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driver_fungalGrowth_singleNutrient.py
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driver_fungalGrowth_singleNutrient.py
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##!/usr/bin/env python3
# -*- coding: utf-8 -*-
"""
Created on Tue Sep 15 14:32:59 2020
@author: jolenebritton
@ Assumption: (Kevin) A segment that undergoes fusion cannot create any new branches. A segment that gets fused to,
@ cannot create any new branches if it has not already done so.
"""
import csv
import numpy as np
# import pandas as pd
import os
import time
import pickle
from joblib import Parallel, delayed
import multiprocessing
import matplotlib.pyplot as plt
import seaborn as sns
import argparse
num_cores = multiprocessing.cpu_count()
# cwd_path ='/Users/libra/Desktop/FBI_FungalGrowth-Bill1'
#cwd_path ='/Users/d3k137/docs/projects/boltzmann/code/06212017/run/fungal_growth/FBI_FungalGrowth2'
#cwd_path = '/scratch/d3k137'
#os.chdir(cwd_path)
import helper_functions as hf
import growth_functions as gf
import nutrient_functions2 as nf
import setup_functions as sf
# Load in parameters as a dictionary
global params
msg = "Adding description"
# Initialize parser
parser = argparse.ArgumentParser(description = msg)
parser.add_argument("-i", "--Input", help = "Show Input")
# Read arguments from command line
args = parser.parse_args()
print(args.Input)
try:
# Load in parameters as a dictionary
params, config = hf.get_configs(args.Input)
except:
print('failed to load parameters from command line')
#params, config = hf.get_configs('parameters.ini')
params, config = hf.get_configs('parameters_env4_noFusion.ini')
# Define string constants
left ='LEFT'
right = 'RIGHT'
left_compartment = 'LEFT_COMPARTMENT'
right_compartment = 'RIGHT_COMPARTMENT'
enzyme_level = 'ENZYME_LEVEL'
deltag0 = 'DGZERO'
deltag0_sigma = 'DGZERO StdDev'
same_compartment = 'Same Compartment?'
full_rxn = 'Full Rxn'
def driver_singleNutrient(run):
t_0 = time.time()
reached_max_branches = False
# ----------------------------------------------------------------------------
# SET UP
# ----------------------------------------------------------------------------
# Load in parameters as a dictionary
#params, config = hf.get_configs('parameters.ini')
# Extract some commonly used parameters
sl = params['sl']
dt_i = params['dt_i']
branch_rate = params['branch_rate']
###########################################################################
###########################################################################
###########################################################################
## Describe the type of simulation we will be running!
# Are we doing calibration? 1 = YES, 0 = NO
isCalibration = 0
print('isCalibration : ', isCalibration)
# Are we using the newer version of the distance to tip calculation (with
# bias given to tips of the same branch)? 1 = YES, 0 = NO
dist2Tip_new = 0
print('dist2Tip_new : ', dist2Tip_new)
# Is the background environment diffusion-capable? 1 = YES, 0 = NO
try:
backDiff = params['diffusion_on']
except: backDiff = 1
print('backDiff : ', backDiff)
# Is the nutrient background variable or fixed?
# variable = 1; fixed = 0
var_nutrient_backgrnd = backDiff
# We may/may not still want excreted compounds to diffuse, though
if backDiff == 0:
glucDiff = 0
trehaDiff = 0
# Is fungal fusion (anastomosis) active? 1 = YES, 0 = NO
try: #(params['fungal_fusion'])
fungal_fusion = params['fungal_fusion']
except NameError: fungal_fusion = 0
print('fungal_fusion : ', fungal_fusion)
# Is chemoattractant released at the tip only? 1 = YES, 0 = NO
try: #(params['isTipRelease'])
isTipRelease = params['isTipRelease']
except NameError: isTipRelease = 0
print('isTipRelease : ', isTipRelease)
# Is the initial condition a line or a cross? 1 = Line, 0 = Cross
whichInitialCondition = 0
print('whichInitialCondition : ', whichInitialCondition)
# Is active transport (translocation) active for both gluc and trehalose in the
# fungal colony? 1 = YES, 0 = NO
isActiveTrans = 1
print('isActiveTrans : ', isActiveTrans)
# Is the branching restricted to N segment away from the tip?
# 0 = Unbiased branching
# 1 = No branching
# N = 2,3,... branching only occur N segment away from the tip.
# Why N = 2 would be the minimum? Because in the current setup, a segment
# directly behind the tip is not enclosed by two septa hence not eligible
# for branching.
try:
restrictBranching = params['restrictBranching']
except NameError: restrictBranching = 3
print('restrictBranching : ', restrictBranching)
# Is the initial background environment with 'patchy' nutrient distribution?
# 1 = YES, 0 = NO
try:
isPatchyExtEnvironment = params['is_pathchyEnv']
except NameError: isPatchyExtEnvironment = 0
print('isPatchyExtEnvironment : ', isPatchyExtEnvironment)
if (isPatchyExtEnvironment == 1):
