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MAOPhot.py
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MAOPhot.py
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4# -*- coding: utf-8 -*-
"""
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##### ### ### ### ###
Welcome to MAOPhot 1.0.0, a PSF Photometry tool using Astropy and Photutils.psf
1.0.0 Revision
- get_comparison_stars enhancement; when more than one match found
for a given comp star or vsx object, then newly matched star gets id
appended with ".1", or ".2", etc. (E.g., comp stars found: 120, 120.1,
120.2; vsx objects: Z Tau, Z Tau.1, Z Tau.2;) Only the comp stars listed
in Settings, and the Object Name listed in Settings is used. User can
changed those settings or change the matching radius. There is a
discussion about this in the documentation.
- Radio buttons "Display all objects" and "Display selected objects only"
allows user to display all detected objects or only ones listed in
the settings
- Check star in settings has priority over saved cvs file. User can change
check star and then re-run "Two Color Photometry".
- Added Non Iterative PSF Photometry option which uses class
PSFPhotometry
- check_star boolean in DataFrame is bool type throughout, not str
- Upgraded to Photutils 1.10.0 and Panda 2.1.4, etc.
- changed "Close" button in Settings window to "OK"
- report JD is DATE-OBS + EXPOSURE/2 (like AAVSO report)
- minor changes to Notes in report to reflect current AAVSO report
- added AMASS to AAVSO report
MAOPhot calculates stellar magnitudes from 2 dimensional digital photographs.
It produces an extended AAVSO (American Association of Variable Star Observers)
report which can be submitted to the AAVSO using the online tool WebObs
(http://www.aavso.org/webobs).
There are many photometry measuring programs available such as VPhot
(http://www.aavso.org/vphot) and AstroImageJ (University of Louisville). VPhot
uses the aperture photometry method.
MAOPhot uses the PSF Photometry method exclusively. PSF (point spread function)
modeling is well suited for measuring stellar magnitudes in crowded fields,
or the magnitude of a star that has a close companion, e.g., Z Tau.
(See https://www.aavso.org/lpv-double-trouble-campaign-0)
MAOPhot is written in Python. It uses many Python 'astropy'
(https://www.astropy.org/) libraries. The astropy package contains key
functionality and common tools for performing astronomy and astrophysics
with Python. Included in the package is Photutils.psf. See "PSF Photometry"
(https://photutils.readthedocs.io/en/stable/psf.html) which describes many of
the classes and methods used in MAOPhot
This program was derived from MetroPSF by Maxym Usatov.
It has been redesigned for AAVSO reporting only and includes, but not limited
to the following enhancements:
- Generation of Effective PSF model, and ability to create a ‘rejection list’
- option to use an Integrated Gaussian PRF (Pixel Response Function) as model
- PSF Photometry using an iterative algorithm to perform point spread function
photometry in crowded fields
- Photometry using an ensemble of comparison stars or a single comp star
- Generation of Two-Color Photometry (B, V), (V, R) or (V, I), and Single Image
Photometry reports in AAVSO extended format
- Use of telescope Transformation Coefficients (needed for Two Color Photometry)
- Image display shows comp star AAVSO label number and name of any found VSX
objects in image field
- Intermediate results are saved as .csv files
- User can optionally enter a AAVSO Chart ID when retrieving comparison star
data
- User can specify check star and list of comp stars to use
More about Single Image Photometry
Single Image Photometry does not utilize the Transformation
coefficients. Simple differential photometry is used.
Only a single comp star is used (which must be the case if the AAVSO
VPhot tool, 'Transform Applier' is to be used).
Var mag = Var IM - Comp IM + Comp (known) mag
Check star mag = Check star IM - Comp IM + Comp (known) mag
where IM is the instrumental magnitude
-2.5 * np.log10(self.results_tab_df["flux_fit"] / exptime)
self.results_tab_df["flux_fit"] represents the fitted flux for the
star (in that row)
More about Two Color Photometry
MAOPhot mimics VPhot's "Two Color Photometry" (Usually B and V)
See spreadsheet: ProcessingMaoImages_202281V1117Her.xlsx
It includes formulas to generate "two color photometry".
The method generate_aavso_report_2color() uses the same formula which
uses the telescope Transformation Coefficients.
