def test_19_matrix_manip(self):
if ONLY and not "19" in ONLY:
return
if CHKTIME:
t0 = time()
hic_data1 = load_hic_data_from_reads("lala-map~", resolution=10000)
hic_map(hic_data1, savedata="lala-map.tsv~", savefig="lala.pdf")
hic_map(hic_data1,
by_chrom="intra",
savedata="lala-maps~",
savefig="lalalo~")
hic_map(hic_data1,
by_chrom="inter",
savedata="lala-maps~",
savefig="lalala~")
# slowest part of the all test:
hic_data2 = read_matrix("lala-map.tsv~", resolution=10000)
self.assertEqual(hic_data1, hic_data2)
# vals = plot_distance_vs_interactions(hic_data1)
# self.assertEqual([round(i, 2) if str(i)!="nan" else 0.0 for i in
# reduce(lambda x, y: x + y, vals)],
# [-1.68, -2.08, 0.02, 2.76, -8.99, 0.0, 0.82, -6.8, 0.0])
a, b = insert_sizes("lala-map~")
self.assertEqual([int(a), int(b)], [43, 1033])
hic_data1 = read_matrix(PATH + "/20Kb/chrT/chrT_A.tsv",
resolution=20000)
hic_data2 = read_matrix(PATH + "/20Kb/chrT/chrT_B.tsv",
resolution=20000)
corr = correlate_matrices(hic_data1, hic_data2)
corr = [round(i, 3) for i in corr[0]]
self.assertEqual(corr, [
0.755, 0.729, 0.804, 0.761, 0.789, 0.776, 0.828, 0.757, 0.797,
0.832
])
ecorr = eig_correlate_matrices(hic_data1,
hic_data2,
savefig='lala3.pdf')
ecorr = [round(i, 3) for i in reduce(lambda x, y: x + y, ecorr)]
self.assertEqual(ecorr, [
0.997, 0.322, 0.442, 0.017, 0.243, 0.014, 0.321, 0.999, 0.01,
0.006, 0.0, 0.007, 0.451, 0.012, 0.996, 0.031, 0.013, 0.004, 0.002,
0.006, 0.029, 0.974, 0.076, 0.03, 0.219, 0.013, 0.031, 0.08, 0.974,
0.018, 0.028, 0.004, 0.0, 0.028, 0.034, 0.89
])
system("rm -rf lala*")
if CHKTIME:
self.assertEqual(True, True)
print "19", time() - t0
def test_19_matrix_manip(self):
if ONLY and ONLY != '19':
return
if CHKTIME:
t0 = time()
hic_data1 = load_hic_data_from_reads('lala-map~', resolution=10000)
hic_map(hic_data1, savedata='lala-map.tsv~', savefig='lala.pdf~')
hic_map(hic_data1,
by_chrom='intra',
savedata='lala-maps~',
savefig='lalalo~')
hic_map(hic_data1,
by_chrom='inter',
savedata='lala-maps~',
savefig='lalala~')
# slowest part of the all test:
hic_data2 = read_matrix('lala-map.tsv~', resolution=10000)
self.assertEqual(hic_data1, hic_data2)
vals = plot_distance_vs_interactions(hic_data1)
self.assertEqual([
round(i, 2) if str(i) != 'nan' else 0.0
for i in reduce(lambda x, y: x + y, vals)
], [-1.68, -2.08, 0.02, 2.76, -8.99, 0.0, 0.82, -6.8, 0.0])
a, b = insert_sizes('lala-map~')
self.assertEqual([int(a), int(b)], [43, 1033])
hic_data1 = read_matrix('20Kb/chrT/chrT_A.tsv', resolution=20000)
hic_data2 = read_matrix('20Kb/chrT/chrT_B.tsv', resolution=20000)
corr = correlate_matrices(hic_data1, hic_data2)
corr = [round(i, 3) for i in corr[0]]
self.assertEqual(corr, [
0.755, 0.729, 0.804, 0.761, 0.789, 0.776, 0.828, 0.757, 0.797,
0.832
])
ecorr = eig_correlate_matrices(hic_data1, hic_data2)
ecorr = [round(i, 3) for i in reduce(lambda x, y: x + y, ecorr)]
self.assertEqual(ecorr, [
0.997, 0.322, 0.442, 0.017, 0.243, 0.014, 0.321, 0.999, 0.01,
0.006, 0.0, 0.007, 0.451, 0.012, 0.996, 0.031, 0.013, 0.004, 0.002,
0.006, 0.029, 0.974, 0.076, 0.03, 0.219, 0.013, 0.031, 0.08, 0.974,
0.018, 0.028, 0.004, 0.0, 0.028, 0.034, 0.89
])
system('rm -rf lala*')
if CHKTIME:
self.assertEqual(True, True)
print '19', time() - t0
def load_hic_read_data(self):
"""
Load the interactions into the HiC-Data data type
This should be used as the primary way of loading the HiC-data as the
data is loaded in the right form for later functions. Options like the
TAD calling also require non-normalised data.
