add 07 figures

python/energy_flow_proper/07_one_bath_systematics/single_qubit_model_experimentation.org

@@ -22,7 +22,7 @@ Init ray and silence stocproc.
 #+end_src
 
 #+RESULTS:
-| node_ip_address | : | 141.30.17.221 | raylet_ip_address | : | 141.30.17.221 | redis_address | : | hline | object_store_address | : | /tmp/ray/session_2022-04-26_13-48-04_707851_3943264/sockets/plasma_store | raylet_socket_name | : | /tmp/ray/session_2022-04-26_13-48-04_707851_3943264/sockets/raylet | webui_url | : | hline | session_dir | : | /tmp/ray/session_2022-04-26_13-48-04_707851_3943264 | metrics_export_port | : | 43755 | gcs_address | : | 141.30.17.221:63388 | address | : | 141.30.17.221:63388 | node_id | : | 8f7d9d5436973542899392aff75eb0d3d44de720533318bb5fd08fa0 |
+: RayContext(dashboard_url='', python_version='3.9.12', ray_version='1.12.0', ray_commit='f18fc31c7562990955556899090f8e8656b48d2d', address_info={'node_ip_address': '141.30.17.221', 'raylet_ip_address': '141.30.17.221', 'redis_address': None, 'object_store_address': '/tmp/ray/session_2022-05-11_19-35-36_875869_38685/sockets/plasma_store', 'raylet_socket_name': '/tmp/ray/session_2022-05-11_19-35-36_875869_38685/sockets/raylet', 'webui_url': '', 'session_dir': '/tmp/ray/session_2022-05-11_19-35-36_875869_38685', 'metrics_export_port': 59149, 'gcs_address': '141.30.17.221:61235', 'address': '141.30.17.221:61235', 'node_id': '2d5374c53f7c14cbfa36dff1f62247d1d327a1acfea62021878d000a'})
 
 #+begin_src jupyter-python :results none :tangle scripts/integrate_slip.py
   from hops.util.logging_setup import logging_setup
@@ -43,8 +43,8 @@ We use a logspaced time to resolve the initial slip.
 
 #+RESULTS:
 :RESULTS:
-| <matplotlib.lines.Line2D | at | 0x7f952bb1aac0> |
-[[file:./.ob-jupyter/93227274c92b68cad6a202f7cfa582dde1bf50f6.svg]]
+| <matplotlib.lines.Line2D | at | 0x7f664ff037c0> |
+[[file:./.ob-jupyter/c358a499ec28802a0a461028cbcf2f52eedcb151.svg]]
 :END:
 
 * Convergence Woes
@@ -71,121 +71,17 @@ We could try the same with another truncation scheme.
   aux.integrate_multi(alt_tol_models, 400_000)
 #+end_src
 
