You signed in with another tab or window. Reload to refresh your session.You signed out in another tab or window. Reload to refresh your session.You switched accounts on another tab or window. Reload to refresh your session.Dismiss alert
This page gives a summary of the functions available in the data assimilation library. Differentiation of persistence diagrams is exploited to optimize data driven model coefficients by minimizing topological differences between model the model forecast and measurements. More information on the details of the TADA algorithm can be found in, "`Topological Approach for Data Assimilation <https://arxiv.org>`_." We plan to implement more data assimilation tools here in the future.
6
+
This page gives a summary of the functions available in the data assimilation library. Differentiation of persistence diagrams is exploited to optimize data driven model coefficients by minimizing topological differences between model the model forecast and measurements. More information on the details of the TADA algorithm can be found in, "`Topological Approach for Data Assimilation <https://arxiv.org/abs/2411.18627>`_." We plan to implement more data assimilation tools here in the future.
7
7
8
8
.. warning::
9
9
`TADA` requires `tensorflow <https://www.tensorflow.org>`_ for optimization features. Please install teaspoon using the command: `pip install "teaspoon[full]"` to install the necessary packages.
@@ -76,3 +76,6 @@ This page gives a summary of the functions available in the data assimilation li
76
76
77
77
print(f"TADA Forecast Time: {tada_time}")
78
78
print(f"LR Forecast Time: {lr_time}")
79
+
80
+
.. note::
81
+
Resulting forecast times may vary depending on the operating system.
Copy file name to clipboardExpand all lines: docs/_modules/teaspoon/MakeData/DynSysLib/medical_data.html
+9-9Lines changed: 9 additions & 9 deletions
Original file line number
Diff line number
Diff line change
@@ -174,23 +174,23 @@ <h1>Source code for teaspoon.MakeData.DynSysLib.medical_data</h1><div class="hig
174
174
<spanclass="sd"> .. [1] Ralph G Andrzejak, Klaus Lehnertz, Florian Mormann, Christoph Rieke, Peter David, and Christian E Elger. Indications of nonlinear deterministic and nite-dimensional structures in time series of brain electrical activity: Dependence on recording region and brain state. Physical Review E, 64(6):061907, 2001.</span>
This page gives a summary of the functions available in the data assimilation library. Differentiation of persistence diagrams is exploited to optimize data driven model coefficients by minimizing topological differences between model the model forecast and measurements. More information on the details of the TADA algorithm can be found in, "`Topological Approach for Data Assimilation <https://arxiv.org>`_." We plan to implement more data assimilation tools here in the future.
6
+
This page gives a summary of the functions available in the data assimilation library. Differentiation of persistence diagrams is exploited to optimize data driven model coefficients by minimizing topological differences between model the model forecast and measurements. More information on the details of the TADA algorithm can be found in, "`Topological Approach for Data Assimilation <https://arxiv.org/abs/2411.18627>`_." We plan to implement more data assimilation tools here in the future.
7
7
8
8
.. warning::
9
9
`TADA` requires `tensorflow <https://www.tensorflow.org>`_ for optimization features. Please install teaspoon using the command: `pip install "teaspoon[full]"` to install the necessary packages.
@@ -76,3 +76,6 @@ This page gives a summary of the functions available in the data assimilation li
76
76
77
77
print(f"TADA Forecast Time: {tada_time}")
78
78
print(f"LR Forecast Time: {lr_time}")
79
+
80
+
.. note::
81
+
Resulting forecast times may vary depending on the operating system.
Copy file name to clipboardExpand all lines: docs/modules/DAF/DataAssimilation.html
+5-1Lines changed: 5 additions & 1 deletion
Original file line number
Diff line number
Diff line change
@@ -156,7 +156,7 @@
156
156
157
157
<sectionid="data-assimilation">
158
158
<h1><spanclass="section-number">2.6.2. </span>Data Assimilation<aclass="headerlink" href="#data-assimilation" title="Link to this heading"></a></h1>
159
-
<p>This page gives a summary of the functions available in the data assimilation library. Differentiation of persistence diagrams is exploited to optimize data driven model coefficients by minimizing topological differences between model the model forecast and measurements. More information on the details of the TADA algorithm can be found in, “<aclass="reference external" href="https://arxiv.org">Topological Approach for Data Assimilation</a>.” We plan to implement more data assimilation tools here in the future.</p>
159
+
<p>This page gives a summary of the functions available in the data assimilation library. Differentiation of persistence diagrams is exploited to optimize data driven model coefficients by minimizing topological differences between model the model forecast and measurements. More information on the details of the TADA algorithm can be found in, “<aclass="reference external" href="https://arxiv.org/abs/2411.18627">Topological Approach for Data Assimilation</a>.” We plan to implement more data assimilation tools here in the future.</p>
160
160
<divclass="admonition warning">
161
161
<pclass="admonition-title">Warning</p>
162
162
<p><cite>TADA</cite> requires <aclass="reference external" href="https://www.tensorflow.org">tensorflow</a> for optimization features. Please install teaspoon using the command: <cite>pip install “teaspoon[full]”</cite> to install the necessary packages.</p>
0 commit comments