import numpy as np
import os
import matplotlib.pyplot as plt
from ..lib import util
from ..lib.plots import figure_filetype
[docs]def binned_lightcurve(meta, log, lc, i, white=False):
'''Plot each spectroscopic light curve. (Figs 4102)
Parameters
----------
meta : eureka.lib.readECF.MetaClass
The metadata object.
log : logedit.Logedit
The open log in which notes from this step can be added.
lc : Xarray Dataset
The Dataset object containing light curve and time data.
i : int
The current bandpass number.
white : bool, optional
Is this figure for the additional white-light light curve
'''
fig = plt.figure(4102, figsize=(8, 6))
fig.clf()
ax = fig.gca()
if white:
fig.suptitle(f'White-light Bandpass {i}: {meta.wave_min:.3f} - '
f'{meta.wave_max:.3f}')
# Normalize the light curve
norm_lcdata, norm_lcerr = util.normalize_spectrum(
meta, lc.flux_white, lc.err_white)
i = 0
fname_tag = 'white'
else:
fig.suptitle(f'Bandpass {i}: {lc.wave_low.values[i]:.3f} - '
f'{lc.wave_hi.values[i]:.3f}')
# Normalize the light curve
norm_lcdata, norm_lcerr = util.normalize_spectrum(meta, lc['data'][i],
lc['err'][i])
ch_number = str(i).zfill(int(np.floor(np.log10(meta.nspecchan))+1))
fname_tag = f'ch{ch_number}'
time_modifier = np.floor(np.ma.min(lc.time.values))
# Plot the normalized light curve
if meta.inst == 'wfc3':
for p in range(2):
iscans = np.where(lc.scandir.values == p)[0]
if len(iscans) > 0:
ax.errorbar(lc.time.values[iscans]-time_modifier,
norm_lcdata[iscans]+0.005*p,
norm_lcerr[iscans], fmt='o', color=f'C{p}',
mec=f'C{p}', alpha=0.2)
mad = util.get_mad_1d(norm_lcdata[iscans])
meta.mad_s4_binned.append(mad)
log.writelog(f' MAD = {np.round(mad).astype(int)} ppm')
plt.text(0.05, 0.075+0.05*p,
f"MAD = {np.round(mad).astype(int)} ppm",
transform=ax.transAxes, color=f'C{p}')
else:
plt.errorbar(lc.time.values-time_modifier, norm_lcdata, norm_lcerr,
fmt='o', color=f'C{i}', mec=f'C{i}', alpha=0.2)
mad = util.get_mad_1d(norm_lcdata)
meta.mad_s4_binned.append(mad)
log.writelog(f' MAD = {np.round(mad).astype(int)} ppm')
plt.text(0.05, 0.1, f"MAD = {np.round(mad).astype(int)} ppm",
transform=ax.transAxes, color='k')
plt.ylabel('Normalized Flux')
time_units = lc.data.attrs['time_units']
plt.xlabel(f'Time [{time_units} - {time_modifier}]')
fig.subplots_adjust(left=0.10, right=0.95, bottom=0.10, top=0.90,
hspace=0.20, wspace=0.3)
fname = f'figs{os.sep}Fig4102_{fname_tag}_1D_LC'+figure_filetype
fig.savefig(meta.outputdir+fname, bbox_inches='tight', dpi=300)
if not meta.hide_plots:
plt.pause(0.2)
[docs]def driftxpos(meta, lc):
'''Plot the 1D drift/jitter results. (Fig 4103)
Parameters
----------
meta : eureka.lib.readECF.MetaClass
The metadata object.
lc : Xarray Dataset
The light curve object containing drift arrays.
