def __init__(self,arg_module,phil_params,verbose=True): # support the many-image-in-one-H5-container paradigm if phil_params.distl.range is not None: # range parameter only intended for H5 files assert len(self.filenames())==1 # can be only one H5 master file if there is a range of image indices if len(phil_params.distl.range)==1: self.unrolled_range = phil_params.distl.range else: self.unrolled_range = range(phil_params.distl.range[0],phil_params.distl.range[1]) self.filenames.FN = [self.filenames.FN[0]]*len(self.unrolled_range) self.frames = self.h5_frames self.imageindex = self.h5_imageindex self.imagepath = self.h5_imagepath import copy for indx,name in enumerate(self.filenames()): if indx==0: A = ImageFactory(name,optional_index=self.unrolled_range[indx]) self.site_modifications(A,self.filenames.FN[indx]) self.images.append(A) else: Acopy = copy.deepcopy(A) Acopy.img_number = self.unrolled_range[indx] self.images.append(Acopy) else: # range is not present; normal behavior for non-H5 images for indx,name in enumerate(self.filenames()): A = ImageFactory(name) self.site_modifications(A,self.filenames.FN[indx]) self.images.append(A)
def main(filenames, map_file, npoints=192, max_resolution=6, reverse_phi=False): rec_range = 1 / max_resolution image = ImageFactory(filenames[0]) panel = image.get_detector()[0] beam = image.get_beam() s0 = beam.get_s0() pixel_size = panel.get_pixel_size() xlim, ylim = image.get_raw_data().all() xy = recviewer.get_target_pixels(panel, s0, xlim, ylim, max_resolution) s1 = panel.get_lab_coord(xy * pixel_size[0]) # FIXME: assumed square pixel s1 = s1 / s1.norms() * (1 / beam.get_wavelength()) # / is not supported... S = s1 - s0 grid = flex.double(flex.grid(npoints, npoints, npoints), 0) cnts = flex.int(flex.grid(npoints, npoints, npoints), 0) for filename in filenames: print "Processing image", filename try: fill_voxels(ImageFactory(filename), grid, cnts, S, xy, reverse_phi, rec_range) except: print " Failed to process. Skipped this." recviewer.normalize_voxels(grid, cnts) uc = uctbx.unit_cell((npoints, npoints, npoints, 90, 90, 90)) ccp4_map.write_ccp4_map(map_file, uc, sgtbx.space_group("P1"), (0, 0, 0), grid.all(), grid, flex.std_string(["cctbx.miller.fft_map"])) return from scitbx import fftpack fft = fftpack.complex_to_complex_3d(grid.all()) grid_complex = flex.complex_double(reals=flex.pow2(grid), imags=flex.double(grid.size(), 0)) grid_transformed = flex.abs(fft.backward(grid_complex)) print flex.max(grid_transformed), flex.min( grid_transformed), grid_transformed.all() ccp4_map.write_ccp4_map(map_file, uc, sgtbx.space_group("P1"), (0, 0, 0), grid.all(), grid_transformed, flex.std_string(["cctbx.miller.fft_map"]))
def __init__(self,arg_module,verbose=True): self.verbose = verbose self.filenames = file_names(arg_module) self.images = [] for indx,name in enumerate(self.filenames()): A = ImageFactory(name) self.images.append(A)
def get_detector_file(image): """ Returns the RAPD detector file given an image file """ # print "get_detector_file %s" % image try: i = ImageFactory(image) # print i.vendortype # print i.parameters["DETECTOR_SN"] except (IOError, AttributeError, RuntimeError): print error return False # print ">>>%s<<<" % i.vendortype # print ">>>%s<<<" % i.parameters["DETECTOR_SN"] v_type = i.vendortype.strip() sn = str(i.parameters["DETECTOR_SN"]).strip() # pprint(detector_list.DETECTORS) if (v_type, sn) in detector_list.DETECTORS: # print "%s: %s %s %s" % (image, detector_list.DETECTORS[(v_type, sn)], v_type, sn) return detector_list.DETECTORS[(v_type, sn)] else: return False
def print_detector_info(image): """ Print out information on the detector given an image """ image_basename = os.path.basename(image) try: i = ImageFactory(image) except IOError as e: if "no format support found for" in e.message: print "No format support for %s" % image_basename return False else: print e return False except AttributeError as e: if "object has no attribute 'detectorbase'" in e.message: print "No format support for %s" % image_basename return False else: print text.red + e.message + text.stop return False print "\nInformation from iotbx ImageFactory" print "=====================================" print "%20s::%s" % ("image", image_basename) print "%20s::%s" % ("vendortype", str(i.vendortype)) # print "%20s" % "Parameters" for key, val in i.parameters.iteritems(): print "%20s::%s" % (key, val)
