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README.md

@@ -1,13 +1,139 @@
----
-title: DeepStruc
-emoji: 📊
-colorFrom: blue
-colorTo: yellow
-sdk: streamlit
-sdk_version: 1.15.2
-app_file: app.py
-pinned: false
-license: apache-2.0
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+[ChemRxiv](https://chemrxiv.org/engage/chemrxiv/article-details/6221f17357a9d20c9a729ecb)  |  [Paper](https://pubs.rsc.org/en/content/articlelanding/2023/dd/d2dd00086e)
+
+# DeepStruc
+Welcome to DeepStruc, a Deep Generative Model (DGM) that learns the relation between PDF and atomic structure and 
+thereby solves a structure from a PDF!
+
+1. [DeepStruc](#deepstruc)
+2. [Getting started (with Colab)](#getting-started-with-colab)
+3. [Getting started (own computer)](#getting-started-own-computer)
+    1. [Install requirements](#install-requirements)
+    2. [Simulate data](#simulate-data)
+    3. [Train model](#train-model)
+    4. [Predict](#predict)
+4. [Author](#author)
+5. [Cite](#cite)
+6. [Acknowledgments](#Acknowledgments)
+7. [License](#license)  
+
+We here apply DeepStruc for the structural analysis of a model system of mono-metallic nanoparticle (MMNPs) with seven
+different structure types and demonstrate the method for both simulated and experimental PDFs. DeepStruc can reconstruct
+simulated data with an average mean absolute error (MAE) of the atom xyz-coordinates on 0.093 ± 0.058 Å after fitting a
+contraction/extraction factor, an ADP and a scale parameter.
+We demonstrate the generative capability of DeepStruc on a dataset of face-centered cubic (fcc), hexagonal closed packed
+(hcp) and stacking faulted structures, where DeepStruc can recognize the stacking faulted structures as an interpolation
+between fcc and hcp and construct new structural models based on a PDF. The MAE is in this example 0.030 ± 0.019 Å.
+
+The MMNPs are provided as a graph-based input to the encoder of DeepStruc. We compare DeepStruc with a similar [DGM](https://github.com/AndyNano/CVAE.git)
+without the graph-based encoder. DeepStruc is able to reconstruct the structures using a smaller dimension of the latent
+space thus having a better generative capabillity. We also compare DeepStruc with a [brute-force modelling](https://github.com/AndyNano/Brute-force-PDF-modelling.git) approach and a [tree-based classification algorithm](https://github.com/AndyNano/MetalFinder.git). The ML models are significantly faster than the brute-force approach, but DeepStruc can furthermore create a latent space from where synthetic structures can be sampled which the tree-based method cannot!
+The baseline models can be found in other repositories: [brute-force](https://github.com/AndyNano/Brute-force-PDF-modelling.git),
+[MetalFinder](https://github.com/AndyNano/MetalFinder.git) and [CVAE](https://github.com/AndyNano/CVAE.git).
+![alt text](img/DeepStruc.png "DeepStruc")
+
+
+# Getting started (with Colab)
+Using DeepStruc on your own PDFs is straightforward and does not require anything installed or downloaded to your computer.
+Follow the instructions in our [Colab notebook](https://colab.research.google.com/github/EmilSkaaning/DeepStruc/blob/main/DeepStruc.ipynb)
+and try to play around. 
+
+# Getting started (own computer)
+Follow these step if you want to train DeepStruc and predict with DeepStruc locally on your own computer.
+
+## Install requirements
+See the [install](/install) folder. 
+
+## Simulate data
+See the [data](/data) folder. 
+
+## Train model
+To train your own DeepStruc model simply run:
+```
+python train.py
+```
+A list of possible arguments or run the '--help' argument for additional information.  
+If you are intersted in changing the architecture of the model go to __train.py__ and change the _model_arch_ dictionary. 
+ 
+| Arg | Description | Example |  
+| --- | --- |  --- |  
+| `-h` or `--help` | Prints help message. |    
+| `-d` or `--data_dir` | Directory containing graph training, validation and test data. __str__| `-d ./data/graphs`  |  
+| `-s` or `--save_dir` | Directory where models will be saved. This is also used for loading a learner. __str__ | `-s bst_model`  |   
+| `-r` or `--resume_model` | If 'True' the save_dir model is loaded and training is continued. __bool__| `-r True`  |
+| `-e` or `--epochs` | Number of maximum epochs. __int__| `-e 100`  |  
+| `-b` or `--batch_size` | Number of graphs in each batch. __int__| `-b 20`  |  
+| `-l` or `--learning_rate` | Learning rate. __float__| `-l 1e-4`  |  
+| `-B` or `--beta` | Initial beta value for scaling KLD. __float__| `-B 0.1`  |  
+| `-i` or `--beta_increase` | Increments of beta when the threshold is met. __float__| `-i 0.1`  |  
+| `-x` or `--beta_max` | Highst value beta can increase to. __float__| `-x 5`  |  
+| `-t` or `--reconstruction_th` | Reconstruction threshold required before beta is increased. __float__| `-t 0.001`  |  
+| `-n` or `--num_files` | Total number of files loaded. Files will be split 60/20/20. If 'None' then all files are loaded. __int__| `-n 500`  |  
+| `-c` or `--compute` | Train model on CPU or GPU. Choices: 'cpu', 'gpu16', 'gpu32' and 'gpu64'. __str__| `-c gpu32`  |  
+| `-L` or `--latent_dim` | Number of latent space dimensions. __int__| `-L 3`  |  
+
+## Predict
+To predict a MMNP using DeepStruc or your own model on a PDF:
+```
+python predict.py
+```
+A list of possible arguments or run the '--help' argument for additional information.  
+ 
+| Arg | Description | Example |  
+| --- | --- |  --- |  
+| `-h` or `--help` | Prints help message. |    
+| `-d` or `--data` | Path to data or data directory. If pointing to data directory all datasets must have same format. __str__| `-d data/experimental_PDFs/JQ_S1.gr`  |  
+| `-m` or `--model` | Path to model. If 'None' GUI will open. __str__ | `-m ./models/DeepStruc`  |   
+| `-n` or `--num_samples` | Number of samples/structures generated for each unique PDF. __int__| `-n 10`  |
+| `-s` or `--sigma` | Sample to '-s' sigma in the normal distribution. __float__| `-s 7`  |  
+| `-p` or `--plot_sampling` | Plots sampled structures on top of DeepStruc training data. Model must be DeepStruc. __bool__| `-p True`  |  
+| `-g` or `--save_path` | Path to directory where predictions will be saved. __bool__| `-g ./best_preds`  |  
+| `-i` or `--index_plot` | Highlights specific reconstruction in the latent space. --data must be specific file and not directory and  '--plot True'. __int__| `-i 4`  |  
+| `-P` or `--plot_data` | If True then the first loaded PDF is plotted and shown after normalization. __bool__| `-P ./best_preds`  |  
+
+
+# Authors
+__Andy S. Anker__<sup>1</sup>   
+__Emil T. S. Kjær__<sup>1</sup>  
+__Marcus N. Weng__<sup>1</sup>  
+__Simon J. L. Billinge__<sup>2, 3</sup>     
+__Raghavendra Selvan__<sup>4, 5</sup>  
+__Kirsten M. Ø. Jensen__<sup>1</sup>    
+ 
+<sup>1</sup> Department of Chemistry and Nano-Science Center, University of Copenhagen, 2100 Copenhagen Ø, Denmark.   
+<sup>2</sup> Department of Applied Physics and Applied Mathematics Science, Columbia University, New York, NY 10027, USA.   
+<sup>3</sup> Condensed Matter Physics and Materials Science Department, Brookhaven National Laboratory, Upton, NY 11973, USA.    
+<sup>4</sup> Department of Computer Science, University of Copenhagen, 2100 Copenhagen Ø, Denmark.   
+<sup>5</sup> Department of Neuroscience, University of Copenhagen, 2200, Copenhagen N.    
+
+Should there be any question, desired improvement or bugs please contact us on GitHub or 
+through email: __andy@chem.ku.dk__ or __etsk@chem.ku.dk__.
+
+# Cite
+If you use our code or our results, please consider citing our papers. Thanks in advance!
+```
+@article{D2DD00086E,
+title={DeepStruc: Towards structure solution from pair distribution function data using deep generative models},
+author={Emil T. S. Kjær, Andy S. Anker, Marcus N. Weng, Simon J. L. Billinge, Raghavendra Selvan, Kirsten M. Ø. Jensen},
+journal={Digital Discovery},
+year={2022}}
+```
+```
+@article{anker2020characterising,
+title={Characterising the atomic structure of mono-metallic nanoparticles from x-ray scattering data using conditional generative models},
+author={Anker, Andy Sode and Kjær, Emil TS and Dam, Erik B and Billinge, Simon JL and Jensen, Kirsten MØ and Selvan, Raghavendra},
+journal={16th international workshop on mining and learning with graphs under KDD2020 conference}
+year={2020}}
+```
+
+# Acknowledgments
+Our code is developed based on the the following publication:
+```
+@article{anker2020characterising,
+title={Characterising the atomic structure of mono-metallic nanoparticles from x-ray scattering data using conditional generative models},
+author={Anker, Andy Sode and Kjær, Emil TS and Dam, Erik B and Billinge, Simon JL and Jensen, Kirsten MØ and Selvan, Raghavendra},
+journal={16th international workshop on mining and learning with graphs under KDD2020 conference}
+year={2020}}
+```
+
+# License
+This project is licensed under the Apache License Version 2.0, January 2004 - see the [LICENSE](LICENSE) file for details.

data/PDFs_experimental/Au144PET_100K-00000.gr

@@ -0,0 +1,3027 @@
+# PDFgetXgui Configuration #
+[PDF]
+wavelength = 0.18597
+dataformat = Qnm
+inputfile = Au144PET_100K-00000.chi
+backgroundfile = Kapton-00000.chi
+mode = xray
+bgscale = 0.9
+composition = Au144 S60 C180
+outputtype = gr
+qmaxinst = 30.0
+qmin = 0.8
+qmax = 26.0
+rmax = 30.0
+rmin = 0.0
+rstep = 0.01
+rpoly = 1.2
+
+[Misc]
+inputdir = /Users/kirstenmariejensen/Documents/JensenDec14/FeSb
+savedir = /Users/kirstenmariejensen/Desktop
+backgroundfilefull = /Users/kirstenmariejensen/Documents/JensenDec14/Kapton Goniometer/Kapton-00000.chi
+
+#### start data
+#S 1
+#L r($\AA$)  G($\AA^{-2}$)
+0 0
+0.01 -0.00413952
+0.02 -0.00801602
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+1 0.166611
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data/PDFs_experimental/Au144pMBA.gr

@@ -0,0 +1,2028 @@
+[DEFAULT]
+
+version = pdfgetx3-1.0-r2239-20130415
+
+# input and output specifications
+dataformat = Qnm
+inputfile = Au144pMBA_100K_ESRF.chi
+backgroundfile =
+outputtype = gr
+
+# PDF calculation setup
+mode = xray
+composition = Au
+bgscale = 1
+rpoly = 0.9
+qmaxinst = 29
+qmin = 1.33
+qmax = 28.0
+rmin = 0
+rmax = 20
+rstep = 0.01
+
+# End of config --------------------------------------------------------------
+
+#### start data
+#S 1
+#L r($\AA$)  G($\AA^{-2}$)
+0 0
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data/PDFs_experimental/JQ_S1.gr

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+# xPDFsuite Configuration #
+[PDF]
+wavelength = 0.1
+dataformat = Qnm
+inputfile = JQ_S1.chi
+backgroundfile = JQ_EG.chi
+mode = xray
+bgscale = 0.9
+composition = Pt
+outputtype = gr
+qmaxinst = 22.0
+qmin = 1.0
+qmax = 21.998245281
+rmax = 50.0
+rmin = 0.0
+rstep = 0.01
+rpoly = 0.9
+
+[Misc]
+inputdir = /Users/kirstenmariejensen/Seafile/KU/KU_Ptnanoparticles_JQ/DESY_Sep17
+savedir = /Users/kirstenmariejensen/Seafile/KU/KU_Ptnanoparticles_JQ/DESY_Sep17
+backgroundfilefull = /Users/kirstenmariejensen/Seafile/KU/KU_Ptnanoparticles_JQ/DESY_Sep17/JQ_EG.chi
+
+#### start data
+#S 1
+#L r($\AA$)  G($\AA^{-2}$)
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data/PDFs_experimental/JQ_S2.gr

