def main(): # check the given arguments if len(sys.argv) < 3: usage() elif len(sys.argv) == 4: if sys.argv[1] == "-c" or sys.argv[1] == "--no-color": shared.terminalRed = "" shared.terminalReset = "" filename = sys.argv[2] spaces = sys.argv[3] else: usage() else: filename = sys.argv[1] spaces = sys.argv[2] # open the input file and check to ensure 'spaces' is an integer f = shared.openFile(filename, "r") spaces = shared.toInt(spaces) # replace spaces with tabs ofile = "" for line in f: count = 0 while line[:spaces] == " " * spaces: line = line[spaces:] count += 1 ofile = ofile + "\t" * count + line f.close() newfile = shared.openFile(filename, "w") newfile.write(ofile)
def main():
print 'Reading command-line arguments...'
args = sys.argv[1:] # Remove the name of the program from the arguments
num_args = len(args)
if num_args == 3: # There are three arguments, each of which is required
input_file = shared.openFile(args[0], 'r') # The input parameter sets
output_fname = args[1]
output_file = shared.openFile(output_fname,
'w') # The output parameter sets
num_output_params = shared.toInt(
args[2]
) # How many parameters each set should have in the output file
else:
usage()
print 'Converting each parameter set...'
num_input_params = -1
for line in input_file: # For every parameter set
if len(line) > 1 and line[0] != '#': # Skip blank lines and comments
input_set = line.split(',')
if num_input_params == -1: # Find the input format based on the first parameter set found
num_input_params = len(input_set)
else:
output_file.write(
'\n') # Print a newline before each non-first set
input_set[num_input_params - 1].replace(
'\n', '') # Get rid of the newline in the last parameter
# Convert the set to the master (88) format
base_set = ['0'] * 88
for par in range(num_input_params):
base_index = set_formats[num_input_params][par]
base_set[base_index] = input_set[par]
# Convert the master format to the specified one
output_set = ['0'] * num_output_params
for par in range(num_output_params):
output_index = set_formats[num_output_params][par]
output_set[par] = base_set[output_index]
# Write the results to the output file
output_file.write(output_set[0])
for par in range(1, num_output_params):
output_file.write(',' + output_set[par])
print 'Closing files...'
input_file.close()
output_file.close()
print 'Done. Your newly formatted parameter sets are stored in ' + output_fname
def main(): # check the given arguments if len(sys.argv) < 3: usage() elif len(sys.argv) == 4: if sys.argv[1] == "-c" or sys.argv[1] == "--no-color": shared.terminalRed = "" shared.terminalReset = "" filename1 = sys.argv[2] filename2 = sys.argv[3] else: usage() else: filename1 = sys.argv[1] filename2 = sys.argv[2] # open input files f1 = shared.openFile(filename1, "r") f2 = shared.openFile(filename2, "r") # make a list for each file containing each line of it as an element list1 = [] list2 = [] for line in f1: list1.append(line) for line in f2: list2.append(line) f1.close() f2.close() size = len(mylist1) unique1 = 0 unique2 = 0 # add lines that are unique and in common for line in list1: if line not in list2: unique1 += 1 continue for line in list2: if line not in list1: unique2 += 1 continue # print the results print filename1, "has", unique1, "unique lines." print filename2, "has", unique2, "unique lines." print "There are", size - unique1, "lines that appear in both files."
def run_sensitivity( sense_args, sim_args, ppn, pbs_count, cluster_name = "biomath"):
if " -a " not in sense_args:
sense_args += " -a "
sense_args += sim_args
if cluster_name == None:
return subprocess.Popen(space_split(sense_args))
else:
memory_limit = "3GB"
disk_limit = "500MB"
wall_time = "24:00:00"
job = shared.openFile("pbs-job-"+str(pbs_count), "w")
job.write('''
#PBS -N ''' + JOB_NAME + '''
#PBS -l nodes=1:ppn='''+str(ppn)+'''
#PBS -l mem=''' + memory_limit + '''
#PBS -l file=''' + disk_limit + '''
#PBS -q ''' + cluster_name + '''
#PBS -j oe
#PBS -o sensitivity_'''+str(pbs_count)+'''.output
#PBS -l walltime='''+wall_time+'''
cd $PBS_O_WORKDIR
''' + sense_args + '''\n''')
job.close()
return subprocess.Popen(["qsub", "pbs-job-"+str(pbs_count)])
def run_sensitivity(sense_args,
sim_args,
ppn,
pbs_count,
cluster_name="biomath"):
if " -a " not in sense_args:
sense_args += " -a "
sense_args += sim_args
if cluster_name == None:
return subprocess.Popen(space_split(sense_args))
else:
memory_limit = "3GB"
disk_limit = "500MB"
wall_time = "24:00:00"
job = shared.openFile("pbs-job-" + str(pbs_count), "w")
job.write('''
#PBS -N ''' + JOB_NAME + '''
#PBS -l nodes=1:ppn=''' + str(ppn) + '''
#PBS -l mem=''' + memory_limit + '''
#PBS -l file=''' + disk_limit + '''
#PBS -q ''' + cluster_name + '''
#PBS -j oe
#PBS -o sensitivity_''' + str(pbs_count) + '''.output
#PBS -l walltime=''' + wall_time + '''
cd $PBS_O_WORKDIR
''' + sense_args + '''\n''')
job.close()
return subprocess.Popen(["qsub", "pbs-job-" + str(pbs_count)])
def parse_files(file_in):
# open the input file and ensure the directory exists
fin = shared.openFile(file_in, "r")
# split the lines to get data
data = []
names = []
for line in fin:
#print line
if names == []:
names = line.split(',')[1:]
data += [line.split(',')[:-1]]
#Number of parameters is the number of lines, minus one for the name line at the top
params = len(data) - 1
#Number of sensitivity values is the length of any one line array, minus one for the "PASSED" or "FAILED" message, and minus one for the set number
features = len(data[1]) - 2
#Create the sense array that will actually contain the data.
sense = numpy.zeros(shape=(params, features))
#Put the sensitivity values into matrix. The +1's take into account that the indexing skips the first item in each loop.
for i in range(1, params + 1):
for j in range(1, features + 1):
num = data[i][j]
if ("FAILED" in num) or (
"PASSED"
in num): #This shouldn't ever be true, but just in case...
num = 0
num = num_check(float(num))
sense[i - 1][j - 1] = num
# close the file
fin.close()
return sense, names
def parse_files(file_in):
# open the input file and ensure the directory exists
fin = shared.openFile(file_in, "r")
# split the lines to get data
data = []
names = []
for line in fin:
#print line
if names == []:
names = line.split(',')[1:]
data += [line.split(',')[:-1] ]
#Number of parameters is the number of lines, minus one for the name line at the top
params = len(data) - 1
#Number of sensitivity values is the length of any one line array, minus one for the "PASSED" or "FAILED" message, and minus one for the set number
features = len(data[1]) - 2
#Create the sense array that will actually contain the data.
sense = numpy.zeros(shape = (params, features))
#Put the sensitivity values into matrix. The +1's take into account that the indexing skips the first item in each loop.
for i in range(1, params+1 ):
for j in range(1, features + 1):
num = data[i][j]
if ("FAILED" in num) or ("PASSED" in num): #This shouldn't ever be true, but just in case...
num = 0
num = num_check(float(num))
sense[i-1][j-1] = num
# close the file
fin.close()
return sense, names
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]
step_size = shared.toFlo(sys.argv[4])
print 'Plotting all the cells from ' + sys.argv[1] + '...'
# split the lines to get data
data = [line.split() for line in f]
max_time = len(data) - 1
# calculate the tissue size
cells_width = shared.toInt(data[0][0])
cells_height = shared.toInt(data[0][1])
total_cells = cells_width * cells_height + 1
# create a matrix to store the concentration values we obtain from the file
cons = numpy.zeros(shape = (max_time, total_cells))
# put the concentration values from the file into the matrix
for i in range(1, max_time + 1):
cons[i - 1][0] = shared.toFlo(data[i][0]) * step_size
for j in range(1, total_cells):
cons[i - 1][j] = shared.toFlo(data[i][j])
# close the file
f.close()
# plot colors
colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']
color = 0
for i in range(0, total_cells,200):
start = 0
# Adjust the plotting interval for each cell to account for different columns being staggered
# as they enter the PSM at intervals of 6 minutes apart frome ach other
while cons[start][i] == -1: # -1 stands for no data in the output file
start += 1
end = max_time - 1
while cons[end][i] == -1:
end -= 1;
if (i % 4 == 0):
pl.plot(cons[start:end, 0], cons[start:end, i], 'r')
#elif (i % 4 == 1):
#pl.plot(cons[start:end, 0], cons[start:end, i], 'g')
#elif (i % 4 == 2):
#pl.plot(cons[start:end, 0], cons[start:end, i], 'b')
#else:
#pl.plot(cons[start:end, 0], cons[start:end, i], 'c')
#pl.ylim((-1,100))
pl.axis([400, 600, 0, 300])
pl.savefig(directory + "/" + image_name + ".png", format = "png")
pl.close()
print 'Done. Your plot is stored in ' + directory + "/" + image_name + ".png"
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] step_size = shared.toFlo(sys.argv[4]) print 'Plotting all the cells from ' + sys.argv[1] + '...' # split the lines to get data data = [line.split() for line in f] max_time = len(data) - 1 # calculate the tissue size cells_width = shared.toInt(data[0][0]) cells_height = shared.toInt(data[0][1]) total_cells = cells_width * cells_height + 1 # create a matrix to store the concentration values we obtain from the file cons = numpy.zeros(shape = (max_time, total_cells)) # put the concentration values from the file into the matrix for i in range(1, max_time + 1): cons[i - 1][0] = shared.toFlo(data[i][0]) * step_size for j in range(1, total_cells): cons[i - 1][j] = shared.toFlo(data[i][j]) # close the file f.close() # plot colors colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k'] color = 0 for i in range(1, total_cells): start = 0 # Adjust the plotting interval for each cell to account for different columns being staggered # as they enter the PSM at intervals of 6 minutes apart frome ach other while cons[start][i] == -1: # -1 stands for no data in the output file start += 1 end = max_time - 1 while cons[end][i] == -1: end -= 1; if (i % 4 == 0): pl.plot(cons[start:end, 0], cons[start:end, i], 'r') elif (i % 4 == 1): pl.plot(cons[start:end, 0], cons[start:end, i], 'g') elif (i % 4 == 2): pl.plot(cons[start:end, 0], cons[start:end, i], 'b') else: pl.plot(cons[start:end, 0], cons[start:end, i], 'c') pl.savefig(directory + "/" + image_name + ".png", format = "png") pl.close() print 'Done. Your plot is stored in ' + directory + "/" + image_name + ".png"
def main():
print 'Reading command-line arguments...'
