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cgca_functions.py
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cgca_functions.py
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# -*- coding: utf-8 -*-
"""
Created on Sat Jun 30 10:21:50 2018
@author: Prosimios
"""
import numpy as np
import matplotlib.pyplot as plt
import time
import matplotlib
#modify some matplotlib parameters to manage the images for illustrator
matplotlib.rcParams['pdf.fonttype'] = 42
matplotlib.rcParams['ps.fonttype'] = 42
def show_grid(grid_array):
plt.figure()
plt.imshow(grid_array, cmap=plt.cm.gray)
plt.show()
def select_cell(grid):
# 2 = growing cell
# 1 = stationary cell
# 0 = empty space
#this rule makes each cell divide only one time per step
g_index = np.nonzero(grid == 2) # growth index = where cell value == 2
#choose a random cell in the dividing state
index_pos = int(np.random.rand(1)[0]*g_index[0].shape[0])
m = g_index[0][index_pos]
n = g_index[1][index_pos]
#save the cell grid index positions
cell_index = [m,n]
return(cell_index)
def check_nbhd(grid, cell_index):
#chek free spaces in the neighbourhood
# fs: array
# index of free spaces in the neighborhood
m = cell_index[0]
n = cell_index[1]
#define the neighborhood
nb = grid[m-1:m+2,n-1:n+2] #nb = neighborhood
#define the free spaces in nb
fs = np.where(nb == 0)
return(fs)
def nb_prob(grid, cell_index, prob_dist = 'contact_linear'):
# assign division probabilities based on empty space cell contacts
# prob_dist: uniform - contact_linear - contact_exp
# contact linear is the default or if another thing is written
# fs: array
# index of free spaces in the neighborhood
# return
# prob: list
# list with the [0,1] probability partition limit of each free space
# e.g. prob = [0.23, 0.81, 1] --> second cell has bigger probability
m = cell_index[0]
n = cell_index[1]
#define neighborhood
nb = grid[m-1:m+2,n-1:n+2] #nb = neighborhood
#define the free spaces in nb
fs = np.where(nb == 0)
#define cell spaces in bn
cs = np.where(nb != 0)
fs_num = len(fs[0])
prob = np.zeros(fs_num)
contacts = np.zeros(fs_num)
if prob_dist != 'uniform':
# if prob_dist is something different from the options, contact_linear is the default
for i in range(fs_num):
mg = m + fs[0][i] - 1 #-1 to convert [0 1 2] to [-1 0 1]
ng = n + fs[1][i] - 1
i_nb = grid[mg-1:mg+2,ng-1:ng+2] # i position neighborhood
occup = np.where(i_nb != 0)
contacts[i] = len(occup[0]) #save the number of contacts of this position
if prob_dist == 'contact_exp':
contacts = np.exp(contacts)
else:
contacts = np.ones(fs_num) #assign uniform values
total = sum(contacts)
prob[0] = (contacts[0]/total)
for i in range(1,fs_num):
prob[i] = prob[i-1]+contacts[i]/total
return(prob)
def cell_divition_uniform(grid, cell_index, fs):
# uniform neighborhood divition probability
#fs: free neighborhood spaces
m = cell_index[0]
n = cell_index[1]
if len(fs[0]) == 1:
grid[m,n] = 1 # then, that position will not divide again
#grown over an empty position
#new_pos = int(np.random.rand(1)[0]*fs[0].shape[0])
new_pos = int(np.random.rand(1)[0]*fs[0].shape[0]) #new pos in the neighbour matrix
m_new = m + fs[0][new_pos] - 1 #-1 to convert [0 1 2] to [-1 0 1]
n_new = n + fs[1][new_pos] - 1
grid[m_new, n_new] = 2 # crates the new cell
ncell_index = [m_new ,n_new]
return(grid, ncell_index)
