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added cross-comparison utilities
This commit is contained in:
P.M. Sutter
parent
cbc7a0299b
commit
1c0964ba54
2 changed files with 96 additions and 20 deletions
79
python_tools/void_python_tools/voidUtil/matchUtil.py
Executable file
79
python_tools/void_python_tools/voidUtil/matchUtil.py
Executable file
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#!/usr/bin/env python
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#+
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# VIDE -- Void IDentification and Examination -- ./crossCompare/analysis/mergerTree.py
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# Copyright (C) 2010-2013 Guilhem Lavaux
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# Copyright (C) 2011-2013 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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from void_python_tools.backend import *
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import imp
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import pickle
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import os
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import matplotlib.pyplot as plt
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import numpy as np
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import argparse
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def compareCatalogs(baseCatalogDir, compareCatalogDir,
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outputDir="./", logDir="./",
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matchMethod="useID", dataPortion="central",
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strictMatch=True,
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pathToCTools="../../../c_tools")
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# reports the overlap between two void catalogs
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# baseCatalogDir: directory of catalog 1
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# compareCatagDir: directory of catalog 2
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# outputDir: directory to place outputs
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# logDir: directory to place log files
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# matchMethod: "useID" to use unique IDs, "prox" to use overlap of Voronoi cells
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# dataPortion: "central" or "all"
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# strictMatch: if True, only attempt to match to trimmed catalog
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# pathToCTools: path to location of VIDE c_tools directory
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if not os.access(outputDir, os.F_OK):
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os.makedirs(outputDir)
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if not os.access(logDir, os.F_OK):
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os.makedirs(logDir)
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outFileName = outputDir + "/" + "voidOverlap" #+ ".dat"
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with open(baseCatalogDir+"/sample_info.dat", 'rb') as input:
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baseSample = pickle.load(input)
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with open(compareCatalogDir+"/sample_info.dat", 'rb') as input:
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sample = pickle.load(input)
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print " Comparing", baseSample.fullName, "to", sample.fullName, "...",
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sys.stdout.flush()
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sampleName = sample.fullName
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binPath = pathToCTools+"/analysis/voidOverlap"
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logFile = logDir+"/compare_"+baseSample.fullName+"_"+sampleName+".out"
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stepOutputFileName = outFileName + "_" + baseSample.fullName + "_" + \
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sampleName + "_"
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launchVoidOverlap(baseSample, sample, baseCatalogDir,
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compareCatalogDir, binPath,
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thisDataPortion=dataPortion, logFile=logFile,
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continueRun=False, workDir=workDir,
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outputFile=stepOutputFileName,
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matchMethod=matchMethod,
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strictMatch=strictMatch)
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print " Done!"
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368
python_tools/void_python_tools/voidUtil/xcorUtil.py
Normal file
368
python_tools/void_python_tools/voidUtil/xcorUtil.py
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@ -0,0 +1,368 @@
