def plot_err_histogram(rel_list, abs_list, title):
# plot relative error histogram.
max_err = 1.0
bin_width = 0.05 # relative err.
bins = np.arange(0. - bin_width / 2. - max_err, max_err + 2 * bin_width,
bin_width)
hist, bin_edges = np.histogram(np.array(rel_list), bins)
mpu.figure()
mpu.plot_histogram(bin_edges[:-1] + bin_width / 2.,
hist,
width=bin_width * 0.8,
xlabel='Relative Error',
plot_title=title)
# plot relative error histogram.
max_err = 20
bin_width = 2 # relative err.
bins = np.arange(0. - bin_width / 2. - max_err, max_err + 2 * bin_width,
bin_width)
hist, bin_edges = np.histogram(np.array(abs_list), bins)
mpu.figure()
mpu.plot_histogram(bin_edges[:-1] + bin_width / 2.,
hist,
width=bin_width * 0.8,
xlabel='Absolute Error',
plot_title=title)
def pull_up_resistor_value(rmax, rmin):
n_r = 200
n_pullups = 4
adc_counts = 1024
pullup_best = math.sqrt(rmax * rmin)
pullup_max = 2 * pullup_best
pullup_min = 0.5 * pullup_best
pullup_max = 500
pullup_min = 50
pullup_step = (pullup_max - pullup_min) / n_pullups
pullup_arr = np.arange(pullup_min, pullup_max, pullup_step)
r_step = (rmax - rmin) / n_r
r_var = np.arange(rmin, rmax, r_step)
v_cc = 5.
v_diff_list = []
mpu.figure()
for r_static in pullup_arr:
v = r_var / (r_static + r_var) * v_cc
pp.plot(r_var, v, mpu.random_color(), label='R1: %.1f' % r_static)
v_diff_list.append(v[-1] - v[0])
pp.xlabel('Variable Resistance')
pp.ylabel('Voltage')
mpu.legend()
mpu.figure()
pp.plot(pullup_arr, v_diff_list)
pp.axvline(pullup_best, c='k', label='Analytically computed optimal value')
pp.xlabel('Pull up resistance (ohms)')
pp.ylabel('Difference in Voltage')
mpu.legend()
l1 = (r_static + rmin) / (r_static + rmax) * adc_counts
l2 = rmin / rmax * (r_static + rmax) / (r_static + rmin) * adc_counts
print 'ADC lost if piezo to GND:', l2
print 'ADC lost if piezo to Vcc:', l1
pp.show()
def pull_up_resistor_value(rmax, rmin):
n_r = 200
n_pullups = 4
adc_counts = 1024
pullup_best = math.sqrt(rmax*rmin)
pullup_max = 2 * pullup_best
pullup_min = 0.5 * pullup_best
pullup_max = 500
pullup_min = 50
pullup_step = (pullup_max - pullup_min) / n_pullups
pullup_arr = np.arange(pullup_min, pullup_max, pullup_step)
r_step = (rmax - rmin) / n_r
r_var = np.arange(rmin, rmax, r_step)
v_cc = 5.
v_diff_list = []
mpu.figure()
for r_static in pullup_arr:
v = r_var / (r_static + r_var) * v_cc
pp.plot(r_var, v, mpu.random_color(), label='R1: %.1f'%r_static)
v_diff_list.append(v[-1] - v[0])
pp.xlabel('Variable Resistance')
pp.ylabel('Voltage')
mpu.legend()
mpu.figure()
pp.plot(pullup_arr, v_diff_list)
pp.axvline(pullup_best, c='k', label='Analytically computed optimal value')
pp.xlabel('Pull up resistance (ohms)')
pp.ylabel('Difference in Voltage')
mpu.legend()
l1 = (r_static + rmin) / (r_static + rmax) * adc_counts
l2 = rmin / rmax * (r_static + rmax) / (r_static + rmin) * adc_counts
print 'ADC lost if piezo to GND:', l2
print 'ADC lost if piezo to Vcc:', l1
pp.show()
def plot_err_histogram(rel_list, abs_list, title):
# plot relative error histogram.
max_err = 1.0
bin_width = 0.05 # relative err.
bins = np.arange(0.-bin_width/2.-max_err, max_err+2*bin_width, bin_width)
hist, bin_edges = np.histogram(np.array(rel_list), bins)
mpu.figure()
mpu.plot_histogram(bin_edges[:-1]+bin_width/2., hist,
width=bin_width*0.8, xlabel='Relative Error',
plot_title=title)
# plot relative error histogram.
max_err = 20
bin_width = 2 # relative err.
bins = np.arange(0.-bin_width/2.-max_err, max_err+2*bin_width, bin_width)
hist, bin_edges = np.histogram(np.array(abs_list), bins)
mpu.figure()
mpu.plot_histogram(bin_edges[:-1]+bin_width/2., hist,
width=bin_width*0.8, xlabel='Absolute Error',
plot_title=title)
def which_variable():
r_static = 2e3
r_min = 800.
r_max = 5e3
r_step = 10.
r_var = np.arange(r_min, r_max, r_step)
v_cc = 5.
