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Merge pull request #164 from eve-le-guillou/mpiExample
[MPI] Add example of use for MPI
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‎docs/index.md

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| [Manifold checks](manifoldCheck/) | ![ExampleImage](https://topology-tool-kit.github.io/img/gallery/manifoldCheck.jpg) |
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| [Cinema IO](cinemaIO/) | <iframe width="100%" height="420" src="https://www.youtube.com/embed/yKyiRzPbs0U" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe> |
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| [Compact Triangulation](compactTriangulation/) | <iframe width="100%" height="420" src="https://www.youtube.com/embed/vDQRh_tuUSA" frameborder="0" allow="accelerometer; autoplay; encrypted-media; gyroscope; picture-in-picture" allowfullscreen></iframe> |
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| [MPI Example](mpiExample) | ![ExampleImage](https://topology-tool-kit.github.io/img/gallery/mpiExample.jpg) |
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‎docs/mpiExample.md

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# MPI Example
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![MPI example Image](https://topology-tool-kit.github.io/img/gallery/mpiExample.jpg)
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This toy example illustrates the usage of TTK in a distributed-memory context with MPI. For this example, the original data set is small. Thus, the pipeline first resamples it to $256^3$ but this dimension can be changed to fit the capabilities of your distributed system.
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For more information about how to run a pipeline in parallel in ParaView with MPI, please refer to the [ParaView documentation](https://docs.paraview.org/en/latest/ReferenceManual/parallelDataVisualization.html).
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Please note both ParaView and TTK need to be compiled with MPI to allow for parallel execution in a distributed context. For that we refer the reader to the ParaView and TTK installation instructions, but, in short, this can be done by setting the following CMake flags `PARAVIEW_USE_MPI=ON` and `TTK_ENABLE_MPI=ON` for ParaView and TTK respectively. Also, for processing large-scale datasets (typically beyond $1024^3$), we recommend to build TTK with 64 bit identifiers (by setting the CMake flag `TTK_ENABLE_64BIT_IDS=ON`).
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## Pipeline description
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The produced visualization captures the covalent and hydrogen bonds within the Adenine-Thymine molecular complex. It also shows where the electronic density experiences rapid changes, indicating transition points occurring within the bond.
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First, the magnitude of the scalar field is computed and the data set is resampled to $256^3$. This dimension can be changed
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(see the corresponding [Python script below](#python-code)).
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Then, both the scalar field and its magnitude are smoothed via [ScalarFieldSmoother](https://topology-tool-kit.github.io/doc/html/classttkScalarFieldSmoother.html) and normalized via [ScalarFieldNormalizer](https://topology-tool-kit.github.io/doc/html/classttkScalarFieldNormalizer.html).
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Next, the critical points of the scalar field are computed via [ScalarFieldCriticalPoints](https://topology-tool-kit.github.io/doc/html/classttkScalarFieldCriticalPoints.html) and used as seeds of the [IntegralLines](https://topology-tool-kit.github.io/doc/html/classttkIntegralLines.html).
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Next, the geometry of the integral lines is smoothed using the [GeometrySmoother](https://topology-tool-kit.github.io/doc/html/classttkGeometrySmoother.html).
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Finally, the critical points of the magnitude are computed on the smoothed geometry of the integral lines.
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## ParaView
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To reproduce the above screenshot on 4 processes and 2 threads, go to your [ttk-data](https://github.com/topology-tool-kit/ttk-data) directory and enter the following command:
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``` bash
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OMP_NUM_THREADS=2 mpirun -n 4 pvserver
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```
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In another command line enter the following command:
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``` bash
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paraview
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```
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Now, follow the procedure described in paragraph $7.2.2$ of the following [ParaView documentation](https://docs.paraview.org/en/latest/ReferenceManual/parallelDataVisualization.html#configuring-a-server-connection) to connect your ParaView server to your client. Once that is done, you can open the state file `states/mpiExample.pvsm` in the ParaView GUI through `File` > `Load State`.
