First push

Documents/school_project/code/Cell_migration/Follow_cell.py

-# -*- coding: utf-8 -*-
-"""
-Created on Mon Apr 25 11:00:48 2022
+#Code following the trace where the cell has been and saving the files in different formats.
 
-@author: encinass1u
-"""
 
 import numpy as np
 import pickle 
@@ -17,7 +13,6 @@ from bio2mslib.inout.inout import WriteData as WD
 import os
 import pandas as pd
 from bio2mslib.analysis.models import CellModels as DM
-import create_graph as cg
 from pandas import DataFrame
 
  #Open te pickle file
@@ -26,7 +21,7 @@ with open('Cell_migration_3'+'.pkl', 'rb') as csv_file:
 im = plt.imshow(Z, cmap="copper_r")
 
 iter= 1 #initial iteration
-itermax = 10 #last iteration
+itermax = 3SSS #last iteration
 L = 12 #longitud of the cell sample
 
 while iter<= itermax:
@@ -41,22 +36,14 @@ while iter<= itermax:
                 im2 = plt.imshow(Z, cmap="copper_r") 
                 plt.show()
                     #Saving the file of pickle
-                with open('Cell_migration_'+str(iter)+'_II.pkl', 'wb') as csv_file:
+                with open('Cell_migration_'+str(iter)+'_II.pkl', 'wb') as pkl_file:
+                    pickle.dump(Z,pkl_file)
+                with open('Cell_migration_'+str(iter)+'_II.xlsx', 'wb') as excel_file:
+                    pickle.dump(Z,excel_file)
+                with open('Cell_migration_'+str(iter)+'_II.cvs', 'wb') as csv_file:
                     pickle.dump(Z,csv_file)
                     iter +=1
-#Definition of saved objects path to use them and create a GIF
-if os.path.isdir(results_path) == False :
-    os.mkdir(results_path)
-if os.path.isdir(path_simulation_gif) == False :
-    os.mkdir(path_simulation_gif)
-WD().create_gif_from_images(files_path=results_path+os.sep+"Migration_22",results_path=path_simulation_gif,filename='Cell_Migration_Trace.gif', time_lapse= time_lapse, deleteOriginFiles=0)
-
-graph_name= 'Cell_Trace'
-data = pd.read_pkl(results_path+os.sep+"Cell_migration_'+str(iter)+'_II.pkl")
-
-    #creation of a gif and a final image of the cells count
-cg.createGraph(data, graph_title='Movement')
-cg.createGIFGraph(data, path_simulation_gif, 1, graph_name) 
+                    
 
 
 

