%global _empty_manifest_terminate_build 0
Name:		python-amplpy
Version:	0.9.3
Release:	1
Summary:	Python API for AMPL
License:	BSD-3
URL:		http://ampl.com/
Source0:	https://mirrors.nju.edu.cn/pypi/web/packages/cf/22/5b01cf9c3cc1a70c81f3fc27901121a544a5e75eea08a20c9953565ac9d2/amplpy-0.9.3.tar.gz

Requires:	python3-future
Requires:	python3-ampltools

%description

# AMPLPY: Python API for AMPL

```python
# Install Python API for AMPL
$ python -m pip install amplpy --upgrade

# Install solver modules (e.g., HiGHS, CBC, Gurobi)
$ python -m amplpy.modules install highs cbc gurobi

# Activate your license (e.g., free https://ampl.com/ce license)
$ python -m amplpy.modules activate <license-uuid>

# Import in Python
$ python
>>> from amplpy import AMPL
>>> ampl = AMPL() # instantiate AMPL object
```

```python
# Minimal example:
from amplpy import AMPL
import pandas as pd
ampl = AMPL()
ampl.eval(r"""
    set A ordered;
    param S{A, A};
    param lb default 0;
    param ub default 1;
    var w{A} >= lb <= ub;
    minimize portfolio_variance:
        sum {i in A, j in A} w[i] * S[i, j] * w[j];
    s.t. portfolio_weights:
        sum {i in A} w[i] = 1;
""")
tickers, cov_matrix = # ... pre-process data in Python
ampl.set["A"] = tickers
ampl.param["S"] = pd.DataFrame(
    cov_matrix, index=tickers, columns=tickers
).unstack()
ampl.option["solver"] = "gurobi"
ampl.option["gurobi_options"] = "outlev=1"
ampl.solve()
assert ampl.get_value("solve_result") == "solved"
sigma = ampl.get_value("sqrt(sum {i in A, j in A} w[i] * S[i, j] * w[j])")
print(f"Volatility: {sigma*100:.1f}%")
# ... post-process solution in Python
```

[[Documentation](https://amplpy.readthedocs.io/)] [[AMPL Modules for Python](https://dev.ampl.com/ampl/python/modules.html)] [[Available on Google Colab](https://colab.ampl.com/)] [[AMPL Community Edition](http://ampl.com/ce)]

`amplpy` is an interface that allows developers to access the features of [AMPL](https://ampl.com) from within Python. For a quick introduction to AMPL see [Quick Introduction to AMPL](https://dev.ampl.com/ampl/introduction.html).

In the same way that AMPL’s syntax matches naturally the mathematical description of the model, the input and output data matches naturally Python lists, sets, dictionaries, `pandas` and `numpy` objects.

All model generation and solver interaction is handled directly by AMPL, which leads to great stability and speed; the library just acts as an intermediary, and the added overhead (in terms of memory and CPU usage) depends mostly on how much data is sent and read back from AMPL, the size of the expanded model as such is irrelevant.

With `amplpy` you can model and solve large scale optimization problems in Python with the performance of heavily optimized C code without losing model readability. The same model can be deployed on applications built on different languages by just switching the API used.

## Documentation

- http://amplpy.readthedocs.io

## Repositories:

* GitHub Repository: https://github.com/ampl/amplpy
* PyPI Repository: https://pypi.python.org/pypi/amplpy


%package -n python3-amplpy
Summary:	Python API for AMPL
Provides:	python-amplpy
BuildRequires:	python3-devel
BuildRequires:	python3-setuptools
BuildRequires:	python3-pip
BuildRequires:	python3-cffi
BuildRequires:	gcc
BuildRequires:	gdb
%description -n python3-amplpy

# AMPLPY: Python API for AMPL

```python
# Install Python API for AMPL
$ python -m pip install amplpy --upgrade

# Install solver modules (e.g., HiGHS, CBC, Gurobi)
$ python -m amplpy.modules install highs cbc gurobi

# Activate your license (e.g., free https://ampl.com/ce license)
$ python -m amplpy.modules activate <license-uuid>

# Import in Python
$ python
>>> from amplpy import AMPL
>>> ampl = AMPL() # instantiate AMPL object
```

```python
# Minimal example:
from amplpy import AMPL
import pandas as pd
ampl = AMPL()
ampl.eval(r"""
    set A ordered;
    param S{A, A};
    param lb default 0;
    param ub default 1;
    var w{A} >= lb <= ub;
    minimize portfolio_variance:
        sum {i in A, j in A} w[i] * S[i, j] * w[j];
    s.t. portfolio_weights:
        sum {i in A} w[i] = 1;
""")
tickers, cov_matrix = # ... pre-process data in Python
ampl.set["A"] = tickers
ampl.param["S"] = pd.DataFrame(
    cov_matrix, index=tickers, columns=tickers
).unstack()
ampl.option["solver"] = "gurobi"
ampl.option["gurobi_options"] = "outlev=1"
ampl.solve()
assert ampl.get_value("solve_result") == "solved"
sigma = ampl.get_value("sqrt(sum {i in A, j in A} w[i] * S[i, j] * w[j])")
print(f"Volatility: {sigma*100:.1f}%")
# ... post-process solution in Python
```

