Oe estimation

README.md

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-fZebrafish somitogenesis simulation
+wfZebrafish somitogenesis simulation
 ==================================
 
 Table of contents

README_2017.md

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+
+********************
+    This is a readme file for 2017 segmentation clock project
+    It contains information about how to complie, run simulation and use plotting scripts
+
+0. Copy everthing onto cluster
+
+1. To compile:
+1.1
+--- simulation:
+cd into simulation folder
+call scons
+1.2
+--- sres:
+cd into sres folder
+for parallel sres: scons mpi=1
+
+
+2. To run:
+on CPU:
+./simulation -x 50 -w 10 -y 4 -i 2017refine2.params -s 1000 -S 0.01 -V 600 -Y 600 -Z 600 -Q 600 -K 600 -r gradient1.txt -u pert.txt -t -D output -L 1
+
+call this line for a normal simulation
+for more details, refer to the general readme file.
+
+
+
+on Cluster:
+use qsub sres.run (sres.run - sres5.run)
+change test name, output file name and ranges file accordingly
+
+
+
+3. 
+
+3.1
+Use extract_para_set.py to extraction all parameter sets from the output above.
+example:
+python extract_para_sets.py ./s51.txt ./ test1 0.0075
+details can be found in extract_para_sets.py
+
+3.2
+****
+NOTE: need to rm features.txt under simulation folder everytime before you run this step.
+****
+For a certain parameter, run it using code from 2. on CPU on cluster (Mac and cluster have different outputs)
+To run multiple sets, use -p flag
+(remember change .params to the output file in 3.1)
+Outputs will be written in features.txt under the same directory as simulation
+
+
+NOTE: need to rm features.txt under simulation folder everytime before you run this step.
+
+
+3.3
+Use scp to copy features.txt from cluster to local 
+
+Convert features.txt to features.csv using the following command line:
+
+python feature_to_csv.py features.txt
+
+New features.csv will be written in the same directory
+
+
+
+
+
+
+
+4. 
+
+Use scripts to plot
+
+
+
+For 4.1 and 4.2,
+Make sure wanted concentration levels are output to file (to change species, change code in mutant create in init.cpp)
+——————————————————————————————————————————————
+4.1
+plot mh1 synchronization 
+
+python plot-snap-sync.py ../simulation/output/wildtype/set_0.cons ./check wt_sync_mh1 0.01 600
+——————————————————————————————————————————————
+4.2
+plot mh1 vs. mespa/mespb complementary
+
+python plot-snap-comp.py ../simulation/output/wildtype/set_0.cons ../simulation/output/MESPAOVER/set_0.cons ./check compl-mh1-mespa 0.01 600
+python plot-snap-comp.py ../simulation/output/wildtype/set_0.cons ../simulation/output/MESPBOVER/set_0.cons ./check compl-mh1-mespb 0.01 600
+
+
+
+
+
+——————————————————————————————————————————————
+
+NOTE:
+
+
+
+——————————————————————————————————————————————
+4.3
+plot mh1 synchronization bar graph
+column 8, 4 ,16
+
+python plot-bar-sync_mh1.py ./check sync_score_mh1
+——————————————————————————————————————————————
+4.4
+plot mesp synchronization bar graph
+column 11, 15, 19
+
+python plot-bar-sync_mesp.py ./check sync_score_mesp
+——————————————————————————————————————————————
+4.5
+plot her1 mRNA amplitude (WT/Delta, WT/DAPT) bar graph
+column 9,17
+
+python plot-bar-amp_mh1-5 ./check amp_score_mh1
+——————————————————————————————————————————————
+4.6
+plot mespa amplitude comparison (WT/Delta, WT/DAPT) bar graph
+column 10, 18
+
+python plot-bar-amp-mmespa-5 ./check amp_score_mespa
+——————————————————————————————————————————————
+4.7
+plot mespb mRNA in Mespa/Mespb amplitude
+column 20, 21
+
+python plot-bar-amp-mesp-1.py ./check amp_score_mesp-1
+——————————————————————————————————————————————
+4.8
+plot wildtype/her1over mRNA amplitude
+column 12,13,14
+
+python plot-bar-amp-her1over-1.py ./ testing features.csv
+
+
+
+
+
+

