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diff --git a/ParticlesToSurfaceBrightness/ARRAKIHS.py b/ParticlesToSurfaceBrightness/ARRAKIHS.py
deleted file mode 100644
index 026b907..0000000
--- a/ParticlesToSurfaceBrightness/ARRAKIHS.py
+++ /dev/null
@@ -1,621 +0,0 @@
-#!/usr/bin/env python3
-###########################################################################################
-# package: pNbody
-# file: __init__.py
-# brief: init file
-# copyright: GPLv3
-# Copyright (C) 2019 EPFL (Ecole Polytechnique Federale de Lausanne)
-# LASTRO - Laboratory of Astrophysics of EPFL
-# author: Yves Revaz <yves.revaz@epfl.ch>
-#
-# This file is part of pNbody.
-###########################################################################################
-
-
-import numpy as np
-
-from astropy import constants as cte
-from astropy import units as u
-
-from pNbody import *
-from pNbody import ic
-from pNbody import mapping
-from pNbody import geometry
-
-import argparse
-
-import matplotlib.pyplot as plt
-import matplotlib.colors as mcolors
-
-from scipy import signal
-
-import stars_class
-
-
-def SetTelecope(opt):
-
-
- ##################
- # set telescope
-
- if opt.telescope=='arrakihs_vis':
-
- opt.ccd_shape=[4096,4096]
- opt.ccd_size=[1.5,1.5]
-
- if opt.filter is None:
- opt.filter = "VIS"
-
- elif opt.telescope=='arrakihs_nir':
-
- opt.ccd_shape=[2048,2048]
- opt.ccd_size=[1.3,1.3]
-
- if opt.filter is None:
- opt.filter = "UKIRT_J"
-
-
- elif opt.telescope=='euclid_vis':
-
- opt.ccd_shape=[24576,24576]
- opt.ccd_size=[0.787,0.787]
-
-
- ##################
- # set filters
-
- if opt.filter=='F475X':
- opt.mlim = 30.12
- opt.mlmin = 29.50
- opt.mlmax = opt.mlim
-
- elif opt.filter=='VISeuclid':
- opt.mlim = 33 #30.23
- opt.mlmin = 29.5 #29.50 # Euclid preparation: XVI. Borlaff 2021
- opt.mlmax = opt.mlim
-
- elif opt.filter=='Yeuclid':
- opt.mlim = 29.59
- opt.mlmin = 29.50
- opt.mlmax = opt.mlim
-
- elif opt.filter=='Jeuclid':
- opt.mlim = 31.37
- opt.mlmin = 29.50
- opt.mlmax = opt.mlim
-
-
-
-
-
-
- ##################################
- # ccd size
- ##################################
-
- # degree to arcsec
- opt.ccd_size = [opt.ccd_size[0]*3600,opt.ccd_size[1]*3600]
- opt.ccd_pixel_size = [opt.ccd_size[0]/opt.ccd_shape[0],opt.ccd_size[1]/opt.ccd_shape[1]]
- opt.ccd_pixel_area = opt.ccd_pixel_size[0]*opt.ccd_pixel_size[1]
-
-
-
-
-def GenerateDwarf(opt):
- '''
- Generate a dwarf model based on a plummer sphere.
- Note that the plummer scaling length is equivalent to
- the projected half light radius.
- '''
-
- # set maximal radius
- rmax = opt.e * opt.rmax_e_ratio
-
- # convert to int
- opt.N = int(opt.N)
-
- # generate the model
- nb = ic.plummer(opt.N,1,1,1,opt.e,rmax,M=opt.M,irand=1,vel='no',name=opt.outputfilename,ftype='swift')
-
- if opt.outputfilename is not None:
- nb.write()
-
- return nb
-
-
-
-def gaussian_filter(kernel_size, sigma=1, muu=0):
-
- # Initializing value of x,y as grid of kernel size
- # in the range of kernel size
-
- x, y = np.meshgrid(np.linspace(-kernel_size, kernel_size, kernel_size),np.linspace(-kernel_size, kernel_size, kernel_size))
- dst = np.sqrt(x**2+y**2)
-
-
- # lower normal part of gaussian
- #normal = 1/np.sqrt(2 * np.pi * sigma**2)
-
- # Calculating Gaussian filter
- gauss = np.exp(-((dst-muu)**2 / (2.0 * sigma**2)))
- gauss = gauss/gauss.sum()
-
- return gauss
-
-
-
-def Project(opt):
- '''
- Project a stellar model
- '''
-
-
- ##################################
- # factor conversion kpc to arcsec
- ##################################
-
- # Mpc to kpc
- distance = opt.distance * 1000
-
- # define a conversion function : kpc -> arcsec
- fct_kpc2arcsec = np.vectorize(lambda x: 3600*180/np.pi* np.arctan(x/distance) )
- opt.fct_kpc2arcsec = lambda x: 3600*180/np.pi* np.arctan(x/distance)
-
- # define a conversion function : arcsec -> kpc
- fct_arcsec2kpc = np.vectorize(lambda x: distance*np.tan(x*np.pi/3600/180) )
- opt.fct_arcsec2kpc = lambda x: distance*np.tan(x*np.pi/3600/180)
-
-
- # image properties
- if opt.fov is not None:
- xmin = -opt.fov/2.
- xmax = +opt.fov/2.
- ymin = -opt.fov/2.
- ymax = +opt.fov/2.
-
- nx = int(opt.fov/opt.ccd_pixel_size[0])+1
- ny = int(opt.fov/opt.ccd_pixel_size[1])+1
-
-
- elif opt.size is not None:
- nx = opt.size[0] + 1 # +1 as the resulting matrix will be -1
- ny = opt.size[1] + 1 # +1 as the resulting matrix will be -1
-
- xmin = -nx*opt.ccd_pixel_size[0] /2.
- xmax = +nx*opt.ccd_pixel_size[0] /2.
- ymin = -ny*opt.ccd_pixel_size[1] /2.
- ymax = +ny*opt.ccd_pixel_size[1] /2.
-
- else:
- nx = int(opt.ccd_shape[0] *opt.ccd_field_fraction)
- ny = int(opt.ccd_shape[1] *opt.ccd_field_fraction)
-
- xmin = -opt.ccd_size[0]/2 *opt.ccd_field_fraction
- xmax = +opt.ccd_size[0]/2 *opt.ccd_field_fraction
- ymin = -opt.ccd_size[1]/2 *opt.ccd_field_fraction
- ymax = +opt.ccd_size[1]/2 *opt.ccd_field_fraction
-
-
-
- # extension in kpc
- opt.xmin_kpc = fct_arcsec2kpc(xmin)
- opt.xmax_kpc = fct_arcsec2kpc(xmax)
- opt.ymin_kpc = fct_arcsec2kpc(ymin)
- opt.ymax_kpc = fct_arcsec2kpc(ymax)
-
-
-
- ##################################
- # open the model
- ##################################
-
- if opt.file is None:
- nb = opt.nb
- else:
- nb = Nbody(opt.file)
- nb.mass = nb.mass*opt.toMsol
-
- # remove doublets
- u,idx = np.unique(nb.rxyz(),return_index=True)
- nb = nb.selectp(lst=nb.num[idx])
-
- if not nb.has_array("rsp"):
- # compute Hsml
- #nb.set_tpe(0)
- #nb.InitSphParameters(DesNumNgb=32, MaxNumNgbDeviation=2)
- #nb.getTree()
- #nb.rsp = nb.get_rsp_approximation()
- nb.ComputeRsp(5)
-
- if not nb.has_array("age"):
- # compute Age [in Gyr]
- print("Compute ages...")
- nb.age = nb.StellarAge(units="Gyr")
- print("done.")
-
- # rotate
- if opt.los is not None:
- nb.align(opt.los)
-
-
- # compute mass
- mass = nb.mass
-
-
-
-
- # scale the model : kpc -> arcsec
- x = fct_kpc2arcsec(nb.x())
- y = fct_kpc2arcsec(nb.y())
- z = fct_kpc2arcsec(nb.z())
-
- nb.pos = np.transpose((x,y,z))
- nb.pos = nb.pos.astype(np.float32)
-
-
- binsx = np.linspace(xmin,xmax,nx)
- binsy = np.linspace(ymin,ymax,ny)
-
-
-
-
- # get the luminosity:
- if opt.filter=="V" or opt.filter=="VIS" or opt.filter=="UKIRT_J" :
-
- # here we assume the mass of the particle to contain its luminosity
-
- # luminosity (L_sun)
- #L = nb.mass.sum()
-
- # map from numpy
- #L_map,xe,ye = np.histogram2d(x, y,bins=[binsx,binsy],weights=nb.mass)
- #L_map = np.rot90(L_map)
-
- # map from pnbody
- params = {}
- params['size'] = (xmax,ymax)
- params['shape'] = (nx,ny)
- params['rendering'] = 'map'
- params['obs'] = None
- params['xp'] = None
- params['view'] = opt.view
- params['mode'] = 'm'
- params['exec'] = None
- params['macro'] = None
- params['frsp'] = opt.frsp
- params['filter_name'] = None
-
- L_map = nb.CombiMap(params)
- L_map = np.rot90(L_map)
- L_map = np.flipud(L_map)
-
- # psf convolution
- if opt.convolve:
- psf = opt.psf/opt.ccd_pixel_size[0]
- # gaussian filter
- psf_map = gaussian_filter(L_map.shape[0],psf)
- # convolve
- L_map = signal.convolve2d(L_map, psf_map, mode='same', boundary='fill', fillvalue=0)
-
-
- # compute the absolute magnitude in each pixel (vband)
- Mvega = 4.81
- M_map = Mvega - 2.5*np.log10(L_map)
-
- # filter stuffs
- M_V = M_map
- L_V = L_map
-
- M_R = M_V -0.45
- M_I = M_R -0.5
- M_J = M_I -0.6
- L_R = np.log10( (M_R-4.43)/(-2.5) )
- L_I = np.log10( (M_R-4.10)/(-2.5) )
- L_J = np.log10( (M_J-3.65)/(-2.5) )
-
- L_VIS = L_V + L_R + L_I
- M_VIS = 3.216 - 2.5*np.log10(L_VIS)
-
- if opt.filter=="V":
- M_map = M_V
- elif opt.filter=="VIS":
- M_map = M_VIS
- elif opt.filter=="UKIRT_J":
- M_map = M_J
-
-
- # compute the apparent magnitude in each pixel
- d = distance*100 # kpc -> 10pc
- m_map = M_map + 5*np.log10(d)
-
- # compute the surface brightness in each pixel
- S_map = m_map + 2.5*np.log10(opt.ccd_pixel_area)
-
-
- #S_map = L_map
-
-
-
- elif opt.filter=="F475X" or opt.filter=="VISeuclid" or opt.filter=="Yeuclid" or opt.filter=="Jeuclid":
-
- if not nb.has_array("MagF475X") or not nb.has_array("MagVISeuclid") or not nb.has_array("MagYeuclid") or not nb.has_array("MagJeuclid"):
-
-
- # compute a magnitude for the filter in each mass bin
- print("Compute magnitudes...")
-
- if opt.filter=="F475X":
- M = stars_class.HST475X_fun(None,nb.age,nb.MH())
-
- elif opt.filter=="VISeuclid":
- M = stars_class.VISeuclid_fun(None,nb.age,nb.MH())
-
- elif opt.filter=="Yeuclid":
- M = stars_class.Yeuclid_fun(None,nb.age,nb.MH())
-
- elif opt.filter=="Jeuclid":
- M = stars_class.Jeuclid_fun(None,nb.age,nb.MH())
-
- # convert to flux (ignore the zero point)
- # to get the correct flux, see notes in Garrotxa
- F = 10**(-M/2.5)
-
- # get the number of stars in each mass bin
- Nstars = stars_class.Stars_fun(mass,None,None, 'normed_3slope')
-
- # sum the contribution of the mass bins
- F = np.sum(F*Nstars, axis=0)
-
- # compute the absolute magnitude in each pixel (as before we ignore the zero point)
- M = - 2.5*np.log10(F)
-
- print("done.")
-
- else:
-
- if opt.filter=="F475X":
- M = nb.MagF475X
-
- elif opt.filter=="VISeuclid":
- M = nb.MagVISeuclid
-
- elif opt.filter=="Yeuclid":
- M = nb.MagYeuclid
-
- elif opt.filter=="Jeuclid":
- M = nb.MagJeuclid
-
-
-
-
- # convert to flux (ignore the zero point)
- F = 10**(-M/2.5)
-
- # store in the mass field
- nb.mass = F.astype(np.float32)
-
- # map from numpy
- #L_map,xe,ye = np.histogram2d(x, y,bins=[binsx,binsy],weights=nb.mass)
- #L_map = np.rot90(L_map)
-
- # map from pnbody
- params = {}
- params['size'] = (xmax,ymax)
- params['shape'] = (nx,ny)
- params['rendering'] = 'map'
- params['obs'] = None
- params['xp'] = None
- params['view'] = opt.view
- params['mode'] = 'm'
- params['exec'] = None
- params['macro'] = None
- params['frsp'] = opt.frsp
- params['filter_name'] = None
-
- L_map = nb.CombiMap(params)
- L_map = np.rot90(L_map)
- L_map = np.flipud(L_map)
-
- # psf convolution
- if opt.convolve:
- psf = opt.psf/opt.ccd_pixel_size[0]
- # gaussian filter
- psf_map = gaussian_filter(L_map.shape[0],psf)
- # convolve
- L_map = signal.convolve2d(L_map, psf_map, mode='same', boundary='fill', fillvalue=0)
-
-
- # compute the absolute magnitude in each pixel (as before we ignore the zero point)
- M_map = - 2.5*np.log10(L_map)
-
- # compute the apparent magnitude in each pixel
- d = distance*100 # kpc -> 10pc
- m_map = M_map + 5*np.log10(d)
-
- # compute the surface brightness in each pixel
- S_map = m_map + 2.5*np.log10(opt.ccd_pixel_area)
-
-
-
-
-
-
-
-
-
-
- return S_map,xmin,xmax,ymin,ymax
-
-
-
-
-def SkySurfaceBrightness(opt,shape):
- '''
- Generate a sky surface brightness
-
- sky background on earth : 21.8 mag/arcsec2 in V
- '''
-
- mean = opt.sky_mean
- std = (mean - opt.mlim)/3.
- sky_image = np.random.normal(mean,std,shape)
-
- print(" mean :",sky_image.mean())
- print(" 3*std :",3*sky_image.std())
- print(" mean-3*std :",sky_image.mean()-3*sky_image.std())
-
- return sky_image
-
-
-
-
-
-def AddSkyBackground(opt,image):
- """
- !!! this must be corrected !!! the filter is bad
- """
-
- if opt.sky_background:
- sky = SkySurfaceBrightness(opt,image.shape)
-
-
- M0 = 4.81
- Fg = 10**((-image+M0)/2.5)
- Fs = 10**((-sky +M0)/2.5)
-
- image = -2.5*np.log10(Fg + Fs) + M0
-
- plt.imshow(image)
- plt.show()
-
-
-
- return image
-
-
-def Display(opt,ax,image,xmin,xmax,ymin,ymax):
-
-
- mmin = 25 # min magnitude
- mmax = 36 # max magnitude
-
-
- if opt.split_colorbar:
- mlim = opt.mlim # limit magnitude
-
- n1 = int(256* (mlim-mmin)/(mmax-mmin))
- n2 = 256-n1
-
- colors1 = plt.cm.gist_heat_r(np.linspace(0.75, 0.25, n1))
- #colors2 = plt.cm.binary(np.linspace(0.25, 0.0, n2))
- colors2 = plt.cm.binary(np.linspace(0.25, 0.0, n2))
-
- # combine them and build a new colormap
- colors = np.vstack((colors1, colors2))
- mymap = mcolors.LinearSegmentedColormap.from_list('my_colormap', colors)
-
- else:
- mymap = plt.cm.Greys_r
-
- # use kpc units
- xmin=opt.xmin_kpc
- xmax=opt.xmax_kpc
- ymin=opt.ymin_kpc
- ymax=opt.ymax_kpc
-
- im = ax.imshow(image,aspect='equal',extent=(xmin,xmax,ymin,ymax),cmap=mymap,interpolation=None,vmin=mmin,vmax=mmax)
-
-
- return im
-
-
-
-
-def DisplayDiff(opt,ax,image,xmin,xmax,ymin,ymax):
-
-
- mmin = 25 # min magnitude
- mmax = 36 # max magnitude
-
-
- if opt.split_colorbar:
- mlim = opt.mlim # limit magnitude
-
- mlmin = opt.mlmin
- mlmax = opt.mlmax
-
- n1 = int(256* (mlmin-mmin)/(mmax-mmin))
- n2 = int(256* (mlmax-mmin)/(mmax-mmin)) - n1
- n3 = 256 - int(256* (mlmax-mmin)/(mmax-mmin))
-
-
- colors1 = plt.cm.binary(np.linspace(0.0, 0.0, n1))
- colors2 = plt.cm.gist_heat_r(np.linspace(0.7, 0.3, n2))
- colors3 = plt.cm.binary(np.linspace(0.0, 0.0, n3))
-
-
- # combine them and build a new colormap
- colors = np.vstack((colors1,colors2,colors3))
- mymap = mcolors.LinearSegmentedColormap.from_list('my_colormap', colors)
-
- else:
- mymap = plt.cm.Greys_r
-
- # use kpc units
- xmin=opt.xmin_kpc
- xmax=opt.xmax_kpc
- ymin=opt.ymin_kpc
- ymax=opt.ymax_kpc
-
- im = ax.imshow(image,aspect='equal',extent=(xmin,xmax,ymin,ymax),cmap=mymap,interpolation=None,vmin=mmin,vmax=mmax)
-
-
- return im
-
-
-
-
-def DisplaySingle(opt,ax,image,xmin,xmax,ymin,ymax):
-
-
-
- mmin = opt.mlmin
- mmax = opt.mlmax
-
- opt.split_colorbar=False
-
- if opt.split_colorbar:
- #mlim = opt.mlim # limit magnitude
-
- #mlmin = opt.mlmin
- #mlmax = opt.mlmax
-
- #n1 = int(256* (mlmin-mmin)/(mmax-mmin))
- #n2 = int(256* (mlmax-mmin)/(mmax-mmin)) - n1
- #n3 = 256 - int(256* (mlmax-mmin)/(mmax-mmin))
-
-
- #colors1 = plt.cm.binary(np.linspace(0.0, 0.0, n1))
- colors2 = plt.cm.gist_heat(np.linspace(0.3, 1.0, 255))
- #colors3 = plt.cm.binary(np.linspace(0.0, 0.0, n3))
-
-
- # combine them and build a new colormap
- #colors = np.vstack((colors1,colors2,colors3))
- mymap = mcolors.LinearSegmentedColormap.from_list('my_colormap', colors2)
-
- #mymap = plt.cm.gist_heat
-
- else:
- mymap = plt.cm.gist_heat
-
- # use kpc units
- xmin=opt.xmin_kpc
- xmax=opt.xmax_kpc
- ymin=opt.ymin_kpc
- ymax=opt.ymax_kpc
-
- im = ax.imshow(image,aspect='equal',extent=(xmin,xmax,ymin,ymax),cmap=mymap,interpolation=None,vmin=mmin,vmax=mmax)
-
-
- return im
diff --git a/ParticlesToSurfaceBrightness/IMF.py b/ParticlesToSurfaceBrightness/IMF.py
deleted file mode 100644
index f82e34d..0000000
--- a/ParticlesToSurfaceBrightness/IMF.py
+++ /dev/null
@@ -1,232 +0,0 @@
-import numpy as np
-
-####################
- ## IMF subroutines
-####################
-
-def slope_imf(x,p1,p2,p3,kn1,kn2):
-
-#Is calculating a three slope IMF
-#INPUT:
-# x = An array of masses for which the IMF should be calculated
-# p1..p3 = the slopes of the power law
-# kn1, kn2 = Where the breaks of the power law are
-#OUTPUT:
-# An array of frequencies matching the mass base array x
- if(x > kn2):
- t = (pow(kn2,p2)/pow(kn2,p3))*pow(x,p3+1)
- elif (x < kn1):
- t = (pow(kn1,p2)/pow(kn1,p1))*pow(x,p1+1)
- else:
- t = pow(x,p2+1)
- return t
-
-
-def lifetime(m,Z):
-
-#here we will calculate the MS lifetime of the star after Argast et al., 2000, A&A, 356, 873
-#INPUT:¡
-# m = mass in Msun
-# Z = metallicity in Zsun
-
-#OUTPUT:
-# returns the lifetime of the star in Gyrs
-
- lm = np.log10(m)
- a0 = 3.79 + 0.24*Z
- a1 = -3.10 - 0.35*Z
- a2 = 0.74 + 0.11*Z
- tmp = a0 + a1*lm + a2*lm*lm
- return np.divide(np.power(10,tmp),1000)
-
-
-class IMF(object):
-
-#This class represents the IMF normed to 1 in units of M_sun.
-#Input for initialisation:
-
-# mmin = minimal mass of the IMF
-
-# mmax = maximal mass of the IMF
-
-# intervals = how many steps inbetween mmin and mmax should be given
-
-#Then one of the IMF functions can be used
-
-# self.x = mass base
-
-# self.dn = the number of stars at x
-
-# self.dm = the masses for each mass interval x
-
- def __init__(self, mmin = 0.08 , mmax = 100., intervals = 5000):
- self.mmin = mmin
- self.mmax = mmax
- self.intervals = intervals
- self.x = np.linspace(mmin,mmax,intervals)
- self.dx = self.x[1]-self.x[0]
-
- def normed_3slope(self,paramet = (-1.3,-2.2,-2.7,0.5,1.0)):
-
-# Three slope IMF, Kroupa 1993 as a default
-
- s1,s2,s3,k1,k2 = paramet
- u = np.zeros_like(self.x)
- v = np.zeros_like(self.x)
- for i in range(len(self.x)):
- u[i] = slope_imf(self.x[i],s1,s2,s3,k1,k2)
- v = np.divide(u,self.x)
- self.dm = np.divide(u,sum(u))
- self.dn = np.divide(self.dm,self.x)
- return(self.dm,self.dn)
-
-
- def Chabrier_1(self, paramet = (0.69, 0.079, -2.3)):
-
-# Chabrier IMF from Chabrier 2003 equation 17 field IMF with variable high mass slope and automatic normalisation
-
- sigma, m_c, expo = paramet
- dn = np.zeros_like(self.x)
- for i in range(len(self.x)):
- if self.x[i] <= 1:
- index_with_mass_1 = i
- dn[i] = (1. / float(self.x[i])) * np.exp(-1*(((np.log10(self.x[i] / m_c))**2)/(2*sigma**2)))
- else:
- dn[i] = (pow(self.x[i],expo))
- # Need to 'attach' the upper to the lower branch
- derivative_at_1 = dn[index_with_mass_1] - dn[index_with_mass_1 - 1]
- target_y_for_m_plus_1 = dn[index_with_mass_1] + derivative_at_1
- rescale = target_y_for_m_plus_1 / dn[index_with_mass_1 + 1]
- dn[np.where(self.x>1.)] *= rescale
- # Normalizing to 1 in mass space
- self.dn = np.divide(dn,sum(dn))
- dm = dn*self.x
- self.dm = np.divide(dm,sum(dm))
- self.dn = np.divide(self.dm,self.x)
- return(self.dm,self.dn)
-
-
- def Chabrier_2(self,paramet = (22.8978, 716.4, 0.25,-2.3)):
-
-# Chabrier IMF from Chabrier 2001, IMF 3 = equation 8 parameters from table 1
-
-
- A,B,sigma,expo = paramet
- expo -= 1. ## in order to get an alpha index normalisation
- dn = np.zeros_like(self.x)
- for i in range(len(self.x)):
- dn[i] = A*(np.exp(-pow((B/self.x[i]),sigma)))*pow(self.x[i],expo)
- self.dn = np.divide(dn,sum(dn))
- dm = dn*self.x
- self.dm = np.divide(dm,sum(dm))
- self.dn = np.divide(self.dm,self.x)
- return(self.dm,self.dn)
-
-
- def salpeter(self, alpha = (2.35)):
-
-# Salpeter IMF
-
-# Input the slope of the IMF
-
- self.alpha = alpha
- temp = np.power(self.x,-self.alpha)
- norm = sum(temp)
- self.dn = np.divide(temp,norm)
- u = self.dn*self.x
- self.dm = np.divide(u,sum(u))
- self.dn = np.divide(self.dm,self.x)
- return (self.dm,self.dn)
-
-
- def BrokenPowerLaw(self, paramet):
- breaks,slopes = paramet
- if len(breaks) != len(slopes)-1:
- print("error in the precription of the power law. It needs one more slope than break value")
- else:
- dn = np.zeros_like(self.x)
- self.breaks = breaks
- self.slopes = slopes
- self.mass_range = np.hstack((self.mmin,breaks,self.mmax))
- for i,item in enumerate(self.slopes):
- cut = np.where(np.logical_and(self.x>=self.mass_range[i],self.x<self.mass_range[i+1]))
- dn[cut] = np.power(self.x[cut],item)
- if i != 0:
- renorm = np.divide(last_dn,dn[cut][0])
- dn[cut] = dn[cut]*renorm
- last_dn = dn[cut][-1]
- last_x = self.x[cut][-1]
- self.dn = np.divide(dn,sum(dn))
- u = self.dn*self.x
- self.dm = np.divide(u,sum(u))
- self.dn = np.divide(self.dm,self.x)
- return (self.dm,self.dn)
-
-
- def imf_mass_fraction(self,mlow,mup):
-
-# Calculates the mass fraction of the IMF sitting between mlow and mup
-
- norm = sum(self.dm)
- cut = np.where(np.logical_and(self.x>=mlow,self.x<mup))
- fraction = np.divide(sum(self.dm[cut]),norm)
- return(fraction)
-
- def imf_number_fraction(self,mlow,mup):
-
-# Calculating the number fraction of stars of the IMF sitting between mlow and mup
-
- norm = sum(self.dn)
- cut = np.where(np.logical_and(self.x>=mlow,self.x<mup))
- fraction = np.divide(sum(self.dn[cut]),norm)
- return(fraction)
-
- def imf_number_stars(self,mlow,mup):
- cut = np.where(np.logical_and(self.x>=mlow,self.x<mup))
- number = sum(self.dn[cut])
- return(number)
-
- def stochastic_sampling(self, mass):
-
-# The analytic IMF will be resampled according to the mass of the SSP.
-# The IMF will still be normalised to 1
-
-# Stochastic sampling is realised by fixing the number of expected stars and then drawing from the probability distribution of the number density
-# Statistical properties are tested for this sampling and are safe: number of stars and masses converge.
-
- number_of_stars = int(round(sum(self.dn) * mass))
- self.dm_copy = np.copy(self.dm)
- self.dn_copy = np.copy(self.dn)
-
- #self.dn_copy = np.divide(self.dn_copy,sum(self.dn_copy))
- random_number = np.random.uniform(low = 0.0, high = sum(self.dn_copy), size = number_of_stars)
- self.dm = np.zeros_like(self.dm)
- self.dn = np.zeros_like(self.dn)
-
- ### This could be favourable if the number of stars drawn is low compared to the imf resolution
-# for i in range(number_of_stars):
- ### the next line randomly draws a mass according to the number distribution of stars
-# cut = np.where(np.abs(np.cumsum(self.dn_copy)-random_number[i])== np.min(np.abs(np.cumsum(self.dn_copy)-random_number[i])))
-# x1 = self.x[cut][0]
- #self.dn[cut] += 0.5
-# self.dn[cut[0]] += 1
-# self.dm[cut[0]] += x1 + self.dx/2.
-# t.append(x1 + self.dx/2.)
-
- counting = np.cumsum(self.dn_copy)
- for i in range(len(counting)-1):
- if i == 0:
- cut = np.where(np.logical_and(random_number>0.,random_number<=counting[i]))
- else:
- cut = np.where(np.logical_and(random_number>counting[i-1],random_number<=counting[i]))
- number_of_stars_in_mass_bin = len(random_number[cut])
- self.dm[i] = number_of_stars_in_mass_bin * self.x[i]
- if number_of_stars:
- self.dm = np.divide(self.dm,sum(self.dm))
- else:
- self.dm = np.divide(self.dm, 1)
- self.dn = np.divide(self.dm,self.x)
- return (self.dm,self.dn)
-########
- ## End IMF subroutines
-########
diff --git a/ParticlesToSurfaceBrightness/Readme b/ParticlesToSurfaceBrightness/Readme
deleted file mode 100644
index 5b4e86f..0000000
--- a/ParticlesToSurfaceBrightness/Readme
+++ /dev/null
@@ -1,52 +0,0 @@
-# Note : this is now obsolete.
-# use https://lastro.epfl.ch/projects/pNbody//rst/MockImages/SurfaceBrightnessMaps.html
-# instead
-
-# requirement : pNbody https://lastro.epfl.ch/projects/pNbody/
-
-
-#################################################
-# download snapshots containing only stars
-# we need to find another repo for big data
-
-stars_1keV_1000kpc.hdf5
-stars_3keV_1000kpc.hdf5
-stars_10keV_1000kpc.hdf5
-stars_30keV_1000kpc.hdf5
-
-#################################################
-# add additional information (age, magnitudes, html)
-
-./arrakihs_addfields.py -o stars_1keV_1000kpc.hdf5 stars_1keV_1000kpc.hdf5
-./arrakihs_addfields.py -o stars_3keV_1000kpc.hdf5 stars_3keV_1000kpc.hdf5
-./arrakihs_addfields.py -o stars_10keV_1000kpc.hdf5 stars_10keV_1000kpc.hdf5
-./arrakihs_addfields.py -o stars_30keV_1000kpc.hdf5 stars_30keV_1000kpc.hdf5
-
-
-################################################
-# compute a set of images
-
-./arrakihs_compute_images.py --telescope arrakihs_vis --filter F475X --fov 5000 --distance 25 --frsp 0.25 --nlos 16 -o F475X_1keV.pkl stars_1keV_1000kpc.hdf5
-./arrakihs_compute_images.py --telescope arrakihs_vis --filter VISeuclid --fov 5000 --distance 25 --frsp 0.25 --nlos 16 -o VISeuclid_3keV.pkl stars_3keV_1000kpc.hdf5
-./arrakihs_compute_images.py --telescope arrakihs_vis --filter Yeuclid --fov 5000 --distance 25 --frsp 0.25 --nlos 16 -o Yeuclid_1keV.pkl stars_1keV_1000kpc.hdf5
-./arrakihs_compute_images.py --telescope arrakihs_vis --filter Jeuclid --fov 5000 --distance 25 --frsp 0.25 --nlos 16 -o Jeuclid_1keV.pkl stars_1keV_1000kpc.hdf5
-
-################################################
-# display or save images in the pkl file
-
-./arrakihs_show_images.py F475X_1keV.pkl -o F475X_1keV.png
-./arrakihs_show_images.py VISeuclid_3keV.pkl -o VISeuclid_3keV.png
-./arrakihs_show_images.py Yeuclid_1keV.pkl -o Yeuclid_1keV.png
-./arrakihs_show_images.py Jeuclid_1keV.pkl -o Jeuclid_1keV.png
-
-###############################################
-# compute and compare the fraction of illuminated pixels
-
-./arrakihs_compute_illuminated.py -o F475X_coverage_fraction.png F475X_1keV.pkl F475X_3keV.pkl F475X_10keV.pkl F475X_30keV.pkl
-./arrakihs_compute_illuminated.py -o VISeuclid_coverage_fraction.png VISeuclid_1keV.pkl VISeuclid_3keV.pkl VISeuclid_10keV.pkl VISeuclid_30keV.pkl
-./arrakihs_compute_illuminated.py -o Yeuclid_coverage_fraction.png Yeuclid_1keV.pkl Yeuclid_3keV.pkl Yeuclid_10keV.pkl Yeuclid_30keV.pkl
-./arrakihs_compute_illuminated.py -o Jeuclid_coverage_fraction.png Jeuclid_1keV.pkl Jeuclid_3keV.pkl Jeuclid_10keV.pkl Jeuclid_30keV.pkl
-
-
-
-
diff --git a/ParticlesToSurfaceBrightness/arrakihs_addfields.py b/ParticlesToSurfaceBrightness/arrakihs_addfields.py
deleted file mode 100755
index 102dfe5..0000000
--- a/ParticlesToSurfaceBrightness/arrakihs_addfields.py
+++ /dev/null
@@ -1,154 +0,0 @@
-#!/usr/bin/env python3
-
-import argparse
-import numpy as np
-from pNbody import *
-
-import stars_class
-
-
-####################################################################
-# option parser
-####################################################################
-
-description=""
-epilog =""""""
-
-parser = argparse.ArgumentParser(description=description,epilog=epilog,formatter_class=argparse.RawDescriptionHelpFormatter)
-
-
-parser.add_argument(action="store",
- dest="files",
- metavar='FILE',
- type=str,
- default=None,
- nargs='*',
- help='a file name')
-
-parser.add_argument("-o",
- action="store",
- type=str,
- dest="outputfilename",
- default=None,
- help="Name of the output file")
-
-####################################################################
-# main
-####################################################################
-
-
-if __name__ == '__main__':
-
- opt = parser.parse_args()
-
- for f in opt.files:
- nb = Nbody(f,ftype="arepo")
-
-
- # remove doublets
- u,idx = np.unique(nb.rxyz(),return_index=True)
- nb = nb.selectp(lst=nb.num[idx])
-
-
- ###################################
- # Stellar ages
- ###################################
- print("Compute ages...")
- nb.age = nb.StellarAge(units="Gyr")
- print("done.")
-
- ###################################
- # compute Hsml
- ###################################
- print("Compute Rsp...")
- #nb.set_tpe(0)
- #nb.InitSphParameters(DesNumNgb=32, MaxNumNgbDeviation=2)
- #nb.getTree()
- #nb.rsp = nb.get_rsp_approximation()
- #nb.set_tpe(4)
- nb.ComputeRsp(5)
- print("done.")
-
- ###################################
- # compute Magnitudes
- ###################################
- print("Compute magnitudes...")
- MH = nb.MH()
-
- # mass in Msol
- mass = nb.mass * 1e10
-
- # get the number of stars in each mass bin
- Nstars = stars_class.Stars_fun(mass,None,None, 'normed_3slope')
-
- ##############################
- # F475X magnitude
-
- M = stars_class.HST475X_fun(None,nb.age,MH)
- # convert to flux (ignore the zero point)
- F = 10**(-M/2.5)
- # sum the contribution of the mass bins
- F = np.sum(F*Nstars, axis=0)
- # compute the absolute magnitude in each pixel (as before we ignore the zero point)
- M = - 2.5*np.log10(F)
- nb.MagF475X = M
-
- ##############################
- # VIS Euclid magnitude
-
- M = stars_class.VISeuclid_fun(None,nb.age,MH)
- # convert to flux (ignore the zero point)
- F = 10**(-M/2.5)
- # sum the contribution of the mass bins
- F = np.sum(F*Nstars, axis=0)
- # compute the absolute magnitude in each pixel (as before we ignore the zero point)
- M = - 2.5*np.log10(F)
- nb.MagVISeuclid = M
-
- ##############################
- # Y Euclid magnitude
-
- M = stars_class.Yeuclid_fun(None,nb.age,MH)
- # convert to flux (ignore the zero point)
- F = 10**(-M/2.5)
- # sum the contribution of the mass bins
- F = np.sum(F*Nstars, axis=0)
- # compute the absolute magnitude in each pixel (as before we ignore the zero point)
- M = - 2.5*np.log10(F)
- nb.MagYeuclid = M
-
- ##############################
- # J Euclid magnitude
-
- M = stars_class.Jeuclid_fun(None,nb.age,MH)
- # convert to flux (ignore the zero point)
- F = 10**(-M/2.5)
- # sum the contribution of the mass bins
- F = np.sum(F*Nstars, axis=0)
- # compute the absolute magnitude in each pixel (as before we ignore the zero point)
- M = - 2.5*np.log10(F)
- nb.MagJeuclid = M
-
-
- print("done.")
