def _robustness_test_(all_snps, phenVals, outputFile, filter=0.1):
import analyzePhenotype
import analyzeHaplotype
import random
new_all_snps = []
for snp in all_snps:
if snp.count(0) > 1 and snp.count(1) > 1:
new_all_snps.append(snp)
print "Filtered", len(all_snps) - len(
new_all_snps), " with minor allele count <2."
all_snps = new_all_snps
def getLeaveOneOutK(K, i):
l = range(0, len(K))
l.pop(i)
new_k = numpy.core.take(K, l, 0)
new_k = numpy.core.take(new_k, l, 1)
return new_k
print "Calculating kinship"
t1 = time.time()
K = calcKinship(all_snps)
t2 = time.time()
print "Took", t2 - t1, "seconds."
"""
Leave one out test..
"""
if filter < 1.0:
snps = random.sample(all_snps, int(len(all_snps) * filter))
print "Number of SNPs:", len(snps)
else:
snps = all_snps
# K = calcKinship(snps)
print "running EMMA"
t1 = time.time()
true_pvals = _runEmma_(snps, phenVals, K)["ps"]
true_pvals = map(float, true_pvals)
t2 = time.time()
print "Took", t2 - t1, "seconds."
log_true_pvals = []
for pval in true_pvals:
log_true_pvals.append(-math.log(pval, 10))
perm_pvalues_list = []
for i in range(0, len(phenVals)):
newPhenvals = phenVals[:]
newPhenvals.pop(i)
newSNPs = []
for snp in snps:
newSNP = snp[:]
newSNP.pop(i)
newSNPs.append(newSNP)
new_k = getLeaveOneOutK(K, i)
print "running EMMA"
t1 = time.time()
pvals = _runEmma_(newSNPs, newPhenvals, new_k)["ps"]
pvals = map(float, pvals)
t2 = time.time()
print "Took", t2 - t1, "seconds."
perm_pvalues_list.append(pvals)
delta_pvals_list = []
delta_log_pvals_list = []
for perm_pvals in perm_pvalues_list:
log_pvals = []
delta_pvals = []
delta_log_pvals = []
for i in range(0, len(true_pvals)):
pval = perm_pvals[i]
true_pval = true_pvals[i]
delta_pvals.append(true_pval - pval)
log_true_pval = log_true_pvals[i]
log_pval = -math.log(pval, 10)
log_pvals.append(log_pval)
delta_log_pvals.append(log_true_pval - log_pval)
delta_pvals_list.append(delta_pvals)
delta_log_pvals_list.append(delta_log_pvals)
sd_log_pvals = []
sd_pvals = []
t_delta_log_pvals_list = map(list, zip(*delta_log_pvals_list))
t_delta_pvals_list = map(list, zip(*delta_pvals_list))
for i in range(0, len(true_pvals)):
sd_log_pvals.append(util.calcSD(t_delta_log_pvals_list[i]))
sd_pvals.append(util.calcSD(t_delta_pvals_list[i]))
#Write SDs out to file, to be able to replot, or plot together with other methods... etc
import csv
sd_log_pval_file = outputFile + ".rob.log_pvals_sd"
f = open(sd_log_pval_file, "w")
w = csv.writer(f)
w.writerow(["log_true_pval", "sd_log_pvals"])
l = zip(log_true_pvals, sd_log_pvals)
w.writerows(l)
f.close()
#Plot things....
pngFile_log_pvals = outputFile + ".rob.log_pval.png"
pngFile_pval = outputFile + ".rob.pval.png"
pngFile_sd_log_pval = outputFile + ".rob.sd_log_pval.png"
pngFile_sd_pval = outputFile + ".rob.sd_pval.png"
min_val = min(true_pvals)
max_val = max(true_pvals)
val_range = max_val - min_val
min_log_val = min(log_true_pvals)
max_log_val = max(log_true_pvals)
log_val_range = max_val - min_val
print "Plotting graphs"
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
plt.figure(figsize=(10, 7))
max_perm_val = 0
min_perm_val = 0
for i in range(0, len(perm_pvalues_list)):
delta_log_pvals = delta_log_pvals_list[i]
plt.plot(log_true_pvals, delta_log_pvals, "b.")
