def __call__(self, **kw):
def make_X_labels(X,start,end,strand,down,up):
flen = end-start
i0 = (list(where(X == 0)[0])+[-1])[0]+1
i1 = (list(where(X == 1)[0])+[len(X)-1])[0]+1
i2 = len(X)-i1
istep = 0.5/(i1-i0)
if down < 1: down *= flen
if up < 1: up *= flen
Xup = (array(range(-i0,0))+.5)*up/i0
Xb = (X[i0:i1]+istep)*flen
Xdown = flen+(array(range(i2))+.5)*down/i2
if strand is None or strand > 0: return start+concatenate([Xup,Xb,Xdown])
else: return end-concatenate([Xup,Xb,Xdown])
chrmeta = "guess"
features = track(kw.get('features'), chrmeta=chrmeta)
#signals = kw.get('SigMulti',{}).get('signals', [])
signals = kw.get('signals', [])
if not isinstance(signals, list): signals = [signals]
signals = [track(sig) for sig in signals]
snames = [sig.name for sig in signals]
labels = None
data = None
upstr = _upstr
downstr = _downstr
if kw.get("upstream") is not None:
_up = int(kw["upstream"])
if _up > 50: upstr = (_up,5)
elif _up > 0: upstr = (_up,1)
else: upstr = (0,0)
if kw.get("downstream") is not None:
_down = int(kw["downstream"])
if _down > 50: downstr = (_down,5)
elif _down > 0: downstr = (_down,1)
else: downstr = (0,0)
if kw.get("nbins") is not None: nbins = max(1,int(kw["nbins"]))
else: nbins = _nbins
if kw.get("noclust") is not None:
noclust = str(kw["noclust"]).lower() in ['1','true','t','on']
else:
noclust = False
try:
ymin = float(kw.get('ymin'))
except (ValueError, TypeError):
ymin = None
try:
ymax = float(kw.get('ymax'))
except (ValueError, TypeError):
ymax = None
for chrom in features.chrmeta:
if 'name' in features.fields: _fread = features.read(chrom)
else: _fread = add_name_field(features.read(chrom))
_l, _d = feature_matrix([s.read(chrom) for s in signals], _fread,
segment=True, nbins=nbins,
upstream=upstr, downstream=downstr)
if _d.size == 0:
continue
if data is None:
labels = _l
data = _d
else:
labels = concatenate((labels, _l))
data = vstack((data, _d))
outf = str(kw.get('output'))
if outf not in output_list:
outf = output_list[0]
pdf = self.temporary_path(fname='plot_features.pdf')
if outf == 'archive':
tarname = self.temporary_path(fname='plot_features.tar.gz')
tarfh = tarfile.open(tarname, "w:gz")
if data is None:
raise ValueError("No data")
mode = kw.get('mode', 0)
if str(mode) in [str(x[0]) for x in plot_types]:
mode = int(mode)
X = array(range(-upstr[1]+1,nbins+downstr[1]+1))/(1.0*nbins)
if mode in plot_types[0]: #heatmap
new = True
if 'name' in features.fields: _fread = features.read(fields=['chr','start','end','name'])
else: _fread = add_name_field(features.read(fields=['chr','start','end']))
order = [where(labels == feat[3])[0][0] for feat in _fread]
for n in range(data.shape[-1]-1):
heatmap(data[order, :, n], output=pdf, new=new, last=False,
rows=labels[order], columns=X, main=snames[n],
orderRows=not(noclust), orderCols=False,
ymin=ymin, ymax=ymax)
new = False
heatmap(data[order, :, -1], output=pdf, new=new, last=True,
rows=labels[order], columns=X, main=snames[-1],
orderRows=not(noclust), orderCols=False,
ymin=ymin, ymax=ymax)
if outf == 'archive':
for n,sn in enumerate(snames):
_datf = self.temporary_path(fname=sn+"_data.txt")
with open(_datf,"w") as dff:
dff.write("\t".join([""]+[str(x) for x in X])+"\n")
for k in order:
dff.write("\t".join([labels[k]]+[str(x) for x in data[k, :, n]])+"\n")
tarfh.add(_datf,arcname=os.path.basename(_datf))
elif mode in plot_types[1]: #average lineplot
Y = data.mean(axis=0)
if ymin is None: ymin = min([x.min() for x in Y]+[0])
if ymax is None: ymax = max([x.max() for x in Y])
lineplot(X, [Y[:, n] for n in range(data.shape[-1])],
output=pdf, new=True, last=True, legend=snames,
ylim=(ymin,ymax))
if outf == 'archive':
_datf = self.temporary_path(fname="lineplot_data.txt")
with open(_datf,"w") as dff:
dff.write("\t".join([""]+[str(x) for x in X])+"\n")
for n,sn in enumerate(snames):
dff.write("\t".join([sn]+[str(x) for x in Y[:, n]])+"\n")
tarfh.add(_datf,arcname=os.path.basename(_datf))
elif mode in plot_types[2]: #mosaic
mfrow = [4,3]
nplot = min(data.shape[0], max_pages*mfrow[0]*mfrow[1])
if ymin is None: ymin = min([data.min(),0])
if ymax is None: ymax = data.max()
_f = ['chr','start','end']
_si = None
if 'strand' in features.fields:
_f.append('strand')
_si = 3
if 'name' in features.fields: _fread = features.read(fields=_f+['name'])
else: _fread = add_name_field(features.read(fields=_f))
order = []
for nf,feat in enumerate(_fread):
reg = where(labels == feat[-1])[0][0]
order.append(reg)
X1 = make_X_labels(X, feat[1], feat[2], feat[_si] if _si else None, downstr[0], upstr[0])
xlim = (X1[0],X1[-1])
Y = [data[reg, :, n] for n in range(data.shape[-1])]
if nf == 0:
lineplot(X1, Y, output=pdf, new=True, last=False, mfrow=mfrow,
main=labels[reg], ylim=(ymin,ymax), xlim=xlim)
elif nf < nplot-1:
lineplot(X1, Y, output=pdf, new=False, last=False,
main=labels[reg], ylim=(ymin,ymax), xlim=xlim)
else:
lineplot(X1, Y, output=pdf, new=False, last=True, legend=snames,
main=labels[reg], ylim=(ymin,ymax), xlim=xlim)
break
if outf == 'archive':
for n,sn in enumerate(snames):
_datf = self.temporary_path(fname=sn+"_data.txt")
with open(_datf,"w") as dff:
dff.write("\t".join([""]+[str(x) for x in X])+"\n")
for k in order:
dff.write("\t".join([labels[k]]+[str(x) for x in data[k, :, n]])+"\n")
tarfh.add(_datf,arcname=os.path.basename(_datf))
else:
raise ValueError("Mode not implemented: %s" % mode)
if outf == 'archive':
tarfh.add(pdf,arcname=os.path.basename(pdf))
tarfh.close()
self.new_file(tarname, 'data_archive')
else:
self.new_file(pdf, 'plot_features')
return self.display_time()
def __call__(self, **kw):
def make_X_labels(X, start, end, strand, down, up):
flen = end - start
i0 = (list(where(X == 0)[0]) + [-1])[0] + 1
i1 = (list(where(X == 1)[0]) + [len(X) - 1])[0] + 1
i2 = len(X) - i1
istep = 0.5 / (i1 - i0)
if down < 1: down *= flen
if up < 1: up *= flen
Xup = (array(range(-i0, 0)) + .5) * up / i0
Xb = (X[i0:i1] + istep) * flen
Xdown = flen + (array(range(i2)) + .5) * down / i2
if strand is None or strand > 0:
return start + concatenate([Xup, Xb, Xdown])
else:
return end - concatenate([Xup, Xb, Xdown])
chrmeta = "guess"
features = track(kw.get('features'), chrmeta=chrmeta)
signals = kw.get('SigMulti', {}).get('signals', [])
if not isinstance(signals, list): signals = [signals]
signals = [track(sig) for sig in signals]
snames = [sig.name for sig in signals]
labels = None
data = None
upstr = _upstr
downstr = _downstr
if kw.get("upstream") is not None:
_up = int(kw["upstream"])
if _up > 50: upstr = (_up, 5)
elif _up > 0: upstr = (_up, 1)
else: upstr = (0, 0)
if kw.get("downstream") is not None:
_down = int(kw["downstream"])
if _down > 50: downstr = (_down, 5)
elif _down > 0: downstr = (_down, 1)
else: downstr = (0, 0)
if kw.get("nbins") is not None: nbins = max(1, int(kw["nbins"]))
else: nbins = _nbins
if kw.get("noclust") is not None:
noclust = str(kw["noclust"]).lower() in ['1', 'true', 't', 'on']
else:
noclust = False
try:
ymin = float(kw.get('ymin'))
