automatic import of python-pystats

3 files changed, 496 insertions, 0 deletions

diff --git a/.gitignore b/.gitignore
index e69de29..6714cb4 100644
--- a/.gitignore
+++ b/.gitignore
@@ -0,0 +1 @@
+/PyStats-0.1.tar.gz
diff --git a/python-pystats.spec b/python-pystats.spec
new file mode 100644
index 0000000..9d4c560
--- /dev/null
+++ b/python-pystats.spec
@@ -0,0 +1,494 @@
+%global _empty_manifest_terminate_build 0
+Name:		python-PyStats
+Version:	0.1
+Release:	1
+Summary:	A Python 3 package of statistical analysis and distributions
+License:	MIT License
+URL:		https://github.com/marcizhu/PyStats
+Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/cb/78/28ced26af1c3511cc864db2c032a93d87761616c93f7d805bb62c19ced60/PyStats-0.1.tar.gz
+
+
+%description
+# PyStats
+
+PyStats is a Python 3 library of statistical analysis and distribution functions with simple **R**-like syntax, scalar/list input/output with OpenMP parallelization.
+
+### Contents
+- [Distributions](#distributions)
+- [Installation](#installation)
+- [Documentation](#documentation)
+- [Syntax and Examples](#syntax-and-examples)
+- [Credits](#credits)
+- [License](#license)
+
+## Distributions
+Functions to compute the cdf, pdf, quantile, as well as random sampling methods, are available for the following distributions:
+
+- Bernoulli
+- Beta
+- Binomial
+- Cauchy
+- Chi-squared
+- Exponential
+- F
+- Gamma
+- Inverse-Gamma
+- Laplace
+- Logistic
+- Log-Normal
+- Normal (Gaussian)
+- Poisson
+- Student's t
+- Uniform
+- Weibull
+
+
+## Installation
+You can use pip to install this library:
+
+```sh
+pip3 install pystats
+```
+
+Alternatively, you can also clone this repository and install the plugin manually using `pip`:
+
+```sh
+$ git clone git@github.com:marcizhu/PyStats.git
+$ pip3 install ./PyStats
+```
+
+After that, you can just `import pystats` (or do `from pystats import *` if you don't want to type `pystats.` before all functions) and you're ready to go.
+
+
+## Documentation
+Full documentation is available online:
+
+[![Documentation Status](https://readthedocs.org/projects/pystats/badge/?version=latest)](https://pystats.readthedocs.io/en/latest/?badge=latest)
+
+A PDF version of the documentation is available [here](https://buildmedia.readthedocs.org/media/pdf/pystats/latest/pystats.pdf).
+
+
+## Syntax and Examples
+Functions are called using an **R**-like syntax. Some general rules:
+
+- Density functions: `pystats.d*`. For example, the Normal (Gaussian) density is called using
+```python3
+pystats.dnorm(<value>, <mean>, <standard deviation>)
+```
+
+- Cumulative distribution functions: `pystats.p*`. For example, the Gamma CDF is called using
+```python3
+pystats.pgamma(<value>, <shape parameter>, <scale parameter>)
+```
+
+- Quantile functions: `pystats.q*`. For example, the Beta quantile is called using
+```python3
+pystats.qbeta(<value>, <a parameter>, <b parameter>)
+```
+
+- Random sampling: `pystats.r*`. For example, to generate a single draw from the Logistic distribution:
+```python3
+pystats.rlogis(<location parameter>, <scale parameter>)
+```
+
+The library also supports lists as input/output:
+- The pdf, cdf and quantile functions can take list arguments. For example:
+```python3
+norm_pdf_vals = pystats.dnorm([x / 10 for x in range(-10, 10, 1)], 1.0, 2.0)
+```
+
+- The randomization functions (`r*`) can output lists of arbitrary size. For example, the following code will generate a 100-item list of iid draws from a Gamma(3,2) distribution:
+```python3
+gamma_rvs = pystats.rgamma(100, 3.0, 2.0)
+```
+
+Additionally, most parameters have defaults to most common values and named parameters are also supported. For example, to generate a single draw from a Normal(0, 2) the following can be used:
+```python3
+norm_draw = pystats.rnorm(sd=2.0)
+```
+
+
+### Examples
+More examples with code:
+```python3
+# Evaluate the normal PDF at x = 1, mu = 0, sigma = 1
+dval_1 = pystats.dnorm(1.0, 0.0, 1.0)
+
+# Evaluate the normal PDF at x = 1, mu = 0, sigma = 1, and return the log value
+dval_2 = pystats.dnorm(1.0, 0.0, 1.0, True)
+
+# Same as above, but using default values and named parameters
+dval_3 = pystats.dnorm(1.0, log=True)
+
+# Evaluate the normal CDF at x = 1, mu = 0, sigma = 1
+pval = pystats.pnorm(1.0, 0.0, 1.0)
+
+# Evaluate the Laplacian quantile at q = 0.1, mu = 0, sigma = 1
+qval = pystats.qlaplace(0.1, 0.0, 1.0)
+
+# Draw from a t-distribution with dof = 30
+rval = pystats.rt(dof=30)
+
+# List output
+beta_rvs = pystats.rbeta(100, 3.0, 2.0)
+
+# List input
+beta_cdf_vals = pystats.pbeta(beta_rvs, 3.0, 2.0)
+```
+
+For more information on default values, parameter names and other examples, check the [documentation](#documentation).
