def test_instrument_and_stellar_convolve_TRES(self):
from StellarSpectra.grid_tools import TRES
instrument = TRES()
low, high = instrument.wl_range
instrument_fine = TRES()
instrument_fine.wl_dict = create_log_lam_grid(low,
high,
min_vc=0.05 / C.c_kms)
log_spec = self.spec.to_LogLambda(instrument=instrument,
min_vc=0.1 / C.c_kms)
print("Before convolve", log_spec.get_min_v(), "km/s")
log_spec2 = log_spec.copy()
log_spec.instrument_and_stellar_convolve(instrument, 20)
print("After convolve", log_spec.get_min_v(), "km/s")
log_spec2.instrument_and_stellar_convolve(instrument_fine, 20)
interp = InterpolatedUnivariateSpline(log_spec.wl, log_spec.fl, k=5)
fl = interp(log_spec2.wl)
assert np.allclose(fl,
log_spec2.fl), "Spectral information not preserved."
def setup_class(self):
self.DataSpectrum = DataSpectrum.open(
"../data/WASP14/WASP-14_2009-06-15_04h13m57s_cb.spec.flux",
orders=np.array([22]))
self.Instrument = TRES()
self.HDF5Interface = HDF5Interface(
"../libraries/PHOENIX_submaster.hdf5")
stellar_Starting = {
"temp": 6000,
"logg": 4.05,
"Z": -0.4,
"vsini": 10.5,
"vz": 15.5,
"logOmega": -19.665
}
stellar_tuple = C.dictkeys_to_tuple(stellar_Starting)
cheb_tuple = ("c1", "c2", "c3")
cov_tuple = ("sigAmp", "logAmp", "l")
region_tuple = ("h", "loga", "mu", "sigma")
self.Model = Model(self.DataSpectrum,
self.Instrument,
self.HDF5Interface,
stellar_tuple=stellar_tuple,
cheb_tuple=cheb_tuple,
cov_tuple=cov_tuple,
region_tuple=region_tuple,
outdir="")
def test_closeness_TRES(self):
'''
First convert self.spec from a Base1DSpectrum to a LogLambdaSpectrum. Then, convert it back to the original
wl_grid and see if all of the information is preserved.
'''
from StellarSpectra.grid_tools import TRES
instrument = TRES()
log_spec = self.spec.to_LogLambda(instrument=instrument,
min_vc=0.1 / C.c_kms)
print(log_spec.get_min_v(), "km/s")
interp = InterpolatedUnivariateSpline(log_spec.wl, log_spec.fl, k=5)
#truncate original wl and fl to instrument ranges
low, high = instrument.wl_range
ind = (self.wl >= low) & (self.wl <= high)
wl = self.spec.wl[ind]
fl = self.spec.fl[ind]
fl_back = interp(wl)
#import matplotlib.pyplot as plt
#fig, ax = plt.subplots(nrows=2, sharex=True)
#ax[0].plot(wl, fl)
#ax[0].plot(wl, fl_back)
#ax[1].plot(wl, fl - fl_back)
#plt.show()
assert np.allclose(fl, fl_back), "Spectral information lost"
def setup_class(self):
from StellarSpectra.grid_tools import HDF5Interface, ModelInterpolator, TRES
#libraries/PHOENIX_submaster.hd5 should have the following bounds
#{"temp":(6000, 7000), "logg":(3.5,5.5), "Z":(-1.0,0.0), "alpha":(0.0,0.4)}
myHDF5Interface = HDF5Interface("../libraries/PHOENIX_submaster.hdf5")
myDataSpectrum = DataSpectrum.open(
"/home/ian/Grad/Research/Disks/StellarSpectra/StellarSpectra/tests/WASP14/WASP-14_2009-06-15_04h13m57s_cb.spec.flux",
orders=np.array([21, 22, 23]))
self.DataSpectrum = myDataSpectrum
myInterpolator = ModelInterpolator(myHDF5Interface, myDataSpectrum)
myInstrument = TRES()
self.model = ModelSpectrum(myInterpolator, myInstrument)
def test_instrument_and_stellar_convolve_TRES(self):
from StellarSpectra.grid_tools import TRES
instrument = TRES()
low, high = instrument.wl_range
instrument_fine = TRES()
instrument_fine.wl_dict = create_log_lam_grid(low, high, min_vc=0.05/C.c_kms)
log_spec = self.spec.to_LogLambda(instrument=instrument, min_vc=0.1/C.c_kms)
print("Before convolve", log_spec.get_min_v(), "km/s")
log_spec2 = log_spec.copy()
log_spec.instrument_and_stellar_convolve(instrument, 20)
print("After convolve", log_spec.get_min_v(), "km/s")
log_spec2.instrument_and_stellar_convolve(instrument_fine, 20)
interp = InterpolatedUnivariateSpline(log_spec.wl, log_spec.fl, k=5)
fl = interp(log_spec2.wl)
assert np.allclose(fl, log_spec2.fl), "Spectral information not preserved."
