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podi_diagnosticplots.py
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podi_diagnosticplots.py
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#!/usr/bin/env python3
#
# Copyright 2012-2013 Ralf Kotulla
#
# This file is part of the ODI QuickReduce pipeline package.
#
# If you find this program or parts thereof please make sure to
# cite it appropriately (please contact the author for the most
# up-to-date reference to use). Also if you find any problems
# or have suggestiosn on how to improve the code or its
# functionality please let me know. Comments and questions are
# always welcome.
#
# The code is made publicly available. Feel free to share the link
# with whoever might be interested. However, I do ask you to not
# publish additional copies on your own website or other sources.
#
# This program is distributed in the hope that it will be useful,
# but WITHOUT ANY WARRANTY; without even the implied warranty of
# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.
#
"""
Module containing all code pertaining to creating the diagnostic plots.
"""
import sys
import os
import astropy.io.fits as pyfits
import numpy
import math
import matplotlib
import matplotlib.colors
# from podi_plotting import *
gain_correct_frames = False
import matplotlib.lines
from podi_definitions import *
from podi_commandline import *
import podi_sitesetup as sitesetup
import podi_focalplanelayout
import queue
import multiprocessing
import podi_logging, logging
import matplotlib.patches
import pickle
import time
from multiprocessing.queues import _ForkingPickler
#####################################################################################
#
#
# This creates the WCS scatter plots
#
#
#####################################################################################
def plot_wcsdiag_scatter(d_ra, d_dec, filename, extension_list,
title="WCS Scatter",
high_s2n=None,
ota_stats = None, ota_global_stats = None):
"""
Function generating the WCS scatter plot from the given data.
"""
logger = logging.getLogger("DiagPlot_WCSScatter")
import matplotlib.pyplot
#fig = matplotlib.pyplot.figure()
fig = matplotlib.pyplot.figure()
ax = fig.add_subplot(111)
count, xedges, yedges = numpy.histogram2d(d_ra*3600., d_dec*3600.,
bins=[60,60], range=[[-3,3], [-3,3]])
# img = ax.imshow(count.T,
# extent=(xedges[0],xedges[-1],yedges[0],yedges[-1]),
# origin='lower',
# cmap=cmap_bluewhite)
# # interpolation='nearest',
# fig.colorbar(img, label='point density')
max_dimension = 3.
n_sigma = 7
if (ota_global_stats is not None):
max_dimension = numpy.max([n_sigma*ota_global_stats['RMS-RA-CLIP'],
n_sigma*ota_global_stats['RMS-DEC-CLIP'],
0.5,])
max_dimension = 3. if max_dimension > 3 else max_dimension
#print "max:",max_dimension
#
# Draw the rings in the center to outline the RMS of the OTA and focal-plane
#
if (ota_global_stats is not None):
x = ota_global_stats['MEDIAN-RA']
y = ota_global_stats['MEDIAN-DEC']
width = ota_global_stats['RMS-RA']
height = ota_global_stats['RMS-DEC']
ellipse = matplotlib.patches.Ellipse(xy=(x,y), width=width, height=height,
edgecolor='r', fc='None', lw=1)
ax.add_patch(ellipse)
global_text = """\
Overall WCS (N=%(STARCOUNT)d):
offset: %(MEDIAN-RA)+0.3f'' / %(MEDIAN-DEC)+0.3f''
R.M.S. %(RMS-RA-CLIP)0.3f'' / %(RMS-DEC-CLIP)0.3f''
%(RMS-CLIP).3f'' (combined)\
""" % ota_global_stats
ax.text(-0.97*max_dimension, -0.97*max_dimension, global_text,
horizontalalignment='right',
verticalalignment='bottom',
fontsize=10, backgroundcolor='white')
if (ota_stats is not None):
x = ota_stats['MEDIAN-RA']
y = ota_stats['MEDIAN-DEC']
width = ota_stats['RMS-RA']
height = ota_stats['RMS-DEC']
ellipse = matplotlib.patches.Ellipse(xy=(x,y), width=width, height=height,
edgecolor='b', fc='None', lw=1)
ax.add_patch(ellipse)
local_text = """\
This OTA (N=%(STARCOUNT)d):
offset: %(MEDIAN-RA)+0.3f'' / %(MEDIAN-DEC)+0.3f''
R.M.S. %(RMS-RA-CLIP)0.3f'' / %(RMS-DEC-CLIP)0.3f''
%(RMS-CLIP).3f'' (combined)\
""" % ota_stats
ax.text(0.97*max_dimension, -0.97*max_dimension, local_text,
horizontalalignment='left',
verticalalignment='bottom',
fontsize=10, backgroundcolor='white')
#
# Add some histograms to the borders to illustrate the distribution
# Only do so if there are at least 5 stars
#
histogram_scale = 0.3*max_dimension
select = [
(numpy.ones(d_ra.shape, dtype=numpy.bool), '#808080')
]
if (high_s2n is not None):
select.append((high_s2n, 'blue'))
if (d_ra.shape[0] > 5):
from scipy.stats import gaussian_kde
x = numpy.linspace(-3,3,600)
for (sample, color) in select:
try:
density_ra = gaussian_kde(d_ra[sample]*3600.)
