def f_simple_observing_log(offset=54034):
"""
Makes a 1D observing log separated by JHK but not by tile.
"""
fig = plt.figure()
sub1= plt.subplot(3,1,1)
j_table = band_cut(variables_photometry, 'j')
h_table = band_cut(variables_photometry, 'h')
k_table = band_cut(variables_photometry, 'k')
j_dates = np.array(list(set(j_table.MEANMJDOBS)))
h_dates = np.array(list(set(h_table.MEANMJDOBS)))
k_dates = np.array(list(set(k_table.MEANMJDOBS)))
plt.plot(j_dates - offset, np.ones(len(j_dates)) + 1/4, 'b.')
plt.plot(h_dates - offset, np.ones(len(h_dates)), 'g.')
plt.plot(k_dates - offset, np.ones(len(k_dates)) - 1/4, 'r.')
plt.ylim(1/3,2-1/3)
plt.xticks([54101-offset, 54282-offset,
54466-offset, 54648-offset,
54832-offset],
["Jan 2007", "July 2007",
"Jan 2008", "July 2008",
"Jan 2009"], rotation=30, fontsize=18)
plt.setp(sub1.get_yticklabels(), visible=False)
plt.show()
return fig
def f_observing_log(title="Observing log for each tile, for each band"):
"""
Makes a graphical observing log.
"""
tile_tables = filter_by_tile()[0]
fig = plt.figure()
for i, tile_table in zip(range(len(tile_tables)), tile_tables):
# How do we J slice? BAND CUT
j_tile_table = band_cut(tile_table, 'j')
h_tile_table = band_cut(tile_table, 'h')
k_tile_table = band_cut(tile_table, 'k')
j_dates = list(set(j_tile_table.MEANMJDOBS))
h_dates = list(set(h_tile_table.MEANMJDOBS))
k_dates = list(set(k_tile_table.MEANMJDOBS))
plt.plot(j_dates, 5/4+i*np.ones(len(j_dates)), 'b.')
plt.plot(h_dates, 1+i*np.ones(len(h_dates)), 'g.')
plt.plot(k_dates, 3/4+i*np.ones(len(k_dates)), 'r.')
plt.xlabel("Modified Julian Date")
plt.ylabel("Tile #", rotation='horizontal')
plt.title(title)
plt.ylim(1-1/3, 16+1/3)
return fig
def get_columns(self, band, max_flag=0, min_flag=0):
"""
Returns relevant columns for a given photometry band.
`band` must be 'j', 'h', or 'k'.
"""
if band.lower() not in ('j', 'h', 'k'):
raise ValueError("Invalid band: {0} not in ('j', 'h', 'k')".format(band.lower()))
b_table = band_cut(self.s_table, band, max_flag=max_flag, min_flag=min_flag)
columns = {}
columns['date'] = b_table['MEANMJDOBS'] - self.date_offset
columns['mag'] = b_table['{0}APERMAG3'.format(band.upper())]
columns['err'] = b_table['{0}APERMAG3ERR'.format(band.upper())]
columns['flag'] = b_table['{0}PPERRBITS'.format(band.upper())]
try:
columns['grade'] = b_table['{0}GRADE'.format(band.upper())]
except:
columns['grade'] = np.zeros_like(columns['mag'])
return columns
def get_colorcolor_columns(self, max_flag=256, min_flag=0):
"""
Returns relevant columns for the J-H, H-K color+color pair.
"""
colorcolor_table = band_cut(band_cut(band_cut(self.s_table, 'k', max_flag=max_flag),
'h', max_flag=max_flag), 'j', max_flag=max_flag)
columns = {}
columns['date'] = colorcolor_table['MEANMJDOBS'] - self.date_offset
columns['jmh'] = colorcolor_table['JMHPNT']
columns['hmk'] = colorcolor_table['HMKPNT']
columns['jmh_err'] = colorcolor_table['JMHPNTERR']
columns['hmk_err'] = colorcolor_table['HMKPNTERR']
return columns
def get_colormag_columns(self, band, max_flag=256, min_flag=0):
"""
Returns relevant columns for color+magnitude pair.
