From a648c0466f0643ad44497d5a5b5e9c1a5d6852a5 Mon Sep 17 00:00:00 2001 From: Fabi Date: Fri, 9 Aug 2024 00:31:02 +0200 Subject: [PATCH 01/11] improved test coverage --- pyrasa/utils/irasa_utils.py | 6 +----- tests/test_compute_slope.py | 30 ++++++++++++++++++++++++++++++ tests/test_irasa_sprint.py | 13 ++++++++++++- 3 files changed, 43 insertions(+), 6 deletions(-) diff --git a/pyrasa/utils/irasa_utils.py b/pyrasa/utils/irasa_utils.py index b700735..47eda76 100644 --- a/pyrasa/utils/irasa_utils.py +++ b/pyrasa/utils/irasa_utils.py @@ -42,11 +42,7 @@ def _gen_irasa( spectrum_dw = irasa_fun(data=data_down, fs=int(fs / h), h=h, time_orig=time, up_down='down') # geometric mean between up and downsampled - # be aware of the input dimensions - if spectra.ndim == 2: # noqa PLR2004 - spectra[i, :] = np.sqrt(spectrum_up * spectrum_dw) - if spectra.ndim == 3: # noqa PLR2004 - spectra[i, :, :] = np.sqrt(spectrum_up * spectrum_dw) + spectra[i, :, :] = np.sqrt(spectrum_up * spectrum_dw) aperiodic_spectrum = np.median(spectra, axis=0) periodic_spectrum = orig_spectrum - aperiodic_spectrum diff --git a/tests/test_compute_slope.py b/tests/test_compute_slope.py index a71f5c8..43f3a33 100644 --- a/tests/test_compute_slope.py +++ b/tests/test_compute_slope.py @@ -2,8 +2,10 @@ import pytest import scipy.signal as dsp +from pyrasa import irasa from pyrasa.utils.aperiodic_utils import compute_slope from pyrasa.utils.fit_funcs import AbstractFitFun +from pyrasa.utils.peak_utils import get_peak_params from .settings import EXPONENT, FS, HIGH_TOLERANCE, MIN_R2, TOLERANCE @@ -62,10 +64,21 @@ def test_slope_fitting_settings( with pytest.raises(AssertionError): compute_slope(psd[freq_logical], freqs[freq_logical], fit_func='fixed', fit_bounds=(1, 1000)) + # test bounds correct + with pytest.raises(AssertionError): + compute_slope(psd[freq_logical], freqs[freq_logical], fit_func='fixed', fit_bounds=(5, 40)) + # test for warning with pytest.warns(UserWarning, match=match_txt): compute_slope(psd[freq_logical], freqs[freq_logical], fit_func='fixed') + # test misspecify string in fit_func + with pytest.raises(AssertionError): + compute_slope(psd[freq_logical], freqs[freq_logical], fit_func='incredible', fit_bounds=(5, 40)) + + # test absence of peaks + get_peak_params(psd[freq_logical], freqs[freq_logical], min_peak_height=10) + # test custom slope fitting functions @pytest.mark.parametrize('exponent, fs', [(-1, 500)], scope='session') @@ -110,3 +123,20 @@ def curve_kwargs(self) -> dict[str, any]: # add a high tolerance assert pytest.approx(np.abs(slope_fit.aperiodic_params['b'][0]), abs=HIGH_TOLERANCE) == np.abs(exponent) + + irasa_spectrum = irasa(fixed_aperiodic_signal, fs, f_range, psd_kwargs={'nperseg': 4 * fs}) + + class CustomFitFun(AbstractFitFun): + log10_aperiodic = True + log10_freq = True + + def func(self, x: np.ndarray, a: float, b: float) -> np.ndarray: + """ + Specparams fixed fitting function. + Use this to model aperiodic activity without a spectral knee + """ + y_hat = a + b * x + + return y_hat + + irasa_spectrum.get_slopes(fit_func=CustomFitFun) diff --git a/tests/test_irasa_sprint.py b/tests/test_irasa_sprint.py index e4ded71..2c1cbbb 100644 --- a/tests/test_irasa_sprint.py +++ b/tests/test_irasa_sprint.py @@ -98,6 +98,17 @@ def test_irasa_sprint_settings(ts4sprint, fs): fs=fs, band=(1, 100), win_func=dsp.windows.dpss, - dpss_settings_time_bandwidth=1, + dpss_settings_time_bandwidth=4, + freq_res=0.5, + ) + + # test ratios + with pytest.raises(ValueError): + irasa_sprint( + ts4sprint[np.newaxis, :], + fs=fs, + band=(1, 100), + win_func=dsp.windows.dpss, + dpss_settings_time_bandwidth=4, freq_res=0.5, ) From e6d947aac65c140cc12cfcbf636e6dd10770c6e9 Mon Sep 17 00:00:00 2001 From: Fabi Date: Fri, 9 Aug 2024 19:33:23 +0200 Subject: [PATCH 02/11] adjusted fitting functions --- examples/basic_functionality.ipynb | 106 ++++++++++----------- examples/custom_fit_functions.ipynb | 56 ++++++------ examples/irasa_mne.ipynb | 137 ++++++++++++++++++++++++---- examples/irasa_sprint.ipynb | 18 ++-- pyrasa/irasa_mne/mne_objs.py | 18 ++-- pyrasa/utils/aperiodic_utils.py | 34 +++---- pyrasa/utils/irasa_spectrum.py | 12 +-- pyrasa/utils/irasa_tf_spectrum.py | 12 +-- pyrasa/utils/types.py | 2 +- tests/test_basic_irasa.py | 4 +- tests/test_compute_slope.py | 34 +++---- tests/test_irasa_knee.py | 8 +- tests/test_irasa_sprint.py | 2 +- tests/test_mne.py | 4 +- 14 files changed, 274 insertions(+), 173 deletions(-) diff --git a/examples/basic_functionality.ipynb b/examples/basic_functionality.ipynb index 9bcbe9b..157ad5c 100644 --- a/examples/basic_functionality.ipynb +++ b/examples/basic_functionality.ipynb @@ -37,7 +37,7 @@ "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -86,7 +86,7 @@ "outputs": [ { "data": { - "image/png": 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KtWvX6p23YsUKdOnSBQEBAVKETUREREREZUiaWIwcORKZmZmYM2cOUlNTERAQgB07duhmeUpNTTVY0yI7OxubN2/GF198IUXIRERERERUDknXsZBCTk4OHB0dkZ2dzW5RRGR2+BlYPfy5EZG5qsrnH1dHIiIiIiKiGmNiQURERERENcbEgoiIiIiIaoyJBRERUQWioqLg7++PkJAQqUMhIpI9JhZEREQVqOkCeaIo4nRajpGjIiKSJyYWMvTLsWvo+tGfOJx8S+pQiIiomoo1Wrzx0zEM+Wo/Dl7MlDocIqJax8RChnafuYG0nAL8fYEVERGRqVIKAu4UlqBIo8ULa+NwPj1X6pCIiGoVEwsZ0prX0iJERPWSQiFg4ciO6NS4AXIKSjBhVSzScwukDouIqNYwsZCh0rxCq2WCQURkyqwslPhmXDCaOtvgyq18hK+OQ15RidRhERHVCiYWMlTaYsG8gojI9DnbWWL1xM5oaGOB41ez8fL6BJRotFKHRURkdEwsZKg0oWCXKCKi+qGpiy2+HR8CS5UCf55Ox/vbEyHyM56I6hkmFjJUmlCw0iEiqj+CmjTEopEdIQjAuoOX8c3ei1KHRERkVEws5EjU+4OIiOqJsHaemD3QDwDw0Y7T+OXYNYkjIiIyHiYWMvTvGAumFkRE9U14D19MCG0KAIjcdBSxl25KGxARkZEwsZAhDt4mIpKHqKgo+Pv7IyQkxGjXFAQB7wz2x6P+7ijSaPH82jhcuHHbaNcnIpIKEwsZ4uBtIiJ5iIiIQGJiImJjY416XaVCwJejAtHBpwGy8ooxYdUhZNwuNOo9iIjqGhMLGRJ1g7clDoSIiGqNtVqJFeOD0djJBik38xG+Jg75RRqpwyIiqjYmFjKk5QJ5RERmwcXOEqsmhqCBjQWOpmThlY0J0PCzn4hMFBMLGdK1WEgcBxER1b7mrnb4Zlww1CoFohOv44NfuMYFEZkmJhYyxDEWRETmJaSpExY80wEAsPrAJazYlyRxREREVcfEQoa0HGNBRGR2Brf3wqywNgCAD3ecwm/HUyWOiIioaphYyJDIFgsiIrP0Qq9mGNu1CUQRmLHpCOIvc40LIjIdTCxkiAvkERGZJ0EQ8O4T/nikjRsKS7SYvCYOSRl3pA6LiKhSmFjIEBfIIyIyXyqlAl+NDkQ7b0fcyivGxFWHkMk1LojIBDCxkKHShgo2WBARmScbtQorJgSjUUNrXMrMw+S1cSgo5hoXRCRvTCxk6N/EgpkFEZG5crO3wuqJIXCwUiEhOQszNh7hGhdEJGtMLGSIYyyIiOQhKioK/v7+CAkJkeT+Ldzs765xoVRg58k0fLTjlCRxEBFVBhMLGeIYCyIieYiIiEBiYiJiY2Mli6FLM2d8+nR7AMCKfUlYtZ9rXBCRPDGxkCEukEdERPcb2tEbbz7eGgAw55dE/H4yTeKIiIgMMbGQId3YCuYVRER0z0u9m+PZzo0hisArGxKQkHxL6pCIiPRInlgsXrwYvr6+sLKyQlBQEPbu3fvA8oWFhZg9ezaaNGkCS0tLNG/eHCtXrqyjaOtGaT7BFgsiIiolCAI+GNoWfVu76ta4uJzJNS6ISD4kTSw2bdqEGTNmYPbs2UhISEDPnj0RFhaG5OTkCs955pln8Oeff2LFihU4c+YMNmzYgDZt2tRh1LWPYyyIiKg8KqUCX4/uhLZeDsi8U4QJq2Jx606R1GEREQGQOLFYsGABwsPDMXnyZPj5+WHRokXw8fHBkiVLyi2/c+dO7NmzBzt27ED//v3RtGlTdO7cGaGhoXUcee3Sau/9yRYLIiIqw9ZShZUTQuDdwBpJGXfwPNe4ICKZkCyxKCoqQnx8PAYMGKC3f8CAAThw4EC55/z8888IDg7G/Pnz4e3tjVatWuH1119Hfn5+XYRcZ0oTCuYVRERUHncHK6yaGAJ7KxXiLt/Caz8chZbN3EQkMZVUN87IyIBGo4G7u7vefnd3d6SllT/bxcWLF7Fv3z5YWVlh69atyMjIwNSpU3Hz5s0Kx1kUFhaisLBQt52Tk2O8h6glImeFIiKih2jlbo9lY4MwfuUh/Ho8FV4NrDB7kL/UYRGRGZN88LYgCHrboiga7Cul1WohCAK+//57dO7cGQMHDsSCBQuwevXqClst5s2bB0dHR93Lx8fH6M9gbGyxICKiyght7oL5T91d4+KbvUn4du9FiSMiInMmWWLh4uICpVJp0DqRnp5u0IpRytPTE97e3nB0dNTt8/PzgyiKuHLlSrnnzJo1C9nZ2bpXSkqK8R6ilnBWKCIiqqxhgY10a1zM/fUUtiaUXx8SEdU2yRILtVqNoKAgREdH6+2Pjo6ucDB29+7dce3aNdy+fVu37+zZs1AoFGjUqFG551haWsLBwUHvJXecFYqIiKripd7NMam7LwDgjR+PYdeZdIkjIiJzJGlXqMjISHz77bdYuXIlTp06hVdffRXJycmYMmUKgLutDePGjdOVHz16NJydnTFx4kQkJiYiJiYGb7zxBiZNmgRra2upHsPodOvjscWCiIgqQRAEvD3ID0M7eqFEK2Lqd4e5gB4R1TnJBm8DwMiRI5GZmYk5c+YgNTUVAQEB2LFjB5o0aQIASE1N1VvTws7ODtHR0Xj55ZcRHBwMZ2dnPPPMM5g7d65Uj1Ar/m2xYGJBRESVo1AI+PSpDriVV4yYszcwcXUsfprSDS3c7KUOjYjMhCCa2dfiOTk5cHR0RHZ2tmy7RfWc/xdSbuajdytXrJnUWepwiKgeMYXPQDkypZ/bncISjP72HxxNyYKXoxV+eikUXg3qT6s+EdWtqnz+ST4rFBkqXSDPrDI+IiIyCltLFVZNCEEzV1tcyy7A+JWHkJXH1bmJqPYxsZAxM2tMIiIiI3GyVWPtpM7wcLDCufTbmLQ6FvlFXJ2biGoXEwsZ4hgLIiJ5iIqKgr+/P0JCQqQOpcoaNbTBmkmd4WClwuHkLESsP4xijVbqsIioHmNiIUO6xIKf/0REkoqIiEBiYiJiY2OlDqVaWnvYY+WEEFiqFPjrdDpmbj7O1nAiqjVMLGSodP0KtlgQEVFNBTd1QtToTlAqBGw+fAUf/3Za6pCIqJ5iYiFDpd8mMa8gIiJj6O/vjnnD2wEAlsVcxDcxFyWOiIjqIyYWMlTaYiFyXigiIjKSZ4J98NbjbQAAH+44hc3xVySOiIjqGyYWMiTqBm9LHAgREdUrU3o3Q3gPXwDAm5uPYdfpdIkjIqL6hImFDHGMBRER1QZBEDB7oB+GBXpDoxUx9fvDOJx8S+qwiKieYGIhQ1q2WBARUS1RKATMf6o9erdyRX6xBpNWx+J8eq7UYRFRPcDEQoZKGyo4JSAREdUGC6UCS8Z0QkefBsjKK8bYFYdwLStf6rCIyMQxsZAhLpBHRES1zUatwqoJIWjuaovU7AKMW3kIt+4USR0WEZkwJhYypOV0s0REVAca2qqxNrwLPByscD79NiatiUVeUYnUYRGRiWJiIUOibvC2tHEQEVH9593AGmvDO8PR2gIJyVmI+P4wijVaqcMiIhPExEKGOMaCiIjqUit3e6ycEAwrCwV2nbmBtzYfg5bfbhFRFTGxkCGOsSAioroW1MQJi5/rBKVCwJbDV/HxztNSh0REJoaJhQxxulkiIpJCvzbu+GREewDA8piLWB5zQeKIiMiUMLGQIS6QR0REUnkqqBFmhbUBAHy04zQ2x1+ROCIiMhVMLGRGb1wF8woiIpLAi72b4/mevgCANzcfw1+nr0scERGZAiYWMnN/XsEWCyIiksqsMD8MC/SGRiti6veHEX/5ptQhEZHMMbGQmfuTCY6xICIiqSgUAuY/1R59WruioFiLSavjcO56rtRhEZGMMbGQGS1bLIiISCYslAosfq4TAhs3QHZ+McatPIRrWflSh0VEMsXEQmbuTyaYVxARkdRs1CqsHB+CFm52SM0uwNgV/+DWnSKpwyIiGWJiITN6Y7eZWRARkQw0tFVj7aTO8HS0woUbdzBxdSzyikqkDouIZIaJhcxwjAUREcmRVwNrrJ3UGQ1sLHAkJQtTvz+MYo1W6rCISEaYWMjM/bkEx1gQEUkrKioK/v7+CAkJkToUWWjpbo8V40NgZaHA7jM38OZPx6Dlt2BEdA8TC5lhiwURkXxEREQgMTERsbGxUociG0FNGmLxc52gVAjYmnAV8347JXVIRCQTTCxkRryvVZljLIiISI76tXHH/BHtAQDf7E3Csj0XJI6IiOSAiYXM6LdYMLEgIiJ5GhHUCP8Z2AYAMO+30/gp/orEERGR1JhYyIzedLMSxkFERPQwL/Rqjhd6NQMAvLX5GA6cz5A4IiKSkuSJxeLFi+Hr6wsrKysEBQVh7969FZbdvXs3BEEweJ0+fboOI65degvkcZAFERHJ3MzH2+DJjl7QaEW8vCEBqdlcQI/IXEmaWGzatAkzZszA7NmzkZCQgJ49eyIsLAzJyckPPO/MmTNITU3VvVq2bFlHEdc+EVwgj4iITIdCIeDjEe3h7+mAzDtFmPr9YRSVcBpaInMkaWKxYMEChIeHY/LkyfDz88OiRYvg4+ODJUuWPPA8Nzc3eHh46F5KpbKOIq599ycTHGNBRESmwMpCiaVjguBgpUJCchY+/DVR6pCISAKSJRZFRUWIj4/HgAED9PYPGDAABw4ceOC5gYGB8PT0xCOPPIJdu3Y9sGxhYSFycnL0XnLG6WaJiMgUNXa2wcKRHQEAa/6+jG0JV6UNiIjqnGSJRUZGBjQaDdzd3fX2u7u7Iy0trdxzPD09sXz5cmzevBlbtmxB69at8cgjjyAmJqbC+8ybNw+Ojo66l4+Pj1Gfw9i0bLEgIiIT9YifO17u1wIAMGvLcZxJy5U4IiKqSyqpAxAEQW9bFEWDfaVat26N1q1b67a7deuGlJQUfPbZZ+jVq1e558yaNQuRkZG67ZycHFknF/cP2GZaQUREpmZG/1Y4kpKFvecyMOW7ePx3Wnc4WFlIHRYR1QHJWixcXFygVCoNWifS09MNWjEepGvXrjh37lyFxy0tLeHg4KD3krP7Gym4QB4REZkapULAF6MC4d3AGkkZd/DGj0dZnxGZCckSC7VajaCgIERHR+vtj46ORmhoaKWvk5CQAE9PT2OHJ5n7Z4XiGAsiIjJFTrZqLH6uE9RKBX4/eR3LYi5KHRIR1QFJu0JFRkZi7NixCA4ORrdu3bB8+XIkJydjypQpAO52Y7p69SrWrl0LAFi0aBGaNm2Ktm3boqioCN999x02b96MzZs3S/kYRsUxFkREVB908GmAd4f4Y/bWE5i/8zTaezsitIWL1GERUS2SNLEYOXIkMjMzMWfOHKSmpiIgIAA7duxAkyZNAACpqal6a1oUFRXh9ddfx9WrV2FtbY22bdvi119/xcCBA6V6BKPTW3lbfPCYEyIiIjkb3bkxDl/OwubDV/DyhgT88koPeDpaSx0WEdUSQTSzjo85OTlwdHREdna2LMdbnE/PRf8F/85ydfGjgVAomFgQkXHI/TNQrvhzq778Ig2GLzmAU6k5CGzcAJte6Aa1StJltIioCqry+cf/2TJTdlyFWWV9RERU71irlVg6phPsuXgeUb3HxEJmyo6r4DgLIiIydU2cbbGIi+cR1XtMLGSmbB7BxIKIiOqDsovnnU7LkTgiIjI2JhYyUzaRYF5BRET1xYz+rdCzpQvyizV4eunf2HgomWtcENUjTCxkhi0WRERUX5UuntfRpwFyC0owc8txjP7mH1zKuCN1aERkBEwsZMZwjIVEgRAREdUCJ1s1Nr8UircH+cHKQoG/L2bisUUxWLrnAko0WqnDI6IaYGIhMwazQrHFgoiI6hmlQsDkns3wx4ze6NHCBYUlWnz822k8uXg/Tl7Lljo8IqomJhYywxYLIiIyF42dbbAuvDPmP9UeDlYqnLiagyFf78cnO0+joFgjdXhEVEVMLGSmbAMFWyyIiKg+EwQBzwT74H+v9cbAdh7QaEUs2X0BYV/sxT8XM6UOj4iqgImFzJRNJNhiQURE5sDN3gqLnwvC0jFBcLO3RFLGHYxcfhAf7TiFYo69IDIJTCxkpmwiwVmhiIjInDwe4IHoyN4YFeIDAFgecxGjvzmI6zkFEkdGRA/DxEJmuPI2ERGZO0drC3w8oj2WPNcJdpYqxF66hUFf7sWB8xlSh0ZED8DEQmYMEgnmFUREZKbC2nli+8s90MbDHhm3izBmxT+I2nUeWvYTJpIlJhZyY9AVSpowiIiI5MDXxRZbp3bH00GNoBWBT38/g/A1scjKK5I6NCIqg4mFzHCMBRERkT5rtRKfPt0B80e0h6VKgV1nbmDQl/twNCVL6tCI6D4qqQMgfRxjQUREVL5nQnzQ1tsBU78/jMuZeXh66d8Y160JmrrYwsPBCh6Od19ONmooFILU4RKZHSYWMlM2kWBeQURE9K+2Xo7Y/nIPvPHjUfx+8jq+3ZdkUMZCKcDN3grN3ezwf4P90cLNToJIicwPu0LJjOECedLEQURUn6SkpKBPnz7w9/dH+/bt8eOPP0odEtWAg5UFlo4JwsKRHfBcl8bo7+eGAG8HuNhZQhCAYo2Iq1n5iDl7AxNXH8LNOxyPQVQX2GIhM+wKRURkfCqVCosWLULHjh2Rnp6OTp06YeDAgbC1tZU6NKomQRAwLLARhgU20ttfrNEiPbcQ17Ly8doPR5F8Mw9TvovHd+FdoFbx+1Si2sT/YTJTNo9gYkFEVHOenp7o2LEjAMDNzQ1OTk64efOmtEFRrbBQKuDdwBohTZ2wYnww7CxVOJR0E//33xMQWacS1SomFjJj2GIhUSBERHUoJiYGTzzxBLy8vCAIArZt22ZQZvHixfD19YWVlRWCgoKwd+/eat0rLi4OWq0WPj4+NYya5K6luz2+ejYQCgHYGJuClfsvSR0SUb1Wra5QKSkpuHTpEvLy8uDq6oq2bdvC0tLS2LGZpbKJBL9dISI5M1Z9cOfOHXTo0AETJ07EiBEjDI5v2rQJM2bMwOLFi9G9e3csW7YMYWFhSExMROPGjQEAQUFBKCwsNDj3jz/+gJeXFwAgMzMT48aNw7ffflvlGMk09W3jhv8M9MPcX0/hw18T0dzVFn1au0kdFlG9VOnE4vLly1i6dCk2bNiAlJQUvV941Wo1evbsiRdeeAEjRoyAQsGGkOoqm0iwxYKI5KY26oOwsDCEhYVVeHzBggUIDw/H5MmTAQCLFi3C77//jiVLlmDevHkAgPj4+Afeo7CwEMOGDcOsWbMQGhpaqbiofgjv4Yuz13PxQ9wVvLw+AVsjQtHCzV7qsIjqnUp94k+fPh3t2rXDuXPnMGfOHJw8eRLZ2dkoKipCWloaduzYgR49euCdd95B+/btERsbW9tx11sGLRZll+ImIpKQFPVBUVER4uPjMWDAAL39AwYMwIEDByp1DVEUMWHCBPTr1w9jx459aPnCwkLk5OTovch0CYKAD54MQEjThsgtLEH4mjjc4kxRREZXqRYLtVqNCxcuwNXV1eCYm5sb+vXrh379+uHdd9/Fjh07cPnyZYSEhBg9WHNgMMZCK1EgRETlkKI+yMjIgEajgbu7u95+d3d3pKWlVeoa+/fvx6ZNm9C+fXvd+I1169ahXbt25ZafN28e3n///RrFTfJiqVJi6ZggDI3aj8uZeRi+5ACau9rB1lIJG7UKtmolbCxV8HWxwcB2nrBUKaUOmcjkVCqx+PTTTyt9wYEDB1Y7GIJB+wRnhSIiOZGyPhAE/ZWURVE02FeRHj16QFuFb2pmzZqFyMhI3XZOTg4He9cDznaWWDE+BMMX70dSxh0kZdwpt9xHO05jYvemeK5LEzhaW9RxlESmi+tYyEzZMRbMK4jI3Lm4uECpVBq0TqSnpxu0YhiLpaUlJyWpp1p72OPP1/rgn6RM5BVpcKew5O6fRSW4XVCCv06nIzW7APN3nkHUX+fxbOfGmNTDF14NrKUOnUj2jJZY/Oc//0FaWhpWrlxprEuaJS6QR0Smztj1gVqtRlBQEKKjozFs2DDd/ujoaAwdOtQo9yDz4uFohaEdvcs9VlSixfaj17A85iLOXM/Ft/uSsPrAJbz6aCtM7dO80q1kRObIaInF1atXkZKSYqzLma2yLfVMLIjI1FSnPrh9+zbOnz+v205KSsKRI0fg5OSExo0bIzIyEmPHjkVwcDC6deuG5cuXIzk5GVOmTDF2+GTm1CoFRgQ1wvBO3th99gaW7bmAgxdv4tPfzyA1Ox/vDwmAUsHkgqg8RpsXds2aNfjrr7+qfF51Fzzav38/VCqVbiXV+qJsIsG0gohMTXXqg7i4OAQGBiIwMBAAEBkZicDAQPzf//0fAGDkyJFYtGgR5syZg44dOyImJgY7duxAkyZNjB4/EXB3TE/f1m7Y+EI3vD+kLQQB+O5gMiK+P4yCYo3U4RHJkqQLTpQueDR79mwkJCSgZ8+eCAsLQ3Jy8gPPy87Oxrhx4/DII4/UUaR1p2wDBRfIIyJz0KdPH4iiaPBavXq1rszUqVNx6dIlFBYWIj4+Hr169ar1uKKiouDv78+ZDs3c+NCmiBrdCWqlAjtPpmHsin+QnVcsdVhEsiOIVfzNdc6cOQ88XvrtUmV06dIFnTp1wpIlS3T7/Pz88OSTT+oWPCrPqFGj0LJlSyiVSmzbtg1Hjhyp9D1zcnLg6OiI7OxsODg4VPq8urIpNhlvbT6u2/5xSjeENHWSMCIiqk+M+RlozPpA7uRed1Dd+PtCJl5YF4fcghK0dLPDmkmdOaib6r2qfP5VeYzF1q1b9baLi4uRlJQElUqF5s2bV7oiKV3waObMmXr7H7bg0apVq3DhwgV89913mDt3blXDl72yC+RpufQ2EcmUseoDIlPRrbkzfpzSDeNXHsK59NsIXxOHrVNDYWXBNS+IgGokFgkJCQb7cnJyMGHCBL3ZOh6mOgsenTt3DjNnzsTevXuhUlUu9MLCQhQWFurFKmeGs0JJFAgR0UMYqz4gMiVtPByw+aVQDPl6P06l5uCTnafx7hNtpQ6LSBaMMsbCwcEBc+bMwTvvvFPlcyu74JFGo8Ho0aPx/vvvo1WrVpW+/rx58+Do6Kh7yX2Bo7KJBMdYEJEpqUl9QGQqGjW0wWdPtwcArNp/CX+eui5xRETyYLTB21lZWcjOzq50+aoueJSbm4u4uDhMmzYNKpUKKpUKc+bMwdGjR6FSqSqcgWTWrFnIzs7WveQ+Ja7BAnkSxUFEVF1VrQ+ITFG/Nu6Y2L0pAOCNn47hek6BtAERyUCVu0J9+eWXetuiKCI1NRXr1q3D448/XunrVHXBIwcHBxw/flxv3+LFi/HXX3/hp59+gq+vb7n3MbXVU8uOqeA6FkQkV8aqD+QsKioKUVFR0Gg4vSgZmhnWBv9cvInE1By8uukI1oV34RoXZNaqnFgsXLhQb1uhUMDV1RXjx4/HrFmzqnSthy14NGvWLFy9ehVr166FQqFAQECA3vlubm6wsrIy2G/KyqYRHGNBRHJlzPpAriIiIhAREaGbFYXofpYqJb4aHYjBX+7DgQuZWBZzAVP7tJA6LCLJVDmxSEpKMtrNR44ciczMTMyZMwepqakICAjQW/AoNTX1oWta1DcGs0KxxYKIZMqY9QGRqWruaof3h7bFmz8dw+d/nEWnxg3RtZmz1GERSaLK61iYOrnPRf7t3ouY++sp3fbKCcHo18ZwzAkRUXXI/TNQrvhzowcRRREzNh3Bf49cg72VCj9O6YY2HnyfUP1Qlc8/ow3eXrx48UMXS6KHM5huVitRIERE1cT6gMyNIAj4ZER7hDRtiNyCEkxYGYtrWflSh0VU54yWWGzevBmrV6821uXMlsF0s9KEQURUbawPyBxZWSjx7bgQtHSzQ1pOAcatPISsvCKpwyKqU0ZLLP78809cvHjRWJczW4YL5DG1ICLTwvqAzJWjjQXWTOoMDwcrnE+/jclr4lBQzBnFyHwYLbEg4yibR5jZEBgiIiKT5tXAGmsmdYaDlQpxl2/hhXXxuFNYInVYRHWiyrNClUpMTERycjKKivSb+YYMGVLjoMxZ2USC080SkdzV5/qA61hQdbT2sMc344IxftUhxJy9gdHfHMTKCSFwtjOddbWIqqPKicXFixcxbNgwHD9+HIIg6H4RFoS7C8Lww7dmON0sEZkKc6gPuI4FVVeXZs5Y/3xXhK+OxdEr2Xhq6d9YO6kzfJxspA6NqNZUuSvU9OnT4evri+vXr8PGxgYnT55ETEwMgoODsXv37loI0bwYjrGQKBAioodgfUD0YJ0aN8RPL4XCu4E1kjLuYPiSAzh5LVvqsIhqTZUTi7///htz5syBq6srFAoFFAoFevTogXnz5uGVV16pjRjNisGsUGyxICKZYn1A9HDNXe2wZWoo2njY40ZuIZ779h+cu54rdVhEtaLKiYVGo4GdnR0AwMXFBdeuXQMANGnSBGfOnDFudGaobCLBvIKI5Ir1AVHluDtYYdOL3dDBpwGy8ooxdsUhXLmVJ3VYREZX5cQiICAAx44dAwB06dIF8+fPx/79+zFnzhw0a9bM6AGam7KJBMdYEJFcsT4gqjxHawusnvDvOhdjVxzCjdxCqcMiMqoqJxZvv/02tPeWg547dy4uX76Mnj17YseOHfjyyy+NHqC54RgLIjIVrA+IqqahrRrrwrvoxlyMX3kIOQXFUodFZDRVnhXqscce0/29WbNmSExMxM2bN9GwYUPdTCBUfZwViohMBesDoqrzcLTCd5O74OmlB5CYmoNxKw5hbXhnOFhZSB0aUY0ZZYE8JycnViJGYjBYm3kFEZkQ1gdED+frYou1k7qggY0FjqRkYcy3/yA7jy0XZPoqlVhMmTIFKSkplbrgpk2b8P3339coKHNm2BWKmQURyYe51QdRUVHw9/dHSEiI1KFQPePv5YD1k7vCyVaNY1eyMfrbg7h1p+jhJxLJWKW6Qrm6uiIgIAChoaEYMmQIgoOD4eXlBSsrK9y6dQuJiYnYt28fNm7cCG9vbyxfvry24663DLtCSRMHEVF5zK0+4AJ5VJv8vRyw4fmueO7bgzh5LQfPfnMQ30/uwhW6yWQJYiUXSkhPT8eKFSuwceNGnDhxQu+Yvb09+vfvjxdeeAEDBgyolUCNpbRyyM7OhoODg9ThGJizPREr9yfptuc+GYAxXZtIGBER1SfG+AysL/VBVci97iDTdj49F89+8w9u5Bailbsdvp/cFa72TC5IHqry+VfpxOJ+WVlZuHz5MvLz8+Hi4oLmzZubTJ9auVcO7/18EqsPXNJtfzC0LcZ2aypZPERUvxj7M9CU64OqkHvdQabvwo3bGP3NQVzPKURzV1tseL4r3ByspA6LqEqff1WeFQoAGjRogAYNGlTnVHqIsnkeu0IRkZyxPiAyjuaudtj0QjeM/uYgLty4g5HLD2L9813g6WgtdWhElWaUWaHIeMomEtVoUCIiIiIT1NTFFpte7KZb52L44gNIvJYjdVhElcbEQma4QB4REZH58nGywaYXu6K5qy1Sswvw9NID2HU6XeqwiCqFiYXMcIE8IiIi89aooQ22vNQdoc2dcadIg/A1sfju4GWpwyJ6qColFqIo6gbpUW3RTySYVxCRHLE+IKpdjjYWWD2xM54JbgStCLy97QRW3zdrJJEcVTmxaNmyJa5cuVJb8Zg9rbbMNjMLIpIh1gdEtU+tUuCTEe3xUp/mAID3tidi7d+XpA2K6AGqlFgoFAq0bNkSmZmZtRWP2eMYCyIyBeZSH3DlbZKaIAh487HWmNL7bnLxf/89iXXsFkUyVeUxFvPnz8cbb7xhsCgSGYfBrFBgZkFE8mQO9UFERAQSExMRGxsrdShkxgRBwFuPt8aLvZsBAP7vvyew80SaxFERGaryOhZjxoxBXl4eOnToALVaDWtr/fmVb968abTgzFHZ6WXZE4qI5Ir1AVHdEQQBMx9vg7xCDdYdvIwZmxKwybEbOvg0kDo0Ip0qJxaLFi2qhTColEFXKPaFIiKZYn1AVLcEQcC7T/gj5VYedp+5gfA1cdgWEYpGDW2kDo0IQDUSi/Hjx9dGHHRP2TSCeQURyRXrA6K6p1Iq8PXoTnhqyQGcTsvF44v2IrS5M3q1csXQjl6wt7KQOkQyY9Vax+LChQt4++238eyzzyI9/e6iLTt37sTJkyeNGpw54joWRGRKWB8Q1T07SxVWTgiBr4stbheW4I/E63h72wmMXHYQBcUaqcMjM1blxGLPnj1o164d/vnnH2zZsgW3b98GABw7dgzvvvuu0QM0N2UTibJjLoiI5IL1AZF0vBpY43+RvfHfiO54fUArONmqkZiag3e21d/JFEj+qpxYzJw5E3PnzkV0dDTUarVuf9++ffH3339XOYDFixfD19cXVlZWCAoKwt69eyssu2/fPnTv3h3Ozs6wtrZGmzZtsHDhwirfU85KEwmFcG9bwliIiB7E2PUBEVWNUiGgg08DTOvXEl8/GwiFAPwYfwWbYpOlDo3MVJUTi+PHj2PYsGEG+11dXas8n/mmTZswY8YMzJ49GwkJCejZsyfCwsKQnFz+fwhbW1tMmzYNMTExOHXqFN5++228/fbbWL58eVUfQ7ZKF8hTKe7+07ArFBHJlTHrAyKqmdAWLnj9sdYAgHf+exLf/3MZRSXah5xFZFxVTiwaNGiA1NRUg/0JCQnw9vau0rUWLFiA8PBwTJ48GX5+fli0aBF8fHywZMmScssHBgbi2WefRdu2bdG0aVOMGTMGjz322ANbOUxNaSKhvNdkwcHbRCRXxqwPiKjmpvRqjkf93VFUosXsrSfQ59Nd2Bx/ReqwyIxUObEYPXo03nrrLaSlpUEQBGi1Wuzfvx+vv/46xo0bV+nrFBUVIT4+HgMGDNDbP2DAABw4cKBS10hISMCBAwfQu3fvKj2DnJXmESpdYsHMgojkyVj1AREZh0Ih4OvRgfi/wf5ws7fEtewCvPbjUaz9+5LUoZGZqHJi8eGHH6Jx48bw9vbG7du34e/vj169eiE0NBRvv/12pa+TkZEBjUYDd3d3vf3u7u5IS3vwapKNGjWCpaUlgoODERERgcmTJ1dYtrCwEDk5OXovOdONsbiXWDCvICK5MlZ9QETGY6lSYlIPX8S82Rcv9ipdqfsktiaw5YJqX5XXsbCwsMD333+POXPmICEhAVqtFoGBgWjZsmW1AhAEQW9bFEWDfWXt3bsXt2/fxsGDBzFz5ky0aNECzz77bLll582bh/fff79asUmhtOuTrisU+0IRkUwZuz4gIuOxslBiZlgbFJZosfrAJbz+4zG42FmiZ0tXqUOjeqzKicW5c+fQsmVLNG/eHM2bN6/2jV1cXKBUKg1aJ9LT0w1aMcry9fUFALRr1w7Xr1/He++9V2FiMWvWLERGRuq2c3Jy4OPjU+24a1vZMRZMK4hIroxVH8hZVFQUoqKioNFwbQAyPYIg4P8G+yMnvxhbEq7irZ+O4Y/I3rCzrPKvf0SVUuWuUK1bt4a3tzdGjx6NZcuW4cyZM9W6sVqtRlBQEKKjo/X2R0dHIzQ0tNLXEUURhYWFFR63tLSEg4OD3kvOShsoOMaCiOTOWPWBnEVERCAxMRGxsbFSh0JULQqFgLnDAtDYyQbXsgswf+dpqUOieqzKiUVqaio+++wzODg4YOHChfDz84OnpydGjRqFpUuXVulakZGR+Pbbb7Fy5UqcOnUKr776KpKTkzFlyhQAd1sb7h8AGBUVhe3bt+PcuXM4d+4cVq1ahc8++wxjxoyp6mPIlli2xYJ5BRHJlDHrAyKqPTZqFeYNbwcAWPv3ZcReuilxRFRfCWINl3Y+f/485s6di++//x5arbbKzcWLFy/G/PnzkZqaioCAACxcuBC9evUCAEyYMAGXLl3C7t27AQBfffUVli1bhqSkJKhUKjRv3hzPP/88XnzxRSgUlcuRcnJy4OjoiOzsbFm2Xoz59h/sO5+Bps42uJSZh3HdmmDO0ACpwyKieqI2PwNrWh/ImdzrDqLKeOunY9gUl4KGNhZ46/E2eCbYRzdZDFFFqvL5V+XE4vbt29i3bx92796NPXv24MiRI/Dz80OfPn3Qu3dvDB06tEbB1za5Vw6jvzmIAxcy0czVFhdv3MGYro0x98l2UodFRPWEMT8DTb0+qAq51x1ElZGdX4xnlx9EYurdGTL9PR3wZKAXwgI84eNkI3F0JFdV+fyr8uidhg0bwsnJCWPHjsXbb7+NHj16wNHRsdrBkj7d4G2BC+QRkbyxPiAyLY7WFvjvtO5Yc+ASFv3vHBJTc5CYmoNPfz+DtZO6oFtzZ6lDJBNX5TEWgwYNgkajwbp167B27VqsX78ep06dqo3YzFLZ6WY5xoKI5Ir1AZHpsVAqMLlnM+x+ow/mDG2LAG8HFGtEfLv3otShUT1Q5cRi27ZtyMjIQHR0NHr06IE///wTffr0gYeHB0aNGlUbMZqV0p5pKqWgt01EJDesD4hMl4udJcZ1a4pFIwMBALvP3kB6boHEUZGpq/ZExu3bt4dGo0FxcTEKCwuxc+dObNmyxZixmaV/WywU97aZWBCRvLE+IDJdLdzsENi4ARKSs/DfhGt4/t5q3UTVUeUWi4ULF2Lo0KFwcnJC586dsWHDBrRu3Rpbt25FRkZGbcRoVnQtFgqOsSAieWN9QFQ/PBXUCADwY3wKe0pQjVS5xeL7779Hnz598Pzzz6NXr16cHcPIdC0WAhfIIyJ5Y31AVD8Mbu+F97cn4uz124g5l4FeLV0gCJyGlqquyolFXFxcbcRB95R+U6BbloN5BRHJFOsDovrB0doCj7X1wPaj1zB+5SE0dbbB2G5N8VyXxrCyUEodHpmQao2xyMrKwooVK3Dq1CkIggA/Pz+Eh4dzmkEjKG2xUHGMBRGZANYHRPXDO4P8kFdYgr3nMnApMw8f/JKIb2Iu4uMR7dCntZvU4ZGJqPIYi7i4ODRv3hwLFy7EzZs3kZGRgYULF6J58+Y4fPhwbcRoVnTrWHCMBRHJHOsDovrDzcEKKyaE4PD/PYoPhwXA09EKaTkFeH5tHKITr0sdHpmIKicWr776KoYMGYJLly5hy5Yt2Lp1K5KSkjB48GDMmDGjFkI0L/+2WHCMBRHJG+sDovrHzlKF57o0wa7X+2BQO08Ua0S89F08Ys7ekDo0MgHVarF46623oFL924tKpVLhzTffZH9bIxDLtFgwryAiuWJ9QFR/WVko8cWojhjc3hMlWhHv/nwSxRqt1GGRzFU5sXBwcEBycrLB/pSUFNjb2xslKHMmlll5my0WRCRX5lAfREVFwd/fHyEhIVKHQlTnVEoFPh7RHk62aiRl3MFP8VekDolkrsqJxciRIxEeHo5NmzYhJSUFV65cwcaNGzF58mQ8++yztRGjWdHqZoViiwURyZs51AcRERFITExEbGys1KEQScLOUoVpfVsAABZGn8UrGxIwbuUhpGVzlW4yVOVZoT777DMIgoBx48ahpKQEAGBhYYGXXnoJH3/8sdEDNDdagwXymFkQkTyxPiAyD891bYwV+5JwNSsfPx+9BgCYsSkB30/uquthQQRUo8VCrVbjiy++wK1bt3DkyBEkJCTg5s2bWLhwISwtLWsjRrNi2BVKwmCIiB6A9QGRebBUKTH/qfbo2swJk3v4wkatxMGLN7F413mpQyOZqXRikZeXh4iICHh7e8PNzQ2TJ0+Gp6cn2rdvDxsbm9qM0ayUbbEQ2WJBRDLD+oDI/HRv4YKNL3TD24P98cHQAADAoj/PIe7STYkjIzmpdGLx7rvvYvXq1Rg0aBBGjRqF6OhovPTSS7UZm1kqTSM4eJuI5Ir1AZF5GxHUCMMCvaHRipi+8Qiy84qlDolkotJjLLZs2YIVK1Zg1KhRAIAxY8age/fu0Gg0UCq53LuxcIE8IpI71gdE9MGTATicfAuXM/Mwa+sxRI3uBEHgeAtzV+kWi5SUFPTs2VO33blzZ6hUKly7dq1WAjNX2ntTRKsUd/9pmFcQkdywPiAiO0sVvno2ECqFgB3H07Dl8FWpQyIZqHRiodFooFar9fapVCrdTCBkHKVjKhQCx1gQkTyxPiAiAGjfqAFefbQVAODdn09iz9kb0LKrhVmrdFcoURQxYcIEvZk+CgoKMGXKFNja2ur2bdmyxbgRmpnS/48qJcdYEJE8sT4golJTejfHrtPpiLt8C+NXHkJbLwesf74rHK0tpA6NJFDpxGL8+PEG+8aMGWPUYKicMRZaKaMhIjLE+oCISikVApaNDcKi/53D1oSrOHktB/N2nMLHI9pLHRpJoNKJxapVq2ozDrpHNyuUwBYLIpIn1gdEdD9nO0t88GQAnujghWeW/Y2NsSno1twZT7T3goIL6JmVKi+QR7VLLNNiwbyCiIiITEFnXyeM69YEADB94xE8unAPrucUSBwV1SUmFjKjG2NRmlhwXigiIiIyEbPC/DCxe1PYW6lw4cYdLNl9QeqQqA4xsZCZ0q5PCq5jQURERCbGWq3Eu0+0xeLnOgEANsWmICuvSOKoqK4wsZCZ0mnaVFx5m4iIiExUjxYu8PN0QH6xBp/+fgY3cgulDonqABMLmSnNI7jyNhEREZkqQRDwYq9mAIDv/0lG70934UhKlrRBUa1jYiEzulmhFFwgj4iIiEzX0I5eeH9IW7R0s0NekQb/998TuJqVjws3bksdGtUSJhYyU9r1iV2hiIiIyJQJgoDxoU2x/vmusLNU4diVbHT/+C88vigGp1JzpA6PaoHkicXixYvh6+sLKysrBAUFYe/evRWW3bJlCx599FG4urrCwcEB3bp1w++//16H0da+fxfIu/tPw7yCiIiITJmrvSWm9Wuh2y7WiFi5L0nCiKi2SJpYbNq0CTNmzMDs2bORkJCAnj17IiwsDMnJyeWWj4mJwaOPPoodO3YgPj4effv2xRNPPIGEhIQ6jrz2lJ1ulmMsiIiIyNQ937MZPn2qPT4Y2hYA8N8j15BxmwO66xtJE4sFCxYgPDwckydPhp+fHxYtWgQfHx8sWbKk3PKLFi3Cm2++iZCQELRs2RIfffQRWrZsie3bt9dx5LVHLDPdLMdYEBERkalTKgQ8HeyDsd2aooNPAxRptHjrp2OciraekSyxKCoqQnx8PAYMGKC3f8CAAThw4EClrqHVapGbmwsnJ6cKyxQWFiInJ0fvJWeGLRZMLIiIiKj+eO3RVrBQCvjzdDqC5v4Pzyz9G9l5xVKHRUYgWWKRkZEBjUYDd3d3vf3u7u5IS0ur1DU+//xz3LlzB88880yFZebNmwdHR0fdy8fHp0Zx17ayLRbsCkVEJJ2oqCj4+/sjJCRE6lCI6o1erVzx05RQtHCzg0Yr4tClm9h25KrUYZERSD54WxAEvW1RFA32lWfDhg147733sGnTJri5uVVYbtasWcjOzta9UlJSahxzbWKLBRGRfERERCAxMRGxsbFSh0JUr3TwaYDoV3vhzcdbAwB+PZ4qcURkDJIlFi4uLlAqlQatE+np6QatGGVt2rQJ4eHh+OGHH9C/f/8HlrW0tISDg4PeS67uH09Ruo4FmFcQERFRPSQIAoZ08AIAxF66ifScAokjopqSLLFQq9UICgpCdHS03v7o6GiEhoZWeN6GDRswYcIErF+/HoMGDartMOvU/d2e2GJBRERE9V2jhjbo4NMAogiMWn4QC/44g5izN/BDbApKNFqpw6MqUkl588jISIwdOxbBwcHo1q0bli9fjuTkZEyZMgXA3W5MV69exdq1awHcTSrGjRuHL774Al27dtW1dlhbW8PR0VGy5zCW+5MIjrEgIiIiczAqxAdHU7JwMeMOvvzrvG7/9ZwCvPxISwkjo6qSNLEYOXIkMjMzMWfOHKSmpiIgIAA7duxAkyZNAACpqal6a1osW7YMJSUliIiIQEREhG7/+PHjsXr16roO3+juTyzYYkFERETm4NnOjRHS1AlHU7Lw1V/ncCkzDwDw1V/nEdrCGUFNKp79k+RF0sQCAKZOnYqpU6eWe6xssrB79+7aD0hC9+cQSkEw2EdERERUH7Vws0MLNzuMCGoEURQxcXUsdp+5gRFL/kZYgAdmhfmhsbON1GHSQ0g+KxT9Sy+x4AJ5REREZIYEQcDCZzpieKA3BAH47UQanv3mIAqKNVKHRg/BxEJG9LpCKTnGgoiIiMxTQ1s1FozsiJ3Te8HT0QpXs/Kx5sAlqcOih2BiISNavelmFQb7iIiIiMxJaw97vDbg7loXUbvO43ZhicQR0YMwsZARbTljLNhiQUREROZsWKA3mrnaIqegBOv/ucxpaGWMiYWMlLdAHsdYEBERkTlTKgS82KsZAOCjHacRNPd/SEi+JXFUVB4mFjJS3uBtdoUiIiIic/dkoDcaNbQGAGTnF2PCqlik3MyTOCoqi4mFjOiPsbj7J9MKIiIiMneWKiW2T+uBLVND4e/pgOz8Yiz63zmpw6IymFjIiOZeYiEIgOLeGAsNB1kQERERoaGtGp0aN8S84e0AAJsPX8Gnv5/mNLQywsRCRoo1d5MIC6UCFveaLJhYEBEREf2rg08DDOngBQCI2nUBr/1wlGNSZYKJhYyUznKgVip061gUc+YDIiIiIj2fPd0Bn4xoBwulgF+Pp+LbvUnIKSiWOiyzx8RCRkqTCJVS0LVYFGtEZuFERERE91GrFBgZ0hj/N9gfAPDhjlNo/94f+PVYKkRRRF4R17uQAhMLGSmvKxQAlLA7FBEREZGBMV2boL+fu247Yv1hDFt8AO3f+wMnrmZLGJl5YmIhI6UtFhYKARb3ukLdv5+IiIiI/iUIAqKeC8TqiSFoYGMBADiSkoUSrYg9Z29IHJ35YWIhI7rEQqXfYlFcwhYLIiIiovJYqpTo09oNX44KRHCThrr9J65mI7+IM0bVJSYWMnJ/VyiV4r4WCy1bLIiIiIgepFcrV/z0UijWTOoMAPjtRBravrsTEd8fRlEJf5eqC0wsZEQ3eFshQBD+7Q7FrlBERERElePv6aD7u1YEfj2eio2xyRJGZD6YWMhIyb0WC7Xq7j9LaXeo0v1ERERE9GCu9pYG+xZEn8Wp1BwJojEvTCxkpOi+Fgvg38SiiC0WRERERJX2Sr8W8HWxxa7X+6CdtyOy8ooRvjqWYy5qGRMLGdEN3laWtliwKxQRERFRVUUOaI1dr/eBr4stvgvvAu8G1riWXYBlMReQcjMPvxy7xnXCagETCxkpuW/w9v1/sisUERERUfU42ljgrbA2AICv/zqPnvN3Ydr6BKz9+7LEkdU/TCxkpEjXYnG3pUJ17092hSIiIiKqvifae+KJDl56iw6/+/NJvP7jUbZcGBETCxmpqMWimFOkEREREVWbIAiY+2SAwf6f4q/g7wuZEkRUPzGxkJGyYyzUpV2htMykiYiIiGrC0doCG57vip4tXfDLyz3Qu5UrAOCtLcdw5VaexNHVD0wsZKSYXaGIiIiIak235s5YF94FAd6O+PSp9vBuYI2Um/n4+LfTUodWLzCxkJHSlbdV7ApFREREVKvcHKywZEwnAMDvJ9Ow80QaTqXm4MTVbI67qCaV1AHQvwynm2VXKCIiIqLa0r5RA3Rq3ACHk7Mw5bt43f7lY4MwoK2HhJGZJrZYyEjJvcRCrSxdII/rWBARERHVppcfaWmwb++5DAkiMX1ssZCRooq6QnEdCyKiGsnNzUW/fv1QXFwMjUaDV155Bc8//7zUYRGRDPRp5YoxXRsjNasAXg2sse7gZaw7eBlWFgrk5JfgvSFtYa1WSh2mSWBiISMVdYViiwURUc3Y2Nhgz549sLGxQV5eHgICAjB8+HA4OztLHRoRSezuVLTtAADXsvKx7uDdhfO+2ZsEAOjg0wCjuzSWLD5Twq5QMlJSZlYodoUiIjIOpVIJGxsbAEBBQQE0Gg0HZxKRAa8G1mjfyBFWFv/+ihxz9oaEEZkWyROLxYsXw9fXF1ZWVggKCsLevXsrLJuamorRo0ejdevWUCgUmDFjRt0FWgeKKlogj12hiKiei4mJwRNPPAEvLy8IgoBt27YZlKlKfVGerKwsdOjQAY0aNcKbb74JFxcXI0VPRPXJT1NCcfidR7H5pVAAwM6TaVj39yUUcZbOh5I0sdi0aRNmzJiB2bNnIyEhAT179kRYWBiSk5PLLV9YWAhXV1fMnj0bHTp0qONoa19pi4VK12LBrlBEZB7u3LmDDh064Ouvvy73eGXqi6CgIAQEBBi8rl27BgBo0KABjh49iqSkJKxfvx7Xr1+vk2cjItOiVilgo1aho08D9Gl9dxG9d/57Ej0++Qu3C0skjk7eJE0sFixYgPDwcEyePBl+fn5YtGgRfHx8sGTJknLLN23aFF988QXGjRsHR0fHOo629hXrZoUqbbG41xWKGTIR1XNhYWGYO3cuhg8fXu7xytQX8fHxOHHihMHLy8tL71ru7u5o3749YmJiKoynsLAQOTk5ei8iMi9KhYBVE0LwqL87ACA9txA/xqVIHJW8SZZYFBUVIT4+HgMGDNDbP2DAABw4cMBo9zGlyqG4oq5QXMeCiMyYMeqL69ev6z7/c3JyEBMTg9atW1dYft68eXB0dNS9fHx8qv8ARGSyBEHA4uc6YUJoUwDAmgOXkHgtB/N+O4UNh8rvYWPOJEssMjIyoNFo4O7urrff3d0daWlpRruPKVUOxWW6QqkU7ApFRGSM+uLKlSvo1asXOnTogB49emDatGlo3759heVnzZqF7Oxs3Sslhd9SEpkrC6UCU/s2BwBcyszDwC/3Ytmei5i15TiejNqP7PxiiSOUD8mnmxUEQW9bFEWDfTUxa9YsREZG6rZzcnJkm1wYTDerYlcoIqJSNakvgoKCcOTIkUrfy9LSEpaWllUJj4jqMTd7q3L3H0nJQof3/8BLfZrjzcdaG/V3WFMkWYuFi4sLlEqlwbdN6enpBt9K1YSlpSUcHBz0XnJVoi3tCnX3TVk61qKEXaGIyIzVVX1BRPQgn4xop7c9uL2n7u9Ldl/A6bTcug5JdiRLLNRqNYKCghAdHa23Pzo6GqGhoRJFJa3SacxKWyxKu0IVsSsUEZkx1hdEJAcjQxrj23HBAIBH2rjh82c6YFTIv71gwr7Yi0mrY6UKTxYk7QoVGRmJsWPHIjg4GN26dcPy5cuRnJyMKVOmALjbjenq1atYu3at7pzSpuzbt2/jxo0bOHLkCNRqNfz9/aV4BKPSjbFQsCsUEZmX27dv4/z587rtpKQkHDlyBE5OTmjcuPFD6wsiorrQ398dW6eGopmrHSxVSnw8oj0KijXYduTutNZ/nU7HjdxCuNqbZ1dKSROLkSNHIjMzE3PmzEFqaioCAgKwY8cONGnSBMDdBfHKrmkRGBio+3t8fDzWr1+PJk2a4NKlS3UZeq0o7fKkVrErFBGZl7i4OPTt21e3XTo2bvz48Vi9evVD64vaEhUVhaioKGg0mlq9DxGZjsDGDfW2m7rY6m2HfPg/BDdpCA9HK8wZGgAnW3VdhicpyQdvT506FVOnTi332OrVqw32iWL9/SXbsCvU3QSDXaGIqL7r06fPQz/fH1Rf1JaIiAhEREQgJyenXq6fREQ119zVzmBf3OVbAICGNmq8P6QtNKKo+/2uPqv/T2hCSlsm/u0Kda/FgokFERERkSw96u+OKb2bw7mclomjV7Iw+tuD6PPpbuQX1f+WTyYWMqJbefteVyjdAnma+ttKQ0RERGTKrCyUmBnWBnFv98cAf3c0c7WF773uUceuZOPgxZu4mpWPo1eypA20DkjeFYr+VaIp02Jxb9pZLpBHREREJG+CIGD5vVmjAODFdXH4/eR13fbSPRfQqKE1GjW0kSK8OsEWCxkpKrtAnpIrbxMRERGZogH+Hnrbu8/cwNgVhySKpm4wsZCRf1fevttSUdpywa5QRERERKalXSPDCR+SMu5g8po4pOcWSBBR7WNiISOlXaFKWypKx1qwxYKISBpRUVHw9/dHSEiI1KEQkYlp5mKL1u72Bvv/d+o6hi8+gIJiTb0b0M3EQkZ0XaFUZbtCscWCiEgKERERSExMRGysea+mS0RVp1Iq8Nv0njj3YRi+Hh2od+zKrXwEvPs7On0QjZSbeRJFaHxMLGSkdFpZC0XZrlBssSAiIiIyNQqFAAulAoPbe+GVfi30jpVoReQXa/C/U3cHeJeuZ2bKOCuUTGi0IkoX2GZXKCIiIqL6ZVq/lrBQKvB7YhpOXM3R7X9/eyL+Op2O/ecz8PkzHTAssJGEUdYMWyxk4v7kQaXUX8eihF2hiIiIiEyaWqXAy4+0RFdfZ4Nje89lQCsC83acliAy42FiIRP3JxalCUVpV6gitlgQERER1Qt37huw/fYgP71jno5WdR2OUTGxkIn7B2iX7QpVwsSCiIiIqF54OvhuV6cuvk54MtBb79jRK9l4cV2cFGEZBRMLmShNHhQCoFTod4XirFBERNLgdLNEZGydGjfE/yJ7Y/XEznC2VRsc//3kdQxfvB8HL2ZKEF3NMLGQibKrbgN3pym7/xgREdUtTjdLRLWhhZsdrNVKCIKA957wRxNnG73jh5OzMGr5Qfwv8bpEEVYPEwuZKC6zON7dv7MrFBEREVF9NqG7L/4X2RvuDpYGxyavjcMPsSkSRFU9TCxkQreGxb1kAgAs7g3e1op3p6MlIiIiovrHQqlAdGRvHH9vAB5v66F37M3NxxCx/jBEUf6/CzKxkInS7k6q+1ssVP/+nWtZEBEREdVfDlYWsLeywKJRHXFw1iMIC/g3wfj1WCrOpd+WMLrK4QJ5MlG6VoW6nK5QwN3EwspCWedxEREREVHdsbJQwsNRiXnD26Gxsw2W7bkIANhwKBlXbuUjtLkzJnb3lTjK8jGxkIniB3SFuntc/s1fRERERGQcDWzUmBXmh7TsAvz3yDWs2n8JABCdeB3Pdm4syy+c2RVKJsrrCqVQCLqpZ9kVioiIiMj8POrvbrBv4Bd7seCPMxJE82BMLGSipJxZoe5uM7EgIiIiMleD23vhg6Ft9fZdzLiDL/86j8ISTQVnSYOJhUyU1xUK+Lc7FLtCERHVPS6QR0RyMLZbU/w8rTuCmzTU2x9/+Ra++vMc0nMKJIpMHxMLmShvHQvg35mhuJYFEVHd4wJ5RCQX7Rs1QGgLF719o7/5B59Hn8Vji2IkikofEwuZKG2xUCnKtFjca8Hg6ttERERE5q25q225+2/lFePPU9cl7xrFxEImShMLtUr/n0TFrlBEREREBKBfGze0crcr91j4mjjM2nIcF27clmwxPSYWMlHR4G01u0IREREREQB7Kwv8Nr0XTs15HK3d7Q2Obzl8FY98vgcbY1MkiI6JhWwUsSsUERERET2EUiHAWq3EL6/0wN43+6K/n5tBmVlbjksQGRML2ShtkbBgVygiIiIieggLpQI+TjYVLpQnRXcoJhYyoZsVqmyLBbtCEREREVEFnuvSpNz9vrN24JUNCXWaYEieWCxevBi+vr6wsrJCUFAQ9u7d+8Dye/bsQVBQEKysrNCsWTMsXbq0jiKtXUW6dSzKTDfLlbeJiIiIqALdmjtj/8x+BmuhAcDPR6/Bd9YONJ35Kx5fFIM7hSW1GoukicWmTZswY8YMzJ49GwkJCejZsyfCwsKQnJxcbvmkpCQMHDgQPXv2REJCAv7zn//glVdewebNm+sk3trM+EoHb6sMVt6+u13ErlBEREREVA7vBtZYOSEE7Rs5wsqi/F/vT6flYkvC1VqNQ9LEYsGCBQgPD8fkyZPh5+eHRYsWwcfHB0uWLCm3/NKlS9G4cWMsWrQIfn5+mDx5MiZNmoTPPvus1mMVRRFvbT6GpXsu1EqCoZtutuzK2+wKRUREREQP0bOlK36e1gO7X++L0V0ao2szJ4MyZ9NyazUGVa1e/QGKiooQHx+PmTNn6u0fMGAADhw4UO45f//9NwYMGKC377HHHsOKFStQXFwMCwuLWov3wIVM/BB3BQBw8loOPn2qPbYfvYZlMRfRxsMeY7s2QZdmzkjKuIOkjNuwVCnRqXFDWKvvDqjRaEWcSs3BP0k3cfJqNrwbWqOjTwM0dbFF3KWb+HrXeQAVd4U6e/02svOLcfNOEf46nQ5nWzWCmzZEiUbElVv5uF1YgkYNrZFTUIwrt/Jx9VY+rtzKh6WFAj1auKCxkw1KtCJu5BZCK4pwtlWjUUMbpOcWQCsCNmolbt9rHlMrFVAqBFy5lY/rOQVQqxSwVCmgFUVk3i6CpYUSDlYq2KpVyM4vBgB4OlrBw9EKapUCOfklyC0oRk5BCfKKSmCrVsHeSgW1SoEbuYW4nluInPxiWFkoYaMufalgo1ZCK96NUSEIsLdSwd3BCgBQWKJBYbEWhSXau38v0d7b1qBYI8LKQglbSyWUCgG37hShWCNCIQAKhQBBEKAQAKUgQCEIEARAIQhQKHBvW7h3DLqypccF4e7PX6sVodGK0IoitCJ0fxdF4F4RCIIAAdBdv/TvxmXcCxo7vtLLCfdd+N99+n9qtLj789Te/ZlqRRH3p+z3h6Y79769Iu6eJ977dxAhQqsFRNydXc1SpYBapTB4xvu/F9D7u14Z/S8P9I/du7sIaMt8xyAIFf8LGevrCFEESrR3/y8Mbu8JG7VkH+NmISoqClFRUdBopF10ioiosjwcrfDRsHb4du9FHLx4U+/YwHaetXpvyWqkjIwMaDQauLu76+13d3dHWlpaueekpaWVW76kpAQZGRnw9DT8YRUWFqKwsFC3nZOTU614Q5s7Y+6TAXjv55PYfvQabt4pRNylWygs0eJ8+m38kXgd4T18sWT3Bd05lioF3B2sIOLuL8sFxQ9vdShNREo52aoBAEv3XMDSPRfKO+Wh1v9TftcyIjJt3Zo5w8aJiUVtioiIQEREBHJycuDo6Ch1OERElVb6O+T9ujV3rtV7Sl4jCWW+ThRF0WDfw8qXt7/UvHnz8P7779cwyrvXH9O1CZq52GLCqljsP58JAOji6wQLpQL7zmfokopW7nbILShBanYBkm/m6a5hb6lCcNOG6ODTAFdu5eNIShYu3riNdt6OaO5mB1EEng7y0bvvfwb6wdnOEpsPX7n3Tf7dN0Xm7SKcS78NtVIBzwZWsLNU4eqtfDjaWMC7gTUaNbSGdwNrZN4pwqGkm7pWADcHSygVAq5nFyA1pwCudpawUCpw517LgkIBFJeIKNZo4e5gBe+G1ijR3P12VCEIaGirRlGJBjn5JbhTVAIHq7utRGk5BUjLLkCxRgsHawvYW6ngYGUBawsl8opKkFNQgqISLVzsLeFubwlHawsUlGiRX1SCvCIN8oo0yC+6+42gi70aAgRk5RchPacQynvfPluqlLC0UOj+XtqSolQIKCjWIr+4BMUaEU42aqjvtbCI974J12j//Xa7tNWh9Pj9rQ/a+46LogiNKEKAoGv9UAoCFIp7LSCKuy0g4n3XElH6p/HH5Bi7B56xO/SVPq/ht/v37buvjEIQ7v0MoWs1Kv1vrN+SIJazD3fPw72WJdw9t7TVSKMVUViiQVGJVndvvU+IMp8XZT89yn6clD1e2vJVej/dM+qeV9RrXanwQtWkVipgoRR0C2gSERGVFRbgidUHLuHYlew6u6dkiYWLiwuUSqVB60R6erpBq0QpDw+PcsurVCo4O5efgc2aNQuRkZG67ZycHPj4+JRbtjJCW7jg4xHtEPnDUdiqlfj8mQ6wt7TA4K/3IuVmPkZ0aoTPnm4PALiUmYebd4oAiHC2tYSPkw2UiqolUg1t1ZgZ1gYzw9qgoFgDUTRs1agurVaEQmGk33SIiIiISDas1Ur8PK0HHlsYgzPXczEqpPq//1aWZImFWq1GUFAQoqOjMWzYMN3+6OhoDB06tNxzunXrhu3bt+vt++OPPxAcHFzh+ApLS0tYWloaL3AAwzs1gq+LLZxtLdGooQ0AYMtL3RF/+Rb6+7npEgVfF1v4utg+8FoPSirKqmgBlOpiUkFERERUv309OhAXbtzGo/4etX4vSdvRIyMj8e2332LlypU4deoUXn31VSQnJ2PKlCkA7rY2jBs3Tld+ypQpuHz5MiIjI3Hq1CmsXLkSK1aswOuvv17nsQc2bojGzja6bVd7Szwe4GEwXSwRERERkVRautvj8QBPg14ztUHSMRYjR45EZmYm5syZg9TUVAQEBGDHjh1o0uTuCoKpqal6a1r4+vpix44dePXVVxEVFQUvLy98+eWXGDFihFSPQEREREREAASxLtf5loHSmT2ys7Ph4OAgdThERHWKn4HVw58bEZmrqnz+sd8OERERERHVGBMLIiIiIiKqMSYWRERERERUY0wsiIiIiIioxphYEBERVSAqKgr+/v4ICQmROhQiItljYkFERFSBiIgIJCYmIjY2VupQiIhkj4kFERERERHVGBMLIiIiIiKqMUlX3pZC6XqAOTk5EkdCRFT3Sj/7zGxt1Bpj3UFE5qoq9YbZJRa5ubkAAB8fH4kjISKSTm5uLhwdHaUOw2Sw7iAic1eZekMQzexrK61Wi2vXrsHe3h6CIDy0fE5ODnx8fJCSkvLQZcxNHZ+1/jGX5wT4rJUliiJyc3Ph5eUFhYK9YStLq9WiVatWiI+Pr7DuCAkJMRjkbYrvy/KeQ873qe51qnpeZcrXtExFx/g+qv171MX7qLJlH1auOsfrqt4wuxYLhUKBRo0aVfk8BwcHk/nPXFN81vrHXJ4T4LNWBlsqqk6hUECtVj/wZ6dUKiv89zCl9+WDnkOO96nudap6XmXK17TMw87n+6j27lEX76PKln1YuZocr+16g19XERERVUJERESNjpuKunoOY92nutep6nmVKV/TMvXlPQTUzbMY8x518T6qbNmaftZI+T4yu65QVZWTkwNHR0dkZ2ebzLcE1cVnrX/M5TkBPivJE/+tyBj4PqKaqqv3EFssHsLS0hLvvvsuLC0tpQ6l1vFZ6x9zeU6Az0ryxH8rMga+j6im6uo9xBYLIiIiIiKqMbZYEBERERFRjTGxICIiIiKiGmNiQURERERENcbEgoiIiIiIaoyJxQMsXrwYvr6+sLKyQlBQEPbu3St1SDU2b948hISEwN7eHm5ubnjyySdx5swZvTKiKOK9996Dl5cXrK2t0adPH5w8eVKiiI1j3rx5EAQBM2bM0O2rb8959epVjBkzBs7OzrCxsUHHjh0RHx+vO14fnrekpARvv/02fH19YW1tjWbNmmHOnDnQarW6Mqb6nDExMXjiiSfg5eUFQRCwbds2veOVea7CwkK8/PLLcHFxga2tLYYMGYIrV67U4VNQVf3yyy9o3bo1WrZsiW+//VbqcMgEDRs2DA0bNsRTTz0ldShkolJSUtCnTx/4+/ujffv2+PHHH6t/MZHKtXHjRtHCwkL85ptvxMTERHH69Omira2tePnyZalDq5HHHntMXLVqlXjixAnxyJEj4qBBg8TGjRuLt2/f1pX5+OOPRXt7e3Hz5s3i8ePHxZEjR4qenp5iTk6OhJFX36FDh8SmTZuK7du3F6dPn67bX5+e8+bNm2KTJk3ECRMmiP/884+YlJQk/u9//xPPnz+vK1Mfnnfu3Lmis7Oz+Msvv4hJSUnijz/+KNrZ2YmLFi3SlTHV59yxY4c4e/ZscfPmzSIAcevWrXrHK/NcU6ZMEb29vcXo6Gjx8OHDYt++fcUOHTqIJSUldfw0VBnFxcViy5YtxStXrog5OTliixYtxMzMTKnDIhPz119/iT///LM4YsQIqUMhE3Xt2jUxISFBFEVRvH79uujt7a33e2FVMLGoQOfOncUpU6bo7WvTpo04c+ZMiSKqHenp6SIAcc+ePaIoiqJWqxU9PDzEjz/+WFemoKBAdHR0FJcuXSpVmNWWm5srtmzZUoyOjhZ79+6tSyzq23O+9dZbYo8ePSo8Xl+ed9CgQeKkSZP09g0fPlwcM2aMKIr15znLJhaVea6srCzRwsJC3Lhxo67M1atXRYVCIe7cubPOYqfK279/v/jkk0/qtl955RVx/fr1EkZEpmrXrl1MLMho2rVrJyYnJ1frXHaFKkdRURHi4+MxYMAAvf0DBgzAgQMHJIqqdmRnZwMAnJycAABJSUlIS0vTe3ZLS0v07t3bJJ89IiICgwYNQv/+/fX217fn/PnnnxEcHIynn34abm5uCAwMxDfffKM7Xl+et0ePHvjzzz9x9uxZAMDRo0exb98+DBw4EED9ec6yKvNc8fHxKC4u1ivj5eWFgIAAk352OXtY9zXgwV1qr127Bm9vb912o0aNcPXq1boInWSipu8hIsC476O4uDhotVr4+PhUKxYmFuXIyMiARqOBu7u73n53d3ekpaVJFJXxiaKIyMhI9OjRAwEBAQCge7768OwbN27E4cOHMW/ePINj9ek5AeDixYtYsmQJWrZsid9//x1TpkzBK6+8grVr1wKoP8/71ltv4dlnn0WbNm1gYWGBwMBAzJgxA88++yyA+vOcZVXmudLS0qBWq9GwYcMKy5Bx3blzBx06dMDXX39d7vFNmzZhxowZmD17NhISEtCzZ0+EhYUhOTkZwN3P4LIEQajVmEleavoeIgKM9z7KzMzEuHHjsHz58mrHoqr2mWag7Ae8KIr16kN/2rRpOHbsGPbt22dwzNSfPSUlBdOnT8cff/wBKyurCsuZ+nOW0mq1CA4OxkcffQQACAwMxMmTJ7FkyRKMGzdOV87Un3fTpk347rvvsH79erRt2xZHjhzBjBkz4OXlhfHjx+vKmfpzVqQ6z1Vfnl2OwsLCEBYWVuHxBQsWIDw8HJMnTwYALFq0CL///juWLFmCefPmwdvbW6+F4sqVK+jSpUutx03yUdP3EBFgnPdRYWEhhg0bhlmzZiE0NLTasbDFohwuLi5QKpUG3/Klp6cbfGNoql5++WX8/PPP2LVrFxo1aqTb7+HhAQAm/+zx8fFIT09HUFAQVCoVVCoV9uzZgy+//BIqlUr3LKb+nKU8PT3h7++vt8/Pz0/3bUR9+Xd94403MHPmTIwaNQrt2rXD2LFj8eqrr+o+GOvLc5ZVmefy8PBAUVERbt26VWEZqjuV6VLbuXNnnDhxAlevXkVubi527NiBxx57TIpwSYbMqVs21Z7KvI9EUcSECRPQr18/jB07tkb3Y2JRDrVajaCgIERHR+vtj46OrlEWJweiKGLatGnYsmUL/vrrL/j6+uod9/X1hYeHh96zFxUVYc+ePSb17I888giOHz+OI0eO6F7BwcF47rnncOTIETRr1qxePGep7t27G0wbfPbsWTRp0gRA/fl3zcvLg0Kh/7GlVCp1083Wl+csqzLPFRQUBAsLC70yqampOHHihEk/u6mqTJdalUqFzz//HH379kVgYCDeeOMNODs7SxEuyVBlu2U/9thjePrpp7Fjxw40atQIsbGxdR0qyVhl3kf79+/Hpk2bsG3bNnTs2BEdO3bE8ePHq3U/doWqQGRkJMaOHYvg4GB069YNy5cvR3JyMqZMmSJ1aDUSERGB9evX47///S/s7e11bypHR0dYW1vr1nr46KOP0LJlS7Rs2RIfffQRbGxsMHr0aImjrzx7e3vduJFStra2cHZ21u2vD89Z6tVXX0VoaCg++ugjPPPMMzh06BCWL1+u6ydZX/5dn3jiCXz44Ydo3Lgx2rZti4SEBCxYsACTJk0CYNrPefv2bZw/f163nZSUhCNHjsDJyQmNGzd+6HM5OjoiPDwcr732GpydneHk5ITXX38d7dq1M5i8gOrOw7qvDRkyBEOGDKnrsMiEPOw99Pvvv9d1SGSCHvQ+6tGjh956UDVS4zmp6rGoqCixSZMmolqtFjt16qSbktWUASj3tWrVKl0ZrVYrvvvuu6KHh4doaWkp9urVSzx+/Lh0QRvJ/dPNimL9e87t27eLAQEBoqWlpdimTRtx+fLlesfrw/Pm5OSI06dPFxs3bixaWVmJzZo1E2fPni0WFhbqypjqc+7atavc/5vjx48XRbFyz5Wfny9OmzZNdHJyEq2trcXBgwdXe8pAqhqUmSK4sLBQVCqV4pYtW/TKvfLKK2KvXr3qODoyBXwPkTFI/T4S7gVBRERE1SQIArZu3Yonn3xSt69Lly4ICgrC4sWLdfv8/f0xdOhQDrwlA3wPkTFI/T5iVygiIqJqeFj3tfrapZaMh+8hMgZZvY+M3gZCRERkBh7WfU0U62eXWjIevofIGOT0PmJXKCIiIiIiqjFON0tERERERDXGxIKIiIiIiGqMiQUREREREdUYEwsiIiIiIqoxJhZE9VRRURFatGiB/fv3G/W6v/zyCwIDA423SicRERHVC0wsyCRMmDABgiAYvO6ft5n0LV++HE2aNEH37t11+wRBwLZt2wzKTpgwQW8xnQcZPHgwBEHA+vXrjRQpERER1QdMLMhkPP7440hNTdV7+fr6GpQrKiqSIDr5+eqrrzB58uRaufbEiRPx1Vdf1cq1iYiIyDQxsSCTYWlpCQ8PD72XUqlEnz59MG3aNERGRsLFxQWPPvooACAxMREDBw6EnZ0d3N3dMXbsWGRkZOiud+fOHYwbNw52dnbw9PTE559/jj59+mDGjBm6MuV9w9+gQQOsXr1at3316lWMHDkSDRs2hLOzM4YOHYpLly7pjpe2Bnz22Wfw9PSEs7MzIiIiUFxcrCtTWFiIN998Ez4+PrC0tETLli2xYsUKiKKIFi1a4LPPPtOL4cSJE1AoFLhw4UK5P6vDhw/j/PnzGDRoUBV/ysClS5fKbR3q06ePrsyQIUNw6NAhXLx4scrXJyIiovqJiQXVC2vWrIFKpcL+/fuxbNkypKamonfv3ujYsSPi4uKwc+dOXL9+Hc8884zunDfeeAO7du3C1q1b8ccff2D37t2Ij4+v0n3z8vLQt29f2NnZISYmBvv27YOdnR0ef/xxvZaTXbt24cKFC9i1axfWrFmD1atX6yUn48aNw8aNG/Hll1/i1KlTWLp0Kezs7CAIAiZNmoRVq1bp3XflypXo2bMnmjdvXm5cMTExaNWqFRwcHKr0PADg4+Oj1yqUkJAAZ2dn9OrVS1emSZMmcHNzw969e6t8fSIiIqqnamU9byIjGz9+vKhUKkVbW1vd66mnnhJFURR79+4tduzYUa/8O++8Iw4YMEBvX0pKighAPHPmjJibmyuq1Wpx48aNuuOZmZmitbW1OH36dN0+AOLWrVv1ruPo6CiuWrVKFEVRXLFihdi6dWtRq9XqjhcWForW1tbi77//rou9SZMmYklJia7M008/LY4cOVIURVE8c+aMCECMjo4u99mvXbsmKpVK8Z9//hFFURSLiopEV1dXcfXq1RX+vKZPny7269fPYD8A0crKSu/naGtrK6pUKnHo0KEG5fPz88UuXbqIgwcPFjUajd6xwMBA8b333qswBiIiMj2FhYVi8+bNxX379hn1utu3bxc7duxoUJdQ/cIWCzIZffv2xZEjR3SvL7/8UncsODhYr2x8fDx27doFOzs73atNmzYAgAsXLuDChQsoKipCt27ddOc4OTmhdevWVYopPj4e58+fh729ve4+Tk5OKCgo0Oum1LZtWyiVSt22p6cn0tPTAQBHjhyBUqlE7969y72Hp6cnBg0ahJUrVwK4OytTQUEBnn766Qrjys/Ph5WVVbnHFi5cqPdzPHLkCIYMGVJu2fDwcOTm5mL9+vVQKPQ/LqytrZGXl1dhDEREUuPEH1XHiT+oJlRSB0BUWba2tmjRokWFx+6n1WrxxBNP4JNPPjEo6+npiXPnzlXqnoIgQBRFvX33j43QarUICgrC999/b3Cuq6ur7u8WFhYG1y2drtXa2vqhcUyePBljx47FwoULsWrVKowcORI2NjYVlndxccHx48fLPebh4WHwc7S3t0dWVpbevrlz52Lnzp04dOgQ7O3tDa5z8+ZNvWckIpKjxx9/3KA7aXmfXUVFRVCr1XUVlmx99dVXeO+992rl2qUTf4wZM6ZWrk/SY4sF1UudOnXCyZMn0bRpU7Ro0ULvVZqgWFhY4ODBg7pzbt26hbNnz+pdx9XVFampqbrtc+fO6X1L36lTJ5w7dw5ubm4G93F0dKxUrO3atYNWq8WePXsqLDNw4EDY2tpiyZIl+O233zBp0qQHXjMwMBCnT582SIoqa/PmzZgzZw5++OGHcsdxlLbIBAYGVuv6RER1hRN//IsTf1BtY2JB9VJERARu3ryJZ599Vvch9scff2DSpEnQaDSws7NDeHg43njjDfz55584ceIEJkyYYNDdp1+/fvj6669x+PBhxMXFYcqUKXqtD8899xxcXFwwdOhQ7N27F0lJSdizZw+mT5+OK1euVCrWpk2bYvz48Zg0aRK2bduGpKQk7N69Gz/88IOujFKpxIQJEzBr1iy0aNFCrwtXefr27Ys7d+7g5MmTVfip3XXixAmMGzcOb731Ftq2bYu0tDSkpaXh5s2bujIHDx6EpaXlQ+MgIpIzTvyhjxN/UI1JPMaDqFLGjx9f7uBiUbw7ePv+Adelzp49Kw4bNkxs0KCBaG1tLbZp00acMWOGbqB1bm6uOGbMGNHGxkZ0d3cX58+fb3Ctq1evigMGDBBtbW3Fli1bijt27NAbvC2KopiamiqOGzdOdHFxES0tLcVmzZqJzz//vJidnV1h7NOnTxd79+6t287PzxdfffVV0dPTU1Sr1WKLFi3ElStX6p1z4cIFEYA4f/78Sv3MRo0aJc6cOVNvH8oZjF42xlWrVokADF73x/vCCy+IL774YqXiICKSCif+4MQfVLc4xoJMwv3f0JS1e/fucve3bNkSW7ZsqfA8Ozs7rFu3DuvWrdPt+/XXX/XKeHl54ffff9fbV3YsgoeHB9asWVPhfcqLfdGiRXrbVlZWWLBgARYsWFDhdVJTU6FSqTBu3LgKy9zvP//5D/r374///Oc/ujESYgVdo+6PccKECZgwYUKF171x4wZ++uknxMXFVSoOIiIp9e3bF0uWLNFt3z8m70ETf5R14cIF5OfnG33ij/tVZuKP0vFzVZn4o3PnzkaZ+KN///56+9566y1oNBqDsqUTf0RHR3PiDzPDxIJI5goLC5GSkoJ33nkHzzzzDNzd3St1Xrt27TB//nxcunQJ7dq1M1o8SUlJWLx4cbmrnhMRyQ0n/uDEH1R3mFgQydyGDRsQHh6Ojh076rWuVMb48eONHk/nzp3RuXNno1+XiEhqnTp1wubNm9G0aVOoVIa/It0/8Ufjxo0B/Dvxx/0tB5WZ+GPTpk1wc3Or1ngGQH/ij7ItCaXKTvwRExPzwGsGBgZiyZIlEEURgiBUOabSiT9+++03Tvxhpjh4m+g+u3fvNuimJLUJEyZAo9EgPj4e3t7eUodDRFRvceIPTvxBNcPEgoiIiAh3x9Xt378fGo0Gjz32GAICAjB9+nQ4OjrqkodPP/0UvXr1wpAhQ9C/f3/06NEDQUFBetf5/PPP4ePjg169emH06NF4/fXX9bog2djYICYmBo0bN8bw4cPh5+eHSZMmIT8/v0otGEuWLMFTTz2FqVOnok2bNnj++edx584dvTLh4eEoKip66DTlAODs7Izhw4eX20XrYeLi4pCXl4e5c+fC09NT9xo+fLiuzIYNG/Dcc889sDsWmTZBrGg0JxERERE9VJ8+fdCxY0fZtXgDwP79+9GnTx9cuXKlUmP0jh8/jv79+5c7uLwmbty4gTZt2iAuLo5j9OoxtlgQERER1TOFhYU4f/58jSb+MCZO/GEeOHibiIiIqJ7hxB8kBXaFIiIiIiKiGmNXKCIiIiIiqjEmFkREREREVGNMLIiIiIiIqMaYWBARERERUY0xsSAiIiIiohpjYkFERERERDXGxIKIiIiIiGqMiQUREREREdUYEwsiIiIiIqqx/wf6KZsa/xnrKwAAAABJRU5ErkJggg==", + "image/png": 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IqOYY3r4+hrarD1EEpqxJwLnUO1KHRERkVkws5Eg0+kFERDWAIAj4aEBztG1QG1nafLyyKhYZ2XlSh0VEZDZMLGTowRgLphZERDWJWqXAouHBqOtkjQtpdzF5TTx0HFBHRDUEEwsZ4uBtIqKay8Veg6UjQ2BtpcCuMzcwd9spqUMiIjILJhYyxMHbREQ1W6CXEz5/vhUAYMmuC9gUf1XiiIiIKo6JhQyJhsHbEgdCRESV5ulWdTGxeyMAwLvr/8W/V25LGxARUQUxsZAhPRfIIyKyCG+GNcUTzdygzdfjlVVxSM3KkTokIqJyY2IhQ4YWC4njICKiyqVUCIh8MQiNXO2QkpmD1344DG2+TuqwiIjKhYmFDHGMBRGR5XC0tsI3I0PgYK1C3OVb+L9Nx7mOERFVS0wsZEjPMRZERBaloas9FgxrA4UArItNwqr9l6UOiYiozJhYyJDIFgsiIlmIiopCQEAA2rZtW+nX6tbEFdP6NAMAzPrtBPadS6v0axIRmRMTCxniAnlERPIQHh6OEydO4NChQ1VyvfGhDTGotRd0ehETVx9G0s17VXJdIiJzYGIhQ1wgj4jIMgmCgDnPtkDLek64fS8P41fF4q42X+qwiIhKhYmFDBU2VLDBgojI8lhbKbF0RAhcHTQ4lZKFiJ8SOP04EVULTCxk6EFiwYqEiMgSeThZY/HwYKiVCmw7fh1f/X1W6pCIiB6LiYUMcYwFEREF+9TG7EGBAIDIv87ij2MpEkdERPRoTCxkiGMsiIgIAAaHeGN0pwYAgIifEnAqJVPagIiIHkHyxGLhwoXw9fWFtbU1goODsXv37keW12q1mDFjBnx8fKDRaNCoUSMsX768iqKtGlwgj4iICs3s549OjergXq4O41fF4tbdXKlDIiIqlqSJxbp16zB16lTMmDED8fHxCA0NRZ8+fZCYmFjiMYMHD8b27duxbNkynD59GmvWrEGzZs2qMOrKJ3KBPCIiuk+lVCBqWBvUd7ZF0s1shK8+jDydXuqwiIhMSJpYzJs3D2PHjsW4cePg7++PyMhIeHt7Y9GiRcWW/+OPP7Br1y5s3boVPXv2RIMGDdCuXTt06tSpiiOvXHoO3iYioofUtlPjm5EhsFUrse98Ov6z5aTUIRERmZAsscjNzUVcXBzCwsKMtoeFhWHfvn3FHvPrr78iJCQEc+fOhZeXF5o0aYK33noL2dnZVRFylRHBMRZERGSsqYcD5g0OAgCs3HcJPx1KkjYgIqIiVFJdOC0tDTqdDu7u7kbb3d3dkZJS/MwXFy5cwJ49e2BtbY2NGzciLS0NEydOxM2bN0scZ6HVaqHVag3PMzPlP/BNf7+Fm2MsiIjoYU8FemBqTz9E/nUWMzcdQyM3ewT71JY6LCIiADIYvC0IgtFzURRNthXS6/UQBAE//vgj2rVrh759+2LevHlYuXJlia0Wc+bMgZOTk+Hh7e1t9nswNz3HWBARUQkmP+GHp5p7IFenx6vfxyE5o2a12hNR9SVZYuHi4gKlUmnSOpGammrSilHI09MTXl5ecHJyMmzz9/eHKIq4cuVKscdMnz4dGRkZhkdSkvybjkXOCkVERCVQKAR8ObgVmnk4IO2OFq9+H4ecPJ3UYRERSZdYqNVqBAcHIzo62mh7dHR0iYOxO3fujGvXruHOnTuGbWfOnIFCoUC9evWKPUaj0cDR0dHoIXdcII+IiB7FTqPCNyNDUMvWCv9eycD0DUc54QcRSU7SrlARERH49ttvsXz5cpw8eRJvvPEGEhMTMWHCBAAFrQ0jR440lB82bBjq1KmDl19+GSdOnEBMTAzefvttjBkzBjY2NlLdhtlxgTwiInocb2dbLBzWBkqFgI3xV7E05oLUIRGRhZNs8DYADBkyBOnp6Zg1axaSk5MRGBiIrVu3wsfHBwCQnJxstKaFvb09oqOj8frrryMkJAR16tTB4MGDMXv2bKluoVIU5hP88omIiB6lU2MX/F//AHzw63HM+f0UlAoB40IbSh0WEVkoSRMLAJg4cSImTpxY7L6VK1eabGvWrJlJ96maRuQ6FkREshAVFYWoqCjodPIdwzCyow+SM3KweNd5zN5yElk5+Zja06/EiVCIiCqL5LNCkSmOsSAikofw8HCcOHEChw4dkjqUEgmCgHefaoq3ezcFAPx3+1nM3nKSX04RUZVjYiFDHGNBRERlIQgCwns0xodPBwAAlu25iGnrj0LHioSIqhATCxniAnlERFQeozv74osXWkEhAOtikzB5bTxy8/VSh0VEFoKJhQyJXCCPiIjK6fngeoga1gZWSgFb/k3Gq9/Hcp0LIqoSTCxkyDArFJhZEBFR2fVp4YlvR7WFtZUCO07fwKjl/yArJ0/qsIiohmNiIUOGMRZsvSYionLq1sQVq8a0h4NGhYMXb2L4twdx626u1GERUQ3GxEKGCsfacYwFERFVRDtfZ6x5pQNq21rhyJUMDFm6H6mZOVKHRUQ1FBMLGeIYCyIiMpdALyf89GpHuDtqcOb6HbywZD+Sbt6TOiwiqoGYWMgQWyyIiMic/Nwd8MuETvB2tsHl9Ht4YfF+nEu9I3VYRFTDMLGQIS6QR0RE5ubtbIufX+0EPzd7pGTmYPCS/Th2NUPqsIioBmFiIUOF+QTTCiIiMicPJ2use7UjWng54ebdXAz95gBiL92UOiwiqiGYWMgQV94mIqLK4mynxo/j26NdA2dk5eRjxLJ/EHPmhtRhEVENwMRChgwtFuwKRURElcDR2grfjWmHbk1ckZ2nw7jvYvHHsRSpwyKiao6JhQxxjAUREVU2G7US34wMQd8WHsjV6RG++jDWx12ROiwiqsaYWMgQF8gjIqKqoFYp8NWLrfF8cD3o9CLe/PkIvtt3SeqwiKiaYmIhQ5xuloiIqopKqcDc51pidKcGAIAPfj2O9zcdQ24+v90iorJhYiFHzCeIiKgKKRQCPng6AG+FNYEgAN8fuIwhS/cjOSNb6tCIqBphYiFDHGNBRERVTRAETHrCD8tHtYWjtQrxibfx9Nd7sP98utShEVE1wcRChjjdLBERSaVHMzf89noo/D0dkXYnF8OXHcTSmPOcqZCIHouJhQxxjAUREUmpfh1bbHitE55t7QWdXsQnW09h0up43NHmSx0aEckYEwuZefgbIeYVREQkFRu1El8OboWPnwmElVLAlqPJeCZqL86l3pE6NCKSKSYWMvNw9ye2WBARkZQEQcCIDj5Y+0pHuDtqcC71Dp6J2os/jiVLHRoRyRATC5lhiwUREclNsE9t/PZ6KNr7OuOONh8TfjiMOb+fRL6OU9IS0QNMLGSGLRZERPIRFRWFgIAAtG3bVupQJOfqoMGP49pjfKgvAGDJrgsYufwfpN3RShwZEckFEwuZ0bPFgohINsLDw3HixAkcOnRI6lBkQaVUYEa/AEQNawNbtRL7zqfj6a/3ID7xltShEZEMMLGQGZEtFkREJHP9Wnrif+Gd0dDVDskZORiy5AB+PHhZ6rCISGJMLGTm4WSCiQUREcmVn7sD/hfeGb2buyNXp8eMjcfw4a/HoeMiTEQWi4mFzBgnFhIGQkRE9BgO1lZYPDwYb/duCgBYue8SXv0+Fvdyud4FkSViYiEzDycTbLAgIiK5EwQB4T0aY+FLbaBRKfDXyVQMWXIAqZk5UodGRFWMiYXcGCUWzCyIiKh66NvCE6vHd4CznRpHr2Zg0MJ9OJ2SJXVYRFSFJE8sFi5cCF9fX1hbWyM4OBi7d+8usezOnTshCILJ49SpU1UYceXiGAsiIqqugn1qY+PETmjoYoert7Px/KJ92H32htRhEVEVkTSxWLduHaZOnYoZM2YgPj4eoaGh6NOnDxITEx953OnTp5GcnGx4+Pn5VVHElY9jLIiIqDrzqWOHDRM7oZ2vM7K0+Xh5xSGsO/Toep2IagZJE4t58+Zh7NixGDduHPz9/REZGQlvb28sWrTokce5ubnBw8PD8FAqlVUUceXjAnlERFTd1bJV4/ux7fBMUF3k60W8u/4oPt92Cnp+Y0ZUo0mWWOTm5iIuLg5hYWFG28PCwrBv375HHtu6dWt4enriySefxI4dOyozzConcoE8IiKqATQqJeYPCcLkJxoDAKJ2nMeUdQnIydNJHBkRVRbJEou0tDTodDq4u7sbbXd3d0dKSkqxx3h6emLp0qVYv349NmzYgKZNm+LJJ59ETExMidfRarXIzMw0esiZnoO3iYiohhAEARFhTfH58y2hUgjYfOQaRiw7iFt3c6UOjYgqgUrqAARBMHouiqLJtkJNmzZF06ZNDc87duyIpKQkfPHFF+jatWuxx8yZMwcfffSR+QKuZCI4xoKIiGqWF0K8UbeWDSb8EIdDl27h2UX7sGxUCBq62ksdGhGZkWQtFi4uLlAqlSatE6mpqSatGI/SoUMHnD17tsT906dPR0ZGhuGRlJRU7pirAsdYEBFRTdS5sQs2vNYJXrVscDHtLvp9tQfL91zkSt1ENYhkiYVarUZwcDCio6ONtkdHR6NTp06lPk98fDw8PT1L3K/RaODo6Gj0kLOHB7YxryAioprEz90BG8M7ob2vM7LzdJj12wkMXrIf51K53gVRTSBpV6iIiAiMGDECISEh6NixI5YuXYrExERMmDABQEFrw9WrV7Fq1SoAQGRkJBo0aIDmzZsjNzcXP/zwA9avX4/169dLeRtmJbLFgoiIajA3B2usGd8Bq/9JxJytJxF3+Rb6/ncPpvT0wytdG8JKKfkSW0RUTpImFkOGDEF6ejpmzZqF5ORkBAYGYuvWrfDx8QEAJCcnG61pkZubi7feegtXr16FjY0Nmjdvji1btqBv375S3YLZcYE8IiKq6RQKAcM7+KBHMze8t+Eodp25gc+3ncbWo8mY+3xLNK/rJHWIRFQOgmhhUw9lZmbCyckJGRkZsuwWdeHGHTzx5S4AgCAAF+f0kzgiIqpJ5P4ZKFd83SqPKIrYcPgqZv12AhnZeVApBEzo1givP9kYGlXNWaeKqLoqy+cf2xtl5uEsTxQ55SwREdVsgiDgueB6iI7oij6BHsjXi1iw4xz6fbUHhxNvSR0eEZUBEwuZKZpIMK8gIiJL4OZgjUXDg7HopTZwsdfgXOodPLdoH77dfUHq0IiolJhYyEzRWfc4zoKIiCxJnxae+CuiK55t7QVRBGZvOYkFf5c8rTwRyQcTC5kpmkhwem8iIrI0tWzVmDckCG/2agIA+OLPM/jyz9PsHkwkc0wsZEavL/KcH6JERGShXn/SD+/1bQYA+Prvc/hk60kmF0QyxsRCZphIEBERPfBK10b4aEBzAMA3uy/ig1+PGy0mS0TywcRC5phoEBGRpRvVqQE+fbYFBAFYtf8y3tt4FDomF0Syw8RCZjjGgoiIyNSL7epj3uBWUAjA2kNJeOvnI8jX6R9/IBFVGSYWMsNZoYiIiIo3qHU9fD20DVQKARvjr2LK2gTkMbkgkg0mFjJTNJEQ+XlJRERk0K+lJxa+1AZqpQJbjibjlVWxyMjOkzosIgITC9kpOtsFWyyIiIiMhTX3wNKRwdCoFNhx+gb6f70bR69kSB0WkcVjYiEzRbtCMa0gIiIy1b2pG36e0BH1atsg6WY2nlu0D98fuMzpaIkkxMRCZop+HrLFgoiIqHgt69XCltdD0SvAHbk6Pd7fdAyT1ybgjjZf6tCILBITC5kxnRWKiQUREVFJnGytsHREMGb284dKIWDzkWsY8PUenErJlDo0IovDxEJmTAZvM68gIiJ6JEEQMC60Ida92gGeTta4kHYXz0TtxU+HkriYHlEVYmIhM+wKRUREVD7BPs7YMjkU3Zq4IidPj3fW/4v2c7bj3V/+xZ/HU3Avl12kiCoTEwuZ4QJ5RETml5SUhO7duyMgIAAtW7bEzz//LHVIVEmc7dRYMbot3u7dFHZqJW5kabEuNgmvfB+HoFnRGL3iH3y//xKSM7KlDpWoxlFJHQAZM1kgj5kFEVGFqVQqREZGIigoCKmpqWjTpg369u0LOzs7qUOjSqBQCAjv0RjjQn3xz8Wb2H4yFX+dvI4rt7Kx8/QN7Dx9A//363F0aeyCIW290SvAHRqVUuqwiao9JhYyw2nyiIjMz9PTE56engAANzc3ODs74+bNm0wsajiNSolQP1eE+rnig6cDcC71DrafSsVfJ64j9vIt7D6bht1n01DL1grPBHlhcIg3Auo6Sh02UbXFrlAywzEWRGSJYmJi8PTTT6Nu3boQBAGbNm0yKbNw4UL4+vrC2toawcHB2L17d7muFRsbC71eD29v7wpGTdWJIAjwc3fAhG6N8MtrnRDzdg+8/kRjeDpZ4/a9PKzcdwl9v9qN5xftQ9LNe1KHS1QtMbGQGY6xICJLdPfuXbRq1QoLFiwodv+6deswdepUzJgxA/Hx8QgNDUWfPn2QmJhoKBMcHIzAwECTx7Vr1wxl0tPTMXLkSCxdurTS74nkrX4dW7wZ1hR73n0CK19ui34tPGGlFBB7+Rb6f70HO0+nSh0iUbUjiBbW9yYzMxNOTk7IyMiAo6P8mjujT1zH+FWxhufb3+yGRq72EkZERDWJ3D8DgYJvljdu3IhnnnnGsK19+/Zo06YNFi1aZNjm7++PZ555BnPmzCnVebVaLXr16oXx48djxIgRZYqpOrxuVHHXbmfjtR8P40jSbQgC8EbPJpjUozEUCkHq0IgkU5bPP7ZYyIzpOhYWlfcREZnIzc1FXFwcwsLCjLaHhYVh3759pTqHKIoYPXo0nnjiiVIlFVqtFpmZmUYPqvnq1rLBT692wLD29SGKwLzoMxi/KhYZ2XlSh0ZULZQrsUhKSsLu3buxbds2HD58GFqt1txxWayiiQS7QhGRnFVFfZCWlgadTgd3d3ej7e7u7khJSSnVOfbu3Yt169Zh06ZNCAoKQlBQEI4ePVpi+Tlz5sDJycnw4HgMy6FRKfHJoBaY+3xLqFUKbD+VigEL9uBcapbUoRHJXqlnhbp8+TIWL16MNWvWICkpyegPYLVajdDQULzyyit47rnnoFCwIaS8iiYSbLAgIrmRqj4QBOPuKKIommwrSZcuXaDX60t9renTpyMiIsLwPDMzk8mFhRkc4o0AT0e8+n0cLqffw6jlh/C/SZ3hYq+ROjQi2SrVJ/6UKVPQokULnD17FrNmzcLx48eRkZGB3NxcpKSkYOvWrejSpQvef/99tGzZEocOHarsuGsszgpFRHImRX3g4uICpVJp0jqRmppq0ophLhqNBo6OjkYPsjyBXk7Y/HoX+LrY4ertbEz4Pg7afJ3UYRHJVqlaLNRqNc6fPw9XV1eTfW5ubnjiiSfwxBNP4IMPPsDWrVtx+fJltG3b1uzBWgLTWaGYWBCRfEhRH6jVagQHByM6OhqDBg0ybI+OjsbAgQMrdG6ix3G2U+PbUSF4JmovYi/fwsyNxzD3+Zalbi0jsiSlSiw+//zzUp+wb9++5Q6Gihu8LVEgRETFqKz64M6dOzh37pzh+cWLF5GQkABnZ2fUr18fERERGDFiBEJCQtCxY0csXboUiYmJmDBhQpniJyqPRq72iBrWBqNX/IOf466gibsDxndtKHVYRLLDlbdlhl2hiMgSxcbGokePHobnheMbRo0ahZUrV2LIkCFIT0/HrFmzkJycjMDAQGzduhU+Pj5ShUwWpmsTV7zfPwAfbT6BT34/icZu9ujRzE3qsIhkxWyj6t577z2MGTPGXKezWFwgj4iqu/LUB927d4coiiaPlStXGspMnDgRly5dglarRVxcHLp27WrmyE1FRUUhICCA3XsJADC6UwMMbecNUQReXxOPM9c5UxTRw8yWWFy9ehWXLl0q83ELFy6Er68vrK2tERwcjN27d5fquL1790KlUiEoKKjM15Qz01mhmFkQUfVS3vpAjsLDw3HixAlOSkIACmYm+2hAINr7OuOONh9v/nQEen4DSGRgtsTiu+++w99//12mY9atW4epU6dixowZiI+PR2hoKPr06YPExMRHHpeRkYGRI0fiySefrEjIssR1LIiouitPfUBUXahVCiwY1gb2GhWOXs3AxvirUodEJBuSLjgxb948jB07FuPGjYO/vz8iIyPh7e2NRYsWPfK4V199FcOGDUPHjh2rKNKqU7SBgi0WRERE8uLqoEF4j8YAgLnbTuFebr7EERHJQ5kHb8+aNeuR+//v//6vVOfJzc1FXFwcpk2bZrQ9LCwM+/btK/G4FStW4Pz58/jhhx8we/bsUl2rOuEYCyKqLsxVHxBVRy93boAfD17GlVvZWLLrAt7o1UTqkIgkV+bEYuPGjUbP8/LycPHiRahUKjRq1KjUFUlaWhp0Op3J4kbu7u4miyAVOnv2LKZNm4bdu3dDpSpd6FqtFlqt1vA8MzOzVMdJpWgiwVmhiEiuzFUfEFVH1lZKvNfXHxN/PIwlMefxYjtveDrZSB0WkaTKnFjEx8ebbMvMzMTo0aONFi4qraILzIiiWOyiMzqdDsOGDcNHH32EJk1K/63AnDlz8NFHH5U5LqlwgTwiqi7MXR8QVTd9Aj3QroEz/rl0E5//cRrzhgRJHRKRpMwyxsLR0RGzZs3C+++/X+pjXFxcoFQqTVonUlNTTVoxACArKwuxsbGYNGkSVCoVVCoVZs2ahSNHjkClUpU4UHD69OnIyMgwPJKSksp2c1XMZEwF8woiqkbKUx/IGaebpUcRBAEz+/sDADbEX8WRpNvSBkQkMbMN3r59+zYyMjJKXV6tViM4OBjR0dFG26Ojo9GpUyeT8o6Ojjh69CgSEhIMjwkTJqBp06ZISEhA+/bti72ORqOBo6Oj0UPOiuYRHGNBRNVNWesDOeN0s/Q4LevVwrNtvAAAH/92gpOukEUrc1eor776yui5KIpITk7G999/j6eeeqpM54qIiMCIESMQEhKCjh07YunSpUhMTMSECRMAFLQ2XL16FatWrYJCoUBgYKDR8W5ubrC2tjbZXp0VnQ+bXaGISK7MWR8QVWfv9G6G34+mIPbyLYxc/g/e6d0MLeo5SR0WUZUrc2Ixf/58o+cKhQKurq4YNWoUpk+fXqZzDRkyBOnp6Zg1axaSk5MRGBiIrVu3wsfHBwCQnJz82DUtahoO3iai6sKc9QFRdebhZI33+wfg//53DLvPpmH32T3oE+iBN8OaorGbvdThEVUZQbSwNrvMzEw4OTkhIyNDlt2ivt19AbO3nDQ8XzG6LXo0c5MwIiKqSeT+GShXfN2oNBLT72H+X2ewKeEqRBFQCEBEryaY9ISf1KERlVtZPv8kXSCPTBVN89hiQUREVD3Ur2OL+UOC8MeUrugV4A69CHzx5xms/ceyel+Q5TJbYrFw4cLHLpZEj1c0kWBeQUTVDesDsnRNPRzwzcgQTH6iYHXuGZuOIebMDYmjIqp8Zkss1q9fj5UrV5rrdBbLdFYoZhZEVL2wPiAq8EavJhjU2gs6vYiJPx7GyWR5L9JLVFFmSyy2b9+OCxcumOt0Fst0gTyJAiEiKqeaVB9wHQuqCEEQ8OlzLdDe1xl3tPkYs/IQUjJypA6LqNJwjIXMmKyPxxYLIiLJcB0LqiiNSomlI0LQyNUOyRk5eGHJPhxOvCV1WESVoszTzRY6ceIEEhMTkZuba7R9wIABFQ7KkpmuYyFRIEREpcT6gOjRnGytsPLldhj6zQEk3czGC4v3442efnite2MoFYLU4RGZTZkTiwsXLmDQoEE4evQoBEEwfKMuCAX/MXQ6nXkjtDBcx4KIqgvWB0Sl5+1siy2TQzFz0zFsPnINX/x5BrvPpuHroa3h5mgtdXhEZlHmrlBTpkyBr68vrl+/DltbWxw/fhwxMTEICQnBzp07KyFEy2IyK5REcRARPQ7rA6KycbKxwlcvBuHz51vCVq3EwYs3MWlNPLs9U41R5sRi//79mDVrFlxdXaFQKKBQKNClSxfMmTMHkydProwYLUrRjxZ+2BCRXLE+ICo7QRDwQog3fp3UGdZWCvxz8Sb+l3BN6rCIzKLMiYVOp4O9fcHy9C4uLrh2reA/g4+PD06fPm3e6CxQ0USCXaGISK5YHxCVX2M3B7x+f0Xu/2w9iaycPIkjIqq4MicWgYGB+PfffwEA7du3x9y5c7F3717MmjULDRs2NHuAlsZkulm9RIEQET0G6wOiihkX6gtfFzvcyNIi8q+zUodDVGFlTixmzpwJ/f2/dmfPno3Lly8jNDQUW7duxVdffWX2AC0NB28TUXXB+oCoYjQqJT54OgAAsHLfJZxOyZI4IqKKKfOsUL179zb8u2HDhjhx4gRu3ryJ2rVrG2YCofIzGbzNvIKIZMoS6oOoqChERUVxhiuqNN2buqF3c3dsO34d7286hsUjguFsp5Y6LKJyMcsCec7OzjWmEpGayQJ5nBeKiKqRmlYfcIE8qgrv9w8oGMh96SZCZkdj8JL9+CbmAm7fy338wUQyUqrEYsKECUhKSirVCdetW4cff/yxQkFZMtPB2xIFQkRUDNYHROZXr7YtFgxtA39PR+hF4J+LN/GfrSfxVORuxF66KXV4RKVWqq5Qrq6uCAwMRKdOnTBgwACEhISgbt26sLa2xq1bt3DixAns2bMHa9euhZeXF5YuXVrZcddYHGNBRHLG+oCocvQMcEfPAHdcuXUPf524jlX7L+NC2l0MWXoAb/duildCG0LBVbpJ5gSxlAslpKamYtmyZVi7di2OHTtmtM/BwQE9e/bEK6+8grCwsEoJ1FwyMzPh5OSEjIwMODo6Sh2OiY82H8eKvZcMzz9+JhAjOvhIFxAR1Sjm+AysKfVBWci97qCa5642HzM2HsWm+2tc9Gvpia9fbM3kgqpcWT7/Sp1YPOz27du4fPkysrOz4eLigkaNGlWbPrVyrxw+/PU4Vu67ZHg+a2BzjOzYQLJ4iKhmMfdnYHWuD8pC7nUH1UyiKGLtoSR88L/jyNXpMbOfP8aFcipnqlpl+fwr86xQAFCrVi3UqlWrPIfSY5iuY8GuUEQkX6wPiCqPIAgY2q4+dHoRMzcdw2d/nELbBs5o5V1L6tCIimWWWaHIfEymm5UoDiIiIpKHl9rXR59AD+TpRLy+Jp6rdJNsMbGQGdPB29LEQURERPIgCAI+fbYlvGrZIPHmPUzfcNRkFkkiOWBiITMm61jwg4OIiMjiOdla4auhraFUCPjt32R8+scpqUMiMlGmxEIURcMgPaocputYMLEgIvmxlPogKioKAQEBaNu2rdShECHYpzbmDGoBAFiy6wKWxpyXOCIiY2VOLPz8/HDlypXKisfimQzeZl5BRDJkKfUBV94muRnc1hvT+zQDAHyy9RR+ii3dgpVEVaFMiYVCoYCfnx/S09MrKx6LxwXyiKg6YH1AJJ1XuzXCq10Lpp19d/2/+CWuZif4VH2UeYzF3Llz8fbbb5ssikTmYTIrFPMKIpIp1gdE0pnWpxleal8fogi8/csRrDuUKHVIRGVfx2L48OG4d+8eWrVqBbVaDRsbG6P9N2/eNFtwloiDt4moumB9QCQdQRAw+5lAqBQCvtt/Ge+uP4o8nYjhHXykDo0sWJkTi8jIyEoIgwqZDt6WKBAiosdgfUAkLUEQ8OGA5lApFVi25yJmbjoGnV7EqE4NpA6NLFSZE4tRo0ZVRhx0H8dYEFF1wfqASHqCIGBmP3+olAKW7LqAD349jjydHuNCG0odGlmgcq1jcf78ecycORNDhw5FamoqAOCPP/7A8ePHzRqcJeKsUERUnbA+IJKeIAiY9lQzTOrRGAAwe8tJLN7FqWip6pU5sdi1axdatGiBgwcPYsOGDbhz5w4A4N9//8UHH3xQ5gAWLlwIX19fWFtbIzg4GLt37y6x7J49e9C5c2fUqVMHNjY2aNasGebPn1/ma8oZx1gQUXVh7vqAiMpPEAS8GdYEU3v6AQA+/f0UFvx9VuKoyNKUObGYNm0aZs+ejejoaKjVasP2Hj16YP/+/WU617p16zB16lTMmDED8fHxCA0NRZ8+fZCYWPzMBnZ2dpg0aRJiYmJw8uRJzJw5EzNnzsTSpUvLehuyVdhioVIIRs+JiOTGnPUBEVWcIAiY2rMJ3gprAgD44s8z+O9fTC6o6pQ5sTh69CgGDRpkst3V1bXM85nPmzcPY8eOxbhx4+Dv74/IyEh4e3tj0aJFxZZv3bo1hg4diubNm6NBgwYYPnw4evfu/chWjuqmMJFQ3k8smFcQkVyZsz4gIvOZ9IQfpt1fRG/+X2ew4TDXuaCqUebEolatWkhOTjbZHh8fDy8vr1KfJzc3F3FxcQgLCzPaHhYWhn379pXqHPHx8di3bx+6detW6uvKXWEi8aDFQsJgiIgewVz1ARGZ34RujQxjLqZtOIp/r9yWNiCyCGVOLIYNG4Z3330XKSkpEAQBer0ee/fuxVtvvYWRI0eW+jxpaWnQ6XRwd3c32u7u7o6UlJRHHluvXj1oNBqEhIQgPDwc48aNK7GsVqtFZmam0UPOChOJBy0WzCyISJ7MVR8QUeWI6NUETzZzQ26+Hq9+H4fUrBypQ6IarsyJxX/+8x/Ur18fXl5euHPnDgICAtC1a1d06tQJM2fOLHMAgiAYPRdF0WRbUbt370ZsbCwWL16MyMhIrFmzpsSyc+bMgZOTk+Hh7e1d5hirklikKxTHWBCRXJm7PiAi81IoBMx/MQgNXe2QnJGDkcv+wc27uVKHRTVYmdexsLKywo8//ohZs2YhPj4eer0erVu3hp+fX5nO4+LiAqVSadI6kZqaatKKUZSvry8AoEWLFrh+/To+/PBDDB06tNiy06dPR0REhOF5ZmamrJOLomMs2BWKiOTKXPWBnEVFRSEqKgo6nU7qUIjKxdHaCstHtcXgJftxKiULL317EGvGt0ctW/XjDyYqozInFmfPnoWfnx8aNWqERo0alfvCarUawcHBiI6ONhr8Fx0djYEDB5b6PKIoQqvVlrhfo9FAo9GUO86qVrQrFFssiEiuzFUfyFl4eDjCw8ORmZkJJycnqcMhKpcGLnZYPb4DXlx6ACeTMzF82UH8OLYDnGytpA6Napgyd4Vq2rQpvLy8MGzYMCxZsgSnT58u98UjIiLw7bffYvny5Th58iTeeOMNJCYmYsKECQAKWhse7qcbFRWFzZs34+zZszh79ixWrFiBL774AsOHDy93DHLzYLrZgl8N8woikitz1gdEVLkau9ljzfj2qGOnxrGrmRix/CAysvOkDotqmDInFsnJyfjiiy/g6OiI+fPnw9/fH56ennjxxRexePHiMp1ryJAhiIyMxKxZsxAUFISYmBhs3boVPj4+hms9vKaFXq/H9OnTERQUhJCQEHz99df49NNPMWvWrLLehuxx8DYRyZ056wMiqnx+7g74cXx71La1wr9XMjBy+T9IzsiWOiyqQQSxgn+5njt3DrNnz8aPP/4IvV4v+36ohc3ZGRkZcHR0lDocEy99ewB7z6WjoYsdLqTdxYgOPvj4mUCpwyKiGqIyPwOrW31QFnKvO4jK4sS1TAz79gBu38uDg7UKHw8MxDOtOUU0Fa8sn39lHmNx584d7NmzBzt37sSuXbuQkJAAf39/vP766zVqPQmp6PUFPxUcY0FEMsf6gKh6CqjriA2vdcIbPx3BkaTbmLouAVk5eRjRsYHUoVE1V+bEonbt2nB2dsaIESMwc+ZMdOnShQPazMgwK5TAWaGISN5YHxBVXw1d7bF+Qkd8/udpLNl1AR9vOYm2vs5o5sEWOSq/Mo+x6NevH3Q6Hb7//nusWrUKq1evxsmTJysjNoskcoE8IqomWB8QVW8qpQLTnmqGJ+4vovf66nhk59acLoxU9cqcWGzatAlpaWmIjo5Gly5dsH37dnTv3h0eHh548cUXKyNGi2KYFUpZmFhIGQ0RUclYHxBVf4Ig4PPnW8LVQYOzqXcwaOFexF2+KXVYVE2VuStUoZYtW0Kn0yEvLw9arRZ//PEHNmzYYM7YLFJhHsF1LIioumB9QFS91bHXYMHQ1nj1hzicSsnCc4v2Y2pPP0x50g/C/a7ZRKVR5haL+fPnY+DAgXB2dka7du2wZs0aNG3aFBs3bkRaWlplxGhRHqxjwTEWRCRvrA+Iao72Devg7ze7Y3BIPQBA5F9n8dkfp9klm8qkzC0WP/74I7p3747x48eja9eunHbPzAoTCYXAMRZEJG+sD4hqFmc7NeY+3wrNPBwx67cTWLzrPNQqBSJ6NZE6NKomypxYxMbGVkYcdF9hIsGuUEQkd6wPiGqmMV18YaUU8P7/juOr7WfRvK4jejf3kDosqgbKNcbi9u3bWLZsGU6ePAlBEODv74+xY8dymkEz0JskFlJGQ0T0aKwPiGqmER0b4ELaXazYewlv/nQEjcLt0djNXuqwSObKPMYiNjYWjRo1wvz583Hz5k2kpaVh/vz5aNSoEQ4fPlwZMVqUwgXyCsdYMK8gIrlifUBUs73X1x/tfZ1xR5uPV7+PRVZOntQhkcyVObF44403MGDAAFy6dAkbNmzAxo0bcfHiRfTv3x9Tp06thBAty4NZoQp+NewKRURyxfqAqGazUiqwYFgbeDha4/yNu3jr5yPQsysFPUK5WizeffddqFQPelGpVCq888477G9rBmKRWaE4eJuI5Ir1AVHN5+qgwaLhbaBWKrDt+HW8/79j0DG5oBKUObFwdHREYmKiyfakpCQ4ODiYJShLZhhjcX+BvMKuUUREcmMJ9UFUVBQCAgLQtm1bqUMhkkzr+rXxybMtIAjAjwcTMfHHONy+lyt1WCRDZU4shgwZgrFjx2LdunVISkrClStXsHbtWowbNw5Dhw6tjBgtSuGXAEqBs0IRkbxZQn0QHh6OEydO4NChQ1KHQiSp54PrYcHQBy0X3T7fiR8OXGbPCjJS5lmhvvjiCwiCgJEjRyI/Px8AYGVlhddeew2ffvqp2QO0NJwVioiqC9YHRJalX0tPeDhp8N6GYzh9PQszNx3DudQ7+L/+AVAouEI3AYJYzlTz3r17OH/+PERRROPGjWFra2vu2CpFZmYmnJyckJGRIcvFnHp8sRMX0+7i+eB6+CXuCnr6u+HbUWyCJyLzqIzPwOpaH5SF3OsOoqqUr9Pj2z0X8envpwAA/Vp44vMXWsJWXa5VDEjmyvL5V+quUPfu3UN4eDi8vLzg5uaGcePGwdPTEy1btqyRlYhU9EUGb7PFgojkhvUBkWVTKRWY0K0R/vtiEKyUArYcTcZzi/Yj6eY9qUMjiZU6sfjggw+wcuVK9OvXDy+++CKio6Px2muvVWZsFqmw/YgrbxORXLE+ICIAGBjkhdXjO8DFXo2TyZkYGLUX+8+nSx0WSajUbVYbNmzAsmXL8OKLLwIAhg8fjs6dO0On00GpVFZagJaGLRZEJHesD4ioUNsGzvh1Uhe88n0sjl3NxKgV/+DvN7uhXm22XlqiUrdYJCUlITQ01PC8Xbt2UKlUuHbtWqUEZqkKGygUXMeCiGSK9QERPaxuLRv8MqET2tSvhdx8PX48aDoNNVmGUicWOp0OarXaaJtKpTLMBELmYZgVitPNEpFMsT4goqKsrZR4pWsjAMC6Q0nIydNJHBFJodRdoURRxOjRo6HRaAzbcnJyMGHCBNjZ2Rm2bdiwwbwRWpiiC+QxryAiuWF9QETF6envhrpO1riWkYMt/ybjueB6UodEVazUicWoUaNMtg0fPtyswdCDMRUqDt4mIplifUBExVEpFXipgw8+33YaH285gQYutgj2cZY6LKpCpU4sVqxYUZlx0H0PZoUq6KXGwdtEJDesD4ioJKM6NUD0ietISLqNYd8cxIaJndC8rpPUYVEVKfUYC6oaYpFZoTh4m4iIiKoLe40Ka8Z3QKifC7T5ery38Rh0/JbUYjCxkBnDGAtON0tERETVkI1aiS9faAV7jQpHkm7jvQ1H8fep6/yy1AIwsZCZwkRCwVmhiIiIqJpyc7TGm2FNAADrYpMwZmUsonackzgqqmylHmNBVaPoAnnMK4iIiKg6GtWxAew0Kuw+m4bNR67hy+gzaO7lhB5N3aQOjSoJWyxk5sHgbY6xICIioupLoRAwOMQbXw9tjaHt6kMUgdd+iMOes2lSh0aVhImFzBgGbys5xoKIiIhqhg8HBKBHU1fk5Okx9rtDuJx+V+qQqBJInlgsXLgQvr6+sLa2RnBwMHbv3l1i2Q0bNqBXr15wdXWFo6MjOnbsiG3btlVhtJVPX6TFgmMsiIiIqLrTqJRYPCIY7X2doc3XY370GalDokogaWKxbt06TJ06FTNmzEB8fDxCQ0PRp08fJCYmFls+JiYGvXr1wtatWxEXF4cePXrg6aefRnx8fBVHXnmKjrFgiwURERHVBBqVEu/3DwAA/O/INUxb/y/+l3AVAHD7Xi67f9cAkiYW8+bNw9ixYzFu3Dj4+/sjMjIS3t7eWLRoUbHlIyMj8c4776Bt27bw8/PDJ598Aj8/P2zevLmKI688YpFZofifjIiIiGqKQC8n9G3hAVEE1h5KwpS1CRi78hCCZkVj+d5LUodHFSRZYpGbm4u4uDiEhYUZbQ8LC8O+fftKdQ69Xo+srCw4O9ec5eKLrmPBvIKIiIhqko8GBGJCt0Z4slnB7FDbT6UCAL7afha5+XopQ6MKkiyxSEtLg06ng7u7u9F2d3d3pKSklOocX375Je7evYvBgweXWEar1SIzM9PoIWemC+QxsyAiIqKaw9VBg2l9mmHxiGB0beIKq/sT1mRk5+GP46X7G5DkSfLB28L9Lj+FRFE02VacNWvW4MMPP8S6devg5lbyfMhz5syBk5OT4eHt7V3hmCtTYRqhUhT8aphYEBFJJyoqCgEBAWjbtq3UoRDVOFZKBb57uS2OftgbU3v6AQBmbDyKL7adhp6DTKslyRILFxcXKJVKk9aJ1NRUk1aMotatW4exY8fip59+Qs+ePR9Zdvr06cjIyDA8kpKSKhx7ZRFFsZh1LCQMiIjIwoWHh+PEiRM4dOiQ1KEQ1UiCIMDaSomRHRugqbsDsnLysWDHOXz9N1fpro4kSyzUajWCg4MRHR1ttD06OhqdOnUq8bg1a9Zg9OjRWL16Nfr16/fY62g0Gjg6Oho95OrhJELFrlBERERkIZzt1Ph9Sig+GtAcADD/rzPo9vkOJCTdljYwKhNJu0JFRETg22+/xfLly3Hy5Em88cYbSExMxIQJEwAUtDaMHDnSUH7NmjUYOXIkvvzyS3To0AEpKSlISUlBRkaGVLdgVg8nEUpON0tEREQWRKEQMKpTA0zq0RhKhYDL6ffw3oajnCGzGpE0sRgyZAgiIyMxa9YsBAUFISYmBlu3boWPjw8AIDk52WhNiyVLliA/Px/h4eHw9PQ0PKZMmSLVLZjVw0mEorArFPifiYiIiCzHW72b4sD0J2GnVuJEciZ+PXJN6pColFRSBzBx4kRMnDix2H0rV640er5z587KD0hCD7dYGLpCcdY1IiIisjCuDhqM7twAUTvOY8raBOw8fQNznm0Bayul1KHRI0g+KxQV78HgbbZYEBERkeWZ1MMPzwfXg0IANsZfxZiVhzhblMwxsZCRYlss+P+HiIiILJCNWokvXmiFH8d1gK1aiX3n0/HXyetSh0WPwMRCRh5OIrhAHhERERHQsVEdvNy5AQDgle/j0OWzv/F//zuGnDydtIGRCSYWMsJZoYiIiIhMjensC1t1wfiKK7eysWr/ZfyawEHdcsPEQkbEhwZqFyYW4KxQREREZOHq2GuwenwHfPlCKzzV3AMA8OeJ69Dm65B0857E0VEhyWeFogfYYkFERERUvCDvWgjyrgV/T0f8cTwFu8/eQI/Pd+JaRg4WDGuN/i3rSh2ixWOLhYw8nENw5W0iIiIiU/6eDvCqZQNtvh7XMnIAAD8cuCxxVAQwsZAV3UPNEwpBMNlGREREZOkEQUBEryYI9HLE5Cf9AAAHL95Eyv0kg6TDxEJGCpMIK6UAlUJhtI2IiIiICjwXXA+/vR6KiF5NEOxTG6IIrP4nUeqwLB4TCxnJ0xWM3lYpFFApC1os8nVMLIiIiIhKUjgV7ZJd53HlFgdyS4mJhYwUJhZWSgFWyoJfTa5Oz9W3iYiIiErQr4Un2vk6Q5uvx7e7L0odjkVjYiEjebrCrlAKWCkFw3Z2hyIiIiIqniAImNi9EQBg5b5LOHs9C6Io8otZCXC6WRl50GKhMLRYFGwXoVJKFRURERGRvHX1c0W92ja4cisbvebHAACcbKywcWInNHS1lzg6y8EWCxkxjLFQCoYxFgCQp9eXdAgRERGRxVMoBEzv4w+16sGfthnZeVi48zxu3c1Fxr08CaOzHEwsZKSwK5RaqYCV4qEWi3wmFkRERESP0q+lJ07OegodGjobtv0SdwWtP45G78gYZOYwuahsTCxkJP+hFguFQjCsvp3PMRZEREREj6VUCFj5cjv8FdEVbRvUNmxPyczBtzEXJIzMMjCxkJHch8ZYFPwsSCxy2WJBREREVCrWVko0dnPA272bGW3/ds9FpN3RShSVZWBiISOFa1aoChOL+92h2GJBREREVDbtfJ3xdu+mGN2pAQK9HHEvV4cZG4/izP1Zo8j8mFjISOHgbfX9lgqr+wOQCrcTERERUemF92iMDwc0x4y+ARAEYNvx6wibH4ON8VelDq1GYmIhI3n3WyZU91sqVPfHWDCxICIiIiq/jo3q4JsRIYbnvx65JmE0NRcTCxkpnP2psKWicKxF4WxRRERERFQ+PQPc8ecbXQEAO0/fwLGrGcjiTFFmxcRCRvLvr1dhdb+lonDwdj5bLIiIiIgqzM/NHl61bAAA/b/eg2ei9kLPsaxmw8RCRnLvt0w8mBVKcX87EwsiIiKiihIEAe881RQu9moAwPkbd7HvfDoHdJsJEwsZeXgdi4Kf92eFYlcoIiIiIrMYGOSFQzN6YlBrLwDA8GUHETY/BnN+PyVxZNUfEwsZeTArlOL+Tw7eJiIiIjI3QRAMiUWhpTEXcDI5U6KIagYmFjKSZ1jHwrjFgoO3iYiIiMwr1M8Fr3ZtaLRt7MpDuHU3V6KIqj8mFjKSV8LK22yxICIiIjIvQRAwva8/Ymf2xLzBrQAA1zJy8ObPRySOrPpiYiEj+SUM3i6cLYqIiIiIzMvFXoM+gZ5o5V0LAPD3qVScuMYuUeXBxEJGHrRYFE43e78rVD67QhERERFVFhu1Ev8L74z+LT0BAH2/2o3fjyZLHFX1w8RCRh6MsSiy8jZbLIiIKiQrKwtt27ZFUFAQWrRogW+++UbqkIhIhl7r3sjw73fX/4ujVzJw5nqWhBFVL5InFgsXLoSvry+sra0RHByM3bt3l1g2OTkZw4YNQ9OmTaFQKDB16tSqC7QKmIyxUBW2WDCxICKqCFtbW+zatQsJCQk4ePAg5syZg/T0dKnDIiKZaV7XCW/3bgoAyMzJx9ML9uC5hftw7GpBgjH3j1OYueko17wogaSJxbp16zB16lTMmDED8fHxCA0NRZ8+fZCYmFhsea1WC1dXV8yYMQOtWrWq4mgrn8nK2/d/5nNFSCKiClEqlbC1tQUA5OTkQKfT8Q8DIipWeI/G6Onvbniepc1H/6/3IGx+DBbuPI8fDiTiXOodCSOUL0kTi3nz5mHs2LEYN24c/P39ERkZCW9vbyxatKjY8g0aNMB///tfjBw5Ek5OTlUcbeXLvT+WorClgitvE5GliImJwdNPP426detCEARs2rTJpExZWriLc/v2bbRq1Qr16tXDO++8AxcXFzNFT0Q1zbNtvB65/0La3SqKpHqRLLHIzc1FXFwcwsLCjLaHhYVh3759EkUlLUOLReEYC668TUQW4u7du2jVqhUWLFhQ7P7StHAHBwcjMDDQ5HHt2jUAQK1atXDkyBFcvHgRq1evxvXr16vk3oio+unbwhNHPwzDgelPoktj0y8h2GJRPJVUF05LS4NOp4O7u7vRdnd3d6SkpJjtOlqtFlqt1vA8M1O+04cVnRWKK28TkaXo06cP+vTpU+L+h1u4ASAyMhLbtm3DokWLMGfOHABAXFxcqa7l7u6Oli1bIiYmBi+88EKxZapT3UFElcPB2goO1lb4YVx7bD5yDa+viTfs237yOvacTcMr3RqiR1M3CaOUF8kHbwuCYPRcFEWTbRUxZ84cODk5GR7e3t5mO7e55RVZx4IrbxMRmaeF+/r164bkIDMzEzExMWjatGmJ5atT3UFEle+pQA+83bspHDQF38kfTryN/RfS8fKKQ5j92wkkZ2RLHKE8SJZYuLi4QKlUmrROpKammrRiVMT06dORkZFheCQlJZnt3OZW2DJROM2sYR0LtlgQkQUzRwv3lStX0LVrV7Rq1QpdunTBpEmT0LJlyxLLV6e6g4gqn5VSgfAejbHrnR4m+77dcxGLd56XICr5kawrlFqtRnBwMKKjozFo0CDD9ujoaAwcONBs19FoNNBoNGY7X2UqHEuhNgzevj8rFBMLIqIKtXAHBwcjISGh1NeqTnUHEVUdZzt1sdv3nef01YDEXaEiIiLw7bffYvny5Th58iTeeOMNJCYmYsKECQAKvjEaOXKk0TEJCQlISEjAnTt3cOPGDSQkJODEiRNShG92uYYWi6KzQrErFBFZrqpq4SYiKo2Z/fzhYG383fzZ1DsYuGAPrt627C5RkiYWQ4YMQWRkJGbNmoWgoCDExMRg69at8PHxAVCwIF7RNS1at26N1q1bIy4uDqtXr0br1q3Rt29fKcI3u/wig7etDLNCscWCiCzXwy3cD4uOjkanTp0kioqILNW40Ib494Mwk+1HrmTg6+1nJYhIPiTrClVo4sSJmDhxYrH7Vq5cabKtJi9oVHTwthVnhSIiC3Hnzh2cO3fO8PzixYtISEiAs7Mz6tevj4iICIwYMQIhISHo2LEjli5datTCTURUlQRBQKdGdbDvfDpq21rh1r08AMD/Eq6hdf1aGNK2vsQRSkPyxIIeeDDdrHFXqDyuvE1ENVxsbCx69HgwKDIiIgIAMGrUKKxcuRJDhgxBeno6Zs2aheTkZAQGBhq1cBMRVbUlI4KRdDMb9evY4ottp7Fy3yVk5+nw7vqjsLZSAgD6t6wLpcJ8s53KHRMLGTHMCnW/paLwZ14+WyyIqGbr3r37Y1ukH9XCXVmioqIQFRUFnU5XpdclIvlzsLZCQF0rAMCHA5pj0hON0eOLncjKyceUtQkAgMvp9zD5ST8Jo6xakq9jQQ/k64t2hVIYbScioqoVHh6OEydO4NChQ1KHQkQy52KvwfzBQUbbFvx9DnvOpkkTkASYWMhIYcvEg8HbHGNBREREVF080cwNAZ6OAAA3Bw1ydXoMX3YQI5YdxIELNX9KWnaFkpFck8HbXCCPiIiIqLpQKAT8MK49rt3Ohp+7Pfr+dzfO37iL3WfTcC71DvZPf1LqECsVWyxkJF9v3GJRuJ5FHtexICIiIqoWnO3UCPRygkalxMTujQ3bkzNyEHf5poSRVT4mFjLyoCtUwa9FreLK20RERETV1XPB9fDzhI4Pni/ajy3/JksYUeViYiEjhdPKqu4nFoUtFlx5m4hIGlFRUQgICEDbtm2lDoWIqqm2DZwR0asJbNUFU9CGrz6M/l/vxu17uRJHZn5MLGQkjytvExHJCmeFIiJzmPykn9H4imNXMxE0Kxpfbz+L8zfuYPDi/dh15oaEEZoHEwuZ0OlFFE7hbqXgyttERERENYmTjRVGdjRe1PPL6DN48std+OfSTYxa/o9EkZkPEwuZeDh5sFIVnRWKXaGIiIiIqruPBjTH+FBfqcOoNEwsZOLhxEKlKLLyNlssiIiIiKo9QRDg6WRT4n5RrN5fJjOxkImHWyUMs0Jx5W0iIiKiGsWnjm2J+z79/RRSM3OqMBrzYmIhE4UDtBUCoDS0WNzvCpXPFgsiIiKimqBbE1f0CfSAu6MGT7eqa7RvScwF9Pt6D/TV9EtlrrwtE7k64zUsCv59vyuUnokFERERUU2gUiqwaHgwRFFErk6P9r7OsNMo8ca6IwCAG1labP73Guo726J1/doSR1s2TCxkIv9+VyjjxIKDt4mIpBQVFYWoqCjodDqpQyGiGkYQBGhUSgzv4IO72nwARwz7pqxNAAD0CnDH58+3RC1btTRBlhG7QslE0TUsCv5d8OvR6cVq2yRGRFSdcR0LIqoKdhoVPn4m0GR79InrWLTzvAQRlQ8TC5kobJVQPdRioXooyWB3KCIiIqKaa0QHH1z4pC/qOlkbbV8Sc6HazBDKxEImCt8w6ocSi4f/nc/uUEREREQ1mkIh4L9DW5ts/+HAZdzV5st+OlomFjKRf79F4uFWisL1LACuZUFERERkCdo2cMaSEcFG2z7afALNP9iG6RuOShRV6TCxkIncfNPB20qFAOF+bsEB3ERERESWoXdzD8S/3wv9W3oabV97KAlt//MXkm7ekyiyR2NiIROGFouHWikEQYCVonBmKLZYEBEREVmK2nZq3NHmm2y/kaXFO7/8K0FEj8fEQiYMYyxUxr+SwlmiOMaCiIiIyLIMCfEudvv+C+k4cS2ziqN5PCYWMmGYFeqhFgvgwSxRuWyxICIiIrIoTwV6YP1rHfFy5wYm+/p+tRvnUrOQkyefdXa4QJ5M5BWz8vbDz/M53SwRUZXjAnlEJCVBEBDs44xgH2dM69MMvx1JxrFrGVix9xIAoOe8GADAm72aYEhbb2jz9fB2tpUsXrZYyERxK28XPC9owcjLZ1coIqKqxgXyiEguNColnguuhw+ebo6tk0ON9n0ZfQbtPtmOpyJjkJmTJ1GETCxkI7eYlbcLnt8fvM0WCyIiIiICEFDXEXOfb2my/W6uDocu3pRsvQsmFjKRX8zK2wXPC1ssmFgQERERUYHBId649Gk/zOjrb7R97Hex+PDX45LExMRCJopbefvh5/l6doUiIiIiImMNXe1Mtn23/zJ+ibtS5bEwsZCJwsRCpSw6K1TBc84KRURERERFlTRY+62fjyD20s0qjUXyxGLhwoXw9fWFtbU1goODsXv37keW37VrF4KDg2FtbY2GDRti8eLFVRRp5corcfD2/RYLrmNBREREREXUq21T4r7nF+9Hg2lbkJFdNQO6JU0s1q1bh6lTp2LGjBmIj49HaGgo+vTpg8TExGLLX7x4EX379kVoaCji4+Px3nvvYfLkyVi/fn0VR25++SUN3ubK20RERERUAlu1Cj+92hFv925aYplWH/2J7SevV3oskiYW8+bNw9ixYzFu3Dj4+/sjMjIS3t7eWLRoUbHlFy9ejPr16yMyMhL+/v4YN24cxowZgy+++KKKIzd1V5uPhKTb5R6FX+I6FirBaD8RERER0cPa+TojvEdj/BXRzeRL6kJjv4tFalZOpcYh2QJ5ubm5iIuLw7Rp04y2h4WFYd++fcUes3//foSFhRlt6927N5YtW4a8vDxYWVlVWrxF5eTpoFQIsFIqcD0zB0O/OYALN+6iWxNXTOvTDE3cHXBHm4/E9HvI1enQwqsW1KqCpCErJw+37+VBoRDg5qBBnk6PzJx8AIBKUWRWqPvPtfl66PUiFA+tzJ2br8el9LsQAGTm5OPoldtQKATYa1Sw16hQx14NtVIJbb4O9tYq1LZVw16jgjZfj3u5+cjO1eHe/Ud2Xr7h3zq9CDuNCg4aFRysVbC3VsFapYRapYCVUgErpQCFQoAAIO1OLu5qC2K3USthr1HBTqOCjZUSN7K0OJWSibPX70AninC2U8PZVo3admo4WquQrxdhpVTARq2ERqXAnZx83M3Nh1IhQCkIUCoEqBQKKBQouLZCgRt3tEjOyMGtu7lwd7SGs50aSkXBAjI2VkqolALS7+TCSqmAo40K2jw9tPk65Nz/CQiwUhact/A+RFGEKAIiAP39fwOAQhCgEArOLQjAg5zxQRkRD28vIAiAcP/n/S0PbSt43QrL6fQibt3LRb5OhMaq4HXQqBSwtip4vXPz9cjKyccdbR50etyPB1AqFFApCl+jgp9KhYB8vYg8nd7Qda7gugIUioKfIkRk5+ogomBiANX916Lw33joNcjXi9Dp9dDpCxZorKqZ64TiPw9LLo8yHlCOa5TnmKqIq0EdO8PnChERUWM3e6MJf0Z19MF3+y8bnl9Ovwc3B+tKu75kiUVaWhp0Oh3c3d2Ntru7uyMlJaXYY1JSUootn5+fj7S0NHh6epoco9VqodVqDc8zMzPLHfMnW0+ieV1H1He2xcsrCxZL6tbEFQcv3ERKZkEGuOvMDew6c8PkWBsrJTydrHE3Nx/XM7Um+wsVtlAYnt9vwXjnl3/xzi//QiEUTEmrViqgzdcZxmYQkeXZ/U4PSVdYJSIi+Xm+TT38HHcF3Zu6wqvI+Iv0OyX/DWoOkiUWhYQiX9GJomiy7XHli9teaM6cOfjoo48qGCVwOPEWlsZcAFDwrXFhMvi/hGsACgbOzH4mEN/tu4SDF2/iXq4OAOBir4YoAul3c3Eh7a7hfNZWCuj0oiExEATAxV6DJ5q6GV03oK4j/nqoT5xeLGipyL2/roW9RmVoOQnydoJapUBWTj4yc/Jx864Wufl6aFRK3NXm43Z2HnT3A1erFLBVK2FrpYSNWglbter+TyVUCgXuavORpc0r+LY8Jx/a+9csOjuVrVoJR2sriBBxL1eHu9p8w2ujVAjwdbFDU3cHaKwUuHk31/C4o82HSiEgTyciJ08Hbb4edmol7DQq6EVAp9ff/8ZcRL5eNNyvk40VPJ2sUcvWCikZOcjKyYdeLCiXnVeQaDnbqZGXr0eWNt/w7b+1lQIalRIiROh0IvL0IvJ1eujutwIJKGihKHgbFbyXRFGEXhShFwu+xTe0ONx/qz3cAlG4rbDlo/B9WdiiIYoixMINeFBGIQioZWd1P1HUG1pWCl9vK6UAB2sr2GtUUCkEQzy6h16bh18rlUKAWqUwtHSJKChfGIMgANZWSghCwYQAeTo98nQFr0WeTgSE+60iuN8SonzQIqIo49fp5WnhEFG2g8p3jXIcU+aDyn6V8txLeVpeqGyioqIQFRUFnU4ndShERKXyf08HoH3DOugV4I7Dl28Z7Uu7k1up15YssXBxcYFSqTRpnUhNTTVplSjk4eFRbHmVSoU6deoUe8z06dMRERFheJ6ZmQlvb+8yxxvg6Yg3ezVB1M5zyMnTo10DZ4zp4ovj1zLg7+mIrk1cYa9RoXtTN+Tr9Lh5LxcOGivYqJXQ60VcSLuD9Du5UCkVaOJuDwdrK+j1IjKy86CxUsDGSllscvRGTz+8EFwPGquCPxbzdQV/3OfrRKhVCng6WT8yEXuYKBb88V3Q7aV83SfE+3/E6+53l9GoFEbXF0UROXl6ZOfpYKtWwtpKWerzPuo+RLEgCXtct4+Hu4s97pxyp9cXJALV+R6Iqrvw8HCEh4cjMzMTTk5OUodDRPRYDtZWeD64HgCge1NXvN27KX48cBnXMnKQVlNbLNRqNYKDgxEdHY1BgwYZtkdHR2PgwIHFHtOxY0ds3rzZaNuff/6JkJCQEsdXaDQaaDSaCsdrbaXE60/64bngejh4MR1PNfeEjVqJpwI9TMqqlAqj/msKhYDGbg5obNwYAYVCQG079SOvKwiC2bo6CIIAW3XFfuWCIBT0y3/Efht1QStIWc/7uP1q1eP/wH54DEp1/4P84XshIiIiKitBEBDeozFa1nPC6ZQshDRwrtTrSdoVKiIiAiNGjEBISAg6duyIpUuXIjExERMmTABQ0Npw9epVrFq1CgAwYcIELFiwABERERg/fjz279+PZcuWYc2aNVUWc91aNhjUul6VXY+IiIiIqCJC/VwR6uda6deRNLEYMmQI0tPTMWvWLCQnJyMwMBBbt26Fj48PACA5OdloTQtfX19s3boVb7zxBqKiolC3bl189dVXeO6556S6BSIiIiIiAiCI5V14oZoq7CebkZEBR0dHqcMhIqpS/AwsH75uRGSpyvL5xwnQiYiIiIiowphYEBERERFRhTGxICIiIiKiCmNiQUREREREFcbEgoiIiIiIKoyJBRERERERVRgTCyIiIiIiqjAmFkRERCWIiopCQEAA2rZtK3UoRESyZ3EL5GVkZKBWrVpISkriIkdEZHEyMzPh7e2N27dvw8nJSepwqg3WHURkqcpSb6iqKCbZyMrKAgB4e3tLHAkRkXSysrKYWJQB6w4isnSlqTcsrsVCr9fj2rVrcHBwgCAIjy1fmKVZwrdUvNeax1LuE+C9lpYoisjKykLdunWhULA3bGnp9Xo0adIEcXFxJdYdbdu2xaFDh4y2Vcf3ZXH3IefrlPc8ZT2uNOUrWqakfXwfVf41quJ9VNqyjytXnv1VVW9YXIuFQqFAvXr1ynyco6NjtfnPXFG815rHUu4T4L2WBlsqyk6hUECtVj/ytVMqlSX+PqrT+/JR9yHH65T3PGU9rjTlK1rmccfzfVR516iK91Fpyz6uXEX2V3a9wa+riIiISiE8PLxC+6uLqroPc12nvOcp63GlKV/RMjXlPQRUzb2Y8xpV8T4qbdmKftZI+T6yuK5QZZWZmQknJydkZGRUm28Jyov3WvNYyn0CvFeSJ/6uyBz4PqKKqqr3EFssHkOj0eCDDz6ARqOROpRKx3uteSzlPgHeK8kTf1dkDnwfUUVV1XuILRZERERERFRhbLEgIiIiIqIKY2JBREREREQVxsSCiIiIiIgqjIkFERERERFVGBOLR1i4cCF8fX1hbW2N4OBg7N69W+qQKmzOnDlo27YtHBwc4ObmhmeeeQanT582KiOKIj788EPUrVsXNjY26N69O44fPy5RxOYxZ84cCIKAqVOnGrbVtPu8evUqhg8fjjp16sDW1hZBQUGIi4sz7K8J95ufn4+ZM2fC19cXNjY2aNiwIWbNmgW9Xm8oU13vMyYmBk8//TTq1q0LQRCwadMmo/2luS+tVovXX38dLi4usLOzw4ABA3DlypUqvAsqq99++w1NmzaFn58fvv32W6nDoWpo0KBBqF27Np5//nmpQ6FqKikpCd27d0dAQABatmyJn3/+ufwnE6lYa9euFa2srMRvvvlGPHHihDhlyhTRzs5OvHz5stShVUjv3r3FFStWiMeOHRMTEhLEfv36ifXr1xfv3LljKPPpp5+KDg4O4vr168WjR4+KQ4YMET09PcXMzEwJIy+/f/75R2zQoIHYsmVLccqUKYbtNek+b968Kfr4+IijR48WDx48KF68eFH866+/xHPnzhnK1IT7nT17tlinTh3xt99+Ey9evCj+/PPPor29vRgZGWkoU13vc+vWreKMGTPE9evXiwDEjRs3Gu0vzX1NmDBB9PLyEqOjo8XDhw+LPXr0EFu1aiXm5+dX8d1QaeTl5Yl+fn7ilStXxMzMTLFx48Zienq61GFRNfP333+Lv/76q/jcc89JHQpVU9euXRPj4+NFURTF69evi15eXkZ/F5YFE4sStGvXTpwwYYLRtmbNmonTpk2TKKLKkZqaKgIQd+3aJYqiKOr1etHDw0P89NNPDWVycnJEJycncfHixVKFWW5ZWVmin5+fGB0dLXbr1s2QWNS0+3z33XfFLl26lLi/ptxvv379xDFjxhhte/bZZ8Xhw4eLolhz7rNoYlGa+7p9+7ZoZWUlrl271lDm6tWrokKhEP/4448qi51Kb+/eveIzzzxjeD558mRx9erVEkZE1dWOHTuYWJDZtGjRQkxMTCzXsewKVYzc3FzExcUhLCzMaHtYWBj27dsnUVSVIyMjAwDg7OwMALh48SJSUlKM7l2j0aBbt27V8t7Dw8PRr18/9OzZ02h7TbvPX3/9FSEhIXjhhRfg5uaG1q1b45tvvjHsryn326VLF2zfvh1nzpwBABw5cgR79uxB3759AdSc+yyqNPcVFxeHvLw8ozJ169ZFYGBgtb53OXtc9zXg0V1qr127Bi8vL8PzevXq4erVq1UROslERd9DRIB530exsbHQ6/Xw9vYuVyxMLIqRlpYGnU4Hd3d3o+3u7u5ISUmRKCrzE0URERER6NKlCwIDAwHAcH814d7Xrl2Lw4cPY86cOSb7atJ9AsCFCxewaNEi+Pn5Ydu2bZgwYQImT56MVatWAag59/vuu+9i6NChaNasGaysrNC6dWtMnToVQ4cOBVBz7rOo0txXSkoK1Go1ateuXWIZMq+7d++iVatWWLBgQbH7161bh6lTp2LGjBmIj49HaGgo+vTpg8TERAAFn8FFCYJQqTGTvFT0PUQEmO99lJ6ejpEjR2Lp0qXljkVV7iMtQNEPeFEUa9SH/qRJk/Dvv/9iz549Jvuq+70nJSVhypQp+PPPP2FtbV1iuep+n4X0ej1CQkLwySefAABat26N48ePY9GiRRg5cqShXHW/33Xr1uGHH37A6tWr0bx5cyQkJGDq1KmoW7cuRo0aZShX3e+zJOW5r5py73LUp08f9OnTp8T98+bNw9ixYzFu3DgAQGRkJLZt24ZFixZhzpw58PLyMmqhuHLlCtq3b1/pcZN8VPQ9RASY532k1WoxaNAgTJ8+HZ06dSp3LGyxKIaLiwuUSqXJt3ypqakm3xhWV6+//jp+/fVX7NixA/Xq1TNs9/DwAIBqf+9xcXFITU1FcHAwVCoVVCoVdu3aha+++goqlcpwL9X9Pgt5enoiICDAaJu/v7/h24ia8nt9++23MW3aNLz44oto0aIFRowYgTfeeMPwwVhT7rOo0tyXh4cHcnNzcevWrRLLUNUpTZfadu3a4dixY7h69SqysrKwdetW9O7dW4pwSYYsqVs2VZ7SvI9EUcTo0aPxxBNPYMSIERW6HhOLYqjVagQHByM6Otpoe3R0dIWyODkQRRGTJk3Chg0b8Pfff8PX19dov6+vLzw8PIzuPTc3F7t27apW9/7kk0/i6NGjSEhIMDxCQkLw0ksvISEhAQ0bNqwR91moc+fOJtMGnzlzBj4+PgBqzu/13r17UCiMP7aUSqVhutmacp9Flea+goODYWVlZVQmOTkZx44dq9b3Xl2VpkutSqXCl19+iR49eqB169Z4++23UadOHSnCJRkqbbfs3r1744UXXsDWrVtRr149HDp0qKpDJRkrzfto7969WLduHTZt2oSgoCAEBQXh6NGj5boeu0KVICIiAiNGjEBISAg6duyIpUuXIjExERMmTJA6tAoJDw/H6tWr8b///Q8ODg6GN5WTkxNsbGwMaz188skn8PPzg5+fHz755BPY2tpi2LBhEkdfeg4ODoZxI4Xs7OxQp04dw/aacJ+F3njjDXTq1AmffPIJBg8ejH/++QdLly419JOsKb/Xp59+Gv/5z39Qv359NG/eHPHx8Zg3bx7GjBkDoHrf5507d3Du3DnD84sXLyIhIQHOzs6oX7/+Y+/LyckJY8eOxZtvvok6derA2dkZb731Flq0aGEyeQFVncd1XxswYAAGDBhQ1WFRNfK499C2bduqOiSqhh71PurSpYvRelAVUuE5qWqwqKgo0cfHR1Sr1WKbNm0MU7JWZwCKfaxYscJQRq/Xix988IHo4eEhajQasWvXruLRo0elC9pMHp5uVhRr3n1u3rxZDAwMFDUajdisWTNx6dKlRvtrwv1mZmaKU6ZMEevXry9aW1uLDRs2FGfMmCFqtVpDmep6nzt27Cj2/+aoUaNEUSzdfWVnZ4uTJk0SnZ2dRRsbG7F///7lnjKQygZFpgjWarWiUqkUN2zYYFRu8uTJYteuXas4OqoO+B4ic5D6fSTcD4KIiIjKSRAEbNy4Ec8884xhW/v27REcHIyFCxcatgUEBGDgwIEceEsm+B4ic5D6fcSuUEREROXwuO5rNbVLLZkP30NkDrJ6H5m9DYSIiMgCPK77mijWzC61ZD58D5E5yOl9xK5QRERERERUYZxuloiIiIiIKoyJBRERERERVRgTCyIiIiIiqjAmFkREREREVGFMLIhqqNzcXDRu3Bh79+4163l/++03tG7d2nyrdBIREVGNwMSCqoXRo0dDEASTx8PzNpOxpUuXwsfHB507dzZsEwQBmzZtMik7evRoo8V0HqV///4QBAGrV682U6RERERUEzCxoGrjqaeeQnJystHD19fXpFxubq4E0cnP119/jXHjxlXKuV9++WV8/fXXlXJuIiIiqp6YWFC1odFo4OHhYfRQKpXo3r07Jk2ahIiICLi4uKBXr14AgBMnTqBv376wt7eHu7s7RowYgbS0NMP57t69i5EjR8Le3h6enp748ssv0b17d0ydOtVQprhv+GvVqoWVK1canl+9ehVDhgxB7dq1UadOHQwcOBCXLl0y7C9sDfjiiy/g6emJOnXqIDw8HHl5eYYyWq0W77zzDry9vaHRaODn54dly5ZBFEU0btwYX3zxhVEMx44dg0KhwPnz54t9rQ4fPoxz586hX79+ZXyVgUuXLhXbOtS9e3dDmQEDBuCff/7BhQsXynx+IiIiqpmYWFCN8N1330GlUmHv3r1YsmQJkpOT0a1bNwQFBSE2NhZ//PEHrl+/jsGDBxuOefvtt7Fjxw5s3LgRf/75J3bu3Im4uLgyXffevXvo0aMH7O3tERMTgz179sDe3h5PPfWUUcvJjh07cP78eezYsQPfffcdVq5caZScjBw5EmvXrsVXX32FkydPYvHixbC3t4cgCBgzZgxWrFhhdN3ly5cjNDQUjRo1KjaumJgYNGnSBI6OjmW6HwDw9vY2ahWKj49HnTp10LVrV0MZHx8fuLm5Yffu3WU+PxEREdVQlbKeN5GZjRo1SlQqlaKdnZ3h8fzzz4uiKIrdunUTg4KCjMq///77YlhYmNG2pKQkEYB4+vRpMSsrS1Sr1eLatWsN+9PT00UbGxtxypQphm0AxI0bNxqdx8nJSVyxYoUoiqK4bNkysWnTpqJerzfs12q1oo2Njbht2zZD7D4+PmJ+fr6hzAsvvCAOGTJEFEVRPH36tAhAjI6OLvber127JiqVSvHgwYOiKIpibm6u6OrqKq5cubLE12vKlCniE088YbIdgGhtbW30OtrZ2YkqlUocOHCgSfns7Gyxffv2Yv/+/UWdTme0r3Xr1uKHH35YYgxERFT9aLVasVGjRuKePXvMet7NmzeLQUFBJnUJ1SxssaBqo0ePHkhISDA8vvrqK8O+kJAQo7JxcXHYsWMH7O3tDY9mzZoBAM6fP4/z588jNzcXHTt2NBzj7OyMpk2blimmuLg4nDt3Dg4ODobrODs7Iycnx6ibUvPmzaFUKg3PPT09kZqaCgBISEiAUqlEt27dir2Gp6cn+vXrh+XLlwMomJUpJycHL7zwQolxZWdnw9rauth98+fPN3odExISMGDAgGLLjh07FllZWVi9ejUUCuOPCxsbG9y7d6/EGIiIpMaJP8qOE39QRaikDoCotOzs7NC4ceMS9z1Mr9fj6aefxmeffWZS1tPTE2fPni3VNQVBgCiKRtseHhuh1+sRHByMH3/80eRYV1dXw7+trKxMzls4XauNjc1j4xg3bhxGjBiB+fPnY8WKFRgyZAhsbW1LLO/i4oKjR48Wu8/Dw8PkdXRwcMDt27eNts2ePRt//PEH/vnnHzg4OJic5+bNm0b3SEQkR0899ZRJd9LiPrtyc3OhVqurKizZ+vrrr/Hhhx9WyrkLJ/4YPnx4pZyfpMcWC6qR2rRpg+PHj6NBgwZo3Lix0aMwQbGyssKBAwcMx9y6dQtnzpwxOo+rqyuSk5MNz8+ePWv0LX2bNm1w9uxZuLm5mVzHycmpVLG2aNECer0eu3btKrFM3759YWdnh0WLFuH333/HmDFjHnnO1q1b49SpUyZJUWmtX78es2bNwk8//VTsOI7CFpnWrVuX6/xERFWFE388wIk/qLIxsaAaKTw8HDdv3sTQoUMNH2J//vknxowZA51OB3t7e4wdOxZvv/02tm/fjmPHjmH06NEm3X2eeOIJLFiwAIcPH0ZsbCwmTJhg1Prw0ksvwcXFBQMHDsTu3btx8eJF7Nq1C1OmTMGVK1dKFWuDBg0watQojBkzBps2bcLFixexc+dO/PTTT4YySqUSo0ePxvTp09G4cWOjLlzF6dGjB+7evYvjx4+X4VUrcOzYMYwcORLvvvsumjdvjpSUFKSkpODmzZuGMgcOHIBGo3lsHEREcsaJP4xx4g+qMInHeBCVyqhRo4odXCyKBYO3Hx5wXejMmTPioEGDxFq1aok2NjZis2bNxKlTpxoGWmdlZYnDhw8XbW1tRXd3d3Hu3Lkm57p69aoYFhYm2tnZiX5+fuLWrVuNBm+LoigmJyeLI0eOFF1cXESNRiM2bNhQHD9+vJiRkVFi7FOmTBG7detmeJ6dnS2+8cYboqenp6hWq8XGjRuLy5cvNzrm/PnzIgBx7ty5pXrNXnzxRXHatGlG21DMYPSiMa5YsUIEYPJ4ON5XXnlFfPXVV0sVBxGRVDjxByf+oKrFMRZULTz8DU1RO3fuLHa7n58fNmzYUOJx9vb2+P777/H9998btm3ZssWoTN26dbFt2zajbUXHInh4eOC7774r8TrFxR4ZGWn03NraGvPmzcO8efNKPE9ycjJUKhVGjhxZYpmHvffee+jZsyfee+89wxgJsYSuUQ/HOHr0aIwePbrE8964cQO//PILYmNjSxUHEZGUevTogUWLFhmePzwm71ETfxR1/vx5ZGdnm33ij4eVZuKPwvFzZZn4o127dmaZ+KNnz55G2959913odDqTsoUTf0RHR3PiDwvDxIJI5rRaLZKSkvD+++9j8ODBcHd3L9VxLVq0wNy5c3Hp0iW0aNHCbPFcvHgRCxcuLHbVcyIiueHEH5z4g6oOEwsimVuzZg3Gjh2LoKAgo9aV0hg1apTZ42nXrh3atWtn9vMSEUmtTZs2WL9+PRo0aACVyvRPpIcn/qhfvz6ABxN/PNxyUJqJP9atWwc3N7dyjWcAjCf+KNqSUKjoxB8xMTGPPGfr1q2xaNEiiKIIQRDKHFPhxB+///47J/6wUBy8TfSQnTt3mnRTktro0aOh0+kQFxcHLy8vqcMhIqqxOPEHJ/6gimFiQURERISCcXV79+6FTqdD7969ERgYiClTpsDJycmQPHz++efo2rUrBgwYgJ49e6JLly4IDg42Os+XX34Jb29vdO3aFcOGDcNbb71l1AXJ1tYWMTExqF+/Pp599ln4+/tjzJgxyM7OLlMLxqJFi/D8889j4sSJaNasGcaPH4+7d+8alRk7dixyc3MfO005ANSpUwfPPvtssV20Hic2Nhb37t3D7Nmz4enpaXg8++yzhjJr1qzBSy+99MjuWFS9CWJJozmJiIiI6LG6d++OoKAg2bV4A8DevXvRvXt3XLlypVRj9I4ePYqePXsWO7i8Im7cuIFmzZohNjaWY/RqMLZYEBEREdUwWq0W586dq9DEH+bEiT8sAwdvExEREdUwnPiDpMCuUEREREREVGHsCkVERERERBXGxIKIiIiIiCqMiQUREREREVUYEwsiIiIiIqowJhZERERERFRhTCyIiIiIiKjCmFgQEREREVGFMbEgIiIiIqIKY2JBREREREQV9v+uExPHiZKIvAAAAABJRU5ErkJggg==", "text/plain": [ "
" ] @@ -163,8 +163,8 @@ " 0\n", " 0\n", " 9.5\n", - " 1.436964\n", - " 0.417941\n", + " 1.437894\n", + " 0.415762\n", " \n", " \n", "\n", @@ -172,7 +172,7 @@ ], "text/plain": [ " ch_name cf bw pw\n", - "0 0 9.5 1.436964 0.417941" + "0 0 9.5 1.437894 0.415762" ] }, "execution_count": 4, @@ -220,8 +220,8 @@ " \n", " \n", " 0\n", - " -1.293737\n", - " 1.013686\n", + " -1.282284\n", + " 1.013071\n", " fixed\n", " 0\n", " \n", @@ -231,7 +231,7 @@ ], "text/plain": [ " Offset Exponent fit_type ch_name\n", - "0 -1.293737 1.013686 fixed 0" + "0 -1.282284 1.013071 fixed 0" ] }, "execution_count": 7, @@ -241,8 +241,8 @@ ], "source": [ "# %% get aperiodic stuff\n", - "slope_fit = irasa_out.get_slopes()\n", - "slope_fit.aperiodic_params" + "aperiodic_fit = irasa_out.fit_aperiodic_model()\n", + "aperiodic_fit.aperiodic_params" ] }, { @@ -282,10 +282,10 @@ " \n", " \n", " 0\n", - " 0.000278\n", - " 0.998247\n", - " -1618.772746\n", - " -1625.359356\n", + " 0.000255\n", + " 0.998391\n", + " -1636.120554\n", + " -1642.707163\n", " fixed\n", " 0\n", " \n", @@ -295,7 +295,7 @@ ], "text/plain": [ " mse r_squared BIC AIC fit_type ch_name\n", - "0 0.000278 0.998247 -1618.772746 -1625.359356 fixed 0" + "0 0.000255 0.998391 -1636.120554 -1642.707163 fixed 0" ] }, "execution_count": 8, @@ -304,7 +304,7 @@ } ], "source": [ - "slope_fit.gof" + "aperiodic_fit.gof" ] }, { @@ -333,7 +333,7 @@ }, { "data": { - "image/png": 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", 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", 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", 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", 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", 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", 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" ] @@ -483,24 +483,24 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 12, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", "text/plain": [ - "
" + "
" ] }, "metadata": {}, @@ -512,7 +512,7 @@ " gmean(psd_up, psd_dw),\n", " gmean(psd_up_19, psd_dw_19)], axis=0)\n", "\n", - "f, ax = plt.subplots(figsize=(8,6))\n", + "f, ax = plt.subplots(figsize=(4,4))\n", "f_max = freq < 100\n", "ax.loglog(freq[f_max], psd[f_max], label='original')\n", "ax.loglog(freq[f_max], aperiodic_spectrum[f_max], label='aperiodic spectrum')\n", @@ -531,22 +531,22 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 13, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -576,12 +576,12 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -630,12 +630,12 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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" ] @@ -661,7 +661,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 18, "metadata": {}, "outputs": [ { @@ -696,8 +696,8 @@ " 0\n", " 0\n", " 10.0\n", - " 1.436404\n", - " 0.423565\n", + " 1.436581\n", + " 0.413776\n", " \n", " \n", "\n", @@ -705,10 +705,10 @@ ], "text/plain": [ " ch_name cf bw pw\n", - "0 0 10.0 1.436404 0.423565" + "0 0 10.0 1.436581 0.413776" ] }, - "execution_count": 20, + "execution_count": 18, "metadata": {}, "output_type": "execute_result" } @@ -725,8 +725,8 @@ "outputs": [], "source": [ "# %% get aperiodic stuff\n", - "slopes_f = irasa_out.get_slopes(fit_func='fixed')\n", - "slopes_k = irasa_out.get_slopes(fit_func='knee')" + "ap_f = irasa_out.fit_aperiodic_model(fit_func='fixed')\n", + "ap_k = irasa_out.fit_aperiodic_model(fit_func='knee')" ] }, { @@ -766,19 +766,19 @@ " \n", " \n", " 0\n", - " 0.019573\n", - " 0.931418\n", - " -772.202215\n", - " -778.788824\n", + " 0.019216\n", + " 0.932464\n", + " -775.861804\n", + " -782.448413\n", " fixed\n", " 0\n", " \n", " \n", " 0\n", - " 0.000028\n", - " 0.999901\n", - " -2062.763593\n", - " -2075.936813\n", + " 0.000027\n", + " 0.999905\n", + " -2072.892462\n", + " -2086.065681\n", " knee\n", " 0\n", " \n", @@ -788,8 +788,8 @@ ], "text/plain": [ " mse r_squared BIC AIC fit_type ch_name\n", - "0 0.019573 0.931418 -772.202215 -778.788824 fixed 0\n", - "0 0.000028 0.999901 -2062.763593 -2075.936813 knee 0" + "0 0.019216 0.932464 -775.861804 -782.448413 fixed 0\n", + "0 0.000027 0.999905 -2072.892462 -2086.065681 knee 0" ] }, "execution_count": 22, @@ -798,7 +798,7 @@ } ], "source": [ - "pd.concat([slopes_f.gof, slopes_k.gof])" + "pd.concat([ap_f.gof, ap_k.gof])" ] }, { diff --git a/examples/custom_fit_functions.ipynb b/examples/custom_fit_functions.ipynb index ed78e5b..b2b2b56 100644 --- a/examples/custom_fit_functions.ipynb +++ b/examples/custom_fit_functions.ipynb @@ -18,12 +18,12 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ "import scipy.signal as dsp\n", - "from pyrasa.utils.aperiodic_utils import compute_slope\n", + "from pyrasa.utils.aperiodic_utils import compute_aperiodic_model\n", "from pyrasa.utils.fit_funcs import AbstractFitFun\n", "from pyrasa import irasa\n", "import numpy as np\n", @@ -51,7 +51,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -70,12 +70,12 @@ " return y_hat\n", " \n", "\n", - "slope_fit = compute_slope(psd, freqs, fit_func=CustomFitFun)\n" + "slope_fit = compute_aperiodic_model(psd, freqs, fit_func=CustomFitFun)\n" ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 4, "metadata": {}, "outputs": [ { @@ -108,8 +108,8 @@ " \n", " \n", " 0\n", - " -1.052551\n", - " 1.510974\n", + " -1.111201\n", + " 1.510531\n", " custom\n", " 0\n", " \n", @@ -119,10 +119,10 @@ ], "text/plain": [ " a b fit_type ch_name\n", - "0 -1.052551 1.510974 custom 0" + "0 -1.111201 1.510531 custom 0" ] }, - "execution_count": 3, + "execution_count": 4, "metadata": {}, "output_type": "execute_result" } @@ -133,16 +133,16 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ - "slope_fit_2 = irasa(sig, fs=fs, band=f_range, psd_kwargs={'nperseg': 4 * fs}).get_slopes(fit_func=CustomFitFun)" + "slope_fit_2 = irasa(sig, fs=fs, band=f_range, psd_kwargs={'nperseg': 4 * fs}).fit_aperiodic_model(fit_func=CustomFitFun)" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -175,8 +175,8 @@ " \n", " \n", " 0\n", - " -1.067699\n", - " 1.500347\n", + " -1.125121\n", + " 1.500167\n", " custom\n", " 0\n", " \n", @@ -186,10 +186,10 @@ ], "text/plain": [ " a b fit_type ch_name\n", - "0 -1.067699 1.500347 custom 0" + "0 -1.125121 1.500167 custom 0" ] }, - "execution_count": 5, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -200,7 +200,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -235,19 +235,19 @@ " \n", " \n", " 0\n", - " 0.006699\n", - " 0.980793\n", - " -2965.641163\n", - " -2974.418286\n", + " 0.007469\n", + " 0.978620\n", + " -2900.899570\n", + " -2909.676693\n", " custom\n", " 0\n", " \n", " \n", " 0\n", - " 0.000291\n", - " 0.999139\n", - " -4832.361294\n", - " -4841.138417\n", + " 0.000350\n", + " 0.998963\n", + " -4721.685378\n", + " -4730.462501\n", " custom\n", " 0\n", " \n", @@ -257,11 +257,11 @@ ], "text/plain": [ " mse r_squared BIC AIC fit_type ch_name\n", - "0 0.006699 0.980793 -2965.641163 -2974.418286 custom 0\n", - "0 0.000291 0.999139 -4832.361294 -4841.138417 custom 0" + "0 0.007469 0.978620 -2900.899570 -2909.676693 custom 0\n", + "0 0.000350 0.998963 -4721.685378 -4730.462501 custom 0" ] }, - "execution_count": 6, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } diff --git a/examples/irasa_mne.ipynb b/examples/irasa_mne.ipynb index 5f51fd0..3058911 100644 --- a/examples/irasa_mne.ipynb +++ b/examples/irasa_mne.ipynb @@ -240,11 +240,11 @@ "name": "stderr", "output_type": "stream", "text": [ - "/var/folders/6x/k2wvgw51691cj5qd77pzcrfw0000gn/T/ipykernel_29797/1044382114.py:4: FutureWarning: The value of `amplitude='auto'` will be removed in MNE 1.8.0, and the new default will be `amplitude=False`.\n", + "/var/folders/6x/k2wvgw51691cj5qd77pzcrfw0000gn/T/ipykernel_62431/1044382114.py:4: FutureWarning: The value of `amplitude='auto'` will be removed in MNE 1.8.0, and the new default will be `amplitude=False`.\n", " raw.compute_psd(method='welch', n_per_seg=nperseg, n_overlap=nperseg//2, fmin=0.25, fmax=50).plot(axes=axes[0])\n", "/Users/fabian.schmidt/git/pyrasa/.pixi/envs/default/lib/python3.12/site-packages/mne/viz/utils.py:167: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown\n", " (fig or plt).show(**kwargs)\n", - "/var/folders/6x/k2wvgw51691cj5qd77pzcrfw0000gn/T/ipykernel_29797/1044382114.py:5: FutureWarning: The value of `amplitude='auto'` will be removed in MNE 1.8.0, and the new default will be `amplitude=False`.\n", + "/var/folders/6x/k2wvgw51691cj5qd77pzcrfw0000gn/T/ipykernel_62431/1044382114.py:5: FutureWarning: The value of `amplitude='auto'` will be removed in MNE 1.8.0, and the new default will be `amplitude=False`.\n", " irasa_results.aperiodic.plot(axes=axes[1])\n" ] }, @@ -262,7 +262,7 @@ "/Users/fabian.schmidt/git/pyrasa/pyrasa/irasa_mne/mne_objs.py:61: UserWarning: Zero value in PSD for channels MEG 0111, MEG 0121, MEG 0131, MEG 0141, MEG 0211, MEG 0221, MEG 0231, MEG 0241, MEG 0311, MEG 0321, MEG 0331, MEG 0341, MEG 0411, MEG 0421, MEG 0431, MEG 0441, MEG 0511, MEG 0521, MEG 0531, MEG 0541, MEG 0611, MEG 0621, MEG 0631, MEG 0641, MEG 0711, MEG 0721, MEG 0731, MEG 0741, MEG 0811, MEG 0821, MEG 0911, MEG 0921, MEG 0931, MEG 0941, MEG 1011, MEG 1021, MEG 1031, MEG 1041, MEG 1111, MEG 1121, MEG 1131, MEG 1141, MEG 1211, MEG 1221, MEG 1231, MEG 1241, MEG 1311, MEG 1321, MEG 1331, MEG 1341, MEG 1411, MEG 1421, MEG 1431, MEG 1441, MEG 1511, MEG 1521, MEG 1531, MEG 1541, MEG 1611, MEG 1621, MEG 1631, MEG 1641, MEG 1711, MEG 1721, MEG 1731, MEG 1741, MEG 1811, MEG 1821, MEG 1831, MEG 1841, MEG 1911, MEG 1921, MEG 1931, MEG 1941, MEG 2011, MEG 2021, MEG 2031, MEG 2041, MEG 2111, MEG 2121, MEG 2131, MEG 2141, MEG 2211, MEG 2221, MEG 2231, MEG 2241, MEG 2311, MEG 2321, MEG 2331, MEG 2341, MEG 2411, MEG 2421, MEG 2431, MEG 2441, MEG 2511, MEG 2521, MEG 2531, MEG 2541, MEG 2611, MEG 2621, MEG 2631, MEG 2641.\n", "These channels might be dead.\n", " super().plot(\n", - "/var/folders/6x/k2wvgw51691cj5qd77pzcrfw0000gn/T/ipykernel_29797/1044382114.py:12: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n", + "/var/folders/6x/k2wvgw51691cj5qd77pzcrfw0000gn/T/ipykernel_62431/1044382114.py:12: UserWarning: This figure includes Axes that are not compatible with tight_layout, so results might be incorrect.\n", " f.tight_layout()\n" ] }, @@ -335,8 +335,8 @@ } ], "source": [ - "fixed_model = irasa_results.aperiodic.get_slopes(fit_func='fixed', scale=True, fit_bounds=[.5, 45])\n", - "knee_model = irasa_results.aperiodic.get_slopes(fit_func='knee', scale=True, fit_bounds=[.5, 45]);\n", + "fixed_model = irasa_results.aperiodic.fit_aperiodic_model(fit_func='fixed', scale=True, fit_bounds=[.5, 45])\n", + "knee_model = irasa_results.aperiodic.fit_aperiodic_model(fit_func='knee', scale=True, fit_bounds=[.5, 45]);\n", "\n", "\n", "f, ax = plt.subplots(ncols=2, figsize=(8,4))\n", @@ -348,20 +348,122 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 14, "metadata": {}, "outputs": [ { "data": { + "text/html": [ + "
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Measurement dateDecember 03, 2002 19:01:10 GMT
ExperimenterMEG
ParticipantUnknown
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Digitized points146 points
Good channels102 Magnetometers
Bad channelsNone
EOG channelsNot available
ECG channelsNot available
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" + ], "text/plain": [ - "(array([ 6., 8., 11., 17., 17., 16., 9., 12., 4., 2.]),\n", - " array([ 8.14227952, 9.53481945, 10.92735938, 12.31989931, 13.71243924,\n", - " 15.10497917, 16.4975191 , 17.89005903, 19.28259896, 20.67513888,\n", - " 22.06767881]),\n", - " )" + " head transform\n", + " dig: 146 items (3 Cardinal, 4 HPI, 61 EEG, 78 Extra)\n", + " events: 1 item (list)\n", + " experimenter: MEG\n", + " file_id: 4 items (dict)\n", + " highpass: 0.1 Hz\n", + " hpi_meas: 1 item (list)\n", + " hpi_results: 1 item (list)\n", + " lowpass: 172.2 Hz\n", + " meas_date: 2002-12-03 19:01:10 UTC\n", + " meas_id: 4 items (dict)\n", + " nchan: 102\n", + " proj_id: 1 item (ndarray)\n", + " proj_name: test\n", + " projs: PCA-v1: off, PCA-v2: off, PCA-v3: off\n", + " sfreq: 600.6 Hz\n", + ">" ] }, - "execution_count": 6, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" }, @@ -397,7 +499,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -422,7 +524,6 @@ " event_id,\n", " tmin,\n", " tmax,\n", - " #picks=picks,\n", " baseline=None,\n", " preload=True,\n", " verbose=False,\n", @@ -457,7 +558,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 16, "metadata": {}, "outputs": [ { @@ -5688,7 +5789,7 @@ } ], "source": [ - "knee_epoched = irasa_epoched.aperiodic.get_slopes(fit_func='knee', scale=True, fit_bounds=[1, 45])" + "knee_epoched = irasa_epoched.aperiodic.fit_aperiodic_model(fit_func='knee', scale=True, fit_bounds=[1, 45])" ] }, { @@ -5708,7 +5809,7 @@ }, { "data": { - "image/png": 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", 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", 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", 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zW6mdkVuA+b9flLEiIiJSAgYgkpVKpcLcQW1gqF88IXr3mZs4du1fGasiIqL6jgGIZNe8kTkmPd1Uq2/2zvPIK+CEaCIiqhkMQKQT3urVHE5WJlI78nYmVh29LmNFRERUnzEAkU4wNTLAbD/tCdH/O3gNN5KzZKqIiKjuEUIgr8TlRNKy83EqNhlC8J6LD2MAIp3R19MOz7o3lNrZ+YWYu/uCjBUREdUdV26lY9B3x3A7vfjWQum5BRjyfRAGfXcMV26ly1id7mEAIp2hUqngP7A1jAyKfywPXLiFQ5eSZKyKiEj3XbmVjqHLg3D2RmqZz5+9kYqhy4MYgkpgACKd4mprhik9m2n1zdkVgZz8QpkqIiLSbUIIvL/lDNJyCipcLi2nAB9sOcPhsPsYgEjnTO7RDK62plI79m4Wlh+OlLEiIiLddToupdwjPw87cyMV4XEpNVtQHcEARDrH2FAf/gNba/UtPxKJmDuZMlVERKS7Ai/cqtLyB6q4fH3FAEQ66Vn3Rni+tb3UzisowpxdETx0S0T0kNTs/Bpdvr5iACKdNdvPEyaG+lL78OXb2B/Bby5ERCVpTAxrdPn6igGIdJajlQmmPtdCq2/u7ghk5VU80Y+ISEnaOFpWafm+nnY1VEndwgBEAAAHjTGcrEzgZGUCB42x3OVIJnRvguaNzKX2zdQcfHvwmowVERHpjoibqZi983yll/dy1qCdi1XNFVSHqAQnVZSSlpYGjUaD1NRUWFpWLVlT9QuK/BejfjwhtQ30VNg37Wk0b2QhY1VERPI6GXUXEwJCkJ5buaPilsYG2PqGL1ra1d+/nVX5/OYRINJ5vs0aYKCXo9QuKBKYtYMToolIuf66dAuvrj6hFX4M9FValxApyctZU+/DT1UxAFGdMPOFVjBXG0jt4Ot3sOvMTRkrIiKSx47T8XhtXRhyC4rv+WWuNsCGCV1w+P2eaGihlvot1AbYPsUXO97sxvDzEAYgqhMaWRrjvT4ttfo++/0i0nN4OicRKcdPQdGYtjkcBUXFR8BtzYzwy2td0bWpLVQqFYz0iz/aLU0M4d3YGiqVSo5ydRoDENUZo31c0cqheEw3KT0X3wRelbEiIqLaIYTA0j+vYs6uCK1+JysT/DrZB22cNDJVVncxAFGdYaCvh/mDta8Q/VNwNC7cTJOpIiKimldUJPDp7gv45s8rWv3NGpphy2QfNGtoXs6aVBEGIKpTOrja4KUOzlK7sEhg1s7zKCrihGgiqn/yC4vw3y1nEBAUrdXf1lmDLZN94WhlIk9h9QADENU50/t7aF3JNCwmGVtP3ZCxIiKi6peTX4g3NoRh++l4rX6fprbYNKkrbMyMZKqsfmAAojrH1lyND/q5a/V9/sclpGTlyVQREVH1SsvJx+g1J/HnxSSt/r6edlg7rpPWWbH0eGQNQH///Tf8/Pzg6OgIlUqFHTt2aD0vhIC/vz8cHR1hYmKCnj17IiIiouyNlbBt2zZ4enpCrVbD09MT27dvr6E9ILmM7NwYbZ2LJ/3dzczDF/svy1gREVH1+DcjFyN/OI6TUXe1+l/q4IzvX24P4xL3SKTHJ2sAyszMhJeXF5YtW1bm84sXL8bXX3+NZcuWISQkBPb29ujTpw/S09PL3WZwcDCGDx+OV199FWfOnMGrr76KYcOG4cSJE+WuQ3WPvp4K8we3QckzOzedjMWZuBTZaiIielI3krPw0opgRDx0csekp5tg8dC2MNDnwE110ZlbYahUKmzfvh2DBw8GcO/oj6OjI6ZNm4aPPvoIAJCbmws7OzssWrQIr7/+epnbGT58ONLS0vDHH39Ifc8//zysra3x888/V6oW3gqj7pi54xw2HI+V2m2dNdg+pRv09XjNCyKqW67eSserq08iMS1Hq/+Dfu6Y0rNZpa/l0+3zvxCfkg3g3mnyx6b3qvZadVW9uBVGVFQUEhMT0bdvX6lPrVajR48eCAoKKne94OBgrXUAoF+/fhWuQ3XXB309YFtiIuDZG6n4+WRsBWsQEeme8LgUDFsZrBV+VCrgs/+0wZvPNueFDGuAzgagxMREAICdnZ1Wv52dnfRceetVdZ3c3FykpaVpPahu0JgaYnp/D62+L/Zfxr8ZuTJVRERUNceu/YtRPx5Hclbxle0N9VX4doQ3Xu7iKmNl9ZvOBqAHHk69QohHJuGqrrNw4UJoNBrp4eLi8vgFU617sb0zOrpaS+3U7Hx8/sclGSsiIqqcfecTMW5tCLLyCqU+E0N9rBrTCX4lbgJN1U9nA5C9vT0AlDpyk5SUVOoIz8PrVXWdGTNmIDU1VXrExcU9QeVU2/T0VJg3uI3WvJ+tYTcQGn23grWIiOT1a0gcpmwMQ15h8U1NLY0NsGFiZ/Ro2VDGypRBZwNQkyZNYG9vj8DAQKkvLy8PR44cga+vb7nr+fj4aK0DAAcOHKhwHbVaDUtLS60H1S2tHCwxxsdNq2/mjvMoKPGHhYhIV/zwdyQ+3HYWJS9i39BCjV8n+6CDq418hSmIrFdSysjIwLVr16R2VFQUwsPDYWNjg8aNG2PatGlYsGABWrRogRYtWmDBggUwNTXFqFGjpHVGjx4NJycnLFy4EADwzjvv4JlnnsGiRYswaNAg7Ny5E3/++SeOHj1a6/tHtevdPi2w5+xNJKXfm/9zKTEdPwXHYEL3JjJXRkR0jxACX+y/jO8PR2r1N7YxxYYJXdDY1lSmypRH1iNAoaGh8Pb2hre3NwDgvffeg7e3N2bPng0A+PDDDzFt2jRMmTIFHTt2RHx8PA4cOAALCwtpG7GxsUhISJDavr6++OWXX7B27Vq0bdsWAQEB2Lx5M7p06VK7O0e1zsLYEDMHeGr1fRN4BbceOqWUiEgOhUUCH28/Xyr8eNhbYOtkH4afWqYz1wHSJbwOUN0lhMDLq04gKPKO1DfQyxHfjvSWsSoiUrq8giK8uzkcv59L0Opv39gKa8d2hsbUsJw1q47XAarj1wEiehwqlQpzB7WBoX7xhOhdZ24i6Nq/MlZFREqWmVuACT+FlAo/PVo2xIaJXao1/FDlMQBRvdO8kTkmPt1Uq2/WzvPIK+CEaCKqXSlZeXhl9Qn8c1X7S9iAtg74cXRHmBrxpqZyYQCieuntXs3hZGUitSNvZ2LV0esyVkRESnMrLQfDVx7H6dgUrf6XuzTG0hHeMDLgR7Cc+L9P9ZKpkQFmPTQh+n8Hr0nj4kRENSn630wMXRGEy7e0b9791rPNMf+h65aRPCodgNq3b4/k5GQAwNy5c5GVlVVjRRFVh36t7dDTvfhiYtn5hZi7O0LGiohICS4mpGHoimDE3dX+wjXzhVZ4v5877+ulIyodgC5evIjMzEwAwKeffoqMjIwaK4qoOqhUKnw6sLXWYeb9Ebdw6HKSjFURUX0WGn0Xw1YGa92PUF9PhS+Gti01N5HkVenZV+3atcO4cePQvXt3CCHw5ZdfwtzcvMxlH1zHh0hurrZmmNKzGZb8eVXq898VAZ9ptjA21JexMiKqbw5dTsIbG8KQk198woWRgR6WjfRG39b2MlZGZal0AAoICMCcOXOwZ88eqFQq/PHHHzAwKL26SqViACKdMrlHM2w/HY+YO/eGbWPuZGHFkUhM691S5sqIqL7YGR6P//56BgUl7m1hZqSPH8d0hG+zBjJWRuWpdAByd3fHL7/8AgDQ09PDwYMH0ahRoxorjKi6GBvqw39ga4xbGyL1fX84Ev/xdoKrrZmMlRFRfbD+eAxm7zyPkpcVtjEzQsC4TmjrbCVbXVSxxzoLbP78+dizZ0+p/jVr1mDRokVPXBRRdXvWvRH6tbaT2nkFRZizKwK8EDoRPS4hBP538Cpm7dAOPw4aY/z6ug/Dj457rAD0ww8/wMPDo1R/69atsWLFiicuiqgmzPZrDZMS834OX76N/RG3ZKyIiOqqoiKBeXsu4qvAK1r9TRuaYesbvmjeqOw5sqQ7HisAJSYmwsHBoVR/w4YNtW5MSqRLnKxM8PZzzbX65u6OQFZegUwVEVFdVFBYhA+2nsWaY1Fa/W2cLLHldR+ti7CS7nqsAOTi4oJjx46V6j927BgcHR2fuCiimjKxe1M0a1g87+dmag7+99c1GSsiorokJ78QkzecwrZTN7T6uzSxwc+TusLWXC1TZVRVjxWAJk6ciGnTpmHt2rWIiYlBTEwM1qxZg3fffReTJk2q7hqJqo2RgR7mDWqj1bfqn+u4lsTrWhFRxdJz8jF27Un8eVF76Lx3Kzv8NL4zLIx5U9O65LHuwvbhhx/i7t27mDJlCvLy8gAAxsbG+OijjzBjxoxqLZCouvk2b4CBXo7YdeYmACC/UGD2zvPYOLELr9BKRGW6k5GLsWtDcC4+Vav/xfbOWPTiUzDQ552l6prHesdUKhUWLVqE27dv4/jx4zhz5gzu3r3L6/9QnTHzhVYwVxfn/6DIO9h9lvPXiKi0+JRsvLQyuFT4Gd+tCb4Y2pbhp456onfN3NwcnTp1Qps2baBWc9yT6o5GlsZ4t4/2hRDn77mA9Jx8mSoiIl10LSkDQ5cH4frtTK3+//ZpiVkDWkGPNzWtsxhbSbHG+LjCw95Caiel5+KbwKsVrEFESnL2RgqGrQxGQmqO1KdSAfMGt8Hbz7XgkHkdxwBEimWgr4f5g7UnRP8UHI2LCWkyVUREuiIo8l+M/OE47mbmSX0GeiosGd4Or3Z1lbEyqi4MQKRoHd1sMLSDs9QuLBKYteM8iop4hWgipdofkYixa0OQmVco9Rkb6uHHMR0xqJ2TjJVRdWIAIsWb3t8DlsbFE6JDY5JLXeODiJRhS2gc3tgQhryC4ju6WxgbYMOELnjWnfe/rE8YgEjxGpir8eHz2rd2+fyPS0jN4oRoIiVZ9c91fLD1LEoeAG5grsavr/ugo5uNfIVRjWAAIgIwsnNjtHXWSO07mXn44sAlGSsiotoihMCX+y9j/u8XtfpdbEyw7Q0ftHKwlKkyqkkMQEQA9PVUmD+4DUqe1LHxRCzO3kiRrSYiqnmFRQIzd5zHskPat8Rxt7PA1sm+cLU1K2dNqusYgIjua+tshVGdG0ttIYCZO86jkBOiieqlvIIivPPLaWw8EavV793YCptf7wo7S2OZKqPawABEVMIH/dxhY2Yktc/eSMXPJ2MrWIOI6qKsvAJMWheKPQ9dAf7pFg2wcWIXWJkalbMm1RcMQEQlWJkaYXp/7QnRX+y/jDsZuTJVRETVLTUrH6+uPokjV25r9b/wlANWjekIU6PHuk0m1TEMQEQPGdreGR1craV2anY+Pv+DE6KJ6oOktBwM/yEYYTHJWv0jOzfGtyO9oTbQl6kyqm0MQEQP0dNTYd6gNih5i58tYTcQGn1XvqKI6InF3snC0BXBuJSYrtU/pWczLPhPG+jzvl6KwgBEVAZPR0uM9W2i1Tdzx3kUFBaVswYR6bJLiWkYuiIIsXeztPo//j8PfPi8B+/rpUAMQETleLdPCzSyUEvtS4npWBccI2NFRPQ4wmKSMWxFMJLSi+fy6amAxS+2xWvPNJOxMpITAxBROSyMDfHJC620+r4OvIJbaTnlrEFEuubIldt4ZdUJpOUUSH1G+nr4/uUOGNbJRcbKSG4MQEQVGOjlCJ+mtlI7I7cAnz10tVgi0k27z9zExJ9CkJ1ffFNTMyN9rB3XCc+3sZexsprloDGGk5UJnKxM4KDhtYzKoxJC8CpvD0lLS4NGo0FqaiosLXkJdKW7lpSO/kv/QX5h8a/Kpold4Nu8gYxVEVFFNp6Iwcwd51HyE87a1BAB4zrDy8VKtrqoZlXl85tHgIgeoXkjC0zo3lSrb9bO81p3iybSZUOXB6Hb53+h2+d/YejyILnLqVFCCHx36Bo+2a4dfuwtjbFlsg/DD0kYgIgqYepzzeFY4lBy5O1MrD4aJWNFRJWXkJqD+JRsxKdkIyG1/s5hE0Jgwd6L+GL/Za3+Jg3MsPUNHzRvZCFTZaSLGICIKsHUyACz/Vpr9X178CriU7JlqoiISiooLMKHW8/ix3+0v5h4Olhiy2QfOFubylQZ6SoGIKJK6tfaDj3dG0rt7PxCzNt9QcaKiAgAcvILMWXjKWwJu6HV37mJDX55vSsamKvLWZOUjAGIqJJUKhU+HdgaRgbFvzb7IhJx6HKSjFURKVtGbgHGB4TgwIVbWv3PeTTCuvGdYWlsKFNlpOsYgIiqwNXWDG/00L5wmv+uCOSUOM2WiGrH3cw8jPrxOIIi72j1/8fbCSte7QBjQ97Xi8rHAERURW/0bIbGNsXzCWLuZGHFkUgZKyJSnpsp2XhpRRDO3kjV6h/r64avXvKCoT4/3qhi/AkhqiJjQ318OlB7QvT3hyMRcydTpoqIlCXydgZeWhGMyNvav3Pv9m6JOX6e0ONNTakSGICIHsOzHo3Q19NOaucVFMF/VwR4XVGimnU+PhXDVgSXOgPz04Gt8U7vFrypKVUaAxDRY5rt5wljw+JfoUOXb5eaiElE1ef49TsY8cNx3MnMk/oM9FRYOqIdxvi6yVcY1UkMQESPydnaFFOfa6HVN3f3BWTlFZSzBhE9rsALtzB6zUlk5Bb/fqkN9PDD6A4Y1M5JxsqormIAInoCE7s3RbOGZlI7PiUby/66JmNFRPXPb6duYPKGMK3bz1gYG2DDxC7o5WFXwZpE5WMAInoCRgZ6mDeojVbfj/9cx7WkDJkqIqpf1hyNwnu/nkFhUfH8ugbmRvjlta7o5GYjY2VU1zEAET0h3+YN4OflKLXzCwVm7zzPCdFET0AIga8PXMbcPdpXW3eyMsGWyb5o7aiRqTKqLxiAiKrBzBdawVxtILWDIu9g99kEGSsiqruKigTm7IrAtw8NJ7doZI5tb/iiSQOzctYkqjwGIKJqYGdpjGm9tSdEz99zAek5+TJVRFQ35RcWYdrmcKwLjtHq93Kxwq+v+8BeYyxTZVTfMAARVZOxvm7wsLeQ2knpuVjy51UZKyKqW7LzCvHaulDsOnNTq7978wbYNLELrM2MZKqM6iMGIKJqYqCvh3mDtSdEBwRF41JimkwVEdUdqdn5GL3mBA5dvq3V37+NPVaP7QizEkPMRNWBAYioGnVys8HQDs5Su7BIYNYOTogmqkhSeg5G/HAcIdHJWv0jOrlg2aj2UBvwpqZU/RiAiKrZ9P4esDQu/rYaEp2MbafiZayISHfF3c3CSyuCcTFB+0jp5B7NsHDIU9Dnfb2ohuh8AEpPT8e0adPg6uoKExMT+Pr6IiQkpMJ1vvvuO7Rq1QomJiZwd3fHunXraqlaIqCBuRofPO+h1bdw70WkZnFCNFFJlxPTMXRFEGLuZGn1T+/vgen9PXhfL6pROh+AJk6ciMDAQKxfvx7nzp1D37590bt3b8THl/2Nevny5ZgxYwb8/f0RERGBTz/9FG+++SZ2795dy5WTko3q3BhtnYuvU3InMw9fHLgkY0VEuuVUbDKGrQzGrbRcqU9PBXw+5ClM7tFMxspIKXQ6AGVnZ2Pbtm1YvHgxnnnmGTRv3hz+/v5o0qQJli9fXuY669evx+uvv47hw4ejadOmGDFiBCZMmIBFixbVcvWkZPp6Kswb1AYlv8BuPBGLszdSZKuJSFf8c/U2Xll1AqnZxUdFjfT18N2o9hjRubGMlZGS6HQAKigoQGFhIYyNta/7YGJigqNHj5a5Tm5ubpnLnzx5Evn5HIKg2uPlYoWRJf6YCwHM2nFe65L+REqz91wCxgeEICuvUOozNdLHmrGd0P8pBxkrI6XR6QBkYWEBHx8fzJs3Dzdv3kRhYSE2bNiAEydOICGh7Kvs9uvXD6tWrUJYWBiEEAgNDcWaNWuQn5+Pf//9t8x1cnNzkZaWpvUgqg4f9nOHTYlrl5y5kYpfQmJlrOieocuD0O3zv9Dt878wdHmQ3OWQQvx8MhZvbTqF/MLiLwFWpobYOLELurdoIGNlpEQ6HYCAe0NaQgg4OTlBrVbj22+/xahRo6CvX/ZpkbNmzUL//v3RtWtXGBoaYtCgQRg7diwAlLvOwoULodFopIeLi0tN7Q4pjJWpEab3154QvXjfZdzJyC1njdqRkJqD+JRsxKdkIyE1R9ZaSBmWH47EjN/OoeQBUDtLNX593Qfeja3lK4wUS+cDULNmzXDkyBFkZGQgLi5OGspq0qRJmcubmJhgzZo1yMrKQnR0NGJjY+Hm5gYLCws0aFD2N4wZM2YgNTVVesTFxdXkLpHCDG3vjA6uxX/gU7PzsWgfJ0STMgghsHDvxVI/8262ptg62Rct7SzKWZOoZul8AHrAzMwMDg4OSE5Oxv79+zFo0KAKlzc0NISzszP09fXxyy+/YMCAAdDTK3t31Wo1LC0ttR5E1UXv/oTokpcz+TX0BsJi7spXFFEtKCwSmL7tHFb+fV2r39PBElsm+8LFxlSmyojqQADav38/9u3bh6ioKAQGBuLZZ5+Fu7s7xo0bB+De0ZvRo0dLy1+5cgUbNmzA1atXcfLkSYwYMQLnz5/HggUL5NoFIng6WmKMr5tW3yfbz6OgsEiegohqWG5BId7ceAqbQ7WPqHdys8bPr3VFQwu1TJUR3aPzASg1NRVvvvkmPDw8MHr0aHTv3h0HDhyAoaEhACAhIQGxscWTSgsLC/HVV1/By8sLffr0QU5ODoKCguDm5ibTHhDd826fllp/9C8lppe64zVRfZCRW4DxASHYF5Go1d/LoxHWje8CjYmhTJURFdP5u8sNGzYMw4YNK/f5gIAArXarVq1w+vTpGq6KqOosjQ0x84VWeOeXcKnv68ArGNDWAY0sjctfkagOSc7Mw9iAEJyJS9HqH9TOEV++5AVDfZ3/3k0KwZ9Eolo00MsRPk1tpXZGbgE+23tRxoqIqk9iag6GrQwuFX7G+Ljim2HtGH5Ip/CnkagWqVQqzBvcGgYlZkTvDL+JoMiyr1FFVFdE/ZuJF5cH4WpShlb/O8+1gP/A1tDjTU1JxzAAEdWy5o0sMPHpplp9s3dGIK+AE6Kpbjofn4qXVgQhPiVbq3+Onyfe7dOSNzUlncQARCSDqc81h6OmeN7PtaQMrDkWJWNFRI/nZNRdjPzhOP7NyJP69PVU+Ga4F8Z1K/t6bUS6gAGISAamRgaY7eep1bf0z6ulvkET6bK/Lt3Cq6tPID23QOpTG+hh5Ssd8B9vZxkrI3o0BiAimfRrbY8eLRtK7ez8QszbfUHGiogqb8fpeExaF4bcEkO3FmoDrBvfGb097WSsjKhyGICIZKJSqfDpwNYwMij+NdwXkYjDl5NkrIro0QKORWHa5nAUlrixl62ZEX5+rSu6lDjLkUiXMQARycitgRkm92im1TdnVwRy8gtlqoiofEIILPnzCvwfOlLpZGWCLZN90MZJI1NlRFXHAEQksyk9m8HFxkRqx9zJwsoj1ytYg6j2FRUJfLr7Apb8eVWrv3kjc2x9wwdNG5rLVBnR42EAIpKZsaE+5g5so9X3/eFriL2TJVNFRNryC4vw3y1nEBAUrdXv5azBr6/7wEFjUvaKRDqMAYhIBzzr0Qh9S0wczS0ogv/uCAghKliLqObl5Bdi8vowbD8dr9Xv28wWGyd1hY2ZkUyVET0ZBiAiHTHbzxPGhsW/kn9dSkLghVsyVkRKl5aTj9GrT+LgJe2J+f1a22HN2E4wV+v87SSJysUARKQjnK1N8XavFlp9n+6+gKy8gnLWIKo5t9NzMWLlcZyMvqvVP6yjM74b1R7GhvoyVUZUPRiAiHTIpKebomlDM6kdn5KNZX9dk7EiUqK4u1kYtjIYFxLStPpfe6YpFr3YFga8qSnVA/wpJtIhRgZ6mDdIe0L0j/9cx7WHbjBJVFOu3krHSyuCEfVvplb/R8974OP/a8X7elG9wQBEpGO6NW+AAW0dpHZ+ocCcXec5IZpqXHhcCl5aGYzEtBypT6UCFvznKbzRs1kFaxLVPQxARDpo5gueMDMqnmNx7Nod7DmbIGNFVN8du/YvRv14HClZ+VKfob4Ky0a2x6gujWWsjKhmMAAR6SB7jTHe7dNSq2/+7xeQkcsJ0VT99p1PwLi1IcjKK74CuYmhPlaP6YQXShyNJKpPGICIdNRYXzd42FtI7VtpuVgSeEXGiqg+2hwSiykbTyGvsPimphoTQ2yY2AXPlLhZL1F9wwBEpKMM9PUwb7D2hOi1QdG4lJhWzhpEVbPySCQ+2nYOJe5pikYWavz6ug86uFrLVxhRLWAAItJhndxs8GJ7Z6ldWCQwawcnRFPlCSG0ju6kZefjVMxdfP7HRSz845LWsq62ptj2hi/cSxx5JKqvGICIdNyM//OApXHxFXdDopOx7VR8BWsQ3XPlVjoGfXcMt9Nzpb703AIMWR6MFQ/dcNfD3gJbJvvAxca0tsskkgUDEJGOa2Cuxgf93LX6Fu69iNQSZ+sQPezKrXQMXR6EszdSH7lsR1drbH7dB40sjGuhMiLdwABEVAeM6uKKp5w0UvtOZh6+PHBZxopIlwkh8P6WM0jLefRZgxbGBlg3vjM0Joa1UBmR7mAAIqoD9PVUmDe4DUpehHfDiRicq8S3e1Ke03EplTryAwDpOQW4fCu9hisi0j0MQER1RDsXK4zsXHxBOiGAmTvPo6iIE6LpnviUbOwMj8fsHeertN6BC7dqqCIi3WXw6EWISFd82M8d+84n4m5mHgDgTFwKfgmJ45V6FaiwSOBiQhrCYpIRGpOMsOi7uJma8+gVy5CazflkpDwMQER1iJWpEaY/74EPt52V+hbtu4R+re1ga66WsTKqaRm5BQiPTUFozF2ERifjdGwyMktcuflJcP4PKREDEFEdM7SDM34JicWp2BQA9769L9p3CYuHeslbGFWrhNRshETfO7ITGpOMiwlpqKnRzr6edjWzYSIdxgBEVMfo3Z8Q7fe/o9IH4q+hNzC8kws6uNrIWxw9lsIigUuJ94ezopMRFpOM+JTsKm3D1Egf7Vys0NHVGu1drfHF/suIuPnoq4Z7OWvQzsXqMSsnqrsYgIjqoNaOGoz2cUNAULTUN3NHBHa/1Q0G+jy3Qddl5hYgPC4FodHJCI25i9OxKVW+0a2dpRodXW3Q0c0aHV1t0MrBQuu9d7QywdDlQRWeCm9pbIAvXvKCquTphUQKwQBEVEe917clfj+XIF3l92JCGtYfj8G4bk1krowelpiaI83dCY25i4sJ6SiswniWSgW421lIYaeDqzWcrU0qDC4t7Syw9Q1fvL/lTJmnxHs5a/DFS15oacfbXpAyMQAR1VGWxoaY+UIrvPNLuNT39YEreOEpBzSy5BV95VJYJHA5MR1hMffm7oRGV304y8Tw/nCWmzU63B/SsjSu+kTllnYW2PlmN3RecFAKyhZqA6yb0BntXKx45IcUjQGIqA4b6OWIX07GIfj6HQD37vO0YO9FLBnhLXNlypGV9+DsrHuno5+OSUZ6FYezGlmo74cdG3Rys0YrB0sYVtNQpkqlglGJbVmaGMK7Me/0TsQARFSHqVQqzB3UGv2X/oOC+0MqO8JvYninxvBpZitzdfXTrbQchEYnIyT6LsJiknEhIe2xhrM6uFpLQ1qPGs4iourHAERUx7Wws8CEp5tgZYm7e8/aeR57pz4NIwNOiH4ShUUCV26lSxcaDI1Jxo3kqg1nGRvq3T87ywYd3KzRvrE1r7tDpAMYgIjqgam9WmBX+E0k3L8S8LWkDKw5FoXJPZrJXFndkpV37+yssOh7w1mnYpORXokbipbU0EKNjq735u50crOBp2P1DWcRUfVhACKqB8zUBpg9wBNvbDwl9S398yoGejnC0cpExsp0W1JajjRROTTmLi7cTJOGEiurpZ05OrrZoKPrveEsFxsOZxHVBQxARPXE823s8UzLhvj7ym0AQHZ+IebtuYDlr3SQuTLdUFQkcCUpXbrQYGjMXcTdrfpwlpezlTR3p31ja2hMOZxFVBcxABHVEyqVCnMHtkbfJX8jr6AIAPDH+UQcuXIbPVo2lLm62pedV3hvOOv+6einYpIrvChgWRqY3xvO6uhmjY5uNvB0sOS8KqJ6ggGIqB5xa2CGyT2a4duDV6W+OTvPY9+0Z2BsqC9jZTUvKT1HmrsTGpOMiPjUKg9ntWhUYjjLzRqNbUw5nEVUTzEAEdUzU3o2w/bTN6Thneg7Wfjh7+uY+lwLmSurPkVFAtduZ9w7Ff1+6Im9m1WlbagN9OB1/95ZHe+fnWVlalRDFRORrmEAIqpnjA318enA1hgfECr1fXfoGga3c0JjW1MZK3t82XmFOHMj5f7NQu9df6fqw1lG9669c/909DaOGg5nESkYAxBRPdTLww59PO0QeOEWACC3oAj+uyOwekzHOjGkczs9997cnftHd84/xnBW80bm6HT/6sodXa3hasvhLCIqxgBEVE/N8fPEP1dvIyf/3oTovy4lIfDCLfRtbS9zZdqKigQib2cgNKb46soxd6o2nGVkoId2zlbo4GaNjq73hrOszTicRUTlYwAiqqecrU3xdq8W+GL/Zanv090X8HQLec8Iy8kvxJm4e/fOCrv/SM3Or9I2bM2MpFtJdHC1QRsnS6gN6vckbyKqXgxARPXYxKebYFvYDVz/NxMAEJ+SjWWHrj5irer1b0bu/Wvv3JWGs/ILqzac1ayhmTR3p5ObDdw4nEVET4gBiKgeUxvoY+6gNnhl9Qmp74e/r8OmhoaHhLg/nBWdjJD7oSf6MYazvJw10tydDq4cziKi6scARFTPdW/RAAPaOmDP2QQAQH6hQEpW1YacypOTX4izN1IRGnPvdPSw2OQqb9vmwXDW/SGtNk4aDmcRUY1jACJSgJkveOLQpSRk5hUCuHdW2ANp2fk4FZsMbxerRw4r3cnIlebuhEbfxfn4NOQVFlW4zsOaNjST7pvVwc0aTRuYcTiLiGodAxCRAthrjPFun5aY//vFUs+l5xZgyPdBaOuswZcveaGlnQWAB8NZmVqno0fdn0tUWUb6enjKWSPdO6uDq3WNDb8REVUFAxCRQvg2s4WeCijvcjpnb6TiP98dw/BOjRF7NxNhMclIruJwlrWp4b25O/dPR2/jpKn3t+AgorqJAYhIAYQQmP7buXLDzwOZeYVYcyyq0ttt2sBMOh29o5sNh7OIqM5gACJSgNNxKTh7I/WJtmGkr4c2TpbSzUI7uFrD1lxdTRUSEdUunQ9A6enpmDVrFrZv346kpCR4e3tj6dKl6NSpU7nrbNy4EYsXL8bVq1eh0Wjw/PPP48svv4StrW0tVk6kOx7cEqMqrEwN7wede0NaT3E4i4jqEZ0PQBMnTsT58+exfv16ODo6YsOGDejduzcuXLgAJyenUssfPXoUo0ePxjfffAM/Pz/Ex8dj8uTJmDhxIrZv3y7DHhDJr6pXWh7Q1gHfjvCGnh6Hs4ioftLpWyFnZ2dj27ZtWLx4MZ555hk0b94c/v7+aNKkCZYvX17mOsePH4ebmxumTp2KJk2aoHv37nj99dcRGhpa5vJESqAxMazS8i42pgw/RFSv6XQAKigoQGFhIYyNjbX6TUxMcPTo0TLX8fX1xY0bN7B3714IIXDr1i1s3boVL7zwQrmvk5ubi7S0NK0HUX3Sx9OuSsv3reLyRER1jU4HIAsLC/j4+GDevHm4efMmCgsLsWHDBpw4cQIJCQllruPr64uNGzdi+PDhMDIygr29PaysrPC///2v3NdZuHAhNBqN9HBxcampXSKShbeLFdo6ayq1rJezBu1crGq2ICIimel0AAKA9evXQwgBJycnqNVqfPvttxg1ahT09cuejHnhwgVMnToVs2fPRlhYGPbt24eoqChMnjy53NeYMWMGUlNTpUdcXFxN7Q6RLFQqFb58yQuWxhVP+7M0NsAXL3nxVHYiqvdUQoiq3ZZZJpmZmUhLS4ODgwOGDx+OjIwM/P7776WWe/XVV5GTk4MtW7ZIfUePHsXTTz+NmzdvwsHB4ZGvlZaWBo1Gg9TUVFhaWlbrfhDJ6cqtdLy/5UyZp8R7OWvwRYkrQVP90e3zvxCfkg0AcLIywbHpvWSuiKhmVOXzW+ePAD1gZmYGBwcHJCcnY//+/Rg0aFCZy2VlZUFPT3u3HhwtqiNZj6jGtLSzwM43u6GhRfH1eyzUBtg+xRc73uzG8ENEiqHzAWj//v3SMFZgYCCeffZZuLu7Y9y4cQDuDV+NHj1aWt7Pzw+//fYbli9fjuvXr+PYsWOYOnUqOnfuDEdHR7l2g0hnqFQqGOkX/+pbmhjCu7E1h72ISFF0/jpAqampmDFjBm7cuAEbGxu8+OKL+Oyzz2BoeO+03oSEBMTGxkrLjx07Funp6Vi2bBn++9//wsrKCr169cKiRYvk2gUiIiLSMTofgIYNG4Zhw4aV+3xAQECpvrfffhtvv/12DVZFREREdZnOD4ERERERVTcGICIiIlIcBiAiIiJSHAYgIiIiUhwGICIiIlIcBiAiIiJSHAYgIiIiUhwGICIiIlIcBiAiIiJSHAYgIiIiUhwGICIiIlIcBiAiIiJSHAYgIiIiUhwGICIiIlIcBiAiIiJSHAYgIiIiUhwGICIiIlIcA7kLICKimuWgMS7z30RKxgBERFTPbX3DV+4SiHQOh8CIiIhIcRiAiIiISHEYgIiIiEhxGICIiIhIcRiAiIiISHEYgIiIiEhxGICIiIhIcRiAiIiISHEYgIiIiEhxGICIiIhIcRiAiIiISHEYgIiIiEhxGICIiIhIcRiAiIiISHEYgIiIiEhxGICIiIhIcRiAiIiISHEYgIiIiEhxGICIiIhIcRiAiIiISHEYgIiIiEhxGICIiIhIcRiAiIiISHEYgIiIiEhxGICIiIhIcRiAiIiISHEYgIiIiEhxGICIiIhIcRiAiIiISHEM5C6AiGqfg8a4zH8TESkFAxCRAm19w1fuEoiIZMUhMCIiIlIcBiAiIiJSHAYgIiIiUhydD0Dp6emYNm0aXF1dYWJiAl9fX4SEhJS7/NixY6FSqUo9WrduXYtVExERkS7T+QA0ceJEBAYGYv369Th37hz69u2L3r17Iz4+vszlly5dioSEBOkRFxcHGxsbvPTSS7VcOREREekqlRBCyF1EebKzs2FhYYGdO3fihRdekPrbtWuHAQMGYP78+Y/cxo4dOzBkyBBERUXB1dW1Uq+blpYGjUaD1NRUWFpaPnb9REREVHuq8vmt00eACgoKUFhYCGNj7euUmJiY4OjRo5XaxurVq9G7d+9Khx8iIiKq/3T6OkAWFhbw8fHBvHnz0KpVK9jZ2eHnn3/GiRMn0KJFi0eun5CQgD/++AObNm2qcLnc3Fzk5uZK7bS0tCeunYiIiHSXTh8BAoD169dDCAEnJyeo1Wp8++23GDVqFPT19R+5bkBAAKysrDB48OAKl1u4cCE0Go30cHFxqabqiYiISBfp9BygkjIzM5GWlgYHBwcMHz4cGRkZ+P3338tdXgiBli1bYsCAAfjmm28q3HZZR4BcXFw4B4iIiKgOqcocIJ0eAivJzMwMZmZmSE5Oxv79+7F48eIKlz9y5AiuXbuGCRMmPHLbarUaarW6ukolIiIiHafzAWj//v0QQsDd3R3Xrl3DBx98AHd3d4wbNw4AMGPGDMTHx2PdunVa661evRpdunRBmzZt5CibiIiIdJjOzwFKTU3Fm2++CQ8PD4wePRrdu3fHgQMHYGhoCODeROfY2NhS62zbtq1SR3+IiIhIeerMHKDalJqaCisrK8TFxXEOEBERUR3xYA5vSkoKNBpNhcvq/BCYHNLT0wGAZ4MRERHVQenp6Y8MQDwCVIaioiLcvHkTFhYWUKlUcpdTax4kZx75Uga+38rC91tZlPp+CyGQnp4OR0dH6OlVPMuHR4DKoKenB2dnZ7nLkI2lpaWifmGUju+3svD9VhYlvt+POvLzgM5PgiYiIiKqbgxAREREpDgMQCRRq9WYM2cOLwqpEHy/lYXvt7Lw/X40ToImIiIixeERICIiIlIcBiAiIiJSHAagOsrf3x/t2rWT2mPHjsXgwYNlq6cmXLp0CV27doWxsbHWvtZ39eW9DQgIgJWVVZXWqav7SlSWh3+Xa1p0dDRUKhXCw8OfeFtubm5YsmSJLK9dWxiAaklQUBD09fXx/PPP18j2ly5dioCAAKnds2dPTJs2rUZeqyJjx47F9OnTAQAqlQo7dux47G3NmTMHZmZmuHz5Mg4ePPhYH6i1Qanv7YOHubk5vLy8tGoEgOHDh+PKlSvVXkdV/zDrurr8IVkRf39/2NnZPfHfAV3l5+eH3r17l/lccHAwVCoVevXqhYMHD9ZyZaXFxMRArVYjLS0N/v7+0u+unp4eHB0d8fLLLyMuLk5rnZCQELz22mvVWoeu/Q1nAKola9aswdtvv42jR4+WunlrddBoNDXyg5WXl1fpZYuKivD7779j0KBB1fLakZGR6N69O1xdXWFra1st26wJSn1v165di4SEBJw5cwbDhw/HuHHjsH//ful5ExMTNGrUqFprrmvq8ofkk4Syixcv4tNPP8XKlSuRkJCA/v3717vgOmHCBPz111+IiYkp9dyaNWvQrl07PPPMMzrxt2vnzp3o2bOndEHE1q1bIyEhATdu3MDmzZtx7tw5DBs2TGudhg0bwtTUVI5yaw0DUC3IzMzEr7/+ijfeeAMDBgzQ+qZcViLesWNHqVtwfP7557Czs4OFhQUmTJiAnJwcredLDh2MHTsWR44cwdKlS6WkHx0dDQA4cuQIOnfuDLVaDQcHB0yfPh0FBQXSdnr27Im33noL7733Hho0aIA+ffpg/PjxGDBggNbrFRQUwN7eHmvWrJH6jh07Bj09PXTp0qVS/y9r165Fq1atYGxsDA8PD3z//ffScyqVCmFhYZg7dy5UKhV69uyJcePGITU1Vdonf3//Sr1OTVLye2tlZQV7e3s0a9YMH3/8MWxsbHDgwIEK93/+/Plo1KgRLCwsMHHiREyfPr3MD9ovv/wSDg4OsLW1xZtvvon8/HxpH2JiYvDuu+9K+6/L6vKH5JOIjIwEAAwaNAj29vb18lTsAQMGoFGjRqWOfGZlZWHz5s2YMGFCqSB5+PBhdO7cGWZmZrCyskK3bt2kn42yhn+nTZuGnj17Su19+/ahe/fusLKygq2tLQYMGCD9X1dk586dGDhwoNQ2MDCAvb09HB0d8fTTT2PSpEk4fvw40tLSpGUeDqyXLl1C9+7dYWxsDE9PT/z5559lHt27fv06nn32WZiamsLLywvBwcHSvuva33AGoFqwefNmuLu7w93dHa+88grWrl2Lqlx94Ndff8WcOXPw2WefITQ0FA4ODlph4WFLly6Fj48PJk2ahISEBCQkJMDFxQXx8fH4v//7P3Tq1AlnzpzB8uXLsXr1asyfP19r/Z9++gkGBgY4duwYVq5ciYkTJ2Lfvn1ISEiQltm7dy8yMjK0vjXs2rULfn5+j7z/CgD8+OOP+OSTT/DZZ5/h4sWLWLBgAWbNmoWffvoJAJCQkIDWrVvjv//9LxISErBr1y4sWbIElpaW0j69//77lf4/rCl8b4HCwkL8+uuvuHv3LgwNDcutfePGjfjss8+waNEihIWFoXHjxli+fHmp5Q4dOoTIyEgcOnQIP/30EwICAqQPmd9++w3Ozs6YO3eutP+6rC5/SFYkPj4ew4cPh7W1NWxtbTFo0CApiPv7+8PPzw/AvdsKPfgCU5eCa2UYGBhg9OjRCAgI0Pqd37JlC/Ly8vDyyy9rLV9QUIDBgwejR48eOHv2LIKDg/Haa69V6f8iMzMT7733HkJCQnDw4EHo6enhP//5D4qKispdJyUlBf/880+5721iYiJ+++036OvrQ19fv8xlioqKMHjwYJiamuLEiRP44Ycf8Mknn5S57CeffIL3338f4eHhaNmyJUaOHImCggL4+vrq3t9wQTXO19dXLFmyRAghRH5+vmjQoIEIDAwUQgixdu1aodFotJbfvn27KPnW+Pj4iMmTJ2st06VLF+Hl5SW1x4wZIwYNGiS1e/ToId555x2tdT7++GPh7u4uioqKpL7vvvtOmJubi8LCQmm9du3aldoHT09PsWjRIqk9ePBgMXbsWK1lWrZsKXbt2iW1AYjt27eX2pYQQri4uIhNmzZp9c2bN0/4+PhIbS8vLzFnzhypXdb/ldyU/N4aGxsLMzMzoa+vLwAIGxsbcfXqVWmZh/e/S5cu4s0339Tabrdu3Urtq6urqygoKJD6XnrpJTF8+HCp7erqKr755ptS+6GrPvjgA+Hm5qb13gQEBAi1Wi3u3r0r5syZI/0f5OfnC41GI95//31x7do1ceHCBREQECBiYmKEEKV/FoQQ4p133hE9evSQ2lu3bhXbtm0TV65cEadPnxZ+fn7iqaeekn4OoqKiBABx+vRpaZ3k5GRhaGgoYmNjhRBCq6aHZWZmihYtWojx48eLs2fPigsXLohRo0YJd3d3kZubK9LT08XatWsFAJGQkCASEhLEnTt3hLOzs5g7d67UVx9cvHhRABB//fWX1PfMM8+IkSNHCiG0/x/v3LkjAIjDhw+Xua3KvLcPS0pKEgDEuXPnhBBlv7cbN24U7du3l9pz5swRenp6wszMTJiYmAgAAoCYOnWq1rZL/p798ccfwsDAQOt9CwwM1Pob/+C1V61aJS0TEREhAIiLFy8KIXTvbziPANWwy5cv4+TJkxgxYgSAe98ahg8frjW88CgXL16Ej4+PVt/D7apsp+Q3jm7duiEjIwM3btyQ+jp27Fhq3YkTJ2Lt2rUAgKSkJPz+++8YP3681rZv3LhR7nyHkm7fvo24uDhMmDAB5ubm0mP+/PmV+qaqK5T+3n7zzTcIDw9HYGAg2rVrh2+++QbNmzcvt8bLly+jc+fOWn0Pt4F78xNKfhN1cHBAUlJSudvVdePHj0d0dDQOHz4s9a1ZswZDhgyBtbW11rJpaWlITU3FgAED0KxZM7Rq1QpjxoxB48aNK/16L774IoYMGYIWLVqgXbt2WL16Nc6dO4cLFy6Uu87evXvx1FNPwcXF5ZHb/+WXX6Cnp4dVq1bhqaeeQqtWrbB27VrExsbi8OHDMDc3l4Y+7e3tYW9vDxsbG+jr68PCwkLqqw88PDzg6+sr/c5HRkbin3/+0fr9ecDGxgZjx45Fv3794Ofnh6VLl1b5CGZkZCRGjRqFpk2bwtLSEk2aNAGACucelnVkz93dHeHh4QgJCcFnn32Gdu3a4bPPPit3G5cvX4aLi4vW+1bW7y4AtG3bVvq3g4MDAOjs7y/vBl/DVq9ejYKCAjg5OUl9QggYGhoiOTkZenp6pYZMHsx3qG5CiFKHWx+8dsl+MzOzUuuOHj0a06dPR3BwMIKDg+Hm5oann35aen7Xrl3o06cPTExMHlnHg8O1P/74Y6n5QuUdgtVFSn9v7e3t0bx5czRv3hxbtmyBt7c3OnbsCE9Pz3LrLK/Gkh4eRlOpVBUe4td1JT8kn332WelDsuR8qQdKfkj26dMHvXv3xrBhw6QPksqIjIzErFmzcPz4cfz777/S/11sbCzatGlT5jpVGf4KCwvDtWvXYGFhodWfk5NTp77AVJcJEybgrbfewnfffYe1a9fC1dUVzz33XJnLrl27FlOnTsW+ffuwefNmzJw5E4GBgejatWul/l74+fnBxcUFP/74IxwdHVFUVIQ2bdqUe0JDfn4+9u3bhxkzZmj1GxkZSV9WWrdujatXr+KNN97A+vXry9xOWX9fylPy9/fBOrr6+8sjQDWooKAA69atw1dffYXw8HDpcebMGbi6umLjxo1o2LAh0tPTkZmZKa338OmprVq1wvHjx7X6Hm4/zMjICIWFhVp9np6eCAoK0volCwoKgoWFhdaHeFlsbW0xePBgrF27FmvXrsW4ceO0nq/KH1A7Ozs4OTnh+vXr0gfog8eDbzSV3Se58L3V1rx5c7z44oul/tCW5O7ujpMnT2r1hYaGVrjdsujSz0FlTZgwAdu2bUNaWlqlPiSDg4Ph6+uLzZs3o2XLltLPRGU/JO/cuYMff/wRJ06cwIkTJwCUf9bfgw/Jyp69WVRUhA4dOmj93IeHh+PKlSsYNWpUpbZRnwwbNgz6+vrYtGkTfvrpJ4wbN67CsODt7Y0ZM2YgKCgIbdq0waZNmwDcO+vq4SNCJf9e3LlzBxcvXsTMmTPx3HPPoVWrVkhOTq6wtkOHDsHKyuqRZ/TNmjULP//8M06dOlXm8x4eHoiNjcWtW7ekvpCQkAq3WRZd+93lEaAatGfPHiQnJ2PChAnQaDRazw0dOhSrV6/GwYMHYWpqio8//hhvv/02Tp48WWrC5DvvvIMxY8agY8eO6N69OzZu3IiIiAg0bdq03Nd2c3PDiRMnEB0dDXNzc9jY2GDKlClYsmQJ3n77bbz11lu4fPky5syZg/fee69SE5cnTpyIAQMGoLCwEGPGjJH6k5KSEBISUua1PqKiokp96Ddv3hz+/v6YOnUqLC0t0b9/f+Tm5iI0NBTJycl47733yt2njIwMHDx4EF5eXjA1NZXtNE2+t6X997//hZeXF0JDQ8scanv77bcxadIkdOzYUfpwP3v2bIX7Wt7+//333xgxYgTUajUaNGhQpfXlMGzYMLzzzjvSh+SkSZMe+SH54IPSx8cHmzZtQteuXdGwYUOcP39ea9nw8HDpW/eDD8mVK1dKR/GOHj1aYW2V/ZB8oH379ti8eTMaNWpUpTPGdO3Dr7qYm5tj+PDh+Pjjj5GamoqxY8eWuVxUVBR++OEHDBw4EI6Ojrh8+TKuXLmC0aNHAwB69eqFL774AuvWrYOPjw82bNiA8+fPw9vbGwCkCec//PADHBwcEBsbK12Xqzy7du2q1BfTpk2bYtCgQZg9ezb27NlT6vk+ffqgWbNmGDNmDBYvXoz09HRpEnRVJnHr0t9wAJwEXZMGDBgg/u///q/M58LCwgQAERYWJrZv3y6aN28ujI2NxYABA8QPP/wgHn5rPvvsM9GgQQNhbm4uxowZIz788MMKJ8pevnxZdO3aVZrkFhUVJYQQ4vDhw6JTp07CyMhI2Nvbi48++kjk5+dL65U1wfaBoqIi4erqWmqfVq1aJbp161ZqedyfXPfw49ChQ0KIe5Pz2rVrJ4yMjIS1tbV45plnxG+//Sat//AkaCGEmDx5srC1tRUASj1Xm/jelj3BvU+fPqJ///5CiLInPM6dO1fa1/Hjx4upU6eKrl27lruvQpSeCBocHCzatm0r1Gp1qf9LXTZhwgRhbW0t9PT0pEnNQmhPlL1+/bqYPn26CAoKEtHR0WL//v3CxsZGfP/990IIIfbt2ydUKpX46aefxJUrV8Ts2bOFpaWl9P9TWFgobG1txSuvvCKuXr0qDh48KDp16lTmZNUHE2XffPNN8dZbb2nVOmfOHNGyZUtx+vRprcfVq1elSdA9e/YUf//9t7h+/bo4fPiwmDp1qoiLixNClJ7sL8S9n42BAweKGzduiNu3b1fz/668goKCBADRt29frf6S721iYqIYPHiwcHBwEEZGRsLV1VXMnj1bmpwuhBCzZ88WdnZ2QqPRiHfffVe89dZbWj/7gYGBolWrVkKtVou2bduKw4cPV/jeuri4SCdllFVTSceOHRMAxPHjx4UQpU82uHjxoujWrZswMjISHh4eYvfu3QKA2LdvX5mvLcS9yfUl/+YLoTt/w4UQou789SDZZWZmCo1GI7Zt26bV7+fnp3UWEdU9cr23vXv3Fq+88kqNbV+X1LUPybK+vDx4nYSEBDF69GjRoEEDoVarRdOmTcWkSZNEamqqEKLsAFRXg2tdFRYWJjQajcjLy6uR7R89elQAENeuXauR7dcGlRBVuGgJKVJRURESExPx1VdfYevWrYiMjISBQfHo6eLFizFy5MhKnUFCuqU239usrCysWLEC/fr1g76+Pn7++WfMnTsXgYGBlTp7kKrfqVOn0KtXL9y+fbvCazhR3XPy5ElcvnwZr776arVsb/v27TA3N0eLFi1w7do1vPPOO7C2tn7kEKsuYwCiR4qOjkaTJk3g7OyMgICAcidvUt1Tm+9tdnY2/Pz8cOrUKeTm5sLd3R0zZ87EkCFDauw1qWLV/SFJ9de6deswb948xMXFoUGDBujduze++uornbiK+eNiACIiIiLF4WnwREREpDgMQERERKQ4DEBERESkOAxAREREpDgMQERERKQ4DEBERI/h8OHDUKlUSElJKXeZgIAA6c7oRKRbGICIiAD07NkT06ZNq/Tyvr6+SEhIKHUvOCKqG3gzVCKix2BkZAR7e3u5yyCix8QjQEQkOyEEFi9ejKZNm8LExAReXl7YunUrioqK4OzsjBUrVmgtf+rUKahUKly/fh0AkJqaitdee026Q3mvXr1w5swZaXl/f3+0a9cO69evh5ubGzQaDUaMGIH09HQAwNixY3HkyBEsXboUKpUKKpUK0dHRFdZc1hBYQEAAGjduDFNTU/znP//BnTt3quc/iIiqHQMQEclu5syZWLt2LZYvX46IiAi8++67eOWVV/DPP/9gxIgR2Lhxo9bymzZtgo+PD5o2bQohBF544QUkJiZi7969CAsLQ/v27fHcc8/h7t270jqRkZHYsWMH9uzZgz179uDIkSP4/PPPAQBLly6Fj48PJk2ahISEBCQkJFT5/mcnTpzA+PHjMWXKFISHh+PZZ5/F/Pnzn/w/h4hqhnz3YSUiEiIjI0MYGxuLoKAgrf4JEyaIkSNHilOnTgmVSiWio6OFEEIUFhYKJycn8d133wkhhDh48KCwtLQUOTk5Wus3a9ZMrFy5Ughx7+7mpqamIi0tTXr+gw8+EF26dJHaPXr0EO+8806l6z506JAAIJKTk4UQQowcOVI8//zzWssMHz5caDSaSm+TiGoPjwARkawuXLiAnJwc9OnTB+bm5tJj3bp1iIyMhLe3Nzw8PPDzzz8DAI4cOYKkpCQMGzYMABAWFoaMjAzY2tpqrR8VFYXIyEjpddzc3GBhYSG1HRwckJSUVG37cfHiRfj4+Gj1PdwmIt3BSdBEJKuioiIAwO+//w4nJyet59RqNQDg5ZdfxqZNmzB9+nRs2rQJ/fr1Q4MGDaT1HRwccPjw4VLbLnkKuqGhodZzKpVKeu3qIHhfaaI6hQGIiGTl6ekJtVqN2NhY9OjRo8xlRo0ahZkzZyIsLAxbt27F8uXLpefat2+PxMREGBgYwM3N7bHrMDIyQmFh4WOv7+npiePHj2v1PdwmIt3BAEREsrKwsMD777+Pd999F0VFRejevTvS0tIQFBQEc3NzjBkzBk2aNIGvry8mTJiAgoICDBo0SFq/d+/e8PHxweDBg7Fo0SK4u7vj5s2b2Lt3LwYPHoyOHTtWqg43NzecOHEC0dHRMDc3h42NDfT0Kj9LYOrUqfD19cXixYsxePBgHDhwAPv27avy/wcR1Q7OASIi2c2bNw+zZ8/GwoUL0apVK/Tr1w+7d+9GkyZNpGVefvllnDlzBkOGDIGJiYnUr1KpsHfvXjzzzDMYP348WrZsiREjRiA6Ohp2dnaVruH999+Hvr4+PD090bBhQ8TGxlZpH7p27YpVq1bhf//7H9q1a4cDBw5g5syZVdoGEdUeleDANRERESkMjwARERGR4jAAERGVYfLkyVqn1Zd8TJ48We7yiOgJcQiMiKgMSUlJSEtLK/M5S0tLNGrUqJYrIqLqxABEREREisMhMCIiIlIcBiAiIiJSHAYgIiIiUhwGICIiIlIcBiAiIiJSHAYgIiIiUhwGICIiIlIcBiAiIiJSnP8H9Z85gHTNcE0AAAAASUVORK5CYII=", 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" ] diff --git a/examples/irasa_sprint.ipynb b/examples/irasa_sprint.ipynb index 7905c87..94a930e 100644 --- a/examples/irasa_sprint.ipynb +++ b/examples/irasa_sprint.ipynb @@ -114,16 +114,16 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ - "slope_spec = irasa_sprint_spectrum.get_slopes()" + "ap_spec = irasa_sprint_spectrum.fit_aperiodic_model()" ] }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -140,13 +140,13 @@ "source": [ "f, ax = plt.subplots(nrows=3, figsize=(8, 7))\n", "\n", - "ax[0].plot(slope_spec.aperiodic_params['time'], slope_spec.aperiodic_params['Offset'])\n", + "ax[0].plot(ap_spec.aperiodic_params['time'], ap_spec.aperiodic_params['Offset'])\n", "ax[0].set_ylabel('Offset')\n", "ax[0].set_xlabel('time (s)')\n", - "ax[1].plot(slope_spec.aperiodic_params['time'], slope_spec.aperiodic_params['Exponent'])\n", + "ax[1].plot(ap_spec.aperiodic_params['time'], ap_spec.aperiodic_params['Exponent'])\n", "ax[1].set_ylabel('Exponent')\n", "ax[1].set_xlabel('time (s)')\n", - "ax[2].plot(slope_spec.aperiodic_params['time'], slope_spec.gof['r_squared'])\n", + "ax[2].plot(ap_spec.aperiodic_params['time'], ap_spec.gof['r_squared'])\n", "ax[2].set_ylabel('R2')\n", "ax[2].set_xlabel('time (s)')\n", "\n", @@ -155,7 +155,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -169,7 +169,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -198,7 +198,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 9, "metadata": {}, "outputs": [ { diff --git a/pyrasa/irasa_mne/mne_objs.py b/pyrasa/irasa_mne/mne_objs.py index 7ce3380..c815349 100644 --- a/pyrasa/irasa_mne/mne_objs.py +++ b/pyrasa/irasa_mne/mne_objs.py @@ -7,9 +7,9 @@ from attrs import define from mne.time_frequency import EpochsSpectrumArray, SpectrumArray -from pyrasa.utils.aperiodic_utils import compute_slope +from pyrasa.utils.aperiodic_utils import compute_aperiodic_model from pyrasa.utils.peak_utils import get_peak_params -from pyrasa.utils.types import SlopeFit +from pyrasa.utils.types import AperiodicFit # FutureWarning: @@ -172,12 +172,12 @@ def __init__( ) ) - def get_slopes( + def fit_aperiodic_model( self: SpectrumArray, fit_func: str = 'fixed', scale: bool = False, fit_bounds: tuple[float, float] | None = None, - ) -> SlopeFit: + ) -> AperiodicFit: """ This method can be used to extract aperiodic parameters from the aperiodic spectrum extracted from IRASA. The algorithm works by applying one of two different curve fit functions and returns the associated parameters, @@ -198,7 +198,7 @@ def get_slopes( """ - return compute_slope( + return compute_aperiodic_model( self.get_data(), self.freqs, ch_names=self.ch_names, @@ -398,12 +398,12 @@ def __init__( ) ) - def get_slopes( + def fit_aperiodic_model( self: SpectrumArray, fit_func: str = 'fixed', scale: bool = False, fit_bounds: tuple[float, float] | None = None, - ) -> SlopeFit: + ) -> AperiodicFit: """ This method can be used to extract aperiodic parameters from the aperiodic spectrum extracted from IRASA. The algorithm works by applying one of two different curve fit functions and returns the associated parameters, @@ -429,7 +429,7 @@ def get_slopes( aps_list, gof_list = [], [] for ix, cur_epoch in enumerate(self.get_data()): - slope_fit = compute_slope( + slope_fit = compute_aperiodic_model( cur_epoch, self.freqs, ch_names=self.ch_names, @@ -443,7 +443,7 @@ def get_slopes( aps_list.append(slope_fit.aperiodic_params.copy()) gof_list.append(slope_fit.gof.copy()) - return SlopeFit(aperiodic_params=pd.concat(aps_list), gof=pd.concat(gof_list)) + return AperiodicFit(aperiodic_params=pd.concat(aps_list), gof=pd.concat(gof_list)) @define diff --git a/pyrasa/utils/aperiodic_utils.py b/pyrasa/utils/aperiodic_utils.py index 1e43f08..03ac49c 100644 --- a/pyrasa/utils/aperiodic_utils.py +++ b/pyrasa/utils/aperiodic_utils.py @@ -7,16 +7,16 @@ import pandas as pd from pyrasa.utils.fit_funcs import AbstractFitFun, FixedFitFun, KneeFitFun -from pyrasa.utils.types import SlopeFit +from pyrasa.utils.types import AperiodicFit -def _compute_slope( +def _compute_aperiodic_model( aperiodic_spectrum: np.ndarray, freq: np.ndarray, fit_func: str | type[AbstractFitFun], scale_factor: float | int = 1, ) -> tuple[pd.DataFrame, pd.DataFrame]: - """get the slope of the aperiodic spectrum""" + """helper function to model the aperiodic spectrum""" if isinstance(fit_func, str): if fit_func == 'fixed': @@ -24,7 +24,7 @@ def _compute_slope( elif fit_func == 'knee': fit_func = KneeFitFun else: - raise ValueError('fit_func should be either "fixed" or "knee"') + raise ValueError('fit_func should be either a string ("fixed", "knee") or of type AbastractFitFun') fit_f = fit_func(freq, aperiodic_spectrum, scale_factor=scale_factor) params, gof = fit_f.fit_func() @@ -32,14 +32,14 @@ def _compute_slope( return params, gof -def compute_slope( +def compute_aperiodic_model( aperiodic_spectrum: np.ndarray, freqs: np.ndarray, fit_func: str | type[AbstractFitFun] = 'fixed', ch_names: Iterable | None = None, scale: bool = False, fit_bounds: tuple[float, float] | None = None, -) -> SlopeFit: +) -> AperiodicFit: """ This function can be used to extract aperiodic parameters from the aperiodic spectrum extracted from IRASA. The algorithm works by applying one of two different curve fit functions and returns the associated parameters, @@ -91,7 +91,7 @@ def compute_slope( if freqs[0] == 0: warnings.warn( - 'The first frequency appears to be 0 this will result in slope fitting problems. ' + 'The first frequency appears to be 0 this will result in aperiodic model fitting problems. ' + 'Frequencies will be evaluated starting from the next highest in Hz' ) freqs = freqs[1:] @@ -113,7 +113,7 @@ def num_zeros(decimal: int) -> float: ap_list, gof_list = [], [] for ix, ch_name in enumerate(ch_names): - params, gof = _compute_slope( + params, gof = _compute_aperiodic_model( aperiodic_spectrum=aperiodic_spectrum[ix], freq=freqs, fit_func=fit_func, @@ -127,10 +127,10 @@ def num_zeros(decimal: int) -> float: gof_list.append(gof) # combine & return - return SlopeFit(aperiodic_params=pd.concat(ap_list), gof=pd.concat(gof_list)) + return AperiodicFit(aperiodic_params=pd.concat(ap_list), gof=pd.concat(gof_list)) -def compute_slope_sprint( +def compute_aperiodic_model_sprint( aperiodic_spectrum: np.ndarray, freqs: np.ndarray, times: np.ndarray, @@ -138,7 +138,7 @@ def compute_slope_sprint( scale: bool = False, ch_names: Iterable | None = None, fit_bounds: tuple[float, float] | None = None, -) -> SlopeFit: +) -> AperiodicFit: """ This function can be used to extract aperiodic parameters from the aperiodic spectrogram extracted from IRASA. The algorithm works by applying one of two different curve fit functions and returns the associated parameters, @@ -169,7 +169,7 @@ def compute_slope_sprint( ap_t_list, gof_t_list = [], [] for ix, t in enumerate(times): - slope_fit = compute_slope( + aperiodic_fit = compute_aperiodic_model( aperiodic_spectrum[:, :, ix], freqs=freqs, fit_func=fit_func, @@ -177,10 +177,10 @@ def compute_slope_sprint( fit_bounds=fit_bounds, scale=scale, ) - slope_fit.aperiodic_params['time'] = t - slope_fit.gof['time'] = t + aperiodic_fit.aperiodic_params['time'] = t + aperiodic_fit.gof['time'] = t - ap_t_list.append(slope_fit.aperiodic_params) - gof_t_list.append(slope_fit.gof) + ap_t_list.append(aperiodic_fit.aperiodic_params) + gof_t_list.append(aperiodic_fit.gof) - return SlopeFit(aperiodic_params=pd.concat(ap_t_list), gof=pd.concat(gof_t_list)) + return AperiodicFit(aperiodic_params=pd.concat(ap_t_list), gof=pd.concat(gof_t_list)) diff --git a/pyrasa/utils/irasa_spectrum.py b/pyrasa/utils/irasa_spectrum.py index 9c7014b..a7d79ae 100644 --- a/pyrasa/utils/irasa_spectrum.py +++ b/pyrasa/utils/irasa_spectrum.py @@ -2,9 +2,9 @@ import pandas as pd from attrs import define -from pyrasa.utils.aperiodic_utils import compute_slope +from pyrasa.utils.aperiodic_utils import compute_aperiodic_model from pyrasa.utils.peak_utils import get_peak_params -from pyrasa.utils.types import SlopeFit +from pyrasa.utils.types import AperiodicFit @define @@ -15,9 +15,9 @@ class IrasaSpectrum: periodic: np.ndarray ch_names: np.ndarray | None - def get_slopes( + def fit_aperiodic_model( self, fit_func: str = 'fixed', scale: bool = False, fit_bounds: tuple[float, float] | None = None - ) -> SlopeFit: + ) -> AperiodicFit: """ This method can be used to extract aperiodic parameters from the aperiodic spectrum extracted from IRASA. The algorithm works by applying one of two different curve fit functions and returns the associated parameters, @@ -31,14 +31,14 @@ def get_slopes( should be None if the whole frequency range is desired. Otherwise a tuple of (lower, upper) - Returns: SlopeFit + Returns: AperiodicFit df_aps: DataFrame DataFrame containing the center frequency, bandwidth and peak height for each channel df_gof: DataFrame DataFrame containing the goodness of fit of the specific fit function for each channel. """ - return compute_slope( + return compute_aperiodic_model( aperiodic_spectrum=self.aperiodic, freqs=self.freqs, ch_names=self.ch_names, diff --git a/pyrasa/utils/irasa_tf_spectrum.py b/pyrasa/utils/irasa_tf_spectrum.py index cb78fea..7727da5 100644 --- a/pyrasa/utils/irasa_tf_spectrum.py +++ b/pyrasa/utils/irasa_tf_spectrum.py @@ -2,9 +2,9 @@ import pandas as pd from attrs import define -from pyrasa.utils.aperiodic_utils import compute_slope_sprint +from pyrasa.utils.aperiodic_utils import compute_aperiodic_model_sprint from pyrasa.utils.peak_utils import get_peak_params_sprint -from pyrasa.utils.types import SlopeFit +from pyrasa.utils.types import AperiodicFit min_ndim = 2 @@ -18,9 +18,9 @@ class IrasaTfSpectrum: periodic: np.ndarray ch_names: np.ndarray | None - def get_slopes( + def fit_aperiodic_model( self, fit_func: str = 'fixed', scale: bool = False, fit_bounds: tuple[float, float] | None = None - ) -> SlopeFit: + ) -> AperiodicFit: """ This method can be used to extract aperiodic parameters from the aperiodic spectrum extracted from IRASA. The algorithm works by applying one of two different curve fit functions and returns the associated parameters, @@ -34,14 +34,14 @@ def get_slopes( should be None if the whole frequency range is desired. Otherwise a tuple of (lower, upper) - Returns: SlopeFit + Returns: AperiodicFit df_aps: DataFrame DataFrame containing the center frequency, bandwidth and peak height for each channel df_gof: DataFrame DataFrame containing the goodness of fit of the specific fit function for each channel. """ - return compute_slope_sprint( + return compute_aperiodic_model_sprint( aperiodic_spectrum=self.aperiodic[np.newaxis, :, :] if self.aperiodic.ndim == min_ndim else self.aperiodic, freqs=self.freqs, times=self.time, diff --git a/pyrasa/utils/types.py b/pyrasa/utils/types.py index ab6f815..31ab85d 100644 --- a/pyrasa/utils/types.py +++ b/pyrasa/utils/types.py @@ -20,6 +20,6 @@ class IrasaSprintKwargsTyped(TypedDict): @define -class SlopeFit: +class AperiodicFit: aperiodic_params: pd.DataFrame gof: pd.DataFrame diff --git a/tests/test_basic_irasa.py b/tests/test_basic_irasa.py index 5c24d6f..d3cc62f 100644 --- a/tests/test_basic_irasa.py +++ b/tests/test_basic_irasa.py @@ -27,8 +27,8 @@ def test_irasa(combined_signal, fs, osc_freq, exponent): r = np.corrcoef(irasa_spectrum.raw_spectrum, psd_cmb)[0, 1] assert r > MIN_CORR_PSD_CMB # test whether we can reconstruct the exponent correctly - slope_fit = irasa_spectrum.get_slopes(fit_func='fixed') - assert bool(np.isclose(slope_fit.aperiodic_params['Exponent'][0], np.abs(exponent), atol=TOLERANCE)) + aperiodic_fit = irasa_spectrum.fit_aperiodic_model(fit_func='fixed') + assert bool(np.isclose(aperiodic_fit.aperiodic_params['Exponent'][0], np.abs(exponent), atol=TOLERANCE)) # test whether we can reconstruct the peak frequency correctly pe_params = irasa_spectrum.get_peaks() assert bool(np.isclose(np.round(pe_params['cf'], 0), osc_freq)) diff --git a/tests/test_compute_slope.py b/tests/test_compute_slope.py index 43f3a33..8e59c5c 100644 --- a/tests/test_compute_slope.py +++ b/tests/test_compute_slope.py @@ -3,7 +3,7 @@ import scipy.signal as dsp from pyrasa import irasa -from pyrasa.utils.aperiodic_utils import compute_slope +from pyrasa.utils.aperiodic_utils import compute_aperiodic_model from pyrasa.utils.fit_funcs import AbstractFitFun from pyrasa.utils.peak_utils import get_peak_params @@ -22,23 +22,23 @@ def test_slope_fitting_fixed(fixed_aperiodic_signal, fs, exponent): freqs, psd = freqs[freq_logical], psd[freq_logical] # test whether we can reconstruct the exponent correctly - slope_fit_f = compute_slope(psd, freqs, fit_func='fixed') - assert pytest.approx(slope_fit_f.aperiodic_params['Exponent'][0], abs=TOLERANCE) == np.abs(exponent) + aperiodic_fit_f = compute_aperiodic_model(psd, freqs, fit_func='fixed') + assert pytest.approx(aperiodic_fit_f.aperiodic_params['Exponent'][0], abs=TOLERANCE) == np.abs(exponent) # test goodness of fit should be close to r_squared == 1 for linear model - assert slope_fit_f.gof['r_squared'][0] > MIN_R2 + assert aperiodic_fit_f.gof['r_squared'][0] > MIN_R2 # test if we can set fit bounds w/o error # _, _ = compute_slope(psd, freqs, fit_func='fixed', fit_bounds=[2, 50]) # bic and aic for fixed model should be better if linear - slope_fit_k = compute_slope(psd, freqs, fit_func='knee') + aperiodic_fit_k = compute_aperiodic_model(psd, freqs, fit_func='knee') # assert gof_k['AIC'][0] > gof['AIC'][0] - assert slope_fit_k.gof['BIC'][0] > slope_fit_f.gof['BIC'][0] + assert aperiodic_fit_k.gof['BIC'][0] > aperiodic_fit_f.gof['BIC'][0] # test the effect of scaling - slope_fit_fs = compute_slope(psd, freqs, fit_func='fixed', scale=True) - assert np.isclose(slope_fit_fs.aperiodic_params['Exponent'], slope_fit_f.aperiodic_params['Exponent']) - assert np.isclose(slope_fit_fs.gof['r_squared'], slope_fit_f.gof['r_squared']) + aperiodic_fit_fs = compute_aperiodic_model(psd, freqs, fit_func='fixed', scale=True) + assert np.isclose(aperiodic_fit_fs.aperiodic_params['Exponent'], aperiodic_fit_f.aperiodic_params['Exponent']) + assert np.isclose(aperiodic_fit_fs.gof['r_squared'], aperiodic_fit_f.gof['r_squared']) @pytest.mark.parametrize('exponent, fs', [(-1, 500)], scope='session') @@ -58,23 +58,23 @@ def test_slope_fitting_settings( ) # test bounds too low with pytest.raises(AssertionError): - compute_slope(psd[freq_logical], freqs[freq_logical], fit_func='fixed', fit_bounds=(0, 200)) + compute_aperiodic_model(psd[freq_logical], freqs[freq_logical], fit_func='fixed', fit_bounds=(0, 200)) # test bounds too high with pytest.raises(AssertionError): - compute_slope(psd[freq_logical], freqs[freq_logical], fit_func='fixed', fit_bounds=(1, 1000)) + compute_aperiodic_model(psd[freq_logical], freqs[freq_logical], fit_func='fixed', fit_bounds=(1, 1000)) # test bounds correct with pytest.raises(AssertionError): - compute_slope(psd[freq_logical], freqs[freq_logical], fit_func='fixed', fit_bounds=(5, 40)) + compute_aperiodic_model(psd[freq_logical], freqs[freq_logical], fit_func='fixed', fit_bounds=(5, 40)) # test for warning with pytest.warns(UserWarning, match=match_txt): - compute_slope(psd[freq_logical], freqs[freq_logical], fit_func='fixed') + compute_aperiodic_model(psd[freq_logical], freqs[freq_logical], fit_func='fixed') # test misspecify string in fit_func with pytest.raises(AssertionError): - compute_slope(psd[freq_logical], freqs[freq_logical], fit_func='incredible', fit_bounds=(5, 40)) + compute_aperiodic_model(psd[freq_logical], freqs[freq_logical], fit_func='incredible', fit_bounds=(5, 40)) # test absence of peaks get_peak_params(psd[freq_logical], freqs[freq_logical], min_peak_height=10) @@ -119,10 +119,10 @@ def curve_kwargs(self) -> dict[str, any]: 'bounds': ((-np.inf, -np.inf), (np.inf, np.inf)), } - slope_fit = compute_slope(np.log10(psd), np.log10(freqs), fit_func=CustomFitFun) + aperiodic_fit = compute_aperiodic_model(np.log10(psd), np.log10(freqs), fit_func=CustomFitFun) # add a high tolerance - assert pytest.approx(np.abs(slope_fit.aperiodic_params['b'][0]), abs=HIGH_TOLERANCE) == np.abs(exponent) + assert pytest.approx(np.abs(aperiodic_fit.aperiodic_params['b'][0]), abs=HIGH_TOLERANCE) == np.abs(exponent) irasa_spectrum = irasa(fixed_aperiodic_signal, fs, f_range, psd_kwargs={'nperseg': 4 * fs}) @@ -139,4 +139,4 @@ def func(self, x: np.ndarray, a: float, b: float) -> np.ndarray: return y_hat - irasa_spectrum.get_slopes(fit_func=CustomFitFun) + irasa_spectrum.fit_aperiodic_model(fit_func=CustomFitFun) diff --git a/tests/test_irasa_knee.py b/tests/test_irasa_knee.py index c858c87..20670dc 100644 --- a/tests/test_irasa_knee.py +++ b/tests/test_irasa_knee.py @@ -23,8 +23,8 @@ def test_irasa_knee_peakless(load_knee_aperiodic_signal, fs, exponent, knee): freq_logical = np.logical_and(freqs_psd >= f_range[0], freqs_psd <= f_range[1]) r = np.corrcoef(psd[freq_logical], psd_cmb)[0, 1] assert r > MIN_CORR_PSD_CMB - slope_fit_k = irasa_out.get_slopes(fit_func='knee') - slope_fit_f = irasa_out.get_slopes(fit_func='fixed') + slope_fit_k = irasa_out.fit_aperiodic_model(fit_func='knee') + slope_fit_f = irasa_out.fit_aperiodic_model(fit_func='fixed') # test whether we can get the first exponent correctly assert bool(np.isclose(slope_fit_k.aperiodic_params['Exponent_1'][0], 0, atol=TOLERANCE)) # test whether we can get the second exponent correctly @@ -54,8 +54,8 @@ def test_irasa_knee_cmb(load_knee_cmb_signal, fs, exponent, knee, osc_freq): freq_logical = np.logical_and(freqs_psd >= f_range[0], freqs_psd <= f_range[1]) r = np.corrcoef(psd[freq_logical], psd_cmb)[0, 1] assert r > MIN_CORR_PSD_CMB - slope_fit_k = irasa_out.get_slopes(fit_func='knee') - slope_fit_f = irasa_out.get_slopes(fit_func='fixed') + slope_fit_k = irasa_out.fit_aperiodic_model(fit_func='knee') + slope_fit_f = irasa_out.fit_aperiodic_model(fit_func='fixed') # test whether we can get the first exponent correctly assert bool(np.isclose(slope_fit_k.aperiodic_params['Exponent_1'][0], 0, atol=TOLERANCE)) # test whether we can get the second exponent correctly diff --git a/tests/test_irasa_sprint.py b/tests/test_irasa_sprint.py index 2c1cbbb..ceaac1c 100644 --- a/tests/test_irasa_sprint.py +++ b/tests/test_irasa_sprint.py @@ -24,7 +24,7 @@ def test_irasa_sprint(ts4sprint, fs, exponent_1, exponent_2): ) # check basic aperiodic detection - slope_fit = irasa_tf.get_slopes(fit_func='fixed') + slope_fit = irasa_tf.fit_aperiodic_model(fit_func='fixed') # irasa_tf.aperiodic[np.newaxis, :, :], freqs=irasa_tf.freqs, times=irasa_tf.time, # ) diff --git a/tests/test_mne.py b/tests/test_mne.py index 6b8ae9d..bd94529 100644 --- a/tests/test_mne.py +++ b/tests/test_mne.py @@ -15,10 +15,10 @@ def test_mne(gen_mne_data_raw): # test raw irasa_raw_result = irasa_raw(mne_data, band=(0.25, 50), duration=2, hset_info=(1.0, 2.0, 0.05)) with pytest.warns(OptimizeWarning): - irasa_raw_result.aperiodic.get_slopes(fit_func='fixed') + irasa_raw_result.aperiodic.fit_aperiodic_model(fit_func='fixed') irasa_raw_result.periodic.get_peaks(smoothing_window=2) # test epochs irasa_epoched_result = irasa_epochs(epochs, band=(0.5, 50), hset_info=(1.0, 2.0, 0.05)) - irasa_epoched_result.aperiodic.get_slopes(fit_func='fixed', scale=True) + irasa_epoched_result.aperiodic.fit_aperiodic_model(fit_func='fixed', scale=True) irasa_epoched_result.periodic.get_peaks(smoothing_window=2) From 5fd96d0e417c57a862980cfc19faae82c256fe18 Mon Sep 17 00:00:00 2001 From: Fabi Date: Fri, 9 Aug 2024 20:21:34 +0200 Subject: [PATCH 03/11] adding new docstrings --- pyrasa/irasa.py | 201 +++++++++++++++++++++++++++--------------------- 1 file changed, 112 insertions(+), 89 deletions(-) diff --git a/pyrasa/irasa.py b/pyrasa/irasa.py index 20dd126..8a7c8f6 100644 --- a/pyrasa/irasa.py +++ b/pyrasa/irasa.py @@ -37,51 +37,70 @@ def irasa( hset_accuracy: int = 4, ) -> IrasaSpectrum: """ - This function can be used to generate aperiodic and periodic power spectra from a time series - using the IRASA algorithm (Wen & Liu, 2016). + Computes the aperiodic and periodic components of the power spectrum from a time series using the + Irregular Resampling Autocorrelation (IRASA) algorithm. - This function gives you maximal control over all parameters so its up to you set things up properly. - - If you have preprocessed your data in mne python we recommend that you use the - irasa_raw or irasa_epochs functions from `pyrasa.irasa_mne`, as they directly work on your - `mne.io.BaseRaw` and `mne.io.BaseEpochs` classes and take care of the necessary checks. + The IRASA algorithm allows for the decomposition of neural signals into fractal (aperiodic) and + oscillatory (periodic) components, providing insight into the underlying dynamics of the data. Parameters ---------- - data : :py:class:˚numpy.ndarray˚ - The timeseries data used to extract aperiodic and periodic power spectra. + data : np.ndarray + Time series data, where the shape is expected to be either (Samples,) or (Channels, Samples). fs : int - The sampling frequency of the data. Can be omitted if data is :py:class:˚mne.io.BaseRaw˚. - band : tuple - A tuple containing the lower and upper band of the frequency range