args = sys.argv[1:] # Remove the name of the program from the arguments
num_args = len(args)
if num_args == 3: # There are three arguments, each of which is required
input_file = shared.openFile(args[0], 'r') # The input parameter sets
output_fname = args[1]
output_file = shared.openFile(output_fname, 'w') # The output parameter sets
num_output_params = shared.toInt(args[2]) # How many parameters each set should have in the output file
else:
usage()
print 'Converting each parameter set...'
num_input_params = -1
for line in input_file: # For every parameter set
if len(line) > 1 and line[0] != '#': # Skip blank lines and comments
input_set = line.split(',')
if num_input_params == -1: # Find the input format based on the first parameter set found
num_input_params = len(input_set)
else:
output_file.write('\n') # Print a newline before each non-first set
input_set[num_input_params - 1].replace('\n', '') # Get rid of the newline in the last parameter
# Convert the set to the master (88) format
base_set = ['0'] * 88
for par in range(num_input_params):
base_index = set_formats[num_input_params][par]
base_set[base_index] = input_set[par]
# Convert the master format to the specified one
output_set = ['0'] * num_output_params
for par in range(num_output_params):
output_index = set_formats[num_output_params][par]
output_set[par] = base_set[output_index]
# Write the results to the output file
output_file.write(output_set[0])
for par in range(1, num_output_params):
output_file.write(',' + output_set[par])
print 'Closing files...'
input_file.close()
output_file.close()
print 'Done. Your newly formatted parameter sets are stored in ' + output_fname
def main():
if len(sys.argv) != 3:
usage()
else:
input_filename = sys.argv[1]
output_filename = sys.argv[2]
input_file = shared.openFile(input_filename, "r")
output_file = shared.openFile(output_filename, "w")
lines = [line for line in input_file]
for i in range(len(lines)):
if "Found a good set" in lines[i]:
good_set = lines[i+1][:]
starting_index = good_set.find(',')
good_set = good_set[starting_index+1:]
output_file.write(good_set)
input_file.close()
output_file.close()
def main():
if len(sys.argv) != 3:
usage()
else:
input_filename = sys.argv[1]
output_filename = sys.argv[2]
input_file = shared.openFile(input_filename, "r")
output_file = shared.openFile(output_filename, "w")
lines = [line for line in input_file]
for i in range(len(lines)):
if "Found a good set" in lines[i]:
good_set = lines[i + 1][:]
starting_index = good_set.find(',')
good_set = good_set[starting_index + 1:]
output_file.write(good_set)
input_file.close()
output_file.close()
def readData(filename):
f = shared.openFile(filename, 'r')
parsedFile = []
index = 0
width = height = 0
for line in f:
line = line.strip()
if (index == 0):
width, height = shared.widthAndHeight(line.split(' '), filename)
if width < 4 or height < 4 or (width % 2 != 0) or (height % 2 != 0):
print 'The size of the tissue must be at least 4x4 and its width and height must be even numbers.'
exit(2)
else:
aux = line.split(' ')
parsedFile.append(aux)
index += 1
f.close()
return width, height, parsedFile
def findMinMax(filename):
f = shared.openFile(filename, 'r')
minCon = float('inf')
maxCon = 0
first = True
for line in f:
if not first:
lineNums = line.split()
for i in range(len(lineNums)):
lineNums[i] = shared.toFlo(lineNums[i])
num = min(lineNums[1:])
if (num < minCon):
minCon = num
num = max(lineNums[1:])
if (num > maxCon):
maxCon = num
first = False
f.close()
return minCon, maxCon
def main():
# check the given arguments
with open(sys.argv[1]) as f:
content = f.readlines()
#f = shared.openFile(, "r")
#directory = sys.argv[2]
'''
data = [line.split() for line in f]
max_time = len(data) - 1
# calculate the tissue size
cons = numpy.zeros(shape = (max_time, 71))
# create array for row plotting
# put the concentration values from the file into the matrix
for i in range(0, len(data)):
for j in range(0, 71):
cons[i][j] = shared.toFlo(data[i][j])
print cons[i][j]
'''
#print content
f.close()
wr= shared.openFile("newset.params","w")
newline=""
needlist= [0,2,3,5,6,8,9,11,12,14,15,17,18,20,21,23,24,29,30,31,33,38,39,40,42,47,48,49,51,53,54,56,57,59,60,62,63,65,66,67,68,69,70]
#print len(needlist)
for i in range(0, len(content)):
#print content[i]
newline=""
newlist= [x.strip() for x in content[i].split(',')]
#print len(newlist)
#print len(needlist)
for j in range(0, len(newlist)):
if j in needlist:
if newline=="":
newline+= newlist[j]
else:
newline+=","+newlist[j]
wr.write(newline)
wr.write("\n")
wr.close()
def write_bar_data(yvals, errorvals, out_file, ynames=None, xnames=None):
data_file = shared.openFile(out_file, "w")
data_file.write("Feature,")
if xnames != None:
for name in xnames:
data_file.write(str(name) + ',')
else:
data_file.write("Parameters...")
data_file.write('\n')
for feature in range(len(yvals)):
data_file.write('\n')
if ynames != None:
data_file.write(str(ynames[feature]) + ',')
for param in range(len(yvals[feature])):
data_file.write(str(yvals[feature][param]) + ',')
data_file.write('\nstdev:,')
for param in range(len(errorvals[feature])):
data_file.write(str(errorvals[feature][param]) + ',')
data_file.close()
return
def write_bar_data(yvals, errorvals, out_file, ynames=None, xnames=None):
data_file = shared.openFile(out_file, "w")
data_file.write("Feature,")
if xnames != None:
for name in xnames:
data_file.write(str(name) + ',')
else:
data_file.write("Parameters...")
data_file.write('\n')
for feature in range(len(yvals)):
data_file.write('\n')
if ynames != None:
data_file.write(str(ynames[feature]) + ',')
for param in range(len(yvals[feature])):
data_file.write(str(yvals[feature][param]) + ',')
data_file.write('\nstdev:,')
for param in range(len(errorvals[feature])):
data_file.write(str(errorvals[feature][param]) + ',')
data_file.close()
return
def main():
print 'Reading command-line arguments...'
args = sys.argv[1:]
if len(args) == 2:
cons_fname = args[0]
directory = args[1]
else:
usage()
print 'Reading the concentrations file...'
min_con = float('inf')
max_con = 0
cons_data = []
if cons_fname.endswith('.cons'): # Read ASCII file
cons_file = shared.openFile(cons_fname, 'r')
width, height = map(lambda num: shared.toInt(num), cons_file.readline().split(' ')) # The first line contains the width and height
checkSize(width, height)
for line in cons_file:
cons = map(lambda num: shared.toFlo(num), line.split(' ')[1:-1]) # Remove the time step column and newline when taking the concentrations
for con in cons:
min_con = min(min_con, con)
max_con = max(max_con, con)
cons_data.append(cons)
elif cons_fname.endswith('.bcons'): # Read binary file
cons_file = shared.openFile(cons_fname, 'rb') # Read the file as a binary
# The first two ints are the width and height
width, = struct.unpack('i', cons_file.read(4))
height, = struct.unpack('i', cons_file.read(4))
checkSize(width, height)
size = width * height
cons = []
cons_length = 0
while True:
con_str = cons_file.read(4)
if con_str == '': # While not EOF
break;
else:
# There are width * height concentration floats per time step
con, = struct.unpack('f', con_str)
min_con = min(min_con, con)
max_con = max(max_con, con)
cons.append(con)
cons_length += 1
if cons_length == height:
cons_data.append(cons)
cons = []
else:
usage()
print 'Creating the directory if necessary...'
directory = shared.ensureDir(directory)
if (directory[-1] != '/'):
directory = directory + '/'
print 'Creating snapshots...'
edge, size = findSizes(width, height) # Configure the hexagon edge and window size based on the grid size
index = 0
for line in cons_data:
if (index % 10 == 0 and index >= 50000):
plotHexagons(directory, size, index, line, min_con, max_con, edge, width, height)
index += 1
print 'Done. Your snapshots are stored in ' + directory
def main():
print 'Reading command line arguments...'
# check the given arguments
if len(sys.argv) < 8:
usage()
else:
folder = sys.argv[1]
parsets = shared.toInt(sys.argv[2])
image_name = sys.argv[3]
feature = sys.argv[4]
ofolder = sys.argv[5]
post_width = shared.toInt(sys.argv[6])
excel_name = sys.argv[7]
num_mutants = 6
index = 0
mutants = ["wildtype", "delta", "her1", "her7", "her7her13", "her13"]
markers = ['o', '^', 's', '*', 'h', 'D']
colors = ['k', 'b', 'g', 'r', 'c', 'm']
features = []
if (feature == "period" or feature == "amplitude"):
features.append(feature)
else:
features.append("period")
features.append("amplitude")
for feat in features:
# Create excel file in which the data used to create the plots will be stored
excel_file = shared.openFile(
ofolder + "/" + excel_name + "-" + feat + ".csv", "w")
print "Plotting ", feat, "..."
first_avg = 0
num_first = 0
for index in range(num_mutants):
mutant = mutants[index]
print ' Running ' + mutant + '...'
marker = markers[index]
color = colors[index]
# open the input file
f = shared.openFile(
folder + "/" + mutant + "/set_0_" + feat + "_mh1.feats", "r")
# split the lines to get data
data = [line.split(",") for line in f]
# calculate the tissue size
height = shared.toInt(data[0][0])
width = shared.toInt(data[0][1])
xmin = 0
xmax = 0.9 * width
buckets = 9 # split the interval into 9 chunks
chunk = (width - post_width) / (
buckets - 1) # the width of the intervals after the posterior
indexes = [0 for i in range(buckets)]
for bucket in range(buckets):
if bucket == 0:
indexes[bucket] = post_width / 2
else:
indexes[bucket] = (post_width +
(bucket - 1) * chunk) + (chunk / 2.0)
averages = [0 for i in range(buckets)]
num_points = [0 for i in range(buckets)]
stderr = [0 for i in range(buckets)]
if mutant == "wildtype":
excel_file.write("mutant,")
for index in indexes:
excel_file.write(str(index) + ",")
excel_file.write("\n")
print ' Averaging the first bucket for the wildtype...' # all other data points will be averaged to this value
for parset in range(parsets):
# open the input file and ensure the directory exists
f = shared.openFile(
folder + "/" + mutant + "/set_" + str(parset) + "_" +
feat + "_mh1.feats", "r")
# split the lines to get data
data = [line.split(",") for line in f]
lines = len(data)
for line in range(1, lines, 2):
for col in range(len(data[line]) - 1):
pos = shared.toInt(data[line][col])
val = shared.toFlo(data[line + 1][col])
if pos < post_width:
first_avg += val
num_first += 1
first_avg /= num_first
for parset in range(parsets):
print ' Normalizing and analyzing data from set ' + str(
parset) + '...'