-#+RESULTS:
-:RESULTS:
-: [INFO    hops.core.integration     3943264] Choosing the nonlinear integrator.
-: [INFO    hops.core.integration     3943264] Using 4 integrators.
-# [goto error]
-#+begin_example
-  ---------------------------------------------------------------------------
-  KeyboardInterrupt                         Traceback (most recent call last)
-  Input In [12], in <cell line: 1>()
-  ----> 1 aux.integrate_multi(alt_tol_models, 400_000)
-
-  File ~/src/two_qubit_model/hiro_models/model_auxiliary.py:81, in integrate_multi(models, *args, **kwargs)
-       74 """Integrate the hops equations for the ``models``.
-       75 Like :any:`integrate` just for many models.
-       76
-       77 A call to :any:`ray.init` may be required.
-       78 """
-       80 for model in models:
-  ---> 81     integrate(model, *args, **kwargs)
-
-  File ~/src/two_qubit_model/hiro_models/model_auxiliary.py:105, in integrate(model, n, data_path, clear_pd)
-       95 # with model_db(data_path) as db:
-       96 #     if hash in db and "data" db[hash]
-       98 supervisor = HOPSSupervisor(
-       99     model.hops_config,
-      100     n,
-      101     data_path=data_path,
-      102     data_name=hash,
-      103 )
-  --> 105 supervisor.integrate(clear_pd)
-      107 with supervisor.get_data(True) as data:
-      108     with model_db(data_path) as db:
-
-  File ~/src/hops/hops/core/integration.py:1245, in HOPSSupervisor.integrate(self, clear_pd)
-     1241     raise RuntimeError("No cpu available for integration!")
-     1243 log.info(f"Using {num_integrators} integrators.")
-  -> 1245 indices = self.get_job_args(data)
-     1247 integrator = ray.put(self.actor(t, self.params, data.result_filter))
-     1249 integration = tqdm(
-     1250     total=len(indices),
-     1251     disable=self._hide_progress,
-     (...)
-     1254     mininterval=1,
-     1255 )
-
-  File ~/src/hops/hops/core/integration.py:1150, in HOPSSupervisor.get_job_args(self, data)
-     1143 """
-     1144 :returns: A list of argument tuples for
-     1145           :any:`hops.core.integration.HOPSActor.integrate`
-     1146           that corresponds to outstanding jobs.
-     1147 """
-     1149 seeds = self.seeds
-  -> 1150 args = [
-     1151     (int(seeds[index]), index)
-     1152     for index in range(
-     1153         self.min_sample_index,
-     1154         self.min_sample_index + self.number_of_samples,
-     1155     )
-     1156     if not data.has_sample(idx=index)
-     1157 ]
-     1159 log.info(f"Some {len(args)} trajectories have to be integrated.")
-     1160 log.debug(f"Trajectories to be integrated: %s", args)
-
-  File ~/src/hops/hops/core/integration.py:1156, in <listcomp>(.0)
-     1143 """
-     1144 :returns: A list of argument tuples for
-     1145           :any:`hops.core.integration.HOPSActor.integrate`
-     1146           that corresponds to outstanding jobs.
-     1147 """
-     1149 seeds = self.seeds
-     1150 args = [
-     1151     (int(seeds[index]), index)
-     1152     for index in range(
-     1153         self.min_sample_index,
-     1154         self.min_sample_index + self.number_of_samples,
-     1155     )
-  -> 1156     if not data.has_sample(idx=index)
-     1157 ]
-     1159 log.info(f"Some {len(args)} trajectories have to be integrated.")
-     1160 log.debug(f"Trajectories to be integrated: %s", args)
-
-  File ~/src/hops/hops/core/hierarchy_data.py:744, in HIData.has_sample(self, idx)
-      741 """Returns whether the sample with the index ``idx`` has been
-      742 processed."""
-      743 if idx < self._idx_cnt:
-  --> 744     return bool(self.tracker[idx])
-      745 else:
-      746     return False
-
-  File h5py/_objects.pyx:54, in h5py._objects.with_phil.wrapper()
-
-  File h5py/_objects.pyx:55, in h5py._objects.with_phil.wrapper()
-
-  File /nix/store/v8al73piyplb4zsr88jnspqc62njjpxq-python3-3.9.10-env/lib/python3.9/site-packages/h5py/_hl/dataset.py:788, in Dataset.__getitem__(self, args, new_dtype)
-      785 if selection.nselect == 0:
-      786     return numpy.ndarray(selection.array_shape, dtype=new_dtype)
-  --> 788 arr = numpy.ndarray(selection.array_shape, new_dtype, order='C')
-      790 # Perform the actual read
-      791 mspace = h5s.create_simple(selection.mshape)
-
-  KeyboardInterrupt:
-#+end_example
-:END:
-
 
 #+begin_src jupyter-python :results none :tangle scripts/integrate_slip.py
   ensmeble_arg = dict(
       every=1000,
-      chunk_size=5_000,
+      chunk_size=500,
   )
 #+end_src
 
 #+begin_src jupyter-python :tangle scripts/integrate_slip.py
   plot_interaction_consistency(
-        alt_tol_models[:1],
+        alt_tol_models,
         # alt_tol_models[0],
         label_fn=lambda m: fr"stocproc=$10^{{{np.log10(m.driving_process_tolerance.integration):.0f}}}$",
         ,**ensmeble_arg
@@ -194,11 +90,8 @@ We could try the same with another truncation scheme.
 
