def on_combobox_lineprops_changed(self, item):
"update the widgets from the active line"
if not self._inited:
return
self._updateson = False
line = self.get_active_line()
ls = line.get_linestyle()
if ls is None:
ls = "None"
self.cbox_linestyles.set_active(self.linestyled[ls])
marker = line.get_marker()
if marker is None:
marker = "None"
self.cbox_markers.set_active(self.markerd[marker])
r, g, b = colorConverter.to_rgb(line.get_color())
color = gtk.gdk.Color(*[int(val * 65535) for val in r, g, b])
button = self.wtree.get_widget("colorbutton_linestyle")
button.set_color(color)
r, g, b = colorConverter.to_rgb(line.get_markerfacecolor())
color = gtk.gdk.Color(*[int(val * 65535) for val in r, g, b])
button = self.wtree.get_widget("colorbutton_markerface")
button.set_color(color)
self._updateson = True
def volume_overlay(ax, opens, closes, volumes,
colorup='k', colordown='r',
width=4, alpha=1.0):
"""
Add a volume overlay to the current axes. The opens and closes
are used to determine the color of the bar. -1 is missing. If a
value is missing on one it must be missing on all
ax : an Axes instance to plot to
width : the bar width in points
colorup : the color of the lines where close >= open
colordown : the color of the lines where close < open
alpha : bar transparency
"""
r,g,b = colorConverter.to_rgb(colorup)
colorup = r,g,b,alpha
r,g,b = colorConverter.to_rgb(colordown)
colordown = r,g,b,alpha
colord = { True : colorup,
False : colordown,
}
colors = [colord[open<close] for open, close in zip(opens, closes) if open!=-1 and close !=-1]
delta = width/2.
bars = [ ( (i-delta, 0), (i-delta, v), (i+delta, v), (i+delta, 0)) for i, v in enumerate(volumes) if v != -1 ]
barCollection = PolyCollection(bars,
facecolors = colors,
edgecolors = ( (0,0,0,1), ),
antialiaseds = (0,),
linewidths = (0.5,),
)
corners = (0, 0), (len(bars), max(volumes))
ax.update_datalim(corners)
ax.autoscale_view()
return barCollection
def _compute_colors(self, array_x, array_y):
# on calcule le maximum absolu de toutes valeurs pour former un carré
abs_maximum = max([max(map(abs,array_x)), max(map(abs,array_y))])
diagonal_length = norm(array([abs_maximum, abs_maximum])) # longueur de la projection
diag = array([diagonal_length, diagonal_length])
anti_diag = array([-diagonal_length, diagonal_length])
# on instancie le gradient de couleur sur le modèle de couleur du centre
linear_normalizer = mpl.colors.Normalize(vmin=-abs_maximum, vmax=abs_maximum)
log_normalizer = mpl.colors.SymLogNorm(abs_maximum/5, vmin=-abs_maximum, vmax=abs_maximum)
r_to_b_gradient = cm.ScalarMappable(norm=linear_normalizer, cmap=redtoblue)
# on calcule le produit scalaire de chaque valeur avec la diagonale
# ensuite, on calcule la couleur à partir de la valeur de la projection sur la diagonale
hex_color_values = []
for i, x in enumerate(array_x):
# on calcule les produits scalaire du point avec la diagonale et l'antidiagonale
scal_p_diag = dot(array([array_x[i], array_y[i]]), diag) / diagonal_length
scal_p_antidiag = dot(array([array_x[i], array_y[i]]), anti_diag) / diagonal_length
#on calcule le gradient de couleur sur la diagonale
on_diag_color = colorConverter.to_rgb(r_to_b_gradient.to_rgba(scal_p_diag))
# puis on utilise cette couleur (en rgb) pour définir un gradient, dont la valeur sera estimée
# sur l'antidiagonale
on_diag_gradient = make_white_gradient(on_diag_color, log_normalizer)
final_color = on_diag_gradient.to_rgba(scal_p_antidiag)
#on traduit en HEX
hex_color_values.append(rgb2hex(colorConverter.to_rgb(final_color)))
return hex_color_values, abs_maximum
def add_bars(self, colorup='g', colordown='r', alpha=0.5, width=1):
r,g,b = colorConverter.to_rgb(colorup)
colorup = r,g,b,alpha
r,g,b = colorConverter.to_rgb(colordown)
colordown = r,g,b,alpha
colord = {True: colorup, False: colordown}
colors = [colord[open<close] for open, close in zip(self.opens, self.closes)]
delta = width/2.0
bars = [((x-delta, 0), (x-delta, y), (x+delta, y), (x+delta, 0))
for x, y in zip(self.dates, self.volumes)]
barCollection = PolyCollection(bars, facecolors = colors)
#self.ax.step(self.dates, self.volumes)
#self.ax.add_collection(barCollection)
#self.ax.bar(self.dates, self.volumes)
#self.ax.plot(self.dates, self.volumes)
self.ax.fill_between(self.dates, self.volumes, alpha=0.5)
xmin, xmax = self.ax.get_xlim()
ys = [y for x, y in zip(self.dates, self.volumes) if xmin<=x<=xmax]
if ys:
self.ax.set_ylim([0, max(ys)*10])
for tick in self.ax.get_yticklabels():
tick.set_visible(False)
def plot(self, widget, width=0.6,
colorup='r', colordown='g', lc='k', alpha=1):
"""docstring for plot"""
delta = self.width/2.
barVerts = [((i-delta, open),
(i-delta, close),
(i+delta, close),
(i+delta, open))
for i, open, close in zip(xrange(len(self.data)),
self.data.open,
self.data.close)
if open != -1 and close != -1]
rangeSegments = [((i, low), (i, high))
for i, low, high in zip(
xrange(len(self.data)),
self.data.low,
self.data.high)
if low != -1]
r, g, b = colorConverter.to_rgb(self.colorup)
colorup = r, g, b, self.alpha
r, g, b = colorConverter.to_rgb(self.colordown)
colordown = r, g, b, self.alpha
colord = {
True: colorup,
False: colordown,
}
colors = [colord[open < close]
for open, close in zip(self.data.open, self.data.close)
if open != -1 and close != -1]
assert(len(barVerts) == len(rangeSegments))
useAA = 0, # use tuple here
lw = 0.5, # and here
r, g, b = colorConverter.to_rgb(self.lc)
linecolor = r, g, b, self.alpha
lineCollection = LineCollection(rangeSegments,
colors=(linecolor,),
linewidths=lw,
antialiaseds=useAA,
zorder=0)
barCollection = PolyCollection(barVerts,
facecolors=colors,
edgecolors=colors,
antialiaseds=useAA,
linewidths=lw,
zorder=1)
#minx, maxx = 0, len(rangeSegments)
#miny = min([low for low in self.data.low if low !=-1])
#maxy = max([high for high in self.data.high if high != -1])
#corners = (minx, miny), (maxx, maxy)
#ax.update_datalim(corners)
widget.autoscale_view()
# add these last
widget.add_collection(barCollection)
widget.add_collection(lineCollection)
#ax.plot(self.data.close, color = 'y')
#lineCollection, barCollection = None, None
return lineCollection, barCollection
def plot(self, widget, data, width=0.6,
colorup='r', colordown='g', lc='k', alpha=1):
if self.lineCollection:
self.lineCollection.remove()
if self.barCollection:
self.barCollection.remove()
self.set_yrange(data.low.values, data.high.values)
self.data = data
"""docstring for plot"""
delta = self.width / 2.
barVerts = [((i - delta, open),
(i - delta, close),
(i + delta, close),
(i + delta, open))
for i, open, close in zip(range(len(self.data)),
self.data.open,
self.data.close)
if open != -1 and close != -1]
rangeSegments = [((i, low), (i, high))
for i, low, high in zip(range(len(self.data)),
self.data.low,
self.data.high)
if low != -1]
r, g, b = colorConverter.to_rgb(self.colorup)
colorup = r, g, b, self.alpha
r, g, b = colorConverter.to_rgb(self.colordown)
colordown = r, g, b, self.alpha
colord = {
True: colorup,
False: colordown,
}
colors = [colord[open < close]
for open, close in zip(self.data.open, self.data.close)
if open != -1 and close != -1]
assert(len(barVerts) == len(rangeSegments))
useAA = 0, # use tuple here
lw = 0.5, # and here
r, g, b = colorConverter.to_rgb(self.lc)
linecolor = r, g, b, self.alpha
self.lineCollection = LineCollection(rangeSegments,
colors=(linecolor,),
linewidths=lw,
antialiaseds=useAA,
zorder=0)
self.barCollection = PolyCollection(barVerts,
facecolors=colors,
edgecolors=colors,
antialiaseds=useAA,
linewidths=lw,
zorder=1)
widget.autoscale_view()
# add these last
widget.add_collection(self.barCollection)
widget.add_collection(self.lineCollection)
return self.lineCollection, self.barCollection
def candlestick2(ax, opens, closes, highs, lows, width=4, colorup="k", colordown="r", alpha=0.75):
"""
Represent the open, close as a bar line and high low range as a
vertical line.