# open the input file and ensure the directory exists
f = shared.openFile(
folder + "/" + mutant + "/set_" + str(parset) + "_" +
feat + "_mh1.feats", "r")
# split the lines to get data
data = [line.split(",") for line in f]
lines = len(data)
for line in range(1, lines, 2):
for col in range(len(data[line]) - 1):
pos = shared.toInt(data[line][col])
val = shared.toFlo(data[line + 1][col]) / first_avg
if pos < post_width:
averages[0] += val
num_points[0] += 1
else:
averages[(pos - post_width) / chunk + 1] += val
num_points[(pos - post_width) / chunk + 1] += 1
# ignore the buckets which don't have data
buckets_with_data = buckets
for bucket in range(buckets):
if post_width + (
(bucket - 1) * chunk) + chunk - 1 > (0.9 * width):
buckets_with_data -= 1
else:
if num_points[bucket] > 0:
averages[bucket] /= num_points[bucket]
elif feat == "amplitude":
averages[bucket] = 0
else:
buckets_with_data -= 1
buckets = buckets_with_data
print ' Calculating standard error...'
for parset in range(parsets):
f = shared.openFile(
folder + "/" + mutant + "/set_" + str(parset) + "_" +
feat + "_mh1.feats", "r")
data = [line.split(",") for line in f]
lines = len(data)
for line in range(1, lines, 2):
for col in range(len(data[line]) - 1):
pos = shared.toInt(data[line][col])
val = shared.toFlo(data[line + 1][col]) / first_avg
if pos < post_width:
stderr[0] += (val - averages[0])**2
else:
stderr[(pos - post_width) / chunk + 1] += (
val -
averages[(pos - post_width) / chunk + 1])**2
for bucket in range(buckets):
if (num_points[bucket] > 0):
stderr[bucket] = math.sqrt(stderr[bucket] /
num_points[bucket])
stderr[bucket] /= math.sqrt(num_points[bucket])
else:
stderr[bucket] = 0
indexes = indexes[:buckets]
averages = averages[:buckets]
stderr = stderr[:buckets]
# Print the means and standard deviations to the excel_file
excel_file.write(mutant + ",")
for average in averages:
excel_file.write(str(average) + ",")
excel_file.write("\n,")
for stder in stderr:
excel_file.write(str(stder) + ",")
excel_file.write("\n")
plt.errorbar(indexes,
averages,
stderr,
fmt='ro',
linestyle='-',
marker=marker,
color=color,
label=mutant)
plt.legend(prop={'size': 8}, loc=2)
pylab.xlim([xmin, xmax])
excel_file.close()
plt.savefig(ofolder + "/" + image_name + "_" + feat + ".png",
format="png")
plt.close()
print "Done. Your " + feat + " plot is stored in " + ofolder + "/" + image_name + "_" + feat + ".png"
print "The data behind the plot can be found in " + ofolder + "/" + excel_name + "-" + feat + ".csv"
def main():
gradients = {
0: ['28', '29'],
1: ['37'],
2: ['41'],
3: ['34', '35'],
4: ['38', '39'],
5: ['4', '5'],
6: ['34', '35', '37'],
7: ['38', '39', '41'],
8: ['4', '5', '7'],
9: ['28', '32', '15']
}
end_rate = [-1, 1001, 1001, 1001, 1001, -1, 1001, 1001, -1, -1]
incs = [-1, 1, 1, 1, 1, -1, 1, 1, -1, -1]
print 'Reading command-line arguments...'
args = sys.argv[1:] # Remove the name of the program from the arguments
num_args = len(args)
num_req_args = 2
req_args = [
False
] * num_req_args # If every required argument was given then req_args will be all true
if num_args >= num_req_args:
# Arguments with default values
base_gradients_file = None
simulation = './simulation'
sim_arguments = ""
for arg in range(0, num_args - 1, 2):
option = args[arg]
value = args[arg + 1]
if option == '-s' or option == '--sets':
sets_fname = value # Parameter sets file
req_args[0] = True
elif option == '-o' or option == '--output-dir':
odir = value
req_args[1] = True
elif option == '-g' or option == '--base-gradients':
base_gradients_file = shared.openFile(
value, 'r'
) # Gradients to add to every simulation on top of the changing ones
elif option == '-S' or option == '--simulation':
simulation = value # Filename of the simulation
elif option == '-a' or option == '--arguments':
for a in range(
arg + 1,
num_args): # Arguments to pass to the simulation
sim_arguments += ' ' + args[a]
break
else:
usage()
for arg in req_args: # Check to ensure every required argument was entered
if not arg:
usage()
else:
usage()
print 'Reading the base gradients file...'
# Add any given base gradients to a string to be passed to each simulation's gradients file
if base_gradients_file is None:
base_gradients = ''
else:
base_gradients = ''
for line in base_gradients_file:
base_gradients += line
print 'Start creating gradient files and pbs-jobs for each gradient combination'
start_point = '9'
end_point = '49'
# For every gradient option
num_gradients = len(gradients)
jobs_to_submit = []
for i in range(num_gradients):
si = str(i)
for j in range(100, end_rate[i], incs[i]):
sj = str(j)
grad_id = '-' + si + '-' + sj
grad_fname = odir + "/grad" + grad_id + ".gradient"
scores_fname = odir + "/scores" + grad_id + ".csv"
subprocess.call(["mkdir", "-p", odir])
grad_file = shared.openFile(grad_fname, 'w')
# Write the base gradient to the file
grad_file.write(base_gradients)
for rate in gradients[i]:
grad_file.write(rate + ' (' + start_point + ' 100) (' +
end_point + ' ' + sj + ')\n')
# Create the PBS job file
pbs_fname = odir + '/pbs-job' + grad_id
jobs_to_submit.append(pbs_fname)
pbs_file = shared.openFile(pbs_fname, 'w')
pbs_file.write("""
#PBS -N gradient-run""" + grad_id + """
#PBS -l nodes=1:ppn=1
#PBS -l mem=500MB
#PBS -l file=500MB
#PBS -q biomath
#PBS -j oe
#PBS -o output.txt
#PBS -l walltime=360:00:00
cd $PBS_O_WORKDIR\n""")
# Write the simulation call to the PBS job file
pbs_file.write(simulation + sim_arguments + ' -i ' + sets_fname +
' -r ' + grad_fname + ' -E ' + scores_fname)
pbs_file.close()
if j % 50 == 0:
queue_jobs = get_num_jobs()
# Add parsing of queue_jobs
while (queue_jobs > 150):
time.sleep(300)
queue_jobs = get_num_jobs()
for job in range(min(100, len(jobs_to_submit))):
print("Submitting job " + jobs_to_submit[job])
subprocess.call(['qsub', jobs_to_submit[job]])
jobs_to_submit = jobs_to_submit[100:]
def main():
# check the given arguments
if len(sys.argv) < 6:
usage()
else:
folder = sys.argv[1]
parsets = shared.toInt(sys.argv[2])
ofolder = sys.argv[3]
image_name = sys.argv[4]
excel_name = sys.argv[5]
mutants = ["wildtype", "delta", "her1", "her7", "her7her13", "her13"]
markers = ['o', '^', 's', '*', 'h', 'D']
colors = ['k', 'b', 'g', 'r', 'c', 'm']
num_mutants = 6
# Create excel file in which the data used to create the plots will be stored
excel_file = shared.openFile(ofolder + "/" + excel_name + "-sync.csv", "w")
for index in range(num_mutants):
mutant = mutants[index]
marker = markers[index]
color = colors[index]
# open the first file to get the height, width and interval
f = shared.openFile(folder + "/" + mutant + "/set_0_sync_mh1.feats",
"r")
# split the lines to get data
data = [line.split(",") for line in f]
# calculate the tissue size
height = shared.toInt(data[0][0])
interval = shared.toFlo(data[0][1])
#split_time = shared.toFlo(data[0][2])
width = len(data[1]) - 1
indexes = [0 for i in range(width)]
averages = [0 for i in range(width)]
stderr = [0 for i in range(width)]
for parset in range(parsets):
f = shared.openFile(
folder + "/" + mutant + "/set_" + str(parset) +
"_sync_mh1.feats", "r")
# split the lines to get data
data = [line.split(",") for line in f]
for col in range(width):
for line in range(1, height + 1):
averages[col] += shared.toFlo(data[line][col])
f.close()
for col in range(width):
indexes[col] = (((interval / 2) * col +
(interval / 2) * col + interval) / 2) / 6
averages[col] /= height * parsets
if mutant == "wildtype":
excel_file.write("mutant,")
for index in indexes:
excel_file.write(str(index) + ",")
excel_file.write("\n")
for parset in range(parsets):
f = shared.openFile(
folder + "/" + mutant + "/set_" + str(parset) +
"_sync_mh1.feats", "r")
data = [line.split(",") for line in f]
# std error = std deviation / sqrt(num data points)
for col in range(width):
for line in range(1, height + 1):
stderr[col] += (shared.toFlo(data[line][col]) -
averages[col])**2
stderr[col] = math.sqrt(stderr[col] / (height * parsets))
stderr[col] /= math.sqrt(height * parsets)
# Print the means and standard deviations to the excel_file
excel_file.write(mutant + ",")
for average in averages:
excel_file.write(str(average) + ",")
excel_file.write("\n,")
for stder in stderr:
excel_file.write(str(stder) + ",")
excel_file.write("\n")
plt.errorbar(indexes,
averages,
stderr,
fmt='ro',
linestyle='-',
marker=marker,
color=color,
label=mutant)
plt.legend(prop={'size': 8}, loc=3)
pylab.xlim([0, (width + 1) * (interval / 2) / 6])
plt.savefig(ofolder + "/" + image_name + ".png", format="png")
plt.close()
def main():
# check the given arguments
if len(sys.argv) < 6:
usage()
else:
f = shared.openFile(sys.argv[1], "r")
directory = sys.argv[2]
image_name = sys.argv[3]
step_size = shared.toFlo(sys.argv[4])
plot_style= sys.argv[5]
plot_helper1= int(shared.toFlo(sys.argv[6]))
plot_helper2= int(shared.toFlo(sys.argv[7]))
print 'Plotting all the cells from ' + sys.argv[1] + '...'