max_perm_val = max(max_perm_val, max(delta_log_pvals))
min_perm_val = min(min_perm_val, min(delta_log_pvals))
perm_val_range = max_perm_val - min_perm_val
v = [
min_log_val - 0.02 * log_val_range, max_log_val + 0.02 * log_val_range,
min_perm_val - 0.02 * perm_val_range,
max_perm_val + 0.02 * perm_val_range
]
plt.axis(v)
plt.savefig(pngFile_log_pvals, format="png")
plt.figure(figsize=(10, 7))
max_perm_val = 0
min_perm_val = 0
for i in range(0, len(perm_pvalues_list)):
delta_pvals = delta_pvals_list[i]
plt.plot(true_pvals, delta_pvals, "b.")
max_perm_val = max(max_perm_val, max(delta_pvals))
min_perm_val = min(min_perm_val, min(delta_pvals))
perm_val_range = max_perm_val - min_perm_val
plt.axis([
min_val - 0.02 * val_range, max_val + 0.02 * val_range,
min_perm_val - 0.02 * perm_val_range,
max_perm_val + 0.02 * perm_val_range
])
plt.savefig(pngFile_pval, format="png")
plt.figure(figsize=(10, 7))
max_sd_log_pval = max(sd_log_pvals)
min_sd_log_pval = min(sd_log_pvals)
sd_val_range = max_sd_log_pval - min_sd_log_pval
plt.plot(log_true_pvals, sd_log_pvals, "b.")
plt.axis([
min_log_val - 0.02 * log_val_range, max_log_val + 0.02 * log_val_range,
min_sd_log_pval - 0.02 * sd_val_range,
max_sd_log_pval + 0.02 * sd_val_range
])
plt.savefig(pngFile_sd_log_pval, format="png")
plt.figure(figsize=(10, 7))
max_sd_pval = max(sd_pvals)
min_sd_pval = min(sd_pvals)
sd_val_range = max_sd_pval - min_sd_pval
plt.plot(true_pvals, sd_pvals, "b.")
plt.axis([
min_val - 0.02 * val_range, max_val + 0.02 * val_range,
min_sd_pval - 0.02 * sd_val_range, max_sd_pval + 0.02 * sd_val_range
])
plt.savefig(pngFile_sd_pval, format="png")
print "Done testing robustness"
def _robustness_test_(all_snps,phenVals,outputFile,filter=0.1):
import analyzePhenotype
import analyzeHaplotype
import random
new_all_snps = []
for snp in all_snps:
if snp.count(0)>1 and snp.count(1)>1:
new_all_snps.append(snp)
print "Filtered",len(all_snps)-len(new_all_snps)," with minor allele count <2."
all_snps = new_all_snps
def getLeaveOneOutK(K,i):
l = range(0,len(K))
l.pop(i)
new_k = numpy.core.take(K,l,0)
new_k = numpy.core.take(new_k,l,1)
return new_k
print "Calculating kinship"
t1 = time.time()
K = calcKinship(all_snps)
t2 = time.time()
print "Took",t2-t1,"seconds."
"""
Leave one out test..
"""
if filter <1.0:
snps = random.sample(all_snps,int(len(all_snps)*filter))
print "Number of SNPs:",len(snps)
else:
snps = all_snps
# K = calcKinship(snps)
print "running EMMA"
t1 = time.time()
true_pvals = _runEmma_(snps,phenVals,K)["ps"]
true_pvals = map(float,true_pvals)
t2 = time.time()
print "Took",t2-t1,"seconds."
log_true_pvals = []
for pval in true_pvals:
log_true_pvals.append(-math.log(pval,10))
perm_pvalues_list = []
for i in range(0,len(phenVals)):
newPhenvals = phenVals[:]
newPhenvals.pop(i)
newSNPs = []
for snp in snps:
newSNP = snp[:]
newSNP.pop(i)
newSNPs.append(newSNP)
new_k = getLeaveOneOutK(K,i)
print "running EMMA"
t1 = time.time()
pvals = _runEmma_(newSNPs,newPhenvals,new_k)["ps"]
pvals = map(float,pvals)
t2 = time.time()
print "Took",t2-t1,"seconds."
