import bge
from ast import literal_eval
from bge.logic import expandPath, globalDict
from scripts.data_loader import load_data
load_data()
def widget(cont):
""" """
# Basic
own = cont.owner
# Sensors
s_mouse_over = [s for s in cont.sensors if 'mouse_over' in s.name][0]
s_lmb = [s for s in cont.sensors if 'lmb' in s.name][0]
# Properties
p_colors = globalDict['database']['colors_gui']
if 'widget_type' in own:
if s_mouse_over.positive:
if not s_lmb.positive:
own.color = p_colors[own['widget_type'] + '_hover']
if s_lmb.positive:
def do_analysis(config, analysis_index):
config_anal = config.analyses[analysis_index]
all_event_count = 0
for max_spill in [config.number_of_spills]:
if max_spill == 0:
continue
data_loader = scripts.data_loader.DataLoader(config, analysis_index)
data_loader.load_data(0, max_spill)
sigma_x = 0.7
sigma_xp = 5e-3
print "Applying recon error with est x error", sigma_x, "est x' error", sigma_xp
recon_error = [[0 for i in range(5)] for j in range(5)]
recon_error[1][1] = sigma_x**2
recon_error[2][2] = sigma_xp**2
recon_error[3][3] = sigma_x**2
recon_error[4][4] = sigma_xp**2
tm_p_list = []
print "Clearing old data"
try:
shutil.rmtree(config_anal["plot_dir"])
except OSError:
pass
os.makedirs(config_anal["plot_dir"])
print "Using p bins", config_anal["p_bins"]
for p_low, p_high in config_anal["p_bins"]:
continue
calculator = TMCalculator(config, analysis_index, p_low, p_high)
calculator.append_data(data_loader.events)
tm, tm_error = calculator.calculate_fit_tm(recon_error)
calculator.scraping_cut(
config_anal["sigma_cut"], 150., config_anal["chi2_cut"], 5,
recon_error) #n_sigma, max_radius, amp_cut, n_steps, use_recon
for cut in config.lattice_scraping_cuts:
p_tot = (p_high + p_low) / 2.
calculator.lattice_scraping_cut(cut, p_tot)
tm, tm_error = calculator.calculate_fit_tm(recon_error)
if tm == None or tm_error == None:
continue
calculator.print_tm()
tm_p_list.append({
"transfer_matrix": tm.get_coefficients_as_matrix(),
"error_matrix": tm_error,
"p_high": p_high,
"p_low": p_low,
"p": (p_high + p_low) / 2.,
})
xboa.common.clear_root()
plot_predicate = TOF12Predicate(config_anal["tof12_cut_low"],
config_anal["tof12_cut_high"])
plotter = DataPlotter(config, analysis_index, data_loader.events,
plot_predicate.test)
plotter.print_cuts_summary()
cov_us, cov_ds = plotter.print_covariance_matrix()
sys.stdout.flush()
plotter.plot_tof12()
plotter.plot_var_2d(0, "upstream", 1, "upstream")
plotter.plot_var_2d(2, "upstream", 3, "upstream")
plotter.plot_var_2d(0, "downstream", 1, "downstream")
plotter.plot_var_2d(2, "downstream", 3, "downstream")
plotter.plot_var_2d(0, "downstream", 0, "residual")
plotter.plot_var_2d(1, "downstream", 1, "residual")
plotter.plot_var_2d(2, "downstream", 2, "residual")
plotter.plot_var_2d(3, "downstream", 3, "residual")
plotter.plot_var_1d(0, "residual")
plotter.plot_var_1d(1, "residual")
plotter.plot_var_1d(2, "residual")
plotter.plot_var_1d(3, "residual")
plotter.plot_var_1d(0, "upstream")
plotter.plot_var_1d(1, "upstream")
plotter.plot_var_1d(2, "upstream")
plotter.plot_var_1d(3, "upstream")
plotter.plot_var_1d(0, "downstream")
plotter.plot_var_1d(1, "downstream")
plotter.plot_var_1d(2, "downstream")
plotter.plot_var_1d(3, "downstream")
plotter.plot_r_ds()
#plotter.plot_chi2()
#plotter.plot_path_length()
#plotter.plot_miss_distance()
plotter.plot_p_tot()
plotter.plot_p_tot_res()
plotter.plot_p_tot_vs_tof()
plotter.plot_p_tot_us_vs_ds()
#for col in range(5):
# for row in range(4):
# plotter.plot_tm_element(row, col)
accepted, accepted_data = plotter.get_cuts_summary()
json_doc = {
"covariance_matrix_us": [cov_us.tolist()], # should be done in p bins
"covariance_matrix_ds": [cov_ds.tolist()], # should be done in p bins
"tm_entries": tm_p_list,
"accepted": accepted, # accepted by all cuts
"accepted_data": accepted_data, # accepted after data cut
"cuts_active": config.cuts_active,
}
fname = config.data_dir + "/tm_" + str(analysis_index) + ".json"
fout = open(fname, "w")
print >> fout, json.dumps(json_doc, indent=2)