"""
if band.lower() not in ('jjh', 'khk'):
raise ValueError("Invalid color-mag combination: {0} not in ('jjh', 'khk')".format(band.lower()))
mag, blue, red = band.lower()
colormag_table = band_cut(band_cut(self.s_table, red, max_flag=max_flag),
blue, max_flag=max_flag)
columns = {}
columns['date'] = colormag_table['MEANMJDOBS'] - self.date_offset
columns['mag'] = colormag_table['{0}APERMAG3'.format(mag.upper())]
columns['color'] = colormag_table['{0}M{1}PNT'.format(blue.upper(), red.upper())]
columns['mag_err'] = colormag_table['{0}APERMAG3ERR'.format(mag.upper())]
columns['color_err'] = colormag_table['{0}M{1}PNTERR'.format(blue.upper(), red.upper())]
return columns
def star_slope( table, sid, xband='hmk', yband='k', flags=0,
verbose=True, null=np.double(-9.99999488e+08)):
"""
Calculates the color slope, given an input table and ID.
Parameters
----------
table : atpy.Table
Table with time-series photometry
sid : int
13-digit WFCAM source ID of star to plot
xband : {'jmh', 'hmk'}
The x-axis array to use for the slope. Default 'k'.
yband : {'j', 'jmh', 'hmk'}
The y-axis array to use for the slope. Default 'hmk'.
flags : int, optional
Maximum ppErrBit quality flags to use (default 0).
verbose : bool. optional
Whether to print a verbose output. Default true.
Returns
-------
slope : float
Slope (in rise/run) of the linear fit.
intercept : float
Y-value where the linear fit intercepts the Y-axis.
slope_error : float
The standard error on the fitted slope: an indication of fit quality.
"""
if (xband not in ['jmh', 'hmk']) or (yband not in ['j', 'jmh', 'k']):
raise ValueError("Incorrect argument to `xband` or `yband`")
# define this dict thing
band_dict = {'j':'JAPERMAG3', 'k':'KAPERMAG3',
'jmh':'JMHPNT', 'hmk':'HMKPNT'}
# Loading data
s_table = data_cut (table, sid, season=0)
if len(s_table) == 0:
print "no data here"
return
j_table = band_cut( s_table, 'j', max_flag=flags)
h_table = band_cut( s_table, 'h', max_flag=flags)
# k_table = band_cut( s_table, 'k', max_flag=flags)
jh_table = band_cut( j_table, 'h', max_flag=flags)
hk_table = band_cut( h_table, 'k', max_flag=flags)
# jk_table = band_cut( j_table, 'k', max_flag=flags)
jhk_table = band_cut( jh_table, 'k', max_flag=flags)
if (xband, yband) == ('hmk', 'k'):
data = hk_table
elif (xband, yband) == ('jmh', 'j'):
data = jh_table
elif (xband, yband) == ('hmk', 'jmh'):
data = jhk_table
else:
data = jhk_table
print "Incorrect combination of `xband`, `yband`."
x_array = data.data[band_dict[xband]]
xerr_array = data.data[band_dict[xband]+"ERR"]
y_array = data.data[band_dict[yband]]
yerr_array = data.data[band_dict[yband]+"ERR"]
return slope(x_array, y_array, xerr_array, yerr_array, verbose)
def Stetson_machine ( s_table, flags=0) :
"""
Computes the Stetson index on the best combination of bands.
There's a lot of internal logic here on how to exactly accomplish
that, and especially which version of the Stetson index to even use.
Parameters
----------
s_table : atpy.Table
Table with time-series photometry of one star
flags : int, optional
Maximum ppErrBit quality flags to use (default 0)
Returns
-------
Stetson : float
The Stetson variability index (either "I" or "J" depending
on whether 2 or 3 bands were used).
choice : str {'jhk', 'hk', 'jh', 'jk'}
Which combination of bands is optimal.
stetson_nights : int
How many nights have all of the optimal combination
(and therefore, how many nights' worth of data is going into
the Stetson calculation)
"""
# First, slice the data to find how many nights have
# a given combination of bands.
j_table = band_cut( s_table, 'j', max_flag=flags)
h_table = band_cut( s_table, 'h', max_flag=flags)
k_table = band_cut( s_table, 'k', max_flag=flags)
jh_table = band_cut( j_table, 'h', max_flag=flags)
hk_table = band_cut( h_table, 'k', max_flag=flags)
jk_table = band_cut( j_table, 'k', max_flag=flags)
jhk_table = band_cut( jh_table, 'k', max_flag=flags)
# Then we'll measure how many nights are in each combination.
jh_len = len(jh_table)
hk_len = len(hk_table)
jk_len = len(jk_table)
jhk_len = len(jhk_table)
# The combination with the most nights (weighted by value^{1})
# will win. Ties are determined in order: JHK, HK, JH, JK
max_len = max(jh_len, hk_len, jk_len, jhk_len*2)
