119 lines
45 KiB
Plaintext
119 lines
45 KiB
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": 1,
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"id": "a540c86a-3bed-4543-b952-342c796d7507",
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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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"import pandas as pd\n",
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"import seaborn as sns\n",
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"import matplotlib.pyplot as plt"
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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": 2,
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"id": "dc1ec4fb-4073-4d32-887f-e488ef0a9437",
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"metadata": {},
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"outputs": [],
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"source": [
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"t = np.arange(0,45)"
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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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"id": "74ff2c1e-6c77-4aef-a26f-e86e5b1ce2d8",
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"metadata": {},
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"outputs": [],
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"source": [
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"# create signal\n",
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"sig = -3*np.ones((6,45))\n",
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"for rownum, start in enumerate([15,12,16,2,14,100]):\n",
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" sig[rownum,start:] = 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": 4,
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"id": "fdc289c2-b05f-4851-9fb2-01a7bda5dd5f",
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"metadata": {},
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"outputs": [],
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"source": [
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"# create measurement\n",
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"meas = np.ones((6,45))\n",
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"data2 = {'well':[], 'cycle': [], 'fluorescence': []}\n",
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"data = {}\n",
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"for row in range(6):\n",
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" value = sig[row,0]\n",
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" for col in range(45):\n",
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" diff = sig[row,col] - value\n",
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" value += 0.4*diff\n",
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" meas[row,col] = value\n",
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" data2['well'].append('well {}'.format(row+1))\n",
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" data2['cycle'].append(col)\n",
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" data2['fluorescence'].append(value)\n",
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" data[f'well {row}'] = meas[row]\n",
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"data[f'cycle'] = t\n",
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"df = pd.DataFrame(data2)"
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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": 5,
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"id": "6e5b1731-6d98-41dd-8a38-f8c168aeaace",
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"metadata": {},
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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 640x480 with 1 Axes>"
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]
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},
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"metadata": {},
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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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"sns.lineplot(data=df, x=\"cycle\", y=\"fluorescence\", hue=\"well\");\n",
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"plt.savefig(\"pcr_results.png\")"
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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": 9,
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"id": "841e114f-dd6d-4136-a093-8070a3069643",
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"metadata": {},
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"outputs": [],
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"source": [
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"df.to_csv('pcr_sample_1.csv', index=False)"
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]
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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 (ipykernel)",
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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": "3.11.10"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 5
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}
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