density_ra.covariance_factor = lambda : .1
density_ra._compute_covariance()
peak_ra = numpy.max(density_ra(x))
ax.plot(x,max_dimension-density_ra(x)/peak_ra*histogram_scale, "-", color=color)#'black')
density_dec = gaussian_kde(d_dec[sample]*3600.)
density_dec.covariance_factor = lambda : .1
density_dec._compute_covariance()
peak_dec = numpy.max(density_dec(x))
ax.plot(density_dec(x)/peak_dec*histogram_scale-max_dimension, x, "-", color=color)#'black')
except ValueError:
pass
if (high_s2n is not None):
# ax.plot(d_ra[high_s2n] * 3600., d_dec[high_s2n] * 3600., "b,", linewidth=0)
ax.scatter(d_ra[~high_s2n] * 3600., d_dec[~high_s2n] * 3600.,
c='#aaaaaa', marker='.', linewidth=0, s=1)
ax.scatter(d_ra[high_s2n] * 3600., d_dec[high_s2n] * 3600.,
c='blue', s=4, marker='.', linewidth=0)
else:
ax.plot(d_ra*3600., d_dec*3600., "b,", linewidth=0)
ax.set_title(title)
ax.set_xlabel("error RA * cos(DEC) [arcsec]")
ax.set_ylabel("error DEC [arcsec]")
ax.set_xlim((max_dimension,-1.*max_dimension))
ax.set_ylim((-1.*max_dimension,max_dimension))
ax.grid(True)
ax.set_aspect('equal')
if (filename is None):
fig.show()
else:
for ext in extension_list:
fig.set_size_inches(8,6)
logger.debug("saving file: %s.%s" % (filename, ext))
fig.savefig(filename+"."+ext, dpi=100,bbox_inches='tight')
matplotlib.pyplot.close(fig)
matplotlib.pyplot.close()
return
def wcsdiag_scatter(matched_radec_odi,
matched_radec_2mass,
matched_ota,
matched_odierror,
filename, options=None, ota_wcs_stats=None,
also_plot_singleOTAs=True,
title_info = None,
):
"""
Function preparing the data for WCS scatter plot and forwarding all plotting
tasks to `plot_wcsdiag_scatter`
"""
logger = logging.getLogger("DiagPlot_WCSScatter")
logger.info("Creating the WCS scatter plot ...")
# Eliminate all matches with somehow illegal coordinates
good_matches = numpy.isfinite(matched_radec_odi[:,0]) & numpy.isfinite(matched_radec_2mass[:,0])
matched_radec_odi = matched_radec_odi[good_matches]
matched_radec_2mass = matched_radec_2mass[good_matches]
matched_ota = matched_ota[good_matches]
# matches_zeroed = matched_cat
# good_matches = matches_zeroed[matches_zeroed[:,2] >= 0]
cos_declination = numpy.cos(numpy.radians(0.5 * (matched_radec_odi[:,1] + matched_radec_2mass[:,1])))
d_ra = (matched_radec_odi[:,0] - matched_radec_2mass[:,0]) * cos_declination
d_dec = matched_radec_odi[:,1] - matched_radec_2mass[:,1]
ota = matched_ota
if (options is None):
extension_list = ['png']
else:
extension_list = options['plotformat']
# Create one plot for the full focalplane
ota_global_stats = None if ota_wcs_stats is None else ota_wcs_stats['full']
title = "WCS Scatter - full focal plane"
if (title_info is not None):
logger.debug("Received information for a more descriptive plot title for WCS scatter")
try:
title = "WCS Scatter (reference: %(ASTRMCAT)s)\n%(OBSID)s (focal-plane) \n%(OBJECT)s - %(FILTER)s - %(EXPTIME)dsec" % title_info
logger.debug(title)
except:
pass
processes = []
plot_args= {"d_ra": d_ra,
"d_dec": d_dec,
"filename": filename,
"extension_list": extension_list,
"ota_stats": None,
"ota_global_stats": ota_global_stats,
"title": title,
"high_s2n": (matched_odierror<0.02),
}
plot_wcsdiag_scatter(**plot_args)
# p = multiprocessing.Process(target=plot_wcsdiag_scatter, kwargs=plot_args)
# p.start()
# processes.append(p)
# plot_wcsdiag_scatter(d_ra, d_dec, filename, extension_list,
# title=title,
# ota_stats=None, ota_global_stats=ota_global_stats)
# Now break down the plots by OTA
if (also_plot_singleOTAs):
list_of_otas = set(ota)
for this_ota in list_of_otas:
in_this_ota = (ota == this_ota)
if (numpy.sum(in_this_ota) <= 0):
continue
extname = "OTA%02d.SCI" % (this_ota)
ota_stats = None
if (ota_wcs_stats is not None):
if (extname in ota_wcs_stats):
ota_stats = ota_wcs_stats[extname]
title = "WSC Scatter - OTA %02d" % (this_ota)
if (title_info is not None):
title_info['OTA'] = this_ota
try:
title = "WCS Scatter (reference: %(ASTRMCAT)s)\n%(OBSID)s - OTA %(OTA)02d\n%(OBJECT)s - %(FILTER)s - %(EXPTIME)dsec" % title_info
except:
pass
logger.debug(extname+" -> "+str(ota_stats))
ota_plotfile = create_qa_otaplot_filename(filename, this_ota, options['structure_qa_subdirs'])
# ota_plotfile = "%s_OTA%02d" % (filename, this_ota)
# if (options['structure_qa_subdirs']):
# ota_plotfile = "%s/OTA%02d" % (filename, this_ota)
plot_args= {"d_ra": d_ra[in_this_ota],
"d_dec": d_dec[in_this_ota],
"high_s2n": (matched_odierror[in_this_ota] < 0.02),
"filename": ota_plotfile,
"extension_list": extension_list,
"title": title,
"ota_stats": ota_stats,
"ota_global_stats": ota_global_stats,
}
plot_wcsdiag_scatter(**plot_args)
# p = multiprocessing.Process(target=plot_wcsdiag_scatter, kwargs=plot_args)
# p.start()
# processes.append(p)
# plot_wcsdiag_scatter(d_ra[in_this_ota], d_dec[in_this_ota], ota_plotfile, extension_list,
# title=title,
# ota_stats=ota_stats, ota_global_stats=ota_global_stats)
for p in processes:
p.join()
# Create some plots for WCS diagnosis
logger.debug("done!")
#####################################################################################
#
#
# WCS shift vector plots
#
#
#####################################################################################
def plot_wcsdiag_shift(radec, d_radec,
# matched_cat,
filename, extension_list=['png'],
ota_outlines=None,
title=None,
):
"""
Function generating the WCS shift plot from the given data.
"""
logger = logging.getLogger("DiagPlot_WCSShift")
import matplotlib.pyplot
fig = matplotlib.pyplot.figure()
ax = fig.add_subplot(111)
ax.ticklabel_format(useOffset=False)
# d_ra = matched_cat[:,0] - matched_cat[:,2]
# d_dec = matched_cat[:,1] - matched_cat[:,3]
# ramin, ramax = numpy.min(matched_cat[:,0]), numpy.max(matched_cat[:,0])
# decmin, decmax = numpy.min(matched_cat[:,1]), numpy.max(matched_cat[:,1])
around_zero = False
if ((numpy.max(radec[:,0]) - numpy.min(radec[:,0])) > 180):
# This means we most likely have to deal with coordinates around 0
radec[:,0][radec[:,0] > 180] -= 360.
around_zero = True
ramin, ramax = numpy.min(radec[:,0]), numpy.max(radec[:,0])
decmin, decmax = numpy.min(radec[:,1]), numpy.max(radec[:,1])
dimension = numpy.min([ramax-ramin, decmax-decmin])
vector_scaling = 10 * dimension/100 * 3600. # size of 1 arcsec in percent of screen size
ax.plot(radec[:,0], radec[:,1],
color='red', marker='o', linestyle='None',
markeredgecolor='none', markersize=4,
)
Q = ax.quiver(radec[:,0], radec[:,1],
d_radec[:,0]*vector_scaling, d_radec[:,1]*vector_scaling,
angles='xy', scale_units='xy', pivot='tail', zorder=99,
scale=1,
headwidth=2, headlength=2, headaxislength=1.8,
width=1e-4, linewidth=1, edgecolor='#000000', color='#000000',
)
# Determine min and max values
ax.set_title(title)
ax.set_xlim((ramin-0.02, ramax+0.02))
ax.set_ylim((decmin-0.02, decmax+0.02))
ax.set_xlabel("RA [degrees]")
ax.set_ylabel("DEC [degrees]")
# Invert the x-axis to have north up and east to the left
ax.set_xlim(ax.get_xlim()[::-1])
# draw some arrow to mark how long the other arrows are
#arrow_x = ramin + 0.05 * (ramax - ramin)
#arrow_y = decmax - 0.05 * (decmax - decmin)
arrow_length = 1 / 3600. # arcsec
# ax.plot(arrow_x, arrow_y, "ro")
# ax.quiver(arrow_x, arrow_y, arrow_length*vector_scaling, 0, linewidth=0,
# angles='xy', scale_units='xy', scale=1, pivot='middle',
# headwidth=0)
logger.debug("Adding quiverkey at pos") #, arrow_x, arrow_y
# qk = ax.quiverkey(Q, arrow_x, arrow_y, arrow_length,
# label="1''", labelpos='S',
# coordinates='data'
# )
# qk = ax.quiverkey(Q, 0.05, 0.95, arrow_length*vector_scaling,
# label="1''", labelpos='S',
# coordinates='axes'
# )
# ax.quiver(arrow_x, arrow_y, arrow_length*vector_scaling, 0, linewidth=0,
# angles='xy', scale_units='xy', scale=1, pivot='middle',
# headwidth=0)
if (ota_outlines is not None):
corners = numpy.array(ota_outlines)
if (around_zero):
corners[:,:,0][corners[:,:,0] > 180.] -= 360.