The following coefficients are used and are added to the list of "pink"
fields. (Pink fields required for report generation)
Tbv
Tv_bv
Tb_bv
These are associated with a particular telescope, eg. MAO
Required data (Pink fields):
Tbv
Tv_bv
Tb_bv
"Select Comp Stars (AAVSO Label)"
"Use Check star (AAVSO Label)"
...
etc.
The following formulas are calculated for each comp star and
var = check star;
then calculated again for each comp star and var = "Object Name".
E.g. V1117 Her
Δv = vvar - vcomp
Δ(B-V) = Tbv * Δ(b-v)
Vvar = Δv + Tv_bv * Δ(B-V) + Vcomp
where:
Δv = IM of variable - IM comp
Vcomp = published V-magnitude of comp
Δ(B-V) = Tbv * difference between standard color of var and standard
color of comp
To calculate these formulas and then generate a report, two (2) sets
of results_tab_df in csv format must exist, one for B and one for V and
must have been derived from the B and V images of the Var under
analysis.
When generate_aavso_report_2color is called, by the menu item,
'Two Color Photometry->Two Color Photometry (B,V)', the user will
be asked to specify the 2 aformentioned csv files.
From these files/Panda databases, the formulas are calculated, and
results are displayed.
Error Estimation :
MAOPhot mimics VPhot when calculating error estimation.
From VPhot documentation:
In an ensemble solution with more than two comp stars,
the magnitude is estimated as the average of the individual
comp stars estimate [of the check star], and the error is taken as
the standard deviation of this sample.
If one or two comp stars are used, the error estimate is
based on the SNR of each measurement (the target measurement
and the comp stars measurements). The standard error of a
measurement is defined as 2.5 * np.log10(1 + 1 / SNR)
[The errors are added in quadruture.]
--
get_comparison_stars
Given the specified Check Star in field "Use Check Star (AAVSO label)"
and the list of comp stars in "Select Comp Stars (AAVSO Label)", these
stars are gotten from the AAVSO and each has an additional attribute,
is_check_star, added to the results_tab_df DB.
MAOPhot derived from original MetroPSF (C) Copyright 2021, Maxym Usatov
<[email protected]> Refer to metropsf.pdf for license information.
This research made use of Photutils, an Astropy package for
detection and photometry of astronomical sources (Bradley et al. 20XX).
"""
#
# Constants
#
__version__ = "1.0.0"
__label_prefix__ = "comp " # prepended to comp stars label's; forces type to str
__empty_cell__ = "%" #this forces cell to be type string
from ast import Assert
from astropy.stats import SigmaClip
from astropy.stats import sigma_clipped_stats
from astropy.stats import gaussian_sigma_to_fwhm
from astropy.table import Table
from astropy.modeling.fitting import LevMarLSQFitter, SLSQPLSQFitter, SimplexLSQFitter
from astropy.nddata import NDData
from astropy.nddata import Cutout2D
from astropy.visualization import SqrtStretch, LogStretch, AsinhStretch, simple_norm
from astropy.coordinates import SkyCoord
from astropy.wcs import WCS
from astropy import units as u
from astropy.time import Time, TimeDelta
from astropy.io import fits
from photutils.background import Background2D
from photutils.background import MedianBackground
from photutils.background import MADStdBackgroundRMS
from photutils.background import LocalBackground
from photutils.detection import find_peaks
from photutils.psf import IterativePSFPhotometry, PSFPhotometry
from photutils.psf import IntegratedGaussianPRF, SourceGrouper
from photutils.detection import IRAFStarFinder
from photutils.psf import extract_stars
from photutils.psf import EPSFBuilder,EPSFFitter
from tkinter import filedialog as fd
from tkinter import ttk
import tkinter as tk
from mpl_toolkits.mplot3d import Axes3D
import math
from matplotlib import cm
from matplotlib.figure import Figure
from matplotlib.backends.backend_tkagg import FigureCanvasTkAgg
from matplotlib.ticker import ScalarFormatter
import matplotlib.pyplot as plt
import matplotlib
from astroquery.vizier import Vizier
from astroquery.astrometry_net import AstrometryNet
from tqdm import tqdm
from PIL import Image, ImageTk, ImageMath
import pandas as pd
import logging
import sys
import requests
import csv
import os.path
import numpy as np
import warnings
import datetime
from time import gmtime, strftime
warnings.filterwarnings("ignore")
matplotlib.use("TkAgg")
