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‎.gitignore

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**/.eegdash_cache/
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**/*/eegdash.egg-info/
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.env
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tests/data/

‎DevNotes.md

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pip install -r requirements.txt
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## signalstore mongodb
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- Check args functions to double check input to db query
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- Create_index to the collection once its created to speed up querying
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- `find` has deserialization to convert timestamp to correct milisecond format and json_schema from bytes to dict
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- `add` has serialization before insert into db

‎notebooks/eegdash.ipynb

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],
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"metadata": {
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"kernelspec": {
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"display_name": "eegdash",
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"display_name": ".venv",
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"language": "python",
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"name": "python3"
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},
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.5"
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"version": "3.10.12"
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}
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},
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"nbformat": 4,

‎notebooks/scratch.ipynb

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"cells": [
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{
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"cell_type": "code",
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"execution_count": 5,
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"execution_count": 1,
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"metadata": {},
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"outputs": [],
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"source": [
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"# To be able to make edits to repo without having to restart notebook\n",
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"%load_ext autoreload\n",
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"%autoreload 2\n",
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"from eegdash import EEGDash\n",
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"from braindecode.datasets import BaseDataset, BaseConcatDataset"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 14,
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"execution_count": 2,
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"metadata": {},
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"outputs": [
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{
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"data": {
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"text/html": [
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"<div>\n",
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"<style scoped>\n",
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" .dataframe tbody tr th:only-of-type {\n",
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" vertical-align: middle;\n",
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" }\n",
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"\n",
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" .dataframe tbody tr th {\n",
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" vertical-align: top;\n",
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" }\n",
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"\n",
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" .dataframe thead th {\n",
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" text-align: right;\n",
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" }\n",
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"</style>\n",
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"<table border=\"1\" class=\"dataframe\">\n",
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" <thead>\n",
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" <tr style=\"text-align: right;\">\n",
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" <th></th>\n",
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" <th>sampling_frequency</th>\n",
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" <th>nchans</th>\n",
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" <th>ntimes</th>\n",
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" </tr>\n",
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" </thead>\n",
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" <tbody>\n",
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" <tr>\n",
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" <th>0</th>\n",
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" <td>250</td>\n",
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" <td>74</td>\n",
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" <td>742500</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>1</th>\n",
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" <td>250</td>\n",
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" <td>74</td>\n",
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" <td>747000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>2</th>\n",
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" <td>250</td>\n",
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" <td>74</td>\n",
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" <td>740000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>3</th>\n",
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" <td>250</td>\n",
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" <td>74</td>\n",
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" <td>738750</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>4</th>\n",
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" <td>250</td>\n",
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" <td>74</td>\n",
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" <td>741750</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>5</th>\n",
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" <td>250</td>\n",
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" <td>74</td>\n",
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" <td>736750</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>6</th>\n",
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" <td>250</td>\n",
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" <td>74</td>\n",
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" <td>749000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>7</th>\n",
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" <td>250</td>\n",
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" <td>74</td>\n",
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" <td>738750</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>8</th>\n",
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" <td>250</td>\n",
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" <td>74</td>\n",
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" <td>747750</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>9</th>\n",
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" <td>250</td>\n",
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" <td>74</td>\n",
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" <td>736500</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>10</th>\n",
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" <td>250</td>\n",
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" <td>74</td>\n",