def cell_divition(grid, cell_index, fs, fs_proba):
#fs: free neighborhood spaces
#fs_proba: free spaces growth probabilities
m = cell_index[0]
n = cell_index[1]
if len(fs[0]) == 1:
grid[m,n] = 1 # then, that position will not divide again
#grown over an empty position
rand_val = np.random.rand(1)[0]
# find the first position which is bigger than rand_val
new_pos = np.where( (fs_proba > rand_val) == True )[0][0] #new pos in the neighbour matrix
m_new = m + fs[0][new_pos] - 1 #-1 to convert [0 1 2] to [-1 0 1]
n_new = n + fs[1][new_pos] - 1
grid[m_new, n_new] = 2 # crates the new cell
ncell_index = [m_new ,n_new]
return(grid, ncell_index)
def initial_plasmids(grid, pattern_num = 0, num_plas = 2, max_copy = 4):
# grid: initial grid
c_index = np.nonzero(grid) # c_index,
#cell_number = c_index[0].shape[0]
gs = grid.shape
pattern = np.zeros((gs[0],gs[1],max_copy)) #initialize the pattern array
# add different patterns
if pattern_num == 0: #random plasmid pattern
for i in range(c_index[0].shape[0]): #assign a random plasmid pattern to each cell position
pattern[c_index[0][i],c_index[1][i],:] = ((num_plas +1 )*np.random.rand(max_copy)).astype(int)
#num_plas +1 to add "no-plasmid" state
elif pattern_num == 1:
pattern = np.ones((grid.shape))
return(pattern)
def role_divideFlag(plasmids):
#plasmids: cell plasmids vector
max_plasmids = plasmids.shape[0]
num_plasmids = np.nonzero(plasmids)[0].shape[0]
divisor = max_plasmids*1.1 #arbitrary defined to make division(max_plasmids number) < 1
# make a cuadratic function of probabilities
probability = (num_plasmids/divisor)**2
#if a cell has no plasmids --> will not divide
if np.random.rand(1) < probability:
return(1) # divide
else:
return(0) # not divide
#Probability tables
#plasmid_nums = np.arange(max_plasmids +1)
#probability = (plasmid_nums/divisor)**2
def create_image(grid, plasgrid):
im_s = plasgrid.shape
aux_imR = np.zeros((im_s[0],im_s[1],im_s[2]))
aux_imG = np.zeros((im_s[0],im_s[1],im_s[2]))
for i in range(im_s[2]):
aux_imR[:,:,i] = 1*(plasgrid[:,:,i]==1)
aux_imG[:,:,i] = 1*(plasgrid[:,:,i]==2)
aux_imR = np.sum(aux_imR,axis=2)
aux_imG = np.sum(aux_imG,axis=2)
aux_transparency = 0.5*(grid[:,:]==1) + 1*(grid[:,:]==2)
# create the image
im_grid = np.zeros((im_s[0],im_s[1],im_s[2]))
im_grid[:,:,0] = np.multiply(np.ones((im_s[0],im_s[1])),aux_imR)
im_grid[:,:,1] = np.multiply(np.ones((im_s[0],im_s[1])),aux_imG)
#im_grid[:,:,2] = np.ones((100,100))*250
im_grid[:,:,3] = np.multiply(np.ones((im_s[0],im_s[1])),aux_transparency)
# stationary cell -> transparency = 0.5)
return(im_grid)
def create_image2(grid, plasgrid):
im_s = plasgrid.shape
aux_imR = np.zeros((im_s[0],im_s[1],im_s[2]))
aux_imG = np.zeros((im_s[0],im_s[1],im_s[2]))
for i in range(im_s[2]):
aux_imR[:,:,i] = 1*(plasgrid[:,:,i]==1)
aux_imG[:,:,i] = 1*(plasgrid[:,:,i]==2)
aux_imR = np.multiply(1*(np.sum(aux_imR,axis=2)>0),50*(grid[:,:]==1)) + 1*(np.sum(aux_imR,axis=2)>0)
aux_imG = np.multiply(1*(np.sum(aux_imG,axis=2)>0),50*(grid[:,:]==1)) + 1*(np.sum(aux_imG,axis=2)>0)
# create the image
im_grid = np.zeros((im_s[0],im_s[1],3))
im_grid[:,:,0] = np.multiply(np.ones((im_s[0],im_s[1])),aux_imR)
im_grid[:,:,1] = np.multiply(np.ones((im_s[0],im_s[1])),aux_imG)
#im_grid[:,:,2] = np.ones((100,100))*250
return(im_grid)
def plasmid_gProb(g_ratio=[1,1], p_types = [1,2]):