# Photometry
# No imports beyond this line.
def save_background_image(stretch_min, stretch_max, zoom_level, image_data):
global FITS_minimum
global FITS_maximum
global background_image
background_image = Image.fromarray(image_data)
width, height = background_image.size
new_size = (int(width * zoom_level), int(height * zoom_level))
background_image = background_image.resize(new_size, Image.LANCZOS)
background_image = ImageMath.eval("(a + " + str(stretch_min / 100 * FITS_maximum) +
") * 255 / " + str(stretch_max / 100 * FITS_maximum), a=background_image)
background_image = ImageMath.eval("convert(a, 'L')", a=background_image)
background_image.save('background.jpg')
def save_image(stretch_min, stretch_max, zoom_level, image_data, filename):
global FITS_minimum
global FITS_maximum
_image = Image.fromarray(image_data)
width, height = _image.size
new_size = (int(width * zoom_level), int(height * zoom_level))
_image = _image.resize(new_size, Image.LANCZOS)
_image = ImageMath.eval("(a + " + str(stretch_min / 100 * FITS_maximum) +
") * 255 / " +
str(stretch_max / 100 * FITS_maximum),
a=background_image)
_image = ImageMath.eval("convert(a, 'L')", a=background_image)
_image.save(filename)
def generate_FITS_thumbnail(stretch_min, stretch_max, zoom_level,
stretching_stringvar):
global generated_image
global image_data
global FITS_minimum
global FITS_maximum
converted_data = image_data.astype(float)
if stretching_stringvar == "Square Root":
stretch = SqrtStretch()
converted_data = (converted_data - np.min(converted_data)
) / np.ptp(converted_data)
converted_data = stretch(converted_data)
if stretching_stringvar == "Log":
stretch = LogStretch()
converted_data = (converted_data - np.min(converted_data)
) / np.ptp(converted_data)
converted_data = stretch(converted_data)
if stretching_stringvar == "Asinh":
stretch = AsinhStretch()
converted_data = (converted_data - np.min(converted_data)
) / np.ptp(converted_data)
converted_data = stretch(converted_data)
generated_image = Image.fromarray(converted_data)
FITS_maximum = np.max(converted_data)
width, height = generated_image.size
new_size = (int(width * zoom_level), int(height * zoom_level))
generated_image = generated_image.resize(new_size, Image.LANCZOS)
generated_image = ImageMath.eval("(a + " +
str(stretch_min / 100 * FITS_maximum) +
") * 255 / " +
str(stretch_max / 100 * FITS_maximum),
a=generated_image)
image_file = ""
image_data = []
class MyGUI:
zoom_level = 1
linreg_error = 0
zoom_step = 0.5
photometry_results_plotted = False
ePSF_samples_plotted = False
results_tab_df = pd.DataFrame()
image_bkg_value = 0
bkg2D = None #if fetched, the Background2D object
fit_shape = 21
error_raised = False
histogram_slider_low = 0
histogram_slider_high = 5
last_clicked_x = 0
last_clicked_y = 0
ensemble_size = 0
jd = 0
image_file = ""
photometry_circles = {}
valid_parameter_list = {}
ePSF_rejection_list = pd.DataFrame({'x':[],'y':[],"stale":[]})
epsf_model = None
stars_tbl = Table()
# Parameter declaration and init
fit_width_entry = None
max_ensemble_magnitude_entry = None
fwhm_entry = None
star_detection_threshold_entry = None
photometry_iterations_entry = None
sharplo_entry = None
bkg_filter_size_entry = None
matching_radius_entry = None
aavso_obscode_entry = None
telescope_entry = None
tbv_entry = None
tv_bv_entry = None
tb_bv_entry = None
tvr_entry = None
tv_vr_entry = None
tr_vr_entry = None
tvi_entry = None
tv_vi_entry = None
ti_vi_entry = None
catalog_stringvar = None
vizier_catalog_entry = None
fitter_stringvar = None
astrometrynet_entry = None
astrometrynet_key_entry = None
object_kref_entry = None
object_sel_comp_entry = None
object_name_entry = None
object_notes_entry = None
display_all_objects = None
#
# The TopLoevel window containing the settings
es_top = None
def console_msg(self, MAOPhot_message, level=logging.INFO):
# add a time stamp
message = datetime.datetime.now().strftime("%d %b %Y %H:%M:%S")
