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We also recommend that a + file or class name and description of purpose be included on the + same "printed page" as the copyright notice for easier + identification within third-party archives. + + Copyright [yyyy] [name of copyright owner] + + Licensed under the Apache License, Version 2.0 (the "License"); + you may not use this file except in compliance with the License. + You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + + Unless required by applicable law or agreed to in writing, software + distributed under the License is distributed on an "AS IS" BASIS, + WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. + See the License for the specific language governing permissions and + limitations under the License. diff --git a/POMFinder.egg-info/PKG-INFO b/POMFinder.egg-info/PKG-INFO new file mode 100644 index 0000000000000000000000000000000000000000..78d87a6ac2a4e00da90e30fc32e678e44c1803b8 --- /dev/null +++ b/POMFinder.egg-info/PKG-INFO @@ -0,0 +1,104 @@ +Metadata-Version: 2.1 +Name: POMFinder +Version: 1.0.0 +Summary: Finds POM clusters from PDF data! +Home-page: https://github.com/AndyNano/POMFinder +Author: Andy S. Anker +Author-email: andy@chem.ku.dk +License: UNKNOWN +Platform: UNKNOWN +Classifier: Programming Language :: Python :: 3 +Classifier: Programming Language :: Python :: 3.6 +Classifier: Programming Language :: Python :: 3.7 +Classifier: Operating System :: OS Independent +Description-Content-Type: text/markdown +License-File: LICENSE.txt + +# POMFinder +Welcome to POMFinder! +This is a simple machine learning tool for structure characterisation of polyoxometalate clusters using total scattering Pair +Distribution Function (PDF) analysis. +Simply provide a PDF and the model will output best best structural models from it structure catalog which contains 443 polyoxometalate clusters. + +1. [Install](#install) +2. [Usage](#usage) +3. [Authors](#authors) +4. [Cite](#cite) +5. [License](#license) + + +## Install +To install POMFindeer you will need to have [Python](https://www.python.org/downloads/) or +[Anaconda](https://www.anaconda.com/products/individual) installed. I recommend running POMFinder on Python version +3.7 or higher. If you have installed Anaconda you can create a new environment and activate it. +``` +conda create --name POMFinder_env python=3.7 +conda activate POMFinder_env +``` +Now you are ready to install what you actually come for! Currently __POMFinder__ is not avaible through PyPI or conda so the +package needs to be downloaded manually +Run the following command to install the __POMFindeer__ package. +``` +pip install . +or +python setup.py install +``` + +To verify that __POMFinder__ have been installed properly try calling the help argument. +``` +POMFinder --help + +>>> usage: POMFinder [-h] -d DATA [-n NYQUIST] [-i QMIN] [-a QMAX] [-m QDAMP] [-f FILE_NAME] +>>> +>>> This is a package which takes a directory of PDF files +>>> or a specific PDF file. It then determines the best structural +>>> candidates based of a polyoxometalate catalog. Results can +>>> be fitted to the PDF. +``` +This should output a list of possible arguments for running __POMFinder__ and indicates that it could find the package! + +## Usage +Now that __POMFinder__ is installed and ready to use, lets discuss the possible arguments. The arguments are described in +greater detail at the end of this section. + +| Arg | Description | Default | +| --- | --- | --- | +| | __Required argument__ | | +| `-h` or `--help` | Prints help message. | +| `-n` or `--nyquist` | Is the data nyquist sampled. __bool__ | `-n True` +| `-i` or `--Qmin` | Qmin