From d1df91b1bfae5eb28674dde27f37aa236501eccd Mon Sep 17 00:00:00 2001
From: "Heneka, Dr. Caroline" <caroline.heneka@uni-hamburg.de>
Date: Wed, 2 Mar 2022 09:46:09 +0000
Subject: [PATCH] Upload New File
---
spectral_classifier_full/fits_to_csv.ipynb | 199 +++++++++++++++++++++
1 file changed, 199 insertions(+)
create mode 100644 spectral_classifier_full/fits_to_csv.ipynb
diff --git a/spectral_classifier_full/fits_to_csv.ipynb b/spectral_classifier_full/fits_to_csv.ipynb
new file mode 100644
index 0000000..26c4490
--- /dev/null
+++ b/spectral_classifier_full/fits_to_csv.ipynb
@@ -0,0 +1,199 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 62,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Länge der Liste wavelength ist: 3267\n",
+ "Länge der Liste wavelength ist: 2690\n",
+ "Länge der Liste wavelength ist: 3483\n",
+ "Länge der Liste wavelength ist: 3521\n",
+ "0.36363636363636365% waren nicht erfolgreich bei der Klasse:AGN\n",
+ "1000 CSV-Dateien angelegt.\n",
+ "Länge der Liste wavelength ist: 3521\n",
+ "Länge der Liste wavelength ist: 3502\n",
+ "Länge der Liste wavelength ist: 3511\n",
+ "Länge der Liste wavelength ist: 3521\n",
+ "Länge der Liste wavelength ist: 3493\n",
+ "Länge der Liste wavelength ist: 3519\n",
+ "Länge der Liste wavelength ist: 3506\n",
+ "Länge der Liste wavelength ist: 3477\n",
+ "Länge der Liste wavelength ist: 3487\n",
+ "Länge der Liste wavelength ist: 3486\n",
+ "Länge der Liste wavelength ist: 3503\n",
+ "Länge der Liste wavelength ist: 3470\n",
+ "Länge der Liste wavelength ist: 3513\n",
+ "Länge der Liste wavelength ist: 3503\n",
+ "Länge der Liste wavelength ist: 3470\n",
+ "Länge der Liste wavelength ist: 3503\n",
+ "Länge der Liste wavelength ist: 3467\n",
+ "Länge der Liste wavelength ist: 3469\n",
+ "Länge der Liste wavelength ist: 3494\n",
+ "Länge der Liste wavelength ist: 3461\n",
+ "Länge der Liste wavelength ist: 3423\n",
+ "Länge der Liste wavelength ist: 3507\n",
+ "Länge der Liste wavelength ist: 3433\n",
+ "Länge der Liste wavelength ist: 3487\n",
+ "Länge der Liste wavelength ist: 3484\n",
+ "Länge der Liste wavelength ist: 3470\n",
+ "Länge der Liste wavelength ist: 3469\n",
+ "Länge der Liste wavelength ist: 3447\n",
+ "Länge der Liste wavelength ist: 3447\n",
+ "Länge der Liste wavelength ist: 3511\n",
+ "2.727272727272727% waren nicht erfolgreich bei der Klasse:galaxy\n",
+ "1000 CSV-Dateien angelegt.\n",
+ "Länge der Liste wavelength ist: 3516\n",
+ "Länge der Liste wavelength ist: 3495\n",
+ "Länge der Liste wavelength ist: 3116\n",
+ "0.27272727272727276% waren nicht erfolgreich bei der Klasse:QSO\n",
+ "1000 CSV-Dateien angelegt.\n",
+ "Länge der Liste wavelength ist: 3461\n",
+ "Länge der Liste wavelength ist: 3472\n",
+ "Länge der Liste wavelength ist: 3475\n",
+ "Länge der Liste wavelength ist: 3468\n",
+ "Länge der Liste wavelength ist: 3444\n",
+ "Länge der Liste wavelength ist: 3469\n",
+ "Länge der Liste wavelength ist: 3439\n",
+ "Länge der Liste wavelength ist: 3459\n",
+ "Länge der Liste wavelength ist: 3424\n",
+ "Länge der Liste wavelength ist: 3431\n",
+ "Länge der Liste wavelength ist: 3521\n",
+ "Länge der Liste wavelength ist: 3441\n",