 #+RESULTS:
 :RESULTS:
-: Loading:   0% 0/80 [00:05<?, ?it/s]
-:
-: KeyboardInterrupt
-:
-[[file:./.ob-jupyter/a73a65aeea47f382c5845aaa1378af12f58242e5.svg]]
+| <Figure | size | 432x288 | with | 1 | Axes> | <AxesSubplot:xlabel= | $τ$ | ylabel= | $\langle H_\mathrm{I}\rangle$ | > |
+[[file:./.ob-jupyter/4d18e7c030d93fef8c85f72b9b1a5a13b645aa66.svg]]
 :END:
 
 #+begin_src jupyter-python
@@ -210,89 +103,24 @@ We could try the same with another truncation scheme.
 #+end_src
 
 #+RESULTS:
-:RESULTS:
-# [goto error]
-#+begin_example
-  ---------------------------------------------------------------------------
-  ValueError                                Traceback (most recent call last)
-  Input In [80], in <cell line: 2>()
-        2 for model, data in aux.model_data_iterator(alt_tol_models[:3]):
-        3     bath = model.bath_energy_flow(data, **ensmeble_arg)
-  ----> 4     fs.plot_with_σ(model.t, bath, bath=0, ax=ax)
-
-  File ~/Documents/Projects/UNI/master/masterarb/python/energy_flow_proper/07_one_bath_systematics/figsaver.py:130, in wrap_plot.<locals>.wrapped(ax, setup_function, *args, **kwargs)
-      127 if not ax:
-      128     fig, ax = setup_function()
-  --> 130 ret_val = f(*args, ax=ax, **kwargs)
-      131 return (fig, ax, ret_val) if ret_val else (fig, ax)
-
-  File ~/Documents/Projects/UNI/master/masterarb/python/energy_flow_proper/07_one_bath_systematics/figsaver.py:254, in plot_with_σ(x, y, ax, transform, bath, **kwargs)
-      251 err = (y.σ[bath] if bath is not None else y.σ).real
-      252 y_final = transform(y.value[bath] if bath is not None else y.value)
-  --> 254 return fancy_error(x, y_final, err, ax=ax, **kwargs)
-
-  File ~/Documents/Projects/UNI/master/masterarb/python/energy_flow_proper/07_one_bath_systematics/figsaver.py:232, in fancy_error(x, y, err, ax, **kwargs)
-      231 def fancy_error(x, y, err, ax=None, **kwargs):
-  --> 232     line = ax.plot(
-      233         x,
-      234         y,
-      235         **kwargs,
-      236     )
-      238     err = ax.fill_between(
-      239         x,
-      240         y + err,
-     (...)
-      243         alpha=0.5,
-      244     )
-      246     return line, err
-
-  File /nix/store/v8al73piyplb4zsr88jnspqc62njjpxq-python3-3.9.10-env/lib/python3.9/site-packages/matplotlib/axes/_axes.py:1632, in Axes.plot(self, scalex, scaley, data, *args, **kwargs)
-     1390 """
-     1391 Plot y versus x as lines and/or markers.
-     1392
-     (...)
-     1629 (``'green'``) or hex strings (``'#008000'``).
-     1630 """
-     1631 kwargs = cbook.normalize_kwargs(kwargs, mlines.Line2D)
-  -> 1632 lines = [*self._get_lines(*args, data=data, **kwargs)]
-     1633 for line in lines:
-     1634     self.add_line(line)
-
-  File /nix/store/v8al73piyplb4zsr88jnspqc62njjpxq-python3-3.9.10-env/lib/python3.9/site-packages/matplotlib/axes/_base.py:312, in _process_plot_var_args.__call__(self, data, *args, **kwargs)
-      310     this += args[0],
-      311     args = args[1:]
-  --> 312 yield from self._plot_args(this, kwargs)
-
-  File /nix/store/v8al73piyplb4zsr88jnspqc62njjpxq-python3-3.9.10-env/lib/python3.9/site-packages/matplotlib/axes/_base.py:498, in _process_plot_var_args._plot_args(self, tup, kwargs, return_kwargs)
-      495     self.axes.yaxis.update_units(y)
-      497 if x.shape[0] != y.shape[0]:
-  --> 498     raise ValueError(f"x and y must have same first dimension, but "
-      499                      f"have shapes {x.shape} and {y.shape}")
-      500 if x.ndim > 2 or y.ndim > 2:
-      501     raise ValueError(f"x and y can be no greater than 2D, but have "
-      502                      f"shapes {x.shape} and {y.shape}")
-
-  ValueError: x and y must have same first dimension, but have shapes (500,) and (1,)
-#+end_example
-[[file:./.ob-jupyter/a2038ed241dfd21af3078c0cbd39629470b58a56.svg]]
-:END:
+[[file:./.ob-jupyter/9cfb3b83095906621ff0e734eb15b48f529baeaa.svg]]
 
