def distribute_Q_over_procs(self, num_procs):
"""
num_Q_per_proc is determined as the largest integer dividing the total_Qsteps number.
the remainder is placed on rank 0 (if there is a remainder...)
"""
# set up array of how many Q on each proc
nQpp_arr = np.zeros(num_procs).astype(int) # Num_Q_Per_Processor_ARRay
proc = 0
for qq in range(self.total_Qsteps):
if proc == num_procs:
proc = 0
nQpp_arr[proc] = nQpp_arr[proc] + 1
proc = proc + 1
# if any procs have 0 Q, print error message and exit
if nQpp_arr.min() == 0:
message = 'atleast one processor will do 0 Qpoints.\n' \
' increase number of procs or decrease number of Q points'
raise PSF_exception(message)
# print parellelism info
message = f'process: 0 Q points: {nQpp_arr[0]:g}\n'
for ii in range(1, num_procs):
message = message + f' process: {ii:g} Q points: {nQpp_arr[ii]:g}\n'
print_stdout(message, msg_type='Q points on each process')
# put the Qpoint indicies for each proc in the list
self.Q_on_procs = []
shift = 0
for ii in range(num_procs):
self.Q_on_procs.append(list(range(shift, shift + nQpp_arr[ii])))
shift = shift + nQpp_arr[ii]
def _check_file(self):
"""
check the key_words in input files and remove comments/blank lines
"""
input_txt = []
for line in self.input_txt:
if len(line.split()) == 0 or line.strip().startswith('#'):
continue
else:
tmp_line = line.split('#')[0].strip()
key_word = tmp_line.split('=')[0].strip()
if key_word not in self.key_words:
message = f'key word \'{key_word}\' is unknown. check the input file'
raise PSF_exception(message)
if key_word in self.doubles:
message = f'key word \'{key_word}\' appears more than once in the input file'
raise PSF_exception(message)
self.doubles.append(key_word)
input_txt.append(tmp_line)
def __init__(self,invars):
"""
open the hdf5 file. had to open it on each process rather than open and copy. mpi4py
complains when trying to pass open files, atleast using the 'pickle' versions of send
& recv.
"""
try:
self.handle = h5py.File(invars.traj_file,'r')
except:
message = 'file \'{invars.traj_file}\' seems borked'
raise PSF_exception(message)
def parse_input(self, input_file):
"""
read the input file
"""
# test that the input file exists/isnt broken
try:
with open(input_file, 'r') as inp:
self.input_txt = inp.readlines()
except:
message = f'input file \'{input_file}\' not found'
raise PSF_exception(message)
# check the key_words in the file, remove empty lines and comments
self._check_file()
# get the variables from file
self.traj_file = self._parse_str('traj_file', self.traj_file)
self.outfile_prefix = self._parse_str('outfile_prefix',
self.outfile_prefix)
self.output_dir = self._parse_str('output_dir', self.output_dir)
self.save_progress = self._parse_bool('save_progress',
self.save_progress)
self.parse_custom = self._parse_bool('parse_custom', self.parse_custom)
self.dt = self._parse_float('dt', self.dt)
self.stride = self._parse_int('stride', self.stride)
self.total_steps = self._parse_int('total_steps', self.total_steps)
self.num_atoms = self._parse_int('num_atoms', self.num_atoms)
self.supercell = self._parse_int_list('supercell', self.supercell)
self.lattice_vectors = self._parse_float_list('lattice_vectors',
self.lattice_vectors)
self.unwrap_pos = self._parse_bool('unwrap_pos', self.unwrap_pos)
self.recalculate_cell_lengths = self._parse_bool(
'recalculate_cell_lengths', self.recalculate_cell_lengths)
self.ins_xlengths = self._parse_float_list('ins_xlengths',
self.ins_xlengths)
self.types = self._parse_str_list('types', self.types)
self.exp_type = self._parse_str('exp_type', self.exp_type)
self.Qpoints_file = self._parse_str('Qpoints_file', self.Qpoints_file)
self.Qmin = self._parse_float_list('Qmin', self.Qmin)
self.Qmax = self._parse_float_list('Qmax', self.Qmax)
self.total_Qsteps = self._parse_int('total_Qsteps', self.total_Qsteps)
self.num_blocks = self._parse_int('num_blocks', self.num_blocks)
self.blocks = list(range(self.num_blocks))
self.blocks = self._parse_int_list('blocks', self.blocks)
self.compute_bragg = self._parse_bool('compute_bragg',
self.compute_bragg)
self.compute_timeavg = self._parse_bool('compute_timeavg',
self.compute_timeavg)
self.compute_sqw = self._parse_bool('compute_sqw', self.compute_sqw)
# check that input variables are valid where applicable
self._check_variables()
def _parse_bool(self, key_word, default):
"""
get bool variable from file
"""
return_value = default
for line in self.input_txt:
if line.split('=')[0].strip() == key_word:
return_value = line.split('=')[-1]
return_value = return_value.split('#')[0].strip()
try:
return_value = bool(int(return_value))
except:
message = f'key word \'{key_word}\' seems wrongs.'