perm_pvalues_list.append(pvals)
delta_pvals_list = []
delta_log_pvals_list = []
for perm_pvals in perm_pvalues_list:
log_pvals = []
delta_pvals = []
delta_log_pvals = []
for i in range(0,len(true_pvals)):
pval = perm_pvals[i]
true_pval = true_pvals[i]
delta_pvals.append(true_pval-pval)
log_true_pval = log_true_pvals[i]
log_pval = -math.log(pval,10)
log_pvals.append(log_pval)
delta_log_pvals.append(log_true_pval-log_pval)
delta_pvals_list.append(delta_pvals)
delta_log_pvals_list.append(delta_log_pvals)
sd_log_pvals = []
sd_pvals = []
t_delta_log_pvals_list = map(list,zip(*delta_log_pvals_list))
t_delta_pvals_list = map(list,zip(*delta_pvals_list))
for i in range(0,len(true_pvals)):
sd_log_pvals.append(util.calcSD(t_delta_log_pvals_list[i]))
sd_pvals.append(util.calcSD(t_delta_pvals_list[i]))
#Write SDs out to file, to be able to replot, or plot together with other methods... etc
import csv
sd_log_pval_file = outputFile+".rob.log_pvals_sd"
f = open(sd_log_pval_file,"w")
w = csv.writer(f)
w.writerow(["log_true_pval","sd_log_pvals"])
l = zip(log_true_pvals,sd_log_pvals)
w.writerows(l)
f.close()
#Plot things....
pngFile_log_pvals = outputFile+".rob.log_pval.png"
pngFile_pval = outputFile+".rob.pval.png"
pngFile_sd_log_pval = outputFile+".rob.sd_log_pval.png"
pngFile_sd_pval = outputFile+".rob.sd_pval.png"
min_val = min(true_pvals)
max_val = max(true_pvals)
val_range = max_val-min_val
min_log_val = min(log_true_pvals)
max_log_val = max(log_true_pvals)
log_val_range = max_val-min_val
print "Plotting graphs"
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
plt.figure(figsize=(10,7))
max_perm_val = 0
min_perm_val = 0
for i in range(0,len(perm_pvalues_list)):
delta_log_pvals = delta_log_pvals_list[i]
plt.plot(log_true_pvals,delta_log_pvals,"b.")
max_perm_val = max(max_perm_val,max(delta_log_pvals))
min_perm_val = min(min_perm_val,min(delta_log_pvals))
perm_val_range = max_perm_val - min_perm_val
v = [min_log_val-0.02*log_val_range, max_log_val+0.02*log_val_range, min_perm_val-0.02*perm_val_range, max_perm_val+0.02*perm_val_range]
plt.axis(v)
plt.savefig(pngFile_log_pvals, format = "png")
plt.figure(figsize=(10,7))
max_perm_val = 0
min_perm_val = 0
for i in range(0,len(perm_pvalues_list)):
delta_pvals = delta_pvals_list[i]
plt.plot(true_pvals,delta_pvals,"b.")
max_perm_val = max(max_perm_val,max(delta_pvals))
min_perm_val = min(min_perm_val,min(delta_pvals))
perm_val_range = max_perm_val - min_perm_val
plt.axis([min_val-0.02*val_range, max_val+0.02*val_range, min_perm_val-0.02*perm_val_range, max_perm_val+0.02*perm_val_range])
plt.savefig(pngFile_pval, format = "png")
plt.figure(figsize=(10,7))
max_sd_log_pval = max(sd_log_pvals)
min_sd_log_pval = min(sd_log_pvals)
sd_val_range = max_sd_log_pval-min_sd_log_pval
plt.plot(log_true_pvals,sd_log_pvals,"b.")
plt.axis([min_log_val-0.02*log_val_range, max_log_val+0.02*log_val_range, min_sd_log_pval-0.02*sd_val_range, max_sd_log_pval+0.02*sd_val_range])
plt.savefig(pngFile_sd_log_pval, format = "png")
plt.figure(figsize=(10,7))
max_sd_pval = max(sd_pvals)
min_sd_pval = min(sd_pvals)
sd_val_range = max_sd_pval-min_sd_pval
plt.plot(true_pvals,sd_pvals,"b.")
plt.axis([min_val-0.02*val_range, max_val+0.02*val_range, min_sd_pval-0.02*sd_val_range, max_sd_pval+0.02*sd_val_range])
plt.savefig(pngFile_sd_pval, format = "png")
print "Done testing robustness"
def _robustness_test_(all_snps,phenVals,outputFile,filter=0.1,test_type = "KW",):
"""
Leave one out test..