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#+
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# VIDE -- Void IDentification and Examination -- ./analysis/xcor.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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#!/usr/bin/env python
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#+
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# VIDE -- Void IDentification and Examination -- ./crossCompare/analysis/mergerTree.py
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# Copyright (C) 2010-2013 Guilhem Lavaux
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# Copyright (C) 2011-2013 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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from void_python_tools.backend import *
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from void_python_tools.plotting import *
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import void_python_tools.xcor as xcorlib
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import imp
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import pickle
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import argparse
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import os
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import string
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import numpy as np
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import matplotlib as mpl
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mpl.use('Agg')
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import matplotlib.pyplot as plt
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import matplotlib.cm as cm
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from matplotlib import rc
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from matplotlib.ticker import NullFormatter
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import random
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import sys
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# ------------------------------------------------------------------------------
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def computeCrossCor(catalogDir,
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outputDir="./", logDir="./",
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matchMethod="useID", dataPortion="central",
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strictMatch=True,
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pathToCTools="../../../c_tools")
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# Computes void-void and void-matter(galaxy) correlations
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# baseCatalogDir: directory of catalog
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# compareCatagDir: directory of catalog 2
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# outputDir: directory to place outputs
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# logDir: directory to place log files
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# matchMethod: "useID" to use unique IDs, "prox" to use overlap of Voronoi cells
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# dataPortion: "central" or "all"
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# strictMatch: if True, only attempt to match to trimmed catalog
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# pathToCTools: path to location of VIDE c_tools directory
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if not os.access(outputDir, os.F_OK):
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os.makedirs(outputDir)
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with open(catalogDir+"/sample_info.dat", 'rb') as input:
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sample = pickle.load(input)
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print " Working with", sample.fullName, "...",
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sys.stdout.flush()
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sampleName = sample.fullName
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# Sim parameters
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Lbox = sample.boxLen # Boxlength [h^(-1)Mpc]
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Lbox -= 2*Lboxcut # Reduced boxlength [h^(-1)Mpc]
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Om = sample.omegaM # Omega_m
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Ol = 1.-Om # Omega_l
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z = sample.zRange[0] # Redshift
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a = 1./(1.+z) # Scale factor
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rho_m = Mpart*(Ni/Lbox)**3 # Background density [(h/Mpc)^3]
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# Input files
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voidDir = voidBaseDir+'/'+sampleDir+'/'
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voidFilename1 = 'centers_central_'+sample.fullName+'.out'
