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 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)
def setup_class(self):
#It is necessary to use a piece of data created on the super computer so we can test interpolation in 4D
self.hdf5interface = HDF5Interface("libraries/PHOENIX_submaster.hdf5")
#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)}
from StellarSpectra.spectrum import DataSpectrum
self.DataSpectrum = DataSpectrum.open("/home/ian/Grad/Research/Disks/StellarSpectra/tests/WASP14/WASP-14_2009-06-15_04h13m57s_cb.spec.flux", orders=np.array([21, 22, 23]))
#TODO: test DataSpectrum with different number of orders, and see how it is truncated.
self.interpolator = ModelInterpolator(self.hdf5interface, self.DataSpectrum, cache_max=20, cache_dump=10)
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 setup_class(self):
#It is necessary to use a piece of data created on the super computer so we can test interpolation in 4D
self.hdf5interface = HDF5Interface("libraries/PHOENIX_submaster.hdf5")
#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)}
from StellarSpectra.spectrum import DataSpectrum
self.DataSpectrum = DataSpectrum.open(
"/home/ian/Grad/Research/Disks/StellarSpectra/tests/WASP14/WASP-14_2009-06-15_04h13m57s_cb.spec.flux",
orders=np.array([21, 22, 23]))
#TODO: test DataSpectrum with different number of orders, and see how it is truncated.
self.interpolator = ModelInterpolator(self.hdf5interface,
self.DataSpectrum,
cache_max=20,
cache_dump=10)
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()
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 StellarSpectra.constants as C
from StellarSpectra.spectrum import DataSpectrum
from StellarSpectra.covariance import CovarianceMatrix
import logging
#Set up the logger
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
filename="log.log",
level=logging.DEBUG,
filemode="w",
datefmt='%m/%d/%Y %I:%M:%S %p')
#interface = HDF5Interface("../libraries/PHOENIX_F.hdf5")
interface = HDF5Interface("../libraries/PHOENIX_TRES_F.hdf5")
dataspec = DataSpectrum.open("../data/WASP14/WASP14-2009-06-14.hdf5",
orders=np.array([22]))
interpolator = Interpolator(interface, dataspec, trilinear=True)
params = {"temp": 6010, "logg": 4.1, "Z": -0.3}
fl, errspec = interpolator(params)
#A good test here would be to create errspec as an arange() so that inside of cov.h we know how we are indexing this
#N = np.prod(errspec.shape)
#testspec = np.arange(N, dtype="f8")
#print(len(fl))
#testspec.shape = errspec.shape
#print(testspec)
#Create a CovarianceMatrix object
cov = CovarianceMatrix(dataspec, 0, 20, debug=True)
def line_classes_strip():
spec = DataSpectrum.open("data/WASP14/WASP-14_2009-06-15_04h13m57s_cb.spec.flux", orders=[22,23])
fl23, fl24 = spec.fls
sig23, sig24 = spec.sigmas
#Model?
wlsz23 = np.load("residuals/wlsz23.npy")[0]
f23 = np.load("residuals/fl23.npy")[0]
wlsz24 = np.load("residuals/wlsz24.npy")[0]
f24 = np.load("residuals/fl24.npy")[0]
#import matplotlib
#font = {'size' : 8}
#matplotlib.rc('font', **font)
#matplotlib.rc('labelsize', **font)
r23 = (fl23 - f23)/sig23
r24 = (fl24 - f24)/sig24
fl23 /= 2e-13
fl24 /= 2e-13
f23 /= 2e-13
f24 /= 2e-13
#Class 0
fig, ax = plt.subplots(nrows=2, figsize=(3,4), sharex=True)
ax[0].plot(wlsz23, fl23, "b", label="data")
ax[0].plot(wlsz23, f23, "r", label="model")
ax[0].legend(loc="lower left")
ax[0].set_title("Class 0")
ax[1].plot(wlsz23, r23, "g")
ax[1].set_xlim(5136.4, 5140.4)
ax[1].set_ylim(-4,4)
labels = ax[1].get_xticklabels()
for label in labels:
label.set_rotation(60)
ax[0].set_ylabel(r"$\propto f_\lambda$")
ax[1].set_ylabel(r"Residuals$/\sigma_P$")
ax[1].xaxis.set_major_formatter(FSF("%.0f"))
ax[1].xaxis.set_major_locator(MultipleLocator(1.))
for i in [0, 1]:
ax[i].tick_params(axis='both', which='major', labelsize=10)
fig.subplots_adjust(left=0.19, hspace=0.1, right=0.96, top=0.92)
fig.savefig("plots/class0.png")
plt.close(fig)
fig, ax = plt.subplots(nrows=2, ncols=2, figsize=(4,4.))
