Update cosmotool 2nd part

external/cosmotool/python_sample/ksz/gal_prof.py

@@ -0,0 +1,175 @@
+import numpy as np
+import numexpr as ne
+from .constants import *
+
+# -----------------------------------------------------------------------------
+# Generic profile generator
+# -----------------------------------------------------------------------------
+
+class KSZ_Profile(object):
+  R_star= 0.0 # 15 kpc/h 
+  L_gal0 = 10**(0.4*(tmpp_cat['Msun']-tmpp_cat['Mstar']))
+
+  def __init__(self,sculpt):
+    """Base class for KSZ profiles
+
+    Arguments:
+      sculpt (float): If negative, do not sculpt. If positive, there will be a 2d 
+                      suppression of the profile with a radius given by sculpt (in arcmins).
+    """
+    self.sculpt = sculpt * np.pi/180/60.
+    self.rGalaxy = 1.0
+
+  def evaluate_profile(self, r):
+    raise NotImplementedError("Abstract function")
+
+  def projected_profile(self, cos_theta,angularDistance):
+
+    idx_base = idx = np.where(cos_theta > 0)[0]
+    tan_theta_2 = 1/(cos_theta[idx]**2) - 1
+    tan_theta_2_max = (self.rGalaxy/angularDistance)**2
+    tan_theta_2_min = (self.R_star/angularDistance)**2
+
+    idx0 = np.where((tan_theta_2 < tan_theta_2_max))
+    idx = idx_base[idx0]
+    tan_theta_2 = tan_theta_2[idx0]
+    tan_theta = np.sqrt(tan_theta_2)
+
+    r = (tan_theta*angularDistance)
+
+    m,idx_mask = self.evaluate_profile(r)
+    idx_mask = idx[idx_mask]
+
+    idx_mask = np.append(idx_mask,idx[np.where(tan_theta_2<tan_theta_2_min)[0]])
+    if tan_theta_2.size > 0:
+      idx_mask = np.append(idx_mask,idx[tan_theta_2.argmin()])
+
+    if self.sculpt > 0:
+      theta = np.arctan(tan_theta)
+      cond = theta < self.sculpt
+      m[cond] *= (theta[cond]/self.sculpt)**2
+
+    return idx,idx_mask,m
+     
+
+# -----------------------------------------------------------------------------
+# Isothermal profile generator
+# -----------------------------------------------------------------------------
+
+class KSZ_Isothermal(KSZ_Profile):
+  sigma_FP=160e3  #m/s
+  R_innergal = 0.030
+
+  def __init__(self, Lgal, x, y=0.0, sculpt=-1):
+    """Support for Isothermal profile
+
+    Arguments:
+      Lgal (float): Galaxy luminosity in solar units
+      x (float): extent of halo in virial radius units
+
+    Keyword arguments:
+      y (float): Inner part where there is no halo
+      sculpt (float): If negative, do not sculpt. If positive, there will be a 2d 
+                      suppression of the profile with a radius given by sculpt (in arcmins).
+    """
+    
+    super(KSZ_Isothermal,self).__init__(sculpt)
+    
+    self.R_gal = 0.226 * x
+    self.R_innergal *= y
+
+    self.rho0 = self.sigma_FP**2/(2*np.pi*G) # * (Lgal/L_gal0)**(2./3)
+    self.rGalaxy = self.R_gal*(Lgal/self.L_gal0)**(1./3)
+    self.rInnerGalaxy = self.R_innergal*(Lgal/self.L_gal0)**(1./3)
+    self._prepare()
+    
+  def _prepare(self):
+    pass
+
+  def evaluate_profile(self,r):
+    rho0, rGalaxy, rInner = self.rho0, self.rGalaxy, self.rInnerGalaxy
+    
+    D = {'rho0':rho0, 'rGalaxy':rGalaxy, 'rInner': rInner, 'Mpc':Mpc }
+    
+    Q = np.zeros(r.size)
+
+    cond = (r<=1e-10)
+    Q[cond] = rho0*2/Mpc * (rGalaxy-rInner)/(rGalaxy*rInner)
+
+    cond = (r>0)*(r <= rInner)
+    D['r'] = r[cond]
+    Q[cond] = ne.evaluate('rho0*2/(Mpc*r) * (arctan(sqrt( (rGalaxy/r)**2 -1 ))  - arctan(sqrt( (rInner/r)**2 - 1 )))',
+                local_dict=D)
+ 
+    cond = (r > rInner)*(r <= rGalaxy)
+    D['r'] = r[cond]
+    Q[cond] = ne.evaluate('rho0*2/(Mpc*r) * arctan(sqrt( (rGalaxy/r)**2 -1 ))',
+                local_dict=D)
+
+    return Q,[] #np.where(r<rInner)[0]
+
+
+# -----------------------------------------------------------------------------
+# NFW profile generator
+# -----------------------------------------------------------------------------
+
+class KSZ_NFW(KSZ_Profile):
+  """ Support for NFW profile
+"""
+
+  def __init__(self,x,y=0.0):
+    from numpy import log, pi
+
+    if 'pre_nfw' not in self:
+      self._prepare()
+
+    kiso = KSZ_Isothermal(x,y)
+    r_is = kiso.rGalaxy
+    rho_is = kiso.rho0
+    r_inner = kiso.rInnerGalaxy
+
+    self.Mgal = rho_is*4*pi*(r_is/args.x)*Mpc #Lgal*M_over_L_galaxy
+    self.Rvir = r_is/x
+
+    cs = self._get_concentration(Mgal)
+
+    self.rs = Rvir/cs
+    b = (log(1.+cs)-cs/(1.+cs))
+    
+    self.rho_s = Mgal/(4*pi*b*(rs*Mpc)**3)
+
+
+  def _prepare(self, _x_min=1e-4, _x_max=1e4):
+    from scipy.integrate import quad
+    from numpy import sqrt, log10
+    from scipy.interpolate import interp1d
+
+    lmin = log10(x_min)
+    lmax = log10(x_max)
+
+    x = 10**(np.arange(100)*(lmax-lmin)/100.+lmin)
+    profile = np.empty(x.size)
+    
+    nu_tilde = lambda u: (1/(u*(1+u)**2))
+
+    for i in range(x.size):
+        if x[i] < args.x:
+            profile[i] = 2*quad(lambda y: (nu_tilde(sqrt(x[i]**2+y**2))), 0, np.sqrt((args.x)**2-x[i]**2))[0]
+        else:
+            profile[i] = 0
+
+    # Insert the interpolator into the class definition
+    KSZ_NFW.pre_nfw = self.pre_nfw = interp1d(x,prof)
+
+  def _get_concentration(self, Mvir):
+  	from numpy import exp, log
+
+  	return exp(0.971 - 0.094*log(Mvir/(1e12*MassSun)))
+
+  def evaluate_profile(self,r):
+    cs = self._get_concentration(self.Mvir)
+    rs = self.Rvir/cs
+    
+    return self.rho_s*rs*Mpc*self.pre_nfw(r/rs),np.array([],dtype=int)
+
+