#!/usr/bin/env python # Mosflm.py # Copyright (C) 2006 CCLRC, Graeme Winter # # This code is distributed under the BSD license, a copy of which is # included in the root directory of this package. # # 23rd June 2006 # # A wrapper for the data processing program Mosflm, with the following # methods to provide functionality: # # index: autoindexing functionality (implemented) # integrate: process a frame or a dataset (implemented) # # Internally this will also require cell refinement and so on, but this # will be implicit - the cell refinement is a local requirement for # mosflm only - though this will provide some useful functionality # for diagnosing wrong indexing solutions. import os import sys import shutil import math import exceptions if not os.environ.has_key('XIA2CORE_ROOT'): raise RuntimeError, 'XIA2CORE_ROOT not defined' if not os.environ.has_key('XIA2_ROOT'): raise RuntimeError, 'XIA2_ROOT not defined' if not os.path.join(os.environ['XIA2CORE_ROOT'], 'Python') in sys.path: sys.path.append(os.path.join(os.environ['XIA2CORE_ROOT'], 'Python')) if not os.environ['XIA2_ROOT'] in sys.path: sys.path.append(os.environ['XIA2_ROOT']) from Background.Background import Background from Driver.DriverFactory import DriverFactory from Decorators.DecoratorFactory import DecoratorFactory # interfaces that this will present from Schema.Interfaces.FrameProcessor import FrameProcessor from Schema.Interfaces.Indexer import Indexer from Schema.Interfaces.Integrater import Integrater # output streams &c. from Handlers.Streams import Chatter, Debug, Journal from Handlers.Citations import Citations from Handlers.Flags import Flags from Handlers.Executables import Executables from Handlers.Files import FileHandler # helpers from MosflmHelpers import _happy_integrate_lp, \ _parse_mosflm_integration_output, decide_integration_resolution_limit, \ _parse_mosflm_index_output, standard_mask, \ _get_indexing_solution_number, detector_class_to_mosflm, \ _parse_summary_file # things we are moving towards... from Modules.Indexer.IndexerSelectImages import index_select_images_lone, \ index_select_images_user from Modules.GainEstimater import gain from Handlers.Files import FileHandler from lib.bits import auto_logfiler, mean_sd from lib.SymmetryLib import lattice_to_spacegroup from Experts.MatrixExpert import transmogrify_matrix, \ get_reciprocal_space_primitive_matrix, reindex_sym_related from Experts.ResolutionExperts import mosflm_mtz_to_list, \ bin_o_tron, digest from Experts.MissetExpert import MosflmMissetExpert # exceptions from Schema.Exceptions.BadLatticeError import BadLatticeError from Schema.Exceptions.NegativeMosaicError import NegativeMosaicError from Schema.Exceptions.IndexingError import IndexingError from Schema.Exceptions.IntegrationError import IntegrationError # other classes which are necessary to implement the integrater # interface (e.g. new version, with reindexing as the finish...) from Wrappers.CCP4.Reindex import Reindex from Wrappers.CCP4.Sortmtz import Sortmtz from Wrappers.XIA.Diffdump import Diffdump from Wrappers.XIA.Printpeaks import Printpeaks from Modules.IceId import IceId # cell refinement image helpers from Modules.Indexer.MosflmCheckIndexerSolution import \ mosflm_check_indexer_solution # jiffy functions for means, standard deviations and outliers from lib.bits import meansd, remove_outliers def Mosflm(DriverType = None): '''A factory for MosflmWrapper classes.''' DriverInstance = DriverFactory.Driver(DriverType) CCP4DriverInstance = DecoratorFactory.Decorate(DriverInstance, 'ccp4') class MosflmWrapper(CCP4DriverInstance.__class__, FrameProcessor, Indexer, Integrater): '''A wrapper for Mosflm, using the CCP4-ified Driver.''' def __init__(self): # generic things CCP4DriverInstance.__class__.__init__(self) # FIXME should this be "hard" coded as the ccp4 one? if Executables.get('ipmosflm'): self.set_executable(Executables.get('ipmosflm')) else: self.set_executable(os.path.join( os.environ['CCP4'], 'bin', 'ipmosflm')) FrameProcessor.__init__(self) Indexer.__init__(self) Integrater.__init__(self) # store the Driver instance used for this for use when working # in parallel - use with DriverFactory.Driver(DriverType) self._mosflm_driver_type = DriverType # local parameters used in autoindexing self._mosflm_autoindex_sol = 0 self._mosflm_autoindex_thresh = None self._mosflm_spot_file = None # local parameters used in cell refinement self._mosflm_cell_ref_images = None self._mosflm_cell_ref_resolution = None self._mosflm_cell_ref_double_mosaic = False # belt + braces for very troublesome cases - this will only # be used in failover / microcrystal mode self._mosflm_cell_ref_add_autoindex = False # and the calculation of the missetting angles self._mosflm_misset_expert = None # local parameters used in integration self._mosflm_refine_profiles = True self._mosflm_postref_fix_mosaic = False self._mosflm_rerun_integration = False self._mosflm_hklout = None self._mosflm_gain = None return def _mosflm_get_header(self, image): '''Return the header for this image.''' name = self.get_image_name(image) dd = Diffdump() dd.set_image(name) return dd.readheader() def _index_prepare(self): if self._indxr_images == []: self._index_select_images() if self._mosflm_autoindex_thresh is None and \ Flags.get_microcrystal(): self._mosflm_autoindex_thresh = 5 return def _index_select_images(self): '''Select correct images based on image headers.''' if Flags.get_small_molecule(): return self._index_select_images_small_molecule() if Flags.get_microcrystal(): return self._index_select_images_microcrystal() phi_width = self.get_header_item('phi_width') images = self.get_matching_images() if Flags.get_interactive(): selected_images = index_select_images_user(phi_width, images, Chatter) else: selected_images = index_select_images_lone(phi_width, images) for image in selected_images: Debug.write('Selected image %s' % image) self.add_indexer_image_wedge(image) return def _index_select_images_small_molecule(self): '''Select correct images based on image headers. This one is for when you have small molecule data so want more images.''' phi_width = self.get_header_item('phi_width') images = self.get_matching_images() Debug.write('Selected image %s' % images[0]) self.add_indexer_image_wedge(images[0]) offset = images[0] - 1 # add an image every 15 degrees up to 90 degrees for j in range(6): image_number = offset + int(15 * (j + 1) / phi_width) if not image_number in images: break Debug.write('Selected image %s' % image_number) self.add_indexer_image_wedge(image_number) return def _index_select_images_microcrystal(self): '''Select images for more difficult cases e.g. microcrystal work. Will apply (up to) 20 images to the task.''' phi_width = self.get_header_item('phi_width') images = self.get_matching_images() spacing = max(1, int(len(images) / 20)) selected = [] for j in range(0, len(images), spacing): selected.append(images[j]) for image in selected[:20]: self.add_indexer_image_wedge(image) return def _refine_select_images(self, mosaic): '''Select images for cell refinement based on image headers.''' cell_ref_images = [] phi_width = self.get_header_item('phi_width') min_images = max(3, int(2 * mosaic / phi_width)) if min_images > 9: min_images = 9 images = self.get_matching_images() if len(images) < 3 * min_images: cell_ref_images.append((min(images), max(images))) return cell_ref_images cell_ref_images = [] cell_ref_images.append((images[0], images[min_images - 1])) ideal_last = int(90.0 / phi_width) + min_images if ideal_last < len(images): ideal_middle = int(45.0 / phi_width) - min_images // 2 cell_ref_images.append((images[ideal_middle - 1], images[ideal_middle - 2 + min_images])) cell_ref_images.append((images[ideal_last - min_images], images[ideal_last])) else: middle = int((max(images) + min(images) - min_images) // 2) cell_ref_images.append((middle - 1, middle - 2 + min_images)) cell_ref_images.append((images[-min_images], images[-1])) return cell_ref_images def _refine_select_twenty(self, mosaic): '''Select images for cell refinement - first 20 in the sweep.''' cell_ref_images = [] images = self.get_matching_images() cell_ref_images = [] if len(images) > 20: cell_ref_images.append((images[0], images[19])) else: cell_ref_images.append((images[0], images[-1])) return cell_ref_images def _index(self): '''Implement the indexer interface.''' Citations.cite('mosflm') # FIXME verify that the spot finding found some spots (and throw # an exception if not!) then perform the indexing on this # spot list. N.B. this would provide the opportunity to index # multiple lattices in a relatively straightforward manner. self.reset() _images = [] for i in self._indxr_images: for j in i: if not j in _images: _images.append(j) _images.sort() images_str = '%d' % _images[0] for i in _images[1:]: images_str += ', %d' % i cell_str = None if self._indxr_input_cell: cell_str = '%.2f %.2f %.2f %.2f %.2f %.2f' % \ self._indxr_input_cell if self._indxr_sweep_name: # then this is a proper autoindexing run - describe this # to the journal entry if len(self._fp_directory) <= 50: dirname = self._fp_directory else: dirname = '...