Slight re-organization of C/C++ tools. Significant modifications to support observational data. Python and pipeline scripts added

python_tools/void_python_tools/apTools/profiles/__init__.py

@@ -0,0 +1,6 @@
+from build import *
+from draw import *
+from fit import *
+from mcmc import *
+from generateExpFigure import *
+from getSurveyProps import *

python_tools/void_python_tools/apTools/profiles/getSurveyProps.py

@@ -0,0 +1,53 @@
+import numpy as np
+import healpy as healpy
+import scipy.integrate
+
+__all__=['getSurveyProps']
+   
+# returns the volume and galaxy density for a given redshit slice
+def getSurveyProps(maskFile, zmin, zmax, selFunMin, selFunMax, portion, selectionFuncFile=None):
+
+  mask = healpy.read_map(maskFile)
+  area = (1.*np.size(np.where(mask > 0)) / np.size(mask)) * 4.*np.pi
+  zmin = zmin * 3000
+  zmax = zmax * 3000
+  volume = area * (zmax**3  - zmin**3) / 3
+
+  if selectionFuncFile != None:
+    selfunc = np.genfromtxt(selectionFuncFile)
+    selfunc = np.array(selfunc)
+    selfunc[:,0] = selfunc[:,0]/100.
+    selfuncUnity = selfunc
+    selfuncUnity[:,1] = 1.0
+    selfuncMin = selfunc[0,0]
+    selfuncMax = selfunc[-1,0]
+    selfuncDx = selfunc[1,0] - selfunc[0,0]
+    selfuncN = np.size(selfunc[:,0])
+  
+    selFunMin *= 3000
+    selFunMax *= 3000
+  
+    selFunMin = max(selFunMin, selfuncMin)
+    selFunMax = min(selFunMax, selfuncMax)
+  
+  
+    def f(z): return selfunc[np.ceil((z-selfuncMin)/selfuncDx), 1]*z**2 
+    def fTotal(z): return selfuncUnity[np.ceil((z-selfuncMin)/selfuncDx), 1]*z**2 
+  
+    zrange = np.linspace(selFunMin, selFunMax)
+  
+    nbar = scipy.integrate.quad(f, selFunMin, selFunMax)
+    nbar = nbar[0]
+  
+    ntotal = scipy.integrate.quad(fTotal, 0.0, max(selfuncUnity[:,0]))
+    #ntotal = scipy.integrate.quad(f, 0.0, max(selfunc[:,0]))
+    ntotal = ntotal[0]
+  
+    nbar = ntotal / area / nbar
+
+  else:
+    nbar = 1.0  
+
+  #print "PROPERTIES: ", volume, nbar
+ 
+  return (volume, nbar)