-
-
- if opt.outputfilename:
- nb.rename(opt.outputfilename)
- nb.write()
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
-
diff --git a/ParticlesToSurfaceBrightness/arrakihs_compute_illuminated.py b/ParticlesToSurfaceBrightness/arrakihs_compute_illuminated.py
deleted file mode 100755
index 45c31d6..0000000
--- a/ParticlesToSurfaceBrightness/arrakihs_compute_illuminated.py
+++ /dev/null
@@ -1,180 +0,0 @@
-#!/usr/bin/env python3
-
-import numpy as np
-
-from astropy import constants as cte
-from astropy import units as u
-
-import argparse
-
-import matplotlib.pyplot as plt
-import matplotlib.colors as mcolors
-
-from pNbody import *
-from pNbody import ic
-import ARRAKIHS
-
-import pickle
-
-
-####################################################################
-# option parser
-####################################################################
-
-description=""
-epilog =""""""
-
-parser = argparse.ArgumentParser(description=description,epilog=epilog,formatter_class=argparse.RawDescriptionHelpFormatter)
-
-
-
-
-
-parser.add_argument(action="store",
- dest="files",
- metavar='FILE',
- type=str,
- default=None,
- nargs='*',
- help='a file name')
-
-parser.add_argument("-o",
- action="store",
- type=str,
- dest="outputfilename",
- default=None,
- help="Name of the output file")
-
-
-
-parser.add_argument('--mlim',
- action="store",
- dest="mlim",
- metavar='FLOAT',
- type=float,
- default=31,
- help='magnitude limit')
-
-
-
-
-
-parser.add_argument("--mlmin",
- action="store",
- dest="mlmin",
- metavar='FLOAT',
- type=float,
- default=None,
- help='mag lim min')
-
-parser.add_argument("--mlmax",
- action="store",
- dest="mlmax",
- metavar='FLOAT',
- type=float,
- default=None,
- help='mag lim max')
-
-parser.add_argument("--nlos",
- action="store",
- dest="nlos",
- metavar='INT',
- type=int,
- default=1,
- help='number of los')
-
-
-
-
-####################################################################
-# main
-####################################################################
-
-cs = ['r','g','b','y']
-nmagbins = 50
-mag0 = 33
-magmax = mag0
-
-if __name__ == '__main__':
-
- opt = parser.parse_args()
-
-
- for j,f in enumerate(opt.files):
-
- print(f)
-
- fd = open(f,"rb")
- images = pickle.load(fd)
- fd.close()
-
- # loop over images of the same model
-
- Npixs = np.zeros((len(images),nmagbins))
-
-
- for k in range(len(images)):
-
- image,xmin,xmax,ymin,ymax = images[k]
-
-
- Npixtot = image.shape[0]*image.shape[1]
-
- # clean
- image = np.where(np.isinf(image),100,image)
-
-
-
- magbins = np.linspace(26,magmax,nmagbins)
- Npix = np.zeros(nmagbins)
-
- for i in tqdm(range(nmagbins)):
- img1 = np.where(image<magbins[i],1,0)
- Npix[i] = np.ravel(img1).sum()
-
- # normalize
- #Npix = Npix/Npixtot
- #idx = np.argmin(np.fabs(magbins - mag0))
- #Npix = Npix/Npix[idx]
- Npix = Npix/Npixtot
-
-
-
- #plt.plot(magbins,Npix,color=cs[j])
-
-
- # sum
- Npixs[k] = Npix
-
-
-
- mean = Npixs.mean(axis=0)
- std = Npixs.std(axis=0)
-
- label = os.path.basename(f)
- label = label[label.find("_")+1:]
- label = label[:-4]
-
-
- plt.plot(magbins,mean,color=cs[j],label=r"$%s$"%label,lw=2)
- #plt.plot(magbins,mean+1*std,color=cs[j],lw=5)
- #plt.plot(magbins,mean-1*std,color=cs[j],lw=5)
-
- ax = plt.gca()
- ax.fill_between(magbins,mean+1*std,mean-1*std,facecolor=cs[j],alpha=0.1)
-
-
-
- plt.legend()
- plt.xlabel(r"$\rm{VIS}\,\,\rm{magnitude}$")
- plt.ylabel(r"$\rm{coverage\,\,fraction}$")
-
- if opt.outputfilename is None:
- plt.show()
- else:
- plt.savefig(opt.outputfilename)
-
-
-
-
-
diff --git a/ParticlesToSurfaceBrightness/arrakihs_compute_images.py b/ParticlesToSurfaceBrightness/arrakihs_compute_images.py
deleted file mode 100755
index e8a3055..0000000
--- a/ParticlesToSurfaceBrightness/arrakihs_compute_images.py
+++ /dev/null
@@ -1,294 +0,0 @@
-#!/usr/bin/env python3
-
-import numpy as np
-
-from astropy import constants as cte
-from astropy import units as u
-
-import argparse
-
-import matplotlib.pyplot as plt
-import matplotlib.colors as mcolors
-
-from pNbody import *
-from pNbody import ic
-import ARRAKIHS
-
-import pickle
-
-
-####################################################################
-# option parser
-####################################################################
-
-description=""
-epilog =""""""
-
-parser = argparse.ArgumentParser(description=description,epilog=epilog,formatter_class=argparse.RawDescriptionHelpFormatter)
-
-
-
-
-
-parser.add_argument(action="store",
- dest="file",
- metavar='FILE',
- type=str,
- default=None,
- nargs='*',
- help='a file name')
-
-parser.add_argument("-o",
- action="store",
- type=str,
- dest="outputfilename",
- default=None,
- help="Name of the output file")
-
-
-parser.add_argument("--ccd_shape",
- action="store",
- dest="ccd_shape",
- metavar='INT INT',
- type=int,
- default=[4096,4096],
- nargs=2,
- help='ccd shape')
-
-parser.add_argument("--size",
- action="store",
- dest="size",
- metavar='INT INT',
- type=int,
- default=None,
- nargs=2,
- help='image size')
-
-
-
-parser.add_argument("--ccd_size",
- action="store",
- dest="ccd_size",
- metavar='FLOAT FLOAT',
- type=float,
- default=[1.5,1.5],
- nargs=2,
- help='ccd size in degree')
-
-
-parser.add_argument("--ccd_field_fraction",
- action="store",
- dest="ccd_field_fraction",
- metavar='FLOAT',
- type=float,
- default=1,
- help='fraction of the field displayed')
-
-
-parser.add_argument("--fov",
- action="store",
- dest="fov",
- metavar='FLOAT',
- type=float,
- default=None,
- help='field of view in arcsec')
-
-
-
-parser.add_argument("--psf",
- action="store",
- dest="psf",
- metavar='FLOAT',
- type=float,
- default=0.8,
- help='psf in arcsec')
-
-
-parser.add_argument("--convolve",
- action="store_true",
- dest="convolve",
- default=False,
- help='convolve with the pdf')
-
-
-parser.add_argument('--distance',
- action="store",
- dest="distance",
- metavar='FLOAT',
- type=float,
- default=20,
- help='distance of the object in Mpc')
-
-
-
-parser.add_argument('--mlim',
- action="store",
- dest="mlim",
- metavar='FLOAT',
- type=float,
- default=31,
- help='magnitude limit')
-
-
-parser.add_argument("--sky_background",
- action="store_true",
- dest="sky_background",
- default=False,
- help='add sky background')
-
-
-parser.add_argument("--sky_N",
- action="store",
- dest="sky_N",
- metavar='FLOAT',
- type=float,
- default=1e6,
- help='number of particles in the sky')
-
-parser.add_argument("--sky_M",
- action="store",
- dest="sky_M",
- metavar='FLOAT',
- type=float,
- default=3.4e5,
- help='total sky mass [Msol]')
-
-parser.add_argument("--sky_e",
- action="store",
- dest="sky_e",
- metavar='FLOAT',
- type=float,
- default=3,
- help='sky size [kpc]')
-
-parser.add_argument("--sky_mean",
- action="store",
- dest="sky_mean",
- metavar='FLOAT',
- type=float,
- default=34,
- help='sky mean surface brighness')
-
-
-
-parser.add_argument("--telescope",
- action="store",
- dest="telescope",
- metavar='STR',
- type=str,
- default='arrakihs_vis',
- help='telescope name')
-
-parser.add_argument("--filter",
- action="store",
- dest="filter",
- metavar='STR',
- type=str,
- default=None,
- help='filter name')
-
-
-parser.add_argument("--toMsol",
- action="store",
- dest="toMsol",
- metavar='FLOAT',
- type=float,
- default=1e10,
- help='mass convertion to solar mass')
-
-parser.add_argument("--frsp",
- action="store",
- dest="frsp",
- metavar='FLOAT',
- type=float,
- default=0,
- help='factor for the image smoothing')
-
-
-parser.add_argument("--view",
- action="store",
- dest="view",
- metavar='STR',
- type=str,
- default='xy',
- help='view xy, yz, zx')
-
-parser.add_argument("--mlmin",
- action="store",
- dest="mlmin",
- metavar='FLOAT',
- type=float,
- default=None,
- help='mag lim min')
-
-parser.add_argument("--mlmax",
- action="store",
- dest="mlmax",
- metavar='FLOAT',
- type=float,
- default=None,
- help='mag lim max')
-
-parser.add_argument("--nlos",
- action="store",
- dest="nlos",
- metavar='INT',
- type=int,
- default=1,
- help='number of los')
-
-
-
-
-####################################################################
-# main
-####################################################################
-
-def get_axes(n=7,irand=0):
-
- np.random.seed(irand)
- random2 = np.random.random(n)
- random3 = np.random.random(n)
-
- phi = random2 * np.pi * 2.
- costh = 1. - 2. * random3
-
- sinth = np.sqrt(1. - costh**2)
-
- x = sinth * np.cos(phi)
- y = sinth * np.sin(phi)
- z = costh
-
- pos = np.transpose(np.array([x, y, z]))
-
- return pos
-
-
-
-
-if __name__ == '__main__':
-
- opt = parser.parse_args()
-
- naxes = opt.nlos
- axes = get_axes(naxes)
-
-
- ARRAKIHS.SetTelecope(opt)
-
- images = []
-
- # loop over axes
- for i in range(naxes):
- opt.los=axes[i]
- image = ARRAKIHS.Project(opt) # here we get: image,xmin,xmax,ymin,ymax
- images.append(image)
-
-
- if opt.outputfilename is not None:
-
- f = open(opt.outputfilename,'wb')
- pickle.dump(images,f)
- f.close()
-
-
diff --git a/ParticlesToSurfaceBrightness/arrakihs_show_images.py b/ParticlesToSurfaceBrightness/arrakihs_show_images.py
deleted file mode 100755
index 1ef266a..0000000
--- a/ParticlesToSurfaceBrightness/arrakihs_show_images.py
+++ /dev/null
@@ -1,203 +0,0 @@
-#!/usr/bin/env python3
-
-import numpy as np
-
-from astropy import constants as cte
-from astropy import units as u
-
-import argparse
-
-import matplotlib.pyplot as plt
-import matplotlib.colors as mcolors
-
-from pNbody import *
-from pNbody import ic
-import ARRAKIHS
-
-import pickle
-import os
-
-####################################################################
-# option parser
-####################################################################
-
-description=""
-epilog =""""""
-
-parser = argparse.ArgumentParser(description=description,epilog=epilog,formatter_class=argparse.RawDescriptionHelpFormatter)
-
-
-
-
-
-parser.add_argument(action="store",
- dest="file",
- metavar='FILE',
- type=str,
- default=None,
- nargs='*',
- help='a file name')
-
-parser.add_argument("-o",
- action="store",
- type=str,
- dest="outputfilename",
- default=None,
- help="Name of the output file")
-
-
-
-
-
-
-parser.add_argument('--mlim',
- action="store",
- dest="mlim",
- metavar='FLOAT',
- type=float,
- default=31,
- help='magnitude limit')
-
-
-
-
-
-parser.add_argument("--mlmin",
- action="store",
- dest="mlmin",
- metavar='FLOAT',
- type=float,
- default=None,
- help='mag lim min')
-
-parser.add_argument("--mlmax",
- action="store",
- dest="mlmax",
- metavar='FLOAT',
- type=float,
- default=None,
- help='mag lim max')
-
-parser.add_argument("--nlos",
- action="store",
- dest="nlos",
- metavar='INT',
- type=int,
- default=1,
- help='number of los')
-
-
-parser.add_argument("--filter",
- action="store",
- dest="filter",
- metavar='STR',
- type=str,
- default=None,
- help='filter name')
-
-
-parser.add_argument("--MDM",
- action="store",
- dest="MDM",
- metavar='FLOAT',
- type=float,
- default=1,
- help='dark mattermass')
-
-
-
-####################################################################
-# main
-####################################################################
-
-
-if __name__ == '__main__':
-
- opt = parser.parse_args()
-
-
- if len(opt.file)==0:
- print("need to provie one file")
- exit()
-
-
- f = open(opt.file[0],"rb")
- images = pickle.load(f)
- f.close()
-
- nx = int(np.sqrt(len(images)))
- ny = nx
-
-
- fig, ax = plt.subplots(nx,ny)
-
- # plot
- fig = plt.gcf()
- fig.set_size_inches(12*1.15, 12)
-
- fig.subplots_adjust(left=0.1)
- fig.subplots_adjust(right=1)
- fig.subplots_adjust(bottom=0.05)
- fig.subplots_adjust(top=0.95)
- fig.subplots_adjust(wspace=0.02)
- fig.subplots_adjust(hspace=0.02)
-
-
- colors2 = plt.cm.gist_heat(np.linspace(0.3, 1.0, 255))
- mymap = mcolors.LinearSegmentedColormap.from_list('my_colormap', colors2)
-
- for i in range(len(images)):
-
- image,xmin,xmax,ymin,ymax = images[i]
-
- #image = np.where(image>26,34,image)
-
-
- ix = i//nx
- iy = i- (ix*nx)
-
-
- #ax[i] = plt.gca()
- ax[ix,iy].set_aspect('equal')
-
- mmin = 24
- mmax = 33
-
- im = ax[ix,iy].imshow(image,aspect='equal',extent=(xmin,xmax,ymin,ymax),interpolation=None,vmin=mmin,vmax=mmax,cmap=mymap)
- #ax[ix,iy].contour(image,levels=[26],colors='r')
-
-
- ax[ix,iy].set_xlabel(r"$x\,[\rm{kpc}]$")
- ax[ix,iy].set_ylabel(r"$y\,[\rm{kpc}]$")
-
-
- if i != nx*(ny-1):
- ax[ix,iy].get_xaxis().set_visible(False)
- ax[ix,iy].get_yaxis().set_visible(False)
- ax[ix,iy].get_xaxis().set_ticks([])
- ax[ix,iy].get_yaxis().set_ticks([])
-
-
- plt.colorbar(im,label="surface brightness [mag/arcsec]",ax=ax)
-
- basename = os.path.basename(opt.file[0])
- opt.filter = basename.split("_")[0]
- opt.MDM = basename.split("_")[1].split("keV")[0]
-
- txt = r"$\rm{%s}\,\,:\,\,%s\,\rm{keV}$"%(opt.filter,opt.MDM)
- fig.suptitle(txt,fontsize=20)
-
-
-
-
- if opt.outputfilename:
- plt.savefig(opt.outputfilename)
- else:
- plt.show()
-
-
-
-
-
-
-
diff --git a/ParticlesToSurfaceBrightness/stars_class.py b/ParticlesToSurfaceBrightness/stars_class.py
deleted file mode 100644
index 811eda3..0000000
--- a/ParticlesToSurfaceBrightness/stars_class.py
+++ /dev/null
@@ -1,1449 +0,0 @@
-import numpy as np
-from IMF import *
-
-
-
-def Stars_fun(mass, age, metals, imf = 'normed_3slope'):
- """
- This function calculates the number of stars for a given stellar mass particle, for different IMFs:
- Parameters
- -------
- mass: numpy array.
- Mass of the particle.
- age: numpy array.
- Age of the particle
- metals: numpy array.
- Metallicity of the particle [M/H]
- imf: str.
- Initial Mass Function. Eligible: 'normed_3slope', 'Chabier_1', 'Chabier_2', 'sapeter' or 'BrokenPowerLaw'. By default: 'normed_3slope'.
-
- """
- init_imf=IMF(mmin=0.05, mmax=100.0, intervals=5000)
- if imf=='Chabrier_1':
- IMF_type=init_imf.Chabrier_1() # Chabrier_1() # Chabrier_2(), salpeter(), BrokenPowerLaw(),
- # normed_3slope() that is Kroupa.
- if imf=='Chabrier_2':
- IMF_type=init_imf.Chabrier_2()
-
- if imf=='salpeter':
- IMF_type=init_imf.salpeter()
-
- if imf=='normed_3slope':
- IMF_type=init_imf.normed_3slope()
-
-
- M1=init_imf.imf_mass_fraction(0.0,0.1)
- M2=init_imf.imf_mass_fraction(0.1,0.2)
- M3=init_imf.imf_mass_fraction(0.2,0.3)
- M4=init_imf.imf_mass_fraction(0.3,0.4)
- M5=init_imf.imf_mass_fraction(0.4,0.5)
- M6=init_imf.imf_mass_fraction(0.5,0.6)
- M7=init_imf.imf_mass_fraction(0.6,0.7)
- M8=init_imf.imf_mass_fraction(0.7,0.8)
- M9=init_imf.imf_mass_fraction(0.8,0.9)
- M10=init_imf.imf_mass_fraction(0.9,1)
- M11=init_imf.imf_mass_fraction(1,2)
- M12=init_imf.imf_mass_fraction(2,3)
- M13=init_imf.imf_mass_fraction(3,4)
- M14=init_imf.imf_mass_fraction(4,5)
- M15=init_imf.imf_mass_fraction(5,6)
- M16=init_imf.imf_mass_fraction(6,7)
- M17=init_imf.imf_mass_fraction(7,8)
- M18=init_imf.imf_mass_fraction(8,101.)
-
-
-
- n_1=[M1*i/0.1 for i in mass]
- n_2=[M2*i/0.2 for i in mass]
- n_3=[M3*i/0.3 for i in mass]
- n_4=[M4*i/0.4 for i in mass]
- n_5=[M5*i/0.5 for i in mass]
- n_6=[M6*i/0.6 for i in mass]
- n_7=[M7*i/0.7 for i in mass]
- n_8=[M8*i/0.8 for i in mass]
- n_9=[M9*i/0.9 for i in mass]
- n_10=[M10*i/1 for i in mass]
- n_11=[M11*i/2 for i in mass]
- n_12=[M12*i/3 for i in mass]
- n_13=[M13*i/4 for i in mass]
- n_14=[M14*i/5 for i in mass]
- n_15=[M15*i/6 for i in mass]
- n_16=[M16*i/7 for i in mass]
- n_17=[M17*i/8 for i in mass]
- n_18=[M18*i/100 for i in mass]
-
-
- return np.array([n_1,n_2, n_3,n_4,n_5,n_6, n_7,n_8,n_9,n_10, n_11, n_12,n_13, n_14, n_15,n_16,n_17,n_18])
-
-
-def Temp_fun(mass, age, metals):
- '''''''''''''''
- This function calculates the temperature of stars from the synthetic CMD:
- Parameters
- -------
- mass: numpy array.
- Mass of the particle.
- age: numpy array.
- Age of the particle
- metals: numpy array.
- '''''''''''''''
- Isoc=np.genfromtxt('./Isochrones/new_CMDs/Isoc_new_euclid.txt',comments='#')
- MH,logAge,Mini,Te,logL=Isoc[:,0],Isoc[:,1],Isoc[:,2],Isoc[:,3],Isoc[:,4]
- # Difererentes edades de las estrellas del cmd
- logAge_iter=[0]
- logAge_iter[0]=logAge[0]
- j=0
- for i in range(0,len(logAge)-1):
- if (logAge_iter[j] != logAge[i]) & (logAge_iter[j] <= logAge[i]):
- j=j+1
- logAge_iter.append(logAge[i])
-
- # Difererentes metalicidades de las estrellas del cmd
- MH_iter=[0]
- MH_iter[0]=MH[0]
- j=0
- for i in range(0,len(MH)-1):
- if MH_iter[j] != MH[i]:
- j=j+1
- MH_iter.append(MH[i])
-
- met_l = np.unique(MH)
- age_l = np.unique(logAge)
- Mass_l = np.unique(Mini)
- met_len=len(met_l)-1
- age_len=len(age_l)-1
- mass_len=len(Mass_l)-1
-
- m=((age_len)*((age*1e9-10**np.min(age_l))/(10**np.max(age_l)-10**np.min(age_l)))).round(0).astype(int)
- m[m<0]=0
- m[m>=age_len]=age_len
-
-
- j=((met_len)*(metals-np.min(met_l))/(np.max(met_l)-np.min(met_l))).astype(int)
-
- j[j<0]=0
- j[j>=met_len]=met_len
-
- k=(j*(age_len+1)*(mass_len+1)+m*(mass_len+1))
-
- t1=Te[k+1]
- t2=Te[k+2]
- t3=Te[k+3]
- t4=Te[k+4]
- t5=Te[k+5]
- t6=Te[k+6]
- t7=Te[k+7]
- t8=Te[k+8]
- t9=Te[k+9]
- t10=Te[k+10]
- t11=Te[k+11]
- t12=Te[k+12]
- t13=Te[k+13]
- t14=Te[k+14]
- t15=Te[k+15]
- t16=Te[k+16]
- t17=Te[k+17]
- t18=Te[k+18]
-
-
- return t1,t2,t3,t4,t5,t6, t7, t8, t9, t10, t11, t12, t13, t14, t15, t16, t17, t18
-
-
-
-def Luminosity_fun(mass, age, metals):
- '''''''''''''''
- This function calculates the intrinsic luminosity of stars from the synthetic CMD:
- Parameters
- -------
- mass: numpy array.
- Mass of the particle.
- age: numpy array.
- Age of the particle
- metals: numpy array.
- '''''''''''''''
- Isoc=np.genfromtxt('./Isochrones/new_CMDs/Isoc_new_euclid.txt',comments='#')
- MH,logAge,Mini,Te,logL=Isoc[:,0],Isoc[:,1],Isoc[:,2],Isoc[:,3],Isoc[:,4]
- # Difererentes edades de las estrellas del cmd
-
- logAge_iter=[0]
- logAge_iter[0]=logAge[0]
- j=0
- for i in range(0,len(logAge)-1):
- if (logAge_iter[j] != logAge[i]) & (logAge_iter[j] <= logAge[i]):
- j=j+1
- logAge_iter.append(logAge[i])
-
- # Difererentes metalicidades de las estrellas del cmd
- MH_iter=[0]
- MH_iter[0]=MH[0]
- j=0
- for i in range(0,len(MH)-1):
- if MH_iter[j] != MH[i]:
- j=j+1
- MH_iter.append(MH[i])
-
- met_l = np.unique(MH)
- age_l = np.unique(logAge)
- Mass_l = np.unique(Mini)
- met_len=len(met_l)-1
- age_len=len(age_l)-1
- mass_len=len(Mass_l)-1
-
-
- m=((age_len)*((age*1e9-10**np.min(age_l))/(10**np.max(age_l)-10**np.min(age_l)))).round(0).astype(int)
- m[m<0]=0
- m[m>=age_len]=age_len
-
-
- j=((met_len)*(metals-np.min(met_l))/(np.max(met_l)-np.min(met_l))).astype(int)
-
- j[j<0]=0
- j[j>=met_len]=met_len
-
- k=(j*(age_len+1)*(mass_len+1)+m*(mass_len+1))
-
-
- l1=10**logL[k+1]
- l2=10**logL[k+2]
- l3=10**logL[k+3]
- l4=10**logL[k+4]
- l5=10**logL[k+5]
- l6=10**logL[k+6]
- l7=10**logL[k+7]
- l8=10**logL[k+8]
- l9=10**logL[k+9]
- l10=10**logL[k+10]
- l11=10**logL[k+11]
- l12=10**logL[k+12]
- l13=10**logL[k+13]
- l14=10**logL[k+14]
- l15=10**logL[k+15]
- l16=10**logL[k+16]
- l17=10**logL[k+17]
- l18=10**logL[k+18]
-
-
- return l1,l2,l3,l4,l5,l6,l7,l8,l9,l10,l11,l12, l13,l14,l15,l16,l17,l18
-
-
-#####################################
-########## HST FUNCTIONS
-#####################################
-
-
-
-
-def HST475X_fun(mass, age, metals):
- '''''''''''''''
- This function calculates the magnitude on HST-F475X from WFC3 of stars from the synthetic CMD:
- Parameters
- -------
- mass: numpy array.
- Mass of the particle.
- age: numpy array.
- Age of the particle
- metals: numpy array.
- '''''''''''''''
- Isoc=np.genfromtxt('./Isochrones/new_CMDs/Isoc_new_hst_wfc3.txt',comments='#')
- MH,logAge,Mini,Te,logL, HST475Xmag=Isoc[:,0],Isoc[:,1],Isoc[:,2],Isoc[:,3],Isoc[:,4], Isoc[:,5]
- # Difererentes edades de las estrellas del cmd
-
- logAge_iter=[0]
- logAge_iter[0]=logAge[0]
- j=0
- for i in range(0,len(logAge)-1):
- if (logAge_iter[j] != logAge[i]) & (logAge_iter[j] <= logAge[i]):
- j=j+1
- logAge_iter.append(logAge[i])
-
-
-
- # Difererentes metalicidades de las estrellas del cmd
- MH_iter=[0]
- MH_iter[0]=MH[0]
- j=0
- for i in range(0,len(MH)-1):
- if MH_iter[j] != MH[i]:
- j=j+1
- MH_iter.append(MH[i])
-
-
- met_l = np.unique(MH)
- age_l = np.unique(logAge)
- Mass_l = np.unique(Mini)
-
- met_len=len(met_l)-1
- age_len=len(age_l)-1
- mass_len=len(Mass_l)-1
-
- # yr: the following lines gives the wrong results !!!
- '''
- m=((age_len)*((age*1e9-10**np.min(age_l))/(10**np.max(age_l)-10**np.min(age_l)))).round(0).astype(int)
- m[m<0]=0
- m[m>=age_len]=age_len
-
- j=((met_len)*(metals-np.min(met_l))/(np.max(met_l)-np.min(met_l))).astype(int)
- j[j<0]=0
- j[j>=met_len]=met_len
-
- k=(j*(age_len+1)*(mass_len+1)+m*(mass_len+1))
- '''
-
- # yr
- get_age_idx = np.vectorize(lambda x:np.argmin( np.fabs( x*1e9 -10**age_l )))
- get_met_idx = np.vectorize(lambda x:np.argmin( np.fabs( x -met_l )))
-
- idx_age = get_age_idx(age)
- idx_met = get_met_idx(metals)
-
- k=(idx_met*(age_len+1)*(mass_len+1)+idx_age*(mass_len+1))
- k = k-1 # really ???
-
-
-
- l1 = HST475Xmag[k+1]
- l2 = HST475Xmag[k+2]
- l3 = HST475Xmag[k+3]
- l4 = HST475Xmag[k+4]
- l5 = HST475Xmag[k+5]
- l6 = HST475Xmag[k+6]
- l7 = HST475Xmag[k+7]
- l8 = HST475Xmag[k+8]
- l9 = HST475Xmag[k+9]
- l10 = HST475Xmag[k+10]
- l11 = HST475Xmag[k+11]
- l12 = HST475Xmag[k+12]
- l13 = HST475Xmag[k+13]
- l14 = HST475Xmag[k+14]
- l15 = HST475Xmag[k+15]
- l16 = HST475Xmag[k+16]
- l17 = HST475Xmag[k+17]
- l18 = HST475Xmag[k+18]
-
- return np.array([l1,l2,l3,l4,l5,l6,l7,l8,l9,l10,l11,l12, l13,l14,l15,l16,l17,l18])
-
-
-
-#####################################
-########## EUCLID FUNCTIONS
-#####################################
-
-def VISeuclid_fun(mass, age, metals):
- '''''''''''''''
- This function calculates the magnitude on Euclid-VIS of stars from the synthetic CMD:
- Parameters
- -------
- mass: numpy array.
- Mass of the particle.
- age: numpy array.
- Age of the particle
- metals: numpy array.
- '''''''''''''''
- Isoc=np.genfromtxt('./Isochrones/new_CMDs/Isoc_new_euclid.txt',comments='#')
- MH,logAge,Mini,Te,logL, VISmag = Isoc[:,0],Isoc[:,1],Isoc[:,2],Isoc[:,3],Isoc[:,4], Isoc[:,5]
- # Difererentes edades de las estrellas del cmd
-
- logAge_iter=[0]
- logAge_iter[0]=logAge[0]
- j=0
- for i in range(0,len(logAge)-1):
- if (logAge_iter[j] != logAge[i]) & (logAge_iter[j] <= logAge[i]):
- j=j+1
- logAge_iter.append(logAge[i])
-
- # Difererentes metalicidades de las estrellas del cmd
- MH_iter=[0]
- MH_iter[0]=MH[0]
- j=0
- for i in range(0,len(MH)-1):
- if MH_iter[j] != MH[i]:
- j=j+1
- MH_iter.append(MH[i])
-
- met_l = np.unique(MH)
- age_l = np.unique(logAge)
- Mass_l = np.unique(Mini)
- met_len=len(met_l)-1
- age_len=len(age_l)-1
- mass_len=len(Mass_l)-1
-
- # yr: the following lines gives the wrong results !!!
- '''
- m=((age_len)*((age*1e9-10**np.min(age_l))/(10**np.max(age_l)-10**np.min(age_l)))).round(0).astype(int)
- m[m<0]=0
- m[m>=age_len]=age_len
-
- j=((met_len)*(metals-np.min(met_l))/(np.max(met_l)-np.min(met_l))).astype(int)
- j[j<0]=0
- j[j>=met_len]=met_len
-
- k=(j*(age_len+1)*(mass_len+1)+m*(mass_len+1))
- '''
-
- # yr
- get_age_idx = np.vectorize(lambda x:np.argmin( np.fabs( x*1e9 -10**age_l )))
- get_met_idx = np.vectorize(lambda x:np.argmin( np.fabs( x -met_l )))
-
- idx_age = get_age_idx(age)
- idx_met = get_met_idx(metals)
-
- k=(idx_met*(age_len+1)*(mass_len+1)+idx_age*(mass_len+1))
- k = k-1 # really ???
-
-
-
- l1 = VISmag[k+1]
- l2 = VISmag[k+2]
- l3 = VISmag[k+3]
- l4 = VISmag[k+4]
- l5 = VISmag[k+5]
- l6 = VISmag[k+6]
- l7 = VISmag[k+7]
- l8 = VISmag[k+8]
- l9 = VISmag[k+9]
- l10 = VISmag[k+10]
- l11 = VISmag[k+11]
- l12 = VISmag[k+12]
- l13 = VISmag[k+13]
- l14 = VISmag[k+14]
- l15 = VISmag[k+15]
- l16 = VISmag[k+16]
- l17 = VISmag[k+17]
- l18 = VISmag[k+18]
-
-
- return np.array([l1,l2,l3,l4,l5,l6,l7,l8,l9,l10,l11,l12, l13,l14,l15,l16,l17,l18])
-
-
-
-def Yeuclid_fun(mass, age, metals):
- '''''''''''''''
- This function calculates the magnitude on Euclid-Y of stars from the synthetic CMD:
- Parameters
- -------
- mass: numpy array.
- Mass of the particle.
- age: numpy array.
- Age of the particle
- metals: numpy array.
- '''''''''''''''
- Isoc=np.genfromtxt('./Isochrones/new_CMDs/Isoc_new_euclid.txt',comments='#')
- MH,logAge,Mini,Te,logL, Ymag = Isoc[:,0],Isoc[:,1],Isoc[:,2],Isoc[:,3],Isoc[:,4], Isoc[:,7]
- # Difererentes edades de las estrellas del cmd
-
- logAge_iter=[0]
- logAge_iter[0]=logAge[0]
- j=0
- for i in range(0,len(logAge)-1):
- if (logAge_iter[j] != logAge[i]) & (logAge_iter[j] <= logAge[i]):
- j=j+1
- logAge_iter.append(logAge[i])
-
- # Difererentes metalicidades de las estrellas del cmd
- MH_iter=[0]
- MH_iter[0]=MH[0]
- j=0
- for i in range(0,len(MH)-1):
- if MH_iter[j] != MH[i]:
- j=j+1
- MH_iter.append(MH[i])
-
- met_l = np.unique(MH)
- age_l = np.unique(logAge)
- Mass_l = np.unique(Mini)
- met_len=len(met_l)-1
- age_len=len(age_l)-1
- mass_len=len(Mass_l)-1
-
- # yr: the following lines gives the wrong results !!!
- '''
- m=((age_len)*((age*1e9-10**np.min(age_l))/(10**np.max(age_l)-10**np.min(age_l)))).round(0).astype(int)
- m[m<0]=0
- m[m>=age_len]=age_len
-
- j=((met_len)*(metals-np.min(met_l))/(np.max(met_l)-np.min(met_l))).astype(int)
- j[j<0]=0
- j[j>=met_len]=met_len
-
- k=(j*(age_len+1)*(mass_len+1)+m*(mass_len+1))
- '''
-
- # yr
- get_age_idx = np.vectorize(lambda x:np.argmin( np.fabs( x*1e9 -10**age_l )))
- get_met_idx = np.vectorize(lambda x:np.argmin( np.fabs( x -met_l )))
-
- idx_age = get_age_idx(age)
- idx_met = get_met_idx(metals)
-
- k=(idx_met*(age_len+1)*(mass_len+1)+idx_age*(mass_len+1))
- k = k-1 # really ???
-
-
- l1 = Ymag[k+1]
- l2 = Ymag[k+2]
- l3 = Ymag[k+3]
- l4 = Ymag[k+4]
- l5 = Ymag[k+5]
- l6 = Ymag[k+6]
- l7 = Ymag[k+7]
- l8 = Ymag[k+8]
- l9 = Ymag[k+9]
- l10 = Ymag[k+10]
- l11 = Ymag[k+11]
- l12 = Ymag[k+12]
- l13 = Ymag[k+13]
- l14 = Ymag[k+14]
- l15 = Ymag[k+15]
- l16 = Ymag[k+16]
- l17 = Ymag[k+17]
- l18 = Ymag[k+18]
-
-
- return np.array([l1,l2,l3,l4,l5,l6,l7,l8,l9,l10,l11,l12, l13,l14,l15,l16,l17,l18])
-
-
-def Jeuclid_fun(mass, age, metals):
- '''''''''''''''
- This function calculates the magnitude on Euclid-J of stars from the synthetic CMD:
- Parameters
- -------
- mass: numpy array.
- Mass of the particle.
- age: numpy array.
- Age of the particle
- metals: numpy array.
- '''''''''''''''
- Isoc=np.genfromtxt('./Isochrones/new_CMDs/Isoc_new_euclid.txt',comments='#')
- MH,logAge,Mini,Te,logL, Jmag = Isoc[:,0],Isoc[:,1],Isoc[:,2],Isoc[:,3],Isoc[:,4], Isoc[:,6]
- # Difererentes edades de las estrellas del cmd
-
- logAge_iter=[0]
- logAge_iter[0]=logAge[0]
- j=0
- for i in range(0,len(logAge)-1):
- if (logAge_iter[j] != logAge[i]) & (logAge_iter[j] <= logAge[i]):
- j=j+1
- logAge_iter.append(logAge[i])
-
- # Difererentes metalicidades de las estrellas del cmd
- MH_iter=[0]
- MH_iter[0]=MH[0]
- j=0
- for i in range(0,len(MH)-1):
- if MH_iter[j] != MH[i]:
- j=j+1
- MH_iter.append(MH[i])
-
- met_l = np.unique(MH)
- age_l = np.unique(logAge)
- Mass_l = np.unique(Mini)
- met_len=len(met_l)-1
- age_len=len(age_l)-1
- mass_len=len(Mass_l)-1
-
- # yr: the following lines gives the wrong results !!!
- '''
- m=((age_len)*((age*1e9-10**np.min(age_l))/(10**np.max(age_l)-10**np.min(age_l)))).round(0).astype(int)
- m[m<0]=0
- m[m>=age_len]=age_len
-
- j=((met_len)*(metals-np.min(met_l))/(np.max(met_l)-np.min(met_l))).astype(int)
- j[j<0]=0
- j[j>=met_len]=met_len
-
- k=(j*(age_len+1)*(mass_len+1)+m*(mass_len+1))
- '''
-
- # yr
- get_age_idx = np.vectorize(lambda x:np.argmin( np.fabs( x*1e9 -10**age_l )))
- get_met_idx = np.vectorize(lambda x:np.argmin( np.fabs( x -met_l )))
-
- idx_age = get_age_idx(age)
- idx_met = get_met_idx(metals)
-
- k=(idx_met*(age_len+1)*(mass_len+1)+idx_age*(mass_len+1))
- k = k-1 # really ???
