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plot_miri_rscd_framereset.py
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plot_miri_rscd_framereset.py
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import argparse
import matplotlib.pyplot as plt
import matplotlib as mpl
from matplotlib.lines import Line2D
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, lincor_data
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=4
)
parser.add_argument(
"--nmax", help="max number groups in linear fit", type=int, default=None
)
parser.add_argument(
"--keepfirst", help="Include the first frame", action="store_true"
)
parser.add_argument(
"--subarray",
choices=["FULL", "4QPM", "SUB64", "SLOW"],
default="FULL",
help="subarray indicates data used",
)
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()
noexp = False
plot_skip = False
max_fr_group = 20
if args.subarray == "SUB64":
all_filenames = [
"Data/MIRI_5709_112_S_20191029-090719_SCE1.fits",
"Data/MIRI_5709_114_S_20191029-092123_SCE1.fits",
"Data/MIRI_5709_120_S_20191029-100335_SCE1.fits",
"Data/MIRI_5709_122_S_20191029-101739_SCE1.fits",
# "Data/MIRI_5709_128_S_20191029-110020_SCE1.fits",
]
frameresets = [0, 1, 4, 8] # , 30]
# ptitle = "SCA106 5709(112, 114, 120, 122, 128) SUB64 subarray"
ptitle = "SCA106 5709(112, 114, 120, 122) SUB64 subarray"
elif args.subarray == "4QPM":
all_filenames = [
"Data/MIRI_5709_76_S_20191029-043558_SCE1.fits",
"Data/MIRI_5709_78_S_20191029-045032_SCE1.fits",
"Data/MIRI_5709_84_S_20191029-053444_SCE1.fits",
"Data/MIRI_5709_86_S_20191029-054928_SCE1.fits",
# "Data/MIRI_5709_92_S_20191029-063439_SCE1.fits",
]
frameresets = [0, 1, 4, 8] # , 30]
# ptitle = "SCA106 5709(76, 78, 84, 86, 92) 4QPM subarray"
ptitle = "SCA106 5709(76, 78, 84, 86) 4QPM subarray"
elif args.subarray == "SLOW":
all_filenames = [
"Data/MIRI_5708_42_S_20191026-173002_SCE1.fits",
"Data/MIRI_5708_41_S_20191026-170526_SCE1.fits",
"Data/MIRI_5708_40_S_20191026-165240_SCE1.fits",
]
frameresets = [0, 0, 0]
ptitle = "SCA106 5708(42) FULL SLOW"
plot_skip = False
max_fr_group = 10
else:
all_filenames = [
"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_18_S_20191028-231009_SCE1.fits",
]
frameresets = [0, 1, 2, 3, 10]
ptitle = "SCA106 5709(34, 32, 30 28, 18) FULL"
# noexp = True
n_files = len(all_filenames)
psize = 3
fig, sax = plt.subplots(
ncols=n_files, nrows=3, figsize=(psize * n_files, 3 * psize), sharey="row",
)
# plotting setup for easier to read plots
fontsize = 14
font = {"size": fontsize}
mpl.rc("font", **font)
mpl.rc("lines", linewidth=2)
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)
for j in range(n_files):
for k in range(3):
sax[k, j].tick_params(axis='both', which='major', labelsize=fontsize)
aveslope = 0.0
fr0_ggnum = []
fr0_gdata_cor = []
for z, cfile in enumerate(all_filenames):
# open the fits file
hdu = fits.open(cfile, memmap=False)
pix_x, pix_y = args.pixel
ngrps = hdu[0].header["NGROUPS"]
nints = min([10, hdu[0].header["NINT"]])
nrej = args.nrej
# for fitting
x = []
y = []
# for plotting
pcol = plt.cm.jet(np.linspace(0, 1, nints))
# plot all integrations folded
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)
sax[0, z].plot(gnum, ydata, label=f"Int #{k+1}", color=pcol[k])