coll = matplotlib.collections.PolyCollection(corners,facecolor='none',edgecolor='#808080', linestyle='-')
ax.add_collection(coll)
# add a line
# x,y = numpy.array([[arrow_x-arrow_length*vector_scaling, arrow_x+arrow_length*vector_scaling], [arrow_y, arrow_y]])
# ax.plot(x,y, linewidth=3, color='black')
# add label saying "2''"
# ax.text(arrow_x, arrow_y-2*vector_scaling/3600., "%d''" % (2*arrow_length*3600),
# horizontalalignment='center')
if (filename is None):
fig.show()
else:
for ext in extension_list:
logger.debug("saving file: %s.%s" % (filename, ext))
fig.set_size_inches(8,6)
fig.savefig(filename+"."+ext, dpi=100,bbox_inches='tight')
matplotlib.pyplot.close(fig)
matplotlib.pyplot.close()
logger.debug("done!")
return
def wcsdiag_shift(matched_radec_odi,
matched_radec_2mass,
matched_ota,
filename,
options=None,
ota_outlines=None,
ota_wcs_stats=None,
also_plot_singleOTAs=True,
title_info = None,
):
"""
Function preparing the data for WCS shift plot and forwarding all plotting
tasks to `plot_wcsdiag_shift`
"""
logger = logging.getLogger("DiagPlot_WCSShift")
logger.info("Creating the WCS offset/shift plot ...")
# Eliminate all matches with somehow illegal coordinates
good_matches = numpy.isfinite(matched_radec_odi[:,0]) & numpy.isfinite(matched_radec_2mass[:,0])
matched_radec_odi = matched_radec_odi[good_matches].copy()
matched_radec_2mass = matched_radec_2mass[good_matches].copy()
matched_ota = matched_ota[good_matches].copy()
around_zero = False
if (((numpy.max(matched_radec_odi[:,0]) - numpy.min(matched_radec_odi[:,0])) > 180) or
((numpy.max(matched_radec_2mass[:,0]) - numpy.min(matched_radec_2mass[:,0])) > 180)):
# This means we most likely have to deal with coordinates around 0
matched_radec_odi[:,0][matched_radec_odi[:,0] > 180] -= 360.
matched_radec_2mass[:,0][matched_radec_2mass[:,0] > 180] -= 360.
