def _one_scatterplot(cl_metadata, xy_data, xy_metadata,
output=None,
lims=None, specs=None,
_spd=sp.ScatterplotData,
_anno=co.namedtuple('_annotation',
CellLineMetadata._fields +
('x', 'y')),
_annopxl=co.namedtuple('_annotated_pixel',
'coords annotation')):
if specs is None:
specs = get_specs(cl_metadata)
readouts = zip(*xy_data)
if lims is None and WITHLIMITS:
lims = limits(readouts)
points = tuple(_spd(*(k + (x, y))) for k, x, y in zip(specs, *xy_data))
fig = sp.scatterplot(points, xy_metadata, lims=lims, outpath=output)
pixels = sp.pixels(points, fig)
annotations = tuple(_anno(*(m + r))
for m, r in zip(cl_metadata, readouts))
return tuple(sorted([_annopxl(p, a) for p, a in zip(pixels, annotations)],
key=lambda r: (r.coords.y, r.coords.x,
r.annotation.cell_line_name)))
def _one_scatterplot(
cl_metadata,
xy_data,
xy_metadata,
output=None,
lims=None,
specs=None,
_spd=sp.ScatterplotData,
_anno=co.namedtuple('_annotation', CellLineMetadata._fields + ('x', 'y')),
_annopxl=co.namedtuple('_annotated_pixel', 'coords annotation')):
if specs is None:
specs = get_specs(cl_metadata)
readouts = zip(*xy_data)
if lims is None and WITHLIMITS:
lims = limits(readouts)
points = tuple(_spd(*(k + (x, y))) for k, x, y in zip(specs, *xy_data))
fig = sp.scatterplot(points, xy_metadata, lims=lims, outpath=output)
pixels = sp.pixels(points, fig)
annotations = tuple(_anno(*(m + r)) for m, r in zip(cl_metadata, readouts))
return tuple(
sorted([_annopxl(p, a) for p, a in zip(pixels, annotations)],
key=lambda r:
(r.coords.y, r.coords.x, r.annotation.cell_line_name)))
def _1j():
rows = readinput(INPUTPATH)
headers = rows[0]
f = mplf.Figure(figsize=(FIGSIZEPX/DPI, FIGSIZEPX/DPI), dpi=DPI)
ax = f.gca()
datarec = co.namedtuple('_datarec',
'cell_line_name cell_line_classification '
'cluster x y')
data = tuple(datarec(*(r[:-2] + map(lambda z: 10**float(z), r[-2:])))
for r in rows[1:])
blue = '#1228B4'
blue = '#4A8CF5'
yellow = '#F9D712'
green = '#5E9563'
black = '#000000'
gray0 = '#666666'
gray0 = '#999999'
gray1 = '#B3B3B3'
gray2 = '#CBCBCB'
red = 'r'
color_lookup = {'1': blue, '2': green, '3': yellow,
'4': gray0, '5': black, '6': black, '7': black}
circle = 'o'
triangle = '^'
square = 's'
marker_lookup = {'TNBC': circle, 'HER2amp': triangle, 'HR+': square}
fixedkwargs = dict(s=200, linewidth=0.5)
for row in sorted(data, key=lambda r: r.cluster):
color = color_lookup[row.cluster]
marker = marker_lookup[row.cell_line_classification]
# see http://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes.scatter
ax.scatter(row.x, row.y, c=color, marker=marker, **fixedkwargs)
ax.set_xscale('log')
ax.set_yscale('log')
xlim = tuple(10**v for v in (-5, -1))
ylim = tuple(10**v for v in (-4.5, 0))
ax.set_xlim(xlim)
ax.set_ylim(ylim)
# this aspect will result in a square plot
ax.set_aspect((ma.log10(xlim[1]/xlim[0])/ma.log10(ylim[1]/ylim[0])))
xlabel = 'Erb3 (pg/cell)'
ylabel = 'EGFR (pg/cell)'
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
fmtr = lambda v, _: ('%g' % v) if v < 1e-4 else ('%.4f' % v).rstrip('.0')
xaxis = ax.xaxis
xaxis.set_major_locator(mplpp.FixedLocator([10**i for i in range(-5, 0)]))
xaxis.set_major_formatter(mplpp.FuncFormatter(fmtr))
xaxis.set_ticks((), minor=True)
yaxis = ax.yaxis
yaxis.set_major_locator(mplpp.FixedLocator([10**i for i in range(-4, 1)]))
yaxis.set_major_formatter(mplpp.FuncFormatter(fmtr))
yaxis.set_ticks((), minor=True)
f.subplots_adjust(left=0.22, bottom=0.2, right=0.97, top=0.95,
wspace=0, hspace=0)
mplpp.setp(f, 'facecolor', 'none')
canvas = mplbb.FigureCanvasAgg(f)
f.set_canvas(canvas)
canvas.print_png(OUTPUTPATH)
htmlrows = [dict(coords=pixel._asdict(), row=make_row(data))
for pixel, data in zip(sp.pixels(data, f), data)]
print ' %s: %s,' % (json.dumps(IMGID), json.dumps(htmlrows))
return f
def _1j():
rows = readinput(INPUTPATH)
headers = rows[0]
f = mplf.Figure(figsize=(FIGSIZEPX / DPI, FIGSIZEPX / DPI), dpi=DPI)
ax = f.gca()
datarec = co.namedtuple(
'_datarec', 'cell_line_name cell_line_classification '
'cluster x y')
data = tuple(
datarec(*(r[:-2] + map(lambda z: 10**float(z), r[-2:])))
for r in rows[1:])
