def init_first(self): AnalysisHook.init_first(self) if self.outg is not None: self.outg.create_dataset('amps', (self.ssize,), float) self.outg.create_dataset('freqs', (self.ssize,), float) self.outg.create_dataset('ac', (self.ssize,), float) self.outg.create_dataset('time', (self.ssize,), float)
def init_first(self): # update the shape if select is present if self.select is not None: self.shape = (len(self.select), ) + self.shape[1:] # compute the number of dimensions, i.e. 3 for atoms self.ndim = 1 for s in self.shape[1:]: self.ndim *= s # allocate working arrays self.last_poss = [ np.zeros(self.shape, float) for i in range(self.mult) ] self.pos = np.zeros(self.shape, float) # prepare the hdf5 output file, if present. AnalysisHook.init_first(self) if self.outg is not None: for m in range(self.mult): self.outg.create_dataset('msd%03i' % (m + 1), shape=(0, ), maxshape=(None, ), dtype=float) self.outg.create_dataset('msdsums', data=self.msdsums) self.outg.create_dataset('msdcounters', data=self.msdcounters) self.outg.create_dataset('pars', shape=(2, ), dtype=float) self.outg.create_dataset('pars_error', shape=(2, ), dtype=float)
def init_first(self): '''Setup some work arrays''' # determine the number of atoms if self.select0 is None: self.natom0 = self.natom else: self.natom0 = len(self.select0) self.pos0 = np.zeros((self.natom0, 3), float) # the number of pairs if self.select1 is None: self.npair = (self.natom0 * (self.natom0 - 1)) // 2 self.pos1 = None else: self.natom1 = len(self.select1) self.pos1 = np.zeros((self.natom1, 3), float) self.npair = self.natom0 * self.natom1 # multiply the number of pairs by all images self.npair *= (1 + 2 * self.nimage)**3 # Prepare the output self.work = np.zeros(self.npair, float) AnalysisHook.init_first(self) if self.outg is not None: self.outg.create_dataset('rdf', (self.nbin, ), float) self.outg.create_dataset('CN', (self.nbin, ), float) self.outg['d'] = self.d if self.pairs_sr is not None: self.outg.create_dataset('rdf_sr', (self.nbin, ), float)
def init_first(self): AnalysisHook.init_first(self) if self.outg is not None: self.outg.create_dataset("amps", (self.ssize,), float) self.outg.create_dataset("freqs", (self.ssize,), float) self.outg.create_dataset("ac", (self.ssize,), float) self.outg.create_dataset("time", (self.ssize,), float)
def init_first(self): AnalysisHook.init_first(self) if self.outg is not None: self.outg.create_dataset('amps', (self.ssize, ), float) self.outg.create_dataset('freqs', (self.ssize, ), float) self.outg.create_dataset('ac', (self.ssize, ), float) self.outg.create_dataset('time', (self.ssize, ), float)
def init_first(self): '''Setup some work arrays''' # determine the number of atoms if self.select0 is None: self.natom0 = self.natom else: self.natom0 = len(self.select0) self.pos0 = np.zeros((self.natom0, 3), float) # the number of pairs if self.select1 is None: self.npair = (self.natom0*(self.natom0-1))/2 self.pos1 = None else: self.natom1 = len(self.select1) self.pos1 = np.zeros((self.natom1, 3), float) self.npair = self.natom0*self.natom1 # multiply the number of pairs by all images self.npair *= (1 + 2*self.nimage)**3 # Prepare the output self.work = np.zeros(self.npair, float) AnalysisHook.init_first(self) if self.outg is not None: self.outg.create_dataset('rdf', (self.nbin,), float) self.outg['d'] = self.d if self.pairs_sr is not None: self.outg.create_dataset('rdf_sr', (self.nbin,), float)
def init_first(self): # update the shape if select is present if self.select is not None: self.shape = (len(self.select),) + self.shape[1:] # compute the number of dimensions, i.e. 3 for atoms self.ndim = 1 for s in self.shape[1:]: self.ndim *= s # allocate working arrays self.last_poss = [np.zeros(self.shape, float) for i in range(self.mult)] self.pos = np.zeros(self.shape, float) # prepare the hdf5 output file, if present. AnalysisHook.init_first(self) if self.outg is not None: for m in range(self.mult): self.outg.create_dataset('msd%03i' % (m+1), shape=(0,), maxshape=(None,), dtype=float) self.outg.create_dataset('msdsums', data=self.msdsums) self.outg.create_dataset('msdcounters', data=self.msdcounters) self.outg.create_dataset('pars', shape=(2,), dtype=float) self.outg.create_dataset('pars_error', shape=(2,), dtype=float)