Documents/school_project/code/Cell_migration/GIF_Uni_cell_prolif.py

+#PRoliferation o a single cell in the Jeong et al. Equation based on the ammount of nutrient
+
+
+import numpy as np
+import pickle 
+import random 
+import matplotlib.pyplot as ptl
+import copy
+from matplotlib import pyplot as plt
+import random
+from scipy import ndimage
+from bio2mslib.inout.inout import WriteData as WD
+from bio2mslib.analysis.models import CellModels as DM
+import os
+from config import *
+
+#Seth path for file saving
+ls_images=[]
+results_path = os.getcwd()+os.sep+"Single_cell"
+path_simulation_gif= results_path
+path_simulation = results_path+os.sep+"prolif"
+if os.path.isdir(path_simulation) == False :
+        os.mkdir(path_simulation)
+        
+#Empty matrix size declaration
+lengthx = 12
+lengthy = 12
+G= np.zeros((lengthx,lengthy))
+ST = copy.deepcopy(G)
+
+#Pickle of the obtained matrix
+with open('Cell_grid.pkl', 'wb') as csv_file:
+    pickle.dump(ST,csv_file)
+
+Z = copy.deepcopy(ST)
+
+
+#insertion of a single cell to track its movement based on the simpspn model
+uni_cell = Z[(5,4)]
+Z[(5,4)] = 1
+iter = 1
+itermax = 5
+Cd =  1*10**-5 #Cell density
+C = 100 #nutrient concentration
+Cdmax = 1 #maximum cell density
+M = 0.001 #Cell motility coefficient
+Lambda = 1 #rate of cell growth
+LambdaC =10**-8 #Consumption rate of nutrients
+D = 2*10**-5 #Diffusion coefficient
+dt = 1 #time step
+P = copy.deepcopy(Z)
+L = 12
+time_lapse=0.5
+
+#Start loop to prolifere
+while iter<= itermax:
+    #Using function of la place to make the initial cell proliferate
+    LaplaTot=ndimage.laplace(Z)
+    # im2 = plt.imshow(LaplaTot, cmap="copper_r")
+    # plt.show()
+
+    for i in range(1,L-1):
+        for j in range(1,L-1):
+            Z[i,j] = P[i,j] + dt*M*LaplaTot[i,j]*(1-(P[i,j]/Cdmax))+Lambda*1*P[i,j]*(1-(P[i,j]/Cdmax))
+            if Z[i,j]>0:
+                Z[i,j]=0.1
+                im2 = plt.imshow(Z, cmap="copper_r")
+                plt.show()
+    iter +=1
+                #Naming the results files
+    name = 'Single_cell_prolif'+str(iter)+'.png'
+    plt.imsave(os.path.join(path_simulation, name), Z, cmap="copper_r")
+    filename = name
+    ls_images.append(filename)
+    
+
+WD().create_gif_from_images(files_path=results_path+os.sep+"prolif",results_path=path_simulation_gif,filename='Proliferation_single_cell.gif', time_lapse= time_lapse, deleteOriginFiles=0)
+
+    
+
+

Documents/school_project/code/Cell_migration/GIF_Uni_cell_prolif_migr.py

 #Model from jeong et al. used to perfome the proliferation model added with the migration.
 
+
 import numpy as np
-import pickle 
-import random 
-import matplotlib.pyplot as ptl
+from bio2mslib.inout.inout import WriteData as WD
+from bio2mslib.analysis.models import CellModels as DM
+import os
+import pandas as pd
+from pandas import DataFrame
 import copy
 from matplotlib import pyplot as plt
 import random
+from config import *
+import pickle
 from scipy import ndimage
-from bio2mslib.inout.inout import WriteData as WD
-import os
 
 
 
+ls_images=[]
+results_path=os.getcwd()+os.sep+"Single_cell"
+path_simulation_gif = os.getcwd()+os.sep+"Single_cell"
+path_simulation = results_path+os.sep+"prolif_migr_"
+if os.path.isdir(path_simulation) == False :
+        os.mkdir(path_simulation)
 
 
-with open('Cell_grid.pkl', 'rb') as csv_file:
-    data_saved = pickle.load(csv_file)
 
-    print(data_saved)
 
-Z = copy.deepcopy(data_saved)
+#Reopening choosen surface
+with open('G_surface_empty.pkl', 'rb') as csv_file:
+     Gst = pickle.load(csv_file)
 
-im2 = plt.imshow(Z, cmap="copper_r")
+# #Visual preentation in image of the matrix
+im1 = plt.imshow(Gst, cmap="copper_r")
 plt.show()
+Gbis = copy.deepcopy(Gst)
 
-#insertion of a single cell to track its movement based on the simpspn model
-uni_cell = Z[(5,4)]
-Z[(5,4)] = 1
-iter = 1
-itermax = 5
-Cd =  1*10**-5 #Cell density
-C = 100 #nutrient concentration
-Cdmax = 1 #maximum cell density
-M = 0.001 #Cell motility coefficient
-Lambda = 1 #rate of cell growth
-LambdaC =10**-8 #Consumption rate of nutrients
-D = 2*10**-5 #Diffusion coefficient
-dt = 1 #time step
-P = copy.deepcopy(Z)
+uni_cell = Gbis[(5,4)]
+Gbis[(5,5)] = 1
 L = 12
-im2 = plt.imshow(Z, cmap="copper_r")
-plt.show()
 
+Cn =  1*10**-5 #Cell density
+NO2 = 10**2 #nutrient concentration
+Cnmax = 10**7 #maximum cell density
+Dm = 0.001 #Cell motility coefficient
+CRgrw = 1 #rate of cell growth
+CRNO2 =10**-8 #Consumption rate of nutrients
+DNO2 = 2*10**-5
+dt = 1 #time step
+iter = 1
 #Start loop to prolifere
-while iter<= itermax:
+while iter <= 5:
     #Using function of la place to make the initial cell proliferate
-    LaplaTot=ndimage.laplace(Z)
+    LaplaTot=ndimage.laplace(Gbis)
 
 
     for i in range(1,L-1):
         for j in range(1,L-1):
-            h = random.randint(-1,1) #horizontal value for the movement of the cell
-            v = random.randint(-1,1) #vertival value for the movement of the cell
-            if Z[i,j]==1 and (h != 0 or v!= 0) and i<L and j<L:
-                Z[h + i, v + j]= 1
+            x = random.randint(-1,1) #horizontal value for the movement of the cell
+            y = random.randint(-1,1) #vertival value for the movement of the cell
+            if Gbis[i,j]==1 and (x != 0 or y!= 0) and i<L and j<L:
+                Gbis[x + i, y + j]= 1
                 #Proliferation after migration
-                Z[i,j] = P[i,j] + dt*M*LaplaTot[i,j]*(1-(P[i,j]/Cdmax))+Lambda*1*P[i,j]*(1-(P[i,j]/Cdmax))
-                if Z[i,j]>0:
-                    Z[i,j]=1
-                    
-    im2 = plt.imshow(Z, cmap="copper_r")
-    plt.show()
-    iter = iter+1
-
+                Gbis[i,j] = P[i,j] + dt*M*LaplaTot[i,j]*(1-(P[i,j]/Cnmax))+CRgrw*1*P[i,j]*(1-(P[i,j]/Cnmax))
+                if Gbis[i,j]>0:
+                    Gbis[i,j]=1
+                im2 = plt.imshow(Gbis, cmap="copper_r")
+                plt.show()
+                iter +=1
+                name = 'Single_cell_migr_prolif'+str(iter)+'.png'
+                plt.imsave(os.path.join(path_simulation, name), Z, cmap="copper_r")
+                filename = name
+                ls_images.append(filename)
+                with open('Cell_g.pkl', 'wb') as csv_file:
+                    pickle.dump(ST,csv_file)
+
+WD().create_gif_from_images(files_path=results_path+os.sep+"prolif_migr",results_path=path_simulation_gif,filename='Proliferation_migration_single_cell.gif', time_lapse= time_lapse, deleteOriginFiles=0)
     
 
 

Documents/school_project/code/Cell_migration/GIF_migration_single_cell.py

+#GIF creation using same libraries
+
+import numpy as np
+from bio2mslib.inout.inout import WriteData as WD
+from bio2mslib.analysis.models import CellModels as DM
+import os
+import pandas as pd
+from pandas import DataFrame
+import copy
+from matplotlib import pyplot as plt
+import random
+from config import *
+
+ls_images=[]
+results_path = os.getcwd()+os.sep+"Migration_Single_cell"
+path_simulation_gif=results_path
+path_simulation = results_path+os.sep+"Migration_Single_cell"
+if os.path.isdir(path_simulation) == False :
+        os.mkdir(path_simulation)
+
+        
+#Empty matrix size declaration
+lengthx = 12
+lengthy = 12
+G= np.zeros((lengthx,lengthy))
+ST = copy.deepcopy(G)
+
+
+#Loop to add the \ surface 
+for i in range (0,12):
+    for j in range (0,12):
+        if (i == j):
+            ST[i,j]=0.5
+Z = copy.deepcopy(ST)
+uni_cell = Z[(5,4)]
+Z[(5,4)] = 1
+iter = 1
+itermax = 3
+time_lapse = 0.5
+
+while iter<= itermax:
+    for i in range(0,12):
+        for j in range(0,12):
+    #         if Zcopy[im,jm]>= 1:
+    #             Zcopyinitial = copy.deepcopy(Zcopy[im,jm])
+
+            h = random.randint(-1,1) #horizontal value for the movement of the cell
+            v = random.randint(-1,1) #vertival value for the movement of the cell
+            if Z[i,j]==1 and (h != 0 or v!= 0):
+                Z[i,j]= 0.7    
+                Z[h + i, v + j]= 1
+                im2 = plt.imshow(Z, cmap="copper_r")
+                plt.show()
+    name = 'Migration_'+str(iter)+'.png'
+    plt.imsave(os.path.join(path_simulation, name), Z, cmap="copper_r")
+    filename = name
+    ls_images.append(filename)
+    iter +=1
+
+WD().create_gif_from_images(files_path=results_path+os.sep+"Migration_Single_cell",results_path=path_simulation_gif,filename='Migration_GIF_single_cell.gif', time_lapse= time_lapse, deleteOriginFiles=0)
\ No newline at end of file

