def main():
out_fname = sys.argv[1]
basedir = '/'.join(
os.path.dirname(os.path.realpath(__file__)).split('/')[:-2])
mm9_methods = {
'distance-corrected':
'%s/Analysis/hifive_mm9_ESC_exp_correlations.txt' % basedir,
'raw': '%s/Analysis/hifive_mm9_ESC_expdist_correlations.txt' % basedir,
}
mm9_data = load_data(mm9_methods)
width = 16.8
spacer = 0.4
plot_width = (width - spacer * 4) / 5
c = canvas.canvas()
mm9_ranges_img, mm9_ranges_height = plot_dataset_ranges(
mm9_data, plot_width, spacer)
mm9_ranges_img.text(0, mm9_ranges_height, 'b',
[text.halign.left, text.valign.top,
text.size(-1)])
c.insert(mm9_ranges_img, [trafo.translate(plot_width + spacer, 0)])
mm9_overall_img = plot_overall(mm9_data, plot_width - spacer,
(mm9_ranges_height - spacer) - 0.3)
c.text(0, mm9_ranges_height, 'a',
[text.halign.left, text.valign.top,
text.size(-1)])
c.insert(mm9_overall_img, [trafo.translate(0, 0.4)])
c.writePDFfile(out_fname)
def plot_dataset_ranges(data, width, label):
methods = data.keys()
binsizes = numpy.unique(data[methods[0]]['binsize'])
ho = 0.4
ho2 = 0.4
vo = 0.6
spacer = 0.25
plot_width = (width - ho * 2 -
(binsizes.shape[0] - 1) * spacer) / binsizes.shape[0] - ho2
plot_height = plot_width
c = canvas.canvas()
for i, binsize in enumerate(binsizes):
img = plot_single_range(data, binsize, plot_width, plot_width)
c.insert(img, [
trafo.translate((plot_width + spacer) * i + ho2 * (i + 1) + ho, vo)
])
c.text(
0, plot_height * 0.5 + vo, "Correlation",
[text.halign.center, text.valign.top,
text.size(-3),
trafo.rotate(90)])
c.text(width, plot_height * 0.5 + vo, label, [
text.halign.center, text.valign.top,
text.size(-3),
trafo.rotate(-90)
])
c.text((plot_width + ho2) * 2 + spacer * 1.5 + ho, 0,
"Interaction Range (bp)",
[text.halign.center, text.valign.bottom,
text.size(-3)])
return c, plot_height + vo + 0.3
def plot_dataset_ranges(data0, data1, width):
methods = data0.keys()
binsizes = numpy.unique(data0[methods[0]]['binsize'])
ho = 0.8
ho2 = 0.25
vo = 0.7
vo2 = 0.4
spacer = 0.0
plot_width = (2 * width - ho * 2 - ho2 - spacer) / 2
plot_height = width - vo - vo2
c = canvas.canvas()
for i, binsize in enumerate(binsizes):
img = plot_single_range(data0, data1, binsize, plot_width, plot_width,
vo, vo2)
c.insert(img, [
trafo.translate(
(plot_width + spacer + ho) * (i % 2) + ho2 + ho,
(1 - i / 2) * (plot_height + spacer + vo + vo2) + vo)
])
c.text(
0, plot_height + 0.5 * spacer + vo + vo2, r"$r_{0K} - r_{500K}$",
[text.halign.center, text.valign.top,
text.size(-2),
trafo.rotate(90)])
c.text(plot_width + ho2 + spacer * 0.5 + ho, 0, "Interaction Range (bp)",
[text.halign.center, text.valign.bottom,
text.size(-3)])
c.text(0, width * 2 + spacer, 'a',
[text.halign.left, text.valign.top,
text.size(-1)])
return c
def plot_dataset_ranges(data0, data1, width, height):
binsizes = numpy.unique(data0['binsize'])
ho = 1.1
ho2 = 0.2
vo = 1.0
plot_width = (width - ho - ho2 * 3) / 4.0
plot_height = height - vo
c = canvas.canvas()
for i, binsize in enumerate(binsizes):
if i == 0:
ylabel = True
else:
ylabel = False
img = plot_single_range(data0, data1, binsize, plot_width, plot_height,
ylabel)
c.insert(img, [trafo.translate(ho + i * (plot_width + ho2), vo - 0.3)])
c.text(
0, plot_height * 0.5 + vo - 0.3, r"$r_{Poisson} - r_{binomial}$",
[text.halign.center, text.valign.top,
text.size(-2),
trafo.rotate(90)])
c.text(plot_width * 2 + ho + 1.5 * ho2, 0, "Interaction Range (bp)",
[text.halign.center, text.valign.bottom,
text.size(-3)])
c.text(0, height, 'a', [text.halign.left, text.valign.top, text.size(-1)])
return c
def plot_dataset_ranges(data, width, spacer):
methods = data.keys()
binsizes = numpy.unique(data[methods[0]]['binsize'])
plot_width = width - 0.4
plot_height = plot_width + 0.2
c = canvas.canvas()
for i, binsize in enumerate(binsizes):
img = plot_single_range(data, binsize, plot_width, plot_height)
c.insert(img, [
trafo.translate(
(plot_width + spacer) * i + 0.4, 0.4 + spacer * 0.5)
])
c.text(
0, plot_height * 0.5 + 0.2 + spacer * 0.75, "Dataset correlation",
[text.halign.center, text.valign.top,
text.size(-3),
trafo.rotate(90)])
c.text(plot_width * 2 + spacer * 1.5 + 0.4, 0.35, "Interaction range (bp)",
[text.halign.center, text.valign.bottom,
text.size(-3)])
c.fill(path.rect(0.4, 0.025, 0.2, 0.2), [color.rgb.black])
c.text(0.7, 0.125, "HiFive-Express using raw reads",
[text.halign.left, text.valign.middle,
text.size(-2)])
c.fill(path.rect(plot_width * 2 + spacer * 1.5, 0.025, 0.2, 0.2),
[color.rgb.red])
c.text(plot_width * 2 + spacer * 1.5 + 0.3, 0.125,
"HiFive-Express using distance-corrected reads",
[text.halign.left, text.valign.middle,
text.size(-2)])
return c, plot_height + 0.4 + spacer
def main():
out_fname = sys.argv[1]
basedir = '/'.join(os.path.dirname(os.path.realpath(__file__)).split('/')[:-2])
mm9_methods = {
'0 pseudo-counts':'%s/Analysis/hifive_mm9_ESC_bin_correlations.txt' % basedir,
'1 pseudo-count':'%s/Analysis/hifive_mm9_ESC_binPC1_correlations.txt' % basedir,
'3 pseudo-counts':'%s/Analysis/hifive_mm9_ESC_binPC3_correlations.txt' % basedir,
'6 pseudo-counts':'%s/Analysis/hifive_mm9_ESC_binPC6_correlations.txt' % basedir,
'HiCPipe':'%s/Analysis/hicpipe_mm9_ESC_correlations.txt' % basedir,
}
mm9_data = load_data(mm9_methods)
width = 16.8
spacer = 0.4
overall_width = (width - spacer * 2) / 2.6
c = canvas.canvas()
prob_ranges_img, prob_ranges_height = plot_dataset_ranges(mm9_data, width)
prob_ranges_img.text(0, prob_ranges_height, 'a',
[text.halign.left, text.valign.top, text.size(-1)])
c.insert(prob_ranges_img)
overall_height = prob_ranges_height * 0.6
hifive_overall_img = plot_overall(mm9_data, overall_width, overall_height, 'cis')
hifive_overall_img.text(0, overall_height + 0.1, 'b',
[text.halign.left, text.valign.top, text.size(-1)])
c.insert(hifive_overall_img, [trafo.translate(0, -overall_height - spacer)])
hifive_overall_img = plot_overall(mm9_data, overall_width, overall_height, 'trans')
hifive_overall_img.text(0, overall_height + 0.1, 'c',
[text.halign.left, text.valign.top, text.size(-1)])
c.insert(hifive_overall_img,
[trafo.translate(width - overall_width, -overall_height - spacer)])
c.insert(plot_key(overall_width * 0.6 + 0.4, overall_height, mm9_data),
[trafo.translate(overall_width + spacer + 0.6, -overall_height - spacer)])
c.writePDFfile(out_fname)
def main():
out_fname = sys.argv[1]
basedir = '/'.join(os.path.dirname(os.path.realpath(__file__)).split('/')[:-2])
hic_fname1 = "%s/Data/HiC/HiFive/mm9_ESC_NcoI_prob.hcp" % basedir
hic_fname2 = "%s/Data/HiC/HiFive/mm9_ESC_HindIII_prob.hcp" % basedir
fivec_fnames = {
"Prob_Phillips":"%s/Data/FiveC/HiFive/Phillips_ESC_prob.fcp" % basedir,
"Prob_Nora":"%s/Data/FiveC/HiFive/Nora_ESC_male_E14_prob.fcp" % basedir,
"Bin_Phillips":"%s/Data/FiveC/HiFive/Phillips_ESC_bin.fcp" % basedir,
"Bin_Nora":"%s/Data/FiveC/HiFive/Nora_ESC_male_E14_bin.fcp" % basedir,
"Exp_Phillips":"%s/Data/FiveC/HiFive/Phillips_ESC_exp.fcp" % basedir,
"Exp_Nora":"%s/Data/FiveC/HiFive/Nora_ESC_male_E14_exp.fcp" % basedir,
"Exp-KR_Phillips":"%s/Data/FiveC/HiFive/Phillips_ESC_expKR.fcp" % basedir,
"Exp-KR_Nora":"%s/Data/FiveC/HiFive/Nora_ESC_male_E14_expKR.fcp" % basedir,
}
hic1 = hifive.HiC(hic_fname1)
hic2 = hifive.HiC(hic_fname2)
hic_hm = { 'Phillips': {} }
fc = hifive.FiveC(fivec_fnames["Prob_Phillips"])
fragments = fc.frags['fragments'][...]
regions = fc.frags['regions'][...]
for i in range(fc.frags['regions'].shape[0]):
binbounds = numpy.hstack((
fragments['start'][regions['start_frag'][i]:regions['stop_frag'][i]].reshape(-1, 1),
fragments['stop'][regions['start_frag'][i]:regions['stop_frag'][i]].reshape(-1, 1)))
binbounds = binbounds[numpy.where(fc.filter[regions['start_frag'][i]:regions['stop_frag'][i]])[0], :]
hic_hm['Phillips'][i] = dynamically_bin(hic1, hic2, regions['chromosome'][i], binbounds)
fc = hifive.FiveC(fivec_fnames["Prob_Nora"])
fragments = fc.frags['fragments'][...]
regions = fc.frags['regions'][...]
binbounds = numpy.hstack((
fragments['start'][regions['start_frag'][0]:regions['stop_frag'][0]].reshape(-1, 1),
fragments['stop'][regions['start_frag'][0]:regions['stop_frag'][0]].reshape(-1, 1)))
binbounds = binbounds[numpy.where(fc.filter[regions['start_frag'][0]:regions['stop_frag'][0]])[0], :]
hic_hm['Nora'] = dynamically_bin(hic1, hic2, regions['chromosome'][0], binbounds)
dist_corr = find_correlations( hic_hm, fivec_fnames, out_fname, True )
nodist_corr = find_correlations( hic_hm, fivec_fnames, out_fname, False )
c = canvas.canvas()
width = 16.8
spacer = 0.4
plot_width = (width - spacer * 2) / 2.5
plot_height = plot_width
key_width = width - (plot_width + spacer) * 2
phillips_img = plot_correlation_diffs(dist_corr, nodist_corr, 'Phillips', plot_width, plot_height)
nora_img = plot_correlation_diffs(dist_corr, nodist_corr, 'Nora', plot_width, plot_height)
key_img = plot_key(key_width, plot_height)
c.insert(phillips_img)
c.insert(nora_img, [trafo.translate(plot_width + spacer, 0)])
c.insert(key_img, [trafo.translate((plot_width + spacer) * 2, 0)])
c.text(0, plot_height, "a",
[text.halign.left, text.valign.top, text.size(-1)])
c.text(plot_width + spacer, plot_height, "b",
[text.halign.left, text.valign.top, text.size(-1)])
c.writePDFfile(out_fname)
def plot_overall(data, width, height, name):
vo = 0.55
ho = 0.7
plot_width = width - ho
plot_height = height - vo - 0.3
c = canvas.canvas()
methods = data.keys()
methods.sort()
bar_colors = []
cis_binsizes = numpy.unique(data[methods[0]]['binsize'][numpy.where(data[methods[0]]['interaction'] == 'cis')])
trans_binsizes = numpy.unique(data[methods[0]]['binsize'][numpy.where(data[methods[0]]['interaction'] == 'trans')])
Y = numpy.zeros((len(methods), cis_binsizes.shape[0] + trans_binsizes.shape[0]), dtype=numpy.float32)
for i, method in enumerate(methods):
for j, binsize in enumerate(cis_binsizes):
where = numpy.where((data[method]['binsize'] == binsize) *
(data[method]['interaction'] == 'cis') *
(data[method]['range'] == 0))
if where[0].shape[0] > 0:
Y[i, j] = data[method]['correlation'][where]
for j, binsize in enumerate(trans_binsizes):
where = numpy.where((data[method]['binsize'] == binsize) *
(data[method]['interaction'] == 'trans') *
(data[method]['range'] == 0))
if where[0].shape[0] > 0:
Y[i, j + cis_binsizes.shape[0]] = data[method]['correlation'][where]
bar_colors.append(method_colors[method])
Y = numpy.array(Y)
g = graph.graphxy(width=plot_width, height=plot_height,
x=graph.axis.nestedbar(painter=graph.axis.painter.bar(nameattrs=None)),
y=graph.axis.lin(painter=painter),
x2=graph.axis.lin(parter=None, min=0, max=1),
y2=graph.axis.lin(parter=None, min=0, max=1))
for i in range(len(methods)):
g.plot(graph.data.points(zip(zip(range(Y.shape[1]), [i] * Y.shape[1]), Y[i, :]), xname=1, y=2),
[graph.style.changebar([method_colors[methods[i]]])])
c.insert(g, [trafo.translate(ho, vo)])
for i, label in enumerate(["10Kb", "50Kb", "250Kb", "1Mb", "250Kb", "1Mb"]):
c.text(ho + plot_width * (i + 0.5) / 6.0, vo - 0.05, "%s" % label,
[text.halign.center, text.valign.top, text.size(-3)])
c.text(ho + plot_width * 2.0 / 6.0, 0.05, "cis",
[text.halign.center, text.valign.bottom, text.size(-3)])
c.stroke(path.line(ho + 0.2, vo * 0.5, ho - 0.2 + plot_width * 4.0 / 6.0, vo * 0.5), [style.linewidth.THin])
c.text(ho + plot_width * 5.0 / 6.0, 0.05, "trans",
[text.halign.center, text.valign.bottom, text.size(-3)])
c.stroke(path.line(ho + 0.2 + plot_width * 4.0 / 6.0, vo * 0.5, ho - 0.2 + plot_width, vo * 0.5), [style.linewidth.THin])
c.text(0, plot_height * 0.5 + vo, "Correlation",
[text.halign.center, text.valign.top, text.size(-3), trafo.rotate(90)])
c.text(plot_width * 0.5 + ho, height, name,
[text.halign.center, text.valign.top, text.size(-3)])
return c
def plot_overall(data, width, height, int_type):
methods = data.keys()
methods.sort()
vo = 0.3
ho = 0.8
plot_width = width - ho
plot_height = height - vo - 0.3
c = canvas.canvas()
bar_colors = []
binsizes = numpy.unique(data[methods[0]]['binsize'][numpy.where(data[methods[0]]['interaction'] == int_type)])
Y = numpy.zeros((len(methods), binsizes.shape[0]), dtype=numpy.float32)
for i, method in enumerate(methods):
for j, binsize in enumerate(binsizes):
where = numpy.where((data[method]['binsize'] == binsize) *
(data[method]['interaction'] == int_type) *
(data[method]['range'] == 0))
if where[0].shape[0] > 0:
Y[i, j] = data[method]['correlation'][where]
bar_colors.append(method_colors[method])
Y = numpy.array(Y)
minY = numpy.amin(Y)
maxY = numpy.amax(Y)
spanY = maxY - minY
minY -= spanY * 0.05
