diff options
Diffstat (limited to 'instrument/scatter/2016.05.02 16-33-15 current delay space/src')
3 files changed, 0 insertions, 273 deletions
diff --git a/instrument/scatter/2016.05.02 16-33-15 current delay space/src/H0.py b/instrument/scatter/2016.05.02 16-33-15 current delay space/src/H0.py deleted file mode 100644 index e040203..0000000 --- a/instrument/scatter/2016.05.02 16-33-15 current delay space/src/H0.py +++ /dev/null @@ -1,171 +0,0 @@ -"""
-@author: Dan
-
-each instance of running this depends on a few initial conditions that have to
-be specified:
- out_group
- rho_0
- wa_central
- a_coupling
- gamma
- dipoles
-
-so create a class where all these can describe the specific instance
-"""
-
-from NISE.lib.misc import *
-
-def gen_w_0(wa_central, a_coupling):
- # convert nice system parameters into system vector indeces
- w_ag = wa_central
- w_2aa = w_ag - a_coupling
- w_2ag = 2*w_ag - a_coupling
- w_gg = 0.
- w_aa = w_gg
- return np.array( [w_gg, w_ag, -w_ag, w_aa, w_2ag, w_ag, w_2aa] )
-
-def gen_Gamma_0(tau_ag, tau_aa, tau_2ag, tau_2aa):
- # same as gen_w_0, but for dephasing/relaxation times
- tau = np.array( [np.inf, tau_ag, tau_ag,
- tau_aa, tau_2ag,
- tau_ag, tau_2aa ] )
- Gamma = 1/tau
- return Gamma
-
-class Omega:
- # record the propagator module used to evolve this hamiltonian
- propagator = 'rk'
- # phase cycling is not valuable in this hamiltonian
- pc = False
- # all attributes should have good initial guesses for parameters
- dm_vector = ['gg1','ag','ga','aa','2ag','ag2','2aa']
- #out_group = [[6,7]]#,[7]]
- out_group = [[5],[6]] # use this to separate alpha/gamma from beta for now
- #--------------------------Oscillator Properties--------------------------
- rho_0 = np.zeros((len(dm_vector)), dtype=np.complex64)
- rho_0[0] = 1.
- # 1S exciton central position
- wa_central = 7000.
- # exciton-exciton coupling
- a_coupling = 0. # cm-1
- # dephasing times, fs
- tau_ag = 50.
- tau_aa = np.inf #1./2000.
- tau_2aa = tau_ag
- tau_2ag = tau_ag
- # transition dipoles (a.u.)
- mu_ag = 1.0
- mu_2aa = 1.0 * mu_ag # HO approx (1.414) vs. uncorr. electron approx. (1.)
- # TOs sets which time-ordered pathways to include (1-6 for TrEE)
- # defaults to include all time-orderings included
- TOs = range(7)[1:]
- #--------------------------Recorded attributes--------------------------
- out_vars = ['dm_vector', 'out_group', 'rho_0', 'mu_ag', 'mu_2aa',
- 'tau_ag', 'tau_aa', 'tau_2aa', 'tau_2ag',
- 'wa_central', 'a_coupling', 'pc', 'propagator',
- 'TOs']
- #--------------------------Methods--------------------------
- def __init__(self, **kwargs):
- # inherit all class attributes unless kwargs has them; then use those
- # values. if kwargs is not an Omega attribute, it gets ignored
- # careful: don't redefine instance methods as class methods!