def main():
from StellarSpectra.spectrum import DataSpectrum
from StellarSpectra.grid_tools import TRES
myDataSpectrum = DataSpectrum.open(
"../../data/WASP14/WASP14-2009-06-14.hdf5", orders=np.array([22]))
myInstrument = TRES()
myComp = AccuracyComparison(myDataSpectrum, myInstrument,
"../../libraries/PHOENIX_submaster.hdf5",
"../../libraries/PHOENIX_objgrid6000.hdf5", {
"temp": 6000,
"logg": 4.5,
"Z": -0.5
}, {
"temp": 10,
"logg": 0.05,
"Z": 0.05
})
myComp.plot_quality()
import numpy as np
from StellarSpectra.model import Model
from StellarSpectra.spectrum import DataSpectrum
from StellarSpectra.grid_tools import TRES, HDF5Interface
from StellarSpectra import utils
myDataSpectrum = DataSpectrum.open("../../data/WASP14/WASP14-2009-06-14.hdf5",
orders=np.array([21, 22, 23]))
myInstrument = TRES()
myHDF5Interface = HDF5Interface("../../libraries/PHOENIX_TRES_F.hdf5")
#Load a model using the JSON file
myModel = Model.from_json("WASP14_PHOENIX_model0_final.json", myDataSpectrum,
myInstrument, myHDF5Interface)
myOrderModel = myModel.OrderModels[1]
model_flux = myOrderModel.get_spectrum()
spec = myModel.get_data()
wl = spec.wls[1]
fl = spec.fls[1]
model_fl = myOrderModel.get_spectrum()
residuals = fl - model_fl
mask = spec.masks[1]
cov = myModel.OrderModels[1].get_Cov().todense()
np.save("PHOENIX_covariance_matrix.npy", cov)
def main():
#Use argparse to determine if we've specified a config file
import argparse
parser = argparse.ArgumentParser(
prog="flot_model.py",
description="Plot the model and residuals using flot.")
parser.add_argument("json", help="*.json file describing the model.")
parser.add_argument("params",
help="*.yaml file specifying run parameters.")
# parser.add_argument("-o", "--output", help="*.html file for output")
args = parser.parse_args()
import json
import yaml
if args.json: #
#assert that we actually specified a *.json file
if ".json" not in args.json:
import sys
sys.exit("Must specify a *.json file.")
if args.params: #
#assert that we actually specified a *.yaml file
if ".yaml" in args.params:
yaml_file = args.params
f = open(args.params)
config = yaml.load(f)
f.close()
else:
import sys
sys.exit("Must specify a *.yaml file.")
yaml_file = args.params
from StellarSpectra.model import Model
from StellarSpectra.spectrum import DataSpectrum
from StellarSpectra.grid_tools import TRES, HDF5Interface
#Figure out what the relative path is to base
import StellarSpectra
base = StellarSpectra.__file__[:-26]
myDataSpectrum = DataSpectrum.open(base + config['data'],
orders=config['orders'])
myInstrument = TRES()
myHDF5Interface = HDF5Interface(base + config['HDF5_path'])
myModel = Model.from_json(args.json, myDataSpectrum, myInstrument,
myHDF5Interface)
for model in myModel.OrderModels:
#If an order has regions, read these out from model_final.json
region_dict = model.get_regions_dict()
print("Region dict", region_dict)
#loop through these to determine the wavelength of each
wl_regions = [value["mu"] for value in region_dict.values()]
#Make vertical markings at the location of the wl_regions.