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+# xPDFsuite Configuration #
+[PDF]
+wavelength = 0.1
+dataformat = Qnm
+inputfile = JQ_S2.chi
+backgroundfile = JQ_EG.chi
+mode = xray
+bgscale = 0.9
+composition = Pt
+outputtype = gr
+qmaxinst = 22.0
+qmin = 1.0
+qmax = 21.998245281
+rmax = 50.0
+rmin = 0.0
+rstep = 0.01
+rpoly = 0.9
+
+[Misc]
+inputdir = /Users/kirstenmariejensen/Seafile/KU/KU_Ptnanoparticles_JQ/DESY_Sep17
+savedir = /Users/kirstenmariejensen/Seafile/KU/KU_Ptnanoparticles_JQ/DESY_Sep17
+backgroundfilefull = /Users/kirstenmariejensen/Seafile/KU/KU_Ptnanoparticles_JQ/DESY_Sep17/JQ_EG.chi
+
+#### start data
+#S 1
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+# xPDFsuite Configuration #
+[PDF]
+wavelength = 0.1
+dataformat = Qnm
+inputfile = JQ_S3.chi
+backgroundfile = JQ_EG.chi
+mode = xray
+bgscale = 0.829
+composition = Pt
+outputtype = gr
+qmaxinst = 22.0
+qmin = 1.0
+qmax = 21.998245281
+rmax = 50.0
+rmin = 0.0
+rstep = 0.01
+rpoly = 0.9
+
+[Misc]
+inputdir = /Users/kirstenmariejensen/Seafile/KU/KU_Ptnanoparticles_JQ/DESY_Sep17
+savedir = /Users/kirstenmariejensen/Seafile/KU/KU_Ptnanoparticles_JQ/DESY_Sep17
+backgroundfilefull = /Users/kirstenmariejensen/Seafile/KU/KU_Ptnanoparticles_JQ/DESY_Sep17/JQ_EG.chi
+
+#### start data
+#S 1
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data/README.md

@@ -0,0 +1,75 @@
+[ChemRxiv](https://chemrxiv.org/engage/chemrxiv/article-details/6221f17357a9d20c9a729ecb)  |  [Paper](https://pubs.rsc.org/en/content/articlelanding/2023/dd/d2dd00086e)
+
+# Data introduction
+This folder contains __graphs__-, __utils__ folder and a gen_data.py script.  
+The __graphs__ folder contains a small set of graph which can be used to verify that DeepStruc is running correctly. To
+generate a broader distribution of mono-metallic nanoparticles (MMNPs) for training, validation and testing the gen_data.py 
+script is needed.
+  
+
+1. [Generate data](#generate-data)
+    1. [Generate data arguments](#generate-data-arguments)
+2. [Generel data structure](#generel-data-structure)
+    1. [Mono-metallic nanoparticles (MMNPs)](#mono-metallic-nanoparticles-mmnps)
+    2. [Graph representation](#graph-representation)
+    3. [Pair Distribution Function (PDF)](#pair-distribution-function-pdf)
+
+# Generate data
+[DiffPy-CMI](https://www.diffpy.org/products/diffpycmi/index.html) in required to simulate PDFs, which only runs on Linux or macOS. To run it on a Windows computer
+please use the [Ubuntu subsystem](https://ubuntu.com/tutorials/ubuntu-on-windows#1-overview). To generate more data run the gen_data.py script. The scripts takes a range of arguments which are all descriped below
+or use the __help__ command to produce the parameter list. The __help__ argument will also show default values.  
+```
+python gen_data.py --help
+>>> usage: gen_data.py [-h] [-d DIRECTORY] [-a ATOMS [ATOMS ...]]
+>>>                    [-t {SC,FCC,BCC,HCP,Ico,Dec,Oct} [{SC,FCC,BCC,HCP,Ico,Dec,Oct} ...]] 
+>>>                    [-n NUM_ATOMS] [-i INTERPOLATION] [-q QMIN] [-Q QMAX]  
+>>>                    [-r RMIN] [-R RMAX] [-rs RSTEP] [-b BISO]    
+>>>
+>>> Generating structures, graphs and conditional PDFs for DeepStruc.    
+>>> ...
+```
+
+## Generate data arguments
+List of possible arguments or run the '--help' argument for additional information.  
+ 
+| Arg | Description | Example |  
+| --- | --- |  --- |  
+| `-h` or `--help` | Prints help message. |    
+| `-d` or `--directory` | Prints help message. __str__ | `-d new_data`  |   
+| `-a` or `--atoms` | An atom or list of atoms. __str__| `-a Nb W Mo`  |
+| `-t` or `--structure_type` | A single or list of structure types. Possible structure types are: SC, FCC, BCC, HCP, Ico, Dec and Oct. __str__| `-t SC Ico`  |  
+| `-n` or `--num_atoms` | Maximum number of possible atoms in structures generated. __int__| `-n 200`  |  
+| `-i` or `--interpolation` | Prints help message. __int__| `-i 3`  |  
+| `-q` or `--qmin` | Smallest scattering amplitude for simulated PDFs. __float__| `-q 0.2`  |  
+| `-Q` or `--qmax` | Largest scattering amplitude for simulated PDFs. __float__| `-Q 22.3`  |  
+| `-p` or `--qdamp` | PDF Gaussian dampening factor due to limited Q-resolution. Not applied when equal to zero. __float__| `-p 0.02`  |  
+| `-r` or `--rmin` | Smallest r-value for simulated PDFs. __float__| `-r 1.5`  |  
+| `-R` or `--rmax` | Largest r-value for simulated PDFs. __float__| `-R 20.0`  |  
+| `-s` or `--rstep` | r-grid spacing for simulated PDFs. __float__| `-s 0.1`  |  
+| `-e` or `--delta2` | Coefficient for (1/r**2) contribution to the peak sharpening. __float__| `-e 3.5`  |  
+| `-b` or `--biso` | Isotropic Atomic Displacement Parameter for simulated PDFs. __float__| `-b 0.2`  |  
+
+  
+# Generel data structure
+A simplified description is shown below. For detailed description of the data format please revisit the paper.
+
+## Mono-metallic nanoparticles (MMNPs)
+To simulate MMNPs we use the [ASE](https://wiki.fysik.dtu.dk/ase/#) library. All of the MMNPs are in a XYZ format where the elements and their euclidian distances are described as seen below:
+
+Atom<sub>1</sub> &nbsp; &nbsp; x<sub>1</sub> &nbsp; &nbsp; y<sub>1</sub> &nbsp; &nbsp; z<sub>1</sub> <br>
+Atom<sub>2</sub> &nbsp; &nbsp; x<sub>2</sub> &nbsp; &nbsp; y<sub>2</sub> &nbsp; &nbsp; z<sub>2</sub> <br>
+...  
+Atom<sub>N</sub> &nbsp; &nbsp; x<sub>N</sub> &nbsp; &nbsp; y<sub>N</sub> &nbsp; &nbsp; z<sub>N</sub> <br>
+
+## Graph representation
+Each structure in graph representation can be described as, G = (X,A), where X ∈ R<sup>N×F</sup> is the node feature matrix which contains F features that can describe each of the N atoms in the structure. We use F = 3 comprising only the Euclidean coordinates of the atom in a 3-dimensional space. The interatomic relationships are captured using the adjacency matrix A ∈ R<sup>N×N</sup>. In our case, the entries of the adjacency matrix are the Euclidean distance between pairs of atoms resulting in a soft adjacency matrix. However, when the distance between any pair of nodes is larger than the lattice constant the corresponding edge weight is set to zero. 
+
+The following figure shows a decahedron consisting of seven atoms alongside the components describing it in our chosen graph representation!
+![alt text](../img/graph_rep.png "Graphs representation of MMNPs.")
+ 
+## Pair Distribution Function (PDF)
+The PDF is the Fourier transform of total scattering data, which can be obtained through x-ray, neutron or electron scattering.
+G(r) can be interpreted as a histogram of real-space interatomic distances and the information is equivalent to that of an unassigned distance matrix. <br> 
+A simulated PDF and how we normalise them are shown below:
+![alt text](../img/PDF.png "Simulated PDF")
+

data/gen_data.py

@@ -0,0 +1,87 @@
+from utils.gen_xyz import gen_xyz
+from utils.remove_duplicates import remove_duplicates
+from utils.gen_graphs import gen_graphs
+import argparse, sys
+
+def main(args=None):
+    print('\nCreating xyz monometallic nanoparticles!')
+    data_base = gen_xyz(args.atoms, args.structure_type, args.num_atoms, args.interpolation, args.directory)
+
+    print('\nCloning unique monometallic nanoparticles!')
+    remove_duplicates(data_base)
+
+    pdf_dict = {
+        'qmin': args.qmin,
+        'qmax': args.qmax,
+        'qdamp': args.qdamp,
+        'rmin': args.rmin,
+        'rmax': args.rmax,
+        'rstep': args.rstep,
+        'delta2': args.delta2,
+        'biso': args.biso
+    }
+    print('\nCreating graphs of monometallic nanoparticles!')
+    gen_graphs(data_base, pdf_dict)
+
+_BANNER = """
+Generating structures, graphs and conditional PDFs
+for DeepStruc.
+"""
+
+parser = argparse.ArgumentParser(description=_BANNER, formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+
+# Generel directory to save data.
+parser.add_argument("-d", "--directory", default=None, type=str,
+                    help="Name or path of output as str")
+
+# Structure parameters
+atom_list = [
+            'Sc', 'Ti', 'V', 'Cr', 'Mn', 'Fe', 'Co',
+            'Ni', 'Cu', 'Zn', 'Y', 'Zr', 'Nb', 'Tc',
+            'Ru', 'Rh', 'Pd', 'Ag', 'Cd', 'Hf', 'Ta',
+            'W', 'Re', 'Os', 'Ir', 'Pt', 'Au', 'Hg'
+            ]
+parser.add_argument("-a", "--atoms", default=atom_list, nargs='+',
+                    help="A list of atoms e.g. '-a W Nb Mo'")
+
+stru_type = [
+            "SC", "FCC", "BCC", "HCP", "Ico", "Dec", "Oct"
+            ]
+parser.add_argument("-t", "--structure_type", default=stru_type, nargs='+',
+                    help="A list of structure types e.g. '-t FCC BCC HCP'",
+                    choices=["SC", "FCC", "BCC", "HCP", "Ico", "Dec", "Oct"])
+
+parser.add_argument("-n", "--num_atoms", default=100, type=int,
+                    help="Positive integer of maximum number of atoms in structures")
+
+parser.add_argument("-i", "--interpolation", default=1, type=int,
+                    help="Positive integer of interpolations per structure")
+
+# PDF parameters
+parser.add_argument("-q", "--qmin", default=0.7, type=float,
+                    help="Smallest scattering amplitude, float")
+
+parser.add_argument("-Q", "--qmax", default=25., type=float,
+                    help="Largest scattering amplitude, float")
+
+parser.add_argument("-p", "--qdamp", default=0.04, type=float,
+                    help="PDF Gaussian dampening factor due to limited Q-resolution. Not applied when equal to zero.")
+
+parser.add_argument("-r", "--rmin", default=0., type=float,
+                    help="Smallest grid value, float")
+
+parser.add_argument("-R", "--rmax", default=30., type=float,
+                    help="Largest grid value, float")
+
+parser.add_argument("-s", "--rstep", default=0.01, type=float,
+                    help="Grid step size, float")
+
+parser.add_argument("-e", "--delta2", default=2., type=float,
+                    help="Coefficient for (1/r**2) contribution to the peak sharpening.")
+
+parser.add_argument("-b", "--biso", default=0.3, type=float,
+                    help="Isotropic Atomic Displacement Parameter (ADP), float")
+
+if __name__ == '__main__':
+    args = parser.parse_args()
+    main(args)

data/graphs/graph_BCC_h_2_k_9_l_9_atom_W_lc_2.6.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:c54adf38193a86885b18b267e1218df67d9fdf59dda097b474855b776e43e0a8
+size 35176

data/graphs/graph_BCC_h_4_k_7_l_8_atom_Cr_lc_2.38.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:8de3e16079c6e1692a27f152dd7a84d389666a2052108585d5a3fc55db0fc997
+size 67576

data/graphs/graph_Decahedron_p_1_q_1_r_1_atom_Ta_lc_2.68.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a7f643911f3e83c2e589454fc12c31bc172ab1b87a06fef24b9f2aa960ff3ccb
+size 31264

