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diff --git a/python-csdmpy.spec b/python-csdmpy.spec new file mode 100644 index 0000000..405850c --- /dev/null +++ b/python-csdmpy.spec @@ -0,0 +1,231 @@ +%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**. + +### 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**. + +### 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**. + +### 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 |