message += " " + MAOPhot_message
self.console.insert(tk.END, message+"\n")
self.console.see(tk.END)
self.window.update_idletasks()
self.our_logger.log(level=level, msg=MAOPhot_message)
"""
file = open("MAOPhotpsf.log", "a+", encoding="utf-8")
file.write(str(message) + "\n")
file.close()
"""
def display_image(self):
if len(image_data) > 0:
self.canvas.delete("all")
global generated_image
generate_FITS_thumbnail(self.histogram_slider_low,
self.histogram_slider_high,
self.zoom_level,
self.stretching_stringvar.get())
self.image = ImageTk.PhotoImage(generated_image)
self.canvas.create_image(0, 0, anchor=tk.NW, image=self.image)
self.canvas.config(scrollregion=self.canvas.bbox(tk.ALL))
self.canvas.bind("<Button-1>", self.mouse_click)
if self.ePSF_samples_plotted:
self.display_ePSF_samples()
self.plot_photometry()
def load_FITS(self, image_file):
global image_figure
global image_data
global image
global image_width
global image_height
global header
global FITS_minimum
global FITS_maximum
global generated_image
try:
self.console_msg("Loading FITS: " + image_file)
with fits.open(image_file) as image:
self.image_file = image_file
self.filename_label['text'] = "FITS: " + image_file
self.canvas.delete("all")
self.zoom_level = 1
self.photometry_results_plotted = False
self.ePSF_samples_plotted = False
#Load previous work if it exists
if os.path.isfile(self.image_file+".csv"):
self.results_tab_df = pd.read_csv(self.image_file + ".csv")
else:
self.results_tab_df = pd.DataFrame()
header = image[0].header
image_data = fits.getdata(image_file)
image_width = image_data.shape[1]
image_height = image_data.shape[0]
self.wcs_header = WCS(image[0].header)
FITS_minimum = np.min(image_data)
FITS_maximum = np.max(image_data)
self.console_msg("Width: " + str(image_width) +
" Height: " + str(image_height))
self.console_msg(
"FITS Minimum: " + str(FITS_minimum) + " Maximum: " +
str(FITS_maximum))
if 'filter' in header:
self.filter = str(header['filter'])
self.set_entry_text(self.filter_entry, self.filter)
self.console_msg("Filter: " + self.filter)
else:
self.console_msg(
"Filter name not in FITS header. Set filter manually.")
if 'airmass' in header:
self.airmass = str(header['airmass'])
self.set_entry_text(self.airmass_entry, self.airmass)
self.console_msg("Airmass: " + self.airmass)
else:
self.console_msg(
"Airmass not in FITS header. Airmass may be required for AAVSO report. Set Airmass manually.")
if 'exptime' in header:
exptime = header['exptime']
self.set_entry_text(self.exposure_entry, exptime)
self.console_msg("Exposure: " + str(exptime))
else:
self.console_msg(
"Exposure (EXPTIME) not in FITS header. Set exposure manually.")
self.jd = 0
if 'date-obs' in header:
try:
date_obs = Time(header['date-obs'])
self.jd = Time(date_obs, format='jd')
self.console_msg("DATE-OBS: " + str(self.jd.to_value('iso')) + " UTC; JD: " + str(self.jd))
self.set_entry_text(self.date_obs_entry, str(self.jd))
except Exception as e:
self.error_raised = True
exc_type, exc_obj, exc_tb = sys.exc_info()
self.console_msg("Exception at line no: " + str(exc_tb.tb_lineno) + " " + str(e), level=logging.ERROR)
pass
if 'jd' in header:
jd = header['jd']
self.console_msg(
"Julian date at the start of exposure (from JD): " + str(jd))
self.jd = jd
self.date_obs_entry.delete(0, tk.END)
self.date_obs_entry.insert(0, str(self.jd))
self.image_bkg_value = np.median(image_data)
self.console_msg("Median background level, ADU: "
+ str(self.image_bkg_value))
self.console_msg("Ready")
except Exception as e:
self.error_raised = True
exc_type, exc_obj, exc_tb = sys.exc_info()
self.console_msg("Exception at line no: " + str(exc_tb.tb_lineno)
+" "+str(e), level=logging.ERROR)
pass
def open_FITS_file(self):
global header
options = {}
options['defaultextension'] = '.fit'
options['filetypes'] = [('FIT', '.fit'),('FITS', '.fits'), ('FTS', '.fts')]
options['title'] = 'Open FIT image file...'
