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# 

# Developed for the LSST Data Management System. 

# This product includes software developed by the LSST Project 

# (http://www.lsst.org). 

# See the COPYRIGHT file at the top-level directory of this distribution 

# for details of code ownership. 

# 

# This program is free software: you can redistribute it and/or modify 

# it under the terms of the GNU General Public License as published by 

# the Free Software Foundation, either version 3 of the License, or 

# (at your option) any later version. 

# 

# 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. See the 

# GNU General Public License for more details. 

# 

# You should have received a copy of the GNU General Public License 

# along with this program. If not, see <http://www.gnu.org/licenses/>. 

# 

 

"""Classes for taking science pipeline outputs and creating data products for 

use in ap_association and the alert production database (APDB). 

""" 

 

__all__ = ["MapApDataConfig", "MapApDataTask", 

"MapDiaSourceConfig", "MapDiaSourceTask", 

"UnpackApdbFlags"] 

 

import numpy as np 

import os 

import yaml 

 

import lsst.afw.table as afwTable 

from lsst.daf.base import DateTime 

import lsst.pipe.base as pipeBase 

import lsst.pex.config as pexConfig 

from lsst.pex.exceptions import RuntimeError 

from lsst.utils import getPackageDir 

from .afwUtils import make_dia_source_schema 

 

 

class MapApDataConfig(pexConfig.Config): 

"""Configuration for the generic MapApDataTask class. 

""" 

copyColumns = pexConfig.DictField( 

keytype=str, 

itemtype=str, 

doc="Mapping of input SciencePipelines columns to output DPDD " 

"columns.", 

default={"id": "id", 

"parent": "parent", 

"coord_ra": "coord_ra", 

"coord_dec": "coord_dec"} 

) 

 

 

class MapApDataTask(pipeBase.Task): 

"""Generic mapper class for copying values from a science pipelines catalog 

into a product for use in ap_association or the APDB. 

""" 

ConfigClass = MapApDataConfig 

_DefaultName = "mapApDataTask" 

 

def __init__(self, inputSchema, outputSchema, **kwargs): 

pipeBase.Task.__init__(self, **kwargs) 

self.inputSchema = inputSchema 

self.outputSchema = outputSchema 

 

self.mapper = afwTable.SchemaMapper(inputSchema, outputSchema) 

 

for inputName, outputName in self.config.copyColumns.items(): 

self.mapper.addMapping( 

self.inputSchema.find(inputName).key, 

outputName, 

True) 

 

def run(self, inputCatalog, exposure=None): 

"""Copy data from the inputCatalog into an output catalog with 

requested columns. 

 

Parameters 

---------- 

inputCatalog: `lsst.afw.table.SourceCatalog` 

Input catalog with data to be copied into new output catalog. 

 

Returns 

------- 

outputCatalog: `lsst.afw.table.SourceCatalog` 

Output catalog with data copied from input and new column names. 

""" 

outputCatalog = afwTable.SourceCatalog(self.outputSchema) 

outputCatalog.extend(inputCatalog, self.mapper) 

 

if not outputCatalog.isContiguous(): 

raise RuntimeError("Output catalogs must be contiguous.") 

 

return outputCatalog 

 

 

class MapDiaSourceConfig(pexConfig.Config): 

"""Config for the DiaSourceMapperTask 

""" 

copyColumns = pexConfig.DictField( 

keytype=str, 

itemtype=str, 

doc="Mapping of input SciencePipelines columns to output DPDD " 

