def calculate_stuff(splits=10, start=0):
'''
Runs the spreadsheet, first splitting it into `splits`
spreadsheets and then joining them.
'''
if type(splits) is not int or type(start) is not int:
raise TypeError
# We are going to split this into 10 smaller pieces through the magic of mod operations! woo.
split_data = []
spreadsheets = []
for i in range(start, splits):
data_i = data.where(data.SOURCEID % splits == i)
split_data.append(data_i)
lookup_i = sp.base_lookup(data_i)
# The parameter "-1" is the season that tells data_cut not to make
# any cuts on the data.
sp_i = sp.spreadsheet_write(data_i,
lookup_i,
-1,
path2 + 'sp%d.fits' % i,
flags=256,
per=True,
graded=False,
rob=True,
colorslope=True)
# EEEEE this is a flag to come and find this section of code
try:
now = datetime.datetime.strftime(datetime.datetime.now(),
"%Y-%m-%d %H:%M:%S")
except:
now = 'sometime'
print "finished chunk %d at %s" % (i, now)
def calculate_stuff( splits = 10, start=0 ):
'''
Runs the spreadsheet, first splitting it into `splits`
spreadsheets and then joining them.
'''
if type(splits) is not int or type(start) is not int:
raise TypeError
# We are going to split this into 10 smaller pieces through the magic of mod operations! woo.
split_data = []
spreadsheets = []
for i in range(start, splits):
data_i = data.where(data.SOURCEID % splits == i)
split_data.append(data_i)
lookup_i = sp.base_lookup(data_i)
# The parameter "-1" is the season that tells data_cut not to make
# any cuts on the data.
sp_i = sp.spreadsheet_write(data_i, lookup_i, -1,
path2+'sp%d.fits'%i, flags=256,
per=True, graded=False, rob=True,
colorslope=True)
# EEEEE this is a flag to come and find this section of code
try:
now = datetime.datetime.strftime(datetime.datetime.now(),
"%Y-%m-%d %H:%M:%S")
except:
now = 'sometime'
print "finished chunk %d at %s" % (i, now)
def tables():
"""
Creates spreadsheets for Wise, Aspin, wise_trans, wise_extras,
and rwa_sources that include color-slope terms and robust stats.
"""
# wise disks
wise_disks_lookup = atpy.Table()
wise_disks_lookup.add_column("SOURCEID", wise_disks[rwd])
wise_disks_lookup.add_column("Designation", wise_disks_names[rwd])
spread3.spreadsheet_write(
watso, wise_disks_lookup, 0,
path2+"Wise/spreadsheet_withcolors.fits",
flags=256, rob=True, colorslope=True, per=True)
# wise special case
wise_v_lookup = atpy.Table()
wise_v_lookup.add_column("SOURCEID", wise_v)
wise_v_lookup.add_column("Designation", wise_v_names)
spread3.spreadsheet_write(
data, wise_v_lookup, 0,
path2+"Wise/spreadsheet_withcolors_wise29434.fits",
flags=256, rob=True, colorslope=True, per=True)
# aspins
aspin_sources_lookup = atpy.Table()
aspin_sources_lookup.add_column("SOURCEID", aspin_sources[ras])
aspin_sources_lookup.add_column("Designation", aspin_sources_names[ras])
spread3.spreadsheet_write(
watso, aspin_sources_lookup, 0,
path2+"Aspin/spreadsheet_withcolors.fits",
flags=256, rob=True, colorslope=True, per=True)
# wise trans
wise_trans_lookup = atpy.Table()
wise_trans_lookup.add_column("SOURCEID", wise_trans[rwt])
wise_trans_lookup.add_column("Designation", wise_trans_names[rwt])
spread3.spreadsheet_write(
watso, wise_trans_lookup, 0,
path2+"transition/spreadsheet_withcolors.fits",
flags=256, rob=True, colorslope=True, per=True)
# wise extras
wise_extras_lookup = atpy.Table()
wise_extras_lookup.add_column("SOURCEID", wise_extras[rwe])
wise_extras_lookup.add_column("Designation", wise_extras_names[rwe])
spread3.spreadsheet_write(
data, wise_extras_lookup, 0,
path2+"Wise_extras/spreadsheet_withcolors.fits",
flags=256, rob=True, colorslope=True)
# special case of whatevers - this is gonna be annoying to disentangle.
# I'll have to see what the error behavior of statcruncher is when
# there's no data for a star. Hopefully null values and no crashing.
welo_lookup = atpy.Table()
welo_lookup.add_column("SOURCEID", welo_sources)
welo_lookup.add_column("Designation", welo_names)
spread3.spreadsheet_write(
welo, welo_lookup, 0,
path2+"Wise_extras/spreadsheet_withcolors_lonely.fits",
flags=256, rob=True, colorslope=True)
# rwa dudes
rwa_lookup = atpy.Table()
rwa_lookup.add_column("SOURCEID", rwa_sources)
rwa_lookup.add_column("Designation", rwa_names)
spread3.spreadsheet_write(
rwa_data, rwa_lookup, 0,
path2+"RWA_sources/spreadsheet_withcolors.fits",
flags=256, rob=True, colorslope=True, per=True)
print "Did the spreadsheets!"
def do_it_all( table, sid_list, name_list, path='',
option=['lc','tables','phase'] ):
"""
Does some stuff. Not sure exactly what yet, but I'll
want it to make tons of plots and tables for a list of
SOURCEIDs and their corresponding data.