"""
filter_reads = self.parsed_reads_dir + '/filtered_map.tsv'
print "\nfilter_reads: " + filter_reads
self.hic_data = load_hic_data_from_reads(filter_reads,
resolution=int(
self.resolution))
def test_19_matrix_manip(self):
if ONLY and ONLY != '19':
return
if CHKTIME:
t0 = time()
hic_data1 = load_hic_data_from_reads('lala-map~', resolution=10000)
hic_map(hic_data1, savedata='lala-map.tsv~', savefig='lala.pdf~')
hic_map(hic_data1, by_chrom='intra', savedata='lala-maps~', savefig='lalalo~')
hic_map(hic_data1, by_chrom='inter', savedata='lala-maps~', savefig='lalala~')
# slowest part of the all test:
hic_data2 = read_matrix('lala-map.tsv~', resolution=10000)
self.assertEqual(hic_data1, hic_data2)
vals = plot_distance_vs_interactions(hic_data1)
self.assertEqual([round(i, 2) if str(i)!='nan' else 0.0 for i in
reduce(lambda x, y: x + y, vals)],
[-1.74, 4.2, 0.52, 1.82, -0.44, 0.0, -0.5, 2.95, 0.0])
a, b = insert_sizes('lala-map~')
self.assertEqual([int(a),int(b)], [43, 1033])
hic_data1 = read_matrix('20Kb/chrT/chrT_A.tsv', resolution=20000)
hic_data2 = read_matrix('20Kb/chrT/chrT_B.tsv', resolution=20000)
corr = correlate_matrices(hic_data1, hic_data2)
corr = [round(i,3) for i in corr[0]]
self.assertEqual(corr, [0.755, 0.729, 0.804, 0.761, 0.789, 0.776, 0.828,
0.757, 0.797, 0.832])
ecorr = eig_correlate_matrices(hic_data1, hic_data2)
ecorr = [round(i,3) for i in reduce(lambda x, y:x+y, ecorr)]
self.assertEqual(ecorr, [0.997, 0.322, 0.442, 0.017, 0.243, 0.014,
0.321, 0.999, 0.01, 0.006, 0.0, 0.007, 0.451,
0.012, 0.996, 0.031, 0.013, 0.004, 0.002,
0.006, 0.029, 0.974, 0.076, 0.03, 0.219, 0.013,
0.031, 0.08, 0.974, 0.018, 0.028, 0.004, 0.0,
0.028, 0.034, 0.89])
system('rm -rf lala*')
if CHKTIME:
self.assertEqual(True, True)
print '19', time() - t0
def hic_map(data, resolution=None, normalized=False, masked=None,
by_chrom=False, savefig=None, show=False, savedata=None,
focus=None, clim=None, cmap='jet', pdf=False, decay=True,
perc=10, name=None, decay_resolution=None, **kwargs):
"""
function to retrieve data from HiC-data object. Data can be stored as
a square matrix, or drawn using matplotlib
:param data: can be either a path to a file with pre-processed reads
(filtered or not), or a Hi-C-data object
:param None resolution: at which to bin the data (try having a dense matrix
with < 10% of cells with zero interaction counts). Note: not necessary
if a hic_data object is passed as 'data'.
:param False normalized: used normalized data, based on precalculated biases
:param masked: a list of columns to be removed. Usually because to few
interactions
:param False by_chrom: data can be stored in a partitioned way. This
parameter can take the values of:
* 'intra': one output per each chromosome will be created
* 'inter': one output per each possible pair of chromosome will be
created
* 'all' : both of the above outputs
:param None savefig: path where to store the output images. Note that, if
the by_chrom option is used, then savefig will be the name of the
directory containing the output files.
:param None savedata: path where to store the output matrices. Note that, if
the by_chrom option is used, then savefig will be the name of the
directory containing the output files.