+ "Länge der Liste wavelength ist: 3515\n",
+ "Länge der Liste wavelength ist: 3237\n",
+ "1.2727272727272727% waren nicht erfolgreich bei der Klasse:star\n",
+ "1000 CSV-Dateien angelegt.\n"
+ ]
+ }
+ ],
+ "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_big\\\\'\n",
+ "data_path = 'F:\\\\data\\\\'\n",
+ "\n",
+ "samples_per_class = 10000\n",
+ "\n",
+ "smallest_wavelength = 4000 # in Angström\n",
+ "biggest_wavelength = 9000 \n",
+ "\n",
+ "########## Program ##########\n",
+ "\n",
+ "\n",
+ "# Funktion zum transponieren von Listen\n",
+ "def transpose_list(list1):\n",
+ " numpy_array = np.array(list1)\n",
+ " transposed = numpy_array.T\n",
+ " return transposed.tolist()\n",
+ "\n",
+ "\n",
+ "# Über alle fits-Dateien iterieren\n",
+ "all_flux = []\n",
+ "\n",
+ "for directory in os.listdir(fits_path):\n",
+ " \n",
+ " count_failed=0\n",
+ " count_added=0\n",
+ " \n",
+ " for filename in os.listdir(fits_path + directory + '\\\\'):\n",
+ " \n",
+ " path = fits_path + '\\\\' + directory + '\\\\' + filename\n",
+ " \n",
+ " # fits-Dateien öffnen und wavelength + flux einlesen\n",
+ " hdul = fits.open(path)\n",
+ " data = hdul[1].data\n",
+ " flux = data['flux']\n",
+ " wavelength = 10**data['loglam']\n",
+ " hdul.close()\n",
+ " \n",
+ " # first und last Index finden\n",
+ " for i in range(len(wavelength)):\n",
+ " if wavelength[i]>smallest_wavelength:\n",
+ " first_index = i\n",
+ " break\n",
+ "\n",
+ " for i in range(len(wavelength)):\n",
+ " if wavelength[i]>biggest_wavelength:\n",
+ " last_index = i\n",
+ " break\n",
+ "\n",
+ " # wavenlength und flux Listen schneiden\n",
+ " wavelength = wavelength[first_index:last_index]\n",
+ " flux = flux[first_index:last_index]\n",
+ " \n",
+ " if len(wavelength) == 3522 and count_added < samples_per_class:\n",
+ " all_flux.append(flux) \n",
+ " count_added += 1 \n",
+ "\n",
+ " if len(wavelength) != 3522:\n",
+ " print(\"Länge der Liste wavelength ist: \" + str(len(wavelength)))\n",
+ " count_failed += 1 \n",
+ " \n",
+ " print(str(count_failed/11000*100) + \"% waren nicht erfolgreich bei der Klasse:\" + directory)\n",
+ " print(str(count_added) + \" CSV-Dateien angelegt.\")\n",
+ " \n",
+ "# Liste mit Einträgen der ersten Zeile in der csv Datei erstellen\n",
+ "row_labels = ['wavelength']\n",
+ "for i in range(1000):\n",
+ " row_labels.append('AGN{} flux'.format(i+1))\n",
+ "for i in range(1000):\n",
+ " row_labels.append('galaxy{} flux'.format(i+1))\n",
+ "for i in range(1000):\n",
+ " row_labels.append('QSO{} flux'.format(i+1))\n",
+ "for i in range(1000):\n",
+ " row_labels.append('star{} flux'.format(i+1)) \n",
+ "\n",
+ "# Daten in csv-Datei abspeichern\n",
+ "file = open(data_path + 'spectral_data_big.csv', 'w') \n",
+ "with file:\n",
+ " writer = csv.writer(file, delimiter=',', lineterminator='\\n')\n",
+ " writer.writerow(row_labels)\n",
+ " \n",
+ " all_flux_transposed = transpose_list(all_flux)\n",
+ " \n",
+ " for i in range(len(all_flux_transposed)):\n",
+ " writer.writerow([wavelength[i]] + all_flux_transposed[i])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "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": 4
+}
--
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