Loco

pySC/correction/loco.py

@@ -0,0 +1,168 @@
+import at
+import numpy as np
+import multiprocessing
+from pySC.lattice_properties.response_model import SCgetModelRM, SCgetModelDispersion
+from pySC.core.constants import SETTING_ADD, TRACK_ORB
+from pySC.core.beam import bpm_reading
+from pySC.utils.sc_tools import SCgetPinv
+import matplotlib.pyplot as plt
+from scipy.optimize import least_squares
+from pySC.utils import logging_tools
+LOGGER = logging_tools.get_logger(__name__)
+
+
+def calculate_jacobian(SC, C_model, dkick, used_cor_ind, bpm_indexes, quads_ind, dk, trackMode=TRACK_ORB,
+                       useIdealRing=True, skewness=False, order=1, method=SETTING_ADD, includeDispersion=False, rf_step=1E3,
+                       cav_ords=None, full_jacobian=True):
+    pool = multiprocessing.Pool()
+    quad_args = [(quad_index, SC, C_model, dkick, used_cor_ind, bpm_indexes, dk, trackMode, useIdealRing,
+                  skewness, order, method, includeDispersion, rf_step, cav_ords) for quad_index in quads_ind]
+    # results = []
+    # for quad_arg in quad_args:
+    #     results.append(generating_quads_response_matrices(quad_arg))
+    results = pool.map(generating_quads_response_matrices, quad_args)
+    pool.close()
+    pool.join()
+
+    results = [result / dk for result in results]
+    if full_jacobian:  # Construct the complete Jacobian matrix for the LOCO
+        # assuming only linear scaling errors of BPMs and corrector magnets
+        n_correctors = len(np.concatenate(used_cor_ind))
+        n_bpms = len(bpm_indexes) * 2  # in both planes
+        j_cor = np.zeros((n_correctors,) + C_model.shape)
+        for i in range(n_correctors):
+            j_cor[i, :, i] = C_model[:, i]   # a single column of response matrix corresponding to a corrector
+        j_bpm = np.zeros((n_bpms,) + C_model.shape)
+        for i in range(n_bpms):
+            j_bpm[i, i, :] = C_model[i, :]  # a single row of response matrix corresponding to a given plane of BPM
+        return np.concatenate((results, j_cor, j_bpm), axis=0)
+    return results
+
+
+def generating_quads_response_matrices(args):
+    (quad_index, SC, C_model, correctors_kick, used_cor_indexes, used_bpm_indexes, dk, trackMode, useIdealRing,
+     skewness, order, method, includeDispersion, rf_step, cav_ords) = args
+    LOGGER.debug('generating response to quad of index', quad_index)
+    if not includeDispersion:
+        SC.set_magnet_setpoints(quad_index, dk, skewness, order, method)
+        C_measured = SCgetModelRM(SC, used_bpm_indexes, used_cor_indexes, dkick=correctors_kick,
+                                  useIdealRing=useIdealRing,
+                                  trackMode=trackMode)
+        SC.set_magnet_setpoints(quad_index, -dk, skewness, order, method)
+        return C_measured - C_model
+
+    dispersion_model = SCgetModelDispersion(SC, used_bpm_indexes, CAVords=cav_ords)
+    SC.set_magnet_setpoints(quad_index, dk, skewness, order, method)
+    C_measured = SCgetModelRM(SC, used_bpm_indexes, used_cor_indexes, dkick=correctors_kick, useIdealRing=useIdealRing,
+                              trackMode=trackMode)
+    dispersion_meas = SCgetModelDispersion(SC, used_bpm_indexes, CAVords=cav_ords, rfStep=rf_step)
+    SC.set_magnet_setpoints(quad_index, -dk, skewness, order, method)
+    return np.hstack((C_measured - C_model, (dispersion_meas - dispersion_model).reshape(-1, 1)))
+
+
+def measure_closed_orbit_response_matrix(SC, bpm_ords, cm_ords, dkick=1e-5):
+    LOGGER.info('Calculating Measure response matrix')
+    n_turns = 1
+    n_bpms = len(bpm_ords)
+    n_cms = len(cm_ords[0]) + len(cm_ords[1])
+    response_matrix = np.full((2 * n_bpms * n_turns, n_cms), np.nan)
+    SC.INJ.trackMode = TRACK_ORB  # TODO may modify SC (not a pure function)!
