Example #1
0
def test_stencil_pxci_lbounds():
    b = stencil_pxci_lbounds(5, 7)
    assert b == [2, 2, 2, 3, 4, 4, 4]

    b = stencil_pxci_lbounds(3, 5, lrefl=True)
    assert b == [0, 1, 2, 3, 3]

    b = stencil_pxci_lbounds(3, 5, rrefl=True)
    assert b == [1, 1, 2, 3, 4]

    b = stencil_pxci_lbounds(5, 7, lrefl=True, rrefl=True)
    assert b == [0, 1, 2, 3, 4, 5, 6]

    b = stencil_pxci_lbounds(3, 4, rrefl=True)
    # [y0, y0, y1, y2, y3, y3]
    assert b == [1, 1, 2, 3]
def test_ReactionDiffusion__only_1_species_diffusion_7bins(log):
    # Diffusion without reaction
    N = 7
    nstencil = 5
    nsidep = (nstencil-1)//2
    t0 = 3.0
    logy, logt = log
    D = 2.0
    y0 = np.array([12, 8, 11, 5, 7, 4, 9], dtype=np.float64)
    x = np.array([3, 5, 13, 17, 23, 25, 35, 37], dtype=np.float64)
    rd = ReactionDiffusion(1, [], [], [], D=[D], x=x,
                           logy=logy, logt=logt, nstencil=nstencil,
                           lrefl=False, rrefl=False)
    weights = [
        [951/8800, -716/2475, 100/297, -75/352, 311/5400],
        [321/8800, -161/2475, 7/297, 3/352, -19/5400],
        [-39/8800, 109/2475, -127/1485, 87/1760, -19/5400],
        [-2/693, 38/675, -129/1100, 7/108, -1/1050],
        [0, 9/160, -7/72, 2/45, -1/288],
        [-8/1575, 9/400, 0, -19/450, 25/1008],
        [16/315, -9/32, 31/72, -13/45, 179/2016]
    ]
    assert np.allclose(rd.D_weight, np.array(weights).flatten())

    lb = stencil_pxci_lbounds(nstencil, N)
    yi = pxci_to_bi(nstencil, N)
    fref = np.array([sum([D*weights[i][j]*y0[yi[j+lb[i]]] for j
                          in range(nstencil)]) for i in range(N)])

    if logy:
        fref /= y0
    if logt:
        fref *= t0

    jref = np.zeros((N, N))
    for i in range(N):
        for j in range(max(0, i-1), min(N, i+2)):
            if logy:
                if j == i+1 or j == i-1:
                    for k in range(nstencil):
                        if yi[k+lb[i]] == j:
                            jref[i, j] += D*weights[i][k]*y0[j]/y0[i]
                else:  # j == i
                    assert i == j
                    for k in range(nstencil):
                        cyi = yi[k+lb[i]]
                        if i == cyi:
                            continue
                        jref[i, i] -= D*weights[i][k]*y0[cyi]/y0[i]
            else:
                if i-1 <= j and j <= i+1:
                    jref[i, j] = D*weights[i][j-lb[i]+nsidep]
    if logt:
        jref *= t0
    t = rd.logb(t0) if logt else t0
    y = rd.logb(y0) if logy else y0
    _test_f_and_dense_jac_rmaj(rd, t, y, fref, jref)

    jout_bnd = np.zeros((4, N), order='F')
    rd.banded_jac_cmaj(t, y, jout_bnd)
    jref_bnd = get_banded(jref, 1, N)
    assert np.allclose(jout_bnd[1:, :], jref_bnd)

    # compressed_jac_cmaj actually use all diagonals
    rd = ReactionDiffusion(1, [], [], [], D=[D], x=x,
                           logy=logy, logt=logt, nstencil=nstencil,
                           lrefl=False, rrefl=False, n_jac_diags=2)
    jout_cmprs = rd.alloc_jout_compressed()
    rd.compressed_jac_cmaj(t, y, jout_cmprs)
    from block_diag_ilu import Compressed_from_dense

    jref2 = np.zeros((N, N))
    for i in range(N):
        for j in range(max(0, i-2), min(N, i+3)):
            if logy:
                if i-2 <= j <= i+2:
                    if i == j:
                        for k in range(nstencil):
                            cyi = yi[k+lb[i]]
                            if i == cyi:
                                continue
                            jref2[i, i] -= D*weights[i][k]*y0[cyi]/y0[i]
                    else:
                        for k in range(nstencil):
                            if yi[k+lb[i]] == j:
                                jref2[i, j] += D*weights[i][k]*y0[j]/y0[i]