# Now note the winning choice, and compute the relevant index.
# If there are no simultaneous observations, choose the most-observed band
# and do a singleband 'Stetson'.
if max_len == 0:
j_len = len(j_table)
h_len = len(h_table)
k_len = len(k_table)
max_len_single = max(j_len, h_len, k_len)
if k_len == max_len_single:
choice = 'k'
vcol = k_table.KAPERMAG3
verr = k_table.KAPERMAG3ERR
elif h_len == max_len_single:
choice = 'h'
vcol = h_table.HAPERMAG3
verr = h_table.HAPERMAG3ERR
else:
choice = 'j'
vcol = j_table.JAPERMAG3
verr = j_table.JAPERMAG3ERR
Stetson = stetson.S_singleton(vcol, verr)
stetson_nights = max_len_single
elif 2*jhk_len == max_len:
choice = 'jhk'
jcol = jhk_table.JAPERMAG3; jerr = jhk_table.JAPERMAG3ERR
hcol = jhk_table.HAPERMAG3; herr = jhk_table.HAPERMAG3ERR
kcol = jhk_table.KAPERMAG3; kerr = jhk_table.KAPERMAG3ERR
Stetson = stetson.S(jcol, jerr, hcol, herr, kcol, kerr)
stetson_nights = jhk_len
else:
if hk_len == max_len:
choice = 'hk'
bcol = hk_table.HAPERMAG3; berr = hk_table.HAPERMAG3ERR
vcol = hk_table.KAPERMAG3; verr = hk_table.KAPERMAG3ERR
elif jh_len == max_len:
choice = 'jh'
bcol = jh_table.JAPERMAG3; berr = jh_table.JAPERMAG3ERR
vcol = jh_table.HAPERMAG3; verr = jh_table.HAPERMAG3ERR
elif jk_len == max_len:
choice = 'jk'
bcol = jk_table.JAPERMAG3; berr = jk_table.JAPERMAG3ERR
vcol = jk_table.KAPERMAG3; verr = jk_table.KAPERMAG3ERR
Stetson = stetson.I(bcol, berr, vcol, verr)
stetson_nights = max_len
# Finally, return S, the band choice, and how many nights
# are going into the Stetson calculation for that choice.
return (Stetson, choice, stetson_nights)
def statcruncher (table, sid, season=0, rob=True, per=True,
graded=False, colorslope=False, flags=0) :
"""
Calculates several statistical properties for a given star.
Will work with "lonely" datapoints (i.e. not all JHK mags are
well-defined). Optionally works with graded data, too!
Parameters
----------
table : atpy.Table
Table with time-series photometry
sid : int
13-digit WFCAM source ID of star to plot
season : int, optional
Which observing season of our dataset (1, 2, 3, or all).
Any value that is not the integers (1, 2, or 3) will be
treated as "no season", and no time-cut will be made.
Note that this is the default behavior.
rob : bool, optional
Use robust statistics, in addition to normal ones?
(takes longer, default True)
per : bool, optional
Run period-finding? Uses fast chi-squared and lomb-scargle.
(takes longer, default True)
graded : bool, optional
Also calculate Stetson indices using quality grades as weights?
Uses stetson_graded; requires that the data has been graded by
night_cleanser.null_cleanser_grader().
colorslope : bool, optional
Calculate color slopes? Runs them over (JvJ-H, KvH-K, J-HvH-K).
Make sure your data has been color-error-corrected! Default False.
flags : int, optional
Maximum ppErrBit quality flags to use (default 0)
Returns
-------
ret : data structure
Contains the computed values.
They can be accessed as attributes
(e.g., "ret.j_mean" or "ret.Stetson").
"""
s_table = data_cut ( table, sid, season=season)
if len(s_table) < 1:
print "no data for %d!" % sid
return None
# First, let's compute single-band statistics. This will require
# separate data_cuts on each band.
full_jtable = band_cut(s_table, 'j')
full_htable = band_cut(s_table, 'h')
full_ktable = band_cut(s_table, 'k')
j_table = band_cut(s_table, 'j', max_flag=flags)
h_table = band_cut(s_table, 'h', max_flag=flags)
k_table = band_cut(s_table, 'k', max_flag=flags)
jmh_table = band_cut(j_table, 'h', max_flag=flags)
hmk_table = band_cut(h_table, 'k', max_flag=flags)
# jhk_table used only for colorslope
jhk_table = band_cut( jmh_table, 'k', max_flag=flags)
# get a date (x-axis) for each
jdate = j_table.MEANMJDOBS
hdate = h_table.MEANMJDOBS
kdate = k_table.MEANMJDOBS
jmhdate = jmh_table.MEANMJDOBS
hmkdate = hmk_table.MEANMJDOBS
# date = s_table.MEANMJDOBS
# get a magnitude and magnitude error for each band
jcol = j_table.JAPERMAG3; jerr = j_table.JAPERMAG3ERR
hcol = h_table.HAPERMAG3; herr = h_table.HAPERMAG3ERR
kcol = k_table.KAPERMAG3; kerr = k_table.KAPERMAG3ERR
jmhcol= jmh_table.JMHPNT; jmherr = jmh_table.JMHPNTERR
hmkcol= hmk_table.HMKPNT; hmkerr = hmk_table.HMKPNTERR
# get the RA and DEC columns, checking for sensible values
racol= s_table.RA[(s_table.RA > 0) & (s_table.RA < 7)]
decol= s_table.DEC[(s_table.DEC > -4) & (s_table.DEC < 4)]
