automatic import of python-csdmpy

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+%global _empty_manifest_terminate_build 0
+Name:		python-csdmpy
+Version:	0.5
+Release:	1
+Summary:	A python module for the core scientific dataset model.
+License:	BSD-3-Clause
+URL:		https://github.com/DeepanshS/csdmpy/
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/fb/0b/3fb267e9131aa261ffb25133397d0297ac278e577d709be4effd86520162/csdmpy-0.5.tar.gz
+BuildArch:	noarch
+
+Requires:	python3-numpy
+Requires:	python3-setuptools
+Requires:	python3-astropy
+Requires:	python3-requests
+Requires:	python3-numexpr
+Requires:	python3-matplotlib
+
+%description
+### Use cases
+The data model is _versatile_ in allowing many **use cases for most spectroscopy,
+diffraction, and imaging techniques**.
+![](/docs/_static/csdm.png "")
+### Data Model
+The model supports multi-component datasets associated with continuous
+physical quantities that are discretely sampled in a multi-dimensional space
+associated with other carefully controlled quantities, for e.g., a mass as a
+function of temperature, a current as a function of voltage and time, a signal
+voltage as a function of magnetic field gradient strength, a color image with
+a red, green, and blue (RGB) light intensity components as a function of two
+independent spatial dimensions, or the six components of the symmetric
+second-rank diffusion tensor MRI as a function of three independent spatial
+dimensions. Additionally, the model supports multiple dependent variables
+sharing the same _d_-dimensional coordinate space. For instance,
+the simultaneous measurement of current and voltage as a function of time.
+Another example would be the simultaneous acquisition of air temperature,
+pressure, wind velocity, and
+solar-flux as a function of Earth’s latitude and longitude coordinates. We
+refer to these dependent variables as _correlated-datasets_.
+**Example**
+```py
+"csdm": {
+  "version": "1.0",
+  # A list of Linear, Monotonic, or Labeled dimensions of the multi-dimensional space.
+  "dimensions": [{
+    "type": "linear",
+    "count": 1608,
+    "increment": "0.08333333333 yr",
+    "coordinates_offset": "1880.0416666667 yr",
+  }],
+  # A list of dependent variables sampling the multi-dimensional space.
+  "dependent_variables": [{
+    "type": "internal",
+    "unit": "mm",
+    "numeric_type": "float32",
+    "quantity_type": "scalar",
+    "component_labels": ["GMSL"],
+    "components": [
+      ["-183.0, -171.125, ..., 59.6875, 58.5"]
+    ]
+  }]
+}
+```
+## Installing _csdmpy_ package
+    $ pip install csdmpy
+## How to cite
+Please cite the following when used in publication.
+1. Srivastava D.J., Vosegaard T., Massiot D., Grandinetti P.J. (2020) Core Scientific Dataset Model: A lightweight and portable model and file format for multi-dimensional scientific data. [PLOS ONE 15(1): e0225953.](https://doi.org/10.1371/journal.pone.0225953)
+## Check out the media coverage.
+- [<img src="https://inc.cnrs.fr/sites/institut_inc/files/styles/top_left/public/image/cnrs_20180120_0025%20%281%29.jpg?itok=i3wlyGBq" height="64" width="64"> Des chimistes élaborent un nouveau format pour le partage de données scientifiques](https://inc.cnrs.fr/fr/cnrsinfo/des-chimistes-elaborent-un-nouveau-format-pour-le-partage-de-donnees-scientifiques)
+- [<img src="https://www.technology.org/texorgwp/wp-content/uploads/2020/01/1920_data-1536x1024.jpg" height="64" width="64"> Simplifying how scientists share data](https://www.technology.org/2020/01/03/simplifying-how-scientists-share-data/)
+
+%package -n python3-csdmpy
+Summary:	A python module for the core scientific dataset model.
+Provides:	python-csdmpy
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+%description -n python3-csdmpy
+### Use cases
+The data model is _versatile_ in allowing many **use cases for most spectroscopy,
+diffraction, and imaging techniques**.
+![](/docs/_static/csdm.png "")
+### Data Model
+The model supports multi-component datasets associated with continuous
+physical quantities that are discretely sampled in a multi-dimensional space
+associated with other carefully controlled quantities, for e.g., a mass as a
+function of temperature, a current as a function of voltage and time, a signal
+voltage as a function of magnetic field gradient strength, a color image with
+a red, green, and blue (RGB) light intensity components as a function of two
+independent spatial dimensions, or the six components of the symmetric
+second-rank diffusion tensor MRI as a function of three independent spatial
+dimensions. Additionally, the model supports multiple dependent variables
+sharing the same _d_-dimensional coordinate space. For instance,
+the simultaneous measurement of current and voltage as a function of time.
+Another example would be the simultaneous acquisition of air temperature,
+pressure, wind velocity, and
+solar-flux as a function of Earth’s latitude and longitude coordinates. We
+refer to these dependent variables as _correlated-datasets_.
+**Example**
+```py
+"csdm": {
+  "version": "1.0",
+  # A list of Linear, Monotonic, or Labeled dimensions of the multi-dimensional space.
