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120 lines
4.1 KiB
Python
120 lines
4.1 KiB
Python
#!/usr/bin/env python
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#+
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# VIDE -- Void IDentification and Examination -- ./python_tools/void_pipeline/datasets/example_observation.py
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# Copyright (C) 2010-2014 Guilhem Lavaux
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# Copyright (C) 2011-2014 P. M. Sutter
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#
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# This program is free software; you can redistribute it and/or modify
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# it under the terms of the GNU General Public License as published by
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# the Free Software Foundation; version 2 of the License.
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#
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#
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# This program is distributed in the hope that it will be useful,
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# but WITHOUT ANY WARRANTY; without even the implied warranty of
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# MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE. See the
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# GNU General Public License for more details.
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#
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# You should have received a copy of the GNU General Public License along
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# with this program; if not, write to the Free Software Foundation, Inc.,
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# 51 Franklin Street, Fifth Floor, Boston, MA 02110-1301 USA.
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#+
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import os
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import numpy as np
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from backend.classes import *
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# if True, will scan log files for last known completed state and run from there
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continueRun = False
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# stages:
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# 1 : extract redshift slices from data
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# 2 : void extraction using zobov
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# 3 : removal of small voids and voids near the edge
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startCatalogStage = 1
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endCatalogStage = 3
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basePath = os.path.dirname(os.path.abspath(__file__))
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# directory for input data files
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inputDataDir = basePath
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# void catalog output directory
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workDir = basePath
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# output directory for log files
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logDir = os.path.join(workDir,"logs","example_observation")
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# output directory for figures
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figDir = os.path.join(workDir,"figs","example_observation")
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# optimization: maximum number of parallel threads to use
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numZobovThreads = 8
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# optimization: number of subdivisions of the volume
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numZobovDivisions = 2
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# Maximum density for merging voids
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# 0 (equivalent to infinitely large value) -> Merge everything (no threshold)
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# 1e-9 (or smaller != 0) -> Do not merge anything
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mergingThreshold = 1e-9
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# when trimming away voids near the bounaries, what multiple of the radius to
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# use for safety
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boundaryTolerance = 1.0
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# don't change this
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dataSampleList = []
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# define your data samples
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newSample = Sample(
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# path to galaxy file is inputDataDir+dataFile
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dataFile = "example_observation.dat",
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# full name for this sample
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fullName = "example_observation",
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# a convenient nickname
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nickName = "exobs",
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# don't change this or nothing will make sense
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dataType = "observation",
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# assume sample is volume-limited?
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volumeLimited = True,
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# HEALpix mask file - set to None to auto-compute
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# NOTE: auto-computed masks are pretty terrible, so
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# only do that if you have no other options
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#maskFile = "",
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maskFile = inputDataDir+"/example_observation_mask.fits",
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# resolution for HEALpix mapping of survey edge contours
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# Set to -1 to use nside from given fits file
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# MUST be set if auto-computing mask
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#nsideForContour = -1,
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nsideForContour = 128,
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# radial selection function (if not volume limited)
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selFunFile = None,
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# max and min redshifts of galaxies in your sample
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zBoundary = (0.0, 0.15),
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# width of redshift boundaries to flag edge galaxies
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# (interpreted as boundaryWidth*(zMax-zMin))
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boundaryWidth = 0.01,
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# leave this at -1 for mean particle separation,
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# or specify your own in Mpc/h
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minVoidRadius = -1,
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# if true, convert to comoving space using LCDM cosmology
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useComoving = True,
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# cosmology assuming flat universe
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omegaM = 0.3,
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)
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dataSampleList.append(newSample)
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# repeat the above block for any other samples
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