ax : an Axes instance to plot to
width : the bar width in points
colorup : the color of the lines where close >= open
colordown : the color of the lines where close < open
alpha : bar transparency
return value is lineCollection, barCollection
"""
# note this code assumes if any value open, close, low, high is
# missing they all are missing
delta = width / 2.0
barVerts = [
((i - delta, open), (i - delta, close), (i + delta, close), (i + delta, open))
for i, open, close in zip(xrange(len(opens)), opens, closes)
if open != -1 and close != -1
]
rangeSegments = [((i, low), (i, high)) for i, low, high in zip(xrange(len(lows)), lows, highs) if low != -1]
r, g, b = colorConverter.to_rgb(colorup)
colorup = r, g, b, alpha
r, g, b = colorConverter.to_rgb(colordown)
colordown = r, g, b, alpha
colord = {True: colorup, False: colordown}
colors = [colord[open < close] for open, close in zip(opens, closes) if open != -1 and close != -1]
assert len(barVerts) == len(rangeSegments)
useAA = (0,) # use tuple here
lw = (0.5,) # and here
rangeCollection = LineCollection(rangeSegments, colors=((0, 0, 0, 1),), linewidths=lw, antialiaseds=useAA)
barCollection = PolyCollection(
barVerts, facecolors=colors, edgecolors=((0, 0, 0, 1),), antialiaseds=useAA, linewidths=lw
)
minx, maxx = 0, len(rangeSegments)
miny = min([low for low in lows if low != -1])
maxy = max([high for high in highs if high != -1])
corners = (minx, miny), (maxx, maxy)
ax.update_datalim(corners)
ax.autoscale_view()
# add these last
ax.add_collection(barCollection)
ax.add_collection(rangeCollection)
return rangeCollection, barCollection
def create_colormap(col1, col2):
c1 = colorConverter.to_rgb(col1)
c2 = colorConverter.to_rgb(col2)
cdict = {
'red': ((0.,c1[0], c1[0]),(1.,c2[0], c2[0])),
'green': ((0.,c1[1], c1[1]),(1.,c2[1], c2[1])),
'blue': ((0.,c1[2], c1[2]),(1.,c2[2], c2[2]))
}
return LinearSegmentedColormap('custom', cdict, 256)
def volume_overlay(ax, opens, closes, volumes, colorup="k", colordown="r", width=4, alpha=1.0):
"""
Add a volume overlay to the current axes. The opens and closes
are used to determine the color of the bar. -1 is missing. If a
value is missing on one it must be missing on all
ax : an Axes instance to plot to
width : the bar width in points
colorup : the color of the lines where close >= open
colordown : the color of the lines where close < open
alpha : bar transparency
"""
r, g, b = colorConverter.to_rgb(colorup)
colorup = r, g, b, alpha
r, g, b = colorConverter.to_rgb(colordown)
colordown = r, g, b, alpha
colord = {True: colorup, False: colordown}
colors = [colord[open < close] for open, close in zip(opens, closes) if open != -1 and close != -1]
right = width / 2.0
left = -width / 2.0
bars = [((left, 0), (left, v), (right, v), (right, 0)) for v in volumes if v != -1]
sx = ax.figure.dpi * (1.0 / 72.0) # scale for points
sy = (ax.bbox.ur().y() - ax.bbox.ll().y()) / (ax.viewLim.ur().y() - ax.viewLim.ll().y())
barTransform = Affine2D().scaled(sx, sy)
offsetsBars = [(i, 0) for i, v in enumerate(volumes) if v != -1]
barCollection = PolyCollection(
bars,
facecolors=colors,
edgecolors=((0, 0, 0, 1),),
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 volumes if v != -1])
corners = (minpy, miny), (maxx, maxy)
ax.update_datalim(corners)
ax.autoscale_view()
# add these last
return barCollection
def _to_rgb(c):
"""
Convert color *c* to a numpy array of *RGB* handling exeption
Parameters
----------
c: Matplotlib color
same as *color* in *colorAlpha_to_rgb*
output
------
rgbs: list of numpy array
list of c converted to *RGB* array
"""
if(getattr(c, '__iter__', False) == False): #if1: if c is a single element (number of string)
rgbs = [np.array(cC.to_rgb(c)),] #list with 1 RGB numpy array
else: #if1, else: if is more that one element
try: #try1: check if c is numberic or not
np.array(c) + 1
except (TypeError, ValueError): #try1: if not numerics is not (only) RGB or RGBA colors
#convert the list/tuble/array of colors into a list of numpy arrays of RGB
rgbs = [np.array( cC.to_rgb(i)) for i in c]
except Exception as e: #try1: if any other exception raised
print("Unexpected error: {}".format(e))
raise e #raise it
else: #try1: if the colors are all numberics
arrc = np.array(c) #convert c to a numpy array
arrcsh = arrc.shape #shape of the array
if len(arrcsh)==1: #if2: if 1D array given
if(arrcsh[0]==3 or arrcsh[0]==4): #if3: if RGB or RBGA
rgbs = [np.array(cC.to_rgb(c)),] #list with 1 RGB numpy array
else: #if3, else: the color cannot be RBG or RGBA
raise ValueError('Invalid rgb arg "{}"'.format(c))
#end if3
elif len(arrcsh)==2: #if2, else: if 2D array
if(arrcsh[1]==3 or arrcsh[1]==4): #if4: if RGB or RBGA
rgbs = [np.array(cC.to_rgb(i)) for i in c] #list with RGB numpy array
else: #if4, else: the color cannot be RBG or RGBA
raise ValueError('Invalid list or array of rgb')
#end if4
else: #if2, else: if more dimention
raise ValueError('The rgb or rgba values must be contained in a 1D or 2D list or array')
#end if2
#end try1
#end if1
return rgbs
def volume_overlay(ax, opens, closes, volumes, colorup="k", colordown="r", width=4, alpha=1.0):
"""Add a volume overlay to the current axes. The opens and closes
are used to determine the color of the bar. -1 is missing. If a
value is missing on one it must be missing on all
Parameters
----------
ax : `Axes`
an Axes instance to plot to
opens : sequence
a sequence of opens
closes : sequence
a sequence of closes
volumes : sequence
a sequence of volumes
width : int
the bar width in points
colorup : color
the color of the lines where close >= open
colordown : color
the color of the lines where close < open
alpha : float
bar transparency
Returns
-------
ret : `barCollection`
The `barrCollection` added to the axes
"""
r, g, b = colorConverter.to_rgb(colorup)
colorup = r, g, b, alpha
r, g, b = colorConverter.to_rgb(colordown)
colordown = r, g, b, alpha
colord = {True: colorup, False: colordown}
colors = [colord[open < close] for open, close in zip(opens, closes) if open != -1 and close != -1]
delta = width / 2.0
bars = [((i - delta, 0), (i - delta, v), (i + delta, v), (i + delta, 0)) for i, v in enumerate(volumes) if v != -1]
barCollection = PolyCollection(
bars, facecolors=colors, edgecolors=((0, 0, 0, 1),), antialiaseds=(0,), linewidths=(0.5,)
)
ax.add_collection(barCollection)
corners = (0, 0), (len(bars), max(volumes))
ax.update_datalim(corners)
ax.autoscale_view()
# add these last
return barCollection
def volume_overlay3(ax, quotes,
colorup='k', colordown='r',