-
-
- l1 = Jmag[k+1]
- l2 = Jmag[k+2]
- l3 = Jmag[k+3]
- l4 = Jmag[k+4]
- l5 = Jmag[k+5]
- l6 = Jmag[k+6]
- l7 = Jmag[k+7]
- l8 = Jmag[k+8]
- l9 = Jmag[k+9]
- l10 = Jmag[k+10]
- l11 = Jmag[k+11]
- l12 = Jmag[k+12]
- l13 = Jmag[k+13]
- l14 = Jmag[k+14]
- l15 = Jmag[k+15]
- l16 = Jmag[k+16]
- l17 = Jmag[k+17]
- l18 = Jmag[k+18]
-
-
- return np.array([l1,l2,l3,l4,l5,l6,l7,l8,l9,l10,l11,l12, l13,l14,l15,l16,l17,l18])
-
-
-
-
-
-
-#####################################
-########## SLOAN FUNCTIONS
-#####################################
-
-
-def rsloan_fun(mass, age, metals):
- '''''''''''''''
- This function calculates the magnitude on sloan r of stars from the synthetic CMD:
- Parameters
- -------
- mass: numpy array.
- Mass of the particle.
- age: numpy array.
- Age of the particle
- metals: numpy array.
- '''''''''''''''
- Isoc=np.genfromtxt('./Isochrones/new_CMDs/Isoc_new_sloan.txt',comments='#')
- MH,logAge,Mini,Te,logL, rmag=Isoc[:,0],Isoc[:,1],Isoc[:,2],Isoc[:,3],Isoc[:,4], Isoc[:,5]
- # Difererentes edades de las estrellas del cmd
-
- logAge_iter=[0]
- logAge_iter[0]=logAge[0]
- j=0
- for i in range(0,len(logAge)-1):
- if (logAge_iter[j] != logAge[i]) & (logAge_iter[j] <= logAge[i]):
- j=j+1
- logAge_iter.append(logAge[i])
-
- # Difererentes metalicidades de las estrellas del cmd
- MH_iter=[0]
- MH_iter[0]=MH[0]
- j=0
- for i in range(0,len(MH)-1):
- if MH_iter[j] != MH[i]:
- j=j+1
- MH_iter.append(MH[i])
-
- met_l = np.unique(MH)
- age_l = np.unique(logAge)
- Mass_l = np.unique(Mini)
- met_len=len(met_l)-1
- age_len=len(age_l)-1
- mass_len=len(Mass_l)-1
-
-
- m=((age_len)*((age*1e9-10**np.min(age_l))/(10**np.max(age_l)-10**np.min(age_l)))).round(0).astype(int)
- m[m<0]=0
- m[m>=age_len]=age_len
-
-
- j=((met_len)*(metals-np.min(met_l))/(np.max(met_l)-np.min(met_l))).astype(int)
-
- j[j<0]=0
- j[j>=met_len]=met_len
-
- k=(j*(age_len+1)*(mass_len+1)+m*(mass_len+1))
-
-
- l1 = rmag[k+1]
- l2 = rmag[k+2]
- l3 = rmag[k+3]
- l4 = rmag[k+4]
- l5 = rmag[k+5]
- l6 = rmag[k+6]
- l7 = rmag[k+7]
- l8 = rmag[k+8]
- l9 = rmag[k+9]
- l10 = rmag[k+10]
- l11 = rmag[k+11]
- l12 = rmag[k+12]
- l13 = rmag[k+13]
- l14 = rmag[k+14]
- l15 = rmag[k+15]
- l16 = rmag[k+16]
- l17 = rmag[k+17]
- l18 = rmag[k+18]
-
-
- return np.array([l1,l2,l3,l4,l5,l6,l7,l8,l9,l10,l11,l12, l13,l14,l15,l16,l17,l18])
-
-
-def gsloan_fun(mass, age, metals):
- '''''''''''''''
- This function calculates the magnitude on sloan g of stars from the synthetic CMD:
- Parameters
- -------
- mass: numpy array.
- Mass of the particle.
- age: numpy array.
- Age of the particle
- metals: numpy array.
- '''''''''''''''
- Isoc=np.genfromtxt('./Isochrones/new_CMDs/Isoc_new_sloan.txt',comments='#')
- MH,logAge,Mini,Te,logL, gmag=Isoc[:,0],Isoc[:,1],Isoc[:,2],Isoc[:,3],Isoc[:,4], Isoc[:,6]
- # Difererentes edades de las estrellas del cmd
-
- logAge_iter=[0]
- logAge_iter[0]=logAge[0]
- j=0
- for i in range(0,len(logAge)-1):
- if (logAge_iter[j] != logAge[i]) & (logAge_iter[j] <= logAge[i]):
- j=j+1
- logAge_iter.append(logAge[i])
-
- # Difererentes metalicidades de las estrellas del cmd
- MH_iter=[0]
- MH_iter[0]=MH[0]
- j=0
- for i in range(0,len(MH)-1):
- if MH_iter[j] != MH[i]:
- j=j+1
- MH_iter.append(MH[i])
-
- met_l = np.unique(MH)
- age_l = np.unique(logAge)
- Mass_l = np.unique(Mini)
- met_len=len(met_l)-1
- age_len=len(age_l)-1
- mass_len=len(Mass_l)-1
-
-
- m=((age_len)*((age*1e9-10**np.min(age_l))/(10**np.max(age_l)-10**np.min(age_l)))).round(0).astype(int)
- m[m<0]=0
- m[m>=age_len]=age_len
-
-
- j=((met_len)*(metals-np.min(met_l))/(np.max(met_l)-np.min(met_l))).astype(int)
-
- j[j<0]=0
- j[j>=met_len]=met_len
-
- k=(j*(age_len+1)*(mass_len+1)+m*(mass_len+1))
-
-
- l1 = gmag[k+1]
- l2 = gmag[k+2]
- l3 = gmag[k+3]
- l4 = gmag[k+4]
- l5 = gmag[k+5]
- l6 = gmag[k+6]
- l7 = gmag[k+7]
- l8 = gmag[k+8]
- l9 = gmag[k+9]
- l10 = gmag[k+10]
- l11 = gmag[k+11]
- l12 = gmag[k+12]
- l13 = gmag[k+13]
- l14 = gmag[k+14]
- l15 = gmag[k+15]
- l16 = gmag[k+16]
- l17 = gmag[k+17]
- l18 = gmag[k+18]
-
-
- return np.array([l1,l2,l3,l4,l5,l6,l7,l8,l9,l10,l11,l12, l13,l14,l15,l16,l17,l18])
-
-
-def isloan_fun(mass, age, metals):
- '''''''''''''''
- This function calculates the magnitude on sloan g of stars from the synthetic CMD:
- Parameters
- -------
- mass: numpy array.
- Mass of the particle.
- age: numpy array.
- Age of the particle
- metals: numpy array.
- '''''''''''''''
- Isoc=np.genfromtxt('./Isochrones/new_CMDs/Isoc_new_sloan.txt',comments='#')
- MH,logAge,Mini,Te,logL, imag=Isoc[:,0],Isoc[:,1],Isoc[:,2],Isoc[:,3],Isoc[:,4], Isoc[:,7]
- # Difererentes edades de las estrellas del cmd
-
- logAge_iter=[0]
- logAge_iter[0]=logAge[0]
- j=0
- for i in range(0,len(logAge)-1):
- if (logAge_iter[j] != logAge[i]) & (logAge_iter[j] <= logAge[i]):
- j=j+1
- logAge_iter.append(logAge[i])
-
- # Difererentes metalicidades de las estrellas del cmd
- MH_iter=[0]
- MH_iter[0]=MH[0]
- j=0
- for i in range(0,len(MH)-1):
- if MH_iter[j] != MH[i]:
- j=j+1
- MH_iter.append(MH[i])
-
- met_l = np.unique(MH)
- age_l = np.unique(logAge)
- Mass_l = np.unique(Mini)
- met_len=len(met_l)-1
- age_len=len(age_l)-1
- mass_len=len(Mass_l)-1
-
-
- m=((age_len)*((age*1e9-10**np.min(age_l))/(10**np.max(age_l)-10**np.min(age_l)))).round(0).astype(int)
- m[m<0]=0
- m[m>=age_len]=age_len
-
-
- j=((met_len)*(metals-np.min(met_l))/(np.max(met_l)-np.min(met_l))).astype(int)
-
- j[j<0]=0
- j[j>=met_len]=met_len
-
- k=(j*(age_len+1)*(mass_len+1)+m*(mass_len+1))
-
-
- l1 = imag[k+1]
- l2 = imag[k+2]
- l3 = imag[k+3]
- l4 = imag[k+4]
- l5 = imag[k+5]
- l6 = imag[k+6]
- l7 = imag[k+7]
- l8 = imag[k+8]
- l9 = imag[k+9]
- l10 = imag[k+10]
- l11 = imag[k+11]
- l12 = imag[k+12]
- l13 = imag[k+13]
- l14 = imag[k+14]
- l15 = imag[k+15]
- l16 = imag[k+16]
- l17 = imag[k+17]
- l18 = imag[k+18]
-
-
- return np.array([l1,l2,l3,l4,l5,l6,l7,l8,l9,l10,l11,l12, l13,l14,l15,l16,l17,l18])
-
-
-#####################################
-########## GALEX FUNCTIONS
-#####################################
-
-def NUVmags_fun(mass, age, metals):
- '''''''''''''''
- This function calculates the magnitude on GALEX NUV of stars from the synthetic CMD:
- Parameters
- -------
- mass: numpy array.
- Mass of the particle.
- age: numpy array.
- Age of the particle
- metals: numpy array.
- '''''''''''''''
- Isoc=np.genfromtxt('./Isochrones/new_CMDs/Isoc_new_galex.txt',comments='#')
- MH,logAge,Mini,Te,logL, NUVmag = Isoc[:,0],Isoc[:,1],Isoc[:,2],Isoc[:,3],Isoc[:,4], Isoc[:,7]
- # Difererentes edades de las estrellas del cmd
-
- logAge_iter=[0]
- logAge_iter[0]=logAge[0]
- j=0
- for i in range(0,len(logAge)-1):
- if (logAge_iter[j] != logAge[i]) & (logAge_iter[j] <= logAge[i]):
- j=j+1
- logAge_iter.append(logAge[i])
-
- # Difererentes metalicidades de las estrellas del cmd
- MH_iter=[0]
- MH_iter[0]=MH[0]
- j=0
- for i in range(0,len(MH)-1):
- if MH_iter[j] != MH[i]:
- j=j+1
- MH_iter.append(MH[i])
-
- met_l = np.unique(MH)
- age_l = np.unique(logAge)
- Mass_l = np.unique(Mini)
- met_len=len(met_l)-1
- age_len=len(age_l)-1
- mass_len=len(Mass_l)-1
-
-
- m=((age_len)*((age*1e9-10**np.min(age_l))/(10**np.max(age_l)-10**np.min(age_l)))).round(0).astype(int)
- m[m<0]=0
- m[m>=age_len]=age_len
-
-
- j=((met_len)*(metals-np.min(met_l))/(np.max(met_l)-np.min(met_l))).astype(int)
-
- j[j<0]=0
- j[j>=met_len]=met_len
-
- k=(j*(age_len+1)*(mass_len+1)+m*(mass_len+1))
-
- l1 = NUVmag[k+1]
- l2 = NUVmag[k+2]
- l3 = NUVmag[k+3]
- l4 = NUVmag[k+4]
- l5 = NUVmag[k+5]
- l6 = NUVmag[k+6]
- l7 = NUVmag[k+7]
- l8 = NUVmag[k+8]
- l9 = NUVmag[k+9]
- l10 = NUVmag[k+10]
- l11 = NUVmag[k+11]
- l12 = NUVmag[k+12]
- l13 = NUVmag[k+13]
- l14 = NUVmag[k+14]
- l15 = NUVmag[k+15]
- l16 = NUVmag[k+16]
- l17 = NUVmag[k+17]
- l18 = NUVmag[k+18]
-
-
- return np.array([l1,l2,l3,l4,l5,l6,l7,l8,l9,l10,l11,l12, l13,l14,l15,l16,l17,l18])
-
-
-
-def FUVmags_fun(mass, age, metals):
- '''''''''''''''
- This function calculates the magnitude on GALEX FUV of stars from the synthetic CMD:
- Parameters
- -------
- mass: numpy array.
- Mass of the particle.
- age: numpy array.
- Age of the particle
- metals: numpy array.
- '''''''''''''''
- Isoc=np.genfromtxt('./Isochrones/new_CMDs/Isoc_new_galex.txt',comments='#')
- MH,logAge,Mini,Te,logL, FUVmag=Isoc[:,0],Isoc[:,1],Isoc[:,2],Isoc[:,3],Isoc[:,4], Isoc[:,6]
- # Difererentes edades de las estrellas del cmd
-
- logAge_iter=[0]
- logAge_iter[0]=logAge[0]
- j=0
- for i in range(0,len(logAge)-1):
- if (logAge_iter[j] != logAge[i]) & (logAge_iter[j] <= logAge[i]):
- j=j+1
- logAge_iter.append(logAge[i])
-
- # Difererentes metalicidades de las estrellas del cmd
- MH_iter=[0]
- MH_iter[0]=MH[0]
- j=0
- for i in range(0,len(MH)-1):
- if MH_iter[j] != MH[i]:
- j=j+1
- MH_iter.append(MH[i])
-
- met_l = np.unique(MH)
- age_l = np.unique(logAge)
- Mass_l = np.unique(Mini)
- met_len=len(met_l)-1
- age_len=len(age_l)-1
- mass_len=len(Mass_l)-1
-
-
- m=((age_len)*((age*1e9-10**np.min(age_l))/(10**np.max(age_l)-10**np.min(age_l)))).round(0).astype(int)
- m[m<0]=0
- m[m>=age_len]=age_len
-
-
- j=((met_len)*(metals-np.min(met_l))/(np.max(met_l)-np.min(met_l))).astype(int)
-
- j[j<0]=0
- j[j>=met_len]=met_len
-
- k=(j*(age_len+1)*(mass_len+1)+m*(mass_len+1))
-
- l1 = FUVmag[k+1]
- l2 = FUVmag[k+2]
- l3 = FUVmag[k+3]
- l4 = FUVmag[k+4]
- l5 = FUVmag[k+5]
- l6 = FUVmag[k+6]
- l7 = FUVmag[k+7]
- l8 = FUVmag[k+8]
- l9 = FUVmag[k+9]
- l10 = FUVmag[k+10]
- l11 = FUVmag[k+11]
- l12 = FUVmag[k+12]
- l13 = FUVmag[k+13]
- l14 = FUVmag[k+14]
- l15 = FUVmag[k+15]
- l16 = FUVmag[k+16]
- l17 = FUVmag[k+17]
- l18 = FUVmag[k+18]
-
-
- return np.array([l1,l2,l3,l4,l5,l6,l7,l8,l9,l10,l11,l12, l13,l14,l15,l16,l17,l18])
-
-
-
-#####################################
-########## LSST FUNCTIONS
-#####################################
-
-def ulsst_fun(mass, age, metals):
- '''''''''''''''
- This function calculates the magnitude on LSST-U of stars from the synthetic CMD:
- Parameters
- -------
- mass: numpy array.
- Mass of the particle.
- age: numpy array.
- Age of the particle
- metals: numpy array.
- '''''''''''''''
- Isoc=np.genfromtxt('./Isochrones/new_CMDs/Isoc_new_lsst.txt',comments='#')
- MH,logAge,Mini,Te,logL, umag=Isoc[:,0],Isoc[:,1],Isoc[:,2],Isoc[:,3],Isoc[:,4], Isoc[:,5]
- # Difererentes edades de las estrellas del cmd
-
- logAge_iter=[0]
- logAge_iter[0]=logAge[0]
- j=0
- for i in range(0,len(logAge)-1):
- if (logAge_iter[j] != logAge[i]) & (logAge_iter[j] <= logAge[i]):
- j=j+1
- logAge_iter.append(logAge[i])
-
- # Difererentes metalicidades de las estrellas del cmd
- MH_iter=[0]
- MH_iter[0]=MH[0]
- j=0
- for i in range(0,len(MH)-1):
- if MH_iter[j] != MH[i]:
- j=j+1
- MH_iter.append(MH[i])
-
- met_l = np.unique(MH)
- age_l = np.unique(logAge)
- Mass_l = np.unique(Mini)
- met_len=len(met_l)-1
- age_len=len(age_l)-1
- mass_len=len(Mass_l)-1
-
-
- m=((age_len)*((age*1e9-10**np.min(age_l))/(10**np.max(age_l)-10**np.min(age_l)))).round(0).astype(int)
- m[m<0]=0
- m[m>=age_len]=age_len
-
-
- j=((met_len)*(metals-np.min(met_l))/(np.max(met_l)-np.min(met_l))).astype(int)
-
- j[j<0]=0
- j[j>=met_len]=met_len
-
- k=(j*(age_len+1)*(mass_len+1)+m*(mass_len+1))
-
- l1 = umag[k+1]
- l2 = umag[k+2]
- l3 = umag[k+3]
- l4 = umag[k+4]
- l5 = umag[k+5]
- l6 = umag[k+6]
- l7 = umag[k+7]
- l8 = umag[k+8]
- l9 = umag[k+9]
- l10 = umag[k+10]
- l11 = umag[k+11]
- l12 = umag[k+12]
- l13 = umag[k+13]
- l14 = umag[k+14]
- l15 = umag[k+15]
- l16 = umag[k+16]
- l17 = umag[k+17]
- l18 = umag[k+18]
-
-
- return np.array([l1,l2,l3,l4,l5,l6,l7,l8,l9,l10,l11,l12, l13,l14,l15,l16,l17,l18])
-
-
-def glsst_fun(mass, age, metals):
- '''''''''''''''
- This function calculates the magnitude on LSST-G of stars from the synthetic CMD:
- Parameters
- -------
- mass: numpy array.
- Mass of the particle.
- age: numpy array.
- Age of the particle
- metals: numpy array.
- '''''''''''''''
- Isoc=np.genfromtxt('./Isochrones/new_CMDs/Isoc_new_lsst.txt',comments='#')
- MH,logAge,Mini,Te,logL, gmag=Isoc[:,0],Isoc[:,1],Isoc[:,2],Isoc[:,3],Isoc[:,4], Isoc[:,6]
- # Difererentes edades de las estrellas del cmd
-
- logAge_iter=[0]
- logAge_iter[0]=logAge[0]
- j=0
- for i in range(0,len(logAge)-1):
- if (logAge_iter[j] != logAge[i]) & (logAge_iter[j] <= logAge[i]):
- j=j+1
- logAge_iter.append(logAge[i])
-
- # Difererentes metalicidades de las estrellas del cmd
- MH_iter=[0]
- MH_iter[0]=MH[0]
- j=0
- for i in range(0,len(MH)-1):
- if MH_iter[j] != MH[i]:
- j=j+1
- MH_iter.append(MH[i])
-
- met_l = np.unique(MH)
- age_l = np.unique(logAge)
- Mass_l = np.unique(Mini)
- met_len=len(met_l)-1
- age_len=len(age_l)-1
- mass_len=len(Mass_l)-1
-
-
- m=((age_len)*((age*1e9-10**np.min(age_l))/(10**np.max(age_l)-10**np.min(age_l)))).round(0).astype(int)
- m[m<0]=0
- m[m>=age_len]=age_len
-
-
- j=((met_len)*(metals-np.min(met_l))/(np.max(met_l)-np.min(met_l))).astype(int)
-
- j[j<0]=0
- j[j>=met_len]=met_len
-
- k=(j*(age_len+1)*(mass_len+1)+m*(mass_len+1))
-
- l1 = gmag[k+1]
- l2 = gmag[k+2]
- l3 = gmag[k+3]
- l4 = gmag[k+4]
- l5 = gmag[k+5]
- l6 = gmag[k+6]
- l7 = gmag[k+7]
- l8 = gmag[k+8]
- l9 = gmag[k+9]
- l10 = gmag[k+10]
- l11 = gmag[k+11]
- l12 = gmag[k+12]
- l13 = gmag[k+13]
- l14 = gmag[k+14]
- l15 = gmag[k+15]
- l16 = gmag[k+16]
- l17 = gmag[k+17]
- l18 = gmag[k+18]
-
- return np.array([l1,l2,l3,l4,l5,l6,l7,l8,l9,l10,l11,l12, l13,l14,l15,l16,l17,l18])
-
-
-
-
-
-
-#####################################
-########## DECAM FUNCTIONS
-#####################################
-
-
-
-
-def g_fun(mass, age, metals):
- '''''''''''''''
- This function calculates the magnitude on DECAM-g of stars from the synthetic CMD:
- Parameters
- -------
- mass: numpy array.
- Mass of the particle.
- age: numpy array.
- Age of the particle
- metals: numpy array.
- '''''''''''''''
- Isoc=np.genfromtxt('./Isochrones/new_CMDs/Isoc_new_decam.txt',comments='#')
- MH,logAge,Mini,Te,logL, gmag = Isoc[:,0],Isoc[:,1],Isoc[:,2],Isoc[:,3],Isoc[:,4], Isoc[:,5]
- # Difererentes edades de las estrellas del cmd
-
- logAge_iter=[0]
- logAge_iter[0]=logAge[0]
- j=0
- for i in range(0,len(logAge)-1):
- if (logAge_iter[j] != logAge[i]) & (logAge_iter[j] <= logAge[i]):
- j=j+1
- logAge_iter.append(logAge[i])
-
- # Difererentes metalicidades de las estrellas del cmd
- MH_iter=[0]
- MH_iter[0]=MH[0]
- j=0
- for i in range(0,len(MH)-1):
- if MH_iter[j] != MH[i]:
- j=j+1
- MH_iter.append(MH[i])
-
- met_l = np.unique(MH)
- age_l = np.unique(logAge)
- Mass_l = np.unique(Mini)
- met_len=len(met_l)-1
- age_len=len(age_l)-1
- mass_len=len(Mass_l)-1
-
-
- m=((age_len)*((age*1e9-10**np.min(age_l))/(10**np.max(age_l)-10**np.min(age_l)))).round(0).astype(int)
- m[m<0]=0
- m[m>=age_len]=age_len
-
-
- j=((met_len)*(metals-np.min(met_l))/(np.max(met_l)-np.min(met_l))).astype(int)
-
- j[j<0]=0
- j[j>=met_len]=met_len
-
- k=(j*(age_len+1)*(mass_len+1)+m*(mass_len+1))
-
-
- l1 = gmag[k+1]
- l2 = gmag[k+2]
- l3 = gmag[k+3]
- l4 = gmag[k+4]
- l5 = gmag[k+5]
- l6 = gmag[k+6]
- l7 = gmag[k+7]
- l8 = gmag[k+8]
- l9 = gmag[k+9]
- l10 = gmag[k+10]
- l11 = gmag[k+11]
- l12 = gmag[k+12]
- l13 = gmag[k+13]
- l14 = gmag[k+14]
- l15 = gmag[k+15]
- l16 = gmag[k+16]
- l17 = gmag[k+17]
- l18 = gmag[k+18]
-
- return np.array([l1,l2,l3,l4,l5,l6,l7,l8,l9,l10,l11,l12, l13,l14,l15,l16,l17,l18])
-
-
-def r_fun(mass, age, metals):
- '''''''''''''''
- This function calculates the magnitude on DECAM-r of stars from the synthetic CMD:
- Parameters
- -------
- mass: numpy array.
- Mass of the particle.
- age: numpy array.
- Age of the particle
- metals: numpy array.
- '''''''''''''''
- Isoc=np.genfromtxt('./Isochrones/new_CMDs/Isoc_new_decam.txt',comments='#')
- MH,logAge,Mini,Te,logL, rmags = Isoc[:,0],Isoc[:,1],Isoc[:,2],Isoc[:,3],Isoc[:,4], Isoc[:,7]
- # Difererentes edades de las estrellas del cmd
-
- logAge_iter=[0]
- logAge_iter[0]=logAge[0]
- j=0
- for i in range(0,len(logAge)-1):
- if (logAge_iter[j] != logAge[i]) & (logAge_iter[j] <= logAge[i]):
- j=j+1
- logAge_iter.append(logAge[i])
-
- # Difererentes metalicidades de las estrellas del cmd
- MH_iter=[0]
- MH_iter[0]=MH[0]
- j=0
- for i in range(0,len(MH)-1):
- if MH_iter[j] != MH[i]:
- j=j+1
- MH_iter.append(MH[i])
-
- met_l = np.unique(MH)
- age_l = np.unique(logAge)
- Mass_l = np.unique(Mini)
- met_len=len(met_l)-1
- age_len=len(age_l)-1
- mass_len=len(Mass_l)-1
-
-
- m=((age_len)*((age*1e9-10**np.min(age_l))/(10**np.max(age_l)-10**np.min(age_l)))).round(0).astype(int)
- m[m<0]=0
- m[m>=age_len]=age_len
-
-
- j=((met_len)*(metals-np.min(met_l))/(np.max(met_l)-np.min(met_l))).astype(int)
-
- j[j<0]=0
- j[j>=met_len]=met_len
-
- k=(j*(age_len+1)*(mass_len+1)+m*(mass_len+1))
-
-
- l1 = rmags[k+1]
- l2 = rmags[k+2]
- l3 = rmags[k+3]
- l4 = rmags[k+4]
- l5 = rmags[k+5]
- l6 = rmags[k+6]
- l7 = rmags[k+7]
- l8 = rmags[k+8]
- l9 = rmags[k+9]
- l10 = rmags[k+10]
- l11 = rmags[k+11]
- l12 = rmags[k+12]
- l13 = rmags[k+13]
- l14 = rmags[k+14]
- l15 = rmags[k+15]
- l16 = rmags[k+16]
- l17 = rmags[k+17]
- l18 = rmags[k+18]
-
- return np.array([l1,l2,l3,l4,l5,l6,l7,l8,l9,l10,l11,l12, l13,l14,l15,l16,l17,l18])
-
-
-
-def z_fun(mass, age, metals):
- '''''''''''''''
- This function calculates the magnitude on DECAM-z of stars from the synthetic CMD:
- Parameters
- -------
- mass: numpy array.
- Mass of the particle.
- age: numpy array.
- Age of the particle
- metals: numpy array.
- '''''''''''''''
- Isoc=np.genfromtxt('./Isochrones/new_CMDs/Isoc_new_decam.txt',comments='#')
- MH,logAge,Mini,Te,logL, zmag = Isoc[:,0],Isoc[:,1],Isoc[:,2],Isoc[:,3],Isoc[:,4], Isoc[:,6]
- # Difererentes edades de las estrellas del cmd
-
- logAge_iter=[0]
- logAge_iter[0]=logAge[0]
- j=0
- for i in range(0,len(logAge)-1):
- if (logAge_iter[j] != logAge[i]) & (logAge_iter[j] <= logAge[i]):
- j=j+1
- logAge_iter.append(logAge[i])
-
- # Difererentes metalicidades de las estrellas del cmd
- MH_iter=[0]
- MH_iter[0]=MH[0]
- j=0
- for i in range(0,len(MH)-1):
- if MH_iter[j] != MH[i]:
- j=j+1
- MH_iter.append(MH[i])
-
- met_l = np.unique(MH)
- age_l = np.unique(logAge)
- Mass_l = np.unique(Mini)
- met_len=len(met_l)-1
- age_len=len(age_l)-1
- mass_len=len(Mass_l)-1
-
-
- m=((age_len)*((age*1e9-10**np.min(age_l))/(10**np.max(age_l)-10**np.min(age_l)))).round(0).astype(int)
- m[m<0]=0
- m[m>=age_len]=age_len
-
-
- j=((met_len)*(metals-np.min(met_l))/(np.max(met_l)-np.min(met_l))).astype(int)
-
- j[j<0]=0
- j[j>=met_len]=met_len
-
- k=(j*(age_len+1)*(mass_len+1)+m*(mass_len+1))
-
-
- l1 = zmag[k+1]
- l2 = zmag[k+2]
- l3 = zmag[k+3]
- l4 = zmag[k+4]
- l5 = zmag[k+5]
- l6 = zmag[k+6]
- l7 = zmag[k+7]
- l8 = zmag[k+8]
- l9 = zmag[k+9]
- l10 = zmag[k+10]
- l11 = zmag[k+11]
- l12 = zmag[k+12]
- l13 = zmag[k+13]
- l14 = zmag[k+14]
- l15 = zmag[k+15]
- l16 = zmag[k+16]
- l17 = zmag[k+17]
- l18 = zmag[k+18]
-
- return np.array([l1,l2,l3,l4,l5,l6,l7,l8,l9,l10,l11,l12, l13,l14,l15,l16,l17,l18])
-
-
-def i_fun(mass, age, metals):
- '''''''''''''''
- This function calculates the magnitude on DECAM-i of stars from the synthetic CMD:
- Parameters
- -------
- mass: numpy array.
- Mass of the particle.
- age: numpy array.
- Age of the particle
- metals: numpy array.