used to extract (a-)periodic spectra. + Sampling frequency of the data in Hz. + band : tuple[float, float] + The frequency range (lower and upper bounds in Hz) over which to compute the spectra. psd_kwargs : dict - A dictionary containing all the keyword arguments that are passed onto `scipy.signal.welch`. - filter_settings : tuple - A tuple containing the cut-off of the High- and Lowpass filter. It is highly advisable to set this - correctly in order to avoid filter artifacts in your evaluated frequency range. - hset_info : tuple, list or :py:class:˚numpy.ndarray˚ - Contains information about the range of the up/downsampling factors. - This should be a tuple, list or :py:class:˚numpy.ndarray˚ of (min, max, step). - hset_accuracy : int - floating point accuracy for the up/downsampling factor of the signal (default=4). + Keyword arguments to be passed to the `scipy.signal.welch` function for PSD estimation. + ch_names : np.ndarray | None, optional + Channel names associated with the data, if available. Default is None. + win_func : Callable, optional + Window function to be used in Welch's method. Default is `dsp.windows.hann`. + win_func_kwargs : dict | None, optional + Additional keyword arguments for the window function. Default is None. + dpss_settings_time_bandwidth : float, optional + Time-bandwidth product for the DPSS windows if used. Default is 2.0. + dpss_settings_low_bias : bool, optional + Keep only tapers with eigenvalues > 0.9. Default is True. + dpss_eigenvalue_weighting : bool, optional + Whether or not to apply eigenvalue weighting in DPSS. If True, spectral estimates weighted by + the concentration ratio of their respective tapers before combining. Default is True. + filter_settings : tuple[float | None, float | None], optional + Cutoff frequencies for highpass and lowpass filtering to avoid artifacts in the evaluated frequency range. + Default is (None, None). + hset_info : tuple[float, float, float], optional + Tuple specifying the range of the resampling factors as (min, max, step). Default is (1.05, 2.0, 0.05). + hset_accuracy : int, optional + Decimal precision for the resampling factors. Default is 4. Returns ------- - freqs : :py:class:`numpy.ndarray` - The Frequencys associated with the (a-)periodic spectra. - aperiodic : :py:class:`numpy.ndarray` - The aperiodic component of the data. - periodic : :py:class:`numpy.ndarray` - The periodic component of the data. + IrasaSpectrum + An object containing the following attributes: + - freqs: np.ndarray + Frequencies corresponding to the computed spectra. + - raw_spectrum: np.ndarray + The raw power spectrum. + - aperiodic: np.ndarray + The aperiodic (fractal) component of the spectrum. + - periodic: np.ndarray + The periodic (oscillatory) component of the spectrum. + - ch_names: np.ndarray + Channel names if provided. + + Notes + ----- + This function provides fine-grained control over the IRASA parameters. For users working with MNE-Python, + the `irasa_raw` and `irasa_epochs` functions from `pyrasa.irasa_mne` are recommended, as they handle + additional preprocessing steps. References ---------- - [1] Wen, H., & Liu, Z. (2016). Separating Fractal and Oscillatory - Components in the Power Spectrum of Neurophysiological Signal. - Brain Topography, 29(1), 13–26. - https://doi.org/10.1007/s10548-015-0448-0 - + Wen, H., & Liu, Z. (2016). Separating Fractal and Oscillatory Components in the Power Spectrum + of Neurophysiological Signal. Brain Topography, 29(1), 13–26. https://doi.org/10.1007/s10548-015-0448-0 """ + # set parameters if win_func_kwargs is None: win_func_kwargs = {} @@ -166,80 +185,84 @@ def irasa_sprint( # noqa PLR0915 C901 hset_accuracy: int = 4, ) -> IrasaTfSpectrum: """ + Computes time-resolved aperiodic and periodic components of the power spectrum from a time series + using the Irregular Resampling Autocorrelation (IRASA) algorithm. - This function can be used to seperate aperiodic from periodic power spectra - using the IRASA algorithm (Wen & Liu, 2016) in a time resolved manner. + This function is useful for analyzing how the aperiodic and periodic components of the power spectrum + change over time, providing a time-frequency decomposition of the signal. Parameters ---------- - data : :py:class:˚numpy.ndarray˚ - The timeseries data used to extract aperiodic and periodic power spectra. + data : np.ndarray + Time series data, where the shape is expected to be either (Samples,) or (Channels, Samples). fs : int - The sampling frequency of the data. - band : tuple - A tuple containing the lower and upper band of the frequency range used to extract (a-)periodic spectra. - freq_res : float - The desired frequency resolution in Hz. - smooth : bool - Whether or not to smooth the time-frequency data before computing IRASA - by averaging over adjacent fft bins using n_avgs. - n_avgs : int - Number indicating the amount of fft bins to average across. - win_duration : float - The time width of window in seconds used to calculate the stffts. - hop : int - Time increment in signal samples for sliding window. - win_duration : float - The time width of window in seconds used to calculate the stffts. - win_func : :py:class:`scipy.signal.windows` - The desired window function. Can be any window function - specified in :py:class:`scipy.signal.windows`. - The default is `scipy.signal.windows.hann`. - win_func_kwargs: dict - A dictionary containing keyword arguments passed to win_func. - dpss_settings: - In case that you want to do multitapering using dpss - we added a "sensible" preconfiguration as `scipy.signal.windows.dpss` - requires more parameters than the window functions in :py:class:`scipy.signal.windows`. - To change the settings adjust the parameter `win_func_kwargs`. - filter_settings : tuple - A tuple containing the cut-off of the High- and Lowpass filter. - It is highly advisable to set this correctly in order to avoid - filter artifacts in your evaluated frequency range. - hset_info : tuple, list or :py:class:˚numpy.ndarray˚ - Contains information about the range of the up/downsampling factors. - This should be a tuple, list or :py:class:˚numpy.ndarray˚ of (min, max, step). - hset_accuracy : int - floating point accuracy for the up/downsampling factor of the signal (default=4). - + Sampling frequency of the data in Hz. + ch_names : np.ndarray | None, optional + Channel names associated with the data, if available. Default is None. + band : tuple[float, float], optional + The frequency range (lower and upper bounds in Hz) over which to compute the spectra. Default is (1.0, 100.0). + freq_res : float, optional + Desired frequency resolution in Hz. Default is 0.5 Hz. + win_duration : float, optional + Duration of the window in seconds used for the short-time Fourier transforms (STFTs). Default is 0.4 seconds. + hop : int, optional + Time increment in signal samples for the sliding window in STFT. Default is 10 samples. + win_func : Callable, optional + Window function to be used in computing the time frequency spectrum. Default is `dsp.windows.hann`. + win_func_kwargs : dict | None, optional + Additional keyword arguments for the window function. Default is None. + dpss_settings_time_bandwidth : float, optional + Time-bandwidth product for the DPSS windows if used. Default is 2.0. + dpss_settings_low_bias : bool, optional + Keep only tapers with eigenvalues > 0.9. Default is True. + dpss_eigenvalue_weighting : bool, optional + Whether or not to apply eigenvalue weighting in DPSS. If True, spectral estimates weighted by + the concentration ratio of their respective tapers before combining. Default is True. + filter_settings : tuple[float | None, float | None], optional + Cutoff frequencies for highpass and lowpass filtering to avoid artifacts in the evaluated frequency range. + Default is (None, None). + hset_info : tuple[float, float, float], optional + Tuple specifying the range of the resampling factors as (min, max, step). Default is (1.05, 2.0, 0.05). + hset_accuracy : int, optional + Decimal precision for the resampling factors. Default is 4. Returns ------- - aperiodic : :py:class:`numpy.ndarray` - The aperiodic component of the data. - periodic : :py:class:`numpy.ndarray` - The periodic component of the data. - freqs : :py:class:`numpy.ndarray` - The Frequencys associated with the (a-)periodic spectra. - time : :py:class:`numpy.ndarray` - The time bins in seconds associated with the (a-)periodic spectra. - + IrasaTfSpectrum + An object containing the following attributes: + - freqs: np.ndarray + Frequencies corresponding to the computed spectra. + - time: np.ndarray + Time bins in seconds associated with the (a-)periodic spectra. + - raw_spectrum: np.ndarray + The raw time-frequency power spectrum. + - aperiodic: np.ndarray + The aperiodic (fractal) component of the spectrum. + - periodic: np.ndarray + The periodic (oscillatory) component of the spectrum. + - ch_names: np.ndarray + Channel names if provided. + + Notes + ----- + This function performs a time-frequency decomposition of the input data, allowing for a time-resolved analysis + of the periodic and aperiodic components of the signal. The STFT is computed for each time window, and IRASA + is applied to separate the spectral components. References ---------- - [1] Wen, H., & Liu, Z. (2016). Separating Fractal and Oscillatory - Components in the Power Spectrum of Neurophysiological Signal. - Brain Topography, 29(1), 13–26.https://doi.org/10.1007/s10548-015-0448-0 - + Wen, H., & Liu, Z. (2016). Separating Fractal and Oscillatory Components in the Power Spectrum of + Neurophysiological Signal. Brain Topography, 29(1), 13–26. https://doi.org/10.1007/s10548-015-0448-0 """ # set parameters if win_func_kwargs is None: win_func_kwargs = {} - # Safety checks - assert isinstance(data, np.ndarray), 'Data should be a numpy array.' - assert data.ndim == 2, 'Data shape needs to be of shape (Channels, Samples).' # noqa PLR2004 + # Minimal safety checks + if data.ndim == 1: + data = data[np.newaxis, :] + assert data.ndim == 2, 'Data shape needs to be either of shape (Channels, Samples) or (Samples, ).' # noqa PLR2004 irasa_params = { 'data': data, From 1a61ffbe8012c5c470f7ba55c27d5b8a9c9e9b1b Mon Sep 17 00:00:00 2001 From: Fabi Date: Fri, 9 Aug 2024 22:42:00 +0200 Subject: [PATCH 04/11] fixed tests --- tests/test_compute_slope.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/tests/test_compute_slope.py b/tests/test_compute_slope.py index 8e59c5c..9819035 100644 --- a/tests/test_compute_slope.py +++ b/tests/test_compute_slope.py @@ -77,7 +77,7 @@ def test_slope_fitting_settings( compute_aperiodic_model(psd[freq_logical], freqs[freq_logical], fit_func='incredible', fit_bounds=(5, 40)) # test absence of peaks - get_peak_params(psd[freq_logical], freqs[freq_logical], min_peak_height=10) + get_peak_params(psd[freq_logical], freqs[freq_logical], min_peak_height=10, fit_bounds=(1, 40)) # test custom slope fitting functions @@ -87,7 +87,7 @@ def test_custom_slope_fitting( exponent, fs, ): - f_range = [1.5, 300] + f_range = [1.5, 100] # test whether recombining periodic and aperiodic spectrum is equivalent to the original spectrum freqs, psd = dsp.welch(fixed_aperiodic_signal, fs, nperseg=int(4 * fs)) freq_logical = np.logical_and(freqs >= f_range[0], freqs <= f_range[1]) From 773ba689a3314f135c0eec7d700416ec71f20728 Mon Sep 17 00:00:00 2001 From: Fabi Date: Fri, 9 Aug 2024 23:10:37 +0200 Subject: [PATCH 05/11] more docstrings for aperiodic_utils --- pyrasa/__version__.py | 2 + pyrasa/irasa.py | 2 + pyrasa/utils/aperiodic_utils.py | 155 +++++++++++++++++++++----------- tests/test_compute_slope.py | 3 +- tests/test_irasa_knee.py | 134 +++++++++++++-------------- 5 files changed, 176 insertions(+), 120 deletions(-) diff --git a/pyrasa/__version__.py b/pyrasa/__version__.py index 7d24cef..4ae33ef 100644 --- a/pyrasa/__version__.py +++ b/pyrasa/__version__.py @@ -1 +1,3 @@ +"""Version information for Pyrasa.""" + __version__ = '0.1.0.dev0' diff --git a/pyrasa/irasa.py b/pyrasa/irasa.py index 8a7c8f6..5712fea 100644 --- a/pyrasa/irasa.py +++ b/pyrasa/irasa.py @@ -1,3 +1,5 @@ +"""Functions to compute the IRASA algorithm.""" + from collections.abc import Callable from typing import TYPE_CHECKING, Any diff --git a/pyrasa/utils/aperiodic_utils.py b/pyrasa/utils/aperiodic_utils.py index 03ac49c..a85b6a9 100644 --- a/pyrasa/utils/aperiodic_utils.py +++ b/pyrasa/utils/aperiodic_utils.py @@ -41,33 +41,58 @@ def compute_aperiodic_model( fit_bounds: tuple[float, float] | None = None, ) -> AperiodicFit: """ - This function can be used to extract aperiodic parameters from the aperiodic spectrum extracted from IRASA. - The algorithm works by applying one of two different curve fit functions and returns the associated parameters, - as well as the respective goodness of fit. - - Parameters: aperiodic_spectrum : 2d array - Power values for the aeriodic spectrum extracted using IRASA shape (channel x frequency) - freqs : 1d array - Frequency values for the aperiodic spectrum - fit_func : string - Can be either "fixed" or "knee". - ch_names : list, optional, default: [] - Channel names ordered according to the periodic spectrum. - If empty channel names are given as numbers in ascending order. - scale : bool - scale the data by a factor of x to improve fitting. - This is helpful when fitting a knee and power values are very small eg. 1e-28, - in which case curve fits struggles to find the proper MSE (seems to be a machine precision issue). - Finally the data are rescaled to return the offset in the magnitude of the original data. - fit_bounds : None, tuple - Lower and upper bound for the fit function, should be None if the whole frequency range is desired. - Otherwise a tuple of (lower, upper) - - Returns: df_aps: DataFrame - DataFrame containing the aperiodic parameters for each channel depending on the fit func. - df_gof: DataFrame - DataFrame containing the goodness of fit of the specific fit function for each channel. - + Computes aperiodic parameters from the aperiodic spectrum using scipy's curve fitting function. + + This function can be used to model the aperiodic (1/f-like) component of the power spectrum. Per default, users can + choose between a fixed or knee model fit or specify their own fit method see examples custom_fit_functions.ipynb + for an example. The function returns the fitted parameters for each channel along with some + goodness of fit metrics. + + Parameters + ---------- + aperiodic_spectrum : np.ndarray + A 1 or 2D array of power values for the aperiodic spectrum where the shape is + expected to be either (Samples,) or (Channels, Samples). + freqs : np.ndarray + A 1D array of frequency values corresponding to the aperiodic spectrum. + fit_func : str or type[AbstractFitFun], optional + The fitting function to use. Can be "fixed" for a linear fit or "knee" for a fit that includes a + knee (bend) in the spectrum or a class that is inherited from AbstractFitFun. The default is 'fixed'. + ch_names : Iterable or None, optional + Channel names corresponding to the aperiodic spectrum. If None, channels will be named numerically + in ascending order. Default is None. + scale : bool, optional + Whether to scale the data to improve fitting accuracy. This is useful in cases where + power values are very small (e.g., 1e-28), which may lead to numerical precision issues during fitting. + After fitting, the parameters are rescaled to match the original data scale. Default is False. + fit_bounds : tuple[float, float] or None, optional + Tuple specifying the lower and upper frequency bounds for the fit function. If None, the entire frequency + range is used. Otherwise, the spectrum is cropped to the specified bounds. Default is None. + + Returns + ------- + AperiodicFit + An object containing two pandas DataFrames: + - aperiodic_params : pd.DataFrame + A DataFrame containing the fitted aperiodic parameters for each channel. + - gof : pd.DataFrame + A DataFrame containing the goodness of fit metrics for each channel. + + Notes + ----- + This function fits the aperiodic component of the power spectrum using scipy's curve fitting function. + The fitting can be performed using either a simple linear model ('fixed') or a more complex model + that includes a "knee" point, where the spectrum bends. The resulting parameters can help in + understanding the underlying characteristics of the aperiodic component in the data. + + If the `fit_bounds` parameter is used, it ensures that only the specified frequency range is considered + for fitting, which can be important to avoid fitting artifacts outside the region of interest. + + The `scale` parameter can be crucial when dealing with data that have extremely small values, + as it helps to mitigate issues related to machine precision during the fitting process. + + The function asserts that the input data are of the correct type and shape, and raises warnings + if the first frequency value is zero, as this can cause issues during model fitting. """ assert isinstance(aperiodic_spectrum, np.ndarray), 'aperiodic_spectrum should be a numpy array.' @@ -134,35 +159,63 @@ def compute_aperiodic_model_sprint( aperiodic_spectrum: np.ndarray, freqs: np.ndarray, times: np.ndarray, - fit_func: str, + fit_func: str | type[AbstractFitFun] = 'fixed', scale: bool = False, ch_names: Iterable | None = None, fit_bounds: tuple[float, float] | None = None, ) -> AperiodicFit: """ - This function can be used to extract aperiodic parameters from the aperiodic spectrogram extracted from IRASA. - The algorithm works by applying one of two different curve fit functions and returns the associated parameters, - as well as the respective goodness of fit. - - Parameters: aperiodic_spectrum : 2d array - Power values for the aeriodic spectrogram extracted using IRASA shape (channel x frequency) - freqs : 1d array - Frequency values for the aperiodic spectrogram - times : 1d array - time values for the aperiodic spectrogram - fit_func : string - Can be either "fixed" or "knee". - ch_names : list, optional, default: [] - Channel names ordered according to the periodic spectrum. - If empty channel names are given as numbers in ascending order. - fit_bounds : None, tuple - Lower and upper bound for the fit function, should be None if the whole frequency range is desired. - Otherwise a tuple of (lower, upper) - - Returns: df_aps: DataFrame - DataFrame containing the center frequency, bandwidth and peak height for each channel - df_gof: DataFrame - DataFrame containing the goodness of fit of the specific fit function for each channel. + Extracts aperiodic parameters from the aperiodic spectrogram using scipy's curve fitting + function. + + This function computes aperiodic parameters for each time point in the spectrogram by applying either one of + two different curve fitting functions (`fixed` or `knee`) or a custom function specified by user to the data. + See examples custom_fit_functions.ipynb. The parameters, along with the goodness of + fit for each time point, are returned in a concatenated format. + + Parameters + ---------- + aperiodic_spectrum : np.ndarray + A 2 or 3D array of power values from the aperiodic spectrogram, with shape (Frequencies, Time) + or (Channels, Frequencies, Time). + freqs : np.ndarray + A 1D array of frequency values corresponding to the aperiodic spectrogram. + times : np.ndarray + A 1D array of time values corresponding to the aperiodic spectrogram. + fit_func : str or type[AbstractFitFun], optional + The fitting function to use. Can be "fixed" for a linear fit or "knee" for a fit that includes a + knee (bend) in the spectrum or a class that is inherited from AbstractFitFun. The default is 'fixed'.. + ch_names : Iterable or None, optional + Channel names corresponding to the aperiodic spectrogram. If None, channels will be named numerically + in ascending order. Default is None. + scale : bool, optional + Whether to scale the data to improve fitting accuracy. This is useful when fitting a knee in cases where + power values are very small, leading to numerical precision issues. Default is False. + fit_bounds : tuple[float, float] or None, optional + Tuple specifying the lower and upper frequency bounds for the fit function. If None, the entire frequency + range is used. Otherwise, the spectrum is cropped to the specified bounds before fitting. Default is None. + + Returns + ------- + AperiodicFit + An object containing two pandas DataFrames: + - aperiodic_params : pd.DataFrame + A DataFrame containing the aperiodic parameters (e.g., center frequency, bandwidth, peak height) + for each channel and each time point. + - gof : pd.DataFrame + A DataFrame containing the goodness of fit metrics for each channel and each time point. + + Notes + ----- + This function iterates over each time point in the provided spectrogram to extract aperiodic parameters + using the specified fit function. It leverages the `compute_aperiodic_model` function for individual fits + at each time point, then combines the results across all time points into comprehensive DataFrames. + + The `fit_bounds` parameter allows for frequency range restrictions during fitting, which can help in focusing + the analysis on a particular frequency band of interest. + + Scaling the data using the `scale` parameter can be particularly important when dealing with very small power + values that might lead to poor fitting due to numerical precision limitations. """ diff --git a/tests/test_compute_slope.py b/tests/test_compute_slope.py index 9819035..881f3cb 100644 --- a/tests/test_compute_slope.py +++ b/tests/test_compute_slope.py @@ -65,8 +65,7 @@ def test_slope_fitting_settings( compute_aperiodic_model(psd[freq_logical], freqs[freq_logical], fit_func='fixed', fit_bounds=(1, 1000)) # test bounds correct - with pytest.raises(AssertionError): - compute_aperiodic_model(psd[freq_logical], freqs[freq_logical], fit_func='fixed', fit_bounds=(5, 40)) + compute_aperiodic_model(psd[freq_logical], freqs[freq_logical], fit_func='fixed', fit_bounds=(5, 40)) # test for warning with pytest.warns(UserWarning, match=match_txt): diff --git a/tests/test_irasa_knee.py b/tests/test_irasa_knee.py index 20670dc..5a3a9be 100644 --- a/tests/test_irasa_knee.py +++ b/tests/test_irasa_knee.py @@ -1,74 +1,74 @@ -import numpy as np -import pytest -import scipy.signal as dsp +# import numpy as np +# import pytest +# import scipy.signal as dsp -from pyrasa import irasa +# from pyrasa import irasa -from .settings import EXP_KNEE_COMBO, FS, KNEE_TOLERANCE, MIN_CORR_PSD_CMB, OSC_FREQ, TOLERANCE +# from .settings import EXP_KNEE_COMBO, FS, KNEE_TOLERANCE, MIN_CORR_PSD_CMB, OSC_FREQ, TOLERANCE -# Estimate periodic and aperiodic components with IRASA -# These tests should cover the basic functionality of the IRASA workflow +# # Estimate periodic and aperiodic components with IRASA +# # These tests should cover the basic functionality of the IRASA workflow -# knee model -@pytest.mark.parametrize('exponent, knee', EXP_KNEE_COMBO, scope='session') -@pytest.mark.parametrize('fs', FS, scope='session') -def test_irasa_knee_peakless(load_knee_aperiodic_signal, fs, exponent, knee): - f_range = [0.1, 100] - irasa_out = irasa(load_knee_aperiodic_signal, fs, f_range, psd_kwargs={'nperseg': 4 * fs}) - # test the shape of the output - assert irasa_out.freqs.shape[0] == irasa_out.aperiodic.shape[1] == irasa_out.periodic.shape[1] - freqs_psd, psd = dsp.welch(load_knee_aperiodic_signal, fs, nperseg=int(4 * fs)) - psd_cmb = irasa_out.aperiodic[0, :] + irasa_out.periodic[0, :] - freq_logical = np.logical_and(freqs_psd >= f_range[0], freqs_psd <= f_range[1]) - r = np.corrcoef(psd[freq_logical], psd_cmb)[0, 1] - assert r > MIN_CORR_PSD_CMB - slope_fit_k = irasa_out.fit_aperiodic_model(fit_func='knee') - slope_fit_f = irasa_out.fit_aperiodic_model(fit_func='fixed') - # test whether we can get the first exponent correctly - assert bool(np.isclose(slope_fit_k.aperiodic_params['Exponent_1'][0], 0, atol=TOLERANCE)) - # test whether we can get the second exponent correctly - assert bool(np.isclose(slope_fit_k.aperiodic_params['Exponent_2'][0], np.abs(exponent), atol=TOLERANCE)) - # test whether we can get the knee correctly - knee_hat = slope_fit_k.aperiodic_params['Knee'][0] ** ( - 1 / (2 * slope_fit_k.aperiodic_params['Exponent_1'][0] + slope_fit_k.aperiodic_params['Exponent_2'][0]) - ) - knee_real = knee ** (1 / np.abs(exponent)) - assert bool(np.isclose(knee_hat, knee_real, atol=KNEE_TOLERANCE)) - # test bic/aic -> should be better for knee - assert slope_fit_k.gof['AIC'][0] < slope_fit_f.gof['AIC'][0] - assert slope_fit_k.gof['BIC'][0] < slope_fit_f.gof['BIC'][0] +# # knee model +# @pytest.mark.parametrize('exponent, knee', EXP_KNEE_COMBO, scope='session') +# @pytest.mark.parametrize('fs', FS, scope='session') +# def test_irasa_knee_peakless(load_knee_aperiodic_signal, fs, exponent, knee): +# f_range = [0.1, 100] +# irasa_out = irasa(load_knee_aperiodic_signal, fs, f_range, psd_kwargs={'nperseg': 4 * fs}) +# # test the shape of the output +# assert irasa_out.freqs.shape[0] == irasa_out.aperiodic.shape[1] == irasa_out.periodic.shape[1] +# freqs_psd, psd = dsp.welch(load_knee_aperiodic_signal, fs, nperseg=int(4 * fs)) +# psd_cmb = irasa_out.aperiodic[0, :] + irasa_out.periodic[0, :] +# freq_logical = np.logical_and(freqs_psd >= f_range[0], freqs_psd <= f_range[1]) +# r = np.corrcoef(psd[freq_logical], psd_cmb)[0, 1] +# assert r > MIN_CORR_PSD_CMB +# slope_fit_k = irasa_out.fit_aperiodic_model(fit_func='knee') +# slope_fit_f = irasa_out.fit_aperiodic_model(fit_func='fixed') +# # test whether we can get the first exponent correctly +# assert bool(np.isclose(slope_fit_k.aperiodic_params['Exponent_1'][0], 0, atol=TOLERANCE)) +# # test whether we can get the second exponent correctly +# assert bool(np.isclose(slope_fit_k.aperiodic_params['Exponent_2'][0], np.abs(exponent), atol=TOLERANCE)) +# # test whether we can get the knee correctly +# knee_hat = slope_fit_k.aperiodic_params['Knee'][0] ** ( +# 1 / (2 * slope_fit_k.aperiodic_params['Exponent_1'][0] + slope_fit_k.aperiodic_params['Exponent_2'][0]) +# ) +# knee_real = knee ** (1 / np.abs(exponent)) +# assert bool(np.isclose(knee_hat, knee_real, atol=KNEE_TOLERANCE)) +# # test bic/aic -> should be better for knee +# assert slope_fit_k.gof['AIC'][0] < slope_fit_f.gof['AIC'][0] +# assert slope_fit_k.gof['BIC'][0] < slope_fit_f.gof['BIC'][0] -# knee model -@pytest.mark.parametrize('exponent, knee', EXP_KNEE_COMBO, scope='session') -@pytest.mark.parametrize('fs', FS, scope='session') -@pytest.mark.parametrize('osc_freq', OSC_FREQ, scope='session') -def test_irasa_knee_cmb(load_knee_cmb_signal, fs, exponent, knee, osc_freq): - f_range = [0.1, 100] - irasa_out = irasa(load_knee_cmb_signal, fs, f_range, psd_kwargs={'nperseg': 4 * fs}) - # test the shape of the output - assert irasa_out.freqs.shape[0] == irasa_out.aperiodic.shape[1] == irasa_out.periodic.shape[1] - freqs_psd, psd = dsp.welch(load_knee_cmb_signal, fs, nperseg=int(4 * fs)) - psd_cmb = irasa_out.aperiodic[0, :] + irasa_out.periodic[0, :] - freq_logical = np.logical_and(freqs_psd >= f_range[0], freqs_psd <= f_range[1]) - r = np.corrcoef(psd[freq_logical], psd_cmb)[0, 1] - assert r > MIN_CORR_PSD_CMB - slope_fit_k = irasa_out.fit_aperiodic_model(fit_func='knee') - slope_fit_f = irasa_out.fit_aperiodic_model(fit_func='fixed') - # test whether we can get the first exponent correctly - assert bool(np.isclose(slope_fit_k.aperiodic_params['Exponent_1'][0], 0, atol=TOLERANCE)) - # test whether we can get the second exponent correctly - assert bool(np.isclose(slope_fit_k.aperiodic_params['Exponent_2'][0], np.abs(exponent), atol=TOLERANCE)) - # test whether we can get the knee correctly - knee_hat = slope_fit_k.aperiodic_params['Knee'][0] ** ( - 1 / (2 * slope_fit_k.aperiodic_params['Exponent_1'][0] + slope_fit_k.aperiodic_params['Exponent_2'][0]) - ) - knee_real = knee ** (1 / np.abs(exponent)) - assert bool(np.isclose(knee_hat, knee_real, atol=KNEE_TOLERANCE)) - # test bic/aic -> should be better for knee - assert slope_fit_k.gof['AIC'][0] < slope_fit_f.gof['AIC'][0] - assert slope_fit_k.gof['BIC'][0] < slope_fit_f.gof['BIC'][0] - # test whether we can reconstruct the peak frequency correctly - pe_params = irasa_out.get_peaks() - assert bool(np.isclose(np.round(pe_params['cf'], 0), osc_freq)) +# # knee model +# @pytest.mark.parametrize('exponent, knee', EXP_KNEE_COMBO, scope='session') +# @pytest.mark.parametrize('fs', FS, scope='session') +# @pytest.mark.parametrize('osc_freq', OSC_FREQ, scope='session') +# def test_irasa_knee_cmb(load_knee_cmb_signal, fs, exponent, knee, osc_freq): +# f_range = [0.1, 100] +# irasa_out = irasa(load_knee_cmb_signal, fs, f_range, psd_kwargs={'nperseg': 4 * fs}) +# # test the shape of the output +# assert irasa_out.freqs.shape[0] == irasa_out.aperiodic.shape[1] == irasa_out.periodic.shape[1] +# freqs_psd, psd = dsp.welch(load_knee_cmb_signal, fs, nperseg=int(4 * fs)) +# psd_cmb = irasa_out.aperiodic[0, :] + irasa_out.periodic[0, :] +# freq_logical = np.logical_and(freqs_psd >= f_range[0], freqs_psd <= f_range[1]) +# r = np.corrcoef(psd[freq_logical], psd_cmb)[0, 1] +# assert r > MIN_CORR_PSD_CMB +# slope_fit_k = irasa_out.fit_aperiodic_model(fit_func='knee') +# slope_fit_f = irasa_out.fit_aperiodic_model(fit_func='fixed') +# # test whether we can get the first exponent correctly +# assert bool(np.isclose(slope_fit_k.aperiodic_params['Exponent_1'][0], 0, atol=TOLERANCE)) +# # test whether we can get the second exponent correctly +# assert bool(np.isclose(slope_fit_k.aperiodic_params['Exponent_2'][0], np.abs(exponent), atol=TOLERANCE)) +# # test whether we can get the knee correctly +# knee_hat = slope_fit_k.aperiodic_params['Knee'][0] ** ( +# 1 / (2 * slope_fit_k.aperiodic_params['Exponent_1'][0] + slope_fit_k.aperiodic_params['Exponent_2'][0]) +# ) +# knee_real = knee ** (1 / np.abs(exponent)) +# assert bool(np.isclose(knee_hat, knee_real, atol=KNEE_TOLERANCE)) +# # test bic/aic -> should be better for knee +# assert slope_fit_k.gof['AIC'][0] < slope_fit_f.gof['AIC'][0] +# assert slope_fit_k.gof['BIC'][0] < slope_fit_f.gof['BIC'][0] +# # test whether we can reconstruct the peak frequency correctly +# pe_params = irasa_out.get_peaks() +# assert bool(np.isclose(np.round(pe_params['cf'], 0), osc_freq)) From c17a334f5f1b610eea13902b4c8e258e13f9222b Mon Sep 17 00:00:00 2001 From: Fabi Date: Sat, 10 Aug 2024 00:14:15 +0200 Subject: [PATCH 06/11] added peak finding doc --- pyrasa/utils/peak_utils.py | 229 ++++++++++++++++++-------- simulations/notebooks/check_peak_p.py | 16 ++ tests/test_compute_slope.py | 16 +- tests/test_irasa_knee.py | 134 +++++++-------- 4 files changed, 245 insertions(+), 150 deletions(-) create mode 100644 simulations/notebooks/check_peak_p.py diff --git a/pyrasa/utils/peak_utils.py b/pyrasa/utils/peak_utils.py index f0e77db..698e56e 100644 --- a/pyrasa/utils/peak_utils.py +++ b/pyrasa/utils/peak_utils.py @@ -18,36 +18,67 @@ def get_peak_params( cut_spectrum: tuple[float, float] | None = None, peak_threshold: float = 1.0, min_peak_height: float = 0.01, - peak_width_limits: tuple[float, float] = (0.5, 6.0), + peak_width_limits: tuple[float, float] = (0.5, 12.0), ) -> pd.DataFrame: """ - This function can be used to extract peak parameters from the periodic spectrum extracted from IRASA. - The algorithm works by smoothing the spectrum, zeroing out negative values and - extracting peaks based on user specified parameters. - - Parameters: periodic_spectrum : 1d or 2d array - Power values for the periodic spectrum extracted using IRASA shape(channel x frequency) - freqs : 1d array - Frequency values for the periodic spectrum - ch_names: list, optional, default: [] - Channel names ordered according to the periodic spectrum. - If empty channel names are given as numbers in ascending order. - smoothing window : int, optional, default: 2 - Smoothing window in Hz handed over to the savitzky-golay filter. - cut_spectrum : tuple of (float, float), optional, default (1, 40) - Cut the periodic spectrum to limit peak finding to a sensible range - peak_threshold : float, optional, default: 1 - Relative threshold for detecting peaks. This threshold is defined in - relative units of the periodic spectrum - min_peak_height : float, optional, default: 0.01 - Absolute threshold for identifying peaks. The threhsold is defined in relative - units of the power spectrum. Setting this is somewhat necessary when a - "knee" is present in the data as it will carry over to the periodic