blue = '#1228B4'
blue = '#4A8CF5'
yellow = '#F9D712'
green = '#5E9563'
black = '#000000'
gray0 = '#666666'
gray0 = '#999999'
gray1 = '#B3B3B3'
gray2 = '#CBCBCB'
red = 'r'
color_lookup = {
'1': blue,
'2': green,
'3': yellow,
'4': gray0,
'5': black,
'6': black,
'7': black
}
circle = 'o'
triangle = '^'
square = 's'
marker_lookup = {'TNBC': circle, 'HER2amp': triangle, 'HR+': square}
fixedkwargs = dict(s=200, linewidth=0.5)
for row in sorted(data, key=lambda r: r.cluster):
color = color_lookup[row.cluster]
marker = marker_lookup[row.cell_line_classification]
# see http://matplotlib.org/api/axes_api.html#matplotlib.axes.Axes.scatter
ax.scatter(row.x, row.y, c=color, marker=marker, **fixedkwargs)
ax.set_xscale('log')
ax.set_yscale('log')
xlim = tuple(10**v for v in (-5, -1))
ylim = tuple(10**v for v in (-4.5, 0))
ax.set_xlim(xlim)
ax.set_ylim(ylim)
# this aspect will result in a square plot
ax.set_aspect((ma.log10(xlim[1] / xlim[0]) / ma.log10(ylim[1] / ylim[0])))
xlabel = 'Erb3 (pg/cell)'
ylabel = 'EGFR (pg/cell)'
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
fmtr = lambda v, _: ('%g' % v) if v < 1e-4 else ('%.4f' % v).rstrip('.0')
xaxis = ax.xaxis
xaxis.set_major_locator(mplpp.FixedLocator([10**i for i in range(-5, 0)]))
xaxis.set_major_formatter(mplpp.FuncFormatter(fmtr))
xaxis.set_ticks((), minor=True)
yaxis = ax.yaxis
yaxis.set_major_locator(mplpp.FixedLocator([10**i for i in range(-4, 1)]))
yaxis.set_major_formatter(mplpp.FuncFormatter(fmtr))
yaxis.set_ticks((), minor=True)
f.subplots_adjust(left=0.22,
bottom=0.2,
right=0.97,
top=0.95,
wspace=0,
hspace=0)
mplpp.setp(f, 'facecolor', 'none')
canvas = mplbb.FigureCanvasAgg(f)
f.set_canvas(canvas)
canvas.print_png(OUTPUTPATH)
htmlrows = [
dict(coords=pixel._asdict(), row=make_row(data))
for pixel, data in zip(sp.pixels(data, f), data)
]
print ' %s: %s,' % (json.dumps(IMGID), json.dumps(htmlrows))
return f
def _4e():
rows = readinput(INPUTPATH)
headers = rows[0]
f = mplf.Figure(figsize=(FIGSIZEPX/DPI, FIGSIZEPX/DPI), dpi=DPI)
ax = f.gca()
datarec = co.namedtuple('_datarec',
'cell_line_name cell_line_classification '
'erlotinib_gi50 x y')
data = tuple(datarec(*(r[:-3] + [float(r[-3])] +
map(lambda z: 10**float(z), r[-2:])))
for r in rows[1:])
circle = 'o'
triangle = '^'
square = 's'
marker_lookup = {'TNBC': circle, 'HER2amp': triangle, 'HR+': square}
pointspec = co.namedtuple('_pointspec', 'x y level marker')
points = tuple(pointspec(rec.x, rec.y, rec.erlotinib_gi50,
marker_lookup[rec.cell_line_classification])
for rec in data)
# eyeballed from PDF
min_gi50 = -5.9 # min log_10 gi50
max_gi50 = -3.7 # max log_10 gi50
fixedkwargs = dict(s=200, linewidth=0.5, vmin=min_gi50, vmax=max_gi50,
cmap=CMAP_BWR)
for marker in (circle, triangle, square):
xs, ys, cs = zip(*((p.x, p.y, p.level)
for p in points if p.marker == marker))
ax.scatter(xs, ys, c=cs, marker=marker, **fixedkwargs)
ax.set_xscale('log')
ax.set_yscale('log')
# eyeballed from the PDF
xlim = (0.8, 25)
ylim = (0.9, 1.9)
ax.set_xlim(xlim)
ax.set_ylim(ylim)
# this aspect will result in a square plot
# ax.set_aspect((xlim[1] - xlim[0])/(ylim[1] - ylim[0]))
ax.set_aspect((ma.log10(xlim[1]/xlim[0])/ma.log10(ylim[1]/ylim[0])))
ax.set_title('Erlotinib')
xlabel = 'pErk[FGF-2$_1$] max (fold change)'
ylabel = 'pAkt[EPR$_1$] max (fold change)'
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel)
xaxis = ax.xaxis
xaxis.set_major_formatter(mplpp.FormatStrFormatter('%d'))
xaxis.set_major_locator(mplpp.FixedLocator([1, 2, 5, 10, 20]))
xaxis.set_ticks((), minor=True)
fmtr = lambda v, _: ('%.1f' % v).rstrip('.0')
yaxis = ax.yaxis
yaxis.set_major_formatter(mplpp.FuncFormatter(fmtr))
yaxis.set_major_locator(mplpp.FixedLocator([1, 1.8]))
yaxis.set_ticks((), minor=True)
f.subplots_adjust(left=0.2, bottom=0.15, right=0.95, top=0.9, wspace=0, hspace=0)
mplpp.setp(f, 'facecolor', 'none')
canvas = mplbb.FigureCanvasAgg(f)
f.set_canvas(canvas)
canvas.print_png(OUTPUTPATH)
htmlrows = [dict(coords=p._asdict(), row=make_row(d))
for p, d in zip(sp.pixels(data, f), data)]
print ' %s: %s,' % (json.dumps(IMGID), json.dumps(htmlrows))
return f
