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dev/_downloads/0c1426a47f20507b843bbb8573dadaa3/plot_batch_simulate.zip
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}, | ||
"outputs": [], | ||
"source": [ | ||
"# Authors: Abdul Samad Siddiqui <[email protected]>\n# Nick Tolley <[email protected]>\n# Ryan Thorpe <[email protected]>\n# Mainak Jas <[email protected]>" | ||
"# Authors: Abdul Samad Siddiqui <[email protected]>\n# Nick Tolley <[email protected]>\n# Ryan Thorpe <[email protected]>\n# Mainak Jas <[email protected]>\n#\n# This project was supported by Google Summer of Code (GSoC) 2024." | ||
] | ||
}, | ||
{ | ||
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dev/_downloads/6f1e7a639e0699d6164445b55e6c116d/auto_examples_jupyter.zip
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# Nick Tolley <[email protected]> | ||
# Ryan Thorpe <[email protected]> | ||
# Mainak Jas <[email protected]> | ||
# | ||
# This project was supported by Google Summer of Code (GSoC) 2024. | ||
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############################################################################### | ||
# Let us import ``hnn_core``. | ||
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@@ -26,7 +26,7 @@ This example shows how to do batch simulations in HNN-core, allowing users to | |
efficiently run multiple simulations with different parameters | ||
for comprehensive analysis. | ||
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.. GENERATED FROM PYTHON SOURCE LINES 10-16 | ||
.. GENERATED FROM PYTHON SOURCE LINES 10-18 | ||
.. code-block:: Python | ||
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@@ -35,6 +35,8 @@ for comprehensive analysis. | |
# Nick Tolley <[email protected]> | ||
# Ryan Thorpe <[email protected]> | ||
# Mainak Jas <[email protected]> | ||
# | ||
# This project was supported by Google Summer of Code (GSoC) 2024. | ||
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@@ -43,11 +45,11 @@ for comprehensive analysis. | |
.. GENERATED FROM PYTHON SOURCE LINES 17-18 | ||
.. GENERATED FROM PYTHON SOURCE LINES 19-20 | ||
Let us import ``hnn_core``. | ||
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.. GENERATED FROM PYTHON SOURCE LINES 18-26 | ||
.. GENERATED FROM PYTHON SOURCE LINES 20-28 | ||
.. code-block:: Python | ||
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@@ -66,7 +68,7 @@ Let us import ``hnn_core``. | |
.. GENERATED FROM PYTHON SOURCE LINES 27-57 | ||
.. GENERATED FROM PYTHON SOURCE LINES 29-59 | ||
.. code-block:: Python | ||
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@@ -107,11 +109,11 @@ Let us import ``hnn_core``. | |
.. GENERATED FROM PYTHON SOURCE LINES 58-59 | ||
.. GENERATED FROM PYTHON SOURCE LINES 60-61 | ||
Define a parameter grid for the batch simulation. | ||
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.. GENERATED FROM PYTHON SOURCE LINES 59-66 | ||
.. GENERATED FROM PYTHON SOURCE LINES 61-68 | ||
.. code-block:: Python | ||
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@@ -129,11 +131,11 @@ Define a parameter grid for the batch simulation. | |
.. GENERATED FROM PYTHON SOURCE LINES 67-68 | ||
.. GENERATED FROM PYTHON SOURCE LINES 69-70 | ||
Define a function to calculate summary statistics | ||
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.. GENERATED FROM PYTHON SOURCE LINES 68-93 | ||
.. GENERATED FROM PYTHON SOURCE LINES 70-95 | ||
.. code-block:: Python | ||
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.. GENERATED FROM PYTHON SOURCE LINES 94-95 | ||
.. GENERATED FROM PYTHON SOURCE LINES 96-97 | ||
Run the batch simulation and collect the results. | ||
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.. GENERATED FROM PYTHON SOURCE LINES 95-112 | ||
.. GENERATED FROM PYTHON SOURCE LINES 97-114 | ||
.. code-block:: Python | ||
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.. code-block:: none | ||
[Parallel(n_jobs=10)]: Using backend MultiprocessingBackend with 10 concurrent workers. | ||
[Parallel(n_jobs=10)]: Done 1 tasks | elapsed: 2.4s | ||
[Parallel(n_jobs=10)]: Done 1 tasks | elapsed: 1.5s | ||
[Parallel(n_jobs=10)]: Using backend MultiprocessingBackend with 10 concurrent workers. | ||
[Parallel(n_jobs=10)]: Done 1 tasks | elapsed: 2.4s | ||
[Parallel(n_jobs=10)]: Done 1 tasks | elapsed: 1.5s | ||
[Parallel(n_jobs=10)]: Using backend MultiprocessingBackend with 10 concurrent workers. | ||
