{
 "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": 29,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(measured['time'], measured['pressure'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "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
}