From 4176620926e4a3ce06fc78ce2093531b01020f60 Mon Sep 17 00:00:00 2001 From: Daniel Ching Date: Fri, 9 Aug 2019 18:13:52 -0500 Subject: [PATCH 01/15] DOC: Remove abandonded releases page from docs --- doc/source/index.rst | 1 - doc/source/release/notes-1.0.0.rst | 86 ------------------------------ doc/source/releases.rst | 8 --- 3 files changed, 95 deletions(-) delete mode 100644 doc/source/release/notes-1.0.0.rst delete mode 100644 doc/source/releases.rst diff --git a/doc/source/index.rst b/doc/source/index.rst index a49e0c143..fa816b534 100644 --- a/doc/source/index.rst +++ b/doc/source/index.rst @@ -23,7 +23,6 @@ Indices and tables data devguide gpu - releases api demo faq diff --git a/doc/source/release/notes-1.0.0.rst b/doc/source/release/notes-1.0.0.rst deleted file mode 100644 index 52b1c326b..000000000 --- a/doc/source/release/notes-1.0.0.rst +++ /dev/null @@ -1,86 +0,0 @@ -TomoPy 1.0.0 Release Notes -========================== - -.. contents:: - :local: - - -New features ------------- - -* `FFTW `_ implementation is now adopted. All functions that rely on FFTs such as gridrec, phase retrieval, stripe removal, etc. are now using the FFTW implementation through `PyFFTW `_. - -* ``sinogram_order`` is added to ``recon`` as an additional argument. It determines whether data is a stack of sinograms (True, y-axis first axis) or a stack of radiographs (False). Default is False, but we plan to make it True in the upcoming release. - -* Reconstruction algorithms only copies data if necessary. - -* Updated library to support new mproc and recon functions. The data is now passed in sinogram order to recon functions. Also updated tests. - -* ``ncores`` and ``nchunks`` are now independent. - -* Setting ``nchunks`` to zero removes the dimension. That allows for the functions work on 2D data rather than 3D data. - -* Sliced data are used so that each process only receives the data it needs. No more ``istart`` and ``iend`` variables for setting up indices in parallel processes. - -* Functions will reuse sharedmem arrays if they can. - - -New functions -------------- - -* `minus_log `_ -* `trim_sinogram `_ - - -New packages in Conda channel ------------------------------ - -* `dxchange 0.1.1 `_ -* `fftw 3.3.4 `_ -* `pyfftw 0.9.2 `_ -* `pywavelets 0.4.0 `_ -* `xraylib 3.1.0 `_ - - -Deprecated features -------------------- - -* All data I/O related functions are deprecated. They are available through `DXchange `_ package. - -* Removed fft.h and fft.c, they are now completely replaced with FFTW. - - -Backward incompatible changes ------------------------------ - -* ``emission`` argument is removed from ``recon``. After this change the tomographic image reconstruction algorithms always assume data to be normalized. - - -Contributors ------------- - -* Arthur Glowacki (`@aglowacki`_) -* Daniel Pelt (`@dmpelt`_) -* Dake Feng (`@dakefeng`_) -* Doga Gursoy (`@dgursoy`_) -* Francesco De Carlo (`@decarlof`_) -* Lin Jiao (`@yxqd`_) -* Luis Barroso-Luque (`@lbluque`_) -* Michael Sutherland (`@michael-sutherland`_) -* Rafael Vescovi (`@ravescovi`_) -* Thomas Caswell (`@tacaswell`_) -* Pete R. Jemian (`@prjemian`_) -* Wei Xu (`@celiafish`_) - -.. _`@aglowacki`: https://github.com/aglowacki -.. _`@dmpelt`: https://github.com/dmpelt -.. _`@dakefeng`: https://github.com/dakefeng -.. _`@dgursoy`: https://github.com/dgursoy -.. _`@decarlof`: https://github.com/decarlof -.. _`@lbluque`: https://github.com/lbluque -.. _`@yxqd`: https://github.com/yxqd -.. _`@michael-sutherland`: https://github.com/michael-sutherland -.. _`@ravescovi`: https://github.com/ravescovi -.. _`@tacaswell`: https://github.com/tacaswell -.. _`@prjemian`: https://github.com/prjemian -.. _`@celiafish`: https://github.com/celiafish diff --git a/doc/source/releases.rst b/doc/source/releases.rst deleted file mode 100644 index 1c6dd8025..000000000 --- a/doc/source/releases.rst +++ /dev/null @@ -1,8 +0,0 @@ -============= -Release Notes -============= - -.. toctree:: - :maxdepth: 1 - - release/notes-1.0.0 \ No newline at end of file From 4264735e9744edd5c54e1bfbf38d8174c34cf255 Mon Sep 17 00:00:00 2001 From: Daniel Ching Date: Fri, 9 Aug 2019 18:40:42 -0500 Subject: [PATCH 02/15] DOC: Reorder navigation bar for better reading priority --- doc/source/{demo.rst => examples.rst} | 0 doc/source/index.rst | 12 ++++++------ doc/source/{credits.rst => zcredits.rst} | 0 3 files changed, 6 insertions(+), 6 deletions(-) rename doc/source/{demo.rst => examples.rst} (100%) rename doc/source/{credits.rst => zcredits.rst} (100%) diff --git a/doc/source/demo.rst b/doc/source/examples.rst similarity index 100% rename from doc/source/demo.rst rename to doc/source/examples.rst diff --git a/doc/source/index.rst b/doc/source/index.rst index fa816b534..d2c222616 100644 --- a/doc/source/index.rst +++ b/doc/source/index.rst @@ -16,14 +16,14 @@ Indices and tables .. toctree:: :hidden: - :maxdepth: 1 + :maxdepth: 4 about + faq + api install + examples data - devguide gpu - api - demo - faq - credits + devguide + zcredits diff --git a/doc/source/credits.rst b/doc/source/zcredits.rst similarity index 100% rename from doc/source/credits.rst rename to doc/source/zcredits.rst From 978c308b8b1fcb19499a1a094acc1c38e2b8d5ad Mon Sep 17 00:00:00 2001 From: Daniel Ching Date: Fri, 9 Aug 2019 18:55:20 -0500 Subject: [PATCH 03/15] DOC: Update FAQ with recent information and reorder questions --- doc/source/faq.rst | 157 +++++++++++++++++++++------------------------ 1 file changed, 73 insertions(+), 84 deletions(-) diff --git a/doc/source/faq.rst b/doc/source/faq.rst index bc4f9ce27..48f00360c 100644 --- a/doc/source/faq.rst +++ b/doc/source/faq.rst @@ -2,121 +2,74 @@ Frequently asked questions ========================== -Here's a list of questions. - -.. contents:: Questions +.. contents:: :local: - + How can I report bugs? ====================== The easiest way to report bugs or get help is to open an issue on GitHub. -Simply go to the `project GitHub page `_, -click on `Issues `_ in the +Simply go to the `project GitHub page `_, +click on `Issues `_ in the right menu tab and submit your report or question. Are there any video tutorials? ============================== -We currently do not have specific plans in this direction, but we agree +We currently do not have specific plans in this direction, but we agree that it would be very helpful. -Can I run this on a HPC cluster? -================================ - -In their default installation packages, TomoPy and the ASTRA toolbox are -limited to running on a multicore single machine. The ASTRA toolbox, and -TomoPy through the presented ASTRA integration, are able to use multiple -GPUs that are installed in a single machine. Both toolboxes can be run on -a HPC cluster through parallelization using MPI, but since installation -and running on a HPC cluster is often cluster­ specific, the default -installation packages do not include these capabilities. - -As such, the integrated packages that is presented in the manuscript -currently does not support running on a HPC cluster. Note that the ASTRA -toolbox provides a separate MPI­ enabled package for use on a HPC cluster. -We refer to :cite:`Bicer:15` for more details about TomoPy's planned HPC -implementation. It is a MapReduce type MPI implementation layer, which -was succesfully used on many clusters, i.e. Stampede, Cori, Mira. -There are plans to allow user access to TomoPy on a HPC cluster -(e.g. through a client or web­portal), but these projects will -take some time before they are being matured for user’s use. - - Are there any segmentation routines? ==================================== -Some data processing operations can be applied after reconstruction. -Examples of these type of operations are image­ based ring­ removal methods, -and gaussian­ filtering or median­ filtering the reconstructed image. Typicaly, -these methods are called "post­processing algorithms, since they occur after -the reconstruction. - -The package does not include segmentation algorithms, since we are currently -focused on tomography, while we feel that segmentation are more part of the -application­ specific data analysis that occurs after tomographic processing. -An important exception is when segmentation steps are used as part of the +No. The package does not include segmentation algorithms, since we are currently +focused on tomography, while we feel that segmentation are more part of the +application­ specific data analysis that occurs after tomographic processing. An +important exception is when segmentation steps are used as part of the tomographic reconstruction algorithm, such as in the DART algorithm. Are there any tools for aligning projections? ============================================= -Yes we have. Please check the -`Examples `_ -section for details. +Yes. Please check the `alignment +`_ +module for details. -What is ASTRA toolbox? -====================== +What is the ASTRA toolbox? +========================== -The ASTRA toolbox provides highly efficient tomographic reconstruction -methods by implementing