def residgraph(w,h0,h1,ax,ay0,ay1,k=None):
"""Canvas with linked-axis graphs for residuals plot"""
c = canvas.canvas()
g0 = c.insert(graph.graphxy(width=w, height=h0, x=ax, y=ay0))
g1 = c.insert(graph.graphxy(width=w, height=h1, ypos=g0.height+0.1, key=k,
x=graph.axis.linkedaxis(g0.axes["x"]), y=ay1))
return c,g0,g1
def example4():
g = graph.graphxy(
width=8,
x=axis.lin(title=r'$x_1$'),
y=axis.lin(min=-1,max=1.5,title=r'$y_1$'),
)
g.plot(
graph.data.function("y(x)=2*exp(-30*x)-exp(-3*x)",
min=0, max=1),
)
insetparams = dict(
#height = width*float(image.shape[0])/image.shape[1]
labeldist=0.2,
labelattrs=[text.size.footnotesize],
)
p=axis.painter.regular(**insetparams)
gi = graph.graphxy(
width=3,
x=axis.lin(min=0, max=2, painter=p),
y=axis.lin(painter=p),
)
gi.plot(graph.data.function("y(x)=cos(20*x)*exp(-2*x)",
min=0,max=2))
x, y = g.vpos(0.9, 0.9)
g.insert(gi, [trafo.translate(x-gi.bbox().right(), y-gi.bbox().top())])
g.writePDFfile("example4.pdf")
def example3():
"""
0,0 is bottom left in pyx land, but it is more common for
"first" to be upper left
"""
c = canvas.canvas()
g1 = graph.graphxy(
width=8,
x=axis.lin(title=r'$x_1$'),
y=axis.lin(title=r'$y_1$'),
)
g1.plot(
[graph.data.function("y(x)=0", min=0, max=1),
graph.data.function("y(x)=2*exp(-30*x)-exp(-3*x)", min=0, max=1)],
)
c.insert(g1)
g2 = graph.graphxy(
width=8,
ypos=g1.height+1.5,
x=axis.lin(title=r'$x_2$'),
y=axis.lin(title=r'$y_2$'),
)
c.insert(g2)
g2.plot(graph.data.function("y(x)=cos(20*x)*exp(-2*x)",min=0,max=2))
g3 = graph.graphxy(
width=8,
ypos=g1.height+1.5,
xpos=g1.width+2.0,
x=axis.lin(title=r'$x_3$'),
y=axis.lin(title=r'$y_3$'),
)
g3.plot(graph.data.function("y(x)=cos(20*x)*exp(-2*x)",min=0,max=2))
c.insert(g3)
g4 = graph.graphxy(
width=8,
#ypos=g1.height,
xpos=g1.width+2.0,
x=axis.lin(title=r'$x_4$'),
y=axis.lin(title=r'$y_4$'),
)
g4.plot(graph.data.function("y(x)=cos(20*x)*exp(-2*x)",min=0,max=2))
c.insert(g4)
c.writePDFfile("example3.pdf")
c.writeGSfile("example3.png")
def plot_scans(fname):
scans = load_scans(fname)
cols = rainbow(len(scans))
gPhi = graph.graphxy(width=10,height=6,
x=graph.axis.lin(title="position [cm]", min = -0.5, max = 3),
y=graph.axis.lin(title="potential [kV]", max = 0.04))
gEt = graph.graphxy(width=12,height=8,
x=graph.axis.lin(title="position [cm]", min = -8, max = 8),
y=graph.axis.lin(title="transverse field [V/cm]", min = -4, max = 0.5),
key=graph.key.key(pos="bl"))
gEa = graph.graphxy(width=10,height=6,
x=graph.axis.lin(title="position [cm]", min = -1, max = 1),
y=graph.axis.lin(title="axial field [V/cm]"))
V = 4.1
BC1 = BesselCalcs(5.5)
BC1.add_endcircle(4.14, V)
BC2 = BesselCalcs(10.)
BC2.add_endcircle(10, 3*V)
BC2.add_endcircle(6.5, -3*V)
BC2.add_endcircle(3.928, V)
BC2 = TwoEndedBessel(10, BC2)
def bccombo(r,z):
if z < 0:
return BC1.Er(r,-z)
return BC2.Er(r,z)
for (n,sc) in enumerate(scans):
print sc.r(), sc.npts
gdat = [(p.x[2], p.phi, p.Er, p.E[0], p.E[1], p.E[2]) for p in sc.pts]
gtitle = "$r = %.1f$ cm"%sc.r()
plotl = [graph.style.line([cols[n], style.linewidth.thin])]
plotl2 = [graph.style.line([cols[n], style.linestyle.dashed, style.linewidth.THin])]
gPhi.plot(graph.data.points(gdat,x=1,y=2), plotl)
gEt.plot(graph.data.points(gdat,x=1,y=3,title=gtitle), plotl)
#gEt.plot(graph.data.points(gdat,x=1,y=5), plotl2)
gEa.plot(graph.data.points(gdat,x=1,y=6), plotl)
gdat_endsim = [(p.x[2], bccombo(p.r(),p.x[2])) for p in sc.pts[::5]]
gEt.plot(graph.data.points(gdat_endsim,x=1,y=2,title=None), plotl2)
gPhi.writetofile("FieldScans.pdf")
gEt.writetofile("FieldScans_Et.pdf")
gEa.writetofile("FieldScans_Ea.pdf")
def example5():
data = numpy.random.normal(loc=0.2, scale=1.2, size=1000)
x = numpy.linspace(-3.5, 3.5, 30)
y = numpy.exp( - 0.5*(x-0.1)**2 )
key=graph.key.key(pos='tl')
g = graph.graphxy(
width=8,
x=axis.lin(title=r'$score$'),
y=axis.lin(min=0, max=1.1),
key=key,
)
d = graph.data.values(
x=x,
y=y,
title='data',
)
lineattrs=[colors('blue')]
styles=[
graph.style.histogram(lineattrs=lineattrs,steps=1),
]
g.plot(d, styles=styles)
g.writePDFfile("example5.pdf")
g.writeGSfile("example5.png")
def plot_gluck1993_a_cxn():
npts = 100
gdat = []
for i in range(npts+1)[1:]:
E = m + i*(D-m)/npts
a0p = a_ideal(E,0)
a0m = a_ideal(E,pi)
r_e = 0.01*gluck_1993_r_e(E)
ap = a0p*(1 + r_e + 0.01*gluck_1993_r_enu(E,0))
am = a0m*(1 + r_e + 0.01*gluck_1993_r_enu(E,pi))
print E, a0p, a0m
print "\t", ap, am
print "\t", gluck_1993_r_enu(E,0), gluck_1993_r_enu(E,pi)
a0 = (a0p-a0m)/(a0p+a0m)
a = (ap-am)/(ap+am)
gdat.append([E-m,(a-a0)/a0*100, 100*alpha*hmg(E)/(2*pi)])
g = graph.graphxy(width=10,height=6,
x=graph.axis.lin(title="kinetic energy [keV]",min=0,max=D-m),
y=graph.axis.lin(title="$(a_{\\rm meas}-a_0)/a_0$ [\\%]"),
key=graph.key.key(pos="bl"))
g.plot(graph.data.points(gdat,x=1,y=2,title='Gl\\"uck'),[graph.style.line([style.linewidth.THick])])
g.plot(graph.data.points(gdat,x=1,y=3,title="Garc\\'ia-Maya"),[graph.style.line([style.linewidth.Thick, style.linestyle.dotted])])
g.writetofile("Gluck_1993_a_cxn.pdf")
def transverse_field_study():
GT = load_Gordon_tables()
Brange = 0.2
gMagF = graph.graphxy(
width=10,
height=6,
x=graph.axis.lin(title="position [m]", min=0, max=3),
y=graph.axis.lin(title="$B_r$ [Gauss]", max=Brange, min=-Brange),
key=graph.key.key(pos="tl", columns=2),
)
nr = 7
cls = rainbow(nr)
for r in range(nr):
ttl = "r = %i cm" % r
gMagF.plot(
graph.data.points(GT[0].get_radius_dat(r, 1), x=1, y=2, title=ttl),
[graph.style.line([style.linewidth.Thick, cls[r]])],
)
M = MPM_Table("Sym_Fat_2")
Bt = transverse_from_axial(M.get_zdat(), 0.01 * (nr - 1), 1) # GT[0].get_radius_dat(0), 0.01*(nr-1))
gMagF.plot(
graph.data.points(Bt, x=1, y=2, title="MPM r=%i" % (nr - 1)), [graph.style.line([style.linewidth.Thick])]
)
gMagF.writetofile("MagF_transverse.pdf")
def errorBand(g, gdat, nx, ny, ndy, xerr=False):
"""error band path"""
g0 = graph.graphxy(width=g.width,
height=g.height,
x=graph.axis.linkedaxis(g.axes["x"]),
y=graph.axis.linkedaxis(g.axes["y"]))
d1, d2 = (None, None)
if xerr:
d1 = g0.plot(
graph.data.points([(g[nx] - g[ndy], g[ny]) for g in gdat],
x=1,
y=2), [graph.style.line()])
d2 = g0.plot(
graph.data.points([(g[nx] + g[ndy], g[ny]) for g in gdat],
x=1,
y=2), [graph.style.line()])
else:
d1 = g0.plot(
graph.data.points([(g[nx], g[ny] - g[ndy]) for g in gdat],
x=1,
y=2), [graph.style.line()])
d2 = g0.plot(
graph.data.points([(g[nx], g[ny] + g[ndy]) for g in gdat],
x=1,
y=2), [graph.style.line()])
g0.finish()
p1 = d1.path
p2 = d2.path
area = (p1 << p2.reversed())
return area
def plot2d(data,
mytitle,
result_dir=None,
xaxistitle="X",
yaxistitle="Y",
mycolor=(0, 0, 0),
minpoint=None,
maxpoint=None):
'''
data: list of tuples of (x,y)
mytitle: determines the name of the of PDF file.