+
+
+## Credits
+This library uses [kthohr/stats](https://github.com/kthohr/stats) for the statistical distribution functions, [kthohr/gcem](https://github.com/kthohr/gcem) (a dependency of the previous library) and [pybind/pybind11](https://github.com/pybind/pybind11) to generate the binding code.
+
+
+## License
+Copyright (c) Marc Izquierdo 2021  
+This library is licensed under the [MIT License](https://choosealicense.com/licenses/mit/). See
+[LICENSE](https://github.com/marcizhu/pystats/blob/master/LICENSE) for more details.
+
+
+
+
+%package -n python3-PyStats
+Summary:	A Python 3 package of statistical analysis and distributions
+Provides:	python-PyStats
+BuildRequires:	python3-devel
+BuildRequires:	python3-setuptools
+BuildRequires:	python3-pip
+BuildRequires:	python3-cffi
+BuildRequires:	gcc
+BuildRequires:	gdb
+%description -n python3-PyStats
+# PyStats
+
+PyStats is a Python 3 library of statistical analysis and distribution functions with simple **R**-like syntax, scalar/list input/output with OpenMP parallelization.
+
+### Contents
+- [Distributions](#distributions)
+- [Installation](#installation)
+- [Documentation](#documentation)
+- [Syntax and Examples](#syntax-and-examples)
+- [Credits](#credits)
+- [License](#license)
+
+## Distributions
+Functions to compute the cdf, pdf, quantile, as well as random sampling methods, are available for the following distributions:
+
+- Bernoulli
+- Beta
+- Binomial
+- Cauchy
+- Chi-squared
+- Exponential
+- F
+- Gamma
+- Inverse-Gamma
+- Laplace
+- Logistic
+- Log-Normal
+- Normal (Gaussian)
+- Poisson
+- Student's t
+- Uniform
+- Weibull
+
+
+## Installation
+You can use pip to install this library:
+
+```sh
+pip3 install pystats
+```
+
+Alternatively, you can also clone this repository and install the plugin manually using `pip`:
+
+```sh
+$ git clone git@github.com:marcizhu/PyStats.git
+$ pip3 install ./PyStats
+```
+
+After that, you can just `import pystats` (or do `from pystats import *` if you don't want to type `pystats.` before all functions) and you're ready to go.
+
+
+## Documentation
+Full documentation is available online:
+
+[![Documentation Status](https://readthedocs.org/projects/pystats/badge/?version=latest)](https://pystats.readthedocs.io/en/latest/?badge=latest)
+
+A PDF version of the documentation is available [here](https://buildmedia.readthedocs.org/media/pdf/pystats/latest/pystats.pdf).