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voidFilename2 = 'voidDesc_central_'+sample.fullName+'.out'
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# Read files
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matter_file = open(matterDir+matterFilename,'r')
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matter_data = []
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count = 0
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sep = 1e8
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for (i,line) in enumerate(matter_file):
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if i/int(sep) == i/sep: print str(round(i*100./Ni**3,1))+' percent of all particles read'
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if random.random() > ss: continue
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count += 1
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matter_data.append(line.split(',')[0:3])
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print str(count)+' particles read in total'
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matter_data = np.asarray(matter_data,dtype=np.float32)
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matter_file.close()
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halo_file = open(haloDir+haloFilename,'r')
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halo_data = np.reshape(halo_file.read().replace('\n',',').split(',')[0:-1],(-1,12)).astype(np.float32)
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halo_file.close()
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void_file = open(voidDir+voidFilename1,'r')
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void_header1 = void_file.readline().split(",")
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void_data1 = np.reshape(void_file.read().split(),(-1,len(void_header))).astype(np.float32)
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void_file.close()
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void_file = open(voidDir+voidFilename2,'r')
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void_header2 = void_file.readline().split(",")+void_file.readline().split(",")
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void_data2 = np.reshape(void_file.read().split(),(-1,11)).astype(np.float32)
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void_file.close()
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# Define arrays
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xm = matter_data[:,0:3]
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xh = halo_data[:,0:3]
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mh = halo_data[:,6]
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xv = void_data1[:,0:3]
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rv = void_data1[:,4]
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vv = void_data1[:,6]
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mv = void_data2[:,8]*Mpart
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# Interpolate to mesh
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dm, wm, ws = xcorlib.cic(xm, Lbox, Lboxcut = Lboxcut, Nmesh = Nmesh)
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dh, wm, ws = xcorlib.cic(xh, Lbox, Lboxcut = Lboxcut, Nmesh = Nmesh)
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dv, wm, ws = xcorlib.cic(xv, Lbox, Lboxcut = Lboxcut, Nmesh = Nmesh)
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# Load dark matter grid
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#output = open('dm_'+str(Nmesh)+'_ss'+str(ss)+'_z'+str(z)+'.dat', 'rb')
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#dm = pickle.load(output)
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#output.close()
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# Save dark matter grid
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#output = open('dm_'+str(Nmesh)+'_ss'+str(ss)+'_z'+str(z)+'.dat', 'wb')
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#pickle.dump(dm,output)
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#output.close()
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# Power spectra & correlation functions
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((Nm, km, Pmm, SPmm),(Nmx, rm, Xmm, SXmm)) = xcorlib.powcor(dm, dm, Lbox, Nmesh = Nmesh, Nbin = Nbin, scale = 'lin', cor = True)
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((Nm, km, Pvm, SPvm),(Nmx, rm, Xvm, SXvm)) = xcorlib.powcor(dv, dm, Lbox, Nmesh = Nmesh, Nbin = Nbin, scale = 'lin', cor = True)
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((Nm, km, Phm, SPhm),(Nmx, rm, Xhm, SXhm)) = xcorlib.powcor(dh, dm, Lbox, Nmesh = Nmesh, Nbin = Nbin, scale = 'lin', cor = True)
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((Nm, km, Pvv, SPvv),(Nmx, rm, Xvv, SXvv)) = xcorlib.powcor(dv, dv, Lbox, Nmesh = Nmesh, Nbin = Nbin, scale = 'lin', cor = True)
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((Nm, km, Pvh, SPvh),(Nmx, rm, Xvh, SXvh)) = xcorlib.powcor(dv, dh, Lbox, Nmesh = Nmesh, Nbin = Nbin, scale = 'lin', cor = True)
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((Nm, km, Phh, SPhh),(Nmx, rm, Xhh, SXhh)) = xcorlib.powcor(dh, dh, Lbox, Nmesh = Nmesh, Nbin = Nbin, scale = 'lin', cor = True)
