Unverified Commit b5e290c2 authored by Tammo Jan Dijkema's avatar Tammo Jan Dijkema
Browse files

Add zonnedemo-2020.ipynb

parent 02ca6730
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import matplotlib.pyplot as plt"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"from astropy.time import Time"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [],
"source": [
"from datetime import datetime"
]
},
{
"cell_type": "code",
"execution_count": 57,
"metadata": {},
"outputs": [],
"source": [
"df_morning = pd.read_csv(\"data1593940824.txt\", index_col=[0], parse_dates=[0])"
]
},
{
"cell_type": "code",
"execution_count": 58,
"metadata": {},
"outputs": [],
"source": [
"df_afternoon = pd.read_csv(\"data1593944189.txt\", index_col=[0], parse_dates=[0])"
]
},
{
"cell_type": "code",
"execution_count": 60,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"fig, ax = plt.subplots()\n",
"df1 = df_morning[df_morning.index > datetime(2020, 7, 5, 9, 32)]\n",
"df2 = df_afternoon[df_afternoon.index > datetime(2020, 7, 5, 11, 0)]\n",
"ax.plot(df1['separation (deg)'], df1[' signal'], '.', label='morning')\n",
"ax.plot(df2['separation (deg)'], df2[' signal'], '.', label='afternoon')\n",
"ax.set_xlim([-2,2]);\n",
"ax.set_title(\"Dwingeloo cross scan Cas A at 1330MHz\\nRecorded 5 July 2020\")\n",
"ax.legend();\n",
"ax.set_xlabel(\"Separation from Cas A (deg)\");\n",
"ax.set_ylabel(\"Uncalibrated intensity\");"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.6.10"
}
},
"nbformat": 4,
"nbformat_minor": 4
}
%% Cell type:code id: tags:
``` python
import matplotlib.pyplot as plt
```
%% Cell type:code id: tags:
``` python
import pandas as pd
```
%% Cell type:code id: tags:
``` python
from astropy.time import Time
```
%% Cell type:code id: tags:
``` python
from datetime import datetime
```
%% Cell type:code id: tags:
``` python
df_morning = pd.read_csv("data1593940824.txt", index_col=[0], parse_dates=[0])
```
%% Cell type:code id: tags:
``` python
df_afternoon = pd.read_csv("data1593944189.txt", index_col=[0], parse_dates=[0])
```
%% Cell type:code id: tags:
``` python
fig, ax = plt.subplots()
df1 = df_morning[df_morning.index > datetime(2020, 7, 5, 9, 32)]
df2 = df_afternoon[df_afternoon.index > datetime(2020, 7, 5, 11, 0)]
ax.plot(df1['separation (deg)'], df1[' signal'], '.', label='morning')
ax.plot(df2['separation (deg)'], df2[' signal'], '.', label='afternoon')
ax.set_xlim([-2,2]);
ax.set_title("Dwingeloo cross scan Cas A at 1330MHz\nRecorded 5 July 2020")
ax.legend();
ax.set_xlabel("Separation from Cas A (deg)");
ax.set_ylabel("Uncalibrated intensity");
```
%%%% Output: display_data
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