maxY += spanY * 0.05
g = graph.graphxy(width=plot_width, height=plot_height,
x=graph.axis.nestedbar(painter=graph.axis.painter.bar(nameattrs=None)),
y=graph.axis.lin(painter=painter, min=minY, max=maxY),
x2=graph.axis.lin(parter=None, min=0, max=1),
y2=graph.axis.lin(parter=None, min=0, max=1))
for i in range(len(methods)):
g.plot(graph.data.points(zip(zip(range(Y.shape[1]), [i] * Y.shape[1]), Y[i, :]), xname=1, y=2),
[graph.style.changebar([method_colors[methods[i]]])])
c.insert(g, [trafo.translate(ho, vo)])
if int_type == 'cis':
for i, label in enumerate(["10Kb", "50Kb", "250Kb", "1Mb"]):
c.text(ho + plot_width * (i + 0.5) / 4.0, vo - 0.05, "%s" % label,
[text.halign.center, text.valign.top, text.size(-3)])
c.text(ho + plot_width * 0.5, height, "Cis",
[text.halign.center, text.valign.top, text.size(-2)])
else:
for i, label in enumerate(["250Kb", "1Mb"]):
c.text(ho + plot_width * (i + 0.5) / 2.0, vo - 0.05, "%s" % label,
[text.halign.center, text.valign.top, text.size(-3)])
c.text(ho + plot_width * 0.5, height, "Trans",
[text.halign.center, text.valign.top, text.size(-2)])
c.text(0, plot_height * 0.5 + vo, "Correlation",
[text.halign.center, text.valign.top, text.size(-3), trafo.rotate(90)])
return c
def plot_correlation_diffs(corr1, corr2, name, width, height):
ho = 1.2
vo = 0.4
plot_width = width - ho
plot_height = height - vo
diffs = {}
ymin = numpy.inf
ymax = -numpy.inf
for n in ['Phillips', 'Nora']:
for meth in meth_names.keys():
cname = "%s_%s" % (meth, n)
diff = corr2[cname] - corr1[cname]
ymin = min(ymin, diff)
ymax = max(ymax, diff)
for meth in meth_names.keys():
cname = "%s_%s" % (meth, name)
diffs[meth] = corr2[cname] - corr1[cname]
yspan = ymax - ymin
ymin -= yspan * 0.05
ymax += yspan * 0.05
yspan = ymax - ymin
c = canvas.canvas()
g = graph.graphxy(
width=plot_width,
height=plot_height,
x=graph.axis.bar(painter=graph.axis.painter.bar(nameattrs=None)),
y=graph.axis.lin(painter=painter, min=ymin, max=ymax),
x2=graph.axis.lin(parter=None, min=0, max=1),
y2=graph.axis.lin(parter=None, min=0, max=1))
w = plot_width / float(len(meth_names) + 1)
y0 = -ymin / yspan * plot_height
for i, meth in enumerate(methods):
col = method_colors[meth]
g.stroke(
path.rect((i + 0.5) * w, y0, w, diffs[meth] / yspan * plot_height),
[deco.filled([col])])
g.stroke(path.line(0, y0, plot_width, y0),
[style.linestyle.dotted, style.linewidth.THin])
c.insert(g, [trafo.translate(ho, vo)])
c.text(
0, plot_height * 0.5 + vo, r"$r_{0K} - r_{50K}$",
[text.halign.center, text.valign.top,
text.size(-3),
trafo.rotate(90)])
c.text(plot_width * 0.5 + ho, vo * 0.5, name,
[text.halign.center, text.valign.middle,
text.size(-3)])
return c
def main():
out_fname = sys.argv[1]
basedir = '/'.join(os.path.dirname(os.path.realpath(__file__)).split('/')[:-2])
mm9_methods = {
'HiFive-Probability':'%s/Analysis/hifive_mm9_ESC_prob_correlations.txt' % basedir,
'HiFive-Express':'%s/Analysis/hifive_mm9_ESC_exp_correlations.txt' % basedir,
'HiFive-Binning':'%s/Analysis/hifive_mm9_ESC_bin_correlations.txt' % basedir,
}
dist_methods = {
'HiFive-Probability':'%s/Analysis/hifive_mm9_ESC_probnodist_correlations.txt' % basedir,
'HiFive-Express':'%s/Analysis/hifive_mm9_ESC_expnodist_correlations.txt' % basedir,
'HiFive-Binning':'%s/Analysis/hifive_mm9_ESC_binnodist_correlations.txt' % basedir,
}
mm9_data = load_data(mm9_methods)
dist_data = load_data(dist_methods)
width = 16.8
spacer = 0.4
range_width = (width - spacer) / 3.0
c = canvas.canvas()
mm9_ranges_img = plot_dataset_ranges(mm9_data, dist_data, range_width)
c.insert(mm9_ranges_img)
mm9_overall_img = plot_overall(mm9_data, dist_data, range_width, range_width)
mm9_overall_img.text(0, range_width, 'b',
[text.halign.left, text.valign.top, text.size(-1)])
c.insert(mm9_overall_img, [trafo.translate(range_width * 2 + spacer, range_width)])
c.insert(plot_key(range_width / 2, range_width / 2),
[trafo.translate(range_width * 2.25 + spacer, range_width * 0.25)])
c.writePDFfile(out_fname)
def text_pyx(g, x_coord, y_coord, text_input, text_size = -2, color = None, rotation = 0.):
"""
Function that draws text in a given plot
INPUTS:
g (Object) A graph-type object to which you want to add the text
x_coord (Double) x-coordinate (in plot units) at which you want to place
the text
y_coord (Double) y-coordinate (in plot units) at which you want to place
the text
text_input (String) Text that you want to add.
text_size (int, optional) Text size of the text added to the plot. Default is -2.
color (instance) Color instance that defines the color that you want the
text to have. Default is black.
"""
# First define the text attributes:
textattrs = [pyx_text.size(text_size),pyx_text.halign.center, pyx_text.vshift.middlezero, trafo.rotate(rotation)]
# Now convert plot positions to pyx's:
x0,y0 = g.pos(x_coord, y_coord)
# If no color is given, draw black text. If color is given, draw text with the input color:
if color is None:
g.text(x0,y0,text_input,textattrs)
else:
# First, check which was the input color palette:
color_dict = color.color
if len(color_dict.keys()) == 4:
color_string = str(color_dict['c'])+','+str(color_dict['m'])+','+str(color_dict['y'])+','+str(color_dict['k'])
color_palette = 'cmyk'
else:
color_string = str(color_dict['r'])+','+str(color_dict['g'])+','+str(color_dict['b'])
color_palette = 'rgb'
# Now draw the text:
g.text(x0, y0, r"\textcolor["+color_palette+"]{"+color_string+"}{"+text_input+"}",textattrs)
def plot_key(width, height):
c = canvas.canvas()
w = height / 7.0
for i, meth in enumerate(methods):
c.fill(path.rect(1.0, (6 - i) * w - 0.1, 0.2, 0.2), [method_colors[meth]])
c.text(1.3, (6 - i) * w, "%s" % meth_names[meth],
[text.halign.left, text.valign.middle, text.size(-3)])
return c
def main():
out_fname = sys.argv[1]
basedir = '/'.join(os.path.dirname(os.path.realpath(__file__)).split('/')[:-2])
mm9_methods = {
'HiFive-Probability':'%s/Analysis/hifive_mm9_ESC_prob_correlations.txt' % basedir,
'HiFive-Express':'%s/Analysis/hifive_mm9_ESC_exp_correlations.txt' % basedir,
'HiFive-Binning':'%s/Analysis/hifive_mm9_ESC_bin_correlations.txt' % basedir,
'HiCNorm':'%s/Analysis/hicnorm_mm9_ESC_correlations.txt' % basedir,
'HiCPipe':'%s/Analysis/hicpipe_mm9_ESC_correlations.txt' % basedir,
'Matrix-Balancing':'%s/Analysis/mb_mm9_ESC_correlations.txt' % basedir,
}
hg19_methods = {
'HiFive-Probability':'%s/Analysis/hifive_hg19_GM12878_prob_correlations.txt' % basedir,
'HiFive-Express':'%s/Analysis/hifive_hg19_GM12878_exp_correlations.txt' % basedir,
'HiFive-Binning':'%s/Analysis/hifive_hg19_GM12878_bin_correlations.txt' % basedir,
'HiCNorm':'%s/Analysis/hicnorm_hg19_GM12878_correlations.txt' % basedir,
'HiCPipe':'%s/Analysis/hicpipe_hg19_GM12878_correlations.txt' % basedir,
'Matrix-Balancing':'%s/Analysis/mb_hg19_GM12878_correlations.txt' % basedir,
}
mm9_data = load_data(mm9_methods)
hg19_data = load_data(hg19_methods)
width = 16.8
spacer = 0.4
overall_width = (width - spacer * 2) / 2.6
c = canvas.canvas()
mm9_ranges_img, mm9_ranges_height = plot_dataset_ranges(mm9_data, width, "MM9 ESC")
mm9_ranges_img.text(0, mm9_ranges_height, 'a',
[text.halign.left, text.valign.top, text.size(-1)])
c.insert(mm9_ranges_img)
hg19_ranges_img, hg19_ranges_height = plot_dataset_ranges(hg19_data, width, "HG19 GM12878")
hg19_ranges_img.text(0, hg19_ranges_height, 'b',
[text.halign.left, text.valign.top, text.size(-1)])
c.insert(hg19_ranges_img, [trafo.translate(0, -hg19_ranges_height - spacer)])
overall_height = mm9_ranges_height * 0.6
mm9_overall_img = plot_overall(mm9_data, overall_width, overall_height, "MM9 ESC")
mm9_overall_img.text(0, overall_height + 0.1, 'c',
[text.halign.left, text.valign.top, text.size(-1)])
c.insert(mm9_overall_img, [trafo.translate(0, -hg19_ranges_height - overall_height - spacer * 2)])
hg19_overall_img = plot_overall(hg19_data, overall_width, overall_height, "HG19 GM12878")
hg19_overall_img.text(0, overall_height + 0.1, 'd',
[text.halign.left, text.valign.top, text.size(-1)])
c.insert(hg19_overall_img, [trafo.translate(overall_width * 1.6 + spacer * 2,
-hg19_ranges_height - overall_height - spacer * 2)])
c.insert(plot_key(overall_width * 0.6 + 0.4, overall_height),
[trafo.translate(overall_width + spacer + 0.6, -hg19_ranges_height - overall_height - spacer * 2)])
c.writePDFfile(out_fname)
def plot_key(width, height):
c = canvas.canvas()
wstep = width / 2.0
hstep = height / 3
for i in range(5):
x = wstep * (i / 3)
y = height - (i % 3 + 0.5) * hstep
c.fill(path.rect(x + 0.1, y - 0.15, 0.3, 0.3), [step_colors[i]])
temp = step_names[str(i)].split(' ')
if len(temp) > 2:
c.text(x + 0.5, y + 0.13, ' '.join(temp[:2]),
[text.halign.left, text.valign.middle, text.size(-2)])
c.text(x + 0.5, y - 0.13, temp[-1],
[text.halign.left, text.valign.middle, text.size(-2)])
else:
c.text(x + 0.5, y, step_names[str(i)],
[text.halign.left, text.valign.middle, text.size(-2)])
return c
def plot_overall(data, width, height):
plot_width = width - 0.4
plot_height = height - 0.4
c = canvas.canvas()
methods = data.keys()
methods.sort()
bar_colors = []
cis_binsizes = numpy.unique(data[methods[0]]['binsize'][numpy.where(data[methods[0]]['interaction'] == 'cis')])
trans_binsizes = numpy.unique(data[methods[0]]['binsize'][numpy.where(data[methods[0]]['interaction'] == 'trans')])
Y = numpy.zeros((len(methods), cis_binsizes.shape[0] + trans_binsizes.shape[0]), dtype=numpy.float32)
for i, method in enumerate(methods):
for j, binsize in enumerate(cis_binsizes):
where = numpy.where((data[method]['binsize'] == binsize) *
(data[method]['interaction'] == 'cis') *
(data[method]['range'] == 0))
if where[0].shape[0] > 0:
Y[i, j] = data[method]['correlation'][where]
for j, binsize in enumerate(trans_binsizes):
where = numpy.where((data[method]['binsize'] == binsize) *
(data[method]['interaction'] == 'trans') *
(data[method]['range'] == 0))
if where[0].shape[0] > 0:
Y[i, j + cis_binsizes.shape[0]] = data[method]['correlation'][where]
bar_colors.append(method_colors[method])
Y = numpy.array(Y)
g = graph.graphxy(width=plot_width, height=plot_height,
x=graph.axis.nestedbar(painter=graph.axis.painter.bar(nameattrs=None)),
y=graph.axis.lin(painter=painter),
x2=graph.axis.lin(parter=None, min=0, max=1),
y2=graph.axis.lin(parter=None, min=0, max=1))
for i in range(len(methods)):
g.plot(graph.data.points(zip(zip(range(Y.shape[1]), [i] * Y.shape[1]), Y[i, :]), xname=1, y=2),
[graph.style.changebar([method_colors[methods[i]]])])
step = plot_width / (cis_binsizes.shape[0] + trans_binsizes.shape[0])
for i, binsize in enumerate(cis_binsizes):
g.text(step * (0.5 + i), -0.05, "%s cis" % (str(binsize/1000) + 'Kb').replace('000Kb', 'Mb'),
[text.halign.right, text.valign.middle, text.size(-4), trafo.rotate(45)])
for i, binsize in enumerate(trans_binsizes):
g.text(step * (0.5 + i + cis_binsizes.shape[0]), -0.05, "%s trans" % (str(binsize/1000) + 'Kb').replace('000Kb', 'Mb'),
[text.halign.right, text.valign.middle, text.size(-4), trafo.rotate(45)])
c.insert(g, [trafo.translate(0.7, 0.4)])
c.text(0, plot_height / 2.0 + 0.4, "Dataset Correlation",
[text.halign.center, text.valign.top, text.size(-3), trafo.rotate(90)])
return c
def plot_key(width, height):
c = canvas.canvas()
w = height / 7.0
for i, meth in enumerate(methods):
c.fill(path.rect(1.0, (6 - i) * w - 0.1, 0.2, 0.2),
[method_colors[meth]])
c.text(1.3, (6 - i) * w, "%s" % meth_names[meth],
[text.halign.left, text.valign.middle,
text.size(-3)])
return c
def plot_dataset_ranges(data, width):
methods = data.keys()
methods.sort()
binsizes = numpy.unique(data[methods[0]]['binsize'])
ho = 0.4
ho2 = 0.4
vo = 0.6
spacer = 0.25
plot_width = (width - ho - (binsizes.shape[0] - 1) * spacer) / binsizes.shape[0] - ho2
plot_height = plot_width
c = canvas.canvas()
for i, binsize in enumerate(binsizes):
img = plot_single_range(data, binsize, plot_width, plot_width, methods)
c.insert(img, [trafo.translate((plot_width + spacer) * i + ho2 * (i + 1) + ho, vo)])
c.text(0, plot_height * 0.5 + vo, "Correlation",
[text.halign.center, text.valign.top, text.size(-3), trafo.rotate(90)])
c.text((plot_width + ho2) * 2 + spacer * 1.5 + ho, 0, "Interaction Range (bp)",
[text.halign.center, text.valign.bottom, text.size(-3)])
return c, plot_height + vo + 0.3
def plot_dataset_ranges(data, width, spacer):
methods = data.keys()
binsizes = numpy.unique(data[methods[0]]['binsize'])
plot_width = width - 0.4
plot_height = plot_width + 0.2
c = canvas.canvas()
for i, binsize in enumerate(binsizes):
img = plot_single_range(data, binsize, plot_width, plot_height)