## There are currently 5 presets of the non-homogeneous randomized
## nutrient distribution. setBackground = 1,2,3,4,or 5 will determine
## the set to use.
## setBackground = 1,2,3 have 50 nutrient foci
## setBackground = 4,5 have 100 nutrient foci
try:
setBackground = params['setPatchyEnv']
except NameError: setBackground = 3
print('Patchy Background : ', setBackground)
# Is the cell wall convection (active transport) scaled by local metabolism
# activity? 1 = YES, 0 = NO
isConvectDependOnMetabo_cw = 1
print('isConvectDependOnMetabo_cw : ', isConvectDependOnMetabo_cw)
# Is the glucose convection (active transport) scaled by local metabolism
# activity? 1 = YES, 0 = NO
isConvectDependOnMetabo_gluc = 1
print('isConvectDependOnMetabo_gluc : ', isConvectDependOnMetabo_gluc)
# Is the trehalose convection (active transport) scaled by local metabolism
# activity? 1 = YES, 0 = NO
isConvectDependOnMetabo_treha = 1
print('isConvectDependOnMetabo_treha : ', isConvectDependOnMetabo_treha)
# What is the probability for fusion to be established?
try:
chance_to_fuse = params['chance_to_fuse']
except NameError: chance_to_fuse = 0.5
print('The probability for fungal fusion is set to : ', chance_to_fuse)
###########################################################################
###########################################################################
###########################################################################
# breakpoint()
# Set up external grid and glucose amounts in each cell
if (isPatchyExtEnvironment == 0):
x_vals, y_vals, sub_e_gluc, sub_e_treha = sf.external_grid()
else:
x_vals, y_vals, sub_e_gluc, sub_e_treha = sf.external_grid_patchy(setBackground, run+1)
folder_string, param_string = hf.get_filepath(params)
# Create appropriate folder
if not os.path.exists('Results/{}/Run{}'.format(param_string, run)):
try:
os.makedirs('Results/{}/Run{}'.format(param_string, run))
except FileExistsError:
print('File exists error:', 'Results/{}/Run{}'.format(param_string, run))
except:
print('Failed to create folder:', 'Results/{}/Run{}'.format(param_string, run))
grid_file = "Results/{}/Run{}/grid_coordinates_temp.txt".format(param_string, run)
thisfile = open(grid_file,'w')
for i in range(np.shape(x_vals)[0]):
print(x_vals[i],y_vals[i],file=thisfile)
thisfile.close()
# Plotting label scaling parameters for external domain
num_ticks = 11 # number of tick labels to appear
yticks = np.linspace(0, len(x_vals)-1, num_ticks, dtype=np.int64)
yticklabels = np.around(np.linspace(-sl*params['grid_scale_val'], sl*params['grid_scale_val'], num_ticks),3)
# File path for saving results
folder_string, param_string = hf.get_filepath(params)
if not os.path.exists('Results/{}/Run{}'.format(param_string, run)):
os.makedirs('Results/{}/Run{}'.format(param_string, run))
# Save copy of the parameters used
params_file = 'Results/{}/Run{}/{}_params.ini'.format(param_string,
run,
param_string)
with open(params_file, 'w') as configfile: # save
config.write(configfile)
# Initialize lists for saving stats
count_branches = []
count_tips = []
count_radii = []
count_max_radii = []
count_avg_radii = []
count_rms_radii = []
count_rms_tip_radii = []
count_times = []
count_biomass = []
total_length_progression = []
# ----------------------------------------------------------------------------
# INITIAL CONDITIONS
# ----------------------------------------------------------------------------
# Initialize mycelia dictionary
# environ_type = 'control' or 'gm41'
mycelia = sf.mycelia_dict()
# Initialize time
current_time = 0
# Initial mycelia centered at origin
num_segs = params['init_segs_count']
if (whichInitialCondition == 1):
mycelia, num_branches, num_total_segs, dtt = sf.initial_conditions_line(mycelia, num_segs, x_vals, y_vals)
elif (whichInitialCondition == 0):
mycelia, num_branches, num_total_segs, dtt = sf.initial_conditions_cross(mycelia, num_segs, x_vals, y_vals)
# ----------------------------------------------------------------------------
# GROW THAT FUNGUS!
# ----------------------------------------------------------------------------
current_step = 2
# Initialize values for storing computational times
time_extend = 0
time_branch = 0
time_external = 0
time_translocation = 0
time_uptake = 0
step_size_extern = 1
#hf.plot_fungus(mycelia, num_total_segs, current_time, folder_string, param_string, params, run)
#hf.plot_fungus_gluc(mycelia, num_total_segs, current_time, folder_string, param_string, params, run)
#hf.plot_fungus_generic(mycelia, num_total_segs, current_time, folder_string, param_string, params, run)
try:
restart = params['restart']
except NameError: restart = 0
if (restart == 1):
try:
restart_file = "Results/{}/Run{}/restart.pkl".format(param_string, run)
except:
print('No restart file found:', restart_file)
elif (restart == 2):
try:
restart_file = params['restart_file']
except NameError: restart_file = "restart_test.pkl"
if (restart > 0):
try:
file = open(restart_file,'rb')
except:
print('No restart file found:', restart_file)
mycelia = pickle.load(file)
num_total_segs = pickle.load(file)
dtt = pickle.load(file)
sub_e_gluc = pickle.load(file)
sub_e_treha = pickle.load(file)
current_time = pickle.load(file)
file.close()
N = round(len(x_vals)/2)-2
if (restart == 0):
# Plot the initial nutrient background:
glucose_ext = sub_e_gluc/params['vol_grid']*1e12 # Convert to molar quantities for display
max_e_gluc = np.max(glucose_ext)
hf.plot_externalsub(sub_e_gluc, yticks, yticklabels, 0.0, max_e_gluc, 'Se', folder_string, param_string, params, run)
print_increment = 1
print('Start: Current time: ', current_time, 'Final time: ', params['final_time'])
while current_time < params['final_time']:
# Convert units
if params['plot_units_time'] == 'days':
plot_time = current_time / (60*60*24)
elif params['plot_units_time'] == 'hours':
plot_time = current_time / (60*60)
elif params['plot_units_time'] == 'minutes':
plot_time = current_time / 60
elif params['plot_units_time'] == 'seconds':
plot_time = current_time
print('Time: ', plot_time, params['plot_units_time'])
#Loop over updates for external diffusion and uptake by fungi
ntimes = np.floor(params['dt_i']/ params['dt_e'])
if (ntimes < 1):
print('Internal time step is less than external time step:')
print('Assumption in simulation is that internal time step is greater than external time step.')