+
+
+## Syntax and Examples
+Functions are called using an **R**-like syntax. Some general rules:
+
+- Density functions: `pystats.d*`. For example, the Normal (Gaussian) density is called using
+```python3
+pystats.dnorm(<value>, <mean>, <standard deviation>)
+```
+
+- Cumulative distribution functions: `pystats.p*`. For example, the Gamma CDF is called using
+```python3
+pystats.pgamma(<value>, <shape parameter>, <scale parameter>)
+```
+
+- Quantile functions: `pystats.q*`. For example, the Beta quantile is called using
+```python3
+pystats.qbeta(<value>, <a parameter>, <b parameter>)
+```
+
+- Random sampling: `pystats.r*`. For example, to generate a single draw from the Logistic distribution:
+```python3
+pystats.rlogis(<location parameter>, <scale parameter>)
+```
+
+The library also supports lists as input/output:
+- The pdf, cdf and quantile functions can take list arguments. For example:
+```python3
+norm_pdf_vals = pystats.dnorm([x / 10 for x in range(-10, 10, 1)], 1.0, 2.0)
+```
+
+- The randomization functions (`r*`) can output lists of arbitrary size. For example, the following code will generate a 100-item list of iid draws from a Gamma(3,2) distribution:
+```python3
+gamma_rvs = pystats.rgamma(100, 3.0, 2.0)
+```
+
+Additionally, most parameters have defaults to most common values and named parameters are also supported. For example, to generate a single draw from a Normal(0, 2) the following can be used:
+```python3
+norm_draw = pystats.rnorm(sd=2.0)
+```
+
+
+### Examples
+More examples with code:
+```python3
+# Evaluate the normal PDF at x = 1, mu = 0, sigma = 1
+dval_1 = pystats.dnorm(1.0, 0.0, 1.0)
+
+# Evaluate the normal PDF at x = 1, mu = 0, sigma = 1, and return the log value
+dval_2 = pystats.dnorm(1.0, 0.0, 1.0, True)
+
+# Same as above, but using default values and named parameters
+dval_3 = pystats.dnorm(1.0, log=True)
+
+# Evaluate the normal CDF at x = 1, mu = 0, sigma = 1
+pval = pystats.pnorm(1.0, 0.0, 1.0)
+
+# Evaluate the Laplacian quantile at q = 0.1, mu = 0, sigma = 1
+qval = pystats.qlaplace(0.1, 0.0, 1.0)
+
+# Draw from a t-distribution with dof = 30
+rval = pystats.rt(dof=30)
+
+# List output
+beta_rvs = pystats.rbeta(100, 3.0, 2.0)
+
+# List input
+beta_cdf_vals = pystats.pbeta(beta_rvs, 3.0, 2.0)
+```
+
+For more information on default values, parameter names and other examples, check the [documentation](#documentation).
+
+
+## Credits
+This library uses [kthohr/stats](https://github.com/kthohr/stats) for the statistical distribution functions, [kthohr/gcem](https://github.com/kthohr/gcem) (a dependency of the previous library) and [pybind/pybind11](https://github.com/pybind/pybind11) to generate the binding code.
+
+
+## License
+Copyright (c) Marc Izquierdo 2021  
+This library is licensed under the [MIT License](https://choosealicense.com/licenses/mit/). See
+[LICENSE](https://github.com/marcizhu/pystats/blob/master/LICENSE) for more details.
+
+
+
+
+%package help
+Summary:	Development documents and examples for PyStats
+Provides:	python3-PyStats-doc
+%description help
+# PyStats
+
+PyStats is a Python 3 library of statistical analysis and distribution functions with simple **R**-like syntax, scalar/list input/output with OpenMP parallelization.
+
+### Contents
+- [Distributions](#distributions)
+- [Installation](#installation)
+- [Documentation](#documentation)
+- [Syntax and Examples](#syntax-and-examples)
+- [Credits](#credits)
+- [License](#license)
+
+## Distributions
+Functions to compute the cdf, pdf, quantile, as well as random sampling methods, are available for the following distributions:
+
+- Bernoulli
+- Beta
+- Binomial
+- Cauchy
+- Chi-squared
+- Exponential
+- F
+- Gamma
+- Inverse-Gamma
+- Laplace
+- Logistic
+- Log-Normal
+- Normal (Gaussian)
+- Poisson
+- Student's t
+- Uniform
+- Weibull
+
+
+## Installation
+You can use pip to install this library:
+
+```sh
+pip3 install pystats
+```
+
+Alternatively, you can also clone this repository and install the plugin manually using `pip`:
+
+```sh
+$ git clone git@github.com:marcizhu/PyStats.git
+$ pip3 install ./PyStats
+```
+
+After that, you can just `import pystats` (or do `from pystats import *` if you don't want to type `pystats.` before all functions) and you're ready to go.
