In [2]:

    

%pylab inline


    

    




Populating the interactive namespace from numpy and matplotlib

In [15]:

    

beta_values = array([1,5,10,15,20.25])
RSD = 0
mock_ids = arange(27)
data_path="../data/skeleton/"
alpha_n = empty(0)
alpha_l = empty(0)
for mock_id in mock_ids:
    n_edges = empty((0))
    l_edges_median = empty((0))
    for beta_value in beta_values:
        filename="edge_xyz_BSkeleton_sdss3_%02d_RSD_%d_OM_0.30_OL_0.70_MASS_5.0E+13_FRAC_0.0100_BETA_%d.dat"%(mock_id, RSD,beta_value)
        edge_data = loadtxt(data_path+filename)
        
        l_edges = sqrt(edge_data[:,0]**2 + edge_data[:,1]**2 + edge_data[:,2]**2)
        l_median = median(l_edges)
        l_edges_median = append(l_edges_median,l_median)
        
        n_edge = shape(edge_data)[0]
        n_edges = append(n_edges,n_edge)
      #  print n_edge
    
    p_l = polyfit(log10(beta_values), log10(l_edges_median),1)
    p_n = polyfit(log10(beta_values), log10(n_edges),1)

    alpha_n = append(alpha_n, p_n[0])
    alpha_l = append(alpha_l, p_l[0])
scatter(alpha_n, alpha_l)
print "alpha_n", average(alpha_n), std(alpha_n)
print "alpha_l", average(alpha_l), std(alpha_l)

#    scatter(log10(beta_values), log10(n_edges))
#    beta_line = linspace(0.5,30,100)
#    plot(log10(beta_line), p[0]*log10(beta_line)+p[1])


    

    




alpha_n -1.06663865175 0.0117296030318
alpha_l -0.38738977401 0.00627045816823
[[ 1.03846154  1.03846154]
 [ 1.03846154  1.03846154]]






    

In [21]:

    

beta_values = array([1,5,10,15,20.25])
RSD = 0
mock_ids = arange(27)
data_path="../data/skeleton/"
alpha_n = empty(0)
alpha_l = empty(0)
for mock_id in mock_ids:
    n_edges = empty((0))
    l_edges_median = empty((0))
    for beta_value in beta_values:
        filename="edge_xyz_BSkeleton_sdss3_%02d_RSD_%d_OM_0.30_OL_0.70_MASS_1.0E+14_FRAC_0.0500_BETA_%d.dat"%(mock_id, RSD,beta_value)
        edge_data = loadtxt(data_path+filename)
        
        l_edges = sqrt(edge_data[:,0]**2 + edge_data[:,1]**2 + edge_data[:,2]**2)
        l_median = median(l_edges)
        l_edges_median = append(l_edges_median,l_median)
        
        n_edge = shape(edge_data)[0]
        n_edges = append(n_edges,n_edge)
      #  print n_edge
    
    p_l = polyfit(log10(beta_values), log10(l_edges_median),1)
    p_n = polyfit(log10(beta_values), log10(n_edges),1)

    alpha_n = append(alpha_n, p_n[0])
    alpha_l = append(alpha_l, p_l[0])
scatter(alpha_n, alpha_l)
print "alpha_n", average(alpha_n), std(alpha_n)
print "alpha_l", average(alpha_l), std(alpha_l)


    

    




alpha_n -1.03332655357 0.0108482423606
alpha_l -0.402527870676 0.00494578220126






    

In [23]:

    

beta_values = array([1,5,10,15,20.25])
RSD = 0
mock_ids = arange(20)
data_path="../data/skeleton/"
alpha_n = empty(0)
alpha_l = empty(0)
for mock_id in mock_ids:
    n_edges = empty((0))
    l_edges_median = empty((0))
    for beta_value in beta_values:
        filename="edge_xyz_BSkeleton_sdss3_%02d_RSD_%d_OM_0.30_OL_0.70_MASS_1.0E+13_FRAC_0.0020_BETA_%d.dat"%(mock_id, RSD,beta_value)
        edge_data = loadtxt(data_path+filename)
        
        l_edges = sqrt(edge_data[:,0]**2 + edge_data[:,1]**2 + edge_data[:,2]**2)
        l_median = median(l_edges)
        l_edges_median = append(l_edges_median,l_median)
        
        n_edge = shape(edge_data)[0]
        n_edges = append(n_edges,n_edge)
      #  print n_edge
    
    p_l = polyfit(log10(beta_values), log10(l_edges_median),1)
    p_n = polyfit(log10(beta_values), log10(n_edges),1)

    alpha_n = append(alpha_n, p_n[0])
    alpha_l = append(alpha_l, p_l[0])
scatter(alpha_n, alpha_l)
print "alpha_n", average(alpha_n), std(alpha_n)
print "alpha_l", average(alpha_l), std(alpha_l)

#    scatter(log10(beta_values), log10(n_edges))
#    beta_line = linspace(0.5,30,100)
#    plot(log10(beta_line), p[0]*log10(beta_line)+p[1])


    

    




alpha_n -1.07756302604 0.00907561505456
alpha_l -0.377715179104 0.00430180472571






    

In [86]:

    

beta_values = array([1,5,10,15,20,25,30])
RSD = 0
mock_ids = [0,1,2,3,4]
data_path="../data/skeleton/"

for mock_id in mock_ids:
    for beta_value in beta_values:
        filename="edge_xyz_BSkeleton_sdss3_%02d_RSD_%d_OM_0.30_OL_0.70_MASS_5.0E+13_FRAC_0.0100_BETA_%d.dat"%(mock_id, RSD,beta_value)
        edge_data = loadtxt(data_path+filename)
       
        print l_median
    
    print p
    scatter(log10(beta_values), log10(l_edges_median))
    beta_line = linspace(0.5,30,100)
    plot(log10(beta_line), p[0]*log10(beta_line)+p[1])


    

    




147.220619162
76.8929370483
59.9619773598
51.2858255458
46.45081669
43.0405011663
39.6651660305
[-0.38228245  2.16236289]
147.40084115
77.6836714715
61.029163989
53.4442968391
48.03720091
44.0307142717
40.3303882961
[-0.37439898  2.16331124]
146.927751832
76.6638807188
59.7200601996
51.6111124837
46.3239371776
42.8324467152
39.7720850797
[-0.38157827  2.16118305]
149.868530159
79.668851842
59.5381202047
50.1285729789
46.2510042407
43.4052697827
41.1214801041
[-0.38601181  2.17006286]
148.146254974
78.0147346346
60.2871534036
50.8368258723
46.0435994036
42.7932822473
40.0376966144
[-0.38574723  2.16660672]






    

In [65]:

    

beta_values = array([1,5,10,15,20,25,30])
RSD = 0
mock_id = 0
data_path="../data/skeleton/"

n_edges = empty((0))
for beta_value in beta_values:
    filename="edge_xyz_BSkeleton_sdss3_%02d_RSD_%d_OM_0.30_OL_0.70_MASS_1.0E+13_FRAC_0.0020_BETA_%d.dat"%(mock_id, RSD,beta_value)
    edge_data = loadtxt(data_path+filename)
    n_edge = shape(edge_data)[0]
    n_edges = append(n_edges,n_edge)
    print n_edge


    

    




36187
5633
2780
1879
1458
1183
992

In [66]:

    

p = polyfit(log10(beta_values), log10(n_edges),1)
print p
scatter(log10(beta_values), log10(n_edges))
beta_line = linspace(0.5,30,100)
plot(log10(beta_line), p[0]*log10(beta_line)+p[1])


    

    




[-1.05356456  4.52705175]






    
Out[66]:





[<matplotlib.lines.Line2D at 0x718bd50>]






    

In [67]:

    

beta_values = array([1,5,10,15,20,25,30])
RSD = 0
mock_id = 0
data_path="../data/skeleton/"

n_edges = empty((0))
for beta_value in beta_values:
    filename="edge_xyz_BSkeleton_sdss3_%02d_RSD_%d_OM_0.30_OL_0.70_MASS_1.0E+14_FRAC_0.0500_BETA_%d.dat"%(mock_id, RSD,beta_value)
    edge_data = loadtxt(data_path+filename)
    n_edge = shape(edge_data)[0]
    n_edges = append(n_edges,n_edge)
    print n_edge


    

    




27832
4494
2385
1654
1318
1096
965

In [68]:

    

p = polyfit(log10(beta_values), log10(n_edges),1)
print p
scatter(log10(beta_values), log10(n_edges))
beta_line = linspace(0.5,30,100)
plot(log10(beta_line), p[0]*log10(beta_line)+p[1])


    

    




[-0.98800135  4.40057183]






    
Out[68]:





[<matplotlib.lines.Line2D at 0x7182d10>]






    

In [69]:

    

beta_values = array([1,5,10,15,20,25,30])
RSD = 0
mock_id = 0
data_path="../data/skeleton/"

n_edges = empty((0))
for beta_value in beta_values:
    filename="edge_xyz_BSkeleton_sdss3_%02d_RSD_%d_OM_0.30_OL_0.70_MASS_5.0E+13_FRAC_0.0200_BETA_%d.dat"%(mock_id, RSD,beta_value)
    edge_data = loadtxt(data_path+filename)
    n_edge = shape(edge_data)[0]
    n_edges = append(n_edges,n_edge)
    print n_edge


    

    




48265
7364
3831
2666
2084
1721
1475

In [70]:

    

p = polyfit(log10(beta_values), log10(n_edges),1)
print p
scatter(log10(beta_values), log10(n_edges))
beta_line = linspace(0.5,30,100)
plot(log10(beta_line), p[0]*log10(beta_line)+p[1])


    

    




[-1.02048856  4.6398585 ]






    
Out[70]:





[<matplotlib.lines.Line2D at 0x8089cd0>]






    

In [59]:

    

beta_values = array([1,5,10,15,20,25,30])
RSD = 0
mock_id = 1
data_path="../data/skeleton/"

n_edges = empty((0))
for beta_value in beta_values:
    filename="edge_xyz_BSkeleton_sdss3_%02d_RSD_%d_OM_0.30_OL_0.70_MASS_5.0E+13_FRAC_0.0100_BETA_%d.dat"%(mock_id, RSD,beta_value)
    edge_data = loadtxt(data_path+filename)
    n_edge = shape(edge_data)[0]
    n_edges = append(n_edges,n_edge)
    print n_edge


    

    




22926
3545
1773
1192
903
727
626

In [60]:

    

p = polyfit(log10(beta_values), log10(n_edges),1)
print p
scatter(log10(beta_values), log10(n_edges))
beta_line = linspace(0.5,30,100)
plot(log10(beta_line), p[0]*log10(beta_line)+p[1])


    

    




[-1.05850456  4.3303433 ]






    
Out[60]:





[<matplotlib.lines.Line2D at 0x7824dd0>]






    

In [61]:

    

beta_values = array([1,5,10,15,20,25,30])
RSD = 0
mock_id = 1
data_path="../data/skeleton/"

n_edges = empty((0))
for beta_value in beta_values:
    filename="edge_xyz_BSkeleton_sdss3_%02d_RSD_%d_OM_0.30_OL_0.70_MASS_1.0E+14_FRAC_0.0500_BETA_%d.dat"%(mock_id, RSD,beta_value)
    edge_data = loadtxt(data_path+filename)
    n_edge = shape(edge_data)[0]
    n_edges = append(n_edges,n_edge)
    print n_edge


    

    




28031
4364
2302
1608
1259
1031
893

In [62]:

    

p = polyfit(log10(beta_values), log10(n_edges),1)
print p
scatter(log10(beta_values), log10(n_edges))
beta_line = linspace(0.5,30,100)
plot(log10(beta_line), p[0]*log10(beta_line)+p[1])


    

    




[-1.00876429  4.40471449]






    
Out[62]:





[<matplotlib.lines.Line2D at 0x736edd0>]






    

In [ ]: