mirror of
https://github.com/galera951/experiment-automation.git
synced 2024-09-20 00:42:52 +03:00
95 lines
42 KiB
Plaintext
95 lines
42 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": "d77ea758-4368-4ffc-86c7-2980c84ceaa9",
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"metadata": {},
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"outputs": [],
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"source": [
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"import matplotlib.pyplot as plt\n",
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"import pandas as pd\n",
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"import numpy as np"
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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": "805a285a-8aaf-4e25-8c23-4503c8cca594",
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"metadata": {},
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"outputs": [],
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"source": [
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"df = pd.read_csv(\"data.txt\")\n",
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"U = df['U']\n",
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"I = df['I']"
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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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"id": "e0b87229-443c-4642-b053-7ce974af2817",
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"metadata": {},
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"outputs": [
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{
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"name": "stderr",
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"output_type": "stream",
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"text": [
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"No artists with labels found to put in legend. Note that artists whose label start with an underscore are ignored when legend() is called with no argument.\n"
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]
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},
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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 2000x1200 with 1 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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"plt.figure(figsize=(10, 6), dpi=200)\n",
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"\n",
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"plt.errorbar(I*6/155, U, xerr=6/155, yerr=0.003*U, linestyle='', marker='.')\n",
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"plt.xlabel(\"Сила тока, мА\")\n",
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"plt.ylabel(\"Напряжение, В\")\n",
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"\n",
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"plt.legend()\n",
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"plt.show()"
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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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"id": "d72fff5c-0148-430a-ba40-219308a57af4",
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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 (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": "3.10.2"
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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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