nclt2ros/extractor/read_ground_truth_covariance.py
def read_gt_cov_csv(self, all_in_one=None):
        """reads the data in the ground_truth_covariance csv file

        :param   all_in_one: Boolean
        :return: ndarray: containing all data, if all_in_one is True
                 else
                 each column seperated as utimes and covariance matrice ...
        """

        if self.ground_truth_covariance_flag:

            gt_cov = np.loadtxt(self.ground_truth_covariance_dir + '/cov_%s.csv' % self.date, delimiter=",")

            if all_in_one is True:

                return gt_cov

            else:
                utimes = gt_cov[:, 0]
                xx = gt_cov[:, 1];  xy = gt_cov[:, 2];  xz = gt_cov[:, 3];  xr = gt_cov[:, 4];  xp = gt_cov[:, 5]; xh = gt_cov[:, 6]
                yy = gt_cov[:, 7];  yz = gt_cov[:, 8];  yr = gt_cov[:, 9];  yp = gt_cov[:, 10]; yh = gt_cov[:, 11]
                zz = gt_cov[:, 12]; zr = gt_cov[:, 13]; zp = gt_cov[:, 14]; zh = gt_cov[:, 15]
                rr = gt_cov[:, 16]; rp = gt_cov[:, 17]; rh = gt_cov[:, 18]
                pp = gt_cov[:, 19]; ph = gt_cov[:, 20]
                hh = gt_cov[:, 21]

                return utimes, xx, xy, xz, xr, xp, xh, yy, yz, yr, yp, yh, zz, zr, zp, zh, rr, rp, rh, pp, ph, hh

        else:
            raise ValueError('no ground_truth_covariance directory available')
reads the data in the ground_truth_covariance csv file

        :param   all_in_one: Boolean
        :return: ndarray: containing all data, if all_in_one is True
                 else
                 each column seperated as utimes and covariance matrice ...
        
reads the data in the ground_truth_covariance csv file
reads the data in the ground_truth_covariance csv file
def read_gt_cov_csv(self, all_in_one=None):
        if self.ground_truth_covariance_flag:
            gt_cov = np.loadtxt(self.ground_truth_covariance_dir + '/cov_%s.csv' % self.date, delimiter=",")
            if all_in_one is True:
                return gt_cov
            else:
                utimes = gt_cov[:, 0]
                xx = gt_cov[:, 1];  xy = gt_cov[:, 2];  xz = gt_cov[:, 3];  xr = gt_cov[:, 4];  xp = gt_cov[:, 5]; xh = gt_cov[:, 6]
                yy = gt_cov[:, 7];  yz = gt_cov[:, 8];  yr = gt_cov[:, 9];  yp = gt_cov[:, 10]; yh = gt_cov[:, 11]
                zz = gt_cov[:, 12]; zr = gt_cov[:, 13]; zp = gt_cov[:, 14]; zh = gt_cov[:, 15]
                rr = gt_cov[:, 16]; rp = gt_cov[:, 17]; rh = gt_cov[:, 18]
                pp = gt_cov[:, 19]; ph = gt_cov[:, 20]
                hh = gt_cov[:, 21]
                return utimes, xx, xy, xz, xr, xp, xh, yy, yz, yr, yp, yh, zz, zr, zp, zh, rr, rp, rh, pp, ph, hh
        else:
            raise ValueError('no ground_truth_covariance directory available')
def read_gt_cov_csv ( self , all_in_one = None ) : if self . ground_truth_covariance_flag : gt_cov = np . loadtxt ( self . ground_truth_covariance_dir + '/cov_%s.csv' % self . date , delimiter = "," ) if all_in_one is True : return gt_cov else : utimes = gt_cov [ : , 0 ] xx = gt_cov [ : , 1 ] ; xy = gt_cov [ : , 2 ] ; xz = gt_cov [ : , 3 ] ; xr = gt_cov [ : , 4 ] ; xp = gt_cov [ : , 5 ] ; xh = gt_cov [ : , 6 ] yy = gt_cov [ : , 7 ] ; yz = gt_cov [ : , 8 ] ; yr = gt_cov [ : , 9 ] ; yp = gt_cov [ : , 10 ] ; yh = gt_cov [ : , 11 ] zz = gt_cov [ : , 12 ] ; zr = gt_cov [ : , 13 ] ; zp = gt_cov [ : , 14 ] ; zh = gt_cov [ : , 15 ] rr = gt_cov [ : , 16 ] ; rp = gt_cov [ : , 17 ] ; rh = gt_cov [ : , 18 ] pp = gt_cov [ : , 19 ] ; ph = gt_cov [ : , 20 ] hh = gt_cov [ : , 21 ] return utimes , xx , xy , xz , xr , xp , xh , yy , yz , yr , yp , yh , zz , zr , zp , zh , rr , rp , rh , pp , ph , hh else : raise ValueError ( 'no ground_truth_covariance directory available' )
reads the data in the ground_truth_covariance csv file
reads the data in the ground_truth_covariance csv file
"""reads the data in the ground_truth_covariance csv file

        :param   all_in_one: Boolean
        :return: ndarray: containing all data, if all_in_one is True
                 else
                 each column seperated as utimes and covariance matrice ...
        """
containing all data, if all_in_one is True
else
each column seperated as utimes and covariance matrice
containing all data if all_in_one is True else each column seperated as utimes and covariance matrice
self
all_in_one
None