# plot the 2pt diffs versus average DN
# ax[1].plot(aveDN, diffDN, label=f"Int #{k+1}", color=pcol[k])
# if k == 0:
# ax[1].set_ylim(0.9 * min(diffDN), 1.4 * max(diffDN))
# accumulate data for fitting
x.append(aveDN)
y.append(diffDN)
if z == 0:
# fit the aveDN versus diffDN combined data from all integrations
# e.g., derive the non-linearity correction
x = np.concatenate(x)
y = np.concatenate(y)
mod = fit_diffs(x, y, noexp=noexp)
if noexp:
polymod = mod
else:
polymod = mod[2]
lincormod = polymod
# more plots
ints = range(nints)
# setup ramp fit
line_init = Linear1D()
fit_line = LinearLSQFitter()
mult_comp = False
intslopes = np.zeros((nints))
linfit_metric = np.zeros((nints))
intexpamp = np.zeros((nints))
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, keepfirst=args.keepfirst, nmax=args.nmax
)
# correct the ramps and plot
gdata_cor = lincor_data(lincormod, gdata, aveDN, diffDN)
# plot the linearied 2pt diffs versus average DN
diffDN = np.diff(gdata_cor)
aveDN = 0.5 * (gdata[:-1] + gdata[1:])
avegnum = 0.5 * (ggnum[:-1] + ggnum[1:])
sax[1, z].plot(avegnum, diffDN, color=pcol[k])
# compute 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)
# 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 slope of the framereset=0 data with the same number of frames
# rejected as the current data framereset value
if z == 0:
fr0_ggnum.append(ggnum)
fr0_gdata_cor.append(gdata_cor)
else:
fr0_nrej = nrej + frameresets[z]
if fr0_nrej < len(fr0_ggnum[k]) - 2:
line_mod = fit_line(
line_init, fr0_ggnum[k][fr0_nrej:], fr0_gdata_cor[k][fr0_nrej:]
)
if plot_skip:
if k == 0:
intslope_nrej_fr = line_mod.slope.value
sax[2, z].plot(
[ints[k] + 1],
[line_mod.slope.value] / intslope_nrej_fr,
"ko",
fillstyle="none",
markersize=10,
)
for k in range(nints):
sax[2, z].plot(
[ints[k] + 1],
[intslopes[k] / intslopes[0]],
"ko",
label=f"Int #{ints[k]+1} slope = {intslopes[k]:.2f} DN/group",
color="k", # pcol[k],
)
# plot the model
mod_x = np.linspace(0.0, 65000.0, num=100)
# sax[z, 1].plot(mod_x, mod(mod_x), "k--", label="Exp1D+Poly1D")
# ax[1].plot(mod_x, polymod(mod_x), "k-.", label="Poly1D only")
# finish the plots
sax[0, z].set_title(f"FRAMERESETS={frameresets[z]}", fontdict=font)
sax[0, z].set_xlabel("group #", fontdict=font)
sax[1, z].set_xlabel("group #", fontdict=font)
sax[2, z].set_xlabel("integration #", fontdict=font)
sax[1, z].set_xlim(0, max_fr_group)
# sax[2, z].set_ylim(0.98, 1.02)
if z == 0:
sax[0, z].set_ylabel("DN", fontdict=font)
sax[1, z].set_ylabel("DN/group", fontdict=font)
sax[2, z].set_ylabel("slope / slope(int=1)", fontdict=font)
sax[0, z].legend(fontsize=8)
# ax[3].set_xlabel("DN")
# ax[3].set_ylabel("DN(linearized)/group")
# ax[1].legend(fontsize=9)
# ax[2].legend(fontsize=9)
# custom legend for the bottom row of plots
if plot_skip:
legend_elements = [
Line2D(
[0],
[0],
marker="o",
color="w",
label="FR=N data",
fillstyle="none",
markerfacecolor="k",
markersize=8,
),
Line2D(
[0],
[0],
marker="o",
color="w",
label="FR=0 skip=N",
fillstyle="none",
markeredgecolor="k",
markersize=10,
),
]
sax[2, 0].legend(handles=legend_elements)
fig.suptitle(f"{ptitle}; Pixel ({pix_x}, {pix_y})")
fig.tight_layout(rect=[0, 0.03, 1, 0.95])
out_basename = f"plot_miri_rscd_frameresest_{args.subarray}_{pix_x}_{pix_y}"
if args.png:
fig.savefig(out_basename + ".png")
elif args.pdf:
fig.savefig(out_basename + ".pdf")
else:
plt.show()