commit d1af5a6188eb9bfd80b2d6804e4a12aad3752bff Author: silaev.vd Date: Mon Oct 3 09:04:20 2022 +0300 3.5.1 and 3.6.1 diff --git a/auto/3.5.1/351 I-V curve plasm.ipynb b/auto/3.5.1/351 I-V curve plasm.ipynb new file mode 100644 index 0000000..f3f21d6 --- /dev/null +++ b/auto/3.5.1/351 I-V curve plasm.ipynb @@ -0,0 +1,118 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 3, + "id": "eb14dc9b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'Ток, мА')" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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3zv7XLWrr3dRLcfzoufPieJHeTc6dS9J8m83q9BsrXoVqQ3v3MVeij45NPUc+XbskqXJmM5w+AiwF1pdfS8ttqkMtzVPPnU/XLkmqnNkMp38EKADnlF9/GRH/V6ULU23qW9dHrunoufNcU46+dX3T/IQkqVJmM5z+fwCrU0ovA0TEp4GtwK2VLEy1qXtFabi9d1Mvo2OjtDS30Leu70i7JGn+zCbEAzg4YftguU11qntFt6EtSTVg2hCPiAUppQPAfwW2R8Tflr96L/CleahNkiTNYKae+A7g2pTSLRHxbSBPqQf+wZTSd+ejOEmSNL2ZQvzIkHlK6UHgwcqXI0mSZmumEF8WEf/PdF+mlG6pQD2SJGmWZgrxRmAxLmKTJKkmzRTiz6WUPjlvlUiSpDmZ6WYv9sAlSaphM4X4unmrQpIkzdm0IZ5S+ul8FiJJkubmmPdOlyRJtckQlyQpowxxZUJhqEDbhjYabm6gbUMbhaFCtUuSpKqbzQNQpKoqDBXo2dhDcbwIwMjYCD0bewB8EIukumZPXDWvd1PvkQA/rDhepHdTb5UqkqTaYIir5o2Ojc6pXZLqhSGumtfS3DKndkmqF4a4al7fuj5yTbmj2nJNOfrW9VWpIkmqDYa4al73im761/fT2txKELQ2t9K/vt9FbZLqXqSUql3DnHR0dKTBwcFqlyFJ0ryJiJ0ppY7J7fbElU3DBbi7De5oKL0Pe924pPpTsRCPiIsi4n9ExGMR8WhEfGSKfd4eEWMR8VD59fuVqkenkOEC7OiB4giQSu87egxySXWnkjd7OQD8TkrpwYg4E9gZEfenlP5h0n7fSSndWME6dKrZ1QsHj75unIPFUnu78+SS6kfFeuIppedSSg+WP78EPAZcUKnfpzpSnOb68OnaJekUNS9z4hHRBqwCtk/x9dqI2BUR34yIq+ajHmVcbprrw6drl6RTVMVDPCIWA18FPppS2jvp6weB1pTSSuBW4O5pjtETEYMRMbhnz56K1qsMWNkHjUdfN05jrtQuSXWkoiEeEU2UAryQUvra5O9TSntTSvvKn+8DmiLi7Cn2608pdaSUOpYtW1bJkpUF7d3Q2Q+5ViBK7539zodLqjsVW9gWEQF8CXgspXTLNPucC+xOKaWI6KT0R8ULlapJp5D2bkNbUt2r5Or0LuCfA0MR8VC57XeBFoCU0m3A+4APRcQBYD/wgZS1u89IklQlFQvxlNIAEMfY5/PA5ytVgyRJpzLv2CZJUkYZ4pIkZZQhLklSRhnikiRllCEuSVJGGeKSJGWUIS5JUkYZ4pIkZZQhLklSRhnikiRllCEuSVJGGeKSJGWUIS5JUkYZ4lINKwwVaNvQRsPNDbRtaKMwVKh2SZJqSCWfJy7pBBSGCvRs7KE4XgRgZGyEno09AHSv6K5maZJqhD1xqUb1buo9EuCHFceL9G7qrVJFkmqNIS7VqNGx0Tm1S6o/hrhUo1qaW+bULqn+GOJSjepb10euKXdUW64pR9+6vipVJKnWGOJSjepe0U3/+n5am1sJgtbmVvrX97uoTdIRkVKqdg1z0tHRkQYHB6tdhiRJ8yYidqaUOia32xOXJCmjDHFJkjLKEJckKaMMcUmSMsoQl+rVcAHuboM7Gkrvw96XXcoa750u1aPhAuzogYPl27oWR0rbAO1ewiZlhT1xqR7t6n0twA87WCy1S8oMQ1yqR8Vp7r8+XbukmmSIS/UoN83916drl1STDHGpHq3sg8aj78tOY67ULikzDHGpHrV3Q2c/5FqBKL139ruoTcoYV6dL9aq929CWMs6euKSTrjBUoG1DGw03N9C2oY3CkNegS5VgT1zSSVUYKtCzsYfieOkStpGxEXo2lq5B9zGq0slliEs1YNu2bXzjG99g0aJFrF69mtWrV7N48eJql3Vcejf1Hgnww4rjRXo39Rri0klmiEs1YM2aNaxZs4Ziscj27du59dZbefnll2loaGDlypV0dXVx7rnnVrvMWRkdm/pa8+naJR0/Q1yqIblcjne84x284x3vAODAgQMMDQ1x1113sXv3blJKXHzxxeTzeS699FIiosoVv15LcwsjYyNTtks6uQxxqYYtWLCAVatWsWrVKgBSSjz55JMMDAzw5S9/GYCzzjqLrq4uVq1axWmnnVbNcgHoW9d31Jw4QK4pR986r0GXTjZDXMqQiKC9vZ329vYjbS+88AJbtmzhj//4jxkfH2fhwoV0dnaydu1alixZMu81Hp737t3Uy+jYKC3NLfSt63M+XKqASClV5sARFwG3A+cCh4D+lNLnJu0TwOeAG4Ai8BsppQdnOm5HR0caHBysSM3SqWD//v0MDg6ydetW9u7dS0Rw9dVXk8/nueCCC6pdnqTjEBE7U0odk9sr2RM/APxOSunBiDgT2BkR96eU/mHCPu8Blpdfq4EvlN8lHadFixbxtre9jbe97W0AHDp0iEcffZR77rmHZ555hojgoosuIp/Pc/nll9PQ4O0ipKyqWIinlJ4Dnit/fikiHgMuACaG+C8Dt6fScMC2iFgaEeeVf1bSSdDQ0MCKFStYsWLFkbbR0VE2b97MnXfeyaFDh1i6dCnXX389b33rW1m4cGEVq5U0F/MyJx4RbcAqYPukry4Anpqw/XS5zRCXKqilpYWWltdWi//sZz9j69atfPrTn+bVV1/ltNNOo6Ojg+uvv56lS5dWr1BJM6p4iEfEYuCrwEdTSnsnfz3Fj7xukj4ieoAe4Kj/8Eg6Od7whjdwww03cMMNNwDwyiuvsHPnTr74xS/y4osvEhFcccUV5PN5/z8o1ZCKLWwDiIgm4F7gv6WUbpni+/8MfDul9Ffl7e8Bb59pON2FbdL8O3ToEI8//jgDAwOMjpZu2nL++eeTz+e56qqraGxsrHKF0qlt3he2lVeefwl4bKoAL7sH+K2IuJPSgrYx58Ol2tPQ0MCVV17JlVdeeaTtmWeeYfPmzfzN3/wNhw4d4swzz2Tt2rVcd911LFq0qIrVSvWjkpeY5YHvAEOULjED+F2gBSCldFs56D8PvJvSJWYfTCnN2M22Jy7Vpr1797Jt2zZ27NjBz3/+cxYsWMC1115LV1cXb3zjG6tdnpRp0/XEKzqcXgmGuJQN4+PjfPe732Xz5s288MILAFx66aXk83na29tr8paxUq0yxCVVVUqJJ554goGBAYaHhwE455xzyOfzvOUtb3FeXZpBNW72IklHRASXXnopl1566ZG23bt3s3nzZu6++24OHjzIGWecwZo1a+js7OSMM86oYrVSNtgTl1Qz9u3bx/bt29m+fTvFYpHGxkauueYaurq6OOecc6pdnlQ1DqdLypwDBw6wa9cuBgYG2LNnDwCXXHIJ+XyeN7/5zc6rq24Y4pIyL6XEj370IwYGBnjiiScAeOMb30g+n+eaa66hqampyhVKleGcuKTMiwguueQSLrnkkiNte/bsYcuWLXzjG9/gwIEDnH766axevZo1a9Zw5plnVrFaqfLsiUs6pRSLRR544AG2bt3Kvn37jjwAJp/Pc95551W7POm4OJwuqS4dPHiQRx55hIGBAX784x+TUqKtrY18Ps9ll1025bx6YahA76ZeRsdGaWluoW9dH90ruqtQvVRiiEsSpXn10dFRBgYGePzxxwFYunQpXV1dXHvttdz1vbvo2dhDcbx45GdyTTn61/cb5KoaQ1ySpvHTn/6ULVu2sHPnTm7ZfAt7D+yF84GLgNNL+7Q2t/LkR5+sYpWqZ4a4JM1Cw80NpPEEzwJPAa9QemjyMnhqw1NceOGF1S1QdcnV6ZI0Cy3NLYyMjUArpRfAITjv5+dx77338vTTTwNw4YUXks/nufLKK2loaHj9gYYLsKsXiqOQa4GVfdDucLxOLkNckiboW9f3+jnxhTk+877PHDUn/tRTT7F582a+8pWvkFJiyZIlXH/99XR0dHD6c1+FHT1wsHyM4khpGwxynVQOp0vSJMezOn1sbIytW7fywAMP8MrDn6Xp0F7e2g7XXwpnLS7vlGuF9z5Z8fp16nFOXJLmyx0NvHog8eAwbP4+vFjukF9+PuQ//iQtLS3eMlZz4py4JM2XXAunFUdYsxzWLC81pQTf+9n53H///YyMjABw7rnnks/nufrqq30Uq46LIS5JJ9vKvqPnxIFYkOPyX/wTLp8wJ/7cc8+xefNmvva1r3Ho0CHOOOMM1q5dS2dnJ4sWLapG5coYh9MlqRKOY3X6Sy+9xPbt29mxYwf79++nsbGRVatW0dXVxdlnnz1PhasWOScuSRkzPj5+5FGsP/nJTwBYvnw5+Xyeiy++2Hn1OmKIS1LGpZT4wQ9+wMDAAD/84Q8BWLZsGfl8npUrV7JggTOkpyoXtklSxkUEy5cvZ/ny5Ufann/+eTZv3sw999zDwYMHyeVyrF69mtWrV7N48eIZjqZTgT1xSTqFvPzyy+zYsYNt27bx8ssv09DQwMqVK8nn87zpTW+qdnk6Tg6nS1IdOnDgAENDQwwMDLB7924ALr74YvL5PMuXL3dePSMMcUkSKSWefPJJBgYG+P73vw/AWWedRVdXF6tWraKpqanKFWoqzolLkogI2tvbaW9vP9L2wgsvsGXLFv7oj/6IAwcOsHDhQjo7O1mzZg1LliypYrU6FnvikqSj7N+/n8HBQbZu3cpLL71ERHD11VeTz+c5//zzq11eXXI4XZJ0XA4dOsSjjz7KwMAAzz77LAAXXXQR+Xyeyy+/fOpHseqkcjhdknRcGhoaWLFiBStWrDjSNjo6yubNm7nzzjtJKdHc3Mz111/PW9/6VhYuXFjFauuLIS5JmrOWlhZaWlqObL/44ots3bqVP/mTP+HVV1+lqamJjo4Orr/+epYuXVq9Qk9xDqdLkk66V155hQcffJAtW7bw4osvEhFcccUV5PN5LrroomqXlzkOp0uS5s3ChQtZu3Yta9euBUrz6t/73vf41re+xejoKADnn38++Xyeq666ynn142SIS5IqrqGhgSuuuIIrrrjiSNszzzzD5s2bueuuu0gpceaZZ7J27Vquu+46Tj/99CP7FYYK9G7qZXRslJbmFvrW9dG9YuYnwtULh9MlSTVh7969bNu2jQceeICf//znLFiwgL1n7eULu7/A/qb9R/bLNeXoX99fV0HuJWaSpEwZHx+n5eMt/PixH8PhDH8j0AItrS2M/PZINcubV86JS5Iypampid1Ld8PackMCXgCGYfS7o/zeT3+Pc845h3w+z1ve8hYaGxurWG11GOKSpJrV0tzCyFi5xx3A2aVXa3Mrf/jRP2T37t1s3ryZr3/960cexbpmzRpWr15NLperZunzwuF0SVLNKgwV6NnYQ3G8eKRtpjnxl19+me3bt7N9+3aKxSKNjY2sXLmSrq4uzjnnnPks/aRyTlySlEknsjr9wIEDPPzwwwwMDLBnzx5SSlxyySXk83ne/OY3Z+ZRrIa4JKnupZQYHh5mYGCAJ554AoCzzz6brq4urrnmGhYsqM1ZZhe2SZLqXkRw8cUXc/HFFx9p+8lPfsKWLVu47777GB8fZ9GiRUcexbp48eIqVntsFeuJR8R/AW4Enk8pXT3F928Hvg4Ml5u+llL65LGOa09cklRJxWKRBx54gG3btrFv374jD4Dp6urivPPOq0pN8z6cHhG/AOwDbp8hxD+WUrpxLsc1xCVJ8+ngwYM88sgjDAwM8OMf/5iUEm1tbeTzeS677LJ5mVef9+H0lNLfR0RbpY4vSdJ8OLzCfeXKlUBpXn10dJSBgQEKhQIAS5cupauri2uvvZbTTjtt3mqr9pz42ojYBTxLqVf+6FQ7RUQP0AMc9eg7SZLmW0TQ2tpKa2vrkbaf/exnbNmyhU996lNcd911vOc975mfWiq5Or3cE793muH0JcChlNK+iLgB+FxKafmxjulwuiSp3kw3nF61Z7+llPamlPaVP98HNEXE2dWqR5KkrKlaiEfEuVFeDRARneVaXqhWPZIkZU3F5sQj4q+AtwNnR8TTwL8HmgBSSrcB7wM+FBEHKD2f5gMpa3eekSSpiiq5Ov1Xj/H954HPV+r3S5J0qqvacLokSToxhrgkqW4Vhgq0bWij4eYG2ja0URgqVLukOan2deKSJFXF5MecjoyN0LOxB2DWT0mrNnvikqS61Lup96jnlAMUx4v0buqtUkVzZ4hLkurS6NjonNprkSEuSapLLc1T38b7t845C+5ugzsaSu/DtTtPbohLkupS37