def set_image(self, file_name_or_data, metrology_matrices=None, get_raw_data=None): self.reset_the_cache() if file_name_or_data is None: self.raw_image = None return if type(file_name_or_data) is type(""): from iotbx.detectors import ImageFactory self.raw_image = ImageFactory(file_name_or_data) self.raw_image.read() else: try: self.raw_image = file_name_or_data._raw except AttributeError: self.raw_image = file_name_or_data # print "SETTING NEW IMAGE",self.raw_image.filename # XXX Since there doesn't seem to be a good way to refresh the # image (yet), the metrology has to be applied here, and not # in frame.py. detector = self.raw_image.get_detector() if len(detector) > 1 and metrology_matrices is not None: self.raw_image.apply_metrology_from_matrices(metrology_matrices) if get_raw_data is not None: self.raw_image.set_raw_data(get_raw_data(self.raw_image)) raw_data = self.raw_image.get_raw_data() if not isinstance(raw_data, tuple): raw_data = (raw_data, ) if len(detector) > 1: self.flex_image = _get_flex_image_multipanel( brightness=self.current_brightness / 100, panels=detector, show_untrusted=self.show_untrusted, raw_data=raw_data, beam=self.raw_image.get_beam(), color_scheme=self.current_color_scheme, ) else: self.flex_image = _get_flex_image( brightness=self.current_brightness / 100, data=raw_data[0], saturation=self.raw_image.get_detector() [0].get_trusted_range()[1], vendortype=self.raw_image.get_vendortype(), show_untrusted=self.show_untrusted, color_scheme=self.current_color_scheme, ) if self.zoom_level >= 0: self.flex_image.adjust(color_scheme=self.current_color_scheme)
def __init__(self, arg_module, phil_params, verbose=True): self.verbose = verbose self.filenames = file_names(arg_module) self.phil_params = phil_params self.images = [] for indx, name in enumerate(self.filenames()): A = ImageFactory(name) self.site_modifications(A, self.filenames.FN[indx]) self.images.append(A) self.acceptable_use_tests_basic()
def __init__ (self, file_name) : screen_params.__init__(self) # XXX major hack - Boost.Python doesn't really deal with Unicode strings if isinstance(file_name, unicode) : file_name = str(file_name) if isinstance(file_name, str) or isinstance(file_name, dict): self.file_name = file_name from iotbx.detectors import ImageFactory, ImageException try : img = ImageFactory(file_name) except ImageException, e : raise Sorry(str(e)) img.read()
def read_cmos_image(f, read_data=True, fast=True): h = {} data = None get_after = lambda l: l[l.index("=") + 1:].rstrip(";\n ") if fast: for l in open(f): if "}" in l: break if l.startswith("SIZE1="): h["size1"] = int(get_after(l)) elif l.startswith("SIZE2="): h["size2"] = int(get_after(l)) elif l.startswith("TYPE="): assert "unsigned_short" in l elif l.startswith("PIXEL_SIZE="): h["pixel_size"] = float(get_after(l)) elif l.startswith("DISTANCE="): h["distance"] = float(get_after(l)) elif l.startswith("WAVELENGTH="): h["wavelength"] = float(get_after(l)) elif l.startswith("BEAM_CENTER_X="): h["beamx"] = float(get_after(l)) elif l.startswith("BEAM_CENTER_Y="): h["beamy"] = float(get_after(l)) h["orgx"], h["orgy"] = h["beamx"] / h["pixel_size"], h["beamy"] / h[ "pixel_size"] if read_data: ifs = open(f, "rb") ifs.seek(-h["size1"] * h["size2"] * 2, 2) data = numpy.fromfile(ifs, dtype=numpy.uint16).reshape( h["size2"], h["size1"]) else: from iotbx.detectors import ImageFactory im = ImageFactory(f) h["orgx"], h[ "orgy"] = im.beamx / im.pixel_size, im.beamy / im.pixel_size h["wavelength"] = im.wavelength h["distance"] = im.distance if read_data: im.read() data = numpy.array(im.linearintdata, dtype=numpy.uint16).reshape(im.size2, im.size1) return h, data
def __init__(self, file_name): screen_params.__init__(self) # XXX major hack - Boost.Python doesn't really deal with Unicode strings if isinstance(file_name, unicode): file_name = str(file_name) if isinstance(file_name, str) or isinstance(file_name, dict): self.file_name = file_name from iotbx.detectors import ImageFactory, ImageException try: img = ImageFactory(file_name) except ImageException as e: raise Sorry(str(e)) img.read() else: img = file_name # assume it's already been read self._raw = img try: img.show_header() except Exception: pass # intentional detector = self._raw.get_detector() if len(detector) == 1: # Image size only makes sense for monolithic detectors. image_size = detector[0].get_image_size() self.set_image_size(w=image_size[0], h=image_size[1]) pixel_size = detector[0].get_pixel_size() for panel in detector: pstest = panel.get_pixel_size() assert pixel_size[0] == pixel_size[1] == pstest[0] == pstest[1] self.set_detector_resolution(pixel_size[0]) try: from spotfinder.command_line.signal_strength import master_params params = master_params.extract() self._raw.initialize_viewer_properties(params) except Exception: pass # intentional self._beam_center = None self._integration = None self._spots = None self._color_scheme = None