# split the lines to get data
data = [line.split() for line in f]
max_time = len(data) - 1
# calculate the tissue size
cells_width = shared.toInt(data[0][0])
cells_height = shared.toInt(data[0][1])
total_cells = cells_width * cells_height + 1
#print cells_width
# create a matrix to store the concentration values we obtain from the file
cons = numpy.zeros(shape = (max_time, total_cells))
cons_t = [0]*max_time
# create array for row plotting
pos = [0]*50
for i in range (0, 49):
pos[i]=i
time = [0]*max_time
for i in range (1,max_time+1):
time[i-1]=i*step_size
# put the concentration values from the file into the matrix
for i in range(1, max_time + 1):
cons[i - 1][0] = shared.toFlo(data[i][0]) * step_size
for j in range(1, total_cells):
cons[i - 1][j] = shared.toFlo(data[i][j])
# close the file
f.close()
# plot colors
colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']
color = 0
# decide which row/column/cells to plot
#plot_col = 1
if (plot_style == "col"):
startpoint = plot_helper1
interval = cells_width
elif (plot_style == "all"):
startpoint =1
interval =1
elif (plot_style =="cell"):
startpoint = plot_helper1
interval = total_cells
elif (plot_style == "col_t"):
startpoint = plot_helper1
interval= cells_width
start_time = plot_helper2
if (plot_style!= "row" and plot_style!="col_t"):
print "not in row"
for j in range(0,plot_helper2):
for i in range(startpoint+j, total_cells, interval):
start = 0
# Adjust the plotting interval for each cell to account for different columns being staggered
# as they enter the PSM at intervals of 6 minutes apart frome ach other
while cons[start][i] == -1: # -1 stands for no data in the output file
start += 1
end = max_time - 1
while cons[end][i] == -1:
end -= 1;
if (i % 4 == 0):
pl.plot(cons[start:end, 0], cons[start:end, i], 'r')
elif (i % 4 == 1):
pl.plot(cons[start:end, 0], cons[start:end, i], 'g')
elif (i % 4 == 2):
pl.plot(cons[start:end, 0], cons[start:end, i], 'b')
else:
pl.plot(cons[start:end, 0], cons[start:end, i], 'c')
elif (plot_style == "row"):
print "in row"
pl.plot(pos[0:49],cons[plot_helper1, 1:50],'r')
pl.plot(pos[0:49],cons[plot_helper1, 51:100],'g')
pl.plot(pos[0:49],cons[plot_helper1, 101:150],'b')
pl.plot(pos[0:49],cons[plot_helper1, 151:200],'c')
elif (plot_style == "col_t"):
difference=0
if start_time > 29999:
difference= start_time-29999
print "tracking column"
if (startpoint <=9):
if start_time<29999:
timetodie= 24000
timetilappear=0
i=1
while i<=29999:
#print i
#print startpoint
#print cons[timetilappear+i,startpoint]
cons_t[timetilappear+i]=cons[timetilappear+i,startpoint]
i+=1
timetilappear=29999
else:
for i in range (start_time):
cons_t[i]=0
timetilappear=start_time
timetodie= (cells_width-1- startpoint)*600-1
elif (startpoint>9 and startpoint <=49):
print ">9"
timetodie= (cells_width-1- startpoint)*600
timetilappear= (startpoint +1-10)*600+29999+difference
#print timetodie
#print timetilappear
for i in range (timetilappear):
cons_t[i]=0
growth=1
i=1
while i<=timetodie:
#print i
#print growth
#print startpoint
#print cons[timetilappear+i,startpoint]
if (timetilappear+i>= 90000):
break
if (startpoint>= 93000):
break
cons_t[timetilappear+i]=cons[timetilappear+i,startpoint]
growth+=1
i+=1
if (growth %600==0 ):
startpoint+=1
growth=1
pl.plot(time[0:max_time], cons_t, 'r')
#new_array=[500,520,540,560,580,600,620,640,660,680,700,720,740,760,780,800,820,840]
#pl.grid(True)
#pl.axis([600, 700,0,1100])
#pl.xticks(new_array)
pl.savefig(directory + "/" + image_name + ".png", format = "png")
pl.close()
print 'Done. Your plot is stored in ' + directory + "/" + image_name + ".png"
def main():
#check the given arguments
print "Reading command-line arguments..."
args = sys.argv[1:]
num_args = len(args)
req_args = [False] * 6
num_seeds = 0
sim_arguments = ""
if num_args >= 6:
for arg in range(0, num_args - 1, 2):
option = args[arg]
value = args[arg + 1]
if option == '-i' or option == '--input-file':
ifile = value
req_args[0] = True
elif option == '-n' or option == '--num-params':
num_params = shared.toInt(value)
req_args[1] = True
elif option == '-p' or option == '--pars-per-job':
pars_per_job = shared.toInt(value)
req_args[2] = True
elif option == '-d' or option == '--directory':
folder = value
req_args[3] = True
elif option == '-s' or option == '--simulation':
simulation = value
req_args[4] = True
elif option == '-S' or option == '--seeds':
num_seeds = int(value)
req_args[5] = True
elif option == '-a' or option == '--arguments':
for a in range(arg + 1, num_args):
sim_arguments += ' ' + args[a]
break
elif option == '-h' or option == '--help':
usage()
else:
usage()
for arg in req_args:
if not arg:
req_args
usage()
else:
usage()
index = 0
input_file = shared.openFile(ifile, "r")
shared.ensureDir(folder)
for parset in range(0, num_params, pars_per_job):
params = shared.openFile(folder + "/input" + str(index) + ".params", "w")
for line in range(pars_per_job):
params.write(input_file.readline())
params.close()
index += 1;
for seeds in range(num_seeds):
seed = (seeds + 1) * 1000
for parset in range(index):
job = shared.openFile(folder + "/pbs-job-" + str(seed) + "-" + str(parset), 'w')
job.write('''
#PBS -N robust-test
#PBS -l nodes=1:ppn=1
#PBS -l mem=500mb
#PBS -l file=300mb
#PBS -q biomath
#PBS -j oe
#PBS -o ''' + folder + '''/output''' + str(seed) + "-" + str(parset) + '''.txt
#PBS -l walltime=06:00:00
cd $PBS_O_WORKDIR
''' + simulation + ' ' + sim_arguments + ' -p ' + str(pars_per_job) + ' -i ' + ifile + ''' -s ''' + str(seed) + " -M 6 -E " + folder + "/scores-" + str(seed) + "-" + str(parset) + ".csv")
job.close()
subprocess.call(["qsub", folder + "/pbs-job-" + str(seed) + "-" + str(parset)])
def main():
print 'Reading command-line arguments...'
args = sys.argv[1:] # Remove the name of the program from the arguments
num_args = len(args)
req_args = [False] * 3 # If every required argument was given then req_args will be all true
if num_args >= 3:
# Arguments with default values
stdevs_away = 2
round_to = 5
for arg in range(0, num_args - 1, 2):
option = args[arg]
value = args[arg + 1]
if option == '-s' or option == '--sets':
sets_file = shared.openFile(value, 'r')
req_args[0] = True
elif option == '-c' or option == '--current-ranges':
cur_ranges_file = shared.openFile(value, 'r')
req_args[1] = True
elif option == '-n' or option == '--new-ranges':
new_ranges_fname = value
new_ranges_file = shared.openFile(new_ranges_fname, 'w')
req_args[2] = True
elif option == '-d' or option == '--standard-dev':
stdevs_away = shared.toInt(value)
elif option == '-r' or option == '--round-to':
round_to = shared.toInt(value)
elif option == '-h' or option == '--help':
usage()
else:
usage()
for arg in req_args: # Check to ensure every required argument was entered
if not arg:
usage()
else:
usage()
print 'Reading the parameter sets file...'
# Parse the sets file to get the list of parameter sets
sets = []
for line in sets_file:
if not(line == '' or line[0] == '#'): # Ignore blank lines and comments
sets.append(line)
if len(sets) < 1: # Ensure at least one set was given
usage()
print 'Reading the current ranges file...'
# Parse the current ranges file to find the existing ranges
par_names = []
cur_ranges = []
for line in cur_ranges_file:
line = line.replace('\t', ' ')
if not(line == '' or line[0] == '#'): # Ignore blank lines and comments
# Get the human-readable description
space = line.find(' ')
if space <= 0:
parsing_error()
par_names.append(line[: space])
line = line[space + 1:] # Skip past the description
# Find the range bounds
start = 0
if line[start] == '\0':
parsing_error()
while line[start] == ' ':
start += 1
if line[start] != '[':
parsing_error()
end = start + 1
while line[end] != ']' and line[end] != '\0':
end += 1
if line[end] == '\0':
parsing_error()
line = line[start + 1 : end]
bounds = map(shared.toFlo, line.split(',')) # Convert the bounds to floats
if len(bounds) != 2:
parsing_error()
cur_ranges.append(bounds)
print 'Calculating new ranges...'
# Calculate each parameter's new range
flo_sets = map(lambda ls: map(shared.toFlo, ls), map(lambda s: s.split(','), sets)) # Convert each parameter set string into an array of floats
num_sets = len(flo_sets)
new_ranges = []
for p in range(len(cur_ranges)): # For every range
# Get the mean range based on every set
vals = []
for s in flo_sets:
vals.append(s[p])
mean = sum(vals) / num_sets
# Calculate the standard deviation from the mean
stdev_sum = 0
for f in vals:
stdev_sum += (f - mean) ** 2
stdev = math.sqrt(stdev_sum / num_sets)
# Define new ranges based on the mean and standard deviation that are at least as narrow as the current ranges
lower_bound = max(cur_ranges[p][0], round(mean - stdev * stdevs_away, round_to))
upper_bound = min(cur_ranges[p][1], round(mean + stdev * stdevs_away, round_to))
new_ranges.append([lower_bound, upper_bound])
print 'Writing the new ranges to the specified output file...'
