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Restructure Jupyter NB

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Mario Hüttel 2020-01-28 23:09:06 +01:00
parent e9a2313221
commit 92fabc0555
1 changed files with 7 additions and 9 deletions
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16
measurement-data/Analog Measurement Analysis.ipynb
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@ -20,6 +20,7 @@
"import pandas as pd\n", "import pandas as pd\n",
"import numpy as np\n", "import numpy as np\n",
"import math\n", "import math\n",
"import scipy\n",
"\n", "\n",
"from __future__ import print_function\n", "from __future__ import print_function\n",
"from ipywidgets import interact, interactive, fixed, interact_manual\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", "two_k_sampling_trafo = pd.read_csv(r'2000OhmSamplingTrafoSupply.csv')\n",
"one_k_sampling_trafo = pd.read_csv(r'1000OhmSamplingTrafoSupply.csv')\n", "one_k_sampling_trafo = pd.read_csv(r'1000OhmSamplingTrafoSupply.csv')\n",
"temperature_measurement = pd.read_csv(r'TempSamplingTrafoSupply.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": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "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", "for df in df_list:\n",
" calculate_temp_for_df(df)\n" " calculate_temp_for_df(df)\n"
] ]
@ -227,11 +229,6 @@
"metadata": {}, "metadata": {},
"outputs": [], "outputs": [],
"source": [ "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", "# Derivateve of temp\n",
"shielded_df['temp_gradient'] = shielded_df['temp_calculated'].diff() / shielded_df['Time'].diff()\n", "shielded_df['temp_gradient'] = shielded_df['temp_calculated'].diff() / shielded_df['Time'].diff()\n",
"\n", "\n",
@ -241,7 +238,7 @@
"last_grad_lf = 0.0\n", "last_grad_lf = 0.0\n",
"\n", "\n",
"alpha = 0.005\n", "alpha = 0.005\n",
"delta_alpha = 0.0\n", "delta_alpha = 0.00\n",
"zeta = 20\n", "zeta = 20\n",
"\n", "\n",
"for index, row in shielded_df.iterrows():\n", "for index, row in shielded_df.iterrows():\n",
@ -288,7 +285,8 @@
"cell_type": "markdown", "cell_type": "markdown",
"metadata": {}, "metadata": {},
"source": [ "source": [
"## Temperature Gradient" "## Temperature Gradient Noise vs Moving Average\n",
"### Time Domain"
] ]
}, },
{ {