def update_probe_nonmodal(self, i, psi_old, psi_new):
d1, d2 = self.positions.data
id1, id2 = d1//1, d2//1
object_intensity_max = (abs(self.object)**2.0).data[0].max()
self.probe.modes[0] += \
CXData.shift(conj(self.object[id1[i] - self.p2:id1[i] + self.p2, id2[i] - self.p2:id2[i] + self.p2]) *
(psi_new-psi_old)[0] / object_intensity_max, d1[i]%1, d2[i]%1)
self.probe.normalise()
def preprocessing(self):
""".. method:: preprocessing()
Collects together all the preprocessing functions that are required to begin phase retrieval.
"""
# Get the scan positions
self.positions = CXData(name='positions', data=[])
self.ptycho_mesh()
if CXP.measurement.simulate_data:
self.simulate_data()
else:
# Read in raw data
self.det_mod = CXData(name = 'det_mod')
if CXP.actions.preprocess_data:
self.det_mod.read_in_data()
else:
self.det_mod.load()
if CXP.io.whitefield_filename:
self.probe_det_mod = CXData(name='probe_det_mod')
self.probe_det_mod.preprocess_data()
self.object = CXData(name='object', data=[sp.zeros((self.ob_p, self.ob_p), complex)])
self.probe_intensity = CXData(name='probe_intensity', data=[sp.zeros((self.p, self.p))])
self.probe = CXModal(modes=[])
self.psi = CXModal(modes=[])
for i in range(CXP.reconstruction.probe_modes):
self.probe.modes.append(CXData(name='probe{:d}'.format(i), data=[sp.zeros((self.p, self.p), complex)]))
self.psi.modes.append(CXData(name='psi{:d}'.format(i), data=[sp.zeros((self.p, self.p), complex) for i in xrange(self.det_mod.len())]))
self.init_probe()
# Calculate STXM image if this is a ptycho scan
if len(self.det_mod.data) > 1:
self.calc_stxm_image()
if CXP.actions.process_dpc:
self.process_dpc()
def update_probe(self, i, psi_old, psi_new):
d1, d2 = self.positions.data
id1, id2 = d1//1, d2//1
object_intensity_max = (abs(self.object)**2.0).data[0].max()
for mode in range(len(self.probe)):
self.probe.modes[mode] += \
CXData.shift(conj(self.object[id1[i] - self.p2:id1[i] + self.p2, id2[i] - self.p2:id2[i] + self.p2]) *
(psi_new-psi_old)[mode] / object_intensity_max, d1[i]%1, d2[i]%1)
self.probe.normalise()
self.probe.orthogonalise()
def update_object(self, i, psi_old, psi_new):
"""
Update the object from a single ptycho position.
"""
then=time.time()
d1, d2 = self.positions.data
id1, id2 = d1//1, d2//1
probe_intensity_max = CXModal.modal_sum(abs(self.probe)**2.0).data[0].max()
self.object[id1[i] - self.p2:id1[i] + self.p2, id2[i] - self.p2:id2[i] + self.p2] += \
CXData.shift(CXModal.modal_sum(conj(self.probe) * (psi_new-psi_old)) / probe_intensity_max,
d1[i]%1, d2[i]%1)
if self.total_its==0 and sp.mod(i, len(self.positions.data[0]) / 10) == 0:
self.update_figure(i)
def init_probe(self, *args, **kwargs):
if CXP.io.initial_probe_guess is not '':
probe = CXData()
probe.load(CXP.io.initial_probe_guess)
self.probe.modes = [CXData(data=[probe.data[0]/(i+1)]) for i in range(CXP.reconstruction.probe_modes)]
self.probe.normalise()
else:
dx_s = CXP.dx_s
p, p2 = CXP.preprocessing.desired_array_shape, CXP.preprocessing.desired_array_shape/2
probe = sp.zeros((p, p), complex)
if CXP.experiment.optic.lower() == 'kb':
if len(CXP.experiment.beam_size)==1:
bsx=bsy=np.round(CXP.experiment.beam_size[0]/dx_s)
elif len(CXP.experiment.beam_size)==2:
bsx, bsy = np.round(CXP.experiment.beam_size[0]/dx_s), np.round(CXP.experiment.beam_size[1]/dx_s)
probe = np.sinc((np.arange(p)-p2)/bsx)[:,np.newaxis]*np.sinc((np.arange(p)-p2)/bsy)[np.newaxis,:]
elif CXP.experiment.optic.lower() == 'zp':
probe = np.sinc(sp.hypot(*sp.ogrid[-p2:p2, -p2:p2])/np.round(3.*CXP.experiment.beam_size[0]/(2*CXP.dx_s)))
ph_func = gauss_smooth(np.random.random(probe.shape), 10)
fwhm = p/2.0
radker = sp.hypot(*sp.ogrid[-p/2:p/2,-p/2:p/2])
gaussian = exp(-1.0*(fwhm/2.35)**-2. * radker**2.0 )
gaussian /= gaussian.max()
probe = abs(gaussian*probe)* exp(complex(0.,np.pi)*ph_func/ph_func.max())
self.probe.modes = [CXData(data=[probe/(i+1)]) for i in range(CXP.reconstruction.probe_modes)]
self.probe.normalise()
def ptycho_mesh(self):
"""
Generate a list of ptycho scan positions.
Outputs
-------
self.data : list of 2xN arrays containing horizontal and vertical scan positions in pixels
self.initial : initial guess at ptycho scan positions (before position correction)
self.initial_skew : initial skew
self.initial_rot : initial rotation
self.initial_scl : initial scaling
self.skew : current best guess at skew
self.rot : current best guess at rotation
self.scl : current best guess at scaling
self.total : total number of ptycho positions
[optional]
self.correct : for simulated data this contains the correct position
"""
CXP.log.info('Getting ptycho position mesh.')