c.insert(img, [trafo.translate((plot_width + spacer) * i + 0.4, 0.4 + spacer * 0.5)])
c.text(0, plot_height * 0.5 + 0.2 + spacer * 0.75, "Dataset correlation",
[text.halign.center, text.valign.top, text.size(-3), trafo.rotate(90)])
c.text(plot_width * 2 + spacer * 1.5 + 0.4, 0.35, "Interaction range (bp)",
[text.halign.center, text.valign.bottom, text.size(-3)])
c.fill(path.rect(0.4, 0.025, 0.2, 0.2), [color.rgb.black])
c.text(0.7, 0.125, "HiFive-Express using raw reads", [text.halign.left, text.valign.middle, text.size(-2)])
c.fill(path.rect(plot_width * 2 + spacer * 1.5, 0.025, 0.2, 0.2), [color.rgb.red])
c.text(plot_width * 2 + spacer * 1.5 + 0.3, 0.125, "HiFive-Express using distance-corrected reads",
[text.halign.left, text.valign.middle, text.size(-2)])
return c, plot_height + 0.4 + spacer
def plot_key(width, height):
c = canvas.canvas()
step = height / float(len(method_colors))
for i, meth in enumerate(
['HiFive-Probability', 'HiFive-Express', 'HiFive-Binning']):
c.fill(path.rect(0.2, height - step * (i + 0.5) - 0.1, 0.2, 0.2),
[method_colors[meth]])
c.text(0.5, height - step * (i + 0.5), meth,
[text.halign.left, text.valign.middle,
text.size(-2)])
return c
def plot_dataset_ranges(data0, data1, width, height):
binsizes = numpy.unique(data0['binsize'])
ho = 1.1
ho2 = 0.2
vo = 1.0
plot_width = (width - ho - ho2 * 3) / 4.0
plot_height = height - vo
c = canvas.canvas()
for i, binsize in enumerate(binsizes):
if i == 0:
ylabel = True
else:
ylabel = False
img = plot_single_range(data0, data1, binsize, plot_width, plot_height, ylabel)
c.insert(img, [trafo.translate(ho + i * (plot_width + ho2), vo - 0.3)])
c.text(0, plot_height * 0.5 + vo - 0.3, r"$r_{Poisson} - r_{binomial}$",
[text.halign.center, text.valign.top, text.size(-2), trafo.rotate(90)])
c.text(plot_width * 2 + ho + 1.5 * ho2, 0, "Interaction Range (bp)",
[text.halign.center, text.valign.bottom, text.size(-3)])
c.text(0, height, 'a', [text.halign.left, text.valign.top, text.size(-1)])
return c
def plot_key(width, height):
c = canvas.canvas()
wstep = width / 2.0
hstep = height / 3
for i in range(5):
x = wstep * (i / 3)
y = height - (i % 3 + 0.5) * hstep
c.fill(path.rect(x + 0.1, y - 0.15, 0.3, 0.3), [step_colors[i]])
temp = step_names[str(i)].split(' ')
if len(temp) > 2:
c.text(x + 0.5, y + 0.13, ' '.join(temp[:2]),
[text.halign.left, text.valign.middle,
text.size(-2)])
c.text(x + 0.5, y - 0.13, temp[-1],
[text.halign.left, text.valign.middle,
text.size(-2)])
else:
c.text(x + 0.5, y, step_names[str(i)],
[text.halign.left, text.valign.middle,
text.size(-2)])
return c
def main():
out_fname = sys.argv[1]
basedir = '/'.join(os.path.dirname(os.path.realpath(__file__)).split('/')[:-2])
mm9_methods = {
'distance-corrected':'%s/Analysis/hifive_mm9_ESC_exp_correlations.txt' % basedir,
'raw':'%s/Analysis/hifive_mm9_ESC_expdist_correlations.txt' % basedir,
}
mm9_data = load_data(mm9_methods)
width = 16.8
spacer = 0.4
plot_width = (width - spacer * 4) / 5
c = canvas.canvas()
mm9_ranges_img, mm9_ranges_height = plot_dataset_ranges(mm9_data, plot_width, spacer)
mm9_ranges_img.text(0, mm9_ranges_height, 'b',
[text.halign.left, text.valign.top, text.size(-1)])
c.insert(mm9_ranges_img, [trafo.translate(plot_width + spacer, 0)])
mm9_overall_img = plot_overall(mm9_data, plot_width - spacer, (mm9_ranges_height - spacer) - 0.3)
c.text(0, mm9_ranges_height, 'a',
[text.halign.left, text.valign.top, text.size(-1)])
c.insert(mm9_overall_img, [trafo.translate(0, 0.4)])
c.writePDFfile(out_fname)
def plot_correlation_diffs(corr1, corr2, name, width, height):
ho = 1.2
vo = 0.4
plot_width = width - ho
plot_height = height - vo
diffs = {}
ymin = numpy.inf
ymax = -numpy.inf
for n in ['Phillips', 'Nora']:
for meth in meth_names.keys():
cname = "%s_%s" % (meth, n)
diff = corr2[cname] - corr1[cname]
ymin = min(ymin, diff)
ymax = max(ymax, diff)
for meth in meth_names.keys():
cname = "%s_%s" % (meth, name)
diffs[meth] = corr2[cname] - corr1[cname]
yspan = ymax - ymin
ymin -= yspan * 0.05
ymax += yspan * 0.05
yspan = ymax - ymin
c = canvas.canvas()
g = graph.graphxy(width=plot_width, height=plot_height,
x=graph.axis.bar(painter=graph.axis.painter.bar(nameattrs=None)),
y=graph.axis.lin(painter=painter, min=ymin, max=ymax),
x2=graph.axis.lin(parter=None, min=0, max=1),
y2=graph.axis.lin(parter=None, min=0, max=1))
w = plot_width / float(len(meth_names) + 1)
y0 = -ymin / yspan * plot_height
for i, meth in enumerate(methods):
col = method_colors[meth]
g.stroke( path.rect((i + 0.5) * w, y0, w, diffs[meth] / yspan * plot_height), [deco.filled([col])])
g.stroke( path.line(0, y0, plot_width, y0), [style.linestyle.dotted, style.linewidth.THin])
c.insert(g, [trafo.translate(ho, vo)])
c.text(0, plot_height * 0.5 + vo, r"$r_{0K} - r_{50K}$",
[text.halign.center, text.valign.top, text.size(-3), trafo.rotate(90)])
c.text(plot_width * 0.5 + ho, vo * 0.5, name,
[text.halign.center, text.valign.middle, text.size(-3)])
return c
def text(g, x_coord, y_coord, text_input, text_size=-2, color=None):
"""
Function that draws text in a given plot
INPUTS:
g (Object) A graph-type object to which you want to add the text
x_coord (Double) x-coordinate (in plot units) at which you want to place
the text
y_coord (Double) y-coordinate (in plot units) at which you want to place
the text
text_input (String) Text that you want to add.
text_size (int, optional) Text size of the text added to the plot. Default is -2.
color (instance) Color instance that defines the color that you want the
text to have. Default is black.
"""
# First define the text attributes:
textattrs = [
pyx_text.size(text_size), pyx_text.halign.center,
pyx_text.vshift.middlezero
]
# Now convert plot positions to pyx's:
x0, y0 = g.pos(x_coord, y_coord)
# If no color is given, draw black text. If color is given, draw text with the input color:
if color is None:
g.text(x0, y0, text_input, textattrs)
else:
# First, check which was the input color palette:
color_dict = color.color
if len(color_dict.keys()) == 4:
color_string = str(color_dict['c']) + ',' + str(
color_dict['m']) + ',' + str(color_dict['y']) + ',' + str(
color_dict['k'])
color_palette = 'cmyk'
else:
color_string = str(color_dict['r']) + ',' + str(
color_dict['g']) + ',' + str(color_dict['b'])
color_palette = 'rgb'
# Now draw the text:
g.text(
x0, y0, r"\textcolor[" + color_palette + "]{" + color_string +
"}{" + text_input + "}", textattrs)
def plot_single_range(data, binsize, width, height):
plot_width = width
plot_height = height
c = canvas.canvas()
xmax = 0.0
methods = data.keys()
methods.sort()
for method in methods:
where = numpy.where((data[method]['binsize'] == binsize) *
(data[method]['interaction'] == 'cis') *
(data[method]['range'] > 0))
if where[0].shape[0] > 0:
xmax = max(xmax, numpy.amax(data[method]['range'][where]))
X = data[method]['range'][where]
X = numpy.r_[0, X]
X[0] = X[1]**2.0 / X[2]
xmin = X[0]
g = graph.graphxy(width=plot_width,
height=plot_height,
x=graph.axis.log(painter=painter, min=X[0], max=xmax),
y=graph.axis.lin(painter=painter),
x2=graph.axis.lin(parter=None, min=0, max=1),
y2=graph.axis.lin(parter=None, min=0, max=1))
for x in X[1:-1]:
pos = ((log(x) - log(xmin)) / (log(xmax) - log(xmin)) * plot_width)
g.stroke(path.line(pos, 0, pos, plot_height),
[style.linestyle.dotted, style.linewidth.THin])
X = (X[1:]**0.5) * (X[:-1]**0.5)
for method in methods:
where = numpy.where((data[method]['binsize'] == binsize) *
(data[method]['interaction'] == 'cis') *
(data[method]['range'] > 0))
if where[0].shape[0] > 0:
Y = data[method]['correlation'][where]
g.plot(graph.data.points(zip(X, Y), x=1, y=2), [
graph.style.line(
lineattrs=[method_colors[method], style.linewidth.Thick])
])
if binsize / 1000000 > 0:
binstring = "%iMb" % (binsize / 1000000)
elif binsize / 1000 > 0:
binstring = "%iKb" % (binsize / 1000)
else:
binstring = str(binsize)
g.text(plot_width / 2, plot_height + 0.3, "%s binning" % (binstring),
[text.halign.center, text.valign.top,
text.size(-2)])
c.insert(g)
return c
def plot_dataset_ranges(data0, data1, width):
methods = data0.keys()
binsizes = numpy.unique(data0[methods[0]]['binsize'])
ho = 0.8
ho2 = 0.25
vo = 0.7
vo2 = 0.4
spacer = 0.0
plot_width = (2 * width - ho * 2 - ho2 - spacer) / 2
plot_height = width - vo - vo2
c = canvas.canvas()
for i, binsize in enumerate(binsizes):
img = plot_single_range(data0, data1, binsize, plot_width, plot_width, vo, vo2)
c.insert(img, [trafo.translate((plot_width + spacer + ho) * (i % 2) + ho2 + ho,
(1 - i /
2) * (plot_height + spacer + vo + vo2) + vo)])
c.text(0, plot_height + 0.5 * spacer + vo + vo2, r"$r_{0K} - r_{500K}$",
[text.halign.center, text.valign.top, text.size(-2), trafo.rotate(90)])
c.text(plot_width + ho2 + spacer * 0.5 + ho, 0, "Interaction Range (bp)",
[text.halign.center, text.valign.bottom, text.size(-3)])
c.text(0, width * 2 + spacer, 'a',
[text.halign.left, text.valign.top, text.size(-1)])
return c
def draw(self):
p = path.path(path.moveto(*self.corners[0]),
path.lineto(*self.corners[1]),
path.lineto(*self.corners[2]),
path.lineto(*self.corners[3]),
path.closepath())
fillcolor = color.hsb(2/3*(1-(self.counter-1)/(self.nsquares-1)), 0.2, 1)
self.c.stroke(p, [deco.filled([fillcolor])])
x, y = 0.5*(self.corners[0]+self.corners[2])
s = int(np.sum(np.abs(self.corners[1]-self.corners[0])))
self.c.text(x, y, str(s),
[text.halign.center, text.valign.middle,
text.size(min(s, 5))])
self.counter = self.counter+1
def draw(self):
p = path.path(path.moveto(*self.corners[0]),
path.lineto(*self.corners[1]),
path.lineto(*self.corners[2]),
path.lineto(*self.corners[3]), path.closepath())
fillcolor = color.hsb(
2 / 3 * (1 - (self.counter - 1) / (self.nsquares - 1)), 0.2, 1)
self.c.stroke(p, [deco.filled([fillcolor])])
x, y = 0.5 * (self.corners[0] + self.corners[2])
s = int(np.sum(np.abs(self.corners[1] - self.corners[0])))
self.c.text(
x, y, str(s),
[text.halign.center, text.valign.middle,
text.size(min(s, 5))])
self.counter = self.counter + 1
def plot_single_range(data, binsize, width, height):
plot_width = width
plot_height = height
c = canvas.canvas()
xmax = 0.0
methods = data.keys()
methods.sort()
for method in methods:
where = numpy.where((data[method]['binsize'] == binsize) *
(data[method]['interaction'] == 'cis') *
(data[method]['range'] > 0))
if where[0].shape[0] > 0:
xmax = max(xmax, numpy.amax(data[method]['range'][where]))
X = data[method]['range'][where]
X = numpy.r_[0, X]
X[0] = X[1] ** 2.0 / X[2]
xmin = X[0]
g = graph.graphxy(width=plot_width, height=plot_height,
x=graph.axis.log(painter=painter, min=X[0], max=xmax),
y=graph.axis.lin(painter=painter),
x2=graph.axis.lin(parter=None, min=0, max=1),
y2=graph.axis.lin(parter=None, min=0, max=1))
for x in X[1:-1]:
pos = ((log(x) - log(xmin)) / (log(xmax) - log(xmin)) * plot_width)
g.stroke(path.line(pos, 0, pos, plot_height), [style.linestyle.dotted, style.linewidth.THin])
X = (X[1:] ** 0.5) * (X[:-1] ** 0.5)
for method in methods:
where = numpy.where((data[method]['binsize'] == binsize) *
(data[method]['interaction'] == 'cis') *
(data[method]['range'] > 0))
if where[0].shape[0] > 0:
Y = data[method]['correlation'][where]
g.plot(graph.data.points(zip(X, Y), x=1, y=2),
[graph.style.line(lineattrs=[method_colors[method], style.linewidth.Thick])])
if binsize / 1000000 > 0:
binstring = "%iMb" % (binsize / 1000000)
elif binsize / 1000 > 0:
binstring = "%iKb" % (binsize / 1000)
else:
binstring = str(binsize)
g.text(plot_width / 2, plot_height + 0.3, "%s binning" % (binstring),
[text.halign.center, text.valign.top, text.size(-2)])
c.insert(g)
return c
def main():
out_fname = sys.argv[1]
basedir = '/'.join(
os.path.dirname(os.path.realpath(__file__)).split('/')[:-2])
mm9_methods = {
'HiFive-Probability':
'%s/Analysis/hifive_mm9_ESC_prob_correlations.txt' % basedir,
'HiFive-Express':
'%s/Analysis/hifive_mm9_ESC_exp_correlations.txt' % basedir,
'HiFive-Binning':
'%s/Analysis/hifive_mm9_ESC_bin_correlations.txt' % basedir,
}
dist_methods = {
'HiFive-Probability':
'%s/Analysis/hifive_mm9_ESC_probnodist_correlations.txt' % basedir,
'HiFive-Express':
'%s/Analysis/hifive_mm9_ESC_expnodist_correlations.txt' % basedir,
'HiFive-Binning':
'%s/Analysis/hifive_mm9_ESC_binnodist_correlations.txt' % basedir,
}
mm9_data = load_data(mm9_methods)
dist_data = load_data(dist_methods)
width = 16.8
spacer = 0.4
range_width = (width - spacer) / 3.0
c = canvas.canvas()
mm9_ranges_img = plot_dataset_ranges(mm9_data, dist_data, range_width)
c.insert(mm9_ranges_img)
