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[ENH] Integrate trials object with Unitary Event Analysis (UE) #643

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[ENH] Integrate trials object with Unitary Event Analysis (UE) #643

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7b24455
add trial handling to jointJ_window_analysis
Moritz-Alexander-Kern Oct 9, 2024
2d4f92d
add tests for trial object
Moritz-Alexander-Kern Oct 9, 2024
1f36584
use trial object in UE tutorial
Moritz-Alexander-Kern Oct 9, 2024
84b8449
update notebook plotting function to accept trial object
Moritz-Alexander-Kern Oct 16, 2024
5e8c4e3
clear notebook outputs
Moritz-Alexander-Kern Oct 16, 2024
6ae9f1b
use decorator for converting trials
Moritz-Alexander-Kern Oct 21, 2024
167361d
clear notebook outputs
Moritz-Alexander-Kern Oct 21, 2024
594e002
Merge branch 'master' into enh/trials_unitary_event_analysis
Moritz-Alexander-Kern Nov 14, 2024
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30 changes: 13 additions & 17 deletions doc/tutorials/unitary_event_analysis.ipynb
View file
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Original file line number Diff line number Diff line change
Expand Up @@ -33,7 +33,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -46,6 +46,8 @@
"\n",
"import elephant.unitary_event_analysis as ue\n",
"from elephant.datasets import download_datasets\n",
"from elephant.trials import TrialsFromBlock, Trials\n",
"from elephant.utils import trials_to_list_of_spiketrainlist\n",
"\n",
"# Fix random seed to guarantee fixed output\n",
"random.seed(1224)"
Expand Down Expand Up @@ -80,7 +82,7 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 3,
"metadata": {
"nbsphinx": "hidden"
},
Expand Down Expand Up @@ -117,7 +119,7 @@
" 'suptitle': None,\n",
"}\n",
"\n",
"\n",
"@trials_to_list_of_spiketrainlist\n",
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  • use Viziphant function here (later)
  • plot_ue should accept trial object (-> later in Viziphant)
  • update plot_ue function in Viziphant
  • copy updated code into this notebook for now

"def plot_ue(spiketrains, Js_dict, significance_level=0.05,\n",
" **plot_params):\n",
" \"\"\"\n",
Expand All @@ -128,7 +130,7 @@
"\n",
" Parameters\n",
" ----------\n",
" spiketrains : list of list of neo.SpikeTrain\n",
" spiketrains : list of list of neo.SpikeTrain or :class:`elephant.trials.Trials`\n",
" A nested list of trials, neurons and their neo.SpikeTrain objects,\n",
" respectively. This should be identical to the one used to generate\n",
" Js_dict.\n",
Expand Down Expand Up @@ -165,7 +167,7 @@
" 'figsize' : tuple of int\n",
" The dimensions for the figure size.\n",
" 'right' : float\n",
" The size of the right margin.\n",
" The size of the right margin.spiketrains\n",
" 'top' : float\n",
" The size of the top margin.\n",
" 'bottom' : float\n",
Expand Down Expand Up @@ -444,17 +446,14 @@
},
{
"cell_type": "code",
"execution_count": null,
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"io = neo.io.NixIO(f\"{filepath}\",'ro')\n",
"block = io.read_block()\n",
"\n",
"spiketrains = []\n",
"# each segment contains a single trial\n",
"for ind in range(len(block.segments)):\n",
" spiketrains.append (block.segments[ind].spiketrains)\n"
"spiketrains = TrialsFromBlock(block)\n"
]
},
{
Expand All @@ -474,18 +473,15 @@
" spiketrains, bin_size=5*pq.ms, win_size=100*pq.ms, win_step=10*pq.ms, pattern_hash=[3])\n",
"\n",
"plot_ue(spiketrains, UE, significance_level=0.05)\n",
"plt.show()"
"plt.show()\n"
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]
}
],
"metadata": {
"interpreter": {
"hash": "623e048a0474aa032839f97d38ba0837cc9041adc49a14b480c72f2df8ea99e3"
},
"kernelspec": {
"display_name": "inm-elephant",
"display_name": "Python 3",
"language": "python",
"name": "inm-elephant"
"name": "python3"
},
"language_info": {
"codemirror_mode": {
Expand All @@ -497,7 +493,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.8.10"
"version": "3.12.5"
},
"latex_envs": {
"LaTeX_envs_menu_present": true,
Expand Down
220 changes: 115 additions & 105 deletions elephant/test/test_unitary_event_analysis.py
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Original file line number Diff line number Diff line change
Expand Up @@ -13,6 +13,7 @@
import quantities as pq
from numpy.testing import assert_array_equal

