add bias field simulations

python/otto_motor/flake.nix

@@ -27,7 +27,7 @@
       shellOverride = (pkgs: oldAttrs: {
         shellHook = ''
                         # export PYTHONPATH=/home/hiro/src/stocproc/:$PYTHONPATH
-                        # export PYTHONPATH=/home/hiro/src/two_qubit_model/:$PYTHONPATH
+                        export PYTHONPATH=/home/hiro/src/two_qubit_model/:$PYTHONPATH
                         # export PYTHONPATH=/home/hiro/src/hops/:$PYTHONPATH
                         # export PYTHONPATH=/home/hiro/src/hopsflow/:$PYTHONPATH
                         export LD_LIBRARY_PATH="${(pkgs.lib.makeLibraryPath [pkgs.arb])}"

python/otto_motor/poetry.lock

@@ -732,7 +732,7 @@ qutip = "^4.6.3"
 type = "git"
 url = "git@github.com:vale981/two_qubit_model.git"
 reference = "main"
-resolved_reference = "1306895b4a44f384f7dca65a02dcbb4bd7d0e8fb"
+resolved_reference = "9c2dd0f625b16c3be723382fdc83796086660578"
 
 [[package]]
 name = "hops"

python/otto_motor/subprojects/cycle_shift/cycle_shift.org

@@ -902,3 +902,50 @@ Interestingly, even with the too-fast switching we have greater power, and great
 [[file:./.ob-jupyter/48801c84dacb82ca9d37f0af0519727e298ec1fd.svg]]
 [[file:./.ob-jupyter/0ef615f736de27bf2129d27452b3f55fb30ff82e.svg]]
 :END:
+
+
+* Rotating Hamiltonian
+Let us brieofly demonstrate the effects of rotating the system
+hamiltonian.
+
+** Construction
+#+begin_src jupyter-python :tangle tangle/rot.py :results none
+  <<boilerplate>>
+  import itertools
+  from hops.util.dynamic_matrix import ConstantMatrix
+#+end_src
+
+#+begin_src jupyter-python :tangle tangle/rot.py
+  rot_models = []
+  weights = [.3, .6]
+  for weight in weights:
+      off_ax = sc.make_model(0, 0)
+      off_ax.H_bias = ConstantMatrix(.3 / 2 * qt.sigmax().full())
+      rot_models.append(off_ax)
+#+end_src
+
+#+RESULTS:
+
+#+begin_src jupyter-python
+  τs = rot_models[0].t
+  #plt.plot(τs, np.einsum('tij,ij->t', rot_models[0].H(τs), qt.sigmay().full()).real)
+  plt.plot(τs, abs(rot_models[0].H(τs)[:, 0, 0]))
+  plt.plot(τs, abs(rot_models[0].H(τs)[:, 0, 1]))
+#+end_src
+
+#+RESULTS:
+:RESULTS:
+: ConstantMatrix(array([[0. +0.j, 0.15+0.j], [0.15+0.j, 0. +0.j]]))
+: ConstantMatrix(array([[0. +0.j, 0.15+0.j], [0.15+0.j, 0. +0.j]]))
+| <matplotlib.lines.Line2D | at | 0x7f12bfd809a0> |
+[[file:./.ob-jupyter/d0d95f8d7e00f7a9dda3749f2a14973988e6887b.svg]]
+:END:
+
+** Integration
+#+begin_src jupyter-python :tangle tangle/rot.py
+  ot.integrate_online_multi(off_ax_models, 10, increment=10, analyze_kwargs=dict(every=10_000))
+#+end_src
+
+#+begin_src jupyter-python
+  aux.import_results(other_data_path="taurus/.data_oa", other_results_path="taurus/results")
+#+end_src

python/otto_motor/subprojects/cycle_shift/tangle/cycle_shift.py

@@ -310,3 +310,10 @@ for (i, model), weight in zip(enumerate(off_ax_models), weights):
     f, a = ot.plot_energy(model)
     a.set_title(rf"$r_y={weight}$")
     fs.export_fig(f"full_energy_offaxis_{weight}", x_scaling=2, y_scaling=1)
+
+τs = rot_models[0].t
+#plt.plot(τs, np.einsum('tij,ij->t', rot_models[0].H(τs), qt.sigmay().full()).real)
+plt.plot(τs, abs(rot_models[0].H(τs)[:, 0, 0]))
+plt.plot(τs, abs(rot_models[0].H(τs)[:, 0, 1]))
+
+aux.import_results(other_data_path="taurus/.data_oa", other_results_path="taurus/results")

python/otto_motor/subprojects/cycle_shift/tangle/rot.py

@@ -0,0 +1,31 @@
+import plot_utils as pu
+from hiro_models.one_qubit_model import StocProcTolerances
+from hiro_models.otto_cycle import OttoEngine
+import hiro_models.model_auxiliary as aux
+import numpy as np
+import qutip as qt
+import utilities as ut
+import stocproc
+import matplotlib.pyplot as plt
+import otto_utilities as ot
+import shift_cycle as sc
+import ray
+import figsaver as fs
+ray.shutdown()
+
+#ray.init(address='auto')
+ray.init()
+from hops.util.logging_setup import logging_setup
+import logging
+logging_setup(logging.INFO)
+import itertools
+from hops.util.dynamic_matrix import ConstantMatrix
+
+rot_models = []
+weights = [.3, .6]
+for weight in weights:
+    off_ax = sc.make_model(0, 0)
+    off_ax.H_bias = ConstantMatrix(.3 / 2 * qt.sigmax().full())
+    rot_models.append(off_ax)
+
+ot.integrate_online_multi(off_ax_models, 10, increment=10, analyze_kwargs=dict(every=10_000))