"columns.", 

default={"id": "id", 

"parent": "parent", 

"coord_ra": "coord_ra", 

"coord_dec": "coord_dec", 

"slot_Centroid_x": "x", 

"slot_Centroid_xErr": "xErr", 

"slot_Centroid_y": "y", 

"slot_Centroid_yErr": "yErr", 

"slot_ApFlux_instFlux": "apFlux", 

"slot_ApFlux_instFluxErr": "apFluxErr", 

"slot_PsfFlux_instFlux": "psFlux", 

"slot_PsfFlux_instFluxErr": "psFluxErr", 

"ip_diffim_DipoleFit_orientation": "dipAngle", 

"ip_diffim_DipoleFit_chi2dof": "dipChi2", 

"ip_diffim_forced_PsfFlux_instFlux": "totFlux", 

"ip_diffim_forced_PsfFlux_instFluxErr": "totFluxErr", 

"ip_diffim_DipoleFit_flag_classification": "isDipole", 

"slot_Shape_xx": "ixx", 

"slot_Shape_xxErr": "ixxErr", 

"slot_Shape_yy": "iyy", 

"slot_Shape_yyErr": "iyyErr", 

"slot_Shape_xy": "ixy", 

"slot_Shape_xyErr": "ixyErr", 

"slot_PsfShape_xx": "ixxPSF", 

"slot_PsfShape_yy": "iyyPSF", 

"slot_PsfShape_xy": "ixyPSF"} 

) 

calibrateColumns = pexConfig.ListField( 

dtype=str, 

doc="Flux columns in the input catalog to calibrate.", 

default=["slot_ApFlux", "slot_PsfFlux", "ip_diffim_forced_PsfFlux"] 

) 

flagMap = pexConfig.Field( 

dtype=str, 

doc="Yaml file specifying SciencePipelines flag fields to bit packs.", 

default=os.path.join(getPackageDir("ap_association"), 

"data", 

"association-flag-map.yaml"), 

) 

dipFluxPrefix = pexConfig.Field( 

dtype=str, 

doc="Prefix of the Dipole measurement column containing negative and " 

"positive flux lobes.", 

default="ip_diffim_DipoleFit", 

) 

dipSepColumn = pexConfig.Field( 

dtype=str, 

doc="Column of the separation of the negative and positive poles of " 

"the dipole.", 

default="ip_diffim_DipoleFit_separation" 

) 

 

 

class MapDiaSourceTask(MapApDataTask): 

"""Task specific for copying columns from science pipelines catalogs, 

calibrating them, for use in ap_association and the APDB. 

 

This task also copies information from the exposure such as the ExpsoureId 

and the exposure date as specified in the DPDD. 

""" 

 

ConfigClass = MapDiaSourceConfig 

_DefaultName = "mapDiaSourceTask" 

 

def __init__(self, inputSchema, **kwargs): 

MapApDataTask.__init__(self, 

inputSchema=inputSchema, 

outputSchema=make_dia_source_schema(), 

**kwargs) 

self._create_bit_pack_mappings() 

 

def _create_bit_pack_mappings(self): 

"""Setup all flag bit packings. 

""" 

self.bit_pack_columns = [] 

with open(self.config.flagMap) as yaml_stream: 

table_list = list(yaml.load_all(yaml_stream)) 

for table in table_list: 

if table['tableName'] == 'DiaSource': 

self.bit_pack_columns = table['columns'] 

break 

 

# Test that all flags requested are present in both the input and 

# output schemas. 

for outputFlag in self.bit_pack_columns: 

try: 

self.outputSchema.find(outputFlag['columnName']) 

except KeyError: 

raise KeyError( 

"Requested column %s not found in MapDiaSourceTask output " 

"schema. Please check that the requested output column " 

"exists." % outputFlag['columnName']) 

bitList = outputFlag['bitList'] 

for bit in bitList: 

try: 

self.inputSchema.find(bit['name']) 

except KeyError: 

raise KeyError( 

"Requested column %s not found in MapDiaSourceTask input " 

"schema. Please check that the requested input column " 

"exists." % outputFlag['columnName']) 

 

def run(self, inputCatalog, exposure, return_pandas=False): 

"""Copy data from the inputCatalog into an output catalog with 

requested columns. 