+
+    closed_orbits0 = bpm_reading(SC, bpm_ords=bpm_ords)[0]
+    cnt = 0
+    for n_dim in range(2):
+        for cm_ord in cm_ords[n_dim]:
+            SC.set_cm_setpoints(cm_ord, dkick, skewness=bool(n_dim), method=SETTING_ADD)
+            closed_orbits1 = bpm_reading(SC, bpm_ords=bpm_ords)[0]
+            SC.set_cm_setpoints(cm_ord, -dkick, skewness=bool(n_dim), method=SETTING_ADD)
+            response_matrix[:, cnt] = np.ravel((closed_orbits1 - closed_orbits0) / dkick)
+            cnt = cnt + 1
+    return response_matrix
+
+
+def loco_correction_lm(initial_guess0, orm_model, orm_measured, Jn, lengths, including_fit_parameters, bounds=(-np.inf, np.inf), weights=1,
+                       verbose=2):
+    mask = _get_parameters_mask(including_fit_parameters, lengths)
+    result = least_squares(lambda delta_params: objective(delta_params, orm_measured - orm_model, Jn[mask, :, :], weights),
+                           initial_guess0[mask], #bounds=bounds,
+                           method="lm",
+                           verbose=verbose)
+    return result.x
+
+
+def loco_correction_ng(initial_guess0, orm_model, orm_measured, J, lengths, including_fit_parameters, s_cut, weights=1):
+    initial_guess = initial_guess0.copy()
+    mask = _get_parameters_mask(including_fit_parameters, lengths)
+    residuals = objective(initial_guess[mask], orm_measured - orm_model, J[mask, :, :], weights)
+    r = residuals.reshape(np.transpose(orm_model).shape)
+    t2 = np.zeros([len(initial_guess[mask]), 1])
+    for i in range(len(initial_guess[mask])):
+        t2[i] = np.sum(np.dot(np.dot(J[i].T, weights), r.T))
+    return get_inverse(J[mask, :, :], t2, s_cut, weights)
+
+
+def objective(masked_params, orm_residuals, masked_jacobian, weights):
+    return np.dot(np.transpose(orm_residuals - np.einsum("ijk,i->jk", masked_jacobian, masked_params)),
+                  np.sqrt(weights)).ravel()
+
+
+def _get_parameters_mask(including_fit_parameters, lengths):
+    len_quads, len_corr, len_bpm = lengths
+    mask = np.zeros(len_quads + len_corr + len_bpm, dtype=bool)
+    mask[:len_quads] = 'quads' in including_fit_parameters
+    mask[len_quads:len_quads + len_corr] = 'cor' in including_fit_parameters
+    mask[len_quads + len_corr:] = 'bpm' in including_fit_parameters
+    return mask
+
+
+def set_correction(SC, r, elem_ind, individuals=True, skewness=False, order=1, method=SETTING_ADD, dipole_compensation=True):
+    if individuals:
+        SC.set_magnet_setpoints(elem_ind, -r, skewness, order, method, dipole_compensation=dipole_compensation)
+        return SC
+
+    for fam_num, quad_fam in enumerate(elem_ind):
+        SC.set_magnet_setpoints(quad_fam, -r[fam_num], skewness, order, method, dipole_compensation=dipole_compensation)
+    return SC
+
+
+def model_beta_beat(ring, twiss, elements_indexes, plot=False):
+    _, _, twiss_error = at.get_optics(ring, elements_indexes)
+    s_pos = twiss_error.s_pos
+    bx = np.array(twiss_error.beta[:, 0] / twiss.beta[:, 0] - 1)
+    by = np.array(twiss_error.beta[:, 1] / twiss.beta[:, 1] - 1)
+    bx_rms = np.sqrt(np.mean(bx ** 2))
+    by_rms = np.sqrt(np.mean(by ** 2))
+
+    if plot:
+        init_font = plt.rcParams["font.size"]
+        plt.rcParams.update({'font.size': 14})
+
+        fig, ax = plt.subplots(nrows=2, sharex="all")
+        betas = [bx, by]
+        letters = ("x", "y")
+        for i in range(2):
+            ax[i].plot(s_pos, betas[i])
+            ax[i].set_xlabel("s_pos [m]")