            else:
                if i-2 <= j <= i+2:
                    jref2[i, j] = D*weights[i][j-lb[i]+nsidep]
    if logt:
        jref2 *= t0
    jref_cmprs = Compressed_from_dense(jref2, N, 1, nsidep).data
    assert np.allclose(jout_cmprs, jref_cmprs)
def test_ReactionDiffusion__3_reactions_4_species_5_bins_k_factor(
        geom_refl):
    # UNSUPPORTED since `bin_k_factor` was replaced with `fields`
    # if a real world scenario need per bin modulation of binary
    # reactions and the functionality is reintroduced, this test
    # is useful
    from sympy import finite_diff_weights
    geom, refl = geom_refl
    lrefl, rrefl = refl
    # r[0]: A + B -> C
    # r[1]: D + C -> D + A + B
    # r[2]: B + B -> D
    #              r[0]     r[1]       r[2]
    stoich_active = [[0, 1], [2, 3],    [1, 1]]
    stoich_prod = [[2],    [0, 1, 3], [3]]
    n = 4
    N = 5
    D = np.array([2.6, 3.7, 5.11, 7.13])*213
    y0 = np.array([
        2.5, 1.2, 3.2, 4.3,
        2.7, 0.8, 1.6, 2.4,
        3.1, 0.3, 1.5, 1.8,
        3.3, 0.6, 1.6, 1.4,
        3.6, 0.9, 1.7, 1.2
    ]).reshape((5, 4))
    x = np.array([11.0, 13.3, 17.0, 23.2, 29.8, 37.2])
    xc_ = x[:-1]+np.diff(x)/2
    xc_ = [x[0]-(xc_[0]-x[0])]+list(xc_)+[x[-1]+(x[-1]-xc_[-1])]
    assert len(xc_) == 7
    k = [31.0, 37.0, 41.0]

    # (r[0], r[1]) modulations over bins
    modulated_rxns = [0, 1]
    modulation = [[i+3 for i in range(N)],
                  [i+4 for i in range(N)]]
    nstencil = 3
    nsidep = 1
    rd = ReactionDiffusion(
        4, stoich_active, stoich_prod, k, N, D=D, x=x,
        geom=geom, nstencil=nstencil, lrefl=lrefl,
        rrefl=rrefl, modulated_rxns=modulated_rxns,
        modulation=modulation)

    assert np.allclose(xc_, rd.xc)

    lb = stencil_pxci_lbounds(nstencil, N, lrefl, rrefl)
    if lrefl:
        if rrefl:
            assert lb == [0, 1, 2, 3, 4]
        else:
            assert lb == [0, 1, 2, 3, 3]
    else:
        if rrefl:
            assert lb == [1, 1, 2, 3, 4]
        else:
            assert lb == [1, 1, 2, 3, 3]
    assert lb == list(map(rd._stencil_bi_lbound, range(N)))

    pxci2bi = pxci_to_bi(nstencil, N)
    assert pxci2bi == [0, 0, 1, 2, 3, 4, 4]
    assert pxci2bi == list(map(rd._xc_bi_map, range(N+2)))

    D_weight = []
    for bi in range(N):
        local_x_serie = xc_[lb[bi]:lb[bi]+nstencil]
        local_x_around = xc_[nsidep+bi]
        w = finite_diff_weights(
            2, local_x_serie, x0=local_x_around
        )
        D_weight.append(w[-1][-1])
        if geom == 'f':
            pass
        elif geom == 'c':
            for wi in range(nstencil):
                # first order derivative
                D_weight[bi][wi] += w[-2][-1][wi]*1/local_x_around
        elif geom == 's':
            for wi in range(nstencil):
                # first order derivative
                D_weight[bi][wi] += w[-2][-1][wi]*2/local_x_around
        else:
            raise RuntimeError
    assert np.allclose(rd.D_weight, np.array(D_weight, dtype=np.float64).flatten())

    def cflux(si, bi):
        f = 0.0
        for k in range(nstencil):
            f += rd.D_weight[bi*nstencil+k]*y0[pxci2bi[lb[bi]+k], si]
        return D[si]*f

    r = [
        [k[0]*modulation[0][bi]*y0[bi, 0]*y0[bi, 1] for
         bi in range(N)],
        [k[1]*modulation[1][bi]*y0[bi, 3]*y0[bi, 2] for
         bi in range(N)],
        [k[2]*y0[bi, 1]**2 for bi in range(N)],
    ]

    fref = np.array([[
        -r[0][bi] + r[1][bi] + cflux(0, bi),
        -r[0][bi] + r[1][bi] - 2*r[2][bi] + cflux(1, bi),
        r[0][bi] - r[1][bi] + cflux(2, bi),
        r[2][bi] + cflux(3, bi)
    ] for bi in range(N)]).flatten()

    # Now let's check that the Jacobian is correctly computed.
    def dfdC(bi, lri, lci):
        v = 0.0
        for ri in range(len(stoich_active)):
            totl = (stoich_prod[ri].count(lri) -
                    stoich_active[ri].count(lri))
            if totl == 0:
                continue
            actv = stoich_active[ri].count(lci)
            if actv == 0:
                continue
            v += actv*totl*r[ri][bi]/y0[bi, lci]
        return v

    def jac_elem(ri, ci):
        bri, bci = ri // n, ci // n
        lri, lci = ri % n,  ci % n
        elem = 0.0

        def _diffusion():
            _elem = 0.0
            for k in range(nstencil):
                if pxci2bi[lb[bri]+k] == bci:
                    _elem += D[lri]*rd.D_weight[bri*nstencil+k]
            return _elem

        if bri == bci:
            # on block diagonal
            elem += dfdC(bri, lri, lci)
            if lri == lci:
                elem += _diffusion()
        elif bri == bci - 1:
            if lri == lci:
                elem = _diffusion()
        elif bri == bci + 1:
            if lri == lci:
                elem = _diffusion()
        return elem

    jref = np.zeros((n*N, n*N), order='C')
    for ri, ci in np.ndindex(n*N, n*N):
        jref[ri, ci] = jac_elem(ri, ci)

