of the virial radius we calculated above." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "hc.add_callback('sphere', radius_field='radius_200', factor=5,\n", " field_parameters=dict(virial_radius=('quantity', 'radius_200')))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using this new sphere, we calculate a gas temperature profile along the virial radius, weighted by the cell mass." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "hc.add_callback('profile', 'virial_radius_fraction', [('gas','temperature')],\n", " storage='virial_profiles',\n", " weight_field='cell_mass', \n", " accumulation=False, output_dir='profiles')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "As profiles are not quantities they will not automatically be written out in the halo catalog; thus in order to be reloadable we must write them out explicitly through a callback of `save_profiles`. This makes sense because they have an extra dimension for each halo along the profile axis. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# Save the profiles\n", "hc.add_callback(\"save_profiles\", storage=\"virial_profiles\", output_dir=\"profiles\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We then create the halo catalog. Remember, no analysis is done before this call to create. By adding callbacks and filters we are simply queuing up the actions we want to take that will all run now." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "hc.create()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Reloading HaloCatalogs" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Finally we load these profiles back in and make a pretty plot. It is not strictly necessary to reload the profiles in this notebook, but we show this process here to illustrate that this step may be performed completely separately from the rest of the script. This workflow allows you to create a single script that will allow you to perform all of the analysis that requires the full dataset. The output can then be saved in a compact form where only the necessarily halo quantities are stored. You can then download this smaller dataset to a local computer and run any further non-computationally intense analysis and design the appropriate plots." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can load a previously saved halo catalog by using the `load` command. We then create a `HaloCatalog` object from just this dataset." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "halos_ds = yt.load(os.path.join(tmpdir, 'halo_catalog/halo_catalog.0.h5'))\n", "\n", "hc_reloaded = HaloCatalog(halos_ds=halos_ds,\n", " output_dir=os.path.join(tmpdir, 'halo_catalog'))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ " Just as profiles are saved seperately throught the `save_profiles` callback they also must be loaded separately using the `load_profiles` callback." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "hc_reloaded.add_callback('load_profiles', storage='virial_profiles',\n", " output_dir='profiles')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Calling `load` is the equivalent of calling `create` earlier, but defaults to to not saving new information. This means that the callback to `load_profiles` is not run until we call `load` here." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "hc_reloaded.load()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Plotting Radial Profiles" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In the future ProfilePlot will be able to properly interpret the loaded profiles of `Halo` and `HaloCatalog` objects, but this functionality is not yet implemented. In the meantime, we show a quick method of viewing a profile for a single halo." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The individual `Halo` objects contained in the `HaloCatalog` can be accessed through the `halo_list` attribute. This gives us access to the dictionary attached to each halo where we stored the radial profiles." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "halo = hc_reloaded.halo_list[0]\n", "\n", "radius = halo.virial_profiles[u\"('index', 'virial_radius_fraction')\"]\n", "temperature = halo.virial_profiles[u\"('gas', 'temperature')\"]\n", "\n", "# Remove output files, that are no longer needed\n", "shutil.rmtree(tmpdir)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Here we quickly use matplotlib to create a basic plot of the radial profile of this halo. When `ProfilePlot` is properly configured to accept Halos and HaloCatalogs the full range of yt plotting tools will be accessible." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np\n", "\n", "plt.plot(np.array(radius), np.array(temperature))\n", "\n", "plt.semilogy()\n", "plt.xlabel(r'$\\rm{R/R_{vir}}$')\n", "plt.ylabel(r'$\\rm{Temperature\\/\\/(K)}$')\n", "\n", "plt.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 } yt-3.3.3/doc/source/cookbook/halo_analysis_example.rst0000644000076500000240000000017712770647750024063 0ustar goldbaumstaff00000000000000.. _halo-analysis-example: Worked Example of Halo Analysis ------------------------------- .. notebook:: Halo_Analysis.ipynb yt-3.3.3/doc/source/cookbook/halo_plotting.py0000644000076500000240000000073513003701576022170 0ustar goldbaumstaff00000000000000import yt from yt.analysis_modules.halo_analysis.halo_catalog import HaloCatalog # Load the dataset ds = yt.load("Enzo_64/RD0006/RedshiftOutput0006") # Load the halo list from a rockstar output for this dataset halos = yt.load('rockstar_halos/halos_0.0.bin') # Create the halo catalog from this halo list hc = HaloCatalog(halos_ds=halos) hc.load() # Create a projection with the halos overplot on top p = yt.ProjectionPlot(ds, "x", "density") p.annotate_halos(hc) p.save() yt-3.3.3/doc/source/cookbook/halo_profiler.py0000644000076500000240000000240413017603554022147 0ustar goldbaumstaff00000000000000import yt from yt.analysis_modules.halo_analysis.api import HaloCatalog # Load the data set with the full simulation information # and rockstar halos data_ds = yt.load('Enzo_64/RD0006/RedshiftOutput0006') halos_ds = yt.load('rockstar_halos/halos_0.0.bin') # Instantiate a catalog using those two paramter files hc = HaloCatalog(data_ds=data_ds, halos_ds=halos_ds) # Filter out less massive halos hc.add_filter("quantity_value", "particle_mass", ">", 1e14, "Msun") # This recipe creates a spherical data container, computes # radial profiles, and calculates r_200 and M_200. hc.add_recipe("calculate_virial_quantities", ["radius", "matter_mass"]) # Create a sphere container with radius 5x r_200. field_params = dict(virial_radius=('quantity', 'radius_200')) hc.add_callback('sphere', radius_field='radius_200', factor=5, field_parameters=field_params) # Compute profiles of T vs. r/r_200 hc.add_callback('profile', ['virial_radius_fraction'], [('gas', 'temperature')], storage='virial_profiles', weight_field='cell_mass', accumulation=False, output_dir='profiles') # Save the profiles hc.add_callback("save_profiles", storage="virial_profiles", output_dir="profiles") hc.create() yt-3.3.3/doc/source/cookbook/hse_field.py0000644000076500000240000000275113017603554021251 0ustar goldbaumstaff00000000000000import numpy as np import yt # Open a dataset from when there's a lot of sloshing going on. ds = yt.load("GasSloshingLowRes/sloshing_low_res_hdf5_plt_cnt_0350") # Define the components of the gravitational acceleration vector field by # taking the gradient of the gravitational potential grad_fields = ds.add_gradient_fields(("gas","gravitational_potential")) # We don't need to do the same for the pressure field because yt already # has pressure gradient fields. Now, define the "degree of hydrostatic # equilibrium" field. def _hse(field, data): # Remember that g is the negative of the potential gradient gx = -data["density"] * data["gravitational_potential_gradient_x"] gy = -data["density"] * data["gravitational_potential_gradient_y"] gz = -data["density"] * data["gravitational_potential_gradient_z"] hx = data["pressure_gradient_x"] - gx hy = data["pressure_gradient_y"] - gy hz = data["pressure_gradient_z"] - gz h = np.sqrt((hx * hx + hy * hy + hz * hz) / (gx * gx + gy * gy + gz * gz)) return h ds.add_field(('gas','HSE'), function=_hse, units="", take_log=False, display_name='Hydrostatic Equilibrium') # The gradient operator requires periodic boundaries. This dataset has # open boundary conditions. We need to hack it for now (this will be fixed # in future version of yt) ds.periodicity = (True, True, True) # Take a slice through the center of the domain slc = yt.SlicePlot(ds, 2, ["density", "HSE"], width=(1, 'Mpc')) slc.save("hse") yt-3.3.3/doc/source/cookbook/image_background_colors.py0000644000076500000240000000231613017603554024166 0ustar goldbaumstaff00000000000000# This shows how to save ImageArray objects, such as those returned from # volume renderings, to pngs with varying backgrounds. # First we use the simple_volume_rendering.py recipe from above to generate # a standard volume rendering. import yt import numpy as np ds = yt.load("Enzo_64/DD0043/data0043") im, sc = yt.volume_render(ds, 'density') im.write_png("original.png", sigma_clip=8.0) # Our image array can now be transformed to include different background # colors. By default, the background color is black. The following # modifications can be used on any image array. # write_png accepts a background keyword argument that defaults to 'black'. # Other choices include: # black (0.,0.,0.,1.) # white (1.,1.,1.,1.) # None (0.,0.,0.,0.) <-- Transparent! # any rgba list/array: [r,g,b,a], bounded by 0..1 # We include the sigma_clip=8 keyword here to bring out more contrast between # the background and foreground, but it is entirely optional. im.write_png('black_bg.png', background='black', sigma_clip=8.0) im.write_png('white_bg.png', background='white', sigma_clip=8.0) im.write_png('green_bg.png', background=[0.,1.,0.,1.], sigma_clip=8.0) im.write_png('transparent_bg.png', background=None, sigma_clip=8.0) yt-3.3.3/doc/source/cookbook/index.rst0000644000076500000240000000270013017603554020610 0ustar goldbaumstaff00000000000000.. _cookbook: The Cookbook ============ yt provides a great deal of functionality to the user, but sometimes it can be a bit complex. This section of the documentation lays out examples recipes for how to do a variety of tasks. Most of the early, simple code demonstrations are small scripts which you can easily copy and paste into your own code; however, as we move to more complex tasks, the recipes move to iPython notebooks to display intermediate steps. All of these recipes are available for download in a link next to the recipe. Getting the Sample Data ----------------------- All of the data used in the cookbook is freely available `here `_, where you will find links to download individual datasets. .. note:: To contribute your own recipes, please follow the instructions on how to contribute documentation code: :ref:`writing_documentation`. Example Scripts --------------- .. toctree:: :maxdepth: 2 simple_plots calculating_information complex_plots cosmological_analysis constructing_data_objects .. _example-notebooks: Example Notebooks ----------------- .. toctree:: :maxdepth: 1 notebook_tutorial custom_colorbar_tickmarks gadget_notebook owls_notebook ../visualizing/transfer_function_helper ../analyzing/analysis_modules/sunyaev_zeldovich fits_radio_cubes fits_xray_images tipsy_notebook halo_analysis_example ../visualizing/volume_rendering_tutorial yt-3.3.3/doc/source/cookbook/light_cone_projection.py0000644000076500000240000000243413017603554023674 0ustar goldbaumstaff00000000000000import yt from yt.analysis_modules.cosmological_observation.api import \ LightCone # Create a LightCone object extending from z = 0 to z = 0.1. # We have already set up the redshift dumps to be # used for this, so we will not use any of the time # data dumps. lc = LightCone('enzo_tiny_cosmology/32Mpc_32.enzo', 'Enzo', 0., 0.1, observer_redshift=0.0, time_data=False) # Calculate a randomization of the solution. lc.calculate_light_cone_solution(seed=123456789, filename="LC/solution.txt") # Choose the field to be projected. field = 'szy' # Use the LightCone object to make a projection with a 600 arcminute # field of view and a resolution of 60 arcseconds. # Set njobs to -1 to have one core work on each projection # in parallel. lc.project_light_cone((600.0, "arcmin"), (60.0, "arcsec"), field, weight_field=None, save_stack=True, save_final_image=True, save_slice_images=True, njobs=-1) # By default, the light cone projections are kept in the LC directory, # but this moves them back to the current directory so that they're rendered # in our cookbook. import shutil, glob for file in glob.glob('LC/*png'): shutil.move(file, '.') yt-3.3.3/doc/source/cookbook/light_ray.py0000644000076500000240000000172713017603554021313 0ustar goldbaumstaff00000000000000import os import yt from yt.analysis_modules.cosmological_observation.api import \ LightRay # Create a directory for the light rays if not os.path.isdir("LR"): os.mkdir('LR') # Create a LightRay object extending from z = 0 to z = 0.1 # and use only the redshift dumps. lr = LightRay("enzo_tiny_cosmology/32Mpc_32.enzo", 'Enzo', 0.0, 0.1, use_minimum_datasets=True, time_data=False) # Make a light ray, and set njobs to -1 to use one core # per dataset. lr.make_light_ray(seed=123456789, solution_filename='LR/lightraysolution.txt', data_filename='LR/lightray.h5', fields=['temperature', 'density'], njobs=-1) # Optionally, we can now overplot the part of this ray that intersects # one output from the source dataset in a ProjectionPlot ds = yt.load('enzo_tiny_cosmology/RD0004/RD0004') p = yt.ProjectionPlot(ds, 'z', 'density') p.annotate_ray(lr) p.save() yt-3.3.3/doc/source/cookbook/multi_plot_3x2_FRB.py0000644000076500000240000000477613017603554022715 0ustar goldbaumstaff00000000000000import yt import numpy as np from yt.visualization.api import get_multi_plot import matplotlib.colorbar as cb from matplotlib.colors import LogNorm fn = "Enzo_64/RD0006/RedshiftOutput0006" # dataset to load # load data and get center value and center location as maximum density location ds = yt.load(fn) v, c = ds.find_max("density") # set up our Fixed Resolution Buffer parameters: a width, resolution, and center width = (1.0, 'unitary') res = [1000, 1000] # get_multi_plot returns a containing figure, a list-of-lists of axes # into which we can place plots, and some axes that we'll put # colorbars. # it accepts: # of x-axis plots, # of y-axis plots, and how the # colorbars are oriented (this also determines where they go: below # in the case of 'horizontal', on the right in the case of # 'vertical'), bw is the base-width in inches (4 is about right for # most cases) orient = 'horizontal' fig, axes, colorbars = get_multi_plot( 2, 3, colorbar=orient, bw = 6) # Now we follow the method of "multi_plot.py" but we're going to iterate # over the columns, which will become axes of slicing. plots = [] for ax in range(3): sli = ds.slice(ax, c[ax]) frb = sli.to_frb(width, res) den_axis = axes[ax][0] temp_axis = axes[ax][1] # here, we turn off the axes labels and ticks, but you could # customize further. for ax in (den_axis, temp_axis): ax.xaxis.set_visible(False) ax.yaxis.set_visible(False) # converting our fixed resolution buffers to NDarray so matplotlib can # render them dens = np.array(frb['density']) temp = np.array(frb['temperature']) plots.append(den_axis.imshow(dens, norm=LogNorm())) plots[-1].set_clim((5e-32, 1e-29)) plots[-1].set_cmap("bds_highcontrast") plots.append(temp_axis.imshow(temp, norm=LogNorm())) plots[-1].set_clim((1e3, 1e8)) plots[-1].set_cmap("hot") # Each 'cax' is a colorbar-container, into which we'll put a colorbar. # the zip command creates triples from each element of the three lists # . Note that it cuts off after the shortest iterator is exhausted, # in this case, titles. titles=[r'$\mathrm{Density}\ (\mathrm{g\ cm^{-3}})$', r'$\mathrm{temperature}\ (\mathrm{K})$'] for p, cax, t in zip(plots, colorbars,titles): # Now we make a colorbar, using the 'image' we stored in plots # above. note this is what is *returned* by the imshow method of # the plots. cbar = fig.colorbar(p, cax=cax, orientation=orient) cbar.set_label(t) # And now we're done! fig.savefig("%s_3x2.png" % ds) yt-3.3.3/doc/source/cookbook/multi_plot_slice_and_proj.py0000644000076500000240000000553613017603554024556 0ustar goldbaumstaff00000000000000import yt import numpy as np from yt.visualization.base_plot_types import get_multi_plot import matplotlib.colorbar as cb from matplotlib.colors import LogNorm fn = "GasSloshing/sloshing_nomag2_hdf5_plt_cnt_0150" # dataset to load orient = 'horizontal' ds = yt.load(fn) # load data # There's a lot in here: # From this we get a containing figure, a list-of-lists of axes into which we # can place plots, and some axes that we'll put colorbars. # We feed it: # Number of plots on the x-axis, number of plots on the y-axis, and how we # want our colorbars oriented. (This governs where they will go, too. # bw is the base-width in inches, but 4 is about right for most cases. fig, axes, colorbars = get_multi_plot(3, 2, colorbar=orient, bw = 4) slc = yt.SlicePlot(ds, 'z', fields=["density","temperature","velocity_magnitude"]) proj = yt.ProjectionPlot(ds, 'z', "density", weight_field="density") slc_frb = slc.data_source.to_frb((1.0, "Mpc"), 512) proj_frb = proj.data_source.to_frb((1.0, "Mpc"), 512) dens_axes = [axes[0][0], axes[1][0]] temp_axes = [axes[0][1], axes[1][1]] vels_axes = [axes[0][2], axes[1][2]] for dax, tax, vax in zip(dens_axes, temp_axes, vels_axes) : dax.xaxis.set_visible(False) dax.yaxis.set_visible(False) tax.xaxis.set_visible(False) tax.yaxis.set_visible(False) vax.xaxis.set_visible(False) vax.yaxis.set_visible(False) # Converting our Fixed Resolution Buffers to numpy arrays so that matplotlib # can render them slc_dens = np.array(slc_frb['density']) proj_dens = np.array(proj_frb['density']) slc_temp = np.array(slc_frb['temperature']) proj_temp = np.array(proj_frb['temperature']) slc_vel = np.array(slc_frb['velocity_magnitude']) proj_vel = np.array(proj_frb['velocity_magnitude']) plots = [dens_axes[0].imshow(slc_dens, origin='lower', norm=LogNorm()), dens_axes[1].imshow(proj_dens, origin='lower', norm=LogNorm()), temp_axes[0].imshow(slc_temp, origin='lower'), temp_axes[1].imshow(proj_temp, origin='lower'), vels_axes[0].imshow(slc_vel, origin='lower', norm=LogNorm()), vels_axes[1].imshow(proj_vel, origin='lower', norm=LogNorm())] plots[0].set_clim((1.0e-27,1.0e-25)) plots[0].set_cmap("bds_highcontrast") plots[1].set_clim((1.0e-27,1.0e-25)) plots[1].set_cmap("bds_highcontrast") plots[2].set_clim((1.0e7,1.0e8)) plots[2].set_cmap("hot") plots[3].set_clim((1.0e7,1.0e8)) plots[3].set_cmap("hot") plots[4].set_clim((1e6, 1e8)) plots[4].set_cmap("gist_rainbow") plots[5].set_clim((1e6, 1e8)) plots[5].set_cmap("gist_rainbow") titles=[r'$\mathrm{Density}\ (\mathrm{g\ cm^{-3}})$', r'$\mathrm{Temperature}\ (\mathrm{K})$', r'$\mathrm{Velocity Magnitude}\ (\mathrm{cm\ s^{-1}})$'] for p, cax, t in zip(plots[0:6:2], colorbars, titles): cbar = fig.colorbar(p, cax=cax, orientation=orient) cbar.set_label(t) # And now we're done! fig.savefig("%s_3x2" % ds) yt-3.3.3/doc/source/cookbook/multi_width_image.py0000644000076500000240000000161613017603554023021 0ustar goldbaumstaff00000000000000import yt # Load the dataset. ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030") # Create a slice plot for the dataset. With no additional arguments, # the width will be the size of the domain and the center will be the # center of the simulation box slc = yt.SlicePlot(ds, 'z', 'density') # Create a list of a couple of widths and units. # (N.B. Mpc (megaparsec) != mpc (milliparsec) widths = [(1, 'Mpc'), (15, 'kpc')] # Loop through the list of widths and units. for width, unit in widths: # Set the width. slc.set_width(width, unit) # Write out the image with a unique name. slc.save("%s_%010d_%s" % (ds, width, unit)) zoomFactors = [2,4,5] # recreate the original slice slc = yt.SlicePlot(ds, 'z', 'density') for zoomFactor in zoomFactors: # zoom in slc.zoom(zoomFactor) # Write out the image with a unique name. slc.save("%s_%i" % (ds, zoomFactor)) yt-3.3.3/doc/source/cookbook/multiplot_2x2.py0000644000076500000240000000300613017603554022045 0ustar goldbaumstaff00000000000000import yt import matplotlib.pyplot as plt from mpl_toolkits.axes_grid1 import AxesGrid fn = "IsolatedGalaxy/galaxy0030/galaxy0030" ds = yt.load(fn) # load data fig = plt.figure() # See http://matplotlib.org/mpl_toolkits/axes_grid/api/axes_grid_api.html # These choices of keyword arguments produce a four panel plot that includes # four narrow colorbars, one for each plot. Axes labels are only drawn on the # bottom left hand plot to avoid repeating information and make the plot less # cluttered. grid = AxesGrid(fig, (0.075,0.075,0.85,0.85), nrows_ncols = (2, 2), axes_pad = 1.0, label_mode = "1", share_all = True, cbar_location="right", cbar_mode="each", cbar_size="3%", cbar_pad="0%") fields = ['density', 'velocity_x', 'velocity_y', 'velocity_magnitude'] # Create the plot. Since SlicePlot accepts a list of fields, we need only # do this once. p = yt.SlicePlot(ds, 'z', fields) # Velocity is going to be both positive and negative, so let's make these # slices use a linear colorbar scale p.set_log('velocity_x', False) p.set_log('velocity_y', False) p.zoom(2) # For each plotted field, force the SlicePlot to redraw itself onto the AxesGrid # axes. for i, field in enumerate(fields): plot = p.plots[field] plot.figure = fig plot.axes = grid[i].axes plot.cax = grid.cbar_axes[i] # Finally, redraw the plot on the AxesGrid axes. p._setup_plots() plt.savefig('multiplot_2x2.png') yt-3.3.3/doc/source/cookbook/multiplot_2x2_coordaxes_slice.py0000644000076500000240000000343013003701576025272 0ustar goldbaumstaff00000000000000import yt import matplotlib.pyplot as plt from mpl_toolkits.axes_grid1 import AxesGrid fn = "IsolatedGalaxy/galaxy0030/galaxy0030" ds = yt.load(fn) # load data fig = plt.figure() # See http://matplotlib.org/mpl_toolkits/axes_grid/api/axes_grid_api.html # These choices of keyword arguments produce two colorbars, both drawn on the # right hand side. This means there are only two colorbar axes, one for Density # and another for temperature. In addition, axes labels will be drawn for all # plots. grid = AxesGrid(fig, (0.075,0.075,0.85,0.85), nrows_ncols = (2, 2), axes_pad = 1.0, label_mode = "all", share_all = True, cbar_location="right", cbar_mode="edge", cbar_size="5%", cbar_pad="0%") cuts = ['x', 'y', 'z', 'z'] fields = ['density', 'density', 'density', 'temperature'] for i, (direction, field) in enumerate(zip(cuts, fields)): # Load the data and create a single plot p = yt.SlicePlot(ds, direction, field) p.zoom(40) # This forces the ProjectionPlot to redraw itself on the AxesGrid axes. plot = p.plots[field] plot.figure = fig plot.axes = grid[i].axes # Since there are only two colorbar axes, we need to make sure we don't try # to set the temperature colorbar to cbar_axes[4], which would if we used i # to index cbar_axes, yielding a plot without a temperature colorbar. # This unecessarily redraws the Density colorbar three times, but that has # no effect on the final plot. if field == 'density': plot.cax = grid.cbar_axes[0] elif field == 'temperature': plot.cax = grid.cbar_axes[1] # Finally, redraw the plot. p._setup_plots() plt.savefig('multiplot_2x2_coordaxes_slice.png') yt-3.3.3/doc/source/cookbook/multiplot_2x2_time_series.py0000644000076500000240000000264413003701576024442 0ustar goldbaumstaff00000000000000import yt import matplotlib.pyplot as plt from mpl_toolkits.axes_grid1 import AxesGrid fns = ['Enzo_64/DD0005/data0005','Enzo_64/DD0015/data0015', 'Enzo_64/DD0025/data0025', 'Enzo_64/DD0035/data0035'] fig = plt.figure() # See http://matplotlib.org/mpl_toolkits/axes_grid/api/axes_grid_api.html # These choices of keyword arguments produce a four panel plot with a single # shared narrow colorbar on the right hand side of the multipanel plot. Axes # labels are drawn for all plots since we're slicing along different directions # for each plot. grid = AxesGrid(fig, (0.075,0.075,0.85,0.85), nrows_ncols = (2, 2), axes_pad = 0.05, label_mode = "L", share_all = True, cbar_location="right", cbar_mode="single", cbar_size="3%", cbar_pad="0%") for i, fn in enumerate(fns): # Load the data and create a single plot ds = yt.load(fn) # load data p = yt.ProjectionPlot(ds, 'z', 'density', width=(55, 'Mpccm')) # Ensure the colorbar limits match for all plots p.set_zlim('density', 1e-4, 1e-2) # This forces the ProjectionPlot to redraw itself on the AxesGrid axes. plot = p.plots['density'] plot.figure = fig plot.axes = grid[i].axes plot.cax = grid.cbar_axes[i] # Finally, this actually redraws the plot. p._setup_plots() plt.savefig('multiplot_2x2_time_series.png') yt-3.3.3/doc/source/cookbook/notebook_tutorial.rst0000644000076500000240000000255313017603554023252 0ustar goldbaumstaff00000000000000.. _notebook-tutorial: Notebook Tutorial ----------------- The IPython notebook is a powerful system for literate codoing - a style of writing code that embeds input, output, and explanatory text into one document. yt has deep integration with the IPython notebook, explained in-depth in the other example notebooks and the rest of the yt documentation. This page is here to give a brief introduction to the notebook its self. To start the notebook, enter the following command at the bash command line: .. code-block:: bash $ ipython notebook Depending on your default web browser and system setup this will open a web browser and direct you to the notebook dashboard. If it does not, you might need to connect to the notebook manually. See the `IPython documentation `_ for more details. For the notebook tutorial, we rely on example notebooks that are part of the IPython documentation. We link to static nbviewer versions of the 'evaluated' versions of these example notebooks. If you would like to run them locally on your own computer, simply download the notebook by clicking the 'Download Notebook' link in the top right corner of each page. 1. `IPython Notebook Tutorials `_ yt-3.3.3/doc/source/cookbook/offaxis_projection.py0000644000076500000240000000240213017603554023213 0ustar goldbaumstaff00000000000000import yt import numpy as np # Load the dataset. ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030") # Choose a center for the render. c = [0.5, 0.5, 0.5] # Our image plane will be normal to some vector. For things like collapsing # objects, you could set it the way you would a cutting plane -- but for this # dataset, we'll just choose an off-axis value at random. This gets normalized # automatically. L = [1.0, 0.0, 0.0] # Our "width" is the width of the image plane as well as the depth. # The first element is the left to right width, the second is the # top-bottom width, and the last element is the back-to-front width # (all in code units) W = [0.04,0.04,0.4] # The number of pixels along one side of the image. # The final image will have Npixel^2 pixels. Npixels = 512 # Create the off axis projection. # Setting no_ghost to False speeds up the process, but makes a # slighly lower quality image. image = yt.off_axis_projection(ds, c, L, W, Npixels, "density", no_ghost=False) # Write out the final image and give it a name # relating to what our dataset is called. # We save the log of the values so that the colors do not span # many orders of magnitude. Try it without and see what happens. yt.write_image(np.log10(image), "%s_offaxis_projection.png" % ds) yt-3.3.3/doc/source/cookbook/offaxis_projection_colorbar.py0000644000076500000240000000325313017603554025103 0ustar goldbaumstaff00000000000000import yt import numpy as np fn = "IsolatedGalaxy/galaxy0030/galaxy0030" # dataset to load ds = yt.load(fn) # load data # Now we need a center of our volume to render. Here we'll just use # 0.5,0.5,0.5, because volume renderings are not periodic. c = [0.5, 0.5, 0.5] # Our image plane will be normal to some vector. For things like collapsing # objects, you could set it the way you would a cutting plane -- but for this # dataset, we'll just choose an off-axis value at random. This gets normalized # automatically. L = [0.5, 0.4, 0.7] # Our "width" is the width of the image plane as well as the depth. # The first element is the left to right width, the second is the # top-bottom width, and the last element is the back-to-front width # (all in code units) W = [0.04,0.04,0.4] # The number of pixels along one side of the image. # The final image will have Npixel^2 pixels. Npixels = 512 # Now we call the off_axis_projection function, which handles the rest. # Note that we set no_ghost equal to False, so that we *do* include ghost # zones in our data. This takes longer to calculate, but the results look # much cleaner than when you ignore the ghost zones. # Also note that we set the field which we want to project as "density", but # really we could use any arbitrary field like "temperature", "metallicity" # or whatever. image = yt.off_axis_projection(ds, c, L, W, Npixels, "density", no_ghost=False) # Image is now an NxN array representing the intensities of the various pixels. # And now, we call our direct image saver. We save the log of the result. yt.write_projection(image, "offaxis_projection_colorbar.png", colorbar_label="Column Density (cm$^{-2}$)") yt-3.3.3/doc/source/cookbook/opaque_rendering.py0000644000076500000240000000410513023400062022633 0ustar goldbaumstaff00000000000000import yt import numpy as np ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030") # We start by building a default volume rendering scene im, sc = yt.volume_render(ds, field=("gas","density"), fname="v0.png", sigma_clip=6.0) sc.camera.set_width(ds.arr(0.1,'code_length')) tf = sc.get_source(0).transfer_function tf.clear() tf.add_layers(4, 0.01, col_bounds = [-27.5,-25.5], alpha=np.logspace(-3,0,4), colormap = 'RdBu_r') sc.render() sc.save("v1.png", sigma_clip=6.0) # In this case, the default alphas used (np.logspace(-3,0,Nbins)) does not # accentuate the outer regions of the galaxy. Let's start by bringing up the # alpha values for each contour to go between 0.1 and 1.0 tf = sc.get_source(0).transfer_function tf.clear() tf.add_layers(4, 0.01, col_bounds = [-27.5,-25.5], alpha=np.logspace(0,0,4), colormap = 'RdBu_r') sc.render() sc.save("v2.png", sigma_clip=6.0) # Now let's set the grey_opacity to True. This should make the inner portions # start to be obcured tf.grey_opacity = True sc.render() sc.save("v3.png", sigma_clip=6.0) # That looks pretty good, but let's start bumping up the opacity. tf.clear() tf.add_layers(4, 0.01, col_bounds = [-27.5,-25.5], alpha=10.0*np.ones(4,dtype='float64'), colormap = 'RdBu_r') sc.render() sc.save("v4.png", sigma_clip=6.0) # Let's bump up again to see if we can obscure the inner contour. tf.clear() tf.add_layers(4, 0.01, col_bounds = [-27.5,-25.5], alpha=30.0*np.ones(4,dtype='float64'), colormap = 'RdBu_r') sc.render() sc.save("v5.png", sigma_clip=6.0) # Now we are losing sight of everything. Let's see if we can obscure the next # layer tf.clear() tf.add_layers(4, 0.01, col_bounds = [-27.5,-25.5], alpha=100.0*np.ones(4,dtype='float64'), colormap = 'RdBu_r') sc.render() sc.save("v6.png", sigma_clip=6.0) # That is very opaque! Now lets go back and see what it would look like with # grey_opacity = False tf.grey_opacity=False sc.render() sc.save("v7.png", sigma_clip=6.0) # That looks pretty different, but the main thing is that you can see that the # inner contours are somewhat visible again. yt-3.3.3/doc/source/cookbook/opengl_ipython.py0000644000076500000240000000144013017603554022357 0ustar goldbaumstaff00000000000000import yt from yt.visualization.volume_rendering.interactive_vr import \ SceneGraph, BlockCollection, TrackballCamera from yt.visualization.volume_rendering.interactive_loop import \ RenderingContext from yt.visualization.volume_rendering import glfw_inputhook rc = RenderingContext(1280, 960) scene = SceneGraph() collection = BlockCollection() ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030") dd = ds.all_data() collection.add_data(dd, "density") scene.add_collection(collection) position = (1.0, 1.0, 1.0) c = TrackballCamera(position=position, focus=ds.domain_center, near_plane=0.1) callbacks = rc.setup_loop(scene, c) rl = rc(scene, c, callbacks) # To make this work from IPython execute: # # glfw_inputhook.inputhook_manager.enable_gui("glfw", app=rl) yt-3.3.3/doc/source/cookbook/opengl_vr.py0000644000076500000240000000134213017603554021315 0ustar goldbaumstaff00000000000000import yt from yt.visualization.volume_rendering.interactive_vr import \ SceneGraph, BlockCollection, TrackballCamera from yt.visualization.volume_rendering.interactive_loop import \ RenderingContext ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030") # Create GLUT window rc = RenderingContext(1280, 960) # Create a 3d Texture from all_data() collection = BlockCollection() dd = ds.all_data() collection.add_data(dd, "density") # Initiliaze basic Scene and pass the data scene = SceneGraph() scene.add_collection(collection) # Create default camera position = (1.0, 1.0, 1.0) c = TrackballCamera(position=position, focus=ds.domain_center, near_plane=0.1) # Start rendering loop rc.start_loop(scene, c) yt-3.3.3/doc/source/cookbook/overplot_grids.py0000644000076500000240000000067613003701576022373 0ustar goldbaumstaff00000000000000import yt # Load the dataset. ds = yt.load("Enzo_64/DD0043/data0043") # Make a density projection. p = yt.ProjectionPlot(ds, "y", "density") # Modify the projection # The argument specifies the region along the line of sight # for which particles will be gathered. # 1.0 signifies the entire domain in the line of sight. p.annotate_grids() # Save the image. # Optionally, give a string as an argument # to name files with a keyword. p.save() yt-3.3.3/doc/source/cookbook/overplot_particles.py0000644000076500000240000000117213003701576023241 0ustar goldbaumstaff00000000000000import yt # Load the dataset. ds = yt.load("Enzo_64/DD0043/data0043") # Make a density projection centered on the 'm'aximum density location # with a width of 10 Mpc.. p = yt.ProjectionPlot(ds, "y", "density", center='m', width=(10, 'Mpc')) # Modify the projection # The argument specifies the region along the line of sight # for which particles will be gathered. # 1.0 signifies the entire domain in the line of sight # p.annotate_particles(1.0) # but in this case we only go 10 Mpc in depth p.annotate_particles((10, 'Mpc')) # Save the image. # Optionally, give a string as an argument # to name files with a keyword. p.save() yt-3.3.3/doc/source/cookbook/owls_notebook.rst0000644000076500000240000000024413017603554022366 0ustar goldbaumstaff00000000000000.. _owls-notebook: Using yt to view and analyze Gadget-OWLS outputs ++++++++++++++++++++++++++++++++++++++++++++++++ .. notebook:: yt_gadget_owls_analysis.ipynb yt-3.3.3/doc/source/cookbook/particle_filter.py0000644000076500000240000000436413017603554022501 0ustar goldbaumstaff00000000000000import yt import numpy as np from yt.data_objects.particle_filters import add_particle_filter # Define filter functions for our particle filters based on stellar age. # In this dataset particles in the initial conditions are given creation # times arbitrarily far into the future, so stars with negative ages belong # in the old stars filter. def stars_10Myr(pfilter, data): age = data.ds.current_time - data["Stars", "creation_time"] filter = np.logical_and(age >= 0, age.in_units('Myr') < 10) return filter def stars_100Myr(pfilter, data): age = (data.ds.current_time - data["Stars", "creation_time"]).in_units('Myr') filter = np.logical_and(age >= 10, age < 100) return filter def stars_old(pfilter, data): age = data.ds.current_time - data["Stars", "creation_time"] filter = np.logical_or(age < 0, age.in_units('Myr') >= 100) return filter # Create the particle filters add_particle_filter("stars_young", function=stars_10Myr, filtered_type='Stars', requires=["creation_time"]) add_particle_filter("stars_medium", function=stars_100Myr, filtered_type='Stars', requires=["creation_time"]) add_particle_filter("stars_old", function=stars_old, filtered_type='Stars', requires=["creation_time"]) # Load a dataset and apply the particle filters filename = "TipsyGalaxy/galaxy.00300" ds = yt.load(filename) ds.add_particle_filter('stars_young') ds.add_particle_filter('stars_medium') ds.add_particle_filter('stars_old') # What are the total masses of different ages of star in the whole simulation # volume? ad = ds.all_data() mass_young = ad['stars_young', 'particle_mass'].in_units('Msun').sum() mass_medium = ad['stars_medium', 'particle_mass'].in_units('Msun').sum() mass_old = ad['stars_old', 'particle_mass'].in_units('Msun').sum() print("Mass of young stars = %g Msun" % mass_young) print("Mass of medium stars = %g Msun" % mass_medium) print("Mass of old stars = %g Msun" % mass_old) # Generate 4 projections: gas density, young stars, medium stars, old stars fields = [('gas', 'density'), ('deposit', 'stars_young_cic'), ('deposit', 'stars_medium_cic'), ('deposit', 'stars_old_cic')] prj = yt.ProjectionPlot(ds, 'z', fields, center="max", width=(100, 'kpc')) prj.save() yt-3.3.3/doc/source/cookbook/particle_filter_sfr.py0000644000076500000240000000204613003701576023344 0ustar goldbaumstaff00000000000000import yt import numpy as np from yt.data_objects.particle_filters import add_particle_filter from matplotlib import pyplot as plt def formed_star(pfilter, data): filter = data["all", "creation_time"] > 0 return filter add_particle_filter("formed_star", function=formed_star, filtered_type='all', requires=["creation_time"]) filename = "IsolatedGalaxy/galaxy0030/galaxy0030" ds = yt.load(filename) ds.add_particle_filter('formed_star') ad = ds.all_data() masses = ad['formed_star', 'particle_mass'].in_units('Msun') formation_time = ad['formed_star', 'creation_time'].in_units('yr') time_range = [0, 5e8] # years n_bins = 1000 hist, bins = np.histogram(formation_time, bins=n_bins, range=time_range,) inds = np.digitize(formation_time, bins=bins) time = (bins[:-1] + bins[1:])/2 sfr = np.array([masses[inds == j].sum()/(bins[j+1]-bins[j]) for j in range(len(time))]) sfr[sfr == 0] = np.nan plt.plot(time/1e6, sfr) plt.xlabel('Time [Myr]') plt.ylabel('SFR [M$_\odot$ yr$^{-1}$]') plt.savefig("filter_sfr.png") yt-3.3.3/doc/source/cookbook/particle_one_color_plot.py0000644000076500000240000000037613017603554024230 0ustar goldbaumstaff00000000000000import yt # load the dataset ds = yt.load('IsolatedGalaxy/galaxy0030/galaxy0030') # create our plot p = yt.ParticlePlot(ds, 'particle_position_x', 'particle_position_y', color='b') # zoom in a little bit p.set_width(500, 'kpc') #save result p.save() yt-3.3.3/doc/source/cookbook/particle_xvz_plot.py0000644000076500000240000000055513017603554023077 0ustar goldbaumstaff00000000000000import yt # load the dataset ds = yt.load('IsolatedGalaxy/galaxy0030/galaxy0030') # create our plot p = yt.ParticlePlot(ds, 'particle_position_x', 'particle_velocity_z', ['particle_mass']) # pick some appropriate units p.set_unit('particle_position_x', 'Mpc') p.set_unit('particle_velocity_z', 'km/s') p.set_unit('particle_mass', 'Msun') # save result p.save() yt-3.3.3/doc/source/cookbook/particle_xy_plot.py0000644000076500000240000000050013017603554022676 0ustar goldbaumstaff00000000000000import yt # load the dataset ds = yt.load('IsolatedGalaxy/galaxy0030/galaxy0030') # create our plot p = yt.ParticlePlot(ds, 'particle_position_x', 'particle_position_y', 'particle_mass', width=(0.5, 0.5)) # pick some appropriate units p.set_axes_unit('kpc') p.set_unit('particle_mass', 'Msun') #save result p.save() yt-3.3.3/doc/source/cookbook/power_spectrum_example.py0000644000076500000240000000565213017603554024123 0ustar goldbaumstaff00000000000000import numpy as np import matplotlib.pyplot as plt import yt """ Make a turbulent KE power spectrum. Since we are stratified, we use a rho**(1/3) scaling to the velocity to get something that would look Kolmogorov (if the turbulence were fully developed). Ultimately, we aim to compute: 1 ^ ^* E(k) = integral - V(k) . V(k) dS 2 n ^ where V = rho U is the density-weighted velocity field, and V is the FFT of V. (Note: sometimes we normalize by 1/volume to get a spectral energy density spectrum). """ def doit(ds): # a FFT operates on uniformly gridded data. We'll use the yt # covering grid for this. max_level = ds.index.max_level ref = int(np.product(ds.ref_factors[0:max_level])) low = ds.domain_left_edge dims = ds.domain_dimensions*ref nx, ny, nz = dims nindex_rho = 1./3. Kk = np.zeros( (nx/2+1, ny/2+1, nz/2+1)) for vel in [("gas", "velocity_x"), ("gas", "velocity_y"), ("gas", "velocity_z")]: Kk += 0.5*fft_comp(ds, ("gas", "density"), vel, nindex_rho, max_level, low, dims) # wavenumbers L = (ds.domain_right_edge - ds.domain_left_edge).d kx = np.fft.rfftfreq(nx)*nx/L[0] ky = np.fft.rfftfreq(ny)*ny/L[1] kz = np.fft.rfftfreq(nz)*nz/L[2] # physical limits to the wavenumbers kmin = np.min(1.0/L) kmax = np.min(0.5*dims/L) kbins = np.arange(kmin, kmax, kmin) N = len(kbins) # bin the Fourier KE into radial kbins kx3d, ky3d, kz3d = np.meshgrid(kx, ky, kz, indexing="ij") k = np.sqrt(kx3d**2 + ky3d**2 + kz3d**2) whichbin = np.digitize(k.flat, kbins) ncount = np.bincount(whichbin) E_spectrum = np.zeros(len(ncount)-1) for n in range(1,len(ncount)): E_spectrum[n-1] = np.sum(Kk.flat[whichbin==n]) k = 0.5*(kbins[0:N-1] + kbins[1:N]) E_spectrum = E_spectrum[1:N] index = np.argmax(E_spectrum) kmax = k[index] Emax = E_spectrum[index] plt.loglog(k, E_spectrum) plt.loglog(k, Emax*(k/kmax)**(-5./3.), ls=":", color="0.5") plt.xlabel(r"$k$") plt.ylabel(r"$E(k)dk$") plt.savefig("spectrum.png") def fft_comp(ds, irho, iu, nindex_rho, level, low, delta ): cube = ds.covering_grid(level, left_edge=low, dims=delta, fields=[irho, iu]) rho = cube[irho].d u = cube[iu].d nx, ny, nz = rho.shape # do the FFTs -- note that since our data is real, there will be # too much information here. fftn puts the positive freq terms in # the first half of the axes -- that's what we keep. Our # normalization has an '8' to account for this clipping to one # octant. ru = np.fft.fftn(rho**nindex_rho * u)[0:nx/2+1,0:ny/2+1,0:nz/2+1] ru = 8.0*ru/(nx*ny*nz) return np.abs(ru)**2 ds = yt.load("maestro_xrb_lores_23437") doit(ds) yt-3.3.3/doc/source/cookbook/profile_with_variance.py0000644000076500000240000000220113003701576023656 0ustar goldbaumstaff00000000000000import matplotlib.pyplot as plt import yt # Load the dataset. ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030") # Create a sphere of radius 1 Mpc centered on the max density location. sp = ds.sphere("max", (1, "Mpc")) # Calculate and store the bulk velocity for the sphere. bulk_velocity = sp.quantities.bulk_velocity() sp.set_field_parameter('bulk_velocity', bulk_velocity) # Create a 1D profile object for profiles over radius # and add a velocity profile. prof = yt.create_profile(sp, 'radius', ('gas', 'velocity_magnitude'), units = {'radius': 'kpc'}, extrema = {'radius': ((0.1, 'kpc'), (1000.0, 'kpc'))}, weight_field='cell_mass') # Create arrays to plot. radius = prof.x.value mean = prof['gas', 'velocity_magnitude'].value variance = prof.variance['gas', 'velocity_magnitude'].value # Plot the average velocity magnitude. plt.loglog(radius, mean, label='Mean') # Plot the variance of the velocity magnitude. plt.loglog(radius, variance, label='Standard Deviation') plt.xlabel('r [kpc]') plt.ylabel('v [cm/s]') plt.legend() plt.savefig('velocity_profiles.png') yt-3.3.3/doc/source/cookbook/rad_velocity.py0000644000076500000240000000233013003701576022002 0ustar goldbaumstaff00000000000000import yt import matplotlib.pyplot as plt ds = yt.load("GasSloshing/sloshing_nomag2_hdf5_plt_cnt_0150") # Get the first sphere sp0 = ds.sphere(ds.domain_center, (500., "kpc")) # Compute the bulk velocity from the cells in this sphere bulk_vel = sp0.quantities.bulk_velocity() # Get the second sphere sp1 = ds.sphere(ds.domain_center, (500., "kpc")) # Set the bulk velocity field parameter sp1.set_field_parameter("bulk_velocity", bulk_vel) # Radial profile without correction rp0 = yt.create_profile(sp0, 'radius', 'radial_velocity', units = {'radius': 'kpc'}, logs = {'radius': False}) # Radial profile with correction for bulk velocity rp1 = yt.create_profile(sp1, 'radius', 'radial_velocity', units = {'radius': 'kpc'}, logs = {'radius': False}) # Make a plot using matplotlib fig = plt.figure() ax = fig.add_subplot(111) ax.plot(rp0.x.value, rp0["radial_velocity"].in_units("km/s").value, rp1.x.value, rp1["radial_velocity"].in_units("km/s").value) ax.set_xlabel(r"$\mathrm{r\ (kpc)}$") ax.set_ylabel(r"$\mathrm{v_r\ (km/s)}$") ax.legend(["Without Correction", "With Correction"]) fig.savefig("%s_profiles.png" % ds) yt-3.3.3/doc/source/cookbook/radial_profile_styles.py0000644000076500000240000000236613017603554023710 0ustar goldbaumstaff00000000000000import yt import matplotlib.pyplot as plt ds = yt.load("GasSloshing/sloshing_nomag2_hdf5_plt_cnt_0150") # Get a sphere object sp = ds.sphere(ds.domain_center, (500., "kpc")) # Bin up the data from the sphere into a radial profile rp = yt.create_profile(sp, 'radius', ['density', 'temperature'], units = {'radius': 'kpc'}, logs = {'radius': False}) # Make plots using matplotlib fig = plt.figure() ax = fig.add_subplot(111) # Plot the density as a log-log plot using the default settings dens_plot = ax.loglog(rp.x.value, rp["density"].value) # Here we set the labels of the plot axes ax.set_xlabel(r"$\mathrm{r\ (kpc)}$") ax.set_ylabel(r"$\mathrm{\rho\ (g\ cm^{-3})}$") # Save the default plot fig.savefig("density_profile_default.png" % ds) # The "dens_plot" object is a list of plot objects. In our case we only have one, # so we index the list by '0' to get it. # Plot using dashed red lines dens_plot[0].set_linestyle("--") dens_plot[0].set_color("red") fig.savefig("density_profile_dashed_red.png") # Increase the line width and add points in the shape of x's dens_plot[0].set_linewidth(5) dens_plot[0].set_marker("x") dens_plot[0].set_markersize(10) fig.savefig("density_profile_thick_with_xs.png") yt-3.3.3/doc/source/cookbook/rendering_with_box_and_grids.py0000644000076500000240000000111613017603554025213 0ustar goldbaumstaff00000000000000import yt # Load the dataset. ds = yt.load("Enzo_64/DD0043/data0043") sc = yt.create_scene(ds, ('gas', 'density')) # You may need to adjust the alpha values to get a rendering with good contrast # For annotate_domain, the fourth color value is alpha. # Draw the domain boundary sc.annotate_domain(ds, color=[1, 1, 1, 0.01]) sc.save("%s_vr_domain.png" % ds, sigma_clip=4) # Draw the grid boundaries sc.annotate_grids(ds, alpha=0.01) sc.save("%s_vr_grids.png" % ds, sigma_clip=4) # Draw a coordinate axes triad sc.annotate_axes(alpha=0.01) sc.save("%s_vr_coords.png" % ds, sigma_clip=4) yt-3.3.3/doc/source/cookbook/rockstar_nest.py0000644000076500000240000000642013017603554022205 0ustar goldbaumstaff00000000000000# You must run this job in parallel. # There are several mpi flags which can be useful in order for it to work OK. # It requires at least 3 processors in order to run because of the way in which # rockstar divides up the work. Make sure you have mpi4py installed as per # http://yt-project.org/docs/dev/analyzing/parallel_computation.html#setting-up-parallel-yt # Usage: mpirun -np --mca btl ^openib python this_script.py import yt from yt.analysis_modules.halo_analysis.halo_catalog import HaloCatalog from yt.data_objects.particle_filters import add_particle_filter from yt.analysis_modules.halo_finding.rockstar.api import RockstarHaloFinder yt.enable_parallelism() # rockstar halofinding requires parallelism # Create a dark matter particle filter # This will be code dependent, but this function here is true for enzo def DarkMatter(pfilter, data): filter = data[("all", "particle_type")] == 1 # DM = 1, Stars = 2 return filter add_particle_filter("dark_matter", function=DarkMatter, filtered_type='all', \ requires=["particle_type"]) # First, we make sure that this script is being run using mpirun with # at least 3 processors as indicated in the comments above. assert(yt.communication_system.communicators[-1].size >= 3) # Load the dataset and apply dark matter filter fn = "Enzo_64/DD0043/data0043" ds = yt.load(fn) ds.add_particle_filter('dark_matter') # Determine highest resolution DM particle mass in sim by looking # at the extrema of the dark_matter particle_mass field. ad = ds.all_data() min_dm_mass = ad.quantities.extrema(('dark_matter','particle_mass'))[0] # Define a new particle filter to isolate all highest resolution DM particles # and apply it to dataset def MaxResDarkMatter(pfilter, data): return data["particle_mass"] <= 1.01 * min_dm_mass add_particle_filter("max_res_dark_matter", function=MaxResDarkMatter, \ filtered_type='dark_matter', requires=["particle_mass"]) ds.add_particle_filter('max_res_dark_matter') # If desired, we can see the total number of DM and High-res DM particles #if yt.is_root(): # print("Simulation has %d DM particles." % # ad['dark_matter','particle_type'].shape) # print("Simulation has %d Highest Res DM particles." % # ad['max_res_dark_matter', 'particle_type'].shape) # Run the halo catalog on the dataset only on the highest resolution dark matter # particles hc = HaloCatalog(data_ds=ds, finder_method='rockstar', \ finder_kwargs={'dm_only':True, 'particle_type':'max_res_dark_matter'}) hc.create() # Or alternatively, just run the RockstarHaloFinder and later import the # output file as necessary. You can skip this step if you've already run it # once, but be careful since subsequent halo finds will overwrite this data. #rhf = RockstarHaloFinder(ds, particle_type="max_res_dark_matter") #rhf.run() # Load the halo list from a rockstar output for this dataset # Create a projection with the halos overplot on top #halos = yt.load('rockstar_halos/halos_0.0.bin') #hc = HaloCatalog(halos_ds=halos) #hc.load() # Regardless of your method of creating the halo catalog, use it to overplot the # halos on a projection. p = yt.ProjectionPlot(ds, "x", "density") p.annotate_halos(hc, annotate_field = 'particle_identifier', width=(10,'Mpc'), factor=2) p.save() yt-3.3.3/doc/source/cookbook/show_hide_axes_colorbar.py0000644000076500000240000000046613017603554024204 0ustar goldbaumstaff00000000000000import yt ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030") slc = yt.SlicePlot(ds, "x", "density") slc.save("default_sliceplot.png") slc.hide_axes() slc.save("no_axes_sliceplot.png") slc.hide_colorbar() slc.save("no_axes_no_colorbar_sliceplot.png") slc.show_axes() slc.save("no_colorbar_sliceplot.png") yt-3.3.3/doc/source/cookbook/sigma_clip.py0000644000076500000240000000116513017603554021434 0ustar goldbaumstaff00000000000000import yt # Load the dataset. ds = yt.load("enzo_tiny_cosmology/RD0009/RD0009") # Create a volume rendering, which will determine data bounds, use the first # acceptable field in the field_list, and set up a default transfer function. # Render and save output images with different levels of sigma clipping. # Sigma clipping removes the highest intensity pixels in a volume render, # which affects the overall contrast of the image. sc = yt.create_scene(ds, field=('gas', 'density')) sc.render() sc.save('clip_0.png') sc.save('clip_2.png', sigma_clip=2) sc.save('clip_4.png', sigma_clip=4) sc.save('clip_6.png', sigma_clip=6) yt-3.3.3/doc/source/cookbook/simple_contour_in_slice.py0000644000076500000240000000167213003701576024235 0ustar goldbaumstaff00000000000000import yt # Load the data file. ds = yt.load("Sedov_3d/sedov_hdf5_chk_0002") # Make a traditional slice plot. sp = yt.SlicePlot(ds, "x", "density") # Overlay the slice plot with thick red contours of density. sp.annotate_contour("density", ncont=3, clim=(1e-2,1e-1), label=True, plot_args={"colors": "red", "linewidths": 2}) # What about some nice temperature contours in blue? sp.annotate_contour("temperature", ncont=3, clim=(1e-8,1e-6), label=True, plot_args={"colors": "blue", "linewidths": 2}) # This is the plot object. po = sp.plots["density"] # Turn off the colormap image, leaving just the contours. po.axes.images[0].set_visible(False) # Remove the colorbar and its label. po.figure.delaxes(po.figure.axes[1]) # Save it and ask for a close fit to get rid of the space used by the colorbar. sp.save(mpl_kwargs={'bbox_inches':'tight'}) yt-3.3.3/doc/source/cookbook/simple_off_axis_projection.py0000644000076500000240000000105613017603554024727 0ustar goldbaumstaff00000000000000import yt # Load the dataset. ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030") # Create a 15 kpc radius sphere, centered on the center of the sim volume sp = ds.sphere("center", (15.0, "kpc")) # Get the angular momentum vector for the sphere. L = sp.quantities.angular_momentum_vector() print("Angular momentum vector: {0}".format(L)) # Create an OffAxisProjectionPlot of density centered on the object with the L # vector as its normal and a width of 25 kpc on a side p = yt.OffAxisProjectionPlot(ds, L, "density", sp.center, (25, "kpc")) p.save() yt-3.3.3/doc/source/cookbook/simple_off_axis_slice.py0000644000076500000240000000104413017603554023647 0ustar goldbaumstaff00000000000000import yt # Load the dataset. ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030") # Create a 15 kpc radius sphere, centered on the center of the sim volume sp = ds.sphere("center", (15.0, "kpc")) # Get the angular momentum vector for the sphere. L = sp.quantities.angular_momentum_vector() print("Angular momentum vector: {0}".format(L)) # Create an OffAxisSlicePlot of density centered on the object with the L # vector as its normal and a width of 25 kpc on a side p = yt.OffAxisSlicePlot(ds, L, "density", sp.center, (25, "kpc")) p.save() yt-3.3.3/doc/source/cookbook/simple_pdf.py0000644000076500000240000000103513017603554021443 0ustar goldbaumstaff00000000000000import yt # Load the dataset. ds = yt.load("GalaxyClusterMerger/fiducial_1to3_b0.273d_hdf5_plt_cnt_0175") # Create a data object that represents the whole box. ad = ds.all_data() # This is identical to the simple phase plot, except we supply # the fractional=True keyword to divide the profile data by the sum. plot = yt.PhasePlot(ad, "density", "temperature", "cell_mass", weight_field=None, fractional=True) # Save the image. # Optionally, give a string as an argument # to name files with a keyword. plot.save() yt-3.3.3/doc/source/cookbook/simple_phase.py0000644000076500000240000000124113003701576021767 0ustar goldbaumstaff00000000000000import yt # Load the dataset. ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030") # Create a sphere of radius 100 kpc in the center of the domain. my_sphere = ds.sphere("c", (100.0, "kpc")) # Create a PhasePlot object. # Setting weight to None will calculate a sum. # Setting weight to a field will calculate an average # weighted by that field. plot = yt.PhasePlot(my_sphere, "density", "temperature", "cell_mass", weight_field=None) # Set the units of mass to be in solar masses (not the default in cgs) plot.set_unit('cell_mass', 'Msun') # Save the image. # Optionally, give a string as an argument # to name files with a keyword. plot.save() yt-3.3.3/doc/source/cookbook/simple_plots.rst0000644000076500000240000002024013017603554022212 0ustar goldbaumstaff00000000000000Making Simple Plots ------------------- One of the easiest ways to interact with yt is by creating simple visualizations of your data. Below we show how to do this, as well as how to extend these plots to be ready for publication. Simple Slices ~~~~~~~~~~~~~ This script shows the simplest way to make a slice through a dataset. See :ref:`slice-plots` for more information. .. yt_cookbook:: simple_slice.py Simple Projections (Non-Weighted) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ This is the simplest way to make a projection through a dataset. There are several different :ref:`projection-types`, but non-weighted line integrals and weighted line integrals are the two most common. Here we create density projections (non-weighted line integral). See :ref:`projection-plots` for more information. .. yt_cookbook:: simple_projection.py Simple Projections (Weighted) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ And here we produce density-weighted temperature projections (weighted line integral) for the same dataset as the non-weighted projections above. See :ref:`projection-plots` for more information. .. yt_cookbook:: simple_projection_weighted.py Simple Phase Plots ~~~~~~~~~~~~~~~~~~ This demonstrates how to make a phase plot. Phase plots can be thought of as two-dimensional histograms, where the value is either the weighted-average or the total accumulation in a cell. See :ref:`how-to-make-2d-profiles` for more information. .. yt_cookbook:: simple_phase.py Simple Probability Distribution Functions ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Often, one wants to examine the distribution of one variable as a function of another. This shows how to see the distribution of mass in a simulation, with respect to the total mass in the simulation. See :ref:`how-to-make-2d-profiles` for more information. .. yt_cookbook:: simple_pdf.py Simple 1D Histograms (Profiles) ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ This is a "profile," which is a 1D histogram. This can be thought of as either the total accumulation (when weight_field is set to ``None``) or the average (when a weight_field is supplied.) See :ref:`how-to-make-1d-profiles` for more information. .. yt_cookbook:: simple_profile.py Simple Radial Profiles ~~~~~~~~~~~~~~~~~~~~~~ This shows how to make a profile of a quantity with respect to the radius. See :ref:`how-to-make-1d-profiles` for more information. .. yt_cookbook:: simple_radial_profile.py 1D Profiles Over Time ~~~~~~~~~~~~~~~~~~~~~ This is a simple example of overplotting multiple 1D profiles from a number of datasets to show how they evolve over time. See :ref:`how-to-make-1d-profiles` for more information. .. yt_cookbook:: time_series_profiles.py .. _cookbook-profile-variance: Profiles with Variance Values ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ This shows how to plot the variance for a 1D profile. In this example, we manually create a 1D profile object, which gives us access to the variance data. See :ref:`how-to-make-1d-profiles` for more information. .. yt_cookbook:: profile_with_variance.py Making Plots of Multiple Fields Simultaneously ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ By adding multiple fields to a single :class:`~yt.visualization.plot_window.SlicePlot` or :class:`~yt.visualization.plot_window.ProjectionPlot` some of the overhead of creating the data object can be reduced, and better performance squeezed out. This recipe shows how to add multiple fields to a single plot. See :ref:`slice-plots` and :ref:`projection-plots` for more information. .. yt_cookbook:: simple_slice_with_multiple_fields.py Off-Axis Slicing ~~~~~~~~~~~~~~~~ One can create slices from any arbitrary angle, not just those aligned with the x,y,z axes. See :ref:`off-axis-slices` for more information. .. yt_cookbook:: simple_off_axis_slice.py .. _cookbook-simple-off-axis-projection: Off-Axis Projection ~~~~~~~~~~~~~~~~~~~ Like off-axis slices, off-axis projections can be created from any arbitrary viewing angle. See :ref:`off-axis-projections` for more information. .. yt_cookbook:: simple_off_axis_projection.py .. _cookbook-simple-particle-plot: Simple Particle Plot ~~~~~~~~~~~~~~~~~~~~ You can also use yt to make particle-only plots. This script shows how to plot all the particle x and y positions in a dataset, using the particle mass to set the color scale. See :ref:`particle-plots` for more information. .. yt_cookbook:: particle_xy_plot.py .. _cookbook-non-spatial-particle-plot: Non-spatial Particle Plots ~~~~~~~~~~~~~~~~~~~~~~~~~~ You are not limited to plotting spatial fields on the x and y axes. This example shows how to plot the particle x-coordinates versus their z-velocities, again using the particle mass to set the colorbar. See :ref:`particle-plots` for more information. .. yt_cookbook:: particle_xvz_plot.py .. _cookbook-single-color-particle-plot: Single-color Particle Plots ~~~~~~~~~~~~~~~~~~~~~~~~~~~ If you don't want to display a third field on the color bar axis, simply pass in a color string instead of a particle field. See :ref:`particle-plots` for more information. .. yt_cookbook:: particle_one_color_plot.py .. _cookbook-simple_volume_rendering: Simple Volume Rendering ~~~~~~~~~~~~~~~~~~~~~~~ Volume renderings are 3D projections rendering isocontours in any arbitrary field (e.g. density, temperature, pressure, etc.) See :ref:`volume_rendering` for more information. .. yt_cookbook:: simple_volume_rendering.py .. _show-hide-axes-colorbar: Showing and Hiding Axis Labels and Colorbars ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ This example illustrates how to create a SlicePlot and then suppress the axes labels and colorbars. This is useful when you don't care about the physical scales and just want to take a closer look at the raw plot data. See :ref:`hiding-colorbar-and-axes` for more information. .. yt_cookbook:: show_hide_axes_colorbar.py .. _matplotlib-primitives: Accessing and Modifying Plots Directly ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ While often the Plot Window, and its affiliated :ref:`callbacks` can cover normal use cases, sometimes more direct access to the underlying Matplotlib engine is necessary. This recipe shows how to modify the plot window :class:`matplotlib.axes.Axes` object directly. See :ref:`matplotlib-customization` for more information. .. yt_cookbook:: simple_slice_matplotlib_example.py Changing the Colormap used in a Plot ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ yt has sensible defaults for colormaps, but there are over a hundred available for customizing your plots. Here we generate a projection and then change its colormap. See :ref:`colormaps` for a list and for images of all the available colormaps. .. yt_cookbook:: colormaps.py Image Background Colors ~~~~~~~~~~~~~~~~~~~~~~~ Here we see how to take an image and save it using different background colors. In this case we use the :ref:`cookbook-simple_volume_rendering` recipe to generate the image, but it works for any NxNx4 image array (3 colors and 1 opacity channel). See :ref:`volume_rendering` for more information. .. yt_cookbook:: image_background_colors.py .. _annotations-recipe: Annotating Plots to Include Lines, Text, Shapes, etc. ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ It can be useful to add annotations to plots to show off certain features and make it easier for your audience to understand the plot's purpose. There are a variety of available :ref:`plot modifications ` one can use to add annotations to their plots. Below includes just a handful, but please look at the other :ref:`plot modifications ` to get a full description of what you can do to highlight your figures. .. yt_cookbook:: annotations.py Annotating Plots with a Timestamp and Physical Scale ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ When creating movies of multiple outputs from the same simulation (see :ref:`time-series-analysis`), it can be helpful to include a timestamp and the physical scale of each individual output. This is simply achieved using the :ref:`annotate_timestamp() ` and :ref:`annotate_scale() ` callbacks on your plots. For more information about similar plot modifications using other callbacks, see the section on :ref:`Plot Modifications `. .. yt_cookbook:: annotate_timestamp_and_scale.py yt-3.3.3/doc/source/cookbook/simple_profile.py0000644000076500000240000000101513017603554022330 0ustar goldbaumstaff00000000000000import yt # Load the dataset. ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030") # Create a 1D profile within a sphere of radius 100 kpc # of the average temperature and average velocity_x # vs. density, weighted by mass. sphere = ds.sphere("c", (100., "kpc")) plot = yt.ProfilePlot(sphere, "density", ["temperature", "velocity_x"], weight_field="cell_mass") plot.set_log("velocity_x", False) # Save the image. # Optionally, give a string as an argument # to name files with a keyword. plot.save() yt-3.3.3/doc/source/cookbook/simple_projection.py0000644000076500000240000000046713003701576023054 0ustar goldbaumstaff00000000000000import yt # Load the dataset. ds = yt.load("GalaxyClusterMerger/fiducial_1to3_b0.273d_hdf5_plt_cnt_0175") # Create projections of the density (non-weighted line integrals). yt.ProjectionPlot(ds, "x", "density").save() yt.ProjectionPlot(ds, "y", "density").save() yt.ProjectionPlot(ds, "z", "density").save() yt-3.3.3/doc/source/cookbook/simple_projection_weighted.py0000644000076500000240000000062713003701576024732 0ustar goldbaumstaff00000000000000import yt # Load the dataset. ds = yt.load("GalaxyClusterMerger/fiducial_1to3_b0.273d_hdf5_plt_cnt_0175") # Create density-weighted projections of temperature (weighted line integrals) yt.ProjectionPlot(ds, "x", "temperature", weight_field="density").save() yt.ProjectionPlot(ds, "y", "temperature", weight_field="density").save() yt.ProjectionPlot(ds, "z", "temperature", weight_field="density").save() yt-3.3.3/doc/source/cookbook/simple_radial_profile.py0000644000076500000240000000105413003701576023645 0ustar goldbaumstaff00000000000000import yt # Load the dataset. ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030") # Create a sphere of radius 100 kpc in the center of the box. my_sphere = ds.sphere("c", (100.0, "kpc")) # Create a profile of the average density vs. radius. plot = yt.ProfilePlot(my_sphere, "radius", "density", weight_field="cell_mass") # Change the units of the radius into kpc (and not the default in cgs) plot.set_unit('radius', 'kpc') # Save the image. # Optionally, give a string as an argument # to name files with a keyword. plot.save() yt-3.3.3/doc/source/cookbook/simple_slice.py0000644000076500000240000000051213003701576021766 0ustar goldbaumstaff00000000000000import yt # Load the dataset. ds = yt.load("GasSloshing/sloshing_nomag2_hdf5_plt_cnt_0150") # Create density slices in all three axes. yt.SlicePlot(ds, 'x', "density", width = (800.0, 'kpc')).save() yt.SlicePlot(ds, 'y', "density", width = (800.0, 'kpc')).save() yt.SlicePlot(ds, 'z', "density", width = (800.0, 'kpc')).save() yt-3.3.3/doc/source/cookbook/simple_slice_matplotlib_example.py0000644000076500000240000000163713003701576025741 0ustar goldbaumstaff00000000000000import yt import numpy as np # Load the dataset. ds = yt.load("GasSloshing/sloshing_nomag2_hdf5_plt_cnt_0150") # Create a slice object slc = yt.SlicePlot(ds,'x','density',width=(800.0,'kpc')) # Get a reference to the matplotlib axes object for the plot ax = slc.plots['density'].axes # Let's adjust the x axis tick labels for label in ax.xaxis.get_ticklabels(): label.set_color('red') label.set_fontsize(16) # Get a reference to the matplotlib figure object for the plot fig = slc.plots['density'].figure # And create a mini-panel of a gaussian histogram inside the plot rect = (0.2,0.2,0.2,0.2) new_ax = fig.add_axes(rect) n, bins, patches = new_ax.hist(np.random.randn(1000)+20, 50, facecolor='black', edgecolor='black') # Make sure its visible new_ax.tick_params(colors='white') # And label it la = new_ax.set_xlabel('Dinosaurs per furlong') la.set_color('white') slc.save() yt-3.3.3/doc/source/cookbook/simple_slice_with_multiple_fields.py0000644000076500000240000000040013017603554026260 0ustar goldbaumstaff00000000000000import yt # Load the dataset ds = yt.load("GasSloshing/sloshing_nomag2_hdf5_plt_cnt_0150") # Create density slices of several fields along the x axis yt.SlicePlot(ds, 'x', ['density','temperature','pressure'], width = (800.0, 'kpc')).save() yt-3.3.3/doc/source/cookbook/simple_volume_rendering.py0000644000076500000240000000054113017603554024237 0ustar goldbaumstaff00000000000000import yt # Load the dataset. ds = yt.load("Enzo_64/DD0043/data0043") # Create a volume rendering, which will determine data bounds, use the first # acceptable field in the field_list, and set up a default transfer function. # This will save a file named 'data0043_Render_density.png' to disk. im, sc = yt.volume_render(ds, field=('gas', 'density')) yt-3.3.3/doc/source/cookbook/simulation_analysis.py0000644000076500000240000000246413017603554023417 0ustar goldbaumstaff00000000000000import yt yt.enable_parallelism() import collections # Enable parallelism in the script (assuming it was called with # `mpirun -np ` ) yt.enable_parallelism() # By using wildcards such as ? and * with the load command, we can load up a # Time Series containing all of these datasets simultaneously. ts = yt.load('enzo_tiny_cosmology/DD????/DD????') # Calculate and store density extrema for all datasets along with redshift # in a data dictionary with entries as tuples # Create an empty dictionary data = {} # Iterate through each dataset in the Time Series (using piter allows it # to happen in parallel automatically across available processors) for ds in ts.piter(): ad = ds.all_data() extrema = ad.quantities.extrema('density') # Fill the dictionary with extrema and redshift information for each dataset data[ds.basename] = (extrema, ds.current_redshift) # Convert dictionary to ordered dictionary to get the right order od = collections.OrderedDict(sorted(data.items())) # Print out all the values we calculated. print("Dataset Redshift Density Min Density Max") print("---------------------------------------------------------") for key, val in od.items(): print("%s %05.3f %5.3g g/cm^3 %5.3g g/cm^3" % \ (key, val[1], val[0][0], val[0][1])) yt-3.3.3/doc/source/cookbook/single_dataset_light_ray.py0000644000076500000240000000155013017603554024353 0ustar goldbaumstaff00000000000000import os import yt from yt.analysis_modules.cosmological_observation.api import \ LightRay ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030") lr = LightRay(ds) # With a single dataset, a start_position and # end_position or trajectory must be given. # These positions can be defined as xyz coordinates, # but here we just use the two opposite corners of the # simulation box. Alternatively, trajectory should # be given as (r, theta, phi) lr.make_light_ray(start_position=ds.domain_left_edge, end_position=ds.domain_right_edge, solution_filename='lightraysolution.txt', data_filename='lightray.h5', fields=['temperature', 'density']) # Optionally, we can now overplot this ray on a projection of the source # dataset p = yt.ProjectionPlot(ds, 'z', 'density') p.annotate_ray(lr) p.save() yt-3.3.3/doc/source/cookbook/smoothed_field.py0000644000076500000240000000327113017603554022312 0ustar goldbaumstaff00000000000000import yt # Load a Gadget dataset following the demonstration notebook. fname = 'GadgetDiskGalaxy/snapshot_200.hdf5' unit_base = {'UnitLength_in_cm' : 3.08568e+21, 'UnitMass_in_g' : 1.989e+43, 'UnitVelocity_in_cm_per_s' : 100000} bbox_lim = 1e5 # kpc bbox = [[-bbox_lim, bbox_lim], [-bbox_lim, bbox_lim], [-bbox_lim, bbox_lim]] ds = yt.load(fname, unit_base=unit_base, bounding_box=bbox) # Create a derived field, the metal density. def _metal_density(field, data): density = data['PartType0', 'Density'] Z = data['PartType0', 'metallicity'] return density * Z # Add it to the dataset. ds.add_field(('PartType0', 'metal_density'), function=_metal_density, units="g/cm**3", particle_type=True) # Add the corresponding smoothed field to the dataset. from yt.fields.particle_fields import add_volume_weighted_smoothed_field add_volume_weighted_smoothed_field('PartType0', 'Coordinates', 'Masses', 'SmoothingLength', 'Density', 'metal_density', ds.field_info) # Define the region where the disk galaxy is. (See the Gadget notebook for # details. Here I make the box a little larger than needed to eliminate the # margin effect.) center = ds.arr([31996, 31474, 28970], "code_length") box_size = ds.quan(250, "code_length") left_edge = center - box_size/2*1.1 right_edge = center + box_size/2*1.1 box = ds.box(left_edge=left_edge, right_edge=right_edge) # And make a projection plot! yt.ProjectionPlot(ds, 'z', ('deposit', 'PartType0_smoothed_metal_density'), center=center, width=box_size, data_source=box).save() yt-3.3.3/doc/source/cookbook/streamlines.py0000644000076500000240000000220213017603554021644 0ustar goldbaumstaff00000000000000import yt import numpy as np import matplotlib.pylab as pl from yt.visualization.api import Streamlines from yt.units import Mpc from mpl_toolkits.mplot3d import Axes3D # Load the dataset ds = yt.load('IsolatedGalaxy/galaxy0030/galaxy0030') # Define c: the center of the box, N: the number of streamlines, # scale: the spatial scale of the streamlines relative to the boxsize, # and then pos: the random positions of the streamlines. c = ds.domain_center N = 100 scale = ds.domain_width[0] pos_dx = np.random.random((N,3))*scale-scale/2. pos = c+pos_dx # Create streamlines of the 3D vector velocity and integrate them through # the box defined above streamlines = Streamlines(ds, pos, 'velocity_x', 'velocity_y', 'velocity_z', length=1.0*Mpc, get_magnitude=True) streamlines.integrate_through_volume() # Create a 3D plot, trace the streamlines throught the 3D volume of the plot fig=pl.figure() ax = Axes3D(fig) for stream in streamlines.streamlines: stream = stream[np.all(stream != 0.0, axis=1)] ax.plot3D(stream[:,0], stream[:,1], stream[:,2], alpha=0.1) # Save the plot to disk. pl.savefig('streamlines.png') yt-3.3.3/doc/source/cookbook/streamlines_isocontour.py0000644000076500000240000000446413017603554024144 0ustar goldbaumstaff00000000000000import yt import numpy as np from yt.visualization.api import Streamlines import matplotlib.pylab as pl from mpl_toolkits.mplot3d import Axes3D from mpl_toolkits.mplot3d.art3d import Poly3DCollection # Load the dataset ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030") # Define c: the center of the box, N: the number of streamlines, # scale: the spatial scale of the streamlines relative to the boxsize, # and then pos: the random positions of the streamlines. c = ds.arr([0.5]*3, 'code_length') N = 30 scale = ds.quan(15, 'kpc').in_units('code_length') # 15 kpc in code units pos_dx = np.random.random((N,3))*scale-scale/2. pos = c+pos_dx # Create the streamlines from these positions with the velocity fields as the # fields to be traced streamlines = Streamlines(ds, pos, 'velocity_x', 'velocity_y', 'velocity_z', length=1.0) streamlines.integrate_through_volume() # Create a 3D matplotlib figure for visualizing the streamlines fig=pl.figure() ax = Axes3D(fig) # Trace the streamlines through the volume of the 3D figure for stream in streamlines.streamlines: stream = stream[np.all(stream != 0.0, axis=1)] # Make the colors of each stream vary continuously from blue to red # from low-x to high-x of the stream start position (each color is R, G, B) # can omit and just set streamline colors to a fixed color x_start_pos = ds.arr(stream[0,0], 'code_length') x_start_pos -= ds.arr(0.5, 'code_length') x_start_pos /= scale x_start_pos += 0.5 color = np.array([x_start_pos, 0, 1-x_start_pos]) # Plot the stream in 3D ax.plot3D(stream[:,0], stream[:,1], stream[:,2], alpha=0.3, color=color) # Create a sphere object centered on the highest density point in the simulation # with radius = 1 Mpc sphere = ds.sphere("max", (1.0, "Mpc")) # Identify the isodensity surface in this sphere with density = 1e-24 g/cm^3 surface = ds.surface(sphere, "density", 1e-24) # Color this isodensity surface according to the log of the temperature field colors = yt.apply_colormap(np.log10(surface["temperature"]), cmap_name="hot") # Render this surface p3dc = Poly3DCollection(surface.triangles, linewidth=0.0) colors = colors[0,:,:]/255. # scale to [0,1] colors[:,3] = 0.3 # alpha = 0.3 p3dc.set_facecolors(colors) ax.add_collection(p3dc) # Save the figure pl.savefig('streamlines_isocontour.png') yt-3.3.3/doc/source/cookbook/sum_mass_in_sphere.py0000644000076500000240000000113613017603554023206 0ustar goldbaumstaff00000000000000import yt # Load the dataset. ds = yt.load("Enzo_64/DD0029/data0029") # Create a 1 Mpc radius sphere, centered on the max density. sp = ds.sphere("max", (1.0, "Mpc")) # Use the total_quantity derived quantity to sum up the # values of the cell_mass and particle_mass fields # within the sphere. baryon_mass, particle_mass = sp.quantities.total_quantity(["cell_mass", "particle_mass"]) print("Total mass in sphere is %0.3e Msun (gas = %0.3e Msun, particles = %0.3e Msun)" % \ ((baryon_mass + particle_mass).in_units('Msun'), \ baryon_mass.in_units('Msun'), particle_mass.in_units('Msun'))) yt-3.3.3/doc/source/cookbook/surface_plot.py0000644000076500000240000000265613017603554022021 0ustar goldbaumstaff00000000000000import yt import numpy as np from mpl_toolkits.mplot3d import Axes3D from mpl_toolkits.mplot3d.art3d import Poly3DCollection import matplotlib.pyplot as plt # Load the dataset ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030") # Create a sphere object centered on the highest density point in the simulation # with radius 1 Mpc sphere = ds.sphere("max", (1.0, "Mpc")) # Identify the isodensity surface in this sphere with density = 1e-24 g/cm^3 surface = ds.surface(sphere, "density", 1e-24) # Color this isodensity surface according to the log of the temperature field colors = yt.apply_colormap(np.log10(surface["temperature"]), cmap_name="hot") # Create a 3D matplotlib figure for visualizing the surface fig = plt.figure() ax = fig.gca(projection='3d') p3dc = Poly3DCollection(surface.triangles, linewidth=0.0) # Set the surface colors in the right scaling [0,1] p3dc.set_facecolors(colors[0,:,:]/255.) ax.add_collection(p3dc) # Let's keep the axis ratio fixed in all directions by taking the maximum # extent in one dimension and make it the bounds in all dimensions max_extent = (surface.vertices.max(axis=1) - surface.vertices.min(axis=1)).max() centers = (surface.vertices.max(axis=1) + surface.vertices.min(axis=1)) / 2 bounds = np.zeros([3,2]) bounds[:,0] = centers[:] - max_extent/2 bounds[:,1] = centers[:] + max_extent/2 ax.auto_scale_xyz(bounds[0,:], bounds[1,:], bounds[2,:]) # Save