diff --git a/.coveragerc b/.coveragerc
new file mode 100644
index 0000000000000000000000000000000000000000..035c59ac4374cd2ffab5e23331968e418349b1ad
--- /dev/null
+++ b/.coveragerc
@@ -0,0 +1,6 @@
+[paths]
+source =
+ cmrsim/
+
+[report]
+include = cmrsim/*
diff --git a/.docker-ci.yml b/.docker-ci.yml
new file mode 100644
index 0000000000000000000000000000000000000000..5dbe1322d2e26c5505b5c7fe70d36ae84fc65b00
--- /dev/null
+++ b/.docker-ci.yml
@@ -0,0 +1,41 @@
+# Templates:
+.docker-build:
+ image:
+ name: gcr.io/kaniko-project/executor:debug
+ entrypoint: [""]
+ before_script:
+ - echo "{\"auths\":{\"$CI_REGISTRY\":{\"username\":\"$CI_REGISTRY_USER\",\"password\":\"$CI_REGISTRY_PASSWORD\"}}}" > /kaniko/.docker/config.json
+ script:
+ - echo $VERSION
+ - echo $CI_REGISTRY_IMAGE/$IMAGE_NAME:$VERSION
+ - cp requirements.txt /builds/jweine/cmrsim/docker/$IMAGE_NAME/python_requirements.txt
+ - cp dist/cmrsim-$VERSION-*.whl /builds/jweine/cmrsim/docker/$IMAGE_NAME/cmrsim-latest-py3-none-any.whl
+ - cd /builds/jweine/cmrsim/docker/$IMAGE_NAME
+ - /kaniko/executor --context ./ --dockerfile ./Dockerfile --destination $CI_REGISTRY_IMAGE/$IMAGE_NAME:$VERSION --destination $CI_REGISTRY_IMAGE/$IMAGE_NAME:latest --cache=true
+
+# Tagged build
+docker-build-gpu:
+ stage: build_dist
+ variables:
+ IMAGE_NAME: gpu
+ VERSION: $CI_COMMIT_TAG
+ extends: .docker-build
+ dependencies:
+ - python_build_tag
+ only:
+ - tags
+ except:
+ - branches
+
+docker-build-juptyer:
+ stage: .post
+ variables:
+ IMAGE_NAME: jupyter
+ VERSION: $CI_COMMIT_TAG
+ extends: .docker-build
+ dependencies:
+ - docker-build-gpu
+ only:
+ - tags
+ except:
+ - branches
diff --git a/.gitlab-ci.yml b/.gitlab-ci.yml
index f7c5c134cc35f7d1a0d382ad437972f0accd700a..d631b1240d45cce71b622f648790baa32c61600f 100644
--- a/.gitlab-ci.yml
+++ b/.gitlab-ci.yml
@@ -1,117 +1,56 @@
stages:
- - base_build
- - build
-
-# Templates:
-.python_build:
- stage: base_build
- image: $CI_REGISTRY/jweine/cmrsim/base:latest
+ - test
+ - pre_build
+ - build_base
+ - build_dist
+ - .post
+
+git describe:
+ image: docker:git
+ stage: pre_build
script:
- - echo $VERSION
- - pip install sphinx sphinx-rtd-theme autodocsumm
- - sphinx-apidoc --separate -o docs/source cmrsim
- - cd docs ; make html; cd ..
- - cp docs/build/html cmrsim/_bin/docs_html -r # Necessary for shipping the docs along with the distribution
- - python setup.py sdist bdist_wheel --version $VERSION
+ - echo "LAST_TAG=$(git describe --tag --abbrev=0)" >> git.env
+ - echo "LAST_MAJOR=$(git describe --tag --abbrev=0 | cut -c1-4)" >> git.env
+ - echo "COMMIT_COUNT=$(git rev-list $(git describe --tag --abbrev=0)..HEAD --count)" >> git.env
artifacts:
- paths:
- - dist/cmrsim-$VERSION-*.whl
- - docs/build/html
-
-.docker-build:
- stage: build
- image:
- name: gcr.io/kaniko-project/executor:debug
- entrypoint: [""]
- before_script:
- - echo "{\"auths\":{\"$CI_REGISTRY\":{\"username\":\"$CI_REGISTRY_USER\",\"password\":\"$CI_REGISTRY_PASSWORD\"}}}" > /kaniko/.docker/config.json
- script:
- - echo $VERSION
- - echo $CI_REGISTRY_IMAGE/$IMAGE_NAME:$VERSION
- - cp requirements.txt /builds/jweine/cmrsim/docker/$IMAGE_NAME/python_requirements.txt
- - cp dist/cmrsim-$VERSION-*.whl /builds/jweine/cmrsim/docker/$IMAGE_NAME/cmrsim-latest-py3-none-any.whl
- - cd /builds/jweine/cmrsim/docker/$IMAGE_NAME
- - /kaniko/executor --context ./ --dockerfile ./Dockerfile --destination $CI_REGISTRY_IMAGE/$IMAGE_NAME:$VERSION --destination $CI_REGISTRY_IMAGE/$IMAGE_NAME:latest --cache=true
-
-# Tagged build
-
-docker-build-base:
- stage: base_build
- image:
- name: gcr.io/kaniko-project/executor:debug
- entrypoint: [""]
- variables:
- IMAGE_NAME: base
- DESTINATION: '${CI_REGISTRY_IMAGE}/${IMAGE_NAME}:${CI_COMMIT_TAG}'
- DESTINATION_LATEST: '${CI_REGISTRY_IMAGE}/${IMAGE_NAME}:latest'
- before_script:
- - echo "{\"auths\":{\"$CI_REGISTRY\":{\"username\":\"$CI_REGISTRY_USER\",\"password\":\"$CI_REGISTRY_PASSWORD\"}}}" > /kaniko/.docker/config.json
- script:
- - printenv
- - cp requirements.txt /builds/jweine/cmrsim/docker/$IMAGE_NAME/python_requirements.txt
- - cd /builds/jweine/cmrsim/docker/base
- - echo $DESTINATION
- - echo $DESTINATION_LATEST
- - /kaniko/executor --context ./ --dockerfile ./Dockerfile --destination ${DESTINATION} --destination ${DESTINATION_LATEST} --cache=true
- only:
+ reports:
+ dotenv: git.env
+ except:
- tags
-python_build_tag:
- extends: .python_build
- variables:
- VERSION: $CI_COMMIT_TAG
- only:
- - tags
-
-pages:
- stage: build
- image: $CI_REGISTRY/jweine/cmrsim/base:latest
+run_unit_test:
+ image: registry.ethz.ch/jweine/cmrsim/gpu:latest
+ stage: test
script:
- - mv docs/build/html/ ./public/
- dependencies:
- - python_build_tag
+ - pip uninstall cmrsim -y
+ - pip install coverage
+ - coverage run -m unittest discover tests/
+ - coverage report
+ - coverage xml
artifacts:
- paths:
- - public
- only:
- - tags
+ reports:
+ cobertura: coverage.xml
-docker-build-gpu:
- variables:
- IMAGE_NAME: gpu
- VERSION: $CI_COMMIT_TAG
- extends: .docker-build
- dependencies:
- - python_build_tag
- only:
- - tags
-
-
-docker-build-juptyer:
+pages:
+ stage: .post
+ image: python:3.8
variables:
- IMAGE_NAME: jupyter
VERSION: $CI_COMMIT_TAG
- extends: .docker-build
+ script:
+ - pip install ./dist/cmrsim-$VERSION-*.whl
+ - pip install sphinx sphinx-rtd-theme autodocsumm
+ - python ./cmrsim/signal_processes/solution_operators/__init__.py
+ - sphinx-apidoc --separate -o docs/source cmrsim
+ - cd docs ; make clean; make html; cd ..
+ - cp -r docs/build/html public
dependencies:
- python_build_tag
+ artifacts:
+ paths:
+ - public
only:
- tags
-
-# Nightly build
-python_build_nightly:
- extends: .python_build
- variables:
- VERSION: nightly
- only:
- - development
-
-docker-build-juptyer-nightly:
- variables:
- IMAGE_NAME: jupyter
- VERSION: nightly
- extends: .docker-build
- only:
- - development
- dependencies:
- - python_build_nightly
\ No newline at end of file
+include:
+ - '.python-ci.yml'
+ - '.docker-ci.yml'
diff --git a/.python-ci.yml b/.python-ci.yml
new file mode 100644
index 0000000000000000000000000000000000000000..c4888c5fd2622b6a632dc4c6c369836f25f7f175
--- /dev/null
+++ b/.python-ci.yml
@@ -0,0 +1,51 @@
+# Templates:
+.python_build:
+ stage: build_base
+ image: python:3.8
+ script:
+ - echo $VERSION
+ - pip install twine anybadge
+ - python setup.py sdist bdist_wheel --version $VERSION
+ - TWINE_PASSWORD=${CI_JOB_TOKEN} TWINE_USERNAME=gitlab-ci-token python -m twine upload --repository-url https://gitlab.ethz.ch/api/v4/projects/${CI_PROJECT_ID}/packages/pypi dist/* --verbose
+
+
+# Tagged build
+python_build_tag:
+ extends: .python_build
+ variables:
+ VERSION: $CI_COMMIT_TAG
+ after_script:
+ - anybadge --label="Version Release" --file=version.svg --value=${VERSION} default_color='green'
+ artifacts:
+ - version.svg
+ only:
+ - tags
+
+# Build experimental on every push
+python_build_experimental:
+ extends: .python_build
+ variables:
+ VERSION: ${LAST_MAJOR}.dev${COMMIT_COUNT}
+ after_script:
+ - anybadge --label="Version Dev" --file=version_dev.svg --value=${VERSION} default_color='green'
+ artifacts:
+ - version_dev.svg
+ only:
+ - development
+ except:
+ - tags
+
+pylint:
+ stage: .post
+ before_script:
+ - pip install pylint pylint-exit anybadge
+ script:
+ - mkdir ./pylint
+ - pylint --output-format=text ./cmrsim | tee ./pylint/pylint.log || pylint-exit $?
+ - PYLINT_SCORE=$(sed -n 's/^Your code has been rated at \([-0-9.]*\)\/.*/\1/p' ./pylint/pylint.log)
+ - anybadge --label=Pylint --file=pylint/pylint.svg --value=$PYLINT_SCORE 2=red 4=orange 8=yellow 10=green
+ - echo "Pylint score is $PYLINT_SCORE"
+ artifacts:
+ paths:
+ - ./pylint/
+
diff --git a/LICENSE b/LICENSE
new file mode 100644
index 0000000000000000000000000000000000000000..0a46ab10f7362a7e2110fc66d96f46fa456e652f
--- /dev/null
+++ b/LICENSE
@@ -0,0 +1,674 @@
+ GNU GENERAL PUBLIC LICENSE
+ Version 3, 29 June 2007
+
+ Copyright (C) 2007 Free Software Foundation, Inc. <https://fsf.org/>
+ Everyone is permitted to copy and distribute verbatim copies
+ of this license document, but changing it is not allowed.
+
+ Preamble
+
+ The GNU General Public License is a free, copyleft license for
+software and other kinds of works.
+
+ The licenses for most software and other practical works are designed
+to take away your freedom to share and change the works. By contrast,
+the GNU General Public License is intended to guarantee your freedom to
+share and change all versions of a program--to make sure it remains free
+software for all its users. We, the Free Software Foundation, use the
+GNU General Public License for most of our software; it applies also to
+any other work released this way by its authors. You can apply it to
+your programs, too.
+
+ When we speak of free software, we are referring to freedom, not
+price. Our General Public Licenses are designed to make sure that you
+have the freedom to distribute copies of free software (and charge for
+them if you wish), that you receive source code or can get it if you
+want it, that you can change the software or use pieces of it in new
+free programs, and that you know you can do these things.
+
+ To protect your rights, we need to prevent others from denying you
+these rights or asking you to surrender the rights. Therefore, you have
+certain responsibilities if you distribute copies of the software, or if
+you modify it: responsibilities to respect the freedom of others.
+
+ For example, if you distribute copies of such a program, whether
+gratis or for a fee, you must pass on to the recipients the same
+freedoms that you received. You must make sure that they, too, receive
+or can get the source code. And you must show them these terms so they
+know their rights.
+
+ Developers that use the GNU GPL protect your rights with two steps:
+(1) assert copyright on the software, and (2) offer you this License
+giving you legal permission to copy, distribute and/or modify it.
+
+ For the developers' and authors' protection, the GPL clearly explains
+that there is no warranty for this free software. For both users' and
+authors' sake, the GPL requires that modified versions be marked as
+changed, so that their problems will not be attributed erroneously to
+authors of previous versions.
+
+ Some devices are designed to deny users access to install or run
+modified versions of the software inside them, although the manufacturer
+can do so. This is fundamentally incompatible with the aim of
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+pattern of such abuse occurs in the area of products for individuals to
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+have designed this version of the GPL to prohibit the practice for those
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+stand ready to extend this provision to those domains in future versions
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+
+ Finally, every program is threatened constantly by software patents.
+States should not allow patents to restrict development and use of
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+avoid the special danger that patents applied to a free program could
+make it effectively proprietary. To prevent this, the GPL assures that
+patents cannot be used to render the program non-free.
+
+ The precise terms and conditions for copying, distribution and
+modification follow.
+
+ TERMS AND CONDITIONS
+
+ 0. Definitions.
+
+ "This License" refers to version 3 of the GNU General Public License.
+
+ "Copyright" also means copyright-like laws that apply to other kinds of
+works, such as semiconductor masks.
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+ "The Program" refers to any copyrightable work licensed under this
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diff --git a/README.md b/README.md
index eee6ffd666b1cb1259c155bf499297745ba9aa15..d3aaf43cf15819b67fe44a36a50ec8fef42f9011 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,289 @@
# CMRsim
-Collaborative project to develop a MRI simulator to be used in the IBT group.
\ No newline at end of file
+**A modular MRI simulation frame-work implemented in Tensorflow 2.3.**
+
+This project is meant to enable code-reuse, ease conducting simulation experiments and provide a clear interface to connect the subgroups *Modeling*, *Imaging* and *MachineLearning* within the IBT-CMR group.
+
+The modularity of this framework allows to easily exchange operators or their parameters of the image generating forward model. Hence, comparing how changes in the forward model effect e.g. reconstruction results can be done with minimum overhead and boiler-plate code. Furthermore, the provides a way to save simulations as tf.graphs making the distribution and reprodcuability of simulation experiments as easy as it should be.
+To facilitate the usage of modelled anatomies, the simulation input not defined on a grid but as a set of material points (see sction 2.1). Therefore, by selecting a subset of mesh-nodes, 2D images can be simulated from a sufficiently dense mesh. Without conceptual changes even 3D acquisitions can be simulated (3D encoding not yet implemented but see section 2.3).
+
+*Note*: This project does not provide a digital phantom / representation of anatomy but defines a generic format (python dictionary of numpy arrays) to handle in arbitrary phantoms (see section "How does it work?").
+
+<div align="center">
+ <img src="/uploads/891a09c547d36b7359160088608782c1/Folie1.PNG" width="800"/>
+</div>
+
+**Hardware**
+
+As the Implementation is based on TF2, the utilization of GPUs is available while it does not require any code changes for prototyping on a computer without a GPU. By default the computations are placed on GPU:0 if available, but every module can be placed on a specific device just by keyword argument in mutli-GPU environments.
+
+**Documentation**
+
+An automatically generated documentation of the latest tagged version is available 
+
+**Contribute**
+
+If you like the concept of this framework but don't yet find all building blocks you need have a look at section 3 on how to implement it yourself quiet easily.
+In case you do have a feature request, a bug to report, or want to contribute a Module or two, open an new  and briefly describe what you have in mind!
+
+
+---
+## Table of Contents
+1. [Getting Started](#1-getting-started)
+ 1.1 [Installation](#11-installation)
+ 1.2 [Basic Examples](#12-basic-examples)
+2. [How Does it work?](#2-how-does-it-work)
+ 2.1 [Definition of digital phantoms](#21-definition-of-digital-phantoms)
+ 2.2 [The Signal Module](#22-the-signal-module)
+ 2.3 [The Encoding Module](#23-the-encoding-module)
+ 2.4 [The Reconstruction Module](#24-the-reconstruction-module)
+ 2.5 [The Simulation Module](#25-the-simulation-module)
+3. [Implementing a new Module](#3-implementing-a-new-module)
+
+
+## 1. Getting Started
+
+### 1.1 Installation
+
+An automatic build pipeline is in place, providing pip-installable python  and docker .
+
+**Tagged versions**:
+Each tagged version produces a package with the same version number in the registry attached to this repository. To install it, use url provided in the registry:
+`pip install cmrsim --extra-index-url https://__token__:<your_personal_token>@gitlab.ethz.ch/api/v4/projects/23798/packages/pypi/simple `
+
+For each tagged version of the framework two containers are build:
+- GPU (Includes all dendencies of the framework and has the cmrsim package installed)
+- Jupyter (Adds Jupyter-lab > 3.0 installation including the interactive widget extension)
+
+To pull e.g. the latest jupyter-lab container use the URL
+`registry.ethz.ch/jweine/cmrsim/jupyter:latest`
+with docker or singularity.
+
+**Development versions**:
+For minor changes, packages marked with 'dev' in version name are build, which can be installed into the containers of the previous tagged version.
+
+### 1.2 Basic Examples
+
+In the directory "./notebooks" there are a couple of jupyter-notebooks, providing basic examples on how to use this simulation frame-work. To try them, pull the repository and run them inside the latest 'jupyter' container provided in the registry.
+
+## 2. How does it work?
+
+This sections highlights the conceptual foundation of the frame-work. In short the in-silico experiment consist of a digital phantom (not provided in this frame work) and a simulation module. The simulation module is called, using the digital phantom as input. Furthermore, the simulation module has three member-submodules: CompositeSignalModule, EncodingModule and ReconstructionModule (optional). If not ReconstructionModule is specified the simulation will yield the k-space as defined by the encoding module. In the following sections, the format of a digital phantom as well as the building blocks of the simulation are explained in more detail.
+
+A graphical overview of the structure is provided above in the summary, which translates into following as UML diagram:
+
+<div align="center">
+ <img src="/uploads/52539d2ca61147f48b16b8de246fa948/cmrsim.jpg" width="500"/>
+</div>
+
+
+### 2.1 Definition of digital phantoms
+
+**General considerations**
+
+This frame-work assumes a generic object representation, where the digital phantom simply is a set of material points carrying the MR-relevant material properties. The data format of the object definition is unstructured, which means that the arrays, representing the properties are flattened over the object dimensions. This has the following consequences:
+1. Each material point $`i`$ needs to have an associated positional vector $`\vec{r}_i`$ as additional property to facilitate the integration/summation over the object inside the fourier-integral (cgl. [2.3](#23-the-encoding-module)).
+2. Not enforcing a grid and hence a predefined, constant volume-element associated with the material point, requires the proton density to be re-formalized as available equilibrium magnetization ($`M_0 = \rho * dV`$). However, if the phantom is defined on a regular grid such that $`dV=const`$ this transformation $`\rho \rightarrow M_0`$ does not change the simulation result up to normalization and can be ommited.
+
+Following from this, every digital phantom must include the properties $`M_0`$ and $`\vec{r}`$ for each material point. All other physical required physical properties, generally depend on the specific simulation as described in [2.2](#22-the-signal-module). As an basic , the evaluating the Spin-Echo signal operator only requires the quantities $`M_0, \vec{r}, T_1, T_2`$. The corresponding python dictionary looks like (the array shapes are explained below):
+
+```python
+digital_phantom = {
+ "M0" : np.ndarray[dtype = np.complex64, shape = (#images, #points, #repetitions, #k-space-samples)],
+ "r_vectors": np.ndarray[dtype = np.float32, shape = (#images, #points, #repetitions, 3(XYZ), #k-space-samples)],
+ "T1" : np.ndarray[dtype = np.float32, shape = (#images, #points, #repetitions, #k-space-samples)],
+ "T2" : np.ndarray[dtype = np.float32, shape = (#images, #points, #repetitions, #k-space-samples)]
+}
+```
+
+**Array shapes**
+To explain the shapes of the stated arrays, it is necessary to understand, how the object is processed streamed to GPU memory during simulation. The meaning of the single axes can be described as:
+
+- *#images*: To be deprecated, should be set to 1.
+
+- *#points*: Number of material points constituting the object
+
+- *#repetitions*: In case the simulation incorporates multiple images with different values of e.g. TE or TR, all images are simulated in one go. To ensure, that the shape is matching during simulation, all arrays are specified in that shape. If the property does not change between acquisitions the dimension the #repetitions axis is trivially set to 1, and the simulation is expected to broadcast the input accordingly.
+
+- *#k-space-samples*: This axis is meant to facilitate, changes of the properties during encoding. The most use-case would be motion/displacement over the course of encoding as demonstrated in . The sampling-time associated which each k-space point is defined by the [encoding module](#23-the-encoding-module). If only the position changes (which is the most sensical thing to happen), the simulation modules are expected to broadcast the other inputs accordingly.
+
+**Dictionary Key-names**
+Naming the property entries, has to adhere to the definitions of the simulation frame_work. A table of currently used properties is given .
+
+
+### 2.2 The Signal Module
+
+All signal modules are derived from a BaseSignalModule and represent the application of a single imaging operator (the encoding operator is handled separately). As the simulation be formulated as a non-interchangeable concatenation of imaging operators, every simulation has a CompositeSignalModule that bundles the signal operators, and calls them in specified order. As the the entire object might be to large for GPU memory it is batched over the #points dimension. Implementation-wise this corresponds to a pipe-and-filter pattern.
+The following code-block shows a generic instantiation of a composite signal-module for the imaging model:
+```math
+M_{xy} = C(m, r) \circ T2star^{\star} \circ GRE(m, r; \alpha, T_E, T_R) \circ O(M_0, \vec{r}, T_1, T_2, T_2^{\star}) \\
+```
+```math
+O : \quad digital \ phantom \ object ,\qquad GRE : \quad Gradient \ echo \ sequence \ operator ,\qquad
+T2star : \quad T_2^{\star}-decay \ operator \ (*e^{-t/T_2^{\star}}) ,\\qquad C: \quad Coil \ sensitivity \ operator
+```
+```python
+from cmrsim.signal_processes.base import CompositeSignalModel
+import cmrsim.signal_processes.solution_operators as solution_operators
+
+# Signal module construction
+gre_module = solution_operators.sequences.GRE(TE, TR)
+t2_star_module = solution_operators.t2_star.StaticT2starDecay(...)
+coil_sensitivities = solution_operators.coil_sensitivities.CoilSensitivity(...)
+
+# Forward model composition
+forward_model = CompositeSignalModel(spinecho_module, t2_star_module, coil_sensitivities)
+```
+The signature for calling an instance CompositeSignalModule to calculate the transverse magnetization before encoding is given as:
+```python
+forward_model(signal_tensor: tf.Tensor, r_vectors: tf.Tensor, **kwargs) -> modified_signal_tensor : tf.Tensor
+```
+The first and second argument both are the dictionary entries "M0" and "r_vectors" of the digital phantom as defined above. They have a special role, as they are mandatory for every simuation. All other properties are passed as keyword-arguments which are automatically forwarded to the sub-modules. Each submodule defines the physical properties, that it expects the material points to have. The CompositeSignalModule creates a set of all required quantities which can be seen after object construction as `forward_model.required_quantities`.
+
+Some signal modules expand the dimension of repetitions. A good example for that is the coil-sensitivity module: the input shape of the signal tensor is (#images, #points_in_batch, n_r, #k-samples) and the output will be (#images, #points_in_batch, n_r * n_coils, #k-samples). If e.g. the GRE module expands the repetition axis by a factor of 2 as well (e.g. 2 different Echo-times) the total output dimension is 16. As it might be desirable to unstack these repetitions per signal module, this functionality is implemented in `CompositeForwardModel.unstack_repetitions(...)`, using the order in which the operators are passed to the composite module on construction.
+
+Section 3 shows how to implement a new module, such that theese functionalities will work.
+
+### 2.3 The Encoding Module
+
+To enable the implementation of different k-space trajectories, while not needing to reimplement the summation of the fourier-integral, encoding modules are derived from a base class and only need to implement the `_calculate_trajectory` method. The base module implements functions to calculate the phase-factors and keep track of the summation for the streamed digital phantom. If the simulated image matrix is realtively large it is beneficial to subdivide the k-space-trajectory into segments. This is conceptually not necessarily the same as a segmented acquisition but it can be used to mimic it.
+The input calling signature and output shape of an encoding module is given as:
+```python
+encoding_module(transverse_magnetization: tf.Tensor, r_vectors: tf.Tensor, segment_index: Union[int, tf.Tensor] = 0, **kwargs) -> k-space
+
+with
+ transverse_magnetization.shape -> (#images, #points_in_batch, n_r * n_coils, #k-samples)
+ k-space.shape -> (#images, #repetitions, #noise-instantiations, #k-space samples)
+```
+The last axis contains the flattened k-space sample dimension. By not enforcing structural information of the k-space samples during calculation, a subclassed module of the BaseEncoding module can implement an arbitrary k-space trajectory. The segment index could be used to subdivide the simulation of e.g. single spokes in a radial acquisition scheme. The segment_index is also available to all module within the signal_operator scope e.g. for modifying the flip angle for each k-space line.
+
+The noise addition is done in k-space, resembling the real acquisition effect. Specifying the added noise intensity is done by setting the member_variable of the encoding module "absolute_noise_std". (*Note:* This is not the same standard deviation as specified by the SNR in image-space, since the noise addition is done k-space. The `auxilliary.snr` contains functionality to compute the necessary noise level for a given target SNR).
+
+As mentioned in the previous section, to obtain a Tensor with an unstacked repetition axis (e.g. `(#images, #coils, #Echo-Times, #noise, #k-space)`), the result can be passed to the CompositeSignalModel.unstack_repetitions() function.
+
+### 2.4 The Reconstruction Module
+
+The Reconstruction module has to be choosen according to the encoding in the simulation. As the k-space-sample axis is flatten, the user has to check if encoding and recon match (e.g. 2D-EPI & iFFT). As it may be desirable to not include the reconstruction into the Simulation call, the specification of the recon module is optional. If a Subclassed module of BaseRecon is specified as recon module, the returned tensor contains data in image space. As no Modules follow after reconstruction there are no restrictions are enforced for the output shape of the reconstructed images.
+
+### 2.5 The Simulation Module
+
+The `cmrsim.simulation.SimulationBase` Module tries to abstract calling all modules described above, away from the user. To do so, while maintaining flexibililty of setting up simulation configurations, the following strategy of building a simulation was choosen.
+
+To implement a new simulation, one has to define a new class, derived from the `SimulationBase` Module und implement the abstract `_build()` function. This function must always return a Tuple with 3 entries. The first entry **must** be an instance of `CompositeSignalModel`, the second **must** be an instance of a `BaseEncoding Subclass`, and the third entry can either be an instance of a `BaseRecon Subclass` (return in image-space) or `None` (return k-space). Following the example forward model from section 2.2, the implementation looks like:
+```python
+import cmrsim.signal_processes.solution_operators as solution_operators
+from cmrsim.signal_processes.base import CompositeSignalModel
+from cmrsim.encoding.epi import EPI
+from cmrsim.reconstruction.cartesian import RoemerMultiCoil
+
+class ExampleSimulation(cmrsim_simulate.SimulationBase):
+ def _build(self):
+
+ # Encoding definition
+ encoding_module = EPI(...)
+
+ # Signal model construction
+ gre_op = solution_operators.sequences.GradientEcho(...)
+ t2_star_op = solution_operators.t2_star.StaticT2starDecay(encoding_module.get_sampling_times())
+ coil_sense_op = solution_operators.coil_sensitivities.CoilSensitivity(...)
+
+ # Forward model composition
+ forward_model = CompositeSignalModel(gre_op, t2_star_op, coil_sense_op)
+
+ # Reconstruction
+ recon_module = RoemerMultiCoil(...)
+
+ return forward_model, encoding_module, recon_module
+
+# Call the simulation
+from cmrsim.anatomy.base import BaseDataset
+
+simulator = ExampleSimulation(name="Example_simulation")
+reconstructed_images = simulator(BaseDataset(digital_phantom_dictionary))
+```
+
+The simulation module has additional functionality to save/load, configurations which can be explored by looking into the documentation or going through the example notebooks.
+
+
+## 3. Implementing a new Module
+
+Each of the building block - types (signal, encoding, recon) do have a base class implemented, that define the necessary structure being enforced on the module. Below a short demonstration of what has to be implemented in which module is provided.
+
+**Signal Module**
+A signal module subclass must implement the functions \_\_init\_\_, \_\_call\_\_ and update. Furthermore, the required quantities have to be specified, as well as a potential expansion of the repetition axis. As an simple example, the module `cmrsim.signal_processes.signal_operator.diffusion_weighting.ConstantGaussianDiffusion` is inspected here. Feel free to have a look at the source code to see the full implementation details.
+
+```python
+import tensorflow as tf
+from cmrsim.signal_processes.base import BaseSignalModel
+
+class ConstantGaussianDiffusion(BaseSignalModel):
+ """ This Docstring should explain the module purpose (e.g. with math/formulas) and assumptions for implementation.
+
+ """
+ required_quantities = ('diffusion_tensor', ) # The required_quantities are specified as class-member / Tuple(str, ...),
+ # this way they can be queryied without instantiating a concrete object
+
+ def __init__(self, b_vectors: tf.Tensor, **kwargs):
+ """ ... (Originial text hidden) Docstring should contain an explanation of the input-arguments
+ to the constructor. To forward the arguments to the Superclass (BaseSignalModel) the **kwargs mechanism is used.
+ """
+ super(ConstantGaussianDiffusion, self).__init__(name="dti_gauss", **kwargs) # Name defines name in tf graph construction
+ self.b_vectors = tf.Variable(b_vectors, shape=(None, 3), dtype=tf.float32, name='b_vectors') # Module parameters should be defined as tf.Variables.
+ self.update() # Most calculations are performed with tf.float32 data-type
+
+
+ @tf.Module.with_name_scope
+ def __call__(self, signal_tensor: tf.Tensor, diffusion_tensor: tf.Tensor, **kwargs) -> tf.Tensor: # The keyword-argument 'diffusion_tensor' must match the
+ # string provided in required_quantities
+ """
+ This Docstring should contain information about the assumed input shapes of the arguments
+
+ :param signal_tensor: (#batch, #voxel, #repetitions, #k-space-samples) last two dimensions can be 1
+ :param diffusion_tensor: diffusion_tensor (#batch, #voxels, 3, 3)
+ :return: (#batch, #voxel, #repetitions * #b_vectors, #k-space-samples)
+ """
+
+ # The following code block expands the repetitions-axis of the signal-tensor by a factor
+ # equal to the number of different b-vectors specified as module parameters. To reflect that change
+ # the object-member self.expansion factor needs to be set accordingly (see function update).
+ with tf.device(self.device):
+ input_shape = tf.shape(signal_tensor)
+ exponent = tf.einsum('ni, bvij, nj -> bvn', self.b_vectors, diffusion_tensor, self.b_vectors)
+ decay_factor = tf.cast(tf.exp(-exponent), dtype=tf.complex64)
+ temp = tf.einsum('bvrk, bvn -> bvrnk', signal_tensor, decay_factor)
+ return tf.reshape(temp, (input_shape[0], input_shape[1], -1, input_shape[3]))
+
+ # The update function of all modules is called in the simulation module right before computing the simulation.
+ # This ensures that potential parameter changes and their dependencies are correctly set.
+ def update(self):
+ super(ConstantGaussianDiffusion, self).update()
+ self.expansion_factor = self.b_vectors.read_value().shape[0]
+
+```
+
+**Encoding Module**
+New encoding modules should be derived from `cmrsim.encoding.base.BaseSampling`. Theoretically only the `_calculate_trajectory` method
+ has to be implemented but to provide parameters on construction the `__init__` function is extended as well.
+```python
+class NewEncodingModule(BaseSampling):
+ """ This Docstring should explain what this module does
+ """
+ def __init__(self, absolute_noise_std: float, ..., **kwargs):
+
+ # Assing input arguments to members of this class...
+ # Calculate k_space_segments and other stuff
+
+ # At last, call the SuperClass constructor (which calls _calculate_trajectory)
+ super(NewEncodingModule, self).__init__(absolute_noise_std, name="new_encoding_module",
+ k_space_segments=1, **kwargs)
+
+ def _calculate_trajectory(self) -> (tf.Tensor, tf.Tensor): # This function must return the k-space vectors (N, 3) and sampling times (N,)
+ """ This Docstring explains how the trajectory is calculated
+ :return: k-space-vectors, sampling-times (tf.Tensor, tf.Tensor)
+ """
+ # Actual calculation using parameters set in __init__
+ return k_vectors, sampling_times
+```
+
+
+
diff --git a/build_docs.py b/build_docs.py
deleted file mode 100644
index 57daee182669a2a1edf4bc7281923f9a044cb8a7..0000000000000000000000000000000000000000
--- a/build_docs.py
+++ /dev/null
@@ -1,19 +0,0 @@
-import glob
-import os
-import subprocess
-
-ROOT_PATH = os.path.abspath(os.path.dirname(__file__))
-
-# Removed old doc rst files
-generic_resource_files = glob.glob('./docs/source/cmrsim.*')
-for f in generic_resource_files:
- os.remove(f)
-
-p = subprocess.run(['sphinx-apidoc', '--separate', f'-o {ROOT_PATH}/docs/source', f'{ROOT_PATH}/cmrsim'])
-bat_file_path = f'{os.path.abspath(os.path.dirname(__file__))}' + os.path.sep + 'docs'
-try:
- os.remove(f'{ROOT_PATH}/docs/source/modules.rst')
-except:
- pass
-p = subprocess.Popen([bat_file_path + os.path.sep + 'make.bat', 'html'], cwd=os.path.normpath(bat_file_path))
-s, r = p.communicate()
diff --git a/cmrsim/_bin/__init__.py b/cmrsim/_bin/__init__.py
deleted file mode 100644
index 0860bbbea17bde986bdda75befdb3052690f0535..0000000000000000000000000000000000000000
--- a/cmrsim/_bin/__init__.py
+++ /dev/null
@@ -1,3 +0,0 @@
-import os
-
-
diff --git a/cmrsim/_bin/cmrsim_demo.py b/cmrsim/_bin/cmrsim_demo.py
deleted file mode 100644
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..0000000000000000000000000000000000000000
diff --git a/cmrsim/_bin/cmrsim_doc.py b/cmrsim/_bin/cmrsim_doc.py
deleted file mode 100644
index 1396ede7ca291f15decdb5545aeaa1c231602a35..0000000000000000000000000000000000000000
--- a/cmrsim/_bin/cmrsim_doc.py
+++ /dev/null
@@ -1,16 +0,0 @@
-import webbrowser
-import os
-
-
-def open_docs():
- url = os.path.join(os.path.dirname(__file__), 'docs_html', 'index.html')
- print(url)
- try:
- webbrowser.get('firefox').open(url)
- except webbrowser.Error:
- ffpath = 'C:/Program Files/Mozilla Firefox/firefox.exe'
- if os.path.exists(ffpath):
- webbrowser.register('firefox', None, webbrowser.GenericBrowser(ffpath))
- webbrowser.get('firefox').open(url)
- else:
- webbrowser.open(url)
diff --git a/cmrsim/anatomy/__init__.py b/cmrsim/anatomy/__init__.py
index e69de29bb2d1d6434b8b29ae775ad8c2e48c5391..86b478c2bbe70024304974fd26e99e89b3b4249d 100644
--- a/cmrsim/anatomy/__init__.py
+++ b/cmrsim/anatomy/__init__.py
@@ -0,0 +1,2 @@
+"""This module is contains functionality to bundle and format input resources in corresponding to the specified
+input format into tf.data.Dataset. This is necessary to manage the streaming of large input arrays efficiently."""
\ No newline at end of file
diff --git a/cmrsim/anatomy/base.py b/cmrsim/anatomy/base.py
index a80928355a5fc5b76073e9401a05f76743d0e47e..4d9a1ca3c5134706bdd569b3cd26453ad8e58b6a 100644
--- a/cmrsim/anatomy/base.py
+++ b/cmrsim/anatomy/base.py
@@ -1,33 +1,37 @@
__all__ = ["BaseDataset"]
from typing import *
from collections import OrderedDict
-from copy import deepcopy
import tensorflow as tf
import numpy as np
class BaseDataset(tf.Module):
- map_names: Tuple
- set_size: int
+ #: Names of simulation quantities passed as dictionary keys on construction
+ map_names: Tuple = None
+ #: Number of anatomies (0th - axis) of the passed simulation quantities
+ set_size: int = None
_array_tuple: Tuple
def __init__(self, array_dictionary: OrderedDict):
""" Initializes a callable module, that yields an iterable tf.Dataset on call. The required inpot
+ :raise: InvalidArgument - if shape of M0 and r_vector entry of the dictionary do not match
+ (1, #MaterialPoints, #Repetitions, #k-space-samples)
+ (1, #MaterialPoints, #Repetitions, 3, #k-space-samples)
+ InvalidArgumentError is raised
+
:param array_dictionary: (OrderedDict) containing the required quantities as keys and the corresponding 2D maps
as Numpy-ndarrays for values. Does a deepcopy of the Dictionary to avoid loosing
the reference.
"""
super(BaseDataset, self).__init__(name='array_dataset')
- arrays = deepcopy(list(array_dictionary.values()))
-
- if len(array_dictionary['M0'].shape) < 4:
- arrays[0] = arrays[0][:, :, :, np.newaxis, np.newaxis]
- elif len(array_dictionary['M0'].shape) < 5:
- arrays[0] = arrays[0][:, :, :, :, np.newaxis]
+ if len(array_dictionary["M0"].shape) != 4:
+ raise ValueError("Shape of input array M0 invalid")
+ if len(array_dictionary["r_vectors"].shape) != 5:
+ raise ValueError("Shape of input array r_vectors invalid")
- self._array_tuple = tuple(arrays)
+ self._array_tuple = tuple(array_dictionary.values())
self.set_size = array_dictionary['M0'].shape[0]
self.map_names = tuple(array_dictionary.keys())
@@ -54,8 +58,6 @@ class BaseDataset(tf.Module):
datasets = tf.data.Dataset.from_tensor_slices(self._array_tuple)
if filter_trivial_grid_points:
datasets = datasets.map(self._filter_inputs)
- else:
- datasets = datasets.map(self._flatten_maps)
datasets = datasets.map(lambda *x: (tf.cast(tf.shape(x[0])[0], tf.int32),
tf.data.Dataset.from_tensor_slices(x).batch(voxel_batch_size).prefetch(prefetch)))
@@ -75,43 +77,6 @@ class BaseDataset(tf.Module):
indices = tf.where(tf.reshape(tf.cast(m0, tf.float32), [-1]) > 0)[:, 0]
return_tuple = ()
for tensor in args:
- original_shape = tf.shape(tensor)
- flat_tensor = tf.reshape(tensor, tf.concat(((-1,), original_shape[2:]), axis=0))
- selection = tf.gather(flat_tensor, indices, axis=0)
- # selection = tf.reshape(selection, tf.concat(((-1,), original_shape[2:]), axis=0))
+ selection = tf.gather(tensor, indices, axis=0)
return_tuple += (selection, )
return return_tuple
- # return tf.data.Dataset.from_tensor_slices(return_tuple)
-
- @staticmethod
- def _flatten_maps(*args):
- return_tuple = ()
- for tensor in args:
- original_shape = tf.shape(tensor)
- flat_tensor = tf.reshape(tensor, tf.concat(((-1,), original_shape[2:]), axis=0))
- return_tuple += (flat_tensor,)
- return return_tuple
-
-
-if __name__ == "__main__":
- """ Example usage of the BaseDataset class for random input"""
- import time
- # Set up data
- x, y = 100, 100
- batch_size = 2
- array_dict = OrderedDict([("M0", np.around(np.random.uniform(0., 1., size=(batch_size, x, y)),
- decimals=0).astype(np.float32)),
- ("diffusion_tensor", tf.eye(3, 3, batch_shape=[batch_size, x, y]).numpy()),
- ("T2star", np.ones((batch_size, x, y), dtype=np.float32)),
- ("r_vectors", np.ones((batch_size, x, y, 1, 3, 1), dtype=np.float32))])
- # Initialize BaseDataset and use its __call__ method to get a tf.data.Dataset that applies the
- # corresponding function mappings to the data
- module = BaseDataset(array_dict)
- start = time.perf_counter()
- for image_idx, (set_size, single_image_dataset) in module(voxel_batch_size=1000).enumerate():
- print(f'\nNew Image ({set_size} voxel): ', image_idx.numpy())
- # Set up data set that divides the full image into voxel-wise batches
- for batch_idx, l in single_image_dataset.enumerate():
- print(f'Batch {batch_idx}', [f'{k}:{v.shape}' for (k, v) in l.items()])
-
-
diff --git a/cmrsim/auxilliary/plot_utilities.py b/cmrsim/auxilliary/plot_utilities.py
deleted file mode 100644
index 2998d4c4eea8adb3bd730e1f8aff8690623b98ef..0000000000000000000000000000000000000000
--- a/cmrsim/auxilliary/plot_utilities.py
+++ /dev/null
@@ -1,106 +0,0 @@
-__all__ = ["SlideShow", ]
-from typing import List
-
-import matplotlib.pyplot as plt
-from mpl_toolkits.axes_grid1 import make_axes_locatable
-
-from matplotlib import animation
-
-import numpy as np
-
-
-class SlideShow(object):
- def __init__(self, data: np.ndarray, axes: List[plt.Axes] = None, figure: plt.Figure = None, **kwargs):
- """
-
- :param data: (subplots, X, Y, #slices)
- :param axes: Optional Iterable[plt.Axes]
- :param figure: Optional plt.figure that contains axes
- :param kwargs: - titles: Iterable[str]
- - colorbars: bool = True
- - register_event: bool = True
- - imshow_kwargs: dict - keyword arguments that are passed through to the imshow call
- - x_labels: Iterable[Iterable[str]]
- """
- # Set up subplots
- n_axes, rows, cols, self.slices = data.shape
- if axes is None:
- self.fig, self.axes = plt.subplots(1, n_axes)
- if n_axes == 1:
- # Allow single axis plot by making self.axes iterable in that case
- self.axes = [self.axes]
- else:
- self.axes = axes
- self.fig = figure
-
- self._ax_titles = kwargs.get("titles", len(self.axes) * [" ", ])
- for ax, title in zip(self.axes, self._ax_titles):
- ax.set_title(title)
- ax.set_xticks([])
- ax.set_yticks([])
-
- # Register event
- if kwargs.get('register_event', True):
- self.fig.canvas.mpl_connect('button_press_event', self.onclick)
-
- # Store data and plot
- self.data = data
- self.ind = self.slices - 1
-
- imshow_kwargs = kwargs.get('imshow_kwargs', {})
- if imshow_kwargs.get('vmax', None) is None:
- max_val_per_channel = np.max(self.data, axis=(1, 2, 3))
- else:
- max_val_per_channel = [imshow_kwargs.get('vmax') for _ in range(n_axes)]
- imshow_kwargs.pop('vmax')
-
- self.ims = [self.axes[i].imshow(x[:, :, self.ind], vmax=max_val_per_channel[i], **imshow_kwargs)
- for i, x in enumerate(self.data)]
-
- default_labels = [n_axes * [f'slice {i}', ] for i in range(self.slices)]
- self._xlabels = kwargs.get('x_labels', default_labels)
-
- # Configure color-bars
- self._plot_cbars = kwargs.get("colorbars", True)
- if self._plot_cbars:
- self._cbars = []
- for ax, im in zip(self.axes, self.ims):
- divider = make_axes_locatable(ax)
- cax = divider.append_axes('right', size='5%', pad=0.05)
- cbar = plt.colorbar(im, cax=cax)
- self._cbars += [cbar, ]
-
- self.update()
-
- def onclick(self, event):
- if str(event.button) == 'MouseButton.LEFT':
- self.ind = (self.ind + 1) % self.slices
- elif str(event.button) == 'MouseButton.RIGHT':
- self.ind = (self.ind - 1) % self.slices
- self.update()
-
- def update(self):
- for idx, ax, im, x in zip(range(len(self.axes)), self.axes, self.ims, self.data):
- im.set_data(x[:, :, self.ind])
- ax.set_xlabel(self._xlabels[self.ind][idx])
- for im in self.ims:
- im.axes.figure.canvas.draw()
-
- def animate(self, save_directory, **kwargs):
- def _init():
- self.ind = 0
- self.update()
-
- def _update(i):
- self.ind = i
- self.update()
-
- ani = animation.FuncAnimation(self.fig, _update, frames=self.slices, init_func=_init, **kwargs)
- ani.save(save_directory, writer='imagemagick')
-
-
-if __name__ == '__main__':
- data = np.random.normal(0, 1., size=(1, 100, 120, 20))
- print(data.shape)
- sl = SlideShow(data)
- sl.animate('../../animation.gif')
diff --git a/cmrsim/auxilliary/sequence/gradients.py b/cmrsim/auxilliary/sequence/gradients.py
new file mode 100644
index 0000000000000000000000000000000000000000..3199203de9d40a7016bcd534f513106294dc5ec4
--- /dev/null
+++ b/cmrsim/auxilliary/sequence/gradients.py
@@ -0,0 +1,169 @@
+__all__ = ['GradientWaveForm', 'ReadOutGradient']
+
+import tensorflow as tf
+import tensorflow_probability as tfp
+import h5py
+import numpy as np
+from scipy import interpolate
+
+
+class GradientWaveForm(tf.Module):
+ #: Time delta used for griding the waveform
+ dt: tf.Tensor = None
+
+ def __init__(self, timing: tf.Tensor, waveform: tf.Tensor):
+ """
+
+ :param timing: (N, 1)
+ :param waveforms: (N, 3)
+ """
+ super(GradientWaveForm, self).__init__(name='gradient_waveform')
+ self.timing = tf.Variable(initial_value=timing, trainable=False, dtype=tf.float32, shape=(None, 1))
+ self.waveform = tf.Variable(initial_value=waveform, trainable=False, dtype=tf.float32, shape=(None, 3))
+ self.dt = timing[1:] - timing[0:-1]
+
+ def __call__(self):
+ """ Creates dataset from waveform tensor.
+ :return:
+ """
+ return
+
+ def regrid(self, dt: float):
+ """ Regrids wave form to a finer or coarser grid. Requires the original to be regular!
+
+ :param dt: new delta t
+ :return:
+ """
+ total_duration = self.timing[-1, 0]
+ start = self.timing[0, 0]
+ n_steps = int(total_duration // dt)
+ dense_time_grid = tf.math.cumsum(tf.ones(n_steps, dtype=tf.float32) * dt) - dt
+
+ wave_form = []
+ for direction in range(3):
+ wave_form += [tfp.math.interp_regular_1d_grid(dense_time_grid, x_ref_min=start, x_ref_max=total_duration,
+ y_ref=self.waveform.read_value()[:, direction]), ]
+
+ self.timing.assign(dense_time_grid[:, tf.newaxis])
+ self.waveform.assign(tf.stack(wave_form, axis=-1))
+ self.dt = tf.constant(self.timing.read_value()[1:] - self.timing.read_value()[:-1])
+
+ def union(self, other: 'GradientWaveForm'):
+ pass
+
+ def calculate_n_moment(self, n: int, cumulative: bool = True):
+ """ Calculates the nth moment of the gradient waveform object either as cumulative function or scalar value per
+ gradient direction.
+ .. math::
+ \int_0 ^t t'^n g(t) dt'
+
+ :param n:
+ :param cumulative:
+ :return: (3, ) or (N, 3)
+ """
+ integrand = self.timing.read_value() ** n * self.waveform.read_value()
+
+ # Manual trapezoidal integration
+ dt = self.timing.read_value()[1:] - self.timing.read_value()[:-1]
+ temp = (integrand[0:-1] + integrand[1:]) / 2 * dt
+ if cumulative:
+ result = tf.cumsum(temp, axis=0)
+ result = tf.concat([tf.zeros((1, 3), dtype=tf.float32), result], axis=0)
+ else:
+ result = tf.reduce_sum(temp, axis=0)
+ return result
+
+ @classmethod
+ def from_jemris_hd5(cls, hd5f_path: str):
+ """ Loads a hd5 file created by JEMRIS and instatiates a GradientWaveform object from it
+
+ :param hd5f_path: str
+ :return: 'GradientWaveForm'
+ """
+ file = h5py.File(hd5f_path, 'r')
+ seq_def = file['seqdiag']
+ g_array = tf.stack([np.array(seq_def[name], dtype=np.float32) for name in ['GX', 'GY', 'GZ']], axis=-1)
+ t_array = tf.constant(seq_def['T'], dtype=tf.float32)
+ return cls(timing=t_array[:, tf.newaxis], waveform=g_array)
+
+ @classmethod
+ def from_gve_file(cls):
+ # Todo: refer to issue #9 on the IBT-CMR global board, where christian guenthner provided matlab code for this.
+ pass
+
+ @classmethod
+ def from_sparse_timings(cls, sparse_timings: tf.Tensor, sparse_waveform: tf.Tensor, dt: float = 1e-3):
+ """ Linearly interpolates the specified switching times/amplitudes to a regular grid of with step width dt.
+
+ :param sparse_timings: (N, 1)
+ :param sparse_waveform: (N, 3)
+ :param dt: float
+ :return:
+ """
+ total_duration = tf.math.reduce_max(sparse_timings)
+ n_steps = int(total_duration // dt)
+ dense_time_grid = tf.math.cumsum(tf.ones(n_steps, dtype=tf.float32) * dt) - dt
+
+ # This will fail if the specified times for switching are not on the grid!
+ switching_indices = tf.searchsorted(dense_time_grid, sparse_timings[:, 0], )
+ assert switching_indices.shape[0] == sparse_timings.shape[0]
+
+ value_segments = []
+ for i in range(sparse_timings.shape[0] - 1):
+ value_left, value_right = sparse_waveform[i:i+2]
+ timing_left, timing_right = sparse_timings[i:i+2]
+ n_steps_per_interval = tf.round((timing_right - timing_left) / dt)
+ slope = (value_right - value_left) / n_steps_per_interval
+ value_segments += [tf.einsum('i, c -> ic', tf.cast(tf.range(0, int(n_steps_per_interval)), tf.float32), slope) + value_left, ]
+ return cls(timing=dense_time_grid[:, tf.newaxis], waveform=tf.concat(value_segments, axis=0))
+
+
+class ReadOutGradient(GradientWaveForm):
+ def __init__(self, timing: tf.Tensor, waveform: tf.Tensor, acquisition_times: tf.Tensor):
+ """
+
+ :param timing: (N, 1)
+ :param waveform: (N, 3)
+ :param acquisition_times: (N, 1)
+ """
+ super(ReadOutGradient, self).__init__(timing=timing, waveform=waveform)
+ self.acquisiton_times = tf.Variable(initial_value=acquisition_times, trainable=False, shape=(None, 1),
+ dtype=tf.float32)
+
+ def get_sampling_vectors(self):
+ """ Integrates the trajectory and interpolates the value at the given sampling times
+ :return: tf.Tensor (N, 3)
+ """
+ m0 = self.calculate_n_moment(n=0, cumulative=True)
+ t = self.timing.read_value()[:, 0]
+ interp_f = interpolate.interp1d(t.numpy(), m0.numpy(), axis=0, assume_sorted=True)
+ sampling_times = self.acquisiton_times.read_value().numpy()
+ k_space_vectors = interp_f(sampling_times[:, 0])
+ return tf.constant(k_space_vectors, dtype=tf.float32)
+
+ def get_sampling_times(self):
+ return self.acquisiton_times.read_value()
+
+ @classmethod
+ def from_jemris_hd5(cls, hd5f_path: str):
+ file = h5py.File(hd5f_path, 'r')
+ seq_def = file['seqdiag']
+ g_array = tf.stack([np.array(seq_def[name], dtype=np.float32) for name in ['GX', 'GY', 'GZ']], axis=-1)
+ t_array = tf.constant(seq_def['T'], dtype=tf.float32)
+
+ acquisition_indices = tf.where(np.array(seq_def['META'], dtype=np.int32) == 2)
+ acquisition_times = tf.gather(t_array, acquisition_indices, axis=0, batch_dims=1)
+ return cls(timing=t_array[:, tf.newaxis], waveform=g_array, acquisition_times=acquisition_times)
+
+ @classmethod
+ def from_sparse_timings(cls, *args, **kwargs):
+ raise AttributeError('Class method not implemented in child-class', cls.__class__)
+
+ @classmethod
+ def from_gve_file(cls, *args, **kwargs):
+ raise AttributeError('Class method not implemented in child-class', cls.__class__)
+
+
+
+
+
diff --git a/cmrsim/auxilliary/snr.py b/cmrsim/auxilliary/snr.py
index 413581fdd92b83eec8270a4e9f7270d4952cdb2b..6a1ca312cedd6d60b9bb41bb252a78650e38a8dc 100644
--- a/cmrsim/auxilliary/snr.py
+++ b/cmrsim/auxilliary/snr.py
@@ -1,4 +1,5 @@
-__all__ = []
+""" Functionality to determine SNR for images """
+__all__ = ["calculate_snr", "compute_noise_std"]
from typing import Union, Optional, Iterable
import warnings
@@ -37,17 +38,18 @@ def calculate_snr(single_coil_images: Union[np.ndarray, tf.Tensor],
def compute_noise_std(noiseless_single_coil_images: Union[np.ndarray, tf.Tensor], target_snr: Iterable[float],
coil_sensitivities: Optional[Union[np.ndarray, tf.Tensor]] = None,
- mask: Optional[Union[np.ndarray, tf.Tensor]] = None, **kwargs):
- """
+ mask: Optional[Union[np.ndarray, tf.Tensor]] = None, **kwargs) -> np.ndarray:
+ """ Computes the standard deviation of the complex gaussian noise for a set of target snr given multiple single
+ coil images, which are combined on reconstruction.
:param noiseless_single_coil_images: (n_coils, X, Y, [Z]) of type complex64
:param target_snr:
:param coil_sensitivities: (n_coils, X, Y, [Z]) of type complex64, Optional: defaults to ones(1, X, Y, [Z])
- Is used for iterative refinement.
+ Is used for iterative refinement.
:param mask: (X, Y, [Z]) Optional: binary mask to specify ROI for SNR computation. Defaults to entire image.
:param kwargs: - iteratively_refine: (bool) default=True. If True and coil_sensitivities are given, uses iterative
- method to refine std estimation.
- :return: estimated_stds np.array of type np.float32 with shape like np.array(target_snr)
+ method to refine std estimation.
+ :return: estimated_stds (np.array) of type np.float32 with shape like np.array(target_snr)
"""
if mask is None:
mask = np.ones(noiseless_single_coil_images.shape[1:], dtype=np.float32)
diff --git a/cmrsim/encoding/base.py b/cmrsim/encoding/base.py
index 6192ce562e8e62470439eb8aab445660920ed145..7092c7b947a727331fecabef5fa161072f79c8a7 100644
--- a/cmrsim/encoding/base.py
+++ b/cmrsim/encoding/base.py
@@ -1,36 +1,44 @@
__all__ = ["BaseSampling", ]
-from typing import Union, Iterable
+from typing import Union, Iterable, TYPE_CHECKING
import abc
import math
import tensorflow as tf
+if TYPE_CHECKING:
+ from cmrsim.auxilliary.sequence.gradients import ReadOutGradient
+
class BaseSampling(tf.Module):
""" Base Module for implementing a time-dependent sampling in k-space. Is meant to be inherited from when specifying
standard trajectories. """
#: Flat and unsegmented tensor of all sampling-event times. Is set by a call of
# abstract function self._calculate_trajectory. Expected shape: (-1, )
- sampling_times: tf.Variable
+ sampling_times: tf.Variable = None
#: Flat and unsegmented tensor of 3D k-space vectors corresponding to sampling-events. Is set by a call of
# abstract function self._calculate_trajectory. Expected shape: (-1, 3)
- k_space_vectors: tf.Variable
- absolute_noise_std: tf.Variable
- k_space_segments: tf.Variable
- number_of_samples: tf.Tensor
- device: str
+ k_space_vectors: tf.Variable = None
+ #: absolute values for the standard deviation of the sampled noise distributions added to the k-space signal
+ absolute_noise_std: tf.Variable = None
+ #: Number of segments used to subdivide the simulation memory load
+ k_space_segments: tf.Variable = None
+ #: Number of k-space samples that are defined by the _calculate trajectory method
+ number_of_samples: tf.Tensor = None
+ #: Name of the device that the module is executed on (defaults to: GPU:0 - CPU:0)
+ device: str = None
_segmented_sampling_times = None # set in update
_segmented_k_vectors = None # set in update
- def __init__(self, absolute_noise_std: Union[float, Iterable[float]], name: str = None, **kwargs):
+ def __init__(self, absolute_noise_std: Union[float, Iterable[float]], name: str = None, k_space_segments: int = 1,
+ device: str = None):
"""
:param absolute_noise_std: if < 0, add_noise() will leave signal unchanged
:param name: (str) defining the module name-scope
- :param kwargs: - k_space_segments: int
- - device
+ :param k_space_segments: int
+ :param device: str e.g. 'GPU:0'
"""
- device = kwargs.get('device', None)
+
if device is None:
if tf.config.get_visible_devices('GPU'):
self.device = 'GPU:0'
@@ -45,8 +53,7 @@ class BaseSampling(tf.Module):
self.absolute_noise_std = tf.Variable(tf.constant(absolute_noise_std), dtype=tf.float32, trainable=False,
shape=[None, ], name='absolute_noise_std')
- self.k_space_segments = tf.Variable(tf.constant(kwargs.get('k_space_segments', 1)),
- dtype=tf.int32, trainable=False)
+ self.k_space_segments = tf.Variable(tf.constant(k_space_segments), dtype=tf.int32, trainable=False)
self.k_space_vectors = tf.Variable(tf.zeros((1, 3), tf.float32), shape=[None, 3], dtype=tf.float32,
trainable=False, name='k_space_vectors')
self.sampling_times = tf.Variable(tf.zeros((1, ), tf.float32), shape=[None, ], dtype=tf.float32,
@@ -73,7 +80,8 @@ class BaseSampling(tf.Module):
self._segmented_k_vectors = tf.RaggedTensor.from_row_lengths(self.k_space_vectors.read_value(), row_lengths)
@tf.Module.with_name_scope
- def __call__(self, transverse_magnetization: tf.Tensor, r_vectors: tf.Tensor, **kwargs):
+ def __call__(self, transverse_magnetization: tf.Tensor, r_vectors: tf.Tensor,
+ segment_index: Union[int, tf.Tensor] = 0, **kwargs):
"""Calculates fourier phases for given object-representation at r-vectors. For multiple different contrasts
#repetitions is the representing axis.
@@ -85,12 +93,11 @@ class BaseSampling(tf.Module):
#repetitions, #k-space-samples can be 1
:param r_vectors: (#batch, #voxel, #repetitions, 3, #k-space-samples), axis #repetitions and #k-space-samples
can be 1 to broadcast for coordinate reuse.
+ :param segment_index: int
:param kwargs: - noise_seed: if not None, sets seed for sampling the noise vector.
- - segment_index: int
:return: tf.Tensor
"""
with tf.device(self.device):
- segment_index = kwargs.get('segment_index', 0)
n_repetitions = tf.shape(transverse_magnetization)[2]
fourier_factors = self._calculate_fourier_phases(r_vectors=r_vectors, segment_index=segment_index)
@@ -118,9 +125,11 @@ class BaseSampling(tf.Module):
:return:
"""
with tf.name_scope('calculate_fourier_phase'):
+ segment_batch_size = tf.size(self._segmented_sampling_times[segment_index])
if tf.shape(r_vectors)[4] == 1:
- r_vectors = tf.tile(r_vectors, [1, 1, 1, 1, tf.size(self._segmented_sampling_times[segment_index])])
-
+ r_vectors = tf.tile(r_vectors, [1, 1, 1, 1, segment_batch_size])
+ else:
+ r_vectors = r_vectors[..., segment_batch_size * segment_index: segment_batch_size * (segment_index+1)]
fourier_phases = tf.einsum('bvrjk, kj -> bvrk', r_vectors, self._segmented_k_vectors[segment_index])
fourier_phases = 1j * tf.cast(tf.scalar_mul(tf.constant(2. * math.pi, tf.float32), fourier_phases), tf.complex64)
fourier_factors = tf.exp(fourier_phases)
@@ -176,3 +185,25 @@ class BaseSampling(tf.Module):
else:
return self._sampling_times.read_value()
+ @classmethod
+ def from_waveform(cls, wave_form_object: 'ReadOutGradient', absolute_noise_std: Union[float, Iterable[float]],
+ k_space_segments: int = 1, name: str = None, device: str = None):
+ """ Instantiates a base object and monkey patches the _calculate_trajectory() method
+ :param wave_form_object:
+ :param absolute_noise_std:
+ :param k_space_segments:
+ :param name:
+ :param device:
+ :return:
+ """
+ k_space_vectors = wave_form_object.get_sampling_vectors()
+ sampling_times = wave_form_object.get_sampling_times()
+
+ def _calc_trajectory(self):
+ return k_space_vectors, sampling_times[:, 0]
+
+ MonkeyPatchSampling = BaseSampling
+ MonkeyPatchSampling._calculate_trajectory = _calc_trajectory
+ encoding_module = MonkeyPatchSampling(absolute_noise_std=absolute_noise_std, name=name,
+ k_space_segments=k_space_segments, device=device)
+ return encoding_module
diff --git a/cmrsim/encoding/cartesian.py b/cmrsim/encoding/cartesian.py
new file mode 100644
index 0000000000000000000000000000000000000000..d4ef771ccff18bd6d6f26d08fdd65fdb9c8ee4ab
--- /dev/null
+++ b/cmrsim/encoding/cartesian.py
@@ -0,0 +1,58 @@
+""" This module contains all modules that are related to cartesian sampling strategies.
+"""
+
+__all__ = ["SingleLinePerShot"]
+
+from typing import List, Tuple, Union, Optional, TYPE_CHECKING
+
+import tensorflow as tf
+if TYPE_CHECKING:
+ import numpy as np
+
+from cmrsim.encoding.base import BaseSampling
+
+
+class SingleLinePerShot(BaseSampling):
+ """ Encoding Module that assumes the handed in signal tensor in images space to be calculated according to one
+ process per k-space line. Number of segments in this implementation corresponds to number of acquired k-space lines
+ """
+ def __init__(self, field_of_view: Union[Tuple[float, float], List[float]],
+ sampling_matrix_size: Union[Tuple[int, int], List[int], 'np.ndarray'],
+ absolute_noise_std: float,
+ read_out_duration: float = None,
+ repetition_time: float = None,
+ acquisition_start: Optional[float] = 0., **kwargs):
+
+ self.fov = tf.Variable(tf.constant(field_of_view), dtype=tf.float32, name='field_of_view')
+ self.matrix_size = tf.Variable(tf.constant(sampling_matrix_size), dtype=tf.int32, name='acquisition_matrix')
+ self.readout_duration = tf.Variable(tf.constant(read_out_duration), dtype=tf.float32, name='readout_duration')
+ self.acquisition_time_offset = tf.Variable(tf.constant(acquisition_start), dtype=tf.float32, name='acquisition_offset')
+ self.repetition_time = tf.Variable(tf.constant(repetition_time), dtype=tf.float32, name='repetition_time')
+ super(SingleLinePerShot, self).__init__(absolute_noise_std, name="single_line_pershot",
+ k_space_segments=sampling_matrix_size[1], **kwargs)
+
+ def _calculate_trajectory(self) -> (tf.Tensor, tf.Tensor):
+ """ Calculates the 2D k-space trajectory for a cartesian readout. The k_z component of all 3D-k-space sampling
+ points is set to 0 to calculate averaging in z-direction.
+
+ For even as well as uneven matrix dimension, the first k-space sample is acquired at [-k_max, -k_max, 0] and
+ it is guaranteed, that the mid, or mid+1/2 sampling point is at the k-space center.
+
+ :return: k-space-vectors, sampling-times (tf.Tensor, tf.Tensor)
+ """
+ ro_steps, pe_steps = tf.cast(self.matrix_size[0], tf.float32), tf.cast(self.matrix_size[1], tf.float32)
+
+ # Calculate k-space-vectors
+ kx, ky, kz = tf.meshgrid(tf.range(self.matrix_size[0]), tf.range(self.matrix_size[1]), 1, indexing='ij')
+ k_vectors = tf.stack((kx, ky, kz), axis=-1)
+ k_vectors = k_vectors - tf.constant([int(ro_steps), int(pe_steps), 0], shape=(3,)) // 2
+ delta_k = tf.math.divide_no_nan(1., self.fov)
+ k_vectors = tf.cast(k_vectors, tf.float32) * tf.concat((delta_k, [0.]), axis=0)
+ k_vectors = tf.reshape(k_vectors, [-1, 3])
+
+ # Calculate Sampling times from matrix size, ADC-duration, blip-duration and leading time offset
+ dwell_time_borders = tf.range(0., ro_steps + 1.) / ro_steps * self.readout_duration
+ dwell_time_centers = dwell_time_borders[:-1] + (dwell_time_borders[1:] - dwell_time_borders[:-1]) / 2
+ sampling_times = tf.tile(dwell_time_centers, [self.matrix_size[1], ]) - self.readout_duration / 2
+
+ return k_vectors, sampling_times
diff --git a/cmrsim/encoding/epi.py b/cmrsim/encoding/epi.py
index 86df54dcee8ff49f5969b3bad620a8f9a77f30eb..5172bdf6b8b9e2bd05275db24457588b3a7508ac 100644
--- a/cmrsim/encoding/epi.py
+++ b/cmrsim/encoding/epi.py
@@ -10,6 +10,9 @@ from cmrsim.encoding.base import BaseSampling
class EPI(BaseSampling):
+ """ Encoding Module implementing a single shot echo planar imaging trajectory. The subdivision into segments is
+ solely used to manage memory limitation during simulation.
+ """
def __init__(self, field_of_view: Union[Tuple[float, float], List[float]],
sampling_matrix_size: Union[Tuple[int, int], List[int], 'np.ndarray'],
absolute_noise_std: float,
@@ -34,13 +37,13 @@ class EPI(BaseSampling):
f'specified. Instead pixel_bandwith: {bandwidth_per_pixel} and '
f'read_out_duration: {read_out_duration}, was given!')
- self._fov = tf.Variable(tf.constant(field_of_view), dtype=tf.float32, name='field_of_view')
- self._matrix_size = tf.Variable(tf.constant(sampling_matrix_size), dtype=tf.int32, name='acquisition_matrix')
+ self.fov = tf.Variable(tf.constant(field_of_view), dtype=tf.float32, name='field_of_view')
+ self.matrix_size = tf.Variable(tf.constant(sampling_matrix_size), dtype=tf.int32, name='acquisition_matrix')
if read_out_duration is None:
read_out_duration = 1000. / bandwidth_per_pixel # noqa
- self._readout_duration = tf.Variable(tf.constant(read_out_duration), dtype=tf.float32, name='readout_duration')
- self._acquisition_time_offset = tf.Variable(tf.constant(acquisition_start), dtype=tf.float32, name='acquisition_offset')
- self._blip_duration = tf.Variable(tf.constant(blip_duration), dtype=tf.float32, name='blip_duration')
+ self.readout_duration = tf.Variable(tf.constant(read_out_duration), dtype=tf.float32, name='readout_duration')
+ self.acquisition_time_offset = tf.Variable(tf.constant(acquisition_start), dtype=tf.float32, name='acquisition_offset')
+ self.blip_duration = tf.Variable(tf.constant(blip_duration), dtype=tf.float32, name='blip_duration')
super(EPI, self).__init__(absolute_noise_std, name="epi", **kwargs)
def _calculate_trajectory(self) -> (tf.Tensor, tf.Tensor):
@@ -52,20 +55,20 @@ class EPI(BaseSampling):
:return: k-space-vectors, sampling-times (tf.Tensor, tf.Tensor)
"""
- ro_steps, pe_steps = tf.cast(self._matrix_size[0], tf.float32), tf.cast(self._matrix_size[1], tf.float32)
+ ro_steps, pe_steps = tf.cast(self.matrix_size[0], tf.float32), tf.cast(self.matrix_size[1], tf.float32)
# Calculate k-space-vectors
- kx, ky, kz = tf.meshgrid(tf.range(self._matrix_size[0]), tf.range(self._matrix_size[1]), 1, indexing='ij')
+ kx, ky, kz = tf.meshgrid(tf.range(self.matrix_size[0]), tf.range(self.matrix_size[1]), 1, indexing='ij')
k_vectors = tf.stack((kx, ky, kz), axis=-1)
k_vectors = k_vectors - tf.constant([int(ro_steps), int(pe_steps), 0], shape=(3, )) // 2
- delta_k = tf.math.divide_no_nan(1., self._fov)
+ delta_k = tf.math.divide_no_nan(1., self.fov)
k_vectors = tf.cast(k_vectors, tf.float32) * tf.concat((delta_k, [0.]), axis=0)
k_vectors = tf.reshape(k_vectors, [-1, 3])
# Calculate Sampling times from matrix size, ADC-duration, blip-duration and leading time offset
- dwell_time_borders = tf.range(0., ro_steps+1.) / ro_steps * self._readout_duration
+ dwell_time_borders = tf.range(0., ro_steps+1.) / ro_steps * self.readout_duration
dwell_time_centers = dwell_time_borders[:-1] + (dwell_time_borders[1:] - dwell_time_borders[:-1])/2
- pe_offsets = tf.range(0., pe_steps) * (self._blip_duration + self._readout_duration)
+ pe_offsets = tf.range(0., pe_steps) * (self.blip_duration + self.readout_duration)
time_slices = ()
for s in range(int(pe_steps)):
@@ -74,6 +77,6 @@ class EPI(BaseSampling):
else:
time_slices += (dwell_time_centers + pe_offsets[s],)
sampling_time_matrix = tf.stack(time_slices, axis=0)
- sampling_times = tf.reshape(sampling_time_matrix, [-1]) - self._acquisition_time_offset
+ sampling_times = tf.reshape(sampling_time_matrix, [-1]) - self.acquisition_time_offset
return k_vectors, sampling_times
diff --git a/cmrsim/reconstruction/base.py b/cmrsim/reconstruction/base.py
index 06da6389885638d7c5b304d233684785fe52f110..257027eb1b39f879dffb8b432827dd2dc826d0f6 100644
--- a/cmrsim/reconstruction/base.py
+++ b/cmrsim/reconstruction/base.py
@@ -7,7 +7,8 @@ import tensorflow as tf
class BaseRecon(tf.Module):
- output_matrix_size: tf.Tensor
+ #: Tensor defining the shape of the returned, reconstructed images which can be different from the sampling matrix
+ output_matrix_size: tf.Tensor = None
def __init__(self, output_matrix_size: Union[Tuple[int, int, int], Tuple[int, ...], tf.Tensor],
name: str, **kwargs):
diff --git a/cmrsim/reconstruction/cartesian.py b/cmrsim/reconstruction/cartesian.py
index 050dac8995a3862541166bb33b0d7d37caca6a68..ce284ca7f6d2956be1308744a0063fb2147300ed 100644
--- a/cmrsim/reconstruction/cartesian.py
+++ b/cmrsim/reconstruction/cartesian.py
@@ -7,8 +7,6 @@ from cmrsim.reconstruction.base import BaseRecon
class Cartesian2D(BaseRecon):
- output_matrix_size: tf.Tensor # defines the shape of the returned, reconstructed images
-
def __init__(self, sample_matrix_size: Union[Tuple[int, int]],
padding: Optional[Union[Tuple[int, int], Iterable[Tuple[int, int]]]] = None, **kwargs):
""" Performs 2D inverse FFT on __call__. If padding argument is given, performs symmetric (per direction)
diff --git a/cmrsim/signal_processes/base.py b/cmrsim/signal_processes/base.py
index 0a3c236225adc9bed122d3c1b1b58685499c0abc..e5dd0c24c73f3ce63c52683b37d13606280cd08b 100644
--- a/cmrsim/signal_processes/base.py
+++ b/cmrsim/signal_processes/base.py
@@ -10,9 +10,12 @@ if TYPE_CHECKING:
class BaseSignalModel(tf.Module):
+ #: Tuple of names specifying the input quantities (anatomy) to run this signal module
required_quantities: Tuple[str] = ()
+ #: Factor by which the repetition axis (2) of the signal_tensor will be expanded (e.g. multiple coils)
expansion_factor: int = None
- device: str
+ #: Name of the device that the module is executed on (defaults to: GPU:0 - CPU:0)
+ device: str = None
def __init__(self, name: str = None, device: str = None):
""" Class to inherit every module from, that multiplies a term \Psi to the integrand in the fourier transform:
diff --git a/cmrsim/signal_processes/solution_operators/__init__.py b/cmrsim/signal_processes/solution_operators/__init__.py
index 6ae1ea86d00804dc33b89c5b47c44e0f69b947f8..a727ad11b39ef8df1b18c81c4602672ef4f55be4 100644
--- a/cmrsim/signal_processes/solution_operators/__init__.py
+++ b/cmrsim/signal_processes/solution_operators/__init__.py
@@ -1,3 +1,68 @@
-__all__ = ["coil_sensitivities", "diffusion_weighting", "sequences", "t2_star"]
+import pkgutil
+import os
+_ABSOLUTE_PATH = os.path.dirname(__file__)
+_ROOT_PATH = os.path.dirname(os.path.dirname(os.path.dirname(_ABSOLUTE_PATH)))
+__all__ = []
+
+for loader, module_name, is_pkg in pkgutil.walk_packages([_ABSOLUTE_PATH, ]):
+ __all__ += [module_name, ]
from cmrsim.signal_processes.solution_operators import *
+
+
+def _get_table_header():
+ h = f"+{'-' * 40}+{'-' * 100}+"
+ h += f"\n|{'Sub - module':<40}|{'Required Quantities':<100}|"
+ h += f"\n+{'=' * 40}+{'=' * 100}+"
+ return h
+
+
+def _format_table_row(lcol_string, rcol_string):
+ s = f"\n|{lcol_string :<40}|{rcol_string:<100}|"
+ s += f"\n+{'-' * 40}+{'-' * 100}+"
+ return s
+
+
+def _format_subheader_row(name):
+ return f"\n|{name :<141}|\n+{'=' * 141}+"
+
+
+def _list_required_properties():
+ import inspect
+ import importlib
+ readme_string = _get_table_header()
+ for _, module_name, _ in pkgutil.iter_modules([os.path.dirname(__file__), ]):
+ i = importlib.import_module(f"cmrsim.signal_processes.solution_operators.{module_name}")
+ # readme_string += _format_subheader_row(module_name)
+
+ for name, obj in inspect.getmembers(i):
+ if name in i.__all__ and inspect.isclass(obj):
+ if obj.__dict__.get('required_quantities') is not None:
+ tuple_str = str(obj.__dict__.get('required_quantities'))
+ readme_string += _format_table_row(name, tuple_str)
+
+ readme_string += "\n"
+ return readme_string
+
+
+def create_readme():
+ _PLACE_HOLDER_TAGS = ["PH_T_REQUIRED_PROPERTIES", ]
+ _REPLACEMENTS = [_list_required_properties(), ]
+
+ with open(f'{_ROOT_PATH}/docs/template.rst', 'r+') as template_file:
+ all_lines = template_file.readlines()
+ template_string = ''.join(all_lines)
+
+ for place_holder_tag, replacement in zip(_PLACE_HOLDER_TAGS, _REPLACEMENTS):
+ template_string = template_string.replace(place_holder_tag, replacement)
+
+ with open(f'{_ROOT_PATH}/docs/source/req_quant_table.rst', 'wb') as readme_file:
+ readme_file.write(template_string.encode('utf-8'))
+
+
+if __name__ == "__main__":
+ import sys
+ print(_ROOT_PATH, _ABSOLUTE_PATH)
+ sys.path.append(_ROOT_PATH)
+ create_readme()
+
diff --git a/cmrsim/signal_processes/solution_operators/coil_sensitivities.py b/cmrsim/signal_processes/solution_operators/coil_sensitivities.py
index c9955f790be46efe4f9526cb79e962c3c7aa8f72..8b61bf459602f735a07a2d4e2901ea676146c363 100644
--- a/cmrsim/signal_processes/solution_operators/coil_sensitivities.py
+++ b/cmrsim/signal_processes/solution_operators/coil_sensitivities.py
@@ -15,27 +15,26 @@ class CoilSensitivity(LookUpTableModule):
_device2: str
def __init__(self, coil_sensitivities: Union['np.ndarray', tf.Tensor], map_dimensions: Tuple[float, float, float],
- **kwargs):
+ device: str = 'GPU:0', device_lookup: str = 'CPU:0', **kwargs):
"""
:param coil_sensitivities: (#channels, X, Y, Z) with dtype complex64
:param map_dimensions: (X, Y, Z)
- :param kwargs: - device: (str) Device on which all operations except for the look-up are placed (default: GPU:0)
- - device_lookup: (str) Device where the lookup table is places (defaults to CPU:0)
+ :param device: (str) Device on which all operations except for the look-up are placed (default: GPU:0)
+ :param device_lookup: (str) Device where the lookup table is places (defaults to CPU:0)
"""
super(CoilSensitivity, self).__init__(look_up_map3d=tf.cast(coil_sensitivities, tf.complex64),
map_dimensions=map_dimensions, name="coil_sensitivities",
- device=kwargs.get('device_lookup', 'CPU:0'))
- self._device2 = kwargs.get('device', 'GPU:0')
+ device=device_lookup)
+ self._device2 = device
@tf.Module.with_name_scope
- def __call__(self, signal_tensor: tf.Tensor, **kwargs):
+ def __call__(self, signal_tensor: tf.Tensor, r_vectors: tf.Tensor, **kwargs):
"""
:param signal_tensor:
- :param kwargs: - r_vectors (#batch, #voxels, #repetitions, 3, #samples)
+ :param r_vectors: (#batch, #voxels, #repetitions, 3, #samples)
:return:
"""
with tf.device(self._device2):
- r_vectors = kwargs.get(self.required_quantities[0])
# sensitivity_factors shape: (#voxels, #repetitions, #samples, n_channels), with axis 0,1,2 taken from r
with tf.name_scope(self.name + "/look_up/"):
sensitivity_factors = super(CoilSensitivity, self).__call__(r_vectors=r_vectors)
diff --git a/cmrsim/signal_processes/solution_operators/diffusion_weighting.py b/cmrsim/signal_processes/solution_operators/diffusion_weighting.py
index 02ce7cd99fc7685f0534e0d6390bb989b0caca98..73299e55e0d1242fe4c0c89e1b1f887ec9c31e16 100644
--- a/cmrsim/signal_processes/solution_operators/diffusion_weighting.py
+++ b/cmrsim/signal_processes/solution_operators/diffusion_weighting.py
@@ -27,16 +27,15 @@ class ConstantGaussianDiffusion(BaseSignalModel):
self.update()
@tf.Module.with_name_scope
- def __call__(self, signal_tensor: tf.Tensor, **kwargs) -> tf.Tensor:
+ def __call__(self, signal_tensor: tf.Tensor, diffusion_tensor: tf.Tensor, **kwargs) -> tf.Tensor:
"""
:param signal_tensor: (#batch, #voxel, #repetitions, #k-space-samples) last two dimensions can be 1
- :param kwargs: diffusion_tensor (#batch, #voxels, 3, 3)
+ :param diffusion_tensor: diffusion_tensor (#batch, #voxels, 3, 3)
:return: (#batch, #voxel, #repetitions * #b_vectors, #k-space-samples)
"""
with tf.device(self.device):
- diff_tensors = kwargs.get('diffusion_tensor')
input_shape = tf.shape(signal_tensor)
- exponent = tf.einsum('ni, bvij, nj -> bvn', self.b_vectors, diff_tensors, self.b_vectors)
+ exponent = tf.einsum('ni, bvij, nj -> bvn', self.b_vectors, diffusion_tensor, self.b_vectors)
decay_factor = tf.cast(tf.exp(-exponent), dtype=tf.complex64)
temp = tf.einsum('bvrk, bvn -> bvrnk', signal_tensor, decay_factor)
return tf.reshape(temp, (input_shape[0], input_shape[1], -1, input_shape[3]))
diff --git a/cmrsim/signal_processes/solution_operators/perfusion.py b/cmrsim/signal_processes/solution_operators/perfusion.py
new file mode 100644
index 0000000000000000000000000000000000000000..d7bbbab09e2b82003f6f9ab3fb72a898e7eaf71e
--- /dev/null
+++ b/cmrsim/signal_processes/solution_operators/perfusion.py
@@ -0,0 +1,93 @@
+__all__ = ['ContrastAgentPerfusion', ]
+
+from typing import Union, Iterable
+import os
+
+import tensorflow as tf
+import tensorflow_probability as tfp
+
+try:
+ import matlab.engine
+except ImportError:
+ import warnings
+ warnings.warn(f'Found no MATLAB installation, registered to the python kernel. Matlab functionality will not work!')
+
+import numpy as np
+from cmrsim.signal_processes.base import BaseSignalModel
+
+
+class ContrastAgentPerfusion(BaseSignalModel):
+ required_quantities = ('T1', 'contrast_dynamic_index')
+ expansion_factor = 1
+
+ def __init__(self, contrast_times: tf.Tensor, sequence_module: BaseSignalModel,
+ relaxivity: float = 5.6 / 1000, integration_start: float = 0., integration_end: float = 100.,
+ n_reference_points: int = 101, **kwargs):
+ """
+
+ :param contrast_times:
+ :param sequence_module: (BaseSignalModel) Module from cmrsim.signal_processes.solution_operator.sequences
+ :param relaxivity: (float) Relaxvity in [1/ms]
+ :param kwargs:
+ """
+
+ super(ContrastAgentPerfusion, self).__init__(name="perfusion_contrast_agent", **kwargs)
+ self.acquisition_times = tf.Variable(contrast_times, dtype=tf.float32, shape=(None, ))
+ self.relaxivity = tf.Variable(relaxivity, dtype=tf.float32, shape=())
+ self.sequence_operator = sequence_module
+ time_grid, concentration_curves = _btex_wrapper(time_start=integration_start, time_end=integration_end,
+ n_points=n_reference_points)
+ self._concentration_curves = tf.constant(concentration_curves, dtype=tf.float32)
+ self._concentration_time_grid = tf.constant(time_grid, dtype=tf.float32)
+ self._interpolated_concentrations = None
+ self.update()
+
+ @tf.Module.with_name_scope
+ def __call__(self, signal_tensor: tf.Tensor, T1: tf.Tensor, contrast_dynamic_index: tf.Tensor,
+ segment_index: tf.Tensor = 0, **kwargs): # noqa
+ """
+ :param contrast_dynamic_index: Assumes shape (N, V, 1, 1)!
+ """
+ concentrations = tf.gather(self._interpolated_concentrations,
+ tf.cast(contrast_dynamic_index[:, :, 0, 0], tf.int32), axis=0)[..., tf.newaxis]
+ r1_per_repetition = tf.math.divide_no_nan(1., T1) + (concentrations * self.relaxivity.read_value())
+ t1_per_repetition = tf.math.divide_no_nan(1., r1_per_repetition)
+ return self.sequence_operator(signal_tensor, T1=t1_per_repetition, segment_index=segment_index)
+
+ def update(self):
+ x = self.acquisition_times.read_value()
+ self.expansion_factor = x.shape[0]
+ self._interpolated_concentrations = tfp.math.interp_regular_1d_grid(x,
+ x_ref_min=self._concentration_time_grid[0],
+ x_ref_max=self._concentration_time_grid[-1],
+ y_ref=self._concentration_curves)
+ self._interpolated_concentrations = tf.concat((tf.zeros_like(self._interpolated_concentrations[0:1]),
+ self._interpolated_concentrations), axis=0)
+
+
+def _btex_wrapper(input_f: Union[float, Iterable[float]] = 2.82/60/2,
+ input_ps: Union[float, Iterable[float]] = 1.32/60/2,
+ input_vp: Union[float, Iterable[float]] = 0.120 / 2,
+ input_vex: Union[float, Iterable[float]] = (0.208 + 0.203) / 2,
+ time_start: float = 0., time_end: float = 100., n_points: int = 101) -> (np.ndarray, np.ndarray):
+ """ Computes the contrast agent concentration according to the btex model, implemented in matlab.
+ :param input_f:
+ :param input_ps:
+ :param input_vp:
+ :param input_vex:
+ :return: time_grid, concentration
+ """
+ matlab_resource_path = os.path.dirname(os.path.abspath(__file__)) + '/perfusion_matlab'
+ print(f"Starting matlab engine from {os.path.abspath(__file__)}")
+ eng = matlab.engine.start_matlab()
+ print(f"Add matlab resources to matlab path: {matlab_resource_path}")
+ eng.eval(f'addpath("{matlab_resource_path}")')
+ input_list = [input_f, input_ps, input_vp, input_vex]
+ input_list = [[_, ] for _ in input_list if isinstance(_, float)]
+
+ result_t, result_concentration = [], []
+ for f, ps, vp, vex in zip(*input_list):
+ t, concentration = eng.btexrunner(f, ps, vp, vex, time_start, time_end, n_points, nargout=2)
+ result_concentration.append(np.array(concentration)[:, 0])
+
+ return np.array(t).reshape(-1), np.stack(result_concentration, axis=0)
diff --git a/cmrsim/signal_processes/solution_operators/perfusion_matlab/BTEX.m b/cmrsim/signal_processes/solution_operators/perfusion_matlab/BTEX.m
new file mode 100644
index 0000000000000000000000000000000000000000..8a72040949c515117a360de5cd4afd84e1d9a0b5
--- /dev/null
+++ b/cmrsim/signal_processes/solution_operators/perfusion_matlab/BTEX.m
@@ -0,0 +1,210 @@
+classdef BTEX < handle
+ %
+ % Implementation of the BTEX model to compute the concentration of the
+ % contrast agent(CA)
+ %
+ % NOTE: In order to use the model all parameters must be set and the
+ % compute method will give the result
+ %
+ % BTEX model:
+ % Variable:
+ % cp(x,t) Concentration CA in plasma [mmol/l]
+ % cex(x,t) Concentration CA in extravascular space (tissue) [mmol/l]
+ %
+ % Parameter:
+ % F Blood Flow [ml/g/s]
+ % L Capillary length [cm]
+ % Dp, Dex Axial dispersion doefficient [cm2/s] plasma and extravascular space (tissue)
+ % Vp, Vex Plasma and extravascular space (tissue) volume
+ %
+ % Governing Equations:
+ % dcpdt = - F*L/Vp*dcpdx + PS/Vp*(cex-cp) + Dp*(ddcpddx)
+ % dcexdt = - PS/Vex*(cex-cp) + Dex*(ddcexddx)
+ %
+ % Inital Values:
+ % cp(x,0) = 0 cex(x,0) = 0
+ %
+ % Boundary Conditions:
+ % cp(0,t) = ca(t) ca(t) : arterial input function
+ % dcp(L,t)dx = 0
+ % dcex(0,t)dx = 0
+ % dcex(L,t)dx = 0
+
+ properties (Access = private)
+ F % Blood Flow [ml/g/s]
+ PS % % Permeability-surface area product across membrane [ml/g/s]
+ Vp % Volume plasma [ml/g]
+ Vex % Volume extravascular space (tissue) [ml/g]
+ Dp % Axial dispersion coefficient plasma [cm2/s]
+ Dex % Axial dispersion coefficient extravascular space (tissue) [cm2/s]
+
+ AIF % Arterial Input Function
+
+ L % Capillary length [cm]
+ Nx % Number of units along axial direction
+
+ T0 % Start time of integration
+ Te % End time of integration
+ Nt % Number of measured time points
+ end
+
+
+
+ methods
+
+ %==================================================================
+ % Setter methods for all parameters
+ %
+ % NOTE: All properties should be initialized
+ function setVolume(obj,vp, vex)
+ obj.Vp = vp;
+ obj.Vex = vex;
+ end
+
+ function setDispersion(obj, dp, dex)
+ obj.Dp = dp;
+ obj.Dex = dex;
+ end
+
+ function setPermeability(obj, ps)
+ obj.PS = ps;
+ end
+
+ function setFlow(obj, f)
+ obj.F = f;
+ end
+
+ function setUnits(obj, l, nx)
+ obj.L = l;
+ obj.Nx = round(nx);
+ end
+
+ function setAIF(obj, t, cin)
+ obj.AIF.t = t;
+ obj.AIF.c = cin;
+ end
+
+ function setTime(obj, t0, te, nt)
+ obj.T0 = t0;
+ obj.Te = te;
+ obj.Nt = nt;
+ end
+
+
+ %==================================================================
+ % Interpolation of the arterial input function
+ function cint = aif(obj, t)
+ cint = interp1(obj.AIF.t, obj.AIF.c ,t,'pchip','extrap');
+ end
+
+
+ %==================================================================
+ % This function should be called from the outside to perform the
+ % computation of the BTEX model. For detailed description of the
+ % integration refer to the part "Numerical Integration"
+ function [tspan, cm] = computeAverage(obj)
+ % Evalution of the BTEX model with the given parameters
+ %
+ % OUTPUT: t 1D Array time steps
+ % cm 2D Array (t,x) concentration of CA in myocardium [mmol/l]
+ %
+
+ [tspan, cp, cex] = obj.compute;
+ vtot = obj.Vp + obj.Vex;
+ cm = (sum(cp,2) * obj.Vp / obj.Nx + sum(cex,2) * obj.Vex / obj.Nx) / vtot;
+ end
+
+
+ %==================================================================
+ % This function should be called from the outside to perform the
+ % computation of the BTEX model. For detailed description of the
+ % integration refer to the part "Numerical Integration"
+ function [t, cp, cex] = compute(obj)
+ % Evalution of the BTEX model with the given parameters
+ %
+ % OUTPUT: t 1D Array time steps
+ % cp 2D Array (t,x) concentration of CA in plasma [mmol/l]
+ % cex 2D Array (t,x) concentration of CA] in extravascular space (tissue) [mmol/l]
+ %
+
+ x = linspace(0, obj.L, obj.Nx);
+ t = linspace(obj.T0, obj.Te , obj.Nt);
+
+ sol = pdepe(0, @(x,t,c,dcdx)obj.pdefun(x,t,c,dcdx), @(x)obj.initCond(x), @(xl,cl,xr,cr,t)obj.boundCond(xl,cl,xr,cr,t), x, t);
+
+ cp = sol(:,:,1);
+ cex = sol(:,:,2);
+ end
+ end
+
+
+ methods (Access = private)
+
+ %==================================================================
+ % Numerical Integration
+ % https://www.mathworks.com/help/matlab/ref/pdepe.html
+ function [a, f, s] = pdefun(obj, ~, ~, c, dcdx)
+ % Implementation of the governing equations given the follwing form
+ %
+ % a(x,t,c,dcdx) * dc/dt = d/dx*(f(x,t,c,dcdx)) + s(x,t,c,dcdx)
+
+ % abbreviated notation:
+ %a = [1; 1];
+ %f = [obj.Dp ; obj.Dex ] .* dcdx;
+ %s = [- obj.F * obj.L / obj.Vp; 0] .* dcdx + (c(2)-c(1)) * [obj.PS / obj.Vp; -obj.PS / obj.Vex];
+
+ % verbose notation:
+ a1 = 1;
+ f1 = obj.Dp * dcdx(1);
+ s1 = (-obj.F * obj.L / obj.Vp) * dcdx(1) + (obj.PS / obj.Vp) * (c(2)-c(1)); % with c(2)=cex and c(1)=cp
+
+ a2 = 1;
+ f2 = obj.Dex * dcdx(2);
+ s2 = (-obj.PS / obj.Vex) * (c(2)-c(1));
+
+ a = [a1; a2];
+ f = [f1; f2];
+ s = [s1; s2];
+ end
+
+
+ function [c] = initCond(~, ~)
+ % Initial condition
+ c = [0; 0];
+ end
+
+
+ function [pl, ql, pr, qr] = boundCond(obj, ~, cl, ~ , ~, t)
+ % Boundary condition
+ % Function:
+ % p(x,t,c) + q(x,t)*f(x,t,c,dcdx) = 0
+ %
+ % with f(x,t,c,dcdx) being = [Dp * dcdx; Dex * dcdx] ?
+ % because Dp and Dex are constant (invariant over t and x), this equals the
+ % boundary conditions stated at the beginning:
+ % cp(0,t) = ca(t) ca(t) : arterial input function
+ % dcp(L,t)dx = 0
+ % dcex(0,t)dx = 0
+ % dcex(L,t)dx = 0
+ %
+ % Input:
+ % xl : left boudary
+ % cl : approximate solution at left boudary
+ % xr : right boundary
+ % cr : approximate solution at right boundary
+ % t : time
+ %
+ % Output:
+ % pl, pr : p evaluated at left and right boundary
+ % ql, qr : q evaluated at left and right bounary
+ %
+ pl = [cl(1) - obj.aif(t); 0];
+ ql = [0; 1.0];
+
+ pr = [0; 0];
+ qr = [1.0; 1.0];
+ end
+
+ end
+
+end
diff --git a/cmrsim/signal_processes/solution_operators/perfusion_matlab/btexrunner.m b/cmrsim/signal_processes/solution_operators/perfusion_matlab/btexrunner.m
new file mode 100644
index 0000000000000000000000000000000000000000..7a6d5bcc3608f4028a4de93cc962bdf4e4775248
--- /dev/null
+++ b/cmrsim/signal_processes/solution_operators/perfusion_matlab/btexrunner.m
@@ -0,0 +1,39 @@
+% btexrunner.m
+
+function [t, cm] = btexrunner(input_F, input_PS, input_Vp, input_Vex, time_start, time_end, n_time_points)
+ % Inititalizes BTEX object, assigns correct parameters and computes the concentration by solving the governing
+ % equations
+ %
+ % Arguments:
+ % input_F : Blood Flow [ml/g/s] | default = (2.82/60 + 0.68/60) / 2
+ % input_PS : Permeability-surface area product across membrane [ml/g/s] | default = (1.32/60 + 1.09/60) / 2
+ % input_VP : Volume plasma [ml/g] | default = (0.120 + 0.097) / 2
+ % input_Vex : Volume extravascular space (tissue) [ml/g] | default = (0.208 + 0.203) / 2
+ % time_start, time_end : Start, end time in [ms] of integration | default (0, 69)
+ % n_time_points : number of measured time points | default = 70
+ %
+ % Returns:
+ % t: Times of concentration measurements
+ % cm: Average concentration (of cex and cp) at times t
+
+ b = BTEX();
+ b.setTime(time_start, time_end, n_time_points);
+
+ t = linspace(time_start, time_end, n_time_points);
+ aif = cin(t);
+ b.setAIF(t, aif);
+
+ b.setFlow(input_F);
+ b.setPermeability(input_PS);
+ b.setVolume(input_Vp, input_Vex);
+
+ Dp = 1e-5; % Axial dispersion coefficient plasma [cm2/s]
+ Dex = 1e-6; % Axial dispersion coefficient extravascular space (tissue) [cm2/s]
+ b.setDispersion(Dp, Dex);
+
+ L = 0.1; % Capillary length [cm]
+ Nx = 60; % Number of units along axial direction
+ b.setUnits(L, Nx);
+
+ % Compute average concentration (intra and extra vascular space) according to set parameters
+ [t, cm] = b.computeAverage();
\ No newline at end of file
diff --git a/cmrsim/signal_processes/solution_operators/perfusion_matlab/cin.m b/cmrsim/signal_processes/solution_operators/perfusion_matlab/cin.m
new file mode 100644
index 0000000000000000000000000000000000000000..8c1bfd8d21d59185e7a11bcfe8ff8f4bc5ff3525
--- /dev/null
+++ b/cmrsim/signal_processes/solution_operators/perfusion_matlab/cin.m
@@ -0,0 +1,14 @@
+function cint = cin(t)
+ % Calculates the arterial input function.
+ % (all values and scaling are taken from the file MRXCAT_CMR_PERF)
+
+ aif005 = [0,0,0,0,0.00088825,0.017793,0.13429,0.5466,1.453,2.8276,4.3365,5.5112,6.0165,5.7934,5.0205,3.9772,2.9161, ...
+ 1.9988,1.2913,0.79165,0.46315,0.25983,0.14035,0.073254,0.037056,0.018216,0.0087227,0.0040768,0.0018632, ...
+ 0.00083405,0.00036621,0.00015793,6.6972e-05,2.7956e-05,1.1499e-05,0,0,0,0,0,0,0,0,0,0,0,0,0,0,0];
+
+ referenceDose = 0.075; % [mmol/kg b.w.]
+ aif01 = aif005 * 12 / max(aif005);
+ ca = aif01 * referenceDose/0.1; % scale to desired dose
+ tm = linspace(0,length(ca)-1, length(ca));
+ cint = interp1(tm,ca,t,'pchip','extrap');
+end
diff --git a/cmrsim/signal_processes/solution_operators/perfusion_matlab/computeSignal.m b/cmrsim/signal_processes/solution_operators/perfusion_matlab/computeSignal.m
new file mode 100644
index 0000000000000000000000000000000000000000..49ce7e195e5933db1c6def5ca5f29ed10fa66b78
--- /dev/null
+++ b/cmrsim/signal_processes/solution_operators/perfusion_matlab/computeSignal.m
@@ -0,0 +1,13 @@
+function sig = computeSignal()
+ % Computes the signal from the current BTEX model.
+ % Output: signal Signal curve with time shift
+
+ % Compute average CA concentration
+ [t, cm] = obj.btex.computeAverage();
+
+ % Compute signal from CA concentration
+ sig = obj.concToSignal(cm);
+
+ % Apply time shift
+ sig = interp1( t+obj.dt, sig, t, 'pchip','extrap' );
+end
\ No newline at end of file
diff --git a/cmrsim/signal_processes/solution_operators/perfusion_matlab/concToSignal.m b/cmrsim/signal_processes/solution_operators/perfusion_matlab/concToSignal.m
new file mode 100644
index 0000000000000000000000000000000000000000..1086f9bfee3d7b5e5924c7dd5ed4ba29a7720151
--- /dev/null
+++ b/cmrsim/signal_processes/solution_operators/perfusion_matlab/concToSignal.m
@@ -0,0 +1,24 @@
+function sig = concToSignal(c)
+ % Computes the signal curve from the concentration curves without time shift.
+ % (implementation adapted from MRXCAT_CMR_PERF)
+ %
+ % Input: c Concentration time curve
+ % Output: sig Signal curve without time shift
+
+ % Signal model (Spoiled GRE)
+ TR = 2.0; % Repetition time [ms]
+ Flip = 15.0; % Flip angle [deg]
+ Tsat = 150.0; % Saturation delay [ms]
+ Nky0 = max(floor((Tsat-70)/TR),1); % Number excitations to ky0 (70 ms: eff. prepulse delay)
+ T1muscle = 800.0;
+ RhoMuscle = 80.0;
+ Relaxivity = 5.6 / 1000;
+ rho = RhoMuscle;
+
+ r1 = 1/T1muscle+c*Relaxivity;
+ a = cos(pi*Flip/180)*exp(-TR*r1);
+ b = 1-exp(-TR*r1);
+ n = Nky0;
+ TD = Tsat;
+ sig = rho*( (1-exp(-TD*r1)).*a.^(n-1) + b.*(1 - a.^(n-1))./(1 - a) );
+end
\ No newline at end of file
diff --git a/cmrsim/signal_processes/solution_operators/sequences.py b/cmrsim/signal_processes/solution_operators/sequences.py
index c90cf11751ea47aadd1d38df42ed84cb58bff7b0..33800a919fd072ed8a77601e701f30e338ba22b4 100644
--- a/cmrsim/signal_processes/solution_operators/sequences.py
+++ b/cmrsim/signal_processes/solution_operators/sequences.py
@@ -1,44 +1,91 @@
-__all__ = ["SpinEcho"]
+__all__ = ["SpinEcho", "SaturationRecoveryGRE"]
-from typing import Union
+from typing import Union, Tuple
import tensorflow as tf
+import numpy as np
from cmrsim.signal_processes.base import BaseSignalModel
class SpinEcho(BaseSignalModel):
required_quantities = ('T1', 'T2')
- TE: tf.Variable
- TR: tf.Variable
+ TE: tf.Variable = None
+ TR: tf.Variable = None
expansion_factor = 1
def __init__(self, echo_time: Union[float, tf.Tensor], repetition_time: Union[float, tf.Tensor], **kwargs):
"""
-
- :param echo_time: in seconds
- :param repetition_time: in seconds
+ :param echo_time: in milliseconds
+ :param repetition_time: in milliseconds
"""
super(SpinEcho, self).__init__(name="spin_echo", **kwargs)
self.TE = tf.Variable(echo_time, dtype=tf.float32, name='TE')
self.TR = tf.Variable(repetition_time, dtype=tf.float32, name='TR')
@tf.Module.with_name_scope
- def __call__(self, signal_tensor: tf.Tensor, **kwargs):
+ def __call__(self, signal_tensor: tf.Tensor, T1: tf.Tensor, T2: tf.Tensor, **kwargs): # noqa
""" Evaluates the signal for the simple Spin-Echo sequence:
.. math::
M_{xy} = M_z(0) \cdot e^{- TE / T_2} \cdot (1 - e^{-TR / T1))
- :param signal_tensor:
- :param kwargs:
+ :param signal_tensor: (#batch, #voxel, #repetitions, #k-space-samples) last two dimensions can be 1
+ :param T1: (#batch, #voxel, 1, 1)
+ :param T2:
:return:
"""
with tf.device(self.device):
- t1 = kwargs.get('T1')
- t2 = kwargs.get('T2')
- t1_term = 1 - tf.exp(-tf.divide(self.TR, t1))
- t2_term = tf.exp(-tf.divide(self.TE, t2))
+ t1_term = 1 - tf.exp(-tf.divide(self.TR, T1))
+ t2_term = tf.exp(-tf.divide(self.TE, T2))
factor = tf.cast(t1_term * t2_term, tf.complex64)
factor = tf.tile(factor, [1, 1, tf.shape(signal_tensor)[2], tf.shape(signal_tensor)[3]])
return signal_tensor * factor
+
+
+class SaturationRecoveryGRE(BaseSignalModel):
+ required_quantities = ('T1', )
+ TE: tf.Variable = None
+ TR: tf.Variable = None
+ expansion_factor = 1
+
+ def __init__(self, repetition_time: Union[float, tf.Tensor], flip_angle: Union[float, tf.Tensor],
+ saturation_delay: float, n_profiles_to_k0: int, **kwargs):
+ """ Evaluates the signal model for a saturation recovery spoiled gradient echo sequence. This modules assumes
+ the cartesian line-by line k-space sampling. This yields the signal equation for a single k-space line:
+
+ .. math::
+ m_{xy} = \\rho(r) \\left[(1 - e^{-T_D \cdot R_1}) (\cos(\\alpha)e^{-T_R \cdot R_1})^{n-1} +
+ (1-e^{-T_R \cdot R_1}) \\frac{1 - (\cos(\\alpha)e^{-T_R \cdot R_1})^{n-1}}{1 - (\cos(\\alpha)e^{-T_R \cdot R_1})} ]
+
+ Where T_D is the saturation_delay, n is the number of profiles left until the k-space center, alpha is the
+ flip_angle and TR is the repetition time.
+
+ for more info refer to https://git.ee.ethz.ch/jweine/cmrsim/-/issues/62
+
+ **Note** This module assumes, that the keyword argument 'segment_index' is used in __call__, such that each
+ segment contains all k-space point acquired in one TR, otherwise the signal equation does not hold!
+
+ :param repetition_time: in ms
+ :param flip_angle: in degree
+ :param saturation_delay: (float) time in ms from saturation pulse (t=0.) to the time of acquisition
+ of the k-space center profile.
+ :param n_profiles_to_k0: (int) profiles (=k-space lines) prior to acquisition of k0
+ """
+ super(SaturationRecoveryGRE, self).__init__(name="saturation_recovery_gre", **kwargs)
+ self.TR = tf.Variable(repetition_time, dtype=tf.float32, name='TR')
+ self.flip_angle = tf.Variable(flip_angle, dtype=tf.float32, name='flip_angle')
+ self.saturation_delay = tf.Variable(saturation_delay, shape=(), dtype=tf.float32, name='TD')
+ self._n_profiles_to_k0 = tf.Variable(n_profiles_to_k0, shape=(), dtype=tf.float32, name='n_tok0')
+
+ @tf.Module.with_name_scope
+ def __call__(self, signal_tensor: tf.Tensor, T1: tf.Tensor, segment_index: tf.Tensor = 0., **kwargs): # noqa
+ with tf.device(self.device):
+ n = tf.maximum(tf.math.floor(self._n_profiles_to_k0 - tf.cast(segment_index, tf.float32)), 1.)
+ a = tf.math.exp(- self.TR / T1)
+ cos_alpha = tf.math.cos(self.flip_angle.read_value() / 180 * tf.constant(np.pi, dtype=tf.float32))
+ b = cos_alpha * a
+ c = 1 - tf.math.exp(-self.saturation_delay / T1)
+ d = tf.pow(b, n - 1.)
+ factor = c * d + (1 - a) * (1 - d) / (1 - b)
+ return signal_tensor * tf.cast(factor, tf.complex64)
diff --git a/cmrsim/signal_processes/solution_operators/t2_star.py b/cmrsim/signal_processes/solution_operators/t2_star.py
index eb0a41cbbc3d6ea43941a48e1732537288c23440..8c2bbdae7b24584debdac7b7cad93f5c381f67d9 100644
--- a/cmrsim/signal_processes/solution_operators/t2_star.py
+++ b/cmrsim/signal_processes/solution_operators/t2_star.py
@@ -18,7 +18,7 @@ class StaticT2starDecay(BaseSignalModel):
""" Initializes a module to calculate the T2* decay at times specified in kwargs. The times are stored
in a tf.Variable. The stored parameters are expected to be passed in in seconds
- :param kwargs: sampling_times - timing of acquisition (ADC) events in ms
+ :param sampling_times: timing of acquisition (ADC) events in milliseconds
"""
super(StaticT2starDecay, self).__init__(name="static_t2star", **kwargs)
with self.name_scope:
@@ -30,18 +30,17 @@ class StaticT2starDecay(BaseSignalModel):
self.sampling_times = sampling_times
@tf.Module.with_name_scope
- def __call__(self, signal_tensor: tf.Tensor, **kwargs):
+ def __call__(self, signal_tensor: tf.Tensor, T2star: tf.Tensor, segment_index: tf.Tensor = 0, **kwargs):
""" Calculates signal decay with stored sampling event timings and given t2-star values. Assumes flattened
tensor of voxels.
:param signal_tensor: (#batch, #voxel, #repetitions, #k-space-samples) #repetitions and #k-space-samples can be1
- :param kwargs: T2star = tf.Tensor (#batch, #voxel, 1, 1)
- segment_index: int, if k-space is segmented for Memory reasons. defaults to 0
+ :param T2star: tf.Tensor (#batch, #voxel, 1, 1) in milliseconds
+ :param segment_index: int, if k-space is segmented for Memory reasons. defaults to 0
:return: signal_tensor (#batch, #voxel, #repetition, #k-space-samples) with #k-space-samples > 1
"""
with tf.device(self.device):
- segment_index = kwargs.get('segment_index', 0)
- t2_star_values = kwargs.get('T2star') * 1e3
+ t2_star_values = T2star * 1e3
sampling_times = self.sampling_times[segment_index]
n_samples = tf.size(sampling_times)
diff --git a/cmrsim/simulation.py b/cmrsim/simulation.py
index 527f35241682c7c877c30bf909c4aafc9697629a..2968001a2b5702fa207accb8adc66d58b335b45c 100644
--- a/cmrsim/simulation.py
+++ b/cmrsim/simulation.py
@@ -1,4 +1,4 @@
-__all__ = ['SimulationBase', 'simulate']
+__all__ = ['SimulationBase', ]
from typing import Tuple, Optional, TYPE_CHECKING
from abc import abstractmethod
@@ -16,8 +16,8 @@ if TYPE_CHECKING:
class SimulationBase(tf.Module):
- forward_model: 'CompositeSignalModel'
- encoding_module: 'BaseSampling'
+ forward_model: 'CompositeSignalModel' = None
+ encoding_module: 'BaseSampling' = None
recon_module: 'BaseRecon' = None
def __init__(self, name: str = None,
@@ -34,6 +34,10 @@ class SimulationBase(tf.Module):
else:
self.forward_model, self.encoding_module, self.recon_module = building_blocks
+ # Inititalize progress bar
+ self.progress_bar = SimulationProgressBar(total_images=1, total_voxels=1, prefix='Run Simulation: ',
+ total_segments=self.encoding_module.k_space_segments.read_value())
+
@abstractmethod
def _build(self) -> ('CompositeSignalModel', 'BaseSampling', 'BaseRecon'):
""" Abstract method that needs to be defined in subclasses to configure specific simulations"""
@@ -41,12 +45,11 @@ class SimulationBase(tf.Module):
return -1
def __call__(self, dataset: 'BaseDataset', voxel_batch_size: int = 1000, execute_eager: Optional[bool] = False,
- graph_log_dir: Optional[str] = None, unstack_repetitions: bool = True) -> tf.Tensor:
+ unstack_repetitions: bool = True) -> tf.Tensor:
""" Wrapper for the decorated (@tf.function) call of the simulation loop.
:param dataset:
:param voxel_batch_size: (int) see documentation of _simulate_segmented_k_space.
:param execute_eager: (bool) Default False: if True, Graph construction is skipped for simulation (debugging)
- :param graph_log_dir: enables graph tracing to save def with tf.summary
:param unstack_repetitions: If False the returned shape will be (#images, #Reps, #noise_levels, #samples).
If True refer to 'return' in docstring.
:return: **(tf.Tensor)** | Stack of images with shape (#images, ..., #noise_levels, #X, #Y, #Z), or k-spaces
@@ -55,29 +58,23 @@ class SimulationBase(tf.Module):
and number of axis in the ellipsis.
"""
self._update()
-
# Check dataset inputs for missing parameter maps
if not all([rq in dataset.map_names for rq in self.forward_model.required_quantities]):
raise AssertionError(
f'{dataset.map_names} does not contain all entries of {self.forward_model.required_quantities}')
# If wanted, disable graph construction with tf.function (designed for debugging)
- tf.config.experimental_run_functions_eagerly(execute_eager)
-
- # If set to True log the graph with tf.summary
- if graph_log_dir is not None:
- os.makedirs(graph_log_dir, exist_ok=True)
- writer = tf.summary.create_file_writer(graph_log_dir)
- tf.summary.trace_on(graph=True)
- print("Graph Tracing activated...")
+ self.progress_bar.total_images.assign(dataset.set_size)
+ if not execute_eager:
+ @tf.function
+ def simulation_execute(dataset):
+ return self._simulation_loop(dataset)
+ else:
+ simulation_execute = self._simulation_loop
# Run actual simulation
- noise_less_k_space_stack = self._simulation_loop(dataset(voxel_batch_size=voxel_batch_size))
-
- if graph_log_dir is not None:
- with writer.as_default(): # noqa
- tf.summary.trace_export(name='graph_def', step=0)
- print(f'Saved Graph-definition to {graph_log_dir}')
+ batched_dataset = dataset(voxel_batch_size=voxel_batch_size)
+ noise_less_k_space_stack = simulation_execute(batched_dataset)
# Act noise_instantiations
k_space_stack = self.encoding_module.add_noise(noise_less_k_space_stack)
@@ -92,7 +89,6 @@ class SimulationBase(tf.Module):
return simulation_result
- @tf.function
def _simulation_loop(self, dataset: tf.data.Dataset):
""" Consumes all object-configuration data (images) and simulates the MR images. The simulation configuration is
exclusively defined by the given modules forward_model and encoding. If the optional reconstruction module is
@@ -104,24 +100,22 @@ class SimulationBase(tf.Module):
"""
# Allocate tensor array to store the simulated images
simulation_result = tf.TensorArray(dtype=tf.complex64, size=1, dynamic_size=True)
- expected_k_space_shape = self.get_k_space_shape()
- s_of_k = tf.zeros(expected_k_space_shape, tf.complex64)
+ s_of_k_temp = tf.zeros(self.get_k_space_shape(), tf.complex64)
# Loop over images
for image_idx, (number_of_isochromates, single_image_dataset) in dataset.enumerate():
- current_voxel_count = tf.constant(0, dtype=tf.int32)
- s_of_k = s_of_k * 0.
+ s_of_k_temp *= (0. + 0.j)
+ self.progress_bar.update(set_image=image_idx), self.progress_bar.print()
+ self.progress_bar.total_voxels.assign(number_of_isochromates), self.progress_bar.reset_voxel()
- # Loop over all voxels in current image
- for batch_idx, batch_dict in single_image_dataset.enumerate():
- current_voxel_count += tf.shape(batch_dict['M0'])[1]
- s_of_k += self._segment_loop(batch_dict)
- tf.print('\rImages: ', image_idx, 'Voxels: ', current_voxel_count, '/', number_of_isochromates,
- end=' ', output_stream=sys.stdout)
+ for batch_dict in single_image_dataset:
+ s_of_k_temp += self._segment_loop(batch_dict)
+ self.progress_bar.update(add_voxels=tf.shape(batch_dict['M0'])[1]), self.progress_bar.print()
# Store k-space of current image (batch-wise Fourier-transformed object)
- simulation_result = simulation_result.write(tf.cast(image_idx, tf.int32), s_of_k)
+ simulation_result = simulation_result.write(tf.cast(image_idx, tf.int32), s_of_k_temp)
+ self.progress_bar.print_final()
return simulation_result.concat()
@tf.function
@@ -129,12 +123,12 @@ class SimulationBase(tf.Module):
""" Fourier transforms the handed batch of object points / isochromates,
:param batch_dict: dictionary of type {'property_name': tf.Tensor(...)} e.g. {'M0': tf.Tensor{}}
- :return: tf.Tensor of shape (1, #repetitions, #noise_levels, #k-space-samples)
+ :return: tf.Tensor of shape (1, #repetitions, #k-space-samples)
"""
# Allocate k-space-Tensor
n_segments = self.encoding_module.k_space_segments.read_value()
s_of_k_segments = tf.TensorArray(dtype=tf.complex64, size=n_segments, dynamic_size=False)
- tf.print("Segments: 00/XX", end="", output_stream=sys.stdout)
+ self.progress_bar.reset_segments()
# Loop over k-space segments, defined by k-space trajectory in self.encoding_module
for segment_index in tf.range(n_segments):
@@ -142,11 +136,9 @@ class SimulationBase(tf.Module):
k_space_segment = self.encoding_module(m_transverse, batch_dict['r_vectors'], segment_index=segment_index)
s_of_k_segments = s_of_k_segments.write(segment_index, tf.transpose(k_space_segment, [2, 1, 0]))
- tf.print('\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b\b',
- tf.cond(segment_index+1 < 10,
- lambda: tf.strings.format("Segments: {}/{}", [segment_index+1, n_segments]),
- lambda: tf.strings.format("Segments: {}/{}", [segment_index+1, n_segments])),
- end="", output_stream=sys.stdout)
+ self.progress_bar.update(add_segment=1)
+ if tf.math.floormod(segment_index, 5) == 0:
+ self.progress_bar.print()
# Concat all segments and transpose axes to match needed format
return tf.transpose(s_of_k_segments.concat(), [2, 1, 0])
@@ -215,3 +207,85 @@ class SimulationBase(tf.Module):
summary['encoding'].update({v.name: {'name': v.name, 'dtype': str(v.dtype), 'shape': str(v.shape),
'is_trainable': str(v.trainable), 'value': v.numpy().tolist()}})
return summary
+
+ def write_graph(self, dataset, graph_log_dir: str):
+ # If set to True log the graph with tf.summary
+ os.makedirs(graph_log_dir, exist_ok=True)
+ writer = tf.summary.create_file_writer(graph_log_dir)
+ print("Graph Tracing activated...")
+ tf.summary.trace_on(graph=True)
+ # TODO: insert dummy simulation
+ _ = self._simulation_loop(dataset(voxel_batch_size=1000))
+ with writer.as_default(): # noqa
+ tf.summary.trace_export(name='graph_def', step=0)
+ print(f'\nSaved Graph-definition to {graph_log_dir}')
+
+
+class SimulationProgressBar(tf.Module):
+ total_images: tf.Variable
+ total_voxels: tf.Variable
+ total_segments: tf.Variable
+ current_image: tf.Variable
+ current_voxel: tf.Variable
+ current_segment: tf.Variable
+
+ def __init__(self, total_images: int = 0, total_voxels: int = 0, total_segments: int = None, prefix: str = '',
+ suffix: str = ''):
+ super(SimulationProgressBar, self).__init__(name='progress_bar')
+ with self.name_scope:
+ self.total_images = tf.Variable(total_images, dtype=tf.int32, trainable=False, name='total_images')
+ self.total_voxels = tf.Variable(total_voxels, dtype=tf.int32, trainable=False, name='total_voxels')
+ self.total_segments = tf.Variable(total_segments, dtype=tf.int32, trainable=False, name='total_segments')
+ self.current_image = tf.Variable(0, dtype=tf.int32, trainable=False, name="current_image")
+ self.current_voxel = tf.Variable(0, dtype=tf.int32, trainable=False, name="current_voxel")
+ self.current_segment = tf.Variable(0, dtype=tf.int32, trainable=False, name="current_segment")
+ self.bar_lengths = (5, 20, 15)
+ self.prefix = prefix
+ self.suffix = suffix
+
+ def update(self, add_image: int = None, add_voxels: int = None, add_segment: int = None,
+ set_image: int = None, set_voxel: int = None, set_segment: int = None):
+ if add_image is not None:
+ self.current_image.assign_add(tf.cast(add_image, tf.int32))
+ if add_voxels is not None:
+ self.current_voxel.assign_add(tf.cast(add_voxels, tf.int32))
+ if add_segment is not None:
+ self.current_segment.assign_add(tf.cast(add_segment, tf.int32))
+ if set_image is not None:
+ self.current_image.assign(tf.cast(set_image, tf.int32))
+ if set_voxel is not None:
+ self.current_voxel.assign(tf.cast(set_voxel, tf.int32))
+ if set_segment is not None:
+ self.current_segment.assign(tf.cast(set_segment, tf.int32))
+
+ def reset_images(self):
+ self.current_image.assign(0)
+
+ def reset_voxel(self):
+ self.current_voxel.assign(0)
+
+ def reset_segments(self):
+ self.current_segment.assign(0)
+
+ def print_final(self):
+ self.update(set_image=self.total_images.read_value(), set_voxel=self.total_voxels.read_value(),
+ set_segment=self.total_segments.read_value())
+ self.print()
+
+ @tf.function
+ def print(self):
+ def bar_string(iteration, total, length):
+ filled_length = tf.cast(length * iteration / total, dtype=tf.int32)
+ bar = tf.concat((tf.repeat('X', filled_length), tf.repeat('-', length - filled_length)), axis=0)
+ ret_list = tf.concat((('|',), bar, ('|', tf.strings.format('{}/{}', [iteration, total]))), axis=0)
+ ret = tf.strings.reduce_join(ret_list)
+ return ret
+
+ image_bar = bar_string(self.current_image.read_value(), self.total_images.read_value(), self.bar_lengths[0])
+ voxel_bar = bar_string(self.current_voxel.read_value(), self.total_voxels.read_value(), self.bar_lengths[1])
+ segment_bar = bar_string(self.current_segment.read_value(), self.total_segments.read_value(),
+ self.bar_lengths[1])
+ full_string = tf.strings.reduce_join(('\r', self.prefix, image_bar, '\t',
+ voxel_bar, '\t', segment_bar, self.suffix))
+ tf.print(full_string, end=' ', output_stream=sys.stdout)
+
diff --git a/docker/base/Dockerfile b/docker/base/Dockerfile
deleted file mode 100644
index ee0792ee7ae28e8f59aab8fc9eb585988cb1f63f..0000000000000000000000000000000000000000
--- a/docker/base/Dockerfile
+++ /dev/null
@@ -1,22 +0,0 @@
-# take the latest GPU python3 tensorflow image as base
-FROM tensorflow/tensorflow:2.2.0-gpu
-
-# maintainter from IBT
-LABEL maintainers="Jonathan Weine (Jonathan@biomed.ee.ethz.ch) \nHannes Dillinger (dillinger@biomed.ee.ethz.ch)"
-LABEL Description="This Docker image is meant to start MR simulations with the CMRsim package"
-
-# Dependencies: Install additional python packages
-COPY python_requirements.txt /requirements.txt
-RUN pip3 install -r /requirements.txt
-
-# Install image magic
-RUN apt-get -y update
-RUN apt-get install -y imagemagick
-
-RUN pip install numpy matplotlib
-
-## Environment ##
-ENV LD_LIBRARY_PATH /usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH
-RUN echo $LD_LIBRARY_PATH
-
-ENV PATH /opt/conda/bin:$PATH
diff --git a/docker/gpu/Dockerfile b/docker/gpu/Dockerfile
index 611078b22f82fedaa53903b67f72265c52829aa8..4fc9d1391815a2be1adc983dc1502bbb54fff0f5 100644
--- a/docker/gpu/Dockerfile
+++ b/docker/gpu/Dockerfile
@@ -1,8 +1,8 @@
-# take the latest GPU python3 tensorflow image as base
-FROM registry.git.ee.ethz.ch/jweine/cmrsim/base:latest
+# take the GPU python3 tensorflow image as base
+FROM tensorflow/tensorflow:2.4.1-gpu
# maintainter from IBT
-LABEL maintainers="Jonathan Weine (Jonathan@biomed.ee.ethz.ch) \nHannes Dillinger (dillinger@biomed.ee.ethz.ch)"
+LABEL maintainers="Jonathan Weine (Jonathan@biomed.ee.ethz.ch)"
LABEL Description="This Docker image is meant to start MR simulations with the CMRsim package"
# Install local project lib
diff --git a/docker/jupyter/Dockerfile b/docker/jupyter/Dockerfile
index a0dea6268b385de4d60dd18f836beaf377c258f6..3debf33e8e62ddb7325cf517649b45df0b922d23 100644
--- a/docker/jupyter/Dockerfile
+++ b/docker/jupyter/Dockerfile
@@ -1,8 +1,7 @@
-# take the latest GPU python3 tensorflow image as base
-FROM tensorflow/tensorflow:2.2.0-gpu-jupyter
+FROM registry.ethz.ch/jweine/cmrsim/gpu:latest
# maintainter from IBT
-LABEL maintainers="Jonathan Weine (Jonathan@biomed.ee.ethz.ch) \nHannes Dillinger (dillinger@biomed.ee.ethz.ch)"
+LABEL maintainers="Jonathan Weine (Jonathan@biomed.ee.ethz.ch)"
LABEL Description="This Docker image is meant to start MR simulations with the CMRsim package"
# Set up jupyter lab with interactive plots
@@ -16,26 +15,11 @@ RUN jupyter labextension install @jupyter-widgets/jupyterlab-manager
RUN jupyter labextension install jupyter-matplotlib
RUN apt-get install -y git
-RUN jupyter labextension install @jupyter-widgets/jupyterlab-manager
-
-# Install image magic
-RUN apt-get install -y imagemagick
-
-# Dependencies: Install additional python packages
-COPY python_requirements.txt /requirements.txt
-RUN pip3 install -r /requirements.txt
-
-# Install local project lib
-COPY cmrsim-latest-py3-none-any.whl /cmrsim-latest-py3-none-any.whl
-RUN pip3 install /cmrsim-latest-py3-none-any.whl
-
## Environment ##
ENV LD_LIBRARY_PATH /usr/local/cuda/extras/CUPTI/lib64:$LD_LIBRARY_PATH
RUN echo $LD_LIBRARY_PATH
-
ENV PATH /opt/conda/bin:$PATH
-##
-# start jupter lab instead of jupyter
-##
-ENTRYPOINT ["jupyter-lab", ""]
+## start jupter lab as entrypoint ##
+EXPOSE 8888
+ENTRYPOINT ["jupyter-lab", "/","--ip=0.0.0.0","--allow-root"]
diff --git a/docs/source/advanced_examples.rst b/docs/source/advanced_examples.rst
deleted file mode 100644
index 4a8cd242a5e7f7f907b74f92719d36c7d3e47ed1..0000000000000000000000000000000000000000
--- a/docs/source/advanced_examples.rst
+++ /dev/null
@@ -1,4 +0,0 @@
-1. Optimize b-values
---------------------
-
-Here are a couple of advanced applications...