except (ValueError, TypeError):
ymin = None
try:
ymax = float(kw.get('ymax'))
except (ValueError, TypeError):
ymax = None
for chrom in features.chrmeta:
if 'name' in features.fields: _fread = features.read(chrom)
else: _fread = add_name_field(features.read(chrom))
_l, _d = feature_matrix([s.read(chrom) for s in signals],
_fread,
segment=True,
nbins=nbins,
upstream=upstr,
downstream=downstr)
if _d.size == 0:
continue
if data is None:
labels = _l
data = _d
else:
labels = concatenate((labels, _l))
data = vstack((data, _d))
outf = str(kw.get('output'))
if outf not in output_list:
outf = output_list[0]
pdf = self.temporary_path(fname='plot_features.pdf')
if outf == 'archive':
tarname = self.temporary_path(fname='plot_features.tar.gz')
tarfh = tarfile.open(tarname, "w:gz")
if data is None:
raise ValueError("No data")
mode = kw.get('mode', 0)
if str(mode) in [str(x[0]) for x in plot_types]:
mode = int(mode)
X = array(range(-upstr[1] + 1, nbins + downstr[1] + 1)) / (1.0 * nbins)
if mode in plot_types[0]: #heatmap
new = True
if 'name' in features.fields:
_fread = features.read(fields=['chr', 'start', 'end', 'name'])
else:
_fread = add_name_field(
features.read(fields=['chr', 'start', 'end']))
order = [where(labels == feat[3])[0][0] for feat in _fread]
for n in range(data.shape[-1] - 1):
heatmap(data[order, :, n],
output=pdf,
new=new,
last=False,
rows=labels[order],
columns=X,
main=snames[n],
orderRows=not (noclust),
orderCols=False,
ymin=ymin,
ymax=ymax)
new = False
heatmap(data[order, :, -1],
output=pdf,
new=new,
last=True,
rows=labels[order],
columns=X,
main=snames[-1],
orderRows=not (noclust),
orderCols=False,
ymin=ymin,
ymax=ymax)
if outf == 'archive':
for n, sn in enumerate(snames):
_datf = self.temporary_path(fname=sn + "_data.txt")
with open(_datf, "w") as dff:
dff.write("\t".join([""] + [str(x) for x in X]) + "\n")
for k in order:
dff.write("\t".join(
[labels[k]] + [str(x)
for x in data[k, :, n]]) + "\n")
tarfh.add(_datf, arcname=os.path.basename(_datf))
elif mode in plot_types[1]: #average lineplot
Y = data.mean(axis=0)
if ymin is None: ymin = min([x.min() for x in Y] + [0])
if ymax is None: ymax = max([x.max() for x in Y])
lineplot(X, [Y[:, n] for n in range(data.shape[-1])],
output=pdf,
new=True,
last=True,
legend=snames,
ylim=(ymin, ymax))
if outf == 'archive':
_datf = self.temporary_path(fname="lineplot_data.txt")
with open(_datf, "w") as dff:
dff.write("\t".join([""] + [str(x) for x in X]) + "\n")
for n, sn in enumerate(snames):
dff.write("\t".join([sn] + [str(x)
for x in Y[:, n]]) + "\n")
tarfh.add(_datf, arcname=os.path.basename(_datf))
elif mode in plot_types[2]: #mosaic
mfrow = [4, 3]
nplot = min(data.shape[0], max_pages * mfrow[0] * mfrow[1])
if ymin is None: ymin = min([data.min(), 0])
if ymax is None: ymax = data.max()
_f = ['chr', 'start', 'end']
_si = None
if 'strand' in features.fields:
_f.append('strand')
_si = 3
if 'name' in features.fields:
_fread = features.read(fields=_f + ['name'])
else:
_fread = add_name_field(features.read(fields=_f))
order = []
for nf, feat in enumerate(_fread):
reg = where(labels == feat[-1])[0][0]
order.append(reg)
X1 = make_X_labels(X, feat[1], feat[2],
feat[_si] if _si else None, downstr[0],
upstr[0])
xlim = (X1[0], X1[-1])
Y = [data[reg, :, n] for n in range(data.shape[-1])]
if nf == 0:
lineplot(X1,
Y,
output=pdf,
new=True,
last=False,
mfrow=mfrow,
main=labels[reg],
ylim=(ymin, ymax),
xlim=xlim)
elif nf < nplot - 1:
lineplot(X1,
Y,
output=pdf,
new=False,
last=False,