value of the experimental PDF. __float__ | `-i 0.7` +| `-a` or `--Qmax` | Qmax value of the experimental PDF. __float__ | `-a 30` +| `-m` or `--Qdamp` | Qdamp value of the experimental PDF. __float__ | `-m 0.04` +| `-f` or `--file_name` | Name of the output file. __str__ | `-o ''` +| `-d` or `--data` | A directory of PDFs or a specific PDF file. __str__ | `-d 5` + +For example +``` +POMFinder --data "Experimental_Data/DanMAX_AlphaKeggin.gr" --nyquist "no" --Qmin 0.7 --Qmax 20 --Qdamp 0.02 + +>>> The 1st guess from the model is: icsd_427457_1_0.9rscale.xyz with 83.29164981842041 % certaincy +>>> The 2nd guess from the model is: icsd_427379_0_0.9rscale.xyz with 13.973137736320496 % certaincy +>>> The 3rd guess from the model is: icsd_281447_0_1.0rscale.xyz with 1.488963421434164 % certaincy +>>> The 4th guess from the model is: icsd_423775_0_0.9rscale.xyz with 0.9325935505330563 % certaincy +>>> The 5th guess from the model is: icsd_172542_0_1.1rscale.xyz with 0.22610558662563562 % certaincy + +``` +# Authors +__Andy S. Anker__1 +__Emil T. S. Kjær__1 +__Kirsten M. Ø. Jensen__1 + +1 Department of Chemistry and Nano-Science Center, University of Copenhagen, 2100 Copenhagen Ø, Denmark. + +Should there be any question, desired improvement or bugs please contact us on GitHub or +through email: __andy@chem.ku.dk__. + +# Cite +If you use our code or our results, please consider citing our paper. Thanks in advance! +``` +``` + +# License +This project is licensed under the Apache License Version 2.0, January 2004 - see the [LICENSE](LICENSE) file for details. + + diff --git a/POMFinder.egg-info/SOURCES.txt b/POMFinder.egg-info/SOURCES.txt new file mode 100644 index 0000000000000000000000000000000000000000..9f72992e047cdefe2ccbc73a8a91003ace24b145 --- /dev/null +++ b/POMFinder.egg-info/SOURCES.txt @@ -0,0 +1,459 @@ +LICENSE.txt +README.md +setup.py +POMFinder.egg-info/PKG-INFO +POMFinder.egg-info/SOURCES.txt +POMFinder.egg-info/dependency_links.txt +POMFinder.egg-info/entry_points.txt +POMFinder.egg-info/not-zip-safe +POMFinder.egg-info/requires.txt +POMFinder.egg-info/top_level.txt +src/__init__.py +src/cli.py +src/main.py +src/Backend/POMFinder_443structures_100Dataset_per_Structure_xPDF_hypercube_sampling_Grmax_Name.h5 +src/Backend/XGBoost_443structures_100PDFperStructure_xPDF_hypercube_sampling_Grmax.model +src/COD_ICSD_XYZs_POMs_unique99/2012944_0_0.9rscale.xyz 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+src/Experimental_Data/DanMAX_AlphaKeggin.gr \ No newline at end of file diff --git a/POMFinder.egg-info/dependency_links.txt b/POMFinder.egg-info/dependency_links.txt new file mode 100644 index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc --- /dev/null +++ b/POMFinder.egg-info/dependency_links.txt @@ -0,0 +1 @@ + diff --git a/POMFinder.egg-info/entry_points.txt b/POMFinder.egg-info/entry_points.txt new file mode 100644 index 0000000000000000000000000000000000000000..582f8610d00e4d9fb9d46e089ff0d3fb16dd874c --- /dev/null +++ b/POMFinder.egg-info/entry_points.txt @@ -0,0 +1,2 @@ +[console_scripts] +POMFinder = POMFinder.cli:main diff --git a/POMFinder.egg-info/not-zip-safe b/POMFinder.egg-info/not-zip-safe new file mode 100644 index 0000000000000000000000000000000000000000..8b137891791fe96927ad78e64b0aad7bded08bdc --- /dev/null +++ b/POMFinder.egg-info/not-zip-safe @@ -0,0 +1 @@ + diff --git a/POMFinder.egg-info/requires.txt b/POMFinder.egg-info/requires.txt new file mode 100644 index 0000000000000000000000000000000000000000..d67319061a1826fc7c14e3e13a1330ea9694f365 --- /dev/null +++ b/POMFinder.egg-info/requires.txt @@ -0,0 +1,5 @@ +xgboost +numpy +matplotlib +sklearn +h5py diff --git a/POMFinder.egg-info/top_level.txt b/POMFinder.egg-info/top_level.txt new file mode 100644 index 0000000000000000000000000000000000000000..491a1f2840c2aea51560982b40a102b1cb9dac05 --- /dev/null +++ b/POMFinder.egg-info/top_level.txt @@ -0,0 +1 @@ +POMFinder diff --git a/README.md b/README.md index 271c3005658874adf2891682b91dd1da4311b937..798635bfaa8f159fab06a8a91c90901348f456c1 100644 --- a/README.md +++ b/README.md @@ -1,12 +1,86 @@ ---- -title: POMFinder App -emoji: 🦀 -colorFrom: green -colorTo: blue -sdk: streamlit -sdk_version: 1.10.0 -app_file: app.py -pinned: false -license: apache-2.0 -duplicated_from: AndySAnker/DeepStruc ---- +# POMFinder +Welcome to POMFinder! +This is a simple machine learning tool for structure characterisation of polyoxometalate clusters using total scattering Pair +Distribution Function (PDF) analysis. +Simply provide a PDF and the model will output best best structural models from it structure catalog which contains 443 polyoxometalate clusters. + +1. [Install](#install) +2. [Usage](#usage) +3. [Authors](#authors) +4. [Cite](#cite) +5. [License](#license) + + +## Install +To install POMFindeer you will need to have [Python](https://www.python.org/downloads/) or +[Anaconda](https://www.anaconda.com/products/individual) installed. I recommend running POMFinder on Python version +3.7 or higher. If you have installed Anaconda you can create a new environment and activate it. +``` +conda create --name POMFinder_env python=3.7 +conda activate POMFinder_env +``` +Now you are ready to install what you actually come for! Currently __POMFinder__ is not avaible through PyPI or conda so the +package needs to be downloaded manually +Run the following command to install the __POMFindeer__ package. +``` +pip install . +or +python setup.py install +``` + +To verify that __POMFinder__ have been installed properly try calling the help argument. +``` +POMFinder --help + +>>> usage: POMFinder [-h] -d DATA [-n NYQUIST] [-i QMIN] [-a QMAX] [-m QDAMP] [-f FILE_NAME] +>>> +>>> This is a package which takes a directory of PDF files +>>> or a specific PDF file. It then determines the best structural +>>> candidates based of a polyoxometalate catalog. Results can +>>> be fitted to the PDF. +``` +This should output a list of possible arguments for running __POMFinder__ and indicates that it could find the package! + +## Usage +Now that __POMFinder__ is installed and ready to use, lets discuss the possible arguments. The arguments are described in +greater detail at the end of this section. + +| Arg | Description | Default | +| --- | --- | --- | +| | __Required argument__ | | +| `-h` or `--help` | Prints help message. | +| `-n` or `--nyquist` | Is the data nyquist sampled. __bool__ | `-n True` +| `-i` or `--Qmin` | Qmin value of the experimental PDF. __float__ | `-i 0.7` +| `-a` or `--Qmax` | Qmax value of the experimental PDF. __float__ | `-a 30` +| `-m` or `--Qdamp` | Qdamp value of the experimental PDF. __float__ | `-m 0.04` +| `-f` or `--file_name` | Name of the output file. __str__ | `-o ''` +| `-d` or `--data` | A directory of PDFs or a specific PDF file. __str__ | `-d 5` + +For example +``` +POMFinder --data "Experimental_Data/DanMAX_AlphaKeggin.gr" --nyquist "no" --Qmin 0.7 --Qmax 20 --Qdamp 0.02 + +>>> The 1st guess from the model is: icsd_427457_1_0.9rscale.xyz +>>> The 2nd guess from the model is: icsd_427379_0_0.9rscale.xyz +>>> The 3rd guess from the model is: icsd_281447_0_1.0rscale.xyz +>>> The 4th guess from the model is: icsd_423775_0_0.9rscale.xyz +>>> The 5th guess from the model is: icsd_172542_0_1.1rscale.xyz + +``` +# Authors +__Andy S. Anker__1 +__Emil T. S. Kjær__1 +__Kirsten M. Ø. Jensen__1 + +1 Department of Chemistry and Nano-Science Center, University of Copenhagen, 2100 Copenhagen Ø, Denmark. + +Should there be any question, desired improvement or bugs please contact us on GitHub or +through email: __andy@chem.ku.dk__. + +# Cite +If you use our code or our results, please consider citing our paper. Thanks in advance! +``` +``` + +# License +This project is licensed under the Apache License Version 2.0, January 2004 - see the [LICENSE](LICENSE) file for details. diff --git a/app.py b/app.py index a10de2a1be150745f2ae0639b5a303328b19c304..a766b12edc073644db3a1289de453d6f7c44f4c6 100644 --- a/app.py +++ b/app.py @@ -1,88 +1,131 @@ -import streamlit as st -import io, os, argparse, random +from sklearn.preprocessing import OrdinalEncoder +import xgboost as xgb import numpy as np +import matplotlib.pyplot as plt +import argparse, h5py, os, re, pkg_resources -seed = 37 -np.random.seed(seed) -random.seed(seed) st.title('POMFinder') st.write('Welcome to DeepStruc that is a Deep Generative Model which has been trained to solve a mono-metallic structure (<200 atoms) based on a PDF!') st.write('Upload a PDF to use DeepStruc to predict the structure.') - # Define the file upload widget pdf_file = st.file_uploader("Upload PDF file in .gr format", type=["gr"]) # Define the form to get the other parameters -num_structures = st.number_input("Number of structures to generate", min_value=1, max_value=100, value=10) -structure_index = st.number_input("Index of structure to visualize", min_value=0, value=3) -sigma = st.number_input("Standard deviation for sampling", min_value=0.1, value=3.0) - -# Define parser -parser = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter) -args = parser.parse_args() -args.num_samples = num_structures -args.index_plot = structure_index -args.sigma = sigma -# Fixed for DeepStruc app -args.model = 'DeepStruc' -args.save_path = './' - -# Define the predict button and its behavior -if st.button("Generate structures"): - if pdf_file is None: - st.warning("Please upload a PDF file.") +num_structures = st.number_input("Qmin value", min_value=0, max_value=2, value=0.7) +#structure_index = st.number_input("Index of structure to visualize", min_value=0, value=3) +#sigma = st.number_input("Standard deviation for sampling", min_value=0.1, value=3.0) + +parser = argparse.ArgumentParser(prog='POMFinder', formatter_class=argparse.ArgumentDefaultsHelpFormatter) + +requiredNamed = parser.add_argument_group('required named arguments') + +requiredNamed.add_argument("-d", "--data", default=None, type=str, + help="a directory of PDFs or a file.", required=True) + +requiredNamed.add_argument("-n", "--nyquist", default="No", type=str, + help="is the data nyquist sampled", required=True) + +parser.add_argument("-i", "--Qmin", default=0.7, type=float, + help="Qmin value of the experimental PDF") + +parser.add_argument("-a", "--Qmax", default=30, type=float, + help="Qmax value of the experimental PDF") + +parser.add_argument("-f", "--file_name", default='', type=str, + help="Name of the output file") + +parser.add_argument("-m", "--Qdamp", default=0.04, type=float, + help="Qdamp value of the experimental PDF") + +def main(args=None): + args = parser.parse_args(args=args) + y, y_onehotenc_cat, y_onehotenc_values, POMFinder = get_POMFinder() + r, Gr = PDF_Preparation(args.data, args.Qmin, args.Qmax, args.Qdamp, rmax=10, nyquist=args.nyquist) + res, y_pred_proba = POMPredicter(POMFinder, Gr, y_onehotenc_cat); + + + +def get_POMFinder(): + # Get file paths + load_files = pkg_resources.resource_listdir(__name__, 'Backend/') + DataBase_path = pkg_resources.resource_filename(__name__, "Backend/"+load_files[0]) + POMFinder_path = pkg_resources.resource_filename(__name__, "Backend/"+load_files[1]) + # Import the Database + hf_name = h5py.File(DataBase_path, "r") + y = hf_name.get('y') + enc = OrdinalEncoder() + y_onehotenc_cat = enc.fit(np.array(y)) + y_onehotenc_values = enc.fit_transform(np.array(y)) + + # Import POMFinder + POMFinder = xgb.XGBClassifier() + POMFinder.load_model(POMFinder_path) + return y, y_onehotenc_cat, y_onehotenc_values, POMFinder + + +def PDF_Preparation(Your_PDF_Name, Qmin, Qmax, Qdamp, rmax, nyquist, plot=True): + for i in range(1000): + with open(Your_PDF_Name, "r") as file: + data = file.read().splitlines(True) + if len(data[0]) == 0: + with open(Your_PDF_Name, 'w') as fout: + fout.writelines(data[1:]) + break + first_line = data[0] + if len(first_line) > 3 and re.match(r'^-?\d+(?:\.\d+)?$', first_line[0]) != None and re.match(r'^-?\d+(?:\.\d+)?$', first_line[1]) == None and re.match(r'^-?\d+(?:\.\d+)?$', first_line[2]) != None: + PDF = np.loadtxt(Your_PDF_Name) + break + else: + with open(Your_PDF_Name, 'w') as fout: + fout.writelines(data[1:]) + + r, Gr = PDF[:,0], PDF[:,1] + if r[0] != 0: # In the case that the Data not start at 0. + Gr = Gr[np.where(r==1)[0][0]:] # Remove Data from 0 to 0.5 AA + Gr = Gr[::10] # Nyquist sample the rest of the Data + Gr = np.concatenate(([0,0,0,0,0,0,0,0,0,0], Gr), axis=0) # Concatenate 0 - 0.5 AA on the Gr. + if nyquist == "No" or nyquist == "no": + Gr = Gr[::10] # Nyquist sample Data + if len(Gr) >= (rmax*10+1): + Gr = Gr[:(rmax*10+1)] # In the case Data is up to more than 30 AA, we do not use it. else: - # Get the contents of the file as bytes - file_bytes = pdf_file.read() + Gr = np.concatenate((Gr, np.zeros((101-len(Gr),))), axis=0) # In case Data is not going to 30 AA, we add 0's. + + Gr[:10] = np.zeros((10,)) + r = np.arange(0, (rmax+0.1), 0.1) + # Normalise it to the data from the database + Gr /= np.max(Gr) + # Add experimental parameters to the Gr + Gr = np.expand_dims(np.concatenate((np.expand_dims(Qmin, axis=0), np.expand_dims(Qmax, axis=0), np.expand_dims(Qdamp, axis=0), Gr), axis=0), axis=0) + + if plot: + # Plot the transformation to make sure everything is alright + plt.plot(PDF[:,0], PDF[:,1], label="Original Data") + plt.plot(r, Gr[0,3:], label="Gr ready for ML") + plt.legend() + plt.title("Original Data vs. normalised Data") + plt.xlabel("r (AA)") + plt.ylabel("Gr") + plt.show() - # Save the contents of the file to disk - with open("uploaded_file.gr", "wb") as f: - f.write(file_bytes) - - df, index_highlight, these_cords = main(args) - - # Plot the latent space - fig = plot_ls(df, index_highlight) - st.pyplot(fig) - st.write('**The two-dimensional latent space with location of the input.** The size of the points relates to the size of the embedded structure. Each point is coloured after its structure type, FCC (light blue), octahedral (dark grey), decahedral (orange), BCC (green), icosahedral (dark blue), HCP (pink) and SC (red). Each point in the latent space corresponds to a structure based on its simulated PDF. Test data point are plotted on top of the training and validation data, which is made semi-transparent. The latent space locations of the reconstructed structures from the input are shown with black markers and the specific reconstructed structure that is shown in the next box is shown with a black and white marker.') - - # Define the save directory and file name - file_name = "DeepStruc_prediction.xyz" - - # Define a download button to download the file - def download_button(file_name, button_text): - with open(file_name, "rb") as f: - bytes = f.read() - st.download_button( - label=button_text, - data=bytes, - file_name=file_name, - mime="text/xyz",) - - # Save