# case I - variable resistor is R2
v1 = r_var / (r_static + r_var) * v_cc
# case II - variable resistor is R1
v2 = r_static / (r_static + r_var) * v_cc
mpu.figure()
pp.plot(r_var, v1, 'b', label='variable R2')
pp.plot(r_var, v2, 'g', label='variable R1')
pp.xlabel('Variable Resistance')
pp.ylabel('Voltage')
mpu.legend()
pp.show()
def which_variable():
r_static = 2e3
r_min = 800.
r_max = 5e3
r_step = 10.
r_var = np.arange(r_min, r_max, r_step)
v_cc = 5.
# case I - variable resistor is R2
v1 = r_var / (r_static + r_var) * v_cc
# case II - variable resistor is R1
v2 = r_static / (r_static + r_var) * v_cc
mpu.figure()
pp.plot(r_var, v1, 'b', label='variable R2')
pp.plot(r_var, v2, 'g', label='variable R1')
pp.xlabel('Variable Resistance')
pp.ylabel('Voltage')
mpu.legend()
pp.show()
rospy.sleep(0.02)
adc_l.append(adc_value)
ft_l.append(ft_client.read()[2,0])
d = {}
d['adc'] = adc_l
d['ft'] = ft_l
d['adc_bias'] = adc_bias
d['pull_up'] = opt.pull_up
d['contact_area'] = opt.ca
ut.save_pickle(d, 'taxel_ft_calib_data.pkl')
if opt.vd:
import matplotlib.pyplot as pp
import hrl_lib.matplotlib_util as mpu
d = ut.load_pickle('taxel_ft_calib_data.pkl')
ft_l = d['ft']
adc_l = (d['adc_bias'] - np.array(d['adc'])).tolist()
print np.max(ft_l)
mpu.figure()
pp.scatter(adc_l, ft_l, marker='x')
pp.xlabel('ADC bias - ADC')
pp.ylabel('FT_z')
pp.show()
opt, args = p.parse_args()
if not opt.direc:
print 'Please specify a directory'
sys.exit()
nm_l = ut.get_bash_command_output('find ' + opt.direc + ' -name "*.pkl"')
color_list = ['r', 'g', 'b']
for nm in nm_l:
c = mpu.random_color()
color_list.append(c)
if True:
mpu.figure()
for nm, c in zip(nm_l, color_list):
d = ut.load_pickle(nm)
#d['adc_bias']=1023 # For stretching experiments with Sarvagya
force_vs_adc(d, c)
#force_vs_adc_2(d, c)
pp.xlim((0, 1000))
pp.ylim((-10, 80))
print 'ADC bias: %d' % d['adc_bias']
# mpu.figure()
# for nm, c in zip(nm_l, color_list):
# d = ut.load_pickle(nm)
# plot_uncertainty_in_force(d, c)
opt, args = p.parse_args()
if not opt.direc:
print 'Please specify a directory'
sys.exit()
nm_l = ut.get_bash_command_output('find '+opt.direc+' -name "*.pkl"')
color_list = ['r', 'g', 'b']
for nm in nm_l:
c = mpu.random_color()
color_list.append(c)
if True:
mpu.figure()
for nm, c in zip(nm_l, color_list):
d = ut.load_pickle(nm)
#d['adc_bias']=1023 # For stretching experiments with Sarvagya
force_vs_adc(d, c)
#force_vs_adc_2(d, c)
pp.xlim((0,1000))
pp.ylim((-10,80))
print 'ADC bias: %d'%d['adc_bias']
# mpu.figure()
# for nm, c in zip(nm_l, color_list):
# d = ut.load_pickle(nm)
# plot_uncertainty_in_force(d, c)
adc_l = []
while rospy.get_time() < t1:
rospy.sleep(0.02)
adc_l.append(adc_value)
ft_l.append(ft_client.read()[2, 0])
d = {}
d['adc'] = adc_l
d['ft'] = ft_l
d['adc_bias'] = adc_bias
d['pull_up'] = opt.pull_up
d['contact_area'] = opt.ca
ut.save_pickle(d, 'taxel_ft_calib_data.pkl')
if opt.vd:
import matplotlib.pyplot as pp
import hrl_lib.matplotlib_util as mpu
d = ut.load_pickle('taxel_ft_calib_data.pkl')
ft_l = d['ft']
adc_l = (d['adc_bias'] - np.array(d['adc'])).tolist()
print np.max(ft_l)
mpu.figure()
pp.scatter(adc_l, ft_l, marker='x')
pp.xlabel('ADC bias - ADC')
pp.ylabel('FT_z')
pp.show()