def calibrate_sensor(sensor, measurements, verbose):
parameters = PARAMETERS[sensor]
if verbose:
print "found %d records" % len(measurements)
flt_meas, flt_idx = utils.filter_meas(measurements,
parameters.noise_window,
parameters.noise_threshold)
if verbose:
print "remaining %d after low pass" % len(flt_meas)
p0 = utils.get_min_max_guess(flt_meas, parameters.sensor_ref)
cp0, np0 = utils.scale_measurements(flt_meas, p0)
print "initial guess : avg %f std %f" % (np0.mean(), np0.std())
def err_func(p, meas, y):
cp, np = utils.scale_measurements(meas, p)
err = y * scipy.ones(len(meas)) - np
return err
p1, success = scipy.optimize.leastsq(err_func,
p0[:],
args=(flt_meas,
parameters.sensor_ref))
cp1, np1 = utils.scale_measurements(flt_meas, p1)
print "optimized guess : avg %f std %f" % (np1.mean(), np1.std())
utils.print_xml(p1, sensor, parameters.sensor_res)
print ""
utils.plot_results(measurements, flt_idx, flt_meas, cp0, np0, cp1, np1,
parameters.sensor_ref)
def calibrate_sensor(sensor, measurements, verbose):
parameters = PARAMETERS[sensor]
if verbose:
print "found %d records" % len(measurements)
flt_meas, flt_idx = utils.filter_meas(measurements, parameters.noise_window, parameters.noise_threshold)
if verbose:
print "remaining %d after low pass" % len(flt_meas)
p0 = utils.get_min_max_guess(flt_meas, parameters.sensor_ref)
cp0, np0 = utils.scale_measurements(flt_meas, p0)
print "initial guess : avg %f std %f" % (np0.mean(), np0.std())
def err_func(p,meas,y):
cp, np = utils.scale_measurements(meas, p)
err = y*scipy.ones(len(meas)) - np
return err
p1, success = scipy.optimize.leastsq(err_func, p0[:], args=(flt_meas, parameters.sensor_ref))
cp1, np1 = utils.scale_measurements(flt_meas, p1)
print "optimized guess : avg %f std %f" % (np1.mean(), np1.std())
utils.print_xml(p1, sensor, parameters.sensor_res)
print ""
utils.plot_results(measurements, flt_idx, flt_meas, cp0, np0, cp1, np1, parameters.sensor_ref)
def err_func(p, meas, y):
cp, np = utils.scale_measurements(meas, p)
err = y * scipy.ones(len(meas)) - np
return err
def err_func(p,meas,y):
cp, np = utils.scale_measurements(meas, p)
err = y*scipy.ones(len(meas)) - np
return err