spctrum in irasa. - peak_width_limits : tuple of (float, float), optional, default (.5, 12) - Limits on possible peak width, in Hz, as (lower_bound, upper_bound) - - Returns: df_peaks: DataFrame - DataFrame containing the center frequency, bandwidth and peak height for each channel + Extracts peak parameters from the periodic spectrum obtained via IRASA. + + This function identifies and extracts peak parameters such as center frequency (cf), bandwidth (bw), + and peak height (pw) from a periodic spectrum using scipy's find_peaks function. + The spectrum can be optionally smoothed prior peak detection. + + Parameters + ---------- + periodic_spectrum : np.ndarray + 1D or 2D array containing power values of the periodic spectrum (shape: [Channels, Frequencies] + or [Frequencies]). + freqs : np.ndarray + 1D array containing frequency values corresponding to the periodic spectrum. + ch_names : Iterable or None, optional + List of channel names corresponding to the periodic spectrum. If None, channels are labeled numerically. + Default is None. + smooth : bool, optional + Whether to smooth the spectrum before peak extraction. Smoothing can help in reducing noise and + better identifying peaks. Default is True. + smoothing_window : int or float, optional + The size of the smoothing window in Hz, passed to the Savitzky-Golay filter. Default is 1 Hz. + polyorder : int, optional + The polynomial order for the Savitzky-Golay filter used in smoothing. The polynomial order must be + less than the window length. Default is 1. + cut_spectrum : tuple of (float, float) or None, optional + Tuple specifying the frequency range (lower_bound, upper_bound) to which the spectrum should be cut + before peak extraction. If None, peaks are detected across the full frequency range. Default is None. + peak_threshold : float, optional + Relative threshold for detecting peaks, defined as a multiple of the standard deviation of the + filtered spectrum. Default is 1.0. + min_peak_height : float, optional + The minimum peak height (in absolute units of the power spectrum) required for a peak to be recognized. + This can be useful for filtering out noise or insignificant peaks, especially when a "knee" is present + in the original data, which may persist in the periodic spectrum. Default is 0.01. + peak_width_limits : tuple of (float, float), optional + The lower and upper bounds for peak widths, in Hz. This helps in constraining the peak detection to + meaningful features. Default is (0.5, 12.0). + + Returns + ------- + pd.DataFrame + A DataFrame containing the detected peak parameters for each channel. The DataFrame includes the + following columns: + - 'ch_name': Channel name + - 'cf': Center frequency of the peak + - 'bw': Bandwidth of the peak + - 'pw': Peak height (power) + + + Notes + ----- + The function works by first optionally smoothing the periodic spectrum using a Savitzky-Golay filter. + Then, it performs peak detection using the `scipy.signal.find_peaks` function, taking into account the + specified peak thresholds and width limits. Peaks that do not meet the minimum height requirement are + filtered out. + + The `cut_spectrum` parameter can be used to focus peak detection on a specific frequency range, which is + particularly useful when the region of interest is known in advance. """ @@ -115,7 +146,7 @@ def get_peak_params( return df_peaks -# %% find peaks in irasa sprint +# % find peaks in irasa sprint def get_peak_params_sprint( @@ -132,36 +163,65 @@ def get_peak_params_sprint( peak_width_limits: tuple[float, float] = (0.5, 12), ) -> pd.DataFrame: """ - This function can be used to extract peak parameters from the periodic spectrum extracted from IRASA. - The algorithm works by smoothing the spectrum, zeroing out negative values and - extracting peaks based on user specified parameters. - - Parameters: periodic_spectrum : 1d or 2d array - Power values for the periodic spectrum extracted using IRASA shape(channel x frequency) - freqs : 1d array - Frequency values for the periodic spectrogram - time : 1d array - time points of the periodic spectrogram - ch_names: list, optional, default: [] - Channel names ordered according to the periodic spectrum. - If empty channel names are given as numbers in ascending order. - smoothing window : int, optional, default: 2 - Smoothing window in Hz handed over to the savitzky-golay filter. - cut_spectrum : tuple of (float, float), optional, default (1, 40) - Cut the periodic spectrum to limit peak finding to a sensible range - peak_threshold : float, optional, default: 1 - Relative threshold for detecting peaks. This threshold is defined in - relative units of the periodic spectrum - min_peak_height : float, optional, default: 0.01 - Absolute threshold for identifying peaks. The threhsold is defined in relative - units of the power spectrum. Setting this is somewhat necessary when a - "knee" is present in the data as it will carry over to the periodic spctrum in irasa. - peak_width_limits : tuple of (float, float), optional, default (.5, 12) - Limits on possible peak width, in Hz, as (lower_bound, upper_bound) - - Returns: df_peaks: DataFrame - DataFrame containing the center frequency, - bandwidth and peak height for each channel and time point. + Extracts peak parameters from a periodic spectrogram obtained via IRASA. + + This function processes a time-resolved periodic spectrum to identify and extract peak parameters such as + center frequency (cf), bandwidth (bw), and peak height (pw) for each time point. It applies smoothing, + peak detection, and thresholding according to user-defined parameters + (see get_peak_params for additional Information). + + Parameters + ---------- + periodic_spectrum : np.ndarray + 2 or 3D array containing power values of the periodic spectrogram (shape: [Channels, Frequencies, Time Points]). + freqs : np.ndarray + 1D array containing frequency values corresponding to the periodic spectrogram. + times : np.ndarray + 1D array containing time points corresponding to the periodic spectrogram. + ch_names : Iterable or None, optional + List of channel names corresponding to the periodic spectrogram. If None, channels are labeled numerically. + Default is None. + smooth : bool, optional + Whether to smooth the spectrum before peak extraction. Smoothing can help in reducing noise and better + identifying peaks. Default is True. + smoothing_window : int or float, optional + The size of the smoothing window in Hz, passed to the Savitzky-Golay filter. Default is 2 Hz. + polyorder : int, optional + The polynomial order for the Savitzky-Golay filter used in smoothing. The polynomial order must be less + than the window length. Default is 1. + cut_spectrum : tuple of (float, float) or None, optional + Tuple specifying the frequency range (lower_bound, upper_bound) to which the spectrum should be cut before + peak extraction. If None, the full frequency range is used. Default is (1, 40). + peak_threshold : int or float, optional + Relative threshold for detecting peaks, defined as a multiple of the standard deviation of the filtered + spectrum. Default is 1. + min_peak_height : float, optional + The minimum peak height (in absolute units of the power spectrum) required for a peak to be recognized. + This can be useful for filtering out noise or insignificant peaks, especially when a "knee" is present + in the data. Default is 0.01. + peak_width_limits : tuple of (float, float), optional + The lower and upper bounds for peak widths, in Hz. This helps in constraining the peak detection to + meaningful features. Default is (0.5, 12). + + Returns + ------- + pd.DataFrame + A DataFrame containing the detected peak parameters for each channel and time point. The DataFrame + includes the following columns: + - 'ch_name': Channel name + - 'cf': Center frequency of the peak + - 'bw': Bandwidth of the peak + - 'pw': Peak height (power) + - 'time': Corresponding time point for the peak + + Notes + ----- + This function iteratively processes each time point in the spectrogram, applying the `get_peak_params` + function to extract peak parameters at each time point. The resulting peak parameters are combined into + a single DataFrame. + + The function is particularly useful for analyzing time-varying spectral features, such as in dynamic or + non-stationary M/EEG data, where peaks may shift in frequency, bandwidth, or amplitude over time. """ @@ -191,20 +251,43 @@ def get_peak_params_sprint( # %% find peaks irasa style def get_band_info(df_peaks: pd.DataFrame, freq_range: tuple[int, int], ch_names: list) -> pd.DataFrame: """ - This function can be used to extract peaks in a specified frequency range - from the Peak DataFrame obtained via "get_peak_params". - - Parameters : df_peaks : DataFrame - DataFrame containing peak parameters obtained via "get_peak_params". - freq_range : tuple (int, int) - Lower and upper limits for the to be extracted frequency range. - ch_names: list - Channel names used in the computation of the periodic spectrum. - This information is needed to fill channels without a peak in the specified range with nans. - - Returns: df_band_peaks: DataFrame - DataFrame containing the center frequency, bandwidth and peak height for each channel - in a specified frequency range + Extract peak information within a specified frequency range from a DataFrame of peak parameters. + + This function filters peaks found in the periodic spectrum or spectrogram to those within + a specified frequency range.It ensures that every channel is represented in the output, + filling in missing channels (i.e., channels without detected peaks in the specified range) with NaN values. + + Parameters + ---------- + df_peaks : pd.DataFrame + DataFrame containing peak parameters obtained from the `get_peak_params` function. + The DataFrame should include columns for 'ch_name' (channel name), 'cf' (center frequency), + 'bw' (bandwidth), and 'pw' (peak height). + freq_range : tuple of (int, int) + Tuple specifying the lower and upper frequency bounds (in Hz) to filter peaks by. Only peaks + with center frequencies (cf) within this range will be included in the output. + ch_names : list + List of channel names used in the computation of the periodic spectrum. This list ensures that + every channel is accounted for in the output, even if no peaks were found in the specified range + for certain channels. + + Returns + ------- + pd.DataFrame + DataFrame containing the peak parameters ('cf', 'bw', 'pw') for each channel within the specified + frequency range. Channels without detected peaks in this range will have NaN values for these parameters. + The DataFrame includes: + - 'ch_name': Channel name + - 'cf': Center frequency of the peak within the specified range + - 'bw': Bandwidth of the peak within the specified range + - 'pw': Peak height (power) within the specified range + + Notes + ----- + This function is useful for isolating and analyzing peaks that occur within specific canonical frequency bands + (e.g., alpha, beta, gamma) across multiple channels in a periodic spectrum. The inclusion of NaN + entries for channels without detected peaks ensures that the output DataFrame is complete and aligned + with the original channel list. """ diff --git a/simulations/notebooks/check_peak_p.py b/simulations/notebooks/check_peak_p.py new file mode 100644 index 0000000..7955217 --- /dev/null +++ b/simulations/notebooks/check_peak_p.py @@ -0,0 +1,16 @@ +#%% +import numpy as np +import scipy.signal as dsp +from pyrasa.utils.peak_utils import get_peak_params +from neurodsp.sim import sim_powerlaw + + +fs = 500 + +sig = sim_powerlaw(n_seconds=60, fs=fs, exponent=-1) +f_range = [0, 100] +#%% test whether recombining periodic and aperiodic spectrum is equivalent to the original spectrum +freqs, psd = dsp.welch(sig, fs, nperseg=int(4 * fs)) +freq_logical = np.logical_and(freqs >= f_range[0], freqs <= f_range[1]) +get_peak_params(psd[freq_logical], freqs[freq_logical], min_peak_height=10) +# %% diff --git a/tests/test_compute_slope.py b/tests/test_compute_slope.py index 881f3cb..bdf2ef1 100644 --- a/tests/test_compute_slope.py +++ b/tests/test_compute_slope.py @@ -5,7 +5,6 @@ from pyrasa import irasa from pyrasa.utils.aperiodic_utils import compute_aperiodic_model from pyrasa.utils.fit_funcs import AbstractFitFun -from pyrasa.utils.peak_utils import get_peak_params from .settings import EXPONENT, FS, HIGH_TOLERANCE, MIN_R2, TOLERANCE @@ -52,10 +51,10 @@ def test_slope_fitting_settings( freqs, psd = dsp.welch(fixed_aperiodic_signal, fs, nperseg=int(4 * fs)) freq_logical = np.logical_and(freqs >= f_range[0], freqs <= f_range[1]) - match_txt = ( - 'The first frequency appears to be 0 this will result in slope fitting problems. ' - + 'Frequencies will be evaluated starting from the next highest in Hz' - ) + # match_txt = ( + # 'The first frequency appears to be 0 this will result in slope fitting problems. ' + # + 'Frequencies will be evaluated starting from the next highest in Hz' + # ) # test bounds too low with pytest.raises(AssertionError): compute_aperiodic_model(psd[freq_logical], freqs[freq_logical], fit_func='fixed', fit_bounds=(0, 200)) @@ -68,16 +67,13 @@ def test_slope_fitting_settings( compute_aperiodic_model(psd[freq_logical], freqs[freq_logical], fit_func='fixed', fit_bounds=(5, 40)) # test for warning - with pytest.warns(UserWarning, match=match_txt): + with pytest.warns(UserWarning): # , match=match_txt): compute_aperiodic_model(psd[freq_logical], freqs[freq_logical], fit_func='fixed') # test misspecify string in fit_func - with pytest.raises(AssertionError): + with pytest.raises(ValueError): compute_aperiodic_model(psd[freq_logical], freqs[freq_logical], fit_func='incredible', fit_bounds=(5, 40)) - # test absence of peaks - get_peak_params(psd[freq_logical], freqs[freq_logical], min_peak_height=10, fit_bounds=(1, 40)) - # test custom slope fitting functions @pytest.mark.parametrize('exponent, fs', [(-1, 500)], scope='session') diff --git a/tests/test_irasa_knee.py b/tests/test_irasa_knee.py index 5a3a9be..20670dc 100644 --- a/tests/test_irasa_knee.py +++ b/tests/test_irasa_knee.py @@ -1,74 +1,74 @@ -# import numpy as np -# import pytest -# import scipy.signal as dsp +import numpy as np +import pytest +import scipy.signal as dsp -# from pyrasa import irasa +from pyrasa import irasa -# from .settings import EXP_KNEE_COMBO, FS, KNEE_TOLERANCE, MIN_CORR_PSD_CMB, OSC_FREQ, TOLERANCE +from .settings import EXP_KNEE_COMBO, FS, KNEE_TOLERANCE, MIN_CORR_PSD_CMB, OSC_FREQ, TOLERANCE -# # Estimate periodic and aperiodic components with IRASA -# # These tests should cover the basic functionality of the IRASA workflow +# Estimate periodic and aperiodic components with IRASA +# These tests should cover the basic functionality of the IRASA workflow -# # knee model -# @pytest.mark.parametrize('exponent, knee', EXP_KNEE_COMBO, scope='session') -# @pytest.mark.parametrize('fs', FS, scope='session') -# def test_irasa_knee_peakless(load_knee_aperiodic_signal, fs, exponent, knee): -# f_range = [0.1, 100] -# irasa_out = irasa(load_knee_aperiodic_signal, fs, f_range, psd_kwargs={'nperseg': 4 * fs}) -# # test the shape of the output -# assert irasa_out.freqs.shape[0] == irasa_out.aperiodic.shape[1] == irasa_out.periodic.shape[1] -# freqs_psd, psd = dsp.welch(load_knee_aperiodic_signal, fs, nperseg=int(4 * fs)) -# psd_cmb = irasa_out.aperiodic[0, :] + irasa_out.periodic[0, :] -# freq_logical = np.logical_and(freqs_psd >= f_range[0], freqs_psd <= f_range[1]) -# r = np.corrcoef(psd[freq_logical], psd_cmb)[0, 1] -# assert r > MIN_CORR_PSD_CMB -# slope_fit_k = irasa_out.fit_aperiodic_model(fit_func='knee') -# slope_fit_f = irasa_out.fit_aperiodic_model(fit_func='fixed') -# # test whether we can get the first exponent correctly -# assert bool(np.isclose(slope_fit_k.aperiodic_params['Exponent_1'][0], 0, atol=TOLERANCE)) -# # test whether we can get the second exponent correctly -# assert bool(np.isclose(slope_fit_k.aperiodic_params['Exponent_2'][0], np.abs(exponent), atol=TOLERANCE)) -# # test whether we can get the knee correctly -# knee_hat = slope_fit_k.aperiodic_params['Knee'][0] ** ( -# 1 / (2 * slope_fit_k.aperiodic_params['Exponent_1'][0] + slope_fit_k.aperiodic_params['Exponent_2'][0]) -# ) -# knee_real = knee ** (1 / np.abs(exponent)) -# assert bool(np.isclose(knee_hat, knee_real, atol=KNEE_TOLERANCE)) -# # test bic/aic -> should be better for knee -# assert slope_fit_k.gof['AIC'][0] < slope_fit_f.gof['AIC'][0] -# assert slope_fit_k.gof['BIC'][0] < slope_fit_f.gof['BIC'][0] +# knee model +@pytest.mark.parametrize('exponent, knee', EXP_KNEE_COMBO, scope='session') +@pytest.mark.parametrize('fs', FS, scope='session') +def test_irasa_knee_peakless(load_knee_aperiodic_signal, fs, exponent, knee): + f_range = [0.1, 100] + irasa_out = irasa(load_knee_aperiodic_signal, fs, f_range, psd_kwargs={'nperseg': 4 * fs}) + # test the shape of the output + assert irasa_out.freqs.shape[0] == irasa_out.aperiodic.shape[1] == irasa_out.periodic.shape[1] + freqs_psd, psd = dsp.welch(load_knee_aperiodic_signal, fs, nperseg=int(4 * fs)) + psd_cmb = irasa_out.aperiodic[0, :] + irasa_out.periodic[0, :] + freq_logical = np.logical_and(freqs_psd >= f_range[0], freqs_psd <= f_range[1]) + r = np.corrcoef(psd[freq_logical], psd_cmb)[0, 1] + assert r > MIN_CORR_PSD_CMB + slope_fit_k = irasa_out.fit_aperiodic_model(fit_func='knee') + slope_fit_f = irasa_out.fit_aperiodic_model(fit_func='fixed') + # test whether we can get the first exponent correctly + assert bool(np.isclose(slope_fit_k.aperiodic_params['Exponent_1'][0], 0, atol=TOLERANCE)) + # test whether we can get the second exponent correctly + assert bool(np.isclose(slope_fit_k.aperiodic_params['Exponent_2'][0], np.abs(exponent), atol=TOLERANCE)) + # test whether we can get the knee correctly + knee_hat = slope_fit_k.aperiodic_params['Knee'][0] ** ( + 1 / (2 * slope_fit_k.aperiodic_params['Exponent_1'][0] + slope_fit_k.aperiodic_params['Exponent_2'][0]) + ) + knee_real = knee ** (1 / np.abs(exponent)) + assert bool(np.isclose(knee_hat, knee_real, atol=KNEE_TOLERANCE)) + # test bic/aic -> should be better for knee + assert slope_fit_k.gof['AIC'][0] < slope_fit_f.gof['AIC'][0] + assert slope_fit_k.gof['BIC'][0] < slope_fit_f.gof['BIC'][0] -# # knee model -# @pytest.mark.parametrize('exponent, knee', EXP_KNEE_COMBO, scope='session') -# @pytest.mark.parametrize('fs', FS, scope='session') -# @pytest.mark.parametrize('osc_freq', OSC_FREQ, scope='session') -# def test_irasa_knee_cmb(load_knee_cmb_signal, fs, exponent, knee, osc_freq): -# f_range = [0.1, 100] -# irasa_out = irasa(load_knee_cmb_signal, fs, f_range, psd_kwargs={'nperseg': 4 * fs}) -# # test the shape of the output -# assert irasa_out.freqs.shape[0] == irasa_out.aperiodic.shape[1] == irasa_out.periodic.shape[1] -# freqs_psd, psd = dsp.welch(load_knee_cmb_signal, fs, nperseg=int(4 * fs)) -# psd_cmb = irasa_out.aperiodic[0, :] + irasa_out.periodic[0, :] -# freq_logical = np.logical_and(freqs_psd >= f_range[0], freqs_psd <= f_range[1]) -# r = np.corrcoef(psd[freq_logical], psd_cmb)[0, 1] -# assert r > MIN_CORR_PSD_CMB -# slope_fit_k = irasa_out.fit_aperiodic_model(fit_func='knee') -# slope_fit_f = irasa_out.fit_aperiodic_model(fit_func='fixed') -# # test whether we can get the first exponent correctly -# assert bool(np.isclose(slope_fit_k.aperiodic_params['Exponent_1'][0], 0, atol=TOLERANCE)) -# # test whether we can get the second exponent correctly -# assert bool(np.isclose(slope_fit_k.aperiodic_params['Exponent_2'][0], np.abs(exponent), atol=TOLERANCE)) -# # test whether we can get the knee correctly -# knee_hat = slope_fit_k.aperiodic_params['Knee'][0] ** ( -# 1 / (2 * slope_fit_k.aperiodic_params['Exponent_1'][0] + slope_fit_k.aperiodic_params['Exponent_2'][0]) -# ) -# knee_real = knee ** (1 / np.abs(exponent)) -# assert bool(np.isclose(knee_hat, knee_real, atol=KNEE_TOLERANCE)) -# # test bic/aic -> should be better for knee -# assert slope_fit_k.gof['AIC'][0] < slope_fit_f.gof['AIC'][0] -# assert slope_fit_k.gof['BIC'][0] < slope_fit_f.gof['BIC'][0] -# # test whether we can reconstruct the peak frequency correctly -# pe_params = irasa_out.get_peaks() -# assert bool(np.isclose(np.round(pe_params['cf'], 0), osc_freq)) +# knee model +@pytest.mark.parametrize('exponent, knee', EXP_KNEE_COMBO, scope='session') +@pytest.mark.parametrize('fs', FS, scope='session') +@pytest.mark.parametrize('osc_freq', OSC_FREQ, scope='session') +def test_irasa_knee_cmb(load_knee_cmb_signal, fs, exponent, knee, osc_freq): + f_range = [0.1, 100] + irasa_out = irasa(load_knee_cmb_signal, fs, f_range, psd_kwargs={'nperseg': 4 * fs}) + # test the shape of the output + assert irasa_out.freqs.shape[0] == irasa_out.aperiodic.shape[1] == irasa_out.periodic.shape[1] + freqs_psd, psd = dsp.welch(load_knee_cmb_signal, fs, nperseg=int(4 * fs)) + psd_cmb = irasa_out.aperiodic[0, :] + irasa_out.periodic[0, :] + freq_logical = np.logical_and(freqs_psd >= f_range[0], freqs_psd <= f_range[1]) + r = np.corrcoef(psd[freq_logical], psd_cmb)[0, 1] + assert r > MIN_CORR_PSD_CMB + slope_fit_k = irasa_out.fit_aperiodic_model(fit_func='knee') + slope_fit_f = irasa_out.fit_aperiodic_model(fit_func='fixed') + # test whether we can get the first exponent correctly + assert bool(np.isclose(slope_fit_k.aperiodic_params['Exponent_1'][0], 0, atol=TOLERANCE)) + # test whether we can get the second exponent correctly + assert bool(np.isclose(slope_fit_k.aperiodic_params['Exponent_2'][0], np.abs(exponent), atol=TOLERANCE)) + # test whether we can get the knee correctly + knee_hat = slope_fit_k.aperiodic_params['Knee'][0] ** ( + 1 / (2 * slope_fit_k.aperiodic_params['Exponent_1'][0] + slope_fit_k.aperiodic_params['Exponent_2'][0]) + ) + knee_real = knee ** (1 / np.abs(exponent)) + assert bool(np.isclose(knee_hat, knee_real, atol=KNEE_TOLERANCE)) + # test bic/aic -> should be better for knee + assert slope_fit_k.gof['AIC'][0] < slope_fit_f.gof['AIC'][0] + assert slope_fit_k.gof['BIC'][0] < slope_fit_f.gof['BIC'][0] + # test whether we can reconstruct the peak frequency correctly + pe_params = irasa_out.get_peaks() + assert bool(np.isclose(np.round(pe_params['cf'], 0), osc_freq)) From 061ff1e134045aa5375d51f9a08612c6b2535328 Mon Sep 17 00:00:00 2001 From: Fabi Date: Sat, 10 Aug 2024 00:58:33 +0200 Subject: [PATCH 07/11] readme updated --- README.md | 77 +++++++++++++++++------ pyrasa/irasa_mne/irasa_mne.py | 115 +++++++++++++++++++--------------- pyrasa/utils/fit_funcs.py | 4 -- 3 files changed, 122 insertions(+), 74 deletions(-) diff --git a/README.md b/README.md index 42880d4..f1ee9f2 100644 --- a/README.md +++ b/README.md @@ -1,4 +1,4 @@ -## PyRASA +# PyRASA - Spectral parameterization in python based on IRASA [![Project Status: WIP – Initial development is in progress, but there has not yet been a stable, usable release suitable for the public.](https://www.repostatus.org/badges/latest/wip.svg)](https://www.repostatus.org/#wip) [![License](https://img.shields.io/badge/License-BSD_2--Clause-orange.svg)](https://opensource.org/licenses/BSD-2-Clause) @@ -6,47 +6,88 @@ [![Coverage Status](https://coveralls.io/repos/github/schmidtfa/pyrasa/badge.svg?branch=main)](https://coveralls.io/github/schmidtfa/pyrasa?branch=main) -Pyrasa is a repository that is build around the IRASA algorithm (Wen & Liu, 2016) to parametrize power and coherence spectra. +PyRASA is a Python library designed to separate and parametrize aperiodic (fractal) and periodic (oscillatory) components in time series data based on the IRASA algorithm (Wen & Liu, 2016). -WARNING - This repository is under heavy development and core functionality may change on a daily basis... +### Features +- **Aperiodic and Periodic Decomposition:** Utilize the IRASA algorithm to decompose power spectra into aperiodic and periodic components, enabling better interpretation of neurophysiological signals. +- **Time Resolved Spectral Parametrization:** Perform time resolved spectral parametrizazion, allowing you to track changes in spectral components over time. +- **Support for Raw and Epoched MNE Objects:** PyRASA provides functions designed for both continuous (Raw) and event-related (Epochs) data, making it versatile for various types of EEG/MEG analyses. +- **Custom Aperiodic Fit Models:** In addition to the built-in "fixed" and "knee" models for aperiodic fitting, users can specify their custom aperiodic fit functions, offering flexibility in how aperiodic components are modeled. -### Documentation -Documentation for PyRASA will soon be available [here]. +## Documentation +Documentation for PyRASA, including detailed descriptions of functions, parameters, and examples, will soon be available [here]. ### Installation To install the latest stable version of PyRASA, you can soon use pip: -``` $ pip install pyrasa ``` +```bash +$ pip install pyrasa +``` -or conda: +or conda -``` $ conda install pyrasa ``` +```bash +$ conda install pyrasa +``` ### Dependencies -The minimum required dependencies to run PyRASA are: -[numpy](https://github.com/numpy/numpy) +PyRASA has the following dependencies: +- **Core Dependencies:** + - [numpy](https://github.com/numpy/numpy) + - [scipy](https://github.com/scipy/scipy) + - [pandas](https://github.com/pandas-dev/pandas) -[scipy](https://github.com/scipy/scipy) +- **Optional Dependencies for Full Functionality:** + - [mne](https://github.com/mne-tools/mne-python): Required for directly working with EEG/MEG data in `Raw` or `Epochs` formats. -[pandas](https://github.com/pandas-dev/pandas) -For full functionality, some functions require: +### Example Usage -[mne](https://github.com/mne-tools/mne-python) +Decompose spectra in periodic and aperiodic ccomponents +```python +from pyrasa.irasa import irasa -### How to contribute -Please take a look at the [CONTRIBUTING.md](CONTRIBUTING.md) file for more information. +irasa_out = irasa(sig, + fs=fs, + band=(1, 100), + psd_kwargs={'nperseg': duration*fs, + 'noverlap': duration*fs*overlap + }, + hset_info=(1, 2, 0.05)) + +``` + +Extract periodic parameters + +```python + +irasa_out.get_peaks() + +``` + +Extract aperiodic parameters + +```python + +irasa_out.fit_aperiodic_model(fit_func='knee') + +``` + + +### How to Contribute + +Contributions to PyRASA are welcome! Whether it's raising issues, improving documentation, fixing bugs, or adding new features, your help is appreciated. Please refer to the [CONTRIBUTING.md](CONTRIBUTING.md) file for more information on how to get involved. ### Reference -If you are using the IRASA algorithm it probably makes sense to cite the smart people who came up with the algorithm: +If you are using IRASA please cite the smart people who came up with the algorithm: -```Wen, H., & Liu, Z. (2016). Separating fractal and oscillatory components in the power spectrum of neurophysiological signal. Brain topography, 29, 13-26.``` +Wen, H., & Liu, Z. (2016). Separating fractal and oscillatory components in the power spectrum of neurophysiological signal. Brain topography, 29, 13-26. If you are using PyRASA it would be nice, if you could additionally cite us (whenever the paper is finally ready): diff --git a/pyrasa/irasa_mne/irasa_mne.py b/pyrasa/irasa_mne/irasa_mne.py index 41c5eae..1d714ab 100644 --- a/pyrasa/irasa_mne/irasa_mne.py +++ b/pyrasa/irasa_mne/irasa_mne.py @@ -20,43 +20,55 @@ def irasa_raw( hset_info: tuple[float, float, float] = (1.05, 2.0, 0.05), ) -> IrasaRaw: """ - This function can be used to seperate aperiodic from periodic power spectra using - the IRASA algorithm (Wen & Liu, 2016). + Separate aperiodic from periodic power spectra using the IRASA algorithm. + + This function applies the Irregular Resampling Auto-Spectral Analysis (IRASA) algorithm + as described by Wen & Liu (2016) to decompose the power spectrum of neurophysiological + signals into aperiodic (fractal) and periodic (oscillatory) components. This function is + essentially a wrapper function for `pyrasa.irasa` Parameters ---------- - data : :py:class:˚mne.io.BaseRaw˚ - The timeseries data used to extract aperiodic and periodic power spectra. - Should be :py:class:˚mne.io.BaseRaw˚ in which case 'fs' and 'filter_settings' - will be automatically extracted. - band : tuple - A tuple containing the lower and upper band of the frequency range used to extract - (a-)periodic spectra. - duration : float - The time window of each segment in seconds used to calculate the psd. - overlap : float - The overlap between segments in percent - hset_info : tuple, list or :py:class:˚numpy.ndarray˚ - Contains information about the range of the up/downsampling factors. - This should be a tuple, list or :py:class:˚numpy.ndarray˚ of (min, max, step). - as_array : bool - The function returns an :py:class:˚mne.time_frequency.SpectrumArray˚ if set to False (default) - if set to True the data is returned as :py:class:`numpy.ndarray`. + data : mne.io.Raw + The time-series data from which the aperiodic and periodic power spectra are extracted. + This should be an instance of `mne.io.Raw`. The function will automatically extract + relevant parameters such as sampling frequency (`sfreq`) and filtering settings from the `mne` object + to make sure the model is specified correctly. + band : tuple of (float, float), optional, default: (1.0, 100.0) + A tuple specifying the lower and upper bounds of the frequency range (in Hz) used + for extracting the aperiodic and periodic spectra. + duration : float, required + The duration (in seconds) of each segment used to calculate the power spectral density (PSD). + This must be less than the total duration of the data. + overlap : float or int, optional, default: 50 + The overlap between segments, specified as a percentage (0-100). + hset_info : tuple of (float, float, float), optional, default: (1.05, 2.0, 0.05) + Contains the range of up/downsampling factors used in the IRASA algorithm. + This should be a tuple specifying the (min, max, step) values for the resampling. Returns ------- - aperiodic : :py:class:˚mne.time_frequency.SpectrumArray˚ - The aperiodic component of the data as an mne.time_frequency.SpectrumArray object. - periodic : :py:class:˚mne.time_frequency.SpectrumArray˚ - The periodic component of the data as mne.time_frequency.SpectrumArray object. - + IrasaRaw + A custom object containing the separated aperiodic and periodic components of the data: + - `periodic`: An instance of `PeriodicSpectrumArray`, which includes the periodic + (oscillatory) component of the power spectrum. + - `aperiodic`: An instance of `AperiodicSpectrumArray`, which includes the aperiodic + (fractal) component of the power spectrum. References ---------- - [1] Wen, H., & Liu, Z. (2016). Separating Fractal and Oscillatory - Components in the Power Spectrum of Neurophysiological Signal. - Brain Topography, 29(1), 13–26. - https://doi.org/10.1007/s10548-015-0448-0 + Wen, H., & Liu, Z. (2016). Separating Fractal and Oscillatory Components in the Power Spectrum + of Neurophysiological Signal. Brain Topography, 29(1), 13–26. + https://doi.org/10.1007/s10548-015-0448-0 + + Notes + ----- + - Ensure that `data` does not contain any bad channels (`data.info['bads']` should be empty), + as this could affect the results. + - The overlap percentage should be carefully chosen to balance between segment independence + and sufficient data for analysis. A value between 0 and 100% is valid. + - The function will raise assertions if the input parameters are not consistent with the + expected formats or if the provided `duration` exceeds the length of the data. """ @@ -109,39 +121,38 @@ def irasa_epochs( hset_info: tuple[float, float, float] = (1.05, 2.0, 0.05), ) -> IrasaEpoched: """ - This function can be used to seperate aperiodic from periodic power spectra - using the IRASA algorithm (Wen & Liu, 2016). + Separate aperiodic from periodic power spectra using the IRASA algorithm for Epochs data. + + This function applies the Irregular Resampling Auto-Spectral Analysis (IRASA) algorithm + as described by Wen & Liu (2016) to decompose the power spectrum of neurophysiological + signals into aperiodic (fractal) and periodic (oscillatory) components. It is specifically + designed for time-series data in `mne.Epochs` format, making it suitable for event-related + EEG/MEG analyses. Parameters ---------- - data : :py:class:˚mne.io.BaseEpochs˚ - The timeseries data used to extract aperiodic and periodic power spectra. - Should be :py:class:˚mne.io.BaseEpochs˚ in which case 'fs', 'filter_settings', - 'duration' and 'overlap' will be automatically extracted. - band : tuple - A tuple containing the lower and upper band of the frequency range used to extract - (a-)periodic spectra. - hset_info : tuple, list or :py:class:˚numpy.ndarray˚ - Contains information about the range of the up/downsampling factors. - This should be a tuple, list or :py:class:˚numpy.ndarray˚ of (min, max, step). - as_array : bool - The function returns an :py:class:˚mne.time_frequency.EpochsSpectrumArray˚ if set to False (default) - if set to True the data is returned as :py:class:`numpy.ndarray`. + data : mne.Epochs + The time-series data used to extract aperiodic and periodic power spectra. + This should be an instance of `mne.Epochs`. + band : tuple of (float, float), optional, default: (1.0, 100.0) + A tuple specifying the lower and upper bounds of the frequency range (in Hz) used + for extracting the aperiodic and periodic spectra. + hset_info : tuple of (float, float, float), optional, default: (1.05, 2.0, 0.05) + Contains the range of up/downsampling factors used in the IRASA algorithm. + This should be a tuple specifying the (min, max, step) values for the resampling. Returns ------- - aperiodic : :py:class:˚mne.time_frequency.EpochsSpectrumArray˚ - The aperiodic component of the data as an mne.time_frequency.EpochsSpectrumArray object. - periodic : :py:class:˚mne.time_frequency.EpochsSpectrumArray˚ - The periodic component of the data as mne.time_frequency.EpochsSpectrumArray object. - + aperiodic : AperiodicEpochsSpectrum + The aperiodic component of the data as an `AperiodicEpochsSpectrum` object. + periodic : PeriodicEpochsSpectrum + The periodic component of the data as a `PeriodicEpochsSpectrum` object. References ---------- - [1] Wen, H., & Liu, Z. (2016). Separating Fractal and Oscillatory - Components in the Power Spectrum of Neurophysiological Signal. - Brain Topography, 29(1), 13–26. - https://doi.org/10.1007/s10548-015-0448-0 + Wen, H., & Liu, Z. (2016). Separating Fractal and Oscillatory Components in the Power Spectrum + of Neurophysiological Signal. Brain Topography, 29(1), 13–26. + https://doi.org/10.1007/s10548-015-0448-0 """ diff --git a/pyrasa/utils/fit_funcs.py b/pyrasa/utils/fit_funcs.py index 45699f7..e04e300 100644 --- a/pyrasa/utils/fit_funcs.py +++ b/pyrasa/utils/fit_funcs.py @@ -29,10 +29,6 @@ def _get_gof(psd: np.ndarray, psd_pred: np.ndarray, k: int, fit_type: str) -> pd mse = np.mean(residuals**2) n = len(psd) - # loglik = -n/2.*(1+np.log(mse)+np.log(2*np.pi)) - # aic = 2 * (k-loglik) - # bic = k * np.log(n)-2 * loglik - bic = n * np.log(mse) + k * np.log(n) aic = n * np.log(mse) + 2 * k From f017f89ed27f4646cfacba715f1d05ee36319870 Mon Sep 17 00:00:00 2001 From: Fabi Date: Sat, 10 Aug 2024 01:16:01 +0200 Subject: [PATCH 08/11] readme updates --- README.md | 23 +++- examples/basic_functionality.ipynb | 182 ++++++++++++++--------------- pixi.lock | 40 ++++++- pyproject.toml | 1 + simulations/example_knee.png | Bin 0 -> 40368 bytes 5 files changed, 148 insertions(+), 98 deletions(-) create mode 100644 simulations/example_knee.png diff --git a/README.md b/README.md index f1ee9f2..bef19fb 100644 --- a/README.md +++ b/README.md @@ -61,6 +61,8 @@ irasa_out = irasa(sig, ``` +![image info](./simulations/example_knee.png) + Extract periodic parameters ```python @@ -68,15 +70,34 @@ Extract periodic parameters irasa_out.get_peaks() ``` +| ch_name | cf | bw | pw | +|----------:|-----:|--------:|-------:| +| 0 | 9.5 | 1.44337 | 0.4146 | Extract aperiodic parameters ```python -irasa_out.fit_aperiodic_model(fit_func='knee') +irasa_out.fit_aperiodic_model(fit_func='knee').aperiodic_params ``` +| Offset | Knee | Exponent_1 | Exponent_2 | fit_type | Knee Frequency (Hz) | ch_name | +|---------:|-------:|-------------:|-------------:|:-----------|----------------------:|----------:| +| 1.38098 | 532.91 | 0.511999 | 1.89448 | knee | 8.59554 | 0 | + +And the goodness of fit + +```python + +irasa_out.fit_aperiodic_model(fit_func='knee').gof + +``` + +| mse | r_squared | BIC | AIC | fit_type | ch_name | +|------------:|------------:|---------:|---------:|:-----------|----------:| +| 3.02402e-05 | 0.999894 | -2049.69 | -2062.86 | knee | 0 | + ### How to Contribute diff --git a/examples/basic_functionality.ipynb b/examples/basic_functionality.ipynb index 157ad5c..8956d5d 100644 --- a/examples/basic_functionality.ipynb +++ b/examples/basic_functionality.ipynb @@ -37,7 +37,7 @@ "outputs": [ { "data": { - "image/png": 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", + "image/png": 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", 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", + "image/png": 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", "text/plain": [ "
