%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. - [ 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) - [ 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. - [ 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) - [ 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. - [ 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) - [ 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 * Wed May 31 2023 Python_Bot - 0.5-1 - Package Spec generated