around_zero = True
cos_declination = numpy.cos(numpy.radians(0.5 * (matched_radec_odi[:,1] + matched_radec_2mass[:,1])))
d_radec = matched_radec_odi - matched_radec_2mass
d_radec[:,0] *= cos_declination
ota = matched_ota
# d_ra = matched_radec_odi[:,0] - matched_radec_2mass[:,0]
# d_dec = matched_radec_odi[:,1] - matched_radec_2mass[:,1]
# matches_zeroed = matched_cat
# good_matches = matches_zeroed[matches_zeroed[:,2] >= 0]
# d_ra = good_matches[:,0] - good_matches[:,2]
# d_dec = good_matches[:,1] - good_matches[:,3]
# ota = good_matches[:,10]
if (options is None):
extension_list = ['png']
else:
extension_list = options['plotformat']
processes = []
# Create one plot for the full focalplane
title = "WCS errors - full focal plane"
if (title_info is not None):
logger.debug("Received information for a more descriptive plot title for WCS scatter")
try:
title = "WCS Errors - %(OBSID)s (focal-plane) \n%(OBJECT)s - %(FILTER)s - %(EXPTIME)dsec" % title_info
logger.debug(title)
except:
pass
plot_args = {"radec": matched_radec_2mass,
"d_radec": d_radec,
"filename": filename,
"extension_list": extension_list,
"ota_outlines": None,
"title": title,
}
plot_wcsdiag_shift(**plot_args)
# p = multiprocessing.Process(target=plot_wcsdiag_shift, kwargs=plot_args)
# p.start()
# processes.append(p)
# Now break down the plots by OTA
if (also_plot_singleOTAs):
list_of_otas = set(ota)
for this_ota in list_of_otas:
in_this_ota = (ota == this_ota)
if (numpy.sum(in_this_ota) <= 0):
continue
extname = "OTA%02d.SCI" % (this_ota)
title = "WSC Error - OTA %02d" % (this_ota)
if (title_info is not None):
title_info['OTA'] = this_ota
try:
title = "WCS Errors - %(OBSID)s OTA %(OTA)02d\n%(OBJECT)s - %(FILTER)s - %(EXPTIME)dsec" % title_info
except:
pass
ota_radec = matched_radec_2mass[in_this_ota]
ota_d_radec = d_radec[in_this_ota]
ota_plotfile = create_qa_otaplot_filename(filename, this_ota, options['structure_qa_subdirs'])
# ota_plotfile = "%s_OTA%02d" % (filename, this_ota)
# if (options['structure_qa_subdirs']):
# ota_plotfile = "%s/OTA%02d" % (filename, this_ota)
plot_args = {"radec": ota_radec,
"d_radec": ota_d_radec,
"filename": ota_plotfile,
"extension_list": extension_list,
"ota_outlines": None,
"title": title,
}
# p = multiprocessing.Process(target=plot_wcsdiag_shift, kwargs=plot_args)
# p.start()
# processes.append(p)
plot_wcsdiag_shift(**plot_args)
for p in processes:
p.join()
# fig, ax = matplotlib.pyplot.subplots()
# #matched_zeroed = matched_cat
# #matched_zeroed[:,0:2] -= wcs_shift_refinement
# #matplotlib.pyplot.plot(matched_zeroed[:,0], matched_zeroed[:,1], ",", color=(1,1,1))
# #matching_radius_arcsec = 3. / 3600.
# #valid = (numpy.fabs(matched_zeroed[:,0]-matched_zeroed[:,2]) < matching_radius_arcsec) & \
# # (numpy.fabs(matched_zeroed[:,1]-matched_zeroed[:,3]) < matching_radius_arcsec)
# #matched_zeroed = matched_zeroed[valid]
# ramin, ramax = numpy.min(matches_zeroed[:,0]), numpy.max(matches_zeroed[:,0])
# decmin, decmax = numpy.min(matches_zeroed[:,1]), numpy.max(matches_zeroed[:,1])
# dimension = numpy.min([ramax-ramin, decmax-decmin])
# vector_scaling = 2 * dimension/100 * 3600. # size of 1 arcsec in percent of screen size
# #vector_scaling = 0.02 * 3600.
# matplotlib.pyplot.quiver(good_matches[:,0], good_matches[:,1],
# d_ra*vector_scaling, d_dec*vector_scaling,
# linewidth=0, angles='xy', scale_units='xy', scale=1, pivot='middle')
# # Determine min and max values
# #ramin, ramax = numpy.min(good_matches[:,0]), numpy.max(good_matches[:,0])
# #decmin, decmax = numpy.min(good_matches[:,1]), numpy.max(good_matches[:,1])
# matplotlib.pyplot.title("WCS misalignment")
# matplotlib.pyplot.xlim((ramin-0.02, ramax+0.02))
# matplotlib.pyplot.ylim((decmin-0.02, decmax+0.02))
# matplotlib.pyplot.xlabel("RA [degrees]")
# matplotlib.pyplot.ylabel("DEC [degrees]")
# # draw some arrow to mark how long the other arrows are
# arrow_x = ramin + 0.05 * (ramax - ramin)
# arrow_y = decmax - 0.05 * (decmax - decmin)
# arrow_length = 1 / 3600. # arcsec
# matplotlib.pyplot.quiver(arrow_x, arrow_y, arrow_length*vector_scaling, 0, linewidth=0,
# angles='xy', scale_units='xy', scale=1, pivot='middle',
# headwidth=0)
# if (not ota_outlines == None):
# corners = numpy.array(ota_outlines)
# coll = matplotlib.collections.PolyCollection(corners,facecolor='none',edgecolor='#808080', linestyle='-')
# ax.add_collection(coll)
# # add a line
# x,y = numpy.array([[arrow_x-arrow_length*vector_scaling, arrow_x+arrow_length*vector_scaling], [arrow_y, arrow_y]])
# matplotlib.pyplot.plot(x,y, linewidth=3, color='black')
# # add label saying "2''"
# matplotlib.pyplot.text(arrow_x, arrow_y-2*vector_scaling/3600., "%d''" % (2*arrow_length*3600),
# horizontalalignment='center')
# # matplotlib.pyplot.text(arrow_x, arrow_y, "%d''" % (2*arrow_length*3600),
# # horizontalalignment='center', verticalalignment='top')
# if (filename == None):
# fig.show()
# else:
# if (not options == None):
# for ext in options['plotformat']:
# if (ext != ''):
# fig.savefig(filename+"."+ext)
# else:
# fig.savefig(filename+".png")
# matplotlib.pyplot.close()
# stdout_write(" done!\n")
return 0
def photocalib_zeropoint(output_filename,
sdss_filtername, odi_filtername,
zp_distribfull=None, zp_distribclipped=None,
title=None,
options=None,
also_plot_singleOTAs=False,
details=None,
plot_formats=None,
):
"""
Generate the photometric zeropoint plot
"""
logger = logging.getLogger("DiagPlot_PhotZP")
logger.info("Creating the photometric calibration plot ...")