" ] @@ -162,17 +162,17 @@ " \n", " 0\n", " 0\n", - " 9.5\n", - " 1.437894\n", - " 0.415762\n", + " 10.0\n", + " 1.43662\n", + " 0.420181\n", " \n", " \n", "\n", "" ], "text/plain": [ - " ch_name cf bw pw\n", - "0 0 9.5 1.437894 0.415762" + " ch_name cf bw pw\n", + "0 0 10.0 1.43662 0.420181" ] }, "execution_count": 4, @@ -187,7 +187,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": {}, "outputs": [ { @@ -220,8 +220,8 @@ " \n", " \n", " 0\n", - " -1.282284\n", - " 1.013071\n", + " -1.323949\n", + " 1.031671\n", " fixed\n", " 0\n", " \n", @@ -231,10 +231,10 @@ ], "text/plain": [ " Offset Exponent fit_type ch_name\n", - "0 -1.282284 1.013071 fixed 0" + "0 -1.323949 1.031671 fixed 0" ] }, - "execution_count": 7, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -247,7 +247,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -282,10 +282,10 @@ " \n", " \n", " 0\n", - " 0.000255\n", - " 0.998391\n", - " -1636.120554\n", - " -1642.707163\n", + " 0.00026\n", + " 0.998414\n", + " -1631.784318\n", + " -1638.370928\n", " fixed\n", " 0\n", " \n", @@ -294,11 +294,11 @@ "" ], "text/plain": [ - " mse r_squared BIC AIC fit_type ch_name\n", - "0 0.000255 0.998391 -1636.120554 -1642.707163 fixed 0" + " mse r_squared BIC AIC fit_type ch_name\n", + "0 0.00026 0.998414 -1631.784318 -1638.370928 fixed 0" ] }, - "execution_count": 8, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -318,7 +318,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -327,13 +327,13 @@ "Text(0.5, 0, 'Frequency (Hz)')" ] }, - "execution_count": 9, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -364,22 +364,22 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 8, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 10, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", + "image/png": 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", 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" ] @@ -431,22 +431,22 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 11, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -483,22 +483,22 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 13, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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" ] @@ -531,22 +531,22 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 11, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "" + "" ] }, - "execution_count": 14, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" }, { "data": { - "image/png": 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", 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", 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" ] @@ -576,12 +576,12 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 12, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", 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", 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" ] @@ -615,7 +615,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -630,12 +630,12 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 24, "metadata": {}, "outputs": [ { "data": { - "image/png": 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", "text/plain": [ "
" ] @@ -645,77 +645,67 @@ } ], "source": [ + "import seaborn as sns\n", + "sns.set_style('ticks')\n", + "sns.set_context('talk')\n", "f, axes = plt.subplots(ncols=2, figsize=(8, 4))\n", - "axes[0].plot(irasa_out.freqs, irasa_out.periodic[0,:], label='periodic')\n", + "axes[0].plot(irasa_out.freqs, irasa_out.periodic[0,:])\n", "axes[0].set_ylabel('Power (a.u.)')\n", "axes[0].set_xlabel('Frequency (Hz)')\n", + "axes[0].set_title('Periodic Spectrum')\n", + "\n", "\n", "axes[1].loglog(freq[1:], psd[1:], label='psd')\n", - "axes[1].loglog(irasa_out.freqs, irasa_out.aperiodic[0,:], label='aperiodic')\n", + "axes[1].loglog(irasa_out.freqs, irasa_out.aperiodic[0,:])\n", "axes[1].set_ylabel('Power (a.u.)')\n", "axes[1].set_xlabel('Frequency (Hz)')\n", - "plt.legend()\n", + "axes[1].set_title('Original + \\n Aperiodic Spectrum')\n", "\n", - "f.tight_layout()" + "f.tight_layout()\n", + "f.savefig('../simulations/example_knee.png')\n" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 16, "metadata": {}, "outputs": [ { - "data": { - "text/html": [ - "
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ch_namecfbwpw
0010.01.4365810.413776
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" - ], - "text/plain": [ - " ch_name cf bw pw\n", - "0 0 10.0 1.436581 0.413776" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" + "name": "stdout", + "output_type": "stream", + "text": [ + "| ch_name | cf | bw | pw |\n", + "|----------:|-----:|--------:|-------:|\n", + "| 0 | 9.5 | 1.44337 | 0.4146 |\n" + ] } ], "source": [ "# %% get periodic stuff\n", - "irasa_out.get_peaks()" + "peaks = irasa_out.get_peaks()\n", + "md = peaks.to_markdown(index=False)\n", + "print(md)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "| mse | r_squared | BIC | AIC | fit_type | ch_name |\n", + "|------------:|------------:|---------:|---------:|:-----------|----------:|\n", + "| 3.02402e-05 | 0.999894 | -2049.69 | -2062.86 | knee | 0 |\n" + ] + } + ], + "source": [ + "ap = irasa_out.fit_aperiodic_model(fit_func='knee').gof\n", + "md = ap.to_markdown(index=False)\n", + "print(md)" ] }, { diff --git a/pixi.lock b/pixi.lock index 5cd5666..ed6e8f0 100644 --- a/pixi.lock +++ b/pixi.lock @@ -398,6 +398,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/stack_data-0.6.2-pyhd8ed1ab_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/statsmodels-0.14.2-py312h085067d_0.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/svt-av1-2.1.0-hac33072_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/tabulate-0.9.0-pyhd8ed1ab_1.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/linux-64/tbb-2021.12.0-h434a139_3.conda - conda: https://conda.anaconda.org/conda-forge/linux-64/tbb-devel-2021.12.0-hfcbfbdb_3.conda - conda: https://conda.anaconda.org/conda-forge/noarch/terminado-0.18.1-pyh0d859eb_0.conda @@ -846,6 +847,7 @@ environments: - conda: https://conda.anaconda.org/conda-forge/noarch/stack_data-0.6.2-pyhd8ed1ab_0.conda - conda: 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https://conda.anaconda.org/conda-forge/win-64/tbb-devel-2021.12.0-h053bfa6_3.conda - conda: https://conda.anaconda.org/conda-forge/noarch/terminado-0.18.1-pyh5737063_0.conda @@ -28539,7 +28559,7 @@ packages: name: pyrasa version: 0.1.0.dev0 path: . - sha256: c5f6392cb2cf7ae255bb2526468a889d68bfaeaefabc20f2ec6cec4722953de5 + sha256: fb7358f4c790842addab7cada866a8a03808d947493ab4f4c000d56caf9df893 requires_dist: - attrs - numpy @@ -32829,6 +32849,24 @@ packages: purls: [] size: 2156817 timestamp: 1719854565230 +- kind: conda + name: tabulate + version: 0.9.0 + build: pyhd8ed1ab_1 + build_number: 1 + subdir: noarch + noarch: python + url: https://conda.anaconda.org/conda-forge/noarch/tabulate-0.9.0-pyhd8ed1ab_1.tar.bz2 + sha256: f6e4a0dd24ba060a4af69ca79d32361a6678e61d78c73eb5e357909b025b4620 + md5: 4759805cce2d914c38472f70bf4d8bcb + depends: + - python >=3.7 + license: MIT + license_family: MIT + purls: + - pkg:pypi/tabulate?source=conda-forge-mapping + size: 35912 + timestamp: 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+++++++++++---- pyrasa/utils/irasa_tf_spectrum.py | 135 ++++++++++---- 5 files changed, 504 insertions(+), 181 deletions(-) diff --git a/README.md b/README.md index bef19fb..87b2dda 100644 --- a/README.md +++ b/README.md @@ -108,7 +108,7 @@ Contributions to PyRASA are welcome! Whether it's raising issues, improving docu If you are using IRASA please cite the smart people who came up with the algorithm: -Wen, H., & Liu, Z. (2016). Separating fractal and oscillatory components in the power spectrum of neurophysiological signal. Brain topography, 29, 13-26. +Wen, H., & Liu, Z. (2016). Separating fractal and oscillatory components in the power spectrum of neurophysiological signal. Brain topography, 29, 13-26. https://doi.org/10.1007/s10548-015-0448-0 If you are using PyRASA it would be nice, if you could additionally cite us (whenever the paper is finally ready): diff --git a/examples/basic_functionality.ipynb b/examples/basic_functionality.ipynb index 8956d5d..9ee1028 100644 --- a/examples/basic_functionality.ipynb +++ b/examples/basic_functionality.ipynb @@ -630,7 +630,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 27, "metadata": {}, "outputs": [ { @@ -645,9 +645,6 @@ } ], "source": [ - "import seaborn as sns\n", - "sns.set_style('ticks')\n", - "sns.set_context('talk')\n", "f, axes = plt.subplots(ncols=2, figsize=(8, 4))\n", "axes[0].plot(irasa_out.freqs, irasa_out.periodic[0,:])\n", "axes[0].set_ylabel('Power (a.u.)')\n", @@ -661,51 +658,133 @@ "axes[1].set_xlabel('Frequency (Hz)')\n", "axes[1].set_title('Original + \\n Aperiodic Spectrum')\n", "\n", - "f.tight_layout()\n", - "f.savefig('../simulations/example_knee.png')\n" + "f.tight_layout()" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 26, "metadata": {}, "outputs": [ { - "name": "stdout", - "output_type": "stream", - "text": [ - "| ch_name | cf | bw | pw |\n", - "|----------:|-----:|--------:|-------:|\n", - "| 0 | 9.5 | 1.44337 | 0.4146 |\n" - ] + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " Offset Knee Exponent_1 Exponent_2 fit_type Knee Frequency (Hz) \\\n", + "0 1.380983 532.909784 0.511999 1.894478 knee 8.59554 \n", + "\n", + " ch_name \n", + "0 0 " + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" } ], "source": [ - "ap = irasa_out.fit_aperiodic_model(fit_func='knee').gof\n", - "md = ap.to_markdown(index=False)\n", - "print(md)" + "irasa_out.fit_aperiodic_model(fit_func='knee').aperiodic_params" ] }, { diff --git a/pyrasa/irasa_mne/mne_objs.py b/pyrasa/irasa_mne/mne_objs.py index c815349..b3e1c59 100644 --- a/pyrasa/irasa_mne/mne_objs.py +++ b/pyrasa/irasa_mne/mne_objs.py @@ -11,8 +11,6 @@ from pyrasa.utils.peak_utils import get_peak_params from pyrasa.utils.types import AperiodicFit -# FutureWarning: - class PeriodicSpectrumArray(SpectrumArray): """Subclass of SpectrumArray""" @@ -100,6 +98,7 @@ def plot_topo( def get_peaks( self: SpectrumArray, + smooth: bool = True, smoothing_window: float | int = 1, cut_spectrum: tuple[float, float] = (1, 40), peak_threshold: float = 2.5, @@ -108,26 +107,56 @@ def get_peaks( peak_width_limits: tuple[float, float] = (0.5, 6), ) -> pd.DataFrame: """ - This method can be used to extract peak parameters from the periodic spectrum extracted from IRASA. - The algorithm works by smoothing the spectrum, zeroing out negative values and - extracting peaks based on user specified parameters. - - Parameters: smoothing window : int, optional, default: 2 - Smoothing window in Hz handed over to the savitzky-golay filter. - cut_spectrum : tuple of (float, float), optional, default (1, 40) - Cut the periodic spectrum to limit peak finding to a sensible range - peak_threshold : float, optional, default: 1 - Relative threshold for detecting peaks. This threshold is defined in - relative units of the periodic spectrum - min_peak_height : float, optional, default: 0.01 - Absolute threshold for identifying peaks. The threhsold is defined in relative - units of the power spectrum. Setting this is somewhat necessary when a - "knee" is present in the data as it will carry over to the periodic spctrum in irasa. - peak_width_limits : tuple of (float, float), optional, default (.5, 12) - Limits on possible peak width, in Hz, as (lower_bound, upper_bound) - - Returns: df_peaks: DataFrame - DataFrame containing the center frequency, bandwidth and peak height for each channel + Extracts peak parameters from the periodic spectrum obtained via IRASA. + + This method identifies and extracts peak parameters such as center frequency (cf), bandwidth (bw), + and peak height (pw) from a periodic spectrum using scipy's find_peaks function. + The spectrum can be optionally smoothed prior to the peak detection. + + Parameters + ---------- + smooth : bool, optional + Whether to smooth the spectrum before peak extraction. Smoothing can help in reducing noise and + better identifying peaks. Default is True. + smoothing_window : int or float, optional + The size of the smoothing window in Hz, passed to the Savitzky-Golay filter. Default is 1 Hz. + polyorder : int, optional + The polynomial order for the Savitzky-Golay filter used in smoothing. The polynomial order must be + less than the window length. Default is 1. + cut_spectrum : tuple of (float, float) or None, optional + Tuple specifying the frequency range (lower_bound, upper_bound) to which the spectrum should be cut + before peak extraction. If None, peaks are detected across the full frequency range. Default is None. + peak_threshold : float, optional + Relative threshold for detecting peaks, defined as a multiple of the standard deviation of the + filtered spectrum. Default is 1.0. + min_peak_height : float, optional + The minimum peak height (in absolute units of the power spectrum) required for a peak to be recognized. + This can be useful for filtering out noise or insignificant peaks, especially when a "knee" is present + in the original data, which may persist in the periodic spectrum. Default is 0.01. + peak_width_limits : tuple of (float, float), optional + The lower and upper bounds for peak widths, in Hz. This helps in constraining the peak detection to + meaningful features. Default is (0.5, 12.0). + + Returns + ------- + pd.DataFrame + A DataFrame containing the detected peak parameters for each channel. The DataFrame includes the + following columns: + - 'ch_name': Channel name + - 'cf': Center frequency of the peak + - 'bw': Bandwidth of the peak + - 'pw': Peak height (power) + + + Notes + ----- + The function works by first optionally smoothing the periodic spectrum using a Savitzky-Golay filter. + Then, it performs peak detection using the `scipy.signal.find_peaks` function, taking into account the + specified peak thresholds and width limits. Peaks that do not meet the minimum height requirement are + filtered out. + + The `cut_spectrum` parameter can be used to focus peak detection on a specific frequency range, which is + particularly useful when the region of interest is known in advance. """ @@ -135,6 +164,7 @@ def get_peaks( self.get_data(), self.freqs, self.ch_names, + smooth=smooth, smoothing_window=smoothing_window, cut_spectrum=cut_spectrum, peak_threshold=peak_threshold, @@ -179,23 +209,53 @@ def fit_aperiodic_model( fit_bounds: tuple[float, float] | None = None, ) -> AperiodicFit: """ - This method can be used to extract aperiodic parameters from the aperiodic spectrum extracted from IRASA. - The algorithm works by applying one of two different curve fit functions and returns the associated parameters, - as well as the respective goodness of fit. - - Parameters: - fit_func : string - Can be either "fixed" or "knee". - fit_bounds : None, tuple - Lower and upper bound for the fit function, - should be None if the whole frequency range is desired. - Otherwise a tuple of (lower, upper) - - Returns: df_aps: DataFrame - DataFrame containing the center frequency, bandwidth and peak height for each channel - df_gof: DataFrame - DataFrame containing the goodness of fit of the specific fit function for each channel. - + Computes aperiodic parameters from the aperiodic spectrum using scipy's curve fitting function. + + This method can be used to model the aperiodic (1/f-like) component of the power spectrum. Per default, + users can choose between a fixed or knee model fit or specify their own fit method see examples + custom_fit_functions.ipynb for an example. + The method returns the fitted parameters for each channel along with some goodness of fit metrics. + + Parameters + ---------- + fit_func : str or type[AbstractFitFun], optional + The fitting function to use. Can be "fixed" for a linear fit or "knee" for a fit that includes a + knee (bend) in the spectrum or a class that is inherited from AbstractFitFun. The default is 'fixed'. + ch_names : Iterable or None, optional + Channel names corresponding to the aperiodic spectrum. If None, channels will be named numerically + in ascending order. Default is None. + scale : bool, optional + Whether to scale the data to improve fitting accuracy. This is useful in cases where + power values are very small (e.g., 1e-28), which may lead to numerical precision issues during fitting. + After fitting, the parameters are rescaled to match the original data scale. Default is False. + fit_bounds : tuple[float, float] or None, optional + Tuple specifying the lower and upper frequency bounds for the fit function. If None, the entire frequency + range is used. Otherwise, the spectrum is cropped to the specified bounds. Default is None. + + Returns + ------- + AperiodicFit + An object containing two pandas DataFrames: + - aperiodic_params : pd.DataFrame + A DataFrame containing the fitted aperiodic parameters for each channel. + - gof : pd.DataFrame + A DataFrame containing the goodness of fit metrics for each channel. + + Notes + ----- + This function fits the aperiodic component of the power spectrum using scipy's curve fitting function. + The fitting can be performed using either a simple linear model ('fixed') or a more complex model + that includes a "knee" point, where the spectrum bends. The resulting parameters can help in + understanding the underlying characteristics of the aperiodic component in the data. + + If the `fit_bounds` parameter is used, it ensures that only the specified frequency range is considered + for fitting, which can be important to avoid fitting artifacts outside the region of interest. + + The `scale` parameter can be crucial when dealing with data that have extremely small values, + as it helps to mitigate issues related to machine precision during the fitting process. + + The function asserts that the input data are of the correct type and shape, and raises warnings + if the first frequency value is zero, as this can cause issues during model fitting. """ return compute_aperiodic_model( @@ -306,6 +366,7 @@ def plot_topo( def get_peaks( self: EpochsSpectrumArray, + smooth: bool = True, smoothing_window: float | int = 1, cut_spectrum: tuple[float, float] = (1.0, 40.0), peak_threshold: float = 2.5, @@ -314,26 +375,56 @@ def get_peaks( peak_width_limits: tuple[float, float] = (0.5, 6.0), ) -> pd.DataFrame: """ - This method can be used to extract peak parameters from the periodic spectrum extracted from IRASA. - The algorithm works by smoothing the spectrum, zeroing out negative values and - extracting peaks based on user specified parameters. - - Parameters: smoothing window : int, optional, default: 2 - Smoothing window in Hz handed over to the savitzky-golay filter. - cut_spectrum : tuple of (float, float), optional, default (1, 40) - Cut the periodic spectrum to limit peak finding to a sensible range - peak_threshold : float, optional, default: 1 - Relative threshold for detecting peaks. This threshold is defined in - relative units of the periodic spectrum - min_peak_height : float, optional, default: 0.01 - Absolute threshold for identifying peaks. The threhsold is defined in relative - units of the power spectrum. Setting this is somewhat necessary when a - "knee" is present in the data as it will carry over to the periodic spctrum in irasa. - peak_width_limits : tuple of (float, float), optional, default (.5, 12) - Limits on possible peak width, in Hz, as (lower_bound, upper_bound) - - Returns: df_peaks: DataFrame - DataFrame containing the center frequency, bandwidth and peak height for each channel + Extracts peak parameters from the periodic spectrum obtained via IRASA. + + This method identifies and extracts peak parameters such as center frequency (cf), bandwidth (bw), + and peak height (pw) from a periodic spectrum using scipy's find_peaks function. + The spectrum can be optionally smoothed prior to the peak detection. + + Parameters + ---------- + smooth : bool, optional + Whether to smooth the spectrum before peak extraction. Smoothing can help in reducing noise and + better identifying peaks. Default is True. + smoothing_window : int or float, optional + The size of the smoothing window in Hz, passed to the Savitzky-Golay filter. Default is 1 Hz. + polyorder : int, optional + The polynomial order for the Savitzky-Golay filter used in smoothing. The polynomial order must be + less than the window length. Default is 1. + cut_spectrum : tuple of (float, float) or None, optional + Tuple specifying the frequency range (lower_bound, upper_bound) to which the spectrum should be cut + before peak extraction. If None, peaks are detected across the full frequency range. Default is None. + peak_threshold : float, optional + Relative threshold for detecting peaks, defined as a multiple of the standard deviation of the + filtered spectrum. Default is 1.0. + min_peak_height : float, optional + The minimum peak height (in absolute units of the power spectrum) required for a peak to be recognized. + This can be useful for filtering out noise or insignificant peaks, especially when a "knee" is present + in the original data, which may persist in the periodic spectrum. Default is 0.01. + peak_width_limits : tuple of (float, float), optional + The lower and upper bounds for peak widths, in Hz. This helps in constraining the peak detection to + meaningful features. Default is (0.5, 12.0). + + Returns + ------- + pd.DataFrame + A DataFrame containing the detected peak parameters for each channel. The DataFrame includes the + following columns: + - 'ch_name': Channel name + - 'cf': Center frequency of the peak + - 'bw': Bandwidth of the peak + - 'pw': Peak height (power) + + + Notes + ----- + The function works by first optionally smoothing the periodic spectrum using a Savitzky-Golay filter. + Then, it performs peak detection using the `scipy.signal.find_peaks` function, taking into account the + specified peak thresholds and width limits. Peaks that do not meet the minimum height requirement are + filtered out. + + The `cut_spectrum` parameter can be used to focus peak detection on a specific frequency range, which is + particularly useful when the region of interest is known in advance. """ @@ -346,6 +437,7 @@ def get_peaks( cur_epoch, self.freqs, self.ch_names, + smooth=smooth, smoothing_window=smoothing_window, cut_spectrum=cut_spectrum, peak_threshold=peak_threshold, @@ -405,23 +497,53 @@ def fit_aperiodic_model( fit_bounds: tuple[float, float] | None = None, ) -> AperiodicFit: """ - This method can be used to extract aperiodic parameters from the aperiodic spectrum extracted from IRASA. - The algorithm works by applying one of two different curve fit functions and returns the associated parameters, - as well as the respective goodness of fit. - - Parameters: - fit_func : string - Can be either "fixed" or "knee". - fit_bounds : None, tuple - Lower and upper bound for the fit function, - should be None if the whole frequency range is desired. - Otherwise a tuple of (lower, upper) - - Returns: df_aps: DataFrame - DataFrame containing the center frequency, bandwidth and peak height for each channel - df_gof: DataFrame - DataFrame containing the goodness of fit of the specific fit function for each channel. - + Computes aperiodic parameters from the aperiodic spectrum using scipy's curve fitting function. + + This method can be used to model the aperiodic (1/f-like) component of the power spectrum. Per default, + users can choose between a fixed or knee model fit or specify their own fit method see examples + custom_fit_functions.ipynb for an example. + The method returns the fitted parameters for each channel along with some goodness of fit metrics. + + Parameters + ---------- + fit_func : str or type[AbstractFitFun], optional + The fitting function to use. Can be "fixed" for a linear fit or "knee" for a fit that includes a + knee (bend) in the spectrum or a class that is inherited from AbstractFitFun. The default is 'fixed'. + ch_names : Iterable or None, optional + Channel names corresponding to the aperiodic spectrum. If None, channels will be named numerically + in ascending order. Default is None. + scale : bool, optional + Whether to scale the data to improve fitting accuracy. This is useful in cases where + power values are very small (e.g., 1e-28), which may lead to numerical precision issues during fitting. + After fitting, the parameters are rescaled to match the original data scale. Default is False. + fit_bounds : tuple[float, float] or None, optional + Tuple specifying the lower and upper frequency bounds for the fit function. If None, the entire frequency + range is used. Otherwise, the spectrum is cropped to the specified bounds. Default is None. + + Returns + ------- + AperiodicFit + An object containing two pandas DataFrames: + - aperiodic_params : pd.DataFrame + A DataFrame containing the fitted aperiodic parameters for each channel. + - gof : pd.DataFrame + A DataFrame containing the goodness of fit metrics for each channel. + + Notes + ----- + This function fits the aperiodic component of the power spectrum using scipy's curve fitting function. + The fitting can be performed using either a simple linear model ('fixed') or a more complex model + that includes a "knee" point, where the spectrum bends. The resulting parameters can help in + understanding the underlying characteristics of the aperiodic component in the data. + + If the `fit_bounds` parameter is used, it ensures that only the specified frequency range is considered + for fitting, which can be important to avoid fitting artifacts outside the region of interest. + + The `scale` parameter can be crucial when dealing with data that have extremely small values, + as it helps to mitigate issues related to machine precision during the fitting process. + + The function asserts that the input data are of the correct type and shape, and raises warnings + if the first frequency value is zero, as this can cause issues during model fitting. """ event_dict = {val: key for key, val in self.event_id.items()} diff --git a/pyrasa/utils/irasa_spectrum.py b/pyrasa/utils/irasa_spectrum.py index a7d79ae..c4abd0e 100644 --- a/pyrasa/utils/irasa_spectrum.py +++ b/pyrasa/utils/irasa_spectrum.py @@ -3,6 +3,7 @@ from attrs import define from pyrasa.utils.aperiodic_utils import compute_aperiodic_model +from pyrasa.utils.fit_funcs import AbstractFitFun from pyrasa.utils.peak_utils import get_peak_params from pyrasa.utils.types import AperiodicFit @@ -16,27 +17,59 @@ class IrasaSpectrum: ch_names: np.ndarray | None def fit_aperiodic_model( - self, fit_func: str = 'fixed', scale: bool = False, fit_bounds: tuple[float, float] | None = None + self, + fit_func: str | type[AbstractFitFun] = 'fixed', + scale: bool = False, + fit_bounds: tuple[float, float] | None = None, ) -> AperiodicFit: """ - This method can be used to extract aperiodic parameters from the aperiodic spectrum extracted from IRASA. - The algorithm works by applying one of two different curve fit functions and returns the associated parameters, - as well as the respective goodness of fit. - - Parameters: - fit_func : string - Can be either "fixed" or "knee". - fit_bounds : None, tuple - Lower and upper bound for the fit function, - should be None if the whole frequency range is desired. - Otherwise a tuple of (lower, upper) - - Returns: AperiodicFit - df_aps: DataFrame - DataFrame containing the center frequency, bandwidth and peak height for each channel - df_gof: DataFrame - DataFrame containing the goodness of fit of the specific fit function for each channel. + Computes aperiodic parameters from the aperiodic spectrum using scipy's curve fitting function. + + This method can be used to model the aperiodic (1/f-like) component of the power spectrum. Per default, + users can choose between a fixed or knee model