Add measurements to analysis of target code for temperature measurements

measurement-data/Analog_Measurement_Analysis.ipynb

@@ -75,6 +75,36 @@
     "    return temp"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def plot_histogram(ax, data, bin_count, title, signal_name):\n",
+    "    n, bins, patches = ax.hist(data, bin_count, density=1, color='navy')\n",
+    "    mu = np.mean(data)\n",
+    "    sigma = np.std(data)\n",
+    "    y = ((1 / (np.sqrt(2 * np.pi) * sigma)) * np.exp(-0.5 * (1 / sigma * (bins - mu))**2))\n",
+    "    ax.plot(bins, y, color='darkorange')\n",
+    "    ax.set_title(title)\n",
+    "    ax.set_ylabel(signal_name + ' probability (normalized)')\n",
+    "    ax.set_xlabel(signal_name)\n",
+    "    # Plot sigma and mu lines\n",
+    "    ax.axvline(x=mu-sigma, ls='--', color='magenta')\n",
+    "    ax.axvline(x=mu+sigma, ls='--', color='magenta')\n",
+    "    ax.axvline(x=mu, ls='--', color='lawngreen')\n",
+    "\n",
+    "    #Plot textbox\n",
+    "    textstr = '\\n'.join((\n",
+    "        r'$\\mu=%.2f$' % (mu, ),\n",
+    "        r'$\\sigma=%.3f$' % (sigma, ),\n",
+    "        r'$N_{Sa} =%d$' % (len(data), )))\n",
+    "    props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)\n",
+    "    ax.text(0.05, 0.95, textstr, transform=ax.transAxes, fontsize=14,\n",
+    "    verticalalignment='top', bbox=props)\n"
+   ]
+  },
   {
    "cell_type": "markdown",
    "metadata": {},
@@ -117,33 +147,14 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "fig, axes = plt.subplots(nrows=3, ncols=3, figsize=(28,20))\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",
+    "signal_list = [('ext_lf_corr', 20), ('temp_calculated', 20)]\n",
+    "\n",
+    "fig, axes = plt.subplots(nrows=len(plot_data), ncols=len(signal_list), figsize=(28,20))\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, color='navy')\n",
+    "        plot_histogram(ax, data_df[sig[0]][start_idx:], sig[1], title,sig[0])\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, color='darkorange')\n",
-    "        ax.set_title(title)\n",
-    "        ax.set_ylabel(sig[0] + ' probability (normalized)')\n",
-    "        ax.set_xlabel(sig[0])\n",
-    "        # Plot sigma and mu lines\n",
-    "        ax.axvline(x=mu-sigma, ls='--', color='magenta')\n",
-    "        ax.axvline(x=mu+sigma, ls='--', color='magenta')\n",
-    "        ax.axvline(x=mu, ls='--', color='lawngreen')\n",
-    "        \n",
-    "        #Plot textbox\n",
-    "        textstr = '\\n'.join((\n",
-    "            r'$\\mu=%.2f$' % (mu, ),\n",
-    "            r'$\\sigma=%.2f$' % (sigma, ),\n",
-    "            r'$N_{Sa} =%d$' % (len(data_df[sig[0]][start_idx:], ))))\n",
-    "        props = dict(boxstyle='round', facecolor='wheat', alpha=0.5)\n",
-    "        ax.text(0.05, 0.95, textstr, transform=ax.transAxes, fontsize=14,\n",
-    "                verticalalignment='top', bbox=props)\n",
     "\n",
     "        \n",
     "plt.tight_layout()\n",
@@ -310,6 +321,66 @@
     "ax.set_title('Cooldown without airflow | Convection has to be taken into account') \n",
     "plt.show()"
    ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "---\n",
+    "# Target Implementation Sampling of 1k Resistor"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "## Histograms"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Read in data\n",
+    "kilo_ohm_sampling1 = pd.read_csv(r'1000OhmSampling1ms.csv')\n",
+    "kilo_ohm_sampling2 = pd.read_csv(r'1000OhmSampling1ms_later.csv')\n",
+    "kilo_ohm_sampling2_fast = pd.read_csv(r'1000OhmSamplingTimerTriggered800ARR.csv')\n",
+    "plot_data_tuples = [(kilo_ohm_sampling1, 'Day 1 Sampling'), (kilo_ohm_sampling2, 'Day 2 sampling'), (kilo_ohm_sampling2_fast,'Day 2 sampling faster (ARR=700)')]\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "# def plot_histogram(ax, data, bin_count, title, signal_name):\n",
+    "# def calculate_temp_for_df(data_frame, resistance_col_name='ext_lf_corr', temp_col_name='temp_calculated'):\n",
+    "\n",
+    "fig, axes = plt.subplots(nrows=len(plot_data_tuples), ncols=2, sharex='col', figsize=(28, 16))\n",
+    "\n",
+    "for df, title in plot_data_tuples:\n",
+    "    calculate_temp_for_df(df, resistance_col_name='pt1000_res_raw_lf')\n",
+    "\n",
+    "for (df,title),ax in zip(plot_data_tuples, axes):\n",
+    "    plot_histogram(ax[0], df['pt1000_res_raw_lf'], 20, title, 'PT1000 Resistance')\n",
+    "    plot_histogram(ax[1], df['temp_calculated'], 20, title, 'Calculated Temperature in °C')\n",
+    "\n",
+    "plt.tight_layout()\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "calc_temp(1097)"
+   ]
   }
  ],
  "metadata": {