breakpoint()
if ((glucDiff ==0) and (trehaDiff == 0)):
ntimes = 1
params['dt_e'] = params['dt_i']
this_time = 0
while(this_time < ntimes):
this_time = this_time + 1
if current_step % step_size_extern == 0:
# EXTERNAL NUTRIENT
tE_0 = time.time()
# sub_e_gluc = nf.rk_update(sub_e_gluc, 'glucose', step_size_extern)
##################################################################
######################## Background Diffusion ####################
##################################################################
if (glucDiff != 0):
sub_e_gluc = nf.diffusion_ADI(sub_e_gluc, params['dt_e'],params)
if (trehaDiff != 0):
sub_e_treha = nf.diffusion_ADI_treha(sub_e_treha, params['dt_e'],params)
# if np.min(sub_e_gluc)<1e-17:
# breakpoint()
if np.min(sub_e_treha)<0:
# if np.min(sub_e_treha)<-1e12:
# breakpoint()
negativeTreha = np.where(sub_e_treha<0.0)[:]
sub_e_treha[negativeTreha] = 0.0
##################################################################
######################## Background Diffusion ####################
##################################################################
# breakpoint()
tE_1 = time.time()
time_external += (tE_1 - tE_0)
# UPTAKE
tU_0 = time.time()
mycelia = nf.uptake(sub_e_gluc, mycelia, num_total_segs, var_nutrient_backgrnd, params['dt_e'],params)
# mycelia = nf.uptake(sub_e_gluc, sub_e_treha, mycelia, num_total_segs, var_nutrient_backgrnd)
tU_1 = time.time()
time_uptake += (tU_1 - tU_0)
# end while
# RELEASE
mycelia = nf.release(sub_e_treha, mycelia, num_total_segs, isTipRelease, params)
if (np.isnan(np.sum(mycelia['cw_i'][:num_total_segs]))):
breakpoint()
# TRANSLOCATION
tT_0 = time.time()
mycelia = nf.transloc(mycelia, params, num_total_segs, dtt, isActiveTrans,
whichInitialCondition,
isConvectDependOnMetabo_cw,
isConvectDependOnMetabo_gluc,
isConvectDependOnMetabo_treha)
if (np.isnan(np.sum(mycelia['cw_i'][:num_total_segs]))):
breakpoint()
tT_1 = time.time()
time_translocation += (tT_1 - tT_0)
# EXTENSION (GROWTH)
tG_0 = time.time()
old_num_total_segs = num_total_segs
# breakpoint()
mycelia, num_total_segs, dtt = gf.extension(mycelia, params, num_total_segs,
dtt, x_vals, y_vals,isCalibration,
dist2Tip_new, fungal_fusion,
chance_to_fuse, branch_rate)
if(np.any(np.isnan(mycelia['cw_i'][:num_total_segs]))):
breakpoint()
if (old_num_total_segs > num_total_segs):
breakpoint()
tG_1 = time.time()
time_extend += (tG_1 - tG_0)
# BRANCHING
#if(num_total_segs > 9):
# breakpoint()
tB_0 = time.time()
old_num_total_segs = num_total_segs
#adj_branch_rate = branch_rate/ntimes
#adj_branch_rate = branch_rate*(params['kg1_wall']/params['sl'])*ntimes#*params['dt_i']
n = (params['sl']/params['kg1_wall'])/params['dt_i']
adj_branch_rate = 1 - (branch_rate**(1/n))
adj_branch_rate = branch_rate
if any(np.where(mycelia['can_branch'])[0]):
reached_max_branches, mycelia, num_total_segs, dtt = gf.branching(mycelia, sub_e_gluc, params,
num_total_segs, dtt, x_vals, y_vals,
isCalibration, dist2Tip_new,
fungal_fusion, restrictBranching,
chance_to_fuse, adj_branch_rate)
if reached_max_branches == True:
print ('Reached maximum number of branches:', np.max(mycelia['cw_i'])[0])
breakpoint()
break
if (old_num_total_segs > num_total_segs):
breakpoint()
tB_1 = time.time()
if (np.isnan(np.sum(mycelia['cw_i'][:num_total_segs]))):
breakpoint()
time_branch += (tB_1 - tB_0)
if current_step % (4*160) == 0:
if (np.isnan(np.sum(mycelia['cw_i'][:num_total_segs]))):
breakpoint()
num_seg = np.where(mycelia['branch_id'][:num_total_segs] > -1)[0]
num_branch = np.max(mycelia['branch_id'][:num_total_segs])
print('Current number of active segments : ', len(num_seg))
print('Current number of active branch : ', (num_branch))
#if(current_time > 24*3600*10):
# grd_density = sf.grid_density(mycelia, sub_e_gluc, num_total_segs)
# print('Max grid density = ', np.max(grd_density))
print_time = 2*3600*print_increment # print every 4 hours
# PLOT & SAVE DATA
if (current_time > print_time):
print_increment = print_increment+1
if(current_time > 24*3600*10):
grd_density = sf.grid_density(mycelia, sub_e_gluc, num_total_segs)
print('Max grid density = ', np.max(grd_density))
#if (current_time > 0.25* print_time):
file_name = "Results/{}/Run{}/restart_t={}.pkl".format(param_string, run, current_time)
file = open(file_name,'wb')
pickle.dump(mycelia,file)
pickle.dump(num_total_segs,file)