 

Parameters 

---------- 

inputCatalog : `lsst.afw.table.SourceCatalog` 

Input catalog with data to be copied into new output catalog. 

exposure: `lsst.afw.image.Exposure` 

Exposure with containing the PhotoCalib object relevant to this 

catalog. 

return_pandas : `bool` 

Return `pandas.DataFrame` instead of `lsst.afw.table.SourceCatalog` 

 

Returns 

------- 

outputCatalog: `lsst.afw.table.SourceCatalog` or `pandas.DataFrame` 

Output catalog with data copied from input and new column names. 

""" 

visit_info = exposure.getInfo().getVisitInfo() 

ccdVisitId = visit_info.getExposureId() 

midPointTaiMJD = visit_info.getDate().get(system=DateTime.MJD) 

filterId = exposure.getFilter().getId() 

filterName = exposure.getFilter().getName() 

wcs = exposure.getWcs() 

 

photoCalib = exposure.getPhotoCalib() 

 

outputCatalog = afwTable.SourceCatalog(self.outputSchema) 

outputCatalog.reserve(len(inputCatalog)) 

 

for inputRecord in inputCatalog: 

outputRecord = outputCatalog.addNew() 

outputRecord.assign(inputRecord, self.mapper) 

self.calibrateFluxes(inputRecord, outputRecord, photoCalib) 

self.computeDipoleFluxes(inputRecord, outputRecord, photoCalib) 

self.computeDipoleSep(inputRecord, outputRecord, wcs) 

self.bitPackFlags(inputRecord, outputRecord) 

outputRecord.set("ccdVisitId", ccdVisitId) 

outputRecord.set("midPointTai", midPointTaiMJD) 

outputRecord.set("filterId", filterId) 

outputRecord.set("filterName", filterName) 

 

if not outputCatalog.isContiguous(): 

raise RuntimeError("Output catalogs must be contiguous.") 

 

if return_pandas: 

return self._convert_to_pandas(outputCatalog) 

return outputCatalog 

 

def calibrateFluxes(self, inputRecord, outputRecord, photoCalib): 

"""Copy flux values into an output record and calibrate them. 

 

Parameters 

---------- 

inputRecord : `lsst.afw.table.SourceRecord` 

Record to copy flux values from. 

outputRecord : `lsst.afw.table.SourceRecord` 

Record to copy and calibrate values into. 

photoCalib : `lsst.afw.image.PhotoCalib` 

Calibration object from the difference exposure. 

""" 

for col_name in self.config.calibrateColumns: 

meas = photoCalib.instFluxToNanojansky(inputRecord, col_name) 

outputRecord.set(self.config.copyColumns[col_name + "_instFlux"], 

meas.value) 

outputRecord.set( 

self.config.copyColumns[col_name + "_instFluxErr"], 

meas.error) 

 

def computeDipoleFluxes(self, inputRecord, outputRecord, photoCalib): 

"""Calibrate and compute dipole mean flux and diff flux. 

 

Parameters 

---------- 

inputRecord : `lsst.afw.table.SourceRecord` 

Record to copy flux values from. 

outputRecord : `lsst.afw.table.SourceRecord` 

Record to copy and calibrate values into. 

photoCalib `lsst.afw.image.PhotoCalib` 

Calibration object from the difference exposure. 

""" 

 

neg_meas = photoCalib.instFluxToNanojansky( 

inputRecord, self.config.dipFluxPrefix + "_neg") 

pos_meas = photoCalib.instFluxToNanojansky( 

inputRecord, self.config.dipFluxPrefix + "_pos") 

outputRecord.set( 

"dipMeanFlux", 

0.5 * (np.abs(neg_meas.value) + np.abs(pos_meas.value))) 

outputRecord.set( 

"dipMeanFluxErr", 

0.5 * np.sqrt(neg_meas.error ** 2 + pos_meas.error ** 2)) 

outputRecord.set( 

"dipFluxDiff", 

np.abs(pos_meas.value) - np.abs(neg_meas.value)) 

outputRecord.set( 

"dipFluxDiffErr", 

np.sqrt(neg_meas.error ** 2 + pos_meas.error ** 2)) 

 

def computeDipoleSep(self, inputRecord, outputRecord, wcs): 

"""Convert the dipole separation from pixels to arcseconds. 

