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plot_miri_ramp.py
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plot_miri_ramp.py
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import argparse
import matplotlib.pyplot as plt
import matplotlib as mpl
import numpy as np
from astropy.io import fits
from astropy.modeling.models import Linear1D
from astropy.modeling.fitting import LinearLSQFitter
from miri_ramp_utils import get_ramp, get_good_ramp, fit_diffs, calc_lincor
if __name__ == "__main__":
# commandline parser
parser = argparse.ArgumentParser()
parser.add_argument(
"--pixel",
help="x y pixel values",
metavar=("x", "y"),
type=int,
nargs=2,
default=[512, 512],
)
parser.add_argument(
"--nrej", help="number of groups to ignore in linear fit", type=int, default=24
)
parser.add_argument(
"--primeonly", help="plot the primary exposure", action="store_true"
)
parser.add_argument("--png", help="save figure as an png file", action="store_true")
parser.add_argument("--pdf", help="save figure as a pdf file", action="store_true")
args = parser.parse_args()
all_filenames = [
"Data/MIRI_5692_18_S_20191017-193412_SCE1.fits",
"Data/MIRI_5692_17_S_20191017-184107_SCE1.fits",
"Data/MIRI_5692_19_S_20191017-202738_SCE1.fits",
"Data/MIRI_5692_20_S_20191017-212044_SCE1.fits",
"Data/MIRI_5692_21_S_20191017-221350_SCE1.fits",
"Data/MIRI_5694_21_S_20191018-170349_SCE1.fits",
"Data/MIRI_5694_22_S_20191018-172524_SCE1.fits",
"Data/MIRI_5694_23_S_20191018-174658_SCE1.fits",
"Data/MIRI_5694_24_S_20191018-180833_SCE1.fits",
"Data/MIRI_5694_25_S_20191018-183008_SCE1.fits",
]
all_filenames = ['Data/MIRI_5700_18_S_20191022-225042_SCE1.fits']
all_filenames = ['Data/MIRI_5701_238_S_20191023-215644_SCE1.fits']
all_filenames = [ # "Data/MIRI_5708_80_S_20191027-053806_SCE1.fits",
"Data/MIRI_5708_137_S_20191027-201705_SCE1.fits",
"Data/MIRI_5708_153_S_20191027-223137_SCE1.fits",
"Data/MIRI_5709_34_S_20191029-011921_SCE1.fits",
"Data/MIRI_5709_32_S_20191029-010437_SCE1.fits",
"Data/MIRI_5709_30_S_20191029-004923_SCE1.fits",
"Data/MIRI_5709_28_S_20191029-003349_SCE1.fits",
"Data/MIRI_5709_24_S_20191029-000141_SCE1.fits",
"Data/MIRI_5709_18_S_20191028-231009_SCE1.fits"]
rampoffvals = [0.0, 0.0,
-4900., -4900., -4900., -4900., -4900., -4900.]
rampoffvals = np.zeros((len(all_filenames)))
# all_filenames = ["Data/MIRI_5709_18_S_20191028-231009_SCE1.fits"]
# all_filenames = all_filenames[::-1]
hdu = fits.open(all_filenames[0], memmap=False)
fig, sax = plt.subplots(ncols=4, nrows=2, figsize=(18, 9))
# fig, sax = plt.subplots(ncols=4, nrows=2, figsize=(8, 6))
ax = [
sax[0, 0],
sax[1, 0],
sax[1, 1],
sax[0, 1],
sax[0, 2],
sax[1, 2],
sax[1, 3],
sax[0, 3],
]
# plotting setup for easier to read plots
# fontsize = 18
fontsize = 8
font = {"size": fontsize}
mpl.rc("font", **font)
mpl.rc("lines", linewidth=1)
mpl.rc("axes", linewidth=2)
mpl.rc("xtick.major", width=2)
mpl.rc("xtick.minor", width=2)
mpl.rc("ytick.major", width=2)
mpl.rc("ytick.minor", width=2)
pix_x, pix_y = args.pixel
ngrps = hdu[0].header["NGROUPS"]
nints = hdu[0].header["NINT"]
nrej = args.nrej
# for fitting
x = []
y = []
# for plotting
pcol = ["b", "g", "r", "c", "y", "b", "g", "r", "c", "y"]
# plot all integrations folded
mm_delta = 0.0
max_ramp_k = -1
rampoffval = rampoffvals[0]
# print("# ints = ", nints)
for k in range(nints):
gnum, ydata = get_ramp(hdu[0], pix_x, pix_y, k, rampoffval=rampoffvals[0])
ggnum, gdata, aveDN, diffDN = get_good_ramp(gnum, ydata)
ax[0].plot(gnum, ydata, label=f"Int #{k+1}", color=pcol[k])
