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@nagos
Created September 23, 2017 13:32
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{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Вычисление амплитуды затущающего сигнала\n",
"=====\n",
"Владимир Яковлев. 2017."
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Импорт модулей"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {
"scrolled": true
},
"outputs": [],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"import matplotlib.pyplot as plt\n",
"np.set_printoptions(precision=3)\n",
"%matplotlib inline"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Загрузка данных"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[[ 1.000e+00 1.412e+03 4.880e+03 -1.704e+04]\n",
" [ 2.000e+00 1.348e+03 4.784e+03 -1.688e+04]\n",
" [ 4.000e+00 1.388e+03 4.768e+03 -1.696e+04]]\n"
]
}
],
"source": [
"data = np.loadtxt(\"data.csv\", delimiter=\",\")\n",
"print(data[:3, :])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Параметры"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"# положение импульса для графиков\n",
"signal_time = [6900, 7600]\n",
"signal_amp = [-20000, 20000]\n",
"# максимальный период колебаний\n",
"freq = 40"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Разделение данных"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [],
"source": [
"data_t = data[:, 0]\n",
"data_av = data[:, 1:4]\n"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Вычислить длину вектора"
]
},
{
"cell_type": "code",
"execution_count": 5,
"metadata": {
"scrolled": false
},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[ 17777.329 17596.538 17675.906]\n"
]
}
],
"source": [
"data_a = np.sqrt(np.sum(data_av**2, axis=1))\n",
"print(data_a[:3])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Удаление среднего уровня"
]
},
{
"cell_type": "code",
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
"data_a_zero = data_a - np.mean(data_a)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Вычисление амплитуды"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [],
"source": [
"amp = pd.rolling_mean(np.sqrt(data_a_zero**2)*np.sqrt(2), freq)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"График\n",
"======"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"data": {
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JARGOiHL3JEQlkLE6w5rhVk18c0gAV/S6gju+uIMx3cfQPLa5d4miYHht6WvM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\nKyQzMysFV0hmZlYK/w+i6xxicljHhAAAAABJRU5ErkJggg==\n",
"text/plain": [
"<matplotlib.figure.Figure at 0x7fd0501ad278>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"plt.plot(data_t, amp)\n",
"plt.plot(data_t, data_a_zero)\n",
"plt.xlim(signal_time)\n",
"plt.ylim(signal_amp)\n",
"plt.xlabel(\"Time, S\")\n",
"plt.ylabel(\"Value, count\")\n",
"plt.grid()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"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": "3.5.2"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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