[[Documentation](https://amplpy.readthedocs.io/)] [[AMPL Modules for Python](https://dev.ampl.com/ampl/python/modules.html)] [[Available on Google Colab](https://colab.ampl.com/)] [[AMPL Community Edition](http://ampl.com/ce)]

`amplpy` is an interface that allows developers to access the features of [AMPL](https://ampl.com) from within Python. For a quick introduction to AMPL see [Quick Introduction to AMPL](https://dev.ampl.com/ampl/introduction.html).

In the same way that AMPL’s syntax matches naturally the mathematical description of the model, the input and output data matches naturally Python lists, sets, dictionaries, `pandas` and `numpy` objects.

All model generation and solver interaction is handled directly by AMPL, which leads to great stability and speed; the library just acts as an intermediary, and the added overhead (in terms of memory and CPU usage) depends mostly on how much data is sent and read back from AMPL, the size of the expanded model as such is irrelevant.

With `amplpy` you can model and solve large scale optimization problems in Python with the performance of heavily optimized C code without losing model readability. The same model can be deployed on applications built on different languages by just switching the API used.

## Documentation

- http://amplpy.readthedocs.io

## Repositories:

* GitHub Repository: https://github.com/ampl/amplpy
* PyPI Repository: https://pypi.python.org/pypi/amplpy


%package help
Summary:	Development documents and examples for amplpy
Provides:	python3-amplpy-doc
%description help

# AMPLPY: Python API for AMPL

```python
# Install Python API for AMPL
$ python -m pip install amplpy --upgrade

# Install solver modules (e.g., HiGHS, CBC, Gurobi)
$ python -m amplpy.modules install highs cbc gurobi

# Activate your license (e.g., free https://ampl.com/ce license)
$ python -m amplpy.modules activate <license-uuid>

# Import in Python
$ python
>>> from amplpy import AMPL
>>> ampl = AMPL() # instantiate AMPL object
```

```python
# Minimal example:
from amplpy import AMPL
import pandas as pd
ampl = AMPL()
ampl.eval(r"""
    set A ordered;
    param S{A, A};
    param lb default 0;
    param ub default 1;
    var w{A} >= lb <= ub;
    minimize portfolio_variance:
        sum {i in A, j in A} w[i] * S[i, j] * w[j];
    s.t. portfolio_weights:
        sum {i in A} w[i] = 1;
""")
tickers, cov_matrix = # ... pre-process data in Python
ampl.set["A"] = tickers
ampl.param["S"] = pd.DataFrame(
    cov_matrix, index=tickers, columns=tickers
).unstack()
ampl.option["solver"] = "gurobi"
ampl.option["gurobi_options"] = "outlev=1"
ampl.solve()
assert ampl.get_value("solve_result") == "solved"
sigma = ampl.get_value("sqrt(sum {i in A, j in A} w[i] * S[i, j] * w[j])")
print(f"Volatility: {sigma*100:.1f}%")
# ... post-process solution in Python
```

[[Documentation](https://amplpy.readthedocs.io/)] [[AMPL Modules for Python](https://dev.ampl.com/ampl/python/modules.html)] [[Available on Google Colab](https://colab.ampl.com/)] [[AMPL Community Edition](http://ampl.com/ce)]

`amplpy` is an interface that allows developers to access the features of [AMPL](https://ampl.com) from within Python. For a quick introduction to AMPL see [Quick Introduction to AMPL](https://dev.ampl.com/ampl/introduction.html).

In the same way that AMPL’s syntax matches naturally the mathematical description of the model, the input and output data matches naturally Python lists, sets, dictionaries, `pandas` and `numpy` objects.

All model generation and solver interaction is handled directly by AMPL, which leads to great stability and speed; the library just acts as an intermediary, and the added overhead (in terms of memory and CPU usage) depends mostly on how much data is sent and read back from AMPL, the size of the expanded model as such is irrelevant.

With `amplpy` you can model and solve large scale optimization problems in Python with the performance of heavily optimized C code without losing model readability. The same model can be deployed on applications built on different languages by just switching the API used.

## Documentation

- http://amplpy.readthedocs.io

## Repositories:

* GitHub Repository: https://github.com/ampl/amplpy
* PyPI Repository: https://pypi.python.org/pypi/amplpy


%prep
%autosetup -n amplpy-0.9.3

%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-amplpy -f filelist.lst
%dir %{python3_sitearch}/*

%files help -f doclist.lst
%{_docdir}/*

%changelog
* Sun Apr 23 2023 Python_Bot <Python_Bot@openeuler.org> - 0.9.3-1
- Package Spec generated