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2 changes: 1 addition & 1 deletion dev/_downloads/20b224a3775181a4359df878ed1f9d35/plot_batch_simulate.ipynb
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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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2 changes: 2 additions & 0 deletions dev/_downloads/ad00e92f96773b3b036745547da3cddf/plot_batch_simulate.py
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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.

###############################################################################
# Let us import ``hnn_core``.
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72 changes: 37 additions & 35 deletions dev/_sources/auto_examples/howto/plot_batch_simulate.rst.txt
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Expand Up @@ -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.

.. GENERATED FROM PYTHON SOURCE LINES 10-16
.. GENERATED FROM PYTHON SOURCE LINES 10-18
.. code-block:: Python
Expand All @@ -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.
Expand All @@ -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``.

.. GENERATED FROM PYTHON SOURCE LINES 18-26
.. GENERATED FROM PYTHON SOURCE LINES 20-28
.. code-block:: Python
Expand All @@ -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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.. GENERATED FROM PYTHON SOURCE LINES 60-61
Define a parameter grid for the batch simulation.

.. GENERATED FROM PYTHON SOURCE LINES 59-66
.. GENERATED FROM PYTHON SOURCE LINES 61-68
.. code-block:: Python
Expand All @@ -129,11 +131,11 @@ Define a parameter grid for the batch simulation.
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.. GENERATED FROM PYTHON SOURCE LINES 69-70
Define a function to calculate summary statistics

.. 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 96-97
Run the batch simulation and collect the results.

.. 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.

.. GENERATED FROM PYTHON SOURCE LINES 116-132
.. GENERATED FROM PYTHON SOURCE LINES 118-134
.. code-block:: Python
Expand Down Expand Up @@ -285,13 +287,13 @@ allowing us to visualize the range of responses across the parameter space.



.. 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.

.. 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

**Total running time of the script:** (0 minutes 25.133 seconds)
**Total running time of the script:** (0 minutes 16.329 seconds)


.. _sphx_glr_download_auto_examples_howto_plot_batch_simulate.py:
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Computation times
=================
**06:56.546** total execution time for 8 files **from auto_examples/howto**:
**00:16.329** total execution time for 8 files **from auto_examples/howto**:

.. container::

Expand All @@ -32,27 +32,27 @@ Computation times
* - Example
- Time
- Mem (MB)
* - :ref:`sphx_glr_auto_examples_howto_plot_connectivity.py` (``plot_connectivity.py``)
- 02:24.040
* - :ref:`sphx_glr_auto_examples_howto_plot_batch_simulate.py` (``plot_batch_simulate.py``)
- 00:16.329
- 0.0
* - :ref:`sphx_glr_auto_examples_howto_plot_hnn_animation.py` (``plot_hnn_animation.py``)
- 01:39.753
* - :ref:`sphx_glr_auto_examples_howto_optimize_evoked.py` (``optimize_evoked.py``)
- 00:00.000
- 0.0
* - :ref:`sphx_glr_auto_examples_howto_plot_record_extracellular_potentials.py` (``plot_record_extracellular_potentials.py``)
- 01:06.091
* - :ref:`sphx_glr_auto_examples_howto_optimize_rhythmic.py` (``optimize_rhythmic.py``)
- 00:00.000
- 0.0
* - :ref:`sphx_glr_auto_examples_howto_plot_firing_pattern.py` (``plot_firing_pattern.py``)
- 00:49.894
* - :ref:`sphx_glr_auto_examples_howto_plot_connectivity.py` (``plot_connectivity.py``)
- 00:00.000
- 0.0
* - :ref:`sphx_glr_auto_examples_howto_plot_simulate_mpi_backend.py` (``plot_simulate_mpi_backend.py``)
- 00:31.635
* - :ref:`sphx_glr_auto_examples_howto_plot_firing_pattern.py` (``plot_firing_pattern.py``)
- 00:00.000
- 0.0
* - :ref:`sphx_glr_auto_examples_howto_plot_batch_simulate.py` (``plot_batch_simulate.py``)
- 00:25.133
* - :ref:`sphx_glr_auto_examples_howto_plot_hnn_animation.py` (``plot_hnn_animation.py``)
- 00:00.000
- 0.0
* - :ref:`sphx_glr_auto_examples_howto_optimize_evoked.py` (``optimize_evoked.py``)
* - :ref:`sphx_glr_auto_examples_howto_plot_record_extracellular_potentials.py` (``plot_record_extracellular_potentials.py``)
- 00:00.000
- 0.0
* - :ref:`sphx_glr_auto_examples_howto_optimize_rhythmic.py` (``optimize_rhythmic.py``)
* - :ref:`sphx_glr_auto_examples_howto_plot_simulate_mpi_backend.py` (``plot_simulate_mpi_backend.py``)
- 00:00.000
- 0.0
8 changes: 4 additions & 4 deletions dev/_sources/sg_execution_times.rst.txt
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Computation times
=================
**00:00.000** total execution time for 13 files **from all galleries**:
**00:16.329** total execution time for 13 files **from all galleries**:

.. container::

Expand All @@ -32,15 +32,15 @@ Computation times
* - Example
- Time
- Mem (MB)
* - :ref:`sphx_glr_auto_examples_howto_plot_batch_simulate.py` (``../examples/howto/plot_batch_simulate.py``)
- 00:16.329
- 0.0
* - :ref:`sphx_glr_auto_examples_howto_optimize_evoked.py` (``../examples/howto/optimize_evoked.py``)
- 00:00.000
- 0.0
* - :ref:`sphx_glr_auto_examples_howto_optimize_rhythmic.py` (``../examples/howto/optimize_rhythmic.py``)
- 00:00.000
- 0.0
* - :ref:`sphx_glr_auto_examples_howto_plot_batch_simulate.py` (``../examples/howto/plot_batch_simulate.py``)
- 00:00.000
- 0.0
* - :ref:`sphx_glr_auto_examples_howto_plot_connectivity.py` (``../examples/howto/plot_connectivity.py``)
- 00:00.000
- 0.0
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