# Write the parameter ranges to the new ranges file
for r in range(len(new_ranges)):
new_ranges_file.write(par_names[r] + ' [' + str(new_ranges[r][0]) + ',' + str(new_ranges[r][1]) + ']\n')
new_ranges_file.close()
print 'Done. The new ranges are in ' + new_ranges_fname
def main():
# check the given arguments
if len(sys.argv) < 3:
usage()
elif len(sys.argv) == 4:
if sys.argv[1] == "-c" or sys.argv[1] == "--no-color":
shared.terminalRed = ""
shared.terminalReset = ""
directory = sys.argv[2]
filename = sys.argv[3]
else:
usage()
else:
directory = sys.argv[1]
filename = sys.argv[2]
# ensure the directory exists and open the output file
directory = shared.ensureDir(directory)
ofile = shared.openFile(filename, "w")
fsize = 0
filename = directory + "/run0.txt"
f = shared.openFile(filename)
width, height = shared.widthAndHeight(f.readline().split(), filename)
for line in f:
data = line.split()
fsize = shared.toFlo(data[0])
avg = [[-1 for i in range(w * h)] for j in range(int(fsize * 10) + 50)]
end = 0
f.close()
for run in range(0, runs):
filename = directory + "/run" + str(run)+ ".txt"
f = open(filename)
width, height = shared.widthAndHeight(f.readline().split(), filename)
for line in f:
lineList = line.split(",")
temptime = shared.toFlo(lineList[0])
place = int(temptime * 10)
if float(place + 1) - (temptime * 10) < (temptime * 10 - float(place)):
index = place + 1
else:
index = place
if (index > end):
end = index
for cell in range(0, w * h):
if (avg[index][cell] == -1):
avg[index][cell] = shared.toFlo(lineList[cell + 1])
else:
avg[index][cell] += shared.toFlo(lineList[cell + 1])
ofile.write(str(width) + " " + str(height) + "\n")
for cell in range(0, width * height):
i = 0
minend = end
while (i < end):
if (avg[i + 1][cell] == -1):
x1 = i
y1 = avg[i][cell]
y2 = 0
x2 = -1
for k in range(i + 2, end + 1):
if (avg[k][cell] != -1):
y2 = avg[k][cell]
x2 = k
break
if (x2 != -1):
m = (y2 - y1) / (x2 - x1)
for k in range (i + 1, x2):
avg[k][cell] = y1 + m * h * (k - i)
i = x2
else:
end = i
if (end < minend):
minend = end
break
else:
i += 1
end = minend
for i in range(1, end + 1):
list = avg[i]
ofile.write(str(float(i) / 10) + " ")
for cell in range(0, w * h):
ofile.write(str(list[cell] / runs) + " ")
ofile.write("\n")
index += 1
ofile.close()
def main():
#check the given arguments
print "Reading command-line arguments..."
args = sys.argv[1:]
num_args = len(args)
req_args = [False] * 6
num_seeds = 0
sim_arguments = ""
if num_args >= 6:
for arg in range(0, num_args - 1, 2):
option = args[arg]
value = args[arg + 1]
if option == '-i' or option == '--input-file':
ifile = value
req_args[0] = True
elif option == '-n' or option == '--num-params':
num_params = shared.toInt(value)
req_args[1] = True
elif option == '-p' or option == '--pars-per-job':
pars_per_job = shared.toInt(value)
req_args[2] = True
elif option == '-d' or option == '--directory':
folder = value
req_args[3] = True
elif option == '-s' or option == '--simulation':
simulation = value
req_args[4] = True
elif option == '-S' or option == '--seeds':
num_seeds = int(value)
req_args[5] = True
elif option == '-a' or option == '--arguments':
for a in range(arg + 1, num_args):
sim_arguments += ' ' + args[a]
break
elif option == '-h' or option == '--help':
usage()
else:
usage()
for arg in req_args:
if not arg:
req_args
usage()
else:
usage()
index = 0
input_file = shared.openFile(ifile, "r")
shared.ensureDir(folder)
for parset in range(0, num_params, pars_per_job):
params = shared.openFile(folder + "/input" + str(index) + ".params",
"w")
for line in range(pars_per_job):
params.write(input_file.readline())
params.close()
index += 1
for seeds in range(num_seeds):
seed = (seeds + 1) * 1000
for parset in range(index):
job = shared.openFile(
folder + "/pbs-job-" + str(seed) + "-" + str(parset), 'w')
job.write('''
#PBS -N robust-test
#PBS -l nodes=1:ppn=1
#PBS -l mem=500mb
#PBS -l file=300mb
#PBS -q biomath
#PBS -j oe
#PBS -o ''' + folder + '''/output''' + str(seed) + "-" + str(parset) + '''.txt
#PBS -l walltime=06:00:00
cd $PBS_O_WORKDIR
''' + simulation + ' ' + sim_arguments + ' -p ' + str(pars_per_job) + ' -i ' +
ifile + ''' -s ''' + str(seed) + " -M 6 -E " + folder +
"/scores-" + str(seed) + "-" + str(parset) + ".csv")
job.close()
subprocess.call(
["qsub", folder + "/pbs-job-" + str(seed) + "-" + str(parset)])
def main():
# check the given arguments
if len(sys.argv) < 6:
usage()
elif len(sys.argv) == 7:
if sys.argv[1] == "-c" or sys.argv[1] == "--no-color":
shared.terminalRed = ""
shared.terminalReset = ""
filename = sys.argv[2]
filename2 = sys.argv[3]
directory = sys.argv[4]
measuring = sys.argv[5]
mutation = sys.argv[6]
else:
usage()
else:
filename = sys.argv[1]
directory = sys.argv[2]
measuring = sys.argv[3]
mutation = sys.argv[4]
# open the input file and ensure the directory exists
f = shared.openFile(filename, "r")
f2 = shared.openFile(filename2, "r")
directory = shared.ensureDir(directory)
# split the lines to get data
data = [line.split() for line in f]
file_len = len(data) - 1
max_x = file_len
f.close()
data2 = [line.split() for line in f2]
file_len2 = len(data2) - 1
max_x2 = file_len2
f2.close()
if (max_x == max_x2):
print "test"
# number of columns we have in the files
cn = shared.toInt(data[0][0]) * shared.toInt(data[0][1]) + 1
cn2 = shared.toInt(data2[0][0]) * shared.toInt(data2[0][1]) + 1
# create matrices to store the data we obtained from the files
m2p = numpy.zeros(shape=(max_x, cn + cn2))
# put the data coming from the files to the matrix
for i in range(2, file_len):
for j in range(0, cn + cn2):
if (j < cn):
m2p[i][j] = shared.toFlo(data[i][j])
elif (j == cn):
print data2[i][j - cn]
else:
m2p[i][j] = 2 * shared.toFlo(data2[i][j - cn])
# plot colors
colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k']
color = 0
for i in range(1, cn + cn2):
if (i % 4 == 0):
pl.plot(m2p[0:max_x, 0], m2p[0:max_x, i], 'r')
elif (i % 4 == 1):
pl.plot(m2p[0:max_x, 0], m2p[0:max_x, i], 'g')
elif (i % 4 == 2):
pl.plot(m2p[0:max_x, 0], m2p[0:max_x, i], 'b')
else:
pl.plot(m2p[0:max_x, 0], m2p[0:max_x, i], 'c')
pl.title(measuring + " " + mutation + " All Cells")
pl.savefig(directory + "/" + mutation + "_all.png", format="png")
pl.close()
# plot the data
average = []
for i in range(0, max_x):
average.append(float(sum(m2p[i][1:])) / float(len(m2p[i][1:])))
pl.plot(m2p[0:max_x, 0], average, colors[color])
if color == len(colors) - 1:
color = 0
else:
color += 1
pl.title(measuring + " " + mutation + " Average")
pl.savefig(directory + "/" + mutation + "_avg.png", format="png")
pl.close()
def main():
print 'Reading command-line arguments...'
args = sys.argv[1:] # Remove the name of the program from the arguments
num_args = len(args)
req_args = [
False
] * 3 # If every required argument was given then req_args will be all true
if num_args >= 3:
# Arguments with default values
stdevs_away = 2
round_to = 5
for arg in range(0, num_args - 1, 2):
option = args[arg]
value = args[arg + 1]
if option == '-s' or option == '--sets':
sets_file = shared.openFile(value, 'r')
req_args[0] = True
elif option == '-c' or option == '--current-ranges':
cur_ranges_file = shared.openFile(value, 'r')
req_args[1] = True
elif option == '-n' or option == '--new-ranges':
new_ranges_fname = value
new_ranges_file = shared.openFile(new_ranges_fname, 'w')
req_args[2] = True
elif option == '-d' or option == '--standard-dev':
stdevs_away = shared.toInt(value)
elif option == '-r' or option == '--round-to':
round_to = shared.toInt(value)
elif option == '-h' or option == '--help':
usage()
else:
usage()
for arg in req_args: # Check to ensure every required argument was entered
if not arg:
usage()
else:
usage()
print 'Reading the parameter sets file...'
# Parse the sets file to get the list of parameter sets
sets = []
for line in sets_file:
if not (line == ''
or line[0] == '#'): # Ignore blank lines and comments
sets.append(line)
if len(sets) < 1: # Ensure at least one set was given
usage()
print 'Reading the current ranges file...'
# Parse the current ranges file to find the existing ranges
par_names = []
cur_ranges = []
for line in cur_ranges_file:
line = line.replace('\t', ' ')
if not (line == ''
or line[0] == '#'): # Ignore blank lines and comments
# Get the human-readable description
space = line.find(' ')
if space <= 0:
parsing_error()
par_names.append(line[:space])
line = line[space + 1:] # Skip past the description
# Find the range bounds
start = 0
if line[start] == '\0':
parsing_error()
while line[start] == ' ':
start += 1
if line[start] != '[':
parsing_error()
end = start + 1
while line[end] != ']' and line[end] != '\0':
end += 1
if line[end] == '\0':
parsing_error()
line = line[start + 1:end]
bounds = map(shared.toFlo,
line.split(',')) # Convert the bounds to floats
if len(bounds) != 2:
parsing_error()
cur_ranges.append(bounds)
print 'Calculating new ranges...'
# Calculate each parameter's new range
flo_sets = map(
lambda ls: map(shared.toFlo, ls),
map(lambda s: s.split(','),
sets)) # Convert each parameter set string into an array of floats
num_sets = len(flo_sets)
new_ranges = []
for p in range(len(cur_ranges)): # For every range
# Get the mean range based on every set
vals = []
for s in flo_sets:
vals.append(s[p])
mean = sum(vals) / num_sets
# Calculate the standard deviation from the mean
stdev_sum = 0
for f in vals:
stdev_sum += (f - mean)**2
stdev = math.sqrt(stdev_sum / num_sets)
# Define new ranges based on the mean and standard deviation that are at least as narrow as the current ranges
lower_bound = max(cur_ranges[p][0],
round(mean - stdev * stdevs_away, round_to))
upper_bound = min(cur_ranges[p][1],
round(mean + stdev * stdevs_away, round_to))
new_ranges.append([lower_bound, upper_bound])
print 'Writing the new ranges to the specified output file...'
