class InstrumentFile(Instrument):
"""
Loads a 2-3 column file with wlgrid
and noise and maybe wlgrid
Parameters
----------
filename: str
Filename of file containing the binning and error
"""
def __init__(self, filename=None):
super().__init__()
self._spectrum = np.loadtxt(filename)
self._wlgrid = self._spectrum[:, 0]
sortedwl = self._wlgrid.argsort()[::-1]
self._wlgrid = self._wlgrid[sortedwl]
self._wngrid = 10000 / self._wlgrid
self._noise = self._spectrum[sortedwl, 1]
try:
self._wlwidths = self._spectrum[sortedwl, 2]
except IndexError:
from taurex.util.util import compute_bin_edges
self._wlwidths - compute_bin_edges(self._wlgrid)[-1]
self.create_wn_widths()
self._binner = FluxBinner(self._wngrid, wngrid_width=self._wnwidths)
def create_wn_widths(self):
self._wnwidths = wnwidth_to_wlwidth(self._wlgrid, self._wlwidths)
def model_noise(self, model, model_res=None, num_observations=1):
if model_res is None:
model_res = model.model()
wngrid, spectrum, error, grid_width = self._binner.bin_model(model_res)
return wngrid, spectrum, self._noise / math.sqrt(
num_observations), grid_width
@classmethod
def input_keywords(self):
return [
'file',
'fromfile',
]
class ExampleInstrument(Instrument):
"""
An example implementation of an instrument
An instument function thats uses the WFC3
spectral grid and applies a gaussian noise
with a scale ``noise_scale`` for each spectrum
point.
"""
def __init__(self, noise_scale=1):
super().__init__()
self._scale = noise_scale
# Wavelength and widths for WFC3
wfc3_grid = np.array([
1.126, 1.156, 1.184, 1.212, 1.238, 1.265, 1.292, 1.318, 1.345,
1.372, 1.399, 1.428, 1.457, 1.487, 1.518, 1.551, 1.586, 1.623,
])
wfc3_wlwidths = np.array([
3.079e-2, 2.930e-2, 2.790e-2, 2.689e-2, 2.649e-2, 2.689e-2,
2.670e-2, 2.629e-2, 2.649e-2, 2.739e-2, 2.800e-2, 2.849e-2,
2.940e-2, 3.079e-2, 3.180e-2, 3.370e-2, 3.600e-2, 3.899e-2,
])
# convert to wavenumber widths
wfc3_wnwidths = wnwidth_to_wlwidth(wfc3_grid, wfc3_wlwidths)
self._wfc3_size = wfc3_grid.shape[0]
# Create our grid resampler
self._binner = FluxBinner(wngrid=10000/wfc3_grid,
wngrid_width=wfc3_wnwidths)
def model_noise(self, model, model_res=None, num_observations=1):
if model_res is None:
model_res = model.model()
# Generate our noise
noise = np.random.normal(scale=self._scale, size=self._wfc3_size)
# Bin down our model
wngrid, depth, _, widths = self._binner.bin_model(model_res)
return wngrid, depth, noise, widths
data = ascii.read("0000_HD 209458 b.csv")
wlgrid0, noise0 = (np.array([]) for k in range(2))
for i in range(len(data)):
wlgrid0 = np.append(wlgrid0, data[i][1])
noise0 = np.append(noise0, data[i][6]) # noise on transit floor
###################### NUMBER OF TRANSITS #######################
num_transits = 1
noise0 = noise0 / np.sqrt(num_transits)
#################################################################
from taurex.binning import FluxBinner
wngrid3 = 10000 / wlgrid0
wngrid3 = np.sort(wngrid3)
bn = FluxBinner(wngrid=wngrid3)
bin_wn, bin_rprs3, _, _ = bn.bin_model(tm.model(wngrid=wngrid3))
x = np.flip(10000 / bin_wn)
y = np.flip(bin_rprs3)
#%%
# =============================================================================
# BINDOWN FROM TIER3 TO TIER2
# =============================================================================
# Load the spectral data, the first column is wavelength, second flux density and third flux density uncertainty
tier2file = np.loadtxt('tier2-grid.txt')
wl = x
wlbinned = tier2file[:, 0]
spectrum = y
noise = noise0