In [1]:
## Here we inspect the result of the isochrone indexing.
# Many different stars get tagged together and their median values are taken
# Here we look into the standard deviation.
# The slicing works fine, except for the photometry.
# Errors in the extinction law are small though!
%pylab inline
In [2]:
ext = np.load('ext.npy')
ext_std = np.load('ext_std.npy')
parsec = np.load('parsec.npy')
parsec_std = np.load('parsec_std.npy')
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ext.shape
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for t in [5,50,95,99,100]:
print(t)
print(np.percentile(ext_std,t,axis = 0))
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parsec
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for item in parsec.dtype.names:
print(item)
for t in [5,50,95,99,100]:
print(t,np.percentile(parsec_std[item],t))
print("#########################")
In [7]:
# We use a bigger font for you to see better on the beamer
font = {'family' : 'normal',
'weight' : 'bold',
'size' : 22}
matplotlib.rc('font', **font)
## We have a problem with those high extincted stars. They have the same properties as the rest
## but strongly diverging photometry. Could be a problem when mapping to other photometry. Write into paper.
sort = np.argsort(parsec_std["gaia_g"])
plt.figure(figsize=(20,10))
plt.scatter(parsec["log_teff"][sort],parsec["log_lum"][sort],c = parsec_std['gaia_g'][sort])
plt.colorbar(label="std in gaia_g")
plt.gca().invert_xaxis()
plt.ylabel("loglum")
plt.xlabel("logteff")
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plt.scatter(parsec["gaia_bpbr"][sort]-parsec['gaia_rp'][sort],parsec["gaia_g"][sort],c = parsec_std['gaia_g'][sort])
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