80 lines
1.6 MiB
Plaintext
80 lines
1.6 MiB
Plaintext
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{
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"cells": [
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{
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"cell_type": "code",
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"execution_count": 9,
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"metadata": {},
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"outputs": [],
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"source": [
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"import numpy as np\n",
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"from plot_note_analysis import plot_all_note_durations\n",
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"\n"
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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": 10,
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"metadata": {
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"scrolled": false
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},
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"outputs": [
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{
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"data": {
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"image/png": "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"text/plain": [
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"<Figure size 1332x9072 with 12 Axes>"
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]
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},
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"metadata": {
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"needs_background": "light"
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},
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"output_type": "display_data"
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}
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],
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"source": [
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" durFn = \"/home/kevin/temp/cache_note_dur.pickle\"\n",
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" yamlFn= \"/home/kevin/src/picadae_ac_3/p_ac.yml\"\n",
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" axisN=12\n",
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" minDurMs=800\n",
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" maxPulseUs=40000\n",
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" plot_all_note_durations(durFn, np.arange(24,108),axisN,yamlFn,minDurMs,maxPulseUs)\n"
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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": null,
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"metadata": {},
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"outputs": [],
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"source": []
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},
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{
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"cell_type": "code",
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"execution_count": null,
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"metadata": {},
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"outputs": [],
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"source": []
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}
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],
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"metadata": {
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"kernelspec": {
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"display_name": "Python 3",
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"language": "python",
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"name": "python3"
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},
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"language_info": {
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"codemirror_mode": {
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"name": "ipython",
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"version": 3
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},
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"file_extension": ".py",
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"mimetype": "text/x-python",
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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": "3.7.5"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 2
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}
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