# Write the parameter ranges to the new ranges file
for r in range(len(new_ranges)):
new_ranges_file.write(par_names[r] + ' [' + str(new_ranges[r][0]) +
',' + str(new_ranges[r][1]) + ']\n')
new_ranges_file.close()
print 'Done. The new ranges are in ' + new_ranges_fname
def main(): # check the given arguments if len(sys.argv) < 3: usage() elif len(sys.argv) == 4: if sys.argv[1] == "-c" or sys.argv[1] == "--no-color": shared.terminalRed = "" shared.terminalReset = "" filename = sys.argv[2] startTime = sys.argv[3] else: usage() else: filename = sys.argv[1] startTime = sys.argv[2] # open the input file and ensure 'startTime' is a number f = shared.openFile(filename, "r") startTime = shared.toFlo(startTime) startIndex = 0 # extract the width and height from the concentrations file width, height = shared.widthAndHeight(f.readline().split(), filename) cellCount = width * height sums = [0 for i in range(cellCount)] cells = [] # for every line in the file, get the sum of the concentration levels as well as each individual value cast as a float, and calculate the starting index based off of the starting time for line in f: data = line.split() curdata = [] if data[0] < startTime: startIndex += 1 for cell in range(cellCount): sums[cell] += shared.toFlo(data[cell + 1]) curdata.append(shared.toFlo(data[cell + 1])) cells.append(curdata) # ensure there was data to retrieve if len(cells) == 0: print shared.terminalRed + "Couldn't get any cell data! Make sure '" + filename + "' is properly formatted. Exit status 3.", shared.terminalReset exit(3) # calculate the mean of each cell means = [] for cell in range(cellCount): means.append(sums[cell] / len(cells)) # calculate the total average score avgscore = 0.0 for cell in range(1, cellCount): numerator = 0 sqr1 = 0 sqr2 = 0 for i in range(startIndex, len(cells)): tstep = cells[i] xi = tstep[0] yi = tstep[cell] numerator += ((xi - means[0]) * (yi - means[cell])) sqr1 += (xi - means[0]) ** 2 sqr2 += (yi - means[cell]) ** 2 sqr1 = math.sqrt(sqr1) sqr2 = math.sqrt(sqr2) if sqr1 == 0 or sqr2 == 0: r = 1 else: r = numerator / (sqr1 * sqr2) avgscore += r # print the synchronization score print round(avgscore / (cellCount - 1), 10) f.close()
def main(): # check the given arguments if len(sys.argv) < 6: usage() elif len(sys.argv) == 7: if sys.argv[1] == "-c" or sys.argv[1] == "--no-color": shared.terminalRed = "" shared.terminalReset = "" filename = sys.argv[2] filename2 = sys.argv[3] directory = sys.argv[4] measuring = sys.argv[5] mutation = sys.argv[6] else: usage() else: filename = sys.argv[1] directory = sys.argv[2] measuring = sys.argv[3] mutation = sys.argv[4] # open the input file and ensure the directory exists f = shared.openFile(filename, "r") f2 = shared.openFile(filename2, "r") directory = shared.ensureDir(directory) # split the lines to get data data = [line.split() for line in f] file_len = len(data) - 1 max_x = file_len f.close() data2 = [line.split() for line in f2] file_len2 = len(data2) - 1 max_x2 = file_len2 f2.close() if (max_x == max_x2): print "test" # number of columns we have in the files cn = shared.toInt(data[0][0]) * shared.toInt(data[0][1]) + 1 cn2 = shared.toInt(data2[0][0]) * shared.toInt(data2[0][1]) + 1 # create matrices to store the data we obtained from the files m2p=numpy.zeros(shape = (max_x,cn + cn2)) # put the data coming from the files to the matrix for i in range(2, file_len): for j in range(0, cn+cn2): if (j <cn): m2p[i][j] = shared.toFlo(data[i][j]) elif (j==cn): print data2[i][j-cn] else: m2p[i][j] = 2*shared.toFlo(data2[i][j-cn]) # plot colors colors = ['b', 'g', 'r', 'c', 'm', 'y', 'k'] color = 0 for i in range(1, cn+cn2): if (i % 4 == 0): pl.plot(m2p[0:max_x, 0], m2p[0:max_x, i], 'r') elif (i % 4 == 1): pl.plot(m2p[0:max_x, 0], m2p[0:max_x, i], 'g') elif (i % 4 == 2): pl.plot(m2p[0:max_x, 0], m2p[0:max_x, i], 'b') else: pl.plot(m2p[0:max_x, 0], m2p[0:max_x, i], 'c') pl.title(measuring + " " + mutation + " All Cells") pl.savefig(directory + "/" + mutation + "_all.png", format = "png") pl.close() # plot the data average = [] for i in range(0, max_x): average.append(float(sum(m2p[i][1:])) / float(len(m2p[i][1:]))) pl.plot(m2p[0:max_x, 0], average, colors[color]) if color == len(colors) - 1: color = 0 else: color += 1 pl.title(measuring + " " + mutation + " Average") pl.savefig(directory + "/" + mutation + "_avg.png", format = "png") pl.close()
def main():
print "Reading command-line arguments..."
args = sys.argv[1:]
if len(args) == 3:
cons_fname1 = args[0]
cons_fname2 = args[1]
directory = args[2]
else:
usage()
print "Reading concentrations file 1..."
min_con1 = float("inf")
max_con1 = 0
cons_data1 = []
if cons_fname1.endswith(".cons"): # Read ASCII file
cons_file1 = shared.openFile(cons_fname1, "r")
width, height = map(
lambda num: shared.toInt(num), cons_file1.readline().split(" ")
) # The first line contains the width and height
checkSize(width, height)
for line in cons_file1:
cons = map(
lambda num: shared.toFlo(num), line.split(" ")[1:-1]
) # Remove the time step column and newline when taking the concentrations
for con in cons:
min_con1 = min(min_con1, con)
max_con1 = max(max_con1, con)
cons_data1.append(cons)
elif cons_fname1.endswith(".bcons"): # Read binary file
cons_file1 = shared.openFile(cons_fname1, "rb") # Read the file as a binary
# The first two ints are the width and height
width, = struct.unpack("i", cons_file1.read(4))
height, = struct.unpack("i", cons_file1.read(4))
checkSize(width, height)
size = width * height
cons1 = []
cons_length1 = 0
while True:
con_str1 = cons_file1.read(4)
if con_str1 == "": # While not EOF
break
else:
# There are width * height concentration floats per time step
con, = struct.unpack("f", con_str1)
min_con1 = min(min_con1, con)
max_con1 = max(max_con1, con)
cons1.append(con)
cons_length1 += 1
if cons_length1 == height:
cons_data1.append(cons)
cons1 = []
else:
usage()
print "Reading concentrations file 2..."
min_con2 = float("inf")
max_con2 = 0
cons_data2 = []
if cons_fname2.endswith(".cons"): # Read ASCII file
cons_file2 = shared.openFile(cons_fname2, "r")
width, height = map(
lambda num: shared.toInt(num), cons_file2.readline().split(" ")
) # The first line contains the width and height
checkSize(width, height)
for line in cons_file2:
cons = map(
lambda num: shared.toFlo(num), line.split(" ")[1:-1]
) # Remove the time step column and newline when taking the concentrations
for con in cons:
min_con2 = min(min_con2, con)
max_con2 = max(max_con2, con)
cons_data2.append(cons)
elif cons_fname2.endswith(".bcons"): # Read binary file
cons_file2 = shared.openFile(cons_fname2, "rb") # Read the file as a binary
# The first two ints are the width and height
width, = struct.unpack("i", cons_file2.read(4))
height, = struct.unpack("i", cons_file2.read(4))
checkSize(width, height)
size = width * height
cons2 = []
cons_length2 = 0
while True:
con_str2 = cons_file2.read(4)
if con_str2 == "": # While not EOF
break
else:
# There are width * height concentration floats per time step
con, = struct.unpack("f", con_str2)
min_con2 = min(min_con2, con)
max_con2 = max(max_con2, con)
cons2.append(con)
cons_length2 += 1
if cons_length2 == height:
cons_data2.append(cons)
cons2 = []
else:
usage()
print "Creating the directory if necessary..."
directory = shared.ensureDir(directory)
if directory[-1] != "/":
directory = directory + "/"
cons_data = combine_cons(cons_data1, cons_data2, max_con1, min_con1, max_con2, min_con2)
print "Creating snapshots..."
edge, size = findSizes(width, height) # Configure the hexagon edge and window size based on the grid size
index = 0
for line in cons_data:
if index % 10 == 0 and index >= 21000:
plotHexagons(directory, size, index, line, edge, width, height)
index += 1
print "Done. Your snapshots are stored in " + directory
def main():
gradients = {0: ['28', '29'], 1: ['37'], 2: ['41'], 3: ['34', '35'], 4: ['38', '39'], 5: ['4', '5'], 6: ['34', '35', '37'], 7: ['38', '39', '41'], 8: ['4', '5', '7'], 9: ['28', '32', '15']}
end_rate = [-1, 1001, 1001, 1001, 1001, -1, 1001, 1001, -1, -1]
incs = [-1, 1, 1, 1, 1, -1, 1, 1, -1, -1]
print 'Reading command-line arguments...'
args = sys.argv[1:] # Remove the name of the program from the arguments
num_args = len(args)
num_req_args = 2
req_args = [False] * num_req_args # If every required argument was given then req_args will be all true
if num_args >= num_req_args:
# Arguments with default values
base_gradients_file = None
simulation = './simulation'
sim_arguments = ""
for arg in range(0, num_args - 1, 2):
option = args[arg]
value = args[arg + 1]
if option == '-s' or option == '--sets':
sets_fname = value # Parameter sets file
req_args[0] = True
elif option == '-o' or option == '--output-dir':
odir = value
req_args[1] = True
elif option == '-g' or option == '--base-gradients':
base_gradients_file = shared.openFile(value, 'r') # Gradients to add to every simulation on top of the changing ones
elif option == '-S' or option == '--simulation':
simulation = value # Filename of the simulation
elif option == '-a' or option == '--arguments':
for a in range(arg + 1, num_args): # Arguments to pass to the simulation
sim_arguments += ' ' + args[a]
break
else:
usage()
for arg in req_args: # Check to ensure every required argument was entered
if not arg:
usage()
else:
usage()
print 'Reading the base gradients file...'