Notes
-----
History:
- Jul 11, 2022 Caroline Piaulet
Edited this function to use the new naming convention for drift
'''
plt.figure(4103, figsize=(8, 4))
plt.clf()
plt.plot(np.arange(meta.n_int)[np.where(~lc.driftmask)],
lc.driftxpos[np.where(~lc.driftmask)], '.',
label='Good Drift Points')
plt.plot(np.arange(meta.n_int)[np.where(lc.driftmask)],
lc.driftxpos[np.where(lc.driftmask)], '.',
label='Interpolated Drift Points')
plt.ylabel('Spectrum Drift Along x')
plt.xlabel('Frame Number')
plt.legend(loc='best')
plt.tight_layout()
fname = 'figs'+os.sep+'fig4103_DriftXPos'+figure_filetype
plt.savefig(meta.outputdir+fname, bbox_inches='tight', dpi=300)
if not meta.hide_plots:
plt.pause(0.2)
[docs]def driftxwidth(meta, lc):
'''Plot the 1D drift width results. (Fig 4104)
Parameters
----------
meta : eureka.lib.readECF.MetaClass
The metadata object.
lc : Xarray Dataset
The light curve object containing drift arrays.
Returns
-------
None
Notes
-----
History:
- Jul 11, 2022 Caroline Piaulet
Created this function
'''
plt.figure(4104, figsize=(8, 4))
plt.clf()
plt.plot(np.arange(meta.n_int)[np.where(~lc.driftmask)],
lc.driftxwidth[np.where(~lc.driftmask)], '.',
label='Good Drift Points')
plt.plot(np.arange(meta.n_int)[np.where(lc.driftmask)],
lc.driftxwidth[np.where(lc.driftmask)], '.',
label='Interpolated Drift Points')
plt.ylabel('Spectrum Drift CC Width Along x')
plt.xlabel('Frame Number')
plt.legend(loc='best')
plt.tight_layout()
fname = 'figs'+os.sep+'fig4104_DriftXWidth'+figure_filetype
plt.savefig(meta.outputdir+fname, bbox_inches='tight', dpi=300)
if not meta.hide_plots:
plt.pause(0.2)
[docs]def lc_driftcorr(meta, wave_1d, optspec, optmask=None):
'''Plot a 2D light curve with drift correction. (Fig 4101)
Parameters
----------
meta : eureka.lib.readECF.MetaClass
The metadata object.
wave_1d : ndarray
Wavelength array with trimmed edges depending on xwindow and ywindow
which have been set in the S3 ecf.
optspec : ndarray
The optimally extracted spectrum.
optmask : ndarray (1D), optional
A mask array to use if optspec is not a masked array. Defaults to None
in which case only the invalid values of optspec will be masked.
'''
optspec = np.ma.masked_invalid(optspec)
optspec = np.ma.masked_where(optmask, optspec)
wmin = meta.wave_min
wmax = meta.wave_max
iwmin = np.nanargmin(np.abs(wave_1d-wmin).values)
iwmax = np.nanargmin(np.abs(wave_1d-wmax).values)
# Normalize the light curve
norm_lcdata = util.normalize_spectrum(meta, optspec[:, iwmin:iwmax])
if not hasattr(meta, 'vmin') or meta.vmin is None:
meta.vmin = 0.97
if not hasattr(meta, 'vmax') or meta.vmin is None:
meta.vmax = 1.03
if not hasattr(meta, 'time_axis') or meta.time_axis is None:
meta.time_axis = 'y'
elif meta.time_axis not in ['y', 'x']:
print("WARNING: meta.time_axis is not one of ['y', 'x']!"
