SPATZ/testing.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "from spatz.transforms.noise import DriftingBias\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "start = 0\n",
    "covariance = 0.005\n",
    "\n",
    "bias = DriftingBias(start, covariance, 400)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "dt = 0.01\n",
    "ts = np.arange(0, 1000, 1) * dt\n",
    "x = [bias(dt, 0) for _ in ts]\n",
    "\n",
    "plt.plot(ts, x)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "measured = pd.read_csv('druckkammer.csv')\n",
    "truth = pd.read_csv('siemens_parsed.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.00043300242654881085"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.var(measured['pressure'][55753:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.plot(measured['time'], measured['pressure'])\n",
    "plt.show()"
   ]
  }
 ],
 "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": "3.12.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}