\ No newline at end of file
diff --git a/docs/source/citation.rst b/docs/source/citation.rst
deleted file mode 100644
index 594e18d63e82290a732c789f0e2bacbee0c49f69..0000000000000000000000000000000000000000
--- a/docs/source/citation.rst
+++ /dev/null
@@ -1,4 +0,0 @@
-Citation
-========
-
-If u like it, refer to it!
\ No newline at end of file
diff --git a/docs/source/conf.py b/docs/source/conf.py
index 59cb91d76ca8518bc6a379485830de862d633b34..0c8e3784a4851ab8a0ae66a884fa0393fe4e9c68 100644
--- a/docs/source/conf.py
+++ b/docs/source/conf.py
@@ -14,6 +14,7 @@ import os
import sys
module_path = os.path.dirname(os.path.dirname(os.path.dirname(__file__)))
+print(module_path), os.listdir(module_path)
sys.path.insert(0, os.path.abspath(module_path))
_ = list(os.walk(os.path.dirname(os.path.dirname(os.path.dirname(__file__))) + '/cmrsim'))
diff --git a/docs/source/index.rst b/docs/source/index.rst
index f783d608deef91ac8bbc39ec996f7e2ed6f1979f..3b6279a74ffceacc234f5aaf528ecb77500312ef 100644
--- a/docs/source/index.rst
+++ b/docs/source/index.rst
@@ -7,15 +7,7 @@ Welcome to cmrsim's documentation!
==================================
A differentiable framework for simulating MRI images, implemented with TensorFlow > 2 in Python
-
-.. toctree::
- :maxdepth: 1
- :caption: Getting started
-
- started
-
-***************************
-
+https://gitlab.ethz.ch/jweine/cmrsim
.. toctree::
:maxdepth: 3
@@ -23,21 +15,18 @@ A differentiable framework for simulating MRI images, implemented with TensorFlo
cmrsim
-****
+***************************
.. toctree::
:maxdepth: 1
- :caption: Who is using it? - Examples
-
- advanced_examples
+ :caption: Required Quantity Overview
-****
+ req_quant_table
-.. toctree::
- :maxdepth: 1
- :caption: How to cite?
+Citation
+========
- citation
+If u like it, refer to it!
****
diff --git a/docs/source/modules.rst b/docs/source/modules.rst
new file mode 100644
index 0000000000000000000000000000000000000000..2938b7b43b11e2ce5b9c414af1478a8c157b376d
--- /dev/null
+++ b/docs/source/modules.rst
@@ -0,0 +1,7 @@
+cmrsim
+======
+
+.. toctree::
+ :maxdepth: 4
+
+ cmrsim
diff --git a/docs/source/req_quant_table.rst b/docs/source/req_quant_table.rst
new file mode 100644
index 0000000000000000000000000000000000000000..ed45b0d4628c1d7d38f2ab06985570f25ff2f700
--- /dev/null
+++ b/docs/source/req_quant_table.rst
@@ -0,0 +1,29 @@
+Signal - processes package
+==========================
+
+All signal - modifying physical processes are implemented in a tf.Module. To ensure extensibility
+and stable functionality all modules need to take the same input arguments and return a tensor
+of the same shape as the input tensor.
+To make needed properties that are stored in the dataset available to the \_\_call\_\_ method
+during runtime, while maintaining the uniform calling-argument signature, they have to be passed
+via **kwargs (keyword-arguments). To make sure they are loaded during runtime, all required
+properties have to be specified in the 'required\_quantities' class attribute. A table of the
+implemented modules, that required input are is shown below:
+
++----------------------------------------+----------------------------------------------------------------------------------------------------+
+|Sub - module |Required Quantities |
++========================================+====================================================================================================+
+|CoilSensitivity |('r_vectors',) |
++----------------------------------------+----------------------------------------------------------------------------------------------------+
+|ConstantGaussianDiffusion |('diffusion_tensor',) |
++----------------------------------------+----------------------------------------------------------------------------------------------------+
+|ContrastAgentPerfusion |('T1', 'contrast_dynamic_index') |
++----------------------------------------+----------------------------------------------------------------------------------------------------+
+|SaturationRecoveryGRE |('T1',) |
++----------------------------------------+----------------------------------------------------------------------------------------------------+
+|SpinEcho |('T1', 'T2') |
++----------------------------------------+----------------------------------------------------------------------------------------------------+
+|StaticT2starDecay |('T2star',) |
++----------------------------------------+----------------------------------------------------------------------------------------------------+
+
+
diff --git a/docs/source/started.rst b/docs/source/started.rst
deleted file mode 100644
index 18dec1a92bbb34a00bccbe1063c937e3f72c16ee..0000000000000000000000000000000000000000
--- a/docs/source/started.rst
+++ /dev/null
@@ -1,4 +0,0 @@
-Simulate a simple image
------------------------
-
-Here are a couple of introductory examples...
\ No newline at end of file
diff --git a/docs/template.rst b/docs/template.rst
new file mode 100644
index 0000000000000000000000000000000000000000..b739b6b60de93d149a8e4f331b1d1a418bb5268d
--- /dev/null
+++ b/docs/template.rst
@@ -0,0 +1,14 @@
+Signal - processes package
+==========================
+
+All signal - modifying physical processes are implemented in a tf.Module. To ensure extensibility
+and stable functionality all modules need to take the same input arguments and return a tensor
+of the same shape as the input tensor.
+To make needed properties that are stored in the dataset available to the \_\_call\_\_ method
+during runtime, while maintaining the uniform calling-argument signature, they have to be passed
+via **kwargs (keyword-arguments). To make sure they are loaded during runtime, all required
+properties have to be specified in the 'required\_quantities' class attribute. A table of the
+implemented modules, that required input are is shown below:
+
+PH_T_REQUIRED_PROPERTIES
+
diff --git a/notebooks/_construct_dataset.py b/notebooks/_construct_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..c181522177bd3b5f9ed14a3baa95762268010389
--- /dev/null
+++ b/notebooks/_construct_dataset.py
@@ -0,0 +1,30 @@
+from cmrsim.anatomy.base import BaseDataset
+import cmrsim.auxilliary.coordinates as cmrsim_coords
+from cmrsim.auxilliary.assign_map_values import assign_to_numerical_label_map
+
+resource_dir = 'example_resources/'
+
+m0_map = np.load(resource_dir + "s0map.npy")
+myo_mask = np.load(resource_dir + "myo_mask.npy")
+non_zero_mask = tf.cast(tf.constant(m0_map, shape=(378, 134), dtype=tf.int32) > 0, tf.int32)
+# This is the standard shape for cmrsim simulation tensors. In this notebook only the first three axis are needed
+batch_shape = (1, 378, 134, 1, 3, 1)
+
+n_images, x, y, _, _, _ = batch_shape
+
+r = cmrsim_coords.get_static_2d_centered_coordinates((378, 134), (0.3024, 0.1072), batch_size=1)
+static_coords = r.numpy()
+static_coords = tf.tile(static_coords, [n_images, 1, 1, 1, 1, 1])
+data_dict = OrderedDict([
+ ('M0', tf.tile(tf.constant(m0_map, shape=(1, x, y, 1, 1), dtype=tf.complex64),
+ [n_images, 1, 1, 1, 1])/ 4.),
+ ('T1', tf.tile(tf.reshape(assign_to_numerical_label_map(non_zero_mask, 1.10, fractions=[1., 1. ]),
+ [1, x, y, 1, 1]), [n_images, 1, 1, 1, 1])),
+ ('T2', tf.tile(tf.reshape(assign_to_numerical_label_map(non_zero_mask, 0.25, fractions=[1., 1.]),
+ [1, x, y, 1, 1]), [n_images, 1, 1, 1, 1])),
+ ('T2star', tf.tile(tf.reshape(assign_to_numerical_label_map(non_zero_mask, 0.035, fractions=[1., 1.]),
+ [1, x, y, 1, 1]), [n_images, 1, 1, 1, 1])),
+ ('r_vectors', static_coords)
+ ])
+
+dataset = BaseDataset(data_dict)
diff --git a/notebooks/basic_simulation_features.ipynb b/notebooks/basic_simulation_features.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..2fe9f29059db65771fd3f0a5842980adf0407a82
--- /dev/null
+++ b/notebooks/basic_simulation_features.ipynb
@@ -0,0 +1,309 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Import external modules"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Available GPUS: \n",
+ "\t PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')\n"
+ ]
+ }
+ ],
+ "source": [
+ "import tensorflow as tf\n",
+ "\n",
+ "physical_devices = tf.config.list_physical_devices('GPU')\n",
+ "print(\"Available GPUS: \\n\\t\", \"\\n\\t\".join([str(_) for _ in physical_devices]))\n",
+ "if physical_devices:\n",
+ " tf.config.experimental.set_visible_devices(physical_devices[0], 'GPU')\n",
+ " tf.config.experimental.set_memory_growth(physical_devices[0], True)\n",
+ "\n",
+ "from collections import OrderedDict\n",
+ "import numpy as np\n",
+ "import math\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib widget "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Import cmrsim modules:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import sys\n",
+ "sys.path.append('..')\n",
+ "import cmrsim.simulation as cmrsim_simulate\n",
+ "import cmrsim.auxilliary.coordinates as cmrsim_coords\n",
+ "import cmrsim.auxilliary.snr as cmrsim_snr\n",
+ "import cmrsim.auxilliary.plot_utilities as cmrsim_plots\n",
+ "from cmrsim.auxilliary.assign_map_values import assign_to_numerical_label_map"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Simulation\n",
+ "\n",
+ "Define subclass of SimulationBase class that implements the `_build` method. The Variables inside the modules can be assigned manually later to configure the simulation parameters."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from cmrsim.anatomy.base import BaseDataset\n",
+ "from cmrsim.signal_processes.base import CompositeSignalModel\n",
+ "import cmrsim.signal_processes.solution_operators as solution_operators\n",
+ "\n",
+ "from cmrsim.encoding.epi import EPI\n",
+ "from cmrsim.reconstruction.cartesian import Cartesian2D, RoemerMultiCoil\n",
+ "\n",
+ "class ExampleSimulation(cmrsim_simulate.SimulationBase):\n",
+ " def _build(self):\n",
+ " n_kx, n_ky = 189, 67\n",
+ " # Encoding definition\n",
+ " encoding_module = EPI(field_of_view=(0.31, 0.112), sampling_matrix_size=(n_kx, n_ky), read_out_duration=0.1,\n",
+ " blip_duration=0.01, k_space_segments=27,\n",
+ " absolute_noise_std=0.\n",
+ " )\n",
+ " # Signal module construction\n",
+ " spinecho_module = solution_operators.sequences.SpinEcho(0.05, 10)\n",
+ " t2_star_module = solution_operators.t2_star.StaticT2starDecay(encoding_module.get_sampling_times())\n",
+ "\n",
+ " # Forward model composition\n",
+ " forward_model = CompositeSignalModel(spinecho_module, t2_star_module)\n",
+ " \n",
+ " # Reconstruction\n",
+ " recon_module = Cartesian2D(sample_matrix_size=(n_kx, n_ky), padding=None)\n",
+ " return forward_model, encoding_module, recon_module"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Set up Dataset by first constructing a dictionary containing the 2D-maps. The axes have to be expanded to the format ```(#images, X, Y, #repetitions, #kspace-samples)```. If the dimension on the two innermost axes are equal to 1, the value is re-used for all subsequent expansions (e.g different b-values in diffusion imaging).\n",
+ "This dictionary serves as input to the cmrsim-dataset class."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%%writefile _construct_dataset.py\n",
+ "from cmrsim.anatomy.base import BaseDataset\n",
+ "resource_dir = 'example_resources/'\n",
+ "\n",
+ "m0_map = np.load(resource_dir + \"s0map.npy\")\n",
+ "myo_mask = np.load(resource_dir + \"myo_mask.npy\")\n",
+ "non_zero_mask = tf.cast(tf.constant(m0_map, shape=(378, 134), dtype=tf.int32) > 0, tf.int32)\n",
+ "# This is the standard shape for cmrsim simulation tensors. In this notebook only the first three axis are needed\n",
+ "batch_shape = (1, 378, 134, 1, 3, 1)\n",
+ "\n",
+ "n_images, x, y, _, _, _ = batch_shape\n",
+ "\n",
+ "r = cmrsim_coords.get_static_2d_centered_coordinates((378, 134), (0.3024, 0.1072), batch_size=1)\n",
+ "static_coords = r.numpy()\n",
+ "static_coords = tf.tile(static_coords, [n_images, 1, 1, 1, 1, 1])\n",
+ "data_dict = OrderedDict([\n",
+ " ('M0', tf.tile(tf.constant(m0_map, shape=(1, x, y, 1, 1), dtype=tf.complex64),\n",
+ " [n_images, 1, 1, 1, 1])/ 4.),\n",
+ " ('T1', tf.tile(tf.reshape(assign_to_numerical_label_map(non_zero_mask, 1.10, fractions=[1., 1. ]),\n",
+ " [1, x, y, 1, 1]), [n_images, 1, 1, 1, 1])),\n",
+ " ('T2', tf.tile(tf.reshape(assign_to_numerical_label_map(non_zero_mask, 0.25, fractions=[1., 1.]),\n",
+ " [1, x, y, 1, 1]), [n_images, 1, 1, 1, 1])),\n",
+ " ('T2star', tf.tile(tf.reshape(assign_to_numerical_label_map(non_zero_mask, 0.035, fractions=[1., 1.]),\n",
+ " [1, x, y, 1, 1]), [n_images, 1, 1, 1, 1])),\n",
+ " ('r_vectors', static_coords)\n",
+ " ])\n",
+ "\n",
+ "dataset = BaseDataset(data_dict)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Run the simulation\n",
+ "\n",
+ "Instantiate an ExampleSimulation:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "simulation = ExampleSimulation(name='simulation_example')"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "After the simulation was performed, the Configured simulation is saved as `tf.train.Checkpoint`. To demonstrate the loading functionality, the `ExampleSimulation` instance is deleted."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "%time simulation_result = simulation(dataset, voxel_batch_size=10000, execute_eager=False)\n",
+ "\n",
+ "import os\n",
+ "chkpt = './model_checkpoints/example_checkpoint'\n",
+ "os.makedirs(os.path.dirname(chkpt), exist_ok=True)\n",
+ "simulation.save(checkpoint_path=chkpt)\n",
+ "del simulation"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Plot Simulation result"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "slide_show = cmrsim_plots.SlideShow(np.abs(simulation_result[..., ::-1, ::-1]).transpose([0,3,2,1]))\n",
+ "slide_show.fig.set_size_inches(5, 1.8)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Intantiate a new `ExampleSimulation` module and load the configuration from the previously saved model checkpoint by using the classmethod `SimulationBase.from_checkpoint(...)`"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "new_simulation = ExampleSimulation.from_checkpoint(chkpt)\n",
+ "\n",
+ "%time simulation_load_result = new_simulation(dataset, voxel_batch_size=10000, execute_eager=False)\n",
+ "print(\"Resulting shape information: (#images, #noise-levels, X, Y)\")\n",
+ "print(\"Simulation result shape:\", simulation_load_result.shape)\n",
+ "print(tf.reduce_sum(simulation_result - simulation_load_result))"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### After instantitation, the variables contained in the submodules can be changed"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "new_simulation.forward_model.spin_echo.TE.assign(0.01)\n",
+ "%time n = new_simulation(dataset, voxel_batch_size=100000)\n",
+ "print('Mean absolute difference for different TE:', tf.reduce_mean(tf.abs(noise_less_k_space - n)))\n",
+ "\n",
+ "new_simulation.forward_model.spin_echo.TE.assign(0.05)\n",
+ "%time n = new_simulation(dataset, voxel_batch_size=100000)\n",
+ "print('Mean absolute difference for same TE:', tf.reduce_mean(tf.abs(noise_less_k_space - n)))\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Summary functionality\n",
+ "The SimulationBase class includes a functionality to create a report of the simulation configuration (aka submodule Variables) as dictionary. This dictionary can be used to generate a JSON string "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import json\n",
+ "from IPython.display import JSON\n",
+ "\n",
+ "summary_dict = new_simulation.get_configuration()\n",
+ "s = json.dumps(summary_dict)\n",
+ "JSON(summary_dict)"
+ ]
+ }
+ ],
+ "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": "3.7.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/notebooks/epi_motion.ipynb b/notebooks/epi_motion.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..47922f8315e4cdd57b85558567f8829694219921
--- /dev/null
+++ b/notebooks/epi_motion.ipynb
@@ -0,0 +1,264 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import tensorflow as tf\n",
+ "import numpy as np\n",
+ "import math\n",
+ "import sys\n",
+ "sys.path.append('../')\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib widget \n",
+ "\n",
+ "from cmrsim.auxilliary import coordinates as cmrsim_coords\n",
+ "from cmrsim.simulation import SimulationBase "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[]"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tf.config.list_physical_devices('GPU')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "def _create_cylinder_phantom(fov=(0.10, 0.125), map_size=(250, 313)):\n",
+ " xx, yy = np.meshgrid(*[range(i) for i in map_size], indexing='ij')\n",
+ " \n",
+ " rrs = [np.sqrt((xx-x0)**2 + (yy-y0)**2) for (x0, y0) in [(75, 100), (125, 200), (175, 100)]]\n",
+ " binarys = [np.where(r<40, np.ones_like(r) * (i+1), np.zeros_like(r)) for i, r in enumerate(rrs)]\n",
+ " class_map = np.sum(np.stack(binarys, axis=0), axis=0)\n",
+ " \n",
+ " t1 = np.where(class_map > 0., np.ones_like(class_map), np.zeros_like(class_map)) * 1000 # ms\n",
+ " t2 = np.where(class_map > 0., np.ones_like(class_map), np.zeros_like(class_map)) * 200 # ms\n",
+ " m0 = np.where(class_map > 0., np.ones_like(class_map), np.zeros_like(class_map)) * 80 # %\n",
+ " m0 *= np.prod([i/j for i,j in zip(fov, map_size)])\n",
+ " \n",
+ " r = cmrsim_coords.get_static_2d_centered_coordinates(map_size=map_size, object_dimensions=fov)\n",
+ " dynamic_index_map = np.where(class_map > 0., np.ones_like(class_map), np.zeros_like(class_map))\n",
+ " \n",
+ " data_dict = {'M0': m0[np.newaxis, ..., np.newaxis, np.newaxis].astype(np.complex64),\n",
+ " 'T1': t1[np.newaxis, ..., np.newaxis, np.newaxis].astype(np.float32),\n",
+ " 'T2': t2[np.newaxis, ..., np.newaxis, np.newaxis].astype(np.float32),\n",
+ " 'r_vectors': -1 * r.numpy().astype(np.float32),\n",
+ " }\n",
+ "\n",
+ " return data_dict\n",
+ " \n",
+ "dd = _create_cylinder_phantom()\n",
+ "plt.close('all')\n",
+ "# f, a = plt.subplots(1, 1)\n",
+ "# a.imshow(np.abs(dd['T1'][0, :, :, 0, 0]))\n",
+ "# a.imshow(dd['r_vectors'][0, :, :, 0, 0, 0])\n",
+ "# a.imshow(np.squeeze(dd['contrast_dynamic_index']))\n",
+ "# plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Required data input of configured simulation: ('M0', 'T1', 'T2')\n"
+ ]
+ }
+ ],
+ "source": [
+ "from cmrsim.encoding.epi import EPI\n",
+ "from cmrsim.signal_processes.solution_operators.sequences import SpinEcho\n",
+ "from cmrsim.signal_processes.base import CompositeSignalModel\n",
+ "from cmrsim.reconstruction.cartesian import Cartesian2D\n",
+ "\n",
+ "class ExampleSimulation(SimulationBase):\n",
+ " def _build(self):\n",
+ " n_kx, n_ky = 50, 50\n",
+ " # Encoding definition\n",
+ " encoding_module = EPI(field_of_view=(0.1, 0.1), sampling_matrix_size=(n_kx, n_ky), read_out_duration=0.1,\n",
+ " blip_duration=0.01, k_space_segments=1, absolute_noise_std=0.)\n",
+ " \n",
+ " # Signal module construction\n",
+ " spinecho_module = SpinEcho(0.05, 10)\n",
+ "\n",
+ " # Forward model composition\n",
+ " forward_model = CompositeSignalModel(spinecho_module)\n",
+ " \n",
+ " # Reconstruction\n",
+ " recon_module = Cartesian2D(sample_matrix_size=(n_kx, n_ky), padding=None)\n",
+ " return forward_model, encoding_module, recon_module\n",
+ " \n",
+ "simulation = ExampleSimulation() \n",
+ "print(f\"Required data input of configured simulation: {simulation.forward_model.required_quantities}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 104,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(1, 250, 313, 1, 3, 2500)\n"
+ ]
+ }
+ ],
+ "source": [
+ "# Add motion in x-direction\n",
+ "dd = _create_cylinder_phantom()\n",
+ "\n",
+ "r = dd['r_vectors']\n",
+ "sampling_times = simulation.encoding_module.sampling_times.read_value().numpy()\n",
+ "velocity = 0.05 # m/s\n",
+ "delta_r = (1/2 * velocity * sampling_times**2)[np.newaxis, np.newaxis] + 1\n",
+ "# delta_r = (velocity * sampling_times[-1]) + 1\n",
+ "\n",
+ "r = np.tile(r, [1, 1, 1, 1, 1, sampling_times.size])\n",
+ "r[0, :, :, 0, 0, :] *= delta_r\n",
+ "r[0, :, :, 0, 1, :] *= delta_r\n",
+ "\n",
+ "dd['r_vectors'] = r\n",
+ "print(r.shape)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 105,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "dict_keys(['M0', 'T1', 'T2', 'r_vectors']) [(1, 250, 313, 1, 1), (1, 250, 313, 1, 1), (1, 250, 313, 1, 1), (1, 250, 313, 1, 3, 2500)]\n"
+ ]
+ }
+ ],
+ "source": [
+ "from cmrsim.anatomy.base import BaseDataset\n",
+ "\n",
+ "print(dd.keys(), [_.shape for _ in dd.values()])\n",
+ "dataset = BaseDataset(dd)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 106,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Run Simulation: |XXXXX|1/1\t|XXXXXXXXXXXXXXXXXXXX|15039/15039\t|XXXXXXXXXXXXXXXXXXXX|1/1 WARNING:tensorflow:11 out of the last 11 calls to <function SimulationBase.__call__.<locals>.simulation_execute at 0x00000212480769D8> triggered tf.function retracing. Tracing is expensive and the excessive number of tracings could be due to (1) creating @tf.function repeatedly in a loop, (2) passing tensors with different shapes, (3) passing Python objects instead of tensors. For (1), please define your @tf.function outside of the loop. For (2), @tf.function has experimental_relax_shapes=True option that relaxes argument shapes that can avoid unnecessary retracing. For (3), please refer to https://www.tensorflow.org/tutorials/customization/performance#python_or_tensor_args and https://www.tensorflow.org/api_docs/python/tf/function for more details.\n"
+ ]
+ }
+ ],
+ "source": [
+ "simulated_images = simulation(dataset, voxel_batch_size=1000, execute_eager=False)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 107,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(1, 1, 50, 50)\n"
+ ]
+ },
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "9ebe530371b04917a9885a917c4ba3bd",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "text/plain": [
+ "(<matplotlib.image.AxesImage at 0x212d2338d88>,\n",
+ " <matplotlib.image.AxesImage at 0x212d2393048>)"
+ ]
+ },
+ "execution_count": 107,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "print(simulated_images.shape)\n",
+ "images = np.squeeze(simulated_images)\n",
+ "magnitude, phase = np.abs(images), np.angle(images)\n",
+ "\n",
+ "f, a = plt.subplots(1, 2)\n",
+ "a[0].imshow(magnitude), a[1].imshow(phase)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "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": "3.7.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/example_resources/model_checkpoints/example_checkpoint.data-00000-of-00002 b/notebooks/example_resources/model_checkpoints/example_checkpoint.data-00000-of-00002
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diff --git a/notebooks/graph_and_optimization.ipynb b/notebooks/graph_and_optimization.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..71e5cf660a6e092b4150bc8c39b6343fbbaeec03
--- /dev/null
+++ b/notebooks/graph_and_optimization.ipynb
@@ -0,0 +1,253 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "## Simulation Graph-Construction and Variable optimization\n",
+ "\n",
+ "This notebook highlights the functionalities of saving the computational Graph of the simulation and displaying it in tensorboard as well as optimizing a sequence parameter defined as a Variable for a very simple set-up"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import os\n",
+ "import tensorflow as tf\n",
+ "\n",
+ "physical_devices = tf.config.list_physical_devices('GPU')\n",
+ "print(\"Available GPUS: \\n\\t\", \"\\n\\t\".join([str(_) for _ in physical_devices]))\n",
+ "if physical_devices:\n",
+ " tf.config.experimental.set_visible_devices(physical_devices[0], 'GPU')\n",
+ " tf.config.experimental.set_memory_growth(physical_devices[0], True)\n",
+ "\n",
+ "from collections import OrderedDict\n",
+ "import numpy as np\n",
+ "import matplotlib.pyplot as plt\n",
+ "import math\n",
+ "\n",
+ "import sys\n",
+ "sys.path.append('..')\n",
+ "import cmrsim.simulation as cmrsim_simulate\n",
+ "import cmrsim.auxilliary.coordinates as cmrsim_coords\n",
+ "import cmrsim.auxilliary.snr as cmrsim_snr\n",
+ "import cmrsim.auxilliary.plot_utilities as cmrsim_plots\n",
+ "from cmrsim.auxilliary.assign_map_values import assign_to_numerical_label_map"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Set up Dataset and Simulation\n",
+ "\n",
+ "First, a dataset exactly as done in the previous notebooks is instantiated (load the code with cell-magic %load and rerun the cell below)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "# %load _construct_dataset.py\n",
+ "from cmrsim.anatomy.base import BaseDataset\n",
+ "resource_dir = 'example_resources/'\n",
+ "\n",
+ "m0_map = np.load(resource_dir + \"s0map.npy\")\n",
+ "myo_mask = np.load(resource_dir + \"myo_mask.npy\")\n",
+ "non_zero_mask = tf.cast(tf.constant(m0_map, shape=(378, 134), dtype=tf.int32) > 0, tf.int32)\n",
+ "# This is the standard shape for cmrsim simulation tensors. In this notebook only the first three axis are needed\n",
+ "batch_shape = (1, 378, 134, 1, 3, 1)\n",
+ "\n",
+ "n_images, x, y, _, _, _ = batch_shape\n",
+ "\n",
+ "r = cmrsim_coords.get_static_2d_centered_coordinates((378, 134), (0.3024, 0.1072), batch_size=1)\n",
+ "static_coords = r.numpy()\n",
+ "static_coords = tf.tile(static_coords, [n_images, 1, 1, 1, 1, 1])\n",
+ "data_dict = OrderedDict([\n",
+ " ('M0', tf.tile(tf.constant(m0_map, shape=(1, x, y, 1, 1), dtype=tf.complex64),\n",
+ " [n_images, 1, 1, 1, 1])/ 4.),\n",
+ " ('T1', tf.tile(tf.reshape(assign_to_numerical_label_map(non_zero_mask, 1.10, fractions=[1., 1. ]),\n",
+ " [1, x, y, 1, 1]), [n_images, 1, 1, 1, 1])),\n",
+ " ('T2', tf.tile(tf.reshape(assign_to_numerical_label_map(non_zero_mask, 0.25, fractions=[1., 1.]),\n",
+ " [1, x, y, 1, 1]), [n_images, 1, 1, 1, 1])),\n",
+ " ('T2star', tf.tile(tf.reshape(assign_to_numerical_label_map(non_zero_mask, 0.035, fractions=[1., 1.]),\n",
+ " [1, x, y, 1, 1]), [n_images, 1, 1, 1, 1])),\n",
+ " ('r_vectors', static_coords)\n",
+ " ])\n",
+ "\n",
+ "dataset = BaseDataset(data_dict)\n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Now define a very simple simulation, whose only parameters are repetition time and echo time, contained in the solution operator for a SpinEcho sequence.\n",
+ "\n",
+ "An overview of the available Variable is shown as output of the cell below"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from cmrsim.signal_processes.base import CompositeSignalModel\n",
+ "import cmrsim.signal_processes.solution_operators as solution_operators\n",
+ "from cmrsim.encoding.epi import EPI\n",
+ "from cmrsim.reconstruction.cartesian import Cartesian2D\n",
+ "\n",
+ "class DifferentiableSimulation(cmrsim_simulate.SimulationBase):\n",
+ " def _build(self):\n",
+ " n_kx, n_ky = 189, 67\n",
+ " # Encoding definition\n",
+ " encoding_module = EPI(field_of_view=(0.31, 0.112), sampling_matrix_size=(n_kx, n_ky), read_out_duration=0.1,\n",
+ " blip_duration=0.01, k_space_segments=27,\n",
+ " absolute_noise_std=0.\n",
+ " )\n",
+ " # Signal module construction\n",
+ " spinecho_module = solution_operators.sequences.SpinEcho(0.05, 10)\n",
+ "\n",
+ " # Forward model composition\n",
+ " forward_model = CompositeSignalModel(spinecho_module)\n",
+ " \n",
+ " # Reconstruction\n",
+ " recon_module = Cartesian2D(sample_matrix_size=(n_kx, n_ky), padding=None)\n",
+ " return forward_model, encoding_module, recon_module\n",
+ " \n",
+ " def _update(self):\n",
+ " super(DifferentiableSimulation, self)._update()\n",
+ "\n",
+ "simulation = DifferentiableSimulation()\n",
+ "list_of_variables = [f\"{v.name :<20} - {str(v.shape) :^15} - trainable = {v.trainable}\" for v in simulation.variables if 'progress_bar' not in v.name]\n",
+ "print(\"Variables within simulation scope:\\n\\n\\t\", '\\n\\t '.join(list_of_variables), end='\\n\\n')\n",
+ "del simulation"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Simulate in graph mode and save the simulation-graph\n",
+ "\n",
+ "The computational graph now is visible in tensorboard (which can be started in bottom cell)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "simulation = DifferentiableSimulation()\n",
+ "result = simulation(dataset, voxel_batch_size=10000, execute_eager=False)\n",
+ "print(result.shape)\n",
+ "_ = simulation.write_graph(dataset=dataset, graph_log_dir='./tensor_board/differentiable_simulation')\n",
+ "del simulation"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Parameter optimization\n",
+ "\n",
+ "In the given example we want to optimize the echo-time of the spin-echo module with respect to minimizing the overall signal intensity. Obviously this results in increasing the echo-time as much as possible. The TE value and the is logged and displayed in tensorboard as well\n",
+ "\n",
+ "**Notes** (To do)**:** \n",
+ "- Unfortunately the gradient is unconnected if the entire simulation loop is constructed as graph (execute_eager=False) \n",
+ "- In eager mode, the gradient tape gets to large to fit GPU memory, therefore its preformed on CPU only (with tf.device('CPU:0')) "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "simulation = DifferentiableSimulation()\n",
+ "variable_to_optimize = simulation.forward_model.spin_echo.TE\n",
+ "print(variable_to_optimize)\n",
+ "\n",
+ "f, a = plt.subplots(1, 10)\n",
+ "file_writer = tf.summary.create_file_writer('./tensor_board/optimization')\n",
+ "\n",
+ "steps = 10\n",
+ "with tf.device('CPU:0'):\n",
+ " optimizer = tf.keras.optimizers.SGD(learning_rate=1e-4)\n",
+ " for step in range(steps):\n",
+ " with tf.GradientTape(watch_accessed_variables=False) as tape:\n",
+ " tape.watch(variable_to_optimize)\n",
+ " _ = simulation(dataset=dataset, voxel_batch_size=10000, execute_eager=True)\n",
+ " dummy_loss = tf.reduce_sum(tf.abs(_))\n",
+ " grad = tape.gradient(_, variable_to_optimize)\n",
+ " optimizer.apply_gradients([(-grad, variable_to_optimize),])\n",
+ " print(dummy_loss, variable_to_optimize.read_value().numpy())\n",
+ " with file_writer.as_default():\n",
+ " tf.summary.scalar('Loss', dummy_loss, step=step)\n",
+ " tf.summary.scalar('TE', variable_to_optimize.read_value(), step=step)\n",
+ " a[step].imshow(np.abs(_)[0, 0], vmax=20, vmin=0), a[step].set_xticks([]), a[step].set_yticks([])\n",
+ " \n"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Start tensorboard inside the Notebook"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%reload_ext tensorboard"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%tensorboard --logdir tensor_board\n"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "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": "3.7.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
\ No newline at end of file
diff --git a/notebooks/multi_coil.ipynb b/notebooks/multi_coil.ipynb
deleted file mode 100644
index 917d77b2ef41c4852737aebc0bc7f4841e35e889..0000000000000000000000000000000000000000
--- a/notebooks/multi_coil.ipynb
+++ /dev/null
@@ -1,524 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Import external modules"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "pycharm": {
- "is_executing": true,
- "name": "#%%\n"
- }
- },
- "outputs": [],
- "source": [
- "import tensorflow as tf\n",
- "physical_devices = tf.config.list_physical_devices('GPU')\n",
- "tf.config.experimental.set_visible_devices(physical_devices[0], 'GPU')\n",
- "tf.config.experimental.set_memory_growth(physical_devices[0], True)\n",
- "\n",
- "from collections import OrderedDict\n",
- "import numpy as np\n",
- "import math\n",
- "\n",
- "import matplotlib.pyplot as plt\n",
- "%matplotlib widget"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Import cmrsim modules:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
- "outputs": [],
- "source": [
- "from cmrsim.anatomy.base import BaseDataset\n",
- "from cmrsim.signal_processes.base import CompositeSignalModel\n",
- "import cmrsim.signal_processes.solution_operators as solution_operators\n",
- "\n",
- "from cmrsim.encoding.epi import EPI\n",
- "from cmrsim.reconstruction.cartesian import Cartesian2D, RoemerMultiCoil\n",
- "from cmrsim.simulation import simulate\n",
- "\n",
- "import cmrsim.auxilliary.coordinates as cmrsim_coords\n",
- "import cmrsim.auxilliary.snr as cmrsim_snr\n",
- "import cmrsim.auxilliary.plot_utilities as cmrsim_plots\n",
- "from cmrsim.auxilliary.assign_map_values import assign_to_numerical_label_map"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Set up data\n",
- "\n",
- "First load the MRXcat maps for simulation:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "a75b49bb64174b0490b6a53e86716ac8",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "resource_dir = 'example_resources/'\n",
- "\n",
- "m0_map = np.load(resource_dir + \"s0map.npy\")\n",
- "coil_sens_maps = np.load(resource_dir + \"coil_sensitivities.npy\").astype(np.complex64)\n",
- "n_coils = coil_sens_maps.shape[0]\n",
- "\n",
- "myo_mask = np.load(resource_dir + \"myo_mask.npy\")\n",
- "non_zero_mask = tf.cast(tf.constant(m0_map, shape=(378, 134), dtype=tf.int32) > 0, tf.int32)\n",
- "\n",
- "fig, axes = plt.subplots(1, 4)\n",
- "for idx, ax, coilmap in zip(range(n_coils), axes, coil_sens_maps):\n",
- " ax.imshow(np.abs(coilmap[..., 0]))\n",
- " ax.set_title(f\"Coil {idx}\")\n",
- " ax.set_xticks([])\n",
- " ax.set_yticks([])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Compute positional vectors of object points from m0_map.\n",
- "For more details see cmrsim.auxilliary.coordinates.get_static_2d_centered_coordinates"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
- "outputs": [],
- "source": [
- "# This is the standard shape for cmrsim simulation tensors. In this notebook only the first three axis are needed\n",
- "batch_shape = (1, 378, 134, 1, 3, 1)\n",
- "n_images, x, y, _, _, _ = batch_shape\n",
- "\n",
- "r = cmrsim_coords.get_static_2d_centered_coordinates((378, 134), (0.3024, 0.1072), batch_size=1)\n",
- "static_coords = r.numpy()\n",
- "static_coords = tf.tile(static_coords, [n_images, 1, 1, 1, 1, 1])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Assign values of T1, T2 and T2* to map and store into Ordered dictionary. Make sure, that the relaxation times, do not\n",
- "contain zeros since it will cause NaN entries in the simulation."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
- "outputs": [],
- "source": [
- "data_dict = OrderedDict([\n",
- " ('M0', tf.tile(tf.constant(m0_map, shape=(1, x, y, 1, 1), dtype=tf.complex64),\n",
- " [n_images, 1, 1, 1, 1])/ 4.),\n",
- " ('T1', tf.tile(tf.reshape(assign_to_numerical_label_map(non_zero_mask, 1.10, fractions=[1., 1. ]),\n",
- " [1, x, y, 1, 1]), [n_images, 1, 1, 1, 1])),\n",
- " ('T2', tf.tile(tf.reshape(assign_to_numerical_label_map(non_zero_mask, 0.25, fractions=[1., 1.]),\n",
- " [1, x, y, 1, 1]), [n_images, 1, 1, 1, 1])),\n",
- " ('T2star', tf.tile(tf.reshape(assign_to_numerical_label_map(non_zero_mask, 0.035, fractions=[1., 1.]),\n",
- " [1, x, y, 1, 1]), [n_images, 1, 1, 1, 1])),\n",
- " ('r_vectors', static_coords)\n",
- " ])\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Simulation\n",
- "Construct the cmrsim simulation modules\n",
- "- EPI encoding\n",
- "- SE sequence\n",
- "- T2 star decay\n",
- "- Coil sensitivities (4 coils)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
- "outputs": [],
- "source": [
- "n_kx, n_ky = 189, 67\n",
- "\n",
- "# Encoding definition\n",
- "encoding_module = EPI(field_of_view=(0.31, 0.112), sampling_matrix_size=(n_kx, n_ky), bandwidth_per_pixel=1300.,\n",
- " k_space_segments=27, absolute_noise_std=tf.constant([0.,], dtype=tf.float32))\n",
- "\n",
- "# Signal module construction\n",
- "spinecho_module = solution_operators.sequences.SpinEcho(0.05, 10)\n",
- "t2_star_module = solution_operators.t2_star.StaticT2starDecay(encoding_module.get_sampling_times())\n",
- "coil_sens_module = solution_operators.coil_sensitivities.CoilSensitivity(coil_sens_maps, (0.3024, 0.1072, 0.))\n",
- "\n",
- "# Forward model composition\n",
- "forward_model = CompositeSignalModel(spinecho_module, t2_star_module, coil_sens_module)\n",
- "\n",
- "# Data stream module\n",
- "dataset = BaseDataset(data_dict)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Run simulation. If recon module is specified, the simulation output are images and if it is None the flattened k-space\n",
- "is returned."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Start building Graph for simulation...\n",
- "Processed images: 0 Current image: 30000 / 32327 Segment index: 26 / 27\n",
- "Resulting shape information: (#images, #coils, #noise-levels, #samples=(kx*ky)\n",
- "Simulation result shape: (1, 4, 1, 12663)\n",
- "Wall time: 7.81 s\n"
- ]
- }
- ],
- "source": [
- "%%time\n",
- "print(\"Start building Graph for simulation...\")\n",
- "noise_less_k_space = simulate(dataset, forward_model, encoding_module, recon_module=None,\n",
- " voxel_batch_size=5000, execute_eager=False)\n",
- "print(\"\\nResulting shape information: (#images, #coils, #noise-levels, #samples=(kx*ky)\")\n",
- "print(\"Simulation result shape:\", noise_less_k_space.shape)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Reconstruct the images for each coil separately and estimated noise std for target SNRs"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "(378, 134)\n",
- "2D recon single coils: (1, 4, 1, 189, 67)\n",
- "Estimated noise level: [0.03448158 0.06693496 0.12864637]\n"
- ]
- }
- ],
- "source": [
- "channel_wise_recon_module = Cartesian2D(sample_matrix_size=(n_kx, n_ky))\n",
- "single_coil_images = channel_wise_recon_module(noise_less_k_space)\n",
- "print(myo_mask.shape)\n",
- "\n",
- "print(\"2D recon single coils:\", single_coil_images.shape)\n",
- "target_snrs = [20., 10., 5.]\n",
- "estimated_std = cmrsim_snr.compute_noise_std(noiseless_single_coil_images=single_coil_images[0, :, 0],\n",
- " coil_sensitivities=coil_sens_maps[:, ::2, ::2, 0],\n",
- " target_snr=target_snrs,\n",
- " mask=myo_mask[::2, ::2].astype(np.float32),\n",
- " iteratively_refine=True)\n",
- "print(\"Estimated noise level:\", estimated_std)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Retrospectively add noise to the simulated k-space according to the estimated noise-levels and\n",
- " use Roemer reconstruction to combine the images from multiple coils."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "Resulting shape information: (#images, #coils, #noise-levels, #samples=(kx*ky)\n",
- "Simulation result shape: (1, 4, 4, 12663)\n"
- ]
- }
- ],
- "source": [
- "encoding_module.absolute_noise_std.assign(tf.constant([0.,] + [_ for _ in estimated_std], tf.float32))\n",
- "simulated_data = encoding_module.add_noise(noise_less_k_space[:, :, 0, :])\n",
- "\n",
- "#Recon definition\n",
- "recon_module = RoemerMultiCoil(sample_matrix_size=(n_kx, n_ky), coil_sensitivities=coil_sens_maps[..., 0],\n",
- " padding=[(math.floor(n_kx/2), math.ceil(n_kx/2)), (math.floor(n_ky/2), math.ceil(n_ky/2))])\n",
- "\n",
- "zero_padded_reconstruction = recon_module(simulated_data, coil_channel_axis=1)\n",
- "\n",
- "print(\"\\nResulting shape information: (#images, #coils, #noise-levels, #samples=(kx*ky)\")\n",
- "print(\"Simulation result shape:\", simulated_data.shape)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot the noisy single coil images for different noise levels"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "pycharm": {
- "is_executing": true,
- "name": "#%%\n"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Channel wise reconstruction: (1, 4, 4, 378, 134)\n"
- ]
- },
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "dc1739bc24fa4769b00df8dcc5c1e452",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "padded_chanel_wise_recon = Cartesian2D(sample_matrix_size=(n_kx, n_ky),\n",
- " padding=[(math.floor(n_kx/2), math.ceil(n_kx/2)),\n",
- " (math.floor(n_ky/2), math.ceil(n_ky/2))])\n",
- "noisy_single_coil_images = padded_chanel_wise_recon(simulated_data)\n",
- "print(\"Channel wise reconstruction: \", noisy_single_coil_images.shape)\n",
- "\n",
- "\n",
- "\n",
- "slide_show1 = cmrsim_plots.SlideShow(np.abs(noisy_single_coil_images[0, ...]).transpose([0, 2, 3, 1]),\n",
- " colorbars=True, titles=[f\"Coil #{idx}\" for idx in range(n_coils)],\n",
- " x_labels=[n_coils * [f'SNR: {s}',] for s in ['inf', ] + target_snrs],\n",
- " imshow_kwargs={'cmap': 'jet', 'vmin':0., 'vmax':1.2})\n",
- "slide_show1.fig.suptitle(f\"Scroll trough target SNRs: {target_snrs}\", fontsize=14, fontweight='bold')\n",
- "slide_show1.fig.set_size_inches(4 * 2.2, 4.5)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Calculate SNR map for the simulated SNRs. Modify ```noise_index_plot``` to choose the corresponding simulation result\n",
- "as map in the plot below"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "pycharm": {
- "is_executing": true,
- "name": "#%%\n"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Current noise std: 0.0\n",
- "Mean signal in simulated imaged: 5.028250694274902\n",
- "Mean snr in LV: nan\n",
- "\n",
- "Current noise std: 0.034481581300497055\n",
- "Mean signal in simulated imaged: 5.04742431640625\n",
- "Mean snr in LV: 19.999818801879883\n",
- "\n",
- "Current noise std: 0.0669349655508995\n",
- "Mean signal in simulated imaged: 5.043673515319824\n",
- "Mean snr in LV: 10.32189655303955\n",
- "\n",
- "Current noise std: 0.1286463737487793\n",
- "Mean signal in simulated imaged: 5.146673202514648\n",
- "Mean snr in LV: 5.519454002380371\n",
- "\n",
- "SNR map shape: (378, 134)\n"
- ]
- }
- ],
- "source": [
- "snr_maps = []\n",
- "for noise_index in range(len(target_snrs)+1):\n",
- " scaling = encoding_module.absolute_noise_std[noise_index]\n",
- "\n",
- " print(f\"Current noise std: {scaling}\")\n",
- " image_shape = recon_module.output_matrix_size\n",
- " noise_vectors = tf.random.normal(shape=(2, n_coils, image_shape[0], image_shape[1]), dtype=tf.float32) * scaling\n",
- " dynamic_noise_scan = tf.complex(noise_vectors[0], noise_vectors[1])\n",
- "\n",
- " snr_map = cmrsim_snr.calculate_snr(single_coil_images=noisy_single_coil_images[0, :, noise_index, ...],\n",
- " dynamic_noise_scan=dynamic_noise_scan,\n",
- " coil_sensitivities=coil_sens_maps[..., 0])\n",
- " \n",
- " snr_maps += [snr_map, ]\n",
- " \n",
- " float_mask = tf.cast(myo_mask, tf.float32)\n",
- " mean_signal_encoded = tf.reduce_sum(tf.abs(zero_padded_reconstruction[0 , noise_index:noise_index+1, ...]) * float_mask) / tf.reduce_sum(float_mask)\n",
- " mean_snr = tf.math.reduce_sum(tf.abs(snr_map) * float_mask) / tf.reduce_sum(float_mask)\n",
- " print(f\"Mean signal in LV: {mean_signal_encoded}\")\n",
- " print(f\"Mean snr in LV: {mean_snr}\\n\")\n",
- "\n",
- "print(\"SNR map shape:\", snr_map.shape)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "pycharm": {
- "name": "#%% md\n"
- }
- },
- "source": [
- "Plot Simulation results:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {
- "pycharm": {
- "is_executing": true,
- "name": "#%%\n"
- }
- },
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "d5d0d4a0f0ab4303aa3e3c5ab53d6b42",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# stack images into shape (#subplots, x, y, #channels)\n",
- "image_stack = np.concatenate((np.abs(zero_padded_reconstruction).transpose([0, 2, 3, 1]),\n",
- " np.nan_to_num(np.stack(snr_maps, axis=2)[np.newaxis, ...], posinf=0.0),\n",
- " np.tile(myo_mask[np.newaxis, :, :, np.newaxis], [1, 1, 1, 1+len(estimated_std)])\n",
- " ), axis=0\n",
- " )\n",
- "s1 = cmrsim_plots.SlideShow(image_stack, colorbars=True, titles=[\"Image\", \"SNR map\", \"Mask\"],\n",
- " x_labels=[[f'SNR:{s :>5}', f'SNR:{s :>5}', ' '] for s in [' inf', ] + target_snrs])\n",
- "s1.fig.set_size_inches((len(target_snrs)+1) * 2.2, 4.5)\n",
- "\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": "3.7.7"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
\ No newline at end of file
diff --git a/notebooks/multi_coil_diffusion.ipynb b/notebooks/multi_coil_diffusion.ipynb
deleted file mode 100644
index 8a5013a59793308dfbda79f4d22f626c0d4a6d78..0000000000000000000000000000000000000000
--- a/notebooks/multi_coil_diffusion.ipynb
+++ /dev/null
@@ -1,637 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Import external modules"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "['anatomy', 'auxilliary', 'encoding', 'reconstruction', 'signal_processes', 'simulation.py', '__init__.py', '__pycache__']\n"
- ]
- }
- ],
- "source": [
- "import tensorflow as tf\n",
- "physical_devices = tf.config.list_physical_devices('GPU')\n",
- "tf.config.experimental.set_visible_devices(physical_devices[0], 'GPU')\n",
- "tf.config.experimental.set_memory_growth(physical_devices[0], True)\n",
- "\n",
- "from collections import OrderedDict\n",
- "from copy import deepcopy\n",
- "import numpy as np\n",
- "import math\n",
- "\n",
- "import matplotlib.pyplot as plt\n",
- "%matplotlib widget"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Import cmrsim modules:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
- "outputs": [],
- "source": [
- "from cmrsim.anatomy.base import BaseDataset\n",
- "from cmrsim.signal_processes.base import CompositeSignalModel\n",
- "import cmrsim.signal_processes.solution_operators as solution_operators\n",
- "\n",
- "from cmrsim.encoding.epi import EPI\n",
- "from cmrsim.reconstruction.cartesian import Cartesian2D, RoemerMultiCoil\n",
- "from cmrsim.simulation import simulate\n",
- "\n",
- "import cmrsim.auxilliary.coordinates as cmrsim_coords\n",
- "import cmrsim.auxilliary.snr as cmrsim_snr\n",
- "import cmrsim.auxilliary.plot_utilities as cmrsim_plots\n",
- "from cmrsim.auxilliary.assign_map_values import assign_to_numerical_label_map"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Set up data\n",
- "\n",
- "First load the MRXcat maps for simulation:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "(4, 378, 134, 1)\n"
- ]
- }
- ],
- "source": [
- "resource_dir = 'example_resources/'\n",
- "\n",
- "m0_map = np.load(resource_dir + \"s0map.npy\")\n",
- "diffusion_tensors = np.load(resource_dir + \"diffusion_tensors.npy\")\n",
- "coil_sens_maps = np.load(resource_dir + \"coil_sensitivities.npy\").astype(np.complex64)\n",
- "n_coils = coil_sens_maps.shape[0]\n",
- "\n",
- "myo_mask = np.load(resource_dir + \"myo_mask.npy\")\n",
- "non_zero_mask = tf.cast(tf.constant(m0_map, shape=(378, 134), dtype=tf.int32) > 0, tf.int32)\n",
- "\n",
- "print(coil_sens_maps.shape)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "This simulation will yield 13 differently diffusion-weighted images. The diffusion related decay is evaluated by the\n",
- "solution of the Bloch-Torrey equation:\n",
- "\n",
- "$$S(b) = S_0 e^{-B_{ij} D_{ij}},$$ where $$B_{ij} = b * g_i g_j $$ \n",
- "\n",
- "The diffusion tensors in this example are only defined in the myocardium of the left ventricle $$Tr(D)=0$$ everywhere else).\n",
- "\n",
- "Additionally, different motion states for acquisitions with different B-Tensors are introduced by using the\n",
- "cmrsim.auxilliary.coordinates module to apply rigid body transformations to the example object (random translation\n",
- "and rotation).\n",
- "\n",
- "The 13 different diffusion weightings will result in a dimension repetititions = (4, 13) (axis 3 and 4) at the end of\n",
- "the simulation. For setting up the data-maps, 13 maps are created.\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
- "outputs": [],
- "source": [
- "batch_shape = (1, 378, 134, 13, 3, 1)\n",
- "n_images, x, y, n_reps, _, n_samples = batch_shape"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
- "outputs": [],
- "source": [
- "# Define 13 diffent diffusion weightings (including b=0)\n",
- "b_values = tf.constant([200, 300, 500, 1000], dtype=tf.float32)\n",
- "g_dirs_cart = tf.constant([[1., 0., 0.], [0., 1., 0.], [0., 0., 1.]], dtype=tf.float32)\n",
- "b_vectors = tf.reshape(tf.einsum('i,jk -> ijk', tf.sqrt(b_values), g_dirs_cart), [-1, 3])\n",
- "b_vectors = tf.concat((tf.zeros((1, 3), tf.float32), b_vectors), axis=0)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
- "outputs": [],
- "source": [
- "# Generate static coordinates\n",
- "r = cmrsim_coords.get_static_2d_centered_coordinates((378, 134), (0.3024, 0.1072), batch_size=1)\n",
- "static_coords = r.numpy()\n",
- "r = tf.tile(r, [1, 1, 1, 13, 1, 1])\n",
- "flat_r = tf.reshape(r, [1, -1, 13, 3, 1])"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Resulting array shape: (1, 378, 134, 13, 3, 1)\n"
- ]
- }
- ],
- "source": [
- "# Sample random transformation matrices and apply the transformations:\n",
- "\n",
- "# 1. Get translation matrix:\n",
- "lower_bounds, upper_bounds = (-0.001, -0.001, -0.001), (0.001, 0.001, 0.001)\n",
- "def transform_bounds(bound):\n",
- " bound = np.array(bound, dtype=np.float32)[np.newaxis, np.newaxis, np.newaxis, np.newaxis, :]\n",
- " return tf.tile(tf.constant(bound), [n_images, x*y, n_reps, n_samples, 1])\n",
- "\n",
- "lower_bounds, upper_bounds = [transform_bounds(b) for b in [lower_bounds, upper_bounds]]\n",
- "normal_translation_sample = tf.random.uniform(shape=(n_images, 1, n_reps, n_samples, 3), minval=0., maxval=1., seed=None)\n",
- "normal_translation_sample = tf.tile(normal_translation_sample, [1, x*y, 1, 1, 1])\n",
- "factor = upper_bounds - lower_bounds\n",
- "scaled_translation_sample = normal_translation_sample * factor + lower_bounds[:, :, :, :]\n",
- "translation_matrix = cmrsim_coords.get_translation_matrix(scaled_translation_sample,\n",
- " batch_dimensions=(n_images, x*y, n_reps, n_samples))\n",
- "\n",
- "# 2. Get rotation matrix\n",
- "rotation_angle = tf.random.uniform([batch_shape[0], 1, batch_shape[3], 1], -1., 1.)\n",
- "rotation_matrix = cmrsim_coords.get_rotation_matrix(rotation_angle, (n_images, x*y, n_reps, n_samples))\n",
- "\n",
- "# 3. Apply transformation for the entire batch\n",
- "displaced_coords = cmrsim_coords.apply_transformations([rotation_matrix, translation_matrix],\n",
- " r_vectors=tf.tile(flat_r, [n_images, 1, 1, 1, 1]))\n",
- "\n",
- "# 4. Tile and reshape the coordinate arrays\n",
- "displaced_coords = tf.reshape(displaced_coords, batch_shape)\n",
- "static_coords = tf.tile(static_coords, [n_images, 1, 1, 1, 1, 1])\n",
- "\n",
- "print(f\"Resulting array shape:\", displaced_coords.shape)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Assign values of T1, T2 and T2* to map and store into Ordered dictionary."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
- "outputs": [],
- "source": [
- "data_dict = OrderedDict([\n",
- " ('M0', tf.tile(tf.constant(m0_map, shape=(1, x, y, 1, 1), dtype=tf.complex64),\n",
- " [n_images, 1, 1, 1, 1])/ 4.),\n",
- " ('diffusion_tensor',\n",
- " tf.tile(tf.constant(diffusion_tensors, shape=(1, x, y, 3, 3), dtype=tf.float32),\n",
- " [n_images, 1, 1, 1, 1])),\n",
- " ('T1', tf.tile(tf.reshape(assign_to_numerical_label_map(non_zero_mask, 1.10, fractions=[1., 1.]),\n",
- " [1, x, y, 1, 1]), [n_images, 1, 1, 1, 1])),\n",
- " ('T2', tf.tile(tf.reshape(assign_to_numerical_label_map(non_zero_mask, 0.25, fractions=[1., 1.]),\n",
- " [1, x, y, 1, 1]), [n_images, 1, 1, 1, 1])),\n",
- " ('T2star', tf.tile(tf.reshape(assign_to_numerical_label_map(non_zero_mask, 0.035, fractions=[1., 1.]),\n",
- " [1, x, y, 1, 1]), [n_images, 1, 1, 1, 1])),\n",
- " ('r_vectors', displaced_coords)\n",
- " ])\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Simulation\n",
- "Construct the cmrsim simulation modules\n",
- "- EPI encoding\n",
- "- SE sequence\n",
- "- T2 star decay\n",
- "- Diffusion decay\n",
- "- Coil sensitivities (4 coils)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 10,
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
- "outputs": [],
- "source": [
- "n_kx, n_ky = 189, 67\n",
- "\n",
- "# Encoding definition\n",
- "encoding_module = EPI(field_of_view=(0.31, 0.112), sampling_matrix_size=(n_kx, n_ky), read_out_duration=0.,\n",
- " blip_duration=0., k_space_segments=27,\n",
- " absolute_noise_std=tf.constant([0.,], dtype=tf.float32))\n",
- "\n",
- "# Signal module construction\n",
- "diffusion_module = solution_operators.diffusion_weighting.ConstantGaussianDiffusion(b_vectors)\n",
- "spinecho_module = solution_operators.sequences.SpinEcho(0.05, 10)\n",
- "t2_star_module = solution_operators.t2_star.StaticT2starDecay(encoding_module.get_sampling_times())\n",
- "coil_sens_module = solution_operators.coil_sensitivities.CoilSensitivity(coil_sens_maps, (0.3024, 0.1072, 0.))\n",
- "\n",
- "# Forward model composition\n",
- "forward_model = CompositeSignalModel(spinecho_module, diffusion_module, t2_star_module, coil_sens_module)\n",
- "\n",
- "# Dataset Module\n",
- "dataset = BaseDataset(data_dict)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### 1. Run reduced simulation without diffusion decay and without displacements."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 11,
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Start building Graph for simulation...\n",
- "Processed images: 0 Current image: 30000 / 32327 Segment index: 26 / 27\n",
- "Resulting shape information: (#images, #coils, #DWIs, #noise-levels, #samples=(kx*ky)\n",
- "Simulation result shape: (1, 4, 1, 12663)\n",
- "Wall time: 5.93 s\n"
- ]
- }
- ],
- "source": [
- "%%time\n",
- "encoding_module.absolute_noise_std.assign(tf.constant([0.,], tf.float32))\n",
- "\n",
- "reduced_forward_model = CompositeSignalModel(spinecho_module, t2_star_module, coil_sens_module)\n",
- "\n",
- "reduce_datadict = deepcopy(data_dict)\n",
- "reduce_datadict['r_vectors'] = static_coords\n",
- "reduced_dataset = BaseDataset(reduce_datadict)\n",
- "\n",
- "print(\"Start building Graph for simulation...\")\n",
- "noise_less_k_space = simulate(reduced_dataset, reduced_forward_model, encoding_module, recon_module=None,\n",
- " voxel_batch_size=10000, execute_eager=True)\n",
- "print(\"\\nResulting shape information: (#images, #coils, #DWIs, #noise-levels, #samples=(kx*ky)\")\n",
- "print(\"Simulation result shape:\", noise_less_k_space.shape)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Reconstruct the images for each coil seperately and estimated noise std for target SNRs. Index 0\n",
- "determines the image with corresponding b-vector that is used to calculate the SNR."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 13,
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "(378, 134)\n",
- "2D recon single coils: (1, 4, 1, 189, 67)\n",
- "Estimated noise level: [0.07049198 0.13958807 0.27641202]\n"
- ]
- }
- ],
- "source": [
- "channel_wise_recon_module = Cartesian2D(sample_matrix_size=(n_kx, n_ky))\n",
- "single_coil_images = channel_wise_recon_module(noise_less_k_space)\n",
- "print(myo_mask.shape)\n",
- "\n",
- "print(\"2D recon single coils:\", single_coil_images.shape)\n",
- "target_snrs = [20., 10., 5]\n",
- "estimated_std = cmrsim_snr.compute_noise_std(noiseless_single_coil_images=single_coil_images[0, :, 0],\n",
- " coil_sensitivities=coil_sens_maps[:, ::2, ::2, 0],\n",
- " target_snr=target_snrs,\n",
- " mask=myo_mask[::2, ::2].astype(np.float32),\n",
- " iteratively_refine=True)\n",
- "print(\"Estimated noise level:\", estimated_std)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### 2. Rerun simulation with estimated noise-levels, forward model including diffusion decay and displacements"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 14,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Start building Graph for simulation...\n",
- "Processed images: 0 Current image: 32000 / 32327 Segment index: 26 / 27Wall time: 1min 24s\n"
- ]
- }
- ],
- "source": [
- "%%time\n",
- "print(\"Start building Graph for simulation...\")\n",
- "encoding_module.absolute_noise_std.assign(tf.constant([0.,] + [_ for _ in estimated_std], tf.float32))\n",
- "simulated_data = simulate(dataset, forward_model, encoding_module, recon_module=None,\n",
- " voxel_batch_size=2000, execute_eager=True)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Use Roemer reconstruction"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 15,
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "\n",
- "Resulting shape information: (#images, #DWIs, #noise-levels, X, Y\n",
- "zero_padded_reconstruction shape: (1, 13, 4, 378, 134)\n",
- "Wall time: 32.8 ms\n"
- ]
- }
- ],
- "source": [
- "%%time\n",
- "#Recon definition\n",
- "recon_module = RoemerMultiCoil(sample_matrix_size=(n_kx, n_ky), coil_sensitivities=coil_sens_maps[..., 0],\n",
- " padding=[(math.floor(n_kx/2), math.ceil(n_kx/2)), (math.floor(n_ky/2), math.ceil(n_ky/2))])\n",
- "\n",
- "zero_padded_reconstruction = recon_module(simulated_data, coil_channel_axis=1)\n",
- "\n",
- "print(\"\\nResulting shape information: (#images, #DWIs, #noise-levels, X, Y\")\n",
- "print(\"zero_padded_reconstruction shape:\", zero_padded_reconstruction.shape)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Calculate SNR map for the b_ref image"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 16,
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Channel wise reconstruction: (1, 4, 13, 4, 378, 134)\n",
- "Current noise std: 0.0\n",
- "Mean signal in simulated imaged: 43.47954177856445\n",
- "Mean snr in LV: nan\n",
- "\n",
- "Current noise std: 0.0704919770359993\n",
- "Mean signal in simulated imaged: 32.163787841796875\n",
- "Mean snr in LV: 20.981002807617188\n",
- "\n",
- "Current noise std: 0.13958807289600372\n",
- "Mean signal in simulated imaged: 32.773582458496094\n",
- "Mean snr in LV: 10.676624298095703\n",
- "\n",
- "Current noise std: 0.2764120101928711\n",
- "Mean signal in simulated imaged: 32.77595901489258\n",
- "Mean snr in LV: 5.45590877532959\n",
- "\n",
- "SNR map shape: (378, 134)\n"
- ]
- }
- ],
- "source": [
- "padded_chanel_wise_recon = Cartesian2D(sample_matrix_size=(n_kx, n_ky),\n",
- " padding=[(math.floor(n_kx/2), math.ceil(n_kx/2)),\n",
- " (math.floor(n_ky/2), math.ceil(n_ky/2))])\n",
- "noisy_single_coil_images = padded_chanel_wise_recon(simulated_data)\n",
- "\n",
- "print(\"Channel wise reconstruction: \", noisy_single_coil_images.shape)\n",
- "snr_maps = []\n",
- "for noise_index in range(len(target_snrs)+1):\n",
- " scaling = encoding_module.absolute_noise_std[noise_index]\n",
- "\n",
- " print(f\"Current noise std: {scaling}\")\n",
- " image_shape = recon_module.output_matrix_size\n",
- " noise_vectors = tf.random.normal(shape=(2, n_coils, image_shape[0], image_shape[1]), dtype=tf.float32) * scaling\n",
- " dynamic_noise_scan = tf.complex(noise_vectors[0], noise_vectors[1])\n",
- "\n",
- " snr_map = cmrsim_snr.calculate_snr(single_coil_images=noisy_single_coil_images[0, :, 0, noise_index,...],\n",
- " dynamic_noise_scan=dynamic_noise_scan,\n",
- " coil_sensitivities=coil_sens_maps[..., 0])\n",
- "\n",
- " snr_maps += [snr_map, ]\n",
- "\n",
- " float_mask = tf.cast(myo_mask, tf.float32)\n",
- " mean_signal_encoded = tf.reduce_sum(tf.abs(zero_padded_reconstruction[0 , noise_index:noise_index+1, ...])\n",
- " * float_mask) / tf.reduce_sum(float_mask)\n",
- " mean_snr = tf.math.reduce_sum(tf.abs(snr_map) * float_mask) / tf.reduce_sum(float_mask)\n",
- " print(f\"Mean signal in simulated imaged: {mean_signal_encoded}\")\n",
- " print(f\"Mean snr in LV: {mean_snr}\\n\")\n",
- "\n",
- "print(\"SNR map shape:\", snr_maps[0].shape)\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Plot slide show of diffusion weighted images for all simulated noise-levels (In a cropped ROI around the left\n",
- "ventricle). Click Left/Right mouse button changes the shown slice index."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {
- "collapsed": false,
- "jupyter": {
- "outputs_hidden": false
- },
- "pycharm": {
- "name": "#%%\n"
- }
- },
- "outputs": [
- {
- "ename": "ImportError",
- "evalue": "cannot import name 'IndexTracker' from 'cmrsim.auxilliary.plot_utilities' (C:/Users/jonat/Promotion/code/cmrsim2/cmrsim\\cmrsim\\auxilliary\\plot_utilities.py)",
- "output_type": "error",
- "traceback": [
- "\u001B[1;31m---------------------------------------------------------------------------\u001B[0m",
- "\u001B[1;31mImportError\u001B[0m Traceback (most recent call last)",
- "\u001B[1;32m<ipython-input-17-db1ccc415f58>\u001B[0m in \u001B[0;36m<module>\u001B[1;34m\u001B[0m\n\u001B[1;32m----> 1\u001B[1;33m \u001B[1;32mfrom\u001B[0m \u001B[0mcmrsim\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mauxilliary\u001B[0m\u001B[1;33m.\u001B[0m\u001B[0mplot_utilities\u001B[0m \u001B[1;32mimport\u001B[0m \u001B[0mIndexTracker\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0m\u001B[0;32m 2\u001B[0m \u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 3\u001B[0m \u001B[1;31m# ROI boundary definition for image cropping\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 4\u001B[0m \u001B[1;31m# lx, ux = 90, 290\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n\u001B[0;32m 5\u001B[0m \u001B[1;31m# ly, uy = 10, 124\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[1;33m\u001B[0m\u001B[0m\n",
- "\u001B[1;31mImportError\u001B[0m: cannot import name 'IndexTracker' from 'cmrsim.auxilliary.plot_utilities' (C:/Users/jonat/Promotion/code/cmrsim2/cmrsim\\cmrsim\\auxilliary\\plot_utilities.py)"
- ]
- }
- ],
- "source": [
- "# ROI boundary definition for image cropping\n",
- "lx, ux = 90, 290\n",
- "ly, uy = 10, 124\n",
- "diffusion_images = np.transpose(zero_padded_reconstruction[0, :, :, lx:ux, ly:uy], [1, 2, 3, 0])\n",
- "\n",
- "title_strings = [\"inf\", ] + [str(_) for _ in target_snrs]\n",
- "slide_show1 = cmrsim_plots.SlideShow(np.abs(diffusion_images), colorbars=True, titles=title_strings)\n",
- "slide_show1.fig.suptitle(f\"Scroll trough DWIs:\", fontsize=14, fontweight='bold')\n",
- "slide_show1.fig.set_size_inches((len(estimated_std)+1) * 2.5, 4)\n"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "8f02fa3853aa41afaf7dc6f5e4e1148f",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# ROI boundary definition for image cropping\n",
- "lx, ux = 90, 290\n",
- "ly, uy = 10, 124\n",
- "diffusion_images = np.transpose(zero_padded_reconstruction[0, :, :, lx:ux, ly:uy], [1, 2, 3, 0])\n",
- "\n",
- "title_strings = [\"inf\", ] + [str(_) for _ in target_snrs]\n",
- "slide_show2 = cmrsim_plots.SlideShow(np.abs(diffusion_images), colorbars=True, titles=title_strings)\n",
- "slide_show2.fig.set_size_inches((len(estimated_std)+1) * 2.5, 4)\n",
- "slide_show2.fig.suptitle(f\"Scroll trough DWIs:\", fontsize=14);\n",
- "\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": "3.7.7"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
\ No newline at end of file
diff --git a/notebooks/multi_coil_simulation.ipynb b/notebooks/multi_coil_simulation.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..47ebe96e8f6fde456ab64ec951b8f68113fe319e
--- /dev/null
+++ b/notebooks/multi_coil_simulation.ipynb
@@ -0,0 +1,355 @@
+{
+ "cells": [
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Import external modules"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Available GPUS: \n",
+ "\t PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')\n"
+ ]
+ }
+ ],
+ "source": [
+ "import tensorflow as tf\n",
+ "physical_devices = tf.config.list_physical_devices('GPU')\n",
+ "print(\"Available GPUS: \\n\\t\", \"\\n\\t\".join([str(_) for _ in physical_devices]))\n",
+ "if physical_devices:\n",
+ " tf.config.experimental.set_visible_devices(physical_devices[0], 'GPU')\n",
+ " tf.config.experimental.set_memory_growth(physical_devices[0], True)\n",
+ "\n",
+ "from collections import OrderedDict\n",
+ "import numpy as np\n",
+ "import math\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib widget "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Import cmrsim modules:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "import sys\n",
+ "sys.path.append('..')\n",
+ "import cmrsim.simulation as cmrsim_simulate\n",
+ "\n",
+ "import cmrsim.auxilliary.coordinates as cmrsim_coords\n",
+ "import cmrsim.auxilliary.snr as cmrsim_snr\n",
+ "import cmrsim.auxilliary.plot_utilities as cmrsim_plots\n",
+ "from cmrsim.auxilliary.assign_map_values import assign_to_numerical_label_map"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "### Simulation\n",
+ "\n",
+ "Define subclass of SimulationBase class that implements the `_build` method. The Variables inside the modules can be assigned manually later to configure the simulation parameters.\n",
+ "\n",
+ "**NOTE**: As the Reconstruction module is None, the returned simulation result is k-space data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 49,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from cmrsim.anatomy.base import BaseDataset\n",
+ "from cmrsim.signal_processes.base import CompositeSignalModel\n",
+ "import cmrsim.signal_processes.solution_operators as solution_operators\n",
+ "\n",
+ "from cmrsim.encoding.epi import EPI\n",
+ "from cmrsim.reconstruction.cartesian import Cartesian2D, RoemerMultiCoil\n",
+ "\n",
+ "from typing import Union, Iterable\n",
+ "\n",
+ "class ExampleSimulation(cmrsim_simulate.SimulationBase):\n",
+ " def _build(self):\n",
+ " n_kx, n_ky = 189, 67\n",
+ " # Encoding definition\n",
+ " encoding_module = EPI(field_of_view=(0.31, 0.112), sampling_matrix_size=(n_kx, n_ky), read_out_duration=0.1,\n",
+ " blip_duration=0.01, k_space_segments=27,\n",
+ " absolute_noise_std=0.\n",
+ " )\n",
+ " # Signal module construction\n",
+ " spinecho_module = solution_operators.sequences.SpinEcho(0.05, 10)\n",
+ " t2_star_module = solution_operators.t2_star.StaticT2starDecay(encoding_module.get_sampling_times())\n",
+ " coil_sens_module = solution_operators.coil_sensitivities.CoilSensitivity(tf.zeros((4, 1, 1, 1), dtype=tf.complex64), (0.3024, 0.1072, 0.))\n",
+ "\n",
+ " # Forward model composition\n",
+ " forward_model = CompositeSignalModel(spinecho_module, t2_star_module, coil_sens_module)\n",
+ " return forward_model, encoding_module, None\n",
+ " \n",
+ " def _update(self):\n",
+ " super(ExampleSimulation, self)._update()\n",
+ " self.forward_model.static_t2star.sampling_times = self.encoding_module.get_sampling_times()\n",
+ " \n",
+ " def estimate_noise_level(self, simulated_k_space: tf.Tensor, mask: Union[np.ndarray, tf.Tensor],\n",
+ " target_snr: np.ndarray):\n",
+ " \"\"\" This function is not defined in the base class but serves the porpose of accesing the modules\n",
+ " and its parameters that are within the scope of the simulation instance!\n",
+ " \n",
+ " It will perform a channelwise (per coil) reconstruction and then estimate the noise-level needed to \n",
+ " yield the target SNR in the Roemer-reconstructed Multi-coil images.\n",
+ " \"\"\"\n",
+ " sampling_matrix_size = self.encoding_module._matrix_size.read_value()\n",
+ " inverse_crime_factor = self.forward_model.coil_sensitivities._bins[0] // sampling_matrix_size[0]\n",
+ " channel_wise_recon = Cartesian2D(sample_matrix_size=sampling_matrix_size)\n",
+ " single_coil_images = channel_wise_recon(simulated_k_space)\n",
+ " coil_sens_maps = self.forward_model.coil_sensitivities.look_up_map3d.read_value()\n",
+ " unstacked_channelwise_images = self.forward_model.unstack_repetitions(single_coil_images)\n",
+ " noise_std_deviations = cmrsim_snr.compute_noise_std(\n",
+ " noiseless_single_coil_images=unstacked_channelwise_images[0, :, 0, ...],\n",
+ " coil_sensitivities=coil_sens_maps[:, ::inverse_crime_factor, ::inverse_crime_factor, 0],\n",
+ " target_snr=target_snr,\n",
+ " mask=mask[::inverse_crime_factor, ::inverse_crime_factor].astype(np.float32),\n",
+ " iteratively_refine=False\n",
+ " )\n",
+ " return noise_std_deviations, single_coil_images\n",
+ " \n",
+ " def delayed_reconstruction(self, simulated_k_space: tf.Tensor):\n",
+ " \"\"\" This method performs a Roemer reconstruction outside of the Graph \"\"\"\n",
+ " sampling_matrix_size = self.encoding_module._matrix_size.read_value()\n",
+ " coil_sens_maps = self.forward_model.coil_sensitivities.look_up_map3d.read_value()\n",
+ " inverse_crime_factor = self.forward_model.coil_sensitivities._bins[0] // sampling_matrix_size[0]\n",
+ " recon_module = RoemerMultiCoil(sample_matrix_size=sampling_matrix_size,\n",
+ " coil_sensitivities=coil_sens_maps[:, ::inverse_crime_factor, ::inverse_crime_factor, 0])\n",
+ " return recon_module(simulated_k_space, coil_channel_axis=1) \n",
+ " "
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Instantiate an ExampleSimulation:"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 50,
+ "metadata": {
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ },
+ "outputs": [],
+ "source": [
+ "simulation = ExampleSimulation(name='simulation_example')\n",
+ "_ = simulation.forward_model.coil_sensitivities.look_up_map3d.assign(np.load(\"example_resources/coil_sensitivities.npy\").astype(np.complex64))\n",
+ "_ = simulation.encoding_module.k_space_segments.assign(54)\n",
+ "simulation._update()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "%load _construct_dataset.py"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "#### Run the simulation"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {
+ "pycharm": {
+ "name": "#%%\n"
+ }
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Run Simulation: |XXXXX|1/1\t|XXXXXXXXXXXXXXXXXXXX|32327/32327\t|XXXXXXXXXXXXXXXXXXXX|54/54 Wall time: 8.33 s\n"
+ ]
+ }
+ ],
+ "source": [
+ "%time noise_less_k_space = simulation(dataset, voxel_batch_size=10000, execute_eager=False)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "Estimate Noise levels and add noise to the k-space data"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 56,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Noiseless shape: (1, 4, 1, 12663)\n",
+ "Noisy shape: (1, 4, 3, 12663)\n",
+ "Images shape: (1, 3, 189, 67)\n"
+ ]
+ }
+ ],
+ "source": [
+ "print('Noiseless shape: ', noise_less_k_space.shape)\n",
+ "estimated_noise_levels, single_coil_images = simulation.estimate_noise_level(noise_less_k_space, myo_mask, [1000, 20, 1])\n",
+ "\n",
+ "simulation.encoding_module.absolute_noise_std.assign(tf.constant(estimated_noise_levels))\n",
+ "simulation._update()\n",
+ "\n",
+ "# Note: second-to-last axis in noise-less-k-space already is a \"noise-axis\". That why the indexing is used.\n",
+ "# Re-running the simulation at this point would yield the same result\n",
+ "noisy_k_space = simulation.encoding_module.add_noise(noise_less_k_space[..., 0, :])\n",
+ "print(\"Noisy shape: \", noisy_k_space.shape)\n",
+ "\n",
+ "images = simulation.delayed_reconstruction(noisy_k_space)\n",
+ "print(\"Images shape: \", images.shape)"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "### Plot images: "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "sl = cmrsim_plots.SlideShow(np.abs(single_coil_images)[0].transpose([0, 2, 3, 1]), colorbars=False, titles=[f\"Coil {i}\" for i in range(4)])\n",
+ "sl.fig.set_size_inches(3, 2.3), sl.fig.suptitle('Noise-less images per coil')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "k_magnitude = np.abs(noisy_k_space)[0].reshape([4, 3, 189, 67]).transpose([1, 2, 3, 0]) \n",
+ "sl = cmrsim_plots.SlideShow(np.log(k_magnitude), colorbars=False,\n",
+ " titles=[f\"SNR {i}\" for i in [1000, 100, 1]], x_labels=[[f\"Coil {i}\", ]*3 for i in range(4)])\n",
+ "sl.fig.set_size_inches(3, 2.8), sl.fig.suptitle('Noisy k-space per coil')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 68,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "application/vnd.jupyter.widget-view+json": {
+ "model_id": "abff566b3fc94fe983b4c071e1867ba0",
+ "version_major": 2,
+ "version_minor": 0
+ },
+ "text/plain": [
+ "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "sl3 = cmrsim_plots.SlideShow(np.abs(images).transpose([1, 2, 3, 0]), x_labels=[[\" \", ] * 3])\n",
+ "# sl3.axes[0].imshow(myo_mask[0::2, 0::2], alpha=0.5)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "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": "3.7.7"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/perfusion_example.ipynb b/notebooks/perfusion_example.ipynb
new file mode 100644
index 0000000000000000000000000000000000000000..c633860198d4d2740352eefda8b6c759d521b500
--- /dev/null
+++ b/notebooks/perfusion_example.ipynb
@@ -0,0 +1,229 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "import tensorflow as tf\n",
+ "import numpy as np\n",
+ "import math\n",
+ "import sys\n",
+ "sys.path.append('../')\n",
+ "\n",
+ "import matplotlib.pyplot as plt\n",
+ "%matplotlib widget \n",
+ "\n",
+ "from cmrsim.auxilliary import coordinates as cmrsim_coords\n",
+ "from cmrsim.simulation import SimulationBase "
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "[PhysicalDevice(name='/physical_device:GPU:0', device_type='GPU')]"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "tf.config.list_physical_devices('GPU')"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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+Y5N9j6d9+wFOyqqb1Hu16zIMSstn5JvNqkOTx/xfrV4kVVRwGWTzPzqS23AHRzcoZEar6RV+k1y5Aaw9a9K++8/2y2HiZd8EwF0SpfSjwqTfN91W3dGZheNfT7rYw+Lcgv60+/uWwF8kyBcVXAZY+cjJ3PnN5xjScD5ZFkvb55zXYCfndfwnAFNfO5yVu3N5q2swL6xc96UFfFx2GJDc1cTC4v0NLWi37F3fMQJLBRdQkexsPv1+N5694x6aROaRHakLpK/c9jcoZxvkbKPww50Mfu9SGl7wMW7PnkCtr9tSmkNtLrgyF6esVNsJD8Sc8795+TBr4nrZ6b5jBEb8G915ZerjvmP8j2MfG0H737xLfNcu31H2mV44h5xIMOcy0+2HH36TDb13+I4RCK+6aQudc/n7j6v+A2b3d3rywt8e8R2jUit+OI4Pru9OpF5wCuXHHwzwHcGLz8p28cbSY33HCDwtogbErgt6sbVTlGcuv4csC+4V298bMZa8BiOot9loOXqW7zhsPxdY4jtFzZtV3ISOP17gO0bgaQ4uAIou7MUPfv0Cy64cS8dYcMttr4JLx/HmNaNZc3cf31GI7yyi/bTLfceoUcWuhFv/+CPfMTKCCs6zPWflM/zX07mi0XrfUb6WxtFsXvv+Pay+1+8Fh11xMXlP1a7DtkpcGUeO8T/3nAkOWnBm9qiZbTKzpRXG7jKz9Wa2KPEzoMLvbjGzVWa2wszOSlfwMIh07cTvxv2JSw/b7DtKUo6qk8NbF43mg1/6nZOzd1ZwwpiRXjPUpAsuql1zrNVxKHNwjwP9Kxm/3znXPfEzA8DMjgMGA10SrxlrZtFUhQ2TOm3bMGXGY/Sul9mTJ7dODu8Oe5CPr+kLET9/FldcTMvRc+n4+Ah2xfd4yVATPivbxRkX/xCbrf3eDtVBC8459yaw9RDfbyAwxTlX7JxbC6wCelYjXyhFunbiqbencnikvu8oKZFlMd69aSxbh/b0VnLEy2h362x6zvuhn8+vAWfddj2Rt97xHSOjVGcd3JVmtjixCNs4MdYK+KjCcwoTY//DzIab2QIzW1BCcTViZJ4R058PTblVNP/X46iT28JrhtKlh7F8T3D200uVCdta0uDjEt8xMk6yBTcO6AB0BzYC9ybGKzulRaV7EjvnJjjn8p1z+TGykoyReTYP70OH2BbfMdJmxbVtwZI7s0kqtL1jNhc8egMbS3d6y5BqD33Wlidu+y51X9JuIV9XUgXnnPvEOVfmnIsDj/DlYmgh0KbCU1sDG6oXMTw+uaov9970J7rUDd/c214rvv8wKx/t4TXDUb+cxYC7f0aJy/xD0KfsaMy0m8+iwXSd7y0ZSRWcmeVWeHg+sHcL6/PAYDPLMrN2QB4wr3oRw2HLsD48+NOx9Ksf9x0lraIWYdkZ41g7pavXHM3HzuLUG0Z5zVBdi/fsZtKwgdT7h/4JJeugRzKY2V+BfkAzMysE7gT6mVl3yhc/1wGXAzjnlpnZVOA9oBQY5VwI/htNgV25xqnBOcIprbIjdbnw2EUs9Lyb5WFT5tI3fgWz7h/vNUcyil0Jt5w2mMiaRb6jZDQdbF8Dii7sxesPjCVWi/aYKXNxus/7P1qe/57vKGz5cR+eun00LaPRwB+Yv7F0Jzuc8dNvXBzo8/EFjQ6298SystjWPlqryg3KF1XbNPqcOm1a+45C04mzuartKZzwwtVsLivyHadKc3aX8b0bb+Cqtqeo3FJEBZdmlteOJdeO9R3DixnHzmDlVW0O/sQa0vGKeZz8z2sDeUWumbuyGHX3lTT82xzfUUJFZxNJI4vV5f2rG/qO4dWJfVeyPf943IKlB39yDeg4fD75v7+O0mYlrD17ou84bCorovez19F4aYRmE3Q9jFRTwaWR1cti7XeCeW63mjK1/WvkdxtB0wDtwtX+ptlEGjTgxKEj2d7nC1af9piXHF0eGkn9Tx15E1Vs6aKCk1opXlRE84dn0fLZlpzWeRitf1XAE23frJHPPuapKzhqZglt/r0AVxLeY2eDQFtR0+jqVe9zTnbtvWbAXqtLdjLqwhG4+cE9M2W0WVOsfn1cVozn/j1t33gEI2rJraouc3HiiQN5Jmw7mhlndQMgvmVroE77HgZVbUXVHFwa9ai7GcjxHcO7DrEc4rFIpcfxBUXZ5i8Pn/tOmy/PD1EwJp+nv/tgUu957cgryXrpv18OxLVltKap4ET2F/9y3/S8q+dy09W9knqbLOanKpEkSbuJpMnnl/Qh29epgwJo3Xeysazac1IFCQYVXJqM+vnfQ3lKpGStHDqOSKPDfceQWkYFJyKhpYITkdBSwYlIaKngRCS0VHAiEloqOBEJLRWciISWCk5EQksFJyKhpYITkdBSwYlIaKngRCS0VHAiEloquDS5b+wgdsZ1Nt+92j07HLdtu+8YUsuo4NKkxYOz2BEv9R0jMPL+Ukx8twpfapYKTkRCSwWXRp+UxXxHCIRt8S+wuP+LG0nto4JLo1u6ftt3hEA4/c7rYM5i3zGkFlLBiUhoqeDSyO3ZQ6f/XOI7hldXru9Fk/e/8B1DaikVXBq54mLa/bLEdwyvXnq9B/b2It8xpJZSwaXb2o/oNHGE7xReDFpzOnmTPvUdQ2oxFVyaxYuKaLok7juGFwVbjqBsxSrfMaQWU8HVgJxp8+j8p5G+Y9So2zadQIvvqdzEr4MWnJm1MbPXzWy5mS0zs2sS403M7BUzK0jcNq7wmlvMbJWZrTCzs9L5B8gIzlFvMyzbUztWthe7El5e3wlXqiM5xK9DmYMrBa53znUGegOjzOw44GbgNedcHvBa4jGJ3w0GugD9gbFmFk1H+EzS/OFZXDz+ehYW7/EdJe3y511G43MKfMcQOXjBOec2Ouf+m7i/A1gOtAIGApMTT5sMnJe4PxCY4pwrds6tBVYBPVMdPBO1unsWQ8dey+qSnb6jpE27GT+m5QXLfccQAb7mOjgzOxo4EZgLtHDObYTyEgSaJ57WCviowssKE2MCtLxnFsv2ND/4EzPUcXduAKfDsiQYDrngzCwHmA5c65w70HlvrJKx//nGm9lwM1tgZgtKKD7UGKHw0I8vDuWplLr9YSRlmzb7jiGyzyEVnJnFKC+3J51zTyeGPzGz3MTvc4FNifFCoE2Fl7cGNuz/ns65Cc65fOdcfoysZPNnpMi/3+Gi/kMpc+HZfeT4B0eS+/ACXEn41zFK5jiUragGTAKWO+fuq/Cr54HLEvcvA56rMD7YzLLMrB2QB8xLXeRwiC99n3MGXsqHpZm9Pm5b/Avy/jKCVr+frXKTwDmUObhTgEuA08xsUeJnAHA3cIaZFQBnJB7jnFsGTAXeA2YCo5xzZWlJn+HcgqVcMvI6Xt6VmadV2lRWRPcXrqH9z2ZrvZsEkrkAfDEPsyaul53uO4Y3n1/Sh+tvf4pBOdt8RzlkO+O7OeGFq+l4hWbOxb9X3bSFzrn8/cfr+AgjX9Xoz7MZHfk+t3RzvPq90bSL5fiOdEDt/vETYlvq0PHW2b6jiByQCi4gGk+eTWPg4ndv5K3fPkiWBXOx9ZgnR9DprsXEi4p8RxE5KB2LGjCNJ8/mzMtH+Y5RqQ5TrqDjr5ap3CRjaA4ugOq9OJ9zTurP+ovaM/emB6hDlKj5+b+o2JVw/sqB2PfjdPxsEWW6MpZkEM3BBZFzlG78mBYPzuLc1j3p+Pow3t4dp6QGN0bPKy7hyR1NObd1T8pO20jpxo912T/JOJqDCzrnOOb/3uGX9CD2Ri7HNPyUVlmfcWOT1Sn/qDd3w9OflW+IKvheS0rXfUglB6GIZAwVXAYp6beR5cDKLvlM/lXvfeN/6zGRLnXrJ/WeJ84fTElZ+cleYv86nOZ/nJX4zYfVTCvinwouA5UtW0GrC758POS669l9RHJzWu3vWKgjECS0VHAhkHvfrIM/qQpaAJUw00YGEQktFZyIhJYKTkRCSwUnIqGlghOR0FLBiUhoqeBEJLRUcCISWio4EQktFZyIhJYKTkRCSwUnIqGlghOR0FLBiUhoqeBEJLRUcCISWio4EQktFZyIhJYKTkRCSwUnIqGlghOR0FLBiUhoqeBEJLRUcCISWio4EQktFZyIhJYKTkRCSwUnIqGlghOR0DLnnO8MmNmnQBGw2XeWQ9SMzMkKmZU3k7JCZuXNpKzw9fK2dc4dsf9gIAoOwMwWOOfyfec4FJmUFTIrbyZlhczKm0lZITV5tYgqIqGlghOR0ApSwU3wHeBryKSskFl5MykrZFbeTMoKKcgbmHVwIiKpFqQ5OBGRlPJecGbW38xWmNkqM7vZd57KmNk6M1tiZovMbEFirImZvWJmBYnbxp6yPWpmm8xsaYWxKrOZ2S2Jab3CzM4KSN67zGx9YvouMrMBQchrZm3M7HUzW25my8zsmsR44KbvAbIGddrWM7N5ZvZuIu8vEuOpnbbOOW8/QBRYDbQH6gLvAsf5zFRFznVAs/3G/gDcnLh/M/B7T9lOBXoASw+WDTguMY2zgHaJaR8NQN67gBsqea7XvEAu0CNxvyGwMpEpcNP3AFmDOm0NyEncjwFzgd6pnra+5+B6Aqucc2ucc3uAKcBAz5kO1UBgcuL+ZOA8HyGcc28CW/cbrirbQGCKc67YObcWWEX530GNqSJvVbzmdc5tdM79N3F/B7AcaEUAp+8BslbF97R1zrmdiYexxI8jxdPWd8G1Aj6q8LiQA/+l+OKAl81soZkNT4y1cM5thPIvF9DcW7r/VVW2IE/vK81scWIRdu9iSWDymtnRwImUz2kEevrulxUCOm3NLGpmi4BNwCvOuZRPW98FZ5WMBXGz7inOuR7A2cAoMzvVd6AkBXV6jwM6AN2BjcC9ifFA5DWzHGA6cK1zbvuBnlrJWI3mrSRrYKetc67MOdcdaA30NLPjD/D0pPL6LrhCoE2Fx62BDZ6yVMk5tyFxuwl4hvJZ40/MLBcgcbvJX8L/UVW2QE5v59wniS97HHiELxc9vOc1sxjlhfGkc+7pxHAgp29lWYM8bfdyzn0OvAH0J8XT1nfBzQfyzKydmdUFBgPPe870FWbWwMwa7r0PnAkspTznZYmnXQY85ydhparK9jww2MyyzKwdkAfM85DvK/Z+oRPOp3z6gue8ZmbAJGC5c+6+Cr8K3PStKmuAp+0RZtYocb8+8G3gfVI9bWtqq8kBtqYMoHyLz2rg577zVJKvPeVbb94Flu3NCDQFXgMKErdNPOX7K+WLHiWU/y837EDZgJ8npvUK4OyA5P0zsARYnPgi5wYhL/ANyheDFgOLEj8Dgjh9D5A1qNO2K/BOItdS4I7EeEqnrY5kEJHQ8r2IKiKSNio4EQktFZyIhJYKTkRCSwUnIqGlghOR0FLBiUhoqeBEJLT+H5ebxLwjjZWiAAAAAElFTkSuQmCC\n",
+ "text/plain": [
+ "<Figure size 432x288 with 1 Axes>"
+ ]
+ },
+ "metadata": {
+ "needs_background": "light"
+ },
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "def _create_cylinder_phantom(fov=(0.10, 0.125), map_size=(250, 313)):\n",
+ " xx, yy = np.meshgrid(*[range(i) for i in map_size], indexing='ij')\n",
+ " \n",
+ " rrs = [np.sqrt((xx-x0)**2 + (yy-y0)**2) for (x0, y0) in [(75, 100), (125, 200), (175, 100)]]\n",
+ " binarys = [np.where(r<40, np.ones_like(r) * (i+1), np.zeros_like(r)) for i, r in enumerate(rrs)]\n",
+ " class_map = np.sum(np.stack(binarys, axis=0), axis=0)\n",
+ " \n",
+ " t1 = np.where(class_map > 0., np.ones_like(class_map), np.zeros_like(class_map)) * 1000 # ms\n",
+ " m0 = np.where(class_map > 0., np.ones_like(class_map), np.zeros_like(class_map)) * 80 # %\n",
+ " m0 *= np.prod([i/j for i,j in zip(fov, map_size)])\n",
+ " \n",
+ " r = cmrsim_coords.get_static_2d_centered_coordinates(map_size=map_size, object_dimensions=fov)\n",
+ " \n",
+ " dynamic_index_map = np.where(class_map > 0., np.ones_like(class_map), np.zeros_like(class_map))\n",
+ " \n",
+ " data_dict = {'M0': m0[np.newaxis, ..., np.newaxis, np.newaxis].astype(np.complex64),\n",
+ " 'T1': t1[np.newaxis, ..., np.newaxis, np.newaxis].astype(np.float32),\n",
+ " 'r_vectors': -1 * r.numpy().astype(np.float32),\n",
+ " 'contrast_dynamic_index': dynamic_index_map[np.newaxis, ..., np.newaxis, np.newaxis].astype(np.float32),\n",
+ " }\n",
+ "\n",
+ " return data_dict\n",
+ " \n",
+ "dd = _create_cylinder_phantom()\n",
+ "plt.close('all')\n",
+ "f, a = plt.subplots(1, 1)\n",
+ "a.imshow(np.abs(dd['T1'][0, :, :, 0, 0]))\n",
+ "# a.imshow(dd['r_vectors'][0, :, :, 0, 1, 0])\n",
+ "a.imshow(np.squeeze(dd['contrast_dynamic_index']))\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 29,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Starting matlab engine from C:\\Users\\Jonat\\Promotion\\code\\cmrsim2\\cmrsim\\cmrsim\\signal_processes\\solution_operators\\perfusion.py\n",
+ "Add matlab resources to matlab path: C:\\Users\\Jonat\\Promotion\\code\\cmrsim2\\cmrsim\\cmrsim\\signal_processes\\solution_operators/perfusion_matlab\n",
+ "Required data input of configured simulation: ('M0', 'T1', 'contrast_dynamic_index')\n"
+ ]
+ }
+ ],
+ "source": [
+ "from cmrsim.encoding.cartesian import SingleLinePerShot\n",
+ "from cmrsim.signal_processes.solution_operators.sequences import SaturationRecoveryGRE\n",
+ "from cmrsim.signal_processes.solution_operators.perfusion import ContrastAgentPerfusion\n",
+ "from cmrsim.signal_processes.base import CompositeSignalModel\n",
+ "from cmrsim.reconstruction.cartesian import Cartesian2D\n",
+ "\n",
+ "class PerfusionSimulation(SimulationBase):\n",
+ " def _build(self):\n",
+ " n_kx, n_ky = 51, 75\n",
+ " encoding_module = SingleLinePerShot(field_of_view=(0.10, 0.10), sampling_matrix_size=(n_kx, n_ky),\n",
+ " absolute_noise_std=0., read_out_duration=1., repetition_time=2.) \n",
+ " \n",
+ " _sequence_module = SaturationRecoveryGRE(repetition_time=2., flip_angle=15., saturation_delay=150., n_profiles_to_k0=n_ky//2)\n",
+ " perfusion_module = ContrastAgentPerfusion(contrast_times=np.array(range(10, 25, 5), dtype=np.float32), sequence_module=_sequence_module,\n",
+ " integration_start=0., integration_end=100., n_reference_points=101.) \n",
+ " forward_model = CompositeSignalModel(perfusion_module) \n",
+ " \n",
+ " recon_module = Cartesian2D(sample_matrix_size=(n_kx, n_ky), padding=None)\n",
+ " return forward_model, encoding_module, recon_module\n",
+ " \n",
+ "simulation = PerfusionSimulation() \n",
+ "print(f\"Required data input of configured simulation: {simulation.forward_model.required_quantities}\")"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<tf.Variable 'UnreadVariable' shape=(None,) dtype=float32, numpy=array([1., 2., 3., 4.], dtype=float32)>"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "simulation.forward_model.perfusion_contrast_agent.acquisition_times.assign(np.array([1., 2., 3., 4.]))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "dict_keys(['M0', 'T1', 'r_vectors', 'contrast_dynamic_index']) [(1, 250, 313, 1, 1), (1, 250, 313, 1, 1), (1, 250, 313, 1, 3, 1), (1, 250, 313, 1, 1)]\n"
+ ]
+ }
+ ],
+ "source": [
+ "from cmrsim.anatomy.base import BaseDataset\n",
+ "\n",
+ "dd = _create_cylinder_phantom()\n",
+ "print(dd.keys(), [_.shape for _ in dd.values()])\n",
+ "dataset = BaseDataset(dd)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 27,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Run Simulation: |XXXXX|1/1\t|XXXXXXXXXXXXXXXXXXXX|15039/15039\t|XXXXXXXXXXXXXXXXXXXX|75/75 "
+ ]
+ }
+ ],
+ "source": [
+ "simulated_images = simulation(dataset, voxel_batch_size=1000, execute_eager=True)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "from cmrsim.auxilliary.plot_utilities import SlideShow\n",
+ "print(simulated_images.shape)\n",
+ "images = np.transpose(simulated_images[0], [1, 2, 3, 0])\n",
+ "\n",
+ "magnitude, phase = np.abs(images), np.angle(images)\n",
+ "\n",
+ "sl = SlideShow(np.concatenate([magnitude, phase]), titles=['Magnitude', 'Phase'], orientation='vertical', x_labels=[[f'Trigger Delay: {i}'] * 2 for i in range(3)])\n",
+ "sl.fig.set_size_inches(4, 5.5)"
+ ]
+ }
+ ],
+ "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": "3.7.9"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
diff --git a/notebooks/simulation_module_example.ipynb b/notebooks/simulation_module_example.ipynb
deleted file mode 100644
index 40b795782ca24d625ca26a01f3667baa5ebb4aa3..0000000000000000000000000000000000000000
--- a/notebooks/simulation_module_example.ipynb
+++ /dev/null
@@ -1,76419 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Import external modules"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
- "outputs": [],
- "source": [
- "import tensorflow as tf\n",
- "physical_devices = tf.config.list_physical_devices('GPU')\n",
- "tf.config.experimental.set_visible_devices(physical_devices[0], 'GPU')\n",
- "tf.config.experimental.set_memory_growth(physical_devices[0], True)\n",
- "\n",
- "from collections import OrderedDict\n",
- "import numpy as np\n",
- "import math\n",
- "\n",
- "import matplotlib.pyplot as plt\n",
- "%matplotlib widget "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Import cmrsim modules:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
- "outputs": [],
- "source": [
- "import sys\n",
- "sys.path.append('..')\n",
- "import cmrsim.simulation as cmrsim_simulate\n",
- "\n",
- "import cmrsim.auxilliary.coordinates as cmrsim_coords\n",
- "import cmrsim.auxilliary.snr as cmrsim_snr\n",
- "import cmrsim.auxilliary.plot_utilities as cmrsim_plots\n",
- "from cmrsim.auxilliary.assign_map_values import assign_to_numerical_label_map"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Simulation\n",
- "\n",
- "Define subclass of SimulationBase class that implements the `_build` method. The Variables inside the modules can be assigned manually later to configure the simulation parameters.\n",
- "\n",
- "**NOTE**: As the Reconstruction module is None, the returned simulation result is k-space data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {},
- "outputs": [],
- "source": [
- "from cmrsim.anatomy.base import BaseDataset\n",
- "from cmrsim.signal_processes.base import CompositeSignalModel\n",
- "import cmrsim.signal_processes.solution_operators as solution_operators\n",
- "\n",
- "from cmrsim.encoding.epi import EPI\n",
- "from cmrsim.reconstruction.cartesian import Cartesian2D, RoemerMultiCoil\n",
- "\n",
- "class ExampleSimulation(cmrsim_simulate.SimulationBase):\n",
- " def _build(self):\n",
- " n_kx, n_ky = 189, 67\n",
- " # Encoding definition\n",
- " encoding_module = EPI(field_of_view=(0.31, 0.112), sampling_matrix_size=(n_kx, n_ky), read_out_duration=0.1,\n",
- " blip_duration=0.01, k_space_segments=27,\n",
- " absolute_noise_std=0.\n",
- " )\n",
- " # Signal module construction\n",
- " spinecho_module = solution_operators.sequences.SpinEcho(0.05, 10)\n",
- " t2_star_module = solution_operators.t2_star.StaticT2starDecay(encoding_module.get_sampling_times())\n",
- "\n",
- " # Forward model composition\n",
- " forward_model = CompositeSignalModel(spinecho_module, t2_star_module)\n",
- " return forward_model, encoding_module, None"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Instantiate an ExampleSimulation:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
- "outputs": [],
- "source": [
- "simulation = ExampleSimulation(name='simulation_example')"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Set up Dataset as described in the previous notebook (single_coil_SE)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {},
- "outputs": [],
- "source": [
- "resource_dir = 'example_resources/'\n",
- "\n",
- "m0_map = np.load(resource_dir + \"s0map.npy\")\n",
- "myo_mask = np.load(resource_dir + \"myo_mask.npy\")\n",
- "non_zero_mask = tf.cast(tf.constant(m0_map, shape=(378, 134), dtype=tf.int32) > 0, tf.int32)\n",
- "# This is the standard shape for cmrsim simulation tensors. In this notebook only the first three axis are needed\n",
- "batch_shape = (1, 378, 134, 1, 3, 1)\n",
- "\n",
- "n_images, x, y, _, _, _ = batch_shape\n",
- "\n",
- "r = cmrsim_coords.get_static_2d_centered_coordinates((378, 134), (0.3024, 0.1072), batch_size=1)\n",
- "static_coords = r.numpy()\n",
- "static_coords = tf.tile(static_coords, [n_images, 1, 1, 1, 1, 1])\n",
- "data_dict = OrderedDict([\n",
- " ('M0', tf.tile(tf.constant(m0_map, shape=(1, x, y, 1, 1), dtype=tf.complex64),\n",
- " [n_images, 1, 1, 1, 1])/ 4.),\n",
- " ('T1', tf.tile(tf.reshape(assign_to_numerical_label_map(non_zero_mask, 1.10, fractions=[1., 1. ]),\n",
- " [1, x, y, 1, 1]), [n_images, 1, 1, 1, 1])),\n",
- " ('T2', tf.tile(tf.reshape(assign_to_numerical_label_map(non_zero_mask, 0.25, fractions=[1., 1.]),\n",
- " [1, x, y, 1, 1]), [n_images, 1, 1, 1, 1])),\n",
- " ('T2star', tf.tile(tf.reshape(assign_to_numerical_label_map(non_zero_mask, 0.035, fractions=[1., 1.]),\n",
- " [1, x, y, 1, 1]), [n_images, 1, 1, 1, 1])),\n",
- " ('r_vectors', static_coords)\n",
- " ])\n",
- "\n",
- "dataset = BaseDataset(data_dict)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Run the simulation\n",
- "\n",
- "After the simulation was performed, the Configured simulation is saved as `tf.train.Checkpoint`. To demonstrate the loading functionality, the `ExampleSimulation` instance is deleted."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Processed image(s): 0 Voxel count: 32327 / 32327 Segment index: 27 / 27 \n",
- "Processed 1 image(s)...\n",
- "Wall time: 5.29 s\n"
- ]
- }
- ],
- "source": [
- "%time noise_less_k_space = simulation(dataset, voxel_batch_size=10000, execute_eager=False)\n",
- "\n",
- "import os\n",
- "chkpt = './model_checkpoints/example_checkpoint'\n",
- "os.makedirs(os.path.dirname(chkpt), exist_ok=True)\n",
- "simulation.save(checkpoint_path=chkpt)\n",
- "del simulation"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Intantiate a new `ExampleSimulation` module and load the configuration from the previously saved model checkpoint by using the classmethod `SimulationBase.from_checkpoint(...)`"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Processed image(s): 0 Voxel count: 32327 / 32327 Segment index: 27 / 27 \n",
- "Processed 1 image(s)...\n",
- "Wall time: 3.63 s\n",
- "Resulting shape information: (#images, #noise-levels, #samples=(kx*ky)\n",
- "Simulation result shape: (1, 1, 12663)\n",
- "tf.Tensor(0j, shape=(), dtype=complex64)\n"
- ]
- }
- ],
- "source": [
- "new_simulation = ExampleSimulation.from_checkpoint(chkpt)\n",
- "\n",
- "%time noise_less_k_space_load = new_simulation(dataset, voxel_batch_size=10000, execute_eager=False)\n",
- "print(\"Resulting shape information: (#images, #noise-levels, #samples=(kx*ky)\")\n",
- "print(\"Simulation result shape:\", noise_less_k_space_load.shape)\n",
- "print(tf.reduce_sum(noise_less_k_space - noise_less_k_space_load))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### After instantitation, the variables contained in the submodules can be changed"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Processed image(s): 0 Voxel count: 32327 / 32327 Segment index: 27 / 27 \n",
- "Processed 1 image(s)...\n",
- "Wall time: 2.7 s\n",
- "Mean absolute difference for different TE: tf.Tensor(40.182934, shape=(), dtype=float32)\n",
- "Processed image(s): 0 Voxel count: 32327 / 32327 Segment index: 27 / 27 \n",
- "Processed 1 image(s)...\n",
- "Wall time: 2.61 s\n",
- "Mean absolute difference for same TE: tf.Tensor(8.297906e-05, shape=(), dtype=float32)\n"
- ]
- }
- ],
- "source": [
- "new_simulation.forward_model.spin_echo.TE.assign(0.01)\n",
- "%time n = new_simulation(dataset, voxel_batch_size=100000)\n",
- "print('Mean absolute difference for different TE:', tf.reduce_mean(tf.abs(noise_less_k_space - n)))\n",
- "\n",
- "new_simulation.forward_model.spin_echo.TE.assign(0.05)\n",
- "%time n = new_simulation(dataset, voxel_batch_size=100000)\n",
- "print('Mean absolute difference for same TE:', tf.reduce_mean(tf.abs(noise_less_k_space - n)))\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Summary functionality\n",
- "The SimulationBase class includes a functionality to create a report of the simulation configuration (aka submodule Variables) as dictionary. This dictionary can be used to generate a JSON string "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "application/json": {
- "example_simulation": {
- "encoding": {
- "Variable:0": {
- "dtype": "<dtype: 'int32'>",
- "is_trainable": "False",
- "name": "Variable:0",
- "shape": "()",
- "value": 27
- },
- "absolute_noise_std:0": {
- "dtype": "<dtype: 'float32'>",
- "is_trainable": "False",
- "name": "absolute_noise_std:0",
- "shape": "(None,)",
- "value": [
- 0
- ]
- },
- "acquisition_matrix:0": {
- "dtype": "<dtype: 'int32'>",
- "is_trainable": "True",
- "name": "acquisition_matrix:0",
- "shape": "(2,)",
- "value": [
- 189,
- 67
- ]
- },
- "acquisition_offset:0": {
- "dtype": "<dtype: 'float32'>",
- "is_trainable": "True",
- "name": "acquisition_offset:0",
- "shape": "()",
- "value": 0
- },
- "blip_duration:0": {
- "dtype": "<dtype: 'float32'>",
- "is_trainable": "True",
- "name": "blip_duration:0",
- "shape": "()",
- "value": 0.009999999776482582
- },
- "field_of_view:0": {
- "dtype": "<dtype: 'float32'>",
- "is_trainable": "True",
- "name": "field_of_view:0",
- "shape": "(2,)",
- "value": [
- 0.3100000023841858,
- 0.1120000034570694
- ]
- },
- "k_space_vectors:0": {
- "dtype": "<dtype: 'float32'>",
- "is_trainable": "False",
- "name": "k_space_vectors:0",
- "shape": "(None, 3)",
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- ]
- }
- },
- "forward model": {
- "modules": [
- "spin_echo",
- "static_t2star"
- ],
- "spin_echo": {
- "Variable:0": {
- "dtype": "<dtype: 'float32'>",
- "is_trainable": "True",
- "name": "Variable:0",
- "shape": "()",
- "value": 10
- }
- },
- "static_t2star": {}
- }
- }
- },
- "text/plain": [
- "<IPython.core.display.JSON object>"
- ]
- },
- "execution_count": 18,
- "metadata": {
- "application/json": {
- "expanded": false,
- "root": "root"
- }
- },
- "output_type": "execute_result"
- }
- ],
- "source": [
- "import json\n",
- "from IPython.display import JSON\n",
- "\n",
- "summary_dict = new_simulation.get_configuration()\n",
- "s = json.dumps(summary_dict)\n",
- "JSON(summary_dict)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "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": "3.7.7"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/notebooks/simulation_module_multi_coil.ipynb b/notebooks/simulation_module_multi_coil.ipynb
deleted file mode 100644
index 8adf8abb3c009e2fbaad553e70cd5ae074a65f1f..0000000000000000000000000000000000000000
--- a/notebooks/simulation_module_multi_coil.ipynb
+++ /dev/null
@@ -1,76433 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Import external modules"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
- "outputs": [],
- "source": [
- "import tensorflow as tf\n",
- "physical_devices = tf.config.list_physical_devices('GPU')\n",
- "tf.config.experimental.set_visible_devices(physical_devices[0], 'GPU')\n",
- "tf.config.experimental.set_memory_growth(physical_devices[0], True)\n",
- "\n",
- "from collections import OrderedDict\n",
- "import numpy as np\n",
- "import math\n",
- "\n",
- "import matplotlib.pyplot as plt\n",
- "%matplotlib widget "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Import cmrsim modules:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
- "outputs": [],
- "source": [
- "import sys\n",
- "sys.path.append('..')\n",
- "import cmrsim.simulation as cmrsim_simulate\n",
- "\n",
- "import cmrsim.auxilliary.coordinates as cmrsim_coords\n",
- "import cmrsim.auxilliary.snr as cmrsim_snr\n",
- "import cmrsim.auxilliary.plot_utilities as cmrsim_plots\n",
- "from cmrsim.auxilliary.assign_map_values import assign_to_numerical_label_map"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Simulation\n",
- "\n",
- "Define subclass of SimulationBase class that implements the `_build` method. The Variables inside the modules can be assigned manually later to configure the simulation parameters.\n",
- "\n",
- "**NOTE**: As the Reconstruction module is None, the returned simulation result is k-space data"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 17,
- "metadata": {},
- "outputs": [],
- "source": [
- "from cmrsim.anatomy.base import BaseDataset\n",
- "from cmrsim.signal_processes.base import CompositeSignalModel\n",
- "import cmrsim.signal_processes.solution_operators as solution_operators\n",
- "\n",
- "from cmrsim.encoding.epi import EPI\n",
- "from cmrsim.reconstruction.cartesian import Cartesian2D, RoemerMultiCoil\n",
- "\n",
- "class ExampleSimulation(cmrsim_simulate.SimulationBase):\n",
- " def _build(self):\n",
- " n_kx, n_ky = 189, 67\n",
- " # Encoding definition\n",
- " encoding_module = EPI(field_of_view=(0.31, 0.112), sampling_matrix_size=(n_kx, n_ky), read_out_duration=0.1,\n",
- " blip_duration=0.01, k_space_segments=27,\n",
- " absolute_noise_std=0.\n",
- " )\n",
- " # Signal module construction\n",
- " spinecho_module = solution_operators.sequences.SpinEcho(0.05, 10)\n",
- " t2_star_module = solution_operators.t2_star.StaticT2starDecay(encoding_module.get_sampling_times())\n",
- " coil_sens_module = solution_operators.coil_sensitivities.CoilSensitivity(tf.zeros((4, 1, 1, 1), dtype=tf.complex64), (0.3024, 0.1072, 0.))\n",
- "\n",
- " # Forward model composition\n",
- " forward_model = CompositeSignalModel(spinecho_module, t2_star_module, coil_sens_module)\n",
- " return forward_model, encoding_module, None\n",
- " \n",
- " def _update(self):\n",
- " self.encoding_module._update_variables()\n",
- " self.forward_model.static_t2star.sampling_times = self.encoding_module.get_sampling_times()"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Instantiate an ExampleSimulation:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "(<cmrsim.signal_processes.solution_operators.coil_sensitivities.CoilSensitivity object at 0x000002240F2D3948>, <cmrsim.signal_processes.solution_operators.sequences.SpinEcho object at 0x000002240FC14B88>, <cmrsim.signal_processes.solution_operators.t2_star.StaticT2starDecay object at 0x000002240F168048>)\n"
- ]
- }
- ],
- "source": [
- "simulation = ExampleSimulation(name='simulation_example')\n",
- "_ = simulation.forward_model.coil_sensitivities.look_up_map3d.assign(np.load(\"example_resources/coil_sensitivities.npy\").astype(np.complex64))\n",
- "_ = simulation.encoding_module.k_space_segments.assign(54)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Set up Dataset as described in the previous notebook (single_coil_SE)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 19,
- "metadata": {},
- "outputs": [],
- "source": [
- "resource_dir = 'example_resources/'\n",
- "\n",
- "m0_map = np.load(resource_dir + \"s0map.npy\")\n",
- "myo_mask = np.load(resource_dir + \"myo_mask.npy\")\n",
- "non_zero_mask = tf.cast(tf.constant(m0_map, shape=(378, 134), dtype=tf.int32) > 0, tf.int32)\n",
- "# This is the standard shape for cmrsim simulation tensors. In this notebook only the first three axis are needed\n",
- "batch_shape = (1, 378, 134, 1, 3, 1)\n",
- "\n",
- "n_images, x, y, _, _, _ = batch_shape\n",
- "\n",
- "r = cmrsim_coords.get_static_2d_centered_coordinates((378, 134), (0.3024, 0.1072), batch_size=1)\n",
- "static_coords = r.numpy()\n",
- "static_coords = tf.tile(static_coords, [n_images, 1, 1, 1, 1, 1])\n",
- "data_dict = OrderedDict([\n",
- " ('M0', tf.tile(tf.constant(m0_map, shape=(1, x, y, 1, 1), dtype=tf.complex64),\n",
- " [n_images, 1, 1, 1, 1])/ 4.),\n",
- " ('T1', tf.tile(tf.reshape(assign_to_numerical_label_map(non_zero_mask, 1.10, fractions=[1., 1. ]),\n",
- " [1, x, y, 1, 1]), [n_images, 1, 1, 1, 1])),\n",
- " ('T2', tf.tile(tf.reshape(assign_to_numerical_label_map(non_zero_mask, 0.25, fractions=[1., 1.]),\n",
- " [1, x, y, 1, 1]), [n_images, 1, 1, 1, 1])),\n",
- " ('T2star', tf.tile(tf.reshape(assign_to_numerical_label_map(non_zero_mask, 0.035, fractions=[1., 1.]),\n",
- " [1, x, y, 1, 1]), [n_images, 1, 1, 1, 1])),\n",
- " ('r_vectors', static_coords)\n",
- " ])\n",
- "\n",
- "dataset = BaseDataset(data_dict)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "#### Run the simulation\n",
- "\n",
- "After the simulation was performed, the Configured simulation is saved as `tf.train.Checkpoint`. To demonstrate the loading functionality, the `ExampleSimulation` instance is deleted."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 20,
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Processed image(s): 0 Voxel count: 32327 / 32327 Segment index: 54 / 54 \n",
- "Processed 1 image(s)...\n",
- "Wall time: 7.74 s\n"
- ]
- }
- ],
- "source": [
- "%time noise_less_k_space = simulation(dataset, voxel_batch_size=10000, execute_eager=False)\n",
- "\n",
- "import os\n",
- "chkpt = './model_checkpoints/example_checkpoint'\n",
- "os.makedirs(os.path.dirname(chkpt), exist_ok=True)\n",
- "simulation.save(checkpoint_path=chkpt)\n",
- "# del simulation"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Intantiate a new `ExampleSimulation` module and load the configuration from the previously saved model checkpoint by using the classmethod `SimulationBase.from_checkpoint(...)`"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Processed image(s): 0 Voxel count: 32327 / 32327 Segment index: 54 / 54 \n",
- "Processed 1 image(s)...\n",
- "Wall time: 5.42 s\n",
- "Resulting shape information: (#images, #noise-levels, #samples=(kx*ky)\n",
- "Simulation result shape: (1, 4, 1, 12663)\n",
- "tf.Tensor(0j, shape=(), dtype=complex64)\n"
- ]
- }
- ],
- "source": [
- "new_simulation = ExampleSimulation.from_checkpoint(chkpt)\n",
- "%time noise_less_k_space_load = new_simulation(dataset, voxel_batch_size=10000, execute_eager=False)\n",
- "print(\"Resulting shape information: (#images, #noise-levels, #samples=(kx*ky)\")\n",
- "print(\"Simulation result shape:\", noise_less_k_space_load.shape)\n",
- "print(tf.reduce_sum(noise_less_k_space - noise_less_k_space_load))"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### After instantitation, the variables contained in the submodules can be changed"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {},
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Processed image(s): 0 Voxel count: 32327 / 32327 Segment index: 27 / 27 \n",
- "Processed 1 image(s)...\n",
- "Wall time: 2.7 s\n",
- "Mean absolute difference for different TE: tf.Tensor(40.182934, shape=(), dtype=float32)\n",
- "Processed image(s): 0 Voxel count: 32327 / 32327 Segment index: 27 / 27 \n",
- "Processed 1 image(s)...\n",
- "Wall time: 2.61 s\n",
- "Mean absolute difference for same TE: tf.Tensor(8.297906e-05, shape=(), dtype=float32)\n"
- ]
- }
- ],
- "source": [
- "new_simulation.forward_model.spin_echo.TE.assign(0.01)\n",
- "%time n = new_simulation(dataset, voxel_batch_size=100000)\n",
- "print('Mean absolute difference for different TE:', tf.reduce_mean(tf.abs(noise_less_k_space - n)))\n",
- "\n",
- "new_simulation.forward_model.spin_echo.TE.assign(0.05)\n",
- "%time n = new_simulation(dataset, voxel_batch_size=100000)\n",
- "print('Mean absolute difference for same TE:', tf.reduce_mean(tf.abs(noise_less_k_space - n)))\n"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Summary functionality\n",
- "The SimulationBase class includes a functionality to create a report of the simulation configuration (aka submodule Variables) as dictionary. This dictionary can be used to generate a JSON string "
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 18,
- "metadata": {},
- "outputs": [
- {
- "data": {
- "application/json": {
- "example_simulation": {
- "encoding": {
- "Variable:0": {
- "dtype": "<dtype: 'int32'>",
- "is_trainable": "False",
- "name": "Variable:0",
- "shape": "()",
- "value": 27
- },
- "absolute_noise_std:0": {
- "dtype": "<dtype: 'float32'>",
- "is_trainable": "False",
- "name": "absolute_noise_std:0",
- "shape": "(None,)",
- "value": [
- 0
- ]
- },
- "acquisition_matrix:0": {
- "dtype": "<dtype: 'int32'>",
- "is_trainable": "True",
- "name": "acquisition_matrix:0",
- "shape": "(2,)",
- "value": [
- 189,
- 67
- ]
- },
- "acquisition_offset:0": {
- "dtype": "<dtype: 'float32'>",
- "is_trainable": "True",
- "name": "acquisition_offset:0",
- "shape": "()",
- "value": 0
- },
- "blip_duration:0": {
- "dtype": "<dtype: 'float32'>",
- "is_trainable": "True",
- "name": "blip_duration:0",
- "shape": "()",
- "value": 0.009999999776482582
- },
- "field_of_view:0": {
- "dtype": "<dtype: 'float32'>",
- "is_trainable": "True",
- "name": "field_of_view:0",
- "shape": "(2,)",
- "value": [
- 0.3100000023841858,
- 0.1120000034570694
- ]
- },
- "k_space_vectors:0": {
- "dtype": "<dtype: 'float32'>",
- "is_trainable": "False",
- "name": "k_space_vectors:0",
- "shape": "(None, 3)",
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- ]
- }
- },
- "forward model": {
- "modules": [
- "spin_echo",
- "static_t2star"
- ],
- "spin_echo": {
- "Variable:0": {
- "dtype": "<dtype: 'float32'>",
- "is_trainable": "True",
- "name": "Variable:0",
- "shape": "()",
- "value": 10
- }
- },
- "static_t2star": {}
- }
- }
- },
- "text/plain": [
- "<IPython.core.display.JSON object>"
- ]
- },
- "execution_count": 18,
- "metadata": {
- "application/json": {
- "expanded": false,
- "root": "root"
- }
- },
- "output_type": "execute_result"
- }
- ],
- "source": [
- "import json\n",
- "from IPython.display import JSON\n",
- "\n",
- "summary_dict = new_simulation.get_configuration()\n",
- "s = json.dumps(summary_dict)\n",
- "JSON(summary_dict)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "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": "3.7.7"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
diff --git a/notebooks/single_coil_SE.ipynb b/notebooks/single_coil_SE.ipynb
deleted file mode 100644
index 036bbf9aa7c1a63c3262642c8fd39992b6923c91..0000000000000000000000000000000000000000
--- a/notebooks/single_coil_SE.ipynb
+++ /dev/null
@@ -1,441 +0,0 @@
-{
- "cells": [
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Import external modules"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 1,
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
- "outputs": [],
- "source": [
- "import tensorflow as tf\n",
- "physical_devices = tf.config.list_physical_devices('GPU')\n",
- "tf.config.experimental.set_visible_devices(physical_devices[0], 'GPU')\n",
- "tf.config.experimental.set_memory_growth(physical_devices[0], True)\n",
- "\n",
- "from collections import OrderedDict\n",
- "import numpy as np\n",
- "import math\n",
- "\n",
- "import matplotlib.pyplot as plt\n",
- "%matplotlib widget "
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Import cmrsim modules:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 2,
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
- "outputs": [],
- "source": [
- "from cmrsim.anatomy.base import BaseDataset\n",
- "from cmrsim.signal_processes.base import CompositeSignalModel\n",
- "import cmrsim.signal_processes.solution_operators as solution_operators\n",
- "\n",
- "from cmrsim.encoding.epi import EPI\n",
- "from cmrsim.reconstruction.cartesian import Cartesian2D, RoemerMultiCoil\n",
- "from cmrsim.simulation import simulate\n",
- "\n",
- "import cmrsim.auxilliary.coordinates as cmrsim_coords\n",
- "import cmrsim.auxilliary.snr as cmrsim_snr\n",
- "import cmrsim.auxilliary.plot_utilities as cmrsim_plots\n",
- "from cmrsim.auxilliary.assign_map_values import assign_to_numerical_label_map"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Set up data\n",
- "\n",
- "First load the MRXcat maps for simulation:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 3,
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
- "outputs": [],
- "source": [
- "resource_dir = 'example_resources/'\n",
- "\n",
- "m0_map = np.load(resource_dir + \"s0map.npy\")\n",
- "myo_mask = np.load(resource_dir + \"myo_mask.npy\")\n",
- "non_zero_mask = tf.cast(tf.constant(m0_map, shape=(378, 134), dtype=tf.int32) > 0, tf.int32)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Compute positional vectors of object points from m0_map.\n",
- "For more details see cmrsim.auxilliary.coordinates.get_static_2d_centered_coordinates"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 4,
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
- "outputs": [],
- "source": [
- "# This is the standard shape for cmrsim simulation tensors. In this notebook only the first three axis are needed\n",
- "batch_shape = (1, 378, 134, 1, 3, 1)\n",
- "\n",
- "n_images, x, y, _, _, _ = batch_shape\n",
- "\n",
- "r = cmrsim_coords.get_static_2d_centered_coordinates((378, 134), (0.3024, 0.1072), batch_size=1)\n",
- "static_coords = r.numpy()\n",
- "static_coords = tf.tile(static_coords, [n_images, 1, 1, 1, 1, 1])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Assign values of T1, T2 and T2* to map and store into Ordered dictionary. Make sure, that the relaxation times, do not\n",
- "contain zeros since it will cause NaN entries in the simulation."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 5,
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
- "outputs": [],
- "source": [
- "data_dict = OrderedDict([\n",
- " ('M0', tf.tile(tf.constant(m0_map, shape=(1, x, y, 1, 1), dtype=tf.complex64),\n",
- " [n_images, 1, 1, 1, 1])/ 4.),\n",
- " ('T1', tf.tile(tf.reshape(assign_to_numerical_label_map(non_zero_mask, 1.10, fractions=[1., 1. ]),\n",
- " [1, x, y, 1, 1]), [n_images, 1, 1, 1, 1])),\n",
- " ('T2', tf.tile(tf.reshape(assign_to_numerical_label_map(non_zero_mask, 0.25, fractions=[1., 1.]),\n",
- " [1, x, y, 1, 1]), [n_images, 1, 1, 1, 1])),\n",
- " ('T2star', tf.tile(tf.reshape(assign_to_numerical_label_map(non_zero_mask, 0.035, fractions=[1., 1.]),\n",
- " [1, x, y, 1, 1]), [n_images, 1, 1, 1, 1])),\n",
- " ('r_vectors', static_coords)\n",
- " ])"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "### Simulation\n",
- "Construct the cmrsim simulation modules\n",
- "- EPI encoding\n",
- "- SE sequence\n",
- "- T2 star decay"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 6,
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
- "outputs": [],
- "source": [
- "n_kx, n_ky = 189, 67\n",
- "\n",
- "# Encoding definition\n",
- "encoding_module = EPI(field_of_view=(0.31, 0.112), sampling_matrix_size=(n_kx, n_ky), read_out_duration=0.1,\n",
- " blip_duration=0.01, k_space_segments=27,\n",
- " absolute_noise_std=0.\n",
- " )\n",
- "# Signal module construction\n",
- "spinecho_module = solution_operators.sequences.SpinEcho(0.05, 10)\n",
- "t2_star_module = solution_operators.t2_star.StaticT2starDecay(encoding_module.get_sampling_times())\n",
- "\n",
- "# Forward model composition\n",
- "forward_model = CompositeSignalModel(spinecho_module, t2_star_module)\n",
- "\n",
- "# Image Data set\n",
- "dataset = BaseDataset(data_dict)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {},
- "source": [
- "Run noiseless simulation. If recon module is specified, the simulation output are images and if it is None the\n",
- "flattened k-space is returned."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 7,
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Start building Graph for simulation without noise...\n",
- "Processed images: 0 Current image: 30000 / 32327 Segment index: 26 / 27Resulting shape information: (#images, #noise-levels, #samples=(kx*ky)\n",
- "Simulation result shape: (1, 1, 12663)\n",
- "Wall time: 5.43 s\n"
- ]
- }
- ],
- "source": [
- "%%time\n",
- "print(\"Start building Graph for simulation without noise...\")\n",
- "noise_less_k_space = simulate(dataset, forward_model, encoding_module, recon_module=None,\n",
- " voxel_batch_size=10000, execute_eager=False)\n",
- "\n",
- "print(\"Resulting shape information: (#images, #noise-levels, #samples=(kx*ky)\")\n",
- "print(\"Simulation result shape:\", noise_less_k_space.shape)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "pycharm": {
- "name": "#%% md\n"
- }
- },
- "source": [
- "Reconstruct noise-less image and determine noise std for target SNR within the myocard mask."
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 8,
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "2D recon single coils: (1, 1, 1, 189, 67)\n",
- "Estimated noise levels for SNRs [20.0, 10.0, 5]: [0.471245 0.94249 1.88498 ]\n"
- ]
- }
- ],
- "source": [
- "# Recon definition\n",
- "recon_module = Cartesian2D(sample_matrix_size=(n_kx, n_ky), padding=None)\n",
- "\n",
- "single_coil_image = recon_module(noise_less_k_space, coil_channel_axis=1)[:, tf.newaxis, ...]\n",
- "print(\"2D recon single coils:\", single_coil_image.shape)\n",
- "\n",
- "target_snrs = [20., 10., 5]\n",
- "estimated_std = cmrsim_snr.compute_noise_std(single_coil_image[0, 0:1, 0, ...], target_snr=target_snrs, mask=myo_mask[::2, ::2])\n",
- "print(f\"Estimated noise levels for SNRs {target_snrs}:\", estimated_std)"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "pycharm": {
- "name": "#%% md\n"
- }
- },
- "source": [
- "Rerun simulation with noise and zero-padded reconstruction"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 9,
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Start building Graph for simulation with noise...\n",
- "Processed images: 0 Current image: 30000 / 32327 Segment index: 26 / 27\n",
- "Simualtion output shape: (1, 4, 378, 134)\n",
- "Wall time: 2.97 s\n"
- ]
- }
- ],
- "source": [
- "%%time\n",
- "encoding_module.absolute_noise_std.assign(tf.constant([0., ] + [_ for _ in estimated_std], dtype=tf.float32))\n",
- "print(\"Start building Graph for simulation with noise...\")\n",
- "simulated_images = simulate(dataset, forward_model, encoding_module, voxel_batch_size=10000, execute_eager=False)\n",
- "padded_recon_module = Cartesian2D(sample_matrix_size=(n_kx, n_ky), padding=[(math.floor(n_kx/2), math.ceil(n_kx/2)),\n",
- " (math.floor(n_ky/2), math.ceil(n_ky/2))])\n",
- "\n",
- "zero_padded_reconstruction = padded_recon_module(simulated_images)\n",
- "print(f\"\\nSimualtion output shape: {zero_padded_reconstruction.shape}\")"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 21,
- "metadata": {
- "pycharm": {
- "name": "#%%\n"
- }
- },
- "outputs": [
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "Noise std: 0.0\n",
- "Mean signal in LV: 9.446754455566406\n",
- "Mean snr in LV: nan\n",
- "\n",
- "Noise std: 0.4712449908256531\n",
- "Mean signal in LV: 9.441938400268555\n",
- "Mean snr in LV: 20.147714614868164\n",
- "\n",
- "Noise std: 0.9424899816513062\n",
- "Mean signal in LV: 9.46458911895752\n",
- "Mean snr in LV: 10.046950340270996\n",
- "\n",
- "Noise std: 1.8849799633026123\n",
- "Mean signal in LV: 9.653931617736816\n",
- "Mean snr in LV: 5.044689178466797\n",
- "\n"
- ]
- }
- ],
- "source": [
- "snr_maps = []\n",
- "\n",
- "for noise_index in range(len(target_snrs)+1):\n",
- " scaling = encoding_module.absolute_noise_std[noise_index]\n",
- " print(f\"Noise std: {scaling}\")\n",
- " image_shape = padded_recon_module.output_matrix_size\n",
- " noise_vectors = tf.random.normal(shape=(2, 1, image_shape[0], image_shape[1]), dtype=tf.float32) * scaling\n",
- " dynamic_noise_scan = tf.complex(noise_vectors[0], noise_vectors[1])\n",
- "\n",
- " snr_map = cmrsim_snr.calculate_snr(single_coil_images=zero_padded_reconstruction[0, 1: , ...],\n",
- " dynamic_noise_scan=dynamic_noise_scan)\n",
- " snr_maps += [snr_map, ]\n",
- " float_mask = tf.cast(myo_mask, tf.float32)\n",
- " mean_signal_encoded = tf.reduce_sum(tf.abs(zero_padded_reconstruction[0 ,noise_index:noise_index+1, ...]) * float_mask) / tf.reduce_sum(float_mask)\n",
- " mean_snr = tf.math.reduce_sum(tf.abs(snr_map) * float_mask) / tf.reduce_sum(float_mask)\n",
- " print(f\"Mean signal in LV: {mean_signal_encoded}\")\n",
- " print(f\"Mean snr in LV: {mean_snr}\\n\")"
- ]
- },
- {
- "cell_type": "markdown",
- "metadata": {
- "pycharm": {
- "name": "#%% md\n"
- }
- },
- "source": [
- "Plot Simulation results:"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": 22,
- "metadata": {
- "jupyter": {
- "outputs_hidden": false
- },
- "pycharm": {
- "name": "#%%\n"
- }
- },
- "outputs": [
- {
- "data": {
- "application/vnd.jupyter.widget-view+json": {
- "model_id": "c678bfc7d89040e09a20aac5e3ce7a68",
- "version_major": 2,
- "version_minor": 0
- },
- "text/plain": [
- "Canvas(toolbar=Toolbar(toolitems=[('Home', 'Reset original view', 'home', 'home'), ('Back', 'Back to previous …"
- ]
- },
- "metadata": {},
- "output_type": "display_data"
- }
- ],
- "source": [
- "# stack images into shape (#subplots, x, y, #channels)\n",
- "image_stack = np.concatenate((np.abs(zero_padded_reconstruction).transpose([0, 2, 3, 1]),\n",
- " np.nan_to_num(np.stack(snr_maps, axis=2)[np.newaxis, ...], posinf=0.0),\n",
- " np.tile(myo_mask[np.newaxis, :, :, np.newaxis], [1, 1, 1, 1+len(estimated_std)])\n",
- " ), axis=0)\n",
- "\n",
- "s1 = cmrsim_plots.SlideShow(image_stack, colorbars=True, titles=[\"Image\", \"SNR map\", \"Mask\"],\n",
- " x_labels=[[f'SNR:{s :>5}', f'SNR:{s :>5}', ' '] for s in [' inf', ] + target_snrs])\n",
- "s1.fig.set_size_inches((len(target_snrs)+1) * 2.2, 4.5)"
- ]
- },
- {
- "cell_type": "code",
- "execution_count": null,
- "metadata": {},
- "outputs": [],
- "source": []
- }
- ],
- "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": "3.7.7"
- }
- },
- "nbformat": 4,
- "nbformat_minor": 4
-}
\ No newline at end of file
diff --git a/public/.buildinfo b/public/.buildinfo
deleted file mode 100644
index f8985e0e8b756a2f314f71122327965f8ebc9eb8..0000000000000000000000000000000000000000
--- a/public/.buildinfo
+++ /dev/null
@@ -1,4 +0,0 @@
-# Sphinx build info version 1
-# This file hashes the configuration used when building these files. When it is not found, a full rebuild will be done.
-config: 9e5048063cc5d9f27a887f497a11e68e
-tags: 645f666f9bcd5a90fca523b33c5a78b7
diff --git a/public/_modules/cmrsim/anatomy/base.html b/public/_modules/cmrsim/anatomy/base.html
deleted file mode 100644
index fe63847380ca9ce2b4d569ca42ec1eb02d2ed739..0000000000000000000000000000000000000000
--- a/public/_modules/cmrsim/anatomy/base.html
+++ /dev/null
@@ -1,312 +0,0 @@
-
-
-<!DOCTYPE html>
-<html class="writer-html5" lang="en" >
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-<p class="caption"><span class="caption-text">Who is using it? - Examples</span></p>
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-<li class="toctree-l1"><a class="reference internal" href="../../../advanced_examples.html">1. Optimize b-values</a></li>
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- <h1>Source code for cmrsim.anatomy.base</h1><div class="highlight"><pre>
-<span></span><span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"BaseDataset"</span><span class="p">]</span>
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="o">*</span>
-<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">OrderedDict</span>
-<span class="kn">from</span> <span class="nn">copy</span> <span class="kn">import</span> <span class="n">deepcopy</span>
-
-<span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-
-<div class="viewcode-block" id="BaseDataset"><a class="viewcode-back" href="../../../cmrsim.anatomy.base.html#cmrsim.anatomy.base.BaseDataset">[docs]</a><span class="k">class</span> <span class="nc">BaseDataset</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
- <span class="n">map_names</span><span class="p">:</span> <span class="n">Tuple</span>
- <span class="n">set_size</span><span class="p">:</span> <span class="nb">int</span>
- <span class="n">_array_tuple</span><span class="p">:</span> <span class="n">Tuple</span>
-
- <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">array_dictionary</span><span class="p">:</span> <span class="n">OrderedDict</span><span class="p">):</span>
- <span class="sd">""" Initializes a callable module, that yields an iterable tf.Dataset on call. The required inpot</span>
-
-<span class="sd"> :param array_dictionary: (OrderedDict) containing the required quantities as keys and the corresponding 2D maps</span>
-<span class="sd"> as Numpy-ndarrays for values. Does a deepcopy of the Dictionary to avoid loosing</span>
-<span class="sd"> the reference.</span>
-<span class="sd"> """</span>
- <span class="nb">super</span><span class="p">(</span><span class="n">BaseDataset</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'array_dataset'</span><span class="p">)</span>
- <span class="n">arrays</span> <span class="o">=</span> <span class="n">deepcopy</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">array_dictionary</span><span class="o">.</span><span class="n">values</span><span class="p">()))</span>
-
- <span class="k">if</span> <span class="nb">len</span><span class="p">(</span><span class="n">array_dictionary</span><span class="p">[</span><span class="s1">'M0'</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o"><</span> <span class="mi">4</span><span class="p">:</span>
- <span class="n">arrays</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">arrays</span><span class="p">[</span><span class="mi">0</span><span class="p">][:,</span> <span class="p">:,</span> <span class="p">:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
- <span class="k">elif</span> <span class="nb">len</span><span class="p">(</span><span class="n">array_dictionary</span><span class="p">[</span><span class="s1">'M0'</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o"><</span> <span class="mi">5</span><span class="p">:</span>
- <span class="n">arrays</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">arrays</span><span class="p">[</span><span class="mi">0</span><span class="p">][:,</span> <span class="p">:,</span> <span class="p">:,</span> <span class="p">:,</span> <span class="n">np</span><span class="o">.</span><span class="n">newaxis</span><span class="p">]</span>
-
- <span class="bp">self</span><span class="o">.</span><span class="n">_array_tuple</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">arrays</span><span class="p">)</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">set_size</span> <span class="o">=</span> <span class="n">array_dictionary</span><span class="p">[</span><span class="s1">'M0'</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">map_names</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">array_dictionary</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
-
- <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">filter_trivial_grid_points</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="kc">True</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">bool</span><span class="p">))</span> <span class="o">-></span> <span class="n">tf</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">Dataset</span><span class="p">:</span>
- <span class="sd">"""</span>
-<span class="sd"> :param filter_trivial_grid_points: If true, only picks entries of the data set that have a magnetization > 0.</span>
-<span class="sd"> Which assumes, that all maps are aligned with respect to the indices.</span>
-<span class="sd"> :return: tf.data.Dataset</span>
-<span class="sd"> """</span>
- <span class="n">datasets</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">Dataset</span><span class="o">.</span><span class="n">from_tensor_slices</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_array_tuple</span><span class="p">)</span>
- <span class="k">if</span> <span class="n">filter_trivial_grid_points</span><span class="p">:</span>
- <span class="n">datasets</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_filter_inputs</span><span class="p">)</span>
- <span class="k">else</span><span class="p">:</span>
- <span class="n">datasets</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">map</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_flatten_maps</span><span class="p">)</span>
-
- <span class="n">datasets</span> <span class="o">=</span> <span class="n">datasets</span><span class="o">.</span><span class="n">prefetch</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">experimental</span><span class="o">.</span><span class="n">AUTOTUNE</span><span class="p">)</span>
- <span class="k">return</span> <span class="n">datasets</span>
-
- <span class="nd">@staticmethod</span>
- <span class="k">def</span> <span class="nf">_filter_inputs</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">):</span>
- <span class="sd">""" Per-image filter function, to select only grid-points that have an initial magnetization > 0. This function</span>
-<span class="sd"> assumes that the magnetization tensor is passed as first argument.</span>
-
-<span class="sd"> :param args: Tensors representing the parameter maps to be filtered.</span>
-<span class="sd"> :return:</span>
-<span class="sd"> """</span>
- <span class="n">m0</span> <span class="o">=</span> <span class="n">args</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
- <span class="n">indices</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">m0</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">),</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span> <span class="o">></span> <span class="mi">0</span><span class="p">)[:,</span> <span class="mi">0</span><span class="p">]</span>
- <span class="n">return_tuple</span> <span class="o">=</span> <span class="p">()</span>
- <span class="k">for</span> <span class="n">tensor</span> <span class="ow">in</span> <span class="n">args</span><span class="p">:</span>
- <span class="n">original_shape</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="n">tensor</span><span class="p">)</span>
- <span class="n">flat_tensor</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">concat</span><span class="p">(((</span><span class="o">-</span><span class="mi">1</span><span class="p">,),</span> <span class="n">original_shape</span><span class="p">[</span><span class="mi">2</span><span class="p">:]),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span>
- <span class="n">selection</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">gather</span><span class="p">(</span><span class="n">flat_tensor</span><span class="p">,</span> <span class="n">indices</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
- <span class="n">selection</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">selection</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">concat</span><span class="p">(((</span><span class="o">-</span><span class="mi">1</span><span class="p">,),</span> <span class="n">original_shape</span><span class="p">[</span><span class="mi">2</span><span class="p">:]),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span>
- <span class="n">return_tuple</span> <span class="o">+=</span> <span class="p">(</span><span class="n">selection</span><span class="p">,</span> <span class="p">)</span>
- <span class="k">return</span> <span class="n">return_tuple</span>
-
- <span class="nd">@staticmethod</span>
- <span class="k">def</span> <span class="nf">_flatten_maps</span><span class="p">(</span><span class="o">*</span><span class="n">args</span><span class="p">):</span>
- <span class="n">return_tuple</span> <span class="o">=</span> <span class="p">()</span>
- <span class="k">for</span> <span class="n">tensor</span> <span class="ow">in</span> <span class="n">args</span><span class="p">:</span>
- <span class="n">original_shape</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="n">tensor</span><span class="p">)</span>
- <span class="n">flat_tensor</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">tensor</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">concat</span><span class="p">(((</span><span class="o">-</span><span class="mi">1</span><span class="p">,),</span> <span class="n">original_shape</span><span class="p">[</span><span class="mi">2</span><span class="p">:]),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span>
- <span class="n">return_tuple</span> <span class="o">+=</span> <span class="p">(</span><span class="n">flat_tensor</span><span class="p">,)</span>
- <span class="k">return</span> <span class="n">return_tuple</span></div>
-
-
-<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">"__main__"</span><span class="p">:</span>
- <span class="sd">""" Example usage of the BaseDataset class for random input"""</span>
- <span class="kn">import</span> <span class="nn">time</span>
- <span class="c1"># Set up data</span>
- <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="mi">1000</span><span class="p">,</span> <span class="mi">1000</span>
- <span class="n">batch_size</span> <span class="o">=</span> <span class="mi">10</span>
- <span class="n">array_dict</span> <span class="o">=</span> <span class="n">OrderedDict</span><span class="p">([(</span><span class="s2">"M0"</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">around</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">uniform</span><span class="p">(</span><span class="mf">0.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">)),</span>
- <span class="n">decimals</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span><span class="o">.</span><span class="n">astype</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)),</span>
- <span class="p">(</span><span class="s2">"diffusion_tensor"</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="n">batch_shape</span><span class="o">=</span><span class="p">[</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">])</span><span class="o">.</span><span class="n">numpy</span><span class="p">()),</span>
- <span class="p">(</span><span class="s2">"T2star"</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)),</span>
- <span class="p">(</span><span class="s2">"r_vectors"</span><span class="p">,</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">batch_size</span><span class="p">,</span> <span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">))])</span>
- <span class="c1"># Initialize BaseDataset and use its __call__ method to get a tf.data.Dataset that applies the</span>
- <span class="c1"># corresponding function mappings to the data</span>
- <span class="n">module</span> <span class="o">=</span> <span class="n">BaseDataset</span><span class="p">(</span><span class="n">array_dict</span><span class="p">)</span>
- <span class="n">start</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">perf_counter</span><span class="p">()</span>
- <span class="k">for</span> <span class="n">_</span><span class="p">,</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">module</span><span class="p">()</span><span class="o">.</span><span class="n">enumerate</span><span class="p">():</span>
- <span class="c1"># Set up data set that divides the full image into voxel-wise batches</span>
- <span class="n">start_inner</span> <span class="o">=</span> <span class="n">time</span><span class="o">.</span><span class="n">perf_counter</span><span class="p">()</span>
- <span class="n">d</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">Dataset</span><span class="o">.</span><span class="n">from_tensor_slices</span><span class="p">(</span><span class="n">i</span><span class="p">)</span>
- <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">a</span> <span class="ow">in</span> <span class="n">d</span><span class="o">.</span><span class="n">batch</span><span class="p">(</span><span class="mi">5000</span><span class="p">)</span><span class="o">.</span><span class="n">enumerate</span><span class="p">():</span>
- <span class="c1"># Do something with the voxel batch...</span>
- <span class="n">_</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reduce_sum</span><span class="p">(</span><span class="n">a</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
- <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="se">\r</span><span class="si">{</span><span class="n">idx</span><span class="si">}</span><span class="s2">, Time per iteration: </span><span class="si">{</span><span class="n">time</span><span class="o">.</span><span class="n">perf_counter</span><span class="p">()</span> <span class="o">-</span> <span class="n">start_inner</span><span class="si">:</span><span class="s2"> 1.4f</span><span class="si">}</span><span class="s2"> "</span>
- <span class="sa">f</span><span class="s2">"</span><span class="se">\t</span><span class="s2"> Tensor shapes: </span><span class="si">{</span><span class="p">[</span><span class="n">j</span><span class="p">[</span><span class="n">tf</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span> <span class="k">for</span> <span class="n">j</span> <span class="ow">in</span> <span class="n">a</span><span class="p">]</span><span class="si">}</span><span class="s2">"</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="s1">''</span><span class="p">)</span>
- <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="se">\n</span><span class="s2">Total time consumed: </span><span class="si">{</span><span class="n">time</span><span class="o">.</span><span class="n">perf_counter</span><span class="p">()</span> <span class="o">-</span> <span class="n">start</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
-</pre></div>
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diff --git a/public/_modules/cmrsim/anatomy/from_files.html b/public/_modules/cmrsim/anatomy/from_files.html
deleted file mode 100644
index 4ed636988ace4bd23494144a3dcd268ad7bb31e7..0000000000000000000000000000000000000000
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- <h1>Source code for cmrsim.anatomy.from_files</h1><div class="highlight"><pre>
-<span></span><span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"MultiFileDataset"</span><span class="p">,</span> <span class="p">]</span>
-
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Iterable</span>
-<span class="kn">import</span> <span class="nn">os</span>
-<span class="kn">import</span> <span class="nn">warnings</span>
-<span class="kn">from</span> <span class="nn">collections</span> <span class="kn">import</span> <span class="n">OrderedDict</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-<span class="kn">from</span> <span class="nn">cmrsim.anatomy.base</span> <span class="kn">import</span> <span class="n">BaseDataset</span>
-
-<span class="kn">from</span> <span class="nn">cmrsim.auxilliary.coordinates</span> <span class="kn">import</span> <span class="n">get_static_2d_centered_coordinates</span>
-
-
-<div class="viewcode-block" id="MultiFileDataset"><a class="viewcode-back" href="../../../cmrsim.anatomy.from_files.html#cmrsim.anatomy.from_files.MultiFileDataset">[docs]</a><span class="k">class</span> <span class="nc">MultiFileDataset</span><span class="p">(</span><span class="n">BaseDataset</span><span class="p">):</span>
-
- <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data_dir</span><span class="p">,</span> <span class="n">object_dimensions</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">],</span> <span class="n">property_names</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span>
- <span class="n">folder_names</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="o">...</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">indices</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Iterable</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
- <span class="sd">""" Loads multiple files for specified properties in data_dir. Assumes that the examples are saved with paths:</span>
-<span class="sd"> data_dir/property_name/index.npy. If folder_names is given, its entries replace the property_name for loading.</span>
-
-<span class="sd"> Uses an Ordered dict, whose keys match 'property_names' and the values are the stacked loaded maps of shape</span>
-<span class="sd"> (len(indices), X, Y, ...) to call super(...).__init__ of BaseDataset to construct a callable dataset module</span>
-
-<span class="sd"> If positional vectors are not found in the directory, a static object is assumed, where the coordinates are</span>
-<span class="sd"> inferred from grid indices. This assumes the center index to be placed in the iso-center</span>
-<span class="sd"> (see cmrsim.auxilliary.coordinates import get_static_2d_centered_coordinates)</span>
-
-<span class="sd"> :param data_dir: directory containing the subdirectories for the required maps</span>
-<span class="sd"> :param object_dimensions: Used to scale relative grid coordinates to absolute coordinates. Will be replaced by</span>
-<span class="sd"> reading a metafile.</span>
-<span class="sd"> :param property_names:</span>
-<span class="sd"> :param folder_names:</span>
-<span class="sd"> :param indices: Iterable[int] Used to select loaded data samples. Defaults to load all.</span>
-<span class="sd"> :return: OrderedDict({str: np.ndarray})</span>
-<span class="sd"> """</span>
- <span class="k">if</span> <span class="n">folder_names</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
- <span class="n">folder_names</span> <span class="o">=</span> <span class="n">property_names</span>
-
- <span class="k">if</span> <span class="n">indices</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
- <span class="n">file_list</span> <span class="o">=</span> <span class="nb">list</span><span class="p">(</span><span class="n">os</span><span class="o">.</span><span class="n">walk</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">data_dir</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="n">folder_names</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="si">}</span><span class="s2">/"</span><span class="p">))[</span><span class="mi">0</span><span class="p">][</span><span class="mi">2</span><span class="p">]</span>
- <span class="n">number_of_files</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="n">file_list</span><span class="p">)</span>
- <span class="n">indices</span> <span class="o">=</span> <span class="nb">range</span><span class="p">(</span><span class="n">number_of_files</span><span class="p">)</span>
- <span class="k">else</span><span class="p">:</span>
- <span class="n">number_of_files</span> <span class="o">=</span> <span class="nb">len</span><span class="p">(</span><span class="nb">list</span><span class="p">(</span><span class="n">indices</span><span class="p">))</span>
-
- <span class="n">shapes</span> <span class="o">=</span> <span class="p">[(</span><span class="n">number_of_files</span><span class="p">,)</span> <span class="o">+</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">data_dir</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="n">f</span><span class="si">}</span><span class="s2">/0.npy"</span><span class="p">)</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span> <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">folder_names</span><span class="p">]</span>
- <span class="n">types</span> <span class="o">=</span> <span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">data_dir</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="n">f</span><span class="si">}</span><span class="s2">/0.npy"</span><span class="p">)</span><span class="o">.</span><span class="n">dtype</span> <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="n">folder_names</span><span class="p">]</span>
-
- <span class="n">array_dict</span> <span class="o">=</span> <span class="n">OrderedDict</span><span class="p">()</span>
- <span class="k">for</span> <span class="n">key</span><span class="p">,</span> <span class="n">folder</span><span class="p">,</span> <span class="n">shape</span><span class="p">,</span> <span class="nb">type</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">property_names</span><span class="p">,</span> <span class="n">folder_names</span><span class="p">,</span> <span class="n">shapes</span><span class="p">,</span> <span class="n">types</span><span class="p">):</span>
- <span class="n">arr</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="nb">type</span><span class="p">)</span>
- <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="n">indices</span><span class="p">:</span>
- <span class="n">arr</span><span class="p">[</span><span class="n">i</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">load</span><span class="p">(</span><span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="n">data_dir</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="n">folder</span><span class="si">}</span><span class="s2">/</span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s2">.npy"</span><span class="p">)</span>
- <span class="n">array_dict</span><span class="o">.</span><span class="n">update</span><span class="p">({</span><span class="n">key</span><span class="p">:</span> <span class="n">arr</span><span class="p">})</span>
-
- <span class="k">if</span> <span class="s1">'r_vectors'</span> <span class="ow">not</span> <span class="ow">in</span> <span class="n">property_names</span><span class="p">:</span>
- <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">"No r_vectors were found in stated data-directory. Inferring coordinates automatically..."</span><span class="p">)</span>
- <span class="n">r_vectors</span> <span class="o">=</span> <span class="n">get_static_2d_centered_coordinates</span><span class="p">(</span><span class="n">shapes</span><span class="p">[</span><span class="mi">0</span><span class="p">][</span><span class="mi">1</span><span class="p">:],</span> <span class="n">object_dimensions</span><span class="o">=</span><span class="n">object_dimensions</span><span class="p">,</span>
- <span class="n">batch_size</span><span class="o">=</span><span class="n">number_of_files</span><span class="p">)</span>
- <span class="n">array_dict</span><span class="o">.</span><span class="n">update</span><span class="p">({</span><span class="s1">'r_vectors'</span><span class="p">:</span> <span class="n">r_vectors</span><span class="p">})</span>
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- <h1>Source code for cmrsim.auxilliary.assign_map_values</h1><div class="highlight"><pre>
-<span></span><span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"assign_random_normal_to_numerical_label_map"</span><span class="p">,</span> <span class="s2">"assign_to_numerical_label_map"</span><span class="p">]</span>
-
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="o">*</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-
-<div class="viewcode-block" id="assign_to_numerical_label_map"><a class="viewcode-back" href="../../../cmrsim.auxilliary.assign_map_values.html#cmrsim.auxilliary.assign_map_values.assign_to_numerical_label_map">[docs]</a><span class="k">def</span> <span class="nf">assign_to_numerical_label_map</span><span class="p">(</span><span class="n">label_map</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">max_value</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span> <span class="n">fractions</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">Iterable</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">List</span><span class="p">]):</span>
- <span class="sd">""" Assigns values to pixels as fractions of the stated maximum value according to class membership. Assumes the</span>
-<span class="sd"> classes to be labeled as increasing integers with increment 1.</span>
-
-<span class="sd"> :param label_map: (N, M) or (#batch, N, M)</span>
-<span class="sd"> :param max_value: float</span>
-<span class="sd"> :param fractions: Iterable of float values. Needs to be at least as long #classes excluding the background</span>
-<span class="sd"> :return: np.ndarray (#batch, N, M)</span>
-<span class="sd"> """</span>
- <span class="n">number_of_classes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">label_map</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span>
- <span class="n">return_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">label_map</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
- <span class="k">for</span> <span class="n">class_number</span><span class="p">,</span> <span class="n">fraction</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">number_of_classes</span><span class="p">),</span> <span class="n">fractions</span><span class="p">):</span>
- <span class="n">return_array</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">label_map</span> <span class="o">==</span> <span class="n">class_number</span><span class="p">)]</span> <span class="o">=</span> <span class="n">fraction</span> <span class="o">*</span> <span class="n">max_value</span>
- <span class="k">return</span> <span class="n">return_array</span></div>
-
-
-<div class="viewcode-block" id="assign_random_normal_to_numerical_label_map"><a class="viewcode-back" href="../../../cmrsim.auxilliary.assign_map_values.html#cmrsim.auxilliary.assign_map_values.assign_random_normal_to_numerical_label_map">[docs]</a><span class="k">def</span> <span class="nf">assign_random_normal_to_numerical_label_map</span><span class="p">(</span><span class="n">label_map</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">means</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">[</span><span class="nb">float</span><span class="p">],</span> <span class="n">stds</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">[</span><span class="nb">float</span><span class="p">]):</span>
- <span class="sd">""" Assigns values to pixels as sample of a normal random variable with the stated mean value and standard deviation\</span>
-<span class="sd"> according to class membership. Assumes the classes to be labeled as increasing integers with increment 1.</span>
-
-<span class="sd"> :param label_map: (N, M) or (#batch, N, M)</span>
-<span class="sd"> :param means: Iterable of float values. Needs to be at least as long #classes excluding the background</span>
-<span class="sd"> :param stds: Iterable of float values. Needs to be at least as long #classes excluding the background</span>
-<span class="sd"> :return: np.ndarray (#batch, N, M)</span>
-<span class="sd"> """</span>
- <span class="n">number_of_classes</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="n">label_map</span><span class="p">)</span>
- <span class="n">return_array</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">label_map</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
- <span class="k">for</span> <span class="n">class_number</span><span class="p">,</span> <span class="n">mean</span><span class="p">,</span> <span class="n">std</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">number_of_classes</span><span class="o">+</span><span class="mi">1</span><span class="p">),</span> <span class="n">means</span><span class="p">,</span> <span class="n">stds</span><span class="p">):</span>
- <span class="n">pixel_indices</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">label_map</span> <span class="o">==</span> <span class="n">class_number</span><span class="p">)</span>
- <span class="n">number_of_pixels</span> <span class="o">=</span> <span class="n">pixel_indices</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
- <span class="n">return_array</span><span class="p">[</span><span class="n">pixel_indices</span><span class="p">]</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="n">mean</span><span class="p">,</span> <span class="n">std</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="n">number_of_pixels</span><span class="p">,</span> <span class="p">))</span>
- <span class="k">return</span> <span class="n">return_array</span></div>
-</pre></div>
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- <h1>Source code for cmrsim.auxilliary.coordinates</h1><div class="highlight"><pre>
-<span></span><span class="sd">""" Module that contains functions to get and transform coordinates in the format required for simulation. """</span>
-
-<span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s1">'get_static_2d_centered_coordinates'</span><span class="p">,</span> <span class="s1">'get_translation_matrix'</span><span class="p">,</span> <span class="s1">'apply_transformations'</span><span class="p">]</span>
-
-
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">List</span><span class="p">,</span> <span class="n">Union</span>
-
-<span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-
-<div class="viewcode-block" id="get_static_2d_centered_coordinates"><a class="viewcode-back" href="../../../cmrsim.auxilliary.coordinates.html#cmrsim.auxilliary.coordinates.get_static_2d_centered_coordinates">[docs]</a><span class="k">def</span> <span class="nf">get_static_2d_centered_coordinates</span><span class="p">(</span><span class="n">map_size</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">],</span>
- <span class="n">object_dimensions</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
- <span class="n">batch_size</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1</span><span class="p">)</span> <span class="o">-></span> <span class="n">tf</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
- <span class="sd">""" Calculates relative coordinates for a dense map with coordinate origin in the center of the map. If the</span>
-<span class="sd"> dimension in map_size is even, the origin is shifted 1/2 with respect to grid indices, if they are uneven, the</span>
-<span class="sd"> origin coincides with a grid point.</span>
-<span class="sd"> Reshapes the output to a shape, used in cmrsim simulations. The singleton-axis is reserved for repetitions /</span>
-<span class="sd"> contrasts.</span>
-
-<span class="sd"> :param map_size: Tuple[int, int], 2D grid dimensions used to calculate coordinates relative to mid-point</span>
-<span class="sd"> :param object_dimensions: Optional Tuple[float, float] used to scale relative coordinates. Defaults to (1., 1.)</span>
-<span class="sd"> :param batch_size: Optional (int) - used to tile the calculated coordinates for a batch. Defaults to 1</span>
-<span class="sd"> :return: (#batch, X, Y, 1, 3)</span>
-<span class="sd"> """</span>
- <span class="k">if</span> <span class="n">object_dimensions</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
- <span class="n">object_dimensions</span> <span class="o">=</span> <span class="p">(</span><span class="mf">1.</span><span class="p">,</span> <span class="mf">1.</span><span class="p">)</span>
-
- <span class="n">map_size</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="n">map_size</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
- <span class="n">x</span><span class="p">,</span> <span class="n">y</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">map_size</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">tf</span><span class="o">.</span><span class="n">range</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">map_size</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="n">indexing</span><span class="o">=</span><span class="s1">'ij'</span><span class="p">)</span>
- <span class="n">grid_coordinates</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">stack</span><span class="p">([</span><span class="n">x</span><span class="p">,</span> <span class="n">y</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">2</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
- <span class="n">offsets</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span> <span class="o">-</span> <span class="n">tf</span><span class="o">.</span><span class="n">math</span><span class="o">.</span><span class="n">mod</span><span class="p">(</span><span class="n">map_size</span><span class="p">,</span> <span class="mi">2</span><span class="p">))</span> <span class="o">/</span> <span class="mi">2</span>
- <span class="n">cell_mid_shift_coords</span> <span class="o">=</span> <span class="n">grid_coordinates</span> <span class="o">-</span> <span class="p">(</span><span class="n">map_size</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">/</span> <span class="mi">2</span> <span class="o">-</span> <span class="n">offsets</span>
- <span class="n">object_scaling_dr</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="n">object_dimensions</span><span class="p">)</span> <span class="o">/</span> <span class="n">map_size</span>
- <span class="n">voxel_center_coords</span> <span class="o">=</span> <span class="n">cell_mid_shift_coords</span> <span class="o">*</span> <span class="n">object_scaling_dr</span>
-
- <span class="c1"># Append zero z coordinate</span>
- <span class="n">voxel_center_coords</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">concat</span><span class="p">((</span><span class="n">voxel_center_coords</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">zeros</span><span class="p">((</span><span class="n">map_size</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">map_size</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">1</span><span class="p">))),</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
-
- <span class="c1"># Reshape to desired shape</span>
- <span class="n">voxel_center_coords</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">voxel_center_coords</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="n">map_size</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">map_size</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
- <span class="k">if</span> <span class="n">batch_size</span> <span class="o">></span> <span class="mi">1</span><span class="p">:</span>
- <span class="n">voxel_center_coords</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">voxel_center_coords</span><span class="p">,</span> <span class="p">[</span><span class="n">batch_size</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
- <span class="k">return</span> <span class="n">voxel_center_coords</span></div>
-
-
-<div class="viewcode-block" id="apply_transformations"><a class="viewcode-back" href="../../../cmrsim.auxilliary.coordinates.html#cmrsim.auxilliary.coordinates.apply_transformations">[docs]</a><span class="k">def</span> <span class="nf">apply_transformations</span><span class="p">(</span><span class="n">matrices</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">tf</span><span class="o">.</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">r_vectors</span><span class="p">:</span> <span class="n">tf</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
- <span class="sd">""" Takes a list of transformation matrix batch dimensions match the ones of r_vectors and performs the</span>
-<span class="sd"> transformation by calculating the innerproduct of the homogeneous transformation matrices and the homogeneous</span>
-<span class="sd"> extension of the input r_vectors.</span>
-
-<span class="sd"> r_new = apply_transformation([R, T, ...], r_old)</span>
-
-<span class="sd"> :param matrices: List of tensors with shape (#batch, #voxel, #repetions, #k_space_samples, 4, 4)</span>
-<span class="sd"> :param r_vectors: tensor of initial positional vectors (#batch, #voxel, #repetitions, 3, #k_space_samples)</span>
-<span class="sd"> :return:</span>
-<span class="sd"> """</span>
- <span class="n">n_batch</span><span class="p">,</span> <span class="n">n_voxels</span><span class="p">,</span> <span class="n">n_repetitions</span><span class="p">,</span> <span class="n">_</span><span class="p">,</span> <span class="n">n_samples</span> <span class="o">=</span> <span class="n">r_vectors</span><span class="o">.</span><span class="n">shape</span>
- <span class="n">homogeneous_extension</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">ones</span><span class="p">((</span><span class="n">n_batch</span><span class="p">,</span> <span class="n">n_voxels</span><span class="p">,</span> <span class="n">n_repetitions</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">n_samples</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
- <span class="n">homogeneous_coordinates</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">concat</span><span class="p">((</span><span class="n">r_vectors</span><span class="p">,</span> <span class="n">homogeneous_extension</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">3</span><span class="p">)</span>
- <span class="k">for</span> <span class="n">transformation_matrix</span> <span class="ow">in</span> <span class="n">matrices</span><span class="p">:</span>
- <span class="n">homogeneous_coordinates</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">'bvrkij, bvrjk -> bvrik'</span><span class="p">,</span> <span class="n">transformation_matrix</span><span class="p">,</span> <span class="n">homogeneous_coordinates</span><span class="p">)</span>
-
- <span class="n">transformed_coordinates</span> <span class="o">=</span> <span class="n">homogeneous_coordinates</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">0</span><span class="p">:</span><span class="mi">3</span><span class="p">,</span> <span class="p">:]</span>
- <span class="k">return</span> <span class="n">transformed_coordinates</span></div>
-
-
-<span class="k">def</span> <span class="nf">get_rotation_matrix</span><span class="p">(</span><span class="n">rotation_angles</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">Tensor</span><span class="p">],</span>
- <span class="n">batch_dimensions</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span>
- <span class="n">rotation_centers</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
- <span class="sd">""" Constructs a transformation matrix for a rotation around a given point in the x-y plane. Each index is treated</span>
-<span class="sd"> separately, so if the same rotation for mor than one element should be performed, one needs to repeat the</span>
-<span class="sd"> arrays such that the same element is place along one axis (e.g. axis 1 to rotate all isochromates by the same</span>
-<span class="sd"> angle around the rotation centers).</span>
-
-<span class="sd"> Meant to be used in apply_transformation!</span>
-
-<span class="sd"> :param rotation_angles: (#images, #isochromates, #repetitions, #samples)</span>
-<span class="sd"> :param batch_dimensions: (#images, #isochromates, #repetitions, #samples)</span>
-<span class="sd"> :param rotation_centers: (#images, #isochromates, #repetitions, #samples) If None defaults to image center</span>
-<span class="sd"> :return: translation matrix (#images, #isochromates, #repetitions, #samples, 4, 4)</span>
-<span class="sd"> """</span>
- <span class="k">if</span> <span class="n">rotation_centers</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
- <span class="n">rotation_centers</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">batch_dimensions</span> <span class="o">+</span> <span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="p">),</span> <span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
-
- <span class="n">forward_shift_matrix</span> <span class="o">=</span> <span class="n">get_translation_matrix</span><span class="p">(</span><span class="n">rotation_centers</span><span class="p">,</span> <span class="n">batch_dimensions</span><span class="p">)</span>
- <span class="n">backward_shift_matrix</span> <span class="o">=</span> <span class="n">get_translation_matrix</span><span class="p">(</span><span class="n">rotation_centers</span><span class="p">,</span> <span class="n">batch_dimensions</span><span class="p">)</span>
-
- <span class="n">rotation_matrix</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="n">batch_shape</span><span class="o">=</span><span class="n">batch_dimensions</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
- <span class="n">sin_a</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">sin</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">deg2rad</span><span class="p">(</span><span class="n">rotation_angles</span><span class="p">))</span>
- <span class="n">cos_a</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">cos</span><span class="p">(</span><span class="n">np</span><span class="o">.</span><span class="n">deg2rad</span><span class="p">(</span><span class="n">rotation_angles</span><span class="p">))</span>
- <span class="n">rotation_matrix</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">cos_a</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
- <span class="n">rotation_matrix</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="n">cos_a</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
- <span class="n">rotation_matrix</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">]</span> <span class="o">=</span> <span class="o">-</span><span class="n">sin_a</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
- <span class="n">rotation_matrix</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]</span> <span class="o">=</span> <span class="n">sin_a</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
-
- <span class="n">full_matrix</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">'bvrkli, bvrkij -> bvrklj'</span><span class="p">,</span> <span class="n">backward_shift_matrix</span><span class="p">,</span> <span class="n">rotation_matrix</span><span class="p">)</span>
- <span class="n">full_matrix</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">'bvrkli, bvrkij -> bvrklj'</span><span class="p">,</span> <span class="n">full_matrix</span><span class="p">,</span> <span class="n">forward_shift_matrix</span><span class="p">)</span>
-
- <span class="k">return</span> <span class="n">full_matrix</span>
-
-
-<div class="viewcode-block" id="get_translation_matrix"><a class="viewcode-back" href="../../../cmrsim.auxilliary.coordinates.html#cmrsim.auxilliary.coordinates.get_translation_matrix">[docs]</a><span class="k">def</span> <span class="nf">get_translation_matrix</span><span class="p">(</span><span class="n">displacement_vectors</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">batch_dimensions</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">]):</span>
- <span class="sd">""" Constructs a transformation matrix for a translation defined by the displacement vectors. Each index is treated</span>
-<span class="sd"> separately, so if the same rotation for mor than one element should be performed, one needs to repeat the</span>
-<span class="sd"> arrays such that the same element is place along one axis (e.g. axis 1 to shift all isochromates by the same</span>
-<span class="sd"> vectors).</span>
-
-<span class="sd"> Meant to be used in apply_transformation!</span>
-
-<span class="sd"> :param displacement_vectors: (#images, #isochromates, #repetitions, #samples, 3)</span>
-<span class="sd"> :param batch_dimensions: (#batch, #voxel, #repetitions, #samples) shape of the tensor of all positional vectors</span>
-<span class="sd"> :return: translation matrix (#images, #isochromates, #repetitions, #samples, 4, 4)</span>
-<span class="sd"> """</span>
- <span class="n">transformation_matrix</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">eye</span><span class="p">(</span><span class="mi">4</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="n">batch_shape</span><span class="o">=</span><span class="n">batch_dimensions</span><span class="p">)</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span>
- <span class="n">transformation_matrix</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="mi">0</span><span class="p">:</span><span class="mi">3</span><span class="p">,</span> <span class="mi">3</span><span class="p">]</span> <span class="o">=</span> <span class="n">displacement_vectors</span>
- <span class="k">return</span> <span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="n">transformation_matrix</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span></div>
-</pre></div>
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diff --git a/public/_modules/cmrsim/auxilliary/plot_utilities.html b/public/_modules/cmrsim/auxilliary/plot_utilities.html
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index 7bc2f869ddc19bc68c1bc426e732a55246ba279e..0000000000000000000000000000000000000000
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-<!DOCTYPE html>
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- <p class="caption"><span class="caption-text">Getting started</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../../../started.html">Simulate a simple image</a></li>
-</ul>
-<p class="caption"><span class="caption-text">Documentation</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../../../cmrsim.html">cmrsim package</a></li>
-</ul>
-<p class="caption"><span class="caption-text">Who is using it? - Examples</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../../../advanced_examples.html">1. Optimize b-values</a></li>
-</ul>
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- <h1>Source code for cmrsim.auxilliary.plot_utilities</h1><div class="highlight"><pre>
-<span></span><span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"SlideShow"</span><span class="p">,</span> <span class="p">]</span>
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span>
-
-<span class="kn">import</span> <span class="nn">matplotlib.pyplot</span> <span class="k">as</span> <span class="nn">plt</span>
-<span class="kn">from</span> <span class="nn">mpl_toolkits.axes_grid1</span> <span class="kn">import</span> <span class="n">make_axes_locatable</span>
-
-<span class="kn">from</span> <span class="nn">matplotlib</span> <span class="kn">import</span> <span class="n">animation</span>
-
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-
-<div class="viewcode-block" id="SlideShow"><a class="viewcode-back" href="../../../cmrsim.auxilliary.plot_utilities.html#cmrsim.auxilliary.plot_utilities.SlideShow">[docs]</a><span class="k">class</span> <span class="nc">SlideShow</span><span class="p">(</span><span class="nb">object</span><span class="p">):</span>
- <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">data</span><span class="p">:</span> <span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">axes</span><span class="p">:</span> <span class="n">List</span><span class="p">[</span><span class="n">plt</span><span class="o">.</span><span class="n">Axes</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">figure</span><span class="p">:</span> <span class="n">plt</span><span class="o">.</span><span class="n">Figure</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
- <span class="sd">"""</span>
-
-<span class="sd"> :param data: (subplots, X, Y, #slices)</span>
-<span class="sd"> :param axes: Optional Iterable[plt.Axes]</span>
-<span class="sd"> :param figure: Optional plt.figure that contains axes</span>
-<span class="sd"> :param kwargs: - titles: Iterable[str]</span>
-<span class="sd"> - colorbars: bool = True</span>
-<span class="sd"> - register_event: bool = True</span>
-<span class="sd"> - imshow_kwargs: dict - keyword arguments that are passed through to the imshow call</span>
-<span class="sd"> - x_labels: Iterable[Iterable[str]]</span>
-<span class="sd"> """</span>
- <span class="c1"># Set up subplots</span>
- <span class="n">n_axes</span><span class="p">,</span> <span class="n">rows</span><span class="p">,</span> <span class="n">cols</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">slices</span> <span class="o">=</span> <span class="n">data</span><span class="o">.</span><span class="n">shape</span>
- <span class="k">if</span> <span class="n">axes</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">fig</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">axes</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">subplots</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">n_axes</span><span class="p">)</span>
- <span class="k">if</span> <span class="n">n_axes</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
- <span class="c1"># Allow single axis plot by making self.axes iterable in that case</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">axes</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">axes</span><span class="p">]</span>
- <span class="k">else</span><span class="p">:</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">axes</span> <span class="o">=</span> <span class="n">axes</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">fig</span> <span class="o">=</span> <span class="n">figure</span>
-
- <span class="bp">self</span><span class="o">.</span><span class="n">_ax_titles</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"titles"</span><span class="p">,</span> <span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">axes</span><span class="p">)</span> <span class="o">*</span> <span class="p">[</span><span class="s2">" "</span><span class="p">,</span> <span class="p">])</span>
- <span class="k">for</span> <span class="n">ax</span><span class="p">,</span> <span class="n">title</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">axes</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_ax_titles</span><span class="p">):</span>
- <span class="n">ax</span><span class="o">.</span><span class="n">set_title</span><span class="p">(</span><span class="n">title</span><span class="p">)</span>
- <span class="n">ax</span><span class="o">.</span><span class="n">set_xticks</span><span class="p">([])</span>
- <span class="n">ax</span><span class="o">.</span><span class="n">set_yticks</span><span class="p">([])</span>
-
- <span class="c1"># Register event</span>
- <span class="k">if</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'register_event'</span><span class="p">,</span> <span class="kc">True</span><span class="p">):</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">fig</span><span class="o">.</span><span class="n">canvas</span><span class="o">.</span><span class="n">mpl_connect</span><span class="p">(</span><span class="s1">'button_press_event'</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">onclick</span><span class="p">)</span>
-
- <span class="c1"># Store data and plot</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">data</span> <span class="o">=</span> <span class="n">data</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">ind</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">slices</span> <span class="o">-</span> <span class="mi">1</span>
-
- <span class="n">imshow_kwargs</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'imshow_kwargs'</span><span class="p">,</span> <span class="p">{})</span>
- <span class="k">if</span> <span class="n">imshow_kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'vmax'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
- <span class="n">max_val_per_channel</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">max</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">))</span>
- <span class="k">else</span><span class="p">:</span>
- <span class="n">max_val_per_channel</span> <span class="o">=</span> <span class="p">[</span><span class="n">imshow_kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'vmax'</span><span class="p">)</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">n_axes</span><span class="p">)]</span>
- <span class="n">imshow_kwargs</span><span class="o">.</span><span class="n">pop</span><span class="p">(</span><span class="s1">'vmax'</span><span class="p">)</span>
-
- <span class="bp">self</span><span class="o">.</span><span class="n">ims</span> <span class="o">=</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">axes</span><span class="p">[</span><span class="n">i</span><span class="p">]</span><span class="o">.</span><span class="n">imshow</span><span class="p">(</span><span class="n">x</span><span class="p">[:,</span> <span class="p">:,</span> <span class="bp">self</span><span class="o">.</span><span class="n">ind</span><span class="p">],</span> <span class="n">vmax</span><span class="o">=</span><span class="n">max_val_per_channel</span><span class="p">[</span><span class="n">i</span><span class="p">],</span> <span class="o">**</span><span class="n">imshow_kwargs</span><span class="p">)</span>
- <span class="k">for</span> <span class="n">i</span><span class="p">,</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">enumerate</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">)]</span>
-
- <span class="n">default_labels</span> <span class="o">=</span> <span class="p">[</span><span class="n">n_axes</span> <span class="o">*</span> <span class="p">[</span><span class="sa">f</span><span class="s1">'slice </span><span class="si">{</span><span class="n">i</span><span class="si">}</span><span class="s1">'</span><span class="p">,</span> <span class="p">]</span> <span class="k">for</span> <span class="n">i</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">slices</span><span class="p">)]</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">_xlabels</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'x_labels'</span><span class="p">,</span> <span class="n">default_labels</span><span class="p">)</span>
-
- <span class="c1"># Configure color-bars</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">_plot_cbars</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"colorbars"</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span>
- <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_plot_cbars</span><span class="p">:</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">_cbars</span> <span class="o">=</span> <span class="p">[]</span>
- <span class="k">for</span> <span class="n">ax</span><span class="p">,</span> <span class="n">im</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">axes</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">ims</span><span class="p">):</span>
- <span class="n">divider</span> <span class="o">=</span> <span class="n">make_axes_locatable</span><span class="p">(</span><span class="n">ax</span><span class="p">)</span>
- <span class="n">cax</span> <span class="o">=</span> <span class="n">divider</span><span class="o">.</span><span class="n">append_axes</span><span class="p">(</span><span class="s1">'right'</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="s1">'5%'</span><span class="p">,</span> <span class="n">pad</span><span class="o">=</span><span class="mf">0.05</span><span class="p">)</span>
- <span class="n">cbar</span> <span class="o">=</span> <span class="n">plt</span><span class="o">.</span><span class="n">colorbar</span><span class="p">(</span><span class="n">im</span><span class="p">,</span> <span class="n">cax</span><span class="o">=</span><span class="n">cax</span><span class="p">)</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">_cbars</span> <span class="o">+=</span> <span class="p">[</span><span class="n">cbar</span><span class="p">,</span> <span class="p">]</span>
-
- <span class="bp">self</span><span class="o">.</span><span class="n">update</span><span class="p">()</span>
-
-<div class="viewcode-block" id="SlideShow.onclick"><a class="viewcode-back" href="../../../cmrsim.auxilliary.plot_utilities.html#cmrsim.auxilliary.plot_utilities.SlideShow.onclick">[docs]</a> <span class="k">def</span> <span class="nf">onclick</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">event</span><span class="p">):</span>
- <span class="k">if</span> <span class="nb">str</span><span class="p">(</span><span class="n">event</span><span class="o">.</span><span class="n">button</span><span class="p">)</span> <span class="o">==</span> <span class="s1">'MouseButton.LEFT'</span><span class="p">:</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">ind</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ind</span> <span class="o">+</span> <span class="mi">1</span><span class="p">)</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">slices</span>
- <span class="k">elif</span> <span class="nb">str</span><span class="p">(</span><span class="n">event</span><span class="o">.</span><span class="n">button</span><span class="p">)</span> <span class="o">==</span> <span class="s1">'MouseButton.RIGHT'</span><span class="p">:</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">ind</span> <span class="o">=</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">ind</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)</span> <span class="o">%</span> <span class="bp">self</span><span class="o">.</span><span class="n">slices</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">update</span><span class="p">()</span></div>
-
-<div class="viewcode-block" id="SlideShow.update"><a class="viewcode-back" href="../../../cmrsim.auxilliary.plot_utilities.html#cmrsim.auxilliary.plot_utilities.SlideShow.update">[docs]</a> <span class="k">def</span> <span class="nf">update</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
- <span class="k">for</span> <span class="n">idx</span><span class="p">,</span> <span class="n">ax</span><span class="p">,</span> <span class="n">im</span><span class="p">,</span> <span class="n">x</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="nb">range</span><span class="p">(</span><span class="nb">len</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">axes</span><span class="p">)),</span> <span class="bp">self</span><span class="o">.</span><span class="n">axes</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">ims</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">data</span><span class="p">):</span>
- <span class="n">im</span><span class="o">.</span><span class="n">set_data</span><span class="p">(</span><span class="n">x</span><span class="p">[:,</span> <span class="p">:,</span> <span class="bp">self</span><span class="o">.</span><span class="n">ind</span><span class="p">])</span>
- <span class="n">ax</span><span class="o">.</span><span class="n">set_xlabel</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_xlabels</span><span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">ind</span><span class="p">][</span><span class="n">idx</span><span class="p">])</span>
- <span class="k">for</span> <span class="n">im</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">ims</span><span class="p">:</span>
- <span class="n">im</span><span class="o">.</span><span class="n">axes</span><span class="o">.</span><span class="n">figure</span><span class="o">.</span><span class="n">canvas</span><span class="o">.</span><span class="n">draw</span><span class="p">()</span></div>
-
-<div class="viewcode-block" id="SlideShow.animate"><a class="viewcode-back" href="../../../cmrsim.auxilliary.plot_utilities.html#cmrsim.auxilliary.plot_utilities.SlideShow.animate">[docs]</a> <span class="k">def</span> <span class="nf">animate</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">save_directory</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
- <span class="k">def</span> <span class="nf">_init</span><span class="p">():</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">ind</span> <span class="o">=</span> <span class="mi">0</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">update</span><span class="p">()</span>
-
- <span class="k">def</span> <span class="nf">_update</span><span class="p">(</span><span class="n">i</span><span class="p">):</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">ind</span> <span class="o">=</span> <span class="n">i</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">update</span><span class="p">()</span>
-
- <span class="n">ani</span> <span class="o">=</span> <span class="n">animation</span><span class="o">.</span><span class="n">FuncAnimation</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">fig</span><span class="p">,</span> <span class="n">_update</span><span class="p">,</span> <span class="n">frames</span><span class="o">=</span><span class="bp">self</span><span class="o">.</span><span class="n">slices</span><span class="p">,</span> <span class="n">init_func</span><span class="o">=</span><span class="n">_init</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
- <span class="n">ani</span><span class="o">.</span><span class="n">save</span><span class="p">(</span><span class="n">save_directory</span><span class="p">,</span> <span class="n">writer</span><span class="o">=</span><span class="s1">'imagemagick'</span><span class="p">)</span></div></div>
-
-
-<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s1">'__main__'</span><span class="p">:</span>
- <span class="n">data</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="mf">1.</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="mi">100</span><span class="p">,</span> <span class="mi">120</span><span class="p">,</span> <span class="mi">20</span><span class="p">))</span>
- <span class="nb">print</span><span class="p">(</span><span class="n">data</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
- <span class="n">sl</span> <span class="o">=</span> <span class="n">SlideShow</span><span class="p">(</span><span class="n">data</span><span class="p">)</span>
- <span class="n">sl</span><span class="o">.</span><span class="n">animate</span><span class="p">(</span><span class="s1">'../../animation.gif'</span><span class="p">)</span>
-</pre></div>
-
- </div>
-
- </div>
- <footer>
-
-
- <hr/>
-
- <div role="contentinfo">
- <p>
-
- © Copyright 2020, Jonathan Weine
-
- </p>
- </div>
-
-
-
- Built with <a href="http://sphinx-doc.org/">Sphinx</a> using a
-
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- <h1>Source code for cmrsim.auxilliary.snr</h1><div class="highlight"><pre>
-<span></span><span class="n">__all__</span> <span class="o">=</span> <span class="p">[]</span>
-
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Union</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">Iterable</span>
-<span class="kn">import</span> <span class="nn">warnings</span>
-
-<span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
-<span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-
-<div class="viewcode-block" id="calculate_snr"><a class="viewcode-back" href="../../../cmrsim.auxilliary.snr.html#cmrsim.auxilliary.snr.calculate_snr">[docs]</a><span class="k">def</span> <span class="nf">calculate_snr</span><span class="p">(</span><span class="n">single_coil_images</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">Tensor</span><span class="p">],</span>
- <span class="n">dynamic_noise_scan</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">Tensor</span><span class="p">],</span>
- <span class="n">coil_sensitivities</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
- <span class="sd">""" Calculates the SNR for a multicoil-acquisition according to doi:10.1002/mrm.21868. To guarantee correct scaling</span>
-<span class="sd"> this assumes, non-zero-filled reconstruction.</span>
-
-<span class="sd"> Previously implemented by Robbert van Gorkum in the MRXCAT project.</span>
-
-<span class="sd"> :param single_coil_images: (#coils, X, Y, [Z]) of type complex64</span>
-<span class="sd"> :param dynamic_noise_scan: (#coils, X, Y, [Z]) of type complex64</span>
-<span class="sd"> :param coil_sensitivities: Optional: (#coils, X, Y, [Z]) of type complex64 defaults to ones(1, X, Y, [Z])</span>
-<span class="sd"> :return: snr_map (X, Y, Z) of type float32</span>
-<span class="sd"> """</span>
- <span class="k">if</span> <span class="n">coil_sensitivities</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
- <span class="n">coil_sensitivities</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">single_coil_images</span><span class="p">)</span>
-
- <span class="n">denominator</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reduce_sum</span><span class="p">(</span><span class="n">coil_sensitivities</span> <span class="o">*</span> <span class="n">tf</span><span class="o">.</span><span class="n">math</span><span class="o">.</span><span class="n">conj</span><span class="p">(</span><span class="n">coil_sensitivities</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
-
- <span class="n">numerator</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reduce_sum</span><span class="p">(</span><span class="n">single_coil_images</span> <span class="o">*</span> <span class="n">tf</span><span class="o">.</span><span class="n">math</span><span class="o">.</span><span class="n">conj</span><span class="p">(</span><span class="n">coil_sensitivities</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
- <span class="n">roemer_noisy_image</span> <span class="o">=</span> <span class="n">numerator</span> <span class="o">/</span> <span class="n">denominator</span>
-
- <span class="n">numerator_noise</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reduce_sum</span><span class="p">(</span><span class="n">dynamic_noise_scan</span> <span class="o">*</span> <span class="n">tf</span><span class="o">.</span><span class="n">math</span><span class="o">.</span><span class="n">conj</span><span class="p">(</span><span class="n">coil_sensitivities</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
- <span class="n">roemer_noise</span> <span class="o">=</span> <span class="n">numerator_noise</span> <span class="o">/</span> <span class="n">denominator</span>
-
- <span class="n">snr_map</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">roemer_noisy_image</span><span class="p">)</span> <span class="o">/</span> <span class="n">tf</span><span class="o">.</span><span class="n">math</span><span class="o">.</span><span class="n">reduce_std</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">math</span><span class="o">.</span><span class="n">real</span><span class="p">(</span><span class="n">roemer_noise</span><span class="p">))</span>
- <span class="k">return</span> <span class="n">snr_map</span></div>
-
-
-<div class="viewcode-block" id="compute_noise_std"><a class="viewcode-back" href="../../../cmrsim.auxilliary.snr.html#cmrsim.auxilliary.snr.compute_noise_std">[docs]</a><span class="k">def</span> <span class="nf">compute_noise_std</span><span class="p">(</span><span class="n">noiseless_single_coil_images</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">target_snr</span><span class="p">:</span> <span class="n">Iterable</span><span class="p">[</span><span class="nb">float</span><span class="p">],</span>
- <span class="n">coil_sensitivities</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
- <span class="n">mask</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
- <span class="sd">"""</span>
-
-<span class="sd"> :param noiseless_single_coil_images: (n_coils, X, Y, [Z]) of type complex64</span>
-<span class="sd"> :param target_snr:</span>
-<span class="sd"> :param coil_sensitivities: (n_coils, X, Y, [Z]) of type complex64, Optional: defaults to ones(1, X, Y, [Z])</span>
-<span class="sd"> Is used for iterative refinement.</span>
-<span class="sd"> :param mask: (X, Y, [Z]) Optional: binary mask to specify ROI for SNR computation. Defaults to entire image.</span>
-<span class="sd"> :param kwargs: - iteratively_refine: (bool) default=True. If True and coil_sensitivities are given, uses iterative</span>
-<span class="sd"> method to refine std estimation.</span>
-<span class="sd"> :return: estimated_stds np.array of type np.float32 with shape like np.array(target_snr)</span>
-<span class="sd"> """</span>
- <span class="k">if</span> <span class="n">mask</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
- <span class="n">mask</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">noiseless_single_coil_images</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">:],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">np</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
-
- <span class="n">n_coils</span> <span class="o">=</span> <span class="n">noiseless_single_coil_images</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-
- <span class="n">flat_image</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">noiseless_single_coil_images</span><span class="p">,</span> <span class="p">[</span><span class="n">n_coils</span><span class="p">,</span> <span class="o">-</span><span class="mi">1</span><span class="p">])</span>
- <span class="n">flat_mask</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">mask</span><span class="p">,</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">]),</span> <span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
- <span class="n">number_of_pixels_in_mask</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reduce_sum</span><span class="p">(</span><span class="n">mask</span><span class="p">)</span>
-
- <span class="n">roi_summed_signal_per_coil</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">'ci, i -> c'</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">flat_image</span><span class="p">),</span> <span class="n">flat_mask</span><span class="p">)</span>
- <span class="n">roi_mean_signal_per_coil</span> <span class="o">=</span> <span class="n">roi_summed_signal_per_coil</span> <span class="o">/</span> <span class="n">tf</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">number_of_pixels_in_mask</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
- <span class="n">sum_of_squares_mean</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">reduce_sum</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">roi_mean_signal_per_coil</span><span class="p">)</span> <span class="o">**</span> <span class="mi">2</span><span class="p">))</span>
- <span class="n">estimated_std</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">sum_of_squares_mean</span> <span class="o">/</span> <span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="n">target_snr</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">))</span>
-
- <span class="k">if</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'iteratively_refine'</span><span class="p">,</span> <span class="kc">True</span><span class="p">)</span> <span class="ow">and</span> <span class="n">coil_sensitivities</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
- <span class="k">assert</span> <span class="nb">all</span><span class="p">([</span><span class="n">s1</span> <span class="o">==</span> <span class="n">s2</span> <span class="k">for</span> <span class="n">s1</span><span class="p">,</span> <span class="n">s2</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">noiseless_single_coil_images</span><span class="o">.</span><span class="n">shape</span><span class="p">,</span> <span class="n">coil_sensitivities</span><span class="o">.</span><span class="n">shape</span><span class="p">)])</span>
- <span class="n">refined_stds</span> <span class="o">=</span> <span class="p">[]</span>
- <span class="k">for</span> <span class="n">target</span><span class="p">,</span> <span class="n">initial_std</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">target_snr</span><span class="p">,</span> <span class="n">estimated_std</span><span class="o">.</span><span class="n">flatten</span><span class="p">()):</span>
- <span class="n">refined_stds</span> <span class="o">+=</span> <span class="p">[</span><span class="n">_iteratively_refine_std_calculation</span><span class="p">(</span><span class="n">noiseless_single_coil_images</span><span class="p">,</span> <span class="n">coil_sensitivities</span><span class="p">,</span>
- <span class="n">target</span><span class="p">,</span> <span class="n">initial_std</span><span class="p">,</span> <span class="n">mask</span><span class="p">),</span> <span class="p">]</span>
- <span class="n">estimated_std</span> <span class="o">=</span> <span class="n">np</span><span class="o">.</span><span class="n">array</span><span class="p">(</span><span class="n">refined_stds</span><span class="p">)</span>
-
- <span class="k">return</span> <span class="n">estimated_std</span></div>
-
-
-<span class="k">def</span> <span class="nf">_iteratively_refine_std_calculation</span><span class="p">(</span><span class="n">noise_less_image</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">Tensor</span><span class="p">],</span>
- <span class="n">coil_sensitivities</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">Tensor</span><span class="p">],</span>
- <span class="n">target_snr</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span> <span class="n">initial_std</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="mf">1.</span><span class="p">,</span>
- <span class="n">mask</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="n">np</span><span class="o">.</span><span class="n">ndarray</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]]</span> <span class="o">=</span> <span class="kc">None</span>
- <span class="p">)</span> <span class="o">-></span> <span class="nb">float</span><span class="p">:</span>
- <span class="sd">"""Iteratively adapts the noise standard deviation while checking the actual Roemer reconstructed SNR for multi</span>
-<span class="sd"> coil data.</span>
-
-<span class="sd"> This is based on the implementation of Robbert van Gorkum in the MRXCAT DIFF package:</span>
-<span class="sd"> https://git.ee.ethz.ch/kozerkes/mrXCAT/-/blob/MRXCAT_2.0/@MRXCAT_CMR_DIFF/MRXCAT_CMR_DIFF.m</span>
-
-<span class="sd"> :param noise_less_image: (n_coils, X, Y, [Z])</span>
-<span class="sd"> :param coil_sensitivities: (n_coils, X, Y, [Z])</span>
-<span class="sd"> :param target_snr: float</span>
-<span class="sd"> :param initial_std: float</span>
-<span class="sd"> :param mask: (X, Y, [Z])</span>
-<span class="sd"> :return: refined_snr: float</span>
-<span class="sd"> """</span>
- <span class="k">if</span> <span class="n">mask</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
- <span class="n">mask</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">ones</span><span class="p">(</span><span class="n">noise_less_image</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">:],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
-
- <span class="c1"># Set up loop variables</span>
- <span class="n">max_iterations</span> <span class="o">=</span> <span class="mi">100</span>
- <span class="n">current_noise_std</span> <span class="o">=</span> <span class="n">initial_std</span>
-
- <span class="n">number_of_pixels_in_mask</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reduce_sum</span><span class="p">(</span><span class="n">mask</span><span class="p">)</span>
- <span class="n">flat_mask</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">mask</span><span class="p">,</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
-
- <span class="c1"># Loop and increase/decrease std-estimation as long as target_snr is not met within the tolerance of 0.5 or the</span>
- <span class="c1"># maximum number of iterations is reached</span>
- <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="n">max_iterations</span><span class="p">):</span>
- <span class="n">noise_vectors</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">2</span><span class="p">,</span> <span class="p">)</span> <span class="o">+</span> <span class="nb">tuple</span><span class="p">(</span><span class="n">noise_less_image</span><span class="o">.</span><span class="n">shape</span><span class="p">))</span> <span class="o">*</span> <span class="n">current_noise_std</span>
- <span class="n">complex_noise_map</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">complex</span><span class="p">(</span><span class="n">noise_vectors</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">noise_vectors</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
- <span class="n">noisy_images</span> <span class="o">=</span> <span class="n">noise_less_image</span> <span class="o">+</span> <span class="n">complex_noise_map</span>
- <span class="n">snr_map</span> <span class="o">=</span> <span class="n">calculate_snr</span><span class="p">(</span><span class="n">single_coil_images</span><span class="o">=</span><span class="n">noisy_images</span><span class="p">,</span>
- <span class="n">dynamic_noise_scan</span><span class="o">=</span><span class="n">complex_noise_map</span><span class="p">,</span>
- <span class="n">coil_sensitivities</span><span class="o">=</span><span class="n">coil_sensitivities</span><span class="p">)</span>
-
- <span class="c1"># Calculate SNR in ROI only (defined by mask argument)</span>
- <span class="n">flat_snr_map</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">snr_map</span><span class="p">,</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span>
- <span class="n">mean_snr_in_roi</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reduce_sum</span><span class="p">(</span><span class="n">flat_snr_map</span> <span class="o">*</span> <span class="n">flat_mask</span><span class="p">)</span> <span class="o">/</span> <span class="n">number_of_pixels_in_mask</span>
- <span class="n">snr_diff_to_target</span> <span class="o">=</span> <span class="n">mean_snr_in_roi</span> <span class="o">-</span> <span class="n">target_snr</span>
-
- <span class="k">if</span> <span class="n">tf</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">snr_diff_to_target</span><span class="p">)</span> <span class="o"><</span> <span class="mf">0.4</span><span class="p">:</span>
- <span class="k">return</span> <span class="n">current_noise_std</span>
- <span class="k">else</span><span class="p">:</span>
- <span class="k">if</span> <span class="n">snr_diff_to_target</span> <span class="o"><</span> <span class="mi">0</span><span class="p">:</span>
- <span class="n">current_noise_std</span> <span class="o">*=</span> <span class="mf">0.99</span>
- <span class="k">else</span><span class="p">:</span>
- <span class="n">current_noise_std</span> <span class="o">*=</span> <span class="mf">1.01</span>
-
- <span class="c1"># This is only reached if tolerance boundary is not reached within 100 iterations.</span>
- <span class="n">warnings</span><span class="o">.</span><span class="n">warn</span><span class="p">(</span><span class="s2">"While iteratively refining the noise standard deviation, the target snr was not met within defined "</span>
- <span class="s2">"tolerance boundary (+- 0.5). Function returns value of last iteration"</span><span class="p">)</span>
- <span class="k">return</span> <span class="n">current_noise_std</span>
-</pre></div>
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- <h1>Source code for cmrsim.encoding.base</h1><div class="highlight"><pre>
-<span></span><span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"BaseSampling"</span><span class="p">,</span> <span class="p">]</span>
-
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Union</span><span class="p">,</span> <span class="n">Iterable</span>
-<span class="kn">import</span> <span class="nn">abc</span>
-<span class="kn">import</span> <span class="nn">math</span>
-
-<span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
-
-
-<div class="viewcode-block" id="BaseSampling"><a class="viewcode-back" href="../../../cmrsim.encoding.base.html#cmrsim.encoding.base.BaseSampling">[docs]</a><span class="k">class</span> <span class="nc">BaseSampling</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
- <span class="sd">""" Base Module for implementing a time-dependent sampling in k-space. Is meant to be inherited from when specifying</span>
-<span class="sd"> standard trajectories. """</span>
- <span class="c1">#: Flat and unsegmented tensor of all sampling-event times. Is set by a call of</span>
- <span class="c1"># abstract function self._calculate_trajectory. Expected shape: (-1, )</span>
- <span class="n">sampling_times</span><span class="p">:</span> <span class="n">tf</span><span class="o">.</span><span class="n">Tensor</span>
- <span class="c1">#: Flat and unsegmented tensor of 3D k-space vectors corresponding to sampling-events. Is set by a call of</span>
- <span class="c1"># abstract function self._calculate_trajectory. Expected shape: (-1, 3)</span>
- <span class="n">k_space_vectors</span><span class="p">:</span> <span class="n">tf</span><span class="o">.</span><span class="n">Tensor</span>
- <span class="n">absolute_noise_std</span><span class="p">:</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span>
- <span class="n">k_space_segments</span><span class="p">:</span> <span class="n">tf</span><span class="o">.</span><span class="n">Tensor</span>
- <span class="n">number_of_samples</span><span class="p">:</span> <span class="n">tf</span><span class="o">.</span><span class="n">Tensor</span>
- <span class="n">device</span><span class="p">:</span> <span class="nb">str</span>
-
- <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">absolute_noise_std</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="n">Iterable</span><span class="p">[</span><span class="nb">float</span><span class="p">]],</span> <span class="n">name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
- <span class="sd">"""</span>
-<span class="sd"> :param absolute_noise_std: if < 0, add_noise() will leave signal unchanged</span>
-<span class="sd"> :param name: (str) defining the module name-scope</span>
-<span class="sd"> :param kwargs: - k_space_segments: int</span>
-<span class="sd"> - device</span>
-<span class="sd"> """</span>
- <span class="n">device</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'device'</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
- <span class="k">if</span> <span class="n">device</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
- <span class="k">if</span> <span class="n">tf</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">get_visible_devices</span><span class="p">(</span><span class="s1">'GPU'</span><span class="p">):</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="s1">'GPU:0'</span>
- <span class="k">else</span><span class="p">:</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="s1">'CPU:0'</span>
- <span class="k">else</span><span class="p">:</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="n">device</span>
-
- <span class="nb">super</span><span class="p">(</span><span class="n">BaseSampling</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">)</span>
- <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">absolute_noise_std</span><span class="p">,</span> <span class="nb">float</span><span class="p">):</span>
- <span class="n">absolute_noise_std</span> <span class="o">=</span> <span class="p">[</span><span class="n">absolute_noise_std</span><span class="p">,</span> <span class="p">]</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">absolute_noise_std</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="n">absolute_noise_std</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">,</span> <span class="n">trainable</span><span class="o">=</span><span class="kc">False</span><span class="p">,</span>
- <span class="n">shape</span><span class="o">=</span><span class="p">[</span><span class="kc">None</span><span class="p">,</span> <span class="p">])</span>
-
- <span class="c1"># Call method defining sampling trajectory</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">k_space_vectors</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">sampling_times</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_calculate_trajectory</span><span class="p">()</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">number_of_samples</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sampling_times</span><span class="p">)</span>
-
- <span class="c1"># Define segmented k-space trajectory for lower GPU memory requirements</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">k_space_segments</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'k_space_segments'</span><span class="p">,</span> <span class="mi">1</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
- <span class="n">segment_size</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">math</span><span class="o">.</span><span class="n">ceil</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">number_of_samples</span> <span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">k_space_segments</span><span class="p">),</span> <span class="n">tf</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
- <span class="n">total_element_count</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">k_space_segments</span> <span class="o">*</span> <span class="n">segment_size</span>
- <span class="n">difference</span> <span class="o">=</span> <span class="n">total_element_count</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">number_of_samples</span>
- <span class="n">row_lengths</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">([</span><span class="nb">int</span><span class="p">(</span><span class="n">segment_size</span><span class="p">)</span> <span class="k">for</span> <span class="n">_</span> <span class="ow">in</span> <span class="n">tf</span><span class="o">.</span><span class="n">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">k_space_segments</span> <span class="o">-</span> <span class="mi">1</span><span class="p">)]</span> <span class="o">+</span>
- <span class="p">[</span><span class="nb">int</span><span class="p">(</span><span class="n">segment_size</span> <span class="o">-</span> <span class="n">difference</span><span class="p">),</span> <span class="p">],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
-
- <span class="bp">self</span><span class="o">.</span><span class="n">_segmented_sampling_times</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">RaggedTensor</span><span class="o">.</span><span class="n">from_row_lengths</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sampling_times</span><span class="p">,</span> <span class="n">row_lengths</span><span class="p">)</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">_segmented_k_vectors</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">RaggedTensor</span><span class="o">.</span><span class="n">from_row_lengths</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">k_space_vectors</span><span class="p">,</span> <span class="n">row_lengths</span><span class="p">)</span>
-
- <span class="nd">@tf</span><span class="o">.</span><span class="n">Module</span><span class="o">.</span><span class="n">with_name_scope</span>
- <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">transverse_magnetization</span><span class="p">:</span> <span class="n">tf</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">r_vectors</span><span class="p">:</span> <span class="n">tf</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
- <span class="sd">"""Calculates fourier phases for given object-representation at r-vectors. For multiple different contrasts</span>
-<span class="sd"> #repetitions is the representing axis.</span>
-
-<span class="sd"> Motion during encoding is captured in the r_vectors input axis #k-space-samples. If this axis has size=1, this</span>
-<span class="sd"> position is reused for all sampling ADC events. If #repetitions = 1 in r_vectors, the same trajectory/constant</span>
-<span class="sd"> position is reused for all contrasts.</span>
-
-<span class="sd"> :param transverse_magnetization: (#batch, #voxel, #repetitions, #k-space-samples) of type tf.complex</span>
-<span class="sd"> #repetitions, #k-space-samples can be 1</span>
-<span class="sd"> :param r_vectors: (#batch, #voxel, #repetitions, 3, #k-space-samples), axis #repetitions and #k-space-samples</span>
-<span class="sd"> can be 1 to broadcast for coordinate reuse.</span>
-<span class="sd"> :param kwargs: - noise_seed: if not None, sets seed for sampling the noise vector.</span>
-<span class="sd"> - segment_index: int</span>
-<span class="sd"> :return: tf.Tensor</span>
-<span class="sd"> """</span>
- <span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">):</span>
- <span class="n">segment_index</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'segment_index'</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
- <span class="n">n_repetitions</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="n">transverse_magnetization</span><span class="p">)[</span><span class="mi">2</span><span class="p">]</span>
- <span class="n">fourier_factors</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_calculate_fourier_phases</span><span class="p">(</span><span class="n">r_vectors</span><span class="o">=</span><span class="n">r_vectors</span><span class="p">,</span> <span class="n">segment_index</span><span class="o">=</span><span class="n">segment_index</span><span class="p">)</span>
-
- <span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">name_scope</span><span class="p">(</span><span class="s1">'shape_handling'</span><span class="p">):</span>
- <span class="k">if</span> <span class="n">tf</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="n">fourier_factors</span><span class="p">)[</span><span class="mi">2</span><span class="p">]</span> <span class="o"><</span> <span class="n">n_repetitions</span><span class="p">:</span>
- <span class="n">multiples</span> <span class="o">=</span> <span class="n">n_repetitions</span> <span class="o">//</span> <span class="n">tf</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="n">fourier_factors</span><span class="p">)[</span><span class="mi">2</span><span class="p">]</span>
- <span class="n">fourier_factors</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">fourier_factors</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">multiples</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
-
- <span class="k">if</span> <span class="n">tf</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="n">transverse_magnetization</span><span class="p">)[</span><span class="mi">3</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
- <span class="n">n_samples</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_segmented_sampling_times</span><span class="p">[</span><span class="n">segment_index</span><span class="p">])</span>
- <span class="n">transverse_magnetization</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">transverse_magnetization</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">n_samples</span><span class="p">])</span>
-
- <span class="n">fourier_transform_batch</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">'bvrk, bvrk -> brk'</span><span class="p">,</span> <span class="n">transverse_magnetization</span><span class="p">,</span> <span class="n">fourier_factors</span><span class="p">)</span>
- <span class="k">return</span> <span class="n">fourier_transform_batch</span>
-
- <span class="k">def</span> <span class="nf">_calculate_fourier_phases</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">r_vectors</span><span class="p">:</span> <span class="n">tf</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">segment_index</span><span class="p">:</span> <span class="n">tf</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
- <span class="sd">""" Calculates the phase factor for encoding a static (during read-out) object for the k-space-trajectory</span>
-<span class="sd"> defined by self.k_space_vectors</span>
-
-<span class="sd"> .. math::</span>
-<span class="sd"> f = e^{2 \pi j\ k_r(t) \cdot r(t) }</span>
-
-<span class="sd"> :param r_vectors:</span>
-<span class="sd"> :param segment_index:</span>
-<span class="sd"> :return:</span>
-<span class="sd"> """</span>
- <span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">name_scope</span><span class="p">(</span><span class="s1">'calculate_fourier_phase'</span><span class="p">):</span>
- <span class="k">if</span> <span class="n">tf</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="n">r_vectors</span><span class="p">)[</span><span class="mi">4</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
- <span class="n">r_vectors</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">r_vectors</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_segmented_sampling_times</span><span class="p">[</span><span class="n">segment_index</span><span class="p">])])</span>
-
- <span class="n">fourier_phases</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">'bvrjk, kj -> bvrk'</span><span class="p">,</span> <span class="n">r_vectors</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_segmented_k_vectors</span><span class="p">[</span><span class="n">segment_index</span><span class="p">])</span>
- <span class="n">fourier_phases</span> <span class="o">=</span> <span class="mi">1</span><span class="n">j</span> <span class="o">*</span> <span class="n">tf</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">scalar_mul</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="mf">2.</span> <span class="o">*</span> <span class="n">math</span><span class="o">.</span><span class="n">pi</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">),</span> <span class="n">fourier_phases</span><span class="p">),</span> <span class="n">tf</span><span class="o">.</span><span class="n">complex64</span><span class="p">)</span>
- <span class="n">fourier_factors</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="n">fourier_phases</span><span class="p">)</span>
- <span class="k">return</span> <span class="n">fourier_factors</span>
-
-<div class="viewcode-block" id="BaseSampling.add_noise"><a class="viewcode-back" href="../../../cmrsim.encoding.base.html#cmrsim.encoding.base.BaseSampling.add_noise">[docs]</a> <span class="k">def</span> <span class="nf">add_noise</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">s_of_k</span><span class="p">:</span> <span class="n">tf</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
- <span class="sd">""" Adds noise to k-space-samples, expands the number of axis by one and appends the different noise</span>
-<span class="sd"> instantiations as second last axis.</span>
-
-<span class="sd"> :param s_of_k: Tensor containing all encoded k-space samples</span>
-<span class="sd"> :return: tf.Tensor</span>
-<span class="sd"> """</span>
- <span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">name_scope</span><span class="p">(</span><span class="s1">'add_noise'</span><span class="p">):</span>
- <span class="n">old_shape</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="n">s_of_k</span><span class="p">)</span>
- <span class="n">s_of_k</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">expand_dims</span><span class="p">(</span><span class="n">s_of_k</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
- <span class="k">if</span> <span class="n">tf</span><span class="o">.</span><span class="n">reduce_any</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">math</span><span class="o">.</span><span class="n">greater</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">absolute_noise_std</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="mf">0.</span><span class="p">))):</span>
- <span class="n">seed</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s2">"seed"</span><span class="p">,</span> <span class="kc">None</span><span class="p">)</span>
-
- <span class="c1"># Set values less that 0 to exactly zero to scales sampled noise vector correctly</span>
- <span class="n">clean_noise_std</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">where</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">math</span><span class="o">.</span><span class="n">greater</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">absolute_noise_std</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="mf">0.</span><span class="p">)),</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">absolute_noise_std</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">absolute_noise_std</span><span class="p">))</span>
- <span class="n">noise_vector_shape</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">concat</span><span class="p">(((</span><span class="mi">2</span><span class="p">,</span> <span class="p">),</span> <span class="n">old_shape</span><span class="p">,</span> <span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">absolute_noise_std</span><span class="p">),)),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
- <span class="n">noise_vector</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">(</span><span class="n">noise_vector_shape</span><span class="p">,</span> <span class="n">mean</span><span class="o">=</span><span class="mf">0.</span><span class="p">,</span> <span class="n">stddev</span><span class="o">=</span><span class="mf">1.</span><span class="p">,</span> <span class="n">seed</span><span class="o">=</span><span class="n">seed</span><span class="p">)</span>
-
- <span class="c1"># Transposition of last two axis on purpose for subsequent addition</span>
- <span class="c1"># Einsum indices: real/imag channel, image_batch, repetitions, pixels, noise_instantiations</span>
- <span class="n">n_half</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">sampling_times</span><span class="p">),</span> <span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span> <span class="o">/</span> <span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="mf">2.</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
- <span class="n">noise_vector</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">'cbrpn, n -> cbrnp'</span><span class="p">,</span> <span class="n">noise_vector</span><span class="p">,</span> <span class="n">clean_noise_std</span> <span class="o">*</span> <span class="n">tf</span><span class="o">.</span><span class="n">sqrt</span><span class="p">(</span><span class="n">n_half</span><span class="p">))</span>
- <span class="n">complex_noise_vector</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">complex</span><span class="p">(</span><span class="n">noise_vector</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">noise_vector</span><span class="p">[</span><span class="mi">1</span><span class="p">])</span>
- <span class="n">s_of_k</span> <span class="o">=</span> <span class="n">s_of_k</span> <span class="o">+</span> <span class="n">complex_noise_vector</span>
- <span class="k">return</span> <span class="n">s_of_k</span></div>
-
- <span class="nd">@abc</span><span class="o">.</span><span class="n">abstractmethod</span>
- <span class="k">def</span> <span class="nf">_calculate_trajectory</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
- <span class="sd">""" Calculates the k_space_trajectory with the given variables of the module. The trajectory is given as tensor</span>
-<span class="sd"> of shape (#samples, 3). Additionally it should return a tensor of times corresponding to the samples</span>
-<span class="sd"> (#samples, 1) in ms</span>
-
-<span class="sd"> :return: k_space_vectors, sampling_times</span>
-<span class="sd"> """</span>
- <span class="k">raise</span> <span class="ne">NotImplementedError</span>
-
-<div class="viewcode-block" id="BaseSampling.get_sampling_times"><a class="viewcode-back" href="../../../cmrsim.encoding.base.html#cmrsim.encoding.base.BaseSampling.get_sampling_times">[docs]</a> <span class="k">def</span> <span class="nf">get_sampling_times</span><span class="p">(</span><span class="bp">self</span><span class="p">):</span>
- <span class="sd">"""Getter for sampling times. Defines format that should be used for all Signal modules that need the timing</span>
-<span class="sd"> of the sampling events.</span>
-
-<span class="sd"> :return: - tf.RaggedTensor or numpy.ndarray</span>
-<span class="sd"> - sampling times as numpy.ndarray in case the trajectory is not segmented. If trajectory is segmented</span>
-<span class="sd"> for simulation, returns a RaggedTensor of shape (n_segment, ...).</span>
-<span class="sd"> """</span>
- <span class="k">if</span> <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">k_space_segments</span><span class="p">)</span> <span class="o">></span> <span class="mi">1</span><span class="p">:</span> <span class="c1"># noqa</span>
- <span class="k">return</span> <span class="bp">self</span><span class="o">.</span><span class="n">_segmented_sampling_times</span>
- <span class="k">else</span><span class="p">:</span>
- <span class="n">_</span><span class="p">,</span> <span class="n">sampling_times</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_calculate_trajectory</span><span class="p">()</span>
- <span class="k">return</span> <span class="n">sampling_times</span><span class="o">.</span><span class="n">numpy</span><span class="p">()</span></div></div>
-
-</pre></div>
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-<li class="toctree-l1"><a class="reference internal" href="../../../advanced_examples.html">1. Optimize b-values</a></li>
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- <h1>Source code for cmrsim.encoding.epi</h1><div class="highlight"><pre>
-<span></span><span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"EPI"</span><span class="p">]</span>
-
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">List</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">Union</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">TYPE_CHECKING</span>
-
-<span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
-<span class="k">if</span> <span class="n">TYPE_CHECKING</span><span class="p">:</span>
- <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-<span class="kn">from</span> <span class="nn">cmrsim.encoding.base</span> <span class="kn">import</span> <span class="n">BaseSampling</span>
-
-
-<div class="viewcode-block" id="EPI"><a class="viewcode-back" href="../../../cmrsim.encoding.epi.html#cmrsim.encoding.epi.EPI">[docs]</a><span class="k">class</span> <span class="nc">EPI</span><span class="p">(</span><span class="n">BaseSampling</span><span class="p">):</span>
- <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">field_of_view</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">],</span> <span class="n">List</span><span class="p">[</span><span class="nb">float</span><span class="p">]],</span>
- <span class="n">sampling_matrix_size</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">],</span> <span class="n">List</span><span class="p">[</span><span class="nb">int</span><span class="p">],</span> <span class="s1">'np.ndarray'</span><span class="p">],</span>
- <span class="n">absolute_noise_std</span><span class="p">:</span> <span class="nb">float</span><span class="p">,</span>
- <span class="n">read_out_duration</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
- <span class="n">bandwidth_per_pixel</span><span class="p">:</span> <span class="nb">float</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span>
- <span class="n">blip_duration</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.</span><span class="p">,</span>
- <span class="n">acquisition_start</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">float</span><span class="p">]</span> <span class="o">=</span> <span class="mf">0.</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
- <span class="sd">""" Calculates a trajectory for a 2D cartesian sampling and initializes a BaseSampling object with the given</span>
-<span class="sd"> k-space vectors.</span>
-
-<span class="sd"> :param field_of_view: Total length in (RO, PE) directions</span>
-<span class="sd"> :param sampling_matrix_size: Integers determining the number of uniform sampling points in RO and PE directions</span>
-<span class="sd"> :param absolute_noise_std: Passed through to base class.</span>
-<span class="sd"> :param read_out_duration: in ms, used to calculate sampling times (centers of dwell time intervals)</span>
-<span class="sd"> :param bandwidth_per_pixel: in Hz, used to calculate read_out_duration (only one can be specified)</span>
-<span class="sd"> :param blip_duration: in ms, time between readout events</span>
-<span class="sd"> :param acquisition_start: in ms, leading offset prior to acquisition (defaults to 0.)</span>
-<span class="sd"> """</span>
- <span class="k">if</span> <span class="p">((</span><span class="n">bandwidth_per_pixel</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">read_out_duration</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">)</span> <span class="ow">or</span>
- <span class="p">(</span><span class="n">bandwidth_per_pixel</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span> <span class="ow">and</span> <span class="n">read_out_duration</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">)):</span>
- <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Only exactly one of the arguments (pixel_bandwith/read_out_duration) must be '</span>
- <span class="sa">f</span><span class="s1">'specified. Instead pixel_bandwith: </span><span class="si">{</span><span class="n">bandwidth_per_pixel</span><span class="si">}</span><span class="s1"> and '</span>
- <span class="sa">f</span><span class="s1">'read_out_duration: </span><span class="si">{</span><span class="n">read_out_duration</span><span class="si">}</span><span class="s1">, was given!'</span><span class="p">)</span>
-
- <span class="bp">self</span><span class="o">.</span><span class="n">_fov</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="n">field_of_view</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">_matrix_size</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="n">sampling_matrix_size</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
- <span class="k">if</span> <span class="n">read_out_duration</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
- <span class="n">read_out_duration</span> <span class="o">=</span> <span class="mf">1000.</span> <span class="o">/</span> <span class="n">bandwidth_per_pixel</span> <span class="c1"># noqa</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">_readout_duration</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="n">read_out_duration</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">_acquisition_time_offset</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="n">acquisition_start</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">_blip_duration</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="n">blip_duration</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
- <span class="nb">super</span><span class="p">(</span><span class="n">EPI</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">absolute_noise_std</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s2">"epi"</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-
- <span class="k">def</span> <span class="nf">_calculate_trajectory</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">Tensor</span><span class="p">):</span>
- <span class="sd">""" Calculates the 2D k-space trajectory for a cartesian readout. The k_z component of all 3D-k-space sampling</span>
-<span class="sd"> points is set to 0 to calculate averaging in z-direction.</span>
-
-<span class="sd"> For even as well as uneven matrix dimension, the first k-space sample is acquired at [-k_max, -k_max, 0] and</span>
-<span class="sd"> it is guaranteed, that the mid, or mid+1/2 sampling point is at the k-space center.</span>
-
-<span class="sd"> :return: k-space-vectors, sampling-times (tf.Tensor, tf.Tensor)</span>
-<span class="sd"> """</span>
- <span class="n">ro_steps</span><span class="p">,</span> <span class="n">pe_steps</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_matrix_size</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">),</span> <span class="n">tf</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_matrix_size</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
-
- <span class="c1"># Calculate k-space-vectors</span>
- <span class="n">kx</span><span class="p">,</span> <span class="n">ky</span><span class="p">,</span> <span class="n">kz</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">meshgrid</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_matrix_size</span><span class="p">[</span><span class="mi">0</span><span class="p">]),</span> <span class="n">tf</span><span class="o">.</span><span class="n">range</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_matrix_size</span><span class="p">[</span><span class="mi">1</span><span class="p">]),</span> <span class="mi">1</span><span class="p">,</span> <span class="n">indexing</span><span class="o">=</span><span class="s1">'ij'</span><span class="p">)</span>
- <span class="n">k_vectors</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">stack</span><span class="p">((</span><span class="n">kx</span><span class="p">,</span> <span class="n">ky</span><span class="p">,</span> <span class="n">kz</span><span class="p">),</span> <span class="n">axis</span><span class="o">=-</span><span class="mi">1</span><span class="p">)</span>
- <span class="n">k_vectors</span> <span class="o">=</span> <span class="n">k_vectors</span> <span class="o">-</span> <span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">([</span><span class="nb">int</span><span class="p">(</span><span class="n">ro_steps</span><span class="p">),</span> <span class="nb">int</span><span class="p">(</span><span class="n">pe_steps</span><span class="p">),</span> <span class="mi">0</span><span class="p">],</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">3</span><span class="p">,</span> <span class="p">))</span> <span class="o">//</span> <span class="mi">2</span>
- <span class="n">delta_k</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">math</span><span class="o">.</span><span class="n">divide_no_nan</span><span class="p">(</span><span class="mf">1.</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_fov</span><span class="p">)</span>
- <span class="n">k_vectors</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">k_vectors</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span> <span class="o">*</span> <span class="n">tf</span><span class="o">.</span><span class="n">concat</span><span class="p">((</span><span class="n">delta_k</span><span class="p">,</span> <span class="p">[</span><span class="mf">0.</span><span class="p">]),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
- <span class="n">k_vectors</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">k_vectors</span><span class="p">,</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span>
-
- <span class="c1"># Calculate Sampling times from matrix size, ADC-duration, blip-duration and leading time offset</span>
- <span class="n">dwell_time_borders</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">range</span><span class="p">(</span><span class="mf">0.</span><span class="p">,</span> <span class="n">ro_steps</span><span class="o">+</span><span class="mf">1.</span><span class="p">)</span> <span class="o">/</span> <span class="n">ro_steps</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">_readout_duration</span>
- <span class="n">dwell_time_centers</span> <span class="o">=</span> <span class="n">dwell_time_borders</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="o">+</span> <span class="p">(</span><span class="n">dwell_time_borders</span><span class="p">[</span><span class="mi">1</span><span class="p">:]</span> <span class="o">-</span> <span class="n">dwell_time_borders</span><span class="p">[:</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span><span class="o">/</span><span class="mi">2</span>
- <span class="n">pe_offsets</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">range</span><span class="p">(</span><span class="mf">0.</span><span class="p">,</span> <span class="n">pe_steps</span><span class="p">)</span> <span class="o">*</span> <span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_blip_duration</span> <span class="o">+</span> <span class="bp">self</span><span class="o">.</span><span class="n">_readout_duration</span><span class="p">)</span>
-
- <span class="n">time_slices</span> <span class="o">=</span> <span class="p">()</span>
- <span class="k">for</span> <span class="n">s</span> <span class="ow">in</span> <span class="nb">range</span><span class="p">(</span><span class="nb">int</span><span class="p">(</span><span class="n">pe_steps</span><span class="p">)):</span>
- <span class="k">if</span> <span class="n">s</span> <span class="o">%</span> <span class="mi">2</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
- <span class="n">time_slices</span> <span class="o">+=</span> <span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">reverse</span><span class="p">(</span><span class="n">dwell_time_centers</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="p">])</span> <span class="o">+</span> <span class="n">pe_offsets</span><span class="p">[</span><span class="n">s</span><span class="p">],</span> <span class="p">)</span>
- <span class="k">else</span><span class="p">:</span>
- <span class="n">time_slices</span> <span class="o">+=</span> <span class="p">(</span><span class="n">dwell_time_centers</span> <span class="o">+</span> <span class="n">pe_offsets</span><span class="p">[</span><span class="n">s</span><span class="p">],)</span>
- <span class="n">sampling_time_matrix</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">stack</span><span class="p">(</span><span class="n">time_slices</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
- <span class="n">sampling_times</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">sampling_time_matrix</span><span class="p">,</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">])</span> <span class="o">-</span> <span class="bp">self</span><span class="o">.</span><span class="n">_acquisition_time_offset</span>
- <span class="k">return</span> <span class="n">k_vectors</span><span class="p">,</span> <span class="n">sampling_times</span></div>
-
-</pre></div>
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- <h1>Source code for cmrsim.reconstruction.base</h1><div class="highlight"><pre>
-<span></span><span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"BaseRecon"</span><span class="p">,</span> <span class="p">]</span>
-
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Union</span><span class="p">,</span> <span class="n">Tuple</span>
-<span class="kn">from</span> <span class="nn">abc</span> <span class="kn">import</span> <span class="n">abstractmethod</span>
-
-<span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
-
-
-<div class="viewcode-block" id="BaseRecon"><a class="viewcode-back" href="../../../cmrsim.reconstruction.base.html#cmrsim.reconstruction.base.BaseRecon">[docs]</a><span class="k">class</span> <span class="nc">BaseRecon</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
- <span class="n">output_matrix_size</span><span class="p">:</span> <span class="n">tf</span><span class="o">.</span><span class="n">Tensor</span>
-
- <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">output_matrix_size</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">],</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="n">tf</span><span class="o">.</span><span class="n">Tensor</span><span class="p">],</span>
- <span class="n">name</span><span class="p">:</span> <span class="nb">str</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
- <span class="sd">""" Base class for type hinting</span>
-
-<span class="sd"> :param output_matrix_size: Stores the resulting image shape after reconstruction</span>
-<span class="sd"> :param name: str Defines the name-space for the reconstruction module</span>
-<span class="sd"> :param kwargs:</span>
-<span class="sd"> """</span>
- <span class="nb">super</span><span class="p">(</span><span class="n">BaseRecon</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">)</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">output_matrix_size</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="n">output_matrix_size</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
-
- <span class="nd">@abstractmethod</span>
- <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flat_samples</span><span class="p">:</span> <span class="n">tf</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
- <span class="sd">""" Performs reconstruction algorithm defined by sub-class for defined input-signature!</span>
-
-<span class="sd"> :param flat_samples: (#batch, #repetitions, x*y) or (#batch, #repetitions, #noise_instatiations, x*y)</span>
-<span class="sd"> representing the discretized k-space</span>
-<span class="sd"> :return: iFFT of input (#batch, #repetitions, [#noise_instantiations], x, y)</span>
-<span class="sd"> """</span>
-
- <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'You tried to call an abstract base-class method. Please refer to another specific,'</span>
- <span class="sa">f</span><span class="s1">'reconstruction-sublass!'</span><span class="p">)</span></div>
-</pre></div>
-
- </div>
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diff --git a/public/_modules/cmrsim/reconstruction/cartesian.html b/public/_modules/cmrsim/reconstruction/cartesian.html
deleted file mode 100644
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--- a/public/_modules/cmrsim/reconstruction/cartesian.html
+++ /dev/null
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-<li class="toctree-l1"><a class="reference internal" href="../../../started.html">Simulate a simple image</a></li>
-</ul>
-<p class="caption"><span class="caption-text">Documentation</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../../../cmrsim.html">cmrsim package</a></li>
-</ul>
-<p class="caption"><span class="caption-text">Who is using it? - Examples</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../../../advanced_examples.html">1. Optimize b-values</a></li>
-</ul>
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- <h1>Source code for cmrsim.reconstruction.cartesian</h1><div class="highlight"><pre>
-<span></span><span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"Cartesian2D"</span><span class="p">,</span> <span class="s2">"RoemerMultiCoil"</span><span class="p">]</span>
-
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Union</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">Iterable</span><span class="p">,</span> <span class="n">Optional</span>
-<span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
-
-<span class="kn">from</span> <span class="nn">cmrsim.reconstruction.base</span> <span class="kn">import</span> <span class="n">BaseRecon</span>
-
-
-<div class="viewcode-block" id="Cartesian2D"><a class="viewcode-back" href="../../../cmrsim.reconstruction.cartesian.html#cmrsim.reconstruction.cartesian.Cartesian2D">[docs]</a><span class="k">class</span> <span class="nc">Cartesian2D</span><span class="p">(</span><span class="n">BaseRecon</span><span class="p">):</span>
- <span class="n">output_matrix_size</span><span class="p">:</span> <span class="n">tf</span><span class="o">.</span><span class="n">Tensor</span> <span class="c1"># defines the shape of the returned, reconstructed images</span>
-
- <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample_matrix_size</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">]],</span>
- <span class="n">padding</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">],</span> <span class="n">Iterable</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">]]]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
- <span class="sd">""" Performs 2D inverse FFT on __call__. If padding argument is given, performs symmetric (per direction)</span>
-<span class="sd"> padding with the given width prior to iFFT.</span>
-
-<span class="sd"> The innermost two dimensions are assumed to represent the image axes after reshaping in __call__.</span>
-
-<span class="sd"> :param sample_matrix_size: (int, int) This is used to reshape the flattened array/tensor of k-space samples into</span>
-<span class="sd"> the correct dimensions, to perform iFFT2d</span>
-<span class="sd"> :param padding: Optional(int, int) Number of zero-element per border and axis. This means for padding=(10, 5) the</span>
-<span class="sd"> dimensions of the k-space array will grow by (20, 10)</span>
-<span class="sd"> Iterable[(int, int)], For assymetric padding. i-th tuple contains the padding length before and</span>
-<span class="sd"> after for the i-th axis of the sampled k-space.</span>
-<span class="sd"> """</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">_sample_matrix_size</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="n">sample_matrix_size</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
-
- <span class="k">if</span> <span class="n">padding</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
- <span class="n">out_matrix_size</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sample_matrix_size</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">_padding</span> <span class="o">=</span> <span class="kc">None</span>
- <span class="k">else</span><span class="p">:</span>
- <span class="n">_padding</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="n">padding</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
- <span class="k">if</span> <span class="n">tf</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="n">_padding</span><span class="p">))</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
- <span class="n">_padding</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">_padding</span><span class="p">,</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">]),</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">_padding</span> <span class="o">=</span> <span class="n">_padding</span>
- <span class="n">out_matrix_size</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">([</span><span class="n">base_size</span> <span class="o">+</span> <span class="n">p</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span> <span class="o">+</span> <span class="n">p</span><span class="p">[</span><span class="mi">1</span><span class="p">]</span> <span class="k">for</span> <span class="n">base_size</span><span class="p">,</span> <span class="n">p</span> <span class="ow">in</span>
- <span class="nb">zip</span><span class="p">(</span><span class="n">sample_matrix_size</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_padding</span><span class="o">.</span><span class="n">numpy</span><span class="p">())])</span>
- <span class="nb">super</span><span class="p">(</span><span class="n">Cartesian2D</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">out_matrix_size</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s2">"cartesian_2d"</span><span class="p">)</span>
-
- <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flat_samples</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
- <span class="sd">""" Reshapes last axis of flat samples into 2D with shape specified in self._sampling_matrix_size, and performs</span>
-<span class="sd"> 2DiFFT over the two innermost dimensions. Number and dimension of all other axis do not have to specified.</span>
-
-<span class="sd"> :param flat_samples: (#image, ..., #noise_levels, #k-space-points)</span>
-<span class="sd"> :param kwargs:</span>
-<span class="sd"> :return:</span>
-<span class="sd"> """</span>
- <span class="c1"># Reshape to 2D k-space</span>
- <span class="n">batch_sizes</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="n">flat_samples</span><span class="p">)[</span><span class="mi">0</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
- <span class="n">k_space_2d</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">flat_samples</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">concat</span><span class="p">((</span><span class="n">batch_sizes</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sample_matrix_size</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span>
-
- <span class="k">if</span> <span class="bp">self</span><span class="o">.</span><span class="n">_padding</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
- <span class="n">batch_padding</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">zeros_like</span><span class="p">(</span><span class="n">batch_sizes</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">int32</span><span class="p">)[:,</span> <span class="n">tf</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
- <span class="n">k_space_2d</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">pad</span><span class="p">(</span><span class="n">k_space_2d</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">concat</span><span class="p">((</span><span class="n">batch_padding</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_padding</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">))</span>
-
- <span class="c1"># Call 2DiFFT on two innermost tensor axes</span>
- <span class="n">innermost_dim_index</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="n">batch_sizes</span><span class="p">)</span> <span class="o">+</span> <span class="mi">1</span>
- <span class="n">images</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">signal</span><span class="o">.</span><span class="n">ifftshift</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">signal</span><span class="o">.</span><span class="n">ifft2d</span><span class="p">(</span><span class="n">k_space_2d</span><span class="p">),</span> <span class="n">axes</span><span class="o">=</span><span class="p">[</span><span class="n">innermost_dim_index</span> <span class="o">-</span> <span class="mi">1</span><span class="p">,</span> <span class="n">innermost_dim_index</span><span class="p">])</span>
- <span class="n">renormalization_factor</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">reduce_prod</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">output_matrix_size</span><span class="o">/</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sample_matrix_size</span><span class="p">),</span> <span class="n">tf</span><span class="o">.</span><span class="n">complex64</span><span class="p">)</span>
- <span class="k">return</span> <span class="n">images</span> <span class="o">*</span> <span class="n">renormalization_factor</span></div>
-
-
-<div class="viewcode-block" id="RoemerMultiCoil"><a class="viewcode-back" href="../../../cmrsim.reconstruction.cartesian.html#cmrsim.reconstruction.cartesian.RoemerMultiCoil">[docs]</a><span class="k">class</span> <span class="nc">RoemerMultiCoil</span><span class="p">(</span><span class="n">Cartesian2D</span><span class="p">):</span>
- <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sample_matrix_size</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">]],</span> <span class="n">coil_sensitivities</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="s1">'np.ndarray'</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">Tensor</span><span class="p">],</span>
- <span class="n">padding</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">],</span> <span class="n">Iterable</span><span class="p">[</span><span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="nb">int</span><span class="p">]]]]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
- <span class="sd">"""</span>
-<span class="sd"> :param sample_matrix_size: (See Cartesian2D)</span>
-<span class="sd"> :param coil_sensitivities: (#n_coils, X, Y)</span>
-<span class="sd"> :param padding: (see Cartesian2D)</span>
-<span class="sd"> :param kwargs:</span>
-<span class="sd"> """</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">_coil_sensitivities</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="n">coil_sensitivities</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">complex64</span><span class="p">)</span>
- <span class="nb">super</span><span class="p">(</span><span class="n">RoemerMultiCoil</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">sample_matrix_size</span><span class="o">=</span><span class="n">sample_matrix_size</span><span class="p">,</span> <span class="n">padding</span><span class="o">=</span><span class="n">padding</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
-
- <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">flat_samples</span><span class="p">:</span> <span class="n">tf</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">coil_channel_axis</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span> <span class="c1"># noqa</span>
- <span class="sd">"""</span>
-
-<span class="sd"> :param flat_samples: (..., #n_coils, ..., #noise_levels, #k-space-points)</span>
-<span class="sd"> :param coil_channel_axis: defines the axis index that matches #n_coils must be less than</span>
-<span class="sd"> len(flat_samples.shape) - 2, since the two innermost axes are exclusively reserved.</span>
-
-<span class="sd"> :param kwargs:</span>
-<span class="sd"> :return: (..., #noise, X, Y, [Z]) reconstructed image batch</span>
-<span class="sd"> """</span>
- <span class="n">single_coil_images</span> <span class="o">=</span> <span class="nb">super</span><span class="p">(</span><span class="n">RoemerMultiCoil</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__call__</span><span class="p">(</span><span class="n">flat_samples</span><span class="p">)</span>
- <span class="n">n_coils</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="n">single_coil_images</span><span class="p">)[</span><span class="n">coil_channel_axis</span><span class="p">:</span><span class="n">coil_channel_axis</span><span class="o">+</span><span class="mi">1</span><span class="p">]</span>
-
- <span class="c1"># tile sens maps to match the shape of the single_coil_images</span>
- <span class="n">batch_shape</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="n">flat_samples</span><span class="p">)[</span><span class="mi">0</span><span class="p">:</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span>
- <span class="n">broad_cast_shape</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">concat</span><span class="p">((</span><span class="n">batch_shape</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_coil_sensitivities</span><span class="p">)[</span><span class="mi">1</span><span class="p">:]),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
- <span class="n">a</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">ones_like</span><span class="p">(</span><span class="n">batch_shape</span><span class="p">)</span>
- <span class="n">expanded_shape</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">concat</span><span class="p">((</span><span class="n">a</span><span class="p">[:</span><span class="n">coil_channel_axis</span><span class="p">],</span> <span class="n">n_coils</span><span class="p">,</span> <span class="n">a</span><span class="p">[</span><span class="n">coil_channel_axis</span><span class="o">+</span><span class="mi">1</span><span class="p">:],</span>
