Various edits

.gitignore

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+**/*.ipynb_checkpoints
+**/*.egg-info
+**/__pycache__

examples/test_M1.py

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+import itertools
+# get_ipython().magic(u'matplotlib inline')
+import numpy as np
+import matplotlib
+# %matplotlib qt5
+import matplotlib.pyplot as plt
+from matplotlib import animation, rc
+# from IPython.display import HTML
+import vertex_model as model
+from vertex_model.run_select import run_simulation_INM, definecolors
+import vertex_model.initialisation as init
+import sys
+
+import time
+import dill
+
+
+type_=4
+
+# parameters of the vertex model
+G=0.075
+L=0.04
+K=1.0
+
+# parameters of the nucleus A-B stochastic dynamics
+# sys.argv to pass from command line
+# call as, e.g.: python test_M1.py 100 0.15
+try:
+	k=float(sys.argv[1]) # 100
+	D=float(sys.argv[2]) # 0.15
+	r=float(sys.argv[3])
+except:
+	print("Missing command line parameters. Execute as\n")
+	print("   python test_M1.py  <k>  <D>  <r = a/k>\n")
+	exit()
+
+# parameters of the crowding force
+s=0.2
+a=r*k
+
+# adding the nuclear movement parameters to 'params'
+params = [K,G,L,k,D,s,a]
+
+def run_sim (run_id):
+    timestart = time.time()
+    history = run_simulation_INM(params,1,rand,type_)  # timend = 1 for test
+    timeend = time.time()
+
+    with open (f"model_1_k{k}_D{D}_run_{run_id}.pkl", "wb") as file:
+        dill.dump(history, file)
+
+    return timeend - timestart
+
+np.random.seed(1999)
+rand = np.random.RandomState(1999)
+no_simulations = 5
+
+for run in range(1,no_simulations+1):
+	run_sim(run)
+
+
+
+

examples/test_M1_analysis.py

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+import itertools
+import numpy as np
+import matplotlib.pyplot as plt
+import vertex_model as model
+import vertex_model.initialisation as init
+from vertex_model.forces import TargetArea, Tension, Perimeter, Pressure
+
+import dill
+with open("history_model_1.pkl", "rb") as file:
+    history = dill.load(file)
+
+last = history[-1]
+
+# Cell cycle length
+"""
+deadcells = np.where(last.empty())[0]
+cell_cycle_lengths = last.properties['age'][deadcells]
+plt.hist(cell_cycle_lengths, bins = 50, density=True)
+plt.title('Cell cycle length distribution Model 1')
+plt.show()
+"""
+
+
+"""
+plt.hist(last.properties['nucl_pos'], bins = 50)
+plt.xlim(0,1)
+plt.title("Nucleus position Model 1")
+plt.show()
+"""
+
+alivecells = np.where(~last.empty())[0]
+
+
+plt.hist(last.mesh.area[alivecells], bins = 50)
+plt.title('Last Area Distribution Model 1')
+plt.show()
+
+
+
+print(len(alivecells))
+print(len(last.mesh.area))
+print(len(last.mesh.area[alivecells]))
+
+

examples/test_M2.py

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+import itertools
+# get_ipython().magic(u'matplotlib inline')
+import numpy as np
+import matplotlib
+# %matplotlib qt5
+import matplotlib.pyplot as plt
+from matplotlib import animation, rc
+# from IPython.display import HTML
+import vertex_model as model
+from vertex_model.run_select import run_simulation_INM, definecolors
+import vertex_model.initialisation as init
+import sys
+
+import time
+timestart = time.time()
+
+type_=5
+
+# parameters of the vertex model
+G=0.075
+L=0.04
+K=1.0
+
+# parameters of the nucleus A-B stochastic dynamics
+# sys.argv to pass from command line
+# call as, e.g.: python test_M1.py 100 0.15
+k=float(sys.argv[1]) # 100
+D=float(sys.argv[2]) # 0.15
+
+# parameters of the crowding force
+s=0.2
+a=1.
+
+# adding the nuclear movement parameters to 'params'
+params = [K,G,L,k,D,s,a]
+
+np.random.seed(1999)
+rand = np.random.RandomState(1999)
+
+no_simulations = 1
+#Cell_cycle_lengths_progenitors = []
+#Cell_cycle_lengths_pois = []
+#final_history = []
+for i in range(no_simulations):
+    history = run_simulation_INM(params,1,rand,type_)  # timend = 1 for test
+    # Rows 32-49 -> copy in ANALYSIS
+    """
+    last = history[-1] # get state of the cells object from the previous step -> this + following rows -> analysis
+    gone = last.empty()
+    divided = gone
+    diffntd = gone
+    if 'poisoned' in last.properties:
+	    pois = last.properties['poisoned']==1
+	    healty = last.properties['poisoned']==0
+	    divided = gone & healty
+	    diffntd = gone & pois
+    progenitor_deadcells_last = np.where(divided)[0]
+    differentiated_last = np.where(diffntd)[0]
+    correct_cell_cycle_lengths = last.properties['age'][progenitor_deadcells_last]
+    pois_cell_cycle_lengths = last.properties['age'][differentiated_last]
+
+    #new_cell_cycle_lengths = last.properties['age'][deadcells_last] # cell cycle lengths = age of deadcells
+    Cell_cycle_lengths_progenitors = np.append(Cell_cycle_lengths_progenitors, correct_cell_cycle_lengths)
+    Cell_cycle_lengths_pois = np.append(Cell_cycle_lengths_pois, pois_cell_cycle_lengths)
+    final_history = np.append(final_history, history) # get a final history array
+
+    print(last.properties['nucl_pos'])
+    print(min(last.properties['nucl_pos']))
+    print(max(last.properties['nucl_pos']))
+    print(last.properties['A0_final'])
+    print(len(last.properties['final_nucl_pos']))
+    """
+
+timeend = time.time()
+print(timeend - timestart)
+
+import dill
+with open ("history_model_2.pkl", "wb") as file:
+    dill.dump(history, file)
+
+
+

examples/test_M2_analysis.py

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+import itertools
+import numpy as np
+import matplotlib.pyplot as plt
+import vertex_model as model
+import vertex_model.initialisation as init
+from vertex_model.forces import TargetArea, Tension, Perimeter, Pressure
+
+import dill
+with open("history_model_2.pkl", "rb") as file:
+    history = dill.load(file)
+
+last = history[-1]
+
+# Cell cycle length
+"""
+deadcells = np.where(last.empty())[0]
+cell_cycle_lengths = last.properties['age'][deadcells]
+plt.hist(cell_cycle_lengths, bins = 50, density=True)
+plt.title('Cell cycle length distribution Model 2')
+plt.show()
+"""
+
+
+"""
+plt.hist(last.properties['nucl_pos'], bins = 50)
+plt.xlim(0,1)
+plt.title("Nucleus position Model 2")
+plt.show()
+"""
+
+#alivecells = np.where(~last.empty())[0]
+plt.hist(last.mesh.area, bins = 50)
+plt.title('Last Area Distribution Model 2')
+plt.show()
+
+
+