#computes the global minimum over a set of two dimensional arrays (stored as files) def timemin(protein): return min([v.min() for v in protein]) proteinList = ["nADP","nATP","ND","NDE","nE"] proteinLabels = ["MinD:ADP (cyto)", "MinD:ATP (cyto)", "MinD:ATP (mem)", "MinE:MinD:ATP (mem)", "MinE (cyto)"] proteins = [0]*len(proteinList) for i in range(len(proteinList)): proteins[i] = load.data(proteinList[i],start_time,end_time) plt.figure(figsize=(9,3.5)) numtimes = int(end_time/dump_time_step)- int(start_time/dump_time_step) numproteins = len(proteins) skip_times = 2 # only plot every skip_times of the snapshots dZ = proteins[0].datashape[1]*1.05 dY = proteins[0].datashape[0]*1.1/skip_times kvals = range(0, int(end_time/dump_time_step)-int(start_time/dump_time_step), skip_times) #axes = plt.subplot(1, 1, 1, axisbg='black') for i in range(len(proteins)): if (proteinList[i]=="ND" or proteinList[i]=="NDE"): maxval = timemax(proteins[i].dataset)/4 else:
from __future__ import division import matplotlib.pyplot as plt import numpy as np import sys import file_loader as load f_shape = sys.argv[1] f_param1 = sys.argv[2] f_param2 = sys.argv[3] f_param3 = sys.argv[4] f_param4 = sys.argv[5] f_param5 = sys.argv[6] natp = load.data(protein="natp") ne = load.data(protein="ne") nadp = load.data(protein="nadp") nd = load.data(protein="nd") areaFname = "data/shape-" + f_shape + "/area_rating_two-" + f_param1 + "-" + f_param2 + "-" + f_param3 + "-" + f_param4 + "-" + f_param5 + ".dat" areaFile = np.loadtxt(areaFname, dtype=float) def splitdata(protein): areaFname = "data/shape-" + f_shape + "/area_rating_two-" + f_param1 + "-" + f_param2 + "-" + f_param3 + "-" + f_param4 + "-" + f_param5 + ".dat" areaFile = np.loadtxt(areaFname, dtype=float) proteinFname = "data/shape-" + f_shape + "/" + str( protein.protein ) + "-avg_density-" + f_shape + "-" + f_param1 + "-" + f_param2 + "-" + f_param3 + "-" + f_param4 + "-" + f_param5 + ".dat" proteinFile = np.loadtxt(proteinFname, dtype=float)
#computes the global minimum over a set of two dimensional arrays (stored as files) def timemin(protein): return min([v.min() for v in protein]) proteinList = ["nADP", "nATP", "ND", "NDE", "nE"] proteinLabels = [ "MinD:ADP (cyto)", "MinD:ATP (cyto)", "MinD:ATP (mem)", "MinE:MinD:ATP (mem)", "MinE (cyto)" ] proteins = [0] * len(proteinList) for i in range(len(proteinList)): proteins[i] = load.data(proteinList[i], start_time, end_time) plt.figure(figsize=(9, 3.5)) numtimes = int(end_time / dump_time_step) - int(start_time / dump_time_step) numproteins = len(proteins) skip_times = 2 # only plot every skip_times of the snapshots dZ = proteins[0].datashape[1] * 1.05 dY = proteins[0].datashape[0] * 1.1 / skip_times kvals = range( 0, int(end_time / dump_time_step) - int(start_time / dump_time_step), skip_times) #axes = plt.subplot(1, 1, 1, axisbg='black') for i in range(len(proteins)):
elif load.f_shape == 'stad': if f_param2 == '2.35': time_stochastic_is_behind = 23.0 if f_param2 == '2.92': time_stochastic_is_behind = 12.0 else: print 'soemthigns wrong!' exit(1) while (time_left > 0): next_end_time = input_start_time + (video_number+1)*video_limit if next_end_time > input_end_time: next_end_time = input_end_time print "next_end_time = ",next_end_time full_video_list = full_video_list + [load.data(protein=protein_name, sim_type='full_array', \ start_time = time_stochastic_is_behind + input_start_time + video_number*video_limit, \ end_time = time_stochastic_is_behind + next_end_time)] exact_video_list = exact_video_list + [load.data(protein=protein_name, sim_type='exact',start_time = input_start_time + video_number*video_limit, end_time = next_end_time)] print 'e',next_end_time print 's',input_start_time print '' time_left -= video_limit video_number += 1 # for lis in full_video_list: # print lis.dataset.shape # for vid in lis.dataset: # print vid[3] # #for line in vid:
print "For ", fname print "This end_time is too great, there are only enough files to support a end_time less than ", total_number_of_files*dump_time_step exit(1) time_left = input_end_time - input_start_time video_limit = 2 #time of each gif created video_list = []#list of video_limit long movies video_number = 0 while (time_left > 0): next_end_time = input_start_time + (video_number+1)*video_limit if next_end_time > input_end_time: next_end_time = input_end_time print "next_end_time = ",next_end_time video_list = video_list + [load.data(protein=protein_name, sim_type=sim_type,start_time = input_start_time + video_number*video_limit, end_time = next_end_time)] time_left -= video_limit video_number += 1 print "video number = ",video_number print "time left = ",time_left print "size video_list = ",len(video_list) # NflE = load.data(protein="NflE") # nATP = load.data(protein="nATP") # nE = load.data(protein="nE") # nADP = load.data(protein="nADP") # ND = load.data(protein="ND") # NDE = load.data(protein="NDE")