image_file = fd.askopenfilename(**options)
if len(image_file) > 0:
try:
self.load_FITS(image_file)
self.display_image()
self.clear_psf_label()
self.clear_epsf_plot()
except Exception as e:
self.error_raised = True
exc_type, exc_obj, exc_tb = sys.exc_info()
self.console_msg("Exception at line no: " + str(exc_tb.tb_lineno) +" "+str(e), level=logging.ERROR)
def save_FITS_file_as(self):
global image
global image_data
global header
options = {}
options['defaultextension'] = '.fit'
options['filetypes'] = [('FIT', '.fit'),('FITS', '.fits'), ('FTS', '.fts')]
options['title'] = 'Save FIT image file as...'
file_name = fd.asksaveasfile(**options)
try:
if len(str(file_name)) > 0:
self.console_msg("Saving FITS as " + str(file_name.name))
fits.writeto(file_name.name, image_data,
header, overwrite=True)
self.console_msg("Saved.")
except Exception as e:
self.error_raised = True
exc_type, exc_obj, exc_tb = sys.exc_info()
self.console_msg("Exception at line no: " + str(exc_tb.tb_lineno) +" "+str(e), level=logging.ERROR)
def save_FITS_file(self):
global image
global image_data
global header
file_name = self.image_file
try:
if len(str(file_name)) > 0:
self.console_msg("Saving FITS as " + str(file_name))
fits.writeto(file_name, image_data, header, overwrite=True)
self.console_msg("Saved.")
except Exception as e:
self.error_raised = True
exc_type, exc_obj, exc_tb = sys.exc_info()
self.console_msg("Exception at line no: " + str(exc_tb.tb_lineno) +" "+str(e), level=logging.ERROR)
def create_ePSF(self):
global header
self.console_msg("Initiating Effective PSF building...")
#make sure an image is loaded
if len(image_data) == 0:
self.console_msg("Cannot proceed; an image must be loaded first; use File->Open...")
return
try:
# Find local peaks in an image that are above a specified
# threshold value. Threshold determined
#
#determine the background, it will be subtrated from image_data, later
bkg_filter_size = int(self.bkg_filter_size_entry.get())
#make sure this is not an even number
if bkg_filter_size % 2 == 0:
bkg_filter_size += 1
sigma_clip = SigmaClip(sigma=3.0)
bkg_estimator = MedianBackground()
self.console_msg("Estimating background...")
self.bkg2D = Background2D(
image_data,
box_size=(self.fit_shape * 10, self.fit_shape * 10),
filter_size=(bkg_filter_size, bkg_filter_size),
edge_method='crop',
sigma_clip=sigma_clip,
bkg_estimator=bkg_estimator)
self.console_msg("Median Background2D level: "
+ str(self.bkg2D.background_median))
peaks_tbl = find_peaks(image_data, threshold=self.bkg2D.background_median * 3)
peaks_tbl_len = len(peaks_tbl)
self.console_msg("ePSF: found " + str(peaks_tbl_len) + " peaks.")
#mask out peaks near the boundary; use twice the aperature entry
#
##Calculate size of cutouts for EPSFBuilder
## make it 10 times the aperture entry
##same size used in display_ePSF_samples
self.fit_shape = int(self.fit_width_entry.get())
size = 10*self.fit_shape + 1
hsize = (size - 1)/2
x = peaks_tbl['x_peak']
y = peaks_tbl['y_peak']
_image = Image.fromarray(image_data)
width, height = _image.size
mask = ((x > hsize) & (x < (width -1 - hsize)) &
(y > hsize) & (y < (height -1 - hsize)))
#prelim_stars_tbl are inbound stars (not to close to the edge)
prelim_stars_tbl = Table()
prelim_stars_tbl['x'] = x[mask]
prelim_stars_tbl['y'] = y[mask]
prelim_stars_tbl['rejected'] = False #init
prelim_stars_tbl_len = len(prelim_stars_tbl)
self.console_msg("ePSF: found and removed " + str(peaks_tbl_len - prelim_stars_tbl_len) + " peaks on edge.")
self.console_msg("ePSF: " + str(prelim_stars_tbl_len) + " peaks remain.")
