Added test_scripts/Exciton_Krylov/tdse_1.py

Added      test_scripts/Exciton_Krylov/tdse_qe_1.py
Added      test_scripts/Exciton_Krylov/tdse_qe_2.py

test_scripts/Exciton_Krylov/tdse_1.py

@@ -0,0 +1,77 @@
+from wave_train.hamilton.exciton import Exciton
+from wave_train.dynamics.tdse import TDSE
+from wave_train.io.logging import TeeLogger
+from os.path import basename, splitext
+
+def exciton_tdse(batch_mode):
+    # Detect name of this script file (without extension)
+    base_name   = basename(__file__)
+    my_file     = splitext(base_name)[0]
+
+    # logging instance: will be initialized with 
+    # class for logging to both console and logfile
+    logger = None
+    if not batch_mode:
+        logger = TeeLogger(log_file=my_file + ".log")
+
+    # Set up the excitonic Hamiltonian for a chain
+    hamilton = Exciton(
+        n_site=12,                       # number of sites
+        periodic=False,                  # periodic boundary conditions
+        homogen=True,                    # Homogeneous chain/ring
+        alpha=1e-1,                      # excitonic site energy
+        beta=-1e-2,                      # coupling strength (NN)
+        eta=0                            # constant energy offset
+    )
+
+    # Set up TT representation of the Hamiltonian
+    hamilton.get_TT(
+        n_basis=2,                       # size of electronic basis set
+        qtt=False                        # using quantized TT format
+    )
+
+    # Set up TDSE solver
+    dynamics = TDSE(
+        hamilton=hamilton,               # choice of Hamiltonian, see above
+        num_steps=100,                    # number of main time steps
+        step_size=50,                    # size of main time steps
+        sub_steps=5,                     # number of sub steps
+        solver='sm',                     # can be 's2' (symmetrized Euler) or 'sm' (Strang-Marchuk splitting) or ...
+        normalize=0,                     # whether|how to normalize the solution, can be 0|2
+        max_rank=4,                      # max rank of solution
+        repeats=50,                      # number of sweeps (implicit ODE solvers only!)
+        threshold=1e-12,                 # threshold in ALS decomposition
+        save_file=my_file+'.pic',        # if not None, generated data will be saved to this file
+        load_file=None,                  # if not None, reference data will be loaded from this file
+        compare=None                     # How to do the comparison with reference data
+    )
+
+    # Set up initial state
+    dynamics.fundamental()               # fundamental excitation near center of chain
+    # dynamics.gaussian()
+    # dynamics.sec_hyp(w_0=0.2)
+
+    # Batch mode
+    if batch_mode:
+        dynamics.solve()                 # Solve TDSE *without* visualization
+
+    # Interactive mode: Setup animated visualization
+    else:
+        from wave_train.graphics.factory import VisualTDSE
+        graphics = VisualTDSE(
+            dynamics=dynamics,           # choice of dynamics (EoM), see above
+            plot_type='Populations',     # select your favorite plot type
+            plot_expect=True,            # toggle plotting of expectation values
+            figure_pos=(100, 50),        # specifying position (x,y) of upper left of figure [in pixels]
+            figure_size=(1050, 450),     # specifying size (w,h) of figure [in pixels]
+            image_file=my_file+'.png',   # if not None, image (last frame) will be written to this file
+            movie_file=my_file+'.mp4',   # if not None, animation will be written to this file
+            snapshots=False,             # save each snapshot 
+            frame_rate=1,                # frames per second in mp4 animation file
+            plot_style={},               # additional plot style information
+        ).create()
+        graphics.solve()                 # Solve TDSE *with* visualization
+
+
+if __name__ == '__main__':
+    exciton_tdse(batch_mode=False)

