automatic import of python-foundry-mlopeneuler20.03

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+/foundry_ml-0.6.3.tar.gz
diff --git a/python-foundry-ml.spec b/python-foundry-ml.spec
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+%global _empty_manifest_terminate_build 0
+Name:		python-foundry-ml
+Version:	0.6.3
+Release:	1
+Summary:	Package to support simplified application of machine learning models to datasets in materials science
+License:	MIT License
+URL:		https://github.com/MLMI2-CSSI/foundry
+Source0:	https://mirrors.aliyun.com/pypi/web/packages/b8/07/cf4adbb29ea6020fe528de44e2ac9955a939c47a2ebc458caa93bc29cf82/foundry_ml-0.6.3.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-mdf-forge
+Requires:	python3-globus-sdk
+Requires:	python3-dlhub-sdk
+Requires:	python3-numpy
+Requires:	python3-pandas
+Requires:	python3-pydantic
+Requires:	python3-mdf-connect-client
+Requires:	python3-h5py
+Requires:	python3-json2table
+
+%description
+
+<picture>
+  <source srcset="https://raw.githubusercontent.com/MLMI2-CSSI/foundry/main/assets/foundry-white.png" height=175" media="(prefers-color-scheme: dark)">
+  <img src="https://raw.githubusercontent.com/MLMI2-CSSI/foundry/main/assets/foundry-black.png" height="175">
+</picture>
+
+[![PyPI](https://img.shields.io/pypi/v/foundry_ml.svg)](https://pypi.python.org/pypi/foundry_ml)
+[![Tests](https://github.com/MLMI2-CSSI/foundry/actions/workflows/tests.yml/badge.svg)](https://github.com/MLMI2-CSSI/foundry/actions/workflows/tests.yml)
+[![Tests](https://github.com/MLMI2-CSSI/foundry/actions/workflows/python-publish.yml/badge.svg)](https://github.com/MLMI2-CSSI/foundry/actions/workflows/python-publish.yml)
+[![NSF-1931306](https://img.shields.io/badge/NSF-1931306-blue)](https://www.nsf.gov/awardsearch/showAward?AWD_ID=1931306&HistoricalAwards=false)
+[<img src="https://img.shields.io/badge/view-documentation-blue">](https://ai-materials-and-chemistry.gitbook.io/foundry/)
+
+
+Foundry-ML simplifies the discovery and usage of ML-ready datasets in materials science and chemistry providing a simple API to access even complex datasets. 
+* Load ML-ready data with just a few lines of code
+* Work with datasets in local or cloud environments. 
+* Publish your own datasets with Foundry to promote community usage
+* (in progress) Run published ML models without hassle
+
+Learn more and see our available datasets on [Foundry-ML.org](https://foundry-ml.org/)
+
+
+
+# Documentation
+Information on how to install and use Foundry is available in our documentation [here](https://ai-materials-and-chemistry.gitbook.io/foundry/v/docs/).
+
+DLHub documentation for model publication and running information can be found [here](https://dlhub-sdk.readthedocs.io/en/latest/servable-publication.html).
+
+# Quick Start
+Install Foundry-ML via command line with:
+`pip install foundry_ml`
+
+You can use the following code to import and instantiate Foundry-ML, then load a dataset.
+
+```python
+from foundry import Foundry
+f = Foundry(index="mdf")
+
+
+f = f.load("10.18126/e73h-3w6n", globus=True)
+```
+If running this code in a notebook, a table of metadata for the dataset will appear:
+
+<img width="903" alt="metadata" src="https://user-images.githubusercontent.com/16869564/197038472-0b6ae559-4a6b-4b20-88e5-679bb6eb4f5c.png">
+
+We can use the data with `f.load_data()` and specifying splits such as `train` for different segments of the dataset, then use matplotlib to visualize it.
+
+```python
+res = f.load_data()
+
+imgs = res['train']['input']['imgs']
+desc = res['train']['input']['metadata']
+coords = res['train']['target']['coords']
+
+n_images = 3
+offset = 150
+key_list = list(res['train']['input']['imgs'].keys())[0+offset:n_images+offset]
+
+fig, axs = plt.subplots(1, n_images, figsize=(20,20))
+for i in range(n_images):
+    axs[i].imshow(imgs[key_list[i]])
+    axs[i].scatter(coords[key_list[i]][:,0], coords[key_list[i]][:,1], s = 20, c = 'r', alpha=0.5)
+```
+<img width="595" alt="Screen Shot 2022-10-20 at 2 22 43 PM" src="https://user-images.githubusercontent.com/16869564/197039252-6d9c78ba-dc09-4037-aac2-d6f7e8b46851.png">
+
+[See full examples](./examples)
+
+# Primary Support
+This work was supported by the National Science Foundation under NSF Award Number: 1931306 "Collaborative Research: Framework: Machine Learning Materials Innovation Infrastructure".