mm9_overall_img = plot_overall(mm9_data, dist_data, range_width,
range_width)
mm9_overall_img.text(0, range_width, 'b',
[text.halign.left, text.valign.top,
text.size(-1)])
c.insert(mm9_overall_img,
[trafo.translate(range_width * 2 + spacer, range_width)])
c.insert(
plot_key(range_width / 2, range_width / 2),
[trafo.translate(range_width * 2.25 + spacer, range_width * 0.25)])
c.writePDFfile(out_fname)
def plot_key(width, height, data):
methods = data.keys()
methods.sort()
c = canvas.canvas()
step = height / float(len(method_colors))
for i, meth in enumerate(methods):
c.fill(path.rect(0.2, height - step * (i + 0.5) - 0.1, 0.2, 0.2),
[method_colors[meth]])
c.text(0.5, height - step * (i + 0.5), meth, [text.halign.left, text.valign.middle, text.size(-2)])
return c
def plot_overall(data0, data1, width, height):
vo = 0.55
ho = 1.1
plot_width = width - ho
plot_height = height - vo
c = canvas.canvas()
methods = data0.keys()
methods.sort()
bar_colors = []
cis_binsizes = numpy.unique(data0[methods[0]]['binsize'][numpy.where(data0[methods[0]]['interaction'] == 'cis')])
trans_binsizes = numpy.unique(data0[methods[0]]['binsize'][numpy.where(data0[methods[0]]['interaction'] == 'trans')])
ymin = numpy.inf
ymax = -numpy.inf
Y = numpy.zeros((len(methods), cis_binsizes.shape[0] + trans_binsizes.shape[0]), dtype=numpy.float32)
for i, method in enumerate(methods):
for j, binsize in enumerate(cis_binsizes):
where = numpy.where((data0[method]['binsize'] == binsize) *
(data0[method]['interaction'] == 'cis') *
(data0[method]['range'] < 0))
where1 = numpy.where((data1[method]['binsize'] == binsize) *
(data1[method]['interaction'] == 'cis') *
(data1[method]['range'] < 0))
if where[0].shape[0] > 0:
Y[i, j] = (data1[method]['correlation'][where1] - data0[method]['correlation'][where])
ymin = min(ymin, Y[i, j])
ymax = max(ymax, Y[i, j])
for j, binsize in enumerate(trans_binsizes):
where = numpy.where((data0[method]['binsize'] == binsize) *
(data0[method]['interaction'] == 'trans') *
(data0[method]['range'] < 0))
where1 = numpy.where((data1[method]['binsize'] == binsize) *
(data1[method]['interaction'] == 'trans') *
(data1[method]['range'] < 0))
if where[0].shape[0] > 0:
Y[i, j + cis_binsizes.shape[0]] = (data1[method]['correlation'][where1] -
data0[method]['correlation'][where])
ymin = min(ymin, Y[i, j + cis_binsizes.shape[0]])
ymax = max(ymax, Y[i, j + cis_binsizes.shape[0]])
bar_colors.append(method_colors[method])
yspan = ymax - ymin
ymin -= yspan * 0.05
ymax += yspan * 0.05
Y = numpy.array(Y)
g = graph.graphxy(width=plot_width, height=plot_height,
x=graph.axis.nestedbar(painter=graph.axis.painter.bar(nameattrs=None)),
y=graph.axis.lin(painter=painter, min=ymin, max=ymax),
x2=graph.axis.lin(parter=None, min=0, max=1),
y2=graph.axis.lin(parter=None, min=0, max=1))
y0 = plot_height * (-ymin) / (ymax - ymin)
g.stroke(path.line(0, plot_height * (-ymin) / (ymax - ymin), plot_width, plot_height * (-ymin) / (ymax - ymin)),
[style.linestyle.dotted, style.linewidth.THin])
w0 = plot_width / Y.shape[1]
w1 = w0 / (len(methods) + 0.5)
for i in range(len(methods)):
for j in range(Y.shape[1]):
x = j * w0 + (i + 0.25) * w1
y = plot_height * (Y[i, j] - ymin) / (ymax - ymin)
g.stroke( path.rect(x, y0, w1, y - y0), [deco.filled([method_colors[methods[i]]])])
c.insert(g, [trafo.translate(ho, vo)])
for i, label in enumerate(["10Kb", "50Kb", "250Kb", "1Mb", "250Kb", "1Mb"]):
c.text(ho + plot_width * (i + 0.5) / 6.0, vo - 0.05, "%s" % label,
[text.halign.center, text.valign.top, text.size(-3)])
c.text(ho + plot_width * 2.0 / 6.0, 0.05, "cis",
[text.halign.center, text.valign.bottom, text.size(-3)])
c.stroke(path.line(ho + 0.2, vo * 0.5, ho - 0.2 + plot_width * 4.0 / 6.0, vo * 0.5), [style.linewidth.THin])
c.text(ho + plot_width * 5.0 / 6.0, 0.05, "trans",
[text.halign.center, text.valign.bottom, text.size(-3)])
c.stroke(path.line(ho + 0.2 + plot_width * 4.0 / 6.0, vo * 0.5, ho - 0.2 + plot_width, vo * 0.5), [style.linewidth.THin])
c.text(0, plot_height * 0.5 + vo, r"$r_{0K} - r_{500K}$",
[text.halign.center, text.valign.top, text.size(-2), trafo.rotate(90)])
return c
def plot_single_range(data0, data1, binsize, width, height, ylabel):
plot_width = width
plot_height = height
c = canvas.canvas()
xmax = 0.0
where = numpy.where((data0['binsize'] == binsize) *
(data0['interaction'] == 'cis') *
(data0['range'] >= 0))
if where[0].shape[0] > 0:
xmax = max(xmax, numpy.amax(data0['range'][where]))
X = data0['range'][where]
X = numpy.r_[0, X]
X[0] = X[1] ** 2.0 / X[2]
xmin = X[0]
ymin = numpy.inf
ymax = -numpy.inf
binsizes = numpy.unique(data0['binsize'])
for b in binsizes:
where = numpy.where((data0['binsize'] == b) *
(data0['interaction'] == 'cis') *
(data0['range'] >= 0))
where1 = numpy.where((data1['binsize'] == b) *
(data1['interaction'] == 'cis') *
(data1['range'] >= 0))
if where[0].shape[0] > 0:
Y = (data1['correlation'][where1] -
data0['correlation'][where] )
ymin = min(ymin, numpy.amin(Y))
ymax = max(ymax, numpy.amax(Y))
yspan = ymax - ymin
ymin -= yspan * 0.05
ymax += yspan * 0.05
if ylabel:
yaxis = graph.axis.lin(painter=painter, min=ymin, max=ymax)
else:
yaxis = graph.axis.lin(painter=None, min=ymin, max=ymax)
g = graph.graphxy(width=plot_width, height=plot_height,
x=graph.axis.log(painter=painter, min=X[0], max=xmax),
y=yaxis,
x2=graph.axis.lin(parter=None, min=0, max=1),
y2=graph.axis.lin(parter=None, min=0, max=1))
g.stroke( path.line(0, 0, 0, plot_height) )
for x in X[1:-1]:
pos = ((log(x) - log(xmin)) / (log(xmax) - log(xmin)) * plot_width)
g.stroke(path.line(pos, 0, pos, plot_height), [style.linestyle.dotted, style.linewidth.THin])
X = (X[1:] ** 0.5) * (X[:-1] ** 0.5)
where = numpy.where((data0['binsize'] == binsize) *
(data0['interaction'] == 'cis') *
(data0['range'] > 0))
where1 = numpy.where((data1['binsize'] == binsize) *
(data1['interaction'] == 'cis') *
(data1['range'] > 0))
if where[0].shape[0] > 0:
Y = ( data1['correlation'][where1] - data0['correlation'][where] )
g.plot(graph.data.points(zip(X, Y), x=1, y=2),
[graph.style.line(lineattrs=[style.linewidth.Thick])])
g.stroke(path.line(0, plot_height * (-ymin) / (ymax - ymin), plot_width, plot_height * (-ymin) / (ymax - ymin)),
[style.linestyle.dotted, style.linewidth.THin])
if binsize / 1000000 > 0:
binstring = "%iMb" % (binsize / 1000000)
elif binsize / 1000 > 0:
binstring = "%iKb" % (binsize / 1000)
else:
binstring = str(binsize)
g.text(plot_width / 2, plot_height + 0.3, "%s binning" % (binstring),
[text.halign.center, text.valign.top, text.size(-2)])
c.insert(g)
return c
def main():
out_fname = sys.argv[1]
basedir = '/'.join(
os.path.dirname(os.path.realpath(__file__)).split('/')[:-2])
mm9_methods = {
'HiFive-Probability':
'%s/Analysis/hifive_mm9_ESC_prob_correlations.txt' % basedir,
'HiFive-Express':
'%s/Analysis/hifive_mm9_ESC_exp_correlations.txt' % basedir,
'HiFive-Binning':
'%s/Analysis/hifive_mm9_ESC_bin_correlations.txt' % basedir,
'HiCNorm': '%s/Analysis/hicnorm_mm9_ESC_correlations.txt' % basedir,
'HiCPipe': '%s/Analysis/hicpipe_mm9_ESC_correlations.txt' % basedir,
'Matrix-Balancing':
'%s/Analysis/mb_mm9_ESC_correlations.txt' % basedir,
}
hg19_methods = {
'HiFive-Probability':
'%s/Analysis/hifive_hg19_GM12878_prob_correlations.txt' % basedir,
'HiFive-Express':
'%s/Analysis/hifive_hg19_GM12878_exp_correlations.txt' % basedir,
'HiFive-Binning':
'%s/Analysis/hifive_hg19_GM12878_bin_correlations.txt' % basedir,
'HiCNorm':
'%s/Analysis/hicnorm_hg19_GM12878_correlations.txt' % basedir,
'HiCPipe':
'%s/Analysis/hicpipe_hg19_GM12878_correlations.txt' % basedir,
'Matrix-Balancing':
'%s/Analysis/mb_hg19_GM12878_correlations.txt' % basedir,
}
mm9_data = load_data(mm9_methods)
hg19_data = load_data(hg19_methods)
width = 16.8
spacer = 0.4
overall_width = (width - spacer * 2) / 2.6
c = canvas.canvas()
mm9_ranges_img, mm9_ranges_height = plot_dataset_ranges(
mm9_data, width, "MM9 ESC")
mm9_ranges_img.text(0, mm9_ranges_height, 'a',
[text.halign.left, text.valign.top,
text.size(-1)])
c.insert(mm9_ranges_img)
hg19_ranges_img, hg19_ranges_height = plot_dataset_ranges(
hg19_data, width, "HG19 GM12878")
hg19_ranges_img.text(0, hg19_ranges_height, 'b',
[text.halign.left, text.valign.top,
text.size(-1)])
c.insert(hg19_ranges_img,
[trafo.translate(0, -hg19_ranges_height - spacer)])
overall_height = mm9_ranges_height * 0.6
mm9_overall_img = plot_overall(mm9_data, overall_width, overall_height,
"MM9 ESC")
mm9_overall_img.text(0, overall_height + 0.1, 'c',
[text.halign.left, text.valign.top,
text.size(-1)])
c.insert(mm9_overall_img, [
trafo.translate(0, -hg19_ranges_height - overall_height - spacer * 2)
])
hg19_overall_img = plot_overall(hg19_data, overall_width, overall_height,
"HG19 GM12878")
hg19_overall_img.text(0, overall_height + 0.1, 'd',
[text.halign.left, text.valign.top,
text.size(-1)])
c.insert(hg19_overall_img, [
trafo.translate(overall_width * 1.6 + spacer * 2,
-hg19_ranges_height - overall_height - spacer * 2)
])
c.insert(plot_key(overall_width * 0.6 + 0.4, overall_height), [
trafo.translate(overall_width + spacer + 0.6,
-hg19_ranges_height - overall_height - spacer * 2)
])
c.writePDFfile(out_fname)
def plot_key(width, height):
c = canvas.canvas()
step = height / float(len(method_colors))
for i, meth in enumerate(['HiFive-Probability', 'HiFive-Express', 'HiFive-Binning',
'HiCNorm', 'HiCPipe', 'Matrix-Balancing']):
c.fill(path.rect(0.2, height - step * (i + 0.5) - 0.1, 0.2, 0.2),
[method_colors[meth]])
c.text(0.5, height - step * (i + 0.5), meth, [text.halign.left, text.valign.middle, text.size(-2)])
return c
def plot_memory(data, width, height):
plot_width = width - 4.0
plot_height = height - 0.8
left_width = plot_width * 0.6
right_width = plot_width * 0.2
prob = data['HiFive-Probability']['3']
norm = data['HiCNorm']['3']
prob_min = prob - right_width * 2.2e4 / left_width * 0.7
prob_max = prob + right_width * 2.2e4 / left_width * 0.3
norm_min = norm - right_width * 2.2e4 / left_width * 0.6
norm_max = norm + right_width * 2.2e4 / left_width * 0.4
c1 = canvas.canvas()
g1 = graph.graphxy(width=left_width, height=plot_height,
y=graph.axis.nestedbar(painter=graph.axis.painter.bar(nameattrs=None)),
x=graph.axis.lin(painter=painter, texter=graph.axis.texter.exponential(mantissaexp=r"{{%s}e%s}", nomantissaexp=r"{e%s}"), min=0, max=2.2e4),
x2=graph.axis.lin(parter=None, min=0, max=1),
y2=graph.axis.lin(painter=None, min=0, max=1))
c1.insert(g1, [trafo.translate(0, 0)])
g2 = graph.graphxy(width=right_width, height=plot_height,
y=graph.axis.lin(painter=None, min=0, max=1),
x=graph.axis.lin(painter=painter, texter=graph.axis.texter.exponential(mantissaexp=r"{{%s}e%s}", nomantissaexp=r"{e%s}"), min=prob_min, max=prob_max),
x2=graph.axis.lin(parter=None, min=0, max=1),
y2=graph.axis.lin(painter=None, min=0, max=1))
c1.insert(g2, [trafo.translate(left_width, 0)])
g3 = graph.graphxy(width=right_width, height=plot_height,
y=graph.axis.lin(painter=None, min=0, max=1),
x=graph.axis.lin(painter=painter, texter=graph.axis.texter.exponential(mantissaexp=r"{{%s}e%s}", nomantissaexp=r"{e%s}"), parter=graph.axis.parter.linear(tickdists=[5000]), min=norm_min, max=norm_max),
x2=graph.axis.lin(parter=None, min=0, max=1),
y2=graph.axis.lin(parter=None, min=0, max=1))
c1.insert(g3, [trafo.translate(left_width + right_width, 0)])
split = canvas.canvas()
split.fill(path.path(path.moveto(-0.15, -0.2), path.lineto(0.05, 0.2), path.lineto(.15, 0.2),
path.lineto(-0.05, -0.2), path.closepath()), [color.cmyk.White])
split.stroke(path.line(-0.15, -0.2, 0.05, 0.2))
split.stroke(path.line(-0.05, -0.2, 0.15, 0.2))
c1.insert(split, [trafo.translate(left_width, 0)])
c1.insert(split, [trafo.translate(left_width, plot_height)])
c1.insert(split, [trafo.translate(left_width + right_width, 0)])
c1.insert(split, [trafo.translate(left_width + right_width, plot_height)])
methods = ['HiCLib', 'HiCPipe', 'HiCNorm', 'HiFive-Probability', 'HiFive-Binning', 'HiFive-Express',
'HiFive-ExpressKR', 'HiFive-ExpressKR w/distance']
hstep = plot_height / len(methods)
substep = hstep / 6.0
scale = left_width / 2.2e4
for i, meth in enumerate(methods[::-1]):
for j in range(5):
if str(j) not in data[meth]:
continue
if meth not in ['HiCNorm', 'HiFive-Probability'] or j != 3:
c1.fill(path.rect(0, hstep * i + (4.5 - j) * substep, data[meth][str(j)] * scale, substep),
[step_colors[j]])
elif meth == 'HiCNorm':
c1.fill(path.rect(0, hstep * i + (4.5 - j) * substep, left_width + right_width * 1.7, substep),
[step_colors[j]])
c1.insert(split, [trafo.translate(left_width, hstep * i + (5 - j) * substep)])