# now set 'rejected' to True for any stars that are proximate to
# another in the same list
for i in range(len(prelim_stars_tbl)):
for ii in range(len(prelim_stars_tbl)):
if ii == i:
continue
i_x = prelim_stars_tbl[i]['x']
i_y = prelim_stars_tbl[i]['y']
ii_x = prelim_stars_tbl[ii]['x']
ii_y = prelim_stars_tbl[ii]['y']
if math.dist([i_x, i_y], [ii_x, ii_y]) <= hsize:
#reject this because it is too close to that companion
prelim_stars_tbl[i]['rejected'] = True
prelim_stars_tbl[ii]['rejected'] = True
x = prelim_stars_tbl['x']
y = prelim_stars_tbl['y']
reject_this = prelim_stars_tbl['rejected']
mask = reject_this == False # only keep ones we don't reject
isolated_stars_tbl = Table()
isolated_stars_tbl['x'] = x[mask]
isolated_stars_tbl['y'] = y[mask]
isolated_stars_tbl['rejected'] = False #init
isolated_stars_tbl_len = len(isolated_stars_tbl)
self.console_msg("ePSF: found and removed " + str(prelim_stars_tbl_len - isolated_stars_tbl_len) + " close companions.")
self.console_msg("ePSF: " + str(isolated_stars_tbl_len) + " peaks remain.")
# now set 'rejected' to True for any stars that are proximate to a
# coordinate in ePSF_rejection_list
for psf_x, psf_y, psf_reject in isolated_stars_tbl:
isolated_stars_tbl.add_index('x')
for index, row in self.ePSF_rejection_list.iterrows():
reject_x = row['x']
reject_y = row['y']
if abs(reject_x - psf_x) <= hsize and abs(reject_y - psf_y) <= hsize:
#user does not want this one
isolated_stars_tbl.loc[psf_x]['rejected'] = True
break
x = isolated_stars_tbl['x']
y = isolated_stars_tbl['y']
reject_this = isolated_stars_tbl['rejected']
mask = reject_this == False # only keep ones we don't reject
self.stars_tbl = Table()
self.stars_tbl['x'] = x[mask]
self.stars_tbl['y'] = y[mask]
stars_tbl_len = len(self.stars_tbl)
self.console_msg("ePSF: found and removed " + str(isolated_stars_tbl_len - stars_tbl_len) + " peaks rejected by user.")
self.console_msg("ePSF: " + str(stars_tbl_len) + " peaks remain for EPSF Builder.")
# Subtract background from image_data
# bkg.background is a 2D ndarray of background image
clean_image = image_data-self.bkg2D.background
working_image = NDData(data=clean_image)
candidate_stars = extract_stars(working_image, self.stars_tbl, size=size)
epsf_builder = EPSFBuilder(oversampling=2, fitter=EPSFFitter(), maxiters=50)
self.console_msg("Starting ePSF Builder...(check console progress bar)")
self.epsf_model, fitted_stars = epsf_builder(candidate_stars)
model_length = len(self.epsf_model.data)
x = np.arange(0, model_length, 1)
y = np.arange(0, model_length, 1)
x, y = np.meshgrid(x, y)
#plot the psf model
self.psf_plot.clear()
self.psf_plot.plot_surface(x, y, self.epsf_model.data, cmap=cm.jet)
self.psf_plot_canvas.draw()
#plot ePSF samples
self.clear_epsf_plot()
self.display_ePSF_samples()
norm = simple_norm(self.epsf_model.data, 'log', percent=99.)
im = self.ePSF_plot.imshow(self.epsf_model.data, norm=norm, origin='lower', cmap='viridis')
self.ePSF_plot.set_title("Effective PSF")
self.fig_ePSF.colorbar(im, ax=self.ePSF_plot)
self.ePSF_plot_canvas.draw()
self.ePSF_samples_plotted = True
self.console_msg("Ready")
except Exception as e:
self.error_raised = True
exc_type, exc_obj, exc_tb = sys.exc_info()
self.console_msg("Exception at line no: " + str(exc_tb.tb_lineno) +" "+str(e), level=logging.ERROR)
def clear_ePSF(self):
global header
self.console_msg("Clearing ePSF model, Rejection List, plot...")