from elephant.trials import TrialsFromLists
import elephant.unitary_event_analysis as ue
from elephant.datasets import download, ELEPHANT_TMP_DIR
from numpy.testing import assert_array_almost_equal
Expand Down Expand Up @@ -324,52 +325,56 @@ def test_jointJ_window_analysis(self):
sts2 = self.sts2_neo

# joinJ_window_analysis requires the following:
# A list of spike trains(neo.SpikeTrain objects) in different trials:
data = list(zip(*[sts1,sts2]))

win_size = 100 * pq.ms
bin_size = 5 * pq.ms
win_step = 20 * pq.ms
pattern_hash = [3]
UE_dic = ue.jointJ_window_analysis(spiketrains=data,
pattern_hash=pattern_hash,
bin_size=bin_size,
win_size=win_size,
win_step=win_step)
expected_Js = np.array(
[0.57953708, 0.47348757, 0.1729669,
0.01883295, -0.21934742, -0.80608759])
expected_n_emp = np.array(
[9., 9., 7., 7., 6., 6.])
expected_n_exp = np.array(
[6.5, 6.85, 6.05, 6.6, 6.45, 8.7])
expected_rate = np.array(
[[0.02166667, 0.01861111],
[0.02277778, 0.01777778],
[0.02111111, 0.01777778],
[0.02277778, 0.01888889],
[0.02305556, 0.01722222],
[0.02388889, 0.02055556]]) * pq.kHz
expected_indecis_tril26 = [4., 4.]
expected_indecis_tril4 = [1.]
assert_array_almost_equal(UE_dic['Js'].squeeze(), expected_Js)
assert_array_almost_equal(UE_dic['n_emp'].squeeze(), expected_n_emp)
assert_array_almost_equal(UE_dic['n_exp'].squeeze(), expected_n_exp)
assert_array_almost_equal(UE_dic['rate_avg'].squeeze(), expected_rate)
assert_array_almost_equal(UE_dic['indices']['trial26'],
expected_indecis_tril26)
assert_array_almost_equal(UE_dic['indices']['trial4'],
expected_indecis_tril4)

# check the input parameters
input_params = UE_dic['input_parameters']
self.assertEqual(input_params['pattern_hash'], pattern_hash)
self.assertEqual(input_params['bin_size'], bin_size)
self.assertEqual(input_params['win_size'], win_size)
self.assertEqual(input_params['win_step'], win_step)
self.assertEqual(input_params['method'], 'analytic_TrialByTrial')
self.assertEqual(input_params['t_start'], 0 * pq.s)
self.assertEqual(input_params['t_stop'], 200 * pq.ms)
Comment on lines -330 to -372
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Only indentation level changed.