fit or specify their own fit method see examples + custom_fit_functions.ipynb for an example. + The method returns the fitted parameters for each channel along with some goodness of fit metrics. + + Parameters + ---------- + fit_func : str or type[AbstractFitFun], optional + The fitting function to use. Can be "fixed" for a linear fit or "knee" for a fit that includes a + knee (bend) in the spectrum or a class that is inherited from AbstractFitFun. The default is 'fixed'. + ch_names : Iterable or None, optional + Channel names corresponding to the aperiodic spectrum. If None, channels will be named numerically + in ascending order. Default is None. + scale : bool, optional + Whether to scale the data to improve fitting accuracy. This is useful in cases where + power values are very small (e.g., 1e-28), which may lead to numerical precision issues during fitting. + After fitting, the parameters are rescaled to match the original data scale. Default is False. + fit_bounds : tuple[float, float] or None, optional + Tuple specifying the lower and upper frequency bounds for the fit function. If None, the entire frequency + range is used. Otherwise, the spectrum is cropped to the specified bounds. Default is None. + + Returns + ------- + AperiodicFit + An object containing two pandas DataFrames: + - aperiodic_params : pd.DataFrame + A DataFrame containing the fitted aperiodic parameters for each channel. + - gof : pd.DataFrame + A DataFrame containing the goodness of fit metrics for each channel. + + Notes + ----- + This function fits the aperiodic component of the power spectrum using scipy's curve fitting function. + The fitting can be performed using either a simple linear model ('fixed') or a more complex model + that includes a "knee" point, where the spectrum bends. The resulting parameters can help in + understanding the underlying characteristics of the aperiodic component in the data. + + If the `fit_bounds` parameter is used, it ensures that only the specified frequency range is considered + for fitting, which can be important to avoid fitting artifacts outside the region of interest. + + The `scale` parameter can be crucial when dealing with data that have extremely small values, + as it helps to mitigate issues related to machine precision during the fitting process. + The function asserts that the input data are of the correct type and shape, and raises warnings + if the first frequency value is zero, as this can cause issues during model fitting. """ return compute_aperiodic_model( aperiodic_spectrum=self.aperiodic, @@ -57,26 +90,56 @@ def get_peaks( peak_width_limits: tuple[float, float] = (0.5, 12), ) -> pd.DataFrame: """ - This method can be used to extract peak parameters from the periodic spectrum extracted from IRASA. - The algorithm works by smoothing the spectrum, zeroing out negative values and - extracting peaks based on user specified parameters. - - Parameters: smoothing window : int, optional, default: 2 - Smoothing window in Hz handed over to the savitzky-golay filter. - cut_spectrum : tuple of (float, float), optional, default (1, 40) - Cut the periodic spectrum to limit peak finding to a sensible range - peak_threshold : float, optional, default: 1 - Relative threshold for detecting peaks. This threshold is defined in - relative units of the periodic spectrum - min_peak_height : float, optional, default: 0.01 - Absolute threshold for identifying peaks. The threhsold is defined in relative - units of the power spectrum. Setting this is somewhat necessary when a - "knee" is present in the data as it will carry over to the periodic spctrum in irasa. - peak_width_limits : tuple of (float, float), optional, default (.5, 12) - Limits on possible peak width, in Hz, as (lower_bound, upper_bound) - - Returns: df_peaks: DataFrame - DataFrame containing the center frequency, bandwidth and peak height for each channel + Extracts peak parameters from the periodic spectrum obtained via IRASA. + + This method identifies and extracts peak parameters such as center frequency (cf), bandwidth (bw), + and peak height (pw) from a periodic spectrum using scipy's find_peaks function. + The spectrum can be optionally smoothed prior to the peak detection. + + Parameters + ---------- + smooth : bool, optional + Whether to smooth the spectrum before peak extraction. Smoothing can help in reducing noise and + better identifying peaks. Default is True. + smoothing_window : int or float, optional + The size of the smoothing window in Hz, passed to the Savitzky-Golay filter. Default is 1 Hz. + polyorder : int, optional + The polynomial order for the Savitzky-Golay filter used in smoothing. The polynomial order must be + less than the window length. Default is 1. + cut_spectrum : tuple of (float, float) or None, optional + Tuple specifying the frequency range (lower_bound, upper_bound) to which the spectrum should be cut + before peak extraction. If None, peaks are detected across the full frequency range. Default is None. + peak_threshold : float, optional + Relative threshold for detecting peaks, defined as a multiple of the standard deviation of the + filtered spectrum. Default is 1.0. + min_peak_height : float, optional + The minimum peak height (in absolute units of the power spectrum) required for a peak to be recognized. + This can be useful for filtering out noise or insignificant peaks, especially when a "knee" is present + in the original data, which may persist in the periodic spectrum. Default is 0.01. + peak_width_limits : tuple of (float, float), optional + The lower and upper bounds for peak widths, in Hz. This helps in constraining the peak detection to + meaningful features. Default is (0.5, 12.0). + + Returns + ------- + pd.DataFrame + A DataFrame containing the detected peak parameters for each channel. The DataFrame includes the + following columns: + - 'ch_name': Channel name + - 'cf': Center frequency of the peak + - 'bw': Bandwidth of the peak + - 'pw': Peak height (power) + + + Notes + ----- + The function works by first optionally smoothing the periodic spectrum using a Savitzky-Golay filter. + Then, it performs peak detection using the `scipy.signal.find_peaks` function, taking into account the + specified peak thresholds and width limits. Peaks that do not meet the minimum height requirement are + filtered out. + + The `cut_spectrum` parameter can be used to focus peak detection on a specific frequency range, which is + particularly useful when the region of interest is known in advance. """ diff --git a/pyrasa/utils/irasa_tf_spectrum.py b/pyrasa/utils/irasa_tf_spectrum.py index 7727da5..26de7b5 100644 --- a/pyrasa/utils/irasa_tf_spectrum.py +++ b/pyrasa/utils/irasa_tf_spectrum.py @@ -3,6 +3,7 @@ from attrs import define from pyrasa.utils.aperiodic_utils import compute_aperiodic_model_sprint +from pyrasa.utils.fit_funcs import AbstractFitFun from pyrasa.utils.peak_utils import get_peak_params_sprint from pyrasa.utils.types import AperiodicFit @@ -19,28 +20,56 @@ class IrasaTfSpectrum: ch_names: np.ndarray | None def fit_aperiodic_model( - self, fit_func: str = 'fixed', scale: bool = False, fit_bounds: tuple[float, float] | None = None + self, + fit_func: str | type[AbstractFitFun] = 'fixed', + scale: bool = False, + fit_bounds: tuple[float, float] | None = None, ) -> AperiodicFit: """ - This method can be used to extract aperiodic parameters from the aperiodic spectrum extracted from IRASA. - The algorithm works by applying one of two different curve fit functions and returns the associated parameters, - as well as the respective goodness of fit. - - Parameters: - fit_func : string - Can be either "fixed" or "knee". - fit_bounds : None, tuple - Lower and upper bound for the fit function, - should be None if the whole frequency range is desired. - Otherwise a tuple of (lower, upper) - - Returns: AperiodicFit - df_aps: DataFrame - DataFrame containing the center frequency, bandwidth and peak height for each channel - df_gof: DataFrame - DataFrame containing the goodness of fit of the specific fit function for each channel. + Extracts aperiodic parameters from the aperiodic spectrogram using scipy's curve fitting + function. + + This function computes aperiodic parameters for each time point in the spectrogram by applying either one of + two different curve fitting functions (`fixed` or `knee`) or a custom function specified by user to the data. + See examples custom_fit_functions.ipynb. The parameters, along with the goodness of + fit for each time point, are returned in a concatenated format. + + Parameters + ---------- + fit_func : str or type[AbstractFitFun], optional + The fitting function to use. Can be "fixed" for a linear fit or "knee" for a fit that includes a + knee (bend) in the spectrum or a class that is inherited from AbstractFitFun. The default is 'fixed'.. + scale : bool, optional + Whether to scale the data to improve fitting accuracy. This is useful when fitting a knee in cases where + power values are very small, leading to numerical precision issues. Default is False. + fit_bounds : tuple[float, float] or None, optional + Tuple specifying the lower and upper frequency bounds for the fit function. If None, the entire frequency + range is used. Otherwise, the spectrum is cropped to the specified bounds before fitting. Default is None. + + Returns + ------- + AperiodicFit + An object containing two pandas DataFrames: + - aperiodic_params : pd.DataFrame + A DataFrame containing the aperiodic parameters (e.g., center frequency, bandwidth, peak height) + for each channel and each time point. + - gof : pd.DataFrame + A DataFrame containing the goodness of fit metrics for each channel and each time point. + + Notes + ----- + This function iterates over each time point in the provided spectrogram to extract aperiodic parameters + using the specified fit function. It leverages the `compute_aperiodic_model` function for individual fits + at each time point, then combines the results across all time points into comprehensive DataFrames. + + The `fit_bounds` parameter allows for frequency range restrictions during fitting, which can help in focusing + the analysis on a particular frequency band of interest. + + Scaling the data using the `scale` parameter can be particularly important when dealing with very small power + values that might lead to poor fitting due to numerical precision limitations. """ + return compute_aperiodic_model_sprint( aperiodic_spectrum=self.aperiodic[np.newaxis, :, :] if self.aperiodic.ndim == min_ndim else self.aperiodic, freqs=self.freqs, @@ -62,26 +91,56 @@ def get_peaks( peak_width_limits: tuple[float, float] = (0.5, 12), ) -> pd.DataFrame: """ - This method can be used to extract peak parameters from the periodic spectrum extracted from IRASA. - The algorithm works by smoothing the spectrum, zeroing out negative values and - extracting peaks based on user specified parameters. - - Parameters: smoothing window : int, optional, default: 2 - Smoothing window in Hz handed over to the savitzky-golay filter. - cut_spectrum : tuple of (float, float), optional, default (1, 40) - Cut the periodic spectrum to limit peak finding to a sensible range - peak_threshold : float, optional, default: 1 - Relative threshold for detecting peaks. This threshold is defined in - relative units of the periodic spectrum - min_peak_height : float, optional, default: 0.01 - Absolute threshold for identifying peaks. The threhsold is defined in relative - units of the power spectrum. Setting this is somewhat necessary when a - "knee" is present in the data as it will carry over to the periodic spctrum in irasa. - peak_width_limits : tuple of (float, float), optional, default (.5, 12) - Limits on possible peak width, in Hz, as (lower_bound, upper_bound) - - Returns: df_peaks: DataFrame - DataFrame containing the center frequency, bandwidth and peak height for each channel + Extracts peak parameters from a periodic spectrogram obtained via IRASA. + + This method processes a time-resolved periodic spectrum to identify and extract peak parameters such as + center frequency (cf), bandwidth (bw), and peak height (pw) for each time point. It applies smoothing, + peak detection, and thresholding according to user-defined parameters + (see get_peak_params for additional Information). + + Parameters + ---------- + smooth : bool, optional + Whether to smooth the spectrum before peak extraction. Smoothing can help in reducing noise and better + identifying peaks. Default is True. + smoothing_window : int or float, optional + The size of the smoothing window in Hz, passed to the Savitzky-Golay filter. Default is 2 Hz. + polyorder : int, optional + The polynomial order for the Savitzky-Golay filter used in smoothing. The polynomial order must be less + than the window length. Default is 1. + cut_spectrum : tuple of (float, float) or None, optional + Tuple specifying the frequency range (lower_bound, upper_bound) to which the spectrum should be cut before + peak extraction. If None, the full frequency range is used. Default is (1, 40). + peak_threshold : int or float, optional + Relative threshold for detecting peaks, defined as a multiple of the standard deviation of the filtered + spectrum. Default is 1. + min_peak_height : float, optional + The minimum peak height (in absolute units of the power spectrum) required for a peak to be recognized. + This can be useful for filtering out noise or insignificant peaks, especially when a "knee" is present + in the data. Default is 0.01. + peak_width_limits : tuple of (float, float), optional + The lower and upper bounds for peak widths, in Hz. This helps in constraining the peak detection to + meaningful features. Default is (0.5, 12). + + Returns + ------- + pd.DataFrame + A DataFrame containing the detected peak parameters for each channel and time point. The DataFrame + includes the following columns: + - 'ch_name': Channel name + - 'cf': Center frequency of the peak + - 'bw': Bandwidth of the peak + - 'pw': Peak height (power) + - 'time': Corresponding time point for the peak + + Notes + ----- + This function iteratively processes each time point in the spectrogram, applying the `get_peak_params` + function to extract peak parameters at each time point. The resulting peak parameters are combined into + a single DataFrame. + + The function is particularly useful for analyzing time-varying spectral features, such as in dynamic or + non-stationary M/EEG data, where peaks may shift in frequency, bandwidth, or amplitude over time. """ From c9b9856d6fc3dc2a8f36e042e1bb5877ba0ca388 Mon Sep 17 00:00:00 2001 From: Fabi Date: Sat, 10 Aug 2024 02:32:42 +0200 Subject: [PATCH 10/11] more docstrings --- pyrasa/irasa.py | 2 +- pyrasa/irasa_mne/__init__.py | 2 + pyrasa/irasa_mne/irasa_mne.py | 2 + pyrasa/irasa_mne/mne_objs.py | 2 + pyrasa/utils/fit_funcs.py | 106 ++++++++++++++++++++++++++++++++-- pyrasa/utils/irasa_utils.py | 1 + pyrasa/utils/types.py | 39 +++++++++++++ 7 files changed, 149 insertions(+), 5 deletions(-) diff --git a/pyrasa/irasa.py b/pyrasa/irasa.py index 5712fea..ec6598e 100644 --- a/pyrasa/irasa.py +++ b/pyrasa/irasa.py @@ -1,4 +1,4 @@ -"""Functions to compute the IRASA algorithm.""" +"""Functions to compute IRASA.""" from collections.abc import Callable from typing import TYPE_CHECKING, Any diff --git a/pyrasa/irasa_mne/__init__.py b/pyrasa/irasa_mne/__init__.py index 438b5d2..a0eb6c2 100644 --- a/pyrasa/irasa_mne/__init__.py +++ b/pyrasa/irasa_mne/__init__.py @@ -1,3 +1,5 @@ +"""Interface to use the IRASA algorithm with MNE objects.""" + from .irasa_mne import irasa_epochs, irasa_raw __all__ = ['irasa_epochs', 'irasa_raw'] diff --git a/pyrasa/irasa_mne/irasa_mne.py b/pyrasa/irasa_mne/irasa_mne.py index 1d714ab..bc65af0 100644 --- a/pyrasa/irasa_mne/irasa_mne.py +++ b/pyrasa/irasa_mne/irasa_mne.py @@ -1,3 +1,5 @@ +"""Interface to use the IRASA algorithm with MNE objects.""" + import mne import numpy as np diff --git a/pyrasa/irasa_mne/mne_objs.py b/pyrasa/irasa_mne/mne_objs.py index b3e1c59..33cd3ca 100644 --- a/pyrasa/irasa_mne/mne_objs.py +++ b/pyrasa/irasa_mne/mne_objs.py @@ -1,3 +1,5 @@ +"""Classes for the MNE Python interface.""" + # %% inherit from spectrum array import matplotlib diff --git a/pyrasa/utils/fit_funcs.py b/pyrasa/utils/fit_funcs.py index e04e300..41d0b81 100644 --- a/pyrasa/utils/fit_funcs.py +++ b/pyrasa/utils/fit_funcs.py @@ -1,3 +1,5 @@ +"""Classes used to model aperiodic spectra""" + import abc import inspect from collections.abc import Callable @@ -10,16 +12,61 @@ def _get_args(f: Callable) -> list: + """ + Extracts the argument names from a function, excluding the first two. + + Parameters + ---------- + f : Callable + The function or method from which to extract argument names. + + Returns + ------- + list + A list of argument names, excluding the first two. + """ + return inspect.getfullargspec(f)[0][2:] def _get_gof(psd: np.ndarray, psd_pred: np.ndarray, k: int, fit_type: str) -> pd.DataFrame: """ - get goodness of fit (i.e. mean squared error and R2) - BIC and AIC currently assume OLS - https://machinelearningmastery.com/probabilistic-model-selection-measures/ + Calculate the goodness of fit metrics for a given model prediction against + actual aperiodic power spectral density (PSD) data. + + This function computes several statistics to evaluate how well the predicted PSD values + match the observed PSD values. The metrics include Mean Squared Error (MSE), R-squared (R²), + Bayesian Information Criterion (BIC), and Akaike Information Criterion (AIC). + + Parameters + ---------- + psd : np.ndarray + The observed power spectral density values. + psd_pred : np.ndarray + The predicted power spectral density values from the model. + k : int + The number of parameters in the curve fitting function used to predict the `psd`. + fit_type : str + A description or label for the type of fit/model used, which will be included in the output DataFrame. + + Returns + ------- + pd.DataFrame + A DataFrame containing the goodness of fit metrics: + - 'mse': Mean Squared Error + - 'r_squared': R-squared value + - 'BIC': Bayesian Information Criterion + - 'AIC': Akaike Information Criterion + - 'fit_type': The type of fit/model used (provided as input) + + Notes + ----- + - BIC and AIC calculations currently assume Ordinary Least Squares (OLS) regression. + + References + ---------- + For further details on BIC and AIC, see: https://machinelearningmastery.com/probabilistic-model-selection-measures/ """ - # k number of parameters in curve fitting function # add np.log10 to psd residuals = psd - psd_pred @@ -39,6 +86,57 @@ def _get_gof(psd: np.ndarray, psd_pred: np.ndarray, k: int, fit_type: str) -> pd @define class AbstractFitFun(abc.ABC): + """ + Abstract base class for fitting functions used to model aperiodic spectra. + + This class provides a framework for defining and fitting models to aperiodic spectra. + It handles common functionality required for fitting a model, such as scaling and goodness-of-fit + computation. Subclasses should implement the `func` method to define the specific fitting function + used for curve fitting. + + Attributes + ---------- + freq : np.ndarray + The frequency values associated with the aperiodic spectrum data. + aperiodic_spectrum : np.ndarray + The aperiodic spectrum data to which the model will be fit. + scale_factor : int | float + A scaling factor used to adjust the fit results. + label : ClassVar[str] + A label to identify the type of fit or model used. Default is 'custom'. + log10_aperiodic : ClassVar[bool] + If True, the aperiodic spectrum values will be transformed using log10. Default is False. + log10_freq : ClassVar[bool] + If True, the frequency values will be transformed using log10. Default is False. + + Methods + ------- + __attrs_post_init__() + Post-initialization method to apply log10 transformations if specified. + func(x: np.ndarray, *args: float) -> np.ndarray + Abstract method to define the model function. Must be implemented by subclasses + and should be applicable to scipy.optimize.curve_fit. + curve_kwargs() -> dict[str, Any] + Returns keyword arguments for the curve fitting process. + add_infos_to_df(df_params: pd.DataFrame) -> pd.DataFrame + Method to add additional information to the parameters DataFrame. Can be overridden by subclasses. + handle_scaling(df_params: pd.DataFrame, scale_factor: float) -> pd.DataFrame + Adjusts the parameters DataFrame based on the scaling factor. Can be overridden by subclasses. + fit_func() -> tuple[pd.DataFrame, pd.DataFrame] + Fits the model to the data and returns DataFrames containing the model parameters and goodness-of-fit metrics. + + Notes + ----- + - Subclasses must implement the `func` method to define the model's functional form. + - The `curve_kwargs` method can be overridden to customize curve fitting options. + - The `add_infos_to_df` and `handle_scaling` methods are intended to be overridden if additional + functionality or specific scaling behavior is required. + + References + ---------- + For details on goodness-of-fit metrics and their calculations, see the documentation for `_get_gof`. + """ + freq: np.ndarray aperiodic_spectrum: np.ndarray scale_factor: int | float diff --git a/pyrasa/utils/irasa_utils.py b/pyrasa/utils/irasa_utils.py index 47eda76..9882430 100644 --- a/pyrasa/utils/irasa_utils.py +++ b/pyrasa/utils/irasa_utils.py @@ -72,6 +72,7 @@ def _get_windows( nperseg: int, dpss_settings: dict, win_func: Callable, win_func_kwargs: dict ) -> tuple[np.ndarray, np.ndarray]: """Generate a window function used for tapering""" + low_bias_ratio = 0.9 min_time_bandwidth = 2.0 win_func_kwargs = copy(win_func_kwargs) diff --git a/pyrasa/utils/types.py b/pyrasa/utils/types.py index 31ab85d..ef49197 100644 --- a/pyrasa/utils/types.py +++ b/pyrasa/utils/types.py @@ -1,3 +1,5 @@ +"""Custom classes for pyrasa.""" + from typing import Protocol, TypedDict import numpy as np @@ -6,12 +8,49 @@ class IrasaFun(Protocol): + """ + A protocol defining the interface for an IRASA function used in the PyRASA library. + + The `IrasaFun` protocol specifies the expected signature of a function used to separate + aperiodic and periodic components of a power spectrum using the IRASA algorithm. + Any function conforming to this protocol can be passed to other PyRASA functions + as a custom IRASA implementation. + + Methods + ------- + __call__(data: np.ndarray, fs: int, h: float, + up_down: str | None, time_orig: np.ndarray | None = None) -> np.ndarray + Separates the input data into its aperiodic and periodic components based on the IRASA method. + + Parameters + ---------- + data : np.ndarray + The input time series data to be analyzed. + fs : int + The sampling frequency of the input data. + h : float + The resampling factor used in the IRASA algorithm. + up_down : str | None + A string indicating the direction of resampling ('up' or 'down'). + If None, no resampling is performed. + time_orig : np.ndarray | None, optional + The original time points of the data, used for interpolation if necessary. + If None, no interpolation is performed. + + Returns + ------- + np.ndarray + The output of the IRASA function. + """ + def __call__( self, data: np.ndarray, fs: int, h: float, up_down: str | None, time_orig: np.ndarray | None = None ) -> np.ndarray: ... class IrasaSprintKwargsTyped(TypedDict): + """TypedDict for the IRASA sprint function.""" + nfft: int hop: int win_duration: float From 5a02bf93318b287a2121403306e11eea316debbb Mon Sep 17 00:00:00 2001 From: Fabi Date: Sat, 10 Aug 2024 20:45:01 +0200 Subject: [PATCH 11/11] finalised docstrings --- pyrasa/utils/fit_funcs.py | 51 +++++++++++++++++ pyrasa/utils/irasa_spectrum.py | 2 + pyrasa/utils/irasa_tf_spectrum.py | 2 + pyrasa/utils/irasa_utils.py | 56 +++++++++++++++---- pyrasa/utils/types.py | 2 + .../notebooks/check_aperiodic_fits.py | 0 .../notebooks/check_basic_functionality.py | 0 .../notebooks}/notebooks/check_irasa_mne.py | 0 .../notebooks/check_irasa_sprint.py | 0 9 files changed, 103 insertions(+), 10 deletions(-) rename {paper => simulations/notebooks}/notebooks/check_aperiodic_fits.py (100%) rename {paper => simulations/notebooks}/notebooks/check_basic_functionality.py (100%) rename {paper => simulations/notebooks}/notebooks/check_irasa_mne.py (100%) rename {paper => simulations/notebooks}/notebooks/check_irasa_sprint.py (100%) diff --git a/pyrasa/utils/fit_funcs.py b/pyrasa/utils/fit_funcs.py index 41d0b81..f806d90 100644 --- a/pyrasa/utils/fit_funcs.py +++ b/pyrasa/utils/fit_funcs.py @@ -192,6 +192,30 @@ def fit_func(self) -> tuple[pd.DataFrame, pd.DataFrame]: class FixedFitFun(AbstractFitFun): + """ + A model for fitting aperiodic activity in power spectra. + + The `FixedFitFun` class extends `AbstractFitFun` to model aperiodic activity in power spectra + using a fixed function that does not include a spectral knee. This model is suitable for + cases where the aperiodic component of the spectrum follows a consistent slope across + the entire frequency range. + + Attributes + ---------- + label : str + A label to identify this fitting model. Default is 'fixed'. + log10_aperiodic : bool + Indicates whether to log-transform the aperiodic spectrum. Default is True. + + Methods + ------- + func(x: np.ndarray, Offset: float, Exponent: float) -> np.ndarray + Defines the model function for aperiodic activity without a spectral knee. + + curve_kwargs() -> dict[str, Any] + Generates initial guess parameters and other keyword arguments for curve fitting. + """ + label = 'fixed' log10_aperiodic = True @@ -222,6 +246,33 @@ def curve_kwargs(self) -> dict[str, Any]: class KneeFitFun(AbstractFitFun): + """ + A model for fitting aperiodic activity in power spectra with a spectral knee. + + The `KneeFitFun` class extends `AbstractFitFun` to model aperiodic activity in power spectra + using a function that includes a spectral knee. This model is particularly useful for + cases where the aperiodic component of the spectrum has a break or knee, representing + a transition between two different spectral slopes. + + Attributes + ---------- + label : str + A label to identify this fitting model. Default is 'knee'. + log10_aperiodic : bool + Indicates whether to log-transform the aperiodic spectrum. Default is True. + + Methods + ------- + func(x: np.ndarray, Offset: float, Knee: float, Exponent_1: float, Exponent_2: float) -> np.ndarray + Defines the model function for aperiodic activity with a spectral knee and pre-knee slope. + + add_infos_to_df(df_params: pd.DataFrame) -> pd.DataFrame + Adds calculated knee frequency to the DataFrame of fit parameters. + + curve_kwargs() -> dict[str, Any] + Generates initial guess parameters and other keyword arguments for curve fitting. + """ + label = 'knee' log10_aperiodic = True diff --git a/pyrasa/utils/irasa_spectrum.py b/pyrasa/utils/irasa_spectrum.py index c4abd0e..75d5862 100644 --- a/pyrasa/utils/irasa_spectrum.py +++ b/pyrasa/utils/irasa_spectrum.py @@ -1,3 +1,5 @@ +"""Output Class of pyrasa.irasa""" + import numpy as np import pandas as pd from attrs import define diff --git a/pyrasa/utils/irasa_tf_spectrum.py b/pyrasa/utils/irasa_tf_spectrum.py index 26de7b5..ef672d1 100644 --- a/pyrasa/utils/irasa_tf_spectrum.py +++ b/pyrasa/utils/irasa_tf_spectrum.py @@ -1,3 +1,5 @@ +"""Output Class of pyrasa.irasa_sprint""" + import numpy as np import pandas as pd from attrs import define diff --git a/pyrasa/utils/irasa_utils.py b/pyrasa/utils/irasa_utils.py index 9882430..608ab4f 100644 --- a/pyrasa/utils/irasa_utils.py +++ b/pyrasa/utils/irasa_utils.py @@ -19,13 +19,49 @@ def _gen_irasa( time: np.ndarray | None = None, ) -> tuple[np.ndarray, np.ndarray, np.ndarray]: """ - This function is implementing the IRASA algorithm using a custom function to - compute a power/cross-spectral density and returns an "original", "periodic" and "aperiodic spectrum". - This implementation of the IRASA algorithm is based on the yasa.irasa function in (Vallat & Walker, 2021). - - [1] Vallat, Raphael, and Matthew P. Walker. “An open-source, - high-performance tool for automated sleep staging.” - Elife 10 (2021). doi: https://doi.org/10.7554/eLife.70092 + Generate original, aperiodic, and periodic spectra using the IRASA algorithm. + + This function implements the IRASA (Irregular Resampling Auto-Spectral Analysis) algorithm + to decompose a power or cross-spectral density into its periodic and aperiodic components. + + Parameters + ---------- + data : np.ndarray + The input time-series data, typically with shape (n_channels, n_times) or similar. + orig_spectrum : np.ndarray + The original power spectral density from which periodic and aperiodic components are to be extracted. + fs : int + The sampling frequency of the input data in Hz. + irasa_fun : IrasaFun + A custom function used to compute power spectral densities. This function should + take resampled data and return the corresponding spectrum. + hset : np.ndarray + An array of up/downsampling factors (e.g., [1.1, 1.2, 1.3, ...]) used in the IRASA algorithm. + time : np.ndarray | None, optional + The time vector associated with the original data. This is only necessary if the IRASA function + requires the time stamps of the original data. + + Returns + ------- + tuple[np.ndarray, np.ndarray, np.ndarray] + A tuple containing: + - `orig_spectrum` (np.ndarray): The original spectrum provided as input. + - `aperiodic_spectrum` (np.ndarray): The median of the geometric mean of up/downsampled spectra, + representing the aperiodic component. + - `periodic_spectrum` (np.ndarray): The difference between the original and the aperiodic spectrum, + representing the periodic component. + + Notes + ----- + This implementation of the IRASA algorithm is based on the `yasa.irasa` function from (Vallat & Walker, 2021). + The IRASA algorithm involves upsampling and downsampling the time-series data by a set of factors (`hset`), + calculating the power spectra of these resampled data, and then taking the geometric mean of the upsampled + and downsampled spectra to isolate the aperiodic component. + + References + ---------- + [1] Vallat, Raphael, and Matthew P. Walker. “An open-source, high-performance tool for automated sleep staging.” + Elife 10 (2021). doi: https://doi.org/10.7554/eLife.70092 """ spectra = np.zeros((len(hset), *orig_spectrum.shape)) @@ -162,7 +198,7 @@ def _compute_psd_welch( axis: int = -1, average: str = 'mean', ) -> tuple[np.ndarray, np.ndarray]: - """Function to compute power spectral densities using welchs method""" + """Compute power spectral densities via scipy.signal.welch""" if nperseg is None: nperseg = data.shape[-1] @@ -206,7 +242,7 @@ def _compute_sgramm( # noqa C901 up_down: str | None = None, time_orig: np.ndarray | None = None, ) -> tuple[np.ndarray, np.ndarray, np.ndarray]: - """Function to compute spectrograms""" + """Compute spectrograms via scipy.signal.stft""" nperseg = int(np.floor(fs * win_duration)) @@ -235,6 +271,6 @@ def _compute_sgramm( # noqa C901 if time_orig is not None: sgramm = sgramm[:, :, : time_orig.shape[-1]] - sgramm = np.squeeze(sgramm) # bring in proper format + sgramm = np.squeeze(sgramm) return freq, time, sgramm diff --git a/pyrasa/utils/types.py b/pyrasa/utils/types.py index ef49197..530a321 100644 --- a/pyrasa/utils/types.py +++ b/pyrasa/utils/types.py @@ -60,5 +60,7 @@ class IrasaSprintKwargsTyped(TypedDict): @define class AperiodicFit: + """Container for the results of aperiodic model fits.""" + aperiodic_params: pd.DataFrame gof: pd.DataFrame diff --git a/paper/notebooks/check_aperiodic_fits.py b/simulations/notebooks/notebooks/check_aperiodic_fits.py similarity index 100% rename from paper/notebooks/check_aperiodic_fits.py rename to simulations/notebooks/notebooks/check_aperiodic_fits.py diff --git a/paper/notebooks/check_basic_functionality.py b/simulations/notebooks/notebooks/check_basic_functionality.py similarity index 100% rename from paper/notebooks/check_basic_functionality.py rename to simulations/notebooks/notebooks/check_basic_functionality.py diff --git a/paper/notebooks/check_irasa_mne.py b/simulations/notebooks/notebooks/check_irasa_mne.py similarity index 100% rename from paper/notebooks/check_irasa_mne.py rename to simulations/notebooks/notebooks/check_irasa_mne.py diff --git a/paper/notebooks/check_irasa_sprint.py b/simulations/notebooks/notebooks/check_irasa_sprint.py similarity index 100% rename from paper/notebooks/check_irasa_sprint.py rename to simulations/notebooks/notebooks/check_irasa_sprint.py