Graphs min/max is automatically chosen as to cover the range with 0,1 being covered.
To override, provide the defaults minpoint (x,y) and maxpoint(x,y)
'''
try:
fnameprefix = '-'.join(mytitle.split()) + '-2d'
if result_dir: fnameprefix = os.path.join(result_dir, fnameprefix)
datafile = fnameprefix + '.dat'
with open(datafile, 'w') as f:
f.write('\n'.join(['%s\t%s' % (x, y) for x, y in data]))
f.close()
print 'Data File written: %s' % datafile
except:
print '%s: Failed to save data file %s' % (mytitle, datafile)
traceback.print_exc()
try:
if minpoint:
xmin, ymin = minpoint
else:
xmin = min(0, min([x for x, y in data]))
ymin = min(0, min([y for x, y in data]))
if maxpoint:
xmax, ymax = maxpoint
else:
xmax = max([x for x, y in data])
ymax = max([y for x, y in data])
print 'xmin=%s, xmax=%s' % (xmin, xmax)
print 'ymin=%s, ymax=%s' % (ymin, ymax)
g = graph.graphxy(width=8,
x=graph.axis.linear(min=xmin,
max=xmax,
title=xaxistitle),
y=graph.axis.linear(min=ymin,
max=ymax,
title=yaxistitle))
g.plot(graph.data.points(data, x=1, y=2),
styles=[graph.style.line([style.linestyle.solid])])
g.text(g.width / 2, g.height + 0.2, mytitle,
[text.halign.center, text.valign.bottom, text.size.large])
g.writePDFfile(fnameprefix)
print 'Graph PDF File written: %s.pdf' % fnameprefix
except:
print '%s: Failed to create Graph' % mytitle
traceback.print_exc()
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 make_sourcecal(sdata):
g = graph.graphxy(width=10,height=6,
x=graph.axis.lin(title="PMT sum [ADC channels]", min=0),
y=graph.axis.lin(title="Expected energy [keV]", min=0),
key = graph.key.key(pos="tl"))
gdat = [(pks["ADC_sum"].center, pks["ADC_sum"].dcenter, pks["MC"].center) for pks in sdata.itervalues()]
g.plot(graph.data.points(gdat, x=1, dx=2, y=3, title=None),[graph.style.symbol(symbol.circle)])
LF = LinearFitter(terms=[polyterm(i) for i in range(2)])
LF.fit(gdat,cols=(0,2))
g.plot(graph.data.points(LF.fitcurve(0,4e4), x=1, y=2, title="$%s$"%LF.toLatex(cfmt=".5g")),[graph.style.line(lineattrs=[style.linewidth.Thick])])
g.writetofile("ADCSum_Cal.pdf")
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(data, xmin, xmax, ymin, ymax):
g = graph.graphxy(height=8,
width=8,
x=graph.axis.linear(min=xmin,
max=xmax,
title=r"$\mathrm{Re}(c)$"),
y=graph.axis.linear(min=ymin,
max=ymax,
title=r'$\mathrm{Im}(c)$'))
g.plot(graph.data.points(data, x=1, y=2, color=3), [
graph.style.density(gradient=color.gradient.ReverseJet, keygraph=None)
])
g.writePDFfile()
g.writeGSfile(device="png16m", resolution=600)
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_correlated(SP1, SP2):
gdat = combo_series_dat((SP1,SP2))
g = graph.graphxy(width=12,height=12, x=SP1.yaxis, y=SP2.yaxis, key=graph.key.key(pos="tl"))
g.plot(graph.data.points(gdat,x=2,dx=3,y=4,dy=5,title=None),[graph.style.errorbar(), graph.style.symbol(symbol.circle,size=0.1,symbolattrs=[deco.filled([color.rgb(1,1,1)])])])
LF = LinearFitter(terms=[polyterm(i) for i in range(2)])
LF.fit(gdat, cols=(1,3,4), errorbarWeights = True)
chi2 = LF.chisquared()
nu = LF.nu()
ttl = "$"+LF.toLatex(cfmt=".3g")+"$; $\\chi^2/\\nu = %.1f/%i$"%(chi2,nu)
if stats:
ttl += " ($P = %.2f$)"%stats.chisqprob(chi2,nu)
g.plot(graph.data.points(LF.fittedpoints(True), x=1,y=3,title=ttl), [graph.style.line(lineattrs=[rgb.red])])
g.writetofile(SP1.basepath+SP2.name+"_VS_"+SP1.name+".pdf")
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 make_graph(self, curs):
self.xaxis = graph.axis.lin(title = "Series")
self.yaxis = graph.axis.lin(title = self.ytitle)
if not self.gdat:
self.make_gdat(curs)
g = graph.graphxy(width=15, height=6, x=self.xaxis, y=self.yaxis, key=graph.key.key(pos="tl"))
g.plot(graph.data.points(self.gdat,x=1,y=2,dy=3,title=None),
[graph.style.errorbar(errorbarattrs=[color.rgb.blue]),
graph.style.symbol(symbol.circle,size=0.1,symbolattrs=[color.rgb.blue, deco.filled([color.rgb(1,1,1)])])])
self.auxfits(g)
outf = self.basepath+self.name+".pdf"
print "Saving plot to",outf
g.writetofile(outf)
def plot_energy_survival():
B = 364 # magnetic field [Gauss]
l = 2.75 # electron collimator radius [cm]
Emax = 1000
g = graph.graphxy(width=10,height=6,
x=graph.axis.lin(title="energy [keV]", min=0, max=Emax),
y=graph.axis.log(title="collimator pass fraction", min=0.001, max=1),
key = graph.key.key(pos="tr"))
gdat = [(KE, sphere_survival_fraction(l, 0, electron_Larmor(KE,B)), distrib_vertex_survival(l,electron_Larmor(KE,B)) ) for KE in unifrange(0, Emax, 500)]
g.plot(graph.data.points(gdat,x=1,y=2,title="centered"),[graph.style.line([style.linewidth.Thick])])
g.plot(graph.data.points(gdat,x=1,y=3,title="volume averaged"),[graph.style.line([style.linewidth.Thick,style.linestyle.dashed])])
g.writetofile("Survival.pdf")
def plot2d(data, mytitle, result_dir=None, xaxistitle="X", yaxistitle="Y", mycolor=(0,0,0), minpoint=None, maxpoint=None):
'''
data: list of tuples of (x,y)
mytitle: determines the name of the of PDF file.
Graphs min/max is automatically chosen as to cover the range with 0,1 being covered.