#Get the data, sigmas, and mask
wl, fl, sigma, mask = model.get_data()
#Get the model flux
flm = model.get_spectrum()
#Get chebyshev
cheb = model.get_Cheb()
name = "Order {}".format(model.order)
plot_data = order_json(wl, fl, sigma, mask, flm, cheb)
plot_data.update({"wl_regions": wl_regions})
print(plot_data['wl_regions'])
render_template(base, plot_data)
def main():
#Use argparse to determine if we've specified a config file
import argparse
parser = argparse.ArgumentParser(
prog="plotly_model.py",
description="Plot the model and residuals using plot.ly")
parser.add_argument("json", help="*.json file describing the model.")
parser.add_argument("params",
help="*.yaml file specifying run parameters.")
# parser.add_argument("-o", "--output", help="*.html file for output")
args = parser.parse_args()
import json
import yaml
if args.json: #
#assert that we actually specified a *.json file
if ".json" not in args.json:
import sys
sys.exit("Must specify a *.json file.")
if args.params: #
#assert that we actually specified a *.yaml file
if ".yaml" in args.params:
yaml_file = args.params
f = open(args.params)
config = yaml.load(f)
f.close()
else:
import sys
sys.exit("Must specify a *.yaml file.")
yaml_file = args.params
from StellarSpectra.model import Model
from StellarSpectra.spectrum import DataSpectrum
from StellarSpectra.grid_tools import TRES, HDF5Interface
#Figure out what the relative path is to base
import StellarSpectra
base = StellarSpectra.__file__[:-26]
myDataSpectrum = DataSpectrum.open(base + config['data'],
orders=config['orders'])
myInstrument = TRES()
myHDF5Interface = HDF5Interface(base + config['HDF5_path'])
myModel = Model.from_json(args.json, myDataSpectrum, myInstrument,
myHDF5Interface)
for model in myModel.OrderModels:
#Get the data
wl, fl = model.get_data()
#Get the model flux
flm = model.get_spectrum()
#Get residuals
residuals = model.get_residuals()
name = "Order {}".format(model.order)
url = plotly_order(name, wl, fl, flm, residuals)
print(url)
"/n/holyscratch/panstarrs/iczekala/master_grids/PHOENIX_master.hdf5")
myInterpolator = Interpolator(myHDF5Interface,
avg_hdr_keys=[
"air", "PHXLUM", "PHXMXLEN", "PHXLOGG",
"PHXDUST", "PHXM_H", "PHXREFF", "PHXXI_L",
"PHXXI_M", "PHXXI_N", "PHXALPHA", "PHXMASS",
"norm", "PHXVER", "PHXTEFF"
])
outKPNO = "/n/home07/iczekala/StellarSpectra/libraries/willie/KPNO/"
outKPNOfnu = "/n/home07/iczekala/StellarSpectra/libraries/willie/KPNOfnu/"
outTRES = "/n/home07/iczekala/StellarSpectra/libraries/willie/TRES/"
outTRESfnu = "/n/home07/iczekala/StellarSpectra/libraries/willie/TRESfnu/"
params_hot = {"temp": 6000, "logg": 4.5, "Z": 0.0, "vsini": 8}
params_cool = {"temp": 4000, "logg": 4.5, "Z": 0.0, "vsini": 4}
KPNOcreator = MasterToFITSIndividual(interpolator=myInterpolator,
instrument=KPNO())
KPNOcreator.process_spectrum(params_hot, out_unit="f_nu_log", out_dir=outKPNO)
KPNOcreator.process_spectrum(params_cool, out_unit="f_nu_log", out_dir=outKPNO)
KPNOcreator.process_spectrum(params_hot, out_unit="f_nu", out_dir=outKPNOfnu)
KPNOcreator.process_spectrum(params_cool, out_unit="f_nu", out_dir=outKPNOfnu)
TREScreator = MasterToFITSIndividual(interpolator=myInterpolator,
instrument=TRES())
TREScreator.process_spectrum(params_hot, out_unit="f_nu_log", out_dir=outTRES)
TREScreator.process_spectrum(params_cool, out_unit="f_nu_log", out_dir=outTRES)
TREScreator.process_spectrum(params_hot, out_unit="f_nu", out_dir=outTRESfnu)
TREScreator.process_spectrum(params_cool, out_unit="f_nu", out_dir=outTRESfnu)
def test_to_LogLambda(self):
from StellarSpectra.grid_tools import TRES
spec = self.spec.to_LogLambda(instrument=TRES(), min_vc=0.06 / C.c_kms)
print(spec.get_min_v(), "km/s")