 
 #+begin_src jupyter-python
-  plot_interaction_consistency_development(
+  fig, ax = plot_interaction_consistency_development(
       alt_tol_models,
       alt_tol_models[0],
       label_fn=lambda m: fr"stocproc=$10^{{{np.log10(m.driving_process_tolerance.integration):.0f}}}$",
       ,**ensmeble_arg
   )
   #plt.xscale("log")
+  fig.set_size_inches(20,5)
 #+end_src
 
 #+RESULTS:
-:RESULTS:
-| <Figure | size | 432x288 | with | 1 | Axes> | <AxesSubplot:xlabel= | $N$ | ylabel= | Consistency [$\%$] | > |
-[[file:./.ob-jupyter/8b54936957f33d5895787c6f4c55f97f5af8381a.svg]]
-:END:
+[[file:./.ob-jupyter/dae34b73b4bcf0a4d2fc95ada6b8d81ed38a52a6.svg]]
+
+Stocproc accuracy really modifies convernge.
 
 ** Cutoff
 #+begin_src jupyter-python :results none :tangle scripts/integrate_slip.py
@@ -388,13 +216,6 @@ We could try the same with another truncation scheme.
 #+end_example
 :END:
 
-#+begin_src jupyter-python :results none :tangle scripts/integrate_slip.py
-  ensmeble_arg = dict(
-      every=1000,
-      chunk_size=1000,
-  )
-#+end_src
-
 #+begin_src jupyter-python :tangle scripts/integrate_slip.py
   plot_interaction_consistency(
         k_models,
@@ -406,134 +227,8 @@ We could try the same with another truncation scheme.
 
