/
reference_images.py
executable file
·572 lines (488 loc) · 29.2 KB
/
reference_images.py
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#!/usr/bin/env python
# -*- coding: utf-8 -*-
'''
Object allows to define and carry out a collection of series of wedges of diffraction images of arbitrary slicing parameter and of arbitrary size at arbitrary reference angles.
'''
import os
import time
import pickle
import logging
import traceback
import numpy as np
import h5py
import gevent
import re
import shutil
import subprocess
try:
import xmlrpclib
except ImportError:
xmlrpclib = None
from omega_scan import omega_scan
class reference_images(omega_scan):
actuator_names = ['Omega']
specific_parameter_fields = [{'name': 'scan_start_angles', 'type': '', 'description': ''},
{'name': 'dose_rate', 'type': '', 'description': ''},
{'name': 'dose_limit', 'type': '', 'description': ''},
{'name': 'vertical_scan_length', 'type': '', 'description': ''},
{'name': 'vertical_step_size', 'type': '', 'description': ''},
{'name': 'inverse_direction', 'type': '', 'description': ''},
{'name': 'vertical_motor_speed', 'type': '', 'description': ''},
{'name': 'exposure_time_per_frame', 'type': 'float', 'description': 'frame time in s'}]
def __init__(self,
name_pattern='ref-test_$id',
directory='/tmp',
scan_range=1.,
scan_exposure_time=1,
scan_start_angles='[0, 90, 180, 270]',
angle_per_frame=0.1,
image_nr_start=1,
vertical_scan_length=0,
vertical_step_size=0.025,
inverse_direction=True,
dose_rate=0.25e6, #Grays per second
dose_limit=20e6, #Grays
i2s_at_highest_resolution=1.,
frames_per_second=None,
position=None,
kappa=None,
phi=None,
photon_energy=None,
resolution=None,
detector_distance=None,
detector_vertical=None,
detector_horizontal=None,
transmission=None,
flux=None,
snapshot=None,
diagnostic=None,
analysis=None,
simulation=None,
parent=None,
treatment_directory='/dev/shm',
xmlrpc_server='http://localhost:60006',
mxcube_parent_id=None,
mxcube_gparent_id=None):
logging.debug('reference_images __init__ len(reference_images.specific_parameter_fields) %d' % len(reference_images.specific_parameter_fields))
if hasattr(self, 'parameter_fields'):
logging.debug('reference_images __init__ len(self.parameter_fields) %d' % len(self.parameter_fields))
self.parameter_fields += reference_images.specific_parameter_fields
else:
self.parameter_fields = reference_images.specific_parameter_fields[:]
logging.debug('reference_images __init__ len(self.parameters_fields) %d' % len(self.parameter_fields))
if isinstance(scan_start_angles, str):
scan_start_angles = eval(scan_start_angles)
self.scan_start_angles = scan_start_angles
self.scan_range = float(scan_range)
self.vertical_scan_length = float(vertical_scan_length)
self.vertical_step_size = float(vertical_step_size)
if self.vertical_scan_length != 0 and self.vertical_scan_length != None:
nimages = int(self.vertical_scan_length/self.vertical_step_size)
angle_per_frame = self.scan_range/nimages
ntrigger = len(self.scan_start_angles)
nimages_per_file = int(self.scan_range/angle_per_frame)
omega_scan.__init__(self,
name_pattern,
directory,
scan_range=scan_range,
scan_start_angle=self.scan_start_angles[0],
scan_exposure_time=scan_exposure_time,
angle_per_frame=angle_per_frame,
image_nr_start=image_nr_start,
frames_per_second=frames_per_second,
position=position,
kappa=kappa,
phi=phi,
photon_energy=photon_energy,
resolution=resolution,
detector_distance=detector_distance,
detector_vertical=detector_vertical,
detector_horizontal=detector_horizontal,
transmission=transmission,
flux=flux,
snapshot=snapshot,
ntrigger=ntrigger,
nimages_per_file=nimages_per_file,
diagnostic=diagnostic,
analysis=analysis,
simulation=simulation,
parent=parent,
mxcube_parent_id=mxcube_parent_id,
mxcube_gparent_id=mxcube_gparent_id)
self.ntrigger = ntrigger
self.total_expected_exposure_time = scan_exposure_time * ntrigger
self.total_expected_wedges = ntrigger
self.inverse_direction = inverse_direction
self.dose_rate = dose_rate
self.dose_limit = dose_limit
self.i2s_at_highest_resolution = i2s_at_highest_resolution
self.saved_parameters = self.load_parameters_from_file()
self.treatment_directory = treatment_directory
self.format_dictionary = {'directory': self.directory, 'name_pattern': self.name_pattern, 'treatment_directory': self.treatment_directory}
self.description = 'Reference images, Proxima 2A, SOLEIL, %s' % time.ctime(self.timestamp)
if xmlrpclib != None:
self.server = xmlrpclib.ServerProxy(xmlrpc_server)
else:
self.server = False
def get_nimages_per_file(self):
if self.saved_parameters is not None:
return self.saved_parameters['nimages_per_file']
return int(self.scan_range/self.angle_per_frame)
def get_exposure_time_per_frame(self):
if self.saved_parameters is not None:
if 'exposure_time_per_frame' in self.saved_parameters:
return self.saved_parameters['exposure_time_per_frame']
return self.scan_exposure_time/self.get_nimages()
def get_nimages(self, epsilon=1e-3):
if self.saved_parameters is not None:
return self.saved_parameters['nimages']
nimages = int(self.scan_range/self.angle_per_frame)
if abs(nimages*self.angle_per_frame - self.scan_range) > epsilon:
nimages += 1
return nimages
def get_ntrigger(self):
if self.saved_parameters is not None:
return self.saved_parameters['ntrigger']
return self.ntrigger
def get_vertical_motor_speed(self):
if self.saved_parameters is not None:
return self.saved_parameters['vertical_motor_speed']
return self.vertical_scan_length/self.scan_exposure_time
def get_angle_per_frame(self):
if self.saved_parameters is not None:
return self.saved_parameters['angle_per_frame']
return self.angle_per_frame
def save_snapshots(self):
logging.info('save_snapshots')
snapshots = []
self.goniometer.insert_backlight()
for k, scan_start_angle in enumerate(self.scan_start_angles):
self.goniometer.set_orientation(scan_start_angle)
imagename, image, image_id = self.camera.save_image('%s_%.2f_rgb.png' % (self.get_template(), scan_start_angle), color=True)
snapshots.append(image)
self.rgbimage = snapshots
self.goniometer.extract_backlight()
def run(self, wait=True):
logging.info('expected files %s' % self.get_expected_files())
if self.snapshot == True:
self.save_snapshots()
self._start = time.time()
task_ids = []
self.md2_task_info = []
vertical_scan_length = self.get_vertical_scan_length()
for k, scan_start_angle in enumerate(self.scan_start_angles):
logging.info('scan_start_angle %s' % scan_start_angle)
if vertical_scan_length == 0:
task_id = self.goniometer.omega_scan(scan_start_angle, self.scan_range, self.scan_exposure_time, wait=wait)
else:
if self.inverse_direction == True:
vertical_scan_length = self.get_vertical_scan_length() * pow(-1, k)
task_id = self.goniometer.vertical_helical_scan(vertical_scan_length, position, scan_start_angle, self.scan_range, self.scan_exposure_time, wait=wait)
task_ids.append(task_id)
self.md2_task_info.append(self.goniometer.get_task_info(task_id))
def clean(self):
_start = time.time()
self.detector.disarm()
logging.info('detector disarm %.4f took' % (time.time() - _start))
self.goniometer.set_position(self.reference_position)
self.collect_parameters()
clean_jobs = []
clean_jobs.append(gevent.spawn(self.save_parameters))
clean_jobs.append(gevent.spawn(self.save_results))
clean_jobs.append(gevent.spawn(self.save_log))
if self.diagnostic == True:
clean_jobs.append(gevent.spawn(self.save_diagnostics))
clean_jobs.append(gevent.spawn(self.wait_for_expected_files))
gevent.joinall(clean_jobs)
logging.info('clean took %.4f seconds' % (time.time() - _start))
def get_scan_start_angles(self):
if os.path.isfile(self.get_parameters_filename()):
return self.get_pickled_file(self.get_parameters_filename())['scan_start_angles']
else:
return self.scan_start_angles
def analyze(self):
logging.info('reference_images analysis expected files %s' % self.get_expected_files())
command = 'reference_images.py'
sense_line = '%s -d %s -n %s -A --scan_start_angles "%s" &' % (command, self.directory, self.name_pattern, self.get_scan_start_angles())
logging.info('analysis line %s' % sense_line)
os.system(sense_line)
#subprocess.call(sense_line, shell=True)
def analyze_online(self):
logging.info('analyze')
try:
self.rectify_master()
except:
logging.info(traceback.format_exc())
#try:
#self.generate_summed_h5()
#except:
#pass
self.run_dozor()
#self.run_xds()
#self.run_best()
#strategy = self.parse_best()
#logging.info('best_strategy')
#logging.info(str(strategy))
return strategy
def rectify_master(self, timeout=15):
logging.info('rectify_master')
_start = time.time()
expected_files = self.get_expected_files()
logging.info('expected files:')
logging.info(str(expected_files))
while not self.expected_files_present() and time.time() - _start < timeout:
gevent.sleep(1)
if not self.expected_files_present():
logging.debug('expected files not present, exiting rectify_master, please check.')