%s' % self._fp_directory[-46:] Journal.block( 'autoindexing', self._indxr_sweep_name, 'mosflm', {'images':images_str, 'target cell':self._indxr_input_cell, 'target lattice':self._indxr_input_lattice, 'template':self._fp_template, 'directory':dirname}) task = 'Autoindex from images:' for i in _images: task += ' %s' % self.get_image_name(i) self.set_task(task) auto_logfiler(self) self.start() if self.get_reversephi(): detector = detector_class_to_mosflm( self.get_header_item('detector_class')) self.input('detector %s reversephi' % detector) if 'pilatus' in self.get_header_item('detector_class'): self.input('detector pilatus') self.input('template "%s"' % self.get_template()) self.input('directory "%s"' % self.get_directory()) self.input('newmat xiaindex.mat') if self.get_beam_prov() == 'user': self.input('beam %f %f' % self.get_beam()) if self.get_wavelength_prov() == 'user': self.input('wavelength %f' % self.get_wavelength()) if self.get_distance_prov() == 'user': self.input('distance %f' % self.get_distance()) if self._indxr_input_cell: self.input('cell %f %f %f %f %f %f' % \ self._indxr_input_cell) if self._indxr_input_lattice != None: spacegroup_number = lattice_to_spacegroup( self._indxr_input_lattice) self.input('symmetry %d' % spacegroup_number) if not self._mosflm_autoindex_thresh: try: min_peaks = 200 Debug.write('Aiming for at least %d spots...' % min_peaks) thresholds = [] for i in _images: p = Printpeaks() p.set_image(self.get_image_name(i)) thresh = p.threshold(min_peaks) Debug.write('Autoindex threshold for image %d: %d' % \ (i, thresh)) thresholds.append(thresh) thresh = min(thresholds) self._mosflm_autoindex_thresh = thresh except exceptions.Exception, e: Debug.write('Error computing threshold: %s' % str(e)) Debug.write('Using default of 20.0') thresh = 20.0 else: thresh = self._mosflm_autoindex_thresh Debug.write('Using autoindex threshold: %d' % thresh) for i in _images: if self._mosflm_autoindex_sol: self.input( 'autoindex dps refine image %d thresh %d solu %d' % \ (i, thresh, self._mosflm_autoindex_sol)) else: self.input( 'autoindex dps refine image %d thresh %d' % \ (i, thresh)) # now forget this to prevent weird things happening later on if self._mosflm_autoindex_sol: self._mosflm_autoindex_sol = 0 # trac 1150 - want to estimate mosaic spread from all images then # calculate the average - though what happens if one of the chosen # images is BLANK? for i in _images: self.input('mosaic estimate %d' % i) self.input('go') self.close_wait() sweep = self.get_indexer_sweep_name() FileHandler.record_log_file( '%s INDEX' % (sweep), self.get_log_file()) output = self.get_all_output() mosaic_spreads = [] for o in output: if 'Final cell (after refinement)' in o: indxr_cell = tuple(map(float, o.split()[-6:])) if min(list(indxr_cell)) < 10.0 and \ indxr_cell[2] / indxr_cell[0] > 6: Debug.write( 'Unrealistic autoindexing solution: ' + '%.2f %.2f %.2f %.2f %.2f %.2f' % indxr_cell) # tweak some parameters and try again... self._mosflm_autoindex_thresh *= 1.5 self.set_indexer_done(False) return intgr_params = { } # look up other possible indexing solutions (not well - in # standard settings only!) This is moved earlier as it could # result in returning if Mosflm has selected the wrong # solution! try: self._indxr_other_lattice_cell = _parse_mosflm_index_output( output) # Change 27/FEB/08 to support user assigned spacegroups if self._indxr_user_input_lattice: lattice_to_spacegroup_dict = { 'aP':1, 'mP':3, 'mC':5, 'oP':16, 'oC':20, 'oF':22, 'oI':23, 'tP':75, 'tI':79, 'hP':143, 'hR':146, 'cP':195, 'cF':196, 'cI':197} for k in self._indxr_other_lattice_cell.keys(): if lattice_to_spacegroup_dict[k] > \ lattice_to_spacegroup_dict[ self._indxr_input_lattice]: del(self._indxr_other_lattice_cell[k]) # check that the selected unit cell matches - and if # not raise a "horrible" exception # FIXME why are these and "False" commented out - see also in (e) if self._indxr_input_cell: for o in output: if 'Final cell (after refinement)' in o: indxr_cell = tuple(map(float, o.split()[-6:])) for j in range(6): if math.fabs(self._indxr_input_cell[j] - indxr_cell[j]) > 2.0: Chatter.write( 'Mosflm autoindexing did not select ' + 'correct (target) unit cell') raise RuntimeError, \ 'something horrible happened in indexing' except RuntimeError, e: # check if mosflm rejected a solution we have it if 'horribl' in str(e): # ok it did - time to break out the big guns... if not self._indxr_input_cell: raise RuntimeError, \ 'error in solution selection when not preset' self._mosflm_autoindex_sol = _get_indexing_solution_number( output, self._indxr_input_cell, self._indxr_input_lattice) # set the fact that we are not done... self.set_indexer_done(False) # and return - hopefully this will restart everything return else: raise e for o in output: if 'Final cell (after refinement)' in o: self._indxr_cell = tuple(map(float, o.split()[-6:])) if 'Beam coordinates of' in o: self._indxr_refined_beam = tuple(map(float, o.split( )[-2:])) # FIXED this may not be there if this is a repeat indexing! if 'Symmetry:' in o: self._indxr_lattice = o.split(':')[1].split()[0] # so we have to resort to this instead... if 'Refining solution #' in o: spagnum = int(o.split(')')[0].split()[-1]) lattice_to_spacegroup_dict = {'aP':1, 'mP':3, 'mC':5, 'oP':16, 'oC':20, 'oF':22, 'oI':23, 'tP':75, 'tI':79, 'hP':143, 'hR':146, 'cP':195, 'cF':196, 'cI':197} spacegroup_to_lattice = { } for k in lattice_to_spacegroup_dict.keys(): spacegroup_to_lattice[ lattice_to_spacegroup_dict[k]] = k self._indxr_lattice = spacegroup_to_lattice[spagnum] # in here I need to check if the mosaic spread estimation # has failed. If it has it is likely that the selected # lattice has too high symmetry, and the "next one down" # is needed if 'The mosaicity has been estimated' in o: ms = float(o.split('>')[1].split()[0]) mosaic_spreads.append(ms) # alternatively this could have failed - which happens # sometimes... if 'The mosaicity estimation has not worked for some' in o: # this is a problem... in particular with the # mosflm built on linux in CCP4 6.0.1... # FIXME this should be a specific kind of # exception e.g. an IndexError # if microcrystal mode, just assume for the moment mosaic # spread is 0.5 degrees... if Flags.get_microcrystal(): self._indxr_mosaic = 0.5 else: raise IndexingError, 'mosaicity estimation failed' # or it may alternatively look like this... if 'The mosaicity has NOT been estimated' in o: # then consider setting it do a default value... # equal to the oscillation width (a good guess) phi_width = self.get_header_item('phi_width') Chatter.write( 'Mosaic estimation failed, so guessing at %4.2f' % \ phi_width) # only consider this if we have thus far no idea on the # mosaic spread... if not mosaic_spreads: mosaic_spreads.append(phi_width) # mosflm doesn't refine this in autoindexing... if 'Crystal to detector distance of' in o: self._indxr_refined_distance = float(o.split( )[5].replace('mm', '')) # but it does complain if it is different to the header # value - so just use the input value in this case... if 'Input crystal to detector distance' in o \ and 'does NOT agree with' in o: self._indxr_refined_distance = self.get_distance() # record raster parameters and so on, useful for the # cell refinement etc - this will be added to a # payload dictionary of mosflm integration keywords # look for "measurement box parameters" if 'parameters have been set to' in o: intgr_params['raster'] = map( int, o.split()[-5:]) if '(currently SEPARATION' in o: intgr_params['separation'] = map( float, o.replace(')', '').split()[-2:]) # get the resolution estimate out... if '99% have resolution' in o: self._indxr_resolution_estimate = float( o.split()[-2]) # compute mosaic as mean(mosaic_spreads) self._indxr_mosaic = sum(mosaic_spreads) / len(mosaic_spreads) self._indxr_payload['mosflm_integration_parameters'] = intgr_params self._indxr_payload['mosflm_orientation_matrix'] = open( os.path.join(self.get_working_directory(), 'xiaindex.mat'), 'r').readlines() # also look at the images given in input to try to decide if # they are icy... ice = [] for i in _images: icy = IceId() icy.set_image(self.get_image_name(i)) icy.set_beam(self._indxr_refined_beam) ice.append(icy.search()) if sum(ice) / len(ice) > 0.45: self._indxr_ice = 1 Debug.write('Autoindexing images look icy: %.3f' % \ (sum(ice) / len(ice))) else: Debug.write('Autoindexing images look ok: %.3f' % \ (sum(ice) / len(ice))) return def _index_finish(self): '''Check that the autoindexing gave a convincing result, and if not (i.e. it gave a centred lattice where a primitive one would be correct) pick up the correct solution.''' if self._indxr_input_lattice: return if self.get_indexer_sweep().get_user_lattice(): return try: status, lattice, matrix, cell = mosflm_check_indexer_solution( self) except: status = None if status is None: # basis is primitive return if status is False: # basis is centred, and passes test return # ok need to update internals... self._indxr_lattice = lattice self._indxr_cell = cell Debug.write('Inserting solution: %s ' % lattice + '%6.2f %6.2f %6.2f %6.2f %6.2f %6.2f' % cell) self._indxr_replace(lattice, cell) self._indxr_payload['mosflm_orientation_matrix'] = matrix return # METHOD to help cell refinement - if the autoindexing has been done # with another program, it could be helpful to run autoindexing # with Mosflm (not to keep the results, mind) to get these parameters # out - for example raster parameters etc. def _mosflm_generate_raster(self, _images): '''Get out the parameters from autoindexing without using the result - this is probably ok as it is quite quick ;o).''' # reset the log file tracking and whatnot - FIXME this should # use the find_spots method... self.reset() # have to get the images to use into here somehow - work through # using