def CF_bam2RPKM(args):
try:
import pysam
except:
print '[ERROR] Cannot load pysam module! Make sure it is insalled'
sys.exit(0)
try:
# read probes table
probe_fn = str(args.probes[0])
probes = cf.loadProbeList(probe_fn)
num_probes = len(probes)
print '[INIT] Successfully read in %d probes from %s' % (num_probes,
probe_fn)
except IOError as e:
print '[ERROR] Cannot read probes file: ', probe_fn
sys.exit(0)
try:
rpkm_f = open(args.output[0], 'w')
except IOError as e:
print '[ERROR] Cannot open rpkm file for writing: ', args.output
sys.exit(0)
print "[RUNNING] Counting total number of reads in bam file..."
total_reads = float(pysam.view("-c", args.input[0])[0].strip("\n"))
print "[RUNNING] Found %d reads" % total_reads
f = pysam.Samfile(args.input[0], "rb")
if not f.has_Index():
print "[ERROR] No index found for bam file (%s)!\n[ERROR] You must first index the bam file and include the .bai file in the same directory as the bam file!" % args.input[
0]
sys.exit(0)
# will be storing values in these arrays
readcount = np.zeros(num_probes)
exon_bp = np.zeros(num_probes)
probeIDs = np.zeros(num_probes)
counter = 0
# detect contig naming scheme here # TODO, add an optional "contigs.txt" file or automatically handle contig naming
bam_contigs = f.references
probes_contigs = [
str(p) for p in set(map(operator.itemgetter("chr"), probes))
]
probes2contigmap = {}
for probes_contig in probes_contigs:
if probes_contig in bam_contigs:
probes2contigmap[probes_contig] = probes_contig
elif cf.chrInt2Str(probes_contig) in bam_contigs:
probes2contigmap[probes_contig] = cf.chrInt2Str(probes_contig)
elif cf.chrInt2Str(probes_contig).replace("chr", "") in bam_contigs:
probes2contigmap[probes_contig] = cf.chrInt2Str(
probes_contig).replace("chr", "")
else:
print "[ERROR] Could not find contig '%s' from %s in bam file! \n[ERROR] Perhaps the contig names for the probes are incompatible with the bam file ('chr1' vs. '1'), or unsupported contig naming is used?" % (
probes_contig, probe_fn)
sys.exit(0)
print "[RUNNING] Calculating RPKM values..."
# loop through each probe
for p in probes:
# f.fetch is a pysam method and returns an iterator for reads overlapping interval
p_chr = probes2contigmap[str(p["chr"])]
p_start = p["start"]
p_stop = p["stop"]
try:
iter = f.fetch(p_chr, p_start, p_stop)
except:
print "[ERROR] Could not retrieve mappings for region %s:%d-%d. Check that contigs are named correctly and the bam file is properly indexed" % (
p_chr, p_start, p_stop)
sys.exit(0)
for i in iter:
if i.pos + 1 >= p_start: #this checks to make sure a read actually starts in an interval
readcount[counter] += 1
exon_bp[counter] = p_stop - p_start
probeIDs[counter] = counter + 1 #probeIDs are 1-based
counter += 1
#calcualte RPKM values for all probes
rpkm = (10**9 * (readcount) / (exon_bp)) / (total_reads)
out = np.vstack([probeIDs, readcount, rpkm])
np.savetxt(rpkm_f, out.transpose(), delimiter='\t', fmt=['%d', '%d', '%f'])
rpkm_f.close()
def CF_bam2RPKM(args):
try:
import pysam
except:
print '[ERROR] Cannot load pysam module! Make sure it is insalled'
sys.exit(0)
try:
# read probes table
probe_fn = str(args.probes[0])
probes = cf.loadProbeList(probe_fn)
num_probes = len(probes)
print '[INIT] Successfully read in %d probes from %s' % (num_probes, probe_fn)
except IOError as e:
print '[ERROR] Cannot read probes file: ', probe_fn
sys.exit(0)
try:
rpkm_f = open(args.output[0],'w')
except IOError as e:
print '[ERROR] Cannot open rpkm file for writing: ', args.output
sys.exit(0)
print "[RUNNING] Counting total number of reads in bam file..."