return -1
else:
logging.info('rectify_master: all files appeared after %.2f seconds' % (time.time()-_start))
for f in expected_files:
shutil.copy2('%s/%s' % (self.directory, f), self.treatment_directory)
m = h5py.File('%s/%s_master.h5' % (self.treatment_directory, self.name_pattern), 'r+')
ntrigger = self.get_ntrigger()
nimages = self.get_nimages()
angle_per_frame = self.get_angle_per_frame()
logging.info('ntrigger %s' % str(ntrigger))
logging.info('nimages %s' % str(nimages))
logging.info('angle_per_frame %s' % str(angle_per_frame))
omega = []
omega_end = []
self.scan_start_angles = self.get_scan_start_angles()
absolute_start = self.scan_start_angles[0]
print('absolute_start', absolute_start)
print('range(len(m["/entry/data"].keys()))', range(len(m['/entry/data'].keys())))
print('self.scan_start_angles', self.scan_start_angles)
for k in range(len(list(m['/entry/data'].keys()))):
start = self.scan_start_angles[k]
end = start + nimages * angle_per_frame
print('in rectify_master start, end', start, end)
omega += list(np.arange(start, end, angle_per_frame)[:nimages])
omega_end += list(np.arange(start+angle_per_frame, end+angle_per_frame, angle_per_frame)[:nimages])
image_nr_low = int(1 + (start - absolute_start)/angle_per_frame)
image_nr_high = image_nr_low + nimages - 1
print('in rectify_master low, high', image_nr_low, image_nr_high)
try:
filename = os.path.basename(m['/entry/data/data_%06d' % (k+1,)].file.filename)
del m['/entry/data/data_%06d' % (k+1,)]
consecutive_wedge_number = int(image_nr_high*angle_per_frame)
m['/entry/data/data_%06d' % (consecutive_wedge_number,)] = h5py.ExternalLink(filename, '/entry/data/data')
m['/entry/data/data_%06d' % (consecutive_wedge_number,)].attrs['image_nr_low'] = image_nr_low
m['/entry/data/data_%06d' % (consecutive_wedge_number,)].attrs['image_nr_high'] = image_nr_high
except:
logging.info('links seem to be already updated')
logging.info('omega %s' % str(omega))
m['/entry/sample/goniometer/omega'].write_direct(np.array(omega))
m['/entry/sample/goniometer/omega_end'].write_direct(np.array(omega_end))
m.close()
for f in expected_files:
shutil.copy2('%s/%s' % (self.treatment_directory, f), self.directory)
logging.info('rectify_master took %.2f seconds' % (time.time()-_start))
def generate_summed_h5(self):
logging.info('generate_summed_h5')
_start = time.time()
if os.path.isfile('{directory}/{name_pattern}_sum10_master.h5'.format(**self.format_dictionary)):
logging.info('summed images already generated')
return
self.format_dictionary['nimages_per_file'] = self.get_nimages_per_file()
self.format_dictionary['treatment_directory'] = self.treatment_directory
generate_summed_h5_line = 'cd {directory}; /usr/local/experimental_methods/summer_devel.py -n {nimages_per_file} -m {name_pattern}_master.h5'.format(**self.format_dictionary)
if os.uname()[1] != 'proxima2a-5':
generate_summed_h5_line = 'ssh proxima2a-5 "%s"' % generate_summed_h5_line
logging.info('generate_summed_h5_line %s' % generate_summed_h5_line)
os.system(generate_summed_h5_line)
for f in ['data_000001.h5', 'master.h5']:
a = '%s/%s_%s_%s' % (self.treatment_directory, self.name_pattern, 'sum%d' % (self.get_nimages_per_file()), f)
logging.info('copying %s to %s ' % (a, self.directory))
shutil.copy2('%s/%s_%s_%s' % (self.treatment_directory, self.name_pattern, 'sum%d' % (self.get_nimages_per_file()), f), self.directory)
os.remove('%s/%s_%s_%s' % (self.treatment_directory, self.name_pattern, 'sum%d' % (self.get_nimages_per_file()), f))
for f in self.get_expected_files():
os.remove(os.path.join(self.treatment_directory, f))
logging.info('summed images generation took %.2f' % (time.time() - _start,))
def generate_cbf(self):
logging.info('generate_cbf')
_start = time.time()
generate_cbf_line = 'cd {treatment_directory}; /usr/local/bin/H5ToCBF.py -m {directory}/{name_pattern}_master.h5 -d {directory}/process'.format(**self.format_dictionary)