"Using 'y' by default.")
meta.time_axis = 'y'
plt.figure(4101, figsize=(8, 8))
plt.clf()
if meta.time_axis == 'y':
plt.imshow(norm_lcdata, origin='lower', aspect='auto',
extent=[wmin, wmax, 0, meta.n_int], vmin=meta.vmin,
vmax=meta.vmax, cmap=plt.cm.RdYlBu_r)
plt.ylabel('Integration Number')
plt.xlabel(r'Wavelength ($\mu m$)')
plt.colorbar(label='Normalized Flux')
if len(meta.wave_low) > 1:
# Insert vertical dashed lines at spectroscopic channel edges
secax = plt.gca().secondary_xaxis('top')
xticks = np.unique(np.concatenate([meta.wave_low, meta.wave_hi]))
xticks_labels = [f'{np.round(xtick, 4):.4f}' for xtick in xticks]
secax.set_xticks(xticks, xticks_labels, rotation=90,
fontsize='xx-small')
plt.vlines(xticks, 0, meta.n_int, '0.3', 'dashed')
else:
plt.imshow(norm_lcdata.swapaxes(0, 1), origin='lower', aspect='auto',
extent=[0, meta.n_int, wmin, wmax], vmin=meta.vmin,
vmax=meta.vmax, cmap=plt.cm.RdYlBu_r)
plt.ylabel(r'Wavelength ($\mu m$)')
plt.xlabel('Integration Number')
plt.colorbar(label='Normalized Flux', pad=0.075)
if len(meta.wave_low) > 1:
# Insert vertical dashed lines at spectroscopic channel edges
secax = plt.gca().secondary_yaxis('right')
yticks = np.unique(np.concatenate([meta.wave_low, meta.wave_hi]))
yticks_labels = [f'{np.round(ytick, 4):.4f}' for ytick in yticks]
secax.set_yticks(yticks, yticks_labels, rotation=0,
fontsize='xx-small')
plt.hlines(yticks, 0, meta.n_int, '0.3', 'dashed')
plt.title(f"MAD = {np.round(meta.mad_s4).astype(int)} ppm")
plt.tight_layout()
fname = 'figs'+os.sep+'fig4101_2D_LC'+figure_filetype
plt.savefig(meta.outputdir+fname, bbox_inches='tight', dpi=300)
if meta.hide_plots:
plt.close()
else:
plt.pause(0.2)
return
[docs]def cc_spec(meta, ref_spec, fit_spec, n):
'''Compare the spectrum used for cross-correlation with the current
spectrum (Fig 4301).
Parameters
----------
meta : eureka.lib.readECF.MetaClass
The metadata object.
ref_spec : ndarray (1D)
The reference spectrum used for cross-correlation.
fit_spec : ndarray (1D)
The extracted spectrum for the current integration.
n : int
The current integration number.
'''
plt.figure(4301, figsize=(8, 8))
plt.clf()
plt.title(f'Cross Correlation - Spectrum {n}')
nx = len(ref_spec)
plt.plot(np.arange(nx), ref_spec, '-', label='Reference Spectrum')
plt.plot(np.arange(meta.drift_range, nx-meta.drift_range), fit_spec, '-',
label='Current Spectrum')
plt.legend(loc='best')
plt.tight_layout()
int_number = str(n).zfill(int(np.floor(np.log10(meta.n_int))+1))
fname = 'figs'+os.sep+f'fig4301_int{int_number}_CC_Spec'+figure_filetype
plt.savefig(meta.outputdir+fname, bbox_inches='tight', dpi=300)
if not meta.hide_plots:
plt.pause(0.2)
[docs]def cc_vals(meta, vals, n):
'''Make the cross-correlation strength plot (Fig 4302).
Parameters
----------
meta : eureka.lib.readECF.MetaClass
The metadata object.
vals : ndarray (1D)
The cross-correlation strength.
n : int
The current integration number.
'''
plt.figure(4302, figsize=(8, 8))
plt.clf()
plt.title(f'Cross Correlation - Values {n}')
plt.plot(np.arange(-meta.drift_range, meta.drift_range+1), vals, '.')
plt.tight_layout()
int_number = str(n).zfill(int(np.floor(np.log10(meta.n_int))+1))
fname = 'figs'+os.sep+f'fig4302_int{int_number}_CC_Vals'+figure_filetype
plt.savefig(meta.outputdir+fname, bbox_inches='tight', dpi=300)
if not meta.hide_plots:
plt.pause(0.2)