To override, provide the defaults minpoint (x,y) and maxpoint(x,y)
'''
try:
fnameprefix = '-'.join(mytitle.split()) + '-2d'
if result_dir: fnameprefix = os.path.join(result_dir, fnameprefix)
datafile = fnameprefix + '.dat'
with open(datafile, 'w') as f:
f.write('\n'.join(['%s\t%s' % (x,y) for x,y in data]))
f.close()
print 'Data File written: %s' % datafile
except:
print '%s: Failed to save data file %s' % (mytitle, datafile)
traceback.print_exc()
try:
if minpoint:
xmin,ymin = minpoint
else:
xmin = min(0,min([x for x,y in data]))
ymin = min(0,min([y for x,y in data]))
if maxpoint:
xmax,ymax = maxpoint
else:
xmax = max([x for x,y in data])
ymax = max([y for x,y in data])
print 'xmin=%s, xmax=%s' % (xmin, xmax)
print 'ymin=%s, ymax=%s' % (ymin, ymax)
g = graph.graphxy(width=8,
x = graph.axis.linear(min=xmin, max=xmax,title=xaxistitle),
y = graph.axis.linear(min=ymin, max=ymax,title=yaxistitle))
g.plot(graph.data.points(data, x=1,y=2),
styles = [ graph.style.line([ style.linestyle.solid]) ] )
g.text(g.width/2, g.height + 0.2, mytitle, [text.halign.center, text.valign.bottom,text.size.large])
g.writePDFfile(fnameprefix)
print 'Graph PDF File written: %s.pdf' % fnameprefix
except:
print '%s: Failed to create Graph' % mytitle
traceback.print_exc()
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 render(logset, width=100, only_current=True, plot_current=True, low=-500, high=500, before='', since='', accounts=''):
all_accts = fsudb.Account.All()
if accounts:
aids = [int(i) for i in accounts.split(',')]
logset = [i for i in logset if i[2] in aids]
if since:
since = time.mktime(time.strptime(since, ISO_TIME))
logset = [i for i in logset if i[0] >= since]
if before:
before = time.mktime(time.strptime(before, ISO_TIME))
logset = [i for i in logset if i[0] <= before]
if plot_current:
for acct in all_accts:
values = [i[5] for i in logset if i[2] == acct.aid]
if not values:
continue
lastval = values[-1]
logset.append([time.time(), 'renderer', acct.aid, 'balance', lastval, acct.balance, 'TEMPORARY entry to plot correctly'])
series = []
accts = set([i[2] for i in logset if i[3] == 'balance'])
if only_current:
accts -= set([i[2] for i in logset if i[3] == 'aid' and i[5] is None])
for acct in sorted(list(accts)):
names = [i.name for i in all_accts if i.aid == acct]
if names:
name = names[-1]
else:
name = ''
points = []
for entry in logset:
if entry[2] != acct or entry[3] != 'balance':
continue
points.extend([(entry[0], clamp(entry[4], low, high)), (entry[0], clamp(entry[5], low, high))])
series.append(graph.data.points(points, x=1, y=2, title=('Account %d (%s)'%(acct, tex_escape(name)))))
g = graph.graphxy(width=width, x=graph.axis.lin(texter=graph_axis_time()), key=graph.key.key(pos='br', dist=0.1))
g.plot(series, [graph.style.line([style.linestyle.solid, color.gradient.Rainbow])])
if plot_current:
all_values = [i[4] for i in logset if i[3] == 'balance' and i[4] is not None] + [i[5] for i in logset if i[3] == 'balance' and i[5] is not None]
actual_min = max(min(all_values), low)
actual_max = min(max(all_values), high)
g.plot(graph.data.values(x=[time.time(), time.time()], y=[actual_min, actual_max], title=time.strftime(ISO_TIME)), [graph.style.line()])
output = StringIO()
g.writeSVGfile(output)
return output
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 errorBand(g,gdat,nx,ny,ndy,xerr=False):
"""error band path"""
g0=graph.graphxy(width=g.width,height=g.height,
x=graph.axis.linkedaxis(g.axes["x"]),
y=graph.axis.linkedaxis(g.axes["y"]))
d1,d2 = (None,None)
if xerr:
d1 = g0.plot(graph.data.points([(g[nx]-g[ndy],g[ny]) for g in gdat],x=1,y=2), [graph.style.line()])
d2 = g0.plot(graph.data.points([(g[nx]+g[ndy],g[ny]) for g in gdat],x=1,y=2), [graph.style.line()])
else:
d1 = g0.plot(graph.data.points([(g[nx],g[ny]-g[ndy]) for g in gdat],x=1,y=2), [graph.style.line()])
d2 = g0.plot(graph.data.points([(g[nx],g[ny]+g[ndy]) for g in gdat],x=1,y=2), [graph.style.line()])
g0.finish()
p1 = d1.path
p2 = d2.path
area = (p1 << p2.reversed())
return area
def plot_bilenkii_a_cxn():
npts = 100
gdat = []
for i in range(npts+1):
E = m + i*(D-m)/npts
a0p = a_ideal(E,0)
a0m = a_ideal(E,pi)
ap = bilenkii_1959_a_cxn(E,0)
am = bilenkii_1959_a_cxn(E,pi)
a0 = (a0p-a0m)/(a0p+a0m)
a = (ap-am)/(ap+am)
gdat.append([E-m,(a-a0)/a0*100,])
g = graph.graphxy(width=10,height=6,
x=graph.axis.lin(title="kinetic energy [keV]",min=0,max=D-m),
y=graph.axis.lin(title="$(a_{\\rm meas}-a_0)/a_0$ [\\%]"))
g.plot(graph.data.points(gdat,x=1,y=2),[graph.style.line([style.linewidth.THIck])])
g.writetofile("Bilenkii_1959_a_cxn.pdf")
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 _get_graph_and_axes(self, **kw):
if 'aspect_ratio' in kw:
kw['ratio'] = kw['aspect_ratio']
key = kw.pop('key',None)
gkw=_unpack_graphxy_keywords(kw)
gkw['width'] = gkw.get('width',8)
height=gkw.get('height',None)
if height is None:
aspect_ratio = gkw
xaxis,xlog,yaxis,ylog = _get_axes(kw)
g = graph.graphxy(
x=xaxis,
y=yaxis,
key=key,
#title='blah',
**gkw)
return g
def gluck_1993_cnxs():
xr_e = [ 0.1, 0.2, 0.3, 0.4, 0.5, 0.6, 0.7, 0.8, 0.9, 0.95 ]
r_e = [ 1.82, 1.74, 1.65, 1.53, 1.40, 1.25, 1.07, 0.84, 0.50, 0.21 ]
# Table V, [[c, [r_enu(x)...]], ... ]
r_enu = [ [0.9, [ 0.03, 0.04, 0.06, 0.08, 0.10, 0.12, 0.14, 0.16 ]],
[0.7, [ 0.03, 0.04, 0.05, 0.07, 0.08, 0.10, 0.11, 0.13 ]],
[0.5, [ 0.02, 0.03, 0.04, 0.05, 0.06, 0.07, 0.08, 0.09 ]],
[0.3, [ 0.02, 0.02, 0.03, 0.03, 0.04, 0.04, 0.05, 0.06 ]],
[0.1, [ 0.01, 0.01, 0.01, 0.02, 0.02, 0.02, 0.02, 0.03 ]],
[-0.1,[ 0.01, 0.00, 0.00, -0.00, -0.00, -0.00, -0.01, -0.00 ]],
[-0.3,[ -0.00, -0.01, -0.01, -0.02, -0.02, -0.03, -0.03, -0.03 ]],
[-0.5,[ -0.01, -0.02, -0.02, -0.05, -0.04, -0.05, -0.05, -0.06 ]],
[-0.7,[ -0.01, -0.02, -0.02, -0.05, -0.06, -0.07, -0.08, -0.08 ]],
[-0.9,[ -0.02, -0.03, -0.05, -0.06, -0.08, -0.09, -0.10, -0.11 ]] ]
# w = F(E)*[1+a*beta*c]*[1 + 0.01*r_e(x) + 0.01*r_enu(x,c)]
gx = graph.graphxy(width=10,height=6,
x=graph.axis.lin(title='$x$',min=0,max=1),
y=graph.axis.lin(title='Gl\\"uck $r_e(x) + r_{e\\nu}(x,c)$'))
gc = graph.graphxy(width=10,height=6,
x=graph.axis.lin(title='$c$',min=-1,max=1),
y=graph.axis.lin(title='Gl\\"uck $r_{e\\nu}(x,c)$'),
key=graph.key.key(pos="tl",columns=2))