scripts/extract_para_sets.py

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+
+import numpy
+import scipy
+import pylab as pl
+import time
+from time import clock
+from scipy.stats.stats import pearsonr
+import sys
+import shared
+def main():
+	# check the given arguments
+	if len(sys.argv) < 5:
+		usage()
+	else:
+		f = shared.openFile(sys.argv[1], "r")
+		directory = sys.argv[2]
+		image_name = sys.argv[3]
+		threshold = shared.toFlo(sys.argv[4])
+
+	print 'Extrating all parameter sets with score less than ' + str(threshold) +' from ' + sys.argv[1]
+        fw = shared.openFile(directory+image_name+'.txt', 'w')
+
+        first_line = True
+	para_count = 0
+	current_set = ''
+
+	data = [line.split() for line in f]
+	for line in range(len(data)):
+
+                if 'generation:' in data[line] and 'best' in data[line] and 'fitness:' in data[line]:
+                        fitness = float(data[line][-1])
+                        if fitness <= threshold:
+                                if not current_set== data[line+1][2]:
+                                        current_set = data[line+1][2]
+
+                                        if not first_line:
+                                                fw.write('\n')
+                                        fw.write(current_set)
+                                        first_line = False
+                                        para_count+=1
+        print 'All parameter sets below threshold are saved into '+ directory + image_name+ '.txt'
+        f.close()
+        fw.close()
+
+
+def usage():
+	print "Usage: python extract_para_sets.py <file with parameter sets> <directory to store extracted parameters> <file name> <threshold for fitness score>"
+	exit(0)
+
+main()
+

scripts/feature_to_csv.py

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+import numpy
+import scipy
+import pylab as pl
+import time
+from time import clock
+from scipy.stats.stats import pearsonr
+import sys
+import shared
+import struct
+import csv
+
+
+def main():
+	# check the given arguments
+	with open(sys.argv[1]) as f, open('features.csv', 'w') as fw:
+                fw = csv.DictWriter(fw, ['1','2','3','4','5','6','7','8','9','10','11','12','13','14','15','16','17','18','19','20','21'])
+                fw.writeheader()
+                for line in f:
+
+                        line = line.split()
+                        #print len(line)
+                        fw.writerow({'1':line[0],
+                                    '2':line[1],
+                                    '3':line[4],
+                                    '4':line[5],
+                                    '5':line[6],
+                                    '6':line[7],
+                                    '7':str(line[2])+"/"+str(line[3]),
+                                    '8':line[8],
+                                    '9':str(line[9])+"/"+str(line[10]),
+                                    '10':str(line[11])+"/"+str(line[12]),
+                                    '11':line[13],
+                                    '12':str(line[14])+"/"+str(line[15]),
+                                    '13':str(line[16])+"/"+str(line[17]),
+                                    '14':str(line[18])+"/"+str(line[19]),
+                                    '15':line[20],
+                                    '16':line[21],
+                                    '17':str(line[22])+"/"+str(line[23]),
+                                    '18':str(line[24])+"/"+str(line[25]),
+                                    '19':line[26],
+                                    '20':str(line[27])+"/"+str(line[28]),
+                                    '21':str(line[29])+"/"+str(line[30])})
+                        #0       1   2      3       4  5     6         7         8        9        10     11  12    13       14      15       16      17
+                        #28.2708 1 30.3207 28.2708 0 0.8134 -0.540569 -0.689723 0.105733 104.724 124.619 0 127.607 0.403748 14.2342 104.657 1.22888 38.6712
+                        #18       19      20       21       22     23      24         25      26      27      28     29       30  
+                        #27.1467 91.2622 0.487785 0.334792 101.69 123.696 0.00204714 118.191 0.18401 27.5067 114.584 19.3371 114.584
+main()