:param None focus: can be either two number (i.e.: (1, 100)) specifying the
start and end position of the sub-matrix to display (start and end, along
the diagonal of the original matrix); or directly a chromosome name; or
two chromosome names (i.e.: focus=('chr2, chrX')), in order to store the
data corresponding to inter chromosomal interactions between these two
chromosomes
:param True decay: plot the correlation between genomic distance and
interactions (usually a decay).
:param False force_image: force to generate an image even if resolution is
crazy...
:param None clim: cutoff for the upper and lower bound in the coloring scale
of the heatmap
:param False pdf: when using the bny_chrom option, to specify the format of
the stored images
:param Reds cmap: color map to be used for the heatmap
:param None decay_resolution: chromatin fragment size to consider when
calculating decay of the number of interactions with genomic distance.
Default is equal to resolution of the matrix.
"""
if isinstance(data, str):
data = load_hic_data_from_reads(data, resolution=resolution, **kwargs)
if not kwargs.get('get_sections', True) and decay:
warn('WARNING: not decay not available when get_sections is off.')
decay = False
hic_data = data
resolution = data.resolution
if not decay_resolution:
decay_resolution = resolution
if hic_data.bads and not masked:
masked = hic_data.bads
# save and draw the data
if by_chrom:
if focus:
raise Exception('Incompatible options focus and by_chrom\n')
if savedata:
mkdir(savedata)
if savefig:
mkdir(savefig)
for i, crm1 in enumerate(hic_data.chromosomes):
for crm2 in hic_data.chromosomes.keys()[i:]:
if by_chrom == 'intra' and crm1 != crm2:
continue
if by_chrom == 'inter' and crm1 == crm2:
continue
try:
subdata = hic_data.get_matrix(focus=(crm1, crm2), normalized=normalized)
start1, _ = hic_data.section_pos[crm1]
start2, _ = hic_data.section_pos[crm2]
masked1 = {}
masked2 = {}
if focus and hic_data.bads:
# rescale masked
masked1 = dict([(m - start1, hic_data.bads[m])
for m in hic_data.bads])
masked2 = dict([(m - start2, hic_data.bads[m])
for m in hic_data.bads])
if masked1 or masked2:
for i in xrange(len(subdata)):
if i in masked1:
subdata[i] = [float('nan')
for j in xrange(len(subdata))]
for j in xrange(len(subdata)):
if j in masked2:
subdata[i][j] = float('nan')
if savedata:
hic_data.write_matrix('%s/%s.mat' % (
savedata, '_'.join(set((crm1, crm2)))),
focus=(crm1, crm2),
normalized=normalized)
if show or savefig:
if (len(subdata) > 10000
and not kwargs.get('force_image', False)):
warn('WARNING: Matrix image not created, more than '
'10000 rows, use a lower resolution to create images')
continue
draw_map(subdata,
OrderedDict([(k, hic_data.chromosomes[k])
for k in hic_data.chromosomes.keys()
if k in [crm1, crm2]]),
hic_data.section_pos,
'%s/%s.%s' % (savefig,
'_'.join(set((crm1, crm2))),
'pdf' if pdf else 'png'),
show, one=True, clim=clim, cmap=cmap,
decay_resolution=decay_resolution, perc=perc,
name=name, cistrans=float('NaN'))
except ValueError, e:
print 'Value ERROR: problem with chromosome %s' % crm1
print str(e)
except IndexError, e:
print 'Index ERROR: problem with chromosome %s' % crm1
print str(e)
def hic_map(data, resolution=None, normalized=False, masked=None,
by_chrom=False, savefig=None, show=False, savedata=None,
focus=None, clim=None, cmap='jet', pdf=False, decay=True,
perc=10, name=None, decay_resolution=None, **kwargs):
"""
function to retrieve data from HiC-data object. Data can be stored as
a square matrix, or drawn using matplotlib
:param data: can be either a path to a file with pre-processed reads
(filtered or not), or a Hi-C-data object
:param None resolution: at which to bin the data (try having a dense matrix
with < 10% of cells with zero interaction counts). Note: not necessary
if a hic_data object is passed as 'data'.
:param False normalized: used normalized data, based on precalculated biases
:param masked: a list of columns to be removed. Usually because to few
interactions
:param False by_chrom: data can be stored in a partitioned way. This
parameter can take the values of:
* 'intra': one output per each chromosome will be created
* 'inter': one output per each possible pair of chromosome will be
created
* 'all' : both of the above outputs
:param None savefig: path where to store the output images. Note that, if
the by_chrom option is used, then savefig will be the name of the
directory containing the output files.