- '''''''''''''''
- Isoc=np.genfromtxt('./Isochrones/new_CMDs/Isoc_new_decam.txt',comments='#')
- MH,logAge,Mini,Te,logL, imag = Isoc[:,0],Isoc[:,1],Isoc[:,2],Isoc[:,3],Isoc[:,4], Isoc[:,8]
- # Difererentes edades de las estrellas del cmd
-
- logAge_iter=[0]
- logAge_iter[0]=logAge[0]
- j=0
- for i in range(0,len(logAge)-1):
- if (logAge_iter[j] != logAge[i]) & (logAge_iter[j] <= logAge[i]):
- j=j+1
- logAge_iter.append(logAge[i])
-
- # Difererentes metalicidades de las estrellas del cmd
- MH_iter=[0]
- MH_iter[0]=MH[0]
- j=0
- for i in range(0,len(MH)-1):
- if MH_iter[j] != MH[i]:
- j=j+1
- MH_iter.append(MH[i])
-
- met_l = np.unique(MH)
- age_l = np.unique(logAge)
- Mass_l = np.unique(Mini)
- met_len=len(met_l)-1
- age_len=len(age_l)-1
- mass_len=len(Mass_l)-1
-
-
- m=((age_len)*((age*1e9-10**np.min(age_l))/(10**np.max(age_l)-10**np.min(age_l)))).round(0).astype(int)
- m[m<0]=0
- m[m>=age_len]=age_len
-
-
- j=((met_len)*(metals-np.min(met_l))/(np.max(met_l)-np.min(met_l))).astype(int)
-
- j[j<0]=0
- j[j>=met_len]=met_len
-
- k=(j*(age_len+1)*(mass_len+1)+m*(mass_len+1))
-
-
- l1 = imag[k+1]
- l2 = imag[k+2]
- l3 = imag[k+3]
- l4 = imag[k+4]
- l5 = imag[k+5]
- l6 = imag[k+6]
- l7 = imag[k+7]
- l8 = imag[k+8]
- l9 = imag[k+9]
- l10 = imag[k+10]
- l11 = imag[k+11]
- l12 = imag[k+12]
- l13 = imag[k+13]
- l14 = imag[k+14]
- l15 = imag[k+15]
- l16 = imag[k+16]
- l17 = imag[k+17]
- l18 = imag[k+18]
-
- return np.array([l1,l2,l3,l4,l5,l6,l7,l8,l9,l10,l11,l12, l13,l14,l15,l16,l17,l18])
-
-
-
diff --git a/Workspace/CumNumOfSat/CDM_PS.dat b/Workspace/CumNumOfSat/CDM_PS.dat
new file mode 100644
index 0000000..076b2aa
--- /dev/null
+++ b/Workspace/CumNumOfSat/CDM_PS.dat
@@ -0,0 +1,1000 @@
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diff --git a/Workspace/CumNumOfSat/CDM_pk.dat b/Workspace/CumNumOfSat/CDM_pk.dat
new file mode 100644
index 0000000..1add843
--- /dev/null
+++ b/Workspace/CumNumOfSat/CDM_pk.dat
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diff --git a/Workspace/CumNumOfSat/HaloDistributionFunction/CDM_pk.dat b/Workspace/CumNumOfSat/HaloDistributionFunction/CDM_pk.dat
new file mode 100644
index 0000000..1add843
--- /dev/null
+++ b/Workspace/CumNumOfSat/HaloDistributionFunction/CDM_pk.dat
@@ -0,0 +1,500 @@
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diff --git a/Workspace/CumNumOfSat/HaloDistributionFunction/MDM_pk.dat b/Workspace/CumNumOfSat/HaloDistributionFunction/MDM_pk.dat
new file mode 100644
index 0000000..4744067
--- /dev/null
+++ b/Workspace/CumNumOfSat/HaloDistributionFunction/MDM_pk.dat
@@ -0,0 +1,500 @@
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diff --git a/Workspace/CumNumOfSat/HaloDistributionFunction/WDM_pk.dat b/Workspace/CumNumOfSat/HaloDistributionFunction/WDM_pk.dat
new file mode 100644
index 0000000..cbacc0f
--- /dev/null
+++ b/Workspace/CumNumOfSat/HaloDistributionFunction/WDM_pk.dat
@@ -0,0 +1,500 @@
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diff --git a/Workspace/CumNumOfSat/HaloDistributionFunction/subhalomf.py b/Workspace/CumNumOfSat/HaloDistributionFunction/subhalomf.py
new file mode 100755
index 0000000..5c9ca0e
--- /dev/null
+++ b/Workspace/CumNumOfSat/HaloDistributionFunction/subhalomf.py
@@ -0,0 +1,173 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.integrate import cumtrapz, trapz
+from classy import Class
+import genmassfct as gmf
+
+
+def ps_class(cosmopars):
+ """
+ Calculate power spectrum with CLASS
+ """
+
+ Om, Ob, As, h0, ns, mWDM, fWDM = cosmopars
+
+
+ if (fWDM>0):
+
+ m_nu = 4.43*mWDM**(4./3)*((Om-Ob)*h0**2/0.1225)**(-1./3)
+
+ CLASSparams = {
+ 'h': h0,
+ 'T_cmb': 2.726,
+ 'Omega_b': Ob,
+ 'Omega_cdm': (1-fWDM)*(Om-Ob),
+ 'Omega_ncdm': fWDM*(Om-Ob),
+ 'N_ur': 3.04,
+ 'N_ncdm': 1,
+ 'm_ncdm': 1000*m_nu,
+ 'T_ncdm': 0.715985,
+ 'n_s': ns,
+ 'A_s': As,
+ 'P_k_max_h/Mpc': 200,
+ 'k_per_decade_for_pk': 10,
+ 'output': 'mPk',
+ 'z_pk': 0.0,
+ 'ncdm_fluid_approximation': 3,
+ }
+ else:
+
+ CLASSparams = {
+ 'h': h0,
+ 'T_cmb': 2.726,
+ 'Omega_b': Ob,
+ 'Omega_cdm': (Om-Ob),
+ 'N_ur': 3.04,
+ 'n_s': ns,
+ 'A_s': As,
+ 'P_k_max_h/Mpc': 200,
+ 'k_per_decade_for_pk': 10,
+ 'output': 'mPk',
+ 'z_pk': 0.0,
+ 'ncdm_fluid_approximation': 1,
+ }
+
+ CLASScosmo = Class()
+ CLASScosmo.set(CLASSparams)
+ CLASScosmo.compute()
+ s8 = CLASScosmo.sigma8()
+ print("s8 = ",s8)
+ k_bin = np.logspace(np.log10(1e-3), np.log10(200), 500) #in [h/Mpc]
+ z_bin = np.array([0])
+ pk_bin = []
+ for i in range(len(k_bin)):
+ pk_bin += [CLASScosmo.pk_lin(k_bin[i]*h0,0)]
+
+ pk_bin = np.array(pk_bin)*h0**3 # [Mpc/h]^3
+
+ CLASScosmo.struct_cleanup()
+ CLASScosmo.empty()
+
+ return k_bin, pk_bin
+
+
+def Nsat_integral(mbin,dNsatdlnm):
+ """
+ Cumulative mass function Nsat(>M)
+ """
+
+ Nsat = trapz(dNsatdlnm/mbin,mbin) - cumtrapz(dNsatdlnm/mbin,mbin,initial=dNsatdlnm[0]/mbin[0])
+
+ return Nsat
+
+
+# MAIN #################################
+
+#Cosmology
+Om = 0.315
+Ob = 0.048
+h0 = 0.681
+As = 2.07e-9
+ns = 0.963
+
+#CDM
+mWDM = 3.0
+fWDM = 0.0
+
+cosmopars = Om, Ob, As, h0, ns, mWDM, fWDM
+
+#calculate power spectrum
+kbin, PSbin = ps_class(cosmopars)
+body = np.transpose([kbin, PSbin])
+filename = 'CDM_pk.dat'
+np.savetxt(filename,body,delimiter='\t')
+
+
+#WDM
+mWDM = 3.0
+fWDM = 1.0
+cosmopars = Om, Ob, As, h0, ns, mWDM, fWDM
+
+#calculate power spectrum
+kbin, PSbin = ps_class(cosmopars)
+body = np.transpose([kbin, PSbin])
+filename = 'WDM_pk.dat'
+np.savetxt(filename,body,delimiter='\t')
+
+
+#CDM+WDM
+mWDM = 0.5
+fWDM = 0.2
+cosmopars = Om, Ob, As, h0, ns, mWDM, fWDM
+
+
+#calculate power spectrum
+kbin, PSbin = ps_class(cosmopars)
+body = np.transpose([kbin, PSbin])
+filename = 'MDM_pk.dat'
+np.savetxt(filename,body,delimiter='\t')
+
+
+#Host halo mass (Msun/h)
+M0 = 1e12
+
+#redshift
+z0 = 0
+
+#initialise parameters
+par = gmf.par()
+par.window.window = "sharpk"
+par.code.rmin = 0.008
+par.mf.q = 1.0
+par.mf.p = 0.3
+par.mf.c = 2.5
+
+
+#Calculate stellite function for CDM
+par.file.psfct = "CDM_pk.dat"
+mbin_CDM, dNsatdlnM_CDM = gmf.dNsatdlnm(M0,z0,par)
+Nsat_CDM = Nsat_integral(mbin_CDM,dNsatdlnM_CDM)
+print(mbin_CDM)
+
+#Calculate stellite function for WDM
+par.file.psfct = "WDM_pk.dat"
+mbin_WDM, dNsatdlnM_WDM = gmf.dNsatdlnm(M0,z0,par)
+Nsat_WDM = Nsat_integral(mbin_WDM,dNsatdlnM_WDM)
+
+#Calculate stellite function for CDM+WDM
+par.file.psfct = "MDM_pk.dat"
+mbin_MDM, dNsatdlnM_MDM = gmf.dNsatdlnm(M0,z0,par)
+Nsat_MDM = Nsat_integral(mbin_MDM,dNsatdlnM_MDM)
+
+
+
+
+f = plt.figure(figsize=(6,4),dpi=140)
+plt.loglog(mbin_CDM, Nsat_CDM, color='black',ls='-')
+plt.loglog(mbin_WDM, Nsat_WDM, color='blue',ls='-')
+plt.loglog(mbin_MDM, Nsat_MDM, color='green',ls='-')
+plt.axis([1e7,4e12,0.0001,1000])
+plt.ylabel(r'Nsat(>M)')
+plt.xlabel(r'M [M$_{\odot}$/h]')
+plt.grid()
+plt.show()
diff --git a/Workspace/CumNumOfSat/HaloDistributionFunction/subhalomf_yr.py b/Workspace/CumNumOfSat/HaloDistributionFunction/subhalomf_yr.py
new file mode 100755
index 0000000..dc87173
--- /dev/null
+++ b/Workspace/CumNumOfSat/HaloDistributionFunction/subhalomf_yr.py
@@ -0,0 +1,155 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.integrate import cumtrapz, trapz
+from classy import Class
+import genmassfct as gmf
+
+
+def ps_class(cosmopars):
+ """
+ Calculate power spectrum with CLASS
+ """
+
+ Om, Ob, As, h0, ns, mWDM, fWDM = cosmopars
+
+
+ if (fWDM>0):
+
+ m_nu = 4.43*mWDM**(4./3)*((Om-Ob)*h0**2/0.1225)**(-1./3)
+
+ CLASSparams = {
+ 'h': h0,
+ 'T_cmb': 2.726,
+ 'Omega_b': Ob,
+ 'Omega_cdm': (1-fWDM)*(Om-Ob),
+ 'Omega_ncdm': fWDM*(Om-Ob),
+ 'N_ur': 3.04,
+ 'N_ncdm': 1,
+ 'm_ncdm': 1000*m_nu,
+ 'T_ncdm': 0.715985,
+ 'n_s': ns,
+ 'A_s': As,
+ 'P_k_max_h/Mpc': 200,
+ 'k_per_decade_for_pk': 10,
+ 'output': 'mPk',
+ 'z_pk': 0.0,
+ 'ncdm_fluid_approximation': 3,
+ }
+ else:
+
+ CLASSparams = {
+ 'h': h0,
+ 'T_cmb': 2.726,
+ 'Omega_b': Ob,
+ 'Omega_cdm': (Om-Ob),
+ 'N_ur': 3.04,
+ 'n_s': ns,
+ 'A_s': As,
+ 'P_k_max_h/Mpc': 200,
+ 'k_per_decade_for_pk': 10,
+ 'output': 'mPk',
+ 'z_pk': 0.0,
+ 'ncdm_fluid_approximation': 1,
+ }
+
+ CLASScosmo = Class()
+ CLASScosmo.set(CLASSparams)
+ CLASScosmo.compute()
+ s8 = CLASScosmo.sigma8()
+ print("s8 = ",s8)
+ k_bin = np.logspace(np.log10(1e-3), np.log10(200), 500) #in [h/Mpc]
+ z_bin = np.array([0])
+ pk_bin = []
+ for i in range(len(k_bin)):
+ pk_bin += [CLASScosmo.pk_lin(k_bin[i]*h0,0)]
+
+ pk_bin = np.array(pk_bin)*h0**3 # [Mpc/h]^3
+
+ CLASScosmo.struct_cleanup()
+ CLASScosmo.empty()
+
+ return k_bin, pk_bin
+
+
+def Nsat_integral(mbin,dNsatdlnm):
+ """
+ Cumulative mass function Nsat(>M)
+ """
+
+ Nsat = trapz(dNsatdlnm/mbin,mbin) - cumtrapz(dNsatdlnm/mbin,mbin,initial=dNsatdlnm[0]/mbin[0])
+
+ return Nsat
+
+
+# MAIN #################################
+
+#Cosmology
+Om = 0.315
+Ob = 0.048
+h0 = 0.681
+As = 2.07e-9
+ns = 0.963
+
+#CDM
+mWDM = None # DM mass in keV
+fWDM = 0.0 # fraction of wdm
+
+cosmopars = Om, Ob, As, h0, ns, mWDM, fWDM
+
+#calculate power spectrum
+kbin, PSbin = ps_class(cosmopars)
+body = np.transpose([kbin, PSbin])
+filename = 'CDM_pk.dat'
+np.savetxt(filename,body,delimiter='\t')
+
+
+#WDM
+mWDM = 1.0 # DM mass in keV
+fWDM = 1.0 # fraction of wdm
+cosmopars = Om, Ob, As, h0, ns, mWDM, fWDM
+
+#calculate power spectrum
+kbin, PSbin = ps_class(cosmopars)
+body = np.transpose([kbin, PSbin])
+filename = 'WDM_pk.dat'
+np.savetxt(filename,body,delimiter='\t')
+
+
+
+#Host halo mass (Msun/h)
+M0 = 1e12
+
+#redshift
+z0 = 0
+
+#initialise parameters
+par = gmf.par()
+par.window.window = "sharpk"
+par.code.rmin = 0.008
+par.mf.q = 1.0
+par.mf.p = 0.3
+par.mf.c = 2.5
+
+
+#Calculate stellite function for CDM
+par.file.psfct = "CDM_pk.dat"
+mbin_CDM, dNsatdlnM_CDM = gmf.dNsatdlnm(M0,z0,par)
+Nsat_CDM = Nsat_integral(mbin_CDM,dNsatdlnM_CDM)
+
+
+#Calculate stellite function for WDM
+par.file.psfct = "WDM_pk.dat"
+mbin_WDM, dNsatdlnM_WDM = gmf.dNsatdlnm(M0,z0,par)
+Nsat_WDM = Nsat_integral(mbin_WDM,dNsatdlnM_WDM)
+
+
+
+
+f = plt.figure(figsize=(6,4),dpi=140)
+plt.loglog(mbin_CDM, Nsat_CDM, color='black',ls='-')
+plt.loglog(mbin_WDM, Nsat_WDM, color='blue',ls='-')
+plt.axis([1e7,4e12,0.0001,1000])
+plt.ylabel(r'Nsat(>M)')
+plt.xlabel(r'M [M$_{\odot}$/h]')
+plt.grid()
+plt.show()
diff --git a/Workspace/CumNumOfSat/HaloDistributionFunction/test.py b/Workspace/CumNumOfSat/HaloDistributionFunction/test.py
new file mode 100644
index 0000000..dcdf149
--- /dev/null
+++ b/Workspace/CumNumOfSat/HaloDistributionFunction/test.py
@@ -0,0 +1,38 @@
+import numpy as np
+import matplotlib.pyplot as plt
+from scipy.integrate import cumtrapz, trapz
+from classy import Class
+import genmassfct as gmf
+
+
+
+#Cosmology
+Om = 0.315
+Ob = 0.048
+h0 = 0.681
+As = 2.07e-9
+ns = 0.963
+
+#CDM
+mWDM = 3.0
+fWDM = 0.0
+
+
+CLASSparams = {
+ 'h': h0,
+ 'T_cmb': 2.726,
+ 'Omega_b': Ob,
+ 'Omega_cdm': (Om-Ob),
+ 'N_ur': 3.04,
+ 'n_s': ns,
+ 'A_s': As,
+ 'P_k_max_h/Mpc': 200,
+ 'k_per_decade_for_pk': 10,
+ 'output': 'mPk',
+ 'z_pk': 0.0,
+ 'ncdm_fluid_approximation': 1,
+ }
+
+CLASScosmo = Class()
+CLASScosmo.set(CLASSparams)
+CLASScosmo.compute()
diff --git a/Workspace/CumNumOfSat/HaloDistributionFunction/var.dat b/Workspace/CumNumOfSat/HaloDistributionFunction/var.dat
new file mode 100644
index 0000000..a5009f8
--- /dev/null
+++ b/Workspace/CumNumOfSat/HaloDistributionFunction/var.dat
@@ -0,0 +1,100 @@
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diff --git a/Workspace/CumNumOfSat/IMF.py b/Workspace/CumNumOfSat/IMF.py
new file mode 120000
index 0000000..32fb2ff
--- /dev/null
+++ b/Workspace/CumNumOfSat/IMF.py
@@ -0,0 +1 @@
+../SurfaceBrightness/IMF.py
\ No newline at end of file
diff --git a/Workspace/CumNumOfSat/Isochrones b/Workspace/CumNumOfSat/Isochrones
new file mode 120000
index 0000000..47886b4
--- /dev/null
+++ b/Workspace/CumNumOfSat/Isochrones
@@ -0,0 +1 @@
+../SurfaceBrightness/Isochrones
\ No newline at end of file
diff --git a/Workspace/CumNumOfSat/MsvsMh_2018.pkl b/Workspace/CumNumOfSat/MsvsMh_2018.pkl
new file mode 100644
index 0000000..9037d19
Binary files /dev/null and b/Workspace/CumNumOfSat/MsvsMh_2018.pkl differ
diff --git a/Workspace/CumNumOfSat/Readme b/Workspace/CumNumOfSat/Readme
new file mode 100644
index 0000000..89faa01
--- /dev/null
+++ b/Workspace/CumNumOfSat/Readme
@@ -0,0 +1,133 @@
+# note:
+Next to classy, you need to pull and pip install this:
+https://bitbucket.org/aurelschneider/genmassfct/src/master/
+
+
+# Note : predictions of the haloes in WDM/CDM
+# https://arxiv.org/abs/2304.09810
+
+
+
+# test the halo function distribution (pour Courbin)
+./satComputeNvsM.py
+
+# test the Mstar vs Mhalo relation
+./satComputeMstarvsMhalo.py --LvvsMh_model rj2018
+
+./satComputeMstarvsMhalo.py --LvvsMh_model model1 -p LvvsMhm1.png
+./satComputeMstarvsMhalo.py --LvvsMh_model model2 -p LvvsMhm2.png
+./satComputeMstarvsMhalo.py --LvvsMh_model model3 -p LvvsMhm3.png
+./satComputeMstarvsMhalo.py --LvvsMh_model model4 -p LvvsMhm4.png
+
+
+
+
+
+# do Ngal realisation and plot the cumulative numbers of satellites
+./satComputeNCum.py --Ngal 80
+
+./satComputeNCumWithErrors.py --Nrealisations=100 --Ngal 100 -p NhCum100.png
+./satComputeNCumWithErrors.py --Nrealisations=100 --Ngal 75 -p NhCum075.png
+./satComputeNCumWithErrors.py --Nrealisations=100 --Ngal 50 -p NhCum050.png
+./satComputeNCumWithErrors.py --Nrealisations=100 --Ngal 25 -p NhCum025.png
+
+
+
+
+# do Ngal realisation and plot the cumulative numbers of satellites vs Luminosity
+./satComputeNCumvsLv.py --LvvsMh_file MsvsMh_2018.pkl --Ngal 80
+
+# same but now we run several realisations
+
+./satComputeNCumvsLvWithErrors.py --LvvsMh_model model1 --data --Nrealisations=100 --Ngal 100 -p LCum100m1.png
+./satComputeNCumvsLvWithErrors.py --LvvsMh_model model1 --data --Nrealisations=100 --Ngal 75 -p LCum075m1.png
+./satComputeNCumvsLvWithErrors.py --LvvsMh_model model1 --data --Nrealisations=100 --Ngal 50 -p LCum050m1.png
+./satComputeNCumvsLvWithErrors.py --LvvsMh_model model1 --data --Nrealisations=100 --Ngal 25 -p LCum025m1.png
+
+
+./satComputeNCumvsLvWithErrors.py --LvvsMh_model model2 --data --Nrealisations=100 --Ngal 100 -p LCum100m2.png
+./satComputeNCumvsLvWithErrors.py --LvvsMh_model model2 --data --Nrealisations=100 --Ngal 75 -p LCum075m2.png
+./satComputeNCumvsLvWithErrors.py --LvvsMh_model model2 --data --Nrealisations=100 --Ngal 50 -p LCum050m2.png
+./satComputeNCumvsLvWithErrors.py --LvvsMh_model model2 --data --Nrealisations=100 --Ngal 25 -p LCum025m2.png
+
+./satComputeNCumvsLvWithErrors.py --LvvsMh_model model3 --data --Nrealisations=100 --Ngal 100 -p LCum100m3.png
+./satComputeNCumvsLvWithErrors.py --LvvsMh_model model3 --data --Nrealisations=100 --Ngal 75 -p LCum075m3.png
+./satComputeNCumvsLvWithErrors.py --LvvsMh_model model3 --data --Nrealisations=100 --Ngal 50 -p LCum050m3.png
+./satComputeNCumvsLvWithErrors.py --LvvsMh_model model3 --data --Nrealisations=100 --Ngal 25 -p LCum025m3.png
+
+./satComputeNCumvsLvWithErrors.py --LvvsMh_model model4 --data --Nrealisations=100 --Ngal 100 -p LCum100m4.png
+./satComputeNCumvsLvWithErrors.py --LvvsMh_model model4 --data --Nrealisations=100 --Ngal 75 -p LCum075m4.png
+./satComputeNCumvsLvWithErrors.py --LvvsMh_model model4 --data --Nrealisations=100 --Ngal 50 -p LCum050m4.png
+./satComputeNCumvsLvWithErrors.py --LvvsMh_model model4 --data --Nrealisations=100 --Ngal 25 -p LCum025m4.png
+
+
+
+
+# variation of the DMmass
+
+./satComputeNCumvsLvWithErrors.py --LvvsMh_model model3 --data --Nrealisations=100 --Ngal 75 --DMmass 1.1 -p LCum075m3DM1.1.png
+./satComputeNCumvsLvWithErrors.py --LvvsMh_model model3 --data --Nrealisations=100 --Ngal 75 --DMmass 2.0 -p LCum075m3DM2.0.png
+./satComputeNCumvsLvWithErrors.py --LvvsMh_model model3 --data --Nrealisations=100 --Ngal 75 --DMmass 3.0 -p LCum075m3DM3.0.png
+./satComputeNCumvsLvWithErrors.py --LvvsMh_model model3 --data --Nrealisations=100 --Ngal 75 --DMmass 4.0 -p LCum075m3DM4.0.png
+
+./satComputeNCumvsLvWithErrors.py --LvvsMh_model model3 --data --Nrealisations=100 --Ngal 75 --DMmass 5.0 -p LCum075m3DM5.0.png
+./satComputeNCumvsLvWithErrors.py --LvvsMh_model model3 --data --Nrealisations=100 --Ngal 75 --DMmass 6.0 -p LCum075m3DM6.0.png
+./satComputeNCumvsLvWithErrors.py --LvvsMh_model model3 --data --Nrealisations=100 --Ngal 75 --DMmass 7.0 -p LCum075m3DM7.0.png
+./satComputeNCumvsLvWithErrors.py --LvvsMh_model model3 --data --Nrealisations=100 --Ngal 75 --DMmass 8.0 -p LCum075m3DM8.0.png
+./satComputeNCumvsLvWithErrors.py --LvvsMh_model model3 --data --Nrealisations=100 --Ngal 75 --DMmass 9.0 -p LCum075m3DM9.0.png
+./satComputeNCumvsLvWithErrors.py --LvvsMh_model model3 --data --Nrealisations=100 --Ngal 75 --DMmass 10.0 -p LCum075m3DM10.0.png
+
+
+# distribution of Main halo mass
+
+./satComputeNCumvsLvWithErrorsWithM0varied.py --LvvsMh_model model3 --data --Nrealisations=100 --Ngal 75 --DMmass 2.0 --M0_var=0 -p LCum075m3DM2.0.d0.0.png
+./satComputeNCumvsLvWithErrorsWithM0varied.py --LvvsMh_model model3 --data --Nrealisations=100 --Ngal 75 --DMmass 2.0 --M0_var=0.1 -p LCum075m3DM2.0.d0.1.png
+./satComputeNCumvsLvWithErrorsWithM0varied.py --LvvsMh_model model3 --data --Nrealisations=100 --Ngal 75 --DMmass 2.0 --M0_var=0.5 -p LCum075m3DM2.0.d0.5.png
+./satComputeNCumvsLvWithErrorsWithM0varied.py --LvvsMh_model model3 --data --Nrealisations=100 --Ngal 75 --DMmass 2.0 --M0_var=1.0 -p LCum075m3DM2.0.d1.0.png
+
+
+
+
+# in magnitude
+./satComputeNCumvsLvWithErrors.py --LvvsMh_model model3 --data --Nrealisations=100 --Ngal 75 --filter VISeuclid --DMmass 1.1 -p LCum_VIS_075m3DM1.1.png
+./satComputeNCumvsLvWithErrors.py --LvvsMh_model model3 --data --Nrealisations=100 --Ngal 75 --filter VISeuclid --DMmass 2.0 -p LCum_VIS_075m3DM2.0.png
+./satComputeNCumvsLvWithErrors.py --LvvsMh_model model3 --data --Nrealisations=100 --Ngal 75 --filter VISeuclid --DMmass 3.0 -p LCum_VIS_075m3DM3.0.png
+./satComputeNCumvsLvWithErrors.py --LvvsMh_model model3 --data --Nrealisations=100 --Ngal 75 --filter VISeuclid --DMmass 4.0 -p LCum_VIS_075m3DM4.0.png
+
+
+./satComputeNCumvsLvWithErrors.py --LvvsMh_model model3 --data --Nrealisations=100 --Ngal 30 --filter VISeuclid --DMmass 4.0 -p LCum_VIS_075m3DM4.0.png
+
+
+
+
+# comments
+
+ - no variation of the MW mass, in all realisation, we assume the same total mass and take
+ values from Sawala 2017
+
+ - the Lv vs Mh relation is obtained from CDM simulations (no WDM)
+
+ - the Lv vs Mh relation is quite uncertain
+
+
+ -> f : Subhalo mass function (SHMF) supression
+ see :
+ Nadler 2019 -> eq 7 - from Schneider 2012
+ Lovell et al. (2014),
+
+
+
+
+dmo resolution (Forouhar): 4 × 10^5
+
+
+
+
+###########################
+# plots for Oliver
+###########################
+
+/home/revaz/ownCloud/papers/2023M83
+
+
+
diff --git a/Workspace/CumNumOfSat/check_hdistrib.py b/Workspace/CumNumOfSat/check_hdistrib.py
new file mode 100755
index 0000000..dbfcd0c
--- /dev/null
+++ b/Workspace/CumNumOfSat/check_hdistrib.py
@@ -0,0 +1,38 @@
+#!/usr/bin/env python3
+
+import sys
+import numpy as np
+import matplotlib.pyplot as plt
+import Ptools as pt
+import pickle
+
+
+
+
+f = open("Mhaloes.pkl","rb")
+Mh = pickle.load(f)
+f.close()
+
+
+mass = np.log10(Mh)
+
+
+logMh = np.linspace(6.9,10,100)
+n, bins = np.histogram(mass, logMh)
+
+
+Ms = 10**bins
+dM = Ms[1:]-Ms[:-1]
+
+dNdM = n/dM
+
+Nc = np.add.accumulate(np.flip(n,0))
+Nc = np.flip(Nc,0)
+
+
+#pt.plot(Ms[1:],dNdM)
+pt.plot(Ms[1:],Nc)
+
+pt.loglog()
+
+pt.show()
diff --git a/Workspace/CumNumOfSat/data/Drlica_raw_all.txt b/Workspace/CumNumOfSat/data/Drlica_raw_all.txt
new file mode 100644
index 0000000..78312fe
--- /dev/null
+++ b/Workspace/CumNumOfSat/data/Drlica_raw_all.txt
@@ -0,0 +1,88 @@
+0.1053709571210737, 57.32295455509422
+0.01558136061859905, 57.32445429983094
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diff --git a/Workspace/CumNumOfSat/data/Drlica_raw_detected.txt b/Workspace/CumNumOfSat/data/Drlica_raw_detected.txt
new file mode 100644
index 0000000..7b222ec
--- /dev/null
+++ b/Workspace/CumNumOfSat/data/Drlica_raw_detected.txt
@@ -0,0 +1,57 @@
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diff --git a/Workspace/CumNumOfSat/data/Drlica_raw_weighted.txt b/Workspace/CumNumOfSat/data/Drlica_raw_weighted.txt
new file mode 100644
index 0000000..edbfdca
--- /dev/null
+++ b/Workspace/CumNumOfSat/data/Drlica_raw_weighted.txt
@@ -0,0 +1,59 @@
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diff --git a/Workspace/CumNumOfSat/data/N(M)_drilca_wagner2020.png b/Workspace/CumNumOfSat/data/N(M)_drilca_wagner2020.png
new file mode 100644
index 0000000..d21c9f0
Binary files /dev/null and b/Workspace/CumNumOfSat/data/N(M)_drilca_wagner2020.png differ
diff --git a/Workspace/CumNumOfSat/data/Nadler_rawb1.txt b/Workspace/CumNumOfSat/data/Nadler_rawb1.txt
new file mode 100644
index 0000000..9f5af20
--- /dev/null
+++ b/Workspace/CumNumOfSat/data/Nadler_rawb1.txt
@@ -0,0 +1,31 @@
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diff --git a/Workspace/CumNumOfSat/data/Nadler_rawb2.txt b/Workspace/CumNumOfSat/data/Nadler_rawb2.txt
new file mode 100644
index 0000000..6ce6094
--- /dev/null
+++ b/Workspace/CumNumOfSat/data/Nadler_rawb2.txt
@@ -0,0 +1,24 @@
+0.10505764874178825, 46.508605897985255
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diff --git a/Workspace/CumNumOfSat/data/Nadler_rawt1.txt b/Workspace/CumNumOfSat/data/Nadler_rawt1.txt
new file mode 100644
index 0000000..4853f60
--- /dev/null
+++ b/Workspace/CumNumOfSat/data/Nadler_rawt1.txt
@@ -0,0 +1,28 @@
+0.10702919903094932, 173.33210477740082
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diff --git a/Workspace/CumNumOfSat/data/Nadler_rawt2.txt b/Workspace/CumNumOfSat/data/Nadler_rawt2.txt
new file mode 100644
index 0000000..aa9429f
--- /dev/null
+++ b/Workspace/CumNumOfSat/data/Nadler_rawt2.txt
@@ -0,0 +1,31 @@
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diff --git a/Workspace/CumNumOfSat/data/Readme b/Workspace/CumNumOfSat/data/Readme
new file mode 100644
index 0000000..aaa433f
--- /dev/null
+++ b/Workspace/CumNumOfSat/data/Readme
@@ -0,0 +1,24 @@
+
+curved obtained from Drlica wagner and
+
+https://apps.automeris.io/wpd/
+
+
+Official data:
+
+mw-sats-1.0.tar.gz
+mw-sats-data.tar.gz
+mw_sats_master.csv
+
+
+Hi Yves
+
+I've uploaded a csv file (mw-sats-master.csv) corresponding to the input used to generate Table 2 of the paper:
+https://github.com/des-science/mw-sats/releases/tag/v1.0
+
+The "Classification" column in Table 2 of the paper maps onto the `type` column in this data file. There are some objects that are classified as star clusters (type=0) and were not included in the table in the paper. Note that we can also provide the TS, SIG, and PDet values in Tables 3 & 4, but we need to think a little bit about exactly how we want to distribute this (I've cc'd Mitch so that he is in the loop).
+
+I've also uploaded the files I was using to over plot the Newton and Nadler functions. Note that the columns are different in each of these files, and that the faint limit of the Nadler file has been extended from Mv = 0 to Mv = 0.1 to match the range of observed dwarfs.