# Now let's get some ability to track errorful data.
# messy_table_j = band_cut( s_table, 'j')
# messy_table_h = band_cut( s_table, 'h')
# messy_table_k = band_cut( s_table, 'k')
# jppcol = messy_table_j.JPPERRBITS
# hppcol = messy_table_h.HPPERRBITS
# kppcol = messy_table_k.KPPERRBITS
# make an empty data structure and just assign it information, then return
# the object itself! then there's no more worrying about indices.
class Empty():
pass
ret = Empty()
# How many nights have observations in each band?
ret.N_j = len(j_table)
ret.N_h = len(h_table)
ret.N_k = len(k_table)
# What's the distribution of flags and nights?
js = full_jtable.JPPERRBITS
hs = full_htable.HPPERRBITS
ks = full_ktable.KPPERRBITS
ret.N_j_noflag = len(js[js == 0])
ret.N_h_noflag = len(hs[hs == 0])
ret.N_k_noflag = len(ks[ks == 0])
ret.N_j_info = len(js[(js < 256) & (js > 0)])
ret.N_h_info = len(hs[(hs < 256) & (hs > 0)])
ret.N_k_info = len(ks[(ks < 256) & (ks > 0)])
ret.N_j_warn = len(js[ js >= 256 ])
ret.N_h_warn = len(hs[ hs >= 256 ])
ret.N_k_warn = len(ks[ ks >= 256 ])
# Mean position of this source
ret.RA = racol.mean()
ret.DEC = decol.mean()
# Calculate the Stetson index...
S, choice, stetson_nights = Stetson_machine (s_table, flags)
ret.Stetson = S
ret.Stetson_choice = choice
ret.Stetson_N = stetson_nights
if graded:
# Calculate the graded Stetson index...
g_S, g_choice, g_stetson_nights = (
graded_Stetson_machine (s_table, flags) )
ret.graded_Stetson = g_S
ret.graded_Stetson_choice = g_choice
ret.graded_Stetson_N = g_stetson_nights
# Calculate PSTAR parameters
ret.pstar_mean = s_table.PSTAR.mean()
ret.pstar_median = np.median(s_table.PSTAR)
ret.pstar_rms = s_table.PSTAR.std()
# Create parallel data structures for each band, so we can iterate
ret.j = Empty(); ret.j.data = jcol; ret.j.err = jerr; ret.j.date = jdate
ret.h = Empty(); ret.h.data = hcol; ret.h.err = herr; ret.h.date = hdate
ret.k = Empty(); ret.k.data = kcol; ret.k.err = kerr; ret.k.date = kdate
ret.jmh = Empty(); ret.jmh.data=jmhcol; ret.jmh.err = jmherr
ret.hmk = Empty(); ret.hmk.data=hmkcol; ret.hmk.err = hmkerr
ret.jmh.date = jmhdate; ret.hmk.date = hmkdate
ret.j.N = ret.N_j ; ret.h.N = ret.N_h ; ret.k.N = ret.N_k
ret.jmh.N = len(jmh_table) ; ret.hmk.N = len(hmk_table)
bands = [ ret.j, ret.h, ret.k, ret.jmh, ret.hmk ]
for b in bands:
# use b.data, b.err
# if this band is empty, don't try to do the following assignments
if b.N == 0: continue
b.rchi2 = reduced_chisq( b.data, b.err )
b.mean = b.data.mean()
b.median = np.median(b.data) # dao
b.rms = b.data.std()
b.min = b.data.min()
b.max = b.data.max()
b.range = b.max - b.min
b.err_mean = b.err.mean() #dao
b.err_median = np.median(b.err) #dao
b.err_rms = b.err.std() #dao
b.err_min = b.err.min() #dao
b.err_max = b.err.max() #dao
b.err_range = b.err_max - b.err_min #dao
# Robust quantifiers simply have an "r" at the end of their names
if rob:
b.datar, b.indr = rb.removeoutliers(b.data, 3, niter=2, retind=True)
b.errr = b.err[b.indr]
b.meanr = rb.meanr(b.data)
b.medianr = rb.medianr(b.data) # dao
b.rmsr = rb.stdr(b.data)
b.minr = b.datar.min()
b.maxr = b.datar.max()
b.ranger = b.maxr - b.minr
b.err_meanr = b.errr.mean() # dao
b.err_medianr = np.median(b.errr) #dao
b.err_rmsr = b.errr.std() #dao
b.err_minr = b.errr.min() #dao
b.err_maxr = b.errr.max() #dao
b.err_ranger = b.err_maxr - b.err_minr #dao
# Period finding... is a little dodgy still, and might take forever
if per==True and b.N > 2:
hifac = lsp_tuning(b.date)
b.lsp = lsp(b.date, b.data, 6., hifac)
Jmax = lsp_mask(b.lsp[0], b.lsp[1])
b.lsp_per = 1./ b.lsp[0][Jmax]
b.lsp_pow = b.lsp[1][Jmax]
b.lsp_sig = getSignificance(b.lsp[0], b.lsp[1], b.lsp[2], 6.)[Jmax]
best_freq, chimin = test_analyze( b.date, b.data, b.err,
ret_chimin=True )
b.fx2_per, b.fx2_chimin = 1./best_freq, chimin
if colorslope:
# J vs J-H : use jmh_table exclusively
(ret.jjh_slope, a, ret.jjh_slope_err) = (
slope( jmh_table.JMHPNT, jmh_table.JAPERMAG3,
jmh_table.JMHPNTERR, jmh_table.JAPERMAG3ERR,
verbose=False) )
# K vs H-K : use hmk_table exclusively
(ret.khk_slope, a, ret.khk_slope_err) = (
slope( hmk_table.HMKPNT, hmk_table.KAPERMAG3,
hmk_table.HMKPNTERR, hmk_table.KAPERMAG3ERR,
verbose=False) )
# J-H vs H-K : use jhk_table exclusively
(ret.jhk_slope, a, ret.jhk_slope_err) = (
slope( jhk_table.HMKPNT, jhk_table.JMHPNT,
jhk_table.HMKPNTERR, jhk_table.JMHPNTERR,
verbose=False) )
# and the pp_max, using the messy table
# (slated for a re-implementation)
# ret.jpp_max = jppcol.max()
# ret.hpp_max = hppcol.max()
# ret.kpp_max = kppcol.max()
return ret
def f_comparison_observing_log():
"""
Zoomed out timeline comparing us to CHS2001 and YSOVAR time coverage.
"""
fig = plt.figure(figsize=(9,2.5))
chs01_start = 51580 #2000 Feb 6 in MJD
chs01_end = 51639 # 2000 Apr 8 in MJD
ysovar_spitzer_start = 55126 #2009 Oct 23 in MJD
ysovar_spitzer_end = 55163 # 2009 Dec 1 in MJD
ysovar_ukirt_start = 55123 #2009 Oct 20 in MJD
ysovar_ukirt_end = 55185 # 2009 Dec 22
ysovar_cfht_start = 55130 # 2009 Oct 27
ysovar_cfht_end = 55142 # 2009 Nov 8
j_wavelength = 1.1 #microns
h_wavelength = 1.6
ks_wavelength = 2.15
k_wavelength = 2.2
irac1_wavelength = 3.6
irac2_wavelength = 4.5
j_table = band_cut(variables_photometry, 'j')
h_table = band_cut(variables_photometry, 'h')
k_table = band_cut(variables_photometry, 'k')
wfcam_j_dates = np.array(list(set(j_table.MEANMJDOBS)))
wfcam_h_dates = np.array(list(set(h_table.MEANMJDOBS)))
wfcam_k_dates = np.array(list(set(k_table.MEANMJDOBS)))
# Our observations
plt.plot(wfcam_j_dates, j_wavelength*np.ones_like(wfcam_j_dates), 'b.')
plt.plot(wfcam_h_dates, h_wavelength*np.ones_like(wfcam_h_dates), 'g.')
plt.plot(wfcam_k_dates, k_wavelength*np.ones_like(wfcam_k_dates), 'r.')