raise PSF_exception(message)
return return_value
def _parse_str_list(self, key_word, default):
"""
get list of ints from file
"""
return_value = default
for line in self.input_txt:
if line.split('=')[0].strip() == key_word:
return_value = line.split('=')[-1]
return_value = return_value.split('#')[0].strip()
return_value = return_value.split()
try:
return_value = [str(x) for x in return_value]
except:
message = f'key word \'{key_word}\' seems wrongs.'
raise PSF_exception(message)
return return_value
def _Qpoints_from_list(self, invars):
"""
Give a csv file of Qpoints. 1 per line, each coord seperated by spaces. Overwrites other
definitions for Q slices if a file name is given.
"""
try:
self.total_reduced_Q = np.loadtxt(
invars.Qpoints_file) # read the Q points
except:
message = f'Qpoints file \'{invars.Qpoints_file}\' is broken'
raise PSF_exception(message)
if len(self.total_reduced_Q.shape
) == 1: # if only 1 Q, reshape to avoid breaking stuff later
self.total_reduced_Q = self.total_reduced_Q.reshape((1, 3))
self.total_Qsteps = self.total_reduced_Q.shape[0] # number of Q points
def _loop_over_blocks(self, invars, Qpoints, lattice, traj_file):
"""
contains outer loop over blocks
info about scattering lengths: there should be 1 length per TYPE, in order
of types. e.g. for 4 types = 1,2,3,4 there should be for lenghts atom 1 : length 1,
atom 2 : lenght 2, etc... i am also assuming that dump_modify sort id was used so
that the order of atoms is the same for each step. this can be changed easily if
not the case using the atom_ids variable, but that will slow down the calc a little.
the b_array variable has shape [num_steps, num_atoms] to vectorize calculating the
neutron weighted density-density correlation fn
"""
for block_index in invars.blocks: # loop over blocks to 'ensemble' average
self.block_index = block_index
# print progress and start timer
if self.rank == 0:
start_time = timer()
message = f'now on block {self.counter} out of {self.num_blocks}'
print_stdout(message, msg_type='NOTE')
# get the positions from file
traj_file.parse_trajectory(invars, self)
# check that the number of b's defined in file are consistent with traj
if self.rank == 0:
if np.unique(self.atom_ids[0, :]).shape[0] != invars.num_types:
message = 'number of types in input file doesnt match simulation'
raise PSF_exception(message)