o+ck25o9p+Y2kTtyx9CYojQCq97+ip2SA3xCVJdal7RTf96/tpbW4lCFqbW7n1/CUsSK8evePBIuyqzXlyV6dLkupW94ruo1ei3zFN37ZYm/Pk9sQlSTosN83jrqdrrzJDXJKkw1b2QePR8+Q05krtNcgQlyTpsPZu6OyHXCsQpffO/lJ7DXJOXJKkidq7aza0J7MnLklSRhnikiRllCEuSVJGGeKSJGWUIS5JUkYZ4pIkZZQhLklSRhnikiRllCEuSVJGGeKSJGVUpJSqXcOcRMQeYOQkHvJs4Ccn8Xj1yvN44jyHJ85zeOI8hyeuEuewNaW0bHJj5kL8ZIuIwZRSR7XryDrP44nzHJ44z+GJ8xyeuPk8hw6nS5KUUYa4JEkZZYhDf7ULOEV4Hk+c5/DEeQ5PnOfwxM3bOaz7OXFJkrLKnrgkSRlVVyEeERdFxP+IiMci4tGI+Ei5/SsR8VD59WREPFTlUmvWDOfwmojYVj6HgxHRWe1aa9UM53BlRGyNiKGI2BgRS6pda62KiNMjYkdE7Cqfw5vL7WdFxP0R8UT5/Q3VrrWWzXAe31/ePhQRrlSfwQzn8DMR8XhEPBwRfxsRSyvy++tpOD0izgPOSyk9GBFnAjuB96aU/mHCPn8KjKWUPlmtOmvZdOcQ2AD8WUrpmxFxA/DxlNLbq1ZoDZvhHP4F8LGU0v+MiN8E2lNKv1fFUmtWRARwRkppX0Q0AQPAR4D/HfhpSulTEfFvgTeklP5NNWutZTOcxzHgEPCfKf07OVjFMmvaDOdwCfB3KaUDEfFpgEr8u1hXPfGU0nMppQfLn18CHgMuOPx9+X+Mfwr8VXUqrH0znMNE6V9agGbg2epUWPtmOIeXAX9f3u1+4J9Up8Lal0r2lTebyq8E/DKlP4Yov793/qvLjunOY0rpsZTS96pYWmbMcA7/e0rpQLl9G3BhJX5/XYX4RBHRBqwCtk9ofhuwO6X0RFWKyphJ5/CjwGci4ings8AnqldZdkw6h48Av1T+6v3ARVUqKxMiorE89fU8cH9KaTvwppTSc1D6Ywk4p4olZsI051FzMItz+JvANyvxu+syxCNiMfBV4KMppb0TvvpV7IXPyhTn8EPAb6eULgJ+G/hSNevLginO4W8CH46IncCZwKvVrK/WpZQOppSuodTD6YyIq6tcUiZ5Hk/cTOcwInqBA0ChEr+77kK8PGfxVaCQUvrahPYFlObTvlKt2rJimnP468Dhz3cBLmybwVTnMKX0eErpH6eU3krpj8kfVrPGrEgpvQh8G3g3sLu85uDw2oPnq1dZtkw6jzoOk89hRPw6cCPQnSq0AK2uQrw85/0l4LGU0i2Tvn4X8HhK6en5ryw7ZjiHzwL/qPz5nYBTEtOY7hxGxDnl9wbg3wG3VafC2hcRyw6v9o2IRZT//wvcQ+kPSsrvX69KgRkxw3nULE13DiPi3cC/AX4ppVSs2O+vs9XpeeA7wBCllZcAv5tSui8i/hzYllLyP5wzmO4cAnuBzwELgJ8D/2dKaWdViqxxM5zD5cCHy9tfAz5Rqb/esy4i3kJp4Vojpc7IX6eUPhkRbwT+GmgBRoH3p5R+Wr1Ka9sM5/F/A24FlgEvAg+llP7XqhVaw2Y4hz8AFgIvlHfdllK66aT/fv8bIUlSNtXVcLokSacSQ1ySpIwyxCVJyihDXJKkjDLEJUnKKENcqjERsW/S9m9ExOerVU+tiYi2iNhffmLerojYEhGXVbsuqRoMcUlZ9MOU0jUppZWUrtH93WoXJFWDIS5lSESsj4jtEfHdiPj/IuJN5fY/iIhnyr3ThyJiLCLeXv5uX0T8aUQ8GBGbImJZub2z3JN9qPyzf1Buvz0ibip//vOIeF9ENETE1yPi/eX2SyLiWxGxMyK+ExGXT9x/Qr2PlHvObRHxSLmtKSJ+dHh0oXzHq69GxAPlV9ccT8sS4GfHfVKlDFtQ7QIkvc6i8hORDjuL0u1EofSs4jUppRQR/xL4OPA75e/+LKX0WYCIuHfCz58BPJhS+p2I+H3g3wO/RemWkH+YUvqbiPgYsLi8/78EvhURP5pwjD8FdqSU7ipv9wM3pZSeiIjVwH+idLvd2egBJk4ZfK5c+0BEtAD/DbjiGMe4pHyOzgRywOpZ/m7plGKIS7Vnf/mJSEBpThzoKG9eCHyl/HCP04DhWRzvEK892Ocvee1BNQcpheBRUkqvRsSnKd2+9DHgXGANcH65nsXA9cBdpdvAA6XbSx72mYj4d+XPl0w8dkTkgA8CXwCuKje/C7hywrGWRMSZ5WetT+eHh89RRPwKpT8qfHCH6o7D6VK23Ap8PqW0AvjXwOnHcYzD91r+A+Bj5Xs8//bhLyOiEfgkpbC9nFJP/lNAb3mXBuDF8pz04dfEnvP/e7id1z+J7aOUAnf/hLYGYO2EY11wjACf7B7gF+awv3TKMMSlbGkGnil//vWZdpygATg8T/1rlIbkAX5MaVj7F4A/m7D/h4Fvp5T+ltJTwD4HfBb4xxFxWfnZ58MT5scjIlbOsvb3Av9lUvt/pzS8T/l415TfOyPi9lkcN4+PbVWdcjhdypY/oDSM/QywDWifxc+8DFwVETuBMeBXyo9D/XNKT/F79vBQdkScC/wrSsPnR6SUDkTER4HPA/8L0A18oTxs3gTcCew6Rh0XAh8rH2ti+/8N/MeIeJjSf5P+HriJ0pPI9r/uKCWH58QDeJXSPL5Ud3yKmXSKi4h9KaXFx96ztkTEZ4Avp5QernYtUq0yxKVTXFZDXNKxGeKSJGWUC9skScooQ1ySpIwyxCVJyihDXJKkjDLEJUnKKENckqSM+v8BmhijPQRQOpsAAAAASUVORK5CYII=\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "\n", + "#d = 0.2 mm, l = 5.2 mm\n", + "\n", + "#217 V -> 32 V \n", + "\n", + "#V\n", + "voltage_up = [31.76, 31.48, 30.25, 29.65, 29.09, 28.33, 27.97, 27.62, 27.20, 27.05, 27] #27 скачет +- 0.2\n", + "#mA / 0.04\n", + "current_up = [35, 40, 46, 52, 57, 65, 70, 79, 90, 99, 110]\n", + "\n", + "for i in range(len(current_up)):\n", + " current_up[i] *= 0.04\n", + "\n", + "#V\n", + "voltage_down = [27.00, 27.03, 27.07, 27.23, 27.48, 27.85, 28.37, 29.37, 30.45, 31.82]\n", + "#mA / 0.04\n", + "current_down = [106, 101, 93, 86, 80, 71, 63, 54, 45, 34]\n", + "\n", + "for i in range(len(current_down)):\n", + " current_down[i] *= 0.04\n", + "\n", + "# 36 V -> 66 V\n", + "\n", + "voltage_lin = [29, 30.25, 31.48, 32]\n", + "current_lin = [0.04 * (46 + 6 * 1.25 / 1.23), 0.04 * 46, 0.04 * 40, 0.04 * (40 - 6 * 0.52 / 1.23)]\n", + "\n", + "plt.figure(figsize=(8, 6))\n", + "plt.scatter(voltage_up, current_up, color = 'green', marker = 'o')\n", + "plt.scatter(voltage_down, current_down, color = 'orange', marker = 'o')\n", + "plt.plot(voltage_lin, current_lin, color = 'black', linewidth = 0.5)\n", + "plt.xlabel('Напряжение, В')\n", + "plt.ylabel('Ток, мА')\n", + "#plt.plot(voltage_up, inverse(voltage_up, *params_up))\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "899d025a", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bd2677a2", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "2395d40a", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/auto/3.5.1/351 I-V curve probe.ipynb b/auto/3.5.1/351 I-V curve probe.ipynb new file mode 100644 index 0000000..88575e1 --- /dev/null +++ b/auto/3.5.1/351 I-V