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Merge pull request #13 from Autostronomy/initR
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Add Init r setting for config file
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@ConnorStoneAstro
ConnorStoneAstro authored Mar 6, 2024
2 parents 0024fd4 + 738d763 commit 8b5e9a1
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Showing 8 changed files with 492 additions and 429 deletions.
289 changes: 176 additions & 113 deletions autoprof/Pipeline.py
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4 changes: 2 additions & 2 deletions autoprof/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,10 +3,11 @@

from . import autoprofutils, Pipeline, pipeline_steps

__version__ = "1.0.2"
__version__ = "1.1.0"
__author__ = "Connor Stone"
__email__ = "[email protected]"


def run_from_terminal():

assert (
Expand All @@ -26,4 +27,3 @@ def run_from_terminal():
PIPELINE = Pipeline.Isophote_Pipeline(loggername=logfile)

PIPELINE.Process_ConfigFile(config_file)

145 changes: 57 additions & 88 deletions autoprof/autoprofutils/Diagnostic_Plots.py
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Original file line number Diff line number Diff line change
Expand Up @@ -45,14 +45,11 @@
"Plot_EllipseModel",
)


def Plot_Background(values, bkgrnd, noise, results, options):

hist, bins = np.histogram(
values[
np.logical_and(
(values - bkgrnd) < 20 * noise, (values - bkgrnd) > -5 * noise
)
],
values[np.logical_and((values - bkgrnd) < 20 * noise, (values - bkgrnd) > -5 * noise)],
bins=max(10, int(np.sqrt(len(values)) / 2)),
)
plt.figure(figsize=(5, 5))
Expand Down Expand Up @@ -90,23 +87,23 @@ def Plot_Background(values, bkgrnd, noise, results, options):
plt.close()


def Plot_PSF_Stars(
IMG, stars_x, stars_y, stars_fwhm, psf, results, options, flagstars=None
):
def Plot_PSF_Stars(IMG, stars_x, stars_y, stars_fwhm, psf, results, options, flagstars=None):
LSBImage(IMG - results["background"], results["background noise"])
AddScale(plt.gca(), IMG.shape[0]*options['ap_pixscale'])
AddScale(plt.gca(), IMG.shape[0] * options["ap_pixscale"])
for i in range(len(stars_fwhm)):
plt.gca().add_patch(
Ellipse(
xy = (stars_x[i], stars_y[i]),
width = 20 * psf,
height = 20 * psf,
angle = 0,
xy=(stars_x[i], stars_y[i]),
width=20 * psf,
height=20 * psf,
angle=0,
fill=False,
linewidth=1.5,
color=autocolours["red1"]
if not flagstars is None and flagstars[i]
else autocolours["blue1"],
color=(
autocolours["red1"]
if not flagstars is None and flagstars[i]
else autocolours["blue1"]
),
)
)
plt.tight_layout()
Expand All @@ -126,32 +123,28 @@ def Plot_Isophote_Init_Ellipse(dat, circ_ellipse_radii, ellip, phase, results, o
ranges = [
[
max(0, int(results["center"]["x"] - circ_ellipse_radii[-1] * 1.5)),
min(
dat.shape[1], int(results["center"]["x"] + circ_ellipse_radii[-1] * 1.5)
),
min(dat.shape[1], int(results["center"]["x"] + circ_ellipse_radii[-1] * 1.5)),
],
[
max(0, int(results["center"]["y"] - circ_ellipse_radii[-1] * 1.5)),
min(
dat.shape[0], int(results["center"]["y"] + circ_ellipse_radii[-1] * 1.5)
),
min(dat.shape[0], int(results["center"]["y"] + circ_ellipse_radii[-1] * 1.5)),
],
]