pickle.dump(dtt,file)
pickle.dump(sub_e_gluc,file)
pickle.dump(sub_e_treha,file)
pickle.dump(current_time,file)
file.flush()
os.fsync(file.fileno())
file.close()
# if current_step % (4*160) == 0:
# if current_step % (1*160) == 0:
# breakpoint()
hf.plot_fungus(mycelia, num_total_segs, current_time, folder_string, param_string, params, run)
hf.plot_fungus_gluc(mycelia, num_total_segs, current_time, folder_string, param_string, params, run)
# hf.plot_fungus_generic(mycelia, num_total_segs, current_time, folder_string, param_string, params, run)
hf.plot_fungus_treha(mycelia, num_total_segs, current_time, folder_string, param_string, params, run)
if 1>0:#params['init_sub_e_gluc'] > 1e-15:
# hf.plot_externalsub(sub_e_gluc, yticks, yticklabels, current_time, params['init_sub_e_gluc'], 'Se', folder_string, param_string, params, run)
glucose_ext = sub_e_gluc/params['vol_grid']*1e12 # Convert to molar quantities for display
max_e_gluc = np.max(glucose_ext)
print('max_e_gluc : ', max_e_gluc)
hf.plot_externalsub(sub_e_gluc, yticks, yticklabels, current_time, max_e_gluc, 'Se', folder_string, param_string, params, run)
hf.plot_externalsub_hyphae(sub_e_gluc, mycelia, num_total_segs, yticks, yticklabels, current_time, params['init_sub_e_gluc'], 'Se', folder_string, param_string, params, run)
treha_ext = sub_e_treha/params['vol_grid']*1e12 # Convert to molar quantities for display
max_e_treha = np.max(treha_ext)
# max_e_treha_fixed = 1e-11
if trehaDiff == 1:
hf.plot_externalsub_treha(sub_e_treha, yticks, yticklabels, current_time, max_e_treha, 'Se', folder_string, param_string, params, run)
hf.plot_externalsub_treha_hyphae(sub_e_treha, mycelia, num_total_segs, yticks, yticklabels, current_time, max_e_treha, 'Se', folder_string, param_string, params, run)
# dist_from_center = []
# for i in range(num_total_segs):
# if mycelia['branch_id'][i]==-1:
# mycelia['dist_from_center'][i] = 0.0
# continue
# mycelia['dist_from_center'][i] = (np.sqrt(mycelia['xy2'][i][0]**2 + mycelia['xy2'][i][1]**2))
DONT_INCLUDE = np.where(mycelia['branch_id'][:num_total_segs] == -1)[0]
DO_INCLUDE = np.where(mycelia['branch_id'][:num_total_segs]>-1)[0]
total_length = 0.0
for i in range(len(DO_INCLUDE)):
mycelia['dist_from_center'][DO_INCLUDE[i]] = (np.sqrt(mycelia['xy2'][DO_INCLUDE[i]][0]**2 + mycelia['xy2'][DO_INCLUDE[i]][1]**2))
total_length += (np.sqrt((mycelia['xy2'][DO_INCLUDE[i]][0] - mycelia['xy1'][DO_INCLUDE[i]][0])**2 + (mycelia['xy2'][DO_INCLUDE[i]][1] - mycelia['xy1'][DO_INCLUDE[i]][1])**2))
total_length_progression.append(total_length)
mycelia['dist_from_center'][DONT_INCLUDE] = 0.0;
hf.plot_hist(mycelia, current_time, num_total_segs, param_string, params, run)
count_branches.append(max(mycelia['branch_id'])[0]+1)
count_tips.append(np.count_nonzero(mycelia['is_tip']))
count_max_radii.append(max(np.sqrt(mycelia['xy2'][:,0]**2 + mycelia['xy2'][:,1]**2)))
count_avg_radii.append(np.mean(np.sqrt(mycelia['xy2'][:,0]**2 + mycelia['xy2'][:,1]**2)))
tip_idx = np.where(mycelia['is_tip'])[0]
count_rms_tip_radii.append(np.sqrt(np.mean(mycelia['xy2'][tip_idx,0]**2 + mycelia['xy2'][tip_idx,1]**2)))
count_rms_radii.append(np.sqrt(np.mean(mycelia['xy2'][:,0]**2 + mycelia['xy2'][:,1]**2)))
count_times.append(current_time)
total_biomass = np.sum(mycelia['seg_length'])*params['cross_area']*params['hy_density']
count_biomass.append(total_biomass)
N = round(len(x_vals)/2)-2
avg_treha_annulus = np.zeros(N)
max_treha_annulus = np.zeros(N)
min_treha_annulus = np.zeros(N)
# breakpoint()
center_x = round(len(x_vals)/2)#np.where(x_vals == 0)[0]
print('center_x : ', center_x)
center_y = round(len(y_vals)/2)#np.where(y_vals == 0)[0]
print('center_y : ', center_y)
## For "pseudo-radial" annulus on center of each cell (with 4 grid points)
for i in range(N):
count = 0
min_treha = 1e5
max_treha = -1e5
if i == 0:
j = 1
block1 = (1/4)*(sub_e_treha[center_x, center_y] +
sub_e_treha[center_x+j, center_y] +
sub_e_treha[center_x, center_y+j] +
sub_e_treha[center_x+j, center_y+j] )
if (block1 >= max_treha):
max_treha = block1
if (block1 <= min_treha):
min_treha = block1
avg_treha_annulus[i] += block1
block2 = (1/4)*(sub_e_treha[center_x, center_y] +
sub_e_treha[center_x+j, center_y] +
sub_e_treha[center_x, center_y-j] +
sub_e_treha[center_x+j, center_y-j] )
if (block2 >= max_treha):
max_treha = block2
