From 92fabc055585ff9b523c3bc371f06215c30c6d1a Mon Sep 17 00:00:00 2001
From: =?UTF-8?q?Mario=20H=C3=BCttel?= <mario.huettel@gmx.net>
Date: Tue, 28 Jan 2020 23:09:06 +0100
Subject: [PATCH] Restructure Jupyter NB

---
 .../Analog Measurement Analysis.ipynb            | 16 +++++++---------
 1 file changed, 7 insertions(+), 9 deletions(-)

diff --git a/measurement-data/Analog Measurement Analysis.ipynb b/measurement-data/Analog Measurement Analysis.ipynb
index 2db9d28..8e3bf52 100644
--- a/measurement-data/Analog Measurement Analysis.ipynb	
+++ b/measurement-data/Analog Measurement Analysis.ipynb	
@@ -20,6 +20,7 @@
     "import pandas as pd\n",
     "import numpy as np\n",
     "import math\n",
+    "import scipy\n",
     "\n",
     "from __future__ import print_function\n",
     "from ipywidgets import interact, interactive, fixed, interact_manual\n",
@@ -42,7 +43,8 @@
     "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')"
+    "constant_sampling = pd.read_csv(r'1000OhmSampling.csv')\n",
+    "shielded_df = pd.read_csv(r'ContactPT1000HeatgunCooldown.csv')"
    ]
   },
   {
@@ -113,7 +115,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "df_list = [one_k_sampling_trafo, two_k_sampling_trafo, temperature_measurement, constant_sampling]\n",
+    "df_list = [one_k_sampling_trafo, two_k_sampling_trafo, temperature_measurement, constant_sampling, shielded_df]\n",
     "for df in df_list:\n",
     "    calculate_temp_for_df(df)\n"
    ]
@@ -227,11 +229,6 @@
    "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",
@@ -241,7 +238,7 @@
     "last_grad_lf = 0.0\n",
     "\n",
     "alpha = 0.005\n",
-    "delta_alpha = 0.0\n",
+    "delta_alpha = 0.00\n",
     "zeta = 20\n",
     "\n",
     "for index, row in shielded_df.iterrows():\n",
@@ -288,7 +285,8 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "## Temperature Gradient"
+    "## Temperature Gradient Noise vs Moving Average\n",
+    "### Time Domain"
    ]
   },
   {