if CXP.measurement.ptycho_scan_mesh == 'generate':
if CXP.measurement.ptycho_scan_type == 'cartesian':
x2 = 0.5*(CXP.measurement.cartesian_scan_dims[0]-1)
y2 = 0.5*(CXP.measurement.cartesian_scan_dims[1]-1)
tmp = map(lambda a: CXP.measurement.cartesian_step_size*a, np.mgrid[-x2:x2+1, -y2:y2+1])
self.positions.data = [tmp[0].flatten(), tmp[1].flatten()]
if CXP.reconstruction.flip_mesh_lr:
self.log.info('Flip ptycho mesh left-right')
self.positions.data[0] = self.data[0][::-1]
if CXP.reconstruction.flip_mesh_ud:
self.log.info('Flip ptycho mesh up-down')
self.positions.data[1] = self.data[1][::-1]
if CXP.reconstruction.flip_fast_axis:
self.log.info('Flip ptycho mesh fast axis')
tmp0, tmp1 = self.data[0], self.data[1]
self.positions.data[0], self.positions.data[1] = tmp1, tmp0
if CXP.measurement.ptycho_scan_type == 'round_roi':
self.positions.data = list(round_roi(CXP.measurement.round_roi_diameter, CXP.measurement.round_roi_step_size))
if CXP.measurement.ptycho_scan_type == 'list':
l = np.genfromtxt(CXP.measurement.list_scan_filename)
x_pos, y_pos = [], []
for element in l:
x_pos.append(element[0])
y_pos.append(element[1])
self.positions.data = [sp.array(x_pos), sp.array(y_pos)]
elif CXP.measurement.ptycho_scan_mesh == 'supplied':
l = np.genfromtxt(CXP.measurement.list_scan_filename)
x_pos, y_pos = [], []
for element in l:
x_pos.append(element[0])
y_pos.append(element[1])
self.positions.data = [sp.array(x_pos), sp.array(y_pos)]
for element in self.positions.data:
element /= CXP.dx_s
element += CXP.ob_p/2
self.positions.total = len(self.positions.data[0])
self.positions.correct = [sp.zeros((self.positions.total))]*2
jit_pix = CXP.reconstruction.initial_position_jitter_radius
search_pix = CXP.reconstruction.ppc_search_radius
self.positions.data[0] += jit_pix * uniform(-1, 1, self.positions.total)
self.positions.data[1] += jit_pix * uniform(-1, 1, self.positions.total)
if CXP.reconstruction.probe_position_correction:
self.positions.correct[0] = self.positions.data[0]+0.25*search_pix * uniform(-1, 1, self.positions.total)
self.positions.correct[1] = self.positions.data[1]+0.25*search_pix * uniform(-1, 1, self.positions.total)
else:
self.positions.correct = [self.positions.data[0].copy(), self.positions.data[1].copy()]
data_copy = CXData(data=list(self.positions.data))
if not CXP.reconstruction.ptycho_subpixel_shift:
self.positions.data = [np.round(self.positions.data[0]), np.round(self.positions.data[1])]
self.positions.correct = [np.round(self.positions.correct[0]), np.round(self.positions.correct[1])]
CXP.rms_rounding_error = [None]*2
for i in range(2):
CXP.rms_rounding_error[i] = sp.sqrt(sp.sum(abs(abs(data_copy.data[i])**2.-abs(self.positions.data[i])**2.)))
CXP.log.info('RMS Rounding Error (Per Position, X, Y):\t {:2.2f}, {:2.2f}'.format(CXP.rms_rounding_error[0]/len(self.positions.data[0]),
CXP.rms_rounding_error[1]/len(self.positions.data[1])))
class CXPhasing(object):
"""
.. class:: CXPhasing(object)
Implements phase retrieval process.
:attr annealing_schedule: Annealing schedule for probe position correction
:type annealing_schedule: lambda function
:attr dict slow_db_queue:
Values to be entered into the slow (once per reconstruction attempt) database.
Entry syntax:
slow_db_queue[db_field] = (value, )
:attr dict fast_db_queue:
Values to be entered into the fast (once per iteration per reconstruction attempt) database.
Entry syntax:
fast_db_queue[db_field] = (iter, value)
:attr int p: side length of state vector array in pixels
:attr int p2: half side length of state vector array in pixels
:attr int ob_p: side length of object array in pixels
:attr int total_its: the total number of iterations
:attr int probe_modes: the number of probe modes
:attr dict algorithms: dictionary of functions implementing iterative phase retrieval algorithms
:attr algorithm: the current phase retrieval algorithm
:type algorithm: lambda function
:attr str em_repr: the update string for Error Reduction iterations
:attr str dm_repr: the update string for Difference Map iterations
:attr str progress_repr: the update string printed once per iteration
:attr log: used for creating a log file and printing data to the terminal
:type log: Logging object
:attr int itnum: the current global iteration number
:attr bool ppc: probe position correction
"""
def __init__(self):
# Annealing schedule for probe position correction
self.annealing_schedule = lambda x: 1 if x ==0 else np.max([0.05,
1. - np.double(x) / CXP.reconstruction.ppc_length])
self.ppc = CXP.reconstruction.probe_position_correction
# MySQL DB Integration
if hasmysql:
self.init_db_conn()
# Values are inserted into the db by adding them to the queue
# The queues are emptied once per iteration
# The slow database has one entry per reconstruction attempt
# The fast database has one entry per iteration per reconstruction attempt
# Entry syntax:
# slow_db_queue[db_field] = (value, )
# fast_db_queue[db_field] = (iter, value)
self.slow_db_queue = {}
self.fast_db_queue = {}
self.p = CXP.p
self.p2 = self.p / 2
self.ob_p = CXP.preprocessing.object_array_shape
self.total_its = 0
self.probe_modes = CXP.reconstruction.probe_modes
self.algorithm = 'er' # Start with error reduction
if CXP.machine.n_processes < 0:
CXP.machine.n_processes = mp.cpu_count()
self.epie_repr = '{:s}\n\tPtychography iteration:{:10d}\n\tPtychography position:{:10d} [{:3.0f}%]'
self.progress_repr = 'Current iteration: {:d}\tPosition: {:d}'
self._sequence_dir = '/'.join([CXP.io.base_dir, CXP.io.scan_id, 'sequences'])
self._cur_sequence_dir = self._sequence_dir+'/sequence_{:d}'.format(CXP.reconstruction.sequence)
def setup(self):
"""
.. method:: setup()
This function implements all of the setup required to begin a phasing attempt.
- Setup directory structure.
- Initiliase the init_figure.
- Log all slow parameters to the db.
:param path: The path to the new CXParams file.
:type path: str.
:returns: int -- the return code.