# set up array of scattering lengths.
for aa in range(invars.num_atoms):
self.b_array[0, aa] = invars.b[self.atom_ids[0, aa] - 1]
self.b_array = np.tile(self.b_array[0, :].reshape(
1, invars.num_atoms),
reps=[self.block_steps, 1])
# box lenghts read from traj file
a = self.box_lengths[0] / invars.supercell[0]
b = self.box_lengths[1] / invars.supercell[1]
c = self.box_lengths[2] / invars.supercell[2]
# print box lengths read from traj file to compare to input file
if self.rank == 0:
message = f'cell lengths from hdf5 file: {a:2.3f} {b:2.3f} {c:2.3f} Angstrom'
print_stdout(message, msg_type='NOTE')
# recall, only ortho lattice vectors used (for now)
if invars.recalculate_cell_lengths: # optionally recalculates from avg in MD file
lattice.lattice_vectors = np.array([[a, 0, 0], [0, b, 0],
[0, 0, c]])
lattice.recompute_lattice(
self.rank) # recompute reciprocal lattice
Qpoints.reconvert_Q_points(
lattice) # convert Q to 1/A in new basis
# do the loop over Q points
if self.rank == 0:
message = (
'printing progess for rank 0, which has >= the number of Q on other procs.\n'
' -- now entering loop over Q -- ')
print_stdout(message, msg_type='NOTE')
for qq in range(Qpoints.Qsteps):
if self.rank == 0:
message = f' now on Q-point {qq+1} out of {Qpoints.Qsteps}'
print_stdout(message)
Q = Qpoints.Qpoints[qq, :].reshape(
(1, 3)) # these are the ones in 1/Angstrom
exp_iQr = np.tile(
Q, reps=[self.block_steps, invars.num_atoms, 1]) * self.pos
exp_iQr = np.exp(1j * exp_iQr.sum(axis=2)) * self.b_array
self.sqw[:, qq] = self.sqw[:, qq] + np.abs(
fft(exp_iQr.sum(axis=1)))**2
# print timing to the log file
if self.rank == 0:
end_time = timer()
elapsed_time = (end_time - start_time) / 60 # minutes
message = f' elapsed time for this block: {elapsed_time:2.3f} minutes'
print_stdout(message, msg_type='TIMING')
message = f' time per Q-point: {elapsed_time*60/Qpoints.Qsteps:2.3f} seconds'
print_stdout(message)
# optionally save progress
if invars.save_progress:
if self.counter != self.num_blocks:
f_name = invars.outfile_prefix + f'_P{self.rank}_B{block_index}.hdf5'
mod_io.save_sqw(invars, Qpoints.reduced_Q, self.meV,
self.sqw / self.counter, f_name)
self.counter = self.counter + 1 # update the counter
"""
def _check_variables(self):
"""
where applicable, do some checks on input variables and exit if need be
"""
# check that the lattice vectors make sense
try:
self.lattice_vectors = np.array(self.lattice_vectors).reshape(
(3, 3))
except:
message = 'lattice vectors seem wrong. should be a list of 9 floats with no commas'
raise PSF_exception(message)
# check that lattice vectors are ortho
# the issue is that positions etc. are in cartesian coords with ortho boxes. different lattice
# vectors should work, but i haven't tested it yet. it will be necessary to convert Q in 1/A
# to cartesian coordinates so that the vectorized multiplication done in mod_sqw._loop_over_blocks
# works.
if (self.lattice_vectors[0, 1] != 0 or self.lattice_vectors[0, 2] != 0
or self.lattice_vectors[1, 0] != 0
or self.lattice_vectors[1, 2] != 0
or self.lattice_vectors[2, 0] != 0
or self.lattice_vectors[2, 1] != 0):
message = 'only ortho. lattice vectors are currently supported. see comments in mod_invars'
raise PSF_exception(message)
# print the traj file
message = f'reading trajectories from file \'{self.traj_file}\''
print_stdout(message, msg_type='NOTE')
# check for user defined scattering lenghts (only for ins)
if self.ins_xlengths != False:
self.num_types = len(self.ins_xlengths)
message = 'using user specified scattering lengths (only works for ins, ignored for xray)'
print_stdout(message, msg_type='NOTE')
else:
self.num_types = len(self.types)
message = 'using scattering lengths from mod_xlengts'
print_stdout(message, msg_type='NOTE')
# check experiment type
if self.exp_type not in ['xray', 'ins']:
message = 'experiment type should be either \'xray\' or \'ins\''
raise PSF_exception(message)
else:
message = f'the experiment type is \'{self.exp_type}\''
print_stdout(message, msg_type='NOTE')
# check that Q paths opts make sense
if len(self.Qmin) != 3:
message = f'variable Qmin should be a list of 3 floats'
raise PSF_exception(message)
if len(self.Qmax) != 3:
message = f'variable Qmax should be a list of 3 floats'
raise PSF_exception(message)
# check that the requested blocks make sense
if max(self.blocks) >= self.num_blocks or len(
self.blocks) > self.num_blocks:
message = f'variable blocks should be a list of the blocks to calculate'
raise PSF_exception(message)
# if the output dir. doesnt exist, create it
if not os.path.exists(self.output_dir):
message = f'creating directory \'{self.output_dir}\''
print_stdout(message, msg_type='NOTE')
os.mkdir(self.output_dir)
# check that atleast one of compute_* is not False
if not self.compute_sqw and not self.compute_timeavg and not self.compute_bragg:
message = (
'there is nothing to do! set atleast one of compute_sqw, \n compute_timeavg,'
' or compute_bragg to 1 in the input file')
raise PSF_exception(message)
# check if traj file opens
if not os.path.exists(self.traj_file):
message = f'file \'{self.traj_file}\' not found'
raise PSF_exception(message)
def _loop_over_blocks(self,invars,Qpoints,lattice,traj_file):
"""
contains outer loop over blocks
info about scattering lengths: there should be 1 length per TYPE, in order
of types. e.g. for 4 types = 1,2,3,4 there should be for lengths atom 1 : length 1,
atom 2 : lenght 2, etc... i am also assuming that dump_modify sort id was used so
that the order of atoms is the same for each step. this can be changed easily if
not the case using the atom_types variable, but that will slow down the calc a little.
the b_array variable has shape [num_steps, num_atoms] to vectorize calculating the
neutron weighted density-density correlation fn
"""
for block_index in invars.blocks: # loop over blocks to 'ensemble' average
# used below
self.block_index = block_index
# print progress and start timer
if self.rank == 0:
start_time = timer()
message = '\n............................................'
print_stdout(message)
message = f' now on block {self.counter} out of {self.num_blocks}'
print_stdout(message,msg_type='NOTE')
# get the positions from file
traj_file.parse_trajectory(invars,self)
# check that the number of b's defined in input file are consistent with traj file
if self.rank == 0:
if np.unique(self.atom_types[0,:]).shape[0] != invars.num_types:
message = 'number of types in input file doesnt match simulation'
raise PSF_exception(message)
# look up ins scattering lengths OR parameters to compute xray form factors.
self.xlengths_tools.map_types_to_data(invars,self)
# box lengths read from traj file
a = self.box_lengths[0]/invars.supercell[0]
b = self.box_lengths[1]/invars.supercell[1]
c = self.box_lengths[2]/invars.supercell[2]
# print box lengths read from traj file to compare to input file
if self.rank == 0:
message = f'cell lengths from hdf5 file: {a:2.3f} {b:2.3f} {c:2.3f} Angstrom'
print_stdout(message,msg_type='NOTE')
# recall, only ortho lattice vectors used (for now)
if invars.recalculate_cell_lengths: # optionally recalculates from avg in MD file
lattice.lattice_vectors = np.array([[a,0,0],[0,b,0],[0,0,c]])
lattice.recompute_lattice(self.rank) # recompute reciprocal lattice
Qpoints.reconvert_Q_points(lattice) # convert Q to 1/A in new basis
# --------------------- enter the loop over Qpoints -----------------------------------
if self.rank == 0:
Q_start_time = timer() # track time per Q not including the read/write time
message = ('printing progess for rank 0, which has >= the number of Q on other procs.\n'