ccols = rainbowDict([r[0] for r in r_enu])
xcols = rainbowDict(xr_e[1:-1])
cdat = [[x,[]] for x in xr_e[1:-1]]
for (c, r) in r_enu:
for (n,rr) in enumerate(r):
cdat[n][1].append([c,rr])
gdat = [(xr_e[n+1], r_e[n+1]+rr) for (n,rr) in enumerate(r)]
gx.plot(graph.data.points(gdat,x=1,y=2),[graph.style.symbol(symbol.circle,size=0.1,symbolattrs=[ccols[c]]), graph.style.line(lineattrs=[ccols[c]])])
gx.writetofile("Gluck_1993_a_cxn_x.pdf")
paramdat = []
for (x,gdat) in cdat:
LF = LinearFitter(terms=[(lambda z: 1), (lambda z: z)])
LF.fit(gdat)
paramdat.append([x,LF.coeffs[0],LF.coeffs[1]])
gc.plot(graph.data.points(gdat,x=1,y=2,title="$x = %g$"%x),[graph.style.symbol(symbol.circle,size=0.1,symbolattrs=[xcols[x]])])
gc.plot(graph.data.points(LF.fitcurve(-1,1),x=1,y=2,title=None),[graph.style.line(lineattrs=[xcols[x]])])
gc.writetofile("Gluck_1993_a_cxn_c.pdf")
gcparam = graph.graphxy(width=10,height=6,
x=graph.axis.lin(title='$x$',min=0,max=1),
y=graph.axis.lin(title='$r_{e\\nu}$ parametrization term',min=0, max=0.2),
key=graph.key.key(pos="tl"))
LF.fit(paramdat,cols=(0,1))
gcparam.plot(graph.data.points(paramdat,x=1,y=2,title="$r_{e\\nu}^0(x) \\approx %.3f+%.3f \\cdot x$"%tuple(LF.coeffs)),[graph.style.symbol(symbol.circle,size=0.1,symbolattrs=[rgb.red])])
gcparam.plot(graph.data.points(LF.fitcurve(0,1),x=1,y=2,title=None),[graph.style.line(lineattrs=[rgb.red])])
LF.fit(paramdat,cols=(0,2))
gcparam.plot(graph.data.points(paramdat,x=1,y=3,title="$r_{e\\nu}^1(x) \\approx %.3f+%.3f \\cdot x$"%tuple(LF.coeffs)),[graph.style.symbol(symbol.circle,size=0.1,symbolattrs=[rgb.blue])])
gcparam.plot(graph.data.points(LF.fitcurve(0,1),x=1,y=2,title=None),[graph.style.line(lineattrs=[rgb.blue])])
gcparam.writetofile("Gluck_1993_a_cxn_cparam.pdf")
with open(cpu + '.dat') as fh:
for line in fh:
nr, t4, t1 = line.rstrip('\n').split()
nr = int(nr)
t1 = float(t1)
t4 = float(t4)
data[cpu].append((t1, t4))
data[cpu] = [(2**nr, data[cpu][0][0] / t4, t1 / t4)
for nr, (t1, t4) in enumerate(data[cpu])]
logparter = graph.axis.parter.log(tickpreexps=[
graph.axis.parter.preexp([graph.axis.tick.rational(1, 1)], 2)
])
g = graph.graphxy(width=8,
x=graph.axis.log(min=1,
max=128,
parter=logparter,
title='number of divisions per axis'),
y=graph.axis.lin(min=0, max=6.5, title='acceleration'))
for nr, cpu in enumerate(cpus):
g.plot(graph.data.points(data[cpu], x=1, y=2), [
graph.style.line(lineattrs=[style.linestyle.dotted]),
graph.style.symbol(symbol=graph.style.symbol.circle,
size=0.1,
symbolattrs=[deco.filled([color.grey(nr)])])
])
g.plot(graph.data.points(data[cpu], x=1, y=3), [
graph.style.line(lineattrs=[style.linestyle.solid]),
graph.style.symbol(symbol=graph.style.symbol.circle,
size=0.1,
symbolattrs=[deco.filled([color.grey(nr)])])
])
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 field_shaper(with_Ed=False):
GT = load_Gordon_tables(with_Ed)
B0 = 370
Bmin = 200
if with_Ed:
Bmin = 0
gMagF = graph.graphxy(
width=10,
height=6,
x=graph.axis.lin(title="position [m]", min=0, max=3),
y=graph.axis.lin(title="$B_z$ [Gauss]", min=Bmin, max=B0 + 10),
key=graph.key.key(pos="bl"),
)
gMagFNarrow = graph.graphxy(
width=10,
height=6,
x=graph.axis.lin(title="position [m]", min=0, max=3),
y=graph.axis.lin(title="$B_z$ [Gauss]", min=350, max=380),
)
if with_Ed:
targf = make_target([p[0] for p in GT[0].get_radius_dat(0)])
LF = LinearFitter(
terms=[fitdict(G.get_radius_dat(0)) for G in GT[1:]], fixparams={1: 1.4, 3: 1.4, 5: 1.4, 8: 5}
)
LF.fit(targf, cols=(0, 1, 2))
print LF.coeffs
gMagF.plot(
graph.data.points(LF.fittedpoints(), x=1, y=3, title=None), [graph.style.symbol(symbol.circle, size=0.02)]
)
gMagF.plot(
graph.data.points(LF.fittedpoints(), x=1, y=2, title=None),
[graph.style.line([style.linewidth.thin, style.linestyle.dotted])],
)
gMagFNarrow.plot(
graph.data.points(LF.fittedpoints(), x=1, y=3, title=None), [graph.style.symbol(symbol.circle, size=0.02)]
)
gMagFNarrow.plot(
graph.data.function("y(x) = 368"), [graph.style.line([style.linewidth.thin, style.linestyle.dotted])]
)
cls = rainbow(len(GT))
for (n, G) in enumerate(GT):
ttl = None
if n and with_Ed:
ttl = "Scaled by %g" % LF.coeffs[n - 1]
# elif with_Ed and not n:
# continue
lstyle = graph.style.line([style.linewidth.Thick, cls[n]])
if n > 6:
lstyle = graph.style.line([style.linewidth.thick, style.linestyle.dotted, cls[n]])
gMagF.plot(graph.data.points(G.get_radius_dat(0, mul=1.0 + (n > 1) * 0), x=1, y=2, title=ttl), [lstyle])
if not n:
gMagFNarrow.plot(
graph.data.points(G.get_radius_dat(0, mul=1.0 + (n > 1) * 0), x=1, y=2, title=ttl),
[graph.style.line([style.linewidth.Thick, cls[n]])],
)
if not with_Ed:
# M = MPM_Table("Baseline")
M = MPM_Table("Baseline")
M2 = MPM_Table("Sym_Fat_2")
gMagF.plot(graph.data.points(M.get_zdat(), x=1, y=2, title=None), [graph.style.line([style.linewidth.thin])])
gMagF.plot(
graph.data.points(M2.get_zdat(), x=1, y=2, title=None),
[graph.style.line([style.linewidth.thin, style.linestyle.dashed])],
)
gMagF.writetofile("MagF.pdf")
gMagFNarrow.writetofile("MagF_Detail.pdf")
def slepc(Gx, Gz, Hx, Hz, Rx, Rz, Pxt, Qx, Pz, Tx, **kw):
name = argv.get("name", "ex3.tmp.c")
print("slepc: name=%s"%name)
r = len(Rx)
n = 2**r
assert (r<40), "ugh"
#code = Code("body.h")
code = Code()
code.append("#define DIMS (%d)"%n)
code.append("static void matmult(PetscScalar *py, const PetscScalar *px, long nx)")
code.begin()
code.append("assert(DIMS == %d);"%n)
code.append("assert(nx == %d);"%n)
code.append("memset(py, 0, sizeof(PetscScalar)*nx);")
offset = argv.get("offset", None)
mz = len(Gz)
t = None
#excite = argv.excite
#if excite is None:
excite = kw.get("excite") or argv.excite
if excite is not None:
print("excite:", excite)
if type(excite) is tuple:
t = Tx[excite[0]]
for i in range(1, len(excite)):
t = (t + Tx[excite[i]])%2
else:
assert type(excite) in (int, int)
t = Tx[excite]
print("t:", shortstr(t))
Gzt = dot2(Gz, t)
print("Gzt:", shortstr(Gzt))
weights = kw.get("weights")
if weights is not None:
assert len(weights)==len(Gx)
RR = dot2(Gz, Rx.transpose())
PxtQx = dot2(Pxt, Qx)
gxs = [getnum(dot2(gx, PxtQx)) for gx in Gx]
gxs.sort()
uniq_gxs = list(set(gxs))
uniq_gxs.sort()
code.append("long v;")
code.append("int k;")
code.append("struct timeval t0, t1;")