:raises: IOError
"""
self.setup_dir_tree()
self.init_figure()
self.log_reconstruction_parameters()
def preprocessing(self):
""".. method:: preprocessing()
Collects together all the preprocessing functions that are required to begin phase retrieval.
"""
# Get the scan positions
self.positions = CXData(name='positions', data=[])
self.ptycho_mesh()
if CXP.measurement.simulate_data:
self.simulate_data()
else:
# Read in raw data
self.det_mod = CXData(name = 'det_mod')
if CXP.actions.preprocess_data:
self.det_mod.read_in_data()
else:
self.det_mod.load()
if CXP.io.whitefield_filename:
self.probe_det_mod = CXData(name='probe_det_mod')
self.probe_det_mod.preprocess_data()
self.object = CXData(name='object', data=[sp.zeros((self.ob_p, self.ob_p), complex)])
self.probe_intensity = CXData(name='probe_intensity', data=[sp.zeros((self.p, self.p))])
self.probe = CXModal(modes=[])
self.psi = CXModal(modes=[])
for i in range(CXP.reconstruction.probe_modes):
self.probe.modes.append(CXData(name='probe{:d}'.format(i), data=[sp.zeros((self.p, self.p), complex)]))
self.psi.modes.append(CXData(name='psi{:d}'.format(i), data=[sp.zeros((self.p, self.p), complex) for i in xrange(self.det_mod.len())]))
self.init_probe()
# Calculate STXM image if this is a ptycho scan
if len(self.det_mod.data) > 1:
self.calc_stxm_image()
if CXP.actions.process_dpc:
self.process_dpc()
def phase_retrieval(self):
""".. method:: phase_retrieval()
Runs the itertaive phase retrieval process.
"""
its = CXP.reconstruction.ptycho_its
if hasmysql:
self.update_slow_table()
beginning = time.time()
for self.itnum in xrange(its):
then = time.time()
self.select_algorithm()
self.ePIE()
now = time.time()
if hasmysql:
self.fast_db_queue['iter_time'] = (self.itnum, now - then)
self.fast_db_queue['iter_time_pptpxit'] = (self.itnum, 1e6*(now - then) / (self.positions.total * self.p**2 * (self.itnum + 1)))
CXP.log.info('{:2.2f} seconds elapsed during iteration {:d} [{:1.2e} sec/pt/pix/it]'.format(now - then, self.itnum + 1,
(now-then)/(self.positions.total * self.p**2 * (self.itnum + 1))))
CXP.log.info('{:5.2f} seconds have elapsed in {:d} iterations [{:2.2f} sec/it]'.format(now-beginning, self.itnum + 1, (now-beginning)/(self.total_its + 1)))
self.calc_mse()
self.total_its += 1
if hasmysql:
self.update_fast_table()
if self.itnum > 0:
self.update_figure(self.itnum)
def postprocessing(self):
""".. method::postprocessing()
Collectes together all the orutines that should be completed after the iterative phase retrieval has successfully completed.
"""
pass
def simulate_data(self):
CXP.log.info('Simulating diffraction patterns.')
self.sample = CXData()
self.sample.load(CXP.io.simulation_sample_filename[0])
self.sample.data[0] = self.sample.data[0].astype(float)
self.sample.normalise(val=0.8)
self.sample.data[0]+=0.2
self.input_probe = CXModal()
if len(CXP.io.simulation_sample_filename)>1:
ph = CXData()
ph.load(CXP.io.simulation_sample_filename[1])
ph.data[0] = ph.data[0].astype(float)
ph.normalise(val=np.pi/3)
self.sample.data[0] = self.sample.data[0]*exp(complex(0., 1.)*ph.data[0])
p = self.sample.data[0].shape[0]
ham_window = sp.hamming(p)[:,np.newaxis]*sp.hamming(p)[np.newaxis,:]
sample_large = CXData(data=sp.zeros((CXP.ob_p, CXP.ob_p), complex))
sample_large.data[0][CXP.ob_p/2-p/2:CXP.ob_p/2+p/2, CXP.ob_p/2-p/2:CXP.ob_p/2+p/2] = self.sample.data[0]*ham_window
ker = sp.arange(0, p)
fwhm = p/3.0
radker = sp.hypot(*sp.ogrid[-p/2:p/2,-p/2:p/2])
gaussian = exp(-1.0*(fwhm/2.35)**-2. * radker**2.0 )
ortho_modes = lambda n1, n2 : gaussian*np.sin(n1*math.pi*ker/p)[:,np.newaxis]*np.sin(n2*math.pi*ker/p)[np.newaxis, :]
mode_generator = lambda : sp.floor(4*sp.random.random(2))+1
used_modes = []
self.input_psi = CXModal()
for mode in range(CXP.reconstruction.probe_modes):
if mode==0:
new_mode = [1,1]
else:
new_mode = list(mode_generator())
while new_mode in used_modes:
new_mode = list(mode_generator())
used_modes.append(new_mode)
CXP.log.info('Simulating mode {:d}: [{:d}, {:d}]'.format(mode, int(new_mode[0]), int(new_mode[1])))
ph_func = gauss_smooth(np.random.random((p,p)), 10)
self.input_probe.modes.append(CXData(name='probe{:d}'.format(mode),
data=ortho_modes(new_mode[0], new_mode[1])*exp(complex(0.,np.pi)*ph_func/ph_func.max())))
self.input_probe.normalise()
self.input_probe.orthogonalise()
for mode in range(CXP.reconstruction.probe_modes):
p2 = p/2
x, y = self.positions.correct
self.input_psi.modes.append(CXData(name='input_psi_mode{:d}'.format(mode), data=[]))
for i in xrange(len(x)):
if i%(len(x)/10)==0.:
CXP.log.info('Simulating diff patt {:d}'.format(i))
tmp = (CXData.shift(sample_large, -1.0*(x[i]-CXP.ob_p/2), -1.0*(y[i]-CXP.ob_p/2))
[CXP.ob_p/2-p2:CXP.ob_p/2+p2, CXP.ob_p/2-p2:CXP.ob_p/2+p2]*
self.input_probe[mode][0])
self.input_psi[mode].data.append(tmp.data[0])
# Add modes incoherently
self.det_mod = CXModal.modal_sum(abs(fft2(self.input_psi)))
self.det_mod.save(path=CXP.io.base_dir+'/'+CXP.io.scan_id+'/raw_data/{:s}.npy'.format('det_mod'))
def pos_correction_transform(self, i, itnum):
# Generates trial position
search_rad = CXP.reconstruction.ppc_search_radius
r = self.annealing_schedule(itnum)
cx = self.positions.data[0][i] + (search_rad * r * uniform(-1, 1))
cy = self.positions.data[1][i] + (search_rad * r * uniform(-1, 1))
# Limit max deviation
if np.abs(cx - self.positions.initial[0][i]) > search_rad:
cx = self.positions.initial[0][i] + search_rad * r * uniform(-1, 1)
if np.abs(cy - self.positions.initial[1][i]) > search_rad:
cy = self.positions.initial[1][i] + search_rad * r * uniform(-1, 1)
if CXP.reconstruction.ptycho_subpixel_shift:
return [cx, cy]
else:
return [np.round(cx), np.round(cy)]
@staticmethod
def M(psi, det_mod):
""".. method:: M(mode, psi_modes, det_mod)
Applies modulus constraint to psi_modes(mode) for a given position.