"""
new_all_snps = []
for snp in all_snps:
if snp.count(0)>1 and snp.count(1)>1:
new_all_snps.append(snp)
print "Filtered",len(all_snps)-len(new_all_snps)," with minor allele count <2."
all_snps = new_all_snps
if filter <1.0:
snps = random.sample(all_snps,int(len(all_snps)*filter))
print "Number of SNPs:",len(snps)
else:
snps = all_snps
if test_type=="KW":
print "running KW"
t1 = time.time()
true_pvals = util.kruskal_wallis(snps,phenVals)["ps"]
t2 = time.time()
print "Took",t2-t1,"seconds."
elif test_type=="Fisher":
print "running Fisher's exact test"
t1 = time.time()
true_pvals = run_fet(snps,phenVals)
t2 = time.time()
print "Took",t2-t1,"seconds."
log_true_pvals = []
for pval in true_pvals:
log_true_pvals.append(-math.log(pval,10))
perm_pvalues_list = []
for i in range(0,len(phenVals)):
newPhenvals = phenVals[:]
newPhenvals.pop(i)
newSNPs = []
for snp in snps:
newSNP = snp[:]
newSNP.pop(i)
newSNPs.append(newSNP)
print i
if test_type=="KW":
print "running KW"
t1 = time.time()
pvals = util.kruskal_wallis(newSNPs,newPhenvals)["ps"]
t2 = time.time()
print "Took",t2-t1,"seconds."
elif test_type=="Fisher":
print "running Fisher's exact test"
t1 = time.time()
pvals = run_fet(newSNPs,newPhenvals)
t2 = time.time()
print "Took",t2-t1,"seconds."
perm_pvalues_list.append(pvals)
delta_pvals_list = []
delta_log_pvals_list = []
for perm_pvals in perm_pvalues_list:
log_pvals = []
delta_pvals = []
delta_log_pvals = []
for i in range(0,len(true_pvals)):
pval = perm_pvals[i]
true_pval = true_pvals[i]
delta_pvals.append(true_pval-pval)
log_true_pval = log_true_pvals[i]
if pval > 0.0:
log_pval = -math.log(pval,10)
else:
print "Damn those random 0 prob. events: event #", i
log_pval = -math.log(true_pval,10)
log_pvals.append(log_pval)
delta_log_pvals.append(log_true_pval-log_pval)
delta_pvals_list.append(delta_pvals)
delta_log_pvals_list.append(delta_log_pvals)
sd_log_pvals = []
sd_pvals = []
t_delta_log_pvals_list = map(list,zip(*delta_log_pvals_list))
t_delta_pvals_list = map(list,zip(*delta_pvals_list))
for i in range(0,len(true_pvals)):
sd_log_pvals.append(util.calcSD(t_delta_log_pvals_list[i]))
sd_pvals.append(util.calcSD(t_delta_pvals_list[i]))
#Write SDs out to file, to be able to replot, or plot together with other methods... etc
import csv
sd_log_pval_file = outputFile+".rob.log_pvals_sd"
f = open(sd_log_pval_file,"w")
w = csv.writer(f)
w.writerow(["log_true_pval","sd_log_pvals"])
l = zip(log_true_pvals,sd_log_pvals)
w.writerows(l)
f.close()
#Plot things....
pngFile_log_pvals = outputFile+".rob.log_pval.png"
pngFile_pval = outputFile+".rob.pval.png"
pngFile_sd_log_pval = outputFile+".rob.sd_log_pval.png"
pngFile_sd_pval = outputFile+".rob.sd_pval.png"
min_val = min(true_pvals)
max_val = max(true_pvals)
val_range = max_val-min_val
min_log_val = min(log_true_pvals)
max_log_val = max(log_true_pvals)
log_val_range = max_val-min_val
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
plt.figure(figsize=(10,7))
max_perm_val = 0
min_perm_val = 0
for i in range(0,len(perm_pvalues_list)):
delta_log_pvals = delta_log_pvals_list[i]
plt.plot(log_true_pvals,delta_log_pvals,"b.")