total_reads = float(pysam.view("-c", args.input[0])[0].strip("\n"))
print "[RUNNING] Found %d reads" % total_reads
f = pysam.Samfile(args.input[0], "rb" )
if not f._hasIndex():
print "[ERROR] No index found for bam file (%s)!\n[ERROR] You must first index the bam file and include the .bai file in the same directory as the bam file!" % args.input[0]
sys.exit(0)
# will be storing values in these arrays
readcount = np.zeros(num_probes)
exon_bp = np.zeros(num_probes)
probeIDs = np.zeros(num_probes)
counter = 0
# detect contig naming scheme here # TODO, add an optional "contigs.txt" file or automatically handle contig naming
bam_contigs = f.references
probes_contigs = [str(p) for p in set(map(operator.itemgetter("chr"),probes))]
probes2contigmap = {}
for probes_contig in probes_contigs:
if probes_contig in bam_contigs:
probes2contigmap[probes_contig] = probes_contig
elif cf.chrInt2Str(probes_contig) in bam_contigs:
probes2contigmap[probes_contig] = cf.chrInt2Str(probes_contig)
elif cf.chrInt2Str(probes_contig).replace("chr","") in bam_contigs:
probes2contigmap[probes_contig] = cf.chrInt2Str(probes_contig).replace("chr","")
else:
print "[ERROR] Could not find contig '%s' from %s in bam file! \n[ERROR] Perhaps the contig names for the probes are incompatible with the bam file ('chr1' vs. '1'), or unsupported contig naming is used?" % (probes_contig, probe_fn)
sys.exit(0)
print "[RUNNING] Calculating RPKM values..."
# loop through each probe
for p in probes:
# f.fetch is a pysam method and returns an iterator for reads overlapping interval
p_chr = probes2contigmap[str(p["chr"])]
p_start = p["start"]
p_stop = p["stop"]
try:
iter = f.fetch(p_chr,p_start,p_stop)
except:
print "[ERROR] Could not retrieve mappings for region %s:%d-%d. Check that contigs are named correctly and the bam file is properly indexed" % (p_chr,p_start,p_stop)
sys.exit(0)
for i in iter:
if i.pos+1 >= p_start: #this checks to make sure a read actually starts in an interval
readcount[counter] += 1
exon_bp[counter] = p_stop-p_start
probeIDs[counter] = counter +1 #probeIDs are 1-based
counter +=1
#calcualte RPKM values for all probes
rpkm = (10**9*(readcount)/(exon_bp))/(total_reads)
out = np.vstack([probeIDs,readcount,rpkm])
np.savetxt(rpkm_f,out.transpose(),delimiter='\t',fmt=['%d','%d','%f'])
rpkm_f.close()
def CF_analyze(args):
# do path/file checks:
try:
# read probes table
probe_fn = str(args.probes[0])
probes = cf.loadProbeList(probe_fn)
num_probes = len(probes)
print '[INIT] Successfully read in %d probes from %s' % (num_probes,
probe_fn)
except IOError as e:
print '[ERROR] Cannot read probes file: ', probe_fn
sys.exit(0)
try:
svd_outfile_fn = str(args.output)
h5file_out = openFile(svd_outfile_fn, mode='w')
probe_group = h5file_out.createGroup("/", "probes", "probes")
except IOError as e:
print '[ERROR] Cannot open SVD output file for writing: ', svd_outfile_fn
sys.exit(0)
if args.write_svals != "":
sval_f = open(args.write_svals, 'w')
if args.plot_scree != "":
try:
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import pylab as P
from matplotlib.lines import Line2D
from matplotlib.patches import Rectangle
except:
print "[ERROR] One or more of the required modules for plotting cannot be loaded! Are matplotlib and pylab installed?"
sys.exit(0)
plt.gcf().clear()
fig = plt.figure(figsize=(10, 5))
ax = fig.add_subplot(111)
rpkm_dir = str(args.rpkm_dir[0])
rpkm_files = glob.glob(rpkm_dir + "/*")
if len(rpkm_files) == 0:
print '[ERROR] Cannot find any files in RPKM directory (or directory path is incorrect): ', rpkm_dir
sys.exit(0)
elif len(rpkm_files) == 1:
print '[ERROR] Found only 1 RPKM file (sample). CoNIFER requires multiple samples (8 or more) to run. Exiting.'