:param list psi_modes: A list of CXData instances containing all modes at a given position.
:param np.ndarray det_mod: Modulus of measured diffraction pattern.
"""
if isinstance(psi, CXData):
return ifft2(det_mod * exp(complex(0., 1.) * angle(fft2(psi))))
elif isinstance(psi, CXModal):
mode_sum = CXModal.modal_sum(abs(fft2(psi))**2.0)**0.5
return ifft2((fft2(psi)/(mode_sum))*det_mod)
def ePIE(self):
""".. method:: initial_update_state_vector(self)
This method uses ePie to generate the initial estimate for psi and object.
"""
d1, d2 = self.positions.data
for i in xrange(self.positions.total):
if i % np.floor(self.positions.total / 10) == 0 and CXP.reconstruction.verbose:
CXP.log.info(self.epie_repr.format(self.algorithm_name, self.itnum, i, 100. * float(i + 1) / self.positions.total))
# Non-modal reconstruction
if self.total_its<CXP.reconstruction.begin_modal_reconstruction:
if self.itnum+i==0:
view=self.probe[0][0].copy()
else:
view = self.probe[0][0] * self.object[d1[i] - self.p2:d1[i] + self.p2, d2[i] - self.p2:d2[i] + self.p2]
if self.algorithm == 'er':
self.psi[0][i] = self.M(view.copy(), self.det_mod[i])
elif self.algorithm == 'dm':
self.psi[0][i] += self.M(2*view-self.psi[0][i], self.det_mod[i]) - view
self.update_object(i, view, self.psi[0][i])
if self.do_update_probe:
self.update_probe_nonmodal(i, view, self.psi[0][i])
else: # Do modal reconstruction
view = self.probe * self.object[d1[i] - self.p2:d1[i] + self.p2, d2[i] - self.p2:d2[i] + self.p2]
if self.algorithm == 'er':
self.psi.setat(i, self.M(view, self.det_mod[i]))
elif self.algorithm == 'dm':
self.psi.setat(i, self.psi.getat(i)+self.M(2*view-self.psi, self.det_mod[i]) - view)
self.update_object(i, view, self.psi.getat(i))
if self.do_update_probe:
self.update_probe(i, view, self.psi.getat(i))
for mode, probe in enumerate(self.probe.modes):
probe.save(path=self._cur_sequence_dir+'/probe_mode{:d}'.format(mode))
self.object.save(path=self._cur_sequence_dir+'/object')
def update_object(self, i, psi_old, psi_new):
"""
Update the object from a single ptycho position.
"""
then=time.time()
d1, d2 = self.positions.data
id1, id2 = d1//1, d2//1
probe_intensity_max = CXModal.modal_sum(abs(self.probe)**2.0).data[0].max()
self.object[id1[i] - self.p2:id1[i] + self.p2, id2[i] - self.p2:id2[i] + self.p2] += \
CXData.shift(CXModal.modal_sum(conj(self.probe) * (psi_new-psi_old)) / probe_intensity_max,
d1[i]%1, d2[i]%1)
if self.total_its==0 and sp.mod(i, len(self.positions.data[0]) / 10) == 0:
self.update_figure(i)
def update_probe_nonmodal(self, i, psi_old, psi_new):
d1, d2 = self.positions.data
id1, id2 = d1//1, d2//1
object_intensity_max = (abs(self.object)**2.0).data[0].max()
self.probe.modes[0] += \
CXData.shift(conj(self.object[id1[i] - self.p2:id1[i] + self.p2, id2[i] - self.p2:id2[i] + self.p2]) *
(psi_new-psi_old)[0] / object_intensity_max, d1[i]%1, d2[i]%1)
self.probe.normalise()
def update_probe(self, i, psi_old, psi_new):
d1, d2 = self.positions.data
id1, id2 = d1//1, d2//1
object_intensity_max = (abs(self.object)**2.0).data[0].max()
for mode in range(len(self.probe)):
self.probe.modes[mode] += \
CXData.shift(conj(self.object[id1[i] - self.p2:id1[i] + self.p2, id2[i] - self.p2:id2[i] + self.p2]) *
(psi_new-psi_old)[mode] / object_intensity_max, d1[i]%1, d2[i]%1)
self.probe.normalise()
self.probe.orthogonalise()
def error(self, psi, det_mod):
""".. method:: error(psi, det_mod)
Calculates the MSE at a given position given the modes at that position.
:param CXModal psi: A list of CXData instances containing all modes at a given position.
:param np.ndarray det_mod: Modulus of measured diffraction pattern.
