03 lgtm, unfortunately not within variance

python/energy_flow_proper/03_gaussian/.envrc

@@ -1 +1,2 @@
 use_flake
+eval "$shellHook"

python/energy_flow_proper/03_gaussian/comparison_with_hops.org

@@ -82,6 +82,9 @@ This will be tangled into the [[file:config.py][config file]] that can be used w
   from hopsflow import util, hopsflow
   import utilities as ut
   from hops.core.hierarchyData import HIMetaData
+  import logging
+  logger = logging.getLogger()
+  logger.setLevel(logging.INFO)
 #+end_src
 
 ** Loading the Data
@@ -89,7 +92,7 @@ Now we can load the data.
 #+begin_src jupyter-python
   class result:
       hd = HIMetaData("data", ".").get_HIData(params, read_only=True)
-      N = 50
+      N = hd.samples
       τ = hd.get_time()
       ψ_1 = hd.aux_states
       ψ = hd.stoc_traj
@@ -98,7 +101,7 @@ Now we can load the data.
 #+end_src
 
 #+RESULTS:
-: 50
+: 10000
 
 We calculate the sytem energy just for fun.
 #+begin_src jupyter-python
@@ -107,12 +110,12 @@ We calculate the sytem energy just for fun.
       system.H_sys,
       result.N,
       params.HiP.nonlinear,
+      save="system_energy",
   )
 #+end_src
 
 #+RESULTS:
-: 100% 49/49 [00:00<00:00, 320.67it/s]
-
+: INFO:root:Loading cache from: results/system_energy_sandwhich_operator_10000_None_80a7565ff3435ccbd1441b90db4c1574f2fff17a482b2e19fb409bcb57834356.npy
 
 #+begin_src jupyter-python
   plt.errorbar(result.τ, e_sys.real, yerr=σ_e_sys.real, ecolor="yellow")
@@ -121,8 +124,9 @@ We calculate the sytem energy just for fun.
 #+RESULTS:
 :RESULTS:
 : <ErrorbarContainer object of 3 artists>
-[[file:./.ob-jupyter/1b0d328de6200b9d791112df06036efd111e92c1.svg]]
+[[file:./.ob-jupyter/e343cdd6804e900e806468abf5a397b8110048dd.svg]]
 :END:
+:RESULTS:
 
 ** Heat Flow
 First we set up some parameter objects for the alogrithm.
@@ -135,20 +139,22 @@ First we set up some parameter objects for the alogrithm.
 And then we calculate the flow.
 #+begin_src jupyter-python
   full_flow = hopsflow.heat_flow_ensemble(
-      iter(result.ψ), iter(result.ψ_1), hf_system, result.N, every=int(result.N // 6)
+      iter(result.ψ), iter(result.ψ_1), hf_system, result.N, every=500, save="heat_flow"
   )
 #+end_src
 
 #+RESULTS:
-: 100% 49/49 [00:00<00:00, 79.80it/s]
+: 100% 9999/9999 [02:56<00:00, 56.60it/s]
+: INFO:root:Writing cache to: results/heat_flow__heat_flow_ensemble_body_10000_500_d1cac423a375f1e0f938b4b34ad4bd79b682a617506460d7b0735849d74ac9da.npy
 
 #+begin_src jupyter-python
   with ut.hiro_style():
-       ut.plot_convergence(result.τ, full_flow, transform=lambda y: -y)
+       _, ax = ut.plot_convergence(result.τ, full_flow, transform=lambda y: -y)
+       ax.legend()
 #+end_src
 
 #+RESULTS:
-[[file:./.ob-jupyter/3f69afd9746a0d8b1834872653cb6718705f5315.svg]]
+[[file:./.ob-jupyter/23c1f43b72822aab508e52b4473ccb7ba92f7965.svg]]
 
 ** Analytic
 #+begin_src jupyter-python :results none
@@ -159,16 +165,20 @@ Setting up the BCFs.
 #+begin_src jupyter-python :results none
   α_0 = gf.BCF(
       params.IntP.t_max,
-      hops_bcf,
-      num_terms=4,
-      resolution=0.001,
+      factors=params.SysP.g,
+      exponents=params.SysP.w,
+      # hops_bcf,
+      # num_terms=6,
+      # resolution=0.001,
   )
 
   α_0_dot = gf.BCF(
       params.IntP.t_max,
-      lambda t: 2 / (1j * np.pi) * (wc / (1 + 1j * wc * t)) ** 3,
-      num_terms=5,
-      resolution=0.001,
+      factors=-params.SysP.w * params.SysP.g,
+      exponents=params.SysP.w,
+      # lambda t: 2 / (1j * np.pi) * (wc / (1 + 1j * wc * t)) ** 3,
+      # num_terms=6,
+      # resolution=0.001,
   )
 #+end_src
 
@@ -180,22 +190,19 @@ We can now initialize the parameter object for the alogrithm.
 Now we can define the flow:
 #+begin_src jupyter-python :results none
   flow = gf.Flow(sys, α_0, α_0_dot, n=1)
-  flow_τ = flow.flow_therm_zero(result.τ) + flow.flow_nontherm(result.τ)
+  flow_τ = flow(result.τ)
 #+end_src
 
 And plot it against the numerically obtained flow.
 #+begin_src jupyter-python
-  fig, ax = ut.plot_convergence(result.τ, full_flow, transform=lambda y: -y)
-  ax.plot(result.τ, flow_τ, label="analytic", color="blue", linestyle="-.")
-  ax.legend()
+  with ut.hiro_style():
+      fig, ax = ut.plot_convergence(result.τ, full_flow, transform=lambda y: -y)
+      ax.plot(result.τ, flow_τ, label="analytic", color="blue", linestyle="-.")
+      ax.legend()
 #+end_src
 
 #+RESULTS:
-:RESULTS:
-: <matplotlib.legend.Legend at 0x7f63ad61afa0>
-[[file:./.ob-jupyter/9997d86dd7ca08d28df98277c986888f8e4d1e39.svg]]
-:END:
-
+[[file:./.ob-jupyter/16c12481f732e64fff702a2919da8c5bed1e4e5c.svg]]
 
 #+begin_src jupyter-python
   for n, flow, flow_std in full_flow[:-1]:
@@ -208,8 +215,8 @@ And plot it against the numerically obtained flow.
 
 #+RESULTS:
 :RESULTS:
-: <matplotlib.legend.Legend at 0x7f63addc3790>
-[[file:./.ob-jupyter/21546601d62462b4554bdb1b43ecc11c3563d970.svg]]
+: <matplotlib.legend.Legend at 0x7f63d74df370>
+[[file:./.ob-jupyter/1a8f9cb60ad50edda813cf32832f0a469c04f69a.svg]]
 :END:
 
 #+begin_src jupyter-python
@@ -219,7 +226,7 @@ And plot it against the numerically obtained flow.
 #+end_src
 
 #+RESULTS:
-[[file:./.ob-jupyter/c18e8f9fdc74df6ae1357f5198960c18672e7135.svg]]
+[[file:./.ob-jupyter/e563b17110269de6e8791f0ecf48387d4b390d0e.svg]]
 
 * Close the Data File
 We need to release the hold on the file.

python/energy_flow_proper/03_gaussian/flake.nix

@@ -10,6 +10,11 @@
     (utils.lib.poetry2nixWrapper nixpkgs {
       name = "01_zero_temp";
       shellPackages = pkgs: with pkgs; [ pyright python3Packages.jupyter ];
+      shellOverride = (oldAttrs: {
+        shellHook = ''
+                    export PYTHONPATH=/home/hiro/src/hopsflow/:$PYTHONPATH
+                    '';
+      });
       noPackage = true;
       poetryArgs = {
         projectDir = ./.;

python/energy_flow_proper/03_gaussian/poetry.lock

@@ -315,7 +315,7 @@ resolved_reference = "b18016185092da74b00581c3925e912b3f59ef18"
 
 [[package]]
 name = "hopsflow"
-version = "1.0.2"
+version = "1.0.3"
 description = "Calculating open system bath energy changes with HOPS and analytically."
 category = "main"
 optional = false
@@ -334,7 +334,7 @@ tqdm = "^4.62.3"
 type = "git"
 url = "https://github.com/vale981/hopsflow"
 reference = "main"
-resolved_reference = "9e33c9ca5085cba3f082ca37f0847db4af741ffa"
+resolved_reference = "cb6fa535f80d87661988e789fe5e84c132e91b74"
 
 [[package]]
 name = "ipykernel"

python/energy_flow_proper/03_gaussian/results/heat_flow__heat_flow_ensemble_body_500_500_740d86c10d0164d6ca0766b5d33d86dd9a783b6e7574188f91faab3edb1ac5cb.json

@@ -0,0 +1 @@
+{"N": 500, "every": 500, "const_args": ["<not-hashed>", null, false], "const_kwargs": {}, "function_name": "_heat_flow_ensemble_body", "first_iterator_value": "[ 0.         -0.03789517 -0.07573306 ...  0.00236136  0.00226609\n  0.00217951]"}