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pp_manident.py
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#!/usr/bin/env python
""" MANIDENT - manual target identification tool for the Photometry Pipeline
v1.0: 2016-08-08, [email protected]
this code is based in part on viewseries.py:
http://spider.wadsworth.org/spider_doc/spider/docs/python/spipylib/examples/viewseries.py
"""
from __future__ import print_function
from __future__ import division
from past.utils import old_div
import os, sys
import numpy
import warnings
from tkinter import *
from PIL import Image
from PIL import ImageTk
from PIL import ImageDraw
import argparse
from astropy.io import fits
from scipy.ndimage import interpolation as interp
from scipy.interpolate import InterpolatedUnivariateSpline
# only import if Python3 is used
if sys.version_info > (3,0):
from future import standard_library
standard_library.install_aliases()
from builtins import range
from builtins import object
# pipeline-specific modules
from catalog import *
### class structure for Tkinter control
class Clicker(object):
def __init__(self, master, zoom, filelist):
self.top = master
self.files = filelist
self.zoom = zoom
self.target_index = [None for i in range(len(self.files))]
self.interp_index = [None for i in range(len(self.files))]
self.index = 0
self.images = []
self.ldac = []
self.mjd = []
### load image data
print('please wait, loading images...', end=' ')
sys.stdout.flush()
self.read_all_fits(self.files)
print('done!')
# create title bar
self.title = Label(text='%s (%d/%d)' %
(self.images[0],
self.index+1, len(self.files)))
self.title.pack()
# select first image
self.tkimage = ImageTk.PhotoImage(self.images[0], palette=256)
self.canvas = Canvas(master, height=self.tkimage.height(), width=
self.tkimage.width())
self.image = self.canvas.create_image(0, 0, anchor='nw',
image=self.tkimage)
# create position indicators:
# green: sources, yellow: inter/extrapolated, red: manually
# selected
self.green_circles = []
self.redcircle_id = self.canvas.create_oval(-100, -100, -100,
-100, outline='red',
width=2)
self.yellowcircle_id = self.canvas.create_oval(-100, -100, -100,
-100, outline='orange',
width=1)
# frame counter variable
self.evar = IntVar()
self.evar.set(1)
self.canvas.pack(side='top', expand=1)
# display image
self.nextframe()
# events
self.canvas.focus_set()
self.canvas.bind("<Key>", self.key)
self.canvas.bind("<Button 1>", self.left_click)
self.canvas.bind("<Button 3>", self.right_click)
def left_click(self, event):
""" select source """
x, y = old_div(event.x,self.zoom), old_div(event.y,self.zoom)
# find source closest to click position in ldac, identify
# source index
residuals = numpy.sqrt((self.ldac[self.index]['XWIN_IMAGE']-x)**2 +
(self.ldac[self.index]['YWIN_IMAGE']-y)**2)
closest_idx = numpy.argmin(residuals)
self.target_index[self.index] = closest_idx
self.nextframe(0)
def right_click(self, event):
""" next frame """
self.nextframe(1)
def key(self, event):
""" keyboard events """
if event.char == 'a':
# previous frame
self.nextframe(-1)
elif event.char == 'd':
# next frame
self.nextframe(1)
elif event.char == 'q':
# quit
self.top.quit()
elif event.char == '+':
# zoom in
if self.zoom < 4.:
self.zoom *= 2
self.nextframe(0)
elif event.char == '-':
# zoom out
if self.zoom > 0.25:
self.zoom /= 2
self.nextframe(0)
def read_all_fits(self, filenames, zoom=0.5):
""" read in all image data, scale images """
for idx, filename in enumerate(filenames):
if idx > 0:
print('\b\b\b\b%3d' % (idx+1), end=' ')
else:
print('%3d' % (idx+1), end=' ')
sys.stdout.flush()
## read image data
hdulist = fits.open(filename, ignore_missing_end=True)
imgdat = hdulist[0].data
# median = numpy.median(imgdat)
# std = numpy.std(imgdat)
median = numpy.median(imgdat[int(imgdat.shape[1]*0.25):
int(imgdat.shape[1]*0.75),
int(imgdat.shape[0]*0.25):
int(imgdat.shape[0]*0.75)])
std = numpy.std(imgdat[int(imgdat.shape[1]*0.25):
int(imgdat.shape[1]*0.75),
int(imgdat.shape[0]*0.25):
int(imgdat.shape[0]*0.75)])
imgdat = old_div(numpy.clip(imgdat, median-0.5*std,
median+0.5*std),(old_div(std,256)))
imgdat = imgdat - numpy.min(imgdat)
imgdat = interp.zoom(imgdat, self.zoom)
self.images.append(Image.fromarray(imgdat))
## read ldac data
cat = catalog(filename)
ldac_filename = filename[:filename.find('.fit')]+'.ldac'
cat.read_ldac(ldac_filename, filename, maxflag=4)
self.ldac.append(cat)
