import matplotlib.pyplot as plt
import numpy as np
import sys
sys.path.append('../../')
import pyterpol
# create the grid
sygri = pyterpol.SyntheticGrid(debug=True, mode='default')
parlis = sygri.select_parameters(order=4, **{'logg':3.9999999999999982, 'z':1.0, 'teff':16000.})
print parlis
parlis = sygri.deselect_exact(parlis, **{'logg':3.9999999999999982, 'z':1.0, 'teff':16000.})
print parlis
import matplotlib.pyplot as plt
import numpy as np
import sys
sys.path.append('../../')
import pyterpol
# create the grid
sygri = pyterpol.SyntheticGrid()
# read properties
sygri.read_list_from_file('b1.dat',
columns=['FILENAME', 'TEFF', 'LOGG', 'Z'],
family='BSTAR')
sygri.read_list_from_file('p1.dat',
columns=['FILENAME', 'TEFF', 'LOGG', 'Z'],
family='POLLUX')
# test of dealing with degeneracies - this should return two pectra
sl = sygri.get_all(z=1.0, teff=15000, logg=4.5)
#print sl
# resolve degeneracy - should raise an exception - checked
#sl = sygri.resolve_degeneracy(sl)
## so we set the grid order and now it should return one spectrum - checked
sygri.set_grid_order(['BSTAR', 'POLLUX'])
sl = sygri.resolve_degeneracy(sl)
#print sl['family']
# this should create a list with intensities of individual
# spectra that will be used for interpolation
""" This script serves a demonstration of the class SyntheticGrid. """ # import the library import pyterpol import matplotlib.pyplot as plt # The handling of the synthetic grid is shadowed from the user, # therefore the interaction of the user with the grid should # restrict to only few methods. # How to create a grid? Ups - we have forgotten, which modes are available.as # So we create a default grid and have a look at the grids that are available # In general user should use default grid, because it spans over all # implemented grids sg = pyterpol.SyntheticGrid() # The method list_modes returns string, so it has to be printed print sg.list_modes() # Now we know the modes, so we can either create the grid again sg = pyterpol.SyntheticGrid(mode='bstar') # or just set mode for the existing one - BSTAR will be our # exemplary grid. sg.set_mode(mode='bstar') # we set up a grid, so we can interpolate. # synthetic spectra should be queried with # the method get_synthetic_spectrum - lets do some calls # Grid parameters have to be wrapped using
import matplotlib.pyplot as plt
import numpy as np
import sys
sys.path.append('../../')
import pyterpol
import time
# create the grid - custom does nothing
sygri = pyterpol.SyntheticGrid('POLLUX')
# try to load a spectrum
keys = ['Z', 'LOGG', 'TEFF']
vals = [1.0, 4.0, 13000]
wmin = 6400
wmax = 6600
# create a dictionary for future look up
specs= {}
for k, v in zip(keys, vals):
specs[k] = v
# load the spectra
t0 = time.time()
print sygri.get_spectra_for_interpolation([vals], keys, wmin=wmin, wmax=wmax)
t1 = time.time()
print t1-t0
# load them again
t0 = time.time()
print sygri.get_spectra_for_interpolation([vals], keys, wmin=wmin, wmax=wmax)
t1 = time.time()
import matplotlib.pyplot as plt
import numpy as np
import sys
sys.path.append('../../')
import pyterpol
import time
# create the grid - custom does nothing
sygri = pyterpol.SyntheticGrid('POLLUX', debug=True)
# interpolate at
wmin = 6500
wmax = 6650
keys = ['Z', 'LOGG', 'TEFF']
values = [1.0, 4.0, 12000]
params = {k: v for k, v in zip(keys, values)}
name = '_'.join([k + '_' + str(v) for k, v in zip(keys, values)])
plt.figure(figsize=(12, 5), dpi=100)
spectrum = sygri.get_synthetic_spectrum(params, wmin, wmax)
w, i = spectrum.get_spectrum()
plt.plot(w, i, 'k-')
plt.savefig(name + '.png')
plt.close()
import matplotlib.pyplot as plt
import numpy as np
import sys
sys.path.append('../../')
import pyterpol
import time
# create the grid - custom does nothing
sygri = pyterpol.SyntheticGrid('BSTAR', debug=True)
# interpolate at
wmin = 6500
wmax = 6650
keys = ['Z', 'LOGG', 'TEFF']
values = [1.0, 3.8, 18400]
params = {k:v for k,v in zip(keys, values)}
name = '_'.join([k+'_'+str(v) for k,v in zip(keys, values)])
ords = np.arange(2,6)
for order in ords:
spectrum = sygri.get_synthetic_spectrum(params, wmin, wmax, order=order)
w,i = spectrum.get_spectrum()
#plot the spectrum
plt.figure(figsize=(12,10), dpi=100)
plt.plot(w, i, 'k-')
plt.savefig(name+"_order_%s" % str(order)+'.png')
plt.close()
# save the spectrum in text file
import matplotlib.pyplot as plt
import numpy as np
import sys
sys.path.append('../../')
import pyterpol
# create the grid - custom does nothing
sygri = pyterpol.SyntheticGrid()
print sygri
## create the grid - BSTAR does nothing
sygri = pyterpol.SyntheticGrid('BSTAR')
print sygri
## create the grid - OSTAR does nothing
sygri = pyterpol.SyntheticGrid('OSTAR')
print sygri
## create the grid - POLLUX does nothing
sygri = pyterpol.SyntheticGrid('POLLUX')
print sygri
## create the grid - POLLUX does nothing
sygri = pyterpol.SyntheticGrid('AMBRE')
print sygri
## create the grid - DEFAULT does nothing
sygri = pyterpol.SyntheticGrid('DEFAULT')
print sygri
import pyterpol import matplotlib.pyplot as plt wmin = 3600 wmax = 4100 sygri = pyterpol.SyntheticGrid(flux_type='absolute') params = dict(teff=9950, logg=3.7, z=1.0) spec1 = sygri.get_synthetic_spectrum(params, [wmin, wmax], order=4, step=0.1) params = dict(teff=10000, logg=3.5, z=1.0) spec2 = sygri.get_synthetic_spectrum(params, [wmin, wmax], order=4, step=0.1) params = dict(teff=10000, logg=4.0, z=1.0) spec3 = sygri.get_synthetic_spectrum(params, [wmin, wmax], order=4, step=0.1) ax = plt.subplot(111) spec1.plot(ax=ax) spec2.plot(ax=ax) spec3.plot(ax=ax) plt.show()