Documents/school_project/code/Cell_migration/GIF_plotter.py

+#GIF creation using same libraries
+
+import numpy as np
+from bio2mslib.inout.inout import WriteData as WD
+from bio2mslib.analysis.models import CellModels as DM
+import os
+import pandas as pd
+from pandas import DataFrame
+import copy
+from matplotlib import pyplot as plt
+import random
+from config import *
+
+ls_images=[]
+results_path = os.getcwd()+os.sep+"GIF_result"
+path_simulation_gif= os.getcwd()+os.sep+"GIF_result"
+path_simulation = results_path+os.sep+"Images_for_GIF"
+if os.path.isdir(path_simulation) == False :
+        os.mkdir(path_simulation)
+
+        
+#Empty matrix size declaration
+lengthx = 12
+lengthy = 12
+G= np.zeros((lengthx,lengthy))
+ST = copy.deepcopy(G)
+
+
+#Loop to add the \ surface 
+for i in range (0,12):
+    for j in range (0,12):
+        if (i == j):
+            ST[i,j]=0.5
+Z = copy.deepcopy(ST)
+uni_cell = Z[(5,4)]
+Z[(5,4)] = 1
+iter = 1
+itermax = 3
+time_lapse = 0.5
+
+while iter<= itermax:
+    for i in range(0,12):
+        for j in range(0,12):
+    #         if Zcopy[im,jm]>= 1:
+    #             Zcopyinitial = copy.deepcopy(Zcopy[im,jm])
+
+            h = random.randint(-1,1) #horizontal value for the movement of the cell
+            v = random.randint(-1,1) #vertival value for the movement of the cell
+            if Z[i,j]==1 and (h != 0 or v!= 0):
+                Z[i,j]= 0.7    
+                Z[h + i, v + j]= 1
+                im2 = plt.imshow(Z, cmap="copper_r")
+                plt.show()
+    name = 'Migration_'+str(iter)+'.png'
+    plt.imsave(os.path.join(path_simulation, name), Z, cmap="copper_r")
+    filename = name
+    ls_images.append(filename)
+    iter +=1
+
+WD().create_gif_from_images(files_path=results_path+os.sep+"Images_for_GIF",results_path=path_simulation_gif,filename='Migration_GIF_single_cell.gif', time_lapse= time_lapse, deleteOriginFiles=0)
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Documents/school_project/code/Cell_migration/Gts_selection.py