the first image from each cell refinement wedge? that # would probably hit the spot... auto_logfiler(self) Debug.write('Running mosflm to generate RASTER, SEPARATION') self.start() if self.get_reversephi(): detector = detector_class_to_mosflm( self.get_header_item('detector_class')) self.input('detector %s reversephi' % detector) if 'pilatus' in self.get_header_item('detector_class'): self.input('detector pilatus') self.input('template "%s"' % self.get_template()) self.input('directory "%s"' % self.get_directory()) self.input('newmat xiaindex.mat') # why would I not want to assign the right beam centre? if self.get_beam_prov() == 'user' or True: self.input('beam %f %f' % self.get_beam()) if self.get_wavelength_prov() == 'user': self.input('wavelength %f' % self.get_wavelength()) if self.get_distance_prov() == 'user': self.input('distance %f' % self.get_distance()) # Added printpeaks check which should be interesting... if not self._mosflm_autoindex_thresh: # miniCBF is not currently supported - so use default # I/sigma of 20 for those... try: min_peaks = 200 Debug.write('Aiming for at least %d spots...' % min_peaks) thresholds = [] for i in _images: p = Printpeaks() p.set_image(self.get_image_name(i)) thresh = p.threshold(min_peaks) Debug.write('Autoindex threshold for image %d: %d' % \ (i, thresh)) thresholds.append(thresh) thresh = min(thresholds) self._mosflm_autoindex_thresh = thresh except exceptions.Exception, e: Debug.write('Error computing threshold: %s' % str(e)) Debug.write('Using default of 20.0') thresh = 20.0 else: thresh = self._mosflm_autoindex_thresh Debug.write('Using autoindex threshold: %d' % thresh) for i in _images: self.input( 'autoindex dps refine image %d thresh %d' % \ (i, thresh)) self.input('mosaic estimate') self.input('go') self.close_wait() intgr_params = { } output = self.get_all_output() for o in output: # record raster parameters and so on, useful for the # cell refinement etc - this will be added to a # payload dictionary of mosflm integration keywords # look for "measurement box parameters" if 'parameters have been set to' in o: intgr_params['raster'] = map( int, o.split()[-5:]) if '(currently SEPARATION' in o: intgr_params['separation'] = map( float, o.replace(')', '').split()[-2:]) return intgr_params def _integrate_prepare(self): '''Prepare for integration - note that if there is a reason why this is needed to be run again, set self._intgr_prepare_done as False.''' self.digest_template() if not self._mosflm_gain and self.get_gain(): self._mosflm_gain = self.get_gain() # if pilatus override GAIN to 1.0 if 'pilatus' in self.get_header_item('detector_class'): self._mosflm_gain = 1.0 if not self._mosflm_cell_ref_images: indxr = self.get_integrater_indexer() mosaic = indxr.get_indexer_mosaic() if Flags.get_microcrystal(): self._mosflm_cell_ref_images = self._refine_select_twenty( mosaic) else: self._mosflm_cell_ref_images = self._refine_select_images( mosaic) indxr = self.get_integrater_indexer() images_str = '%d to %d' % self._mosflm_cell_ref_images[0] for i in self._mosflm_cell_ref_images[1:]: images_str += ', %d to %d' % i cell_str = '%.2f %.2f %.2f %.2f %.2f %.2f' % \ indxr.get_indexer_cell() if len(self._fp_directory) <= 50: dirname = self._fp_directory else: dirname = '...%s' % self._fp_directory[-46:] Journal.block('cell refining', self._intgr_sweep_name, 'mosflm', {'images':images_str, 'start cell':cell_str, 'target lattice':indxr.get_indexer_lattice(), 'template':self._fp_template, 'directory':dirname}) # in here, check to see if we have the raster parameters and # separation from indexing - if we used a different indexer # we may not, so if this is the case call a function to generate # them... if not indxr.get_indexer_payload( 'mosflm_integration_parameters'): # generate a list of first images images = [] for cri in self._mosflm_cell_ref_images: images.append(cri[0]) images.sort() integration_params = self._mosflm_generate_raster(images) # copy them over to where they are needed if integration_params.has_key('separation'): self.set_integrater_parameter( 'mosflm', 'separation', '%f %f' % tuple(integration_params['separation'])) if integration_params.has_key('raster'): self.set_integrater_parameter( 'mosflm', 'raster', '%d %d %d %d %d' % tuple(integration_params['raster'])) # next test the cell refinement with the correct lattice # and P1 and see how the numbers stack up... # FIXME in here 19/JUNE/08 - if I get a negative mosaic spread # in here it may be worth doubling the estimated mosaic spread # and having another go. If it fails a second time, then # let the exception through... - this should now raise a # NegativeMosaicError not a BadLatticeError # copy the cell refinement resolution in... self._mosflm_cell_ref_resolution = indxr.get_indexer_resolution() Debug.write( 'Using resolution limit of %.2f for cell refinement' % \ self._mosflm_cell_ref_resolution) # now trap NegativeMosaicError exception - once! try: # now reading the background residual values as well - if these # are > 10 it would indicate that the images are blank (assert) # so ignore from the analyis / comparison if Flags.get_no_lattice_test() or \ self.get_integrater_sweep().get_user_lattice(): rms_deviations_p1 = [] br_p1 = [] else: self.reset() auto_logfiler(self) rms_deviations_p1, br_p1 = self._mosflm_test_refine_cell( 'aP') self.reset() auto_logfiler(self) rms_deviations, br = self._mosflm_refine_cell() except NegativeMosaicError, nme: # need to handle cases where the mosaic spread refines to # a negative value when the lattice is right - this could # be caused by the starting value being too small so # try doubling - if this fails, reject lattice as duff... if self._mosflm_cell_ref_double_mosaic: # reset flag; half mosaic; raise BadLatticeError Debug.write('Mosaic negative even x2 -> BadLattice') self._mosflm_cell_ref_double_mosaic = False raise BadLatticeError, 'negative mosaic spread' else: # set flag, double mosaic, return to try again Debug.write('Mosaic negative -> try x2') self._mosflm_cell_ref_double_mosaic = True self.set_integrater_prepare_done(False) return if not self.get_integrater_prepare_done(): # cell refinement failed so no point getting the # results of refinement in P1... now this is # ignored as it is important we refine the cell # in P1 first... return # run the cell refinement again with the refined parameters # in the correct lattice as this will give a fair comparison # with the P1 refinement (see bug # 2539) - would also be # interesting to see how much better these are... images = [] for cri in self._mosflm_cell_ref_images: for j in range(cri[0], cri[1] + 1): images.append(j) if rms_deviations and rms_deviations_p1: cycles = [] j = 1 while rms_deviations.has_key(j) and \ rms_deviations_p1.has_key(j): cycles.append(j) j += 1 Debug.write('Cell refinement comparison:') Debug.write('Image correct triclinic') ratio = 0.0 ratios = [] for c in cycles: Debug.write('Cycle %d' % c) for j, image in enumerate(images): background_residual = max(br_p1[c][image], br[c][image]) if background_residual > 10: Debug.write('. %4d %.2f %.2f (ignored)' % \ (images[j], rms_deviations[c][j], rms_deviations_p1[c][j])) continue Debug.write('. %4d %.2f %.2f' % \ (images[j], rms_deviations[c][j], rms_deviations_p1[c][j])) ratio += rms_deviations[c][j] / rms_deviations_p1[c][j] ratios.append( (rms_deviations[c][j] / rms_deviations_p1[c][j])) # fixme in here only run this if lattice != aP if False and self.get_integrater_indexer( ).get_indexer_lattice() != 'aP': good, bad = remove_outliers(ratios, 6) m, s = meansd(good) bs = '' for b in bad: bs += '%.3f ' % b Debug.write('%d outlier ratios: %s' % (len(bad), bs)) Debug.write('Of the good: %.3f +- %.3f' % (m, s)) Debug.write('Average ratio: %.2f' % \ (ratio / len(ratios))) if (ratio / (max(cycles) * len(images))) > \ Flags.get_rejection_threshold() and \ not self.get_integrater_sweep().get_user_lattice(): raise BadLatticeError, 'incorrect lattice constraints' else: Debug.write('Cell refinement in P1 failed... or was not run') # also look for the images we want to integrate... since this # is part of the preparation and was causing fun with # bug # 2040 - going quickly! this resets the integration done # flag... cell_str = '%.2f %.2f %.2f %.2f %.2f %.2f' % \ self._intgr_cell Journal.entry({'refined cell':cell_str}) if not self._intgr_wedge: images = self.get_matching_images() self.set_integrater_wedge(min(images), max(images)) return def _integrate(self): '''Implement the integrater interface.''' # cite the program Citations.cite('mosflm') images_str = '%d to %d' % self._intgr_wedge cell_str = '%.2f %.2f %.2f %.2f %.2f %.2f' % self._intgr_cell if len(self._fp_directory) <= 50: dirname = self._fp_directory else: dirname = '...