[ENH] Integrate trials object with Unitary Event Analysis (UE) #643
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| 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) | ||
|
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||
| # 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) |
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| 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} | ||
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| 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) |
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| "}\n", | ||
| "\n", | ||
| "\n", | ||
| "@trials_to_list_of_spiketrainlist\n", |
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- use Viziphant function here (later)
plot_ueshould accept trial object (-> later in Viziphant)- update
plot_uefunction in Viziphant - copy updated code into this notebook for now
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This pull request introduces an enhancement to Unitary Event Analysis (UE) using the the
elephant.trialsmodule to represent trials. This change is part of a greater effort to provide a more structured and unified approach for handling trial data in Elephant.Trial representations
trial object: The new trial_object is now used as the primary input structure for representing trials. This object handles trial data, see https://elephant.readthedocs.io/en/latest/reference/trials.html .
list of lists: The previous implementation relied on a nested structure of lists to represent trials.
Changes
jointJ_window_analysisno handles trial object