' -- now entering loop over Q -- ')
print_stdout(message,msg_type='NOTE')
for qq in range(Qpoints.Qsteps):
if self.rank == 0:
message = f' now on Q-point {qq+1} out of {Qpoints.Qsteps}'
print_stdout(message)
# the Qpoint to do
Q = Qpoints.Qpoints[qq,:].reshape((1,3)) # 1/Angstrom
self.Q_norm = np.sqrt(Q[0,0]**2+Q[0,1]**2+Q[0,2]**2)
# if xray, need to compute f(|Q|), which are placed in self.xlengths
if invars.exp_type == 'xray':
self.xlengths_tools.compute_xray_form_fact(self,invars)
# space FT by vectorized Q.r dot products and sum over atoms. (tile prepends new axes)
exp_iQr = np.tile(Q,reps=[self.block_steps,invars.num_atoms,1])*self.pos # Q.r
exp_iQr = np.exp(1j*exp_iQr.sum(axis=2))*self.xlengths # sum over x, y, z
exp_iQr = exp_iQr.sum(axis=1) # sum over atoms
# compute bragg intensity = |<rho(Q,t)>|**2
if invars.compute_bragg:
self.bragg[qq] = self.bragg[qq]+np.abs((exp_iQr).mean())**2/self.common_rescale
# compute timeavg intensity = <|rho(Q,t)|**2>
if invars.compute_timeavg:
self.timeavg[qq] = self.timeavg[qq]+(np.abs(exp_iQr)**2).mean()/self.common_rescale
# compute dynamical intensity = |rho(Q,w)|**2
if invars.compute_sqw:
self.sqw[:,qq] = (self.sqw[:,qq]+np.abs(fft(exp_iQr))**2/
self.sqw_norm/self.common_rescale)
# -------------------------------------------------------------------------------------
# optionally save progress
if invars.save_progress:
if self.counter != self.num_blocks:
if invars.compute_sqw:
f_name = invars.outfile_prefix+f'_SQW_P{self.rank}_B{block_index}.hdf5'
mod_io.save_sqw(invars,Qpoints.reduced_Q,self.meV,self.sqw/self.counter,f_name)
if invars.compute_bragg:
f_name = invars.outfile_prefix+f'_BRAGG_P{self.rank}_B{block_index}.hdf5'
mod_io.save_bragg(invars,Qpoints.reduced_Q,self.bragg,f_name)
if invars.compute_timeavg:
f_name = invars.outfile_prefix+f'_TIMEAVG_P{self.rank}_B{block_index}.hdf5'
mod_io.save_timeavg(invars,Qpoints.reduced_Q,self.timeavg,f_name)
# print timing to the log file
if self.rank == 0:
end_time = timer()
elapsed_time = end_time-start_time
Q_time = end_time-Q_start_time
io_time = elapsed_time-Q_time
Q_time = Q_time/Qpoints.Qsteps # avg over all Q
message = f' avg time per Q-point: {Q_time:2.3f} seconds'
print_stdout(message,msg_type='TIMING')
message = f' total io time: {io_time:2.3f} seconds'
print_stdout(message)
message = (f' total time for this block: {elapsed_time:2.3f} seconds'
f' ({elapsed_time/60:2.3f} minutes)')
print_stdout(message)
self.counter = self.counter+1 # update the counter
def _loop_over_blocks(self,invars,Qpoints,lattice,traj_file):
"""
contains outer loop over blocks
info about scattering lengths: there should be 1 length per TYPE, in order
of types. e.g. for 4 types = 1,2,3,4 there should be for lengths atom 1 : length 1,
atom 2 : lenght 2, etc... i am also assuming that dump_modify sort id was used so
that the order of atoms is the same for each step. this can be changed easily if
not the case using the atom_types variable, but that will slow down the calc a little.
the b_array variable has shape [num_steps, num_atoms] to vectorize calculating the
neutron weighted density-density correlation fn
"""
for block_index in invars.blocks: # loop over blocks to 'ensemble' average
# used below
self.block_index = block_index
# print progress and start timer
start_time = timer()
message = '\n............................................'
print_stdout(message)
message = f' now on block {self.counter} out of {self.num_blocks}'
print_stdout(message,msg_type='NOTE')
# get the positions from file
traj_file.parse_trajectory(invars,self)
# check that the number of b's defined in input file are consistent with traj file
if np.unique(self.atom_types[0,:]).shape[0] != invars.num_types:
message = 'number of types in input file doesnt match simulation'
raise PSF_exception(message)