"""
mode_sum = CXModal.modal_sum(abs(fft2(psi)))
return (sp.sum((abs(mode_sum - det_mod) ** 2.).data[0]) / sp.sum(det_mod.data[0] ** 2.))**0.5
def select_algorithm(self):
try:
self.algorithm_count
except AttributeError:
self.algorithm_count = 0
if self.algorithm == 'er':
if self.algorithm_count>=CXP.reconstruction.algorithm['er']:
self.algorithm = 'dm'
self.algorithm_name = 'Difference Map'
self.algorithm_count = 0
else:
self.algorithm_name = 'Error Reduction'
elif self.algorithm == 'dm':
if self.algorithm_count>=CXP.reconstruction.algorithm['dm']:
self.algorithm = 'er'
self.algorithm_name = 'Error Reduction'
self.algorithm_count = 0
else:
self.algorithm_name = 'Difference Map'
if self.total_its>CXP.reconstruction.ptycho_its-100:
self.algorithm = 'er'
self.algorithm_name = 'Error Reduction'
if self.total_its>CXP.reconstruction.begin_updating_probe:# and self.algorithm=='er':
self.do_update_probe = True
else:
self.do_update_probe=False
self.algorithm_count += 1
self.fast_db_queue['algorithm'] = (self.itnum, self.algorithm)
def init_figure(self):
pylab.ion()
self.f1=pylab.figure(1, figsize=(12, 10))
thismanager = pylab.get_current_fig_manager()
thismanager.window.wm_geometry("+600+0")
try:
itnum = self.itnum
except AttributeError:
itnum = 0
try:
mse = self.av_mse
except AttributeError:
mse = -1.0
pylab.suptitle('Sequence: {:d}, Iteration: {:d}, MSE: {:3.2f}%'.format(CXP.reconstruction.sequence, itnum, 100*mse))
def update_figure(self, i=0):
cur_cmap = cm.RdGy_r
self.f1.clf()
self.init_figure()
wh = sp.where(abs(self.object.data[0]) > 0.1 * (abs(self.object.data[0]).max()))
try:
x1, x2 = min(wh[0]), max(wh[0])
y1, y2 = min(wh[1]), max(wh[1])
except (ValueError, IndexError):
x1, x2 = 0, self.ob_p
y1, y2 = 0, self.ob_p
# Plot magnitude of object
s1 = pylab.subplot(231)
s1_im = s1.imshow(abs(self.object).data[0][x1:x2, y1:y2], cmap=cm.Greys_r)
s1.set_title('|object|')
plt.axis('off')
pylab.colorbar(s1_im)
# Plot phase of object
s2 = pylab.subplot(232)
s2_im = s2.imshow(sp.angle(self.object.data[0][x1:x2, y1:y2]), cmap=cm.hsv)
s2.set_title('phase(object)')
plt.axis('off')
pylab.colorbar(s2_im)
# Complex HSV plot of object
s3 = pylab.subplot(233)
h = ((angle(self.object).data[0][x1:x2, y1:y2] + np.pi) / (2*np.pi)) % 1.0
s = np.ones_like(h)
l = abs(self.object).data[0][x1:x2, y1:y2]
l-=l.min()
l/=l.max()
s3_im = s3.imshow(np.dstack(v_hls_to_rgb(h,l,s)))
s3.set_title('Complex plot of Object')
plt.axis('off')
# Plot probe mode 0
s4 = pylab.subplot(234)
s4_im = s4.imshow(abs(self.probe.modes[0].data[0]), cmap=cur_cmap)
s4.set_title('|probe0|')
plt.axis('off')
pylab.colorbar(s4_im)
if CXP.reconstruction.probe_modes>1:
s5 = pylab.subplot(235)
s5_im = s5.imshow(abs(self.probe.modes[1].data[0]), cmap=cur_cmap)
s5.set_title('|probe1|')
plt.axis('off')
pylab.colorbar(s5_im)
else:
pass
if self.ppc:
s6 = self.f1.add_subplot(236)
s6_im = s6.scatter(self.positions.data[0], self.positions.data[1], s=10,
c='b', marker='o', alpha=0.5, edgecolors='none', label='current')
patches = []
for m in range(self.positions.total):
patches.append(Circle((self.positions.initial[0][m], self.positions.initial[1][m]),
radius=CXP.reconstruction.ppc_search_radius))
collection = PatchCollection(patches, color='tomato', alpha=0.2, edgecolors=None)
s4.add_collection(collection)
if CXP.measurement.simulate_data:
s4_im = s4.scatter(self.positions.correct[0], self.positions.correct[1], s=10,
c='g', marker='o', alpha=0.5, edgecolors='none', label='correct')
CXP.log.info('RMS position deviation from correct: [x:{:3.2f},y:{:3.2f}] pixels'.format(
sp.sqrt(sp.mean((self.positions.data[0] - self.positions.correct[0])**2.)),
sp.sqrt(sp.mean((self.positions.data[1] - self.positions.correct[1])**2.))))
lines=[]
for m in range(self.positions.total):
lines.append(((self.positions.correct[0][m], self.positions.correct[1][m]),
(self.positions.data[0][m], self.positions.data[1][m])))
for element in lines:
x, y = zip(*element)
s4.plot(x, y, 'g-')
else:
lines = []
for m in range(self.positions.total):
lines.append(((self.positions.initial[0][m], self.positions.initial[1][m]),
(self.positions.data[0][m], self.positions.data[1][m])))
for element in lines:
x, y = zip(*element)
s6.plot(x, y, 'g-')
CXP.log.info('RMS position deviation from initial: [x:{:3.2f},y:{:3.2f}] pixels'.format(
sp.sqrt(sp.mean((self.positions.data[0] - self.positions.initial[0])**2.)),
sp.sqrt(sp.mean((self.positions.data[1] - self.positions.initial[1])**2.))))
