linopy/test/test_constraints.py at 2f3e87eac6ccdfca5fe9d9bf0a5b433b10f65bea · PyPSA/linopy · GitHub

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#!/usr/bin/env python3
"""
Created on Wed Mar 10 11:23:13 2021.

@author: fabulous
"""

import dask
import dask.array.core
import numpy as np
import pandas as pd
import pytest
import xarray as xr

from linopy import EQUAL, GREATER_EQUAL, LESS_EQUAL, Model
from linopy.testing import assert_conequal

# Test model functions


def test_constraint_assignment() -> None:
    m: Model = Model()

    lower: xr.DataArray = xr.DataArray(
        np.zeros((10, 10)), coords=[range(10), range(10)]
    )
    upper: xr.DataArray = xr.DataArray(np.ones((10, 10)), coords=[range(10), range(10)])
    x = m.add_variables(lower, upper, name="x")
    y = m.add_variables(name="y")

    con0 = m.add_constraints(1 * x + 10 * y, EQUAL, 0)

    for attr in m.constraints.dataset_attrs:
        assert "con0" in getattr(m.constraints, attr)

    assert m.constraints.labels.con0.shape == (10, 10)
    assert np.issubdtype(m.constraints.labels.con0.dtype, np.integer)
    assert m.constraints.coeffs.con0.dtype in (int, float)
    assert np.issubdtype(m.constraints.vars.con0.dtype, np.integer) or np.issubdtype(
        m.constraints.vars.con0.dtype, np.floating
    )
    assert m.constraints.rhs.con0.dtype in (int, float)

    assert_conequal(m.constraints.con0, con0)


def test_constraint_equality() -> None:
    m: Model = Model()

    lower: xr.DataArray = xr.DataArray(
        np.zeros((10, 10)), coords=[range(10), range(10)]
    )
    upper: xr.DataArray = xr.DataArray(np.ones((10, 10)), coords=[range(10), range(10)])
    x = m.add_variables(lower, upper, name="x")
    y = m.add_variables(name="y")

    con0 = m.add_constraints(1 * x + 10 * y, EQUAL, 0)

    assert_conequal(con0, 1 * x + 10 * y == 0, strict=False)
    assert_conequal(1 * x + 10 * y == 0, 1 * x + 10 * y == 0, strict=False)

    with pytest.raises(AssertionError):
        assert_conequal(con0, 1 * x + 10 * y <= 0, strict=False)

    with pytest.raises(AssertionError):
        assert_conequal(con0, 1 * x + 10 * y >= 0, strict=False)

    with pytest.raises(AssertionError):
        assert_conequal(10 * y + 2 * x == 0, 1 * x + 10 * y == 0, strict=False)


def test_constraints_getattr_formatted() -> None:
    m: Model = Model()
    x = m.add_variables(0, 10, name="x")
    m.add_constraints(1 * x == 0, name="con-0")
    assert_conequal(m.constraints.con_0, m.constraints["con-0"])


def test_anonymous_constraint_assignment() -> None:
    m: Model = Model()

    lower = xr.DataArray(np.zeros((10, 10)), coords=[range(10), range(10)])
    upper = xr.DataArray(np.ones((10, 10)), coords=[range(10), range(10)])
    x = m.add_variables(lower, upper, name="x")
    y = m.add_variables(name="y")
    con = 1 * x + 10 * y == 0
    m.add_constraints(con)

    for attr in m.constraints.dataset_attrs:
        assert "con0" in getattr(m.constraints, attr)

    assert m.constraints.labels.con0.shape == (10, 10)
    assert np.issubdtype(m.constraints.labels.con0.dtype, np.integer)
    assert m.constraints.coeffs.con0.dtype in (int, float)
    assert np.issubdtype(m.constraints.vars.con0.dtype, np.integer) or np.issubdtype(
        m.constraints.vars.con0.dtype, np.floating
    )
    assert m.constraints.rhs.con0.dtype in (int, float)


def test_constraint_assignment_with_tuples() -> None:
    m: Model = Model()

    lower = xr.DataArray(np.zeros((10, 10)), coords=[range(10), range(10)])
    upper = xr.DataArray(np.ones((10, 10)), coords=[range(10), range(10)])
    x = m.add_variables(lower, upper)
    y = m.add_variables()

    m.add_constraints([(1, x), (10, y)], EQUAL, 0, name="c")
    for attr in m.constraints.dataset_attrs:
        assert "c" in getattr(m.constraints, attr)
    assert m.constraints.labels.c.shape == (10, 10)