+
+Best,
+Alex
diff --git a/Workspace/CumNumOfSat/data/mw-sats-1.0.tar.gz b/Workspace/CumNumOfSat/data/mw-sats-1.0.tar.gz
new file mode 100644
index 0000000..6c2818b
Binary files /dev/null and b/Workspace/CumNumOfSat/data/mw-sats-1.0.tar.gz differ
diff --git a/Workspace/CumNumOfSat/data/mw-sats-data.tar.gz b/Workspace/CumNumOfSat/data/mw-sats-data.tar.gz
new file mode 100644
index 0000000..ac45d53
Binary files /dev/null and b/Workspace/CumNumOfSat/data/mw-sats-data.tar.gz differ
diff --git a/Workspace/CumNumOfSat/data/mw_sats_master.csv b/Workspace/CumNumOfSat/data/mw_sats_master.csv
new file mode 100644
index 0000000..7fb3fff
--- /dev/null
+++ b/Workspace/CumNumOfSat/data/mw_sats_master.csv
@@ -0,0 +1,77 @@
+key,name,abbreviation,other_name,type,type2,survey,category,ra,dec,distance_modulus,distance_kpc,ellipticity,a_h,r_physical,m_v,velocity_dispersion,surface_brightness,structure_ref,distance_ref,kinematics_ref
+am_1,AM 1,AM 1,,0,0,ps1,classical,58.7608,-49.6152,20.45,123.3,0.07,0.45,15.010137,-5.03,nan,24.4225364,2018ApJ...860...66M,,
+antlia_2,Antlia II,Ant II,,4,4,,gaia,143.8868,-36.7673,20.6,131.8256739,0.38,76.2,2300.789747,-9.03,nan,31.3497774,2019MNRAS.488.2743T,2019MNRAS.488.2743T,
+aquarius_2,Aquarius II,Aqr II,,4,5,ps1,atlas,338.4813,-9.3274,20.16,107.6465214,0.39,5.1,124.7272109,-4.4,5.4,29.6504182,2016MNRAS.463..712T,2016MNRAS.463..712T,2016MNRAS.463..712T
+bootes_1,Bootes I,Boo I,,4,5,ps1,sdss,210.02,14.5135,19.1,66.0693448,0.3,9.97,160.3135737,-6.02,4.6,28.5754604,2018ApJ...860...66M,2012ApJ...744...96O,2011ApJ...736..146K
+bootes_2,Bootes II,Boo II,,4,5,ps1,sdss,209.5141,12.8553,18.1,41.68693835,0.25,3.17,33.29016986,-2.94,10.5,28.2421928,2018ApJ...860...66M,2008ApJ...688..245W,2009ApJ...690..453K
+bootes_3,Bootes III,Boo III,,4,4,ps1,sdss,209.3,26.8,18.35,46.77351413,0.5,30,288.6239554,-5.75,14,30.1222475,2009ApJ...693.1118G,arXiv:1204.1562,2012AJ....144....4M
+bootes_4,Bootes IV,Boo IV,,3,3,ps1,hsc,233.689,43.726,21.6,208.9296131,0.64,7.6,277.1347336,-4.53,nan,31.2540667,2019PASJ..tmp...91H,2019PASJ..tmp...91H,None
+canis_major_1,Canis Major,CMa,,0,0,ps1,classical,108.1458333,-27.66666667,14.29,7.211074792,nan,nan,nan,-14.39,nan,nan,2012AJ....144....4M,arXiv:1204.1562,
+canes_venatici_1,Canes Venatici I,CVn I,,4,5,ps1,sdss,202.0091,33.5521,21.69,217.7709772,0.44,7.12,337.5204756,-8.8,7.6,27.412112,2018ApJ...860...66M,2008ApJ...674L..81K,2007ApJ...670..313S
+canes_venatici_2,Canes Venatici II,CVn II,SDSS J1257+3419,4,5,ps1,sdss,194.2927,34.3226,21.02,159.9558029,0.4,1.52,54.78293934,-5.17,14.6,27.0938397,2018ApJ...860...66M,2008ApJ...675L..73G,2007ApJ...670..313S
+carina_1,Carina,Car,,4,5,,classical,100.4065,-50.9593,20.106,105,0.36,10.1,248.1632556,-9.43,6.44,26.1142965,2018ApJ...860...66M,2018ApJ...860...66M,2009AJ....137.3100W
+carina_2,Carina II,Car II,,4,5,,maglites,114.1066,-57.9991,17.79,36.14098626,0.34,8.69,76.79233291,-4.5,3.4,28.4971992,2018MNRAS.475.5085T,2018MNRAS.475.5085T,2018ApJ...857..145L
+carina_3,Carina III,Car III,,4,4,,maglites,114.6298,-57.8997,17.22,27.79713268,0.55,3.75,20.34059449,-2.4,5.6,27.712431,2018MNRAS.475.5085T,2018MNRAS.475.5085T,2018ApJ...857..145L
+centaurus_1,Centaurus I,Cen I,,3,3,,delve,189.585,-40.902,20.33,116.4126029,0.4,2.9,76.06760456,-5.55,nan,27.4266116,arXiv:1912.03301,arXiv:1912.03301,
+cetus_2,Cetus II,Cet II,DES J0117-1725,3,3,ps1 des,des,19.47,-17.42,17.38,29.92264637,nan,1.9,16.5378755,0.0,nan,29.6830115,2015ApJ...813..109D,2015ApJ...813..109D,None
+cetus_3,Cetus III,Cet III,,3,3,ps1 des,hsc,31.331,-4.27,22,251.1886432,0.76,1.23,44.02880005,-2.5,nan,29.2892974,2018PASJ...70S..18H,2018PASJ...70S..18H,None
+columba_1,Columba I,Col I,DES J0531-2801,3,3,ps1 des,des,82.86,-28.01,21.31,182.8100216,0.3,2.2,97.88086936,-4.2,nan,29.324102,2017AJ....154..267C,2017AJ....154..267C,None
+coma_berenices,Coma Berenices,Com,,4,5,ps1,sdss,186.7454,23.9069,18.22,44.05548635,0.37,5.64,57.36876436,-4.38,nan,27.9839894,2018ApJ...860...66M,2009ApJ...695L..83M,
+crater_2,Crater II,Crt II,,4,4,ps1,atlas,177.31,-18.413,20.35,117.4897555,nan,31.2,1066.303197,-8.2,2.7,30.5099571,2016MNRAS.459.2370T,2016MNRAS.459.2370T,2017ApJ...839...20C
+des_sag_1,DES J0111-1341,DESJ0111,DES 2,0,0,des,des,17.7929,-13.6848,17.12,26.54605562,0.27,0.59,3.892598034,0.3,nan,26.8218109,2017MNRAS.468...97L,2017MNRAS.468...97L,None
+des_sag_2,DES J0225+0304,DESJ0225,,1,1,des,des,36.4267,3.0695,16.88,23.76840287,0.61,2.68,11.57159472,-1.1,nan,27.7875791,2017MNRAS.468...97L,2017MNRAS.468...97L,None
+draco_1,Draco,Dra,UGC 10822; DDO 208,4,5,ps1,classical,260.0684,57.9185,19.4,75.8577575,0.29,9.67,179.7967204,-8.71,9.12,26.1345179,2018ApJ...860...66M,2004AJ....127..861B,2015MNRAS.448.2717W
+draco_2,Draco II,Dra II,Laevens 4,3,3,ps1,ps1,238.174,64.579,16.67,21.57744409,0.23,3,16.52314578,-0.8,2.9,28.8610764,2018MNRAS.480.2609L,2018MNRAS.480.2609L,2016MNRAS.458L..59M
+eridanus,Eridanus,Eri,,0,0,ps1,,66.1853,-21.1876,19.77,90.1,0.09,0.64,15.26413767,-4.93,nan,24.5589745,2018ApJ...860...66M,2018ApJ...860...66M,
+eridanus_2,Eridanus II,Eri II,DES J0222.7-5217,4,5,des,des,56.0925,-43.5329,22.9,380.1893963,0.35,1.77,157.817828,-7.21,6.9,27.3513933,2018ApJ...860...66M,2016ApJ...824L..14C,2017ApJ...838....8L
+eridanus_3,Eridanus III,Eri III,DES J0222.7-5217,0,0,des,des,35.6952,-52.2838,19.7,87.096359,0.58,0.3,4.925746043,-2.37,nan,24.6629732,2018ApJ...860...66M,arXiv:1204.1562,None
+fornax_1,Fornax,For,,4,5,des,classical,39.9583,-34.4997,20.84,147.2312502,0.29,19.6,707.3125484,-13.46,10.59,24.3586533,2018ApJ...860...66M,2009AJ....138..459P,2009AJ....137.3100W
+gaia_1,Gaia 1,Gaia1,,0,0,des,gaia,101.47,-16.75,13.3,4.570881896,nan,6.5,8.642501704,-5,nan,23.2538079,2017MNRAS.470.2702K,2017MNRAS.470.2702K,None
+gaia_2,Gaia 2,Gaia2,,0,0,des,gaia,28.124,53.04,13.65,5.370317964,0.18,1.9,2.626555877,-2,nan,23.6675462,2017MNRAS.470.2702K,2017MNRAS.470.2702K,None
+grus_1,Grus I,Gru I,,3,3,des,des,344.1797,-50.18,20.4,120.2264435,0.45,0.81,21.00837596,-3.47,-9999,26.7125755,2018ApJ...860...66M,2015ApJ...805..130K,2016ApJ...819...53W
+grus_2,Grus II,Gru II,DES J2204-4626,3,3,des,des,331.02,-46.44,18.62,52.96634439,nan,6,92.44371023,-3.9,nan,29.4999978,2015ApJ...813..109D,2015ApJ...813..109D,None
+hercules,Hercules,Her,,4,5,ps1,sdss,247.7722,12.7852,20.6,131.8256739,0.69,5.63,120.2030144,-5.83,nan,28.1401894,2018ApJ...860...66M,2012ApJ...756..121M,
+horologium_1,Horologium I,Hor I,DES J0255.4-5406,4,4,des,des,43.8813,-54.116,19.488,78.99507213,0.27,1.59,31.2165333,-3.55,4.9,27.4925364,2018ApJ...860...66M,2015ApJ...805..130K,2015ApJ...811...62K
+horologium_2,Horologium II,Hor II,,3,3,des,des,49.1077,-50.0486,19.46,77.98301105,0.52,2.09,32.84679666,-2.6,nan,28.5530781,2015ApJ...808L..39K,2015ApJ...808L..39K,None
+hydra_1,Hydra I,Hyd I,,0,0,ps1,,133.9,3.6,15.52,12.70574105,nan,nan,nan,-2.5,8.4,nan,2016ApJ...818...39H,2016ApJ...818...39H,2016ApJ...818...39H
+hydra_2,Hydra II,Hyd II,Hydra 2,3,3,,smash,185.4251,-31.986,20.89,150.6607066,0.24,1.52,58.07329945,-4.6,4.5,27.7904956,2018ApJ...860...66M,2016AJ....151..118V,2015ApJ...810...56K
+hydrus_1,Hydrus I,Hyi I,,4,5,,decam,37.389,-79.3089,17.2,27.54228703,0.21,7.42,52.83765905,-4.71,2.69,27.4753283,2018MNRAS.479.5343K,2018MNRAS.479.5343K,2018arXiv180406430K
+indus_2,Indus II,Ind II,DES J2038-4609,1,1,des,des,309.72,-46.16,21.65,213.796209,nan,2.9,180.3533091,-4.3,nan,30.5512332,2015ApJ...813..109D,2015ApJ...813..109D,None
+kim_1,Kim 1,Kim 1,,0,0,ps1,,332.9214,7.0271,16.48,19.7696964,0.59,1.09,4.013696627,0.73,nan,27.3183358,2018ApJ...860...66M,2018ApJ...860...66M,None
+indus_1,Kim 2,Kim 2,Indus I; DES J2108.8-5109,2,2,des,,317.202,-51.1671,20,100,0.32,0.48,12.05651556,-3.32,nan,25.6567222,2018ApJ...860...66M,2015ApJ...803...63K,None
+koposov_1,Koposov 1,Kop 1,,0,0,ps1,sdss,179.8253,12.2615,18.5,50.11872336,0.45,0.62,6.703461994,-1.04,nan,26.6621089,2018ApJ...860...66M,2007ApJ...669..337K,None
+koposov_2,Koposov 2,Kop 2,,0,0,ps1,sdss,119.5715,26.2574,18,39.81071706,0.43,0.44,3.846947164,-0.92,nan,25.5761943,2018ApJ...860...66M,2007ApJ...669..337K,None
+crater_1,Laevens 1,Lae I,Crater I,2,2,ps1,,174.0668,-10.8772,20.807,145.0106836,0.17,0.51,19.71675044,-4.8,2.04,25.2447898,2018ApJ...860...66M,2018ApJ...860...66M,2016MNRAS.460.3384V
+laevens_3,Laevens 3,Lae 3,,0,0,ps1,,316.7263,14.98,19.14,67.29766563,0.21,0.4,6.959846386,-4.4,nan,23.3836114,2015ApJ...813...44L,2015ApJ...813...44L,None
+lmc,LMC,LMC,Large Magellanic Cloud,4,5,,classical,80.89375,-69.75611111,18.4935,49.968924,0.148862,323,4735,-18.12,20.2,23.39,2014A&A...570A..13M,2013Natur.495...76P,2012AJ....144....4M
+leo_1,Leo I,Leo I,UGC 5470; DDO 74; Regulus Dwarf,4,5,ps1,classical,152.1146,12.3059,22.024,253.9802814,0.3,3.65,225.6150163,-11.78,9.2,23.5574526,2018ApJ...860...66M,2014PASP..126..616S,2008ApJ...675..201M
+leo_2,Leo II,Leo II,Leo B; UGC 6253; DDO 93,4,5,ps1,classical,168.3627,22.1529,21.84,233.3458062,0.07,2.52,164.9560058,-9.74,7.4,24.9174535,2018ApJ...860...66M,2005MNRAS.360..185B,2017ApJ...836..202S
+leo_4,Leo IV,Leo IV,,4,5,ps1,sdss,173.2405,-0.5453,20.94,154.1700453,0.17,2.54,103.7764667,-4.99,3.3,28.6611072,2018ApJ...860...66M,2009ApJ...699L.125M,2007ApJ...670..313S
+leo_5,Leo V,Leo V,,4,5,ps1,sdss,172.7857,2.2194,21.25,177.827941,0.43,1,39.05382223,-4.4,3.7,27.1289309,2018ApJ...860...66M,2018ApJ...860...66M,2012AJ....144....4M
+munoz_1,Munoz 1,Mun1,,0,0,ps1,,225.449,66.9682,18.266,44.99870339,0.34,0.49,5.220284413,-0.49,4.7,26.669084,2018ApJ...860...66M,2018ApJ...860...66M,2012ApJ...753L..15M
+pegasus_3,Pegasus III,Peg III,,4,5,ps1,sdss,336.102,5.405,21.66,214.7830474,0.38,0.85,41.81584599,-3.4,5.4,28.2899449,2016ApJ...833...16K,2016ApJ...833...16K,2016ApJ...833...16K
+phoenix_2,Phoenix II,Phe II,DES J2339.9-5424,4,5,des,des,354.996,-54.4115,19.595,82.98507675,0.67,1.49,20.66185719,-3.3,nan,26.8464599,2018ApJ...860...66M,2018arXiv180408627M,None
+pictor_1,Pictor I,Pic I,DES J0443.8-5017,3,3,des,des,70.949,-50.2854,20.28,113.7627286,0.63,0.88,17.71371494,-3.45,nan,26.3621614,2018ApJ...860...66M,2015ApJ...805..130K,None
+pictor_2,Pictor II,Pic II,,3,3,,maglites,101.18,-59.897,18.3,45.70881896,0.13,3.8,47.1269507,-3.2,nan,28.7369591,2016ApJ...833L...5D,2016ApJ...833L...5D,None
+pisces_2,Pisces II,Psc II,,4,5,ps1,sdss,344.6345,5.9526,21.3,181.9700859,0.34,1.12,48.16328579,-4.22,5.4,27.7641936,2018ApJ...860...66M,2012ApJ...756...79S,2015ApJ...810...56K
+reticulum_2,Reticulum II,Ret II,DES J0335.6-5403,4,5,des,des,53.9203,-54.0513,17.4,30.1995172,0.58,5.52,31.42605311,-3.88,3.3,27.1770616,2018ApJ...860...66M,2018arXiv180408627M,2015ApJ...808...95S
+reticulum_3,Reticulum III,Ret III,DES J0345-6026,3,3,des,des,56.36,-60.45,19.81,91.62204901,nan,2.4,63.96425691,-3.3,nan,29.2902996,2015ApJ...813..109D,2015ApJ...813..109D,None
+sagittarius,Sagittarius,Sgr,,4,5,,classical,283.83125,-30.545278,17.1,26.30267992,0.64,342,1565,-13.5,nan,26.0406263,2012AJ....144....4M,arXiv:1204.1562,
+sagittarius_2,Sagittarius II,Sgr II,Laevens 5,4,5,ps1,ps1,298.1647083,-22.0650528,19.2,69.18309709,nan,1.6,32.19927549,-5.2,nan,25.9098435,2018ApJ...863...25M,2018ApJ...863...25M,2019arXiv190202780L
+sculptor_1,Sculptor,Scl,,4,5,des,classical,15.0183,-33.7186,19.62,83.94599865,0.33,11.17,223.2629669,-10.82,8.8,24.4946915,2018ApJ...860...66M,2015MNRAS.454.1509M,2009AJ....137.3100W
+segue_1,Segue 1,Seg 1,,4,4,ps1,sdss,151.7504,16.0756,16.8,22.90867653,0.33,3.62,19.74567975,-1.3,3.7,28.7479731,2018ApJ...860...66M,arXiv:1204.1562,2011ApJ...733...46S
+segue_2,Segue 2,Seg 2,,3,3,ps1,sdss,34.8226,20.1624,17.72,34.9945167,0.22,3.76,33.80346842,-1.86,-9999,29.3554188,2018ApJ...860...66M,2013AJ....146...94B,2013ApJ...770...16K
+segue_3,Segue 3,Seg 3,,0,0,ps1,,320.3795,19.1178,17.16,27.03958364,0.23,0.49,3.381952938,-0.87,1.2,25.3464509,2018ApJ...860...66M,2018ApJ...860...66M,2011AJ....142...88F
+sextans_1,Sextans,Sex,,4,4,ps1,classical,153.2628,-1.6133,19.67,85.90135215,0.3,16.5,344.9523289,-8.72,7.09,27.5393969,2018ApJ...860...66M,2017MNRAS.467..208O,2009AJ....137.3100W
+smc,SMC,SMC,Small Magellanic Cloud,4,4,,classical,13.18666667,-72.82861111,18.965,62.0869,0.3974,151,2728,-17.18,27.6,23.56,2014A&A...570A..13M,2014ApJ...780...59G,2012AJ....144....4M
+smash_1,SMASH 1,SMASH1,,0,0,,,95.2496,-80.3966,18.8,57.54399373,0.62,0.57,5.881563242,-1,nan,26.418077,2016ApJ...830L..10M,2016ApJ...830L..10M,None
+triangulum_2,Triangulum II,Tri II,Laevens 2,3,3,ps1,ps1,33.3252,36.1702,17.39,30.06076303,0.46,1.99,12.78721813,-1.6,-9999,27.5044934,2018ApJ...860...66M,2017AJ....154..267C,2017ApJ...838...83K
+tucana_2,Tucana II,Tuc II,DES J2251.2-5836,4,4,des,des,342.9796,-58.5689,18.8,57.54399373,0.39,12.586,164.5427541,-3.8,8.6,30.8520011,2015ApJ...805..130K,2015ApJ...805..130K,2016ApJ...819...53W
+tucana_3,Tucana III,Tuc III,DES J2356-5935,3,3,des,des,359.15,-59.6,17.01,25.23480772,0,6,44.04304809,-2.4,-9999,29.3899978,2015ApJ...813..109D,2015ApJ...813..109D,2017ApJ...838...11S
+tucana_4,Tucana IV,Tuc IV,DES J0002-6051,4,5,des,des,0.73,-60.85,18.41,48.08393484,0.4,11.8,127.8450006,-3.5,nan,30.6040268,2015ApJ...813..109D,2015ApJ...813..109D,None
+tucana_5,Tucana V,Tuc V,DES J2337-6316,3,3,des,des,354.35,-63.27,18.71,55.20774393,0.7,1.8,15.83285556,-1.6,nan,27.9684094,2015ApJ...813..109D,2015ApJ...813..109D,None
+ursa_major_1,Ursa Major I,UMa I,,4,5,ps1,sdss,158.7706,51.9479,19.94,97.27472238,0.59,8.31,150.5632807,-5.12,7.6,29.3392068,2018ApJ...860...66M,2013ApJ...767...62G,2007ApJ...670..313S
+ursa_major_2,Ursa Major II,UMa II,,4,5,ps1,sdss,132.8726,63.1335,17.53,32.06269325,0.56,13.8,85.37516221,-4.25,6.7,28.9772657,2018ApJ...860...66M,2012ApJ...752...42D,2007ApJ...670..313S
+ursa_minor_1,Ursa Minor,UMi,UGC 9749; DDO 199,4,5,ps1,classical,227.242,67.2221,19.41,76.207901,0.55,18.3,272.1344127,-9.03,9.4,26.7145213,2018ApJ...860...66M,2002AJ....124.3222B,None
+virgo_1,Virgo I,Vir I,,3,3,ps1,hsc,180.038,-0.681,19.8,91.20108394,0.59,1.76,29.89721354,-0.33,nan,30.6187667,2018PASJ...70S..18H,2018PASJ...70S..18H,None
+willman_1,Willman 1,Wil 1,SDSSJ1049+5103,4,4,ps1,sdss,162.3436,51.0501,17.9,38.01893963,0.47,2.51,20.20867193,-2.53,4,27.5683018,2018ApJ...860...66M,2005AJ....129.2692W,2011AJ....142..128W
\ No newline at end of file
diff --git a/Workspace/CumNumOfSat/sales2022/MsMh_high_1.txt b/Workspace/CumNumOfSat/sales2022/MsMh_high_1.txt
new file mode 100644
index 0000000..2e22bfd
--- /dev/null
+++ b/Workspace/CumNumOfSat/sales2022/MsMh_high_1.txt
@@ -0,0 +1,10 @@
+8.0102564 3.32967032967
+8.2666666 3.74411302982
+8.4256410 4.25274725274
+8.6410256 4.71428571428
+8.8051282 5.25117739403
+9.2358974 6.02354788069
+9.6256410 6.58869701726
+9.9179487 7.13500784929
+10.220512 7.73783359497
+11 9
diff --git a/Workspace/CumNumOfSat/sales2022/MsMh_high_2.txt b/Workspace/CumNumOfSat/sales2022/MsMh_high_2.txt
new file mode 100644
index 0000000..27f1abd
--- /dev/null
+++ b/Workspace/CumNumOfSat/sales2022/MsMh_high_2.txt
@@ -0,0 +1,9 @@
+7.989743589 5.2982731554160125
+8.256410256 5.609105180533752
+8.528205128 5.938775510204082
+8.856410256 6.390894819466248
+9.246153846 6.861852433281005
+9.738461538 7.408163265306123
+10.10769230 7.756671899529042
+10.36923076 8.048665620094193
+11 9
diff --git a/Workspace/CumNumOfSat/sales2022/MsMh_high_3.txt b/Workspace/CumNumOfSat/sales2022/MsMh_high_3.txt
new file mode 100644
index 0000000..a17c3e1
--- /dev/null
+++ b/Workspace/CumNumOfSat/sales2022/MsMh_high_3.txt
@@ -0,0 +1,9 @@
+7.969230769230769 4.987441130298274
+8.307692307692308 5.467817896389326
+8.76923076923077 6.287284144427002
+9.169230769230769 7.125588697017269
+9.328205128205129 7.718995290423862
+9.687179487179488 8.293563579277865
+10.03076923076923 8.773940345368917
+10.34871794871795 9.188383045525903
+11 10
diff --git a/Workspace/CumNumOfSat/sales2022/MsMh_high_4.txt b/Workspace/CumNumOfSat/sales2022/MsMh_high_4.txt
new file mode 100644
index 0000000..601b57d
--- /dev/null
+++ b/Workspace/CumNumOfSat/sales2022/MsMh_high_4.txt
@@ -0,0 +1,9 @@
+7.99547511312 5.80939226519337
+8.62895927601 6.596685082872929
+8.94117647058 6.928176795580111
+9.31221719457 7.417127071823205
+9.70588235294 7.997237569060774
+9.94570135746 8.42817679558011
+10.2398190045 8.85082872928177
+10.4977375565 9.290055248618785
+11 10
diff --git a/Workspace/CumNumOfSat/sales2022/MsMh_low_1.txt b/Workspace/CumNumOfSat/sales2022/MsMh_low_1.txt
new file mode 100644
index 0000000..6aee599
--- /dev/null
+++ b/Workspace/CumNumOfSat/sales2022/MsMh_low_1.txt
@@ -0,0 +1,8 @@
+8.01025641025641 1.9544740973312393
+8.34871794871795 2.208791208791209
+8.92820512820512 2.8587127158555727
+9.24615384615384 3.4992150706436425
+9.77948717948718 4.233908948194663
+10.1487179487179 5.185243328100471
+10.3435897435897 6.324960753532182
+11 8.2
diff --git a/Workspace/CumNumOfSat/sales2022/MsMh_low_2.txt b/Workspace/CumNumOfSat/sales2022/MsMh_low_2.txt
new file mode 100644
index 0000000..8115858
--- /dev/null
+++ b/Workspace/CumNumOfSat/sales2022/MsMh_low_2.txt
@@ -0,0 +1,10 @@
+7.953846153 1.9167974
+8.369230769 2.3124018
+8.820512820 2.8587127
+9.2 3.4050235
+9.615384615 4.1114599
+10.05641025 4.9497645
+10.13846153 5.5714285
+10.32307692 6.7111459
+10.53846153 7.3893249
+11 8.2
diff --git a/Workspace/CumNumOfSat/sales2022/MsMh_low_3.txt b/Workspace/CumNumOfSat/sales2022/MsMh_low_3.txt
new file mode 100644
index 0000000..940acff
--- /dev/null
+++ b/Workspace/CumNumOfSat/sales2022/MsMh_low_3.txt
@@ -0,0 +1,10 @@
+7.953846153 1.9167974
+8.369230769 2.3124018
+8.820512820 2.8587127
+9.2 3.4050235
+9.615384615 4.1114599
+10.05641025 4.9497645
+10.13846153 5.5714285
+10.32307692 6.7111459
+10.53846153 7.3893249
+11. 8.2
diff --git a/Workspace/CumNumOfSat/sales2022/MsMh_low_4.txt b/Workspace/CumNumOfSat/sales2022/MsMh_low_4.txt
new file mode 100644
index 0000000..743f51e
--- /dev/null
+++ b/Workspace/CumNumOfSat/sales2022/MsMh_low_4.txt
@@ -0,0 +1,9 @@
+7.97285067873303 1.9475138121546962
+8.25339366515837 2.3784530386740332
+8.66968325791855 3.2569060773480674
+9.02714932126696 4.0773480662983435
+9.51583710407239 4.947513812154696
+9.99095022624434 5.850828729281768
+10.1945701357466 6.488950276243094
+10.4343891402714 7.383977900552487
+11. 8.2
diff --git a/Workspace/CumNumOfSat/satComputeMstarvsMhalo.py b/Workspace/CumNumOfSat/satComputeMstarvsMhalo.py
new file mode 100755
index 0000000..3ce29b7
--- /dev/null
+++ b/Workspace/CumNumOfSat/satComputeMstarvsMhalo.py
@@ -0,0 +1,114 @@
+#!/usr/bin/env python3
+
+import sys,os,string
+
+import argparse
+import satlib
+import numpy as np
+import Ptools as pt
+import pickle
+from scipy.interpolate import splrep,splev
+
+####################################################################
+# option parser
+####################################################################
+
+description=""
+epilog =""""""
+
+parser = argparse.ArgumentParser(description=description,epilog=epilog,formatter_class=argparse.RawDescriptionHelpFormatter)
+
+
+
+parser.add_argument("--LvvsMh_model",
+ action="store",
+ type=str,
+ dest="LvvsMh_model",
+ default = None,
+ help="Lv Mh model")
+
+
+parser.add_argument("-p",
+ action="store",
+ dest="ps",
+ metavar='FILE NAME',
+ type=str,
+ default=None,
+ help='output file name')
+
+parser.add_argument("--dpi",
+ action="store",
+ dest="dpi",
+ type=float,
+ default=300,
+ help="DPI of the saved file",
+ metavar=" FLOAT")
+
+
+
+
+####################################################################
+# main
+####################################################################
+
+
+def MakePlot(opt):
+
+
+
+ # get Mh vs Lv relation
+ Mhbins,Lvmins,Lvmaxs = satlib.GetStellarMassHaloMassRelation(model=opt.LvvsMh_model)
+
+ ax = pt.gca()
+ ax.fill_between(Mhbins,Lvmins,Lvmaxs,facecolor='orange',alpha=0.5,label=r"$\textrm{model=%s}$"%opt.LvvsMh_model)
+
+ #pt.plot(Mhbins,Lvmins)
+ #pt.plot(Mhbins,Lvmaxs)
+
+ ###########################
+ # finalize
+ ###########################
+
+ xmin = 1e8
+ xmax = 1e11
+ ymin = 1e2
+ ymax = 1e10
+
+ xlabel = r"$\rm{Halo\,\,Mass\,\,(M_{\rm halo})}\,\,[M_{\odot}]$"
+ ylabel = r"$\rm{Stellar\,\,Mass\,\,(M_{\star})}\,\,[M_{\odot}]$"
+
+ pt.SetAxis(xmin,xmax,ymin,ymax,log="xy")
+ pt.xlabel(xlabel,fontsize=pt.labelfont)
+ pt.ylabel(ylabel,fontsize=pt.labelfont)
+ pt.grid(False)
+
+ pt.legend()
+
+ pt.title(r"$\textrm{model=%s}$"%opt.LvvsMh_model,fontsize=pt.labelfont)
+
+
+if __name__ == '__main__':
+
+ opt = parser.parse_args()
+ files = []
+
+ pt.InitPlot(files, opt)
+ pt.labelfont=20
+
+ # pt.figure(figsize=(8*2,6*2))
+ # pt.figure(dpi=10)
+
+ #fig = pt.gcf()
+ # fig.subplots_adjust(left=0.1)
+ # fig.subplots_adjust(right=1)
+ # fig.subplots_adjust(bottom=0.12)
+ # fig.subplots_adjust(top=0.95)
+ # fig.subplots_adjust(wspace=0.25)
+ # fig.subplots_adjust(hspace=0.02)
+
+ MakePlot(opt)
+ pt.EndPlot(files, opt)
+
+
+
+
diff --git a/Workspace/CumNumOfSat/satComputeNCum.py b/Workspace/CumNumOfSat/satComputeNCum.py
new file mode 100755
index 0000000..64641fe
--- /dev/null
+++ b/Workspace/CumNumOfSat/satComputeNCum.py
@@ -0,0 +1,180 @@
+#!/usr/bin/env python3
+
+import sys,os,string
+
+import argparse
+import satlib
+import numpy as np
+import Ptools as pt
+import pickle
+from scipy.interpolate import splrep,splev
+
+####################################################################
+# option parser
+####################################################################
+
+description=""
+epilog =""""""
+
+parser = argparse.ArgumentParser(description=description,epilog=epilog,formatter_class=argparse.RawDescriptionHelpFormatter)
+
+
+
+parser.add_argument("--Mmin",
+ action="store",
+ dest="Mmin",
+ metavar='FLOAT',
+ type=float,
+ default=5e7,
+ help='the min halo mass')
+
+
+parser.add_argument("--Mmax",
+ action="store",
+ dest="Mmax",
+ metavar='FLOAT',
+ type=float,
+ default=1e10,
+ help='the max halo mass')
+
+parser.add_argument("-N",
+ action="store",
+ dest="N",
+ metavar='INT',
+ type=int,
+ default=1000,
+ help='number of bins')
+
+
+parser.add_argument("--Ngal",
+ action="store",
+ dest="Ngal",
+ metavar='INT',
+ type=int,
+ default=100,
+ help='number of galaxies')
+
+
+
+####################################################################
+# main
+####################################################################
+
+
+opt = parser.parse_args()
+
+
+# halo bins
+lnMh = np.linspace(np.log10(opt.Mmin),np.log10(opt.Mmax),opt.N)
+Mh0 = 10**lnMh
+
+# cumulative number of haloes
+NM_CDM = satlib.CDM_CumulativeMass(Mh0)
+NM_WDM = satlib.fctWDM_CumulativeMass(Mh0)
+
+
+pt.plot(Mh0,NM_WDM)
+pt.plot(Mh0,NM_CDM)
+
+
+
+# define the CDM sampling function
+fct_CDM_Sample = np.vectorize( lambda x: 10**np.interp(x, NM_CDM[::-1]/NM_CDM.max(), np.log10(Mh0[::-1])) )
+maxCDM = (NM_CDM/NM_CDM.max()).min()
+
+# define the WDM sampling function
+fct_WDM_Sample = np.vectorize( lambda x: 10**np.interp(x, NM_WDM[::-1]/NM_WDM.max(), np.log10(Mh0[::-1])) )
+maxWDM = (NM_WDM/NM_WDM.max()).min()
+
+
+
+# number of haloes for the CDM
+Nh_CDM = int(satlib.CDM_CumulativeMass(opt.Mmin))
+
+# number of haloes for the WDM
+Nh_WDM = int(satlib.fctWDM_CumulativeMass(opt.Mmin))
+
+
+Nb = 100
+
+Ncs_CDM = np.zeros((opt.Ngal,Nb-1))
+Ncs_WDM = np.zeros((opt.Ngal,Nb-1))
+
+
+for j in range(opt.Ngal):
+
+ print(j)
+
+ # generate N haloes for the CDM
+ Mhs = fct_CDM_Sample(np.random.random(Nh_CDM))
+ lnm = np.log10(Mhs)
+ # do the histogram
+ logMh = np.linspace(np.log10(opt.Mmin),10,Nb)
+ n, bins = np.histogram(lnm, logMh)
+ Ms = 10**bins
+ dM = Ms[1:]-Ms[:-1]
+ dNdM = n/dM
+ Nc_CDM = np.add.accumulate(np.flip(n,0))
+ Nc_CDM = np.flip(Nc_CDM,0)
+
+
+ # generate N haloes for the WDM
+ Mhs = fct_WDM_Sample(np.random.random(Nh_WDM))
+ lnm = np.log10(Mhs)
+ # do the histogram
+ logMh = np.linspace(np.log10(opt.Mmin),10,Nb)
+ n, bins = np.histogram(lnm, logMh)
+ Ms = 10**bins
+ dM = Ms[1:]-Ms[:-1]
+ dNdM = n/dM
+ Nc_WDM = np.add.accumulate(np.flip(n,0))
+ Nc_WDM = np.flip(Nc_WDM,0)
+ Ncs_CDM[j] = Nc_CDM
+ Ncs_WDM[j] = Nc_WDM
+
+
+# mean
+Nc_CDM_mean = Ncs_CDM.mean(axis=0)
+Nc_WDM_mean = Ncs_WDM.mean(axis=0)
+
+# std
+Nc_CDM_std = Ncs_CDM.std(axis=0)
+Nc_WDM_std = Ncs_WDM.std(axis=0)
+
+
+
+pt.plot(Ms[1:],Nc_CDM_mean,'k')
+pt.plot(Ms[1:],Nc_WDM_mean,'k')
+
+#pt.plot(Ms[1:],Nc_CDM_mean+Nc_CDM_std,'k')
+#pt.plot(Ms[1:],Nc_CDM_mean-Nc_CDM_std,'k')
+
+#pt.plot(Ms[1:],Nc_WDM_mean+Nc_WDM_std,'k')
+#pt.plot(Ms[1:],Nc_WDM_mean-Nc_WDM_std,'k')
+
+ax = pt.gca()
+ax.fill_between(Ms[1:],Nc_CDM_mean+Nc_CDM_std,Nc_CDM_mean-Nc_CDM_std,facecolor='k',alpha=0.1,label=r"$\textrm{CDM}$")
+ax.fill_between(Ms[1:],Nc_WDM_mean+Nc_WDM_std,Nc_WDM_mean-Nc_WDM_std,facecolor='k',alpha=0.1,label=r"$\textrm{WDM}$")
+
+
+###########################
+# finalize
+###########################
+
+xmin = opt.Mmin
+xmax = opt.Mmax
+ymin = None
+ymax = None
+
+xlabel = r"$\rm{Halo\,\,Mass}$"
+ylabel = r"$\rm{Cumulative\,\,number\,\,of\,\,galaxies}\,\,(D<300\,\rm{kpc})$"
+
+pt.SetAxis(xmin,xmax,ymin,ymax,log="xy")
+pt.xlabel(xlabel)
+pt.ylabel(ylabel)
+pt.grid(False)
+
+pt.legend()
+
+
+pt.show()
diff --git a/Workspace/CumNumOfSat/satComputeNCumWithErrors.py b/Workspace/CumNumOfSat/satComputeNCumWithErrors.py
new file mode 100755
index 0000000..a76e79f
--- /dev/null
+++ b/Workspace/CumNumOfSat/satComputeNCumWithErrors.py
@@ -0,0 +1,267 @@
+#!/usr/bin/env python3
+
+import sys,os,string
+
+import argparse
+import satlib
+import numpy as np
+import Ptools as pt
+import pickle
+from scipy.interpolate import splrep,splev
+from tqdm import tqdm
+
+
+####################################################################
+# option parser
+####################################################################
+
+description=""
+epilog =""""""
+
+parser = argparse.ArgumentParser(description=description,epilog=epilog,formatter_class=argparse.RawDescriptionHelpFormatter)
+
+parser.add_argument("--M0",
+ action="store",
+ dest="M0",
+ metavar='FLOAT',
+ type=float,
+ default=1e12,
+ help='host halo mass')
+
+parser.add_argument("--Mmin",
+ action="store",
+ dest="Mmin",
+ metavar='FLOAT',
+ type=float,
+ default=5e7,
+ help='the min halo mass')
+
+
+parser.add_argument("--Mmax",
+ action="store",
+ dest="Mmax",
+ metavar='FLOAT',
+ type=float,
+ default=1e11,
+ help='the max halo mass')
+
+parser.add_argument("--DMmass",
+ action="store",
+ dest="DMmass",
+ metavar='FLOAT',
+ type=float,
+ default=2.0,
+ help='dark matter particle mass')
+
+
+parser.add_argument("-N",
+ action="store",
+ dest="N",
+ metavar='INT',
+ type=int,
+ default=1000,
+ help='number of bins')
+
+
+parser.add_argument("--Ngal",
+ action="store",
+ dest="Ngal",
+ metavar='INT',
+ type=int,
+ default=100,
+ help='number of galaxies')
+
+parser.add_argument("--Nrealisations",
+ action="store",
+ dest="Nrealisations",
+ metavar='INT',
+ type=int,
+ default=100,
+ help='number of realizations')
+
+parser.add_argument("-p",
+ action="store",
+ dest="ps",
+ metavar='FILE NAME',
+ type=str,
+ default=None,
+ help='output file name')
+
+parser.add_argument("--dpi",
+ action="store",
+ dest="dpi",
+ type=float,
+ default=300,
+ help="DPI of the saved file",
+ metavar=" FLOAT")
+
+
+####################################################################
+# main
+####################################################################
+
+def MakePlot(opt):
+
+
+ # halo bins
+ lnMh = np.linspace(np.log10(opt.Mmin),np.log10(opt.Mmax),opt.N)
+ Mh0 = 10**lnMh
+
+ # define the DM models
+ #CDM = satlib.CDM_Sawala_Model(opt.Mmin,opt.Mmax,opt.N)
+ #WDM = satlib.CDM_Forouhar_Model(opt.Mmin,opt.Mmax,opt.N)
+
+ CDM = satlib.GEN_Model(opt.Mmin,opt.Mmax,opt.N,MDM=None,fWDM=0,M0=opt.M0)
+ WDM = satlib.GEN_Model(opt.Mmin,opt.Mmax,opt.N,MDM=opt.DMmass,fWDM=1,M0=opt.M0)
+
+
+ # cumulative number of haloes
+ NM_CDM = CDM.CumulativeMass()
+ NM_WDM = WDM.CumulativeMass()
+
+ pt.plot(Mh0,NM_WDM,c='k',ls='--',label=r"$\rm{theory}$")
+ pt.plot(Mh0,NM_CDM,c='k',ls='--')
+
+
+ # number of haloes for the CDM
+ Nh_CDM = CDM.NumberOfHaloes()
+
+ # number of haloes for the WDM
+ Nh_WDM = WDM.NumberOfHaloes()
+
+
+
+ Nb = 100
+
+ Ncs_CDM = np.zeros((opt.Ngal,Nb-1))
+ Ncs_WDM = np.zeros((opt.Ngal,Nb-1))
+
+
+ Nc_CDM_realisations = np.zeros((opt.Nrealisations,Nb-1))
+ Nc_WDM_realisations = np.zeros((opt.Nrealisations,Nb-1))
+
+
+ for realisation in tqdm(range(opt.Nrealisations)):
+
+ for j in range(opt.Ngal):
+
+ # generate N haloes for the CDM
+ Mhs = CDM.fctSample(np.random.random(Nh_CDM))
+
+ lnm = np.log10(Mhs)
+ # do the histogram
+ logMh = np.linspace(np.log10(opt.Mmin),np.log10(opt.Mmax),Nb)
+ n, bins = np.histogram(lnm, logMh)
+ Ms = 10**bins
+ dM = Ms[1:]-Ms[:-1]
+ dNdM = n/dM
+ Nc_CDM = np.add.accumulate(np.flip(n,0))
+ Nc_CDM = np.flip(Nc_CDM,0)
+ Ncs_CDM[j] = Nc_CDM
+
+
+ # generate N haloes for the WDM
+ #Mhs = fct_WDM_Sample(np.random.random(Nh_WDM))
+ Mhs = WDM.fctSample(np.random.random(Nh_WDM))
+
+ lnm = np.log10(Mhs)
+ # do the histogram
+ logMh = np.linspace(np.log10(opt.Mmin),np.log10(opt.Mmax),Nb)
+ n, bins = np.histogram(lnm, logMh)
+ Ms = 10**bins
+ dM = Ms[1:]-Ms[:-1]
+ dNdM = n/dM
+ Nc_WDM = np.add.accumulate(np.flip(n,0))
+ Nc_WDM = np.flip(Nc_WDM,0)
+ Ncs_WDM[j] = Nc_WDM
+
+
+ #pt.plot(Ms[1:],Nc_CDM,'blue',alpha=0.5)
+ #pt.plot(Ms[1:],Nc_WDM,'red',alpha=0.5)
+
+
+
+ # compute the mean over the Ngal "galaxies"
+ Nc_CDM_mean = Ncs_CDM.mean(axis=0)
+ Nc_WDM_mean = Ncs_WDM.mean(axis=0)
+
+ Nc_CDM_realisations[realisation] = Nc_CDM_mean
+ Nc_WDM_realisations[realisation] = Nc_WDM_mean
+
+
+ #pt.plot(Ms[1:],Nc_CDM_mean,'blue',alpha=0.5)
+ #pt.plot(Ms[1:],Nc_WDM_mean,'red',alpha=0.5)
+
+
+ Nc_CDM_realisations_mean = Nc_CDM_realisations.mean(axis=0)
+ Nc_CDM_realisations_std = Nc_CDM_realisations.std(axis=0)
+
+ Nc_CDM_meanp = Nc_CDM_realisations_mean + 3*Nc_CDM_realisations_std
+ Nc_CDM_meanm = Nc_CDM_realisations_mean - 3*Nc_CDM_realisations_std
+
+
+ Nc_WDM_realisations_mean = Nc_WDM_realisations.mean(axis=0)
+ Nc_WDM_realisations_std = Nc_WDM_realisations.std(axis=0)
+
+ Nc_WDM_meanp = Nc_WDM_realisations_mean + 3*Nc_WDM_realisations_std
+ Nc_WDM_meanm = Nc_WDM_realisations_mean - 3*Nc_WDM_realisations_std
+
+
+
+ ax = pt.gca()
+ ax.fill_between(Ms[1:],Nc_CDM_meanp,Nc_CDM_meanm,facecolor='blue',alpha=0.25,label=r"$\textrm{CDM}$")
+ ax.fill_between(Ms[1:],Nc_WDM_meanp,Nc_WDM_meanm,facecolor='red',alpha=0.25,label=r"$\textrm{WDM}$")
+
+
+
+
+
+
+ ###########################
+ # finalize
+ ###########################
+
+ xmin = opt.Mmin
+ xmax = opt.Mmax
+ ymin = None
+ ymax = None
+
+ xlabel = r"$\rm{Halo\,\,Mass\,\,(M_{\rm halo})}\,\,[M_{\odot}]$"
+ ylabel = r"$N_{\rm halo}(>M_{\rm halo})$"
+
+ pt.SetAxis(xmin,xmax,ymin,ymax,log="xy")
+ pt.xlabel(xlabel,fontsize=pt.labelfont)
+ pt.ylabel(ylabel,fontsize=pt.labelfont)
+ pt.grid(False)
+
+ pt.title(r"$\rm{%d\,\,galaxies\,\,observed}$"%opt.Ngal,fontsize=pt.labelfont)
+ pt.legend()
+
+
+
+
+
+
+
+if __name__ == '__main__':
+
+ opt = parser.parse_args()
+ files = []
+
+ pt.InitPlot(files, opt)
+ pt.labelfont=20
+
+ # pt.figure(figsize=(8*2,6*2))
+ # pt.figure(dpi=10)
+
+ #fig = pt.gcf()
+ # fig.subplots_adjust(left=0.1)
+ # fig.subplots_adjust(right=1)
+ # fig.subplots_adjust(bottom=0.12)
+ # fig.subplots_adjust(top=0.95)
+ # fig.subplots_adjust(wspace=0.25)
+ # fig.subplots_adjust(hspace=0.02)
+
+ MakePlot(opt)
+ pt.EndPlot(files, opt)
+
diff --git a/Workspace/CumNumOfSat/satComputeNCumWithErrors_Fred.py b/Workspace/CumNumOfSat/satComputeNCumWithErrors_Fred.py
new file mode 100755
index 0000000..b61e3e3
--- /dev/null
+++ b/Workspace/CumNumOfSat/satComputeNCumWithErrors_Fred.py
@@ -0,0 +1,267 @@
+#!/usr/bin/env python3
+
+import sys,os,string
+
+import argparse
+import satlib
+import numpy as np
+import Ptools as pt
+import pickle
+from scipy.interpolate import splrep,splev
+from tqdm import tqdm
+
+
+####################################################################
+# option parser
+####################################################################
+
+description=""
+epilog =""""""
+
+parser = argparse.ArgumentParser(description=description,epilog=epilog,formatter_class=argparse.RawDescriptionHelpFormatter)
+
+parser.add_argument("--M0",
+ action="store",
+ dest="M0",
+ metavar='FLOAT',
+ type=float,
+ default=1e12,
+ help='host halo mass')
+
+parser.add_argument("--Mmin",
+ action="store",
+ dest="Mmin",
+ metavar='FLOAT',
+ type=float,
+ default=5e6,
+ help='the min halo mass')
+
+
+parser.add_argument("--Mmax",
+ action="store",
+ dest="Mmax",
+ metavar='FLOAT',
+ type=float,
+ default=1e9,
+ help='the max halo mass')
+
+parser.add_argument("--DMmass",
+ action="store",
+ dest="DMmass",
+ metavar='FLOAT',
+ type=float,
+ default=2.0,
+ help='dark matter particle mass')
+
+
+parser.add_argument("-N",
+ action="store",
+ dest="N",
+ metavar='INT',
+ type=int,
+ default=1000,
+ help='number of bins')
+
+
+parser.add_argument("--Ngal",
+ action="store",
+ dest="Ngal",
+ metavar='INT',
+ type=int,
+ default=100,
+ help='number of galaxies')
+
+parser.add_argument("--Nrealisations",
+ action="store",
+ dest="Nrealisations",
+ metavar='INT',
+ type=int,
+ default=100,
+ help='number of realizations')
+
+parser.add_argument("-p",
+ action="store",
+ dest="ps",
+ metavar='FILE NAME',
+ type=str,
+ default=None,
+ help='output file name')
+
+parser.add_argument("--dpi",
+ action="store",
+ dest="dpi",
+ type=float,
+ default=300,
+ help="DPI of the saved file",
+ metavar=" FLOAT")
+
+
+####################################################################
+# main
+####################################################################
+
+def MakePlot(opt):
+
+
+ # halo bins
+ lnMh = np.linspace(np.log10(opt.Mmin),np.log10(opt.Mmax),opt.N)
+ Mh0 = 10**lnMh
+
+ # define the DM models
+ #CDM = satlib.CDM_Sawala_Model(opt.Mmin,opt.Mmax,opt.N)
+ #WDM = satlib.CDM_Forouhar_Model(opt.Mmin,opt.Mmax,opt.N)
+
+ CDM = satlib.GEN_Model(opt.Mmin,opt.Mmax,opt.N,MDM=None,fWDM=0,M0=opt.M0)
+ WDM = satlib.GEN_Model(opt.Mmin,opt.Mmax,opt.N,MDM=opt.DMmass,fWDM=1,M0=opt.M0)
+
+
+ # cumulative number of haloes
+ NM_CDM = CDM.CumulativeMass()
+ NM_WDM = WDM.CumulativeMass()
+
+ pt.plot(Mh0,NM_WDM,c='k',ls='--',label=r"$\rm{theory}$")
+ pt.plot(Mh0,NM_CDM,c='k',ls='--')
+
+
+ # number of haloes for the CDM
+ Nh_CDM = CDM.NumberOfHaloes()
+
+ # number of haloes for the WDM
+ Nh_WDM = WDM.NumberOfHaloes()
+
+
+
+ Nb = 100
+
+ Ncs_CDM = np.zeros((opt.Ngal,Nb-1))
+ Ncs_WDM = np.zeros((opt.Ngal,Nb-1))
+
+
+ Nc_CDM_realisations = np.zeros((opt.Nrealisations,Nb-1))
+ Nc_WDM_realisations = np.zeros((opt.Nrealisations,Nb-1))
+
+
+ for realisation in tqdm(range(opt.Nrealisations)):
+
+ for j in range(opt.Ngal):
+
+ # generate N haloes for the CDM
+ Mhs = CDM.fctSample(np.random.random(Nh_CDM))
+
+ lnm = np.log10(Mhs)
+ # do the histogram
+ logMh = np.linspace(np.log10(opt.Mmin),np.log10(opt.Mmax),Nb)
+ n, bins = np.histogram(lnm, logMh)
+ Ms = 10**bins
+ dM = Ms[1:]-Ms[:-1]
+ dNdM = n/dM
+ Nc_CDM = np.add.accumulate(np.flip(n,0))
+ Nc_CDM = np.flip(Nc_CDM,0)
+ Ncs_CDM[j] = Nc_CDM
+
+
+ # generate N haloes for the WDM
+ #Mhs = fct_WDM_Sample(np.random.random(Nh_WDM))
+ Mhs = WDM.fctSample(np.random.random(Nh_WDM))
+
+ lnm = np.log10(Mhs)
+ # do the histogram
+ logMh = np.linspace(np.log10(opt.Mmin),np.log10(opt.Mmax),Nb)
+ n, bins = np.histogram(lnm, logMh)
+ Ms = 10**bins
+ dM = Ms[1:]-Ms[:-1]
+ dNdM = n/dM
+ Nc_WDM = np.add.accumulate(np.flip(n,0))
+ Nc_WDM = np.flip(Nc_WDM,0)
+ Ncs_WDM[j] = Nc_WDM
+
+
+ #pt.plot(Ms[1:],Nc_CDM,'blue',alpha=0.5)
+ #pt.plot(Ms[1:],Nc_WDM,'red',alpha=0.5)
+
+
+
+ # compute the mean over the Ngal "galaxies"
+ Nc_CDM_mean = Ncs_CDM.mean(axis=0)
+ Nc_WDM_mean = Ncs_WDM.mean(axis=0)
+
+ Nc_CDM_realisations[realisation] = Nc_CDM_mean
+ Nc_WDM_realisations[realisation] = Nc_WDM_mean
+
+
+ #pt.plot(Ms[1:],Nc_CDM_mean,'blue',alpha=0.5)
+ #pt.plot(Ms[1:],Nc_WDM_mean,'red',alpha=0.5)
+
+
+ Nc_CDM_realisations_mean = Nc_CDM_realisations.mean(axis=0)
+ Nc_CDM_realisations_std = Nc_CDM_realisations.std(axis=0)
+
+ Nc_CDM_meanp = Nc_CDM_realisations_mean + 3*Nc_CDM_realisations_std
+ Nc_CDM_meanm = Nc_CDM_realisations_mean - 3*Nc_CDM_realisations_std
+
+
+ Nc_WDM_realisations_mean = Nc_WDM_realisations.mean(axis=0)
+ Nc_WDM_realisations_std = Nc_WDM_realisations.std(axis=0)
+
+ Nc_WDM_meanp = Nc_WDM_realisations_mean + 3*Nc_WDM_realisations_std
+ Nc_WDM_meanm = Nc_WDM_realisations_mean - 3*Nc_WDM_realisations_std
+
+
+
+ ax = pt.gca()
+ ax.fill_between(Ms[1:],Nc_CDM_meanp,Nc_CDM_meanm,facecolor='blue',alpha=0.25,label=r"$\textrm{CDM}$")
+ ax.fill_between(Ms[1:],Nc_WDM_meanp,Nc_WDM_meanm,facecolor='red',alpha=0.25,label=r"$\textrm{WDM}$")
+
+
+
+
+
+
+ ###########################
+ # finalize
+ ###########################
+
+ xmin = opt.Mmin
+ xmax = opt.Mmax
+ ymin = None
+ ymax = None
+
+ xlabel = r"$\rm{Halo\,\,Mass\,\,(M_{\rm halo})}\,\,[M_{\odot}]$"
+ ylabel = r"$N_{\rm halo}(>M_{\rm halo})$"
+
+ pt.SetAxis(xmin,xmax,ymin,ymax,log="xy")
+ pt.xlabel(xlabel,fontsize=pt.labelfont)
+ pt.ylabel(ylabel,fontsize=pt.labelfont)
+ pt.grid(False)
+
+ pt.title(r"$\rm{%d\,\,galaxies\,\,observed}$"%opt.Ngal,fontsize=pt.labelfont)
+ pt.legend()
+
+
+
+
+
+
+
+if __name__ == '__main__':
+
+ opt = parser.parse_args()
+ files = []
+
+ pt.InitPlot(files, opt)
+ pt.labelfont=20
+
+ # pt.figure(figsize=(8*2,6*2))
+ # pt.figure(dpi=10)
+
+ #fig = pt.gcf()
+ # fig.subplots_adjust(left=0.1)
+ # fig.subplots_adjust(right=1)
+ # fig.subplots_adjust(bottom=0.12)
+ # fig.subplots_adjust(top=0.95)
+ # fig.subplots_adjust(wspace=0.25)
+ # fig.subplots_adjust(hspace=0.02)
+
+ MakePlot(opt)
+ pt.EndPlot(files, opt)
+
diff --git a/Workspace/CumNumOfSat/satComputeNCumvsLv.py b/Workspace/CumNumOfSat/satComputeNCumvsLv.py
new file mode 100755
index 0000000..a5836f9
--- /dev/null
+++ b/Workspace/CumNumOfSat/satComputeNCumvsLv.py
@@ -0,0 +1,318 @@
+#!/usr/bin/env python3
+
+import sys,os,string
+
+import argparse
+import satlib
+import numpy as np
+import Ptools as pt
+import pickle
+from scipy.interpolate import splrep,splev
+
+####################################################################
+# option parser
+####################################################################
+
+description=""
+epilog =""""""
+
+parser = argparse.ArgumentParser(description=description,epilog=epilog,formatter_class=argparse.RawDescriptionHelpFormatter)
+
+
+
+parser.add_argument("--Mmin",
+ action="store",
+ dest="Mmin",
+ metavar='FLOAT',
+ type=float,
+ default=5e7,
+ help='the min halo mass')
+
+
+parser.add_argument("--Mmax",
+ action="store",
+ dest="Mmax",
+ metavar='FLOAT',
+ type=float,
+ default=1e10,
+ help='the max halo mass')
+
+parser.add_argument("-N",
+ action="store",
+ dest="N",
+ metavar='INT',
+ type=int,
+ default=1000,
+ help='number of bins')
+
+
+parser.add_argument("--Ngal",
+ action="store",
+ dest="Ngal",
+ metavar='INT',
+ type=int,
+ default=100,
+ help='number of galaxies')
+
+
+parser.add_argument("--LvvsMh_file",
+ action="store",
+ type=str,
+ dest="LvvsMh_file",
+ default = None,
+ help="Lv Mh file")
+
+
+
+
+####################################################################
+# main
+####################################################################
+
+
+opt = parser.parse_args()
+
+
+myblue = (0,128/255.,255/255.)