#drop all the rows but keep the 'x' and 'y' column
self.ePSF_rejection_list.drop(self.ePSF_rejection_list.index, inplace=True)
self.epsf_model = None #reset
self.stars_tbl = Table()
self.clear_psf_label()
self.clear_epsf_plot()
self.ePSF_samples_plotted = False
self.display_image()
self.console_msg("Ready")
return
def load_ePSF_rejection_list(self):
global header
try:
options = {}
options['defaultextension'] = '.csv'
options['filetypes'] = [('CSV', '.csv')]
options['title'] = 'Choose the ' + self.image_file + '-rejection.csv'
file_name = fd.askopenfilename(**options)
if len(str(file_name)) > 0 and os.path.isfile(str(file_name)):
self.console_msg("Loading Rejection list from: " + str(file_name))
self.ePSF_rejection_list = pd.read_csv(str(file_name))
self.ePSF_rejection_list["stale"] = True #reset
self.ePSF_samples_plotted = True
self.display_image()
else:
return
self.console_msg("Ready")
except Exception as e:
self.error_raised = True
exc_type, exc_obj, exc_tb = sys.exc_info()
self.console_msg("Exception at line no: " + str(exc_tb.tb_lineno) +" "+str(e), level=logging.ERROR)
return
def save_as_ePSF_rejection_list(self):
global header
try:
options = {}
options['defaultextension'] = '.csv'
options['filetypes'] = [('CSV', '.csv')]
options['title'] = 'Save Rejection List As... ' + self.image_file + '-rejection.csv'
options['initialfile'] = self.object_name_entry.get() + '-rejection.csv'
file_name = fd.asksaveasfile(**options)
if len(str(file_name)) > 0:
self.console_msg("Saving Rejection List as " + file_name.name)
dir_path = os.path.dirname(os.path.realpath(file_name.name)) + "\\"
self.ePSF_rejection_list.to_csv(file_name.name, index=False)
self.console_msg("Ready")
except Exception as e:
self.error_raised = True
exc_type, exc_obj, exc_tb = sys.exc_info()
self.console_msg("Exception at line no: " + str(exc_tb.tb_lineno) +" "+str(e), level=logging.ERROR)
return
def execute_noniterative_psf_photometry(self):
global header
self.console_msg(
"Initiating Non-iterative PSF photometry...")
if len(image_data) == 0:
self.console_msg("Cannot proceed; an image must be loaded first; use File->Open...")
return
try:
#determine the background, it will be subtrated from image_data, later
bkg_filter_size = int(self.bkg_filter_size_entry.get())
#make sure this is not an even number
if bkg_filter_size % 2 == 0:
bkg_filter_size += 1
self.fit_shape = int(self.fit_width_entry.get())
sigma_clip = SigmaClip(sigma=3.0)
bkg_estimator = MedianBackground()
self.console_msg("Estimating background...")
#Backgound2D used because background may vary over FOV
self.bkg2D = Background2D(
image_data,
box_size=(self.fit_shape * 10, self.fit_shape * 10),
filter_size=(bkg_filter_size, bkg_filter_size),
edge_method='crop',
sigma_clip=sigma_clip,
bkg_estimator=bkg_estimator)
self.console_msg("Median Background2D level: "
+ str(self.bkg2D.background_median))
# Subtract background from image_data
# bkg.background is a 2D ndarray of background image
clean_image = image_data-self.bkg2D.background
working_image = NDData(data=clean_image)
fwhm = float(self.fwhm_entry.get())
star_detection_threshold = float(
self.star_detection_threshold_entry.get())
sharplo = float(self.sharplo_entry.get())
star_find = IRAFStarFinder(threshold = star_detection_threshold,
fwhm = fwhm,
#minsep_fwhm = 1,
exclude_border = True,
roundhi = 3.0,
roundlo = -5.0,
sharplo = sharplo,
sharphi = 2.0
)
# the 2.5 in the following is from
# https://photutils.readthedocs.io/en/stable/grouping.html#getting-started
#grouper = SourceGrouper(2.5 * fwhm)
local_bkg = LocalBackground(inner_radius=fwhm*4, outer_radius=fwhm*8)
if self.fitter_stringvar.get() == "Sequential LS Programming":
self.console_msg(
"Setting fitter to Sequential Least Squares Programming")
selected_fitter = SLSQPLSQFitter()
elif self.fitter_stringvar.get() == "Simplex LS":
self.console_msg(
"Setting fitter to Simplex and Least Squares Statistic")
selected_fitter = SimplexLSQFitter()
else: # self.fitter_stringvar.get() == "Levenberg-Marquardt":