them on graphic processing units (GPUs). It -includes advanced iterative methods and allows for very flexible scanning -geometries. The ASTRA toolbox also includes building blocks which can be -used to develop new reconstruction methods, allowing for easy and efficient -implementation and modification of advanced reconstruction methods. -However, the toolbox is only focused on reconstruction, and does not -include pre-processing or post-processing methods that are typically -required for correctly processing synchrotron data. Furthermore, no -routines to read data from disk are provided by the toolbox. +The ASTRA toolbox provides highly efficient tomographic reconstruction methods +by implementing them on graphic processing units (GPUs). It includes advanced +iterative methods and allows for very flexible scanning geometries. The ASTRA +toolbox also includes building blocks which can be used to develop new +reconstruction methods, allowing for easy and efficient implementation and +modification of advanced reconstruction methods. However, the toolbox is only +focused on reconstruction, and does not include pre-processing or +post-processing methods that are typically required for correctly processing +synchrotron data. Furthermore, no routines to read data from disk are provided +by the toolbox. -Why TomoPy and ASTRA were integrated? +Why were TomoPy and ASTRA integrated? ===================================== -The TomoPy toolbox is specifically designed to be easy to use and deploy -at a synchrotron facility beamline. It supports reading many common -synchrotron data formats from disk :cite:`DeCarlo:14a`, and includes -several other processing algorithms commonly used for synchrotron data. -TomoPy also includes several reconstruction algorithms, which can be run -on multi-core workstations and large-scale computing facilities. The -algorithms in TomoPy are all CPU-based, however, which can make them -prohibitively slow in the case of iterative methods, which are often -required for advanced tomographic experiments. - -By integrating the ASTRA toolbox in the TomoPy framework, the optimized -GPU-based reconstruction methods become easily available for synchrotron -beamline users, and users of the ASTRA toolbox can more easily read data -and use TomoPy’s other functionality for data filtering and cleaning. - - -Which platforms are supported? -============================== - -TomoPy supports Linux and Mac OS X, and the ASTRA toolbox supports Linux -and Windows. As such, the combined package currently supports only Linux, -but we are working on supporting more operating systems. +The TomoPy toolbox is specifically designed to be easy to use and deploy at a +synchrotron facility beamline. It supports reading many common synchrotron data +formats from disk through the companion project, DXChange :cite:`DeCarlo:14a`, +and includes several other processing algorithms commonly used for synchrotron +data. The algorithms in TomoPy are all CPU-based, however, which can make them +prohibitively slow in the case of iterative methods, which are often required +for advanced tomographic experiments. - -Does ASTRA support all GPUs? -============================ - -The GPU algorithms are all implemented used nVidia CUDA. As a result, -only nVidia CUDA­ enabled video cards can be used to run them. +By integrating the ASTRA toolbox in the TomoPy framework, the optimized +GPU-based reconstruction methods become easily available for synchrotron +beamline users, and users of the ASTRA toolbox can more easily read data and use +TomoPy’s other functionality for data filtering and cleaning. What is UFO? @@ -125,9 +78,45 @@ What is UFO? UFO is a general purpose image processing framework, optimized for heterogeneous compute systems and streams of data. Arbitrary data processing tasks are plugged together to form larger processing pipelines. These pipelines are then mapped to -the hardware resources available at run-time, i.e. both multiple GPUs and CPUs. +the hardware resources available at run-time, i.e. all available multiple GPUs +and CPUs. One specific use case that has been integrated into the TomoPy is fast reconstruction using the filtered backprojection and direct Fourier inversion methods although others for pre- and post-processing might be added in the future. + + +Which platforms are supported? +============================== + +TomoPy supports Windows, Linux, and macOS. The ASTRA toolbox supports Linux +and Windows. Project UFO supports Linux and macOS. + + +Do TomoPy, ASTRA, and UFO support all GPUs? +=========================================== + +The GPU algorithms in TomoPy and the ASTRA toolbox are all implemented using +nVidia's CUDA. As a result, only nVidia CUDA­ enabled GPUs can be used to run +them. UFO uses OpenCL, so it supports both AMD and nVidia OpenCL compatible +GPUs. + + +Can I run this on a HPC cluster? +================================ + +Maybe. In their default installation packages, TomoPy and the ASTRA toolbox are +limited to running on a single multi-core and multi-GPU machine. Both toolboxes +can be run on a HPC cluster through parallelization using MPI, but since +installation and running on a HPC cluster is often cluster­ specific, the +default installation packages do not include these capabilities. + +As such, the integrated packages that is presented in the manuscript currently +does not support running on a HPC cluster. Note that the ASTRA toolbox provides +a separate MPI­ enabled package for use on a HPC cluster. We refer to +:cite:`Bicer:15` for more details about TomoPy's planned HPC implementation. It +is a MapReduce type MPI implementation layer, which was successfully used on +many clusters, i.e. Stampede, Cori, Mira. There are plans to allow user access +to TomoPy on a HPC cluster (e.g. through a client or web­portal), but these +projects will take some time before they are being matured for user’s use. From e21c7663beccd7f103a7db2e5ab908922e3569e2 Mon Sep 17 00:00:00 2001 From: Daniel Ching Date: Fri, 9 Aug 2019 19:00:54 -0500 Subject: [PATCH 04/15] DOC: Update layman install directions --- doc/source/install.rst | 27 ++++++++------------------- 1 file changed, 8 insertions(+), 19 deletions(-) diff --git a/doc/source/install.rst b/doc/source/install.rst index dec3be4df..a1c11ea5c 100644 --- a/doc/source/install.rst +++ b/doc/source/install.rst @@ -5,39 +5,28 @@ Install directions This section covers the basics of how to download and install TomoPy. -Supported Environments ----------------------- -TomoPy is tested, built, and distributed for python 2.7 3.6 3.7 on Linux/macOS -and python 3.6 3.7 on Windows 10. - - -OPTION 1: Installing from Conda (Recommended) ---------------------------------------------- +Installing from Conda +--------------------- If you only want to run TomoPy, not develop it, then you should install through a package manager. Conda, our supported package manager, can install TomoPy and its dependencies for you. -First, you must have `Conda `_ installed, -then open a terminal or a command prompt window and run:: +First, you must have `Conda `_ installed, then +open a terminal or a command prompt window and run:: $ conda install -c conda-forge tomopy This will install TomoPy and all the dependencies from the conda-forge channel. + Updating the installation -========================= +------------------------- TomoPy is an active project, so we suggest you update your installation frequently. To update the installation run:: $ conda update -c conda-forge tomopy -For some more information about using Conda, please refer to the -`docs `__. - - -OPTION 2: Installing from source -------------------------------------------- - -Please read the devguide for instructions on how to install TomoPy from source code. +For some more information about using Conda, please refer to the `docs +`__. From 1f473de14f0a26dd227047aa6403b3c3459f7034 Mon Sep 17 00:00:00 2001 From: Daniel Ching Date: Fri, 9 Aug 2019 19:17:17 -0500 Subject: [PATCH 05/15] DOC: Update text of development guide --- doc/source/devguide.rst | 120 +++++++++++++++++++++------------------- 1 file changed, 62 insertions(+), 58 deletions(-) diff --git a/doc/source/devguide.rst b/doc/source/devguide.rst index 86b32d6b3..905820b31 100644 --- a/doc/source/devguide.rst +++ b/doc/source/devguide.rst @@ -1,6 +1,6 @@ -=========== -Development -=========== +================= +Development Guide +================= This section explains the basics for developers who wish to contribute to the TomoPy project. @@ -13,17 +13,17 @@ Cloning the repository ====================== The project is maintained on GitHub, which is a version control and a -collaboration platform for software developers. To start first register -on `GitHub `_ and fork the TomoPy repository by -clicking the **Fork** button in the header of the -`TomoPy repository `__: +collaboration platform for software developers. First register on `GitHub +`_ and fork (make your own copy of) the TomoPy repository by +clicking the **Fork** button in the header of the `TomoPy repository +`__: .. image:: img/fork-repo.png -This creates a copy of the project in your personal -GitHub space. The next thing you want to do is to clone it to your -local machine. You can do this by clicking the **Clone in Desktop** -button in the bottom of the right hand side bar: +This creates a remote copy of the project in your personal GitHub space. Next, +clone a copy of your fork of the project to your local machine. You can do this +by clicking the **Clone in Desktop** button in the bottom of the right hand side +bar: .. image:: img/clone-in-desktop.png @@ -39,10 +39,11 @@ Installing dependencies To build and run TomoPy, you will need to install at least the dependencies listed in ``envs/{platform}-{version}.yml`` plus additional dependencies based on your build environment. For example, installing requirements for building -the Python 3.6 version on Unix can be accomplished as follows:: +the Python 3.6 version on Linux can be accomplished as follows:: $ conda env create -f envs/linux-36.yml + Building TomoPy =============== @@ -54,27 +55,28 @@ running the install script in the typical Python way:: Running the Tests ================= + TomoPy has a suite of Python unit tests that live in the ``/test`` directory, where they follow the same tree structure as the packages under ``/tomopy``. -These are automatically run by TravisCI when you make a pull request -(See below for how to do that). You can run them manually using pytest, -or whichever python test runner you prefer. To make it easier to run tests -on the changes you make to the code, it is recommended that you install TomoPy -in development mode. (`python setup.py develop`) +These are automatically run by TravisCI when you make a pull request (See below +for how to do that). You can run them manually using pytest, or whichever python +test runner you prefer. To make it easier to run tests on the changes you make +to the code, it is recommended that you install TomoPy in development mode. +(`python setup.py develop`) The `pytest test runner `__, is available through pip or anaconda. -To run the tests open a terminal, navigate to your project folder, -then run ``py.test``. +To run the tests open a terminal, navigate to your project folder, then run +``py.test``. + +To run sections of tests, pass py.test a directory or filepath, as in ``py.test +test/test_recon`` or ``py.test test/test_recon/test_rotation.py``. -To run sections of tests, pass py.test a -directory or filepath, as in ``py.test test/test_recon`` or -``py.test test/test_recon/test_rotation.py``. +When writing tests, at minimum we try to check all function returns with +synthetic data, together with some dimension, type, etc. Writing tests is highly +encouraged! -When writing tests, at minimum we try to check all function -returns with synthetic data, together with some dimension, type, etc. -Writing tests is highly encouraged! Coding conventions ================== @@ -107,58 +109,60 @@ parameters:: """ return a+b + Package versioning ================== -We follow the X.Y.Z (Major.Minor.Patch) semantic for package versioning. -The version should be updated before each pull request accordingly. The -patch number is incremented for minor changes and bug fixes which do not -change the software's API. The minor version is incremented for releases -which add new, but backward-compatible, API features, and the major version -is incremented for API changes which are not backward-compatible. For -example, software which relies on version 2.1.5 of an API is compatible -with version 2.2.3, but not necessarily with 3.2.4. +We follow the X.Y.Z (Major.Minor.Patch) semantic for package versioning. The +patch number is incremented for minor changes and bug fixes which do not change +the software's API. The minor version is incremented for releases which add new, +but backward-compatible, API features, and the major version is incremented for +API changes which are not backward-compatible. For example, software which +relies on version 2.1.5 of an API is compatible with version 2.2.3, but not +necessarily with 3.2.4. + +We manage our versions using git tags, and the `setuptools_scm` package. + Committing changes ================== -After making some changes in the code, you may want to take a -*snapshot* of the edits you made. That's when you make a *commit*. -To do this, launch the GitHub desktop application and it should -provide you all the changes in your code since your last commit. -Write a brief *Summary* and *Description* about the changes you +After making some changes in the code, take a *snapshot* of the edits you made. +That's when you make a *commit*. To do this, launch the GitHub desktop +application and it should provide you all the changes in your code since your +last commit. Write a brief *Summary* and *Description* about the changes you made and click the **Commit** button: .. image:: img/commit-screen.png -You can continue to make changes, add modules, write your own functions, -and take more *Commit snapshots* of your code writing process. +Remember that you are writing these messages to help other people understand the +changes that you have made to the code. -Contributing back -================= -Once you feel that the functionality you added would benefit the community, -then you should consider contributing back to the TomoPy project. You will -need to push your local commits to GitHub, then go to your online GitHub -repository of the project and click on the *green* button to compare, review, -and create a pull request. +Publishing your changes +======================= + +Once you feel that the functionality you added would benefit the community, then +you should consider contributing back to the TomoPy project. You will need to +push your local commits to GitHub, then go to your online GitHub repository of +the project and click on the *green* button to compare, review, and create a +pull request. .. image:: img/create-revision.png -After clicking on this button, you are presented with a review page -where you can get a high-level overview of what exactly has changed -between your forked branch and the original TomoPy repository. -When you're ready to submit your pull request, click -**Create pull request**: +After clicking on this button, you are presented with a review page where you +can get a high-level overview of what exactly has changed between your forked +branch and the original TomoPy repository. When you're ready to submit your pull +request, click **Create pull request**: .. image:: img/create-pr.png -Clicking on **Create pull request** sends you to a discussion page, -where you can enter a title and optional description. It’s important to -provide as much useful information and a rationale for why you’re making -this Pull Request in the first place. +Clicking on **Create pull request** sends you to a discussion page, where you +can enter a title and helpful description. It’s important to provide as much +useful information and a rationale for why you’re making this Pull Request in +the first place. -When you’re ready typing out your heartfelt argument, click on **Send -pull request**. You’re done! +When you’re ready typing out your heartfelt argument, click on **Send pull +request**. You’re done! .. This text is partially adopted from GitHub guides and Wikipedia. From e4185c02a6c6fa81d0a13f3ba6934a100de1293e Mon Sep 17 00:00:00 2001 From: Daniel Ching Date: Fri, 9 Aug 2019 19:48:52 -0500 Subject: [PATCH 06/15] DOC: Move data reading page into example section --- doc/source/examples.rst | 10 +++++----- doc/source/index.rst | 1 - doc/source/{ => ipynb}/data.rst | 16 ++++++++-------- 3 files changed, 13 insertions(+), 14 deletions(-) rename doc/source/{ => ipynb}/data.rst (52%) diff --git a/doc/source/examples.rst b/doc/source/examples.rst index 0cafce5a1..589b8cd48 100644 --- a/doc/source/examples.rst +++ b/doc/source/examples.rst @@ -2,9 +2,9 @@ Examples ======== -This section contains `Jupyter Notebooks `_ and -Python scripts examples for various tomoPy functions. - +This section contains examples for using various TomoPy functions and plugins +from other packages. + To `run `_ these examples in a `notebooks `_ install `Jupyter `_ or run the python @@ -14,6 +14,7 @@ scripts from `here `_ .. toctree:: :titlesonly: + ipynb/data.rst ipynb/tomopy.rst ipynb/astra.rst ipynb/ufo.rst @@ -24,5 +25,4 @@ scripts from `here `_ .. automodule:: tomopy :members: :undoc-members: - :show-inheritance: - + :show-inheritance: diff --git a/doc/source/index.rst b/doc/source/index.rst index d2c222616..5ca9834e3 100644 --- a/doc/source/index.rst +++ b/doc/source/index.rst @@ -23,7 +23,6 @@ Indices and tables api install examples - data gpu devguide zcredits diff --git a/doc/source/data.rst b/doc/source/ipynb/data.rst similarity index 52% rename from doc/source/data.rst rename to doc/source/ipynb/data.rst index 0d77f0fd9..c44fd6f58 100644 --- a/doc/source/data.rst +++ b/doc/source/ipynb/data.rst @@ -2,16 +2,16 @@ How to read tomographic data ============================ -For reading tomography files formatted in different ways, please -go check the `DXchange `_ package. There -are various examples and demonstration scripts about how to use the -package for loading your datasets. +For reading tomography files formatted in different ways, please see the +`DXchange `_ package where there are various +examples and demonstration scripts about how to use the package for loading your +datasets. -The package can be installed by simply running the following -in a terminal:: +The package can be installed alongside TomoPy with Conda by simply running the +following in a terminal:: conda install -c conda-forge dxchange -For a repository of experimental and simulated data sets please check -`TomoBank `_ :cite:`DeCarlo:17`. \ No newline at end of file +For a repository of experimental and simulated data sets please check +`TomoBank `_ :cite:`DeCarlo:17`. From 9b6c757fbfea09535df487748a79b1d269f2e7bb Mon Sep 17 00:00:00 2001 From: Daniel Ching Date: Tue, 13 Aug 2019 14:45:32 -0700 Subject: [PATCH 07/15] DOC: Remove redundant text from examples These examples are no longer interactive jupyter notebooks, so there is no reason for all of the examples to have the same introduction section explaining how to load data using DXchange. It's sufficient to do data loading and import once in the TomoPy only example. --- doc/source/examples.rst | 5 +- doc/source/ipynb/astra.rst | 225 +++++++++--------------------------- doc/source/ipynb/data.rst | 2 +- doc/source/ipynb/lprec.rst | 209 ++++++++++++--------------------- doc/source/ipynb/tomopy.rst | 59 ++++------ doc/source/ipynb/ufo.rst | 121 +++---------------- 6 files changed, 171 insertions(+), 450 deletions(-) diff --git a/doc/source/examples.rst b/doc/source/examples.rst index 589b8cd48..3042e0f56 100644 --- a/doc/source/examples.rst +++ b/doc/source/examples.rst @@ -6,20 +6,21 @@ This section contains examples for using various TomoPy functions and plugins from other packages. To `run `_ these examples in a -`notebooks `_ install +`notebook `_ install `Jupyter `_ or run the python scripts from `here `_ .. toctree:: :titlesonly: + :hidden: ipynb/data.rst ipynb/tomopy.rst ipynb/astra.rst + ipynb/lprec.rst ipynb/ufo.rst ipynb/vector.rst - ipynb/lprec.rst .. automodule:: tomopy diff --git a/doc/source/ipynb/astra.rst b/doc/source/ipynb/astra.rst index 17a87d61a..b9b87282f 100644 --- a/doc/source/ipynb/astra.rst +++ b/doc/source/ipynb/astra.rst @@ -1,206 +1,85 @@ -Using the ASTRA toolbox through TomoPy +TomoPy with the ASTRA toolbox -------------------------------------- -Here is an example on how to use the `ASTRA -toolbox `__ through its integration with -`TomoPy `__, as published in :cite:`Pelt:16a`. +Here is an example on how to use the `ASTRA toolbox +`__ through its integration with +`TomoPy `__, as published in +:cite:`Pelt:16a`. -.. code:: python - - %pylab inline - -Install the `ASTRA -toolbox `__ and -`TomoPy `__ then: - -.. code:: python - - import tomopy - -`DXchange `__ is installed with tomopy -to provide support for tomographic data loading. Various data format -from all major -`synchrotron `__ -facilities are supported. - -.. code:: python - - import dxchange - -matplotlib provide plotting of the result in this notebook. -`Paraview `__ or other tools are available for -more sophisticated 3D rendering. - -.. code:: python - - import matplotlib.pyplot as plt - -Set the path to the micro-CT data to reconstruct. - -.. code:: python - - fname = '../../tomopy/data/tooth.h5' - -Select the sinogram range to reconstruct. - -.. code:: python - - start = 0 - end = 2 - -This data set file format follows the `APS `__ -beamline `2-BM and 32-ID `__ -definition. Other file format readers are available at -`DXchange `__. - -.. code:: python - - proj, flat, dark, theta = dxchange.read_aps_32id(fname, sino=(start, end)) - -Plot the sinogram: - -.. code:: python - - plt.imshow(proj[:, 0, :], cmap='Greys_r') - plt.show() - - - -.. image:: astra_files/output_15_0.png - - -If the angular information is not avaialable from the raw data you need -to set the data collection angles. In this case theta is set as equally -spaced between 0-180 degrees. - -.. code:: python - - if (theta is None): - theta = tomopy.angles(proj.shape[0]) - else: - pass - -Perform the flat-field correction of raw data: - -.. math:: \frac{proj - dark} {flat - dark} - -.. code:: python - - proj = tomopy.normalize(proj, flat, dark) - -Tomopy provides various methods to `find rotation -center `__. - -.. code:: python - - rot_center = tomopy.find_center(proj, theta, init=290, ind=0, tol=0.5) - -Calculate - -.. math:: -log(proj) - -.. code:: python - - proj = tomopy.minus_log(proj) - -Reconstruction with TomoPy -^^^^^^^^^^^^^^^^^^^^^^^^^^ - -Reconstruction can be performed using either TomoPy's algorithms, or the -algorithms of the ASTRA toolbox. - -To compare, we first show how to reconstruct an image using TomoPy's -Gridrec algorithm: - -.. code:: python - - recon = tomopy.recon(proj, theta, center=rot_center, algorithm='gridrec') - -Mask each reconstructed slice with a circle. - -.. code:: python - - recon = tomopy.circ_mask(recon, axis=0, ratio=0.95) - -.. code:: python - - plt.imshow(recon[0, :,:], cmap='Greys_r') - plt.show() - - - -.. image:: astra_files/output_28_0.png - - -Reconstruction with the ASTRA toolbox -^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ - -To reconstruct the image with the ASTRA toolbox instead of TomoPy, -change the ``algorithm`` keyword to ``tomopy.astra``, and specify the -projection kernel to use (``proj_type``) and which ASTRA algorithm to -reconstruct with (``method``) in the ``options`` keyword. - -More information about the projection kernels and algorithms that are -supported by the ASTRA toolbox can be found in the documentation: -`projection kernels `__ -and `algorithms `__. -Note that only the 2D (i.e. slice-based) algorithms are supported when -reconstructing through TomoPy. +To reconstruct the image with the ASTRA toolbox instead of TomoPy, change the +``algorithm`` keyword to ``tomopy.astra``. Specify which projection kernel to +use (``proj_type``) and which ASTRA algorithm to reconstruct with (``method``) +in the ``options`` keyword. For example, to use a line-based CPU kernel and the FBP method, use: .. code:: python - options = {'proj_type':'linear', 'method':'FBP'} - recon = tomopy.recon(proj, theta, center=rot_center, algorithm=tomopy.astra, options=options) + options = {'proj_type': 'linear', 'method': 'FBP'} + recon = tomopy.recon(proj, + theta, + center=rot_center, + algorithm=tomopy.astra, + options=options) recon = tomopy.circ_mask(recon, axis=0, ratio=0.95) - plt.imshow(recon[0, :,:], cmap='Greys_r') + plt.imshow(recon[0, :, :], cmap='Greys_r') plt.show() - - .. image:: astra_files/output_30_0.png - -If you have a CUDA-capable NVIDIA GPU, reconstruction times can be -greatly reduced by using GPU-based algorithms of the ASTRA toolbox, -especially for iterative reconstruction methods. +If you have a CUDA-capable NVIDIA GPU, reconstruction times can be greatly +reduced by using GPU-based algorithms of the ASTRA toolbox, especially for +iterative reconstruction methods. To use the GPU, change the ``proj_type`` option to ``'cuda'``, and use CUDA-specific algorithms (e.g. ``'FBP_CUDA'`` for FBP): .. code:: python - options = {'proj_type':'cuda', 'method':'FBP_CUDA'} - recon = tomopy.recon(proj, theta, center=rot_center, algorithm=tomopy.astra, options=options) + options = {'proj_type': 'cuda', 'method': 'FBP_CUDA'} + recon = tomopy.recon(proj, + theta, + center=rot_center, + algorithm=tomopy.astra, + options=options) recon = tomopy.circ_mask(recon, axis=0, ratio=0.95) - plt.imshow(recon[0, :,:], cmap='Greys_r') + plt.imshow(recon[0, :, :], cmap='Greys_r') plt.show() - - .. image:: astra_files/output_32_0.png - -Many algorithms of the ASTRA toolbox support additional options, which -can be found in the -`documentation `__. -These options can be specified using the ``extra_options`` keyword. +Many algorithms of the ASTRA toolbox support additional options, which can be +found in the `documentation +`__. These options can be +specified using the ``extra_options`` keyword. For example, to use the GPU-based iterative SIRT method with a -nonnegativity constraint, use: - -.. code:: python - - extra_options ={'MinConstraint':0} - options = {'proj_type':'cuda', 'method':'SIRT_CUDA', 'num_iter':200, 'extra_options':extra_options} - recon = tomopy.recon(proj, theta, center=rot_center, algorithm=tomopy.astra, options=options) +non-negativity constraint, use: + +.. code:: python + + extra_options = {'MinConstraint': 0} + options = { + 'proj_type': 'cuda', + 'method': 'SIRT_CUDA', + 'num_iter': 200, + 'extra_options': extra_options + } + recon = tomopy.recon(proj, + theta, + center=rot_center, + algorithm=tomopy.astra, + options=options) recon = tomopy.circ_mask(recon, axis=0, ratio=0.95) - plt.imshow(recon[0, :,:], cmap='Greys_r') + plt.imshow(recon[0, :, :], cmap='Greys_r') plt.show() - .. image:: astra_files/output_34_0.png +More information