 

Parameters 

---------- 

inputRecord : `lsst.afw.table.SourceRecord` 

Record to copy flux values from. 

outputRecord : `lsst.afw.table.SourceRecord` 

Record to copy and calibrate values into. 

wcs : `lsst.afw.geom.SkyWcs` 

Wcs of image inputRecords was observed. 

""" 

pixScale = wcs.getPixelScale(inputRecord.getCentroid()) 

dipSep = pixScale * inputRecord.get(self.config.dipSepColumn) 

outputRecord.set("dipLength", dipSep.asArcseconds()) 

 

def bitPackFlags(self, inputRecord, outputRecord): 

"""Pack requested flag columns in inputRecord into single columns in 

outputRecord. 

 

Parameters 

---------- 

inputRecord : `lsst.afw.table.SourceRecord` 

Record to copy flux values from. 

outputRecord : `lsst.afw.table.SourceRecord` 

Record to copy and calibrate values into. 

""" 

for outputFlag in self.bit_pack_columns: 

bitList = outputFlag['bitList'] 

value = 0 

for bit in bitList: 

value += inputRecord[bit['name']] * 2 ** bit['bit'] 

outputRecord.set(outputFlag['columnName'], value) 

 

def _convert_to_pandas(self, inputCatalog): 

"""Convert input afw table to pandas. 

 

Using afwTable.toAstropy().to_pandas() alone is not sufficient to 

properly store data in the Apdb. We must also convert the RA/DEC values 

from radians to degrees and rename several columns. 

 

Parameters 

---------- 

inputCatalog : `lsst.afw.table.SourceCatalog` 

Catalog to convert to panads and rename columns. 

 

Returns 

------- 

catalog : `pandas.DataFrame` 

""" 

catalog = inputCatalog.asAstropy().to_pandas() 

catalog.rename(columns={"coord_ra": "ra", 

"coord_dec": "decl", 

"id": "diaSourceId", 

"parent": "parentDiaSourceId"}, 

inplace=True) 

catalog["ra"] = np.degrees(catalog["ra"]) 

catalog["decl"] = np.degrees(catalog["decl"]) 

 

return catalog 

 

 

class UnpackApdbFlags: 

"""Class for unpacking bits from integer flag fields stored in the Apdb. 

 

Attributes 

---------- 

flag_map_file : `str` 

Absolute or relative path to a yaml file specifiying mappings of flags 

to integer bits. 

table_name : `str` 

Name of the Apdb table the integer bit data are coming from. 

""" 

 

def __init__(self, flag_map_file, table_name): 

self.bit_pack_columns = [] 

with open(flag_map_file) as yaml_stream: 

table_list = list(yaml.load_all(yaml_stream)) 

for table in table_list: 

if table['tableName'] == table_name: 

self.bit_pack_columns = table['columns'] 

break 

 

self.output_flag_columns = {} 

 

for column in self.bit_pack_columns: 

names = [] 

for bit in column["bitList"]: 

names.append((bit["name"], np.bool)) 

self.output_flag_columns[column["columnName"]] = names 

 

def unpack(self, input_flag_values, flag_name): 

"""Determine individual boolean flags from an input array of unsigned 

ints. 

 

Parameters 

---------- 

input_flag_values : array-like of type uint 

Input integer flags to unpack. 

flag_name : `str` 

Apdb column name of integer flags to unpack. Names of packed int 

flags are given by the flag_map_file. 

 

Returns 

------- 

output_flags : `numpy.ndarray` 

Numpy named tuple of booleans. 

""" 

bit_names_types = self.output_flag_columns[flag_name] 

output_flags = np.zeros(len(input_flag_values), dtype=bit_names_types) 

 

for bit_idx, (bit_name, dtypes) in enumerate(bit_names_types): 

masked_bits = np.bitwise_and(input_flag_values, 2 ** bit_idx) 

output_flags[bit_name] = masked_bits 

 

return output_flags