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# Number densities
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nm = np.empty(len(km))
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nh = np.empty(len(km))
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nv = np.empty(len(km))
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nm[:] = Npart/Lbox**3
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nh[:] = len(xh)/Lbox**3
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nv[:] = len(xv)/Lbox**3
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# Number functions
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Mbin = 40
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Vbin = 40
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Nh, Mh = np.histogram(mh, bins = mh.min()*(mh.max()/mh.min())**(np.arange(Mbin+1)/float(Mbin)))
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Nvm, Mv = np.histogram(mv, bins = mv.min()*(mv.max()/mv.min())**(np.arange(Vbin+1)/float(Vbin)))
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Nvv, Vv = np.histogram(vv, bins = vv.min()*(vv.max()/vv.min())**(np.arange(Vbin+1)/float(Vbin)))
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# Bias
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b_hh = np.sqrt(Phh/Pmm)
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b_vv = np.sqrt(Pvv/Pmm)
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b_hm = Phm/Pmm
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b_vm = Pvm/Pmm
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b_vh = Pvh/Phh
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knl = 0.04 # Wavenumber above which nonlinearities kick in [h/Mpc]
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idls = np.where(km <= knl)[0]
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bm_hm = np.average(b_hm[idls],weights=Nm[idls])
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bm_vm = np.average(b_vm[idls],weights=Nm[idls])
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bm_vh = np.average(b_vh[idls],weights=Nm[idls])
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# Shot Noise
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sn_hh = Phh - Phm**2/Pmm
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sn_vh = Pvh - Pvm*Phm/Pmm
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sn_vv = Pvv - Pvm**2/Pmm
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# Plots
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ms = 4
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mew = 0.2
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margin = 1.2
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kmin = km.min()/margin
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kmax = km.max()*margin
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rmin = rm.min()/margin
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rmax = rm.max()*margin
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plt.imshow(np.sum(dm+1,2)/Nmesh,extent=[0,Lbox,0,Lbox],aspect='equal',cmap='YlGnBu_r',interpolation='gaussian')
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plt.xlabel(r'$x \;[h^{-1}\mathrm{Mpc}]$')
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plt.ylabel(r'$y \;[h^{-1}\mathrm{Mpc}]$')
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plt.title(r'Dark matter')
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plt.colorbar()
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plt.savefig(outputDir+'/dm_'+sample.fullName+'.pdf', format='pdf', bbox_inches="tight")
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plt.clf()
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plt.imshow(np.sum(dv+1,2)/Nmesh,extent=[0,Lbox,0,Lbox],aspect='equal',cmap='YlGnBu_r',interpolation='gaussian')
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plt.xlabel(r'$x \;[h^{-1}\mathrm{Mpc}]$')
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plt.ylabel(r'$y \;[h^{-1}\mathrm{Mpc}]$')
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plt.title(r'Voids')
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plt.colorbar()
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plt.savefig(outputDir+'/dv_'+sample.fullName+'.pdf', format='pdf', bbox_inches="tight")
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plt.clf()
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plt.imshow(np.sum(dh+1,2)/Nmesh,extent=[0,Lbox,0,Lbox],aspect='equal',cmap='YlGnBu_r',interpolation='gaussian')
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plt.xlabel(r'$x \;[h^{-1}\mathrm{Mpc}]$')
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plt.ylabel(r'$y \;[h^{-1}\mathrm{Mpc}]$')
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plt.title(r'Halos')
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plt.colorbar()
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plt.savefig(outputDir+'/dh_'+sample.fullName+'.pdf', format='pdf', bbox_inches="tight")
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plt.clf()