the figure plt.savefig("%s_Surface.png" % ds) yt-3.3.3/doc/source/cookbook/tests/0000755000076500000240000000000013023400160020073 5ustar goldbaumstaff00000000000000yt-3.3.3/doc/source/cookbook/tests/test_cookbook.py0000644000076500000240000000414613017603554023336 0ustar goldbaumstaff00000000000000# -*- coding: utf-8 -*- """Module for cookbook testing This test should be run from main yt directory. Example: $ sed -e '/where/d' -i nose.cfg setup.cfg $ nosetests doc/source/cookbook/tests/test_cookbook.py -P -v """ import glob import os import sys import subprocess def run_with_capture(*args, **kwargs): sp = subprocess.Popen(*args, stdout=subprocess.PIPE, stderr=subprocess.PIPE, **kwargs) out, err = sp.communicate() if out: sys.stdout.write(out.decode("UTF-8")) if err: sys.stderr.write(err.decode("UTF-8")) if sp.returncode != 0: retstderr = " ".join(args[0]) retstderr += "\n\nTHIS IS THE REAL CAUSE OF THE FAILURE:\n" retstderr += err.decode("UTF-8") + "\n" raise subprocess.CalledProcessError(sp.returncode, retstderr) return sp.returncode PARALLEL_TEST = {"rockstar_nest.py": "3"} BLACKLIST = ["opengl_ipython.py", "opengl_vr.py"] if sys.version_info >= (3,0,0): BLACKLIST.append("rockstar_nest.py") def test_recipe(): '''Dummy test grabbing all cookbook's recipes''' for fname in glob.glob("doc/source/cookbook/*.py"): recipe = os.path.basename(fname) if recipe in BLACKLIST: continue check_recipe.description = "Testing recipe: %s" % recipe if recipe in PARALLEL_TEST: yield check_recipe, \ ["mpiexec", "-n", PARALLEL_TEST[recipe], "python", fname] else: yield check_recipe, ["python", fname] def check_recipe(cmd): '''Run single recipe''' proc = subprocess.Popen(cmd, stdout=subprocess.PIPE, stderr=subprocess.PIPE) out, err = proc.communicate() if out: sys.stdout.write(out.decode("utf8")) if err: sys.stderr.write(err.decode("utf8")) if proc.returncode != 0: retstderr = " ".join(cmd) retstderr += "\n\nTHIS IS THE REAL CAUSE OF THE FAILURE:\n" retstderr += err.decode("UTF-8") + "\n" raise subprocess.CalledProcessError(proc.returncode, retstderr) yt-3.3.3/doc/source/cookbook/thin_slice_projection.py0000644000076500000240000000212613003701576023676 0ustar goldbaumstaff00000000000000import yt # Load the dataset. ds = yt.load("Enzo_64/DD0030/data0030") # Make a projection that is the full width of the domain, # but only 5 Mpc in depth. This is done by creating a # region object with this exact geometry and providing it # as a data_source for the projection. # Center on the domain center center = ds.domain_center # First make the left and right corner of the region based # on the full domain. left_corner = ds.domain_left_edge right_corner = ds.domain_right_edge # Now adjust the size of the region along the line of sight (x axis). depth = ds.quan(5.0,'Mpc') left_corner[0] = center[0] - 0.5 * depth right_corner[0] = center[0] + 0.5 * depth # Create the region region = ds.box(left_corner, right_corner) # Create a density projection and supply the region we have just created. # Only cells within the region will be included in the projection. # Try with another data container, like a sphere or disk. plot = yt.ProjectionPlot(ds, "x", "density", weight_field="density", data_source=region) # Save the image with the keyword. plot.save("Thin_Slice") yt-3.3.3/doc/source/cookbook/time_series.py0000644000076500000240000000264213017603554021636 0ustar goldbaumstaff00000000000000import yt import matplotlib.pyplot as plt import numpy as np # Enable parallelism in the script (assuming it was called with # `mpirun -np ` ) yt.enable_parallelism() # By using wildcards such as ? and * with the load command, we can load up a # Time Series containing all of these datasets simultaneously. ts = yt.load('GasSloshingLowRes/sloshing_low_res_hdf5_plt_cnt_0*') storage = {} # By using the piter() function, we can iterate on every dataset in # the TimeSeries object. By using the storage keyword, we can populate # a dictionary where the dataset is the key, and sto.result is the value # for later use when the loop is complete. # The serial equivalent of piter() here is just "for ds in ts:" . for store, ds in ts.piter(storage=storage): # Create a sphere of radius 100 kpc at the center of the dataset volume sphere = ds.sphere("c", (100., "kpc")) # Calculate the entropy within that sphere entr = sphere["entropy"].sum() # Store the current time and sphere entropy for this dataset in our # storage dictionary as a tuple store.result = (ds.current_time.in_units('Gyr'), entr) # Convert the storage dictionary values to a Nx2 array, so the can be easily # plotted arr = np.array(list(storage.values())) # Plot up the results: time versus entropy plt.semilogy(arr[:,0], arr[:,1], 'r-') plt.xlabel("Time (Gyr)") plt.ylabel("Entropy (ergs/K)") plt.savefig("time_versus_entropy.png") yt-3.3.3/doc/source/cookbook/time_series_profiles.py0000644000076500000240000000161313017603554023536 0ustar goldbaumstaff00000000000000import yt # Create a time-series object. sim = yt.simulation("enzo_tiny_cosmology/32Mpc_32.enzo", "Enzo") sim.get_time_series(redshifts=[5, 4, 3, 2, 1, 0]) # Lists to hold profiles, labels, and plot specifications. profiles = [] labels = [] plot_specs = [] # Loop over each dataset in the time-series. for ds in sim: # Create a data container to hold the whole dataset. ad = ds.all_data() # Create a 1d profile of density vs. temperature. profiles.append(yt.create_profile(ad, ["density"], fields=["temperature"])) # Add labels and linestyles. labels.append("z = %.2f" % ds.current_redshift) plot_specs.append(dict(linewidth=2, alpha=0.7)) # Create the profile plot from the list of profiles. plot = yt.ProfilePlot.from_profiles(profiles, labels=labels, plot_specs=plot_specs) # Save the image. plot.save() yt-3.3.3/doc/source/cookbook/tipsy_and_yt.ipynb0000644000076500000240000001213313017603554022521 0ustar goldbaumstaff00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading Files" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Alright, let's start with some basics. Before we do anything, we will need to load a snapshot. You can do this using the ```load``` convenience function. yt will autodetect that you have a tipsy snapshot, and automatically set itself up appropriately." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import yt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We will be looking at a fairly low resolution dataset. In the next cell, the `ds` object has an atribute called `n_ref` that tells the oct-tree how many particles to refine on. The default is 64, but we'll get prettier plots (at the expense of a deeper tree) with 8. Just passing the argument `n_ref=8` to load does this for us." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ ">This dataset is available for download at http://yt-project.org/data/TipsyGalaxy.tar.gz (10 MB)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ds = yt.load('TipsyGalaxy/galaxy.00300', n_ref=8)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We now have a `TipsyDataset` object called `ds`. Let's see what fields it has." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ds.field_list" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "yt also defines so-called \"derived\" fields. These fields are functions of the on-disk fields that live in the `field_list`. There is a `derived_field_list` attribute attached to the `Dataset` object - let's take look at the derived fields in this dataset:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ds.derived_field_list" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "All of the field in the `field_list` are arrays containing the values for the associated particles. These haven't been smoothed or gridded in any way. We can grab the array-data for these particles using `ds.all_data()`. For example, let's take a look at a temperature-colored scatterplot of the gas particles in this output." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline\n", "import matplotlib.pyplot as plt\n", "import numpy as np" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "dd = ds.all_data()\n", "xcoord = dd['Gas','Coordinates'][:,0].v\n", "ycoord = dd['Gas','Coordinates'][:,1].v\n", "logT = np.log10(dd['Gas','Temperature'])\n", "plt.scatter(xcoord, ycoord, c=logT, s=2*logT, marker='o', edgecolor='none', vmin=2, vmax=6)\n", "plt.xlim(-20,20)\n", "plt.ylim(-20,20)\n", "cb = plt.colorbar()\n", "cb.set_label('$\\log_{10}$ Temperature')\n", "plt.gcf().set_size_inches(15,10)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Making Smoothed Images" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "yt will automatically generate smoothed versions of these fields that you can use to plot. Let's make a temperature slice and a density projection." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "yt.SlicePlot(ds, 'z', ('gas','density'), width=(40, 'kpc'), center='m')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "yt.ProjectionPlot(ds, 'z', ('gas','density'), width=(40, 'kpc'), center='m')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Not only are the values in the tipsy snapshot read and automatically smoothed, the auxiliary files that have physical significance are also smoothed. Let's look at a slice of Iron mass fraction." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "yt.SlicePlot(ds, 'z', ('gas', 'Fe_fraction'), width=(40, 'kpc'), center='m')" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 } yt-3.3.3/doc/source/cookbook/tipsy_notebook.rst0000644000076500000240000000025212770647750022564 0ustar goldbaumstaff00000000000000.. _tipsy-notebook: Using yt to view and analyze Tipsy outputs from Gasoline ++++++++++++++++++++++++++++++++++++++++++++++++++++++++ .. notebook:: tipsy_and_yt.ipynb yt-3.3.3/doc/source/cookbook/various_lens.py0000644000076500000240000001140113017603554022030 0ustar goldbaumstaff00000000000000import yt from yt.visualization.volume_rendering.api import Scene, VolumeSource import numpy as np field = ("gas", "density") # normal_vector points from camera to the center of tbe final projection. # Now we look at the positive x direction. normal_vector = [1., 0., 0.] # north_vector defines the "top" direction of the projection, which is # positive z direction here. north_vector = [0., 0., 1.] # Follow the simple_volume_rendering cookbook for the first part of this. ds = yt.load("IsolatedGalaxy/galaxy0030/galaxy0030") sc = Scene() vol = VolumeSource(ds, field=field) tf = vol.transfer_function tf.grey_opacity = True # Plane-parallel lens cam = sc.add_camera(ds, lens_type='plane-parallel') # Set the resolution of tbe final projection. cam.resolution = [250, 250] # Set the location of the camera to be (x=0.2, y=0.5, z=0.5) # For plane-parallel lens, the location info along the normal_vector (here # is x=0.2) is ignored. cam.position = ds.arr(np.array([0.2, 0.5, 0.5]), 'code_length') # Set the orientation of the camera. cam.switch_orientation(normal_vector=normal_vector, north_vector=north_vector) # Set the width of the camera, where width[0] and width[1] specify the length and # height of final projection, while width[2] in plane-parallel lens is not used. cam.set_width(ds.domain_width * 0.5) sc.add_source(vol) sc.render() sc.save('lens_plane-parallel.png', sigma_clip=6.0) # Perspective lens cam = sc.add_camera(ds, lens_type='perspective') cam.resolution = [250, 250] # Standing at (x=0.2, y=0.5, z=0.5), we look at the area of x>0.2 (with some open angle # specified by camera width) along the positive x direction. cam.position = ds.arr([0.2, 0.5, 0.5], 'code_length') cam.switch_orientation(normal_vector=normal_vector, north_vector=north_vector) # Set the width of the camera, where width[0] and width[1] specify the length and # height of the final projection, while width[2] specifies the distance between the # camera and the final image. cam.set_width(ds.domain_width * 0.5) sc.add_source(vol) sc.render() sc.save('lens_perspective.png', sigma_clip=6.0) # Stereo-perspective lens cam = sc.add_camera(ds, lens_type='stereo-perspective') # Set the size ratio of the final projection to be 2:1, since stereo-perspective lens # will generate the final image with both left-eye and right-eye ones jointed together. cam.resolution = [500, 250] cam.position = ds.arr([0.2, 0.5, 0.5], 'code_length') cam.switch_orientation(normal_vector=normal_vector, north_vector=north_vector) cam.set_width(ds.domain_width*0.5) # Set the distance between left-eye and right-eye. cam.lens.disparity = ds.domain_width[0] * 1.e-3 sc.add_source(vol) sc.render() sc.save('lens_stereo-perspective.png', sigma_clip=6.0) # Fisheye lens dd = ds.sphere(ds.domain_center, ds.domain_width[0] / 10) cam = sc.add_camera(dd, lens_type='fisheye') cam.resolution = [250, 250] v, c = ds.find_max(field) cam.set_position(c - 0.0005 * ds.domain_width) cam.switch_orientation(normal_vector=normal_vector, north_vector=north_vector) cam.set_width(ds.domain_width) cam.lens.fov = 360.0 sc.add_source(vol) sc.render() sc.save('lens_fisheye.png', sigma_clip=6.0) # Spherical lens cam = sc.add_camera(ds, lens_type='spherical') # Set the size ratio of the final projection to be 2:1, since spherical lens # will generate the final image with length of 2*pi and height of pi. # Recommended resolution for YouTube 360-degree videos is [3840, 2160] cam.resolution = [500, 250] # Standing at (x=0.4, y=0.5, z=0.5), we look in all the radial directions # from this point in spherical coordinate. cam.position = ds.arr([0.4, 0.5, 0.5], 'code_length') cam.switch_orientation(normal_vector=normal_vector, north_vector=north_vector) # In (stereo)spherical camera, camera width is not used since the entire volume # will be rendered sc.add_source(vol) sc.render() sc.save('lens_spherical.png', sigma_clip=6.0) # Stereo-spherical lens cam = sc.add_camera(ds, lens_type='stereo-spherical') # Set the size ratio of the final projection to be 1:1, since spherical-perspective lens # will generate the final image with both left-eye and right-eye ones jointed together, # with left-eye image on top and right-eye image on bottom. # Recommended resolution for YouTube virtual reality videos is [3840, 2160] cam.resolution = [500, 500] cam.position = ds.arr([0.4, 0.5, 0.5], 'code_length') cam.switch_orientation(normal_vector=normal_vector, north_vector=north_vector) # In (stereo)spherical camera, camera width is not used since the entire volume # will be rendered # Set the distance between left-eye and right-eye. cam.lens.disparity = ds.domain_width[0] * 1.e-3 sc.add_source(vol) sc.render() sc.save('lens_stereo-spherical.png', sigma_clip=6.0) yt-3.3.3/doc/source/cookbook/velocity_vectors_on_slice.py0000644000076500000240000000033013003701576024572 0ustar goldbaumstaff00000000000000import yt # Load the dataset. ds = yt.load("GasSloshing/sloshing_nomag2_hdf5_plt_cnt_0150") p = yt.SlicePlot(ds, "x", "density") # Draw a velocity vector every 16 pixels. p.annotate_velocity(factor = 16) p.save() yt-3.3.3/doc/source/cookbook/vol-annotated.py0000755000076500000240000000170213017603554022100 0ustar goldbaumstaff00000000000000import yt ds = yt.load('Enzo_64/DD0043/data0043') sc = yt.create_scene(ds, lens_type='perspective') source = sc[0] source.set_field('density') source.set_log(True) # Set up the camera parameters: focus, width, resolution, and image orientation sc.camera.focus = ds.domain_center sc.camera.resolution = 1024 sc.camera.north_vector = [0, 0, 1] sc.camera.position = [1.7, 1.7, 1.7] # You may need to adjust the alpha values to get an image with good contrast. # For the annotate_domain call, the fourth value in the color tuple is the # alpha value. sc.annotate_axes(alpha=.02) sc.annotate_domain(ds, color=[1, 1, 1, .01]) text_string = "T = {} Gyr".format(float(ds.current_time.to('Gyr'))) # save an annotated version of the volume rendering including a representation # of the transfer function and a nice label showing the simulation time. sc.save_annotated("vol_annotated.png", sigma_clip=6, text_annotate=[[(.1, 1.05), text_string]]) yt-3.3.3/doc/source/cookbook/vol-lines.py0000644000076500000240000000072213017603554021233 0ustar goldbaumstaff00000000000000import yt import numpy as np from yt.visualization.volume_rendering.api import LineSource from yt.units import kpc ds = yt.load('IsolatedGalaxy/galaxy0030/galaxy0030') sc = yt.create_scene(ds) np.random.seed(1234567) nlines = 50 vertices = (np.random.random([nlines, 2, 3]) - 0.5) * 200 * kpc colors = np.random.random([nlines, 4]) colors[:, 3] = 0.1 lines = LineSource(vertices, colors) sc.add_source(lines) sc.camera.width = 300*kpc sc.save(sigma_clip=4.0) yt-3.3.3/doc/source/cookbook/vol-points.py0000644000076500000240000000113113017603554021430 0ustar goldbaumstaff00000000000000import yt import numpy as np from yt.visualization.volume_rendering.api import PointSource from yt.units import kpc ds = yt.load('IsolatedGalaxy/galaxy0030/galaxy0030') sc = yt.create_scene(ds) np.random.seed(1234567) npoints = 1000 # Random particle positions vertices = np.random.random([npoints, 3])*200*kpc # Random colors colors = np.random.random([npoints, 4]) # Set alpha value to something that produces a good contrast with the volume # rendering colors[:, 3] = 0.1 points = PointSource(vertices, colors=colors) sc.add_source(points) sc.camera.width = 300*kpc sc.save(sigma_clip=5) yt-3.3.3/doc/source/cookbook/yt_gadget_analysis.ipynb0000644000076500000240000001434213017603554023671 0ustar goldbaumstaff00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading the data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we set up our imports:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import yt\n", "import numpy as np\n", "import yt.units as units\n", "import pylab" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "First we load the data set, specifying both the unit length/mass/velocity, as well as the size of the bounding box (which should encapsulate all the particles in the data set)\n", "\n", "At the end, we flatten the data into \"ad\" in case we want access to the raw simulation data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ ">This dataset is available for download at http://yt-project.org/data/GadgetDiskGalaxy.tar.gz (430 MB)." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fname = 'GadgetDiskGalaxy/snapshot_200.hdf5'\n", "\n", "unit_base = {'UnitLength_in_cm' : 3.08568e+21,\n", " 'UnitMass_in_g' : 1.989e+43,\n", " 'UnitVelocity_in_cm_per_s' : 100000}\n", "\n", "bbox_lim = 1e5 #kpc\n", "\n", "bbox = [[-bbox_lim,bbox_lim],\n", " [-bbox_lim,bbox_lim],\n", " [-bbox_lim,bbox_lim]]\n", " \n", "ds = yt.load(fname,unit_base=unit_base,bounding_box=bbox)\n", "ds.index\n", "ad= ds.all_data()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's make a projection plot to look at the entire volume" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "px = yt.ProjectionPlot(ds, 'x', ('gas', 'density'))\n", "px.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's print some quantities about the domain, as well as the physical properties of the simulation\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print ('left edge: ',ds.domain_left_edge)\n", "print ('right edge: ',ds.domain_right_edge)\n", "print ('center: ',ds.domain_center)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also see the fields that are available to query in the dataset" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sorted(ds.field_list)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Let's create a data object that represents the full simulation domain, and find the total mass in gas and dark matter particles contained in it:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ad = ds.all_data()\n", "\n", "# total_mass returns a list, representing the total gas and dark matter + stellar mass, respectively\n", "print ([tm.in_units('Msun') for tm in ad.quantities.total_mass()])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now let's say we want to zoom in on the box (since clearly the bounding we chose initially is much larger than the volume containing the gas particles!), and center on wherever the highest gas density peak is. First, let's find this peak:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "density = ad[(\"PartType0\",\"density\")]\n", "wdens = np.where(density == np.max(density))\n", "coordinates = ad[(\"PartType0\",\"Coordinates\")]\n", "center = coordinates[wdens][0]\n", "print ('center = ',center)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Set up the box to zoom into" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "new_box_size = ds.quan(250,'code_length')\n", "\n", "left_edge = center - new_box_size/2\n", "right_edge = center + new_box_size/2\n", "\n", "print (new_box_size.in_units('Mpc'))\n", "print (left_edge.in_units('Mpc'))\n", "print (right_edge.in_units('Mpc'))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ad2= ds.region(center=center, left_edge=left_edge, right_edge=right_edge)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using this new data object, let's confirm that we're only looking at a subset of the domain by first calculating thte total mass in gas and particles contained in the subvolume:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print ([tm.in_units('Msun') for tm in ad.quantities.total_mass()])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "And then by visualizing what the new zoomed region looks like" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "px = yt.ProjectionPlot(ds, 'x', ('gas', 'density'), center=center, width=new_box_size)\n", "px.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Cool - there's a disk galaxy there!" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 } yt-3.3.3/doc/source/cookbook/yt_gadget_owls_analysis.ipynb0000644000076500000240000001375713023400062024730 0ustar goldbaumstaff00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# OWLS Examples" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Setup" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The first thing you will need to run these examples is a working installation of yt. The author or these examples followed the instructions under \"Get yt: from source\" at http://yt-project.org/ to install an up to date development version of yt.\n", "\n", "Next you should set the default ``test_data_dir`` in the ``.yt/config`` file in your home directory. Note that you may have to create the directory and file if it doesn't exist already.\n", "\n", "> [yt]\n", "\n", "> test_data_dir=/home/galtay/yt-data\n", "\n", "Now you should create the directory referenced above (in this case ``/home/galtay/yt-data``). The first time you request ion fields from yt it will download a supplementary data file to this directory. \n", "\n", "Next we will get an example OWLS snapshot. There is a snapshot available on the yt data site, http://yt-project.org/data/snapshot_033.tar.gz (269 MB). Save this tar.gz file in the same directory as this notebook, then unzip and untar it. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we will tell the notebook that we want figures produced inline. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "%matplotlib inline" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import yt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we will load the snapshot. See the docs (http://yt-project.org/docs/dev/examining/loading_data.html#indexing-criteria) for a description of ``n_ref`` and ``over_refine_factor``." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fname = 'snapshot_033/snap_033.0.hdf5'\n", "ds = yt.load(fname, n_ref=64, over_refine_factor=1)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Set a ``YTRegion`` that contains all the data." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ad = ds.all_data()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Inspecting " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The dataset can tell us what fields it knows about, " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ds.field_list" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ds.derived_field_list" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Note that the ion fields follow the naming convention described in YTEP-0003 http://ytep.readthedocs.org/en/latest/YTEPs/YTEP-0003.html#molecular-and-atomic-species-names" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Accessing Particle Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The raw particle data can be accessed using the particle types. This corresponds directly with what is in the hdf5 snapshots. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ad['PartType0', 'Coordinates']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ad['PartType4', 'IronFromSNIa']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ad['PartType1', 'ParticleIDs']" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ad['PartType0', 'Hydrogen']" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Projection Plots" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The projection plots make use of derived fields that store the smoothed particle data (particles smoothed onto an oct-tree). Below we make a projection of all hydrogen gas followed by only the neutral hydrogen gas. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pz = yt.ProjectionPlot(ds, 'z', ('gas', 'H_density'))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pz.show()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pz = yt.ProjectionPlot(ds, 'z', ('gas', 'H_p0_density'))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pz.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 } yt-3.3.3/doc/source/cookbook/zoomin_frames.py0000644000076500000240000000240013003701576022164 0ustar goldbaumstaff00000000000000import yt import numpy as np # load data fn = "IsolatedGalaxy/galaxy0030/galaxy0030" ds = yt.load(fn) # This is the number of frames to make -- below, you can see how this is used. n_frames = 5 # This is the minimum size in smallest_dx of our last frame. # Usually it should be set to something like 400, but for THIS # dataset, we actually don't have that great of resolution. min_dx = 40 frame_template = "frame_%05i" # Template for frame filenames p = yt.SlicePlot(ds, "z", "density") # Add our slice, along z p.annotate_contour("temperature") # We'll contour in temperature # What we do now is a bit fun. "enumerate" returns a tuple for every item -- # the index of the item, and the item itself. This saves us having to write # something like "i = 0" and then inside the loop "i += 1" for ever loop. The # argument to enumerate is the 'logspace' function, which takes a minimum and a # maximum and the number of items to generate. It returns 10^power of each # item it generates. for i,v in enumerate(np.logspace(0, np.log10(ds.index.get_smallest_dx()*min_dx), n_frames)): # We set our width as necessary for this frame p.set_width(v, 'unitary') # save p.save(frame_template % (i)) yt-3.3.3/doc/source/developing/0000755000076500000240000000000013023400160017257 5ustar goldbaumstaff00000000000000yt-3.3.3/doc/source/developing/building_the_docs.rst0000644000076500000240000002304313017603554023477 0ustar goldbaumstaff00000000000000.. _documentation: Documentation ============= .. _writing_documentation: How to Write Documentation -------------------------- Writing documentation is one of the most important but often overlooked tasks for increasing yt's impact in the community. It is the way in which the world will understand how to use our code, so it needs to be done concisely and understandably. Typically, when a developer submits some piece of code with new functionality, she should also include documentation on how to use that functionality (as per :ref:`requirements-for-code-submission`). Depending on the nature of the code addition, this could be a new narrative docs section describing how the new code works and how to use it, it could include a recipe in the cookbook section, or it could simply be adding a note in the relevant docs text somewhere. The documentation exists in the main mercurial code repository for yt in the ``doc`` directory (i.e. ``$YT_HG/doc/source`` where ``$YT_HG`` is the path of the yt mercurial repository). It is organized hierarchically into the main categories of: * Visualizing * Analyzing * Analysis Modules * Examining * Cookbook * Quickstart * Developing * Reference * FAQ * Help You will have to figure out where your new/modified doc fits into this, but browsing through the existing documentation is a good way to sort that out. All the source for the documentation is written in `Sphinx `_, which uses ReST for markup. ReST is very straightforward to markup in a text editor, and if you are new to it, we recommend just using other .rst files in the existing yt documentation as templates or checking out the `ReST reference documentation `_. New cookbook recipes (see :ref:`cookbook`) are very helpful for the community as they provide simple annotated recipes on how to use specific functionality. To add one, create a concise python script which demonstrates some functionality and pare it down to its minimum. Add some comment lines to describe what it is that you're doing along the way. Place this ``.py`` file in the ``source/cookbook/`` directory, and then link to it explicitly in one of the relevant ``.rst`` files in that directory (e.g. ``complex_plots.rst``, ``cosmological_analysis.rst``, etc.), and add some description of what the script actually does. We recommend that you use one of the `sample data sets `_ in your recipe. When the full docs are built, each of the cookbook recipes are executed dynamically on a system which has access to all of the sample datasets. Any output images generated by your script will then be attached inline in the built documentation directly following your script. After you have made your modifications to the docs, you will want to make sure that they render the way you expect them to render. For more information on this, see the section on :ref:`docs_build`. Unless you're contributing cookbook recipes or notebooks which require a dynamical build, you can probably get away with just doing a 'quick' docs build. When you have completed your documentation additions, commit your changes to your repository and make a pull request in the same way you would contribute a change to the codebase, as described in the section on :ref:`sharing-changes`. .. _docs_build: Building the Documentation -------------------------- The yt documentation makes heavy use of the sphinx documentation automation suite. Sphinx, written in python, was originally created for the documentation of the python project and has many nice capabilities for managing the documentation of python code. While much of the yt documentation is static text, we make heavy use of cross-referencing with API documentation that is automatically generated at build time by sphinx. We also use sphinx to run code snippets (e.g. the cookbook and the notebooks) and embed resulting images and example data. You will want to make sure you have both Sphinx and the sphinx bootstrap theme installed. This installation is easily performed by running this at the command line: .. code-block:: bash pip install sphinx sphinx_bootstrap_theme Quick versus Full Documentation Builds ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Building the entire set of yt documentation is a laborious task, since you need to have a large number of packages in order to successfully execute and render all of the notebooks and yt recipes drawing from every corner of the yt source. As an quick alternative, one can do a ``quick`` build of the documentation, which eschews the need for downloading all of these dependencies, but it only produces the static docs. The static docs do not include the cookbook outputs and the notebooks, but this is good enough for most cases of people testing out whether or not their documentation contributions look OK before submitting them to the yt repository. If you want to create the full documentation locally, then you'll need to follow the instructions for building the ``full`` docs, so that you can dynamically execute and render the cookbook recipes, the notebooks, etc. Building the Docs (Quick) ^^^^^^^^^^^^^^^^^^^^^^^^^ You will need to have the yt repository available on your computer, which is done by default if you have yt installed. In addition, you need a current version of Sphinx_ (1.1.3) documentation software installed, as well as the Sphinx `Bootstrap theme `_, which can be installed via ``pip install sphinx_bootstrap_theme``. In order to tell sphinx not to do all of the dynamical building, you must set the ``$READTHEDOCS`` environment variable to be True by typing this at the command line: .. code-block:: bash export READTHEDOCS=True # for bash setenv READTHEDOCS True # for csh This variable is set for automated builds on the free ReadTheDocs service but can be used by anyone to force a quick, minimal build. Now all you need to do is execute sphinx on the yt doc source. Go to the documentation directory and build the docs: .. code-block:: bash cd $YT_HG/doc make html This will produce an html version of the documentation locally in the ``$YT_HG/doc/build/html`` directory. You can now go there and open up ``index.html`` or whatever file you wish in your web browser. Building the Docs (Full) ^^^^^^^^^^^^^^^^^^^^^^^^ As alluded to earlier, building the full documentation is a bit more involved than simply building the static documentation. The full documentation makes heavy use of custom sphinx extensions to transform recipes, notebooks, and inline code snippets into python scripts, IPython_ notebooks, or notebook cells that are executed when the docs are built. To do this, we use Jupyter's nbconvert module to transform notebooks into HTML. to simplify versioning of the notebook JSON format, we store notebooks in an unevaluated state. To build the full documentation, you will need yt, jupyter, and all dependencies needed for yt's analysis modules installed. The following dependencies were used to generate the yt documentation during the release of yt 3.2 in 2015. * Sphinx_ 1.3.1 * Jupyter 1.0.0 * RunNotebook 0.1 * pandoc_ 1.13.2 * Rockstar halo finder 0.99.6 * SZpack_ 1.1.1 * ffmpeg_ 2.7.1 (compiled with libvpx support) * Astropy_ 0.4.4 .. _SZpack: http://www.cita.utoronto.ca/~jchluba/Science_Jens/SZpack/SZpack.html .. _Astropy: http://astropy.org/ .. _Sphinx: http://sphinx-doc.org/ .. _pandoc: http://johnmacfarlane.net/pandoc/ .. _ffmpeg: http://www.ffmpeg.org/ .. _IPython: https://ipython.org/ You will also need the full yt suite of `yt test data `_, including the larger datasets that are not used in the answer tests. You will need to ensure that your testing configuration is properly configured and that all of the yt test data is in the testing directory. See :ref:`run_answer_testing` for more details on how to set up the testing configuration. Now that you have everything set up properly, go to the documentation directory and build it using sphinx: .. code-block:: bash cd $YT_HG/doc make html If all of the dependencies are installed and all of the test data is in the testing directory, this should churn away for a while (several hours) and eventually generate a docs build. We suggest setting :code:`suppressStreamLogging = True` in your yt configuration (See :ref:`configuration-file`) to suppress large amounts of debug output from yt. To clean the docs build, use :code:`make clean`. By default, :code:`make clean` will not delete the autogenerated API docs, so use :code:`make fullclean` to delete those as well. Building the Docs (Hybrid) ^^^^^^^^^^^^^^^^^^^^^^^^^^ It's also possible to create a custom sphinx build that builds a restricted set of notebooks or scripts. This can be accomplished by editing the Sphinx :code:`conf.py` file included in the :code:`source` directory at the top level of the docs. The extensions included in the build are contained in the :code:`extensions` list. To disable an extension, simply remove it from the list. Doing so will raise a warning when sphinx encounters the directive in the docs and will prevent sphinx from evaluating the directive. As a concrete example, if one wanted to include the :code:`notebook`, and :code:`notebook-cell` directives, but not the :code:`python-script` or :code:`autosummary` directives, one would just need to comment out the lines that append these extensions to the :code:`extensions` list. The resulting docs build will be significantly quicker since it would avoid executing the lengthy API autodocumentation as well as a large number of python script snippets in the narrative docs. yt-3.3.3/doc/source/developing/creating_datatypes.rst0000644000076500000240000000511413017603554023703 0ustar goldbaumstaff00000000000000.. _creating-objects: Creating Data Objects ===================== The three-dimensional datatypes in yt follow a fairly simple protocol. The basic principle is that if you want to define a region in space, that region must be identifiable from some sort of cut applied against the cells -- typically, in yt, this is done by examining the geometry. Creating a new data object requires modifications to two different files, one of which is in Python and the other in Cython. First, a subclass of :class:`~yt.data_objects.data_containers.YTDataContainer` must be defined; typically you actually want to subclass one of: :class:`~yt.data_objects.data_containers.YTSelectionContainer0D` :class:`~yt.data_objects.data_containers.YTSelectionContainer1D` :class:`~yt.data_objects.data_containers.YTSelectionContainer2D` :class:`~yt.data_objects.data_containers.YTSelectionContainer3D`. The following attributes must be defined: * ``_type_name`` - this is the short name by which the object type will be known as. Remember this for later, as we will have to use it when defining the underlying selector. * ``_con_args`` - this is the set of arguments passed to the object, and their names as attributes on the data object. * ``_container_fields`` - any fields that are generated by the object, rather than by another derived field in yt. The rest of the object can be defined in Cython, in the file ``yt/geometry/selection_routines.pyx``. You must define a subclass of ``SelectorObject``, which will require implementation of the following methods: * ``fill_mask`` - this takes a grid object and fills a mask of which zones should be included. It must take into account the child mask of the grid. * ``select_cell`` - this routine accepts a position and a width, and returns either zero or one for whether or not that cell is included in the selector. * ``select_sphere`` - this routine returns zero or one whether a sphere (point and radius) is included in the selector. * ``select_point`` - this identifies whether or not a point is included in the selector. It should be identical to selecting a cell or a sphere with zero extent. * ``select_bbox`` - this returns whether or not a bounding box (i.e., grid) is included in the selector. * ``_hash_vals`` - this must return some combination of parameters that semi-uniquely