the coordinates to a file and display a download button - np.savetxt(file_name, these_cords, fmt="%s") - download_button(file_name, "Download XYZ file") - - - -st.subheader('Cite') - -st.write('If you use DeepStruc, our code or results, please consider citing our papers. Thanks in advance!') - -st.write('DeepStruc: Towards structure solution from pair distribution function data using deep generative models **2023** (https://pubs.rsc.org/en/content/articlehtml/2022/dd/d2dd00086e)') -st.write('Characterising the atomic structure of mono-metallic nanoparticles from x-ray scattering data using conditional generative models **2020** (https://par.nsf.gov/biblio/10300745)') - -st.subheader('LICENSE') - -st.write('This project is licensed under the Apache License Version 2.0, January 2004 - see the LICENSE file at https://github.com/EmilSkaaning/DeepStruc/blob/main/LICENSE.md for details.') -st.write("") - -st.subheader('Github') -st.write('https://github.com/EmilSkaaning/DeepStruc') + return r, Gr + +def POMPredicter(POMFinder, Gr, y_onehotenc_cat): + y_pred_proba = POMFinder.predict_proba(Gr); + y_pred_proba = y_pred_proba[:,1]; + #print (np.shape(y_pred_proba)) + #y_pred_proba = y_pred_proba[0]; + res = sorted(range(len(y_pred_proba)), key = lambda sub: y_pred_proba[sub]); + res.reverse(); + print ("The 1st guess from the model is: ", str(y_onehotenc_cat.categories_[0][res[0]])[2:-2]+"cale.xyz") + print ("The 2nd guess from the model is: ", str(y_onehotenc_cat.categories_[0][res[1]])[2:-2]+"cale.xyz") + print ("The 3rd guess from the model is: ", str(y_onehotenc_cat.categories_[0][res[2]])[2:-2]+"cale.xyz") + print ("The 4th guess from the model is: ", str(y_onehotenc_cat.categories_[0][res[3]])[2:-2]+"cale.xyz") + print ("The 5th guess from the model is: ", str(y_onehotenc_cat.categories_[0][res[4]])[2:-2]+"cale.xyz") + + return res, y_pred_proba -st.subheader('Questions') -st.write('andy@chem.ku.dk or etsk@chem.ku.dk') diff --git a/requirements.txt b/requirements.txt index afa83751baa82ecbe6ba067c11394d76d687e3ba..706b96d6908276a0a76c9e0a4160530450f55c33 100644 --- a/requirements.txt +++ b/requirements.txt @@ -1,3 +1,3 @@ -streamlit -matplotlib==3.4.3 -POMFinder \ No newline at end of file +xgboost +scikit-learn +matplotlib \ No newline at end of file diff --git a/setup.py b/setup.py new file mode 100644 index 0000000000000000000000000000000000000000..8ebf41eacf84557c3f1717e782eb3ce82cdc78f4 --- /dev/null +++ b/setup.py @@ -0,0 +1,43 @@ +from setuptools import setup + +with open("README.md", "r") as fh: + long_description = fh.read() + +setup( + name='POMFinder', + version='1.0.1', + author='Andy S. Anker', + author_email='andy@chem.ku.dk', + url='https://github.com/AndyNano/POMFinder', + description='Finds POM clusters from PDF data!', + long_description=long_description, + long_description_content_type="text/markdown", + py_modules=['POMFinder'], + packages=['POMFinder'], + package_dir={'POMFinder': 'src'}, + package_data={'POMFinder': ['Backend/*.model', 'Backend/*.h5', 'COD_ICSD_XYZs_POMs_unique99/*.xyz', 'Experimental_Data/*.gr']}, + + entry_points={'console_scripts': [ + 'POMFinder=POMFinder.cli:main', + ], + }, + classifiers=[ + 'Programming Language :: Python :: 3', + 'Programming Language :: Python :: 3.6', + 'Programming Language :: Python :: 3.7', + 'Operating System :: OS Independent', + ], + include_package_data = True, + zip_safe=False, + + install_requires=[ + 'xgboost', + 'numpy', + 'matplotlib', + 'sklearn', + 'h5py' + ], +) + +# requirements.txt for deployment on machines that you control. +# pip freeze to genereate requirements.txt file.