sys.exit(0)
elif len(rpkm_files) < 8:
print '[WARNING] Only found %d samples... this is less than the recommended minimum, and CoNIFER may not analyze this dataset correctly!' % len(
rpkm_files)
elif len(rpkm_files) <= int(args.svd):
print '[ERROR] The number of SVD values specified (%d) must be less than the number of samples (%d). Either add more samples to the analysis or reduce the --svd parameter! Exiting.' % (
len(rpkm_files), int(args.svd))
sys.exit(0)
else:
print '[INIT] Found %d RPKM files in %s' % (len(rpkm_files), rpkm_dir)
# read in samples names and generate file list
samples = {}
for f in rpkm_files:
s = '.'.join(f.split('/')[-1].split('.')[0:-1])
print "[INIT] Mapping file to sampleID: %s --> %s" % (f, s)
samples[s] = f
#check uniqueness and total # of samples
if len(set(samples)) != len(set(rpkm_files)):
print '[ERROR] Could not successfully derive sample names from RPKM filenames. There are probably non-unique sample names! Please rename files using <sampleID>.txt format!'
sys.exit(0)
# LOAD RPKM DATA
RPKM_data = np.zeros([num_probes, len(samples)], dtype=np.float)
failed_samples = 0
for i, s in enumerate(samples.keys()):
t = np.loadtxt(samples[s],
dtype=np.float,
delimiter="\t",
skiprows=0,
usecols=[2])
if len(t) != num_probes:
print "[WARNING] Number of RPKM values for %s in file %s does not match number of defined probes in %s. **This sample will be dropped from analysis**!" % (
s, samples[s], probe_fn)
_ = samples.pop(s)
failed_samples += 1
else:
RPKM_data[:, i] = t
print "[INIT] Successfully read RPKM data for sampleID: %s" % s
RPKM_data = RPKM_data[:, 0:len(samples)]
print "[INIT] Finished reading RPKM files. Total number of samples in experiment: %d (%d failed to read properly)" % (
len(samples), failed_samples)
if len(samples) <= int(args.svd):
print '[ERROR] The number of SVD values specified (%d) must be less than the number of samples (%d). Either add more samples to the analysis or reduce the --svd parameter! Exiting.' % (
int(args.svd), len(samples))
sys.exit(0)
# BEGIN
chrs_to_process = set(map(operator.itemgetter("chr"), probes))
chrs_to_process_str = ', '.join(
[cf.chrInt2Str(c) for c in chrs_to_process])
print '[INIT] Attempting to process chromosomes: ', chrs_to_process_str
for chr in chrs_to_process:
print "[RUNNING: chr%d] Now on: %s" % (chr, cf.chrInt2Str(chr))
chr_group_name = "chr%d" % chr
chr_group = h5file_out.createGroup("/", chr_group_name, chr_group_name)
chr_probes = filter(lambda i: i["chr"] == chr, probes)
num_chr_probes = len(chr_probes)
start_probeID = chr_probes[0]['probeID']
stop_probeID = chr_probes[-1]['probeID']
print "[RUNNING: chr%d] Found %d probes; probeID range is [%d-%d]" % (
chr, len(chr_probes), start_probeID - 1, stop_probeID
) # probeID is 1-based and slicing is 0-based, hence the start_probeID-1 term
rpkm = RPKM_data[start_probeID:stop_probeID, :]
print "[RUNNING: chr%d] Calculating median RPKM" % chr
median = np.median(rpkm, 1)
sd = np.std(rpkm, 1)
probe_mask = median >= float(args.min_rpkm)
print "[RUNNING: chr%d] Masking %d probes with median RPKM < %f" % (
chr, np.sum(probe_mask == False), float(args.min_rpkm))
rpkm = rpkm[probe_mask, :]
num_chr_probes = np.sum(probe_mask)
if num_chr_probes <= len(samples):
print "[ERROR] This chromosome has fewer informative probes than there are samples in the analysis! There are probably no mappings on this chromosome. Please remove these probes from the probes.txt file"
sys.exit(0)
probeIDs = np.array(map(operator.itemgetter("probeID"),
chr_probes))[probe_mask]
probe_starts = np.array(map(operator.itemgetter("start"),
chr_probes))[probe_mask]
probe_stops = np.array(map(operator.itemgetter("stop"),
chr_probes))[probe_mask]