identifies the selector. Once the object has been defined, it must then be aliased within ``selection_routines.pyx`` as ``typename_selector``. For instance, ``ray_selector`` or ``sphere_selector`` for ``_type_name`` values of ``ray`` and ``sphere``, respectively. yt-3.3.3/doc/source/developing/creating_derived_fields.rst0000644000076500000240000003201713017603554024657 0ustar goldbaumstaff00000000000000.. _creating-derived-fields: Creating Derived Fields ======================= One of the more powerful means of extending yt is through the usage of derived fields. These are fields that describe a value at each cell in a simulation. Defining a New Field -------------------- Once a new field has been conceived of, the best way to create it is to construct a function that performs an array operation -- operating on a collection of data, neutral to its size, shape, and type. A simple example of this is the pressure field, which demonstrates the ease of this approach. .. code-block:: python import yt def _pressure(field, data): return (data.ds.gamma - 1.0) * \ data["density"] * data["thermal_energy"] Note that we do a couple different things here. We access the ``gamma`` parameter from the dataset, we access the ``density`` field and we access the ``thermal_energy`` field. ``thermal_energy`` is, in fact, another derived field! We don't do any loops, we don't do any type-checking, we can simply multiply the three items together. In this example, the ``density`` field will return data with units of ``g/cm**3`` and the ``thermal_energy`` field will return data units of ``erg/g``, so the result will automatically have units of pressure, ``erg/cm**3``. This assumes the unit system is set to the default, which is CGS: if a different unit system is selected, the result will be in the same dimensions of pressure but different units. See :ref:`unit_systems` for more information. Once we've defined our function, we need to notify yt that the field is available. The :func:`add_field` function is the means of doing this; it has a number of fairly specific parameters that can be passed in, but here we'll only look at the most basic ones needed for a simple scalar baryon field. .. note:: There are two different :func:`add_field` functions. For the differences, see :ref:`faq-add-field-diffs`. .. code-block:: python yt.add_field(("gas", "pressure"), function=_pressure, units="dyne/cm**2") We feed it the name of the field, the name of the function, and the units. Note that the units parameter is a "raw" string, in the format that yt uses in its :ref:`symbolic units implementation ` (e.g., employing only unit names, numbers, and mathematical operators in the string, and using ``"**"`` for exponentiation). For cosmological datasets and fields, see :ref:`cosmological-units`. We suggest that you name the function that creates a derived field with the intended field name prefixed by a single underscore, as in the ``_pressure`` example above. Field definitions return array data with units. If the field function returns data in a dimensionally equivalent unit (e.g. a ``"dyne"`` versus a ``"N"``), the field data will be converted to the units specified in ``add_field`` before being returned in a data object selection. If the field function returns data with dimensions that are incompatible with units specified in ``add_field``, you will see an error. To clear this error, you must ensure that your field function returns data in the correct units. Often, this means applying units to a dimensionless float or array. If your field definition includes physical constants rather than defining a constant as a float, you can import it from ``yt.units`` to get a predefined version of the constant with the correct units. If you know the units your data is supposed to have ahead of time, you can also import unit symbols like ``g`` or ``cm`` from the ``yt.units`` namespace and multiply the return value of your field function by the appropriate combination of unit symbols for your field's units. You can also convert floats or NumPy arrays into :class:`~yt.units.yt_array.YTArray` or :class:`~yt.units.yt_array.YTQuantity` instances by making use of the :func:`~yt.data_objects.static_output.Dataset.arr` and :func:`~yt.data_objects.static_output.Dataset.quan` convenience functions. Lastly, if you do not know the units of your field ahead of time, you can specify ``units='auto'`` in the call to ``add_field`` for your field. This will automatically determine the appropriate units based on the units of the data returned by the field function. This is also a good way to let your derived fields be automatically converted to the units of the :ref:`unit system ` in your dataset. If ``units='auto'`` is set, it is also required to set the ``dimensions`` keyword argument so that error-checking can be done on the derived field to make sure that the dimensionality of the returned array and the field are the same: .. code-block:: python import yt from yt.units import dimensions def _pressure(field, data): return (data.ds.gamma - 1.0) * \ data["density"] * data["thermal_energy"] yt.add_field(("gas","pressure"), function=_pressure, units="auto", dimensions=dimensions.pressure) If ``dimensions`` is not set, an error will be thrown. The ``dimensions`` keyword can be a SymPy ``symbol`` object imported from ``yt.units.dimensions``, a compound dimension of these, or a string corresponding to one of these objects. :func:`add_field` can be invoked in two other ways. The first is by the function decorator :func:`derived_field`. The following code is equivalent to the previous example: .. code-block:: python from yt import derived_field @derived_field(name="pressure", units="dyne/cm**2") def _pressure(field, data): return (data.ds.gamma - 1.0) * \ data["density"] * data["thermal_energy"] The :func:`derived_field` decorator takes the same arguments as :func:`add_field`, and is often a more convenient shorthand in cases where you want to quickly set up a new field. Defining derived fields in the above fashion must be done before a dataset is loaded, in order for the dataset to recognize it. If you want to set up a derived field after you have loaded a dataset, or if you only want to set up a derived field for a particular dataset, there is an :func:`~yt.data_objects.static_output.Dataset.add_field` method that hangs off dataset objects. The calling syntax is the same: .. code-block:: python ds = yt.load("GasSloshing/sloshing_nomag2_hdf5_plt_cnt_0100") ds.add_field(("gas", "pressure"), function=_pressure, units="dyne/cm**2") If you specify fields in this way, you can take advantage of the dataset's :ref:`unit system ` to define the units for you, so that the units will be returned in the units of that system: .. code-block:: python ds.add_field(("gas", "pressure"), function=_pressure, units=ds.unit_system["pressure"]) Since the :class:`yt.units.unit_systems.UnitSystem` object returns a :class:`yt.units.unit_object.Unit` object when queried, you're not limited to specifying units in terms of those already available. You can specify units for fields using basic arithmetic if necessary: .. code-block:: python ds.add_field(("gas", "my_acceleration"), function=_my_acceleration, units=ds.unit_system["length"]/ds.unit_system["time"]**2) If you find yourself using the same custom-defined fields over and over, you should put them in your plugins file as described in :ref:`plugin-file`. A More Complicated Example -------------------------- But what if we want to do something a bit more fancy? Here's an example of getting parameters from the data object and using those to define the field; specifically, here we obtain the ``center`` and ``bulk_velocity`` parameters and use those to define a field for radial velocity (there is already a ``radial_velocity`` field in yt, but we create this one here just as a transparent and simple example). .. code-block:: python from yt.fields.api import ValidateParameter import numpy as np def _my_radial_velocity(field, data): if data.has_field_parameter("bulk_velocity"): bv = data.get_field_parameter("bulk_velocity").in_units("cm/s") else: bv = data.ds.arr(np.zeros(3), "cm/s") xv = data["gas","velocity_x"] - bv[0] yv = data["gas","velocity_y"] - bv[1] zv = data["gas","velocity_z"] - bv[2] center = data.get_field_parameter('center') x_hat = data["x"] - center[0] y_hat = data["y"] - center[1] z_hat = data["z"] - center[2] r = np.sqrt(x_hat*x_hat+y_hat*y_hat+z_hat*z_hat) x_hat /= r y_hat /= r z_hat /= r return xv*x_hat + yv*y_hat + zv*z_hat yt.add_field(("gas","my_radial_velocity"), function=_my_radial_velocity, units="cm/s", take_log=False, validators=[ValidateParameter('center'), ValidateParameter('bulk_velocity')]) Note that we have added a few parameters below the main function; we specify that we do not wish to display this field as logged, that we require both ``bulk_velocity`` and ``center`` to be present in a given data object we wish to calculate this for, and we say that it should not be displayed in a drop-down box of fields to display. This is done through the parameter *validators*, which accepts a list of :class:`~yt.fields.derived_field.FieldValidator` objects. These objects define the way in which the field is generated, and when it is able to be created. In this case, we mandate that parameters ``center`` and ``bulk_velocity`` are set before creating the field. These are set via :meth:`~yt.data_objects.data_containers.set_field_parameter`, which can be called on any object that has fields: .. code-block:: python ds = yt.load("GasSloshing/sloshing_nomag2_hdf5_plt_cnt_0100") sp = ds.sphere("max", (200.,"kpc")) sp.set_field_parameter("bulk_velocity", yt.YTArray([-100.,200.,300.], "km/s")) In this case, we already know what the ``center`` of the sphere is, so we do not set it. Also, note that ``center`` and ``bulk_velocity`` need to be :class:`~yt.units.yt_array.YTArray` objects with units. Other examples for creating derived fields can be found in the cookbook recipe :ref:`cookbook-simple-derived-fields`. .. _derived-field-options: Field Options ------------- The arguments to :func:`add_field` are passed on to the constructor of :class:`DerivedField`. There are a number of options available, but the only mandatory ones are ``name``, ``units``, and ``function``. ``name`` This is the name of the field -- how you refer to it. For instance, ``pressure`` or ``magnetic_field_strength``. ``function`` This is a function handle that defines the field ``units`` This is a string that describes the units, or a query to a :ref:`UnitSystem ` object, e.g. ``ds.unit_system["energy"]``. Powers must be in Python syntax (``**`` instead of ``^``). Alternatively, it may be set to ``"auto"`` to have the units determined automatically. In this case, the ``dimensions`` keyword must be set to the correct dimensions of the field. ``display_name`` This is a name used in the plots, for instance ``"Divergence of Velocity"``. If not supplied, the ``name`` value is used. ``take_log`` This is *True* or *False* and describes whether the field should be logged when plotted. ``particle_type`` Is this field a *particle* field? ``validators`` (*Advanced*) This is a list of :class:`FieldValidator` objects, for instance to mandate spatial data. ``display_field`` (*Advanced*) Should this field appear in the dropdown box in Reason? ``not_in_all`` (*Advanced*) If this is *True*, the field may not be in all the grids. ``output_units`` (*Advanced*) For fields that exist on disk, which we may want to convert to other fields or that get aliased to themselves, we can specify a different desired output unit than the unit found on disk. ``force_override`` (*Advanced*) Overrides the definition of an old field if a field with the same name has already been defined. ``dimensions`` Set this if ``units="auto"``. Can be either a string or a dimension object from ``yt.units.dimensions``. Debugging a Derived Field ------------------------- If your derived field is not behaving as you would like, you can insert a call to ``data._debug()`` to spawn an interactive interpreter whenever that line is reached. Note that this is slightly different from calling ``pdb.set_trace()``, as it will *only* trigger when the derived field is being called on an actual data object, rather than during the field detection phase. The starting position will be one function lower in the stack than you are likely interested in, but you can either step through back to the derived field function, or simply type ``u`` to go up a level in the stack. For instance, if you had defined this derived field: .. code-block:: python @yt.derived_field(name = ("gas","funthings")) def funthings(field, data): return data["sillythings"] + data["humorousthings"]**2.0 And you wanted to debug it, you could do: .. code-block:: python @yt.derived_field(name = ("gas","funthings")) def funthings(field, data): data._debug() return data["sillythings"] + data["humorousthings"]**2.0 And now, when that derived field is actually used, you will be placed into a debugger. yt-3.3.3/doc/source/developing/creating_derived_quantities.rst0000644000076500000240000000303212770647750025605 0ustar goldbaumstaff00000000000000.. _creating_derived_quantities: Creating Derived Quantities --------------------------- .. warning:: This section is not yet updated to work with yt 3.0. If you have a question about making a custom derived quantity, please contact the mailing list. The basic idea is that you need to be able to operate both on a set of data, and a set of sets of data. (If this is not possible, the quantity needs to be added with the ``force_unlazy`` option.) Two functions are necessary. One will operate on arrays of data, either fed from each grid individually or fed from the entire data object at once. The second one takes the results of the first, either as lists of arrays or as single arrays, and returns the final values. For an example, we look at the ``TotalMass`` function: .. code-block:: python def _TotalMass(data): baryon_mass = data["cell_mass"].sum() particle_mass = data["ParticleMassMsun"].sum() return baryon_mass, particle_mass def _combTotalMass(data, baryon_mass, particle_mass): return baryon_mass.sum() + particle_mass.sum() add_quantity("TotalMass", function=_TotalMass, combine_function=_combTotalMass, n_ret = 2) Once the two functions have been defined, we then call :func:`add_quantity` to tell it the function that defines the data, the collator function, and the number of values that get passed between them. In this case we return both the particle and the baryon mass, so we have two total values passed from the main function into the collator. yt-3.3.3/doc/source/developing/creating_frontend.rst0000644000076500000240000003135013017603554023525 0ustar goldbaumstaff00000000000000.. _creating_frontend: Creating A New Code Frontend ============================ .. warning: This section is not yet updated to work with yt 3.0. If you have a question about making a custom derived quantity, please contact the mailing list. yt is designed to support analysis and visualization of data from multiple different simulation codes. For a list of codes and the level of support they enjoy, see :ref:`code-support`. We'd like to support a broad range of codes, both Adaptive Mesh Refinement (AMR)-based and otherwise. To add support for a new code, a few things need to be put into place. These necessary structures can be classified into a couple categories: * Data meaning: This is the set of parameters that convert the data into physically relevant units; things like spatial and mass conversions, time units, and so on. * Data localization: These are structures that help make a "first pass" at data loading. Essentially, we need to be able to make a first pass at guessing where data in a given physical region would be located on disk. With AMR data, this is typically quite easy: the grid patches are the "first pass" at localization. * Data reading: This is the set of routines that actually perform a read of either all data in a region or a subset of that data. Data Meaning Structures ----------------------- If you are interested in adding a new code, be sure to drop us a line on `yt-dev `_! To get started, make a new directory in ``yt/frontends`` with the name of your code and add the name into ``yt/frontends/api.py``. Copying the contents of the ``yt/frontends/_skeleton`` directory will add a lot of boilerplate for the required classes and methods that are needed. In particular, you'll have to create a subclass of ``Dataset`` in the data_structures.py file. This subclass will need to handle conversion between the different physical units and the code units (typically in the ``_set_code_unit_attributes()`` method), read in metadata describing the overall data on disk (via the ``_parse_parameter_file()`` method), and provide a ``classmethod`` called ``_is_valid()`` that lets the ``yt.load`` method help identify an input file as belonging to *this* particular ``Dataset`` subclass. For the most part, the examples of ``yt.frontends.boxlib.data_structures.OrionDataset`` and ``yt.frontends.enzo.data_structures.EnzoDataset`` should be followed, but ``yt.frontends.chombo.data_structures.ChomboDataset``, as a slightly newer addition, can also be used as an instructive example. A new set of fields must be added in the file ``fields.py`` in your new directory. For the most part this means subclassing ``FieldInfoContainer`` and adding the necessary fields specific to your code. Here is a snippet from the base BoxLib field container: .. code-block:: python from yt.fields.field_info_container import FieldInfoContainer class BoxlibFieldInfo(FieldInfoContainer): known_other_fields = ( ("density", (rho_units, ["density"], None)), ("eden", (eden_units, ["energy_density"], None)), ("xmom", (mom_units, ["momentum_x"], None)), ("ymom", (mom_units, ["momentum_y"], None)), ("zmom", (mom_units, ["momentum_z"], None)), ("temperature", ("K", ["temperature"], None)), ("Temp", ("K", ["temperature"], None)), ("x_velocity", ("cm/s", ["velocity_x"], None)), ("y_velocity", ("cm/s", ["velocity_y"], None)), ("z_velocity", ("cm/s", ["velocity_z"], None)), ("xvel", ("cm/s", ["velocity_x"], None)), ("yvel", ("cm/s", ["velocity_y"], None)), ("zvel", ("cm/s", ["velocity_z"], None)), ) known_particle_fields = ( ("particle_mass", ("code_mass", [], None)), ("particle_position_x", ("code_length", [], None)), ("particle_position_y", ("code_length", [], None)), ("particle_position_z", ("code_length", [], None)), ("particle_momentum_x", (mom_units, [], None)), ("particle_momentum_y", (mom_units, [], None)), ("particle_momentum_z", (mom_units, [], None)), ("particle_angmomen_x", ("code_length**2/code_time", [], None)), ("particle_angmomen_y", ("code_length**2/code_time", [], None)), ("particle_angmomen_z", ("code_length**2/code_time", [], None)), ("particle_id", ("", ["particle_index"], None)), ("particle_mdot", ("code_mass/code_time", [], None)), ) The tuples, ``known_other_fields`` and ``known_particle_fields`` contain entries, which are tuples of the form ``("name", ("units", ["fields", "to", "alias"], "display_name"))``. ``"name"`` is the name of a field stored on-disk in the dataset. ``"units"`` corresponds to the units of that field. The list ``["fields", "to", "alias"]`` allows you to specify additional aliases to this particular field; for example, if your on-disk field for the x-direction velocity were ``"x-direction-velocity"``, maybe you'd prefer to alias to the more terse name of ``"xvel"``. ``"display_name"`` is an optional parameter that can be used to specify how you want the field to be displayed on a plot; this can be LaTeX code, for example the density field could have a display name of ``r"\rho"``. Omitting the ``"display_name"`` will result in using a capitalized version of the ``"name"``. .. _bfields-frontend: Creating Aliases for Magnetic Fields ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Setting up access to the magnetic fields in your dataset requires special handling, because in different unit systems magnetic fields have different dimensions (see :ref:`bfields` for an explanation). If your dataset includes magnetic fields, you should include them in ``known_other_fields``, but do not set up aliases for them--instead use the special handling function :meth:`~yt.fields.magnetic_fields.setup_magnetic_field_aliases`. It takes as arguments the ``FieldInfoContainer`` instance, the field type of the frontend, and the list of magnetic fields from the frontend. Here is an example of how this is implemented in the FLASH frontend: .. code-block:: python class FLASHFieldInfo(FieldInfoContainer): known_other_fields = ( ... ("magx", (b_units, [], "B_x")), # Note there is no alias here ("magy", (b_units, [], "B_y")), ("magz", (b_units, [], "B_z")), ... ) def setup_fluid_fields(self): from yt.fields.magnetic_field import \ setup_magnetic_field_aliases ... setup_magnetic_field_aliases(self, "flash", ["mag%s" % ax for ax in "xyz"]) This function should always be imported and called from within the ``setup_fluid_fields`` method of the ``FieldInfoContainer``. If this function is used, converting between magnetic fields in different :ref:`unit systems ` will be handled automatically. Data Localization Structures ---------------------------- These functions and classes let yt know about how the arrangement of data on disk corresponds to the physical arrangement of data within the simulation. yt has grid datastructures for handling both patch-based and octree-based AMR codes. The terms 'patch-based' and 'octree-based' are used somewhat loosely here. For example, traditionally, the FLASH code used the paramesh AMR library, which is based on a tree structure, but the FLASH frontend in yt utilizes yt's patch-based datastructures. It is up to the frontend developer to determine which yt datastructures best match the datastructures of their simulation code. Both approaches -- patch-based and octree-based -- have a concept of a *Hierarchy* or *Index* (used somewhat interchangeably in the code) of datastructures and something that describes the elements that make up the Hierarchy or Index. For patch-based codes, the Index is a collection of ``AMRGridPatch`` objects that describe a block of zones. For octree-based codes, the Index contains datastructures that hold information about the individual octs, namely an ``OctreeContainer``. Hierarchy or Index ^^^^^^^^^^^^^^^^^^ To set up data localization, a ``GridIndex`` subclass for patch-based codes or an ``OctreeIndex`` subclass for octree-based codes must be added in the file ``data_structures.py``. Examples of these different types of ``Index`` can be found in, for example, the ``yt.frontends.chombo.data_structures.ChomboHierarchy`` for patch-based codes and ``yt.frontends.ramses.data_structures.RAMSESIndex`` for octree-based codes. For the most part, the ``GridIndex`` subclass must override (at a minimum) the following methods: * ``_detect_output_fields()``: ``self.field_list`` must be populated as a list of strings corresponding to "native" fields in the data files. * ``_count_grids()``: this must set ``self.num_grids`` to be the total number of grids (equivalently ``AMRGridPatch``'es) in the simulation. * ``_parse_index()``: this must fill in ``grid_left_edge``, ``grid_right_edge``, ``grid_particle_count``, ``grid_dimensions`` and ``grid_levels`` with the appropriate information. Each of these variables is an array, with an entry for each of the ``self.num_grids`` grids. Additionally, ``grids`` must be an array of ``AMRGridPatch`` objects that already know their IDs. * ``_populate_grid_objects()``: this initializes the grids by calling ``_prepare_grid()`` and ``_setup_dx()`` on all of them. Additionally, it should set up ``Children`` and ``Parent`` lists on each grid object. The ``OctreeIndex`` has somewhat analogous methods, but often with different names; both ``OctreeIndex`` and ``GridIndex`` are subclasses of the ``Index`` class. In particular, for the ``OctreeIndex``, the method ``_initialize_oct_handler()`` setups up much of the oct metadata that is analogous to the grid metadata created in the ``GridIndex`` methods ``_count_grids()``, ``_parse_index()``, and ``_populate_grid_objects()``. Grids ^^^^^ .. note:: This section only applies to the approach using yt's patch-based datastructures. For the octree-based approach, one does not create a grid object, but rather an ``OctreeSubset``, which has methods for filling out portions of the octree structure. Again, see the code in ``yt.frontends.ramses.data_structures`` for an example of the octree approach. A new grid object, subclassing ``AMRGridPatch``, will also have to be added in ``data_structures.py``. For the most part, this may be all that is needed: .. code-block:: python class ChomboGrid(AMRGridPatch): _id_offset = 0 __slots__ = ["_level_id"] def __init__(self, id, index, level = -1): AMRGridPatch.__init__(self, id, filename = index.index_filename, index = index) self.Parent = [] self.Children = [] self.Level = level Even one of the more complex grid objects, ``yt.frontends.boxlib.BoxlibGrid``, is still relatively simple. Data Reading Functions ---------------------- In ``io.py``, there are a number of IO handlers that handle the mechanisms by which data is read off disk. To implement a new data reader, you must subclass ``BaseIOHandler``. The various frontend IO handlers are stored in an IO registry - essentially a dictionary that uses the name of the frontend as a key, and the specific IO handler as a value. It is important, therefore, to set the ``dataset_type`` attribute of your subclass, which is what is used as the key in the IO registry. For example: .. code-block:: python class IOHandlerBoxlib(BaseIOHandler): _dataset_type = "boxlib_native" ... At a minimum, one should also override the following methods * ``_read_fluid_selection()``: this receives a collection of data "chunks", a selector describing which "chunks" you are concerned with, a list of fields, and the size of the data to read. It should create and return a dictionary whose keys are the fields, and whose values are numpy arrays containing the data. The data should actually be read via the ``_read_chunk_data()`` method. * ``_read_chunk_data()``: this method receives a "chunk" of data along with a list of fields we want to read. It loops over all the grid objects within the "chunk" of data and reads from disk the specific fields, returning a dictionary whose keys are the fields and whose values are numpy arrays of the data. If your dataset has particle information, you'll want to override the ``_read_particle_coords()`` and ``read_particle_fields()`` methods as well. Each code is going to read data from disk in a different fashion, but the ``yt.frontends.boxlib.io.IOHandlerBoxlib`` is a decent place to start. And that just about covers it. Please feel free to email `yt-users `_ or `yt-dev `_ with any questions, or to let us know you're thinking about adding a new code to yt. yt-3.3.3/doc/source/developing/debugdrive.rst0000644000076500000240000001044413017603554022153 0ustar goldbaumstaff00000000000000.. _debug-drive: Debugging yt ============ There are several different convenience functions that allow you to control yt in perhaps unexpected and unorthodox manners. These will allow you to conduct in-depth debugging of processes that may be running in parallel on multiple processors, as well as providing a mechanism of signalling to yt that you need more information about a running process. Additionally, yt has a built-in mechanism for optional reporting of errors to a central server. All of these allow for more rapid development and debugging of any problems you might encounter. Additionally, yt is able to leverage existing developments in the IPython community for parallel, interactive analysis. This allows you to initialize multiple yt processes through ``mpirun`` and interact with all of them from a single, unified interactive prompt. This enables and facilitates parallel analysis without sacrificing interactivity and flexibility. .. _pastebin: Pastebin -------- A pastebin is a website where you can easily copy source code and error messages to share with yt developers or your collaborators. At http://paste.yt-project.org/ a pastebin is available for placing scripts. With yt the script ``yt_lodgeit.py`` is distributed and wrapped with the ``pastebin`` and ``pastebin_grab`` commands, which allow for commandline uploading and downloading of pasted snippets. To upload a script you would supply it to the command: .. code-block:: bash $ yt pastebin some_script.py The URL will be returned. If you'd like it to be marked 'private' and not show up in the list of pasted snippets, supply the argument ``--private``. All snippets are given either numbers or hashes. To download a pasted snippet, you would use the ``pastebin_grab`` option: .. code-block:: bash $ yt pastebin_grab 1768 The snippet will be output to the window, so output redirection can be used to store it in a file. Use the Python Debugger ----------------------- yt is almost entirely composed of python code, so it makes sense to use the `python debugger`_ as your first stop in trying to debug it. .. _python debugger: https://docs.python.org/2/library/pdb.html Signaling yt to Do Something ---------------------------- During startup, yt inserts handlers for two operating system-level signals. These provide two diagnostic methods for interacting with a running process. Signalling the python process that is running your script with these signals will induce the requested behavior. SIGUSR1 This will cause the python code to print a stack trace, showing exactly where in the function stack it is currently executing. SIGUSR1 This will cause the python code to insert an IPython session wherever it currently is, with all local variables in the local namespace. It should allow you to change the state variables. If your yt-running process has PID 5829, you can signal it to print a traceback with: .. code-block:: bash $ kill -SIGUSR1 5829 Note, however, that if the code is currently inside a C function, the signal will not be handled, and the stacktrace will not be printed, until it returns from that function. .. _remote-debugging: Remote and Disconnected Debugging --------------------------------- If you are running a parallel job that fails, often it can be difficult to do a post-mortem analysis to determine what went wrong. To facilitate this, yt has implemented an `XML-RPC `_ interface to the Python debugger (``pdb``) event loop. Running with the ``--rpdb`` command will cause any uncaught exception during execution to spawn this interface, which will sit and wait for commands, exposing the full Python debugger. Additionally, a frontend to this is provided through the yt command. So if you run the command: .. code-block:: bash $ mpirun -np 4 python2.7 some_script.py --parallel --rpdb and it reaches an error or an exception, it will launch the debugger. Additionally, instructions will be printed for connecting to the debugger. Each of the four processes will be accessible via: .. code-block:: bash $ yt rpdb 0 where ``0`` here indicates the process 0. For security reasons, this will only work on local processes; to connect on a cluster, you will have to execute the command ``yt rpdb`` on the node on which that process was launched. yt-3.3.3/doc/source/developing/developing.rst0000644000076500000240000000004713017603554022165 0ustar goldbaumstaff00000000000000.. include:: ../../../CONTRIBUTING.rst yt-3.3.3/doc/source/developing/extensions.rst0000644000076500000240000000437313017603554022236 0ustar goldbaumstaff00000000000000.. _extensions: Extension Packages ================== .. note:: For some additional discussion, see `YTEP-0029 `_, where this plan was designed. As of version 3.3 of yt, we have put into place new methods for easing the process of developing "extensions" to yt. Extensions might be analysis packages, visualization tools, or other software projects that use yt as a base engine but that are versioned, developed and distributed separately. This brings with it the advantage of retaining control over the versioning, contribution guidelines, scope, etc, while also providing a mechanism for disseminating information about it, and potentially a method of interacting with other extensions. We have created a few pieces of infrastructure for developing extensions, making them discoverable, and distributing them to collaborators. If you have a module you would like to retain some external control over, or that you don't feel would fit into yt, we encourage you to build it as an extension module and distribute and version it independently. Hooks for Extensions -------------------- Starting with version 3.3 of yt, any package named with the prefix ``yt_`` is importable from the namespace ``yt.extensions``. For instance, the ``yt_interaction`` package ( https://bitbucket.org/data-exp-lab/yt_interaction ) is importable as ``yt.extensions.interaction``. In subsequent versions, we plan to include in yt a catalog of known extensions and where to find them; this will put discoverability directly into the code base. Extension Template ------------------ A template for starting an extension module (or converting an existing set of code to an extension module) can be found at https://bitbucket.org/yt_analysis/yt_extension_template . To get started, download a zipfile of the template ( https://bitbucket.org/yt_analysis/yt_extension_template/get/tip.zip ) and follow the directions in ``README.md`` to modify the metadata. Distributing Extensions ----------------------- We encourage you to version on your choice of hosting platform (Bitbucket, GitHub, etc), and to distribute your extension widely. We are presently working on deploying a method for listing extension modules on the yt webpage. yt-3.3.3/doc/source/developing/external_analysis.rst0000644000076500000240000003644713017603554023573 0ustar goldbaumstaff00000000000000.. _external-analysis-tools: Using yt with External Analysis Tools ===================================== yt can be used as a ``glue`` code between simulation data and other methods of analyzing data. Its facilities for understanding units, disk IO and data selection set it up ideally to use other mechanisms for analyzing, processing and visualizing data. Calling External Python Codes ----------------------------- Calling external Python codes very straightforward. For instance, if you had a Python code that accepted a set of structured meshes and then post-processed them to apply radiative feedback, one could imagine calling it directly: .. code-block:: python import yt import radtrans ds = yt.load("DD0010/DD0010") rt_grids = [] for grid in ds.index.grids: rt_grid = radtrans.RegularBox( grid.LeftEdge, grid.RightEdge, grid["density"], grid["temperature"], grid["metallicity"]) rt_grids.append(rt_grid) grid.clear_data() radtrans.process(rt_grids) Or if you wanted to run a population synthesis module on a set of star particles (and you could fit them all into memory) it might look something like this: .. code-block:: python import yt import pop_synthesis ds = yt.load("DD0010/DD0010") ad = ds.all_data() star_masses = ad["StarMassMsun"] star_metals = ad["StarMetals"] pop_synthesis.CalculateSED(star_masses, star_metals) If you have a code that's written in Python that you are having trouble getting data into from yt, please feel encouraged to email the users list and we'll help out. Calling Non-Python External Codes --------------------------------- Independent of its ability to process, analyze and visualize data, yt can also serve as a mechanism for reading and selecting simulation data. In this way, it can be used to supply data to an external analysis routine written in Fortran, C or C++. This document describes how to supply that data, using the example of a simple code that calculates the best axes that describe a distribution of particles as a starting point. (The underlying method is left as an exercise for the reader; we're only currently interested in the function specification and structs.) If you have written a piece of code that performs some analysis function, and you would like to include it in the base distribution of yt, we would be happy to do so; drop us a line or see :ref:`contributing-code` for more information. To accomplish the process of linking Python with our external code, we will be using a language called `Cython `_, which is essentially a superset of Python that compiles down to C. It is aware of NumPy arrays, and it is able to massage data between the interpreted language Python and C, Fortran or C++. It will be much easier to utilize routines and analysis code that have been separated into subroutines that accept data structures, so we will assume that our halo axis calculator accepts a set of structs. Our Example Code ++++++++++++++++ Here is the ``axes.h`` file in our imaginary code, which we will then wrap: .. code-block:: c typedef struct structParticleCollection { long npart; double *xpos; double *ypos; double *zpos; } ParticleCollection; void calculate_axes(ParticleCollection *part, double *ax1, double *ax2, double *ax3); There are several components to this analysis routine which we will have to wrap. #. We have to wrap the creation of an instance of ``ParticleCollection``. #. We have to transform a set of NumPy arrays into pointers to doubles. #. We have to create a set of doubles into which ``calculate_axes`` will be placing the values of the axes it calculates. #. We have to turn the return values back into Python objects. Each of these steps can be handled in turn, and we'll be doing it using Cython as our interface code. Setting Up and Building Our Wrapper +++++++++++++++++++++++++++++++++++ To get started, we'll need to create two files: .. code-block:: bash axes_calculator.pyx axes_calculator_setup.py These can go anywhere, but it might be useful to put them in their own directory. The contents of ``axes_calculator.pyx`` will be left for the next section, but we will need to put some boilerplate code into ``axes_calculator_setup.pyx``. As a quick sidenote, you should call these whatever is most appropriate for the external code you are wrapping; ``axes_calculator`` is probably not the best bet. Here's a rough outline of what should go in ``axes_calculator_setup.py``: .. code-block:: python NAME = "axes_calculator" EXT_SOURCES = [] EXT_LIBRARIES = ["axes_utils", "m"] EXT_LIBRARY_DIRS = ["/home/rincewind/axes_calculator/"] EXT_INCLUDE_DIRS = [] DEFINES = [] from distutils.core import setup from distutils.extension import Extension from Cython.Distutils import build_ext ext_modules = [Extension(NAME, [NAME+".pyx"] + EXT_SOURCES, libraries = EXT_LIBRARIES, library_dirs = EXT_LIBRARY_DIRS, include_dirs = EXT_INCLUDE_DIRS, define_macros = DEFINES) ] setup( name = NAME, cmdclass = {'build_ext': build_ext}, ext_modules = ext_modules ) The only variables you should have to change in this are the first six, and possibly only the first one. We'll