:param None savedata: path where to store the output matrices. Note that, if
the by_chrom option is used, then savefig will be the name of the
directory containing the output files.
:param None focus: can be either two number (i.e.: (1, 100)) specifying the
start and end position of the sub-matrix to display (start and end, along
the diagonal of the original matrix); or directly a chromosome name; or
two chromosome names (i.e.: focus=('chr2, chrX')), in order to store the
data corresponding to inter chromosomal interactions between these two
chromosomes
:param True decay: plot the correlation between genomic distance and
interactions (usually a decay).
:param None clim: cutoff for the upper and lower bound in the coloring scale
of the heatmap
:param False pdf: when using the bny_chrom option, to specify the format of
the stored images
:param Reds cmap: color map to be used for the heatmap
:param None decay_resolution: chromatin fragment size to consider when
calculating decay of the number of interactions with genomic distance.
Default is equal to resolution of the matrix.
"""
if isinstance(data, str):
data = load_hic_data_from_reads(data, resolution=resolution, **kwargs)
if not kwargs.get('get_sections', True) and decay:
warn('WARNING: not decay not available when get_sections is off.')
decay = False
hic_data = data
resolution = data.resolution
if not decay_resolution:
decay_resolution = resolution
if hic_data.bads and not masked:
masked = hic_data.bads
# save and draw the data
if by_chrom:
if focus:
raise Exception('Incompatible options focus and by_chrom\n')
os.system('mkdir -p ' + (savedata if savedata else savefig))
for i, crm1 in enumerate(hic_data.chromosomes):
for crm2 in hic_data.chromosomes.keys()[i:]:
if by_chrom == 'intra' and crm1 != crm2:
continue
if by_chrom == 'inter' and crm1 == crm2:
continue
subdata = hic_data.get_matrix(focus=(crm1, crm2), normalized=normalized)
if savedata:
hic_data.write_matrix('%s/%s.mat' % (
savedata, '_'.join(set((crm1, crm2)))),
focus=(crm1, crm2),
normalized=normalized)
if show or savefig:
draw_map(subdata,
OrderedDict([(k, hic_data.chromosomes[k])
for k in hic_data.chromosomes.keys()
if k in [crm1, crm2]]),
hic_data.section_pos,
'%s/%s.%s' % (savefig,
'_'.join(set((crm1, crm2))),
'pdf' if pdf else 'png'),
show, one=True, clim=clim, cmap=cmap,
decay_resolution=decay_resolution, perc=perc,
name=name, cistrans=float('NaN'))
else:
if savedata:
hic_data.write_matrix(savedata, focus=focus,
normalized=normalized)
if show or savefig:
subdata = hic_data.get_matrix(focus=focus, normalized=normalized)
if focus and masked:
# rescale masked
masked = dict([(m - focus[0], masked[m]) for m in masked])
if masked:
for i in xrange(len(subdata)):
if i in masked:
subdata[i] = [float('nan')
for j in xrange(len(subdata))]
for j in xrange(len(subdata)):
if j in masked:
subdata[i][j] = float('nan')
draw_map(subdata,
{} if focus else hic_data.chromosomes,
hic_data.section_pos, savefig, show,
one = True if focus else False, decay=decay,
clim=clim, cmap=cmap, decay_resolution=decay_resolution,
perc=perc, normalized=normalized,
max_diff=kwargs.get('max_diff', None),
name=name, cistrans=float('NaN') if focus else
hic_data.cis_trans_ratio(kwargs.get('normalized', False),
kwargs.get('exclude', None),
kwargs.get('diagonal', True),
kwargs.get('equals', None),
kwargs.get('verbose', False)))
def hic_map(
data,
resolution=None,
normalized=False,
masked=None,
by_chrom=False,
savefig=None,
show=False,
savedata=None,
focus=None,
clim=None,
cmap="jet",
pdf=False,
decay=True,
perc=10,
name=None,
decay_resolution=None,
**kwargs
):
"""
function to retrieve data from HiC-data object. Data can be stored as
a square matrix, or drawn using matplotlib
:param data: can be either a path to a file with pre-processed reads
(filtered or not), or a Hi-C-data object
:param None resolution: at which to bin the data (try having a dense matrix
with < 10% of cells with zero interaction counts). Note: not necessary
if a hic_data object is passed as 'data'.