width=4, alpha=1.0):
"""
Add a volume overlay to the current axes. quotes is a list of (d,
open, close, high, low, volume) and close-open is used to
determine the color of the bar
kwarg
width : the bar width in points
colorup : the color of the lines where close1 >= close0
colordown : the color of the lines where close1 < close0
alpha : bar transparency
"""
r,g,b = colorConverter.to_rgb(colorup)
colorup = r,g,b,alpha
r,g,b = colorConverter.to_rgb(colordown)
colordown = r,g,b,alpha
colord = { True : colorup,
False : colordown,
}
dates, opens, closes, highs, lows, volumes = zip(*quotes)
colors = [colord[close1>=close0] for close0, close1 in zip(closes[:-1], closes[1:]) if close0!=-1 and close1 !=-1]
colors.insert(0,colord[closes[0]>=opens[0]])
right = width/2.0
left = -width/2.0
bars = [ ( (left, 0), (left, volume), (right, volume), (right, 0)) for d, open, close, high, low, volume in quotes]
sx = ax.figure.dpi * (1.0/72.0) # scale for points
sy = ax.bbox.height / ax.viewLim.height
barTransform = Affine2D().scale(sx,sy)
dates = [d for d, open, close, high, low, volume in quotes]
offsetsBars = [(d, 0) for d in dates]
useAA = 0, # use tuple here
lw = 0.5, # and here
barCollection = PolyCollection(bars,
facecolors = colors,
edgecolors = ( (0,0,0,1), ),
antialiaseds = useAA,
linewidths = lw,
offsets = offsetsBars,
transOffset = ax.transData,
)
barCollection.set_transform(barTransform)
minpy, maxx = (min(dates), max(dates))
miny = 0
maxy = max([volume for d, open, close, high, low, volume in quotes])
corners = (minpy, miny), (maxx, maxy)
ax.update_datalim(corners)
ax.add_collection(barCollection)
ax.autoscale_view()
return barCollection
def plot_hmm(means_, transmat, covars, initProbs, axes=None, clr=None, transition_arrows=True):
if axes != None:
axes(axes)
# f, axes = subplots(2)#,sharex=True, sharey=True)
# sca(axes[0])
global annotations
annotations = []
global means
means = []
colors = clr
# color_map = colors #[colorConverter.to_rgb(colors[i]) for i in range(len(means_))]
for i, mean in enumerate(means_):
# print_n_flush( "MEAN:", tuple(mean))
means.append(scatter(*tuple(mean), color=colorConverter.to_rgb(colors[i]), picker=10, label="State%i" % i))
annotate(s="%d" % i, xy=mean, xytext=(-10, -10), xycoords="data", textcoords="offset points",
alpha=1, bbox=dict(boxstyle='round,pad=0.2', fc=colorConverter.to_rgb(colors[i]), alpha=0.3))
# gca().add_patch(Ellipse(xy = means_[i], width = np.diag(covars[i])[0], height = np.diag(covars[i])[1],
# alpha=.15, color=colorConverter.to_rgb(colors[i])))
plot_cov_ellipse(covars[i], mean, alpha=.15, color=colorConverter.to_rgb(colors[i]))
x0, y0 = mean
prob_string = "P(t0)=%f" % initProbs[i]
for j, p in enumerate(transmat[i]):
xdif = 10
ydif = 5
s = "P(%d->%d)=%f" % (i, j, p)
# print_n_flush( "State%d: %s" % (i, s))
prob_string = "%s\n%s" % (prob_string, s)
if transition_arrows:
if i != j:
x1, y1 = means_[j]
# if transmat[i][j] is too low, we get an underflow here
# q = quiver([x0], [y0], [x1-x0], [y1-y0], alpha = 10000 * (transmat[i][j]**2),
alpha = 10 ** -300
if p > 10 ** -100:
alpha = (100 * p) ** 2
q = quiver([x0], [y0], [x1 - x0], [y1 - y0], alpha=1 / log(alpha),
scale_units='xy', angles='xy', scale=1, width=0.005, label="P(%d->%d)=%f" % (i, j, p))
# legend()
annotations.append(annotate(s=prob_string, xy=mean, xytext=(0, 10), xycoords="data", textcoords="offset points",
alpha=1,
bbox=dict(boxstyle='round,pad=0.2', fc=colorConverter.to_rgb(colors[i]), alpha=0.3),
picker=True,
visible=False))
# print_n_flush( "State%i is %s" % (i, colors[i]))
cid = gcf().canvas.mpl_connect('pick_event', on_pick)
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 add_aliasing_edges(G, only_states, belief_mdp, pomdp): # @UnusedVariable
for s1 in only_states:
for s2 in only_states:
if s1 == s2:
continue
obs1 = pomdp.get_observations_dist_given_belief(s1)
obs2 = pomdp.get_observations_dist_given_belief(s2)
can_distinguish = len(set(obs1) & set(obs2)) == 0
# if not len(obs1) == 0 or not len(obs2) == 0:
# print('not deterministic, %s , %s , %s ,
# %s ' % (s1, obs1, s2, obs2))
# continue
if not can_distinguish:
# aliasing!
G.add_edge(s1, s2)
G.edge[s1][s2]['type'] = 'aliasing'
G.edge[s1][s2]['edge_color'] = colorConverter.to_rgb('y')
G.edge[s1][s2]['edge_style'] = 'dotted'
return G
def pastel(colour, weight=2.4):
'''
Convert colour into a nice pastel shade
'''
rgb = asarray(colorConverter.to_rgb(colour))
# scale colour
maxc = max(rgb)
if maxc < 1.0 and maxc > 0:
# scale colour
scale = 1.0 / maxc
rgb = rgb * scale
# now decrease saturation
total = sum(rgb)
slack = 0
for x in rgb:
slack += 1.0 - x
# want to increase weight from total to weight
# pick x s.t. slack * x == weight - total
# x = (weight - total) / slack
x = (weight - total) / slack
rgb = [c + (x * (1.0-c)) for c in rgb]
return rgb
def from_list(name, colors, N=256, gamma=1.0):
"""
Make a linear segmented colormap with *name* from a sequence
of *colors* which evenly transitions from colors[0] at val=0
to colors[-1] at val=1. *N* is the number of rgb quantization
levels.
Alternatively, a list of (value, color) tuples can be given
to divide the range unevenly.
"""
if not cbook.iterable(colors):
raise ValueError('colors must be iterable')
if cbook.iterable(colors[0]) and len(colors[0]) == 2 and \
not cbook.is_string_like(colors[0]):
# List of value, color pairs
vals, colors = zip(*colors)
else:
vals = np.linspace(0., 1., len(colors))
cdict = dict(red=[], green=[], blue=[])
for val, color in zip(vals, colors):
r,g,b = colorConverter.to_rgb(color)
cdict['red'].append((val, r, r))
cdict['green'].append((val, g, g))
cdict['blue'].append((val, b, b))
return MixedAlphaColormap(name, cdict, N, gamma)
def gradient(cmin, cmax):
if isinstance(cmin, str):
cmin = colorConverter.to_rgb(cmin)
if isinstance(cmax, str):
cmax = colorConverter.to_rgb(cmax)
cdict = {
'red': [(0, 0, cmin[0]),
(1, cmax[0], 1)],
'green': [(0, 0, cmin[1]),
(1, cmax[1], 1)],
'blue': [(0, 0, cmin[2]),
(1, cmax[2], 1)]
}
return mpl.colors.LinearSegmentedColormap('cmap', cdict, N=1000)
def __init__(self, label=None, marker="o", markersize=10,
color="blue", saturation=1, opaqcity=1, zorder=0):
self.label = label
self.marker = marker
self.markersize = markersize
self.color_rgb = colorConverter.to_rgb(color)
self.saturation = saturation
self.opaqcity = opaqcity
self.zorder = zorder
def get_selection_box_colour(series):
"""
Returns a colour (as an RGB tuple) that will be visible against the
background of the subplot.
"""
subplot = series.get_subplot()
bkgd_col = colorConverter.to_rgb(subplot.get_mpl_axes().get_axis_bgcolor())
return tuple([1.0 - c for c in bkgd_col])
def _register_cmap_transparent(name, color):
"""Create a color map from a given color to transparent."""
from matplotlib.colors import colorConverter, LinearSegmentedColormap
red, green, blue = colorConverter.to_rgb(color)
cdict = {'red': ((0, red, red), (1, red, red)),
'green': ((0, green, green), (1, green, green)),
'blue': ((0, blue, blue), (1, blue, blue)),
'alpha': ((0, 0, 0), (1, 1, 1))}
cmap = LinearSegmentedColormap(name, cdict)
_plt.cm.register_cmap(cmap=cmap)
def colorAlpha_to_rgb(colors, alpha, bg='w'):
"""
Given a Matplotlib color and a value of alpha, it returns
a RGB color which mimic the RGBA colors on the given background
Parameters
----------
colors: Matplotlib color (documentation from matplotlib.colors.colorConverter.to_rgb),
list/tuple/numpy array of colors
Can be an *RGB* or *RGBA* sequence or a string in any of
several forms:
1) a letter from the set 'rgbcmykw'
2) a hex color string, like '#00FFFF'
3) a standard name, like 'aqua'
4) a float, like '0.4', indicating gray on a 0-1 scale
if *color* is *RGBA*, the *A* will simply be discarded.
alpha: float [0,1] or list/tuple/numpy array with len(colors) elements
Value of alpha to mimic.
bg: Matplotlib color (optional, default='w')
Color of the background. Can be of any type shown in *color*
output
------
rgb: *RGB* color
example
-------
import mimic_alpha as ma
print(ma.colorAlpha_to_rgb('r', 0.5))
>>> [array([ 1. , 0.5, 0.5])]
print(ma.colorAlpha_to_rgb(['r', 'g'], 0.5))
>>> [array([ 1. , 0.5, 0.5]), array([ 0.5 , 0.75, 0.5 ])]
print(ma.colorAlpha_to_rgb(['r', 'g'], [0.5, 0.3]))
>>> [array([ 1. , 0.5, 0.5]), array([ 0.7 , 0.85, 0.7 ])]
print(ma.colorAlpha_to_rgb(['r', [1,0,0]], 0.5))
>>> [array([ 1. , 0.5, 0.5]), array([ 1. , 0.5, 0.5])]
print( ma.colorAlpha_to_rgb([[0,1,1], [1,0,0]], 0.5) )
>>> [array([ 0.5, 1. , 1. ]), array([ 1. , 0.5, 0.5])]
print(ma.colorAlpha_to_rgb(np.array([[0,1,1], [1,0,0]]), 0.5))
>>> [array([ 0.5, 1. , 1. ]), array([ 1. , 0.5, 0.5])]
print(ma.colorAlpha_to_rgb(np.array([[0,1,1], [1,0,0]]), 0.5, bg='0.5'))
>>> [array([ 0.25, 0.75, 0.75]), array([ 0.75, 0.25, 0.25])]
"""
colors = _to_rgb(colors) #convert the color and save in a list of np arrays
bg = np.array(cC.to_rgb(bg)) #convert the background
#check if alpha has 1 or len(colors) elements and return a list of len(color) alpha
alpha = _check_alpha(alpha, len(colors))
#interpolate between background and color
rgb = [(1.-a) * bg + a*c for c,a in zip(colors, alpha)]
return rgb
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 plot_hit_matrix(hit_matrix, k, m, fold, kmers, virus_family, sequence_type, project_path):
background_colors = {
'white' : np.array([255,255,255]).reshape(1,3)/255.,
'black' : np.array([0,0,0]).reshape(1,3)/255.,
'grey' : np.array([38,38,38]).reshape(1,3)/255.,
'darkgrey' : np.array([18,18,18]).reshape(1,3)/255.,
'offwhite' : np.array([235,235,235]).reshape(1,3)/255.,
}
kmer_colors = ['red','green','blue','purple','cyan','orange','magenta','black','hotpink']
(num_proteins,C,ig) = hit_matrix.shape
C = C-1
V = min([9,len(kmers)])
data = np.zeros((num_proteins,C,3),dtype='float')
for i in range(V):
data += hit_matrix[:,1:,i:i+1] * np.array(list(convert.to_rgb(kmer_colors[i])))
idx = (hit_matrix[:,0,0]==1).nonzero()[0]
data[idx,:,:] = data[idx,:,:] + (1-(data[idx,:,:].sum(2)>0)).reshape(idx.size,C,1) * background_colors['white']
idx = (hit_matrix[:,0,0]==2).nonzero()[0]
data[idx,:,:] = data[idx,:,:] + (1-(data[idx,:,:].sum(2)>0)).reshape(idx.size,C,1) * background_colors['offwhite']
# idx = (hit_matrix[:,0,0]==3).nonzero()[0]
# data[idx,:,:] = data[idx,:,:] + (1-(data[idx,:,:].sum(2)>0)).reshape(idx.size,C,1)*color_scheme['white']
fig = plot.figure()
im = fig.add_subplot(111)
im.set_position([0.03,0.07,0.80,0.88])
im.imshow(data,aspect='auto',interpolation='nearest')
im.axis([0,hit_matrix.shape[1]-1,0,hit_matrix.shape[0]])
im.set_xticks([0,hit_matrix.shape[1]-1])
im.set_xticklabels((0,1))
im.set_xlabel('Relative location')
y_labels = ('Plant','Animal')
y_label_loc = []
for c in np.unique(hit_matrix[:,0,0]):
y_label_loc.append(int(np.mean((hit_matrix[:,0,0]==c).nonzero()[0])))
im.set_yticks(y_label_loc)
im.set_yticklabels(y_labels, rotation=90)
for line in im.get_yticklines():
line.set_markersize(0)
im.set_title('k = %d, m = %d' % (k,m))
# a figtext bbox for legend
kmer_locs = np.linspace(0.5+V/2*0.04,0.5-V/2*0.04,V)
for kidx in range(V):
kmer = kmers[kidx]
try:
plot.figtext(0.84, kmer_locs[kidx], kmer, fontsize=9, color=kmer_colors[kidx], horizontalalignment='left', verticalalignment='center')
except IndexError:
pdb.set_trace()
fname = project_path + 'fig/%s_%s_kmer_visualization_%d_%d_%d.pdf' % (virus_family, sequence_type, k, m, fold)
fig.savefig(fname,dpi=(300),format='pdf')
def create_colormap(*args):
col = [colorConverter.to_rgb(c) for c in args]
step = 1.0 / (len(col) - 1)
cdict = {
'red': [[i * step, col[i][0], col[i][0]] for i in range(len(col))],
'green': [[i * step, col[i][1], col[i][1]] for i in range(len(col))],
'blue': [[i * step, col[i][2], col[i][2]] for i in range(len(col))]
}
return LinearSegmentedColormap('custom', cdict, 256)
def _register_cmap_transparent(name, color):
"""Create a color map from a given color to transparent."""
from matplotlib.colors import colorConverter, LinearSegmentedColormap
red, green, blue = colorConverter.to_rgb(color)
cdict = {
'red': ((0, red, red), (1, red, red)),
'green': ((0, green, green), (1, green, green)),
'blue': ((0, blue, blue), (1, blue, blue)),
'alpha': ((0, 0, 0), (1, 1, 1))
}
cmap = LinearSegmentedColormap(name, cdict)
plt.cm.register_cmap(cmap=cmap)
def show(self):
self.fr1=Frame(self.fig.fr_curves, padx=3, pady=3)
self.fr1.pack(side=TOP)
l=Label(self.fr1, text="%s(%s)"%(self.y, self.x)); l.pack(side=LEFT)
Button(self.fr1, text="Del", command=self.destroy).pack(side=LEFT)
# self.line, = self.fig.ax.plot(self.sode[self.x], self.sode[self.y],'.-',label="%s(%s)"%(self.y, self.x))
self.line, = self.fig.ax.plot(self.sode[self.x], self.sode[self.y],label="%s(%s)"%(self.y, self.x))
self.fr1["bg"] = rgb2hex(colorConverter.to_rgb(self.line.get_color()))
# self.ax.legend()
# self.ax.figure.canvas.draw()
self.fig.cnvs.update_idletasks()
self.fig.cnvs["scrollregion"]=self.fig.cnvs.bbox(ALL)
def plot_day_summary3(
ax,
closes,
ticksize=4,
color='k',
):
"""
Represent the time, open, close, high, low as a vertical line
ranging from low to high. The left tick is the open and the right
tick is the close.
ax : an Axes instance to plot to
ticksize : size of open and close ticks in points
color : the color of the lines
return value is a list of lines added
"""
rangeSegments = []
pfrom = (0, closes[0])
for i in range(0, len(closes)):
if closes[i] >= 0.0:
pto = (i, closes[i])
rangeSegments.append((pfrom, pto))
pfrom = pto
r, g, b = colorConverter.to_rgb(color)
color = r, g, b, 1
useAA = 0, # use tuple here
if ticksize > 1:
lw = 0.5, # and here
else:
lw = 0.2,
rangeCollection = LineCollection(
rangeSegments,
colors=color,
linewidths=lw,
antialiaseds=useAA,
)
minx, maxx = (0, len(rangeSegments))
miny = min([low for low in closes if low != -1])
maxy = max([high for high in closes if high != -1])
corners = (minx, miny), (maxx, maxy)
ax.update_datalim(corners)
ax.autoscale_view()
# add these last
ax.add_collection(rangeCollection)
return rangeCollection
def create_colormap(*args):
col = [colorConverter.to_rgb(c) for c in args]
step = 1.0 / (len(col) - 1)
cdict = {
'red': [[i * step, col[i][0], col[i][0]] for i in range(len(col))],
'green': [[i * step, col[i][1], col[i][1]] for i in range(len(col))],
'blue': [[i * step, col[i][2], col[i][2]] for i in range(len(col))]
}
return LinearSegmentedColormap('custom', cdict, 256)
def _get_rgb_arrowface(self):
"""Get the color of the arrow face.
"""
from matplotlib.cbook import is_string_like
from matplotlib.colors import colorConverter
facecolor = self._arrowfacecolor
if is_string_like(facecolor) and facecolor.lower() == 'none':
rgb_face = None
else:
rgb_face = colorConverter.to_rgb(facecolor)
return rgb_face
def make_cmap(colors, position=None, bit=False, webcolors=False):
'''
from: http://schubert.atmos.colostate.edu/~cslocum/custom_cmap.html
(added support for web colors)
make_cmap takes a list of tuples which contain RGB values. The RGB
values may either be in 8-bit [0 to 255] (in which bit must be set to
True when called) or arithmetic [0 to 1] (default). make_cmap returns
a cmap with equally spaced colors.
Arrange your tuples so that the first color is the lowest value for the
colorbar and the last is the highest.
position contains values from 0 to 1 to dictate the location of each color.
'''
import matplotlib as mpl
import numpy as np
bit_rgb = np.linspace(0,1,256)
if position == None:
position = np.linspace(0,1,len(colors))
else:
if len(position) != len(colors):
sys.exit("position length must be the same as colors")
elif position[0] != 0 or position[-1] != 1:
sys.exit("position must start with 0 and end with 1")
'''
if webcolors:
bit = False
try:
for i in range(len(colors)):
colors[i] = colorConverter.to_rgb(colors[i])
except ValueError:
print("invalid html web color {}.".format(colors[i]))
'''
if bit:
try:
for i in range(len(colors)):
colors[i] = (bit_rgb[colors[i][0]],
bit_rgb[colors[i][1]],
bit_rgb[colors[i][2]])
except:
try:
for i in range(len(colors)):
colors[i] = colorConverter.to_rgb(colors[i])
except ValueError:
print("invalid html web color {}.".format(colors[i]))
cdict = {'red':[], 'green':[], 'blue':[]}
for pos, color in zip(position, colors):
cdict['red'].append((pos, color[0], color[0]))
cdict['green'].append((pos, color[1], color[1]))
cdict['blue'].append((pos, color[2], color[2]))
cmap = mpl.colors.LinearSegmentedColormap('my_colormap',cdict,256)
return cmap
def update_color_physical_network(node_list, colors='b'):
global plt_fig, phy_g, virt_g, pos_phy, pos_virt, phy_subplot, virt_subplot, phy_sp_xlim, phy_sp_ylim, \
virt_sp_xlim, virt_sp_ylim, canvas, node_mappings, continue_button, host_color_map
phy_subplot = plt_fig.add_subplot(121)
plt.axis('off')
cl = []
if type(node_list) is not list:
nodes = [node_list]
else:
nodes = node_list
if type(colors) is list:
for n,c in zip(nodes, colors):
host_color_map[n] = colorConverter.to_rgb(c)
else:
for n in nodes:
host_color_map[n] = colorConverter.to_rgb(colors)
for node in phy_g.nodes():
cl.append(host_color_map[node])
nx.draw_networkx(phy_g, pos=pos_phy,ax=phy_subplot, with_labels=True, node_color=cl)
phy_subplot.set_xlim(phy_sp_xlim)
phy_subplot.set_ylim(phy_sp_ylim)
plt.axis('off')
def Jmol_color(in_color):
if isinstance(in_color, str) and in_color[:2] == "[x" and in_color[-1] == ']':
# I guess the user knows what they're doing
return in_color
#----------------------------------------#
if _have_mpl_colors:
color = colorConverter.to_rgb(in_color)
hex_str = rgb2hex(color)
hex_str = "[x" + hex_str[1:] + "]"
return hex_str
else:
# I can't help you, sorry
return in_color
def __init__(self, figure):
super(ColorCodePatchBuilder, self).__init__(figure, None)
self.y_unit = 10**-6
"""Unit on the y-axis"""
# permission composition shorthands
load_store = CheriCapPerm.LOAD | CheriCapPerm.STORE
load_exec = CheriCapPerm.LOAD | CheriCapPerm.EXEC
store_exec = CheriCapPerm.STORE | CheriCapPerm.EXEC
load_store_exec = (CheriCapPerm.STORE | CheriCapPerm.LOAD
| CheriCapPerm.EXEC)
self._collection_map = {
0: [],
CheriCapPerm.LOAD: [],
CheriCapPerm.STORE: [],
CheriCapPerm.EXEC: [],
load_store: [],
load_exec: [],
store_exec: [],
load_store_exec: [],
"call": [],
}
"""Map capability permission to the set where the line should go"""
self._colors = {
0: colorConverter.to_rgb("#bcbcbc"),
CheriCapPerm.LOAD: colorConverter.to_rgb("k"),
CheriCapPerm.STORE: colorConverter.to_rgb("y"),
CheriCapPerm.EXEC: colorConverter.to_rgb("m"),
load_store: colorConverter.to_rgb("c"),
load_exec: colorConverter.to_rgb("b"),
store_exec: colorConverter.to_rgb("g"),
load_store_exec: colorConverter.to_rgb("r"),
"call": colorConverter.to_rgb("#31c648"),
}
"""Map capability permission to line colors"""
self._patches = None
"""List of generated patches"""
self._node_map = SortedDict()
"""Maps the Y axis coordinate to the graph node at that position"""
def make_cmap(colors, position=None, bit=False, webcolors=False):
'''
from: http://schubert.atmos.colostate.edu/~cslocum/custom_cmap.html
(added support for web colors)
make_cmap takes a list of tuples which contain RGB values. The RGB
values may either be in 8-bit [0 to 255] (in which bit must be set to
True when called) or arithmetic [0 to 1] (default). make_cmap returns
a cmap with equally spaced colors.
Arrange your tuples so that the first color is the lowest value for the
colorbar and the last is the highest.
position contains values from 0 to 1 to dictate the location of each color.
'''
import matplotlib as mpl
import numpy as np
bit_rgb = np.linspace(0, 1, 256)
if position == None:
position = np.linspace(0, 1, len(colors))
else:
if len(position) != len(colors):
sys.exit("position length must be the same as colors")
elif position[0] != 0 or position[-1] != 1:
sys.exit("position must start with 0 and end with 1")
'''
if webcolors:
bit = False
try:
for i in range(len(colors)):
colors[i] = colorConverter.to_rgb(colors[i])
except ValueError:
print("invalid html web color {}.".format(colors[i]))
'''
if bit:
try:
for i in range(len(colors)):
colors[i] = (bit_rgb[colors[i][0]], bit_rgb[colors[i][1]],
bit_rgb[colors[i][2]])
except:
try:
for i in range(len(colors)):
colors[i] = colorConverter.to_rgb(colors[i])
except ValueError:
print("invalid html web color {}.".format(colors[i]))
cdict = {'red': [], 'green': [], 'blue': []}
for pos, color in zip(position, colors):
cdict['red'].append((pos, color[0], color[0]))
cdict['green'].append((pos, color[1], color[1]))
cdict['blue'].append((pos, color[2], color[2]))
cmap = mpl.colors.LinearSegmentedColormap('my_colormap', cdict, 256)
return cmap
def colorAlpha_to_rgb(colors, alpha, bg='w'):
"""
Given a Matplotlib color and a value of alpha, it returns
a RGB color which mimic the RGBA colors on the given background
Parameters
----------
colors: Matplotlib color (documentation from matplotlib.colors.colorConverter.to_rgb),
list/tuple/numpy array of colors
Can be an *RGB* or *RGBA* sequence or a string in any of
several forms:
1) a letter from the set 'rgbcmykw'
2) a hex color string, like '#00FFFF'
3) a standard name, like 'aqua'
4) a float, like '0.4', indicating gray on a 0-1 scale
if *color* is *RGBA*, the *A* will simply be discarded.
alpha: float [0,1] or list/tuple/numpy array with len(colors) elements
Value of alpha to mimic.
bg: Matplotlib color (optional, default='w')
Color of the background. Can be of any type shown in *color*
output
------
rgb: *RGB* color
example
-------
import mimic_alpha as ma
print(ma.colorAlpha_to_rgb('r', 0.5))
>>> [array([ 1. , 0.5, 0.5])]
print(ma.colorAlpha_to_rgb(['r', 'g'], 0.5))
>>> [array([ 1. , 0.5, 0.5]), array([ 0.5 , 0.75, 0.5 ])]
print(ma.colorAlpha_to_rgb(['r', 'g'], [0.5, 0.3]))
>>> [array([ 1. , 0.5, 0.5]), array([ 0.7 , 0.85, 0.7 ])]
print(ma.colorAlpha_to_rgb(['r', [1,0,0]], 0.5))
>>> [array([ 1. , 0.5, 0.5]), array([ 1. , 0.5, 0.5])]
print( ma.colorAlpha_to_rgb([[0,1,1], [1,0,0]], 0.5) )
>>> [array([ 0.5, 1. , 1. ]), array([ 1. , 0.5, 0.5])]
print(ma.colorAlpha_to_rgb(np.array([[0,1,1], [1,0,0]]), 0.5))
>>> [array([ 0.5, 1. , 1. ]), array([ 1. , 0.5, 0.5])]
print(ma.colorAlpha_to_rgb(np.array([[0,1,1], [1,0,0]]), 0.5, bg='0.5'))
>>> [array([ 0.25, 0.75, 0.75]), array([ 0.75, 0.25, 0.25])]
"""
colors = _to_rgb(
colors) #convert the color and save in a list of np arrays
bg = np.array(cC.to_rgb(bg)) #convert the background
#check if alpha has 1 or len(colors) elements and return a list of len(color) alpha
alpha = _check_alpha(alpha, len(colors))
#interpolate between background and color
rgb = [(1. - a) * bg + a * c for c, a in zip(colors, alpha)]
return rgb
def plot_navpoints(self):
for npid in self.navpoints:
name, (x, y, z) = self.navpoints[npid]
if npid in navpointcolors:
color = navpointcolors[npid]
else:
color = colorConverter.to_rgb('white')
s = 7
label = self.navpoints[npid][0].split('.')[1]
self.ax.plot([x], [y], [z],
markerfacecolor=color,
markersize=s,
marker='s',
label=label)
def ring_color(start_angle, level):
from matplotlib.colors import colorConverter
# f: [1 - 0.26]
# rel: [0 - 198]
# icolor: [0 - 5]
if level == 1:
return colorConverter.to_rgb('#808080')
f = 1 - (((level-1) * 0.3) / 8)
rel = start_angle / 180. * 99
icolor = int(rel / (100./3))
next_icolor = (icolor + 1) % 6
# Interpolate (?)
color = colorConverter.to_rgb(tango_colors[icolor])
next_color = colorConverter.to_rgb(tango_colors[next_icolor])
p = (rel - icolor * 100./3) / (100./3)
color = [f * (c - p * (c - n)) for c, n in zip(color, next_color)]
return color
def Jmol_color(in_color):
if isinstance(in_color,
str) and in_color[:2] == "[x" and in_color[-1] == ']':
# I guess the user knows what they're doing
return in_color
#----------------------------------------#
if _have_mpl_colors:
color = colorConverter.to_rgb(in_color)
hex_str = rgb2hex(color)
hex_str = "[x" + hex_str[1:] + "]"
return hex_str
else:
# I can't help you, sorry
return in_color
def _repr_html_(self):
html = '<table style="border: 0;">'
for c in self:
hx = rgb2hex(colorConverter.to_rgb(c))
html += '<tr style="border: 0;">' '<td style="background-color: {0}; ' 'border: 0;">' '<code style="background-color: {0};">'.format(
hx)
html += c + '</code></td>'
html += '<td style="border: 0"><code>'
html += repr(self[c]) + '</code>'
html += '</td></tr>'
html += '</table>'
return html
def plot_axis(self, axis):
axis_data = self.evaluate_single_axis(axis)
# acquire mutex data
mut_speed = axis_data["lock_speedup"]
two_std = list(map(lambda x: 2 * sqrt(x), axis_data["lock_var"]))
mut_upper_bound = list(map(lambda x, y: x + y, mut_speed, two_std))
mut_lower_bound = list(map(lambda x, y: x - y, mut_speed, two_std))
mut_speed = np.array(mut_speed)
mut_upper_bound = np.array(mut_upper_bound)
mut_lower_bound = np.array(mut_lower_bound)
# acquire local list data
list_speed = axis_data["list_speedup"]
two_std = list(map(lambda x: 2 * sqrt(x), axis_data["list_var"]))
list_upper_bound = list(map(lambda x, y: x + y, list_speed, two_std))
list_lower_bound = list(map(lambda x, y: x - y, list_speed, two_std))
list_speed = np.array(list_speed)
list_upper_bound = np.array(list_upper_bound)
list_lower_bound = np.array(list_lower_bound)
fig = plt.figure(1, figsize=(7, 2.5))
x_axis = np.array(axis_data["x_axis"])
self.plot_mean_and_CI(x_axis,
mut_speed,
mut_upper_bound,
mut_lower_bound,
color_mean='k',
color_shading='k')
self.plot_mean_and_CI(x_axis,
list_speed,
list_upper_bound,
list_lower_bound,
color_mean='r--',
color_shading='r')
bg = np.array([1, 1, 1]) # background of the legend is white
colors = ['black', 'red']
# with alpha = .5, the faded color is the average of the background and color
colors_faded = [(np.array(cc.to_rgb(color)) + bg) / 2.0
for color in colors]
plt.xlabel('threads')
plt.ylabel('speed up')
plt.legend(
[0, 1], ['Lock', 'Local List'],
handler_map={
0: LegendObject(colors[0], colors_faded[0]),
1: LegendObject(colors[1], colors_faded[1], dashed=True),
})
plt.title('aggregated speed up')
#plt.tight_layout()
plt.grid()
plt.show()
def on_combobox_lineprops_changed(self, item):
'update the widgets from the active line'
if not self._inited: return
self._updateson = False
line = self.get_active_line()
ls = line.get_linestyle()
if ls is None: ls = 'None'
self.cbox_linestyles.set_active(self.linestyled[ls])
marker = line.get_marker()
if marker is None: marker = 'None'
self.cbox_markers.set_active(self.markerd[marker])
r, g, b = colorConverter.to_rgb(line.get_color())
color = gtk.gdk.Color(*[int(val * 65535) for val in r, g, b])
button = self.wtree.get_widget('colorbutton_linestyle')
button.set_color(color)
r, g, b = colorConverter.to_rgb(line.get_markerfacecolor())
color = gtk.gdk.Color(*[int(val * 65535) for val in r, g, b])
button = self.wtree.get_widget('colorbutton_markerface')
button.set_color(color)
self._updateson = True
def blend_colors(color, bg, factor):
"""Blend color with background
Parameters
----------
color
Color that will be blended.
bg
Background color.
factor : float
Blend factor: 0 to 1.
"""
from matplotlib.colors import colorConverter
color, bg = (np.array(colorConverter.to_rgb(c)) for c in (color, bg))
return (1 - factor) * bg + factor * color
def latex_colors(colors, name=''):
""" Print a \definecolor command for LaTeX.
Parameters
----------
colors: matplotlib color or dict of matplotlib colors
A dictionary with names and rgb hex-strings of colors
or a single matplotlib color.
name: string
If colors is a single color, then name is the name of the color.
"""
if isinstance(colors, dict):
for cn in colors:
latex_colors(colors[cn], cn)
else:
r, g, b = cc.to_rgb(colors)
print('\\definecolor{%s}{RGB}{%.3f,%.3f,%.3f}' % (name, r, g, b))
def lighter(color, lightness):
"""Make a color lighter
From bendalab/plottools package.
Parameters
----------
color: dict or matplotlib color spec
A matplotlib color (hex string, name color string, rgb tuple)
or a dictionary with an 'color' or 'facecolor' key.
lightness: float
The smaller the lightness, the lighter the returned color.
A lightness of 0 returns white.
A lightness of 1 leaves the color untouched.
A lightness of 2 returns black.
Returns
-------
color: string or dict
The lighter color as a hexadecimal RGB string (e.g. '#rrggbb').
If `color` is a dictionary, a copy of the dictionary is returned
with the value of 'color' or 'facecolor' set to the lighter color.
"""
try:
c = color['color']
cd = dict(**color)
cd['color'] = lighter(c, lightness)
return cd
except (KeyError, TypeError):
try:
c = color['facecolor']
cd = dict(**color)
cd['facecolor'] = lighter(c, lightness)
return cd
except (KeyError, TypeError):
if lightness > 2:
lightness = 2
if lightness > 1:
return darker(color, 2.0 - lightness)
if lightness < 0:
lightness = 0
r, g, b = cc.to_rgb(color)
rl = r + (1.0 - lightness) * (1.0 - r)
gl = g + (1.0 - lightness) * (1.0 - g)
bl = b + (1.0 - lightness) * (1.0 - b)
return to_hex((rl, gl, bl)).upper()
def darker(color, saturation):
""" Make a color darker.
From bendalab/plottools package.
Parameters
----------
color: dict or matplotlib color spec
A matplotlib color (hex string, name color string, rgb tuple)
or a dictionary with an 'color' or 'facecolor' key.
saturation: float
The smaller the saturation, the darker the returned color.
A saturation of 0 returns black.
A saturation of 1 leaves the color untouched.
A saturation of 2 returns white.
Returns
-------
color: string or dictionary
The darker color as a hexadecimal RGB string (e.g. '#rrggbb').
If `color` is a dictionary, a copy of the dictionary is returned
with the value of 'color' or 'facecolor' set to the darker color.
"""
try:
c = color['color']
cd = dict(**color)
cd['color'] = darker(c, saturation)
return cd
except (KeyError, TypeError):
try:
c = color['facecolor']
cd = dict(**color)
cd['facecolor'] = darker(c, saturation)
return cd
except (KeyError, TypeError):
if saturation > 2:
sauration = 2
if saturation > 1:
return lighter(color, 2.0 - saturation)
if saturation < 0:
saturation = 0
r, g, b = cc.to_rgb(color)
rd = r * saturation
gd = g * saturation
bd = b * saturation
return to_hex((rd, gd, bd)).upper()
def get_color_map(color):
'''Generate a LinearSegmentedColormap with a single color and varying
transparency. Bad values and values below the lower limit are set to be
completely transparent.
Parameters
----------
color: string or array-like with shape (3,)
The color to use when overlaying the survey footprint. Either a
string that can be input to colorConverter.to_rgb() or rgb triplet.
Returns
-------
colormap1: LinearSegmentedColormap
A color map that is a single color but varies its opacity
from 0.5 to 1. Bad values and values below the minimum are completely
transparent.
'''
from matplotlib.colors import LinearSegmentedColormap
from matplotlib.colors import colorConverter
# if the type is a string it is assumed to be an input to allow us
# to get an rgb value. If it is not a string and length is 3, it is
# assumed to be an actual rgb value
if isinstance(color, str):
rgb = colorConverter.to_rgb(color)
elif len(color) == 3:
rgb = color
else:
raise ValueError('Bad color input')
cdict = {
'red': [(0, rgb[0], rgb[0]), (1, rgb[0], rgb[0])],
'green': [(0, rgb[1], rgb[1]), (1, rgb[1], rgb[1])],
'blue': [(0, rgb[2], rgb[2]), (1, rgb[2], rgb[2])],
'alpha': [(0, 0.5, 0.5), (1, 1, 1)]
}
colormap1 = LinearSegmentedColormap('FootprintCM', cdict)
colormap1.set_bad(alpha=0.0)
colormap1.set_under(alpha=0.0)
return colormap1
def __init__(self, *args, **kwargs):
super().__init__(*args, **kwargs)
# permission composition shorthands
load_store = CheriCapPerm.LOAD | CheriCapPerm.STORE
load_exec = CheriCapPerm.LOAD | CheriCapPerm.EXEC
store_exec = CheriCapPerm.STORE | CheriCapPerm.EXEC
load_store_exec = (CheriCapPerm.STORE | CheriCapPerm.LOAD
| CheriCapPerm.EXEC)
self._colors = {
0: colorConverter.to_rgb("#bcbcbc"),
CheriCapPerm.LOAD: colorConverter.to_rgb("k"),
CheriCapPerm.STORE: colorConverter.to_rgb("y"),
CheriCapPerm.EXEC: colorConverter.to_rgb("m"),
load_store: colorConverter.to_rgb("c"),
load_exec: colorConverter.to_rgb("b"),
store_exec: colorConverter.to_rgb("g"),
load_store_exec: colorConverter.to_rgb("r"),
}
def create_plot(self, frame, fig):
dkey = list(self.keys())[0]
pmap = dataclasses.I3RecoPulseSeriesMap.from_frame(frame, dkey)
calib = frame['I3Calibration']
status = frame['I3DetectorStatus']
keys = self.setting('OMKeys')
axes = fig.add_subplot(1, 1, 1)
# protect against missed clicks
keys = [k for k in keys if k in pmap]
# FIXME: think of a better way to pick colors and line styles
for color, k in colorize(keys):
pulses = pmap[k]
cal = calib.dom_cal[k]
stat = status.dom_status[k]
label = 'OM(%d,%d): %.1f PE' % (k.string, k.om,
sum(p.charge for p in pulses))
trange = (min(p.time for p in pulses), max(p.time for p in pulses))
wf = dataclasses.I3Waveform()
WaveformPlotter.plot_pulses(axes,
trange,
wf,
pulses,
cal,
stat,
color=color,
label=label)
if ('I3Geometry' in frame):
self.addOverlayLine(frame['I3Geometry'].omgeo[k].position,
mplcol.to_rgb(color))
axes.set_xlabel('Time [microseconds]', fontsize='smaller')
axes.set_ylabel('Toroid output voltage [mV]', fontsize='smaller')
if (self.setting('logscale')):
axes.set_yscale('log')
if (self.setting("Animated")):
mintime = min(pulses[0].time for pulses in pmap.values()) - 100
self.timeline = axes.axvline(mintime / 1e3, color='black')
if (self.setting('legend')):
axes.legend(loc='best', prop={'size': 'small'})
def get_color_arr(c, n, flip_rb=False):
"""
Convert string c to carr array (N x 3) format
"""
carr = None;
if isinstance(c, str): # single color
carr = np.tile(np.array(colorConverter.to_rgb(c)), [n,1])
elif isinstance(c, float):
carr = np.tile(np.array(color_func(c)), [n,1])
else:
carr = reshape_arr(c)
if flip_rb:
b, r = carr[:,0], carr[:,2]
carr[:,0], carr[:,2] = r.copy(), b.copy()
# return floating point with values in [0,1]
return carr.astype(np.float32) / 255.0 if carr.dtype == np.uint8 else carr.astype(np.float32)
def headerData(self, section, orientation, role):
if orientation == QtCore.Qt.Horizontal and role == QtCore.Qt.DisplayRole:
columns = [
'No.',
'Timestamp',
'Offset',
] + [
'Head %s' % head.serial for head in self.analyzer.bottle.heads
]
return columns[section]
elif (orientation == QtCore.Qt.Horizontal
and role == QtCore.Qt.ForegroundRole and section >= 3
and matplotlib):
return QtGui.QBrush(
QtGui.QColor(*(i * 255 for i in colorConverter.to_rgb(
matplotlib.rcParams['axes.color_cycle']
[(section - 3) %
len(matplotlib.rcParams['axes.color_cycle'])]))))
elif orientation == QtCore.Qt.Vertical and role == QtCore.Qt.DisplayRole:
return section
def _check_color(color):
from matplotlib.colors import colorConverter
if isinstance(color, str):
color = colorConverter.to_rgb(color)
elif isinstance(color, collections.abc.Iterable):
np_color = np.array(color)
if np_color.size % 3 != 0 and np_color.size % 4 != 0:
raise ValueError("The expected valid format is RGB or RGBA.")
if np_color.dtype == np.int:
if (np_color < 0).any() or (np_color > 255).any():
raise ValueError("Values out of range [0, 255].")
elif np_color.dtype == np.float:
if (np_color < 0.0).any() or (np_color > 1.0).any():
raise ValueError("Values out of range [0.0, 1.0].")
else:
raise TypeError("Expected data type is `np.int` or `np.float` but "
"{} was given.".format(np_color.dtype))
else:
raise TypeError("Expected type is `str` or iterable but "
"{} was given.".format(type(color)))
return color
def _repr_html_(self):
html = '<table style="border: 0;">'
for c in self:
col, i = c.split('_')
hx = rgb2hex(colorConverter.to_rgb(col))
if self.map_to_id:
self._map_pathname_to_id(c)
else:
self.c_ids = self[c]
html += '<tr style="border: 0;">' \
'<td style="background-color: {0}; ' \
'border: 0;">' \
'<code style="background-color: {0};">'.format(hx)
html += f'Module_{int(i) + 1}' + '</code></td>'
html += '<td style="border: 0"><code>'
html += ', '.join(self.c_ids) + '</code>'
html += '</td></tr>'
html += '</table>'
return html
def __init__(self, axes):
"""
Construct the VM-map patch builder.
:param axes: the address-space axes where the patches
will be rendered
:type axes: :class:`matplotlib.axes.Axes`
"""
super(VMMapPatchBuilder, self).__init__()
self.patches = []
"""List of rectangles"""
self.patch_colors = []
"""List of colors for the patches"""
self.annotations = []
"""Text labels"""
self.label_y = 0.5
""" Y position of the label in Axes coordinates (in [0,1])"""
self._colors = {
"": colorConverter.to_rgb("#bcbcbc"),
"r": colorConverter.to_rgb("k"),
"w": colorConverter.to_rgb("y"),
"x": colorConverter.to_rgb("m"),
"rw": colorConverter.to_rgb("c"),
"rx": colorConverter.to_rgb("b"),
"wx": colorConverter.to_rgb("g"),
"rwx": colorConverter.to_rgb("r")
}
"""Map section permission to line colors"""
self.transform = transforms.blended_transform_factory(
axes.transData, axes.transAxes)
"""Transform used by the patches"""
def kmeans_plot(X, y, cluster_centers, ax=None):
import matplotlib.patheffects as path_effects
from sklearn.metrics.pairwise import pairwise_distances_argmin_min
if ax is None:
ax = plt.gca()
# colors = cm.spectral(y.astype(float) / len(cluster_centers))
cmap = cm.get_cmap("Spectral")
colors = cmap(y.astype(float) / len(cluster_centers))
ax.scatter(*list(zip(*X)), lw=0, c=colors, s=30)
offset = max(list(zip(*cluster_centers))[0]) * 0.2
for i, cluster in enumerate(cluster_centers):
index, _ = pairwise_distances_argmin_min(cluster.reshape(1, -1), Y=X)
cluster_color = colorConverter.to_rgb(colors[index[0]])
if is_luminous(cluster_color) is False:
cluster_color = darken_rgb(cluster_color, 0.35)
label = ax.text(x=cluster[0] + offset,
y=cluster[1],
s='{:d}'.format(i + 1),
color=cluster_color)
label.set_path_effects([
path_effects.Stroke(lw=2, foreground='white'),
path_effects.Normal()
])
limit = max(*ax.get_xlim(), *ax.get_xlim())
ax.set_xlim(0, limit)
ax.set_ylim(0, limit)
ax.set_xlabel("Feature space for the 1st feature")
ax.set_ylabel("Feature space for the 2nd feature")
return ax
def pastel(colour, weight=2.4):
""" Convert colour into a nice pastel shade"""
rgb = np.asarray(colorConverter.to_rgb(colour))
# scale colour
maxc = max(rgb)
if maxc < 1.0 and maxc > 0:
# scale colour
scale = 1.0 / maxc
rgb = rgb * scale
# now decrease saturation
total = rgb.sum()
slack = 0
for x in rgb:
slack += 1.0 - x
# want to increase weight from total to weight
# pick x s.t. slack * x == weight - total
# x = (weight - total) / slack
x = (weight - total) / slack
rgb = [c + (x * (1.0 - c)) for c in rgb]
return rgb
def discrete_cmap(N=8,
colors=None,
cmap='viridis',
norm=None,
use_bounds=False,
zero=None):
if (colors is None):
if (norm is None):
if (use_bounds):
norm = Normalize(vmin=0, vmax=N - 1)
else:
norm = Normalize(vmin=-0.5, vmax=N - 0.5)
sm = cm.ScalarMappable(cmap=cmap, norm=norm)
cols = sm.to_rgba(range(N))[:, :3]
else:
cols = colors
N = len(colors) # Number of rows
if (zero is not None):
zcol = colorConverter.to_rgb(zero)
cols = np.insert(cols, 0, zcol, axis=0)
return ListedColormap(cols)
def get_stock_signal_data():
fname = os.path.join(os.getcwd(), 'data', 'stock_data', '_IF000.csv')
price_data = csv2frame(fname)
from matplotlib.colors import colorConverter
info = load_tradeinfo("_djtrend2_IF000")
entry_x = []
entry_y = info['entry_price'].tolist()
exit_x = []
exit_y = info['exit_price'].tolist()
colors = []
for t in info.index:
entry_x.append(price_data.index.searchsorted(t))
for t in info['exit_datetime'].values:
exit_x.append(price_data.index.searchsorted(t))
for i in range(len(info)):
tr = info.ix[i]
if tr['islong']:
c = 'r' if tr['exit_price']>tr['entry_price'] else 'w'
else:
c = 'r' if tr['exit_price']<tr['entry_price'] else 'w'
r,g,b = colorConverter.to_rgb(c)
colors.append((r,g,b,1))
return price_data, entry_x, entry_y, exit_x, exit_y, colors
def mpl_to_plotly_cmap(cmap) -> List:
"""Convert a matplotlib cmap for use with Plotly.
https://plotly.com/python/v3/matplotlib-colorscales/
Args:
cmap: The matplotlib colormap
Returns:
Plotly colorscale
"""
pl_rgb = []
norm = Normalize(vmin=0, vmax=255)
for i in range(0, 255):
k = colorConverter.to_rgb(cmap(norm(i)))
pl_rgb.append(k)
pl_entries = 255
h = 1.0 / (pl_entries - 1)
pl_colorscale = []
for k in range(pl_entries):
C = list(map(np.uint8, np.array(cmap(k * h)[:3]) * 255))
pl_colorscale.append([k * h, "rgb" + str((C[0], C[1], C[2]))])
return pl_colorscale
def get_stock_signal_data():
from matplotlib.colors import colorConverter
signal_data = pd.read_csv('./data/signal_IF000.csv', index_col=0, parse_dates=True)
price_data = pd.read_csv('./data/IF000.csv' , index_col=0, parse_dates=True)
info = sugar.process_signal(signal_data, price_data)
entry_x = []
entry_y = info['entry_price'].tolist()
exit_x = []
exit_y = info['exit_price'].tolist()
colors = []
for t in info.index:
entry_x.append(price_data.index.searchsorted(t))
for t in info['exit_datetime'].values:
exit_x.append(price_data.index.searchsorted(t))
for i in range(len(info)):
tr = info.ix[i]
if tr['islong']:
c = 'r' if tr['exit_price']>tr['entry_price'] else 'b'
else:
c = 'r' if tr['exit_price']<tr['entry_price'] else 'b'
r,g,b = colorConverter.to_rgb(c)
colors.append((r,g,b,1))
return price_data, entry_x, entry_y, exit_x, exit_y, colors