# Add any given base gradients to a string to be passed to each simulation's gradients file
if base_gradients_file is None:
base_gradients = ''
else:
base_gradients = ''
for line in base_gradients_file:
base_gradients += line
print 'Start creating gradient files and pbs-jobs for each gradient combination'
start_point = '9'
end_point = '49'
# For every gradient option
num_gradients = len(gradients)
jobs_to_submit = []
for i in range(num_gradients):
si = str(i)
for j in range(100, end_rate[i], incs[i]):
sj = str(j)
grad_id = '-' + si + '-' + sj
grad_fname = odir + "/grad" + grad_id + ".gradient"
scores_fname = odir + "/scores" + grad_id + ".csv"
subprocess.call(["mkdir", "-p", odir])
grad_file = shared.openFile(grad_fname, 'w')
# Write the base gradient to the file
grad_file.write(base_gradients)
for rate in gradients[i]:
grad_file.write(rate + ' (' + start_point + ' 100) (' + end_point + ' ' + sj + ')\n')
# Create the PBS job file
pbs_fname = odir + '/pbs-job' + grad_id
jobs_to_submit.append(pbs_fname)
pbs_file = shared.openFile(pbs_fname, 'w')
pbs_file.write("""
#PBS -N gradient-run""" + grad_id + """
#PBS -l nodes=1:ppn=1
#PBS -l mem=500MB
#PBS -l file=500MB
#PBS -q biomath
#PBS -j oe
#PBS -o output.txt
#PBS -l walltime=360:00:00
cd $PBS_O_WORKDIR\n""")
# Write the simulation call to the PBS job file
pbs_file.write(simulation + sim_arguments + ' -i ' + sets_fname + ' -r ' + grad_fname + ' -E ' + scores_fname)
pbs_file.close()
if j % 50 == 0:
queue_jobs = get_num_jobs()
# Add parsing of queue_jobs
while (queue_jobs > 150):
time.sleep(300)
queue_jobs = get_num_jobs()
for job in range(min(100, len(jobs_to_submit))):
print ("Submitting job " + jobs_to_submit[job])
subprocess.call(['qsub', jobs_to_submit[job]])
jobs_to_submit = jobs_to_submit[100:]
def main():
print 'Reading command-line arguments...'
args = sys.argv[1:] # Remove the name of the program from the arguments
num_args = len(args)
cons_file = None # Concentrations file
figure_fname = 'st' # Filename to give the figure minus its extension
image_format = 'png' # Format in which to save the figure, also serving as its extension
image_width = 1000 # Width of the image in pixels
image_height = 250 # Height of the image in pixels
steps_til_growth = 60000 # Steps before growth starts
steps_to_split = 600 # Steps each split takes
initial_width = 10 # Initial width of the PSM in cells
granularity = 1 # Use each <granularity> time steps of data
start_step = 0 # Start time step relative to steps_til_growth
end_step = 60000 # End time step relative to steps_til_growth
for arg in range(0, num_args - 1, 2):
option = args[arg]
value = args[arg + 1]
if option == '-c' or option == '--cons-file':
cons_file = shared.openFile(value, 'r')
elif option == '-f' or option == '--figure-name':
figure_fname = value
elif option == '-i' or option == '--image-format':
image_format = value
elif option == '-w' or option == '--image-width':
image_width = shared.toInt(value)
elif option == '-h' or option == '--image-height':
image_height = shared.toInt(value)
elif option == '-G' or option == '--steps-til-growth':
steps_til_growth = shared.toInt(value)
elif option == '-S' or option == '--steps-to-split':
steps_to_split = shared.toInt(value)
elif option == '-n' or option == '--initial-width':
initial_width = shared.toInt(value)
elif option == '-g' or option == '--granularity':
granularity = shared.toInt(value)
elif option == '-s' or option == '--start-step':
start_step = shared.toInt(value)
elif option == '-e' or option == '--end-step':
end_step = shared.toInt(value)
elif option == '-h' or option == '--help':
usage()
else:
usage()
if cons_file is None: # The concentrations file is required
usage()
print 'Parsing concentrations file...'
raw_data = [line.split() for line in cons_file] # Split the data into lines and split each line by spaces into an array
cons_file.close()
print 'Converting data to the appropriate sizes...'
# Take the width and height from the first line of the file
psm_width = shared.toInt(raw_data[0][0])
psm_height = shared.toInt(raw_data[0][1])
raw_data = raw_data[1 + steps_til_growth:] # Remove all data before growth starts
# Adjust step sizes for the given granularity
steps_til_growth /= granularity
steps_to_split /= granularity
data = [] # Like raw data, but takes only each <granularity> time steps of data and removes the time steps column
for line in range(len(raw_data)):
if line % granularity == 0:
data.append(raw_data[line])
total_steps = len(data)
for row in range(total_steps):
data[row] = data[row][1:] # Remove the time steps column
steps_when_full = (psm_width - initial_width) * steps_to_split # When the PSM is done growing
total_width = psm_width + (total_steps - steps_when_full) / steps_to_split # The width of every cell that exists at any point
table = [[0 for i in range(total_steps)] for j in range(total_width)] # A table containing the data formatted more closely to what the figure requires
print 'Accounting for cell growth and averaging cell columns...'
min_con = float('inf')
max_con = 0
# Fill in the table with all data from when the PSM is growing
current_width = initial_width
row_start = current_width - 1
steps_elapsed = 0
for column in range(steps_when_full):
for row in range(current_width):
avg_con = 0
cell_x = row_start - row # Posterior cells should be printed on the right
for cell_y in range(psm_height): # Average each column of cells
cell_index = cell_y * psm_width + cell_x
avg_con += shared.toFlo(data[column][cell_index])
avg_con /= psm_height
table[row][column] = avg_con
# Update the minimum and maximum concentrations
min_con = min(min_con, avg_con)
max_con = max(max_con, avg_con)
for row in range(current_width, total_width): # Nonexistent cells get concentrations of 0
table[row][column] = -10
steps_elapsed += 1
if steps_elapsed == steps_to_split: # Split the PSM every steps_to_split time steps
current_width += 1
row_start += 1 # Adjust because the first cell in data is the new, posterior-most cell
steps_elapsed = 0
for column in range(steps_when_full, total_steps):
for row in range(current_width, total_width):
table[row][column] = -10
# Fill in the table with all data from when the PSM is done growing
arrested_cells = []
row_start = psm_width - 1
row_offset = 0
for column in range(steps_when_full, total_steps):
for row in range(psm_width):
avg_con = 0
cell_x = (row_start - row) % psm_width # Posterior cells should be printed on the right
for cell_y in range(psm_height): # Average each column of cells
cell_index = cell_y * psm_width + cell_x
avg_con += shared.toFlo(data[column][cell_index])
avg_con /= psm_height
table[row + row_offset][column] = avg_con
# Update the minimum and maximum concentrations
min_con = min(min_con, avg_con)
max_con = max(max_con, avg_con)
for cell in arrested_cells: # Print the last value each arrested cell had for the rest of time
table[cell[0]][column] = -10
steps_elapsed += 1
if steps_elapsed == steps_to_split: # Split the PSM every steps_to_split time steps
arrested_cells.append((row_offset, table[row_offset][column]))
row_offset += 1
steps_elapsed = 0
max_con += 1
print 'Cropping to the specified time range...'
start_step /= granularity
end_step /= granularity
total_steps = end_step - start_step
for row in range(total_width):
table[row] = table[row][start_step:end_step]
print 'Creating a blank image...'
im = Image.new('RGB', (image_width, image_height), rgb('FFFFFF')) # Make an image with a blank, white canvas
draw = ImageDraw.Draw(im) # Get the drawing object
print 'Filling the image with the concentrations...'
# Find the factors to scale the table data into an image_width by image_height sized figure
x_factor = shared.toFlo(total_steps) / image_width
y_factor = shared.toFlo(total_width) / image_height
# Darker shades indicate higher concentrations
shades = [rgb('FEB4EF'), rgb('FEB4EF'), rgb('FE5A77'), rgb('FE2D3B'), rgb('FF0000'), rgb('BF0000'), rgb('7F0000'), rgb('3F0000'), rgb('000000'), rgb('FFFFFF')]
num_shades = len(shades)
for i in range(image_width):
x = shared.toInt(i * x_factor)
for j in range(image_height):
reverse_j = image_height - j - 1 # In the figure, cell 0 is at the bottom, not top
y = shared.toInt(reverse_j * y_factor)
con = table[y][x]
if con == -10:
color = rgb('EEE5DE')
else:
color = shades[int((con - min_con) / (max_con - min_con) * (num_shades - 1))] # Find the color matching the concentration
draw.point((i, j), fill = color)
print 'Saving the image...'
figure_fname_full = figure_fname + '.' + image_format.lower()
im.save(figure_fname_full, image_format.upper())
print 'Done. Your figure is stored in ' + figure_fname_full
def main():
print 'Reading command-line arguments...'
args = sys.argv[1:]
if len(args) == 3:
cons_fname1 = args[0]
cons_fname2 = args[1]
directory = args[2]
else:
usage()
print 'Reading concentrations file 1...'
min_con1 = float('inf')
max_con1 = 0
cons_data1 = []
if cons_fname1.endswith('.cons'): # Read ASCII file
cons_file1 = shared.openFile(cons_fname1, 'r')
width, height = map(
lambda num: shared.toInt(num),
cons_file1.readline().split(
' ')) # The first line contains the width and height
checkSize(width, height)
for line in cons_file1:
cons = map(
lambda num: shared.toFlo(num),
line.split(' ')[1:-1]
) # Remove the time step column and newline when taking the concentrations
for con in cons:
min_con1 = min(min_con1, con)
max_con1 = max(max_con1, con)
cons_data1.append(cons)
elif cons_fname1.endswith('.bcons'): # Read binary file
cons_file1 = shared.openFile(cons_fname1,
'rb') # Read the file as a binary
# The first two ints are the width and height
width, = struct.unpack('i', cons_file1.read(4))
height, = struct.unpack('i', cons_file1.read(4))
checkSize(width, height)
size = width * height
cons1 = []
cons_length1 = 0
while True:
con_str1 = cons_file1.read(4)
if con_str1 == '': # While not EOF
break
else:
# There are width * height concentration floats per time step
con, = struct.unpack('f', con_str1)
min_con1 = min(min_con1, con)
max_con1 = max(max_con1, con)
cons1.append(con)
cons_length1 += 1
if cons_length1 == height:
cons_data1.append(cons)
cons1 = []
else:
usage()
print 'Reading concentrations file 2...'