LSBImage(
dat[ranges[1][0] : ranges[1][1], ranges[0][0] : ranges[0][1]],
results["background noise"],
)
AddScale(plt.gca(), (ranges[0][1] - ranges[0][0])*options['ap_pixscale'])
AddScale(plt.gca(), (ranges[0][1] - ranges[0][0]) * options["ap_pixscale"])
plt.gca().add_patch(
Ellipse(
xy =(
xy=(
results["center"]["x"] - ranges[0][0],
results["center"]["y"] - ranges[1][0],
),
width = 2 * circ_ellipse_radii[-1],
height = 2 * circ_ellipse_radii[-1] * (1.0 - ellip),
angle = phase * 180 / np.pi,
width=2 * circ_ellipse_radii[-1],
height=2 * circ_ellipse_radii[-1] * (1.0 - ellip),
angle=phase * 180 / np.pi,
fill=False,
linewidth=1,
color=autocolours["blue1"],
Expand Down Expand Up @@ -229,11 +222,7 @@ def Plot_Isophote_Fit(dat, sample_radii, parameters, results, options):
Rlim = sample_radii[-1] * (
1.0
if parameters[-1]["m"] is None
else np.exp(
sum(
np.abs(parameters[-1]["Am"][m]) for m in range(len(parameters[-1]["m"]))
)
)
else np.exp(sum(np.abs(parameters[-1]["Am"][m]) for m in range(len(parameters[-1]["m"]))))
)
ranges = [
[
Expand All @@ -249,7 +238,7 @@ def Plot_Isophote_Fit(dat, sample_radii, parameters, results, options):
dat[ranges[1][0] : ranges[1][1], ranges[0][0] : ranges[0][1]],
results["background noise"],
)
AddScale(plt.gca(), (ranges[0][1] - ranges[0][0])*options['ap_pixscale'])
AddScale(plt.gca(), (ranges[0][1] - ranges[0][0]) * options["ap_pixscale"])

for i in range(len(sample_radii)):
N = max(15, int(0.9 * 2 * np.pi * sample_radii[i]))
Expand Down Expand Up @@ -278,7 +267,7 @@ def Plot_Isophote_Fit(dat, sample_radii, parameters, results, options):
R * X + results["center"]["x"] - ranges[0][0],
R * Y + results["center"]["y"] - ranges[1][0],
)

plt.plot(
list(X) + [X[0]],
list(Y) + [Y[0]],
Expand All @@ -304,11 +293,7 @@ def _Plot_Isophotes(dat, R, parameters, results, options):
Rlim = R[-1] * (
1.0
if parameters[-1]["m"] is None
else np.exp(
sum(
np.abs(parameters[-1]["Am"][m]) for m in range(len(parameters[-1]["m"]))
)
)
else np.exp(sum(np.abs(parameters[-1]["Am"][m]) for m in range(len(parameters[-1]["m"]))))
)
ranges = [
[
Expand All @@ -324,7 +309,7 @@ def _Plot_Isophotes(dat, R, parameters, results, options):
dat[ranges[1][0] : ranges[1][1], ranges[0][0] : ranges[0][1]],
results["background noise"],
)
AddScale(plt.gca(), (ranges[0][1] - ranges[0][0])*options['ap_pixscale'])
AddScale(plt.gca(), (ranges[0][1] - ranges[0][0]) * options["ap_pixscale"])
fitlim = results["fit R"][-1] if "fit R" in results else np.inf
for i in range(len(R)):
N = max(15, int(0.9 * 2 * np.pi * R[i]))
Expand Down Expand Up @@ -380,14 +365,14 @@ def Plot_SB_Profile(dat, R, SB, SB_e, parameters, results, options):
errscale = 1.0
if np.all(SB_e[CHOOSE] < 0.5):
errscale = 1 / np.max(SB_e[CHOOSE])
if 'ap_plot_sbprof_set_errscale' in options:
errscale = options['ap_plot_sbprof_set_errscale']
if "ap_plot_sbprof_set_errscale" in options:
errscale = options["ap_plot_sbprof_set_errscale"]