## read header data (MJD)
self.mjd.append(float(hdulist[0].header['MIDTIMJD']))
def nextframe(self,i=1, imgnum=-1):
""" display frame using iterator i"""
if imgnum == -1:
self.index += i
else:
self.index = imgnum - 1
if self.index >= len(self.files):
self.index = 0
elif self.index < 0:
self.index = len(self.files) - 1
filename = self.files[self.index]
if not os.path.exists(filename):
print("Unable to find %s" % filename)
self.top.quit()
self.evar.set(self.index+1)
self.title.configure(text='%s (%d/%d)' %
(os.path.basename(filename),
self.index+1, len(self.files)))
im = self.images[self.index]
## draw red circle, if target has been identified
# yellow circle if extrapolation
interp_idx = None
if self.target_index[self.index] is not None:
idx = self.target_index[self.index]
self.canvas.coords(self.redcircle_id,
(self.ldac[self.index][idx]['XWIN_IMAGE']*self.zoom-4,
self.ldac[self.index][idx]['YWIN_IMAGE']*self.zoom-4,
self.ldac[self.index][idx]['XWIN_IMAGE']*self.zoom+4,
self.ldac[self.index][idx]['YWIN_IMAGE']*self.zoom+4))
self.canvas.coords(self.yellowcircle_id, (-100, -100, -100, -100))
else:
# if no coordinates available, move red circle out of canvas
self.canvas.coords(self.redcircle_id, (-100, -100, -100, -100))
# use yellow circle if sufficient target positions known
interp_idx = self.extrapolate(self.mjd[self.index])
if interp_idx is not None:
self.canvas.coords(self.yellowcircle_id,
(self.ldac[self.index][interp_idx]['XWIN_IMAGE']*self.zoom-4,
self.ldac[self.index][interp_idx]['YWIN_IMAGE']*self.zoom-4,
self.ldac[self.index][interp_idx]['XWIN_IMAGE']*self.zoom+4,
self.ldac[self.index][interp_idx]['YWIN_IMAGE']*self.zoom+4))
else:
self.canvas.coords(self.yellowcircle_id,
(-100, -100, -100, -100))
self.interp_index[self.index] = interp_idx
# plot all sources
for circ in self.green_circles:
self.canvas.delete(circ)
x = self.ldac[self.index]['XWIN_IMAGE']
y = self.ldac[self.index]['YWIN_IMAGE']
indices = list(range(len(x)))
if self.target_index[self.index] is not None:
indices.pop(self.target_index[self.index])
if interp_idx is not None:
indices.pop(interp_idx)
self.green_circles = \
[self.canvas.create_oval(x[i]*self.zoom-4,
y[i]*self.zoom-4,
x[i]*self.zoom+4,
y[i]*self.zoom+4,
outline='green',
width=1)
for i in indices]
self.tkimage.paste(im)
def extrapolate(self, time):
"""fit path with spline, identify nearest source"""
x, y, t = [], [], []
for i in range(len(self.files)):
if self.target_index[i] is not None:
t.append(self.mjd[i])
x.append(self.ldac[i][self.target_index[i]]['XWIN_IMAGE'])
y.append(self.ldac[i][self.target_index[i]]['YWIN_IMAGE'])
k = min([len(t), 3])-1
if k >= 1:
# extrapolate position
fx = InterpolatedUnivariateSpline(numpy.array(t), numpy.array(x),
k=k)
fy = InterpolatedUnivariateSpline(numpy.array(t), numpy.array(y),
k=k)
# identify closest source
residuals = numpy.sqrt((self.ldac[self.index]['XWIN_IMAGE']-
fx(time))**2 +
(self.ldac[self.index]['YWIN_IMAGE']-
fy(time))**2)
return numpy.argmin(residuals)
else:
return None
# --------------------------------------------------------------------
if __name__ == "__main__":
############# here
# define command line arguments
parser = argparse.ArgumentParser(description='manual target identification')
parser.add_argument('-zoom', help='image zoom factor', default=0.5)
parser.add_argument('images', help='images to process', nargs='+')
args = parser.parse_args()
zoom = float(args.zoom)
filenames = args.images
root = Tk()
app = Clicker(root, zoom, filenames)
root.mainloop()
outf = open('positions.dat', 'w')
outf.write('# filename midtime_JD RA Dec\n' +
'# note that RA and Dec might be based on a fake plate solution and hence are not astrometric\n')
# recalculate target coordinates and write them into a file
for image_idx, image_name in enumerate(app.files):
# accurate position
if app.target_index[image_idx] is not None:
outf.write('%50.50s %9.5f %9.5f %15.7f\n' % (image_name,
app.ldac[image_idx][app.target_index[image_idx]]['ra.deg'],
app.ldac[image_idx][app.target_index[image_idx]]['dec.deg'],
app.mjd[image_idx]))
# interpolated position
else:
app.index = image_idx
interp_idx = app.extrapolate(app.mjd[image_idx])
outf.write('%50.50s %9.5f %9.5f %15.7f\n' % (image_name,
app.ldac[image_idx][interp_idx]['ra.deg'],
app.ldac[image_idx][interp_idx]['dec.deg'],
app.mjd[image_idx]))
print(image_idx+1, 'target positions written to file positions.dat')
outf.close()