# look up ins scattering lengths OR parameters to compute xray form factors.
self.xlengths_tools.map_types_to_data(invars,self)
# box lengths read from traj file
a = self.box_lengths[0]/invars.supercell[0]
b = self.box_lengths[1]/invars.supercell[1]
c = self.box_lengths[2]/invars.supercell[2]
# print box lengths read from traj file to compare to input file
message = f'cell lengths from hdf5 file: {a:2.3f} {b:2.3f} {c:2.3f} Angstrom'
print_stdout(message,msg_type='NOTE')
# recall, only ortho lattice vectors used (for now)
if invars.recalculate_cell_lengths: # optionally recalculates from avg in MD file
lattice.lattice_vectors = np.array([[a,0,0],[0,b,0],[0,0,c]])
lattice.recompute_lattice() # recompute reciprocal lattice
Qpoints.reconvert_Q_points(lattice) # convert Q to 1/A in new basis
Q_start_time = timer() # track time per Q not including the read/write time
message = ('printing progess for process 0, which has >= the number of Q on other' \
' processes.\n -- now entering loop over Q -- ')
print_stdout(message,msg_type='NOTE')
# -----------------------------------------------------------------------------------------
# ------------- multiprocessing part. -------------
# -----------------------------------------------------------------------------------------
# a Queue to hold the retured SQW data
self.mp_queue = mp.Queue()
# a container to hold the processes
procs = []
# loop over processes, setting up the loop over Q on each.
for pp in range(invars.num_processes):
procs.append(mp.Process(target=self._loop_over_Q, args=(pp,invars,Qpoints)))
# now start running the function on each process
for proc in procs:
proc.start()
# note, doing it this way with the queue 'blocks' until the next processes adds to queue if
# it is empty. I dont know if this will freeze the whole calculation or just the background
# proc that is running the queue. anyway, everything with the queue has to be done before
# joining the procs or the data will corrupt/crash the program.
# get the stuff calculated on each proc
for pp in range(invars.num_processes):
sqw_pp, bragg_pp, timeavg_pp, proc = self.mp_queue.get()
Q_inds = Qpoints.Q_on_procs[proc]
# now put it into main arrays if requested
if invars.compute_sqw:
self.sqw[:,Q_inds] = self.sqw[:,Q_inds]+sqw_pp
if invars.compute_bragg:
self.bragg[Q_inds] = self.bragg[Q_inds]+bragg_pp
if invars.compute_timeavg:
self.timeavg[Q_inds] = self.timeavg[Q_inds]+timeavg_pp
# now close the queue and rejoin its proc
self.mp_queue.close()
self.mp_queue.join_thread()
# wait here for all to finish before moving on to the next block
for proc in procs:
proc.join()
# -----------------------------------------------------------------------------------------
# ------------ end of multiprocessing part ----------------
# -----------------------------------------------------------------------------------------
# optionally save progress
if invars.save_progress:
if self.counter != self.num_blocks: # dont write if this is the last block
if invars.compute_sqw:
f_name = invars.outfile_prefix+f'_SQW_B{block_index}.hdf5'
mod_io.save_sqw(invars,Qpoints.reduced_Q,self.meV,self.sqw/self.counter,f_name)
if invars.compute_bragg:
f_name = invars.outfile_prefix+f'_BRAGG_B{block_index}.hdf5'
mod_io.save_bragg(invars,Qpoints.reduced_Q,self.bragg/self.counter,f_name)
if invars.compute_timeavg:
f_name = invars.outfile_prefix+f'_TIMEAVG_B{block_index}.hdf5'
mod_io.save_timeavg(invars,Qpoints.reduced_Q,self.timeavg/self.counter,f_name)
# print timing to screen
end_time = timer()
elapsed_time = end_time-start_time
Q_time = end_time-Q_start_time
io_time = elapsed_time-Q_time
# time per Qpoint
Q_time = Q_time/len(Qpoints.Q_on_procs[0]) # avg over all Q
message = f' avg time per Q-point: {Q_time:2.3f} seconds'
print_stdout(message,msg_type='TIMING')
# time spent in i/o
message = f' total io time: {io_time:2.3f} seconds'
print_stdout(message)
# total time for in this method
message = (f' total time for this block: {elapsed_time:2.3f} seconds'
f' ({elapsed_time/60:2.3f} minutes)')
print_stdout(message)