+
+# Other Support
+Foundry-ML brings together many components in the materials data ecosystem. Including [MAST-ML](https://mastmldocs.readthedocs.io/en/latest/), the [Data and Learning Hub for Science](https://www.dlhub.org) (DLHub), and the [Materials Data Facility](https://materialsdatafacility.org) (MDF).
+
+## MAST-ML
+This work was supported by the National Science Foundation (NSF) SI2 award No. 1148011 and DMREF award number DMR-1332851
+
+## The Data and Learning Hub for Science (DLHub)
+This material is based upon work supported by Laboratory Directed Research and Development (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357.
+https://www.dlhub.org
+
+## The Materials Data Facility
+This work was performed under financial assistance award 70NANB14H012 from U.S. Department of Commerce, National Institute of Standards and Technology as part of the [Center for Hierarchical Material Design (CHiMaD)](http://chimad.northwestern.edu). This work was performed under the following financial assistance award 70NANB19H005 from U.S. Department of Commerce, National Institute of Standards and Technology as part of the Center for Hierarchical Materials Design (CHiMaD). This work was also supported by the National Science Foundation as part of the [Midwest Big Data Hub](http://midwestbigdatahub.org) under NSF Award Number: 1636950 "BD Spokes: SPOKE: MIDWEST: Collaborative: Integrative Materials Design (IMaD): Leverage, Innovate, and Disseminate".
+https://www.materialsdatafacility.org
+
+
+%package -n python3-foundry-ml
+Summary:	Package to support simplified application of machine learning models to datasets in materials science
+Provides:	python-foundry-ml
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-foundry-ml
+
+<picture>
+  <source srcset="https://raw.githubusercontent.com/MLMI2-CSSI/foundry/main/assets/foundry-white.png" height=175" media="(prefers-color-scheme: dark)">
+  <img src="https://raw.githubusercontent.com/MLMI2-CSSI/foundry/main/assets/foundry-black.png" height="175">
+</picture>
+
+[![PyPI](https://img.shields.io/pypi/v/foundry_ml.svg)](https://pypi.python.org/pypi/foundry_ml)
+[![Tests](https://github.com/MLMI2-CSSI/foundry/actions/workflows/tests.yml/badge.svg)](https://github.com/MLMI2-CSSI/foundry/actions/workflows/tests.yml)
+[![Tests](https://github.com/MLMI2-CSSI/foundry/actions/workflows/python-publish.yml/badge.svg)](https://github.com/MLMI2-CSSI/foundry/actions/workflows/python-publish.yml)
+[![NSF-1931306](https://img.shields.io/badge/NSF-1931306-blue)](https://www.nsf.gov/awardsearch/showAward?AWD_ID=1931306&HistoricalAwards=false)
+[<img src="https://img.shields.io/badge/view-documentation-blue">](https://ai-materials-and-chemistry.gitbook.io/foundry/)
+
+
+Foundry-ML simplifies the discovery and usage of ML-ready datasets in materials science and chemistry providing a simple API to access even complex datasets. 
+* Load ML-ready data with just a few lines of code
+* Work with datasets in local or cloud environments. 
+* Publish your own datasets with Foundry to promote community usage
+* (in progress) Run published ML models without hassle
+
+Learn more and see our available datasets on [Foundry-ML.org](https://foundry-ml.org/)
+
+
+
+# Documentation
+Information on how to install and use Foundry is available in our documentation [here](https://ai-materials-and-chemistry.gitbook.io/foundry/v/docs/).
+
+DLHub documentation for model publication and running information can be found [here](https://dlhub-sdk.readthedocs.io/en/latest/servable-publication.html).
+
+# Quick Start
+Install Foundry-ML via command line with:
+`pip install foundry_ml`
+
+You can use the following code to import and instantiate Foundry-ML, then load a dataset.
+
+```python
+from foundry import Foundry
+f = Foundry(index="mdf")
+
+
+f = f.load("10.18126/e73h-3w6n", globus=True)
+```
+If running this code in a notebook, a table of metadata for the dataset will appear:
+
+<img width="903" alt="metadata" src="https://user-images.githubusercontent.com/16869564/197038472-0b6ae559-4a6b-4b20-88e5-679bb6eb4f5c.png">
+
+We can use the data with `f.load_data()` and specifying splits such as `train` for different segments of the dataset, then use matplotlib to visualize it.
