added hsw profile function

python_tools/void_python_tools/voidUtil/profileUtil.py

@@ -102,17 +102,104 @@ def fitHSWProfile(radii, densities, sigmas):
 
 
 # -----------------------------------------------------------------------------
-def getHSWProfile(den, radius):
+def getHSWProfile(density, radius):
 
 # returns the HSW profile for the given sample density and void size
-# (interpolated from best-fit values)
-#  density: density of sample
+#   will interpolate/extrapole the radius
+
+#  density: choice of sample (see arXiv:1309.5087):
+#           maxDM: DM at 1 particles per cubic Mpc/h
+#           fullDM: DM at 0.01 particles per cubic Mpc/h
+#           denseDM: DM at 4.e-3 particles per cubic Mpc/h
+#           sparseDM: DM at 3.e-4 particles per cubic Mpc/h
+#           
+#           denseHalos: halos at 4.e-3 particles per cubic Mpc/h
+#           sparseHalos: halos at 3.e-4 particles per cubic Mpc/h
+#          
+#           denseGal: galaxies at 4.e-3 particles per cubic Mpc/h
+#           sparseGal: galaxies at 3.e-4 particles per cubic Mpc/h
+#
 #  radius: void size in Mpc/h
 
 # returns:
+#   (rs, dc): best-fit values
 #   binCenters: array of radii in binned profile
 #   stackedProfile: the density profile
 
-  sample = catalog.sampleInfo
-  data = catalog.voids[:].radius
+  samples = [
+    {'name': 'maxDM',
+     'rv': [8.74041095,  11.65557095,  15.54746657,  20.94913774, 28.41063131,  38.61523696,  51.85944898,  69.42297033],
+     'rs': [0.74958738,  0.79650829,  0.86245251,  0.93960051, 1.01595177,  1.13159483,  1.31457096,  1.65611709],
+     'dc': [-0.95353184, -0.94861939, -0.91888455, -0.84480086, -0.73431544, -0.62614422, -0.54908132, -0.4912146],
+    },
+    {'name': 'fullDM',
+     'rv': [10, 15, 20, 25, 30, 35],
+     'rs': [ 0.76986779,  0.80980775,  0.86590177,  0.93732629,  1.02643542,
+        1.12875503],
+     'dc': [-0.81021   , -0.78115474, -0.78326026, -0.78670109, -0.76626508,
+       -0.72531084],
+    },
+    {'name': 'denseDM',
+     'rv': [10, 15, 20, 25, 30, 35, 40],
+     'rs': [0.7477462 ,  0.79932797,  0.84369297,  0.90309363,  0.92990401,
+        1.06970842,  1.16393474],
+     'dc': [-0.78333107, -0.75780925, -0.71586397, -0.74669512, -0.74902649,
+       -0.75342964, -0.76598043],
+    },
+    {'name': 'sparseDM',
+     'rv': [25, 30, 35, 40, 45, 50, 55, 60],
+     'rs': [0.78351117,  0.79751047,  0.8225573 ,  0.83751894,  0.85443167,
+        0.86346031,  0.85692501,  0.91470448],
+     'dc': [-0.67407548, -0.62389586, -0.59125414, -0.55341724, -0.54659457,
+       -0.5297821 , -0.534683  , -0.51055946],
+    },
+    {'name': 'denseHalos',
+     'rv': [15, 20, 25, 30, 35, 40, 45, 50, 55],
+     'rs': [0.75393528,  0.7758442 ,  0.79720886,  0.81560553,  0.83797299,
+        0.84377082,  0.84900783,  0.8709897 ,  0.86886687],
+     'dc': [-0.81348968, -0.77362777, -0.74336192, -0.72571135, -0.67928029,
+       -0.6279349 , -0.6313316 , -0.55188564, -0.48096026],
+    },
+    {'name': 'sparseHalos',
+     'rv': [25, 30, 35, 40, 45, 50, 55, 60, 65, 70],
+     'rs': [0.76420703,  0.78939067,  0.80480265,  0.82315275,  0.8158607 ,
+        0.82553517,  0.83843323,  0.85759226,  0.8471268 ,  0.89286939],
+     'dc': [-0.79317192, -0.81661415, -0.76770778, -0.7151494 , -0.718561  ,
+       -0.70858856, -0.68995608, -0.67415305, -0.63706798, -0.5303759],
+    },
+    {'name': 'denseGal',
+     'rv': [10, 15, 20, 25, 30, 35, 40, 45],
+     'rs': [0.70048139,  0.73717884,  0.75338516,  0.76782043,  0.79292536,
+        0.80157122,  0.8207239 ,  0.8091386],
+     'dc': [-0.83166549, -0.86505329, -0.81066899, -0.7662453 , -0.72840363,
+       -0.65163607, -0.57937656, -0.57125164],
+    },
+    {'name': 'sparseGal',
+     'rv': [25, 30, 35, 40, 45, 50, 55, 60, 65, 70, 75],
+     'rs': [0.75928922,  0.76622648,  0.77695425,  0.79963152,  0.8045125 ,
+        0.81892965,  0.83439691,  0.86600085,  0.83166875,  0.85283258,
+        0.83971344],
+     'dc': [-0.82413212, -0.87483536, -0.8221596 , -0.78459706, -0.75290061,
+       -0.77513988, -0.70012913, -0.67487994, -0.69903308, -0.65811992,
+       -0.57929526],
+    },
+  ]
 
+  if not np.any(density == samples[:]['name']):
+    print "Sample", density," not found! Use one of ", samples[:]['name']
+    return -1
+
+  # interpolate the radii
+  for sample in samples:
+    if not density == sample['name']: continue
+ 
+    rsFunc = interp1d( sample['rv'], sample['rs'], kind='cubic' )
+    dcFunc = interp1d( sample['rv'], sample['dc'], kind='cubic' )
+    
+    rs = rsFunc(radius)
+    dc = dcFunc(radius)  
+
+  # return best-fits
+  rVals = np.linspace(0.0, 3*radius, 100) / radius
+  return (rs,dc), rVals, HamausProfile(rVals,rs,dc)
+