update 07 to use new hashing

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_2022-04-21_16-15-09_598127_25703/sockets/plasma_store | raylet_socket_name | : | /tmp/ray/session_2022-04-21_16-15-09_598127_25703/sockets/raylet | webui_url | : | hline | session_dir | : | /tmp/ray/session_2022-04-21_16-15-09_598127_25703 | metrics_export_port | : | 64861 | gcs_address | : | 141.30.17.221:45866 | address | : | 141.30.17.221:45866 | node_id | : | fbe39e432feebd7f9fcc6bc8fee33c1b97fd050aad1349cfc0bbb757 |
+| node_ip_address | : | 141.30.17.221 | raylet_ip_address | : | 141.30.17.221 | redis_address | : | hline | object_store_address | : | /tmp/ray/session_2022-04-21_16-23-22_125157_28182/sockets/plasma_store | raylet_socket_name | : | /tmp/ray/session_2022-04-21_16-23-22_125157_28182/sockets/raylet | webui_url | : | hline | session_dir | : | /tmp/ray/session_2022-04-21_16-23-22_125157_28182 | metrics_export_port | : | 65168 | gcs_address | : | 141.30.17.221:54526 | address | : | 141.30.17.221:54526 | node_id | : | 806bca127b0340571172a7c98f4bebb739adc17d1aba1100723d6722 |
 
 #+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 | 0x7f130b64e940> |
-[[file:./.ob-jupyter/752676933111c678082642f6e3e60bb7666a5df0.svg]]
+| <matplotlib.lines.Line2D | at | 0x7f7218506be0> |
+[[file:./.ob-jupyter/fc9b4a3f486432abd1d441383ea3ba30f2594ffb.svg]]
 :END:
 
 * Convergence Woes
@@ -247,8 +247,134 @@ We could try the same with another truncation scheme.
 
 #+RESULTS:
 :RESULTS:
-| <Figure | size | 432x288 | with | 1 | Axes> | <AxesSubplot:xlabel= | $τ$ | ylabel= | $\langle H_\mathrm{I}\rangle$ | > |
-[[file:./.ob-jupyter/4d65797073284e10d6239710532004a5b8ae0fc3.svg]]
+: 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]]
 :END:
 
 #+begin_src jupyter-python