data/graphs/graph_Decahedron_p_5_q_1_r_0_atom_Re_lc_2.56.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2a2946a1602d09dea6673dba645a190e7a95e138aa5736e9aaf48eb1d39ca19e
+size 52408

data/graphs/graph_FCC_h_3_k_6_l_7_atom_Zn_lc_2.42.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:1229120080397c9d6a6087ad05edea2b87367854487141687aff890505fcf450
+size 68488

data/graphs/graph_HCP_Size1_2_Size2_2_Size3_7_atom_Fe_lc1_2.34_lc2_3.82122.h5

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+version https://git-lfs.github.com/spec/v1
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+size 37168

data/graphs/graph_HCP_Size1_4_Size2_4_Size3_4_atom_Os_lc1_2.52_lc2_4.11516.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:4dd1ca0f2be63e506be0b34fb5ae11925b193fc52067e56274943679b4fe9411
+size 56272

data/graphs/graph_Icosahedron_shell_2_atom_Mn_lc_2.36.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:86511fc4bd701cc64ba722e45cff2b19b4ef16838b644a7289fb0fbcb576355f
+size 30144

data/graphs/graph_Icosahedron_shell_3_atom_V_lc_2.44.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:a66649264d4eeae81860454f0d373c585100132d8340d2d9f250b86ad272faa4
+size 33448

data/graphs/graph_Octahedron_length_6_atom_Zn_lc_2.42.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:491b0f06e47639fddfe29784ae0fd06fc0fb53cf476bda6fb0ddbef9a3e4b86b
+size 63280

data/graphs/graph_SC_h_2_k_2_l_7_atom_Ti_lc_2.64.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:d3588d4aac88bd9c5c4cae82077ffbde4499fe23d88e8dff6afdaeea30c5129e
+size 31768

data/graphs/graph_SC_h_4_k_4_l_5_atom_V_lc_2.44.h5

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+version https://git-lfs.github.com/spec/v1
+oid sha256:7e980e9b638a0a03d57c40619950efbed75addae5a51fac9fdf1cc374588645c
+size 51928

data/utils/gen_graphs.py

@@ -0,0 +1,146 @@
+import os,yaml
+from diffpy.Structure import loadStructure, Lattice
+import numpy as np
+from diffpy.srreal.pdfcalculator import DebyePDFCalculator
+import matplotlib.pyplot as plt
+from tqdm import tqdm
+import h5py
+
+class graph_maker_xyz_nn():
+    def __init__(self, path, save_dir, pdf_dict):
+        self.stru_dir = path
+        self.save_dir = save_dir
+        self.strus = sorted(os.listdir(path))
+        print('Number of structures:', len(self.strus))
+
+        print('PDF simulation parameters:')
+        for key in pdf_dict.keys():
+            print(f'{key} = {pdf_dict[key]:.2f}')
+
+        # Set parameters
+        self.qmin = pdf_dict['qmin']
+        self.qmax = pdf_dict['qmax']
+        self.qdamp = pdf_dict['qdamp']
+        self.rmin = pdf_dict['rmin']
+        self.rmax = pdf_dict['rmax']
+        self.rstep = pdf_dict['rstep']
+        self.delta2 = pdf_dict['delta2']
+        self.biso = pdf_dict['biso']
+
+    def setup_calculator(self):
+        dbc = DebyePDFCalculator()
+        dbc.qmin = self.qmin
+        dbc.qmax = self.qmax
+        dbc.rmin = self.rmin
+        dbc.rmax = self.rmax
+        dbc.rstep = self.rstep
+        dbc.delta2 = self.delta2
+        dbc.qdamp = self.qdamp
+
+        return dbc
+
+    def gen_graphs(self):
+        dbc = self.setup_calculator()
+
+        pbar = tqdm(total=len(self.strus))
+
+        for stru in self.strus:
+            if os.path.isfile(self.save_dir+'/'+'graph_{}.h5'.format(stru[:-4])):
+                pbar.update()
+                continue
+            lc = self.read_lc(stru)
+            lc *= 1.02
+            cluster = loadStructure(self.stru_dir + '/' + stru)
+
+            cluster.B11 = self.biso
+            cluster.B22 = self.biso
+            cluster.B33 = self.biso
+            cluster.B12 = 0
+            cluster.B13 = 0
+            cluster.B23 = 0
+
+            dbc.setStructure(cluster)
+            r, g0 = dbc()
+
+            # Todo: make this a function
+            #dist_mat = np.zeros((len(cluster),len(cluster)))  # Edge info
+
+            node_mat = []  # For angles Todo: add atom label
+            index_list1 = []
+            index_list2 = []
+            edge_f_list = []
+            for i in range(len(cluster)):
+
+                for j in range(i,len(cluster)):
+                    if i == j: continue
+                        #dist_mat[i][j] = 99  # Todo: this is not a pretty solution
+                    else:
+                        position_0 = np.array([cluster.x[i], cluster.y[i], cluster.z[i]])
+                        position_1 = np.array([cluster.x[j], cluster.y[j], cluster.z[j]])
+
+                        dist = calc_dist(position_0, position_1)
+                        #dist_mat[i][j] = dist
+                        #dist_mat[j][i] = dist
+
+                    if dist <= lc:
+                        index_list1.append(i)
+                        index_list2.append(j)
+                        edge_f_list.append(dist)
+                        index_list1.append(j)
+                        index_list2.append(i)
+                        edge_f_list.append(dist)
+
+
+
+                node_mat.append([cluster.x[i], cluster.y[i], cluster.z[i]])
+
+            #dist_mat = np.array(#dist_mat)
+            #np.fill_diagonal(#dist_mat, 0)
+            edge_f_list = np.array(edge_f_list)
+
+            node_mat = np.array(node_mat)
+            direction = np.array([index_list1, index_list2])
+            g0 = np.array(g0)
+
+            h5_file = h5py.File(self.save_dir+'/'+'graph_{}.h5'.format(stru[:-4]), 'w')
+            h5_file.create_dataset('Edge Feature Matrix', data=edge_f_list)
+            h5_file.create_dataset('Node Feature Matrix', data=node_mat)
+            h5_file.create_dataset('Edge Directions', data=direction)
+            h5_file.create_dataset('PDF label', data=g0)
+            h5_file.close()
+
+            pbar.update()
+        pbar.close
+        return None
+
+    def read_lc(self, str_name):
+        if 'lc1' in str_name:
+            lc1_idx = str_name.index('lc1_') + 4
+            lc2_idx = str_name.index('lc2_') - 1
+            bd = float(str_name[lc1_idx:lc2_idx])
+        else:
+            lc_idx = str_name.index('lc_') + 3
+            bd = float(str_name[lc_idx:-4])
+
+        return bd
+
+
+
+def calc_dist(position_0, position_1):
+    """ Returns the distance between vectors position_0 and position_1 """
+    return np.sqrt((position_0[0] - position_1[0]) ** 2 + (position_0[1] - position_1[1]) ** 2 + (
+            position_0[2] - position_1[2]) ** 2)
+
+def gen_graphs(data_base, pdf_dict):
+    try:
+        os.makedirs(f'{data_base}/graphs')
+    except FileExistsError:
+        pass
+
+    with open(f'{data_base}/PDF_simulation_parameters.yaml', 'w') as outfile:
+        yaml.dump(pdf_dict, outfile, allow_unicode=True, default_flow_style=False)
+
+    obj = graph_maker_xyz_nn(f'{data_base}/xyz_unique', f'{data_base}/graphs', pdf_dict)
+    obj.gen_graphs()
+
+

