updates of project 06

python/energy_flow_proper/06_two_qubit_first_experiments/results/flow__heat_flow_ensemble_body_10_None_cffb41f74178527dd6c91e3e8e3db24b062c15e1f42032c564dc439ed83da381.json

@@ -1 +0,0 @@
-{"N": 10, "every": null, "const_args": ["<not serializable>", "<not serializable>", false], "const_kwargs": {}, "function_name": "_heat_flow_ensemble_body", "first_iterator_value": "<not serializable>"}

python/energy_flow_proper/06_two_qubit_first_experiments/results/flow__heat_flow_ensemble_body_2000_None_4e00cd09f78377b70056b323ac4103a85b6eb8a74227b73ae056c6cc6b0c4493.json

@@ -1 +0,0 @@
-{"N": 2000, "every": null, "const_args": ["<not serializable>", null, false], "const_kwargs": {}, "function_name": "_heat_flow_ensemble_body", "first_iterator_value": "<not serializable>"}

python/energy_flow_proper/06_two_qubit_first_experiments/results/flow__heat_flow_ensemble_body_2000_None_d521c22bbf3af78e231554d83329012349636ae11b8a0cda7748e77f7ac733b2.json

@@ -1 +0,0 @@
-{"N": 2000, "every": null, "const_args": ["<not serializable>", "<not serializable>", false], "const_kwargs": {}, "function_name": "_heat_flow_ensemble_body", "first_iterator_value": "<not serializable>"}

python/energy_flow_proper/06_two_qubit_first_experiments/results/flow__heat_flow_ensemble_body_500_None_a539842987daf9e1d4838c65e1aa6d7ef66614c7b5f45c4f61fd88f5bd829d8a.json

@@ -0,0 +1 @@
+{"N": 500, "every": null, "const_args": ["<not serializable>", null, false], "const_kwargs": {}, "function_name": "_heat_flow_ensemble_body", "first_iterator_value": "<not serializable>"}

python/energy_flow_proper/06_two_qubit_first_experiments/utilities.py

@@ -50,18 +50,30 @@ def plot_complex(x, y, *args, ax=None, label="", **kwargs):
 
 @wrap_plot
 def plot_convergence(
-    x, y, ax=None, label="", transform=lambda y: y, slice=None, linestyle="-"
+    x,
+    y,
+    ax=None,
+    label="",
+    transform=lambda y: y,
+    slice=None,
+    linestyle="-",
+    bath: int = 0,
 ):
     label = label + ", " if (len(label) > 0) else ""
     slice = (0, -1) if not slice else slice
     for n, val, _ in y[slice[0] : slice[1]]:
         plt.plot(
-            x, transform(val), label=f"{label}n={n}", alpha=n / y[-1][0], linestyle="--"
+            x,
+            transform(val[bath]),
+            label=f"{label}n={n}",
+            alpha=n / y[-1][0],
+            linestyle="--",
         )
+
     ax.errorbar(
         x,
-        transform(y[-1][1]),
-        yerr=y[-1][2],
+        transform(y[-1][1][bath]),
+        yerr=y[-1][2][bath],
         ecolor="yellow",
         label=f"{label}n={y[-1][0]}",
         color="red",

python/energy_flow_proper/06_two_qubit_first_experiments/zz_xx_test.org

@@ -20,7 +20,7 @@ We have to find the name of the data file.
 #+end_src
 
 #+RESULTS: data_file
-: .zz_xx_test/_7/data_77a4c2652ef00388cafbeded1407b887_1.h5
+: .zz_xx_test/_5/data_5ede531da3f2d49c7f3a7de1e5225c79_1.h5
 
 * Set up the Python Model Stuff
 Let's define the model.
@@ -65,7 +65,7 @@ Let's plot the density matrix.
 #+end_src
 
 #+RESULTS:
-[[file:./.ob-jupyter/db3b91250a9b801150682ff6685622cb783cce55.svg]]
+[[file:./.ob-jupyter/68c2783708648f0a403bca88c15ae30aebdedda5.svg]]
 
 And the variance.
 #+begin_src jupyter-python
@@ -75,8 +75,8 @@ And the variance.
 