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pa, = plt.plot(Mh[:-1], Nh, 'ro-', ms=ms, mew=mew)
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pb, = plt.plot(Vv[:-1]*1e9, Nvv, 'bo-', ms=ms, mew=mew)
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plt.xlabel(r'$M \;[h^{-1}M_{\odot}]$ , $V \;[h^{-3}\mathrm{kpc}^3]$')
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plt.ylabel(r'$N(M,V)$')
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plt.title(r'Number of halos and voids')
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plt.xscale('log')
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plt.yscale('log')
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plt.xlim(min(10**np.floor(np.log10(Vv.min()))*1e9,10**np.floor(np.log10(Mh.min()))), max(10**np.ceil(np.log10(Mh.max())),10**np.ceil(np.log10(Vv.max()))*1e8))
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plt.ylim(10**np.floor(np.log10(Nh.min())), 10**np.ceil(np.log10(Nh.max())))
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plt.annotate(r'$\frac{4\pi}{3}\langle r_\mathrm{v}\rangle^3$', xy=(4./3.*np.pi*rv.mean()**3*1e9,1.1), xytext=(-50,235),textcoords='offset points',arrowprops=dict(fc='k',arrowstyle="->",connectionstyle="angle,angleA=0,angleB=90,rad=10"))
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plt.legend([pa,pb],['halos','voids'],'best' )
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plt.savefig(outputDir+'/number_'+sample.fullName+'.pdf', format='pdf', bbox_inches="tight")
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plt.clf()
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plt.subplot(211)
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plt.subplots_adjust(wspace=0,hspace=0)
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plt.plot(np.sort(vv), mv[np.argsort(vv)], 'r-', ms=ms, mew=mew, lw=0.01)
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plt.plot(np.sort(vv), np.sort(mv), 'k-', ms=ms, mew=mew)
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plt.plot(np.sort(vv), np.sort(vv)*1e9, 'k--', ms=ms, mew=mew)
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plt.title(r'Mass-volume relation of voids')
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plt.ylabel(r'$M \;[h^{-1}M_\odot]$')
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plt.xscale('log')
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plt.yscale('log')
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plt.subplot(211).xaxis.set_major_formatter(NullFormatter())
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plt.subplot(212)
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plt.plot(np.sort(vv), mv[np.argsort(vv)]/np.sort(vv)/rho_m-1., 'b-', ms=ms, mew=mew, lw=0.01)
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plt.plot(np.sort(vv), np.sort(mv)/np.sort(vv)/rho_m-1., 'k-', ms=ms, mew=mew)
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plt.xlabel(r'$V \;[h^{-3}\mathrm{Mpc}^3]$')
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plt.ylabel(r'$\delta$')
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plt.xscale('log')
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plt.yscale('linear')
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plt.ylim(-1.01,-0.861)
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plt.savefig(outputDir+'/massvol_'+sample.fullName+'.pdf', format='pdf', bbox_inches="tight")
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plt.clf()
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pa ,= plt.plot(km, Phh, 'r-', ms=ms, mew=mew)
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plt.plot(km, Phh-sn_hh, 'r:', ms=ms, mew=mew)
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plt.fill_between(km, Phh+SPhh, abs(Phh-SPhh), color='r', alpha=0.2)
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pb ,= plt.plot(km, Phm, 'y-', ms=ms, mew=mew)
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plt.fill_between(km, Phm+SPhm, abs(Phm-SPhm), color='y', alpha=0.2)
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pc ,= plt.plot(km, Pmm, 'k-', ms=ms, mew=mew)
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plt.plot(km, Pmm-1./nm, 'k:', ms=ms, mew=mew)
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plt.fill_between(km, Pmm+SPmm, abs(Pmm-SPmm), color='k', alpha=0.2)
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pd ,= plt.plot(km, Pvh, 'g-', ms=ms, mew=mew)
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plt.plot(km, -Pvh, 'g--', ms=ms, mew=mew)
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plt.plot(km, abs(Pvh-sn_vh), 'g:', ms=ms, mew=mew)
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plt.fill_between(km, abs(Pvh+SPvh), abs(Pvh-SPvh), color='g', alpha=0.2)
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pe ,= plt.plot(km, Pvm, 'm-', ms=ms, mew=mew)
|
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plt.plot(km, -Pvm, 'm--', ms=ms, mew=mew)
|
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plt.fill_between(km, abs(Pvm+SPvm), abs(Pvm-SPvm), color='m', alpha=0.2)
|
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pf ,= plt.plot(km, Pvv, 'b-', ms=ms, mew=mew)
|