# A list of spike trains(neo.SpikeTrain objects) in different trials, or trials.Trial object
test_cases = (
list(zip(*[sts1, sts2])), # list
TrialsFromLists(list(zip(*[sts1, sts2]))), # Trial object
)
for data in test_cases:
with self.subTest(data=data):
win_size = 100 * pq.ms
bin_size = 5 * pq.ms
win_step = 20 * pq.ms
pattern_hash = [3]
UE_dic = ue.jointJ_window_analysis(spiketrains=data,
pattern_hash=pattern_hash,
bin_size=bin_size,
win_size=win_size,
win_step=win_step)
expected_Js = np.array(
[0.57953708, 0.47348757, 0.1729669,
0.01883295, -0.21934742, -0.80608759])
expected_n_emp = np.array(
[9., 9., 7., 7., 6., 6.])
expected_n_exp = np.array(
[6.5, 6.85, 6.05, 6.6, 6.45, 8.7])
expected_rate = np.array(
[[0.02166667, 0.01861111],
[0.02277778, 0.01777778],
[0.02111111, 0.01777778],
[0.02277778, 0.01888889],
[0.02305556, 0.01722222],
[0.02388889, 0.02055556]]) * pq.kHz
expected_indecis_tril26 = [4., 4.]
expected_indecis_tril4 = [1.]
assert_array_almost_equal(UE_dic['Js'].squeeze(), expected_Js)
assert_array_almost_equal(UE_dic['n_emp'].squeeze(), expected_n_emp)
assert_array_almost_equal(UE_dic['n_exp'].squeeze(), expected_n_exp)
assert_array_almost_equal(UE_dic['rate_avg'].squeeze(), expected_rate)
assert_array_almost_equal(UE_dic['indices']['trial26'],
expected_indecis_tril26)
assert_array_almost_equal(UE_dic['indices']['trial4'],
expected_indecis_tril4)

# check the input parameters
input_params = UE_dic['input_parameters']
self.assertEqual(input_params['pattern_hash'], pattern_hash)
self.assertEqual(input_params['bin_size'], bin_size)
self.assertEqual(input_params['win_size'], win_size)
self.assertEqual(input_params['win_step'], win_step)
self.assertEqual(input_params['method'], 'analytic_TrialByTrial')
self.assertEqual(input_params['t_start'], 0 * pq.s)
self.assertEqual(input_params['t_stop'], 200 * pq.ms)

@staticmethod
def load_gdf2Neo(fname, trigger, t_pre, t_post):
Expand Down Expand Up @@ -501,69 +506,74 @@ def test_multiple_neurons(self):
np.random.seed(12)

# Create a list of lists containing 3 Trials with 5 spiketrains
spiketrains = \
spiketrains_poisson = \
[StationaryPoissonProcess(
rate=50 * pq.Hz, t_stop=1 * pq.s).generate_n_spiketrains(5)
for _ in range(3)]

spiketrains = list(zip(*spiketrains))
UE_dic = ue.jointJ_window_analysis(spiketrains, bin_size=5 * pq.ms,
win_size=300 * pq.ms,
win_step=100 * pq.ms)

js_expected = [[0.3978179],
[0.08131966],
[-1.4239882],
[-0.9377029],
[-0.3374434],
[-0.2043383],
[-1.001536],
[-np.inf]]
indices_expected = \
{'trial3': [12, 27, 31, 34, 27, 31, 34, 136, 136, 136],
'trial4': [4, 60, 60, 60, 117, 117, 117]}
n_emp_expected = [[5.],
[4.],
[1.],
[2.],
[2.],
[2.],
[1.],
[0.]]
n_exp_expected = [[3.5591667],
[3.4536111],
[3.3158333],
[3.8466666],
[2.370278],
[2.0811112],
[2.4011111],
[3.0533333]]
rate_expected = [[[0.042, 0.03933334, 0.048]],
[[0.04533333, 0.038, 0.05]],
[[0.046, 0.04, 0.04666667]],
[[0.05066667, 0.042, 0.046]],
[[0.04466667, 0.03666667, 0.04066667]],
[[0.04066667, 0.03533333, 0.04333333]],
[[0.03933334, 0.038, 0.038]],
[[0.04066667, 0.04866667, 0.03666667]]] * (1. / pq.ms)
input_parameters_expected = {'pattern_hash': [7],
'bin_size': 5 * pq.ms,
'win_size': 300 * pq.ms,
'win_step': 100 * pq.ms,
'method': 'analytic_TrialByTrial',
't_start': 0 * pq.s,
't_stop': 1 * pq.s, 'n_surrogates': 100}

assert_array_almost_equal(UE_dic['Js'], js_expected)
assert_array_almost_equal(UE_dic['n_emp'], n_emp_expected)
assert_array_almost_equal(UE_dic['n_exp'], n_exp_expected)
assert_array_almost_equal(UE_dic['rate_avg'], rate_expected)
self.assertEqual(sorted(UE_dic['indices'].keys()),
sorted(indices_expected.keys()))
for trial_key in indices_expected.keys():
assert_array_equal(indices_expected[trial_key],
UE_dic['indices'][trial_key])
self.assertEqual(UE_dic['input_parameters'], input_parameters_expected)
Comment on lines -510 to -566
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Only indentation level changed.