c1.insert(split, [trafo.translate(left_width + right_width, hstep * i + (5 - j) * substep)])
else:
c1.fill(path.rect(0, hstep * i + (4.5 - j) * substep, left_width + right_width * 0.6, substep),
[step_colors[j]])
c1.insert(split, [trafo.translate(left_width, hstep * i + (5 - j) * substep)])
c = canvas.canvas()
c.insert(c1, [trafo.translate(4.0, 0.8)])
for i, meth in enumerate(methods):
c.text(3.9, height - plot_height / len(methods) * (i + 0.5), meth,
[text.halign.right, text.valign.middle, text.size(-2)])
c.text(4.0 + plot_width / 2, 0, "Maximum RAM usage (resident set size, Mbytes)",
[text.halign.center, text.valign.bottom, text.size(-2)])
return c
def plot_overall(data, width, height, name):
vo = 0.55
ho = 0.7
plot_width = width - ho
plot_height = height - vo - 0.3
c = canvas.canvas()
methods = data.keys()
methods.sort()
bar_colors = []
cis_binsizes = numpy.unique(data[methods[0]]['binsize'][numpy.where(
data[methods[0]]['interaction'] == 'cis')])
trans_binsizes = numpy.unique(data[methods[0]]['binsize'][numpy.where(
data[methods[0]]['interaction'] == 'trans')])
Y = numpy.zeros(
(len(methods), cis_binsizes.shape[0] + trans_binsizes.shape[0]),
dtype=numpy.float32)
for i, method in enumerate(methods):
for j, binsize in enumerate(cis_binsizes):
where = numpy.where((data[method]['binsize'] == binsize) *
(data[method]['interaction'] == 'cis') *
(data[method]['range'] == 0))
if where[0].shape[0] > 0:
Y[i, j] = data[method]['correlation'][where]
for j, binsize in enumerate(trans_binsizes):
where = numpy.where((data[method]['binsize'] == binsize) *
(data[method]['interaction'] == 'trans') *
(data[method]['range'] == 0))
if where[0].shape[0] > 0:
Y[i, j +
cis_binsizes.shape[0]] = data[method]['correlation'][where]
bar_colors.append(method_colors[method])
Y = numpy.array(Y)
g = graph.graphxy(
width=plot_width,
height=plot_height,
x=graph.axis.nestedbar(painter=graph.axis.painter.bar(nameattrs=None)),
y=graph.axis.lin(painter=painter),
x2=graph.axis.lin(parter=None, min=0, max=1),
y2=graph.axis.lin(parter=None, min=0, max=1))
for i in range(len(methods)):
g.plot(
graph.data.points(zip(zip(range(Y.shape[1]), [i] * Y.shape[1]),
Y[i, :]),
xname=1,
y=2),
[graph.style.changebar([method_colors[methods[i]]])])
c.insert(g, [trafo.translate(ho, vo)])
for i, label in enumerate(["10Kb", "50Kb", "250Kb", "1Mb", "250Kb",
"1Mb"]):
c.text(ho + plot_width * (i + 0.5) / 6.0, vo - 0.05, "%s" % label,
[text.halign.center, text.valign.top,
text.size(-3)])
c.text(ho + plot_width * 2.0 / 6.0, 0.05, "cis",
[text.halign.center, text.valign.bottom,
text.size(-3)])
c.stroke(
path.line(ho + 0.2, vo * 0.5, ho - 0.2 + plot_width * 4.0 / 6.0,
vo * 0.5), [style.linewidth.THin])
c.text(ho + plot_width * 5.0 / 6.0, 0.05, "trans",
[text.halign.center, text.valign.bottom,
text.size(-3)])
c.stroke(
path.line(ho + 0.2 + plot_width * 4.0 / 6.0, vo * 0.5,
ho - 0.2 + plot_width, vo * 0.5), [style.linewidth.THin])
c.text(
0, plot_height * 0.5 + vo, "Correlation",
[text.halign.center, text.valign.top,
text.size(-3),
trafo.rotate(90)])
c.text(plot_width * 0.5 + ho, height, name,
[text.halign.center, text.valign.top,
text.size(-3)])
return c
circle = path.circle
line = path.line
# TODO: configs
BLACK = color.rgb.black
RED = color.rgb.red
GREEN = color.rgb.green
POINT_RADIUS = 4
POINT_STD_COLOR = BLACK
POINT_LABEL_DEFAULT_OFFSET = (10, 10)
LINE_DEF_COLOR = BLACK
LINE_DEF_WIDTH = 2
TEXT_DEFAULT_STYLE = [text.halign.center, text.size(5), BLACK]
class point:
def __init__(self, x, y, name=None):
self.x = x
self.y = y
self.name = name
def get_xy(self):
return (self.x, self.y)
def mark(self, canv, r=POINT_RADIUS, color=POINT_STD_COLOR):
circle = path.circle(self.x, self.y, r)
style = [color]
def main():
out_fname = sys.argv[1]
basedir = '/'.join(os.path.dirname(os.path.realpath(__file__)).split('/')[:-2])
hic_phillips_fname1 = "%s/Data/HiC/HiCPipe/HM/mm9_ESC_NcoI_Phillips.hch" % basedir
hic_phillips_fname2 = "%s/Data/HiC/HiCPipe/HM/mm9_ESC_HindIII_Phillips.hch" % basedir
hic_nora_fname1 = "%s/Data/HiC/HiCPipe/HM/mm9_ESC_NcoI_Nora.hch" % basedir
hic_nora_fname2 = "%s/Data/HiC/HiCPipe/HM/mm9_ESC_HindIII_Nora.hch" % basedir
hic_phillips1 = h5py.File(hic_phillips_fname1, 'r')
hic_phillips2 = h5py.File(hic_phillips_fname2, 'r')
hic_nora1 = h5py.File(hic_nora_fname1, 'r')
hic_nora2 = h5py.File(hic_nora_fname2, 'r')
hm_phillips = {}
hm_nora = {}
for key in hic_phillips1.keys():
if key.count('unbinned_counts') == 0:
continue
region = int(key.split('.')[0])
hm_phillips[region] = dynamically_bin(hic_phillips1, hic_phillips2, region)
for key in hic_nora1.keys():
if key.count('unbinned_counts') == 0:
continue
region = int(key.split('.')[0])
hm_nora[region] = dynamically_bin(hic_nora1, hic_nora2, region)
fivec_fnames = {
"Prob_Phillips":"%s/Data/FiveC/HiFive/Phillips_ESC_probnodist.fcp" % basedir,
"Prob_Nora":"%s/Data/FiveC/HiFive/Nora_ESC_male_E14_probnodist.fcp" % basedir,
"Bin_Phillips":"%s/Data/FiveC/HiFive/Phillips_ESC_binnodist.fcp" % basedir,
"Bin_Nora":"%s/Data/FiveC/HiFive/Nora_ESC_male_E14_binnodist.fcp" % basedir,
"Exp_Phillips":"%s/Data/FiveC/HiFive/Phillips_ESC_expnodist.fcp" % basedir,
"Exp_Nora":"%s/Data/FiveC/HiFive/Nora_ESC_male_E14_expnodist.fcp" % basedir,
"Exp-KR_Phillips":"%s/Data/FiveC/HiFive/Phillips_ESC_expKRnodist.fcp" % basedir,
"Exp-KR_Nora":"%s/Data/FiveC/HiFive/Nora_ESC_male_E14_expKRnodist.fcp" % basedir,
}
data = {}
imgs = {}
ratio1 = 0
ratio2 = 0
for meth in ['Prob', 'Bin', 'Exp', 'Exp-KR']:
fc = hifive.FiveC(fivec_fnames["%s_Phillips" % meth])
fragments = fc.frags['fragments'][...]
regions = fc.frags['regions'][...]
counts = numpy.zeros(0, dtype=numpy.float64)
expected = numpy.zeros(0, dtype=numpy.float64)
hic_counts = numpy.zeros(0, dtype=numpy.float64)
hic_expected = numpy.zeros(0, dtype=numpy.float64)
skipped = []
for i in range(fc.frags['regions'].shape[0]):
temp = fc.cis_heatmap(i, datatype='fragment', arraytype='compact', binsize=0, skipfiltered=True)
if temp is None:
skipped.append(i)
continue
counts = numpy.hstack((counts, temp[:, :, 0].ravel()))
expected = numpy.hstack((expected, temp[:, :, 1].ravel()))
if i == 6:
ratio1 = temp.shape[1] / float(temp.shape[0])
imgs["%s_Phillips" % meth] = hifive.plotting.plot_full_array(temp, symmetricscaling=False)
if meth == 'Prob':
temp1 = numpy.zeros((temp.shape[0], temp.shape[1]), dtype=numpy.float32)
temp1[numpy.where(temp[:, :, 0] > 0.0)] = 1
if i == 6:
imgs["Raw_Phillips"] = hifive.plotting.plot_full_array(
numpy.dstack((temp[:, :, 0], temp1)), symmetricscaling=False)
binbounds = numpy.hstack((
fragments['start'][regions['start_frag'][i]:regions['stop_frag'][i]].reshape(-1, 1),
fragments['stop'][regions['start_frag'][i]:regions['stop_frag'][i]].reshape(-1, 1)))
valid = numpy.where(fc.filter[regions['start_frag'][i]:regions['stop_frag'][i]])[0]
binbounds = binbounds[valid, :]
temp = hm_phillips[i]
strands = fragments['strand'][regions['start_frag'][i]:regions['stop_frag'][i]][valid]
temp = temp[numpy.where(strands == 0)[0], :, :][:, numpy.where(strands == 1)[0], :]
hic_counts = numpy.hstack((hic_counts, temp[:, :, 0].ravel()))
hic_expected = numpy.hstack((hic_expected, temp[:, :, 1].ravel()))
if i == 6:
imgs["HiC_Phillips"] = hifive.plotting.plot_full_array(temp, symmetricscaling=False)
if meth == 'Prob':
data["Raw_Phillips"] = numpy.copy(counts)
where = numpy.where(hic_expected > 0.0)[0]
hic_counts[where] /= hic_expected[where]
data["HiC_Phillips"] = numpy.copy(hic_counts)
where = numpy.where(expected > 0.0)[0]
counts[where] /= expected[where]
data["%s_Phillips" % meth] = numpy.copy(counts)
fc = hifive.FiveC(fivec_fnames["%s_Nora" % meth])
temp = fc.cis_heatmap(0, datatype='fragment', arraytype='compact', binsize=0, skipfiltered=True)
ratio2 = temp.shape[1] / float(temp.shape[0])
imgs["%s_Nora" % meth] = hifive.plotting.plot_full_array(temp, symmetricscaling=False)
counts = temp[:, :, 0].ravel()
expected = temp[:, :, 1].ravel()
if meth == 'Prob':
temp1 = numpy.zeros((temp.shape[0], temp.shape[1]), dtype=numpy.float32)
temp1[numpy.where(temp[:, :, 0] > 0.0)] = 1
imgs["Raw_Nora"] = hifive.plotting.plot_full_array(
numpy.dstack((temp[:, :, 0], temp1)), symmetricscaling=False)
data["Raw_Nora"] = numpy.copy(counts)
fragments = fc.frags['fragments'][...]
regions = fc.frags['regions'][...]
binbounds = numpy.hstack((
fragments['start'][regions['start_frag'][0]:regions['stop_frag'][0]].reshape(-1, 1),
fragments['stop'][regions['start_frag'][0]:regions['stop_frag'][0]].reshape(-1, 1)))
binbounds = binbounds[numpy.where(fc.filter[regions['start_frag'][0]:regions['stop_frag'][0]])[0], :]
temp = hm_nora[0]
strands = fragments['strand'][regions['start_frag'][0]:regions['stop_frag'][0]]
temp = temp[numpy.where(strands==0)[0], :, :][:, numpy.where(strands == 1)[0], :]
imgs["HiC_Nora"] = hifive.plotting.plot_full_array(temp, symmetricscaling=False)
hic_counts = temp[:, :, 0].ravel()
hic_expected = temp[:, :, 1].ravel()
where = numpy.where(hic_expected > 0.0)[0]
hic_counts[where] /= hic_expected[where]
data["HiC_Nora"] = numpy.copy(hic_counts)
where = numpy.where(expected > 0.0)[0]
counts[where] /= expected[where]
data["%s_Nora" % meth] = numpy.copy(counts)
correlations = {}
output = open(out_fname.replace('pdf', 'txt'), 'w')
print >> output, "Method\tPhillips\tNora"
for meth in methods:
temp = [meth]
for name in ["Phillips", "Nora"]:
valid = numpy.where((data["%s_%s" % (meth, name)] > 0.0) * (data["HiC_%s" % name] > 0.0))
correlations["%s_%s" % (meth, name)] = numpy.corrcoef(numpy.log(data["%s_%s" % (meth, name)][valid]),
numpy.log(data["HiC_%s" % name][valid]))[0, 1]
temp.append(str(correlations["%s_%s" % (meth, name)]))
print >> output, '\t'.join(temp)
output.close()
width = 16.8
spacer = 0.3
c = canvas.canvas()
plot_width = (width - spacer * 3.0 - 0.4) / 4.0
for i, meth in enumerate(["Raw", "Prob", "HiC"]):
meth_names = {"Raw":"Raw", "Prob":"HiFive", "HiC":"HiC"}
c.text(plot_width * (i + 1.5) + spacer * (i + 1), (ratio1 + ratio2) * plot_width + spacer + 0.1,
"%s" % meth_names[meth], [text.halign.center, text.valign.bottom, text.size(-2)])
c.insert(bitmap.bitmap(0, 0, imgs["%s_Phillips" % meth], width=plot_width),
[trafo.translate((i + 1) * (plot_width + spacer), plot_width * ratio2 + spacer)])
c.insert(bitmap.bitmap(0, 0, imgs["%s_Nora" % meth], width=plot_width),
[trafo.translate((i + 1) * (plot_width + spacer), 0)])
g = graph.graphxy(width=plot_width - 0.8, height=plot_width * ratio1,
x=graph.axis.nestedbar(painter=graph.axis.painter.bar(nameattrs=None)),
y=graph.axis.lin(painter=painter),
x2=graph.axis.lin(parter=None, min=0, max=1),
y2=graph.axis.lin(parter=None, min=0, max=1))
for i, meth in enumerate(methods):
Y = numpy.zeros(2, dtype=numpy.float32)
col = method_colors[meth]
for j, name in enumerate(["Phillips", "Nora"]):
Y[j] = correlations["%s_%s" % (meth, name)]
g.plot(graph.data.points(zip(zip(range(Y.shape[0]), [i] * Y.shape[0]), Y), xname=1, y=2),
[graph.style.changebar([col])])
g.text(-0.8, plot_width * ratio1 * 0.5, "Correlation",
[text.halign.center, text.valign.top, text.size(-3), trafo.rotate(90)])
g.text((plot_width - 0.8) * 0.25, -0.1, "Phillips",
[text.halign.center, text.valign.top, text.size(-3)])
g.text((plot_width - 0.8) * 0.75, -0.1, "Nora",
[text.halign.center, text.valign.top, text.size(-3)])
c.insert(g, [trafo.translate(0.8, plot_width * ratio2 + spacer)])
c.text(width, (ratio1 + ratio2 * 0.5) * plot_width + spacer, "Phillips",
[text.halign.center, text.valign.top, trafo.rotate(-90), text.size(-2)])
c.text(width, ratio1 * 0.5 * plot_width, "Nora",
[text.halign.center, text.valign.top, trafo.rotate(-90), text.size(-2)])
meth_names = {"Raw":"Raw", "Prob":"HiFive-Probability", "Exp":"HiFive-Express", "Bin":"HiFive-Binning",
"Exp-KR":"HiFive-ExpressKR", "Exp-KR-dist":"HiFive-ExpressKR-dist"}
for i, meth in enumerate(methods):
c.fill(path.rect(1.0, plot_width * ratio1 - 1.0 - i * 0.5, 0.2, 0.2), [method_colors[meth]])
c.text(1.3, plot_width * ratio1 - 0.9 - i * 0.5, "%s" % meth_names[meth],
[text.halign.left, text.valign.middle, text.size(-3)])
c.writePDFfile(out_fname)
def plot_overall(data, width, height):
plot_width = width - 0.4
plot_height = height - 0.4
c = canvas.canvas()
methods = data.keys()
methods.sort()
bar_colors = []
cis_binsizes = numpy.unique(data[methods[0]]['binsize'][numpy.where(
data[methods[0]]['interaction'] == 'cis')])
trans_binsizes = numpy.unique(data[methods[0]]['binsize'][numpy.where(
data[methods[0]]['interaction'] == 'trans')])