# Carpenter
plt.plot([chs01_start, chs01_end], [j_wavelength]*2, 'b',lw=4)
plt.plot([chs01_start, chs01_end], [h_wavelength]*2, 'g', lw=4)
plt.plot([chs01_start, chs01_end], [ks_wavelength]*2, 'r', lw=4)
# YSOVAR
# spitzer
plt.plot([ysovar_spitzer_start, ysovar_spitzer_end],
[irac1_wavelength]*2, 'm', lw=4)
plt.plot([ysovar_spitzer_start, ysovar_spitzer_end],
[irac2_wavelength]*2, 'k', lw=4)
# UKIRT
plt.plot([ysovar_ukirt_start, ysovar_ukirt_end], [j_wavelength]*2,
'b', lw=4)
# CFHT
plt.plot([ysovar_cfht_start, ysovar_cfht_end], [j_wavelength]*2,
'b', lw=4)
plt.plot([ysovar_cfht_start, ysovar_cfht_end], [ks_wavelength]*2,
'r', lw=4)
# plt.gca().invert_yaxis()
plt.ylabel("Wavelength (microns)")
plt.ylim(6, 0.1)
xticks_values = [51544, 51910, 52275, 52640, 53005,
53371, 53736, 54101, 54466, 54832, 55197]
xticklabels_values = ["20%02d"%x for x in range(11)]
plt.xticks(xticks_values[::2], xticklabels_values[::2])
plt.title("History of infrared monitoring campaigns in the ONC")
plt.text(51550, 0.8, "Carpenter et al. 2001")
plt.text(55050, 0.8, "YSOVAR$^a$")
plt.text(54250, 0.8, "This paper")
plt.text(55220, 4.5, "[4.5]", color='k', size=14, verticalalignment='center')
plt.text(55220, 3.6, "[3.6]", color='m', size=14, verticalalignment='center')
plt.text(55200, 2.5, "$K_s$", color='r', size=16)
plt.text(55230, 1.3, "$J$", color='b', size=16)
plt.text(53900, 2.5, "$K$", color='red', size=16)
plt.text(53900, 1.8, "$H$", color='green', size=16)
plt.text(53915, 1.1, "$J$", color='blue', size=16)
plt.show()
return fig
def quadrant_corrector(data, j_constants, h_constants, k_constants,
min_grade=None, max_grade=None):
"""
Corrects magnitudes using a network of constant stars.
Parameters
----------
data : atpy.Table
Table with UKIRT time-series photometry.
j_constants, h_constants, k_constants : atpy.Table
Table with 'spreadsheet' information on J, H, and K constants.
Requires robust statistical information.
Must be pre-cleaned (we'll use all the constants you give us)
min_grade, max_grade : float, optional
What range of grades to correct data for. Default is all of them.
Returns
-------
new_data : atpy.Table
The corrected data table, with photometry corrected using the network.
"""
# Make a copy of the data table
new_data = data.where(data.SOURCEID != 0)
# new_data.add_column
# glue your set of constant lists together
cdict = {'j':j_constants, 'h':h_constants, 'k':k_constants}
for band in ['j', 'h', 'k']:
# Grab the timestamps we'll be iterating over
bdata = band_cut(data, band, max_flag=256)
col = band.upper()+"APERMAG3"
bandmean = band.lower()+"_meanr"
bandgrade = band.upper()+"GRADE"
timestamp_list = list(set(list(bdata.MEANMJDOBS)))
timestamp_list.sort()
for date in timestamp_list:
this_night = bdata.where(bdata.MEANMJDOBS == date)
# Can we skip this night due to a sufficient grade?
print( "min_grade: %f, max_grade: %f, grade: %f" %
(min_grade, max_grade, this_night.data[bandgrade][0]))
if min_grade == 0.0 and max_grade == 1.0:
pass
elif ((this_night.data[bandgrade][0] < min_grade) or
(this_night.data[bandgrade][0] > max_grade)):
print "Night %s skipped re:quality" % str(date)
