1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
|
%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
|