about the projection kernels and algorithms that are supported +by the ASTRA toolbox can be found in the documentation: `projection kernels +`__ and `algorithms +`__. Note that only the 2D +(i.e. slice-based) algorithms are supported when reconstructing through TomoPy. diff --git a/doc/source/ipynb/data.rst b/doc/source/ipynb/data.rst index c44fd6f58..0e7517714 100644 --- a/doc/source/ipynb/data.rst +++ b/doc/source/ipynb/data.rst @@ -1,5 +1,5 @@ ============================ -How to read tomographic data +Loading tomographic data ============================ For reading tomography files formatted in different ways, please see the diff --git a/doc/source/ipynb/lprec.rst b/doc/source/ipynb/lprec.rst index b8fb98348..0239bec21 100644 --- a/doc/source/ipynb/lprec.rst +++ b/doc/source/ipynb/lprec.rst @@ -1,218 +1,163 @@ -LPrec ------ - -Here is an example on how to use the log-polar based method -(https://github.com/math-vrn/lprec) for reconstruction in Tomopy - -.. code:: ipython3 - - %pylab inline - - - -Install lprec from github, then - -.. code:: ipython3 - - import tomopy - -`DXchange `__ is installed with tomopy -to provide support for tomographic data loading. Various data format -from all major -`synchrotron `__ -facilities are supported. - -.. code:: ipython3 - - import dxchange - -matplotlib provide plotting of the result in this notebook. -`Paraview `__ or other tools are available for -more sophisticated 3D rendering. - -.. code:: ipython3 - - import matplotlib.pyplot as plt - -Set the path to the micro-CT data to reconstruct. - -.. code:: ipython3 - - fname = '../../tomopy/data/tooth.h5' - -Select the sinogram range to reconstruct. - -.. code:: ipython3 - - start = 0 - end = 2 - -This data set file format follows the `APS `__ -beamline `2-BM and 32-ID `__ -definition. Other file format readers are available at -`DXchange `__. - -.. code:: ipython3 - - proj, flat, dark, theta = dxchange.read_aps_32id(fname, sino=(start, end)) - -Plot the sinogram: - -.. code:: ipython3 - - plt.imshow(proj[:, 0, :], cmap='Greys_r') - plt.show() +TomoPy with LPrec +----------------- +Here is an example on how to use the `log-polar based method +`__ for reconstruction with TomoPy. +To reconstruct the image with the LPrec instead of TomoPy, change the +``algorithm`` keyword to ``tomopy.lprec``. Specify which LPrec algorithm to +reconstruct with the ``lpmethod`` keyword. -.. image:: lprec_files/output_15_0.png - - -If the angular information is not avaialable from the raw data you need -to set the data collection angles. In this case theta is set as equally -spaced between 0-180 degrees. - -.. code:: ipython3 - - theta = tomopy.angles(proj.shape[0]) - -Perform the flat-field correction of raw data: - -.. math:: \frac{proj - dark} {flat - dark} - -.. code:: ipython3 - - proj = tomopy.normalize(proj, flat, dark) - -Select the rotation center manually - -.. code:: ipython3 - - rot_center = 296 - -Calculate - -.. math:: -log(proj) +Note that with LPrec, there can be no negative values after the transmission +tomography linearization: .. code:: ipython3 proj = tomopy.minus_log(proj) - proj[proj<0] = 0 + proj[proj < 0] = 0 # no values less than zero with lprec Reconstruction using FBP method with the log-polar coordinates -.. math:: \hat{f}=\mathcal{W}\mathcal{R}^* g +.. math:: \hat{f}=\mathcal{W}\mathcal{R}^* g .. code:: ipython3 - recon = tomopy.recon(proj, theta, center=rot_center, algorithm=tomopy.lprec, lpmethod='fbp', filter_name='parzen') + recon = tomopy.recon(proj, + theta, + center=rot_center, + algorithm=tomopy.lprec, + lpmethod='fbp', + filter_name='parzen') recon = tomopy.circ_mask(recon, axis=0, ratio=0.95) - plt.imshow(recon[0, :,:], cmap='Greys_r') + plt.imshow(recon[0, :, :], cmap='Greys_r') plt.show() - - .. image:: lprec_files/output_25_0.png - Reconstruction using the gradient descent method with the log-polar coordinates -.. math:: \hat{f} = \text{argmin}_f\lVert\mathcal{R}f-g \rVert_2^2 +.. math:: \hat{f} = \text{argmin}_f\lVert\mathcal{R}f-g \rVert_2^2 .. code:: ipython3 - recon = tomopy.recon(proj, theta, center=rot_center, algorithm=tomopy.lprec, lpmethod='grad', ncore=1, num_iter=64, reg_par=-1) + recon = tomopy.recon(proj, + theta, + center=rot_center, + algorithm=tomopy.lprec, + lpmethod='grad', + ncore=1, + num_iter=64, + reg_par=-1) recon = tomopy.circ_mask(recon, axis=0, ratio=0.95) - plt.imshow(recon[0, :,:], cmap='Greys_r') + plt.imshow(recon[0, :, :], cmap='Greys_r') plt.show() - - .. image:: lprec_files/output_27_0.png - Reconstruction using the conjugate gradient method with the log-polar coordinates -.. math:: \hat{f} = \text{argmin}_f\lVert\mathcal{R}f-g \rVert_2^2 +.. math:: \hat{f} = \text{argmin}_f\lVert\mathcal{R}f-g \rVert_2^2 .. code:: ipython3 - recon = tomopy.recon(proj, theta, center=rot_center, algorithm=tomopy.lprec, lpmethod='cg', ncore=1, num_iter=16, reg_par=-1) + recon = tomopy.recon(proj, + theta, + center=rot_center, + algorithm=tomopy.lprec, + lpmethod='cg', + ncore=1, + num_iter=16, + reg_par=-1) recon = tomopy.circ_mask(recon, axis=0, ratio=0.95) - plt.imshow(recon[0, :,:], cmap='Greys_r') + plt.imshow(recon[0, :, :], cmap='Greys_r') plt.show() - .. image:: lprec_files/output_29_0.png - Reconstruction using the TV method with the log-polar coordinates. It gives piecewise constant reconstructions and can be used for denoising. -.. math:: \hat{f} = \text{argmin}_f\lVert\mathcal{R}f-g \rVert_2^2 + \lambda \lVert\nabla f\rVert_1 +.. math:: \hat{f} = \text{argmin}_f\lVert\mathcal{R}f-g \rVert_2^2 + \lambda \lVert\nabla f\rVert_1 .. code:: ipython3 - recon = tomopy.recon(proj, theta, center=rot_center, algorithm=tomopy.lprec, lpmethod='tv', ncore=1, num_iter=512, reg_par=5e-4) + recon = tomopy.recon(proj, + theta, + center=rot_center, + algorithm=tomopy.lprec, + lpmethod='tv', + ncore=1, + num_iter=512, + reg_par=5e-4) recon = tomopy.circ_mask(recon, axis=0, ratio=0.95) - plt.imshow(recon[0, :,:], cmap='Greys_r') + plt.imshow(recon[0, :, :], cmap='Greys_r') plt.show() - - .. image:: lprec_files/output_31_0.png - Reconstruction using the TV-entropy method with the log-polar coordinates. It can be used for suppressing Poisson noise. -.. math:: \hat{f} = \text{argmin}_f \lambda \lVert\nabla f\rVert_1+\int_\Omega\mathcal{R}f-g\log(\mathcal{R}f)df +.. math:: \hat{f} = \text{argmin}_f \lambda \lVert\nabla f\rVert_1+\int_\Omega\mathcal{R}f-g\log(\mathcal{R}f)df .. code:: ipython3 - recon = tomopy.recon(proj, theta, center=rot_center, algorithm=tomopy.lprec, lpmethod='tve', ncore=1, num_iter=512, reg_par=2e-4) + recon = tomopy.recon(proj, + theta, + center=rot_center, + algorithm=tomopy.lprec, + lpmethod='tve', + ncore=1, + num_iter=512, + reg_par=2e-4) recon = tomopy.circ_mask(recon, axis=0, ratio=0.95) - plt.imshow(recon[0, :,:], cmap='Greys_r') + plt.imshow(recon[0, :, :], cmap='Greys_r') plt.show() - .. image:: lprec_files/output_33_0.png - Reconstruction using the TV-l1 method with the log-polar coordinates. It can be used to remove structures of an image of a certain scale, and the regularization parameter :math:`\lambda` can be used for scale selection. -.. math:: \hat{f} = \text{argmin}_f\lVert\mathcal{R}f-g \rVert_1 + \lambda \lVert\nabla f\rVert_1 +.. math:: \hat{f} = \text{argmin}_f\lVert\mathcal{R}f-g \rVert_1 + \lambda \lVert\nabla f\rVert_1 .. code:: ipython3 - recon = tomopy.recon(proj, theta, center=rot_center, algorithm=tomopy.lprec, lpmethod='tvl1', ncore=1, num_iter=512, reg_par=3e-2) + recon = tomopy.recon(proj, + theta, + center=rot_center, + algorithm=tomopy.lprec, + lpmethod='tvl1', + ncore=1, + num_iter=512, + reg_par=3e-2) recon = tomopy.circ_mask(recon, axis=0, ratio=0.95) - plt.imshow(recon[0, :,:], cmap='Greys_r') + plt.imshow(recon[0, :, :], cmap='Greys_r') plt.show() - - .. image:: lprec_files/output_35_0.png - Reconstruction using the MLEM method with the log-polar coordinates .. code:: ipython3 - recon = tomopy.recon(proj, theta, center=rot_center, algorithm=tomopy.lprec, lpmethod='em', ncore=1, num_iter=64, reg_par=0.05) + recon = tomopy.recon(proj, + theta, + center=rot_center, + algorithm=tomopy.lprec, + lpmethod='em', + ncore=1, + num_iter=64, + reg_par=0.05) recon = tomopy.circ_mask(recon, axis=0, ratio=0.95) - plt.imshow(recon[0, :,:], cmap='Greys_r') + plt.imshow(recon[0, :, :], cmap='Greys_r') plt.show() - - .. image:: lprec_files/output_37_0.png - diff --git a/doc/source/ipynb/tomopy.rst b/doc/source/ipynb/tomopy.rst index 5bf163ddf..c30351404 100644 --- a/doc/source/ipynb/tomopy.rst +++ b/doc/source/ipynb/tomopy.rst @@ -1,16 +1,11 @@ -Gridrec -------- - -Here is an example on how to use the gridrec :cite:`Dowd:99` -reconstruction algorithm with `TomoPy `__ -:cite:`Gursoy:14a`. -You can download the python script :download:`here<../../../doc/demo/gridrec.py>` -or the Jupyter notebook :download:`here<../../../doc/demo/gridrec.ipynb>` - -.. code:: python - - %pylab inline +Reconstruction with TomoPy +-------------------------- +Here is an example on how to use the gridrec :cite:`Dowd:99` reconstruction +algorithm with `TomoPy `__ +:cite:`Gursoy:14a`. You can download the python script +:download:`here<../../../doc/demo/gridrec.py>` or the Jupyter notebook +:download:`here<../../../doc/demo/gridrec.ipynb>` `Install `__ TomoPy then: @@ -19,14 +14,14 @@ then: import tomopy -Tomographic data input in TomoPy is supported by -`DXchange `__. +Tomographic data input in TomoPy is supported by `DXchange +`__. .. code:: python import dxchange -matplotlib provide plotting of the result in this notebook. +Matplotlib provides plotting of the result in this notebook. `Paraview `__ or other tools are available for more sophisticated 3D rendering. @@ -47,9 +42,9 @@ Select the sinogram range to reconstruct. start = 0 end = 2 -:download:`tooth.h5<../../../tomopy/data/tooth.h5>` data set file format -follows the `APS `__ beamline `2-BM and 32-ID -`__ +:download:`tooth.h5<../../../source/tomopy/data/tooth.h5>` data set file format +follows the `APS `__ beamline `2-BM and 32-ID +`__ `data-exchange `__ file format definition. Major synchrotron file format `examples `__ are available at `DXchange `__. @@ -65,12 +60,10 @@ Plot the sinogram: plt.imshow(proj[:, 0, :], cmap='Greys_r') plt.show() - - .. image:: tomopy_files/tomopy_15_0.png -If the angular information is not avaialable from the raw data you need +If the angular information is not available from the raw data you need to set the data collection angles. In this case theta is set as equally spaced between 0-180 degrees. @@ -83,20 +76,20 @@ spaced between 0-180 degrees. Perform the flat-field correction of raw data: -.. math:: \frac{proj - dark} {flat - dark} +.. math:: \frac{proj - dark} {flat - dark} .. code:: python proj = tomopy.normalize(proj, flat, dark) -Tomopy provides various methods (:cite:`Donath:06`, :cite:`Vo:14`, :cite:`Guizar:08`) -to find the `rotation center `__. +Tomopy provides various methods (:cite:`Donath:06`, :cite:`Vo:14`, +:cite:`Guizar:08`) to find the `rotation center +`__. .. code:: python rot_center = tomopy.find_center(proj, theta, init=290, ind=0, tol=0.5) - .. parsed-literal:: tomopy.rotation:Trying center: [ 290.] @@ -114,19 +107,15 @@ to find the `rotation center `__ -methods including the one part of the `ASTRA -toolbox `__. +Reconstruction using Gridrec algorithm. Tomopy provides various `reconstruction +`__ +methods. .. code:: python @@ -143,8 +132,4 @@ Mask each reconstructed slice with a circle. plt.imshow(recon[0, :,:], cmap='Greys_r') plt.show() - - .. image:: tomopy_files/tomopy_28_0.png - - diff --git a/doc/source/ipynb/ufo.rst b/doc/source/ipynb/ufo.rst index a0892a90a..01b81e24f 100644 --- a/doc/source/ipynb/ufo.rst +++ b/doc/source/ipynb/ufo.rst @@ -1,120 +1,31 @@ -Reconstruction with UFO ------------------------ - -`UFO `__ is a general-purpose image -processing framework developed at the Karlsruhe Institute of Technology and uses -OpenCL to execute processing tasks on multiple accelerator devices such as -NVIDIA and AMD GPUs, AMD and Intel CPUs as well as Intel Xeon Phi cards. +TomoPy with UFO +--------------- Here is an example on how to use `TomoPy `__ with UFO and its accompanying reconstruction algorithms. -Install -`TomoPy `__, -`ufo-core `__ and -`ufo-filters `__. Make sure to -install the Python Numpy interfaces in the ``python`` subdirectory of ufo-core. - -`DXchange `__ is installed with tomopy -to provide support for tomographic data loading. Various data format -from all major -`synchrotron `__ -facilities are supported. - -.. code:: python - - import dxchange - -matplotlib allows us to plot the result in this notebook. - -.. code:: python - - import matplotlib.pyplot as plt - -Set the path to the micro-CT dataset and the sinogram range to -reconstruct. - -.. code:: python - - fname = 'tooth.h5' - start, end = (0, 2) - -This dataset file format follows the `APS `__ -beamline `2-BM and 32-ID `__ -definition. Other file format readers are available at -`DXchange `__. - -.. code:: python - - proj, flat, dark, theta = dxchange.read_aps_32id(fname, sino=(start, end)) - -Plot the sinogram: - -.. code:: python - - plt.imshow(proj[:, 0, :], cmap='Greys_r') - plt.show() - - - -.. image:: ufo_files/ufo_13_0.png - - -If the angular information is not available from the raw data you need -to set the data collection angles. In this case theta is set as equally -spaced between 0-180 degrees. - -.. code:: python - - if (theta is None): - theta = tomopy.angles(proj.shape[0]) - else: - pass - -Perform the flat-field correction of raw data: - -.. math:: \frac{proj - dark} {flat - dark} - -.. code:: python - - proj = tomopy.normalize(proj, flat, dark) - -Tomopy provides various methods to `find rotation -center `__. - -.. code:: python - - center = tomopy.find_center(proj, theta, init=290, ind=0, tol=0.5) - -Calculate - -.. math:: -\log(proj) - -.. code:: python - - proj = tomopy.minus_log(proj) - -Now, reconstruct using UFO's filtered backprojection algorithm. Note, -that we *must* set ``ncore`` to 1 in order to let UFO do the -multi-threading. If left to the default value or set to a value other -than 1 will crash the reconstruction. - -.. code:: python +`UFO `__ is a general-purpose image +processing framework developed at the Karlsruhe Institute of Technology and uses +OpenCL to execute processing tasks on multiple accelerator devices such as +NVIDIA and AMD GPUs, AMD and Intel CPUs as well as Intel Xeon Phi cards. - recon = tomopy.recon(proj, theta, center=center, algorithm=ufo_fbp, ncore=1) +The `ufo-core `__ and `ufo-filters +`__ packages must be installed +separately from TomoPy. Make sure to install the Python NumPy interfaces in the +``python`` subdirectory of ufo-core. -Mask each reconstructed slice with a circle. +For example, to reconstruct using UFO's filtered back-projection algorithm by +setting the algorithm to ``ufo_fbp``. Note, that we *must* set ``ncore`` to 1 in +order to let UFO do the multi-threading. If left to the default value or set to +a value other than 1 will crash the reconstruction. .. code:: python + recon = tomopy.recon(proj, theta, center=rot_center, algorithm=ufo_fbp, ncore=1) recon = tomopy.circ_mask(recon, axis=0, ratio=0.95) - -.. code:: python - - plt.imshow(recon[0, :,:], cmap='Greys_r') + plt.imshow(recon[0, :, :], cmap='Greys_r') plt.show() - .. image:: ufo_files/ufo_26_0.png From f3e9c19aeb8c2828bf7c43bc4c0aed4018b4eed2 Mon Sep 17 00:00:00 2001 From: Daniel Ching Date: Wed, 14 Aug 2019 09:58:42 -0700 Subject: [PATCH 08/15] DOC: Have users create a conda environment when installing Conda environments prevent dependency nightmares when users are also using a bunch of other packages for unrelated things --- doc/source/install.rst | 21 +++++++++++++-------- 1 file changed, 13 insertions(+), 8 deletions(-) diff --git a/doc/source/install.rst b/doc/source/install.rst index a1c11ea5c..297fea945 100644 --- a/doc/source/install.rst +++ b/doc/source/install.rst @@ -9,24 +9,29 @@ Installing from Conda --------------------- If you only want to run TomoPy, not develop it, then you should install through -a package manager. Conda, our supported package manager, can install TomoPy and -its dependencies for you. +Conda, our supported package and environment manager. Conda can install TomoPy +and its dependencies for you. -First, you must have `Conda `_ installed, then -open a terminal or a command prompt window and run:: +First, you must have `Conda `_ installed. - $ conda install -c conda-forge tomopy +Next, install TomoPy and all its dependencies into a new Conda environment +called ``tomopy`` by running:: -This will install TomoPy and all the dependencies from the conda-forge channel. + $ conda create --name tomopy --channel conda-forge tomopy + +Use this TomoPy installation by activating this environment:: + + $ conda activate tomopy Updating the installation ------------------------- TomoPy is an active project, so we suggest you update your installation -frequently. To update the installation run:: +frequently. To update the installation, activate the Conda environment +containing TomoPy and run:: - $ conda update -c conda-forge tomopy + $ conda update --channel conda-forge tomopy For some more information about using Conda, please refer to the `docs `__. From 2f8541cde0bbb98b767b83ea62f1b74973dc0ff3 Mon Sep 17 00:00:00 2001 From: Daniel Ching Date: Wed, 14 Aug 2019 09:59:43 -0700 Subject: [PATCH 09/15] DOC: Reformat citations requests for readability --- doc/source/zcredits.rst | 10 ++++++---- 1 file changed, 6 insertions(+), 4 deletions(-) diff --git a/doc/source/zcredits.rst b/doc/source/zcredits.rst index 2b7c878ef..5574d84fd 100644 --- a/doc/source/zcredits.rst +++ b/doc/source/zcredits.rst @@ -2,10 +2,12 @@ Credits ======= -We kindly request that you cite the following article(s) :cite:`Gursoy:14a` if -you use TomoPy (and also cite :cite:`Pelt:16a` if you use ASTRA or -:cite:`Vogelgesang:12` if you use UFO). For vector reconstructions please -additionally cite :cite:`Hierro-Rodriguez:18`. +We kindly request that you cite the following article(s): + +- cite :cite:`Gursoy:14a` if you use TomoPy +- cite :cite:`Pelt:16a` if you use ASTRA +- cite :cite:`Hierro-Rodriguez:18` if you use vector reconstructions +- cite :cite:`Vogelgesang:12` if you use UFO .. bibliography:: bibtex/zcite.bib :style: unsrt From 75f3d2f2393095af8a45e24c3bd5b331dcb2986d Mon Sep 17 00:00:00 2001 From: Daniel Ching Date: Tue, 20 Aug 2019 13:24:52 -0500 Subject: [PATCH 10/15] DOC: Update filepaths in gridrec demos --- doc/demo/gridrec.ipynb | 223 +++++++++++++++-------------------------- doc/demo/gridrec.py | 174 ++++++++++++++++---------------- 2 files changed, 170 insertions(+), 227 deletions(-) diff --git a/doc/demo/gridrec.ipynb b/doc/demo/gridrec.ipynb index 5fea9e232..fa64366c7 100644 --- a/doc/demo/gridrec.ipynb +++ b/doc/demo/gridrec.ipynb @@ -4,102 +4,41 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "## Gridrec\n", + "# Gridrec\n", "\n", - "Here is an example on how to use [TomoPy](http://tomopy.readthedocs.io/en/latest/) with the gridrec algorithm." + "Here is an example on how to use [TomoPy](http://tomopy.readthedocs.io/en/latest/) with the gridrec algorithm. \n", + "\n", + "In this example, [DXchange](http://dxchange.readthedocs.io) is used to import data from the Advanced Photon Source. Data from all major [synchrotron](http://dxchange.readthedocs.io/en/latest/source/demo.html) facilities may also be imported using DXchange.