+  "dimensions": [{
+    "type": "linear",
+    "count": 1608,
+    "increment": "0.08333333333 yr",
+    "coordinates_offset": "1880.0416666667 yr",
+  }],
+  # A list of dependent variables sampling the multi-dimensional space.
+  "dependent_variables": [{
+    "type": "internal",
+    "unit": "mm",
+    "numeric_type": "float32",
+    "quantity_type": "scalar",
+    "component_labels": ["GMSL"],
+    "components": [
+      ["-183.0, -171.125, ..., 59.6875, 58.5"]
+    ]
+  }]
+}
+```
+## Installing _csdmpy_ package
+    $ pip install csdmpy
+## How to cite
+Please cite the following when used in publication.
+1. Srivastava D.J., Vosegaard T., Massiot D., Grandinetti P.J. (2020) Core Scientific Dataset Model: A lightweight and portable model and file format for multi-dimensional scientific data. [PLOS ONE 15(1): e0225953.](https://doi.org/10.1371/journal.pone.0225953)
+## Check out the media coverage.
+- [<img src="https://inc.cnrs.fr/sites/institut_inc/files/styles/top_left/public/image/cnrs_20180120_0025%20%281%29.jpg?itok=i3wlyGBq" height="64" width="64"> Des chimistes élaborent un nouveau format pour le partage de données scientifiques](https://inc.cnrs.fr/fr/cnrsinfo/des-chimistes-elaborent-un-nouveau-format-pour-le-partage-de-donnees-scientifiques)
+- [<img src="https://www.technology.org/texorgwp/wp-content/uploads/2020/01/1920_data-1536x1024.jpg" height="64" width="64"> Simplifying how scientists share data](https://www.technology.org/2020/01/03/simplifying-how-scientists-share-data/)
+
+%package help
+Summary:	Development documents and examples for csdmpy
+Provides:	python3-csdmpy-doc
+%description help
+### Use cases
+The data model is _versatile_ in allowing many **use cases for most spectroscopy,
+diffraction, and imaging techniques**.
+![](/docs/_static/csdm.png "")
+### Data Model
+The model supports multi-component datasets associated with continuous
+physical quantities that are discretely sampled in a multi-dimensional space
+associated with other carefully controlled quantities, for e.g., a mass as a
+function of temperature, a current as a function of voltage and time, a signal
+voltage as a function of magnetic field gradient strength, a color image with
+a red, green, and blue (RGB) light intensity components as a function of two
+independent spatial dimensions, or the six components of the symmetric
+second-rank diffusion tensor MRI as a function of three independent spatial
+dimensions. Additionally, the model supports multiple dependent variables
+sharing the same _d_-dimensional coordinate space. For instance,
+the simultaneous measurement of current and voltage as a function of time.
+Another example would be the simultaneous acquisition of air temperature,
+pressure, wind velocity, and
+solar-flux as a function of Earth’s latitude and longitude coordinates. We
+refer to these dependent variables as _correlated-datasets_.
+**Example**
+```py
+"csdm": {
+  "version": "1.0",
+  # A list of Linear, Monotonic, or Labeled dimensions of the multi-dimensional space.
+  "dimensions": [{
+    "type": "linear",
+    "count": 1608,
+    "increment": "0.08333333333 yr",
+    "coordinates_offset": "1880.0416666667 yr",
+  }],
+  # A list of dependent variables sampling the multi-dimensional space.
+  "dependent_variables": [{
+    "type": "internal",
+    "unit": "mm",
+    "numeric_type": "float32",
+    "quantity_type": "scalar",
+    "component_labels": ["GMSL"],
+    "components": [
+      ["-183.0, -171.125, ..., 59.6875, 58.5"]
+    ]
+  }]
+}
+```
+## Installing _csdmpy_ package
+    $ pip install csdmpy
+## How to cite
+Please cite the following when used in publication.
+1. Srivastava D.J., Vosegaard T., Massiot D., Grandinetti P.J. (2020) Core Scientific Dataset Model: A lightweight and portable model and file format for multi-dimensional scientific data. [PLOS ONE 15(1): e0225953.](https://doi.org/10.1371/journal.pone.0225953)
+## Check out the media coverage.
+- [<img src="https://inc.cnrs.fr/sites/institut_inc/files/styles/top_left/public/image/cnrs_20180120_0025%20%281%29.jpg?itok=i3wlyGBq" height="64" width="64"> Des chimistes élaborent un nouveau format pour le partage de données scientifiques](https://inc.cnrs.fr/fr/cnrsinfo/des-chimistes-elaborent-un-nouveau-format-pour-le-partage-de-donnees-scientifiques)
+- [<img src="https://www.technology.org/texorgwp/wp-content/uploads/2020/01/1920_data-1536x1024.jpg" height="64" width="64"> Simplifying how scientists share data](https://www.technology.org/2020/01/03/simplifying-how-scientists-share-data/)
+
+%prep
+%autosetup -n csdmpy-0.5
+
+%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-csdmpy -f filelist.lst
+%dir %{python3_sitelib}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon May 29 2023 Python_Bot <Python_Bot@openeuler.org> - 0.5-1
+- Package Spec generated