if (details['photref_col_mag'] < 0):
logger.debug("Error: No valid reference photometry found!")
return
import matplotlib.pyplot
fig = matplotlib.pyplot.figure()
ax = fig.add_subplot(111)
#small_errors = details['odi_sdss_matched_smallerrors']
use_for_calibration = details['use_for_calibration_mask']
small_errors = details['odi_sdss_matched'][use_for_calibration]
sdss_mag = small_errors[:, details['photref_col_mag']]
sdss_magerr = small_errors[:, details['photref_col_err']]
# zp = small_errors[:, photref_col_mag] - small_errors[:, SXcolumn[col_mag]+2]
# zp_err = numpy.hypot(small_errors[:, photref_col_err], small_errors[:, SXcolumn[col_magerr]+2])
# zp_raw = sdss_mag - odi_mag
# zp_err = numpy.hypot(sdss_magerr, odi_magerr)
# if (zp_raw.shape[0] < 5):
# zp_clipped = zp_raw
# clipped = numpy.isfinite(zp_clipped)
# else:
# zp_clipped, clipped = three_sigma_clip(zp_raw, return_mask=True)
zp_median = details['median']
zp_std = details['std']
delta = 0.3 if zp_std < 0.3 else zp_std
# close_to_median = (zp_raw > zp_median - 3 * delta) & (zp_raw < zp_median + 3 * delta)
#zp_max, zp_min = zp_median+3*delta, zp_median-3*delta
zp_min = zp_median-sitesetup.diagplot__zeropoint_ZPrange[0] if \
(sitesetup.diagplot__zeropoint_ZPrange[0] > 0) else zp_median-5*zp_std-0.3
zp_max = zp_median+sitesetup.diagplot__zeropoint_ZPrange[1] if \
(sitesetup.diagplot__zeropoint_ZPrange[1] > 0) else zp_median+5*zp_std+0.3
# Determine the min and max sdss magnitudes
sdss_min = numpy.min(details['odi_sdss_matched'][:,details['photref_col_mag']] -
details['odi_sdss_matched'][:,details['photref_col_err']]) - 1
sdss_max = numpy.max(details['odi_sdss_matched'][:,details['photref_col_mag']] +
details['odi_sdss_matched'][:,details['photref_col_err']])
# now round them to the nearest integer
sdss_minint = sitesetup.diagplot__zeropoint_magrange[0] if \
(sitesetup.diagplot__zeropoint_magrange[0] > 0) else int(math.floor(sdss_min))
sdss_maxint = sitesetup.diagplot__zeropoint_magrange[1] if \
(sitesetup.diagplot__zeropoint_magrange[1] > 0) else int(math.ceil(sdss_max))
# Prepare some helpers for horizontal lines
# There has to be a more elegant way to do this
x_values = numpy.linspace(sdss_minint, sdss_maxint, 10)
y_values = numpy.ones(shape=x_values.shape)