\n", + "\n", + "Matplotlib is used to plot the results in this notebook. [Paraview](http://www.paraview.org/) or other tools are available for more sophisticated 3D rendering." ] }, { "cell_type": "code", "execution_count": 1, - "metadata": { - "collapsed": false, - "scrolled": true - }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Populating the interactive namespace from numpy and matplotlib\n" - ] - } - ], - "source": [ - "%pylab inline" - ] - }, - { - "cell_type": "markdown", "metadata": {}, - "source": [ - "[Install](http://tomopy.readthedocs.io/en/latest/install.html) TomoPy then:" - ] - }, - { - "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true - }, "outputs": [], "source": [ + "import dxchange\n", + "import matplotlib.pyplot as plt\n", + "import numpy\n", "import tomopy" ] }, { "cell_type": "markdown", "metadata": {}, - "source": [ - "[DXchange](http://dxchange.readthedocs.io) is installed with tomopy to provide support \n", - "for tomographic data loading. Various data format from all major [synchrotron](http://dxchange.readthedocs.io/en/latest/source/demo.html) facilities are supported. " - ] - }, - { - "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [ - "import dxchange" - ] - }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "matplotlib provide plotting of the result in this notebook. [Paraview](http://www.paraview.org/) or other tools are available for more sophisticated 3D rendering." - ] - }, - { - "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": false - }, - "outputs": [], - "source": [ - "import matplotlib.pyplot as plt" - ] - }, - { - "cell_type": "raw", - "metadata": {}, "source": [ "Set the path to the micro-CT data to reconstruct." ] }, { "cell_type": "code", - "execution_count": 5, - "metadata": { - "collapsed": true - }, + "execution_count": 2, + "metadata": {}, "outputs": [], "source": [ - "fname = '../../tomopy/data/tooth.h5'" + "fname = '../../source/tomopy/data/tooth.h5'" ] }, { @@ -111,10 +50,8 @@ }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": true - }, + "execution_count": 3, + "metadata": {}, "outputs": [], "source": [ "start = 0\n", @@ -130,10 +67,8 @@ }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": false - }, + "execution_count": 4, + "metadata": {}, "outputs": [], "source": [ "proj, flat, dark, theta = dxchange.read_aps_32id(fname, sino=(start, end))" @@ -148,24 +83,24 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": false - }, + "execution_count": 5, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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3+/lEwMlkknc3bm9vT8lT+dZ6EOD9vstDQxTalp6Oab1zM+yhbRrxpM+oRxa1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\nAKqqquxxrqys5PPD6aW3Wi0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Z+OSl6qcace97ml7byPuKGwJvdy9XeVP9842BUX4RIEb3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\nlH/yxfL5f3d3N7/CzVejRG2seTKv0WiEg4MDbGx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jo6P8QgsHYvXG1MvXbfHAvVev6Uaa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\nIbQA9AUtaEELeofQAtAXtKAFLegdQv8/uLE+hPFr3f8AAAAASUVORK5CYII=\n", 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ - "plt.imshow(proj[:, 0, :], cmap='Greys_r')\n", + "plt.imshow(proj[:, 0, :])\n", "plt.show()" ] }, @@ -178,13 +113,12 @@ }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": false - }, + "execution_count": 6, + "metadata": {}, "outputs": [], "source": [ - "theta = tomopy.angles(proj.shape[0])" + "if theta is None:\n", + " theta = tomopy.angles(proj.shape[0])" ] }, { @@ -196,10 +130,8 @@ }, { "cell_type": "code", - "execution_count": 10, - "metadata": { - "collapsed": true - }, + "execution_count": 7, + "metadata": {}, "outputs": [], "source": [ "proj = tomopy.normalize(proj, flat, dark)" @@ -214,11 +146,29 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": false - }, - "outputs": [], + "execution_count": 8, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reconstructing 48 slice groups with 1 master threads...\n", + "Reconstructing 48 slice groups with 1 master threads...\n", + "Reconstructing 48 slice groups with 1 master threads...\n", + "Reconstructing 48 slice groups with 1 master threads...\n", + "Reconstructing 48 slice groups with 1 master threads...\n", + "Reconstructing 48 slice groups with 1 master threads...\n", + "Reconstructing 48 slice groups with 1 master threads...\n", + "Reconstructing 48 slice groups with 1 master threads...\n", + "Reconstructing 48 slice groups with 1 master threads...\n", + "Reconstructing 48 slice groups with 1 master threads...\n", + "Reconstructing 48 slice groups with 1 master threads...\n", + "Reconstructing 48 slice groups with 1 master threads...\n", + "Reconstructing 48 slice groups with 1 master threads...\n" + ] + } + ], "source": [ "rot_center = tomopy.find_center(proj, theta, init=290, ind=0, tol=0.5)" ] @@ -232,10 +182,8 @@ }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": false - }, + "execution_count": 9, + "metadata": {}, "outputs": [], "source": [ "proj = tomopy.minus_log(proj)" @@ -250,13 +198,19 @@ }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": true - }, - "outputs": [], + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Reconstructing 48 slice groups with 2 master threads...\n" + ] + } + ], "source": [ - "recon = tomopy.recon(proj, theta, center=rot_center, algorithm='gridrec')" + "recon = tomopy.recon(proj, theta, center=rot_center, algorithm='gridrec', sinogram_order=False)" ] }, { @@ -268,10 +222,8 @@ }, { "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": true - }, + "execution_count": 11, + "metadata": {}, "outputs": [], "source": [ "recon = tomopy.circ_mask(recon, axis=0, ratio=0.95)" @@ -279,56 +231,47 @@ }, { "cell_type": "code", - "execution_count": 15, - "metadata": { - "collapsed": false - }, + "execution_count": 12, + "metadata": {}, "outputs": [ { "data": { - "image/png": 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MvF6vbxB6RMzVamVCGw5cJYeH+UdoI2mjPZp0BZUd96ZWq2k4HGo8HhsauJFk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w18Fzwh2Jg5uxGlgv4WSkYckJQHlBSUuhDGzEYIw9DUAxKP/y6DwUDNZtOcmt4U91quN0xAkIY3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\n4+PzPr/nz/f5PvI/pqamsrxd2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+ "image/png": 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\n", 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" ] }, - "metadata": {}, + "metadata": { + "needs_background": "light" + }, "output_type": "display_data" } ], "source": [ - "plt.imshow(recon[0, :,:], cmap='Greys_r')\n", + "plt.imshow(recon[0, :,:])\n", "plt.show()" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": { - "collapsed": true - }, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 2", + "display_name": "Python [conda env:tike]", "language": "python", - "name": "python2" + "name": "conda-env-tike-py" }, "language_info": { "codemirror_mode": { "name": "ipython", - "version": 2 + "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", - "pygments_lexer": "ipython2", - "version": "2.7.11" + "pygments_lexer": "ipython3", + "version": "3.7.1" } }, "nbformat": 4, - "nbformat_minor": 0 + "nbformat_minor": 1 } diff --git a/doc/demo/gridrec.py b/doc/demo/gridrec.py index 014b88281..dc470c8ff 100644 --- a/doc/demo/gridrec.py +++ b/doc/demo/gridrec.py @@ -1,87 +1,87 @@ -#!