test_cases = (
list(zip(*spiketrains_poisson)), # list
TrialsFromLists(list(zip(*spiketrains_poisson))), # Trial object
)
for spiketrains in test_cases:
with self.subTest(data=spiketrains):
UE_dic = ue.jointJ_window_analysis(spiketrains, bin_size=5 * pq.ms,
win_size=300 * pq.ms,
win_step=100 * pq.ms)

js_expected = [[0.3978179],
[0.08131966],
[-1.4239882],
[-0.9377029],
[-0.3374434],
[-0.2043383],
[-1.001536],
[-np.inf]]
indices_expected = \
{'trial3': [12, 27, 31, 34, 27, 31, 34, 136, 136, 136],
'trial4': [4, 60, 60, 60, 117, 117, 117]}
n_emp_expected = [[5.],
[4.],
[1.],
[2.],
[2.],
[2.],
[1.],
[0.]]
n_exp_expected = [[3.5591667],
[3.4536111],
[3.3158333],
[3.8466666],
[2.370278],
[2.0811112],
[2.4011111],
[3.0533333]]
rate_expected = [[[0.042, 0.03933334, 0.048]],
[[0.04533333, 0.038, 0.05]],
[[0.046, 0.04, 0.04666667]],
[[0.05066667, 0.042, 0.046]],
[[0.04466667, 0.03666667, 0.04066667]],
[[0.04066667, 0.03533333, 0.04333333]],
[[0.03933334, 0.038, 0.038]],
[[0.04066667, 0.04866667, 0.03666667]]] * (1. / pq.ms)
input_parameters_expected = {'pattern_hash': [7],
'bin_size': 5 * pq.ms,
'win_size': 300 * pq.ms,
'win_step': 100 * pq.ms,
'method': 'analytic_TrialByTrial',
't_start': 0 * pq.s,
't_stop': 1 * pq.s, 'n_surrogates': 100}

assert_array_almost_equal(UE_dic['Js'], js_expected)
assert_array_almost_equal(UE_dic['n_emp'], n_emp_expected)
assert_array_almost_equal(UE_dic['n_exp'], n_exp_expected)
assert_array_almost_equal(UE_dic['rate_avg'], rate_expected)
self.assertEqual(sorted(UE_dic['indices'].keys()),
sorted(indices_expected.keys()))
for trial_key in indices_expected.keys():
assert_array_equal(indices_expected[trial_key],
UE_dic['indices'][trial_key])
self.assertEqual(UE_dic['input_parameters'], input_parameters_expected)


if __name__ == '__main__':
Expand Down
5 changes: 3 additions & 2 deletions elephant/unitary_event_analysis.py
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Original file line number Diff line number Diff line change
Expand Up @@ -60,7 +60,7 @@
import scipy

import elephant.conversion as conv
from elephant.utils import is_binary
from elephant.utils import is_binary, trials_to_list_of_spiketrainlist

__all__ = [
"hash_from_pattern",
Expand Down Expand Up @@ -689,6 +689,7 @@ def _UE(mat, pattern_hash, method='analytic_TrialByTrial', n_surrogates=1):
return Js, rate_avg, n_exp, n_emp, indices


@trials_to_list_of_spiketrainlist
def jointJ_window_analysis(spiketrains, bin_size=5 * pq.ms,
win_size=100 * pq.ms, win_step=5 * pq.ms,
pattern_hash=None, method='analytic_TrialByTrial',
Expand All @@ -701,7 +702,7 @@ def jointJ_window_analysis(spiketrains, bin_size=5 * pq.ms,

Parameters
----------
spiketrains : list
spiketrains : :class:`elephant.trials.Trials`, list
A list of spike trains (`neo.SpikeTrain` objects) in different trials:
* 0-axis --> Trials

Expand Down
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