opsimsummary.summarize_opsim module

Class to summarize the OpSim output

class opsimsummary.summarize_opsim.SynOpSim(pointings, opsimversion='lsstv3', raCol='ditheredRA', decCol='ditheredDec', angleUnit='degrees', indexCol='obsHistID', usePointingTree=False, subset='None')

Bases: object

Class with a summary of OpSim data base output.

Attributes:

pointingTree

if self.usePointingTree is False, this is set to None. Otherewise

Methods

df_subset_columns(df, subset)	Return the dataframe df with only a subset of the columns as specified in the list subset
fromOpSimDB(dbname[, subset, opsimversion, …])	Class Method to instantiate this from an OpSim sqlite database output which largely uses OpSimOutput.fromOpSimDB
observedVisitsinRegion([nside, nest, …])	return a pd.DataFrame with the healpixelID=’hid’, ra and dec of the healpixels ‘hpix_ra’, ‘hpix_dec’ in radians, and count the number of visits for all healpixels that have visits satisfying minVisits <= count <= maxVisits .
pointingsEnclosing(ra, dec[, circRadius, …])	Helper method returning a generator of pointings overlapping with circles of radius circRadius around a sequence of positions given in terms of `ra and dec.
sampleRegion([numFields, minVisits, nest, …])	This method samples a number numFields fields provided they have a minimal number of visits minVisits

static df_subset_columns(df, subset)

Return the dataframe df with only a subset of the columns as specified in the list subset

Parameters:

df : pd.DataFrame

the input dataframe to be returned with subset of the columns

subset: (list of strings| ‘all’)

if ‘all’, df is returned. Otherwise, return df[subset], with the same index.

..note: subset=[] returns only the index

classmethod fromOpSimDB(dbname, subset='combined', opsimversion='lsstv3', zeroDDFDithers=True, user_propIDs=None, dithercolumns=None, add_dithers=False, tableNames=('Summary', 'Proposal'), usePointingTree=False, **kwargs)

Class Method to instantiate this from an OpSim sqlite database output which largely uses OpSimOutput.fromOpSimDB

Parameters:

dbname : string

absolute path to database file

subset : string, optional, defaults to ‘combined’

one of {‘_all’, ‘unique_all’, ‘wfd’, ‘ddf’, ‘combined’} determines a sequence of propIDs for selecting observations appropriate for the OpSim database in use

propIDs : sequence of integers, defaults to None

proposal ID values. If present, overrides the use of subset

tableNames : tuple of strings, defaults to (‘Summary’, ‘Proposal’)

names of tables read from the OpSim database

zeroDDFDithers : bool, defaults to True

if True, set dithers in DDF to 0, by setting ditheredRA, ditheredDec to fieldRA, fieldDec

opsimversion: {‘lsstv3’|’sstf’|’lsstv4’}

version of OpSim corresponding to the output format.

dithercolumns: `pd.DataFrame`, defaults to None

a pandas dataframe with the columns ditheredRA, ditheredDec and index obsHistID

add_dithers : Bool, defaults to False

if add_dithers is True, the ditheredRA, ditheredDec columns will be removed and additional dithered columns either by dithercolumns or get_dithercolumns will be used.

observedVisitsinRegion(nside=256, nest=True, minVisits=1, maxVisits=None, outFile=None, writeFile=False)

return a pd.DataFrame with the healpixelID=’hid’, ra and dec of the healpixels ‘hpix_ra’, ‘hpix_dec’ in radians, and count the number of visits for all healpixels that have visits satisfying minVisits <= count <= maxVisits . This will also write out a csv outFile clobbering past version with this dataframe.

pointingTree

if self.usePointingTree is False, this is set to None. Otherewise contains a PointingTree Object. This contains a BallTree of the pointings, and a method to find all pointings enclosed in a given radii.

pointingsEnclosing(ra, dec, circRadius=0.0, pointingRadius=1.75, usePointingTree=None, transform=None, subset='all')

Helper method returning a generator of pointings overlapping with circles of radius circRadius around a sequence of positions given in terms of `ra and dec. The calculation uses a Tree to make the calculations more efficient if usePointingTree is True, or uses direct calculations if this variable is set to False. A user may choose to obtain a subset of the pointing by supplying a subset in the form a list via the parameter subset.

Parameters:

ra : np.ndarray or a float, unit of degrees

a float or an array of floats representing the ra values

dec : np.ndarray or a float, unit of degrees

a float or an array of floats representing the dec values

circRadius: float, unit of degrees, defaults to 0.

a circle around each of the

pointingRadius : degrees, defaults to 1.75

radius of the field of view

usePointingTree: {None|True|False}, defaults to `None`

if None, usePointingTree = self.usePointingTree else the variable takes the {True|False} values assigned

transform: function, Not implemented

subset: (list of strings| ‘all’)

if ‘all’, df is returned. Otherwise, return df[subset], with the same index.

Returns:

A generator with the pointings that overlap with ra, dec

.. note: 1. the values in the generator may be accessed by next(generator)

2. subset=[] returns only the index

sampleRegion(numFields=50000, minVisits=1, nest=True, nside=256, rng=<mtrand.RandomState object>, outfile=None, usePointingTree=True, subset='wfd', mwebv=0.0)

This method samples a number numFields fields provided they have a minimal number of visits minVisits

Parameters:

numFields : int, mandatory

Number of fields to sample

minVisits : int, number, defaults to 1

minimal number of visits required to consider the tile.

nest : Bool, defaults to True

use the nest method rather than ring

nside : int, defaults to 256

Healpix.NSIDE

rng :

outfile :

usePointingTree : Bool, defaults to True

whether to use PointingTree or not

subset : {‘wfd’|’ddf’|’combined’}

which subset to use.

class opsimsummary.summarize_opsim.PointingTree(pointings, raCol='_ra', decCol='_dec', indexCol='obsHistID', leafSize=50)

Bases: object

Methods

pointingsEnclosing(ra, dec, circRadius[, …])	Parameters:
validatePointings(pointings, raCol, decCol)	Validates pointings as having required properties according to list of tests below

pointingsEnclosing(ra, dec, circRadius, pointingRadius=1.75)

Parameters:

ra : float or sequence, degrees

ra of the coordinates

dec : float or sequence, degrees

dec of the coordinates

circRadius : degrees, mandatory

radius of circle around point

pointingRadius : degrees, defaults to 1.75

radius of the field of view

static validatePointings(pointings, raCol, decCol)

Validates pointings as having required properties according to list of tests below

Parameters:

raCol : column name that should be in pointings

decCol : column name that should be in pointings

List of Tests:

1. raCol and decCol should be in the columns of `pointings`

opsimsummary.summarize_opsim.add_simlibCols(opsimtable, pixSize=0.2)

Parameters:

opsimtable: `~pandas.DataFrame` object, mandatory

containing an opsim Output of version X. The main requirements here are that the columns ‘finSeeing’, ‘fiveSigmaDepth’, and ‘filtSkyBrightness’ are defined. If the opsim output has differently named variables or transformed variables, these should be changed to meet the criteria.

pixSize: float, optional, defaults to LSST value of 0.2

pixel Size in units of arc seconds.

Returns:

DataFrame with additional columns of ‘simLibPsf’, ‘simLibZPTAVG’, and

‘simLibSkySig’

.. note :: This was written from a piece of f77 code by David

Cinabro sent by email on May 26, 2015.