def test_constraint_assignment_chunked() -> None:
    # setting bounds with one pd.DataFrame and one pd.Series
    m: Model = Model(chunk=5)
    lower = pd.DataFrame(np.zeros((10, 10)))
    upper = pd.Series(np.ones(10))
    x = m.add_variables(lower, upper)
    m.add_constraints(x, GREATER_EQUAL, 0, name="c")
    assert m.constraints.coeffs.c.data.shape == (
        10,
        10,
        1,
    )
    assert isinstance(m.constraints.coeffs.c.data, dask.array.core.Array)


def test_constraint_assignment_with_reindex() -> None:
    m: Model = Model()

    lower = xr.DataArray(np.zeros((10, 10)), coords=[range(10), range(10)])
    upper = xr.DataArray(np.ones((10, 10)), coords=[range(10), range(10)])
    x = m.add_variables(lower, upper, name="x")
    y = m.add_variables(name="y")

    m.add_constraints(1 * x + 10 * y, EQUAL, 0)

    shuffled_coords = [2, 1, 3, 4, 6, 5, 7, 9, 8, 0]

    con = x.loc[shuffled_coords] + y >= 10
    assert (con.coords["dim_0"].values == shuffled_coords).all()


def test_wrong_constraint_assignment_repeated() -> None:
    # repeated variable assignment is forbidden
    m: Model = Model()
    x = m.add_variables()
    m.add_constraints(x, LESS_EQUAL, 0, name="con")
    with pytest.raises(ValueError):
        m.add_constraints(x, LESS_EQUAL, 0, name="con")


def test_masked_constraints() -> None:
    m: Model = Model()

    lower = xr.DataArray(np.zeros((10, 10)), coords=[range(10), range(10)])
    upper = xr.DataArray(np.ones((10, 10)), coords=[range(10), range(10)])
    x = m.add_variables(lower, upper)
    y = m.add_variables()

    mask = pd.Series([True] * 5 + [False] * 5)
    m.add_constraints(1 * x + 10 * y, EQUAL, 0, mask=mask)
    assert (m.constraints.labels.con0[0:5, :] != -1).all()
    assert (m.constraints.labels.con0[5:10, :] == -1).all()


def test_non_aligned_constraints() -> None:
    m: Model = Model()

    lower = xr.DataArray(np.zeros(10), coords=[range(10)])
    x = m.add_variables(lower, name="x")

    lower = xr.DataArray(np.zeros(8), coords=[range(8)])
    y = m.add_variables(lower, name="y")

    m.add_constraints(x == 0.0)
    m.add_constraints(y == 0.0)

    with pytest.warns(UserWarning):
        m.constraints.labels

        for dtype in m.constraints.labels.dtypes.values():
            assert np.issubdtype(dtype, np.integer)

        for dtype in m.constraints.coeffs.dtypes.values():
            assert np.issubdtype(dtype, np.floating)

        for dtype in m.constraints.vars.dtypes.values():
            assert np.issubdtype(dtype, np.integer)

        for dtype in m.constraints.rhs.dtypes.values():
            assert np.issubdtype(dtype, np.floating)


def test_constraints_flat() -> None:
    m: Model = Model()

    lower = xr.DataArray(np.zeros((10, 10)), coords=[range(10), range(10)])
    upper = xr.DataArray(np.ones((10, 10)), coords=[range(10), range(10)])
    x = m.add_variables(lower, upper)
    y = m.add_variables()

    assert isinstance(m.constraints.flat, pd.DataFrame)
    assert m.constraints.flat.empty
    with pytest.raises(ValueError):
        m.constraints.to_matrix()

    m.add_constraints(1 * x + 10 * y, EQUAL, 0)
    m.add_constraints(1 * x + 10 * y, LESS_EQUAL, 0)
    m.add_constraints(1 * x + 10 * y, GREATER_EQUAL, 0)

    assert isinstance(m.constraints.flat, pd.DataFrame)
    assert not m.constraints.flat.empty


def test_sanitize_infinities() -> None:
    m: Model = Model()

    lower = xr.DataArray(np.zeros((10, 10)), coords=[range(10), range(10)])
    upper = xr.DataArray(np.ones((10, 10)), coords=[range(10), range(10)])
    x = m.add_variables(lower, upper, name="x")
    y = m.add_variables(name="y")

    # Test correct infinities
    m.add_constraints(x <= np.inf, name="con_inf")
    m.add_constraints(y >= -np.inf, name="con_neg_inf")
    m.constraints.sanitize_infinities()
    assert (m.constraints["con_inf"].labels == -1).all()
    assert (m.constraints["con_neg_inf"].labels == -1).all()

    # Test incorrect infinities
    with pytest.raises(ValueError):
        m.add_constraints(x >= np.inf, name="con_wrong_inf")
    with pytest.raises(ValueError):
        m.add_constraints(y <= -np.inf, name="con_wrong_neg_inf")