{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from IPython.core.display import display, HTML\n",
    "display(HTML(\"<style>.container { width:100% !important; }</style>\"))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "\n",
    "from __future__ import print_function\n",
    "from ipywidgets import interact, interactive, fixed, interact_manual\n",
    "import ipywidgets as widgets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read in Measurements"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "two_k_sampling_trafo = pd.read_csv(r'2000OhmSamplingTrafoSupply.csv')\n",
    "one_k_sampling_trafo = pd.read_csv(r'1000OhmSamplingTrafoSupply.csv')\n",
    "temperature_measurement = pd.read_csv(r'TempSamplingTrafoSupply.csv')\n",
    "constant_sampling = pd.read_csv(r'1000OhmSampling.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Calculation Function for $\\vartheta(R_{PT1000})$\n",
    "$\\vartheta(R_{PT1000}) = \\frac{-\\alpha R_0 + \\sqrt{\\alpha^2R_0^2 - 4\\beta R_0 \\left(R_0 - R_{PT1000}\\right)}}{2\\beta R_0}$\n",
    "\n",
    "with\n",
    "* $\\alpha = 3.9083 \\cdot 10^{-3}$\n",
    "* $\\beta = -5.7750 \\cdot 10^{-7}$\n",
    "* $R_0 = 1000~\\Omega$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "R_zero = 1000.0\n",
    "A = 3.9083E-3\n",
    "B = -5.7750E-7\n",
    "\n",
    "def calc_temp(resistance):\n",
    "    temp = (-R_zero * A + np.sqrt(R_zero*R_zero * A * A - 4* R_zero * B * (R_zero - resistance)))/(2*R_zero*B)\n",
    "    return temp"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Description of ADC Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "print(constant_sampling['adc_results.pa2_raw'].describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "--------------------\n",
    "# Calculate Temperature from Resistance Value"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "df_list = [one_k_sampling_trafo, two_k_sampling_trafo, temperature_measurement, constant_sampling]\n",
    "for df in df_list:\n",
    "    df['temp_calculated'] = df.apply(lambda row: calc_temp(row['ext_lf_corr']) , axis=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Histograms -- Starting from Index 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, axes = plt.subplots(nrows=3, ncols=3, figsize=(28,15))\n",
    "plot_data = [(one_k_sampling_trafo, '1 kOhm Sampling Transformer powered', 0), (two_k_sampling_trafo, '2 kOhm Sampling Transformer powered' , 0), (constant_sampling, '1 kOhm Sampling', 100)]\n",
    "signal_list = [('adc_results.pa2_raw', 20), ('ext_lf_corr', 20), ('temp_calculated', 20)]\n",
    "\n",
    "for (data_df, title, start_idx), ax_rows in zip(plot_data, axes):\n",
    "    for ax,sig in zip(ax_rows, signal_list):\n",
    "        n, bins, patches = ax.hist(data_df[sig[0]][start_idx:], sig[1], density=1)\n",
    "        mu = np.mean(data_df[sig[0]][start_idx:])\n",
    "        sigma = np.std(data_df[sig[0]][start_idx:])\n",
    "        y = ((1 / (np.sqrt(2 * np.pi) * sigma)) * np.exp(-0.5 * (1 / sigma * (bins - mu))**2))\n",
    "        ax.plot(bins, y)\n",
    "        ax.set_title('Histogram of '+sig[0]+' for '+title)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Startup of Moving Average Filter with $\\alpha' = 0.005$\n",
    "\n",
    "Filter difference equation: $y[n] = (1-\\alpha')y[n-1] + \\alpha'x[n]$"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "fig, ax = plt.subplots(nrows=2, ncols=1, figsize=(28,6), sharex=True)\n",
    "data = constant_sampling['ext_lf_corr'][:20]\n",
    "ax[0].plot(constant_sampling['Time'][:20], data)\n",
    "ax[1].plot(constant_sampling['Time'][:20], constant_sampling['temp_calculated'][:20])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Temperature Plotting"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "idx_count = len(temperature_measurement.index)\n",
    "@interact(low=(0,idx_count -1,10), high=(0, idx_count-1, 10))\n",
    "def plot_temp(low=0, high=idx_count-1):\n",
    "    fig, ax = plt.subplots(nrows=3, ncols=1, figsize=(28,9*3), sharex=True)\n",
    "    ax[0].plot(temperature_measurement['Time'][low:high], temperature_measurement['ext_lf_corr'][low:high])\n",
    "    ax[1].plot(temperature_measurement['Time'][low:high], temperature_measurement['adc_results.pa2_raw'][low:high])\n",
    "    ax[2].plot(temperature_measurement['Time'][low:high], temperature_measurement['temp_calculated'][low:high])\n",
    "    plt.plot()"
   ]
  }
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