continue
# first, grab the sourceids that are in this here night
source_list = this_night.SOURCEID
ra_list = this_night.RA
dec_list = this_night.DEC
print str(len(source_list)) +" sources on night %f" % date
# And also grab the constants that are in this here night!
ref_phot = cdict[band].where(
np.in1d(cdict[band].SOURCEID, source_list) )
for s, ra, dec in zip(source_list, ra_list, dec_list):
# Find four nearby constants (one in each column)
(sid_list, offset_list,
ra_list, dec_list) = quadrant_match(
np.degrees(ra), np.degrees(dec), ref_phot,
max_match=600)
# print offset_list, "sup"
# Get the deviation of each constant
deviation = []
for sid in sid_list:
this_stars_phot = this_night.where(
this_night.SOURCEID == sid)
this_stars_avg = ref_phot.where(
ref_phot.SOURCEID == sid)
deviation.append(this_stars_phot.data[col][0] -
this_stars_avg.data[bandmean][0])
# Calculate the relevant offset
adjustment = magnitude_adjustment(deviation, offset_list)
# Apply the offset to our working table
new_data.data[col][(new_data.SOURCEID == s) &
(new_data.MEANMJDOBS == date)] += adjustment
if s%23 == 0:
print ("%d: adjusted by %f mag (%s) on night %d using %d ref. stars" % (s, adjustment, band.upper(), date, len(offset_list)))
# break
return new_data
def exposure_grader(data, spreadsheet, band, min_mag=17):
"""
Investigates the quality of all exposures by checking
a) how many constant stars are detected
b) how many fall inside of, versus outside of, \pm .05 mag deviation
Very similar to count_constants_calc_ratio(), but this one goes on
a per-exposure, rather than per-night, basis.
Parameters
----------
data : atpy.Table
Table that contains all the photometry data.
spreadsheet : atpy.Table
Table that contains median photometry and stuff
band : str {'j'|'h'|'k'}
Which band to use.
Returns
-------
date : np.ndarray
Array of MJD timestamps corresponding to times of observation.
n_const : np.ndarray
Number of constant stars detected per exposure
ratio : np.ndarray
Ratio of (well-behaved)/(deviant) constants per exposure
"""
if band.lower() not in ('j','h','k'):
raise(ValueError("`band` must be 'j','h', or 'k'"))
col = band.upper()+"APERMAG3"
bandmean = band.lower()+"_meanr"
pperrbits = band.upper()+"PPERRBITS"
# First, let's make the list of dates that we are iterating through.
date_list = list(set(list(data.MEANMJDOBS)))
date_list.sort()
print len(date_list), " timestamps in this dataset"
dates = np.array(date_list)
n_const = np.zeros_like(dates, dtype='int')
ratio = np.zeros_like(dates, dtype='float')
# Now we iterate over our date list.
for night, i in zip(date_list, range(len(date_list))):
# Grab this night's photometry that corresponds to the input constant
# star list.
# relevant data
rdata = band_cut(data, band, max_flag=256)
this_nights_phot = rdata.where(
(rdata.MEANMJDOBS == night) &
(np.in1d(rdata.SOURCEID, spreadsheet.SOURCEID)) &
(rdata.data[col] < min_mag))
# Grab the spreadsheet info that corresponds exactly to this night's
# stars. ("reference photometry")
ref_phot = spreadsheet.where(
np.in1d(spreadsheet.SOURCEID, this_nights_phot.SOURCEID) )
print "For night %s:" % night
print len(this_nights_phot), len(ref_phot)
# Now compute the deviation for each star.
# (I'd love to do this in an array-based way, but some stars have
# two observations per night, and that breaks my array-based method...)
deviation = np.zeros_like( ref_phot.data[bandmean] )
for j in range(len(deviation)):
this_stars_phot = this_nights_phot.where(
this_nights_phot.SOURCEID == ref_phot.SOURCEID[j])
deviation[j] = (
this_stars_phot.data[col][0] - ref_phot.data[bandmean][j])
# NOW count how many stars there are and the ratio that fall inside
# versus outside the \pm .05 mag bands
n_const[i] = len(deviation)
goods = len( deviation[np.abs(deviation) < 0.05] )
if n_const[i] > 0:
ratio[i] = (goods / n_const[i])
else:
ratio[i] = 0
return dates, n_const, ratio
def mapmaker(data, spreadsheet, band, path, min_mag=17):
"""
A function to make variability map movies.
Produces only the PNGs required to make the movies. In order
to glue them together, manually run a command like:
mencoder mf://*.png -mf fps=1:type=png -ovc copy -o k_movie_slow.avi
to create a .avi video.
Parameters
----------
data : atpy.Table
Table that contains all the photometry data.
spreadsheet : atpy.Table
Table that contains median photometry and stuff
band : str {'j'|'h'|'k'}
Which band to use.
path : string
Place to save to.