if os.uname()[1] != 'process1':
generate_cbf_line = 'ssh process1 "%s"' % generate_cbf_line
logging.info('generate_cbf_line %s' % generate_cbf_line)
os.system(generate_cbf_line)
os.system('touch {directory}'.format(**self.format_dictionary))
self.create_ordered_cbf_links()
logging.info('generate_cbf took %.2f' % (time.time() - _start,))
def get_transmission(self):
if self.saved_parameters is not None:
return self.saved_parameters['transmission']
elif self.transmission is not None:
return self.transmission
else:
self.transmission_motor.get_transmission()
def run_xds(self):
logging.info('run_xds')
if os.path.isfile('{directory}/process/xdsme_auto_{name_pattern}/CORRECT.LP'.format(**self.format_dictionary)):
xds_line = ''
else:
os.makedirs('{directory}/process/xdsme_auto_{name_pattern}'.format(**self.format_dictionary))
xds_line = 'cd {directory}/process; ref_xdsme -p auto_{name_pattern} -i "LIB= /nfs/data/xds-zcbf.so" {directory}/{name_pattern}_cbf/{name_pattern}_??????.cbf.gz'.format(**self.format_dictionary)
if os.uname()[1] != 'process1':
xds_line = "ssh process1 '%s'" % xds_line
logging.info('xds_line %s' % xds_line)
best_log_file = '{directory}/{name_pattern}_cbf/process/{name_pattern}_best.log'.format(**self.format_dictionary)
if os.path.isfile(best_log_file) and os.stat(best_log_file).st_size > 200:
return
os.environ['besthome'] = '/usr/local/bin'
best_line = 'echo besthome $besthome; export besthome=/usr/local/bin; /usr/local/bin/best -f eiger9m -t {exposure_time} -e none -i2s 1. -M 0.005 -S 120 -Trans {transmission} -w 0.001 -GpS {dose_rate} -dna {directory}/process/{name_pattern}_best_strategy.xml -xds {directory}/process/xdsme_auto_{name_pattern}/CORRECT.LP {directory}/xdsme_auto_{name_pattern}/BKGINIT.cbf {directory}/process/xdsme_auto_{name_pattern}/XDS_ASCII.HKL | tee {directory}/process/{name_pattern}_best.log '.format(**{'directory': self.directory, 'name_pattern': self.name_pattern, 'exposure_time': self.get_exposure_time_per_frame(), 'dose_rate': self.get_dose_rate(), 'dose_limit': self.get_dose_limit(), 'transmission': self.get_transmission()})
logging.info('best_line %s' % best_line)
if xds_line != '':
total_line = '%s && %s' % (xds_line, best_line)
else:
total_line = best_line
logging.info('total_line %s' % total_line)
subprocess.call(total_line, shell=True)
def run_best(self, sleeptime=1., timeout=120.):
logging.info('run_best')
best_log_file = '{directory}/process/{name_pattern}_best.log'.format(**self.format_dictionary)
if os.path.isfile(best_log_file) and os.stat(best_log_file).st_size > 200:
return
best_line = 'best -f eiger9m -t {exposure_time} -e none -M 0.005 -S 120 -Trans {transmission} -w 0.001 -GpS {dose_rate} -DMAX {dose_limit} -dna {directory}/process/{name_pattern}_best_strategy.xml -xds {directory}/process/xdsme_auto_{name_pattern}/CORRECT.LP {directory}/process/xdsme_auto_{name_pattern}/BKGINIT.cbf {directory}/process/xdsme_auto_{name_pattern}/XDS_ASCII.HKL | tee {directory}/process/{name_pattern}_best.log '.format(**{'directory': self.directory, 'name_pattern': self.name_pattern, 'exposure_time': self.get_exposure_time_per_frame(), 'dose_rate': self.get_dose_rate(), 'dose_limit': self.get_dose_limit(), 'transmission': self.get_transmission()})
correct_file = '{directory}/process/xdsme_auto_{name_pattern}/CORRECT.LP'.format(**{'directory': self.directory, 'name_pattern': self.name_pattern})
xds_ascii_file = '{directory}/process/xdsme_auto_{name_pattern}/XDS_ASCII.HKL'.format(**{'directory': self.directory, 'name_pattern': self.name_pattern})
bkginit_file = '{directory}/process/xdsme_auto_{name_pattern}/BKGINIT.cbf'.format(**{'directory': self.directory, 'name_pattern': self.name_pattern})
start = time.time()
while (not os.path.isfile(correct_file) or not os.path.isfile(xds_ascii_file) or not os.path.isfile(bkginit_file)) and time.time() - start < timeout:
os.system('touch {directory}/process/xdsme_auto_{name_pattern}'.format(**{'directory': self.directory, 'name_pattern': self.name_pattern}))
gevent.sleep(sleeptime)
for f in (correct_file, xds_ascii_file, bkginit_file):
if os.path.isfile(f):
logging.info('file is created %s' % f)
else:
logging.info('file not created %s' % f)
#if os.uname()[1] != 'proxima2a-10':
#best_line = 'ssh proxima2a-10 "%s"' % best_line
logging.info('best_line %s' % best_line)
xds_dir_content = subprocess.getoutput('ls {directory}/process/xdsme_auto_{name_pattern}'.format(**{'directory': self.directory, 'name_pattern': self.name_pattern}))
logging.info('xds_dir_content')
logging.info(str(xds_dir_content))
os.system('touch {directory}/process/xdsme_auto_{name_pattern}'.format(**{'directory': self.directory, 'name_pattern': self.name_pattern}))
xds_dir_content2 = subprocess.getoutput('ls {directory}/process/xdsme_auto_{name_pattern}'.format(**{'directory': self.directory, 'name_pattern': self.name_pattern}))
os.system('touch {directory}/process'.format(**{'directory': self.directory, 'name_pattern': self.name_pattern}))
xds_dir_content2 = subprocess.getoutput('ls {directory}/process/xdsme_auto_{name_pattern}'.format(**{'directory': self.directory, 'name_pattern': self.name_pattern}))
logging.info('xds_dir_content2')
logging.info(str(xds_dir_content2))
os.system('echo %s' % best_line)
os.system(best_line)
def parse_best(self):
l = open('{directory}/process/{name_pattern}_best.log'.format(**{'directory': self.directory, 'name_pattern': self.name_pattern})).read()
print('BEST strategy')
print(l)
''' Main Wedge
================
Resolution limit is set according to the given max.time
Resolution limit =2.48 Angstrom Transmission = 10.0% Distance = 275.5mm
-----------------------------------------------------------------------------------------
WEDGE PARAMETERS || INFORMATION
----------------------------------||-----------------------------------------------------
sub-| Phi |Rot. | Exposure| N.of||Over|sWedge|Exposure|Exposure| Dose | Dose |Comple-
We-|start |width | /image | ima-||-lap| width| /sWedge| total |/sWedge| total |teness
dge|degree|degree| s | ges|| |degree| s | s | MGy | MGy | %
----------------------------------||-----------------------------------------------------
1 74.00 0.15 0.015 954|| No 143.10 14.2 14.2 3.540 3.540 100.0
-----------------------------------------------------------------------------------------
'''
try:
resolution = float(re.findall('Resolution limit =([\d\.]*) Angstrom', l)[0])
transmission = float(re.findall('Transmission[\s=]*([\d\.]*)%', l)[0])
distance = float(re.findall('Distance[\s=]*([\d\.]*)mm', l)[0])
subwedge = ' (\d)\s*'
start = '([\d\.]*)\s*'
width = '([\d\.]*)\s*'
exposure = '([\d\.]*)\s*'
nimages = '([\d]*)\|\|'
search = subwedge + start + width + exposure + nimages
wedges = re.findall(search, l)
except IndexError:
resolution = None
transmission = None
distance = None
wedges = None
if self.server != False:
self.server.log_message('BEST analysis did not succeed')
return
'''
[('1', '74.00', '0.15', '0.063', '767'),
('2', '189.05', '0.15', '0.161', '187')]
'''
strategy_text = ''
ls = l.split('\n')
flag = False
for line in ls:
if 'Main Wedge' in line:
flag = True
if 'Phi_start' in line:
flag = False
break
if flag == True:
strategy_text += '%s\n' % line
strategy = []
for wedge in wedges:
wedge_parameters = {}
wedge_parameters['resolution'] = float(resolution)
wedge_parameters['transmission'] = float(transmission)
wedge_parameters['distance'] = float(distance)
wedge_parameters['order'] = int(wedge[0])
wedge_parameters['scan_start_angle'] = float(wedge[1])
wedge_parameters['angle_per_frame'] = float(wedge[2])