code.append("gettimeofday(&t0, NULL);")
code.append("for(v=0; v<%d; v++)"%n)
code.begin()
code.append("double pxv = px[v];")
if n >= 128:
code.append(r'if((v+1) %% %d == 0)' % (n//128))
code.begin()
code.append("gettimeofday(&t1, NULL);")
code.append("long delta = t1.tv_sec-t0.tv_sec;")
code.append("if(delta>1)")
code.append('{printf("[%lds]", delta);fflush(stdout);}')
code.append('t0 = t1;')
code.end()
code.append("k = 0;")
for i, row in enumerate(RR):
if t is not None and Gzt[i]==1:
code.append("k += (countbits_fast(v&%s)+1) %% 2;" % getnum(row))
else:
code.append("k += countbits_fast(v&%s) %% 2;" % getnum(row))
cutoff = argv.cutoff
if cutoff is not None:
code.append("if(k>%d) continue; // <-------- continue" % cutoff)
else:
code.append("if(k>cutoff) continue; // <-------- continue")
code.append("py[v] += pxv * (%d - 2*k);" % mz)
if weights is None:
for gx in uniq_gxs:
s = '+'.join(['pxv']*gxs.count(gx))
code.append("py[v^%s] += %s;" % (gx, s))
else:
gxs = [getnum(dot2(gx, PxtQx)) for gx in Gx]
for i, gx in enumerate(gxs):
code.append("py[v^%s] += %s*pxv;" % (gx, weights[i]))
code.end()
code.end()
if name is None:
return
s = code.output()
src = open("ex3.c").read()
match = '\n#include "body.h"\n'
assert match in src
src = src.replace(match, s)
assert name and name.endswith(".c")
f = open(name, 'w')
tag = hash(src)
print(("hash(src):", tag))
f.write(src)
f.close()
import socket
host = socket.gethostname()
if host == "bucket":
cmd = "gcc MATCH.c -O3 -o MATCH -I/home/simon/local/petsc/arch-linux2-c-debug/include -I/home/simon/local/petsc/include/petsc/mpiuni -I/home/simon/local/petsc/include -I/home/simon/local/slepc-3.7.1/include -I/home/simon/local/slepc-3.7.1/arch-linux2-c-debug/include/ -L/home/simon/local/petsc/arch-linux2-c-debug/lib -L/home/simon/local/slepc-3.7.1/arch-linux2-c-debug/lib -lpetsc -lslepc"
elif host == "hero":
cmd = "gcc MATCH.c -O3 -o MATCH -I/usr/include/openmpi -I/usr/include/petsc -I/usr/include/slepc -lpetsc -lslepc -lmpi"
else:
cmd = "gcc -O3 MATCH.c -I/suphys/sburton/include/ -o MATCH -lpetsc -L$PETSC_DIR/$PETSC_ARCH/lib -L$SLEPC_DIR/$PETSC_ARCH/lib -lslepc"
cmd = cmd.replace("MATCH.c", name)
stem = name[:-2]
cmd = cmd.replace("MATCH", stem)
rval = os.system(cmd)
assert rval == 0
#print("exec:", hash(open(stem).read()))
nev = argv.get("nev", 1)
cmd = "./%s -eps_nev %d -eps_ncv %d -eps_largest_real"
if argv.plot:
cmd += " -eps_view_vectors binary:evec.bin "
cmd = cmd%(stem, nev, max(2*nev, 4))
eps_tol = argv.get("eps_tol", 1e-4)
if eps_tol is not None:
cmd += " -eps_tol %s "%eps_tol
#cmd += " -eps_type arnoldi -info -eps_monitor -eps_tol 1e-3"
print(cmd)
#rval = os.system(cmd)
#assert rval == 0
f = os.popen(cmd)
s = f.read()
#print(s)
lines = s.split('\n')
vals = []
for line in lines:
line = line.strip()
flds = line.split()
#print("parse", flds)
try:
a, b = flds
a = float(a)
b = float(b)
vals.append(a)
except:
pass
if not argv.plot:
print(("vals:", vals))
return vals
assert argv.plot.endswith(".pdf")
s = open("evec.bin").read()
sz = 8*2**r
if len(s)==sz+8:
s = s[8:]
elif len(s)==sz+16:
s = s[16:]
#elif len(s)==2*(sz+16): # got two vectors
# s = s[16:16+sz] # pick the first vector
elif len(s)%(sz+16) == 0:
count = len(s)/(sz+16)
# s = s[16:16+sz] # pick the first vector
ev_idx = argv.get("ev_idx", 0)
s = s[16+ev_idx*(16+sz):(ev_idx+1)*(16+sz)]
else:
assert 0, "sz=%d but s=%s"%(sz, len(s))
vec0 = numpy.fromstring(s, dtype=">d")
assert len(vec0)==2**r
assert excite is None
print("graphing...")
gz, n = Gz.shape
xdata = []
lookup = {}
GzRxt = dot2(Gz, Rx.transpose())
for i, v in enumerate(genidx((2,)*r)):
v = array2(v)
lookup[v.tobytes()] = i
syndrome = dot2(GzRxt, v)
value = gz - 2*syndrome.sum()
xdata.append(value)
pdata = {}
ndata = {}
my = 20. # mul y
EPSILON = argv.get("EPSILON", 1e-6)
def yfunc(y):
y = log2(abs(y))
y = int(round(my*y))
return y
for i in range(len(vec0)):
x = xdata[i] # integer
y = vec0[i]
if abs(y) < EPSILON:
continue
if y > 0.:
y = yfunc(y)
pdata[x, y] = pdata.get((x, y), 0) + 1
else:
y = yfunc(y)
ndata[x, y] = ndata.get((x, y), 0) + 1
from pyx import graph, canvas, path, trafo, color, deco, text
north = [text.halign.boxcenter, text.valign.top]
northeast = [text.halign.boxright, text.valign.top]
northwest = [text.halign.boxleft, text.valign.top]
south = [text.halign.boxcenter, text.valign.bottom]
southeast = [text.halign.boxright, text.valign.bottom]
southwest = [text.halign.boxleft, text.valign.bottom]
east = [text.halign.boxright, text.valign.middle]
west = [text.halign.boxleft, text.valign.middle]
center = [text.halign.boxcenter, text.valign.middle]
c = canvas.canvas()
sx = 0.4
sy = 1.4
tr = trafo.scale(sx, sy)
green = color.rgb(0.2,0.6,0.2)
brown = color.rgb(0.8,0.2,0.2)
grey = color.rgb(0.4,0.4,0.4)
lgrey = color.rgb(0.8,0.8,0.8)
W = 2*gz
H = log2(EPSILON)
dy = 0.5 * 1.2/my
X0 = -gz
Y0 = 0.
def showp(gx, radius):
v = dot2(gx, PxtQx)
syndrome = dot2(GzRxt, v)
x = gz - 2*syndrome.sum()
i = lookup[v.tobytes()]
#print syndrome, syndrome.sum(), vec0[i]
y = (1./my)*yfunc(vec0[i]) + 0.5*dy
#y = 0.5*dy + log2(abs(vec0[i]))
c.fill(path.circle(-x*sx, y*sy, radius), [lgrey])
showp(zeros2(n), 0.8)
for gx in Gx:
showp(gx, 0.4)
for gx0 in Gx:
for gx1 in Gx:
gx = (gx0+gx1)%2
if gx.sum()==0:
continue
showp(gx, 0.2)
for i in range(0, gz+1):
x, y = X0+2*i, Y0
c.stroke(path.line(x, y, x, y+H), [tr, grey])
if i%2 == 0:
c.text(x*sx, y*sy + 0.2, "%d"%i, south)
c.stroke(path.line(X0, Y0, X0+1.0*W+3.5, Y0), [tr, deco.earrow(size=0.5)])
c.stroke(path.line(X0, Y0, X0, Y0+1.0*H-0.5), [tr, deco.earrow(size=0.5)])
y = 1.0
i = 0
while y > EPSILON:
x = X0*sx
y1 = sy*(1./my)*yfunc(y)
c.stroke(path.line(x, y1, x-0.1, y1))
c.text(x-0.3, y1, "%d"%i, east)
y /= 2.
i -= 1
R = 0.15
for key, value in list(pdata.items()):
x, y = key
y = y/my
x = -x
value = 1 + math.log(value)
r = R*value
#c.stroke(path.circle(x, y, r))
#c.stroke(path.line(x, y, x+r, y), [brown, tr])
c.fill(path.rect(x, y, r, dy), [brown, tr])
for key, value in list(ndata.items()):
x, y = key
y = y/my
x = -x
value = 1 + math.log(value)
r = R*value
#c.stroke(path.circle(x, y, r))
#c.stroke(path.line(x-r, y, x, y), [green, tr])
c.fill(path.rect(x-r, y, r, dy), [green, tr])
c.writePDFfile(argv.plot)
if 0:
print("graph..")