+myred = (255/255.,137/255.,137/255.)
+
+
+# read Mh vs Lv
+f = open(opt.LvvsMh_file,"rb")
+Mhbins = pickle.load(f,encoding="latin1")
+Lvmins = pickle.load(f,encoding="latin1")
+Lvmaxs = pickle.load(f,encoding="latin1")
+f.close()
+
+
+
+
+# halo bins
+lnMh = np.linspace(np.log10(opt.Mmin),np.log10(opt.Mmax),opt.N)
+Mh0 = 10**lnMh
+
+# cumulative number of haloes
+NM_CDM = satlib.CDM_CumulativeMass(Mh0)
+NM_WDM = satlib.fctWDM_CumulativeMass(Mh0)
+
+
+# define the CDM sampling function
+fct_CDM_Sample = np.vectorize( lambda x: 10**np.interp(x, NM_CDM[::-1]/NM_CDM.max(), np.log10(Mh0[::-1])) )
+maxCDM = (NM_CDM/NM_CDM.max()).min()
+
+# define the WDM sampling function
+fct_WDM_Sample = np.vectorize( lambda x: 10**np.interp(x, NM_WDM[::-1]/NM_WDM.max(), np.log10(Mh0[::-1])) )
+maxWDM = (NM_WDM/NM_WDM.max()).min()
+
+
+
+
+
+
+Nbins = 100
+Nsas_CDM = np.zeros((opt.Ngal,Nbins-1))
+Nsas_WDM = np.zeros((opt.Ngal,Nbins-1))
+
+
+
+for j in range(opt.Ngal):
+
+ print(j)
+
+ #################################################
+ # generate N haloes for the CDM
+ #################################################
+ Nh = int(satlib.CDM_CumulativeMass(opt.Mmin))
+ Mhs = fct_CDM_Sample(np.random.random(Nh))
+
+ ###################
+ # loop over haloes
+ logLv = np.zeros(len(Mhs))
+
+ for i,Mh in enumerate(Mhs):
+ if Mh < 1e8:
+ continue
+
+ # find the nearest values in Mbins
+ # and set a Luminosity
+
+ idx = np.argmin( np.fabs( Mh-Mhbins ) )
+
+ logLvmins = np.log10(Lvmins[idx])
+ logLvmaxs = np.log10(Lvmaxs[idx])
+
+ logLv[i] = np.random.uniform(logLvmins,logLvmaxs)
+
+
+ ######################
+ # cumulative
+
+ bins = np.linspace(2,10,Nbins)
+ Ns,Lv = np.histogram(logLv,bins)
+
+ # now, need to cumulate
+ # invert vector first and reinvert after
+ Nsa = np.add.accumulate(Ns[-np.arange(1,len(Ns)+1)])
+ Nsa = Nsa[-np.arange(1,len(Nsa)+1)]
+ Lv = Lv[1:]
+ # add
+ Nsas_CDM[j] = Nsa
+
+
+
+
+ #################################################
+ # generate N haloes for the WDM
+ #################################################
+ Nh = int(satlib.fctWDM_CumulativeMass(opt.Mmin))
+ Mhs = fct_WDM_Sample(np.random.random(Nh))
+
+
+ ###################
+ # loop over haloes
+ logLv = np.zeros(len(Mhs))
+
+ for i,Mh in enumerate(Mhs):
+ if Mh < 1e8:
+ continue
+
+ # find the nearest values in Mbins
+ # and set a Luminosity
+
+ idx = np.argmin( np.fabs( Mh-Mhbins ) )
+
+ logLvmins = np.log10(Lvmins[idx])
+ logLvmaxs = np.log10(Lvmaxs[idx])
+
+ logLv[i] = np.random.uniform(logLvmins,logLvmaxs)
+
+
+ ######################
+ # cumulative
+
+ bins = np.linspace(2,10,Nbins)
+ Ns,Lv = np.histogram(logLv,bins)
+
+ # now, need to cumulate
+ # invert vector first and reinvert after
+ Nsa = np.add.accumulate(Ns[-np.arange(1,len(Ns)+1)])
+ Nsa = Nsa[-np.arange(1,len(Nsa)+1)]
+ Lv = Lv[1:]
+ # add
+ Nsas_WDM[j] = Nsa
+
+
+
+
+
+
+
+
+Ns_CDM_mean = Nsas_CDM.mean(axis=0)
+Ns_CDM_std = Nsas_CDM.std(axis=0)
+
+Ns_CDM_meanp = Ns_CDM_mean + Ns_CDM_std
+Ns_CDM_meanm = Ns_CDM_mean - Ns_CDM_std
+
+
+Ns_WDM_mean = Nsas_WDM.mean(axis=0)
+Ns_WDM_std = Nsas_WDM.std(axis=0)
+
+Ns_WDM_meanp = Ns_WDM_mean + Ns_WDM_std
+Ns_WDM_meanm = Ns_WDM_mean - Ns_WDM_std
+
+pt.plot(10**Lv,Ns_CDM_mean,label=r"$\textrm{CDM}$",lw=5)
+pt.plot(10**Lv,Ns_WDM_mean,label=r"$\textrm{WDM}$",lw=5)
+
+
+#ax = pt.gca()
+#ax.fill_between(10**Lv,Ns_CDM_meanp,Ns_CDM_meanm,facecolor='k',alpha=0.1,label=r"$\textrm{CDM}$")
+#ax.fill_between(10**Lv,Ns_WDM_meanp,Ns_WDM_meanm,facecolor='k',alpha=0.1,label=r"$\textrm{WDM}$")
+
+
+data = True
+
+if data:
+
+ ################################################################################
+ # add data from Nadler, Drlica etc.
+
+ ax = pt.gca()
+ s = 0.0
+ k = 1
+
+ def read_data(f):
+ data = np.loadtxt(f,delimiter=',')
+ Mv = data[:,0]
+ N = data[:,1]
+ Lv = pow(10, (4.74 - Mv) / 2.5)
+ return Lv, N
+
+
+ ###################
+ # Nadler
+
+ Lvp, Np = read_data("data/Nadler_rawt1.txt")
+ Lvm, Nm = read_data("data/Nadler_rawb1.txt")
+
+ ap = splrep(Lvp,Np,s=s,k=k)
+ am = splrep(Lvm,Nm,s=s,k=k)
+
+ Np= splev(Lvp,ap)
+ Nm= splev(Lvp,am)
+
+ #pt.plot(Lvp,Np,'k-')
+ #pt.plot(Lvm,Nm,'k-')
+ ax.fill_between(Lvp,Np,Nm,facecolor='blue',alpha=0.1,label=r"$\textrm{Nadler\,\,et\,\,al.\,\,2018a}$")
+
+
+ Lvp, Np = read_data("data/Nadler_rawt2.txt")
+ Lvm, Nm = read_data("data/Nadler_rawb2.txt")
+
+ ap = splrep(Lvp,Np,s=s,k=k)
+ am = splrep(Lvm,Nm,s=s,k=k)
+
+ Np= splev(Lvp,ap)
+ Nm= splev(Lvp,am)
+
+ #pt.plot(Lvp,Np,'k-')
+ #pt.plot(Lvm,Nm,'k-')
+ ax.fill_between(Lvp,Np,Nm,facecolor='blue',alpha=0.1)
+
+
+ ###################
+ # Drilca
+
+ Lv, N = read_data("data/Drlica_raw_weighted.txt")
+ pt.plot(Lv,N,'k-',label=r"$\textrm{DES+PS1,\,\,weighted\,\,(Drlica-Wagner\,\,et\,\,al.\,\,2020)}$")
+
+ Lv, N = read_data("data/Drlica_raw_detected.txt")
+ pt.plot(Lv,N,'k--',label=r"$\textrm{DES+PS1,\,\,detected\,\,(Drlica-Wagner\,\,et\,\,al.\,\,2020)}$")
+
+ Lv, N = read_data("data/Drlica_raw_all.txt")
+ pt.plot(Lv,N,'r-',label=r"$\textrm{All\,\,known\,\,satellites\,\,(Drlica-Wagner\,\,et\,\,al.\,\,2020)}$")
+
+
+
+
+###########################
+# finalize
+###########################
+
+xmin = 5e2
+xmax = 5e9
+ymin = 1
+ymax = 500
+
+xlabel = r"$\rm{V-band\,\,Luminosity}$"
+ylabel = r"$\rm{Cumulative\,\,number\,\,of\,\,galaxies}\,\,(D<300\,\rm{kpc})$"
+
+pt.SetAxis(xmin,xmax,ymin,ymax,log="xy")
+pt.xlabel(xlabel)
+pt.ylabel(ylabel)
+pt.grid(False)
+
+pt.legend()
+
+
+pt.show()
diff --git a/Workspace/CumNumOfSat/satComputeNCumvsLvWithErrors.py b/Workspace/CumNumOfSat/satComputeNCumvsLvWithErrors.py
new file mode 100755
index 0000000..fb36a65
--- /dev/null
+++ b/Workspace/CumNumOfSat/satComputeNCumvsLvWithErrors.py
@@ -0,0 +1,455 @@
+#!/usr/bin/env python3
+
+import sys,os,string
+
+import argparse
+import satlib
+import numpy as np
+import Ptools as pt
+import pickle
+from scipy.interpolate import splrep,splev
+from tqdm import tqdm
+
+####################################################################
+# option parser
+####################################################################
+
+description=""
+epilog =""""""
+
+parser = argparse.ArgumentParser(description=description,epilog=epilog,formatter_class=argparse.RawDescriptionHelpFormatter)
+
+
+parser.add_argument("--M0",
+ action="store",
+ dest="M0",
+ metavar='FLOAT',
+ type=float,
+ default=1e12,
+ help='host halo mass')
+
+
+parser.add_argument("--Mmin",
+ action="store",
+ dest="Mmin",
+ metavar='FLOAT',
+ type=float,
+ default=5e7,
+ help='the min halo mass')
+
+
+parser.add_argument("--Mmax",
+ action="store",
+ dest="Mmax",
+ metavar='FLOAT',
+ type=float,
+ default=1e11,
+ help='the max halo mass')
+
+parser.add_argument("--DMmass",
+ action="store",
+ dest="DMmass",
+ metavar='FLOAT',
+ type=float,
+ default=2.0,
+ help='dark matter particle mass')
+
+
+parser.add_argument("-N",
+ action="store",
+ dest="N",
+ metavar='INT',
+ type=int,
+ default=1000,
+ help='number of bins')
+
+
+parser.add_argument("--Ngal",
+ action="store",
+ dest="Ngal",
+ metavar='INT',
+ type=int,
+ default=100,
+ help='number of galaxies')
+
+
+parser.add_argument("--Nrealisations",
+ action="store",
+ dest="Nrealisations",
+ metavar='INT',
+ type=int,
+ default=100,
+ help='number of realizations')
+
+
+
+parser.add_argument("--LvvsMh_model",
+ action="store",
+ type=str,
+ dest="LvvsMh_model",
+ default = 'rj2018',
+ help="Lv Mh model")
+
+
+parser.add_argument("-p",
+ action="store",
+ dest="ps",
+ metavar='FILE NAME',
+ type=str,
+ default=None,
+ help='output file name')
+
+parser.add_argument("--dpi",
+ action="store",
+ dest="dpi",
+ type=float,
+ default=300,
+ help="DPI of the saved file",
+ metavar=" FLOAT")
+
+parser.add_argument("--data",
+ action="store_true",
+ dest="data",
+ default=False,
+ help='add data')
+
+
+parser.add_argument("--filter",
+ action="store",
+ dest="filter",
+ metavar='STR',
+ type=str,
+ default=None,
+ help='filter name')
+
+parser.add_argument("--distance",
+ action="store",
+ dest="distance",
+ type=float,
+ default=35,
+ help="distance in Mpc",
+ metavar=" FLOAT")
+
+####################################################################
+# main
+####################################################################
+
+
+def MakePlot(opt):
+
+
+ # get Mh vs Lv relation
+ Mhbins,Lvmins,Lvmaxs = satlib.GetStellarMassHaloMassRelation(model=opt.LvvsMh_model)
+
+
+
+ # halo bins
+ lnMh = np.linspace(np.log10(opt.Mmin),np.log10(opt.Mmax),opt.N)
+ Mh0 = 10**lnMh
+
+
+ # define the DM models
+ #CDM = satlib.CDM_Sawala_Model(opt.Mmin,opt.Mmax,opt.N)
+ #WDM = satlib.CDM_Forouhar_Model(opt.Mmin,opt.Mmax,opt.N)
+ CDM = satlib.GEN_Model(opt.Mmin,opt.Mmax,opt.N,MDM=None,fWDM=0,M0=opt.M0)
+ WDM = satlib.GEN_Model(opt.Mmin,opt.Mmax,opt.N,MDM=opt.DMmass,fWDM=1,M0=opt.M0)
+
+
+
+ # number of haloes for the CDM
+ Nh_CDM = CDM.NumberOfHaloes()
+
+ # number of haloes for the WDM
+ Nh_WDM = WDM.NumberOfHaloes()
+
+
+
+
+
+ Nbins = 100
+ Nsats_CDM = np.zeros((opt.Ngal,Nbins-1))
+ Nsats_WDM = np.zeros((opt.Ngal,Nbins-1))
+
+
+ Ns_CDM_realisations = np.zeros((opt.Nrealisations,Nbins-1))
+ Ns_WDM_realisations = np.zeros((opt.Nrealisations,Nbins-1))
+
+ for realisation in tqdm(range(opt.Nrealisations)):
+
+ for j in range(opt.Ngal):
+
+ #print(realisation,j)
+
+ #################################################
+ # generate N haloes for the CDM
+ #################################################
+ Mhs = CDM.fctSample(np.random.random(Nh_CDM))
+
+ ###################
+ # loop over haloes
+ logLv = np.zeros(len(Mhs))
+
+ for i,Mh in enumerate(Mhs):
+ if Mh < 1e8:
+ continue
+
+ # find the nearest values in Mbins
+ # and set a Luminosity
+
+ idx = np.argmin( np.fabs( Mh-Mhbins ) )
+
+ logLvmins = np.log10(Lvmins[idx])
+ logLvmaxs = np.log10(Lvmaxs[idx])
+
+ logLv[i] = np.random.uniform(logLvmins,logLvmaxs)
+
+
+ ######################
+ # cumulative
+
+ bins = np.linspace(2,12,Nbins)
+ Ns,Lv = np.histogram(logLv,bins)
+
+ # now, need to cumulate
+ # invert vector first and reinvert after
+ Nsat = np.add.accumulate(Ns[-np.arange(1,len(Ns)+1)])
+ Nsat = Nsat[-np.arange(1,len(Nsat)+1)]
+ Lv = Lv[1:]
+ # add
+ Nsats_CDM[j] = Nsat
+
+
+
+
+ #################################################
+ # generate N haloes for the WDM
+ #################################################
+ Mhs = WDM.fctSample(np.random.random(Nh_WDM))
+
+
+ ###################
+ # loop over haloes
+ logLv = np.zeros(len(Mhs))
+
+ for i,Mh in enumerate(Mhs):
+ if Mh < 1e8:
+ continue
+
+ # find the nearest values in Mbins
+ # and set a Luminosity
+
+ idx = np.argmin( np.fabs( Mh-Mhbins ) )
+
+ logLvmins = np.log10(Lvmins[idx])
+ logLvmaxs = np.log10(Lvmaxs[idx])
+
+ logLv[i] = np.random.uniform(logLvmins,logLvmaxs)
+
+
+ ######################
+ # cumulative
+
+ bins = np.linspace(2,12,Nbins)
+ Ns,Lv = np.histogram(logLv,bins)
+
+ # now, need to cumulate
+ # invert vector first and reinvert after
+ Nsat = np.add.accumulate(Ns[-np.arange(1,len(Ns)+1)])
+ Nsat = Nsat[-np.arange(1,len(Nsat)+1)]
+ Lv = Lv[1:]
+ # add
+ Nsats_WDM[j] = Nsat
+
+
+ # plot individual galaxies
+ #pt.plot(10**Lv,Nsats_CDM[j],'blue',alpha=0.5)
+ #pt.plot(10**Lv,Nsats_WDM[j],'red' ,alpha=0.5)
+
+
+
+
+ Ns_CDM_mean = Nsats_CDM.mean(axis=0)
+ Ns_WDM_mean = Nsats_WDM.mean(axis=0)
+
+ Ns_CDM_realisations[realisation] = Ns_CDM_mean
+ Ns_WDM_realisations[realisation] = Ns_WDM_mean
+
+ # plot means
+ #pt.plot(10**Lv,Ns_CDM_mean,lw=1,alpha=0.5, c='blue')
+ #pt.plot(10**Lv,Ns_WDM_mean,lw=1,alpha=0.5, c='red')
+
+
+
+
+
+ Ns_CDM_realisations_mean = Ns_CDM_realisations.mean(axis=0)
+ Ns_CDM_realisations_std = Ns_CDM_realisations.std(axis=0)
+
+ Ns_CDM_meanp = Ns_CDM_realisations_mean + 3*Ns_CDM_realisations_std
+ Ns_CDM_meanm = Ns_CDM_realisations_mean - 3*Ns_CDM_realisations_std
+
+
+ Ns_WDM_realisations_mean = Ns_WDM_realisations.mean(axis=0)
+ Ns_WDM_realisations_std = Ns_WDM_realisations.std(axis=0)
+
+ Ns_WDM_meanp = Ns_WDM_realisations_mean + 3*Ns_WDM_realisations_std
+ Ns_WDM_meanm = Ns_WDM_realisations_mean - 3*Ns_WDM_realisations_std
+
+
+
+ if opt.filter is not None:
+ # convert Lv assumed to be Msol in Mag
+
+ Mag = np.zeros(len(Lv))
+ for i in range(len(Lv)):
+ Mag[i] = satlib.Msol2Mag(10**Lv[i],filter=opt.filter)
+
+ # to apparent magnitude
+ opt.distance = opt.distance * 1e6 # Mpc to pc
+ Mag = Mag + 5*np.log10(opt.distance) - 5
+
+
+ ax = pt.gca()
+ ax.fill_between(Mag,Ns_CDM_meanp,Ns_CDM_meanm,facecolor='blue',alpha=0.5,label=r"$\textrm{CDM}$")
+ ax.fill_between(Mag,Ns_WDM_meanp,Ns_WDM_meanm,facecolor='red',alpha=0.5,label=r"$\textrm{WDM}$")
+
+ xmin = 30
+ xmax = 15
+ ymin = 1
+ ymax = 500
+
+ pt.SetAxis(xmin,xmax,ymin,ymax,log="y")
+ xlabel = r"$\rm{%s\,\,mag}$"%(opt.filter)
+
+ # 1e5 Msol
+ pt.plot([23.2,23.2],[0.1,1000],c='k',ls=':')
+
+
+ else:
+ ax = pt.gca()
+ ax.fill_between(10**Lv,Ns_CDM_meanp,Ns_CDM_meanm,facecolor='blue',alpha=0.5,label=r"$\textrm{CDM}$")
+ ax.fill_between(10**Lv,Ns_WDM_meanp,Ns_WDM_meanm,facecolor='red',alpha=0.5,label=r"$\textrm{WDM}$")
+
+ xmin = 5e2
+ xmax = 5e9
+ ymin = 1
+ ymax = 500
+
+ pt.SetAxis(xmin,xmax,ymin,ymax,log="xy")
+ xlabel = r"$\rm{V-band\,\,Luminosity}$"
+
+ pt.plot([1e5,1e5],[0.1,1000],c='k',ls=':')
+
+
+ if opt.data:
+
+ ################################################################################
+ # add data from Nadler, Drlica etc.