- <span class="n">tf</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_coil_sensitivities</span><span class="p">)[</span><span class="mi">1</span><span class="p">:]),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
- <span class="n">expanded_sens_maps</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_coil_sensitivities</span><span class="p">,</span> <span class="n">expanded_shape</span><span class="p">)</span>
- <span class="n">tiled_sensitivity_maps</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">broadcast_to</span><span class="p">(</span><span class="n">expanded_sens_maps</span><span class="p">,</span> <span class="n">broad_cast_shape</span><span class="p">)</span>
-
- <span class="n">numerator</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reduce_sum</span><span class="p">(</span><span class="n">single_coil_images</span> <span class="o">*</span> <span class="n">tf</span><span class="o">.</span><span class="n">math</span><span class="o">.</span><span class="n">conj</span><span class="p">(</span><span class="n">tiled_sensitivity_maps</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="n">coil_channel_axis</span><span class="p">)</span>
- <span class="n">denominator</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reduce_sum</span><span class="p">(</span><span class="n">tiled_sensitivity_maps</span> <span class="o">*</span> <span class="n">tf</span><span class="o">.</span><span class="n">math</span><span class="o">.</span><span class="n">conj</span><span class="p">(</span><span class="n">tiled_sensitivity_maps</span><span class="p">),</span> <span class="n">axis</span><span class="o">=</span><span class="n">coil_channel_axis</span><span class="p">)</span>
- <span class="n">roemer_image</span> <span class="o">=</span> <span class="n">numerator</span> <span class="o">/</span> <span class="n">denominator</span>
- <span class="k">return</span> <span class="n">roemer_image</span></div>
-
-
-<span class="k">if</span> <span class="vm">__name__</span> <span class="o">==</span> <span class="s2">"__main__"</span><span class="p">:</span>
- <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
- <span class="n">rmrecon</span> <span class="o">=</span> <span class="n">RoemerMultiCoil</span><span class="p">((</span><span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">),</span> <span class="n">coil_sensitivities</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">((</span><span class="mi">4</span><span class="p">,</span> <span class="mi">10</span><span class="p">,</span> <span class="mi">10</span><span class="p">)),</span> <span class="n">tf</span><span class="o">.</span><span class="n">complex64</span><span class="p">))</span>
- <span class="n">a</span> <span class="o">=</span> <span class="n">rmrecon</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">random</span><span class="o">.</span><span class="n">normal</span><span class="p">((</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">10</span><span class="o">*</span><span class="mi">10</span><span class="p">)),</span> <span class="n">tf</span><span class="o">.</span><span class="n">complex64</span><span class="p">),</span> <span class="n">coil_channel_axis</span><span class="o">=</span><span class="mi">2</span><span class="p">)</span>
- <span class="nb">print</span><span class="p">(</span><span class="n">a</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span>
-</pre></div>
-
- </div>
-
- </div>
- <footer>
-
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- <hr/>
-
- <div role="contentinfo">
- <p>
-
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-
- </p>
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- <h1>Source code for cmrsim.signal_processes.base</h1><div class="highlight"><pre>
-<span></span><span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"BaseSignalModel"</span><span class="p">,</span> <span class="s2">"LookUpTableModule"</span><span class="p">,</span> <span class="s2">"CompositeSignalModel"</span><span class="p">]</span>
-
-<span class="kn">from</span> <span class="nn">abc</span> <span class="kn">import</span> <span class="n">abstractmethod</span>
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Union</span><span class="p">,</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">TYPE_CHECKING</span>
-
-<span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
-
-<span class="k">if</span> <span class="n">TYPE_CHECKING</span><span class="p">:</span>
- <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-
-<div class="viewcode-block" id="BaseSignalModel"><a class="viewcode-back" href="../../../cmrsim.signal_processes.base.html#cmrsim.signal_processes.base.BaseSignalModel">[docs]</a><span class="k">class</span> <span class="nc">BaseSignalModel</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
- <span class="n">required_quantities</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="p">()</span>
- <span class="n">expansion_factor</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="kc">None</span>
- <span class="n">device</span><span class="p">:</span> <span class="nb">str</span>
-
- <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">name</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">device</span><span class="p">:</span> <span class="nb">str</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
- <span class="sd">""" Class to inherit every module from, that multiplies a term \Psi to the integrand in the fourier transform:</span>
-
-<span class="sd"> .. math::</span>
-<span class="sd"> s(\mathbf{k}(t)) = \int_V \mathrm{d}V m( \mathbf{r} , 0)</span>
-<span class="sd"> \Psi(\mathbf{r}, t) e^{-2\pi j \mathbf{k}(t) \cdot \mathbf{r}}</span>
-
-
-<span class="sd"> The purpose of this parent class is to ensure the required quantities field is set and the __call__ method is</span>
-<span class="sd"> implemented</span>
-<span class="sd"> :param name:</span>
-<span class="sd"> :param device: Device on which the graph of this module is executed. Defaults to GPU:0 (CPU:0 if no GPU is</span>
-<span class="sd"> available)</span>
-<span class="sd"> """</span>
- <span class="k">if</span> <span class="n">device</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
- <span class="k">if</span> <span class="n">tf</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">get_visible_devices</span><span class="p">(</span><span class="s1">'GPU'</span><span class="p">):</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="s1">'GPU:0'</span>
- <span class="k">else</span><span class="p">:</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="s1">'CPU:0'</span>
- <span class="k">else</span><span class="p">:</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">device</span> <span class="o">=</span> <span class="n">device</span>
-
- <span class="nb">super</span><span class="p">(</span><span class="n">BaseSignalModel</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">)</span>
-
- <span class="o">@</span> <span class="n">abstractmethod</span>
- <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">signal_tensor</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
- <span class="sd">""" Defines the calculation which represent the factor multiplied to the integrand in the signal equation.</span>
-<span class="sd"> Tensors and arguments that are listed in the self._required_quantities field are automatically passed via</span>
-<span class="sd"> **kwargs if the module is inserted into a CompositeSignalModel.</span>
-
-<span class="sd"> :param signal_tensor: tf.complex64 - Tensor of points representing local magnetization multiplied with factors</span>
-<span class="sd"> of previously executed modules</span>
-<span class="sd"> :param kwargs: all available tensors representing quantities at given points</span>
-<span class="sd"> :return: signal_tensor * (defined_factor)</span>
-<span class="sd"> """</span>
- <span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">):</span>
- <span class="c1"># Calculate factor.....</span>
- <span class="c1"># factor = ....</span>
- <span class="k">pass</span>
- <span class="k">return</span> <span class="c1"># signal_tensor * factor</span>
-
-<div class="viewcode-block" id="BaseSignalModel.check_inputs"><a class="viewcode-back" href="../../../cmrsim.signal_processes.base.html#cmrsim.signal_processes.base.BaseSignalModel.check_inputs">[docs]</a> <span class="k">def</span> <span class="nf">check_inputs</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
- <span class="sd">""" This method is meant to be called by CompositeSignalModel and asserts that all required_quantities are</span>
-<span class="sd"> passed in kwargs during graph construction.</span>
-
-<span class="sd"> :raises: ValueError if *kwargs is missing a specified required quantity</span>
-<span class="sd"> """</span>
- <span class="n">key_check</span> <span class="o">=</span> <span class="p">[</span><span class="n">key</span> <span class="ow">in</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">keys</span><span class="p">()</span> <span class="k">for</span> <span class="n">key</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">required_quantities</span><span class="p">]</span>
- <span class="k">if</span> <span class="ow">not</span> <span class="nb">all</span><span class="p">(</span><span class="n">key_check</span><span class="p">):</span>
- <span class="k">raise</span> <span class="ne">ValueError</span><span class="p">(</span><span class="sa">f</span><span class="s2">"For calling the Signal model </span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s2"> following keyword arguments are necessary:"</span>
- <span class="sa">f</span><span class="s2">"</span><span class="si">{</span><span class="bp">self</span><span class="o">.</span><span class="n">required_quantities</span><span class="si">}</span><span class="s2"> while these were given: </span><span class="si">{</span><span class="n">key_check</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span></div></div>
-
-
-<div class="viewcode-block" id="LookUpTableModule"><a class="viewcode-back" href="../../../cmrsim.signal_processes.base.html#cmrsim.signal_processes.base.LookUpTableModule">[docs]</a><span class="k">class</span> <span class="nc">LookUpTableModule</span><span class="p">(</span><span class="n">BaseSignalModel</span><span class="p">):</span>
- <span class="n">required_quantities</span> <span class="o">=</span> <span class="p">(</span><span class="s1">'r_vectors'</span><span class="p">,)</span>
-
- <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">look_up_map3d</span><span class="p">,</span> <span class="n">map_dimensions</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">],</span> <span class="n">name</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
- <span class="sd">"""</span>
-
-<span class="sd"> :param look_up_map3d: (#channels, N, M, L) where N, M, L are a the number of bins of the 3D map. The channels</span>
-<span class="sd"> are assumed to be aligned and centered, such that negative coordinates are mapped to</span>
-<span class="sd"> bins with n,m < N/2,M/2.</span>
-<span class="sd"> :param map_dimensions: Extend of the map in scanner coordinate system. Used to sort isochromat positions</span>
-<span class="sd"> into bins. This is assumed to be the 'length' in each spatial direction of</span>
-<span class="sd"> :param name: defines Module name-scope</span>
-<span class="sd"> :param kwargs: - device: str defaults to GPU:0</span>
-<span class="sd"> """</span>
- <span class="nb">super</span><span class="p">(</span><span class="n">LookUpTableModule</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="n">name</span><span class="p">,</span> <span class="n">device</span><span class="o">=</span><span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'device'</span><span class="p">,</span> <span class="kc">None</span><span class="p">))</span>
-
- <span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">):</span>
- <span class="n">lookup</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="n">look_up_map3d</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">look_up_map3d</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">map_dimensions</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="n">map_dimensions</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">_bins</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="n">look_up_map3d</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">1</span><span class="p">:],</span> <span class="n">dtype</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">_n_channels</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="n">look_up_map3d</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">map_dtype</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">as_dtype</span><span class="p">(</span><span class="n">look_up_map3d</span><span class="o">.</span><span class="n">dtype</span><span class="p">)</span>
- <span class="c1"># TODO: Transposition and flip was done heuristically for maps generate with matlab code...</span>
- <span class="n">flipped_lookup</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reverse</span><span class="p">(</span><span class="n">lookup</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">_flat_look_up</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">flipped_lookup</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">0</span><span class="p">]),</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_n_channels</span><span class="p">])</span>
-
- <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
- <span class="sd">""" Rounds r_vectors to map-resolution to get look-up indices and gathers the required values from the stored</span>
-<span class="sd"> 3D table.</span>
-
-<span class="sd"> :param signal_tensor:</span>
-<span class="sd"> :param kwargs: r_vectors (1, #v, #r, 3, #k)</span>
-<span class="sd"> :return: (#v, #r, #k, self._n_channels)</span>
-<span class="sd"> """</span>
- <span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">):</span>
- <span class="n">r_vectors</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">gather</span><span class="p">(</span><span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'r_vectors'</span><span class="p">),</span> <span class="mi">0</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
- <span class="n">r_vectors</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">r_vectors</span><span class="p">,</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">,</span> <span class="mi">2</span><span class="p">])</span>
- <span class="n">r_shape</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="n">r_vectors</span><span class="p">)</span>
- <span class="n">relative_r_vectors</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">math</span><span class="o">.</span><span class="n">divide_no_nan</span><span class="p">(</span><span class="n">r_vectors</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">map_dimensions</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">))</span> <span class="o">+</span> <span class="mf">0.5</span>
- <span class="n">r_as_bin_indices</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">math</span><span class="o">.</span><span class="n">floor</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">relative_r_vectors</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">float64</span><span class="p">)</span> <span class="o">*</span> <span class="n">tf</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_bins</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">float64</span><span class="p">))</span>
- <span class="n">flat_r</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">r_as_bin_indices</span><span class="p">,</span> <span class="p">[</span><span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="mi">3</span><span class="p">]),</span> <span class="n">tf</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
- <span class="n">row_indices</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reduce_sum</span><span class="p">(</span><span class="n">flat_r</span> <span class="o">*</span> <span class="p">[</span><span class="bp">self</span><span class="o">.</span><span class="n">_bins</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">_bins</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">_bins</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="mi">1</span><span class="p">],</span> <span class="n">axis</span><span class="o">=</span><span class="mi">1</span><span class="p">)</span>
- <span class="n">look_up_values</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">gather</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_flat_look_up</span><span class="p">,</span> <span class="n">row_indices</span><span class="p">,</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
- <span class="n">return_shape</span> <span class="o">=</span> <span class="p">(</span><span class="n">r_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">r_shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">r_shape</span><span class="p">[</span><span class="mi">2</span><span class="p">],</span> <span class="bp">self</span><span class="o">.</span><span class="n">_n_channels</span><span class="p">)</span>
- <span class="k">return</span> <span class="n">tf</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">look_up_values</span><span class="p">,</span> <span class="n">return_shape</span><span class="p">)</span></div>
-
-
-<div class="viewcode-block" id="CompositeSignalModel"><a class="viewcode-back" href="../../../cmrsim.signal_processes.base.html#cmrsim.signal_processes.base.CompositeSignalModel">[docs]</a><span class="k">class</span> <span class="nc">CompositeSignalModel</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">Module</span><span class="p">):</span>
- <span class="n">required_quantities</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="p">(</span><span class="s1">'M0'</span><span class="p">,</span> <span class="p">)</span>
- <span class="n">_sub_module_order</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="p">()</span>
- <span class="n">_expansion_factors</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="p">()</span>
- <span class="n">_expansion_names</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="p">()</span>
-
- <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="o">*</span><span class="n">args</span><span class="p">):</span>
- <span class="sd">""" Container module to concatenate and sequentially call subclasses of BaseSignalModules. Calling order is</span>
-<span class="sd"> equal to order arguments.</span>
-
-<span class="sd"> :param args: All signal modules that need to be concatenated.</span>
-<span class="sd"> """</span>
- <span class="nb">super</span><span class="p">(</span><span class="n">CompositeSignalModel</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">"composite_signal_model"</span><span class="p">)</span>
-
- <span class="k">with</span> <span class="bp">self</span><span class="o">.</span><span class="n">name_scope</span><span class="p">:</span>
- <span class="k">for</span> <span class="n">module</span> <span class="ow">in</span> <span class="n">args</span><span class="p">:</span>
- <span class="k">if</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">module</span><span class="p">,</span> <span class="n">BaseSignalModel</span><span class="p">):</span>
- <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="n">module</span><span class="o">.</span><span class="n">name</span><span class="p">]</span> <span class="o">=</span> <span class="n">module</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">required_quantities</span> <span class="o">+=</span> <span class="n">module</span><span class="o">.</span><span class="n">required_quantities</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">_sub_module_order</span> <span class="o">+=</span> <span class="p">(</span><span class="n">module</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="p">)</span>
- <span class="k">if</span> <span class="n">module</span><span class="o">.</span><span class="n">expansion_factor</span> <span class="ow">is</span> <span class="kc">None</span> <span class="ow">or</span> <span class="n">module</span><span class="o">.</span><span class="n">expansion_factor</span> <span class="o"><</span> <span class="mi">1</span><span class="p">:</span>
- <span class="k">raise</span> <span class="ne">NotImplementedError</span><span class="p">(</span><span class="sa">f</span><span class="s2">"The module </span><span class="si">{</span><span class="n">module</span><span class="o">.</span><span class="n">name</span><span class="si">}</span><span class="s2"> seems to not have set the expansion"</span>
- <span class="sa">f</span><span class="s2">"factor for the #repetition-axis. If the module does not increase"</span>
- <span class="sa">f</span><span class="s2">"the number of repetitions make sure it is set to 1! Currently it "</span>
- <span class="sa">f</span><span class="s2">"is: </span><span class="si">{</span><span class="n">module</span><span class="o">.</span><span class="n">expansion_factor</span><span class="si">}</span><span class="s2">"</span><span class="p">)</span>
- <span class="k">else</span><span class="p">:</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">_expansion_factors</span> <span class="o">+=</span> <span class="p">(</span><span class="n">module</span><span class="o">.</span><span class="n">expansion_factor</span><span class="p">,</span> <span class="p">)</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">_expansion_names</span> <span class="o">+=</span> <span class="p">(</span><span class="n">module</span><span class="o">.</span><span class="n">name</span><span class="p">,</span> <span class="p">)</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">required_quantities</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">(</span><span class="nb">dict</span><span class="o">.</span><span class="n">fromkeys</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">required_quantities</span><span class="p">)</span><span class="o">.</span><span class="n">keys</span><span class="p">())</span>
-
- <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">signal_tensor</span><span class="p">:</span> <span class="n">tf</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="n">segment_index</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="n">Union</span><span class="p">[</span><span class="nb">int</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">Tensor</span><span class="p">]]</span> <span class="o">=</span> <span class="mi">0</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
- <span class="sd">""" Consecutively calls submodules which the signal change to the passed in tensor.</span>
-
-<span class="sd"> :param kwargs: dictionary of tensors containing all required quantities. Is forwarded to BaseSignalModule.</span>
-<span class="sd"> """</span>
- <span class="n">kwargs</span><span class="o">.</span><span class="n">update</span><span class="p">({</span><span class="s1">'segment_index'</span><span class="p">:</span> <span class="n">segment_index</span><span class="p">})</span>
- <span class="k">for</span> <span class="n">sub_mod_name</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_sub_module_order</span><span class="p">:</span>
- <span class="n">sub_mod</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="vm">__dict__</span><span class="p">[</span><span class="n">sub_mod_name</span><span class="p">]</span>
- <span class="n">signal_tensor</span> <span class="o">=</span> <span class="n">sub_mod</span><span class="p">(</span><span class="n">signal_tensor</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
- <span class="k">return</span> <span class="n">signal_tensor</span>
-
- <span class="nd">@property</span>
- <span class="k">def</span> <span class="nf">expected_number_of_repetitions</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="n">tf</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
- <span class="k">return</span> <span class="n">tf</span><span class="o">.</span><span class="n">reduce_prod</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_expansion_factors</span><span class="p">)</span>
-
- <span class="nd">@property</span>
- <span class="k">def</span> <span class="nf">unstacked_axis_names</span><span class="p">(</span><span class="bp">self</span><span class="p">)</span> <span class="o">-></span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">str</span><span class="p">]:</span>
- <span class="sd">""" returns the name of modules with expansion factors > 1 in the order that is used in unstack_repetitions:</span>
-<span class="sd"> """</span>
- <span class="n">names</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">([</span><span class="nb">str</span><span class="p">(</span><span class="n">n</span><span class="p">)</span> <span class="k">for</span> <span class="p">(</span><span class="n">n</span><span class="p">,</span> <span class="n">f</span><span class="p">)</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_expansion_names</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_expansion_factors</span><span class="p">)</span> <span class="k">if</span> <span class="n">f</span> <span class="o">></span> <span class="mi">1</span><span class="p">])</span>
- <span class="k">return</span> <span class="n">names</span>
-
-<div class="viewcode-block" id="CompositeSignalModel.unstack_repetitions"><a class="viewcode-back" href="../../../cmrsim.signal_processes.base.html#cmrsim.signal_processes.base.CompositeSignalModel.unstack_repetitions">[docs]</a> <span class="k">def</span> <span class="nf">unstack_repetitions</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">simulation_result</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">tf</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="s1">'np.ndarray'</span><span class="p">])</span> <span class="o">-></span> <span class="n">tf</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
- <span class="sd">""" Uses the dimension expansion information from the sub modules to unstack the simulated tensor of k-space,</span>
-<span class="sd"> samples or images. In both cases the second axis (index=1) is assumed to represent the stacked repetitions.</span>
-<span class="sd"> """</span>
- <span class="n">input_shape</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="n">simulation_result</span><span class="p">)</span>
- <span class="n">unstacked_repetitions_shape</span> <span class="o">=</span> <span class="nb">tuple</span><span class="p">([</span><span class="n">f</span> <span class="k">for</span> <span class="n">f</span> <span class="ow">in</span> <span class="bp">self</span><span class="o">.</span><span class="n">_expansion_factors</span><span class="p">[::</span><span class="o">-</span><span class="mi">1</span><span class="p">]</span> <span class="k">if</span> <span class="n">f</span> <span class="o">></span> <span class="mi">1</span><span class="p">])</span>
- <span class="n">resulting_shape</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">concat</span><span class="p">((</span><span class="n">input_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">:</span><span class="mi">1</span><span class="p">],</span> <span class="n">unstacked_repetitions_shape</span><span class="p">,</span> <span class="n">input_shape</span><span class="p">[</span><span class="mi">2</span><span class="p">:]),</span> <span class="n">axis</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
- <span class="k">return</span> <span class="n">tf</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">simulation_result</span><span class="p">,</span> <span class="n">resulting_shape</span><span class="p">)</span></div></div>
-</pre></div>
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diff --git a/public/_modules/cmrsim/signal_processes/solution_operators/coil_sensitivities.html b/public/_modules/cmrsim/signal_processes/solution_operators/coil_sensitivities.html
deleted file mode 100644
index d78e942b9595b6e0cd236c5985f6b0d573ed3005..0000000000000000000000000000000000000000
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- <h1>Source code for cmrsim.signal_processes.solution_operators.coil_sensitivities</h1><div class="highlight"><pre>
-<span></span><span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"CoilSensitivity"</span><span class="p">]</span>
-
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">Union</span><span class="p">,</span> <span class="n">TYPE_CHECKING</span>
-
-<span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
-
-<span class="k">if</span> <span class="n">TYPE_CHECKING</span><span class="p">:</span>
- <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-<span class="kn">from</span> <span class="nn">cmrsim.signal_processes.base</span> <span class="kn">import</span> <span class="n">LookUpTableModule</span>
-
-
-<div class="viewcode-block" id="CoilSensitivity"><a class="viewcode-back" href="../../../../cmrsim.signal_processes.solution_operators.coil_sensitivities.html#cmrsim.signal_processes.solution_operators.coil_sensitivities.CoilSensitivity">[docs]</a><span class="k">class</span> <span class="nc">CoilSensitivity</span><span class="p">(</span><span class="n">LookUpTableModule</span><span class="p">):</span>
- <span class="n">required_quantities</span> <span class="o">=</span> <span class="p">(</span><span class="s1">'r_vectors'</span><span class="p">,</span> <span class="p">)</span>
- <span class="n">_device2</span><span class="p">:</span> <span class="nb">str</span>
-
- <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">coil_sensitivities</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="s1">'np.ndarray'</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">map_dimensions</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">,</span> <span class="nb">float</span><span class="p">],</span>
- <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
- <span class="sd">"""</span>
-<span class="sd"> :param coil_sensitivities: (#channels, X, Y, Z) with dtype complex64</span>
-<span class="sd"> :param map_dimensions: (X, Y, Z)</span>
-<span class="sd"> :param kwargs: - device: (str) Device on which all operations except for the look-up are placed (default: GPU:0)</span>
-<span class="sd"> - device_lookup: (str) Device where the lookup table is places (defaults to CPU:0)</span>
-<span class="sd"> """</span>
- <span class="nb">super</span><span class="p">(</span><span class="n">CoilSensitivity</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">look_up_map3d</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">coil_sensitivities</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">complex64</span><span class="p">),</span>
- <span class="n">map_dimensions</span><span class="o">=</span><span class="n">map_dimensions</span><span class="p">,</span> <span class="n">name</span><span class="o">=</span><span class="s2">"coil_sensitivities"</span><span class="p">,</span>
- <span class="n">device</span><span class="o">=</span><span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'device_lookup'</span><span class="p">,</span> <span class="s1">'CPU:0'</span><span class="p">))</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">expansion_factor</span> <span class="o">=</span> <span class="nb">int</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_n_channels</span><span class="p">)</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">_device2</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'device'</span><span class="p">,</span> <span class="s1">'GPU:0'</span><span class="p">)</span>
-
- <span class="nd">@tf</span><span class="o">.</span><span class="n">Module</span><span class="o">.</span><span class="n">with_name_scope</span>
- <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">signal_tensor</span><span class="p">:</span> <span class="n">tf</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
- <span class="sd">""" </span>
-<span class="sd"> :param signal_tensor:</span>
-<span class="sd"> :param kwargs: - r_vectors (#batch, #voxels, #repetitions, 3, #samples)</span>
-<span class="sd"> :return:</span>
-<span class="sd"> """</span>
- <span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">_device2</span><span class="p">):</span>
- <span class="n">r_vectors</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">required_quantities</span><span class="p">[</span><span class="mi">0</span><span class="p">])</span>
- <span class="c1"># sensitivity_factors shape: (#voxels, #repetitions, #samples, n_channels), with axis 0,1,2 taken from r</span>
- <span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">name_scope</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">+</span> <span class="s2">"/look_up/"</span><span class="p">):</span>
- <span class="n">sensitivity_factors</span> <span class="o">=</span> <span class="nb">super</span><span class="p">(</span><span class="n">CoilSensitivity</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__call__</span><span class="p">(</span><span class="n">r_vectors</span><span class="o">=</span><span class="n">r_vectors</span><span class="p">)</span>
-
- <span class="n">old_shape</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="n">signal_tensor</span><span class="p">)</span>
-
- <span class="c1"># In case all repetitions have the same position, reuse the sensitivity factors</span>
- <span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">name_scope</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">+</span> <span class="s2">"/shape_handling/"</span><span class="p">):</span>
- <span class="k">if</span> <span class="n">tf</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="n">sensitivity_factors</span><span class="p">)[</span><span class="mi">1</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
- <span class="n">multiples</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="n">signal_tensor</span><span class="p">)[</span><span class="mi">2</span><span class="p">]</span>
- <span class="n">sensitivity_factors</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">sensitivity_factors</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="n">multiples</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">])</span>
-
- <span class="n">temp</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">signal_tensor</span><span class="p">[</span><span class="o">...</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">newaxis</span><span class="p">],</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">_n_channels</span><span class="p">])</span>
- <span class="n">product</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">multiply</span><span class="p">(</span><span class="n">temp</span><span class="p">,</span> <span class="n">sensitivity_factors</span><span class="p">[</span><span class="n">tf</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,</span> <span class="o">...</span><span class="p">])</span>
-
- <span class="n">product</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">product</span><span class="p">,</span> <span class="p">[</span><span class="mi">0</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">4</span><span class="p">,</span> <span class="mi">2</span><span class="p">,</span> <span class="mi">3</span><span class="p">])</span>
- <span class="n">result</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">product</span><span class="p">,</span> <span class="p">[</span><span class="n">old_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">old_shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="n">old_shape</span><span class="p">[</span><span class="mi">2</span><span class="p">]</span> <span class="o">*</span> <span class="bp">self</span><span class="o">.</span><span class="n">_n_channels</span><span class="p">,</span>
- <span class="n">old_shape</span><span class="p">[</span><span class="mi">3</span><span class="p">]])</span>
- <span class="k">return</span> <span class="n">result</span></div>
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- <h1>Source code for cmrsim.signal_processes.solution_operators.diffusion_weighting</h1><div class="highlight"><pre>
-<span></span><span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"ConstantGaussianDiffusion"</span><span class="p">]</span>
-
-<span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
-
-<span class="kn">from</span> <span class="nn">cmrsim.signal_processes.base</span> <span class="kn">import</span> <span class="n">BaseSignalModel</span>
-
-
-<div class="viewcode-block" id="ConstantGaussianDiffusion"><a class="viewcode-back" href="../../../../cmrsim.signal_processes.solution_operators.diffusion_weighting.html#cmrsim.signal_processes.solution_operators.diffusion_weighting.ConstantGaussianDiffusion">[docs]</a><span class="k">class</span> <span class="nc">ConstantGaussianDiffusion</span><span class="p">(</span><span class="n">BaseSignalModel</span><span class="p">):</span>
- <span class="n">required_quantities</span> <span class="o">=</span> <span class="p">(</span><span class="s1">'diffusion_tensor'</span><span class="p">,</span> <span class="p">)</span>
-
- <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">b_vectors</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
- <span class="sd">""" Intantiates a module that evaluates the diffusion signal representation:</span>
-
-<span class="sd"> .. math::</span>
-<span class="sd"> F = \exp(-\mathbf{b}^T\mathbf{D}\mathbf{b}}</span>
-
-<span class="sd"> Assumes the b-vector to be scaled with</span>
-<span class="sd"> .. math::</span>
-<span class="sd"> \sqrt{b}</span>
-
-<span class="sd"> Instead of two dimensions for direction and b-value just uses one dimension of scaled b-vectors.</span>
-
-<span class="sd"> :param b_vectors: - (#directions * #bvalues, 3)</span>
-<span class="sd"> """</span>
- <span class="nb">super</span><span class="p">(</span><span class="n">ConstantGaussianDiffusion</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">"dti_gauss"</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">b_vectors</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">b_vectors</span><span class="p">)</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">expansion_factor</span> <span class="o">=</span> <span class="n">b_vectors</span><span class="o">.</span><span class="n">shape</span><span class="p">[</span><span class="mi">0</span><span class="p">]</span>
-
- <span class="nd">@tf</span><span class="o">.</span><span class="n">Module</span><span class="o">.</span><span class="n">with_name_scope</span>
- <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">signal_tensor</span><span class="p">:</span> <span class="n">tf</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span> <span class="o">-></span> <span class="n">tf</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
- <span class="sd">"""</span>
-<span class="sd"> :param signal_tensor: (#batch, #voxel, #repetitions, #k-space-samples) last two dimensions can be 1</span>
-<span class="sd"> :param kwargs: diffusion_tensor (#batch, #voxels, 3, 3)</span>
-<span class="sd"> :return: (#batch, #voxel, #repetitions * #b_vectors, #k-space-samples)</span>
-<span class="sd"> """</span>
- <span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">):</span>
- <span class="n">diff_tensors</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'diffusion_tensor'</span><span class="p">)</span>
- <span class="n">input_shape</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="n">signal_tensor</span><span class="p">)</span>
- <span class="n">exponent</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">'ni, bvij, nj -> bvn'</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">b_vectors</span><span class="p">,</span> <span class="n">diff_tensors</span><span class="p">,</span> <span class="bp">self</span><span class="o">.</span><span class="n">b_vectors</span><span class="p">)</span>
- <span class="n">decay_factor</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">exponent</span><span class="p">),</span> <span class="n">dtype</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">complex64</span><span class="p">)</span>
- <span class="n">temp</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">'bvrk, bvn -> bvrnk'</span><span class="p">,</span> <span class="n">signal_tensor</span><span class="p">,</span> <span class="n">decay_factor</span><span class="p">)</span>
- <span class="k">return</span> <span class="n">tf</span><span class="o">.</span><span class="n">reshape</span><span class="p">(</span><span class="n">temp</span><span class="p">,</span> <span class="p">(</span><span class="n">input_shape</span><span class="p">[</span><span class="mi">0</span><span class="p">],</span> <span class="n">input_shape</span><span class="p">[</span><span class="mi">1</span><span class="p">],</span> <span class="o">-</span><span class="mi">1</span><span class="p">,</span> <span class="n">input_shape</span><span class="p">[</span><span class="mi">3</span><span class="p">]))</span></div>
-</pre></div>
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-<li class="toctree-l1"><a class="reference internal" href="../../../../cmrsim.html">cmrsim package</a></li>
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-<p class="caption"><span class="caption-text">Who is using it? - Examples</span></p>
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-<li class="toctree-l1"><a class="reference internal" href="../../../../advanced_examples.html">1. Optimize b-values</a></li>
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- <h1>Source code for cmrsim.signal_processes.solution_operators.sequences</h1><div class="highlight"><pre>
-<span></span><span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"SpinEcho"</span><span class="p">]</span>
-
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Union</span>
-
-<span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
-
-<span class="kn">from</span> <span class="nn">cmrsim.signal_processes.base</span> <span class="kn">import</span> <span class="n">BaseSignalModel</span>
-
-
-<div class="viewcode-block" id="SpinEcho"><a class="viewcode-back" href="../../../../cmrsim.signal_processes.solution_operators.sequences.html#cmrsim.signal_processes.solution_operators.sequences.SpinEcho">[docs]</a><span class="k">class</span> <span class="nc">SpinEcho</span><span class="p">(</span><span class="n">BaseSignalModel</span><span class="p">):</span>
- <span class="n">required_quantities</span> <span class="o">=</span> <span class="p">(</span><span class="s1">'T1'</span><span class="p">,</span> <span class="s1">'T2'</span><span class="p">)</span>
- <span class="n">TE</span><span class="p">:</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span>
- <span class="n">TR</span><span class="p">:</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span>
- <span class="n">expansion_factor</span> <span class="o">=</span> <span class="mi">1</span>
-
- <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">echo_time</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">Tensor</span><span class="p">],</span> <span class="n">repetition_time</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="nb">float</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">Tensor</span><span class="p">],</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
- <span class="sd">"""</span>
-
-<span class="sd"> :param echo_time: in seconds</span>
-<span class="sd"> :param repetition_time: in seconds</span>
-<span class="sd"> """</span>
- <span class="nb">super</span><span class="p">(</span><span class="n">SpinEcho</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">"spin_echo"</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">TE</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">echo_time</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">TR</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">Variable</span><span class="p">(</span><span class="n">repetition_time</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">float32</span><span class="p">)</span>
-
- <span class="nd">@tf</span><span class="o">.</span><span class="n">Module</span><span class="o">.</span><span class="n">with_name_scope</span>
- <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">signal_tensor</span><span class="p">:</span> <span class="n">tf</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
- <span class="sd">""" Evaluates the signal for the simple Spin-Echo sequence:</span>
-
-<span class="sd"> .. math::</span>
-<span class="sd"> M_{xy} = M_z(0) \cdot e^{- TE / T_2} \cdot (1 - e^{-TR / T1))</span>
-
-<span class="sd"> :param signal_tensor:</span>
-<span class="sd"> :param kwargs:</span>
-<span class="sd"> :return:</span>
-<span class="sd"> """</span>
- <span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">):</span>
- <span class="n">t1</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'T1'</span><span class="p">)</span>
- <span class="n">t2</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'T2'</span><span class="p">)</span>
- <span class="n">t1_term</span> <span class="o">=</span> <span class="mi">1</span> <span class="o">-</span> <span class="n">tf</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">tf</span><span class="o">.</span><span class="n">divide</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">TR</span><span class="p">,</span> <span class="n">t1</span><span class="p">))</span>
- <span class="n">t2_term</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">tf</span><span class="o">.</span><span class="n">divide</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">TE</span><span class="p">,</span> <span class="n">t2</span><span class="p">))</span>
- <span class="n">factor</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">t1_term</span> <span class="o">*</span> <span class="n">t2_term</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">complex64</span><span class="p">)</span>
- <span class="n">factor</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">factor</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="n">signal_tensor</span><span class="p">)[</span><span class="mi">2</span><span class="p">],</span> <span class="n">tf</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="n">signal_tensor</span><span class="p">)[</span><span class="mi">3</span><span class="p">]])</span>
- <span class="k">return</span> <span class="n">signal_tensor</span> <span class="o">*</span> <span class="n">factor</span></div>
-</pre></div>
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deleted file mode 100644
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- <h1>Source code for cmrsim.signal_processes.solution_operators.t2_star</h1><div class="highlight"><pre>
-<span></span><span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"StaticT2starDecay"</span><span class="p">]</span>
-
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Union</span><span class="p">,</span> <span class="n">TYPE_CHECKING</span>
-<span class="k">if</span> <span class="n">TYPE_CHECKING</span><span class="p">:</span>
- <span class="kn">import</span> <span class="nn">numpy</span> <span class="k">as</span> <span class="nn">np</span>
-
-<span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
-
-<span class="kn">from</span> <span class="nn">cmrsim.signal_processes.base</span> <span class="kn">import</span> <span class="n">BaseSignalModel</span>
-
-
-<div class="viewcode-block" id="StaticT2starDecay"><a class="viewcode-back" href="../../../../cmrsim.signal_processes.solution_operators.t2_star.html#cmrsim.signal_processes.solution_operators.t2_star.StaticT2starDecay">[docs]</a><span class="k">class</span> <span class="nc">StaticT2starDecay</span><span class="p">(</span><span class="n">BaseSignalModel</span><span class="p">):</span>
- <span class="n">required_quantities</span> <span class="o">=</span> <span class="p">(</span><span class="s1">'T2star'</span><span class="p">,</span> <span class="p">)</span>
- <span class="n">sampling_times</span> <span class="o">=</span> <span class="kc">None</span> <span class="c1"># tf.Tensor or tf.RaggedTensor</span>
- <span class="n">expansion_factor</span> <span class="o">=</span> <span class="mi">1</span>
-
- <span class="k">def</span> <span class="fm">__init__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">sampling_times</span><span class="p">:</span> <span class="n">Union</span><span class="p">[</span><span class="n">tf</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="s1">'np.ndarray'</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">RaggedTensor</span><span class="p">],</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
- <span class="sd">""" Initializes a module to calculate the T2* decay at times specified in kwargs. The times are stored</span>
-<span class="sd"> in a tf.Variable. The stored parameters are expected to be passed in in seconds</span>
-
-<span class="sd"> :param kwargs: sampling_times - timing of acquisition (ADC) events in ms</span>
-<span class="sd"> """</span>
- <span class="nb">super</span><span class="p">(</span><span class="n">StaticT2starDecay</span><span class="p">,</span> <span class="bp">self</span><span class="p">)</span><span class="o">.</span><span class="fm">__init__</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s2">"static_t2star"</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
- <span class="k">with</span> <span class="bp">self</span><span class="o">.</span><span class="n">name_scope</span><span class="p">:</span>
- <span class="k">assert</span> <span class="nb">len</span><span class="p">(</span><span class="n">sampling_times</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">1</span> <span class="ow">or</span> <span class="nb">len</span><span class="p">(</span><span class="n">sampling_times</span><span class="o">.</span><span class="n">shape</span><span class="p">)</span> <span class="o">==</span> <span class="mi">2</span>
-
- <span class="k">if</span> <span class="ow">not</span> <span class="nb">isinstance</span><span class="p">(</span><span class="n">sampling_times</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">RaggedTensor</span><span class="p">):</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">sampling_times</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">abs</span><span class="p">(</span><span class="n">sampling_times</span><span class="p">),</span> <span class="n">shape</span><span class="o">=</span><span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="n">sampling_times</span><span class="p">)))</span>
- <span class="k">else</span><span class="p">:</span>
- <span class="bp">self</span><span class="o">.</span><span class="n">sampling_times</span> <span class="o">=</span> <span class="n">sampling_times</span>
-
- <span class="nd">@tf</span><span class="o">.</span><span class="n">Module</span><span class="o">.</span><span class="n">with_name_scope</span>
- <span class="k">def</span> <span class="fm">__call__</span><span class="p">(</span><span class="bp">self</span><span class="p">,</span> <span class="n">signal_tensor</span><span class="p">:</span> <span class="n">tf</span><span class="o">.</span><span class="n">Tensor</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">):</span>
- <span class="sd">""" Calculates signal decay with stored sampling event timings and given t2-star values. Assumes flattened</span>
-<span class="sd"> tensor of voxels.</span>
-
-<span class="sd"> :param signal_tensor: (#batch, #voxel, #repetitions, #k-space-samples) #repetitions and #k-space-samples can be1</span>
-<span class="sd"> :param kwargs: T2star = tf.Tensor (#batch, #voxel, 1, 1)</span>
-<span class="sd"> segment_index: int, if k-space is segmented for Memory reasons. defaults to 0</span>
-<span class="sd"> :return: signal_tensor (#batch, #voxel, #repetition, #k-space-samples) with #k-space-samples > 1</span>
-<span class="sd"> """</span>
- <span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">device</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">device</span><span class="p">):</span>
- <span class="n">segment_index</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'segment_index'</span><span class="p">,</span> <span class="mi">0</span><span class="p">)</span>
- <span class="n">t2_star_values</span> <span class="o">=</span> <span class="n">kwargs</span><span class="o">.</span><span class="n">get</span><span class="p">(</span><span class="s1">'T2star'</span><span class="p">)</span> <span class="o">*</span> <span class="mf">1e3</span>
- <span class="n">sampling_times</span> <span class="o">=</span> <span class="bp">self</span><span class="o">.</span><span class="n">sampling_times</span><span class="p">[</span><span class="n">segment_index</span><span class="p">]</span>
- <span class="n">n_samples</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">size</span><span class="p">(</span><span class="n">sampling_times</span><span class="p">)</span>
-
- <span class="n">t2_star_values</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">t2_star_values</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">n_samples</span><span class="p">])</span>
- <span class="n">exponent</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">einsum</span><span class="p">(</span><span class="s1">'k, bvrk -> bvrk'</span><span class="p">,</span> <span class="n">sampling_times</span><span class="p">,</span> <span class="mi">1</span><span class="o">/</span><span class="n">t2_star_values</span><span class="p">)</span>
- <span class="n">factor</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">exp</span><span class="p">(</span><span class="o">-</span><span class="n">exponent</span><span class="p">),</span> <span class="n">tf</span><span class="o">.</span><span class="n">complex64</span><span class="p">)</span>
-
- <span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">name_scope</span><span class="p">(</span><span class="bp">self</span><span class="o">.</span><span class="n">name</span> <span class="o">+</span> <span class="s1">'/handle_shape/'</span><span class="p">):</span>
- <span class="k">if</span> <span class="n">tf</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="n">signal_tensor</span><span class="p">)[</span><span class="mi">3</span><span class="p">]</span> <span class="o">==</span> <span class="mi">1</span><span class="p">:</span>
- <span class="n">signal_tensor</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">tile</span><span class="p">(</span><span class="n">signal_tensor</span><span class="p">,</span> <span class="p">[</span><span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="n">n_samples</span><span class="p">])</span>
-
- <span class="k">return</span> <span class="n">signal_tensor</span> <span class="o">*</span> <span class="n">factor</span></div>
-</pre></div>
-
- </div>
-
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- <p>
-
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-
- <a href="https://github.com/rtfd/sphinx_rtd_theme">theme</a>
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- <h1>Source code for cmrsim.simulation</h1><div class="highlight"><pre>
-<span></span><span class="n">__all__</span> <span class="o">=</span> <span class="p">[</span><span class="s2">"simulate"</span><span class="p">]</span>
-
-<span class="kn">from</span> <span class="nn">typing</span> <span class="kn">import</span> <span class="n">Tuple</span><span class="p">,</span> <span class="n">Optional</span><span class="p">,</span> <span class="n">TYPE_CHECKING</span>
-<span class="kn">import</span> <span class="nn">sys</span>
-<span class="kn">import</span> <span class="nn">os</span>
-
-<span class="kn">import</span> <span class="nn">tensorflow</span> <span class="k">as</span> <span class="nn">tf</span>
-
-
-<span class="k">if</span> <span class="n">TYPE_CHECKING</span><span class="p">:</span>
- <span class="kn">from</span> <span class="nn">cmrsim.anatomy.base</span> <span class="kn">import</span> <span class="n">BaseDataset</span>
- <span class="kn">from</span> <span class="nn">cmrsim.encoding.base</span> <span class="kn">import</span> <span class="n">BaseSampling</span>
- <span class="kn">from</span> <span class="nn">cmrsim.reconstruction.base</span> <span class="kn">import</span> <span class="n">BaseRecon</span>
- <span class="kn">from</span> <span class="nn">cmrsim.signal_processes.base</span> <span class="kn">import</span> <span class="n">CompositeSignalModel</span>
-
-
-<div class="viewcode-block" id="simulate"><a class="viewcode-back" href="../../cmrsim.simulation.html#cmrsim.simulation.simulate">[docs]</a><span class="k">def</span> <span class="nf">simulate</span><span class="p">(</span><span class="n">dataset</span><span class="p">:</span> <span class="s1">'BaseDataset'</span><span class="p">,</span> <span class="n">forward_model</span><span class="p">:</span> <span class="s1">'CompositeSignalModel'</span><span class="p">,</span> <span class="n">encoding_module</span><span class="p">:</span> <span class="s1">'BaseSampling'</span><span class="p">,</span>
- <span class="n">recon_module</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="s1">'BaseRecon'</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">voxel_batch_size</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="mi">1000</span><span class="p">,</span>
- <span class="n">execute_eager</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">bool</span><span class="p">]</span> <span class="o">=</span> <span class="kc">False</span><span class="p">,</span> <span class="n">graph_log_dir</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">str</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">):</span>
- <span class="sd">""" Wrapper for the decorated (@tf.function) call of the simulation loop.</span>
-
-<span class="sd"> :param dataset:</span>
-<span class="sd"> :param forward_model:</span>
-<span class="sd"> :param encoding_module:</span>
-<span class="sd"> :param recon_module: Subclass of BaseRecon Module, Optional: If none the return tensor are the flat k-space images.</span>
-<span class="sd"> :param voxel_batch_size: (int) see documentation of _simulate_segmented_k_space.</span>
-<span class="sd"> :param execute_eager: (bool) Default False: if True, Graph construction is skipped for simulation (allows debugging)</span>
-<span class="sd"> :param graph_log_dir: enables graph tracing to save def with tf.summary</span>
-<span class="sd"> :return: **(tf.Tensor)** | Stack of images with shape (#images, ..., #noise_levels, #X, #Y, #Z), or k-spaces with shape</span>
-<span class="sd"> (#images, ..., #noise_levels, #samples) where the ellipsis (...) depends on the specific forward model.</span>
-<span class="sd"> The order in which the *CompositeSignalModel* calls the submodules determines the order of dimensions and number of</span>
-<span class="sd"> axis in the ellipsis.</span>
-<span class="sd"> """</span>
-
- <span class="c1"># Check dataset inputs for missing parameter maps</span>
- <span class="k">if</span> <span class="ow">not</span> <span class="nb">all</span><span class="p">([</span><span class="n">rq</span> <span class="ow">in</span> <span class="n">dataset</span><span class="o">.</span><span class="n">map_names</span> <span class="k">for</span> <span class="n">rq</span> <span class="ow">in</span> <span class="n">forward_model</span><span class="o">.</span><span class="n">required_quantities</span><span class="p">]):</span>
- <span class="k">raise</span> <span class="ne">AssertionError</span><span class="p">(</span><span class="sa">f</span><span class="s1">'</span><span class="si">{</span><span class="n">dataset</span><span class="o">.</span><span class="n">map_names</span><span class="si">}</span><span class="s1"> does not contain all entries of </span><span class="si">{</span><span class="n">forward_model</span><span class="o">.</span><span class="n">required_quantities</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
-
- <span class="c1"># If wanted, disable graph construction with tf.function (designed for debugging)</span>
- <span class="n">tf</span><span class="o">.</span><span class="n">config</span><span class="o">.</span><span class="n">experimental_run_functions_eagerly</span><span class="p">(</span><span class="n">execute_eager</span><span class="p">)</span>
-
- <span class="c1"># If set to True log the graph with tf.summary</span>
- <span class="k">if</span> <span class="n">graph_log_dir</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
- <span class="n">os</span><span class="o">.</span><span class="n">makedirs</span><span class="p">(</span><span class="n">graph_log_dir</span><span class="p">,</span> <span class="n">exist_ok</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
- <span class="n">writer</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">summary</span><span class="o">.</span><span class="n">create_file_writer</span><span class="p">(</span><span class="n">graph_log_dir</span><span class="p">)</span>
- <span class="n">tf</span><span class="o">.</span><span class="n">summary</span><span class="o">.</span><span class="n">trace_on</span><span class="p">(</span><span class="n">graph</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
- <span class="nb">print</span><span class="p">(</span><span class="s2">"Graph Tracing activated..."</span><span class="p">)</span>
-
- <span class="c1"># Run actual simulation</span>
- <span class="n">stack_of_images</span> <span class="o">=</span> <span class="n">_simulate_batch_of_images</span><span class="p">(</span><span class="n">dataset</span><span class="p">(),</span> <span class="n">dataset</span><span class="o">.</span><span class="n">map_names</span><span class="p">,</span> <span class="n">dataset</span><span class="o">.</span><span class="n">set_size</span><span class="p">,</span> <span class="n">forward_model</span><span class="p">,</span>
- <span class="n">encoding_module</span><span class="p">,</span> <span class="n">recon_module</span><span class="p">,</span> <span class="n">voxel_batch_size</span><span class="p">)</span>
-
- <span class="k">if</span> <span class="n">graph_log_dir</span> <span class="ow">is</span> <span class="ow">not</span> <span class="kc">None</span><span class="p">:</span>
- <span class="k">with</span> <span class="n">writer</span><span class="o">.</span><span class="n">as_default</span><span class="p">():</span> <span class="c1"># noqa</span>
- <span class="n">tf</span><span class="o">.</span><span class="n">summary</span><span class="o">.</span><span class="n">trace_export</span><span class="p">(</span><span class="n">name</span><span class="o">=</span><span class="s1">'graph_def'</span><span class="p">,</span> <span class="n">step</span><span class="o">=</span><span class="mi">0</span><span class="p">)</span>
- <span class="nb">print</span><span class="p">(</span><span class="sa">f</span><span class="s1">'Saved Graph-definition to </span><span class="si">{</span><span class="n">graph_log_dir</span><span class="si">}</span><span class="s1">'</span><span class="p">)</span>
- <span class="k">return</span> <span class="n">forward_model</span><span class="o">.</span><span class="n">unstack_repetitions</span><span class="p">(</span><span class="n">stack_of_images</span><span class="p">)</span></div>
-
-
-<span class="k">def</span> <span class="nf">_simulate_batch_of_images</span><span class="p">(</span><span class="n">dataset</span><span class="p">:</span> <span class="n">tf</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">Dataset</span><span class="p">,</span> <span class="n">map_names</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">str</span><span class="p">,</span> <span class="o">...</span><span class="p">],</span> <span class="n">set_size</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span>
- <span class="n">forward_model</span><span class="p">:</span> <span class="s1">'CompositeSignalModel'</span><span class="p">,</span> <span class="n">encoding_module</span><span class="p">:</span> <span class="s1">'BaseSampling'</span><span class="p">,</span>
- <span class="n">recon_module</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="s1">'BaseRecon'</span><span class="p">]</span> <span class="o">=</span> <span class="kc">None</span><span class="p">,</span> <span class="n">voxel_batch_size</span><span class="p">:</span> <span class="nb">int</span> <span class="o">=</span> <span class="mi">1000</span><span class="p">)</span> <span class="o">-></span> <span class="n">tf</span><span class="o">.</span><span class="n">Tensor</span><span class="p">:</span>
- <span class="sd">""" Consumes all object-configuration data (images) and simulates the MR images. The simulation configuration is</span>
-<span class="sd"> exclusively defined by the given modules forward_model and encoding. If the optional reconstruction module is</span>
-<span class="sd"> given, the returned tensor contains images otherwise it will return the k-space data.</span>
-
-<span class="sd"> :param dataset: tf.Dataset that yields a batch of flat image-parameters where the first</span>
-<span class="sd"> dimensions are (#batch, #isochromates, #repetitions, ...). This assumes M0 to be the first</span>
-<span class="sd"> entry of the yielded tuple of tensors.</span>
-<span class="sd"> :param map_names: (passed through) Tuple of strings matching the required quantities of forward model</span>
-<span class="sd"> :param set_size: Number of images in the Dataset. necessary for allocation.</span>
-<span class="sd"> :param forward_model:</span>
-<span class="sd"> :param encoding_module:</span>
-<span class="sd"> :param recon_module:</span>
-<span class="sd"> :param voxel_batch_size: If the images are to large for processing them at ones, this parameter is</span>
-<span class="sd"> used to batch the image into smaller subsets of isochromates.</span>
-<span class="sd"> :return: Stack of images as tensor with shape (#images, #repetitions, #noise_instances, #X, #Y, [#Z]) or stack of</span>
-<span class="sd"> k-space-images as tensor with shape (#images, #repetitions, #noise_instance, #samples).</span>
-<span class="sd"> The returned images are only 3D if the input data, encoding and recon module are 3D as well.</span>
-<span class="sd"> """</span>
-
- <span class="c1"># Allocate tensor array to store the simulated images</span>
- <span class="n">simulation_result</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">TensorArray</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">complex64</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">set_size</span><span class="p">,</span> <span class="n">dynamic_size</span><span class="o">=</span><span class="kc">False</span><span class="p">)</span>
-
- <span class="n">image_idx</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">int64</span><span class="p">)</span>
- <span class="k">for</span> <span class="n">image_idx</span><span class="p">,</span> <span class="n">single_image_dataset</span> <span class="ow">in</span> <span class="n">dataset</span><span class="o">.</span><span class="n">enumerate</span><span class="p">():</span>
- <span class="c1"># Batching with size 1 removes first dimension -> index 0 now is #voxels</span>
- <span class="n">current_object_isochromat_count</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="n">single_image_dataset</span><span class="p">[</span><span class="mi">0</span><span class="p">])[</span><span class="mi">0</span><span class="p">]</span>
-
- <span class="c1"># Sub-batch #voxels</span>
- <span class="n">single_image_dataset</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">Dataset</span><span class="o">.</span><span class="n">from_tensor_slices</span><span class="p">(</span><span class="n">single_image_dataset</span><span class="p">)</span>
- <span class="n">single_image_dataset</span> <span class="o">=</span> <span class="n">single_image_dataset</span><span class="o">.</span><span class="n">batch</span><span class="p">(</span><span class="n">voxel_batch_size</span><span class="p">)</span><span class="o">.</span><span class="n">enumerate</span><span class="p">()</span>
- <span class="n">single_image_dataset</span> <span class="o">=</span> <span class="n">single_image_dataset</span><span class="o">.</span><span class="n">prefetch</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">experimental</span><span class="o">.</span><span class="n">AUTOTUNE</span><span class="p">)</span>
-
- <span class="c1"># Simulate discretized k-space according to forward model and encoding module</span>
- <span class="n">s_of_k</span> <span class="o">=</span> <span class="n">_single_image_k_space_loop</span><span class="p">(</span><span class="n">single_image_dataset</span><span class="p">,</span> <span class="n">encoding_module</span><span class="p">,</span> <span class="n">forward_model</span><span class="p">,</span> <span class="n">map_names</span><span class="p">,</span>
- <span class="n">current_object_isochromat_count</span><span class="p">,</span> <span class="n">image_idx</span><span class="p">)</span>
-
- <span class="k">if</span> <span class="n">recon_module</span> <span class="ow">is</span> <span class="kc">None</span><span class="p">:</span>
- <span class="n">simulation_result</span> <span class="o">=</span> <span class="n">simulation_result</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">image_idx</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">int32</span><span class="p">),</span> <span class="n">s_of_k</span><span class="p">)</span>
- <span class="k">else</span><span class="p">:</span> <span class="c1"># Apply reconstruction</span>
- <span class="n">image</span> <span class="o">=</span> <span class="n">recon_module</span><span class="p">(</span><span class="n">s_of_k</span><span class="p">)</span>
- <span class="n">simulation_result</span> <span class="o">=</span> <span class="n">simulation_result</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">tf</span><span class="o">.</span><span class="n">cast</span><span class="p">(</span><span class="n">image_idx</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">int32</span><span class="p">),</span> <span class="n">image</span><span class="p">)</span>
- <span class="n">tf</span><span class="o">.</span><span class="n">print</span><span class="p">(</span><span class="s1">'</span><span class="se">\n</span><span class="s1">Processed'</span><span class="p">,</span> <span class="n">image_idx</span><span class="o">+</span><span class="mi">1</span><span class="p">,</span> <span class="s1">'image(s)...'</span><span class="p">)</span>
- <span class="k">return</span> <span class="n">simulation_result</span><span class="o">.</span><span class="n">concat</span><span class="p">()</span>
-
-
-<span class="nd">@tf</span><span class="o">.</span><span class="n">function</span>
-<span class="k">def</span> <span class="nf">_single_image_k_space_loop</span><span class="p">(</span><span class="n">single_image_dataset</span><span class="p">:</span> <span class="n">tf</span><span class="o">.</span><span class="n">data</span><span class="o">.</span><span class="n">Dataset</span><span class="p">,</span> <span class="n">encoding_module</span><span class="p">:</span> <span class="s1">'BaseSampling'</span><span class="p">,</span>
- <span class="n">forward_model</span><span class="p">:</span> <span class="s1">'CompositeSignalModel'</span><span class="p">,</span> <span class="n">map_names</span><span class="p">:</span> <span class="n">Tuple</span><span class="p">[</span><span class="nb">str</span><span class="p">],</span>
- <span class="n">total_count</span><span class="p">:</span> <span class="nb">int</span><span class="p">,</span> <span class="n">image_index</span><span class="p">:</span> <span class="n">Optional</span><span class="p">[</span><span class="nb">int</span><span class="p">]</span> <span class="o">=</span> <span class="mi">0</span><span class="p">):</span>
- <span class="sd">""" Consumes the all object - datapoints (isochromates/voxels), evaluates the defined forward model and encodes</span>
-<span class="sd"> data to k-space with defined encoding module.</span>
-
-<span class="sd"> Additional explanations for defined variables:</span>
-<span class="sd"> - expected_shape: Tuple[int] Used to allocate a zero tensor to enable evaluating the fourier integral in</span>
-<span class="sd"> batches. This shape is determined by the axis expansions performed by the modules and is</span>
-<span class="sd"> given as : (#image_batch, #repetitions, #k-space-samples).</span>
-<span class="sd"> The noise dimension is added after evaluating the Fourier integral.</span>
-
-<span class="sd"> :param single_image_dataset: tf.data.Dataset that yields a tuple of tensors containing the properties in the same</span>
-<span class="sd"> order specified in map_names - argument</span>
-<span class="sd"> :param encoding_module: Instance of Subclass of cmrsim.encoding.base.BaseSampling module</span>
-<span class="sd"> :param forward_model: Instance of cmrsim.signal_process.base.CompositeSignalModel</span>
-<span class="sd"> :param map_names: Tuple[str] containing the ordered names of the tensors streamed by single_image_dataset</span>
-<span class="sd"> :param total_count: (Only used for verbose print statement)</span>
-<span class="sd"> :param image_index: (Only used for verbose print statement)</span>
-<span class="sd"> :return: tf.Tensor of shape (1, #repetitions, #noise_levels, #k-space-samples)</span>
-<span class="sd"> """</span>
- <span class="c1"># Allocate k-space-Tensor</span>
- <span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">name_scope</span><span class="p">(</span><span class="s1">'k_space_loop/'</span><span class="p">):</span>
- <span class="n">n_segments</span> <span class="o">=</span> <span class="n">encoding_module</span><span class="o">.</span><span class="n">k_space_segments</span>
- <span class="n">s_of_k_segments</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">TensorArray</span><span class="p">(</span><span class="n">dtype</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">complex64</span><span class="p">,</span> <span class="n">size</span><span class="o">=</span><span class="n">n_segments</span><span class="p">,</span> <span class="n">dynamic_size</span><span class="o">=</span><span class="kc">True</span><span class="p">)</span>
- <span class="n">expected_k_space_shape</span> <span class="o">=</span> <span class="p">(</span><span class="mi">1</span><span class="p">,</span> <span class="n">forward_model</span><span class="o">.</span><span class="n">expected_number_of_repetitions</span><span class="p">,</span> <span class="n">encoding_module</span><span class="o">.</span><span class="n">number_of_samples</span><span class="p">)</span>
- <span class="n">current_voxel_count</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">constant</span><span class="p">(</span><span class="mi">0</span><span class="p">,</span> <span class="n">dtype</span><span class="o">=</span><span class="n">tf</span><span class="o">.</span><span class="n">int32</span><span class="p">)</span>
- <span class="c1"># allocation for cumulative sum that represents the Volume integral over the transverse magnetization</span>
- <span class="n">s_of_k</span> <span class="o">=</span> <span class="n">tf</span><span class="o">.</span><span class="n">zeros</span><span class="p">(</span><span class="n">expected_k_space_shape</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">complex64</span><span class="p">)</span>
-
- <span class="k">for</span> <span class="n">voxel_index</span><span class="p">,</span> <span class="n">voxel_batch</span> <span class="ow">in</span> <span class="n">single_image_dataset</span><span class="p">:</span>
- <span class="c1"># Add batch axis for compatibility reasons</span>
- <span class="n">kwargs</span> <span class="o">=</span> <span class="p">{</span><span class="n">name</span><span class="p">:</span> <span class="n">tensor</span><span class="p">[</span><span class="n">tf</span><span class="o">.</span><span class="n">newaxis</span><span class="p">,</span> <span class="o">...</span><span class="p">]</span> <span class="k">for</span> <span class="n">name</span><span class="p">,</span> <span class="n">tensor</span> <span class="ow">in</span> <span class="nb">zip</span><span class="p">(</span><span class="n">map_names</span><span class="p">,</span> <span class="n">voxel_batch</span><span class="p">)}</span>
-
- <span class="c1"># Evaluate contribution of voxel batch to all k-space point and add to cumulative integration (s_of_k)</span>
- <span class="k">for</span> <span class="n">segment_index</span> <span class="ow">in</span> <span class="n">tf</span><span class="o">.</span><span class="n">range</span><span class="p">(</span><span class="n">n_segments</span><span class="p">):</span>
- <span class="n">m_transverse_segment</span> <span class="o">=</span> <span class="n">forward_model</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">"M0"</span><span class="p">],</span> <span class="n">segment_index</span><span class="o">=</span><span class="n">segment_index</span><span class="p">,</span> <span class="o">**</span><span class="n">kwargs</span><span class="p">)</span>
- <span class="n">k_space_segment</span> <span class="o">=</span> <span class="n">encoding_module</span><span class="p">(</span><span class="n">m_transverse_segment</span><span class="p">,</span> <span class="n">kwargs</span><span class="p">[</span><span class="s1">'r_vectors'</span><span class="p">],</span>
- <span class="n">segment_index</span><span class="o">=</span><span class="n">segment_index</span><span class="p">)</span>
-
- <span class="n">s_of_k_segments</span> <span class="o">=</span> <span class="n">s_of_k_segments</span><span class="o">.</span><span class="n">write</span><span class="p">(</span><span class="n">segment_index</span><span class="p">,</span> <span class="n">tf</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">k_space_segment</span><span class="p">,</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">]))</span>
- <span class="n">tf</span><span class="o">.</span><span class="n">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\r</span><span class="s2">Processed image(s):"</span><span class="p">,</span> <span class="n">image_index</span><span class="p">,</span> <span class="s2">"Voxel count:"</span><span class="p">,</span> <span class="n">current_voxel_count</span><span class="p">,</span> <span class="s2">"/"</span><span class="p">,</span> <span class="n">total_count</span><span class="p">,</span>
- <span class="s2">"Segment index:"</span><span class="p">,</span> <span class="n">segment_index</span><span class="p">,</span> <span class="s2">"/"</span><span class="p">,</span> <span class="n">n_segments</span><span class="p">,</span> <span class="mi">10</span><span class="o">*</span><span class="s1">' '</span><span class="p">,</span> <span class="n">output_stream</span><span class="o">=</span><span class="n">sys</span><span class="o">.</span><span class="n">stdout</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="s2">""</span><span class="p">)</span>
-
- <span class="k">with</span> <span class="n">tf</span><span class="o">.</span><span class="n">name_scope</span><span class="p">(</span><span class="s1">'add_batch_to_k_space/'</span><span class="p">):</span>
- <span class="n">current_voxel_count</span> <span class="o">=</span> <span class="n">current_voxel_count</span> <span class="o">+</span> <span class="n">tf</span><span class="o">.</span><span class="n">shape</span><span class="p">(</span><span class="n">kwargs</span><span class="p">[</span><span class="s2">"M0"</span><span class="p">])[</span><span class="mi">1</span><span class="p">]</span>
- <span class="n">s_of_k</span> <span class="o">+=</span> <span class="n">tf</span><span class="o">.</span><span class="n">transpose</span><span class="p">(</span><span class="n">s_of_k_segments</span><span class="o">.</span><span class="n">concat</span><span class="p">(),</span> <span class="p">[</span><span class="mi">2</span><span class="p">,</span> <span class="mi">1</span><span class="p">,</span> <span class="mi">0</span><span class="p">])</span>
-
- <span class="n">tf</span><span class="o">.</span><span class="n">print</span><span class="p">(</span><span class="s2">"</span><span class="se">\r</span><span class="s2">Processed image(s):"</span><span class="p">,</span> <span class="n">image_index</span><span class="p">,</span> <span class="s2">"Voxel count:"</span><span class="p">,</span> <span class="n">total_count</span><span class="p">,</span> <span class="s2">"/"</span><span class="p">,</span> <span class="n">total_count</span><span class="p">,</span> <span class="s2">"Segment index:"</span><span class="p">,</span>
- <span class="n">n_segments</span><span class="p">,</span> <span class="s2">"/"</span><span class="p">,</span> <span class="n">n_segments</span><span class="p">,</span> <span class="mi">10</span><span class="o">*</span><span class="s1">' '</span><span class="p">,</span> <span class="n">output_stream</span><span class="o">=</span><span class="n">sys</span><span class="o">.</span><span class="n">stdout</span><span class="p">,</span> <span class="n">end</span><span class="o">=</span><span class="s2">""</span><span class="p">)</span>
-
- <span class="n">s_of_k</span> <span class="o">=</span> <span class="n">encoding_module</span><span class="o">.</span><span class="n">add_noise</span><span class="p">(</span><span class="n">s_of_k</span><span class="p">)</span>
- <span class="k">return</span> <span class="n">s_of_k</span>
-
-</pre></div>
-
- </div>
-
- </div>
- <footer>
-
-
- <hr/>
-
- <div role="contentinfo">
- <p>
-
- © Copyright 2020, Jonathan Weine
-
- </p>
- </div>
-
-
-
- Built with <a href="http://sphinx-doc.org/">Sphinx</a> using a
-
- <a href="https://github.com/rtfd/sphinx_rtd_theme">theme</a>
-
- provided by <a href="https://readthedocs.org">Read the Docs</a>.
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- });
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diff --git a/public/_modules/index.html b/public/_modules/index.html
deleted file mode 100644
index d6080ba5a1abbb1086e6fbcbc5a2b5c77f9b384f..0000000000000000000000000000000000000000
--- a/public/_modules/index.html
+++ /dev/null
@@ -1,224 +0,0 @@
-
-
-<!DOCTYPE html>
-<html class="writer-html5" lang="en" >
-<head>
- <meta charset="utf-8">
-
- <meta name="viewport" content="width=device-width, initial-scale=1.0">
-
- <title>Overview: module code — cmrsim 1.0alpha documentation</title>
-
-
-
- <link rel="stylesheet" href="../_static/css/theme.css" type="text/css" />
- <link rel="stylesheet" href="../_static/pygments.css" type="text/css" />
-
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- <!--[if lt IE 9]>
- <script src="../_static/js/html5shiv.min.js"></script>
- <![endif]-->
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-
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- <link rel="index" title="Index" href="../genindex.html" />
- <link rel="search" title="Search" href="../search.html" />
-</head>
-
-<body class="wy-body-for-nav">
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- <div class="wy-grid-for-nav">
-
- <nav data-toggle="wy-nav-shift" class="wy-nav-side">
- <div class="wy-side-scroll">
- <div class="wy-side-nav-search" >
-
-
-
- <a href="../index.html" class="icon icon-home" alt="Documentation Home"> cmrsim
-
-
-
- </a>
-
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-
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-<div role="search">
- <form id="rtd-search-form" class="wy-form" action="../search.html" method="get">
- <input type="text" name="q" placeholder="Search docs" />
- <input type="hidden" name="check_keywords" value="yes" />
- <input type="hidden" name="area" value="default" />
- </form>
-</div>
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-
-
- <div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="main navigation">
-
-
-
-
-
-
- <p class="caption"><span class="caption-text">Getting started</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../started.html">Simulate a simple image</a></li>
-</ul>
-<p class="caption"><span class="caption-text">Documentation</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../cmrsim.html">cmrsim package</a></li>
-</ul>
-<p class="caption"><span class="caption-text">Who is using it? - Examples</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../advanced_examples.html">1. Optimize b-values</a></li>
-</ul>
-<p class="caption"><span class="caption-text">How to cite?</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="../citation.html">Citation</a></li>
-</ul>
-
-
-
- </div>
-
- </div>
- </nav>
-
- <section data-toggle="wy-nav-shift" class="wy-nav-content-wrap">
-
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- <nav class="wy-nav-top" aria-label="top navigation">
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- <div class="wy-nav-content">
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-
-<div role="navigation" aria-label="breadcrumbs navigation">
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- <ul class="wy-breadcrumbs">
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- <li><a href="../index.html" class="icon icon-home"></a> »</li>
-
- <li>Overview: module code</li>
-
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- <li class="wy-breadcrumbs-aside">
-
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- </ul>
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- <hr/>
-</div>
- <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
- <div itemprop="articleBody">
-
- <h1>All modules for which code is available</h1>
-<ul><li><a href="cmrsim/anatomy/base.html">cmrsim.anatomy.base</a></li>
-<li><a href="cmrsim/anatomy/from_files.html">cmrsim.anatomy.from_files</a></li>
-<li><a href="cmrsim/auxilliary/assign_map_values.html">cmrsim.auxilliary.assign_map_values</a></li>
-<li><a href="cmrsim/auxilliary/coordinates.html">cmrsim.auxilliary.coordinates</a></li>
-<li><a href="cmrsim/auxilliary/plot_utilities.html">cmrsim.auxilliary.plot_utilities</a></li>
-<li><a href="cmrsim/auxilliary/snr.html">cmrsim.auxilliary.snr</a></li>
-<li><a href="cmrsim/encoding/base.html">cmrsim.encoding.base</a></li>
-<li><a href="cmrsim/encoding/epi.html">cmrsim.encoding.epi</a></li>
-<li><a href="cmrsim/reconstruction/base.html">cmrsim.reconstruction.base</a></li>
-<li><a href="cmrsim/reconstruction/cartesian.html">cmrsim.reconstruction.cartesian</a></li>
-<li><a href="cmrsim/signal_processes/base.html">cmrsim.signal_processes.base</a></li>
-<li><a href="cmrsim/signal_processes/solution_operators/coil_sensitivities.html">cmrsim.signal_processes.solution_operators.coil_sensitivities</a></li>
-<li><a href="cmrsim/signal_processes/solution_operators/diffusion_weighting.html">cmrsim.signal_processes.solution_operators.diffusion_weighting</a></li>
-<li><a href="cmrsim/signal_processes/solution_operators/sequences.html">cmrsim.signal_processes.solution_operators.sequences</a></li>
-<li><a href="cmrsim/signal_processes/solution_operators/t2_star.html">cmrsim.signal_processes.solution_operators.t2_star</a></li>
-<li><a href="cmrsim/simulation.html">cmrsim.simulation</a></li>
-</ul>
-
- </div>
-
- </div>
- <footer>
-
-
- <hr/>
-
- <div role="contentinfo">
- <p>
-
- © Copyright 2020, Jonathan Weine
-
- </p>
- </div>
-
-
-
- Built with <a href="http://sphinx-doc.org/">Sphinx</a> using a
-
- <a href="https://github.com/rtfd/sphinx_rtd_theme">theme</a>
-
- provided by <a href="https://readthedocs.org">Read the Docs</a>.
-
-</footer>
-
- </div>
- </div>
-
- </section>
-
- </div>
-
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- <script type="text/javascript">
- jQuery(function () {
- SphinxRtdTheme.Navigation.enable(true);
- });
- </script>
-
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-</body>
-</html>
\ No newline at end of file
diff --git a/public/_sources/advanced_examples.rst.txt b/public/_sources/advanced_examples.rst.txt
deleted file mode 100644
index 4a8cd242a5e7f7f907b74f92719d36c7d3e47ed1..0000000000000000000000000000000000000000
--- a/public/_sources/advanced_examples.rst.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-1. Optimize b-values
---------------------
-
-Here are a couple of advanced applications...
\ No newline at end of file
diff --git a/public/_sources/citation.rst.txt b/public/_sources/citation.rst.txt
deleted file mode 100644
index 594e18d63e82290a732c789f0e2bacbee0c49f69..0000000000000000000000000000000000000000
--- a/public/_sources/citation.rst.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-Citation
-========
-
-If u like it, refer to it!
\ No newline at end of file
diff --git a/public/_sources/cmrsim.anatomy.base.rst.txt b/public/_sources/cmrsim.anatomy.base.rst.txt
deleted file mode 100644
index 1f8141443707dd3e4f97152ccbc9abf02bdf1f19..0000000000000000000000000000000000000000
--- a/public/_sources/cmrsim.anatomy.base.rst.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-cmrsim.anatomy.base module
-==========================
-
-.. automodule:: cmrsim.anatomy.base
- :members:
- :undoc-members:
- :show-inheritance:
diff --git a/public/_sources/cmrsim.anatomy.from_files.rst.txt b/public/_sources/cmrsim.anatomy.from_files.rst.txt
deleted file mode 100644
index c22899481d0d98d0c13d912b56df6f5faf806d54..0000000000000000000000000000000000000000
--- a/public/_sources/cmrsim.anatomy.from_files.rst.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-cmrsim.anatomy.from\_files module
-=================================
-
-.. automodule:: cmrsim.anatomy.from_files
- :members:
- :undoc-members:
- :show-inheritance:
diff --git a/public/_sources/cmrsim.anatomy.rst.txt b/public/_sources/cmrsim.anatomy.rst.txt
deleted file mode 100644
index 74c0eed0fcb1ec3e4cc16c69fbd17f666fa9db7e..0000000000000000000000000000000000000000
--- a/public/_sources/cmrsim.anatomy.rst.txt
+++ /dev/null
@@ -1,19 +0,0 @@
-cmrsim.anatomy package
-======================
-
-Submodules
-----------
-
-.. toctree::
- :maxdepth: 4
-
- cmrsim.anatomy.base
- cmrsim.anatomy.from_files
-
-Module contents
----------------
-
-.. automodule:: cmrsim.anatomy
- :members:
- :undoc-members:
- :show-inheritance:
diff --git a/public/_sources/cmrsim.auxilliary.assign_map_values.rst.txt b/public/_sources/cmrsim.auxilliary.assign_map_values.rst.txt
deleted file mode 100644
index d93314062134457f032d2b52e7cb3be41e8a1441..0000000000000000000000000000000000000000
--- a/public/_sources/cmrsim.auxilliary.assign_map_values.rst.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-cmrsim.auxilliary.assign\_map\_values module
-============================================
-
-.. automodule:: cmrsim.auxilliary.assign_map_values
- :members:
- :undoc-members:
- :show-inheritance:
diff --git a/public/_sources/cmrsim.auxilliary.coordinates.rst.txt b/public/_sources/cmrsim.auxilliary.coordinates.rst.txt
deleted file mode 100644
index 61dc40a9da8641ac1c4146adef3702dea6196fbf..0000000000000000000000000000000000000000
--- a/public/_sources/cmrsim.auxilliary.coordinates.rst.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-cmrsim.auxilliary.coordinates module
-====================================
-
-.. automodule:: cmrsim.auxilliary.coordinates
- :members:
- :undoc-members:
- :show-inheritance:
diff --git a/public/_sources/cmrsim.auxilliary.plot_utilities.rst.txt b/public/_sources/cmrsim.auxilliary.plot_utilities.rst.txt
deleted file mode 100644
index 5777dbf408698f88e641f03bb79439314ef4de7a..0000000000000000000000000000000000000000
--- a/public/_sources/cmrsim.auxilliary.plot_utilities.rst.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-cmrsim.auxilliary.plot\_utilities module
-========================================
-
-.. automodule:: cmrsim.auxilliary.plot_utilities
- :members:
- :undoc-members:
- :show-inheritance:
diff --git a/public/_sources/cmrsim.auxilliary.rst.txt b/public/_sources/cmrsim.auxilliary.rst.txt
deleted file mode 100644
index 7fc1c92e3dbb74ddf12121df17b179b3d9672dd4..0000000000000000000000000000000000000000
--- a/public/_sources/cmrsim.auxilliary.rst.txt
+++ /dev/null
@@ -1,29 +0,0 @@
-cmrsim.auxilliary package
-=========================
-
-Subpackages
------------
-
-.. toctree::
- :maxdepth: 4
-
- cmrsim.auxilliary.sequence
-
-Submodules
-----------
-
-.. toctree::
- :maxdepth: 4
-
- cmrsim.auxilliary.assign_map_values
- cmrsim.auxilliary.coordinates
- cmrsim.auxilliary.plot_utilities
- cmrsim.auxilliary.snr
-
-Module contents
----------------
-
-.. automodule:: cmrsim.auxilliary
- :members:
- :undoc-members:
- :show-inheritance:
diff --git a/public/_sources/cmrsim.auxilliary.sequence.rst.txt b/public/_sources/cmrsim.auxilliary.sequence.rst.txt
deleted file mode 100644
index 68df01bb209fcec62fcb1d0f88e3376ea41cc4f2..0000000000000000000000000000000000000000
--- a/public/_sources/cmrsim.auxilliary.sequence.rst.txt
+++ /dev/null
@@ -1,10 +0,0 @@
-cmrsim.auxilliary.sequence package
-==================================
-
-Module contents
----------------
-
-.. automodule:: cmrsim.auxilliary.sequence
- :members:
- :undoc-members:
- :show-inheritance:
diff --git a/public/_sources/cmrsim.auxilliary.snr.rst.txt b/public/_sources/cmrsim.auxilliary.snr.rst.txt
deleted file mode 100644
index 17eef305434d282e316354d4961d83ce4a77cc02..0000000000000000000000000000000000000000
--- a/public/_sources/cmrsim.auxilliary.snr.rst.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-cmrsim.auxilliary.snr module
-============================
-
-.. automodule:: cmrsim.auxilliary.snr
- :members:
- :undoc-members:
- :show-inheritance:
diff --git a/public/_sources/cmrsim.encoding.base.rst.txt b/public/_sources/cmrsim.encoding.base.rst.txt
deleted file mode 100644
index 3353703463aabd043b78b99cca67614dfac87f9b..0000000000000000000000000000000000000000
--- a/public/_sources/cmrsim.encoding.base.rst.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-cmrsim.encoding.base module
-===========================
-
-.. automodule:: cmrsim.encoding.base
- :members:
- :undoc-members:
- :show-inheritance:
diff --git a/public/_sources/cmrsim.encoding.epi.rst.txt b/public/_sources/cmrsim.encoding.epi.rst.txt
deleted file mode 100644
index 3940bea5a3f221f8b7903b322ab3804e12655f4b..0000000000000000000000000000000000000000
--- a/public/_sources/cmrsim.encoding.epi.rst.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-cmrsim.encoding.epi module
-==========================
-
-.. automodule:: cmrsim.encoding.epi
- :members:
- :undoc-members:
- :show-inheritance:
diff --git a/public/_sources/cmrsim.encoding.rst.txt b/public/_sources/cmrsim.encoding.rst.txt
deleted file mode 100644
index 905b8dbe2754ac72c41171ca0140558bc5f3bb95..0000000000000000000000000000000000000000
--- a/public/_sources/cmrsim.encoding.rst.txt
+++ /dev/null
@@ -1,19 +0,0 @@
-cmrsim.encoding package
-=======================
-
-Submodules
-----------
-
-.. toctree::
- :maxdepth: 4
-
- cmrsim.encoding.base
- cmrsim.encoding.epi
-
-Module contents
----------------
-
-.. automodule:: cmrsim.encoding
- :members:
- :undoc-members:
- :show-inheritance:
diff --git a/public/_sources/cmrsim.reconstruction.base.rst.txt b/public/_sources/cmrsim.reconstruction.base.rst.txt
deleted file mode 100644
index 1201b68dc650c09aa8f1398f27a795e4d97e4d36..0000000000000000000000000000000000000000
--- a/public/_sources/cmrsim.reconstruction.base.rst.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-cmrsim.reconstruction.base module
-=================================
-
-.. automodule:: cmrsim.reconstruction.base
- :members:
- :undoc-members:
- :show-inheritance:
diff --git a/public/_sources/cmrsim.reconstruction.cartesian.rst.txt b/public/_sources/cmrsim.reconstruction.cartesian.rst.txt
deleted file mode 100644
index 1ff15ad7f0b9849ac3edd069e8166e09035e0711..0000000000000000000000000000000000000000
--- a/public/_sources/cmrsim.reconstruction.cartesian.rst.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-cmrsim.reconstruction.cartesian module
-======================================
-
-.. automodule:: cmrsim.reconstruction.cartesian
- :members:
- :undoc-members:
- :show-inheritance:
diff --git a/public/_sources/cmrsim.reconstruction.rst.txt b/public/_sources/cmrsim.reconstruction.rst.txt
deleted file mode 100644
index 84d42c02fb1e52f4d49eeb5092829809667e97c1..0000000000000000000000000000000000000000
--- a/public/_sources/cmrsim.reconstruction.rst.txt
+++ /dev/null
@@ -1,19 +0,0 @@
-cmrsim.reconstruction package
-=============================
-
-Submodules
-----------
-
-.. toctree::
- :maxdepth: 4
-
- cmrsim.reconstruction.base
- cmrsim.reconstruction.cartesian
-
-Module contents
----------------
-
-.. automodule:: cmrsim.reconstruction
- :members:
- :undoc-members:
- :show-inheritance:
diff --git a/public/_sources/cmrsim.rst.txt b/public/_sources/cmrsim.rst.txt
deleted file mode 100644
index 23fbb486cea98af20474f444984f8d6d3e7697af..0000000000000000000000000000000000000000
--- a/public/_sources/cmrsim.rst.txt
+++ /dev/null
@@ -1,30 +0,0 @@
-cmrsim package
-==============
-
-Subpackages
------------
-
-.. toctree::
- :maxdepth: 4
-
- cmrsim.anatomy
- cmrsim.auxilliary
- cmrsim.encoding
- cmrsim.reconstruction
- cmrsim.signal_processes
-
-Submodules
-----------
-
-.. toctree::
- :maxdepth: 4
-
- cmrsim.simulation
-
-Module contents
----------------
-
-.. automodule:: cmrsim
- :members:
- :undoc-members:
- :show-inheritance:
diff --git a/public/_sources/cmrsim.signal_processes.base.rst.txt b/public/_sources/cmrsim.signal_processes.base.rst.txt
deleted file mode 100644
index 9ccdffca351c88a0e3611cc40a250900565e44c5..0000000000000000000000000000000000000000
--- a/public/_sources/cmrsim.signal_processes.base.rst.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-cmrsim.signal\_processes.base module
-====================================
-
-.. automodule:: cmrsim.signal_processes.base
- :members:
- :undoc-members:
- :show-inheritance:
diff --git a/public/_sources/cmrsim.signal_processes.bloch_simulation.rst.txt b/public/_sources/cmrsim.signal_processes.bloch_simulation.rst.txt
deleted file mode 100644
index b7e96cb4542f8fbde54bc6767c64bd032227f1b1..0000000000000000000000000000000000000000
--- a/public/_sources/cmrsim.signal_processes.bloch_simulation.rst.txt
+++ /dev/null
@@ -1,10 +0,0 @@
-cmrsim.signal\_processes.bloch\_simulation package
-==================================================
-
-Module contents
----------------
-
-.. automodule:: cmrsim.signal_processes.bloch_simulation
- :members:
- :undoc-members:
- :show-inheritance:
diff --git a/public/_sources/cmrsim.signal_processes.rst.txt b/public/_sources/cmrsim.signal_processes.rst.txt
deleted file mode 100644
index c322887828d6eef0958d9629906aae012b004d97..0000000000000000000000000000000000000000
--- a/public/_sources/cmrsim.signal_processes.rst.txt
+++ /dev/null
@@ -1,27 +0,0 @@
-cmrsim.signal\_processes package
-================================
-
-Subpackages
------------
-
-.. toctree::
- :maxdepth: 4
-
- cmrsim.signal_processes.bloch_simulation
- cmrsim.signal_processes.solution_operators
-
-Submodules
-----------
-
-.. toctree::
- :maxdepth: 4
-
- cmrsim.signal_processes.base
-
-Module contents
----------------
-
-.. automodule:: cmrsim.signal_processes
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diff --git a/public/_sources/cmrsim.signal_processes.solution_operators.coil_sensitivities.rst.txt b/public/_sources/cmrsim.signal_processes.solution_operators.coil_sensitivities.rst.txt
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--- a/public/_sources/cmrsim.signal_processes.solution_operators.coil_sensitivities.rst.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-cmrsim.signal\_processes.solution\_operators.coil\_sensitivities module
-=======================================================================
-
-.. automodule:: cmrsim.signal_processes.solution_operators.coil_sensitivities
- :members:
- :undoc-members:
- :show-inheritance:
diff --git a/public/_sources/cmrsim.signal_processes.solution_operators.diffusion_weighting.rst.txt b/public/_sources/cmrsim.signal_processes.solution_operators.diffusion_weighting.rst.txt
deleted file mode 100644
index 366cec517655c303c0e190e367db4edd6d2f0034..0000000000000000000000000000000000000000
--- a/public/_sources/cmrsim.signal_processes.solution_operators.diffusion_weighting.rst.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-cmrsim.signal\_processes.solution\_operators.diffusion\_weighting module
-========================================================================
-
-.. automodule:: cmrsim.signal_processes.solution_operators.diffusion_weighting
- :members:
- :undoc-members:
- :show-inheritance:
diff --git a/public/_sources/cmrsim.signal_processes.solution_operators.rst.txt b/public/_sources/cmrsim.signal_processes.solution_operators.rst.txt
deleted file mode 100644
index 9235929501df927c78f2b4482f642c1b8a52f41c..0000000000000000000000000000000000000000
--- a/public/_sources/cmrsim.signal_processes.solution_operators.rst.txt
+++ /dev/null
@@ -1,21 +0,0 @@
-cmrsim.signal\_processes.solution\_operators package
-====================================================
-
-Submodules
-----------
-
-.. toctree::
- :maxdepth: 4
-
- cmrsim.signal_processes.solution_operators.coil_sensitivities
- cmrsim.signal_processes.solution_operators.diffusion_weighting
- cmrsim.signal_processes.solution_operators.sequences
- cmrsim.signal_processes.solution_operators.t2_star
-
-Module contents
----------------
-
-.. automodule:: cmrsim.signal_processes.solution_operators
- :members:
- :undoc-members:
- :show-inheritance:
diff --git a/public/_sources/cmrsim.signal_processes.solution_operators.sequences.rst.txt b/public/_sources/cmrsim.signal_processes.solution_operators.sequences.rst.txt
deleted file mode 100644
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--- a/public/_sources/cmrsim.signal_processes.solution_operators.sequences.rst.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-cmrsim.signal\_processes.solution\_operators.sequences module
-=============================================================
-
-.. automodule:: cmrsim.signal_processes.solution_operators.sequences
- :members:
- :undoc-members:
- :show-inheritance:
diff --git a/public/_sources/cmrsim.signal_processes.solution_operators.t2_star.rst.txt b/public/_sources/cmrsim.signal_processes.solution_operators.t2_star.rst.txt
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--- a/public/_sources/cmrsim.signal_processes.solution_operators.t2_star.rst.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-cmrsim.signal\_processes.solution\_operators.t2\_star module
-============================================================
-
-.. automodule:: cmrsim.signal_processes.solution_operators.t2_star
- :members:
- :undoc-members:
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diff --git a/public/_sources/cmrsim.simulation.rst.txt b/public/_sources/cmrsim.simulation.rst.txt
deleted file mode 100644
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--- a/public/_sources/cmrsim.simulation.rst.txt
+++ /dev/null
@@ -1,7 +0,0 @@
-cmrsim.simulation module
-========================
-
-.. automodule:: cmrsim.simulation
- :members:
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diff --git a/public/_sources/index.rst.txt b/public/_sources/index.rst.txt
deleted file mode 100644
index f783d608deef91ac8bbc39ec996f7e2ed6f1979f..0000000000000000000000000000000000000000
--- a/public/_sources/index.rst.txt
+++ /dev/null
@@ -1,49 +0,0 @@
-.. cmrsim documentation master file, created by
- sphinx-quickstart on Thu Jul 30 15:43:01 2020.
- You can adapt this file completely to your liking, but it should at least
- contain the root `toctree` directive.
-
-Welcome to cmrsim's documentation!
-==================================
-
-A differentiable framework for simulating MRI images, implemented with TensorFlow > 2 in Python
-
-.. toctree::
- :maxdepth: 1
- :caption: Getting started
-
- started
-
-***************************
-
-
-.. toctree::
- :maxdepth: 3
- :caption: Documentation
-
- cmrsim
-
-****
-
-.. toctree::
- :maxdepth: 1
- :caption: Who is using it? - Examples
-
- advanced_examples
-
-****
-
-.. toctree::
- :maxdepth: 1
- :caption: How to cite?
-
- citation
-
-****
-
-Indices and tables
-==================
-
-* :ref:`genindex`
-* :ref:`modindex`
-* :ref:`search`
diff --git a/public/_sources/started.rst.txt b/public/_sources/started.rst.txt
deleted file mode 100644
index 18dec1a92bbb34a00bccbe1063c937e3f72c16ee..0000000000000000000000000000000000000000
--- a/public/_sources/started.rst.txt
+++ /dev/null
@@ -1,4 +0,0 @@
-Simulate a simple image
------------------------
-
-Here are a couple of introductory examples...
\ No newline at end of file
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diff --git a/public/_static/doctools.js b/public/_static/doctools.js
deleted file mode 100644
index daccd209dab05a75f5627390e37a6b764e93f547..0000000000000000000000000000000000000000
--- a/public/_static/doctools.js
+++ /dev/null
@@ -1,315 +0,0 @@
-/*
- * doctools.js
- * ~~~~~~~~~~~
- *
- * Sphinx JavaScript utilities for all documentation.
- *
- * :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS.
- * :license: BSD, see LICENSE for details.
- *
- */
-
-/**
- * select a different prefix for underscore
- */
-$u = _.noConflict();
-
-/**
- * make the code below compatible with browsers without
- * an installed firebug like debugger
-if (!window.console || !console.firebug) {
- var names = ["log", "debug", "info", "warn", "error", "assert", "dir",
- "dirxml", "group", "groupEnd", "time", "timeEnd", "count", "trace",
- "profile", "profileEnd"];
- window.console = {};
- for (var i = 0; i < names.length; ++i)
- window.console[names[i]] = function() {};
-}
- */
-
-/**
- * small helper function to urldecode strings
- */
-jQuery.urldecode = function(x) {
- return decodeURIComponent(x).replace(/\+/g, ' ');
-};
-
-/**
- * small helper function to urlencode strings
- */
-jQuery.urlencode = encodeURIComponent;
-
-/**
- * This function returns the parsed url parameters of the
- * current request. Multiple values per key are supported,
- * it will always return arrays of strings for the value parts.
- */
-jQuery.getQueryParameters = function(s) {
- if (typeof s === 'undefined')
- s = document.location.search;
- var parts = s.substr(s.indexOf('?') + 1).split('&');
- var result = {};
- for (var i = 0; i < parts.length; i++) {
- var tmp = parts[i].split('=', 2);
- var key = jQuery.urldecode(tmp[0]);
- var value = jQuery.urldecode(tmp[1]);
- if (key in result)
- result[key].push(value);
- else
- result[key] = [value];
- }
- return result;
-};
-
-/**
- * highlight a given string on a jquery object by wrapping it in
- * span elements with the given class name.
- */
-jQuery.fn.highlightText = function(text, className) {
- function highlight(node, addItems) {
- if (node.nodeType === 3) {
- var val = node.nodeValue;
- var pos = val.toLowerCase().indexOf(text);
- if (pos >= 0 &&
- !jQuery(node.parentNode).hasClass(className) &&
- !jQuery(node.parentNode).hasClass("nohighlight")) {
- var span;
- var isInSVG = jQuery(node).closest("body, svg, foreignObject").is("svg");
- if (isInSVG) {
- span = document.createElementNS("http://www.w3.org/2000/svg", "tspan");
- } else {
- span = document.createElement("span");
- span.className = className;
- }
- span.appendChild(document.createTextNode(val.substr(pos, text.length)));
- node.parentNode.insertBefore(span, node.parentNode.insertBefore(
- document.createTextNode(val.substr(pos + text.length)),
- node.nextSibling));
- node.nodeValue = val.substr(0, pos);
- if (isInSVG) {
- var rect = document.createElementNS("http://www.w3.org/2000/svg", "rect");
- var bbox = node.parentElement.getBBox();
- rect.x.baseVal.value = bbox.x;
- rect.y.baseVal.value = bbox.y;
- rect.width.baseVal.value = bbox.width;
- rect.height.baseVal.value = bbox.height;
- rect.setAttribute('class', className);
- addItems.push({
- "parent": node.parentNode,
- "target": rect});
- }
- }
- }
- else if (!jQuery(node).is("button, select, textarea")) {
- jQuery.each(node.childNodes, function() {
- highlight(this, addItems);
- });
- }
- }
- var addItems = [];
- var result = this.each(function() {
- highlight(this, addItems);
- });
- for (var i = 0; i < addItems.length; ++i) {
- jQuery(addItems[i].parent).before(addItems[i].target);
- }
- return result;
-};
-
-/*
- * backward compatibility for jQuery.browser
- * This will be supported until firefox bug is fixed.
- */
-if (!jQuery.browser) {
- jQuery.uaMatch = function(ua) {
- ua = ua.toLowerCase();
-
- var match = /(chrome)[ \/]([\w.]+)/.exec(ua) ||
- /(webkit)[ \/]([\w.]+)/.exec(ua) ||
- /(opera)(?:.*version|)[ \/]([\w.]+)/.exec(ua) ||
- /(msie) ([\w.]+)/.exec(ua) ||
- ua.indexOf("compatible") < 0 && /(mozilla)(?:.*? rv:([\w.]+)|)/.exec(ua) ||
- [];
-
- return {
- browser: match[ 1 ] || "",
- version: match[ 2 ] || "0"
- };
- };
- jQuery.browser = {};
- jQuery.browser[jQuery.uaMatch(navigator.userAgent).browser] = true;
-}
-
-/**
- * Small JavaScript module for the documentation.
- */
-var Documentation = {
-
- init : function() {
- this.fixFirefoxAnchorBug();
- this.highlightSearchWords();
- this.initIndexTable();
- if (DOCUMENTATION_OPTIONS.NAVIGATION_WITH_KEYS) {
- this.initOnKeyListeners();
- }
- },
-
- /**
- * i18n support
- */
- TRANSLATIONS : {},
- PLURAL_EXPR : function(n) { return n === 1 ? 0 : 1; },
- LOCALE : 'unknown',
-
- // gettext and ngettext don't access this so that the functions
- // can safely bound to a different name (_ = Documentation.gettext)
- gettext : function(string) {
- var translated = Documentation.TRANSLATIONS[string];
- if (typeof translated === 'undefined')
- return string;
- return (typeof translated === 'string') ? translated : translated[0];
- },
-
- ngettext : function(singular, plural, n) {
- var translated = Documentation.TRANSLATIONS[singular];
- if (typeof translated === 'undefined')
- return (n == 1) ? singular : plural;
- return translated[Documentation.PLURALEXPR(n)];
- },
-
- addTranslations : function(catalog) {
- for (var key in catalog.messages)
- this.TRANSLATIONS[key] = catalog.messages[key];
- this.PLURAL_EXPR = new Function('n', 'return +(' + catalog.plural_expr + ')');
- this.LOCALE = catalog.locale;
- },
-
- /**
- * add context elements like header anchor links
- */
- addContextElements : function() {
- $('div[id] > :header:first').each(function() {
- $('<a class="headerlink">\u00B6</a>').
- attr('href', '#' + this.id).
- attr('title', _('Permalink to this headline')).
- appendTo(this);
- });
- $('dt[id]').each(function() {
- $('<a class="headerlink">\u00B6</a>').
- attr('href', '#' + this.id).
- attr('title', _('Permalink to this definition')).
- appendTo(this);
- });
- },
-
- /**
- * workaround a firefox stupidity
- * see: https://bugzilla.mozilla.org/show_bug.cgi?id=645075
- */
- fixFirefoxAnchorBug : function() {
- if (document.location.hash && $.browser.mozilla)
- window.setTimeout(function() {
- document.location.href += '';
- }, 10);
- },
-
- /**
- * highlight the search words provided in the url in the text
- */
- highlightSearchWords : function() {
- var params = $.getQueryParameters();
- var terms = (params.highlight) ? params.highlight[0].split(/\s+/) : [];
- if (terms.length) {
- var body = $('div.body');
- if (!body.length) {
- body = $('body');
- }
- window.setTimeout(function() {
- $.each(terms, function() {
- body.highlightText(this.toLowerCase(), 'highlighted');
- });
- }, 10);
- $('<p class="highlight-link"><a href="javascript:Documentation.' +
- 'hideSearchWords()">' + _('Hide Search Matches') + '</a></p>')
- .appendTo($('#searchbox'));
- }
- },
-
- /**
- * init the domain index toggle buttons
- */
- initIndexTable : function() {
- var togglers = $('img.toggler').click(function() {
- var src = $(this).attr('src');
- var idnum = $(this).attr('id').substr(7);
- $('tr.cg-' + idnum).toggle();
- if (src.substr(-9) === 'minus.png')
- $(this).attr('src', src.substr(0, src.length-9) + 'plus.png');
- else
- $(this).attr('src', src.substr(0, src.length-8) + 'minus.png');
- }).css('display', '');
- if (DOCUMENTATION_OPTIONS.COLLAPSE_INDEX) {
- togglers.click();
- }
- },
-
- /**
- * helper function to hide the search marks again
- */
- hideSearchWords : function() {
- $('#searchbox .highlight-link').fadeOut(300);
- $('span.highlighted').removeClass('highlighted');
- },
-
- /**
- * make the url absolute
- */
- makeURL : function(relativeURL) {
- return DOCUMENTATION_OPTIONS.URL_ROOT + '/' + relativeURL;
- },
-
- /**
- * get the current relative url
- */
- getCurrentURL : function() {
- var path = document.location.pathname;
- var parts = path.split(/\//);
- $.each(DOCUMENTATION_OPTIONS.URL_ROOT.split(/\//), function() {
- if (this === '..')
- parts.pop();
- });
- var url = parts.join('/');
- return path.substring(url.lastIndexOf('/') + 1, path.length - 1);
- },
-
- initOnKeyListeners: function() {
- $(document).keydown(function(event) {
- var activeElementType = document.activeElement.tagName;
- // don't navigate when in search box or textarea
- if (activeElementType !== 'TEXTAREA' && activeElementType !== 'INPUT' && activeElementType !== 'SELECT'
- && !event.altKey && !event.ctrlKey && !event.metaKey && !event.shiftKey) {
- switch (event.keyCode) {
- case 37: // left
- var prevHref = $('link[rel="prev"]').prop('href');
- if (prevHref) {
- window.location.href = prevHref;
- return false;
- }
- case 39: // right
- var nextHref = $('link[rel="next"]').prop('href');
- if (nextHref) {
- window.location.href = nextHref;
- return false;
- }
- }
- }
- });
- }
-};
-
-// quick alias for translations
-_ = Documentation.gettext;
-
-$(document).ready(function() {
- Documentation.init();
-});
diff --git a/public/_static/documentation_options.js b/public/_static/documentation_options.js
deleted file mode 100644
index f9f32c173d4cc220c39a6104c38fa5ae9040d2f5..0000000000000000000000000000000000000000
--- a/public/_static/documentation_options.js
+++ /dev/null
@@ -1,12 +0,0 @@
-var DOCUMENTATION_OPTIONS = {
- URL_ROOT: document.getElementById("documentation_options").getAttribute('data-url_root'),
- VERSION: '1.0alpha',
- LANGUAGE: 'None',
- COLLAPSE_INDEX: false,
- BUILDER: 'html',
- FILE_SUFFIX: '.html',
- LINK_SUFFIX: '.html',
- HAS_SOURCE: true,
- SOURCELINK_SUFFIX: '.txt',
- NAVIGATION_WITH_KEYS: false
-};
\ No newline at end of file
diff --git a/public/_static/file.png b/public/_static/file.png
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diff --git a/public/_static/fonts/Lato/lato-bold.eot b/public/_static/fonts/Lato/lato-bold.eot
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deleted file mode 100644
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diff --git a/public/_static/fonts/Lato/lato-bolditalic.eot b/public/_static/fonts/Lato/lato-bolditalic.eot
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deleted file mode 100644
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diff --git a/public/_static/fonts/RobotoSlab/roboto-slab-v7-bold.eot b/public/_static/fonts/RobotoSlab/roboto-slab-v7-bold.eot
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diff --git a/public/_static/fonts/lato-bold-italic.woff b/public/_static/fonts/lato-bold-italic.woff
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diff --git a/public/_static/jquery-3.5.1.js b/public/_static/jquery-3.5.1.js
deleted file mode 100644
index edd637b284d6b115205dcd1365acc408dec7e889..0000000000000000000000000000000000000000
--- a/public/_static/jquery-3.5.1.js
+++ /dev/null
@@ -1,10872 +0,0 @@
-/*!
- * jQuery JavaScript Library v3.5.1
- * https://jquery.com/
- *
- * Includes Sizzle.js
- * https://sizzlejs.com/
- *
- * Copyright JS Foundation and other contributors
- * Released under the MIT license
- * https://jquery.org/license
- *
- * Date: 2020-05-04T22:49Z
- */
-( function( global, factory ) {
-
- "use strict";
-
- if ( typeof module === "object" && typeof module.exports === "object" ) {
-
- // For CommonJS and CommonJS-like environments where a proper `window`
- // is present, execute the factory and get jQuery.
- // For environments that do not have a `window` with a `document`
- // (such as Node.js), expose a factory as module.exports.
- // This accentuates the need for the creation of a real `window`.
- // e.g. var jQuery = require("jquery")(window);
- // See ticket #14549 for more info.
- module.exports = global.document ?
- factory( global, true ) :
- function( w ) {
- if ( !w.document ) {
- throw new Error( "jQuery requires a window with a document" );
- }
- return factory( w );
- };
- } else {
- factory( global );
- }
-
-// Pass this if window is not defined yet
-} )( typeof window !== "undefined" ? window : this, function( window, noGlobal ) {
-
-// Edge <= 12 - 13+, Firefox <=18 - 45+, IE 10 - 11, Safari 5.1 - 9+, iOS 6 - 9.1
-// throw exceptions when non-strict code (e.g., ASP.NET 4.5) accesses strict mode
-// arguments.callee.caller (trac-13335). But as of jQuery 3.0 (2016), strict mode should be common
-// enough that all such attempts are guarded in a try block.
-"use strict";
-
-var arr = [];
-
-var getProto = Object.getPrototypeOf;
-
-var slice = arr.slice;
-
-var flat = arr.flat ? function( array ) {
- return arr.flat.call( array );
-} : function( array ) {
- return arr.concat.apply( [], array );
-};
-
-
-var push = arr.push;
-
-var indexOf = arr.indexOf;
-
-var class2type = {};
-
-var toString = class2type.toString;
-
-var hasOwn = class2type.hasOwnProperty;
-
-var fnToString = hasOwn.toString;
-
-var ObjectFunctionString = fnToString.call( Object );
-
-var support = {};
-
-var isFunction = function isFunction( obj ) {
-
- // Support: Chrome <=57, Firefox <=52
- // In some browsers, typeof returns "function" for HTML <object> elements
- // (i.e., `typeof document.createElement( "object" ) === "function"`).
- // We don't want to classify *any* DOM node as a function.
- return typeof obj === "function" && typeof obj.nodeType !== "number";
- };
-
-
-var isWindow = function isWindow( obj ) {
- return obj != null && obj === obj.window;
- };
-
-
-var document = window.document;
-
-
-
- var preservedScriptAttributes = {
- type: true,
- src: true,
- nonce: true,
- noModule: true
- };
-
- function DOMEval( code, node, doc ) {
- doc = doc || document;
-
- var i, val,
- script = doc.createElement( "script" );
-
- script.text = code;
- if ( node ) {
- for ( i in preservedScriptAttributes ) {
-
- // Support: Firefox 64+, Edge 18+
- // Some browsers don't support the "nonce" property on _bin.
- // On the other hand, just using `getAttribute` is not enough as
- // the `nonce` attribute is reset to an empty string whenever it
- // becomes browsing-context connected.
- // See https://github.com/whatwg/html/issues/2369
- // See https://html.spec.whatwg.org/#nonce-attributes
- // The `node.getAttribute` check was added for the sake of
- // `jQuery.globalEval` so that it can fake a nonce-containing node
- // via an object.
- val = node[ i ] || node.getAttribute && node.getAttribute( i );
- if ( val ) {
- script.setAttribute( i, val );
- }
- }
- }
- doc.head.appendChild( script ).parentNode.removeChild( script );
- }
-
-
-function toType( obj ) {
- if ( obj == null ) {
- return obj + "";
- }
-
- // Support: Android <=2.3 only (functionish RegExp)
- return typeof obj === "object" || typeof obj === "function" ?
- class2type[ toString.call( obj ) ] || "object" :
- typeof obj;
-}
-/* global Symbol */
-// Defining this global in .eslintrc.json would create a danger of using the global
-// unguarded in another place, it seems safer to define global only for this module
-
-
-
-var
- version = "3.5.1",
-
- // Define a local copy of jQuery
- jQuery = function( selector, context ) {
-
- // The jQuery object is actually just the init constructor 'enhanced'
- // Need init if jQuery is called (just allow error to be thrown if not included)
- return new jQuery.fn.init( selector, context );
- };
-
-jQuery.fn = jQuery.prototype = {
-
- // The current version of jQuery being used
- jquery: version,
-
- constructor: jQuery,
-
- // The default length of a jQuery object is 0
- length: 0,
-
- toArray: function() {
- return slice.call( this );
- },
-
- // Get the Nth element in the matched element set OR
- // Get the whole matched element set as a clean array
- get: function( num ) {
-
- // Return all the elements in a clean array
- if ( num == null ) {
- return slice.call( this );
- }
-
- // Return just the one element from the set
- return num < 0 ? this[ num + this.length ] : this[ num ];
- },
-
- // Take an array of elements and push it onto the stack
- // (returning the new matched element set)
- pushStack: function( elems ) {
-
- // Build a new jQuery matched element set
- var ret = jQuery.merge( this.constructor(), elems );
-
- // Add the old object onto the stack (as a reference)
- ret.prevObject = this;
-
- // Return the newly-formed element set
- return ret;
- },
-
- // Execute a callback for every element in the matched set.
- each: function( callback ) {
- return jQuery.each( this, callback );
- },
-
- map: function( callback ) {
- return this.pushStack( jQuery.map( this, function( elem, i ) {
- return callback.call( elem, i, elem );
- } ) );
- },
-
- slice: function() {
- return this.pushStack( slice.apply( this, arguments ) );
- },
-
- first: function() {
- return this.eq( 0 );
- },
-
- last: function() {
- return this.eq( -1 );
- },
-
- even: function() {
- return this.pushStack( jQuery.grep( this, function( _elem, i ) {
- return ( i + 1 ) % 2;
- } ) );
- },
-
- odd: function() {
- return this.pushStack( jQuery.grep( this, function( _elem, i ) {
- return i % 2;
- } ) );
- },
-
- eq: function( i ) {
- var len = this.length,
- j = +i + ( i < 0 ? len : 0 );
- return this.pushStack( j >= 0 && j < len ? [ this[ j ] ] : [] );
- },
-
- end: function() {
- return this.prevObject || this.constructor();
- },
-
- // For internal use only.
- // Behaves like an Array's method, not like a jQuery method.
- push: push,
- sort: arr.sort,
- splice: arr.splice
-};
-
-jQuery.extend = jQuery.fn.extend = function() {
- var options, name, src, copy, copyIsArray, clone,
- target = arguments[ 0 ] || {},
- i = 1,
- length = arguments.length,
- deep = false;
-
- // Handle a deep copy situation
- if ( typeof target === "boolean" ) {
- deep = target;
-
- // Skip the boolean and the target
- target = arguments[ i ] || {};
- i++;
- }
-
- // Handle case when target is a string or something (possible in deep copy)
- if ( typeof target !== "object" && !isFunction( target ) ) {
- target = {};
- }
-
- // Extend jQuery itself if only one argument is passed
- if ( i === length ) {
- target = this;
- i--;
- }
-
- for ( ; i < length; i++ ) {
-
- // Only deal with non-null/undefined values
- if ( ( options = arguments[ i ] ) != null ) {
-
- // Extend the build object
- for ( name in options ) {
- copy = options[ name ];
-
- // Prevent Object.prototype pollution
- // Prevent never-ending loop
- if ( name === "__proto__" || target === copy ) {
- continue;
- }
-
- // Recurse if we're merging plain objects or arrays
- if ( deep && copy && ( jQuery.isPlainObject( copy ) ||
- ( copyIsArray = Array.isArray( copy ) ) ) ) {
- src = target[ name ];
-
- // Ensure proper type for the source value
- if ( copyIsArray && !Array.isArray( src ) ) {
- clone = [];
- } else if ( !copyIsArray && !jQuery.isPlainObject( src ) ) {
- clone = {};
- } else {
- clone = src;
- }
- copyIsArray = false;
-
- // Never move original objects, clone them
- target[ name ] = jQuery.extend( deep, clone, copy );
-
- // Don't bring in undefined values
- } else if ( copy !== undefined ) {
- target[ name ] = copy;
- }
- }
- }
- }
-
- // Return the modified object
- return target;
-};
-
-jQuery.extend( {
-
- // Unique for each copy of jQuery on the page
- expando: "jQuery" + ( version + Math.random() ).replace( /\D/g, "" ),
-
- // Assume jQuery is ready without the ready module
- isReady: true,
-
- error: function( msg ) {
- throw new Error( msg );
- },
-
- noop: function() {},
-
- isPlainObject: function( obj ) {
- var proto, Ctor;
-
- // Detect obvious negatives
- // Use toString instead of jQuery.type to catch host objects
- if ( !obj || toString.call( obj ) !== "[object Object]" ) {
- return false;
- }
-
- proto = getProto( obj );
-
- // Objects with no prototype (e.g., `Object.create( null )`) are plain
- if ( !proto ) {
- return true;
- }
-
- // Objects with prototype are plain iff they were constructed by a global Object function
- Ctor = hasOwn.call( proto, "constructor" ) && proto.constructor;
- return typeof Ctor === "function" && fnToString.call( Ctor ) === ObjectFunctionString;
- },
-
- isEmptyObject: function( obj ) {
- var name;
-
- for ( name in obj ) {
- return false;
- }
- return true;
- },
-
- // Evaluates a script in a provided context; falls back to the global one
- // if not specified.
- globalEval: function( code, options, doc ) {
- DOMEval( code, { nonce: options && options.nonce }, doc );
- },
-
- each: function( obj, callback ) {
- var length, i = 0;
-
- if ( isArrayLike( obj ) ) {
- length = obj.length;
- for ( ; i < length; i++ ) {
- if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
- break;
- }
- }
- } else {
- for ( i in obj ) {
- if ( callback.call( obj[ i ], i, obj[ i ] ) === false ) {
- break;
- }
- }
- }
-
- return obj;
- },
-
- // results is for internal usage only
- makeArray: function( arr, results ) {
- var ret = results || [];
-
- if ( arr != null ) {
- if ( isArrayLike( Object( arr ) ) ) {
- jQuery.merge( ret,
- typeof arr === "string" ?
- [ arr ] : arr
- );
- } else {
- push.call( ret, arr );
- }
- }
-
- return ret;
- },
-
- inArray: function( elem, arr, i ) {
- return arr == null ? -1 : indexOf.call( arr, elem, i );
- },
-
- // Support: Android <=4.0 only, PhantomJS 1 only
- // push.apply(_, arraylike) throws on ancient WebKit
- merge: function( first, second ) {
- var len = +second.length,
- j = 0,
- i = first.length;
-
- for ( ; j < len; j++ ) {
- first[ i++ ] = second[ j ];
- }
-
- first.length = i;
-
- return first;
- },
-
- grep: function( elems, callback, invert ) {
- var callbackInverse,
- matches = [],
- i = 0,
- length = elems.length,
- callbackExpect = !invert;
-
- // Go through the array, only saving the items
- // that pass the validator function
- for ( ; i < length; i++ ) {
- callbackInverse = !callback( elems[ i ], i );
- if ( callbackInverse !== callbackExpect ) {
- matches.push( elems[ i ] );
- }
- }
-
- return matches;
- },
-
- // arg is for internal usage only
- map: function( elems, callback, arg ) {
- var length, value,
- i = 0,
- ret = [];
-
- // Go through the array, translating each of the items to their new values
- if ( isArrayLike( elems ) ) {
- length = elems.length;
- for ( ; i < length; i++ ) {
- value = callback( elems[ i ], i, arg );
-
- if ( value != null ) {
- ret.push( value );
- }
- }
-
- // Go through every key on the object,
- } else {
- for ( i in elems ) {
- value = callback( elems[ i ], i, arg );
-
- if ( value != null ) {
- ret.push( value );
- }
- }
- }
-
- // Flatten any nested arrays
- return flat( ret );
- },
-
- // A global GUID counter for objects
- guid: 1,
-
- // jQuery.support is not used in Core but other projects attach their
- // properties to it so it needs to exist.
- support: support
-} );
-
-if ( typeof Symbol === "function" ) {
- jQuery.fn[ Symbol.iterator ] = arr[ Symbol.iterator ];
-}
-
-// Populate the class2type map
-jQuery.each( "Boolean Number String Function Array Date RegExp Object Error Symbol".split( " " ),
-function( _i, name ) {
- class2type[ "[object " + name + "]" ] = name.toLowerCase();
-} );
-
-function isArrayLike( obj ) {
-
- // Support: real iOS 8.2 only (not reproducible in simulator)
- // `in` check used to prevent JIT error (gh-2145)
- // hasOwn isn't used here due to false negatives
- // regarding Nodelist length in IE
- var length = !!obj && "length" in obj && obj.length,
- type = toType( obj );
-
- if ( isFunction( obj ) || isWindow( obj ) ) {
- return false;
- }
-
- return type === "array" || length === 0 ||
- typeof length === "number" && length > 0 && ( length - 1 ) in obj;
-}
-var Sizzle =
-/*!
- * Sizzle CSS Selector Engine v2.3.5
- * https://sizzlejs.com/
- *
- * Copyright JS Foundation and other contributors
- * Released under the MIT license
- * https://js.foundation/
- *
- * Date: 2020-03-14
- */
-( function( window ) {
-var i,
- support,
- Expr,
- getText,
- isXML,
- tokenize,
- compile,
- select,
- outermostContext,
- sortInput,
- hasDuplicate,
-
- // Local document vars
- setDocument,
- document,
- docElem,
- documentIsHTML,
- rbuggyQSA,
- rbuggyMatches,
- matches,
- contains,
-
- // Instance-specific data
- expando = "sizzle" + 1 * new Date(),
- preferredDoc = window.document,
- dirruns = 0,
- done = 0,
- classCache = createCache(),
- tokenCache = createCache(),
- compilerCache = createCache(),
- nonnativeSelectorCache = createCache(),
- sortOrder = function( a, b ) {
- if ( a === b ) {
- hasDuplicate = true;
- }
- return 0;
- },
-
- // Instance methods
- hasOwn = ( {} ).hasOwnProperty,
- arr = [],
- pop = arr.pop,
- pushNative = arr.push,
- push = arr.push,
- slice = arr.slice,
-
- // Use a stripped-down indexOf as it's faster than native
- // https://jsperf.com/thor-indexof-vs-for/5
- indexOf = function( list, elem ) {
- var i = 0,
- len = list.length;
- for ( ; i < len; i++ ) {
- if ( list[ i ] === elem ) {
- return i;
- }
- }
- return -1;
- },
-
- booleans = "checked|selected|async|autofocus|autoplay|controls|defer|disabled|hidden|" +
- "ismap|loop|multiple|open|readonly|required|scoped",
-
- // Regular expressions
-
- // http://www.w3.org/TR/css3-selectors/#whitespace
- whitespace = "[\\x20\\t\\r\\n\\f]",
-
- // https://www.w3.org/TR/css-syntax-3/#ident-token-diagram
- identifier = "(?:\\\\[\\da-fA-F]{1,6}" + whitespace +
- "?|\\\\[^\\r\\n\\f]|[\\w-]|[^\0-\\x7f])+",
-
- // Attribute selectors: http://www.w3.org/TR/selectors/#attribute-selectors
- attributes = "\\[" + whitespace + "*(" + identifier + ")(?:" + whitespace +
-
- // Operator (capture 2)
- "*([*^$|!~]?=)" + whitespace +
-
- // "Attribute values must be CSS identifiers [capture 5]
- // or strings [capture 3 or capture 4]"
- "*(?:'((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\"|(" + identifier + "))|)" +
- whitespace + "*\\]",
-
- pseudos = ":(" + identifier + ")(?:\\((" +
-
- // To reduce the number of selectors needing tokenize in the preFilter, prefer arguments:
- // 1. quoted (capture 3; capture 4 or capture 5)
- "('((?:\\\\.|[^\\\\'])*)'|\"((?:\\\\.|[^\\\\\"])*)\")|" +
-
- // 2. simple (capture 6)
- "((?:\\\\.|[^\\\\()[\\]]|" + attributes + ")*)|" +
-
- // 3. anything else (capture 2)
- ".*" +
- ")\\)|)",
-
- // Leading and non-escaped trailing whitespace, capturing some non-whitespace characters preceding the latter
- rwhitespace = new RegExp( whitespace + "+", "g" ),
- rtrim = new RegExp( "^" + whitespace + "+|((?:^|[^\\\\])(?:\\\\.)*)" +
- whitespace + "+$", "g" ),
-
- rcomma = new RegExp( "^" + whitespace + "*," + whitespace + "*" ),
- rcombinators = new RegExp( "^" + whitespace + "*([>+~]|" + whitespace + ")" + whitespace +
- "*" ),
- rdescend = new RegExp( whitespace + "|>" ),
-
- rpseudo = new RegExp( pseudos ),
- ridentifier = new RegExp( "^" + identifier + "$" ),
-
- matchExpr = {
- "ID": new RegExp( "^#(" + identifier + ")" ),
- "CLASS": new RegExp( "^\\.(" + identifier + ")" ),
- "TAG": new RegExp( "^(" + identifier + "|[*])" ),
- "ATTR": new RegExp( "^" + attributes ),
- "PSEUDO": new RegExp( "^" + pseudos ),
- "CHILD": new RegExp( "^:(only|first|last|nth|nth-last)-(child|of-type)(?:\\(" +
- whitespace + "*(even|odd|(([+-]|)(\\d*)n|)" + whitespace + "*(?:([+-]|)" +
- whitespace + "*(\\d+)|))" + whitespace + "*\\)|)", "i" ),
- "bool": new RegExp( "^(?:" + booleans + ")$", "i" ),
-
- // For use in libraries implementing .is()
- // We use this for POS matching in `select`
- "needsContext": new RegExp( "^" + whitespace +
- "*[>+~]|:(even|odd|eq|gt|lt|nth|first|last)(?:\\(" + whitespace +
- "*((?:-\\d)?\\d*)" + whitespace + "*\\)|)(?=[^-]|$)", "i" )
- },
-
- rhtml = /HTML$/i,
- rinputs = /^(?:input|select|textarea|button)$/i,
- rheader = /^h\d$/i,
-
- rnative = /^[^{]+\{\s*\[native \w/,
-
- // Easily-parseable/retrievable ID or TAG or CLASS selectors
- rquickExpr = /^(?:#([\w-]+)|(\w+)|\.([\w-]+))$/,
-
- rsibling = /[+~]/,
-
- // CSS escapes
- // http://www.w3.org/TR/CSS21/syndata.html#escaped-characters
- runescape = new RegExp( "\\\\[\\da-fA-F]{1,6}" + whitespace + "?|\\\\([^\\r\\n\\f])", "g" ),
- funescape = function( escape, nonHex ) {
- var high = "0x" + escape.slice( 1 ) - 0x10000;
-
- return nonHex ?
-
- // Strip the backslash prefix from a non-hex escape sequence
- nonHex :
-
- // Replace a hexadecimal escape sequence with the encoded Unicode code point
- // Support: IE <=11+
- // For values outside the Basic Multilingual Plane (BMP), manually construct a
- // surrogate pair
- high < 0 ?
- String.fromCharCode( high + 0x10000 ) :
- String.fromCharCode( high >> 10 | 0xD800, high & 0x3FF | 0xDC00 );
- },
-
- // CSS string/identifier serialization
- // https://drafts.csswg.org/cssom/#common-serializing-idioms
- rcssescape = /([\0-\x1f\x7f]|^-?\d)|^-$|[^\0-\x1f\x7f-\uFFFF\w-]/g,
- fcssescape = function( ch, asCodePoint ) {
- if ( asCodePoint ) {
-
- // U+0000 NULL becomes U+FFFD REPLACEMENT CHARACTER
- if ( ch === "\0" ) {
- return "\uFFFD";
- }
-
- // Control characters and (dependent upon position) numbers get escaped as code points
- return ch.slice( 0, -1 ) + "\\" +
- ch.charCodeAt( ch.length - 1 ).toString( 16 ) + " ";
- }
-
- // Other potentially-special ASCII characters get backslash-escaped
- return "\\" + ch;
- },
-
- // Used for iframes
- // See setDocument()
- // Removing the function wrapper causes a "Permission Denied"
- // error in IE
- unloadHandler = function() {
- setDocument();
- },
-
- inDisabledFieldset = addCombinator(
- function( elem ) {
- return elem.disabled === true && elem.nodeName.toLowerCase() === "fieldset";
- },
- { dir: "parentNode", next: "legend" }
- );
-
-// Optimize for push.apply( _, NodeList )
-try {
- push.apply(
- ( arr = slice.call( preferredDoc.childNodes ) ),
- preferredDoc.childNodes
- );
-
- // Support: Android<4.0
- // Detect silently failing push.apply
- // eslint-disable-next-line no-unused-expressions
- arr[ preferredDoc.childNodes.length ].nodeType;
-} catch ( e ) {
- push = { apply: arr.length ?
-
- // Leverage slice if possible
- function( target, els ) {
- pushNative.apply( target, slice.call( els ) );
- } :
-
- // Support: IE<9
- // Otherwise append directly
- function( target, els ) {
- var j = target.length,
- i = 0;
-
- // Can't trust NodeList.length
- while ( ( target[ j++ ] = els[ i++ ] ) ) {}
- target.length = j - 1;
- }
- };
-}
-
-function Sizzle( selector, context, results, seed ) {
- var m, i, elem, nid, match, groups, newSelector,
- newContext = context && context.ownerDocument,
-
- // nodeType defaults to 9, since context defaults to document
- nodeType = context ? context.nodeType : 9;
-
- results = results || [];
-
- // Return early from calls with invalid selector or context
- if ( typeof selector !== "string" || !selector ||
- nodeType !== 1 && nodeType !== 9 && nodeType !== 11 ) {
-
- return results;
- }
-
- // Try to shortcut find operations (as opposed to filters) in HTML documents
- if ( !seed ) {
- setDocument( context );
- context = context || document;
-
- if ( documentIsHTML ) {
-
- // If the selector is sufficiently simple, try using a "get*By*" DOM method
- // (excepting DocumentFragment context, where the methods don't exist)
- if ( nodeType !== 11 && ( match = rquickExpr.exec( selector ) ) ) {
-
- // ID selector
- if ( ( m = match[ 1 ] ) ) {
-
- // Document context
- if ( nodeType === 9 ) {
- if ( ( elem = context.getElementById( m ) ) ) {
-
- // Support: IE, Opera, Webkit
- // TODO: identify versions
- // getElementById can match elements by name instead of ID
- if ( elem.id === m ) {
- results.push( elem );
- return results;
- }
- } else {
- return results;
- }
-
- // Element context
- } else {
-
- // Support: IE, Opera, Webkit
- // TODO: identify versions
- // getElementById can match elements by name instead of ID
- if ( newContext && ( elem = newContext.getElementById( m ) ) &&
- contains( context, elem ) &&
- elem.id === m ) {
-
- results.push( elem );
- return results;
- }
- }
-
- // Type selector
- } else if ( match[ 2 ] ) {
- push.apply( results, context.getElementsByTagName( selector ) );
- return results;
-
- // Class selector
- } else if ( ( m = match[ 3 ] ) && support.getElementsByClassName &&
- context.getElementsByClassName ) {
-
- push.apply( results, context.getElementsByClassName( m ) );
- return results;
- }
- }
-
- // Take advantage of querySelectorAll
- if ( support.qsa &&
- !nonnativeSelectorCache[ selector + " " ] &&
- ( !rbuggyQSA || !rbuggyQSA.test( selector ) ) &&
-
- // Support: IE 8 only
- // Exclude object elements
- ( nodeType !== 1 || context.nodeName.toLowerCase() !== "object" ) ) {
-
- newSelector = selector;
- newContext = context;
-
- // qSA considers elements outside a scoping root when evaluating child or
- // descendant combinators, which is not what we want.
- // In such cases, we work around the behavior by prefixing every selector in the
- // list with an ID selector referencing the scope context.
- // The technique has to be used as well when a leading combinator is used
- // as such selectors are not recognized by querySelectorAll.
- // Thanks to Andrew Dupont for this technique.
