From 1b66cf20d0f40741d89d39b901716341beeabeca Mon Sep 17 00:00:00 2001 From: Blaise Thompson Date: Mon, 16 Oct 2017 21:05:20 -0500 Subject: structure --- .../2016.05.02 17-48-41 old delay space/src/H0.py | 171 --------------------- .../src/class_maps.p | 17 -- .../src/inhom.py | 85 ---------- 3 files changed, 273 deletions(-) delete mode 100644 figures/instrument/scatter/2016.05.02 17-48-41 old delay space/src/H0.py delete mode 100644 figures/instrument/scatter/2016.05.02 17-48-41 old delay space/src/class_maps.p delete mode 100644 figures/instrument/scatter/2016.05.02 17-48-41 old delay space/src/inhom.py (limited to 'figures/instrument/scatter/2016.05.02 17-48-41 old delay space/src') diff --git a/figures/instrument/scatter/2016.05.02 17-48-41 old delay space/src/H0.py b/figures/instrument/scatter/2016.05.02 17-48-41 old delay space/src/H0.py deleted file mode 100644 index e040203..0000000 --- a/figures/instrument/scatter/2016.05.02 17-48-41 old 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/figures/instrument/scatter/2016.05.02 17-48-41 old delay space/src/class_maps.p b/figures/instrument/scatter/2016.05.02 17-48-41 old delay space/src/class_maps.p deleted file mode 100644 index 44e2a40..0000000 --- a/figures/instrument/scatter/2016.05.02 17-48-41 old 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/figures/instrument/scatter/2016.05.02 17-48-41 old delay space/src/inhom.py b/figures/instrument/scatter/2016.05.02 17-48-41 old delay space/src/inhom.py deleted file mode 100644 index 1438846..0000000 --- a/figures/instrument/scatter/2016.05.02 17-48-41 old 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 -- cgit v1.2.3