 #+RESULTS:
 :RESULTS:
-| <matplotlib.lines.Line2D | at | 0x7f1e701e1fa0> |
-[[file:./.ob-jupyter/49ad46289dc4ac413c6a6d14ae516dc06b1490c0.svg]]
+| <matplotlib.lines.Line2D | at | 0x7fa737875910> |
+[[file:./.ob-jupyter/8fd846327bc2132be1f74839e8128cc26f444b70.svg]]
 :END:
 
 
@@ -92,7 +92,7 @@ And the variance.
 #+RESULTS:
 :RESULTS:
 | <Figure | size | 432x288 | with | 1 | Axes> | <AxesSubplot:> |
-[[file:./.ob-jupyter/c59f93f046036dd58b3259c61f571f117c5d8eb4.svg]]
+[[file:./.ob-jupyter/bee3e7c767880ffe022e3c03fc9cb9da2d199030.svg]]
 :END:
 
 #+begin_src jupyter-python
@@ -103,7 +103,7 @@ And the variance.
 #+RESULTS:
 :RESULTS:
 | <Figure | size | 576x432 | with | 2 | Axes> | <AxesSubplot:> |
-[[file:./.ob-jupyter/e8b80d1adb1ae3ff23448492623b2a1b8a0858b3.svg]]
+[[file:./.ob-jupyter/c9d57a382cbc0b162aa1972b17a17a32811d0c13.svg]]
 :END:
 
 Let's calculate the system energy.
@@ -117,8 +117,8 @@ Let's calculate the system energy.
 
 #+RESULTS:
 :RESULTS:
-| <matplotlib.lines.Line2D | at | 0x7f1e6f9507f0> |
-[[file:./.ob-jupyter/5f14fbb8395caa26acf96099923202fbfec63393.svg]]
+| <matplotlib.lines.Line2D | at | 0x7fa72e94e820> |
+[[file:./.ob-jupyter/7d1ecd2e5b40e4635aca3fe209e0e51cfb7a6cfd.svg]]
 :END:
 
 The density matrix is not thermal.
@@ -127,10 +127,10 @@ The density matrix is not thermal.
 #+end_src
 
 #+RESULTS:
-: array([[0.00028261, 0.00012042, 0.00017604, 0.04725103],
-:        [0.00012042, 0.03759883, 0.00297758, 0.00044194],
-:        [0.00017604, 0.00297758, 0.03759883, 0.00047171],
-:        [0.04725103, 0.00044194, 0.00047171, 0.00028261]])
+: array([[0.00028215, 0.00011577, 0.00017104, 0.04726899],
+:        [0.00011577, 0.03761938, 0.00298941, 0.00044884],
+:        [0.00017104, 0.00298941, 0.03761938, 0.00047356],
+:        [0.04726899, 0.00044884, 0.00047356, 0.00028215]])
 
 Let's split up the energy.
 #+begin_src jupyter-python
@@ -161,8 +161,8 @@ Let's split up the energy.
 
 #+RESULTS:
 :RESULTS:
-: <matplotlib.legend.Legend at 0x7f1e7000da90>
-[[file:./.ob-jupyter/7d395d8ab10acdc617ffa5e353f1587dc305ff11.svg]]
+: <matplotlib.legend.Legend at 0x7fa72e581e20>
+[[file:./.ob-jupyter/1e597c3e4009902f3b031333cf0f8ac533c2ac8f.svg]]
 :END:
 
 * Energy Flow
@@ -188,6 +188,45 @@ Let's split up the energy.
   therm_run = hf.ThermalRunParams(therm_params, data.rng_seed[0])
 #+end_src
 