+
+```python
+res = f.load_data()
+
+imgs = res['train']['input']['imgs']
+desc = res['train']['input']['metadata']
+coords = res['train']['target']['coords']
+
+n_images = 3
+offset = 150
+key_list = list(res['train']['input']['imgs'].keys())[0+offset:n_images+offset]
+
+fig, axs = plt.subplots(1, n_images, figsize=(20,20))
+for i in range(n_images):
+    axs[i].imshow(imgs[key_list[i]])
+    axs[i].scatter(coords[key_list[i]][:,0], coords[key_list[i]][:,1], s = 20, c = 'r', alpha=0.5)
+```
+<img width="595" alt="Screen Shot 2022-10-20 at 2 22 43 PM" src="https://user-images.githubusercontent.com/16869564/197039252-6d9c78ba-dc09-4037-aac2-d6f7e8b46851.png">
+
+[See full examples](./examples)
+
+# Primary Support
+This work was supported by the National Science Foundation under NSF Award Number: 1931306 "Collaborative Research: Framework: Machine Learning Materials Innovation Infrastructure".
+
+# Other Support
+Foundry-ML brings together many components in the materials data ecosystem. Including [MAST-ML](https://mastmldocs.readthedocs.io/en/latest/), the [Data and Learning Hub for Science](https://www.dlhub.org) (DLHub), and the [Materials Data Facility](https://materialsdatafacility.org) (MDF).
+
+## MAST-ML
+This work was supported by the National Science Foundation (NSF) SI2 award No. 1148011 and DMREF award number DMR-1332851
+
+## The Data and Learning Hub for Science (DLHub)
+This material is based upon work supported by Laboratory Directed Research and Development (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357.
+https://www.dlhub.org
+
+## The Materials Data Facility
+This work was performed under financial assistance award 70NANB14H012 from U.S. Department of Commerce, National Institute of Standards and Technology as part of the [Center for Hierarchical Material Design (CHiMaD)](http://chimad.northwestern.edu). This work was performed under the following financial assistance award 70NANB19H005 from U.S. Department of Commerce, National Institute of Standards and Technology as part of the Center for Hierarchical Materials Design (CHiMaD). This work was also supported by the National Science Foundation as part of the [Midwest Big Data Hub](http://midwestbigdatahub.org) under NSF Award Number: 1636950 "BD Spokes: SPOKE: MIDWEST: Collaborative: Integrative Materials Design (IMaD): Leverage, Innovate, and Disseminate".
+https://www.materialsdatafacility.org
+
+
+%package help
+Summary:	Development documents and examples for foundry-ml
+Provides:	python3-foundry-ml-doc
+%description help
+
+<picture>
+  <source srcset="https://raw.githubusercontent.com/MLMI2-CSSI/foundry/main/assets/foundry-white.png" height=175" media="(prefers-color-scheme: dark)">
+  <img src="https://raw.githubusercontent.com/MLMI2-CSSI/foundry/main/assets/foundry-black.png" height="175">
+</picture>
+
+[![PyPI](https://img.shields.io/pypi/v/foundry_ml.svg)](https://pypi.python.org/pypi/foundry_ml)
+[![Tests](https://github.com/MLMI2-CSSI/foundry/actions/workflows/tests.yml/badge.svg)](https://github.com/MLMI2-CSSI/foundry/actions/workflows/tests.yml)
+[![Tests](https://github.com/MLMI2-CSSI/foundry/actions/workflows/python-publish.yml/badge.svg)](https://github.com/MLMI2-CSSI/foundry/actions/workflows/python-publish.yml)
+[![NSF-1931306](https://img.shields.io/badge/NSF-1931306-blue)](https://www.nsf.gov/awardsearch/showAward?AWD_ID=1931306&HistoricalAwards=false)
+[<img src="https://img.shields.io/badge/view-documentation-blue">](https://ai-materials-and-chemistry.gitbook.io/foundry/)
+
+
+Foundry-ML simplifies the discovery and usage of ML-ready datasets in materials science and chemistry providing a simple API to access even complex datasets. 
+* Load ML-ready data with just a few lines of code
+* Work with datasets in local or cloud environments. 
+* Publish your own datasets with Foundry to promote community usage
+* (in progress) Run published ML models without hassle
+
+Learn more and see our available datasets on [Foundry-ML.org](https://foundry-ml.org/)
+
+
+
+# Documentation
+Information on how to install and use Foundry is available in our documentation [here](https://ai-materials-and-chemistry.gitbook.io/foundry/v/docs/).
+
+DLHub documentation for model publication and running information can be found [here](https://dlhub-sdk.readthedocs.io/en/latest/servable-publication.html).
+
+# Quick Start
+Install Foundry-ML via command line with:
+`pip install foundry_ml`
+
+You can use the following code to import and instantiate Foundry-ML, then load a dataset.