+            ax[i].set_ylabel(rf'$\Delta\beta_{letters[i]}$ / $\beta_{letters[i]}$')
+            ax[i].ticklabel_format(axis='y', style='sci', scilimits=(0, 0))
+            ax[i].grid(True, which='both', linestyle=':', color='gray')
+
+        fig.show()
+        plt.rcParams.update({'font.size': init_font})
+
+    return bx_rms, by_rms
+
+
+def select_equally_spaced_elements(total_elements, num_elements):
+    step = len(total_elements) // (num_elements - 1)
+    return total_elements[::step]
+
+
+def get_inverse(jacobian, B, s_cut, weights, plot=False):
+    n_resp_mats = len(jacobian)
+    sum_corr = np.sum(jacobian, axis=2)          # Sum over i and j for all planes
+    matrix = np.dot(np.dot(sum_corr, weights), sum_corr.T)
+    inv_matrix = SCgetPinv(matrix, num_removed=n_resp_mats - min(n_resp_mats, s_cut), plot=plot)
+    results = np.ravel(np.dot(inv_matrix, B))
+    # e = np.ravel(np.dot(matrix, results)) - np.ravel(B)
+    return results

pySC/correction/loco_wrapper.py

@@ -15,8 +15,8 @@ def loco_model(SC, **kwargs):
              ))
     flags.update(**kwargs)
     ring_data, init = DotDict(), DotDict()
-    ring_data.CavityFrequency = SC.IDEALRING[SC.ORD.RF].Frequency
-    ring_data.CavityHarmNumber = SC.IDEALRING[SC.ORD.RF].HarmNumber
+    ring_data.CavityFrequency = SC.IDEALRING[SC.ORD.RF][0].Frequency
+    ring_data.CavityHarmNumber = SC.IDEALRING[SC.ORD.RF][0].HarmNumber
     ring_data.Lattice = SC.IDEALRING
     init.SC = SC
     return ring_data, flags, init

tests/test_loco.py

@@ -0,0 +1,103 @@
+import at
+import numpy as np
+import pytest
+from pySC.core.simulated_commissioning import SimulatedCommissioning
+from pySC.utils.sc_tools import SCgetOrds
+from pySC.utils import logging_tools
+from pySC.correction import loco
+from pySC.lattice_properties.response_model import SCgetModelRM, SCgetModelDispersion
+
+LOGGER = logging_tools.get_logger(__name__)
+
+
+def test_loco_hmba(at_ring):
+    np.random.seed(12345678)
+    sc = SimulatedCommissioning(at_ring)
+    sc.register_bpms(SCgetOrds(sc.RING, 'BPM'), Roll=0.0, CalError=1E-2 * np.ones(2), NoiseCO=np.array([1e-7, 1E-7]))
+    sc.register_magnets(SCgetOrds(sc.RING, 'QF|QD'), CalErrorB=np.array([0, 1E-2]))  # relative
+    sc.register_magnets(SCgetOrds(sc.RING, 'CXY'), CalErrorA=np.array([1E-4, 0]), CalErrorB=np.array([1E-4, 0]))
+    sc.register_magnets(SCgetOrds(sc.RING, 'BEND'))
+    sc.register_magnets(SCgetOrds(sc.RING, 'SF|SD'))  # [1/m]
+    sc.register_cavities(SCgetOrds(sc.RING, 'RFC'))
+    sc.apply_errors()
+    cor_ords = SCgetOrds(sc.RING, 'CXY')
+
+    used_correctors1 = loco.select_equally_spaced_elements(cor_ords, 10)
+    used_correctors2 = loco.select_equally_spaced_elements(cor_ords, 10)
+    used_cor_ords = [used_correctors1, used_correctors2]
+    used_bpms_ords = loco.select_equally_spaced_elements(sc.ORD.BPM, len(sc.ORD.BPM))
+    cav_ords = SCgetOrds(sc.RING, 'RFC')
+    quads_ords = [SCgetOrds(sc.RING, 'QF'), SCgetOrds(sc.RING, 'QD')]
+
+    CMstep = np.array([1.e-4])  # correctors change [rad]
+    dk = 1.e-4  # quads change
+    RFstep = 1e3
+
+    _, _, twiss = at.get_optics(sc.IDEALRING, sc.ORD.BPM)
+    orbit_response_matrix_model = SCgetModelRM(sc, used_bpms_ords, used_cor_ords, trackMode='ORB', useIdealRing=True, dkick=CMstep)
+    model_dispersion = SCgetModelDispersion(sc, used_bpms_ords, cav_ords, trackMode='ORB', Z0=np.zeros(6), nTurns=1,
+                                            rfStep=RFstep, useIdealRing=True)
+    Jn = loco.calculate_jacobian(sc, orbit_response_matrix_model, CMstep, used_cor_ords, used_bpms_ords, np.concatenate(quads_ords), dk,