main=labels[reg],
ylim=(ymin, ymax),
xlim=xlim)
else:
lineplot(X1,
Y,
output=pdf,
new=False,
last=True,
legend=snames,
main=labels[reg],
ylim=(ymin, ymax),
xlim=xlim)
break
if outf == 'archive':
for n, sn in enumerate(snames):
_datf = self.temporary_path(fname=sn + "_data.txt")
with open(_datf, "w") as dff:
dff.write("\t".join([""] + [str(x) for x in X]) + "\n")
for k in order:
dff.write("\t".join(
[labels[k]] + [str(x)
for x in data[k, :, n]]) + "\n")
tarfh.add(_datf, arcname=os.path.basename(_datf))
else:
raise ValueError("Mode not implemented: %s" % mode)
if outf == 'archive':
tarfh.add(pdf, arcname=os.path.basename(pdf))
tarfh.close()
self.new_file(tarname, 'data_archive')
else:
self.new_file(pdf, 'plot_features')
return self.display_time()
def __call__(self, **kw):
feature_type = int(kw.get("feature_type") or 0)
individual = kw.get("individual", False)
if isinstance(individual, basestring):
individual = individual.lower() in ["1", "true", "t", "on"]
if individual and int(kw["mode"]) != 1:
raise ValueError("Only correlation plots can work with the 'individual' option.")
assembly_id = kw.get("assembly") or None
chrmeta = "guess"
if assembly_id:
assembly = genrep.Assembly(assembly_id)
chrmeta = assembly.chrmeta
genes = assembly.gene_track
exons = assembly.exon_track
elif not (feature_type == 3):
raise ValueError("Please specify an assembly")
# signals = kw.get('SigMulti',{}).get('signals', [])
signals = kw.get("signals", [])
if not isinstance(signals, list):
signals = [signals]
signals = [track(sig, chrmeta=chrmeta) for sig in signals]
snames = [sig.name for sig in signals]
if feature_type == 0: # bodies
features = genes
elif feature_type == 1: # promoters
prom_pars = {
"before_start": int(kw.get("upstream") or prom_up_def),
"after_start": int(kw.get("downstream") or prom_down_def),
"on_strand": True,
}
features = lambda c: neighborhood(genes(c), **prom_pars)
elif feature_type == 2: # exons
features = exons
elif feature_type == 3: # custom track
_t = track(kw.get("features"), chrmeta=chrmeta)
chrmeta = _t.chrmeta
features = _t.read
else:
raise ValueError("Feature type not known: %i" % feature_type)
# highlights = kw.get('HiMulti',{}).get('highlights', [])
highlights = kw.get("highlights", [])
if not isinstance(highlights, list):
highlights = [highlights]
if highlights is not None:
highlights = [track(hi, chrmeta=chrmeta) for hi in highlights]
hinames = [t.name for t in highlights]
pdf = self.temporary_path(fname="plot_pairs.pdf")
narr = None
set_index = []
set_labels = []
_new = True
if int(kw["mode"]) == 1: # correl
cormax = int(kw.get("cormax") or _cormax)
xarr = array(range(-cormax, cormax + 1))
_f = ["chr", "start", "end", "score"]
features = [x[:3] for chrom in chrmeta for x in sorted_stream(features(chrom))]
table = self.temporary_path(fname="table.txt")
with open(table, "w") as t:
t.write("\t".join(["chr", "start", "end", "max(correlation)", "lag_max"]) + "\n")
if individual:
for nplot, feature in enumerate(features):
if narr is not None and nplot < _MAX_PLOTS_:
pairs(narr, xarr, labels=snames, output=pdf, new=_new, last=False)
_new = False
narr = correlation([s.read(fields=_f) for s in signals], [feature], (-cormax, cormax), True)
list_corr = list(narr[0][0])
max_corr = max(list_corr)
lag_max = list_corr.index(max_corr) - cormax
t.write("\t".join([str(x) for x in feature[:3] + (max_corr, lag_max)]) + "\n")
else:
narr = correlation([s.read(fields=_f) for s in signals], features, (-cormax, cormax), True)