go through these variables one at a time. ``NAME`` This is the name of our source file, minus the ``.pyx``. We're also mandating that it be the name of the module we import. You're free to modify this. ``EXT_SOURCES`` Any additional sources can be listed here. For instance, if you are only linking against a single ``.c`` file, you could list it here -- if our axes calculator were fully contained within a file called ``calculate_my_axes.c`` we could link against it using this variable, and then we would not have to specify any libraries. This is usually the simplest way to do things, and in fact, yt makes use of this itself for things like HEALPix and interpolation functions. ``EXT_LIBRARIES`` Any libraries that will need to be linked against (like ``m``!) should be listed here. Note that these are the name of the library minus the leading ``lib`` and without the trailing ``.so``. So ``libm.so`` would become ``m`` and ``libluggage.so`` would become ``luggage``. ``EXT_LIBRARY_DIRS`` If the libraries listed in ``EXT_LIBRARIES`` reside in some other directory or directories, those directories should be listed here. For instance, ``["/usr/local/lib", "/home/rincewind/luggage/"]`` . ``EXT_INCLUDE_DIRS`` If any header files have been included that live in external directories, those directories should be included here. ``DEFINES`` Any define macros that should be passed to the C compiler should be listed here; if they just need to be defined, then they should be specified to be defined as "None." For instance, if you wanted to pass ``-DTWOFLOWER``, you would set this to equal: ``[("TWOFLOWER", None)]``. To build our extension, we would run: .. code-block:: bash $ python2.7 axes_calculator_setup.py build_ext -i Note that since we don't yet have an ``axes_calculator.pyx``, this will fail. But once we have it, it ought to run. Writing and Calling our Wrapper +++++++++++++++++++++++++++++++ Now we begin the tricky part, of writing our wrapper code. We've already figured out how to build it, which is halfway to being able to test that it works, and we now need to start writing Cython code. For a more detailed introduction to Cython, see the Cython documentation at http://docs.cython.org/ . We'll cover a few of the basics for wrapping code however. To start out with, we need to open up and edit our file, ``axes_calculator.pyx``. Open this in your favorite version of vi (mine is vim) and we will get started by declaring the struct we need to pass in. But first, we need to include some header information: .. code-block:: cython import numpy as np cimport numpy as np cimport cython from stdlib cimport malloc, free These lines simply import and "Cython import" some common routines. For more information about what is already available, see the Cython documentation. For now, we need to start translating our data. To do so, we tell Cython both where the struct should come from, and then we describe the struct itself. One fun thing to note is that if you don't need to set or access all the values in a struct, and it just needs to be passed around opaquely, you don't have to include them in the definition. For an example of this, see the ``png_writer.pyx`` file in the yt repository. Here's the syntax for pulling in (from a file called ``axes_calculator.h``) a struct like the one described above: .. code-block:: cython cdef extern from "axes_calculator.h": ctypedef struct ParticleCollection: long npart double *xpos double *ypos double *zpos So far, pretty easy! We've basically just translated the declaration from the ``.h`` file. Now that we have done so, any other Cython code can create and manipulate these ``ParticleCollection`` structs -- which we'll do shortly. Next up, we need to declare the function we're going to call, which looks nearly exactly like the one in the ``.h`` file. (One common problem is that Cython doesn't know what ``const`` means, so just remove it wherever you see it.) Declare it like so: .. code-block:: cython void calculate_axes(ParticleCollection *part, double *ax1, double *ax2, double *ax3) Note that this is indented one level, to indicate that it, too, comes from ``axes_calculator.h``. The next step is to create a function that accepts arrays and converts them to the format the struct likes. We declare our function just like we would a normal Python function, using ``def``. You can also use ``cdef`` if you only want to call a function from within Cython. We want to call it from Python, too, so we just use ``def``. Note that we don't here specify types for the various arguments. In a moment we'll refine this to have better argument types. .. code-block:: cython def examine_axes(xpos, ypos, zpos): cdef double ax1[3], ax2[3], ax3[3] cdef ParticleCollection particles cdef int i particles.npart = len(xpos) particles.xpos = malloc(particles.npart * sizeof(double)) particles.ypos = malloc(particles.npart * sizeof(double)) particles.zpos = malloc(particles.npart * sizeof(double)) for i in range(particles.npart): particles.xpos[i] = xpos[i] particles.ypos[i] = ypos[i] particles.zpos[i] = zpos[i] calculate_axes(&particles, ax1, ax2, ax3) free(particles.xpos) free(particles.ypos) free(particles.zpos) return ( (ax1[0], ax1[1], ax1[2]), (ax2[0], ax2[1], ax2[2]), (ax3[0], ax3[1], ax3[2]) ) This does the rest. Note that we've weaved in C-type declarations (ax1, ax2, ax3) and Python access to the variables fed in. This function will probably be quite slow -- because it doesn't know anything about the variables xpos, ypos, zpos, it won't be able to speed up access to them. Now we will see what we can do by declaring them to be of array-type before we start handling them at all. We can do that by annotating in the function argument list. But first, let's test that it works. From the directory in which you placed these files, run: .. code-block:: bash $ python2.6 setup.py build_ext -i Now, create a sample file that feeds in the particles: .. code-block:: python import axes_calculator axes_calculator.examine_axes(xpos, ypos, zpos) Most of the time in that function is spent in converting the data. So now we can go back and we'll try again, rewriting our converter function to believe that its being fed arrays from NumPy: .. code-block:: cython def examine_axes(np.ndarray[np.float64_t, ndim=1] xpos, np.ndarray[np.float64_t, ndim=1] ypos, np.ndarray[np.float64_t, ndim=1] zpos): cdef double ax1[3], ax2[3], ax3[3] cdef ParticleCollection particles cdef int i particles.npart = len(xpos) particles.xpos = malloc(particles.npart * sizeof(double)) particles.ypos = malloc(particles.npart * sizeof(double)) particles.zpos = malloc(particles.npart * sizeof(double)) for i in range(particles.npart): particles.xpos[i] = xpos[i] particles.ypos[i] = ypos[i] particles.zpos[i] = zpos[i] calculate_axes(&particles, ax1, ax2, ax3) free(particles.xpos) free(particles.ypos) free(particles.zpos) return ( (ax1[0], ax1[1], ax1[2]), (ax2[0], ax2[1], ax2[2]), (ax3[0], ax3[1], ax3[2]) ) This should be substantially faster, assuming you feed it arrays. Now, there's one last thing we can try. If we know our function won't modify our arrays, and they are C-Contiguous, we can simply grab pointers to the data: .. code-block:: cython def examine_axes(np.ndarray[np.float64_t, ndim=1] xpos, np.ndarray[np.float64_t, ndim=1] ypos, np.ndarray[np.float64_t, ndim=1] zpos): cdef double ax1[3], ax2[3], ax3[3] cdef ParticleCollection particles cdef int i particles.npart = len(xpos) particles.xpos = xpos.data particles.ypos = ypos.data particles.zpos = zpos.data for i in range(particles.npart): particles.xpos[i] = xpos[i] particles.ypos[i] = ypos[i] particles.zpos[i] = zpos[i] calculate_axes(&particles, ax1, ax2, ax3) return ( (ax1[0], ax1[1], ax1[2]), (ax2[0], ax2[1], ax2[2]), (ax3[0], ax3[1], ax3[2]) ) But note! This will break or do weird things if you feed it arrays that are non-contiguous. At this point, you should have a mostly working piece of wrapper code. And it was pretty easy! Let us know if you run into any problems, or if you are interested in distributing your code with yt. A complete set of files is available with this documentation. These are slightly different, so that the whole thing will simply compile, but they provide a useful example. * `axes.c <../_static/axes.c>`_ * `axes.h <../_static/axes.h>`_ * `axes_calculator.pyx <../_static/axes_calculator.pyx>`_ * `axes_calculator_setup.py <../_static/axes_calculator_setup.txt>`_ Exporting Data from yt ---------------------- yt is installed alongside h5py. If you need to export your data from yt, to share it with people or to use it inside another code, h5py is a good way to do so. You can write out complete datasets with just a few commands. You have to import, and then save things out into a file. .. code-block:: python import h5py f = h5py.File("some_file.h5") f.create_dataset("/data", data=some_data) This will create ``some_file.h5`` if necessary and add a new dataset (``/data``) to it. Writing out in ASCII should be relatively straightforward. For instance: .. code-block:: python f = open("my_file.txt", "w") for halo in halos: x, y, z = halo.center_of_mass() f.write("%0.2f %0.2f %0.2f\n", x, y, z) f.close() This example could be extended to work with any data object's fields, as well. yt-3.3.3/doc/source/developing/index.rst0000644000076500000240000000165113017603554021142 0ustar goldbaumstaff00000000000000Developing in yt ================ yt is an open-source project with a community of contributing scientists. While you can use the existing framework within yt to help answer questions about your own datasets, yt thrives by the addition of new functionality by users just like yourself. Maybe you have a new data format that you would like supported, a new derived quantity that you feel should be included, or a new way of visualizing data--please add them to the code base! We are eager to help you make it happen. There are many ways to get involved with yt -- participating in the mailing list, helping people out in IRC, providing suggestions for the documentation, and contributing code! .. toctree:: :maxdepth: 2 developing building_the_docs testing extensions debugdrive releasing creating_datatypes creating_derived_fields creating_derived_quantities creating_frontend external_analysis yt-3.3.3/doc/source/developing/releasing.rst0000644000076500000240000002316613017603554022011 0ustar goldbaumstaff00000000000000How to Do a Release ------------------- Periodically, the yt development community issues new releases. Since yt follows `semantic versioning `_, the type of release can be read off from the version number used. Version numbers should follow the scheme ``MAJOR.MINOR.PATCH``. There are three kinds of possible releases: * Bugfix releases These releases are regularly scheduled and will optimally happen approximately once a month. These releases should contain only fixes for bugs discovered in earlier releases and should not contain new features or API changes. Bugfix releases should increment the ``PATCH`` version number. Bugfix releases should *not* be generated by merging from the ``yt`` branch, instead bugfix pull requests should be manually backported using the PR backport script, described below. Version ``3.2.2`` is a bugfix release. * Minor releases These releases happen when new features are deemed ready to be merged into the ``stable`` branch and should not happen on a regular schedule. Minor releases can also include fixes for bugs if the fix is determined to be too invasive for a bugfix release. Minor releases should *not* inlucde backwards-incompatible changes and should not change APIs. If an API change is deemed to be necessary, the old API should continue to function but might trigger deprecation warnings. Minor releases should happen by merging the ``yt`` branch into the ``stable`` branch. Minor releases should increment the ``MINOR`` version number and reset the ``PATCH`` version number to zero. Version ``3.3.0`` is a minor release. * Major releases These releases happen when the development community decides to make major backwards-incompatible changes. In principle a major version release could include arbitrary changes to the library. Major version releases should only happen after extensive discussion and vetting among the developer and user community. Like minor releases, a major release should happen by merging the ``yt`` branch into the ``stable`` branch. Major releases should increment the ``MAJOR`` version number and reset the ``MINOR`` and ``PATCH`` version numbers to zero. If it ever happens, version ``4.0.0`` will be a major release. The job of doing a release differs depending on the kind of release. Below, we describe the necessary steps for each kind of release in detail. Doing a Bugfix Release ~~~~~~~~~~~~~~~~~~~~~~ As described above, bugfix releases are regularly scheduled updates for minor releases to ensure fixes for bugs make their way out to users in a timely manner. Since bugfix releases should not include new features, we do not issue bugfix releases by simply merging from the development ``yt`` branch into the ``stable`` branch. Instead, we make use of the ``pr_backport.py`` script to manually cherry-pick bugfixes from the from ``yt`` branch onto the ``stable`` branch. The backport script issues interactive prompts to backport individual pull requests to the ``stable`` branch in a temporary clone of the main yt mercurial repository on bitbucket. The script is written this way to to avoid editing history in a clone of the repository that a developer uses for day-to-day work and to avoid mixing work-in-progress changes with changes that have made their way to the "canonical" yt repository on bitbucket. Rather than automatically manipulating the temporary repository by scripting mercurial commands using ``python-hglib``, the script must be "operated" by a human who is ready to think carefully about what the script is telling them to do. Most operations will merely require copy/pasting a suggested mercurial command. However, some changes will require manual backporting. To run the backport script, first open two terminal windows. The first window will be used to run the backport script. The second terminal will be used to manipulate a temporary clone of the yt mercurial repository. In the first window, navigate to the ``scripts`` directory at the root of the yt repository and run the backport script, .. code-block:: bash $ cd $YT_HG/scripts $ python pr_backport.py You will then need to wait for about a minute (depending on the speed of your internet connection and bitbucket's servers) while the script makes a clone of the main yt repository and then gathers information about pull requests that have been merged since the last tagged release. Once this step finishes, you will be prompted to navigate to the temporary folder in a new separate terminal session. Do so, and then hit the enter key in the original terminal session. For each pull request in the set of pull requests that were merged since the last tagged release that were pointed at the "main" line of development (e.g. not the ``experimental`` bookmark), you will be prompted by the script with the PR number, title, description, and a suggested mercurial command to use to backport the pull request. If the pull request consists of a single changeset, you will be prompted to use ``hg graft``. If it contains more than one changeset, you will be prompted to use ``hg rebase``. Note that ``rebase`` is an optional extension for mercurial that is not turned on by default. To enable it, add a section like the following in your ``.hgrc`` file: .. code-block:: none [extensions] rebase= Since ``rebase`` is bundled with core mercurial, you do not need to specify a path to the rebase extension, just say ``rebase=`` and mercurial will find the version of ``rebase`` bundled with mercurial. Note also that mercurial does not automatically update to the tip of the rebased head after executing ``hg rebase`` so you will need to manually issue ``hg update stable`` to move your working directory to the new head of the stable branch. The backport script should prompt you with a suggestion to update as well. If the pull request contains merge commits, you must take care to *not* backport commits that merge with the main line of development on the ``yt`` branch. Doing so may bring unrelated changes, including new features, into a bugfix release. If the pull request you'd like to backport contains merge commits, the backport script should warn you to be extra careful. Once you've finished backporting, the script will let you know that you are done and warn you to push your work. The temporary repository you have been working with will be deleted as soon as the script exits, so take care to push your work on the ``stable`` branch to your fork on bitbucket. Once you've pushed to your fork, you will be able to issue a pull request containing the backported fixes just like any other yt pull request. Doing a Minor or Major Release ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ This is much simpler than a bugfix release. All that needs to happen is the ``yt`` branch must get merged into the ``stable`` branch, and any conflicts that happen must be resolved, almost certainly in favor of the yt branch. This can happen either using a merge tool like ``vimdiff`` and ``kdiff3`` or by telling mercurial to write merge markers. If you prefer merge markers, the following configuration options should be turned on in your ``hgrc`` to get more detail during the merge: .. code-block:: none [ui] merge = internal:merge3 mergemarkers = detailed The first option tells mercurial to write merge markers that show the state of the conflicted region of the code on both sides of the merge as well as the "base" most recent common ancestor changeset. The second option tells mercurial to add extra information about the code near the merge markers. Incrementing Version Numbers and Tagging a Release ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Before creating the tag for the release, you must increment the version numbers that are hard-coded in a few files in the yt source so that version metadata for the code is generated correctly. This includes things like ``yt.__version__`` and the version that gets read by the Python Package Index (PyPI) infrastructure. The paths relative to the root of the repository for the three files that need to be edited are: * ``doc/source/conf.py`` The ``version`` and ``release`` variables need to be updated. * ``setup.py`` The ``VERSION`` variable needs to be updated * ``yt/__init__.py`` The ``__version__`` variable must be updated. Once these files have been updated, commit these updates. This is the commit we will tag for the release. To actually create the tag, issue the following command: .. code-block:: bash hg tag Where ```` follows the project's naming scheme for tags (e.g. ``yt-3.2.1``). Commit the tag, and you should be ready to upload the release to pypi. If you are doing a minor or major version number release, you will also need to update back to the development branch and update the development version numbers in the same files. Uploading to PyPI ~~~~~~~~~~~~~~~~~ To actually upload the release to the Python Package Index, you just need to issue the following command: .. code-block:: bash python setup.py sdist upload -r https://pypi.python.org/pypi You will be prompted for your PyPI credentials and then the package should upload. Note that for this to complete successfully, you will need an account on PyPI and that account will need to be registered as an "owner" of the yt package. Right now there are three owners: Matt Turk, Britton Smith, and Nathan Goldbaum. After the release is uploaded to PyPI, you should send out an announcement e-mail to the yt mailing lists as well as other possibly interested mailing lists for all but bugfix releases. In addition, you should contact John ZuHone about uploading binary wheels to PyPI for Windows and OS X users and contact Nathan Goldbaum about getting the Anaconda packages updated. yt-3.3.3/doc/source/developing/testing.rst0000644000076500000240000005217713023400062021503 0ustar goldbaumstaff00000000000000.. _testing: Testing ======= yt includes a testing suite which one can run on the codebase to assure that no breaks in functionality have occurred. This testing suite is based on the Nose_ testing framework. The suite consists of two components, unit tests and answer tests. Unit tests confirm that an isolated piece of functionality runs without failure for inputs with known correct outputs. Answer tests verify the integration and compatibility of the individual code unit by generating output from user-visible yt functions and comparing and matching the results against outputs of the same function produced using older versions of the yt codebase. This ensures consistency in results as development proceeds. .. _nosetests: The testing suite should be run locally by developers to make sure they aren't checking in any code that breaks existing functionality. To further this goal, an automatic buildbot runs the test suite after each code commit to confirm that yt hasn't broken recently. To supplement this effort, we also maintain a `continuous integration server `_ that runs the tests with each commit to the yt version control repository. .. _unit_testing: Unit Testing ------------ What do Unit Tests Do ^^^^^^^^^^^^^^^^^^^^^ Unit tests are tests that operate on some small set of machinery, and verify that the machinery works. yt uses the `Nose `_ framework for running unit tests. In practice, what this means is that we write scripts that assert statements, and Nose identifies those scripts, runs them, and verifies that the assertions are true and the code runs without crashing. How to Run the Unit Tests ^^^^^^^^^^^^^^^^^^^^^^^^^ One can run the unit tests very straightforwardly from any python interpreter that can import the yt module: .. code-block:: python import yt yt.run_nose() If you are developing new functionality, it is sometimes more convenient to use the Nose command line interface, ``nosetests``. You can run the unit tests using ``nose`` by navigating to the base directory of the yt mercurial repository and invoking ``nosetests``: .. code-block:: bash $ cd $YT_HG $ nosetests where ``$YT_HG`` is the path to the root of the yt mercurial repository. If you want to specify a specific unit test to run (and not run the entire suite), you can do so by specifying the path of the test relative to the ``$YT_HG/yt`` directory -- note that you strip off one yt more than you normally would! For example, if you want to run the plot_window tests, you'd run: .. code-block:: bash $ nosetests yt/visualization/tests/test_plotwindow.py How to Write Unit Tests ^^^^^^^^^^^^^^^^^^^^^^^ yt provides several pieces of testing assistance, all in the ``yt.testing`` module. Describing them in detail is somewhat outside the scope of this document, as in some cases they belong to other packages. However, a few come in handy: * :func:`~yt.testing.fake_random_ds` provides the ability to create a random dataset, with several fields and divided into several different grids, that can be operated on. * :func:`~yt.testing.assert_equal` can operate on arrays. * :func:`~yt.testing.assert_almost_equal` can operate on arrays and accepts a relative allowable difference. * :func:`~yt.testing.assert_allclose_units` raises an error if two arrays are not equal up to a desired absolute or relative tolerance. This wraps numpy's assert_allclose to correctly verify unit consistency as well. * :func:`~yt.testing.amrspace` provides the ability to create AMR grid structures. * :func:`~yt.testing.expand_keywords` provides the ability to iterate over many values for keywords. To create new unit tests: #. Create a new ``tests/`` directory next to the file containing the functionality you want to test and add an empty ``__init__.py`` file to it. #. Inside that directory, create a new python file prefixed with ``test_`` and including the name of the functionality. #. Inside that file, create one or more routines prefixed with ``test_`` that accept no arguments. The test function should do some work that tests some functionality and should also verify that the results are correct using assert statements or functions. #. Tests can ``yield`` a tuple of the form ``function``, ``argument_one``, ``argument_two``, etc. For example ``yield my_test, 'banana', 2.0`` would be captured by nose and the ``my_test`` function will be run with the provided arguments. #. Use ``fake_random_ds`` to test on datasets, and be sure to test for several combinations of ``nproc``, so that domain decomposition can be tested as well. #. Test multiple combinations of options by using the :func:`~yt.testing.expand_keywords` function, which will enable much easier iteration over options. For an example of how to write unit tests, look at the file ``yt/data_objects/tests/test_covering_grid.py``, which covers a great deal of functionality. Debugging failing tests ^^^^^^^^^^^^^^^^^^^^^^^ When writing new tests, often one exposes bugs or writes a test incorrectly, causing an exception to be raised or a failed test. To help debug issues like this, ``nose`` can drop into a debugger whenever a test fails or raises an exception. This can be accomplished by passing ``--pdb`` and ``--pdb-failures`` to the ``nosetests`` executable. These options will drop into the pdb debugger whenever an error is raised or a failure happens, respectively. Inside the debugger you can interactively print out variables and go up and down the call stack to determine the context for your failure or error. .. code-block:: bash nosetests --pdb --pdb-failures In addition, one can debug more crudely using print statements. To do this, you can add print statements to the code as normal. However, the test runner will capture all print output by default. To ensure that output gets printed to your terminal while the tests are running, pass ``-s`` to the ``nosetests`` executable. Lastly, to quickly debug a specific failing test, it is best to only run that one test during your testing session. This can be accomplished by explicitly passing the name of the test function or class to ``nosetests``, as in the following example: .. code-block:: bash $ nosetests yt.visualization.tests.test_plotwindow:TestSetWidth This nosetests invocation will only run the tests defined by the ``TestSetWidth`` class. Finally, to determine which test is failing while the tests are running, it helps to run the tests in "verbose" mode. This can be done by passing the ``-v`` option to the ``nosetests`` executable. All of the above ``nosetests`` options can be combined. So, for example to run the ``TestSetWidth`` tests with verbose output, letting the output of print statements come out on the terminal prompt, and enabling pdb debugging on errors or test failures, one would do: .. code-block:: bash $ nosetests --pdb --pdb-failures -v -s yt.visualization.tests.test_plotwindow:TestSetWidth .. _answer_testing: Answer Testing -------------- What do Answer Tests Do ^^^^^^^^^^^^^^^^^^^^^^^ Answer tests test **actual data**, and many operations on that data, to make sure that answers don't drift over time. This is how we test frontends, as opposed to operations, in yt. .. _run_answer_testing: How to Run the Answer Tests ^^^^^^^^^^^^^^^^^^^^^^^^^^^ The very first step is to make a directory and copy over the data against which you want to test. Currently, we test: NMSU ART ~~~~~~~~ * ``D9p_500/10MpcBox_HartGal_csf_a0.500.d`` ARTIO ~~~~~ * ``sizmbhloz-clref04SNth-rs9_a0.9011/sizmbhloz-clref04SNth-rs9_a0.9011.art`` Athena ~~~~~~ * ``ShockCloud/id0/Cloud.0050.vtk`` * ``MHDBlast/id0/Blast.0100.vtk`` * ``RamPressureStripping/id0/rps.0062.vtk`` * ``MHDSloshing/virgo_low_res.0054.vtk`` Boxlib ~~~~~~ * ``RadAdvect/plt00000`` * ``RadTube/plt00500`` * ``StarParticles/plrd01000`` Chombo ~~~~~~ * ``TurbBoxLowRes/data.0005.3d.hdf5`` * ``GaussianCloud/data.0077.3d.hdf5`` * ``IsothermalSphere/data.0000.3d.hdf5`` * ``ZeldovichPancake/plt32.2d.hdf5`` * ``KelvinHelmholtz/data.0004.hdf5`` Enzo ~~~~ * ``DD0010/moving7_0010`` (available in ``tests/`` in the yt distribution) * ``IsolatedGalaxy/galaxy0030/galaxy0030`` * ``enzo_tiny_cosmology/DD0046/DD0046`` * ``enzo_cosmology_pluts/DD0046/DD0046`` FITS ~~~~ * ``radio_fits/grs-50-cube.fits`` * ``UnigridData/velocity_field_20.fits`` FLASH ~~~~~ * ``WindTunnel/windtunnel_4lev_hdf5_plt_cnt_0030`` * ``GasSloshingLowRes/sloshing_low_res_hdf5_plt_cnt_0300`` Gadget ~~~~~~ * ``IsothermalCollapse/snap_505`` * ``IsothermalCollapse/snap_505.hdf5`` * ``GadgetDiskGalaxy/snapshot_200.hdf5`` GAMER ~~~~~~ * ``InteractingJets/jet_000002`` * ``WaveDarkMatter/psiDM_000020`` Halo Catalog ~~~~~~~~~~~~ * ``owls_fof_halos/groups_001/group_001.0.hdf5`` * ``owls_fof_halos/groups_008/group_008.0.hdf5`` * ``gadget_fof_halos/groups_005/fof_subhalo_tab_005.0.hdf5`` * ``gadget_fof_halos/groups_042/fof_subhalo_tab_042.0.hdf5`` * ``rockstar_halos/halos_0.0.bin`` MOAB ~~~~ * ``c5/c5.h5m`` RAMSES ~~~~~~ * ``output_00080/info_00080.txt`` Tipsy ~~~~~ * ``halo1e11_run1.00400/halo1e11_run1.00400`` * ``agora_1e11.00400/agora_1e11.00400`` * ``TipsyGalaxy/galaxy.00300`` OWLS ~~~~ * ``snapshot_033/snap_033.0.hdf5`` These datasets are available at http://yt-project.org/data/. Next, modify the file ``~/.yt/config`` to include a section ``[yt]`` with the parameter ``test_data_dir``. Set this to point to the directory with the test data you want to test with. Here is an example config file: .. code-block:: none [yt] test_data_dir = /Users/tomservo/src/yt-data More data will be added over time. To run the answer tests, you must first generate a set of test answers locally on a "known good" revision, then update to the revision you want to test, and run the tests again using the locally stored answers. Let's focus on running the answer tests for a single frontend. It's possible to run the answer tests for **all** the frontends, but due to the large number of test datasets we currently use this is not normally done except on the yt project's contiguous integration server. .. code-block:: bash $ cd $YT_HG $ nosetests --with-answer-testing --local --local-dir $HOME/Documents/test --answer-store --answer-name=local-tipsy yt.frontends.tipsy This command will create a set of local answers from the tipsy frontend tests and store them in ``$HOME/Documents/test`` (this can but does not have to be the same directory as the ``test_data_dir`` configuration variable defined in your ``.yt/config`` file) in a file named ``local-tipsy``. To run the tipsy frontend's answer tests using a different yt changeset, update to that changeset, recompile if necessary, and run the tests using the following command: .. code-block:: bash $ nosetests --with-answer-testing --local --local-dir $HOME/Documents/test --answer-name=local-tipsy yt.frontends.tipsy The results from a nose testing session are pretty straightforward to understand, the results for each test are printed directly to STDOUT. If a test passes, nose prints a period, F if a test fails, and E if the test encounters an exception or errors out for some reason. Explicit descriptions for each test are also printed if you pass ``-v`` to the ``nosetests`` executable. If you want to also run tests for the 'big' datasets, then you will need to pass ``--answer-big-data`` to ``nosetests``. For example, to run the tests for the OWLS frontend, do the following: .. code-block:: bash $ nosetests --with-answer-testing --local --local-dir $HOME/Documents/test --answer-store --answer-big-data yt.frontends.owls How to Write Answer Tests ^^^^^^^^^^^^^^^^^^^^^^^^^ Tests can be added in the file ``yt/utilities/answer_testing/framework.py`` . You can find examples there of how to write a test. Here is a trivial example: .. code-block:: python #!python class MaximumValueTest(AnswerTestingTest): _type_name = "MaximumValue" _attrs = ("field",) def __init__(self, ds_fn, field): super(MaximumValueTest, self).__init__(ds_fn) self.field = field def run(self): v, c = self.ds.find_max(self.field) result = np.empty(4, dtype="float64") result[0] = v result[1:] = c return result def compare(self, new_result, old_result): assert_equal(new_result, old_result) What this does is calculate the location and value of the maximum of a field. It then puts that into the variable result, returns that from ``run`` and then in ``compare`` makes sure that all are exactly equal. To write a new test: * Subclass ``AnswerTestingTest`` * Add the attributes ``_type_name`` (a string) and ``_attrs`` (a tuple of strings, one for each attribute that defines the test -- see how this is done for projections, for instance) * Implement the two routines ``run`` and ``compare`` The first should return a result and the second should compare a result to an old result. Neither should yield, but instead actually return. If you need additional arguments to the test, implement an ``__init__`` routine. * Keep in mind that *everything* returned from ``run`` will be stored. So if you are going to return a huge amount of data, please ensure that the test only gets run for small data. If you want a fast way to measure something as being similar or different, either an md5 hash (see the grid values test) or a sum and std of an array act as good proxies. If you must store a large amount of data for some reason, try serializing the data to a string (e.g. using ``numpy.ndarray.dumps``), and then compressing the data stream using ``zlib.compress``. * Typically for derived values, we compare to 10 or 12 decimal places. For exact values, we compare exactly. How To Write Answer Tests for a Frontend ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ To add a new frontend answer test, first write a new set of tests for the data. The Enzo example in ``yt/frontends/enzo/tests/test_outputs.py`` is considered canonical. Do these things: * Create a new directory, ``tests`` inside the frontend's directory. * Create a new file, ``test_outputs.py`` in the frontend's ``tests`` directory. * Create a new routine that operates similarly to the routines you can see in Enzo's output tests. * This routine should test a number of different fields and data objects. * The test routine itself should be decorated with ``@requires_ds(test_dataset_name)``. This decorator can accept the argument ``big_data=True`` if the test is expensive. The ``test_dataset_name`` should be a string containing the path you would pass to the ``yt.load`` function. It does not need to be the full path to the dataset, since the path will be automatically prepended with the location of the test data directory. See :ref:`configuration-file` for more information about the ``test_data-dir`` configuration option. * There are ``small_patch_amr`` and ``big_patch_amr`` routines that you can yield from to execute a bunch of standard tests. In addition we have created ``sph_answer`` which is more suited for particle SPH datasets. This is where you should start, and then yield additional tests that stress the outputs in whatever ways are necessary to ensure functionality. If you are adding to a frontend that has a few tests already, skip the first two steps. How to Write Image Comparison Tests ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ We have a number of tests designed to compare images as part of yt. We make use of some functionality from matplotlib to automatically compare images and detect differences, if any. Image comparison tests are used in the plotting and volume rendering machinery. The easiest way to use the image comparison tests is to make use of the ``GenericImageTest`` class. This class takes three arguments: * A dataset instance (e.g. something you load with ``yt.load`` or ``data_dir_load``) * A function the test machinery can call which will save an image to disk. The test class will then find any images that get created and compare them with the stored "correct" answer. * An integer specifying the number of decimal places to use when comparing images. A smaller number of decimal places will produce a less stringent test. Matplotlib uses an L2 norm on the full image to do the comparison tests, so this is not a pixel-by-pixel measure, and surprisingly large variations will still pass the test if the strictness of the comparison is not high enough. You *must* decorate your test function with ``requires_ds``, otherwise the answer testing machinery will not be properly set up. Here is an example test function: .. code-block:: python from yt.utilities.answer_testing.framework import \ GenericImageTest, requires_ds, data_dir_load from matplotlib import pyplot as plt @requires_ds(my_ds) def test_my_ds(): ds = data_dir_load(my_ds) def create_image(filename_prefix): plt.plot([1, 2], [1, 2]) plt.savefig(filename_prefix) test = GenericImageTest(ds, create_image, 12) # this ensures the test has a unique key in the # answer test storage file test.prefix = "my_unique_name" # this ensures a nice test name in nose's output test_my_ds.