lnlist = []
if errscale > 1.01 or 'ap_plot_sbprof_set_errscale' in options:
errlabel = ' (err$\\times$%.1f)' % errscale
if errscale > 1.01 or "ap_plot_sbprof_set_errscale" in options:
errlabel = " (err$\\times$%.1f)" % errscale
else:
errlabel = ''
errlabel = ""
lnlist.append(
plt.errorbar(
R[CHOOSE],
Expand All @@ -413,12 +398,12 @@ def Plot_SB_Profile(dat, R, SB, SB_e, parameters, results, options):
)
plt.xlabel("Semi-Major-Axis [arcsec]", fontsize=16)
plt.ylabel("Surface Brightness [mag arcsec$^{-2}$]", fontsize=16)
if 'ap_plot_sbprof_xlim' in options:
plt.xlim(options['ap_plot_sbprof_xlim'])
if "ap_plot_sbprof_xlim" in options:
plt.xlim(options["ap_plot_sbprof_xlim"])
else:
plt.xlim([0, None])
if 'ap_plot_sbprof_ylim' in options:
plt.ylim(options['ap_plot_sbprof_ylim'])
if "ap_plot_sbprof_ylim" in options:
plt.ylim(options["ap_plot_sbprof_ylim"])
bkgrdnoise = (
-2.5 * np.log10(results["background noise"])
+ zeropoint
Expand Down Expand Up @@ -491,12 +476,12 @@ def Plot_I_Profile(dat, R, I, I_e, parameters, results, options):
plt.xlabel("Semi-Major-Axis [arcsec]", fontsize=16)
plt.ylabel("Intensity [flux arcsec$^{-2}$]", fontsize=16)
plt.yscale("log")
if 'ap_plot_sbprof_xlim' in options:
plt.xlim(options['ap_plot_sbprof_xlim'])
if "ap_plot_sbprof_xlim" in options:
plt.xlim(options["ap_plot_sbprof_xlim"])
else:
plt.xlim([0, None])
if 'ap_plot_sbprof_ylim' in options:
plt.ylim(options['ap_plot_sbprof_ylim'])
if "ap_plot_sbprof_ylim" in options:
plt.ylim(options["ap_plot_sbprof_ylim"])
bkgrdnoise = results["background noise"] / (options["ap_pixscale"] ** 2)
lnlist.append(
plt.axhline(
Expand Down Expand Up @@ -569,11 +554,8 @@ def Plot_Phase_Profile(R, parameters, results, options):
)
plt.plot(
R,
list(
p["Phim"][m] / (np.pi * parameters[0]["m"][m]) for p in parameters
),
label="$\\phi_%i$ [rad/%i$\\pi$]"
% (parameters[0]["m"][m], parameters[0]["m"][m]),
list(p["Phim"][m] / (np.pi * parameters[0]["m"][m]) for p in parameters),
label="$\\phi_%i$ [rad/%i$\\pi$]" % (parameters[0]["m"][m], parameters[0]["m"][m]),
)
plt.legend()
plt.xlabel("Semi-Major-Axis [arcsec]", fontsize=16)
Expand Down Expand Up @@ -650,9 +632,7 @@ def Plot_Meas_Fmodes(R, parameters, results, options):
plt.close()


def Plot_Radial_Profiles(
dat, R, sb, sbE, pa, nwedges, wedgeangles, wedgewidth, results, options
):
def Plot_Radial_Profiles(dat, R, sb, sbE, pa, nwedges, wedgeangles, wedgewidth, results, options):

zeropoint = options["ap_zeropoint"] if "ap_zeropoint" in options else 22.5
ranges = [
Expand All @@ -665,7 +645,7 @@ def Plot_Radial_Profiles(
min(dat.shape[0], int(results["center"]["y"] + 1.5 * R[-1] + 2)),
],
]

cmap = cm.get_cmap("hsv")
colorind = (np.linspace(0, 1 - 1 / nwedges, nwedges) + 0.1) % 1.0
for sa_i in range(len(wedgeangles)):
Expand Down Expand Up @@ -714,7 +694,7 @@ def Plot_Radial_Profiles(
dat[ranges[1][0] : ranges[1][1], ranges[0][0] : ranges[0][1]],
results["background noise"],
)
AddScale(plt.gca(), (ranges[0][1] - ranges[0][0])*options['ap_pixscale'])
AddScale(plt.gca(), (ranges[0][1] - ranges[0][0]) * options["ap_pixscale"])

cx, cy = (
results["center"]["x"] - ranges[0][0],
Expand Down Expand Up @@ -787,15 +767,11 @@ def Plot_Axial_Profiles(dat, R, sb, sbE, pa, results, options):
for ang in [1, -1]:
key = (rd, ang)
# cmap = matplotlib.cm.get_cmap('viridis_r')
norm = matplotlib.colors.Normalize(
vmin=0, vmax=R[-1] * options["ap_pixscale"]
)
norm = matplotlib.colors.Normalize(vmin=0, vmax=R[-1] * options["ap_pixscale"])
for pi, pR in enumerate(R):
if pi % 3 != 0:
continue
CHOOSE = np.logical_and(
np.array(sb[key][pi]) < 99, np.array(sbE[key][pi]) < 1
)
CHOOSE = np.logical_and(np.array(sb[key][pi]) < 99, np.array(sbE[key][pi]) < 1)
plt.errorbar(
np.array(R)[CHOOSE] * options["ap_pixscale"],
np.array(sb[key][pi])[CHOOSE],
Expand All @@ -810,22 +786,20 @@ def Plot_Axial_Profiles(dat, R, sb, sbE, pa, results, options):
"%s-axis position on line [arcsec]"
% (
"Major"
if "ap_axialprof_parallel" in options
and options["ap_axialprof_parallel"]
if "ap_axialprof_parallel" in options and options["ap_axialprof_parallel"]
else "Minor"
),
fontsize=16,
)
plt.ylabel("Surface Brightness [mag arcsec$^{-2}$]", fontsize=16)
cb1 = plt.colorbar(
matplotlib.cm.ScalarMappable(norm=norm, cmap=autocmap.reversed())
matplotlib.cm.ScalarMappable(norm=norm, cmap=autocmap.reversed()), ax=plt.gca()
)
cb1.set_label(
"%s-axis position of line [arcsec]"
% (
"Minor"
if "ap_axialprof_parallel" in options
and options["ap_axialprof_parallel"]
if "ap_axialprof_parallel" in options and options["ap_axialprof_parallel"]
else "Major"
),
fontsize=16,
Expand Down Expand Up @@ -866,9 +840,7 @@ def Plot_Axial_Profiles(dat, R, sb, sbE, pa, results, options):
firstbad = np.argmax(np.logical_not(CHOOSE))
if firstbad > 3:
CHOOSE[firstbad:] = False
outto = (
np.array(results["prof data"]["R"])[CHOOSE][-1] * 1.5 / options["ap_pixscale"]
)
outto = np.array(results["prof data"]["R"])[CHOOSE][-1] * 1.5 / options["ap_pixscale"]
ranges = [
[
max(0, int(results["center"]["x"] - outto - 2)),
Expand All @@ -883,11 +855,11 @@ def Plot_Axial_Profiles(dat, R, sb, sbE, pa, results, options):
dat[ranges[1][0] : ranges[1][1], ranges[0][0] : ranges[0][1]],
results["background noise"],
)
AddScale(plt.gca(), (ranges[0][1] - ranges[0][0])*options['ap_pixscale'])
AddScale(plt.gca(), (ranges[0][1] - ranges[0][0]) * options["ap_pixscale"])
count = 0
cmap = matplotlib.cm.get_cmap("hsv")
colorind = (np.linspace(0, 1 - 1 / 4, 4) + 0.1) % 1
colours = list(cmap(c) for c in colorind)
colours = list(cmap(c) for c in colorind)
for rd in [1, -1]:
for ang in [1, -1]:
key = (rd, ang)
Expand All @@ -912,11 +884,10 @@ def Plot_Axial_Profiles(dat, R, sb, sbE, pa, results, options):
linewidth=0.5,
color=colours[count],
label=(
"%sR : pa%s90"
% ("+" if rd > 0 else "-", "+" if ang > 0 else "-")
)
if pi == 0
else None,
("%sR : pa%s90" % ("+" if rd > 0 else "-", "+" if ang > 0 else "-"))
if pi == 0
else None
),
)
count += 1
plt.legend()
Expand Down Expand Up @@ -949,9 +920,7 @@ def Plot_EllipseModel(IMG, Model, R, modeltype, results, options):
plt.figure(figsize=(7, 7))
autocmap.set_under("k", alpha=0)
showmodel = Model[ranges[1][0] : ranges[1][1], ranges[0][0] : ranges[0][1]].copy()
showmodel[showmodel > 0] += np.max(showmodel) / (10 ** 3.5) - np.min(
showmodel[showmodel > 0]
)
showmodel[showmodel > 0] += np.max(showmodel) / (10**3.5) - np.min(showmodel[showmodel > 0])
plt.imshow(
showmodel,
origin="lower",
Expand Down
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