+#Gst generator,it is possible to choose a type of texture for the cell couture.
+import numpy as np
+import pickle 
+import random 
+import matplotlib.pyplot as ptl
+import copy
+from matplotlib import pyplot as plt
+import random
+from scipy import ndimage
+from bio2mslib.inout.inout import WriteData as WD
+import os
+
+#Selection of desire treated surface, Gts
+First_Pickle= True
+square_grid=False
+highs_grid=False
+diagonal=True
+
+if First_Pickle== True:
+    #L is the matrix dimension
+    L =12
+    Lx =np.linspace(0, L, 12)
+    Ly =np.linspace(0, L, 12)
+    #G is the ground, an empty matrix to add the desire surface
+    G= np.zeros((L,L))
+    #Gts us the ground for treated surface
+    Gts = copy.deepcopy(G)
+    if square_grid==True:
+        # #Square grid
+        for i in range (1,len(Lx)-1):
+            for j in range(1,len(Ly)-1):
+                if ((i % 2) !=0) or ((j % 2) ==0):
+                    Gts[i,j] = 0.5
+    elif highs_grid==True:
+        for i in range (1,len(Lx)-1):
+            for j in range(1,len(Ly)-1):
+                if i==0 or j==0 or i==len(Lx)-1 or j==len(Ly)-1:
+                    Gst[i,j] = 0.5
+                elif i==1 or j==1 or i==len(Lx)-2 or j==len(Ly)-2:
+                    Gts[i,j] = 0.7
+    elif diagonal==True:
+        for i in range (1,len(Lx)-1):
+            for j in range (1,len(Ly)-1):
+                if (i == j):
+                    Gts[i,j]=0.5
+else:
+    with open('G_surface_empty.pkl', 'rb') as csv_file:
+        Gts = pickle.load(csv_file)
+
+with open('G_surface_empty.pkl', 'wb') as csv_file:
+    pickle.dump(Gts,csv_file)
+
+im = plt.imshow(Gts, cmap="copper_r")
+plt.show()
+

Documents/school_project/code/Cell_migration/Multi_cell_prolif.py

+#PRoliferation o a single cell in the Jeong et al. Equation based on the ammount of nutrient
+
+
+import numpy as np
+import pickle 
+import random 
+import matplotlib.pyplot as ptl
+import copy
+from matplotlib import pyplot as plt
+import random
+from scipy import ndimage
+from bio2mslib.inout.inout import WriteData as WD
+import os
+
+#Empty matrix size declaration
+lengthx = 12
+lengthy = 12
+G= np.zeros((lengthx,lengthy))
+Gbis = copy.deepcopy(G)
+
+for i in range (4,lengthx-4):
+    for j in range (4,lengthy-4):
+        G[i,j]==1
+
+im2 = plt.imshow(G, cmap="copper_r")
+plt.show()
+
+#insertion of a single cell to track its movement based on the simpspn model
+
+# iter = 1
+# itermax = 5
+# Cn =  1*10**-5 #Cell density
+# NO2 = 100 #nutrient concentration
+# Cnmax = 1 #maximum cell density
+# Dm = 0.001 #Cell motility coefficient
+# Ncgrw = 1 #rate of cell growth
+# CRNO2 =10**-8 #Consumption rate of nutrients
+# Dn = 2*10**-5 #Diffusion coefficient
+# dt = 1 #time step
+# P = copy.deepcopy(Gbis)
+# L = 12
+# im2 = plt.imshow(Gbis, cmap="copper_r")
+# plt.show()
+
+# #Start loop to prolifere
+# while iter<= itermax:
+#     #Using function of la place to make the initial cell proliferate
+#     LaplaTot=ndimage.laplace(Gbis)
+#     # im2 = plt.imshow(LaplaTot, cmap="copper_r")
+#     # plt.show()
+
+#     for i in range(1,L-1):
+#         for j in range(1,L-1):
+#             Gbis[i,j] = P[i,j] + dt*Dm*LaplaTot[i,j]*(1-(P[i,j]/Cnmax))+Ncgrw*1*P[i,j]*(1-(P[i,j]/Cnmax))
+#             if Gbis[i,j]>0:
+#                 Gbis[i,j]=0.1
+#     im2 = plt.imshow(Gbis, cmap="copper_r")
+#     plt.show()
+#     iter = iter+1
+
+    
+
+