Y = numpy.zeros(
(len(methods), cis_binsizes.shape[0] + trans_binsizes.shape[0]),
dtype=numpy.float32)
for i, method in enumerate(methods):
for j, binsize in enumerate(cis_binsizes):
where = numpy.where((data[method]['binsize'] == binsize) *
(data[method]['interaction'] == 'cis') *
(data[method]['range'] == 0))
if where[0].shape[0] > 0:
Y[i, j] = data[method]['correlation'][where]
for j, binsize in enumerate(trans_binsizes):
where = numpy.where((data[method]['binsize'] == binsize) *
(data[method]['interaction'] == 'trans') *
(data[method]['range'] == 0))
if where[0].shape[0] > 0:
Y[i, j +
cis_binsizes.shape[0]] = data[method]['correlation'][where]
bar_colors.append(method_colors[method])
Y = numpy.array(Y)
g = graph.graphxy(
width=plot_width,
height=plot_height,
x=graph.axis.nestedbar(painter=graph.axis.painter.bar(nameattrs=None)),
y=graph.axis.lin(painter=painter),
x2=graph.axis.lin(parter=None, min=0, max=1),
y2=graph.axis.lin(parter=None, min=0, max=1))
for i in range(len(methods)):
g.plot(
graph.data.points(zip(zip(range(Y.shape[1]), [i] * Y.shape[1]),
Y[i, :]),
xname=1,
y=2),
[graph.style.changebar([method_colors[methods[i]]])])
step = plot_width / (cis_binsizes.shape[0] + trans_binsizes.shape[0])
for i, binsize in enumerate(cis_binsizes):
g.text(step * (0.5 + i), -0.05,
"%s cis" % (str(binsize / 1000) + 'Kb').replace('000Kb', 'Mb'),
[
text.halign.right, text.valign.middle,
text.size(-4),
trafo.rotate(45)
])
for i, binsize in enumerate(trans_binsizes):
g.text(
step * (0.5 + i + cis_binsizes.shape[0]), -0.05,
"%s trans" % (str(binsize / 1000) + 'Kb').replace('000Kb', 'Mb'), [
text.halign.right, text.valign.middle,
text.size(-4),
trafo.rotate(45)
])
c.insert(g, [trafo.translate(0.7, 0.4)])
c.text(
0, plot_height / 2.0 + 0.4, "Dataset Correlation",
[text.halign.center, text.valign.top,
text.size(-3),
trafo.rotate(90)])
return c
def plot_bargraph(data, width, height):
methods = ['HiCLib', 'HiCPipe', 'HiCNorm', 'HiFive-Probability', 'HiFive-Binning', 'HiFive-Express',
'HiFive-ExpressKR', 'HiFive-ExpressKR w/distance']
ho = 4.0
left_width = (width - ho) * 0.45
mid_width1 = (width - ho) * 0.3
mid_width2 = (width - ho) * 0.125
right_width = (width - ho) * 0.125
bar_height = height / len(methods) - 0.1
data_totals = {}
ranges = numpy.zeros((4, 2), dtype=numpy.float32)
for meth in data:
data_totals[meth] = find_total(data[meth])
if meth == 'HiCPipe':
ranges[1, 1] = data_totals[meth]
elif meth == 'HiCNorm':
ranges[2, 1] = data_totals[meth]
elif meth == 'HiFive-Probability':
ranges[3, 1] = data_totals[meth]
else:
ranges[0, 1] = max(ranges[0, 1], data_totals[meth])
ranges /= 60.0
ranges[0, 1] = 28.0
ranges[1, 0] = ranges[1, 1] - ranges[0, 1] / 0.45 * 0.3 * 0.9
ranges[1, 1] = ranges[1, 1] + ranges[0, 1] / 0.45 * 0.3 * 0.1
ranges[2, 0] = ranges[2, 1] - ranges[0, 1] / 0.45 * 0.125 * 0.5
ranges[2, 1] = ranges[2, 1] + ranges[0, 1] / 0.45 * 0.125 * 0.5
ranges[3, 0] = ranges[3, 1] - ranges[0, 1] / 0.45 * 0.125 * 0.5
ranges[3, 1] = ranges[3, 1] + ranges[0, 1] / 0.45 * 0.125 * 0.5
c = canvas.canvas()
g1 = graph.graphxy(width=left_width, height=height,
x=graph.axis.lin(painter=painter, min=0, max=ranges[0, 1]),
x2=graph.axis.lin(parter=None, min=0, max=ranges[0, 1]),
y=graph.axis.lin(parter=None, min=0, max=1),
y2=graph.axis.lin(painter=None, min=0, max=1))
c.insert(g1)
g2 = graph.graphxy(width=mid_width1, height=height,
x=graph.axis.lin(painter=painter, min=ranges[1, 0], max=ranges[1, 1]),
x2=graph.axis.lin(parter=None, min=ranges[1, 0], max=ranges[1, 1]),
y2=graph.axis.lin(painter=None, min=0, max=1),
y=graph.axis.lin(painter=None, min=0, max=1))
c.insert(g2, [trafo.translate(left_width, 0)])
g3 = graph.graphxy(width=mid_width2, height=height,
x=graph.axis.lin(painter=painter, min=ranges[2, 0], max=ranges[2, 1]),
x2=graph.axis.lin(parter=None, min=ranges[2, 0], max=ranges[2, 1]),
y2=graph.axis.lin(painter=None, min=0, max=1),
y=graph.axis.lin(painter=None, min=0, max=1))
c.insert(g3, [trafo.translate(left_width + mid_width1, 0)])
g4 = graph.graphxy(width=right_width, height=height,
x=graph.axis.lin(painter=painter, min=ranges[3, 0], max=ranges[3, 1]),
x2=graph.axis.lin(parter=None, min=ranges[3, 0], max=ranges[3, 1]),
y2=graph.axis.lin(parter=None, min=0, max=1),
y=graph.axis.lin(painter=None, min=0, max=1))
c.insert(g4, [trafo.translate(left_width + mid_width1 + mid_width2, 0)])
split = canvas.canvas()
split.fill(path.path(path.moveto(-0.15, -0.2), path.lineto(0.05, 0.2), path.lineto(.15, 0.2),
path.lineto(-0.05, -0.2), path.closepath()), [color.cmyk.White])
split.stroke(path.line(-0.15, -0.2, 0.05, 0.2))
split.stroke(path.line(-0.05, -0.2, 0.15, 0.2))
c.insert(split, [trafo.translate(left_width, 0)])
c.insert(split, [trafo.translate(left_width, height)])
c.insert(split, [trafo.translate(left_width + mid_width1, 0)])
c.insert(split, [trafo.translate(left_width + mid_width1, height)])
c.insert(split, [trafo.translate(left_width + mid_width1 + mid_width2, 0)])
c.insert(split, [trafo.translate(left_width + mid_width1 + mid_width2, height)])
for i, meth in enumerate(methods):
c.insert(plot_bar(data[meth], ranges, bar_height, left_width / ranges[0, 1], split),
[trafo.translate(0, height - 0.05 - bar_height * (i + 1) - i * 0.1)])
c.text(-0.1, height * (len(methods) - i - 0.5) / len(methods), meth,
[text.halign.right, text.valign.middle, text.size(-2)])
c.text((width - ho) / 2.0, -0.35, "Runtime (minutes)",
[text.halign.center, text.valign.top, text.size(-2)])
return c
def plot_single_range(data0, data1, binsize, width, height, ylabel):
plot_width = width
plot_height = height
c = canvas.canvas()
xmax = 0.0
where = numpy.where(
(data0['binsize'] == binsize) * (data0['interaction'] == 'cis') *
(data0['range'] >= 0))
if where[0].shape[0] > 0:
xmax = max(xmax, numpy.amax(data0['range'][where]))
X = data0['range'][where]
X = numpy.r_[0, X]
X[0] = X[1]**2.0 / X[2]
xmin = X[0]
ymin = numpy.inf
ymax = -numpy.inf
binsizes = numpy.unique(data0['binsize'])
for b in binsizes:
where = numpy.where(
(data0['binsize'] == b) * (data0['interaction'] == 'cis') *
(data0['range'] >= 0))
where1 = numpy.where(
(data1['binsize'] == b) * (data1['interaction'] == 'cis') *
(data1['range'] >= 0))
if where[0].shape[0] > 0:
Y = (data1['correlation'][where1] - data0['correlation'][where])
ymin = min(ymin, numpy.amin(Y))
ymax = max(ymax, numpy.amax(Y))
yspan = ymax - ymin
ymin -= yspan * 0.05
ymax += yspan * 0.05
if ylabel:
yaxis = graph.axis.lin(painter=painter, min=ymin, max=ymax)
else:
yaxis = graph.axis.lin(painter=None, min=ymin, max=ymax)
g = graph.graphxy(width=plot_width,
height=plot_height,
x=graph.axis.log(painter=painter, min=X[0], max=xmax),
y=yaxis,
x2=graph.axis.lin(parter=None, min=0, max=1),
y2=graph.axis.lin(parter=None, min=0, max=1))
g.stroke(path.line(0, 0, 0, plot_height))
for x in X[1:-1]:
pos = ((log(x) - log(xmin)) / (log(xmax) - log(xmin)) * plot_width)
g.stroke(path.line(pos, 0, pos, plot_height),
[style.linestyle.dotted, style.linewidth.THin])
X = (X[1:]**0.5) * (X[:-1]**0.5)
where = numpy.where((data0['binsize'] == binsize) *
(data0['interaction'] == 'cis') * (data0['range'] > 0))
where1 = numpy.where(
(data1['binsize'] == binsize) * (data1['interaction'] == 'cis') *
(data1['range'] > 0))
if where[0].shape[0] > 0:
Y = (data1['correlation'][where1] - data0['correlation'][where])
g.plot(graph.data.points(zip(X, Y), x=1, y=2),
[graph.style.line(lineattrs=[style.linewidth.Thick])])
g.stroke(
path.line(0,
plot_height * (-ymin) / (ymax - ymin), plot_width,
plot_height * (-ymin) / (ymax - ymin)),
[style.linestyle.dotted, style.linewidth.THin])
if binsize / 1000000 > 0:
binstring = "%iMb" % (binsize / 1000000)
elif binsize / 1000 > 0:
binstring = "%iKb" % (binsize / 1000)
else:
binstring = str(binsize)
g.text(plot_width / 2, plot_height + 0.3, "%s binning" % (binstring),
[text.halign.center, text.valign.top,
text.size(-2)])
c.insert(g)
return c
def plot_overall(data0, data1, width, height):
vo = 1.15
ho = 1.15
plot_width = width - ho
plot_height = height - vo
c = canvas.canvas()
cis_binsizes = numpy.unique(
data0['binsize'][numpy.where(data0['interaction'] == 'cis')])
trans_binsizes = numpy.unique(
data0['binsize'][numpy.where(data0['interaction'] == 'trans')])
ymin = numpy.inf
ymax = -numpy.inf
Y = numpy.zeros((cis_binsizes.shape[0] + trans_binsizes.shape[0]),
dtype=numpy.float32)
for j, binsize in enumerate(cis_binsizes):
where = numpy.where(
(data0['binsize'] == binsize) * (data0['interaction'] == 'cis') *
(data0['range'] < 0))
where1 = numpy.where(
(data1['binsize'] == binsize) * (data1['interaction'] == 'cis') *
(data1['range'] < 0))
if where[0].shape[0] > 0:
Y[j] = (data1['correlation'][where1] - data0['correlation'][where])
ymin = min(ymin, Y[j])
ymax = max(ymax, Y[j])
for j, binsize in enumerate(trans_binsizes):
where = numpy.where(
(data0['binsize'] == binsize) * (data0['interaction'] == 'trans') *
(data0['range'] < 0))
where1 = numpy.where(
(data1['binsize'] == binsize) * (data1['interaction'] == 'trans') *
(data1['range'] < 0))
if where[0].shape[0] > 0:
Y[j + cis_binsizes.shape[0]] = (data1['correlation'][where1] -
data0['correlation'][where])
ymin = min(ymin, Y[j + cis_binsizes.shape[0]])
ymax = max(ymax, Y[j + cis_binsizes.shape[0]])
yspan = ymax - ymin
ymin -= yspan * 0.05
ymax += yspan * 0.05
Y = numpy.array(Y)
g = graph.graphxy(
width=plot_width,
height=plot_height,
x=graph.axis.bar(painter=graph.axis.painter.bar(nameattrs=None)),
y=graph.axis.lin(painter=painter, min=ymin, max=ymax),
x2=graph.axis.lin(parter=None, min=0, max=1),
y2=graph.axis.lin(parter=None, min=0, max=1))
y0 = plot_height * (-ymin) / (ymax - ymin)
g.stroke(
path.line(0,
plot_height * (-ymin) / (ymax - ymin), plot_width,
plot_height * (-ymin) / (ymax - ymin)),
[style.linestyle.dotted, style.linewidth.THin])
w0 = plot_width / Y.shape[0]
w1 = w0 / 1.5
for j in range(Y.shape[0]):
x = j * w0 + 0.25 * w1
y = plot_height * (Y[j] - ymin) / (ymax - ymin)
g.fill(path.rect(x, y0, w1, y - y0))
c.insert(g, [trafo.translate(ho, vo)])
for i, label in enumerate(["10Kb", "50Kb", "250Kb", "1Mb", "250Kb",
"1Mb"]):
c.text(ho + plot_width * (i + 0.5) / 6.0, vo - 0.05, "%s" % label, [
text.halign.right, text.valign.middle,
text.size(-3),
trafo.rotate(90)
])
c.text(ho + plot_width * 2.0 / 6.0, 0, "cis",
[text.halign.center, text.valign.bottom,
text.size(-3)])
c.stroke(path.line(ho + 0.2, 0.3, ho - 0.2 + plot_width * 4.0 / 6.0, 0.3),
[style.linewidth.THin])
c.text(ho + plot_width * 5.0 / 6.0, 0, "trans",
[text.halign.center, text.valign.bottom,
text.size(-3)])
c.stroke(
path.line(ho + 0.2 + plot_width * 4.0 / 6.0, 0.3,
ho - 0.2 + plot_width, 0.3), [style.linewidth.THin])
c.text(
0, plot_height * 0.5 + vo, r"$r_{Poisson} - r_{binomial}$",
[text.halign.center, text.valign.top,
text.size(-2),
trafo.rotate(90)])
c.text(0, height, 'b', [text.halign.left, text.valign.top, text.size(-1)])
return c
def main():
out_fname = sys.argv[1]
basedir = '/'.join(
os.path.dirname(os.path.realpath(__file__)).split('/')[:-2])
hic_fname1 = "%s/Data/HiC/HiFive/mm9_ESC_NcoI_prob.hcp" % basedir
hic_fname2 = "%s/Data/HiC/HiFive/mm9_ESC_HindIII_prob.hcp" % basedir
fivec_fnames = {
"Prob_Phillips":
"%s/Data/FiveC/HiFive/Phillips_ESC_prob.fcp" % basedir,
"Prob_Nora":
"%s/Data/FiveC/HiFive/Nora_ESC_male_E14_prob.fcp" % basedir,
"Bin_Phillips":
"%s/Data/FiveC/HiFive/Phillips_ESC_bin.fcp" % basedir,
"Bin_Nora":
"%s/Data/FiveC/HiFive/Nora_ESC_male_E14_bin.fcp" % basedir,
"Exp_Phillips":
"%s/Data/FiveC/HiFive/Phillips_ESC_exp.fcp" % basedir,
"Exp_Nora":
"%s/Data/FiveC/HiFive/Nora_ESC_male_E14_exp.fcp" % basedir,
"Exp-KR_Phillips":
"%s/Data/FiveC/HiFive/Phillips_ESC_expKR.fcp" % basedir,
"Exp-KR_Nora":
"%s/Data/FiveC/HiFive/Nora_ESC_male_E14_expKR.fcp" % basedir,
}
hic1 = hifive.HiC(hic_fname1)
hic2 = hifive.HiC(hic_fname2)
hic_hm = {'Phillips': {}}
fc = hifive.FiveC(fivec_fnames["Prob_Phillips"])
fragments = fc.frags['fragments'][...]
regions = fc.frags['regions'][...]
for i in range(fc.frags['regions'].shape[0]):
binbounds = numpy.hstack(
(fragments['start']
[regions['start_frag'][i]:regions['stop_frag'][i]].reshape(-1, 1),
fragments['stop']
[regions['start_frag'][i]:regions['stop_frag'][i]].reshape(-1,
1)))
binbounds = binbounds[numpy.where(
fc.filter[regions['start_frag'][i]:regions['stop_frag'][i]])[0], :]
hic_hm['Phillips'][i] = dynamically_bin(hic1, hic2,
regions['chromosome'][i],
binbounds)
fc = hifive.FiveC(fivec_fnames["Prob_Nora"])
fragments = fc.frags['fragments'][...]
regions = fc.frags['regions'][...]
