LHCMeasurementTools/LHC_BQM.py at master · PyCOMPLETE/LHCMeasurementTools · GitHub

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import numpy as np
from . import TimberManager as tm

class filled_buckets:
    def __init__(self, timber_variable, beam=0, verbose=False):

        if not (beam == 1 or beam == 2):
                raise ValueError('You need to specify which beam! (1 or 2)')

        if type(timber_variable) is str:
            dict_timber = tm.parse_timber_file(timber_variable, verbose=True)
            timber_variable_filled = dict_timber[get_variable_dict(beam)['FILLED_BUCKETS']]
        elif hasattr(timber_variable, '__getitem__'):
            timber_variable_filled = timber_variable[get_variable_dict(beam)['FILLED_BUCKETS']]
        elif isinstance(timber_variable, tm.timber_variable_list):
            timber_variable_filled = timber_variable
        else:
            raise ValueError('Type of timber variable is not understood')


        self.t_stamps = np.array(timber_variable_filled.t_stamps)
        self.fillbuck = timber_variable_filled.values
        self.fillbuck = [np.array([int(float(y)) for y in x]) for x in self.fillbuck]
        self.fillbuck = [(x-1)/10 for x in self.fillbuck]
        self.fillbuck = [x[x>=0] for x in self.fillbuck]
        self.fillbuck = [x.astype(int) for x in self.fillbuck]

        self.Nbun = list(map(len, self.fillbuck))
        N_acq = len(self.Nbun)

        # self.flag_filled = []
        # for ii in xrange(N_acq):
        #     self.flag_filled.append(np.array(map(lambda x: x in self.fillbuck[ii], range(3564))))
        N_slots = 3564
        self.flag_filled = np.array(N_acq * [N_slots * [False]])
        array_slots = np.array(list(range(N_slots)))
        if verbose:
            print('Start building fillbucket matrix')
        for ii in range(N_acq):
           #self.flag_filled[ii,:] = [x in self.fillbuck[ii] for x in array_slots]
           self.flag_filled[ii, self.fillbuck[ii]] = True
        if verbose:
            print('Done')


    def nearest_older_sample(self, t_obs):
        ind_min = np.argmin(np.abs(self.t_stamps - t_obs))
        if self.t_stamps[ind_min] > t_obs:
            ind_min -= 1

        if ind_min == -1:
            return 0.*self.fillbuck[ind_min]
        else:
            return self.fillbuck[ind_min]

    def nearest_older_sample_flag_filled_Nbun(self, t_obs):
        ind_min = np.argmin(np.abs(self.t_stamps - t_obs))
        if self.t_stamps[ind_min] > t_obs:
            ind_min -= 1

        if ind_min == -1:
            return [], 0
        else:
            return self.flag_filled[ind_min], self.Nbun[ind_min]


class blength:
    def __init__(self,timber_variable_blength, timber_variable_filled_bucket=None, beam=0):

        if type(timber_variable_blength) is str:
            if not (beam == 1 or beam == 2):
                raise ValueError('You need to specify which beam! (1 or 2)')
            dict_timber = tm.parse_timber_file(timber_variable_blength, verbose=True)
            timber_variable_blength = dict_timber[get_variable_dict(beam)['BUNCH_LENGTH']]
            if timber_variable_filled_bucket == None:
                timber_variable_filled_bucket = dict_timber[get_variable_dict(beam)['FILLED_BUCKETS']]

        elif hasattr(timber_variable_blength, '__getitem__'):
            dict_timber = timber_variable_blength
            timber_variable_blength = dict_timber[get_variable_dict(beam)['BUNCH_LENGTH']]
            if timber_variable_filled_bucket == None:
                timber_variable_filled_bucket = dict_timber[get_variable_dict(beam)['FILLED_BUCKETS']]


        if isinstance(timber_variable_filled_bucket,tm.timber_variable_list):
            fillbuck_obj = filled_buckets(timber_variable_filled_bucket, beam=beam)
        elif type(timber_variable_filled_bucket) is str:
            if not (beam == 1 or beam == 2):
                raise ValueError('You need to specify which beam! (1 or 2)')
            dict_timber = tm.parse_timber_file(timber_variable_filled_bucket, verbose=True)
            timber_variable_filled_bucket = dict_timber[get_variable_dict(beam)['FILLED_BUCKETS']]
            fillbuck_obj = filled_buckets(timber_variable_filled_bucket, beam = beam)
        elif isinstance(timber_variable_filled_bucket, filled_buckets):
            fillbuck_obj = timber_variable_filled_bucket
        elif timber_variable_filled_bucket == None:
            raise TypeError('You need to provide a filled_bucket file or object!')

        self.t_stamps = timber_variable_blength.t_stamps
        self.blen = []
        blen_timberstyle = timber_variable_blength.values
        blen_timberstyle = [np.array(list(map(float, x))) for x in blen_timberstyle]

        N_acq = len(self.t_stamps)

        bad_points = []
        for ii in range(N_acq):
            blen_vect = np.zeros(3564)
            flag_filled_curr, Nbun_curr = fillbuck_obj.nearest_older_sample_flag_filled_Nbun(self.t_stamps[ii])
            if sum(flag_filled_curr) != len(blen_timberstyle[ii][:Nbun_curr]):
                bad_points.append(ii)
                continue
            blen_vect[flag_filled_curr] = (blen_timberstyle[ii][:Nbun_curr])
            self.blen.append(blen_vect)

        self.t_stamps = list(self.t_stamps)
        for ii in bad_points[::-1]:
            del self.t_stamps[ii]

        self.t_stamps = np.array(self.t_stamps)
        self.avblen = np.array(list(map(mean_nonzero, self.blen)))
        self.blen = np.array(self.blen)

    def nearest_older_sample(self, t_obs, flag_return_time=False):
        ind_min = np.argmin(np.abs(self.t_stamps - t_obs))
        if self.t_stamps[ind_min]>t_obs:
            ind_min -= 1

        if flag_return_time:
            if ind_min == -1:
                return 0.*self.blen[ind_min], -1
            else:
                return self.blen[ind_min], self.t_stamps[ind_min]
        else:
            if ind_min == -1:
                return 0.*self.blen[ind_min]
            else:
                return self.blen[ind_min]


def mean_nonzero(x):
    mask_nonzero = x > 0
    if np.sum(mask_nonzero) > 0:
        return np.mean(x[mask_nonzero])
    else:
        return 0.


def get_variable_dict(beam):
    var_dict = {}
    var_dict['FILLED_BUCKETS'] = 'LHC.BQM.B%d:FILLED_BUCKETS'%beam
    var_dict['BUNCH_LENGTH'] = 'LHC.BQM.B%d:BUNCH_LENGTHS'%beam

    return var_dict


def variable_list(beams = [1,2]):
    var_list = []
    for beam in beams:
                var_list += list(get_variable_dict(beam).values())

    return var_list