#define a growtn probability (=velocity) based on the plasmids
#g_ratio: ratio of growth rate between genotypes (i.e. plasmids)
#p_types: plasmids types or labels
#built the probability class vector
cat_len = len(g_ratio)
probs = np.zeros(cat_len)
denominator = sum(g_ratio)
probs[0] = g_ratio[0]/denominator
for i in range(1,cat_len):
probs[i] = probs[i-1]+g_ratio[i]/denominator
return(probs)
def plasm_g_test(plasmids,probs):
#perform the probability test
rand_val = np.random.rand(1) #random value
growth = False
ptypes = np.unique(plasmids[np.where(plasmids>0)]) #plasmid types in the vector
if ptypes.size == 1: #if has one type of plasmid
#determine the first position > random value
pos = np.where( (probs > rand_val) == True )[0][0]
growth_type = pos + 1 #to transform position to the corresponding plasmid type
#found = np.where(plasmids == ptype)[0]
#ptotal = np.where(plasmids > 0)[0] #total num plasmids
#pdif = ptotal.size - found.size
if growth_type == ptypes:
growth = True
#if found.size>0:
else: #if has more than one type of plasmid
mean_prob = probs[-1]/probs.size #mean plasmid probability
if rand_val < mean_prob:
growth = True
return(growth)
def cell_ratio(plasmgrid, ptype = [1,2]):
c_num_plasm = np.sum(plasmgrid>0, axis=2) #number of plasmids in each grid
plasm_sum = np.sum(plasmgrid, axis = 2)
divition = np.divide(plasm_sum,c_num_plasm)
#total = np.sum(np.isnan(divition) == False, axis = (0,1)) #it include cells with mix plasmids
found = np.zeros(len(ptype))
total = 0
for i in range(len(ptype)):
found[i] = len(np.where(divition == ptype[i])[0])
total += found[i]
ratio = found[0]/total
return(ratio)
def plasmid_update(plasmids, pos_index):
#plasmids: vector with plasmids. e.g [0,1,1,0,2]
state = 2 # cell state = growing state
plasmids_pos = np.nonzero(plasmids)
empty_pos = np.where(plasmids == 0)
num_plas = plasmids_pos[0].shape[0]
if num_plas == 0:
#it means no plasmid in the cell
state = 1 #to not evaluate this cell in the loop again
elif num_plas == plasmids.shape[0]:
#it means all plasmids positions are full
return(plasmids, state)
else:
copied_pos = np.random.randint(num_plas)
plasmids[empty_pos[0][0]] = plasmids[plasmids_pos[0][copied_pos]]
#copy the plasmid in the first free space
return(plasmids, state)
def divide_plasmids(plasmids):
#plasmids: cell plasmids
p_size = plasmids.size
mother_p = np.zeros(p_size)
child_p = np.zeros(p_size)
np.random.shuffle(plasmids) #shuffle the plasmids
if (p_size & 1) == 1: #odd case
#sum a random value to choose which cell keep more plasmids
rand_val = np.random.rand(1)
half_p = int(p_size/2 + rand_val)
else: #even case
half_p = int(p_size/2)
mother_p[:half_p] = plasmids[:half_p]
child_p[half_p:]= plasmids[half_p:]
return(mother_p, child_p)
def initial_pattern(grid, pattern_num):
pattern = {} #initiate initial pattern dictionary
# add different patterns
pattern[0] = np.array([[2]])
pattern[1] = np.array([[0, 0, 2, 0, 0],[0,2,2,2,0],[2,2,1,2,2],[0,2,2,2,0],[0,0,2,0,0]])
pattern[2] = np.ones((2,35))*2
#make elements which are not in the border to be = 1
fixed_pat = pattern[pattern_num]
#put the pattern in the grid
gs = grid.shape
m0 = int(gs[0]/2)
n0 = int(gs[1]/2)
ps = fixed_pat.shape
mpm = int(ps[0]/2)
npm = int(ps[1]/2)
for i in range(ps[0]):
for j in range(ps[1]):
m = m0 + (i - mpm)
n = n0 + (j - npm)
grid[m,n] = fixed_pat[i,j]
return(grid)