curve probe.ipynb @@ -0,0 +1,123 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 2, + "id": "6939d4fc", + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "82.65368591439173 44.342458599117634 21.767499999999995\n" + ] + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "import numpy as np\n", + "\n", + "#5 mA\n", + "#V\n", + "voltage_5 = [25, 22, 19, 16, 13, 10, 8, 6.04, 3.95, 2.07, 0.48, -0.38, -2, -4, -6.04, -8, -10, -13, -16.07, -19, -22, -25]\n", + "#muA\n", + "current_5 = [120.02, 116.93, 113.78, 110.29, 105.35, 97.18, 89.20, 78.43, 63.32, 46.90, 25.17, 3.59, -18.41, -37.95, -55.38, -68.57, -78.62, -88.28, -94.05, -97.70, -101.01, -104.42]\n", + "\n", + "#3 mA\n", + "#V\n", + "voltage_3 = [25, 22, 19, 16, 13, 10, 8, 6.03, 4.05, 2, 0.29, -0.73, -2, -4, -6, -8, -10, -13, -16.07, -19, -22, -25]\n", + "#muA\n", + "current_3 = [69.05, 66.95, 64.84, 62.73, 60.27, 56.44, 52.25, 46.12, 37.51, 25.78, 14.42, 4.48, -4.80, -17.45, -27.78, -35.60, -40.79, -45.21, -47.58, -49.35, -51.09, -52.93]\n", + "\n", + "#1.5 mA\n", + "#V\n", + "voltage_15 = [25, 22, 19, 16, 13, 10, 8, 6, 4, 2, 0.36, -0.30, -2, -4, -6, -8, -10, -13, -16, -19, -22, -25]\n", + "#muA\n", + "current_15 = [36.20, 34.93, 33.65, 32.39, 31.05, 29.13, 27.02, 23.91, 19.54, 13.68, 7.97, 5.06, -1.01, -7.63, -12.91, -16.76, -19.39, -21.44, -22.38, -23.16, -23.95, -24.77]\n", + "\n", + "k_5_r, b_5_r = np.polyfit(voltage_5[:4], current_5[:4], deg = 1)\n", + "k_5_l, b_5_l = np.polyfit(voltage_5[17:], current_5[17:], deg = 1)\n", + "k_3_r, b_3_r = np.polyfit(voltage_3[:4], current_3[:4], deg = 1)\n", + "k_3_l, b_3_l = np.polyfit(voltage_3[17:], current_3[17:], deg = 1)\n", + "k_15_r, b_15_r = np.polyfit(voltage_15[:4], current_15[:4], deg = 1)\n", + "k_15_l, b_15_l = np.polyfit(voltage_15[17:], current_15[17:], deg = 1)\n", + "\n", + "current_5_r = []\n", + "current_5_l = []\n", + "current_3_r = []\n", + "current_3_l = []\n", + "current_15_r = []\n", + "current_15_l = []\n", + "for i in range(11):\n", + " current_5_r.append(k_5_r * voltage_5[i] + b_5_r) \n", + " current_5_l.append(k_5_l * voltage_5[11 + i] + b_5_l)\n", + " current_3_r.append(k_3_r * voltage_3[i] + b_3_r) \n", + " current_3_l.append(k_3_l * voltage_3[11 + i] + b_3_l)\n", + " current_15_r.append(k_15_r * voltage_15[i] + b_15_r) \n", + " current_15_l.append(k_15_l * voltage_15[11 + i] + b_15_l)\n", + "\n", + "plt.figure(figsize=(8, 6))\n", + "plt.scatter(voltage_5, current_5, color = 'green', marker = 'o')\n", + "plt.scatter(voltage_3, current_3, color = 'orange', marker = 'o')\n", + "plt.scatter(voltage_15, current_15, color = 'purple', marker = 'o')\n", + "plt.plot(voltage_5[:11], current_5_r, color = 'black', linewidth = 0.5)\n", + "plt.plot(voltage_5[11:], current_5_l, color = 'black', linewidth = 0.5)\n", + "plt.plot(voltage_3[:11], current_3_r, color = 'black', linewidth = 0.5)\n", + "plt.plot(voltage_3[11:], current_3_l, color = 'black', linewidth = 0.5)\n", + "plt.plot(voltage_15[:11], current_15_r, color = 'black', linewidth = 0.5)\n", + "plt.plot(voltage_15[11:], current_15_l, color = 'black', linewidth = 0.5)\n", + "plt.plot(voltage_5, current_5, color = 'green')\n", + "plt.plot(voltage_3, current_3, color = 'orange')\n", + "plt.plot(voltage_15, current_15, color = 'purple')\n", + "plt.axhline(y=0, color ='k')\n", + "plt.axvline(x=0, color ='k')\n", + "plt.xlabel('Напряжение, В')\n", + "plt.ylabel('Ток, мкА')\n", + "\n", + "print((b_5_r - b_5_l) / 2, (b_3_r - b_3_l) / 2, (b_15_r - b_15_l) / 2)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e4a91142", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.9.12" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/auto/3.5.1/lab351.pdf b/auto/3.5.1/lab351.pdf new file mode 100644 index 0000000..230af79 Binary files /dev/null and b/auto/3.5.1/lab351.pdf differ diff --git a/auto/3.6.1/361.ipynb b/auto/3.6.1/361.ipynb new file mode 100644 index 0000000..837a56c --- /dev/null +++ b/auto/3.6.1/361.ipynb @@ -0,0 +1,151 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 9, + "id": "01d8887a", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Text(0, 0.5, 'k')" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "#прямоугольники\n", + "\n", + "#картинки\n", + "#1) tau = 50 мкс, f = 1 кГц\n", + "#2) tau = 50 мкс, f = 1.5 кГц\n", + "#3) tau = 50 мкс, f = 0.5 кГц\n", + "#4) tau = 100 мкс, f = 1 кГц\n", + "#5) tau = 75 мкс, f = 1 кГц\n", + "\n", + "#tau = 50 мкс, f = 1 кГц\n", + "#кГц\n", + "a_n = [1, 2.003, 3.005, 4.007, 5.01, 6.012, 7.014]\n", + "#мВ\n", + "f_n = [415.9, 