%s' % self._fp_directory[-46:] Journal.block( 'integrating', self._intgr_sweep_name, 'mosflm', {'images':images_str, 'cell':cell_str, 'lattice':self.get_integrater_indexer().get_indexer_lattice(), 'template':self._fp_template, 'directory':dirname, 'resolution':'%.2f' % self._intgr_reso_high}) self._mosflm_rerun_integration = False wd = self.get_working_directory() try: self.reset() auto_logfiler(self) if self.get_integrater_sweep_name(): pname, xname, dname = self.get_integrater_project_info() FileHandler.record_log_file( '%s %s %s %s mosflm integrate' % \ (self.get_integrater_sweep_name(), pname, xname, dname), self.get_log_file()) if Flags.get_parallel() > 1: Debug.write('Parallel integration: %d jobs' % Flags.get_parallel()) self._mosflm_hklout = self._mosflm_parallel_integrate() else: self._mosflm_hklout = self._mosflm_integrate() # record integration output for e.g. BLEND. sweep = self.get_integrater_sweep_name() if sweep: FileHandler.record_more_data_file( '%s %s %s %s INTEGRATE' % (pname, xname, dname, sweep), self._mosflm_hklout) except IntegrationError, e: if 'negative mosaic spread' in str(e): if self._mosflm_postref_fix_mosaic: Chatter.write( 'Negative mosaic spread - stopping integration') raise BadLatticeError, 'negative mosaic spread' Chatter.write( 'Negative mosaic spread - rerunning integration') self.set_integrater_done(False) self._mosflm_postref_fix_mosaic = True if self._mosflm_rerun_integration and not Flags.get_quick(): # make sure that this is run again... Chatter.write('Need to rerun the integration...') self.set_integrater_done(False) return self._mosflm_hklout def _integrate_finish(self): '''Finish the integration - if necessary performing reindexing based on the pointgroup and the reindexing operator.''' # FIXME this should rely on self.get_integrater_raw_intensities() # not self._mosflm_hklout... if self._intgr_reindex_operator is None and \ self._intgr_spacegroup_number == lattice_to_spacegroup( self.get_integrater_indexer().get_indexer_lattice()): return self._mosflm_hklout if self._intgr_reindex_operator is None and \ self._intgr_spacegroup_number == 0: return self._mosflm_hklout Debug.write('Reindexing to spacegroup %d (%s)' % \ (self._intgr_spacegroup_number, self._intgr_reindex_operator)) hklin = self._mosflm_hklout reindex = Reindex() reindex.set_working_directory(self.get_working_directory()) auto_logfiler(reindex) reindex.set_operator(self._intgr_reindex_operator) if self._intgr_spacegroup_number: reindex.set_spacegroup(self._intgr_spacegroup_number) hklout = '%s_reindex.mtz' % hklin[:-4] reindex.set_hklin(hklin) reindex.set_hklout(hklout) reindex.reindex() return hklout def _mosflm_test_refine_cell(self, test_lattice): '''Test performing cell refinement in with a different lattice to the one which was selected by the autoindex procedure. This should not change anything in the class.''' indxr = self.get_integrater_indexer() lattice = indxr.get_indexer_lattice() mosaic = indxr.get_indexer_mosaic() beam = indxr.get_indexer_beam() distance = indxr.get_indexer_distance() matrix = indxr.get_indexer_payload('mosflm_orientation_matrix') if mosaic < 0.25 * self.get_header_item('phi_width'): mosaic = 0.25 * self.get_header_item('phi_width') input_matrix = '' for m in matrix: input_matrix += '%s\n' % m new_matrix = transmogrify_matrix(lattice, input_matrix, test_lattice, self.get_wavelength(), self.get_working_directory()) spacegroup_number = lattice_to_spacegroup(test_lattice) if not self._mosflm_cell_ref_images: raise RuntimeError, 'wedges must be assigned already' open(os.path.join(self.get_working_directory(), 'test-xiaindex-%s.mat' % lattice), 'w').write(new_matrix) self.start() if self._mosflm_gain: self.input('gain %5.2f' % self._mosflm_gain) if self.get_reversephi(): detector = detector_class_to_mosflm( self.get_header_item('detector_class')) self.input('detector %s reversephi' % detector) if 'pilatus' in self.get_header_item('detector_class'): self.input('detector pilatus') self.input('template "%s"' % self.get_template()) self.input('directory "%s"' % self.get_directory()) self.input('matrix test-xiaindex-%s.mat' % lattice) self.input('newmat test-xiarefine.mat') self.input('beam %f %f' % beam) self.input('distance %f' % distance) self.input('symmetry %s' % spacegroup_number) # FIXME 18/JUN/08 - it may help to have an overestimate # of the mosaic spread in here as it *may* refine down # better than up... - this is not a good idea as it may # also not refine at all! - 12972 # integration failed # Bug # 3103 if self._mosflm_cell_ref_double_mosaic: self.input('mosaic %f' % (2.0 * mosaic)) else: self.input('mosaic %f' % mosaic) # if set, use the resolution for cell refinement - see # bug # 2078... if self._mosflm_cell_ref_resolution and not \ Flags.get_microcrystal(): self.input('resolution %f' % \ self._mosflm_cell_ref_resolution) if self._mosflm_postref_fix_mosaic: self.input('postref fix mosaic') if Flags.get_microcrystal(): self.input('postref sdfac 2.0') # note well that the beam centre is coming from indexing so # should be already properly handled if self.get_wavelength_prov() == 'user': self.input('wavelength %f' % self.get_wavelength()) # belt + braces mode - only to be used when considering failover, # will run an additional step of autoindexing prior to cell # refinement, to be used only after proving that not going it # will result in cell refinement failure - will use the first # wedge... N.B. this is only useful if the indexer is Labelit # not Mosflm... if self._mosflm_cell_ref_add_autoindex: assert(Flags.get_failover()) cri = self._mosflm_cell_ref_images[0] for j in range(cri[0], 1 + cri[1]): self.input('autoindex dps refine image %d' % j) self.input('go') # get all of the stored parameter values parameters = self.get_integrater_parameters('mosflm') self.input('!parameters from autoindex run') for p in parameters.keys(): self.input('%s %s' % (p, str(parameters[p]))) detector_width = self._fp_header['size'][0] * \ self._fp_header['pixel'][0] detector_height = self._fp_header['size'][1] * \ self._fp_header['pixel'][1] lim_x = 0.5 * detector_width lim_y = 0.5 * detector_height Debug.write('Scanner limits: %.1f %.1f' % (lim_x, lim_y)) self.input('limits xscan %f yscan %f' % (lim_x, lim_y)) self.input('separation close') self.input('refinement residual 15.0') self.input('refinement include partials') self._reorder_cell_refinement_images() self.input('postref multi segments %d repeat 10' % \ len(self._mosflm_cell_ref_images)) self.input('postref maxresidual 5.0') genfile = os.path.join(os.environ['CCP4_SCR'], '%d_mosflm.gen' % self.get_xpid()) self.input('genfile %s' % genfile) for cri in self._mosflm_cell_ref_images: self.input('process %d %d' % cri) self.input('go') # that should be everything self.close_wait() # get the log file output = self.get_all_output() rms_values_last = None rms_values = None new_cycle_number = 0 new_rms_values = { } new_image_counter = None new_ignore_update = False parse_cycle = 1 parse_image = 0 background_residual = { } for i in range(len(output)): o = output[i] if 'Processing Image' in o: new_image_counter = int(o.split()[2]) parse_image = int(o.split()[2]) if 'Repeating the entire run' in o: parse_cycle += 1 if 'Background residual' in o: res = float(o.replace('residual=', '').split()[8]) if not parse_cycle in background_residual: background_residual[parse_cycle] = { } background_residual[parse_cycle][parse_image] = res if 'As this is near to the start' in o: new_ignore_update = True if 'Post-refinement will use partials' in o: if new_ignore_update: new_ignore_update = False else: new_cycle_number += 1 new_rms_values[new_cycle_number] = { } if 'Final rms residual' in o: rv = float(o.replace('mm', ' ').split()[3]) new_rms_values[new_cycle_number][new_image_counter] = rv if 'Rms positional error (mm) as a function of' in o and True: images = [] cycles = [] rms_values = { } j = i + 1 while output[j].split(): if 'Image' in output[j]: for image in map(int, output[j].replace( 'Image', '').split()): images.append(image) else: cycle = int(output[j].replace( 'Cycle', '').split()[0]) if not cycle in cycles: cycles.append(cycle) rms_values[cycle] = [] record = [output[j][k:k + 6] \ for k in range( 11, len(output[j]), 6)] data = [] for r in record: if r.strip(): data.append(r.strip()) record = data try: values = map(float, record) for v in values: rms_values[cycle].append(v) except ValueError, e: Debug.write( 'Error parsing %s as floats' % \ output[j][12:]) j += 1 # now transform back the new rms residual values # into the old structure... messy but effective! for cycle in new_rms_values.keys(): images = new_rms_values[cycle].keys() images.sort() rms_values[cycle] = [] for i in images: rms_values[cycle].append( new_rms_values[cycle][i]) rms_values_last = rms_values[max(cycles)] return rms_values, background_residual def _mosflm_refine_cell(self, set_spacegroup = None): '''Perform the refinement of the unit cell. This will populate all of the information needed to perform the integration.''' # FIXME this will die after #1285 if not self.get_integrater_indexer(): self.set_integrater_indexer(self) indxr = self.get_integrater_indexer() if not indxr.get_indexer_payload('mosflm_orientation_matrix'): raise RuntimeError, 'unexpected situation in indexing' lattice = indxr.get_indexer_lattice() mosaic = indxr.get_indexer_mosaic() cell = indxr.get_indexer_cell() beam = indxr.get_indexer_beam() # bug # 3174 - if mosaic is very small (here defined to be # 0.25 x osc_width) then set to this minimum value. if mosaic < 0.25 * self.get_header_item('phi_width'): mosaic = 0.25 * self.get_header_item('phi_width') if indxr.get_indexer_payload('mosflm_beam_centre'): beam = indxr.get_indexer_payload('mosflm_beam_centre') distance = indxr.get_indexer_distance() matrix = indxr.get_indexer_payload('mosflm_orientation_matrix') integration_params = indxr.get_indexer_payload( 'mosflm_integration_parameters') if integration_params: if integration_params.has_key('separation'): self.set_integrater_parameter( 'mosflm', 'separation', '%f %f' % tuple(integration_params['separation'])) if integration_params.has_key('raster'): self.set_integrater_parameter( 'mosflm', 'raster', '%d %d %d %d %d' % tuple(integration_params['raster'])) indxr.set_indexer_payload('mosflm_integration_parameters', None) # copy these into myself for later reference, if indexer # is not myself - everything else is copied via the # cell refinement process... if indxr != self: self.set_indexer_input_lattice(lattice) self.set_indexer_beam(beam) spacegroup_number = lattice_to_spacegroup(lattice) # FIXME surely these have been assigned further up?! if not self._mosflm_cell_ref_images: self._mosflm_cell_ref_images = self._refine_select_images( mosaic) f = open(os.path.join(self.get_working_directory(), 'xiaindex-%s.mat' % lattice), 'w') for m in matrix: f.write(m) f.close() # then start the cell refinement self.start() if self._mosflm_gain: self.input('gain %5.2f' % self._mosflm_gain) if self.get_reversephi(): detector = detector_class_to_mosflm( self.get_header_item('detector_class')) self.input('detector %s reversephi' % detector) if 'pilatus' in self.get_header_item('detector_class'): self.input('detector pilatus') self.input('template "%s"' % self.get_template()) self.input('directory "%s"' % self.get_directory()) self.input('matrix xiaindex-%s.mat' % lattice) self.input('newmat xiarefine.mat') self.input('beam %f %f' % beam) self.input('distance %f' % distance) if set_spacegroup: self.input('symmetry %s' % set_spacegroup) else: self.input('symmetry %s' % spacegroup_number) # FIXME 18/JUN/08 - it may help to have an overestimate # of the mosaic spread in here as it *may* refine down # better than up... - this is not a good idea as it may # also not refine at all! - 12972 # integration failed # Bug # 3103 if self._mosflm_cell_ref_double_mosaic: self.input('mosaic %f' % (2.0 * mosaic)) else: self.input('mosaic %f' % mosaic) if self._mosflm_postref_fix_mosaic: self.input('postref fix mosaic') # if set, use the resolution for cell refinement - see # bug # 2078... if self._mosflm_cell_ref_resolution and not \ Flags.get_microcrystal(): self.input('resolution %f' % \ self._mosflm_cell_ref_resolution) if Flags.get_microcrystal(): self.input('postref sdfac 2.0') if self.get_wavelength_prov() == 'user': self.input('wavelength %f' % self.get_wavelength()) # belt + braces mode - only to be used when considering failover, # will run an additional step of autoindexing prior to cell # refinement, to be used only after proving that not going it # will result in cell refinement failure - will use the first # wedge... if self._mosflm_cell_ref_add_autoindex: assert(Flags.get_failover()) cri = self._mosflm_cell_ref_images[0] for j in range(cri[0], 1 + cri[1]): self.input('autoindex dps refine image %d' % j) self.input('go') parameters = self.get_integrater_parameters('mosflm') self.input('!parameters from autoindex run') for p in parameters.keys(): self.input('%s %s' % (p, str(parameters[p]))) self.input('separation close') self.input('refinement residual 15') self.input('refinement include partials') detector_width = self._fp_header['size'][0] * \ self._fp_header['pixel'][0] detector_height = self._fp_header['size'][1] * \ self._fp_header['pixel'][1] lim_x = 0.5 * detector_width lim_y = 0.5 * detector_height Debug.write('Scanner limits: %.1f %.1f' % (lim_x, lim_y)) self.input('limits xscan %f yscan %f' % (lim_x, lim_y)) self._reorder_cell_refinement_images() self.input('postref multi segments %d repeat 10' % \ len(self._mosflm_cell_ref_images)) self.input('postref maxresidual 5.0') genfile = os.path.join(os.environ['CCP4_SCR'], '%d_mosflm.gen' % self.get_xpid()) self.input('genfile %s' % genfile) for cri in self._mosflm_cell_ref_images: self.input('process %d %d' % cri) self.input('go') # that should be everything self.close_wait() # get the log file output = self.get_all_output() # then look to see if the cell refinement worked ok - if it # didn't then this may indicate that the lattice was wrongly # selected. cell_refinement_ok = False for o in output: if 'Cell refinement is complete' in o: cell_refinement_ok = True if not cell_refinement_ok: if Flags.get_failover() and not \ self._mosflm_cell_ref_add_autoindex: Debug.write('Repeating cell refinement...') self.set_integrater_prepare_done(False) self._mosflm_cell_ref_add_autoindex = True return [0.0], [0.0] Chatter.write( 'Looks like cell refinement failed - more follows...') rms_values_last = None rms_values = None new_cycle_number = 0 new_rms_values = { } new_image_counter = None new_ignore_update = False parse_cycle = 1 parse_image = 0 background_residual = { } for i in range(len(output)): o = output[i] if 'Processing will be aborted' in o: raise BadLatticeError, 'cell refinement failed' if 'An unrecoverable error has occurred in MOSFLM' in o: raise BadLatticeError, 'cell refinement failed' if 'Processing Image' in o: new_image_counter = int(o.split()[2]) if 'As this is near to the start' in o: new_ignore_update = True if 'Post-refinement will use partials' in o: if new_ignore_update: new_ignore_update = False else: new_cycle_number += 1 new_rms_values[new_cycle_number] = { } if 'Final rms residual' in o: rv = float(o.replace('mm', ' ').split()[3]) new_rms_values[new_cycle_number][new_image_counter] = rv if 'Rms positional error (mm) as a function of' in o and True: images = [] cycles = [] rms_values = { } j = i + 1 while output[j].split(): if 'Image' in output[j]: for image in map(int, output[j].replace( 'Image', '').split()): images.append(image) else: cycle = int(output[j].replace( 'Cycle', '').split()[0]) if not cycle in cycles: cycles.append(cycle) rms_values[cycle] = [] record = [output[j][k:k + 6] \ for k in range( 11, len(output[j]), 6)] data = [] for r in record: if r.strip(): data.append(r.strip()) record = data try: values = map(float, record) for v in values: rms_values[cycle].append(v) except ValueError, e: Debug.write( 'Error parsing %s as floats' % \ output[j][12:]) j += 1 for cycle in new_rms_values.keys(): images = new_rms_values[cycle].keys() images.sort() rms_values[cycle] = [] for i in images: rms_values[cycle].append( new_rms_values[cycle][i]) if cycles: rms_values_last = rms_values[max(cycles)] else: rms_values_last = None # look for "error" type problems if 'is greater than the maximum allowed' in o and \ 'FINAL weighted residual' in o: Debug.write('Large weighted residual... ignoring') if 'INACCURATE CELL PARAMETERS' in o: # get the inaccurate cell parameters in question parameters = output[i + 3].lower().split() # and the standard deviations - so we can decide # if it really has failed sd_record = output[i + 5].replace( 'A', ' ').replace(',', ' ').split() sds = map(float, [sd_record[j] for j in range(1, 12, 2)]) Debug.write('Standard deviations:') Debug.write('A %4.2f B %4.2f C %4.2f' % \ (tuple(sds[:3]))) Debug.write('Alpha %4.2f Beta %4.2f Gamma %4.2f' % \ (tuple(sds[3:6]))) Debug.write( 'In cell refinement, the following cell parameters') Debug.write( 'have refined poorly:') for p in parameters: Debug.write('... %s' % p) Debug.write( 'However, will continue to integration.') if 'One or more cell parameters has changed by more' in o: # this is a more severe example of the above problem... Debug.write( 'Cell refinement is unstable...') raise BadLatticeError, 'Cell refinement failed' # other possible problems in the cell refinement - a # negative mosaic spread, for instance if 'Refined mosaic spread (excluding safety factor)' in o: mosaic = float(o.split()[-1]) if mosaic < 0.05: Debug.write('Negative mosaic spread (%5.2f)' % mosaic) raise NegativeMosaicError, 'refinement failed' parse_cycle = 1 parse_image = 0 background_residual = { } for i, o in enumerate(output): if 'Processing Image' in o: parse_image = int(o.split()[2]) if 'Repeating the entire run' in o: parse_cycle += 1 if 'Background residual' in o: res = float(o.replace('residual=', '').split()[8]) if not parse_cycle in background_residual: background_residual[parse_cycle] = { } background_residual[parse_cycle][parse_image] = res if 'Cell refinement is complete' in o: j = i + 2 refined_cell = map(float, output[j].split()[2:]) error = map(float, output[j + 1].split()[1:]) names = ['A', 'B', 'C', 'Alpha', 'Beta', 'Gamma'] Debug.write('Errors in cell parameters (relative %)') for j in range(6): Debug.write('%s\t%7.3f %5.3f %.3f' % \ (names[j], refined_cell[j], error[j], 100.0 * error[j] / refined_cell[j])) if 'Refined cell' in o: indxr._indxr_cell = tuple(map(float, o.split()[-6:])) self._intgr_cell = tuple(map(float, o.split()[-6:])) # FIXME with these are they really on one line? if 'Detector distance as a' in o: j = i + 1 while output[j].strip() != '': j += 1 distances = map(float, output[j - 1].split()[2:]) distance = 0.0 for d in distances: distance += d distance /= len(distances) indxr._indxr_refined_distance = distance if 'YSCALE as a function' in o: j = i + 1 while output[j].strip() != '': j += 1 yscales = map(float, output[j - 1].split()[2:]) yscale = 0.0 for y in yscales: yscale += y yscale /= len(yscales) self.set_integrater_parameter('mosflm', 'distortion yscale', yscale) if 'Final optimised raster parameters:' in o: self.set_integrater_parameter('mosflm', 'raster', o.split(':')[1].strip()) if 'Separation parameters updated to' in o: tokens = o.replace('mm', ' ').split() self.set_integrater_parameter('mosflm', 'separation', '%s %s' % \ (tokens[4], tokens[8])) if 'XCEN YCEN XTOFRA' in o: numbers = output[i + 1].split() self.set_integrater_parameter('mosflm', 'beam', '%s %s' % \ (numbers[0], numbers[1])) self.set_integrater_parameter('mosflm', 'distance', numbers[3]) self.set_integrater_parameter('mosflm', 