#
# Draw a grey-shaded region outlining the 1-sigma range
# Plot it as a horizontal bar histogram with a single bin.
#
#ax.barh(zp_median-zp_std, (sdss_maxint-sdss_minint), height=2*zp_std,
# ax.barh(y=0, # CHECK
# bottom=zp_median, height=2*zp_std,
# left=sdss_minint, width=(sdss_maxint - sdss_minint),
# label=u"1$\sigma$ range",
# color="#a0a0a0", edgecolor='#a0a0a0')
ax.add_patch(matplotlib.patches.Rectangle(
xy=(sdss_minint, zp_median-zp_std),
width=sdss_maxint-sdss_minint,
height=2*zp_std,
facecolor="#a0a0a0", edgecolor='#a0a0a0',
))
#
# Compute and plot a histogram showing the distribution of ZPs
#
# Prepare a histogram to illustrate the distribution of ZP values
binwidth = numpy.max([(0.2 * zp_std), 0.025])
nbins = int(math.ceil((zp_max - zp_min) / binwidth))
small_errors = details['odi_sdss_matched_smallerrors']
zp_good = small_errors[:, details['photref_col_mag']] \
- small_errors[:, details['odi_col_mag']]
count, edges = numpy.histogram(zp_good, bins=nbins, range=[zp_min, zp_max], density=True)
# Normalize histogram
count = count * (zp_std*math.sqrt(2*math.pi)) / numpy.sum(count) / binwidth
# also add a gaussian with the determined distribution overlayed on the histogram
gauss_y = numpy.linspace(zp_min, zp_max, 1000)
gauss_x = float(sdss_minint) + numpy.exp(-(gauss_y-zp_median)**2/(2*zp_std**2)) #/(zp_std*math.sqrt(2*math.pi))
#/(zp_std*math.sqrt(2*math.pi))*binwidth*numpy.sum(clipped)
ax.barh(edges[:-1], count*1.5, height=binwidth, left=sdss_minint,
edgecolor='green', color='green',
label='distribution')
matplotlib.pyplot.plot(gauss_x, gauss_y, linewidth=1, ls='-', color='black')
#print gauss_y, gauss_x
#
# Count the number of sources in each of the categories
#
n_ref = numpy.sum(use_for_calibration) # details['odi_sdss_matched_ref'].shape[0]
n_outlier = details['odi_sdss_matched_outlier'].shape[0]
n_all = details['odi_sdss_matched'].shape[0]
n_ignored = n_all-(n_ref+n_outlier)
#
# Plot the actual measurments and values deemed to be outliers
#
# full_cat = details['odi_sdss_matched']
# full_sdss_mag = full_cat[:, details['photref_col_mag']]
# full_odi_mag = full_cat[:, details['odi_col_mag']]
# full_sdss_err = full_cat[:, details['photref_col_err']]
# full_odi_err = full_cat[:, details['odi_col_err']]
# full_zp = full_sdss_mag - full_odi_mag
# within_std = numpy.fabs(full_zp - zp_median) <= zp_std
# outside_std = numpy.fabs(full_zp - zp_median) > zp_std
#
# Plot sources with large (i.e.exceeding the limit specified
# in the sitesetup configuration) errors
#
# largeerr_cat = details['odi_sdss_matched_largeerrors']
# largeerr_sdss_mag = largeerr_cat[:, details['photref_col_mag']]
# largeerr_odi_mag = largeerr_cat[:, details['odi_col_mag']]
# largeerr_sdss_err = largeerr_cat[:, details['photref_col_err']]
# largeerr_odi_err = largeerr_cat[:, details['odi_col_err']]
# largeerr_zp = largeerr_sdss_mag - largeerr_odi_mag
# ax.errorbar(largeerr_sdss_mag,
# largeerr_zp,
# xerr=largeerr_sdss_err,
# yerr=numpy.hypot(largeerr_sdss_err, largeerr_odi_err),
# capsize=0,
# fmt='.', ms=0, color='#606060',
# label='ignored (#: %d)' % n_ignored)
# smallerr_cat = details['odi_sdss_matched_smallerrors']
# smallerr_sdss_mag = smallerr_cat[:, details['photref_col_mag']]
# smallerr_odi_mag = smallerr_cat[:, details['odi_col_mag']]
# smallerr_sdss_err = smallerr_cat[:, details['photref_col_err']]
# smallerr_odi_err = smallerr_cat[:, details['odi_col_err']]
# smallerr_zp = smallerr_sdss_mag - smallerr_odi_mag
#
# And for completeness, also plot sources deemed outliers
#
outlier_cat = details['odi_sdss_matched_outlier']
ax.errorbar(outlier_cat[:, details['photref_col_mag']],
outlier_cat[:, details['photref_col_mag']]-outlier_cat[:, details['odi_col_mag']],
xerr=outlier_cat[:, details['photref_col_err']],
yerr=numpy.hypot(outlier_cat[:, details['photref_col_err']],
outlier_cat[:, details['odi_col_err']]),
capsize=0,
fmt="+", ms=5, color='red', linewidth=1,
label='outlier (#: %d)' % n_outlier)
#
# Plot all valid calibrator sources
#
ref_cat = details['odi_sdss_matched_ref']