if (block2 <= min_treha):
min_treha = block2
avg_treha_annulus[i] += block2
block3 = (1/4)*(sub_e_treha[center_x, center_y] +
sub_e_treha[center_x-j, center_y] +
sub_e_treha[center_x, center_y+j] +
sub_e_treha[center_x-j, center_y+j] )
if (block3 >= max_treha):
max_treha = block3
if (block3 <= min_treha):
min_treha = block3
avg_treha_annulus[i] += block3
block4 = (1/4)*(sub_e_treha[center_x, center_y] +
sub_e_treha[center_x-j, center_y] +
sub_e_treha[center_x, center_y-j] +
sub_e_treha[center_x-j, center_y-j] )
if (block4 >= max_treha):
max_treha = block4
if (block4 <= min_treha):
min_treha = block4
avg_treha_annulus[i] += block4
count = 4
else:
for j in range(0,i+1):
if (j < i):
block = (1/4)*(sub_e_treha[center_x+j, center_y+i] +
sub_e_treha[center_x+j, center_y+i+1] +
sub_e_treha[center_x+j+1, center_y+i] +
sub_e_treha[center_x+j+1, center_y+i+1] )
if (block >= max_treha):
max_treha = block
if (block <= min_treha):
min_treha = block
count +=1
avg_treha_annulus[i] += block
block = (1/4)*(sub_e_treha[center_x+j, center_y-i] +
sub_e_treha[center_x+j, center_y-i-1] +
sub_e_treha[center_x+j+1, center_y-i] +
sub_e_treha[center_x+j+1, center_y-i-1] )
if (block >= max_treha):
max_treha = block
if (block <= min_treha):
min_treha = block
count +=1
avg_treha_annulus[i] += block
block = (1/4)*(sub_e_treha[center_x-j, center_y+i] +
sub_e_treha[center_x-j, center_y+i+1] +
sub_e_treha[center_x-j-1, center_y+i] +
sub_e_treha[center_x-j-1, center_y+i+1] )
if (block >= max_treha):
max_treha = block
if (block <= min_treha):
min_treha = block
count +=1
avg_treha_annulus[i] += block
block = (1/4)*(sub_e_treha[center_x-j, center_y-i] +
sub_e_treha[center_x-j, center_y-i-1] +
sub_e_treha[center_x-j-1, center_y-i] +
sub_e_treha[center_x-j-1, center_y-i-1] )
if (block >= max_treha):
max_treha = block
if (block <= min_treha):
min_treha = block
count +=1
avg_treha_annulus[i] += block
if (j == i):
for k in range(0, i+1): # Say, i = 1, k varies between 0 ~ 1
block = (1/4)*(sub_e_treha[center_x+j, center_y+k] +
sub_e_treha[center_x+j, center_y+k+1] +
sub_e_treha[center_x+j+1, center_y+k] +
sub_e_treha[center_x+j+1, center_y+k+1] )
if (block >= max_treha):
max_treha = block
if (block <= min_treha):
min_treha = block
count +=1
avg_treha_annulus[i] += block
block = (1/4)*(sub_e_treha[center_x+j, center_y-k] +
sub_e_treha[center_x+j, center_y-k-1] +
sub_e_treha[center_x+j+1, center_y-k] +
sub_e_treha[center_x+j+1, center_y-k-1] )
if (block >= max_treha):
max_treha = block
if (block <= min_treha):
min_treha = block
count +=1
avg_treha_annulus[i] += block
block = (1/4)*(sub_e_treha[center_x-j, center_y+k] +
sub_e_treha[center_x-j, center_y+k+1] +
sub_e_treha[center_x-j-1, center_y+k] +
sub_e_treha[center_x-j-1, center_y+k+1] )
if (block >= max_treha):
max_treha = block
if (block <= min_treha):
min_treha = block
count +=1
avg_treha_annulus[i] += block
block = (1/4)*(sub_e_treha[center_x-j, center_y-k] +
sub_e_treha[center_x-j, center_y-k-1] +
sub_e_treha[center_x-j-1, center_y-k] +
sub_e_treha[center_x-j-1, center_y-k-1] )
if (block >= max_treha):
max_treha = block
if (block <= min_treha):
min_treha = block
count +=1
avg_treha_annulus[i] += block
avg_treha_annulus[i] = avg_treha_annulus[i]/count
max_treha_annulus[i] = max_treha
min_treha_annulus[i] = min_treha
## For "pseudo-raidal" annulus on grid points only
# for i in range(N):
# count = 0
# min_treha = 1e5
# max_treha = -1e5
# if i == 0:
# # continue
# avg_treha_annulus[i] = sub_e_treha[center_x,center_y]
# count+=1
# # elif i == 1:
# elif i == 1:
# avg_treha_annulus[i] += sub_e_treha[center_x+1, center_y+0]
# if (sub_e_treha[center_x+1, center_y+0] >= max_treha):
# max_treha = sub_e_treha[center_x+1, center_y+0]
# if (sub_e_treha[center_x+1, center_y+0] <= min_treha):
# min_treha = sub_e_treha[center_x+1, center_y+0]
# avg_treha_annulus[i] += sub_e_treha[center_x+1, center_y+1]
# if (sub_e_treha[center_x+1, center_y+1] >= max_treha):
# max_treha = sub_e_treha[center_x+1, center_y+1]
# if (sub_e_treha[center_x+1, center_y+1] <= min_treha):
# min_treha = sub_e_treha[center_x+1, center_y+1]
# avg_treha_annulus[i] += sub_e_treha[center_x+1, center_y-1]
# if (sub_e_treha[center_x+1, center_y-1] >= max_treha):
# max_treha = sub_e_treha[center_x+1, center_y-1]
# if (sub_e_treha[center_x+1, center_y-1] <= min_treha):