'distortion tilt', numbers[5]) self.set_integrater_parameter('mosflm', 'distortion twist', numbers[6]) if 'Refined mosaic spread' in o: indxr._indxr_mosaic = float(o.split()[-1]) self.set_indexer_done(True) self.set_indexer_payload('mosflm_orientation_matrix', open( os.path.join(self.get_working_directory(), 'xiarefine.mat'), 'r').readlines()) indxr.set_indexer_payload('mosflm_orientation_matrix', open( os.path.join(self.get_working_directory(), 'xiarefine.mat'), 'r').readlines()) return rms_values, background_residual def _mosflm_integrate(self): '''Perform the actual integration, based on the results of the cell refinement or indexing (they have the equivalent form.)''' if not self.get_integrater_indexer(): self.set_integrater_indexer(self) indxr = self.get_integrater_indexer() if not indxr.get_indexer_payload('mosflm_orientation_matrix'): raise RuntimeError, 'unexpected situation in indexing' lattice = indxr.get_indexer_lattice() mosaic = indxr.get_indexer_mosaic() cell = indxr.get_indexer_cell() beam = indxr.get_indexer_beam() distance = indxr.get_indexer_distance() matrix = indxr.get_indexer_payload('mosflm_orientation_matrix') integration_params = indxr.get_indexer_payload( 'mosflm_integration_parameters') if integration_params: if integration_params.has_key('separation'): self.set_integrater_parameter( 'mosflm', 'separation', '%f %f' % tuple(integration_params['separation'])) if integration_params.has_key('raster'): self.set_integrater_parameter( 'mosflm', 'raster', '%d %d %d %d %d' % tuple(integration_params['raster'])) indxr.set_indexer_payload('mosflm_integration_parameters', None) spacegroup_number = lattice_to_spacegroup(lattice) f = open(os.path.join(self.get_working_directory(), 'xiaintegrate.mat'), 'w') for m in matrix: f.write(m) f.close() # then start the integration summary_file = 'summary_%s.log' % spacegroup_number self.add_command_line('SUMMARY') self.add_command_line(summary_file) self.start() if not self._mosflm_refine_profiles: self.input('profile nooptimise') pname, xname, dname = self.get_integrater_project_info() if pname != None and xname != None and dname != None: Debug.write('Harvesting: %s/%s/%s' % (pname, xname, dname)) harvest_dir = os.path.join(os.environ['HARVESTHOME'], 'DepositFiles', pname) if not os.path.exists(harvest_dir): Debug.write('Creating harvest directory...') os.makedirs(harvest_dir) # harvest file name will be %s.mosflm_run_start_end % dname self.input('harvest on') self.input('pname %s' % pname) self.input('xname %s' % xname) temp_dname = '%s_%s' % \ (dname, self.get_integrater_sweep_name()) self.input('dname %s' % temp_dname) # self.input('ucwd') if self.get_reversephi(): detector = detector_class_to_mosflm( self.get_header_item('detector_class')) self.input('detector %s reversephi' % detector) if 'pilatus' in self.get_header_item('detector_class'): self.input('detector pilatus') self.input('template "%s"' % self.get_template()) self.input('directory "%s"' % self.get_directory()) # check for ice - and if so, exclude (ranges taken from # XDS documentation) if self.get_integrater_ice() != 0: Debug.write('Excluding ice rings') for record in open(os.path.join( os.environ['XIA2_ROOT'], 'Data', 'ice-rings.dat')).readlines(): resol = tuple(map(float, record.split()[:2])) self.input('resolution exclude %.2f %.2f' % (resol)) # exclude specified resolution ranges if len(self.get_integrater_excluded_regions()) != 0: regions = self.get_integrater_excluded_regions() Debug.write('Excluding regions: %s' % `regions`) for upper, lower in regions: self.input('resolution exclude %.2f %.2f' % (upper, lower)) mask = standard_mask(self._fp_header['detector_class']) for m in mask: self.input(m) self.input('matrix xiaintegrate.mat') self.input('beam %f %f' % beam) self.input('distance %f' % distance) self.input('symmetry %s' % spacegroup_number) self.input('mosaic %f' % mosaic) self.input('refinement include partials') if self.get_wavelength_prov() == 'user': self.input('wavelength %f' % self.get_wavelength()) parameters = self.get_integrater_parameters('mosflm') for p in parameters.keys(): self.input('%s %s' % (p, str(parameters[p]))) if self._mosflm_gain: self.input('gain %5.2f' % self._mosflm_gain) # check for resolution limits if self._intgr_reso_high > 0.0: if self._intgr_reso_low: self.input('resolution %f %f' % (self._intgr_reso_high, self._intgr_reso_low)) else: self.input('resolution %f' % self._intgr_reso_high) if Flags.get_mask(): mask = Flags.get_mask().calculate_mask_mosflm( self.get_header()) record = 'limits quad' for m in mask: record += ' %.1f %.1f' % m self.input(record) # set up the integration self.input('postref fix all') self.input('postref maxresidual 5.0') detector_width = self._fp_header['size'][0] * \ self._fp_header['pixel'][0] detector_height = self._fp_header['size'][1] * \ self._fp_header['pixel'][1] lim_x = 0.5 * detector_width lim_y = 0.5 * detector_height Debug.write('Scanner limits: %.1f %.1f' % (lim_x, lim_y)) self.input('limits xscan %f yscan %f' % (lim_x, lim_y)) if self._mosflm_postref_fix_mosaic: self.input('postref fix mosaic') self.input('separation close') # FIXME this is a horrible hack - I at least need to # sand box this ... if self.get_header_item('detector') == 'raxis': self.input('adcoffset 0') offset = self.get_frame_offset() genfile = os.path.join(os.environ['CCP4_SCR'], '%d_mosflm.gen' % self.get_xpid()) self.input('genfile %s' % genfile) # add an extra chunk of orientation refinement if Flags.get_microcrystal(): a = self._intgr_wedge[0] - offset if self._intgr_wedge[1] - self._intgr_wedge[0] > 20: b = a + 20 else: b = self._intgr_wedge[1] - offset self.input('postref segment 1 fix all') self.input('process %d %d' % (a, b)) self.input('go') self.input('postref nosegment') self.input('process %d %d block %d' % \ (self._intgr_wedge[0] - offset, self._intgr_wedge[1] - offset, 1 + self._intgr_wedge[1] - self._intgr_wedge[0])) else: self.input('process %d %d' % (self._intgr_wedge[0] - offset, self._intgr_wedge[1] - offset)) self.input('go') # that should be everything self.close_wait() # get the log file output = self.get_all_output() integrated_images_first = 1.0e6 integrated_images_last = -1.0e6 # look for major errors for i in range(len(output)): o = output[i] if 'LWBAT: error in ccp4_lwbat' in o: raise RuntimeError, 'serious mosflm error - inspect %s' % \ self.get_log_file() mosaics = [] for i in range(len(output)): o = output[i] if 'Integrating Image' in o: batch = int(o.split()[2]) if batch < integrated_images_first: integrated_images_first = batch if batch > integrated_images_last: integrated_images_last = batch if 'Smoothed value for refined mosaic' in o: mosaics.append(float(o.split()[-1])) if 'ERROR IN DETECTOR GAIN' in o: # ignore for photon counting detectors if 'pilatus' in self.get_header_item('detector_class'): continue # look for the correct gain for j in range(i, i + 10): if output[j].split()[:2] == ['set', 'to']: gain = float(output[j].split()[-1][:-1]) # check that this is not the input # value... Bug # 3374 if self._mosflm_gain: if math.fabs(gain - self._mosflm_gain) > 0.02: self.set_integrater_parameter( 'mosflm', 'gain', gain) self.set_integrater_export_parameter( 'mosflm', 'gain', gain) Debug.write('GAIN updated to %f' % gain) self._mosflm_gain = gain self._mosflm_rerun_integration = True else: self.set_integrater_parameter( 'mosflm', 'gain', gain) self.set_integrater_export_parameter( 'mosflm', 'gain', gain) Debug.write('GAIN found to be %f' % gain) self._mosflm_gain = gain self._mosflm_rerun_integration = True # FIXME if mosaic spread refines to a negative value # once the lattice has passed the triclinic postrefinement # test then fix this by setting "POSTREF FIX MOSAIC" and # restarting. if 'Smoothed value for refined mosaic spread' in o: mosaic = float(o.split()[-1]) if mosaic < 0.0: raise IntegrationError, 'negative mosaic spread' if 'WRITTEN OUTPUT MTZ FILE' in o: self._mosflm_hklout = os.path.join( self.get_working_directory(), output[i + 1].split()[-1]) Debug.write('Integration output: %s' % \ self._mosflm_hklout) if 'Number of Reflections' in o: self._intgr_n_ref = int(o.split()[-1]) # if a BGSIG error happened try not refining the # profile and running again... if 'BGSIG too large' in o: if not self._mosflm_refine_profiles: raise RuntimeError, 'BGSIG error with profiles fixed' Debug.write( 'BGSIG error detected - try fixing profile...') self._mosflm_refine_profiles = False self.set_integrater_done(False) return if 'An unrecoverable error has occurred in GETPROF' in o: Debug.write( 'GETPROF error detected - try fixing profile...') self._mosflm_refine_profiles = False self.set_integrater_done(False) return if 'MOSFLM HAS TERMINATED EARLY' in o: Chatter.write('Mosflm has failed in integration') message = 'The input was:\n\n' for input in self.get_all_input(): message += ' %s' % input Chatter.write(message) raise RuntimeError, \ 'integration failed: reason unknown (log %s)' % \ self.get_log_file() if not self._mosflm_hklout: raise RuntimeError, 'processing abandoned' self._intgr_batches_out = (integrated_images_first, integrated_images_last) if mosaics and len(mosaics) > 0: self.set_integrater_mosaic_min_mean_max( min(mosaics), sum(mosaics) / len(mosaics), max(mosaics)) else: m = indxr.get_indexer_mosaic() self.set_integrater_mosaic_min_mean_max(m, m, m) Chatter.write('Processed batches %d to %d' % \ self._intgr_batches_out) # write the report for each image as .