/usr/bin/env python -# -*- coding: utf-8 -*- - -# ######################################################################### -# Copyright (c) 2019, UChicago Argonne, LLC. All rights reserved. # -# # -# Copyright 2019. UChicago Argonne, LLC. This software was produced # -# under U.S. Government contract DE-AC02-06CH11357 for Argonne National # -# Laboratory (ANL), which is operated by UChicago Argonne, LLC for the # -# U.S. Department of Energy. The U.S. Government has rights to use, # -# reproduce, and distribute this software. NEITHER THE GOVERNMENT NOR # -# UChicago Argonne, LLC MAKES ANY WARRANTY, EXPRESS OR IMPLIED, OR # -# ASSUMES ANY LIABILITY FOR THE USE OF THIS SOFTWARE. If software is # -# modified to produce derivative works, such modified software should # -# be clearly marked, so as not to confuse it with the version available # -# from ANL. # -# # -# Additionally, 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 UChicago Argonne, LLC, Argonne National # -# Laboratory, ANL, the U.S. Government, 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 UChicago Argonne, LLC 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 UChicago # -# Argonne, LLC 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. # -# ######################################################################### - -""" -TomoPy example script to reconstruct the tomography data as -with gridrec. -""" -from __future__ import print_function -import tomopy -import dxchange - -if __name__ == '__main__': - - # Set path to the micro-CT data to reconstruct. - fname = '../../../tomopy/data/tooth.h5' - - # Select the sinogram range to reconstruct. - start = 0 - end = 2 - - # Read the APS 2-BM 0r 32-ID raw data. - proj, flat, dark, theta = dxchange.read_aps_32id(fname, sino=(start, end)) - - # Set data collection angles as equally spaced between 0-180 degrees. - theta = tomopy.angles(proj.shape[0]) - - # Set data collection angles as equally spaced between 0-180 degrees. - proj = tomopy.normalize(proj, flat, dark) - - # Set data collection angles as equally spaced between 0-180 degrees. - rot_center = tomopy.find_center(proj, theta, init=290, ind=0, tol=0.5) - - proj = tomopy.minus_log(proj) - - # Reconstruct object using Gridrec algorithm. - recon = tomopy.recon(proj, theta, center=rot_center, algorithm='gridrec') - - # Mask each reconstructed slice with a circle. - recon = tomopy.circ_mask(recon, axis=0, ratio=0.95) - - # Write data as stack of TIFs. - dxchange.write_tiff_stack(recon, fname='recon_dir/recon') +#!/usr/bin/env python +# -*- coding: utf-8 -*- + +# ######################################################################### +# Copyright (c) 2019, UChicago Argonne, LLC. All rights reserved. # +# # +# Copyright 2019. UChicago Argonne, LLC. This software was produced # +# under U.S. Government contract DE-AC02-06CH11357 for Argonne National # +# Laboratory (ANL), which is operated by UChicago Argonne, LLC for the # +# U.S. Department of Energy. The U.S. Government has rights to use, # +# reproduce, and distribute this software. NEITHER THE GOVERNMENT NOR # +# UChicago Argonne, LLC MAKES ANY WARRANTY, EXPRESS OR IMPLIED, OR # +# ASSUMES ANY LIABILITY FOR THE USE OF THIS SOFTWARE. If software is # +# modified to produce derivative works, such modified software should # +# be clearly marked, so as not to confuse it with the version available # +# from ANL. # +# # +# Additionally, 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 UChicago Argonne, LLC, Argonne National # +# Laboratory, ANL, the U.S. Government, 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 UChicago Argonne, LLC 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 UChicago # +# Argonne, LLC 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. # +# ######################################################################### + +""" +TomoPy example script to reconstruct the tomography data as +with gridrec. +""" +from __future__ import print_function +import tomopy +import dxchange + +if __name__ == '__main__': + + # Set path to the micro-CT data to reconstruct. + fname = '../../../source/tomopy/data/tooth.h5' + + # Select the sinogram range to reconstruct. + start = 0 + end = 2 + + # Read the APS 2-BM 0r 32-ID raw data. + proj, flat, dark, theta = dxchange.read_aps_32id(fname, sino=(start, end)) + + # Set data collection angles as equally spaced between 0-180 degrees. + theta = tomopy.angles(proj.shape[0]) + + # Set data collection angles as equally spaced between 0-180 degrees. + proj = tomopy.normalize(proj, flat, dark) + + # Set data collection angles as equally spaced between 0-180 degrees. + rot_center = tomopy.find_center(proj, theta, init=290, ind=0, tol=0.5) + + proj = tomopy.minus_log(proj) + + # Reconstruct object using Gridrec algorithm. + recon = tomopy.recon(proj, theta, center=rot_center, algorithm='gridrec') + + # Mask each reconstructed slice with a circle. + recon = tomopy.circ_mask(recon, axis=0, ratio=0.95) + + # Write data as stack of TIFs. + dxchange.write_tiff_stack(recon, fname='recon_dir/recon') From 48183f213ffc9b1d370719b515a18d3c385d64bf Mon Sep 17 00:00:00 2001 From: Daniel Ching Date: Tue, 20 Aug 2019 13:53:28 -0500 Subject: [PATCH 11/15] DOC: Add logging to quick start example --- doc/source/ipynb/tomopy.rst | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/doc/source/ipynb/tomopy.rst b/doc/source/ipynb/tomopy.rst index c30351404..b8e5a9197 100644 --- a/doc/source/ipynb/tomopy.rst +++ b/doc/source/ipynb/tomopy.rst @@ -29,6 +29,13 @@ more sophisticated 3D rendering. import matplotlib.pyplot as plt +Import and activate Python's built in logging module if desired. + +.. code:: python + + import logging + logging.basicConfig(level=logging.INFO) + Set the path to the micro-CT data to reconstruct. .. code:: python From 6ee9a48816a0f7c5b036299e70a8ae1ad20db20b Mon Sep 17 00:00:00 2001 From: Daniel Ching Date: Tue, 24 Sep 2019 16:38:40 -0500 Subject: [PATCH 12/15] DOC: Add link to bug tracker on front page. --- README.rst | 1 + 1 file changed, 1 insertion(+) diff --git a/README.rst b/README.rst index b1d1477da..3cd769f5b 100644 --- a/README.rst +++ b/README.rst @@ -62,6 +62,7 @@ You can try notebooks now using mybinder.org Contribute ========== +* Bugs Tracker: https://github.com/tomopy/tomopy/projects/1 * Issue Tracker: https://github.com/tomopy/tomopy/issues * Documentation: https://github.com/tomopy/tomopy/tree/master/doc * Source Code: https://github.com/tomopy/tomopy/tree/master/tomopy From f59fbd3f80effe8967f83f755577e5758a2b0509 Mon Sep 17 00:00:00 2001 From: Daniel Ching Date: Tue, 24 Sep 2019 16:56:53 -0500 Subject: [PATCH 13/15] DOC: Remove astra from binder because dependency resolution The only way to get tomopy and matplotlib and dxchange is using python=3.7, but astra doesn't offer a 3.7 build. --- binder/environment.yml | 5 +++-- 1 file changed, 3 insertions(+), 2 deletions(-) diff --git a/binder/environment.yml b/binder/environment.yml index badb10369..0321942e5 100644 --- a/binder/environment.yml +++ b/binder/environment.yml @@ -2,7 +2,8 @@ name: tomopy channels: - conda-forge - defaults - - astra-toolbox dependencies: + - dxchange + - python + - matplotlib - tomopy - - astra-toolbox From 7b707260a3cccba1e1a3f0d4e33de032d6a6cac8 Mon Sep 17 00:00:00 2001 From: Daniel Ching Date: Tue, 24 Sep 2019 17:09:25 -0500 Subject: [PATCH 14/15] DOC: Ignore ipynb checkpoints during sphinx build --- doc/source/conf.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/doc/source/conf.py b/doc/source/conf.py index e1d784f44..9c24f6933 100644 --- a/doc/source/conf.py +++ b/doc/source/conf.py @@ -93,7 +93,7 @@ # List of patterns, relative to source directory, that match files and # directories to ignore when looking for source files. -exclude_patterns = ['_build'] +exclude_patterns = ['_build', '**.ipynb_checkpoints'] # The reST default role (used for this markup: `text`) to use for all # documents. From f6f2f0ba6574a3530e74626df48a448d47c63cf7 Mon Sep 17 00:00:00 2001 From: Daniel Ching Date: Tue, 24 Sep 2019 17:57:30 -0500 Subject: [PATCH 15/15] DOC: Automatically link to binder from all ipynb in docs This parameter automatically generates a link from a notebook page to opening the notebook on the binder website. --- doc/source/conf.py | 30 ++++++++++++++++++++++++++++++ 1 file changed, 30 insertions(+) diff --git a/doc/source/conf.py b/doc/source/conf.py index 9c24f6933..73273cfcc 100644 --- a/doc/source/conf.py +++ b/doc/source/conf.py @@ -122,6 +122,36 @@ # -- Options for HTML output ---------------------------------------------- +nbsphinx_execute_arguments = [ + "--InlineBackend.figure_formats={'svg', 'pdf'}", + "--InlineBackend.rc={'figure.dpi': 96}", +] + +# This is processed by Jinja2 and inserted before each notebook +nbsphinx_prolog = r""" +{% set docname = env.doc2path(env.docname, base='doc/source') %} + +.. only:: html + + .. role:: raw-html(raw) + :format: html + + .. nbinfo:: + + This page was generated from `{{ docname }}`__. + Interactive online version: + :raw-html:`Binder badge` + + __ https://github.com/tomopy/tomopy/blob/ + {{ env.config.release }}/{{ docname }} + +.. raw:: latex + + \nbsphinxstartnotebook{\scriptsize\noindent\strut + \textcolor{gray}{The following section was generated from + \sphinxcode{\sphinxupquote{\strut {{ docname | escape_latex }}}} \dotfill}} +""" + # The theme to use for HTML and HTML Help pages. See the documentation for # a list of builtin themes. html_theme = 'default'