Documents/school_project/code/Cell_migration/Pluri_cell_prolif_migr.py

+import numpy as np
+import pickle 
+import random 
+import matplotlib.pyplot as ptl
+import copy
+from matplotlib import pyplot as plt
+import random
+from scipy import ndimage
+from bio2mslib.inout.inout import WriteData as WD
+import os
+from Gts_selection import L, Lx, Ly
+from bio2mslib.analysis.models import CellModels as DM
+from matplotlib.pyplot import figure
+
+#Declaring saving paths for results
+ls_images=[]
+results_path = os.getcwd()+os.sep+"Single_cell"
+path_simulation_gif =os.getcwd()+os.sep+"Single_cell"
+path_simulation = results_path+os.sep+"prolif_migr"
+if os.path.isdir(path_simulation) == False :
+    os.mkdir(path_simulation)
+        
+#Reopening choosen surface
+with open('G_surface_empty.pkl', 'rb') as csv_file:
+     Gts = pickle.load(csv_file)
+
+# #Visual preentation in image of the matrix
+# im1 = plt.imshow(Gts, cmap="copper_r")
+# plt.show()
+#Gs is ground seeded, a copy of the choosen surface to seed the cells,
+Gs = copy.deepcopy(Gts)
+
+#Milticelular seeding
+for i in range (4,L-6):
+    for j in range (4,L-7):
+        if Gs[i,j]==0.5:
+            Gs[i,j]=1.5
+        else:
+            Gs[i,j]=1
+            
+#images of the cell in the surface
+couture= plt.imshow(Gs, cmap="copper_r")
+plt.show()
+
+            
+
+
+#Variable deffinition for PDE proliferation & migration model
+Cd =  1*10**-5 #Cell density
+NO2 = 10**2 #nutrient concentration
+Cdmax = 10**7 #maximum cell density
+Dm = 0.001 #Cell motility coefficient
+CRgrw = 1 #rate of cell growth
+CRNO2 =10**-8 #Consumption rate of nutrients
+DNO2 = 2*10**-5
+dt = 1 #time step
+time_lapse = 0.1
+
+Gscopy = copy.deepcopy(Gs)
+Laplace = ndimage.laplace(Gs)
+#Migration 
+iter = 0
+while iter<= 15:
+    for mi in range(1, len(Lx)-1):
+        for mj in range(1, len(Ly)-1):
+                x = random.randint(-1,1)#horizontal value for the movement of the cell
+                y = random.randint(-1,1)#vertival value for the movement of the cell
+                if Gs[mi,mj]==1 and (x != 0 or y!= 0) and ((mi+x)<len(Lx)) and ((mj+y)< len(Ly)):
+                    Gs[mi+x,mj+y]= 1
+                    Gs[mi,mj]=0
+                    #Proliferation after migration
+                    Gs[mi,mj] = Gscopy[mi,mj] + dt*Dm*Laplace[mi,mj]*(1-(Gscopy[mi,mj]/Cdmax))+CRgrw*1*Gscopy[mi,mj]*(1-(Gscopy[mi,mj]/Cdmax))                
+                    if Gs[mi,mj]>0 and Gs[mi,mj]!=0.7 and Gs[mi,mj]!=0.5 :
+                        Gs[mi,mj]=1
+                        im2 = plt.imshow(Gs, cmap="copper_r", norm = None, aspect ='equal', vmin=0 ,vmax=12)
+                        plt.show()
+                        name = 'Single_cell_migr_prolif'+str(iter)+'.png'
+                        # plt.imsave(os.path.join(path_simulation, name), Gs, cmap="copper_r")
+                        filename = name
+                        ls_images.append(im2)
+                        iter+=1
+                        
+WD().create_gif_from_images(files_path=results_path+os.sep+"prolif_migr",results_path=path_simulation_gif,filename='Cell_behavior.gif', time_lapse= time_lapse, deleteOriginFiles=0)
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Documents/school_project/code/Cell_migration/Proliferation_Migration.py