def run(img_in): im = ImageFactory(img_in) im.read() print dir(im) print im.size2, im.size1 data = numpy.array(im.linearintdata, dtype=numpy.uint16).reshape(im.size2, im.size1) print data, data.dtype prefix = os.path.basename(img_in) of = h5py.File("%s_byteoffset.h5" % prefix, "w") grp = of.create_group("data") dset = grp.create_dataset(prefix, data.shape, dtype=data.dtype, compression=CBF_BYTE_OFFSET) dset[...] = data of.close()
def _try_as_img(self): from iotbx.detectors import ImageFactory img = ImageFactory(self.file_name) img.read() self._file_type = "img" self._file_object = img
from __future__ import division from __future__ import print_function import sys import numpy from iotbx.detectors import ImageFactory from matplotlib import pyplot as plt image = ImageFactory(sys.argv[1]) image.read() nfast = image.parameters["SIZE1"] nslow = image.parameters["SIZE2"] data = image.get_raw_data() print("here 1") data2d = numpy.reshape(numpy.array(data, dtype=float), (nfast, nslow)) print("here 2") data2dsmoth = numpy.zeros(nfast * nslow, dtype=float).reshape(nfast, nslow) diffdata2d = numpy.zeros(nfast * nslow, dtype=float).reshape(nfast, nslow) print("nslow, nfast =", nslow, nfast) print("max(data2d) =", numpy.max(data2d)) print("min(data2d) =", numpy.min(data2d)) for f in range(1, nfast - 1): for s in range(1, nslow - 1): pscan = float(numpy.sum(data2d[f - 1 : f + 1, s - 1 : s + 1]) / 9.0) data2dsmoth[f, s] = pscan print("max(data2dsmoth) =", numpy.max(data2dsmoth))
def createInput(image_dir, site, logger): logger.debug('createInput') #import glob from iotbx.detectors import ImageFactory try: img_site_id, img_site, c_site, imgs = site l1 = [] l2 = [] d = {} pids = [] vendortype = ImageFactory(imgs[0]).vendortype if vendortype == 'ADSC-HF4M': from detectors.rapd_adsc import Hf4mReadHeader as readHeader elif vendortype == 'Pilatus-6M': from detectors.rapd_pilatus import pilatus_read_header as readHeader elif vendortype == 'ADSC': from detectors.rapd_adsc import Q315ReadHeader as readHeader else: from detectors.mar import MarReadHeader as readHeader l = [p for p in imgs if p.count('priming_shot') == False ] #Remove priming shot (NE-CAT ONLY) for x in range(2): for i in l: if x == 0: #Get headers first header = readHeader(i) #Send images to ImageFactory to read the header (TODO). #Send images to RAM on all cluster nodes first. if RAM == True: image_path = os.path.join( '/dev/shm', os.path.basename(header.get('fullname'))) command = 'cp %s %s' % (header.get('fullname'), image_path) #ONLY work at NE-CAT job = Process(target=Utils.rocksCommand, args=(command, logger)) job.start() pids.append(job) else: image_path = header.get('fullname') x1, y1 = calc_beamcenter(round(header.get('distance'))) l1.append({#'beam_center_x' : round(header.get('beam_center_x'),3), #'beam_center_y' : round(header.get('beam_center_y'),3), #'beam_center_x' : 150.049, #BM #'beam_center_y' : 151.148, #BM #'beam_center_x' : 151.186, #ID #'beam_center_y' : 144.821, #ID #'beam_center_x' : 150.31, #BM #'beam_center_y' : 149.53, #BM 'beam_center_x' : x1, #BM 'beam_center_y' : y1, #BM 'spacegroup' : SPACEGROUP, 'fullname' : image_path, 'distance' : round(header.get('distance')), 'vendortype' : vendortype, }) else: #Then sort by distance for input l3 = [] if os.path.basename(i) not in (l2): for y in range(2): for z in range(len(l1)): if y == 0: if os.path.basename(i) == os.path.basename( l1[z].get('fullname')): dist = l1[z].get('distance') l3.append(l1[z]) l2.append( os.path.basename( l1[z].get('fullname'))) elif l1[z].get('distance') == dist: if os.path.basename( l1[z].get('fullname')) not in l2: l3.append(l1[z]) l2.append( os.path.basename( l1[z].get('fullname'))) d[str(dist)] = tuple(l3[:2]) #wait for images to be copied to RAM. if RAM == True: while len(pids) != 0: for job in pids: if job.is_alive() == False: pids.remove(job) time.sleep(1) inp = { 'directories': { 'work': WORK_DIR }, 'info': d, 'command': 'INDEX+STRATEGY', 'preferences': { "sample_type": "Protein", "beam_flip": "False", "multiprocessing": "True", "a": 0.0, "b": 0.0, "c": 0.0, "alpha": 0.0, "beta": 0.0, "gamma": 0.0 }, 'site_parameters': { 'img_site_id': img_site_id, 'img_site': img_site, 'cluster_site': c_site }, 'return_address': ("127.0.0.1", 50000) } Utils.pp(inp) return (inp) except: logger.exception('**Error in Handler.postprocess**') print 'Could not create input script from folder specified!' return (None)