data/utils/gen_xyz.py

@@ -0,0 +1,375 @@
+#!/usr/bin/env python3
+# -*- coding: utf-8 -*-
+"""
+Created on Sat May  9 14:52:34 2020
+
+@author: andysodeanker
+"""
+import sys
+
+from diffpy.Structure import Structure
+from diffpy.Structure import Atom
+from diffpy.Structure import loadStructure, Structure, Lattice
+import numpy as np
+from ase.data import covalent_radii, atomic_numbers, chemical_symbols
+from ase.cluster.cubic import FaceCenteredCubic, BodyCenteredCubic, SimpleCubic
+from ase.lattice.hexagonal import Hexagonal, HexagonalClosedPacked, Graphite
+from ase.cluster.decahedron import Decahedron
+from ase.cluster.icosahedron import Icosahedron
+from ase.cluster.octahedron import Octahedron
+from tqdm import tqdm
+import mendeleev, datetime
+import os, pdb
+from shutil import copyfile
+
+#from simPDFs import *
+
+"""
+The structure geometries are set into categories.
+Sc: 0, FCC: 1, BCC: 2, HCP: 3, Icosahedron: 4, Decahedron: 5, Octahedron: 6
+The  lattice constant is chossen between 90-110 % of the normal covalent_radii*2 for the atom.
+"""
+
+import shutil
+
+
+def new_structure_checker(Input_array, list_search):
+	for i in range(len(list_search)):
+		if np.all(Input_array == list_search[i]) == True:
+			return True
+		else:
+			pass
+	return False
+
+def find_geometry(structure_type):
+	if structure_type==0: 
+		Geometry = "SC"
+	if structure_type==1: 
+		Geometry = "FCC"
+	if structure_type==2: 
+		Geometry = "BCC"
+	if structure_type==3:
+		Geometry = "HCP"
+	if structure_type==4:
+		Geometry = "Ico"
+	if structure_type==5:
+		Geometry = "Dec"
+	if structure_type==6:
+		Geometry = "Oct"
+	return Geometry
+
+def find_structure_type(Geometry):
+	if Geometry=="SC": 
+		structure_type = 0
+	if Geometry=="FCC": 
+		structure_type = 1
+	if Geometry=="BCC": 
+		structure_type = 2
+	if Geometry=="HCP":
+		structure_type = 3
+	if Geometry=="Ico":
+		structure_type = 4
+	if Geometry=="Dec":
+		structure_type = 5
+	if Geometry=="Oct":
+		structure_type = 6
+	return structure_type
+
+def structure_maker(Geometry):
+	global h, k, l, atom, lc, lc1, lc2, size1, size2, size3, shell, p, q, r, length
+	if Geometry=="SC": 
+		stru = SimpleCubic(atom, surfaces=[[1,0,0], [1,1,0], [1,1,1]], layers=(h,k,l), latticeconstant=lc)
+		structure_type = 0
+	if Geometry=="FCC": 
+		stru = FaceCenteredCubic(atom, surfaces=[[1,0,0], [1,1,0], [1,1,1]], layers=(h,k,l), latticeconstant=2*np.sqrt(0.5*lc**2))
+		structure_type = 1
+	if Geometry=="BCC": 
+		stru = BodyCenteredCubic(atom, surfaces=[[1,0,0], [1,1,0], [1,1,1]], layers=(h,k,l), latticeconstant=lc)
+		structure_type = 2
+	if Geometry=="HCP": 
+		stru = HexagonalClosedPacked(symbol=atom, latticeconstant=(lc1, lc2), size=(size1, size2, size3))
+		structure_type = 3
+	if Geometry=="Ico":
+		stru = Icosahedron(atom, shell, latticeconstant=2*np.sqrt(0.5*lc**2))
+		structure_type = 4
+	if Geometry=="Dec":
+		stru = Decahedron(atom, p, q, r, latticeconstant=2*np.sqrt(0.5*lc**2))
+		structure_type = 5
+	if Geometry=="Oct":
+		stru = Octahedron(atom, length, latticeconstant=2*np.sqrt(0.5*lc**2))
+		structure_type = 6
+	return stru, structure_type
+
+def make_data(atoms, path, numberatoms, Geometry, minimum_atoms=0, numberOfBondLengths=1):
+	global ATOM_LIST
+	ATOM_LIST = atoms
+	if Geometry=="SC" or Geometry=="FCC" or Geometry=="BCC":
+		make_data_SC_FCC_BCC(path, numberatoms, Geometry, minimum_atoms, numberOfBondLengths)
+	if Geometry=="HCP":
+		make_data_HCP(path, numberatoms, Geometry, minimum_atoms, numberOfBondLengths)
+	if Geometry=="Ico":
+		make_data_Icosahedron(path, numberatoms, Geometry, minimum_atoms, numberOfBondLengths)
+	if Geometry=="Dec":
+		make_data_Decahedron(path, numberatoms, Geometry, minimum_atoms, numberOfBondLengths)
+	if Geometry=="Oct":
+		make_data_Octahedron(path, numberatoms, Geometry, minimum_atoms, numberOfBondLengths)
+	return None
+
+
+def make_data_SC_FCC_BCC(path, numberatoms, Geometry, minimum_atoms=0, numberOfBondLengths=1):
+	global h, k, l, atom, lc
+	if numberatoms < 101:
+		h_list = [1, 2, 3, 4, 5, 6, 7, 8]
+		k_list = [1, 2, 3, 4, 5, 6, 7]
+		l_list = [1, 2, 3, 4, 5, 6, 7]
+	elif numberatoms < 201 and numberatoms > 101:
+		h_list = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+		k_list = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+		l_list = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+	elif numberatoms > 201:
+		h_list = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
+		k_list = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
+		l_list = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
+
+	possible_structures = len(h_list)*len(k_list)*len(l_list)*len(ATOM_LIST)*numberOfBondLengths
+	pbar = tqdm(total=possible_structures)
+	for atom in ATOM_LIST:
+		structure_list = []
+		for h in h_list:
+			for k in k_list:
+				for l in l_list:	
+					if h <= k and h <= l and k <= l:	
+						#atom_cov_radii = covalent_radii[atomic_numbers[atom]]
+						atom_cov_radii =  mendeleev.element(atom).metallic_radius/100
+						lc_list = np.linspace(atom_cov_radii*2*0.99 , atom_cov_radii*2*1.01, numberOfBondLengths)
+						if numberOfBondLengths == 1:
+							lc_list = [atom_cov_radii*2]
+						for lc in lc_list:
+							stru1, structure_type = structure_maker(Geometry)
+							xyz1 = stru1.get_positions()
+							if np.shape(xyz1)[0] > 1 and np.shape(xyz1)[0] <= numberatoms and  np.shape(xyz1)[0] >= minimum_atoms:
+								cluster = Structure([Atom(atom, xi) for xi in xyz1])
+								if new_structure_checker(xyz1, structure_list) == False:
+									structure = np.zeros((numberatoms, 4))
+									for i in range(len(xyz1)):
+										structure[i, 0] = atomic_numbers[atom]
+										structure[i, 1:] = xyz1[i]
+									structure_list.append(xyz1)
+									cluster.write(path+str(Geometry)+"_h_"+str(h)+"_k_"+str(k)+"_l_"+str(l)+"_atom_"+str(atom)+"_lc_"+str(lc)+".xyz", format="xyz")
+									#generator = simPDFs(path+str(Geometry)+"_h_"+str(h)+"_k_"+str(k)+"_l_"+str(l)+"_atom_"+str(atom)+"_lc_"+str(lc)+".xyz", "./")
+									#r, Gr = generator.getPDF()
+									#y = [structure_type, h, k, l, 0, 0, 0, 0, 0, 0, 0, 0, atomic_numbers[atom], lc, 0]+Gr.tolist()
+									#np.savetxt(path+str(Geometry)+"_h_"+str(h)+"_k_"+str(k)+"_l_"+str(l)+"_atom_"+str(atom)+"_lc_"+str(lc)+"_LABEL.txt", y)
+					pbar.update(1)
+	pbar.close()     
+	return None
+
+def make_data_HCP(path, numberatoms, Geometry, minimum_atoms=0, numberOfBondLengths=1):
+	global size1, size2, size3, atom, lc1, lc2
+	if numberatoms < 101:
+		size1_list = [1, 2, 3, 4, 5, 6, 7, 8]
+		size2_list = [1, 2, 3, 4, 5, 6, 7, 8]
+		size3_list = [1, 2, 3, 4, 5, 6, 7, 8]
+	elif numberatoms < 201 and numberatoms > 101:
+		size1_list = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+		size2_list = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+		size3_list = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10]
+	elif numberatoms > 201:
+		size1_list = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
+		size2_list = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
+		size3_list = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15]
+
+	possible_structures = len(size1_list)*len(size2_list)*len(size3_list)*len(ATOM_LIST)*numberOfBondLengths
+	pbar = tqdm(total=possible_structures)
+	for atom in ATOM_LIST:
+		structure_list = []
+		for size1 in size1_list:
+			for size2 in size2_list:
+				for size3 in size3_list:
+					if size1 <= size2 and size1 <= size3 and size2 <= size3:	
+						#atom_cov_radii = covalent_radii[atomic_numbers[atom]]
+						atom_cov_radii =  mendeleev.element(atom).metallic_radius/100
+						lc1_list = np.linspace(atom_cov_radii*2*0.99 , atom_cov_radii*2*1.01, numberOfBondLengths)
+						if numberOfBondLengths == 1:
+							lc1_list = [atom_cov_radii*2]
+						for lc1 in lc1_list:
+							#lc2_list = np.linspace(lc1*2*0.99 , lc1*2*1.01, 10)*1.633
+							lc2_list = np.asarray([lc1])*1.633
+							for lc2 in lc2_list:
+								stru1, structure_type = structure_maker(Geometry)
+								xyz1 = stru1.get_positions()
+								if np.shape(xyz1)[0] > 1 and np.shape(xyz1)[0] <= numberatoms and  np.shape(xyz1)[0] >= minimum_atoms:
+									cluster = Structure([Atom(atom, xi) for xi in xyz1])
+									if new_structure_checker(xyz1, structure_list) == False:
+										structure = np.zeros((numberatoms, 4))
+										for i in range(len(xyz1)):
+											structure[i, 0] = atomic_numbers[atom]
+											structure[i, 1:] = xyz1[i]
+										structure_list.append(xyz1)
+										cluster.write(path+str(Geometry)+"_Size1_"+str(size1)+"_Size2_"+str(size2)+"_Size3_"+str(size3)+"_atom_"+str(atom)+"_lc1_"+str(lc1)+"_lc2_"+str(lc2)+".xyz", format="xyz")
+										#generator = simPDFs(path+str(Geometry)+"_Size1_"+str(size1)+"_Size2_"+str(size2)+"_Size3_"+str(size3)+"_atom_"+str(atom)+"_lc1_"+str(lc1)+"_lc2_"+str(lc2)+".xyz", "./")
+										#r, Gr = generator.getPDF()
+										#y = [structure_type, 0, 0, 0, size1, size2, size3, 0, 0, 0, 0, 0, atomic_numbers[atom], lc1, lc2]+Gr.tolist()
+										#np.savetxt(path+str(Geometry)+"_Size1_"+str(size1)+"_Size2_"+str(size2)+"_Size3_"+str(size3)+"_atom_"+str(atom)+"_lc1_"+str(lc1)+"_lc2_"+str(lc2)+"_LABEL.txt", y)
+								pbar.update(1)
+	pbar.close()
+	return None
+
+def make_data_Icosahedron(path, numberatoms, Geometry, minimum_atoms=0, numberOfBondLengths=1):
+	global atom, shell, lc
+	if numberatoms < 101:
+		shell_list = [1, 2, 3, 4]
+	elif numberatoms < 201 and numberatoms > 101:
+		shell_list = [1, 2, 3, 4, 5]
+	elif numberatoms > 201:
+		shell_list = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
+
+	possible_structures = len(shell_list)*len(ATOM_LIST)*numberOfBondLengths
+	pbar = tqdm(total=possible_structures)
+	for atom in ATOM_LIST:
+		structure_list = []
+		for shell in shell_list:	
+			#atom_cov_radii = covalent_radii[atomic_numbers[atom]]
+			atom_cov_radii =  mendeleev.element(atom).metallic_radius/100
+			lc_list = np.linspace(atom_cov_radii*2*0.99 , atom_cov_radii*2*1.01, numberOfBondLengths)
+			if numberOfBondLengths == 1:
+				lc_list = [atom_cov_radii*2]
+			for lc in lc_list:
+				stru1, structure_type = structure_maker(Geometry)
+				xyz1 = stru1.get_positions()
+				if np.shape(xyz1)[0] > 1 and np.shape(xyz1)[0] <= numberatoms and  np.shape(xyz1)[0] >= minimum_atoms:
+					cluster = Structure([Atom(atom, xi) for xi in xyz1])
+					if new_structure_checker(xyz1, structure_list) == False:
+						structure = np.zeros((numberatoms, 4))
+						for i in range(len(xyz1)):
+							structure[i, 0] = atomic_numbers[atom]
+							structure[i, 1:] = xyz1[i]
+						structure_list.append(xyz1)
+						cluster.write(path+str(Geometry)+"_shell_"+str(shell)+"_atom_"+str(atom)+"_lc_"+str(lc)+".xyz", format="xyz")
+						#generator = simPDFs(path+str(Geometry)+"_shell_"+str(shell)+"_atom_"+str(atom)+"_lc_"+str(lc)+".xyz", "./")
+						#r, Gr = generator.getPDF()
+						#y = [structure_type, 0, 0, 0, 0, 0, 0, shell, 0, 0, 0, 0, atomic_numbers[atom], lc, 0]+Gr.tolist()
+						#np.savetxt(path+str(Geometry)+"_shell_"+str(shell)+"_atom_"+str(atom)+"_lc_"+str(lc)+"_LABEL.txt", y)
+				pbar.update(1)
+	pbar.close()
+	return None
+
+def make_data_Decahedron(path, numberatoms, Geometry, minimum_atoms=0, numberOfBondLengths=1):
+	global atom, p, q, r, lc
+	if numberatoms < 101:
+		p_list = [1, 2, 3, 4]
+		q_list = [1, 2, 3, 4]
+		r_list = [0, 1, 2, 3, 4]
+	elif numberatoms < 201 and numberatoms > 101:
+		p_list = [1, 2, 3, 4, 5]
+		q_list = [1, 2, 3, 4, 5]
+		r_list = [0, 1, 2, 3, 4, 5]
+	elif numberatoms > 201:
+		p_list = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
+		q_list = [1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
+		r_list = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
+
+	possible_structures = len(p_list)*len(q_list)*len(r_list)*len(ATOM_LIST)*numberOfBondLengths
+	pbar = tqdm(total=possible_structures)
+	for atom in ATOM_LIST:
+		structure_list = []
+		for p in p_list:
+			for q in q_list:
+				for r in r_list:	
+					#if p <= q and p <= r and q <= r:	
+					#atom_cov_radii = covalent_radii[atomic_numbers[atom]]
+					atom_cov_radii =  mendeleev.element(atom).metallic_radius/100
+					lc_list = np.linspace(atom_cov_radii*2*0.99 , atom_cov_radii*2*1.01, numberOfBondLengths)
+					if numberOfBondLengths == 1:
+						lc_list = [atom_cov_radii*2]
+					for lc in lc_list:
+						stru1, structure_type = structure_maker(Geometry)
+						xyz1 = stru1.get_positions()
+						if np.shape(xyz1)[0] > 1 and np.shape(xyz1)[0] <= numberatoms and  np.shape(xyz1)[0] >= minimum_atoms:
+							cluster = Structure([Atom(atom, xi) for xi in xyz1])
+							if new_structure_checker(xyz1, structure_list) == False:
+								structure = np.zeros((numberatoms, 4))
+								for i in range(len(xyz1)):
+									structure[i, 0] = atomic_numbers[atom]
+									structure[i, 1:] = xyz1[i]
+								structure_list.append(xyz1)
+								cluster.write(path+str(Geometry)+"_p_"+str(p)+"_q_"+str(q)+"_r_"+str(r)+"_atom_"+str(atom)+"_lc_"+str(lc)+".xyz", format="xyz")
+								#generator = simPDFs(path+str(Geometry)+"_p_"+str(p)+"_q_"+str(q)+"_r_"+str(r)+"_atom_"+str(atom)+"_lc_"+str(lc)+".xyz", "./")
+								#r_PDF, Gr = generator.getPDF()
+								#y = [structure_type, 0, 0, 0, 0, 0, 0, 0, p, q, r, 0, atomic_numbers[atom], lc, 0]+Gr.tolist()
+								#np.savetxt(path+str(Geometry)+"_p_"+str(p)+"_q_"+str(q)+"_r_"+str(r)+"_atom_"+str(atom)+"_lc_"+str(lc)+"_LABEL.txt", y)
+						pbar.update(1)
+	pbar.close()
+	return None
+
+def make_data_Octahedron(path, numberatoms, Geometry, minimum_atoms=0, numberOfBondLengths=1):
+	global atom, length, lc
+	if numberatoms < 101:
+		length_list = [2, 3, 4, 5]
+	elif numberatoms < 201 and numberatoms > 101:
+		length_list = [2, 3, 4, 5, 6, 7]
+	elif numberatoms > 201:
+		length_list = [2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12]
+
+	possible_structures = len(length_list)*len(ATOM_LIST)*numberOfBondLengths
+	pbar = tqdm(total=possible_structures)
+	for atom in ATOM_LIST:
+		structure_list = []
+		for length in length_list:	
+			#atom_cov_radii = covalent_radii[atomic_numbers[atom]]
+			atom_cov_radii =  mendeleev.element(atom).metallic_radius/100
+			lc_list = np.linspace(atom_cov_radii*2*0.99 , atom_cov_radii*2*1.01, numberOfBondLengths)
+			if numberOfBondLengths == 1:
+				lc_list = [atom_cov_radii*2]
+			for lc in lc_list:
+				stru1, structure_type = structure_maker(Geometry)
+				xyz1 = stru1.get_positions()
+				if np.shape(xyz1)[0] > 1 and np.shape(xyz1)[0] <= numberatoms and  np.shape(xyz1)[0] >= minimum_atoms:
+					cluster = Structure([Atom(atom, xi) for xi in xyz1])
+					if new_structure_checker(xyz1, structure_list) == False:
+						structure = np.zeros((numberatoms, 4))
+						for i in range(len(xyz1)):
+							structure[i, 0] = atomic_numbers[atom]
+							structure[i, 1:] = xyz1[i]
+						structure_list.append(xyz1)
+						cluster.write(path+str(Geometry)+"_length_"+str(length)+"_atom_"+str(atom)+"_lc_"+str(lc)+".xyz", format="xyz")
+						#generator = simPDFs(path+str(Geometry)+"_length_"+str(length)+"_atom_"+str(atom)+"_lc_"+str(lc)+".xyz", "./")
+						#r, Gr = generator.getPDF()
+						#y = [structure_type, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, length, atomic_numbers[atom], lc, 0]+Gr.tolist()
+						#np.savetxt(path+str(Geometry)+"_length_"+str(length)+"_atom_"+str(atom)+"_lc_"+str(lc)+"_LABEL.txt", y)
+				pbar.update(1)
+	pbar.close()
+	return None
+
+
+
+def new_structure_checker(Input_array, list_search):
+	for i in range(len(list_search)):
+		if np.all(Input_array == list_search[i]) == True:
+			return True
+		else:
+			pass
+	return False
+
+
+
+def gen_xyz(atoms, type_list, max_atoms, interpolate, directory_base):
+	if directory_base==None:
+		ct = str(datetime.datetime.now()).replace(' ', '_').replace(':', '-').replace('.', '-')
+		directory_base = f'./data_{ct}'
+		print(f'\nProject name is: {directory_base}')
+	elif directory_base[-1] != '/':
+		directory_base = f'{directory_base}/'
+
+	directory = f"{directory_base}/xyz_raw/"
+	try:
+		os.makedirs(directory)
+	except FileExistsError:
+		pass
+
+	for type in type_list:
+		print(f'Generating structure type: {type}')
+		make_data(atoms, directory, max_atoms, type, minimum_atoms=5, numberOfBondLengths=interpolate)
+
+
+	return directory_base

data/utils/remove_duplicates.py

@@ -0,0 +1,100 @@
+import os, sys
+import numpy as np
+from scipy import spatial
+import shutil
+from tqdm import tqdm
+
+def remove_duplicates(data_base):
+    src, dst = copy_files(data_base)
+
+    """dir1 = f'{data_base}/xyz_raw'
+    files1 = sorted(os.listdir(dir1))
+
+    print(f'{len(files1)} files are being checked.')
+    moved_files, identical_files = 0, 0
+    pbar = tqdm(total=len(files1))
+    for idx1 in range(len(files1)):
+        clone = False
+        file1 = files1[idx1]
+        dat1 = np.loadtxt(dir1+'/'+file1, skiprows=1, dtype=np.str)
+        dat1 = np.array(dat1.T[1:], dtype=np.float)
+        dist_mat1 = spatial.distance.cdist(dat1.T, dat1.T, 'euclidean')
+        dist_mat1 = np.sort(dist_mat1.reshape(-1))
+        pbar.update()
+        for idx2 in range(idx1+1, len(files1)):
+            file2 = files1[idx2]
+
+            dat2 = np.loadtxt(dir1+'/'+file2, skiprows=1, dtype=np.str)
+            dat2 = np.array(dat2.T[1:], dtype=np.float)
+
+            if dat1.shape == dat2.shape:
+                dist_mat2 = spatial.distance.cdist(dat2.T, dat2.T, 'euclidean')
+                dist_mat2 = np.sort(dist_mat2.reshape(-1))
+
+                val = np.array_equal(dist_mat1, dist_mat2)
+
+                if val == True:
+                    clone = True
+                    break
+
+        if clone:
+            identical_files+=1
+        else:
+            moved_files += 1
+            shutil.copy2(f'{dir1}/{file1}', f'{data_base}/xyz_unique/{file1}')
+
+    pbar.close()"""
+
+    files1 = sorted(os.listdir(dst))
+    remove_list = []
+
+    pbar = tqdm(total=len(files1))
+    for idx1 in range(len(files1)):
+        file1 = files1[idx1]
+        dat1 = np.loadtxt(dst + '/' + file1, skiprows=1, dtype=np.str)
+        dat1 = np.array(dat1.T[1:], dtype=np.float)
+        dist_mat1 = spatial.distance.cdist(dat1.T, dat1.T, 'euclidean')
+        dist_mat1 = np.sort(dist_mat1.reshape(-1))
+        pbar.update()
+        for idx2 in range(idx1, len(files1)):
+            file2 = files1[idx2]
+
+            if file1 == file2:
+                continue
+            elif file2 in remove_list or file1 in remove_list:
+                continue
+
+            dat2 = np.loadtxt(dst + '/' + file2, skiprows=1, dtype=np.str)
+            dat2 = np.array(dat2.T[1:], dtype=np.float)
+
+            if dat1.shape == dat2.shape:
+                dist_mat2 = spatial.distance.cdist(dat2.T, dat2.T, 'euclidean')
+                dist_mat2 = np.sort(dist_mat2.reshape(-1))
+
+                val = np.array_equal(dist_mat1, dist_mat2)
+
+                if val == True:
+                    remove_list.append(file1)
+                    os.remove(dst + '/' + file1)
+
+    print(f'{len(os.listdir(dst))} were moved and {len(remove_list)} were clones.')
+
+    return None
+
+
+def copy_files(data_base):
+    dst = f'{data_base}/xyz_unique'
+    src = f'{data_base}/xyz_raw'
+    try:
+        os.makedirs(dst)
+    except FileExistsError:
+        pass
+
+    xyz_files = os.listdir(src)
+    pbar = tqdm(total=len(xyz_files))
+    for file in xyz_files:
+        shutil.copyfile(src+'/'+file,
+                        dst+'/'+file)
+        pbar.update()
+    pbar.close()
+    return src, dst