 #+RESULTS:
 :RESULTS:
-: Loading:   0% 0/400 [00:03<?, ?it/s]
-# [goto error]
-#+begin_example
-  ---------------------------------------------------------------------------
-  KeyboardInterrupt                         Traceback (most recent call last)
-  Input In [10], in <cell line: 1>()
-  ----> 1 plot_interaction_consistency(
-        2       k_models,
-        3       #k_models[-1],
-        4       label_fn=lambda m: fr"k=${m.k_max}$",
-        5       **ensmeble_arg
-        6   )
-
-  File ~/Documents/Projects/UNI/master/masterarb/python/energy_flow_proper/07_one_bath_systematics/figsaver.py:317, in plot_interaction_consistency(models, reference, label_fn, **kwargs)
-      315 for model in models:
-      316     with aux.get_data(model) as data:
-  --> 317         energy = model.interaction_energy(data, **kwargs)
-      318         interaction_ref = model.interaction_energy_from_conservation(data, **kwargs)
-      319         diff = abs(interaction_ref - energy)
-
-  File ~/src/two_qubit_model/hiro_models/model_base.py:310, in Model.interaction_energy(self, data, **kwargs)
-      299 """Calculates interaction energy from the hierarchy data
-      300 ``data``.
-      301
-     (...)
-      305 :returns: See :any:`hopsflow.util.interaction_energy_ensemble`.
-      306 """
-      308 N, kwargs = _get_N_kwargs(kwargs, data)
-  --> 310 return hopsflow.hopsflow.interaction_energy_ensemble(
-      311     data.valid_sample_iterator(data.stoc_traj),  # type: ignore
-      312     data.valid_sample_iterator(data.aux_states),  # type: ignore
-      313     self.hopsflow_system,
-      314     (data.valid_sample_iterator(data.rng_seed), self.hopsflow_therm(data.time[:])),  # type: ignore
-      315     N=N,
-      316     save=f"interaction_{self.hexhash}",
-      317     **kwargs,
-      318 )
-
-  File ~/src/hopsflow/hopsflow/hopsflow.py:485, in interaction_energy_ensemble(ψ_0s, ψ_1s, params, therm_args, **kwargs)
-      481         energy += interaction_energy_therm(run, therm_run)
-      483     return energy
-  --> 485 return util.ensemble_mean(
-      486     iter(zip(ψ_0s, ψ_1s, therm_args[0]))
-      487     if therm_args
-      488     else iter(zip(ψ_0s, ψ_1s, itertools.repeat(0))),
-      489     interaction_energy_task,
-      490     **kwargs,
-      491 )
-
-  File ~/src/hopsflow/hopsflow/util.py:655, in ensemble_mean(arg_iter, function, N, every, save, overwrite_cache, chunk_size)
-      651 @ray.remote
-      652 def remote_function(chunk: tuple):
-      653     return [function(arg) for arg in chunk]
-  --> 655 handles = [
-      656     remote_function.remote(chunk)
-      657     for chunk in tqdm(
-      658         _grouper(
-      659             chunk_size, itertools.islice(arg_iter, None, N - 1 if N else None)
-      660         ),
-      661         total=int((N - 1) / chunk_size + 1) if N is not None else None,
-      662         desc="Loading",
-      663     )
-      664 ]
-      666 progress = tqdm(total=len(handles), desc="Processing")
-      668 for ref in handles:
-
-  File ~/src/hopsflow/hopsflow/util.py:655, in <listcomp>(.0)
-      651 @ray.remote
-      652 def remote_function(chunk: tuple):
-      653     return [function(arg) for arg in chunk]
-  --> 655 handles = [
-      656     remote_function.remote(chunk)
-      657     for chunk in tqdm(
-      658         _grouper(
-      659             chunk_size, itertools.islice(arg_iter, None, N - 1 if N else None)
-      660         ),
-      661         total=int((N - 1) / chunk_size + 1) if N is not None else None,
-      662         desc="Loading",
-      663     )
-      664 ]
-      666 progress = tqdm(total=len(handles), desc="Processing")
-      668 for ref in handles:
-
-  File /nix/store/v8al73piyplb4zsr88jnspqc62njjpxq-python3-3.9.10-env/lib/python3.9/site-packages/tqdm/std.py:1195, in tqdm.__iter__(self)
-     1192 time = self._time
-     1194 try:
-  -> 1195     for obj in iterable:
-     1196         yield obj
-     1197         # Update and possibly print the progressbar.
-     1198         # Note: does not call self.update(1) for speed optimisation.
-
-  File ~/src/hopsflow/hopsflow/util.py:587, in _grouper(n, iterable)
-      584 """Groups the iteartor into tuples of at most length ``n``."""
-      586 while True:
-  --> 587     chunk = tuple(itertools.islice(iterable, n))
-      588     if not chunk:
-      589         return
-
-  File ~/src/hops/hops/core/hierarchy_data.py:1223, in HIData.valid_sample_iterator(self, iterator)
-     1216 def valid_sample_iterator(self, iterator: Iterator[T]) -> Iterator[T]:
-     1217     """
-     1218     Takes an ``iterator`` that yields a sequence of items related to
-     1219     the sequence of samples and yields them if the sample is
-     1220     actually present in the data.
-     1221     """
-  -> 1223     for i, item in enumerate(iterator):
-     1224         if self.has_sample(i):
-     1225             yield item
-
-  File /nix/store/v8al73piyplb4zsr88jnspqc62njjpxq-python3-3.9.10-env/lib/python3.9/site-packages/h5py/_hl/dataset.py:664, in Dataset.__iter__(self)
-      662     raise TypeError("Can't iterate over a scalar dataset")
-      663 for i in range(shape[0]):
-  --> 664     yield self[i]
-
-  File h5py/_objects.pyx:54, in h5py._objects.with_phil.wrapper()
-
-  File h5py/_objects.pyx:55, in h5py._objects.with_phil.wrapper()
-
-  File /nix/store/v8al73piyplb4zsr88jnspqc62njjpxq-python3-3.9.10-env/lib/python3.9/site-packages/h5py/_hl/dataset.py:793, in Dataset.__getitem__(self, args, new_dtype)
-      791 mspace = h5s.create_simple(selection.mshape)
-      792 fspace = selection.id
-  --> 793 self.id.read(mspace, fspace, arr, mtype, dxpl=self._dxpl)
-      795 # Patch up the output for NumPy
-      796 if arr.shape == ():
-
-  KeyboardInterrupt:
-#+end_example
-[[file:./.ob-jupyter/d81cd0cee8f06820ba9fdad3107ae9833dcff026.svg]]
+| <Figure | size | 432x288 | with | 1 | Axes> | <AxesSubplot:xlabel= | $τ$ | ylabel= | $\langle H_\mathrm{I}\rangle$ | > |
+[[file:./.ob-jupyter/f594d6229b908607d0c38db3b06860a758679103.svg]]
 :END:
 