Parameters
----------
table : atpy.Table
The WFCAM time-series data to be extracted
sid_list : (list or array) of int
SOURCEIDs of stars to be analyzed
name_list : list of str
Names that correspond to each SOURCEID: will be used in
the filename and title section of each plot, and in the tables
path : str
The parent file path to save the output to.
Please make sure it has a "/" at the end of it.
option : list of str, optional
Which sub-components of this function you'd like to actually
call. Default is all (lc, tables, phase).
Returns
-------
I don't know yet. Probably nothing.
Produces a bunch of files, though!
Notes:
The input data must already be cleaned via some other method.
"""
# until we figure out orion seasons...
season = 0
if path=='' or type(path) is not str:
print "`path` must be a string. Exiting without action."
return
# Force `path` to have a trailing forward slash
path = path.rstrip('/')+'/'
## First, make directories for everything (or try, at least).
# Structure of directories:
# path/lc
# path/phase
# path/tables
# within each of those (now it's a *),
# path/*/(s1 | s2 | s3 | s123)
# Within lc/s*, we put the files directly in.
# Within phase/s*, we do
# path/phase/s*/(h_fx2 | h_lsp | j_fx2 | j_lsp | k_fx2 | k_lsp | lsp_power)
# Within tables/s*, we put the files directly in.
mkdir_p(path+"lc/")
mkdir_p(path+"phase/")
mkdir_p(path+"tables/")
# ss = ['s1', 's2', 's3', 's123']
types = ['h_fx2', 'h_lsp', 'j_fx2', 'j_lsp', 'k_fx2', 'k_lsp', 'lsp_power']
# for s in ss:
mkdir_p(path+"lc/")
mkdir_p(path+"tables/")
for t in types:
mkdir_p("%sphase/%s"%(path,t))
# We should now be done making directories. Let's test this.
# Tested! Woo.
## Second, let's make tables.
tables = path+"tables/"
if 'tables' in option:
tables = path+"tables/"
# Make a lookup table. It should have a column with the `name_list`
# parameter we fed into this function, as well as a column with the
# SOURCEIDs.
# They should be named "SOURCEID" and "Designation", respectively.
lookup = atpy.Table()
lookup.add_column("SOURCEID", sid_list)
lookup.add_column("Designation", name_list)
# for season, s in zip([1,2,3,123], ss):
# Write the spreadsheet and save it to the relevant directory.
spread3.spreadsheet_write(table, lookup, season,
tables+s+'/spreadsheet.fits',
flags=256, per=True)
# What command do we want to make plots?
# Probably plot3.lc and plot3.phase, which are going to be almost
# identical to plot2 equivalents except that they can handle missing data
# and flags and stuff.
## Third, make lightcurves.
# And put the gorram Stetson index in the title!
# for season, s in zip([1,2,3,123], ss):
s_stats = atpy.Table(tables+'/spreadsheet.fits')
for name, sid in zip(name_list, sid_list):
# The specific plot command we use here depends a lot
# on what functions are available.
if 'lc' in option:
tplot.graded_lc(table, sid, season=0, name=name, #flags=16,
outfile=path+"lc/"+name, png_too=True,
abridged=True, color_slope=True)
#png, eps, pdf
# A bunch of the following code will be substantially rewritten
# once "spread3" is functional.
# Well... spread3 is now functional!
if 'phase' in option:
for t in types:
if t == 'lsp_power':
tplot.lsp_power(table, sid, season=0, name=name,
outfile=path+"phase/lsp_power/"+name,
png_too=True)
else:
per = s_stats.data[t+"_per"][s_stats.SOURCEID == sid]
tplot.graded_phase(table, sid, period=per, season=0,
name=name, color_slope=True,
outfile=path+"phase/"+t+"/"+name,
png_too=True)
# elif t == 'k_fx2':
# tplot.phase(table, sid, season=season, name=name,
# outfile=path+"phase/"+s+"/k_fx2/"+name,
# png_too=True)
# elif t == 'k_lsp':
# tplot.phase(table, sid, season=season, name=name,
# period='lsp',
# outfile=path+"phase/"+s+"/k_lsp/"+name,
# png_too=True)
# else:
# pass
return