__name__ = test.description yield test Another good example of an image comparison test is the ``PlotWindowAttributeTest`` defined in the answer testing framework and used in ``yt/visualization/tests/test_plotwindow.py``. This test shows how a new answer test subclass can be used to programmatically test a variety of different methods of a complicated class using the same test class. This sort of image comparison test is more useful if you are finding yourself writing a ton of boilerplate code to get your image comparison test working. The ``GenericImageTest`` is more useful if you only need to do a one-off image comparison test. Enabling Answer Tests on Jenkins ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Before any code is added to or modified in the yt codebase, each incoming changeset is run against all available unit and answer tests on our `continuous integration server `_. While unit tests are autodiscovered by `nose `_ itself, answer tests require definition of which set of tests constitute to a given answer. Configuration for the integration server is stored in *tests/tests.yaml* in the main yt repository: .. code-block:: yaml answer_tests: local_artio_000: - yt/frontends/artio/tests/test_outputs.py # ... other_tests: unittests: - '-v' - '-s' Each element under *answer_tests* defines answer name (*local_artio_000* in above snippet) and specifies a list of files/classes/methods that will be validated (*yt/frontends/artio/tests/test_outputs.py* in above snippet). On the testing server it is translated to: .. code-block:: bash $ nosetests --with-answer-testing --local --local-dir ... --answer-big-data \ --answer-name=local_artio_000 \ yt/frontends/artio/tests/test_outputs.py If the answer doesn't exist on the server yet, ``nosetests`` is run twice and during first pass ``--answer-store`` is added to the commandline. Updating Answers ~~~~~~~~~~~~~~~~ In order to regenerate answers for a particular set of tests it is sufficient to change the answer name in *tests/tests.yaml* e.g.: .. code-block:: diff --- a/tests/tests.yaml +++ b/tests/tests.yaml @@ -25,7 +25,7 @@ - yt/analysis_modules/halo_finding/tests/test_rockstar.py - yt/frontends/owls_subfind/tests/test_outputs.py - local_owls_000: + local_owls_001: - yt/frontends/owls/tests/test_outputs.py local_pw_000: would regenerate answers for OWLS frontend. When adding tests to an existing set of answers (like ``local_owls_000`` or ``local_varia_000``), it is considered best practice to first submit a pull request adding the tests WITHOUT incrementing the version number. Then, allow the tests to run (resulting in "no old answer" errors for the missing answers). If no other failures are present, you can then increment the version number to regenerate the answers. This way, we can avoid accidently covering up test breakages. Adding New Answer Tests ~~~~~~~~~~~~~~~~~~~~~~~ In order to add a new set of answer tests, it is sufficient to extend the *answer_tests* list in *tests/tests.yaml* e.g.: .. code-block:: diff --- a/tests/tests.yaml +++ b/tests/tests.yaml @@ -60,6 +60,10 @@ - yt/analysis_modules/absorption_spectrum/tests/test_absorption_spectrum.py:test_absorption_spectrum_non_cosmo - yt/analysis_modules/absorption_spectrum/tests/test_absorption_spectrum.py:test_absorption_spectrum_cosmo + local_gdf_000: + - yt/frontends/gdf/tests/test_outputs.py + + other_tests: unittests: Restricting Python Versions for Answer Tests ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ If for some reason a test can be run only for a specific version of python it is possible to indicate this by adding a ``[py2]`` or ``[py3]`` tag. For example: .. code-block:: yaml answer_tests: local_test_000: - yt/test_A.py # [py2] - yt/test_B.py # [py3] would result in ``test_A.py`` being run only for *python2* and ``test_B.py`` being run only for *python3*. yt-3.3.3/doc/source/examining/0000755000076500000240000000000013023400160017102 5ustar goldbaumstaff00000000000000yt-3.3.3/doc/source/examining/generic_array_data.rst0000644000076500000240000000020012770647750023461 0ustar goldbaumstaff00000000000000.. _loading-numpy-array: Loading Generic Array Data ========================== .. notebook:: Loading_Generic_Array_Data.ipynb yt-3.3.3/doc/source/examining/generic_particle_data.rst0000644000076500000240000000021312770647750024152 0ustar goldbaumstaff00000000000000.. _generic-particle-data: Loading Generic Particle Data ----------------------------- .. notebook:: Loading_Generic_Particle_Data.ipynb yt-3.3.3/doc/source/examining/index.rst0000644000076500000240000000126213017603554020763 0ustar goldbaumstaff00000000000000.. _examining-data: Loading and Examining Data ========================== Nominally, one should just be able to run ``yt.load()`` on a dataset and start computing; however, there may be additional notes associated with different data formats as described below. Furthermore, we provide methods for loading data from unsupported data formats in :ref:`loading-numpy-array`, :ref:`generic-particle-data`, and :ref:`loading-spherical-data`. Lastly, if you want to examine the raw data for your particular dataset, visit :ref:`low-level-data-inspection`. .. toctree:: :maxdepth: 2 loading_data generic_array_data generic_particle_data spherical_data low_level_inspection yt-3.3.3/doc/source/examining/loading_data.rst0000644000076500000240000016744613023400062022265 0ustar goldbaumstaff00000000000000.. _loading-data: Loading Data ============ This section contains information on how to load data into yt, as well as some important caveats about different data formats. .. _loading-art-data: ART Data -------- ART data has been supported in the past by Christopher Moody and is currently cared for by Kenza Arraki. Please contact the ``yt-dev`` mailing list if you are interested in using yt for ART data, or if you are interested in assisting with development of yt to work with ART data. To load an ART dataset you can use the ``yt.load`` command and provide it the gas mesh file. It will search for and attempt to find the complementary dark matter and stellar particle header and data files. However, your simulations may not follow the same naming convention. .. code-block:: python import yt ds = yt.load("D9p_500/10MpcBox_HartGal_csf_a0.500.d") It will search for and attempt to find the complementary dark matter and stellar particle header and data files. However, your simulations may not follow the same naming convention. For example, the single snapshot given in the sample data has a series of files that look like this: .. code-block:: none 10MpcBox_HartGal_csf_a0.500.d #Gas mesh PMcrda0.500.DAT #Particle header PMcrs0a0.500.DAT #Particle data (positions,velocities) stars_a0.500.dat #Stellar data (metallicities, ages, etc.) The ART frontend tries to find the associated files matching the above, but if that fails you can specify ``file_particle_header``, ``file_particle_data``, and ``file_particle_stars``, in addition to specifying the gas mesh. Note that the ``pta0.500.dat`` or ``pt.dat`` file containing particle time steps is not loaded by yt. You also have the option of gridding particles and assigning them onto the meshes. This process is in beta, and for the time being it's probably best to leave ``do_grid_particles=False`` as the default. To speed up the loading of an ART file, you have a few options. You can turn off the particles entirely by setting ``discover_particles=False``. You can also only grid octs up to a certain level, ``limit_level=5``, which is useful when debugging by artificially creating a 'smaller' dataset to work with. Finally, when stellar ages are computed we 'spread' the ages evenly within a smoothing window. By default this is turned on and set to 10Myr. To turn this off you can set ``spread=False``, and you can tweak the age smoothing window by specifying the window in seconds, ``spread=1.0e7*365*24*3600``. There is currently preliminary support for dark matter only ART data. To load a dataset use the ``yt.load`` command and provide it the particle data file. It will search for the complementary particle header file. .. code-block:: python import yt ds = yt.load("PMcrs0a0.500.DAT") Important: This should not be used for loading just the dark matter data for a 'regular' hydrodynamical data set as the units and IO are different! .. _loading-artio-data: ARTIO Data ---------- ARTIO data has a well-specified internal parameter system and has few free parameters. However, for optimization purposes, the parameter that provides the most guidance to yt as to how to manage ARTIO data is ``max_range``. This governs the maximum number of space-filling curve cells that will be used in a single "chunk" of data read from disk. For small datasets, setting this number very large will enable more data to be loaded into memory at any given time; for very large datasets, this parameter can be left alone safely. By default it is set to 1024; it can in principle be set as high as the total number of SFC cells. To load ARTIO data, you can specify a command such as this: .. code-block:: python ds = load("./A11QR1/s11Qzm1h2_a1.0000.art") .. _loading_athena_data: Athena Data ----------- Athena 4.x VTK data is *mostly* supported and cared for by John ZuHone. Both uniform grid and SMR datasets are supported. .. note: yt also recognizes Fargo3D data written to VTK files as Athena data, but support for Fargo3D data is preliminary. Loading Athena datasets is slightly different depending on whether your dataset came from a serial or a parallel run. If the data came from a serial run or you have joined the VTK files together using the Athena tool ``join_vtk``, you can load the data like this: .. code-block:: python import yt ds = yt.load("kh.0010.vtk") The filename corresponds to the file on SMR level 0, whereas if there are multiple levels the corresponding files will be picked up automatically, assuming they are laid out in ``lev*`` subdirectories under the directory where the base file is located. For parallel datasets, yt assumes that they are laid out in directories named ``id*``, one for each processor number, each with ``lev*`` subdirectories for additional refinement levels. To load this data, call ``load`` with the base file in the ``id0`` directory: .. code-block:: python import yt ds = yt.load("id0/kh.0010.vtk") which will pick up all of the files in the different ``id*`` directories for the entire dataset. yt works in cgs ("Gaussian") units by default, but Athena data is not normally stored in these units. If you would like to convert data to cgs units, you may supply conversions for length, time, and mass to ``load`` using the ``units_override`` functionality: .. code-block:: python import yt units_override = {"length_unit":(1.0,"Mpc"), "time_unit"(1.0,"Myr"), "mass_unit":(1.0e14,"Msun")} ds = yt.load("id0/cluster_merger.0250.vtk", units_override=units_override) This means that the yt fields, e.g. ``("gas","density")``, ``("gas","x-velocity")``, ``("gas","magnetic_field_x")``, will be in cgs units, but the Athena fields, e.g., ``("athena","density")``, ``("athena","velocity_x")``, ``("athena","cell_centered_B_x")``, will be in code units. Some 3D Athena outputs may have large grids (especially parallel datasets subsequently joined with the `join_vtk` script), and may benefit from being subdivided into "virtual grids". For this purpose, one can pass in the `nprocs` parameter: .. code-block:: python import yt ds = yt.load("sloshing.0000.vtk", nprocs=8) which will subdivide each original grid into `nprocs` grids. .. note:: Virtual grids are only supported (and really only necessary) for 3D data. Alternative values for the following simulation parameters may be specified using a ``parameters`` dict, accepting the following keys: * ``Gamma``: ratio of specific heats, Type: Float * ``geometry``: Geometry type, currently accepts ``"cartesian"`` or ``"cylindrical"`` * ``periodicity``: Is the domain periodic? Type: Tuple of boolean values corresponding to each dimension .. code-block:: python import yt parameters = {"gamma":4./3., "geometry":"cylindrical", "periodicity":(False,False,False)} ds = yt.load("relativistic_jet_0000.vtk", parameters=parameters) .. rubric:: Caveats * yt primarily works with primitive variables. If the Athena dataset contains conservative variables, the yt primitive fields will be generated from the conserved variables on disk. * Special relativistic datasets may be loaded, but are not fully supported. In particular, the relationships between quantities such as pressure and thermal energy will be incorrect, as it is currently assumed that their relationship is that of an ideal a :math:`\gamma`-law equation of state. * Domains may be visualized assuming periodicity. * Particle list data is currently unsupported. .. note:: The old behavior of supplying unit conversions using a ``parameters`` dict supplied to ``load`` for Athena datasets is still supported, but is being deprecated in favor of ``units_override``, which provides the same functionality. .. _loading-orion-data: BoxLib Data ----------- yt has been tested with BoxLib data generated by Orion, Nyx, Maestro and Castro. Currently it is cared for by a combination of Andrew Myers, Chris Malone, Matthew Turk, and Mike Zingale. To load a BoxLib dataset, you can use the ``yt.load`` command on the plotfile directory name. In general, you must also have the ``inputs`` file in the base directory, but Maestro and Castro will get all the necessary parameter information from the ``job_info`` file in the plotfile directory. For instance, if you were in a directory with the following files: .. code-block:: none inputs pltgmlcs5600/ pltgmlcs5600/Header pltgmlcs5600/Level_0 pltgmlcs5600/Level_0/Cell_H pltgmlcs5600/Level_1 pltgmlcs5600/Level_1/Cell_H pltgmlcs5600/Level_2 pltgmlcs5600/Level_2/Cell_H pltgmlcs5600/Level_3 pltgmlcs5600/Level_3/Cell_H pltgmlcs5600/Level_4 pltgmlcs5600/Level_4/Cell_H You would feed it the filename ``pltgmlcs5600``: .. code-block:: python import yt ds = yt.load("pltgmlcs5600") For Maestro and Castro, you would not need the ``inputs`` file, and you would have a ``job_info`` file in the plotfile directory. .. rubric:: Caveats * yt does not read the Maestro base state (although you can have Maestro map it to a full Cartesian state variable before writing the plotfile to get around this). E-mail the dev list if you need this support. * yt does not know about particles in Maestro. * For Maestro, yt aliases either "tfromp" or "tfromh to" ``temperature`` depending on the value of the ``use_tfromp`` runtime parameter. * For Maestro, some velocity fields like ``velocity_magnitude`` or ``mach_number`` will always use the on-disk value, and not have yt derive it, due to the complex interplay of the base state velocity. .. _loading-pluto-data: Pluto Data ---------- Support for Pluto AMR data is provided through the Chombo frontend, which is currently maintained by Andrew Myers. Pluto output files that don't use the Chombo HDF5 format are currently not supported. To load a Pluto dataset, you can use the ``yt.load`` command on the ``*.hdf5`` files. For example, the KelvinHelmholtz sample dataset is a directory that contains the following files: .. code-block:: none data.0004.hdf5 pluto.ini To load it, you can navigate into that directory and do: .. code-block:: python import yt ds = yt.load("data.0004.hdf5") The ``pluto.ini`` file must also be present alongside the HDF5 file. By default, all of the Pluto fields will be in code units. .. _loading-enzo-data: Enzo Data --------- Enzo data is fully supported and cared for by Matthew Turk. To load an Enzo dataset, you can use the ``yt.load`` command and provide it the dataset name. This would be the name of the output file, and it contains no extension. For instance, if you have the following files: .. code-block:: none DD0010/ DD0010/data0010 DD0010/data0010.index DD0010/data0010.cpu0000 DD0010/data0010.cpu0001 DD0010/data0010.cpu0002 DD0010/data0010.cpu0003 You would feed the ``load`` command the filename ``DD0010/data0010`` as mentioned. .. code-block:: python import yt ds = yt.load("DD0010/data0010") .. rubric:: Caveats * There are no major caveats for Enzo usage * Units should be correct, if you utilize standard unit-setting routines. yt will notify you if it cannot determine the units, although this notification will be passive. * 2D and 1D data are supported, but the extraneous dimensions are set to be of length 1.0 in "code length" which may produce strange results for volume quantities. .. _loading-exodusii-data: Exodus II Data -------------- .. note:: To load Exodus II data, you need to have the `netcdf4 `_ python interface installed. Exodus II is a file format for Finite Element datasets that is used by the MOOSE framework for file IO. Support for this format (and for unstructured mesh data in general) is a new feature as of yt 3.3, so while we aim to fully support it, we also expect there to be some buggy features at present. Currently, yt can visualize quads, hexes, triangles, and tetrahedral element types at first order. Additionally, there is experimental support for the high-order visualization of 20-node hex elements. Development of more high-order visualization capability is a work in progress. To load an Exodus II dataset, you can use the ``yt.load`` command on the Exodus II file: .. code-block:: python import yt ds = yt.load("MOOSE_sample_data/out.e-s010", step=0) Because Exodus II datasets can have multiple steps (which can correspond to time steps, picard iterations, non-linear solve iterations, etc...), you can also specify a step argument when you load an Exodus II data that defines the index at which to look when you read data from the file. Omitting this argument is the same as passing in 0, and setting ``step=-1`` selects the last time output in the file. You can access the connectivity information directly by doing: .. code-block:: python import yt ds = yt.load("MOOSE_sample_data/out.e-s010", step=-1) print(ds.index.meshes[0].connectivity_coords) print(ds.index.meshes[0].connectivity_indices) print(ds.index.meshes[1].connectivity_coords) print(ds.index.meshes[1].connectivity_indices) This particular dataset has two meshes in it, both of which are made of 8-node hexes. yt uses a field name convention to access these different meshes in plots and data objects. To see all the fields found in a particular dataset, you can do: .. code-block:: python import yt ds = yt.load("MOOSE_sample_data/out.e-s010") print(ds.field_list) This will give you a list of field names like ``('connect1', 'diffused')`` and ``('connect2', 'convected')``. Here, fields labelled with ``'connect1'`` correspond to the first mesh, and those with ``'connect2'`` to the second, and so on. To grab the value of the ``'convected'`` variable at all the nodes in the first mesh, for example, you would do: .. code-block:: python import yt ds = yt.load("MOOSE_sample_data/out.e-s010") ad = ds.all_data() # geometric selection, this just grabs everything print(ad['connect1', 'convected']) In this dataset, ``('connect1', 'convected')`` is nodal field, meaning that the field values are defined at the vertices of the elements. If we examine the shape of the returned array: .. code-block:: python import yt ds = yt.load("MOOSE_sample_data/out.e-s010") ad = ds.all_data() print(ad['connect1', 'convected'].shape) we see that this mesh has 12480 8-node hexahedral elements, and that we get 8 field values for each element. To get the vertex positions at which these field values are defined, we can do, for instance: .. code-block:: python import yt ds = yt.load("MOOSE_sample_data/out.e-s010") ad = ds.all_data() print(ad['connect1', 'vertex_x']) If we instead look at an element-centered field, like ``('connect1', 'conv_indicator')``, we get: .. code-block:: python import yt ds = yt.load("MOOSE_sample_data/out.e-s010") ad = ds.all_data() print(ad['connect1', 'conv_indicator'].shape) we instead get only one field value per element. For information about visualizing unstructured mesh data, including Exodus II datasets, please see :ref:`unstructured-mesh-slices` and :ref:`unstructured_mesh_rendering`. Displacement Fields ^^^^^^^^^^^^^^^^^^^ Finite element codes often solve for the displacement of each vertex from its original position as a node variable, rather than updating the actual vertex positions with time. For analysis and visualization, it is often useful to turn these displacements on or off, and to be able to scale them arbitrarily to emphasize certain features of the solution. To allow this, if ``yt`` detects displacement fields in an Exodus II dataset (using the convention that they will be named ``disp_x``, ``disp_y``, etc...), it will optionally add these to the mesh vertex positions for the purposes of visualization. Displacement fields can be controlled when a dataset is loaded by passing in an optional dictionary to the ``yt.load`` command. This feature is turned off by default, meaning that a dataset loaded as .. code-block:: python import yt ds = yt.load("MOOSE_sample_data/mps_out.e") will not include the displacements in the vertex positions. The displacements can be turned on separately for each mesh in the file by passing in a a tuple of (scale, offset) pairs for the meshes you want to enable displacements for. For example, the following code snippet turns displacements on for the second mesh, but not the first: .. code-block:: python import yt ds = yt.load("MOOSE_sample_data/mps_out.e", step=10, displacements={'connect2': (1.0, [0.0, 0.0, 0.0])}) The displacements can also be scaled by an arbitrary factor before they are added in to the vertex positions. The following code turns on displacements for both ``connect1`` and ``connect2``, scaling the former by a factor of 5.0 and the later by a factor of 10.0: .. code-block:: python import yt ds = yt.load("MOOSE_sample_data/mps_out.e", step=10, displacements={'connect1': (5.0, [0.0, 0.0, 0.0]), 'connect2': (10.0, [0.0, 0.0, 0.0])}) Finally, we can also apply an arbitrary offset to the mesh vertices after the scale factor is applied. For example, the following code scales all displacements in the second mesh by a factor of 5.0, and then shifts each vertex in the mesh by 1.0 unit in the z-direction: .. code-block:: python import yt ds = yt.load("MOOSE_sample_data/mps_out.e", step=10, displacements={'connect2': (5.0, [0.0, 0.0, 1.0])}) .. _loading-fits-data: FITS Data --------- FITS data is *mostly* supported and cared for by John ZuHone. In order to read FITS data, `AstroPy `_ must be installed. FITS data cubes can be loaded in the same way by yt as other datasets. yt can read FITS image files that have the following (case-insensitive) suffixes: * fits * fts * fits.gz * fts.gz yt can read two kinds of FITS files: FITS image files and FITS binary table files containing positions, times, and energies of X-ray events. .. note:: AstroPy is necessary due to the requirements of both FITS file reading and WCS coordinates. Since new releases of `PyFITS `_ are to be discontinued, individual installations of this package and the `PyWCS `_ package are not supported. Though a FITS image is composed of a single array in the FITS file, upon being loaded into yt it is automatically decomposed into grids: .. code-block:: python import yt ds = yt.load("m33_hi.fits") ds.print_stats() .. parsed-literal:: level # grids # cells # cells^3 ---------------------------------------------- 0 512 981940800 994 ---------------------------------------------- 512 981940800 yt will generate its own domain decomposition, but the number of grids can be set manually by passing the ``nprocs`` parameter to the ``load`` call: .. code-block:: python ds = load("m33_hi.fits", nprocs=1024) Making the Most of yt for FITS Data ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ yt will load data without WCS information and/or some missing header keywords, but the resulting field information will necessarily be incomplete. For example, field names may not be descriptive, and units will not be correct. To get the full use out of yt for FITS files, make sure that for each image the following header keywords have sensible values: * ``CDELTx``: The pixel width in along axis ``x`` * ``CRVALx``: The coordinate value at the reference position along axis ``x`` * ``CRPIXx``: The reference pixel along axis ``x`` * ``CTYPEx``: The projection type of axis ``x`` * ``CUNITx``: The units of the coordinate along axis ``x`` * ``BTYPE``: The type of the image * ``BUNIT``: The units of the image FITS header keywords can easily be updated using AstroPy. For example, to set the ``BTYPE`` and ``BUNIT`` keywords: .. code-block:: python import astropy.io.fits as pyfits f = pyfits.open("xray_flux_image.fits", mode="update") f[0].header["BUNIT"] = "cts/s/pixel" f[0].header["BTYPE"] = "flux" f.flush() f.close() FITS Coordinates ^^^^^^^^^^^^^^^^ For FITS datasets, the unit of ``code_length`` is always the width of one pixel. yt will attempt to use the WCS information in the FITS header to construct information about the coordinate system, and provides support for the following dataset types: 1. Rectilinear 2D/3D images with length units (e.g., Mpc, AU, etc.) defined in the ``CUNITx`` keywords 2. 2D images in some celestial coordinate systems (RA/Dec, galactic latitude/longitude, defined in the ``CTYPEx`` keywords), and X-ray binary table event files 3. 3D images with celestial coordinates and a third axis for another quantity, such as velocity, frequency, wavelength, etc. 4. 4D images with the first three axes like Case 3, where the slices along the 4th axis are interpreted as different fields. If your data is of the first case, yt will determine the length units based on the information in the header. If your data is of the second or third cases, no length units will be assigned, but the world coordinate information about the axes will be stored in separate fields. If your data is of the fourth type, the coordinates of the first three axes will be determined according to cases 1-3. .. note:: Linear length-based coordinates (Case 1 above) are only supported if all dimensions have the same value for ``CUNITx``. WCS coordinates are only supported for Cases 2-4. Fields in FITS Datasets ^^^^^^^^^^^^^^^^^^^^^^^ Multiple fields can be included in a FITS dataset in several different ways. The first way, and the simplest, is if more than one image HDU is contained within the same file. The field names will be determined by the value of ``BTYPE`` in the header, and the field units will be determined by the value of ``BUNIT``. The second way is if a dataset has a fourth axis, with each slice along this axis corresponding to a different field. In this case, the field names will be determined by the value of the ``CTYPE4`` keyword and the index of the slice. So, for example, if ``BTYPE`` = ``"intensity"`` and ``CTYPE4`` = ``"stokes"``, then the fields will be named ``"intensity_stokes_1"``, ``"intensity_stokes_2"``, and so on. The third way is if auxiliary files are included along with the main file, like so: .. code-block:: python ds = load("flux.fits", auxiliary_files=["temp.fits","metal.fits"]) The image blocks in each of these files will be loaded as a separate field, provided they have the same dimensions as the image blocks in the main file. Additionally, fields corresponding to the WCS coordinates will be generated. based on the corresponding ``CTYPEx`` keywords. When queried, these fields will be generated from the pixel coordinates in the file using the WCS transformations provided by AstroPy. X-ray event data will be loaded as particle fields in yt, but a grid will be constructed from the WCS information in the FITS header. There is a helper function, ``setup_counts_fields``, which may be used to make deposited image fields from the event data for different energy bands (for an example see :ref:`xray_fits`). .. note:: Each FITS image from a single dataset, whether from one file or from one of multiple files, must have the same dimensions and WCS information as the first image in the primary file. If this is not the case, yt will raise a warning and will not load this field. .. _additional_fits_options: Additional Options ^^^^^^^^^^^^^^^^^^ The following are additional options that may be passed to the ``load`` command when analyzing FITS data: ``nan_mask`` """""""""""" FITS image data may include ``NaNs``. If you wish to mask this data out, you may supply a ``nan_mask`` parameter, which may either be a single floating-point number (applies to all fields) or a Python dictionary containing different mask values for different fields: .. code-block:: python # passing a single float ds = load("m33_hi.fits", nan_mask=0.0) # passing a dict ds = load("m33_hi.fits", nan_mask={"intensity":-1.0,"temperature":0.0}) ``suppress_astropy_warnings`` """"""""""""""""""""""""""""" Generally, AstroPy may generate a lot of warnings about individual FITS files, many of which you may want to ignore. If you want to see these warnings, set ``suppress_astropy_warnings = False``. ``z_axis_decomp`` """"""""""""""""" For some applications, decomposing 3D FITS data into grids that span the x-y plane with short strides along the z-axis may result in a significant improvement in I/O speed. To enable this feature, set ``z_axis_decomp=True``. ``spectral_factor`` """"""""""""""""""" Often, the aspect ratio of 3D spectral cubes can be far from unity. Because yt sets the pixel scale as the ``code_length``, certain visualizations (such as volume renderings) may look extended or distended in ways that are undesirable. To adjust the width in ``code_length`` of the spectral axis, set ``spectral_factor`` equal to a constant which gives the desired scaling, or set it to ``"auto"`` to make the width the same as the largest axis in the sky plane. Miscellaneous Tools for Use with FITS Data ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ A number of tools have been prepared for use with FITS data that enhance yt's visualization and analysis capabilities for this particular type of data. These are included in the ``yt.frontends.fits.misc`` module, and can be imported like so: .. code-block:: python from yt.frontends.fits.misc import setup_counts_fields, PlotWindowWCS, ds9_region ``setup_counts_fields`` """"""""""""""""""""""" This function can be used to create image fields from X-ray counts data in different energy bands: .. code-block:: python ebounds = [(0.1,2.0),(2.0,5.0)] # Energies are in keV setup_counts_fields(ds, ebounds) which would make two fields, ``"counts_0.1-2.0"`` and ``"counts_2.0-5.0"``, and add them to the field registry for the dataset ``ds``. ``ds9_region`` """""""""""""" This function takes a `ds9 `_ region and creates a "cut region" data container from it, that can be used to select the cells in the FITS dataset that fall within the region. To use this functionality, the `pyregion `_ package must be installed. .. code-block:: python ds = yt.load("m33_hi.fits") circle_region = ds9_region(ds, "circle.reg") print(circle_region.quantities.extrema("flux")) ``PlotWindowWCS`` """"""""""""""""" This class takes a on-axis ``SlicePlot`` or ``ProjectionPlot`` of FITS data and adds celestial coordinates to the plot axes. To use it, the `WCSAxes `_ package must be installed. .. code-block:: python wcs_slc = PlotWindowWCS(slc) wcs_slc.show() # for the IPython notebook wcs_slc.save() ``WCSAxes`` is still in an experimental state, but as its functionality improves it will be utilized more here. ``create_spectral_slabs`` """"""""""""""""""""""""" .. note:: The following functionality requires the `spectral-cube `_ library to be installed. If you have a spectral intensity dataset of some sort, and would like to extract emission in particular slabs along the spectral axis of a certain width, ``create_spectral_slabs`` can be used to generate a dataset with these slabs as different fields. In this example, we use it to extract individual lines from an intensity cube: .. code-block:: python slab_centers = {'13CN': (218.03117, 'GHz'), 'CH3CH2CHO': (218.284256, 'GHz'), 'CH3NH2': (218.40956, 'GHz')} slab_width = (0.05, "GHz") ds = create_spectral_slabs("intensity_cube.fits", slab_centers, slab_width, nan_mask=0.0) All keyword arguments to `create_spectral_slabs` are passed on to `load` when creating the dataset (see :ref:`additional_fits_options` above). In the returned dataset, the different slabs will be different fields, with the field names taken from the keys in ``slab_centers``. The WCS coordinates on the spectral axis are reset so that the center of the domain along this axis is zero, and the left and right edges of the domain along this axis are :math:`\pm` ``0.5*slab_width``. Examples of Using FITS Data ^^^^^^^^^^^^^^^^^^^^^^^^^^^ The following IPython notebooks show examples of working with FITS data in yt, which we recommend you look at in the following order: * :ref:`radio_cubes` * :ref:`xray_fits` .. _loading-flash-data: FLASH Data ---------- FLASH HDF5 data is *mostly* supported and cared for by John ZuHone. To load a FLASH dataset, you can use the ``yt.load`` command and provide it the file name of a plot file, checkpoint file, or particle file. Particle files require special handling depending on the situation, the main issue being that they typically lack grid information. The first case is when you have a plotfile and a particle file that you would like to load together. In the simplest case, this occurs automatically. For instance, if you were in a directory with the following files: .. code-block:: none radio_halo_1kpc_hdf5_plt_cnt_0100 # plotfile radio_halo_1kpc_hdf5_part_0100 # particle file where the plotfile and the particle file were created at the same time (therefore having particle data consistent with the grid structure of the former). Notice also that the prefix ``"radio_halo_1kpc_"`` and the file number ``100`` are the same. In this special case, the particle file will be loaded automatically when ``yt.load`` is called on the plotfile. This also works when loading a number of files in a time series. If the two files do not have the same prefix and number, but they nevertheless have the same grid structure and are at the same simulation time, the particle data may be loaded with the ``particle_filename`` optional argument to ``yt.load``: .. code-block:: python import yt ds = yt.load("radio_halo_1kpc_hdf5_plt_cnt_0100", particle_filename="radio_halo_1kpc_hdf5_part_0100") However, if you don't have a corresponding plotfile for a particle file, but would still like to load the particle data, you can still call ``yt.load`` on the file. However, the grid information will not be available, and the particle data will be loaded in a fashion similar to SPH data. .. rubric:: Caveats * Please be careful that the units are correctly utilized; yt assumes cgs by default, but conversion to other :ref:`unit systems ` is also possible. .. _loading-gadget-data: Gadget Data ----------- yt has support for reading Gadget data in both raw binary and HDF5 formats. It is able to access the particles as it would any other particle dataset, and it can apply smoothing kernels to the data to produce both quantitative analysis and visualization. See :ref:`loading-sph-data` for more details and :ref:`gadget-notebook` for a detailed example of loading, analyzing, and visualizing a Gadget dataset. An example which makes use of a Gadget snapshot from the OWLS project can be found at :ref:`owls-notebook`. Gadget data in HDF5 format can be loaded with the ``load`` command: .. code-block:: python import yt ds = yt.load("snapshot_061.hdf5") Gadget data in raw binary format can also be loaded with the ``load`` command. This is only supported for snapshots created with the ``SnapFormat`` parameter set to 1 (the standard for Gadget-2). .. code-block:: python import yt ds = yt.load("snapshot_061") .. _particle-bbox: Units and Bounding Boxes ^^^^^^^^^^^^^^^^^^^^^^^^ There are two additional pieces of information that may be needed. If your simulation is cosmological, yt can often guess the bounding box and the units of the simulation. However, for isolated simulations and for cosmological simulations with non-standard units, these must be supplied. For example, if a length unit of 1.0 corresponds to a kiloparsec, you can supply this in the constructor. yt can accept units such as ``Mpc``, ``kpc``, ``cm``, ``Mpccm/h`` and so on. In particular, note that ``Mpc/h`` and ``Mpccm/h`` (``cm`` for comoving here) are usable unit definitions. yt will attempt to use units for ``mass``, ``length`` and ``time`` as supplied in the argument ``unit_base``. The ``bounding_box`` argument is a list of two-item tuples or lists that describe the left and right extents of the particles. .. code-block:: python ds = GadgetDataset("snap_004", unit_base = {'length': ('kpc', 1.0)}, bounding_box = [[-600.0, 600.0], [-600.0, 600.0], [-600.0, 600.0]]) .. _particle-indexing-criteria: Indexing Criteria ^^^^^^^^^^^^^^^^^ yt generates a global mesh index via octree that governs the resolution of volume elements. This is governed by two parameters, ``n_ref`` and ``over_refine_factor``. They are weak proxies for each other. The first, ``n_ref``, governs how many particles in an oct results in that oct being refined into eight child octs. Lower values mean higher resolution; the default is 64. The second parameter, ``over_refine_factor``, governs how many cells are in a given oct; the default value of 1 corresponds to 8 cells. The number of cells in an oct is defined by the expression ``2**(3*over_refine_factor)``. It's recommended that if you want higher-resolution, try reducing the value of ``n_ref`` to 32 or 16. Also yt can be set to generate the global mesh index according to a specific type of particles instead of all the particles through the parameter ``index_ptype``. For example, to build the octree only according to the ``"PartType0"`` particles, you can do: .. code-block:: python ds = yt.load("snapshot_061.hdf5", index_ptype="PartType0") By default, ``index_ptype`` is set to ``"all"``, which means all the particles. Currently this feature only works for the Gadget HDF5 and OWLS datasets. To bring the feature to other frontends, it's recommended to refer to this `PR `_ for implementation details. .. _gadget-field-spec: Field Specifications ^^^^^^^^^^^^^^^^^^^^ Binary Gadget outputs often have additional fields or particle types that are non-standard from the default Gadget distribution format. These can be specified in the call to ``GadgetDataset`` by either supplying one of the sets of field specifications as a string or by supplying a field specification itself. As an example, yt has built-in definitions for ``default`` (the default) and ``agora_unlv``. Field specifications must be tuples, and must be of this format: .. code-block:: python default = ( "Coordinates", "Velocities", "ParticleIDs", "Mass", ("InternalEnergy", "Gas"), ("Density", "Gas"), ("SmoothingLength", "Gas"), ) This is the default specification used by the Gadget frontend. It means that the fields are, in order, Coordinates, Velocities, ParticleIDs, Mass, and the fields InternalEnergy, Density and SmoothingLength *only* for Gas particles. So for example, if you have defined a Metallicity field for the particle type Halo, which comes right after ParticleIDs in the file, you could define it like this: .. code-block:: python my_field_def = ( "Coordinates", "Velocities", "ParticleIDs", ("Metallicity", "Halo"), "Mass", ("InternalEnergy", "Gas"), ("Density", "Gas"), ("SmoothingLength", "Gas"), ) To save time, you can utilize the plugins file for yt and use it to add items to the dictionary where these definitions are stored. You could do this like so: .. code-block:: python from yt.frontends.gadget.definitions import gadget_field_specs gadget_field_specs["my_field_def"] = my_field_def Please also feel free to issue a pull request with any new field specifications, as we're happy to include them in the main distribution! .. _gadget-ptype-spec: Particle Type Definitions ^^^^^^^^^^^^^^^^^^^^^^^^^ In some cases, research groups add new particle types or re-order them. You can supply alternate particle types by using the keyword ``ptype_spec`` to the ``GadgetDataset`` call. The default for Gadget binary data is: .. code-block:: python ( "Gas", "Halo", "Disk", "Bulge", "Stars", "Bndry" ) You can specify alternate names, but note that this may cause problems with the field specification if none of the names match old names. .. _gadget-header-spec: Header Specification ^^^^^^^^^^^^^^^^^^^^ If you have modified the header in your Gadget binary file, you can specify an alternate header specification with the keyword ``header_spec``. This can either be a list of strings corresponding to individual header types known to yt, or it can be a combination of strings and header specifications. The default header specification (found in ``yt/frontends/sph/definitions.py``) is: .. code-block:: python default = (('Npart', 6, 'i'), ('Massarr', 6, 'd'), ('Time', 1, 'd'), ('Redshift', 1, 'd'), ('FlagSfr', 1, 'i'), ('FlagFeedback', 1, 