<jupyter_start><jupyter_text>Orthogonal cuts<jupyter_code>"""
Main function
Generate all configurations from combinations functions 
"""

l = generate_binary(NCcell_x*NCcell_y, [])
listBinary = findCombinations_rs_yxy_wo_detached(l, NCcell_x, NCcell_y,12)

start = 0
end  = len(listBinary)

listAll =[]
listDensity=[]
data_base=[]
#careful with this '4'! 
for rd in range(start, end, 4):
    
    #file name
    filename = "geo.kirigami_d0.20_sAC_orthgonal"
    
    stringsBinary = listBinary[rd]
    cutConfigurations = toArray(stringsBinary)

    
    #create genome_ID in string format
    genome_ID = toString(cutConfigurations)  
    
    #create PRISTINE inner region
    inner = generateInnerCell(NCcell_x, NCcell_y, ncell_x, ncell_y)
    #cut inner region
    inner_wCuts = makeCutsonCell(cutConfigurations, inner, NCcell_x, NCcell_y, ncell_x, ncell_y)
    #add edges with not cuts for loading in LAMMPS simulations 
    kirigami = addEdges(inner_wCuts, ncell_x, ncell_y)

    #create graphene lattice with cuts
    la = generateLattice(kirigami, NCcell_x, NCcell_y, ncell_x, ncell_y, cell_x, cell_y, cell_z, Lx, Ly, Lz)
    
    #get boundary 
    xhi, xlo, yhi, ylo, zhi, zlo = getSize(la)
    listAll.append(la)
    
    #UNFLAG this to save LAMMPS input output files 
    #writeInput(rd//4, la, filename)
"""
Function to visualize kirigami
"""
import matplotlib.pyplot as plt

fig = plt.figure()
#sns.set_context("paper", font_scale=2, rc={"lines.linewidth": 2.5})


#for j in range(len(listAll)):
for j in range(0, 10):
    #j=0
    listAtoms = listAll[j]
    X = np.zeros(len(listAtoms))
    Y = np.zeros(len(listAtoms))
    Z = np.zeros(len(listAtoms))
    for i in  range(len(listAtoms)):
        X[i] = listAtoms[i][0]
        Y[i] = listAtoms[i][1]
        Z[i] = listAtoms[i][2]
    
    plt.scatter(X, Y, alpha=1, s=10, color='grey')
    #plt.xlabel('X(A)')
    #plt.ylabel('Y(A)')
    plt.tick_params(top='off', bottom='off', left='off', right='off', labelleft='off', labelbottom='off')
    #fig.patch.set_visible(False)
    for spine in plt.gca().spines.values():
        spine.set_visible(False)
    plt.show()<jupyter_output><empty_output><jupyter_text>Parallel cuts<jupyter_code>"""
Main function
Generate all configurations from combinations functions 
"""

l = generate_binary(NCcell_x*NCcell_y, [])
listBinary = findCombinations_rs_yxy(l, NCcell_x, NCcell_y,12)

start = 0
end  = len(listBinary)

listAll =[]
listDensity=[]
data_base=[]
orientation = -1

#careful with this '4'! 
for rd in range(start, end, 4):
    
    #file name
    filename = "geo.kirigami_d0.20_sAC_parallel"
    
    stringsBinary = listBinary[rd]
    cutConfigurations = toArray(stringsBinary)
    if orientation == -1:
        for i in range(len(cutConfigurations)):
            if cutConfigurations[i]==0: 
                cutConfigurations[i]=-1

    
    #create genome_ID in string format
    genome_ID = toString(cutConfigurations)  
    
    #create PRISTINE inner region
    inner = generateInnerCell(NCcell_x, NCcell_y, ncell_x, ncell_y)
    #cut inner region
    inner_wCuts = makeCutsonCell(cutConfigurations, inner, NCcell_x, NCcell_y, ncell_x, ncell_y)
    #add edges with not cuts for loading in LAMMPS simulations 
    kirigami = addEdges(inner_wCuts, ncell_x, ncell_y)

    #create graphene lattice with cuts
    la = generateLattice(kirigami, NCcell_x, NCcell_y, ncell_x, ncell_y, cell_x, cell_y, cell_z, Lx, Ly, Lz)
    
    #get boundary 
    xhi, xlo, yhi, ylo, zhi, zlo = getSize(la)
    listAll.append(la)
    
    #UNFLAG this to save LAMMPS input output files 
    #writeInput(rd//4, la, filename)
"""
Function to visualize kirigami
"""
import matplotlib.pyplot as plt

fig = plt.figure()
#sns.set_context("paper", font_scale=2, rc={"lines.linewidth": 2.5})


#for j in range(len(listAll)):
for j in range(0, 10):
    #j=0
    listAtoms = listAll[j]
    X = np.zeros(len(listAtoms))
    Y = np.zeros(len(listAtoms))
    Z = np.zeros(len(listAtoms))
    for i in  range(len(listAtoms)):
        X[i] = listAtoms[i][0]
        Y[i] = listAtoms[i][1]
        Z[i] = listAtoms[i][2]
    
    plt.scatter(X, Y, alpha=1, s=10, color='grey')
    #plt.xlabel('X(A)')
    #plt.ylabel('Y(A)')
    plt.tick_params(top='off', bottom='off', left='off', right='off', labelleft='off', labelbottom='off')
    #fig.patch.set_visible(False)
    for spine in plt.gca().spines.values():
        spine.set_visible(False)
    plt.show()<jupyter_output><empty_output>
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