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https://github.com/vale981/master-thesis
synced 2025-03-04 17:41:43 -05:00
look at consistency
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parent
b07f047e10
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1 changed files with 143 additions and 31 deletions
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@ -9,10 +9,6 @@
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#+end_src
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#+RESULTS:
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: The autoreload extension is already loaded. To reload it, use:
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: %reload_ext autoreload
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: The jupyter_spaces extension is already loaded. To reload it, use:
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: %reload_ext jupyter_spaces
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** Matplotlib
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#+begin_src jupyter-python
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@ -105,9 +101,9 @@ Basic parameters.
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#+RESULTS:
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:RESULTS:
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| <matplotlib.lines.Line2D | at | 0x7f6be57ecfd0> |
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| <matplotlib.lines.Line2D | at | 0x7f6be579f8e0> |
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| <matplotlib.lines.Line2D | at | 0x7f6bf5de3430> |
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| <matplotlib.lines.Line2D | at | 0x7f32d17675e0> |
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| <matplotlib.lines.Line2D | at | 0x7f32d1767880> |
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| <matplotlib.lines.Line2D | at | 0x7f32d1767df0> |
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[[file:./.ob-jupyter/252b4713c37e957d1909f4354fd107d3803ecda2.png]]
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:END:
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@ -273,19 +269,89 @@ Initialize Hierarchy.
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Run the integration.
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#+begin_src jupyter-python
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myHierarchy.integrate_simple(data_name="energy_flow.data")
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myHierarchy.integrate_simple(data_name="energy_flow_new.data", overwrite=True)
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#+end_src
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#+RESULTS:
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: samples :0.0%
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: integration :0.0%
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: [2A[8m[0msamples : 100%
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: integration :0.0%
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: [0A[8m[0m
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#+begin_example
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samples :0.0%
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integration :0.0%
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[2A[8m[0msamples :2.0%
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integration :40.4%
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[2A[8m[0msamples :5.0%
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integration :19.0%
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[2A[8m[0msamples :8.0%
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integration :4.6%
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[2A[8m[0msamples :11.0%
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integration :1.8%
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integration :1.0%
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[2A[8m[0msamples :16.0%
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integration :99.8%
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integration :99.8%
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integration :0.8%
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integration :87.8%
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integration :69.0%
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integration :62.0%
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integration :55.6%
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integration :49.8%
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integration :39.6%
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integration :39.4%
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integration :28.2%
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integration :28.0%
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integration :20.4%
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integration :17.2%
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integration :12.6%
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integration :18.6%
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integration :10.2%
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integration :82.8%
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integration :55.6%
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integration :44.8%
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integration :41.2%
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integration :33.2%
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integration :27.8%
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integration :30.0%
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integration :20.0%
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integration :11.2%
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integration :10.2%
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integration :7.8%
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integration :0.8%
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integration :0.0%
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[0A[8m[0m
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#+end_example
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Get the samples.
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#+begin_src jupyter-python
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# to access the data the 'hi_key' is used to find the data in the hdf5 file
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with hierarchyData.HIMetaData(hid_name="energy_flow.data", hid_path=".") as metaData:
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with hierarchyData.HIMetaData(hid_name="energy_flow_new.data", hid_path=".") as metaData:
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with metaData.get_HIData(hi_key, read_only=True) as data:
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smp = data.get_samples()
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print("{} samples found in database".format(smp))
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@ -299,7 +365,7 @@ Get the samples.
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#+end_src
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#+RESULTS:
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: 122 samples found in database
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: 200 samples found in database
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Calculate energy.
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#+begin_src jupyter-python
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@ -309,8 +375,8 @@ Calculate energy.
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#+RESULTS:
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:RESULTS:
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| <matplotlib.lines.Line2D | at | 0x7f6bf5cdef10> |
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[[file:./.ob-jupyter/009521d0047fb3d6927063413a3290f039a1cbe3.png]]
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| <matplotlib.lines.Line2D | at | 0x7f32e51e6d00> |
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[[file:./.ob-jupyter/be6c2de12726698c7cc02ae53a819ba08e14f319.png]]
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:END:
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#+begin_src jupyter-python
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@ -320,8 +386,8 @@ Calculate energy.
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#+RESULTS:
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:RESULTS:
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| <matplotlib.lines.Line2D | at | 0x7f6bf5be1700> |
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[[file:./.ob-jupyter/6b4898a15426ff4e99c43e8db1649e9fe9f1ec28.png]]
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| <matplotlib.lines.Line2D | at | 0x7f32e5154ca0> |
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[[file:./.ob-jupyter/3f92fa53823636bc644ef2400693567d7f641b4e.png]]
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:END:
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* Energy Flow
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@ -342,25 +408,24 @@ Let's look at the norm.
