Classes |
| class | process_dir_estimate.Scan |
Namespaces |
| namespace | process_dir_estimate |
Functions |
| def | process_dir_estimate.desired_ant |
| def | process_dir_estimate.get_mean |
Variables |
| tuple | process_dir_estimate.bins = (XBINS,YBINS) |
| tuple | process_dir_estimate.bins_ind_x = np.sum( xy[0][:,np.newaxis] > xedges[:-1], axis = 1 ) |
| tuple | process_dir_estimate.bins_ind_y = np.sum( xy[1][:,np.newaxis] > yedges[:-1], axis = 1 ) |
| tuple | process_dir_estimate.d_new = pkl.load( f ) |
| list | process_dir_estimate.dist = npstats[2] |
| tuple | process_dir_estimate.f = open( yaml_fname ) |
| tuple | process_dir_estimate.fnames = reduce( lambda x,y: x+y, [ glob.glob(i) for i in yaml_config['glob_files'] ], [] ) |
| string | process_dir_estimate.help = 'yaml file that describes this run.' |
| list | process_dir_estimate.herr = npstats[5] |
| tuple | process_dir_estimate.ind_d = np.sum( dist[:,np.newaxis] > bins_d[:-1], axis=1) |
| tuple | process_dir_estimate.ind_r = np.sum( rssi[:,np.newaxis] > bins_r[:-1], axis=1) |
| tuple | process_dir_estimate.magherr = np.abs( herr ) |
| tuple | process_dir_estimate.magherr_md = np.array([ np.mean( magherr[ np.where( ind_d == i )[0] ]) for i in xrange(len(h_d)) ]) |
| tuple | process_dir_estimate.magherr_mr = np.array([ np.mean( magherr[ np.where( ind_r == i )[0] ]) for i in xrange(len(h_r)) ]) |
| tuple | process_dir_estimate.magherr_sd = np.array([ np.std( magherr[ np.where( ind_d == i )[0] ]) for i in xrange(len(h_d)) ]) |
| tuple | process_dir_estimate.magherr_sr = np.array([ np.std( magherr[ np.where( ind_r == i )[0] ]) for i in xrange(len(h_r)) ]) |
| tuple | process_dir_estimate.magherr_Z = np.ma.array( np.copy(H.T), mask=(H.T < 1) ) |
| tuple | process_dir_estimate.magserr = np.abs( serr ) |
| tuple | process_dir_estimate.magserr_md = np.array([ np.mean( magserr[ np.where( ind_d == i )[0] ]) for i in xrange(len(h_d)) ]) |
| tuple | process_dir_estimate.magserr_mr = np.array([ np.mean( magserr[ np.where( ind_r == i )[0] ]) for i in xrange(len(h_r)) ]) |
| tuple | process_dir_estimate.magserr_sd = np.array([ np.std( magserr[ np.where( ind_d == i )[0] ]) for i in xrange(len(h_d)) ]) |
| tuple | process_dir_estimate.magserr_sr = np.array([ np.std( magserr[ np.where( ind_r == i )[0] ]) for i in xrange(len(h_r)) ]) |
| tuple | process_dir_estimate.magserr_Z = np.ma.array( np.copy(H.T), mask=(H.T < 1) ) |
| tuple | process_dir_estimate.npstats = np.array( stats ) |
| tuple | process_dir_estimate.num_d = np.array([ len( np.where( ind_d == i )[0] ) for i in xrange(len(h_d)) ]) |
| tuple | process_dir_estimate.num_r = np.array([ len( np.where( ind_r == i )[0] ) for i in xrange(len(h_r)) ]) |
| tuple | process_dir_estimate.p = optparse.OptionParser() |
| | process_dir_estimate.PLOT = False |
| list | process_dir_estimate.range |
| list | process_dir_estimate.rssi = npstats[3] |
| list | process_dir_estimate.scans = [] |
| list | process_dir_estimate.serr = npstats[7] |
| int | process_dir_estimate.skipped = 0 |
| tuple | process_dir_estimate.sr = string.rfind( f, '/' ) |
| list | process_dir_estimate.stats = [] |
| int | process_dir_estimate.XBINS = 9 |
| float | process_dir_estimate.XMAX = 11.0 |
| float | process_dir_estimate.XMIN = 0.0 |
| tuple | process_dir_estimate.xy |
| tuple | process_dir_estimate.yaml_config = yaml.load( f ) |
| | process_dir_estimate.yaml_fname = opt.yaml |
| int | process_dir_estimate.YBINS = 5 |
| float | process_dir_estimate.YMAX = 7.0 |
| float | process_dir_estimate.YMIN = 0.0 |