+                            trackMode='ORB', useIdealRing=False, skewness=False, order=1, method='add',
+                            includeDispersion=False, rf_step=RFstep, cav_ords=cav_ords)
+    weights = np.eye(len(used_bpms_ords) * 2)
+    n_singular_values = 20
+
+    _, _, twiss_err = at.get_optics(sc.RING, sc.ORD.BPM)
+    bx_rms_err, by_rms_err = loco.model_beta_beat(sc.RING, twiss, sc.ORD.BPM, plot=False)
+    info_tab = 14 * " "
+    LOGGER.info("RMS Beta-beating before LOCO:\n"
+                f"{info_tab}{bx_rms_err * 100:04.2f}% horizontal\n{info_tab}{by_rms_err * 100:04.2f}% vertical  ")
+    n_iter = 3
+
+    for x in range(n_iter):  # optics correction using QF and QD
+        LOGGER.info(f'LOCO iteration {x}')
+        orbit_response_matrix_measured = loco.measure_closed_orbit_response_matrix(sc, used_bpms_ords, used_cor_ords, CMstep)
+        n_quads, n_corrs, n_bpms = len(np.concatenate(quads_ords)), len(np.concatenate(used_cor_ords)), len(used_bpms_ords) * 2
+        bx_rms_err, by_rms_err = loco.model_beta_beat(sc.RING, twiss, sc.ORD.BPM, plot=False)
+        total_length = n_bpms + n_corrs + n_quads
+        lengths = [n_quads, n_corrs, n_bpms]
+        including_fit_parameters = ['quads', 'cor', 'bpm']
+        initial_guess = np.zeros(total_length)
+
+        initial_guess[:lengths[0]] = 1e-6
+        initial_guess[lengths[0]:lengths[0] + lengths[1]] = 1e-6
+        initial_guess[lengths[0] + lengths[1]:] = 1e-6
+
+        # method lm (least squares)
+        fit_parameters = loco.loco_correction_lm(initial_guess, orbit_response_matrix_model,
+                                                 orbit_response_matrix_measured, Jn, lengths,
+                                                 including_fit_parameters, bounds=(-0.03, 0.03), weights=weights, verbose=2)
+
+        # method ng
+        # fit_parameters = loco.loco_correction_ng(initial_guess, orbit_response_matrix_model,
+        #                                          orbit_response_matrix_measured, Jn, lengths,
+        #                                          including_fit_parameters, n_singular_values, weights=weights)
+
+        dg = fit_parameters[:lengths[0]] if len(fit_parameters) > n_quads else fit_parameters
+        sc = loco.set_correction(sc, dg, np.concatenate(quads_ords))
+        bx_rms_cor, by_rms_cor = loco.model_beta_beat(sc.RING, twiss, sc.ORD.BPM, plot=False)
+        LOGGER.info(f"RMS Beta-beating after {x + 1} LOCO iterations:\n"
+                    f"{info_tab}{bx_rms_cor * 100:04.2f}% horizontal\n{info_tab}{by_rms_cor * 100:04.2f}% vertical  ")
+        LOGGER.info(f"Correction reduction: \n"
+                    f"    beta_x: {(1 - bx_rms_cor / bx_rms_err) * 100:.2f}%\n"
+                    f"    beta_y: {(1 - by_rms_cor / by_rms_err) * 100:.2f}%\n")
+    assert bx_rms_cor < 0.002
+    assert by_rms_cor < 0.002
+
+
+@pytest.fixture
+def at_ring():
+    ring = at.load_mat('inputs/hmba.mat')
+    bpm_indexes = at.get_refpts(ring, at.elements.Monitor)
+    for bpm_index in reversed(bpm_indexes):
+        corrector = at.elements.Corrector(f'CXY{bpm_index}', length=0, kick_angle=[0, 0], PolynomA=[0, 0],
+                                          PolynomB=[0, 0])
+        ring.insert(bpm_index + 1, corrector)
+    ring.enable_6d()
+    at.set_cavity_phase(ring)
+    at.set_rf_frequency(ring)
+
+    ring.tapering(niter=3, quadrupole=True, sextupole=True)
+    ring = at.Lattice(ring)
+    return ring