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#+RESULTS:
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:RESULTS:
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| <matplotlib.lines.Line2D | at | 0x7f6bf5a5d280> |
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[[file:./.ob-jupyter/542a9e20ddb61f23548be094780da540cd1d60b2.png]]
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| <matplotlib.lines.Line2D | at | 0x7f32e50c4a90> |
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[[file:./.ob-jupyter/05059610be839a612c11be140a5ad0e2d6bd2ab4.png]]
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:END:
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And try to calculate the energy flow.
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#+begin_src jupyter-python
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def flow_for_traj(ψ_0, ψ_1):
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a = np.array((L @ ψ_0.T).T)
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return np.array(2 * (1j * -W * np.sum(a.conj() * ψ_1, axis=1)).real).flatten()
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def flow_for_traj_alt(ψ_0, y):
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Eta.new_process(y)
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eta_dot = scipy.misc.derivative(Eta, τ, dx=1e-6)
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a = L @ ψ_0.T
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eta_dot = scipy.misc.derivative(Eta, τ, dx=1e-8)
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a = np.array((L @ ψ_0.T).T)
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return (
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eta_dot * np.array(2 * (1j * np.sum(ψ_0.conj() * a, axis=1))).flatten()
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return -(
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2j * eta_dot.conj() * np.array((np.sum(ψ_0.conj() * a, axis=1))).flatten()
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).real
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#+end_src
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@ -428,11 +493,58 @@ With this we can retrieve the energy of the interaction Hamiltonian.
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#+RESULTS:
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:RESULTS:
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| <matplotlib.lines.Line2D | at | 0x7f6bcb4ce610> |
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| <matplotlib.lines.Line2D | at | 0x7f6bcb4cea60> |
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| <matplotlib.lines.Line2D | at | 0x7f6bcb4ce7f0> |
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| <matplotlib.lines.Line2D | at | 0x7f32e501ec40> |
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| <matplotlib.lines.Line2D | at | 0x7f32e502d070> |
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| <matplotlib.lines.Line2D | at | 0x7f32e502d3a0> |
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: Text(0.5, 0, 'τ')
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: <matplotlib.legend.Legend at 0x7f6bcb4da160>
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[[file:./.ob-jupyter/54dcf3f70f069181a1bba0ac9b3037900cb69e2f.png]]
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: <matplotlib.legend.Legend at 0x7f32e50892e0>
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[[file:./.ob-jupyter/45f86f6728fd17bb4bfc0b6d9e8f5ad1fc14898b.png]]
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:END:
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#+RESULTS:
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* System + Interaction Energy
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:PROPERTIES:
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:ID: cbc95df0-609d-4b1f-a51d-ebca7b680ec7
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:END:
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#+begin_src jupyter-python
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def h_si_for_traj(ψ_0, ψ_1):
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a = np.array((L @ ψ_0.T).T)
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b = np.array((H_s @ ψ_0.T).T)
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E_i = np.array(2 * (-1j * np.sum(a.conj() * ψ_1, axis=1)).real).flatten()
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E_s = np.array(np.sum(b.conj() * ψ_0, axis=1)).flatten().real
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return E_i + E_s
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def h_si_for_traj_alt(ψ_0, y):
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Eta.new_process(y)
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a = np.array((L.conj().T @ ψ_0.T).T)
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b = np.array((H_s @ ψ_0.T).T)
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E_i = np.array(2 * (Eta(τ) * 1j * np.sum(a.conj() * ψ_0, axis=1)).real).flatten()
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E_s = np.array(np.sum(b.conj() * ψ_0, axis=1)).flatten().real
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return E_i + E_s
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#+end_src
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#+RESULTS:
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#+begin_src jupyter-python
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e_si = np.zeros_like(τ)
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for i in range(0, N):
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e_si += h_si_for_traj(ψ_0[i], ψ_1[i])
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e_si /= N
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#+end_src
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#+RESULTS:
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Checks out.
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#+begin_src jupyter-python
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plt.plot(τ, e_si)
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plt.plot(τ, E_I + energy)
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#+end_src
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#+RESULTS:
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:RESULTS:
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| <matplotlib.lines.Line2D | at | 0x7f32e4ebbc10> |
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[[file:./.ob-jupyter/db7e2b07936c085337617799cbca51600a0ad57e.png]]
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:END:
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