+
+ ax = pt.gca()
+ s = 0.0
+ k = 1
+
+ def read_data(f):
+ data = np.loadtxt(f,delimiter=',')
+ Mv = data[:,0]
+ N = data[:,1]
+ Lv = pow(10, (4.74 - Mv) / 2.5)
+ return Lv, N
+
+
+ ###################
+ # Nadler
+
+ Lvp, Np = read_data("data/Nadler_rawt1.txt")
+ Lvm, Nm = read_data("data/Nadler_rawb1.txt")
+
+ ap = splrep(Lvp,Np,s=s,k=k)
+ am = splrep(Lvm,Nm,s=s,k=k)
+
+ Np= splev(Lvp,ap)
+ Nm= splev(Lvp,am)
+
+ #pt.plot(Lvp,Np,'k-')
+ #pt.plot(Lvm,Nm,'k-')
+ ax.fill_between(Lvp,Np,Nm,facecolor='green',alpha=0.05,label=r"$\textrm{Nadler\,\,et\,\,al.\,\,2018a}$")
+
+
+ Lvp, Np = read_data("data/Nadler_rawt2.txt")
+ Lvm, Nm = read_data("data/Nadler_rawb2.txt")
+
+ ap = splrep(Lvp,Np,s=s,k=k)
+ am = splrep(Lvm,Nm,s=s,k=k)
+
+ Np= splev(Lvp,ap)
+ Nm= splev(Lvp,am)
+
+ #pt.plot(Lvp,Np,'k-')
+ #pt.plot(Lvm,Nm,'k-')
+ ax.fill_between(Lvp,Np,Nm,facecolor='green',alpha=0.05)
+
+
+ ###################
+ # Drilca
+
+ Lv, N = read_data("data/Drlica_raw_weighted.txt")
+ pt.plot(Lv,N,'k-',label=r"$\textrm{DES+PS1,\,\,weighted\,\,(Drlica-Wagner\,\,et\,\,al.\,\,2020)}$",alpha=0.5)
+
+ Lv, N = read_data("data/Drlica_raw_detected.txt")
+ pt.plot(Lv,N,'k--',label=r"$\textrm{DES+PS1,\,\,detected\,\,(Drlica-Wagner\,\,et\,\,al.\,\,2020)}$",alpha=0.5)
+
+ Lv, N = read_data("data/Drlica_raw_all.txt")
+ pt.plot(Lv,N,'r-',label=r"$\textrm{All\,\,known\,\,satellites\,\,(Drlica-Wagner\,\,et\,\,al.\,\,2020)}$",alpha=0.5)
+
+
+
+
+ ###########################
+ # finalize
+ ###########################
+
+
+
+
+
+ ylabel = r"$\rm{Cumulative\,\,number\,\,of\,\,galaxies}\,\,(D<300\,\rm{kpc})$"
+
+
+ pt.xlabel(xlabel,fontsize=pt.labelfont)
+ pt.ylabel(ylabel,fontsize=pt.labelfont)
+ pt.grid(False)
+ pt.legend()
+
+ pt.title(r"$\rm{%d\,\,galaxies\,\,observed\,\,+\,\,model=%s}\,\,+\,\,M_{\rm{DM}}=%3.1f\,\rm{keV}$"%(opt.Ngal,opt.LvvsMh_model,opt.DMmass),fontsize=pt.labelfont)
+
+ #pt.plot([1e5,1e5],[0.1,1000],c='k',ls=':')
+
+
+
+
+
+if __name__ == '__main__':
+
+ opt = parser.parse_args()
+ files = []
+
+ pt.InitPlot(files, opt)
+ pt.labelfont=20
+
+ # pt.figure(figsize=(8*2,6*2))
+ # pt.figure(dpi=10)
+
+ #fig = pt.gcf()
+ # fig.subplots_adjust(left=0.1)
+ # fig.subplots_adjust(right=1)
+ # fig.subplots_adjust(bottom=0.12)
+ # fig.subplots_adjust(top=0.95)
+ # fig.subplots_adjust(wspace=0.25)
+ # fig.subplots_adjust(hspace=0.02)
+
+ MakePlot(opt)
+ pt.EndPlot(files, opt)
+
+
+
+
diff --git a/Workspace/CumNumOfSat/satComputeNCumvsLvWithErrorsWithM0varied.py b/Workspace/CumNumOfSat/satComputeNCumvsLvWithErrorsWithM0varied.py
new file mode 100755
index 0000000..cf88483
--- /dev/null
+++ b/Workspace/CumNumOfSat/satComputeNCumvsLvWithErrorsWithM0varied.py
@@ -0,0 +1,448 @@
+#!/usr/bin/env python3
+
+import sys,os,string
+
+import argparse
+import satlib
+import numpy as np
+import Ptools as pt
+import pickle
+from scipy.interpolate import splrep,splev
+from tqdm import tqdm
+
+####################################################################
+# option parser
+####################################################################
+
+description=""
+epilog =""""""
+
+parser = argparse.ArgumentParser(description=description,epilog=epilog,formatter_class=argparse.RawDescriptionHelpFormatter)
+
+
+parser.add_argument("--M0",
+ action="store",
+ dest="M0",
+ metavar='FLOAT',
+ type=float,
+ default=1e12,
+ help='host halo mass')
+
+
+
+parser.add_argument("--Mmin",
+ action="store",
+ dest="Mmin",
+ metavar='FLOAT',
+ type=float,
+ default=5e7,
+ help='the min halo mass')
+
+
+parser.add_argument("--Mmax",
+ action="store",
+ dest="Mmax",
+ metavar='FLOAT',
+ type=float,
+ default=1e11,
+ help='the max halo mass')
+
+parser.add_argument("--DMmass",
+ action="store",
+ dest="DMmass",
+ metavar='FLOAT',
+ type=float,
+ default=2.0,
+ help='dark matter particle mass')
+
+
+parser.add_argument("-N",
+ action="store",
+ dest="N",
+ metavar='INT',
+ type=int,
+ default=1000,
+ help='number of bins')
+
+
+parser.add_argument("--Ngal",
+ action="store",
+ dest="Ngal",
+ metavar='INT',
+ type=int,
+ default=100,
+ help='number of galaxies')
+
+
+parser.add_argument("--Nrealisations",
+ action="store",
+ dest="Nrealisations",
+ metavar='INT',
+ type=int,
+ default=100,
+ help='number of realizations')
+
+
+
+parser.add_argument("--LvvsMh_model",
+ action="store",
+ type=str,
+ dest="LvvsMh_model",
+ default = 'rj2018',
+ help="Lv Mh model")
+
+
+parser.add_argument("-p",
+ action="store",
+ dest="ps",
+ metavar='FILE NAME',
+ type=str,
+ default=None,
+ help='output file name')
+
+parser.add_argument("--dpi",
+ action="store",
+ dest="dpi",
+ type=float,
+ default=300,
+ help="DPI of the saved file",
+ metavar=" FLOAT")
+
+parser.add_argument("--data",
+ action="store_true",
+ dest="data",
+ default=False,
+ help='add data')
+
+
+parser.add_argument("--M0_var",
+ action="store",
+ dest="M0_var",
+ metavar='FLOAT',
+ type=float,
+ default=1,
+ help='variation of the mass of the main halo in dex')
+
+parser.add_argument("--M0_N",
+ action="store",
+ dest="M0_N",
+ metavar='INT',
+ type=int,
+ default=11,
+ help='number of bins for the variation of the mass of the main halo')
+
+
+
+
+
+####################################################################
+# main
+####################################################################
+
+
+def MakePlot(opt):
+
+
+ # get Mh vs Lv relation
+ Mhbins,Lvmins,Lvmaxs = satlib.GetStellarMassHaloMassRelation(model=opt.LvvsMh_model)
+
+
+
+ # halo bins
+ lnMh = np.linspace(np.log10(opt.Mmin),np.log10(opt.Mmax),opt.N)
+ Mh0 = 10**lnMh
+
+
+ # define the DM models
+
+ # CDM
+ lnfmin = -opt.M0_var
+ lnfmax = +opt.M0_var
+ M0min = 10**(lnfmin) * opt.M0
+ M0max = 10**(lnfmax) * opt.M0
+ M0s = 10**np.linspace(lnfmin,lnfmax,opt.M0_N) * opt.M0
+
+ print("Minimal main halo mass:",M0min/opt.M0)
+ print("Maximal main halo mass:",M0max/opt.M0)
+
+ CDMs = []
+ WDMs = []
+ for M0 in M0s:
+ CDMs.append(satlib.GEN_Model(opt.Mmin,opt.Mmax,opt.N,MDM=None,fWDM=0,M0=M0))
+ WDMs.append(satlib.GEN_Model(opt.Mmin,opt.Mmax,opt.N,MDM=opt.DMmass,fWDM=1,M0=M0))
+
+
+
+ Nbins = 100
+ Nsats_CDM = np.zeros((opt.Ngal,Nbins-1))
+ Nsats_WDM = np.zeros((opt.Ngal,Nbins-1))
+
+
+ Ns_CDM_realisations = np.zeros((opt.Nrealisations,Nbins-1))
+ Ns_WDM_realisations = np.zeros((opt.Nrealisations,Nbins-1))
+
+ for realisation in tqdm(range(opt.Nrealisations)):
+
+ for j in range(opt.Ngal):
+
+ # find a random host halo mass between the desired mass range
+ rand = opt.M0* 10**np.random.uniform(lnfmin,lnfmax)
+ dd = np.absolute(M0s-rand)
+ # get the index
+ idx = np.argmin(dd)
+
+ # get the proper model
+ CDM = CDMs[idx]
+ WDM = WDMs[idx]
+
+
+ # number of haloes for the CDM
+ Nh_CDM = CDM.NumberOfHaloes()
+
+ # number of haloes for the WDM
+ Nh_WDM = WDM.NumberOfHaloes()
+
+
+
+
+ #################################################
+ # generate N haloes for the CDM
+ #################################################
+ Mhs = CDM.fctSample(np.random.random(Nh_CDM))
+
+ ###################
+ # loop over haloes
+ logLv = np.zeros(len(Mhs))
+
+ for i,Mh in enumerate(Mhs):
+ if Mh < 1e8:
+ continue
+
+ # find the nearest values in Mbins
+ # and set a Luminosity
+
+ idx = np.argmin( np.fabs( Mh-Mhbins ) )
+
+ logLvmins = np.log10(Lvmins[idx])
+ logLvmaxs = np.log10(Lvmaxs[idx])
+
+ logLv[i] = np.random.uniform(logLvmins,logLvmaxs)
+
+
+ ######################
+ # cumulative
+
+ bins = np.linspace(2,12,Nbins)
+ Ns,Lv = np.histogram(logLv,bins)
+
+ # now, need to cumulate
+ # invert vector first and reinvert after
+ Nsat = np.add.accumulate(Ns[-np.arange(1,len(Ns)+1)])
+ Nsat = Nsat[-np.arange(1,len(Nsat)+1)]
+ Lv = Lv[1:]
+ # add
+ Nsats_CDM[j] = Nsat
+
+
+
+
+ #################################################
+ # generate N haloes for the WDM
+ #################################################
+ Mhs = WDM.fctSample(np.random.random(Nh_WDM))
+
+
+ ###################
+ # loop over haloes
+ logLv = np.zeros(len(Mhs))
+
+ for i,Mh in enumerate(Mhs):
+ if Mh < 1e8:
+ continue
+
+ # find the nearest values in Mbins
+ # and set a Luminosity
+
+ idx = np.argmin( np.fabs( Mh-Mhbins ) )
+
+ logLvmins = np.log10(Lvmins[idx])
+ logLvmaxs = np.log10(Lvmaxs[idx])
+
+ logLv[i] = np.random.uniform(logLvmins,logLvmaxs)
+
+
+ ######################
+ # cumulative
+
+ bins = np.linspace(2,12,Nbins)
+ Ns,Lv = np.histogram(logLv,bins)
+
+ # now, need to cumulate
+ # invert vector first and reinvert after
+ Nsat = np.add.accumulate(Ns[-np.arange(1,len(Ns)+1)])
+ Nsat = Nsat[-np.arange(1,len(Nsat)+1)]
+ Lv = Lv[1:]
+ # add
+ Nsats_WDM[j] = Nsat
+
+
+ # plot individual galaxies
+ #pt.plot(10**Lv,Nsats_CDM[j],'blue',alpha=0.5)
+ #pt.plot(10**Lv,Nsats_WDM[j],'red' ,alpha=0.5)
+
+
+
+
+ Ns_CDM_mean = Nsats_CDM.mean(axis=0)
+ Ns_WDM_mean = Nsats_WDM.mean(axis=0)
+
+ Ns_CDM_realisations[realisation] = Ns_CDM_mean
+ Ns_WDM_realisations[realisation] = Ns_WDM_mean
+
+ # plot means
+ #pt.plot(10**Lv,Ns_CDM_mean,lw=1,alpha=0.5, c='blue')
+ #pt.plot(10**Lv,Ns_WDM_mean,lw=1,alpha=0.5, c='red')
+
+
+
+
+
+ Ns_CDM_realisations_mean = Ns_CDM_realisations.mean(axis=0)
+ Ns_CDM_realisations_std = Ns_CDM_realisations.std(axis=0)
+
+ Ns_CDM_meanp = Ns_CDM_realisations_mean + 3*Ns_CDM_realisations_std
+ Ns_CDM_meanm = Ns_CDM_realisations_mean - 3*Ns_CDM_realisations_std
+
+
+ Ns_WDM_realisations_mean = Ns_WDM_realisations.mean(axis=0)
+ Ns_WDM_realisations_std = Ns_WDM_realisations.std(axis=0)
+
+ Ns_WDM_meanp = Ns_WDM_realisations_mean + 3*Ns_WDM_realisations_std
+ Ns_WDM_meanm = Ns_WDM_realisations_mean - 3*Ns_WDM_realisations_std
+
+
+
+ ax = pt.gca()
+ ax.fill_between(10**Lv,Ns_CDM_meanp,Ns_CDM_meanm,facecolor='blue',alpha=0.5,label=r"$\textrm{CDM}$")
+ ax.fill_between(10**Lv,Ns_WDM_meanp,Ns_WDM_meanm,facecolor='red',alpha=0.5,label=r"$\textrm{WDM}$")
+
+
+
+
+
+ if opt.data:
+
+ ################################################################################
+ # add data from Nadler, Drlica etc.
+
+ ax = pt.gca()
+ s = 0.0
+ k = 1
+
+ def read_data(f):
+ data = np.loadtxt(f,delimiter=',')
+ Mv = data[:,0]
+ N = data[:,1]
+ Lv = pow(10, (4.74 - Mv) / 2.5)
+ return Lv, N
+
+
+ ###################
+ # Nadler
+
+ Lvp, Np = read_data("data/Nadler_rawt1.txt")
+ Lvm, Nm = read_data("data/Nadler_rawb1.txt")
+
+ ap = splrep(Lvp,Np,s=s,k=k)
+ am = splrep(Lvm,Nm,s=s,k=k)
+
+ Np= splev(Lvp,ap)
+ Nm= splev(Lvp,am)
+
+ #pt.plot(Lvp,Np,'k-')
+ #pt.plot(Lvm,Nm,'k-')
+ ax.fill_between(Lvp,Np,Nm,facecolor='green',alpha=0.05,label=r"$\textrm{Nadler\,\,et\,\,al.\,\,2018a}$")
+
+
+ Lvp, Np = read_data("data/Nadler_rawt2.txt")
+ Lvm, Nm = read_data("data/Nadler_rawb2.txt")
+
+ ap = splrep(Lvp,Np,s=s,k=k)
+ am = splrep(Lvm,Nm,s=s,k=k)
+
+ Np= splev(Lvp,ap)
+ Nm= splev(Lvp,am)
+
+ #pt.plot(Lvp,Np,'k-')
+ #pt.plot(Lvm,Nm,'k-')
+ ax.fill_between(Lvp,Np,Nm,facecolor='green',alpha=0.05)
+
+
+ ###################
+ # Drilca
+
+ Lv, N = read_data("data/Drlica_raw_weighted.txt")
+ pt.plot(Lv,N,'k-',label=r"$\textrm{DES+PS1,\,\,weighted\,\,(Drlica-Wagner\,\,et\,\,al.\,\,2020)}$",alpha=0.5)
+
+ Lv, N = read_data("data/Drlica_raw_detected.txt")
+ pt.plot(Lv,N,'k--',label=r"$\textrm{DES+PS1,\,\,detected\,\,(Drlica-Wagner\,\,et\,\,al.\,\,2020)}$",alpha=0.5)
+
+ Lv, N = read_data("data/Drlica_raw_all.txt")
+ pt.plot(Lv,N,'r-',label=r"$\textrm{All\,\,known\,\,satellites\,\,(Drlica-Wagner\,\,et\,\,al.\,\,2020)}$",alpha=0.5)
+
+
+
+
+ ###########################
+ # finalize
+ ###########################
+
+ xmin = 5e2
+ xmax = 5e9
+ ymin = 1
+ ymax = 500
+
+
+
+ xlabel = r"$\rm{V-band\,\,Luminosity}$"
+ ylabel = r"$\rm{Cumulative\,\,number\,\,of\,\,galaxies}\,\,(D<300\,\rm{kpc})$"
+
+ pt.SetAxis(xmin,xmax,ymin,ymax,log="xy")
+ pt.xlabel(xlabel,fontsize=pt.labelfont)
+ pt.ylabel(ylabel,fontsize=pt.labelfont)
+ pt.grid(False)
+ pt.legend()
+
+ pt.title(r"$\rm{%d\,\,galaxies\,\,observed\,\,+\,\,model=%s}\,\,+\,\,M_{\rm{DM}}=%3.1f\,\rm{keV}$"%(opt.Ngal,opt.LvvsMh_model,opt.DMmass),fontsize=pt.labelfont)
+
+ pt.plot([1e5,1e5],[0.1,1000],c='k',ls=':')
+
+
+
+
+
+if __name__ == '__main__':
+
+ opt = parser.parse_args()
+ files = []
+
+ pt.InitPlot(files, opt)
+ pt.labelfont=20
+
+ # pt.figure(figsize=(8*2,6*2))
+ # pt.figure(dpi=10)
+
+ #fig = pt.gcf()
+ # fig.subplots_adjust(left=0.1)
+ # fig.subplots_adjust(right=1)
+ # fig.subplots_adjust(bottom=0.12)
+ # fig.subplots_adjust(top=0.95)
+ # fig.subplots_adjust(wspace=0.25)
+ # fig.subplots_adjust(hspace=0.02)
+
+ MakePlot(opt)
+ pt.EndPlot(files, opt)
+
+
+
+
diff --git a/Workspace/CumNumOfSat/satComputeNCumvsLvWithErrors_forOliver.py b/Workspace/CumNumOfSat/satComputeNCumvsLvWithErrors_forOliver.py
new file mode 100755
index 0000000..53c9718
--- /dev/null
+++ b/Workspace/CumNumOfSat/satComputeNCumvsLvWithErrors_forOliver.py
@@ -0,0 +1,411 @@
+#!/usr/bin/env python3
+
+import sys,os,string
+
+import argparse
+import satlib
+import numpy as np
+import Ptools as pt
+import pickle
+from scipy.interpolate import splrep,splev
+from tqdm import tqdm
+
+####################################################################
+# option parser
+####################################################################
+
+description=""
+epilog =""""""
+
+parser = argparse.ArgumentParser(description=description,epilog=epilog,formatter_class=argparse.RawDescriptionHelpFormatter)
+
+
+parser.add_argument("--M0",
+ action="store",
+ dest="M0",
+ metavar='FLOAT',
+ type=float,
+ default=1e12,
+ help='host halo mass')
+
+
+parser.add_argument("--Mmin",
+ action="store",
+ dest="Mmin",
+ metavar='FLOAT',
+ type=float,
+ default=5e7,
+ help='the min halo mass')
+
+
+parser.add_argument("--Mmax",
+ action="store",
+ dest="Mmax",
+ metavar='FLOAT',
+ type=float,
+ default=1e11,
+ help='the max halo mass')
+
+parser.add_argument("--DMmass",
+ action="store",
+ dest="DMmass",
+ metavar='FLOAT',
+ type=float,
+ default=2.0,
+ help='dark matter particle mass')
+
+
+parser.add_argument("-N",
+ action="store",
+ dest="N",
+ metavar='INT',
+ type=int,
+ default=1000,
+ help='number of bins')
+
+
+parser.add_argument("--Ngal",
+ action="store",
+ dest="Ngal",
+ metavar='INT',
+ type=int,
+ default=100,
+ help='number of galaxies')
+
+
+parser.add_argument("--Nrealisations",
+ action="store",
+ dest="Nrealisations",
+ metavar='INT',
+ type=int,
+ default=100,
+ help='number of realizations')
+
+
+
+parser.add_argument("--LvvsMh_model",
+ action="store",
+ type=str,
+ dest="LvvsMh_model",
+ default = 'rj2018',
+ help="Lv Mh model")
+
+
+parser.add_argument("-p",
+ action="store",
+ dest="ps",
+ metavar='FILE NAME',
+ type=str,
+ default=None,
+ help='output file name')
+
+parser.add_argument("--dpi",
+ action="store",
+ dest="dpi",
+ type=float,
+ default=300,
+ help="DPI of the saved file",
+ metavar=" FLOAT")
+
+parser.add_argument("--data",
+ action="store_true",
+ dest="data",
+ default=False,
+ help='add data')
+
+
+parser.add_argument("--filter",
+ action="store",
+ dest="filter",
+ metavar='STR',
+ type=str,
+ default=None,
+ help='filter name')
+
+parser.add_argument("--distance",
+ action="store",
+ dest="distance",
+ type=float,
+ default=35,
+ help="distance in Mpc",
+ metavar=" FLOAT")
+
+####################################################################
+# main
+####################################################################
+
+
+def MakePlot(opt):
+
+
+ # get Mh vs Lv relation
+ Mhbins,Lvmins,Lvmaxs = satlib.GetStellarMassHaloMassRelation(model=opt.LvvsMh_model)
+
+
+
+ # halo bins
+ lnMh = np.linspace(np.log10(opt.Mmin),np.log10(opt.Mmax),opt.N)
+ Mh0 = 10**lnMh
+
+
+ # define the DM models
+ #CDM = satlib.CDM_Sawala_Model(opt.Mmin,opt.Mmax,opt.N)
+ #WDM = satlib.CDM_Forouhar_Model(opt.Mmin,opt.Mmax,opt.N)
+ CDM = satlib.GEN_Model(opt.Mmin,opt.Mmax,opt.N,MDM=None,fWDM=0,M0=opt.M0)
+
+
+
+ # number of haloes for the CDM
+ Nh_CDM = CDM.NumberOfHaloes()
+
+
+ Nbins = 100
+ Nsats_CDM = np.zeros((opt.Ngal,Nbins-1))
+
+ Ns_CDM_realisations = np.zeros((opt.Nrealisations,Nbins-1))
+
+ for realisation in tqdm(range(opt.Nrealisations)):
+
+ for j in range(opt.Ngal):
+
+ #print(realisation,j)
+
+ #################################################
+ # generate N haloes for the CDM
+ #################################################
+ Mhs = CDM.fctSample(np.random.random(Nh_CDM))
+
+ ###################
+ # loop over haloes
+ logLv = np.zeros(len(Mhs))
+
+ for i,Mh in enumerate(Mhs):
+ if Mh < 1e8:
+ continue
+
+ # find the nearest values in Mbins
+ # and set a Luminosity
+
+ idx = np.argmin( np.fabs( Mh-Mhbins ) )
+
+ logLvmins = np.log10(Lvmins[idx])
+ logLvmaxs = np.log10(Lvmaxs[idx])
+
+ logLv[i] = np.random.uniform(logLvmins,logLvmaxs)
+
+
+ ######################
+ # cumulative
+
+ bins = np.linspace(2,12,Nbins)
+ Ns,Lv = np.histogram(logLv,bins)
+
+ # now, need to cumulate
+ # invert vector first and reinvert after
+ Nsat = np.add.accumulate(Ns[-np.arange(1,len(Ns)+1)])
+ Nsat = Nsat[-np.arange(1,len(Nsat)+1)]
+ Lv = Lv[1:]
+ # add
+ Nsats_CDM[j] = Nsat
+
+
+
+
+
+ Ns_CDM_mean = Nsats_CDM.mean(axis=0)
+
+ Ns_CDM_realisations[realisation] = Ns_CDM_mean
+
+ # plot means
+ #pt.plot(10**Lv,Ns_CDM_mean,lw=1,alpha=0.5, c='blue')
+ #pt.plot(10**Lv,Ns_WDM_mean,lw=1,alpha=0.5, c='red')
+
+
+
+
+
+ Ns_CDM_realisations_mean = Ns_CDM_realisations.mean(axis=0)
+ Ns_CDM_realisations_std = Ns_CDM_realisations.std(axis=0)
+
+ Ns_CDM_meanp = Ns_CDM_realisations_mean + 3*Ns_CDM_realisations_std
+ Ns_CDM_meanm = Ns_CDM_realisations_mean - 3*Ns_CDM_realisations_std
+
+
+
+ if opt.filter is not None:
+ # convert Lv assumed to be Msol in Mag
+
+ Mag = np.zeros(len(Lv))
+ for i in range(len(Lv)):
+ Mag[i] = satlib.Msol2Mag(10**Lv[i],filter=opt.filter)
+
+ # to apparent magnitude
+ opt.distance = opt.distance * 1e6 # Mpc to pc
+ Mag = Mag + 5*np.log10(opt.distance) - 5
+
+
+ ax = pt.gca()
+ ax.fill_between(Mag,Ns_CDM_meanp,Ns_CDM_meanm,facecolor='blue',alpha=0.5,label=r"$\textrm{CDM}$")
+
+ xmin = 30
+ xmax = 15
+ ymin = 1
+ ymax = 500
+
+ pt.SetAxis(xmin,xmax,ymin,ymax,log="y")
+ xlabel = r"$\rm{%s\,\,mag}$"%(opt.filter)
+
+ # 1e5 Msol
+ pt.plot([23.2,23.2],[0.1,1000],c='k',ls=':')
+
+
+ else:
+ ax = pt.gca()
+ ax.fill_between(10**Lv,Ns_CDM_meanp,Ns_CDM_meanm,facecolor='blue',alpha=0.5,label=r"$\textrm{CDM}$")
+
+ xmin = 5e2
+ xmax = 5e9
+ ymin = 1
+ ymax = 500
+
+ pt.SetAxis(xmin,xmax,ymin,ymax,log="xy")
+ xlabel = r"$\rm{V-band\,\,Luminosity}$"
+
+ pt.plot([1e5,1e5],[0.1,1000],c='k',ls=':')
+
+
+ '''
+
+ if opt.data:
+
+ ################################################################################
+ # add data from Nadler, Drlica etc.
+
+ ax = pt.gca()
+ s = 0.0
+ k = 1
+
+ def read_data(f):
+ data = np.loadtxt(f,delimiter=',')
+ Mv = data[:,0]
+ N = data[:,1]
+ Lv = pow(10, (4.74 - Mv) / 2.5)
+ return Lv, N
+
+
+ ###################
+ # Nadler
+
+ Lvp, Np = read_data("data/Nadler_rawt1.txt")
+ Lvm, Nm = read_data("data/Nadler_rawb1.txt")
+
+ ap = splrep(Lvp,Np,s=s,k=k)
+ am = splrep(Lvm,Nm,s=s,k=k)
+
+ Np= splev(Lvp,ap)
+ Nm= splev(Lvp,am)
+
+ #pt.plot(Lvp,Np,'k-')
+ #pt.plot(Lvm,Nm,'k-')
+ ax.fill_between(Lvp,Np,Nm,facecolor='green',alpha=0.05,label=r"$\textrm{Nadler\,\,et\,\,al.\,\,2018a}$")
+
+
+ Lvp, Np = read_data("data/Nadler_rawt2.txt")
+ Lvm, Nm = read_data("data/Nadler_rawb2.txt")
+
+ ap = splrep(Lvp,Np,s=s,k=k)
+ am = splrep(Lvm,Nm,s=s,k=k)
+
+ Np= splev(Lvp,ap)
+ Nm= splev(Lvp,am)
+
+ #pt.plot(Lvp,Np,'k-')
+ #pt.plot(Lvm,Nm,'k-')
+ ax.fill_between(Lvp,Np,Nm,facecolor='green',alpha=0.05)
+
+
+ ###################
+ # Drilca
+
+ Lv, N = read_data("data/Drlica_raw_weighted.txt")
+ pt.plot(Lv,N,'k-',label=r"$\textrm{DES+PS1,\,\,weighted\,\,(Drlica-Wagner\,\,et\,\,al.\,\,2020)}$",alpha=0.5)
+
+ Lv, N = read_data("data/Drlica_raw_detected.txt")
+ pt.plot(Lv,N,'k--',label=r"$\textrm{DES+PS1,\,\,detected\,\,(Drlica-Wagner\,\,et\,\,al.\,\,2020)}$",alpha=0.5)
+
+ Lv, N = read_data("data/Drlica_raw_all.txt")
+ pt.plot(Lv,N,'r-',label=r"$\textrm{All\,\,known\,\,satellites\,\,(Drlica-Wagner\,\,et\,\,al.\,\,2020)}$",alpha=0.5)
+
+ '''
+
+ # Oliver data
+ Lv = np.array([5.91561634e+06, 2.14783047e+07, 8.55066713e+07, 1.12719746e+06,
+ 3.73250158e+07, 1.95884467e+08, 2.83139200e+06, 3.73250158e+07,
+ 1.78648757e+06, 1.02801630e+08, 2.83139200e+05, 4.09260660e+08,
+ 5.91561634e+06])
+
+ logLv = np.log10(Lv)
+
+ bins = np.array([5,5.5,6,6.5,7,7.5,8])
+ bins = np.linspace(4,9,20)
+
+ h,bins = np.histogram(logLv, bins=bins)
+
+ h = np.flip(h)
+ h = np.add.accumulate(h)
+ h = np.flip(h)
+
+ #pt.scatter(10**bins[1:],h,marker='s',color="k",s=50,label=r'$\rm{M83}$')
+ pt.plot(10**bins[1:],h,color="k",label=r'$\rm{M83}$')
+
+
+
+ ###########################
+ # finalize
+ ###########################
+
+
+
+
+
+ ylabel = r"$\rm{Cumulative\,\,number\,\,of\,\,galaxies}\,\,(D<300\,\rm{kpc})$"
+
+
+ pt.xlabel(xlabel,fontsize=pt.labelfont)
+ pt.ylabel(ylabel,fontsize=pt.labelfont)
+ pt.grid(False)
+ pt.legend()
+
+ pt.title(r"$\rm{%d\,\,galaxies\,\,observed\,\,+\,\,model=%s}$"%(opt.Ngal,opt.LvvsMh_model),fontsize=pt.labelfont)
+
+ #pt.plot([1e5,1e5],[0.1,1000],c='k',ls=':')
+
+
+
+
+
+if __name__ == '__main__':
+
+ opt = parser.parse_args()
+ files = []
+
+ pt.InitPlot(files, opt)
+ pt.labelfont=20
+
+ # pt.figure(figsize=(8*2,6*2))
+ # pt.figure(dpi=10)
+
+ #fig = pt.gcf()
+ # fig.subplots_adjust(left=0.1)
+ # fig.subplots_adjust(right=1)
+ # fig.subplots_adjust(bottom=0.12)
+ # fig.subplots_adjust(top=0.95)
+ # fig.subplots_adjust(wspace=0.25)
+ # fig.subplots_adjust(hspace=0.02)
+
+ MakePlot(opt)
+ pt.EndPlot(files, opt)
+
+
+
+
diff --git a/Workspace/CumNumOfSat/satComputeNvsM.py b/Workspace/CumNumOfSat/satComputeNvsM.py
new file mode 100755
index 0000000..0c29e17
--- /dev/null
+++ b/Workspace/CumNumOfSat/satComputeNvsM.py
@@ -0,0 +1,176 @@
+#!/usr/bin/env python3
+
+import sys,os,string
+
+import argparse
+import satlib
+import numpy as np
+import Ptools as pt
+import pickle
+from scipy.interpolate import splrep,splev
+from tqdm import tqdm
+
+
+####################################################################
+# option parser
+####################################################################
+
+description=""
+epilog =""""""
+
+parser = argparse.ArgumentParser(description=description,epilog=epilog,formatter_class=argparse.RawDescriptionHelpFormatter)
+
+parser.add_argument("--M0",
+ action="store",
+ dest="M0",
+ metavar='FLOAT',
+ type=float,
+ default=1e12,
+ help='host halo mass')
+
+parser.add_argument("--Mmin",
+ action="store",
+ dest="Mmin",
+ metavar='FLOAT',
+ type=float,
+ default=5e6,
+ help='the min halo mass')
+
+
+parser.add_argument("--Mmax",
+ action="store",
+ dest="Mmax",
+ metavar='FLOAT',
+ type=float,
+ default=1e9,
+ help='the max halo mass')
+
+parser.add_argument("--DMmass",
+ action="store",
+ dest="DMmass",
+ metavar='FLOAT',
+ type=float,
+ default=2.0,
+ help='dark matter particle mass')
+
+
+parser.add_argument("-N",
+ action="store",
+ dest="N",
+ metavar='INT',
+ type=int,
+ default=1000,
+ help='number of bins')
+
+
+parser.add_argument("--Ngal",
+ action="store",
+ dest="Ngal",
+ metavar='INT',
+ type=int,
+ default=100,
+ help='number of galaxies')
+
+parser.add_argument("--Nrealisations",
+ action="store",
+ dest="Nrealisations",
+ metavar='INT',
+ type=int,
+ default=100,
+ help='number of realizations')
+
+parser.add_argument("-p",
+ action="store",
+ dest="ps",
+ metavar='FILE NAME',
+ type=str,
+ default=None,
+ help='output file name')
+
+parser.add_argument("--dpi",
+ action="store",
+ dest="dpi",
+ type=float,
+ default=300,
+ help="DPI of the saved file",
+ metavar=" FLOAT")
+
+
+####################################################################
+# main
+####################################################################
+
+def MakePlot(opt):
+
+
+ # halo bins
+ lnMh = np.linspace(np.log10(opt.Mmin),np.log10(opt.Mmax),opt.N)
+ Mh0 = 10**lnMh
+
+ # define the DM models
+ #CDM = satlib.CDM_Sawala_Model(opt.Mmin,opt.Mmax,opt.N)
+ #WDM = satlib.CDM_Forouhar_Model(opt.Mmin,opt.Mmax,opt.N)
+
+ CDM = satlib.GEN_Model(opt.Mmin,opt.Mmax,opt.N,MDM=None,fWDM=0,M0=opt.M0)
+ pt.plot(CDM.mbin,CDM.Nsat,label=r"$\rm{CDM}$")
+
+ Ms = [3,5,7,9]
+
+ for DMmass in Ms:
+ print(DMmass)
+
+ WDM = satlib.GEN_Model(opt.Mmin,opt.Mmax,opt.N,MDM=DMmass,fWDM=1,M0=opt.M0)
+ pt.plot(WDM.mbin,WDM.Nsat,label=r"$%d\,\rm{keV}$"%DMmass)
+
+
+
+
+
+ ###########################
+ # finalize
+ ###########################
+
+ xmin = opt.Mmin
+ xmax = opt.Mmax
+ ymin = 8
+ ymax = 3e2
+
+ xlabel = r"$\rm{Halo\,\,Mass\,\,(M_{\rm halo})}\,\,[M_{\odot}]$"
+ ylabel = r"$dN/dm$"
+
+ pt.SetAxis(xmin,xmax,ymin,ymax,log="xy")
+ pt.xlabel(xlabel,fontsize=pt.labelfont)
+ pt.ylabel(ylabel,fontsize=pt.labelfont)
+ pt.grid(False)
+
+ #pt.title(r"$\rm{%d\,\,galaxies\,\,observed}$"%opt.Ngal,fontsize=pt.labelfont)
+ pt.legend()
+
+
+
+
+
+
+
+if __name__ == '__main__':
+
+ opt = parser.parse_args()
+ files = []
+
+ pt.InitPlot(files, opt)
+ pt.labelfont=20
+
+ # pt.figure(figsize=(8*2,6*2))
+ # pt.figure(dpi=10)
+
+ #fig = pt.gcf()
+ # fig.subplots_adjust(left=0.1)
+ # fig.subplots_adjust(right=1)
+ # fig.subplots_adjust(bottom=0.12)
+ # fig.subplots_adjust(top=0.95)
+ # fig.subplots_adjust(wspace=0.25)
+ # fig.subplots_adjust(hspace=0.02)
+
+ MakePlot(opt)
+ pt.EndPlot(files, opt)
+
diff --git a/Workspace/CumNumOfSat/satComputedNdM.py b/Workspace/CumNumOfSat/satComputedNdM.py
new file mode 100755
index 0000000..4116cd5
--- /dev/null
+++ b/Workspace/CumNumOfSat/satComputedNdM.py
@@ -0,0 +1,304 @@
+#!/usr/bin/env python3
+
+import sys,os,string
+
+import argparse
+import satlib
+import numpy as np
+import Ptools as pt
+import pickle
+from scipy.interpolate import splrep,splev
+
+from scipy.integrate import cumtrapz, trapz
+from classy import Class
+import genmassfct as gmf
+
+####################################################################
+# option parser
+####################################################################
+
+description=""
+epilog =""""""
+
+parser = argparse.ArgumentParser(description=description,epilog=epilog,formatter_class=argparse.RawDescriptionHelpFormatter)
+
+
+
+parser.add_argument("--Mmin",
+ action="store",
+ dest="Mmin",
+ metavar='FLOAT',
+ type=float,
+ default=5e7,
+ help='the min halo mass')
+
+
+parser.add_argument("--Mmax",
+ action="store",
+ dest="Mmax",
+ metavar='FLOAT',
+ type=float,
+ default=1e11,
+ help='the max halo mass')
+
+
+parser.add_argument("--Mh",
+ action="store",
+ dest="Mh",
+ metavar='FLOAT',
+ type=float,
+ default=1e12,
+ help='halo mass')
+
+
+parser.add_argument("--DMmass",
+ action="store",
+ dest="DMmass",
+ metavar='FLOAT',
+ type=float,
+ default=2.0,
+ help='dark matter particle mass')
+
+
+
+parser.add_argument("-o","--outputfile",
+ action="store",
+ type=str,
+ dest="outputfile",
+ default = None,
+ help="output file name")
+
+parser.add_argument("-p",
+ action="store",
+ dest="ps",
+ metavar='FILE NAME',
+ type=str,
+ default=None,
+ help='output file name')
+
+parser.add_argument("--dpi",
+ action="store",
+ dest="dpi",
+ type=float,
+ default=300,
+ help="DPI of the saved file",
+ metavar=" FLOAT")
+
+
+
+parser.add_argument("-N",