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" <td>738250</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>11</th>\n",
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" <td>250</td>\n",
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" <td>74</td>\n",
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" <td>739500</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>12</th>\n",
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" <td>250</td>\n",
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" <td>74</td>\n",
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" <td>749000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>13</th>\n",
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" <td>250</td>\n",
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" <td>74</td>\n",
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" <td>741500</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>14</th>\n",
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" <td>250</td>\n",
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" <td>74</td>\n",
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" <td>743000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>15</th>\n",
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" <td>250</td>\n",
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" <td>74</td>\n",
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" <td>744000</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>16</th>\n",
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" <td>250</td>\n",
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" <td>74</td>\n",
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" <td>742500</td>\n",
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" </tr>\n",
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" <tr>\n",
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" <th>17</th>\n",
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" <td>250</td>\n",
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" <td>74</td>\n",
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" <td>743250</td>\n",
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" </tr>\n",
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" </tbody>\n",
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"</table>\n",
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"</div>"
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],
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"text/plain": [
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" sampling_frequency nchans ntimes\n",
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"0 250 74 742500\n",
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"1 250 74 747000\n",
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"2 250 74 740000\n",
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"3 250 74 738750\n",
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"4 250 74 741750\n",
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"5 250 74 736750\n",
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"6 250 74 749000\n",
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"7 250 74 738750\n",
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"8 250 74 747750\n",
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"9 250 74 736500\n",
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"10 250 74 738250\n",
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"11 250 74 739500\n",
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"12 250 74 749000\n",
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"13 250 74 741500\n",
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"14 250 74 743000\n",
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"15 250 74 744000\n",
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"16 250 74 742500\n",
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"17 250 74 743250"
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]
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},
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"execution_count": 14,
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"metadata": {},
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"output_type": "execute_result"
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{'_id': ObjectId('677db70295291b67a99cada1'), 'schema_ref': 'eeg_signal', 'data_name': 'ds002718_sub-014_task-FaceRecognition_eeg.set', 'dataset': 'ds002718', 'bidspath': 'ds002718/sub-014/eeg/sub-014_task-FaceRecognition_eeg.set', 'subject': '014', 'task': 'FaceRecognition', 'session': '', 'run': '', 'sampling_frequency': 250, 'modality': 'EEG', 'has_file': True, 'version_timestamp': 0, 'time_of_save': 1738946443346607, 'time_of_removal': None, 'nchans': 74, 'ntimes': 742500, 'channel_types': ['EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'HEOG', 'VEOG', 'ECG', 'ECG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG', 'EEG'], 'channel_names': ['EEG001', 'EEG002', 'EEG003', 'EEG004', 'EEG005', 'EEG006', 'EEG007', 'EEG008', 'EEG009', 'EEG010', 'EEG011', 'EEG012', 'EEG013', 'EEG014', 'EEG015', 'EEG016', 'EEG017', 'EEG018', 'EEG019', 'EEG020', 'EEG021', 'EEG022', 'EEG023', 'EEG024', 'EEG025', 'EEG026', 'EEG027', 'EEG028', 'EEG029', 'EEG030', 'EEG031', 'EEG032', 'EEG033', 'EEG034', 'EEG035', 'EEG036', 'EEG037', 'EEG038', 'EEG039', 'EEG040', 'EEG041', 'EEG042', 'EEG043', 'EEG044', 'EEG045', 'EEG046', 'EEG047', 'EEG048', 'EEG049', 'EEG050', 'EEG051', 'EEG052', 'EEG053', 'EEG054', 'EEG055', 'EEG056', 'EEG057', 'EEG058', 'EEG059', 'EEG060', 'EEG061', 'EEG062', 'EEG063', 'EEG064', 'EEG065', 'EEG066', 'EEG067', 'EEG068', 'EEG069', 'EEG070', 'EEG071', 'EEG072', 'EEG073', 'EEG074']}\n"
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]
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}
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],
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"source": [
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"eeg = EEGDash(is_public=False)\n",
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"# eeg.update_noref({'data_name': record['data_name']})\n",
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"datasets = eeg.findrecord({'dataset': 'ds002718'})\n",
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"print(datasets[0])"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 3,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"{'data_name': 'test2', 'dataset': 'test', 'bidspath': 'test', 'subject': 'test'}\n"
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]
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}
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],
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"source": [
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"eeg = EEGDash()\n",
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"datasets = eeg.find({'dataset': 'ds002718'})\n",
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"datasets.description"
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"record = {\n",
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" 'data_name': 'test2',\n",
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" 'dataset': 'test',\n",
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" 'bidspath': 'test',\n",
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" 'subject': 'test',\n",
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"}\n",
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"eeg.addnoref(record)"
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]
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},
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{

‎notebooks/testspeed.ipynb

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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.11.9"
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"version": "3.10.12"
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}
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},
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"nbformat": 4,

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