'i'), ('Nall', 6, 'i'), ('FlagCooling', 1, 'i'), ('NumFiles', 1, 'i'), ('BoxSize', 1, 'd'), ('Omega0', 1, 'd'), ('OmegaLambda', 1, 'd'), ('HubbleParam', 1, 'd'), ('FlagAge', 1, 'i'), ('FlagMEtals', 1, 'i'), ('NallHW', 6, 'i'), ('unused', 16, 'i')) These items will all be accessible inside the object ``ds.parameters``, which is a dictionary. You can add combinations of new items, specified in the same way, or alternately other types of headers. The other string keys defined are ``pad32``, ``pad64``, ``pad128``, and ``pad256`` each of which corresponds to an empty padding in bytes. For example, if you have an additional 256 bytes of padding at the end, you can specify this with: .. code-block:: python header_spec = ["default", "pad256"] This can then be supplied to the constructor. Note that you can also do this manually, for instance with: .. code-block:: python header_spec = ["default", (('some_value', 8, 'd'), ('another_value', 1, 'i'))] The letters correspond to data types from the Python struct module. Please feel free to submit alternate header types to the main yt repository. .. _specifying-gadget-units: Specifying Units ^^^^^^^^^^^^^^^^ If you are running a cosmology simulation, yt will be able to guess the units with some reliability. However, if you are not and you do not specify a dataset, yt will not be able to and will use the defaults of length being 1.0 Mpc/h (comoving), velocity being in cm/s, and mass being in 10^10 Msun/h. You can specify alternate units by supplying the ``unit_base`` keyword argument of this form: .. code-block:: python unit_base = {'length': (1.0, 'cm'), 'mass': (1.0, 'g'), 'time': (1.0, 's')} yt will utilize length, mass and time to set up all other units. .. _loading-gamer-data: GAMER Data ---------- GAMER HDF5 data is supported and cared for by Hsi-Yu Schive. You can load the data like this: .. code-block:: python import yt ds = yt.load("InteractingJets/jet_000002") Currently GAMER does not assume any unit for non-cosmological simulations. To specify the units for yt, you need to supply conversions for length, time, and mass to ``load`` using the ``units_override`` functionality: .. code-block:: python import yt code_units = { "length_unit":(1.0,"kpc"), "time_unit" :(3.08567758096e+13,"s"), "mass_unit" :(1.4690033e+36,"g") } ds = yt.load("InteractingJets/jet_000002", units_override=code_units) This means that the yt fields, e.g., ``("gas","density")``, will be in cgs units, but the GAMER fields, e.g., ``("gamer","Dens")``, will be in code units. .. rubric:: Caveats * GAMER data in raw binary format (i.e., OPT__OUTPUT_TOTAL = C-binary) is not supported. .. _loading-amr-data: Generic AMR Data ---------------- See :ref:`loading-numpy-array` and :func:`~yt.frontends.stream.data_structures.load_amr_grids` for more detail. It is possible to create native yt dataset from Python's dictionary that describes set of rectangular patches of data of possibly varying resolution. .. code-block:: python import yt grid_data = [ dict(left_edge = [0.0, 0.0, 0.0], right_edge = [1.0, 1.0, 1.], level = 0, dimensions = [32, 32, 32], number_of_particles = 0) dict(left_edge = [0.25, 0.25, 0.25], right_edge = [0.75, 0.75, 0.75], level = 1, dimensions = [32, 32, 32], number_of_particles = 0) ] for g in grid_data: g["density"] = np.random.random(g["dimensions"]) * 2**g["level"] ds = yt.load_amr_grids(grid_data, [32, 32, 32], 1.0) Particle fields are supported by adding 1-dimensional arrays and setting the ``number_of_particles`` key to each ``grid``'s dict: .. code-block:: python for g in grid_data: g["number_of_particles"] = 100000 g["particle_position_x"] = np.random.random((g["number_of_particles"])) .. rubric:: Caveats * Some functions may behave oddly, and parallelism will be disappointing or non-existent in most cases. * No consistency checks are performed on the index * Data must already reside in memory. * Consistency between particle positions and grids is not checked; ``load_amr_grids`` assumes that particle positions associated with one grid are not bounded within another grid at a higher level, so this must be ensured by the user prior to loading the grid data. Generic Array Data ------------------ See :ref:`loading-numpy-array` and :func:`~yt.frontends.stream.data_structures.load_uniform_grid` for more detail. Even if your data is not strictly related to fields commonly used in astrophysical codes or your code is not supported yet, you can still feed it to yt to use its advanced visualization and analysis facilities. The only requirement is that your data can be represented as one or more uniform, three dimensional numpy arrays. Assuming that you have your data in ``arr``, the following code: .. code-block:: python import yt data = dict(Density = arr) bbox = np.array([[-1.5, 1.5], [-1.5, 1.5], [1.5, 1.5]]) ds = yt.load_uniform_grid(data, arr.shape, 3.08e24, bbox=bbox, nprocs=12) will create yt-native dataset ``ds`` that will treat your array as density field in cubic domain of 3 Mpc edge size (3 * 3.08e24 cm) and simultaneously divide the domain into 12 chunks, so that you can take advantage of the underlying parallelism. Particle fields are detected as one-dimensional fields. The number of particles is set by the ``number_of_particles`` key in ``data``. Particle fields are then added as one-dimensional arrays in a similar manner as the three-dimensional grid fields: .. code-block:: python import yt data = dict(Density = dens, number_of_particles = 1000000, particle_position_x = posx_arr, particle_position_y = posy_arr, particle_position_z = posz_arr) bbox = np.array([[-1.5, 1.5], [-1.5, 1.5], [1.5, 1.5]]) ds = yt.load_uniform_grid(data, arr.shape, 3.08e24, bbox=bbox, nprocs=12) where in this example the particle position fields have been assigned. ``number_of_particles`` must be the same size as the particle arrays. If no particle arrays are supplied then ``number_of_particles`` is assumed to be zero. .. rubric:: Caveats * Particles may be difficult to integrate. * Data must already reside in memory. Semi-Structured Grid Data ------------------------- See :ref:`loading-numpy-array`, :func:`~yt.frontends.stream.data_structures.hexahedral_connectivity`, :func:`~yt.frontends.stream.data_structures.load_hexahedral_mesh` for more detail. In addition to uniform grids as described above, you can load in data with non-uniform spacing between datapoints. To load this type of data, you must first specify a hexahedral mesh, a mesh of six-sided cells, on which it will live. You define this by specifying the x,y, and z locations of the corners of the hexahedral cells. The following code: .. code-block:: python import yt import numpy xgrid = numpy.array([-1, -0.65, 0, 0.65, 1]) ygrid = numpy.array([-1, 0, 1]) zgrid = numpy.array([-1, -0.447, 0.447, 1]) coordinates,connectivity = yt.hexahedral_connectivity(xgrid,ygrid,zgrid) will define the (x,y,z) coordinates of the hexahedral cells and information about that cell's neighbors such that the cell corners will be a grid of points constructed as the Cartesion product of xgrid, ygrid, and zgrid. Then, to load your data, which should be defined on the interiors of the hexahedral cells, and thus should have the shape, ``(len(xgrid)-1, len(ygrid)-1, len(zgrid)-1)``, you can use the following code: .. code-block:: python bbox = numpy.array([[numpy.min(xgrid),numpy.max(xgrid)], [numpy.min(ygrid),numpy.max(ygrid)], [numpy.min(zgrid),numpy.max(zgrid)]]) data = {"density" : arr} ds = yt.load_hexahedral_mesh(data,conn,coords,1.0,bbox=bbox) to load your data into the dataset ``ds`` as described above, where we have assumed your data is stored in the three-dimensional array ``arr``. .. rubric:: Caveats * Integration is not implemented. * Some functions may behave oddly or not work at all. * Data must already reside in memory. Unstructured Grid Data ---------------------- See :ref:`loading-numpy-array`, :func:`~yt.frontends.stream.data_structures.load_unstructured_mesh` for more detail. In addition to the above grid types, you can also load data stored on unstructured meshes. This type of mesh is used, for example, in many finite element calculations. Currently, hexahedral and tetrahedral mesh elements are supported. To load an unstructured mesh, you need to specify the following. First, you need to have a coordinates array, which should be an (L, 3) array that stores the (x, y, z) positions of all of the vertices in the mesh. Second, you need to specify a connectivity array, which describes how those vertices are connected into mesh elements. The connectivity array should be (N, M), where N is the number of elements and M is the connectivity length, i.e. the number of vertices per element. Finally, you must also specify a data dictionary, where the keys should be the names of the fields and the values should be numpy arrays that contain the field data. These arrays can either supply the cell-averaged data for each element, in which case they would be (N, 1), or they can have node-centered data, in which case they would also be (N, M). Here is an example of how to load an in-memory, unstructured mesh dataset: .. code-block:: python import yt import numpy from yt.utilities.exodusII_reader import get_data coords, connectivity, data = get_data("MOOSE_sample_data/out.e-s010") This uses a publically available `MOOSE ` dataset along with the get_data function to parse the coords, connectivity, and data. Then, these can be loaded as an in-memory dataset as follows: .. code-block:: python mesh_id = 0 ds = yt.load_unstructured_mesh(data[mesh_id], connectivity[mesh_id], coords[mesh_id]) Note that load_unstructured_mesh can take either a single or a list of meshes. Here, we have selected only the first mesh to load. .. rubric:: Caveats * Integration is not implemented. * Some functions may behave oddly or not work at all. * Data must already reside in memory. Generic Particle Data --------------------- See :ref:`generic-particle-data` and :func:`~yt.frontends.stream.data_structures.load_particles` for more detail. You can also load generic particle data using the same ``stream`` functionality discussed above to load in-memory grid data. For example, if your particle positions and masses are stored in ``positions`` and ``massess``, a vertically-stacked array of particle x,y, and z positions, and a 1D array of particle masses respectively, you would load them like this: .. code-block:: python import yt data = dict(particle_position=positions, particle_mass=masses) ds = yt.load_particles(data) You can also load data using 1D x, y, and z position arrays: .. code-block:: python import yt data = dict(particle_position_x=posx, particle_position_y=posy, particle_position_z=posz, particle_mass=masses) ds = yt.load_particles(data) The ``load_particles`` function also accepts the following keyword parameters: ``length_unit`` The units used for particle positions. ``mass_unit`` The units of the particle masses. ``time_unit`` The units used to represent times. This is optional and is only used if your data contains a ``creation_time`` field or a ``particle_velocity`` field. ``velocity_unit`` The units used to represent velocities. This is optional and is only used if you supply a velocity field. If this is not supplied, it is inferred from the length and time units. ``bbox`` The bounding box for the particle positions. .. _loading-gizmo-data: Gizmo Data ---------- Gizmo datasets, including FIRE outputs, can be loaded into yt in the usual manner. Like other SPH data formats, yt loads Gizmo data as particle fields and then uses smoothing kernels to deposit those fields to an underlying grid structure as spatial fields as described in :ref:`loading-gadget-data`. To load Gizmo datasets using the standard HDF5 output format:: import yt ds = yt.load("snapshot_600.hdf5") Because the Gizmo output format is similar to the Gadget format, yt may load Gizmo datasets as Gadget depending on the circumstances, but this should not pose a problem in most situations. FIRE outputs will be loaded accordingly due to the number of metallicity fields found (11 or 17). For Gizmo outputs written as raw binary outputs, you may have to specify a bounding box, field specification, and units as are done for standard Gadget outputs. See :ref:`loading-gadget-data` for more information. .. _halo-catalog-data: Halo Catalog Data ----------------- yt has support for reading halo catalogs produced by Rockstar and the inline FOF/SUBFIND halo finders of Gadget and OWLS. The halo catalogs are treated as particle datasets where each particle represents a single halo. For example, this means that the `particle_mass` field refers to the mass of the halos. For Gadget FOF/SUBFIND catalogs, the member particles for a given halo can be accessed by creating `halo` data containers. See :ref:`halo_containers` for more information. If you have access to both the halo catalog and the simulation snapshot from the same redshift, additional analysis can be performed for each halo using :ref:`halo_catalog`. .. _rockstar: Rockstar ^^^^^^^^ Rockstar halo catalogs are loaded by providing the path to one of the .bin files. In the case where multiple files were produced, one need only provide the path to a single one of them. The field type for all fields is "halos". Some fields of note available from Rockstar are: +----------------+---------------------------+ | Rockstar field | yt field name | +================+===========================+ | halo id | particle_identifier | +----------------+---------------------------+ | virial mass | particle_mass | +----------------+---------------------------+ | virial radius | virial_radius | +----------------+---------------------------+ | halo position | particle_position_(x,y,z) | +----------------+---------------------------+ | halo velocity | particle_velocity_(x,y,z) | +----------------+---------------------------+ Numerous other Rockstar fields exist. To see them, check the field list by typing `ds.field_list` for a dataset loaded as `ds`. Like all other datasets, fields must be accessed through :ref:`Data-objects`. .. code-block:: python import yt ds = yt.load("rockstar_halos/halos_0.0.bin") ad = ds.all_data() # halo masses print(ad["halos", "particle_mass"]) # halo radii print(ad["halos", "virial_radius"]) .. _gadget_fof: Gadget FOF/SUBFIND ^^^^^^^^^^^^^^^^^^ Gadget FOF/SUBFIND halo catalogs work in the same way as those created by :ref:`rockstar`, except there are two field types: `FOF` for friend-of-friends groups and `Subhalo` for halos found with the SUBFIND substructure finder. Also like Rockstar, there are a number of fields specific to these halo catalogs. +-------------------+---------------------------+ | FOF/SUBFIND field | yt field name | +===================+===========================+ | halo id | particle_identifier | +-------------------+---------------------------+ | halo mass | particle_mass | +-------------------+---------------------------+ | halo position | particle_position_(x,y,z) | +-------------------+---------------------------+ | halo velocity | particle_velocity_(x,y,z) | +-------------------+---------------------------+ | num. of particles | particle_number | +-------------------+---------------------------+ | num. of subhalos | subhalo_number (FOF only) | +-------------------+---------------------------+ Many other fields exist, especially for SUBFIND subhalos. Check the field list by typing `ds.field_list` for a dataset loaded as `ds`. Like all other datasets, fields must be accessed through :ref:`Data-objects`. .. code-block:: python import yt ds = yt.load("gadget_fof_halos/groups_042/fof_subhalo_tab_042.0.hdf5") ad = ds.all_data() # The halo mass print(ad["Group", "particle_mass"]) print(ad["Subhalo", "particle_mass"]) # Halo ID print(ad["Group", "particle_identifier"]) print(ad["Subhalo", "particle_identifier"]) # positions print(ad["Group", "particle_position_x"]) # velocities print(ad["Group", "particle_velocity_x"]) Multidimensional fields can be accessed through the field name followed by an underscore and the index. .. code-block:: python # x component of the spin print(ad["Subhalo", "SubhaloSpin_0"]) .. _halo_containers: Halo Data Containers ^^^^^^^^^^^^^^^^^^^^ Halo member particles are accessed by creating halo data containers with the type of halo ("Group" or "Subhalo") and the halo id. Scalar values for halos can be accessed in the same way. Halos also have mass, position, and velocity attributes. .. code-block:: python halo = ds.halo("Group", 0) # member particles for this halo print halo["member_ids"] # halo virial radius print halo["Group_R_Crit200"] # halo mass print halo.mass Subhalos containers can be created using either their absolute ids or their subhalo ids. .. code-block:: python # first subhalo of the first halo subhalo = ds.halo("Subhalo", (0, 0)) # this subhalo's absolute id print subhalo.group_identifier # member particles print subhalo["member_ids"] OWLS FOF/SUBFIND ^^^^^^^^^^^^^^^^ OWLS halo catalogs have a very similar structure to regular Gadget halo catalogs. The two field types are `FOF` and `SUBFIND`. See :ref:`gadget_fof` for more information. At this time, halo member particles cannot be loaded. .. code-block:: python import yt ds = yt.load("owls_fof_halos/groups_008/group_008.0.hdf5") ad = ds.all_data() # The halo mass print(ad["FOF", "particle_mass"]) .. _loading-pyne-data: PyNE Data --------- `PyNE `_ is an open source nuclear engineering toolkit maintained by the PyNE developement team (pyne-dev@googlegroups.com). PyNE meshes utilize the Mesh-Oriented datABase `(MOAB) `_ and can be Cartesian or tetrahedral. In addition to field data, pyne meshes store pyne Material objects which provide a rich set of capabilities for nuclear engineering tasks. PyNE Cartesian (Hex8) meshes are supported by yt. To create a pyne mesh: .. code-block:: python from pyne.mesh import Mesh num_divisions = 50 coords = linspace(-1, 1, num_divisions) m = Mesh(structured=True, structured_coords=[coords, coords, coords]) Field data can then be added: .. code-block:: python from pyne.mesh import iMeshTag m.neutron_flux = IMeshTag() # neutron_flux_data is a list or numpy array of size num_divisions^3 m.neutron_flux[:] = neutron_flux_data Any field data or material data on the mesh can then be viewed just like any other yt dataset! .. code-block:: python import yt pf = yt.frontends.moab.data_structures.PyneMoabHex8Dataset(m) s = yt.SlicePlot(pf, 'z', 'neutron_flux') s.display() .. _loading-ramses-data: RAMSES Data ----------- In yt-3.0, RAMSES data is fully supported. If you are interested in taking a development or stewardship role, please contact the yt-dev mailing list. To load a RAMSES dataset, you can use the ``yt.load`` command and provide it the ``info*.txt`` filename. For instance, if you were in a directory with the following files: .. code-block:: none output_00007 output_00007/amr_00007.out00001 output_00007/grav_00007.out00001 output_00007/hydro_00007.out00001 output_00007/info_00007.txt output_00007/part_00007.out00001 You would feed it the filename ``output_00007/info_00007.txt``: .. code-block:: python import yt ds = yt.load("output_00007/info_00007.txt") yt will attempt to guess the fields in the file. You may also specify a list of fields by supplying the ``fields`` keyword in your call to ``load``. .. _loading-sph-data: SPH Particle Data ----------------- For all of the SPH frontends, yt uses cython-based SPH smoothing onto an in-memory octree to create deposited mesh fields from individual SPH particle fields. This uses a standard M4 smoothing kernel and the ``smoothing_length`` field to calculate SPH sums, filling in the mesh fields. This gives you the ability to both track individual particles (useful for tasks like following contiguous clouds of gas that would be require a clump finder in grid data) as well as doing standard grid-based analysis (i.e. slices, projections, and profiles). The ``smoothing_length`` variable is also useful for determining which particles can interact with each other, since particles more distant than twice the smoothing length do not typically see each other in SPH simulations. By changing the value of the ``smoothing_length`` and then re-depositing particles onto the grid, you can also effectively mimic what your data would look like at lower resolution. .. _loading-tipsy-data: Tipsy Data ---------- See :ref:`tipsy-notebook` and :ref:`loading-sph-data` for more details. yt also supports loading Tipsy data. Many of its characteristics are similar to how Gadget data is loaded; specifically, it shares its definition of indexing and mesh-identification with that described in :ref:`particle-indexing-criteria`. .. code-block:: python ds = load("./halo1e11_run1.00400") .. _specifying-cosmology-tipsy: Specifying Tipsy Cosmological Parameters and Setting Default Units ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Cosmological parameters can be specified to Tipsy to enable computation of default units. For example do the following, to load a Tipsy dataset whose path is stored in the variable ``my_filename`` with specified cosmology parameters: .. code-block:: python cosmology_parameters = {'current_redshift': 0.0, 'omega_lambda': 0.728, 'omega_matter': 0.272, 'hubble_constant': 0.702} ds = yt.load(my_filename, cosmology_parameters=cosmology_parameters) If you wish to set the unit system directly, you can do so by using the ``unit_base`` keyword in the load statement. .. code-block:: python import yt ds = yt.load(filename, unit_base={'length', (1.0, 'Mpc')}) See the documentation for the :class:`~yt.frontends.tipsy.data_structures.TipsyDataset` class for more information. Loading Cosmological Simulations ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ If you are not using a parameter file (i.e. non-Gasoline users), then you must use keyword ``cosmology_parameters`` when loading your data set to indicate to yt that it is a cosmological data set. If you do not wish to set any non-default cosmological parameters, you may pass an empty dictionary. .. code-block:: python import yt ds = yt.load(filename, cosmology_parameters={}) yt-3.3.3/doc/source/examining/Loading_Generic_Array_Data.ipynb0000644000076500000240000004770313017603554025277 0ustar goldbaumstaff00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "Even if your data is not strictly related to fields commonly used in\n", "astrophysical codes or your code is not supported yet, you can still feed it to\n", "yt to use its advanced visualization and analysis facilities. The only\n", "requirement is that your data can be represented as three-dimensional NumPy arrays with a consistent grid structure. What follows are some common examples of loading in generic array data that you may find useful. " ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generic Unigrid Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The simplest case is that of a single grid of data spanning the domain, with one or more fields. The data could be generated from a variety of sources; we'll just give three common examples:" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Data generated \"on-the-fly\"" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The most common example is that of data that is generated in memory from the currently running script or notebook. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import yt\n", "import numpy as np" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this example, we'll just create a 3-D array of random floating-point data using NumPy:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "arr = np.random.random(size=(64,64,64))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To load this data into yt, we need associate it with a field. The `data` dictionary consists of one or more fields, each consisting of a tuple of a NumPy array and a unit string. Then, we can call `load_uniform_grid`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data = dict(density = (arr, \"g/cm**3\"))\n", "bbox = np.array([[-1.5, 1.5], [-1.5, 1.5], [-1.5, 1.5]])\n", "ds = yt.load_uniform_grid(data, arr.shape, length_unit=\"Mpc\", bbox=bbox, nprocs=64)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`load_uniform_grid` takes the following arguments and optional keywords:\n", "\n", "* `data` : This is a dict of numpy arrays, where the keys are the field names\n", "* `domain_dimensions` : The domain dimensions of the unigrid\n", "* `length_unit` : The unit that corresponds to `code_length`, can be a string, tuple, or floating-point number\n", "* `bbox` : Size of computational domain in units of `code_length`\n", "* `nprocs` : If greater than 1, will create this number of subarrays out of data\n", "* `sim_time` : The simulation time in seconds\n", "* `mass_unit` : The unit that corresponds to `code_mass`, can be a string, tuple, or floating-point number\n", "* `time_unit` : The unit that corresponds to `code_time`, can be a string, tuple, or floating-point number\n", "* `velocity_unit` : The unit that corresponds to `code_velocity`\n", "* `magnetic_unit` : The unit that corresponds to `code_magnetic`, i.e. the internal units used to represent magnetic field strengths.\n", "* `periodicity` : A tuple of booleans that determines whether the data will be treated as periodic along each axis\n", "\n", "This example creates a yt-native dataset `ds` that will treat your array as a\n", "density field in cubic domain of 3 Mpc edge size and simultaneously divide the \n", "domain into `nprocs` = 64 chunks, so that you can take advantage\n", "of the underlying parallelism. \n", "\n", "The optional unit keyword arguments allow for the default units of the dataset to be set. They can be:\n", "* A string, e.g. `length_unit=\"Mpc\"`\n", "* A tuple, e.g. `mass_unit=(1.0e14, \"Msun\")`\n", "* A floating-point value, e.g. `time_unit=3.1557e13`\n", "\n", "In the latter case, the unit is assumed to be cgs. \n", "\n", "The resulting `ds` functions exactly like a dataset like any other yt can handle--it can be sliced, and we can show the grid boundaries:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "slc = yt.SlicePlot(ds, \"z\", [\"density\"])\n", "slc.set_cmap(\"density\", \"Blues\")\n", "slc.annotate_grids(cmap=None)\n", "slc.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Particle fields are detected as one-dimensional fields. The number of\n", "particles is set by the `number_of_particles` key in\n", "`data`. Particle fields are then added as one-dimensional arrays in\n", "a similar manner as the three-dimensional grid fields:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "posx_arr = np.random.uniform(low=-1.5, high=1.5, size=10000)\n", "posy_arr = np.random.uniform(low=-1.5, high=1.5, size=10000)\n", "posz_arr = np.random.uniform(low=-1.5, high=1.5, size=10000)\n", "data = dict(density = (np.random.random(size=(64,64,64)), \"Msun/kpc**3\"), \n", " number_of_particles = 10000,\n", " particle_position_x = (posx_arr, 'code_length'), \n", " particle_position_y = (posy_arr, 'code_length'),\n", " particle_position_z = (posz_arr, 'code_length'))\n", "bbox = np.array([[-1.5, 1.5], [-1.5, 1.5], [-1.5, 1.5]])\n", "ds = yt.load_uniform_grid(data, data[\"density\"][0].shape, length_unit=(1.0, \"Mpc\"), mass_unit=(1.0,\"Msun\"), \n", " bbox=bbox, nprocs=4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this example only the particle position fields have been assigned. `number_of_particles` must be the same size as the particle\n", "arrays. If no particle arrays are supplied then `number_of_particles` is assumed to be zero. Take a slice, and overlay particle positions:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "slc = yt.SlicePlot(ds, \"z\", [\"density\"])\n", "slc.set_cmap(\"density\", \"Blues\")\n", "slc.annotate_particles(0.25, p_size=12.0, col=\"Red\")\n", "slc.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### HDF5 data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "HDF5 is a convenient format to store data. If you have unigrid data stored in an HDF5 file, it is possible to load it into memory and then use `load_uniform_grid` to get it into yt:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import h5py\n", "from yt.config import ytcfg\n", "data_dir = ytcfg.get('yt','test_data_dir')\n", "from yt.utilities.physical_ratios import cm_per_kpc\n", "f = h5py.File(data_dir+\"/UnigridData/turb_vels.h5\", \"r\") # Read-only access to the file" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The HDF5 file handle's keys correspond to the datasets stored in the file:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print (f.keys())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We need to add some unit information. It may be stored in the file somewhere, or we may know it from another source. In this case, the units are simply cgs:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "units = [\"gauss\",\"gauss\",\"gauss\", \"g/cm**3\", \"erg/cm**3\", \"K\", \n", " \"cm/s\", \"cm/s\", \"cm/s\", \"cm/s\", \"cm/s\", \"cm/s\"]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can iterate over the items in the file handle and the units to get the data into a dictionary, which we will then load:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data = {k:(v.value,u) for (k,v), u in zip(f.items(),units)}\n", "bbox = np.array([[-0.5, 0.5], [-0.5, 0.5], [-0.5, 0.5]])" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ds = yt.load_uniform_grid(data, data[\"Density\"][0].shape, length_unit=250.*cm_per_kpc, bbox=bbox, nprocs=8, \n", " periodicity=(False,False,False))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In this case, the data came from a simulation which was 250 kpc on a side. An example projection of two fields:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "prj = yt.ProjectionPlot(ds, \"z\", [\"z-velocity\",\"Temperature\",\"Bx\"], weight_field=\"Density\")\n", "prj.set_log(\"z-velocity\", False)\n", "prj.set_log(\"Bx\", False)\n", "prj.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### Volume Rendering Loaded Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Volume rendering requires defining a `TransferFunction` to map data to color and opacity and a `camera` to create a viewport and render the image." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "#Find the min and max of the field\n", "mi, ma = ds.all_data().quantities.extrema('Temperature')\n", "#Reduce the dynamic range\n", "mi = mi.value + 1.5e7\n", "ma = ma.value - 0.81e7" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Define the properties and size of the `camera` viewport:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "# Choose a vector representing the viewing direction.\n", "L = [0.5, 0.5, 0.5]\n", "# Define the center of the camera to be the domain center\n", "c = ds.domain_center[0]\n", "# Define the width of the image\n", "W = 1.5*ds.domain_width[0]\n", "# Define the number of pixels to render\n", "Npixels = 512" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Create a `camera` object and " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sc = yt.create_scene(ds, 'Temperature')\n", "dd = ds.all_data()\n", "\n", "source = sc[0]\n", "\n", "source.log_field = False\n", "\n", "tf = yt.ColorTransferFunction((mi, ma), grey_opacity=False)\n", "tf.map_to_colormap(mi, ma, scale=15.0, colormap='algae')\n", "\n", "source.set_transfer_function(tf)\n", "\n", "sc.add_source(source)\n", "\n", "cam = sc.add_camera()\n", "cam.width = W\n", "cam.center = c\n", "cam.normal_vector = L\n", "cam.north_vector = [0, 0, 1]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "sc.show(sigma_clip=4)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "### FITS image data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The FITS file format is a common astronomical format for 2-D images, but it can store three-dimensional data as well. The [AstroPy](http://www.astropy.org) project has modules for FITS reading and writing, which were incorporated from the [PyFITS](http://www.stsci.edu/institute/software_hardware/pyfits) library." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "import astropy.io.fits as pyfits\n", "# Or, just import pyfits if that's what you have installed" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Using `pyfits` we can open a FITS file. If we call `info()` on the file handle, we can figure out some information about the file's contents. The file in this example has a primary HDU (header-data-unit) with no data, and three HDUs with 3-D data. In this case, the data consists of three velocity fields:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "f = pyfits.open(data_dir+\"/UnigridData/velocity_field_20.fits\")\n", "f.info()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can put it into a dictionary in the same way as before, but we slice the file handle `f` so that we don't use the `PrimaryHDU`. `hdu.name` is the field name and `hdu.data` is the actual data. Each of these velocity fields is in km/s. We can check that we got the correct fields. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data = {}\n", "for hdu in f:\n", " name = hdu.name.lower()\n", " data[name] = (hdu.data,\"km/s\")\n", "print (data.keys())" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The velocity field names in this case are slightly different than the standard yt field names for velocity fields, so we will reassign the field names:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "data[\"velocity_x\"] = data.pop(\"x-velocity\")\n", "data[\"velocity_y\"] = data.pop(\"y-velocity\")\n", "data[\"velocity_z\"] = data.pop(\"z-velocity\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now we load the data into yt. Let's assume that the box size is a Mpc. Since these are velocity fields, we can overlay velocity vectors on slices, just as if we had loaded in data from a supported code. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ds = yt.load_uniform_grid(data, data[\"velocity_x\"][0].shape, length_unit=(1.0,\"Mpc\"))\n", "slc = yt.SlicePlot(ds, \"x\", [\"velocity_x\",\"velocity_y\",\"velocity_z\"])\n", "for ax in \"xyz\":\n", " slc.set_log(\"velocity_%s\" % (ax), False)\n", "slc.annotate_velocity()\n", "slc.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Generic AMR Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "In a similar fashion to unigrid data, data gridded into rectangular patches at varying levels of resolution may also be loaded into yt. In this case, a list of grid dictionaries should be provided, with the requisite information about each grid's properties. This example sets up two grids: a top-level grid (`level == 0`) covering the entire domain and a subgrid at `level == 1`. " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "grid_data = [\n", " dict(left_edge = [0.0, 0.0, 0.0],\n", " right_edge = [1.0, 1.0, 1.0],\n", " level = 0,\n", " dimensions = [32, 32, 32]), \n", " dict(left_edge = [0.25, 0.25, 0.25],\n", " right_edge = [0.75, 0.75, 0.75],\n", " level = 1,\n", " dimensions = [32, 32, 32])\n", " ]" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We'll just fill each grid with random density data, with a scaling with the grid refinement level." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "for g in grid_data: \n", " g[\"density\"] = (np.random.random(g[\"dimensions\"]) * 2**g[\"level\"], \"g/cm**3\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Particle fields are supported by adding 1-dimensional arrays to each `grid` and\n", "setting the `number_of_particles` key in each `grid`'s dict. If a grid has no particles, set `number_of_particles = 0`, but the particle fields still have to be defined since they are defined elsewhere; set them to empty NumPy arrays:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "grid_data[0][\"number_of_particles\"] = 0 # Set no particles in the top-level grid\n", "grid_data[0][\"particle_position_x\"] = (np.array([]), \"code_length\") # No particles, so set empty arrays\n", "grid_data[0][\"particle_position_y\"] = (np.array([]), \"code_length\")\n", "grid_data[0][\"particle_position_z\"] = (np.array([]), \"code_length\")\n", "grid_data[1][\"number_of_particles\"] = 1000\n", "grid_data[1][\"particle_position_x\"] = (np.random.uniform(low=0.25, high=0.75, size=1000), \"code_length\")\n", "grid_data[1][\"particle_position_y\"] = (np.random.uniform(low=0.25, high=0.75, size=1000), \"code_length\")\n", "grid_data[1][\"particle_position_z\"] = (np.random.uniform(low=0.25, high=0.75, size=1000), \"code_length\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Then, call `load_amr_grids`:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ds = yt.load_amr_grids(grid_data, [32, 32, 32])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "`load_amr_grids` also takes the same keywords `bbox` and `sim_time` as `load_uniform_grid`. We could have also specified the length, time, velocity, and mass units in the same manner as before. Let's take a slice:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "slc = yt.SlicePlot(ds, \"z\", [\"density\"])\n", "slc.annotate_particles(0.25, p_size=15.0, col=\"Pink\")\n", "slc.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Caveats for Loading Generic Array Data" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "* Particles may be difficult to integrate.\n", "* Data must already reside in memory before loading it in to yt, whether it is generated at runtime or loaded from disk. \n", "* Some functions may behave oddly, and parallelism will be disappointing or non-existent in most cases.\n", "* No consistency checks are performed on the hierarchy\n", "* Consistency between particle positions and grids is not checked; `load_amr_grids` assumes that particle positions associated with one grid are not bounded within another grid at a higher level, so this must be ensured by the user prior to loading the grid data. " ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 } yt-3.3.3/doc/source/examining/Loading_Generic_Particle_Data.ipynb0000644000076500000240000001142413017603554025753 0ustar goldbaumstaff00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "This example creates a fake in-memory particle dataset and then loads it as a yt dataset using the `load_particles` function.\n", "\n", "Our \"fake\" dataset will be numpy arrays filled with normally distributed randoml particle positions and uniform particle masses. Since real data is often scaled, I arbitrarily multiply by 1e6 to show how to deal with scaled data." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "\n", "n_particles = 5e6\n", "\n", "ppx, ppy, ppz = 1e6*np.random.normal(size=[3, n_particles])\n", "\n", "ppm = np.ones(n_particles)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `load_particles` function accepts a dictionary populated with particle data fields loaded in memory as numpy arrays or python lists:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "data = {'particle_position_x': ppx,\n", " 'particle_position_y': ppy,\n", " 'particle_position_z': ppz,\n", " 'particle_mass': ppm}" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "To hook up with yt's internal field system, the dictionary keys must be 'particle_position_x', 'particle_position_y', 'particle_position_z', and 'particle_mass', as well as any other particle field provided by one of the particle frontends." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `load_particles` function transforms the `data` dictionary into an in-memory yt `Dataset` object, providing an interface for further analysis with yt. The example below illustrates how to load the data dictionary we created above." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import yt\n", "from yt.units import parsec, Msun\n", "\n", "bbox = 1.1*np.array([[min(ppx), max(ppx)], [min(ppy), max(ppy)], [min(ppz), max(ppz)]])\n", "\n", "ds = yt.load_particles(data, length_unit=parsec, mass_unit=1e8*Msun, n_ref=256, bbox=bbox)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "The `length_unit` and `mass_unit` are the conversion from the units used in the `data` dictionary to CGS. I've arbitrarily chosen one parsec and 10^8 Msun for this example. \n", "\n", "The `n_ref` parameter controls how many particle it takes to accumulate in an oct-tree cell to trigger refinement. Larger `n_ref` will decrease poisson noise at the cost of resolution in the octree. \n", "\n", "Finally, the `bbox` parameter is a bounding box in the units of the dataset that contains all of the particles. This is used to set the size of the base octree block." ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "This new dataset acts like any other yt `Dataset` object, and can be used to create data objects and query for yt fields. This example shows how to access \"deposit\" fields:" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ad = ds.all_data()\n", "\n", "# This is generated with \"cloud-in-cell\" interpolation.\n", "cic_density = ad[\"deposit\", \"all_cic\"]\n", "\n", "# These three are based on nearest-neighbor cell deposition\n", "nn_density = ad[\"deposit\", \"all_density\"]\n", "nn_deposited_mass = ad[\"deposit\", \"all_mass\"]\n", "particle_count_per_cell = ad[\"deposit\", \"all_count\"]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ds.field_list" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ds.derived_field_list" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "slc = yt.SlicePlot(ds, 2, ('deposit', 'all_cic'))\n", "slc.set_width((8, 'Mpc'))" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 } yt-3.3.3/doc/source/examining/Loading_Spherical_Data.ipynb0000644000076500000240000001572213017603554024473 0ustar goldbaumstaff00000000000000{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Loading Spherical Data\n", "\n", "With version 3.0 of yt, it has gained the ability to load data from non-Cartesian systems. This support is still being extended, but here is an example of how to load spherical data from a regularly-spaced grid. For irregularly spaced grids, a similar setup can be used, but the `load_hexahedral_mesh` method will have to be used instead.\n", "\n", "Note that in yt, \"spherical\" means that it is ordered $r$, $\\theta$, $\\phi$, where $\\theta$ is the declination from the azimuth (running from $0$ to $\\pi$ and $\\phi$ is the angle around the zenith (running from $0$ to $2\\pi$).\n", "\n", "We first start out by loading yt." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "import numpy as np\n", "import yt" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "Now, we create a few derived fields. The first three are just straight translations of the Cartesian coordinates, so that we can see where we are located in the data, and understand what we're seeing. The final one is just a fun field that is some combination of the three coordinates, and will vary in all dimensions." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "@yt.derived_field(name = \"sphx\", units = \"cm\", take_log=False)\n", "def sphx(field, data):\n", " return np.cos(data[\"phi\"]) * np.sin(data[\"theta\"])*data[\"r\"]\n", "@yt.derived_field(name = \"sphy\", units = \"cm\", take_log=False)\n", "def sphy(field, data):\n", " return np.sin(data[\"phi\"]) * np.sin(data[\"theta\"])*data[\"r\"]\n", "@yt.derived_field(name = \"sphz\", units = \"cm\", take_log=False)\n", "def sphz(field, data):\n", " return np.cos(data[\"theta\"])*data[\"r\"]\n", "@yt.derived_field(name = \"funfield\", units=\"cm\", take_log=False)\n", "def funfield(field, data):\n", " return (np.sin(data[\"phi\"])**2 + np.cos(data[\"theta\"])**2) * (1.0*data[\"r\"].uq+data[\"r\"])" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Loading Data\n", "\n", "Now we can actually load our data. We use the `load_uniform_grid` function here. Normally, the first argument would be a dictionary of field data, where the keys were the field names and the values the field data arrays. Here, we're just going to look at derived fields, so we supply an empty one.\n", "\n", "The next few arguments are the number of dimensions, the bounds, and we then specify the geometry as spherical." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ds = yt.load_uniform_grid({}, [128, 128, 128],\n", " bbox=np.array([[0.0, 1.0], [0.0, np.pi], [0.0, 2*np.pi]]),\n", " geometry=\"spherical\")" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "## Looking at Data\n", "\n", "Now we can take slices. The first thing we will try is making a slice of data along the \"phi\" axis, here $\\pi/2$, which will be along the y axis in the positive direction. We use the `.slice` attribute, which creates a slice, and then we convert this into a plot window. Note that here 2 is used to indicate the third axis (0-indexed) which for spherical data is $\\phi$.\n", "\n", "This is the manual way of creating a plot -- below, we'll use the standard, automatic ways. Note that the coordinates run from $-r$ to $r$ along the $z$ axis and from $0$ to $r$ along the $R$ axis. We use the capital $R$ to indicate that it's the $R$ along the $x-y$ plane." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "s = ds.slice(2, np.pi/2)\n", "p = s.to_pw(\"funfield\", origin=\"native\")\n", "p.set_zlim(\"all\", 0.0, 4.0)\n", "p.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also slice along $r$. For now, this creates a regular grid with *incorrect* units for phi and theta. We are currently exploring two other options -- a simple aitoff projection, and fixing it to use the correct units as-is." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "s = yt.SlicePlot(ds, \"r\", \"funfield\")\n", "s.set_zlim(\"all\", 0.0, 4.0)\n", "s.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can also slice at constant $\\theta$. But, this is a weird thing! We're slicing at a constant declination from the azimuth. What this means is that when thought of in a Cartesian domain, this slice is actually a cone. The axes have been labeled appropriately, to indicate that these are not exactly the $x$ and $y$ axes, but instead differ by a factor of $\\sin(\\theta))$." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "s = yt.SlicePlot(ds, \"theta\", \"funfield\")\n", "s.set_zlim(\"all\", 0.0, 4.0)\n", "s.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We've seen lots of the `funfield` plots, but we can also look at the Cartesian axes. This next plot plots the Cartesian $x$, $y$ and $z$ values on a $\\theta$ slice. Because we're not supplying an argument to the `center` parameter, yt will place it at the center of the $\\theta$ axis, which will be at $\\pi/2$, where it will be aligned with the $x-y$ plane. The slight change in `sphz` results from the cells themselves migrating, and plotting the center of those cells." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "s = yt.SlicePlot(ds, \"theta\", [\"sphx\", \"sphy\", \"sphz\"])\n", "s.show()" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "We can do the same with the $\\phi$ axis." ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "s = yt.SlicePlot(ds, \"phi\", [\"sphx\", \"sphy\", \"sphz\"])\n", "s.show()" ] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.5.1" } }, "nbformat": 4, "nbformat_minor": 0 } yt-3.3.3/doc/source/examining/low_level_inspection.rst0000644000076500000240000002524513017603554024106 0ustar goldbaumstaff00000000000000.. _low-level-data-inspection: Low-Level Data Inspection: Accessing Raw Data ============================================= yt can not only provide high-level access to data, such as through slices, projections, object queries and the like, but it can also provide low-level access to the raw data. .. note:: This section is tuned for patch- or block-based simulations. Future versions of yt will enable more direct access to particle and oct based simulations. For now, these are represented as patches, with the attendant properties. For a more basic introduction, see :ref:`quickstart` and more specifically :ref:`data_inspection`. .. _examining-grid-hierarchies: Examining Grid Hierarchies -------------------------- yt organizes grids in a hierarchical fashion; a coarser grid that contains (or overlaps with) a finer grid is referred to as its parent. yt organizes these only a single level of refinement at a time. To access grids, the ``grids`` attribute on a :class:`~yt.geometry.grid_geometry_handler.GridIndex` object. (For fast operations, a number of additional arrays prefixed with ``grid`` are also available, such as ``grid_left_edges`` and so on.) This returns an instance of :class:`~yt.data_objects.grid_patch.AMRGridPatch`, which can be queried for either data or index information. The :class:`~yt.data_objects.grid_patch.AMRGridPatch` object itself provides the following attributes: * ``Children``: a list of grids contained within this one, of one higher level of refinement * ``Parent``: a single object or a list of objects this grid is contained within, one level of refinement coarser * ``child_mask``: a mask of 0's and 1's, representing where no finer data is available in refined grids (1) or where this grid is covered by finer regions (0). Note that to get back the final data contained within a grid, one can multiple a field by this attribute. * ``child_indices``: a mask of booleans, where False indicates no finer data is available. This is essentially the inverse of ``child_mask``. * ``child_index_mask``: a mask of indices into the ``ds.index.grids`` array of the child grids. * ``LeftEdge``: the left edge, in native code coordinates, of this grid * ``RightEdge``: the right edge, in native code coordinates, of this grid * ``dds``: the width of a cell in this grid * ``id``: the id (not necessarily the index) of this grid. Defined such that subtracting the property ``_id_offset`` gives the index into ``ds.index.grids``. * ``NumberOfParticles``: the number of particles in this grid * ``OverlappingSiblings``: a list of sibling grids that this grid overlaps with. Likely only defined for Octree-based codes. In addition, the method :meth:`~yt.data_objects.grid_patch.AMRGridPatch.get_global_startindex` can be used to get the integer coordinates of the upper left edge. These integer coordinates are defined with respect to the current level; this means that they are the offset of the left edge, with respect to the left edge of the domain, divided by the local ``dds``. To traverse a series of grids, this type of construction can be used: .. code-block:: python g = ds.index.grids[1043] g2 = g.Children[1].Children[0] print(g2.LeftEdge) .. _examining-grid-data: Examining Grid Data ------------------- Once you have identified a grid you wish to inspect, there are two ways to examine data. You can either ask the grid to read the data and pass it to you as normal, or you can manually intercept the data from the IO handler and examine it before it has been unit converted. This allows for much more raw data inspection. To access data that has been read in the typical fashion and unit-converted as normal, you can access the grid as you would a normal object: .. code-block:: python g = ds.index.grids[1043] print(g["density"]) print(g["density"].min()) To access the raw data, you have to call the IO handler from the index instead. This is somewhat more low-level. .. code-block:: python g = ds.index.grids[1043] rho = ds.index.io.pop(g, "density") This field will be the raw data found in the file. .. _finding-data-at-fixed-points: Finding Data at Fixed Points ---------------------------- One of the most common questions asked of data is, what is the value *at this specific point*. While there are several ways to find out the answer to this question, a few helper routines are provided as well. To identify the finest-resolution (i.e., most canonical) data at a given point, use :meth:`~yt.data_objects.index.AMRHierarchy.find_field_value_at_point`. This accepts a position (in coordinates of the domain) and returns the field values for one or multiple fields. To identify all the grids that intersect a given point, the function :meth:`~yt.data_objects.index.AMRHierarchy.find_point` will return indices and objects that correspond to it. For instance: .. code-block:: python gs, gi = ds.find_point((0.5, 0.6, 0.9)) for g in gs: print(g.Level, g.LeftEdge, g.RightEdge) Note that this doesn't just return the canonical output, but also all of the parent grids that overlap with that point. .. _examining-grid-data-in-a-fixed-resolution-array: Examining Grid Data in a Fixed Resolution Array ----------------------------------------------- If you have a dataset, either AMR or single resolution, and you want to just stick it into a fixed resolution numpy array for later examination, then you want to use a :ref:`Covering Grid `. You must specify the maximum level at which to sample the data, a left edge of the data where you will start, and the resolution at which you want to sample. For example, let's use the :ref:`sample dataset ` ``Enzo_64``. This dataset is at a resolution of 64^3 with 5 levels of AMR, so if we want a 64^3 array covering the entire volume and sampling just the lowest level data, we run: .. code-block:: python import yt ds = yt.load('Enzo_64/DD0043/data0043') all_data_level_0 = ds.covering_grid(level=0, left_edge=[0,0.0,0.0], dims=[64, 64, 64]) Note that we can also get the same result and rely on the dataset to know its own underlying dimensions: .. code-block:: python all_data_level_0 = ds.covering_grid(level=0, left_edge=[0,0.0,0.0], dims=ds.domain_dimensions) We can now access our underlying data at the lowest level by specifying what :ref:`field ` we want to examine: .. code-block:: python print(all_data_level_0['density'].shape) (64, 64, 64) print(all_data_level_0['density']) array([[[ 1.92588925e-31, 1.74647692e-31, 2.54787518e-31, ..., print(all_data_level_0['temperature'].shape) (64, 64, 64) If you create a covering grid that spans two child grids of a single parent grid, it will fill those zones covered by a zone of a child grid with the data from that child grid. Where it is covered only by the parent grid, the cells from the parent grid will be duplicated (appropriately) to fill the covering grid. Let's say we now want to look at that entire data volume and sample it at a higher resolution (i.e. level 2). As stated above, we'll be oversampling under-refined regions, but that's OK. We must also increase the resolution of our output array by a factor of 2^2 in each direction to hold this new larger dataset: .. code-block:: python all_data_level_2 = ds.covering_grid(level=2, left_edge=[0,0.0,0.0], dims=ds.domain_dimensions * 2**2) And let's see what's the density in the central location: .. code-block:: python print(all_data_level_2['density'].shape) (256, 256, 256) print(all_data_level_2['density'][128, 128, 128]) 1.7747457571203124e-31 There are two different types of covering grids: unsmoothed and smoothed. Smoothed grids will be filled through a cascading interpolation process; they will be filled at level 0, interpolated to level 1, filled at level 1, interpolated to level 2, filled at level 2, etc. This will help to reduce edge effects. Unsmoothed covering grids will not be interpolated, but rather values will be duplicated multiple times. To sample our dataset from above with a smoothed covering grid in order to reduce edge effects, it is a nearly identical process: .. code-block:: python all_data_level_2_s = ds.smoothed_covering_grid(2, [0.0, 0.0, 0.0], ds.domain_dimensions * 2**2) print(all_data_level_2_s['density'].shape) (256, 256, 256) print(all_data_level_2_s['density'][128, 128, 128]) 1.763744852165591e-31 .. _examining-image-data-in-a-fixed-resolution-array: Examining Image Data in a Fixed Resolution Array ------------------------------------------------ In the same way that one can sample a multi-resolution 3D dataset by placing it into a fixed resolution 3D array as a :ref:`Covering Grid `, one can also access the raw image data that is returned from various yt functions directly as a fixed resolution array. This provides a means for bypassing the yt method for generating plots, and allows the user the freedom to use whatever interface they wish for displaying and saving their image data. You can use the :class:`~yt.visualization.fixed_resolution.FixedResolutionBuffer` to accomplish this as described in :ref:`fixed-resolution-buffers`. High-level Information about Particles -------------------------------------- There are a number of high-level helpers attached to ``Dataset`` objects to find out information about the particles in an output file. First, one can check if there are any particles in a dataset at all by examining ``ds.particles_exist``. This will be ``True`` for datasets the include particles and ``False`` otherwise. One can also see which particle types are available in a dataset. Particle types that are available in the dataset's on-disk output are known as "raw" particle types, and they will appear in ``ds.particle_types_raw``. Particle types that are dynamically defined via a particle filter of a particle union will also appear in the ``ds.particle_types`` list. If the simulation only has one particle type on-disk, its name will by ``'io'``. If there is more than one particle type, the names of the particle types will be inferred from the output file. For example, Gadget HDF5 files have particle type names like ``PartType0`` and ``PartType1``, while Enzo data, which usually only has one particle type, will only have a particle named ``io``. Finally, one can see the number of each particle type by inspecting ``ds.particle_type_counts``. This will be a dictionary mappying the names of particle types in ``ds.particle_types_raw`` to the number of each particle type in a simulation output. yt-3.3.3/doc/source/examining/spherical_data.rst0000644000076500000240000000016712770647750022635 0ustar goldbaumstaff00000000000000.. _loading-spherical-data: Loading Spherical Data ====================== .. notebook:: Loading_Spherical_Data.ipynb yt-3.3.3/doc/source/faq/0000755000076500000240000000000013023400160015672 5ustar goldbaumstaff00000000000000yt-3.3.3/doc/source/faq/index.rst0000644000076500000240000004175613023400062017551 0ustar goldbaumstaff00000000000000.. _faq: Frequently Asked Questions ========================== .. contents:: :depth: 2 :local: :backlinks: none Version & Installation ---------------------- .. _determining-version: How can I tell what version of yt I'm using? ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ If you run into problems with yt and you're writing to the mailing list or contacting developers on IRC, they will likely want to know what version of yt you're using. Oftentimes, you'll want to know both the yt version, as well as the last changeset that was committed to the branch you're using. To reveal this, go to a command line and type: .. code-block:: bash $ yt version yt module located at: /Users/username/src/yt-x86_64/src/yt-hg The supplemental repositories are located at: /Users/username/src/yt-x86_64/src/yt-supplemental The current version and changeset for the code is: --- Version = 2.7-dev Changeset = 6bffc737a67a --- This installation CAN be automatically updated. yt dependencies were last updated on Wed Dec 4 15:47:40 MST 2013 To update all dependencies, run "yt update --all". If the changeset is displayed followed by a "+", it means you have made modifications to the code since the last changeset. For more information on this topic, see :ref:`updating-yt`. .. _yt-3.0-problems: I upgraded to yt 3.0 but my code no longer works. What do I do? ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Because there are a lot of backwards-incompatible changes in yt 3.0 (see :ref:`yt3differences`, it can be a daunting effort in transitioning old scripts from yt 2.x to 3.0. We have tried to describe the basic process of making that transition in :ref:`transitioning-to-3.0`. If you just want to change back to yt 2.x for a while until you're ready to make the transition, you can follow the instructions in :ref:`switching-between-yt-versions`. Code Errors and Failures ------------------------ yt fails saying that it cannot import yt modules ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ This is commonly exhibited with this error: ``ImportError: cannot import name obtain_rvec``. This is likely because you need to rebuild the source. You can do this automatically by running: .. code-block:: bash cd $YT_HG python setup.py develop where ``$YT_HG`` is the path to the yt mercurial repository. This error tends to occur when there are changes in the underlying cython files that need to be rebuilt, like after a major code update or in switching from 2.x to 3.x. For more information on this, see :ref:`switching-between-yt-versions`. .. _faq-mpi4py: yt complains that it needs the mpi4py module ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ For yt to be able to incorporate parallelism on any of its analysis (see :ref:`parallel-computation`), it needs to be able to use MPI libraries. This requires the ``mpi4py`` module to be installed in your version of python. Unfortunately, installation of ``mpi4py`` is *just* tricky enough to elude the yt batch installer. So if you get an error in yt complaining about mpi4py like: .. code-block:: bash ImportError: No module named mpi4py then you should install ``mpi4py``. The easiest way to install it is through the pip interface. At the command line, type: .. code-block:: bash pip install mpi4py What this does is it finds your default installation of python (presumably in the yt source directory), and it installs the mpi4py module. If this action is successful, you should never have to worry about your aforementioned problems again. If, on the other hand, this installation fails (as it does on such machines as NICS Kraken, NASA Pleaides and more), then you will have to take matters into your own hands. Usually when it fails, it is due to pip being unable to find your MPI C/C++ compilers (look at the error message). If this is the case, you can specify them explicitly as per: .. code-block:: bash env MPICC=/path/to/MPICC pip install mpi4py So for example, on Kraken, I switch to the gnu C compilers (because yt doesn't work with the portland group C compilers), then I discover that cc is the mpi-enabled C compiler (and it is in my path), so I run: .. code-block:: bash module swap PrgEnv-pgi PrgEnv-gnu env MPICC=cc pip install mpi4py And voila! It installs! If this *still* fails for you, then you can build and install from source and specify the mpi-enabled c and c++ compilers in the mpi.cfg file. See the `mpi4py installation page `_ for details. Units ----- .. _conversion-factors: How do I get the convert between code units and physical units for my dataset? ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Converting between physical units and code units is a common task. In yt-2.x, the syntax for getting conversion factors was in the units dictionary (``pf.units['kpc']``). So in order to convert a variable ``x`` in code units to kpc, you might run: .. code-block:: python x = x*pf.units['kpc'] In yt-3.0, this no longer works. Conversion factors are tied up in the ``length_unit``, ``times_unit``, ``mass_unit``, and ``velocity_unit`` attributes, which can be converted to any arbitrary desired physical unit: .. code-block:: python print("Length unit: ", ds.length_unit) print("Time unit: ", ds.time_unit) print("Mass unit: ", ds.mass_unit) print("Velocity unit: ", ds.velocity_unit) print("Length unit: ", ds.length_unit.in_units('code_length')) print("Time unit: ", ds.time_unit.in_units('code_time')) print("Mass unit: ", ds.mass_unit.in_units('kg')) print("Velocity unit: ", ds.velocity_unit.in_units('Mpc/year')) So to accomplish the example task of converting a scalar variable ``x`` in code units to kpc in yt-3.0, you can do one of two things. If ``x`` is already a YTQuantity with units in ``code_length``, you can run: .. code-block:: python x.in_units('kpc') However, if ``x`` is just a numpy array or native python variable without units, you can convert it to a YTQuantity with units of ``kpc`` by running: .. code-block:: python x = x*ds.length_unit.in_units('kpc') For more information about unit conversion, see :ref:`data_selection_and_fields`. How do I make a YTQuantity tied to a specific dataset's units? ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ If you want to create a variable or array that is tied to a particular dataset (and its specific conversion factor to code units), use the ``ds.quan`` (for individual variables) and ``ds.arr`` (for arrays): .. code-block:: python import yt ds = yt.load(filename) one_Mpc = ds.quan(1, 'Mpc') x_vector = ds.arr([1,0,0], 'code_length') You can then naturally exploit the units system: .. code-block:: python print("One Mpc in code_units:", one_Mpc.in_units('code_length')) print("One Mpc in AU:", one_Mpc.in_units('AU')) print("One Mpc in comoving kpc:", one_Mpc.in_units('kpccm')) For more information about unit conversion, see :ref:`data_selection_and_fields`. .. _accessing-unitless-data: How do I access the unitless data in a YTQuantity or YTArray? ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ While there are numerous benefits to having units tied to individual quantities in yt, they can also produce issues when simply trying to combine YTQuantities with numpy arrays or native python floats that lack units. A simple example of this is:: # Create a YTQuantity that is 1 kpc in length and tied to the units of # dataset ds >>> x = ds.quan(1, 'kpc') # Try to add this to some non-dimensional quantity >>> print(x + 1) YTUnitOperationError: The addition operator for YTArrays with units (kpc) and (1) is not well defined. The solution to this means using the YTQuantity and YTArray objects for all of one's computations, but this isn't always feasible. A quick fix for this is to just grab the unitless data out of a YTQuantity or YTArray object with the ``value`` and ``v`` attributes, which return a copy, or with the ``d`` attribute, which returns the data itself: .. code-block:: python x = ds.quan(1, 'kpc') x_val = x.v print(x_val) array(1.0) # Try to add this to some non-dimensional quantity print(x + 1) 2.0 For more information about this functionality with units, see :ref:`data_selection_and_fields`. Fields ------ .. _faq-handling-log-vs-linear-space: How do I modify whether or not yt takes the log of a particular field? ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ yt sets up defaults for many fields for whether or not a field is presented in log or linear space. To override this behavior, you can modify the ``field_info`` dictionary. For example, if you prefer that ``density`` not be logged, you could type: .. code-block:: python ds = load("my_data") ds.index ds.field_info['density'].take_log = False From that point forward, data products such as slices, projections, etc., would be presented in linear space. Note that you have to instantiate ds.index before you can access ds.field info. For more information see the documentation on :ref:`fields` and :ref:`creating-derived-fields`. .. _faq-new-field: I added a new field to my simulation data, can yt see it? ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Yes! yt identifies all the fields in the simulation's output file and will add them to its ``field_list`` even if they aren't listed in :ref:`field-list`. These can then be accessed in the usual manner. For example, if you have created a field for the potential called ``PotentialField``, you could type: .. code-block:: python ds = load("my_data") ad = ds.all_data() potential_field = ad["PotentialField"] The same applies to fields you might derive inside your yt script via :ref:`creating-derived-fields`. To check what fields are available, look at the properties ``field_list`` and ``derived_field_list``: .. code-block:: python print(ds.field_list) print(ds.derived_field_list) or for a more legible version, try: .. code-block:: python for field in ds.derived_field_list: print(field) .. _faq-add-field-diffs: What is the difference between ``yt.add_field()`` and ``ds.add_field()``? ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ The global ``yt.add_field()`` (:meth:`~yt.fields.field_info_container.FieldInfoContainer.add_field`) function is for adding a field for every subsequent dataset that is loaded in a particular python session, whereas ``ds.add_field()`` (:meth:`~yt.data_objects.static_output.Dataset.add_field`) will only add it to dataset ``ds``. Data Objects ------------ .. _ray-data-ordering: Why are the values in my Ray object out of order? ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Using the Ray objects (:class:`~yt.data_objects.selection_data_containers.YTOrthoRay` and :class:`~yt.data_objects.selection_data_containers.YTRay`) with AMR data gives non-contiguous cell information in the Ray's data array. The higher-resolution cells are appended to the end of the array. Unfortunately, due to how data is loaded by chunks for data containers, there is really no easy way to fix this internally. However, there is an easy workaround. One can sort the ``Ray`` array data by the ``t`` field, which is the value of the parametric variable that goes from 0 at the start of the ray to 1 at the end. That way the data will always be ordered correctly. As an example you can: .. code-block:: python my_ray = ds.ray(...) ray_sort = np.argsort(my_ray["t"]) density = my_ray["density"][ray_sort] There is also a full example in the :ref:`manual-line-plots` section of the docs. Developing ---------- .. _making-a-PR: Someone asked me to make a Pull Request (PR) to yt. How do I do that? ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ A pull request is the action by which you contribute code to yt. You make modifications in your local copy of the source code, then *request* that other yt developers review and accept your changes to the main code base. For a full description of the steps necessary to successfully contribute code and issue a pull request (or manage multiple versions of the source code) please see :ref:`sharing-changes`. .. _making-an-issue: Someone asked me to file an issue or a bug report for a bug I found. How? ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ See :ref:`reporting-a-bug` and :ref:`sharing-changes`. Miscellaneous ------------- .. _getting-sample-data: How can I get some sample data for yt? ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ Many different sample datasets can be found at http://yt-project.org/data/ . These can be downloaded, unarchived, and they will each create their own directory. It is generally straight forward to load these datasets, but if you have any questions about loading data from a code with which you are unfamiliar, please visit :ref:`loading-data`. To make things easier to load these sample datasets, you can add the parent directory to your downloaded sample data to your *yt path*. If you set the option ``test_data_dir``, in the section ``[yt]``, in ``~/.yt/config``, yt will search this path for them. This means you can download these datasets to ``/big_drive/data_for_yt`` , add the appropriate item to ``~/.yt/config``, and no matter which directory you are in when running yt, it will also check in *that* directory. .. _faq-scroll-up: I can't scroll-up to previous commands inside python ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ If the up-arrow key does not recall the most recent commands, there is probably an issue with the readline library. To ensure the yt python environment can use readline, run the following command: .. code-block:: bash $ ~/yt/bin/pip install gnureadline .. _faq-old-data: yt seems to be plotting from old data ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ yt does check the time stamp of the simulation so that if you overwrite your data outputs, the new set will be read in fresh by yt. However, if you have problems or the yt output seems to be in someway corrupted, try deleting the ``.yt`` and ``.harray`` files from inside your data directory. If this proves to be a persistent problem add the line: .. code-block:: python from yt.config import ytcfg; ytcfg["yt","serialize"] = "False" to the very top of your yt script. Turning off serialization is the default behavior in yt-3.0. .. _faq-log-level: How can I change yt's log level? ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ yt's default log level is ``INFO``. However, you may want less voluminous logging, especially if you are in an IPython notebook or running a long or parallel script. On the other hand, you may want it to output a lot more, since you can't figure out exactly what's going wrong, and you want to output some debugging information. The yt log level can be changed using the :ref:`configuration-file`, either by setting it in the ``$HOME/.yt/config`` file: .. code-block:: bash [yt] loglevel = 10 # This sets the log level to "DEBUG" which would produce debug (as well as info, warning, and error) messages, or at runtime: .. code-block:: python from yt.funcs import mylog mylog.setLevel(40) # This sets the log level to "ERROR" which in this case would suppress everything below error messages. For reference, the numerical values corresponding to different log levels are: .. csv-table:: :header: Level, Numeric Value :widths: 10, 10 ``CRITICAL``,50 ``ERROR``,40 ``WARNING``,30 ``INFO``,20 ``DEBUG``,10 ``NOTSET``,0 Can I always load custom data objects, fields, quantities, and colormaps with every dataset? ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ The :ref:`plugin-file` provides a means for always running custom code whenever yt is loaded up. This custom code can be new data objects, or fields, or colormaps, which will then be accessible in any future session without having modified the source code directly. See the description in :ref:`plugin-file` for more details. How do I cite yt? ^^^^^^^^^^^^^^^^^ If you use yt in a publication, we'd very much appreciate a citation! You should feel free to cite the `ApJS paper `_ with the following BibTeX entry: :: @ARTICLE{2011ApJS..192....9T, author = {{Turk}, M.~J. and {Smith}, B.~D. and {Oishi}, J.~S. and {Skory}, S. and {Skillman}, S.~W. and {Abel}, T. and {Norman}, M.~L.}, title = "{yt: A Multi-code Analysis Toolkit for Astrophysical Simulation Data}", journal = {The Astrophysical Journal Supplement Series}, archivePrefix = "arXiv", eprint = {1011.3514}, primaryClass = "astro-ph.IM", keywords = {cosmology: theory, methods: data analysis, methods: numerical }, year = 2011, month = jan, volume = 192, eid = {9}, pages = {9}, doi = {10.1088/0067-0049/192/1/9}, adsurl = {http://adsabs.harvard.edu/abs/2011ApJS..192....9T}, adsnote = {Provided by the SAO/NASA Astrophysics Data System} } yt-3.3.3/doc/source/help/0000755000076500000240000000000013023400160016053 5ustar goldbaumstaff00000000000000yt-3.3.3/doc/source/help/index.rst0000644000076500000240000002152713017603554017742 0ustar goldbaumstaff00000000000000.. _asking-for-help: What to do if you run into problems =================================== If you run into problems with yt, there are a number of steps to follow to come to a solution. The first handful of options are things you can do on your own, but if those don't yield results, we have provided a number of ways to connect with our community of users and developers to solve the problem together. To summarize, here are the steps in order: .. contents:: :depth: 1 :local: :backlinks: none .. _dont-panic: Don't panic and don't give up ----------------------------- This may seem silly, but it's effective. While yt is a robust code with lots of functionality, like all actively-developed codes sometimes there are bugs. Chances are good that your problems have a quick fix, either because someone encountered it before and fixed it, the documentation is out of date, or some other simple solution. Don't give up! We want to help you succeed! .. _update-the-code: Update to the latest version ---------------------------- Sometimes the pace of development is pretty fast on yt, particularly in the development branch, so a fix to your problem may have already been developed by the time you encounter it. Many users' problems can simply be corrected by updating to the latest version of the code and/or its dependencies. You can update yt's source by running: .. code-block:: bash $ yt update or you could update yt's source as well as any software dependencies by running: .. code-block:: bash $ yt update --all .. _update-errors: Update errors ^^^^^^^^^^^^^ If for some reason the ``update`` command fails with errors, or any attempt at loading yt either from the command line or from within python also fails, it may simply mean you need to rebuild the yt source (some of the c-code in yt needs to be rebuilt after major changes). You can do this by navigating to the root of the yt mercurial repository. If you installed with the all-in-one installer script, this is the ``yt-/src/yt-hg`` directory. Then execute these commands: .. code-block:: bash $ python setup.py develop Now try running yt again with: .. code-block:: bash $ yt --help If you continue to see errors, you should try contacting us via IRC or email but you may have to reinstall yt (see :ref:`getting-and-installing-yt`). .. _search-the-documentation: Search the documentation, FAQ, and mailing lists ------------------------------------------------ The documentation has a lot of the answers to everyday problems. This doesn't mean you have to read all of the docs top-to-bottom, but you should at least run a search to see if relevant topics have been answered in the docs. Click on the search field to the right of this window and enter your text. Another good place to look for answers in the documentation is our :ref:`faq` page. OK, so there was no obvious solution to your problem in the documentation. It is possible that someone else experienced the problem before you did, and wrote to the mailing list about it. You can easily check the mailing list archive with the other search field to the right of this window (or you can use the search field below). .. raw:: html

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