binbounds = numpy.hstack(
(fragments['start']
[regions['start_frag'][0]:regions['stop_frag'][0]].reshape(-1, 1),
fragments['stop']
[regions['start_frag'][0]:regions['stop_frag'][0]].reshape(-1, 1)))
binbounds = binbounds[numpy.where(
fc.filter[regions['start_frag'][0]:regions['stop_frag'][0]])[0], :]
hic_hm['Nora'] = dynamically_bin(hic1, hic2, regions['chromosome'][0],
binbounds)
dist_corr = find_correlations(hic_hm, fivec_fnames, out_fname, True)
nodist_corr = find_correlations(hic_hm, fivec_fnames, out_fname, False)
c = canvas.canvas()
width = 16.8
spacer = 0.4
plot_width = (width - spacer * 2) / 2.5
plot_height = plot_width
key_width = width - (plot_width + spacer) * 2
phillips_img = plot_correlation_diffs(dist_corr, nodist_corr, 'Phillips',
plot_width, plot_height)
nora_img = plot_correlation_diffs(dist_corr, nodist_corr, 'Nora',
plot_width, plot_height)
key_img = plot_key(key_width, plot_height)
c.insert(phillips_img)
c.insert(nora_img, [trafo.translate(plot_width + spacer, 0)])
c.insert(key_img, [trafo.translate((plot_width + spacer) * 2, 0)])
c.text(0, plot_height, "a",
[text.halign.left, text.valign.top,
text.size(-1)])
c.text(plot_width + spacer, plot_height, "b",
[text.halign.left, text.valign.top,
text.size(-1)])
c.writePDFfile(out_fname)
import sys
import os
from glob import glob
from math import floor
import numpy
from pyx import canvas, text, path, graph, color, trafo, unit, attr, deco, style, bitmap
unit.set(defaultunit="cm")
text.set(mode="latex")
text.preamble(r"\usepackage{times}")
text.preamble(r"\usepackage{sansmath}")
text.preamble(r"\sansmath")
text.preamble(r"\renewcommand*\familydefault{\sfdefault}")
painter = graph.axis.painter.regular(labeldist=0.1,
labelattrs=[text.size(-3)],
titleattrs=[text.size(-3)])
painter2 = graph.axis.painter.regular(labelattrs=None)
step_colors = {
0: color.cmyk.MidnightBlue,
1: color.cmyk.Mahogany,
2: color.cmyk.CadetBlue,
3: color.cmyk.Dandelion,
4: color.cmyk.OliveGreen,
}
step_names = {
'0': 'Loading data',
'1': 'Filtering and preprocessing',
'2': 'Binning',
def plot_overall(data0, data1, width, height):
vo = 0.55
ho = 1.1
plot_width = width - ho
plot_height = height - vo
c = canvas.canvas()
methods = data0.keys()
methods.sort()
bar_colors = []
cis_binsizes = numpy.unique(data0[methods[0]]['binsize'][numpy.where(
data0[methods[0]]['interaction'] == 'cis')])
trans_binsizes = numpy.unique(data0[methods[0]]['binsize'][numpy.where(
data0[methods[0]]['interaction'] == 'trans')])
ymin = numpy.inf
ymax = -numpy.inf
Y = numpy.zeros(
(len(methods), cis_binsizes.shape[0] + trans_binsizes.shape[0]),
dtype=numpy.float32)
for i, method in enumerate(methods):
for j, binsize in enumerate(cis_binsizes):
where = numpy.where((data0[method]['binsize'] == binsize) *
(data0[method]['interaction'] == 'cis') *
(data0[method]['range'] < 0))
where1 = numpy.where((data1[method]['binsize'] == binsize) *
(data1[method]['interaction'] == 'cis') *
(data1[method]['range'] < 0))
if where[0].shape[0] > 0:
Y[i, j] = (data1[method]['correlation'][where1] -
data0[method]['correlation'][where])
ymin = min(ymin, Y[i, j])
ymax = max(ymax, Y[i, j])
for j, binsize in enumerate(trans_binsizes):
where = numpy.where((data0[method]['binsize'] == binsize) *
(data0[method]['interaction'] == 'trans') *
(data0[method]['range'] < 0))
where1 = numpy.where((data1[method]['binsize'] == binsize) *
(data1[method]['interaction'] == 'trans') *
(data1[method]['range'] < 0))
if where[0].shape[0] > 0:
Y[i, j + cis_binsizes.shape[0]] = (
data1[method]['correlation'][where1] -
data0[method]['correlation'][where])
ymin = min(ymin, Y[i, j + cis_binsizes.shape[0]])
ymax = max(ymax, Y[i, j + cis_binsizes.shape[0]])
bar_colors.append(method_colors[method])
yspan = ymax - ymin
ymin -= yspan * 0.05
ymax += yspan * 0.05
Y = numpy.array(Y)
g = graph.graphxy(
width=plot_width,
height=plot_height,
x=graph.axis.nestedbar(painter=graph.axis.painter.bar(nameattrs=None)),
y=graph.axis.lin(painter=painter, min=ymin, max=ymax),
x2=graph.axis.lin(parter=None, min=0, max=1),
y2=graph.axis.lin(parter=None, min=0, max=1))
y0 = plot_height * (-ymin) / (ymax - ymin)
g.stroke(
path.line(0,
plot_height * (-ymin) / (ymax - ymin), plot_width,
plot_height * (-ymin) / (ymax - ymin)),
[style.linestyle.dotted, style.linewidth.THin])
w0 = plot_width / Y.shape[1]
w1 = w0 / (len(methods) + 0.5)
for i in range(len(methods)):
for j in range(Y.shape[1]):
x = j * w0 + (i + 0.25) * w1
y = plot_height * (Y[i, j] - ymin) / (ymax - ymin)
g.stroke(path.rect(x, y0, w1, y - y0),
[deco.filled([method_colors[methods[i]]])])
c.insert(g, [trafo.translate(ho, vo)])
for i, label in enumerate(["10Kb", "50Kb", "250Kb", "1Mb", "250Kb",
"1Mb"]):
c.text(ho + plot_width * (i + 0.5) / 6.0, vo - 0.05, "%s" % label,
[text.halign.center, text.valign.top,
text.size(-3)])
c.text(ho + plot_width * 2.0 / 6.0, 0.05, "cis",
[text.halign.center, text.valign.bottom,
text.size(-3)])
c.stroke(
path.line(ho + 0.2, vo * 0.5, ho - 0.2 + plot_width * 4.0 / 6.0,
vo * 0.5), [style.linewidth.THin])
c.text(ho + plot_width * 5.0 / 6.0, 0.05, "trans",
[text.halign.center, text.valign.bottom,
text.size(-3)])
c.stroke(
path.line(ho + 0.2 + plot_width * 4.0 / 6.0, vo * 0.5,
ho - 0.2 + plot_width, vo * 0.5), [style.linewidth.THin])
c.text(
0, plot_height * 0.5 + vo, r"$r_{0K} - r_{500K}$",
[text.halign.center, text.valign.top,
text.size(-2),
trafo.rotate(90)])
return c
def plot_bargraph(data, width, height):
methods = [
'HiCLib', 'HiCPipe', 'HiCNorm', 'HiFive-Probability', 'HiFive-Binning',
'HiFive-Express', 'HiFive-ExpressKR', 'HiFive-ExpressKR w/distance'
]
ho = 4.0
left_width = (width - ho) * 0.45
mid_width1 = (width - ho) * 0.3
mid_width2 = (width - ho) * 0.125
right_width = (width - ho) * 0.125
bar_height = height / len(methods) - 0.1
data_totals = {}
ranges = numpy.zeros((4, 2), dtype=numpy.float32)
for meth in data:
data_totals[meth] = find_total(data[meth])
if meth == 'HiCPipe':
ranges[1, 1] = data_totals[meth]
elif meth == 'HiCNorm':
ranges[2, 1] = data_totals[meth]
elif meth == 'HiFive-Probability':
ranges[3, 1] = data_totals[meth]
else:
ranges[0, 1] = max(ranges[0, 1], data_totals[meth])
ranges /= 60.0
ranges[0, 1] = 28.0
ranges[1, 0] = ranges[1, 1] - ranges[0, 1] / 0.45 * 0.3 * 0.9
ranges[1, 1] = ranges[1, 1] + ranges[0, 1] / 0.45 * 0.3 * 0.1
ranges[2, 0] = ranges[2, 1] - ranges[0, 1] / 0.45 * 0.125 * 0.5
ranges[2, 1] = ranges[2, 1] + ranges[0, 1] / 0.45 * 0.125 * 0.5
ranges[3, 0] = ranges[3, 1] - ranges[0, 1] / 0.45 * 0.125 * 0.5
ranges[3, 1] = ranges[3, 1] + ranges[0, 1] / 0.45 * 0.125 * 0.5
c = canvas.canvas()
g1 = graph.graphxy(width=left_width,
height=height,
x=graph.axis.lin(painter=painter,
min=0,
max=ranges[0, 1]),
x2=graph.axis.lin(parter=None, min=0, max=ranges[0, 1]),
y=graph.axis.lin(parter=None, min=0, max=1),
y2=graph.axis.lin(painter=None, min=0, max=1))
c.insert(g1)
g2 = graph.graphxy(width=mid_width1,
height=height,
x=graph.axis.lin(painter=painter,
min=ranges[1, 0],
max=ranges[1, 1]),
x2=graph.axis.lin(parter=None,
min=ranges[1, 0],
max=ranges[1, 1]),
y2=graph.axis.lin(painter=None, min=0, max=1),
y=graph.axis.lin(painter=None, min=0, max=1))
c.insert(g2, [trafo.translate(left_width, 0)])
g3 = graph.graphxy(width=mid_width2,
height=height,
x=graph.axis.lin(painter=painter,
min=ranges[2, 0],
max=ranges[2, 1]),
x2=graph.axis.lin(parter=None,
min=ranges[2, 0],
max=ranges[2, 1]),
y2=graph.axis.lin(painter=None, min=0, max=1),
y=graph.axis.lin(painter=None, min=0, max=1))
c.insert(g3, [trafo.translate(left_width + mid_width1, 0)])
g4 = graph.graphxy(width=right_width,
height=height,
x=graph.axis.lin(painter=painter,
min=ranges[3, 0],
max=ranges[3, 1]),
x2=graph.axis.lin(parter=None,
min=ranges[3, 0],
max=ranges[3, 1]),
y2=graph.axis.lin(parter=None, min=0, max=1),
y=graph.axis.lin(painter=None, min=0, max=1))
c.insert(g4, [trafo.translate(left_width + mid_width1 + mid_width2, 0)])
split = canvas.canvas()
split.fill(
path.path(path.moveto(-0.15, -0.2), path.lineto(0.05, 0.2),
path.lineto(.15, 0.2), path.lineto(-0.05, -0.2),
path.closepath()), [color.cmyk.White])
split.stroke(path.line(-0.15, -0.2, 0.05, 0.2))
split.stroke(path.line(-0.05, -0.2, 0.15, 0.2))
c.insert(split, [trafo.translate(left_width, 0)])
c.insert(split, [trafo.translate(left_width, height)])
c.insert(split, [trafo.translate(left_width + mid_width1, 0)])
c.insert(split, [trafo.translate(left_width + mid_width1, height)])
c.insert(split, [trafo.translate(left_width + mid_width1 + mid_width2, 0)])
c.insert(split,
[trafo.translate(left_width + mid_width1 + mid_width2, height)])
for i, meth in enumerate(methods):
c.insert(
plot_bar(data[meth], ranges, bar_height, left_width / ranges[0, 1],
split),
[
trafo.translate(0, height - 0.05 - bar_height *
(i + 1) - i * 0.1)
])
c.text(-0.1,
height * (len(methods) - i - 0.5) / len(methods), meth,
[text.halign.right, text.valign.middle,
text.size(-2)])
c.text((width - ho) / 2.0, -0.35, "Runtime (minutes)",
[text.halign.center, text.valign.top,
text.size(-2)])
return c
('file 2', (0, 1, 2, 3, 5)),
('file 3', (1, 4, 5)))):
nr_revisions = max(nr_revisions, max(versions))
hue = hueoff+nr/3
cf.text(0, -nr*dy, name, [color.hsb(hue, 1, 0.5), text.valign.middle])
for nver, (v1, v2) in enumerate(zip(versions[:-1], versions[1:])):
y = -(nr+0.4)*dy
lv = len(versions)-1
xll = v1*dx+versionoff+0.1
yll = y
width = (v2-v1)*dx-0.2
height = 0.8*dy
cf.fill(path.rect(xll, yll, width, height),
[color.hsb(hue, 1-(lv-1-nver)/(lv-1)*0.7, 0.6)])
cf.text(xll+0.5*width, yll+0.5*height, gethashstring(),
[text.size(-4), text.halign.center, text.valign.middle, color.grey(1)])
for n in range(nr_revisions):
xcenter = (n+0.5)*dx+versionoff
y = 0.5
cf.text(xcenter, y, gethashstring(), [text.size(-4), text.halign.center])
if n:
yshift = 0.1
cf.stroke(path.line(xcenter-0.33*dx, y+yshift, xcenter-0.67*dx, y+yshift),
[deco.earrow])
cf.stroke(path.rect(3*dx+versionoff, -2.6*dy, dx, 2.6*dy+1.0),
[style.linewidth.THIck, deformer.smoothed(0.3)])
c.insert(cf, [trafo.translate(4.5, 1)])
c.writePDFfile()
import os
import numpy
from pyx import canvas, text, path, graph, color, trafo, unit, attr, deco, style, bitmap
import h5py
import hifive
unit.set(defaultunit="cm")
text.set(mode="latex")
text.preamble(r"\usepackage{times}")
text.preamble(r"\usepackage{sansmath}")
text.preamble(r"\sansmath")
text.preamble(r"\renewcommand*\familydefault{\sfdefault}")
painter = graph.axis.painter.regular( labeldist=0.1, labelattrs=[text.size(-3)], titleattrs=[text.size(-3)] )
methods = ['Raw', 'Prob', 'Exp', 'Bin', 'Exp-KR']
method_colors = {
'Prob':color.cmyk.Black,
'Exp':color.cmyk.CadetBlue,
'Bin':color.cmyk.MidnightBlue,
'Raw':color.cmyk.Dandelion,
'Exp-KR':color.cmyk.Mahogany,
}
def main():
out_fname = sys.argv[1]
basedir = '/'.join(os.path.dirname(os.path.realpath(__file__)).split('/')[:-2])
hic_phillips_fname1 = "%s/Data/HiC/HiCPipe/HM/mm9_ESC_NcoI_Phillips.hch" % basedir
def draw_continent_circle(con,
name="",
draw_upper_landscape=True,
draw_lower_landscape=False,
draw_upper_green=True,
draw_lower_purple=False,
draw_train_tracks=False,
draw_foliation=True,
foliation_style_old=False,
foliation_style_split=False,
foliation_style_cusp_leaves=True,
foliation_style_boundary_leaves=True,
shade_triangles=False,
draw_fund_domain=False,
fund_dom_tets=None,
draw_fund_domain_edges=False,
draw_tetrahedron_rectangles=[]):
global_scale_up = 10.0
edge_thickness = 0.02
track_thickness = 0.02
leaf_thickness = 0.03
edge_colours = {
True: color.rgb(0.9, 0.3, 0),
False: color.rgb(0, 0.3, 0.9)
}
green = color.rgb(0.0, 0.5, 0.0)
purple = color.rgb(0.5, 0.0, 0.5)
scl = trafo.trafo(matrix=((global_scale_up, 0), (0, global_scale_up)),
vector=(0, 0))
canv = canvas.canvas()
canv.stroke(path.circle(0, 0, global_scale_up), [style.linewidth(0.02)])
n = len(con.coast)
for v in con.coast:
i = v.coastal_index
t = 2 * pi * float(i) / float(n)
v.circle_pos = complex(cos(t), sin(t))
vert_pos = v.circle_pos * 1.01 * global_scale_up
canv.text(vert_pos.real,
vert_pos.imag,
"$" + str(con.vertices.index(v)) + "$",
textattrs=[
text.size(-4), text.halign.left, text.valign.middle,
trafo.rotate(
(180 / pi) * atan2(vert_pos.imag, vert_pos.real))
])
# vert_pos2 = v.circle_pos * 1.2 * global_scale_up
# p = path.path(path.moveto(vert_pos.real, vert_pos.imag), path.lineto(vert_pos2.real, vert_pos2.imag))
# canv.stroke(p, [deco.curvedtext("$"+str(con.vertices.index(v))+"$")])
### highlight vertices of tetrahedra in a fundamental domain
if draw_fund_domain:
if fund_dom_tets == None:
fund_dom_tets = get_fund_domain_tetrahedra(con)
for con_tet in fund_dom_tets:
if type(
con_tet
) == continent_tetrahedron: ### could be an integer if we didnt find this tet
if draw_fund_domain_edges:
for e in con_tet.edges():
col = edge_colours[e.is_red]
u, v = e.vertices
p = make_arc(u.circle_pos, v.circle_pos)
p = p.transformed(scl)
canv.stroke(p, [
style.linewidth(edge_thickness),
style.linecap.round, col
])
update_fund_dom_tet_nums(con, fund_dom_tets)
for v in [v for v in con.coast if v.fund_dom_tet_nums != []]:
vert_pos = v.circle_pos * 1.03 * global_scale_up
canv.text(vert_pos.real,
vert_pos.imag,
"$" + str(v.fund_dom_tet_nums) + "$",
textattrs=[
text.size(-4), text.halign.left, text.valign.middle,
trafo.rotate(
(180 / pi) * atan2(vert_pos.imag, vert_pos.real))
])
# lower_colours = {True: color.rgb(0.5,0.3,0), False: color.rgb(0,0.3,0.5)}
# upper_colours = {True: color.rgb(0.9,0.3,0), False: color.rgb(0,0.3,0.9)}
landscape_edges = [con.lower_landscape_edges, con.upper_landscape_edges]
# colours = [lower_colours, upper_colours]
upper_tris = con.upper_landscape_triangles
lower_tris = con.lower_landscape_triangles
boundary_tris = [lower_tris, upper_tris]
if shade_triangles:
u, v, w = con.triangle_path[0].vertices
p = make_arc(u.circle_pos, v.circle_pos)
q = make_arc(v.circle_pos, w.circle_pos)
r = make_arc(w.circle_pos, u.circle_pos)
p.append(q[1])
p.append(r[1]) ### remove extraneous moveto commands
p = p.transformed(scl)
canv.stroke(
p, [deco.filled([color.transparency(0.8)]),
style.linewidth(0)])
u, v, w = con.triangle_path[-1].vertices
p = make_arc(u.circle_pos, v.circle_pos)
q = make_arc(v.circle_pos, w.circle_pos)
r = make_arc(w.circle_pos, u.circle_pos)
p.append(q[1])
p.append(r[1]) ### remove extraneous moveto commands
p = p.transformed(scl)
canv.stroke(
p, [deco.filled([color.transparency(0.8)]),
style.linewidth(0)])
# for triangle in con.triangle_path:
# u,v,w = triangle.vertices
# p = make_arc(u.circle_pos, v.circle_pos)
# q = make_arc(v.circle_pos, w.circle_pos)
# r = make_arc(w.circle_pos, u.circle_pos)
# p.append(q[1])
# p.append(r[1]) ### remove extraneous moveto commands
# p = p.transformed(scl)
# canv.stroke(p, [deco.filled([color.transparency(0.8)]), style.linewidth(0)])
to_do = []
if draw_lower_landscape:
to_do.append(0)
if draw_upper_landscape:
to_do.append(1)
for i in to_do:
for e in landscape_edges[i]:
col = edge_colours[e.is_red]
transp = []
if i == 0:
transp = [color.transparency(0.75)]
u, v = e.vertices
p = make_arc(u.circle_pos, v.circle_pos)
p = p.transformed(scl)
canv.stroke(
p,
[style.linewidth(edge_thickness), style.linecap.round, col] +
transp)
for tri in boundary_tris[i]:
center = incenter(tri.vertices[0].circle_pos,
tri.vertices[1].circle_pos,
tri.vertices[2].circle_pos)
# canv.fill(path.circle(global_scale_up*center[0], global_scale_up*center[1], 0.1))
canv.text(global_scale_up * center[0],
global_scale_up * center[1],
"$" + str(tri.index) + "$",
textattrs=[
text.size(-2), text.halign.center, text.valign.middle
] + transp)
### train tracks...
purple_train_routes = [
] ### pairs of coastal edges corresponding to a train route
green_train_routes = []
if draw_lower_purple:
if draw_train_tracks:
for tri in lower_tris:
midpts = []
is_reds = []
for e in tri.edges:
is_reds.append(e.is_red)
u, v = e.vertices
p, midpt = make_arc(u.circle_pos,
v.circle_pos,
return_midpt=True)
midpts.append(midpt)
for i in range(3):
if (is_reds[i] == is_reds[(i + 1) % 3]) or (
not is_reds[i] and is_reds[(i + 1) % 3]):
p = make_arc(midpts[i], midpts[(i + 1) % 3])
p = p.transformed(scl)
canv.stroke(p, [
style.linewidth(track_thickness),
style.linecap.round, purple
])
if draw_foliation:
for edge in con.lower_landscape_edges:
leaf_end_edges = []
if edge.is_coastal():
if not edge.is_coastal_sink(upper=False):
leaf_end_edges.append(edge)
for tri in edge.boundary_triangles:
if not tri.is_upper:
last_tri = con.flow(tri)[0]
last_edge = last_tri.edges[
last_tri.downriver_index()]
leaf_end_edges.append(last_edge)
else:
if edge.is_watershed():
for tri in edge.boundary_triangles:
last_tri = con.flow(tri)[0]
last_edge = last_tri.edges[
last_tri.downriver_index()]
leaf_end_edges.append(last_edge)
if len(leaf_end_edges) == 2:
purple_train_routes.append(leaf_end_edges)
if foliation_style_old:
leaf_ends = []
for e in leaf_end_edges:
endpts = e.vertices
_, midpt = make_arc(endpts[0].circle_pos,
endpts[1].circle_pos,
return_midpt=True)
leaf_ends.append(midpt)
p = make_arc(leaf_ends[0], leaf_ends[1])
p = p.transformed(scl)
canv.stroke(p, [
style.linewidth(leaf_thickness),
style.linecap.round, purple
])
if draw_upper_green:
if draw_train_tracks:
for tri in upper_tris:
midpts = []
is_reds = []
for e in tri.edges:
is_reds.append(e.is_red)
u, v = e.vertices
p, midpt = make_arc(u.circle_pos,
v.circle_pos,
return_midpt=True)
midpts.append(midpt)
for i in range(3):
if (is_reds[i] == is_reds[(i + 1) % 3]) or (
is_reds[i] and not is_reds[(i + 1) % 3]):
p = make_arc(midpts[i], midpts[(i + 1) % 3])
p = p.transformed(scl)
canv.stroke(p, [
style.linewidth(track_thickness),
style.linecap.round, green
])
if draw_foliation:
for edge in con.upper_landscape_edges:
leaf_end_edges = []
if edge.is_coastal():
if not edge.is_coastal_sink(upper=True):
leaf_end_edges.append(edge)
for tri in edge.boundary_triangles:
if tri.is_upper:
last_tri = con.flow(tri)[0]
last_edge = last_tri.edges[
last_tri.downriver_index()]
leaf_end_edges.append(last_edge)
else:
if edge.is_watershed():
for tri in edge.boundary_triangles:
last_tri = con.flow(tri)[0]
last_edge = last_tri.edges[
last_tri.downriver_index()]
leaf_end_edges.append(last_edge)
if len(leaf_end_edges) == 2:
green_train_routes.append(leaf_end_edges)
if foliation_style_old:
leaf_ends = []
for e in leaf_end_edges:
endpts = e.vertices
_, midpt = make_arc(endpts[0].circle_pos,
endpts[1].circle_pos,
return_midpt=True)
leaf_ends.append(midpt)
p = make_arc(leaf_ends[0], leaf_ends[1])
p = p.transformed(scl)
canv.stroke(p, [
style.linewidth(leaf_thickness),
style.linecap.round, green
])
if draw_foliation and (foliation_style_split or foliation_style_cusp_leaves
or foliation_style_boundary_leaves):
for e in con.coastal_edges:
e.purple_ends = []
e.green_ends = []
for e1, e2 in purple_train_routes:
e1.purple_ends.append(e2)
e2.purple_ends.append(e1)
for e1, e2 in green_train_routes:
e1.green_ends.append(e2)
e2.green_ends.append(e1)
for i, e in enumerate(con.coastal_edges):
rotated_coastal_edges = con.coastal_edges[
i:] + con.coastal_edges[:i]
e.purple_ends.sort(
key=lambda e_other: rotated_coastal_edges.index(e_other),
reverse=True)
e.green_ends.sort(
key=lambda e_other: rotated_coastal_edges.index(e_other),
reverse=True)
if e.is_red:
e.ends = e.green_ends + e.purple_ends
else:
e.ends = e.purple_ends + e.green_ends
if foliation_style_split:
for e1, e2 in purple_train_routes:
p1 = end_pos(e2, e1)
p2 = end_pos(e1, e2)
p = make_arc(p1, p2)
p = p.transformed(scl)
canv.stroke(p, [
style.linewidth(leaf_thickness), style.linecap.round,
purple
])
for e1, e2 in green_train_routes:
p1 = end_pos(e2, e1)
p2 = end_pos(e1, e2)
p = make_arc(p1, p2)
p = p.transformed(scl)
canv.stroke(p, [
style.linewidth(leaf_thickness), style.linecap.round, green
])
if foliation_style_cusp_leaves or foliation_style_boundary_leaves:
for i, c in enumerate(con.coast):
c.purple_thorn_end_positions = [] ### complex numbers
c.purple_thorn_ends = [
] ### [coastal arc, position along that arc]
e = con.coastal_edges[i]
e1 = e.purple_ends[0]
while True:
index = e1.purple_ends.index(e)
if index == len(e1.purple_ends) - 1:
break
else:
c.purple_thorn_end_positions.append(
end_pos(e, e1, offset=0.5))
c.purple_thorn_ends.append((e1, e1.ends.index(e)))
e, e1 = e1, e1.purple_ends[index + 1]
if foliation_style_boundary_leaves:
e_before = con.coastal_edges[(i - 1) % len(con.coast)]
e_after = con.coastal_edges[i]
first_pos = end_pos(e_after.purple_ends[0],
e_after,
offset=-0.25)
last_pos = end_pos(e_before.purple_ends[-1],
e_before,
offset=0.25)
c.purple_thorn_end_positions = [
first_pos
] + c.purple_thorn_end_positions + [last_pos]
arcs = []
for i in range(len(c.purple_thorn_end_positions) - 1):
arcs.append(
make_arc(c.purple_thorn_end_positions[i],
c.purple_thorn_end_positions[i + 1]))
for p in arcs:
p = p.transformed(scl)
canv.stroke(p, [
style.linewidth(leaf_thickness),
style.linecap.round, purple
])
if foliation_style_cusp_leaves:
for thorn_end in c.purple_thorn_ends:
thorn_end_pos = end_pos2(thorn_end)
p = make_arc(c.circle_pos, thorn_end_pos)
p = p.transformed(scl)
canv.stroke(p, [
style.linewidth(leaf_thickness),
style.linecap.round, purple
])
for i, c in enumerate(con.coast):
c.green_thorn_end_positions = [] ### complex numbers
c.green_thorn_ends = [
] ### [coastal arc, position along that arc]
e = con.coastal_edges[i]
e1 = e.green_ends[0]
while True:
index = e1.green_ends.index(e)
if index == len(e1.green_ends) - 1:
break
else:
c.green_thorn_end_positions.append(
end_pos(e, e1, offset=0.5))
c.green_thorn_ends.append((e1, e1.ends.index(e)))
e, e1 = e1, e1.green_ends[index + 1]
if foliation_style_boundary_leaves:
e_before = con.coastal_edges[(i - 1) % len(con.coast)]
e_after = con.coastal_edges[i]
first_pos = end_pos(e_after.green_ends[0],
e_after,
offset=-0.25)
last_pos = end_pos(e_before.green_ends[-1],
e_before,
offset=0.25)
c.green_thorn_end_positions = [
first_pos
] + green_thorn_end_positions + [last_pos]
arcs = []
for i in range(len(c.green_thorn_end_positions) - 1):
arcs.append(
make_arc(c.green_thorn_end_positions[i],
c.green_thorn_end_positions[i + 1]))
for p in arcs:
p = p.transformed(scl)
canv.stroke(p, [
style.linewidth(leaf_thickness),
style.linecap.round, green
])
if foliation_style_cusp_leaves:
for thorn_end in c.green_thorn_ends:
thorn_end_pos = end_pos2(thorn_end)
p = make_arc(c.circle_pos, thorn_end_pos)
p = p.transformed(scl)
canv.stroke(p, [
style.linewidth(leaf_thickness),
style.linecap.round, green
])
for tet in draw_tetrahedron_rectangles:
purple_sides = tet_purple_rectangle_sides(
tet, actually_do_green=False)
green_sides = tet_purple_rectangle_sides(
tet, actually_do_green=True)
for side in purple_sides:
for cusp_leaf in side:
if cusp_leaf != None:
v, thorn_end = cusp_leaf
thorn_end_pos = end_pos2(thorn_end)
p = make_arc(v.circle_pos, thorn_end_pos)
p = p.transformed(scl)
canv.stroke(p, [
style.linewidth(2 * leaf_thickness),
style.linecap.round, purple
])
for side in green_sides:
for cusp_leaf in side:
if cusp_leaf != None:
v, thorn_end = cusp_leaf
thorn_end_pos = end_pos2(thorn_end)
p = make_arc(v.circle_pos, thorn_end_pos)
p = p.transformed(scl)
canv.stroke(p, [
style.linewidth(2 * leaf_thickness),
style.linecap.round, green
])
output_filename = 'Images/CircleContinent/' + name + '.pdf'
canv.writePDFfile(output_filename)
def cross(x, y, r=0.1):
c.stroke(path.line(x-r, y, x+r, y))
c.stroke(path.line(x, y-r, x, y+r))
cross(0.1, 0.1)
cross(w-0.1, 0.1)
cross(0.1, h-0.1)
cross(w-0.1, h-0.1)
y = h-2*dr
c.text(w/2, y,
r"{\huge Error Correction in a Fibonacci Fusion Code}",
[text.halign.boxcenter, text.valign.top, text.size(1)])
y -= 2*dr
c.text(w/2, y,
r"{\large Simon Burton$^1$, Courtney G. Brell$^{1,2}$, Steven T. Flammia$^1$}",
[text.halign.boxcenter, text.valign.top])
#c.text(w/2, y,
# r"{\large Simon Burton{\tiny(1)}, Courtney G. Brell\(1,2\), Steven T. Flammia{(1)}}",
# [text.halign.boxcenter, text.valign.top])
y -= dr
c.text(w/2, y,
r"{\it $^1$ Centre for Engineered Quantum Systems, School of Physics, The University of Sydney, Sydney, Australia}",
[text.halign.boxcenter, text.valign.top])
def plot_overall(data0, data1, width, height):
vo = 1.15
ho = 1.15
plot_width = width - ho
plot_height = height - vo
c = canvas.canvas()
cis_binsizes = numpy.unique(data0['binsize'][numpy.where(data0['interaction'] == 'cis')])
trans_binsizes = numpy.unique(data0['binsize'][numpy.where(data0['interaction'] == 'trans')])
ymin = numpy.inf
ymax = -numpy.inf
Y = numpy.zeros((cis_binsizes.shape[0] + trans_binsizes.shape[0]), dtype=numpy.float32)
for j, binsize in enumerate(cis_binsizes):
where = numpy.where((data0['binsize'] == binsize) *
(data0['interaction'] == 'cis') *
(data0['range'] < 0))
where1 = numpy.where((data1['binsize'] == binsize) *
(data1['interaction'] == 'cis') *
(data1['range'] < 0))
if where[0].shape[0] > 0:
Y[j] = (data1['correlation'][where1] - data0['correlation'][where])
ymin = min(ymin, Y[j])
ymax = max(ymax, Y[j])
for j, binsize in enumerate(trans_binsizes):
where = numpy.where((data0['binsize'] == binsize) *
(data0['interaction'] == 'trans') *
(data0['range'] < 0))
where1 = numpy.where((data1['binsize'] == binsize) *
(data1['interaction'] == 'trans') *
(data1['range'] < 0))
if where[0].shape[0] > 0:
Y[j + cis_binsizes.shape[0]] = (data1['correlation'][where1] - data0['correlation'][where])
ymin = min(ymin, Y[j + cis_binsizes.shape[0]])
ymax = max(ymax, Y[j + cis_binsizes.shape[0]])
yspan = ymax - ymin
ymin -= yspan * 0.05
ymax += yspan * 0.05
Y = numpy.array(Y)
g = graph.graphxy(width=plot_width, height=plot_height,
x=graph.axis.bar(painter=graph.axis.painter.bar(nameattrs=None)),
y=graph.axis.lin(painter=painter, min=ymin, max=ymax),
x2=graph.axis.lin(parter=None, min=0, max=1),
y2=graph.axis.lin(parter=None, min=0, max=1))
y0 = plot_height * (-ymin) / (ymax - ymin)
g.stroke(path.line(0, plot_height * (-ymin) / (ymax - ymin), plot_width, plot_height * (-ymin) / (ymax - ymin)),
[style.linestyle.dotted, style.linewidth.THin])
w0 = plot_width / Y.shape[0]
w1 = w0 / 1.5
for j in range(Y.shape[0]):
x = j * w0 + 0.25 * w1
y = plot_height * (Y[j] - ymin) / (ymax - ymin)
g.fill( path.rect(x, y0, w1, y - y0) )
c.insert(g, [trafo.translate(ho, vo)])
for i, label in enumerate(["10Kb", "50Kb", "250Kb", "1Mb", "250Kb", "1Mb"]):
c.text(ho + plot_width * (i + 0.5) / 6.0, vo - 0.05, "%s" % label,
[text.halign.right, text.valign.middle, text.size(-3), trafo.rotate(90)])
c.text(ho + plot_width * 2.0 / 6.0, 0, "cis",
[text.halign.center, text.valign.bottom, text.size(-3)])
c.stroke(path.line(ho + 0.2, 0.3, ho - 0.2 + plot_width * 4.0 / 6.0, 0.3), [style.linewidth.THin])
c.text(ho + plot_width * 5.0 / 6.0, 0, "trans",
[text.halign.center, text.valign.bottom, text.size(-3)])
c.stroke(path.line(ho + 0.2 + plot_width * 4.0 / 6.0, 0.3, ho - 0.2 + plot_width, 0.3), [style.linewidth.THin])
c.text(0, plot_height * 0.5 + vo, r"$r_{Poisson} - r_{binomial}$",
[text.halign.center, text.valign.top, text.size(-2), trafo.rotate(90)])
c.text(0, height, 'b', [text.halign.left, text.valign.top, text.size(-1)])
return c
