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3dce2593d9
Some general housekeeping, making sure some new parameters are passed around correctly, and removing the storage of some unused files. This update is considered HIGHLY UNSTABLE. It will almost certainly break somewhere for simulations. Still under active development.
127 lines
4.3 KiB
Python
127 lines
4.3 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 = 2
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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 = 2
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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 = 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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# max and min redshifts where you want to find voids
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zRange = (0.1, 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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# density of mock particles in cubic Mpc/h
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# (make this as high as you can afford)
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### DEPRECATED
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fakeDensity = 0.05,
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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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