+ action="store",
+ dest="N",
+ metavar='INT',
+ type=int,
+ default=1000,
+ help='number of bins')
+
+
+
+
+def ps_class(cosmopars):
+ """
+ Calculate power spectrum with CLASS
+ """
+
+ Om, Ob, As, h0, ns, mWDM, fWDM = cosmopars
+
+
+ if (fWDM>0):
+
+ m_nu = 4.43*mWDM**(4./3)*((Om-Ob)*h0**2/0.1225)**(-1./3)
+
+ CLASSparams = {
+ 'h': h0,
+ 'T_cmb': 2.726,
+ 'Omega_b': Ob,
+ 'Omega_cdm': (1-fWDM)*(Om-Ob),
+ 'Omega_ncdm': fWDM*(Om-Ob),
+ 'N_ur': 3.04,
+ 'N_ncdm': 1,
+ 'm_ncdm': 1000*m_nu,
+ 'T_ncdm': 0.715985,
+ 'n_s': ns,
+ 'A_s': As,
+ 'P_k_max_h/Mpc': 200,
+ 'k_per_decade_for_pk': 10,
+ 'output': 'mPk',
+ 'z_pk': 0.0,
+ 'ncdm_fluid_approximation': 3,
+ }
+ else:
+
+ CLASSparams = {
+ 'h': h0,
+ 'T_cmb': 2.726,
+ 'Omega_b': Ob,
+ 'Omega_cdm': (Om-Ob),
+ 'N_ur': 3.04,
+ 'n_s': ns,
+ 'A_s': As,
+ 'P_k_max_h/Mpc': 200,
+ 'k_per_decade_for_pk': 10,
+ 'output': 'mPk',
+ 'z_pk': 0.0,
+ 'ncdm_fluid_approximation': 1,
+ }
+
+ CLASScosmo = Class()
+ CLASScosmo.set(CLASSparams)
+ CLASScosmo.compute()
+ s8 = CLASScosmo.sigma8()
+ print("s8 = ",s8)
+ k_bin = np.logspace(np.log10(1e-3), np.log10(200), 500) #in [h/Mpc]
+ z_bin = np.array([0])
+ pk_bin = []
+ for i in range(len(k_bin)):
+ pk_bin += [CLASScosmo.pk_lin(k_bin[i]*h0,0)]
+
+ pk_bin = np.array(pk_bin)*h0**3 # [Mpc/h]^3
+
+ CLASScosmo.struct_cleanup()
+ CLASScosmo.empty()
+
+ return k_bin, pk_bin
+
+
+def Nsat_integral(mbin,dNsatdlnm):
+ """
+ Cumulative mass function Nsat(>M)
+ """
+
+ Nsat = trapz(dNsatdlnm/mbin,mbin) - cumtrapz(dNsatdlnm/mbin,mbin,initial=dNsatdlnm[0]/mbin[0])
+
+ return Nsat
+
+
+
+
+
+####################################################################
+# main
+####################################################################
+
+
+
+
+
+
+def MakePlot(opt):
+
+ #Cosmology
+ Om = 0.315
+ Ob = 0.048
+ h0 = 0.681
+ As = 2.07e-9
+ ns = 0.963
+
+ ##################################
+ # CDM
+ ##################################
+
+ mWDM = None # DM mass in keV
+ fWDM = 0.0 # fraction of wdm
+ cosmopars = Om, Ob, As, h0, ns, mWDM, fWDM
+
+ #calculate power spectrum
+ kbin, PSbin = ps_class(cosmopars)
+ body = np.transpose([kbin, PSbin])
+ filename = 'CDM_pk.dat'
+ np.savetxt(filename,body,delimiter='\t')
+
+ #WDM
+ mWDM = opt.DMmass # DM mass in keV
+ fWDM = 1.0 # fraction of wdm
+ cosmopars = Om, Ob, As, h0, ns, mWDM, fWDM
+
+ #calculate power spectrum
+ kbin, PSbin = ps_class(cosmopars)
+ body = np.transpose([kbin, PSbin])
+ filename = 'WDM_pk.dat'
+ np.savetxt(filename,body,delimiter='\t')
+
+
+ #Host halo mass (Msun/h)
+ M0 = 1e12
+
+ #redshift
+ z0 = 0
+
+ #initialise parameters
+ par = gmf.par()
+ par.window.window = "sharpk"
+ par.code.rmin = 0.008
+ par.mf.q = 1.0
+ par.mf.p = 0.3
+ par.mf.c = 2.5
+
+
+ #Calculate stellite function for CDM
+ par.file.psfct = "CDM_pk.dat"
+ mbin_CDM, dNsatdlnM_CDM = gmf.dNsatdlnm(M0,z0,par)
+ Nsat_CDM = Nsat_integral(mbin_CDM,dNsatdlnM_CDM)
+
+
+ #Calculate stellite function for WDM
+ par.file.psfct = "WDM_pk.dat"
+ mbin_WDM, dNsatdlnM_WDM = gmf.dNsatdlnm(M0,z0,par)
+ Nsat_WDM = Nsat_integral(mbin_WDM,dNsatdlnM_WDM)
+
+
+ pt.loglog(mbin_CDM, Nsat_CDM, color='black',ls='-',label="CDM")
+ pt.loglog(mbin_WDM, Nsat_WDM, color='blue' ,ls='-',label="WDM")
+
+ #pt.axis([1e7,4e12,0.0001,1000])
+
+ pt.ylabel(r'Nsat(>M)')
+ pt.xlabel(r'M [M$_{\odot}$/h]')
+
+
+ ########################
+ # add sawala
+
+ Mh0 = M0
+
+ import satlib
+
+ lnMh = np.linspace(np.log10(opt.Mmin),np.log10(opt.Mmax),opt.N)
+ Mh = 10**lnMh
+
+
+ NM_CDM = satlib.CDM_CumulativeMass(Mh)
+ NM_WDM = satlib.fctWDM_CumulativeMass(Mh)
+
+ pt.loglog(Mh,NM_WDM,label="yrWDM")
+ pt.loglog(Mh,NM_CDM,label="yrCDM")
+
+
+ pt.axis([1e7,4e12,0.0001,1000])
+
+ pt.legend()
+
+
+if __name__ == '__main__':
+
+ opt = parser.parse_args()
+ files = []
+
+ pt.InitPlot(files, opt)
+ pt.labelfont=20
+
+ # pt.figure(figsize=(8*2,6*2))
+ # pt.figure(dpi=10)
+
+ #fig = pt.gcf()
+ # fig.subplots_adjust(left=0.1)
+ # fig.subplots_adjust(right=1)
+ # fig.subplots_adjust(bottom=0.12)
+ # fig.subplots_adjust(top=0.95)
+ # fig.subplots_adjust(wspace=0.25)
+ # fig.subplots_adjust(hspace=0.02)
+
+ MakePlot(opt)
+ pt.EndPlot(files, opt)
+
+
diff --git a/Workspace/CumNumOfSat/satlib.py b/Workspace/CumNumOfSat/satlib.py
new file mode 100644
index 0000000..0e990ac
--- /dev/null
+++ b/Workspace/CumNumOfSat/satlib.py
@@ -0,0 +1,644 @@
+
+import numpy as np
+from scipy import special
+from scipy.integrate import quad
+
+
+a = 1.859e9 # to fit Sawala 2017
+b = -1.915 # to fit Sawala 2017
+
+def CDM_DistributionFunction(Mh):
+ '''
+ dN/dM :
+ '''
+ dNdM = a *(Mh)**b
+ return dNdM
+
+
+def CDM_CumulativeMass(Mh):
+ NM = -a*(Mh)**(b+1)/(b+1)
+ return NM
+
+
+
+def WDMCDMRatio(Mhs):
+ """
+ Ratio of the halo mass function of the WDM to CDM (Forouhar 2023)
+ """
+ lnMhs = np.log10(Mhs)
+ f = np.array([0,0.004,0.008,0.025,0.092,0.253,0.396,0.485,0.620,0.746,0.827,0.869,0.873,0.936,1])
+ lnMh = np.array([7,7.176,7.549,7.934,8.367,8.900,9.244,9.457,9.742,10.00,10.35,10.55,11.10,11.38,11.63])
+ fs = np.interp(lnMhs,lnMh,f)
+ return fs
+
+
+# cumulative mass fct from CDM_CumulativeMass
+fctCDM_CumulativeMass = np.vectorize(lambda x: quad( lambda M: CDM_DistributionFunction(M) ,x, 1e12)[0])
+
+
+# cumulative mass fct from CDM_CumulativeMass
+fctWDM_CumulativeMass = np.vectorize(lambda x: quad( lambda M: CDM_DistributionFunction(M)*WDMCDMRatio(M) ,x, 1e12)[0])
+
+
+
+
+
+def GetStellarMassHaloMassRelation(model='rj2018'):
+
+ if model=="rj2018":
+
+ import pickle
+ # read Mh vs Lv
+ f = open("MsvsMh_2018.pkl","rb")
+ Mhbins = pickle.load(f,encoding="latin1")
+ Lvmins = pickle.load(f,encoding="latin1")
+ Lvmaxs = pickle.load(f,encoding="latin1")
+ f.close()
+
+ return Mhbins,Lvmins,Lvmaxs
+
+
+ elif model=="model1":
+
+ Mhbins = np.linspace(7,11,20)
+
+ data = np.loadtxt("sales2022/MsMh_low_1.txt")
+ Mh1 = data[:,0]
+ Msl = data[:,1]
+ Lvmins = np.interp(Mhbins,Mh1,Msl)
+
+ data = np.loadtxt("sales2022/MsMh_high_1.txt")
+ Mh1 = data[:,0]
+ Msu = data[:,1]
+ Lvmaxs = np.interp(Mhbins,Mh1,Msu)
+
+ return 10**Mhbins,10**Lvmins,10**Lvmaxs
+
+
+ elif model=="model2":
+
+ Mhbins = np.linspace(7,11,20)
+
+ data = np.loadtxt("sales2022/MsMh_low_2.txt")
+ Mh1 = data[:,0]
+ Msl = data[:,1]
+ Lvmins = np.interp(Mhbins,Mh1,Msl)
+
+ data = np.loadtxt("sales2022/MsMh_high_2.txt")
+ Mh1 = data[:,0]
+ Msu = data[:,1]
+ Lvmaxs = np.interp(Mhbins,Mh1,Msu)
+
+ return 10**Mhbins,10**Lvmins,10**Lvmaxs
+
+
+ elif model=="model3":
+
+ Mhbins = np.linspace(7,11,20)
+
+ data = np.loadtxt("sales2022/MsMh_low_3.txt")
+ Mh1 = data[:,0]
+ Msl = data[:,1]
+ Lvmins = np.interp(Mhbins,Mh1,Msl)
+
+ data = np.loadtxt("sales2022/MsMh_high_3.txt")
+ Mh1 = data[:,0]
+ Msu = data[:,1]
+ Lvmaxs = np.interp(Mhbins,Mh1,Msu)
+
+ return 10**Mhbins,10**Lvmins,10**Lvmaxs
+
+
+
+ elif model=="model4":
+
+ Mhbins = np.linspace(7,11,20)
+
+ data = np.loadtxt("sales2022/MsMh_low_4.txt")
+ Mh1 = data[:,0]
+ Msl = data[:,1]
+ Lvmins = np.interp(Mhbins,Mh1,Msl)
+
+ data = np.loadtxt("sales2022/MsMh_high_4.txt")
+ Mh1 = data[:,0]
+ Msu = data[:,1]
+ Lvmaxs = np.interp(Mhbins,Mh1,Msu)
+
+ return 10**Mhbins,10**Lvmins,10**Lvmaxs
+
+
+
+
+class DM_Model():
+
+ def __init__(self,Mmin,Mmax,N):
+
+ self.Mmin = Mmin # minimal halo mass
+ self.Mmax = Mmax # maximal halo mass
+ self.N = N # number of mass bins
+
+ # halo mass bins
+ self.Mh = 10**np.linspace(np.log10(self.Mmin),np.log10(self.Mmax),self.N)
+
+
+
+ def DistributionFunction(self,Mh):
+ '''
+ return the dark matter distribution function
+ i.e., dN/dM
+ '''
+ pass
+
+
+ def CumulativeMass(self,Mh):
+ '''
+ return the cumulative halo mass
+ '''
+ pass
+
+
+ def NumberOfHaloes(self):
+ '''
+ return the number of haloes
+ '''
+ return int(self.CumulativeMass(self.Mmin))
+
+
+##############################################
+# CDM from Sawala 2017 fit
+##############################################
+
+class CDM_Sawala_Model(DM_Model):
+
+ def __init__(self,Mmin,Mmax,N):
+
+
+ # do the initialisation
+ super().__init__(Mmin,Mmax,N)
+
+
+ self.a = 1.859e9 # to fit Sawala 2017
+ self.b = -1.915 # to fit Sawala 2017
+
+ # cumulative number of haloes
+ NM = self.CumulativeMass()
+
+ # compute the sampling function
+ self.fctSample = np.vectorize( lambda x: 10**np.interp(x, NM[::-1]/NM.max(), np.log10(self.Mh[::-1])) )
+
+
+
+ def DistributionFunction(self,Mh=None):
+ '''
+ return the dark matter distribution function
+ i.e., dN/dM
+ '''
+ if Mh is None:
+ Mh = self.Mh
+
+ dNdM = self.a *(Mh)**self.b
+ return dNdM
+
+
+ def CumulativeMass(self,Mh=None):
+ '''
+ return the cumulative halo mass
+ '''
+
+ if Mh is None:
+ Mh = self.Mh
+
+ NM = -self.a*(Mh)**(self.b+1)/(self.b+1)
+ return NM
+
+
+
+
+
+##############################################
+# WDM from Sawala 2017 + Forouhar 2023
+##############################################
+
+class CDM_Forouhar_Model(DM_Model):
+
+ def __init__(self,Mmin,Mmax,N):
+
+
+ # do the initialisation
+ super().__init__(Mmin,Mmax,N)
+
+ self.CDM = CDM_Sawala_Model(Mmin,Mmax,N)
+
+ # cumulative mass fct from CDM_CumulativeMass
+ self.fctCumulativeMass = np.vectorize(lambda x: quad( lambda M: self.DistributionFunction(M),x, 1e12)[0])
+
+
+
+ # cumulative number of haloes
+ NM = self.CumulativeMass()
+
+ # compute the sampling function
+ self.fctSample = np.vectorize( lambda x: 10**np.interp(x, NM[::-1]/NM.max(), np.log10(self.Mh[::-1])) )
+
+
+
+
+ def DistributionFunction(self,Mh=None):
+ '''
+ return the dark matter distribution function
+ i.e., dN/dM
+ '''
+ if Mh is None:
+ Mh = self.Mh
+
+ dNdM = self.CDM.DistributionFunction(Mh)*self.WDMCDMRatio(Mh)
+ return dNdM
+
+
+ def CumulativeMass(self,Mh=None):
+ '''
+ return the cumulative halo mass
+ '''
+ if Mh is None:
+ Mh = self.Mh
+
+ return self.fctCumulativeMass(Mh)
+
+
+
+
+ def WDMCDMRatio(self,Mh=None):
+ """
+ Ratio of the halo mass function of the WDM to CDM (Forouhar 2023)
+ """
+ if Mh is None:
+ Mh = self.Mh
+
+ lnMhs = np.log10(Mh)
+ f = np.array([0,0.004,0.008,0.025,0.092,0.253,0.396,0.485,0.620,0.746,0.827,0.869,0.873,0.936,1])
+ lnMh = np.array([7,7.176,7.549,7.934,8.367,8.900,9.244,9.457,9.742,10.00,10.35,10.55,11.10,11.38,11.63])
+ fs = np.interp(lnMhs,lnMh,f)
+ return fs
+
+
+##############################################
+# WDM from Bode, Ostriker & Turok (2001)
+# Viel 2005 (nu updated)
+##############################################
+# Note : here we avoid to use classy to get
+# the power spectrum
+
+from scipy.integrate import cumtrapz, trapz
+import genmassfct as gm
+
+
+class GEN_Model(DM_Model):
+
+ def __init__(self,Mmin,Mmax,N,MDM=2,fWDM=0,M0=1e12,Om=0.315,h0=0.674):
+
+ # do the initialisation
+ super().__init__(Mmin,Mmax,N)
+
+ # unperturbed power spectrum filename
+ filename = './CDM_PS.dat'
+ # temporary file
+ self.filename = '/tmp/powerspectrum_pk.dat'
+
+ k, pk = np.loadtxt(filename, unpack=True, usecols = (0,1))
+ if MDM is not None:
+ Pk = self.Transfer(k,pk,MDM,Om,h0)
+ else:
+ Pk = pk
+
+ # save (needed for )
+ body = np.transpose([k, Pk])
+ np.savetxt(self.filename,body,delimiter='\t')
+
+
+ # initialise parameters
+ par = gmf.par()
+ par.window.window = "sharpk"
+ par.code.rmin = 0.008
+ par.mf.q = 1.0
+ par.mf.p = 0.3
+ par.mf.c = 2.5
+
+ # redshift
+ z0 = 0
+
+ # calculate stellite function
+ par.file.psfct = self.filename
+ self.mbin, dNsatdlnM = gmf.dNsatdlnm(M0,z0,par)
+ self.Nsat = Nsat_integral(self.mbin,dNsatdlnM)
+
+ # cumulative number of haloes
+ NM = self.CumulativeMass()
+
+ # compute the sampling function
+ self.fctSample = np.vectorize( lambda x: 10**np.interp(x, NM[::-1]/NM.max(), np.log10(self.Mh[::-1])) )
+
+
+
+
+
+ def CumulativeMass(self,Mh=None):
+ '''
+ return the cumulative halo mass
+ '''
+ if Mh is None:
+ Mh = self.Mh
+
+ return np.interp(Mh,self.mbin,self.Nsat)
+
+
+
+
+
+
+
+
+
+ def Transfer(self,k,pk,DMmass,OmegaWDM,h):
+ """
+ initially from Bode, Ostriker & Turok (2001)
+ but values from Viel 2005 (nu updated)
+ """
+
+ OmegaWDM = OmegaWDM/0.25
+ h = h/0.7
+
+ nu = 1.12
+ alpha = 0.049*DMmass**(-1.11) * (OmegaWDM)**(0.11) * (h)**(1.22)
+
+
+ T = (1+(alpha*k)**(2*nu))**(-5/nu)
+ pk = pk*T**2
+ return pk
+
+
+
+
+
+
+
+
+
+
+
+
+
+##############################################
+# Generic model (Schneider 2014)
+##############################################
+# Note : deprecated
+
+from scipy.integrate import cumtrapz, trapz
+from classy import Class
+import genmassfct as gmf
+
+
+def ps_class(cosmopars):
+ """
+ Calculate power spectrum with CLASS
+ """
+
+ Om, Ob, As, h0, ns, mWDM, fWDM = cosmopars
+
+
+ if (fWDM>0):
+
+ m_nu = 4.43*mWDM**(4./3)*((Om-Ob)*h0**2/0.1225)**(-1./3)
+
+ CLASSparams = {
+ 'h': h0,
+ 'T_cmb': 2.726,
+ 'Omega_b': Ob,
+ 'Omega_cdm': (1-fWDM)*(Om-Ob),
+ 'Omega_ncdm': fWDM*(Om-Ob),
+ 'N_ur': 3.04,
+ 'N_ncdm': 1,
+ 'm_ncdm': 1000*m_nu,
+ 'T_ncdm': 0.715985,
+ 'n_s': ns,
+ 'A_s': As,
+ 'P_k_max_h/Mpc': 200,
+ 'k_per_decade_for_pk': 10,
+ 'output': 'mPk',
+ 'z_pk': 0.0,
+ 'ncdm_fluid_approximation': 3,
+ }
+ else:
+
+ CLASSparams = {
+ 'h': h0,
+ 'T_cmb': 2.726,
+ 'Omega_b': Ob,
+ 'Omega_cdm': (Om-Ob),
+ 'N_ur': 3.04,
+ 'n_s': ns,
+ 'A_s': As,
+ 'P_k_max_h/Mpc': 200,
+ 'k_per_decade_for_pk': 10,
+ 'output': 'mPk',
+ 'z_pk': 0.0,
+ 'ncdm_fluid_approximation': 1,
+ }
+
+ CLASScosmo = Class()
+ CLASScosmo.set(CLASSparams)
+ CLASScosmo.compute()
+ s8 = CLASScosmo.sigma8()
+ print("s8 = ",s8)
+ k_bin = np.logspace(np.log10(1e-3), np.log10(200), 500) #in [h/Mpc]
+ z_bin = np.array([0])
+ pk_bin = []
+ for i in range(len(k_bin)):
+ pk_bin += [CLASScosmo.pk_lin(k_bin[i]*h0,0)]
+
+ pk_bin = np.array(pk_bin)*h0**3 # [Mpc/h]^3
+
+ CLASScosmo.struct_cleanup()
+ CLASScosmo.empty()
+
+ return k_bin, pk_bin
+
+
+def Nsat_integral(mbin,dNsatdlnm):
+ """
+ Cumulative mass function Nsat(>M)
+ """
+
+ Nsat = trapz(dNsatdlnm/mbin,mbin) - cumtrapz(dNsatdlnm/mbin,mbin,initial=dNsatdlnm[0]/mbin[0])
+
+ return Nsat
+
+
+
+
+
+class oldGEN_Model(DM_Model):
+
+ def __init__(self,Mmin,Mmax,N,MDM=2,fWDM=0,M0=1e12):
+
+ # do the initialisation
+ super().__init__(Mmin,Mmax,N)
+
+ # temporary file
+ self.filename = '/tmp/powerspectrum_pk.dat'
+
+
+ # Cosmologycal parameters
+ Om = 0.315
+ Ob = 0.048
+ h0 = 0.681
+ As = 2.07e-9
+ ns = 0.963
+
+ # redshift
+ z0 = 0
+
+ self.MDM = MDM # dark matter mass in keV
+ self.fWDM = fWDM # warm dark matter fraction
+ self.M0 = M0 # Host halo mass (Msun/h)
+
+ cosmopars = Om, Ob, As, h0, ns, self.MDM, fWDM
+
+
+ # calculate power spectrum
+ kbin, PSbin = ps_class(cosmopars)
+ body = np.transpose([kbin, PSbin])
+ np.savetxt(self.filename,body,delimiter='\t')
+
+
+ # initialise parameters
+ par = gmf.par()
+ par.window.window = "sharpk"
+ par.code.rmin = 0.008
+ par.mf.q = 1.0
+ par.mf.p = 0.3
+ par.mf.c = 2.5
+
+
+ # calculate stellite function
+ par.file.psfct = self.filename
+ self.mbin, dNsatdlnM = gmf.dNsatdlnm(M0,z0,par)
+ self.Nsat = Nsat_integral(self.mbin,dNsatdlnM)
+
+
+
+ # cumulative number of haloes
+ NM = self.CumulativeMass()
+
+ # compute the sampling function
+ self.fctSample = np.vectorize( lambda x: 10**np.interp(x, NM[::-1]/NM.max(), np.log10(self.Mh[::-1])) )
+
+
+
+
+
+ def CumulativeMass(self,Mh=None):
+ '''
+ return the cumulative halo mass
+ '''
+ if Mh is None:
+ Mh = self.Mh
+
+ return np.interp(Mh,self.mbin,self.Nsat)
+
+
+
+
+def Msol2Mag(mass,Age=12,MH=-2,filter="F475X"):
+ '''
+ mass : in Msol
+ Age : in Gyr
+ MH : in [M/H]
+ '''
+
+ import stars_class
+
+ mass = np.array([mass])
+ Age = np.array([Age])
+ MH = np.array([MH])
+
+
+ ###################################
+ # compute Magnitudes
+ ###################################
+
+ # get the number of stars in each mass bin
+ Nstars = stars_class.Stars_fun(mass,None,None, 'normed_3slope')
+
+
+
+ ##############################
+ # F475X magnitude
+
+ if filter=="F475X":
+ M = stars_class.HST475X_fun(None,Age,MH)
+ # convert to flux (ignore the zero point)
+ F = 10**(-M/2.5)
+ # sum the contribution of the mass bins
+ F = np.sum(F*Nstars, axis=0)
+ # compute the absolute magnitude in each pixel (as before we ignore the zero point)
+ M = - 2.5*np.log10(F)
+ return M[0]
+
+ ##############################
+ # VIS Euclid magnitude
+
+ if filter=="VISeuclid":
+ M = stars_class.VISeuclid_fun(None,Age,MH)
+ # convert to flux (ignore the zero point)
+ F = 10**(-M/2.5)
+ # sum the contribution of the mass bins
+ F = np.sum(F*Nstars, axis=0)
+ # compute the absolute magnitude in each pixel (as before we ignore the zero point)
+ M = - 2.5*np.log10(F)
+ return M[0]
+
+ ##############################
+ # Y Euclid magnitude
+
+ if filter=="Yeuclid":
+ M = stars_class.Yeuclid_fun(None,Age,MH)
+ # convert to flux (ignore the zero point)
+ F = 10**(-M/2.5)
+ # sum the contribution of the mass bins
+ F = np.sum(F*Nstars, axis=0)
+ # compute the absolute magnitude in each pixel (as before we ignore the zero point)
+ M = - 2.5*np.log10(F)
+ return M[0]
+
+ ##############################
+ # J Euclid magnitude
+
+ if filter=="Jeuclid":
+ M = stars_class.Jeuclid_fun(None,Age,MH)
+ # convert to flux (ignore the zero point)
+ F = 10**(-M/2.5)
+ # sum the contribution of the mass bins
+ F = np.sum(F*Nstars, axis=0)
+ # compute the absolute magnitude in each pixel (as before we ignore the zero point)
+ M = - 2.5*np.log10(F)
+ return M[0]
+
+
+ ##############################
+ # Vasdekhis
+
+ if filter=="V":
+ from pNbody.SSP import libvazdekis
+ LObj = libvazdekis.VazdekisLuminosities("/home/revaz/.pNbody/opt/SSP/vazdekis_kb_mu1.3.txt",band="V")
+ LObj.ExtrapolateMatrix(order=1,s=0)
+ LObj.CreateInterpolator()
+ LObj.Extrapolate2DMatrix()
+
+ # luminosity
+ Lv = LObj.Luminosities(MH,Age)*mass
+
+ # Magnitude
+ Mag_Vega = 4.81
+ Mv = Mag_Vega - 2.5*np.log10(Lv)
+ return Mv
+
diff --git a/Workspace/CumNumOfSat/stars_class.py b/Workspace/CumNumOfSat/stars_class.py
new file mode 120000
index 0000000..b7b3b64
--- /dev/null
+++ b/Workspace/CumNumOfSat/stars_class.py
@@ -0,0 +1 @@
+../SurfaceBrightness/stars_class.py
\ No newline at end of file
diff --git a/Workspace/CumNumOfSat/sto.satComputeNCumvsLvWithErrors.py b/Workspace/CumNumOfSat/sto.satComputeNCumvsLvWithErrors.py
new file mode 100755
index 0000000..f74b730
--- /dev/null
+++ b/Workspace/CumNumOfSat/sto.satComputeNCumvsLvWithErrors.py
@@ -0,0 +1,390 @@
+#!/usr/bin/env python3
+
+import sys,os,string
+
+import argparse
+import satlib
+import numpy as np
+import Ptools as pt
+import pickle
+from scipy.interpolate import splrep,splev
+from tqdm import tqdm
+
+####################################################################
+# option parser
+####################################################################
+
+description=""
+epilog =""""""
+
+parser = argparse.ArgumentParser(description=description,epilog=epilog,formatter_class=argparse.RawDescriptionHelpFormatter)
+
+
+
+parser.add_argument("--Mmin",
+ action="store",
+ dest="Mmin",
+ metavar='FLOAT',
+ type=float,
+ default=5e7,
+ help='the min halo mass')
+
+
+parser.add_argument("--Mmax",
+ action="store",
+ dest="Mmax",
+ metavar='FLOAT',
+ type=float,
+ default=1e11,
+ help='the max halo mass')
+
+parser.add_argument("-N",
+ action="store",
+ dest="N",
+ metavar='INT',
+ type=int,
+ default=1000,
+ help='number of bins')
+
+
+parser.add_argument("--Ngal",
+ action="store",
+ dest="Ngal",
+ metavar='INT',
+ type=int,
+ default=100,
+ help='number of galaxies')
+
+
+parser.add_argument("--Nrealisations",
+ action="store",
+ dest="Nrealisations",
+ metavar='INT',
+ type=int,
+ default=100,
+ help='number of realizations')
+
+
+
+parser.add_argument("--LvvsMh_model",
+ action="store",
+ type=str,
+ dest="LvvsMh_model",
+ default = 'rj2018',
+ help="Lv Mh model")
+
+
+parser.add_argument("-p",
+ action="store",
+ dest="ps",
+ metavar='FILE NAME',
+ type=str,
+ default=None,
+ help='output file name')
+
+parser.add_argument("--dpi",
+ action="store",
+ dest="dpi",
+ type=float,
+ default=300,
+ help="DPI of the saved file",
+ metavar=" FLOAT")
+
+parser.add_argument("--data",
+ action="store_true",
+ dest="data",
+ default=False,
+ help='add data')
+
+
+####################################################################
+# main
+####################################################################
+
+
+def MakePlot(opt):
+
+
+ # get Mh vs Lv relation
+ Mhbins,Lvmins,Lvmaxs = satlib.GetStellarMassHaloMassRelation(model=opt.LvvsMh_model)
+
+
+
+ # halo bins
+ lnMh = np.linspace(np.log10(opt.Mmin),np.log10(opt.Mmax),opt.N)
+ Mh0 = 10**lnMh
+
+ # cumulative number of haloes
+ NM_CDM = satlib.CDM_CumulativeMass(Mh0)
+ NM_WDM = satlib.fctWDM_CumulativeMass(Mh0)
+
+
+ # define the CDM sampling function
+ fct_CDM_Sample = np.vectorize( lambda x: 10**np.interp(x, NM_CDM[::-1]/NM_CDM.max(), np.log10(Mh0[::-1])) )
+ maxCDM = (NM_CDM/NM_CDM.max()).min()
+
+ # define the WDM sampling function
+ fct_WDM_Sample = np.vectorize( lambda x: 10**np.interp(x, NM_WDM[::-1]/NM_WDM.max(), np.log10(Mh0[::-1])) )
+ maxWDM = (NM_WDM/NM_WDM.max()).min()
+
+
+ Nh_CDM = int(satlib.CDM_CumulativeMass(opt.Mmin))
+ Nh_WDM = int(satlib.fctWDM_CumulativeMass(opt.Mmin))
+
+
+
+ Nbins = 100
+ Nsats_CDM = np.zeros((opt.Ngal,Nbins-1))
+ Nsats_WDM = np.zeros((opt.Ngal,Nbins-1))
+
+
+ Ns_CDM_realisations = np.zeros((opt.Nrealisations,Nbins-1))
+ Ns_WDM_realisations = np.zeros((opt.Nrealisations,Nbins-1))
+
+ for realisation in tqdm(range(opt.Nrealisations)):
+
+ for j in range(opt.Ngal):
+
+ #print(realisation,j)
+
+ #################################################
+ # generate N haloes for the CDM
+ #################################################
+ Mhs = fct_CDM_Sample(np.random.random(Nh_CDM))
+
+ ###################
+ # loop over haloes
+ logLv = np.zeros(len(Mhs))
+
+ for i,Mh in enumerate(Mhs):
+ if Mh < 1e8:
+ continue
+
+ # find the nearest values in Mbins
+ # and set a Luminosity
+
+ idx = np.argmin( np.fabs( Mh-Mhbins ) )
+
+ logLvmins = np.log10(Lvmins[idx])
+ logLvmaxs = np.log10(Lvmaxs[idx])
+
+ logLv[i] = np.random.uniform(logLvmins,logLvmaxs)
+
+
+ ######################
+ # cumulative
+
+ bins = np.linspace(2,12,Nbins)
+ Ns,Lv = np.histogram(logLv,bins)
+
+ # now, need to cumulate
+ # invert vector first and reinvert after
+ Nsat = np.add.accumulate(Ns[-np.arange(1,len(Ns)+1)])
+ Nsat = Nsat[-np.arange(1,len(Nsat)+1)]
+ Lv = Lv[1:]
+ # add
+ Nsats_CDM[j] = Nsat
+
+
+
+
+ #################################################
+ # generate N haloes for the WDM
+ #################################################
+ Mhs = fct_WDM_Sample(np.random.random(Nh_WDM))
+
+
+ ###################
+ # loop over haloes
+ logLv = np.zeros(len(Mhs))
+
+ for i,Mh in enumerate(Mhs):
+ if Mh < 1e8:
+ continue
+
+ # find the nearest values in Mbins
+ # and set a Luminosity
+
+ idx = np.argmin( np.fabs( Mh-Mhbins ) )
+
+ logLvmins = np.log10(Lvmins[idx])
+ logLvmaxs = np.log10(Lvmaxs[idx])
+
+ logLv[i] = np.random.uniform(logLvmins,logLvmaxs)
+
+
+ ######################
+ # cumulative
+
+ bins = np.linspace(2,12,Nbins)
+ Ns,Lv = np.histogram(logLv,bins)
+
+ # now, need to cumulate
+ # invert vector first and reinvert after
+ Nsat = np.add.accumulate(Ns[-np.arange(1,len(Ns)+1)])
+ Nsat = Nsat[-np.arange(1,len(Nsat)+1)]
+ Lv = Lv[1:]
+ # add
+ Nsats_WDM[j] = Nsat
+
+
+ # plot individual galaxies
+ #pt.plot(10**Lv,Nsats_CDM[j],'blue',alpha=0.5)
+ #pt.plot(10**Lv,Nsats_WDM[j],'red' ,alpha=0.5)
+
+
+
+
+ Ns_CDM_mean = Nsats_CDM.mean(axis=0)
+ Ns_WDM_mean = Nsats_WDM.mean(axis=0)
+
+ Ns_CDM_realisations[realisation] = Ns_CDM_mean
+ Ns_WDM_realisations[realisation] = Ns_WDM_mean
+
+ # plot means
+ #pt.plot(10**Lv,Ns_CDM_mean,lw=1,alpha=0.5, c='blue')
+ #pt.plot(10**Lv,Ns_WDM_mean,lw=1,alpha=0.5, c='red')
+
+
+
+
+
+ Ns_CDM_realisations_mean = Ns_CDM_realisations.mean(axis=0)
+ Ns_CDM_realisations_std = Ns_CDM_realisations.std(axis=0)
+
+ Ns_CDM_meanp = Ns_CDM_realisations_mean + 3*Ns_CDM_realisations_std
+ Ns_CDM_meanm = Ns_CDM_realisations_mean - 3*Ns_CDM_realisations_std
+
+
+ Ns_WDM_realisations_mean = Ns_WDM_realisations.mean(axis=0)
+ Ns_WDM_realisations_std = Ns_WDM_realisations.std(axis=0)
+
+ Ns_WDM_meanp = Ns_WDM_realisations_mean + 3*Ns_WDM_realisations_std
+ Ns_WDM_meanm = Ns_WDM_realisations_mean - 3*Ns_WDM_realisations_std
+
+
+
+ ax = pt.gca()
+ ax.fill_between(10**Lv,Ns_CDM_meanp,Ns_CDM_meanm,facecolor='blue',alpha=0.5,label=r"$\textrm{CDM}$")
+ ax.fill_between(10**Lv,Ns_WDM_meanp,Ns_WDM_meanm,facecolor='red',alpha=0.5,label=r"$\textrm{WDM}$")
+
+
+
+
+
+ if opt.data:
+
+ ################################################################################
+ # add data from Nadler, Drlica etc.
+
+ ax = pt.gca()
+ s = 0.0
+ k = 1
+
+ def read_data(f):
+ data = np.loadtxt(f,delimiter=',')
+ Mv = data[:,0]
+ N = data[:,1]
+ Lv = pow(10, (4.74 - Mv) / 2.5)
+ return Lv, N
+
+
+ ###################
+ # Nadler
+
+ Lvp, Np = read_data("data/Nadler_rawt1.txt")
+ Lvm, Nm = read_data("data/Nadler_rawb1.txt")
+
+ ap = splrep(Lvp,Np,s=s,k=k)
+ am = splrep(Lvm,Nm,s=s,k=k)
+
+ Np= splev(Lvp,ap)
+ Nm= splev(Lvp,am)
+
+ #pt.plot(Lvp,Np,'k-')
+ #pt.plot(Lvm,Nm,'k-')
+ ax.fill_between(Lvp,Np,Nm,facecolor='green',alpha=0.05,label=r"$\textrm{Nadler\,\,et\,\,al.\,\,2018a}$")
+
+
+ Lvp, Np = read_data("data/Nadler_rawt2.txt")
+ Lvm, Nm = read_data("data/Nadler_rawb2.txt")
+
+ ap = splrep(Lvp,Np,s=s,k=k)
+ am = splrep(Lvm,Nm,s=s,k=k)
+
+ Np= splev(Lvp,ap)
+ Nm= splev(Lvp,am)
+
+ #pt.plot(Lvp,Np,'k-')
+ #pt.plot(Lvm,Nm,'k-')
+ ax.fill_between(Lvp,Np,Nm,facecolor='green',alpha=0.05)
+
+
+ ###################
+ # Drilca
+
+ Lv, N = read_data("data/Drlica_raw_weighted.txt")
+ pt.plot(Lv,N,'k-',label=r"$\textrm{DES+PS1,\,\,weighted\,\,(Drlica-Wagner\,\,et\,\,al.\,\,2020)}$",alpha=0.5)
+
+ Lv, N = read_data("data/Drlica_raw_detected.txt")
+ pt.plot(Lv,N,'k--',label=r"$\textrm{DES+PS1,\,\,detected\,\,(Drlica-Wagner\,\,et\,\,al.\,\,2020)}$",alpha=0.5)
+
+ Lv, N = read_data("data/Drlica_raw_all.txt")
+ pt.plot(Lv,N,'r-',label=r"$\textrm{All\,\,known\,\,satellites\,\,(Drlica-Wagner\,\,et\,\,al.\,\,2020)}$",alpha=0.5)
+
+
+
+
+ ###########################
+ # finalize
+ ###########################
+
+ xmin = 5e2
+ xmax = 5e9
+ ymin = 1
+ ymax = 500
+
+
+
+ xlabel = r"$\rm{V-band\,\,Luminosity}$"
+ ylabel = r"$\rm{Cumulative\,\,number\,\,of\,\,galaxies}\,\,(D<300\,\rm{kpc})$"
+
+ pt.SetAxis(xmin,xmax,ymin,ymax,log="xy")
+ pt.xlabel(xlabel,fontsize=pt.labelfont)
+ pt.ylabel(ylabel,fontsize=pt.labelfont)
+ pt.grid(False)
+ pt.legend()
+
+ pt.title(r"$\rm{%d\,\,galaxies\,\,observed\,\,+\,\,model=%s}$"%(opt.Ngal,opt.LvvsMh_model),fontsize=pt.labelfont)
+
+ pt.plot([1e5,1e5],[0.1,1000],c='k',ls=':')
+
+
+
+
+
+if __name__ == '__main__':
+
+ opt = parser.parse_args()
+ files = []
+
+ pt.InitPlot(files, opt)
+ pt.labelfont=20
+
+ # pt.figure(figsize=(8*2,6*2))
+ # pt.figure(dpi=10)
+
+ #fig = pt.gcf()
+ # fig.subplots_adjust(left=0.1)
+ # fig.subplots_adjust(right=1)
+ # fig.subplots_adjust(bottom=0.12)
+ # fig.subplots_adjust(top=0.95)
+ # fig.subplots_adjust(wspace=0.25)
+ # fig.subplots_adjust(hspace=0.02)
+
+ MakePlot(opt)
+ pt.EndPlot(files, opt)
+
+
+
+

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