gene_names = np.array(map(operator.itemgetter("name"),
chr_probes))[probe_mask]
dt = np.dtype([('probeID', np.uint32), ('start', np.uint32),
('stop', np.uint32), ('name', np.str_, 20)])
out_probes = np.empty(num_chr_probes, dtype=dt)
out_probes['probeID'] = probeIDs
out_probes['start'] = probe_starts
out_probes['stop'] = probe_stops
out_probes['name'] = gene_names
probe_table = h5file_out.createTable(probe_group, "probes_chr%d" % chr,
cf.probe, "chr%d" % chr)
probe_table.append(out_probes)
print "[RUNNING: chr%d] Calculating ZRPKM scores..." % chr
rpkm = np.apply_along_axis(cf.zrpkm, 0, rpkm, median[probe_mask],
sd[probe_mask])
# svd transform
print "[RUNNING: chr%d] SVD decomposition..." % chr
components_removed = int(args.svd)
U, S, Vt = np.linalg.svd(rpkm, full_matrices=False)
new_S = np.diag(
np.hstack([np.zeros([components_removed]),
S[components_removed:]]))
if args.write_svals != "":
sval_f.write('chr' + str(chr) + '\t' +
'\t'.join([str(_i) for _i in S]) + "\n")
if args.plot_scree != "":
ax.plot(S, label='chr' + str(chr), lw=0.5)
# reconstruct data matrix
rpkm = np.dot(U, np.dot(new_S, Vt))
# save to HDF5 file
print "[RUNNING: chr%d] Saving SVD-ZRPKM values" % chr
for i, s in enumerate(samples):
out_data = np.empty(num_chr_probes, dtype='u4,f8')
out_data['f0'] = probeIDs
out_data['f1'] = rpkm[:, i]
sample_tbl = h5file_out.createTable(chr_group, "sample_" + str(s),
cf.rpkm_value, "%s" % str(s))
sample_tbl.append(out_data)
print "[RUNNING] Saving sampleIDs to file..."
sample_group = h5file_out.createGroup("/", "samples", "samples")
sample_table = h5file_out.createTable(sample_group, "samples", cf.sample,
"samples")
dt = np.dtype([('sampleID', np.str_, 100)])
out_samples = np.empty(len(samples.keys()), dtype=dt)
out_samples['sampleID'] = np.array(samples.keys())
sample_table.append(out_samples)
if args.write_sd != "":
print "[RUNNING] Calculating standard deviations for all samples (this can take a while)..."
sd_file = open(args.write_sd, 'w')
for i, s in enumerate(samples):
# collect all SVD-ZRPKM values
count = 1
for chr in chrs_to_process:
if count == 1:
sd_out = h5file_out.root._f_getChild(
"chr%d" % chr)._f_getChild(
"sample_%s" % s).read(field="rpkm").flatten()
else:
sd_out = np.hstack([
sd_out,
out.h5file_out.root._f_getChild(
"chr%d" % chr)._f_getChild(
"sample_%s" % s).read(field="rpkm").flatten()
])
sd = np.std(sd_out)
sd_file.write("%s\t%f\n" % (s, sd))
sd_file.close()
if args.plot_scree != "":
plt.title("Scree plot")
if len(samples) < 50:
plt.xlim([0, len(samples)])
plt.xlabel("S values")
else:
plt.xlim([0, 50])
plt.xlabel("S values (only first 50 plotted)")
plt.ylabel("Relative contributed variance")
plt.savefig(args.plot_scree)
print "[FINISHED]"
h5file_out.close()
sys.exit(0)
def CF_analyze(args):
# do path/file checks:
try:
# read probes table
probe_fn = str(args.probes[0])
probes = cf.loadProbeList(probe_fn)
num_probes = len(probes)
print '[INIT] Successfully read in %d probes from %s' % (num_probes, probe_fn)
except IOError as e:
print '[ERROR] Cannot read probes file: ', probe_fn
sys.exit(0)
try:
svd_outfile_fn = str(args.output)
h5file_out = openFile(svd_outfile_fn, mode='w')
probe_group = h5file_out.createGroup("/","probes","probes")
except IOError as e:
print '[ERROR] Cannot open SVD output file for writing: ', svd_outfile_fn
sys.exit(0)
if args.write_svals != "":
sval_f = open(args.write_svals,'w')
if args.plot_scree != "":
try:
import matplotlib
matplotlib.use('Agg')
import matplotlib.pyplot as plt
import pylab as P
from matplotlib.lines import Line2D
from matplotlib.patches import Rectangle
except:
print "[ERROR] One or more of the required modules for plotting cannot be loaded! Are matplotlib and pylab installed?"