# min_treha = sub_e_treha[center_x+1, center_y-1]
# avg_treha_annulus[i] += sub_e_treha[center_x, center_y+1]
# if (sub_e_treha[center_x, center_y+1] >= max_treha):
# max_treha = sub_e_treha[center_x, center_y+1]
# if (sub_e_treha[center_x, center_y+1] <= min_treha):
# min_treha = sub_e_treha[center_x, center_y+1]
# avg_treha_annulus[i] += sub_e_treha[center_x, center_y-1]
# if (sub_e_treha[center_x, center_y-1] >= max_treha):
# max_treha = sub_e_treha[center_x, center_y-1]
# if (sub_e_treha[center_x, center_y-1] <= min_treha):
# min_treha = sub_e_treha[center_x, center_y-1]
# avg_treha_annulus[i] += sub_e_treha[center_x-1, center_y+0]
# if (sub_e_treha[center_x-1, center_y+0] >= max_treha):
# max_treha = sub_e_treha[center_x-1, center_y+0]
# if (sub_e_treha[center_x-1, center_y+0] <= min_treha):
# min_treha = sub_e_treha[center_x-1, center_y+0]
# avg_treha_annulus[i] += sub_e_treha[center_x-1, center_y+1]
# if (sub_e_treha[center_x-1, center_y+1] >= max_treha):
# max_treha = sub_e_treha[center_x-1, center_y+1]
# if (sub_e_treha[center_x-1, center_y+1] <= min_treha):
# min_treha = sub_e_treha[center_x-1, center_y+1]
# avg_treha_annulus[i] += sub_e_treha[center_x-1, center_y-1]
# if (sub_e_treha[center_x-1, center_y-1] >= max_treha):
# max_treha = sub_e_treha[center_x-1, center_y-1]
# if (sub_e_treha[center_x-1, center_y-1] <= min_treha):
# min_treha = sub_e_treha[center_x-1, center_y-1]
# avg_treha_annulus[i] = avg_treha_annulus[i]/8.0
# max_treha_annulus[i] = max_treha
# min_treha_annulus[i] = min_treha
# else:
# for j in range(0,i):
# if j <= (i-1):
# avg_treha_annulus[i] += sub_e_treha[center_x+j, center_y+i]
# if (sub_e_treha[center_x+j, center_y+i] >= max_treha):
# max_treha = sub_e_treha[center_x+j, center_y+i]
# if (sub_e_treha[center_x+j, center_y+i] <= min_treha):
# min_treha = sub_e_treha[center_x+j, center_y+i]
# avg_treha_annulus[i] += sub_e_treha[center_x+j, center_y-i]
# if (sub_e_treha[center_x+j, center_y-i] >= max_treha):
# max_treha = sub_e_treha[center_x+j, center_y-i]
# if (sub_e_treha[center_x+j, center_y-i] <= min_treha):
# min_treha = sub_e_treha[center_x+j, center_y-i]
# avg_treha_annulus[i] += sub_e_treha[center_x-j, center_y+i]
# if (sub_e_treha[center_x-j, center_y+i] >= max_treha):
# max_treha = sub_e_treha[center_x-j, center_y+i]
# if (sub_e_treha[center_x-j, center_y+i] <= min_treha):
# min_treha = sub_e_treha[center_x-j, center_y+i]
# avg_treha_annulus[i] += sub_e_treha[center_x-j, center_y-i]
# if (sub_e_treha[center_x-j, center_y-i] >= max_treha):
# max_treha = sub_e_treha[center_x-j, center_y-i]
# if (sub_e_treha[center_x-j, center_y-i] <= min_treha):
# min_treha = sub_e_treha[center_x-j, center_y-i]
# count += 4
# else:
# for k in range(0,i-1):
# if k == 0:
# avg_treha_annulus[i] += sub_e_treha[center_x+j, center_y+k]
# if (sub_e_treha[center_x+j, center_y+k] >= max_treha):
# max_treha = sub_e_treha[center_x+j, center_y+k]
# if (sub_e_treha[center_x+j, center_y+k] <= min_treha):
# min_treha = sub_e_treha[center_x+j, center_y+k]
# avg_treha_annulus[i] += sub_e_treha[center_x-j, center_y+k]
# if (sub_e_treha[center_x-j, center_y+k] >= max_treha):
# max_treha = sub_e_treha[center_x-j, center_y+k]
# if (sub_e_treha[center_x-j, center_y+k] <= min_treha):
# min_treha = sub_e_treha[center_x-j, center_y+k]
# count += 2
# else:
# avg_treha_annulus[i] += sub_e_treha[center_x+j, center_y+k]
# if (sub_e_treha[center_x+j, center_y+k] >= max_treha):
# max_treha = sub_e_treha[center_x+j, center_y+k]
# if (sub_e_treha[center_x+j, center_y+k] <= min_treha):
# min_treha = sub_e_treha[center_x+j, center_y+k]
# avg_treha_annulus[i] += sub_e_treha[center_x+j, center_y-k]
# if (sub_e_treha[center_x+j, center_y-i] >= max_treha):
# max_treha = sub_e_treha[center_x+j, center_y-k]
# if (sub_e_treha[center_x+j, center_y-i] <= min_treha):
# min_treha = sub_e_treha[center_x+j, center_y-k]
# avg_treha_annulus[i] += sub_e_treha[center_x-j, center_y+k]
# if (sub_e_treha[center_x-j, center_y+k] >= max_treha):
# max_treha = sub_e_treha[center_x-j, center_y+k]
# if (sub_e_treha[center_x-j, center_y+k] <= min_treha):
# min_treha = sub_e_treha[center_x-j, center_y+k]
# avg_treha_annulus[i] += sub_e_treha[center_x-j, center_y-k]
# if (sub_e_treha[center_x-j, center_y-i] >= max_treha):
# max_treha = sub_e_treha[center_x-j, center_y-k]
# if (sub_e_treha[center_x-j, center_y-i] <= min_treha):
# min_treha = sub_e_treha[center_x+j, center_y-k]
# count += 4
# avg_treha_annulus[i] = avg_treha_annulus[i]/count
# max_treha_annulus[i] = max_treha
# min_treha_annulus[i] = min_treha
# breakpoint()
# ----------------------------------------------------------------------------
# Write Current RESULTS to CSV
# ----------------------------------------------------------------------------
t_1 = time.time()