wedge_parameters['exposure_per_frame'] = float(wedge[3])
wedge_parameters['nimages'] = int(wedge[4])
wedge_parameters['scan_exposure_time'] = wedge_parameters['nimages'] * wedge_parameters['exposure_per_frame']
strategy.append(wedge_parameters)
try:
if self.server != False:
self.server.log_message('BEST recomends the following parameters:')
for wedge_parameters in strategy:
for key in ['scan_start_angle', 'resolution', 'transmission', 'angle_per_frame', 'exposure_per_frame', 'nimages']:
self.server.log_message('%s: %s' % (key, wedge_parameters[key]))
except:
logging.info('BEST recomends the following parameters:')
for wedge_parameters in strategy:
for key in ['scan_start_angle', 'resolution', 'transmission', 'angle_per_frame', 'exposure_per_frame', 'nimages']:
logging.info('%s: %s' % (key, wedge_parameters[key]))
if resolution > self.get_resolution():
logging.getLogger('user_level_log').warning('Best results indicate the current sample diffracts beyond currently set resolution, please consider approaching detector or increasing photon energy to measure diffraction to higher resolution.')
return strategy
def main():
import optparse
parser = optparse.OptionParser()
parser.add_option('-n', '--name_pattern', default='ref-test_$id', type=str, help='Prefix default=%default')
parser.add_option('-d', '--directory', default='/nfs/data/default', type=str, help='Destination directory default=%default')
parser.add_option('-r', '--scan_range', default=1.2, type=float, help='Scan range [deg]')
parser.add_option('-e', '--scan_exposure_time', default=0.25, type=float, help='Scan exposure time [s]')
parser.add_option('-s', '--scan_start_angles', default='[0, 90, 180, 225, 315]', type=str, help='Scan start angles [deg]')
parser.add_option('-a', '--angle_per_frame', default=0.1, type=float, help='Angle per frame [deg]')
parser.add_option('-f', '--image_nr_start', default=1, type=int, help='Start image number [int]')
parser.add_option('-v', '--vertical_scan_length', default=0, type=float, help='Vertical scan length [mm]')
parser.add_option('-V', '--vertical_step_size', default=0.025, type=float, help='Vertical steps size [mm]')
#parser.add_option('-I', '--inverse_dierction', action='store_true', help='If set will invese direction of subsequent vertical scans')
parser.add_option('-R', '--dose_rate', default=0.25e6, type=float, help='Dose rate in Grays per second (default=%default)')
parser.add_option('-L', '--dose_limit', default=15e6, type=float, help='Dose limit in Grays (default=%default)')
parser.add_option('-i', '--position', default=None, type=str, help='Gonio alignment position [dict]')
parser.add_option('-p', '--photon_energy', default=None, type=float, help='Photon energy ')
parser.add_option('-t', '--detector_distance', default=None, type=float, help='Detector distance')
parser.add_option('-o', '--resolution', default=None, type=float, help='Resolution [Angstroem]')
parser.add_option('-x', '--flux', default=None, type=float, help='Flux [ph/s]')
parser.add_option('-m', '--transmission', default=None, type=float, help='Transmission. Number in range between 0 and 1.')
parser.add_option('-T', '--snapshot', action='store_true', help='If set will record snapshots.')
parser.add_option('-A', '--analysis', action='store_true', help='If set will perform automatic analysis.')
parser.add_option('-D', '--diagnostic', action='store_true', help='If set will record diagnostic information.')
parser.add_option('-S', '--simulation', action='store_true', help='If set will record diagnostic information.')
options, args = parser.parse_args()
print('options', options)
print('args', args)
ri = reference_images(**vars(options))
filename = '%s_parameters.pickle' % ri.get_template()
if not os.path.isfile(filename):
ri.execute()
elif options.analysis == True:
ri.analyze_online()
if __name__ == '__main__':
main()