g = graph.graphxy(
width=16,
x=graph.axis.linear(reverse=True),
y=graph.axis.linear())
#y=graph.axis.log(min=0.8*vec0.min(), max=1.2*vec0.max()))
g.plot(graph.data.values(x=xdata, y=ydata))
g.writePDFfile(argv.plot)
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
from pyx import color, deco, graph, style
with open('numba_parallel.dat') as fh:
t_cpu = float(fh.readline().rstrip('\n').split()[1])
t_parallel = [(1, t_cpu)]
for _ in range(7):
elems = fh.readline().rstrip('\n').split()
t_parallel.append((int(elems[0]), float(elems[1])))
t_parallel = [(n, t_cpu / t) for n, t in t_parallel]
g = graph.graphxy(width=8,
x=graph.axis.linear(min=1, max=8, title="number of threads"),
y=graph.axis.linear(title="acceleration"))
g.plot(graph.data.points(t_parallel, x=1, y=2), [
graph.style.line([]),
graph.style.symbol(symbol=graph.style.symbol.circle,
size=0.1,
symbolattrs=[deco.filled([color.grey(1)])])
])
g.writePDFfile()
g.writeGSfile(device="png16m", resolution=600)
import numpy as np
import numpy.polynomial.polynomial as P
from pyx import color, deco, graph, style, text, unit
np.random.seed(987)
x = np.pi*np.linspace(0, 1, 100)
y = np.sin(x)+0.1*np.random.rand(100)
fit = P.Polynomial(P.polyfit(x, y, 2))
text.set(text.LatexRunner)
text.preamble(r'\usepackage[sfdefault,scaled=.85,lining]{FiraSans}\usepackage{newtxsf}')
unit.set(xscale=1.2)
g = graph.graphxy(width=8,
x=graph.axis.lin(title=r'\Large $x$', divisor=np.pi,
texter=graph.axis.texter.rational(suffix=r'\pi')),
y=graph.axis.lin(min=0, max=1.1, title=r'\Large $y$',
parter=graph.axis.parter.lin(tickdists=[0.2])))
origdata = list(zip(x, y))
symbolattrs = [deco.filled, color.hsb(0.6, 1, 0.7)]
g.plot(graph.data.points(origdata, x=1, y=2),
[graph.style.symbol(graph.style.symbol.circle, 0.07,
symbolattrs=symbolattrs)])
fitdata = list(zip(x, fit(x)))
lineattrs = [color.hsb(0.05, 1, 0.7), style.linewidth.THick]
g.plot(graph.data.points(fitdata, x=1, y=2),
[graph.style.line(lineattrs=lineattrs)])
g.writePDFfile()
titleattrs=[text.halign.right])
painter_y = painter.regular(basepathattrs=[deco.earrow],
titlepos=1.03,
titledist=0.15,
titleattrs=[text.valign.top],
titledirection=None)
g1 = c.insert(
graphxy(width=5,
height=2,
xaxisat=0,
yaxisat=0,
x=axis.linear(title="$x$",
min=0,
max=xrange,
painter=painter_x,
parter=None),
y=axis.linear(title=r"$I(x)$",
max=1.2,
painter=painter_y,
parter=None)))
x2 = np.linspace(0, (0.93 * xrange)**2, 800)
factor = sqrt(0.5 * pi)
_, integral = fresnel(np.sqrt(x2) / factor)
integral = factor * integral
g1.plot(data.points(list(zip(np.sqrt(x2), integral)), x=1, y=2),
lineproperties)
g2 = c.insert(
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
from math import exp, expm1
from pyx import color, deco, graph, style, text, unit
text.set(text.LatexEngine)
mypainter = graph.axis.painter.regular(innerticklength=None,
outerticklength=graph.axis.painter.ticklength.normal)
g = graph.graphxy(width=8,
x=graph.axis.log(title="$x$",
painter=mypainter),
y=graph.axis.lin(title=r"$f(x)-1$",
min=-1.1e-6,
max=1.1e-6,
painter=mypainter,
parter=graph.axis.parter.lin(tickdists=[1e-6])),
key=graph.key.key(pos="tr")
)
g.plot(graph.data.function("y(x)=(exp(x)-1)/x-1", min=1e-10, max=1e-6,
title=r"\textsf{\big(exp(x)-1\big)/x}"),
[graph.style.symbol(symbol=graph.style.symbol.circle,
size=0.15*unit.v_cm,
symbolattrs=[deco.filled([color.rgb(0.2, 0.2, 1)]),
deco.stroked([color.grey(1)])])])
g.plot(graph.data.function("y(x)=expm1(x)/x-1", min=1e-10, max=1e-6,
title=r"\textsf{expm1(x)/x}",
context={"expm1": expm1}),
[graph.style.line([color.rgb(1, 0.5, 0), style.linewidth.Thick])])
g.writeGSfile("expm1.png", resolution=300)
from pyx import color, deco, graph, style, unit
unit.set(xscale=1.3)
g = graph.graphxy(width=8,
x=graph.axis.linear(title="$x$"),
y=graph.axis.linear(title="$y$"))
g.plot(graph.data.file("pyx1.dat", x=1, y=2), [
graph.style.line([style.linestyle.dashed,
color.rgb(0, 0, 1)]),
graph.style.symbol(graph.style.symbol.circle,
size=0.1,
symbolattrs=[
deco.filled([color.rgb.red]),
deco.stroked([color.grey(0)])
])
])
g.writePDFfile()
g.writeGSfile(device="png16m", resolution=800)
from pyx import graph
g = graph.graphxy(width=8)
g.plot(graph.data.file("pyx1.dat", x=1, y=2))
g.writePDFfile()
g.writeGSfile(device="pnggray", resolution=800)
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_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(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 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 doplot(self,
d,
xlabel=None,
xlog=False,
ymin=-0.0029,
ymax=0.0029,
xmin=None,
xmax=None,
nocorr=False,
fitlines=False,
**kw):
"""
plot the results
parameters
----------
d: dict
result of running something like process_run or process_flist
**kw:
extra plotting keywords
"""
import pyxtools
from pyx import graph, deco, style
from pyx.graph import axis
if xlabel is None:
xlabel = 'x'
xdensity = kw.get('xdensity', 1.5)
ydensity = kw.get('ydensity', 1.5)
red = pyxtools.colors('red')
blue = pyxtools.colors('blue')
xvals = self._extract_xvals(d)
if nocorr:
sh = d['shear_nocorr']['shear']
sherr = d['shear_nocorr']['shear_err']
else:
sh = d['shear']['shear']
sherr = d['shear']['shear_err']
if xlog:
xcls = axis.log
else:
xcls = axis.lin
xaxis = xcls(
title=xlabel,
density=xdensity,
min=xmin,
max=xmax,
)
yaxis = axis.lin(
title=r"$\langle g \rangle$",
density=ydensity,
min=ymin,
max=ymax,
)
key = graph.key.key(pos='bl')
g = graph.graphxy(
width=8,
ratio=1.2,
key=key,
x=xaxis,
y=yaxis,
)
c = graph.data.function(
"y(x)=0",
title=None,
min=xvals[0],
max=xvals[-1],
)
g.plot(c)
g1values = graph.data.values(
x=list(xvals),
y=list(sh[:, 0]),
dy=list(sherr[:, 0]),
title=r'$g_1$',
)
g2values = graph.data.values(
x=list(xvals),
y=list(sh[:, 1]),
dy=list(sherr[:, 1]),
title=r'$g_2$',
)
symbol1 = graph.style.symbol(
symbol=graph.style.symbol.circle,
symbolattrs=[red, deco.filled([red])],
size=0.1,
)
symbol2 = graph.style.symbol(
symbol=graph.style.symbol.triangle,
symbolattrs=[blue, deco.filled([blue])],
size=0.1,
)
g.plot(g1values, [symbol1, graph.style.errorbar(errorbarattrs=[red])])
g.plot(g2values, [symbol2, graph.style.errorbar(errorbarattrs=[blue])])
use_errors = True
if fitlines and not use_errors:
res1 = fitline(xvals, sh[:, 0])
res2 = fitline(xvals, sh[:, 1])
fmt = '(%(slope).3g +/- %(slope_err).3g) x + (%(offset).3g +/- %(offset_err).3g)'
print("line1: " + fmt % res1)
print("line2: " + fmt % res2)