s6.legend(prop={'size': 6})
s6.set_title('Position Correction')
s6.set_aspect('equal')
extent = s6.get_window_extent().transformed(self.f1.dpi_scale_trans.inverted())
pylab.savefig(self._cur_sequence_dir + '/ppc_{:d}.png'.format(self.total_its), bbox_inches=extent.expanded(1.2, 1.2), dpi=100)
s6.set_aspect('auto')
else:
s6 = pylab.subplot(236)
if CXP.measurement.simulate_data:
s6_im = s6.imshow(abs(self.input_probe[1].data[0]), cmap = cur_cmap)
s6.set_title('|input_probe1|')
else:
s6_im = s6.imshow(nlog(fftshift(self.det_mod[np.mod(i,self.positions.total)])).data[0], cmap=cur_cmap)
s6.set_title('Diff Patt: {:d}'.format(i))
plt.axis('off')
pylab.colorbar(s6_im)
pylab.draw()
pylab.savefig(self._cur_sequence_dir + '/recon_{:d}.png'.format(self.total_its), dpi=60)
def init_db_conn(self):
# Make db connection
self.db = SimpleDB()
self.dbconn = self.db.conn
# Select the CXParams db
self.db.use(CXP.db.master_db)
self.db.get_cursor()
# Create table interface
self.t_slow_params = self.db.tables['slow_params']
self.t_fast_params = self.db.tables['fast_params']
self.recon_id = self.t_slow_params.get_new_recon_id()
CXP.log.info('MySQL Reconstruction ID: {}'.format(self.recon_id))
def update_slow_table(self):
for element in CXP.param_store.instances:
for key, value in getattr(CXP, element).__dict__.iteritems():
self.slow_db_queue[key] = (value,)
then = time.time()
cnt = 0
for k, (v,) in self.slow_db_queue.iteritems():
if isinstance(v, (list, tuple)):
v=str(v)
self.t_slow_params.insert_on_duplicate_key_update(primary={'id': self.recon_id}, update={k: v})
cnt += 1
now = time.time()
self.slow_db_queue['time_per_slow_db_entry'] = (now - then)/cnt
CXP.log.info('{:3.2f} seconds elapsed entering {:d} values into slow db [{:3.2f} msec/entry]'.format(now-then,
cnt, 1e3*(now - then) / cnt))
def update_fast_table(self):
if not self.t_fast_params.check_columns(self.fast_db_queue.keys()):
for key, (itnum, value) in self.fast_db_queue.iteritems():
if not self.t_fast_params.check_columns([key]):
CXP.log.warning('MYSQL: Adding column {} to fast_params.'.format(key))
ftype = 'double'
if isinstance(value, (list, tuple)):
value = str(value)
if isinstance(value, str):
ftype = 'text'
def_val = ''
elif isinstance(value, bool):
ftype = 'bool'
def_val = ''
elif isinstance(value, (int, float)):
ftype = 'double'
def_val = 0
else:
ftype = 'blob'
def_val = ''
self.t_fast_params.add_column(col_name=key, var_type=ftype, default_value=def_val)
self.t_fast_params.update_fieldtypes()
then = time.time()
cnt = 0
for k, (itnum, v) in self.fast_db_queue.iteritems():
if isinstance(v, (list, tuple)):
v=str(v)
self.t_fast_params.insert_on_duplicate_key_update(
primary={'slow_id': self.recon_id, 'iter': itnum}, update={k: v})
cnt+=1
now = time.time()
self.fast_db_queue['time_per_fast_db_entry'] = (self.itnum, (now - then) / cnt)
CXP.log.info('{:3.2f} seconds elapsed entering {:d} values into fast db [{:3.2f} msec/entry]'.format(now-then,
cnt, 1e3 * (now - then) / cnt))
def calc_mse(self):
then = time.time()
multip = multiprocess.multiprocess(self.mse_worker)
d1, d2 = self.positions.data
for i_range in list(split_seq(range(self.positions.total),
CXP.machine.n_processes)):
multip.add_job((i_range, self.psi, self.det_mod))
results = multip.close_out()
self.av_mse = sp.mean(list(itertools.chain(*results)))
CXP.log.info('Mean square error: {:3.2f}%'.format(100 * self.av_mse))
self.fast_db_queue['error'] = (self.itnum, self.av_mse)
now = time.time()
CXP.log.info('Calculating MSE took {:3.2f}sec [{:3.2f}msec/position]'.format(now - then,
1e3*(now - then) / self.positions.total))
@staticmethod
@multiprocess.worker
def mse_worker(args):
i_range, psi, det_mod = args
indvdl_mse = []
p = det_mod[0].data[0].shape[0]
for i in i_range:
psi_sum = CXModal.modal_sum(abs(fft2(psi.getat(i))))
indvdl_mse.append(sp.sum((abs(psi_sum - det_mod[i]) ** 2.).data[0]) / sp.sum(det_mod[i].data[0] ** 2.))
return indvdl_mse
def log_reconstruction_parameters(self):
"""
h - object size\nz - sam-det dist\npix - # of pix\ndel_x_d - pixel size
"""
dx_d = CXP.experiment.dx_d
x = (CXP.p/2.)*dx_d
l = energy_to_wavelength(CXP.experiment.energy)
h = min(CXP.experiment.beam_size)
pix = CXP.p
z=CXP.experiment.z
NF = lambda nh, nl, nz: nh**2./(nl*nz)
del_x_s = lambda l, z, x: (l*z)/(2.*x)
nNF = NF(h, l, z)
OS = lambda l, z, x, h, pix: ((pix*del_x_s(l, z, x))**2.)/(h**2.)
nOS = OS(l, z, x, h, pix)
NA = sp.sin(sp.arctan(x/z))
axial_res = 2*l/NA**2.
lateral_res = l/(2.*NA)
CXP.log.info('Fresnel number: {:2.2e}'.format(nNF))
CXP.log.info('Oversampling: {:3.2f}'.format(nOS))
CXP.log.info('Detector pixel size: {:3.2f} [micron]'.format(1e6*dx_d))
CXP.log.info('Detector width: {:3.2f} [mm]'.format(1e3*pix*dx_d))
CXP.log.info('Sample pixel size: {:3.2f} [nm]'.format(1e9*del_x_s(l, z, x)))
CXP.log.info('Sample FOV: {:3.2f} [micron]'.format(1e6*del_x_s(l, z, x)*pix))
CXP.log.info('Numerical aperture: {:3.2f}'.format(NA))
CXP.log.info('Axial resolution: {:3.2f} [micron]'.format(1e6*axial_res))
CXP.log.info('Lateral resolution: {:3.2f} [nm]'.format(1e9*lateral_res))
self.slow_db_queue['fresnel_number'] = (nNF,)
self.slow_db_queue['oversampling'] = (nOS,)
self.slow_db_queue['dx_s'] = (del_x_s(l, z, x),)
self.slow_db_queue['sample_fov'] = (del_x_s(l, z, x)*pix,)
self.slow_db_queue['numerical_aperture'] = (NA,)
self.slow_db_queue['axial_resolution'] = (axial_res,)
def setup_dir_tree(self):
"""Setup the directory structure for a new scan id"""
_top_dir = '/'.join([CXP.io.base_dir, CXP.io.scan_id])
_sequence_dir = '/'.join([CXP.io.base_dir, CXP.io.scan_id, 'sequences'])
_cur_sequence_dir = _sequence_dir+'/sequence_{:d}'.format(CXP.reconstruction.sequence)
_raw_data_dir = '/'.join([CXP.io.base_dir, CXP.io.scan_id, 'raw_data'])
_dpc_dir = '/'.join([CXP.io.base_dir, CXP.io.scan_id, 'dpc'])
_CXP_dir = '/'.join([CXP.io.base_dir, CXP.io.scan_id, '.CXPhasing'])
_py_dir = '/'.join([CXP.io.base_dir, CXP.io.scan_id, 'python'])
if not os.path.exists(_top_dir):
CXP.log.info('Setting up new scan directory...')