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## Python code
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``` python linenums="1"
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--8<-- "python/mpiExample.py"
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```
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To run the above Python script using 2 threads and 4 processes, go to your [ttk-data](https://github.com/topology-tool-kit/ttk-data) directory and enter the following command:
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``` bash
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OMP_NUM_THREADS=2 mpirun -n 4 pvbatch python/mpiExample.py
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```
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By default, the dataset is resampled to $256^3$. To resample to a higher dimension, for example $2048^3$, enter the following command:
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```bash
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OMP_NUM_THREADS=2 mpirun -n 4 pvbatch python/mpiExample.py 2048
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```
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Be aware that this will require a lot of memory to execute and will most likely not be possible on a regular laptop.
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## Inputs
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- [at.vti](https://github.com/topology-tool-kit/ttk-data/raw/dev/at.vti): A molecular dataset: a three-dimensional regular grid with one scalar field, the electronic density for the Adenine Thymine complex.
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## Outputs
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- `integralLines.pvtu`: the geometry of the smoothed integral lines capturing the covalent and hydrogen bonds of the molecule, as extracted by the analysis pipeline.
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- `criticalPoints.pvtp`: the critical points computed on the geometry of the integral lines, showing the rapid changes in the bonds.
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## C++/Python API
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[ArrayPreconditioning](https://topology-tool-kit.github.io/doc/html/classttkArrayPreconditioning.html)
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[GeometrySmoother](https://topology-tool-kit.github.io/doc/html/classttkGeometrySmoother.html)
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[IntegralLines](https://topology-tool-kit.github.io/doc/html/classttkIntegralLines.html)
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[PointDataSelector](https://topology-tool-kit.github.io/doc/html/classttkPointDataSelector.html)
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[ScalarFieldCriticalPoints](https://topology-tool-kit.github.io/doc/html/classttkScalarFieldCriticalPoints.html)
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[ScalarFieldNormalizer](https://topology-tool-kit.github.io/doc/html/classttkScalarFieldNormalizer.html)
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[ScalarFieldSmoother](https://topology-tool-kit.github.io/doc/html/classttkScalarFieldSmoother.html)
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‎python/mpiExample.py

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#### import the simple module from the paraview
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from paraview.simple import *
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# ----------------------------------------------------------------
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# Choose the resampling dimension for the example
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# ----------------------------------------------------------------
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if len(sys.argv) == 2:
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dim = int(sys.argv[1])
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else:
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dim = 256
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# create a new 'XML Image Data Reader'
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atvti = XMLImageDataReader(FileName=["at.vti"])
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atvti.PointArrayStatus = ["density"]
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calculator1 = Calculator(Input=atvti)
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calculator1.ResultArrayType = "Float"
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calculator1.ResultArrayName = "density"
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calculator1.Function = "density"
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# create a new 'Compute Derivatives'
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computeDerivatives1 = ComputeDerivatives(Input=calculator1)
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computeDerivatives1.Scalars = ["POINTS", "density"]
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computeDerivatives1.Vectors = ["POINTS", "1"]
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# create a new 'Calculator'
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calculator2 = Calculator(Input=computeDerivatives1)
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calculator2.AttributeType = "Cell Data"
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calculator2.ResultArrayName = "gradientMagnitude"
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calculator2.Function = "mag(ScalarGradient)"
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calculator2.ResultArrayType = "Float"
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# create a new 'Cell Data to Point Data'
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cellDatatoPointData1 = CellDatatoPointData(Input=calculator2)
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cellDatatoPointData1.CellDataArraytoprocess = ["gradientMagnitude"]
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# create a new 'TTK PointDataSelector'
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tTKPointDataSelector1 = TTKPointDataSelector(Input=cellDatatoPointData1)
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tTKPointDataSelector1.ScalarFields = ["density", "gradientMagnitude"]
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tTKPointDataSelector1.RangeId = [0, 2]
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# create a new 'Cell Data to Point Data'
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cellDatatoPointData2 = CellDatatoPointData(Input=tTKPointDataSelector1)