 #+begin_src jupyter-python
@@ -546,11 +241,9 @@ We could try the same with another truncation scheme.
 #+end_src
 
 #+RESULTS:
-[[file:./.ob-jupyter/9e634499e8f3ff92f542cbfd461189dcca25ee31.svg]]
+[[file:./.ob-jupyter/ea78f4382a8b8edc415fa918b10305558ee5300d.svg]]
 
-No cauchy sequence. HA LOL. This was a bug. Stil no cauchy.
-Interesting that consistency is that weird. We see the one ver square
-root development clearly.
+Ok this is interesting. For longer memory stocproc has to still more accurate it appears.
 
 #+begin_src jupyter-python
   fig, ax = plt.subplots()
@@ -565,7 +258,7 @@ root development clearly.
 #+RESULTS:
 :RESULTS:
 | 0.0 | 1.0 |
-[[file:./.ob-jupyter/c0967591b335e645c36e304065600e883dcee7bf.svg]]
+[[file:./.ob-jupyter/4e42c5e70e08ea25036789b8c98a6e319f12fd2c.svg]]
 :END:
 
 #+begin_src jupyter-python
@@ -576,7 +269,7 @@ root development clearly.
 #+end_src
 
 #+RESULTS:
-[[file:./.ob-jupyter/1b43b7b801fe70101b68e251547723a01c23dbfb.svg]]
+[[file:./.ob-jupyter/fbb339bd2e6b71d0b2e6af08d8d21c0a07e49358.svg]]
 
 
 * Initial Slip Cutoff Frequency