# plot the 2pt diffs
ax[1].plot(ggnum[:-1], diffDN, label=f"Int #{k+1}", color=pcol[k])
# plot the 2pt diffs versus average DN
ax[2].plot(aveDN, diffDN, label=f"Int #{k+1}", color=pcol[k])
if k == 0:
ax[1].set_ylim(0.9 * min(diffDN), 1.1 * max(diffDN))
ax[2].set_ylim(0.9 * min(diffDN), 1.1 * max(diffDN))
# accumulate data for later plotting
x.append(aveDN)
y.append(diffDN)
# find the ramp that spans the largest range of DN
# and save some info -> needed for creating the correction
# if (gdata.max() - gdata.min()) > mm_delta:
# mm_delta = gdata.max() - gdata.min()
if k == 3:
max_ramp_k = k
max_ramp_gdata = gdata
max_ramp_aveDN = aveDN
# fit the aveDN versus diffDN combined data from all integrations
x = np.concatenate(x)
y = np.concatenate(y)
mod = fit_diffs(x, y)
polymod = mod[2]
# plot the model
mod_x = np.linspace(0.0, 65000.0, num=100)
# for k in range(nints):
# gnum, ydata = get_ramp(hdu[0], pix_x, pix_y, k)
# ggnum, gdata, aveDN, diffDN = get_good_ramp(gnum, ydata)
# (DN_exp, cor, cor_mod) = calc_lincor(mod[1], gdata, args.startDN)
# ax[3].plot(DN_exp, cor, "--", label=f"Int #{k+1}")
startDNvals = np.arange(0.0, 20000.0, 200.0)
chival = np.zeros((nints, len(startDNvals)))
ints = range(nints)
for i, startDN in enumerate(startDNvals):
(DN_exp, cor, cor_mod) = calc_lincor(polymod, max_ramp_aveDN, startDN)
for k in ints:
gnum, ydata = get_ramp(hdu[0], pix_x, pix_y, k, rampoffval=rampoffvals[0])
ggnum, gdata, aveDN, diffDN = get_good_ramp(gnum, ydata)
# correct the ramps
ycor = cor_mod(gdata)
gdata_cor = gdata * ycor
# calculate the chisqr for each integration set of differences
# from the expected flat line
diffDN = np.diff(gdata_cor)
aveDN = 0.5 * (gdata[:-1] + gdata[1:])
cindxs, = np.where(aveDN > 10000.0)
chival[k, i] = np.sum((diffDN[aveDN > 15000.0] - polymod.c0) ** 2)
minindx = np.zeros((nints), dtype=int)
for k in ints[1:]:
# ax[6].plot(startDNvals, chival[k, :], label=f"Int #{k+1}", color=pcol[k])
minindx[k] = np.argmin(chival[k, :])
startDN = startDNvals[minindx[max_ramp_k]]
# get the correction
(DN_exp, cor, cor_mod) = calc_lincor(polymod, max_ramp_aveDN, startDN)
ax[3].plot(DN_exp, cor, "ko", label=f"Int #{max_ramp_k+1} StartDN={startDN:.1f}")
ax[3].plot(mod_x, cor_mod(mod_x), "k--", label="Cor Poly1D")
# apply the correction
line_init = Linear1D()
fit_line = LinearLSQFitter()
intslopes = np.zeros((nints))
linfit_metric = np.zeros((nints))
for k in range(nints):
gnum, ydata = get_ramp(hdu[0], pix_x, pix_y, k, rampoffval=rampoffvals[0])
ggnum, gdata, aveDN, diffDN = get_good_ramp(gnum, ydata)
# correct the ramps and plot
ycor = cor_mod(gdata)
gdata_cor = gdata * ycor
ax[0].plot(ggnum, gdata_cor, "--", label=f"Cor Int #{k+1}", color=pcol[k])
# plot the corrected ramp divided by a linear fit
line_mod = fit_line(line_init, ggnum[nrej:], gdata_cor[nrej:])
intslopes[k] = line_mod.slope.value
linfit_ratio = gdata_cor / line_mod(ggnum)
ax[4].plot(ggnum, linfit_ratio, "--", label=f"Int #{k+1}", color=pcol[k])
ax[4].plot(ggnum, np.full((len(ggnum)), 1.0), "k--")
# compute metric on deviations from the linear fit
linfit_metric[k] = np.sum(np.power(linfit_ratio[nrej:] - 1.0, 2.0)) / len(
linfit_ratio[nrej:]
)
# plot the 2pt diffs versus average DN
diffDN = np.diff(gdata_cor)
aveDN = 0.5 * (gdata[:-1] + gdata[1:])
ax[5].plot(aveDN, diffDN, label=f"Int #{k+1}", color=pcol[k])
# diffDN_orig = np.diff(gdata)
# ax[2].plot(aveDN, diffDN_orig - mod[0](aveDN), '--')
if k == 0:
ax[5].set_ylim(0.9 * min(diffDN), 1.1 * max(diffDN))