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cellDatatoPointData2.CellDataArraytoprocess = ["ScalarGradient", "gradientMagnitude"]
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# create a new 'Resample To Image'
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resampleToImage1 = ResampleToImage(Input=cellDatatoPointData2)
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resampleToImage1.SamplingDimensions = [dim, dim, dim]
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resampleToImage1.SamplingBounds = [0.0, 176.0, 0.0, 94.0, 0.0, 47.0]
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# create a new 'TTK ScalarFieldSmoother'
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tTKScalarFieldSmoother1 = TTKScalarFieldSmoother(Input=resampleToImage1)
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tTKScalarFieldSmoother1.ScalarField = ["POINTS", "density"]
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tTKScalarFieldSmoother1.IterationNumber = 1
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# create a new 'TTK ScalarFieldSmoother'
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tTKScalarFieldSmoother2 = TTKScalarFieldSmoother(Input=tTKScalarFieldSmoother1)
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tTKScalarFieldSmoother2.ScalarField = ["POINTS", "gradientMagnitude"]
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tTKScalarFieldSmoother2.IterationNumber = 10
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# create a new 'TTK ScalarFieldNormalizer'
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tTKScalarFieldNormalizer2 = TTKScalarFieldNormalizer(Input=tTKScalarFieldSmoother2)
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tTKScalarFieldNormalizer2.ScalarField = ["POINTS", "density"]
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# create a new 'TTK ArrayPreconditioning'
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tTKArrayPreconditioning1 = TTKArrayPreconditioning(Input=tTKScalarFieldNormalizer2)
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tTKArrayPreconditioning1.PointDataArrays = ["density"]
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# create a new 'TTK ScalarFieldCriticalPoints'
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tTKScalarFieldCriticalPoints2 = TTKScalarFieldCriticalPoints(
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Input=tTKArrayPreconditioning1
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)
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tTKScalarFieldCriticalPoints2.ScalarField = ["POINTS", "density"]
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tTKScalarFieldCriticalPoints2.Backend = "Default generic backend"
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# create a new 'Mask Points'
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maskPoints2 = MaskPoints(Input=tTKScalarFieldCriticalPoints2)
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maskPoints2.OnRatio = 1
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maskPoints2.MaximumNumberofPoints = 99999999
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maskPoints2.ProportionallyDistributeMaximumNumberOfPoints = 1
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maskPoints2.RandomSampling = 1
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maskPoints2.RandomSamplingMode = "Random Sampling"
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maskPoints2.GenerateVertices = 1
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maskPoints2.SingleVertexPerCell = 1
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# create a new 'Threshold'
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threshold1 = Threshold(Input=maskPoints2)
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threshold1.Scalars = ["POINTS", "CriticalType"]
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threshold1.LowerThreshold = 1.0
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threshold1.UpperThreshold = 1.0
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# create a new 'Threshold'
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threshold4 = Threshold(Input=threshold1)
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threshold4.Scalars = ["POINTS", "IsOnBoundary"]
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# create a new 'TTK IntegralLines'
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tTKIntegralLines1 = TTKIntegralLines(Domain=tTKArrayPreconditioning1, Seeds=threshold4)
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tTKIntegralLines1.ScalarField = ["POINTS", "density"]
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tTKIntegralLines1.Direction = "Backward"
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tTKIntegralLines1.Vertexidentifierfield = ["POINTS", "CriticalType"]
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tTKIntegralLines1.EnableForking = 1
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# create a new 'Clean to Grid'
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cleantoGrid1 = CleantoGrid(Input=tTKIntegralLines1)
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# create a new 'TTK GeometrySmoother'
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tTKGeometrySmoother2 = TTKGeometrySmoother(Input=cleantoGrid1)
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tTKGeometrySmoother2.IterationNumber = 200
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# create a new 'Resample With Dataset'
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resampleWithDataset1 = ResampleWithDataset(
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SourceDataArrays=cellDatatoPointData1, DestinationMesh=tTKGeometrySmoother2
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)
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resampleWithDataset1.CellLocator = "Static Cell Locator"
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resampleWithDataset1.PassCellArrays = 1
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resampleWithDataset1.PassPointArrays = 1
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# create a new 'TTK ScalarFieldCriticalPoints'
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tTKScalarFieldCriticalPoints1 = TTKScalarFieldCriticalPoints(Input=resampleWithDataset1)
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tTKScalarFieldCriticalPoints1.ScalarField = ["POINTS", "gradientMagnitude"]
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tTKScalarFieldCriticalPoints1.Backend = "Default generic backend"
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SaveData("integralLines.pvtu", tTKGeometrySmoother2)
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SaveData("criticalPoints.pvtp", tTKScalarFieldCriticalPoints1)

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