min_con2 = float('inf')
max_con2 = 0
cons_data2 = []
if cons_fname2.endswith('.cons'): # Read ASCII file
cons_file2 = shared.openFile(cons_fname2, 'r')
width, height = map(
lambda num: shared.toInt(num),
cons_file2.readline().split(
' ')) # The first line contains the width and height
checkSize(width, height)
for line in cons_file2:
cons = map(
lambda num: shared.toFlo(num),
line.split(' ')[1:-1]
) # Remove the time step column and newline when taking the concentrations
for con in cons:
min_con2 = min(min_con2, con)
max_con2 = max(max_con2, con)
cons_data2.append(cons)
elif cons_fname2.endswith('.bcons'): # Read binary file
cons_file2 = shared.openFile(cons_fname2,
'rb') # Read the file as a binary
# The first two ints are the width and height
width, = struct.unpack('i', cons_file2.read(4))
height, = struct.unpack('i', cons_file2.read(4))
checkSize(width, height)
size = width * height
cons2 = []
cons_length2 = 0
while True:
con_str2 = cons_file2.read(4)
if con_str2 == '': # While not EOF
break
else:
# There are width * height concentration floats per time step
con, = struct.unpack('f', con_str2)
min_con2 = min(min_con2, con)
max_con2 = max(max_con2, con)
cons2.append(con)
cons_length2 += 1
if cons_length2 == height:
cons_data2.append(cons)
cons2 = []
else:
usage()
print 'Creating the directory if necessary...'
directory = shared.ensureDir(directory)
if (directory[-1] != '/'):
directory = directory + '/'
cons_data = combine_cons(cons_data1, cons_data2, max_con1, min_con1,
max_con2, min_con2)
print 'Creating snapshots...'
edge, size = findSizes(
width, height
) # Configure the hexagon edge and window size based on the grid size
index = 0
for line in cons_data:
if (index % 10 == 0 and index >= 21000):
plotHexagons(directory, size, index, line, edge, width, height)
index += 1
print 'Done. Your snapshots are stored in ' + directory
def main():
print 'Reading command line arguments...'
# check the given arguments
if len(sys.argv) < 8:
usage()
else:
folder = sys.argv[1]
parsets = shared.toInt(sys.argv[2])
image_name = sys.argv[3]
feature = sys.argv[4]
ofolder = sys.argv[5]
post_width = shared.toInt(sys.argv[6])
excel_name = sys.argv[7]
num_mutants = 6
index = 0
mutants = ["wildtype", "delta", "her1", "her7", "her7her13", "her13"]
markers = ['o', '^', 's', '*', 'h', 'D']
colors = ['k', 'b', 'g', 'r', 'c', 'm']
features = []
if (feature == "period" or feature == "amplitude"):
features.append(feature)
else:
features.append("period")
features.append("amplitude")
for feat in features:
# Create excel file in which the data used to create the plots will be stored
excel_file = shared.openFile(ofolder + "/" + excel_name + "-" + feat + ".csv", "w")
print "Plotting ", feat, "..."
first_avg = 0
num_first = 0
for index in range(num_mutants):
mutant = mutants[index]
print ' Running ' + mutant + '...'
marker = markers[index]
color = colors[index]
# open the input file
f = shared.openFile(folder + "/" + mutant + "/set_0_" + feat + "_mh1.feats", "r")
# split the lines to get data
data = [line.split(",") for line in f]
# calculate the tissue size
height = shared.toInt(data[0][0])
width = shared.toInt(data[0][1])
xmin = 0
xmax = 0.9 * width
buckets = 9 # split the interval into 9 chunks
chunk = (width - post_width) / (buckets - 1) # the width of the intervals after the posterior
indexes = [0 for i in range(buckets)]
for bucket in range(buckets):
if bucket == 0:
indexes[bucket] = post_width / 2
else:
indexes[bucket] = (post_width + (bucket - 1) * chunk) + (chunk / 2.0)
averages = [0 for i in range(buckets)]
num_points = [0 for i in range(buckets)]
stderr = [0 for i in range(buckets)]
if mutant == "wildtype":
excel_file.write("mutant,")
for index in indexes:
excel_file.write(str(index) + ",")
excel_file.write("\n")
print ' Averaging the first bucket for the wildtype...' # all other data points will be averaged to this value
for parset in range(parsets):
# open the input file and ensure the directory exists
f = shared.openFile(folder + "/" + mutant + "/set_" + str(parset) + "_" + feat + "_mh1.feats", "r")
# split the lines to get data
data = [line.split(",") for line in f]
lines = len(data)
for line in range(1, lines, 2):
for col in range(len(data[line]) - 1):
pos = shared.toInt(data[line][col])
val = shared.toFlo(data[line + 1][col])
if pos < post_width:
first_avg += val
num_first += 1
first_avg /= num_first
for parset in range(parsets):
print ' Normalizing and analyzing data from set ' + str(parset) + '...'
# open the input file and ensure the directory exists
f = shared.openFile(folder + "/" + mutant + "/set_" + str(parset) + "_" + feat + "_mh1.feats", "r")
# split the lines to get data
data = [line.split(",") for line in f]
lines = len(data)
for line in range(1, lines, 2):
for col in range(len(data[line]) - 1):
pos = shared.toInt(data[line][col])
val = shared.toFlo(data[line + 1][col]) / first_avg
if pos < post_width:
averages[0] += val
num_points[0] += 1
else:
averages[(pos - post_width) / chunk + 1] += val
num_points[(pos - post_width) / chunk + 1] += 1
# ignore the buckets which don't have data
buckets_with_data = buckets
for bucket in range(buckets):
if post_width + ((bucket - 1) * chunk) + chunk - 1 > (0.9 * width):
buckets_with_data -= 1
else:
if num_points[bucket] > 0:
averages[bucket] /= num_points[bucket]
elif feat == "amplitude":
averages[bucket] = 0
else:
buckets_with_data -= 1
buckets = buckets_with_data
print ' Calculating standard error...'
for parset in range(parsets):
f = shared.openFile(folder + "/" + mutant + "/set_" + str(parset) + "_" + feat + "_mh1.feats", "r")
data = [line.split(",") for line in f]
lines = len(data)
for line in range(1, lines, 2):
for col in range(len(data[line]) - 1):
pos = shared.toInt(data[line][col])
val = shared.toFlo(data[line + 1][col]) / first_avg
if pos < post_width:
stderr[0] += (val - averages[0]) ** 2
else:
stderr[(pos - post_width) / chunk + 1] += (val - averages[(pos - post_width) / chunk + 1]) ** 2
for bucket in range(buckets):
if (num_points[bucket] > 0):
stderr[bucket] = math.sqrt(stderr[bucket] / num_points[bucket])
stderr[bucket] /= math.sqrt(num_points[bucket])
else:
stderr[bucket] = 0
indexes = indexes[:buckets]
averages = averages[:buckets]
stderr = stderr[:buckets]
# Print the means and standard deviations to the excel_file
excel_file.write(mutant + ",")
for average in averages:
excel_file.write(str(average) + ",")
excel_file.write("\n,")
for stder in stderr:
excel_file.write(str(stder) + ",")
excel_file.write("\n")
plt.errorbar(indexes, averages, stderr, fmt='ro', linestyle='-', marker=marker, color=color, label=mutant)
plt.legend(prop={'size':8}, loc=2)
pylab.xlim([xmin, xmax])
excel_file.close()
plt.savefig(ofolder + "/" + image_name + "_" + feat + ".png", format = "png")
plt.close()
print "Done. Your " + feat + " plot is stored in " + ofolder + "/" + image_name + "_" + feat + ".png"
print "The data behind the plot can be found in " + ofolder + "/" + excel_name + "-" + feat + ".csv"
def main():
# check the given arguments
if len(sys.argv) < 6:
usage()
else:
folder = sys.argv[1]
parsets = shared.toInt(sys.argv[2])
ofolder = sys.argv[3]
image_name = sys.argv[4]
excel_name = sys.argv[5]
mutants = ["wildtype", "delta", "her1", "her7", "her7her13", "her13"]
markers = ['o', '^', 's', '*', 'h', 'D']
colors = ['k', 'b', 'g', 'r', 'c', 'm']
num_mutants = 6
# Create excel file in which the data used to create the plots will be stored
excel_file = shared.openFile(ofolder + "/" + excel_name + "-sync.csv", "w")
for index in range(num_mutants):
mutant = mutants[index]
marker = markers[index]
color = colors[index]
# open the first file to get the height, width and interval
f = shared.openFile(folder + "/" + mutant + "/set_0_sync_mh1.feats", "r")
# split the lines to get data
data = [line.split(",") for line in f]
# calculate the tissue size
height = shared.toInt(data[0][0])
interval = shared.toFlo(data[0][1])
#split_time = shared.toFlo(data[0][2])
width = len(data[1]) - 1
indexes = [0 for i in range(width)]
averages = [0 for i in range(width)]
stderr = [0 for i in range(width)]
for parset in range(parsets):
f = shared.openFile(folder + "/" + mutant + "/set_" + str(parset) + "_sync_mh1.feats", "r")
# split the lines to get data
data = [line.split(",") for line in f]
for col in range(width):
for line in range(1, height + 1):
averages[col] += shared.toFlo(data[line][col])
f.close()
for col in range(width):
indexes[col] = (((interval / 2) * col + (interval / 2) * col + interval) / 2) / 6
averages[col] /= height * parsets
if mutant == "wildtype":
excel_file.write("mutant,")
for index in indexes:
excel_file.write(str(index) + ",")
excel_file.write("\n")
for parset in range(parsets):
f = shared.openFile(folder + "/" + mutant + "/set_" + str(parset) + "_sync_mh1.feats", "r")
data = [line.split(",") for line in f]
# std error = std deviation / sqrt(num data points)
for col in range(width):
for line in range(1, height + 1):
stderr[col] += (shared.toFlo(data[line][col]) - averages[col]) ** 2
stderr[col] = math.sqrt(stderr[col] / (height * parsets))
stderr[col] /= math.sqrt(height * parsets)
# Print the means and standard deviations to the excel_file
excel_file.write(mutant + ",")
for average in averages:
excel_file.write(str(average) + ",")
excel_file.write("\n,")
for stder in stderr:
excel_file.write(str(stder) + ",")
excel_file.write("\n")
plt.errorbar(indexes, averages, stderr, fmt='ro', linestyle='-', marker=marker, color=color, label=mutant)
plt.legend(prop={'size':8}, loc=3)
pylab.xlim([0, (width + 1) * (interval / 2) / 6])
plt.savefig(ofolder + "/" + image_name + ".png", format = "png")
plt.close()