    # Compare to what is calculated using our C++ callback
    _test_f_and_dense_jac_rmaj(rd, 0, y0.flatten(), fref, jref)

    jout_cmaj = np.zeros((n*N, n*N), order='F')
    rd.dense_jac_cmaj(0.0, y0.flatten(), jout_cmaj)
    assert np.allclose(jout_cmaj, jref)

    ref_banded_j = get_banded(jref, n, N)

    ref_banded_j_symbolic = rd.alloc_jout(order='F', pad=0)
    symrd = SymRD.from_rd(rd)
    symrd.banded_jac(0.0, y0.flatten(), ref_banded_j_symbolic)
    assert np.allclose(ref_banded_j_symbolic, ref_banded_j)

    jout_bnd_packed_cmaj = np.zeros((3*n+1, n*N), order='F')
    rd.banded_jac_cmaj(0.0, y0.flatten(), jout_bnd_packed_cmaj)

    if os.environ.get('plot_tests', False):
        import matplotlib
        matplotlib.use('Agg')
        import matplotlib.pyplot as plt
        from chemreac.util.plotting import coloured_spy
        fig = plt.figure()
        ax = fig.add_subplot(3, 1, 1)
        coloured_spy(ref_banded_j, ax=ax)
        plt.title('ref_banded_j')
        ax = fig.add_subplot(3, 1, 2)
        coloured_spy(jout_bnd_packed_cmaj[n:, :], ax=ax)
        plt.title('jout_bnd_packed_cmaj')
        ax = fig.add_subplot(3, 1, 3)
        coloured_spy(ref_banded_j-jout_bnd_packed_cmaj[n:, :], ax=ax)
        plt.title('diff')
        plt.savefig(__file__+'.png')

    assert np.allclose(jout_bnd_packed_cmaj[n:, :], ref_banded_j)
def test_ReactionDiffusion__lrefl_7(log):
    # Diffusion without reaction (7 bins)
    from sympy import finite_diff_weights
    lrefl, rrefl = True, False
    N = 7
    t0 = 3.0
    logy, logt = log
    D = 17.0
    nstencil = 5
    nsidep = (nstencil-1)//2
    x = np.array([3, 5, 13, 17, 23, 25, 35, 37], dtype=np.float64)
    y0 = np.array([12, 8, 11, 5, 7, 4, 9], dtype=np.float64)
    xc_ = padded_centers(x, nsidep)
    lb = stencil_pxci_lbounds(nstencil, N, lrefl=lrefl, rrefl=rrefl)

    rd = ReactionDiffusion(1, [], [], [], D=[D], x=x, logy=logy,
                           logt=logt, N=N, nstencil=nstencil,
                           lrefl=lrefl, rrefl=rrefl)

    y = rd.logb(y0) if logy else y0
    t = rd.logb(t0) if logt else t0
    le = nsidep if lrefl else 0
    D_weight_ref = np.array([
        finite_diff_weights(
            2, xc_[lb[i]:lb[i]+nstencil], x0=xc_[le+i])[-1][-1]
        for i in range(N)], dtype=np.float64)
    assert np.allclose(rd.D_weight, D_weight_ref.flatten())

    yi = pxci_to_bi(nstencil, N)

    fref = D*np.array([
        sum([rd.D_weight[i*nstencil+j]*y0[yi[lb[i]+j]]
             for j in range(nstencil)])
        for i in range(N)
    ])

    if logy:
        fref /= y0
    if logt:
        fref *= t0

    _test_f(rd, t, y, fref)

    if logy:
        def cb(i, j):
            if abs(i-j) > 1:
                return 0  # imperfect Jacobian
            elif i == j:
                res = 0
                for k in range(nstencil):
                    cyi = yi[lb[i] + k]  # current y index
                    if cyi != i:
                        res -= y0[cyi]/y0[i]*rd.D_weight[i*nstencil + k]
                return res
            else:
                res = 0
                for k in range(nstencil):
                    cyi = yi[lb[i] + k]  # current y index
                    if cyi == j:
                        res += y0[j]/y0[i]*rd.D_weight[i*nstencil + k]
                return res
    else:
        def cb(i, j):
            if abs(i-j) > 1:
                return 0  # imperfect Jacobian
            res = 0
            for k in range(nstencil):
                if yi[lb[i]+k] == j:
                    res += rd.D_weight[i*nstencil + k]
            return res
    jref = D*np.array([cb(i, j) for i, j in product(
        range(N), range(N))]).reshape(N, N)
    if logt:
        jref *= t0

    _test_dense_jac_rmaj(rd, t, y, jref)