aveslope = np.average(intslopes)
ax[7].plot(
np.array(ints) + 1,
intslopes / aveslope,
"ko",
label=f"Exp 1: Ave = {aveslope:.2f}",
)
ax[6].plot(
np.array(ints) + 1,
np.sqrt(linfit_metric),
"ko",
label=f"Exp 1: Ave = {aveslope:.2f}",
)
# ***
if args.primeonly:
filenames = []
else:
filenames = all_filenames[1:]
lin_off_val = 0.01
prev_ints = nints
for z, cfile in enumerate(filenames):
hdu = fits.open(cfile)
nints = hdu[0].header["NINT"]
ints = range(nints)
# off_int = (z + 1) * nints
off_int = prev_ints
prev_ints += nints
# plot all integrations folded
for k in range(nints):
gnum, ydata = get_ramp(hdu[0], pix_x, pix_y, k, rampoffval=rampoffvals[z + 1])
ggnum, gdata, aveDN, diffDN = get_good_ramp(gnum, ydata)
ax[0].plot(gnum, ydata, color=pcol[k])
# plot the 2pt diffs
ax[1].plot(ggnum[:-1], diffDN, color=pcol[k])
# plot the 2pt diffs versus average DN
ax[2].plot(aveDN, diffDN, color=pcol[k])
# apply the correction
line_init = Linear1D()
fit_line = LinearLSQFitter()
intslopes = np.zeros((nints))
linfit_metric = np.zeros((nints))
for k in range(nints):
gnum, ydata = get_ramp(hdu[0], pix_x, pix_y, k, rampoffval=rampoffvals[z + 1])
ggnum, gdata, aveDN, diffDN = get_good_ramp(gnum, ydata)
# correct the ramps and plot
ycor = cor_mod(gdata)
gdata_cor = gdata * ycor
ax[0].plot(ggnum, gdata_cor, "--", color=pcol[k])
# plot the corrected ramp divided by a linear fit
line_mod = fit_line(line_init, ggnum[nrej:], gdata_cor[nrej:])
intslopes[k] = line_mod.slope.value
linfit_ratio = gdata_cor / line_mod(ggnum)
ax[4].plot(ggnum, linfit_ratio + (z + 1) * lin_off_val, "--", color=pcol[k])
ax[4].plot(ggnum, (z + 1) * lin_off_val + np.full((len(ggnum)), 1.0), "k--")
# compute metric on deviations from the linear fit
linfit_metric[k] = np.sum(np.power(linfit_ratio[nrej:] - 1.0, 2.0)) / len(
linfit_ratio[nrej:]
)
# plot the 2pt diffs versus average DN
diffDN = np.diff(gdata_cor)
aveDN = 0.5 * (gdata[:-1] + gdata[1:])
ax[5].plot(aveDN, diffDN)
aveslope = np.average(intslopes)
ax[7].plot(
np.array(ints) + 1 + off_int,
intslopes / aveslope,
"o",
label=f"Exp {z+2}: Ave = {aveslope:.2f}",
)
ax[6].plot(
np.array(ints) + 1 + off_int,
np.sqrt(linfit_metric),
"o",
label=f"Exp {z+2}: Ave = {aveslope:.2f}",
)
# ***
ax[2].plot(mod_x, mod(mod_x), "k--", label="Exp1D+Poly1D")
ax[2].plot(mod_x, polymod(mod_x), "k-.", label="Poly1D only")
ax[5].plot(ax[5].get_xlim(), [polymod.c0, polymod.c0], "k--", label="c_0")
ax[7].set_xlabel("integration #")
ax[7].set_ylabel("slope / ave")
ax[7].set_ylim(0.99, 1.05)
# finish the plots
ax[0].set_xlabel("group #")
ax[0].set_ylabel("DN")
ax[4].set_xlabel("group #")
ax[4].set_ylabel("DN_cor/line_fit")
ax[4].set_ylim(0.99, 1.01 + lin_off_val * len(filenames))
ax[4].plot(nrej * np.full((2), 1.0), ax[4].get_ylim(), "k--", label="nrej")
ax[1].set_xlabel("group #")
ax[1].set_ylabel("DN/group")
ax[5].set_xlabel("DN")
ax[5].set_ylabel("DN_cor/group")
ax[2].set_xlabel("DN")
ax[2].set_ylabel("DN/group")
ax[3].set_xlabel("DN")
ax[3].set_ylabel("Mult Correction")
ax[6].set_xlabel("integration #")
ax[6].set_ylabel(r"$\sigma$ dev from linfit per group")
for k in range(len(ax)):
ax[k].legend()
ax[7].legend(ncol=2)
ax[6].legend().set_visible(False)
fig.suptitle(f"{all_filenames[0]}; Pixel ({pix_x}, {pix_y})")
fig.tight_layout(rect=[0, 0.03, 1, 0.95])
out_basename = f"plot_miri_ramp_{pix_x}_{pix_y}_nreg{args.nrej+1}"
if args.primeonly:
out_basename += "_primeonly"
if args.png:
fig.savefig(out_basename)
elif args.pdf:
fig.savefig(out_basename)
else:
plt.show()