max_perm_val = max(max_perm_val,max(delta_log_pvals))
min_perm_val = min(min_perm_val,min(delta_log_pvals))
perm_val_range = max_perm_val - min_perm_val
plt.axis([min_log_val-0.02*log_val_range, max_log_val+0.02*log_val_range, min_perm_val-0.02*perm_val_range, max_perm_val+0.02*perm_val_range])
plt.savefig(pngFile_log_pvals, format = "png")
plt.figure(figsize=(10,7))
max_perm_val = 0
min_perm_val = 0
for i in range(0,len(perm_pvalues_list)):
delta_pvals = delta_pvals_list[i]
plt.plot(true_pvals,delta_pvals,"b.")
max_perm_val = max(max_perm_val,max(delta_pvals))
min_perm_val = min(min_perm_val,min(delta_pvals))
perm_val_range = max_perm_val - min_perm_val
plt.axis([min_val-0.02*val_range, max_val+0.02*val_range, min_perm_val-0.02*perm_val_range, max_perm_val+0.02*perm_val_range])
plt.savefig(pngFile_pval, format = "png")
plt.figure(figsize=(10,7))
max_sd_log_pval = max(sd_log_pvals)
min_sd_log_pval = min(sd_log_pvals)
sd_val_range = max_sd_log_pval-min_sd_log_pval
plt.plot(log_true_pvals,sd_log_pvals,"b.")
plt.axis([min_log_val-0.02*log_val_range, max_log_val+0.02*log_val_range, min_sd_log_pval-0.02*sd_val_range, max_sd_log_pval+0.02*sd_val_range])
plt.savefig(pngFile_sd_log_pval, format = "png")
plt.figure(figsize=(10,7))
max_sd_pval = max(sd_pvals)
min_sd_pval = min(sd_pvals)
sd_val_range = max_sd_pval-min_sd_pval
plt.plot(true_pvals,sd_pvals,"b.")
plt.axis([min_val-0.02*val_range, max_val+0.02*val_range, min_sd_pval-0.02*sd_val_range, max_sd_pval+0.02*sd_val_range])
plt.savefig(pngFile_sd_pval, format = "png")
def _robustness_test_(all_snps,phenVals,outputFile,filter=0.1,test_type = "KW",):
"""
Leave one out test..
"""
new_all_snps = []
for snp in all_snps:
if snp.count(0)>1 and snp.count(1)>1:
new_all_snps.append(snp)
print "Filtered",len(all_snps)-len(new_all_snps)," with minor allele count <2."
all_snps = new_all_snps
if filter <1.0:
snps = random.sample(all_snps,int(len(all_snps)*filter))
print "Number of SNPs:",len(snps)
else:
snps = all_snps
if test_type=="KW":
print "running KW"
t1 = time.time()
true_pvals = util.kruskal_wallis(snps,phenVals)["ps"]
t2 = time.time()
print "Took",t2-t1,"seconds."
elif test_type=="Fisher":
print "running Fisher's exact test"
t1 = time.time()
true_pvals = run_fet(snps,phenVals)
t2 = time.time()
print "Took",t2-t1,"seconds."
log_true_pvals = []
for pval in true_pvals:
log_true_pvals.append(-math.log(pval,10))
perm_pvalues_list = []
for i in range(0,len(phenVals)):
newPhenvals = phenVals[:]
newPhenvals.pop(i)
newSNPs = []
for snp in snps:
newSNP = snp[:]
newSNP.pop(i)
newSNPs.append(newSNP)
print i
if test_type=="KW":
print "running KW"
t1 = time.time()
pvals = util.kruskal_wallis(newSNPs,newPhenvals)["ps"]
t2 = time.time()
print "Took",t2-t1,"seconds."
elif test_type=="Fisher":
print "running Fisher's exact test"
t1 = time.time()
pvals = run_fet(newSNPs,newPhenvals)
t2 = time.time()
print "Took",t2-t1,"seconds."
