In [1]:

    

%pylab inline


    

    




Populating the interactive namespace from numpy and matplotlib

In [2]:

    

r = np.load("result_aida42159.npy")
# first dimension thin disk normalisation
x = np.linspace(0.6,1.0,41)
# second dimension thick disk normalisation
y = np.array([1.0,1.15,1.3,1.45,1.6,1.75,1.9,2.05])
# third dimension count parameter
value = ["stellardiskmass","wddiskmass","totalcount","starcounts","likelihood","diffplus","diffminus"]


    

In [3]:

    

for i,item in enumerate(value):
    plt.imshow(r[:,:,i],extent=(y.min(), y.max(), x.max(), x.min()), cmap=cm.gist_rainbow, aspect = 'auto')
    plt.colorbar(label =item)
    plt.gca().invert_yaxis()
    plt.xlabel("thick disk normalisation")
    plt.ylabel("thin disk normalisation")
    plt.show()
    plt.clf()
    plt.close()
plt.imshow(r[:,:,5]+r[:,:,6],extent=(y.min(), y.max(), x.max(), x.min()), cmap=cm.gist_rainbow, aspect = 'auto')
plt.colorbar(label ="total difference in numbers")
plt.gca().invert_yaxis()
plt.xlabel("thick disk normalisation")
plt.ylabel("thin disk normalisation")
plt.show()


    

In [8]:

    

r = np.load("result_extended.npy")
# first dimension thin disk normalisation
x = np.linspace(0.75,1.2,16)
# second dimension thick disk normalisation
y = np.linspace(0.5,1.0,16)
# third dimension count parameter
value = ["stellardiskmass","wddiskmass","totalcount","starcounts","likelihood","diffplus","diffminus"]


    

In [9]:

    

for i,item in enumerate(value):
    plt.imshow(r[:,:,i],extent=(y.min(), y.max(), x.max(), x.min()), cmap=cm.gist_rainbow, aspect = 'auto')
    plt.colorbar(label =item)
    plt.gca().invert_yaxis()
    plt.xlabel("thick disk normalisation")
    plt.ylabel("thin disk normalisation")
    plt.show()
    plt.clf()
    plt.close()
plt.imshow(r[:,:,5]+r[:,:,6],extent=(y.min(), y.max(), x.max(), x.min()), cmap=cm.gist_rainbow, aspect = 'auto')
plt.colorbar(label ="total difference in numbers")
plt.gca().invert_yaxis()
plt.xlabel("thick disk normalisation")
plt.ylabel("thin disk normalisation")
plt.show()


    

In [ ]:

    

# in the end we took: thin disk normalisation == 0.9, thick dist normalisation  = 0.8