def fsexatodeg(ra_sexa, dec_sexa):
'''
Converts Right Ascension and Declination coordinates from the sexagismal system to decimal degrees.
Note: valid input formats are e.g., "00 05 08.83239 +67 50 24.0135" or “00:05:08.83239 -67:50:24.0135”.
Spaces or colons are allowed as separators for the individual components of the input coordinates.
Input
ra_sexa : Right Ascension coordinates in the sexagismal system; can be single-valued or a list/array
dec_sexa : Declination coordinates in the sexagismal system; can be single-valued or a list/array
Output
ra_deg : Right Ascension coordinates in degrees
dec_deg : Declination coordinates in degrees
'''
if (isinstance(ra_sexa, str) == True):
# If input is a single coordinate.
sexa = ra_sexa + " " + dec_sexa
ra_deg, dec_deg = pyasl.coordsSexaToDeg(sexa)
elif (isinstance(ra_sexa, np.ndarray) == True):
# If input is an array of coordinates.
ra_deg_list = []
dec_deg_list = []
for i in range(len(ra_sexa)):
ra_sexa_i = ra_sexa[i]
dec_sexa_i = dec_sexa[i]
sexa_i = ra_sexa_i + " +" + dec_sexa_i
ra_deg_i, dec_deg_i = pyasl.coordsSexaToDeg(sexa_i)
ra_deg_list.append(ra_deg_i)
dec_deg_list.append(dec_deg_i)
ra_deg = np.array(ra_deg_list)
dec_deg = np.array(dec_deg_list)
return ra_deg, dec_deg
def coordsSexaToDeg(ra_dec):
try:
return pyasl.coordsSexaToDeg(ra_dec)
except:
ra, dec = ra_dec.split('+')
ra = pad_with_spaces(ra)
dec = pad_with_spaces(dec)
ra_dec = ra + ' +' + dec
return pyasl.coordsSexaToDeg(ra_dec)
def astro(coordenadas):
ra, dec = pyasl.coordsSexaToDeg(coordenadas)
print("*****************************************************************")
print("*Las coordenadas del astro en grados son: %010.6f %+09.6f *" % (ra, dec))
Pra, Pdec = pyasl.coordsSexaToDeg("02 31 50.59 +89 15 51.4")
print("*Las coordenadas de Polaris en grados son: %010.6f %+09.6f*" % (Pra, Pdec))
Dra = ra - Pra
Ddec = Pdec - dec
print("*Diferecia en DEG: %010.6f %+09.6f *" % (Dra, Ddec))
return Dra, Ddec
def translate_source(ra, dec, sexi):
valid_true = ['true', 'TRUE', 'True', 't', 'T']
if sexi in valid_true:
if dec[0] != "+" or dec[0] != "-":
dec = "+%s" % (dec)
hd1 = ra + " " + dec
ra, dec = pyasl.coordsSexaToDeg(hd1)
# print(pyasl.coordsDegToSexa(float(ra),float(dec)))
return float(ra), float(dec)
def MAGIC_Field_CR(ra_pos, dec_pos):
import numpy as np
from astroquery.vizier import Vizier
import astropy.units as u
import astropy.coordinates as coord
from PyAstronomy import pyasl
import matplotlib.pyplot as plt
#from tabulate import tabulate
from astropy.table import Table, Column, MaskedColumn, QTable
from quantiphy import Quantity
from astropy.io import ascii
Vizier.ROW_LIMIT = -1
result = Vizier.query_region(coord.SkyCoord(ra=ra_pos,
dec=dec_pos,
unit=(u.deg, u.deg),
frame='icrs'),
width="7d",
catalog="V/125")
if result == []:
data = np.array([ra_pos, dec_pos, 0, 0, 0, 0, 0, 0])
#data = data.transpose
return data
t = result['V/125/obcat']
#t.columns
#use ALS IDs to ensure uniqueness
ALS0 = np.array(t['ALS'])
ra = np.array(t['RAJ2000'])
dec = np.array(t['DEJ2000'])
#ALS0[0:3]
t.columns
#Section 3 of Jupyter Notebook
from astroquery.simbad import Simbad
#Right now that's B, V, sptype, but later might want more...