test_scripts/Exciton_Krylov/tdse_qe_1.py

@@ -0,0 +1,75 @@
+from wave_train.hamilton.exciton import Exciton
+from wave_train.dynamics.tdse import TDSE
+from wave_train.io.logging import TeeLogger
+from os.path import basename, splitext
+
+def exciton_qe_1(batch_mode):
+    # Detect name of this script file (without extension)
+    base_name   = basename(__file__)
+    my_file     = splitext(base_name)[0]
+
+    # logging instance: will be initialized with 
+    # class for logging to both console and logfile
+    logger = None
+    if not batch_mode:
+        logger = TeeLogger(log_file=my_file + ".log")
+
+    # Set up the excitonic Hamiltonian for a chain
+    hamilton = Exciton(
+        n_site=12,                       # number of sites
+        periodic=False,                  # periodic boundary conditions
+        homogen=True,                    # homogeneous chain/ring
+        alpha=1e-1,                      # excitonic site energy
+        beta=-1e-2,                      # coupling strength (NN)
+        eta=0                            # constant energy offset
+    )
+
+    # Set up TT representation of the Hamiltonian
+    hamilton.get_TT(
+        n_basis=2,                       # size of electronic basis set
+        qtt=False                        # using quantized TT format
+    )
+
+    # Set up TDSE solver
+    dynamics = TDSE(
+        hamilton=hamilton,               # choice of Hamiltonian, see above
+        num_steps=100,                   # number of main time steps
+        step_size=50,                    # size of main time steps
+        sub_steps=None,                  # dummy
+        solver='qe',                     # quasi-exact
+        normalize=None,                  # dummy
+        max_rank=None,                   # dummy
+        repeats=None,                    # dummy
+        threshold=None,                  # dummy
+        save_file='exact_12.pic',        # if not None, generated data will be saved to this file
+        load_file=None,                  # if not None, reference data will be loaded from this file
+        compare=None                     # How to do the comparison with reference data
+    )
+
+    # Set up initial state
+    dynamics.fundamental()               # fundamental excitation near center of chain
+
+    # Batch mode
+    if batch_mode:
+        dynamics.solve()                 # Solve TDSE *without* visualization
+
+    # Interactive mode: Setup animated visualization
+    else:
+        from wave_train.graphics.factory import VisualTDSE
+        graphics = VisualTDSE(
+            dynamics=dynamics,           # choice of dynamics (EoM), see above
+            plot_type='Populations',     # select your favorite plot type
+            plot_expect=True,            # toggle plotting of expectation values
+            figure_pos=(100, 50),        # specifying position (x,y) of upper left of figure [in pixels]
+            figure_size=(1050, 450),     # specifying size (w,h) of figure [in pixels]
+            image_file=my_file+'.png',   # if not None, image (last frame) will be written to this file
+            movie_file=my_file+'.mp4',   # if not None, animation will be written to this file
+            snapshots=False,             # save each snapshot
+            frame_rate=1,                # frames per second in mp4 animation file
+            plot_style={}                # additional plot style information
+        ).create()
+        graphics.solve()                 # Solve TDSE *with* visualization
+
+
+if __name__ == '__main__':
+    exciton_qe_1(batch_mode=False)

test_scripts/Exciton_Krylov/tdse_qe_2.py

@@ -0,0 +1,75 @@
+from wave_train.hamilton.exciton import Exciton
+from wave_train.dynamics.tdse import TDSE
+from wave_train.io.logging import TeeLogger
+from os.path import basename, splitext
+
+def exciton_qe_2(batch_mode):
+    # Detect name of this script file (without extension)
+    base_name   = basename(__file__)
+    my_file     = splitext(base_name)[0]
+
+    # logging instance: will be initialized with 
+    # class for logging to both console and logfile
+    logger = None
+    if not batch_mode:
+        logger = TeeLogger(log_file=my_file + ".log")
+
+    # Set up the excitonic Hamiltonian for a chain
+    hamilton = Exciton(
+        n_site=12,                       # number of sites
+        periodic=False,                  # periodic boundary conditions
+        homogen=True,                    # homogeneous chain/ring
+        alpha=1e-1,                      # excitonic site energy
+        beta=-1e-2,                      # coupling strength (NN)
+        eta=0                            # constant energy offset
+    )
+
+    # Set up TT representation of the Hamiltonian
+    hamilton.get_TT(
+        n_basis=2,                       # size of electronic basis set
+        qtt=False                        # using quantized TT format
+    )
+
+    # Set up TDSE solver
+    dynamics = TDSE(
+        hamilton=hamilton,               # choice of Hamiltonian, see above
+        num_steps=100,                   # number of main time steps
+        step_size=50,                    # size of main time steps
+        sub_steps=10,                    # number of sub steps
+        solver='k4',                     # can be 's2' (symmetrized Euler) or 'sm' (Strang-Marchuk splitting) or ...
+        normalize=0,                     # whether|how to normalize the solution, can be 0|2
+        max_rank=4,                      # max rank of solution
+        repeats=50,                      # number of sweeps (implicit ODE solvers only!)
+        threshold=1e-12,                 # threshold in ALS decomposition
+        save_file=my_file+'.pic',        # if not None, generated data will be saved to this file
+        load_file='exact_12.pic',        # if not None, reference data will be loaded from this file
+        compare='psi'                    # How to do the comparison with reference data
+    )
+
+    # Set up initial state
+    dynamics.fundamental()               # fundamental excitation near center of chain
+
+    # Batch mode
+    if batch_mode:
+        dynamics.solve()                 # Solve TDSE *without* visualization
+
+    # Interactive mode: Setup animated visualization
+    else:
+        from wave_train.graphics.factory import VisualTDSE
+        graphics = VisualTDSE(
+            dynamics=dynamics,           # choice of dynamics (EoM), see above
+            plot_type='Populations',     # select your favorite plot type
+            plot_expect=True,            # toggle plotting of expectation values
+            figure_pos=(100, 50),        # specifying position (x,y) of upper left of figure [in pixels]
+            figure_size=(1050, 450),     # specifying size (w,h) of figure [in pixels]
+            image_file=my_file+'.png',   # if not None, image (last frame) will be written to this file
+            movie_file=my_file+'.mp4',   # if not None, animation will be written to this file
+            snapshots=False,             # save each snapshot
+            frame_rate=1,                # frames per second in mp4 animation file
+            plot_style={}                # additional plot style information
+        ).create()
+        graphics.solve()                 # Solve TDSE *with* visualization
+
+
+if __name__ == '__main__':
+    exciton_qe_2(batch_mode=False)