self.console_msg("Setting fitter to Levenberg-Marquardt")
selected_fitter = LevMarLSQFitter()
if self.epsf_model != None:
self.console_msg("Using derived Effective PSF Model")
psf_model = self.epsf_model
else:
#Use Gausian
sigma = 2.00 * fwhm / gaussian_sigma_to_fwhm
self.console_msg("Using Gaussian PRF for model; sigma = "+format(sigma, '.3f')+\
"; fwhm = "+format(fwhm, '.1f')+\
"; fitting width/height = "+str(self.fit_shape))
psf_model = IntegratedGaussianPRF(sigma)
photometry = PSFPhotometry(
psf_model = psf_model,
fit_shape = self.fit_shape,
finder = star_find,
grouper = None,
fitter = selected_fitter,
fitter_maxiters = 100,
localbkg_estimator = local_bkg,
aperture_radius=1.5*fwhm,
progress_bar=True
)
self.console_msg("Starting Photometry...(check console progress bar)")
result_tab = photometry(data=working_image)
if 'message' in selected_fitter.fit_info:
self.console_msg("Done. PSF fitter message(s): " + str(selected_fitter.fit_info['message']))
else:
self.console_msg("Done. PSF fitter; no message available")
self.results_tab_df = result_tab[result_tab.colnames[0:14]].to_pandas() # only need first 15 columns
self.results_tab_df["removed_from_ensemble"] = False
self.results_tab_df["date-obs"] = float(self.date_obs_entry.get())
if len(self.airmass_entry.get()) > 0:
self.results_tab_df["AMASS"] = float(self.airmass_entry.get())
else:
self.results_tab_df["AMASS"] = "na"
# Calculate instrumental magnitudes
# Following added for "True" inst mag used in AAVSO report
image_exposure_time = float(self.exposure_entry.get())
self.results_tab_df["inst_mag"] = -2.5 * np.log10(self.results_tab_df["flux_fit"] / image_exposure_time)
#record for later
self.results_tab_df["exposure"] = image_exposure_time
self.results_tab_df.to_csv(self.image_file + ".csv", index=False)
self.console_msg("Photometry saved to " + str(self.image_file + ".csv") + "; len = " + str(len(self.results_tab_df)))
self.plot_photometry()
except Exception as e:
self.error_raised = True
exc_type, exc_obj, exc_tb = sys.exc_info()
self.console_msg("Exception at line no: " + str(exc_tb.tb_lineno) +" "+str(e), level=logging.ERROR)
def execute_iterative_psf_photometry(self):
global header
self.console_msg(
"Initiating iterative PSF photometry...")
if len(image_data) == 0:
self.console_msg("Cannot proceed; an image must be loaded first; use File->Open...")
return
try:
#determine the background, it will be subtrated from image_data, later
bkg_filter_size = int(self.bkg_filter_size_entry.get())
#make sure this is not an even number
if bkg_filter_size % 2 == 0:
bkg_filter_size += 1
self.fit_shape = int(self.fit_width_entry.get())
sigma_clip = SigmaClip(sigma=3.0)
bkg_estimator = MedianBackground()
self.console_msg("Estimating background...")
#Backgound2D used because background may vary over FOV
self.bkg2D = Background2D(
image_data,
box_size=(self.fit_shape * 10, self.fit_shape * 10),
filter_size=(bkg_filter_size, bkg_filter_size),
edge_method='crop',
sigma_clip=sigma_clip,
bkg_estimator=bkg_estimator)
self.console_msg("Median Background2D level: "
+ str(self.bkg2D.background_median))
# Subtract background from image_data
# bkg.background is a 2D ndarray of background image
clean_image = image_data-self.bkg2D.background
working_image = NDData(data=clean_image)
fwhm = float(self.fwhm_entry.get())
star_detection_threshold = float(
self.star_detection_threshold_entry.get())
iterations = int(self.photometry_iterations_entry.get()) if self.photometry_iterations_entry.get().isnumeric() else None
sharplo = float(self.sharplo_entry.get())
#bkg_filter_size = int(self.bkg_filter_size_entry.get())
star_find = IRAFStarFinder(threshold = star_detection_threshold,
fwhm = fwhm,
minsep_fwhm = 1,
exclude_border = True,
roundhi = 3.0,
roundlo = -5.0,
sharplo = sharplo,
sharphi = 2.0
)
local_bkg = LocalBackground(inner_radius=fwhm*4, outer_radius=fwhm*8)
if self.fitter_stringvar.get() == "Sequential LS Programming":
self.console_msg(
"Setting fitter to Sequential Least Squares Programming")
selected_fitter = SLSQPLSQFitter()