customSimbad = Simbad()
customSimbad.add_votable_fields('sptype', 'fe_h', 'flux(V)', 'flux(B)',
'flux(U)')
vmag = np.zeros(len(ALS0))
bmag = np.zeros(len(ALS0))
st = np.empty(len(ALS0), dtype='S2')
#vmag = np.zeros(200)
#bmag = np.zeros(200)
for ii in range(len(ALS0)):
result_table = customSimbad.query_object('ALS' +
np.array2string(ALS0[ii]))
#print(str(result_table[0][11])[0:2])
#print(ALS0[ii])
if result_table is None:
#print('None 2')
vmag[ii] = np.nan
bmag[ii] = np.nan
st[ii] = 'XX'
else:
vmag[ii] = np.asarray(result_table['FLUX_V'])
bmag[ii] = np.asarray(result_table['FLUX_B'])
st[ii] = str(result_table[0][11])[0:2]
#print('bmag1')
#print(bmag)
idx = (np.isnan(vmag))
#print('idx1')
#print(idx)
vmag = vmag[~idx]
bmag = bmag[~idx]
st = st[~idx]
ra = ra[~idx]
dec = dec[~idx]
idx = (np.isnan(bmag))
#print('idx2')
#print(idx)
vmag = vmag[~idx]
bmag = bmag[~idx]
st = st[~idx]
ra = ra[~idx]
dec = dec[~idx]
#print('bmag2')
#print(bmag)
#print(bmag.size)
if bmag.size == 0 or vmag.size == 0:
#print('in if')
data = np.array([ra_pos, dec_pos, 0, 0, 0, 0, 0, 0])
#data = data.transpose
return data
#Start of section 4 in Jupyter notebook
#collect spectral type, V magnitude information for each star
#Use SIMBAD spectral types, recorded in st string array
#Do some temporary cleanup here. For stars with generic OB type, assign type O5V. For Wolf-Rayet stars (W..),
#also assign type O5V. Note that this is definitely too cool, but right now don't have the models to assign a hotter type
st_vals = list()
for ii in range(len(st)):
temp1 = np.array2string(st[ii])[2:4]
if len(temp1) == 2 and temp1[1] == "'":
temp1 = temp1[0] + '5'
if temp1 == "OB":
temp1 = "O5"
if temp1[0] == "W":
temp1 = "O5"
if temp1[0] == "'":
temp1 = "O5"
if len(temp1) == 1 and temp1[1] == "'":
temp1 = "G5"
temp1 = temp1 + 'V'
st_vals.append(temp1)
st_vals = np.asarray(st_vals)
len(st_vals)
#MeanStars is a python package which includes Mamajek mean stars catalog
from MeanStars import MeanStars
ms = MeanStars()
ms_st = ms.data
#ms_st are spectral types from Mamajek mean stars catalog
ms_st = ms_st['SpT']
#ms_temp = ms_st['Teff']
#ms_temp are temperatures from Mamajek catalog
ms_temp = ms.data['Teff']
#match up rows in Mamajek catalog with observed STs
sloc = []
for x in range(len(st_vals)):
sloc_temp = np.nonzero(st_vals[x] == ms_st)
sloc.append(sloc_temp[0])
#star_temp = temperature of stars suitable for calling to function
#Note for stars with no temperature value, assign T<10,000K so they will be ignored later
#For stars with T>40000, assign 40000 (model fits for >40,000 are broken for now)
star_temp = []
for x in range(len(st_vals)):
star_temp.append(ms_temp[sloc[x]])
if len(star_temp[x]) == 0:
star_temp[x] = 9999
if star_temp[x] > 40000:
star_temp[x] = 40000
#Estimate count rate for each star based on spectral type, magnitude, model atmosphere fits
#Polynomial fits to the atmosphere fits derived from my MATLAB code (which needs to be ported to Python!)
#cr1 -0.3375 -34.1223 227.7543 -172.6182 34.0610
#cr2 3.2407 -51.6510 219.8193 -91.3053 5.2321
cr1 = np.zeros(len(st_vals))
cr2 = np.zeros(len(st_vals))
for x in range(len(st_vals)):
cr2[x] = (2.512**-bmag[x]) * 1e4 * np.polyval(
[-0.3375, -34.1223, 227.7543, -172.6182, 34.0610],
1e-4 * star_temp[x])
cr1[x] = (2.512**-bmag[x]) * 1e4 * np.polyval(
[3.2407, -51.6510, 219.8193, -91.3053, 5.2321],
1e-4 * star_temp[x])
#plt.figure(1)
#plt.plot(star_temp,cr1,'.')