num_branches = max(mycelia['branch_id'])[0]+1
num_segs = num_total_segs
num_tips = np.count_nonzero(mycelia['is_tip'])
max_radii = max(np.sqrt(mycelia['xy2'][:,0]**2 + mycelia['xy2'][:,1]**2))
avg_radii = np.mean(np.sqrt(mycelia['xy2'][:,0]**2 + mycelia['xy2'][:,1]**2))
time_total = t_1 - t_0
time_mins, time_secs = divmod(time_total, 60)
min_seg_length_nonTipIdx = (np.where(mycelia['is_tip'][:num_total_segs] == False))[0]
min_seg_length_nonTipIdx2 = (np.where(mycelia['branch_id'][min_seg_length_nonTipIdx] != -1))[0]
max_seg_length = max(mycelia['seg_length'])[0]
min_seg_length = 0.0
if np.any(mycelia['seg_length'][min_seg_length_nonTipIdx[min_seg_length_nonTipIdx2]]):
min_seg_length = min(mycelia['seg_length'][min_seg_length_nonTipIdx[min_seg_length_nonTipIdx2]])
# breakpoint()
potential_CFL_fail_segment = np.where(mycelia['seg_length'][:num_total_segs] < params['dt_i']*params['kg1_wall'])[0]
CFL_fail_segment = len(np.where(mycelia['is_tip'][potential_CFL_fail_segment] == False)[0])
#total_biomass = np.sum(mycelia['seg_length'])*params['cross_area']*params['hy_density']
#count_branches.append(num_branches)
#count_tips.append(num_tips)
#count_radii.append(max_radii)
#count_times.append(current_time)
#count_biomass.append(total_biomass)
#count_branches = np.array(count_branches)
#count_tips = np.array(count_tips)
#count_radii = np.array(count_radii)
#count_times = np.array(count_times)
#count_biomass = np.array(count_biomass)
#avg_treha_annulus = np.array(avg_treha_annulus)
#max_treha_annulus = np.array(max_treha_annulus)
#min_treha_annulus = np.array(min_treha_annulus)
output_dict = {
'run_num' : run,
'total_run_time': t_1-t_0,
'array_times' : count_times,
'total_biomass' : count_biomass,
'max_radii' : count_max_radii,
'avg_radii' : count_avg_radii,
'rms_radii' : count_rms_radii,
'rms_tip_radii' : count_rms_tip_radii,
'array_num_branches' : count_branches,
'array_num_tips' : count_tips,
'array_branching_density' : np.array(count_branches)/count_tips,
'array_radii' : count_radii,
'array_avg_radii' : count_avg_radii,
'num_segments_at_end' : num_segs,
'max_seg_length' : max_seg_length,
'min_seg_length_nonTip' : min_seg_length,
'CFL_fail_segment ' : CFL_fail_segment,
'isCalibration ': isCalibration,
'dist2Tip_new ': dist2Tip_new,
'backDiff ': backDiff,
'fungal_fusion' : fungal_fusion,
'isTipRelease' : isTipRelease,
'isActiveTrans' : isActiveTrans,
'whichInitialCondition' : whichInitialCondition,
'restrictBranching' : restrictBranching,
'isPatchyExtEnvironment' : isPatchyExtEnvironment,
'isConvectDependOnMetabo_cw' : isConvectDependOnMetabo_cw,
'isConvectDependOnMetabo_gluc' : isConvectDependOnMetabo_gluc,
'isConvectDependOnMetabo_treha' : isConvectDependOnMetabo_treha,
'chance_to_fuse' : chance_to_fuse,
'total_length_progression' : np.array(total_length_progression),
'avg_treha_annulus' : avg_treha_annulus,
'max_treha_annulus' : max_treha_annulus,
'min_treha_annulus' : min_treha_annulus}
output_file_csv = "Results/{}/Run{}/{}_outputdata_{}_t={:0.2f}.csv".format(param_string,
run,
param_string,
run,
current_time)
#with open(output_file_csv, 'w') as csv_file:
# writer = csv.writer(csv_file)
# for key, value in output_dict.items():
# writer.writerow([key, value])
# Write to output csv file
csv_file = open(output_file_csv, 'w')
writer = csv.writer(csv_file)
for key, value in output_dict.items():
writer.writerow([key, value])
csv_file.flush()
os.fsync(csv_file.fileno())
csv_file.close()
# end xxx
WWWW = np.where(avg_treha_annulus == np.max(avg_treha_annulus))[0]
print('Max avg treha at contour : ', WWWW)
#hf.plot_avg_treha_annulus(avg_treha_annulus,np.max(avg_treha_annulus), np.min(avg_treha_annulus), 'Avgerage Trehalose Per Annulus', folder_string, param_string, current_time, params, run)
hf.plot_max_treha_annulus(max_treha_annulus,1.0, 'Max Trehalose Per Annulus', folder_string, param_string, current_time, params, run)