:param False normalized: used normalized data, based on precalculated biases
:param masked: a list of columns to be removed. Usually because to few
interactions
:param False by_chrom: data can be stored in a partitioned way. This
parameter can take the values of:
* 'intra': one output per each chromosome will be created
* 'inter': one output per each possible pair of chromosome will be
created
* 'all' : both of the above outputs
:param None savefig: path where to store the output images. Note that, if
the by_chrom option is used, then savefig will be the name of the
directory containing the output files.
:param None savedata: path where to store the output matrices. Note that, if
the by_chrom option is used, then savefig will be the name of the
directory containing the output files.
:param None focus: can be either two number (i.e.: (1, 100)) specifying the
start and end position of the sub-matrix to display (start and end, along
the diagonal of the original matrix); or directly a chromosome name; or
two chromosome names (i.e.: focus=('chr2, chrX')), in order to store the
data corresponding to inter chromosomal interactions between these two
chromosomes
:param True decay: plot the correlation between genomic distance and
interactions (usually a decay).
:param None clim: cutoff for the upper and lower bound in the coloring scale
of the heatmap
:param False pdf: when using the bny_chrom option, to specify the format of
the stored images
:param Reds cmap: color map to be used for the heatmap
:param None decay_resolution: chromatin fragment size to consider when
calculating decay of the number of interactions with genomic distance.
Default is equal to resolution of the matrix.
"""
if isinstance(data, str):
data = load_hic_data_from_reads(data, resolution=resolution, **kwargs)
if not kwargs.get("get_sections", True) and decay:
warn("WARNING: not decay not available when get_sections is off.")
decay = False
hic_data = data
resolution = data.resolution
if not decay_resolution:
decay_resolution = resolution
if hic_data.bads and not masked:
masked = hic_data.bads
# save and draw the data
if by_chrom:
if focus:
raise Exception("Incompatible options focus and by_chrom\n")
os.system("mkdir -p " + (savedata if savedata else savefig))
for i, crm1 in enumerate(hic_data.chromosomes):
for crm2 in hic_data.chromosomes.keys()[i:]:
if by_chrom == "intra" and crm1 != crm2:
continue
if by_chrom == "inter" and crm1 == crm2:
continue
subdata = hic_data.get_matrix(focus=(crm1, crm2), normalized=normalized)
if savedata:
hic_data.write_matrix(
"%s/%s.mat" % (savedata, "_".join(set((crm1, crm2)))), focus=(crm1, crm2), normalized=normalized
)
if show or savefig:
draw_map(
subdata,
OrderedDict(
[(k, hic_data.chromosomes[k]) for k in hic_data.chromosomes.keys() if k in [crm1, crm2]]
),
hic_data.section_pos,
"%s/%s.%s" % (savefig, "_".join(set((crm1, crm2))), "pdf" if pdf else "png"),
show,
one=True,
clim=clim,
cmap=cmap,
decay_resolution=decay_resolution,
perc=perc,
name=name,
cistrans=float("NaN"),
)
else:
if savedata:
hic_data.write_matrix(savedata, focus=focus, normalized=normalized)
if show or savefig:
subdata = hic_data.get_matrix(focus=focus, normalized=normalized)
if focus and masked:
# rescale masked
masked = dict([(m - focus[0], masked[m]) for m in masked])
if masked:
for i in xrange(len(subdata)):
if i in masked:
subdata[i] = [float("nan") for j in xrange(len(subdata))]
for j in xrange(len(subdata)):
if j in masked:
subdata[i][j] = float("nan")
draw_map(
subdata,
{} if focus else hic_data.chromosomes,
hic_data.section_pos,
savefig,
show,
one=True if focus else False,
decay=decay,
clim=clim,
cmap=cmap,
decay_resolution=decay_resolution,
perc=perc,
normalized=normalized,
max_diff=kwargs.get("max_diff", None),
name=name,
cistrans=float("NaN")
if focus
else hic_data.cis_trans_ratio(
kwargs.get("normalized", False),
kwargs.get("exclude", None),
kwargs.get("diagonal", True),
kwargs.get("equals", None),
kwargs.get("verbose", False),
),
)
def hic_map(data, resolution=None, normalized=False, masked=None,
by_chrom=False, savefig=None, show=False, savedata=None,
focus=None, clim=None, cmap='tadbit', pdf=False, decay=True,
perc=10, name=None, **kwargs):
"""
function to retrieve data from HiC-data object. Data can be stored as
a square matrix, or drawn using matplotlib
:param data: can be either a path to a file with pre-processed reads
(filtered or not), or a Hi-C-data object
:param None resolution: at which to bin the data (try having a dense matrix
with < 10% of cells with zero interaction counts). Note: not necessary
if a hic_data object is passed as 'data'.