[Parallel(n_jobs=10)]: Done 1 tasks | elapsed: 2.4s | ||
[Parallel(n_jobs=10)]: Done 1 tasks | elapsed: 1.6s | ||
[Parallel(n_jobs=10)]: Using backend MultiprocessingBackend with 10 concurrent workers. | ||
[Parallel(n_jobs=10)]: Done 1 tasks | elapsed: 2.4s | ||
[Parallel(n_jobs=10)]: Done 1 tasks | elapsed: 1.6s | ||
[Parallel(n_jobs=10)]: Using backend MultiprocessingBackend with 10 concurrent workers. | ||
[Parallel(n_jobs=10)]: Done 1 tasks | elapsed: 2.4s | ||
[Parallel(n_jobs=10)]: Done 1 tasks | elapsed: 1.6s | ||
[Parallel(n_jobs=10)]: Using backend MultiprocessingBackend with 10 concurrent workers. | ||
[Parallel(n_jobs=10)]: Done 1 tasks | elapsed: 2.4s | ||
[Parallel(n_jobs=10)]: Done 1 tasks | elapsed: 1.5s | ||
[Parallel(n_jobs=10)]: Using backend MultiprocessingBackend with 10 concurrent workers. | ||
[Parallel(n_jobs=10)]: Done 1 tasks | elapsed: 2.4s | ||
[Parallel(n_jobs=10)]: Done 1 tasks | elapsed: 1.5s | ||
[Parallel(n_jobs=10)]: Using backend MultiprocessingBackend with 10 concurrent workers. | ||
[Parallel(n_jobs=10)]: Done 1 tasks | elapsed: 2.4s | ||
[Parallel(n_jobs=10)]: Done 1 tasks | elapsed: 1.5s | ||
[Parallel(n_jobs=10)]: Using backend MultiprocessingBackend with 10 concurrent workers. | ||
[Parallel(n_jobs=10)]: Done 1 tasks | elapsed: 2.4s | ||
[Parallel(n_jobs=10)]: Done 1 tasks | elapsed: 1.6s | ||
[Parallel(n_jobs=10)]: Using backend MultiprocessingBackend with 10 concurrent workers. | ||
[Parallel(n_jobs=10)]: Done 1 tasks | elapsed: 2.4s | ||
[Parallel(n_jobs=10)]: Done 1 tasks | elapsed: 1.5s | ||
Simulation results: {'summary_statistics': [[{'min_peak': -1.9487233699162363e-05, 'max_peak': 2.438299811172486e-05}], [{'min_peak': -1.9487233699162363e-05, 'max_peak': 5.406961258004377e-05}], [{'min_peak': -1.9487233699162363e-05, 'max_peak': 0.00011922352353898099}], [{'min_peak': -0.0006636604554164317, 'max_peak': 0.0011404645089040637}], [{'min_peak': -1.9487233699162363e-05, 'max_peak': 0.0009097461915339956}], [{'min_peak': -1.9487233699162363e-05, 'max_peak': 0.0011362095936085991}], [{'min_peak': -1.9487233699162363e-05, 'max_peak': 0.001168458230350182}], [{'min_peak': -1.9487233699162363e-05, 'max_peak': 0.0012737618628301784}], [{'min_peak': -1.9487233699162363e-05, 'max_peak': 0.0014391544369764205}], [{'min_peak': -1.9487233699162363e-05, 'max_peak': 0.001616939552849071}]], 'simulated_data': [[{'net': <Network | 3 x 3 Pyramidal cells (L2, L5) | ||
3 L2 basket cells | ||
3 L5 basket cells>, 'param_values': {'weight_basket': 9.999999999999999e-06, 'weight_pyr': 9.999999999999999e-05}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7f5d9712b670>]}], [{'net': <Network | 3 x 3 Pyramidal cells (L2, L5) | ||
3 L5 basket cells>, 'param_values': {'weight_basket': 9.999999999999999e-06, 'weight_pyr': 9.999999999999999e-05}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7f7a3c1c8550>]}], [{'net': <Network | 3 x 3 Pyramidal cells (L2, L5) | ||
3 L2 basket cells | ||
3 L5 basket cells>, 'param_values': {'weight_basket': 2.0235896477251556e-05, 'weight_pyr': 0.00021544346900318845}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7f5da811a070>]}], [{'net': <Network | 3 x 3 Pyramidal cells (L2, L5) | ||
3 L5 basket cells>, 'param_values': {'weight_basket': 2.0235896477251556e-05, 'weight_pyr': 0.00021544346900318845}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7f7a3c17f1f0>]}], [{'net': <Network | 3 x 3 Pyramidal cells (L2, L5) | ||
3 L2 basket cells | ||
3 L5 basket cells>, 'param_values': {'weight_basket': 4.094915062380427e-05, 'weight_pyr': 0.00046415888336127773}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7f5d9f02d3d0>]}], [{'net': <Network | 3 x 3 Pyramidal cells (L2, L5) | ||
3 L5 basket cells>, 'param_values': {'weight_basket': 4.094915062380427e-05, 'weight_pyr': 0.00046415888336127773}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7f7a3c1a5e50>]}], [{'net': <Network | 3 x 3 Pyramidal cells (L2, L5) | ||
3 L2 basket cells | ||
3 L5 basket cells>, 'param_values': {'weight_basket': 8.286427728546842e-05, 'weight_pyr': 0.001}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7f5d9ec85c70>]}], [{'net': <Network | 3 x 3 Pyramidal cells (L2, L5) | ||
3 L5 basket cells>, 'param_values': {'weight_basket': 8.286427728546842e-05, 'weight_pyr': 0.001}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7f7a3c157a30>]}], [{'net': <Network | 3 x 3 Pyramidal cells (L2, L5) | ||