os.mkdir(_top_dir)
os.mkdir(_sequence_dir)
os.mkdir(_cur_sequence_dir)
os.mkdir(_raw_data_dir)
os.mkdir(_dpc_dir)
os.mkdir(_CXP_dir)
os.mkdir(_py_dir)
try:
shutil.copy(CXP.io.code_dir+'/CXParams.py', _py_dir)
except IOError:
CXP.log.error('Was unable to save a copy of CXParams.py to {}'.format(_py_dir))
else:
CXP.log.info('Dir tree already exists.')
if not os.path.exists(_sequence_dir):
os.mkdir(_sequence_dir)
if not os.path.exists(_cur_sequence_dir):
CXP.log.info('Making new sequence directory')
os.mkdir(_cur_sequence_dir)
try:
shutil.copy(CXP.io.code_dir+'/CXParams.py', _py_dir)
shutil.copy(CXP.io.code_dir+'/CXParams.py',
_cur_sequence_dir+'/CXParams_sequence{}.py'.format(CXP.reconstruction.sequence))
except IOError:
CXP.log.error('Was unable to save a copy of CXParams.py to {}'.format(_py_dir))
def ptycho_mesh(self):
"""
Generate a list of ptycho scan positions.
Outputs
-------
self.data : list of 2xN arrays containing horizontal and vertical scan positions in pixels
self.initial : initial guess at ptycho scan positions (before position correction)
self.initial_skew : initial skew
self.initial_rot : initial rotation
self.initial_scl : initial scaling
self.skew : current best guess at skew
self.rot : current best guess at rotation
self.scl : current best guess at scaling
self.total : total number of ptycho positions
[optional]
self.correct : for simulated data this contains the correct position
"""
CXP.log.info('Getting ptycho position mesh.')
if CXP.measurement.ptycho_scan_mesh == 'generate':
if CXP.measurement.ptycho_scan_type == 'cartesian':
x2 = 0.5*(CXP.measurement.cartesian_scan_dims[0]-1)
y2 = 0.5*(CXP.measurement.cartesian_scan_dims[1]-1)
tmp = map(lambda a: CXP.measurement.cartesian_step_size*a, np.mgrid[-x2:x2+1, -y2:y2+1])
self.positions.data = [tmp[0].flatten(), tmp[1].flatten()]
if CXP.reconstruction.flip_mesh_lr:
self.log.info('Flip ptycho mesh left-right')
self.positions.data[0] = self.data[0][::-1]
if CXP.reconstruction.flip_mesh_ud:
self.log.info('Flip ptycho mesh up-down')
self.positions.data[1] = self.data[1][::-1]
if CXP.reconstruction.flip_fast_axis:
self.log.info('Flip ptycho mesh fast axis')
tmp0, tmp1 = self.data[0], self.data[1]
self.positions.data[0], self.positions.data[1] = tmp1, tmp0
if CXP.measurement.ptycho_scan_type == 'round_roi':
self.positions.data = list(round_roi(CXP.measurement.round_roi_diameter, CXP.measurement.round_roi_step_size))
if CXP.measurement.ptycho_scan_type == 'list':
l = np.genfromtxt(CXP.measurement.list_scan_filename)
x_pos, y_pos = [], []
for element in l:
x_pos.append(element[0])
y_pos.append(element[1])
self.positions.data = [sp.array(x_pos), sp.array(y_pos)]
elif CXP.measurement.ptycho_scan_mesh == 'supplied':
l = np.genfromtxt(CXP.measurement.list_scan_filename)
x_pos, y_pos = [], []
for element in l:
x_pos.append(element[0])
y_pos.append(element[1])
self.positions.data = [sp.array(x_pos), sp.array(y_pos)]
for element in self.positions.data:
element /= CXP.dx_s
element += CXP.ob_p/2
self.positions.total = len(self.positions.data[0])
self.positions.correct = [sp.zeros((self.positions.total))]*2
jit_pix = CXP.reconstruction.initial_position_jitter_radius
search_pix = CXP.reconstruction.ppc_search_radius
self.positions.data[0] += jit_pix * uniform(-1, 1, self.positions.total)
self.positions.data[1] += jit_pix * uniform(-1, 1, self.positions.total)
if CXP.reconstruction.probe_position_correction:
self.positions.correct[0] = self.positions.data[0]+0.25*search_pix * uniform(-1, 1, self.positions.total)
self.positions.correct[1] = self.positions.data[1]+0.25*search_pix * uniform(-1, 1, self.positions.total)
else:
self.positions.correct = [self.positions.data[0].copy(), self.positions.data[1].copy()]
data_copy = CXData(data=list(self.positions.data))
if not CXP.reconstruction.ptycho_subpixel_shift:
self.positions.data = [np.round(self.positions.data[0]), np.round(self.positions.data[1])]
self.positions.correct = [np.round(self.positions.correct[0]), np.round(self.positions.correct[1])]
CXP.rms_rounding_error = [None]*2
for i in range(2):
CXP.rms_rounding_error[i] = sp.sqrt(sp.sum(abs(abs(data_copy.data[i])**2.-abs(self.positions.data[i])**2.)))