sys.exit(0)
plt.gcf().clear()
fig = plt.figure(figsize=(10,5))
ax = fig.add_subplot(111)
rpkm_dir = str(args.rpkm_dir[0])
rpkm_files = glob.glob(rpkm_dir + "/*")
if len(rpkm_files) == 0:
print '[ERROR] Cannot find any files in RPKM directory (or directory path is incorrect): ', rpkm_dir
sys.exit(0)
elif len(rpkm_files) == 1:
print '[ERROR] Found only 1 RPKM file (sample). CoNIFER requires multiple samples (8 or more) to run. Exiting.'
sys.exit(0)
elif len(rpkm_files) < 8:
print '[WARNING] Only found %d samples... this is less than the recommended minimum, and CoNIFER may not analyze this dataset correctly!' % len(rpkm_files)
elif len(rpkm_files) <= int(args.svd):
print '[ERROR] The number of SVD values specified (%d) must be less than the number of samples (%d). Either add more samples to the analysis or reduce the --svd parameter! Exiting.' % (len(rpkm_files), int(args.svd))
sys.exit(0)
else:
print '[INIT] Found %d RPKM files in %s' % (len(rpkm_files), rpkm_dir)
# read in samples names and generate file list
samples = {}
for f in rpkm_files:
s = '.'.join(f.split('/')[-1].split('.')[0:-1])
print "[INIT] Mapping file to sampleID: %s --> %s" % (f, s)
samples[s] = f
#check uniqueness and total # of samples
if len(set(samples)) != len(set(rpkm_files)):
print '[ERROR] Could not successfully derive sample names from RPKM filenames. There are probably non-unique sample names! Please rename files using <sampleID>.txt format!'
sys.exit(0)
# LOAD RPKM DATA
RPKM_data = np.zeros([num_probes,len(samples)], dtype=np.float)
failed_samples = 0
for i,s in enumerate(samples.keys()):
t = cf.loadRPKM(samples[s])
if len(t) != num_probes:
print "[WARNING] Number of RPKM values for %s in file %s does not match number of defined probes in %s. **This sample will be dropped from analysis**!" % (s, samples[s], probe_fn)
_ = samples.pop(s)
failed_samples += 1
else:
RPKM_data[:,i] = t
print "[INIT] Successfully read RPKM data for sampleID: %s" % s
RPKM_data = RPKM_data[:,0:len(samples)]
print "[INIT] Finished reading RPKM files. Total number of samples in experiment: %d (%d failed to read properly)" % (len(samples), failed_samples)
if len(samples) <= int(args.svd):
print '[ERROR] The number of SVD values specified (%d) must be less than the number of samples (%d). Either add more samples to the analysis or reduce the --svd parameter! Exiting.' % (int(args.svd), len(samples))
sys.exit(0)
# BEGIN
chrs_to_process = set(map(operator.itemgetter("chr"),probes))
chrs_to_process_str = ', '.join([cf.chrInt2Str(c) for c in chrs_to_process])
print '[INIT] Attempting to process chromosomes: ', chrs_to_process_str
for chr in chrs_to_process:
print "[RUNNING: chr%d] Now on: %s" %(chr, cf.chrInt2Str(chr))
chr_group_name = "chr%d" % chr
chr_group = h5file_out.createGroup("/",chr_group_name,chr_group_name)
chr_probes = filter(lambda i: i["chr"] == chr, probes)
num_chr_probes = len(chr_probes)
start_probeID = chr_probes[0]['probeID']
stop_probeID = chr_probes[-1]['probeID']
print "[RUNNING: chr%d] Found %d probes; probeID range is [%d-%d]" % (chr, len(chr_probes), start_probeID-1, stop_probeID) # probeID is 1-based and slicing is 0-based, hence the start_probeID-1 term
rpkm = RPKM_data[start_probeID:stop_probeID,:]
print "[RUNNING: chr%d] Calculating median RPKM" % chr
median = np.median(rpkm,1)
sd = np.std(rpkm,1)
probe_mask = median >= float(args.min_rpkm)