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plt.plot(km, Pvv-sn_vv, 'b:', ms=ms, mew=mew)
|
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plt.fill_between(km, Pvv+SPvv, abs(Pvv-SPvv), color='b', alpha=0.2)
|
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plt.annotate(r'$\frac{\pi}{\langle r_\mathrm{v}\rangle}$', xy=(np.pi/(rv.mean()),1.01*10**np.floor(np.log10(abs(Pvh).min()))/margin), xytext=(10,280),textcoords='offset points',arrowprops=dict(fc='k',arrowstyle="->",connectionstyle="angle,angleA=0,angleB=90,rad=10"))
|
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plt.xlabel(r'$k \;[h\mathrm{Mpc}^{-1}]$')
|
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plt.ylabel(r'$P(k) \;[h^{-3}\mathrm{Mpc}^3]$')
|
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plt.title(r'Power spectra')
|
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plt.xscale('log')
|
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plt.yscale('log')
|
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plt.xlim(kmin,kmax)
|
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plt.ylim(10**np.floor(np.log10(abs(Pvh).min()))/margin, max(10**np.ceil(np.log10(Phh.max())),10**np.ceil(np.log10(Pvv.max())))*margin)
|
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plt.legend([pa, pb, pc, pd, pe, pf],['hh', 'hm', 'mm', 'vh', 'vm', 'vv'],'lower left' )
|
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plt.savefig(outputDir+'/power_'+sample.fullName+'.pdf', format='pdf', bbox_inches="tight")
|
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plt.clf()
|
||||
|
||||
|
||||
pa ,= plt.plot(rm, Xhh, 'r-', ms=ms, mew=mew)
|
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plt.fill_between(rm, abs(Xhh+SXhh), abs(Xhh-SXhh), color='r', alpha=0.2)
|
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pb ,= plt.plot(rm, Xhm, 'y-', ms=ms, mew=mew)
|
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plt.fill_between(rm, abs(Xhm+SXhm), abs(Xhm-SXhm), color='y', alpha=0.2)
|
||||
pc ,= plt.plot(rm, Xmm, 'k-', ms=ms, mew=mew)
|
||||
plt.fill_between(rm, abs(Xmm+SXmm), abs(Xmm-SXmm), color='k', alpha=0.2)
|
||||
pd ,= plt.plot(rm, Xvh, 'g-', ms=ms, mew=mew)
|
||||
plt.plot(rm, -Xvh, 'g--', ms=ms, mew=mew)
|
||||
plt.fill_between(rm, abs(Xvh+SXvh), abs(Xvh-SXvh), color='g', alpha=0.2)
|
||||
pe ,= plt.plot(rm, Xvm, 'm-', ms=ms, mew=mew)
|
||||
plt.plot(rm, -Xvm, 'm--', ms=ms, mew=mew)
|
||||
plt.fill_between(rm, abs(Xvm+SXvm), abs(Xvm-SXvm), color='m', alpha=0.2)
|
||||
pf ,= plt.plot(rm, Xvv, 'b-', ms=ms, mew=mew)
|
||||
plt.fill_between(rm, abs(Xvv+SXvv), abs(Xvv-SXvv), color='b', alpha=0.2)
|
||||
plt.annotate(r'$\langle r_\mathrm{v}\rangle$', xy=(rv.mean(),1.01*10**np.floor(np.log10(abs(Xvh).min()))/margin), xytext=(10,300),textcoords='offset points',arrowprops=dict(fc='k',arrowstyle="->",connectionstyle="angle,angleA=0,angleB=90,rad=10"))
|
||||
plt.xlabel(r'$r \;[h^{-1}\mathrm{Mpc}]$')
|
||||
plt.ylabel(r'$\xi(r)$')
|
||||
plt.title(r'Correlation functions')
|
||||
plt.xscale('log')
|
||||
plt.yscale('log')
|
||||
plt.xlim(rmin,rmax)
|
||||
plt.ylim(10**np.floor(np.log10(abs(Xvh).min()))/margin, max(10**np.ceil(np.log10(Xhh.max())),10**np.ceil(np.log10(Xvv.max())))*margin)
|
||||
plt.legend([pa, pb, pc, pd, pe, pf],['hh', 'hm', 'mm', 'vh', 'vm', 'vv'],'lower left' )
|
||||
plt.savefig(outputDir+'/correlation_'+sample.fullName+'.pdf', format='pdf', bbox_inches="tight")
|
||||
plt.clf()
|
||||
|
||||
|
||||
pa, = plt.plot(km, b_hh, 'r-', ms=ms, mew=mew)
|
||||
pb, = plt.plot(km, b_hm, 'r--', ms=ms, mew=mew)
|
||||
pc, = plt.plot(km, b_vv, 'b-', ms=ms, mew=mew)
|
||||
pd, = plt.plot(km, b_vm, 'b--', ms=ms, mew=mew)
|
||||
pe, = plt.plot(km, b_vh/bm_vh, 'g-', ms=ms, mew=mew)
|
||||
plt.plot(km, np.sin(km*rv.mean())/(km*rv.mean()), 'k:', ms=ms, mew=mew)
|
||||
plt.xlabel(r'$k \;[h\mathrm{Mpc}^{-1}]$')
|
||||
plt.ylabel(r'$b(k)$')
|
||||
plt.title(r'Bias')
|
||||
plt.xscale('log')
|
||||
plt.yscale('linear')
|
||||
plt.xlim(kmin,kmax)
|
||||
plt.ylim(np.floor(b_vm.min()),np.ceil(max(b_hh.max(),b_vv.max())))
|
||||
plt.legend([pa,pb,pc,pd,pe],['hh', 'hm', 'vv', 'vm', r'$\bar{u}_\mathrm{v}(k)$'],'best' )
|
||||
plt.savefig(outputDir+'/bias_'+sample.fullName+'.pdf', format='pdf', bbox_inches="tight")
|
||||
plt.clf()
|
||||
|
||||
|
||||
pa, = plt.plot(km, sn_hh, 'r-', ms=ms, mew=mew)
|
||||
pb, = plt.plot(km, sn_vh, 'g-', ms=ms, mew=mew)
|
||||
pc, = plt.plot(km, sn_vv, 'b-', ms=ms, mew=mew)
|
||||
plt.plot(km, abs(sn_vh), 'g:')
|
||||
pd, = plt.plot(km, 1/nh, 'r--')
|
||||
pe, = plt.plot(km, 1/nv, 'b-.')
|
||||
plt.xlabel(r'$k \;[h\mathrm{Mpc}^{-1}]$')
|
||||
plt.ylabel(r'$\sigma^2(k)$')
|
||||
plt.title(r'Shotnoise')
|
||||
plt.xscale('log')
|
||||
plt.yscale('log')
|
||||
plt.xlim(kmin,kmax)
|
||||
plt.ylim(10**np.floor(np.log10(abs(sn_vh).min())), 10**np.ceil(np.log10(sn_vv.max())))
|
||||
plt.legend([pa,pb,pc,pd,pe],['hh', 'vh', 'vv', r'$\bar{n}_\mathrm{h}^{-1}$', r'$\bar{n}_\mathrm{v}^{-1}$'],'best' )
|
||||
plt.savefig(outputDir+'/shotnoise_'+sample.fullName+'.pdf', format='pdf', bbox_inches="tight")
|
||||
plt.clf()
|
||||
Loading…
Add table
Add a link
Reference in a new issue