perm_pvalues_list.append(pvals)
delta_pvals_list = []
delta_log_pvals_list = []
for perm_pvals in perm_pvalues_list:
log_pvals = []
delta_pvals = []
delta_log_pvals = []
for i in range(0,len(true_pvals)):
pval = perm_pvals[i]
true_pval = true_pvals[i]
delta_pvals.append(true_pval-pval)
log_true_pval = log_true_pvals[i]
if pval > 0.0:
log_pval = -math.log(pval,10)
else:
print "Damn those random 0 prob. events: event #", i
log_pval = -math.log(true_pval,10)
log_pvals.append(log_pval)
delta_log_pvals.append(log_true_pval-log_pval)
delta_pvals_list.append(delta_pvals)
delta_log_pvals_list.append(delta_log_pvals)
sd_log_pvals = []
sd_pvals = []
t_delta_log_pvals_list = map(list,zip(*delta_log_pvals_list))
t_delta_pvals_list = map(list,zip(*delta_pvals_list))
for i in range(0,len(true_pvals)):
sd_log_pvals.append(util.calcSD(t_delta_log_pvals_list[i]))
sd_pvals.append(util.calcSD(t_delta_pvals_list[i]))
#Write SDs out to file, to be able to replot, or plot together with other methods... etc
import csv
sd_log_pval_file = outputFile+".rob.log_pvals_sd"
f = open(sd_log_pval_file,"w")
w = csv.writer(f)
w.writerow(["log_true_pval","sd_log_pvals"])
l = zip(log_true_pvals,sd_log_pvals)
w.writerows(l)
f.close()
#Plot things....
pngFile_log_pvals = outputFile+".rob.log_pval.png"
pngFile_pval = outputFile+".rob.pval.png"
pngFile_sd_log_pval = outputFile+".rob.sd_log_pval.png"
pngFile_sd_pval = outputFile+".rob.sd_pval.png"
min_val = min(true_pvals)
max_val = max(true_pvals)
val_range = max_val-min_val
min_log_val = min(log_true_pvals)
max_log_val = max(log_true_pvals)
log_val_range = max_val-min_val
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
plt.figure(figsize=(10,7))
max_perm_val = 0
min_perm_val = 0
for i in range(0,len(perm_pvalues_list)):
delta_log_pvals = delta_log_pvals_list[i]
plt.plot(log_true_pvals,delta_log_pvals,"b.")
max_perm_val = max(max_perm_val,max(delta_log_pvals))
min_perm_val = min(min_perm_val,min(delta_log_pvals))
perm_val_range = max_perm_val - min_perm_val
plt.axis([min_log_val-0.02*log_val_range, max_log_val+0.02*log_val_range, min_perm_val-0.02*perm_val_range, max_perm_val+0.02*perm_val_range])
plt.savefig(pngFile_log_pvals, format = "png")
plt.figure(figsize=(10,7))
max_perm_val = 0
min_perm_val = 0
for i in range(0,len(perm_pvalues_list)):
delta_pvals = delta_pvals_list[i]
plt.plot(true_pvals,delta_pvals,"b.")
max_perm_val = max(max_perm_val,max(delta_pvals))
min_perm_val = min(min_perm_val,min(delta_pvals))
perm_val_range = max_perm_val - min_perm_val
plt.axis([min_val-0.02*val_range, max_val+0.02*val_range, min_perm_val-0.02*perm_val_range, max_perm_val+0.02*perm_val_range])
plt.savefig(pngFile_pval, format = "png")
plt.figure(figsize=(10,7))
max_sd_log_pval = max(sd_log_pvals)
min_sd_log_pval = min(sd_log_pvals)
sd_val_range = max_sd_log_pval-min_sd_log_pval
plt.plot(log_true_pvals,sd_log_pvals,"b.")
plt.axis([min_log_val-0.02*log_val_range, max_log_val+0.02*log_val_range, min_sd_log_pval-0.02*sd_val_range, max_sd_log_pval+0.02*sd_val_range])
plt.savefig(pngFile_sd_log_pval, format = "png")
plt.figure(figsize=(10,7))
max_sd_pval = max(sd_pvals)
min_sd_pval = min(sd_pvals)
sd_val_range = max_sd_pval-min_sd_pval
plt.plot(true_pvals,sd_pvals,"b.")
plt.axis([min_val-0.02*val_range, max_val+0.02*val_range, min_sd_pval-0.02*sd_val_range, max_sd_pval+0.02*sd_val_range])
plt.savefig(pngFile_sd_pval, format = "png")