if self.element is not None:
tit1 = r'$g_1=%.2g ~g^{psf}_%d + %.2g$' % (
res1['slope'], self.element + 1, res1['offset'])
tit2 = r'$g_2=%.2g ~g^{psf}_%d + %.2g$' % (
res2['slope'], self.element + 1, res2['offset'])
else:
tit1 = r'$g_1=%.2g ~x + %.2g$' % (res1['slope'],
res1['offset'])
tit2 = r'$g_2=%.2g ~x + %.2g$' % (res1['slope'],
res1['offset'])
c1 = graph.data.function(
"y(x)=%g*x + %g" % tuple((res1['slope'], res1['offset'])),
title=tit1,
min=xvals[0],
max=xvals[-1],
)
c2 = graph.data.function(
"y(x)=%g*x + %g" % tuple((res2['slope'], res2['offset'])),
title=tit2,
min=xvals[0],
max=xvals[-1],
)
styles1 = [
graph.style.line([red, style.linestyle.solid]),
]
styles2 = [
graph.style.line([blue, style.linestyle.dashed]),
]
g.plot(c1, styles1)
g.plot(c2, styles2)
if fitlines and use_errors:
# old code
import fitting
l1 = fitting.fit_line(xvals, sh[:, 0], yerr=sherr[:, 0])
l2 = fitting.fit_line(xvals, sh[:, 1], yerr=sherr[:, 1])
res1 = l1.get_result()
res2 = l2.get_result()
pars1, err1 = res1['pars'], res1['perr']
pars2, err2 = res2['pars'], res2['perr']
fmt = '(%.3g +/- %.3g) x + (%.3g +/- %.3g)'
print("line1: " + fmt % (pars1[0], err1[0], pars1[1], err1[1]))
print("line2: " + fmt % (pars2[0], err2[0], pars2[1], err2[1]))
if self.element is not None:
tit1 = r'$g_1=%.2g ~g^{psf}_%d + %.2g$' % (
pars1[0], self.element + 1, pars1[1])
tit2 = r'$g_2=%.2g ~g^{psf}_%d + %.2g$' % (
pars2[0], self.element + 1, pars2[1])
else:
tit1 = r'$g_1=%.2g ~x + %.2g$' % (pars1[0], pars1[1])
tit2 = r'$g_2=%.2g ~x + %.2g$' % (pars2[0], pars2[1])
c1 = graph.data.function(
"y(x)=%g*x + %g" % tuple(pars1),
title=tit1,
min=xvals[0],
max=xvals[-1],
)
c2 = graph.data.function(
"y(x)=%g*x + %g" % tuple(pars2),
title=tit2,
min=xvals[0],
max=xvals[-1],
)
styles1 = [
graph.style.line([red, style.linestyle.solid]),
]
styles2 = [
graph.style.line([blue, style.linestyle.dashed]),
]
g.plot(c1, styles1)
g.plot(c2, styles2)
if 'file' in kw:
print("writing:", kw['file'])
pyxtools.write(g, kw['file'], dpi=200)
if fitlines and 'fitfile' in kw:
print("writing:", kw['fitfile'])
with open(kw['fitfile'], 'w') as fobj:
fobj.write(repr(l1))
fobj.write('\n')
fobj.write(repr(l2))
fobj.write('\n')
return g
julia_c = -0.8 + 0.156j
schwelle = 50000
maxit = 600
julia_daten = []
for y in np.linspace(ymin, ymax, seitenlaenge):
for x in np.linspace(xmin, xmax, seitenlaenge):
iterationen = julia_iteration(x, y, julia_c, schwelle, maxit)
julia_daten.append((x, y, iterationen / maxit))
c = canvas.canvas()
dx = 0.3
h = 4
w = 6.5
p = path.rect(-dx, -dx, w + 2 * dx, h + 2 * dx)
p = deformer.smoothed(0.5).deform(p)
c.fill(p, [color.grey(0.5), trafo.translate(0.05, -0.05)])
c1 = canvas.canvas([canvas.clip(p)])
g = graph.graphxy(height=8,
width=8,
x=graph.axis.linear(min=xmin, max=xmax),
y=graph.axis.linear(min=ymin, max=ymax))
g.plot(graph.data.points(julia_daten, x=1, y=2, color=3, title="iterations"),
[graph.style.density(gradient=color.rgbgradient.Rainbow)])
c1.insert(g, [trafo.translate(-0.1 * w, -0.5 * h)])
c.insert(c1)
c.writeGSfile(device="png16m", resolution=300)
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
for r in results:
nx, ny, procid, partialdata = r
for mx, my in product(range(nlen), repeat=2):
cval = c[nx * nlen + mx, ny * nlen + my]
data.append((cval.real, cval.imag, partialdata[mx, my]))
procdict[(nx, ny)] = procid
procids = set(procdict.values())
colors = [
color.hsb(n / (len(procids) - 1) * 0.67, 1, 1) for n in range(len(procids))
]
proccolors = dict(zip(procids, colors))
text.set(text.LatexRunner)
text.preamble(r'\usepackage{arev}\usepackage[T1]{fontenc}')
g = graph.graphxy(width=8,
height=8,
x=graph.axis.lin(title=r'$\mathrm{Re}(c)$'),
y=graph.axis.lin(title=r'$\mathrm{Im}(c)$'))
g.plot(graph.data.points(data, x=1, y=2, color=3),
[graph.style.density(keygraph=None)])
dx = (xmax - xmin) / n
dy = (ymax - ymin) / n
for k, v in procdict.items():
nx, ny = k
tilecolor = proccolors[v]
xll, yll = g.pos(xmin + dx * nx, ymin + dy * ny)
xur, yur = g.pos(xmin + dx * (nx + 1), ymin + dy * (ny + 1))
g.fill(path.rect(xll, yll, xur - xll, yur - yll),
[deco.stroked([color.grey(0)]), tilecolor,
color.transparency(0.5)])
g.writePDFfile()
VermillonU = rgb256pyx(244, 65, 0)
text.set(cls=text.LatexRunner, texenc='utf-8')
text.preamble(r'\usepackage[utf8]{inputenc}')
text.preamble(r'\usepackage{textgreek}')
text.preamble(r'\usepackage{fixltx2e}')
text.preamble(r'\usepackage{libertine}')
text.preamble(r'\renewcommand{\familydefault}{\sfdefault}')
text.preamble(r'\usepackage{sansmath}')
g = graphxy(
width=5.0,
height=2.4,
x=fakebaraxis(ticks=protein_keys,
texter=texter.decimal(labelattrs=[text.clearmathmode])),
x2=emptyaxis(),
y=axis.linear(min=0.0,
max=12.0,
parter=parter.linear(tickdists=[4.0, 2.0]),
texter=texter.decimal(labelattrs=[text.clearmathmode]),
title=r"C\textsubscript{α} RMSD ± std (Å)"))
i = 0
time, rmsd = read_dat("rmsd_Ahc.xvg", [float, float])
rmsd = [x * 10 for x in rmsd]
cloudplot(g, i, clouddata(rmsd), BlueU, ya=True)
boxplot(g, i, boxdata(rmsd), ya=True)
i = 1
time, rmsd = read_dat("rmsd_B2m.xvg", [float, float])
rmsd = [x * 10 for x in rmsd]
titledist=-0.4,
titleattrs=[text.halign.right])
painter_y = graph.axis.painter.regular(basepathattrs=[deco.earrow],
titlepos=1.03,
titledist=0.15,
titleattrs=[text.valign.top],
titledirection=None)
g = graph.graphxy(width=5,
height=2,
xaxisat=0,
yaxisat=0,
x=graph.axis.linear(title="$t$",
min=0,
max=trange,
painter=painter_x,
parter=None),
y=graph.axis.linear(title=r"$x$",
max=1.2,
painter=painter_y,
parter=None))
for np in range(-3, 4):
p = 0.2 * np
g.plot(
graph.data.function("y(x) = 0.5*cos(x)+{}*sin(x)".format(p),
max=trange - 0.7),
[graph.style.line([style.linestyle.solid, linecolor])])
textattrs = [text.halign.center]
print "Recovered phase shift: %.2f pi" % (recovered_phase_shift/pi)
pyplot.plot(t, )
pyplot.show()
quit()
from pyx import canvas, graph, text, color, style, trafo, unit
from pyx.graph import axis, key
text.set(mode="latex")
text.preamble(r"\usepackage{txfonts}")
figwidth = 12
gkey = key.key(pos=None, hpos=0.05, vpos=0.8)
xaxis = axis.linear(title=r"Time, \(t\)")
yaxis = axis.linear(title="Signal", min=-5, max=17)
g = graph.graphxy(width=figwidth, x=xaxis, y=yaxis, key=gkey)
plotdata = [graph.data.values(x=t, y=signal+offset, title=label) for label, signal, offset in (r"\(A(t) = \mathrm{square}(2\pi t/T)\)", A, 2.5), (r"\(B(t) = \mathrm{sawtooth}(\phi + 2 \pi t/T)\)", B, -2.5)]