+#+RESULTS:
+#+begin_example
+  /home/hiro/src/hops/hops/util/bcf.py:265: UserWarning: this implementation uses mpmath to evaluate the zeta_function! for a better performance consider the 'OhmEnv' package
+    warnings.warn(
+  stocproc.stocproc - INFO - non neg freq only
+  INFO:stocproc.stocproc:non neg freq only
+  stocproc.stocproc - INFO - get_dt_for_accurate_interpolation, please wait ...
+  INFO:stocproc.stocproc:get_dt_for_accurate_interpolation, please wait ...
+  stocproc.method_ft - INFO - acc interp N 33 dt 1.88e+00 -> diff 2.19e-01
+  INFO:stocproc.method_ft:acc interp N 33 dt 1.88e+00 -> diff 2.19e-01
+  stocproc.method_ft - INFO - acc interp N 65 dt 9.38e-01 -> diff 8.26e-02
+  INFO:stocproc.method_ft:acc interp N 65 dt 9.38e-01 -> diff 8.26e-02
+  stocproc.method_ft - INFO - acc interp N 129 dt 4.69e-01 -> diff 1.88e-02
+  INFO:stocproc.method_ft:acc interp N 129 dt 4.69e-01 -> diff 1.88e-02
+  stocproc.method_ft - INFO - acc interp N 257 dt 2.34e-01 -> diff 4.03e-03
+  INFO:stocproc.method_ft:acc interp N 257 dt 2.34e-01 -> diff 4.03e-03
+  stocproc.method_ft - INFO - acc interp N 513 dt 1.17e-01 -> diff 9.62e-04
+  INFO:stocproc.method_ft:acc interp N 513 dt 1.17e-01 -> diff 9.62e-04
+  stocproc.method_ft - INFO - acc interp N 1025 dt 5.86e-02 -> diff 2.38e-04
+  INFO:stocproc.method_ft:acc interp N 1025 dt 5.86e-02 -> diff 2.38e-04
+  stocproc.method_ft - INFO - acc interp N 2049 dt 2.93e-02 -> diff 5.93e-05
+  INFO:stocproc.method_ft:acc interp N 2049 dt 2.93e-02 -> diff 5.93e-05
+  stocproc.stocproc - INFO - requires dt < 2.930e-02
+  INFO:stocproc.stocproc:requires dt < 2.930e-02
+  stocproc.stocproc - INFO - yields N = 1025 (time domain)
+  INFO:stocproc.stocproc:yields N = 1025 (time domain)
+  stocproc.stocproc - INFO - find accurate discretisation in frequency domain
+  INFO:stocproc.stocproc:find accurate discretisation in frequency domain
+  stocproc.stocproc - INFO - wmax:8.977470233936439
+  INFO:stocproc.stocproc:wmax:8.977470233936439
+  n:32 d:0.6692598501571181 tol:0.0001
+  n:64 d:0.41535419936251716 tol:0.0001
+  n:128 d:0.15058338362442436 tol:0.0001
+  stocproc.stocproc - INFO - perform numeric check of entire time axis [0,30.0] N:1025
+  INFO:stocproc.stocproc:perform numeric check of entire time axis [0,30.0] N:1025
+  n:256 d:9.75406609938787e-06 tol:0.0001
+  done!
+#+end_example
+
 
 #+begin_src jupyter-python :results none
   flow = hf.flow_trajectory(run, sys, therm_run)
@@ -200,28 +239,36 @@ Let's split up the energy.
 
 #+RESULTS:
 :RESULTS:
-| <matplotlib.lines.Line2D | at | 0x7f1e560af730> |
-[[file:./.ob-jupyter/c75cf2b5621a8d86f86ed96baac9cad2e4abf0ef.svg]]
+| <matplotlib.lines.Line2D | at | 0x7fa72e7cff10> |
+[[file:./.ob-jupyter/0a0e43bd788d4fbd01a3bd724a2feaada5234730.svg]]
 :END:
 
 #+begin_src jupyter-python
-  flow_total = hf.heat_flow_ensemble(iter(data.stoc_traj), iter(data.aux_states), sys, 2000, None, save="flow")
+  flow_total = hf.heat_flow_ensemble(iter(data.stoc_traj), iter(data.aux_states), sys, data.samples, (data.rng_seed, therm_params))
 #+end_src
 
 #+RESULTS:
-: 100% 1999/1999 [00:15<00:00, 129.99it/s]
+: 100% 99999/99999 [30:05<00:00, 55.38it/s]
 
 #+begin_src jupyter-python
+  plt.plot(τ, flow_total[1][0])
   plt.plot(τ, flow_total[1][1])
-  #plt.plot(τ, flow_total[1][1])
+  plt.plot(τ, flow_total[1][0] + flow_total[1][1])
 #+end_src
 
 #+RESULTS:
 :RESULTS:
-| <matplotlib.lines.Line2D | at | 0x7f1e5601e3d0> |
-[[file:./.ob-jupyter/ddf2da7a776aeeb7e02e2ff0c8f0170ff36b0587.svg]]
+| <matplotlib.lines.Line2D | at | 0x7fa72e021130> |
+[[file:./.ob-jupyter/2a673d1b36801d45148847ff35d293ba4aed3c72.svg]]
 :END:
 
+Sweet. Right as expected. A modest flow. :P.
+
+#+begin_src jupyter-python :results none
+  ut.plot_convergence(τ, flow_total, bath=0)
+#+end_src
+
+
 * Conculisions
 - steady state reached very fast
 - some fluctuations in steady state -> statistics?