list_corr = list(narr[0][0])
max_corr = max(list_corr)
lag_max = list_corr.index(max_corr) - cormax
t.write("\t".join(["-", "-", "-"] + [str(max_corr), str(lag_max)]) + "\n")
elif int(kw["mode"]) == 0: # density
xarr = None
for chrom in chrmeta:
feat = features(chrom)
if "name" not in feat.fields:
feat = add_name_field(feat)
means = score_by_feature([s.read(chrom) for s in signals], feat)
mf = means.fields[len(feat.fields) :]
_n, _l = score_array(means, mf)
if _n.size == 0:
continue
if narr is None:
narr = _n
else:
narr = vstack((narr, _n))
set_index = [narr.shape[0]]
for hitrack in highlights:
for chrom in chrmeta:
hiread = hitrack.read(chrom)
if "name" not in hiread.fields:
hiread = add_name_field(hiread)
means = score_by_feature([s.read(chrom) for s in signals], hiread)
mf = means.fields[len(hiread.fields) :]
_n, _l = score_array(means, mf)
if _n.size == 0:
continue
narr = vstack((narr, _n))
set_labels.extend(_l)
set_index.append(narr.shape[0])
else:
raise ValueError("Mode not implemented: %s" % kw["mode"])
if narr is None:
raise ValueError("No data")
pairs(narr, xarr, labels=snames, output=pdf, highlights=[set_index, set_labels], new=_new, last=True)
if int(kw["mode"]) == 1:
self.new_file(table, "table")
self.new_file(pdf, "plot_pairs")
return self.display_time()
def __call__(self, **kw):
feature_type = int(kw.get('feature_type') or 0)
assembly_id = kw.get('assembly') or None
chrmeta = "guess"
if assembly_id:
assembly = genrep.Assembly(assembly_id)
chrmeta = assembly.chrmeta
genes = assembly.gene_track
exons = assembly.exon_track
elif not (feature_type == 3):
raise ValueError("Please specify an assembly")
signals = kw.get('SigMulti', {}).get('signals', [])
if not isinstance(signals, list): signals = [signals]
signals = [track(sig, chrmeta=chrmeta) for sig in signals]
snames = [sig.name for sig in signals]
if feature_type == 0: #bodies
features = genes
elif feature_type == 1: #promoters
prom_pars = {
'before_start': int(kw.get('upstream') or prom_up_def),
'after_start': int(kw.get('downstream') or prom_down_def),
'on_strand': True
}
features = lambda c: neighborhood(genes(c), **prom_pars)
elif feature_type == 2: #exons
features = exons
elif feature_type == 3: #custom track
_t = track(kw.get('features'), chrmeta=chrmeta)
chrmeta = _t.chrmeta
features = _t.read
else:
raise ValueError("Feature type not known: %i" % feature_type)
highlights = kw.get('HiMulti', {}).get('highlights', [])
if not isinstance(highlights, list): highlights = [highlights]
if highlights is not None:
highlights = [track(hi, chrmeta=chrmeta) for hi in highlights]
hinames = [t.name for t in highlights]
pdf = self.temporary_path(fname='plot_pairs.pdf')
narr = None
set_index = []
set_labels = []
if int(kw['mode']) == 0: #correl
cormax = int(kw.get('cormax') or _cormax)
xarr = array(range(-cormax, cormax + 1))
srtdchrom = sorted(chrmeta.keys())
features = [
x[:3] for chrom in srtdchrom
for x in sorted_stream(features(chrom))
]
_f = ['chr', 'start', 'end', 'score']
narr = correlation([s.read(fields=_f) for s in signals], features,
(-cormax, cormax), True)
elif int(kw['mode']) == 1: #density
xarr = None
for chrom in chrmeta:
feat = features(chrom)
if 'name' not in feat.fields:
feat = add_name_field(feat)
means = score_by_feature([s.read(chrom) for s in signals],
feat)
mf = means.fields[len(feat.fields):]
_n, _l = score_array(means, mf)
if _n.size == 0: continue
if narr is None: narr = _n
else: narr = vstack((narr, _n))
