#############################################################################
#Filtrations and persitence computation 
#############################################################################

#Currently, the function to assign a filtration value to a simplex that is 
# already in the filtration has not been included in the Python version of 
#Gudhi. It will be in the next release (but it is already existing in the C++
#library. A trick to overcome this issue is the following:
    
st2 = gd.SimplexTree() #The new filtered complex
L = st.get_filtration()
for splx in L: 
    #We assign to each simplex its dimension as filtration value
    st2.insert(splx[0],filtration=len(splx[0])-1.0)
    
L = st2.get_filtration()
for splx in L:
    print(splx)

#To compute the persistence diagram of the filtered simplex
st2.initialize_filtration()
diag2=st2.persistence()
print(diag2)
#To plot a persistence diagram
gd.plot_persistence_diagram(diag2)
gd.plot_persistence_barcode(diag2)

#To compute bottleneck distance between diagrams
st3 = gd.SimplexTree()
st3.insert([0,1],filtration=0.0)
st3.insert([1,2],filtration=0.1)
st3.insert([2,0],filtration=0.2)
st3.insert([0,1,2],filtration=0.5)
st3.set_dimension(2)
st3.initialize_filtration()
diag3 = st3.persistence()
gd.plot_persistence_diagram(diag3)


diag2_0 = st2.persistence_intervals_in_dimension(0)
diag3_0 = st3.persistence_intervals_in_dimension(0)
dB0 = gd.bottleneck_distance(diag2_0,diag3_0)



diag2_1 = st2.persistence_intervals_in_dimension(1)
diag3_1 = st3.persistence_intervals_in_dimension(1)
dB1 = gd.bottleneck_distance(diag2_1,diag3_1)