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Heneka, Dr. Caroline
ML-for-Astro-tutorial-Sternwarte
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a5d7eef7
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a5d7eef7
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3 years ago
by
Heneka, Dr. Caroline
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spectral_classifier_full/find_spectrum.ipynb
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a5d7eef7
{
"cells": [
{
"cell_type": "code",
"execution_count": 62,
"id": "b1144fb9",
"metadata": {},
"outputs": [],
"source": [
"import numpy as np\n",
"from astropy.io import fits\n",
"import matplotlib.pyplot as plt\n",
"from astropy.wcs import WCS\n",
"import os\n",
"import csv\n",
"\n",
"########## Input ##########\n",
"\n",
"fits_path = 'F:\\\\data\\\\spectral_fits\\\\'\n",
"\n",
"samples_per_class = 1000\n",
"\n",
"search_number = 454"
]
},
{
"cell_type": "code",
"execution_count": 63,
"id": "3917f5ac",
"metadata": {},
"outputs": [
{
"data": {
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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"########## Program ##########\n",
"\n",
"number = 0\n",
"\n",
"filenames = np.load(fits_path + 'filenames.npy')\n",
"\n",
"directories = ['AGN', 'galaxy', 'QSO', 'star']\n",
"directory = directories[int(search_number/1000)]\n",
" \n",
"for filename in os.listdir(fits_path + directory + '\\\\'):\n",
"\n",
" path = fits_path + '\\\\' + directory + '\\\\' + filename\n",
"\n",
" if filename == filenames[search_number]:\n",
"\n",
" hdul = fits.open(path)\n",
" data = hdul[1].data\n",
" flux = data['flux']\n",
" wavelength = 10**data['loglam']\n",
"\n",
" plt.plot(wavelength, flux) \n",
" plt.xlabel('wavelength(Å)') \n",
" plt.ylabel('flux (10-17 ergs/s/cm2/Å)') \n",
" plt.grid(True)\n",
" plt.title(str(search_number)+\": \"+filename)\n",
" plt.show()\n",
"\n",
" number += 1"
]
},
{
"cell_type": "code",
"execution_count": 22,
"id": "43d7a808",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"0"
]
},
"execution_count": 22,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"int(0.934)"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "bfee957d",
"metadata": {},
"outputs": [],
"source": []
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"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": "3.8.5"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
%% Cell type:code id:b1144fb9 tags:
```
python
import
numpy
as
np
from
astropy.io
import
fits
import
matplotlib.pyplot
as
plt
from
astropy.wcs
import
WCS
import
os
import
csv
########## Input ##########
fits_path
=
'
F:
\\
data
\\
spectral_fits
\\
'
samples_per_class
=
1000
search_number
=
454
```
%% Cell type:code id:3917f5ac tags:
```
python
########## Program ##########
number
=
0
filenames
=
np
.
load
(
fits_path
+
'
filenames.npy
'
)
directories
=
[
'
AGN
'
,
'
galaxy
'
,
'
QSO
'
,
'
star
'
]
directory
=
directories
[
int
(
search_number
/
1000
)]
for
filename
in
os
.
listdir
(
fits_path
+
directory
+
'
\\
'
):
path
=
fits_path
+
'
\\
'
+
directory
+
'
\\
'
+
filename
if
filename
==
filenames
[
search_number
]:
hdul
=
fits
.
open
(
path
)
data
=
hdul
[
1
].
data
flux
=
data
[
'
flux
'
]
wavelength
=
10
**
data
[
'
loglam
'
]
plt
.
plot
(
wavelength
,
flux
)
plt
.
xlabel
(
'
wavelength(Å)
'
)
plt
.
ylabel
(
'
flux (10-17 ergs/s/cm2/Å)
'
)
plt
.
grid
(
True
)
plt
.
title
(
str
(
search_number
)
+
"
:
"
+
filename
)
plt
.
show
()
number
+=
1
```
%% Output
%% Cell type:code id:43d7a808 tags:
```
python
int
(
0.934
)
```
%% Output
0
%% Cell type:code id:bfee957d tags:
```
python
```
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