install/README.md

@@ -0,0 +1,23 @@
+[ChemRxiv](https://chemrxiv.org/engage/chemrxiv/article-details/6221f17357a9d20c9a729ecb)  |  [Paper](https://pubs.rsc.org/en/content/articlelanding/2023/dd/d2dd00086e)
+
+# Install
+To run DeepStruc you will need some packages, which are dependent on your computer specifications. 
+This includes [PyTorch](https://pytorch.org/) and 
+[PyTorch-Geometric](https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html), make sure that
+the versions match and [PyTorch-Geometric](https://pytorch-geometric.readthedocs.io/en/latest/notes/installation.html) is version 1.7.2.
+All other packages can be install through the requirement file. 
+First go to your desired conda environment.
+ ```
+conda activate <env_name>
+``` 
+Or create one and activate it afterwards.
+```
+conda create --name env_name python=3.7
+``` 
+Now install the required packages through the requirement files. Install [DiffPy-CMI](https://www.diffpy.org/products/diffpycmi/index.html) (see how to [HERE](https://www.diffpy.org/products/diffpycmi/index.html))
+if you want to be able to use the gen_data script.
+```
+pip install -r requirements.txt
+``` 
+
+

install/requirements.txt

@@ -0,0 +1,11 @@
+torch-geometric==1.7.2
+pytorch-lightning
+h5py
+matplotlib>=3.4.3
+numpy
+PyYAML
+tqdm
+pandas
+scipy
+mendeleev
+ase

models/DeepStruc/model_arch.yaml

@@ -0,0 +1,33 @@
+PDF_len: 2800
+decoder:
+  d0: 32
+  d1: 64
+  d2: 128
+  d3: 256
+  d4: 512
+  d5: 1024
+  out_dim: 200
+encoder:
+  e0: 1024
+  e1: 512
+  e2: 256
+  e3: 128
+  e4: 64
+latent_space: 2
+mlps:
+  m0: 256
+  m1: 128
+  m2: 64
+node_features: 3
+norm_vals:
+  x: 25.92
+  y: 25.92
+  z: 50.2637
+posterior:
+  prior_0: 384
+  prior_1: 192
+  prior_2: 24
+prior:
+  prior_0: 384
+  prior_1: 192
+  prior_2: 24

models/DeepStruc/models/DeepStruc.ckpt

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b272c760a1df8398b876ea62c52ed700997c00b0fa075e7fa6357ccad04655dd
+size 82960030

models/README.md

@@ -0,0 +1,5 @@
+[ChemRxiv](https://chemrxiv.org/engage/chemrxiv/article-details/6221f17357a9d20c9a729ecb)  |  [Paper](https://pubs.rsc.org/en/content/articlelanding/2023/dd/d2dd00086e)
+
+# Models
+This folder contain the DeepStruc model and all other trained models will be save here with the folder name:
+DeepStruc-year-month-day-time. 

predict.py

@@ -0,0 +1,76 @@
+import sys, argparse
+from tools.module import Net
+import torch, random
+import numpy as np
+import pytorch_lightning as pl
+from tools.utils import get_data, format_predictions, plot_ls, get_model, save_predictions
+
+seed = 37
+torch.manual_seed(seed)
+pl.seed_everything(seed)
+torch.manual_seed(seed)
+np.random.seed(seed)
+random.seed(seed)
+
+def main(args):
+    data, data_name, project_name = get_data(args)
+    model_path, model_arch = get_model(args.model)
+
+    Net(model_arch=model_arch)
+    DeepStruc = Net.load_from_checkpoint(model_path,model_arch=model_arch)
+    xyz_pred, latent_space, kl, mu, sigma = DeepStruc(data, mode='prior', sigma_scale=args.sigma)
+    samling_pairs = format_predictions(latent_space, data_name, mu, sigma, args.sigma)
+
+    if args.plot_sampling == True and args.model == 'DeepStruc':
+        plot_ls(samling_pairs, project_name, args.index_plot)
+    elif args.plot_sampling == True and args.model != 'DeepStruc':
+        print("Argument '--model' needs to be default DeepStruc value for plot to be generated!")
+
+    save_predictions(xyz_pred, samling_pairs, project_name, model_arch, args)
+
+    return None
+
+_BANNER = """
+Predict data.
+"""
+
+parser = argparse.ArgumentParser(description=_BANNER, formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+
+parser.add_argument("-d", "--data", default='data/PDFs_simulated', type=str,
+                    help="Path to data or data directory. If pointing to data directory all datasets must have same format.")
+
+parser.add_argument("-m", "--model", default='DeepStruc', type=str,
+                    help="Path to model. 'DeepStruc' to use pretrained model.")
+
+parser.add_argument("-n", "--num_samples", default=5, type=int,
+                    help="Number of samples/structures generated.")
+
+parser.add_argument("-s", "--sigma", default=3, type=float,
+                    help="Multiplier of the normaldistributions sigma.")
+
+parser.add_argument("-p", "--plot_sampling", default=False, type=bool,
+                    help="Plots sampled structures ontop of DeepStruc training data. Model must be DeepStruc.")
+
+parser.add_argument("-g", "--save_path", default='.', type=str,  # todo: add in README
+                    help="Save predictions path.")
+
+parser.add_argument("-i", "--index_plot", default=-1, type=int,  # todo: add in README
+                    help="Highlights specific reconstruction in the latent space. --data must be specific file "
+                         "and not directory and  '--plot True'.")
+
+parser.add_argument("-P", "--plot_data", default=True, type=bool,  # todo: add in README
+                    help="If True then the first loaded PDF is plotted and shown after normalization.")
+
+if __name__=='__main__':
+    args = parser.parse_args()
+    main(args)
+
+
+
+
+
+
+
+
+
+

tools/data_loader.py

@@ -0,0 +1,235 @@
+import os, torch, h5py, random, sys, shutil, yaml
+from pytorch_lightning.callbacks import ModelCheckpoint
+import numpy as np
+from torch_geometric.data import Data, DataLoader
+from tqdm import tqdm
+import pytorch_lightning as pl
+
+
+class graph_loader(pl.LightningDataModule):
+    def __init__(self, data_dir, cluster_size=None, num_files=None, batchsize=1, shuffle=True, num_workers=0):
+        super(graph_loader, self).__init__()
+        """
+
+        Parameters
+        ----------
+        data_dir
+        num_files
+        batchsize
+        shuffle
+
+        Returns
+        -------
+
+        """
+        self.batchsize = int(batchsize)
+        self.num_workers = num_workers
+        self.files_sorted = sorted(os.listdir(data_dir))
+        self.cluster_size = cluster_size
+        files = self.files_sorted.copy()
+        # files = [file for file in files if 'FCC' in file]
+
+        if shuffle == True:
+            random.shuffle(files)
+        if files != None:
+            files = files[:num_files]
+        else:
+            pass
+
+        nTrain = int(0.6 * len(files))
+        nValid = int((len(files) - nTrain) / 2)
+        nTest = len(files) - (nTrain + nValid)
+
+        print('\nBatch size: {}'.format(batchsize))
+        print('Total number of graphs {}.'.format(len(files)))
+        print('\tTraining files:', nTrain)
+        print('\tValidation files:', nValid)
+        print('\tTest files:', nTest, '\n')
+
+        self.trSamples, self.vlSamples, self.teSamples = list(), list(), list()
+        print('Loading graphs:')
+
+        for idx in range(len(files)):
+            h5f = h5py.File(data_dir + '/' + files[idx], 'r')
+            b = h5f['Node Feature Matrix'][:]
+            h5f.close()
+
+            if self.cluster_size == None:
+                self.cluster_size = len(b)
+            elif len(b) > self.cluster_size:
+                self.cluster_size = len(b)
+
+        largest_x_dist, largest_y_dist, largest_z_dist, edge_f_max = 0, 0, 0, 0
+        for idx in range(nTrain):
+            h5f = h5py.File(data_dir + '/' + files[idx], 'r')
+            a = h5f['Edge Feature Matrix'][:]
+            b = h5f['Node Feature Matrix'][:]
+
+            h5f.close()
+
+            diff_ph = abs(np.amin(b, axis=0)) + np.amax(b, axis=0)
+            if largest_x_dist < diff_ph[0]:
+                largest_x_dist = diff_ph[0]
+            if largest_y_dist < diff_ph[1]:
+                largest_y_dist = diff_ph[1]
+            if largest_z_dist < diff_ph[2]:
+                largest_z_dist = diff_ph[2]
+            if np.amax(a) > edge_f_max:
+                edge_f_max = np.amax(a)
+
+        self.largest_x_dist = largest_x_dist
+        self.largest_y_dist = largest_y_dist
+        self.largest_z_dist = largest_z_dist
+
+        for idx in tqdm(range(len(files))):
+            h5f = h5py.File(data_dir + '/' + files[idx], 'r')
+            a = h5f['Edge Feature Matrix'][:]  # todo: norm this
+            b = h5f['Node Feature Matrix'][:]
+            c = h5f['Edge Directions'][:]
+            d = h5f['PDF label'][:]
+            h5f.close()
+
+            a /= edge_f_max
+            min_vals = np.amin(b, axis=0)
+            if min_vals[0] < 0.0:  # Make all coordinates positive
+                b[:, 0] -= min_vals[0]
+            if min_vals[1] < 0.0:  # Make all coordinates positive
+                b[:, 1] -= min_vals[1]
+            if min_vals[2] < 0.0:  # Make all coordinates positive
+                b[:, 2] -= min_vals[2]
+
+            b[:, 0] /= largest_x_dist
+            b[:, 1] /= largest_y_dist
+            b[:, 2] /= largest_z_dist
+
+            cord_ph = np.zeros((self.cluster_size, np.shape(b)[1])) - 1
+            cord_ph[:np.shape(b)[0]] = b
+
+            d /= np.amax(d)  # Standardize PDF
+
+            pdf = torch.tensor([d], dtype=torch.float)
+            x = torch.tensor(b, dtype=torch.float)
+            y = torch.tensor([cord_ph], dtype=torch.float)
+            edge_index = torch.tensor(c, dtype=torch.long)
+            edge_attr = torch.tensor(a, dtype=torch.float)
+            name_idx = torch.tensor(self.files_sorted.index(files[idx]), dtype=torch.int16)
+
+            if idx < nTrain:
+                self.trSamples.append(
+                    tuple((Data(x=x, y=y, edge_index=edge_index, edge_attr=edge_attr), pdf.T, name_idx)))
+            elif idx < nTrain + nValid:
+                self.vlSamples.append(
+                    tuple((Data(x=x, y=y, edge_index=edge_index, edge_attr=edge_attr), pdf.T, name_idx)))
+            else:
+                self.teSamples.append(
+                    tuple((Data(x=x, y=y, edge_index=edge_index, edge_attr=edge_attr), pdf.T, name_idx)))
+
+    def train_dataloader(self):
+        return DataLoader(self.trSamples, batch_size=self.batchsize, shuffle=True, num_workers=self.num_workers)
+
+    def val_dataloader(self):
+        return DataLoader(self.vlSamples, batch_size=self.batchsize, num_workers=self.num_workers)
+
+    def test_dataloader(self):
+        return DataLoader(self.teSamples, batch_size=self.batchsize, num_workers=self.num_workers)
+
+
+def save_xyz_file(save_dir, cords, file_name, xyz_scale=[1,1,1]):
+
+    cords = [xyz for xyz in cords if np.mean(xyz) >= -0.2]
+    cords = np.array(cords)
+    cords[:,0] -= cords[:,0].mean()
+    cords[:,1] -= cords[:,1].mean()
+    cords[:,2] -= cords[:,2].mean()
+    these_cords = []
+    for count, xyz in enumerate(cords):
+        if count == 0:
+            these_cords.append(['{:d}'.format(len(cords))])
+            these_cords.append([''])
+
+        these_cords.append(['W   {:.4f}   {:.4f}   {:.4f}'.format(xyz[0]*xyz_scale[0], xyz[1]*xyz_scale[1], xyz[2]*xyz_scale[2])])
+
+    np.savetxt(save_dir + '/{}.xyz'.format(file_name), these_cords, fmt='%s')
+    return None
+
+
+def folder_manager(input_dict, model_arch):
+    this_trainer = None
+    epoch = input_dict['epochs']
+    if not os.path.isdir(input_dict['save_dir']):
+        os.mkdir(input_dict['save_dir'])
+        os.mkdir(input_dict['save_dir'] + '/models')
+        shutil.copy2('train.py', input_dict['save_dir'] + '/train.py')
+        shutil.copy2('./tools/data_loader.py', input_dict['save_dir'] + '/data_loader.py')
+        shutil.copy2('./tools/module.py', input_dict['save_dir'] + '/module.py')
+        os.mkdir(input_dict['save_dir'] + '/prior')
+        os.mkdir(input_dict['save_dir'] + '/posterior')
+    else:
+        shutil.copy2('train.py', input_dict['save_dir'] + '/train.py')
+        shutil.copy2('./tools/data_loader.py', input_dict['save_dir'] + '/data_loader.py')
+        shutil.copy2('./tools/module.py', input_dict['save_dir'] + '/module.py')
+
+    if input_dict['load_trainer']:
+        best_model = sorted(os.listdir(input_dict['save_dir'] + '/models'))
+        print(f'\nUsing {best_model[0]} as starting model!\n')
+        this_trainer = input_dict['save_dir'] + '/models/' + best_model[0]
+        #input_dict = yaml.load(f'{input_dict["save_dir"]}/input_dict.yaml', Loader=yaml.FullLoader)
+
+        try:
+            with open(f'{input_dict["save_dir"]}/input_dict.yaml') as file:
+                input_dict = yaml.full_load(file)
+            input_dict['load_trainer'] = True
+            input_dict['epochs'] = epoch
+            with open(f'{input_dict["save_dir"]}/model_arch.yaml') as file:
+                model_arch = yaml.full_load(file)
+        except FileNotFoundError:  # todo: transition - need to be deleted at some point
+            with open(f'{input_dict["save_dir"]}/input_dict.yaml', 'w') as outfile:
+                yaml.dump(input_dict, outfile, allow_unicode=True, default_flow_style=False)
+
+            with open(f'{input_dict["save_dir"]}/model_arch.yaml', 'w') as outfile:
+                yaml.dump(model_arch, outfile, allow_unicode=True, default_flow_style=False)
+    else:
+        with open(f'{input_dict["save_dir"]}/input_dict.yaml', 'w') as outfile:
+            yaml.dump(input_dict, outfile, allow_unicode=True, default_flow_style=False)
+
+        with open(f'{input_dict["save_dir"]}/model_arch.yaml', 'w') as outfile:
+            yaml.dump(model_arch, outfile, allow_unicode=True, default_flow_style=False)
+    return this_trainer, input_dict, model_arch
+
+
+def get_callbacks(save_dir):
+    checkpoint_callback_tot = ModelCheckpoint(
+        monitor='vld_tot',
+        dirpath=save_dir + '/models',
+        filename='model-{vld_tot:.5f}-{beta:.3f}-{vld_rec_pdf:.5f}-{epoch:010d}',
+        save_top_k=5,
+        mode='min',
+        save_last=True,
+    )
+
+    checkpoint_callback_rec = ModelCheckpoint(
+        monitor='vld_rec',
+        dirpath=save_dir + '/models',
+        filename='model-{vld_rec:.5f}-{beta:.3f}-{vld_rec_pdf:.5f}-{vld_tot:.5f}-{epoch:010d}',
+        save_top_k=5,
+        mode='min',
+    )
+
+    checkpoint_callback_kld = ModelCheckpoint(
+        monitor='vld_kld',
+        dirpath=save_dir + '/models',
+        filename='model-{vld_kld:.5f}-{beta:.3f}-{vld_rec_pdf:.5f}-{vld_tot:.5f}-{epoch:010d}',
+        save_top_k=5,
+        mode='min',
+    )
+
+    checkpoint_callback_vld_rec_pdf = ModelCheckpoint(
+        monitor='vld_rec_pdf',
+        dirpath=save_dir + '/models',
+        filename='model-{vld_rec_pdf:.5f}-{beta:.3f}-{vld_tot:.5f}-{epoch:010d}',
+        save_top_k=5,
+        mode='min',
+    )
+
+    return [checkpoint_callback_tot, checkpoint_callback_rec, checkpoint_callback_kld,
+            checkpoint_callback_vld_rec_pdf]

tools/module.py

@@ -0,0 +1,364 @@
+import torch.nn as nn
+import torch, sys
+import torch.nn.functional as F
+import torch.nn
+from torch_geometric.nn import GATConv
+import pytorch_lightning as pl
+from collections import OrderedDict
+from torch_geometric.nn.glob import global_add_pool, GlobalAttention
+from torch.distributions import Normal, Independent
+from torch.distributions.kl import kl_divergence as KLD
+
+class Net(pl.LightningModule):
+    def __init__(self, model_arch, lr=1e-4, beta=0, beta_inc=0.001, beta_max=1, rec_th=0.0001):
+        super(Net, self).__init__()
+        self.actFunc = nn.LeakyReLU()
+        self.actFunc_ReLU = nn.ReLU()
+        self.cluster_size = int(model_arch['decoder']['out_dim'])
+        self.latent_space = model_arch['latent_space']
+        self.beta = beta  # starting val
+        self.beta_inc = beta_inc  # beta increase
+        self.rec_th = rec_th  # Update beta if loss_rec is =< this value
+        self.last_beta_update = 0
+        self.beta_max = beta_max
+        self.lr = lr
+        self.num_node_features = model_arch['node_features']
+        self.encoder_layers = self.Encoder(model_arch['node_features'], model_arch['encoder'], model_arch['mlps']['m0'])
+        self.decoder_layers = self.Decoder(model_arch['node_features'], model_arch['decoder'], model_arch['latent_space'])
+        self.mlp_layers = self.MLPs(model_arch['mlps'], model_arch['latent_space'])
+
+        self.prior_layers = self.conditioning_nw(model_arch['PDF_len'], model_arch['prior'], self.latent_space * 2)
+        self.posterior_layers = self.conditioning_nw(model_arch['PDF_len'], model_arch['posterior'], model_arch['mlps']['m0'])  # Posterior
+        self.glob_at = GlobalAttention(torch.nn.Linear(model_arch['mlps']['m0'], 1), torch.nn.Linear(model_arch['mlps']['m0'], model_arch['mlps']['m0']))
+
+
+    def MLPs(self, model_arch, latent_dim):
+        layers = OrderedDict()
+
+        for idx, key in enumerate(model_arch.keys()):
+            if idx == 0:
+                layers[str(key)] = torch.nn.Linear(model_arch[key]*2, model_arch[key])
+            else:
+                layers[str(key)] = torch.nn.Linear(former_nhid, model_arch[key])
+
+            former_nhid = model_arch[key]
+
+        layers['-1'] = torch.nn.Linear(former_nhid, latent_dim*2)
+
+
+        return nn.Sequential(layers)
+
+
+    def Encoder(self, init_data, model_arch, out_dim):
+        layers = OrderedDict()
+
+        for idx, key in enumerate(model_arch.keys()):
+            if idx == 0:
+                layers[str(key)] = GATConv(init_data, model_arch[key])
+            else:
+                layers[str(key)] = GATConv(former_nhid, model_arch[key])
+
+            former_nhid = model_arch[key]
+
+
+        #layers['-1'] = GATConv(former_nhid, model_arch['m0'])
+        layers[str('e{}'.format(idx + 1))] = GATConv(former_nhid, out_dim)
+
+        return nn.Sequential(layers)
+
+    def Decoder(self, init_data, model_arch, latent_dim):
+        layers = OrderedDict()
+
+        for idx, key in enumerate(model_arch.keys()):
+            if idx == 0 :
+                layers[str(key)] = nn.Linear(latent_dim, model_arch[key])
+            elif key == 'out_dim':
+                continue
+            else:
+                layers[str(key)] = nn.Linear(former_nhid, model_arch[key])
+
+            former_nhid = model_arch[key]
+
+
+        layers[str('d{}'.format(idx+1))] = nn.Linear(former_nhid, model_arch['out_dim']*init_data)
+
+        return nn.Sequential(layers)
+
+    def conditioning_nw(self, pdf, model_arch, out):
+        ### Conditioning network on prior for atom list
+        ### Creates additional node features per node
+        ### Assumes 1xself.atomRangex1 one hot encoding vector as input
+        ### Output: 1x2*latent_dimx1
+        """conditioning_layers = nn.Sequential(
+            GatedConv1d(pdf, 48, kernel_size=1, stride=1), nn.ReLU(),
+            GatedConv1d(48, 24, kernel_size=1, stride=1), nn.ReLU(),
+            GatedConv1d(24, out, kernel_size=1, stride=1))"""
+
+
+        conditioning_layers = torch.nn.Sequential()
+        for idx, key in enumerate(model_arch.keys()):
+            if idx == 0:
+                conditioning_layers.add_module(str(key), GatedConv1d(pdf, model_arch[key], kernel_size=1, stride=1))
+            else:
+                conditioning_layers.add_module(str(key), GatedConv1d(former_nhid, model_arch[key], kernel_size=1, stride=1))
+
+            former_nhid = model_arch[key]
+        conditioning_layers.add_module('-1', GatedConv1d(former_nhid, out, kernel_size=1, stride=1))
+
+        return conditioning_layers
+
+
+    def forward(self, data, mode='posterior', sigma_scale=1):
+        """
+
+        Parameters
+        ----------
+        data :
+        mode : str - posterior, prior or generate
+
+        Returns
+        -------
+
+        """
+        self.sigma_scale = sigma_scale
+        if mode == 'posterior':
+            pdf_cond = data[1].to(self.device)
+            data = data[0].to(self.device)
+            try:
+                this_batch_size = len(data.batch.unique())
+            except:
+                this_batch_size = 1
+
+            # Prior
+            prior = self.get_prior_dist(pdf_cond)
+
+            # Posterior
+            posterior = self.get_posterior_dist(data, pdf_cond, this_batch_size)
+
+            # Divergence between posterior and prior
+            kl = KLD(posterior, prior) / this_batch_size
+
+            # Draw z from posterior distribution
+            z_sample = posterior.rsample()
+            z = z_sample.clone()
+
+        elif mode == 'prior':
+            try:
+                hej = data.clone()
+                pdf_cond = data.to(self.device)
+                this_batch_size = len(data)
+            except:
+                #print(data)
+                pdf_cond = data[1].to(self.device)
+                this_batch_size = 1
+
+
+            # Prior
+            prior = self.get_prior_dist(pdf_cond)
+
+            # Draw z from prior distribution
+            z_sample = prior.rsample()
+            z = z_sample.clone()
+            kl = torch.zeros(this_batch_size) -1
+
+        elif mode == 'generate':
+            # Set is given
+            z = data.clone()
+            z_sample = data.clone()
+            this_batch_size = 1
+            kl = torch.zeros(this_batch_size) -1
+
+        # Decoder
+        for idx, layer in enumerate(self.decoder_layers):
+            if idx == len(self.decoder_layers)-1:
+                z_sample = layer(z_sample)
+            else:
+                z_sample = self.actFunc(layer(z_sample))
+
+        z_sample = z_sample.view(this_batch_size, self.cluster_size, self.num_node_features)  # Output
+
+        return z_sample, z, kl, self.mu, self.sigma#.mean()
+
+
+    def get_prior_dist(self, pdf_cond):
+        cond_prior = pdf_cond.clone()
+
+        for idx, layer in enumerate(self.prior_layers):
+            if idx == len(self.prior_layers) - 1:
+                cond_prior = layer(cond_prior)
+            else:
+                cond_prior = self.actFunc(layer(cond_prior))
+
+        cond_prior = cond_prior.squeeze(-1)
+        prior = self.get_distribution(cond_prior)
+        return prior
+
+
+    def get_posterior_dist(self, data, pdf_cond, this_batch_size):
+        cond_post = pdf_cond.clone()
+
+        # Posterior
+        for idx, layer in enumerate(self.posterior_layers):
+            if idx == len(self.posterior_layers) - 1:
+                cond_post = layer(cond_post)
+            else:
+                cond_post = self.actFunc(layer(cond_post))
+
+        # Encoder
+        z = data.x.clone()
+        for idx, layer in enumerate(self.encoder_layers):
+            if idx == len(self.encoder_layers) - 1:
+                z = layer(z, data.edge_index)
+            else:
+                edge_index = data.edge_index
+
+                z = self.actFunc(layer(z, edge_index))
+        test = z.clone()
+
+        #z = global_add_pool(z, data.batch, size=this_batch_size)  # Sum note features
+        z = self.glob_at(test, data.batch, size=this_batch_size)
+
+        cond_post = cond_post.squeeze(-1)
+
+        z = torch.cat((z, cond_post), -1)
+
+        for idx, layer in enumerate(self.mlp_layers):
+            if idx == len(self.mlp_layers) - 1:
+                z = layer(z)
+            else:
+                z = self.actFunc(layer(z))
+
+        # Draw from distribution
+        posterior = self.get_distribution(z)
+        return posterior
+
+
+    def get_distribution(self, z):
+        mu, log_var = torch.chunk(z, 2, dim=-1)
+        log_var = nn.functional.softplus(log_var)  # Sigma can't be negative
+        sigma = torch.exp(log_var / 2) * self.sigma_scale
+        self.sigma = sigma
+        self.mu = mu
+        distribution = Independent(Normal(loc=mu, scale=sigma), 2)
+        return distribution
+
+
+    def training_step(self, batch, batch_nb):
+        prediction, _, kl, _, _ = self.forward(batch)
+
+        loss = weighted_mse_loss(prediction, batch[0]['y'], self.device)
+
+        #loss = F.mse_loss(prediction, batch[0]['y'])
+        log_loss = loss#torch.log(loss)
+
+        tot_loss = log_loss + (self.beta * kl)
+
+        self.log('trn_tot', tot_loss, prog_bar=False, on_step=False, on_epoch=True)
+        self.log('trn_rec', loss, prog_bar=False, on_step=False, on_epoch=True)
+        self.log('trn_log_rec', log_loss, prog_bar=False, on_step=False, on_epoch=True)
+        self.log('trn_kld', kl, prog_bar=False, on_step=False, on_epoch=True)
+
+        return tot_loss
+
+
+    def validation_step(self, batch, batch_nb):
+        prediction, _, kl, _, _ = self.forward(batch)
+        prediction_pdf, _, _, _, _ = self.forward(batch[1], mode='prior')
+
+        #loss = weighted_mse_loss(prediction, batch[0]['y'], self.device, node_weight=5)
+        #loss_pdf = weighted_mse_loss(prediction_pdf, batch[0]['y'], self.device, node_weight=5)
+
+        loss = F.mse_loss(prediction, batch[0]['y'])
+        loss_pdf = F.mse_loss(prediction_pdf, batch[0]['y'])
+
+        log_loss = loss#torch.log(loss)
+
+        tot_loss = log_loss + (self.beta * kl)
+
+        if (self.last_beta_update != self.current_epoch and self.beta < self.beta_max) and loss <= self.rec_th:
+            self.beta += self.beta_inc
+            self.last_beta_update = self.current_epoch
+
+        beta = self.beta
+        self.log('vld_tot', tot_loss, prog_bar=True, on_epoch=True)
+        self.log('vld_rec', loss, prog_bar=True, on_epoch=True)
+        self.log('vld_log_rec', log_loss, prog_bar=True, on_epoch=True)
+        self.log('vld_rec_pdf', loss_pdf, prog_bar=True, on_epoch=True)
+        self.log('vld_kld', kl, prog_bar=True, on_epoch=True)
+        self.log('beta', beta, prog_bar=True, on_step=False, on_epoch=True)
+
+        return tot_loss
+
+
+    def test_step(self, batch, batch_nb):
+        prediction, _, kl, _, _ = self.forward(batch)
+        prediction_pdf, _, _, _, _ = self.forward(batch[1], mode='prior')
+
+        #loss = weighted_mse_loss(prediction, batch[0]['y'], self.device, node_weight=5)
+        #loss_pdf = weighted_mse_loss(prediction_pdf, batch[0]['y'], self.device, node_weight=5)
+
+        loss = F.mse_loss(prediction, batch[0]['y'])