@@ -33,8 +33,8 @@ for i in range (0,12):
 #Pickle of the obtained matrix
 with open('Cell_grid.pkl', 'wb') as csv_file:
     pickle.dump(ST,csv_file)
-Laplace = ndimage.laplace(Z)
-P = copy.deepcopy(Z)
+Laplace = ndimage.laplace(ST)
+P = copy.deepcopy(ST)
 iter= 1 #initial iteration
 itermax = 5 #last iteration
 L = 12 #longitud of the cell sample
@@ -48,13 +48,13 @@ D = 2*10**-5
 dt = 1 #time step
 
 while iter<= itermax:
-    LaplaTot=ndimage.laplace(Z)
+    LaplaTot=ndimage.laplace(ST)
     for i in range(0,L):
         for j in range(0,L):
             h = random.randint(-1,1) #horizontal value for the movement of the cell
             v = random.randint(-1,1) #vertival value for the movement of the cell
             if Z[i,j]==1 and (h != 0 or v!= 0) and i<L and j<L:
-                Z[i,j] = P[i,j] + dt*M*Laplace[i,j]*(1-(P[i,j]/Cdmax))+Lambda*1*P[i,j]*(1-(P[i,j]/Cdmax))
+                ST[i,j] = P[i,j] + dt*M*Laplace[i,j]*(1-(P[i,j]/Cdmax))+Lambda*1*P[i,j]*(1-(P[i,j]/Cdmax))
                 im2 = plt.imshow(Z, cmap="copper_r") 
                 plt.show()
                 

Documents/school_project/code/Cell_migration/Single_cell/Cell_behavior.gif

+;
\ No newline at end of file

Documents/school_project/code/Cell_migration/Testing_Gts.py

+#Gst generator,it is possible to choose a type of texture for the cell couture.
+import numpy as np
+import pickle 
+import random 
+import matplotlib.pyplot as ptl
+import copy
+from matplotlib import pyplot as plt
+import random
+from scipy import ndimage
+from bio2mslib.inout.inout import WriteData as WD
+import os
+import pyvista as pv
+
+#Selection of desire treated surface, Gts
+First_Pickle= True
+square_grid=False
+highs_grid=True
+diagonal=False
+
+# 
+
+with open('Surface_SMAT.vtk', 'rb') as vtk_file:
+     tata = pickle.load(vtk_file)
+
+surface = pv.read(tata)
+
+tata.plot()

Documents/school_project/code/Cell_migration/Texture.py

@@ -9,6 +9,15 @@ from scipy import ndimage
 from bio2mslib.inout.inout import WriteData as WD
 import os
 
+First_Pickle= False
+
+if First_Pickle== True:
+    with open('Cell_grid.pkl', 'wb') as csv_file:
+        pickle.dump(ST,csv_file)
+else: 
+    with open('Cell_grid.pkl', 'rb') as csv_file:
+        data_saved = pickle.load(csv_file)
+        
 #Empty matrix size declaration
 lengthx = 12
 lengthy = 12
@@ -84,24 +93,3 @@ while iter<= 5:
                 plt.show()
     iter+=1
                         
-                # elif h==0 and v != 0 and Zcopy[im,jm-v]==0: 
-                #     Zcopy[im,jm]=0
-                #     Zcopy[im,jm-v]=Zcopyinitial
-                # elif v==0 and h != 0 and Zcopy[im+l,jm]==0:
-                #             Zcopy[im,jm]=0
-                #             Zcopy[im+h,jm]=Zcopyinitial
-
-
-
-
-#Proliferation process for the cells, regarding that we only have cells in the 
-# for i in range (4,lengthx-4):
-#     for j in range (4,lengthy-4):
-#         if Z[i,j]<=1:
-#             for i in range (4,lengthx-4):
-#                 for j in range (4,lengthy-4):
-#                     Z[i,j] = Zcopy[i,j] + M*Laplace[i,j]*(1-Zcopy[i,j]/Cdmax) + Lambda*Zcopy[i,j]*(1-Zcopy[i,j]/Cdmax)
-                    # im2 = plt.imshow(Z, cmap="copper_r")
-                    # plt.show()
-                
-#WD().create_gif_from_images(files_path=results_path+os.sep+"cell_results_var",results_path=path_simulation_gif,filename='Nbr_cells_graph_nutri_variable_migration.gif', time_lapse= time_lapse, deleteOriginFiles=0)