def __init__(self, ax, data,labels=None, color_on='r', color_off='k'):
self.axes = ax
self.canvas = ax.figure.canvas
self.data = data
self.call_list = []
self.Nxy = data.shape[0]
self.color_on = colorConverter.to_rgba(color_on)
self.color_off = colorConverter.to_rgba(color_off)
facecolors = [self.color_on for _ in range(self.Nxy)]
fig = ax.figure
self.collection = RegularPolyCollection(
fig.dpi, 6, sizes=(1,),
facecolors=facecolors,
edgecolors=facecolors,
offsets = self.data,
transOffset = ax.transData)
ax.add_collection(self.collection, autolim=True)
ax.autoscale_view()
if labels is not None:
ax.set_xlabel(labels[0])
ax.set_ylabel(labels[1])
self.cid = self.canvas.mpl_connect('button_press_event', self.onpress)
self.ind = None
self.canvas.draw()
def __init__(self, axis, **poly_kwargs):
"""
Supply an axis on which to plot the boxes
"""
self.ax = axis
self.rois = []
self.selector = None
dflt_kwargs = dict(fc='#0000b3',
fa=0.25,
ec='k',
ea=1.0,
lw=1)
dflt_kwargs.update(poly_kwargs)
poly_kwargs = dflt_kwargs
# Allow 'face_alpha' / 'fa' and 'edge_alpha' / 'ea'
face_alpha = poly_kwargs.pop('fa', None)
face_alpha = poly_kwargs.pop('face_alpha', face_alpha)
face_color = poly_kwargs.pop('fc', None)
face_color = poly_kwargs.pop('face_color', face_color)
poly_kwargs['fc'] = colorConverter.to_rgba(face_color, alpha=face_alpha)
edge_alpha = poly_kwargs.pop('ea', None)
edge_alpha = poly_kwargs.pop('edge_alpha', edge_alpha)
edge_color = poly_kwargs.pop('ec', None)
edge_color = poly_kwargs.pop('edge_color', edge_color)
poly_kwargs['ec'] = colorConverter.to_rgba(edge_color, alpha=edge_alpha)
self.poly_kwargs = poly_kwargs
def index_bar(ax, vals,
facecolor='b', edgecolor='l',
width=4, alpha=1.0, ):
"""
Add a bar collection graph with height vals (-1 is missing).
ax : an Axes instance to plot to
width : the bar width in points
alpha : bar transparency
"""
facecolors = (colorConverter.to_rgba(facecolor, alpha),)
edgecolors = (colorConverter.to_rgba(edgecolor, alpha),)
right = width/2.0
left = -width/2.0
bars = [ ( (left, 0), (left, v), (right, v), (right, 0)) for v in vals if v != -1 ]
sx = ax.figure.dpi * (1.0/72.0) # scale for points
sy = ax.bbox.height / ax.viewLim.height
barTransform = Affine2D().scale(sx,sy)
offsetsBars = [ (i, 0) for i,v in enumerate(vals) if v != -1 ]
barCollection = PolyCollection(bars,
facecolors = facecolors,
edgecolors = edgecolors,
antialiaseds = (0,),
linewidths = (0.5,),
offsets = offsetsBars,
transOffset = ax.transData,
)
barCollection.set_transform(barTransform)
minpy, maxx = (0, len(offsetsBars))
miny = 0
maxy = max([v for v in vals if v!=-1])
corners = (minpy, miny), (maxx, maxy)
ax.update_datalim(corners)
ax.autoscale_view()
ax.add_collection(barCollection)
return barCollection
def show_window(self, subwindow):
obstable_img = np.transpose(subwindow[0, :, :])
alt_var_img = np.transpose(subwindow[1, :, :])
# generate the colors for your colormap
color1 = colorConverter.to_rgba('white')
color2 = colorConverter.to_rgba('blue')
# make the colormaps
cmap1 = mpl.colors.LinearSegmentedColormap.from_list('my_cmap', ['white', 'black'], 256)
cmap2 = mpl.colors.LinearSegmentedColormap.from_list('my_cmap2', [color1, color2], 256)
cmap2._init() # create the _lut array, with rgba values
# create your alpha array and fill the colormap with them.
# here it is progressive, but you can create whathever you want
alphas = np.linspace(0., 1.0, cmap2.N + 3)
cmap2._lut[:, -1] = alphas
plt.figure()
img3 = plt.imshow(obstable_img, interpolation='none', vmin=0, vmax=1, cmap=cmap1, origin='lower')
plt.hold(True)
img2 = plt.imshow(alt_var_img, interpolation='none', vmin=0, vmax=1, cmap=cmap2, origin='lower')
plt.colorbar()
plt.hold(False)
plt.show()
def _plot_from_line(plot, suffix, line):
"""
Plot lines in Chaco Plot object `plot` given MPL Line2D `line`.
"""
xname = "lx_{0}".format(suffix)
yname = "ly_{0}".format(suffix)
plot.data.set_data(xname, line.get_xdata())
plot.data.set_data(yname, line.get_ydata())
ls = line.get_linestyle()
if ls != "None":
plot.plot(
(xname, yname),
line_style=line_trans.get(ls, "solid"),
color=colorConverter.to_rgba(line.get_color()))
marker = line.get_marker()
if marker != "None":
chaco_marker = marker_trans.get(marker, "circle")
if chaco_marker == 'down triangle':
# Workaround the bug in Chaco shell.
# (https://github.com/enthought/chaco/issues/70)
chaco_marker = 'inverted_triangle'
plot.plot(
(xname, yname),
type="scatter",
marker=chaco_marker,
color=colorConverter.to_rgba(line.get_markerfacecolor()))
def plot_band(obj=None, step=False, emptybins=True, ax=None, **kwargs):
""" Produce an errorbar plots with or without connecting lines.
Args:
obj: Mplobj representation of a root object.
ax: Axis to plot on. If not specified current global axis will be used.
x_err: If True, x errorbars will be plotted.
yerr: If True, y errorbars will be plotted.
emptybins: Not Implemented. Supposed to ignore/plot empty bins.
"""
# Convert root object to mpl readable object
obj = R2npObject1D(obj)
# if no axis passed use current global axis
if ax is None:
ax = plt.gca()
x = obj.x
y = obj.y
y_errl = obj.yerrl
y_erru = obj.yerru
if step:
x = steppify_bin(obj.xbinedges, isx=True)
y = steppify_bin(y)
y_errl = steppify_bin(y_errl)
y_erru = steppify_bin(y_erru)
y_le = y - y_errl
y_ue = y + y_erru
if 'clip_vals' in kwargs:
y_le = np.clip(y - y_errl, kwargs['clip_vals'][0], kwargs['clip_vals'][1])
y_ue = np.clip(y + y_erru, kwargs['clip_vals'][0], kwargs['clip_vals'][1])
if kwargs['facecolor'] == 'none':
fill = False
else:
fill = True
kwargs['facecolor'] = colorConverter.to_rgba(kwargs['facecolor'], kwargs.get('alpha', 1.0))
kwargs['edgecolor'] = colorConverter.to_rgba(kwargs['edgecolor'], kwargs.get('edgealpha', 1.0))
# plot without hatch
fill_between_kwargs = {k: v for k, v in kwargs.items() if
k in ['label', 'facecolor', 'edgecolor', 'zorder', 'rasterized', 'linewidth']}
artist = ax.fill_between(x, y_le, y_ue, **fill_between_kwargs)
# work around okular bug present when hatch+color is plotted (plot
if 'hatch' in kwargs:
fill_between_kwargs2 = {k: v for k, v in kwargs.items() if
k in ['label', 'edgecolor', 'zorder', 'hatch', 'rasterized', 'linewidth']}
artist = ax.fill_between(x, y_le, y_ue, color='none', **fill_between_kwargs2)
p = matplotlib.patches.Rectangle((0, 0), 0, 0, hatch=kwargs.get('hatch', ''), **fill_between_kwargs)
ax.add_patch(p)
return p
def color(value):
"""
A valid matplotlib color
"""
from matplotlib.colors import colorConverter
try:
colorConverter.to_rgba(value)
except ValueError:
return False
def draw3DComplex(K, axes, dimensions = [0,1,2,3]):
if isinstance(K, CH.CubicalComplex):
if 0 in dimensions:
points = [map(lambda x:x[0], cube.intervals) for cube in K(0) if not K.isCritical(cube)]
if points:
x, y, z = map(np.array, zip(*points))
axes.scatter(y, z, -x, c='b', marker='o')
criticalPoints = [map(lambda x:x[0], cube.intervals) for cube in K(0) if K.isCritical(cube)]
if criticalPoints:
x, y, z = map(np.array, zip(*criticalPoints))
axes.scatter(y, z, -x, c='r', marker='o')
r1 = 0.85
r3 = 0.8
a1 = 0.9
a2 = 0.8
if 1 in dimensions:
for edge in K(1):
c = 'g-' if K.isCritical(edge) else 'b--'
points = list(product(*map(set, edge.intervals)))
V = [homothetic(edge.center, r1, P) for P in points]
x, y, z = map(np.array, zip(*V))
axes.plot(y, z, -x, c, linewidth=2, )
if 2 in dimensions:
for face in K(2):
points = list(product(*map(set, face.intervals)))
V = [homothetic(face.center, r1, P) for P in points]
x, y, z = map(np.array, zip(*V))
X, Y, Z = map(lambda x: x.copy(), [x, y, z])
X[2], X[3] = x[3], x[2]
Y[2], Y[3] = y[3], y[2]
Z[2], Z[3] = z[3], z[2]
poly = Poly3DCollection(
[zip(Y, Z, -X)],
facecolor=colorConverter.to_rgba('g', a2))
axes.add_collection3d(poly)
if 3 in dimensions:
for cube in K(3):
for face in cube.border():
points = list(product(*map(set, face.intervals)))
V = [homothetic(cube.center, r3, P) for P in points]
x, y, z = map(np.array, zip(*V))
X, Y, Z = map(lambda x: x.copy(), [x, y, z])
X[2], X[3] = x[3], x[2]
Y[2], Y[3] = y[3], y[2]
Z[2], Z[3] = z[3], z[2]
poly = Poly3DCollection(
[zip(Y, Z, -X)],
facecolor=colorConverter.to_rgba('r', 1))
axes.add_collection3d(poly)
elif isinstance(K, ACH.CubicalComplex):
draw3DComplex(K.toCubicalComplex(), axes, dimension)
def export_color(color):
"""Convert matplotlib color code to hex color or RGBA color"""
if color is None or colorConverter.to_rgba(color)[3] == 0:
return 'none'
elif colorConverter.to_rgba(color)[3] == 1:
rgb = colorConverter.to_rgb(color)
return '#{0:02X}{1:02X}{2:02X}'.format(*(int(255 * c) for c in rgb))
else:
c = colorConverter.to_rgba(color)
return "rgba(" + ", ".join(str(int(np.round(val * 255)))
for val in c[:3])+', '+str(c[3])+")"
def _shade_colors(self, color, normals):
"""
Shade *color* using normal vectors given by *normals*.
*color* can also be an array of the same length as *normals*.
"""
shade = []
for n in normals:
n = n / proj3d.mod(n)
shade.append(np.dot(n, [-1, -1, 0.5]))
shade = np.array(shade)
mask = ~np.isnan(shade)
if len(shade[mask]) > 0:
norm = Normalize(min(shade[mask]), max(shade[mask]))
if art3d.iscolor(color):
color = color.copy()
color[3] = 1
colors = [color * (0.5 + norm(v) * 0.5) for v in shade]
else:
colors = [np.array(colorConverter.to_rgba(c)) * (0.5 + norm(v) * 0.5) for c, v in zip(color, shade)]
else:
colors = color.copy()
return colors
def poly3d(df, elev=0, azim=0, **pltkwds):
''' Written by evelyn, updated by Adam 12/1/12.'''
xlabel, ylabel, title, pltkwds=pu.smart_label(df, pltkwds)
zlabel_def=''
zlabel=pltkwds.pop('zlabel', zlabel_def)
zs=df.columns
verts=[zip(df.index, df[col]) for col in df] #don't have to say df.columns
fig = plt.figure()
ax = fig.gca(projection='3d')
### Convert verts(type:list) to poly(type:mpl_toolkits.mplot3d.art3d.Poly3DCollection)
### poly used in plotting function ax.add_collection3d to do polygon plot
cc = lambda arg: colorConverter.to_rgba(arg, alpha=0.6)
poly = PolyCollection((verts), facecolors = [cc('b'), cc('g'), cc('r'),
cc('y'),cc('m'), cc('c'), cc('b'),cc('g'),cc('r'), cc('y')])
poly.set_alpha(0.2)
### zdir is the direction used to plot,here we use time so y axis
ax.add_collection3d(poly, zs=zs, zdir='x')
ax.set_xlabel(xlabel)
ax.set_ylabel(ylabel) #Y
ax.set_zlabel(zlabel) #data
ax.set_title(title)
ax.set_ylim3d(min(df.index), max(df.index))
ax.set_xlim3d(min(df.columns), max(df.columns)) #x
ax.set_zlim3d(min(df.min()), max(df.max())) #How to get absolute min/max of df values
ax.view_init(elev, azim)
return ax
def plot4MainDir(degVector):
fourDirVector = allDeg24Directions(degVector['Value'])
pHours = 24 # periodo considerado
sampling = 60 # 10min de amostragem
base = pHours*60/sampling
totDays = len(fourDirVector)/base # Dias multiplo de 5, para graficos poly 3d
days = np.arange(totDays)+1
hours = np.arange(0,pHours*60,sampling)
meshTime, indices = np.meshgrid(hours, days)
meshProfile = np.zeros(meshTime.shape)
profileList = []
ii = 1
for i in range(totDays):
dataPeriod = fourDirVector[i*base:ii*base]
profileList.append( dataPeriod )
ii +=1
profileMatrix = np.array(profileList)
for i in range( indices.shape[0] ):
for j in range( indices.shape[1] ):
meshProfile[(i,j)] = profileMatrix[(i,j)]
fig = plt.figure()
ax = fig.gca(projection='3d')
X = meshTime
Y = indices
Z = meshProfile
ax.plot_surface(X, Y, Z, rstride=1, cstride=1, cmap='coolwarm', alpha=0.8) # ou a linha abaixo
ax.set_xlabel('minutos')
ax.set_ylabel('dia')
ax.set_zlabel(r'$^oC$')
# Visao apenas dos perfis
fig2 = plt.figure()
ax2 = fig2.gca(projection='3d')
cc = lambda arg: colorConverter.to_rgba(arg, alpha=0.6)
verts = []
cs = [cc('r'), cc('g'), cc('b'), cc('y'), cc('c')]*(totDays/5)
k = 0
for d in days:
verts.append(list(zip(hours, meshProfile[k])))
k += 1
poly = PolyCollection(verts, facecolors = cs)
poly.set_alpha(0.7)
ax2.add_collection3d(poly, zs=days, zdir='y')
""" OK! Mostra grafico de barras
cs = ['r', 'g', 'b', 'y','c']*(totDays/5)
k = 0
for c, d in zip(cs, days):
cc = [c]*len(hours)
ax2.bar(hours, meshProfile[k], zs=d, zdir='y', color=cc, alpha=0.5)
k += 1
"""
ax2.set_xlabel('minutos')
ax2.set_xlim3d(0, hours[-1])
ax2.set_ylabel('dia')
ax2.set_ylim3d(0, days[-1])
ax2.set_zlabel('Dir')
ax2.set_zlim3d(0, 360)
plt.show()
def funDisplayDifferenceCurveIn3D(vecDigitLevel, inputData_x, dataToDisplay_y, xLabelText, yLabelText, zLabelText, titleText, figureName):
'''
Exactly the same as the function above, but in 3D, yes in 3D, it is the future here.
'''
fig = plt.figure()
ax = fig.gca(projection='3d')
cc = lambda arg: colorConverter.to_rgba(arg, alpha=0.2)
xs = inputData_x
verts = []
tabColor = []
zs = vecDigitLevel
for ii in np.arange(0,np.size(vecDigitLevel)):
ys = dataToDisplay_y[ii,:]
ys[0], ys[-1] = 0, 0
verts.append(list(zip(xs, ys)))
tabColor.append(list(cc(repr(vecDigitLevel[ii]/255.))))
poly = PolyCollection(verts, facecolors = tabColor)
poly.set_alpha(0.7)
ax.add_collection3d(poly, zs=zs, zdir='y')
ax.set_xlabel(xLabelText)#'level search')
ax.set_xlim3d(0, 255)
ax.set_ylabel(yLabelText)#'level tested')
ax.set_ylim3d(-1, 256)
ax.set_zlabel(zLabelText)#L difference')
ax.set_zlim3d(0, 1)
plt.title(titleText)#'Various difference curves in 3D')
plt.draw()
plt.savefig(figureName)# dirToSaveResults+prefixName+'_c1_2.png')
def set_data(self, zname, zdata, zcolor):
if zdata!=None:
if self.overall_plot_type=="polygon":
if zname not in self.clts: #plottables['plotted']:#self.pd.list_data():
clt=PolyCollection(zdata, alpha=0.5, antialiased=True)#, rasterized=False, antialiased=False)
clt.set_color(colorConverter.to_rgba(zcolor))
self.clts[zname]=clt
self.axe.add_collection(self.clts[zname], autolim=True)
else:
self.clts[zname].set_verts(zdata)
if self.overall_plot_type=="XY":
if zname not in self.clts:
clt = LineCollection(zdata)#, offsets=offs)
clt.set_color(colors)
#print dir(clt)
self.clts[zname]=clt
self.axe.add_collection(self.clts[zname], autolim=True)
self.axe.autoscale_view()
else:
self.clts[zname].set_segments(zdata)
if self.overall_plot_type=="img":
if zname not in self.clts:
axeimg=self.axe.imshow( Magvec,
vmin=amin(Magvec),
vmax=0.001, #amax(Magvec),
aspect="auto", origin="lower",
extent=[amin(yoko),amax(yoko), amin(freq),amax(freq)],
#cmap='RdBu'
)
self.fig.colorbar(axeimg)
def plot_3dfreq_sweep_error(error, ampl):
from mpl_toolkits.mplot3d import axes3d, Axes3D
import matplotlib.pyplot as plt
from matplotlib.collections import PolyCollection
from matplotlib.colors import colorConverter
import numpy as np
cc = lambda arg: colorConverter.to_rgba(arg, alpha=0.6)
fig = plt.figure()
ax = Axes3D(fig)
ax.add_collection3d(PolyCollection(error))
# ax.set_xlabel('X')
ax.set_xlim3d(0, 20000)
# ax.set_ylabel('Y')
# ax.set_ylim3d(-1, 4)
# ax.set_zlabel('Z')
# ax.set_zlim3d(0, 1)
plt.show()
return
def make_image(x):
''' x wil be a matrix data structure. '''
fig = plt.figure()
ax = fig.gca(projection='3d')
#
cc = lambda arg: colorConverter.to_rgba(arg, alpha=0.6)
#
xs = np.arange(0, 10, 0.4)
verts = x.get_verts('freq')
for i in xrange(17):
print verts[0][i],'\n'
for i in xrange(17):
print verts[1][i],'\n'
zs = [0.0, 1.0, 2.0, 3.0]
# for z in zs:
# ys = np.random.rand(len(xs))
# ys[0], ys[-1] = 0, 0
# verts.append(list(zip(xs, ys)))
# poly = PolyCollection(verts, facecolors = [cc('r'), cc('g'), cc('b'),cc('y')])
poly = PolyCollection(verts)
poly.set_alpha(0.3)
# ax.add_collection3d(poly, zs=zs, zdir='y')
ax.add_collection3d(poly, zs=zs, zdir='y')
#
ax.set_xlabel('X')
ax.set_xlim3d(0, 123456)
ax.set_ylabel('Y')
ax.set_ylim3d(0, 65536)
ax.set_zlabel('Z')
ax.set_zlim3d(0, 1000)
#
plt.show()
def color_to_hex(color):
"""Convert matplotlib color code to hex color code"""
if color is None or colorConverter.to_rgba(color)[3] == 0:
return 'none'
else:
rgb = colorConverter.to_rgb(color)
return '#{0:02X}{1:02X}{2:02X}'.format(*(int(255 * c) for c in rgb))
def test_something(self):
fig = plt.figure()
ax = fig.gca(projection='3d')
cc = lambda arg: colorConverter.to_rgba(arg, alpha=0.6)
xs = np.arange(0, 10, 0.4)
verts = []
zs = [0.0, 1.0, 2.0, 3.0]
for z in zs:
ys = np.random.rand(len(xs))
ys[0], ys[-1] = 0, 0
verts.append(list(zip(xs, ys)))
poly = PolyCollection(verts, facecolors = [cc('r'), cc('g'), cc('b'),
cc('y')])
poly.set_alpha(0.7)
ax.add_collection3d(poly, zs=zs, zdir='y')
ax.set_xlabel('Coordinate')
ax.set_xlim3d(0, 10)
ax.set_ylabel('hypothesis#')
ax.set_ylim3d(-1, 4)
ax.set_zlabel('Concentration')
ax.set_zlim3d(0, 1)
plt.show()
def test_switching_region_color(self):
from matplotlib.colors import colorConverter
from numpy.testing import assert_almost_equal
self.run_example('switching_region_color.py')
actual_colors = mapcall('get_facecolor', self.ax.collections)
desired_colors = [[colorConverter.to_rgba('gray')]] * 3
assert_almost_equal(actual_colors, desired_colors)
def doTracelines(xstart,ystart,zstart,step,tmax,Nmax):
global ActiveAxis, ActiveCanvas, ActiveTimmlModel, ActiveSettings
setActiveWindow()
win = getActiveWindow()
ActiveAxis.set_autoscale_on(False)
width = 0.5
color = []
for j in range(getActiveNumberLayers()):
color.append( ActiveSettings.get_color('Trace',j) )
color[j] = colorConverter.to_rgba( color[j] )
for i in range( len(xstart) ):
xyz, time, reason, pylayers = ActiveTimmlModel.\
traceline(xstart[i],ystart[i],zstart[i],step,tmax,Nmax,tstart=0.0,window=win,labfrac = 2.0, Hfrac = 2.0)
trace_color = []
for j in range(len(xyz)-1): # Number of segments one less than number of points
trace_color.append( color[ pylayers[j] ] )
points = zip( xyz[:,0], xyz[:,1] )
segments = zip( points[:-1], points[1:] )
LC = LineCollection(segments, colors = trace_color)
LC.set_linewidth(width)
ActiveAxis.add_collection(LC)
#ActiveAxis.plot( xyz[:,0], xyz[:,1], 'b' )
ActiveAxis.set_xlim(win[0],win[2])
ActiveAxis.set_ylim(win[1],win[3])
ActiveCanvas.draw()
def plot_weights(weights_list, title="Neurons weights progress", y_lim = None):
# Plot
# Make a list of colors cycling through the rgbcmyk series.
colors = [colorConverter.to_rgba(c) for c in ('k', 'r', 'g', 'b', 'c', 'y', 'm')]
axes = plt.axes()
ax4 = axes # unpack the axes
ncurves = 1
offs = (0.0, 0.0)
segs = []
for i in range(ncurves):
curve = weights_list
segs.append(curve)
col = collections.LineCollection(segs, offsets=offs)
ax4.add_collection(col, autolim=True)
col.set_color(colors)
ax4.autoscale_view()
ax4.set_title(title)
ax4.set_xlabel('Time ms')
ax4.set_ylabel('Weight pA')
if y_lim :
ax4.set_ylim(0, y_lim)
plt.show()
def test_polys():
from matplotlib.collections import LineCollection, PolyCollection
from matplotlib.colors import colorConverter
cc = lambda arg: colorConverter.to_rgba(arg, alpha=0.6)
ax = Axes3D()
xs = npy.arange(0,10,0.4)
verts = []
zs = [0.0,1.0,2.0,3.0]
for z in zs:
ys = [random.random() for x in xs]
ys[0],ys[-1] = 0,0
verts.append(zip(xs,ys))
poly = PolyCollection(verts, facecolors = [cc('r'),cc('g'),cc('b'),
cc('y')])
#patches = art3d.Poly3DCollectionW(poly, zs=zs, dir='y')
#poly = PolyCollection(verts)
ax.add_collection(poly,zs=zs,dir='y')
#ax.wrapped.add_collection(poly)
#
ax.plot(xs,ys, z=z, dir='y', c='r')
ax.set_xlim(0,10)
ax.set_ylim(-1,4)
ax.set_zlim(0,1)
def dhsv(color, dh=0., ds=0., dv=0., da=0.):
"""
Modify color on hsv scale.
*dv* change intensity, e.g., +0.1 to brighten, -0.1 to darken.
*dh* change hue
*ds* change saturation
*da* change transparency
Color can be any valid matplotlib color. The hsv scale is [0,1] in
each dimension. Saturation, value and alpha scales are clipped to [0,1]
after changing. The hue scale wraps between red to violet.
:Example:
Make sea green 10% darker:
>>> from bumps.plotutil import dhsv
>>> darker = dhsv('seagreen', dv=-0.1)
>>> print([int(v*255) for v in darker])
[37, 113, 71, 255]
"""
from matplotlib.colors import colorConverter
from colorsys import rgb_to_hsv, hsv_to_rgb
from numpy import clip, array, fmod
r, g, b, a = colorConverter.to_rgba(color)
# print "from color",r,g,b,a
h, s, v = rgb_to_hsv(r, g, b)
s, v, a = [clip(val, 0., 1.) for val in (s + ds, v + dv, a + da)]
h = fmod(h + dh, 1.)
r, g, b = hsv_to_rgb(h, s, v)
# print "to color",r,g,b,a
return array((r, g, b, a))
def _convert_color(color, byte=True):
"""Convert 3- or 4-element color into OpenGL usable color"""
color = (0., 0., 0., 0.) if color is None else color
color = 255 * np.array(colorConverter.to_rgba(color))
color = color.astype(np.uint8)
if not byte:
color = (color / 255.).astype(np.float32)
return tuple(color)
def polygon3D(verts, axes, color='b', alpha=0.6): x = [verts[i][0] for i in xrange(len(verts))] y = [verts[i][1] for i in xrange(len(verts))] z = [verts[i][2] for i in xrange(len(verts))] poly = Poly3DCollection( [zip(x, y, z)], facecolor=[colorConverter.to_rgba(color, alpha)]) axes.add_collection3d(poly)
def set_data(self, zname, zdata, zcolor):
if zdata!=None:
if zname not in self.clts: #plottables['plotted']:#self.pd.list_data():
clt=PolyCollection(zdata, alpha=0.5, antialiased=True)#, rasterized=False, antialiased=False)
clt.set_color(colorConverter.to_rgba(zcolor))
self.clts[zname]=clt
self.axe.add_collection(self.clts[zname], autolim=True)
else:
self.clts[zname].set_verts(zdata)
def bar3d(self, x, y, z, dx, dy, dz, color='b'):
'''
Generate a 3D bar, or multiple bars.
When generating multiple bars, x, y, z have to be arrays.
dx, dy, dz can still be scalars.
'''
had_data = self.has_data()
if not cbook.iterable(x):
x, y, z = [x], [y], [z]
if not cbook.iterable(dx):
dx, dy, dz = [dx], [dy], [dz]
if len(dx) == 1:
dx = dx * len(x)
dy = dy * len(x)
dz = dz * len(x)
minx, miny, minz = 1e20, 1e20, 1e20
maxx, maxy, maxz = -1e20, -1e20, -1e20
polys = []
for xi, yi, zi, dxi, dyi, dzi in zip(x, y, z, dx, dy, dz):
minx = min(xi, minx)
maxx = max(xi + dxi, maxx)
miny = min(yi, miny)
maxy = max(yi + dyi, maxy)
minz = min(zi, minz)
maxz = max(zi + dzi, maxz)
polys.extend([
((xi, yi, zi), (xi + dxi, yi, zi),
(xi + dxi, yi + dyi, zi), (xi, yi + dyi, zi)),
((xi, yi, zi + dzi), (xi + dxi, yi, zi + dzi),
(xi + dxi, yi + dyi, zi + dzi), (xi, yi + dyi, zi + dzi)),
((xi, yi, zi), (xi + dxi, yi, zi),
(xi + dxi, yi, zi + dzi), (xi, yi, zi + dzi)),
((xi, yi + dyi, zi), (xi + dxi, yi + dyi, zi),
(xi + dxi, yi + dyi, zi + dzi), (xi, yi + dyi, zi + dzi)),
((xi, yi, zi), (xi, yi + dyi, zi),
(xi, yi + dyi, zi + dzi), (xi, yi, zi + dzi)),
((xi + dxi, yi, zi), (xi + dxi, yi + dyi, zi),
(xi + dxi, yi + dyi, zi + dzi), (xi + dxi, yi, zi + dzi)),
])
color = np.array(colorConverter.to_rgba(color))
normals = self._generate_normals(polys)
colors = self._shade_colors(color, normals)
col = art3d.Poly3DCollection(polys, facecolor=colors)
self.add_collection(col)
self.auto_scale_xyz((minx, maxx), (miny, maxy), (minz, maxz), had_data)
def draw3DCube(cube, axes, color):
r1 = 0.85
r3 = 0.8
a1 = 0.9
a2 = 0.8
if isinstance(cube, CH.Cube):
if cube.dim == 0:
points = map(lambda x:x[0], cube.intervals)
print cube
raw_input('>>')
x, y, z = map(np.array, zip(*points))
axes.scatter(y, z, -x, c=color, marker='o')
elif cube.dim == 1:
points = list(product(*map(set, cube.intervals)))
V = [homothetic(cube.center, r1, P) for P in points]
x, y, z = map(np.array, zip(*V))
axes.plot(y, z, -x, color, linewidth=2, )
elif cube.dim == 2:
points = list(product(*map(set, cube.intervals)))
V = [homothetic(cube.center, r1, P) for P in points]
x, y, z = map(np.array, zip(*V))
X, Y, Z = map(lambda x: x.copy(), [x, y, z])
X[2], X[3] = x[3], x[2]
Y[2], Y[3] = y[3], y[2]
Z[2], Z[3] = z[3], z[2]
poly = Poly3DCollection(
[zip(Y, Z, -X)],
facecolor=colorConverter.to_rgba(color, a2))
axes.add_collection3d(poly)
elif cube.dim == 3:
for face in cube.border():
points = list(product(*map(set, face.intervals)))
V = [homothetic(cube.center, r3, P) for P in points]
x, y, z = map(np.array, zip(*V))
X, Y, Z = map(lambda x: x.copy(), [x, y, z])
X[2], X[3] = x[3], x[2]
Y[2], Y[3] = y[3], y[2]
Z[2], Z[3] = z[3], z[2]
poly = Poly3DCollection(
[zip(Y, Z, -X)],
facecolor=colorConverter.to_rgba(color, 1))
axes.add_collection3d(poly)
def colored_with_affinity(arr, domains, affinity=False, chr=None, cc=None):
arr = np.ma.fix_invalid(arr)
# PuBu
colored = cm.jet(pltcol.LogNorm()(arr))
normalizer = Normalize(vmin=-1, vmax=3)
non_empty_domains = remove_empty_domains(arr, topify(domains))
if affinity:
doms_affinity = domains_affinity(arr, non_empty_domains)
heatmap_notlog(np.clip(doms_affinity, 0, 10))
doms_affinity_log = np.log(doms_affinity)
affinity_thr = np.nanmean(doms_affinity_log) + np.nanstd(doms_affinity_log)
affinity_thr = np.exp(affinity_thr)
affinity_thr = np.nanmean(doms_affinity) + 1 * np.nanstd(doms_affinity)
affinity_normalizer = Normalize(vmin=affinity_thr, vmax=10)
for i, dom in enumerate(domains):
begin, end = dom.get_begin(), dom.get_end()
if end - begin < DOM_THR:
continue
color = dom.color or BORDER_COLOR
try:
color_num = int(dom.color)
if color_num >=0 :
color = COLOR_LIST[color_num]
else:
color = COLORS['0']
except:
pass
if cc:
color = COLORS[cc.get((chr, dom.color), '0')]
color = colorConverter.to_rgba(color)
for i in range(begin, end + 1):
if i != end:
colored[i, end] = color
if i != begin:
colored[begin, i] = color
#colored[begin, begin] = colorConverter.to_rgba('black')
#colored[end, end] = colorConverter.to_rgba('black')
if affinity:
for i, dom in enumerate(non_empty_domains):
begin, end = dom.get_begin(), dom.get_end()
for j, dom2 in enumerate(non_empty_domains):
if j <= i:
continue
begin2, end2 = dom2.get_begin(), dom2.get_end()
if doms_affinity[i, j] < affinity_thr:
continue
color = cm.gnuplot(affinity_normalizer(doms_affinity[i, j]))
for k in range(begin, end + 1):
colored[k, begin2] = color
colored[k, end2] = color
for k in range(begin2, end2 + 1):
colored[begin, k] = color
colored[end, k] = color
return colored
def mpl_to_css_color(color, alpha=None, isRGB=True):
"""Convert Matplotlib color spec (or rgb tuple + alpha) to CSS color string."""
if not isRGB:
r, g, b, alpha = colorConverter.to_rgba(color)
color = (r, g, b)
if alpha is None and len(color) == 4:
alpha = color[3]
if alpha is None:
return rgb2hex(color[:3])
else:
return 'rgba(%d, %d, %d, %.3g)' % (color[0] * 255, color[1] * 255, color[2] * 255, alpha)
def plot_band_weight(kslist,
ekslist,
wkslist=None,
efermi=None,
shift_efermi=False,
yrange=None,
output=None,
style='alpha',
color='blue',
axis=None,
width=10,
fatness=4,
xticks=None,
cmap=mpl.cm.bwr,
weight_min=-0.1,
weight_max=0.6):
if axis is None:
fig, a = plt.subplots()
else:
a = axis
if efermi is not None and shift_efermi:
ekslist = np.array(ekslist) - efermi
else:
ekslist = np.array(ekslist)
xmax = max(kslist[0])
if yrange is None:
yrange = (np.array(ekslist).flatten().min() - 0.66,
np.array(ekslist).flatten().max() + 0.66)
if wkslist is not None:
for i in range(len(kslist)):
x = kslist[i]
y = ekslist[i]
#lwidths=np.ones(len(x))
points = np.array([x, y]).T.reshape(-1, 1, 2)
segments = np.concatenate([points[:-1], points[1:]], axis=1)
if style == 'width':
lwidths = np.array(wkslist[i]) * width
lc = LineCollection(segments, linewidths=lwidths, colors=color)
elif style == 'alpha':
lwidths = np.array(wkslist[i]) * width
# The alpha values sometimes goes above 1 so in those cases we will normalize
# the alpha values. -Uthpala
alpha_values = [lwidth / (width + 0.05) for lwidth in lwidths]
if max(alpha_values) > 1:
print('alpha is larger than 1. Renormalizing values.')
alpha_values = [alpha_i / max(alpha_values) for alpha_i in alpha_values]
lc = LineCollection(
segments,
linewidths=[fatness] * len(x),
colors=[colorConverter.to_rgba(color, alpha=alpha_i) for alpha_i in alpha_values])
elif style == 'color' or style == 'colormap':
lwidths = np.array(wkslist[i]) * width
norm = mpl.colors.Normalize(vmin=weight_min, vmax=weight_max)
#norm = mpl.colors.SymLogNorm(linthresh=0.03,vmin=weight_min, vmax=weight_max)
m = mpl.cm.ScalarMappable(norm=norm, cmap=cmap)
#lc = LineCollection(segments,linewidths=np.abs(norm(lwidths)-0.5)*1, colors=[m.to_rgba(lwidth) for lwidth in lwidths])
lc = LineCollection(
segments,
linewidths=lwidths,
colors=[m.to_rgba(lwidth) for lwidth in lwidths])
a.add_collection(lc)
if axis is None:
for ks, eks in zip(kslist, ekslist):
a.plot(ks, eks, color='gray', linewidth=0.01)
#a.set_xlim(0, xmax)
#a.set_ylim(yrange)
if xticks is not None:
a.set_xticks(xticks[1])
a.set_xticklabels(xticks[0])
for x in xticks[1]:
a.axvline(x, alpha=0.6, color='black', linewidth=0.7)
if efermi is not None:
if shift_efermi:
a.axhline(linestyle='--', color='black')
else:
a.axhline(efermi, linestyle='--', color='black')
return a
def draw_networkx_edges(G, pos,
edgelist=None,
width=1.0,
edge_color='k',
style='solid',
alpha=None,
edge_cmap=None,
edge_vmin=None,
edge_vmax=None,
ax=None,
arrows=True,
label=None,
**kwds):
"""Draw the edges of the graph G.
This draws only the edges of the graph G.
Parameters
----------
G : graph
A networkx graph
pos : dictionary
A dictionary with nodes as keys and positions as values.
If not specified a spring layout positioning will be computed.
See networkx.layout for functions that compute node positions.
edgelist : collection of edge tuples
Draw only specified edges(default=G.edges())
width : float
Line width of edges (default =1.0)
edge_color : color string, or array of floats
Edge color. Can be a single color format string (default='r'),
or a sequence of colors with the same length as edgelist.
If numeric values are specified they will be mapped to
colors using the edge_cmap and edge_vmin,edge_vmax parameters.
style : string
Edge line style (default='solid') (solid|dashed|dotted,dashdot)
alpha : float
The edge transparency (default=1.0)
edge_ cmap : Matplotlib colormap
Colormap for mapping intensities of edges (default=None)
edge_vmin,edge_vmax : floats
Minimum and maximum for edge colormap scaling (default=None)
ax : Matplotlib Axes object, optional
Draw the graph in the specified Matplotlib axes.
arrows : bool, optional (default=True)
For directed graphs, if True draw arrowheads.
label : [None| string]
Label for legend
Notes
-----
For directed graphs, "arrows" (actually just thicker stubs) are drawn
at the head end. Arrows can be turned off with keyword arrows=False.
Yes, it is ugly but drawing proper arrows with Matplotlib this
way is tricky.
Examples
--------
>>> G=nx.dodecahedral_graph()
>>> edges=nx.draw_networkx_edges(G,pos=nx.spring_layout(G))
Also see the NetworkX drawing examples at
http://networkx.lanl.gov/gallery.html
See Also
--------
draw()
draw_networkx()
draw_networkx_nodes()
draw_networkx_labels()
draw_networkx_edge_labels()
"""
try:
import matplotlib
import matplotlib.pylab as pylab
import matplotlib.cbook as cb
from matplotlib.colors import colorConverter,Colormap
from matplotlib.collections import LineCollection
import numpy
except ImportError:
raise ImportError("Matplotlib required for draw()")
except RuntimeError:
print("Matplotlib unable to open display")
raise
if ax is None:
ax=pylab.gca()
if edgelist is None:
edgelist=G.edges()
if not edgelist or len(edgelist)==0: # no edges!
return None
# set edge positions
edge_pos=numpy.asarray([(pos[e[0]],pos[e[1]]) for e in edgelist])
if not cb.iterable(width):
lw = (width,)
else:
lw = width
if not cb.is_string_like(edge_color) \
and cb.iterable(edge_color) \
and len(edge_color)==len(edge_pos):
if numpy.alltrue([cb.is_string_like(c)
for c in edge_color]):
# (should check ALL elements)
# list of color letters such as ['k','r','k',...]
edge_colors = tuple([colorConverter.to_rgba(c,alpha)
for c in edge_color])
elif numpy.alltrue([not cb.is_string_like(c)
for c in edge_color]):
# If color specs are given as (rgb) or (rgba) tuples, we're OK
if numpy.alltrue([cb.iterable(c) and len(c) in (3,4)
for c in edge_color]):
edge_colors = tuple(edge_color)
else:
# numbers (which are going to be mapped with a colormap)
edge_colors = None
else:
raise ValueError('edge_color must consist of either color names or numbers')
else:
if cb.is_string_like(edge_color) or len(edge_color)==1:
edge_colors = ( colorConverter.to_rgba(edge_color, alpha), )
else:
raise ValueError('edge_color must be a single color or list of exactly m colors where m is the number or edges')
edge_collection = LineCollection(edge_pos,
colors = edge_colors,
linewidths = lw,
antialiaseds = (1,),
linestyle = style,
transOffset = ax.transData,
)
edge_collection.set_zorder(1) # edges go behind nodes
edge_collection.set_label(label)
ax.add_collection(edge_collection)
# Note: there was a bug in mpl regarding the handling of alpha values for
# each line in a LineCollection. It was fixed in matplotlib in r7184 and
# r7189 (June 6 2009). We should then not set the alpha value globally,
# since the user can instead provide per-edge alphas now. Only set it
# globally if provided as a scalar.
if cb.is_numlike(alpha):
edge_collection.set_alpha(alpha)
if edge_colors is None:
if edge_cmap is not None:
assert(isinstance(edge_cmap, Colormap))
edge_collection.set_array(numpy.asarray(edge_color))
edge_collection.set_cmap(edge_cmap)
if edge_vmin is not None or edge_vmax is not None:
edge_collection.set_clim(edge_vmin, edge_vmax)
else:
edge_collection.autoscale()
pylab.sci(edge_collection)
arrow_collection=None
if G.is_directed() and arrows:
# a directed graph hack
# draw thick line segments at head end of edge
# waiting for someone else to implement arrows that will work
arrow_colors = edge_colors
a_pos=[]
p=1.0-0.25 # make head segment 25 percent of edge length
for src,dst in edge_pos:
x1,y1=src
x2,y2=dst
dx=x2-x1 # x offset
dy=y2-y1 # y offset
d=numpy.sqrt(float(dx**2+dy**2)) # length of edge
if d==0: # source and target at same position
continue
if dx==0: # vertical edge
xa=x2
ya=dy*p+y1
if dy==0: # horizontal edge
ya=y2
xa=dx*p+x1
else:
theta=numpy.arctan2(dy,dx)
xa=p*d*numpy.cos(theta)+x1
ya=p*d*numpy.sin(theta)+y1
a_pos.append(((xa,ya),(x2,y2)))
arrow_collection = LineCollection(a_pos,
colors = arrow_colors,
linewidths = [4*ww for ww in lw],
antialiaseds = (1,),
transOffset = ax.transData,
)
arrow_collection.set_zorder(1) # edges go behind nodes
arrow_collection.set_label(label)
ax.add_collection(arrow_collection)
# update view
minx = numpy.amin(numpy.ravel(edge_pos[:,:,0]))
maxx = numpy.amax(numpy.ravel(edge_pos[:,:,0]))
miny = numpy.amin(numpy.ravel(edge_pos[:,:,1]))
maxy = numpy.amax(numpy.ravel(edge_pos[:,:,1]))
w = maxx-minx
h = maxy-miny
padx, pady = 0.05*w, 0.05*h
corners = (minx-padx, miny-pady), (maxx+padx, maxy+pady)
ax.update_datalim( corners)
ax.autoscale_view()
# if arrow_collection:
return edge_collection
def set_bad(self, color='k', alpha=None):
"""Set color to be used for masked values.
"""
self._rgba_bad = colorConverter.to_rgba(color, alpha)
def draw_roll(self):
roll = self.get_roll()
# build and set fig obj
plt.ioff()
fig = plt.figure(figsize=(4, 3))
a1 = fig.add_subplot(111)
a1.axis("equal")
a1.set_facecolor("black")
# change unit of time axis from tick to second
tick = self.get_total_ticks()
second = mido.tick2second(tick, self.ticks_per_beat, self.get_tempo())
#print(second)
if second > 10:
x_label_period_sec = second // 10
else:
x_label_period_sec = second / 10 # ms
#print(x_label_period_sec)
x_label_interval = mido.second2tick(x_label_period_sec, self.ticks_per_beat, self.get_tempo()) / self.sr
#print(x_label_interval)
plt.xticks([int(x * x_label_interval) for x in range(20)], [round(x * x_label_period_sec, 2) for x in range(20)])
# change scale and label of y axis
plt.yticks([y*16 for y in range(8)], [y*16 for y in range(8)])
# build colors
channel_nb = 16
transparent = colorConverter.to_rgba('black')
colors = [mpl.colors.to_rgba(mpl.colors.hsv_to_rgb((i / channel_nb, 1, 1)), alpha=1) for i in range(channel_nb)]
cmaps = [mpl.colors.LinearSegmentedColormap.from_list('my_cmap', [transparent, colors[i]], 128) for i in
range(channel_nb)]
# build color maps
for i in range(channel_nb):
cmaps[i]._init()
# create your alpha array and fill the colormap with them.
alphas = np.linspace(0, 1, cmaps[i].N + 3)
# create the _lut array, with rgba values
cmaps[i]._lut[:, -1] = alphas
# draw piano roll and stack image on a1
for i in range(channel_nb):
try:
a1.imshow(roll[i], origin="lower", interpolation='nearest', cmap=cmaps[i], aspect='auto')
except IndexError:
pass
# draw color bar
colors = [mpl.colors.hsv_to_rgb((i / channel_nb, 1, 1)) for i in range(channel_nb)]
cmap = mpl.colors.LinearSegmentedColormap.from_list('my_cmap', colors, 16)
a2 = fig.add_axes([0.05, 0.80, 0.9, 0.15])
cbar = mpl.colorbar.ColorbarBase(a2, cmap=cmap,
orientation='horizontal',
ticks=list(range(16)))
# show piano roll
plt.draw()
plt.ion()
plt.show(block=True)
def dd(arg):
return colorConverter.to_rgba(arg, alpha=0.1)
def draw_edges(G,
pos,
ax,
edgelist=None,
width=1.0,
width_adjuster=50,
edge_color='k',
style='solid',
alpha=None,
edge_cmap=None,
edge_vmin=None,
edge_vmax=None,
traversal_weight=1.0,
edge_delengthify=0.15,
arrows=True,
label=None,
zorder=1,
**kwds):
"""
Code cleaned-up version of networkx.draw_networkx_edges
New args:
width_adjuster - the line width is generated from the weight if present, use this adjuster to thicken the lines (multiply)
"""
if edgelist is None:
edgelist = G.edges()
if not edgelist or len(edgelist) == 0: # no edges!
return None
# set edge positions
edge_pos = [(pos[e[0]], pos[e[1]]) for e in edgelist]
new_ep = []
for e in edge_pos:
x, y = e[0]
dx, dy = e[1]
# Get edge length
elx = (dx - x) * edge_delengthify
ely = (dy - y) * edge_delengthify
x += elx
y += ely
dx -= elx
dy -= ely
new_ep.append(((x, y), (dx, dy)))
edge_pos = numpy.asarray(new_ep)
if not cb.iterable(width):
#print [G.get_edge_data(n[0], n[1])['weight'] for n in edgelist]
# see if I can find an edge attribute:
if 'weight' in G.get_edge_data(edgelist[0][0],
edgelist[0][1]): # Test an edge
lw = [
0.5 +
((G.get_edge_data(n[0], n[1])['weight'] - traversal_weight) *
width_adjuster) for n in edgelist
]
else:
lw = (width, )
else:
lw = width
if not is_string_like(edge_color) and cb.iterable(edge_color) and len(
edge_color) == len(edge_pos):
if numpy.alltrue([cb.is_string_like(c) for c in edge_color]):
# (should check ALL elements)
# list of color letters such as ['k','r','k',...]
edge_colors = tuple(
[colorConverter.to_rgba(c, alpha) for c in edge_color])
elif numpy.alltrue([not cb.is_string_like(c) for c in edge_color]):
# If color specs are given as (rgb) or (rgba) tuples, we're OK
if numpy.alltrue(
[cb.iterable(c) and len(c) in (3, 4) for c in edge_color]):
edge_colors = tuple(edge_color)
else:
# numbers (which are going to be mapped with a colormap)
edge_colors = None
else:
raise ValueError(
'edge_color must consist of either color names or numbers')
else:
if is_string_like(edge_color) or len(edge_color) == 1:
edge_colors = (colorConverter.to_rgba(edge_color, alpha), )
else:
raise ValueError(
'edge_color must be a single color or list of exactly m colors where m is the number or edges'
)
edge_collection = LineCollection(edge_pos,
colors=edge_colors,
linewidths=lw,
antialiaseds=(1, ),
linestyle=style,
transOffset=ax.transData,
zorder=zorder)
edge_collection.set_label(label)
ax.add_collection(edge_collection)
if cb.is_numlike(alpha):
edge_collection.set_alpha(alpha)
if edge_colors is None:
if edge_cmap is not None:
assert (isinstance(edge_cmap, Colormap))
edge_collection.set_array(numpy.asarray(edge_color))
edge_collection.set_cmap(edge_cmap)
if edge_vmin is not None or edge_vmax is not None:
edge_collection.set_clim(edge_vmin, edge_vmax)
else:
edge_collection.autoscale()
# update view
'''
minx = numpy.amin(numpy.ravel(edge_pos[:,:,0]))
maxx = numpy.amax(numpy.ravel(edge_pos[:,:,0]))
miny = numpy.amin(numpy.ravel(edge_pos[:,:,1]))
maxy = numpy.amax(numpy.ravel(edge_pos[:,:,1]))
w = maxx-minx
h = maxy-miny
padx, pady = 0.05*w, 0.05*h
corners = (minx-padx, miny-pady), (maxx+padx, maxy+pady)
ax.update_datalim(corners)
ax.autoscale_view()
'''
return (edge_collection)
#------------------------------------------------------------------------------
# Set up string variables for the input files/data
#------------------------------------------------------------------------------
startFilename = "point_stat_"
midFilename = "0000L_"
endFilename = "0000V_cts.txt"
#inputRootDir = "/discover/nobackup/projects/nu-wrf/members/bvanaart/SkillScoreRuns/MET_output/PointStat/round"
#exptDirList = ["0_default","2_bl_pbl_1_sf_sfclay_11","2_bl_pbl_2_sf_sfclay_2","3_ra_lw_phys_4_sw_phys_4","3_ra_lw_phys_56_sw_phys_56","4_moist_adv_opt_2","4_moist_adv_opt_4"]
exptDirList = nuwrfRunDirs
#exptLabels = [" Def_Goddard4"," YSU PBL, MM5 M-O SL", " MYJ TKE PBL, M-O-J SL", " RRTMG"," 2014 Goddard rad"," Monotonic advec"," 5th-order WENO advec"]
exptLabels = nuwrfRunLabels
myColors = [
colorConverter.to_rgba(c)
for c in ('red', 'black', 'goldenrod', 'blue', 'darkseagreen', 'darkgreen',
'mediumpurple', 'indigo', 'darkturquoise')
]
numExpts = len(exptLabels)
#Set output PDF file name, which will host several plots
eLabel = '-'.join(str(x) for x in exptLabels[0:2])
pdfName = 'T-Q-U-V_SkillScores_' + ''.join(eLabel.split()) + '.pdf'
pp = PdfPages(pdfName)
#------------------------------------------------------------------------------
# Find total # of time-periods to plot
#------------------------------------------------------------------------------
startDate = datetime.datetime(int(beginYear), int(beginMon), int(beginDate),
def makeGrey(col_list):
return [
colorConverter.to_rgba("#cccccc", 0.1) for i in range(len(col_list))
]
# print "itercategories:"
# print itercategories(dict(yaxis.items() + singletons.items()))
# print "others:"
# print singletons
if options.verbose:
sep = ","
print sep.join(metadata)
for tup in formatted_data:
print sep.join(map(str, tup))
# Produce Speedup Graph
N = len(column(formatted_data,0))
ind = np.arange(N) + 0.15 # the x locations for the groups
width = (0.7/len(metadata[1:])) # the width of the bars
#old_colors = map(lambda c: colorConverter.to_rgba(c, 0.5), ["#00C12B", "#133AAC", "#FFAE00", "#FF1800", "#24913C", "#062170", "#A67100", "#A61000"])
colors = map(lambda c: colorConverter.to_rgba(c, 0.5), ["#0000DD", "#FF8800", "#24913C", "#062170", "#123456", "#654321"])
xlabel_offset = 0
xbar_offset = 0
xbar_space = 0.05
bars = []
names = []
legend_colors = []
fig, ax = plt.subplots()
if options.logscale:
ax.set_yscale("log", basey=options.logscale, nonposy="clip")
ax.get_yaxis().set_major_formatter(ScalarFormatter())
for i in range(1,len(formatted_data[0])):
def add_label(self,
label,
color=None,
alpha=1,
scalar_thresh=None,
borders=False,
hemi=None,
subdir=None):
"""Add an ROI label to the image.
Parameters
----------
label : str | instance of Label
label filepath or name. Can also be an instance of
an object with attributes "hemi", "vertices", "name", and
optionally "color" and "values" (if scalar_thresh is not None).
color : matplotlib-style color | None
anything matplotlib accepts: string, RGB, hex, etc. (default
"crimson")
alpha : float in [0, 1]
alpha level to control opacity
scalar_thresh : None or number
threshold the label ids using this value in the label
file's scalar field (i.e. label only vertices with
scalar >= thresh)
borders : bool | int
Show only label borders. If int, specify the number of steps
(away from the true border) along the cortical mesh to include
as part of the border definition.
hemi : str | None
If None, it is assumed to belong to the hemipshere being
shown.
subdir : None | str
If a label is specified as name, subdir can be used to indicate
that the label file is in a sub-directory of the subject's
label directory rather than in the label directory itself (e.g.
for ``$SUBJECTS_DIR/$SUBJECT/label/aparc/lh.cuneus.label``
``brain.add_label('cuneus', subdir='aparc')``).
Notes
-----
To remove previously added labels, run Brain.remove_labels().
"""
from matplotlib.colors import colorConverter
if isinstance(label, str):
if color is None:
color = "crimson"
if os.path.isfile(label):
filepath = label
label = read_label(filepath)
hemi = label.hemi
label_name = os.path.basename(filepath).split('.')[1]
else:
hemi = self._check_hemi(hemi)
label_name = label
label_fname = ".".join([hemi, label_name, 'label'])
if subdir is None:
filepath = pjoin(self._subjects_dir, self._subject_id,
'label', label_fname)
else:
filepath = pjoin(self._subjects_dir, self._subject_id,
'label', subdir, label_fname)
if not os.path.exists(filepath):
raise ValueError('Label file %s does not exist' % filepath)
label = read_label(filepath)
ids = label.vertices
scalars = label.values
else:
# try to extract parameters from label instance
try:
hemi = label.hemi
ids = label.vertices
if label.name is None:
label_name = 'unnamed'
else:
label_name = str(label.name)
if color is None:
if hasattr(label, 'color') and label.color is not None:
color = label.color
else:
color = "crimson"
if scalar_thresh is not None:
scalars = label.values
except Exception:
raise ValueError('Label was not a filename (str), and could '
'not be understood as a class. The class '
'must have attributes "hemi", "vertices", '
'"name", and (if scalar_thresh is not None)'
'"values"')
hemi = self._check_hemi(hemi)
if scalar_thresh is not None:
ids = ids[scalars >= scalar_thresh]
# XXX: add support for label_name
self._label_name = label_name
label = np.zeros(self.geo[hemi].coords.shape[0])
label[ids] = 1
color = colorConverter.to_rgba(color, alpha)
cmap = np.array([(
0,
0,
0,
0,
), color])
ctable = np.round(cmap * 255).astype(np.uint8)
for ri, v in enumerate(self._views):
if self._hemi != 'split':
ci = 0
else:
ci = 0 if hemi == 'lh' else 1
views_dict = lh_views_dict if hemi == 'lh' else rh_views_dict
self._renderer.subplot(ri, ci)
if borders:
surface = {
'rr': self.geo[hemi].coords,
'tris': self.geo[hemi].faces,
}
self._renderer.contour(surface,
label, [1.0],
color=color,
kind='tube')
else:
self._renderer.mesh(x=self.geo[hemi].coords[:, 0],
y=self.geo[hemi].coords[:, 1],
z=self.geo[hemi].coords[:, 2],
triangles=self.geo[hemi].faces,
scalars=label,
color=None,
colormap=ctable,
backface_culling=False)
self._renderer.set_camera(azimuth=views_dict[v].azim,
elevation=views_dict[v].elev)
xx, yy = np.meshgrid(xs1, xs2) # create the grid
# Initialize and fill the classification plane
classification_plane = np.zeros((nb_of_xs, nb_of_xs))
for i in range(nb_of_xs):
for j in range(nb_of_xs):
# classification_plane[i,j] = nn_predict(xx[i,j], yy[i,j])
classification_plane[i, j] = sess.run(y_pred,
feed_dict={
x: [[xx[i, j], yy[i, j]]],
keep_prob: 1.0
})
classification_plane[i, j] = int(classification_plane[i, j])
# Create a color map to show the classification colors of each grid point
cmap = ListedColormap([
colorConverter.to_rgba('r', alpha=0.30),
colorConverter.to_rgba('b', alpha=0.30)
])
# Plot the classification plane with decision boundary and input samples
plt.contourf(xx, yy, classification_plane, cmap=cmap)
plt.show()
xTrain, yTrain = generate(12, num_classes, [[3.0, 0], [3.0, 3.0], [0, 3.0]],
True)
yTrain = yTrain % 2
# colors = ['r' if l == 0.0 else 'b' for l in yTrain[:]]
# plt.scatter(xTrain[:,0], xTrain[:,1], c=colors)
xr = []
xb = []
for (l, k) in zip(yTrain[:], xTrain[:]):
def _plot_morphology3D(morpho, figure, colors, values, value_norm,
value_colormap,
show_diameters=True,
show_compartments=False):
import mayavi.mlab as mayavi
if values is not None:
# calculate color for the soma
vmin, vmax = value_norm
if vmin is None:
vmin = min(values)
if vmax is None:
vmax = max(values)
normed_value = (values[0] - vmin)/(vmax - vmin)
colors = np.vstack(value_colormap([normed_value]))
else:
colors = np.vstack([colorConverter.to_rgba(c) for c in colors])
flat_morpho = FlatMorphology(morpho)
if isinstance(morpho, Soma):
start_idx = 1
# Plot the Soma
mayavi.points3d(flat_morpho.x[0]/float(um),
flat_morpho.y[0]/float(um),
flat_morpho.z[0]/float(um),
flat_morpho.diameter[0]/float(um),
figure=figure, color=tuple(colors[0, :-1]),
resolution=16, scale_factor=1)
else:
start_idx = 0
if show_compartments:
# plot points at center of compartment
if show_diameters:
diameters = flat_morpho.diameter[start_idx:]/float(um)/10
else:
diameters = np.ones(len(flat_morpho.diameter) - start_idx)
mayavi.points3d(flat_morpho.x[start_idx:]/float(um),
flat_morpho.y[start_idx:]/float(um),
flat_morpho.z[start_idx:]/float(um),
diameters,
figure=figure, color=(0, 0, 0),
resolution=16, scale_factor=1)
# Plot all other compartments
start_points = np.vstack([flat_morpho.start_x[start_idx:]/float(um),
flat_morpho.start_y[start_idx:]/float(um),
flat_morpho.start_z[start_idx:]/float(um)]).T
end_points = np.vstack([flat_morpho.end_x[start_idx:]/float(um),
flat_morpho.end_y[start_idx:]/float(um),
flat_morpho.end_z[start_idx:]/float(um)]).T
points = np.empty((2*len(start_points), 3))
points[::2, :] = start_points
points[1::2, :] = end_points
# Create the points at start and end of the compartments
if values is not None:
scatter_values = values[start_idx:].repeat(2)
else:
scatter_values = flat_morpho.depth[start_idx:].repeat(2)
src = mayavi.pipeline.scalar_scatter(points[:, 0],
points[:, 1],
points[:, 2],
scatter_values,
scale_factor=1)
# Create the lines between compartments
connections = []
for start, end in zip(flat_morpho.starts[1:], flat_morpho.ends[1:]):
# we only need the lines within the sections
new_connections = [((idx-1)*2, (idx-1)*2 + 1)
for idx in range(start, end)]
connections.extend(new_connections)
connections = np.vstack(connections)
src.mlab_source.dataset.lines = connections
if show_diameters:
radii = flat_morpho.diameter[start_idx:].repeat(2)/float(um)/2
src.mlab_source.dataset.point_data.add_array(radii)
src.mlab_source.dataset.point_data.get_array(1).name = 'radius'
src.update()
lines = mayavi.pipeline.stripper(src)
if show_diameters:
lines = mayavi.pipeline.set_active_attribute(lines,
point_scalars='radius')
tubes = mayavi.pipeline.tube(lines)
tubes.filter.vary_radius = 'vary_radius_by_absolute_scalar'
tubes = mayavi.pipeline.set_active_attribute(tubes,
point_scalars='scalars')
else:
tubes = mayavi.pipeline.tube(lines, tube_radius=1)
max_depth = max(flat_morpho.depth)
if values is not None:
surf = mayavi.pipeline.surface(tubes, colormap='prism', line_width=1,
opacity=0.5, vmin=vmin, vmax=vmax)
surf.module_manager.scalar_lut_manager.lut.number_of_colors = 256
cmap = np.array(np.vstack(value_colormap(np.linspace(0., 1., num=256, endpoint=True)))*255.,
dtype=np.uint8)
else:
surf = mayavi.pipeline.surface(tubes, colormap='prism', line_width=1,
opacity=0.5,
vmin=0, vmax=max(flat_morpho.depth))
surf.module_manager.scalar_lut_manager.lut.number_of_colors = max_depth + start_idx
cmap = np.int_(np.round(255*colors[np.arange(max_depth + start_idx)%len(colors), :]))
surf.module_manager.scalar_lut_manager.lut.table = cmap
src.update()
return surf
def __call__(self, plot):
x0, x1 = plot.xlim
y0, y1 = plot.ylim
xx0, xx1 = plot._axes.get_xlim()
yy0, yy1 = plot._axes.get_ylim()
(dx, dy) = self.pixel_scale(plot)
(xpix, ypix) = plot.image._A.shape
ax = plot.data.axis
px_index = plot.data.ds.coordinates.x_axis[ax]
py_index = plot.data.ds.coordinates.y_axis[ax]
DW = plot.data.ds.domain_width
if self.periodic:
pxs, pys = np.mgrid[-1:1:3j, -1:1:3j]
else:
pxs, pys = np.mgrid[0:0:1j, 0:0:1j]
GLE, GRE, levels = [], [], []
for block, mask in plot.data.blocks:
GLE.append(block.LeftEdge.in_units("code_length"))
GRE.append(block.RightEdge.in_units("code_length"))
levels.append(block.Level)
if len(GLE) == 0: return
# Retain both units and registry
GLE = YTArray(GLE, input_units=GLE[0].units)
GRE = YTArray(GRE, input_units=GRE[0].units)
levels = np.array(levels)
min_level = self.min_level or 0
max_level = self.max_level or levels.max()
# sorts the three arrays in order of ascending level - this makes images look nicer
new_indices = np.argsort(levels)
levels = levels[new_indices]
GLE = GLE[new_indices]
GRE = GRE[new_indices]
for px_off, py_off in zip(pxs.ravel(), pys.ravel()):
pxo = px_off * DW[px_index]
pyo = py_off * DW[py_index]
left_edge_x = np.array((GLE[:, px_index] + pxo - x0) * dx) + xx0
left_edge_y = np.array((GLE[:, py_index] + pyo - y0) * dy) + yy0
right_edge_x = np.array((GRE[:, px_index] + pxo - x0) * dx) + xx0
right_edge_y = np.array((GRE[:, py_index] + pyo - y0) * dy) + yy0
xwidth = xpix * (right_edge_x - left_edge_x) / (xx1 - xx0)
ywidth = ypix * (right_edge_y - left_edge_y) / (yy1 - yy0)
visible = np.logical_and(
np.logical_and(xwidth > self.min_pix, ywidth > self.min_pix),
np.logical_and(levels >= min_level, levels <= max_level))
# Grids can either be set by edgecolors OR a colormap.
if self.edgecolors is not None:
edgecolors = colorConverter.to_rgba(self.edgecolors,
alpha=self.alpha)
else: # use colormap if not explicity overridden by edgecolors
if self.cmap is not None:
color_bounds = [0, plot.data.ds.index.max_level]
edgecolors = apply_colormap(
levels[visible] * 1.0,
color_bounds=color_bounds,
cmap_name=self.cmap)[0, :, :] * 1.0 / 255.
edgecolors[:, 3] = self.alpha
else:
edgecolors = (0.0, 0.0, 0.0, self.alpha)
if visible.nonzero()[0].size == 0: continue
verts = np.array([
(left_edge_x, left_edge_x, right_edge_x, right_edge_x),
(left_edge_y, right_edge_y, right_edge_y, left_edge_y)
])
verts = verts.transpose()[visible, :, :]
grid_collection = matplotlib.collections.PolyCollection(
verts,
facecolors="none",
edgecolors=edgecolors,
linewidth=self.linewidth)
plot._axes.hold(True)
plot._axes.add_collection(grid_collection)
if self.draw_ids:
visible_ids = np.logical_and(
np.logical_and(xwidth > self.min_pix_ids,
ywidth > self.min_pix_ids),
np.logical_and(levels >= min_level, levels <= max_level))
active_ids = np.unique(plot.data['grid_indices'])
for i in np.where(visible_ids)[0]:
plot._axes.text(left_edge_x[i] + (2 * (xx1 - xx0) / xpix),
left_edge_y[i] + (2 * (yy1 - yy0) / ypix),
"%d" % active_ids[i],
clip_on=True)
plot._axes.hold(False)
def draw_embedding(G,
layout,
emb,
embedded_graph=None,
interaction_edges=None,
chain_color=None,
unused_color=(0.9, 0.9, 0.9, 1.0),
cmap=None,
show_labels=False,
overlapped_embedding=False,
**kwargs):
"""Draws an embedding onto the graph G, according to layout.
If interaction_edges is not None, then only display the couplers in that
list. If embedded_graph is not None, the only display the couplers between
chains with intended couplings according to embedded_graph.
Parameters
----------
G : NetworkX graph
The graph to be drawn
layout : dict
A dict of coordinates associated with each node in G. Should
be of the form {node: coordinate, ...}. Coordinates will be
treated as vectors, and should all have the same length.
emb : dict
A dict of chains associated with each node in G. Should be
of the form {node: chain, ...}. Chains should be iterables
of qubit labels (qubits are nodes in G).
embedded_graph : NetworkX graph (optional, default None)
A graph which contains all keys of emb as nodes. If specified,
edges of G will be considered interactions if and only if they
exist between two chains of emb if their keys are connected by
an edge in embedded_graph
interaction_edges : list (optional, default None)
A list of edges which will be used as interactions.
show_labels: boolean (optional, default False)
If show_labels is True, then each chain in emb is labelled with its key.
chain_color : dict (optional, default None)
A dict of colors associated with each key in emb. Should be
of the form {node: rgba_color, ...}. Colors should be length-4
tuples of floats between 0 and 1 inclusive. If chain_color is None,
each chain will be assigned a different color.
cmap : str or matplotlib colormap (optional, default None)
A matplotlib colormap for coloring of chains. Only used if chain_color
is None.
unused_color : tuple or color string (optional, default (0.9,0.9,0.9,1.0))
The color to use for nodes and edges of G which are not involved
in chains, and edges which are neither chain edges nor interactions.
If unused_color is None, these nodes and edges will not be shown at all.
overlapped_embedding: boolean (optional, default False)
If overlapped_embedding is True, then chains in emb may overlap (contain
the same vertices in G), and the drawing will display these overlaps as
concentric circles.
kwargs : optional keywords
See networkx.draw_networkx() for a description of optional keywords,
with the exception of the `pos` parameter which is not used by this
function. If `linear_biases` or `quadratic_biases` are provided,
any provided `node_color` or `edge_color` arguments are ignored.
"""
try:
import matplotlib.pyplot as plt
import matplotlib as mpl
except ImportError:
raise ImportError("Matplotlib and numpy required for draw_chimera()")
if nx.utils.is_string_like(unused_color):
from matplotlib.colors import colorConverter
alpha = kwargs.get('alpha', 1.0)
unused_color = colorConverter.to_rgba(unused_color, alpha)
if chain_color is None:
import matplotlib.cm
n = max(1., len(emb) - 1.)
if cmap:
color = matplotlib.cm.get_cmap(cmap)
else:
color = distinguishable_color_map(int(n + 1))
var_i = {v: i for i, v in enumerate(emb)}
chain_color = {v: color(i / n) for i, v in enumerate(emb)}
if overlapped_embedding:
bags = compute_bags(G, emb)
base_node_size = kwargs.get('node_size', 100)
node_size_dict = {v: base_node_size for v in G.nodes()}
G, emb, interaction_edges = unoverlapped_embedding(
G, emb, interaction_edges)
for node, data in G.nodes(data=True):
if 'dummy' in data:
v, x = node
layout[node] = layout[v]
for v, bag in bags.items():
for i, x in enumerate(bag):
node_size_dict[(v, x)] = base_node_size * (len(bag) - i)**2
kwargs['node_size'] = [node_size_dict[p] for p in G.nodes()]
qlabel = {q: v for v, chain in emb.items() for q in chain}
edgelist = []
edge_color = []
background_edgelist = []
background_edge_color = []
if interaction_edges is not None:
interactions = nx.Graph()
interactions.add_edges_from(interaction_edges)
def show(p, q, u, v):
return interactions.has_edge(p, q)
elif embedded_graph is not None:
def show(p, q, u, v):
return embedded_graph.has_edge(u, v)
else:
def show(p, q, u, v):
return True
for (p, q) in G.edges():
u = qlabel.get(p)
v = qlabel.get(q)
if u is None or v is None:
ec = unused_color
elif u == v:
ec = chain_color.get(u)
elif show(p, q, u, v):
ec = (0, 0, 0, 1)
else:
ec = unused_color
if ec == unused_color:
background_edgelist.append((p, q))
background_edge_color.append(ec)
elif ec is not None:
edgelist.append((p, q))
edge_color.append(ec)
nodelist = []
node_color = []
for p in G.nodes():
u = qlabel.get(p)
if u is None:
pc = unused_color
else:
pc = chain_color.get(u)
if pc is not None:
nodelist.append(p)
node_color.append(pc)
labels = {}
if show_labels:
if overlapped_embedding:
node_labels = {q: [] for q in bags.keys()}
node_index = {p: i for i, p in enumerate(G.nodes())}
for v in emb.keys():
v_labelled = False
chain = emb[v]
for node in chain:
(q, _) = node
if len(bags[q]) == 1:
# if there's a node that only has this label, use that
labels[q] = str(v)
v_labelled = True
break
if not v_labelled and chain:
# otherwise, pick a random node for this label
node = random.choice(list(chain))
(q, _) = node
node_labels[q].append(v)
for q, label_vars in node_labels.items():
x, y = layout[q]
# TODO: find a better way of placing labels around the outside of nodes.
# Currently, if the graph is resized, labels will appear at a strange distance from the vertices.
# To fix this, the "scale" value below, rather than being a fixed constant, should be determined using
# both the size of the nodes and the size of the coordinate space of the graph.
scale = 0.1
# spread the labels evenly around the node.
for i, v in enumerate(label_vars):
theta = 2 * math.pi * i / len(label_vars)
new_x = x + scale * math.sin(theta)
new_y = y + scale * math.cos(theta)
plt.text(new_x,
new_y,
str(v),
color=node_color[node_index[(q, v)]],
horizontalalignment='center',
verticalalignment='center')
else:
for v in emb.keys():
c = emb[v]
labels[list(c)[0]] = str(v)
# draw the background (unused) graph first
if unused_color is not None:
draw(G,
layout,
nodelist=nodelist,
edgelist=background_edgelist,
node_color=node_color,
edge_color=background_edge_color,
**kwargs)
draw(G,
layout,
nodelist=nodelist,
edgelist=edgelist,
node_color=node_color,
edge_color=edge_color,
labels=labels,
**kwargs)
import scipy.cluster.hierarchy as sch
import cmap.io.gct as gct
from eVIP_predict import max_diff
from eVIP_compare import getSelfConnectivity, getConnectivity
#############
# CONSTANTS #
#############
DIFF_THRESH = 5
INFINITY = 10
DEF_PRED_COL = "prediction"
#RGB CODES
#GOF_COL = "#e31a1c"
GOF_COL = colorConverter.to_rgba("#ca0020", 1)
COF_PLUS_COL = colorConverter.to_rgba("#c2a5cf", 1)
COF_COL = colorConverter.to_rgba("#6a3d9a", 1)
COF_MINUS_COL = colorConverter.to_rgba("#c2a5cf", 1)
#LOF_COL = "#1f78b4"
LOF_COL = colorConverter.to_rgba("#0571b0", 1)
EQ_COL = colorConverter.to_rgba("#5aae61", 1)
INERT_COL = colorConverter.to_rgba("#000000", 1)
NI_COL = colorConverter.to_rgba("#ffffff", 1)
MAIN_MARKER = 'o'
NEG_MARKER = 'o'
XMIN = 0
XMAX = 4
YMIN = -3
def draw_networkx_edges(G, pos,
edgelist=None,
width=1.0,
edge_color='k',
style='solid',
alpha=1.0,
arrowstyle='-|>',
arrowsize=10,
edge_cmap=None,
edge_vmin=None,
edge_vmax=None,
ax=None,
arrows=True,
label=None,
node_size=300,
nodelist=None,
node_shape="o",
**kwds):
"""Draw the edges of the graph G.
This draws only the edges of the graph G.
Parameters
----------
G : graph
A networkx graph
pos : dictionary
A dictionary with nodes as keys and positions as values.
Positions should be sequences of length 2.
edgelist : collection of edge tuples
Draw only specified edges(default=G.edges())
width : float, or array of floats
Line width of edges (default=1.0)
edge_color : color string, or array of floats
Edge color. Can be a single color format string (default='r'),
or a sequence of colors with the same length as edgelist.
If numeric values are specified they will be mapped to
colors using the edge_cmap and edge_vmin,edge_vmax parameters.
style : string
Edge line style (default='solid') (solid|dashed|dotted,dashdot)
alpha : float
The edge transparency (default=1.0)
edge_ cmap : Matplotlib colormap
Colormap for mapping intensities of edges (default=None)
edge_vmin,edge_vmax : floats
Minimum and maximum for edge colormap scaling (default=None)
ax : Matplotlib Axes object, optional
Draw the graph in the specified Matplotlib axes.
arrows : bool, optional (default=True)
For directed graphs, if True draw arrowheads.
Note: Arrows will be the same color as edges.
arrowstyle : str, optional (default='-|>')
For directed graphs, choose the style of the arrow heads.
See :py:class: `matplotlib.patches.ArrowStyle` for more
options.
arrowsize : int, optional (default=10)
For directed graphs, choose the size of the arrow head head's length and
width. See :py:class: `matplotlib.patches.FancyArrowPatch` for attribute
`mutation_scale` for more info.
label : [None| string]
Label for legend
Returns
-------
matplotlib.collection.LineCollection
`LineCollection` of the edges
list of matplotlib.patches.FancyArrowPatch
`FancyArrowPatch` instances of the directed edges
Depending whether the drawing includes arrows or not.
Notes
-----
For directed graphs, arrows are drawn at the head end. Arrows can be
turned off with keyword arrows=False. Be sure to include `node_size` as a
keyword argument; arrows are drawn considering the size of nodes.
Examples
--------
>>> G = nx.dodecahedral_graph()
>>> edges = nx.draw_networkx_edges(G, pos=nx.spring_layout(G))
>>> G = nx.DiGraph()
>>> G.add_edges_from([(1, 2), (1, 3), (2, 3)])
>>> arcs = nx.draw_networkx_edges(G, pos=nx.spring_layout(G))
>>> alphas = [0.3, 0.4, 0.5]
>>> for i, arc in enumerate(arcs): # change alpha values of arcs
... arc.set_alpha(alphas[i])
Also see the NetworkX drawing examples at
https://networkx.github.io/documentation/latest/auto_examples/index.html
See Also
--------
draw()
draw_networkx()
draw_networkx_nodes()
draw_networkx_labels()
draw_networkx_edge_labels()
"""
try:
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.cbook as cb
from matplotlib.colors import colorConverter, Colormap, Normalize
from matplotlib.collections import LineCollection
from matplotlib.patches import FancyArrowPatch
import numpy as np
except ImportError:
raise ImportError("Matplotlib required for draw()")
except RuntimeError:
print("Matplotlib unable to open display")
raise
if ax is None:
ax = plt.gca()
if edgelist is None:
edgelist = list(G.edges())
if not edgelist or len(edgelist) == 0: # no edges!
return None
if nodelist is None:
nodelist = list(G.nodes())
# set edge positions
edge_pos = np.asarray([(pos[e[0]], pos[e[1]]) for e in edgelist])
if not cb.iterable(width):
lw = (width,)
else:
lw = width
if not is_string_like(edge_color) \
and cb.iterable(edge_color) \
and len(edge_color) == len(edge_pos):
if np.alltrue([is_string_like(c) for c in edge_color]):
# (should check ALL elements)
# list of color letters such as ['k','r','k',...]
edge_colors = tuple([colorConverter.to_rgba(c, alpha)
for c in edge_color])
elif np.alltrue([not is_string_like(c) for c in edge_color]):
# If color specs are given as (rgb) or (rgba) tuples, we're OK
if np.alltrue([cb.iterable(c) and len(c) in (3, 4)
for c in edge_color]):
edge_colors = tuple(edge_color)
else:
# numbers (which are going to be mapped with a colormap)
edge_colors = None
else:
raise ValueError('edge_color must contain color names or numbers')
else:
if is_string_like(edge_color) or len(edge_color) == 1:
edge_colors = (colorConverter.to_rgba(edge_color, alpha), )
else:
msg = 'edge_color must be a color or list of one color per edge'
raise ValueError(msg)
if (not G.is_directed() or not arrows):
edge_collection = LineCollection(edge_pos,
colors=edge_colors,
linewidths=lw,
antialiaseds=(1,),
linestyle=style,
transOffset=ax.transData,
)
edge_collection.set_zorder(1) # edges go behind nodes
edge_collection.set_label(label)
ax.add_collection(edge_collection)
# Note: there was a bug in mpl regarding the handling of alpha values
# for each line in a LineCollection. It was fixed in matplotlib by
# r7184 and r7189 (June 6 2009). We should then not set the alpha
# value globally, since the user can instead provide per-edge alphas
# now. Only set it globally if provided as a scalar.
if isinstance(alpha, Number):
edge_collection.set_alpha(alpha)
if edge_colors is None:
if edge_cmap is not None:
assert(isinstance(edge_cmap, Colormap))
edge_collection.set_array(np.asarray(edge_color))
edge_collection.set_cmap(edge_cmap)
if edge_vmin is not None or edge_vmax is not None:
edge_collection.set_clim(edge_vmin, edge_vmax)
else:
edge_collection.autoscale()
return edge_collection
arrow_collection = None
if G.is_directed() and arrows:
# Note: Waiting for someone to implement arrow to intersection with
# marker. Meanwhile, this works well for polygons with more than 4
# sides and circle.
def to_marker_edge(marker_size, marker):
if marker in "s^>v<d": # `large` markers need extra space
return np.sqrt(2 * marker_size) / 2
else:
return np.sqrt(marker_size) / 2
# Draw arrows with `matplotlib.patches.FancyarrowPatch`
arrow_collection = []
mutation_scale = arrowsize # scale factor of arrow head
arrow_colors = edge_colors
if arrow_colors is None:
if edge_cmap is not None:
assert(isinstance(edge_cmap, Colormap))
else:
edge_cmap = plt.get_cmap() # default matplotlib colormap
if edge_vmin is None:
edge_vmin = min(edge_color)
if edge_vmax is None:
edge_vmax = max(edge_color)
color_normal = Normalize(vmin=edge_vmin, vmax=edge_vmax)
for i, (src, dst) in enumerate(edge_pos):
x1, y1 = src
x2, y2 = dst
arrow_color = None
line_width = None
shrink_source = 0 # space from source to tail
shrink_target = 0 # space from head to target
if cb.iterable(node_size): # many node sizes
src_node, dst_node = edgelist[i]
index_node = nodelist.index(dst_node)
marker_size = node_size[index_node]
shrink_target = to_marker_edge(marker_size, node_shape)
else:
shrink_target = to_marker_edge(node_size, node_shape)
if arrow_colors is None:
arrow_color = edge_cmap(color_normal(edge_color[i]))
elif len(arrow_colors) > 1:
arrow_color = arrow_colors[i]
else:
arrow_color = arrow_colors[0]
if len(lw) > 1:
line_width = lw[i]
else:
line_width = lw[0]
arrow = FancyArrowPatch((x1, y1), (x2, y2),
arrowstyle=arrowstyle,
shrinkA=shrink_source,
shrinkB=shrink_target,
mutation_scale=mutation_scale,
color=arrow_color,
linewidth=line_width,
zorder=1) # arrows go behind nodes
# There seems to be a bug in matplotlib to make collections of
# FancyArrowPatch instances. Until fixed, the patches are added
# individually to the axes instance.
arrow_collection.append(arrow)
ax.add_patch(arrow)
# update view
minx = np.amin(np.ravel(edge_pos[:, :, 0]))
maxx = np.amax(np.ravel(edge_pos[:, :, 0]))
miny = np.amin(np.ravel(edge_pos[:, :, 1]))
maxy = np.amax(np.ravel(edge_pos[:, :, 1]))
w = maxx - minx
h = maxy - miny
padx, pady = 0.05 * w, 0.05 * h
corners = (minx - padx, miny - pady), (maxx + padx, maxy + pady)
ax.update_datalim(corners)
ax.autoscale_view()
return arrow_collection
def eVIP_run_main(pred_file=None,
ref_allele_mode=None,
y_thresh=None,
x_thresh=None,
use_c_pval=None,
annotate=None,
by_gene_color=None,
pdf=None,
xmin=None,
xmax=None,
ymin=None,
ymax=None,
out_dir=None):
x_thresh = float(x_thresh)
y_thresh = float(y_thresh)
# setting default values
xmin = float(xmin) if xmin != None else float(0.0)
xmax = float(xmax) if xmax != None else float(4.0)
ymin = float(ymin) if ymin != None else float(-3.0)
ymax = float(ymax) if ymax != None else float(3.0)
pred_file = open(pred_file)
if pdf:
format = "pdf"
else:
format = "png"
if os.path.exists(out_dir):
out_dir = os.path.abspath(out_dir)
else:
os.mkdir(out_dir)
out_dir = os.path.abspath(out_dir)
print "Creating output directory: %s" % out_dir
x_thresh = getNegLog10(x_thresh, xmax)
y_thresh = getNegLog10(y_thresh, ymax)
annotate = annotate
pred_col = DEF_PRED_COL
ref_allele_mode = ref_allele_mode
gene2type = None
if by_gene_color:
gene2type = parseGeneColor(by_gene_color)
(gene2mut_wt, gene2mut_wt_rep_p, gene2neg_log_p, gene2diff_score,
gene2allele, gene2col, gene_type2pred2count,
gene2markerstyle) = parse_pred_file(pred_file, x_thresh, y_thresh,
pred_col, use_c_pval, gene2type,
ref_allele_mode, xmax, ymax)
if gene2type:
# Print out gene type and prediction counts
# for gene_type in gene_type2pred2count:
# print "###%s###" % gene_type
# for pred in gene_type2pred2count[gene_type]:
# print "%s\t%s" % (pred, gene_type2pred2count[gene_type][pred])
gene_type2data = {
"ONC": {
"mut_wt": [],
"mut_wt_rep_p": [],
"neg_log_p": [],
"markerstyle": [],
"col": []
},
"TSG": {
"mut_wt": [],
"mut_wt_rep_p": [],
"neg_log_p": [],
"markerstyle": [],
"col": []
},
"TSG_noTP53": {
"mut_wt": [],
"mut_wt_rep_p": [],
"neg_log_p": [],
"markerstyle": [],
"col": []
},
"ONC-NEG": {
"mut_wt": [],
"mut_wt_rep_p": [],
"neg_log_p": [],
"markerstyle": [],
"col": []
}
}
all_mut_wt = []
all_mut_wt_rep_p = []
all_neg_log_p = []
all_col = []
all_diff_score = []
all_markerstyle = []
recs = []
predictions = ["GOF", "LOF", "COF", "Neutral"]
for gene in gene2allele:
this_fig = plt.figure()
ax = this_fig.add_subplot(111)
# plt.axhline(y=0, color="grey")
all_mut_wt.extend(gene2mut_wt[gene])
all_mut_wt_rep_p.extend(gene2mut_wt_rep_p[gene])
all_neg_log_p.extend(gene2neg_log_p[gene])
all_col.extend(gene2col[gene])
all_diff_score.extend(gene2diff_score[gene])
all_markerstyle.extend(gene2markerstyle[gene])
if gene not in gene2type:
gene2type[gene] = "UNKN"
# Add to gene-type specific plot data
for gene_type in gene_type2data:
gene_type2data[gene_type]["mut_wt"].extend(gene2mut_wt[gene])
gene_type2data[gene_type]["mut_wt_rep_p"].extend(
gene2mut_wt_rep_p[gene])
gene_type2data[gene_type]["neg_log_p"].extend(gene2neg_log_p[gene])
gene_type2data[gene_type]["markerstyle"].extend(
gene2markerstyle[gene])
gene_root = gene.split("_")[0]
if gene_type == "TSG_noTP53":
if gene2type[gene_root] == "TSG" and gene != "TP53":
gene_type2data[gene_type]["col"].extend(gene2col[gene])
else:
gene_type2data[gene_type]["col"].extend(
makeGrey(gene2col[gene]))
else:
if gene2type[gene_root] == gene_type:
gene_type2data[gene_type]["col"].extend(gene2col[gene])
else:
gene_type2data[gene_type]["col"].extend(
makeGrey(gene2col[gene]))
(main_markers, neg_markers) = split_data(gene2markerstyle[gene],
gene2neg_log_p[gene],
gene2mut_wt_rep_p[gene],
gene2col[gene])
try:
plt.scatter(main_markers["x"],
main_markers["y"],
s=MARKER_SIZE,
c=main_markers["col"],
marker=MAIN_MARKER,
edgecolors="none",
linewidth=0)
except:
pdb.set_trace()
plt.scatter(neg_markers["x"],
neg_markers["y"],
s=MARKER_SIZE,
c=neg_markers["col"],
marker=NEG_MARKER,
linewidth=4)
for i in range(len(gene2neg_log_p[gene])):
this_col = gene2col[gene][i]
if this_col == colorConverter.to_rgba("#ffffff", 1): # white
this_col = "black"
plt.plot([0, gene2neg_log_p[gene][i]],
[0, gene2mut_wt_rep_p[gene][i]],
color=this_col,
linewidth=SPARKLER_LINEWIDTH)
if annotate:
for i in range(len(gene2allele[gene])):
ax.annotate(
gene2allele[gene][i],
(gene2neg_log_p[gene][i], gene2mut_wt_rep_p[gene][i]),
textcoords='data')
plt.axvline(x=x_thresh, color="grey", ls=THRESH_LS)
plt.xlim(xmin, xmax)
plt.ylim(ymin, ymax)
if use_c_pval:
ax.set_xlabel("-log10(corrected p-val)")
else:
ax.set_xlabel("-log10(p-val)")
ax.set_ylabel("impact direction score")
ax.text(100,
-7,
"MUT robustness < WT robustness",
fontsize="small",
ha="center")
ax.text(100,
7,
"MUT robustness > WT robustness",
fontsize="small",
ha="center")
predictions = ["GOF", "LOF", "COF", "Neutral"]
colors = [GOF_COL, LOF_COL, COF_COL, "black"]
for i in range(len(colors)):
recs.append(mpatches.Rectangle((0, 0), 1, 1, fc=colors[i]))
this_fig.savefig("%s/%s_spark_plots.%s" % (out_dir, gene, format),
format=format)
plt.close(this_fig)
# GENE-TYPE plots
for legend_flag in ["legend_on", "legend_off"]:
for gene_type in gene_type2data:
this_fig = plt.figure()
ax = this_fig.add_subplot(111)
# plt.axhline(y=0, color="grey")
# Add lines
for i in range(len(gene_type2data[gene_type]["mut_wt"])):
this_col = gene_type2data[gene_type]["col"][i]
if this_col == colorConverter.to_rgba("#ffffff", 1): # white
this_col = INERT_COL
plt.plot(
[0, gene_type2data[gene_type]["neg_log_p"][i]],
[0, gene_type2data[gene_type]["mut_wt_rep_p"][i]],
# [0, gene_type2data[gene_type]["mut_wt"][i]],
color=this_col,
# alpha=0.5,
linewidth=SPARKLER_LINEWIDTH)
(main_markers, neg_markers) = split_data(
gene_type2data[gene_type]["markerstyle"],
gene_type2data[gene_type]["neg_log_p"],
gene_type2data[gene_type]["mut_wt_rep_p"],
gene_type2data[gene_type]["col"])
plt.scatter(main_markers["x"],
main_markers["y"],
s=MARKER_SIZE,
c=main_markers["col"],
marker=MAIN_MARKER,
edgecolors="none",
linewidth=0)
plt.scatter(neg_markers["x"],
neg_markers["y"],
s=MARKER_SIZE,
c=neg_markers["col"],
marker=NEG_MARKER,
linewidth=4)
plt.axvline(x=x_thresh, color="grey", ls=THRESH_LS)
plt.xlim(xmin, xmax)
plt.ylim(ymin, ymax)
if use_c_pval:
ax.set_xlabel("-log10(corrected p-val)")
else:
ax.set_xlabel("-log10(p-val)")
ax.set_ylabel("impact direction score")
ax.text(100,
-7,
"MUT robustness < WT robustness",
fontsize="small",
ha="center")
ax.text(100,
7,
"MUT robustness > WT robustness",
fontsize="small",
ha="center")
if legend_flag == "legend_on":
plt.legend(recs,
predictions,
loc="lower right",
fontsize='xx-small',
title="prediction")
this_fig.savefig("%s/%s_spark_plots_%s.%s" %
(out_dir, gene_type, legend_flag, format),
format=format)
plt.close(this_fig)
# final plot
this_fig = plt.figure()
ax = this_fig.add_subplot(111)
# Add lines
for i in range(len(all_diff_score)):
this_col = all_col[i]
if this_col == "white":
this_col = "black"
plt.plot([0, all_neg_log_p[i]], [0, all_mut_wt_rep_p[i]],
color=this_col,
alpha=0.25,
linewidth=ALL_SPARKLER_LINEWIDTH)
(main_markers, neg_markers) = split_data(all_markerstyle, all_neg_log_p,
all_mut_wt_rep_p, all_col)
plt.scatter(main_markers["x"],
main_markers["y"],
s=MARKER_SIZE,
c=main_markers["col"],
marker=MAIN_MARKER,
edgecolors="none",
linewidth=0)
plt.scatter(neg_markers["x"],
neg_markers["y"],
s=MARKER_SIZE,
c=neg_markers["col"],
marker=NEG_MARKER,
linewidth=4)
plt.axvline(x=x_thresh, color="grey", ls=THRESH_LS)
plt.xlim(xmin, xmax)
plt.ylim(ymin, ymax)
if use_c_pval:
ax.set_xlabel("-log10(corrected p-val)")
else:
ax.set_xlabel("-log10(p-val)")
ax.set_ylabel("impact direction score")
this_fig.savefig("%s/all_spark_plots.%s" % (out_dir, format),
format=format)
plt.close(this_fig)
def draw_nx_tapered_edges(G,
pos,
edgelist=None,
width=0.5,
edge_color='k',
style='solid',
alpha=1.0,
edge_cmap=None,
edge_vmin=None,
edge_vmax=None,
ax=None,
label=None,
highlight=None,
tapered=False,
**kwds):
"""Draw the edges of the graph G.
This draws only the edges of the graph G.
Parameters
----------
G : graph
A networkx graph
pos : dictionary
A dictionary with nodes as keys and positions as values.
Positions should be sequences of length 2.
edgelist : collection of edge tuples
Draw only specified edges(default=G.edges())
width : float, or array of floats
Line width of edges (default=1.0)
edge_color : color string, or array of floats
Edge color. Can be a single color format string (default='r'),
or a sequence of colors with the same length as edgelist.
If numeric values are specified they will be mapped to
colors using the edge_cmap and edge_vmin,edge_vmax parameters.
style : string
Edge line style (default='solid') (solid|dashed|dotted,dashdot)
alpha : float
The edge transparency (default=1.0)
edge_ cmap : Matplotlib colormap
Colormap for mapping intensities of edges (default=None)
edge_vmin,edge_vmax : floats
Minimum and maximum for edge colormap scaling (default=None)
ax : Matplotlib Axes object, optional
Draw the graph in the specified Matplotlib axes.
label : [None| string]
Label for legend
Returns
-------
matplotlib.collection.LineCollection
`LineCollection` of the edges
Examples
--------
>>> G=nx.dodecahedral_graph()
>>> edges=nx.draw_networkx_edges(G,pos=nx.spring_layout(G))
Also see the NetworkX drawing examples at
http://networkx.github.io/documentation/latest/gallery.html
See Also
--------
draw()
draw_networkx()
draw_networkx_nodes()
draw_networkx_labels()
draw_networkx_edge_labels()
"""
if ax is None:
ax = plt.gca()
if edgelist is None:
edgelist = list(G.edges())
if not edgelist or len(edgelist) == 0: # no edges!
return None
if highlight is not None and (isinstance(edge_color, basestring)
or not cb.iterable(edge_color)):
idMap = {}
nodes = G.nodes()
for i in range(len(nodes)):
idMap[nodes[i]] = i
ecol = [edge_color] * len(edgelist)
eHighlight = [
highlight[idMap[edge[0]]] or highlight[idMap[edge[1]]]
for edge in edgelist
]
for i in range(len(eHighlight)):
if eHighlight[i]:
ecol[i] = '0.0'
edge_color = ecol
# set edge positions
if not cb.iterable(width):
lw = np.full(len(edgelist), width)
else:
lw = width
edge_pos = []
wdScale = 0.01
for i in range(len(edgelist)):
e = edgelist[i]
w = wdScale * lw[i] / 2
p0 = pos[e[0]]
p1 = pos[e[1]]
dx = p1[0] - p0[0]
dy = p1[1] - p0[1]
l = math.sqrt(dx * dx + dy * dy)
edge_pos.append(
((p0[0] + w * dy / l, p0[1] - w * dx / l),
(p0[0] - w * dy / l, p0[1] + w * dx / l), (p1[0], p1[1])))
edge_vertices = np.asarray(edge_pos)
if not isinstance(edge_color, basestring) \
and cb.iterable(edge_color) \
and len(edge_color) == len(edge_vertices):
if np.alltrue([isinstance(c, basestring) for c in edge_color]):
# (should check ALL elements)
# list of color letters such as ['k','r','k',...]
edge_colors = tuple(
[colorConverter.to_rgba(c, alpha) for c in edge_color])
elif np.alltrue([not isinstance(c, basestring) for c in edge_color]):
# If color specs are given as (rgb) or (rgba) tuples, we're OK
if np.alltrue(
[cb.iterable(c) and len(c) in (3, 4) for c in edge_color]):
edge_colors = tuple(edge_color)
else:
# numbers (which are going to be mapped with a colormap)
edge_colors = None
else:
raise ValueError(
'edge_color must consist of either color names or numbers')
else:
if isinstance(edge_color, basestring) or len(edge_color) == 1:
edge_colors = (colorConverter.to_rgba(edge_color, alpha), )
else:
raise ValueError(
'edge_color must be a single color or list of exactly m colors where m is the number or edges'
)
if tapered:
edge_collection = PolyCollection(
edge_vertices,
facecolors=edge_colors,
linewidths=0,
antialiaseds=(1, ),
transOffset=ax.transData,
)
else:
edge_collection = LineCollection(
edge_pos,
colors=edge_colors,
linewidths=lw,
antialiaseds=(1, ),
linestyle=style,
transOffset=ax.transData,
)
edge_collection.set_zorder(1) # edges go behind nodes
edge_collection.set_label(label)
ax.add_collection(edge_collection)
# Note: there was a bug in mpl regarding the handling of alpha values for
# each line in a LineCollection. It was fixed in matplotlib in r7184 and
# r7189 (June 6 2009). We should then not set the alpha value globally,
# since the user can instead provide per-edge alphas now. Only set it
# globally if provided as a scalar.
if cb.is_numlike(alpha):
edge_collection.set_alpha(alpha)
if edge_colors is None:
if edge_cmap is not None:
assert (isinstance(edge_cmap, Colormap))
edge_collection.set_array(np.asarray(edge_color))
edge_collection.set_cmap(edge_cmap)
if edge_vmin is not None or edge_vmax is not None:
edge_collection.set_clim(edge_vmin, edge_vmax)
else:
edge_collection.autoscale()
# update view
minx = np.amin(np.ravel(edge_vertices[:, :, 0]))
maxx = np.amax(np.ravel(edge_vertices[:, :, 0]))
miny = np.amin(np.ravel(edge_vertices[:, :, 1]))
maxy = np.amax(np.ravel(edge_vertices[:, :, 1]))
w = maxx - minx
h = maxy - miny
padx, pady = 0.05 * w, 0.05 * h
corners = (minx - padx, miny - pady), (maxx + padx, maxy + pady)
ax.update_datalim(corners)
ax.autoscale_view()
return edge_collection
def main():
opt_parser = OptionParser()
# Add Options. Required options should have default=None
opt_parser.add_option("--pred_file",
dest="pred_file",
type="string",
help="""File containing the mutation impact
predictions""",
default=None)
opt_parser.add_option("--ref_allele_mode",
dest="ref_allele_mode",
action="store_true",
help="""Instead of organizing plots by gene, will use
the wt column to determine what are the
reference alleles.""",
default=False)
opt_parser.add_option("--x_thresh",
dest="x_thresh",
type="float",
help="Threshold of significance",
default=None)
opt_parser.add_option("--y_thresh",
dest="y_thresh",
type="float",
help="Threshold of impact direction",
default=None)
opt_parser.add_option("--use_c_pval",
dest="use_c_pval",
action="store_true",
help="Use corrected p-val instead of raw p-val",
default=False)
opt_parser.add_option("--annotate",
dest="annotate",
action="store_true",
help="Will add allele labels to points.",
default=False)
opt_parser.add_option("--by_gene_color",
dest="by_gene_color",
type="string",
help="""File containing labels and colors for
gene-cenric plot""",
default=None)
opt_parser.add_option("--out_dir",
dest="out_dir",
type="string",
help="Output directory to put figures",
default=None)
opt_parser.add_option("--pdf",
dest="pdf",
action="store_true",
help="Will print plots in pdf format instead of png",
default=False)
opt_parser.add_option("--xmin",
dest="xmin",
type="float",
help="Min value of x-axis. DEF=%d" % XMIN,
default=XMIN)
opt_parser.add_option("--xmax",
dest="xmax",
type="float",
help="Max value of x-axis. DEF=%d" % XMAX,
default=XMAX)
opt_parser.add_option("--ymin",
dest="ymin",
type="float",
help="Min value of y-axis. DEF=%d" % YMIN,
default=YMIN)
opt_parser.add_option("--ymax",
dest="ymax",
type="float",
help="Max value of y-axis. DEF=%d" % YMAX,
default=YMAX)
(options, args) = opt_parser.parse_args()
# validate the command line arguments
opt_parser.check_required("--pred_file")
opt_parser.check_required("--x_thresh")
opt_parser.check_required("--y_thresh")
opt_parser.check_required("--by_gene_color")
opt_parser.check_required("--out_dir")
pred_file = open(options.pred_file)
if options.pdf:
format = "pdf"
else:
format = "png"
if os.path.exists(options.out_dir):
out_dir = os.path.abspath(options.out_dir)
else:
os.mkdir(options.out_dir)
out_dir = os.path.abspath(options.out_dir)
print "Creating output directory: %s" % out_dir
x_thresh = getNegLog10(options.x_thresh, options.xmax)
y_thresh = getNegLog10(options.y_thresh, options.ymax)
annotate = options.annotate
pred_col = DEF_PRED_COL
ref_allele_mode = options.ref_allele_mode
use_c_pval = options.use_c_pval
xmin = options.xmin
xmax = options.xmax
ymin = options.ymin
ymax = options.ymax
gene2type = None
if options.by_gene_color:
gene2type = parseGeneColor(options.by_gene_color)
(gene2mut_wt, gene2mut_wt_rep_p, gene2neg_log_p, gene2diff_score,
gene2allele, gene2col, gene_type2pred2count,
gene2markerstyle) = parse_pred_file(pred_file, x_thresh, y_thresh,
pred_col, use_c_pval, gene2type,
ref_allele_mode, xmax, ymax)
if gene2type:
# Print out gene type and prediction counts
# for gene_type in gene_type2pred2count:
# print "###%s###" % gene_type
# for pred in gene_type2pred2count[gene_type]:
# print "%s\t%s" % (pred, gene_type2pred2count[gene_type][pred])
gene_type2data = {
"ONC": {
"mut_wt": [],
"mut_wt_rep_p": [],
"neg_log_p": [],
"markerstyle": [],
"col": []
},
"TSG": {
"mut_wt": [],
"mut_wt_rep_p": [],
"neg_log_p": [],
"markerstyle": [],
"col": []
},
"TSG_noTP53": {
"mut_wt": [],
"mut_wt_rep_p": [],
"neg_log_p": [],
"markerstyle": [],
"col": []
},
"ONC-NEG": {
"mut_wt": [],
"mut_wt_rep_p": [],
"neg_log_p": [],
"markerstyle": [],
"col": []
}
}
all_mut_wt = []
all_mut_wt_rep_p = []
all_neg_log_p = []
all_col = []
all_diff_score = []
all_markerstyle = []
predictions = ["GOF", "LOF", "COF", "Neutral"]
for gene in gene2allele:
this_fig = plt.figure()
ax = this_fig.add_subplot(111)
# plt.axhline(y=0, color="grey")
all_mut_wt.extend(gene2mut_wt[gene])
all_mut_wt_rep_p.extend(gene2mut_wt_rep_p[gene])
all_neg_log_p.extend(gene2neg_log_p[gene])
all_col.extend(gene2col[gene])
all_diff_score.extend(gene2diff_score[gene])
all_markerstyle.extend(gene2markerstyle[gene])
if gene not in gene2type:
gene2type[gene] = "UNKN"
# Add to gene-type specific plot data
for gene_type in gene_type2data:
gene_type2data[gene_type]["mut_wt"].extend(gene2mut_wt[gene])
gene_type2data[gene_type]["mut_wt_rep_p"].extend(
gene2mut_wt_rep_p[gene])
gene_type2data[gene_type]["neg_log_p"].extend(gene2neg_log_p[gene])
gene_type2data[gene_type]["markerstyle"].extend(
gene2markerstyle[gene])
gene_root = gene.split("_")[0]
if gene_type == "TSG_noTP53":
if gene2type[gene_root] == "TSG" and gene != "TP53":
gene_type2data[gene_type]["col"].extend(gene2col[gene])
else:
gene_type2data[gene_type]["col"].extend(
makeGrey(gene2col[gene]))
else:
if gene2type[gene_root] == gene_type:
gene_type2data[gene_type]["col"].extend(gene2col[gene])
else:
gene_type2data[gene_type]["col"].extend(
makeGrey(gene2col[gene]))
(main_markers, neg_markers) = split_data(gene2markerstyle[gene],
gene2neg_log_p[gene],
gene2mut_wt_rep_p[gene],
gene2col[gene])
try:
plt.scatter(main_markers["x"],
main_markers["y"],
s=MARKER_SIZE,
c=main_markers["col"],
marker=MAIN_MARKER,
edgecolors="none",
linewidth=0)
except:
pdb.set_trace()
plt.scatter(neg_markers["x"],
neg_markers["y"],
s=MARKER_SIZE,
c=neg_markers["col"],
marker=NEG_MARKER,
linewidth=4)
for i in range(len(gene2neg_log_p[gene])):
this_col = gene2col[gene][i]
if this_col == colorConverter.to_rgba("#ffffff", 1): # white
this_col = "black"
plt.plot([0, gene2neg_log_p[gene][i]],
[0, gene2mut_wt_rep_p[gene][i]],
color=this_col,
linewidth=SPARKLER_LINEWIDTH)
if annotate:
for i in range(len(gene2allele[gene])):
ax.annotate(
gene2allele[gene][i],
(gene2neg_log_p[gene][i], gene2mut_wt_rep_p[gene][i]),
textcoords='data')
plt.axvline(x=x_thresh, color="grey", ls=THRESH_LS)
plt.xlim(xmin, xmax)
plt.ylim(ymin, ymax)
if use_c_pval:
ax.set_xlabel("-log10(corrected p-val)")
else:
ax.set_xlabel("-log10(p-val)")
ax.set_ylabel("impact direction score")
ax.text(100,
-7,
"MUT robustness < WT robustness",
fontsize="small",
ha="center")
ax.text(100,
7,
"MUT robustness > WT robustness",
fontsize="small",
ha="center")
# predictions = ["GOF", "LOF", "COF", "Neutral"]
colors = [GOF_COL, LOF_COL, COF_COL, "black"]
recs = []
for i in range(len(colors)):
recs.append(mpatches.Rectangle((0, 0), 1, 1, fc=colors[i]))
this_fig.savefig("%s/%s_spark_plots.%s" % (out_dir, gene, format),
format=format)
plt.close(this_fig)
# GENE-TYPE plots
for legend_flag in ["legend_on", "legend_off"]:
for gene_type in gene_type2data:
this_fig = plt.figure()
ax = this_fig.add_subplot(111)
# plt.axhline(y=0, color="grey")
# Add lines
for i in range(len(gene_type2data[gene_type]["mut_wt"])):
this_col = gene_type2data[gene_type]["col"][i]
if this_col == colorConverter.to_rgba("#ffffff", 1): # white
this_col = INERT_COL
plt.plot(
[0, gene_type2data[gene_type]["neg_log_p"][i]],
[0, gene_type2data[gene_type]["mut_wt_rep_p"][i]],
# [0, gene_type2data[gene_type]["mut_wt"][i]],
color=this_col,
# alpha=0.5,
linewidth=SPARKLER_LINEWIDTH)
(main_markers, neg_markers) = split_data(
gene_type2data[gene_type]["markerstyle"],
gene_type2data[gene_type]["neg_log_p"],
gene_type2data[gene_type]["mut_wt_rep_p"],
gene_type2data[gene_type]["col"])
plt.scatter(main_markers["x"],
main_markers["y"],
s=MARKER_SIZE,
c=main_markers["col"],
marker=MAIN_MARKER,
edgecolors="none",
linewidth=0)
plt.scatter(neg_markers["x"],
neg_markers["y"],
s=MARKER_SIZE,
c=neg_markers["col"],
marker=NEG_MARKER,
linewidth=4)
plt.axvline(x=x_thresh, color="grey", ls=THRESH_LS)
plt.xlim(xmin, xmax)
plt.ylim(ymin, ymax)
if use_c_pval:
ax.set_xlabel("-log10(corrected p-val)")
else:
ax.set_xlabel("-log10(p-val)")
ax.set_ylabel("impact direction score")
ax.text(100,
-7,
"MUT robustness < WT robustness",
fontsize="small",
ha="center")
ax.text(100,
7,
"MUT robustness > WT robustness",
fontsize="small",
ha="center")
if legend_flag == "legend_on":
plt.legend(recs,
predictions,
loc="lower right",
fontsize='xx-small',
title="prediction")
this_fig.savefig("%s/%s_spark_plots_%s.%s" %
(out_dir, gene_type, legend_flag, format),
format=format)
plt.close(this_fig)
# final plot
this_fig = plt.figure()
ax = this_fig.add_subplot(111)
# Add lines
for i in range(len(all_diff_score)):
this_col = all_col[i]
if this_col == "white":
this_col = "black"
plt.plot([0, all_neg_log_p[i]], [0, all_mut_wt_rep_p[i]],
color=this_col,
alpha=0.25,
linewidth=ALL_SPARKLER_LINEWIDTH)
(main_markers, neg_markers) = split_data(all_markerstyle, all_neg_log_p,
all_mut_wt_rep_p, all_col)
plt.scatter(main_markers["x"],
main_markers["y"],
s=MARKER_SIZE,
c=main_markers["col"],
marker=MAIN_MARKER,
edgecolors="none",
linewidth=0)
plt.scatter(neg_markers["x"],
neg_markers["y"],
s=MARKER_SIZE,
c=neg_markers["col"],
marker=NEG_MARKER,
linewidth=4)
plt.axvline(x=x_thresh, color="grey", ls=THRESH_LS)
plt.xlim(xmin, xmax)
plt.ylim(ymin, ymax)
if use_c_pval:
ax.set_xlabel("-log10(corrected p-val)")
else:
ax.set_xlabel("-log10(p-val)")
ax.set_ylabel("impact direction score")
this_fig.savefig("%s/all_spark_plots.%s" % (out_dir, format),
format=format)
plt.close(this_fig)
sys.exit(0)
test_set = Ising(DATA_PATH, train=False)
test_loader = DataLoader(test_set, batch_size=BATCH_SIZE, shuffle=True)
net = Network()
net.load_state_dict(torch.load(MODEL_PATH))
if CUDA:
net.to(torch.device('cuda'))
net.eval()
losses, targets, predictions = evaluate(net, test_loader, CUDA=CUDA)
r = spearmanr(targets, predictions).correlation
loss_color = colorConverter.to_rgba('mediumseagreen', alpha=.5)
markerOptions = dict(markerfacecolor='none', markeredgewidth=1.5)
fig, ax = plt.subplots(figsize=(10, 6))
ax.title.set_text(f"Spearman Correlation Coefficient r={r:.4f}")
ax.plot(targets,
predictions,
linestyle='None',
marker='s',
color='blue',
**markerOptions)
ax.set_ylabel('Predicted Temperature T/T$_C$')
ax.set_xlabel("Ground truth T/T$_C$")
def apply_alpha(colors, alpha, elem_list, cmap=None, vmin=None, vmax=None):
"""Apply an alpha (or list of alphas) to the colors provided.
Parameters
----------
colors : color string, or array of floats
Color of element. Can be a single color format string (default='r'),
or a sequence of colors with the same length as nodelist.
If numeric values are specified they will be mapped to
colors using the cmap and vmin,vmax parameters. See
matplotlib.scatter for more details.
alpha : float or array of floats
Alpha values for elements. This can be a single alpha value, in
which case it will be applied to all the elements of color. Otherwise,
if it is an array, the elements of alpha will be applied to the colors
in order (cycling through alpha multiple times if necessary).
elem_list : array of networkx objects
The list of elements which are being colored. These could be nodes,
edges or labels.
cmap : matplotlib colormap
Color map for use if colors is a list of floats corresponding to points
on a color mapping.
vmin, vmax : float
Minimum and maximum values for normalizing colors if a color mapping is
used.
Returns
-------
rgba_colors : numpy ndarray
Array containing RGBA format values for each of the node colours.
"""
from itertools import islice, cycle
try:
import numpy as np
from matplotlib.colors import colorConverter
import matplotlib.cm as cm
except ImportError:
raise ImportError("Matplotlib required for draw()")
# If we have been provided with a list of numbers as long as elem_list,
# apply the color mapping.
if len(colors) == len(elem_list) and isinstance(colors[0], Number):
mapper = cm.ScalarMappable(cmap=cmap)
mapper.set_clim(vmin, vmax)
rgba_colors = mapper.to_rgba(colors)
# Otherwise, convert colors to matplotlib's RGB using the colorConverter
# object. These are converted to numpy ndarrays to be consistent with the
# to_rgba method of ScalarMappable.
else:
try:
rgba_colors = np.array([colorConverter.to_rgba(colors)])
except ValueError:
rgba_colors = np.array([colorConverter.to_rgba(color)
for color in colors])
# Set the final column of the rgba_colors to have the relevant alpha values
try:
# If alpha is longer than the number of colors, resize to the number of
# elements. Also, if rgba_colors.size (the number of elements of
# rgba_colors) is the same as the number of elements, resize the array,
# to avoid it being interpreted as a colormap by scatter()
if len(alpha) > len(rgba_colors) or rgba_colors.size == len(elem_list):
rgba_colors = np.resize(rgba_colors, (len(elem_list), 4))
rgba_colors[1:, 0] = rgba_colors[0, 0]
rgba_colors[1:, 1] = rgba_colors[0, 1]
rgba_colors[1:, 2] = rgba_colors[0, 2]
rgba_colors[:, 3] = list(islice(cycle(alpha), len(rgba_colors)))
except TypeError:
rgba_colors[:, -1] = alpha
return rgba_colors
def cc(arg): return colorConverter.to_rgba(arg, alpha=0.6)
colors = [cc('r'), cc('g'), cc('b'), cc('y')]
#
fig = plt.figure(1)
plt.plot(aa, bb, linewidth=1.0, color='b')
plt.grid(True)
plt.xlabel('Time (sec)')
plt.title('Input Time History')
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
from matplotlib.colors import colorConverter
fig = plt.figure(2)
ax = fig.gca(projection='3d')
cc = lambda arg: colorConverter.to_rgba(arg, alpha=0.6)
verts = []
ys = zeros(mk, 'f')
zs = zeros(mk, 'f')
maxz = 0
for i in range(0, NW):
for j in range(0, mk):
zs[j] = store_p[i][j]
if zs[j] > maxz:
maxz = zs[j]
def plot_surface(self, X, Y, Z, *args, **kwargs):
'''
Create a surface plot.
By default it will be colored in shades of a solid color,
but it also supports color mapping by supplying the *cmap*
argument.
============= ================================================
Argument Description
============= ================================================
*X*, *Y*, *Z* Data values as numpy.arrays
*rstride* Array row stride (step size)
*cstride* Array column stride (step size)
*color* Color of the surface patches
*cmap* A colormap for the surface patches.
*facecolors* Face colors for the individual patches
*norm* An instance of Normalize to map values to colors
*vmin* Minimum value to map
*vmax* Maximum value to map
*shade* Whether to shade the facecolors
============= ================================================
Other arguments are passed on to
:func:`~mpl_toolkits.mplot3d.art3d.Poly3DCollection.__init__`
'''
had_data = self.has_data()
rows, cols = Z.shape
rstride = kwargs.pop('rstride', 10)
cstride = kwargs.pop('cstride', 10)
if 'facecolors' in kwargs:
fcolors = kwargs.pop('facecolors')
else:
color = np.array(colorConverter.to_rgba(kwargs.pop('color', 'b')))
fcolors = None
cmap = kwargs.get('cmap', None)
norm = kwargs.pop('norm', None)
vmin = kwargs.pop('vmin', None)
vmax = kwargs.pop('vmax', None)
linewidth = kwargs.get('linewidth', None)
shade = kwargs.pop('shade', cmap is None)
lightsource = kwargs.pop('lightsource', None)
# Shade the data
if shade and cmap is not None and fcolors is not None:
fcolors = self._shade_colors_lightsource(Z, cmap, lightsource)
polys = []
# Only need these vectors to shade if there is no cmap
if cmap is None and shade :
totpts = int(np.ceil(float(rows - 1) / rstride) *
np.ceil(float(cols - 1) / cstride))
v1 = np.empty((totpts, 3))
v2 = np.empty((totpts, 3))
# This indexes the vertex points
which_pt = 0
#colset contains the data for coloring: either average z or the facecolor
colset = []
for rs in xrange(0, rows-1, rstride):
for cs in xrange(0, cols-1, cstride):
ps = []
for a in (X, Y, Z) :
ztop = a[rs,cs:min(cols, cs+cstride+1)]
zleft = a[rs+1:min(rows, rs+rstride+1),
min(cols-1, cs+cstride)]
zbase = a[min(rows-1, rs+rstride), cs:min(cols, cs+cstride+1):][::-1]
zright = a[rs:min(rows-1, rs+rstride):, cs][::-1]
z = np.concatenate((ztop, zleft, zbase, zright))
ps.append(z)
# The construction leaves the array with duplicate points, which
# are removed here.
ps = zip(*ps)
lastp = np.array([])
ps2 = [ps[0]] + [ps[i] for i in xrange(1, len(ps)) if ps[i] != ps[i-1]]
avgzsum = sum(p[2] for p in ps2)
polys.append(ps2)
if fcolors is not None:
colset.append(fcolors[rs][cs])
else:
colset.append(avgzsum / len(ps2))
# Only need vectors to shade if no cmap
if cmap is None and shade:
i1, i2, i3 = 0, int(len(ps2)/3), int(2*len(ps2)/3)
v1[which_pt] = np.array(ps2[i1]) - np.array(ps2[i2])
v2[which_pt] = np.array(ps2[i2]) - np.array(ps2[i3])
which_pt += 1
if cmap is None and shade:
normals = np.cross(v1, v2)
else :
normals = []
polyc = art3d.Poly3DCollection(polys, *args, **kwargs)
if fcolors is not None:
if shade:
colset = self._shade_colors(colset, normals)
polyc.set_facecolors(colset)
polyc.set_edgecolors(colset)
elif cmap:
colset = np.array(colset)
polyc.set_array(colset)
if vmin is not None or vmax is not None:
polyc.set_clim(vmin, vmax)
if norm is not None:
polyc.set_norm(norm)
else:
if shade:
colset = self._shade_colors(color, normals)
else:
colset = color
polyc.set_facecolors(colset)
self.add_collection(polyc)
self.auto_scale_xyz(X, Y, Z, had_data)
return polyc
def hist2d(x,
y,
bins=20,
range=None,
weights=None,
levels=None,
smooth=None,
ax=None,
color=None,
quiet=False,
plot_datapoints=True,
plot_density=True,
plot_contours=True,
no_fill_contours=False,
fill_contours=False,
contour_kwargs=None,
contourf_kwargs=None,
data_kwargs=None,
**kwargs):
"""
Plot a 2-D histogram of samples.
Parameters
----------
x : array_like[nsamples,]
The samples.
y : array_like[nsamples,]
The samples.
quiet : bool
If true, suppress warnings for small datasets.
levels : array_like
The contour levels to draw.
ax : matplotlib.Axes
A axes instance on which to add the 2-D histogram.
plot_datapoints : bool
Draw the individual data points.
plot_density : bool
Draw the density colormap.
plot_contours : bool
Draw the contours.
no_fill_contours : bool
Add no filling at all to the contours (unlike setting
``fill_contours=False``, which still adds a white fill at the densest
points).
fill_contours : bool
Fill the contours.
contour_kwargs : dict
Any additional keyword arguments to pass to the `contour` method.
contourf_kwargs : dict
Any additional keyword arguments to pass to the `contourf` method.
data_kwargs : dict
Any additional keyword arguments to pass to the `plot` method when
adding the individual data points.
"""
if ax is None:
ax = pl.gca()
# Set the default range based on the data range if not provided.
if range is None:
if "extent" in kwargs:
logging.warn("Deprecated keyword argument 'extent'. "
"Use 'range' instead.")
range = kwargs["extent"]
else:
range = [[x.min(), x.max()], [y.min(), y.max()]]
# Set up the default plotting arguments.
if color is None:
color = "k"
# Choose the default "sigma" contour levels.
if levels is None:
levels = 1.0 - np.exp(-0.5 * np.arange(0.5, 2.1, 0.5)**2)
# This is the color map for the density plot, over-plotted to indicate the
# density of the points near the center.
density_cmap = LinearSegmentedColormap.from_list("density_cmap",
[color, (1, 1, 1, 0)])
# This color map is used to hide the points at the high density areas.
white_cmap = LinearSegmentedColormap.from_list("white_cmap", [(1, 1, 1),
(1, 1, 1)],
N=2)
# This "color map" is the list of colors for the contour levels if the
# contours are filled.
rgba_color = colorConverter.to_rgba(color)
contour_cmap = [list(rgba_color) for l in levels] + [rgba_color]
for i, l in enumerate(levels):
contour_cmap[i][-1] *= float(i) / (len(levels) + 1)
# We'll make the 2D histogram to directly estimate the density.
try:
H, X, Y = np.histogram2d(x.flatten(),
y.flatten(),
bins=bins,
range=list(map(np.sort, range)),
weights=weights)
except ValueError:
raise ValueError("It looks like at least one of your sample columns "
"have no dynamic range. You could try using the "
"'range' argument.")
if smooth is not None:
if gaussian_filter is None:
raise ImportError("Please install scipy for smoothing")
H = gaussian_filter(H, smooth)
if plot_contours or plot_density:
# Compute the density levels.
Hflat = H.flatten()
inds = np.argsort(Hflat)[::-1]
Hflat = Hflat[inds]
sm = np.cumsum(Hflat)
sm /= sm[-1]
V = np.empty(len(levels))
for i, v0 in enumerate(levels):
try:
V[i] = Hflat[sm <= v0][-1]
except:
V[i] = Hflat[0]
V.sort()
m = np.diff(V) == 0
if np.any(m) and not quiet:
logging.warning("Too few points to create valid contours")
while np.any(m):
V[np.where(m)[0][0]] *= 1.0 - 1e-4
m = np.diff(V) == 0
V.sort()
# Compute the bin centers.
X1, Y1 = 0.5 * (X[1:] + X[:-1]), 0.5 * (Y[1:] + Y[:-1])
# Extend the array for the sake of the contours at the plot edges.
H2 = H.min() + np.zeros((H.shape[0] + 4, H.shape[1] + 4))
H2[2:-2, 2:-2] = H
H2[2:-2, 1] = H[:, 0]
H2[2:-2, -2] = H[:, -1]
H2[1, 2:-2] = H[0]
H2[-2, 2:-2] = H[-1]
H2[1, 1] = H[0, 0]
H2[1, -2] = H[0, -1]
H2[-2, 1] = H[-1, 0]
H2[-2, -2] = H[-1, -1]
X2 = np.concatenate([
X1[0] + np.array([-2, -1]) * np.diff(X1[:2]),
X1,
X1[-1] + np.array([1, 2]) * np.diff(X1[-2:]),
])
Y2 = np.concatenate([
Y1[0] + np.array([-2, -1]) * np.diff(Y1[:2]),
Y1,
Y1[-1] + np.array([1, 2]) * np.diff(Y1[-2:]),
])
if plot_datapoints:
if data_kwargs is None:
data_kwargs = dict()
data_kwargs["color"] = data_kwargs.get("color", color)
data_kwargs["ms"] = data_kwargs.get("ms", 2.0)
data_kwargs["mec"] = data_kwargs.get("mec", "none")
data_kwargs["alpha"] = data_kwargs.get("alpha", 0.1)
ax.plot(x, y, "o", zorder=-1, rasterized=True, **data_kwargs)
# Plot the base fill to hide the densest data points.
if (plot_contours or plot_density) and not no_fill_contours:
ax.contourf(X2,
Y2,
H2.T, [V.min(), H.max()],
cmap=white_cmap,
antialiased=False)
if plot_contours and fill_contours:
if contourf_kwargs is None:
contourf_kwargs = dict()
contourf_kwargs["colors"] = contourf_kwargs.get("colors", contour_cmap)
contourf_kwargs["antialiased"] = contourf_kwargs.get(
"antialiased", False)
ax.contourf(X2, Y2, H2.T,
np.concatenate([[0], V, [H.max() * (1 + 1e-4)]]),
**contourf_kwargs)
# Plot the density map. This can't be plotted at the same time as the
# contour fills.
elif plot_density:
ax.pcolor(X, Y, H.max() - H.T, cmap=density_cmap)
# Plot the contour edge colors.
if plot_contours:
if contour_kwargs is None:
contour_kwargs = dict()
contour_kwargs["colors"] = contour_kwargs.get("colors", color)
ax.contour(X2, Y2, H2.T, V, **contour_kwargs)
ax.set_xlim(range[0])
ax.set_ylim(range[1])
q = args.q
I = args.I
sigq = args.sigq
f = plt.figure(figsize=[6,6])
gs = gridspec.GridSpec(2, 1, height_ratios=[3,1])
ax0 = plt.subplot(gs[0])
#handle sigq values whose error bounds would go negative and be missing on the log scale
#sigq2 = np.copy(sigq)
#sigq2[sigq>I] = I[sigq>I]*.999
sigq2 = np.interp(qraw, q, sigq)
sigq2[sigq2>Iraw] = Iraw[sigq2>Iraw]*.999
#ax0.errorbar(q, I, fmt='k-', yerr=[sigq2[q<=q[-1]],sigq[q<=q[-1]]], capsize=0, elinewidth=0.1, ecolor=cc.to_rgba('0',alpha=0.5),label='Supplied Data')
#ax0.plot(q, I, 'k.',alpha=0.1,mec='none',label='Raw Data')
ax0.errorbar(qraw, Iraw, fmt='k.', yerr=sigq2, mec='none', mew=0, ms=3, alpha=0.3, capsize=0, elinewidth=0.1, ecolor=cc.to_rgba('0',alpha=0.5),label='Supplied Data')
ax0.plot(q, I, 'k--',alpha=0.7,lw=1,label='Supplied Fit')
ax0.plot(qdata[qdata<=q[-1]], Idata[qdata<=q[-1]], 'bo',alpha=0.5,label='Interpolated')
ax0.plot(qbinsc[qdata<=q[-1]],Imean[qdata<=q[-1]],'r.',label='DENSS Map')
handles,labels = ax0.get_legend_handles_labels()
handles = [handles[3], handles[0], handles[1],handles[2]]
labels = [labels[3], labels[0], labels[1], labels[2]]
xmax = np.min([qraw.max(),q.max(),qdata.max()])*1.1
ymin = np.min([np.min(I[q<=xmax]),np.min(Idata[qdata<=xmax]),np.min(Imean[qdata<=xmax])])
ymax = np.max([np.max(I[q<=xmax]),np.max(Idata[qdata<=xmax]),np.max(Imean[qdata<=xmax])])
ax0.set_xlim([-xmax*.05,xmax])
ax0.set_ylim([0.5*ymin,1.5*ymax])
ax0.legend(handles,labels)
ax0.semilogy()
ax0.set_ylabel('I(q)')
npts = 100
# Make some spirals
r = N.array(range(nverts))
theta = N.array(range(nverts)) * (2*N.pi)/(nverts-1)
xx = r * N.sin(theta)
yy = r * N.cos(theta)
spiral = zip(xx,yy)
# Make some offsets
xo = N.random.randn(npts)
yo = N.random.randn(npts)
xyo = zip(xo, yo)
# Make a list of colors cycling through the rgbcmyk series.
colors = [colorConverter.to_rgba(c) for c in ('r','g','b','c','y','m','k')]
fig = P.figure()
a = fig.add_subplot(2,2,1)
col = collections.LineCollection([spiral], offsets=xyo,
transOffset=a.transData)
trans = transforms.Affine2D().scale(fig.dpi/72.0)
col.set_transform(trans) # the points to pixels transform
# Note: the first argument to the collection initializer
# must be a list of sequences of x,y tuples; we have only
# one sequence, but we still have to put it in a list.
a.add_collection(col, autolim=True)
# autolim=True enables autoscaling. For collections with
# offsets like this, it is neither efficient nor accurate,
# but it is good enough to generate a plot that you can use
def cc(arg):
return colorConverter.to_rgba(arg, alpha=0.6)
def draw_networkx_edges(G, pos,
edgelist=None,
width=1.0,
edge_color='k',
style='solid',
alpha=None,
edge_cmap=None,
edge_vmin=None,
edge_vmax=None,
ax=None,
arrows=True,
**kwds):
"""Draw the edges of the graph G
This draws only the edges of the graph G.
pos is a dictionary keyed by vertex with a two-tuple
of x-y positions as the value.
See networkx.layout for functions that compute node positions.
edgelist is an optional list of the edges in G to be drawn.
If provided, only the edges in edgelist will be drawn.
edgecolor can be a list of matplotlib color letters such as 'k' or
'b' that lists the color of each edge; the list must be ordered in
the same way as the edge list. Alternatively, this list can contain
numbers and those number are mapped to a color scale using the color
map edge_cmap. Finally, it can also be a list of (r,g,b) or (r,g,b,a)
tuples, in which case these will be used directly to color the edges. If
the latter mode is used, you should not provide a value for alpha, as it
would be applied globally to all lines.
For directed graphs, "arrows" (actually just thicker stubs) are drawn
at the head end. Arrows can be turned off with keyword arrows=False.
See draw_networkx for the list of other optional parameters.
"""
try:
import matplotlib
import matplotlib.pylab as pylab
import matplotlib.cbook as cb
from matplotlib.colors import colorConverter,Colormap
from matplotlib.collections import LineCollection
import numpy
except ImportError:
raise ImportError, "Matplotlib required for draw()"
except RuntimeError:
pass # unable to open display
if ax is None:
ax=pylab.gca()
if edgelist is None:
edgelist=G.edges()
if not edgelist or len(edgelist)==0: # no edges!
return None
# set edge positions
edge_pos=numpy.asarray([(pos[e[0]],pos[e[1]]) for e in edgelist])
if not cb.iterable(width):
lw = (width,)
else:
lw = width
if not cb.is_string_like(edge_color) \
and cb.iterable(edge_color) \
and len(edge_color)==len(edge_pos):
if numpy.alltrue([cb.is_string_like(c)
for c in edge_color]):
# (should check ALL elements)
# list of color letters such as ['k','r','k',...]
edge_colors = tuple([colorConverter.to_rgba(c,alpha)
for c in edge_color])
elif numpy.alltrue([not cb.is_string_like(c)
for c in edge_color]):
# If color specs are given as (rgb) or (rgba) tuples, we're OK
if numpy.alltrue([cb.iterable(c) and len(c) in (3,4)
for c in edge_color]):
edge_colors = tuple(edge_color)
else:
# numbers (which are going to be mapped with a colormap)
edge_colors = None
else:
raise ValueError('edge_color must consist of either color names or numbers')
else:
if len(edge_color)==1:
edge_colors = ( colorConverter.to_rgba(edge_color, alpha), )
else:
raise ValueError('edge_color must be a single color or list of exactly m colors where m is the number or edges')
edge_collection = LineCollection(edge_pos,
colors = edge_colors,
linewidths = lw,
antialiaseds = (1,),
linestyle = style,
transOffset = ax.transData,
)
# Note: there was a bug in mpl regarding the handling of alpha values for
# each line in a LineCollection. It was fixed in matplotlib in r7184 and
# r7189 (June 6 2009). We should then not set the alpha value globally,
# since the user can instead provide per-edge alphas now. Only set it
# globally if provided as a scalar.
if cb.is_numlike(alpha):
edge_collection.set_alpha(alpha)
# need 0.87.7 or greater for edge colormaps
mpl_version=matplotlib.__version__
if mpl_version.endswith('svn'):
mpl_version=matplotlib.__version__[0:-3]
if mpl_version.endswith('pre'):
mpl_version=matplotlib.__version__[0:-3]
if map(int,mpl_version.split('.'))>=[0,87,7]:
if edge_colors is None:
if edge_cmap is not None: assert(isinstance(edge_cmap, Colormap))
edge_collection.set_array(numpy.asarray(edge_color))
edge_collection.set_cmap(edge_cmap)
if edge_vmin is not None or edge_vmax is not None:
edge_collection.set_clim(edge_vmin, edge_vmax)
else:
edge_collection.autoscale()
pylab.sci(edge_collection)
# else:
# sys.stderr.write(\
# """matplotlib version >= 0.87.7 required for colormapped edges.
# (version %s detected)."""%matplotlib.__version__)
# raise UserWarning(\
# """matplotlib version >= 0.87.7 required for colormapped edges.
# (version %s detected)."""%matplotlib.__version__)
arrow_collection=None
if G.is_directed() and arrows:
# a directed graph hack
# draw thick line segments at head end of edge
# waiting for someone else to implement arrows that will work
arrow_colors = ( colorConverter.to_rgba('k', alpha), )
a_pos=[]
p=1.0-0.25 # make head segment 25 percent of edge length
for src,dst in edge_pos:
x1,y1=src
x2,y2=dst
dx=x2-x1 # x offset
dy=y2-y1 # y offset
d=numpy.sqrt(float(dx**2+dy**2)) # length of edge
if d==0: # source and target at same position
continue
if dx==0: # vertical edge
xa=x2
ya=dy*p+y1
if dy==0: # horizontal edge
ya=y2
xa=dx*p+x1
else:
theta=numpy.arctan2(dy,dx)
xa=p*d*numpy.cos(theta)+x1
ya=p*d*numpy.sin(theta)+y1
a_pos.append(((xa,ya),(x2,y2)))
arrow_collection = LineCollection(a_pos,
colors = arrow_colors,
linewidths = [4*ww for ww in lw],
antialiaseds = (1,),
transOffset = ax.transData,
)
# update view
minx = numpy.amin(numpy.ravel(edge_pos[:,:,0]))
maxx = numpy.amax(numpy.ravel(edge_pos[:,:,0]))
miny = numpy.amin(numpy.ravel(edge_pos[:,:,1]))
maxy = numpy.amax(numpy.ravel(edge_pos[:,:,1]))
w = maxx-minx
h = maxy-miny
padx, pady = 0.05*w, 0.05*h
corners = (minx-padx, miny-pady), (maxx+padx, maxy+pady)
ax.update_datalim( corners)
ax.autoscale_view()
edge_collection.set_zorder(1) # edges go behind nodes
ax.add_collection(edge_collection)
if arrow_collection:
arrow_collection.set_zorder(1) # edges go behind nodes
ax.add_collection(arrow_collection)
return edge_collection
def index_bar(
ax,
vals,
facecolor='b',
edgecolor='l',
width=4,
alpha=1.0,
):
"""Add a bar collection graph with height vals (-1 is missing).
Parameters
----------
ax : `Axes`
an Axes instance to plot to
vals : sequence
a sequence of values
facecolor : color
the color of the bar face
edgecolor : color
the color of the bar edges
width : int
the bar width in points
alpha : float
bar transparency
Returns
-------
ret : `barCollection`
The `barrCollection` added to the axes
"""
facecolors = (colorConverter.to_rgba(facecolor, alpha), )
edgecolors = (colorConverter.to_rgba(edgecolor, alpha), )
right = width / 2.0
left = -width / 2.0
bars = [((left, 0), (left, v), (right, v), (right, 0)) for v in vals
if v != -1]
sx = ax.figure.dpi * (1.0 / 72.0) # scale for points
sy = ax.bbox.height / ax.viewLim.height
barTransform = Affine2D().scale(sx, sy)
offsetsBars = [(i, 0) for i, v in enumerate(vals) if v != -1]
barCollection = PolyCollection(
bars,
facecolors=facecolors,
edgecolors=edgecolors,
antialiaseds=(0, ),
linewidths=(0.5, ),
offsets=offsetsBars,
transOffset=ax.transData,
)
barCollection.set_transform(barTransform)
minpy, maxx = (0, len(offsetsBars))
miny = 0
maxy = max([v for v in vals if v != -1])
corners = (minpy, miny), (maxx, maxy)
ax.update_datalim(corners)
ax.autoscale_view()
# add these last
ax.add_collection(barCollection)
return barCollection