+
+
+## Documentation
+Full documentation is available online:
+
+[![Documentation Status](https://readthedocs.org/projects/pystats/badge/?version=latest)](https://pystats.readthedocs.io/en/latest/?badge=latest)
+
+A PDF version of the documentation is available [here](https://buildmedia.readthedocs.org/media/pdf/pystats/latest/pystats.pdf).
+
+
+## Syntax and Examples
+Functions are called using an **R**-like syntax. Some general rules:
+
+- Density functions: `pystats.d*`. For example, the Normal (Gaussian) density is called using
+```python3
+pystats.dnorm(<value>, <mean>, <standard deviation>)
+```
+
+- Cumulative distribution functions: `pystats.p*`. For example, the Gamma CDF is called using
+```python3
+pystats.pgamma(<value>, <shape parameter>, <scale parameter>)
+```
+
+- Quantile functions: `pystats.q*`. For example, the Beta quantile is called using
+```python3
+pystats.qbeta(<value>, <a parameter>, <b parameter>)
+```
+
+- Random sampling: `pystats.r*`. For example, to generate a single draw from the Logistic distribution:
+```python3
+pystats.rlogis(<location parameter>, <scale parameter>)
+```
+
+The library also supports lists as input/output:
+- The pdf, cdf and quantile functions can take list arguments. For example:
+```python3
+norm_pdf_vals = pystats.dnorm([x / 10 for x in range(-10, 10, 1)], 1.0, 2.0)
+```
+
+- The randomization functions (`r*`) can output lists of arbitrary size. For example, the following code will generate a 100-item list of iid draws from a Gamma(3,2) distribution:
+```python3
+gamma_rvs = pystats.rgamma(100, 3.0, 2.0)
+```
+
+Additionally, most parameters have defaults to most common values and named parameters are also supported. For example, to generate a single draw from a Normal(0, 2) the following can be used:
+```python3
+norm_draw = pystats.rnorm(sd=2.0)
+```
+
+
+### Examples
+More examples with code:
+```python3
+# Evaluate the normal PDF at x = 1, mu = 0, sigma = 1
+dval_1 = pystats.dnorm(1.0, 0.0, 1.0)
+
+# Evaluate the normal PDF at x = 1, mu = 0, sigma = 1, and return the log value
+dval_2 = pystats.dnorm(1.0, 0.0, 1.0, True)
+
+# Same as above, but using default values and named parameters
+dval_3 = pystats.dnorm(1.0, log=True)
+
+# Evaluate the normal CDF at x = 1, mu = 0, sigma = 1
+pval = pystats.pnorm(1.0, 0.0, 1.0)
+
+# Evaluate the Laplacian quantile at q = 0.1, mu = 0, sigma = 1
+qval = pystats.qlaplace(0.1, 0.0, 1.0)
+
+# Draw from a t-distribution with dof = 30
+rval = pystats.rt(dof=30)
+
+# List output
+beta_rvs = pystats.rbeta(100, 3.0, 2.0)
+
+# List input
+beta_cdf_vals = pystats.pbeta(beta_rvs, 3.0, 2.0)
+```
+
+For more information on default values, parameter names and other examples, check the [documentation](#documentation).
+
+
+## Credits
+This library uses [kthohr/stats](https://github.com/kthohr/stats) for the statistical distribution functions, [kthohr/gcem](https://github.com/kthohr/gcem) (a dependency of the previous library) and [pybind/pybind11](https://github.com/pybind/pybind11) to generate the binding code.
+
+
+## License
+Copyright (c) Marc Izquierdo 2021  
+This library is licensed under the [MIT License](https://choosealicense.com/licenses/mit/). See
+[LICENSE](https://github.com/marcizhu/pystats/blob/master/LICENSE) for more details.
+
+
+
+
+%prep
+%autosetup -n PyStats-0.1
+
+%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-PyStats -f filelist.lst
+%dir %{python3_sitearch}/*
+
+%files help -f doclist.lst
+%{_docdir}/*
+
+%changelog
+* Mon May 29 2023 Python_Bot <Python_Bot@openeuler.org> - 0.1-1
+- Package Spec generated
diff --git a/sources b/sources
new file mode 100644
index 0000000..53e2bb0
--- /dev/null
+++ b/sources
@@ -0,0 +1 @@
+0e6329d8056d4f5cceb4f64b690f6bbf  PyStats-0.1.tar.gz