- if ( nodeType === 1 &&
- ( rdescend.test( selector ) || rcombinators.test( selector ) ) ) {
-
- // Expand context for sibling selectors
- newContext = rsibling.test( selector ) && testContext( context.parentNode ) ||
- context;
-
- // We can use :scope instead of the ID hack if the browser
- // supports it & if we're not changing the context.
- if ( newContext !== context || !support.scope ) {
-
- // Capture the context ID, setting it first if necessary
- if ( ( nid = context.getAttribute( "id" ) ) ) {
- nid = nid.replace( rcssescape, fcssescape );
- } else {
- context.setAttribute( "id", ( nid = expando ) );
- }
- }
-
- // Prefix every selector in the list
- groups = tokenize( selector );
- i = groups.length;
- while ( i-- ) {
- groups[ i ] = ( nid ? "#" + nid : ":scope" ) + " " +
- toSelector( groups[ i ] );
- }
- newSelector = groups.join( "," );
- }
-
- try {
- push.apply( results,
- newContext.querySelectorAll( newSelector )
- );
- return results;
- } catch ( qsaError ) {
- nonnativeSelectorCache( selector, true );
- } finally {
- if ( nid === expando ) {
- context.removeAttribute( "id" );
- }
- }
- }
- }
- }
-
- // All others
- return select( selector.replace( rtrim, "$1" ), context, results, seed );
-}
-
-/**
- * Create key-value caches of limited size
- * @returns {function(string, object)} Returns the Object data after storing it on itself with
- * property name the (space-suffixed) string and (if the cache is larger than Expr.cacheLength)
- * deleting the oldest entry
- */
-function createCache() {
- var keys = [];
-
- function cache( key, value ) {
-
- // Use (key + " ") to avoid collision with native prototype properties (see Issue #157)
- if ( keys.push( key + " " ) > Expr.cacheLength ) {
-
- // Only keep the most recent entries
- delete cache[ keys.shift() ];
- }
- return ( cache[ key + " " ] = value );
- }
- return cache;
-}
-
-/**
- * Mark a function for special use by Sizzle
- * @param {Function} fn The function to mark
- */
-function markFunction( fn ) {
- fn[ expando ] = true;
- return fn;
-}
-
-/**
- * Support testing using an element
- * @param {Function} fn Passed the created element and returns a boolean result
- */
-function assert( fn ) {
- var el = document.createElement( "fieldset" );
-
- try {
- return !!fn( el );
- } catch ( e ) {
- return false;
- } finally {
-
- // Remove from its parent by default
- if ( el.parentNode ) {
- el.parentNode.removeChild( el );
- }
-
- // release memory in IE
- el = null;
- }
-}
-
-/**
- * Adds the same handler for all of the specified attrs
- * @param {String} attrs Pipe-separated list of attributes
- * @param {Function} handler The method that will be applied
- */
-function addHandle( attrs, handler ) {
- var arr = attrs.split( "|" ),
- i = arr.length;
-
- while ( i-- ) {
- Expr.attrHandle[ arr[ i ] ] = handler;
- }
-}
-
-/**
- * Checks document order of two siblings
- * @param {Element} a
- * @param {Element} b
- * @returns {Number} Returns less than 0 if a precedes b, greater than 0 if a follows b
- */
-function siblingCheck( a, b ) {
- var cur = b && a,
- diff = cur && a.nodeType === 1 && b.nodeType === 1 &&
- a.sourceIndex - b.sourceIndex;
-
- // Use IE sourceIndex if available on both nodes
- if ( diff ) {
- return diff;
- }
-
- // Check if b follows a
- if ( cur ) {
- while ( ( cur = cur.nextSibling ) ) {
- if ( cur === b ) {
- return -1;
- }
- }
- }
-
- return a ? 1 : -1;
-}
-
-/**
- * Returns a function to use in pseudos for input types
- * @param {String} type
- */
-function createInputPseudo( type ) {
- return function( elem ) {
- var name = elem.nodeName.toLowerCase();
- return name === "input" && elem.type === type;
- };
-}
-
-/**
- * Returns a function to use in pseudos for buttons
- * @param {String} type
- */
-function createButtonPseudo( type ) {
- return function( elem ) {
- var name = elem.nodeName.toLowerCase();
- return ( name === "input" || name === "button" ) && elem.type === type;
- };
-}
-
-/**
- * Returns a function to use in pseudos for :enabled/:disabled
- * @param {Boolean} disabled true for :disabled; false for :enabled
- */
-function createDisabledPseudo( disabled ) {
-
- // Known :disabled false positives: fieldset[disabled] > legend:nth-of-type(n+2) :can-disable
- return function( elem ) {
-
- // Only certain elements can match :enabled or :disabled
- // https://html.spec.whatwg.org/multipage/scripting.html#selector-enabled
- // https://html.spec.whatwg.org/multipage/scripting.html#selector-disabled
- if ( "form" in elem ) {
-
- // Check for inherited disabledness on relevant non-disabled elements:
- // * listed form-associated elements in a disabled fieldset
- // https://html.spec.whatwg.org/multipage/forms.html#category-listed
- // https://html.spec.whatwg.org/multipage/forms.html#concept-fe-disabled
- // * option elements in a disabled optgroup
- // https://html.spec.whatwg.org/multipage/forms.html#concept-option-disabled
- // All such elements have a "form" property.
- if ( elem.parentNode && elem.disabled === false ) {
-
- // Option elements defer to a parent optgroup if present
- if ( "label" in elem ) {
- if ( "label" in elem.parentNode ) {
- return elem.parentNode.disabled === disabled;
- } else {
- return elem.disabled === disabled;
- }
- }
-
- // Support: IE 6 - 11
- // Use the isDisabled shortcut property to check for disabled fieldset ancestors
- return elem.isDisabled === disabled ||
-
- // Where there is no isDisabled, check manually
- /* jshint -W018 */
- elem.isDisabled !== !disabled &&
- inDisabledFieldset( elem ) === disabled;
- }
-
- return elem.disabled === disabled;
-
- // Try to winnow out elements that can't be disabled before trusting the disabled property.
- // Some victims get caught in our net (label, legend, menu, track), but it shouldn't
- // even exist on them, let alone have a boolean value.
- } else if ( "label" in elem ) {
- return elem.disabled === disabled;
- }
-
- // Remaining elements are neither :enabled nor :disabled
- return false;
- };
-}
-
-/**
- * Returns a function to use in pseudos for positionals
- * @param {Function} fn
- */
-function createPositionalPseudo( fn ) {
- return markFunction( function( argument ) {
- argument = +argument;
- return markFunction( function( seed, matches ) {
- var j,
- matchIndexes = fn( [], seed.length, argument ),
- i = matchIndexes.length;
-
- // Match elements found at the specified indexes
- while ( i-- ) {
- if ( seed[ ( j = matchIndexes[ i ] ) ] ) {
- seed[ j ] = !( matches[ j ] = seed[ j ] );
- }
- }
- } );
- } );
-}
-
-/**
- * Checks a node for validity as a Sizzle context
- * @param {Element|Object=} context
- * @returns {Element|Object|Boolean} The input node if acceptable, otherwise a falsy value
- */
-function testContext( context ) {
- return context && typeof context.getElementsByTagName !== "undefined" && context;
-}
-
-// Expose support vars for convenience
-support = Sizzle.support = {};
-
-/**
- * Detects XML nodes
- * @param {Element|Object} elem An element or a document
- * @returns {Boolean} True iff elem is a non-HTML XML node
- */
-isXML = Sizzle.isXML = function( elem ) {
- var namespace = elem.namespaceURI,
- docElem = ( elem.ownerDocument || elem ).documentElement;
-
- // Support: IE <=8
- // Assume HTML when documentElement doesn't yet exist, such as inside loading iframes
- // https://bugs.jquery.com/ticket/4833
- return !rhtml.test( namespace || docElem && docElem.nodeName || "HTML" );
-};
-
-/**
- * Sets document-related variables once based on the current document
- * @param {Element|Object} [doc] An element or document object to use to set the document
- * @returns {Object} Returns the current document
- */
-setDocument = Sizzle.setDocument = function( node ) {
- var hasCompare, subWindow,
- doc = node ? node.ownerDocument || node : preferredDoc;
-
- // Return early if doc is invalid or already selected
- // Support: IE 11+, Edge 17 - 18+
- // IE/Edge sometimes throw a "Permission denied" error when strict-comparing
- // two documents; shallow comparisons work.
- // eslint-disable-next-line eqeqeq
- if ( doc == document || doc.nodeType !== 9 || !doc.documentElement ) {
- return document;
- }
-
- // Update global variables
- document = doc;
- docElem = document.documentElement;
- documentIsHTML = !isXML( document );
-
- // Support: IE 9 - 11+, Edge 12 - 18+
- // Accessing iframe documents after unload throws "permission denied" errors (jQuery #13936)
- // Support: IE 11+, Edge 17 - 18+
- // IE/Edge sometimes throw a "Permission denied" error when strict-comparing
- // two documents; shallow comparisons work.
- // eslint-disable-next-line eqeqeq
- if ( preferredDoc != document &&
- ( subWindow = document.defaultView ) && subWindow.top !== subWindow ) {
-
- // Support: IE 11, Edge
- if ( subWindow.addEventListener ) {
- subWindow.addEventListener( "unload", unloadHandler, false );
-
- // Support: IE 9 - 10 only
- } else if ( subWindow.attachEvent ) {
- subWindow.attachEvent( "onunload", unloadHandler );
- }
- }
-
- // Support: IE 8 - 11+, Edge 12 - 18+, Chrome <=16 - 25 only, Firefox <=3.6 - 31 only,
- // Safari 4 - 5 only, Opera <=11.6 - 12.x only
- // IE/Edge & older browsers don't support the :scope pseudo-class.
- // Support: Safari 6.0 only
- // Safari 6.0 supports :scope but it's an alias of :root there.
- support.scope = assert( function( el ) {
- docElem.appendChild( el ).appendChild( document.createElement( "div" ) );
- return typeof el.querySelectorAll !== "undefined" &&
- !el.querySelectorAll( ":scope fieldset div" ).length;
- } );
-
- /* Attributes
- ---------------------------------------------------------------------- */
-
- // Support: IE<8
- // Verify that getAttribute really returns attributes and not properties
- // (excepting IE8 booleans)
- support.attributes = assert( function( el ) {
- el.className = "i";
- return !el.getAttribute( "className" );
- } );
-
- /* getElement(s)By*
- ---------------------------------------------------------------------- */
-
- // Check if getElementsByTagName("*") returns only elements
- support.getElementsByTagName = assert( function( el ) {
- el.appendChild( document.createComment( "" ) );
- return !el.getElementsByTagName( "*" ).length;
- } );
-
- // Support: IE<9
- support.getElementsByClassName = rnative.test( document.getElementsByClassName );
-
- // Support: IE<10
- // Check if getElementById returns elements by name
- // The broken getElementById methods don't pick up programmatically-set names,
- // so use a roundabout getElementsByName test
- support.getById = assert( function( el ) {
- docElem.appendChild( el ).id = expando;
- return !document.getElementsByName || !document.getElementsByName( expando ).length;
- } );
-
- // ID filter and find
- if ( support.getById ) {
- Expr.filter[ "ID" ] = function( id ) {
- var attrId = id.replace( runescape, funescape );
- return function( elem ) {
- return elem.getAttribute( "id" ) === attrId;
- };
- };
- Expr.find[ "ID" ] = function( id, context ) {
- if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
- var elem = context.getElementById( id );
- return elem ? [ elem ] : [];
- }
- };
- } else {
- Expr.filter[ "ID" ] = function( id ) {
- var attrId = id.replace( runescape, funescape );
- return function( elem ) {
- var node = typeof elem.getAttributeNode !== "undefined" &&
- elem.getAttributeNode( "id" );
- return node && node.value === attrId;
- };
- };
-
- // Support: IE 6 - 7 only
- // getElementById is not reliable as a find shortcut
- Expr.find[ "ID" ] = function( id, context ) {
- if ( typeof context.getElementById !== "undefined" && documentIsHTML ) {
- var node, i, elems,
- elem = context.getElementById( id );
-
- if ( elem ) {
-
- // Verify the id attribute
- node = elem.getAttributeNode( "id" );
- if ( node && node.value === id ) {
- return [ elem ];
- }
-
- // Fall back on getElementsByName
- elems = context.getElementsByName( id );
- i = 0;
- while ( ( elem = elems[ i++ ] ) ) {
- node = elem.getAttributeNode( "id" );
- if ( node && node.value === id ) {
- return [ elem ];
- }
- }
- }
-
- return [];
- }
- };
- }
-
- // Tag
- Expr.find[ "TAG" ] = support.getElementsByTagName ?
- function( tag, context ) {
- if ( typeof context.getElementsByTagName !== "undefined" ) {
- return context.getElementsByTagName( tag );
-
- // DocumentFragment nodes don't have gEBTN
- } else if ( support.qsa ) {
- return context.querySelectorAll( tag );
- }
- } :
-
- function( tag, context ) {
- var elem,
- tmp = [],
- i = 0,
-
- // By happy coincidence, a (broken) gEBTN appears on DocumentFragment nodes too
- results = context.getElementsByTagName( tag );
-
- // Filter out possible comments
- if ( tag === "*" ) {
- while ( ( elem = results[ i++ ] ) ) {
- if ( elem.nodeType === 1 ) {
- tmp.push( elem );
- }
- }
-
- return tmp;
- }
- return results;
- };
-
- // Class
- Expr.find[ "CLASS" ] = support.getElementsByClassName && function( className, context ) {
- if ( typeof context.getElementsByClassName !== "undefined" && documentIsHTML ) {
- return context.getElementsByClassName( className );
- }
- };
-
- /* QSA/matchesSelector
- ---------------------------------------------------------------------- */
-
- // QSA and matchesSelector support
-
- // matchesSelector(:active) reports false when true (IE9/Opera 11.5)
- rbuggyMatches = [];
-
- // qSa(:focus) reports false when true (Chrome 21)
- // We allow this because of a bug in IE8/9 that throws an error
- // whenever `document.activeElement` is accessed on an iframe
- // So, we allow :focus to pass through QSA all the time to avoid the IE error
- // See https://bugs.jquery.com/ticket/13378
- rbuggyQSA = [];
-
- if ( ( support.qsa = rnative.test( document.querySelectorAll ) ) ) {
-
- // Build QSA regex
- // Regex strategy adopted from Diego Perini
- assert( function( el ) {
-
- var input;
-
- // Select is set to empty string on purpose
- // This is to test IE's treatment of not explicitly
- // setting a boolean content attribute,
- // since its presence should be enough
- // https://bugs.jquery.com/ticket/12359
- docElem.appendChild( el ).innerHTML = "<a id='" + expando + "'></a>" +
- "<select id='" + expando + "-\r\\' msallowcapture=''>" +
- "<option selected=''></option></select>";
-
- // Support: IE8, Opera 11-12.16
- // Nothing should be selected when empty strings follow ^= or $= or *=
- // The test attribute must be unknown in Opera but "safe" for WinRT
- // https://msdn.microsoft.com/en-us/library/ie/hh465388.aspx#attribute_section
- if ( el.querySelectorAll( "[msallowcapture^='']" ).length ) {
- rbuggyQSA.push( "[*^$]=" + whitespace + "*(?:''|\"\")" );
- }
-
- // Support: IE8
- // Boolean attributes and "value" are not treated correctly
- if ( !el.querySelectorAll( "[selected]" ).length ) {
- rbuggyQSA.push( "\\[" + whitespace + "*(?:value|" + booleans + ")" );
- }
-
- // Support: Chrome<29, Android<4.4, Safari<7.0+, iOS<7.0+, PhantomJS<1.9.8+
- if ( !el.querySelectorAll( "[id~=" + expando + "-]" ).length ) {
- rbuggyQSA.push( "~=" );
- }
-
- // Support: IE 11+, Edge 15 - 18+
- // IE 11/Edge don't find elements on a `[name='']` query in some cases.
- // Adding a temporary attribute to the document before the selection works
- // around the issue.
- // Interestingly, IE 10 & older don't seem to have the issue.
- input = document.createElement( "input" );
- input.setAttribute( "name", "" );
- el.appendChild( input );
- if ( !el.querySelectorAll( "[name='']" ).length ) {
- rbuggyQSA.push( "\\[" + whitespace + "*name" + whitespace + "*=" +
- whitespace + "*(?:''|\"\")" );
- }
-
- // Webkit/Opera - :checked should return selected option elements
- // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
- // IE8 throws error here and will not see later tests
- if ( !el.querySelectorAll( ":checked" ).length ) {
- rbuggyQSA.push( ":checked" );
- }
-
- // Support: Safari 8+, iOS 8+
- // https://bugs.webkit.org/show_bug.cgi?id=136851
- // In-page `selector#id sibling-combinator selector` fails
- if ( !el.querySelectorAll( "a#" + expando + "+*" ).length ) {
- rbuggyQSA.push( ".#.+[+~]" );
- }
-
- // Support: Firefox <=3.6 - 5 only
- // Old Firefox doesn't throw on a badly-escaped identifier.
- el.querySelectorAll( "\\\f" );
- rbuggyQSA.push( "[\\r\\n\\f]" );
- } );
-
- assert( function( el ) {
- el.innerHTML = "<a href='' disabled='disabled'></a>" +
- "<select disabled='disabled'><option/></select>";
-
- // Support: Windows 8 Native Apps
- // The type and name attributes are restricted during .innerHTML assignment
- var input = document.createElement( "input" );
- input.setAttribute( "type", "hidden" );
- el.appendChild( input ).setAttribute( "name", "D" );
-
- // Support: IE8
- // Enforce case-sensitivity of name attribute
- if ( el.querySelectorAll( "[name=d]" ).length ) {
- rbuggyQSA.push( "name" + whitespace + "*[*^$|!~]?=" );
- }
-
- // FF 3.5 - :enabled/:disabled and hidden elements (hidden elements are still enabled)
- // IE8 throws error here and will not see later tests
- if ( el.querySelectorAll( ":enabled" ).length !== 2 ) {
- rbuggyQSA.push( ":enabled", ":disabled" );
- }
-
- // Support: IE9-11+
- // IE's :disabled selector does not pick up the children of disabled fieldsets
- docElem.appendChild( el ).disabled = true;
- if ( el.querySelectorAll( ":disabled" ).length !== 2 ) {
- rbuggyQSA.push( ":enabled", ":disabled" );
- }
-
- // Support: Opera 10 - 11 only
- // Opera 10-11 does not throw on post-comma invalid pseudos
- el.querySelectorAll( "*,:x" );
- rbuggyQSA.push( ",.*:" );
- } );
- }
-
- if ( ( support.matchesSelector = rnative.test( ( matches = docElem.matches ||
- docElem.webkitMatchesSelector ||
- docElem.mozMatchesSelector ||
- docElem.oMatchesSelector ||
- docElem.msMatchesSelector ) ) ) ) {
-
- assert( function( el ) {
-
- // Check to see if it's possible to do matchesSelector
- // on a disconnected node (IE 9)
- support.disconnectedMatch = matches.call( el, "*" );
-
- // This should fail with an exception
- // Gecko does not error, returns false instead
- matches.call( el, "[s!='']:x" );
- rbuggyMatches.push( "!=", pseudos );
- } );
- }
-
- rbuggyQSA = rbuggyQSA.length && new RegExp( rbuggyQSA.join( "|" ) );
- rbuggyMatches = rbuggyMatches.length && new RegExp( rbuggyMatches.join( "|" ) );
-
- /* Contains
- ---------------------------------------------------------------------- */
- hasCompare = rnative.test( docElem.compareDocumentPosition );
-
- // Element contains another
- // Purposefully self-exclusive
- // As in, an element does not contain itself
- contains = hasCompare || rnative.test( docElem.contains ) ?
- function( a, b ) {
- var adown = a.nodeType === 9 ? a.documentElement : a,
- bup = b && b.parentNode;
- return a === bup || !!( bup && bup.nodeType === 1 && (
- adown.contains ?
- adown.contains( bup ) :
- a.compareDocumentPosition && a.compareDocumentPosition( bup ) & 16
- ) );
- } :
- function( a, b ) {
- if ( b ) {
- while ( ( b = b.parentNode ) ) {
- if ( b === a ) {
- return true;
- }
- }
- }
- return false;
- };
-
- /* Sorting
- ---------------------------------------------------------------------- */
-
- // Document order sorting
- sortOrder = hasCompare ?
- function( a, b ) {
-
- // Flag for duplicate removal
- if ( a === b ) {
- hasDuplicate = true;
- return 0;
- }
-
- // Sort on method existence if only one input has compareDocumentPosition
- var compare = !a.compareDocumentPosition - !b.compareDocumentPosition;
- if ( compare ) {
- return compare;
- }
-
- // Calculate position if both inputs belong to the same document
- // Support: IE 11+, Edge 17 - 18+
- // IE/Edge sometimes throw a "Permission denied" error when strict-comparing
- // two documents; shallow comparisons work.
- // eslint-disable-next-line eqeqeq
- compare = ( a.ownerDocument || a ) == ( b.ownerDocument || b ) ?
- a.compareDocumentPosition( b ) :
-
- // Otherwise we know they are disconnected
- 1;
-
- // Disconnected nodes
- if ( compare & 1 ||
- ( !support.sortDetached && b.compareDocumentPosition( a ) === compare ) ) {
-
- // Choose the first element that is related to our preferred document
- // Support: IE 11+, Edge 17 - 18+
- // IE/Edge sometimes throw a "Permission denied" error when strict-comparing
- // two documents; shallow comparisons work.
- // eslint-disable-next-line eqeqeq
- if ( a == document || a.ownerDocument == preferredDoc &&
- contains( preferredDoc, a ) ) {
- return -1;
- }
-
- // Support: IE 11+, Edge 17 - 18+
- // IE/Edge sometimes throw a "Permission denied" error when strict-comparing
- // two documents; shallow comparisons work.
- // eslint-disable-next-line eqeqeq
- if ( b == document || b.ownerDocument == preferredDoc &&
- contains( preferredDoc, b ) ) {
- return 1;
- }
-
- // Maintain original order
- return sortInput ?
- ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
- 0;
- }
-
- return compare & 4 ? -1 : 1;
- } :
- function( a, b ) {
-
- // Exit early if the nodes are identical
- if ( a === b ) {
- hasDuplicate = true;
- return 0;
- }
-
- var cur,
- i = 0,
- aup = a.parentNode,
- bup = b.parentNode,
- ap = [ a ],
- bp = [ b ];
-
- // Parentless nodes are either documents or disconnected
- if ( !aup || !bup ) {
-
- // Support: IE 11+, Edge 17 - 18+
- // IE/Edge sometimes throw a "Permission denied" error when strict-comparing
- // two documents; shallow comparisons work.
- /* eslint-disable eqeqeq */
- return a == document ? -1 :
- b == document ? 1 :
- /* eslint-enable eqeqeq */
- aup ? -1 :
- bup ? 1 :
- sortInput ?
- ( indexOf( sortInput, a ) - indexOf( sortInput, b ) ) :
- 0;
-
- // If the nodes are siblings, we can do a quick check
- } else if ( aup === bup ) {
- return siblingCheck( a, b );
- }
-
- // Otherwise we need full lists of their ancestors for comparison
- cur = a;
- while ( ( cur = cur.parentNode ) ) {
- ap.unshift( cur );
- }
- cur = b;
- while ( ( cur = cur.parentNode ) ) {
- bp.unshift( cur );
- }
-
- // Walk down the tree looking for a discrepancy
- while ( ap[ i ] === bp[ i ] ) {
- i++;
- }
-
- return i ?
-
- // Do a sibling check if the nodes have a common ancestor
- siblingCheck( ap[ i ], bp[ i ] ) :
-
- // Otherwise nodes in our document sort first
- // Support: IE 11+, Edge 17 - 18+
- // IE/Edge sometimes throw a "Permission denied" error when strict-comparing
- // two documents; shallow comparisons work.
- /* eslint-disable eqeqeq */
- ap[ i ] == preferredDoc ? -1 :
- bp[ i ] == preferredDoc ? 1 :
- /* eslint-enable eqeqeq */
- 0;
- };
-
- return document;
-};
-
-Sizzle.matches = function( expr, elements ) {
- return Sizzle( expr, null, null, elements );
-};
-
-Sizzle.matchesSelector = function( elem, expr ) {
- setDocument( elem );
-
- if ( support.matchesSelector && documentIsHTML &&
- !nonnativeSelectorCache[ expr + " " ] &&
- ( !rbuggyMatches || !rbuggyMatches.test( expr ) ) &&
- ( !rbuggyQSA || !rbuggyQSA.test( expr ) ) ) {
-
- try {
- var ret = matches.call( elem, expr );
-
- // IE 9's matchesSelector returns false on disconnected nodes
- if ( ret || support.disconnectedMatch ||
-
- // As well, disconnected nodes are said to be in a document
- // fragment in IE 9
- elem.document && elem.document.nodeType !== 11 ) {
- return ret;
- }
- } catch ( e ) {
- nonnativeSelectorCache( expr, true );
- }
- }
-
- return Sizzle( expr, document, null, [ elem ] ).length > 0;
-};
-
-Sizzle.contains = function( context, elem ) {
-
- // Set document vars if needed
- // Support: IE 11+, Edge 17 - 18+
- // IE/Edge sometimes throw a "Permission denied" error when strict-comparing
- // two documents; shallow comparisons work.
- // eslint-disable-next-line eqeqeq
- if ( ( context.ownerDocument || context ) != document ) {
- setDocument( context );
- }
- return contains( context, elem );
-};
-
-Sizzle.attr = function( elem, name ) {
-
- // Set document vars if needed
- // Support: IE 11+, Edge 17 - 18+
- // IE/Edge sometimes throw a "Permission denied" error when strict-comparing
- // two documents; shallow comparisons work.
- // eslint-disable-next-line eqeqeq
- if ( ( elem.ownerDocument || elem ) != document ) {
- setDocument( elem );
- }
-
- var fn = Expr.attrHandle[ name.toLowerCase() ],
-
- // Don't get fooled by Object.prototype properties (jQuery #13807)
- val = fn && hasOwn.call( Expr.attrHandle, name.toLowerCase() ) ?
- fn( elem, name, !documentIsHTML ) :
- undefined;
-
- return val !== undefined ?
- val :
- support.attributes || !documentIsHTML ?
- elem.getAttribute( name ) :
- ( val = elem.getAttributeNode( name ) ) && val.specified ?
- val.value :
- null;
-};
-
-Sizzle.escape = function( sel ) {
- return ( sel + "" ).replace( rcssescape, fcssescape );
-};
-
-Sizzle.error = function( msg ) {
- throw new Error( "Syntax error, unrecognized expression: " + msg );
-};
-
-/**
- * Document sorting and removing duplicates
- * @param {ArrayLike} results
- */
-Sizzle.uniqueSort = function( results ) {
- var elem,
- duplicates = [],
- j = 0,
- i = 0;
-
- // Unless we *know* we can detect duplicates, assume their presence
- hasDuplicate = !support.detectDuplicates;
- sortInput = !support.sortStable && results.slice( 0 );
- results.sort( sortOrder );
-
- if ( hasDuplicate ) {
- while ( ( elem = results[ i++ ] ) ) {
- if ( elem === results[ i ] ) {
- j = duplicates.push( i );
- }
- }
- while ( j-- ) {
- results.splice( duplicates[ j ], 1 );
- }
- }
-
- // Clear input after sorting to release objects
- // See https://github.com/jquery/sizzle/pull/225
- sortInput = null;
-
- return results;
-};
-
-/**
- * Utility function for retrieving the text value of an array of DOM nodes
- * @param {Array|Element} elem
- */
-getText = Sizzle.getText = function( elem ) {
- var node,
- ret = "",
- i = 0,
- nodeType = elem.nodeType;
-
- if ( !nodeType ) {
-
- // If no nodeType, this is expected to be an array
- while ( ( node = elem[ i++ ] ) ) {
-
- // Do not traverse comment nodes
- ret += getText( node );
- }
- } else if ( nodeType === 1 || nodeType === 9 || nodeType === 11 ) {
-
- // Use textContent for elements
- // innerText usage removed for consistency of new lines (jQuery #11153)
- if ( typeof elem.textContent === "string" ) {
- return elem.textContent;
- } else {
-
- // Traverse its children
- for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
- ret += getText( elem );
- }
- }
- } else if ( nodeType === 3 || nodeType === 4 ) {
- return elem.nodeValue;
- }
-
- // Do not include comment or processing instruction nodes
-
- return ret;
-};
-
-Expr = Sizzle.selectors = {
-
- // Can be adjusted by the user
- cacheLength: 50,
-
- createPseudo: markFunction,
-
- match: matchExpr,
-
- attrHandle: {},
-
- find: {},
-
- relative: {
- ">": { dir: "parentNode", first: true },
- " ": { dir: "parentNode" },
- "+": { dir: "previousSibling", first: true },
- "~": { dir: "previousSibling" }
- },
-
- preFilter: {
- "ATTR": function( match ) {
- match[ 1 ] = match[ 1 ].replace( runescape, funescape );
-
- // Move the given value to match[3] whether quoted or unquoted
- match[ 3 ] = ( match[ 3 ] || match[ 4 ] ||
- match[ 5 ] || "" ).replace( runescape, funescape );
-
- if ( match[ 2 ] === "~=" ) {
- match[ 3 ] = " " + match[ 3 ] + " ";
- }
-
- return match.slice( 0, 4 );
- },
-
- "CHILD": function( match ) {
-
- /* matches from matchExpr["CHILD"]
- 1 type (only|nth|...)
- 2 what (child|of-type)
- 3 argument (even|odd|\d*|\d*n([+-]\d+)?|...)
- 4 xn-component of xn+y argument ([+-]?\d*n|)
- 5 sign of xn-component
- 6 x of xn-component
- 7 sign of y-component
- 8 y of y-component
- */
- match[ 1 ] = match[ 1 ].toLowerCase();
-
- if ( match[ 1 ].slice( 0, 3 ) === "nth" ) {
-
- // nth-* requires argument
- if ( !match[ 3 ] ) {
- Sizzle.error( match[ 0 ] );
- }
-
- // numeric x and y parameters for Expr.filter.CHILD
- // remember that false/true cast respectively to 0/1
- match[ 4 ] = +( match[ 4 ] ?
- match[ 5 ] + ( match[ 6 ] || 1 ) :
- 2 * ( match[ 3 ] === "even" || match[ 3 ] === "odd" ) );
- match[ 5 ] = +( ( match[ 7 ] + match[ 8 ] ) || match[ 3 ] === "odd" );
-
- // other types prohibit arguments
- } else if ( match[ 3 ] ) {
- Sizzle.error( match[ 0 ] );
- }
-
- return match;
- },
-
- "PSEUDO": function( match ) {
- var excess,
- unquoted = !match[ 6 ] && match[ 2 ];
-
- if ( matchExpr[ "CHILD" ].test( match[ 0 ] ) ) {
- return null;
- }
-
- // Accept quoted arguments as-is
- if ( match[ 3 ] ) {
- match[ 2 ] = match[ 4 ] || match[ 5 ] || "";
-
- // Strip excess characters from unquoted arguments
- } else if ( unquoted && rpseudo.test( unquoted ) &&
-
- // Get excess from tokenize (recursively)
- ( excess = tokenize( unquoted, true ) ) &&
-
- // advance to the next closing parenthesis
- ( excess = unquoted.indexOf( ")", unquoted.length - excess ) - unquoted.length ) ) {
-
- // excess is a negative index
- match[ 0 ] = match[ 0 ].slice( 0, excess );
- match[ 2 ] = unquoted.slice( 0, excess );
- }
-
- // Return only captures needed by the pseudo filter method (type and argument)
- return match.slice( 0, 3 );
- }
- },
-
- filter: {
-
- "TAG": function( nodeNameSelector ) {
- var nodeName = nodeNameSelector.replace( runescape, funescape ).toLowerCase();
- return nodeNameSelector === "*" ?
- function() {
- return true;
- } :
- function( elem ) {
- return elem.nodeName && elem.nodeName.toLowerCase() === nodeName;
- };
- },
-
- "CLASS": function( className ) {
- var pattern = classCache[ className + " " ];
-
- return pattern ||
- ( pattern = new RegExp( "(^|" + whitespace +
- ")" + className + "(" + whitespace + "|$)" ) ) && classCache(
- className, function( elem ) {
- return pattern.test(
- typeof elem.className === "string" && elem.className ||
- typeof elem.getAttribute !== "undefined" &&
- elem.getAttribute( "class" ) ||
- ""
- );
- } );
- },
-
- "ATTR": function( name, operator, check ) {
- return function( elem ) {
- var result = Sizzle.attr( elem, name );
-
- if ( result == null ) {
- return operator === "!=";
- }
- if ( !operator ) {
- return true;
- }
-
- result += "";
-
- /* eslint-disable max-len */
-
- return operator === "=" ? result === check :
- operator === "!=" ? result !== check :
- operator === "^=" ? check && result.indexOf( check ) === 0 :
- operator === "*=" ? check && result.indexOf( check ) > -1 :
- operator === "$=" ? check && result.slice( -check.length ) === check :
- operator === "~=" ? ( " " + result.replace( rwhitespace, " " ) + " " ).indexOf( check ) > -1 :
- operator === "|=" ? result === check || result.slice( 0, check.length + 1 ) === check + "-" :
- false;
- /* eslint-enable max-len */
-
- };
- },
-
- "CHILD": function( type, what, _argument, first, last ) {
- var simple = type.slice( 0, 3 ) !== "nth",
- forward = type.slice( -4 ) !== "last",
- ofType = what === "of-type";
-
- return first === 1 && last === 0 ?
-
- // Shortcut for :nth-*(n)
- function( elem ) {
- return !!elem.parentNode;
- } :
-
- function( elem, _context, xml ) {
- var cache, uniqueCache, outerCache, node, nodeIndex, start,
- dir = simple !== forward ? "nextSibling" : "previousSibling",
- parent = elem.parentNode,
- name = ofType && elem.nodeName.toLowerCase(),
- useCache = !xml && !ofType,
- diff = false;
-
- if ( parent ) {
-
- // :(first|last|only)-(child|of-type)
- if ( simple ) {
- while ( dir ) {
- node = elem;
- while ( ( node = node[ dir ] ) ) {
- if ( ofType ?
- node.nodeName.toLowerCase() === name :
- node.nodeType === 1 ) {
-
- return false;
- }
- }
-
- // Reverse direction for :only-* (if we haven't yet done so)
- start = dir = type === "only" && !start && "nextSibling";
- }
- return true;
- }
-
- start = [ forward ? parent.firstChild : parent.lastChild ];
-
- // non-xml :nth-child(...) stores cache data on `parent`
- if ( forward && useCache ) {
-
- // Seek `elem` from a previously-cached index
-
- // ...in a gzip-friendly way
- node = parent;
- outerCache = node[ expando ] || ( node[ expando ] = {} );
-
- // Support: IE <9 only
- // Defend against cloned attroperties (jQuery gh-1709)
- uniqueCache = outerCache[ node.uniqueID ] ||
- ( outerCache[ node.uniqueID ] = {} );
-
- cache = uniqueCache[ type ] || [];
- nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
- diff = nodeIndex && cache[ 2 ];
- node = nodeIndex && parent.childNodes[ nodeIndex ];
-
- while ( ( node = ++nodeIndex && node && node[ dir ] ||
-
- // Fallback to seeking `elem` from the start
- ( diff = nodeIndex = 0 ) || start.pop() ) ) {
-
- // When found, cache indexes on `parent` and break
- if ( node.nodeType === 1 && ++diff && node === elem ) {
- uniqueCache[ type ] = [ dirruns, nodeIndex, diff ];
- break;
- }
- }
-
- } else {
-
- // Use previously-cached element index if available
- if ( useCache ) {
-
- // ...in a gzip-friendly way
- node = elem;
- outerCache = node[ expando ] || ( node[ expando ] = {} );
-
- // Support: IE <9 only
- // Defend against cloned attroperties (jQuery gh-1709)
- uniqueCache = outerCache[ node.uniqueID ] ||
- ( outerCache[ node.uniqueID ] = {} );
-
- cache = uniqueCache[ type ] || [];
- nodeIndex = cache[ 0 ] === dirruns && cache[ 1 ];
- diff = nodeIndex;
- }
-
- // xml :nth-child(...)
- // or :nth-last-child(...) or :nth(-last)?-of-type(...)
- if ( diff === false ) {
-
- // Use the same loop as above to seek `elem` from the start
- while ( ( node = ++nodeIndex && node && node[ dir ] ||
- ( diff = nodeIndex = 0 ) || start.pop() ) ) {
-
- if ( ( ofType ?
- node.nodeName.toLowerCase() === name :
- node.nodeType === 1 ) &&
- ++diff ) {
-
- // Cache the index of each encountered element
- if ( useCache ) {
- outerCache = node[ expando ] ||
- ( node[ expando ] = {} );
-
- // Support: IE <9 only
- // Defend against cloned attroperties (jQuery gh-1709)
- uniqueCache = outerCache[ node.uniqueID ] ||
- ( outerCache[ node.uniqueID ] = {} );
-
- uniqueCache[ type ] = [ dirruns, diff ];
- }
-
- if ( node === elem ) {
- break;
- }
- }
- }
- }
- }
-
- // Incorporate the offset, then check against cycle size
- diff -= last;
- return diff === first || ( diff % first === 0 && diff / first >= 0 );
- }
- };
- },
-
- "PSEUDO": function( pseudo, argument ) {
-
- // pseudo-class names are case-insensitive
- // http://www.w3.org/TR/selectors/#pseudo-classes
- // Prioritize by case sensitivity in case custom pseudos are added with uppercase letters
- // Remember that setFilters inherits from pseudos
- var args,
- fn = Expr.pseudos[ pseudo ] || Expr.setFilters[ pseudo.toLowerCase() ] ||
- Sizzle.error( "unsupported pseudo: " + pseudo );
-
- // The user may use createPseudo to indicate that
- // arguments are needed to create the filter function
- // just as Sizzle does
- if ( fn[ expando ] ) {
- return fn( argument );
- }
-
- // But maintain support for old signatures
- if ( fn.length > 1 ) {
- args = [ pseudo, pseudo, "", argument ];
- return Expr.setFilters.hasOwnProperty( pseudo.toLowerCase() ) ?
- markFunction( function( seed, matches ) {
- var idx,
- matched = fn( seed, argument ),
- i = matched.length;
- while ( i-- ) {
- idx = indexOf( seed, matched[ i ] );
- seed[ idx ] = !( matches[ idx ] = matched[ i ] );
- }
- } ) :
- function( elem ) {
- return fn( elem, 0, args );
- };
- }
-
- return fn;
- }
- },
-
- pseudos: {
-
- // Potentially complex pseudos
- "not": markFunction( function( selector ) {
-
- // Trim the selector passed to compile
- // to avoid treating leading and trailing
- // spaces as combinators
- var input = [],
- results = [],
- matcher = compile( selector.replace( rtrim, "$1" ) );
-
- return matcher[ expando ] ?
- markFunction( function( seed, matches, _context, xml ) {
- var elem,
- unmatched = matcher( seed, null, xml, [] ),
- i = seed.length;
-
- // Match elements unmatched by `matcher`
- while ( i-- ) {
- if ( ( elem = unmatched[ i ] ) ) {
- seed[ i ] = !( matches[ i ] = elem );
- }
- }
- } ) :
- function( elem, _context, xml ) {
- input[ 0 ] = elem;
- matcher( input, null, xml, results );
-
- // Don't keep the element (issue #299)
- input[ 0 ] = null;
- return !results.pop();
- };
- } ),
-
- "has": markFunction( function( selector ) {
- return function( elem ) {
- return Sizzle( selector, elem ).length > 0;
- };
- } ),
-
- "contains": markFunction( function( text ) {
- text = text.replace( runescape, funescape );
- return function( elem ) {
- return ( elem.textContent || getText( elem ) ).indexOf( text ) > -1;
- };
- } ),
-
- // "Whether an element is represented by a :lang() selector
- // is based solely on the element's language value
- // being equal to the identifier C,
- // or beginning with the identifier C immediately followed by "-".
- // The matching of C against the element's language value is performed case-insensitively.
- // The identifier C does not have to be a valid language name."
- // http://www.w3.org/TR/selectors/#lang-pseudo
- "lang": markFunction( function( lang ) {
-
- // lang value must be a valid identifier
- if ( !ridentifier.test( lang || "" ) ) {
- Sizzle.error( "unsupported lang: " + lang );
- }
- lang = lang.replace( runescape, funescape ).toLowerCase();
- return function( elem ) {
- var elemLang;
- do {
- if ( ( elemLang = documentIsHTML ?
- elem.lang :
- elem.getAttribute( "xml:lang" ) || elem.getAttribute( "lang" ) ) ) {
-
- elemLang = elemLang.toLowerCase();
- return elemLang === lang || elemLang.indexOf( lang + "-" ) === 0;
- }
- } while ( ( elem = elem.parentNode ) && elem.nodeType === 1 );
- return false;
- };
- } ),
-
- // Miscellaneous
- "target": function( elem ) {
- var hash = window.location && window.location.hash;
- return hash && hash.slice( 1 ) === elem.id;
- },
-
- "root": function( elem ) {
- return elem === docElem;
- },
-
- "focus": function( elem ) {
- return elem === document.activeElement &&
- ( !document.hasFocus || document.hasFocus() ) &&
- !!( elem.type || elem.href || ~elem.tabIndex );
- },
-
- // Boolean properties
- "enabled": createDisabledPseudo( false ),
- "disabled": createDisabledPseudo( true ),
-
- "checked": function( elem ) {
-
- // In CSS3, :checked should return both checked and selected elements
- // http://www.w3.org/TR/2011/REC-css3-selectors-20110929/#checked
- var nodeName = elem.nodeName.toLowerCase();
- return ( nodeName === "input" && !!elem.checked ) ||
- ( nodeName === "option" && !!elem.selected );
- },
-
- "selected": function( elem ) {
-
- // Accessing this property makes selected-by-default
- // options in Safari work properly
- if ( elem.parentNode ) {
- // eslint-disable-next-line no-unused-expressions
- elem.parentNode.selectedIndex;
- }
-
- return elem.selected === true;
- },
-
- // Contents
- "empty": function( elem ) {
-
- // http://www.w3.org/TR/selectors/#empty-pseudo
- // :empty is negated by element (1) or content nodes (text: 3; cdata: 4; entity ref: 5),
- // but not by others (comment: 8; processing instruction: 7; etc.)
- // nodeType < 6 works because attributes (2) do not appear as children
- for ( elem = elem.firstChild; elem; elem = elem.nextSibling ) {
- if ( elem.nodeType < 6 ) {
- return false;
- }
- }
- return true;
- },
-
- "parent": function( elem ) {
- return !Expr.pseudos[ "empty" ]( elem );
- },
-
- // Element/input types
- "header": function( elem ) {
- return rheader.test( elem.nodeName );
- },
-
- "input": function( elem ) {
- return rinputs.test( elem.nodeName );
- },
-
- "button": function( elem ) {
- var name = elem.nodeName.toLowerCase();
- return name === "input" && elem.type === "button" || name === "button";
- },
-
- "text": function( elem ) {
- var attr;
- return elem.nodeName.toLowerCase() === "input" &&
- elem.type === "text" &&
-
- // Support: IE<8
- // New HTML5 attribute values (e.g., "search") appear with elem.type === "text"
- ( ( attr = elem.getAttribute( "type" ) ) == null ||
- attr.toLowerCase() === "text" );
- },
-
- // Position-in-collection
- "first": createPositionalPseudo( function() {
- return [ 0 ];
- } ),
-
- "last": createPositionalPseudo( function( _matchIndexes, length ) {
- return [ length - 1 ];
- } ),
-
- "eq": createPositionalPseudo( function( _matchIndexes, length, argument ) {
- return [ argument < 0 ? argument + length : argument ];
- } ),
-
- "even": createPositionalPseudo( function( matchIndexes, length ) {
- var i = 0;
- for ( ; i < length; i += 2 ) {
- matchIndexes.push( i );
- }
- return matchIndexes;
- } ),
-
- "odd": createPositionalPseudo( function( matchIndexes, length ) {
- var i = 1;
- for ( ; i < length; i += 2 ) {
- matchIndexes.push( i );
- }
- return matchIndexes;
- } ),
-
- "lt": createPositionalPseudo( function( matchIndexes, length, argument ) {
- var i = argument < 0 ?
- argument + length :
- argument > length ?
- length :
- argument;
- for ( ; --i >= 0; ) {
- matchIndexes.push( i );
- }
- return matchIndexes;
- } ),
-
- "gt": createPositionalPseudo( function( matchIndexes, length, argument ) {
- var i = argument < 0 ? argument + length : argument;
- for ( ; ++i < length; ) {
- matchIndexes.push( i );
- }
- return matchIndexes;
- } )
- }
-};
-
-Expr.pseudos[ "nth" ] = Expr.pseudos[ "eq" ];
-
-// Add button/input type pseudos
-for ( i in { radio: true, checkbox: true, file: true, password: true, image: true } ) {
- Expr.pseudos[ i ] = createInputPseudo( i );
-}
-for ( i in { submit: true, reset: true } ) {
- Expr.pseudos[ i ] = createButtonPseudo( i );
-}
-
-// Easy API for creating new setFilters
-function setFilters() {}
-setFilters.prototype = Expr.filters = Expr.pseudos;
-Expr.setFilters = new setFilters();
-
-tokenize = Sizzle.tokenize = function( selector, parseOnly ) {
- var matched, match, tokens, type,
- soFar, groups, preFilters,
- cached = tokenCache[ selector + " " ];
-
- if ( cached ) {
- return parseOnly ? 0 : cached.slice( 0 );
- }
-
- soFar = selector;
- groups = [];
- preFilters = Expr.preFilter;
-
- while ( soFar ) {
-
- // Comma and first run
- if ( !matched || ( match = rcomma.exec( soFar ) ) ) {
- if ( match ) {
-
- // Don't consume trailing commas as valid
- soFar = soFar.slice( match[ 0 ].length ) || soFar;
- }
- groups.push( ( tokens = [] ) );
- }
-
- matched = false;
-
- // Combinators
- if ( ( match = rcombinators.exec( soFar ) ) ) {
- matched = match.shift();
- tokens.push( {
- value: matched,
-
- // Cast descendant combinators to space
- type: match[ 0 ].replace( rtrim, " " )
- } );
- soFar = soFar.slice( matched.length );
- }
-
- // Filters
- for ( type in Expr.filter ) {
- if ( ( match = matchExpr[ type ].exec( soFar ) ) && ( !preFilters[ type ] ||
- ( match = preFilters[ type ]( match ) ) ) ) {
- matched = match.shift();
- tokens.push( {
- value: matched,
- type: type,
- matches: match
- } );
- soFar = soFar.slice( matched.length );
- }
- }
-
- if ( !matched ) {
- break;
- }
- }
-
- // Return the length of the invalid excess
- // if we're just parsing
- // Otherwise, throw an error or return tokens
- return parseOnly ?
- soFar.length :
- soFar ?
- Sizzle.error( selector ) :
-
- // Cache the tokens
- tokenCache( selector, groups ).slice( 0 );
-};
-
-function toSelector( tokens ) {
- var i = 0,
- len = tokens.length,
- selector = "";
- for ( ; i < len; i++ ) {
- selector += tokens[ i ].value;
- }
- return selector;
-}
-
-function addCombinator( matcher, combinator, base ) {
- var dir = combinator.dir,
- skip = combinator.next,
- key = skip || dir,
- checkNonElements = base && key === "parentNode",
- doneName = done++;
-
- return combinator.first ?
-
- // Check against closest ancestor/preceding element
- function( elem, context, xml ) {
- while ( ( elem = elem[ dir ] ) ) {
- if ( elem.nodeType === 1 || checkNonElements ) {
- return matcher( elem, context, xml );
- }
- }
- return false;
- } :
-
- // Check against all ancestor/preceding elements
- function( elem, context, xml ) {
- var oldCache, uniqueCache, outerCache,
- newCache = [ dirruns, doneName ];
-
- // We can't set arbitrary data on XML nodes, so they don't benefit from combinator caching
- if ( xml ) {
- while ( ( elem = elem[ dir ] ) ) {
- if ( elem.nodeType === 1 || checkNonElements ) {
- if ( matcher( elem, context, xml ) ) {
- return true;
- }
- }
- }
- } else {
- while ( ( elem = elem[ dir ] ) ) {
- if ( elem.nodeType === 1 || checkNonElements ) {
- outerCache = elem[ expando ] || ( elem[ expando ] = {} );
-
- // Support: IE <9 only
- // Defend against cloned attroperties (jQuery gh-1709)
- uniqueCache = outerCache[ elem.uniqueID ] ||
- ( outerCache[ elem.uniqueID ] = {} );
-
- if ( skip && skip === elem.nodeName.toLowerCase() ) {
- elem = elem[ dir ] || elem;
- } else if ( ( oldCache = uniqueCache[ key ] ) &&
- oldCache[ 0 ] === dirruns && oldCache[ 1 ] === doneName ) {
-
- // Assign to newCache so results back-propagate to previous elements
- return ( newCache[ 2 ] = oldCache[ 2 ] );
- } else {
-
- // Reuse newcache so results back-propagate to previous elements
- uniqueCache[ key ] = newCache;
-
- // A match means we're done; a fail means we have to keep checking
- if ( ( newCache[ 2 ] = matcher( elem, context, xml ) ) ) {
- return true;
- }
- }
- }
- }
- }
- return false;
- };
-}
-
-function elementMatcher( matchers ) {
- return matchers.length > 1 ?
- function( elem, context, xml ) {
- var i = matchers.length;
- while ( i-- ) {
- if ( !matchers[ i ]( elem, context, xml ) ) {
- return false;
- }
- }
- return true;
- } :
- matchers[ 0 ];
-}
-
-function multipleContexts( selector, contexts, results ) {
- var i = 0,
- len = contexts.length;
- for ( ; i < len; i++ ) {
- Sizzle( selector, contexts[ i ], results );
- }
- return results;
-}
-
-function condense( unmatched, map, filter, context, xml ) {
- var elem,
- newUnmatched = [],
- i = 0,
- len = unmatched.length,
- mapped = map != null;
-
- for ( ; i < len; i++ ) {
- if ( ( elem = unmatched[ i ] ) ) {
- if ( !filter || filter( elem, context, xml ) ) {
- newUnmatched.push( elem );
- if ( mapped ) {
- map.push( i );
- }
- }
- }
- }
-
- return newUnmatched;
-}
-
-function setMatcher( preFilter, selector, matcher, postFilter, postFinder, postSelector ) {
- if ( postFilter && !postFilter[ expando ] ) {
- postFilter = setMatcher( postFilter );
- }
- if ( postFinder && !postFinder[ expando ] ) {
- postFinder = setMatcher( postFinder, postSelector );
- }
- return markFunction( function( seed, results, context, xml ) {
- var temp, i, elem,
- preMap = [],
- postMap = [],
- preexisting = results.length,
-
- // Get initial elements from seed or context
- elems = seed || multipleContexts(
- selector || "*",
- context.nodeType ? [ context ] : context,
- []
- ),
-
- // Prefilter to get matcher input, preserving a map for seed-results synchronization
- matcherIn = preFilter && ( seed || !selector ) ?
- condense( elems, preMap, preFilter, context, xml ) :
- elems,
-
- matcherOut = matcher ?
-
- // If we have a postFinder, or filtered seed, or non-seed postFilter or preexisting results,
- postFinder || ( seed ? preFilter : preexisting || postFilter ) ?
-
- // ...intermediate processing is necessary
- [] :
-
- // ...otherwise use results directly
- results :
- matcherIn;
-
- // Find primary matches
- if ( matcher ) {
- matcher( matcherIn, matcherOut, context, xml );
- }
-
- // Apply postFilter
- if ( postFilter ) {
- temp = condense( matcherOut, postMap );
- postFilter( temp, [], context, xml );
-
- // Un-match failing elements by moving them back to matcherIn
- i = temp.length;
- while ( i-- ) {
- if ( ( elem = temp[ i ] ) ) {
- matcherOut[ postMap[ i ] ] = !( matcherIn[ postMap[ i ] ] = elem );
- }
- }
- }
-
- if ( seed ) {
- if ( postFinder || preFilter ) {
- if ( postFinder ) {
-
- // Get the final matcherOut by condensing this intermediate into postFinder contexts
- temp = [];
- i = matcherOut.length;
- while ( i-- ) {
- if ( ( elem = matcherOut[ i ] ) ) {
-
- // Restore matcherIn since elem is not yet a final match
- temp.push( ( matcherIn[ i ] = elem ) );
- }
- }
- postFinder( null, ( matcherOut = [] ), temp, xml );
- }
-
- // Move matched elements from seed to results to keep them synchronized
- i = matcherOut.length;
- while ( i-- ) {
- if ( ( elem = matcherOut[ i ] ) &&
- ( temp = postFinder ? indexOf( seed, elem ) : preMap[ i ] ) > -1 ) {
-
- seed[ temp ] = !( results[ temp ] = elem );
- }
- }
- }
-
- // Add elements to results, through postFinder if defined
- } else {
- matcherOut = condense(
- matcherOut === results ?
- matcherOut.splice( preexisting, matcherOut.length ) :
- matcherOut
- );
- if ( postFinder ) {
- postFinder( null, results, matcherOut, xml );
- } else {
- push.apply( results, matcherOut );
- }
- }
- } );
-}
-
-function matcherFromTokens( tokens ) {
- var checkContext, matcher, j,
- len = tokens.length,
- leadingRelative = Expr.relative[ tokens[ 0 ].type ],
- implicitRelative = leadingRelative || Expr.relative[ " " ],
- i = leadingRelative ? 1 : 0,
-
- // The foundational matcher ensures that elements are reachable from top-level context(s)
- matchContext = addCombinator( function( elem ) {
- return elem === checkContext;
- }, implicitRelative, true ),
- matchAnyContext = addCombinator( function( elem ) {
- return indexOf( checkContext, elem ) > -1;
- }, implicitRelative, true ),
- matchers = [ function( elem, context, xml ) {
- var ret = ( !leadingRelative && ( xml || context !== outermostContext ) ) || (
- ( checkContext = context ).nodeType ?
- matchContext( elem, context, xml ) :
- matchAnyContext( elem, context, xml ) );
-
- // Avoid hanging onto element (issue #299)
- checkContext = null;
- return ret;
- } ];
-
- for ( ; i < len; i++ ) {
- if ( ( matcher = Expr.relative[ tokens[ i ].type ] ) ) {
- matchers = [ addCombinator( elementMatcher( matchers ), matcher ) ];
- } else {
- matcher = Expr.filter[ tokens[ i ].type ].apply( null, tokens[ i ].matches );
-
- // Return special upon seeing a positional matcher
- if ( matcher[ expando ] ) {
-
- // Find the next relative operator (if any) for proper handling
- j = ++i;
- for ( ; j < len; j++ ) {
- if ( Expr.relative[ tokens[ j ].type ] ) {
- break;
- }
- }
- return setMatcher(
- i > 1 && elementMatcher( matchers ),
- i > 1 && toSelector(
-
- // If the preceding token was a descendant combinator, insert an implicit any-element `*`
- tokens
- .slice( 0, i - 1 )
- .concat( { value: tokens[ i - 2 ].type === " " ? "*" : "" } )
- ).replace( rtrim, "$1" ),
- matcher,
- i < j && matcherFromTokens( tokens.slice( i, j ) ),
- j < len && matcherFromTokens( ( tokens = tokens.slice( j ) ) ),
- j < len && toSelector( tokens )
- );
- }
- matchers.push( matcher );
- }
- }
-
- return elementMatcher( matchers );
-}
-
-function matcherFromGroupMatchers( elementMatchers, setMatchers ) {
- var bySet = setMatchers.length > 0,
- byElement = elementMatchers.length > 0,
- superMatcher = function( seed, context, xml, results, outermost ) {
- var elem, j, matcher,
- matchedCount = 0,
- i = "0",
- unmatched = seed && [],
- setMatched = [],
- contextBackup = outermostContext,
-
- // We must always have either seed elements or outermost context
- elems = seed || byElement && Expr.find[ "TAG" ]( "*", outermost ),
-
- // Use integer dirruns iff this is the outermost matcher
- dirrunsUnique = ( dirruns += contextBackup == null ? 1 : Math.random() || 0.1 ),
- len = elems.length;
-
- if ( outermost ) {
-
- // Support: IE 11+, Edge 17 - 18+
- // IE/Edge sometimes throw a "Permission denied" error when strict-comparing
- // two documents; shallow comparisons work.
- // eslint-disable-next-line eqeqeq
- outermostContext = context == document || context || outermost;
- }
-
- // Add elements passing elementMatchers directly to results
- // Support: IE<9, Safari
- // Tolerate NodeList properties (IE: "length"; Safari: <number>) matching elements by id
- for ( ; i !== len && ( elem = elems[ i ] ) != null; i++ ) {
- if ( byElement && elem ) {
- j = 0;
-
- // Support: IE 11+, Edge 17 - 18+
- // IE/Edge sometimes throw a "Permission denied" error when strict-comparing
- // two documents; shallow comparisons work.
- // eslint-disable-next-line eqeqeq
- if ( !context && elem.ownerDocument != document ) {
- setDocument( elem );
- xml = !documentIsHTML;
- }
- while ( ( matcher = elementMatchers[ j++ ] ) ) {
- if ( matcher( elem, context || document, xml ) ) {
- results.push( elem );
- break;
- }
- }
- if ( outermost ) {
- dirruns = dirrunsUnique;
- }
- }
-
- // Track unmatched elements for set filters
- if ( bySet ) {
-
- // They will have gone through all possible matchers
- if ( ( elem = !matcher && elem ) ) {
- matchedCount--;
- }
-
- // Lengthen the array for every element, matched or not
- if ( seed ) {
- unmatched.push( elem );
- }
- }
- }
-
- // `i` is now the count of elements visited above, and adding it to `matchedCount`
- // makes the latter nonnegative.
- matchedCount += i;
-
- // Apply set filters to unmatched elements
- // NOTE: This can be skipped if there are no unmatched elements (i.e., `matchedCount`
- // equals `i`), unless we didn't visit _any_ elements in the above loop because we have
- // no element matchers and no seed.
- // Incrementing an initially-string "0" `i` allows `i` to remain a string only in that
- // case, which will result in a "00" `matchedCount` that differs from `i` but is also
- // numerically zero.
- if ( bySet && i !== matchedCount ) {
- j = 0;
- while ( ( matcher = setMatchers[ j++ ] ) ) {
- matcher( unmatched, setMatched, context, xml );
- }
-
- if ( seed ) {
-
- // Reintegrate element matches to eliminate the need for sorting
- if ( matchedCount > 0 ) {
- while ( i-- ) {
- if ( !( unmatched[ i ] || setMatched[ i ] ) ) {
- setMatched[ i ] = pop.call( results );
- }
- }
- }
-
- // Discard index placeholder values to get only actual matches
- setMatched = condense( setMatched );
- }
-
- // Add matches to results
- push.apply( results, setMatched );
-
- // Seedless set matches succeeding multiple successful matchers stipulate sorting
- if ( outermost && !seed && setMatched.length > 0 &&
- ( matchedCount + setMatchers.length ) > 1 ) {
-
- Sizzle.uniqueSort( results );
- }
- }
-
- // Override manipulation of globals by nested matchers
- if ( outermost ) {
- dirruns = dirrunsUnique;
- outermostContext = contextBackup;
- }
-
- return unmatched;
- };
-
- return bySet ?
- markFunction( superMatcher ) :
- superMatcher;
-}
-
-compile = Sizzle.compile = function( selector, match /* Internal Use Only */ ) {
- var i,
- setMatchers = [],
- elementMatchers = [],
- cached = compilerCache[ selector + " " ];
-
- if ( !cached ) {
-
- // Generate a function of recursive functions that can be used to check each element
- if ( !match ) {
- match = tokenize( selector );
- }
- i = match.length;
- while ( i-- ) {
- cached = matcherFromTokens( match[ i ] );
- if ( cached[ expando ] ) {
- setMatchers.push( cached );
- } else {
- elementMatchers.push( cached );
- }
- }
-
- // Cache the compiled function
- cached = compilerCache(
- selector,
- matcherFromGroupMatchers( elementMatchers, setMatchers )
- );
-
- // Save selector and tokenization
- cached.selector = selector;
- }
- return cached;
-};
-
-/**
- * A low-level selection function that works with Sizzle's compiled
- * selector functions
- * @param {String|Function} selector A selector or a pre-compiled
- * selector function built with Sizzle.compile
- * @param {Element} context
- * @param {Array} [results]
- * @param {Array} [seed] A set of elements to match against
- */
-select = Sizzle.select = function( selector, context, results, seed ) {
- var i, tokens, token, type, find,
- compiled = typeof selector === "function" && selector,
- match = !seed && tokenize( ( selector = compiled.selector || selector ) );
-
- results = results || [];
-
- // Try to minimize operations if there is only one selector in the list and no seed
- // (the latter of which guarantees us context)
- if ( match.length === 1 ) {
-
- // Reduce context if the leading compound selector is an ID
- tokens = match[ 0 ] = match[ 0 ].slice( 0 );
- if ( tokens.length > 2 && ( token = tokens[ 0 ] ).type === "ID" &&
- context.nodeType === 9 && documentIsHTML && Expr.relative[ tokens[ 1 ].type ] ) {
-
- context = ( Expr.find[ "ID" ]( token.matches[ 0 ]
- .replace( runescape, funescape ), context ) || [] )[ 0 ];
- if ( !context ) {
- return results;
-
- // Precompiled matchers will still verify ancestry, so step up a level
- } else if ( compiled ) {
- context = context.parentNode;
- }
-
- selector = selector.slice( tokens.shift().value.length );
- }
-
- // Fetch a seed set for right-to-left matching
- i = matchExpr[ "needsContext" ].test( selector ) ? 0 : tokens.length;
- while ( i-- ) {
- token = tokens[ i ];
-
- // Abort if we hit a combinator
- if ( Expr.relative[ ( type = token.type ) ] ) {
- break;
- }
- if ( ( find = Expr.find[ type ] ) ) {
-
- // Search, expanding context for leading sibling combinators
- if ( ( seed = find(
- token.matches[ 0 ].replace( runescape, funescape ),
- rsibling.test( tokens[ 0 ].type ) && testContext( context.parentNode ) ||
- context
- ) ) ) {
-
- // If seed is empty or no tokens remain, we can return early
- tokens.splice( i, 1 );
- selector = seed.length && toSelector( tokens );
- if ( !selector ) {
- push.apply( results, seed );
- return results;
- }
-
- break;
- }
- }
- }
- }
-
- // Compile and execute a filtering function if one is not provided
- // Provide `match` to avoid retokenization if we modified the selector above
- ( compiled || compile( selector, match ) )(
- seed,
- context,
- !documentIsHTML,
- results,
- !context || rsibling.test( selector ) && testContext( context.parentNode ) || context
- );
- return results;
-};
-
-// One-time assignments
-
-// Sort stability
-support.sortStable = expando.split( "" ).sort( sortOrder ).join( "" ) === expando;
-
-// Support: Chrome 14-35+
-// Always assume duplicates if they aren't passed to the comparison function
-support.detectDuplicates = !!hasDuplicate;
-
-// Initialize against the default document
-setDocument();
-
-// Support: Webkit<537.32 - Safari 6.0.3/Chrome 25 (fixed in Chrome 27)
-// Detached nodes confoundingly follow *each other*
-support.sortDetached = assert( function( el ) {
-
- // Should return 1, but returns 4 (following)
- return el.compareDocumentPosition( document.createElement( "fieldset" ) ) & 1;
-} );
-
-// Support: IE<8
-// Prevent attribute/property "interpolation"
-// https://msdn.microsoft.com/en-us/library/ms536429%28VS.85%29.aspx
-if ( !assert( function( el ) {
- el.innerHTML = "<a href='#'></a>";
- return el.firstChild.getAttribute( "href" ) === "#";
-} ) ) {
- addHandle( "type|href|height|width", function( elem, name, isXML ) {
- if ( !isXML ) {
- return elem.getAttribute( name, name.toLowerCase() === "type" ? 1 : 2 );
- }
- } );
-}
-
-// Support: IE<9
-// Use defaultValue in place of getAttribute("value")
-if ( !support.attributes || !assert( function( el ) {
- el.innerHTML = "<input/>";
- el.firstChild.setAttribute( "value", "" );
- return el.firstChild.getAttribute( "value" ) === "";
-} ) ) {
- addHandle( "value", function( elem, _name, isXML ) {
- if ( !isXML && elem.nodeName.toLowerCase() === "input" ) {
- return elem.defaultValue;
- }
- } );
-}
-
-// Support: IE<9
-// Use getAttributeNode to fetch booleans when getAttribute lies
-if ( !assert( function( el ) {
- return el.getAttribute( "disabled" ) == null;
-} ) ) {
- addHandle( booleans, function( elem, name, isXML ) {
- var val;
- if ( !isXML ) {
- return elem[ name ] === true ? name.toLowerCase() :
- ( val = elem.getAttributeNode( name ) ) && val.specified ?
- val.value :
- null;
- }
- } );
-}
-
-return Sizzle;
-
-} )( window );
-
-
-
-jQuery.find = Sizzle;
-jQuery.expr = Sizzle.selectors;
-
-// Deprecated
-jQuery.expr[ ":" ] = jQuery.expr.pseudos;
-jQuery.uniqueSort = jQuery.unique = Sizzle.uniqueSort;
-jQuery.text = Sizzle.getText;
-jQuery.isXMLDoc = Sizzle.isXML;
-jQuery.contains = Sizzle.contains;
-jQuery.escapeSelector = Sizzle.escape;
-
-
-
-
-var dir = function( elem, dir, until ) {
- var matched = [],
- truncate = until !== undefined;
-
- while ( ( elem = elem[ dir ] ) && elem.nodeType !== 9 ) {
- if ( elem.nodeType === 1 ) {
- if ( truncate && jQuery( elem ).is( until ) ) {
- break;
- }
- matched.push( elem );
- }
- }
- return matched;
-};
-
-
-var siblings = function( n, elem ) {
- var matched = [];
-
- for ( ; n; n = n.nextSibling ) {
- if ( n.nodeType === 1 && n !== elem ) {
- matched.push( n );
- }
- }
-
- return matched;
-};
-
-
-var rneedsContext = jQuery.expr.match.needsContext;
-
-
-
-function nodeName( elem, name ) {
-
- return elem.nodeName && elem.nodeName.toLowerCase() === name.toLowerCase();
-
-};
-var rsingleTag = ( /^<([a-z][^\/\0>:\x20\t\r\n\f]*)[\x20\t\r\n\f]*\/?>(?:<\/\1>|)$/i );
-
-
-
-// Implement the identical functionality for filter and not
-function winnow( elements, qualifier, not ) {
- if ( isFunction( qualifier ) ) {
- return jQuery.grep( elements, function( elem, i ) {
- return !!qualifier.call( elem, i, elem ) !== not;
- } );
- }
-
- // Single element
- if ( qualifier.nodeType ) {
- return jQuery.grep( elements, function( elem ) {
- return ( elem === qualifier ) !== not;
- } );
- }
-
- // Arraylike of elements (jQuery, arguments, Array)
- if ( typeof qualifier !== "string" ) {
- return jQuery.grep( elements, function( elem ) {
- return ( indexOf.call( qualifier, elem ) > -1 ) !== not;
- } );
- }
-
- // Filtered directly for both simple and complex selectors
- return jQuery.filter( qualifier, elements, not );
-}
-
-jQuery.filter = function( expr, elems, not ) {
- var elem = elems[ 0 ];
-
- if ( not ) {
- expr = ":not(" + expr + ")";
- }
-
- if ( elems.length === 1 && elem.nodeType === 1 ) {
- return jQuery.find.matchesSelector( elem, expr ) ? [ elem ] : [];
- }
-
- return jQuery.find.matches( expr, jQuery.grep( elems, function( elem ) {
- return elem.nodeType === 1;
- } ) );
-};
-
-jQuery.fn.extend( {
- find: function( selector ) {
- var i, ret,
- len = this.length,
- self = this;
-
- if ( typeof selector !== "string" ) {
- return this.pushStack( jQuery( selector ).filter( function() {
- for ( i = 0; i < len; i++ ) {
- if ( jQuery.contains( self[ i ], this ) ) {
- return true;
- }
- }
- } ) );
- }
-
- ret = this.pushStack( [] );
-
- for ( i = 0; i < len; i++ ) {
- jQuery.find( selector, self[ i ], ret );
- }
-
- return len > 1 ? jQuery.uniqueSort( ret ) : ret;
- },
- filter: function( selector ) {
- return this.pushStack( winnow( this, selector || [], false ) );
- },
- not: function( selector ) {
- return this.pushStack( winnow( this, selector || [], true ) );
- },
- is: function( selector ) {
- return !!winnow(
- this,
-
- // If this is a positional/relative selector, check membership in the returned set
- // so $("p:first").is("p:last") won't return true for a doc with two "p".
- typeof selector === "string" && rneedsContext.test( selector ) ?
- jQuery( selector ) :
- selector || [],
- false
- ).length;
- }
-} );
-
-
-// Initialize a jQuery object
-
-
-// A central reference to the root jQuery(document)
-var rootjQuery,
-
- // A simple way to check for HTML strings
- // Prioritize #id over <tag> to avoid XSS via location.hash (#9521)
- // Strict HTML recognition (#11290: must start with <)
- // Shortcut simple #id case for speed
- rquickExpr = /^(?:\s*(<[\w\W]+>)[^>]*|#([\w-]+))$/,
-
- init = jQuery.fn.init = function( selector, context, root ) {
- var match, elem;
-
- // HANDLE: $(""), $(null), $(undefined), $(false)
- if ( !selector ) {
- return this;
- }
-
- // Method init() accepts an alternate rootjQuery
- // so migrate can support jQuery.sub (gh-2101)
- root = root || rootjQuery;
-
- // Handle HTML strings
- if ( typeof selector === "string" ) {
- if ( selector[ 0 ] === "<" &&
- selector[ selector.length - 1 ] === ">" &&
- selector.length >= 3 ) {
-
- // Assume that strings that start and end with <> are HTML and skip the regex check
- match = [ null, selector, null ];
-
- } else {
- match = rquickExpr.exec( selector );
- }
-
- // Match html or make sure no context is specified for #id
- if ( match && ( match[ 1 ] || !context ) ) {
-
- // HANDLE: $(html) -> $(array)
- if ( match[ 1 ] ) {
- context = context instanceof jQuery ? context[ 0 ] : context;
-
- // Option to run _bin is true for back-compat
- // Intentionally let the error be thrown if parseHTML is not present
- jQuery.merge( this, jQuery.parseHTML(
- match[ 1 ],
- context && context.nodeType ? context.ownerDocument || context : document,
- true
- ) );
-
- // HANDLE: $(html, props)
- if ( rsingleTag.test( match[ 1 ] ) && jQuery.isPlainObject( context ) ) {
- for ( match in context ) {
-
- // Properties of context are called as methods if possible
- if ( isFunction( this[ match ] ) ) {
- this[ match ]( context[ match ] );
-
- // ...and otherwise set as attributes
- } else {
- this.attr( match, context[ match ] );
- }
- }
- }
-
- return this;
-
- // HANDLE: $(#id)
- } else {
- elem = document.getElementById( match[ 2 ] );
-
- if ( elem ) {
-
- // Inject the element directly into the jQuery object
- this[ 0 ] = elem;
- this.length = 1;
- }
- return this;
- }
-
- // HANDLE: $(expr, $(...))
- } else if ( !context || context.jquery ) {
- return ( context || root ).find( selector );
-
- // HANDLE: $(expr, context)
- // (which is just equivalent to: $(context).find(expr)
- } else {
- return this.constructor( context ).find( selector );
- }
-
- // HANDLE: $(DOMElement)
- } else if ( selector.nodeType ) {
- this[ 0 ] = selector;
- this.length = 1;
- return this;
-
- // HANDLE: $(function)
- // Shortcut for document ready
- } else if ( isFunction( selector ) ) {
- return root.ready !== undefined ?
- root.ready( selector ) :
-
- // Execute immediately if ready is not present
- selector( jQuery );
- }
-
- return jQuery.makeArray( selector, this );
- };
-
-// Give the init function the jQuery prototype for later instantiation
-init.prototype = jQuery.fn;
-
-// Initialize central reference
-rootjQuery = jQuery( document );
-
-
-var rparentsprev = /^(?:parents|prev(?:Until|All))/,
-
- // Methods guaranteed to produce a unique set when starting from a unique set
- guaranteedUnique = {
- children: true,
- contents: true,
- next: true,
- prev: true
- };
-
-jQuery.fn.extend( {
- has: function( target ) {
- var targets = jQuery( target, this ),
- l = targets.length;
-
- return this.filter( function() {
- var i = 0;
- for ( ; i < l; i++ ) {
- if ( jQuery.contains( this, targets[ i ] ) ) {
- return true;
- }
- }
- } );
- },
-
- closest: function( selectors, context ) {
- var cur,
- i = 0,
- l = this.length,
- matched = [],
- targets = typeof selectors !== "string" && jQuery( selectors );
-
- // Positional selectors never match, since there's no _selection_ context
- if ( !rneedsContext.test( selectors ) ) {
- for ( ; i < l; i++ ) {
- for ( cur = this[ i ]; cur && cur !== context; cur = cur.parentNode ) {
-
- // Always skip document fragments
- if ( cur.nodeType < 11 && ( targets ?
- targets.index( cur ) > -1 :
-
- // Don't pass non-elements to Sizzle
- cur.nodeType === 1 &&
- jQuery.find.matchesSelector( cur, selectors ) ) ) {
-
- matched.push( cur );
- break;
- }
- }
- }
- }
-
- return this.pushStack( matched.length > 1 ? jQuery.uniqueSort( matched ) : matched );
- },
-
- // Determine the position of an element within the set
- index: function( elem ) {
-
- // No argument, return index in parent
- if ( !elem ) {
- return ( this[ 0 ] && this[ 0 ].parentNode ) ? this.first().prevAll().length : -1;
- }
-
- // Index in selector
- if ( typeof elem === "string" ) {
- return indexOf.call( jQuery( elem ), this[ 0 ] );
- }
-
- // Locate the position of the desired element
- return indexOf.call( this,
-
- // If it receives a jQuery object, the first element is used
- elem.jquery ? elem[ 0 ] : elem
- );
- },
-
- add: function( selector, context ) {
- return this.pushStack(
- jQuery.uniqueSort(
- jQuery.merge( this.get(), jQuery( selector, context ) )
- )
- );
- },
-
- addBack: function( selector ) {
- return this.add( selector == null ?
- this.prevObject : this.prevObject.filter( selector )
- );
- }
-} );
-
-function sibling( cur, dir ) {
- while ( ( cur = cur[ dir ] ) && cur.nodeType !== 1 ) {}
- return cur;
-}
-
-jQuery.each( {
- parent: function( elem ) {
- var parent = elem.parentNode;
- return parent && parent.nodeType !== 11 ? parent : null;
- },
- parents: function( elem ) {
- return dir( elem, "parentNode" );
- },
- parentsUntil: function( elem, _i, until ) {
- return dir( elem, "parentNode", until );
- },
- next: function( elem ) {
- return sibling( elem, "nextSibling" );
- },
- prev: function( elem ) {
- return sibling( elem, "previousSibling" );
- },
- nextAll: function( elem ) {
- return dir( elem, "nextSibling" );
- },
- prevAll: function( elem ) {
- return dir( elem, "previousSibling" );
- },
- nextUntil: function( elem, _i, until ) {
- return dir( elem, "nextSibling", until );
- },
- prevUntil: function( elem, _i, until ) {
- return dir( elem, "previousSibling", until );
- },
- siblings: function( elem ) {
- return siblings( ( elem.parentNode || {} ).firstChild, elem );
- },
- children: function( elem ) {
- return siblings( elem.firstChild );
- },
- contents: function( elem ) {
- if ( elem.contentDocument != null &&
-
- // Support: IE 11+
- // <object> elements with no `data` attribute has an object
- // `contentDocument` with a `null` prototype.
- getProto( elem.contentDocument ) ) {
-
- return elem.contentDocument;
- }
-
- // Support: IE 9 - 11 only, iOS 7 only, Android Browser <=4.3 only
- // Treat the template element as a regular one in browsers that
- // don't support it.
- if ( nodeName( elem, "template" ) ) {
- elem = elem.content || elem;
- }
-
- return jQuery.merge( [], elem.childNodes );
- }
-}, function( name, fn ) {
- jQuery.fn[ name ] = function( until, selector ) {
- var matched = jQuery.map( this, fn, until );
-
- if ( name.slice( -5 ) !== "Until" ) {
- selector = until;
- }
-
- if ( selector && typeof selector === "string" ) {
- matched = jQuery.filter( selector, matched );
- }
-
- if ( this.length > 1 ) {
-
- // Remove duplicates
- if ( !guaranteedUnique[ name ] ) {
- jQuery.uniqueSort( matched );
- }
-
- // Reverse order for parents* and prev-derivatives
- if ( rparentsprev.test( name ) ) {
- matched.reverse();
- }
- }
-
- return this.pushStack( matched );
- };
-} );
-var rnothtmlwhite = ( /[^\x20\t\r\n\f]+/g );
-
-
-
-// Convert String-formatted options into Object-formatted ones
-function createOptions( options ) {
- var object = {};
- jQuery.each( options.match( rnothtmlwhite ) || [], function( _, flag ) {
- object[ flag ] = true;
- } );
- return object;
-}
-
-/*
- * Create a callback list using the following parameters:
- *
- * options: an optional list of space-separated options that will change how
- * the callback list behaves or a more traditional option object
- *
- * By default a callback list will act like an event callback list and can be
- * "fired" multiple times.
- *
- * Possible options:
- *
- * once: will ensure the callback list can only be fired once (like a Deferred)
- *
- * memory: will keep track of previous values and will call any callback added
- * after the list has been fired right away with the latest "memorized"
- * values (like a Deferred)
- *
- * unique: will ensure a callback can only be added once (no duplicate in the list)
- *
- * stopOnFalse: interrupt callings when a callback returns false
- *
- */
-jQuery.Callbacks = function( options ) {
-
- // Convert options from String-formatted to Object-formatted if needed
- // (we check in cache first)
- options = typeof options === "string" ?
- createOptions( options ) :
- jQuery.extend( {}, options );
-
- var // Flag to know if list is currently firing
- firing,
-
- // Last fire value for non-forgettable lists
- memory,
-
- // Flag to know if list was already fired
- fired,
-
- // Flag to prevent firing
- locked,
-
- // Actual callback list
- list = [],
-
- // Queue of execution data for repeatable lists
- queue = [],
-
- // Index of currently firing callback (modified by add/remove as needed)
- firingIndex = -1,
-
- // Fire callbacks
- fire = function() {
-
- // Enforce single-firing
- locked = locked || options.once;
-
- // Execute callbacks for all pending executions,
- // respecting firingIndex overrides and runtime changes
- fired = firing = true;
- for ( ; queue.length; firingIndex = -1 ) {
- memory = queue.shift();
- while ( ++firingIndex < list.length ) {
-
- // Run callback and check for early termination
- if ( list[ firingIndex ].apply( memory[ 0 ], memory[ 1 ] ) === false &&
- options.stopOnFalse ) {
-
- // Jump to end and forget the data so .add doesn't re-fire
- firingIndex = list.length;
- memory = false;
- }
- }
- }
-
- // Forget the data if we're done with it
- if ( !options.memory ) {
- memory = false;
- }
-
- firing = false;
-
- // Clean up if we're done firing for good
- if ( locked ) {
-
- // Keep an empty list if we have data for future add calls
- if ( memory ) {
- list = [];
-
- // Otherwise, this object is spent
- } else {
- list = "";
- }
- }
- },
-
- // Actual Callbacks object
- self = {
-
- // Add a callback or a collection of callbacks to the list
- add: function() {
- if ( list ) {
-
- // If we have memory from a past run, we should fire after adding
- if ( memory && !firing ) {
- firingIndex = list.length - 1;
- queue.push( memory );
- }
-
- ( function add( args ) {
- jQuery.each( args, function( _, arg ) {
- if ( isFunction( arg ) ) {
- if ( !options.unique || !self.has( arg ) ) {
- list.push( arg );
- }
- } else if ( arg && arg.length && toType( arg ) !== "string" ) {
-
- // Inspect recursively
- add( arg );
- }
- } );
- } )( arguments );
-
- if ( memory && !firing ) {
- fire();
- }
- }
- return this;
- },
-
- // Remove a callback from the list
- remove: function() {
- jQuery.each( arguments, function( _, arg ) {
- var index;
- while ( ( index = jQuery.inArray( arg, list, index ) ) > -1 ) {
- list.splice( index, 1 );
-
- // Handle firing indexes
- if ( index <= firingIndex ) {
- firingIndex--;
- }
- }
- } );
- return this;
- },
-
- // Check if a given callback is in the list.
- // If no argument is given, return whether or not list has callbacks attached.
- has: function( fn ) {
- return fn ?
- jQuery.inArray( fn, list ) > -1 :
- list.length > 0;
- },
-
- // Remove all callbacks from the list
- empty: function() {
- if ( list ) {
- list = [];
- }
- return this;
- },
-
- // Disable .fire and .add
- // Abort any current/pending executions
- // Clear all callbacks and values
- disable: function() {
- locked = queue = [];
- list = memory = "";
- return this;
- },
- disabled: function() {
- return !list;
- },
-
- // Disable .fire
- // Also disable .add unless we have memory (since it would have no effect)
- // Abort any pending executions
- lock: function() {
- locked = queue = [];
- if ( !memory && !firing ) {
- list = memory = "";
- }
- return this;
- },
- locked: function() {
- return !!locked;
- },
-
- // Call all callbacks with the given context and arguments
- fireWith: function( context, args ) {
- if ( !locked ) {
- args = args || [];
- args = [ context, args.slice ? args.slice() : args ];
- queue.push( args );
- if ( !firing ) {
- fire();
- }
- }
- return this;
- },
-
- // Call all the callbacks with the given arguments
- fire: function() {
- self.fireWith( this, arguments );
- return this;
- },
-
- // To know if the callbacks have already been called at least once
- fired: function() {
- return !!fired;
- }
- };
-
- return self;
-};
-
-
-function Identity( v ) {
- return v;
-}
-function Thrower( ex ) {
- throw ex;
-}
-
-function adoptValue( value, resolve, reject, noValue ) {
- var method;
-
- try {
-
- // Check for promise aspect first to privilege synchronous behavior
- if ( value && isFunction( ( method = value.promise ) ) ) {
- method.call( value ).done( resolve ).fail( reject );
-
- // Other thenables
- } else if ( value && isFunction( ( method = value.then ) ) ) {
- method.call( value, resolve, reject );
-
- // Other non-thenables
- } else {
-
- // Control `resolve` arguments by letting Array#slice cast boolean `noValue` to integer:
- // * false: [ value ].slice( 0 ) => resolve( value )
- // * true: [ value ].slice( 1 ) => resolve()
- resolve.apply( undefined, [ value ].slice( noValue ) );
- }
-
- // For Promises/A+, convert exceptions into rejections
- // Since jQuery.when doesn't unwrap thenables, we can skip the extra checks appearing in
- // Deferred#then to conditionally suppress rejection.
- } catch ( value ) {
-
- // Support: Android 4.0 only
- // Strict mode functions invoked without .call/.apply get global-object context
- reject.apply( undefined, [ value ] );
- }
-}
-
-jQuery.extend( {
-
- Deferred: function( func ) {
- var tuples = [
-
- // action, add listener, callbacks,
- // ... .then handlers, argument index, [final state]
- [ "notify", "progress", jQuery.Callbacks( "memory" ),
- jQuery.Callbacks( "memory" ), 2 ],
- [ "resolve", "done", jQuery.Callbacks( "once memory" ),
- jQuery.Callbacks( "once memory" ), 0, "resolved" ],
- [ "reject", "fail", jQuery.Callbacks( "once memory" ),
- jQuery.Callbacks( "once memory" ), 1, "rejected" ]
- ],
- state = "pending",
- promise = {
- state: function() {
- return state;
- },
- always: function() {
- deferred.done( arguments ).fail( arguments );
- return this;
- },
- "catch": function( fn ) {
- return promise.then( null, fn );
- },
-
- // Keep pipe for back-compat
- pipe: function( /* fnDone, fnFail, fnProgress */ ) {
- var fns = arguments;
-
- return jQuery.Deferred( function( newDefer ) {
- jQuery.each( tuples, function( _i, tuple ) {
-
- // Map tuples (progress, done, fail) to arguments (done, fail, progress)
- var fn = isFunction( fns[ tuple[ 4 ] ] ) && fns[ tuple[ 4 ] ];
-
- // deferred.progress(function() { bind to newDefer or newDefer.notify })
- // deferred.done(function() { bind to newDefer or newDefer.resolve })
- // deferred.fail(function() { bind to newDefer or newDefer.reject })
- deferred[ tuple[ 1 ] ]( function() {
- var returned = fn && fn.apply( this, arguments );
- if ( returned && isFunction( returned.promise ) ) {
- returned.promise()
- .progress( newDefer.notify )
- .done( newDefer.resolve )
- .fail( newDefer.reject );
- } else {
- newDefer[ tuple[ 0 ] + "With" ](
- this,
- fn ? [ returned ] : arguments
- );
- }
- } );
- } );
- fns = null;
- } ).promise();
- },
- then: function( onFulfilled, onRejected, onProgress ) {
- var maxDepth = 0;
- function resolve( depth, deferred, handler, special ) {
- return function() {
- var that = this,
- args = arguments,
- mightThrow = function() {
- var returned, then;
-
- // Support: Promises/A+ section 2.3.3.3.3
- // https://promisesaplus.com/#point-59
- // Ignore double-resolution attempts
- if ( depth < maxDepth ) {
- return;
- }
-
- returned = handler.apply( that, args );
-
- // Support: Promises/A+ section 2.3.1
- // https://promisesaplus.com/#point-48
- if ( returned === deferred.promise() ) {
- throw new TypeError( "Thenable self-resolution" );
- }
-
- // Support: Promises/A+ sections 2.3.3.1, 3.5
- // https://promisesaplus.com/#point-54
- // https://promisesaplus.com/#point-75
- // Retrieve `then` only once
- then = returned &&
-
- // Support: Promises/A+ section 2.3.4
- // https://promisesaplus.com/#point-64
- // Only check objects and functions for thenability
- ( typeof returned === "object" ||
- typeof returned === "function" ) &&
- returned.then;
-
- // Handle a returned thenable
- if ( isFunction( then ) ) {
-
- // Special processors (notify) just wait for resolution
- if ( special ) {
- then.call(
- returned,
- resolve( maxDepth, deferred, Identity, special ),
- resolve( maxDepth, deferred, Thrower, special )
- );
-
- // Normal processors (resolve) also hook into progress
- } else {
-
- // ...and disregard older resolution values
- maxDepth++;
-
- then.call(
- returned,
- resolve( maxDepth, deferred, Identity, special ),
- resolve( maxDepth, deferred, Thrower, special ),
- resolve( maxDepth, deferred, Identity,
- deferred.notifyWith )
- );
- }
-
- // Handle all other returned values
- } else {
-
- // Only substitute handlers pass on context
- // and multiple values (non-spec behavior)
- if ( handler !== Identity ) {
- that = undefined;
- args = [ returned ];
- }
-
- // Process the value(s)
- // Default process is resolve
- ( special || deferred.resolveWith )( that, args );
- }
- },
-
- // Only normal processors (resolve) catch and reject exceptions
- process = special ?
- mightThrow :
- function() {
- try {
- mightThrow();
- } catch ( e ) {
-
- if ( jQuery.Deferred.exceptionHook ) {
- jQuery.Deferred.exceptionHook( e,
- process.stackTrace );
- }
-
- // Support: Promises/A+ section 2.3.3.3.4.1
- // https://promisesaplus.com/#point-61
- // Ignore post-resolution exceptions
- if ( depth + 1 >= maxDepth ) {
-
- // Only substitute handlers pass on context
- // and multiple values (non-spec behavior)
- if ( handler !== Thrower ) {
- that = undefined;
- args = [ e ];
- }
-
- deferred.rejectWith( that, args );
- }
- }
- };
-
- // Support: Promises/A+ section 2.3.3.3.1
- // https://promisesaplus.com/#point-57
- // Re-resolve promises immediately to dodge false rejection from
- // subsequent errors
- if ( depth ) {
- process();
- } else {
-
- // Call an optional hook to record the stack, in case of exception
- // since it's otherwise lost when execution goes async
- if ( jQuery.Deferred.getStackHook ) {
- process.stackTrace = jQuery.Deferred.getStackHook();
- }
- window.setTimeout( process );
- }
- };
- }
-
- return jQuery.Deferred( function( newDefer ) {
-
- // progress_handlers.add( ... )
- tuples[ 0 ][ 3 ].add(
- resolve(
- 0,
- newDefer,
- isFunction( onProgress ) ?
- onProgress :
- Identity,
- newDefer.notifyWith
- )
- );
-
- // fulfilled_handlers.add( ... )
- tuples[ 1 ][ 3 ].add(
- resolve(
- 0,
- newDefer,
- isFunction( onFulfilled ) ?
- onFulfilled :
- Identity
- )
- );
-
- // rejected_handlers.add( ... )
- tuples[ 2 ][ 3 ].add(
- resolve(
- 0,
- newDefer,
- isFunction( onRejected ) ?
- onRejected :
- Thrower
- )
- );
- } ).promise();
- },
-
- // Get a promise for this deferred
- // If obj is provided, the promise aspect is added to the object
- promise: function( obj ) {
- return obj != null ? jQuery.extend( obj, promise ) : promise;
- }
- },
- deferred = {};
-
- // Add list-specific methods
- jQuery.each( tuples, function( i, tuple ) {
- var list = tuple[ 2 ],
- stateString = tuple[ 5 ];
-
- // promise.progress = list.add
- // promise.done = list.add
- // promise.fail = list.add
- promise[ tuple[ 1 ] ] = list.add;
-
- // Handle state
- if ( stateString ) {
- list.add(
- function() {
-
- // state = "resolved" (i.e., fulfilled)
- // state = "rejected"
- state = stateString;
- },
-
- // rejected_callbacks.disable
- // fulfilled_callbacks.disable
- tuples[ 3 - i ][ 2 ].disable,
-
- // rejected_handlers.disable
- // fulfilled_handlers.disable
- tuples[ 3 - i ][ 3 ].disable,
-
- // progress_callbacks.lock
- tuples[ 0 ][ 2 ].lock,
-
- // progress_handlers.lock
- tuples[ 0 ][ 3 ].lock
- );
- }
-
- // progress_handlers.fire
- // fulfilled_handlers.fire
- // rejected_handlers.fire
- list.add( tuple[ 3 ].fire );
-
- // deferred.notify = function() { deferred.notifyWith(...) }
- // deferred.resolve = function() { deferred.resolveWith(...) }
- // deferred.reject = function() { deferred.rejectWith(...) }
- deferred[ tuple[ 0 ] ] = function() {
- deferred[ tuple[ 0 ] + "With" ]( this === deferred ? undefined : this, arguments );
- return this;
- };
-
- // deferred.notifyWith = list.fireWith
- // deferred.resolveWith = list.fireWith
- // deferred.rejectWith = list.fireWith
- deferred[ tuple[ 0 ] + "With" ] = list.fireWith;
- } );
-
- // Make the deferred a promise
- promise.promise( deferred );
-
- // Call given func if any
- if ( func ) {
- func.call( deferred, deferred );
- }
-
- // All done!
- return deferred;
- },
-
- // Deferred helper
- when: function( singleValue ) {
- var
-
- // count of uncompleted subordinates
- remaining = arguments.length,
-
- // count of unprocessed arguments
- i = remaining,
-
- // subordinate fulfillment data
- resolveContexts = Array( i ),
- resolveValues = slice.call( arguments ),
-
- // the master Deferred
- master = jQuery.Deferred(),
-
- // subordinate callback factory
- updateFunc = function( i ) {
- return function( value ) {
- resolveContexts[ i ] = this;
- resolveValues[ i ] = arguments.length > 1 ? slice.call( arguments ) : value;
- if ( !( --remaining ) ) {
- master.resolveWith( resolveContexts, resolveValues );
- }
- };
- };
-
- // Single- and empty arguments are adopted like Promise.resolve
- if ( remaining <= 1 ) {
- adoptValue( singleValue, master.done( updateFunc( i ) ).resolve, master.reject,
- !remaining );
-
- // Use .then() to unwrap secondary thenables (cf. gh-3000)
- if ( master.state() === "pending" ||
- isFunction( resolveValues[ i ] && resolveValues[ i ].then ) ) {
-
- return master.then();
- }
- }
-
- // Multiple arguments are aggregated like Promise.all array elements
- while ( i-- ) {
- adoptValue( resolveValues[ i ], updateFunc( i ), master.reject );
- }
-
- return master.promise();
- }
-} );
-
-
-// These usually indicate a programmer mistake during development,
-// warn about them ASAP rather than swallowing them by default.
-var rerrorNames = /^(Eval|Internal|Range|Reference|Syntax|Type|URI)Error$/;
-
-jQuery.Deferred.exceptionHook = function( error, stack ) {
-
- // Support: IE 8 - 9 only
- // Console exists when dev tools are open, which can happen at any time
- if ( window.console && window.console.warn && error && rerrorNames.test( error.name ) ) {
- window.console.warn( "jQuery.Deferred exception: " + error.message, error.stack, stack );
- }
-};
-
-
-
-
-jQuery.readyException = function( error ) {
- window.setTimeout( function() {
- throw error;
- } );
-};
-
-
-
-
-// The deferred used on DOM ready
-var readyList = jQuery.Deferred();
-
-jQuery.fn.ready = function( fn ) {
-
- readyList
- .then( fn )
-
- // Wrap jQuery.readyException in a function so that the lookup
- // happens at the time of error handling instead of callback
- // registration.
- .catch( function( error ) {
- jQuery.readyException( error );
- } );
-
- return this;
-};
-
-jQuery.extend( {
-
- // Is the DOM ready to be used? Set to true once it occurs.
- isReady: false,
-
- // A counter to track how many items to wait for before
- // the ready event fires. See #6781
- readyWait: 1,
-
- // Handle when the DOM is ready
- ready: function( wait ) {
-
- // Abort if there are pending holds or we're already ready
- if ( wait === true ? --jQuery.readyWait : jQuery.isReady ) {
- return;
- }
-
- // Remember that the DOM is ready
- jQuery.isReady = true;
-
- // If a normal DOM Ready event fired, decrement, and wait if need be
- if ( wait !== true && --jQuery.readyWait > 0 ) {
- return;
- }
-
- // If there are functions bound, to execute
- readyList.resolveWith( document, [ jQuery ] );
- }
-} );
-
-jQuery.ready.then = readyList.then;
-
-// The ready event handler and self cleanup method
-function completed() {
- document.removeEventListener( "DOMContentLoaded", completed );
- window.removeEventListener( "load", completed );
- jQuery.ready();
-}
-
-// Catch cases where $(document).ready() is called
-// after the browser event has already occurred.
-// Support: IE <=9 - 10 only
-// Older IE sometimes signals "interactive" too soon
-if ( document.readyState === "complete" ||
- ( document.readyState !== "loading" && !document.documentElement.doScroll ) ) {
-
- // Handle it asynchronously to allow _bin the opportunity to delay ready
- window.setTimeout( jQuery.ready );
-
-} else {
-
- // Use the handy event callback
- document.addEventListener( "DOMContentLoaded", completed );
-
- // A fallback to window.onload, that will always work
- window.addEventListener( "load", completed );
-}
-
-
-
-
-// Multifunctional method to get and set values of a collection
-// The value/s can optionally be executed if it's a function
-var access = function( elems, fn, key, value, chainable, emptyGet, raw ) {
- var i = 0,
- len = elems.length,
- bulk = key == null;
-
- // Sets many values
- if ( toType( key ) === "object" ) {
- chainable = true;
- for ( i in key ) {
- access( elems, fn, i, key[ i ], true, emptyGet, raw );
- }
-
- // Sets one value
- } else if ( value !== undefined ) {
- chainable = true;
-
- if ( !isFunction( value ) ) {
- raw = true;
- }
-
- if ( bulk ) {
-
- // Bulk operations run against the entire set
- if ( raw ) {
- fn.call( elems, value );
- fn = null;
-
- // ...except when executing function values
- } else {
- bulk = fn;
- fn = function( elem, _key, value ) {
- return bulk.call( jQuery( elem ), value );
- };
- }
- }
-
- if ( fn ) {
- for ( ; i < len; i++ ) {
- fn(
- elems[ i ], key, raw ?
- value :
- value.call( elems[ i ], i, fn( elems[ i ], key ) )
- );
- }
- }
- }
-
- if ( chainable ) {
- return elems;
- }
-
- // Gets
- if ( bulk ) {
- return fn.call( elems );
- }
-
- return len ? fn( elems[ 0 ], key ) : emptyGet;
-};
-
-
-// Matches dashed string for camelizing
-var rmsPrefix = /^-ms-/,
- rdashAlpha = /-([a-z])/g;
-
-// Used by camelCase as callback to replace()
-function fcamelCase( _all, letter ) {
- return letter.toUpperCase();
-}
-
-// Convert dashed to camelCase; used by the css and data modules
-// Support: IE <=9 - 11, Edge 12 - 15
-// Microsoft forgot to hump their vendor prefix (#9572)
-function camelCase( string ) {
- return string.replace( rmsPrefix, "ms-" ).replace( rdashAlpha, fcamelCase );
-}
-var acceptData = function( owner ) {
-
- // Accepts only:
- // - Node
- // - Node.ELEMENT_NODE
- // - Node.DOCUMENT_NODE
- // - Object
- // - Any
- return owner.nodeType === 1 || owner.nodeType === 9 || !( +owner.nodeType );
-};
-
-
-
-
-function Data() {
- this.expando = jQuery.expando + Data.uid++;
-}
-
-Data.uid = 1;
-
-Data.prototype = {
-
- cache: function( owner ) {
-
- // Check if the owner object already has a cache
- var value = owner[ this.expando ];
-
- // If not, create one
- if ( !value ) {
- value = {};
-
- // We can accept data for non-element nodes in modern browsers,
- // but we should not, see #8335.
- // Always return an empty object.
- if ( acceptData( owner ) ) {
-
- // If it is a node unlikely to be stringify-ed or looped over
- // use plain assignment
- if ( owner.nodeType ) {
- owner[ this.expando ] = value;
-
- // Otherwise secure it in a non-enumerable property
- // configurable must be true to allow the property to be
- // deleted when data is removed
- } else {
- Object.defineProperty( owner, this.expando, {
- value: value,
- configurable: true
- } );
- }
- }
- }
-
- return value;
- },
- set: function( owner, data, value ) {
- var prop,
- cache = this.cache( owner );
-
- // Handle: [ owner, key, value ] args
- // Always use camelCase key (gh-2257)
- if ( typeof data === "string" ) {
- cache[ camelCase( data ) ] = value;
-
- // Handle: [ owner, { properties } ] args
- } else {
-
- // Copy the properties one-by-one to the cache object
- for ( prop in data ) {
- cache[ camelCase( prop ) ] = data[ prop ];
- }
- }
- return cache;
- },
- get: function( owner, key ) {
- return key === undefined ?
- this.cache( owner ) :
-
- // Always use camelCase key (gh-2257)
- owner[ this.expando ] && owner[ this.expando ][ camelCase( key ) ];
- },
- access: function( owner, key, value ) {
-
- // In cases where either:
- //
- // 1. No key was specified
- // 2. A string key was specified, but no value provided
- //
- // Take the "read" path and allow the get method to determine
- // which value to return, respectively either:
- //
- // 1. The entire cache object
- // 2. The data stored at the key
- //
- if ( key === undefined ||
- ( ( key && typeof key === "string" ) && value === undefined ) ) {
-
- return this.get( owner, key );
- }
-
- // When the key is not a string, or both a key and value
- // are specified, set or extend (existing objects) with either:
- //
- // 1. An object of properties
- // 2. A key and value
- //
- this.set( owner, key, value );
-
- // Since the "set" path can have two possible entry points
- // return the expected data based on which path was taken[*]
- return value !== undefined ? value : key;
- },
- remove: function( owner, key ) {
- var i,
- cache = owner[ this.expando ];
-
- if ( cache === undefined ) {
- return;
- }
-
- if ( key !== undefined ) {
-
- // Support array or space separated string of keys
- if ( Array.isArray( key ) ) {
-
- // If key is an array of keys...
- // We always set camelCase keys, so remove that.
- key = key.map( camelCase );
- } else {
- key = camelCase( key );
-
- // If a key with the spaces exists, use it.
- // Otherwise, create an array by matching non-whitespace
- key = key in cache ?
- [ key ] :
- ( key.match( rnothtmlwhite ) || [] );
- }
-
- i = key.length;
-
- while ( i-- ) {
- delete cache[ key[ i ] ];
- }
- }
-
- // Remove the expando if there's no more data
- if ( key === undefined || jQuery.isEmptyObject( cache ) ) {
-
- // Support: Chrome <=35 - 45
- // Webkit & Blink performance suffers when deleting properties
- // from DOM nodes, so set to undefined instead
- // https://bugs.chromium.org/p/chromium/issues/detail?id=378607 (bug restricted)
- if ( owner.nodeType ) {
- owner[ this.expando ] = undefined;
- } else {
- delete owner[ this.expando ];
- }
- }
- },
- hasData: function( owner ) {
- var cache = owner[ this.expando ];
- return cache !== undefined && !jQuery.isEmptyObject( cache );
- }
-};
-var dataPriv = new Data();
-
-var dataUser = new Data();
-
-
-
-// Implementation Summary
-//
-// 1. Enforce API surface and semantic compatibility with 1.9.x branch
-// 2. Improve the module's maintainability by reducing the storage
-// paths to a single mechanism.
-// 3. Use the same single mechanism to support "private" and "user" data.
-// 4. _Never_ expose "private" data to user code (TODO: Drop _data, _removeData)
-// 5. Avoid exposing implementation details on user objects (eg. expando properties)
-// 6. Provide a clear path for implementation upgrade to WeakMap in 2014
-
-var rbrace = /^(?:\{[\w\W]*\}|\[[\w\W]*\])$/,
- rmultiDash = /[A-Z]/g;
-
-function getData( data ) {
- if ( data === "true" ) {
- return true;
- }
-
- if ( data === "false" ) {
- return false;
- }
-
- if ( data === "null" ) {
- return null;
- }
-
- // Only convert to a number if it doesn't change the string
- if ( data === +data + "" ) {
- return +data;
- }
-
- if ( rbrace.test( data ) ) {
- return JSON.parse( data );
- }
-
- return data;
-}
-
-function dataAttr( elem, key, data ) {
- var name;
-
- // If nothing was found internally, try to fetch any
- // data from the HTML5 data-* attribute
- if ( data === undefined && elem.nodeType === 1 ) {
- name = "data-" + key.replace( rmultiDash, "-$&" ).toLowerCase();
- data = elem.getAttribute( name );
-
- if ( typeof data === "string" ) {
- try {
- data = getData( data );
- } catch ( e ) {}
-
- // Make sure we set the data so it isn't changed later
- dataUser.set( elem, key, data );
- } else {
- data = undefined;
- }
- }
- return data;
-}
-
-jQuery.extend( {
- hasData: function( elem ) {
- return dataUser.hasData( elem ) || dataPriv.hasData( elem );
- },
-
- data: function( elem, name, data ) {
- return dataUser.access( elem, name, data );
- },
-
- removeData: function( elem, name ) {
- dataUser.remove( elem, name );
- },
-
- // TODO: Now that all calls to _data and _removeData have been replaced
- // with direct calls to dataPriv methods, these can be deprecated.
- _data: function( elem, name, data ) {
- return dataPriv.access( elem, name, data );
- },
-
- _removeData: function( elem, name ) {
- dataPriv.remove( elem, name );
- }
-} );
-
-jQuery.fn.extend( {
- data: function( key, value ) {
- var i, name, data,
- elem = this[ 0 ],
- attrs = elem && elem.attributes;
-
- // Gets all values
- if ( key === undefined ) {
- if ( this.length ) {
- data = dataUser.get( elem );
-
- if ( elem.nodeType === 1 && !dataPriv.get( elem, "hasDataAttrs" ) ) {
- i = attrs.length;
- while ( i-- ) {
-
- // Support: IE 11 only
- // The attrs elements can be null (#14894)
- if ( attrs[ i ] ) {
- name = attrs[ i ].name;
- if ( name.indexOf( "data-" ) === 0 ) {
- name = camelCase( name.slice( 5 ) );
- dataAttr( elem, name, data[ name ] );
- }
- }
- }
- dataPriv.set( elem, "hasDataAttrs", true );
- }
- }
-
- return data;
- }
-
- // Sets multiple values
- if ( typeof key === "object" ) {
- return this.each( function() {
- dataUser.set( this, key );
- } );
- }
-
- return access( this, function( value ) {
- var data;
-
- // The calling jQuery object (element matches) is not empty
- // (and therefore has an element appears at this[ 0 ]) and the
- // `value` parameter was not undefined. An empty jQuery object
- // will result in `undefined` for elem = this[ 0 ] which will
- // throw an exception if an attempt to read a data cache is made.
- if ( elem && value === undefined ) {
-
- // Attempt to get data from the cache
- // The key will always be camelCased in Data
- data = dataUser.get( elem, key );
- if ( data !== undefined ) {
- return data;
- }
-
- // Attempt to "discover" the data in
- // HTML5 custom data-* attrs
- data = dataAttr( elem, key );
- if ( data !== undefined ) {
- return data;
- }
-
- // We tried really hard, but the data doesn't exist.
- return;
- }
-
- // Set the data...
- this.each( function() {
-
- // We always store the camelCased key
- dataUser.set( this, key, value );
- } );
- }, null, value, arguments.length > 1, null, true );
- },
-
- removeData: function( key ) {
- return this.each( function() {
- dataUser.remove( this, key );
- } );
- }
-} );
-
-
-jQuery.extend( {
- queue: function( elem, type, data ) {
- var queue;
-
- if ( elem ) {
- type = ( type || "fx" ) + "queue";
- queue = dataPriv.get( elem, type );
-
- // Speed up dequeue by getting out quickly if this is just a lookup
- if ( data ) {
- if ( !queue || Array.isArray( data ) ) {
- queue = dataPriv.access( elem, type, jQuery.makeArray( data ) );
- } else {
- queue.push( data );
- }
- }
- return queue || [];
- }
- },
-
- dequeue: function( elem, type ) {
- type = type || "fx";
-
- var queue = jQuery.queue( elem, type ),
- startLength = queue.length,
- fn = queue.shift(),
- hooks = jQuery._queueHooks( elem, type ),
- next = function() {
- jQuery.dequeue( elem, type );
- };
-
- // If the fx queue is dequeued, always remove the progress sentinel
- if ( fn === "inprogress" ) {
- fn = queue.shift();
- startLength--;
- }
-
- if ( fn ) {
-
- // Add a progress sentinel to prevent the fx queue from being
- // automatically dequeued
- if ( type === "fx" ) {
- queue.unshift( "inprogress" );
- }
-
- // Clear up the last queue stop function
- delete hooks.stop;
- fn.call( elem, next, hooks );
- }
-
- if ( !startLength && hooks ) {
- hooks.empty.fire();
- }
- },
-
- // Not public - generate a queueHooks object, or return the current one
- _queueHooks: function( elem, type ) {
- var key = type + "queueHooks";
- return dataPriv.get( elem, key ) || dataPriv.access( elem, key, {
- empty: jQuery.Callbacks( "once memory" ).add( function() {
- dataPriv.remove( elem, [ type + "queue", key ] );
- } )
- } );
- }
-} );
-
-jQuery.fn.extend( {
- queue: function( type, data ) {
- var setter = 2;
-
- if ( typeof type !== "string" ) {
- data = type;
- type = "fx";
- setter--;
- }
-
- if ( arguments.length < setter ) {
- return jQuery.queue( this[ 0 ], type );
- }
-
- return data === undefined ?
- this :
- this.each( function() {
- var queue = jQuery.queue( this, type, data );
-
- // Ensure a hooks for this queue
- jQuery._queueHooks( this, type );
-
- if ( type === "fx" && queue[ 0 ] !== "inprogress" ) {
- jQuery.dequeue( this, type );
- }
- } );
- },
- dequeue: function( type ) {
- return this.each( function() {
- jQuery.dequeue( this, type );
- } );
- },
- clearQueue: function( type ) {
- return this.queue( type || "fx", [] );
- },
-
- // Get a promise resolved when queues of a certain type
- // are emptied (fx is the type by default)
- promise: function( type, obj ) {
- var tmp,
- count = 1,
- defer = jQuery.Deferred(),
- elements = this,
- i = this.length,
- resolve = function() {
- if ( !( --count ) ) {
- defer.resolveWith( elements, [ elements ] );
- }
- };
-
- if ( typeof type !== "string" ) {
- obj = type;
- type = undefined;
- }
- type = type || "fx";
-
- while ( i-- ) {
- tmp = dataPriv.get( elements[ i ], type + "queueHooks" );
- if ( tmp && tmp.empty ) {
- count++;
- tmp.empty.add( resolve );
- }
- }
- resolve();
- return defer.promise( obj );
- }
-} );
-var pnum = ( /[+-]?(?:\d*\.|)\d+(?:[eE][+-]?\d+|)/ ).source;
-
-var rcssNum = new RegExp( "^(?:([+-])=|)(" + pnum + ")([a-z%]*)$", "i" );
-
-
-var cssExpand = [ "Top", "Right", "Bottom", "Left" ];
-
-var documentElement = document.documentElement;
-
-
-
- var isAttached = function( elem ) {
- return jQuery.contains( elem.ownerDocument, elem );
- },
- composed = { composed: true };
-
- // Support: IE 9 - 11+, Edge 12 - 18+, iOS 10.0 - 10.2 only
- // Check attachment across shadow DOM boundaries when possible (gh-3504)
- // Support: iOS 10.0-10.2 only
- // Early iOS 10 versions support `attachShadow` but not `getRootNode`,
- // leading to errors. We need to check for `getRootNode`.
- if ( documentElement.getRootNode ) {
- isAttached = function( elem ) {
- return jQuery.contains( elem.ownerDocument, elem ) ||
- elem.getRootNode( composed ) === elem.ownerDocument;
- };
- }
-var isHiddenWithinTree = function( elem, el ) {
-
- // isHiddenWithinTree might be called from jQuery#filter function;
- // in that case, element will be second argument
- elem = el || elem;
-
- // Inline style trumps all
- return elem.style.display === "none" ||
- elem.style.display === "" &&
-
- // Otherwise, check computed style
- // Support: Firefox <=43 - 45
- // Disconnected elements can have computed display: none, so first confirm that elem is
- // in the document.
- isAttached( elem ) &&
-
- jQuery.css( elem, "display" ) === "none";
- };
-
-
-
-function adjustCSS( elem, prop, valueParts, tween ) {
- var adjusted, scale,
- maxIterations = 20,
- currentValue = tween ?
- function() {
- return tween.cur();
- } :
- function() {
- return jQuery.css( elem, prop, "" );
- },
- initial = currentValue(),
- unit = valueParts && valueParts[ 3 ] || ( jQuery.cssNumber[ prop ] ? "" : "px" ),
-
- // Starting value computation is required for potential unit mismatches
- initialInUnit = elem.nodeType &&
- ( jQuery.cssNumber[ prop ] || unit !== "px" && +initial ) &&
- rcssNum.exec( jQuery.css( elem, prop ) );
-
- if ( initialInUnit && initialInUnit[ 3 ] !== unit ) {
-
- // Support: Firefox <=54
- // Halve the iteration target value to prevent interference from CSS upper bounds (gh-2144)
- initial = initial / 2;
-
- // Trust units reported by jQuery.css
- unit = unit || initialInUnit[ 3 ];
-
- // Iteratively approximate from a nonzero starting point
- initialInUnit = +initial || 1;
-
- while ( maxIterations-- ) {
-
- // Evaluate and update our best guess (doubling guesses that zero out).
- // Finish if the scale equals or crosses 1 (making the old*new product non-positive).
- jQuery.style( elem, prop, initialInUnit + unit );
- if ( ( 1 - scale ) * ( 1 - ( scale = currentValue() / initial || 0.5 ) ) <= 0 ) {
- maxIterations = 0;
- }
- initialInUnit = initialInUnit / scale;
-
- }
-
- initialInUnit = initialInUnit * 2;
- jQuery.style( elem, prop, initialInUnit + unit );
-
- // Make sure we update the tween properties later on
- valueParts = valueParts || [];
- }
-
- if ( valueParts ) {
- initialInUnit = +initialInUnit || +initial || 0;
-
- // Apply relative offset (+=/-=) if specified
- adjusted = valueParts[ 1 ] ?
- initialInUnit + ( valueParts[ 1 ] + 1 ) * valueParts[ 2 ] :
- +valueParts[ 2 ];
- if ( tween ) {
- tween.unit = unit;
- tween.start = initialInUnit;
- tween.end = adjusted;
- }
- }
- return adjusted;
-}
-
-
-var defaultDisplayMap = {};
-
-function getDefaultDisplay( elem ) {
- var temp,
- doc = elem.ownerDocument,
- nodeName = elem.nodeName,
- display = defaultDisplayMap[ nodeName ];
-
- if ( display ) {
- return display;
- }
-
- temp = doc.body.appendChild( doc.createElement( nodeName ) );
- display = jQuery.css( temp, "display" );
-
- temp.parentNode.removeChild( temp );
-
- if ( display === "none" ) {
- display = "block";
- }
- defaultDisplayMap[ nodeName ] = display;
-
- return display;
-}
-
-function showHide( elements, show ) {
- var display, elem,
- values = [],
- index = 0,
- length = elements.length;
-
- // Determine new display value for elements that need to change
- for ( ; index < length; index++ ) {
- elem = elements[ index ];
- if ( !elem.style ) {
- continue;
- }
-
- display = elem.style.display;
- if ( show ) {
-
- // Since we force visibility upon cascade-hidden elements, an immediate (and slow)
- // check is required in this first loop unless we have a nonempty display value (either
- // inline or about-to-be-restored)
- if ( display === "none" ) {
- values[ index ] = dataPriv.get( elem, "display" ) || null;
- if ( !values[ index ] ) {
- elem.style.display = "";
- }
- }
- if ( elem.style.display === "" && isHiddenWithinTree( elem ) ) {
- values[ index ] = getDefaultDisplay( elem );
- }
- } else {
- if ( display !== "none" ) {
- values[ index ] = "none";
-
- // Remember what we're overwriting
- dataPriv.set( elem, "display", display );
- }
- }
- }
-
- // Set the display of the elements in a second loop to avoid constant reflow
- for ( index = 0; index < length; index++ ) {
- if ( values[ index ] != null ) {
- elements[ index ].style.display = values[ index ];
- }
- }
-
- return elements;
-}
-
-jQuery.fn.extend( {
- show: function() {
- return showHide( this, true );
- },
- hide: function() {
- return showHide( this );
- },
- toggle: function( state ) {
- if ( typeof state === "boolean" ) {
- return state ? this.show() : this.hide();
- }
-
- return this.each( function() {
- if ( isHiddenWithinTree( this ) ) {
- jQuery( this ).show();
- } else {
- jQuery( this ).hide();
- }
- } );
- }
-} );
-var rcheckableType = ( /^(?:checkbox|radio)$/i );
-
-var rtagName = ( /<([a-z][^\/\0>\x20\t\r\n\f]*)/i );
-
-var rscriptType = ( /^$|^module$|\/(?:java|ecma)script/i );
-
-
-
-( function() {
- var fragment = document.createDocumentFragment(),
- div = fragment.appendChild( document.createElement( "div" ) ),
- input = document.createElement( "input" );
-
- // Support: Android 4.0 - 4.3 only
- // Check state lost if the name is set (#11217)
- // Support: Windows Web Apps (WWA)
- // `name` and `type` must use .setAttribute for WWA (#14901)
- input.setAttribute( "type", "radio" );
- input.setAttribute( "checked", "checked" );
- input.setAttribute( "name", "t" );
-
- div.appendChild( input );
-
- // Support: Android <=4.1 only
- // Older WebKit doesn't clone checked state correctly in fragments
- support.checkClone = div.cloneNode( true ).cloneNode( true ).lastChild.checked;
-
- // Support: IE <=11 only
- // Make sure textarea (and checkbox) defaultValue is properly cloned
- div.innerHTML = "<textarea>x</textarea>";
- support.noCloneChecked = !!div.cloneNode( true ).lastChild.defaultValue;
-
- // Support: IE <=9 only
- // IE <=9 replaces <option> tags with their contents when inserted outside of
- // the select element.
- div.innerHTML = "<option></option>";
- support.option = !!div.lastChild;
-} )();
-
-
-// We have to close these tags to support XHTML (#13200)
-var wrapMap = {
-
- // XHTML parsers do not magically insert elements in the
- // same way that tag soup parsers do. So we cannot shorten
- // this by omitting <tbody> or other required elements.
- thead: [ 1, "<table>", "</table>" ],
- col: [ 2, "<table><colgroup>", "</colgroup></table>" ],
- tr: [ 2, "<table><tbody>", "</tbody></table>" ],
- td: [ 3, "<table><tbody><tr>", "</tr></tbody></table>" ],
-
- _default: [ 0, "", "" ]
-};
-
-wrapMap.tbody = wrapMap.tfoot = wrapMap.colgroup = wrapMap.caption = wrapMap.thead;
-wrapMap.th = wrapMap.td;
-
-// Support: IE <=9 only
-if ( !support.option ) {
- wrapMap.optgroup = wrapMap.option = [ 1, "<select multiple='multiple'>", "</select>" ];
-}
-
-
-function getAll( context, tag ) {
-
- // Support: IE <=9 - 11 only
- // Use typeof to avoid zero-argument method invocation on host objects (#15151)
- var ret;
-
- if ( typeof context.getElementsByTagName !== "undefined" ) {
- ret = context.getElementsByTagName( tag || "*" );
-
- } else if ( typeof context.querySelectorAll !== "undefined" ) {
- ret = context.querySelectorAll( tag || "*" );
-
- } else {
- ret = [];
- }
-
- if ( tag === undefined || tag && nodeName( context, tag ) ) {
- return jQuery.merge( [ context ], ret );
- }
-
- return ret;
-}
-
-
-// Mark _bin as having already been evaluated
-function setGlobalEval( elems, refElements ) {
- var i = 0,
- l = elems.length;
-
- for ( ; i < l; i++ ) {
- dataPriv.set(
- elems[ i ],
- "globalEval",
- !refElements || dataPriv.get( refElements[ i ], "globalEval" )
- );
- }
-}
-
-
-var rhtml = /<|&#?\w+;/;
-
-function buildFragment( elems, context, scripts, selection, ignored ) {
- var elem, tmp, tag, wrap, attached, j,
- fragment = context.createDocumentFragment(),
- nodes = [],
- i = 0,
- l = elems.length;
-
- for ( ; i < l; i++ ) {
- elem = elems[ i ];
-
- if ( elem || elem === 0 ) {
-
- // Add nodes directly
- if ( toType( elem ) === "object" ) {
-
- // Support: Android <=4.0 only, PhantomJS 1 only
- // push.apply(_, arraylike) throws on ancient WebKit
- jQuery.merge( nodes, elem.nodeType ? [ elem ] : elem );
-
- // Convert non-html into a text node
- } else if ( !rhtml.test( elem ) ) {
- nodes.push( context.createTextNode( elem ) );
-
- // Convert html into DOM nodes
- } else {
- tmp = tmp || fragment.appendChild( context.createElement( "div" ) );
-
- // Deserialize a standard representation
- tag = ( rtagName.exec( elem ) || [ "", "" ] )[ 1 ].toLowerCase();
- wrap = wrapMap[ tag ] || wrapMap._default;
- tmp.innerHTML = wrap[ 1 ] + jQuery.htmlPrefilter( elem ) + wrap[ 2 ];
-
- // Descend through wrappers to the right content
- j = wrap[ 0 ];
- while ( j-- ) {
- tmp = tmp.lastChild;
- }
-
- // Support: Android <=4.0 only, PhantomJS 1 only
- // push.apply(_, arraylike) throws on ancient WebKit
- jQuery.merge( nodes, tmp.childNodes );
-
- // Remember the top-level container
- tmp = fragment.firstChild;
-
- // Ensure the created nodes are orphaned (#12392)
- tmp.textContent = "";
- }
- }
- }
-
- // Remove wrapper from fragment
- fragment.textContent = "";
-
- i = 0;
- while ( ( elem = nodes[ i++ ] ) ) {
-
- // Skip elements already in the context collection (trac-4087)
- if ( selection && jQuery.inArray( elem, selection ) > -1 ) {
- if ( ignored ) {
- ignored.push( elem );
- }
- continue;
- }
-
- attached = isAttached( elem );
-
- // Append to fragment
- tmp = getAll( fragment.appendChild( elem ), "script" );
-
- // Preserve script evaluation history
- if ( attached ) {
- setGlobalEval( tmp );
- }
-
- // Capture executables
- if ( scripts ) {
- j = 0;
- while ( ( elem = tmp[ j++ ] ) ) {
- if ( rscriptType.test( elem.type || "" ) ) {
- scripts.push( elem );
- }
- }
- }
- }
-
- return fragment;
-}
-
-
-var
- rkeyEvent = /^key/,
- rmouseEvent = /^(?:mouse|pointer|contextmenu|drag|drop)|click/,
- rtypenamespace = /^([^.]*)(?:\.(.+)|)/;
-
-function returnTrue() {
- return true;
-}
-
-function returnFalse() {
- return false;
-}
-
-// Support: IE <=9 - 11+
-// focus() and blur() are asynchronous, except when they are no-op.
