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PKG Add optlang package (pyodide#1093)
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package: | ||
name: glpk | ||
version: 4.65 | ||
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source: | ||
sha256: 4281e29b628864dfe48d393a7bedd781e5b475387c20d8b0158f329994721a10 | ||
url: https://ftp.gnu.org/gnu/glpk/glpk-4.65.tar.gz | ||
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build: | ||
library: true | ||
script: | | ||
CFLAGS="-fPIC" emconfigure ./configure --prefix=$PWD | ||
emmake make -j ${PYODIDE_JOBS:-3} |
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package: | ||
name: optlang | ||
version: 1.4.7 | ||
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source: | ||
sha256: 0c9d7aae9babd5f9eac296b6f975a0e475545ac7ced9790f43796671157eb017 | ||
url: https://files.pythonhosted.org/packages/47/85/9d028fbc971b1db9b8c3e58072237e8a7fda5cd936dd73f0b179805b5464/optlang-1.4.7.tar.gz | ||
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requirements: | ||
run: | ||
- sympy | ||
- six | ||
- swiglpk | ||
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test: | ||
imports: | ||
- optlang | ||
- optlang.glpk_interface | ||
- optlang.symbolics |
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import pytest | ||
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def test_optlang(selenium): | ||
selenium.load_package("optlang") | ||
selenium.run( | ||
""" | ||
from optlang import Model, Variable, Constraint, Objective | ||
# All the (symbolic) variables are declared, with a name and optionally a lower and/or upper bound. | ||
x1 = Variable('x1', lb=0) | ||
x2 = Variable('x2', lb=0) | ||
x3 = Variable('x3', lb=0) | ||
# A constraint is constructed from an expression of variables and a lower and/or upper bound (lb and ub). | ||
c1 = Constraint(x1 + x2 + x3, ub=100) | ||
c2 = Constraint(10 * x1 + 4 * x2 + 5 * x3, ub=600) | ||
c3 = Constraint(2 * x1 + 2 * x2 + 6 * x3, ub=300) | ||
# An objective can be formulated | ||
obj = Objective(10 * x1 + 6 * x2 + 4 * x3, direction='max') | ||
# Variables, constraints and objective are combined in a Model object, which can subsequently be optimized. | ||
model = Model(name='Simple model') | ||
model.objective = obj | ||
model.add([c1, c2, c3]) | ||
status = model.optimize() | ||
""" | ||
) | ||
result = selenium.run("model.status") | ||
assert result == "optimal" | ||
result = selenium.run("model.objective.value") | ||
assert result == pytest.approx(733.3333, abs=1e-4) | ||
result = selenium.run("model.variables['x1'].primal") | ||
assert result == pytest.approx(33.3333, abs=1e-4) | ||
result = selenium.run("model.variables['x2'].primal") | ||
assert result == pytest.approx(66.6667, abs=1e-4) | ||
result = selenium.run("model.variables['x3'].primal") | ||
assert result == pytest.approx(0.0000, abs=1e-4) |
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package: | ||
name: swiglpk | ||
version: 4.65.1 | ||
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source: | ||
sha256: 0216db2930a6fe2c07ac7f0e28e76e9a2711a647836a3a4067113091c7ae221e | ||
url: https://files.pythonhosted.org/packages/0e/c9/9b0ec3e2ca942b6d067e84e52d72818fa9c3a1a1524671fcbec4841b54b2/swiglpk-4.65.1.tar.gz | ||
extras: | ||
- [../glpk/build/glpk-4.65/src/glpk.h, ./glpk.h] | ||
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build: | ||
ldflags: -L../../../glpk/build/glpk-4.65/src/.libs/ | ||
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requirements: | ||
run: | ||
- glpk | ||
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test: | ||
imports: | ||
- swiglpk |
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def test_swiglpk(selenium): | ||
selenium.load_package("swiglpk") | ||
selenium.run( | ||
""" | ||
from swiglpk import * | ||
ia = intArray(1+1000); ja = intArray(1+1000); | ||
ar = doubleArray(1+1000); | ||
lp = glp_create_prob(); | ||
glp_set_prob_name(lp, "sample"); | ||
glp_set_obj_dir(lp, GLP_MAX); | ||
glp_add_rows(lp, 3); | ||
glp_set_row_name(lp, 1, "p"); | ||
glp_set_row_bnds(lp, 1, GLP_UP, 0.0, 100.0); | ||
glp_set_row_name(lp, 2, "q"); | ||
glp_set_row_bnds(lp, 2, GLP_UP, 0.0, 600.0); | ||
glp_set_row_name(lp, 3, "r"); | ||
glp_set_row_bnds(lp, 3, GLP_UP, 0.0, 300.0); | ||
glp_add_cols(lp, 3); | ||
glp_set_col_name(lp, 1, "x1"); | ||
glp_set_col_bnds(lp, 1, GLP_LO, 0.0, 0.0); | ||
glp_set_obj_coef(lp, 1, 10.0); | ||
glp_set_col_name(lp, 2, "x2"); | ||
glp_set_col_bnds(lp, 2, GLP_LO, 0.0, 0.0); | ||
glp_set_obj_coef(lp, 2, 6.0); | ||
glp_set_col_name(lp, 3, "x3"); | ||
glp_set_col_bnds(lp, 3, GLP_LO, 0.0, 0.0); | ||
glp_set_obj_coef(lp, 3, 4.0); | ||
ia[1] = 1; ja[1] = 1; ar[1] = 1.0; # a[1,1] = 1 | ||
ia[2] = 1; ja[2] = 2; ar[2] = 1.0; # a[1,2] = 1 | ||
ia[3] = 1; ja[3] = 3; ar[3] = 1.0; # a[1,3] = 1 | ||
ia[4] = 2; ja[4] = 1; ar[4] = 10.0; # a[2,1] = 10 | ||
ia[5] = 3; ja[5] = 1; ar[5] = 2.0; # a[3,1] = 2 | ||
ia[6] = 2; ja[6] = 2; ar[6] = 4.0; # a[2,2] = 4 | ||
ia[7] = 3; ja[7] = 2; ar[7] = 2.0; # a[3,2] = 2 | ||
ia[8] = 2; ja[8] = 3; ar[8] = 5.0; # a[2,3] = 5 | ||
ia[9] = 3; ja[9] = 3; ar[9] = 6.0; # a[3,3] = 6 | ||
glp_load_matrix(lp, 9, ia, ja, ar); | ||
glp_simplex(lp, None); | ||
Z = glp_get_obj_val(lp); | ||
x1 = glp_get_col_prim(lp, 1); | ||
x2 = glp_get_col_prim(lp, 2); | ||
x3 = glp_get_col_prim(lp, 3); | ||
glp_delete_prob(lp); | ||
""" | ||
) | ||
result = selenium.run("""'Z = %g; x1 = %g; x2 = %g; x3 = %g' % (Z, x1, x2, x3)""") | ||
assert result == "Z = 733.333; x1 = 33.3333; x2 = 66.6667; x3 = 0" |