ax.errorbar(ref_cat[:, details['photref_col_mag']],
ref_cat[:, details['photref_col_mag']]-ref_cat[:, details['odi_col_mag']],
xerr=ref_cat[:, details['photref_col_err']],
yerr=numpy.hypot(ref_cat[:, details['photref_col_err']],
ref_cat[:, details['odi_col_err']]),
capsize=0,
fmt="+", ms=5, color='blue', linewidth=1,
label='valid calibrator (#: %d)' % n_ref)
#
# Overplot a white line to illustrate the median value
#
matplotlib.pyplot.plot(x_values+1, y_values*zp_median, linewidth=1, ls='-',
color='white', zorder=99)
# ax.hline(y=zp_median, color='white', zorder=99)
#
# Add some additional data about the restricted fitting range and
# the slope of phot. ZP with SDSS magnitude
#
if (details is not None):
# Add some information about the fitted slope
if (details['zp_magnitude_slope'] is not None):
fit, err = details['zp_magnitude_slope']
full_cat = details['odi_sdss_matched']
minx = numpy.min(full_cat[:, details['photref_col_mag']])
maxx = numpy.max(full_cat[:, details['photref_col_mag']])
slopefit_x = numpy.linspace(minx-0.1*(maxx-minx), maxx+0.1*(maxx-minx), 100)
slopefit_y = fit[0] + fit[1] * slopefit_x
# ax.plot(slopefit_x, slopefit_y, "k-", label="fit ZP(%s-ODI)" % (details['catalog']))
ax.grid(True)
# ax.legend(loc='upper left', borderaxespad=0.5, prop={'size':9})
rms = details['rms']
sem = details['sem']
photzp_text = u"ZP = %.3f \u00b1 %.3f mag\nr.m.s/s.e.m. = %.3f/%.3f mag" % (
zp_median, zp_std, rms, sem,
)
ax.text(0.96, 0.05, photzp_text, fontsize=15,
horizontalalignment='right',
verticalalignment='bottom',
transform=ax.transAxes,
bbox=dict(facecolor='white', alpha=0.8, edgecolor='none', linewidth=0))
title_string = title if (title != None) else ""
matplotlib.pyplot.title(title_string)
ref_mag_name = "reference"
ref_filter = "none"
if (details is not None and 'catalog' in details):
ref_mag_name = details['catalog']
ref_filter = details['reference_filter']
matplotlib.pyplot.xlabel("%s magnitude in %s" % (ref_mag_name, ref_filter), labelpad=7)
matplotlib.pyplot.ylabel("zeropoint (%s [%s] - odi [%s])" % (ref_mag_name, ref_filter, odi_filtername), labelpad=20)
matplotlib.pyplot.xlim((sdss_minint, sdss_maxint))
matplotlib.pyplot.ylim((zp_min, zp_max))
# matplotlib.pyplot.axes().set_aspect('equal')
if (output_filename is None):
fig.show()
else:
fig.set_size_inches(8,6)
if (options is not None):
for ext in options['plotformat']:
if (ext != ''):
fig.savefig(output_filename+"."+ext, bbox_inches='tight')
logger.info("plot saved as %s.%s" % (output_filename, ext))
else:
fig.savefig(output_filename+".png", dpi=100,bbox_inches='tight')
matplotlib.pyplot.close(fig)
matplotlib.pyplot.close()
logger.debug("done!")
#####################################################################################
#
#
# Following are the routines to create the zeropoint maps
#
#
#####################################################################################
def plot_zeropoint_map(details, ota_select, ota_outlines, output_filename, zp_range, options=None, plot_formats=None):
"""
Plot the photometric zeropoint, color-coded, as function of position
"""
logger = logging.getLogger("DiagPlot_ZPmap")
import matplotlib.pyplot
fig = matplotlib.pyplot.figure()
ax = fig.add_subplot(111)
ax.ticklabel_format(useOffset=False)
zp_min, zp_max = zp_range
full_catalog = details['odi_sdss_matched_smallerrors'].copy()
if (ota_select is not None):
in_this_ota = full_catalog[:, SXcolumn['ota']+2] == ota_select
full_catalog = full_catalog[in_this_ota]
ra = full_catalog[:, 0]
dec = full_catalog[:, 1]
zp = full_catalog[:, details['photref_col_mag']] - full_catalog[:, details['odi_col_mag']]
around_zero = False
min_ra, max_ra = numpy.min(ra), numpy.max(ra)
if ((max_ra - min_ra) > 180 or (max_ra > 0 and min_ra < 0) or max_ra > 360):
# This means we most likely have to deal with coordinates around 0
ra[ra > 180] -= 360.
around_zero = True
# if (ra.shape[0] < 5):
# zp_clipped = zp
# clipped = numpy.isfinite(zp)
# else:
# zp_clipped, clipped = three_sigma_clip(zp, return_mask=True)
zp_median_ota = numpy.median(zp)