-// So expect focus to be synchronous when the element is already active,
-// and blur to be synchronous when the element is not already active.
-// (focus and blur are always synchronous in other supported browsers,
-// this just defines when we can count on it).
-function expectSync( elem, type ) {
- return ( elem === safeActiveElement() ) === ( type === "focus" );
-}
-
-// Support: IE <=9 only
-// Accessing document.activeElement can throw unexpectedly
-// https://bugs.jquery.com/ticket/13393
-function safeActiveElement() {
- try {
- return document.activeElement;
- } catch ( err ) { }
-}
-
-function on( elem, types, selector, data, fn, one ) {
- var origFn, type;
-
- // Types can be a map of types/handlers
- if ( typeof types === "object" ) {
-
- // ( types-Object, selector, data )
- if ( typeof selector !== "string" ) {
-
- // ( types-Object, data )
- data = data || selector;
- selector = undefined;
- }
- for ( type in types ) {
- on( elem, type, selector, data, types[ type ], one );
- }
- return elem;
- }
-
- if ( data == null && fn == null ) {
-
- // ( types, fn )
- fn = selector;
- data = selector = undefined;
- } else if ( fn == null ) {
- if ( typeof selector === "string" ) {
-
- // ( types, selector, fn )
- fn = data;
- data = undefined;
- } else {
-
- // ( types, data, fn )
- fn = data;
- data = selector;
- selector = undefined;
- }
- }
- if ( fn === false ) {
- fn = returnFalse;
- } else if ( !fn ) {
- return elem;
- }
-
- if ( one === 1 ) {
- origFn = fn;
- fn = function( event ) {
-
- // Can use an empty set, since event contains the info
- jQuery().off( event );
- return origFn.apply( this, arguments );
- };
-
- // Use same guid so caller can remove using origFn
- fn.guid = origFn.guid || ( origFn.guid = jQuery.guid++ );
- }
- return elem.each( function() {
- jQuery.event.add( this, types, fn, data, selector );
- } );
-}
-
-/*
- * Helper functions for managing events -- not part of the public interface.
- * Props to Dean Edwards' addEvent library for many of the ideas.
- */
-jQuery.event = {
-
- global: {},
-
- add: function( elem, types, handler, data, selector ) {
-
- var handleObjIn, eventHandle, tmp,
- events, t, handleObj,
- special, handlers, type, namespaces, origType,
- elemData = dataPriv.get( elem );
-
- // Only attach events to objects that accept data
- if ( !acceptData( elem ) ) {
- return;
- }
-
- // Caller can pass in an object of custom data in lieu of the handler
- if ( handler.handler ) {
- handleObjIn = handler;
- handler = handleObjIn.handler;
- selector = handleObjIn.selector;
- }
-
- // Ensure that invalid selectors throw exceptions at attach time
- // Evaluate against documentElement in case elem is a non-element node (e.g., document)
- if ( selector ) {
- jQuery.find.matchesSelector( documentElement, selector );
- }
-
- // Make sure that the handler has a unique ID, used to find/remove it later
- if ( !handler.guid ) {
- handler.guid = jQuery.guid++;
- }
-
- // Init the element's event structure and main handler, if this is the first
- if ( !( events = elemData.events ) ) {
- events = elemData.events = Object.create( null );
- }
- if ( !( eventHandle = elemData.handle ) ) {
- eventHandle = elemData.handle = function( e ) {
-
- // Discard the second event of a jQuery.event.trigger() and
- // when an event is called after a page has unloaded
- return typeof jQuery !== "undefined" && jQuery.event.triggered !== e.type ?
- jQuery.event.dispatch.apply( elem, arguments ) : undefined;
- };
- }
-
- // Handle multiple events separated by a space
- types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
- t = types.length;
- while ( t-- ) {
- tmp = rtypenamespace.exec( types[ t ] ) || [];
- type = origType = tmp[ 1 ];
- namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
-
- // There *must* be a type, no attaching namespace-only handlers
- if ( !type ) {
- continue;
- }
-
- // If event changes its type, use the special event handlers for the changed type
- special = jQuery.event.special[ type ] || {};
-
- // If selector defined, determine special event api type, otherwise given type
- type = ( selector ? special.delegateType : special.bindType ) || type;
-
- // Update special based on newly reset type
- special = jQuery.event.special[ type ] || {};
-
- // handleObj is passed to all event handlers
- handleObj = jQuery.extend( {
- type: type,
- origType: origType,
- data: data,
- handler: handler,
- guid: handler.guid,
- selector: selector,
- needsContext: selector && jQuery.expr.match.needsContext.test( selector ),
- namespace: namespaces.join( "." )
- }, handleObjIn );
-
- // Init the event handler queue if we're the first
- if ( !( handlers = events[ type ] ) ) {
- handlers = events[ type ] = [];
- handlers.delegateCount = 0;
-
- // Only use addEventListener if the special events handler returns false
- if ( !special.setup ||
- special.setup.call( elem, data, namespaces, eventHandle ) === false ) {
-
- if ( elem.addEventListener ) {
- elem.addEventListener( type, eventHandle );
- }
- }
- }
-
- if ( special.add ) {
- special.add.call( elem, handleObj );
-
- if ( !handleObj.handler.guid ) {
- handleObj.handler.guid = handler.guid;
- }
- }
-
- // Add to the element's handler list, delegates in front
- if ( selector ) {
- handlers.splice( handlers.delegateCount++, 0, handleObj );
- } else {
- handlers.push( handleObj );
- }
-
- // Keep track of which events have ever been used, for event optimization
- jQuery.event.global[ type ] = true;
- }
-
- },
-
- // Detach an event or set of events from an element
- remove: function( elem, types, handler, selector, mappedTypes ) {
-
- var j, origCount, tmp,
- events, t, handleObj,
- special, handlers, type, namespaces, origType,
- elemData = dataPriv.hasData( elem ) && dataPriv.get( elem );
-
- if ( !elemData || !( events = elemData.events ) ) {
- return;
- }
-
- // Once for each type.namespace in types; type may be omitted
- types = ( types || "" ).match( rnothtmlwhite ) || [ "" ];
- t = types.length;
- while ( t-- ) {
- tmp = rtypenamespace.exec( types[ t ] ) || [];
- type = origType = tmp[ 1 ];
- namespaces = ( tmp[ 2 ] || "" ).split( "." ).sort();
-
- // Unbind all events (on this namespace, if provided) for the element
- if ( !type ) {
- for ( type in events ) {
- jQuery.event.remove( elem, type + types[ t ], handler, selector, true );
- }
- continue;
- }
-
- special = jQuery.event.special[ type ] || {};
- type = ( selector ? special.delegateType : special.bindType ) || type;
- handlers = events[ type ] || [];
- tmp = tmp[ 2 ] &&
- new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" );
-
- // Remove matching events
- origCount = j = handlers.length;
- while ( j-- ) {
- handleObj = handlers[ j ];
-
- if ( ( mappedTypes || origType === handleObj.origType ) &&
- ( !handler || handler.guid === handleObj.guid ) &&
- ( !tmp || tmp.test( handleObj.namespace ) ) &&
- ( !selector || selector === handleObj.selector ||
- selector === "**" && handleObj.selector ) ) {
- handlers.splice( j, 1 );
-
- if ( handleObj.selector ) {
- handlers.delegateCount--;
- }
- if ( special.remove ) {
- special.remove.call( elem, handleObj );
- }
- }
- }
-
- // Remove generic event handler if we removed something and no more handlers exist
- // (avoids potential for endless recursion during removal of special event handlers)
- if ( origCount && !handlers.length ) {
- if ( !special.teardown ||
- special.teardown.call( elem, namespaces, elemData.handle ) === false ) {
-
- jQuery.removeEvent( elem, type, elemData.handle );
- }
-
- delete events[ type ];
- }
- }
-
- // Remove data and the expando if it's no longer used
- if ( jQuery.isEmptyObject( events ) ) {
- dataPriv.remove( elem, "handle events" );
- }
- },
-
- dispatch: function( nativeEvent ) {
-
- var i, j, ret, matched, handleObj, handlerQueue,
- args = new Array( arguments.length ),
-
- // Make a writable jQuery.Event from the native event object
- event = jQuery.event.fix( nativeEvent ),
-
- handlers = (
- dataPriv.get( this, "events" ) || Object.create( null )
- )[ event.type ] || [],
- special = jQuery.event.special[ event.type ] || {};
-
- // Use the fix-ed jQuery.Event rather than the (read-only) native event
- args[ 0 ] = event;
-
- for ( i = 1; i < arguments.length; i++ ) {
- args[ i ] = arguments[ i ];
- }
-
- event.delegateTarget = this;
-
- // Call the preDispatch hook for the mapped type, and let it bail if desired
- if ( special.preDispatch && special.preDispatch.call( this, event ) === false ) {
- return;
- }
-
- // Determine handlers
- handlerQueue = jQuery.event.handlers.call( this, event, handlers );
-
- // Run delegates first; they may want to stop propagation beneath us
- i = 0;
- while ( ( matched = handlerQueue[ i++ ] ) && !event.isPropagationStopped() ) {
- event.currentTarget = matched.elem;
-
- j = 0;
- while ( ( handleObj = matched.handlers[ j++ ] ) &&
- !event.isImmediatePropagationStopped() ) {
-
- // If the event is namespaced, then each handler is only invoked if it is
- // specially universal or its namespaces are a superset of the event's.
- if ( !event.rnamespace || handleObj.namespace === false ||
- event.rnamespace.test( handleObj.namespace ) ) {
-
- event.handleObj = handleObj;
- event.data = handleObj.data;
-
- ret = ( ( jQuery.event.special[ handleObj.origType ] || {} ).handle ||
- handleObj.handler ).apply( matched.elem, args );
-
- if ( ret !== undefined ) {
- if ( ( event.result = ret ) === false ) {
- event.preventDefault();
- event.stopPropagation();
- }
- }
- }
- }
- }
-
- // Call the postDispatch hook for the mapped type
- if ( special.postDispatch ) {
- special.postDispatch.call( this, event );
- }
-
- return event.result;
- },
-
- handlers: function( event, handlers ) {
- var i, handleObj, sel, matchedHandlers, matchedSelectors,
- handlerQueue = [],
- delegateCount = handlers.delegateCount,
- cur = event.target;
-
- // Find delegate handlers
- if ( delegateCount &&
-
- // Support: IE <=9
- // Black-hole SVG <use> instance trees (trac-13180)
- cur.nodeType &&
-
- // Support: Firefox <=42
- // Suppress spec-violating clicks indicating a non-primary pointer button (trac-3861)
- // https://www.w3.org/TR/DOM-Level-3-Events/#event-type-click
- // Support: IE 11 only
- // ...but not arrow key "clicks" of radio inputs, which can have `button` -1 (gh-2343)
- !( event.type === "click" && event.button >= 1 ) ) {
-
- for ( ; cur !== this; cur = cur.parentNode || this ) {
-
- // Don't check non-elements (#13208)
- // Don't process clicks on disabled elements (#6911, #8165, #11382, #11764)
- if ( cur.nodeType === 1 && !( event.type === "click" && cur.disabled === true ) ) {
- matchedHandlers = [];
- matchedSelectors = {};
- for ( i = 0; i < delegateCount; i++ ) {
- handleObj = handlers[ i ];
-
- // Don't conflict with Object.prototype properties (#13203)
- sel = handleObj.selector + " ";
-
- if ( matchedSelectors[ sel ] === undefined ) {
- matchedSelectors[ sel ] = handleObj.needsContext ?
- jQuery( sel, this ).index( cur ) > -1 :
- jQuery.find( sel, this, null, [ cur ] ).length;
- }
- if ( matchedSelectors[ sel ] ) {
- matchedHandlers.push( handleObj );
- }
- }
- if ( matchedHandlers.length ) {
- handlerQueue.push( { elem: cur, handlers: matchedHandlers } );
- }
- }
- }
- }
-
- // Add the remaining (directly-bound) handlers
- cur = this;
- if ( delegateCount < handlers.length ) {
- handlerQueue.push( { elem: cur, handlers: handlers.slice( delegateCount ) } );
- }
-
- return handlerQueue;
- },
-
- addProp: function( name, hook ) {
- Object.defineProperty( jQuery.Event.prototype, name, {
- enumerable: true,
- configurable: true,
-
- get: isFunction( hook ) ?
- function() {
- if ( this.originalEvent ) {
- return hook( this.originalEvent );
- }
- } :
- function() {
- if ( this.originalEvent ) {
- return this.originalEvent[ name ];
- }
- },
-
- set: function( value ) {
- Object.defineProperty( this, name, {
- enumerable: true,
- configurable: true,
- writable: true,
- value: value
- } );
- }
- } );
- },
-
- fix: function( originalEvent ) {
- return originalEvent[ jQuery.expando ] ?
- originalEvent :
- new jQuery.Event( originalEvent );
- },
-
- special: {
- load: {
-
- // Prevent triggered image.load events from bubbling to window.load
- noBubble: true
- },
- click: {
-
- // Utilize native event to ensure correct state for checkable inputs
- setup: function( data ) {
-
- // For mutual compressibility with _default, replace `this` access with a local var.
- // `|| data` is dead code meant only to preserve the variable through minification.
- var el = this || data;
-
- // Claim the first handler
- if ( rcheckableType.test( el.type ) &&
- el.click && nodeName( el, "input" ) ) {
-
- // dataPriv.set( el, "click", ... )
- leverageNative( el, "click", returnTrue );
- }
-
- // Return false to allow normal processing in the caller
- return false;
- },
- trigger: function( data ) {
-
- // For mutual compressibility with _default, replace `this` access with a local var.
- // `|| data` is dead code meant only to preserve the variable through minification.
- var el = this || data;
-
- // Force setup before triggering a click
- if ( rcheckableType.test( el.type ) &&
- el.click && nodeName( el, "input" ) ) {
-
- leverageNative( el, "click" );
- }
-
- // Return non-false to allow normal event-path propagation
- return true;
- },
-
- // For cross-browser consistency, suppress native .click() on links
- // Also prevent it if we're currently inside a leveraged native-event stack
- _default: function( event ) {
- var target = event.target;
- return rcheckableType.test( target.type ) &&
- target.click && nodeName( target, "input" ) &&
- dataPriv.get( target, "click" ) ||
- nodeName( target, "a" );
- }
- },
-
- beforeunload: {
- postDispatch: function( event ) {
-
- // Support: Firefox 20+
- // Firefox doesn't alert if the returnValue field is not set.
- if ( event.result !== undefined && event.originalEvent ) {
- event.originalEvent.returnValue = event.result;
- }
- }
- }
- }
-};
-
-// Ensure the presence of an event listener that handles manually-triggered
-// synthetic events by interrupting progress until reinvoked in response to
-// *native* events that it fires directly, ensuring that state changes have
-// already occurred before other listeners are invoked.
-function leverageNative( el, type, expectSync ) {
-
- // Missing expectSync indicates a trigger call, which must force setup through jQuery.event.add
- if ( !expectSync ) {
- if ( dataPriv.get( el, type ) === undefined ) {
- jQuery.event.add( el, type, returnTrue );
- }
- return;
- }
-
- // Register the controller as a special universal handler for all event namespaces
- dataPriv.set( el, type, false );
- jQuery.event.add( el, type, {
- namespace: false,
- handler: function( event ) {
- var notAsync, result,
- saved = dataPriv.get( this, type );
-
- if ( ( event.isTrigger & 1 ) && this[ type ] ) {
-
- // Interrupt processing of the outer synthetic .trigger()ed event
- // Saved data should be false in such cases, but might be a leftover capture object
- // from an async native handler (gh-4350)
- if ( !saved.length ) {
-
- // Store arguments for use when handling the inner native event
- // There will always be at least one argument (an event object), so this array
- // will not be confused with a leftover capture object.
- saved = slice.call( arguments );
- dataPriv.set( this, type, saved );
-
- // Trigger the native event and capture its result
- // Support: IE <=9 - 11+
- // focus() and blur() are asynchronous
- notAsync = expectSync( this, type );
- this[ type ]();
- result = dataPriv.get( this, type );
- if ( saved !== result || notAsync ) {
- dataPriv.set( this, type, false );
- } else {
- result = {};
- }
- if ( saved !== result ) {
-
- // Cancel the outer synthetic event
- event.stopImmediatePropagation();
- event.preventDefault();
- return result.value;
- }
-
- // If this is an inner synthetic event for an event with a bubbling surrogate
- // (focus or blur), assume that the surrogate already propagated from triggering the
- // native event and prevent that from happening again here.
- // This technically gets the ordering wrong w.r.t. to `.trigger()` (in which the
- // bubbling surrogate propagates *after* the non-bubbling build), but that seems
- // less bad than duplication.
- } else if ( ( jQuery.event.special[ type ] || {} ).delegateType ) {
- event.stopPropagation();
- }
-
- // If this is a native event triggered above, everything is now in order
- // Fire an inner synthetic event with the original arguments
- } else if ( saved.length ) {
-
- // ...and capture the result
- dataPriv.set( this, type, {
- value: jQuery.event.trigger(
-
- // Support: IE <=9 - 11+
- // Extend with the prototype to reset the above stopImmediatePropagation()
- jQuery.extend( saved[ 0 ], jQuery.Event.prototype ),
- saved.slice( 1 ),
- this
- )
- } );
-
- // Abort handling of the native event
- event.stopImmediatePropagation();
- }
- }
- } );
-}
-
-jQuery.removeEvent = function( elem, type, handle ) {
-
- // This "if" is needed for plain objects
- if ( elem.removeEventListener ) {
- elem.removeEventListener( type, handle );
- }
-};
-
-jQuery.Event = function( src, props ) {
-
- // Allow instantiation without the 'new' keyword
- if ( !( this instanceof jQuery.Event ) ) {
- return new jQuery.Event( src, props );
- }
-
- // Event object
- if ( src && src.type ) {
- this.originalEvent = src;
- this.type = src.type;
-
- // Events bubbling up the document may have been marked as prevented
- // by a handler lower down the tree; reflect the correct value.
- this.isDefaultPrevented = src.defaultPrevented ||
- src.defaultPrevented === undefined &&
-
- // Support: Android <=2.3 only
- src.returnValue === false ?
- returnTrue :
- returnFalse;
-
- // Create target properties
- // Support: Safari <=6 - 7 only
- // Target should not be a text node (#504, #13143)
- this.target = ( src.target && src.target.nodeType === 3 ) ?
- src.target.parentNode :
- src.target;
-
- this.currentTarget = src.currentTarget;
- this.relatedTarget = src.relatedTarget;
-
- // Event type
- } else {
- this.type = src;
- }
-
- // Put explicitly provided properties onto the event object
- if ( props ) {
- jQuery.extend( this, props );
- }
-
- // Create a timestamp if incoming event doesn't have one
- this.timeStamp = src && src.timeStamp || Date.now();
-
- // Mark it as fixed
- this[ jQuery.expando ] = true;
-};
-
-// jQuery.Event is based on DOM3 Events as specified by the ECMAScript Language Binding
-// https://www.w3.org/TR/2003/WD-DOM-Level-3-Events-20030331/ecma-script-binding.html
-jQuery.Event.prototype = {
- constructor: jQuery.Event,
- isDefaultPrevented: returnFalse,
- isPropagationStopped: returnFalse,
- isImmediatePropagationStopped: returnFalse,
- isSimulated: false,
-
- preventDefault: function() {
- var e = this.originalEvent;
-
- this.isDefaultPrevented = returnTrue;
-
- if ( e && !this.isSimulated ) {
- e.preventDefault();
- }
- },
- stopPropagation: function() {
- var e = this.originalEvent;
-
- this.isPropagationStopped = returnTrue;
-
- if ( e && !this.isSimulated ) {
- e.stopPropagation();
- }
- },
- stopImmediatePropagation: function() {
- var e = this.originalEvent;
-
- this.isImmediatePropagationStopped = returnTrue;
-
- if ( e && !this.isSimulated ) {
- e.stopImmediatePropagation();
- }
-
- this.stopPropagation();
- }
-};
-
-// Includes all common event props including KeyEvent and MouseEvent specific props
-jQuery.each( {
- altKey: true,
- bubbles: true,
- cancelable: true,
- changedTouches: true,
- ctrlKey: true,
- detail: true,
- eventPhase: true,
- metaKey: true,
- pageX: true,
- pageY: true,
- shiftKey: true,
- view: true,
- "char": true,
- code: true,
- charCode: true,
- key: true,
- keyCode: true,
- button: true,
- buttons: true,
- clientX: true,
- clientY: true,
- offsetX: true,
- offsetY: true,
- pointerId: true,
- pointerType: true,
- screenX: true,
- screenY: true,
- targetTouches: true,
- toElement: true,
- touches: true,
-
- which: function( event ) {
- var button = event.button;
-
- // Add which for key events
- if ( event.which == null && rkeyEvent.test( event.type ) ) {
- return event.charCode != null ? event.charCode : event.keyCode;
- }
-
- // Add which for click: 1 === left; 2 === middle; 3 === right
- if ( !event.which && button !== undefined && rmouseEvent.test( event.type ) ) {
- if ( button & 1 ) {
- return 1;
- }
-
- if ( button & 2 ) {
- return 3;
- }
-
- if ( button & 4 ) {
- return 2;
- }
-
- return 0;
- }
-
- return event.which;
- }
-}, jQuery.event.addProp );
-
-jQuery.each( { focus: "focusin", blur: "focusout" }, function( type, delegateType ) {
- jQuery.event.special[ type ] = {
-
- // Utilize native event if possible so blur/focus sequence is correct
- setup: function() {
-
- // Claim the first handler
- // dataPriv.set( this, "focus", ... )
- // dataPriv.set( this, "blur", ... )
- leverageNative( this, type, expectSync );
-
- // Return false to allow normal processing in the caller
- return false;
- },
- trigger: function() {
-
- // Force setup before trigger
- leverageNative( this, type );
-
- // Return non-false to allow normal event-path propagation
- return true;
- },
-
- delegateType: delegateType
- };
-} );
-
-// Create mouseenter/leave events using mouseover/out and event-time checks
-// so that event delegation works in jQuery.
-// Do the same for pointerenter/pointerleave and pointerover/pointerout
-//
-// Support: Safari 7 only
-// Safari sends mouseenter too often; see:
-// https://bugs.chromium.org/p/chromium/issues/detail?id=470258
-// for the description of the bug (it existed in older Chrome versions as well).
-jQuery.each( {
- mouseenter: "mouseover",
- mouseleave: "mouseout",
- pointerenter: "pointerover",
- pointerleave: "pointerout"
-}, function( orig, fix ) {
- jQuery.event.special[ orig ] = {
- delegateType: fix,
- bindType: fix,
-
- handle: function( event ) {
- var ret,
- target = this,
- related = event.relatedTarget,
- handleObj = event.handleObj;
-
- // For mouseenter/leave call the handler if related is outside the target.
- // NB: No relatedTarget if the mouse left/entered the browser window
- if ( !related || ( related !== target && !jQuery.contains( target, related ) ) ) {
- event.type = handleObj.origType;
- ret = handleObj.handler.apply( this, arguments );
- event.type = fix;
- }
- return ret;
- }
- };
-} );
-
-jQuery.fn.extend( {
-
- on: function( types, selector, data, fn ) {
- return on( this, types, selector, data, fn );
- },
- one: function( types, selector, data, fn ) {
- return on( this, types, selector, data, fn, 1 );
- },
- off: function( types, selector, fn ) {
- var handleObj, type;
- if ( types && types.preventDefault && types.handleObj ) {
-
- // ( event ) dispatched jQuery.Event
- handleObj = types.handleObj;
- jQuery( types.delegateTarget ).off(
- handleObj.namespace ?
- handleObj.origType + "." + handleObj.namespace :
- handleObj.origType,
- handleObj.selector,
- handleObj.handler
- );
- return this;
- }
- if ( typeof types === "object" ) {
-
- // ( types-object [, selector] )
- for ( type in types ) {
- this.off( type, selector, types[ type ] );
- }
- return this;
- }
- if ( selector === false || typeof selector === "function" ) {
-
- // ( types [, fn] )
- fn = selector;
- selector = undefined;
- }
- if ( fn === false ) {
- fn = returnFalse;
- }
- return this.each( function() {
- jQuery.event.remove( this, types, fn, selector );
- } );
- }
-} );
-
-
-var
-
- // Support: IE <=10 - 11, Edge 12 - 13 only
- // In IE/Edge using regex groups here causes severe slowdowns.
- // See https://connect.microsoft.com/IE/feedback/details/1736512/
- rnoInnerhtml = /<script|<style|<link/i,
-
- // checked="checked" or checked
- rchecked = /checked\s*(?:[^=]|=\s*.checked.)/i,
- rcleanScript = /^\s*<!(?:\[CDATA\[|--)|(?:\]\]|--)>\s*$/g;
-
-// Prefer a tbody over its parent table for containing new rows
-function manipulationTarget( elem, content ) {
- if ( nodeName( elem, "table" ) &&
- nodeName( content.nodeType !== 11 ? content : content.firstChild, "tr" ) ) {
-
- return jQuery( elem ).children( "tbody" )[ 0 ] || elem;
- }
-
- return elem;
-}
-
-// Replace/restore the type attribute of script elements for safe DOM manipulation
-function disableScript( elem ) {
- elem.type = ( elem.getAttribute( "type" ) !== null ) + "/" + elem.type;
- return elem;
-}
-function restoreScript( elem ) {
- if ( ( elem.type || "" ).slice( 0, 5 ) === "true/" ) {
- elem.type = elem.type.slice( 5 );
- } else {
- elem.removeAttribute( "type" );
- }
-
- return elem;
-}
-
-function cloneCopyEvent( src, dest ) {
- var i, l, type, pdataOld, udataOld, udataCur, events;
-
- if ( dest.nodeType !== 1 ) {
- return;
- }
-
- // 1. Copy private data: events, handlers, etc.
- if ( dataPriv.hasData( src ) ) {
- pdataOld = dataPriv.get( src );
- events = pdataOld.events;
-
- if ( events ) {
- dataPriv.remove( dest, "handle events" );
-
- for ( type in events ) {
- for ( i = 0, l = events[ type ].length; i < l; i++ ) {
- jQuery.event.add( dest, type, events[ type ][ i ] );
- }
- }
- }
- }
-
- // 2. Copy user data
- if ( dataUser.hasData( src ) ) {
- udataOld = dataUser.access( src );
- udataCur = jQuery.extend( {}, udataOld );
-
- dataUser.set( dest, udataCur );
- }
-}
-
-// Fix IE bugs, see support tests
-function fixInput( src, dest ) {
- var nodeName = dest.nodeName.toLowerCase();
-
- // Fails to persist the checked state of a cloned checkbox or radio button.
- if ( nodeName === "input" && rcheckableType.test( src.type ) ) {
- dest.checked = src.checked;
-
- // Fails to return the selected option to the default selected state when cloning options
- } else if ( nodeName === "input" || nodeName === "textarea" ) {
- dest.defaultValue = src.defaultValue;
- }
-}
-
-function domManip( collection, args, callback, ignored ) {
-
- // Flatten any nested arrays
- args = flat( args );
-
- var fragment, first, scripts, hasScripts, node, doc,
- i = 0,
- l = collection.length,
- iNoClone = l - 1,
- value = args[ 0 ],
- valueIsFunction = isFunction( value );
-
- // We can't cloneNode fragments that contain checked, in WebKit
- if ( valueIsFunction ||
- ( l > 1 && typeof value === "string" &&
- !support.checkClone && rchecked.test( value ) ) ) {
- return collection.each( function( index ) {
- var self = collection.eq( index );
- if ( valueIsFunction ) {
- args[ 0 ] = value.call( this, index, self.html() );
- }
- domManip( self, args, callback, ignored );
- } );
- }
-
- if ( l ) {
- fragment = buildFragment( args, collection[ 0 ].ownerDocument, false, collection, ignored );
- first = fragment.firstChild;
-
- if ( fragment.childNodes.length === 1 ) {
- fragment = first;
- }
-
- // Require either new content or an interest in ignored elements to invoke the callback
- if ( first || ignored ) {
- scripts = jQuery.map( getAll( fragment, "script" ), disableScript );
- hasScripts = scripts.length;
-
- // Use the original fragment for the last item
- // instead of the first because it can end up
- // being emptied incorrectly in certain situations (#8070).
- for ( ; i < l; i++ ) {
- node = fragment;
-
- if ( i !== iNoClone ) {
- node = jQuery.clone( node, true, true );
-
- // Keep references to cloned _bin for later restoration
- if ( hasScripts ) {
-
- // Support: Android <=4.0 only, PhantomJS 1 only
- // push.apply(_, arraylike) throws on ancient WebKit
- jQuery.merge( scripts, getAll( node, "script" ) );
- }
- }
-
- callback.call( collection[ i ], node, i );
- }
-
- if ( hasScripts ) {
- doc = scripts[ scripts.length - 1 ].ownerDocument;
-
- // Reenable _bin
- jQuery.map( scripts, restoreScript );
-
- // Evaluate executable _bin on first document insertion
- for ( i = 0; i < hasScripts; i++ ) {
- node = scripts[ i ];
- if ( rscriptType.test( node.type || "" ) &&
- !dataPriv.access( node, "globalEval" ) &&
- jQuery.contains( doc, node ) ) {
-
- if ( node.src && ( node.type || "" ).toLowerCase() !== "module" ) {
-
- // Optional AJAX dependency, but won't run _bin if not present
- if ( jQuery._evalUrl && !node.noModule ) {
- jQuery._evalUrl( node.src, {
- nonce: node.nonce || node.getAttribute( "nonce" )
- }, doc );
- }
- } else {
- DOMEval( node.textContent.replace( rcleanScript, "" ), node, doc );
- }
- }
- }
- }
- }
- }
-
- return collection;
-}
-
-function remove( elem, selector, keepData ) {
- var node,
- nodes = selector ? jQuery.filter( selector, elem ) : elem,
- i = 0;
-
- for ( ; ( node = nodes[ i ] ) != null; i++ ) {
- if ( !keepData && node.nodeType === 1 ) {
- jQuery.cleanData( getAll( node ) );
- }
-
- if ( node.parentNode ) {
- if ( keepData && isAttached( node ) ) {
- setGlobalEval( getAll( node, "script" ) );
- }
- node.parentNode.removeChild( node );
- }
- }
-
- return elem;
-}
-
-jQuery.extend( {
- htmlPrefilter: function( html ) {
- return html;
- },
-
- clone: function( elem, dataAndEvents, deepDataAndEvents ) {
- var i, l, srcElements, destElements,
- clone = elem.cloneNode( true ),
- inPage = isAttached( elem );
-
- // Fix IE cloning issues
- if ( !support.noCloneChecked && ( elem.nodeType === 1 || elem.nodeType === 11 ) &&
- !jQuery.isXMLDoc( elem ) ) {
-
- // We eschew Sizzle here for performance reasons: https://jsperf.com/getall-vs-sizzle/2
- destElements = getAll( clone );
- srcElements = getAll( elem );
-
- for ( i = 0, l = srcElements.length; i < l; i++ ) {
- fixInput( srcElements[ i ], destElements[ i ] );
- }
- }
-
- // Copy the events from the original to the clone
- if ( dataAndEvents ) {
- if ( deepDataAndEvents ) {
- srcElements = srcElements || getAll( elem );
- destElements = destElements || getAll( clone );
-
- for ( i = 0, l = srcElements.length; i < l; i++ ) {
- cloneCopyEvent( srcElements[ i ], destElements[ i ] );
- }
- } else {
- cloneCopyEvent( elem, clone );
- }
- }
-
- // Preserve script evaluation history
- destElements = getAll( clone, "script" );
- if ( destElements.length > 0 ) {
- setGlobalEval( destElements, !inPage && getAll( elem, "script" ) );
- }
-
- // Return the cloned set
- return clone;
- },
-
- cleanData: function( elems ) {
- var data, elem, type,
- special = jQuery.event.special,
- i = 0;
-
- for ( ; ( elem = elems[ i ] ) !== undefined; i++ ) {
- if ( acceptData( elem ) ) {
- if ( ( data = elem[ dataPriv.expando ] ) ) {
- if ( data.events ) {
- for ( type in data.events ) {
- if ( special[ type ] ) {
- jQuery.event.remove( elem, type );
-
- // This is a shortcut to avoid jQuery.event.remove's overhead
- } else {
- jQuery.removeEvent( elem, type, data.handle );
- }
- }
- }
-
- // Support: Chrome <=35 - 45+
- // Assign undefined instead of using delete, see Data#remove
- elem[ dataPriv.expando ] = undefined;
- }
- if ( elem[ dataUser.expando ] ) {
-
- // Support: Chrome <=35 - 45+
- // Assign undefined instead of using delete, see Data#remove
- elem[ dataUser.expando ] = undefined;
- }
- }
- }
- }
-} );
-
-jQuery.fn.extend( {
- detach: function( selector ) {
- return remove( this, selector, true );
- },
-
- remove: function( selector ) {
- return remove( this, selector );
- },
-
- text: function( value ) {
- return access( this, function( value ) {
- return value === undefined ?
- jQuery.text( this ) :
- this.empty().each( function() {
- if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
- this.textContent = value;
- }
- } );
- }, null, value, arguments.length );
- },
-
- append: function() {
- return domManip( this, arguments, function( elem ) {
- if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
- var target = manipulationTarget( this, elem );
- target.appendChild( elem );
- }
- } );
- },
-
- prepend: function() {
- return domManip( this, arguments, function( elem ) {
- if ( this.nodeType === 1 || this.nodeType === 11 || this.nodeType === 9 ) {
- var target = manipulationTarget( this, elem );
- target.insertBefore( elem, target.firstChild );
- }
- } );
- },
-
- before: function() {
- return domManip( this, arguments, function( elem ) {
- if ( this.parentNode ) {
- this.parentNode.insertBefore( elem, this );
- }
- } );
- },
-
- after: function() {
- return domManip( this, arguments, function( elem ) {
- if ( this.parentNode ) {
- this.parentNode.insertBefore( elem, this.nextSibling );
- }
- } );
- },
-
- empty: function() {
- var elem,
- i = 0;
-
- for ( ; ( elem = this[ i ] ) != null; i++ ) {
- if ( elem.nodeType === 1 ) {
-
- // Prevent memory leaks
- jQuery.cleanData( getAll( elem, false ) );
-
- // Remove any remaining nodes
- elem.textContent = "";
- }
- }
-
- return this;
- },
-
- clone: function( dataAndEvents, deepDataAndEvents ) {
- dataAndEvents = dataAndEvents == null ? false : dataAndEvents;
- deepDataAndEvents = deepDataAndEvents == null ? dataAndEvents : deepDataAndEvents;
-
- return this.map( function() {
- return jQuery.clone( this, dataAndEvents, deepDataAndEvents );
- } );
- },
-
- html: function( value ) {
- return access( this, function( value ) {
- var elem = this[ 0 ] || {},
- i = 0,
- l = this.length;
-
- if ( value === undefined && elem.nodeType === 1 ) {
- return elem.innerHTML;
- }
-
- // See if we can take a shortcut and just use innerHTML
- if ( typeof value === "string" && !rnoInnerhtml.test( value ) &&
- !wrapMap[ ( rtagName.exec( value ) || [ "", "" ] )[ 1 ].toLowerCase() ] ) {
-
- value = jQuery.htmlPrefilter( value );
-
- try {
- for ( ; i < l; i++ ) {
- elem = this[ i ] || {};
-
- // Remove element nodes and prevent memory leaks
- if ( elem.nodeType === 1 ) {
- jQuery.cleanData( getAll( elem, false ) );
- elem.innerHTML = value;
- }
- }
-
- elem = 0;
-
- // If using innerHTML throws an exception, use the fallback method
- } catch ( e ) {}
- }
-
- if ( elem ) {
- this.empty().append( value );
- }
- }, null, value, arguments.length );
- },
-
- replaceWith: function() {
- var ignored = [];
-
- // Make the changes, replacing each non-ignored context element with the new content
- return domManip( this, arguments, function( elem ) {
- var parent = this.parentNode;
-
- if ( jQuery.inArray( this, ignored ) < 0 ) {
- jQuery.cleanData( getAll( this ) );
- if ( parent ) {
- parent.replaceChild( elem, this );
- }
- }
-
- // Force callback invocation
- }, ignored );
- }
-} );
-
-jQuery.each( {
- appendTo: "append",
- prependTo: "prepend",
- insertBefore: "before",
- insertAfter: "after",
- replaceAll: "replaceWith"
-}, function( name, original ) {
- jQuery.fn[ name ] = function( selector ) {
- var elems,
- ret = [],
- insert = jQuery( selector ),
- last = insert.length - 1,
- i = 0;
-
- for ( ; i <= last; i++ ) {
- elems = i === last ? this : this.clone( true );
- jQuery( insert[ i ] )[ original ]( elems );
-
- // Support: Android <=4.0 only, PhantomJS 1 only
- // .get() because push.apply(_, arraylike) throws on ancient WebKit
- push.apply( ret, elems.get() );
- }
-
- return this.pushStack( ret );
- };
-} );
-var rnumnonpx = new RegExp( "^(" + pnum + ")(?!px)[a-z%]+$", "i" );
-
-var getStyles = function( elem ) {
-
- // Support: IE <=11 only, Firefox <=30 (#15098, #14150)
- // IE throws on elements created in popups
- // FF meanwhile throws on frame elements through "defaultView.getComputedStyle"
- var view = elem.ownerDocument.defaultView;
-
- if ( !view || !view.opener ) {
- view = window;
- }
-
- return view.getComputedStyle( elem );
- };
-
-var swap = function( elem, options, callback ) {
- var ret, name,
- old = {};
-
- // Remember the old values, and insert the new ones
- for ( name in options ) {
- old[ name ] = elem.style[ name ];
- elem.style[ name ] = options[ name ];
- }
-
- ret = callback.call( elem );
-
- // Revert the old values
- for ( name in options ) {
- elem.style[ name ] = old[ name ];
- }
-
- return ret;
-};
-
-
-var rboxStyle = new RegExp( cssExpand.join( "|" ), "i" );
-
-
-
-( function() {
-
- // Executing both pixelPosition & boxSizingReliable tests require only one layout
- // so they're executed at the same time to save the second computation.
- function computeStyleTests() {
-
- // This is a singleton, we need to execute it only once
- if ( !div ) {
- return;
- }
-
- container.style.cssText = "position:absolute;left:-11111px;width:60px;" +
- "margin-top:1px;padding:0;border:0";
- div.style.cssText =
- "position:relative;display:block;box-sizing:border-box;overflow:scroll;" +
- "margin:auto;border:1px;padding:1px;" +
- "width:60%;top:1%";
- documentElement.appendChild( container ).appendChild( div );
-
- var divStyle = window.getComputedStyle( div );
- pixelPositionVal = divStyle.top !== "1%";
-
- // Support: Android 4.0 - 4.3 only, Firefox <=3 - 44
- reliableMarginLeftVal = roundPixelMeasures( divStyle.marginLeft ) === 12;
-
- // Support: Android 4.0 - 4.3 only, Safari <=9.1 - 10.1, iOS <=7.0 - 9.3
- // Some styles come back with percentage values, even though they shouldn't
- div.style.right = "60%";
- pixelBoxStylesVal = roundPixelMeasures( divStyle.right ) === 36;
-
- // Support: IE 9 - 11 only
- // Detect misreporting of content dimensions for box-sizing:border-box elements
- boxSizingReliableVal = roundPixelMeasures( divStyle.width ) === 36;
-
- // Support: IE 9 only
- // Detect overflow:scroll screwiness (gh-3699)
- // Support: Chrome <=64
- // Don't get tricked when zoom affects offsetWidth (gh-4029)
- div.style.position = "absolute";
- scrollboxSizeVal = roundPixelMeasures( div.offsetWidth / 3 ) === 12;
-
- documentElement.removeChild( container );
-
- // Nullify the div so it wouldn't be stored in the memory and
- // it will also be a sign that checks already performed
- div = null;
- }
-
- function roundPixelMeasures( measure ) {
- return Math.round( parseFloat( measure ) );
- }
-
- var pixelPositionVal, boxSizingReliableVal, scrollboxSizeVal, pixelBoxStylesVal,
- reliableTrDimensionsVal, reliableMarginLeftVal,
- container = document.createElement( "div" ),
- div = document.createElement( "div" );
-
- // Finish early in limited (non-browser) environments
- if ( !div.style ) {
- return;
- }
-
- // Support: IE <=9 - 11 only
- // Style of cloned element affects source element cloned (#8908)
- div.style.backgroundClip = "content-box";
- div.cloneNode( true ).style.backgroundClip = "";
- support.clearCloneStyle = div.style.backgroundClip === "content-box";
-
- jQuery.extend( support, {
- boxSizingReliable: function() {
- computeStyleTests();
- return boxSizingReliableVal;
- },
- pixelBoxStyles: function() {
- computeStyleTests();
- return pixelBoxStylesVal;
- },
- pixelPosition: function() {
- computeStyleTests();
- return pixelPositionVal;
- },
- reliableMarginLeft: function() {
- computeStyleTests();
- return reliableMarginLeftVal;
- },
- scrollboxSize: function() {
- computeStyleTests();
- return scrollboxSizeVal;
- },
-
- // Support: IE 9 - 11+, Edge 15 - 18+
- // IE/Edge misreport `getComputedStyle` of table rows with width/height
- // set in CSS while `offset*` properties report correct values.
- // Behavior in IE 9 is more subtle than in newer versions & it passes
- // some versions of this test; make sure not to make it pass there!
- reliableTrDimensions: function() {
- var table, tr, trChild, trStyle;
- if ( reliableTrDimensionsVal == null ) {
- table = document.createElement( "table" );
- tr = document.createElement( "tr" );
- trChild = document.createElement( "div" );
-
- table.style.cssText = "position:absolute;left:-11111px";
- tr.style.height = "1px";
- trChild.style.height = "9px";
-
- documentElement
- .appendChild( table )
- .appendChild( tr )
- .appendChild( trChild );
-
- trStyle = window.getComputedStyle( tr );
- reliableTrDimensionsVal = parseInt( trStyle.height ) > 3;
-
- documentElement.removeChild( table );
- }
- return reliableTrDimensionsVal;
- }
- } );
-} )();
-
-
-function curCSS( elem, name, computed ) {
- var width, minWidth, maxWidth, ret,
-
- // Support: Firefox 51+
- // Retrieving style before computed somehow
- // fixes an issue with getting wrong values
- // on detached elements
- style = elem.style;
-
- computed = computed || getStyles( elem );
-
- // getPropertyValue is needed for:
- // .css('filter') (IE 9 only, #12537)
- // .css('--customProperty) (#3144)
- if ( computed ) {
- ret = computed.getPropertyValue( name ) || computed[ name ];
-
- if ( ret === "" && !isAttached( elem ) ) {
- ret = jQuery.style( elem, name );
- }
-
- // A tribute to the "awesome hack by Dean Edwards"
- // Android Browser returns percentage for some values,
- // but width seems to be reliably pixels.
- // This is against the CSSOM draft spec:
- // https://drafts.csswg.org/cssom/#resolved-values
- if ( !support.pixelBoxStyles() && rnumnonpx.test( ret ) && rboxStyle.test( name ) ) {
-
- // Remember the original values
- width = style.width;
- minWidth = style.minWidth;
- maxWidth = style.maxWidth;
-
- // Put in the new values to get a computed value out
- style.minWidth = style.maxWidth = style.width = ret;
- ret = computed.width;
-
- // Revert the changed values
- style.width = width;
- style.minWidth = minWidth;
- style.maxWidth = maxWidth;
- }
- }
-
- return ret !== undefined ?
-
- // Support: IE <=9 - 11 only
- // IE returns zIndex value as an integer.
- ret + "" :
- ret;
-}
-
-
-function addGetHookIf( conditionFn, hookFn ) {
-
- // Define the hook, we'll check on the first run if it's really needed.
- return {
- get: function() {
- if ( conditionFn() ) {
-
- // Hook not needed (or it's not possible to use it due
- // to missing dependency), remove it.
- delete this.get;
- return;
- }
-
- // Hook needed; redefine it so that the support test is not executed again.
- return ( this.get = hookFn ).apply( this, arguments );
- }
- };
-}
-
-
-var cssPrefixes = [ "Webkit", "Moz", "ms" ],
- emptyStyle = document.createElement( "div" ).style,
- vendorProps = {};
-
-// Return a vendor-prefixed property or undefined
-function vendorPropName( name ) {
-
- // Check for vendor prefixed names
- var capName = name[ 0 ].toUpperCase() + name.slice( 1 ),
- i = cssPrefixes.length;
-
- while ( i-- ) {
- name = cssPrefixes[ i ] + capName;
- if ( name in emptyStyle ) {
- return name;
- }
- }
-}
-
-// Return a potentially-mapped jQuery.cssProps or vendor prefixed property
-function finalPropName( name ) {
- var final = jQuery.cssProps[ name ] || vendorProps[ name ];
-
- if ( final ) {
- return final;
- }
- if ( name in emptyStyle ) {
- return name;
- }
- return vendorProps[ name ] = vendorPropName( name ) || name;
-}
-
-
-var
-
- // Swappable if display is none or starts with table
- // except "table", "table-cell", or "table-caption"
- // See here for display values: https://developer.mozilla.org/en-US/docs/CSS/display
- rdisplayswap = /^(none|table(?!-c[ea]).+)/,
- rcustomProp = /^--/,
- cssShow = { position: "absolute", visibility: "hidden", display: "block" },
- cssNormalTransform = {
- letterSpacing: "0",
- fontWeight: "400"
- };
-
-function setPositiveNumber( _elem, value, subtract ) {
-
- // Any relative (+/-) values have already been
- // normalized at this point
- var matches = rcssNum.exec( value );
- return matches ?
-
- // Guard against undefined "subtract", e.g., when used as in cssHooks
- Math.max( 0, matches[ 2 ] - ( subtract || 0 ) ) + ( matches[ 3 ] || "px" ) :
- value;
-}
-
-function boxModelAdjustment( elem, dimension, box, isBorderBox, styles, computedVal ) {
- var i = dimension === "width" ? 1 : 0,
- extra = 0,
- delta = 0;
-
- // Adjustment may not be necessary
- if ( box === ( isBorderBox ? "border" : "content" ) ) {
- return 0;
- }
-
- for ( ; i < 4; i += 2 ) {
-
- // Both box models exclude margin
- if ( box === "margin" ) {
- delta += jQuery.css( elem, box + cssExpand[ i ], true, styles );
- }
-
- // If we get here with a content-box, we're seeking "padding" or "border" or "margin"
- if ( !isBorderBox ) {
-
- // Add padding
- delta += jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
-
- // For "border" or "margin", add border
- if ( box !== "padding" ) {
- delta += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
-
- // But still keep track of it otherwise
- } else {
- extra += jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
- }
-
- // If we get here with a border-box (content + padding + border), we're seeking "content" or
- // "padding" or "margin"
- } else {
-
- // For "content", subtract padding
- if ( box === "content" ) {
- delta -= jQuery.css( elem, "padding" + cssExpand[ i ], true, styles );
- }
-
- // For "content" or "padding", subtract border
- if ( box !== "margin" ) {
- delta -= jQuery.css( elem, "border" + cssExpand[ i ] + "Width", true, styles );
- }
- }
- }
-
- // Account for positive content-box scroll gutter when requested by providing computedVal
- if ( !isBorderBox && computedVal >= 0 ) {
-
- // offsetWidth/offsetHeight is a rounded sum of content, padding, scroll gutter, and border
- // Assuming integer scroll gutter, subtract the rest and round down
- delta += Math.max( 0, Math.ceil(
- elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
- computedVal -
- delta -
- extra -
- 0.5
-
- // If offsetWidth/offsetHeight is unknown, then we can't determine content-box scroll gutter
- // Use an explicit zero to avoid NaN (gh-3964)
- ) ) || 0;
- }
-
- return delta;
-}
-
-function getWidthOrHeight( elem, dimension, extra ) {
-
- // Start with computed style
- var styles = getStyles( elem ),
-
- // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-4322).
- // Fake content-box until we know it's needed to know the true value.
- boxSizingNeeded = !support.boxSizingReliable() || extra,
- isBorderBox = boxSizingNeeded &&
- jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
- valueIsBorderBox = isBorderBox,
-
- val = curCSS( elem, dimension, styles ),
- offsetProp = "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 );
-
- // Support: Firefox <=54
- // Return a confounding non-pixel value or feign ignorance, as appropriate.
- if ( rnumnonpx.test( val ) ) {
- if ( !extra ) {
- return val;
- }
- val = "auto";
- }
-
-
- // Support: IE 9 - 11 only
- // Use offsetWidth/offsetHeight for when box sizing is unreliable.
- // In those cases, the computed value can be trusted to be border-box.
- if ( ( !support.boxSizingReliable() && isBorderBox ||
-
- // Support: IE 10 - 11+, Edge 15 - 18+
- // IE/Edge misreport `getComputedStyle` of table rows with width/height
- // set in CSS while `offset*` properties report correct values.
- // Interestingly, in some cases IE 9 doesn't suffer from this issue.
- !support.reliableTrDimensions() && nodeName( elem, "tr" ) ||
-
- // Fall back to offsetWidth/offsetHeight when value is "auto"
- // This happens for inline elements with no explicit setting (gh-3571)
- val === "auto" ||
-
- // Support: Android <=4.1 - 4.3 only
- // Also use offsetWidth/offsetHeight for misreported inline dimensions (gh-3602)
- !parseFloat( val ) && jQuery.css( elem, "display", false, styles ) === "inline" ) &&
-
- // Make sure the element is visible & connected
- elem.getClientRects().length ) {
-
- isBorderBox = jQuery.css( elem, "boxSizing", false, styles ) === "border-box";
-
- // Where available, offsetWidth/offsetHeight approximate border box dimensions.
- // Where not available (e.g., SVG), assume unreliable box-sizing and interpret the
- // retrieved value as a content box dimension.
- valueIsBorderBox = offsetProp in elem;
- if ( valueIsBorderBox ) {
- val = elem[ offsetProp ];
- }
- }
-
- // Normalize "" and auto
- val = parseFloat( val ) || 0;
-
- // Adjust for the element's box model
- return ( val +
- boxModelAdjustment(
- elem,
- dimension,
- extra || ( isBorderBox ? "border" : "content" ),
- valueIsBorderBox,
- styles,
-
- // Provide the current computed size to request scroll gutter calculation (gh-3589)
- val
- )
- ) + "px";
-}
-
-jQuery.extend( {
-
- // Add in style property hooks for overriding the default
- // behavior of getting and setting a style property
- cssHooks: {
- opacity: {
- get: function( elem, computed ) {
- if ( computed ) {
-
- // We should always get a number back from opacity
- var ret = curCSS( elem, "opacity" );
- return ret === "" ? "1" : ret;
- }
- }
- }
- },
-
- // Don't automatically add "px" to these possibly-unitless properties
- cssNumber: {
- "animationIterationCount": true,
- "columnCount": true,
- "fillOpacity": true,
- "flexGrow": true,
- "flexShrink": true,
- "fontWeight": true,
- "gridArea": true,
- "gridColumn": true,
- "gridColumnEnd": true,
- "gridColumnStart": true,
- "gridRow": true,
- "gridRowEnd": true,
- "gridRowStart": true,
- "lineHeight": true,
- "opacity": true,
- "order": true,
- "orphans": true,
- "widows": true,
- "zIndex": true,
- "zoom": true
- },
-
- // Add in properties whose names you wish to fix before
- // setting or getting the value
- cssProps: {},
-
- // Get and set the style property on a DOM Node
- style: function( elem, name, value, extra ) {
-
- // Don't set styles on text and comment nodes
- if ( !elem || elem.nodeType === 3 || elem.nodeType === 8 || !elem.style ) {
- return;
- }
-
- // Make sure that we're working with the right name
- var ret, type, hooks,
- origName = camelCase( name ),
- isCustomProp = rcustomProp.test( name ),
- style = elem.style;
-
- // Make sure that we're working with the right name. We don't
- // want to query the value if it is a CSS custom property
- // since they are user-defined.
- if ( !isCustomProp ) {
- name = finalPropName( origName );
- }
-
- // Gets hook for the prefixed version, then unprefixed version
- hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
-
- // Check if we're setting a value
- if ( value !== undefined ) {
- type = typeof value;
-
- // Convert "+=" or "-=" to relative numbers (#7345)
- if ( type === "string" && ( ret = rcssNum.exec( value ) ) && ret[ 1 ] ) {
- value = adjustCSS( elem, name, ret );
-
- // Fixes bug #9237
- type = "number";
- }
-
- // Make sure that null and NaN values aren't set (#7116)
- if ( value == null || value !== value ) {
- return;
- }
-
- // If a number was passed in, add the unit (except for certain CSS properties)
- // The isCustomProp check can be removed in jQuery 4.0 when we only auto-append
- // "px" to a few hardcoded values.
- if ( type === "number" && !isCustomProp ) {
- value += ret && ret[ 3 ] || ( jQuery.cssNumber[ origName ] ? "" : "px" );
- }
-
- // background-* props affect original clone's values
- if ( !support.clearCloneStyle && value === "" && name.indexOf( "background" ) === 0 ) {
- style[ name ] = "inherit";
- }
-
- // If a hook was provided, use that value, otherwise just set the specified value
- if ( !hooks || !( "set" in hooks ) ||
- ( value = hooks.set( elem, value, extra ) ) !== undefined ) {
-
- if ( isCustomProp ) {
- style.setProperty( name, value );
- } else {
- style[ name ] = value;
- }
- }
-
- } else {
-
- // If a hook was provided get the non-computed value from there
- if ( hooks && "get" in hooks &&
- ( ret = hooks.get( elem, false, extra ) ) !== undefined ) {
-
- return ret;
- }
-
- // Otherwise just get the value from the style object
- return style[ name ];
- }
- },
-
- css: function( elem, name, extra, styles ) {
- var val, num, hooks,
- origName = camelCase( name ),
- isCustomProp = rcustomProp.test( name );
-
- // Make sure that we're working with the right name. We don't
- // want to modify the value if it is a CSS custom property
- // since they are user-defined.
- if ( !isCustomProp ) {
- name = finalPropName( origName );
- }
-
- // Try prefixed name followed by the unprefixed name
- hooks = jQuery.cssHooks[ name ] || jQuery.cssHooks[ origName ];
-
- // If a hook was provided get the computed value from there
- if ( hooks && "get" in hooks ) {
- val = hooks.get( elem, true, extra );
- }
-
- // Otherwise, if a way to get the computed value exists, use that
- if ( val === undefined ) {
- val = curCSS( elem, name, styles );
- }
-
- // Convert "normal" to computed value
- if ( val === "normal" && name in cssNormalTransform ) {
- val = cssNormalTransform[ name ];
- }
-
- // Make numeric if forced or a qualifier was provided and val looks numeric
- if ( extra === "" || extra ) {
- num = parseFloat( val );
- return extra === true || isFinite( num ) ? num || 0 : val;
- }
-
- return val;
- }
-} );
-
-jQuery.each( [ "height", "width" ], function( _i, dimension ) {
- jQuery.cssHooks[ dimension ] = {
- get: function( elem, computed, extra ) {
- if ( computed ) {
-
- // Certain elements can have dimension info if we invisibly show them
- // but it must have a current display style that would benefit
- return rdisplayswap.test( jQuery.css( elem, "display" ) ) &&
-
- // Support: Safari 8+
- // Table columns in Safari have non-zero offsetWidth & zero
- // getBoundingClientRect().width unless display is changed.
- // Support: IE <=11 only
- // Running getBoundingClientRect on a disconnected node
- // in IE throws an error.
- ( !elem.getClientRects().length || !elem.getBoundingClientRect().width ) ?
- swap( elem, cssShow, function() {
- return getWidthOrHeight( elem, dimension, extra );
- } ) :
- getWidthOrHeight( elem, dimension, extra );
- }
- },
-
- set: function( elem, value, extra ) {
- var matches,
- styles = getStyles( elem ),
-
- // Only read styles.position if the test has a chance to fail
- // to avoid forcing a reflow.
- scrollboxSizeBuggy = !support.scrollboxSize() &&
- styles.position === "absolute",
-
- // To avoid forcing a reflow, only fetch boxSizing if we need it (gh-3991)
- boxSizingNeeded = scrollboxSizeBuggy || extra,
- isBorderBox = boxSizingNeeded &&
- jQuery.css( elem, "boxSizing", false, styles ) === "border-box",
- subtract = extra ?
- boxModelAdjustment(
- elem,
- dimension,
- extra,
- isBorderBox,
- styles
- ) :
- 0;
-
- // Account for unreliable border-box dimensions by comparing offset* to computed and
- // faking a content-box to get border and padding (gh-3699)
- if ( isBorderBox && scrollboxSizeBuggy ) {
- subtract -= Math.ceil(
- elem[ "offset" + dimension[ 0 ].toUpperCase() + dimension.slice( 1 ) ] -
- parseFloat( styles[ dimension ] ) -
- boxModelAdjustment( elem, dimension, "border", false, styles ) -
- 0.5
- );
- }
-
- // Convert to pixels if value adjustment is needed
- if ( subtract && ( matches = rcssNum.exec( value ) ) &&
- ( matches[ 3 ] || "px" ) !== "px" ) {
-
- elem.style[ dimension ] = value;
- value = jQuery.css( elem, dimension );
- }
-
- return setPositiveNumber( elem, value, subtract );
- }
- };
-} );
-
-jQuery.cssHooks.marginLeft = addGetHookIf( support.reliableMarginLeft,
- function( elem, computed ) {
- if ( computed ) {
- return ( parseFloat( curCSS( elem, "marginLeft" ) ) ||
- elem.getBoundingClientRect().left -
- swap( elem, { marginLeft: 0 }, function() {
- return elem.getBoundingClientRect().left;
- } )
- ) + "px";
- }
- }
-);
-
-// These hooks are used by animate to expand properties
-jQuery.each( {
- margin: "",
- padding: "",
- border: "Width"
-}, function( prefix, suffix ) {
- jQuery.cssHooks[ prefix + suffix ] = {
- expand: function( value ) {
- var i = 0,
- expanded = {},
-
- // Assumes a single number if not a string
- parts = typeof value === "string" ? value.split( " " ) : [ value ];
-
- for ( ; i < 4; i++ ) {
- expanded[ prefix + cssExpand[ i ] + suffix ] =
- parts[ i ] || parts[ i - 2 ] || parts[ 0 ];
- }
-
- return expanded;
- }
- };
-
- if ( prefix !== "margin" ) {
- jQuery.cssHooks[ prefix + suffix ].set = setPositiveNumber;
- }
-} );
-
-jQuery.fn.extend( {
- css: function( name, value ) {
- return access( this, function( elem, name, value ) {
- var styles, len,
- map = {},
- i = 0;
-
- if ( Array.isArray( name ) ) {
- styles = getStyles( elem );
- len = name.length;
-
- for ( ; i < len; i++ ) {
- map[ name[ i ] ] = jQuery.css( elem, name[ i ], false, styles );
- }
-
- return map;
- }
-
- return value !== undefined ?
- jQuery.style( elem, name, value ) :
- jQuery.css( elem, name );
- }, name, value, arguments.length > 1 );
- }
-} );
-
-
-function Tween( elem, options, prop, end, easing ) {
- return new Tween.prototype.init( elem, options, prop, end, easing );
-}
-jQuery.Tween = Tween;
-
-Tween.prototype = {
- constructor: Tween,
- init: function( elem, options, prop, end, easing, unit ) {
- this.elem = elem;
- this.prop = prop;
- this.easing = easing || jQuery.easing._default;
- this.options = options;
- this.start = this.now = this.cur();
- this.end = end;
- this.unit = unit || ( jQuery.cssNumber[ prop ] ? "" : "px" );
- },
- cur: function() {
- var hooks = Tween.propHooks[ this.prop ];
-
- return hooks && hooks.get ?
- hooks.get( this ) :
- Tween.propHooks._default.get( this );
- },
- run: function( percent ) {
- var eased,
- hooks = Tween.propHooks[ this.prop ];
-
- if ( this.options.duration ) {
- this.pos = eased = jQuery.easing[ this.easing ](
- percent, this.options.duration * percent, 0, 1, this.options.duration
- );
- } else {
- this.pos = eased = percent;
- }
- this.now = ( this.end - this.start ) * eased + this.start;
-
- if ( this.options.step ) {
- this.options.step.call( this.elem, this.now, this );
- }
-
- if ( hooks && hooks.set ) {
- hooks.set( this );
- } else {
- Tween.propHooks._default.set( this );
- }
- return this;
- }
-};
-
-Tween.prototype.init.prototype = Tween.prototype;
-
-Tween.propHooks = {
- _default: {
- get: function( tween ) {
- var result;
-
- // Use a property on the element directly when it is not a DOM element,
- // or when there is no matching style property that exists.
- if ( tween.elem.nodeType !== 1 ||
- tween.elem[ tween.prop ] != null && tween.elem.style[ tween.prop ] == null ) {
- return tween.elem[ tween.prop ];
- }
-
- // Passing an empty string as a 3rd parameter to .css will automatically
- // attempt a parseFloat and fallback to a string if the parse fails.
- // Simple values such as "10px" are parsed to Float;
- // complex values such as "rotate(1rad)" are returned as-is.
- result = jQuery.css( tween.elem, tween.prop, "" );
-
- // Empty strings, null, undefined and "auto" are converted to 0.
- return !result || result === "auto" ? 0 : result;
- },
- set: function( tween ) {
-
- // Use step hook for back compat.
- // Use cssHook if its there.
- // Use .style if available and use plain properties where available.
- if ( jQuery.fx.step[ tween.prop ] ) {
- jQuery.fx.step[ tween.prop ]( tween );
- } else if ( tween.elem.nodeType === 1 && (
- jQuery.cssHooks[ tween.prop ] ||
- tween.elem.style[ finalPropName( tween.prop ) ] != null ) ) {
- jQuery.style( tween.elem, tween.prop, tween.now + tween.unit );
- } else {
- tween.elem[ tween.prop ] = tween.now;
- }
- }
- }
-};
-
-// Support: IE <=9 only
-// Panic based approach to setting things on disconnected nodes
-Tween.propHooks.scrollTop = Tween.propHooks.scrollLeft = {
- set: function( tween ) {
- if ( tween.elem.nodeType && tween.elem.parentNode ) {
- tween.elem[ tween.prop ] = tween.now;
- }
- }
-};
-
-jQuery.easing = {
- linear: function( p ) {
- return p;
- },
- swing: function( p ) {
- return 0.5 - Math.cos( p * Math.PI ) / 2;
- },
- _default: "swing"
-};
-
-jQuery.fx = Tween.prototype.init;
-
-// Back compat <1.8 extension point
-jQuery.fx.step = {};
-
-
-
-
-var
- fxNow, inProgress,
- rfxtypes = /^(?:toggle|show|hide)$/,
- rrun = /queueHooks$/;
-
-function schedule() {
- if ( inProgress ) {
- if ( document.hidden === false && window.requestAnimationFrame ) {
- window.requestAnimationFrame( schedule );
- } else {
- window.setTimeout( schedule, jQuery.fx.interval );
- }
-
- jQuery.fx.tick();
- }
-}
-
-// Animations created synchronously will run synchronously
-function createFxNow() {
- window.setTimeout( function() {
- fxNow = undefined;
- } );
- return ( fxNow = Date.now() );
-}
-
-// Generate parameters to create a standard animation
-function genFx( type, includeWidth ) {
- var which,
- i = 0,
- attrs = { height: type };
-
- // If we include width, step value is 1 to do all cssExpand values,
- // otherwise step value is 2 to skip over Left and Right
- includeWidth = includeWidth ? 1 : 0;
- for ( ; i < 4; i += 2 - includeWidth ) {
- which = cssExpand[ i ];
- attrs[ "margin" + which ] = attrs[ "padding" + which ] = type;
- }
-
- if ( includeWidth ) {
- attrs.opacity = attrs.width = type;
- }
-
- return attrs;
-}
-
-function createTween( value, prop, animation ) {
- var tween,
- collection = ( Animation.tweeners[ prop ] || [] ).concat( Animation.tweeners[ "*" ] ),
- index = 0,
- length = collection.length;
- for ( ; index < length; index++ ) {
- if ( ( tween = collection[ index ].call( animation, prop, value ) ) ) {
-
- // We're done with this property
- return tween;
- }
- }
-}
-
-function defaultPrefilter( elem, props, opts ) {
- var prop, value, toggle, hooks, oldfire, propTween, restoreDisplay, display,
- isBox = "width" in props || "height" in props,
- anim = this,
- orig = {},
- style = elem.style,
- hidden = elem.nodeType && isHiddenWithinTree( elem ),
- dataShow = dataPriv.get( elem, "fxshow" );
-
- // Queue-skipping animations hijack the fx hooks
- if ( !opts.queue ) {
- hooks = jQuery._queueHooks( elem, "fx" );
- if ( hooks.unqueued == null ) {
- hooks.unqueued = 0;
- oldfire = hooks.empty.fire;
- hooks.empty.fire = function() {
- if ( !hooks.unqueued ) {
- oldfire();
- }
- };
- }
- hooks.unqueued++;
-
- anim.always( function() {
-
- // Ensure the complete handler is called before this completes
- anim.always( function() {
- hooks.unqueued--;
- if ( !jQuery.queue( elem, "fx" ).length ) {
- hooks.empty.fire();
- }
- } );
- } );
- }
-
- // Detect show/hide animations
- for ( prop in props ) {
- value = props[ prop ];
- if ( rfxtypes.test( value ) ) {
- delete props[ prop ];
- toggle = toggle || value === "toggle";
- if ( value === ( hidden ? "hide" : "show" ) ) {
-
- // Pretend to be hidden if this is a "show" and
- // there is still data from a stopped show/hide
- if ( value === "show" && dataShow && dataShow[ prop ] !== undefined ) {
- hidden = true;
-
- // Ignore all other no-op show/hide data
- } else {
- continue;
- }
- }
- orig[ prop ] = dataShow && dataShow[ prop ] || jQuery.style( elem, prop );
- }
- }
-
- // Bail out if this is a no-op like .hide().hide()
- propTween = !jQuery.isEmptyObject( props );
- if ( !propTween && jQuery.isEmptyObject( orig ) ) {
- return;
- }
-
- // Restrict "overflow" and "display" styles during box animations
- if ( isBox && elem.nodeType === 1 ) {
-
- // Support: IE <=9 - 11, Edge 12 - 15
- // Record all 3 overflow attributes because IE does not infer the shorthand
- // from identically-valued overflowX and overflowY and Edge just mirrors
- // the overflowX value there.
- opts.overflow = [ style.overflow, style.overflowX, style.overflowY ];
-
- // Identify a display type, preferring old show/hide data over the CSS cascade
- restoreDisplay = dataShow && dataShow.display;
- if ( restoreDisplay == null ) {
- restoreDisplay = dataPriv.get( elem, "display" );
- }
- display = jQuery.css( elem, "display" );
- if ( display === "none" ) {
- if ( restoreDisplay ) {
- display = restoreDisplay;
- } else {
-
- // Get nonempty value(s) by temporarily forcing visibility
- showHide( [ elem ], true );
- restoreDisplay = elem.style.display || restoreDisplay;
- display = jQuery.css( elem, "display" );
- showHide( [ elem ] );
- }
- }
-
- // Animate inline elements as inline-block
- if ( display === "inline" || display === "inline-block" && restoreDisplay != null ) {
- if ( jQuery.css( elem, "float" ) === "none" ) {
-
- // Restore the original display value at the end of pure show/hide animations
- if ( !propTween ) {
- anim.done( function() {
- style.display = restoreDisplay;
- } );
- if ( restoreDisplay == null ) {
- display = style.display;
- restoreDisplay = display === "none" ? "" : display;
- }
- }
- style.display = "inline-block";
- }
- }
- }
-
- if ( opts.overflow ) {
- style.overflow = "hidden";
- anim.always( function() {
- style.overflow = opts.overflow[ 0 ];
- style.overflowX = opts.overflow[ 1 ];
- style.overflowY = opts.overflow[ 2 ];
- } );
- }
-
- // Implement show/hide animations
- propTween = false;
- for ( prop in orig ) {
-
- // General show/hide setup for this element animation
- if ( !propTween ) {
- if ( dataShow ) {
- if ( "hidden" in dataShow ) {
- hidden = dataShow.hidden;
- }
- } else {
- dataShow = dataPriv.access( elem, "fxshow", { display: restoreDisplay } );
- }
-
- // Store hidden/visible for toggle so `.stop().toggle()` "reverses"
- if ( toggle ) {
- dataShow.hidden = !hidden;
- }
-
- // Show elements before animating them
- if ( hidden ) {
- showHide( [ elem ], true );
- }
-
- /* eslint-disable no-loop-func */
-
- anim.done( function() {
-
- /* eslint-enable no-loop-func */
-
- // The final step of a "hide" animation is actually hiding the element
- if ( !hidden ) {
- showHide( [ elem ] );
- }
- dataPriv.remove( elem, "fxshow" );
- for ( prop in orig ) {
- jQuery.style( elem, prop, orig[ prop ] );
- }
- } );
- }
-
- // Per-property setup
- propTween = createTween( hidden ? dataShow[ prop ] : 0, prop, anim );
- if ( !( prop in dataShow ) ) {
- dataShow[ prop ] = propTween.start;
- if ( hidden ) {
- propTween.end = propTween.start;
- propTween.start = 0;
- }
- }
- }
-}
-
-function propFilter( props, specialEasing ) {
- var index, name, easing, value, hooks;
-
- // camelCase, specialEasing and expand cssHook pass
- for ( index in props ) {
- name = camelCase( index );
- easing = specialEasing[ name ];
- value = props[ index ];
- if ( Array.isArray( value ) ) {
- easing = value[ 1 ];
- value = props[ index ] = value[ 0 ];
- }
-
- if ( index !== name ) {
- props[ name ] = value;
- delete props[ index ];
- }
-
- hooks = jQuery.cssHooks[ name ];
- if ( hooks && "expand" in hooks ) {
- value = hooks.expand( value );
- delete props[ name ];
-
- // Not quite $.extend, this won't overwrite existing keys.
- // Reusing 'index' because we have the correct "name"
- for ( index in value ) {
- if ( !( index in props ) ) {
- props[ index ] = value[ index ];
- specialEasing[ index ] = easing;
- }
- }
- } else {
- specialEasing[ name ] = easing;
- }
- }
-}
-
-function Animation( elem, properties, options ) {
- var result,
- stopped,
- index = 0,
- length = Animation.prefilters.length,
- deferred = jQuery.Deferred().always( function() {
-
- // Don't match elem in the :animated selector
- delete tick.elem;
- } ),
- tick = function() {
- if ( stopped ) {
- return false;
- }
- var currentTime = fxNow || createFxNow(),
- remaining = Math.max( 0, animation.startTime + animation.duration - currentTime ),
-
- // Support: Android 2.3 only
- // Archaic crash bug won't allow us to use `1 - ( 0.5 || 0 )` (#12497)
- temp = remaining / animation.duration || 0,
- percent = 1 - temp,
- index = 0,
- length = animation.tweens.length;
-
- for ( ; index < length; index++ ) {
- animation.tweens[ index ].run( percent );
- }
-
- deferred.notifyWith( elem, [ animation, percent, remaining ] );
-
- // If there's more to do, yield
- if ( percent < 1 && length ) {
- return remaining;
- }
-
- // If this was an empty animation, synthesize a final progress notification
- if ( !length ) {
- deferred.notifyWith( elem, [ animation, 1, 0 ] );
- }
-
- // Resolve the animation and report its conclusion
- deferred.resolveWith( elem, [ animation ] );
- return false;
- },
- animation = deferred.promise( {
- elem: elem,
- props: jQuery.extend( {}, properties ),
- opts: jQuery.extend( true, {
- specialEasing: {},
- easing: jQuery.easing._default
- }, options ),
- originalProperties: properties,
- originalOptions: options,
- startTime: fxNow || createFxNow(),
- duration: options.duration,
- tweens: [],
- createTween: function( prop, end ) {
- var tween = jQuery.Tween( elem, animation.opts, prop, end,
- animation.opts.specialEasing[ prop ] || animation.opts.easing );
- animation.tweens.push( tween );
- return tween;
- },
- stop: function( gotoEnd ) {
- var index = 0,
-
- // If we are going to the end, we want to run all the tweens
- // otherwise we skip this part
- length = gotoEnd ? animation.tweens.length : 0;
- if ( stopped ) {
- return this;
- }
- stopped = true;
- for ( ; index < length; index++ ) {
- animation.tweens[ index ].run( 1 );
- }
-
- // Resolve when we played the last frame; otherwise, reject
- if ( gotoEnd ) {
- deferred.notifyWith( elem, [ animation, 1, 0 ] );
- deferred.resolveWith( elem, [ animation, gotoEnd ] );
- } else {
- deferred.rejectWith( elem, [ animation, gotoEnd ] );
- }
- return this;
- }
- } ),
- props = animation.props;
-
- propFilter( props, animation.opts.specialEasing );
-
- for ( ; index < length; index++ ) {
- result = Animation.prefilters[ index ].call( animation, elem, props, animation.opts );
- if ( result ) {
- if ( isFunction( result.stop ) ) {
- jQuery._queueHooks( animation.elem, animation.opts.queue ).stop =
- result.stop.bind( result );
- }
- return result;
- }
- }
-
- jQuery.map( props, createTween, animation );
-
- if ( isFunction( animation.opts.start ) ) {
- animation.opts.start.call( elem, animation );
- }
-
- // Attach callbacks from options
- animation
- .progress( animation.opts.progress )
- .done( animation.opts.done, animation.opts.complete )
- .fail( animation.opts.fail )
- .always( animation.opts.always );
-
- jQuery.fx.timer(
- jQuery.extend( tick, {
- elem: elem,
- anim: animation,
- queue: animation.opts.queue
- } )
- );
-
- return animation;
-}
-
-jQuery.Animation = jQuery.extend( Animation, {
-
- tweeners: {
- "*": [ function( prop, value ) {
- var tween = this.createTween( prop, value );
- adjustCSS( tween.elem, prop, rcssNum.exec( value ), tween );
- return tween;
- } ]
- },
-
- tweener: function( props, callback ) {
- if ( isFunction( props ) ) {
- callback = props;
- props = [ "*" ];
- } else {
- props = props.match( rnothtmlwhite );
- }
-
- var prop,
- index = 0,
- length = props.length;
-
- for ( ; index < length; index++ ) {
- prop = props[ index ];
- Animation.tweeners[ prop ] = Animation.tweeners[ prop ] || [];
- Animation.tweeners[ prop ].unshift( callback );
- }
- },
-
- prefilters: [ defaultPrefilter ],
-
- prefilter: function( callback, prepend ) {
- if ( prepend ) {
- Animation.prefilters.unshift( callback );
- } else {
- Animation.prefilters.push( callback );
- }
- }
-} );
-
-jQuery.speed = function( speed, easing, fn ) {
- var opt = speed && typeof speed === "object" ? jQuery.extend( {}, speed ) : {
- complete: fn || !fn && easing ||
- isFunction( speed ) && speed,
- duration: speed,
- easing: fn && easing || easing && !isFunction( easing ) && easing
- };
-
- // Go to the end state if fx are off
- if ( jQuery.fx.off ) {
- opt.duration = 0;
-
- } else {
- if ( typeof opt.duration !== "number" ) {
- if ( opt.duration in jQuery.fx.speeds ) {
- opt.duration = jQuery.fx.speeds[ opt.duration ];
-
- } else {
- opt.duration = jQuery.fx.speeds._default;
- }
- }
- }
-
- // Normalize opt.queue - true/undefined/null -> "fx"
- if ( opt.queue == null || opt.queue === true ) {
- opt.queue = "fx";
- }
-
- // Queueing
- opt.old = opt.complete;
-
- opt.complete = function() {
- if ( isFunction( opt.old ) ) {
- opt.old.call( this );
- }
-
- if ( opt.queue ) {
- jQuery.dequeue( this, opt.queue );
- }
- };
-
- return opt;
-};
-
-jQuery.fn.extend( {
- fadeTo: function( speed, to, easing, callback ) {
-
- // Show any hidden elements after setting opacity to 0
- return this.filter( isHiddenWithinTree ).css( "opacity", 0 ).show()
-
- // Animate to the value specified
- .end().animate( { opacity: to }, speed, easing, callback );
- },
- animate: function( prop, speed, easing, callback ) {
- var empty = jQuery.isEmptyObject( prop ),
- optall = jQuery.speed( speed, easing, callback ),
- doAnimation = function() {
-
- // Operate on a copy of prop so per-property easing won't be lost
- var anim = Animation( this, jQuery.extend( {}, prop ), optall );
-
- // Empty animations, or finishing resolves immediately
- if ( empty || dataPriv.get( this, "finish" ) ) {
- anim.stop( true );
- }
- };
- doAnimation.finish = doAnimation;
-
- return empty || optall.queue === false ?
- this.each( doAnimation ) :
- this.queue( optall.queue, doAnimation );
- },
- stop: function( type, clearQueue, gotoEnd ) {
- var stopQueue = function( hooks ) {
- var stop = hooks.stop;
- delete hooks.stop;
- stop( gotoEnd );
- };
-
- if ( typeof type !== "string" ) {
- gotoEnd = clearQueue;
- clearQueue = type;
- type = undefined;
- }
- if ( clearQueue ) {
- this.queue( type || "fx", [] );
- }
-
- return this.each( function() {
- var dequeue = true,
- index = type != null && type + "queueHooks",
- timers = jQuery.timers,
- data = dataPriv.get( this );
-
- if ( index ) {
- if ( data[ index ] && data[ index ].stop ) {
- stopQueue( data[ index ] );
- }
- } else {
- for ( index in data ) {
- if ( data[ index ] && data[ index ].stop && rrun.test( index ) ) {
- stopQueue( data[ index ] );
- }
- }
- }
-
- for ( index = timers.length; index--; ) {
- if ( timers[ index ].elem === this &&
- ( type == null || timers[ index ].queue === type ) ) {
-
- timers[ index ].anim.stop( gotoEnd );
- dequeue = false;
- timers.splice( index, 1 );
- }
- }
-
- // Start the next in the queue if the last step wasn't forced.
- // Timers currently will call their complete callbacks, which
- // will dequeue but only if they were gotoEnd.
- if ( dequeue || !gotoEnd ) {
- jQuery.dequeue( this, type );
- }
- } );
- },
- finish: function( type ) {
- if ( type !== false ) {
- type = type || "fx";
- }
- return this.each( function() {
- var index,
- data = dataPriv.get( this ),
- queue = data[ type + "queue" ],
- hooks = data[ type + "queueHooks" ],
- timers = jQuery.timers,
- length = queue ? queue.length : 0;
-
- // Enable finishing flag on private data
- data.finish = true;
-
- // Empty the queue first
- jQuery.queue( this, type, [] );
-
- if ( hooks && hooks.stop ) {
- hooks.stop.call( this, true );
- }
-
- // Look for any active animations, and finish them
- for ( index = timers.length; index--; ) {
- if ( timers[ index ].elem === this && timers[ index ].queue === type ) {
- timers[ index ].anim.stop( true );
- timers.splice( index, 1 );
- }
- }
-
- // Look for any animations in the old queue and finish them
- for ( index = 0; index < length; index++ ) {
- if ( queue[ index ] && queue[ index ].finish ) {
- queue[ index ].finish.call( this );
- }
- }
-
- // Turn off finishing flag
- delete data.finish;
- } );
- }
-} );
-
-jQuery.each( [ "toggle", "show", "hide" ], function( _i, name ) {
- var cssFn = jQuery.fn[ name ];
- jQuery.fn[ name ] = function( speed, easing, callback ) {
- return speed == null || typeof speed === "boolean" ?
- cssFn.apply( this, arguments ) :
- this.animate( genFx( name, true ), speed, easing, callback );
- };
-} );
-
-// Generate shortcuts for custom animations
-jQuery.each( {
- slideDown: genFx( "show" ),
- slideUp: genFx( "hide" ),
- slideToggle: genFx( "toggle" ),
- fadeIn: { opacity: "show" },
- fadeOut: { opacity: "hide" },
- fadeToggle: { opacity: "toggle" }
-}, function( name, props ) {
- jQuery.fn[ name ] = function( speed, easing, callback ) {
- return this.animate( props, speed, easing, callback );
- };
-} );
-
-jQuery.timers = [];
-jQuery.fx.tick = function() {
- var timer,
- i = 0,
- timers = jQuery.timers;
-
- fxNow = Date.now();
-
- for ( ; i < timers.length; i++ ) {
- timer = timers[ i ];
-
- // Run the timer and safely remove it when done (allowing for external removal)
- if ( !timer() && timers[ i ] === timer ) {
- timers.splice( i--, 1 );
- }
- }
-
- if ( !timers.length ) {
- jQuery.fx.stop();
- }
- fxNow = undefined;
-};
-
-jQuery.fx.timer = function( timer ) {
- jQuery.timers.push( timer );
- jQuery.fx.start();
-};
-
-jQuery.fx.interval = 13;
-jQuery.fx.start = function() {
- if ( inProgress ) {
- return;
- }
-
- inProgress = true;
- schedule();
-};
-
-jQuery.fx.stop = function() {
- inProgress = null;
-};
-
-jQuery.fx.speeds = {
- slow: 600,
- fast: 200,
-
- // Default speed
- _default: 400
-};
-
-
-// Based off of the plugin by Clint Helfers, with permission.
-// https://web.archive.org/web/20100324014747/http://blindsignals.com/index.php/2009/07/jquery-delay/
-jQuery.fn.delay = function( time, type ) {
- time = jQuery.fx ? jQuery.fx.speeds[ time ] || time : time;
- type = type || "fx";
-
- return this.queue( type, function( next, hooks ) {
- var timeout = window.setTimeout( next, time );
- hooks.stop = function() {
- window.clearTimeout( timeout );
- };
- } );
-};
-
-
-( function() {
- var input = document.createElement( "input" ),
- select = document.createElement( "select" ),
- opt = select.appendChild( document.createElement( "option" ) );
-
- input.type = "checkbox";
-
- // Support: Android <=4.3 only
- // Default value for a checkbox should be "on"
- support.checkOn = input.value !== "";
-
- // Support: IE <=11 only
- // Must access selectedIndex to make default options select
- support.optSelected = opt.selected;
-
- // Support: IE <=11 only
- // An input loses its value after becoming a radio
- input = document.createElement( "input" );
- input.value = "t";
- input.type = "radio";
- support.radioValue = input.value === "t";
-} )();
-
-
-var boolHook,
- attrHandle = jQuery.expr.attrHandle;
-
-jQuery.fn.extend( {
- attr: function( name, value ) {
- return access( this, jQuery.attr, name, value, arguments.length > 1 );
- },
-
- removeAttr: function( name ) {
- return this.each( function() {
- jQuery.removeAttr( this, name );
- } );
- }
-} );
-
-jQuery.extend( {
- attr: function( elem, name, value ) {
- var ret, hooks,
- nType = elem.nodeType;
-
- // Don't get/set attributes on text, comment and attribute nodes
- if ( nType === 3 || nType === 8 || nType === 2 ) {
- return;
- }
-
- // Fallback to prop when attributes are not supported
- if ( typeof elem.getAttribute === "undefined" ) {
- return jQuery.prop( elem, name, value );
- }
-
- // Attribute hooks are determined by the lowercase version
- // Grab necessary hook if one is defined
- if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
- hooks = jQuery.attrHooks[ name.toLowerCase() ] ||
- ( jQuery.expr.match.bool.test( name ) ? boolHook : undefined );
- }
-
- if ( value !== undefined ) {
- if ( value === null ) {
- jQuery.removeAttr( elem, name );
- return;
- }
-
- if ( hooks && "set" in hooks &&
- ( ret = hooks.set( elem, value, name ) ) !== undefined ) {
- return ret;
- }
-
- elem.setAttribute( name, value + "" );
- return value;
- }
-
- if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
- return ret;
- }
-
- ret = jQuery.find.attr( elem, name );
-
- // Non-existent attributes return null, we normalize to undefined
- return ret == null ? undefined : ret;
- },
-
- attrHooks: {
- type: {
- set: function( elem, value ) {
- if ( !support.radioValue && value === "radio" &&
- nodeName( elem, "input" ) ) {
- var val = elem.value;
- elem.setAttribute( "type", value );
- if ( val ) {
- elem.value = val;
- }
- return value;
- }
- }
- }
- },
-
- removeAttr: function( elem, value ) {
- var name,
- i = 0,
-
- // Attribute names can contain non-HTML whitespace characters
- // https://html.spec.whatwg.org/multipage/syntax.html#attributes-2
- attrNames = value && value.match( rnothtmlwhite );
-
- if ( attrNames && elem.nodeType === 1 ) {
- while ( ( name = attrNames[ i++ ] ) ) {
- elem.removeAttribute( name );
- }
- }
- }
-} );
-
-// Hooks for boolean attributes
-boolHook = {
- set: function( elem, value, name ) {
- if ( value === false ) {
-
- // Remove boolean attributes when set to false
- jQuery.removeAttr( elem, name );
- } else {
- elem.setAttribute( name, name );
- }
- return name;
- }
-};
-
-jQuery.each( jQuery.expr.match.bool.source.match( /\w+/g ), function( _i, name ) {
- var getter = attrHandle[ name ] || jQuery.find.attr;
-
- attrHandle[ name ] = function( elem, name, isXML ) {
- var ret, handle,
- lowercaseName = name.toLowerCase();
-
- if ( !isXML ) {
-
- // Avoid an infinite loop by temporarily removing this function from the getter
- handle = attrHandle[ lowercaseName ];
- attrHandle[ lowercaseName ] = ret;
- ret = getter( elem, name, isXML ) != null ?
- lowercaseName :
- null;
- attrHandle[ lowercaseName ] = handle;
- }
- return ret;
- };
-} );
-
-
-
-
-var rfocusable = /^(?:input|select|textarea|button)$/i,
- rclickable = /^(?:a|area)$/i;
-
-jQuery.fn.extend( {
- prop: function( name, value ) {
- return access( this, jQuery.prop, name, value, arguments.length > 1 );
- },
-
- removeProp: function( name ) {
- return this.each( function() {
- delete this[ jQuery.propFix[ name ] || name ];
- } );
- }
-} );
-
-jQuery.extend( {
- prop: function( elem, name, value ) {
- var ret, hooks,
- nType = elem.nodeType;
-
- // Don't get/set properties on text, comment and attribute nodes
- if ( nType === 3 || nType === 8 || nType === 2 ) {
- return;
- }
-
- if ( nType !== 1 || !jQuery.isXMLDoc( elem ) ) {
-
- // Fix name and attach hooks
- name = jQuery.propFix[ name ] || name;
- hooks = jQuery.propHooks[ name ];
- }
-
- if ( value !== undefined ) {
- if ( hooks && "set" in hooks &&
- ( ret = hooks.set( elem, value, name ) ) !== undefined ) {
- return ret;
- }
-
- return ( elem[ name ] = value );
- }
-
- if ( hooks && "get" in hooks && ( ret = hooks.get( elem, name ) ) !== null ) {
- return ret;
- }
-
- return elem[ name ];
- },
-
- propHooks: {
- tabIndex: {
- get: function( elem ) {
-
- // Support: IE <=9 - 11 only
- // elem.tabIndex doesn't always return the
- // correct value when it hasn't been explicitly set
- // https://web.archive.org/web/20141116233347/http://fluidproject.org/blog/2008/01/09/getting-setting-and-removing-tabindex-values-with-javascript/
- // Use proper attribute retrieval(#12072)
- var tabindex = jQuery.find.attr( elem, "tabindex" );
-
- if ( tabindex ) {
- return parseInt( tabindex, 10 );
- }
-
- if (
- rfocusable.test( elem.nodeName ) ||
- rclickable.test( elem.nodeName ) &&
- elem.href
- ) {
- return 0;
- }
-
- return -1;
- }
- }
- },
-
- propFix: {
- "for": "htmlFor",
- "class": "className"
- }
-} );
-
-// Support: IE <=11 only
-// Accessing the selectedIndex property
-// forces the browser to respect setting selected
-// on the option
-// The getter ensures a default option is selected
-// when in an optgroup
-// eslint rule "no-unused-expressions" is disabled for this code
-// since it considers such accessions noop
-if ( !support.optSelected ) {
- jQuery.propHooks.selected = {
- get: function( elem ) {
-
- /* eslint no-unused-expressions: "off" */
-
- var parent = elem.parentNode;
- if ( parent && parent.parentNode ) {
- parent.parentNode.selectedIndex;
- }
- return null;
- },
- set: function( elem ) {
-
- /* eslint no-unused-expressions: "off" */
-
- var parent = elem.parentNode;
- if ( parent ) {
- parent.selectedIndex;
-
- if ( parent.parentNode ) {
- parent.parentNode.selectedIndex;
- }
- }
- }
- };
-}
-
-jQuery.each( [
- "tabIndex",
- "readOnly",
- "maxLength",
- "cellSpacing",
- "cellPadding",
- "rowSpan",
- "colSpan",
- "useMap",
- "frameBorder",
- "contentEditable"
-], function() {
- jQuery.propFix[ this.toLowerCase() ] = this;
-} );
-
-
-
-
- // Strip and collapse whitespace according to HTML spec
- // https://infra.spec.whatwg.org/#strip-and-collapse-ascii-whitespace
- function stripAndCollapse( value ) {
- var tokens = value.match( rnothtmlwhite ) || [];
- return tokens.join( " " );
- }
-
-
-function getClass( elem ) {
- return elem.getAttribute && elem.getAttribute( "class" ) || "";
-}
-
-function classesToArray( value ) {
- if ( Array.isArray( value ) ) {
- return value;
- }
- if ( typeof value === "string" ) {
- return value.match( rnothtmlwhite ) || [];
- }
- return [];
-}
-
-jQuery.fn.extend( {
- addClass: function( value ) {
- var classes, elem, cur, curValue, clazz, j, finalValue,
- i = 0;
-
- if ( isFunction( value ) ) {
- return this.each( function( j ) {
- jQuery( this ).addClass( value.call( this, j, getClass( this ) ) );
- } );
- }
-
- classes = classesToArray( value );
-
- if ( classes.length ) {
- while ( ( elem = this[ i++ ] ) ) {
- curValue = getClass( elem );
- cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
-
- if ( cur ) {
- j = 0;
- while ( ( clazz = classes[ j++ ] ) ) {
- if ( cur.indexOf( " " + clazz + " " ) < 0 ) {
- cur += clazz + " ";
- }
- }
-
- // Only assign if different to avoid unneeded rendering.
- finalValue = stripAndCollapse( cur );
- if ( curValue !== finalValue ) {
- elem.setAttribute( "class", finalValue );
- }
- }
- }
- }
-
- return this;
- },
-
- removeClass: function( value ) {
- var classes, elem, cur, curValue, clazz, j, finalValue,
- i = 0;
-
- if ( isFunction( value ) ) {
- return this.each( function( j ) {
- jQuery( this ).removeClass( value.call( this, j, getClass( this ) ) );
- } );
- }
-
- if ( !arguments.length ) {
- return this.attr( "class", "" );
- }
-
- classes = classesToArray( value );
-
- if ( classes.length ) {
- while ( ( elem = this[ i++ ] ) ) {
- curValue = getClass( elem );
-
- // This expression is here for better compressibility (see addClass)
- cur = elem.nodeType === 1 && ( " " + stripAndCollapse( curValue ) + " " );
-
- if ( cur ) {
- j = 0;
- while ( ( clazz = classes[ j++ ] ) ) {
-
- // Remove *all* instances
- while ( cur.indexOf( " " + clazz + " " ) > -1 ) {
- cur = cur.replace( " " + clazz + " ", " " );
- }
- }
-
- // Only assign if different to avoid unneeded rendering.
- finalValue = stripAndCollapse( cur );
- if ( curValue !== finalValue ) {
- elem.setAttribute( "class", finalValue );
- }
- }
- }
- }
-
- return this;
- },
-
- toggleClass: function( value, stateVal ) {
- var type = typeof value,
- isValidValue = type === "string" || Array.isArray( value );
-
- if ( typeof stateVal === "boolean" && isValidValue ) {
- return stateVal ? this.addClass( value ) : this.removeClass( value );
- }
-
- if ( isFunction( value ) ) {
- return this.each( function( i ) {
- jQuery( this ).toggleClass(
- value.call( this, i, getClass( this ), stateVal ),
- stateVal
- );
- } );
- }
-
- return this.each( function() {
- var className, i, self, classNames;
-
- if ( isValidValue ) {
-
- // Toggle individual class names
- i = 0;
- self = jQuery( this );
- classNames = classesToArray( value );
-
- while ( ( className = classNames[ i++ ] ) ) {
-
- // Check each className given, space separated list
- if ( self.hasClass( className ) ) {
- self.removeClass( className );
- } else {
- self.addClass( className );
- }
- }
-
- // Toggle whole class name
- } else if ( value === undefined || type === "boolean" ) {
- className = getClass( this );
- if ( className ) {
-
- // Store className if set
- dataPriv.set( this, "__className__", className );
- }
-
- // If the element has a class name or if we're passed `false`,
- // then remove the whole classname (if there was one, the above saved it).
- // Otherwise bring back whatever was previously saved (if anything),
- // falling back to the empty string if nothing was stored.
- if ( this.setAttribute ) {
- this.setAttribute( "class",
- className || value === false ?
- "" :
- dataPriv.get( this, "__className__" ) || ""
- );
- }
- }
- } );
- },
-
- hasClass: function( selector ) {
- var className, elem,
- i = 0;
-
- className = " " + selector + " ";
- while ( ( elem = this[ i++ ] ) ) {
- if ( elem.nodeType === 1 &&
- ( " " + stripAndCollapse( getClass( elem ) ) + " " ).indexOf( className ) > -1 ) {
- return true;
- }
- }
-
- return false;
- }
-} );
-
-
-
-
-var rreturn = /\r/g;
-
-jQuery.fn.extend( {
- val: function( value ) {
- var hooks, ret, valueIsFunction,
- elem = this[ 0 ];
-
- if ( !arguments.length ) {
- if ( elem ) {
- hooks = jQuery.valHooks[ elem.type ] ||
- jQuery.valHooks[ elem.nodeName.toLowerCase() ];
-
- if ( hooks &&
- "get" in hooks &&
- ( ret = hooks.get( elem, "value" ) ) !== undefined
- ) {
- return ret;
- }
-
- ret = elem.value;
-
- // Handle most common string cases
- if ( typeof ret === "string" ) {
- return ret.replace( rreturn, "" );
- }
-
- // Handle cases where value is null/undef or number
- return ret == null ? "" : ret;
- }
-
- return;
- }
-
- valueIsFunction = isFunction( value );
-
- return this.each( function( i ) {
- var val;
-
- if ( this.nodeType !== 1 ) {
- return;
- }
-
- if ( valueIsFunction ) {
- val = value.call( this, i, jQuery( this ).val() );
- } else {
- val = value;
- }
-
- // Treat null/undefined as ""; convert numbers to string
- if ( val == null ) {
- val = "";
-
- } else if ( typeof val === "number" ) {
- val += "";
-
- } else if ( Array.isArray( val ) ) {
- val = jQuery.map( val, function( value ) {
- return value == null ? "" : value + "";
- } );
- }
-
- hooks = jQuery.valHooks[ this.type ] || jQuery.valHooks[ this.nodeName.toLowerCase() ];
-
- // If set returns undefined, fall back to normal setting
- if ( !hooks || !( "set" in hooks ) || hooks.set( this, val, "value" ) === undefined ) {
- this.value = val;
- }
- } );
- }
-} );
-
-jQuery.extend( {
- valHooks: {
- option: {
- get: function( elem ) {
-
- var val = jQuery.find.attr( elem, "value" );
- return val != null ?
- val :
-
- // Support: IE <=10 - 11 only
- // option.text throws exceptions (#14686, #14858)
- // Strip and collapse whitespace
- // https://html.spec.whatwg.org/#strip-and-collapse-whitespace
- stripAndCollapse( jQuery.text( elem ) );
- }
- },
- select: {
- get: function( elem ) {
- var value, option, i,
- options = elem.options,
- index = elem.selectedIndex,
- one = elem.type === "select-one",
- values = one ? null : [],
- max = one ? index + 1 : options.length;
-
- if ( index < 0 ) {
- i = max;
-
- } else {
- i = one ? index : 0;
- }
-
- // Loop through all the selected options
- for ( ; i < max; i++ ) {
- option = options[ i ];
-
- // Support: IE <=9 only
- // IE8-9 doesn't update selected after form reset (#2551)
- if ( ( option.selected || i === index ) &&
-
- // Don't return options that are disabled or in a disabled optgroup
- !option.disabled &&
- ( !option.parentNode.disabled ||
- !nodeName( option.parentNode, "optgroup" ) ) ) {
-
- // Get the specific value for the option
- value = jQuery( option ).val();
-
- // We don't need an array for one selects
- if ( one ) {
- return value;
- }
-
- // Multi-Selects return an array
- values.push( value );
- }
- }
-
- return values;
- },
-
- set: function( elem, value ) {
- var optionSet, option,
- options = elem.options,
- values = jQuery.makeArray( value ),
- i = options.length;
-
- while ( i-- ) {
- option = options[ i ];
-
- /* eslint-disable no-cond-assign */
-
- if ( option.selected =
- jQuery.inArray( jQuery.valHooks.option.get( option ), values ) > -1
- ) {
- optionSet = true;
- }
-
- /* eslint-enable no-cond-assign */
- }
-
- // Force browsers to behave consistently when non-matching value is set
- if ( !optionSet ) {
- elem.selectedIndex = -1;
- }
- return values;
- }
- }
- }
-} );
-
-// Radios and checkboxes getter/setter
-jQuery.each( [ "radio", "checkbox" ], function() {
- jQuery.valHooks[ this ] = {
- set: function( elem, value ) {
- if ( Array.isArray( value ) ) {
- return ( elem.checked = jQuery.inArray( jQuery( elem ).val(), value ) > -1 );
- }
- }
- };
- if ( !support.checkOn ) {
- jQuery.valHooks[ this ].get = function( elem ) {
- return elem.getAttribute( "value" ) === null ? "on" : elem.value;
- };
- }
-} );
-
-
-
-
-// Return jQuery for attributes-only inclusion
-
-
-support.focusin = "onfocusin" in window;
-
-
-var rfocusMorph = /^(?:focusinfocus|focusoutblur)$/,
- stopPropagationCallback = function( e ) {
- e.stopPropagation();
- };
-
-jQuery.extend( jQuery.event, {
-
- trigger: function( event, data, elem, onlyHandlers ) {
-
- var i, cur, tmp, bubbleType, ontype, handle, special, lastElement,
- eventPath = [ elem || document ],
- type = hasOwn.call( event, "type" ) ? event.type : event,
- namespaces = hasOwn.call( event, "namespace" ) ? event.namespace.split( "." ) : [];
-
- cur = lastElement = tmp = elem = elem || document;
-
- // Don't do events on text and comment nodes
- if ( elem.nodeType === 3 || elem.nodeType === 8 ) {
- return;
- }
-
- // focus/blur morphs to focusin/out; ensure we're not firing them right now
- if ( rfocusMorph.test( type + jQuery.event.triggered ) ) {
- return;
- }
-
- if ( type.indexOf( "." ) > -1 ) {
-
- // Namespaced trigger; create a regexp to match event type in handle()
- namespaces = type.split( "." );
- type = namespaces.shift();
- namespaces.sort();
- }
- ontype = type.indexOf( ":" ) < 0 && "on" + type;
-
- // Caller can pass in a jQuery.Event object, Object, or just an event type string
- event = event[ jQuery.expando ] ?
- event :
- new jQuery.Event( type, typeof event === "object" && event );
-
- // Trigger bitmask: & 1 for native handlers; & 2 for jQuery (always true)
- event.isTrigger = onlyHandlers ? 2 : 3;
- event.namespace = namespaces.join( "." );
- event.rnamespace = event.namespace ?
- new RegExp( "(^|\\.)" + namespaces.join( "\\.(?:.*\\.|)" ) + "(\\.|$)" ) :
- null;
-
- // Clean up the event in case it is being reused
- event.result = undefined;
- if ( !event.target ) {
- event.target = elem;
- }
-
- // Clone any incoming data and prepend the event, creating the handler arg list
- data = data == null ?
- [ event ] :
- jQuery.makeArray( data, [ event ] );
-
- // Allow special events to draw outside the lines
- special = jQuery.event.special[ type ] || {};
- if ( !onlyHandlers && special.trigger && special.trigger.apply( elem, data ) === false ) {
- return;
- }
-
- // Determine event propagation path in advance, per W3C events spec (#9951)
- // Bubble up to document, then to window; watch for a global ownerDocument var (#9724)
- if ( !onlyHandlers && !special.noBubble && !isWindow( elem ) ) {
-
- bubbleType = special.delegateType || type;
- if ( !rfocusMorph.test( bubbleType + type ) ) {
- cur = cur.parentNode;
- }
- for ( ; cur; cur = cur.parentNode ) {
- eventPath.push( cur );
- tmp = cur;
- }
-
- // Only add window if we got to document (e.g., not plain obj or detached DOM)
- if ( tmp === ( elem.ownerDocument || document ) ) {
- eventPath.push( tmp.defaultView || tmp.parentWindow || window );
- }
- }
-
- // Fire handlers on the event path
- i = 0;
- while ( ( cur = eventPath[ i++ ] ) && !event.isPropagationStopped() ) {
- lastElement = cur;
- event.type = i > 1 ?
- bubbleType :
- special.bindType || type;
-
- // jQuery handler
- handle = (
- dataPriv.get( cur, "events" ) || Object.create( null )
- )[ event.type ] &&
- dataPriv.get( cur, "handle" );
- if ( handle ) {
- handle.apply( cur, data );
- }
-
- // Native handler
- handle = ontype && cur[ ontype ];
- if ( handle && handle.apply && acceptData( cur ) ) {
- event.result = handle.apply( cur, data );
- if ( event.result === false ) {
- event.preventDefault();
- }
- }
- }
- event.type = type;
-
- // If nobody prevented the default action, do it now
- if ( !onlyHandlers && !event.isDefaultPrevented() ) {
-
- if ( ( !special._default ||
- special._default.apply( eventPath.pop(), data ) === false ) &&
- acceptData( elem ) ) {
-
- // Call a native DOM method on the target with the same name as the event.
- // Don't do default actions on window, that's where global variables be (#6170)
- if ( ontype && isFunction( elem[ type ] ) && !isWindow( elem ) ) {
-
- // Don't re-trigger an onFOO event when we call its FOO() method
- tmp = elem[ ontype ];
-
- if ( tmp ) {
- elem[ ontype ] = null;
- }
-
- // Prevent re-triggering of the same event, since we already bubbled it above
- jQuery.event.triggered = type;
-
- if ( event.isPropagationStopped() ) {
- lastElement.addEventListener( type, stopPropagationCallback );
- }
-
- elem[ type ]();
-
- if ( event.isPropagationStopped() ) {
- lastElement.removeEventListener( type, stopPropagationCallback );
- }
-
- jQuery.event.triggered = undefined;
-
- if ( tmp ) {
- elem[ ontype ] = tmp;
- }
- }
- }
- }
-
- return event.result;
- },
-
- // Piggyback on a donor event to simulate a different one
- // Used only for `focus(in | out)` events
- simulate: function( type, elem, event ) {
- var e = jQuery.extend(
- new jQuery.Event(),
- event,
- {
- type: type,
- isSimulated: true
- }
- );
-
- jQuery.event.trigger( e, null, elem );
- }
-
-} );
-
-jQuery.fn.extend( {
-
- trigger: function( type, data ) {
- return this.each( function() {
- jQuery.event.trigger( type, data, this );
- } );
- },
- triggerHandler: function( type, data ) {
- var elem = this[ 0 ];
- if ( elem ) {
- return jQuery.event.trigger( type, data, elem, true );
- }
- }
-} );
-
-
-// Support: Firefox <=44
-// Firefox doesn't have focus(in | out) events
-// Related ticket - https://bugzilla.mozilla.org/show_bug.cgi?id=687787
-//
-// Support: Chrome <=48 - 49, Safari <=9.0 - 9.1
-// focus(in | out) events fire after focus & blur events,
-// which is spec violation - http://www.w3.org/TR/DOM-Level-3-Events/#events-focusevent-event-order
-// Related ticket - https://bugs.chromium.org/p/chromium/issues/detail?id=449857
-if ( !support.focusin ) {
- jQuery.each( { focus: "focusin", blur: "focusout" }, function( orig, fix ) {
-
- // Attach a single capturing handler on the document while someone wants focusin/focusout
- var handler = function( event ) {
- jQuery.event.simulate( fix, event.target, jQuery.event.fix( event ) );
- };
-
- jQuery.event.special[ fix ] = {
- setup: function() {
-
- // Handle: regular nodes (via `this.ownerDocument`), window
- // (via `this.document`) & document (via `this`).
- var doc = this.ownerDocument || this.document || this,
- attaches = dataPriv.access( doc, fix );
-
- if ( !attaches ) {
- doc.addEventListener( orig, handler, true );
- }
- dataPriv.access( doc, fix, ( attaches || 0 ) + 1 );
- },
- teardown: function() {
- var doc = this.ownerDocument || this.document || this,
- attaches = dataPriv.access( doc, fix ) - 1;
-
- if ( !attaches ) {
- doc.removeEventListener( orig, handler, true );
- dataPriv.remove( doc, fix );
-
- } else {
- dataPriv.access( doc, fix, attaches );
- }
- }
- };
- } );
-}
-var location = window.location;
-
-var nonce = { guid: Date.now() };
-
-var rquery = ( /\?/ );
-
-
-
-// Cross-browser xml parsing
-jQuery.parseXML = function( data ) {
- var xml;
- if ( !data || typeof data !== "string" ) {
- return null;
- }
-
- // Support: IE 9 - 11 only
- // IE throws on parseFromString with invalid input.
- try {
- xml = ( new window.DOMParser() ).parseFromString( data, "text/xml" );
- } catch ( e ) {
- xml = undefined;
- }
-
- if ( !xml || xml.getElementsByTagName( "parsererror" ).length ) {
- jQuery.error( "Invalid XML: " + data );
- }
- return xml;
-};
-
-
-var
- rbracket = /\[\]$/,
- rCRLF = /\r?\n/g,
- rsubmitterTypes = /^(?:submit|button|image|reset|file)$/i,
- rsubmittable = /^(?:input|select|textarea|keygen)/i;
-
-function buildParams( prefix, obj, traditional, add ) {
- var name;
-
- if ( Array.isArray( obj ) ) {
-
- // Serialize array item.
- jQuery.each( obj, function( i, v ) {
- if ( traditional || rbracket.test( prefix ) ) {
-
- // Treat each array item as a scalar.
- add( prefix, v );
-
- } else {
-
- // Item is non-scalar (array or object), encode its numeric index.
- buildParams(
- prefix + "[" + ( typeof v === "object" && v != null ? i : "" ) + "]",
- v,
- traditional,
- add
- );
- }
- } );
-
- } else if ( !traditional && toType( obj ) === "object" ) {
-
- // Serialize object item.
- for ( name in obj ) {
- buildParams( prefix + "[" + name + "]", obj[ name ], traditional, add );
- }
-
- } else {
-
- // Serialize scalar item.
- add( prefix, obj );
- }
-}
-
-// Serialize an array of form elements or a set of
-// key/values into a query string
-jQuery.param = function( a, traditional ) {
- var prefix,
- s = [],
- add = function( key, valueOrFunction ) {
-
- // If value is a function, invoke it and use its return value
- var value = isFunction( valueOrFunction ) ?
- valueOrFunction() :
- valueOrFunction;
-
- s[ s.length ] = encodeURIComponent( key ) + "=" +
- encodeURIComponent( value == null ? "" : value );
- };
-
- if ( a == null ) {
- return "";
- }
-
- // If an array was passed in, assume that it is an array of form elements.
- if ( Array.isArray( a ) || ( a.jquery && !jQuery.isPlainObject( a ) ) ) {
-
- // Serialize the form elements
- jQuery.each( a, function() {
- add( this.name, this.value );
- } );
-
- } else {
-
- // If traditional, encode the "old" way (the way 1.3.2 or older
- // did it), otherwise encode params recursively.
- for ( prefix in a ) {
- buildParams( prefix, a[ prefix ], traditional, add );
- }
- }
-
- // Return the resulting serialization
- return s.join( "&" );
-};
-
-jQuery.fn.extend( {
- serialize: function() {
- return jQuery.param( this.serializeArray() );
- },
- serializeArray: function() {
- return this.map( function() {
-
- // Can add propHook for "elements" to filter or add form elements
- var elements = jQuery.prop( this, "elements" );
- return elements ? jQuery.makeArray( elements ) : this;
- } )
- .filter( function() {
- var type = this.type;
-
- // Use .is( ":disabled" ) so that fieldset[disabled] works
- return this.name && !jQuery( this ).is( ":disabled" ) &&
- rsubmittable.test( this.nodeName ) && !rsubmitterTypes.test( type ) &&
- ( this.checked || !rcheckableType.test( type ) );
- } )
- .map( function( _i, elem ) {
- var val = jQuery( this ).val();
-
- if ( val == null ) {
- return null;
- }
-
- if ( Array.isArray( val ) ) {
- return jQuery.map( val, function( val ) {
- return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
- } );
- }
-
- return { name: elem.name, value: val.replace( rCRLF, "\r\n" ) };
- } ).get();
- }
-} );
-
-
-var
- r20 = /%20/g,
- rhash = /#.*$/,
- rantiCache = /([?&])_=[^&]*/,
- rheaders = /^(.*?):[ \t]*([^\r\n]*)$/mg,
-
- // #7653, #8125, #8152: local protocol detection
- rlocalProtocol = /^(?:about|app|app-storage|.+-extension|file|res|widget):$/,
- rnoContent = /^(?:GET|HEAD)$/,
- rprotocol = /^\/\//,
-
- /* Prefilters
- * 1) They are useful to introduce custom dataTypes (see ajax/jsonp.js for an example)
- * 2) These are called:
- * - BEFORE asking for a transport
- * - AFTER param serialization (s.data is a string if s.processData is true)
- * 3) key is the dataType
- * 4) the catchall symbol "*" can be used
- * 5) execution will start with transport dataType and THEN continue down to "*" if needed
- */
- prefilters = {},
-
- /* Transports bindings
- * 1) key is the dataType
- * 2) the catchall symbol "*" can be used
- * 3) selection will start with transport dataType and THEN go to "*" if needed
- */
- transports = {},
-
- // Avoid comment-prolog char sequence (#10098); must appease lint and evade compression
- allTypes = "*/".concat( "*" ),
-
- // Anchor tag for parsing the document origin
- originAnchor = document.createElement( "a" );
- originAnchor.href = location.href;
-
-// Base "constructor" for jQuery.ajaxPrefilter and jQuery.ajaxTransport
-function addToPrefiltersOrTransports( structure ) {
-
- // dataTypeExpression is optional and defaults to "*"
- return function( dataTypeExpression, func ) {
-
- if ( typeof dataTypeExpression !== "string" ) {
- func = dataTypeExpression;
- dataTypeExpression = "*";
- }
-
- var dataType,
- i = 0,
- dataTypes = dataTypeExpression.toLowerCase().match( rnothtmlwhite ) || [];
-
- if ( isFunction( func ) ) {
-
- // For each dataType in the dataTypeExpression
- while ( ( dataType = dataTypes[ i++ ] ) ) {
-
- // Prepend if requested
- if ( dataType[ 0 ] === "+" ) {
- dataType = dataType.slice( 1 ) || "*";
- ( structure[ dataType ] = structure[ dataType ] || [] ).unshift( func );
-
- // Otherwise append
- } else {
- ( structure[ dataType ] = structure[ dataType ] || [] ).push( func );
- }
- }
- }
- };
-}
-
-// Base inspection function for prefilters and transports
-function inspectPrefiltersOrTransports( structure, options, originalOptions, jqXHR ) {
-
- var inspected = {},
- seekingTransport = ( structure === transports );
-
- function inspect( dataType ) {
- var selected;
- inspected[ dataType ] = true;
- jQuery.each( structure[ dataType ] || [], function( _, prefilterOrFactory ) {
- var dataTypeOrTransport = prefilterOrFactory( options, originalOptions, jqXHR );
- if ( typeof dataTypeOrTransport === "string" &&
- !seekingTransport && !inspected[ dataTypeOrTransport ] ) {
-
- options.dataTypes.unshift( dataTypeOrTransport );
- inspect( dataTypeOrTransport );
- return false;
- } else if ( seekingTransport ) {
- return !( selected = dataTypeOrTransport );
- }
- } );
- return selected;
- }
-
- return inspect( options.dataTypes[ 0 ] ) || !inspected[ "*" ] && inspect( "*" );
-}
-
-// A special extend for ajax options
-// that takes "flat" options (not to be deep extended)
-// Fixes #9887
-function ajaxExtend( target, src ) {
- var key, deep,
- flatOptions = jQuery.ajaxSettings.flatOptions || {};
-
- for ( key in src ) {
- if ( src[ key ] !== undefined ) {
- ( flatOptions[ key ] ? target : ( deep || ( deep = {} ) ) )[ key ] = src[ key ];
- }
- }
- if ( deep ) {
- jQuery.extend( true, target, deep );
- }
-
- return target;
-}
-
-/* Handles responses to an ajax request:
- * - finds the right dataType (mediates between content-type and expected dataType)
- * - returns the corresponding response
- */
-function ajaxHandleResponses( s, jqXHR, responses ) {
-
- var ct, type, finalDataType, firstDataType,
- contents = s.contents,
- dataTypes = s.dataTypes;
-
- // Remove auto dataType and get content-type in the process
- while ( dataTypes[ 0 ] === "*" ) {
- dataTypes.shift();
- if ( ct === undefined ) {
- ct = s.mimeType || jqXHR.getResponseHeader( "Content-Type" );
- }
- }
-
- // Check if we're dealing with a known content-type
- if ( ct ) {
- for ( type in contents ) {
- if ( contents[ type ] && contents[ type ].test( ct ) ) {
- dataTypes.unshift( type );
- break;
- }
- }
- }
-
- // Check to see if we have a response for the expected dataType
- if ( dataTypes[ 0 ] in responses ) {
- finalDataType = dataTypes[ 0 ];
- } else {
-
- // Try convertible dataTypes
- for ( type in responses ) {
- if ( !dataTypes[ 0 ] || s.converters[ type + " " + dataTypes[ 0 ] ] ) {
- finalDataType = type;
- break;
- }
- if ( !firstDataType ) {
- firstDataType = type;
- }
- }
-
- // Or just use first one
- finalDataType = finalDataType || firstDataType;
- }
-
- // If we found a dataType
- // We add the dataType to the list if needed
- // and return the corresponding response
- if ( finalDataType ) {
- if ( finalDataType !== dataTypes[ 0 ] ) {
- dataTypes.unshift( finalDataType );
- }
- return responses[ finalDataType ];
- }
-}
-
-/* Chain conversions given the request and the original response
- * Also sets the responseXXX fields on the jqXHR instance
- */
-function ajaxConvert( s, response, jqXHR, isSuccess ) {
- var conv2, current, conv, tmp, prev,
- converters = {},
-
- // Work with a copy of dataTypes in case we need to modify it for conversion
- dataTypes = s.dataTypes.slice();
-
- // Create converters map with lowercased keys
- if ( dataTypes[ 1 ] ) {
- for ( conv in s.converters ) {
- converters[ conv.toLowerCase() ] = s.converters[ conv ];
- }
- }
-
- current = dataTypes.shift();
-
- // Convert to each sequential dataType
- while ( current ) {
-
- if ( s.responseFields[ current ] ) {
- jqXHR[ s.responseFields[ current ] ] = response;
- }
-
- // Apply the dataFilter if provided
- if ( !prev && isSuccess && s.dataFilter ) {
- response = s.dataFilter( response, s.dataType );
- }
-
- prev = current;
- current = dataTypes.shift();
-
- if ( current ) {
-
- // There's only work to do if current dataType is non-auto
- if ( current === "*" ) {
-
- current = prev;
-
- // Convert response if prev dataType is non-auto and differs from current
- } else if ( prev !== "*" && prev !== current ) {
-
- // Seek a direct converter
- conv = converters[ prev + " " + current ] || converters[ "* " + current ];
-
- // If none found, seek a pair
- if ( !conv ) {
- for ( conv2 in converters ) {
-
- // If conv2 outputs current
- tmp = conv2.split( " " );
- if ( tmp[ 1 ] === current ) {
-
- // If prev can be converted to accepted input
- conv = converters[ prev + " " + tmp[ 0 ] ] ||
- converters[ "* " + tmp[ 0 ] ];
- if ( conv ) {
-
- // Condense equivalence converters
- if ( conv === true ) {
- conv = converters[ conv2 ];
-
- // Otherwise, insert the intermediate dataType
- } else if ( converters[ conv2 ] !== true ) {
- current = tmp[ 0 ];
- dataTypes.unshift( tmp[ 1 ] );
- }
- break;
- }
- }
- }
- }
-
- // Apply converter (if not an equivalence)
- if ( conv !== true ) {
-
- // Unless errors are allowed to bubble, catch and return them
- if ( conv && s.throws ) {
- response = conv( response );
- } else {
- try {
- response = conv( response );
- } catch ( e ) {
- return {
- state: "parsererror",
- error: conv ? e : "No conversion from " + prev + " to " + current
- };
- }
- }
- }
- }
- }
- }
-
- return { state: "success", data: response };
-}
-
-jQuery.extend( {
-
- // Counter for holding the number of active queries
- active: 0,
-
- // Last-Modified header cache for next request
- lastModified: {},
- etag: {},
-
- ajaxSettings: {
- url: location.href,
- type: "GET",
- isLocal: rlocalProtocol.test( location.protocol ),
- global: true,
- processData: true,
- async: true,
- contentType: "application/x-www-form-urlencoded; charset=UTF-8",
-
- /*
- timeout: 0,
- data: null,
- dataType: null,
- username: null,
- password: null,
- cache: null,
- throws: false,
- traditional: false,
- headers: {},
- */
-
- accepts: {
- "*": allTypes,
- text: "text/plain",
- html: "text/html",
- xml: "application/xml, text/xml",
- json: "application/json, text/javascript"
- },
-
- contents: {
- xml: /\bxml\b/,
- html: /\bhtml/,
- json: /\bjson\b/
- },
-
- responseFields: {
- xml: "responseXML",
- text: "responseText",
- json: "responseJSON"
- },
-
- // Data converters
- // Keys separate source (or catchall "*") and destination types with a single space
- converters: {
-
- // Convert anything to text
- "* text": String,
-
- // Text to html (true = no transformation)
- "text html": true,
-
- // Evaluate text as a json expression
- "text json": JSON.parse,
-
- // Parse text as xml
- "text xml": jQuery.parseXML
- },
-
- // For options that shouldn't be deep extended:
- // you can add your own custom options here if
- // and when you create one that shouldn't be
- // deep extended (see ajaxExtend)
- flatOptions: {
- url: true,
- context: true
- }
- },
-
- // Creates a full fledged settings object into target
- // with both ajaxSettings and settings fields.
- // If target is omitted, writes into ajaxSettings.
- ajaxSetup: function( target, settings ) {
- return settings ?