*-#$ to Chatter - # detailed report will be written automagically to science... parsed_output = _parse_mosflm_integration_output(output) spot_status = _happy_integrate_lp(parsed_output) # inspect the output for e.g. very high weighted residuals images = parsed_output.keys() images.sort() max_weighted_residual = 0.0 # FIXME bug 2175 this should probably look at the distribution # of values rather than the peak, since this is probably a better # diagnostic of a poor lattice. residuals = [] for i in images: if parsed_output[i].has_key('weighted_residual'): residuals.append(parsed_output[i]['weighted_residual']) mean, sd = mean_sd(residuals) Chatter.write('Weighted RMSD: %.2f (%.2f)' % \ (mean, sd)) for i in images: data = parsed_output[i] if data.has_key('weighted_residual'): if data['weighted_residual'] > max_weighted_residual: max_weighted_residual = data['weighted_residual'] if len(spot_status) > 60: Chatter.write('Integration status per image (60/record):') else: Chatter.write('Integration status per image:') for chunk in [spot_status[i:i + 60] \ for i in range(0, len(spot_status), 60)]: Chatter.write(chunk) Chatter.write( '"o" => good "%" => ok "!" => bad rmsd') Chatter.write( '"O" => overloaded "#" => many bad "." => blank') Chatter.write( '"@" => abandoned') Chatter.write('Mosaic spread: %.3f < %.3f < %.3f' % \ self.get_integrater_mosaic_min_mean_max()) # gather the statistics from the postrefinement try: postref_result = _parse_summary_file( os.path.join(self.get_working_directory(), summary_file)) except AssertionError, e: postref_result = { } # now write this to a postrefinement log postref_log = os.path.join(self.get_working_directory(), 'postrefinement.log') fout = open(postref_log, 'w') fout.write('$TABLE: Postrefinement for %s:\n' % \ self._intgr_sweep_name) fout.write('$GRAPHS: Missetting angles:A:1, 2, 3, 4: $$\n') fout.write('Batch PhiX PhiY PhiZ $$ Batch PhiX PhiY PhiZ $$\n') for image in sorted(postref_result): phix = postref_result[image].get('phix', 0.0) phiy = postref_result[image].get('phiy', 0.0) phiz = postref_result[image].get('phiz', 0.0) fout.write('%d %5.2f %5.2f %5.2f\n' % \ (image, phix, phiy, phiz)) fout.write('$$\n') fout.close() if self.get_integrater_sweep_name(): pname, xname, dname = self.get_integrater_project_info() FileHandler.record_log_file('%s %s %s %s postrefinement' % \ (self.get_integrater_sweep_name(), pname, xname, dname), postref_log) return self._mosflm_hklout def _mosflm_parallel_integrate(self): '''Perform the integration as before, but this time as a number of parallel Mosflm jobs (hence, in separate directories) and including a step of pre-refinement of the mosaic spread and missets. This will all be kind of explicit and hence probably messy!''' # ok, in here try to get the missetting angles at two "widely # spaced" points, so that the missetting angle calculating # expert can do it's stuff. figured = False if figured: offset = self.get_frame_offset() start = self._intgr_wedge[0] - offset end = self._intgr_wedge[1] - offset next = start + \ int(round(90.0 / self.get_header_item('phi_width'))) if next > end: next = end end -= 3 # right, run: wd = os.path.join(self.get_working_directory(), 'misset' % j) if not os.path.exists(wd): os.makedirs(wd) # create the Driver, configure job = DriverFactory.Driver(self._mosflm_driver_type) job.set_executable(self.get_executable()) job.set_working_directory(wd) auto_logfiler(job) # FIXME why am I getting the cell constants and so on from the # indexer?! Because that is where the _integrate_prepare step # stores them... interesting! if not self.get_integrater_indexer(): # should I raise a RuntimeError here?! self.set_integrater_indexer(self) indxr = self.get_integrater_indexer() lattice = indxr.get_indexer_lattice() spacegroup_number = lattice_to_spacegroup(lattice) mosaic = indxr.get_indexer_mosaic() cell = indxr.get_indexer_cell() beam = indxr.get_indexer_beam() distance = indxr.get_indexer_distance() matrix = indxr.get_indexer_payload('mosflm_orientation_matrix') # integration parameters - will have to be copied to all # of the running Mosflm instances... integration_params = indxr.get_indexer_payload( 'mosflm_integration_parameters') if integration_params: if integration_params.has_key('separation'): self.set_integrater_parameter( 'mosflm', 'separation', '%f %f' % tuple(integration_params['separation'])) if integration_params.has_key('raster'): self.set_integrater_parameter( 'mosflm', 'raster', '%d %d %d %d %d' % tuple(integration_params['raster'])) indxr.set_indexer_payload('mosflm_integration_parameters', None) pname, xname, dname = self.get_integrater_project_info() # what follows below should (i) be run in separate directories # and (ii) be repeated N=parallel times. parallel = Flags.get_parallel() if not parallel: raise RuntimeError, 'parallel not set' if parallel < 2: raise RuntimeError, 'parallel not parallel: %s' % parallel jobs = [] hklouts = [] # reindex_ops = [] nref = 0 # calculate the chunks to use offset = self.get_frame_offset() start = self._intgr_wedge[0] - offset end = self._intgr_wedge[1] - offset left_images = 1 + end - start left_chunks = parallel chunks = [] while left_images > 0: size = left_images // left_chunks chunks.append((start, start + size - 1)) start += size left_images -= size left_chunks -= 1 summary_files = [] for j in range(parallel): # make some working directories, as necessary - chunk-(0:N-1) wd = os.path.join(self.get_working_directory(), 'chunk-%d' % j) if not os.path.exists(wd): os.makedirs(wd) # create the Driver, configure job = DriverFactory.Driver(self._mosflm_driver_type) job.set_executable(self.get_executable()) job.set_working_directory(wd) auto_logfiler(job) # copy in the clibd/syminfo.lib to the working directory syminfo = os.path.join(os.environ['CLIBD'], 'syminfo.lib') shutil.copyfile(syminfo, os.path.join(wd, 'syminfo.lib')) # then tell the job about it job.set_working_environment('CLIBD', wd) l = indxr.get_indexer_lattice() # create the starting point f = open(os.path.join(wd, 'xiaintegrate-%s.mat' % l), 'w') for m in matrix: f.write(m) f.close() spacegroup_number = lattice_to_spacegroup(lattice) summary_file = 'summary_%s.log' % spacegroup_number job.add_command_line('SUMMARY') job.add_command_line(summary_file) summary_files.append(os.path.join(wd, summary_file)) job.start() if not self._mosflm_refine_profiles: job.input('profile nooptimise') # N.B. for harvesting need to append N to dname. if pname != None and xname != None and dname != None: Debug.write('Harvesting: %s/%s/%s' % (pname, xname, dname)) harvest_dir = os.path.join(os.environ['HARVESTHOME'], 'DepositFiles', pname) if not os.path.exists(harvest_dir): Debug.write('Creating harvest directory...') os.makedirs(harvest_dir) job.input('harvest on') job.input('pname %s' % pname) job.input('xname %s' % xname) temp_dname = '%s_%s' % \ (dname, self.get_integrater_sweep_name()) job.input('dname %s' % temp_dname) if self.get_reversephi(): detector = detector_class_to_mosflm( self.get_header_item('detector_class')) job.input('detector %s reversephi' % detector) if 'pilatus' in self.get_header_item('detector_class'): job.input('detector pilatus') job.input('template "%s"' % self.get_template()) job.input('directory "%s"' % self.get_directory()) # check for ice - and if so, exclude (ranges taken from # XDS documentation) if self.get_integrater_ice() != 0: Debug.write('Excluding ice rings') for record in open(os.path.join( os.environ['XIA2_ROOT'], 'Data', 'ice-rings.dat')).readlines(): resol = tuple(map(float, record.split()[:2])) job.input('resolution exclude %.2f %.2f' % (resol)) # exclude specified resolution ranges if len(self.get_integrater_excluded_regions()) != 0: regions = self.get_integrater_excluded_regions() Debug.write('Excluding regions: %s' % `regions`) for upper, lower in regions: job.input('resolution exclude %.2f %.2f' % \ (upper, lower)) # generate the mask information from the detector class mask = standard_mask(self._fp_header['detector_class']) for m in mask: job.input(m) # suggestion from HRP 10/AUG/09 job.input('matrix xiaintegrate-%s.mat' % l) # job.input('target xiaintegrate.mat') job.input('beam %f %f' % beam) job.input('distance %f' % distance) job.input('symmetry %s' % spacegroup_number) job.input('mosaic %f' % mosaic) # TEST: re-autoindex the pattern to see if the problem # with the cell refinement convergence radius goes away... # # This doesn't work, lots of other problems => calculate the # right missets ab initio. # # a, b = chunks[j] # job.input('autoindex dps refine image %d' % a) # job.input('autoindex dps refine image %d' % b) # job.input('newmat processed.mat') # job.input('go') if self._mosflm_postref_fix_mosaic: job.input('postref fix mosaic') job.input('refinement include partials') # note well that the beam centre is coming from indexing so # should be already properly handled - likewise the distance if self.get_wavelength_prov() == 'user': job.input('wavelength %f' % self.get_wavelength()) # get all of the stored parameter values parameters = self.get_integrater_parameters('mosflm') for p in parameters.keys(): job.input('%s %s' % (p, str(parameters[p]))) # in here I need to get the GAIN parameter from the sweep # or from somewhere in memory.... if self._mosflm_gain: job.input('gain %5.2f' % self._mosflm_gain) # check for resolution limits if self._intgr_reso_high > 0.0: if self._intgr_reso_low: job.input('resolution %f %f' % (self._intgr_reso_high, self._intgr_reso_low)) else: job.input('resolution %f' % self._intgr_reso_high) if Flags.get_mask(): mask = Flags.get_mask().calculate_mask_mosflm( self.get_header()) record = 'limits quad' for m in mask: record += ' %.1f %.1f' % m job.input(record) # set up the integration job.input('postref fix all') # fudge this needs to be fixed. FIXME! job.input('postref maxresidual 5.0') # compute the detector limits to use for this... # these are w.r.t. the beam centre and are there to # ensure that spots are not predicted off the detector # (see bug # 2551) detector_width = self._fp_header['size'][0] * \ self._fp_header['pixel'][0] detector_height = self._fp_header['size'][1] * \ self._fp_header['pixel'][1] lim_x = 0.5 * detector_width lim_y = 0.5 * detector_height Debug.write('Scanner limits: %.1f %.1f' % (lim_x, lim_y)) job.input('limits xscan %f yscan %f' % (lim_x, lim_y)) # FIXME somewhere here need to include the pre-refinement # step... start with reprocessing first 4 images... a, b = chunks[j] if b - a > 3: b = a + 3 job.input('postref multi segments 1') job.input('process %d %d' % (a, b)) job.input('go') job.input('postref nosegment') if self._mosflm_postref_fix_mosaic: job.input('postref fix mosaic') job.input('separation close') # FIXME this is a horrible hack - I at least need to # sand box this ... if self.get_header_item('detector') == 'raxis': job.input('adcoffset 0') genfile = os.path.join(os.environ['CCP4_SCR'], '%d_%d_mosflm.gen' % (self.get_xpid(), j)) job.input('genfile %s' % genfile) job.input('process %d %d' % chunks[j]) job.input('go') # these are now running so ... jobs.append(job) continue # ok, at this stage I need to ... # # (i) accumulate the statistics as a function of batch # (ii) mong them into a single block # # This is likely to be a pain in the arse! first_integrated_batch = 1.0e6 last_integrated_batch = -1.0e6 all_residuals = [] all_spot_status = '' threads = [] for j in range(parallel): job = jobs[j] # now wait for them to finish - first wait will really be the # first one, then all should be finished... thread = Background(job, 'close_wait') thread.start() threads.append(thread) mosaics = [] for j in range(parallel): thread = threads[j] thread.stop() job = jobs[j] # get the log file output = job.get_all_output() # record a copy of it, perhaps - though not if parallel if self.get_integrater_sweep_name() and False: pname, xname, dname = self.get_integrater_project_info() FileHandler.record_log_file( '%s %s %s %s mosflm integrate' % \ (self.get_integrater_sweep_name(), pname, xname, '%s_%d' % (dname, j)), job.get_log_file()) # look for things that we want to know... # that is, the output reflection file name, the updated # value for the gain (if present,) any warnings, errors, # or just interesting facts. integrated_images_first = 1.0e6 integrated_images_last = -1.0e6 # look for major errors for i in range(len(output)): o = output[i] if 'LWBAT: error in ccp4_lwbat' in o: raise RuntimeError, 'serious error - inspect %s' % \ self.get_log_file() for i in range(len(output)): o = output[i] if 'Integrating Image' in o: batch = int(o.replace('Image', 'Image ').split()[2]) if batch < integrated_images_first: integrated_images_first = batch if batch > integrated_images_last: integrated_images_last = batch if batch < first_integrated_batch: first_integrated_batch = batch if batch > last_integrated_batch: last_integrated_batch = batch if 'Smoothed value for refined mosaic' in o: mosaics.append(float(o.split()[-1])) if 'ERROR IN DETECTOR GAIN' in o: # ignore for photon counting detectors if 'pilatus' in self.get_header_item('detector_class'): continue # look for the correct gain for j in range(i, i + 10): if output[j].split()[:2] == ['set', 'to']: gain = float(output[j].split()[-1][:-1]) # check that this is not the input # value... Bug # 3374 if self._mosflm_gain: if math.fabs( gain - self._mosflm_gain) > 0.02: self.set_integrater_parameter( 'mosflm', 'gain', gain) self.set_integrater_export_parameter( 'mosflm', 'gain', gain) Debug.write( 'GAIN updated to %f' % gain) self._mosflm_gain = gain self._mosflm_rerun_integration = True else: self.set_integrater_parameter( 'mosflm', 'gain', gain) self.set_integrater_export_parameter( 'mosflm', 'gain', gain) Debug.write('GAIN found to be %f' % gain) self._mosflm_gain = gain self._mosflm_rerun_integration = True # FIXME if mosaic spread refines to a negative value # once the lattice has passed the triclinic postrefinement # test then fix this by setting "POSTREF FIX MOSAIC" and # restarting. if 'Smoothed value for refined mosaic spread' in o: mosaic = float(o.split()[-1]) if mosaic < 0.0: raise IntegrationError, 'negative mosaic spread' if 'WRITTEN OUTPUT MTZ FILE' in o: hklout = os.path.join( job.get_working_directory(), output[i + 1].split()[-1]) Debug.write('Integration output: %s' % hklout) hklouts.append(hklout) if 'Number of Reflections' in o: nref += int(o.split()[-1]) if 'BGSIG too large' in o: Debug.write( 'BGSIG error detected - try fixing profile...') self._mosflm_refine_profiles = False self.set_integrater_done(False) return if 'An unrecoverable error has occurred in GETPROF' in o: Debug.write( 'GETPROF error detected - try fixing profile...') self._mosflm_refine_profiles = False self.set_integrater_done(False) return if 'MOSFLM HAS TERMINATED EARLY' in o: Chatter.write('Mosflm has failed in integration') message = 'The input was:\n\n' for input in self.get_all_input(): message += ' %s' % input Chatter.write(message) raise RuntimeError, \ 'integration failed: reason unknown (log %s)' % \ self.get_log_file() # here # write the report for each image as .*-#$ to Chatter - # detailed report will be written automagically to science... parsed_output = _parse_mosflm_integration_output(output) spot_status = _happy_integrate_lp(parsed_output) # inspect the output for e.g. very high weighted residuals images = parsed_output.keys() images.sort() max_weighted_residual = 0.0 residuals = [] for i in images: if parsed_output[i].has_key('weighted_residual'): residuals.append(parsed_output[i]['weighted_residual']) for r in residuals: all_residuals.append(r) for s in spot_status: all_spot_status += s # concatenate all of the output lines to our own output # channel (may be messy, but nothing better presents itself... # yuck, this involves delving in to the Driver interface... for record in output: self._standard_output_records.append(record) if not self._log_file is None: self._log_file.write(record) self._intgr_batches_out = (first_integrated_batch, last_integrated_batch) if mosaics and len(mosaics) > 0: self.set_integrater_mosaic_min_mean_max( min(mosaics), sum(mosaics) / len(mosaics), max(mosaics)) else: m = indxr.get_indexer_mosaic() self.set_integrater_mosaic_min_mean_max(m, m, m) Chatter.write('Processed batches %d to %d' % \ self._intgr_batches_out) spot_status = all_spot_status if len(spot_status) > 60: Chatter.write('Integration status per image (60/record):') else: Chatter.write('Integration status per image:') for chunk in [spot_status[i:i + 60] \ for i in range(0, len(spot_status), 60)]: Chatter.write(chunk) Chatter.write( '"o" => good "%" => ok "!" => bad rmsd') Chatter.write( '"O" => overloaded "#" => many bad "." => blank') Chatter.write( '"@" => abandoned') Chatter.write('Mosaic spread: %.3f < %.3f < %.3f' % \ self.get_integrater_mosaic_min_mean_max()) # gather the statistics from the postrefinement for all sweeps postref_result = { } for summary in summary_files: try: update = _parse_summary_file(summary) except AssertionError, e: update = { } postref_result.update(update) # now write this to a postrefinement log postref_log = os.path.join(self.get_working_directory(), 'postrefinement.log') fout = open(postref_log, 'w') fout.write('$TABLE: Postrefinement for %s:\n' % \ self._intgr_sweep_name) fout.write('$GRAPHS: Missetting angles:A:1, 2, 3, 4: $$\n') fout.write('Batch PhiX PhiY PhiZ $$ Batch PhiX PhiY PhiZ $$\n') for image in sorted(postref_result): phix = postref_result[image].get('phix', 0.0) phiy = postref_result[image].get('phiy', 0.0) phiz = postref_result[image].get('phiz', 0.0) fout.write('%d %5.2f %5.2f %5.2f\n' % \ (image, phix, phiy, phiz)) fout.write('$$\n') fout.close() if self.get_integrater_sweep_name(): pname, xname, dname = self.get_integrater_project_info() FileHandler.record_log_file('%s %s %s %s postrefinement' % \ (self.get_integrater_sweep_name(), pname, xname, dname), postref_log) hklouts.sort() hklout = os.path.join(self.get_working_directory(), os.path.split(hklouts[0])[-1]) Debug.write('Sorting data to %s' % hklout) for hklin in hklouts: Debug.write('<= %s' % hklin) sortmtz = Sortmtz() sortmtz.set_hklout(hklout) for hklin in hklouts: sortmtz.add_hklin(hklin) sortmtz.sort() self._mosflm_hklout = hklout return self._mosflm_hklout def _reorder_cell_refinement_images(self): if not self._mosflm_cell_ref_images: raise RuntimeError, 'no cell refinement images to reorder' hashmap = { } for m in self._mosflm_cell_ref_images: hashmap[m[0]] = m[1] keys = hashmap.keys() keys.sort() cell_ref_images = [(k, hashmap[k]) for k in keys] self._mosflm_cell_ref_images = cell_ref_images return def set_integrater_resolution(self, dmin, dmax, user = False): if user: Integrater.set_integrater_resolution(self, dmin, dmax, user) return def set_integrater_high_resolution(self, dmin, user = False): if user: Integrater.set_integrater_high_resolution(self, dmin, user) return def set_integrater_low_resolution(self, dmax, user = False): self._intgr_reso_low = dmax return return MosflmWrapper()