395.6, 366.0, 327.2, 299.4, 279.1, 251.4]\n", + "\n", + "#мкс\n", + "tau = [20, 40, 60, 80, 100, 120, 140, 160, 180, 200] #со 180 скачет\n", + "#кГц\n", + "delta_nu1 = [50, 25, 17.01, 12.51, 10.04, 8.012, 7.01, 6.008, 5.571, 4.928] \n", + "\n", + "#цуги\n", + "\n", + "#6) f = 50 кГц, T = 1 мс, N = 5\n", + "#7) f = 50 кГц, T = 1 мс, N = 7\n", + "#8) f = 50 кГц, T = 1 мс, N = 10\n", + "#9) f = 50 кГц, T = 1.5 мс, N = 5\n", + "#10) f = 50 кГц, T = 0.5 мс, N = 5\n", + "#11) f = 100 кГц, T = 1 мс, N = 5\n", + "\n", + "#мс\n", + "T = [0.5, 1, 1.5, 2, 2.5, 3, 3.5, 4, 4.5, 5]\n", + "#кГц\n", + "delta_nu2 = [2, 1.02, 0.65, 0.5, 0.39, 0.33, 0.29, 0.25, 0.21, 0.19]\n", + "\n", + "#гаусс(???)\n", + "\n", + "#12) f = 1 кГц\n", + "#13) f = 1 кГц\n", + "#14) f = 1 кГц\n", + "\n", + "#am\n", + "\n", + "# A_max = 1.26 V, A_min = 0.42 V\n", + "\n", + "#15) f = 50 кГц, f_mod = 2 кГц, m = 0.5\n", + "\n", + "m = [0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 1]\n", + "#mV\n", + "a_max = 537.9\n", + "a_min = [27.73, 53.6, 81.33, 105.4, 131.2, 162.7, 186.7, 212.6, 238.4, 266.2]\n", + "\n", + "m_2 =[]\n", + "for i in range(10):\n", + " a_min[i] /= a_max\n", + " m_2.append(m[i]/2) \n", + "\n", + "#pm\n", + "\n", + "#16) f = 50 кГц, f_mod = 2 кГц, phi = 10\n", + "#17) f = 50 кГц, f_mod = 2 кГц, phi = 50\n", + "#18) f = 50 кГц, f_mod = 2 кГц, phi = 90\n", + "#19) f = 50 кГц, f_mod = 10 кГц, phi = 10\n", + "#20) f = 100 кГц, f_mod = 2 кГц, phi = 10\n", + "\n", + "\n", + "\n", + "\n", + "#21) T = 6 мкс, tau = 0.3 мкс осциллограмма\n", + "#22) T = 6 мкс, tau = 0.3 мкс спектр\n", + "#23) T = 6 мкс, tau = 0.5 мкс осциллограмма\n", + "#24) T = 6 мкс, tau = 0.5 мкс спектр\n", + "#25) T = 6 мкс, tau = 0.1 мкс осциллограмма\n", + "#26) T = 6 мкс, tau = 0.1 мкс спектр\n", + "\n", + "#мВ\n", + "a_filter = [68.39, 35.12, 22.18, 16.64, 12.01]\n", + "a_0 = [250.5, 244.9, 254.2, 244.9, 217.2]\n", + "\n", + "\n", + "plt.scatter(m, a_min)\n", + "plt.plot(m, m_2, color = 'black', linewidth = 0.5)\n", + "plt.xlabel('m')\n", + "plt.ylabel('k')\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "718aeb2a", + "metadata": {}, + "outputs": [], + "source": [] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": 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