from __future__ import division import numpy as np import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt import os import sys import time import file_loader as load #create data objects (see file_loader.py) NflE = load.data(protein="NflE") NflD = load.data(protein="NflD") nATP = load.data(protein="nATP") nE = load.data(protein="nE") nADP = load.data(protein="nADP") ND = load.data(protein="ND") NDE = load.data(protein="NDE") f_shape = sys.argv[1] f_param1 = sys.argv[2] f_param2 = sys.argv[3] f_param3 = sys.argv[4] f_param4 = sys.argv[5] f_param5 = sys.argv[6] #computes the maximum of a two dimensional array - REPLACE with method def maxnum(page): Z = [0. for i in range(page.shape[0])] for i in range(page.shape[0]): Z[i] = max(page[i])
max_y = 0 for x in range(max_data.shape[0]): for y in range(max_data.shape[1]): if max_data[x,y] > maxima: maxima = max_data[x,y] max_x = x max_y = y p_file.write('%g %g %g %g\n'%(input_start_time+num*dump_time_step,maxima,max_x,max_y)) if (num%20 == 0 and num > 1): contour_values = job_string +'ave-time/contour-values-' + str(protein) +'-'+ str(int(input_start_time))+'-' \ +str(int(input_start_time+num*dump_time_step))+'.dat' unsmeared_values = job_string +'ave-time/unsmeared-values-' + str(protein) +'-'+ str(int(input_start_time))+'-' \ +str(int(input_start_time+num*dump_time_step))+'.dat' print contour_values c_file = open(contour_values,'w') unsmeared_file = open(unsmeared_values,'w') for x in range(new.shape[0]): for y in range(new.shape[1]): c_file.write("%g "%(new[x,y]/num/dx**2)) unsmeared_file.write("%g "%(unsmeared[x,y]/num/dx**2)) c_file.write('\n') unsmeared_file.write('\n') c_file.close() unsmeared_file.close() p_file.close() return new/data.shape[0] data = load.data(protein=protein_name, sim_type=sim_type,start_time = input_start_time, end_time = input_end_time) smeared_data = gaussian_smear(data.dataset,.509,data.protein) #this is in microns green light at 500nm,
f_shape = sys.argv[1] f_param1 = sys.argv[2] f_param2 = sys.argv[3] f_param3 = sys.argv[4] f_param4 = sys.argv[5] f_param5 = sys.argv[6] par1 = str(int(10*(float(f_param1)))) par2 = str(int(10*(float(f_param2)))) par3 = str(int(10*(float(f_param3)))) par4 = str(int(10*(float(f_param4)))) par5 = str(int(10*(float(f_param5)))) NflE = load.data(protein="NflE") NflD = load.data(protein="NflD") nATP = load.data(protein="nATP") nE = load.data(protein="nE") nADP = load.data(protein="nADP") Nd = load.data(protein="Nd") Nde = load.data(protein="Nde") mid = False if "-mid" in sys.argv: mid = True print "The -mid flag was set so we'll add the middle point data to the plot" norm = False if "-norm" in sys.argv: norm = True
from __future__ import division import matplotlib matplotlib.use('Agg') import matplotlib.pyplot as plt import numpy as np import file_loader as load import sys #create protein data objects (see file_loader.py) NflE = load.data(protein="NflE") NflD = load.data(protein="NflD") nATP = load.data(protein="nATP") nE = load.data(protein="nE") nADP = load.data(protein="nADP") Nd = load.data(protein="Nd") Nde = load.data(protein="Nde") #compute time average (removing first 10% happens in file loading) def average_location(dataset): tsum = np.zeros_like(dataset[0]) for t in range(nATP.tsteps): tsum += dataset[t] return tsum/(nATP.tsteps) #plot each of the data set time maps individually. for p in [NflE, NflD, nATP, nE, nADP, Nd]: plt.figure() plt.contourf(p.axes[0],p.axes[1],average_location(p.dataset),500) plt.axes().set_aspect('equal', 'datalim') plt.colorbar() plt.savefig(load.print_string("time-map",p))
from __future__ import division import matplotlib.pyplot as plt import numpy as np import sys import file_loader as load f_shape = sys.argv[1] f_param1 = sys.argv[2] f_param2 = sys.argv[3] f_param3 = sys.argv[4] f_param4 = sys.argv[5] f_param5 = sys.argv[6] natp = load.data(protein="natp") ne = load.data(protein="ne") nadp = load.data(protein="nadp") nd = load.data(protein="nd") areaFname = ( "data/shape-" + f_shape + "/area_rating_two-" + f_param1 + "-" + f_param2 + "-" + f_param3 + "-" + f_param4 + "-" + f_param5