# update the block counter
self.counter = self.counter+1
def parse_input(self, input_file):
"""
read the input file
"""
# test that the input file exists/isnt broken
try:
with open(input_file, 'r') as inp:
self.input_txt = inp.readlines()
except:
message = f'input file \'{input_file}\' not found'
raise PSF_exception(message)
# check the key_words in the file, remove empty lines and comments
self._check_file()
# get the variables from file
self.traj_file = self._parse_str('traj_file', self.traj_file)
self.outfile_prefix = self._parse_str('outfile_prefix',
self.outfile_prefix)
self.output_dir = self._parse_str('output_dir', self.output_dir)
self.save_progress = self._parse_bool('save_progress',
self.save_progress)
self.parse_custom = self._parse_bool('parse_custom', self.parse_custom)
self.dt = self._parse_float('dt', self.dt)
self.stride = self._parse_int('stride', self.stride)
self.total_steps = self._parse_int('total_steps', self.total_steps)
self.num_atoms = self._parse_int('num_atoms', self.num_atoms)
self.supercell = self._parse_int_list('supercell', self.supercell)
self.lattice_vectors = self._parse_float_list('lattice_vectors',
self.lattice_vectors)
self.unwrap_pos = self._parse_bool('unwrap_pos', self.unwrap_pos)
self.recalculate_cell_lengths = self._parse_bool(
'recalculate_cell_lengths', self.recalculate_cell_lengths)
self.b = self._parse_float_list('b', self.b)
self.num_types = len(self.b)
self.Qpoints_file = self._parse_str('Qpoints_file', self.Qpoints_file)
self.Qmin = self._parse_float_list('Qmin', self.Qmin)
self.Qmax = self._parse_float_list('Qmax', self.Qmax)
self.total_Qsteps = self._parse_int('total_Qsteps', self.total_Qsteps)
self.num_blocks = self._parse_int('num_blocks', self.num_blocks)
self.blocks = list(range(self.num_blocks))
self.blocks = self._parse_int_list('blocks', self.blocks)
# check that the lattice vectors make sense
try:
self.lattice_vectors = np.array(self.lattice_vectors).reshape(
(3, 3))
except:
message = 'lattice vectors seem wrong. should be a list of 9 floats with no commas'
raise PSF_exception(message)
# check that lattice vectors are ortho
# the issue is that positions etc. are in cartesian coords with ortho boxes. different lattice
# vectors should work, but i haven't tested it yet. it will be necessary to convert Q in 1/A
# to cartesian coordinates so that the vectorized multiplication done in mod_sqw._loop_over_blocks
# works.
if (self.lattice_vectors[0, 1] != 0 or self.lattice_vectors[0, 2] != 0
or self.lattice_vectors[1, 0] != 0
or self.lattice_vectors[1, 2] != 0
or self.lattice_vectors[2, 0] != 0
or self.lattice_vectors[2, 1] != 0):
message = 'only ortho. lattice vectors are currently supported. see comments in mod_invars'
raise PSF_exception(message)
# print the traj file
message = f'reading trajectories from file \'{self.traj_file}\''
print_stdout(message, msg_type='NOTE')
# print the scattering lengths to file
message = f' atom-type: 0 b: {self.b[0]: 2.4f}\n'
for bb in range(1, self.num_types):
message = message + f' atom-type: {bb:2g} b: {self.b[bb]: 2.4f}\n'
print_stdout(message, msg_type='scattering lengths (b) in femtometers')
# now convert b to Angstrom. 'intensities' are of order 1 this way. with FM, theyre
# of order 1e16. this is purely for convenience and doesnt change the physics, since the
# intensities are arbitrary units anyway
for bb in range(self.num_types):
self.b[bb] = self.b[bb] * 1e-5
# check that Q paths opts make sense
if len(self.Qmin) != 3:
message = f'variable Qmin should be a list of 3 floats'
raise PSF_exception(message)
if len(self.Qmax) != 3:
message = f'variable Qmax should be a list of 3 floats'
raise PSF_exception(message)
# check that the requested blocks make sense
if max(self.blocks) >= self.num_blocks or len(
self.blocks) > self.num_blocks:
message = f'variable blocks should be a list of the blocks to calculate'
raise PSF_exception(message)
# if the output dir. doesnt exist, create it
if not os.path.exists(self.output_dir):
message = f'creating directory \'{self.output_dir}\''
print_stdout(message, msg_type='NOTE')
os.mkdir(self.output_dir)