#plt.show()
#Summed total count rates over field in both bands, as well as max local CRs in field in both bands
#print('cr1')
#print(cr1)
tot_cr1 = np.nansum(cr1)
tot_cr2 = np.nansum(cr2)
count1 = np.count_nonzero(cr1 > 100)
count2 = np.count_nonzero(cr2 > 100)
#np.nanmax(cr1)
#np.nanmax(cr2)
np.nanmedian(cr1)
#np.nanmedian(cr2)
#Histogram of count rates in field
#plt.figure(2)
#plt.hist(np.log10(cr1[~np.isnan(cr1)]),bins=40)
#plt.show()
#Scatter plot
#pyastro and future import was moved to the beginning of the file
#plt.plot(ra,dec,'x')
#plt.show()
ra_d = np.zeros(len(ra))
dec_d = np.zeros(len(dec))
for ii in range(len(ra)):
temp = str(ra[ii])
temp2 = str(dec[ii])
ra_d[ii], dec_d[ii] = pyasl.coordsSexaToDeg(temp[0:-1] + " " +
temp2[0:-1])
#plt.figure(3)
#plt.scatter(ra_d,dec_d,cr1*0.01,alpha=0.3)
#plt.axis('square')
#plt.xlabel('RA')
#plt.ylabel('Dec')
#plt.show()
#Number of stars with T>10000
tstar = np.asarray(star_temp)
len(tstar[tstar > 10000])
b_i, v_i = cr1, cr2
#print(b_i, v_i)
maxcr1 = np.max(b_i)
#x=f"{maxcr1.value:0.03f}"
maxcr2 = np.max(v_i)
in_cr = maxcr1 + maxcr2
data = np.array(
[ra_pos, dec_pos, maxcr1, maxcr2, tot_cr1, tot_cr2, count1, count2])
#data = data.transpose
#print(data)
#print(maxcr2)
#data=(np.array(x,dtype=object,copy=False, order='C'))
#fopen = open("sample.txt", "a")
#print(fopen)
#np.savetxt("sample.txt", data, fmt='%10.5f', newline=' ')
#np.savetxt("Datamagic3",data, fmt='%10.5f',newline=' ')
# data=(np.array(x,dtype=object,copy=False, order='C')
#ascii.write(data, 'values.dat', names=['x'], overwrite=True)
#ascii.write(data,'values.csv',format='csv',fast_writer=False)
# print(x)
# y = maxcr2
#data1=QTable([maxcr1], names=['Maxcr1'])
#lines = ['in_CR & maxCr1 & maxCr2 ','----------------------- & ----------------- & -------------',' 32876.33701130775 & 18225.588784508154 & 14650.748916622626']
# data = ascii.read(lines, data_start=2, delimiter='&')
# print(data)
# plt.hist(np.log10(cr1[~np.isnan(cr1)]),bins=40)
# return plt.hist(np.log10(cr1[~np.isnan(cr1)]),bins=40)
return data
'''
Transform the sexagecimal system to decimal system the equatorial coordinate
'''
from __future__ import print_function, division
from PyAstronomy import pyasl
import numpy as np
list_=[]
f = open("Belist", 'r')
header1 = f.readline()
for line in f:
line = line.strip()
columns = line.split()
list_.append(line)
#asciifile = "Belist-sdss.txt"
asciifile = "Belist-LAMOST.txt"
iii=[ii for ii in range(len(list_))]
for x, i in zip(iii, list_):
ra, dec = pyasl.coordsSexaToDeg(i)
#print(x, ra, dec)
print(ra, dec, '2.0')
imgName = str(galaxies.iloc[x][0]) + ".jpeg"
maxValue = 0
shapeIndex = ellipticIndex
for y in xrange(ellipticIndex,combinedIndex + 1):
if galaxies.iloc[x][y] > maxValue:
maxValue = galaxies.iloc[x][y]
shapeIndex = y
hd2 = galaxies.iloc[x][1] + " " + galaxies.iloc[x][2]
# Obtain decimal representation
ra1, dec1 = pyasl.coordsSexaToDeg(hd2)
img = SkyServer.getJpegImgCutout(ra=ra1, dec=dec1, width=512, height=512, scale=0.1,
dataRelease="DR13",opt="I",
query="SELECT TOP 1 g.objID, g.ra, g.dec, g.r FROM fGetObjFromRectEq(ra1-0.5,dec1-0.5,ra1+0.5,dec1+0.5) n, Galaxy g WHERE n.objID=g.objID")
im = Image.fromarray(img)
im.save(imgName)
with open('shapes.csv', 'a') as f:
writer = csv.writer(f)
writer.writerow([str(galaxies.iloc[x][0]), str(shapeIndex)])