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twogaussian.py
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twogaussian.py
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from __future__ import print_function
import sys
from functions import *
def twogaussian_fit(num_param, str_param, project_name='twogauss'):
try:
project_name = str_param['project_name']
except KeyError:
pass
# will look like onegauss_2016-8-31-12-58-48
prefix = project_name + '_' + propertime()
os.mkdir(prefix)
os.chdir(prefix)
log = open('%s.log' % prefix, mode='w')
screen = sys.stdout
graph_print = boolean_translate(str_param, 'graph_print')
table_print = boolean_translate(str_param, 'table_print')
dat_print = boolean_translate(str_param, 'dat_print')
normalize = boolean_translate(str_param, 'normalize')
table = str_param['table']
try:
xserver = str_param['xserver']
except KeyError:
xserver = True
table = get_grd(table, xserver=xserver)
bragg = num_param['bragg'].value
template = str_param['template']
real_data = str_param['data']
if template == 'xy':
theta, yelid = get_dat(real_data, bragg=bragg, normalize=normalize, template=template)
yelid_errors = None
elif template == 'xyy' or template == 'xyyerror':
theta, yelid, yelid_errors = get_dat(real_data, bragg=bragg, normalize=normalize, template=template)
elif template == 'xxerroryyerror':
theta, theta_error, yelid, yelid_errors = get_dat(real_data, bragg=bragg, normalize=normalize,
template=template)
logprint(log, screen, """
Data succesfully read from:
prefix = %s
table = %s
data = %s
bragg = %f
normalize = %s
graph_print = %s
table_print = %s
""" % (prefix, str_param['table'], str_param['data'], bragg, normalize, graph_print, table_print))
# now we have yelid with correct angles (not relative to bragg),
# errors and standing wave table as np.array() with correct order
logprint(log, screen, "\n")
logprint(log, screen, """
************************
* MINIMIZATION STARTED *
************************""")
logprint(log, screen, """
Initial parameters are:\n""")
sys.stdout = log
num_param.pretty_print()
print('\n')
for key in str_param.keys():
print(key, str_param[key], sep=':\t')
sys.stdout = screen
num_param.pretty_print()
print('\n')
for key in str_param.keys():
print(key, str_param[key], sep=':\t')
sys.stdout = screen
print("Chisquared\trfactor\tx01\tx02\tsigma1\tsigma2\tamp1")
start = time.time()
out = minimize(residual_twogaussians, num_param, args=(table, theta, yelid, yelid_errors))
stop = time.time()
logprint(log, screen, """
MINIMIZATION FINISHED
Time consumed is: %.2f s
""" % (stop - start))
logprint(log, screen, """
Fitted parameters are:
""")
sys.stdout = log
out.params.pretty_print()
sys.stdout = screen
out.params.pretty_print()
model, ibar = intensity_twogaussians(table,
theta,
out.params['amp1'],
out.params['ratio'],
out.params['sigma1'],
out.params['sigma2'],
out.params['x01'],
out.params['x02'],
out.params['zmax'],
out.params['angle_slope'],
out.params['zmin'],
get_ibar=True
)
if yelid_errors is not None: # if we have errors
chisquared = np.sum((yelid - model) ** 2 / yelid_errors ** 2) / \
(len(theta) - 7) # that must be chi-squared criteria with errors
else:
chisquared = np.sum((yelid - model) ** 2) / \
(len(theta) - 7) # that must be chi-squared criteria with similar errors
rfactor = sum(abs(model - yelid) / sum(yelid))
logprint(log, screen, """
Rfactor:\t\t %f
Chisquared:\t\t %f
""" % (rfactor, chisquared))
amp1 = out.params['amp1']
ratio = out.params['ratio']
sigma1 = out.params['sigma1']
sigma2 = out.params['sigma2']
x01 = out.params['x01']
x02 = out.params['x02']
zmax = out.params['zmax']
angle_slope = out.params['angle_slope']
zmin = out.params['zmin']
if dat_print:
fout = open('data_%s.dat' % prefix, 'w')
for i in range(len(theta)):
print(theta[i], model[i], file=fout, sep='\t')
fout.close()
if graph_print:
plt.plot(theta, model, 'b')
plt.plot(theta, yelid, 'ko')
plt.title('x01=%.1f, x02=%.1f, sigma1=%.1f, sigma2=%.1f, ratio=%.1f' % (x01, x02,
sigma1, sigma2,
ratio))
plt.savefig('yelid_%s.png' % prefix)
plt.clf()
if table_print:
gauss_first = lambda coord: (amp1) * \
np.exp(-(coord - x01) ** 2 / 2.0 / sigma1 ** 2)
gauss_second = lambda coord: (amp1 * sigma1 / sigma2 / ratio) * \
np.exp(-(coord - x02) ** 2 / 2.0 / sigma2 ** 2)
gauss = lambda coord: gauss_first(coord) + gauss_second(coord)
plt.subplot(3, 1, 1)
plt.plot(theta, model, 'b')
plt.plot(theta, yelid, 'ko')
plt.title('x01=%.1f, x02=%.1f, sigma1=%.1f, sigma2=%.1f, ratio=%.1f' % (x01, x02,
sigma1, sigma2,
ratio))
plt.subplot(3, 1, 2)
plt.imshow(ibar,
aspect='auto',
extent=(zmin, zmax, np.min(theta), np.max(theta)))
plt.colorbar()
plt.subplot(3, 1, 3)
x = np.linspace(zmin, zmax, int(abs(zmax - zmin) * 2))
y = gauss(x)
plt.plot(x, y)
plt.savefig('tables_%s.png' % prefix)
print(os.getcwd())
log.close()
return out.params, theta, model, yelid, chisquared, rfactor
parameters = sys.argv[1]
num_param, str_param = get_initials(parameters)
params, theta, model, yelid, chisquared, rfactor = twogaussian_fit(num_param, str_param)
plt.show()