"""
if not (len(band)==1 and type(band) is str):
raise(ValueError)
col = band.upper()+"APERMAG3"
bandmean = band.lower()+"_meanr"
pperrbits = band.upper()+"PPERRBITS"
# First, let's make the list of dates that we are iterating through.
date_list = list(set(list(np.trunc(data.MEANMJDOBS))))
date_list.sort()
# Now we iterate over our date list.
for night in date_list:
# Grab this night's photometry that corresponds to the input constant
# star list.
# relevant data
rdata = band_cut(data, band, max_flag=256)
this_nights_phot = rdata.where(
(np.trunc(rdata.MEANMJDOBS) == night) &
(np.in1d(rdata.SOURCEID, spreadsheet.SOURCEID)) &
(rdata.data[col] < min_mag))
# Grab the spreadsheet info that corresponds exactly to this night's
# stars. ("reference photometry")
ref_phot = spreadsheet.where(
np.in1d(spreadsheet.SOURCEID, this_nights_phot.SOURCEID) )
print "For night %s:" % night
print len(this_nights_phot), len(ref_phot)
# Now compute the deviation for each star.
# (I'd love to do this in an array-based way, but some stars have
# two observations per night, and that breaks my array-based method...)
deviation = np.zeros_like( ref_phot.data[bandmean] )
for i in range(len(deviation)):
this_stars_phot = this_nights_phot.where(
this_nights_phot.SOURCEID == ref_phot.SOURCEID[i])
deviation[i] = (
this_stars_phot.data[col][0] - ref_phot.data[bandmean][i])
try:
fig = plt.figure()
plt.scatter(np.degrees(ref_phot.RA), np.degrees(ref_phot.DEC),
s = (19 - ref_phot.data[bandmean])**2,
c = deviation, cmap='RdBu_r', vmin=-0.15, vmax=0.15)
cbar = plt.colorbar()
cbar.set_label("Deviation from mean magnitude")
cbar.ax.invert_yaxis()
fig.gca().invert_xaxis()
fig.gca().set_aspect('equal')
plt.xlabel("Right Ascension (degrees)")
plt.ylabel("Declination (degrees)")
plt.xlim(84.3, 83.2)
plt.ylim(-5.95, -4.9)
plt.title("Night: MJD = %s (%d since 01/01/2000)" %
(str(night), night - 51544))
plt.savefig(path+'%s.png' % str(night))
plt.close()
except ValueError:
continue
def deviation_plot(data, spreadsheet, band, path, min_mag=17):
"""
Plots the deviation of each constant star as a function of magnitude.
Parameters
----------
data : atpy.Table
Table that contains all the photometry data.
spreadsheet : atpy.Table
Table that contains median photometry and stuff
band : str {'j'|'h'|'k'}
Which band to use.
path : string
Place to save to.
"""
if not (len(band)==1 and type(band) is str):
raise(ValueError)
col = band.upper()+"APERMAG3"
bandmean = band.lower()+"_meanr"
pperrbits = band.upper()+"PPERRBITS"
colordict = {'k':'r', 'h':'g', 'j':'b'}
# First, let's make the list of dates that we are iterating through.
date_list = list(set(list(np.trunc(data.MEANMJDOBS))))
date_list.sort()
# Now we iterate over our date list.
for night in date_list:
# Grab this night's photometry that corresponds to the input constant
# star list.
# relevant data
rdata = band_cut(data, band, max_flag=256)
this_nights_phot = rdata.where(
(np.trunc(rdata.MEANMJDOBS) == night) &
(np.in1d(rdata.SOURCEID, spreadsheet.SOURCEID)) &
(rdata.data[col] < min_mag))
# Grab the spreadsheet info that corresponds exactly to this night's
# stars. ("reference photometry")
ref_phot = spreadsheet.where(
np.in1d(spreadsheet.SOURCEID, this_nights_phot.SOURCEID) )
print "For night %s:" % night
print len(this_nights_phot), len(ref_phot)
# Now compute the deviation for each star.
# (I'd love to do this in an array-based way, but some stars have
# two observations per night, and that breaks my array-based method...)
deviation = np.zeros_like( ref_phot.data[bandmean] )
for i in range(len(deviation)):
this_stars_phot = this_nights_phot.where(
this_nights_phot.SOURCEID == ref_phot.SOURCEID[i])
deviation[i] = (
this_stars_phot.data[col][0] - ref_phot.data[bandmean][i])
try:
fig = plt.figure()
plt.plot( ref_phot.data[bandmean], deviation,
colordict[band.lower()]+'.')
plt.plot( [5, 20], [0, 0], 'k--')
plt.plot( [5, 20], [0.05, 0.05], 'k:')
plt.plot( [5, 20], [-0.05, -0.05], 'k:')
plt.xlabel("Mean %s magnitude" % band.upper())
plt.ylabel("Deviation (mag)")
plt.title("Night: MJD = %s (%d since 01/01/2000)" %
(str(night), night - 51544))
plt.xlim(11, min_mag+0.25)
plt.ylim(-1,1)
plt.gca().invert_yaxis()
plt.savefig(path+'%s_dev.png' % str(night))
plt.close()
# if night > 54040.0:
# break
except:
continue
return None