set_index = [narr.shape[0]]
for hitrack in highlights:
for chrom in chrmeta:
hiread = hitrack.read(chrom)
if 'name' not in hiread.fields:
hiread = add_name_field(hiread)
means = score_by_feature([s.read(chrom) for s in signals],
hiread)
mf = means.fields[len(hiread.fields):]
_n, _l = score_array(means, mf)
if _n.size == 0: continue
narr = vstack((narr, _n))
set_labels.extend(_l)
set_index.append(narr.shape[0])
else:
raise ValueError("Mode not implemented: %s" % kw['mode'])
if narr is None:
raise ValueError("No data")
pairs(narr,
xarr,
labels=snames,
output=pdf,
highlights=[set_index, set_labels])
self.new_file(pdf, 'plot_pairs')
return self.display_time()
def __call__(self, **kw):
feature_type = int(kw.get('feature_type') or 0)
assembly_id = kw.get('assembly') or None
chrmeta = "guess"
if assembly_id:
assembly = genrep.Assembly(assembly_id)
chrmeta = assembly.chrmeta
genes = assembly.gene_track
exons = assembly.exon_track
elif not(feature_type == 3):
raise ValueError("Please specify an assembly")
signals = kw.get('SigMulti',{}).get('signals', [])
if not isinstance(signals, list): signals = [signals]
signals = [track(sig, chrmeta=chrmeta) for sig in signals]
snames = [sig.name for sig in signals]
if feature_type == 0: #bodies
features = genes
elif feature_type == 1: #promoters
prom_pars = {'before_start': int(kw.get('upstream') or prom_up_def),
'after_start': int(kw.get('downstream') or prom_down_def),
'on_strand': True}
features = lambda c: neighborhood(genes(c), **prom_pars)
elif feature_type == 2: #exons
features = exons
elif feature_type == 3: #custom track
_t = track(kw.get('features'), chrmeta=chrmeta)
chrmeta = _t.chrmeta
features = _t.read
else:
raise ValueError("Feature type not known: %i" % feature_type)
highlights = kw.get('HiMulti',{}).get('highlights', [])
if not isinstance(highlights, list): highlights = [highlights]
if highlights is not None:
highlights = [track(hi, chrmeta=chrmeta) for hi in highlights]
hinames = [t.name for t in highlights]
pdf = self.temporary_path(fname='plot_pairs.pdf')
narr = None
set_index = []
set_labels = []
if int(kw['mode']) == 0: #correl
cormax = int(kw.get('cormax') or _cormax)
xarr = array(range(-cormax, cormax + 1))
srtdchrom = sorted(chrmeta.keys())
features = [x[:3] for chrom in srtdchrom
for x in sorted_stream(features(chrom))]
_f = ['chr', 'start', 'end', 'score']
narr = correlation([s.read(fields=_f) for s in signals],
features, (-cormax, cormax), True)
elif int(kw['mode']) == 1: #density
xarr = None
for chrom in chrmeta:
feat = features(chrom)
if 'name' not in feat.fields:
feat = add_name_field(feat)
means = score_by_feature([s.read(chrom) for s in signals], feat)
mf = means.fields[len(feat.fields):]
_n, _l = score_array(means, mf)
if _n.size == 0: continue
if narr is None: narr = _n
else: narr = vstack((narr, _n))
set_index = [narr.shape[0]]
for hitrack in highlights:
for chrom in chrmeta:
hiread = hitrack.read(chrom)
if 'name' not in hiread.fields:
hiread = add_name_field(hiread)
means = score_by_feature([s.read(chrom) for s in signals], hiread)
mf = means.fields[len(hiread.fields):]
_n, _l = score_array(means, mf)
if _n.size == 0: continue
narr = vstack((narr, _n))
set_labels.extend(_l)
set_index.append(narr.shape[0])
else:
raise ValueError("Mode not implemented: %s" % kw['mode'])
if narr is None:
raise ValueError("No data")
pairs(narr, xarr, labels=snames, output=pdf, highlights=[set_index,set_labels])
self.new_file(pdf, 'plot_pairs')
return self.display_time()