CXP.log.info('RMS Rounding Error (Per Position, X, Y):\t {:2.2f}, {:2.2f}'.format(CXP.rms_rounding_error[0]/len(self.positions.data[0]),
CXP.rms_rounding_error[1]/len(self.positions.data[1])))
def init_probe(self, *args, **kwargs):
if CXP.io.initial_probe_guess is not '':
probe = CXData()
probe.load(CXP.io.initial_probe_guess)
self.probe.modes = [CXData(data=[probe.data[0]/(i+1)]) for i in range(CXP.reconstruction.probe_modes)]
self.probe.normalise()
else:
dx_s = CXP.dx_s
p, p2 = CXP.preprocessing.desired_array_shape, CXP.preprocessing.desired_array_shape/2
probe = sp.zeros((p, p), complex)
if CXP.experiment.optic.lower() == 'kb':
if len(CXP.experiment.beam_size)==1:
bsx=bsy=np.round(CXP.experiment.beam_size[0]/dx_s)
elif len(CXP.experiment.beam_size)==2:
bsx, bsy = np.round(CXP.experiment.beam_size[0]/dx_s), np.round(CXP.experiment.beam_size[1]/dx_s)
probe = np.sinc((np.arange(p)-p2)/bsx)[:,np.newaxis]*np.sinc((np.arange(p)-p2)/bsy)[np.newaxis,:]
elif CXP.experiment.optic.lower() == 'zp':
probe = np.sinc(sp.hypot(*sp.ogrid[-p2:p2, -p2:p2])/np.round(3.*CXP.experiment.beam_size[0]/(2*CXP.dx_s)))
ph_func = gauss_smooth(np.random.random(probe.shape), 10)
fwhm = p/2.0
radker = sp.hypot(*sp.ogrid[-p/2:p/2,-p/2:p/2])
gaussian = exp(-1.0*(fwhm/2.35)**-2. * radker**2.0 )
gaussian /= gaussian.max()
probe = abs(gaussian*probe)* exp(complex(0.,np.pi)*ph_func/ph_func.max())
self.probe.modes = [CXData(data=[probe/(i+1)]) for i in range(CXP.reconstruction.probe_modes)]
self.probe.normalise()
def calc_stxm_image(self):
path = '/'.join([CXP.io.base_dir, CXP.io.scan_id, 'sequences/sequence_{:d}/stxm_regular_grid.png'.format(CXP.reconstruction.sequence)])
CXP.log.info('Calculating STXM image.\nSTXM saved to:\n\t{}'.format(path))
image_sum = sp.array([sp.sum(data) for data in self.det_mod.data])
x, y = self.positions.data
fig = Figure(figsize=(6, 6))
canvas = FigureCanvas(fig)
ax = fig.add_subplot(111)
ax.set_title('STXM Image', fontsize=14)
ax.set_xlabel('Position [micron]', fontsize=12)
ax.set_ylabel('Position [micron]', fontsize=12)
if CXP.measurement.ptycho_scan_type == 'cartesian':
ax.hexbin(x, y, C=image_sum, gridsize=CXP.measurement.cartesian_scan_dims, cmap=cm.RdGy)
canvas.print_figure('/'.join([CXP.io.base_dir, CXP.io.scan_id, 'sequences/sequence_{:d}/stxm_scatter.png'.format(CXP.reconstruction.sequence)]), dpi=500)
ax.imshow(image_sum.reshape(CXP.measurement.cartesian_scan_dims), cmap=cm.RdGy)
else:
ax.hexbin(x, y, C=image_sum, cmap=cm.RdGy)
canvas.print_figure(path, dpi=500)
def simulate_data(self):
CXP.log.info('Simulating diffraction patterns.')
self.sample = CXData()
self.sample.load(CXP.io.simulation_sample_filename[0])
self.sample.data[0] = self.sample.data[0].astype(float)
self.sample.normalise(val=0.8)
self.sample.data[0]+=0.2
self.input_probe = CXModal()
if len(CXP.io.simulation_sample_filename)>1:
ph = CXData()
ph.load(CXP.io.simulation_sample_filename[1])
ph.data[0] = ph.data[0].astype(float)
ph.normalise(val=np.pi/3)
self.sample.data[0] = self.sample.data[0]*exp(complex(0., 1.)*ph.data[0])
p = self.sample.data[0].shape[0]
ham_window = sp.hamming(p)[:,np.newaxis]*sp.hamming(p)[np.newaxis,:]
sample_large = CXData(data=sp.zeros((CXP.ob_p, CXP.ob_p), complex))
sample_large.data[0][CXP.ob_p/2-p/2:CXP.ob_p/2+p/2, CXP.ob_p/2-p/2:CXP.ob_p/2+p/2] = self.sample.data[0]*ham_window
ker = sp.arange(0, p)
fwhm = p/3.0
radker = sp.hypot(*sp.ogrid[-p/2:p/2,-p/2:p/2])
gaussian = exp(-1.0*(fwhm/2.35)**-2. * radker**2.0 )
ortho_modes = lambda n1, n2 : gaussian*np.sin(n1*math.pi*ker/p)[:,np.newaxis]*np.sin(n2*math.pi*ker/p)[np.newaxis, :]
mode_generator = lambda : sp.floor(4*sp.random.random(2))+1
used_modes = []
self.input_psi = CXModal()
for mode in range(CXP.reconstruction.probe_modes):
if mode==0:
new_mode = [1,1]
else:
new_mode = list(mode_generator())
while new_mode in used_modes:
new_mode = list(mode_generator())
used_modes.append(new_mode)
CXP.log.info('Simulating mode {:d}: [{:d}, {:d}]'.format(mode, int(new_mode[0]), int(new_mode[1])))
ph_func = gauss_smooth(np.random.random((p,p)), 10)
self.input_probe.modes.append(CXData(name='probe{:d}'.format(mode),
data=ortho_modes(new_mode[0], new_mode[1])*exp(complex(0.,np.pi)*ph_func/ph_func.max())))
self.input_probe.normalise()
self.input_probe.orthogonalise()
for mode in range(CXP.reconstruction.probe_modes):
p2 = p/2
x, y = self.positions.correct
self.input_psi.modes.append(CXData(name='input_psi_mode{:d}'.format(mode), data=[]))
for i in xrange(len(x)):
if i%(len(x)/10)==0.:
CXP.log.info('Simulating diff patt {:d}'.format(i))
tmp = (CXData.shift(sample_large, -1.0*(x[i]-CXP.ob_p/2), -1.0*(y[i]-CXP.ob_p/2))
[CXP.ob_p/2-p2:CXP.ob_p/2+p2, CXP.ob_p/2-p2:CXP.ob_p/2+p2]*
self.input_probe[mode][0])
self.input_psi[mode].data.append(tmp.data[0])
# Add modes incoherently
self.det_mod = CXModal.modal_sum(abs(fft2(self.input_psi)))
self.det_mod.save(path=CXP.io.base_dir+'/'+CXP.io.scan_id+'/raw_data/{:s}.npy'.format('det_mod'))
