From e9a2313221412a17196d15c07137b896cf938de9 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Mario=20H=C3=BCttel?= Date: Tue, 28 Jan 2020 22:42:40 +0100 Subject: [PATCH] Do further analysis in jupyter notebook --- .../Analog Measurement Analysis.ipynb | 182 ++++++++++++++++-- 1 file changed, 165 insertions(+), 17 deletions(-) diff --git a/measurement-data/Analog Measurement Analysis.ipynb b/measurement-data/Analog Measurement Analysis.ipynb index d06df96..2db9d28 100644 --- a/measurement-data/Analog Measurement Analysis.ipynb +++ b/measurement-data/Analog Measurement Analysis.ipynb @@ -19,6 +19,7 @@ "import matplotlib.pyplot as plt\n", "import pandas as pd\n", "import numpy as np\n", + "import math\n", "\n", "from __future__ import print_function\n", "from ipywidgets import interact, interactive, fixed, interact_manual\n", @@ -102,16 +103,8 @@ "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" + "def calculate_temp_for_df(data_frame, resistance_col_name='ext_lf_corr', temp_col_name='temp_calculated'):\n", + " data_frame[temp_col_name] = data_frame.apply(lambda row: calc_temp(row[resistance_col_name]) , axis=1)" ] }, { @@ -120,18 +113,53 @@ "metadata": {}, "outputs": [], "source": [ - "fig, axes = plt.subplots(nrows=3, ncols=3, figsize=(28,15))\n", + "df_list = [one_k_sampling_trafo, two_k_sampling_trafo, temperature_measurement, constant_sampling]\n", + "for df in df_list:\n", + " calculate_temp_for_df(df)\n" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Histograms -- Starting from Index 100 (Uncalibrated)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "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", "\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", + " n, bins, patches = ax.hist(data_df[sig[0]][start_idx:], sig[1], density=1, color='navy')\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", + " 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", + " 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", "plt.show()" ] }, @@ -161,7 +189,9 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "# Temperature Plotting" + "# Temperature Plotting\n", + "\n", + "Noise is visible as soon as the temperature sensor is touched or connected to ground in an improper way." ] }, { @@ -174,11 +204,129 @@ "@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[0].plot(temperature_measurement['Time'][low:high], temperature_measurement['adc_results.pa2_raw'][low:high])\n", + " ax[1].plot(temperature_measurement['Time'][low:high], temperature_measurement['ext_lf_corr'][low:high])\n", " ax[2].plot(temperature_measurement['Time'][low:high], temperature_measurement['temp_calculated'][low:high])\n", + " titles = ['Raw ADC Results', 'Low Pass Filtered Resistance Reading', 'Calculated Low Frequency Temperature']\n", + " for i, title in zip(range(0,3), titles):\n", + " ax[i].grid()\n", + " ax[i].set_title(title)\n", " plt.plot()" ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "# Temperature Plotting With Proper Grounding of Circuit and the Cable Shield" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "shielded_df = pd.read_csv(r'ContactPT1000HeatgunCooldown.csv')\n", + "\n", + "# Calculate temperature\n", + "calculate_temp_for_df(shielded_df)\n", + "\n", + "# Derivateve of temp\n", + "shielded_df['temp_gradient'] = shielded_df['temp_calculated'].diff() / shielded_df['Time'].diff()\n", + "\n", + "# Low pass filter gradient with moving average\n", + "shielded_df['temp_gradient_lf'] = 0.0\n", + "shielded_df['temp_gradient_lf_2'] = 0.0\n", + "last_grad_lf = 0.0\n", + "\n", + "alpha = 0.005\n", + "delta_alpha = 0.0\n", + "zeta = 20\n", + "\n", + "for index, row in shielded_df.iterrows():\n", + " if index == 0:\n", + " pass\n", + " else:\n", + " current_gradient = row['temp_gradient']\n", + " if last_grad_lf != 0.0:\n", + " alpha_corr = abs(current_gradient) / zeta * delta_alpha\n", + " else:\n", + " alpha_corr = 0\n", + " last_grad_lf = last_grad_lf * (1-(alpha+alpha_corr)) + (alpha+alpha_corr) * current_gradient\n", + " shielded_df.at[index, 'temp_gradient_lf'] = last_grad_lf\n", + " \n", + "# Derivateve of grad is grad2\n", + "shielded_df['temp_gradient_lf_2'] = shielded_df['temp_gradient_lf'].diff() / shielded_df['Time'].diff()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Full curve" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(28,9))\n", + "tau = 25\n", + "tau2 = 0\n", + "ax.plot(shielded_df['Time'], shielded_df['temp_calculated'], label='Uncalibrated Temperature')\n", + "ax.plot(shielded_df['Time'], shielded_df['temp_calculated']+shielded_df['temp_gradient_lf']*tau + shielded_df['temp_gradient_lf_2']*tau2, label=r'PT1 corrected with $\\tau = %f$' % tau)\n", + "ax.grid()\n", + "ax.legend()\n", + "ax.set_title('Temperature measurement with proper ground connection')\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Temperature Gradient" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(28,8))\n", + "tau = 30\n", + "ax.plot(shielded_df['Time'], shielded_df['temp_gradient'], label=r\"$\\dot{\\vartheta} = \\frac{\\partial\\vartheta}{\\partial t}$\")\n", + "ax.plot(shielded_df['Time'], shielded_df['temp_gradient_lf'], label=r'$\\tilde{\\dot{\\vartheta}}$')\n", + "ax.legend()\n", + "plt.show()" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## Cooldown to Room Temperature in Detail" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "fig, ax = plt.subplots(figsize=(28,9))\n", + "start_time = -320\n", + "filtered_cooldown = shielded_df[shielded_df['Time'] > start_time]\n", + "ax.plot(filtered_cooldown['Time'], filtered_cooldown['temp_calculated'], label='Measured Cooldown')\n", + "ax.plot(filtered_cooldown['Time'], filtered_cooldown['temp_calculated']+filtered_cooldown['temp_gradient_lf']*tau, label='Calculated Exterior Temperature')\n", + "ax.grid()\n", + "ax.set_title('Cooldown without airflow | Convection has to be taken into account') \n", + "plt.show()" + ] } ], "metadata": {