print "[RUNNING: chr%d] Masking %d probes with median RPKM < %f" % (chr, np.sum(probe_mask==False), float(args.min_rpkm))
rpkm = rpkm[probe_mask, :]
num_chr_probes = np.sum(probe_mask)
if num_chr_probes <= len(samples):
print "[ERROR] This chromosome has fewer informative probes than there are samples in the analysis! There are probably no mappings on this chromosome. Please remove these probes from the probes.txt file"
sys.exit(0)
probeIDs = np.array(map(operator.itemgetter("probeID"),chr_probes))[probe_mask]
probe_starts = np.array(map(operator.itemgetter("start"),chr_probes))[probe_mask]
probe_stops = np.array(map(operator.itemgetter("stop"),chr_probes))[probe_mask]
gene_names = np.array(map(operator.itemgetter("name"),chr_probes))[probe_mask]
dt = np.dtype([('probeID',np.uint32),('start',np.uint32),('stop',np.uint32), ('name', np.str_, 20)])
out_probes = np.empty(num_chr_probes,dtype=dt)
out_probes['probeID'] = probeIDs
out_probes['start'] = probe_starts
out_probes['stop'] = probe_stops
out_probes['name'] = gene_names
probe_table = h5file_out.createTable(probe_group,"probes_chr%d" % chr,cf.probe,"chr%d" % chr)
probe_table.append(out_probes)
print "[RUNNING: chr%d] Calculating ZRPKM scores..." % chr
rpkm = np.apply_along_axis(cf.zrpkm, 0, rpkm, median[probe_mask], sd[probe_mask])
# svd transform
print "[RUNNING: chr%d] SVD decomposition..." % chr
components_removed = int(args.svd)
U, S, Vt = np.linalg.svd(rpkm,full_matrices=False)
new_S = np.diag(np.hstack([np.zeros([components_removed]),S[components_removed:]]))
if args.write_svals != "":
sval_f.write('chr' + str(chr) + '\t' + '\t'.join([str(_i) for _i in S]) + "\n")
if args.plot_scree != "":
ax.plot(S, label='chr' + str(chr),lw=0.5)
# reconstruct data matrix
rpkm = np.dot(U, np.dot(new_S, Vt))
# save to HDF5 file
print "[RUNNING: chr%d] Saving SVD-ZRPKM values" % chr
for i,s in enumerate(samples):
out_data = np.empty(num_chr_probes,dtype='u4,f8')
out_data['f0'] = probeIDs
out_data['f1'] = rpkm[:,i]
sample_tbl = h5file_out.createTable(chr_group,"sample_" + str(s),cf.rpkm_value,"%s" % str(s))
sample_tbl.append(out_data)
print "[RUNNING] Saving sampleIDs to file..."
sample_group = h5file_out.createGroup("/","samples","samples")
sample_table = h5file_out.createTable(sample_group,"samples",cf.sample,"samples")
dt = np.dtype([('sampleID',np.str_,100)])
out_samples = np.empty(len(samples.keys()),dtype=dt)
out_samples['sampleID'] = np.array(samples.keys())
sample_table.append(out_samples)
if args.write_sd != "":
print "[RUNNING] Calculating standard deviations for all samples (this can take a while)..."
sd_file = open(args.write_sd,'w')
for i,s in enumerate(samples):
# collect all SVD-ZRPKM values
count = 1
for chr in chrs_to_process:
if count == 1:
sd_out = h5file_out.root._f_getChild("chr%d" % chr)._f_getChild("sample_%s" % s).read(field="rpkm").flatten()
else:
sd_out = np.hstack([sd_out,out.h5file_out.root._f_getChild("chr%d" % chr)._f_getChild("sample_%s" % s).read(field="rpkm").flatten()])
sd = np.std(sd_out)
sd_file.write("%s\t%f\n" % (s,sd))
sd_file.close()
if args.plot_scree != "":
plt.title("Scree plot")
if len(samples) < 50:
plt.xlim([0,len(samples)])
plt.xlabel("S values")
else:
plt.xlim([0,50])
plt.xlabel("S values (only first 50 plotted)")
plt.ylabel("Relative contributed variance")
plt.savefig(args.plot_scree)
print "[FINISHED]"
h5file_out.close()
sys.exit(0)