#Class I
ax[0,0].plot(wlsz23, fl23, "b")
ax[0,0].plot(wlsz23, f23, "r")
ax[0,0].set_title("Class I")
ax[0,0].set_ylim(0.4, 1.1)
ax[1,0].plot(wlsz23, r23, "g")
ax[0,0].set_xlim(5188, 5189.5)
ax[1,0].set_xlim(5188, 5189.5)
#ax[1,1].set_ylim(-4,4)
#Class II
ax[0,1].plot(wlsz24, fl24, "b", label='data')
ax[0,1].plot(wlsz24, f24, "r", label='model')
ax[0,1].set_ylim(0.3, 1.1)
ax[0,1].legend(loc="lower center", prop={'size':10})
ax[0,1].set_title("Class II")
ax[1,1].plot(wlsz24, r24, "g")
ax[0,1].set_xlim(5258, 5260)
ax[1,1].set_xlim(5258, 5260)
ax[0,0].set_ylabel(r"$\propto f_\lambda$")
ax[1,0].set_ylabel(r"Residuals$/\sigma_P$")
for i in [0,1]:
for j in [0,1]:
ax[i,j].xaxis.set_major_formatter(FSF("%.1f"))
ax[i,j].xaxis.set_major_locator(MultipleLocator(0.5))
ax[i,j].tick_params(axis='both', which='major', labelsize=10)
ax[0,i].xaxis.set_ticklabels([])
for j in range(2):
labels = ax[1,j].get_xticklabels()
for label in labels:
label.set_rotation(60)
fig.subplots_adjust(left=0.15, hspace=0.1, wspace=0.23, right=0.96, top=0.92, bottom=0.15)
fig.savefig("plots/classI_II.png")
plt.close(fig)
#Class III
fig, ax = plt.subplots(nrows=2, figsize=(3,4), sharex=True)
ax[0].plot(wlsz24, fl24, "b", label="data")
ax[0].plot(wlsz24, f24, "r", label="model")
ax[0].legend(loc="lower left")
ax[0].set_title("Class III")
ax[1].plot(wlsz24, r24, "g")
ax[1].set_xlim(5260, 5271)
ax[1].set_ylim(-15, 15)
ax[0].set_ylabel(r"$\propto f_\lambda$")
ax[1].set_ylabel(r"Residuals$/\sigma_P$")
ax[1].xaxis.set_major_formatter(FSF("%.0f"))
ax[1].xaxis.set_major_locator(MultipleLocator(2.))
for i in [0, 1]:
ax[i].tick_params(axis='both', which='major', labelsize=10)
labels = ax[1].get_xticklabels()
for label in labels:
label.set_rotation(60)
fig.subplots_adjust(left=0.19, hspace=0.1, right=0.96, top=0.92)
fig.savefig("plots/classIII.png")
plt.close(fig)
from StellarSpectra.spectrum import DataSpectrum
from StellarSpectra.covariance import CovarianceMatrix
import logging
# Set up the logger
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
filename="log.log",
level=logging.DEBUG,
filemode="w",
datefmt="%m/%d/%Y %I:%M:%S %p",
)
# interface = HDF5Interface("../libraries/PHOENIX_F.hdf5")
interface = HDF5Interface("../libraries/PHOENIX_TRES_F.hdf5")
dataspec = DataSpectrum.open("../data/WASP14/WASP14-2009-06-14.hdf5", orders=np.array([22]))
interpolator = Interpolator(interface, dataspec, trilinear=True)
params = {"temp": 6010, "logg": 4.1, "Z": -0.3}
fl, errspec = interpolator(params)
# A good test here would be to create errspec as an arange() so that inside of cov.h we know how we are indexing this
# N = np.prod(errspec.shape)
# testspec = np.arange(N, dtype="f8")
# print(len(fl))
# testspec.shape = errspec.shape
# print(testspec)
# Create a CovarianceMatrix object
cov = CovarianceMatrix(dataspec, 0, 20, debug=True)
import numpy as np
from StellarSpectra.model import Model
from StellarSpectra.spectrum import DataSpectrum
from StellarSpectra.grid_tools import SPEX, HDF5Interface
from StellarSpectra import utils
myDataSpectrum = DataSpectrum.open("../../data/Gl51/Gl51RA.hdf5", orders=np.array([0]))
myInstrument = SPEX()
myHDF5Interface = HDF5Interface("../../libraries/PHOENIX_SPEX_M.hdf5")
#Load a model using the JSON file
#Taken from:
# /home/ian/Grad/Research/Disks/StellarSpectra/output/Gl51/PHOENIX/RA/region/logg/4_8sig/
myModel = Model.from_json("Gl51_model0_final.json", myDataSpectrum, myInstrument, myHDF5Interface)
myOrderModel = myModel.OrderModels[0]
model_flux = myOrderModel.get_spectrum()
spec = myModel.get_data()
wl = spec.wls[0]
fl = spec.fls[0]
model_fl = myOrderModel.get_spectrum()
