def process(args, raw_hud_lines):
"""
@param args: user options from the web or cmdline
@param hud_lines: raw lines of a .hud file
@return: results in convenient text form
"""
out = StringIO()
names, data = hud.decode(raw_hud_lines)
C_full = np.array(data, dtype=float)
pcs = eigenpop.get_scaled_eigenvectors(C_full, args.diploid_and_biallelic)
axis_index = args.axis - 1
# check for sufficient number of eigenvectors
if axis_index >= len(pcs):
msg = 'the requested axis is not available'
raise ValueError(msg)
# compute the correlation of each SNP vector the requested PC
pc = pcs[axis_index]
corrs = [mycorr(snp, pc) for snp in C_full.T]
sqcorrs = [mycorr(snp, pc)**2 for snp in C_full.T]
if args.rank_squared:
keys = sqcorrs
else:
keys = corrs
corr_index_pairs = [(cor, i) for i, cor in enumerate(keys)]
sorted_pairs = list(reversed(sorted(corr_index_pairs)))
indices = zip(*sorted_pairs)[1]
if args.locus_from_1:
nominal_indices = [i+1 for i in indices]
else:
nominal_indices = indices
rows = [(nom_i, corrs[i]) for i, nom_i in zip(indices, nominal_indices)]
lines = ['\t'.join(str(x) for x in row) for row in rows]
return '\n'.join(lines) + '\n'
def process(args, raw_hud_lines):
"""
@param args: user options from the web or cmdline
@param hud_lines: raw lines of a .hud file
@return: results in convenient text form
"""
out = StringIO()
names, data = hud.decode(raw_hud_lines)
# normalize the names of the isolates
if args.clean_isolates:
names = [Carbone.clean_isolate_element(x) for x in names]
# get the pcs
C_full = np.array(data, dtype=float)
pcs = eigenpop.get_scaled_eigenvectors(C_full, args.diploid_and_biallelic)
# check for sufficient number of eigenvectors
if len(pcs) < args.npcs:
msg_a = 'the number of requested principal components '
msg_b = 'must be no more than the number of OTUs'
raise ValueError(msg_a + msg_b)
# create the R frame
headers = ['otu'] + ['pc%d' % (i+1) for i in range(args.npcs)]
print >> out, '\t'.join(headers)
for i, name in enumerate(names):
typed_row = [name] + [pcs[j][i] for j in range(args.npcs)]
if args.add_indices:
typed_row = [i+1] + typed_row
row = [str(x) for x in typed_row]
print >> out, '\t'.join(row)
return out.getvalue()
def process(args, raw_hud_lines):
"""
@param args: user options from the web or cmdline
@param hud_lines: raw lines of a .hud file
@return: results in convenient text form
"""
out = StringIO()
names, data = hud.decode(raw_hud_lines)
# normalize the names of the isolates
if args.clean_isolates:
names = [Carbone.clean_isolate_element(x) for x in names]
# get the pcs
C_full = np.array(data, dtype=float)
pcs = eigenpop.get_scaled_eigenvectors(C_full, args.diploid_and_biallelic)
# check for sufficient number of eigenvectors
if len(pcs) < args.npcs:
msg_a = 'the number of requested principal components '
msg_b = 'must be no more than the number of OTUs'
raise ValueError(msg_a + msg_b)
# create the R frame
headers = ['otu'] + ['pc%d' % (i + 1) for i in range(args.npcs)]
print >> out, '\t'.join(headers)
for i, name in enumerate(names):
typed_row = [name] + [pcs[j][i] for j in range(args.npcs)]
if args.add_indices:
typed_row = [i + 1] + typed_row
row = [str(x) for x in typed_row]
print >> out, '\t'.join(row)
return out.getvalue()
def process(args, raw_hud_lines):
"""
@param args: user options from the web or cmdline
@param hud_lines: raw lines of a .hud file
@return: results in convenient text form
"""
out = StringIO()
names, data = hud.decode(raw_hud_lines)
C_full = np.array(data, dtype=float)
pcs = eigenpop.get_scaled_eigenvectors(C_full, args.diploid_and_biallelic)
# check for sufficient number of eigenvectors
if len(pcs) < args.ncoords:
raise ValueError('the number of requested principal components '
'must be no more than the number of OTUs')
# compute the correlation of each SNP vector with each principal PC
mylist = []
for snp in C_full.T:
row = [mycorr(snp, pc) for pc in pcs[:args.ncoords]]
mylist.append(row)
np.set_printoptions(linewidth=300, threshold=10000)
return str(np.array(mylist))
def process(args, raw_hud_lines):
"""
@param args: user options from the web or cmdline
@param hud_lines: raw lines of a .hud file
@return: results in convenient text form
"""
out = StringIO()
names, data = hud.decode(raw_hud_lines)
C_full = np.array(data, dtype=float)
pcs = eigenpop.get_scaled_eigenvectors(C_full, args.diploid_and_biallelic)
# check for sufficient number of eigenvectors
if len(pcs) < args.ncoords:
raise ValueError(
'the number of requested principal components '
'must be no more than the number of OTUs')
# compute the correlation of each SNP vector with each principal PC
mylist = []
for snp in C_full.T:
row = [mycorr(snp, pc) for pc in pcs[:args.ncoords]]
mylist.append(row)
np.set_printoptions(linewidth=300, threshold=10000)
return str(np.array(mylist))