Join the 1-day Testing & QA Summit featuring 15+ Expert Speakers.Register for FREE! Join TestMu Conference

How to use _combine_constraints method in pandera

Best Python code snippet using pandera_python

truncation.py

Source:truncation.py

...185    if chi_max is not None:186        # keep at most chi_max values187        good2 = np.zeros(len(piv), dtype=np.bool_)188        good2[-chi_max:] = True189        good = _combine_constraints(good, good2, "chi_max")190    if chi_min is not None and chi_min > 1:191        # keep at most chi_max values192        good2 = np.ones(len(piv), dtype=np.bool_)193        good2[-chi_min + 1:] = False194        good = _combine_constraints(good, good2, "chi_min")195    if deg_tol:196        # don't cut between values (cut-1, cut) with ``log(S[cut]/S[cut-1]) < deg_tol``197        # this is equivalent to198        # ``(S[cut] - S[cut-1])/S[cut-1] < exp(deg_tol) - 1 = deg_tol + O(deg_tol^2)``199        good2 = np.empty(len(piv), np.bool_)200        good2[0] = True201        good2[1:] = np.greater_equal(logS[1:] - logS[:-1], deg_tol)202        good = _combine_constraints(good, good2, "degeneracy_tol")203    if svd_min is not None:204        # keep only values S[i] >= svd_min205        good2 = np.greater_equal(logS, np.log(svd_min))206        good = _combine_constraints(good, good2, "svd_min")207    if trunc_cut is not None:208        good2 = (np.cumsum(S[piv]**2) > trunc_cut * trunc_cut)209        good = _combine_constraints(good, good2, "trunc_cut")210    cut = np.nonzero(good)[0][0]  # smallest possible cut: keep as many S as allowed211    mask = np.zeros(len(S), dtype=np.bool_)212    np.put(mask, piv[cut:], True)213    norm_new = np.linalg.norm(S[mask])214    return mask, norm_new, TruncationError.from_S(S[np.logical_not(mask)]),215def svd_theta(theta, trunc_par, qtotal_LR=[None, None], inner_labels=['vR', 'vL']):216    """Performs SVD of a matrix `theta` (= the wavefunction) and truncates it.217    Perform a singular value decomposition (SVD) with :func:`~tenpy.linalg.np_conserved.svd`218    and truncates with :func:`truncate`.219    The result is an approximation220    ``theta ~= tensordot(U.scale_axis(S*renormalization, 1), VH, axes=1)``221    Parameters222    ----------223    theta : :class:`~tenpy.linalg.np_conserved.Array`, shape ``(M, N)``224        The matrix, on which the singular value decomposition (SVD) is performed.225        Usually, `theta` represents the wavefunction, such that the SVD is a Schmidt decomposition.226    trunc_par : dict227        truncation parameters as described in :func:`truncate`.228    qtotalLR : (charges, charges)229        The total charges for the returned `U` and `VH`.230    inner_labels : (string, string)231        Labels for the `U` and `VH` on the newly-created bond.232    Returns233    -------234    U : :class:`~tenpy.linalg.np_conserved.Array`235        Matrix with left singular vectors as columns.236        Shape ``(M, M)`` or ``(M, K)`` depending on `full_matrices`.237    S : 1D ndarray238        The singluar values of the array.239        If no `cutoff` is given, it has lenght ``min(M, N)``.240        Normalized to ``np.linalg.norm(S)==1``.241    VH : :class:`~tenpy.linalg.np_conserved.Array`242        Matrix with right singular vectors as rows.243        Shape ``(N, N)`` or ``(K, N)`` depending on `full_matrices`.244    err : :class:`TruncationError`245        The truncation error introduced.246    renormalization : float247        Factor, by which S was renormalized.248    """249    U, S, VH = npc.svd(theta,250                       full_matrices=False,251                       compute_uv=True,252                       qtotal_LR=qtotal_LR,253                       inner_labels=inner_labels)254    renormalization = np.linalg.norm(S)255    S = S / renormalization256    piv, new_norm, err = truncate(S, trunc_par)257    new_len_S = np.sum(piv, dtype=np.int_)258    if new_len_S * 100 < len(S) and (trunc_par['chi_max'] is None259                                     or new_len_S != trunc_par['chi_max']):260        msg = "Catastrophic reduction in chi: {0:d} -> {1:d}".format(len(S), new_len_S)261        # NANs are excluded in npc.svd262        UHU = npc.tensordot(U.conj(), U, axes=[[0], [0]])263        msg += " |U^d U - 1| = {0:f}".format(npc.norm(UHU - npc.eye_like(UHU)))264        VHV = npc.tensordot(VH, VH.conj(), axes=[[1], [1]])265        msg += " |V V - 1| = {0:f}".format(npc.norm(VHV - npc.eye_like(VHV)))266        warnings.warn(msg, stacklevel=2)267    S = S[piv] / new_norm268    renormalization *= new_norm269    U.iproject(piv, axes=1)  # U = U[:, piv]270    VH.iproject(piv, axes=0)  # VH = VH[piv, :]271    return U, S, VH, err, renormalization272def _combine_constraints(good1, good2, warn):273    """return logical_and(good1, good2) if there remains at least one `True` entry.274    Otherwise print a warning and return just `good1`.275    """276    res = np.logical_and(good1, good2)277    if np.any(res):278        return res279    warnings.warn("truncation: can't satisfy constraint for " + warn, stacklevel=3)...

test_translator.py

Source:test_translator.py

...20    m = three_parameter_problem.m21    assert y.size == m22def test_translate_equality_constraints(three_parameter_problem):23    translator = Translator(three_parameter_problem)24    translated_eqcon = translator._combine_constraints("eq")25    # Get the Jacobian of the translated eqcon26    x = np.zeros(translated_eqcon.dim)27    jac1 = translated_eqcon.jac(x)[:, :-1]28    # Get the Jacobian of the original constraint29    x1 = np.zeros(three_parameter_problem.shape[0])30    x2 = np.zeros(three_parameter_problem.shape[1])31    jac0 = three_parameter_problem.constraints[0].jac(x1, x2)32    assert np.isclose(jac1, jac0).all()33def test_translate_inequality_constraint(three_parameter_problem):34    translator = Translator(three_parameter_problem)35    translated_incon = translator._combine_constraints("ineq")36    n1 = three_parameter_problem.shape[0]37    # Get Jacobian of the original inequality constraint38    y = np.zeros(three_parameter_problem.constraints[1].dim)39    jac0 = three_parameter_problem.constraints[1].jac(y)40    # Get corresponding part of the Jacobian of the translated inequality constraint.41    x = np.zeros(translated_incon.dim)42    jac1 = translated_incon.jac(x)43    assert np.isclose(jac1[:, n1:-1], jac0).all()44def test_translate_unconstrained(unconstrained_problem):45    translator = Translator(unconstrained_problem)46    eqcon = translator._combine_constraints("eq")47    incon = translator._combine_constraints("ineq")48    assert isinstance(eqcon, NullConstraint)...

Automation Testing Tutorials

Learn to execute automation testing from scratch with LambdaTest Learning Hub. Right from setting up the prerequisites to run your first automation test, to following best practices and diving deeper into advanced test scenarios. LambdaTest Learning Hubs compile a list of step-by-step guides to help you be proficient with different test automation frameworks i.e. Selenium, Cypress, TestNG etc.