linestyles = [style.linestyle.solid, style.linejoin.round, style.linewidth.Thick, color.gradient.Rainbow, color.transparency(0.5)]
plotstyles = [graph.style.line(linestyles)]
g.plot(plotdata, plotstyles)
g.plot(graph.data.values(x=t, y=listX, title="Blah"), plotstyles)
g.text(10*unit.x_pt, 0.56*figwidth, r"\textbf{Cross correlation of noisy anharmonic signals}")
g.text(10*unit.x_pt, 0.33*figwidth, "Phase shift: input \(\phi = %.2f \,\pi\), recovered \(\phi = %.2f \,\pi\)" % (phase_shift/pi, recovered_phase_shift/pi))
xxaxis = axis.linear(title=r"Time Lag, \(\Delta t\)", min=-1.5, max=1.5)
yyaxis = axis.linear(title=r"\(A(t) \star B(t)\)")
gg = graph.graphxy(width=0.2*figwidth, x=xxaxis, y=yyaxis)
plotstyles = [graph.style.line(linestyles + [color.rgb(0.2,0.5,0.2)])]
#gg.plot(graph.data.values(x=dt, y=xcorr), plotstyles)
gg.plot(graph.data.values(x=dt, y=xcorr, title="Blah"), plotstyles)
gg.stroke(gg.xgridpath(recovered_time_shift), [style.linewidth.THIck, color.gray(0.5), color.transparency(0.7)])
ggtrafos = [trafo.translate(0.75*figwidth, 0.45*figwidth)]
from math import pi, cos
from pyx import *
from pyx.deco import barrow, earrow
from pyx.style import linewidth, linestyle
from pyx.graph import graphxy
from pyx.graph.axis import linear
from pyx.graph.axis.painter import regular
from pyx.graph.style import line
from pyx.graph.data import function
mypainter = regular(basepathattrs=[earrow.normal], titlepos=1)
def mycos(x): return -cos(x)+.10*x
g = graphxy(height=5, x2=None, y2=None,
x=linear(min=-2.5*pi, max=3.3*pi, parter=None,
painter=mypainter, title=r"$\delta\phi$"),
y=linear(min=-2.3, max=2, painter=None))
g.plot(function("y(x)=mycos(x)", context=locals()),
[line(lineattrs=[linewidth.Thick])])
g.finish()
x1, y1 = g.pos(-pi+.1, mycos(-pi+.1))
x2, y2 = g.pos(-.1, mycos(-.1))
x3, y3 = g.pos(pi+.1, mycos(pi+.1))
g.stroke(path.line(x1-.5, y1, x1+.5, y1), [linestyle.dashed])
g.stroke(path.line(x1-.5, y3, x3+.5, y3), [linestyle.dashed])
g.stroke(path.line(x2-.5, y2, x3+.5, y2), [linestyle.dashed])
g.stroke(path.line(x1, y1, x1, y3), [barrow.normal, earrow.normal])
g.stroke(path.line(x3, y2, x3, y3), [barrow.normal, earrow.normal])
g.text(x1+.2, 0.5*(y1+y3), r"$2\pi\gamma k\Omega$", [text.vshift.middlezero])
def dense(Gx, Gz, Hx, Hz, Rx, Rz, Pxt, Qx, Pz, Tx, **kw):
r, n = Rx.shape
N = 2**r
gz = len(Gz)
# print "Hz:"
# print shortstr(Hz)
print("Hx|Tx:")
print(shortstrx(Hx, Tx))
print("Hx:")
for i, h in enumerate(Hx):
print(i, shortstr(h), h.sum())
print("GzTx")
GzTx = dot2(Gz, Tx.transpose())
for i, h in enumerate(GzTx.transpose()):
print(i, shortstr(h), h.sum())
# print "Rx:"
# print shortstr(Rx)
print("Tx:", len(Tx))
#print shortstr(Tx)
RR = dot2(Gz, Rx.transpose())
PxtQx = dot2(Pxt, Qx)
excite = argv.excite
if excite is None:
excite = kw.get("excite")
if excite:
excites = [excite]
else:
excites = genidx((2,)*len(Tx))
vec0 = None
eigvals = []
for excite in excites:
print("excite:", excite)
assert len(excite)==len(Tx)
t = zeros2(n)
for i, ex in enumerate(excite):
if ex:
t = (t + Tx[i])%2
#print "t:", shortstr(t)
Gzt = dot2(Gz, t)
#print "Gzt:", shortstr(Gzt)
if N<=1024:
H = numpy.zeros((N, N))
else:
H = None
A = {}
U = []
#for i in range(N):
pos = neg = 0
for i, v in enumerate(genidx((2,)*r)):
v = array2(v)
syndrome = (dot2(Gz, Rx.transpose(), v) + Gzt)%2
value = gz - 2*syndrome.sum()
#print shortstr(dot2(Rx.transpose(), v)), value
if H is not None:
H[i, i] = value
U.append(value)
for i, v in enumerate(genidx((2,)*r)):
v = array2(v)
#print shortstr(v),
for g in Gx:
u = (v + dot2(g, PxtQx))%2
j = eval('0b'+shortstr(u, zero='0'))
if H is not None:
H[i, j] += 1
A[i, j] = A.get((i, j), 0) + 1
#print H
if argv.orbigraph:
vals, vecs = do_orbigraph(A, U)
show_eigs(vals)
#if vec0 is not None:
# Hv = numpy.dot(H, vec0)
# print numpy.dot(vec0, Hv),
# Hv /= numpy.linalg.norm(Hv)
# print numpy.dot(vec0, Hv)
if argv.solve:
assert N <= 1024
assert numpy.allclose(H, H.transpose())
vals, vecs = numpy.linalg.eigh(H)
if argv.show_eigs:
show_eigs(vals)
vals = list(vals)
vals.sort()
val0 = vals[-1] # top one is last
assert vals[-2] < val0 - 1e-4
#print "excite:", excite,
print("eigval:", val0)
eigvals.append(val0)
vec0 = vecs[:,-1]
if vec0[0] < 0:
vec0 = -vec0
#break
if argv.plot:
from pyx import graph
xdata = U
ydata = list(vec0)
g = graph.graphxy(
width=16,
x=graph.axis.linear(reverse=True),
y=graph.axis.log(min=0.8*vec0.min(), max=1.0))
# either provide lists of the individual coordinates
g.plot(graph.data.values(x=xdata, y=ydata))
# or provide one list containing the whole points
#g.plot(graph.data.points(list(zip(range(10), range(10))), x=1, y=2))
g.writePDFfile("pic-groundstate.pdf")
return
if eigvals:
top = max(eigvals)
idx = eigvals.index(top)
eigvals.pop(idx)
second = max(eigvals)
print("gap:", top-second)
BC1.cns[n], BC2.cns[n] = (BC1.cns[n]-ez*BC2.cns[n])*k, (-ez*BC1.cns[n]+BC2.cns[n])*k
self.BC1 = BC1
self.BC2 = BC2
self.dz = dz
def V(self, r, z):
return self.BC1.V(r,z) + self.BC2.V(r, self.dz-z)
def Er(self, r, z):
return self.BC1.Er(r,z) + self.BC2.Er(r, self.dz-z)
def Ez(self, r, z):
return self.BC1.Ez(r,z) - self.BC2.Ez(r, self.dz-z)
if __name__ == "__main__":
BC = BesselCalcs()
print BC.j0n
for n in range(200)[1:]:
print "\t%i\t& %g\t\\\\"%(n, BC.cn(n))
g = graph.graphxy(width=10,height=6,
x=graph.axis.lin(title="radial position $r/r_0$"),
y=graph.axis.lin(title="endcap contribution $V(r,z)/V_1$"))
for z in [0.1*n for n in range(25)]:
npts = 50
if z==0:
npts = 600
gdat = [ (r, BC.V(r,z)) for r in numpy.linspace(0,1,400) ]
g.plot(graph.data.points(gdat,x=1,y=2),[graph.style.line(lineattrs=[style.linewidth.thin,rgb.blue])])
g.writetofile("endfield.pdf")
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)
p = painter.regular(basepathattrs=[earrow.normal],
titlepos=0.95,
outerticklength=painter.ticklength.normal,
titledist=-0.3,
titledirection=None) # horizontal text
g = graphxy(
width=10,
xaxisat=0,
yaxisat=0,
x=axis.linear(
title="$x$",
min=-range,
max=+range,
painter=p,
# suppress some ticks by overwriting ...
manualticks=[tick.tick(0, None, None),
tick.tick(range, None, None)]),
y=axis.linear(
title=r"$x\sin(x^2)$",
painter=p,
manualticks=[tick.tick(0, None, None),
tick.tick(3, None, None)]))
g.plot(
data.function("y(x)=x*sin(x**2)"),
# Line style is set before symbol style -> symbols will be draw
# above the line.
[
graph.style.line([style.linewidth.Thin, style.linestyle.solid]),