+
+```python
+from foundry import Foundry
+f = Foundry(index="mdf")
+
+
+f = f.load("10.18126/e73h-3w6n", globus=True)
+```
+If running this code in a notebook, a table of metadata for the dataset will appear:
+
+<img width="903" alt="metadata" src="https://user-images.githubusercontent.com/16869564/197038472-0b6ae559-4a6b-4b20-88e5-679bb6eb4f5c.png">
+
+We can use the data with `f.load_data()` and specifying splits such as `train` for different segments of the dataset, then use matplotlib to visualize it.
+
+```python
+res = f.load_data()
+
+imgs = res['train']['input']['imgs']
+desc = res['train']['input']['metadata']
+coords = res['train']['target']['coords']
+
+n_images = 3
+offset = 150
+key_list = list(res['train']['input']['imgs'].keys())[0+offset:n_images+offset]
+
+fig, axs = plt.subplots(1, n_images, figsize=(20,20))
+for i in range(n_images):
+    axs[i].imshow(imgs[key_list[i]])
+    axs[i].scatter(coords[key_list[i]][:,0], coords[key_list[i]][:,1], s = 20, c = 'r', alpha=0.5)
+```
+<img width="595" alt="Screen Shot 2022-10-20 at 2 22 43 PM" src="https://user-images.githubusercontent.com/16869564/197039252-6d9c78ba-dc09-4037-aac2-d6f7e8b46851.png">
+
+[See full examples](./examples)
+
+# Primary Support
+This work was supported by the National Science Foundation under NSF Award Number: 1931306 "Collaborative Research: Framework: Machine Learning Materials Innovation Infrastructure".
+
+# Other Support
+Foundry-ML brings together many components in the materials data ecosystem. Including [MAST-ML](https://mastmldocs.readthedocs.io/en/latest/), the [Data and Learning Hub for Science](https://www.dlhub.org) (DLHub), and the [Materials Data Facility](https://materialsdatafacility.org) (MDF).
+
+## MAST-ML
+This work was supported by the National Science Foundation (NSF) SI2 award No. 1148011 and DMREF award number DMR-1332851
+
+## The Data and Learning Hub for Science (DLHub)
+This material is based upon work supported by Laboratory Directed Research and Development (LDRD) funding from Argonne National Laboratory, provided by the Director, Office of Science, of the U.S. Department of Energy under Contract No. DE-AC02-06CH11357.
+https://www.dlhub.org
+
+## The Materials Data Facility
+This work was performed under financial assistance award 70NANB14H012 from U.S. Department of Commerce, National Institute of Standards and Technology as part of the [Center for Hierarchical Material Design (CHiMaD)](http://chimad.northwestern.edu). This work was performed under the following financial assistance award 70NANB19H005 from U.S. Department of Commerce, National Institute of Standards and Technology as part of the Center for Hierarchical Materials Design (CHiMaD). This work was also supported by the National Science Foundation as part of the [Midwest Big Data Hub](http://midwestbigdatahub.org) under NSF Award Number: 1636950 "BD Spokes: SPOKE: MIDWEST: Collaborative: Integrative Materials Design (IMaD): Leverage, Innovate, and Disseminate".
+https://www.materialsdatafacility.org
+
+
+%prep
+%autosetup -n foundry_ml-0.6.3
+
+%build
+%py3_build
+
+%install
+%py3_install
+install -d -m755 %{buildroot}/%{_pkgdocdir}
+if [ -d doc ]; then cp -arf doc %{buildroot}/%{_pkgdocdir}; fi
+if [ -d docs ]; then cp -arf docs %{buildroot}/%{_pkgdocdir}; fi
+if [ -d example ]; then cp -arf example %{buildroot}/%{_pkgdocdir}; fi
+if [ -d examples ]; then cp -arf examples %{buildroot}/%{_pkgdocdir}; fi
+pushd %{buildroot}
+if [ -d usr/lib ]; then
+	find usr/lib -type f -printf "\"/%h/%f\"\n" >> filelist.lst
+fi
+if [ -d usr/lib64 ]; then
+	find usr/lib64 -type f -printf "\"/%h/%f\"\n" >> filelist.lst
+fi
+if [ -d usr/bin ]; then
+	find usr/bin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
+fi
+if [ -d usr/sbin ]; then
+	find usr/sbin -type f -printf "\"/%h/%f\"\n" >> filelist.lst
+fi
+touch doclist.lst
+if [ -d usr/share/man ]; then
+	find usr/share/man -type f -printf "\"/%h/%f.gz\"\n" >> doclist.lst
+fi
+popd
+mv %{buildroot}/filelist.lst .
+mv %{buildroot}/doclist.lst .
+
+%files -n python3-foundry-ml -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Tue Jun 20 2023 Python_Bot <Python_Bot@openeuler.org> - 0.6.3-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..0dd7732
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+62d6eba741e096af6f03001a7ac17d1c  foundry_ml-0.6.3.tar.gz