2018-02-27 23:58

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