residuals = fl - model_fl
mask = spec.masks[0]
cov = myModel.OrderModels[0].get_Cov().todense()
np.save("Gl51_covariance_matrix.npy", cov)
import sys
sys.exit()
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)
#Open the yaml file in this directory
yaml_file = args.run + "/input.yaml"
f = open(yaml_file)
config = yaml.load(f)
f.close()
#Use the model_final.json to figure out how many orders there are
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.run + "/model_final.json", myDataSpectrum,
myInstrument, myHDF5Interface)
orders = [orderModel.order for orderModel in myModel.OrderModels]
flot_plots = {22: "Hi"}
#If the Jinja templater is going to work, it needs a list of orders. It also needs a list of how many regions
# are in each order
# each order, there is dictionary
#of global
#Set the categories as the decomposition of the run directory, excluding
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
myDataSpectrum = DataSpectrum.open(config['data_dir'], orders=config['orders'])
myInstrument = TRES()
myHDF5Interface = HDF5Interface(config['HDF5_path'])
stellar_Starting = config['stellar_params']
stellar_tuple = C.dictkeys_to_tuple(stellar_Starting)
cheb_Starting = config['cheb_params']
cheb_tuple = ("logc0", "c1", "c2", "c3")
cov_Starting = config['cov_params']
cov_tuple = C.dictkeys_to_cov_global_tuple(cov_Starting)
region_tuple = ("h", "loga", "mu", "sigma")
region_MH_cov = np.array([0.05, 0.04, 0.02, 0.02])**2 * np.identity(
len(region_tuple))
yaml_file = args.run + "/input.yaml"
f = open(yaml_file)
config = yaml.load(f)
f.close()
# Use the model_final.json to figure out how many orders there are
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.run + "/model_final.json", myDataSpectrum, myInstrument, myHDF5Interface)
orders = [orderModel.order for orderModel in myModel.OrderModels]
flot_plots = {22: "Hi"}
# If the Jinja templater is going to work, it needs a list of orders. It also needs a list of how many regions
# are in each order
# each order, there is dictionary
# of global
# Set the categories as the decomposition of the run directory, excluding
# output and the "run00" directory.
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)
import numpy as np
from StellarSpectra.model import Model
from StellarSpectra.spectrum import DataSpectrum
from StellarSpectra.grid_tools import TRES, SPEX, 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/Kurucz_TRES.hdf5")
# Load a model using the JSON file
# Taken from:
# /n/home07/iczekala/StellarSpectra/output/WASP14/Kurucz/21_22_23/logg/cov/2014-08-06/run18
myModel = Model.from_json("WASP14_Kurucz_logg_model_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()
draws = utils.random_draws(cov, num=50)
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)
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
myDataSpectrum = DataSpectrum.open(config['data_dir'], orders=config['orders'])
myInstrument = TRES()
myHDF5Interface = HDF5Interface(config['HDF5_path'])
stellar_Starting = config['stellar_params']
stellar_tuple = C.dictkeys_to_tuple(stellar_Starting)
cheb_Starting = config['cheb_params']
cheb_tuple = ("logc0", "c1", "c2", "c3")
cov_Starting = config['cov_params']
cov_tuple = C.dictkeys_to_cov_global_tuple(cov_Starting)
region_tuple = ("h", "loga", "mu", "sigma")
region_MH_cov = np.array([0.05, 0.04, 0.02, 0.02])**2 * np.identity(len(region_tuple))
def line_classes_panel():
fig, ax = plt.subplots(nrows=2, ncols=4, figsize=(6,4.))
spec = DataSpectrum.open("data/WASP14/WASP-14_2009-06-15_04h13m57s_cb.spec.flux", orders=[22,23])
fl23, fl24 = spec.fls
sig23, sig24 = spec.sigmas
#Model?