-
- // Building a settings object
- ajaxExtend( ajaxExtend( target, jQuery.ajaxSettings ), settings ) :
-
- // Extending ajaxSettings
- ajaxExtend( jQuery.ajaxSettings, target );
- },
-
- ajaxPrefilter: addToPrefiltersOrTransports( prefilters ),
- ajaxTransport: addToPrefiltersOrTransports( transports ),
-
- // Main method
- ajax: function( url, options ) {
-
- // If url is an object, simulate pre-1.5 signature
- if ( typeof url === "object" ) {
- options = url;
- url = undefined;
- }
-
- // Force options to be an object
- options = options || {};
-
- var transport,
-
- // URL without anti-cache param
- cacheURL,
-
- // Response headers
- responseHeadersString,
- responseHeaders,
-
- // timeout handle
- timeoutTimer,
-
- // Url cleanup var
- urlAnchor,
-
- // Request state (becomes false upon send and true upon completion)
- completed,
-
- // To know if global events are to be dispatched
- fireGlobals,
-
- // Loop variable
- i,
-
- // uncached part of the url
- uncached,
-
- // Create the final options object
- s = jQuery.ajaxSetup( {}, options ),
-
- // Callbacks context
- callbackContext = s.context || s,
-
- // Context for global events is callbackContext if it is a DOM node or jQuery collection
- globalEventContext = s.context &&
- ( callbackContext.nodeType || callbackContext.jquery ) ?
- jQuery( callbackContext ) :
- jQuery.event,
-
- // Deferreds
- deferred = jQuery.Deferred(),
- completeDeferred = jQuery.Callbacks( "once memory" ),
-
- // Status-dependent callbacks
- statusCode = s.statusCode || {},
-
- // Headers (they are sent all at once)
- requestHeaders = {},
- requestHeadersNames = {},
-
- // Default abort message
- strAbort = "canceled",
-
- // Fake xhr
- jqXHR = {
- readyState: 0,
-
- // Builds headers hashtable if needed
- getResponseHeader: function( key ) {
- var match;
- if ( completed ) {
- if ( !responseHeaders ) {
- responseHeaders = {};
- while ( ( match = rheaders.exec( responseHeadersString ) ) ) {
- responseHeaders[ match[ 1 ].toLowerCase() + " " ] =
- ( responseHeaders[ match[ 1 ].toLowerCase() + " " ] || [] )
- .concat( match[ 2 ] );
- }
- }
- match = responseHeaders[ key.toLowerCase() + " " ];
- }
- return match == null ? null : match.join( ", " );
- },
-
- // Raw string
- getAllResponseHeaders: function() {
- return completed ? responseHeadersString : null;
- },
-
- // Caches the header
- setRequestHeader: function( name, value ) {
- if ( completed == null ) {
- name = requestHeadersNames[ name.toLowerCase() ] =
- requestHeadersNames[ name.toLowerCase() ] || name;
- requestHeaders[ name ] = value;
- }
- return this;
- },
-
- // Overrides response content-type header
- overrideMimeType: function( type ) {
- if ( completed == null ) {
- s.mimeType = type;
- }
- return this;
- },
-
- // Status-dependent callbacks
- statusCode: function( map ) {
- var code;
- if ( map ) {
- if ( completed ) {
-
- // Execute the appropriate callbacks
- jqXHR.always( map[ jqXHR.status ] );
- } else {
-
- // Lazy-add the new callbacks in a way that preserves old ones
- for ( code in map ) {
- statusCode[ code ] = [ statusCode[ code ], map[ code ] ];
- }
- }
- }
- return this;
- },
-
- // Cancel the request
- abort: function( statusText ) {
- var finalText = statusText || strAbort;
- if ( transport ) {
- transport.abort( finalText );
- }
- done( 0, finalText );
- return this;
- }
- };
-
- // Attach deferreds
- deferred.promise( jqXHR );
-
- // Add protocol if not provided (prefilters might expect it)
- // Handle falsy url in the settings object (#10093: consistency with old signature)
- // We also use the url parameter if available
- s.url = ( ( url || s.url || location.href ) + "" )
- .replace( rprotocol, location.protocol + "//" );
-
- // Alias method option to type as per ticket #12004
- s.type = options.method || options.type || s.method || s.type;
-
- // Extract dataTypes list
- s.dataTypes = ( s.dataType || "*" ).toLowerCase().match( rnothtmlwhite ) || [ "" ];
-
- // A cross-domain request is in order when the origin doesn't match the current origin.
- if ( s.crossDomain == null ) {
- urlAnchor = document.createElement( "a" );
-
- // Support: IE <=8 - 11, Edge 12 - 15
- // IE throws exception on accessing the href property if url is malformed,
- // e.g. http://example.com:80x/
- try {
- urlAnchor.href = s.url;
-
- // Support: IE <=8 - 11 only
- // Anchor's host property isn't correctly set when s.url is relative
- urlAnchor.href = urlAnchor.href;
- s.crossDomain = originAnchor.protocol + "//" + originAnchor.host !==
- urlAnchor.protocol + "//" + urlAnchor.host;
- } catch ( e ) {
-
- // If there is an error parsing the URL, assume it is crossDomain,
- // it can be rejected by the transport if it is invalid
- s.crossDomain = true;
- }
- }
-
- // Convert data if not already a string
- if ( s.data && s.processData && typeof s.data !== "string" ) {
- s.data = jQuery.param( s.data, s.traditional );
- }
-
- // Apply prefilters
- inspectPrefiltersOrTransports( prefilters, s, options, jqXHR );
-
- // If request was aborted inside a prefilter, stop there
- if ( completed ) {
- return jqXHR;
- }
-
- // We can fire global events as of now if asked to
- // Don't fire events if jQuery.event is undefined in an AMD-usage scenario (#15118)
- fireGlobals = jQuery.event && s.global;
-
- // Watch for a new set of requests
- if ( fireGlobals && jQuery.active++ === 0 ) {
- jQuery.event.trigger( "ajaxStart" );
- }
-
- // Uppercase the type
- s.type = s.type.toUpperCase();
-
- // Determine if request has content
- s.hasContent = !rnoContent.test( s.type );
-
- // Save the URL in case we're toying with the If-Modified-Since
- // and/or If-None-Match header later on
- // Remove hash to simplify url manipulation
- cacheURL = s.url.replace( rhash, "" );
-
- // More options handling for requests with no content
- if ( !s.hasContent ) {
-
- // Remember the hash so we can put it back
- uncached = s.url.slice( cacheURL.length );
-
- // If data is available and should be processed, append data to url
- if ( s.data && ( s.processData || typeof s.data === "string" ) ) {
- cacheURL += ( rquery.test( cacheURL ) ? "&" : "?" ) + s.data;
-
- // #9682: remove data so that it's not used in an eventual retry
- delete s.data;
- }
-
- // Add or update anti-cache param if needed
- if ( s.cache === false ) {
- cacheURL = cacheURL.replace( rantiCache, "$1" );
- uncached = ( rquery.test( cacheURL ) ? "&" : "?" ) + "_=" + ( nonce.guid++ ) +
- uncached;
- }
-
- // Put hash and anti-cache on the URL that will be requested (gh-1732)
- s.url = cacheURL + uncached;
-
- // Change '%20' to '+' if this is encoded form body content (gh-2658)
- } else if ( s.data && s.processData &&
- ( s.contentType || "" ).indexOf( "application/x-www-form-urlencoded" ) === 0 ) {
- s.data = s.data.replace( r20, "+" );
- }
-
- // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
- if ( s.ifModified ) {
- if ( jQuery.lastModified[ cacheURL ] ) {
- jqXHR.setRequestHeader( "If-Modified-Since", jQuery.lastModified[ cacheURL ] );
- }
- if ( jQuery.etag[ cacheURL ] ) {
- jqXHR.setRequestHeader( "If-None-Match", jQuery.etag[ cacheURL ] );
- }
- }
-
- // Set the correct header, if data is being sent
- if ( s.data && s.hasContent && s.contentType !== false || options.contentType ) {
- jqXHR.setRequestHeader( "Content-Type", s.contentType );
- }
-
- // Set the Accepts header for the server, depending on the dataType
- jqXHR.setRequestHeader(
- "Accept",
- s.dataTypes[ 0 ] && s.accepts[ s.dataTypes[ 0 ] ] ?
- s.accepts[ s.dataTypes[ 0 ] ] +
- ( s.dataTypes[ 0 ] !== "*" ? ", " + allTypes + "; q=0.01" : "" ) :
- s.accepts[ "*" ]
- );
-
- // Check for headers option
- for ( i in s.headers ) {
- jqXHR.setRequestHeader( i, s.headers[ i ] );
- }
-
- // Allow custom headers/mimetypes and early abort
- if ( s.beforeSend &&
- ( s.beforeSend.call( callbackContext, jqXHR, s ) === false || completed ) ) {
-
- // Abort if not done already and return
- return jqXHR.abort();
- }
-
- // Aborting is no longer a cancellation
- strAbort = "abort";
-
- // Install callbacks on deferreds
- completeDeferred.add( s.complete );
- jqXHR.done( s.success );
- jqXHR.fail( s.error );
-
- // Get transport
- transport = inspectPrefiltersOrTransports( transports, s, options, jqXHR );
-
- // If no transport, we auto-abort
- if ( !transport ) {
- done( -1, "No Transport" );
- } else {
- jqXHR.readyState = 1;
-
- // Send global event
- if ( fireGlobals ) {
- globalEventContext.trigger( "ajaxSend", [ jqXHR, s ] );
- }
-
- // If request was aborted inside ajaxSend, stop there
- if ( completed ) {
- return jqXHR;
- }
-
- // Timeout
- if ( s.async && s.timeout > 0 ) {
- timeoutTimer = window.setTimeout( function() {
- jqXHR.abort( "timeout" );
- }, s.timeout );
- }
-
- try {
- completed = false;
- transport.send( requestHeaders, done );
- } catch ( e ) {
-
- // Rethrow post-completion exceptions
- if ( completed ) {
- throw e;
- }
-
- // Propagate others as results
- done( -1, e );
- }
- }
-
- // Callback for when everything is done
- function done( status, nativeStatusText, responses, headers ) {
- var isSuccess, success, error, response, modified,
- statusText = nativeStatusText;
-
- // Ignore repeat invocations
- if ( completed ) {
- return;
- }
-
- completed = true;
-
- // Clear timeout if it exists
- if ( timeoutTimer ) {
- window.clearTimeout( timeoutTimer );
- }
-
- // Dereference transport for early garbage collection
- // (no matter how long the jqXHR object will be used)
- transport = undefined;
-
- // Cache response headers
- responseHeadersString = headers || "";
-
- // Set readyState
- jqXHR.readyState = status > 0 ? 4 : 0;
-
- // Determine if successful
- isSuccess = status >= 200 && status < 300 || status === 304;
-
- // Get response data
- if ( responses ) {
- response = ajaxHandleResponses( s, jqXHR, responses );
- }
-
- // Use a noop converter for missing script
- if ( !isSuccess && jQuery.inArray( "script", s.dataTypes ) > -1 ) {
- s.converters[ "text script" ] = function() {};
- }
-
- // Convert no matter what (that way responseXXX fields are always set)
- response = ajaxConvert( s, response, jqXHR, isSuccess );
-
- // If successful, handle type chaining
- if ( isSuccess ) {
-
- // Set the If-Modified-Since and/or If-None-Match header, if in ifModified mode.
- if ( s.ifModified ) {
- modified = jqXHR.getResponseHeader( "Last-Modified" );
- if ( modified ) {
- jQuery.lastModified[ cacheURL ] = modified;
- }
- modified = jqXHR.getResponseHeader( "etag" );
- if ( modified ) {
- jQuery.etag[ cacheURL ] = modified;
- }
- }
-
- // if no content
- if ( status === 204 || s.type === "HEAD" ) {
- statusText = "nocontent";
-
- // if not modified
- } else if ( status === 304 ) {
- statusText = "notmodified";
-
- // If we have data, let's convert it
- } else {
- statusText = response.state;
- success = response.data;
- error = response.error;
- isSuccess = !error;
- }
- } else {
-
- // Extract error from statusText and normalize for non-aborts
- error = statusText;
- if ( status || !statusText ) {
- statusText = "error";
- if ( status < 0 ) {
- status = 0;
- }
- }
- }
-
- // Set data for the fake xhr object
- jqXHR.status = status;
- jqXHR.statusText = ( nativeStatusText || statusText ) + "";
-
- // Success/Error
- if ( isSuccess ) {
- deferred.resolveWith( callbackContext, [ success, statusText, jqXHR ] );
- } else {
- deferred.rejectWith( callbackContext, [ jqXHR, statusText, error ] );
- }
-
- // Status-dependent callbacks
- jqXHR.statusCode( statusCode );
- statusCode = undefined;
-
- if ( fireGlobals ) {
- globalEventContext.trigger( isSuccess ? "ajaxSuccess" : "ajaxError",
- [ jqXHR, s, isSuccess ? success : error ] );
- }
-
- // Complete
- completeDeferred.fireWith( callbackContext, [ jqXHR, statusText ] );
-
- if ( fireGlobals ) {
- globalEventContext.trigger( "ajaxComplete", [ jqXHR, s ] );
-
- // Handle the global AJAX counter
- if ( !( --jQuery.active ) ) {
- jQuery.event.trigger( "ajaxStop" );
- }
- }
- }
-
- return jqXHR;
- },
-
- getJSON: function( url, data, callback ) {
- return jQuery.get( url, data, callback, "json" );
- },
-
- getScript: function( url, callback ) {
- return jQuery.get( url, undefined, callback, "script" );
- }
-} );
-
-jQuery.each( [ "get", "post" ], function( _i, method ) {
- jQuery[ method ] = function( url, data, callback, type ) {
-
- // Shift arguments if data argument was omitted
- if ( isFunction( data ) ) {
- type = type || callback;
- callback = data;
- data = undefined;
- }
-
- // The url can be an options object (which then must have .url)
- return jQuery.ajax( jQuery.extend( {
- url: url,
- type: method,
- dataType: type,
- data: data,
- success: callback
- }, jQuery.isPlainObject( url ) && url ) );
- };
-} );
-
-jQuery.ajaxPrefilter( function( s ) {
- var i;
- for ( i in s.headers ) {
- if ( i.toLowerCase() === "content-type" ) {
- s.contentType = s.headers[ i ] || "";
- }
- }
-} );
-
-
-jQuery._evalUrl = function( url, options, doc ) {
- return jQuery.ajax( {
- url: url,
-
- // Make this explicit, since user can override this through ajaxSetup (#11264)
- type: "GET",
- dataType: "script",
- cache: true,
- async: false,
- global: false,
-
- // Only evaluate the response if it is successful (gh-4126)
- // dataFilter is not invoked for failure responses, so using it instead
- // of the default converter is kludgy but it works.
- converters: {
- "text script": function() {}
- },
- dataFilter: function( response ) {
- jQuery.globalEval( response, options, doc );
- }
- } );
-};
-
-
-jQuery.fn.extend( {
- wrapAll: function( html ) {
- var wrap;
-
- if ( this[ 0 ] ) {
- if ( isFunction( html ) ) {
- html = html.call( this[ 0 ] );
- }
-
- // The elements to wrap the target around
- wrap = jQuery( html, this[ 0 ].ownerDocument ).eq( 0 ).clone( true );
-
- if ( this[ 0 ].parentNode ) {
- wrap.insertBefore( this[ 0 ] );
- }
-
- wrap.map( function() {
- var elem = this;
-
- while ( elem.firstElementChild ) {
- elem = elem.firstElementChild;
- }
-
- return elem;
- } ).append( this );
- }
-
- return this;
- },
-
- wrapInner: function( html ) {
- if ( isFunction( html ) ) {
- return this.each( function( i ) {
- jQuery( this ).wrapInner( html.call( this, i ) );
- } );
- }
-
- return this.each( function() {
- var self = jQuery( this ),
- contents = self.contents();
-
- if ( contents.length ) {
- contents.wrapAll( html );
-
- } else {
- self.append( html );
- }
- } );
- },
-
- wrap: function( html ) {
- var htmlIsFunction = isFunction( html );
-
- return this.each( function( i ) {
- jQuery( this ).wrapAll( htmlIsFunction ? html.call( this, i ) : html );
- } );
- },
-
- unwrap: function( selector ) {
- this.parent( selector ).not( "body" ).each( function() {
- jQuery( this ).replaceWith( this.childNodes );
- } );
- return this;
- }
-} );
-
-
-jQuery.expr.pseudos.hidden = function( elem ) {
- return !jQuery.expr.pseudos.visible( elem );
-};
-jQuery.expr.pseudos.visible = function( elem ) {
- return !!( elem.offsetWidth || elem.offsetHeight || elem.getClientRects().length );
-};
-
-
-
-
-jQuery.ajaxSettings.xhr = function() {
- try {
- return new window.XMLHttpRequest();
- } catch ( e ) {}
-};
-
-var xhrSuccessStatus = {
-
- // File protocol always yields status code 0, assume 200
- 0: 200,
-
- // Support: IE <=9 only
- // #1450: sometimes IE returns 1223 when it should be 204
- 1223: 204
- },
- xhrSupported = jQuery.ajaxSettings.xhr();
-
-support.cors = !!xhrSupported && ( "withCredentials" in xhrSupported );
-support.ajax = xhrSupported = !!xhrSupported;
-
-jQuery.ajaxTransport( function( options ) {
- var callback, errorCallback;
-
- // Cross domain only allowed if supported through XMLHttpRequest
- if ( support.cors || xhrSupported && !options.crossDomain ) {
- return {
- send: function( headers, complete ) {
- var i,
- xhr = options.xhr();
-
- xhr.open(
- options.type,
- options.url,
- options.async,
- options.username,
- options.password
- );
-
- // Apply custom fields if provided
- if ( options.xhrFields ) {
- for ( i in options.xhrFields ) {
- xhr[ i ] = options.xhrFields[ i ];
- }
- }
-
- // Override mime type if needed
- if ( options.mimeType && xhr.overrideMimeType ) {
- xhr.overrideMimeType( options.mimeType );
- }
-
- // X-Requested-With header
- // For cross-domain requests, seeing as conditions for a preflight are
- // akin to a jigsaw puzzle, we simply never set it to be sure.
- // (it can always be set on a per-request basis or even using ajaxSetup)
- // For same-domain requests, won't change header if already provided.
- if ( !options.crossDomain && !headers[ "X-Requested-With" ] ) {
- headers[ "X-Requested-With" ] = "XMLHttpRequest";
- }
-
- // Set headers
- for ( i in headers ) {
- xhr.setRequestHeader( i, headers[ i ] );
- }
-
- // Callback
- callback = function( type ) {
- return function() {
- if ( callback ) {
- callback = errorCallback = xhr.onload =
- xhr.onerror = xhr.onabort = xhr.ontimeout =
- xhr.onreadystatechange = null;
-
- if ( type === "abort" ) {
- xhr.abort();
- } else if ( type === "error" ) {
-
- // Support: IE <=9 only
- // On a manual native abort, IE9 throws
- // errors on any property access that is not readyState
- if ( typeof xhr.status !== "number" ) {
- complete( 0, "error" );
- } else {
- complete(
-
- // File: protocol always yields status 0; see #8605, #14207
- xhr.status,
- xhr.statusText
- );
- }
- } else {
- complete(
- xhrSuccessStatus[ xhr.status ] || xhr.status,
- xhr.statusText,
-
- // Support: IE <=9 only
- // IE9 has no XHR2 but throws on binary (trac-11426)
- // For XHR2 non-text, let the caller handle it (gh-2498)
- ( xhr.responseType || "text" ) !== "text" ||
- typeof xhr.responseText !== "string" ?
- { binary: xhr.response } :
- { text: xhr.responseText },
- xhr.getAllResponseHeaders()
- );
- }
- }
- };
- };
-
- // Listen to events
- xhr.onload = callback();
- errorCallback = xhr.onerror = xhr.ontimeout = callback( "error" );
-
- // Support: IE 9 only
- // Use onreadystatechange to replace onabort
- // to handle uncaught aborts
- if ( xhr.onabort !== undefined ) {
- xhr.onabort = errorCallback;
- } else {
- xhr.onreadystatechange = function() {
-
- // Check readyState before timeout as it changes
- if ( xhr.readyState === 4 ) {
-
- // Allow onerror to be called first,
- // but that will not handle a native abort
- // Also, save errorCallback to a variable
- // as xhr.onerror cannot be accessed
- window.setTimeout( function() {
- if ( callback ) {
- errorCallback();
- }
- } );
- }
- };
- }
-
- // Create the abort callback
- callback = callback( "abort" );
-
- try {
-
- // Do send the request (this may raise an exception)
- xhr.send( options.hasContent && options.data || null );
- } catch ( e ) {
-
- // #14683: Only rethrow if this hasn't been notified as an error yet
- if ( callback ) {
- throw e;
- }
- }
- },
-
- abort: function() {
- if ( callback ) {
- callback();
- }
- }
- };
- }
-} );
-
-
-
-
-// Prevent auto-execution of _bin when no explicit dataType was provided (See gh-2432)
-jQuery.ajaxPrefilter( function( s ) {
- if ( s.crossDomain ) {
- s.contents.script = false;
- }
-} );
-
-// Install script dataType
-jQuery.ajaxSetup( {
- accepts: {
- script: "text/javascript, application/javascript, " +
- "application/ecmascript, application/x-ecmascript"
- },
- contents: {
- script: /\b(?:java|ecma)script\b/
- },
- converters: {
- "text script": function( text ) {
- jQuery.globalEval( text );
- return text;
- }
- }
-} );
-
-// Handle cache's special case and crossDomain
-jQuery.ajaxPrefilter( "script", function( s ) {
- if ( s.cache === undefined ) {
- s.cache = false;
- }
- if ( s.crossDomain ) {
- s.type = "GET";
- }
-} );
-
-// Bind script tag hack transport
-jQuery.ajaxTransport( "script", function( s ) {
-
- // This transport only deals with cross domain or forced-by-attrs requests
- if ( s.crossDomain || s.scriptAttrs ) {
- var script, callback;
- return {
- send: function( _, complete ) {
- script = jQuery( "<script>" )
- .attr( s.scriptAttrs || {} )
- .prop( { charset: s.scriptCharset, src: s.url } )
- .on( "load error", callback = function( evt ) {
- script.remove();
- callback = null;
- if ( evt ) {
- complete( evt.type === "error" ? 404 : 200, evt.type );
- }
- } );
-
- // Use native DOM manipulation to avoid our domManip AJAX trickery
- document.head.appendChild( script[ 0 ] );
- },
- abort: function() {
- if ( callback ) {
- callback();
- }
- }
- };
- }
-} );
-
-
-
-
-var oldCallbacks = [],
- rjsonp = /(=)\?(?=&|$)|\?\?/;
-
-// Default jsonp settings
-jQuery.ajaxSetup( {
- jsonp: "callback",
- jsonpCallback: function() {
- var callback = oldCallbacks.pop() || ( jQuery.expando + "_" + ( nonce.guid++ ) );
- this[ callback ] = true;
- return callback;
- }
-} );
-
-// Detect, normalize options and install callbacks for jsonp requests
-jQuery.ajaxPrefilter( "json jsonp", function( s, originalSettings, jqXHR ) {
-
- var callbackName, overwritten, responseContainer,
- jsonProp = s.jsonp !== false && ( rjsonp.test( s.url ) ?
- "url" :
- typeof s.data === "string" &&
- ( s.contentType || "" )
- .indexOf( "application/x-www-form-urlencoded" ) === 0 &&
- rjsonp.test( s.data ) && "data"
- );
-
- // Handle iff the expected data type is "jsonp" or we have a parameter to set
- if ( jsonProp || s.dataTypes[ 0 ] === "jsonp" ) {
-
- // Get callback name, remembering preexisting value associated with it
- callbackName = s.jsonpCallback = isFunction( s.jsonpCallback ) ?
- s.jsonpCallback() :
- s.jsonpCallback;
-
- // Insert callback into url or form data
- if ( jsonProp ) {
- s[ jsonProp ] = s[ jsonProp ].replace( rjsonp, "$1" + callbackName );
- } else if ( s.jsonp !== false ) {
- s.url += ( rquery.test( s.url ) ? "&" : "?" ) + s.jsonp + "=" + callbackName;
- }
-
- // Use data converter to retrieve json after script execution
- s.converters[ "script json" ] = function() {
- if ( !responseContainer ) {
- jQuery.error( callbackName + " was not called" );
- }
- return responseContainer[ 0 ];
- };
-
- // Force json dataType
- s.dataTypes[ 0 ] = "json";
-
- // Install callback
- overwritten = window[ callbackName ];
- window[ callbackName ] = function() {
- responseContainer = arguments;
- };
-
- // Clean-up function (fires after converters)
- jqXHR.always( function() {
-
- // If previous value didn't exist - remove it
- if ( overwritten === undefined ) {
- jQuery( window ).removeProp( callbackName );
-
- // Otherwise restore preexisting value
- } else {
- window[ callbackName ] = overwritten;
- }
-
- // Save back as free
- if ( s[ callbackName ] ) {
-
- // Make sure that re-using the options doesn't screw things around
- s.jsonpCallback = originalSettings.jsonpCallback;
-
- // Save the callback name for future use
- oldCallbacks.push( callbackName );
- }
-
- // Call if it was a function and we have a response
- if ( responseContainer && isFunction( overwritten ) ) {
- overwritten( responseContainer[ 0 ] );
- }
-
- responseContainer = overwritten = undefined;
- } );
-
- // Delegate to script
- return "script";
- }
-} );
-
-
-
-
-// Support: Safari 8 only
-// In Safari 8 documents created via document.implementation.createHTMLDocument
-// collapse sibling forms: the second one becomes a child of the first one.
-// Because of that, this security measure has to be disabled in Safari 8.
-// https://bugs.webkit.org/show_bug.cgi?id=137337
-support.createHTMLDocument = ( function() {
- var body = document.implementation.createHTMLDocument( "" ).body;
- body.innerHTML = "<form></form><form></form>";
- return body.childNodes.length === 2;
-} )();
-
-
-// Argument "data" should be string of html
-// context (optional): If specified, the fragment will be created in this context,
-// defaults to document
-// keepScripts (optional): If true, will include _bin passed in the html string
-jQuery.parseHTML = function( data, context, keepScripts ) {
- if ( typeof data !== "string" ) {
- return [];
- }
- if ( typeof context === "boolean" ) {
- keepScripts = context;
- context = false;
- }
-
- var base, parsed, scripts;
-
- if ( !context ) {
-
- // Stop _bin or inline event handlers from being executed immediately
- // by using document.implementation
- if ( support.createHTMLDocument ) {
- context = document.implementation.createHTMLDocument( "" );
-
- // Set the build href for the created document
- // so any parsed elements with URLs
- // are based on the document's URL (gh-2965)
- base = context.createElement( "base" );
- base.href = document.location.href;
- context.head.appendChild( base );
- } else {
- context = document;
- }
- }
-
- parsed = rsingleTag.exec( data );
- scripts = !keepScripts && [];
-
- // Single tag
- if ( parsed ) {
- return [ context.createElement( parsed[ 1 ] ) ];
- }
-
- parsed = buildFragment( [ data ], context, scripts );
-
- if ( scripts && scripts.length ) {
- jQuery( scripts ).remove();
- }
-
- return jQuery.merge( [], parsed.childNodes );
-};
-
-
-/**
- * Load a url into a page
- */
-jQuery.fn.load = function( url, params, callback ) {
- var selector, type, response,
- self = this,
- off = url.indexOf( " " );
-
- if ( off > -1 ) {
- selector = stripAndCollapse( url.slice( off ) );
- url = url.slice( 0, off );
- }
-
- // If it's a function
- if ( isFunction( params ) ) {
-
- // We assume that it's the callback
- callback = params;
- params = undefined;
-
- // Otherwise, build a param string
- } else if ( params && typeof params === "object" ) {
- type = "POST";
- }
-
- // If we have elements to modify, make the request
- if ( self.length > 0 ) {
- jQuery.ajax( {
- url: url,
-
- // If "type" variable is undefined, then "GET" method will be used.
- // Make value of this field explicit since
- // user can override it through ajaxSetup method
- type: type || "GET",
- dataType: "html",
- data: params
- } ).done( function( responseText ) {
-
- // Save response for use in complete callback
- response = arguments;
-
- self.html( selector ?
-
- // If a selector was specified, locate the right elements in a dummy div
- // Exclude _bin to avoid IE 'Permission Denied' errors
- jQuery( "<div>" ).append( jQuery.parseHTML( responseText ) ).find( selector ) :
-
- // Otherwise use the full result
- responseText );
-
- // If the request succeeds, this function gets "data", "status", "jqXHR"
- // but they are ignored because response was set above.
- // If it fails, this function gets "jqXHR", "status", "error"
- } ).always( callback && function( jqXHR, status ) {
- self.each( function() {
- callback.apply( this, response || [ jqXHR.responseText, status, jqXHR ] );
- } );
- } );
- }
-
- return this;
-};
-
-
-
-
-jQuery.expr.pseudos.animated = function( elem ) {
- return jQuery.grep( jQuery.timers, function( fn ) {
- return elem === fn.elem;
- } ).length;
-};
-
-
-
-
-jQuery.offset = {
- setOffset: function( elem, options, i ) {
- var curPosition, curLeft, curCSSTop, curTop, curOffset, curCSSLeft, calculatePosition,
- position = jQuery.css( elem, "position" ),
- curElem = jQuery( elem ),
- props = {};
-
- // Set position first, in-case top/left are set even on static elem
- if ( position === "static" ) {
- elem.style.position = "relative";
- }
-
- curOffset = curElem.offset();
- curCSSTop = jQuery.css( elem, "top" );
- curCSSLeft = jQuery.css( elem, "left" );
- calculatePosition = ( position === "absolute" || position === "fixed" ) &&
- ( curCSSTop + curCSSLeft ).indexOf( "auto" ) > -1;
-
- // Need to be able to calculate position if either
- // top or left is auto and position is either absolute or fixed
- if ( calculatePosition ) {
- curPosition = curElem.position();
- curTop = curPosition.top;
- curLeft = curPosition.left;
-
- } else {
- curTop = parseFloat( curCSSTop ) || 0;
- curLeft = parseFloat( curCSSLeft ) || 0;
- }
-
- if ( isFunction( options ) ) {
-
- // Use jQuery.extend here to allow modification of coordinates argument (gh-1848)
- options = options.call( elem, i, jQuery.extend( {}, curOffset ) );
- }
-
- if ( options.top != null ) {
- props.top = ( options.top - curOffset.top ) + curTop;
- }
- if ( options.left != null ) {
- props.left = ( options.left - curOffset.left ) + curLeft;
- }
-
- if ( "using" in options ) {
- options.using.call( elem, props );
-
- } else {
- if ( typeof props.top === "number" ) {
- props.top += "px";
- }
- if ( typeof props.left === "number" ) {
- props.left += "px";
- }
- curElem.css( props );
- }
- }
-};
-
-jQuery.fn.extend( {
-
- // offset() relates an element's border box to the document origin
- offset: function( options ) {
-
- // Preserve chaining for setter
- if ( arguments.length ) {
- return options === undefined ?
- this :
- this.each( function( i ) {
- jQuery.offset.setOffset( this, options, i );
- } );
- }
-
- var rect, win,
- elem = this[ 0 ];
-
- if ( !elem ) {
- return;
- }
-
- // Return zeros for disconnected and hidden (display: none) elements (gh-2310)
- // Support: IE <=11 only
- // Running getBoundingClientRect on a
- // disconnected node in IE throws an error
- if ( !elem.getClientRects().length ) {
- return { top: 0, left: 0 };
- }
-
- // Get document-relative position by adding viewport scroll to viewport-relative gBCR
- rect = elem.getBoundingClientRect();
- win = elem.ownerDocument.defaultView;
- return {
- top: rect.top + win.pageYOffset,
- left: rect.left + win.pageXOffset
- };
- },
-
- // position() relates an element's margin box to its offset parent's padding box
- // This corresponds to the behavior of CSS absolute positioning
- position: function() {
- if ( !this[ 0 ] ) {
- return;
- }
-
- var offsetParent, offset, doc,
- elem = this[ 0 ],
- parentOffset = { top: 0, left: 0 };
-
- // position:fixed elements are offset from the viewport, which itself always has zero offset
- if ( jQuery.css( elem, "position" ) === "fixed" ) {
-
- // Assume position:fixed implies availability of getBoundingClientRect
- offset = elem.getBoundingClientRect();
-
- } else {
- offset = this.offset();
-
- // Account for the *real* offset parent, which can be the document or its root element
- // when a statically positioned element is identified
- doc = elem.ownerDocument;
- offsetParent = elem.offsetParent || doc.documentElement;
- while ( offsetParent &&
- ( offsetParent === doc.body || offsetParent === doc.documentElement ) &&
- jQuery.css( offsetParent, "position" ) === "static" ) {
-
- offsetParent = offsetParent.parentNode;
- }
- if ( offsetParent && offsetParent !== elem && offsetParent.nodeType === 1 ) {
-
- // Incorporate borders into its offset, since they are outside its content origin
- parentOffset = jQuery( offsetParent ).offset();
- parentOffset.top += jQuery.css( offsetParent, "borderTopWidth", true );
- parentOffset.left += jQuery.css( offsetParent, "borderLeftWidth", true );
- }
- }
-
- // Subtract parent offsets and element margins
- return {
- top: offset.top - parentOffset.top - jQuery.css( elem, "marginTop", true ),
- left: offset.left - parentOffset.left - jQuery.css( elem, "marginLeft", true )
- };
- },
-
- // This method will return documentElement in the following cases:
- // 1) For the element inside the iframe without offsetParent, this method will return
- // documentElement of the parent window
- // 2) For the hidden or detached element
- // 3) For body or html element, i.e. in case of the html node - it will return itself
- //
- // but those exceptions were never presented as a real life use-cases
- // and might be considered as more preferable results.
- //
- // This logic, however, is not guaranteed and can change at any point in the future
- offsetParent: function() {
- return this.map( function() {
- var offsetParent = this.offsetParent;
-
- while ( offsetParent && jQuery.css( offsetParent, "position" ) === "static" ) {
- offsetParent = offsetParent.offsetParent;
- }
-
- return offsetParent || documentElement;
- } );
- }
-} );
-
-// Create scrollLeft and scrollTop methods
-jQuery.each( { scrollLeft: "pageXOffset", scrollTop: "pageYOffset" }, function( method, prop ) {
- var top = "pageYOffset" === prop;
-
- jQuery.fn[ method ] = function( val ) {
- return access( this, function( elem, method, val ) {
-
- // Coalesce documents and windows
- var win;
- if ( isWindow( elem ) ) {
- win = elem;
- } else if ( elem.nodeType === 9 ) {
- win = elem.defaultView;
- }
-
- if ( val === undefined ) {
- return win ? win[ prop ] : elem[ method ];
- }
-
- if ( win ) {
- win.scrollTo(
- !top ? val : win.pageXOffset,
- top ? val : win.pageYOffset
- );
-
- } else {
- elem[ method ] = val;
- }
- }, method, val, arguments.length );
- };
-} );
-
-// Support: Safari <=7 - 9.1, Chrome <=37 - 49
-// Add the top/left cssHooks using jQuery.fn.position
-// Webkit bug: https://bugs.webkit.org/show_bug.cgi?id=29084
-// Blink bug: https://bugs.chromium.org/p/chromium/issues/detail?id=589347
-// getComputedStyle returns percent when specified for top/left/bottom/right;
-// rather than make the css module depend on the offset module, just check for it here
-jQuery.each( [ "top", "left" ], function( _i, prop ) {
- jQuery.cssHooks[ prop ] = addGetHookIf( support.pixelPosition,
- function( elem, computed ) {
- if ( computed ) {
- computed = curCSS( elem, prop );
-
- // If curCSS returns percentage, fallback to offset
- return rnumnonpx.test( computed ) ?
- jQuery( elem ).position()[ prop ] + "px" :
- computed;
- }
- }
- );
-} );
-
-
-// Create innerHeight, innerWidth, height, width, outerHeight and outerWidth methods
-jQuery.each( { Height: "height", Width: "width" }, function( name, type ) {
- jQuery.each( { padding: "inner" + name, content: type, "": "outer" + name },
- function( defaultExtra, funcName ) {
-
- // Margin is only for outerHeight, outerWidth
- jQuery.fn[ funcName ] = function( margin, value ) {
- var chainable = arguments.length && ( defaultExtra || typeof margin !== "boolean" ),
- extra = defaultExtra || ( margin === true || value === true ? "margin" : "border" );
-
- return access( this, function( elem, type, value ) {
- var doc;
-
- if ( isWindow( elem ) ) {
-
- // $( window ).outerWidth/Height return w/h including scrollbars (gh-1729)
- return funcName.indexOf( "outer" ) === 0 ?
- elem[ "inner" + name ] :
- elem.document.documentElement[ "client" + name ];
- }
-
- // Get document width or height
- if ( elem.nodeType === 9 ) {
- doc = elem.documentElement;
-
- // Either scroll[Width/Height] or offset[Width/Height] or client[Width/Height],
- // whichever is greatest
- return Math.max(
- elem.body[ "scroll" + name ], doc[ "scroll" + name ],
- elem.body[ "offset" + name ], doc[ "offset" + name ],
- doc[ "client" + name ]
- );
- }
-
- return value === undefined ?
-
- // Get width or height on the element, requesting but not forcing parseFloat
- jQuery.css( elem, type, extra ) :
-
- // Set width or height on the element
- jQuery.style( elem, type, value, extra );
- }, type, chainable ? margin : undefined, chainable );
- };
- } );
-} );
-
-
-jQuery.each( [
- "ajaxStart",
- "ajaxStop",
- "ajaxComplete",
- "ajaxError",
- "ajaxSuccess",
- "ajaxSend"
-], function( _i, type ) {
- jQuery.fn[ type ] = function( fn ) {
- return this.on( type, fn );
- };
-} );
-
-
-
-
-jQuery.fn.extend( {
-
- bind: function( types, data, fn ) {
- return this.on( types, null, data, fn );
- },
- unbind: function( types, fn ) {
- return this.off( types, null, fn );
- },
-
- delegate: function( selector, types, data, fn ) {
- return this.on( types, selector, data, fn );
- },
- undelegate: function( selector, types, fn ) {
-
- // ( namespace ) or ( selector, types [, fn] )
- return arguments.length === 1 ?
- this.off( selector, "**" ) :
- this.off( types, selector || "**", fn );
- },
-
- hover: function( fnOver, fnOut ) {
- return this.mouseenter( fnOver ).mouseleave( fnOut || fnOver );
- }
-} );
-
-jQuery.each( ( "blur focus focusin focusout resize scroll click dblclick " +
- "mousedown mouseup mousemove mouseover mouseout mouseenter mouseleave " +
- "change select submit keydown keypress keyup contextmenu" ).split( " " ),
- function( _i, name ) {
-
- // Handle event binding
- jQuery.fn[ name ] = function( data, fn ) {
- return arguments.length > 0 ?
- this.on( name, null, data, fn ) :
- this.trigger( name );
- };
- } );
-
-
-
-
-// Support: Android <=4.0 only
-// Make sure we trim BOM and NBSP
-var rtrim = /^[\s\uFEFF\xA0]+|[\s\uFEFF\xA0]+$/g;
-
-// Bind a function to a context, optionally partially applying any
-// arguments.
-// jQuery.proxy is deprecated to promote standards (specifically Function#bind)
-// However, it is not slated for removal any time soon
-jQuery.proxy = function( fn, context ) {
- var tmp, args, proxy;
-
- if ( typeof context === "string" ) {
- tmp = fn[ context ];
- context = fn;
- fn = tmp;
- }
-
- // Quick check to determine if target is callable, in the spec
- // this throws a TypeError, but we will just return undefined.
- if ( !isFunction( fn ) ) {
- return undefined;
- }
-
- // Simulated bind
- args = slice.call( arguments, 2 );
- proxy = function() {
- return fn.apply( context || this, args.concat( slice.call( arguments ) ) );
- };
-
- // Set the guid of unique handler to the same of original handler, so it can be removed
- proxy.guid = fn.guid = fn.guid || jQuery.guid++;
-
- return proxy;
-};
-
-jQuery.holdReady = function( hold ) {
- if ( hold ) {
- jQuery.readyWait++;
- } else {
- jQuery.ready( true );
- }
-};
-jQuery.isArray = Array.isArray;
-jQuery.parseJSON = JSON.parse;
-jQuery.nodeName = nodeName;
-jQuery.isFunction = isFunction;
-jQuery.isWindow = isWindow;
-jQuery.camelCase = camelCase;
-jQuery.type = toType;
-
-jQuery.now = Date.now;
-
-jQuery.isNumeric = function( obj ) {
-
- // As of jQuery 3.0, isNumeric is limited to
- // strings and numbers (primitives or objects)
- // that can be coerced to finite numbers (gh-2662)
- var type = jQuery.type( obj );
- return ( type === "number" || type === "string" ) &&
-
- // parseFloat NaNs numeric-cast false positives ("")
- // ...but misinterprets leading-number strings, particularly hex literals ("0x...")
- // subtraction forces infinities to NaN
- !isNaN( obj - parseFloat( obj ) );
-};
-
-jQuery.trim = function( text ) {
- return text == null ?
- "" :
- ( text + "" ).replace( rtrim, "" );
-};
-
-
-
-// Register as a named AMD module, since jQuery can be concatenated with other
-// files that may use define, but not via a proper concatenation script that
-// understands anonymous AMD modules. A named AMD is safest and most robust
-// way to register. Lowercase jquery is used because AMD module names are
-// derived from file names, and jQuery is normally delivered in a lowercase
-// file name. Do this after creating the global so that if an AMD module wants
-// to call noConflict to hide this version of jQuery, it will work.
-
-// Note that for maximum portability, libraries that are not jQuery should
-// declare themselves as anonymous modules, and avoid setting a global if an
-// AMD loader is present. jQuery is a special case. For more information, see
-// https://github.com/jrburke/requirejs/wiki/Updating-existing-libraries#wiki-anon
-
-if ( typeof define === "function" && define.amd ) {
- define( "jquery", [], function() {
- return jQuery;
- } );
-}
-
-
-
-
-var
-
- // Map over jQuery in case of overwrite
- _jQuery = window.jQuery,
-
- // Map over the $ in case of overwrite
- _$ = window.$;
-
-jQuery.noConflict = function( deep ) {
- if ( window.$ === jQuery ) {
- window.$ = _$;
- }
-
- if ( deep && window.jQuery === jQuery ) {
- window.jQuery = _jQuery;
- }
-
- return jQuery;
-};
-
-// Expose jQuery and $ identifiers, even in AMD
-// (#7102#comment:10, https://github.com/jquery/jquery/pull/557)
-// and CommonJS for browser emulators (#13566)
-if ( typeof noGlobal === "undefined" ) {
- window.jQuery = window.$ = jQuery;
-}
-
-
-
-
-return jQuery;
-} );
diff --git a/public/_static/jquery.js b/public/_static/jquery.js
deleted file mode 100644
index b0614034ad3a95e4ae9f53c2b015eeb3e8d68bde..0000000000000000000000000000000000000000
--- a/public/_static/jquery.js
+++ /dev/null
@@ -1,2 +0,0 @@
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diff --git a/public/_static/language_data.js b/public/_static/language_data.js
deleted file mode 100644
index d2b4ee91b0da7308cf5693804779c7b18c26252a..0000000000000000000000000000000000000000
--- a/public/_static/language_data.js
+++ /dev/null
@@ -1,297 +0,0 @@
-/*
- * language_data.js
- * ~~~~~~~~~~~~~~~~
- *
- * This script contains the language-specific data used by searchtools.js,
- * namely the list of stopwords, stemmer, scorer and splitter.
- *
- * :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS.
- * :license: BSD, see LICENSE for details.
- *
- */
-
-var stopwords = ["a","and","are","as","at","be","but","by","for","if","in","into","is","it","near","no","not","of","on","or","such","that","the","their","then","there","these","they","this","to","was","will","with"];
-
-
-/* Non-minified version JS is _stemmer.js if file is provided */
-/**
- * Porter Stemmer
- */
-var Stemmer = function() {
-
- var step2list = {
- ational: 'ate',
- tional: 'tion',
- enci: 'ence',
- anci: 'ance',
- izer: 'ize',
- bli: 'ble',
- alli: 'al',
- entli: 'ent',
- eli: 'e',
- ousli: 'ous',
- ization: 'ize',
- ation: 'ate',
- ator: 'ate',
- alism: 'al',
- iveness: 'ive',
- fulness: 'ful',
- ousness: 'ous',
- aliti: 'al',
- iviti: 'ive',
- biliti: 'ble',
- logi: 'log'
- };
-
- var step3list = {
- icate: 'ic',
- ative: '',
- alize: 'al',
- iciti: 'ic',
- ical: 'ic',
- ful: '',
- ness: ''
- };
-
- var c = "[^aeiou]"; // consonant
- var v = "[aeiouy]"; // vowel
- var C = c + "[^aeiouy]*"; // consonant sequence
- var V = v + "[aeiou]*"; // vowel sequence
-
- var mgr0 = "^(" + C + ")?" + V + C; // [C]VC... is m>0
- var meq1 = "^(" + C + ")?" + V + C + "(" + V + ")?$"; // [C]VC[V] is m=1
- var mgr1 = "^(" + C + ")?" + V + C + V + C; // [C]VCVC... is m>1
- var s_v = "^(" + C + ")?" + v; // vowel in stem
-
- this.stemWord = function (w) {
- var stem;
- var suffix;
- var firstch;
- var origword = w;
-
- if (w.length < 3)
- return w;
-
- var re;
- var re2;
- var re3;
- var re4;
-
- firstch = w.substr(0,1);
- if (firstch == "y")
- w = firstch.toUpperCase() + w.substr(1);
-
- // Step 1a
- re = /^(.+?)(ss|i)es$/;
- re2 = /^(.+?)([^s])s$/;
-
- if (re.test(w))
- w = w.replace(re,"$1$2");
- else if (re2.test(w))
- w = w.replace(re2,"$1$2");
-
- // Step 1b
- re = /^(.+?)eed$/;
- re2 = /^(.+?)(ed|ing)$/;
- if (re.test(w)) {
- var fp = re.exec(w);
- re = new RegExp(mgr0);
- if (re.test(fp[1])) {
- re = /.$/;
- w = w.replace(re,"");
- }
- }
- else if (re2.test(w)) {
- var fp = re2.exec(w);
- stem = fp[1];
- re2 = new RegExp(s_v);
- if (re2.test(stem)) {
- w = stem;
- re2 = /(at|bl|iz)$/;
- re3 = new RegExp("([^aeiouylsz])\\1$");
- re4 = new RegExp("^" + C + v + "[^aeiouwxy]$");
- if (re2.test(w))
- w = w + "e";
- else if (re3.test(w)) {
- re = /.$/;
- w = w.replace(re,"");
- }
- else if (re4.test(w))
- w = w + "e";
- }
- }
-
- // Step 1c
- re = /^(.+?)y$/;
- if (re.test(w)) {
- var fp = re.exec(w);
- stem = fp[1];
- re = new RegExp(s_v);
- if (re.test(stem))
- w = stem + "i";
- }
-
- // Step 2
- re = /^(.+?)(ational|tional|enci|anci|izer|bli|alli|entli|eli|ousli|ization|ation|ator|alism|iveness|fulness|ousness|aliti|iviti|biliti|logi)$/;
- if (re.test(w)) {
- var fp = re.exec(w);
- stem = fp[1];
- suffix = fp[2];
- re = new RegExp(mgr0);
- if (re.test(stem))
- w = stem + step2list[suffix];
- }
-
- // Step 3
- re = /^(.+?)(icate|ative|alize|iciti|ical|ful|ness)$/;
- if (re.test(w)) {
- var fp = re.exec(w);
- stem = fp[1];
- suffix = fp[2];
- re = new RegExp(mgr0);
- if (re.test(stem))
- w = stem + step3list[suffix];
- }
-
- // Step 4
- re = /^(.+?)(al|ance|ence|er|ic|able|ible|ant|ement|ment|ent|ou|ism|ate|iti|ous|ive|ize)$/;
- re2 = /^(.+?)(s|t)(ion)$/;
- if (re.test(w)) {
- var fp = re.exec(w);
- stem = fp[1];
- re = new RegExp(mgr1);
- if (re.test(stem))
- w = stem;
- }
- else if (re2.test(w)) {
- var fp = re2.exec(w);
- stem = fp[1] + fp[2];
- re2 = new RegExp(mgr1);
- if (re2.test(stem))
- w = stem;
- }
-
- // Step 5
- re = /^(.+?)e$/;
- if (re.test(w)) {
- var fp = re.exec(w);
- stem = fp[1];
- re = new RegExp(mgr1);
- re2 = new RegExp(meq1);
- re3 = new RegExp("^" + C + v + "[^aeiouwxy]$");
- if (re.test(stem) || (re2.test(stem) && !(re3.test(stem))))
- w = stem;
- }
- re = /ll$/;
- re2 = new RegExp(mgr1);
- if (re.test(w) && re2.test(w)) {
- re = /.$/;
- w = w.replace(re,"");
- }
-
- // and turn initial Y back to y
- if (firstch == "y")
- w = firstch.toLowerCase() + w.substr(1);
- return w;
- }
-}
-
-
-
-
-
-var splitChars = (function() {
- var result = {};
- var singles = [96, 180, 187, 191, 215, 247, 749, 885, 903, 907, 909, 930, 1014, 1648,
- 1748, 1809, 2416, 2473, 2481, 2526, 2601, 2609, 2612, 2615, 2653, 2702,
- 2706, 2729, 2737, 2740, 2857, 2865, 2868, 2910, 2928, 2948, 2961, 2971,
- 2973, 3085, 3089, 3113, 3124, 3213, 3217, 3241, 3252, 3295, 3341, 3345,
- 3369, 3506, 3516, 3633, 3715, 3721, 3736, 3744, 3748, 3750, 3756, 3761,
- 3781, 3912, 4239, 4347, 4681, 4695, 4697, 4745, 4785, 4799, 4801, 4823,
- 4881, 5760, 5901, 5997, 6313, 7405, 8024, 8026, 8028, 8030, 8117, 8125,
- 8133, 8181, 8468, 8485, 8487, 8489, 8494, 8527, 11311, 11359, 11687, 11695,
- 11703, 11711, 11719, 11727, 11735, 12448, 12539, 43010, 43014, 43019, 43587,
- 43696, 43713, 64286, 64297, 64311, 64317, 64319, 64322, 64325, 65141];
- var i, j, start, end;
- for (i = 0; i < singles.length; i++) {
- result[singles[i]] = true;
- }
- var ranges = [[0, 47], [58, 64], [91, 94], [123, 169], [171, 177], [182, 184], [706, 709],
- [722, 735], [741, 747], [751, 879], [888, 889], [894, 901], [1154, 1161],
- [1318, 1328], [1367, 1368], [1370, 1376], [1416, 1487], [1515, 1519], [1523, 1568],
- [1611, 1631], [1642, 1645], [1750, 1764], [1767, 1773], [1789, 1790], [1792, 1807],
- [1840, 1868], [1958, 1968], [1970, 1983], [2027, 2035], [2038, 2041], [2043, 2047],
- [2070, 2073], [2075, 2083], [2085, 2087], [2089, 2307], [2362, 2364], [2366, 2383],
- [2385, 2391], [2402, 2405], [2419, 2424], [2432, 2436], [2445, 2446], [2449, 2450],
- [2483, 2485], [2490, 2492], [2494, 2509], [2511, 2523], [2530, 2533], [2546, 2547],
- [2554, 2564], [2571, 2574], [2577, 2578], [2618, 2648], [2655, 2661], [2672, 2673],
- [2677, 2692], [2746, 2748], [2750, 2767], [2769, 2783], [2786, 2789], [2800, 2820],
- [2829, 2830], [2833, 2834], [2874, 2876], [2878, 2907], [2914, 2917], [2930, 2946],
- [2955, 2957], [2966, 2968], [2976, 2978], [2981, 2983], [2987, 2989], [3002, 3023],
- [3025, 3045], [3059, 3076], [3130, 3132], [3134, 3159], [3162, 3167], [3170, 3173],
- [3184, 3191], [3199, 3204], [3258, 3260], [3262, 3293], [3298, 3301], [3312, 3332],
- [3386, 3388], [3390, 3423], [3426, 3429], [3446, 3449], [3456, 3460], [3479, 3481],
- [3518, 3519], [3527, 3584], [3636, 3647], [3655, 3663], [3674, 3712], [3717, 3718],
- [3723, 3724], [3726, 3731], [3752, 3753], [3764, 3772], [3774, 3775], [3783, 3791],
- [3802, 3803], [3806, 3839], [3841, 3871], [3892, 3903], [3949, 3975], [3980, 4095],
- [4139, 4158], [4170, 4175], [4182, 4185], [4190, 4192], [4194, 4196], [4199, 4205],
- [4209, 4212], [4226, 4237], [4250, 4255], [4294, 4303], [4349, 4351], [4686, 4687],
- [4702, 4703], [4750, 4751], [4790, 4791], [4806, 4807], [4886, 4887], [4955, 4968],
- [4989, 4991], [5008, 5023], [5109, 5120], [5741, 5742], [5787, 5791], [5867, 5869],
- [5873, 5887], [5906, 5919], [5938, 5951], [5970, 5983], [6001, 6015], [6068, 6102],
- [6104, 6107], [6109, 6111], [6122, 6127], [6138, 6159], [6170, 6175], [6264, 6271],
- [6315, 6319], [6390, 6399], [6429, 6469], [6510, 6511], [6517, 6527], [6572, 6592],
- [6600, 6607], [6619, 6655], [6679, 6687], [6741, 6783], [6794, 6799], [6810, 6822],
- [6824, 6916], [6964, 6980], [6988, 6991], [7002, 7042], [7073, 7085], [7098, 7167],
- [7204, 7231], [7242, 7244], [7294, 7400], [7410, 7423], [7616, 7679], [7958, 7959],
- [7966, 7967], [8006, 8007], [8014, 8015], [8062, 8063], [8127, 8129], [8141, 8143],
- [8148, 8149], [8156, 8159], [8173, 8177], [8189, 8303], [8306, 8307], [8314, 8318],
- [8330, 8335], [8341, 8449], [8451, 8454], [8456, 8457], [8470, 8472], [8478, 8483],
- [8506, 8507], [8512, 8516], [8522, 8525], [8586, 9311], [9372, 9449], [9472, 10101],
- [10132, 11263], [11493, 11498], [11503, 11516], [11518, 11519], [11558, 11567],
- [11622, 11630], [11632, 11647], [11671, 11679], [11743, 11822], [11824, 12292],
- [12296, 12320], [12330, 12336], [12342, 12343], [12349, 12352], [12439, 12444],
- [12544, 12548], [12590, 12592], [12687, 12689], [12694, 12703], [12728, 12783],
- [12800, 12831], [12842, 12880], [12896, 12927], [12938, 12976], [12992, 13311],
- [19894, 19967], [40908, 40959], [42125, 42191], [42238, 42239], [42509, 42511],
- [42540, 42559], [42592, 42593], [42607, 42622], [42648, 42655], [42736, 42774],
- [42784, 42785], [42889, 42890], [42893, 43002], [43043, 43055], [43062, 43071],
- [43124, 43137], [43188, 43215], [43226, 43249], [43256, 43258], [43260, 43263],
- [43302, 43311], [43335, 43359], [43389, 43395], [43443, 43470], [43482, 43519],
- [43561, 43583], [43596, 43599], [43610, 43615], [43639, 43641], [43643, 43647],
- [43698, 43700], [43703, 43704], [43710, 43711], [43715, 43738], [43742, 43967],
- [44003, 44015], [44026, 44031], [55204, 55215], [55239, 55242], [55292, 55295],
- [57344, 63743], [64046, 64047], [64110, 64111], [64218, 64255], [64263, 64274],
- [64280, 64284], [64434, 64466], [64830, 64847], [64912, 64913], [64968, 65007],
- [65020, 65135], [65277, 65295], [65306, 65312], [65339, 65344], [65371, 65381],
- [65471, 65473], [65480, 65481], [65488, 65489], [65496, 65497]];
- for (i = 0; i < ranges.length; i++) {
- start = ranges[i][0];
- end = ranges[i][1];
- for (j = start; j <= end; j++) {
- result[j] = true;
- }
- }
- return result;
-})();
-
-function splitQuery(query) {
- var result = [];
- var start = -1;
- for (var i = 0; i < query.length; i++) {
- if (splitChars[query.charCodeAt(i)]) {
- if (start !== -1) {
- result.push(query.slice(start, i));
- start = -1;
- }
- } else if (start === -1) {
- start = i;
- }
- }
- if (start !== -1) {
- result.push(query.slice(start));
- }
- return result;
-}
-
-
diff --git a/public/_static/minus.png b/public/_static/minus.png
deleted file mode 100644
index d96755fdaf8bb2214971e0db9c1fd3077d7c419d..0000000000000000000000000000000000000000
Binary files a/public/_static/minus.png and /dev/null differ
diff --git a/public/_static/plus.png b/public/_static/plus.png
deleted file mode 100644
index 7107cec93a979b9a5f64843235a16651d563ce2d..0000000000000000000000000000000000000000
Binary files a/public/_static/plus.png and /dev/null differ
diff --git a/public/_static/pygments.css b/public/_static/pygments.css
deleted file mode 100644
index 631bc92ffa57bc202c1dac78d96bbeaea624571e..0000000000000000000000000000000000000000
--- a/public/_static/pygments.css
+++ /dev/null
@@ -1,69 +0,0 @@
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-.highlight { background: #f8f8f8; }
-.highlight .c { color: #408080; font-style: italic } /* Comment */
-.highlight .err { border: 1px solid #FF0000 } /* Error */
-.highlight .k { color: #008000; font-weight: bold } /* Keyword */
-.highlight .o { color: #666666 } /* Operator */
-.highlight .ch { color: #408080; font-style: italic } /* Comment.Hashbang */
-.highlight .cm { color: #408080; font-style: italic } /* Comment.Multiline */
-.highlight .cp { color: #BC7A00 } /* Comment.Preproc */
-.highlight .cpf { color: #408080; font-style: italic } /* Comment.PreprocFile */
-.highlight .c1 { color: #408080; font-style: italic } /* Comment.Single */
-.highlight .cs { color: #408080; font-style: italic } /* Comment.Special */
-.highlight .gd { color: #A00000 } /* Generic.Deleted */
-.highlight .ge { font-style: italic } /* Generic.Emph */
-.highlight .gr { color: #FF0000 } /* Generic.Error */
-.highlight .gh { color: #000080; font-weight: bold } /* Generic.Heading */
-.highlight .gi { color: #00A000 } /* Generic.Inserted */
-.highlight .go { color: #888888 } /* Generic.Output */
-.highlight .gp { color: #000080; font-weight: bold } /* Generic.Prompt */
-.highlight .gs { font-weight: bold } /* Generic.Strong */
-.highlight .gu { color: #800080; font-weight: bold } /* Generic.Subheading */
-.highlight .gt { color: #0044DD } /* Generic.Traceback */
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-.highlight .m { color: #666666 } /* Literal.Number */
-.highlight .s { color: #BA2121 } /* Literal.String */
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-.highlight .w { color: #bbbbbb } /* Text.Whitespace */
-.highlight .mb { color: #666666 } /* Literal.Number.Bin */
-.highlight .mf { color: #666666 } /* Literal.Number.Float */
-.highlight .mh { color: #666666 } /* Literal.Number.Hex */
-.highlight .mi { color: #666666 } /* Literal.Number.Integer */
-.highlight .mo { color: #666666 } /* Literal.Number.Oct */
-.highlight .sa { color: #BA2121 } /* Literal.String.Affix */
-.highlight .sb { color: #BA2121 } /* Literal.String.Backtick */
-.highlight .sc { color: #BA2121 } /* Literal.String.Char */
-.highlight .dl { color: #BA2121 } /* Literal.String.Delimiter */
-.highlight .sd { color: #BA2121; font-style: italic } /* Literal.String.Doc */
-.highlight .s2 { color: #BA2121 } /* Literal.String.Double */
-.highlight .se { color: #BB6622; font-weight: bold } /* Literal.String.Escape */
-.highlight .sh { color: #BA2121 } /* Literal.String.Heredoc */
-.highlight .si { color: #BB6688; font-weight: bold } /* Literal.String.Interpol */
-.highlight .sx { color: #008000 } /* Literal.String.Other */
-.highlight .sr { color: #BB6688 } /* Literal.String.Regex */
-.highlight .s1 { color: #BA2121 } /* Literal.String.Single */
-.highlight .ss { color: #19177C } /* Literal.String.Symbol */
-.highlight .bp { color: #008000 } /* Name.Builtin.Pseudo */
-.highlight .fm { color: #0000FF } /* Name.Function.Magic */
-.highlight .vc { color: #19177C } /* Name.Variable.Class */
-.highlight .vg { color: #19177C } /* Name.Variable.Global */
-.highlight .vi { color: #19177C } /* Name.Variable.Instance */
-.highlight .vm { color: #19177C } /* Name.Variable.Magic */
-.highlight .il { color: #666666 } /* Literal.Number.Integer.Long */
\ No newline at end of file
diff --git a/public/_static/searchtools.js b/public/_static/searchtools.js
deleted file mode 100644
index ab56499655111429619df63a1dc6687646d22a3e..0000000000000000000000000000000000000000
--- a/public/_static/searchtools.js
+++ /dev/null
@@ -1,515 +0,0 @@
-/*
- * searchtools.js
- * ~~~~~~~~~~~~~~~~
- *
- * Sphinx JavaScript utilities for the full-text search.
- *
- * :copyright: Copyright 2007-2020 by the Sphinx team, see AUTHORS.
- * :license: BSD, see LICENSE for details.
- *
- */
-
-if (!Scorer) {
- /**
- * Simple result scoring code.
- */
- var Scorer = {
- // Implement the following function to further tweak the score for each result
- // The function takes a result array [filename, title, anchor, descr, score]
- // and returns the new score.
- /*
- score: function(result) {
- return result[4];
- },
- */
-
- // query matches the full name of an object
- objNameMatch: 11,
- // or matches in the last dotted part of the object name
- objPartialMatch: 6,
- // Additive scores depending on the priority of the object
- objPrio: {0: 15, // used to be importantResults
- 1: 5, // used to be objectResults
- 2: -5}, // used to be unimportantResults
- // Used when the priority is not in the mapping.
- objPrioDefault: 0,
-
- // query found in title
- title: 15,
- partialTitle: 7,
- // query found in terms
- term: 5,
- partialTerm: 2
- };
-}
-
-if (!splitQuery) {
- function splitQuery(query) {
- return query.split(/\s+/);
- }
-}
-
-/**
- * Search Module
- */
-var Search = {
-
- _index : null,
- _queued_query : null,
- _pulse_status : -1,
-
- htmlToText : function(htmlString) {
- var htmlElement = document.createElement('span');
- htmlElement.innerHTML = htmlString;
- $(htmlElement).find('.headerlink').remove();
- docContent = $(htmlElement).find('[role=main]')[0];
- if(docContent === undefined) {
- console.warn("Content block not found. Sphinx search tries to obtain it " +
- "via '[role=main]'. Could you check your theme or template.");
- return "";
- }
- return docContent.textContent || docContent.innerText;
- },
-
- init : function() {
- var params = $.getQueryParameters();
- if (params.q) {
- var query = params.q[0];
- $('input[name="q"]')[0].value = query;
- this.performSearch(query);
- }
- },
-
- loadIndex : function(url) {
- $.ajax({type: "GET", url: url, data: null,
- dataType: "script", cache: true,
- complete: function(jqxhr, textstatus) {
- if (textstatus != "success") {
- document.getElementById("searchindexloader").src = url;
- }
- }});
- },
-
- setIndex : function(index) {
- var q;
- this._index = index;
- if ((q = this._queued_query) !== null) {
- this._queued_query = null;
- Search.query(q);
- }
- },
-
- hasIndex : function() {
- return this._index !== null;
- },
-
- deferQuery : function(query) {
- this._queued_query = query;
- },
-
- stopPulse : function() {
- this._pulse_status = 0;
- },
-
- startPulse : function() {
- if (this._pulse_status >= 0)
- return;
- function pulse() {
- var i;
- Search._pulse_status = (Search._pulse_status + 1) % 4;
- var dotString = '';
- for (i = 0; i < Search._pulse_status; i++)
- dotString += '.';
- Search.dots.text(dotString);
- if (Search._pulse_status > -1)
- window.setTimeout(pulse, 500);
- }
- pulse();
- },
-
- /**
- * perform a search for something (or wait until index is loaded)
- */
- performSearch : function(query) {
- // create the required interface elements
- this.out = $('#search-results');
- this.title = $('<h2>' + _('Searching') + '</h2>').appendTo(this.out);
- this.dots = $('<span></span>').appendTo(this.title);
- this.status = $('<p class="search-summary"> </p>').appendTo(this.out);
- this.output = $('<ul class="search"/>').appendTo(this.out);
-
- $('#search-progress').text(_('Preparing search...'));
- this.startPulse();
-
- // index already loaded, the browser was quick!
- if (this.hasIndex())
- this.query(query);
- else
- this.deferQuery(query);
- },
-
- /**
- * execute search (requires search index to be loaded)
- */
- query : function(query) {
- var i;
-
- // stem the searchterms and add them to the correct list
- var stemmer = new Stemmer();
- var searchterms = [];
- var excluded = [];
- var hlterms = [];
- var tmp = splitQuery(query);
- var objectterms = [];
- for (i = 0; i < tmp.length; i++) {
- if (tmp[i] !== "") {
- objectterms.push(tmp[i].toLowerCase());
- }
-
- if ($u.indexOf(stopwords, tmp[i].toLowerCase()) != -1 || tmp[i].match(/^\d+$/) ||
- tmp[i] === "") {
- // skip this "word"
- continue;
- }
- // stem the word
- var word = stemmer.stemWord(tmp[i].toLowerCase());
- // prevent stemmer from cutting word smaller than two chars
- if(word.length < 3 && tmp[i].length >= 3) {
- word = tmp[i];
- }
- var toAppend;
- // select the correct list
- if (word[0] == '-') {
- toAppend = excluded;
- word = word.substr(1);
- }
- else {
- toAppend = searchterms;
- hlterms.push(tmp[i].toLowerCase());
- }
- // only add if not already in the list
- if (!$u.contains(toAppend, word))
- toAppend.push(word);
- }
- var highlightstring = '?highlight=' + $.urlencode(hlterms.join(" "));
-
- // console.debug('SEARCH: searching for:');
- // console.info('required: ', searchterms);
- // console.info('excluded: ', excluded);
-
- // prepare search
- var terms = this._index.terms;
- var titleterms = this._index.titleterms;
-
- // array of [filename, title, anchor, descr, score]
- var results = [];
- $('#search-progress').empty();
-
- // lookup as object
- for (i = 0; i < objectterms.length; i++) {
- var others = [].concat(objectterms.slice(0, i),
- objectterms.slice(i+1, objectterms.length));
- results = results.concat(this.performObjectSearch(objectterms[i], others));
- }
-
- // lookup as search terms in fulltext
- results = results.concat(this.performTermsSearch(searchterms, excluded, terms, titleterms));
-
- // let the scorer override scores with a custom scoring function
- if (Scorer.score) {
- for (i = 0; i < results.length; i++)
- results[i][4] = Scorer.score(results[i]);
- }
-
- // now sort the results by score (in opposite order of appearance, since the
- // display function below uses pop() to retrieve items) and then
- // alphabetically
- results.sort(function(a, b) {
- var left = a[4];
- var right = b[4];
- if (left > right) {
- return 1;
- } else if (left < right) {
- return -1;
- } else {
- // same score: sort alphabetically
- left = a[1].toLowerCase();
- right = b[1].toLowerCase();
- return (left > right) ? -1 : ((left < right) ? 1 : 0);
- }
- });
-
- // for debugging
- //Search.lastresults = results.slice(); // a copy
- //console.info('search results:', Search.lastresults);
-
- // print the results
- var resultCount = results.length;
- function displayNextItem() {
- // results left, load the summary and display it
- if (results.length) {
- var item = results.pop();
- var listItem = $('<li style="display:none"></li>');
- var requestUrl = "";
- var linkUrl = "";
- if (DOCUMENTATION_OPTIONS.BUILDER === 'dirhtml') {
- // dirhtml builder
- var dirname = item[0] + '/';
- if (dirname.match(/\/index\/$/)) {
- dirname = dirname.substring(0, dirname.length-6);
- } else if (dirname == 'index/') {
- dirname = '';
- }
- requestUrl = DOCUMENTATION_OPTIONS.URL_ROOT + dirname;
- linkUrl = requestUrl;
-
- } else {
- // normal html builders
- requestUrl = DOCUMENTATION_OPTIONS.URL_ROOT + item[0] + DOCUMENTATION_OPTIONS.FILE_SUFFIX;
- linkUrl = item[0] + DOCUMENTATION_OPTIONS.LINK_SUFFIX;
- }
- listItem.append($('<a/>').attr('href',
- linkUrl +
- highlightstring + item[2]).html(item[1]));
- if (item[3]) {
- listItem.append($('<span> (' + item[3] + ')</span>'));
- Search.output.append(listItem);
- listItem.slideDown(5, function() {
- displayNextItem();
- });
- } else if (DOCUMENTATION_OPTIONS.HAS_SOURCE) {
- $.ajax({url: requestUrl,
- dataType: "text",
- complete: function(jqxhr, textstatus) {
- var data = jqxhr.responseText;
- if (data !== '' && data !== undefined) {
- listItem.append(Search.makeSearchSummary(data, searchterms, hlterms));
- }
- Search.output.append(listItem);
- listItem.slideDown(5, function() {
- displayNextItem();
- });
- }});
- } else {
- // no source available, just display title
- Search.output.append(listItem);
- listItem.slideDown(5, function() {
- displayNextItem();
- });
- }
- }
- // search finished, update title and status message
- else {
- Search.stopPulse();
- Search.title.text(_('Search Results'));
- if (!resultCount)
- Search.status.text(_('Your search did not match any documents. Please make sure that all words are spelled correctly and that you\'ve selected enough categories.'));
- else
- Search.status.text(_('Search finished, found %s page(s) matching the search query.').replace('%s', resultCount));
- Search.status.fadeIn(500);
- }
- }
- displayNextItem();
- },
-
- /**
- * search for object names
- */
- performObjectSearch : function(object, otherterms) {
- var filenames = this._index.filenames;
- var docnames = this._index.docnames;
- var objects = this._index.objects;
- var objnames = this._index.objnames;
- var titles = this._index.titles;
-
- var i;
- var results = [];
-
- for (var prefix in objects) {
- for (var name in objects[prefix]) {
- var fullname = (prefix ? prefix + '.' : '') + name;
- var fullnameLower = fullname.toLowerCase()
- if (fullnameLower.indexOf(object) > -1) {
- var score = 0;
- var parts = fullnameLower.split('.');
- // check for different match types: exact matches of full name or
- // "last name" (i.e. last dotted part)
- if (fullnameLower == object || parts[parts.length - 1] == object) {
- score += Scorer.objNameMatch;
- // matches in last name
- } else if (parts[parts.length - 1].indexOf(object) > -1) {
- score += Scorer.objPartialMatch;
- }
- var match = objects[prefix][name];
- var objname = objnames[match[1]][2];
- var title = titles[match[0]];
- // If more than one term searched for, we require other words to be
- // found in the name/title/description
- if (otherterms.length > 0) {
- var haystack = (prefix + ' ' + name + ' ' +
- objname + ' ' + title).toLowerCase();
- var allfound = true;
- for (i = 0; i < otherterms.length; i++) {
- if (haystack.indexOf(otherterms[i]) == -1) {
- allfound = false;
- break;
- }
- }
- if (!allfound) {
- continue;
- }
- }
- var descr = objname + _(', in ') + title;
-
- var anchor = match[3];
- if (anchor === '')
- anchor = fullname;
- else if (anchor == '-')
- anchor = objnames[match[1]][1] + '-' + fullname;
- // add custom score for some objects according to scorer
- if (Scorer.objPrio.hasOwnProperty(match[2])) {
- score += Scorer.objPrio[match[2]];
- } else {
- score += Scorer.objPrioDefault;
- }
- results.push([docnames[match[0]], fullname, '#'+anchor, descr, score, filenames[match[0]]]);
- }
- }
- }
-
- return results;
- },
-
- /**
- * search for full-text terms in the index
- */
- performTermsSearch : function(searchterms, excluded, terms, titleterms) {
- var docnames = this._index.docnames;
- var filenames = this._index.filenames;
- var titles = this._index.titles;
-
- var i, j, file;
- var fileMap = {};
- var scoreMap = {};
- var results = [];
-
- // perform the search on the required terms
- for (i = 0; i < searchterms.length; i++) {
- var word = searchterms[i];
- var files = [];
- var _o = [
- {files: terms[word], score: Scorer.term},
- {files: titleterms[word], score: Scorer.title}
- ];
- // add support for partial matches
- if (word.length > 2) {
- for (var w in terms) {
- if (w.match(word) && !terms[word]) {
- _o.push({files: terms[w], score: Scorer.partialTerm})
- }
- }
- for (var w in titleterms) {
- if (w.match(word) && !titleterms[word]) {
- _o.push({files: titleterms[w], score: Scorer.partialTitle})
- }
- }
- }
-
- // no match but word was a required one
- if ($u.every(_o, function(o){return o.files === undefined;})) {
- break;
- }
- // found search word in contents
- $u.each(_o, function(o) {
- var _files = o.files;
- if (_files === undefined)
- return
-
- if (_files.length === undefined)
- _files = [_files];
- files = files.concat(_files);
-
- // set score for the word in each file to Scorer.term
- for (j = 0; j < _files.length; j++) {
- file = _files[j];
- if (!(file in scoreMap))
- scoreMap[file] = {};
- scoreMap[file][word] = o.score;
- }
- });
-
- // create the mapping
- for (j = 0; j < files.length; j++) {
- file = files[j];
- if (file in fileMap && fileMap[file].indexOf(word) === -1)
- fileMap[file].push(word);
- else
- fileMap[file] = [word];
- }
- }
-
- // now check if the files don't contain excluded terms
- for (file in fileMap) {
- var valid = true;
-
- // check if all requirements are matched
- var filteredTermCount = // as search terms with length < 3 are discarded: ignore
- searchterms.filter(function(term){return term.length > 2}).length
- if (
- fileMap[file].length != searchterms.length &&
- fileMap[file].length != filteredTermCount
- ) continue;
-
- // ensure that none of the excluded terms is in the search result
- for (i = 0; i < excluded.length; i++) {
- if (terms[excluded[i]] == file ||
- titleterms[excluded[i]] == file ||
- $u.contains(terms[excluded[i]] || [], file) ||
- $u.contains(titleterms[excluded[i]] || [], file)) {
- valid = false;
- break;
- }
- }
-
- // if we have still a valid result we can add it to the result list
- if (valid) {
- // select one (max) score for the file.
- // for better ranking, we should calculate ranking by using words statistics like basic tf-idf...
- var score = $u.max($u.map(fileMap[file], function(w){return scoreMap[file][w]}));
- results.push([docnames[file], titles[file], '', null, score, filenames[file]]);
- }
- }
- return results;
- },
-
- /**
- * helper function to return a node containing the
- * search summary for a given text. keywords is a list
- * of stemmed words, hlwords is the list of normal, unstemmed
- * words. the first one is used to find the occurrence, the
- * latter for highlighting it.
- */
- makeSearchSummary : function(htmlText, keywords, hlwords) {
- var text = Search.htmlToText(htmlText);
- var textLower = text.toLowerCase();
- var start = 0;
- $.each(keywords, function() {
- var i = textLower.indexOf(this.toLowerCase());
- if (i > -1)
- start = i;
- });
- start = Math.max(start - 120, 0);
- var excerpt = ((start > 0) ? '...' : '') +
- $.trim(text.substr(start, 240)) +
- ((start + 240 - text.length) ? '...' : '');
- var rv = $('<div class="context"></div>').text(excerpt);
- $.each(hlwords, function() {
- rv = rv.highlightText(this, 'highlighted');
- });
- return rv;
- }
-};
-
-$(document).ready(function() {
- Search.init();
-});
diff --git a/public/_static/underscore-1.3.1.js b/public/_static/underscore-1.3.1.js
deleted file mode 100644
index 208d4cd890c3183d946092ebe982738ade565061..0000000000000000000000000000000000000000
--- a/public/_static/underscore-1.3.1.js
+++ /dev/null
@@ -1,999 +0,0 @@
-// Underscore.js 1.3.1
-// (c) 2009-2012 Jeremy Ashkenas, DocumentCloud Inc.
-// Underscore is freely distributable under the MIT license.
-// Portions of Underscore are inspired or borrowed from Prototype,
-// Oliver Steele's Functional, and John Resig's Micro-Templating.
-// For all details and documentation:
-// http://documentcloud.github.com/underscore
-
-(function() {
-
- // Baseline setup
- // --------------
-
- // Establish the root object, `window` in the browser, or `global` on the server.
- var root = this;
-
- // Save the previous value of the `_` variable.
- var previousUnderscore = root._;
-
- // Establish the object that gets returned to break out of a loop iteration.
- var breaker = {};
-
- // Save bytes in the minified (but not gzipped) version:
- var ArrayProto = Array.prototype, ObjProto = Object.prototype, FuncProto = Function.prototype;
-
- // Create quick reference variables for speed access to core prototypes.
- var slice = ArrayProto.slice,
- unshift = ArrayProto.unshift,
- toString = ObjProto.toString,
- hasOwnProperty = ObjProto.hasOwnProperty;
-
- // All **ECMAScript 5** native function implementations that we hope to use
- // are declared here.
- var
- nativeForEach = ArrayProto.forEach,
- nativeMap = ArrayProto.map,
- nativeReduce = ArrayProto.reduce,
- nativeReduceRight = ArrayProto.reduceRight,
- nativeFilter = ArrayProto.filter,
- nativeEvery = ArrayProto.every,
- nativeSome = ArrayProto.some,
- nativeIndexOf = ArrayProto.indexOf,
- nativeLastIndexOf = ArrayProto.lastIndexOf,
- nativeIsArray = Array.isArray,
- nativeKeys = Object.keys,
- nativeBind = FuncProto.bind;
-
- // Create a safe reference to the Underscore object for use below.
- var _ = function(obj) { return new wrapper(obj); };
-
- // Export the Underscore object for **Node.js**, with
- // backwards-compatibility for the old `require()` API. If we're in
- // the browser, add `_` as a global object via a string identifier,
- // for Closure Compiler "advanced" mode.
- if (typeof exports !== 'undefined') {
- if (typeof module !== 'undefined' && module.exports) {
- exports = module.exports = _;
- }
- exports._ = _;
- } else {
- root['_'] = _;
- }
-
- // Current version.
- _.VERSION = '1.3.1';
-
- // Collection Functions
- // --------------------
-
- // The cornerstone, an `each` implementation, aka `forEach`.
- // Handles objects with the built-in `forEach`, arrays, and raw objects.
- // Delegates to **ECMAScript 5**'s native `forEach` if available.
- var each = _.each = _.forEach = function(obj, iterator, context) {
- if (obj == null) return;
- if (nativeForEach && obj.forEach === nativeForEach) {
- obj.forEach(iterator, context);
- } else if (obj.length === +obj.length) {
- for (var i = 0, l = obj.length; i < l; i++) {
- if (i in obj && iterator.call(context, obj[i], i, obj) === breaker) return;
- }
- } else {
- for (var key in obj) {
- if (_.has(obj, key)) {
- if (iterator.call(context, obj[key], key, obj) === breaker) return;
- }
- }
- }
- };
-
- // Return the results of applying the iterator to each element.
- // Delegates to **ECMAScript 5**'s native `map` if available.
- _.map = _.collect = function(obj, iterator, context) {
- var results = [];
- if (obj == null) return results;
- if (nativeMap && obj.map === nativeMap) return obj.map(iterator, context);
- each(obj, function(value, index, list) {
- results[results.length] = iterator.call(context, value, index, list);
- });
- if (obj.length === +obj.length) results.length = obj.length;
- return results;
- };
-
- // **Reduce** builds up a single result from a list of values, aka `inject`,
- // or `foldl`. Delegates to **ECMAScript 5**'s native `reduce` if available.
- _.reduce = _.foldl = _.inject = function(obj, iterator, memo, context) {
- var initial = arguments.length > 2;
- if (obj == null) obj = [];
- if (nativeReduce && obj.reduce === nativeReduce) {
- if (context) iterator = _.bind(iterator, context);
- return initial ? obj.reduce(iterator, memo) : obj.reduce(iterator);
- }
- each(obj, function(value, index, list) {
- if (!initial) {
- memo = value;
- initial = true;
- } else {
- memo = iterator.call(context, memo, value, index, list);
- }
- });
- if (!initial) throw new TypeError('Reduce of empty array with no initial value');
- return memo;
- };
-
- // The right-associative version of reduce, also known as `foldr`.
- // Delegates to **ECMAScript 5**'s native `reduceRight` if available.
- _.reduceRight = _.foldr = function(obj, iterator, memo, context) {
- var initial = arguments.length > 2;
- if (obj == null) obj = [];
- if (nativeReduceRight && obj.reduceRight === nativeReduceRight) {
- if (context) iterator = _.bind(iterator, context);
- return initial ? obj.reduceRight(iterator, memo) : obj.reduceRight(iterator);
- }
- var reversed = _.toArray(obj).reverse();
- if (context && !initial) iterator = _.bind(iterator, context);
- return initial ? _.reduce(reversed, iterator, memo, context) : _.reduce(reversed, iterator);
- };
-
- // Return the first value which passes a truth test. Aliased as `detect`.
- _.find = _.detect = function(obj, iterator, context) {
- var result;
- any(obj, function(value, index, list) {
- if (iterator.call(context, value, index, list)) {
- result = value;
- return true;
- }
- });
- return result;
- };
-
- // Return all the elements that pass a truth test.
- // Delegates to **ECMAScript 5**'s native `filter` if available.
- // Aliased as `select`.
- _.filter = _.select = function(obj, iterator, context) {
- var results = [];
- if (obj == null) return results;
- if (nativeFilter && obj.filter === nativeFilter) return obj.filter(iterator, context);
- each(obj, function(value, index, list) {
- if (iterator.call(context, value, index, list)) results[results.length] = value;
- });
- return results;
- };
-
- // Return all the elements for which a truth test fails.
- _.reject = function(obj, iterator, context) {
- var results = [];
- if (obj == null) return results;
- each(obj, function(value, index, list) {
- if (!iterator.call(context, value, index, list)) results[results.length] = value;
- });
- return results;
- };
-
- // Determine whether all of the elements match a truth test.
- // Delegates to **ECMAScript 5**'s native `every` if available.
- // Aliased as `all`.
- _.every = _.all = function(obj, iterator, context) {
- var result = true;
- if (obj == null) return result;
- if (nativeEvery && obj.every === nativeEvery) return obj.every(iterator, context);
- each(obj, function(value, index, list) {
- if (!(result = result && iterator.call(context, value, index, list))) return breaker;
- });
- return result;
- };
-
- // Determine if at least one element in the object matches a truth test.
- // Delegates to **ECMAScript 5**'s native `some` if available.
- // Aliased as `any`.
- var any = _.some = _.any = function(obj, iterator, context) {
- iterator || (iterator = _.identity);
- var result = false;
- if (obj == null) return result;
- if (nativeSome && obj.some === nativeSome) return obj.some(iterator, context);
- each(obj, function(value, index, list) {
- if (result || (result = iterator.call(context, value, index, list))) return breaker;
- });
- return !!result;
- };
-
- // Determine if a given value is included in the array or object using `===`.
- // Aliased as `contains`.
- _.include = _.contains = function(obj, target) {
- var found = false;
- if (obj == null) return found;
- if (nativeIndexOf && obj.indexOf === nativeIndexOf) return obj.indexOf(target) != -1;
- found = any(obj, function(value) {
- return value === target;
- });
- return found;
- };
-
- // Invoke a method (with arguments) on every item in a collection.
- _.invoke = function(obj, method) {
- var args = slice.call(arguments, 2);
- return _.map(obj, function(value) {
- return (_.isFunction(method) ? method || value : value[method]).apply(value, args);
- });
- };
-
- // Convenience version of a common use case of `map`: fetching a property.
- _.pluck = function(obj, key) {
- return _.map(obj, function(value){ return value[key]; });
- };
-
- // Return the maximum element or (element-based computation).
- _.max = function(obj, iterator, context) {
- if (!iterator && _.isArray(obj)) return Math.max.apply(Math, obj);
- if (!iterator && _.isEmpty(obj)) return -Infinity;
- var result = {computed : -Infinity};
- each(obj, function(value, index, list) {
- var computed = iterator ? iterator.call(context, value, index, list) : value;
- computed >= result.computed && (result = {value : value, computed : computed});
- });
- return result.value;
- };
-
- // Return the minimum element (or element-based computation).
- _.min = function(obj, iterator, context) {
- if (!iterator && _.isArray(obj)) return Math.min.apply(Math, obj);
- if (!iterator && _.isEmpty(obj)) return Infinity;
- var result = {computed : Infinity};
- each(obj, function(value, index, list) {
- var computed = iterator ? iterator.call(context, value, index, list) : value;
- computed < result.computed && (result = {value : value, computed : computed});
- });
- return result.value;
- };
-
- // Shuffle an array.
- _.shuffle = function(obj) {
- var shuffled = [], rand;
- each(obj, function(value, index, list) {
- if (index == 0) {
- shuffled[0] = value;
- } else {
- rand = Math.floor(Math.random() * (index + 1));
- shuffled[index] = shuffled[rand];
- shuffled[rand] = value;
- }
- });
- return shuffled;
- };
-
- // Sort the object's values by a criterion produced by an iterator.
- _.sortBy = function(obj, iterator, context) {
- return _.pluck(_.map(obj, function(value, index, list) {
- return {
- value : value,
- criteria : iterator.call(context, value, index, list)
- };
- }).sort(function(left, right) {
- var a = left.criteria, b = right.criteria;
- return a < b ? -1 : a > b ? 1 : 0;
- }), 'value');
- };
-
- // Groups the object's values by a criterion. Pass either a string attribute
- // to group by, or a function that returns the criterion.
- _.groupBy = function(obj, val) {
- var result = {};
- var iterator = _.isFunction(val) ? val : function(obj) { return obj[val]; };
- each(obj, function(value, index) {
- var key = iterator(value, index);
- (result[key] || (result[key] = [])).push(value);
- });
- return result;
- };
-
- // Use a comparator function to figure out at what index an object should
- // be inserted so as to maintain order. Uses binary search.
- _.sortedIndex = function(array, obj, iterator) {
- iterator || (iterator = _.identity);
- var low = 0, high = array.length;
- while (low < high) {
- var mid = (low + high) >> 1;
- iterator(array[mid]) < iterator(obj) ? low = mid + 1 : high = mid;
- }
- return low;
- };
-
- // Safely convert anything iterable into a real, live array.
- _.toArray = function(iterable) {
- if (!iterable) return [];
- if (iterable.toArray) return iterable.toArray();
- if (_.isArray(iterable)) return slice.call(iterable);
- if (_.isArguments(iterable)) return slice.call(iterable);
- return _.values(iterable);
- };
-
- // Return the number of elements in an object.
- _.size = function(obj) {
- return _.toArray(obj).length;
- };
-
- // Array Functions
- // ---------------
-
- // Get the first element of an array. Passing **n** will return the first N
- // values in the array. Aliased as `head`. The **guard** check allows it to work
- // with `_.map`.
- _.first = _.head = function(array, n, guard) {
- return (n != null) && !guard ? slice.call(array, 0, n) : array[0];
- };
-
- // Returns everything but the last entry of the array. Especcialy useful on
- // the arguments object. Passing **n** will return all the values in
- // the array, excluding the last N. The **guard** check allows it to work with
- // `_.map`.
- _.initial = function(array, n, guard) {
- return slice.call(array, 0, array.length - ((n == null) || guard ? 1 : n));
- };
-
- // Get the last element of an array. Passing **n** will return the last N
- // values in the array. The **guard** check allows it to work with `_.map`.
- _.last = function(array, n, guard) {
- if ((n != null) && !guard) {
- return slice.call(array, Math.max(array.length - n, 0));
- } else {
- return array[array.length - 1];
- }
- };
-
- // Returns everything but the first entry of the array. Aliased as `tail`.
- // Especially useful on the arguments object. Passing an **index** will return
- // the rest of the values in the array from that index onward. The **guard**
- // check allows it to work with `_.map`.
- _.rest = _.tail = function(array, index, guard) {
- return slice.call(array, (index == null) || guard ? 1 : index);
- };
-
- // Trim out all falsy values from an array.
- _.compact = function(array) {
- return _.filter(array, function(value){ return !!value; });
- };
-
- // Return a completely flattened version of an array.
- _.flatten = function(array, shallow) {
- return _.reduce(array, function(memo, value) {
- if (_.isArray(value)) return memo.concat(shallow ? value : _.flatten(value));
- memo[memo.length] = value;
- return memo;
- }, []);
- };
-
- // Return a version of the array that does not contain the specified value(s).
- _.without = function(array) {
- return _.difference(array, slice.call(arguments, 1));
- };
-
- // Produce a duplicate-free version of the array. If the array has already
- // been sorted, you have the option of using a faster algorithm.
- // Aliased as `unique`.
- _.uniq = _.unique = function(array, isSorted, iterator) {
- var initial = iterator ? _.map(array, iterator) : array;
- var result = [];
- _.reduce(initial, function(memo, el, i) {
- if (0 == i || (isSorted === true ? _.last(memo) != el : !_.include(memo, el))) {
- memo[memo.length] = el;
- result[result.length] = array[i];
- }
- return memo;
- }, []);
- return result;
- };
-
- // Produce an array that contains the union: each distinct element from all of
- // the passed-in arrays.
- _.union = function() {
- return _.uniq(_.flatten(arguments, true));
- };
-
- // Produce an array that contains every item shared between all the
- // passed-in arrays. (Aliased as "intersect" for back-compat.)
- _.intersection = _.intersect = function(array) {
- var rest = slice.call(arguments, 1);
- return _.filter(_.uniq(array), function(item) {
- return _.every(rest, function(other) {
- return _.indexOf(other, item) >= 0;
- });
- });
- };
-
- // Take the difference between one array and a number of other arrays.
- // Only the elements present in just the first array will remain.
- _.difference = function(array) {
- var rest = _.flatten(slice.call(arguments, 1));
- return _.filter(array, function(value){ return !_.include(rest, value); });
- };
-
- // Zip together multiple lists into a single array -- elements that share
- // an index go together.
- _.zip = function() {
- var args = slice.call(arguments);
- var length = _.max(_.pluck(args, 'length'));
- var results = new Array(length);
- for (var i = 0; i < length; i++) results[i] = _.pluck(args, "" + i);
- return results;
- };
-
- // If the browser doesn't supply us with indexOf (I'm looking at you, **MSIE**),
- // we need this function. Return the position of the first occurrence of an
- // item in an array, or -1 if the item is not included in the array.
- // Delegates to **ECMAScript 5**'s native `indexOf` if available.
- // If the array is large and already in sort order, pass `true`
- // for **isSorted** to use binary search.
- _.indexOf = function(array, item, isSorted) {
- if (array == null) return -1;
- var i, l;
- if (isSorted) {
- i = _.sortedIndex(array, item);
- return array[i] === item ? i : -1;
- }
- if (nativeIndexOf && array.indexOf === nativeIndexOf) return array.indexOf(item);
- for (i = 0, l = array.length; i < l; i++) if (i in array && array[i] === item) return i;
- return -1;
- };
-
- // Delegates to **ECMAScript 5**'s native `lastIndexOf` if available.
- _.lastIndexOf = function(array, item) {
- if (array == null) return -1;
- if (nativeLastIndexOf && array.lastIndexOf === nativeLastIndexOf) return array.lastIndexOf(item);
- var i = array.length;
- while (i--) if (i in array && array[i] === item) return i;
- return -1;
- };
-
- // Generate an integer Array containing an arithmetic progression. A port of
- // the native Python `range()` function. See
- // [the Python documentation](http://docs.python.org/library/functions.html#range).
- _.range = function(start, stop, step) {
- if (arguments.length <= 1) {
- stop = start || 0;
- start = 0;
- }
- step = arguments[2] || 1;
-
- var len = Math.max(Math.ceil((stop - start) / step), 0);
- var idx = 0;
- var range = new Array(len);
-
- while(idx < len) {
- range[idx++] = start;
- start += step;
- }
-
- return range;
- };
-
- // Function (ahem) Functions
- // ------------------
-
- // Reusable constructor function for prototype setting.
- var ctor = function(){};
-
- // Create a function bound to a given object (assigning `this`, and arguments,
- // optionally). Binding with arguments is also known as `curry`.
- // Delegates to **ECMAScript 5**'s native `Function.bind` if available.
- // We check for `func.bind` first, to fail fast when `func` is undefined.
- _.bind = function bind(func, context) {
- var bound, args;
- if (func.bind === nativeBind && nativeBind) return nativeBind.apply(func, slice.call(arguments, 1));
- if (!_.isFunction(func)) throw new TypeError;
- args = slice.call(arguments, 2);
- return bound = function() {
- if (!(this instanceof bound)) return func.apply(context, args.concat(slice.call(arguments)));
- ctor.prototype = func.prototype;
- var self = new ctor;
- var result = func.apply(self, args.concat(slice.call(arguments)));
- if (Object(result) === result) return result;
- return self;
- };
- };
-
- // Bind all of an object's methods to that object. Useful for ensuring that
- // all callbacks defined on an object belong to it.
- _.bindAll = function(obj) {
- var funcs = slice.call(arguments, 1);
- if (funcs.length == 0) funcs = _.functions(obj);
- each(funcs, function(f) { obj[f] = _.bind(obj[f], obj); });
- return obj;
- };
-
- // Memoize an expensive function by storing its results.
- _.memoize = function(func, hasher) {
- var memo = {};
- hasher || (hasher = _.identity);
- return function() {
- var key = hasher.apply(this, arguments);
- return _.has(memo, key) ? memo[key] : (memo[key] = func.apply(this, arguments));
- };
- };
-
- // Delays a function for the given number of milliseconds, and then calls
- // it with the arguments supplied.
- _.delay = function(func, wait) {
- var args = slice.call(arguments, 2);
- return setTimeout(function(){ return func.apply(func, args); }, wait);
- };
-
- // Defers a function, scheduling it to run after the current call stack has
- // cleared.
- _.defer = function(func) {
- return _.delay.apply(_, [func, 1].concat(slice.call(arguments, 1)));
- };
-
- // Returns a function, that, when invoked, will only be triggered at most once
- // during a given window of time.
- _.throttle = function(func, wait) {
- var context, args, timeout, throttling, more;
- var whenDone = _.debounce(function(){ more = throttling = false; }, wait);
- return function() {
- context = this; args = arguments;
- var later = function() {
- timeout = null;
- if (more) func.apply(context, args);
- whenDone();
- };
- if (!timeout) timeout = setTimeout(later, wait);
- if (throttling) {
- more = true;
- } else {
- func.apply(context, args);
- }
- whenDone();
- throttling = true;
- };
- };
-
- // Returns a function, that, as long as it continues to be invoked, will not
- // be triggered. The function will be called after it stops being called for
- // N milliseconds.
- _.debounce = function(func, wait) {
- var timeout;
- return function() {
- var context = this, args = arguments;
- var later = function() {
- timeout = null;
- func.apply(context, args);
- };
- clearTimeout(timeout);
- timeout = setTimeout(later, wait);
- };
- };
-
- // Returns a function that will be executed at most one time, no matter how
- // often you call it. Useful for lazy initialization.
- _.once = function(func) {
- var ran = false, memo;
- return function() {
- if (ran) return memo;
- ran = true;
- return memo = func.apply(this, arguments);
- };
- };
-
- // Returns the first function passed as an argument to the second,
- // allowing you to adjust arguments, run code before and after, and
- // conditionally execute the original function.
- _.wrap = function(func, wrapper) {
- return function() {
- var args = [func].concat(slice.call(arguments, 0));
- return wrapper.apply(this, args);
- };
- };
-
- // Returns a function that is the composition of a list of functions, each
- // consuming the return value of the function that follows.
- _.compose = function() {
- var funcs = arguments;
- return function() {
- var args = arguments;
- for (var i = funcs.length - 1; i >= 0; i--) {
- args = [funcs[i].apply(this, args)];
- }
- return args[0];
- };
- };
-
- // Returns a function that will only be executed after being called N times.
- _.after = function(times, func) {
- if (times <= 0) return func();
- return function() {
- if (--times < 1) { return func.apply(this, arguments); }
- };
- };
-
- // Object Functions
- // ----------------
-
- // Retrieve the names of an object's properties.
- // Delegates to **ECMAScript 5**'s native `Object.keys`
- _.keys = nativeKeys || function(obj) {
- if (obj !== Object(obj)) throw new TypeError('Invalid object');
- var keys = [];
- for (var key in obj) if (_.has(obj, key)) keys[keys.length] = key;
- return keys;
- };
-
- // Retrieve the values of an object's properties.
- _.values = function(obj) {
- return _.map(obj, _.identity);
- };
-
- // Return a sorted list of the function names available on the object.
- // Aliased as `methods`
- _.functions = _.methods = function(obj) {
- var names = [];
- for (var key in obj) {
- if (_.isFunction(obj[key])) names.push(key);
- }
- return names.sort();
- };
-
- // Extend a given object with all the properties in passed-in object(s).
- _.extend = function(obj) {
- each(slice.call(arguments, 1), function(source) {
- for (var prop in source) {
- obj[prop] = source[prop];
- }
- });
- return obj;
- };
-
- // Fill in a given object with default properties.
- _.defaults = function(obj) {
- each(slice.call(arguments, 1), function(source) {
- for (var prop in source) {
- if (obj[prop] == null) obj[prop] = source[prop];
- }
- });
- return obj;
- };
-
- // Create a (shallow-cloned) duplicate of an object.
- _.clone = function(obj) {
- if (!_.isObject(obj)) return obj;
- return _.isArray(obj) ? obj.slice() : _.extend({}, obj);
- };
-
- // Invokes interceptor with the obj, and then returns obj.
- // The primary purpose of this method is to "tap into" a method chain, in
- // order to perform operations on intermediate results within the chain.
- _.tap = function(obj, interceptor) {
- interceptor(obj);
- return obj;
- };
-
- // Internal recursive comparison function.
- function eq(a, b, stack) {
- // Identical objects are equal. `0 === -0`, but they aren't identical.
- // See the Harmony `egal` proposal: http://wiki.ecmascript.org/doku.php?id=harmony:egal.
- if (a === b) return a !== 0 || 1 / a == 1 / b;
- // A strict comparison is necessary because `null == undefined`.
- if (a == null || b == null) return a === b;
- // Unwrap any wrapped objects.
- if (a._chain) a = a._wrapped;
- if (b._chain) b = b._wrapped;
- // Invoke a custom `isEqual` method if one is provided.
- if (a.isEqual && _.isFunction(a.isEqual)) return a.isEqual(b);
- if (b.isEqual && _.isFunction(b.isEqual)) return b.isEqual(a);
- // Compare `[[Class]]` names.
- var className = toString.call(a);
- if (className != toString.call(b)) return false;
- switch (className) {
- // Strings, numbers, dates, and booleans are compared by value.
- case '[object String]':
- // Primitives and their corresponding object wrappers are equivalent; thus, `"5"` is
- // equivalent to `new String("5")`.
- return a == String(b);
- case '[object Number]':
- // `NaN`s are equivalent, but non-reflexive. An `egal` comparison is performed for
- // other numeric values.
- return a != +a ? b != +b : (a == 0 ? 1 / a == 1 / b : a == +b);
- case '[object Date]':
- case '[object Boolean]':
- // Coerce dates and booleans to numeric primitive values. Dates are compared by their
- // millisecond representations. Note that invalid dates with millisecond representations
- // of `NaN` are not equivalent.
- return +a == +b;
- // RegExps are compared by their source patterns and flags.
- case '[object RegExp]':
- return a.source == b.source &&
- a.global == b.global &&
- a.multiline == b.multiline &&
- a.ignoreCase == b.ignoreCase;
- }
- if (typeof a != 'object' || typeof b != 'object') return false;
- // Assume equality for cyclic structures. The algorithm for detecting cyclic
- // structures is adapted from ES 5.1 section 15.12.3, abstract operation `JO`.
- var length = stack.length;
- while (length--) {
- // Linear search. Performance is inversely proportional to the number of
- // unique nested structures.
- if (stack[length] == a) return true;
- }
- // Add the first object to the stack of traversed objects.
- stack.push(a);
- var size = 0, result = true;
- // Recursively compare objects and arrays.
- if (className == '[object Array]') {
- // Compare array lengths to determine if a deep comparison is necessary.
- size = a.length;
- result = size == b.length;
- if (result) {
- // Deep compare the contents, ignoring non-numeric properties.
- while (size--) {
- // Ensure commutative equality for sparse arrays.
- if (!(result = size in a == size in b && eq(a[size], b[size], stack))) break;
- }
- }
- } else {
- // Objects with different constructors are not equivalent.
- if ('constructor' in a != 'constructor' in b || a.constructor != b.constructor) return false;
- // Deep compare objects.
- for (var key in a) {
- if (_.has(a, key)) {
- // Count the expected number of properties.
- size++;
- // Deep compare each member.
- if (!(result = _.has(b, key) && eq(a[key], b[key], stack))) break;
- }
- }
- // Ensure that both objects contain the same number of properties.
- if (result) {
- for (key in b) {
- if (_.has(b, key) && !(size--)) break;
- }
- result = !size;
- }
- }
- // Remove the first object from the stack of traversed objects.
- stack.pop();
- return result;
- }
-
- // Perform a deep comparison to check if two objects are equal.
- _.isEqual = function(a, b) {
- return eq(a, b, []);
- };
-
- // Is a given array, string, or object empty?
- // An "empty" object has no enumerable own-properties.
- _.isEmpty = function(obj) {
- if (_.isArray(obj) || _.isString(obj)) return obj.length === 0;
- for (var key in obj) if (_.has(obj, key)) return false;
- return true;
- };
-
- // Is a given value a DOM element?
- _.isElement = function(obj) {
- return !!(obj && obj.nodeType == 1);
- };
-
- // Is a given value an array?
- // Delegates to ECMA5's native Array.isArray
- _.isArray = nativeIsArray || function(obj) {
- return toString.call(obj) == '[object Array]';
- };
-
- // Is a given variable an object?
- _.isObject = function(obj) {
- return obj === Object(obj);
- };
-
- // Is a given variable an arguments object?
- _.isArguments = function(obj) {
- return toString.call(obj) == '[object Arguments]';
- };
- if (!_.isArguments(arguments)) {
- _.isArguments = function(obj) {
- return !!(obj && _.has(obj, 'callee'));
- };
- }
-
- // Is a given value a function?
- _.isFunction = function(obj) {
- return toString.call(obj) == '[object Function]';
- };
-
- // Is a given value a string?
- _.isString = function(obj) {
- return toString.call(obj) == '[object String]';
- };
-
- // Is a given value a number?
- _.isNumber = function(obj) {
- return toString.call(obj) == '[object Number]';
- };
-
- // Is the given value `NaN`?
- _.isNaN = function(obj) {
- // `NaN` is the only value for which `===` is not reflexive.
- return obj !== obj;
- };
-
- // Is a given value a boolean?
- _.isBoolean = function(obj) {
- return obj === true || obj === false || toString.call(obj) == '[object Boolean]';
- };
-
- // Is a given value a date?
- _.isDate = function(obj) {
- return toString.call(obj) == '[object Date]';
- };
-
- // Is the given value a regular expression?
- _.isRegExp = function(obj) {
- return toString.call(obj) == '[object RegExp]';
- };
-
- // Is a given value equal to null?
- _.isNull = function(obj) {
- return obj === null;
- };
-
- // Is a given variable undefined?
- _.isUndefined = function(obj) {
- return obj === void 0;
- };
-
- // Has own property?
- _.has = function(obj, key) {
- return hasOwnProperty.call(obj, key);
- };
-
- // Utility Functions
- // -----------------
-
- // Run Underscore.js in *noConflict* mode, returning the `_` variable to its
- // previous owner. Returns a reference to the Underscore object.
- _.noConflict = function() {
- root._ = previousUnderscore;
- return this;
- };
-
- // Keep the identity function around for default iterators.
- _.identity = function(value) {
- return value;
- };
-
- // Run a function **n** times.
- _.times = function (n, iterator, context) {
- for (var i = 0; i < n; i++) iterator.call(context, i);
- };
-
- // Escape a string for HTML interpolation.
- _.escape = function(string) {
- return (''+string).replace(/&/g, '&').replace(/</g, '<').replace(/>/g, '>').replace(/"/g, '"').replace(/'/g, ''').replace(/\//g,'/');
- };
-
- // Add your own custom functions to the Underscore object, ensuring that
- // they're correctly added to the OOP wrapper as well.
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-<span id="cmrsim-anatomy-base-module"></span><h1>cmrsim.anatomy.base module<a class="headerlink" href="#module-cmrsim.anatomy.base" title="Permalink to this headline">¶</a></h1>
-<p><strong>Classes</strong></p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#cmrsim.anatomy.base.BaseDataset" title="cmrsim.anatomy.base.BaseDataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BaseDataset</span></code></a>(array_dictionary)</p></td>
-<td><p></p></td>
-</tr>
-</tbody>
-</table>
-<dl class="py class">
-<dt id="cmrsim.anatomy.base.BaseDataset">
-<em class="property">class </em><code class="sig-prename descclassname">cmrsim.anatomy.base.</code><code class="sig-name descname">BaseDataset</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">array_dictionary</span><span class="p">:</span> <span class="n">collections.OrderedDict</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/cmrsim/anatomy/base.html#BaseDataset"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#cmrsim.anatomy.base.BaseDataset" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">tensorflow.python.module.module.Module</span></code></p>
-<p><strong>Attributes</strong></p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#cmrsim.anatomy.base.BaseDataset.map_names" title="cmrsim.anatomy.base.BaseDataset.map_names"><code class="xref py py-obj docutils literal notranslate"><span class="pre">map_names</span></code></a></p></td>
-<td><p>The most base type</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#cmrsim.anatomy.base.BaseDataset.set_size" title="cmrsim.anatomy.base.BaseDataset.set_size"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_size</span></code></a></p></td>
-<td><p>The most base type</p></td>
-</tr>
-</tbody>
-</table>
-<dl class="py attribute">
-<dt id="cmrsim.anatomy.base.BaseDataset.map_names">
-<code class="sig-name descname">map_names</code><em class="property">: Tuple</em><a class="headerlink" href="#cmrsim.anatomy.base.BaseDataset.map_names" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py attribute">
-<dt id="cmrsim.anatomy.base.BaseDataset.set_size">
-<code class="sig-name descname">set_size</code><em class="property">: int</em><a class="headerlink" href="#cmrsim.anatomy.base.BaseDataset.set_size" title="Permalink to this definition">¶</a></dt>
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-<p><strong>Classes</strong></p>
-<table class="longtable docutils align-default">
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-<col style="width: 10%" />
-<col style="width: 90%" />
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-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#cmrsim.anatomy.from_files.MultiFileDataset" title="cmrsim.anatomy.from_files.MultiFileDataset"><code class="xref py py-obj docutils literal notranslate"><span class="pre">MultiFileDataset</span></code></a>(data_dir, …)</p></td>
-<td><p></p></td>
-</tr>
-</tbody>
-</table>
-<dl class="py class">
-<dt id="cmrsim.anatomy.from_files.MultiFileDataset">
-<em class="property">class </em><code class="sig-prename descclassname">cmrsim.anatomy.from_files.</code><code class="sig-name descname">MultiFileDataset</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data_dir</span></em>, <em class="sig-param"><span class="n">object_dimensions</span><span class="p">:</span> <span class="n">Tuple<span class="p">[</span>float<span class="p">, </span>float<span class="p">]</span></span></em>, <em class="sig-param"><span class="n">property_names</span><span class="p">:</span> <span class="n">Tuple[str, …]</span></em>, <em class="sig-param"><span class="n">folder_names</span><span class="p">:</span> <span class="n">Optional[Tuple[str, …]]</span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">indices</span><span class="p">:</span> <span class="n">Optional<span class="p">[</span>Iterable<span class="p">]</span></span> <span class="o">=</span> <span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/cmrsim/anatomy/from_files.html#MultiFileDataset"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#cmrsim.anatomy.from_files.MultiFileDataset" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <a class="reference internal" href="cmrsim.anatomy.base.html#cmrsim.anatomy.base.BaseDataset" title="cmrsim.anatomy.base.BaseDataset"><code class="xref py py-class docutils literal notranslate"><span class="pre">cmrsim.anatomy.base.BaseDataset</span></code></a></p>
-<p><strong>Attributes</strong></p>
-<table class="longtable docutils align-default">
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-<col style="width: 10%" />
-<col style="width: 90%" />
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-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#cmrsim.anatomy.from_files.MultiFileDataset.map_names" title="cmrsim.anatomy.from_files.MultiFileDataset.map_names"><code class="xref py py-obj docutils literal notranslate"><span class="pre">map_names</span></code></a></p></td>
-<td><p>The most base type</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#cmrsim.anatomy.from_files.MultiFileDataset.set_size" title="cmrsim.anatomy.from_files.MultiFileDataset.set_size"><code class="xref py py-obj docutils literal notranslate"><span class="pre">set_size</span></code></a></p></td>
-<td><p>The most base type</p></td>
-</tr>
-</tbody>
-</table>
-<dl class="py attribute">
-<dt id="cmrsim.anatomy.from_files.MultiFileDataset.map_names">
-<code class="sig-name descname">map_names</code><a class="headerlink" href="#cmrsim.anatomy.from_files.MultiFileDataset.map_names" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
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-<dt id="cmrsim.anatomy.from_files.MultiFileDataset.set_size">
-<code class="sig-name descname">set_size</code><a class="headerlink" href="#cmrsim.anatomy.from_files.MultiFileDataset.set_size" title="Permalink to this definition">¶</a></dt>
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-<span id="cmrsim-auxilliary-assign-map-values-module"></span><h1>cmrsim.auxilliary.assign_map_values module<a class="headerlink" href="#module-cmrsim.auxilliary.assign_map_values" title="Permalink to this headline">¶</a></h1>
-<p><strong>Functions</strong></p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#cmrsim.auxilliary.assign_map_values.assign_random_normal_to_numerical_label_map" title="cmrsim.auxilliary.assign_map_values.assign_random_normal_to_numerical_label_map"><code class="xref py py-obj docutils literal notranslate"><span class="pre">assign_random_normal_to_numerical_label_map</span></code></a>(…)</p></td>
-<td><p>Assigns values to pixels as sample of a normal random variable with the stated mean value and standard deviation according to class membership.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#cmrsim.auxilliary.assign_map_values.assign_to_numerical_label_map" title="cmrsim.auxilliary.assign_map_values.assign_to_numerical_label_map"><code class="xref py py-obj docutils literal notranslate"><span class="pre">assign_to_numerical_label_map</span></code></a>(label_map, …)</p></td>
-<td><p>Assigns values to pixels as fractions of the stated maximum value according to class membership.</p></td>
-</tr>
-</tbody>
-</table>
-<dl class="py function">
-<dt id="cmrsim.auxilliary.assign_map_values.assign_random_normal_to_numerical_label_map">
-<code class="sig-prename descclassname">cmrsim.auxilliary.assign_map_values.</code><code class="sig-name descname">assign_random_normal_to_numerical_label_map</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">label_map</span><span class="p">:</span> <span class="n">numpy.ndarray</span></em>, <em class="sig-param"><span class="n">means</span><span class="p">:</span> <span class="n">Iterable<span class="p">[</span>float<span class="p">]</span></span></em>, <em class="sig-param"><span class="n">stds</span><span class="p">:</span> <span class="n">Iterable<span class="p">[</span>float<span class="p">]</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/cmrsim/auxilliary/assign_map_values.html#assign_random_normal_to_numerical_label_map"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#cmrsim.auxilliary.assign_map_values.assign_random_normal_to_numerical_label_map" title="Permalink to this definition">¶</a></dt>
-<dd><p>Assigns values to pixels as sample of a normal random variable with the stated mean value and standard deviation according to class membership. Assumes the classes to be labeled as increasing integers with increment 1.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>label_map</strong> – (N, M) or (#batch, N, M)</p></li>
-<li><p><strong>means</strong> – Iterable of float values. Needs to be at least as long #classes excluding the background</p></li>
-<li><p><strong>stds</strong> – Iterable of float values. Needs to be at least as long #classes excluding the background</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><p>np.ndarray (#batch, N, M)</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt id="cmrsim.auxilliary.assign_map_values.assign_to_numerical_label_map">
-<code class="sig-prename descclassname">cmrsim.auxilliary.assign_map_values.</code><code class="sig-name descname">assign_to_numerical_label_map</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">label_map</span><span class="p">:</span> <span class="n">numpy.ndarray</span></em>, <em class="sig-param"><span class="n">max_value</span><span class="p">:</span> <span class="n">float</span></em>, <em class="sig-param"><span class="n">fractions</span><span class="p">:</span> <span class="n">Union<span class="p">[</span>Iterable<span class="p">, </span>Tuple<span class="p">, </span>List<span class="p">]</span></span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/cmrsim/auxilliary/assign_map_values.html#assign_to_numerical_label_map"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#cmrsim.auxilliary.assign_map_values.assign_to_numerical_label_map" title="Permalink to this definition">¶</a></dt>
-<dd><p>Assigns values to pixels as fractions of the stated maximum value according to class membership. Assumes the
-classes to be labeled as increasing integers with increment 1.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>label_map</strong> – (N, M) or (#batch, N, M)</p></li>
-<li><p><strong>max_value</strong> – float</p></li>
-<li><p><strong>fractions</strong> – Iterable of float values. Needs to be at least as long #classes excluding the background</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><p>np.ndarray (#batch, N, M)</p>
-</dd>
-</dl>
-</dd></dl>
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-<span id="cmrsim-auxilliary-coordinates-module"></span><h1>cmrsim.auxilliary.coordinates module<a class="headerlink" href="#module-cmrsim.auxilliary.coordinates" title="Permalink to this headline">¶</a></h1>
-<p>Module that contains functions to get and transform coordinates in the format required for simulation.</p>
-<p><strong>Functions</strong></p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#cmrsim.auxilliary.coordinates.get_static_2d_centered_coordinates" title="cmrsim.auxilliary.coordinates.get_static_2d_centered_coordinates"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_static_2d_centered_coordinates</span></code></a>(map_size, …)</p></td>
-<td><p>Calculates relative coordinates for a dense map with coordinate origin in the center of the map.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#cmrsim.auxilliary.coordinates.get_translation_matrix" title="cmrsim.auxilliary.coordinates.get_translation_matrix"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_translation_matrix</span></code></a>(displacement_vectors, …)</p></td>
-<td><p>Constructs a transformation matrix for a translation defined by the displacement vectors.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#cmrsim.auxilliary.coordinates.apply_transformations" title="cmrsim.auxilliary.coordinates.apply_transformations"><code class="xref py py-obj docutils literal notranslate"><span class="pre">apply_transformations</span></code></a>(matrices, r_vectors)</p></td>
-<td><p>Takes a list of transformation matrix batch dimensions match the ones of r_vectors and performs the transformation by calculating the innerproduct of the homogeneous transformation matrices and the homogeneous extension of the input r_vectors.</p></td>
-</tr>
-</tbody>
-</table>
-<dl class="py function">
-<dt id="cmrsim.auxilliary.coordinates.apply_transformations">
-<code class="sig-prename descclassname">cmrsim.auxilliary.coordinates.</code><code class="sig-name descname">apply_transformations</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">matrices</span><span class="p">:</span> <span class="n">List<span class="p">[</span>tensorflow.python.framework.ops.Tensor<span class="p">]</span></span></em>, <em class="sig-param"><span class="n">r_vectors</span><span class="p">:</span> <span class="n">tensorflow.python.framework.ops.Tensor</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/cmrsim/auxilliary/coordinates.html#apply_transformations"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#cmrsim.auxilliary.coordinates.apply_transformations" title="Permalink to this definition">¶</a></dt>
-<dd><p>Takes a list of transformation matrix batch dimensions match the ones of r_vectors and performs the
-transformation by calculating the innerproduct of the homogeneous transformation matrices and the homogeneous
-extension of the input r_vectors.</p>
-<p>r_new = apply_transformation([R, T, …], r_old)</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>matrices</strong> – List of tensors with shape (#batch, #voxel, #repetions, #k_space_samples, 4, 4)</p></li>
-<li><p><strong>r_vectors</strong> – tensor of initial positional vectors (#batch, #voxel, #repetitions, 3, #k_space_samples)</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><p></p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt id="cmrsim.auxilliary.coordinates.get_static_2d_centered_coordinates">
-<code class="sig-prename descclassname">cmrsim.auxilliary.coordinates.</code><code class="sig-name descname">get_static_2d_centered_coordinates</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">map_size</span><span class="p">:</span> <span class="n">Tuple<span class="p">[</span>int<span class="p">, </span>int<span class="p">]</span></span></em>, <em class="sig-param"><span class="n">object_dimensions</span><span class="p">:</span> <span class="n">Optional<span class="p">[</span>Tuple<span class="p">[</span>float<span class="p">, </span>float<span class="p">]</span><span class="p">]</span></span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">batch_size</span><span class="p">:</span> <span class="n">Optional<span class="p">[</span>int<span class="p">]</span></span> <span class="o">=</span> <span class="default_value">1</span></em><span class="sig-paren">)</span> → tensorflow.python.framework.ops.Tensor<a class="reference internal" href="_modules/cmrsim/auxilliary/coordinates.html#get_static_2d_centered_coordinates"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#cmrsim.auxilliary.coordinates.get_static_2d_centered_coordinates" title="Permalink to this definition">¶</a></dt>
-<dd><p>Calculates relative coordinates for a dense map with coordinate origin in the center of the map. If the
-dimension in map_size is even, the origin is shifted 1/2 with respect to grid indices, if they are uneven, the
-origin coincides with a grid point.
