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https://github.com/vale981/master-thesis
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113 lines
5.8 KiB
Python
113 lines
5.8 KiB
Python
import hierarchyLib
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import hierarchyData
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import numpy as np
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from stocproc.stocproc import StocProc_FFT
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import bcf
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t_max = 25
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coupling_strength = 0.3
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# setup hierarchy parameters, here use a simple triangular hierarchy up to level 5
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# the comments show the default values
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# NOTE! the normalized version of HOPS is still buggy !!!
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HierachyParam = hierarchyData.HiP(k_max = 5,
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#g_scale=None,
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#sample_method='random',
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#seed=0,
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#nonlinear=True,
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#normalized=False,
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#terminator=False,
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#result_type= hierarchyData.RESULT_TYPE_ZEROTH_ORDER_ONLY,
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#accum_only=None,
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#rand_skip=None
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)
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# setup the integration parameters (see scipy ode)
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IntegrationParam = hierarchyData.IntP(t_max = t_max, t_steps = 500,
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#integrator_name='zvode',
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#atol=1e-8,
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#rtol=1e-8,
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#order=5,
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#nsteps=5000,
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#method='bdf',
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#t_steps_skip=1
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)
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# setup the system parameters
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# here a simple Spin Boson system, single qubit coupled to a sub-ohmic environment
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# how to get the g, w is a different story
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SystemParam = hierarchyData.SysP(H_sys = np.asarray([[0,1],[1,0]]), # SIGMA X
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L = np.asarray([[-1,0],[0,1]]), # SIGME Z
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psi0 = np.asarray([0,1]), # excited qubit
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g = np.asarray([-0.00783725-0.03065741j, # fit for sub-ohmic SD / BCF
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-0.00959114-0.12684515j,
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0.12327218-0.34116637j,
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0.71838859-0.3019772j,
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0.52298016+0.66270003j,
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-0.20912288+0.13755251j]),
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w = np.asarray([0.08064116+7.08122348e-03j, # fit for sub-ohmic SD / BCF
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0.40868336+4.73041920e-02j,
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1.18403114+2.25925466e-01j,
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2.81218754+9.72139059e-01j,
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5.63558761+3.41859384e+00j,
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9.49687769+9.75760546e+00j]),
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H_dynamic = [],
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bcf_scale = coupling_strength, # some coupling strength (scaling of the fit parameters 'g_i')
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gw_hash = None, # this is used to load g,w from some database
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len_gw = None)
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# setup the stochastic process for the subohmic SD
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# for each sample, the StocProc_FFT class is seeded differently which results in a different realization.
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# for the HOPS solver, this looks like a simple callable z(t) returning complex values
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Eta = StocProc_FFT(spectral_density = bcf.OSD(s=0.25, eta=1, gamma=3),
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t_max = t_max,
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alpha = bcf.OBCF(s=0.25, eta=1, gamma=3),
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intgr_tol=1e-2,
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intpl_tol=1e-2,
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seed=None,
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negative_frequencies=False,
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scale=coupling_strength)
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# no thermal noise -> zero temperature, however, this works ion the same fashion as above
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EtaTherm = None
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# this guy must be digestable by the 'binfootprint' class, generating a unique binary representation of hi_key
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# this allows to generate a hash value of the binary to access the produced data from some hdf5 data (see hierarchyData.py)
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# NOTE! this requires that the ENTIRE sub-structure of 'hi_key' is digestable by 'binfootprint', therefore the special classes
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# representing the spectral density and bath correlation function defined in bcf.py
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# I developed 'binfootprit' since pickle and co. are not guaranteed to lead to unique binary data,
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hi_key = hierarchyData.HIMetaKey_type(HiP = HierachyParam,
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IntP = IntegrationParam,
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SysP = SystemParam,
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Eta = Eta,
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EtaTherm = EtaTherm)
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# here the hierarchy structure it set up ...
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myHierarchy = hierarchyLib.HI(hi_key,
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number_of_samples=100,
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desc="run a test case")
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# ... which allows to perform the integration, the data is stored in a file called 'myHierarchy.data'
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# running the script again only crunches samples not done before!
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myHierarchy.integrate_simple(data_name='myHierarchy.data')
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# to access the data the 'hi_key' is used to find the data in the hdf5 file
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with hierarchyData.HIMetaData(hid_name='myHierarchy.data', hid_path='.') as metaData:
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with metaData.get_HIData(hi_key, read_only=True) as data:
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smp = data.get_samples()
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print("{} samples found in database".format(smp))
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t = data.get_time()
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rho_t = data.get_rho_t()
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psi_zt_0 = data.get_stoc_traj(idx = 0)
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# to show some dynamics
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import matplotlib.pyplot as plt
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plt.plot(t, rho_t[:,0,0].real, label="average ({})".format(smp))
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plt.plot(t, np.abs(psi_zt_0[:,0]), label="single psi_z_t")
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plt.plot(t, np.sqrt(np.sum(np.abs(psi_zt_0)**2, axis=(1,))), label="|psi_z_t|", color='0.4')
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plt.ylabel("rho_(0,0)")
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plt.xlabel("time")
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plt.legend()
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plt.show()
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