:param False normalized: used normalized data, based on precalculated biases
:param masked: a list of columns to be removed. Usually because to few
interactions
:param False by_chrom: data can be stored in a partitioned way. This
parameter can take the values of:
* 'intra': one output per each chromosome will be created
* 'inter': one output per each possible pair of chromosome will be
created
* 'all' : both of the above outputs
:param None savefig: path where to store the output images. Note that, if
the by_chrom option is used, then savefig will be the name of the
directory containing the output files.
:param None savedata: path where to store the output matrices. Note that, if
the by_chrom option is used, then savefig will be the name of the
directory containing the output files.
:param None focus: can be either two number (i.e.: (1, 100)) specifying the
start and end position of the sub-matrix to display (start and end, along
the diagonal of the original matrix); or directly a chromosome name; or
two chromosome names (i.e.: focus=('chr2, chrX')), in order to store the
data corresponding to inter chromosomal interactions between these two
chromosomes
:param True decay: plot the correlation between genomic distance and
interactions (usually a decay).
:param None clim: cutoff for the upper and lower bound in the coloring scale
of the heatmap
:param False pdf: when using the bny_chrom option, to specify the format of
the stored images
:param Reds cmap: color map to be used for the heatmap
:param False get_sections: for very very high resolution, when the column
index does not fit in memory
"""
if isinstance(data, str):
data = load_hic_data_from_reads(data, resolution=resolution, **kwargs)
if not kwargs.get('get_sections', True) and decay:
warn('WARNING: not decay not available when get_sections is off.')
decay = False
hic_data = data
if hic_data.bads and not masked:
masked = hic_data.bads
# save and draw the data
if by_chrom:
if focus:
raise Exception('Incompatible options focus and by_chrom\n')
os.system('mkdir -p ' + (savedata if savedata else savefig))
for i, crm1 in enumerate(hic_data.chromosomes):
for crm2 in hic_data.chromosomes.keys()[i:]:
if by_chrom == 'intra' and crm1 != crm2:
continue
if by_chrom == 'inter' and crm1 == crm2:
continue
subdata = hic_data.get_matrix(focus=(crm1, crm2), normalized=normalized)
if savedata:
out = open('%s/%s.mat' % (
savedata, '_'.join(set((crm1, crm2)))), 'w')
out.write('\n'.join(['\t'.join([str(i) for i in d])
for d in subdata]) + '\n')
out.close()
if show or savefig:
draw_map(subdata,
OrderedDict([(k, hic_data.chromosomes[k])
for k in hic_data.chromosomes.keys()
if k in [crm1, crm2]]),
hic_data.section_pos,
'%s/%s.%s' % (savefig,
'_'.join(set((crm1, crm2))),
'pdf' if pdf else 'png'),
show, one=True, clim=clim, cmap=cmap,
resolution=resolution, perc=perc,
name=name, cistrans=float('NaN'))
else:
if savedata:
out = open(savedata, 'w')
out.write('\n'.join(
['\t'.join([str(i) for i in line])
for line in hic_data.get_matrix(
focus=focus, normalized=normalized)]) + '\n')
out.close()
if show or savefig:
subdata = hic_data.get_matrix(focus=focus, normalized=normalized)
if focus and masked:
# rescale masked
masked = dict([(m - focus[0], masked[m]) for m in masked])
if masked:
for i in xrange(len(subdata)):
if i in masked:
subdata[i] = [float('nan')
for j in xrange(len(subdata))]
for j in xrange(len(subdata)):
if j in masked:
subdata[i][j] = float('nan')
draw_map(subdata,
{} if focus else hic_data.chromosomes,
hic_data.section_pos, savefig, show,
one = True if focus else False, decay=decay,
clim=clim, cmap=cmap, resolution=resolution, perc=perc,
name=name, cistrans=float('NaN') if focus else
hic_data.cis_trans_ratio(kwargs.get('normalized', False),
kwargs.get('exclude', None),
kwargs.get('diagonal', False),
kwargs.get('equals', None),
kwargs.get('verbose', False)))
