def dump_sample(input_dir):
bm = BigCountMatrix.open(input_dir)
for i in xrange(bm.shape[0]):
a = bm.counts[i, 0]
if np.isnan(a):
a = 'NA'
print bm.rownames[i], str(a)
def dump_sample(input_dir):
bm = BigCountMatrix.open(input_dir)
for i in xrange(bm.shape[0]):
a = bm.counts[i,0]
if np.isnan(a):
a = 'NA'
print bm.rownames[i], str(a)
def main(argv=None):
'''Command line options.'''
# create instance of run configuration
# Setup argument parser
parser = argparse.ArgumentParser()
# Add command line parameters
parser.add_argument("matrix_dir", help="directory containing matrix file")
parser.add_argument("-nr",
"--rows",
dest='num_rows',
default=1,
help='number of rows in matrix to grab')
parser.add_argument("-nc",
"--cols",
dest='num_cols',
default=1,
help='number of rows in matrix to grab')
parser.add_argument("-in",
"--input_number",
dest="in_num",
action="store_true",
default=False,
help="import defined number of rows/cols")
# Process arguments
args = parser.parse_args()
# setup logging
level = logging.INFO
logging.basicConfig(
level=level,
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s")
bcm = BigCountMatrix.open(args.matrix_dir)
rows = bcm.rownames
cols = bcm.colnames
matrix = bcm.counts
if args.in_num == True:
num_rows = int(args.num_rows)
num_cols = int(args.num_cols)
for row in xrange(num_rows):
for col in xrange(num_cols):
d = {
't_id': row,
's_id': col,
'value': float(matrix[row, col])
}
print json.dumps(d)
else:
for row in xrange(len(rows)):
for col in xrange(len(cols)):
d = {
't_id': row,
's_id': col,
'value': float(matrix[row, col])
}
print json.dumps(d)
def search_for_all_nan(input_dir):
logging.debug("Opening matrix memmap files")
bm = BigCountMatrix.open(input_dir)
for i in xrange(bm.shape[0]):
a = bm.counts[i, :]
a = np.array(a, dtype=np.float)
b = a[np.isfinite(a)]
if len(b) < len(a):
print i, len(b), len(a)
def search_for_all_nan(input_dir):
logging.debug("Opening matrix memmap files")
bm = BigCountMatrix.open(input_dir)
for i in xrange(bm.shape[0]):
a = bm.counts[i,:]
a = np.array(a, dtype=np.float)
b = a[np.isfinite(a)]
if len(b) < len(a):
print i, len(b), len(a)
def main(argv=None):
'''Command line options.'''
# create instance of run configuration
# Setup argument parser
parser = argparse.ArgumentParser()
# Add command line parameters
parser.add_argument("matrix_dir",
help="directory containing matrix file")
parser.add_argument("-nr", "--rows", dest = 'num_rows',
default = 1,
help = 'number of rows in matrix to grab')
parser.add_argument("-nc", "--cols", dest = 'num_cols',
default = 1,
help = 'number of rows in matrix to grab')
parser.add_argument("-in", "--input_number", dest="in_num",
action="store_true", default=False,
help="import defined number of rows/cols")
# Process arguments
args = parser.parse_args()
# setup logging
level = logging.INFO
logging.basicConfig(level=level,
format="%(asctime)s - %(name)s - %(levelname)s - %(message)s")
bcm = BigCountMatrix.open(args.matrix_dir)
rows = bcm.rownames
cols = bcm.colnames
matrix = bcm.counts
if args.in_num == True:
num_rows = int(args.num_rows)
num_cols = int(args.num_cols)
for row in xrange(num_rows):
for col in xrange(num_cols):
d = {'t_id': row,
's_id': col,
'value': float(matrix[row,col])}
print json.dumps(d)
else:
for row in xrange(len(rows)):
for col in xrange(len(cols)):
d = {'t_id': row,
's_id': col,
'value': float(matrix[row,col])}
print json.dumps(d)
def normalize_count_data(input_dir):
# setup matrix
logging.debug("Opening matrix memmap files")
bm = BigCountMatrix.open(input_dir)
r = RandomState()
for i in xrange(bm.shape[0]):
a = bm.counts[i, :]
a = np.array(a, dtype=np.float)
b = a.copy()
normalize_counts(b,
bm.size_factors,
r,
resample=True,
add_noise=True,
noise_loc=1.0,
noise_scale=1.0)
logging.debug(str(i))
y1 = np.log2((a / bm.size_factors) + 1)
y2 = np.log2(b + 1)
order = y1.argsort()[::-1]
y1 = y1[order]
y2 = y2[order]
x = np.arange(len(y1))
f = plt.figure()
plt.plot(x, y2, 'ro', ms=1, mew=0, label='Resampled counts')
plt.plot(x, y1, 'b-', lw=3, label='Original counts')
plt.legend(markerscale=5, numpoints=3)
plt.xlabel('Samples sorted by counts')
plt.ylabel('log2(counts + 1)')
plt.savefig('/home/mkiyer/Documents/t%d.png' % (i))
plt.show()
print 'y1', y1.min(), y1.max(), y1.mean(), np.median(y1)
print 'y2', y2.min(), y2.max(), y2.mean(), np.median(y2)
if i == 100:
break
def normalize_count_data(input_dir):
# setup matrix
logging.debug("Opening matrix memmap files")
bm = BigCountMatrix.open(input_dir)
r = RandomState()
for i in xrange(bm.shape[0]):
a = bm.counts[i,:]
a = np.array(a, dtype=np.float)
b = a.copy()
normalize_counts(b, bm.size_factors, r,
resample=True,
add_noise=True,
noise_loc=1.0,
noise_scale=1.0)
logging.debug(str(i))
y1 = np.log2((a / bm.size_factors) + 1)
y2 = np.log2(b + 1)
order = y1.argsort()[::-1]
y1 = y1[order]
y2 = y2[order]
x = np.arange(len(y1))
f = plt.figure()
plt.plot(x, y2, 'ro', ms=1, mew=0, label='Resampled counts')
plt.plot(x, y1, 'b-', lw=3, label='Original counts')
plt.legend(markerscale=5, numpoints=3)
plt.xlabel('Samples sorted by counts')
plt.ylabel('log2(counts + 1)')
plt.savefig('/home/mkiyer/Documents/t%d.png' % (i))
plt.show()
print 'y1', y1.min(), y1.max(), y1.mean(), np.median(y1)
print 'y2', y2.min(), y2.max(), y2.mean(), np.median(y2)
if i == 100:
break
def rpkm_versus_count(input_dir):
bm = BigCountMatrix.open(input_dir)
for j in xrange(bm.shape[1]):
a = bm.counts_t[j, :]
a = a[np.isfinite(a)]
print bm.colnames[j], a.sum()
def rpkm_versus_count(input_dir):
bm = BigCountMatrix.open(input_dir)
for j in xrange(bm.shape[1]):
a = bm.counts_t[j,:]
a = a[np.isfinite(a)]
print bm.colnames[j], a.sum()