hf.plot_min_treha_annulus(min_treha_annulus,1.0, 'Min Trehalose Per Annulus', folder_string, param_string, current_time, params, run)
# Update time
# breakpoint()
current_time += dt_i
current_step += 1
# end
# end
print('End: Current time: ', current_time, 'Final time: ', params['final_time'])
# Write out a restart file
file_name = "Results/{}/Run{}/restart_t={}.pkl".format(param_string, run, current_time)
file = open(file_name,'wb')
pickle.dump(mycelia,file)
pickle.dump(num_total_segs,file)
pickle.dump(dtt,file)
pickle.dump(sub_e_gluc,file)
pickle.dump(sub_e_treha,file)
pickle.dump(current_time,file)
file.flush()
os.fsync(file.fileno())
file.close()
# Plot results at final time
hf.plot_fungus(mycelia, num_total_segs, current_time, folder_string, param_string, params, run)
hf.plot_fungus_gluc(mycelia, num_total_segs, current_time, folder_string, param_string, params, run)
hf.plot_fungus_treha(mycelia, num_total_segs, current_time, folder_string, param_string, params, run)
if params['init_sub_e_gluc'] > 1e-15:
hf.plot_externalsub(sub_e_gluc, yticks, yticklabels, current_time, params['init_sub_e_gluc'], 'Se', folder_string, param_string, params, run)
hf.plot_externalsub_hyphae(sub_e_gluc, mycelia, num_total_segs, yticks, yticklabels, current_time, params['init_sub_e_gluc'], 'Se', folder_string, param_string, params, run)
if trehaDiff == 1:
max_e_treha = np.max(sub_e_treha)
hf.plot_externalsub_treha(sub_e_treha, yticks, yticklabels, current_time, max_e_treha, 'Se', folder_string, param_string, params, run)
hf.plot_externalsub_treha_hyphae(sub_e_treha, mycelia, num_total_segs, yticks, yticklabels, current_time, max_e_treha, 'Se', folder_string, param_string, params, run)
for i in range(num_total_segs):
if mycelia['branch_id'][i]==-1:
continue
mycelia['dist_from_center'][i] = (np.sqrt(mycelia['xy2'][i][0]**2 + mycelia['xy2'][i][1]**2))
# mycelia['true_seg_length'][i] = (np.sqrt((mycelia['xy2'][i][0]-mycelia['xy1'][i][0])**2 + (mycelia['xy2'][i][1]-mycelia['xy1'][i][1])**2))
# hf.plot_hist(mycelia, num_total_segs, param_string, params, run)
# np.ceil(mycelia['dist_from_center'][:num_total_segs])
# density_per_unit_radius = np.zeros(2000)
# density_per_unit_annulus = np.zeros(2000)
# breakpoint()
center_x = np.where(x_vals == 0)[0]
# print('center_x : ', center_x)
center_y = np.where(y_vals == 0)[0]
# print('center_y : ', center_y)
avg_treha_annulus = np.zeros(N)
max_treha_annulus = np.zeros(N)
min_treha_annulus = np.zeros(N)
# breakpoint()
center_x = round(len(x_vals)/2)#np.where(x_vals == 0)[0]
print('center_x : ', center_x)
center_y = round(len(y_vals)/2)#np.where(y_vals == 0)[0]
print('center_y : ', center_y)
## For "pseudo-raidal" annulus
for i in range(N):
count = 0
min_treha = 1e5
max_treha = -1e5
if i == 0:
# continue
avg_treha_annulus[i] = sub_e_treha[center_x,center_y]
count+=1
# elif i == 1:
elif i == 1:
avg_treha_annulus[i] += sub_e_treha[center_x+1, center_y+0]
if (sub_e_treha[center_x+1, center_y+0] >= max_treha):
max_treha = sub_e_treha[center_x+1, center_y+0]
if (sub_e_treha[center_x+1, center_y+0] <= min_treha):
min_treha = sub_e_treha[center_x+1, center_y+0]
avg_treha_annulus[i] += sub_e_treha[center_x+1, center_y+1]
if (sub_e_treha[center_x+1, center_y+1] >= max_treha):
max_treha = sub_e_treha[center_x+1, center_y+1]
if (sub_e_treha[center_x+1, center_y+1] <= min_treha):
min_treha = sub_e_treha[center_x+1, center_y+1]
avg_treha_annulus[i] += sub_e_treha[center_x+1, center_y-1]
if (sub_e_treha[center_x+1, center_y-1] >= max_treha):
max_treha = sub_e_treha[center_x+1, center_y-1]
if (sub_e_treha[center_x+1, center_y-1] <= min_treha):
min_treha = sub_e_treha[center_x+1, center_y-1]
avg_treha_annulus[i] += sub_e_treha[center_x, center_y+1]
if (sub_e_treha[center_x, center_y+1] >= max_treha):
max_treha = sub_e_treha[center_x, center_y+1]
if (sub_e_treha[center_x, center_y+1] <= min_treha):
min_treha = sub_e_treha[center_x, center_y+1]
avg_treha_annulus[i] += sub_e_treha[center_x, center_y-1]
if (sub_e_treha[center_x, center_y-1] >= max_treha):
max_treha = sub_e_treha[center_x, center_y-1]
if (sub_e_treha[center_x, center_y-1] <= min_treha):
min_treha = sub_e_treha[center_x, center_y-1]
avg_treha_annulus[i] += sub_e_treha[center_x-1, center_y+0]
if (sub_e_treha[center_x-1, center_y+0] >= max_treha):