3 L2 basket cells | ||
3 L5 basket cells>, 'param_values': {'weight_basket': 0.00016768329368110083, 'weight_pyr': 0.002154434690031882}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7f5da9caa160>]}], [{'net': <Network | 3 x 3 Pyramidal cells (L2, L5) | ||
3 L5 basket cells>, 'param_values': {'weight_basket': 0.00016768329368110083, 'weight_pyr': 0.002154434690031882}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7f7a3c107610>]}], [{'net': <Network | 3 x 3 Pyramidal cells (L2, L5) | ||
3 L2 basket cells | ||
3 L5 basket cells>, 'param_values': {'weight_basket': 0.00033932217718953293, 'weight_pyr': 0.004641588833612777}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7f5d9ed861c0>]}], [{'net': <Network | 3 x 3 Pyramidal cells (L2, L5) | ||
3 L5 basket cells>, 'param_values': {'weight_basket': 0.00033932217718953293, 'weight_pyr': 0.004641588833612777}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7f7a3c1361f0>]}], [{'net': <Network | 3 x 3 Pyramidal cells (L2, L5) | ||
3 L2 basket cells | ||
3 L5 basket cells>, 'param_values': {'weight_basket': 0.0006866488450042998, 'weight_pyr': 0.01}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7f5d97138910>]}], [{'net': <Network | 3 x 3 Pyramidal cells (L2, L5) | ||
3 L5 basket cells>, 'param_values': {'weight_basket': 0.0006866488450042998, 'weight_pyr': 0.01}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7f7a3c0dfd90>]}], [{'net': <Network | 3 x 3 Pyramidal cells (L2, L5) | ||
3 L2 basket cells | ||
3 L5 basket cells>, 'param_values': {'weight_basket': 0.0013894954943731376, 'weight_pyr': 0.021544346900318822}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7f5d970cfdf0>]}], [{'net': <Network | 3 x 3 Pyramidal cells (L2, L5) | ||
3 L5 basket cells>, 'param_values': {'weight_basket': 0.0013894954943731376, 'weight_pyr': 0.021544346900318822}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7f7a3c08e8e0>]}], [{'net': <Network | 3 x 3 Pyramidal cells (L2, L5) | ||
3 L2 basket cells | ||
3 L5 basket cells>, 'param_values': {'weight_basket': 0.002811768697974231, 'weight_pyr': 0.046415888336127774}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7f5d9e649460>]}], [{'net': <Network | 3 x 3 Pyramidal cells (L2, L5) | ||
3 L5 basket cells>, 'param_values': {'weight_basket': 0.002811768697974231, 'weight_pyr': 0.046415888336127774}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7f7a3c0be580>]}], [{'net': <Network | 3 x 3 Pyramidal cells (L2, L5) | ||
3 L2 basket cells | ||
3 L5 basket cells>, 'param_values': {'weight_basket': 0.005689866029018299, 'weight_pyr': 0.09999999999999999}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7f5da9923160>]}]]} | ||
3 L5 basket cells>, 'param_values': {'weight_basket': 0.005689866029018299, 'weight_pyr': 0.09999999999999999}, 'dpl': [<hnn_core.dipole.Dipole object at 0x7f7a3c06f0a0>]}]]} | ||
.. GENERATED FROM PYTHON SOURCE LINES 113-116 | ||
.. GENERATED FROM PYTHON SOURCE LINES 115-118 | ||
This plot shows an overlay of all smoothed dipole waveforms from the | ||
batch simulation. Each line represents a different set of parameters, | ||
allowing us to visualize the range of responses across the parameter space. | ||
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.. GENERATED FROM PYTHON SOURCE LINES 116-132 | ||
.. GENERATED FROM PYTHON SOURCE LINES 118-134 | ||
.. code-block:: Python | ||
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.. GENERATED FROM PYTHON SOURCE LINES 133-136 | ||
.. GENERATED FROM PYTHON SOURCE LINES 135-138 | ||
This plot displays the minimum and maximum dipole peaks across | ||
different synaptic strengths. This allows us to see how the range of | ||
dipole activity changes as we vary the synaptic strength parameter. | ||
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.. GENERATED FROM PYTHON SOURCE LINES 136-157 | ||
.. GENERATED FROM PYTHON SOURCE LINES 138-159 | ||
.. code-block:: Python | ||
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.. rst-class:: sphx-glr-timing | ||
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**Total running time of the script:** (0 minutes 25.133 seconds) | ||
**Total running time of the script:** (0 minutes 16.329 seconds) | ||
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.. _sphx_glr_download_auto_examples_howto_plot_batch_simulate.py: | ||
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