+        loss_pdf = F.mse_loss(prediction_pdf, batch[0]['y'])
+
+        log_loss = loss#torch.log(loss)
+
+        tot_loss = log_loss + (self.beta * kl)
+
+        self.log('tst_tot', tot_loss, prog_bar=False, on_epoch=True)
+        self.log('tst_rec', loss, prog_bar=False, on_epoch=True)
+        self.log('tst_log_rec', log_loss, prog_bar=False, on_epoch=True)
+        self.log('tst_rec_pdf', loss_pdf, prog_bar=False, on_epoch=True)
+        self.log('tst_kld', kl, prog_bar=False, on_epoch=True)
+
+        return tot_loss
+
+
+    def configure_optimizers(self):
+        return torch.optim.Adam(self.parameters(), lr=self.lr)
+
+
+class GatedConv1d(nn.Module):
+    def __init__(self, input_channels, output_channels,
+                 kernel_size, stride, padding=0, dilation=1, activation=None):
+        super(GatedConv1d, self).__init__()
+
+        self.activation = activation
+        self.sigmoid = nn.Sigmoid()
+
+        self.h = nn.Conv1d(input_channels, output_channels, kernel_size,
+                           stride, padding, dilation)
+        self.g = nn.Conv1d(input_channels, output_channels, kernel_size,
+                           stride, padding, dilation)
+
+    def forward(self, x):
+        if self.activation is None:
+            h = self.h(x)
+        else:
+            h = self.activation(self.h(x))
+        g = self.sigmoid(self.g(x))
+
+        return h * g
+
+
+def weighted_mse_loss(pred, label,device, dummy_weight=0.1, node_weight=1):
+    """
+
+    Parameters
+    ----------
+    pred : Predictions. (tensor)
+    label : True labels. (tensor)
+    dummy_weight : Weight of dummy nodes, default is 0.1. (float)
+
+    Returns
+    -------
+    this_loss : Computed loss. (tensor)
+    """
+    mask = torch.ones(label.shape).to(device)
+    mask[label == -1.] = dummy_weight
+    mask[label >= -0] = node_weight
+
+    loss_func = nn.MSELoss(reduction='none')
+    this_loss = loss_func(pred, label)
+    this_loss = this_loss*mask
+
+    return this_loss.mean()

tools/utils.py

@@ -0,0 +1,270 @@
+import torch, os, yaml, sys
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+from tqdm import tqdm
+from matplotlib.patches import Ellipse
+import matplotlib.lines as mlines
+from matplotlib.gridspec import GridSpec
+import datetime
+from tools.data_loader import save_xyz_file
+
+
+def get_data(args):  # Todo: write your own dataloader.
+    ct = str(datetime.datetime.now()).replace(' ', '_').replace(':','-').replace('.','-')
+    project_name = f'{args.save_path}/DeepStruc_{ct}'
+    print(f'\nProject name is: {project_name}')
+    if not os.path.isdir(f'{project_name}'):
+        os.mkdir(f'{project_name}')
+
+    this_path = args.data
+    samples = args.num_samples
+    if os.path.isdir(this_path):
+        files = sorted(os.listdir(this_path))
+    else:
+        files = [this_path]
+        this_path = '.'
+
+    x_list, y_list, name_list = [], [], []
+    idxx = 0
+    np_data = np.zeros((len(files)*samples, 2800))
+    for idx, file in enumerate(files):
+        for skip_row in range(100):
+            try:
+                data = np.loadtxt(f'{this_path}/{file}', skiprows=skip_row)
+            except ValueError:
+                continue
+            data = data.T
+            x_list.append(data[0])
+            y_list.append(data[1])
+            Gr_ph = data[1]
+            if round(data[0][1] - data[0][0],2) != 0.01:
+                raise ValueError("The PDF does not have an r-step of 0.01 Å")
+            try:
+                start_PDF = np.where((data[0] > 1.995) & (data[0] < 2.005))[0][0]
+            except:
+                Gr_ph = np.concatenate((np.zeros((int((data[0][0])/0.01))), Gr_ph))
+                print("The PDFs first value is above 2 Å. We have added 0's down to 2 Å as a quick fix.")
+            try:
+                end_PDF = np.where((data[0] > 29.995) & (data[0] < 30.005))[0][0]
+            except:
+                Gr_ph = np.concatenate((Gr_ph, np.zeros((3000-len(Gr_ph)))))
+                print("The PDFs last value is before 30 Å. We have added 0's up to 30 Å as a quick fix.")
+            Gr_ph = Gr_ph[200:3000]
+
+            for i in range(samples):
+                np_data[idxx] = Gr_ph
+                np_data[idxx] /= np.amax(np_data[idxx])
+                idxx += 1
+                name_list.append(file)
+            break
+
+    if args.plot_data==True:
+        fig, ax = plt.subplots()
+
+        plt.plot(x_list[0], y_list[0], label="Input PDF")
+        plt.plot(np.arange(2, 30, 0.01), np_data[0], label="DeepStruc PDF")
+        ax.set_xlabel(r'r / $\mathtt{\AA}$')
+        ax.set_ylabel('G(r) / a.u.')
+
+        ax.set_xlim(0,30)
+        plt.legend()
+        plt.title(f'{files[0]}')
+        plt.tight_layout()
+        plt.savefig(f'{project_name}/PDFs.png', dpi=300)
+
+    np_data = np_data.reshape((len(files)*samples, 2800, 1))
+    np_data = torch.tensor(np_data, dtype=torch.float)
+    return np_data, name_list, project_name
+
+
+def format_predictions(latent_space, data_names, mus, sigmas, sigma_inc):
+    df_preds = pd.DataFrame(columns=['x', 'y', 'file_name', 'mu', 'sigma', 'sigma_inc'])
+    for i,j, mu, sigma in zip(latent_space, data_names, mus, sigmas):
+        if '/' in j:
+            j = j.split('/')[-1]
+
+        if '.' in j:
+            j_idx = j.rindex('.')
+            j = j[:j_idx]
+
+        info_dict = {
+            'x': i[0].detach().cpu().numpy(),
+            'y': i[1].detach().cpu().numpy(),
+            'file_name': j,
+            'mu': mu.detach().cpu().numpy(),
+            'sigma': sigma.detach().cpu().numpy(),
+            'sigma_inc': sigma_inc,
+
+        }
+        df_preds = df_preds.append(info_dict, ignore_index=True)
+    return df_preds
+
+
+def plot_ls(df, mk_dir, index_highlight):
+    if not os.path.isdir(mk_dir):
+        os.mkdir(mk_dir)
+    ideal_ls = './tools/ls_points.csv'
+    color_dict = {
+        'FCC': '#19ADFF',
+        'BCC': '#4F8F00',
+        'SC': '#941100',
+        'Octahedron': '#212121',
+        'Icosahedron': '#005493',
+        'Decahedron': '#FF950E',
+        'HCP': '#FF8AD8',
+    }
+    df_ideal = pd.read_csv(ideal_ls, index_col=0)  # Get latent space data
+    # Plotting inputs
+    ## Training and validation data
+    MARKER_SIZE_TR = 60
+    EDGE_LINEWIDTH_TR = 0.0
+    ALPHA_TR = 0.3
+
+    ## Figure
+    FIG_SIZE = (10, 4)
+    MARKER_SIZE_FG = 60
+    MARKER_FONT_SIZE = 10
+    MARKER_SCALE = 1.5
+
+    fig = plt.figure(figsize=FIG_SIZE)
+    gs = GridSpec(1, 5, figure=fig)
+    ax = fig.add_subplot(gs[0, :4])
+    ax_legend = fig.add_subplot(gs[0, 4])
+
+    if index_highlight >= len(df):
+        print(f'\nIndex argument is to large! Need to be smaller than {len(df)} but was {index_highlight}')
+        raise IndexError
+    elif index_highlight < -1:
+        print(f'\nIndex argument invalid! Must be integer from -1 to number of samples generated.')
+        raise ValueError
+    elif index_highlight==-1:
+        pass
+    elif len(df['file_name'].unique()) > 1:
+        print(f'\nCan only show highlight index if --data is specific file but {len(df["file_name"].unique())} files were loaded.')
+    else:
+        print(f'\nHighlighting index {index_highlight} from the {df["file_name"].unique()[0]} sampling pool.')
+        ax.scatter(df.iloc[index_highlight]['x'], df.iloc[index_highlight]['y'], c='k', s=40,
+                   linewidth=0.0, marker='o', zorder=3)
+        ax.scatter(df.iloc[index_highlight]['x'], df.iloc[index_highlight]['y'], c='w', s=25,
+                   linewidth=0.0, marker='o', zorder=3)
+        ax.scatter(df.iloc[index_highlight]['x'], df.iloc[index_highlight]['y'], c='k', s=10,
+                   linewidth=0.0, marker='o', zorder=3)
+        ax.scatter(df.iloc[index_highlight]['x'], df.iloc[index_highlight]['y'], c='w', s=1,
+                   linewidth=0.0, marker='o', zorder=3)
+
+    print('\nPlotting DeepStruc training + validation data.')
+    pbar = tqdm(total=len(df_ideal))
+    for idx in range(len(df_ideal)):
+        ax.scatter(df_ideal.iloc[idx]['x'], df_ideal.iloc[idx]['y'],
+                   c=color_dict[df_ideal.iloc[idx]['stru_type']], s=MARKER_SIZE_TR * df_ideal.iloc[idx]['size'],
+                   edgecolors='k', linewidth=EDGE_LINEWIDTH_TR,
+                   alpha=ALPHA_TR)
+
+        pbar.update()
+    pbar.close()
+
+    mlines_list = []
+    for key in color_dict.keys():
+        mlines_list.append(
+            mlines.Line2D([], [], MARKER_SIZE_FG, marker='o', c=color_dict[key], linestyle='None', label=key,
+                          mew=1))
+
+    from matplotlib import cm
+    cm_subsection = np.linspace(0, 1, len(df.file_name.unique()))
+    data_color = [cm.magma(x) for x in cm_subsection]
+
+    print('\nPlotting DeepStruc structure sampling.')
+    pbar = tqdm(total=len(df.file_name.unique()))
+    for idx, file_name in enumerate(df.file_name.unique()):
+        this_c = np.array([data_color[idx]])
+
+        df_ph = df[df.file_name==file_name]
+        df_ph.reset_index(drop=True, inplace=True)
+
+        ax.scatter(df_ph['mu'][0][0],df_ph['mu'][0][1], c=this_c, s=10, edgecolors='k',
+                   linewidth=0.5, marker='D',zorder=1)
+        ellipse = Ellipse((df_ph['mu'][0][0],df_ph['mu'][0][1]),df_ph['sigma'][0][0],df_ph['sigma'][0][1], ec='k', fc=this_c, alpha=0.5, fill=True, zorder=-1)
+        ax.add_patch(ellipse)
+
+        ellipse = Ellipse((df_ph['mu'][0][0],df_ph['mu'][0][1]),df_ph['x'].var(),df_ph['y'].var(), ec='k', fc=this_c, alpha=0.2, fill=True, zorder=-1)
+        ax.add_patch(ellipse)
+
+        mlines_list.append(
+            mlines.Line2D([], [], MARKER_SIZE_FG, marker='D', c=this_c, linestyle='None', label=file_name, mec='k',
+                          mew=1))
+
+        for index, sample in df_ph.iterrows():
+            ax.scatter(sample['x'], sample['y'], c=this_c, s=10, edgecolors='k',
+                       linewidth=0.8, marker='o', zorder=2)
+        pbar.update()
+    pbar.close()
+    ax_legend.legend(handles=mlines_list,fancybox=True, #ncol=2,  #, bbox_to_anchor=(0.8, 0.5)
+          markerscale=MARKER_SCALE, fontsize=MARKER_FONT_SIZE, loc='upper right')
+
+    ax.set_xlabel('Latent space $\mathregular{z_0}$', size=10)  # Latent Space Feature 1
+    ax.set_ylabel('Latent space $\mathregular{z_1}$', size=10)
+
+    ax_legend.spines['top'].set_visible(False)
+    ax_legend.spines['right'].set_visible(False)
+    ax_legend.spines['bottom'].set_visible(False)
+    ax_legend.spines['left'].set_visible(False)
+    ax_legend.get_xaxis().set_ticks([])
+    ax_legend.get_yaxis().set_ticks([])
+    ax.get_xaxis().set_ticks([])
+    ax.get_yaxis().set_ticks([])
+
+    plt.tight_layout()
+    plt.savefig(f'{mk_dir}/ls.png',dpi=300)
+    return None
+
+
+def get_model(model_dir):
+    if model_dir == 'DeepStruc':
+        with open(f'./models/DeepStruc/model_arch.yaml') as file:
+            model_arch = yaml.full_load(file)
+        model_path = './models/DeepStruc/models/DeepStruc.ckpt'
+        return model_path, model_arch
+    if os.path.isdir(model_dir):
+        if 'models' in os.listdir(model_dir):
+            models = sorted(os.listdir(f'{model_dir}/models'))
+            models = [model for model in models if '.ckpt' in model]
+            print(f'No specific model was provided. {models[0]} was chosen.')
+            print('Dataloader might not be sufficient in loading dimensions.')
+            model_path = f'{model_dir}/models/{models[0]}'
+            with open(f'{model_dir}/model_arch.yaml') as file:
+                model_arch = yaml.full_load(file)
+
+            return model_path, model_arch
+        else:
+            print(f'Path not understood: {model_dir}')
+    else:
+        idx = model_dir.rindex('/')
+        with open(f'{model_dir[:idx-6]}model_arch.yaml') as file:
+            model_arch = yaml.full_load(file)
+
+        return model_dir, model_arch
+
+
+def save_predictions(xyz_pred, df, project_name, model_arch, args):
+    print('\nSaving predicted structures as XYZ files.')
+    if not os.path.isdir(f'{project_name}'):
+        os.mkdir(f'{project_name}')
+
+    with open(f'{project_name}/args.yaml', 'w') as outfile:
+        yaml.dump(vars(args), outfile, allow_unicode=True, default_flow_style=False)
+
+    pbar = tqdm(total=len(df))
+    for count, (idx, row) in enumerate(df.iterrows()):
+        if not os.path.isdir(f'{project_name}/{row["file_name"]}'):
+            os.mkdir(f'{project_name}/{row["file_name"]}')
+        x = f'{float(row["x"]):+.3f}'.replace('.', '-')
+        y = f'{float(row["y"]):+.3f}'.replace('.', '-')
+
+        save_xyz_file(f'{project_name}/{row["file_name"]}',
+                      xyz_pred[idx].detach().cpu().numpy(),
+                      f'{row["file_name"]}_{count:05}_ls_{x}_{y}',
+                      [model_arch['norm_vals']['x'],model_arch['norm_vals']['y'],model_arch['norm_vals']['z']])
+        pbar.update()
+    pbar.close()
+    return None