wlsz23 = np.load("residuals/wlsz23.npy")[0]
f23 = np.load("residuals/fl23.npy")[0]
wlsz24 = np.load("residuals/wlsz24.npy")[0]
f24 = np.load("residuals/fl24.npy")[0]
#import matplotlib
#font = {'size' : 8}
#matplotlib.rc('font', **font)
#matplotlib.rc('labelsize', **font)
r23 = (fl23 - f23)/sig23
r24 = (fl24 - f24)/sig24
fl23 /= 2e-13
fl24 /= 2e-13
f23 /= 2e-13
f24 /= 2e-13
ax[0,0].plot(wlsz23, fl23, "b", label="data")
ax[0,0].plot(wlsz23, f23, "r", label="model")
ax[1,0].plot(wlsz23, r23, "g")
ax[0,0].set_xlim(5136.4, 5140.4)
ax[1,0].set_xlim(5136.4, 5140.4)
ax[1,0].set_ylim(-4,4)
ax[0,1].plot(wlsz23, fl23, "b")
ax[0,1].plot(wlsz23, f23, "r")
ax[1,1].plot(wlsz23, r23, "g")
ax[0,1].set_xlim(5188, 5189.5)
ax[1,1].set_xlim(5188, 5189.5)
#ax[1,1].set_ylim(-4,4)
ax[0,2].plot(wlsz24, fl24, "b", label='data')
ax[0,2].plot(wlsz24, f24, "r", label='model')
ax[0,2].legend(loc="lower center", prop={'size':10})
ax[1,2].plot(wlsz24, r24, "g")
ax[0,2].set_xlim(5258, 5260)
ax[1,2].set_xlim(5258, 5260)
ax[0,3].plot(wlsz24, fl24, "b")
ax[0,3].plot(wlsz24, f24, "r")
ax[1,3].plot(wlsz24, r24, "g")
ax[0,3].set_xlim(5260, 5271)
ax[1,3].set_xlim(5260, 5271)
ax[1,3].set_ylim(-15, 15)
for j in range(4):
labels = ax[1,j].get_xticklabels()
for label in labels:
label.set_rotation(60)
ax[0,0].set_ylabel(r"$\propto f_\lambda$")
ax[1,0].set_ylabel(r"Residuals$/\sigma_P$")
for i in range(2):
for j in range(4):
ax[i,j].xaxis.set_major_formatter(FSF("%.0f"))
ax[i,j].xaxis.set_major_locator(MultipleLocator(1.))
ax[i,j].tick_params(axis='both', which='major', labelsize=10)
for i in [0,1]:
for j in [1,2]:
ax[i,j].xaxis.set_major_formatter(FSF("%.1f"))
ax[i,j].xaxis.set_major_locator(MultipleLocator(0.5))
for i in [0,1]:
ax[i,3].xaxis.set_major_formatter(FSF("%.0f"))
ax[i,3].xaxis.set_major_locator(MultipleLocator(2))
class_label = ["0", "I", "II", "III"]
for j in range(4):
ax[0,j].set_title(class_label[j])
ax[0,j].xaxis.set_ticklabels([])
ax[0,j].set_ylim(0.25, 1.15)
if j != 0:
ax[0,j].yaxis.set_ticklabels([])
fig.subplots_adjust(left=0.09, right=0.99, top=0.94, bottom=0.18, hspace=0.1, wspace=0.27)
fig.text(0.48, 0.02, r"$\lambda$ (\AA)")
fig.savefig("plots/badlines.png")
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)
from StellarSpectra.spectrum import DataSpectrum
from StellarSpectra.grid_tools import TRES, HDF5Interface
import StellarSpectra.constants as C
import numpy as np
import sys
from emcee.utils import MPIPool
myDataSpectrum = DataSpectrum.open("../data/WASP14/WASP-14_2009-06-15_04h13m57s_cb.spec.flux", orders=np.array([22]))
myInstrument = TRES()
myHDF5Interface = HDF5Interface("../libraries/PHOENIX_submaster.hdf5")
stellar_Starting = {"temp":(6000, 6100), "logg":(3.9, 4.2), "Z":(-0.6, -0.3), "vsini":(4, 6), "vz":(15.0, 16.0), "logOmega":(-19.665, -19.664)}
stellar_tuple = C.dictkeys_to_tuple(stellar_Starting)
#cheb_Starting = {"c1": (-.02, -0.015), "c2": (-.0195, -0.0165), "c3": (-.005, .0)}
cheb_Starting = {"logc0": (-0.02, 0.02), "c1": (-.02, 0.02), "c2": (-0.02, 0.02), "c3": (-.02, 0.02)}
cov_Starting = {"sigAmp":(0.9, 1.1), "logAmp":(-14.4, -14), "l":(0.1, 0.25)}
cov_tuple = C.dictkeys_to_covtuple(cov_Starting)
myModel = Model(myDataSpectrum, myInstrument, myHDF5Interface, stellar_tuple=stellar_tuple, cov_tuple=cov_tuple)
def eval0():
myModel.evaluate()
def eval1():
myModel.evaluate()