- for key, value in kwargs.items():
- if key in Omega.__dict__.keys():
- setattr(self, key, value)
- else:
- print 'did not recognize attribute {0}. No assignment made'.format(key)
- # with this set, initialize parameter vectors
- self.w_0 = gen_w_0(self.wa_central, self.a_coupling)
- self.Gamma = gen_Gamma_0(self.tau_ag, self.tau_aa, self.tau_2ag,
- self.tau_2aa)
-
- def o(self, efields, t, wl):
- # combine the two pulse permutations to produce one output array
- E1, E2, E3 = efields[0:3]
-
- out1 = self._gen_matrix(E1, E2, E3, t, wl, w1first = True)
- out2 = self._gen_matrix(E1, E2, E3, t, wl, w1first = False)
-
- return np.array([out1, out2], dtype=np.complex64)
-
- def _gen_matrix(self, E1, E2, E3, t, wl, w1first = True):
- """
- creates the coupling array given the input e-fields values for a specific time, t
- w1first selects whether w1 or w2p is the first interacting positive field
-
- Currently neglecting pathways where w2 and w3 require different frequencies
- (all TRIVE space, or DOVE on diagonal)
-
- Matrix formulated such that dephasing/relaxation is accounted for
- outside of the matrix
- """
- wag = wl[1]
- w2aa = wl[6]
-
- mu_ag = self.mu_ag
- mu_2aa = self.mu_2aa
-
- if w1first==True:
- first = E1
- second = E3
- else:
- first = E3
- second = E1
-
- O = np.zeros((len(t), len(wl), len(wl)), dtype=np.complex64)
- # from gg1
- O[:,1,0] = mu_ag * first * rotor(-wag*t)
- if w1first and 3 in self.TOs:
- O[:,2,0] = -mu_ag * E2 * rotor(wag*t)
- if not w1first and 5 in self.TOs:
- O[:,2,0] = -mu_ag * E2 * rotor(wag*t)
- # from ag1
- # to DQC
- if w1first and 2 in self.TOs:
- O[:,4,1] = mu_2aa * second * rotor(-w2aa*t)
- if not w1first and 4 in self.TOs:
- O[:,4,1] = mu_2aa * second * rotor(-w2aa*t)
- # to pop
- if w1first and 1 in self.TOs:
- O[:,3,1] = -mu_ag * E2 * rotor(wag*t)
- if not w1first and 6 in self.TOs:
- O[:,3,1] = -mu_ag * E2 * rotor(wag*t)
- # from ga
- O[:,3,2] = mu_ag * first * rotor(-wag*t)
- # from gg-aa
- O[:,5,3] = -mu_ag * second * rotor(-wag*t) * mu_ag
- # because of alpha and gamma pathways, count twice
- O[:,5,3] -= mu_ag * second * rotor(-wag*t) * mu_ag
- O[:,6,3] = mu_2aa * second * rotor(-w2aa*t) * mu_2aa
- # from 2ag
- O[:,6,4] = mu_ag * E2 * rotor(wag*t) * mu_2aa
- O[:,5,4] = -mu_2aa * E2 * rotor(w2aa*t) * mu_ag
-
- # make complex according to Liouville Equation
- O *= complex(0,0.5)
-
- # include coherence decay rates:
- for i in range(O.shape[-1]):
- O[:,i,i] = -self.Gamma[i]
-
- return O
-
- def ws(self, inhom_object):
- """
- creates the correspondence of oscillator energies to the state vector
- contains instructions for how energies change as subsets are changed
- """
- z = inhom_object.zeta
-
- wg = 0.0 + 0*z
- wa = z + self.wa_central
- w2a = 2*wa - self.a_coupling
-
- w_ag = wa - wg
- w_aa = wa - wa
- w_gg = wg - wg
- w_2ag = w2a - wg
- w_2aa = w2a - wa
- #array aggregates all frequencies to match state vectors
- w = np.array( [w_gg, w_ag, -w_ag, w_aa, w_2ag, w_ag, w_2aa] )
- return w
diff --git a/instrument/scatter/2016.05.02 16-33-15 current delay space/src/class_maps.p b/instrument/scatter/2016.05.02 16-33-15 current delay space/src/class_maps.p deleted file mode 100644 index 44e2a40..0000000 --- a/instrument/scatter/2016.05.02 16-33-15 current delay space/src/class_maps.p +++ /dev/null @@ -1,17 +0,0 @@ -(dp1 -S'H' -(lp2 -S'Omega' -p3 -aS'NISE.hamiltonians.H0' -p4 -aS'H0.py' -p5 -asS'Inhom' -p6 -(lp7 -g6 -ag6 -aS'inhom.py' -p8 -as.
\ No newline at end of file diff --git a/instrument/scatter/2016.05.02 16-33-15 current delay space/src/inhom.py b/instrument/scatter/2016.05.02 16-33-15 current delay space/src/inhom.py deleted file mode 100644 index 1438846..0000000 --- a/instrument/scatter/2016.05.02 16-33-15 current delay space/src/inhom.py +++ /dev/null @@ -1,85 +0,0 @@ -# -*- coding: utf-8 -*-
-"""
-Created on Sat Jun 21 14:07:53 2014
-
-@author: Dan
-"""
-
-from NISE.lib.misc import *
-
-class Inhom():
- # class contains the list of weights and sampling values to use
- #--------------------------Recorded attributes--------------------------
- out_vars = ['inhom_sampling', 'dist_params']
- #--------------------------Methods--------------------------
- def __init__(self, inhom_sampling=None, **dist_params):
- """
- generates the list of sampling points in the distribution and their weights
- inhom dists should be normalized (int(f, dzeta) = 1.)
- """
- # inherit all class attributes unless kwargs has them; then use those
- # values. if kwargs is not an Omega attribute, it gets ignored
- for key, value in dist_params.items():
- setattr(self, key, value)
- #print self.__dict__.items()
- # eliminating other quadrature methods; linear works best anyways
- if inhom_sampling == 'linear':
- # currently the only inhomogeneity parameter that can normalize well
- # in relation to the case of no inhomogeneity
- if isinstance(dist_params.get('num'), int):
- num = dist_params.get('num')
- else:
- try:
- num = int(num)
- except TypeError:
- print 'no distribution sampling number specified; using 10 points as default'
- num = 10
- if 'zeta_bound' in dist_params.keys():
- zeta_bound = dist_params.get('zeta_bound')
- else:
- zeta_bound = 3
- zeta = np.linspace(-zeta_bound, zeta_bound, num=num)
- # need parameter 'sigma'
- sigma = dist_params.get('sigma')
- # scale our sampling intervals according to sigma
- zeta = zeta * sigma
- self.zweight = 1 / (np.sqrt(2*np.pi)*sigma) * np.exp(- 0.5 * ((zeta / sigma)**2))
- self.dzeta = np.abs(zeta[1] - zeta[0])
- self.zeta = zeta
- elif inhom_sampling == 'rect':
- w = dist_params.get('w')
- if isinstance(dist_params.get('num'), int):
- num = dist_params['num']
- else:
- try:
- num = int(num)
- except TypeError:
- print 'no distribution sampling number specified; using 10 points as default'
- num = 10
- self.zeta = np.linspace(-w,w,num=num)
- self.dzeta = np.abs(self.zeta[1] - self.zeta[0])
- self.zweight = np.ones(self.zeta.shape)
- elif inhom_sampling == 'gh':
- import NISE.hamiltonians.params.gauss_hermite as gh
- # gaussian-hermite quadrature
- # see http://en.wikipedia.org/wiki/Gauss%E2%80%93Hermite_quadrature
- # for details
- n = dist_params.get('n')
- try:
- gh.quad[n]
- except KeyError:
- print 'no table for quadrature of number {0} is available'.format(n)
- print 'available quadrature numbers: {0}'.format(str(gh.quad.keys()))
- sigma = dist_params.get('sigma')
- self.zeta = np.array(gh.quad[n])[0]
- self.zweight = np.array(gh.quad[n])[1]
- self.dzeta = 1.
- # substitution to inhom variables yields the following scaling:
- self.zeta*= np.sqrt(2) * sigma
- self.zweight*= np.pi**-0.5
- else:
- self.zeta = np.array([0])
- self.zweight = [1.0]
- self.dzeta = 1.0
- self.inhom_sampling = inhom_sampling
- self.dist_params = dist_params
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