-Reshapes the output to a shape, used in cmrsim simulations. The singleton-axis is reserved for repetitions /
-contrasts.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>map_size</strong> – Tuple[int, int], 2D grid dimensions used to calculate coordinates relative to mid-point</p></li>
-<li><p><strong>object_dimensions</strong> – Optional Tuple[float, float] used to scale relative coordinates. Defaults to (1., 1.)</p></li>
-<li><p><strong>batch_size</strong> – Optional (int) - used to tile the calculated coordinates for a batch. Defaults to 1</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><p>(#batch, X, Y, 1, 3)</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt id="cmrsim.auxilliary.coordinates.get_translation_matrix">
-<code class="sig-prename descclassname">cmrsim.auxilliary.coordinates.</code><code class="sig-name descname">get_translation_matrix</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">displacement_vectors</span><span class="p">:</span> <span class="n">Union<span class="p">[</span>numpy.ndarray<span class="p">, </span>tensorflow.python.framework.ops.Tensor<span class="p">]</span></span></em>, <em class="sig-param"><span class="n">batch_dimensions</span><span class="p">:</span> <span class="n">Tuple[int, …]</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/cmrsim/auxilliary/coordinates.html#get_translation_matrix"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#cmrsim.auxilliary.coordinates.get_translation_matrix" title="Permalink to this definition">¶</a></dt>
-<dd><p>Constructs a transformation matrix for a translation defined by the displacement vectors. Each index is treated
-separately, so if the same rotation for mor than one element should be performed, one needs to repeat the
-arrays such that the same element is place along one axis (e.g. axis 1 to shift all isochromates by the same
-vectors).</p>
-<p>Meant to be used in apply_transformation!</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>displacement_vectors</strong> – (#images, #isochromates, #repetitions, #samples, 3)</p></li>
-<li><p><strong>batch_dimensions</strong> – (#batch, #voxel, #repetitions, #samples) shape of the tensor of all positional vectors</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><p>translation matrix (#images, #isochromates, #repetitions, #samples, 4, 4)</p>
-</dd>
-</dl>
-</dd></dl>
-
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-<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-cmrsim.auxilliary" title="Permalink to this headline">¶</a></h2>
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-<span id="cmrsim-auxilliary-plot-utilities-module"></span><h1>cmrsim.auxilliary.plot_utilities module<a class="headerlink" href="#module-cmrsim.auxilliary.plot_utilities" title="Permalink to this headline">¶</a></h1>
-<p><strong>Classes</strong></p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#cmrsim.auxilliary.plot_utilities.SlideShow" title="cmrsim.auxilliary.plot_utilities.SlideShow"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SlideShow</span></code></a>(data, axes, figure, **kwargs)</p></td>
-<td><p></p></td>
-</tr>
-</tbody>
-</table>
-<dl class="py class">
-<dt id="cmrsim.auxilliary.plot_utilities.SlideShow">
-<em class="property">class </em><code class="sig-prename descclassname">cmrsim.auxilliary.plot_utilities.</code><code class="sig-name descname">SlideShow</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">data</span><span class="p">:</span> <span class="n">numpy.ndarray</span></em>, <em class="sig-param"><span class="n">axes</span><span class="p">:</span> <span class="n">List<span class="p">[</span>matplotlib.axes._axes.Axes<span class="p">]</span></span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">figure</span><span class="p">:</span> <span class="n">matplotlib.figure.Figure</span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/cmrsim/auxilliary/plot_utilities.html#SlideShow"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#cmrsim.auxilliary.plot_utilities.SlideShow" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">object</span></code></p>
-<p><strong>Methods</strong></p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#cmrsim.auxilliary.plot_utilities.SlideShow.animate" title="cmrsim.auxilliary.plot_utilities.SlideShow.animate"><code class="xref py py-obj docutils literal notranslate"><span class="pre">animate</span></code></a>(save_directory, **kwargs)</p></td>
-<td><p></p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#cmrsim.auxilliary.plot_utilities.SlideShow.onclick" title="cmrsim.auxilliary.plot_utilities.SlideShow.onclick"><code class="xref py py-obj docutils literal notranslate"><span class="pre">onclick</span></code></a>(event)</p></td>
-<td><p></p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#cmrsim.auxilliary.plot_utilities.SlideShow.update" title="cmrsim.auxilliary.plot_utilities.SlideShow.update"><code class="xref py py-obj docutils literal notranslate"><span class="pre">update</span></code></a>()</p></td>
-<td><p></p></td>
-</tr>
-</tbody>
-</table>
-<dl class="py method">
-<dt id="cmrsim.auxilliary.plot_utilities.SlideShow.animate">
-<code class="sig-name descname">animate</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">save_directory</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/cmrsim/auxilliary/plot_utilities.html#SlideShow.animate"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#cmrsim.auxilliary.plot_utilities.SlideShow.animate" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py method">
-<dt id="cmrsim.auxilliary.plot_utilities.SlideShow.onclick">
-<code class="sig-name descname">onclick</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">event</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/cmrsim/auxilliary/plot_utilities.html#SlideShow.onclick"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#cmrsim.auxilliary.plot_utilities.SlideShow.onclick" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py method">
-<dt id="cmrsim.auxilliary.plot_utilities.SlideShow.update">
-<code class="sig-name descname">update</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/cmrsim/auxilliary/plot_utilities.html#SlideShow.update"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#cmrsim.auxilliary.plot_utilities.SlideShow.update" title="Permalink to this definition">¶</a></dt>
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-<span id="cmrsim-auxilliary-snr-module"></span><h1>cmrsim.auxilliary.snr module<a class="headerlink" href="#module-cmrsim.auxilliary.snr" title="Permalink to this headline">¶</a></h1>
-<p><strong>Functions</strong></p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#cmrsim.auxilliary.snr.calculate_snr" title="cmrsim.auxilliary.snr.calculate_snr"><code class="xref py py-obj docutils literal notranslate"><span class="pre">calculate_snr</span></code></a>(single_coil_images, …)</p></td>
-<td><p>Calculates the SNR for a multicoil-acquisition according to doi:10.1002/mrm.21868.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#cmrsim.auxilliary.snr.compute_noise_std" title="cmrsim.auxilliary.snr.compute_noise_std"><code class="xref py py-obj docutils literal notranslate"><span class="pre">compute_noise_std</span></code></a>(…)</p></td>
-<td><p><dl class="field-list simple">
-<dt class="field-odd">param noiseless_single_coil_images</dt>
-<dd class="field-odd"><p>(n_coils, X, Y, [Z]) of type complex64</p>
-</dd>
-</dl>
-</p></td>
-</tr>
-</tbody>
-</table>
-<dl class="py function">
-<dt id="cmrsim.auxilliary.snr.calculate_snr">
-<code class="sig-prename descclassname">cmrsim.auxilliary.snr.</code><code class="sig-name descname">calculate_snr</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">single_coil_images</span><span class="p">:</span> <span class="n">Union<span class="p">[</span>numpy.ndarray<span class="p">, </span>tensorflow.python.framework.ops.Tensor<span class="p">]</span></span></em>, <em class="sig-param"><span class="n">dynamic_noise_scan</span><span class="p">:</span> <span class="n">Union<span class="p">[</span>numpy.ndarray<span class="p">, </span>tensorflow.python.framework.ops.Tensor<span class="p">]</span></span></em>, <em class="sig-param"><span class="n">coil_sensitivities</span><span class="p">:</span> <span class="n">Optional<span class="p">[</span>Union<span class="p">[</span>numpy.ndarray<span class="p">, </span>tensorflow.python.framework.ops.Tensor<span class="p">]</span><span class="p">]</span></span> <span class="o">=</span> <span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/cmrsim/auxilliary/snr.html#calculate_snr"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#cmrsim.auxilliary.snr.calculate_snr" title="Permalink to this definition">¶</a></dt>
-<dd><p>Calculates the SNR for a multicoil-acquisition according to doi:10.1002/mrm.21868. To guarantee correct scaling
-this assumes, non-zero-filled reconstruction.</p>
-<p>Previously implemented by Robbert van Gorkum in the MRXCAT project.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>single_coil_images</strong> – (#coils, X, Y, [Z]) of type complex64</p></li>
-<li><p><strong>dynamic_noise_scan</strong> – (#coils, X, Y, [Z]) of type complex64</p></li>
-<li><p><strong>coil_sensitivities</strong> – Optional: (#coils, X, Y, [Z]) of type complex64 defaults to ones(1, X, Y, [Z])</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><p>snr_map (X, Y, Z) of type float32</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py function">
-<dt id="cmrsim.auxilliary.snr.compute_noise_std">
-<code class="sig-prename descclassname">cmrsim.auxilliary.snr.</code><code class="sig-name descname">compute_noise_std</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">noiseless_single_coil_images</span><span class="p">:</span> <span class="n">Union<span class="p">[</span>numpy.ndarray<span class="p">, </span>tensorflow.python.framework.ops.Tensor<span class="p">]</span></span></em>, <em class="sig-param"><span class="n">target_snr</span><span class="p">:</span> <span class="n">Iterable<span class="p">[</span>float<span class="p">]</span></span></em>, <em class="sig-param"><span class="n">coil_sensitivities</span><span class="p">:</span> <span class="n">Optional<span class="p">[</span>Union<span class="p">[</span>numpy.ndarray<span class="p">, </span>tensorflow.python.framework.ops.Tensor<span class="p">]</span><span class="p">]</span></span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">mask</span><span class="p">:</span> <span class="n">Optional<span class="p">[</span>Union<span class="p">[</span>numpy.ndarray<span class="p">, </span>tensorflow.python.framework.ops.Tensor<span class="p">]</span><span class="p">]</span></span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/cmrsim/auxilliary/snr.html#compute_noise_std"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#cmrsim.auxilliary.snr.compute_noise_std" title="Permalink to this definition">¶</a></dt>
-<dd><dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>noiseless_single_coil_images</strong> – (n_coils, X, Y, [Z]) of type complex64</p></li>
-<li><p><strong>target_snr</strong> – </p></li>
-<li><p><strong>coil_sensitivities</strong> – (n_coils, X, Y, [Z]) of type complex64, Optional: defaults to ones(1, X, Y, [Z])
-Is used for iterative refinement.</p></li>
-<li><p><strong>mask</strong> – (X, Y, [Z]) Optional: binary mask to specify ROI for SNR computation. Defaults to entire image.</p></li>
-<li><p><strong>kwargs</strong> – <ul>
-<li><p>iteratively_refine: (bool) default=True. If True and coil_sensitivities are given, uses iterative</p></li>
-</ul>
-<p>method to refine std estimation.</p>
-</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><p>estimated_stds np.array of type np.float32 with shape like np.array(target_snr)</p>
-</dd>
-</dl>
-</dd></dl>
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- <div class="section" id="module-cmrsim.encoding.base">
-<span id="cmrsim-encoding-base-module"></span><h1>cmrsim.encoding.base module<a class="headerlink" href="#module-cmrsim.encoding.base" title="Permalink to this headline">¶</a></h1>
-<p><strong>Classes</strong></p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#cmrsim.encoding.base.BaseSampling" title="cmrsim.encoding.base.BaseSampling"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BaseSampling</span></code></a>(absolute_noise_std, …)</p></td>
-<td><p>Base Module for implementing a time-dependent sampling in k-space.</p></td>
-</tr>
-</tbody>
-</table>
-<dl class="py class">
-<dt id="cmrsim.encoding.base.BaseSampling">
-<em class="property">class </em><code class="sig-prename descclassname">cmrsim.encoding.base.</code><code class="sig-name descname">BaseSampling</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">absolute_noise_std</span><span class="p">:</span> <span class="n">Union<span class="p">[</span>float<span class="p">, </span>Iterable<span class="p">[</span>float<span class="p">]</span><span class="p">]</span></span></em>, <em class="sig-param"><span class="n">name</span><span class="p">:</span> <span class="n">str</span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/cmrsim/encoding/base.html#BaseSampling"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#cmrsim.encoding.base.BaseSampling" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">tensorflow.python.module.module.Module</span></code></p>
-<p>Base Module for implementing a time-dependent sampling in k-space. Is meant to be inherited from when specifying
-standard trajectories.</p>
-<p><strong>Methods</strong></p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#cmrsim.encoding.base.BaseSampling.add_noise" title="cmrsim.encoding.base.BaseSampling.add_noise"><code class="xref py py-obj docutils literal notranslate"><span class="pre">add_noise</span></code></a>(s_of_k, **kwargs)</p></td>
-<td><p>Adds noise to k-space-samples, expands the number of axis by one and appends the different noise instantiations as second last axis.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#cmrsim.encoding.base.BaseSampling.get_sampling_times" title="cmrsim.encoding.base.BaseSampling.get_sampling_times"><code class="xref py py-obj docutils literal notranslate"><span class="pre">get_sampling_times</span></code></a>()</p></td>
-<td><p>Getter for sampling times.</p></td>
-</tr>
-</tbody>
-</table>
-<p><strong>Attributes</strong></p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#cmrsim.encoding.base.BaseSampling.sampling_times" title="cmrsim.encoding.base.BaseSampling.sampling_times"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sampling_times</span></code></a></p></td>
-<td><p>The most base type</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#cmrsim.encoding.base.BaseSampling.k_space_vectors" title="cmrsim.encoding.base.BaseSampling.k_space_vectors"><code class="xref py py-obj docutils literal notranslate"><span class="pre">k_space_vectors</span></code></a></p></td>
-<td><p>The most base type</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#cmrsim.encoding.base.BaseSampling.absolute_noise_std" title="cmrsim.encoding.base.BaseSampling.absolute_noise_std"><code class="xref py py-obj docutils literal notranslate"><span class="pre">absolute_noise_std</span></code></a></p></td>
-<td><p>The most base type</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#cmrsim.encoding.base.BaseSampling.k_space_segments" title="cmrsim.encoding.base.BaseSampling.k_space_segments"><code class="xref py py-obj docutils literal notranslate"><span class="pre">k_space_segments</span></code></a></p></td>
-<td><p>The most base type</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#cmrsim.encoding.base.BaseSampling.number_of_samples" title="cmrsim.encoding.base.BaseSampling.number_of_samples"><code class="xref py py-obj docutils literal notranslate"><span class="pre">number_of_samples</span></code></a></p></td>
-<td><p>The most base type</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#cmrsim.encoding.base.BaseSampling.device" title="cmrsim.encoding.base.BaseSampling.device"><code class="xref py py-obj docutils literal notranslate"><span class="pre">device</span></code></a></p></td>
-<td><p>The most base type</p></td>
-</tr>
-</tbody>
-</table>
-<dl class="py method">
-<dt id="cmrsim.encoding.base.BaseSampling.add_noise">
-<code class="sig-name descname">add_noise</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">s_of_k</span><span class="p">:</span> <span class="n">tensorflow.python.framework.ops.Tensor</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/cmrsim/encoding/base.html#BaseSampling.add_noise"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#cmrsim.encoding.base.BaseSampling.add_noise" title="Permalink to this definition">¶</a></dt>
-<dd><p>Adds noise to k-space-samples, expands the number of axis by one and appends the different noise
-instantiations as second last axis.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><p><strong>s_of_k</strong> – Tensor containing all encoded k-space samples</p>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><p>tf.Tensor</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py method">
-<dt id="cmrsim.encoding.base.BaseSampling.get_sampling_times">
-<code class="sig-name descname">get_sampling_times</code><span class="sig-paren">(</span><span class="sig-paren">)</span><a class="reference internal" href="_modules/cmrsim/encoding/base.html#BaseSampling.get_sampling_times"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#cmrsim.encoding.base.BaseSampling.get_sampling_times" title="Permalink to this definition">¶</a></dt>
-<dd><p>Getter for sampling times. Defines format that should be used for all Signal modules that need the timing
-of the sampling events.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Returns</dt>
-<dd class="field-odd"><p><ul class="simple">
-<li><p>tf.RaggedTensor or numpy.ndarray</p></li>
-<li><p>sampling times as numpy.ndarray in case the trajectory is not segmented. If trajectory is segmented
-for simulation, returns a RaggedTensor of shape (n_segment, …).</p></li>
-</ul>
-</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py attribute">
-<dt id="cmrsim.encoding.base.BaseSampling.absolute_noise_std">
-<code class="sig-name descname">absolute_noise_std</code><em class="property">: tf.Variable</em><a class="headerlink" href="#cmrsim.encoding.base.BaseSampling.absolute_noise_std" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py attribute">
-<dt id="cmrsim.encoding.base.BaseSampling.device">
-<code class="sig-name descname">device</code><em class="property">: str</em><a class="headerlink" href="#cmrsim.encoding.base.BaseSampling.device" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py attribute">
-<dt id="cmrsim.encoding.base.BaseSampling.k_space_segments">
-<code class="sig-name descname">k_space_segments</code><em class="property">: tf.Tensor</em><a class="headerlink" href="#cmrsim.encoding.base.BaseSampling.k_space_segments" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py attribute">
-<dt id="cmrsim.encoding.base.BaseSampling.k_space_vectors">
-<code class="sig-name descname">k_space_vectors</code><em class="property">: tf.Tensor</em><a class="headerlink" href="#cmrsim.encoding.base.BaseSampling.k_space_vectors" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py attribute">
-<dt id="cmrsim.encoding.base.BaseSampling.number_of_samples">
-<code class="sig-name descname">number_of_samples</code><em class="property">: tf.Tensor</em><a class="headerlink" href="#cmrsim.encoding.base.BaseSampling.number_of_samples" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py attribute">
-<dt id="cmrsim.encoding.base.BaseSampling.sampling_times">
-<code class="sig-name descname">sampling_times</code><em class="property">: tf.Tensor</em><a class="headerlink" href="#cmrsim.encoding.base.BaseSampling.sampling_times" title="Permalink to this definition">¶</a></dt>
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-<span id="cmrsim-encoding-epi-module"></span><h1>cmrsim.encoding.epi module<a class="headerlink" href="#module-cmrsim.encoding.epi" title="Permalink to this headline">¶</a></h1>
-<p><strong>Classes</strong></p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#cmrsim.encoding.epi.EPI" title="cmrsim.encoding.epi.EPI"><code class="xref py py-obj docutils literal notranslate"><span class="pre">EPI</span></code></a>(field_of_view, float], List[float]], …)</p></td>
-<td><p></p></td>
-</tr>
-</tbody>
-</table>
-<dl class="py class">
-<dt id="cmrsim.encoding.epi.EPI">
-<em class="property">class </em><code class="sig-prename descclassname">cmrsim.encoding.epi.</code><code class="sig-name descname">EPI</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">field_of_view</span><span class="p">:</span> <span class="n">Union<span class="p">[</span>Tuple<span class="p">[</span>float<span class="p">, </span>float<span class="p">]</span><span class="p">, </span>List<span class="p">[</span>float<span class="p">]</span><span class="p">]</span></span></em>, <em class="sig-param"><span class="n">sampling_matrix_size</span><span class="p">:</span> <span class="n">Union<span class="p">[</span>Tuple<span class="p">[</span>int<span class="p">, </span>int<span class="p">]</span><span class="p">, </span>List<span class="p">[</span>int<span class="p">]</span><span class="p">, </span>np.ndarray<span class="p">]</span></span></em>, <em class="sig-param"><span class="n">absolute_noise_std</span><span class="p">:</span> <span class="n">float</span></em>, <em class="sig-param"><span class="n">read_out_duration</span><span class="p">:</span> <span class="n">float</span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">bandwidth_per_pixel</span><span class="p">:</span> <span class="n">float</span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">blip_duration</span><span class="p">:</span> <span class="n">Optional<span class="p">[</span>float<span class="p">]</span></span> <span class="o">=</span> <span class="default_value">0.0</span></em>, <em class="sig-param"><span class="n">acquisition_start</span><span class="p">:</span> <span class="n">Optional<span class="p">[</span>float<span class="p">]</span></span> <span class="o">=</span> <span class="default_value">0.0</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/cmrsim/encoding/epi.html#EPI"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#cmrsim.encoding.epi.EPI" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <a class="reference internal" href="cmrsim.encoding.base.html#cmrsim.encoding.base.BaseSampling" title="cmrsim.encoding.base.BaseSampling"><code class="xref py py-class docutils literal notranslate"><span class="pre">cmrsim.encoding.base.BaseSampling</span></code></a></p>
-<p><strong>Attributes</strong></p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#cmrsim.encoding.epi.EPI.sampling_times" title="cmrsim.encoding.epi.EPI.sampling_times"><code class="xref py py-obj docutils literal notranslate"><span class="pre">sampling_times</span></code></a></p></td>
-<td><p>The most base type</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#cmrsim.encoding.epi.EPI.k_space_vectors" title="cmrsim.encoding.epi.EPI.k_space_vectors"><code class="xref py py-obj docutils literal notranslate"><span class="pre">k_space_vectors</span></code></a></p></td>
-<td><p>The most base type</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#cmrsim.encoding.epi.EPI.absolute_noise_std" title="cmrsim.encoding.epi.EPI.absolute_noise_std"><code class="xref py py-obj docutils literal notranslate"><span class="pre">absolute_noise_std</span></code></a></p></td>
-<td><p>The most base type</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#cmrsim.encoding.epi.EPI.k_space_segments" title="cmrsim.encoding.epi.EPI.k_space_segments"><code class="xref py py-obj docutils literal notranslate"><span class="pre">k_space_segments</span></code></a></p></td>
-<td><p>The most base type</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#cmrsim.encoding.epi.EPI.number_of_samples" title="cmrsim.encoding.epi.EPI.number_of_samples"><code class="xref py py-obj docutils literal notranslate"><span class="pre">number_of_samples</span></code></a></p></td>
-<td><p>The most base type</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#cmrsim.encoding.epi.EPI.device" title="cmrsim.encoding.epi.EPI.device"><code class="xref py py-obj docutils literal notranslate"><span class="pre">device</span></code></a></p></td>
-<td><p>The most base type</p></td>
-</tr>
-</tbody>
-</table>
-<dl class="py attribute">
-<dt id="cmrsim.encoding.epi.EPI.absolute_noise_std">
-<code class="sig-name descname">absolute_noise_std</code><a class="headerlink" href="#cmrsim.encoding.epi.EPI.absolute_noise_std" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py attribute">
-<dt id="cmrsim.encoding.epi.EPI.device">
-<code class="sig-name descname">device</code><a class="headerlink" href="#cmrsim.encoding.epi.EPI.device" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py attribute">
-<dt id="cmrsim.encoding.epi.EPI.k_space_segments">
-<code class="sig-name descname">k_space_segments</code><a class="headerlink" href="#cmrsim.encoding.epi.EPI.k_space_segments" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py attribute">
-<dt id="cmrsim.encoding.epi.EPI.k_space_vectors">
-<code class="sig-name descname">k_space_vectors</code><a class="headerlink" href="#cmrsim.encoding.epi.EPI.k_space_vectors" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py attribute">
-<dt id="cmrsim.encoding.epi.EPI.number_of_samples">
-<code class="sig-name descname">number_of_samples</code><a class="headerlink" href="#cmrsim.encoding.epi.EPI.number_of_samples" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py attribute">
-<dt id="cmrsim.encoding.epi.EPI.sampling_times">
-<code class="sig-name descname">sampling_times</code><a class="headerlink" href="#cmrsim.encoding.epi.EPI.sampling_times" title="Permalink to this definition">¶</a></dt>
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-<span id="module-contents"></span><h2>Module contents<a class="headerlink" href="#module-cmrsim.encoding" title="Permalink to this headline">¶</a></h2>
-<p>The submodules contained in this module are implementations of the encoding process in MRI. The
-shared calculation (namely the evaluating the fourier phase for each k-space point) are implemented
-in the base class. The actual definition of the k-space trajectory is done in a corresponding
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-<span id="cmrsim-reconstruction-base-module"></span><h1>cmrsim.reconstruction.base module<a class="headerlink" href="#module-cmrsim.reconstruction.base" title="Permalink to this headline">¶</a></h1>
-<p><strong>Classes</strong></p>
-<table class="longtable docutils align-default">
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-<col style="width: 10%" />
-<col style="width: 90%" />
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-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#cmrsim.reconstruction.base.BaseRecon" title="cmrsim.reconstruction.base.BaseRecon"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BaseRecon</span></code></a>(output_matrix_size, int, int], …)</p></td>
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-<dl class="py class">
-<dt id="cmrsim.reconstruction.base.BaseRecon">
-<em class="property">class </em><code class="sig-prename descclassname">cmrsim.reconstruction.base.</code><code class="sig-name descname">BaseRecon</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">output_matrix_size</span><span class="p">:</span> <span class="n">Union[Tuple[int, int, int], Tuple[int, …], tensorflow.python.framework.ops.Tensor]</span></em>, <em class="sig-param"><span class="n">name</span><span class="p">:</span> <span class="n">str</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/cmrsim/reconstruction/base.html#BaseRecon"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#cmrsim.reconstruction.base.BaseRecon" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">tensorflow.python.module.module.Module</span></code></p>
-<p><strong>Attributes</strong></p>
-<table class="longtable docutils align-default">
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-<col style="width: 10%" />
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-<tr class="row-odd"><td><p><a class="reference internal" href="#cmrsim.reconstruction.base.BaseRecon.output_matrix_size" title="cmrsim.reconstruction.base.BaseRecon.output_matrix_size"><code class="xref py py-obj docutils literal notranslate"><span class="pre">output_matrix_size</span></code></a></p></td>
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- <div class="section" id="module-cmrsim.reconstruction.cartesian">
-<span id="cmrsim-reconstruction-cartesian-module"></span><h1>cmrsim.reconstruction.cartesian module<a class="headerlink" href="#module-cmrsim.reconstruction.cartesian" title="Permalink to this headline">¶</a></h1>
-<p><strong>Classes</strong></p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#cmrsim.reconstruction.cartesian.Cartesian2D" title="cmrsim.reconstruction.cartesian.Cartesian2D"><code class="xref py py-obj docutils literal notranslate"><span class="pre">Cartesian2D</span></code></a>(sample_matrix_size, int], …)</p></td>
-<td><p></p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#cmrsim.reconstruction.cartesian.RoemerMultiCoil" title="cmrsim.reconstruction.cartesian.RoemerMultiCoil"><code class="xref py py-obj docutils literal notranslate"><span class="pre">RoemerMultiCoil</span></code></a>(sample_matrix_size, int], …)</p></td>
-<td><p></p></td>
-</tr>
-</tbody>
-</table>
-<dl class="py class">
-<dt id="cmrsim.reconstruction.cartesian.Cartesian2D">
-<em class="property">class </em><code class="sig-prename descclassname">cmrsim.reconstruction.cartesian.</code><code class="sig-name descname">Cartesian2D</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sample_matrix_size</span><span class="p">:</span> <span class="n">Tuple<span class="p">[</span>int<span class="p">, </span>int<span class="p">]</span></span></em>, <em class="sig-param"><span class="n">padding</span><span class="p">:</span> <span class="n">Optional<span class="p">[</span>Union<span class="p">[</span>Tuple<span class="p">[</span>int<span class="p">, </span>int<span class="p">]</span><span class="p">, </span>Iterable<span class="p">[</span>Tuple<span class="p">[</span>int<span class="p">, </span>int<span class="p">]</span><span class="p">]</span><span class="p">]</span><span class="p">]</span></span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/cmrsim/reconstruction/cartesian.html#Cartesian2D"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#cmrsim.reconstruction.cartesian.Cartesian2D" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <a class="reference internal" href="cmrsim.reconstruction.base.html#cmrsim.reconstruction.base.BaseRecon" title="cmrsim.reconstruction.base.BaseRecon"><code class="xref py py-class docutils literal notranslate"><span class="pre">cmrsim.reconstruction.base.BaseRecon</span></code></a></p>
-<p><strong>Attributes</strong></p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#cmrsim.reconstruction.cartesian.Cartesian2D.output_matrix_size" title="cmrsim.reconstruction.cartesian.Cartesian2D.output_matrix_size"><code class="xref py py-obj docutils literal notranslate"><span class="pre">output_matrix_size</span></code></a></p></td>
-<td><p>The most base type</p></td>
-</tr>
-</tbody>
-</table>
-<dl class="py attribute">
-<dt id="cmrsim.reconstruction.cartesian.Cartesian2D.output_matrix_size">
-<code class="sig-name descname">output_matrix_size</code><em class="property">: tf.Tensor</em><a class="headerlink" href="#cmrsim.reconstruction.cartesian.Cartesian2D.output_matrix_size" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt id="cmrsim.reconstruction.cartesian.RoemerMultiCoil">
-<em class="property">class </em><code class="sig-prename descclassname">cmrsim.reconstruction.cartesian.</code><code class="sig-name descname">RoemerMultiCoil</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sample_matrix_size</span><span class="p">:</span> <span class="n">Tuple<span class="p">[</span>int<span class="p">, </span>int<span class="p">]</span></span></em>, <em class="sig-param"><span class="n">coil_sensitivities</span><span class="p">:</span> <span class="n">Union<span class="p">[</span>np.ndarray<span class="p">, </span>tensorflow.python.framework.ops.Tensor<span class="p">]</span></span></em>, <em class="sig-param"><span class="n">padding</span><span class="p">:</span> <span class="n">Optional<span class="p">[</span>Union<span class="p">[</span>Tuple<span class="p">[</span>int<span class="p">, </span>int<span class="p">]</span><span class="p">, </span>Iterable<span class="p">[</span>Tuple<span class="p">[</span>int<span class="p">, </span>int<span class="p">]</span><span class="p">]</span><span class="p">]</span><span class="p">]</span></span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/cmrsim/reconstruction/cartesian.html#RoemerMultiCoil"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#cmrsim.reconstruction.cartesian.RoemerMultiCoil" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <a class="reference internal" href="#cmrsim.reconstruction.cartesian.Cartesian2D" title="cmrsim.reconstruction.cartesian.Cartesian2D"><code class="xref py py-class docutils literal notranslate"><span class="pre">cmrsim.reconstruction.cartesian.Cartesian2D</span></code></a></p>
-<p><strong>Attributes</strong></p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#cmrsim.reconstruction.cartesian.RoemerMultiCoil.output_matrix_size" title="cmrsim.reconstruction.cartesian.RoemerMultiCoil.output_matrix_size"><code class="xref py py-obj docutils literal notranslate"><span class="pre">output_matrix_size</span></code></a></p></td>
-<td><p>The most base type</p></td>
-</tr>
-</tbody>
-</table>
-<dl class="py attribute">
-<dt id="cmrsim.reconstruction.cartesian.RoemerMultiCoil.output_matrix_size">
-<code class="sig-name descname">output_matrix_size</code><a class="headerlink" href="#cmrsim.reconstruction.cartesian.RoemerMultiCoil.output_matrix_size" title="Permalink to this definition">¶</a></dt>
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- <div class="section" id="module-cmrsim.signal_processes.base">
-<span id="cmrsim-signal-processes-base-module"></span><h1>cmrsim.signal_processes.base module<a class="headerlink" href="#module-cmrsim.signal_processes.base" title="Permalink to this headline">¶</a></h1>
-<p><strong>Classes</strong></p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#cmrsim.signal_processes.base.BaseSignalModel" title="cmrsim.signal_processes.base.BaseSignalModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">BaseSignalModel</span></code></a>(name, device)</p></td>
-<td><p></p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#cmrsim.signal_processes.base.LookUpTableModule" title="cmrsim.signal_processes.base.LookUpTableModule"><code class="xref py py-obj docutils literal notranslate"><span class="pre">LookUpTableModule</span></code></a>(look_up_map3d, …)</p></td>
-<td><p></p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#cmrsim.signal_processes.base.CompositeSignalModel" title="cmrsim.signal_processes.base.CompositeSignalModel"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CompositeSignalModel</span></code></a>(*args)</p></td>
-<td><p></p></td>
-</tr>
-</tbody>
-</table>
-<dl class="py class">
-<dt id="cmrsim.signal_processes.base.BaseSignalModel">
-<em class="property">class </em><code class="sig-prename descclassname">cmrsim.signal_processes.base.</code><code class="sig-name descname">BaseSignalModel</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">name</span><span class="p">:</span> <span class="n">str</span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">device</span><span class="p">:</span> <span class="n">str</span> <span class="o">=</span> <span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/cmrsim/signal_processes/base.html#BaseSignalModel"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#cmrsim.signal_processes.base.BaseSignalModel" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">tensorflow.python.module.module.Module</span></code></p>
-<p><strong>Methods</strong></p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#cmrsim.signal_processes.base.BaseSignalModel.check_inputs" title="cmrsim.signal_processes.base.BaseSignalModel.check_inputs"><code class="xref py py-obj docutils literal notranslate"><span class="pre">check_inputs</span></code></a>(**kwargs)</p></td>
-<td><p>This method is meant to be called by CompositeSignalModel and asserts that all required_quantities are passed in kwargs during graph construction.</p></td>
-</tr>
-</tbody>
-</table>
-<p><strong>Attributes</strong></p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#cmrsim.signal_processes.base.BaseSignalModel.required_quantities" title="cmrsim.signal_processes.base.BaseSignalModel.required_quantities"><code class="xref py py-obj docutils literal notranslate"><span class="pre">required_quantities</span></code></a></p></td>
-<td><p>Built-in immutable sequence.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#cmrsim.signal_processes.base.BaseSignalModel.device" title="cmrsim.signal_processes.base.BaseSignalModel.device"><code class="xref py py-obj docutils literal notranslate"><span class="pre">device</span></code></a></p></td>
-<td><p>The most base type</p></td>
-</tr>
-</tbody>
-</table>
-<dl class="py method">
-<dt id="cmrsim.signal_processes.base.BaseSignalModel.check_inputs">
-<code class="sig-name descname">check_inputs</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/cmrsim/signal_processes/base.html#BaseSignalModel.check_inputs"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#cmrsim.signal_processes.base.BaseSignalModel.check_inputs" title="Permalink to this definition">¶</a></dt>
-<dd><p>This method is meant to be called by CompositeSignalModel and asserts that all required_quantities are
-passed in kwargs during graph construction.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Raises</dt>
-<dd class="field-odd"><p>ValueError if <a href="#id1"><span class="problematic" id="id2">*</span></a>kwargs is missing a specified required quantity</p>
-</dd>
-</dl>
-</dd></dl>
-
-<dl class="py attribute">
-<dt id="cmrsim.signal_processes.base.BaseSignalModel.device">
-<code class="sig-name descname">device</code><em class="property">: str</em><a class="headerlink" href="#cmrsim.signal_processes.base.BaseSignalModel.device" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py attribute">
-<dt id="cmrsim.signal_processes.base.BaseSignalModel.expansion_factor">
-<code class="sig-name descname">expansion_factor</code><em class="property">: int</em><em class="property"> = None</em><a class="headerlink" href="#cmrsim.signal_processes.base.BaseSignalModel.expansion_factor" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py attribute">
-<dt id="cmrsim.signal_processes.base.BaseSignalModel.required_quantities">
-<code class="sig-name descname">required_quantities</code><em class="property">: Tuple<span class="p">[</span>str<span class="p">]</span></em><em class="property"> = ()</em><a class="headerlink" href="#cmrsim.signal_processes.base.BaseSignalModel.required_quantities" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt id="cmrsim.signal_processes.base.CompositeSignalModel">
-<em class="property">class </em><code class="sig-prename descclassname">cmrsim.signal_processes.base.</code><code class="sig-name descname">CompositeSignalModel</code><span class="sig-paren">(</span><em class="sig-param"><span class="o">*</span><span class="n">args</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/cmrsim/signal_processes/base.html#CompositeSignalModel"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#cmrsim.signal_processes.base.CompositeSignalModel" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <code class="xref py py-class docutils literal notranslate"><span class="pre">tensorflow.python.module.module.Module</span></code></p>
-<p><strong>Attributes</strong></p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#cmrsim.signal_processes.base.CompositeSignalModel.expected_number_of_repetitions" title="cmrsim.signal_processes.base.CompositeSignalModel.expected_number_of_repetitions"><code class="xref py py-obj docutils literal notranslate"><span class="pre">expected_number_of_repetitions</span></code></a></p></td>
-<td><p></p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#cmrsim.signal_processes.base.CompositeSignalModel.required_quantities" title="cmrsim.signal_processes.base.CompositeSignalModel.required_quantities"><code class="xref py py-obj docutils literal notranslate"><span class="pre">required_quantities</span></code></a></p></td>
-<td><p>Built-in immutable sequence.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#cmrsim.signal_processes.base.CompositeSignalModel.unstacked_axis_names" title="cmrsim.signal_processes.base.CompositeSignalModel.unstacked_axis_names"><code class="xref py py-obj docutils literal notranslate"><span class="pre">unstacked_axis_names</span></code></a></p></td>
-<td><p>returns the name of modules with expansion factors > 1 in the order that is used in unstack_repetitions:</p></td>
-</tr>
-</tbody>
-</table>
-<p><strong>Methods</strong></p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#cmrsim.signal_processes.base.CompositeSignalModel.unstack_repetitions" title="cmrsim.signal_processes.base.CompositeSignalModel.unstack_repetitions"><code class="xref py py-obj docutils literal notranslate"><span class="pre">unstack_repetitions</span></code></a>(simulation_result, …)</p></td>
-<td><p>Uses the dimension expansion information from the sub modules to unstack the simulated tensor of k-space, samples or images.</p></td>
-</tr>
-</tbody>
-</table>
-<dl class="py method">
-<dt id="cmrsim.signal_processes.base.CompositeSignalModel.unstack_repetitions">
-<code class="sig-name descname">unstack_repetitions</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">simulation_result</span><span class="p">:</span> <span class="n">Union<span class="p">[</span>tensorflow.python.framework.ops.Tensor<span class="p">, </span>np.ndarray<span class="p">]</span></span></em><span class="sig-paren">)</span> → tensorflow.python.framework.ops.Tensor<a class="reference internal" href="_modules/cmrsim/signal_processes/base.html#CompositeSignalModel.unstack_repetitions"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#cmrsim.signal_processes.base.CompositeSignalModel.unstack_repetitions" title="Permalink to this definition">¶</a></dt>
-<dd><p>Uses the dimension expansion information from the sub modules to unstack the simulated tensor of k-space,
-samples or images. In both cases the second axis (index=1) is assumed to represent the stacked repetitions.</p>
-</dd></dl>
-
-<dl class="py method">
-<dt id="cmrsim.signal_processes.base.CompositeSignalModel.expected_number_of_repetitions">
-<em class="property">property </em><code class="sig-name descname">expected_number_of_repetitions</code><a class="headerlink" href="#cmrsim.signal_processes.base.CompositeSignalModel.expected_number_of_repetitions" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py attribute">
-<dt id="cmrsim.signal_processes.base.CompositeSignalModel.required_quantities">
-<code class="sig-name descname">required_quantities</code><em class="property">: Tuple<span class="p">[</span>str<span class="p">]</span></em><em class="property"> = ('M0',)</em><a class="headerlink" href="#cmrsim.signal_processes.base.CompositeSignalModel.required_quantities" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py method">
-<dt id="cmrsim.signal_processes.base.CompositeSignalModel.unstacked_axis_names">
-<em class="property">property </em><code class="sig-name descname">unstacked_axis_names</code><a class="headerlink" href="#cmrsim.signal_processes.base.CompositeSignalModel.unstacked_axis_names" title="Permalink to this definition">¶</a></dt>
-<dd><p>returns the name of modules with expansion factors > 1 in the order that is used in unstack_repetitions:</p>
-</dd></dl>
-
-</dd></dl>
-
-<dl class="py class">
-<dt id="cmrsim.signal_processes.base.LookUpTableModule">
-<em class="property">class </em><code class="sig-prename descclassname">cmrsim.signal_processes.base.</code><code class="sig-name descname">LookUpTableModule</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">look_up_map3d</span></em>, <em class="sig-param"><span class="n">map_dimensions</span><span class="p">:</span> <span class="n">Tuple<span class="p">[</span>float<span class="p">, </span>float<span class="p">, </span>float<span class="p">]</span></span></em>, <em class="sig-param"><span class="n">name</span><span class="p">:</span> <span class="n">Optional<span class="p">[</span>str<span class="p">]</span></span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/cmrsim/signal_processes/base.html#LookUpTableModule"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#cmrsim.signal_processes.base.LookUpTableModule" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <a class="reference internal" href="#cmrsim.signal_processes.base.BaseSignalModel" title="cmrsim.signal_processes.base.BaseSignalModel"><code class="xref py py-class docutils literal notranslate"><span class="pre">cmrsim.signal_processes.base.BaseSignalModel</span></code></a></p>
-<p><strong>Attributes</strong></p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#cmrsim.signal_processes.base.LookUpTableModule.required_quantities" title="cmrsim.signal_processes.base.LookUpTableModule.required_quantities"><code class="xref py py-obj docutils literal notranslate"><span class="pre">required_quantities</span></code></a></p></td>
-<td><p>Built-in immutable sequence.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#cmrsim.signal_processes.base.LookUpTableModule.device" title="cmrsim.signal_processes.base.LookUpTableModule.device"><code class="xref py py-obj docutils literal notranslate"><span class="pre">device</span></code></a></p></td>
-<td><p>The most base type</p></td>
-</tr>
-</tbody>
-</table>
-<dl class="py attribute">
-<dt id="cmrsim.signal_processes.base.LookUpTableModule.device">
-<code class="sig-name descname">device</code><a class="headerlink" href="#cmrsim.signal_processes.base.LookUpTableModule.device" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py attribute">
-<dt id="cmrsim.signal_processes.base.LookUpTableModule.required_quantities">
-<code class="sig-name descname">required_quantities</code><em class="property">: Tuple<span class="p">[</span>str<span class="p">]</span></em><em class="property"> = ('r_vectors',)</em><a class="headerlink" href="#cmrsim.signal_processes.base.LookUpTableModule.required_quantities" title="Permalink to this definition">¶</a></dt>
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-<span id="cmrsim-signal-processes-solution-operators-coil-sensitivities-module"></span><h1>cmrsim.signal_processes.solution_operators.coil_sensitivities module<a class="headerlink" href="#module-cmrsim.signal_processes.solution_operators.coil_sensitivities" title="Permalink to this headline">¶</a></h1>
-<p><strong>Classes</strong></p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#cmrsim.signal_processes.solution_operators.coil_sensitivities.CoilSensitivity" title="cmrsim.signal_processes.solution_operators.coil_sensitivities.CoilSensitivity"><code class="xref py py-obj docutils literal notranslate"><span class="pre">CoilSensitivity</span></code></a>(coil_sensitivities, …)</p></td>
-<td><p></p></td>
-</tr>
-</tbody>
-</table>
-<dl class="py class">
-<dt id="cmrsim.signal_processes.solution_operators.coil_sensitivities.CoilSensitivity">
-<em class="property">class </em><code class="sig-prename descclassname">cmrsim.signal_processes.solution_operators.coil_sensitivities.</code><code class="sig-name descname">CoilSensitivity</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">coil_sensitivities</span><span class="p">:</span> <span class="n">Union<span class="p">[</span>np.ndarray<span class="p">, </span>tensorflow.python.framework.ops.Tensor<span class="p">]</span></span></em>, <em class="sig-param"><span class="n">map_dimensions</span><span class="p">:</span> <span class="n">Tuple<span class="p">[</span>float<span class="p">, </span>float<span class="p">, </span>float<span class="p">]</span></span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/cmrsim/signal_processes/solution_operators/coil_sensitivities.html#CoilSensitivity"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#cmrsim.signal_processes.solution_operators.coil_sensitivities.CoilSensitivity" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <a class="reference internal" href="cmrsim.signal_processes.base.html#cmrsim.signal_processes.base.LookUpTableModule" title="cmrsim.signal_processes.base.LookUpTableModule"><code class="xref py py-class docutils literal notranslate"><span class="pre">cmrsim.signal_processes.base.LookUpTableModule</span></code></a></p>
-<p><strong>Attributes</strong></p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#cmrsim.signal_processes.solution_operators.coil_sensitivities.CoilSensitivity.required_quantities" title="cmrsim.signal_processes.solution_operators.coil_sensitivities.CoilSensitivity.required_quantities"><code class="xref py py-obj docutils literal notranslate"><span class="pre">required_quantities</span></code></a></p></td>
-<td><p>Built-in immutable sequence.</p></td>
-</tr>
-</tbody>
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-<span id="cmrsim-signal-processes-solution-operators-diffusion-weighting-module"></span><h1>cmrsim.signal_processes.solution_operators.diffusion_weighting module<a class="headerlink" href="#module-cmrsim.signal_processes.solution_operators.diffusion_weighting" title="Permalink to this headline">¶</a></h1>
-<p><strong>Classes</strong></p>
-<table class="longtable docutils align-default">
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-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#cmrsim.signal_processes.solution_operators.diffusion_weighting.ConstantGaussianDiffusion" title="cmrsim.signal_processes.solution_operators.diffusion_weighting.ConstantGaussianDiffusion"><code class="xref py py-obj docutils literal notranslate"><span class="pre">ConstantGaussianDiffusion</span></code></a>(b_vectors, **kwargs)</p></td>
-<td><p></p></td>
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-<dl class="py class">
-<dt id="cmrsim.signal_processes.solution_operators.diffusion_weighting.ConstantGaussianDiffusion">
-<em class="property">class </em><code class="sig-prename descclassname">cmrsim.signal_processes.solution_operators.diffusion_weighting.</code><code class="sig-name descname">ConstantGaussianDiffusion</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">b_vectors</span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/cmrsim/signal_processes/solution_operators/diffusion_weighting.html#ConstantGaussianDiffusion"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#cmrsim.signal_processes.solution_operators.diffusion_weighting.ConstantGaussianDiffusion" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <a class="reference internal" href="cmrsim.signal_processes.base.html#cmrsim.signal_processes.base.BaseSignalModel" title="cmrsim.signal_processes.base.BaseSignalModel"><code class="xref py py-class docutils literal notranslate"><span class="pre">cmrsim.signal_processes.base.BaseSignalModel</span></code></a></p>
-<p><strong>Attributes</strong></p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#cmrsim.signal_processes.solution_operators.diffusion_weighting.ConstantGaussianDiffusion.required_quantities" title="cmrsim.signal_processes.solution_operators.diffusion_weighting.ConstantGaussianDiffusion.required_quantities"><code class="xref py py-obj docutils literal notranslate"><span class="pre">required_quantities</span></code></a></p></td>
-<td><p>Built-in immutable sequence.</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#cmrsim.signal_processes.solution_operators.diffusion_weighting.ConstantGaussianDiffusion.device" title="cmrsim.signal_processes.solution_operators.diffusion_weighting.ConstantGaussianDiffusion.device"><code class="xref py py-obj docutils literal notranslate"><span class="pre">device</span></code></a></p></td>
-<td><p>The most base type</p></td>
-</tr>
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-<dt id="cmrsim.signal_processes.solution_operators.diffusion_weighting.ConstantGaussianDiffusion.device">
-<code class="sig-name descname">device</code><a class="headerlink" href="#cmrsim.signal_processes.solution_operators.diffusion_weighting.ConstantGaussianDiffusion.device" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
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-<code class="sig-name descname">required_quantities</code><em class="property">: Tuple<span class="p">[</span>str<span class="p">]</span></em><em class="property"> = ('diffusion_tensor',)</em><a class="headerlink" href="#cmrsim.signal_processes.solution_operators.diffusion_weighting.ConstantGaussianDiffusion.required_quantities" title="Permalink to this definition">¶</a></dt>
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-<span id="cmrsim-signal-processes-solution-operators-sequences-module"></span><h1>cmrsim.signal_processes.solution_operators.sequences module<a class="headerlink" href="#module-cmrsim.signal_processes.solution_operators.sequences" title="Permalink to this headline">¶</a></h1>
-<p><strong>Classes</strong></p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#cmrsim.signal_processes.solution_operators.sequences.SpinEcho" title="cmrsim.signal_processes.solution_operators.sequences.SpinEcho"><code class="xref py py-obj docutils literal notranslate"><span class="pre">SpinEcho</span></code></a>(echo_time, …)</p></td>
-<td><p></p></td>
-</tr>
-</tbody>
-</table>
-<dl class="py class">
-<dt id="cmrsim.signal_processes.solution_operators.sequences.SpinEcho">
-<em class="property">class </em><code class="sig-prename descclassname">cmrsim.signal_processes.solution_operators.sequences.</code><code class="sig-name descname">SpinEcho</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">echo_time</span><span class="p">:</span> <span class="n">Union<span class="p">[</span>float<span class="p">, </span>tensorflow.python.framework.ops.Tensor<span class="p">]</span></span></em>, <em class="sig-param"><span class="n">repetition_time</span><span class="p">:</span> <span class="n">Union<span class="p">[</span>float<span class="p">, </span>tensorflow.python.framework.ops.Tensor<span class="p">]</span></span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/cmrsim/signal_processes/solution_operators/sequences.html#SpinEcho"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#cmrsim.signal_processes.solution_operators.sequences.SpinEcho" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <a class="reference internal" href="cmrsim.signal_processes.base.html#cmrsim.signal_processes.base.BaseSignalModel" title="cmrsim.signal_processes.base.BaseSignalModel"><code class="xref py py-class docutils literal notranslate"><span class="pre">cmrsim.signal_processes.base.BaseSignalModel</span></code></a></p>
-<p><strong>Attributes</strong></p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#cmrsim.signal_processes.solution_operators.sequences.SpinEcho.expansion_factor" title="cmrsim.signal_processes.solution_operators.sequences.SpinEcho.expansion_factor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">expansion_factor</span></code></a></p></td>
-<td><p>int([x]) -> integer</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#cmrsim.signal_processes.solution_operators.sequences.SpinEcho.required_quantities" title="cmrsim.signal_processes.solution_operators.sequences.SpinEcho.required_quantities"><code class="xref py py-obj docutils literal notranslate"><span class="pre">required_quantities</span></code></a></p></td>
-<td><p>Built-in immutable sequence.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#cmrsim.signal_processes.solution_operators.sequences.SpinEcho.TE" title="cmrsim.signal_processes.solution_operators.sequences.SpinEcho.TE"><code class="xref py py-obj docutils literal notranslate"><span class="pre">TE</span></code></a></p></td>
-<td><p>The most base type</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#cmrsim.signal_processes.solution_operators.sequences.SpinEcho.TR" title="cmrsim.signal_processes.solution_operators.sequences.SpinEcho.TR"><code class="xref py py-obj docutils literal notranslate"><span class="pre">TR</span></code></a></p></td>
-<td><p>The most base type</p></td>
-</tr>
-</tbody>
-</table>
-<dl class="py attribute">
-<dt id="cmrsim.signal_processes.solution_operators.sequences.SpinEcho.TE">
-<code class="sig-name descname">TE</code><em class="property">: tf.Variable</em><a class="headerlink" href="#cmrsim.signal_processes.solution_operators.sequences.SpinEcho.TE" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py attribute">
-<dt id="cmrsim.signal_processes.solution_operators.sequences.SpinEcho.TR">
-<code class="sig-name descname">TR</code><em class="property">: tf.Variable</em><a class="headerlink" href="#cmrsim.signal_processes.solution_operators.sequences.SpinEcho.TR" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py attribute">
-<dt id="cmrsim.signal_processes.solution_operators.sequences.SpinEcho.expansion_factor">
-<code class="sig-name descname">expansion_factor</code><em class="property"> = 1</em><a class="headerlink" href="#cmrsim.signal_processes.solution_operators.sequences.SpinEcho.expansion_factor" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py attribute">
-<dt id="cmrsim.signal_processes.solution_operators.sequences.SpinEcho.required_quantities">
-<code class="sig-name descname">required_quantities</code><em class="property"> = ('T1', 'T2')</em><a class="headerlink" href="#cmrsim.signal_processes.solution_operators.sequences.SpinEcho.required_quantities" title="Permalink to this definition">¶</a></dt>
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-<span id="cmrsim-signal-processes-solution-operators-t2-star-module"></span><h1>cmrsim.signal_processes.solution_operators.t2_star module<a class="headerlink" href="#module-cmrsim.signal_processes.solution_operators.t2_star" title="Permalink to this headline">¶</a></h1>
-<p><strong>Classes</strong></p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#cmrsim.signal_processes.solution_operators.t2_star.StaticT2starDecay" title="cmrsim.signal_processes.solution_operators.t2_star.StaticT2starDecay"><code class="xref py py-obj docutils literal notranslate"><span class="pre">StaticT2starDecay</span></code></a>(sampling_times, …)</p></td>
-<td><p></p></td>
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-<dl class="py class">
-<dt id="cmrsim.signal_processes.solution_operators.t2_star.StaticT2starDecay">
-<em class="property">class </em><code class="sig-prename descclassname">cmrsim.signal_processes.solution_operators.t2_star.</code><code class="sig-name descname">StaticT2starDecay</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">sampling_times</span><span class="p">:</span> <span class="n">Union<span class="p">[</span>tensorflow.python.framework.ops.Tensor<span class="p">, </span>np.ndarray<span class="p">, </span>tensorflow.python.ops.ragged.ragged_tensor.RaggedTensor<span class="p">]</span></span></em>, <em class="sig-param"><span class="o">**</span><span class="n">kwargs</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/cmrsim/signal_processes/solution_operators/t2_star.html#StaticT2starDecay"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#cmrsim.signal_processes.solution_operators.t2_star.StaticT2starDecay" title="Permalink to this definition">¶</a></dt>
-<dd><p>Bases: <a class="reference internal" href="cmrsim.signal_processes.base.html#cmrsim.signal_processes.base.BaseSignalModel" title="cmrsim.signal_processes.base.BaseSignalModel"><code class="xref py py-class docutils literal notranslate"><span class="pre">cmrsim.signal_processes.base.BaseSignalModel</span></code></a></p>
-<p><strong>Attributes</strong></p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#cmrsim.signal_processes.solution_operators.t2_star.StaticT2starDecay.expansion_factor" title="cmrsim.signal_processes.solution_operators.t2_star.StaticT2starDecay.expansion_factor"><code class="xref py py-obj docutils literal notranslate"><span class="pre">expansion_factor</span></code></a></p></td>
-<td><p>int([x]) -> integer</p></td>
-</tr>
-<tr class="row-even"><td><p><a class="reference internal" href="#cmrsim.signal_processes.solution_operators.t2_star.StaticT2starDecay.required_quantities" title="cmrsim.signal_processes.solution_operators.t2_star.StaticT2starDecay.required_quantities"><code class="xref py py-obj docutils literal notranslate"><span class="pre">required_quantities</span></code></a></p></td>
-<td><p>Built-in immutable sequence.</p></td>
-</tr>
-<tr class="row-odd"><td><p><a class="reference internal" href="#cmrsim.signal_processes.solution_operators.t2_star.StaticT2starDecay.device" title="cmrsim.signal_processes.solution_operators.t2_star.StaticT2starDecay.device"><code class="xref py py-obj docutils literal notranslate"><span class="pre">device</span></code></a></p></td>
-<td><p>The most base type</p></td>
-</tr>
-</tbody>
-</table>
-<dl class="py attribute">
-<dt id="cmrsim.signal_processes.solution_operators.t2_star.StaticT2starDecay.device">
-<code class="sig-name descname">device</code><a class="headerlink" href="#cmrsim.signal_processes.solution_operators.t2_star.StaticT2starDecay.device" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py attribute">
-<dt id="cmrsim.signal_processes.solution_operators.t2_star.StaticT2starDecay.expansion_factor">
-<code class="sig-name descname">expansion_factor</code><em class="property">: int</em><em class="property"> = 1</em><a class="headerlink" href="#cmrsim.signal_processes.solution_operators.t2_star.StaticT2starDecay.expansion_factor" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py attribute">
-<dt id="cmrsim.signal_processes.solution_operators.t2_star.StaticT2starDecay.required_quantities">
-<code class="sig-name descname">required_quantities</code><em class="property">: Tuple<span class="p">[</span>str<span class="p">]</span></em><em class="property"> = ('T2star',)</em><a class="headerlink" href="#cmrsim.signal_processes.solution_operators.t2_star.StaticT2starDecay.required_quantities" title="Permalink to this definition">¶</a></dt>
-<dd></dd></dl>
-
-<dl class="py attribute">
-<dt id="cmrsim.signal_processes.solution_operators.t2_star.StaticT2starDecay.sampling_times">
-<code class="sig-name descname">sampling_times</code><em class="property"> = None</em><a class="headerlink" href="#cmrsim.signal_processes.solution_operators.t2_star.StaticT2starDecay.sampling_times" title="Permalink to this definition">¶</a></dt>
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-<span id="cmrsim-simulation-module"></span><h1>cmrsim.simulation module<a class="headerlink" href="#module-cmrsim.simulation" title="Permalink to this headline">¶</a></h1>
-<p><strong>Functions</strong></p>
-<table class="longtable docutils align-default">
-<colgroup>
-<col style="width: 10%" />
-<col style="width: 90%" />
-</colgroup>
-<tbody>
-<tr class="row-odd"><td><p><a class="reference internal" href="#cmrsim.simulation.simulate" title="cmrsim.simulation.simulate"><code class="xref py py-obj docutils literal notranslate"><span class="pre">simulate</span></code></a>(dataset, forward_model, …)</p></td>
-<td><p>Wrapper for the decorated (@tf.function) call of the simulation loop.</p></td>
-</tr>
-</tbody>
-</table>
-<dl class="py function">
-<dt id="cmrsim.simulation.simulate">
-<code class="sig-prename descclassname">cmrsim.simulation.</code><code class="sig-name descname">simulate</code><span class="sig-paren">(</span><em class="sig-param"><span class="n">dataset</span><span class="p">:</span> <span class="n">BaseDataset</span></em>, <em class="sig-param"><span class="n">forward_model</span><span class="p">:</span> <span class="n">CompositeSignalModel</span></em>, <em class="sig-param"><span class="n">encoding_module</span><span class="p">:</span> <span class="n">BaseSampling</span></em>, <em class="sig-param"><span class="n">recon_module</span><span class="p">:</span> <span class="n">Optional<span class="p">[</span>BaseRecon<span class="p">]</span></span> <span class="o">=</span> <span class="default_value">None</span></em>, <em class="sig-param"><span class="n">voxel_batch_size</span><span class="p">:</span> <span class="n">Optional<span class="p">[</span>int<span class="p">]</span></span> <span class="o">=</span> <span class="default_value">1000</span></em>, <em class="sig-param"><span class="n">execute_eager</span><span class="p">:</span> <span class="n">Optional<span class="p">[</span>bool<span class="p">]</span></span> <span class="o">=</span> <span class="default_value">False</span></em>, <em class="sig-param"><span class="n">graph_log_dir</span><span class="p">:</span> <span class="n">Optional<span class="p">[</span>str<span class="p">]</span></span> <span class="o">=</span> <span class="default_value">None</span></em><span class="sig-paren">)</span><a class="reference internal" href="_modules/cmrsim/simulation.html#simulate"><span class="viewcode-link">[source]</span></a><a class="headerlink" href="#cmrsim.simulation.simulate" title="Permalink to this definition">¶</a></dt>
-<dd><p>Wrapper for the decorated (@tf.function) call of the simulation loop.</p>
-<dl class="field-list simple">
-<dt class="field-odd">Parameters</dt>
-<dd class="field-odd"><ul class="simple">
-<li><p><strong>dataset</strong> – </p></li>
-<li><p><strong>forward_model</strong> – </p></li>
-<li><p><strong>encoding_module</strong> – </p></li>
-<li><p><strong>recon_module</strong> – Subclass of BaseRecon Module, Optional: If none the return tensor are the flat k-space images.</p></li>
-<li><p><strong>voxel_batch_size</strong> – (int) see documentation of _simulate_segmented_k_space.</p></li>
-<li><p><strong>execute_eager</strong> – (bool) Default False: if True, Graph construction is skipped for simulation (allows debugging)</p></li>
-<li><p><strong>graph_log_dir</strong> – enables graph tracing to save def with tf.summary</p></li>
-</ul>
-</dd>
-<dt class="field-even">Returns</dt>
-<dd class="field-even"><p><strong>(tf.Tensor)</strong> | Stack of images with shape (#images, …, #noise_levels, #X, #Y, #Z), or k-spaces with shape
-(#images, …, #noise_levels, #samples) where the ellipsis (…) depends on the specific forward model.
-The order in which the <em>CompositeSignalModel</em> calls the submodules determines the order of dimensions and number of
-axis in the ellipsis.</p>
-</dd>
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- <li><a href="cmrsim.encoding.base.html#cmrsim.encoding.base.BaseSampling.absolute_noise_std">absolute_noise_std (cmrsim.encoding.base.BaseSampling attribute)</a>
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- <li><a href="cmrsim.auxilliary.coordinates.html#cmrsim.auxilliary.coordinates.apply_transformations">apply_transformations() (in module cmrsim.auxilliary.coordinates)</a>
-</li>
- <li><a href="cmrsim.auxilliary.assign_map_values.html#cmrsim.auxilliary.assign_map_values.assign_random_normal_to_numerical_label_map">assign_random_normal_to_numerical_label_map() (in module cmrsim.auxilliary.assign_map_values)</a>
-</li>
- <li><a href="cmrsim.auxilliary.assign_map_values.html#cmrsim.auxilliary.assign_map_values.assign_to_numerical_label_map">assign_to_numerical_label_map() (in module cmrsim.auxilliary.assign_map_values)</a>
-</li>
- </ul></td>
-</tr></table>
-
-<h2 id="B">B</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="cmrsim.anatomy.base.html#cmrsim.anatomy.base.BaseDataset">BaseDataset (class in cmrsim.anatomy.base)</a>
-</li>
- <li><a href="cmrsim.reconstruction.base.html#cmrsim.reconstruction.base.BaseRecon">BaseRecon (class in cmrsim.reconstruction.base)</a>
-</li>
- </ul></td>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="cmrsim.encoding.base.html#cmrsim.encoding.base.BaseSampling">BaseSampling (class in cmrsim.encoding.base)</a>
-</li>
- <li><a href="cmrsim.signal_processes.base.html#cmrsim.signal_processes.base.BaseSignalModel">BaseSignalModel (class in cmrsim.signal_processes.base)</a>
-</li>
- </ul></td>
-</tr></table>
-
-<h2 id="C">C</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="cmrsim.auxilliary.snr.html#cmrsim.auxilliary.snr.calculate_snr">calculate_snr() (in module cmrsim.auxilliary.snr)</a>
-</li>
- <li><a href="cmrsim.reconstruction.cartesian.html#cmrsim.reconstruction.cartesian.Cartesian2D">Cartesian2D (class in cmrsim.reconstruction.cartesian)</a>
-</li>
- <li><a href="cmrsim.signal_processes.base.html#cmrsim.signal_processes.base.BaseSignalModel.check_inputs">check_inputs() (cmrsim.signal_processes.base.BaseSignalModel method)</a>
-</li>
- <li>
- cmrsim
-
- <ul>
- <li><a href="cmrsim.html#module-cmrsim">module</a>
-</li>
- </ul></li>
- <li>
- cmrsim.anatomy
-
- <ul>
- <li><a href="cmrsim.anatomy.html#module-cmrsim.anatomy">module</a>
-</li>
- </ul></li>
- <li>
- cmrsim.anatomy.base
-
- <ul>
- <li><a href="cmrsim.anatomy.base.html#module-cmrsim.anatomy.base">module</a>
-</li>
- </ul></li>
- <li>
- cmrsim.anatomy.from_files
-
- <ul>
- <li><a href="cmrsim.anatomy.from_files.html#module-cmrsim.anatomy.from_files">module</a>
-</li>
- </ul></li>
- <li>
- cmrsim.auxilliary
-
- <ul>
- <li><a href="cmrsim.auxilliary.html#module-cmrsim.auxilliary">module</a>
-</li>
- </ul></li>
- <li>
- cmrsim.auxilliary.assign_map_values
-
- <ul>
- <li><a href="cmrsim.auxilliary.assign_map_values.html#module-cmrsim.auxilliary.assign_map_values">module</a>
-</li>
- </ul></li>
- <li>
- cmrsim.auxilliary.coordinates
-
- <ul>
- <li><a href="cmrsim.auxilliary.coordinates.html#module-cmrsim.auxilliary.coordinates">module</a>
-</li>
- </ul></li>
- <li>
- cmrsim.auxilliary.plot_utilities
-
- <ul>
- <li><a href="cmrsim.auxilliary.plot_utilities.html#module-cmrsim.auxilliary.plot_utilities">module</a>
-</li>
- </ul></li>
- <li>
- cmrsim.auxilliary.sequence
-
- <ul>
- <li><a href="cmrsim.auxilliary.sequence.html#module-cmrsim.auxilliary.sequence">module</a>
-</li>
- </ul></li>
- <li>
- cmrsim.auxilliary.snr
-
- <ul>
- <li><a href="cmrsim.auxilliary.snr.html#module-cmrsim.auxilliary.snr">module</a>
-</li>
- </ul></li>
- <li>
- cmrsim.encoding
-
- <ul>
- <li><a href="cmrsim.encoding.html#module-cmrsim.encoding">module</a>
-</li>
- </ul></li>
- <li>
- cmrsim.encoding.base
-
- <ul>
- <li><a href="cmrsim.encoding.base.html#module-cmrsim.encoding.base">module</a>
-</li>
- </ul></li>
- <li>
- cmrsim.encoding.epi
-
- <ul>
- <li><a href="cmrsim.encoding.epi.html#module-cmrsim.encoding.epi">module</a>
-</li>
- </ul></li>
- </ul></td>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li>
- cmrsim.reconstruction
-
- <ul>
- <li><a href="cmrsim.reconstruction.html#module-cmrsim.reconstruction">module</a>
-</li>
- </ul></li>
- <li>
- cmrsim.reconstruction.base
-
- <ul>
- <li><a href="cmrsim.reconstruction.base.html#module-cmrsim.reconstruction.base">module</a>
-</li>
- </ul></li>
- <li>
- cmrsim.reconstruction.cartesian
-
- <ul>
- <li><a href="cmrsim.reconstruction.cartesian.html#module-cmrsim.reconstruction.cartesian">module</a>
-</li>
- </ul></li>
- <li>
- cmrsim.signal_processes
-
- <ul>
- <li><a href="cmrsim.signal_processes.html#module-cmrsim.signal_processes">module</a>
-</li>
- </ul></li>
- <li>
- cmrsim.signal_processes.base
-
- <ul>
- <li><a href="cmrsim.signal_processes.base.html#module-cmrsim.signal_processes.base">module</a>
-</li>
- </ul></li>
- <li>
- cmrsim.signal_processes.bloch_simulation
-
- <ul>
- <li><a href="cmrsim.signal_processes.bloch_simulation.html#module-cmrsim.signal_processes.bloch_simulation">module</a>
-</li>
- </ul></li>
- <li>
- cmrsim.signal_processes.solution_operators
-
- <ul>
- <li><a href="cmrsim.signal_processes.solution_operators.html#module-cmrsim.signal_processes.solution_operators">module</a>
-</li>
- </ul></li>
- <li>
- cmrsim.signal_processes.solution_operators.coil_sensitivities
-
- <ul>
- <li><a href="cmrsim.signal_processes.solution_operators.coil_sensitivities.html#module-cmrsim.signal_processes.solution_operators.coil_sensitivities">module</a>
-</li>
- </ul></li>
- <li>
- cmrsim.signal_processes.solution_operators.diffusion_weighting
-
- <ul>
- <li><a href="cmrsim.signal_processes.solution_operators.diffusion_weighting.html#module-cmrsim.signal_processes.solution_operators.diffusion_weighting">module</a>
-</li>
- </ul></li>
- <li>
- cmrsim.signal_processes.solution_operators.sequences
-
- <ul>
- <li><a href="cmrsim.signal_processes.solution_operators.sequences.html#module-cmrsim.signal_processes.solution_operators.sequences">module</a>
-</li>
- </ul></li>
- <li>
- cmrsim.signal_processes.solution_operators.t2_star
-
- <ul>
- <li><a href="cmrsim.signal_processes.solution_operators.t2_star.html#module-cmrsim.signal_processes.solution_operators.t2_star">module</a>
-</li>
- </ul></li>
- <li>
- cmrsim.simulation
-
- <ul>
- <li><a href="cmrsim.simulation.html#module-cmrsim.simulation">module</a>
-</li>
- </ul></li>
- <li><a href="cmrsim.signal_processes.solution_operators.coil_sensitivities.html#cmrsim.signal_processes.solution_operators.coil_sensitivities.CoilSensitivity">CoilSensitivity (class in cmrsim.signal_processes.solution_operators.coil_sensitivities)</a>
-</li>
- <li><a href="cmrsim.signal_processes.base.html#cmrsim.signal_processes.base.CompositeSignalModel">CompositeSignalModel (class in cmrsim.signal_processes.base)</a>
-</li>
- <li><a href="cmrsim.auxilliary.snr.html#cmrsim.auxilliary.snr.compute_noise_std">compute_noise_std() (in module cmrsim.auxilliary.snr)</a>
-</li>
- <li><a href="cmrsim.signal_processes.solution_operators.diffusion_weighting.html#cmrsim.signal_processes.solution_operators.diffusion_weighting.ConstantGaussianDiffusion">ConstantGaussianDiffusion (class in cmrsim.signal_processes.solution_operators.diffusion_weighting)</a>
-</li>
- </ul></td>
-</tr></table>
-
-<h2 id="D">D</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="cmrsim.encoding.base.html#cmrsim.encoding.base.BaseSampling.device">device (cmrsim.encoding.base.BaseSampling attribute)</a>
-
- <ul>
- <li><a href="cmrsim.encoding.epi.html#cmrsim.encoding.epi.EPI.device">(cmrsim.encoding.epi.EPI attribute)</a>
-</li>
- <li><a href="cmrsim.signal_processes.base.html#cmrsim.signal_processes.base.BaseSignalModel.device">(cmrsim.signal_processes.base.BaseSignalModel attribute)</a>
-</li>
- <li><a href="cmrsim.signal_processes.base.html#cmrsim.signal_processes.base.LookUpTableModule.device">(cmrsim.signal_processes.base.LookUpTableModule attribute)</a>
-</li>
- <li><a href="cmrsim.signal_processes.solution_operators.diffusion_weighting.html#cmrsim.signal_processes.solution_operators.diffusion_weighting.ConstantGaussianDiffusion.device">(cmrsim.signal_processes.solution_operators.diffusion_weighting.ConstantGaussianDiffusion attribute)</a>
-</li>
- <li><a href="cmrsim.signal_processes.solution_operators.t2_star.html#cmrsim.signal_processes.solution_operators.t2_star.StaticT2starDecay.device">(cmrsim.signal_processes.solution_operators.t2_star.StaticT2starDecay attribute)</a>
-</li>
- </ul></li>
- </ul></td>
-</tr></table>
-
-<h2 id="E">E</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="cmrsim.encoding.epi.html#cmrsim.encoding.epi.EPI">EPI (class in cmrsim.encoding.epi)</a>
-</li>
- <li><a href="cmrsim.signal_processes.base.html#cmrsim.signal_processes.base.BaseSignalModel.expansion_factor">expansion_factor (cmrsim.signal_processes.base.BaseSignalModel attribute)</a>
-
- <ul>
- <li><a href="cmrsim.signal_processes.solution_operators.sequences.html#cmrsim.signal_processes.solution_operators.sequences.SpinEcho.expansion_factor">(cmrsim.signal_processes.solution_operators.sequences.SpinEcho attribute)</a>
-</li>
- <li><a href="cmrsim.signal_processes.solution_operators.t2_star.html#cmrsim.signal_processes.solution_operators.t2_star.StaticT2starDecay.expansion_factor">(cmrsim.signal_processes.solution_operators.t2_star.StaticT2starDecay attribute)</a>
-</li>
- </ul></li>
- </ul></td>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="cmrsim.signal_processes.base.html#cmrsim.signal_processes.base.CompositeSignalModel.expected_number_of_repetitions">expected_number_of_repetitions() (cmrsim.signal_processes.base.CompositeSignalModel property)</a>
-</li>
- </ul></td>
-</tr></table>
-
-<h2 id="G">G</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="cmrsim.encoding.base.html#cmrsim.encoding.base.BaseSampling.get_sampling_times">get_sampling_times() (cmrsim.encoding.base.BaseSampling method)</a>
-</li>
- </ul></td>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="cmrsim.auxilliary.coordinates.html#cmrsim.auxilliary.coordinates.get_static_2d_centered_coordinates">get_static_2d_centered_coordinates() (in module cmrsim.auxilliary.coordinates)</a>
-</li>
- <li><a href="cmrsim.auxilliary.coordinates.html#cmrsim.auxilliary.coordinates.get_translation_matrix">get_translation_matrix() (in module cmrsim.auxilliary.coordinates)</a>
-</li>
- </ul></td>
-</tr></table>
-
-<h2 id="K">K</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="cmrsim.encoding.base.html#cmrsim.encoding.base.BaseSampling.k_space_segments">k_space_segments (cmrsim.encoding.base.BaseSampling attribute)</a>
-
- <ul>
- <li><a href="cmrsim.encoding.epi.html#cmrsim.encoding.epi.EPI.k_space_segments">(cmrsim.encoding.epi.EPI attribute)</a>
-</li>
- </ul></li>
- </ul></td>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="cmrsim.encoding.base.html#cmrsim.encoding.base.BaseSampling.k_space_vectors">k_space_vectors (cmrsim.encoding.base.BaseSampling attribute)</a>
-
- <ul>
- <li><a href="cmrsim.encoding.epi.html#cmrsim.encoding.epi.EPI.k_space_vectors">(cmrsim.encoding.epi.EPI attribute)</a>
-</li>
- </ul></li>
- </ul></td>
-</tr></table>
-
-<h2 id="L">L</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="cmrsim.signal_processes.base.html#cmrsim.signal_processes.base.LookUpTableModule">LookUpTableModule (class in cmrsim.signal_processes.base)</a>
-</li>
- </ul></td>
-</tr></table>
-
-<h2 id="M">M</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="cmrsim.anatomy.base.html#cmrsim.anatomy.base.BaseDataset.map_names">map_names (cmrsim.anatomy.base.BaseDataset attribute)</a>
-
- <ul>
- <li><a href="cmrsim.anatomy.from_files.html#cmrsim.anatomy.from_files.MultiFileDataset.map_names">(cmrsim.anatomy.from_files.MultiFileDataset attribute)</a>
-</li>
- </ul></li>
- <li>
- module
-
- <ul>
- <li><a href="cmrsim.html#module-cmrsim">cmrsim</a>
-</li>
- <li><a href="cmrsim.anatomy.html#module-cmrsim.anatomy">cmrsim.anatomy</a>
-</li>
- <li><a href="cmrsim.anatomy.base.html#module-cmrsim.anatomy.base">cmrsim.anatomy.base</a>
-</li>
- <li><a href="cmrsim.anatomy.from_files.html#module-cmrsim.anatomy.from_files">cmrsim.anatomy.from_files</a>
-</li>
- <li><a href="cmrsim.auxilliary.html#module-cmrsim.auxilliary">cmrsim.auxilliary</a>
-</li>
- <li><a href="cmrsim.auxilliary.assign_map_values.html#module-cmrsim.auxilliary.assign_map_values">cmrsim.auxilliary.assign_map_values</a>
-</li>
- <li><a href="cmrsim.auxilliary.coordinates.html#module-cmrsim.auxilliary.coordinates">cmrsim.auxilliary.coordinates</a>
-</li>
- <li><a href="cmrsim.auxilliary.plot_utilities.html#module-cmrsim.auxilliary.plot_utilities">cmrsim.auxilliary.plot_utilities</a>
-</li>
- <li><a href="cmrsim.auxilliary.sequence.html#module-cmrsim.auxilliary.sequence">cmrsim.auxilliary.sequence</a>
-</li>
- <li><a href="cmrsim.auxilliary.snr.html#module-cmrsim.auxilliary.snr">cmrsim.auxilliary.snr</a>
-</li>
- <li><a href="cmrsim.encoding.html#module-cmrsim.encoding">cmrsim.encoding</a>
-</li>
- <li><a href="cmrsim.encoding.base.html#module-cmrsim.encoding.base">cmrsim.encoding.base</a>
-</li>
- <li><a href="cmrsim.encoding.epi.html#module-cmrsim.encoding.epi">cmrsim.encoding.epi</a>
-</li>
- <li><a href="cmrsim.reconstruction.html#module-cmrsim.reconstruction">cmrsim.reconstruction</a>
-</li>
- <li><a href="cmrsim.reconstruction.base.html#module-cmrsim.reconstruction.base">cmrsim.reconstruction.base</a>
-</li>
- <li><a href="cmrsim.reconstruction.cartesian.html#module-cmrsim.reconstruction.cartesian">cmrsim.reconstruction.cartesian</a>
-</li>
- <li><a href="cmrsim.signal_processes.html#module-cmrsim.signal_processes">cmrsim.signal_processes</a>
-</li>
- <li><a href="cmrsim.signal_processes.base.html#module-cmrsim.signal_processes.base">cmrsim.signal_processes.base</a>
-</li>
- <li><a href="cmrsim.signal_processes.bloch_simulation.html#module-cmrsim.signal_processes.bloch_simulation">cmrsim.signal_processes.bloch_simulation</a>
-</li>
- <li><a href="cmrsim.signal_processes.solution_operators.html#module-cmrsim.signal_processes.solution_operators">cmrsim.signal_processes.solution_operators</a>
-</li>
- <li><a href="cmrsim.signal_processes.solution_operators.coil_sensitivities.html#module-cmrsim.signal_processes.solution_operators.coil_sensitivities">cmrsim.signal_processes.solution_operators.coil_sensitivities</a>
-</li>
- <li><a href="cmrsim.signal_processes.solution_operators.diffusion_weighting.html#module-cmrsim.signal_processes.solution_operators.diffusion_weighting">cmrsim.signal_processes.solution_operators.diffusion_weighting</a>
-</li>
- <li><a href="cmrsim.signal_processes.solution_operators.sequences.html#module-cmrsim.signal_processes.solution_operators.sequences">cmrsim.signal_processes.solution_operators.sequences</a>
-</li>
- <li><a href="cmrsim.signal_processes.solution_operators.t2_star.html#module-cmrsim.signal_processes.solution_operators.t2_star">cmrsim.signal_processes.solution_operators.t2_star</a>
-</li>
- <li><a href="cmrsim.simulation.html#module-cmrsim.simulation">cmrsim.simulation</a>
-</li>
- </ul></li>
- </ul></td>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="cmrsim.anatomy.from_files.html#cmrsim.anatomy.from_files.MultiFileDataset">MultiFileDataset (class in cmrsim.anatomy.from_files)</a>
-</li>
- </ul></td>
-</tr></table>
-
-<h2 id="N">N</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="cmrsim.encoding.base.html#cmrsim.encoding.base.BaseSampling.number_of_samples">number_of_samples (cmrsim.encoding.base.BaseSampling attribute)</a>
-
- <ul>
- <li><a href="cmrsim.encoding.epi.html#cmrsim.encoding.epi.EPI.number_of_samples">(cmrsim.encoding.epi.EPI attribute)</a>
-</li>
- </ul></li>
- </ul></td>
-</tr></table>
-
-<h2 id="O">O</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="cmrsim.auxilliary.plot_utilities.html#cmrsim.auxilliary.plot_utilities.SlideShow.onclick">onclick() (cmrsim.auxilliary.plot_utilities.SlideShow method)</a>
-</li>
- <li><a href="cmrsim.reconstruction.base.html#cmrsim.reconstruction.base.BaseRecon.output_matrix_size">output_matrix_size (cmrsim.reconstruction.base.BaseRecon attribute)</a>
-
- <ul>
- <li><a href="cmrsim.reconstruction.cartesian.html#cmrsim.reconstruction.cartesian.Cartesian2D.output_matrix_size">(cmrsim.reconstruction.cartesian.Cartesian2D attribute)</a>
-</li>
- <li><a href="cmrsim.reconstruction.cartesian.html#cmrsim.reconstruction.cartesian.RoemerMultiCoil.output_matrix_size">(cmrsim.reconstruction.cartesian.RoemerMultiCoil attribute)</a>
-</li>
- </ul></li>
- </ul></td>
-</tr></table>
-
-<h2 id="R">R</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="cmrsim.signal_processes.base.html#cmrsim.signal_processes.base.BaseSignalModel.required_quantities">required_quantities (cmrsim.signal_processes.base.BaseSignalModel attribute)</a>
-
- <ul>
- <li><a href="cmrsim.signal_processes.base.html#cmrsim.signal_processes.base.CompositeSignalModel.required_quantities">(cmrsim.signal_processes.base.CompositeSignalModel attribute)</a>
-</li>
- <li><a href="cmrsim.signal_processes.base.html#cmrsim.signal_processes.base.LookUpTableModule.required_quantities">(cmrsim.signal_processes.base.LookUpTableModule attribute)</a>
-</li>
- <li><a href="cmrsim.signal_processes.solution_operators.coil_sensitivities.html#cmrsim.signal_processes.solution_operators.coil_sensitivities.CoilSensitivity.required_quantities">(cmrsim.signal_processes.solution_operators.coil_sensitivities.CoilSensitivity attribute)</a>
-</li>
- <li><a href="cmrsim.signal_processes.solution_operators.diffusion_weighting.html#cmrsim.signal_processes.solution_operators.diffusion_weighting.ConstantGaussianDiffusion.required_quantities">(cmrsim.signal_processes.solution_operators.diffusion_weighting.ConstantGaussianDiffusion attribute)</a>
-</li>
- <li><a href="cmrsim.signal_processes.solution_operators.sequences.html#cmrsim.signal_processes.solution_operators.sequences.SpinEcho.required_quantities">(cmrsim.signal_processes.solution_operators.sequences.SpinEcho attribute)</a>
-</li>
- <li><a href="cmrsim.signal_processes.solution_operators.t2_star.html#cmrsim.signal_processes.solution_operators.t2_star.StaticT2starDecay.required_quantities">(cmrsim.signal_processes.solution_operators.t2_star.StaticT2starDecay attribute)</a>
-</li>
- </ul></li>
- </ul></td>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="cmrsim.reconstruction.cartesian.html#cmrsim.reconstruction.cartesian.RoemerMultiCoil">RoemerMultiCoil (class in cmrsim.reconstruction.cartesian)</a>
-</li>
- </ul></td>
-</tr></table>
-
-<h2 id="S">S</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
- <td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="cmrsim.encoding.base.html#cmrsim.encoding.base.BaseSampling.sampling_times">sampling_times (cmrsim.encoding.base.BaseSampling attribute)</a>
-
- <ul>
- <li><a href="cmrsim.encoding.epi.html#cmrsim.encoding.epi.EPI.sampling_times">(cmrsim.encoding.epi.EPI attribute)</a>
-</li>
- <li><a href="cmrsim.signal_processes.solution_operators.t2_star.html#cmrsim.signal_processes.solution_operators.t2_star.StaticT2starDecay.sampling_times">(cmrsim.signal_processes.solution_operators.t2_star.StaticT2starDecay attribute)</a>
-</li>
- </ul></li>
- <li><a href="cmrsim.anatomy.base.html#cmrsim.anatomy.base.BaseDataset.set_size">set_size (cmrsim.anatomy.base.BaseDataset attribute)</a>
-
- <ul>
- <li><a href="cmrsim.anatomy.from_files.html#cmrsim.anatomy.from_files.MultiFileDataset.set_size">(cmrsim.anatomy.from_files.MultiFileDataset attribute)</a>
-</li>
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-</li>
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-</li>
- <li><a href="cmrsim.signal_processes.solution_operators.t2_star.html#cmrsim.signal_processes.solution_operators.t2_star.StaticT2starDecay">StaticT2starDecay (class in cmrsim.signal_processes.solution_operators.t2_star)</a>
-</li>
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-
-<h2 id="T">T</h2>
-<table style="width: 100%" class="indextable genindextable"><tr>
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-</li>
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-</li>
- </ul></td>
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-<table style="width: 100%" class="indextable genindextable"><tr>
- <td style="width: 33%; vertical-align: top;"><ul>
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-</li>
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- <td style="width: 33%; vertical-align: top;"><ul>
- <li><a href="cmrsim.signal_processes.base.html#cmrsim.signal_processes.base.CompositeSignalModel.unstacked_axis_names">unstacked_axis_names() (cmrsim.signal_processes.base.CompositeSignalModel property)</a>
-</li>
- <li><a href="cmrsim.auxilliary.plot_utilities.html#cmrsim.auxilliary.plot_utilities.SlideShow.update">update() (cmrsim.auxilliary.plot_utilities.SlideShow method)</a>
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- <table class="indextable modindextable">
- <tr class="pcap"><td></td><td> </td><td></td></tr>
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method"]},objtypes:{"0":"py:module","1":"py:class","2":"py:attribute","3":"py:function","4":"py:method"},terms:{"case":[13,19],"class":[4,5,7,9,12,13,14,16,17,19,22,23,24,25],"default":[8,11,26],"float":[5,7,8,11,13,14,19,22,24],"function":[6,7,8,11,26],"int":[4,8,14,16,17,19,25,26],"long":7,"return":[7,8,11,13,19,26],"true":[11,26],Axes:9,The:[6,8,12,26],Uses:19,_ax:9,_simulate_segmented_k_spac:26,absolute_noise_std:[13,14],accord:[7,11],acquisit:11,acquisition_start:14,actual:12,add:13,add_nois:13,advanc:0,all:[8,13,19],allow:26,along:8,analys:6,anatomi:[2,27],anim:9,append:13,applic:0,apply_transform:8,arg:19,arrai:[8,11],array_dictionari:4,assert:19,assign:7,assign_map_valu:[2,6],assign_random_normal_to_numerical_label_map:7,assign_to_numerical_label_map:7,assum:[7,11,19],attribut:[4,5,13,14,16,17,19,22,23,24,25],auxilliari:[2,27],axes:9,axi:[8,13,19,26],b_vector:23,background:7,bandwidth_per_pixel:14,base:[2,3,5,9,12,14,15,17,18,22,23,24,25],basedataset:[4,5,26],baserecon:[16,17,26],basesampl:[13,14,26],basesignalmodel:[19,23,24,25],batch:[7,8],batch_dimens:8,batch_siz:8,binari:11,blip_dur:14,bloch_simul:[2,18],bool:[11,26],both:19,calcul:[6,8,11,12],calculate_snr:11,call:[19,26],cartesian2d:17,cartesian:[2,15],center:8,check_input:19,citat:27,cite:27,coil:11,coil_sensit:[11,17,18,21],coilsensit:22,coincid:8,collect:[4,6],commonli:6,complex64:11,compositesignalmodel:[19,26],comput:11,compute_noise_std:11,constantgaussiandiffus:23,construct:[8,19,26],contain:[6,8,12,13],content:27,contrast:8,coordin:[2,6],correct:11,correspond:12,coupl:[0,28],data:[6,9],data_dir:5,dataset:26,debug:26,decor:26,def:26,defin:[8,13],definit:12,dens:8,depend:[13,26],determin:26,deviat:7,devic:[13,14,19,23,25],differ:13,differenti:27,diffusion_tensor:23,diffusion_weight:[18,21],dimens:[8,19,26],displac:8,displacement_vector:8,document:26,doi:11,done:12,dure:19,dynamic_noise_scan:11,each:[8,12],echo_tim:24,element:8,ellipsi:26,enabl:26,encod:[2,27],encoding_modul:26,entir:11,epi:[2,12],estim:11,estimated_std:11,evalu:12,even:8,event:[9,13],exampl:[27,28],exclud:7,execute_eag:26,expand:13,expans:19,expansion_factor:[19,24,25],expected_number_of_repetit:19,extens:8,factor:19,fals:26,field_of_view:14,figur:9,fill:11,flat:26,float32:11,folder_nam:5,format:[8,13],forward:26,forward_model:26,fourier:12,fraction:7,framework:[8,11,13,16,17,19,22,24,25,27],from:[13,19],from_fil:[2,3],get:[8,27],get_sampling_tim:13,get_static_2d_centered_coordin:8,get_translation_matrix:8,getter:13,given:11,gorkum:11,graph:[19,26],graph_log_dir:26,grid:8,guarante:11,here:[0,28],homogen:8,how:27,imag:[8,11,19,26,27],implement:[11,12,13,27],increas:7,increment:7,index:[8,19,27],indic:[5,8],inform:19,inherit:13,initi:8,innerproduct:8,input:8,inspect:6,instanti:13,integ:7,introductori:28,isochrom:8,iter:[5,7,11,13,17],iteratively_refin:11,k_space_sampl:8,k_space_seg:[13,14],k_space_vector:[13,14],kwarg:[9,11,13,14,16,17,19,22,23,24,25],label:7,label_map:7,last:13,least:7,like:[1,11],list:[7,8,9,14],look_up_map3d:19,lookuptablemodul:[19,22],loop:26,map:8,map_dimens:[19,22],map_nam:[4,5],map_siz:8,mask:11,match:8,matplotlib:9,matric:8,matrix:8,max_valu:7,maximum:7,mean:7,meant:[8,13,19],membership:7,method:[9,11,13,19],mid:8,miss:19,model:26,modul:27,mor:8,mri:[12,27],mrm:11,mrxcat:11,multicoil:11,multifiledataset:5,n_coil:11,n_segment:13,name:[12,13,16,19],ndarrai:[7,8,9,11,13,14,17,19,22,25],need:[7,8,13],nois:13,noise_level:26,noiseless_single_coil_imag:11,non:11,none:[5,8,9,11,13,14,17,19,25,26],normal:7,number:[13,26],number_of_sampl:[13,14],numpi:[7,8,9,11,13],object:9,object_dimens:[5,8],onclick:9,one:[8,13],ones:[8,11],ops:[8,11,13,16,17,19,22,24,25],optim:27,option:[5,8,11,14,17,19,26],order:[19,26],ordereddict:4,origin:8,output:8,output_matrix_s:[16,17],packag:27,pad:17,page:27,paramet:[7,8,11,13,26],pass:19,perform:8,phase:12,pixel:7,place:8,plot_util:[2,6],point:[8,12],posit:8,prepar:6,previous:11,process:12,project:11,properti:19,property_nam:5,python:[4,8,11,13,16,17,19,22,24,25,27],quantiti:19,r_new:8,r_old:8,r_vector:[8,19,22],rag:25,ragged_tensor:25,raggedtensor:[13,25],rais:19,random:7,read_out_dur:14,recon_modul:26,reconstruct:[2,11,27],refer:1,refin:11,rel:8,repeat:8,repet:8,repetit:[8,19],repetition_tim:24,repres:19,requir:[8,19],required_quant:[19,22,23,24,25],reserv:8,reshap:8,respect:8,result:6,robbert:11,roemermulticoil:17,roi:11,rotat:8,s_of_k:13,same:8,sampl:[7,8,13,19,26],sample_matrix_s:17,sampling_matrix_s:14,sampling_tim:[13,14,25],save:26,save_directori:9,scale:[8,11],search:27,second:[13,19],see:26,segment:13,separ:8,sequenc:[2,6,18,21],set_siz:[4,5],shape:[8,11,13,26],share:12,shift:8,should:[8,13],signal:13,signal_process:[2,27],simpl:27,simul:[2,6,8,13,19,27],simulation_result:19,single_coil_imag:11,singleton:8,skip:26,slideshow:9,snr:[2,6],snr_map:11,solution_oper:[2,18],sourc:[4,5,7,8,9,11,13,14,16,17,19,22,23,24,25,26],space:[12,13,19,26],specif:26,specifi:[11,13,19],spinecho:24,stack:[19,26],standard:[7,13],start:27,state:7,statict2stardecai:25,std:[7,11],str:[5,13,16,19,23,25,26],sub:19,subclass:[12,26],submodul:[26,27],subpackag:27,summari:26,t2_star:[18,21],t2star:25,take:8,target_snr:11,tensor:[8,11,13,16,17,19,22,24,25,26],tensorflow:[4,8,11,13,16,17,19,22,24,25,27],than:8,thei:8,thi:[6,11,12,19],tile:8,time:13,trace:26,trajectori:[12,13],transform:8,translat:8,treat:8,tupl:[4,5,7,8,14,16,17,19,22,23,25],type:11,uneven:8,union:[7,8,11,13,14,16,17,19,22,24,25],unstack:19,unstack_repetit:19,unstacked_axis_nam:19,updat:9,used:[6,8,11,13,19],uses:11,using:27,valu:[7,27],valueerror:19,van:11,variabl:[7,13,24],vector:8,voxel:8,voxel_batch_s:26,when:13,where:26,which:26,who:27,wrapper:26,zero:11},titles:["1. Optimize b-values","Citation","cmrsim package","cmrsim.anatomy package","cmrsim.anatomy.build module","cmrsim.anatomy.from_files module","cmrsim.auxilliary package","cmrsim.auxilliary.assign_map_values module","cmrsim.auxilliary.coordinates module","cmrsim.auxilliary.plot_utilities module","cmrsim.auxilliary.sequence package","cmrsim.auxilliary.snr module","cmrsim.encoding package","cmrsim.encoding.build module","cmrsim.encoding.epi module","cmrsim.reconstruction package","cmrsim.reconstruction.build module","cmrsim.reconstruction.cartesian module","cmrsim.signal_processes package","cmrsim.signal_processes.build module","cmrsim.signal_processes.bloch_simulation package","cmrsim.signal_processes.solution_operators package","cmrsim.signal_processes.solution_operators.coil_sensitivities module","cmrsim.signal_processes.solution_operators.diffusion_weighting module","cmrsim.signal_processes.solution_operators.sequences module","cmrsim.signal_processes.solution_operators.t2_star module","cmrsim.simulation module","Welcome to cmrsim\u2019s documentation!","Simulate a simple image"],titleterms:{anatomi:[3,4,5],assign_map_valu:7,auxilliari:[6,7,8,9,10,11],base:[4,13,16,19],bloch_simul:20,cartesian:17,citat:1,cmrsim:[2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26,27],coil_sensit:22,content:[2,3,6,10,12,15,18,20,21],coordin:8,diffusion_weight:23,document:27,encod:[12,13,14],epi:14,from_fil:5,imag:28,indic:27,modul:[2,3,4,5,6,7,8,9,10,11,12,13,14,15,16,17,18,19,20,21,22,23,24,25,26],optim:0,packag:[2,3,6,10,12,15,18,20,21],plot_util:9,reconstruct:[15,16,17],sequenc:[10,24],signal_process:[18,19,20,21,22,23,24,25],simpl:28,simul:[26,28],snr:11,solution_oper:[21,22,23,24,25],submodul:[2,3,6,12,15,18,21],subpackag:[2,6,18],t2_star:25,tabl:27,valu:0,welcom:27}})
\ No newline at end of file
diff --git a/public/started.html b/public/started.html
deleted file mode 100644
index 207f250165be84cd9325d08ff3c10ab8ac8602b8..0000000000000000000000000000000000000000
--- a/public/started.html
+++ /dev/null
@@ -1,226 +0,0 @@
-
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-<!DOCTYPE html>
-<html class="writer-html5" lang="en" >
-<head>
- <meta charset="utf-8">
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- <link rel="stylesheet" href="_static/pygments.css" type="text/css" />
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- <script src="_static/js/html5shiv.min.js"></script>
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- <link rel="search" title="Search" href="search.html" />
- <link rel="next" title="cmrsim package" href="cmrsim.html" />
- <link rel="prev" title="Welcome to cmrsim’s documentation!" href="index.html" />
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- <div class="wy-side-scroll">
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-<div role="search">
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- <input type="text" name="q" placeholder="Search docs" />
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- </form>
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- <div class="wy-menu wy-menu-vertical" data-spy="affix" role="navigation" aria-label="main navigation">
-
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-
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- <p class="caption"><span class="caption-text">Getting started</span></p>
-<ul class="current">
-<li class="toctree-l1 current"><a class="current reference internal" href="#">Simulate a simple image</a></li>
-</ul>
-<p class="caption"><span class="caption-text">Documentation</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="cmrsim.html">cmrsim package</a></li>
-</ul>
-<p class="caption"><span class="caption-text">Who is using it? - Examples</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="advanced_examples.html">1. Optimize b-values</a></li>
-</ul>
-<p class="caption"><span class="caption-text">How to cite?</span></p>
-<ul>
-<li class="toctree-l1"><a class="reference internal" href="citation.html">Citation</a></li>
-</ul>
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-</div>
- <div role="main" class="document" itemscope="itemscope" itemtype="http://schema.org/Article">
- <div itemprop="articleBody">
-
- <div class="section" id="simulate-a-simple-image">
-<h1>Simulate a simple image<a class="headerlink" href="#simulate-a-simple-image" title="Permalink to this headline">¶</a></h1>
-<p>Here are a couple of introductory examples…</p>
-</div>
-
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- </div>
-
- </div>
- <footer>
-
- <div class="rst-footer-buttons" role="navigation" aria-label="footer navigation">
-
- <a href="cmrsim.html" class="btn btn-neutral float-right" title="cmrsim package" accesskey="n" rel="next">Next <span class="fa fa-arrow-circle-right"></span></a>
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-
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- </div>
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diff --git a/requirements.txt b/requirements.txt
index c478c67e58cd0f5cb2c5abece24eb0d2124b6209..1c74e3ff0f31d83a6a12c030ccbed4d30a4b03c8 100644
--- a/requirements.txt
+++ b/requirements.txt
@@ -1,5 +1,14 @@
-numpy>=1.18.1
-matplotlib>=3.1.3
+numpy~=1.18.5
+matplotlib==3.3.2
setuptools>=49.1.0
keras-tqdm
click>=7.0
+
+tensorflow-probability==0.11.1
+matlab
+
+h5py~=2.10.0
+scipy>=1.4.1
+
+#tensorflow~=2.3.0
+#tensorflow-gpu~=2.3.0
\ No newline at end of file
diff --git a/setup.py b/setup.py
index 9af6bcaa39375043d16195b0e5733b938ddf4ed1..aa3b9bdd9425cbc5dbbd58f10e1d8478804e9f77 100644
--- a/setup.py
+++ b/setup.py
@@ -3,7 +3,6 @@
import setuptools
import os
-import glob
import shutil
import sys
@@ -15,7 +14,7 @@ with open(f'{ROOT_PATH}/README.md', 'r') as file:
project_name = "cmrsim"
author_list = ["Jonathan Weine", ]
author_email_list = ["weine@biomed.ee.ethz.ch", ]
-url = "https://git.ee.ethz.ch/jweine/cmrsim"
+url = "https://gitlab.ethz.ch/jweine/cmrsim"
# Get version tag
if '--version' in sys.argv:
@@ -29,18 +28,6 @@ else:
with open('cmrsim/__init__.py', 'a') as module_header_file:
module_header_file.write(f'__version__ = "{current_version}"')
-# Prepare package_data
-notebooks = [f for f in glob.glob(f'{ROOT_PATH}/notebooks/**.ipynb', recursive=True)]
-resource_files = [f for f in glob.glob(f'{ROOT_PATH}/notebooks/**/*.npy', recursive=True)]
-os.makedirs('cmrsim/_bin/notebooks'), os.makedirs('cmrsim/_bin/notebooks/example_resources')
-for nb in notebooks:
- shutil.copy(nb, f'cmrsim/_bin/notebooks/{os.path.basename(nb)}')
-for res in resource_files:
- shutil.copy(res, f'cmrsim/_bin/notebooks/example_resources/{os.path.basename(res)}')
-
-# This requires a local build of the documentation ... -> managed by CI
-# shutil.copytree(f'{ROOT_PATH}/docs/build/html', f'{ROOT_PATH}/cmrsim/_bin/docs_html')
-
setuptools.setup(
name=project_name,
url=url,
@@ -50,19 +37,6 @@ setuptools.setup(
long_description=long_description,
packages=setuptools.find_packages(exclude=["*.tests", "*.tests.*", "tests.*", "tests"]),
include_package_data=True,
- entry_points={'console_scripts': ['cmrsimdoc = cmrsim._bin.cmrsim_doc:open_docs']},
+ install_requires=["tensorflow==2.4", "tensorflow-probability", "h5py~=2.10.0", "scipy>=1.4.1"],
python_requires=">=3.6"
)
-
-# copy latest requirements and distribution to docker folder
-package_name = f"{project_name}-{current_version}-py3-none-any.whl"
-latest_name = f"{project_name}-latest-py3-none-any.whl"
-image_names = list(os.walk('./docker'))[0][1]
-for image in image_names:
- try:
- os.remove(f"docker/{image}/{latest_name}")
- except FileNotFoundError:
- pass
- shutil.copyfile(f"dist/{package_name}", f"docker/{image}/{latest_name}")
- shutil.copyfile('requirements.txt', f'docker/{image}/python_requirements.txt')
-
diff --git a/tests/__init__.py b/tests/__init__.py
new file mode 100644
index 0000000000000000000000000000000000000000..864ba33809212ae98eef6df7dde3e550144cb906
--- /dev/null
+++ b/tests/__init__.py
@@ -0,0 +1,5 @@
+import sys
+sys.path.append("../")
+
+import tensorflow as tf
+tf.config.set_visible_devices([], "GPU:0")
diff --git a/tests/resources/seq.h5 b/tests/resources/seq.h5
new file mode 100644
index 0000000000000000000000000000000000000000..aca1ae72ba369635e01b29077dc2a6e6090f1291
Binary files /dev/null and b/tests/resources/seq.h5 differ
diff --git a/tests/test_auxilliary_snr.py b/tests/test_auxilliary_snr.py
index 75015eef72540005d3f002f6231f5059edaeaa01..8cc6bba612ea2b8cd7267b645f6b47dfceffcab3 100644
--- a/tests/test_auxilliary_snr.py
+++ b/tests/test_auxilliary_snr.py
@@ -72,7 +72,7 @@ class TestCalculateSNR(unittest.TestCase):
noise_vectors = tf.random.normal(shape=(2,) + tuple(image_noiseless.shape)) * noise_std
complex_noise_map = tf.complex(noise_vectors[0], noise_vectors[1])
noisy_image = image_noiseless + complex_noise_map
- snr_map = cmrsim_snr.calculate_snr(images=noisy_image,
+ snr_map = cmrsim_snr.calculate_snr(single_coil_images=noisy_image,
dynamic_noise_scan=complex_noise_map,
coil_sensitivities=coil_sensitivities)
mean_snr = tf.reduce_mean(snr_map)
@@ -90,7 +90,8 @@ class TestCalculateSNR(unittest.TestCase):
noise_vectors = tf.random.normal(shape=(2,) + tuple(image_noiseless.shape)) * noise_std
complex_noise_map = tf.complex(noise_vectors[0], noise_vectors[1])
noisy_image = image_noiseless + complex_noise_map
- snr_map = cmrsim_snr.calculate_snr(images=noisy_image,
- dynamic_noise_scan=complex_noise_map,)
+ snr_map = cmrsim_snr.calculate_snr(single_coil_images=noisy_image,
+ dynamic_noise_scan=complex_noise_map,
+ coil_sensitivities=coil_sensitivities)
mean_snr = tf.reduce_mean(snr_map)
self.assertTrue(np.allclose(mean_snr, 1/noise_std, rtol=0.2))
diff --git a/tests/test_base_dataset.py b/tests/test_base_dataset.py
new file mode 100644
index 0000000000000000000000000000000000000000..7d02c997192e908dd7664b76d962e35c4e799bf0
--- /dev/null
+++ b/tests/test_base_dataset.py
@@ -0,0 +1,33 @@
+import unittest
+import collections
+
+import numpy as np
+from cmrsim.anatomy.base import BaseDataset
+
+
+class TestBaseDataset(unittest.TestCase):
+ def setUp(self) -> None:
+ batch_size, x, y, = 1, 100, 100
+ valid_data = dict(M0=np.around(np.random.uniform(0., 1., size=(batch_size, x * y, 1, 1)), decimals=0),
+ r_vectors=np.ones((batch_size, x * y, 1, 3, 1), dtype=np.float32),
+ T1=np.ones((batch_size, x * y, 1, 1)),
+ T2=np.ones((batch_size, x * y, 1, 1)))
+ self.valid_data = collections.OrderedDict({k: v.astype(np.float32) for k, v in valid_data.items()})
+
+ def test_valid_shape(self):
+ """ Constructs dataset from valid input data and tests if the keys, type and batch-shape is correct when
+ iterating over the dataset."""
+ data_set_module = BaseDataset(self.valid_data)
+ for image_idx, (set_size, single_image_dataset) in data_set_module(voxel_batch_size=1000).enumerate():
+ remainder = set_size % 1000
+ for batch_idx, l in single_image_dataset.enumerate():
+ self.assertListEqual(list(self.valid_data.keys()), list(l.keys()),
+ msg="Keys in voxel batch is unequal to data keys")
+ self.assertTrue(all([(v.shape[1] == 1000 or v.shape[1] == remainder) for v in l.values()]),
+ msg="Batch shape does not match expected batch size")
+
+ def test_invalid_shape(self):
+ """ Tries to construct a dataset from invalid data (without the unitary dimensions via np.squeeze) and
+ asserts that a ValueError is raised"""
+ invalid_data = collections.OrderedDict([(k, np.squeeze(v)) for k, v in self.valid_data.items()])
+ self.assertRaises(ValueError, lambda: BaseDataset(invalid_data))
diff --git a/tests/test_encoding_base.py b/tests/test_encoding_base.py
index 1ed685cc3e630506ad5f834c8ffcafcfa6e400d2..b94db9fa3d4aed379602282e70d02b3a5c118428 100644
--- a/tests/test_encoding_base.py
+++ b/tests/test_encoding_base.py
@@ -34,9 +34,7 @@ class TestInputShapes(unittest.TestCase):
target_shape = (n_batch, actual_number_of_contrasts, self.n_sampling_points)
m_transverse = tf.ones((n_batch, n_voxels, actual_number_of_contrasts, timings), dtype=tf.complex64)
r_vectors = tf.random.normal((n_batch, n_voxels, reps, 3, timings))
- # print("input: r: {}, m_T: {}".format(r_vectors.numpy().shape, m_transverse.numpy().shape))
k = self.module(m_transverse, r_vectors)
- print("Output: ", k.shape)
self.assertTrue(all(i == j for i, j in zip(target_shape, tuple(k.shape))))
@@ -49,7 +47,3 @@ class TestValues(unittest.TestCase):
bandwidth_per_pixel=100., acquisition_start=1., blip_duration=0., )
constant_k_space = encoding_module(m0, r)
self.assertTrue(np.allclose(0., np.abs(constant_k_space - 1.)))
-
-
-if __name__ == '__main__':
- unittest.main()
diff --git a/tests/test_encoding_cartesian.py b/tests/test_encoding_cartesian.py
new file mode 100644
index 0000000000000000000000000000000000000000..95d6141bcb54c2f85facb1572ab742941f2a1646
--- /dev/null
+++ b/tests/test_encoding_cartesian.py
@@ -0,0 +1,40 @@
+import unittest
+
+import tensorflow as tf
+import numpy as np
+from cmrsim.encoding.cartesian import SingleLinePerShot
+
+
+class TestSingleLinePerShot(unittest.TestCase):
+ def test_sampling_time_calculation(self):
+ """ Construct single line per shot """
+ encoding_module = SingleLinePerShot(field_of_view=(0.34, 0.11), sampling_matrix_size=(13, 7),
+ repetition_time=2., read_out_duration=1., acquisition_start=0.,
+ absolute_noise_std=0.)
+ k_of_t = encoding_module.sampling_times.numpy()
+
+ # check if sampling times per line are identical
+ temp = k_of_t.reshape(7, 13) - k_of_t[:13][: np.newaxis]
+ self.assertEqual(np.sum(temp), 0.)
+
+ @unittest.skip
+ def test_plot_trajectory(self):
+ import matplotlib.pyplot as plt
+ import matplotlib
+ matplotlib.use('TkAgg')
+
+ single_line_module = SingleLinePerShot(field_of_view=(0.34, 0.11), sampling_matrix_size=(13, 7),
+ repetition_time=2., read_out_duration=1., acquisition_start=0.,
+ absolute_noise_std=0.)
+ y = single_line_module.sampling_times.numpy()
+ f, a = plt.subplots(1, 1)
+ a.plot(y, '.')
+ a.grid(True)
+
+ f2, a2 = plt.subplots(1, 1)
+ k_vex = single_line_module.k_space_vectors.read_value()
+ print(k_vex.shape)
+ x, y = k_vex.numpy()[:, 0:2].T
+ a2.scatter(x, y)
+ a2.grid(True)
+ plt.show()
diff --git a/tests/test_gradients.py b/tests/test_gradients.py
new file mode 100644
index 0000000000000000000000000000000000000000..d04ff43f522ce8426f328ba461a13b839af1bd11
--- /dev/null
+++ b/tests/test_gradients.py
@@ -0,0 +1,54 @@
+import unittest
+import os
+
+import tensorflow as tf
+import numpy as np
+
+import cmrsim.auxilliary.sequence.gradients as gradient_module
+
+
+class TestWaveforms(unittest.TestCase):
+ def plot_results(self, gwf: 'gradient_module.GradientWaveForm'):
+ import matplotlib.pyplot as plt
+ import matplotlib
+ matplotlib.use('TkAgg')
+ f, axes = plt.subplots(3, 1)
+ for i in range(3):
+ axes[i].plot(gwf.timing.numpy()[:, 0], gwf.waveform.numpy()[:, i])
+ axes[i].grid(True)
+ m = gwf.calculate_n_moment(0, cumulative=True)
+ for i in range(3):
+ axes[i].plot(gwf.timing.numpy()[:, 0], m[:, i])
+ axes[i].grid(True)
+
+ def test_classmethod_from_jemris(self):
+ gwf = gradient_module.GradientWaveForm.from_jemris_hd5(f'{os.path.dirname(__file__)}/resources/seq.h5')
+ # self.plot_results(gwf)
+
+ def test_classmethod_from_sparse_timings(self):
+ sparse_timings = tf.constant([0, 10, 10.5, 14, 14.5, 30, 30.5, 34, 34.5, 35], dtype=tf.float32,
+ shape=(10, 1))
+ wave_forms_x = tf.constant([0, 0, 1, 1, 0, 0, -1, -1, 0, 0], dtype=tf.float32)
+ sparse_wave_forms = tf.stack([wave_forms_x, tf.zeros_like(wave_forms_x), tf.zeros_like(wave_forms_x)],
+ axis=-1)
+ gwf = gradient_module.GradientWaveForm.from_sparse_timings(sparse_timings, sparse_wave_forms)
+ # self.plot_results(gwf)
+
+ def test_readout_waveform(self):
+ gwf = gradient_module.ReadOutGradient.from_jemris_hd5(f'{os.path.dirname(__file__)}/resources/seq.h5')
+ # f2, a2 = plt.subplots(1, 1)
+ k_space_vectors = gwf.get_sampling_vectors()
+ # a2.scatter(k_space_vectors[:, 0].numpy(), k_space_vectors[:, 1].numpy())
+
+
+class TestEncodingInteraction(unittest.TestCase):
+ def test_construction(self):
+ from cmrsim.encoding.base import BaseSampling
+ np.load(f'{os.path.dirname(__file__)}/resources/coil_sensitivities.npy')
+ gwf = gradient_module.ReadOutGradient.from_jemris_hd5(f'{os.path.dirname(__file__)}/resources/seq.h5')
+ encoding_module = BaseSampling.from_waveform(gwf, 0., k_space_segments=32, name='test')
+ # f2, a2 = plt.subplots(1, 1)
+ k_space_vectors = encoding_module.k_space_vectors.read_value()
+ # a2.scatter(k_space_vectors[:, 0].numpy(), k_space_vectors[:, 1].numpy(), marker='x')
+ # plt.show()
+
diff --git a/tests/test_look_up_module.py b/tests/test_look_up_module.py
index 24f776225487907db20f1d541a356dea250968e1..e73d7897440153e37f870d7e999cb26b0ca2458b 100644
--- a/tests/test_look_up_module.py
+++ b/tests/test_look_up_module.py
@@ -7,37 +7,56 @@ import numpy as np
from cmrsim.signal_processes.base import LookUpTableModule
-class MyTestCase(unittest.TestCase):
+class TestLookupModule(unittest.TestCase):
def setUp(self) -> None:
- """ Sets up dummy module with multiple repetitions and map channels.
-
- :return:
+ """ Sets up dummy module with multiple repetitions and map channels """
+ # Set up lookup table with shape (channels, #Xbins, #Ybins, #Zbins)
+ # spanning the physical dimensions (X, Y, Z) mm
+ self.lut_shape = (n_channels, xbin, ybin, zbin) = (8, 150, 150, 10)
+ self.dimensions = (150., 150., 10.)
+
+ lookup = np.linspace(0., 1., xbin * ybin * zbin, dtype=np.float32).reshape([xbin, ybin, zbin])
+ lookup = lookup[np.newaxis] * np.arange(0, n_channels)[:, np.newaxis, np.newaxis, np.newaxis]
+ assert all([s1 == s2 for s1, s2 in zip(lookup.shape, self.lut_shape)])
+ self.look_up_module = LookUpTableModule(look_up_map3d=lookup, map_dimensions=self.dimensions)
+
+ def test_valid_lookup(self):
+ """ As described in the docstring test random lookup input with correct shape that lies within
+ the range (-dims/2, dims/2). Subtest with multiple repetitions and sampling points are performed.
+ This test just expects not to throw any errors
"""
- n_voxels, n_repetitions, n_samples, n_channels = 100, 18, 1, 8
- _dtype, bins, dims = tf.float32, (150, 150, 1), (150., 150., 0.)
- look_up_dtype = np.complex64
- lookup = np.ones((n_channels,) + bins, dtype=look_up_dtype)
-
- self.mod = LookUpTableModule(look_up_map3d=lookup, map_dimensions=dims)
- self.loop_reps = 10
- reps = 100
- signal_tensor = tf.ones((1,), tf.complex64)
- self.r_vectors_ref = tf.random.uniform((self.loop_reps, n_voxels * reps + 5, n_repetitions, 3, n_samples),
- minval=0, maxval=100, dtype=_dtype)
- _ = self.mod(r_vectors=self.r_vectors_ref[0:1])
+ for voxels in [1, 249]:
+ for reps in [1, 4]:
+ for samps in [1, 64*64]:
+ with self.subTest(msg=f"Look-up with {voxels} points {reps} repetitions"
+ f" and {samps} k-space samples"):
+ rvec_shape = (_, n_voxels, n_repetitions, _, n_samples) = 1, voxels, reps, 3, samps
+ pseudo_rvecs = tf.random.uniform(rvec_shape, minval=0., maxval=1., dtype=tf.float32)
+ scaling = tf.constant(np.array(self.dimensions).reshape([1, 1, 1, 3, 1]), dtype=tf.float32)
+ pseudo_rvecs = (pseudo_rvecs - 1/2) * scaling
+ _ = self.look_up_module(r_vectors=pseudo_rvecs)
+
+ def test_invalid_lookup(self):
+ """ Test whether a position out of range of the look up table causes the module to fail"""
+ from tensorflow.python.framework.errors_impl import InvalidArgumentError
+
+ pseudo_rvecs_too_large = tf.reshape(tf.constant(self.dimensions), [1, 1, 1, 3, 1])
+ self.assertRaises(InvalidArgumentError, lambda: self.look_up_module(r_vectors=pseudo_rvecs_too_large))
+
+ pseudo_rvecs_negative_too_large = -tf.reshape(tf.constant(self.dimensions), [1, 1, 1, 3, 1])
+ self.assertRaises(InvalidArgumentError, lambda: self.look_up_module(r_vectors=pseudo_rvecs_negative_too_large))
def test_speed(self):
- """ Repeatingly looks up values and reports duration.
- Asserts: None
- """
+ """ Repeating looks up values and reports duration """
+ rvec_shape = (_, n_voxels, n_repetitions, _, n_samples) = 1, 100, 1, 3, 128*128
+ pseudo_rvecs = tf.random.uniform(rvec_shape, minval=0., maxval=1., dtype=tf.float32)
+ scaling = tf.constant(np.array(self.dimensions).reshape([1, 1, 1, 3, 1]), dtype=tf.float32)
+ pseudo_rvecs = (pseudo_rvecs - 1 / 2) * scaling
+
start = time.perf_counter()
- for idx in range(self.loop_reps):
+ for idx in range(10):
loop_start = time.perf_counter()
- _ = self.mod(r_vectors=self.r_vectors_ref[idx:idx + 1])
+ _ = self.look_up_module(r_vectors=pseudo_rvecs)
print(f"\rIteration index: {idx}, iteration duration: {time.perf_counter() - loop_start}", end='')
print(f"\nTotal duration: {time.perf_counter() - start}, "
- f"Total number of lookups: {int(tf.reduce_prod(self.r_vectors_ref.shape) / 3)}")
-
-
-if __name__ == '__main__':
- unittest.main()
+ f"Total number of lookups: {int(tf.reduce_prod(pseudo_rvecs.shape) / 3)}")
diff --git a/tests/test_signal_so_coilsens.py b/tests/test_signal_so_coilsens.py
index 125f4a614948a7b0b9ad18dad8493e64194b96fe..c75c2e36d5a300402e869db96918818ffab490e7 100644
--- a/tests/test_signal_so_coilsens.py
+++ b/tests/test_signal_so_coilsens.py
@@ -1,4 +1,5 @@
import unittest
+import os
import tensorflow as tf
import numpy as np
@@ -50,7 +51,7 @@ class TestInputShape(unittest.TestCase):
class TestValues(unittest.TestCase):
def setUp(self) -> None:
- self.sensitivity_maps = np.load('./resources/coil_sensitivities.npy')
+ self.sensitivity_maps = np.load(f'{os.path.dirname(__file__)}/resources/coil_sensitivities.npy')
ncoils, nx, ny, nz = self.sensitivity_maps.shape
map_dimensions = (0.1, 0.1, 0.)
self.signal_tensor = tf.ones((1, (nx * ny * nz), 1, 1), tf.complex64)
diff --git a/tests/test_signal_so_perfusion.py b/tests/test_signal_so_perfusion.py
new file mode 100644
index 0000000000000000000000000000000000000000..70789dc4f8dc6a0b65d6f793e3a94a68c6b28433
--- /dev/null
+++ b/tests/test_signal_so_perfusion.py
@@ -0,0 +1,34 @@
+import unittest
+
+import tensorflow as tf
+import numpy as np
+
+
+from cmrsim.signal_processes.solution_operators.sequences import SaturationRecoveryGRE
+from cmrsim.signal_processes.solution_operators.perfusion import ContrastAgentPerfusion
+
+
+class TestPerfusionModule(unittest.TestCase):
+ @unittest.skip
+ def test_basic(self):
+ import matplotlib.pyplot as plt
+ m0 = tf.ones(shape=(1, 10, 1, 1), dtype=tf.complex64)
+ T1 = tf.ones(shape=(1, 10, 1, 1), dtype=tf.float32)
+ spoiled_gre = SaturationRecoveryGRE(repetition_time=1000., flip_angle=15.,
+ saturation_delay=150., n_profiles_to_k0=21)
+
+ perfusion_module = ContrastAgentPerfusion(
+ contrast_times=tf.constant(np.array(range(10, 80, 5)), dtype=tf.float32),
+ sequence_module=spoiled_gre)
+
+ t, y = perfusion_module._concentration_time_grid.numpy(), perfusion_module._concentration_curves.numpy()
+ t_, y_ = perfusion_module.acquisition_times.read_value(), perfusion_module._interpolated_concentrations.numpy()
+ print(t.shape, y.shape, t_.shape, y_.shape)
+
+ ca_indices = tf.reshape(tf.round(tf.range(0.1, 1.1, 0.1)), [1, 10, 1, 1])
+ perfusion_module(m0, T1=T1, contrast_dynamic_index=ca_indices, segment_index=0)
+
+ f, ax = plt.subplots(1, 1)
+ ax.plot(t, y[0], 'b-')
+ ax.plot(t_, y_[1], 'kx')
+ plt.show()
diff --git a/tests/test_signal_so_sequences.py b/tests/test_signal_so_sequences.py
new file mode 100644
index 0000000000000000000000000000000000000000..6b800847a4e76e005a61a49db2af93d3292ab7e7
--- /dev/null
+++ b/tests/test_signal_so_sequences.py
@@ -0,0 +1,17 @@
+import unittest
+
+import tensorflow as tf
+
+from cmrsim.signal_processes.solution_operators.sequences import SaturationRecoveryGRE
+
+
+class TestSaturationRecoveryGRE(unittest.TestCase):
+ def test_calculate_trajectory(self):
+ m0 = tf.ones(shape=(1, 10, 1, 1), dtype=tf.complex64)
+ T1 = tf.ones(shape=(1, 10, 1, 1), dtype=tf.float32)
+
+ spoiled_gre = SaturationRecoveryGRE(repetition_time=1000., flip_angle=15.,
+ saturation_delay=150., n_profiles_to_k0=21)
+ a = spoiled_gre(m0, T1=T1)
+ print(a.shape)
+
diff --git a/tests/test_simulation.py b/tests/test_simulation.py
index c0e635ded065842550ffec77bc2c53c9f082589a..36ccd55f845bcbd95071a07ba9c2ff8b7012a110 100644
--- a/tests/test_simulation.py
+++ b/tests/test_simulation.py
@@ -1,4 +1,6 @@
import unittest
+import collections
+import os
from cmrsim.simulation import SimulationBase
import tensorflow as tf
@@ -20,7 +22,9 @@ class DummySimulation(SimulationBase):
bandwidth_per_pixel=1300., blip_duration=0., k_space_segments=43,
acquisition_start=-25.7692, absolute_noise_std=0.)
- sens_maps = tf.constant(np.load('./resources/coil_sensitivities.npy'), dtype=tf.complex64)
+ sens_maps = tf.constant(np.load(f'{os.path.dirname(__file__)}/resources/coil_sensitivities.npy'),
+ dtype=tf.complex64)
+
# Signal module construction
coil_sens_module = solution_operators.coil_sensitivities.CoilSensitivity(sens_maps, (0.31, 0.11, 0.))
@@ -35,15 +39,18 @@ class DummySimulation(SimulationBase):
class TestBasic(unittest.TestCase):
def setUp(self) -> None:
- r = utils.coordinates.get_static_2d_centered_coordinates((605, 215), (0.3024, 0.1072), batch_size=1)
+ r = utils.coordinates.get_static_2d_centered_coordinates((242, 86), (0.3024, 0.1072), batch_size=1)
static_coords = r.numpy()
- static_coords = tf.tile(static_coords, [1, 1, 1, 1, 1, 1])
- datadict = {
- 'M0': np.random.normal(1, 0.1, size=(1, 605, 215, 1, 1)).astype(np.complex64),
- 'r_vectors': static_coords
- }
+ datadict = collections.OrderedDict([
+ ('M0', np.random.normal(1, 0.1, size=(1, 242*86, 1, 1)).astype(np.complex64)),
+ ('r_vectors', static_coords.reshape(1, -1, 1, 3, 1))
+ ])
self.dataset = BaseDataset(datadict)
- def test_run(self):
+ def test_run_eager(self):
+ simulator = DummySimulation()
+ _ = simulator(self.dataset, execute_eager=True, voxel_batch_size=1000)
+
+ def test_run_function(self):
simulator = DummySimulation()
- _ = simulator(self.dataset, execute_eager=False, voxel_batch_size=5000)
+ _ = simulator(self.dataset, execute_eager=True, voxel_batch_size=1000)