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])
rgba = mcolors.to_rgba(line.get_color())
color = gtk.gdk.Color(*[int(val * 65535) for val in rgba[:3]])
button = self.wtree.get_widget("colorbutton_linestyle")
button.set_color(color)
rgba = mcolors.to_rgba(line.get_markerfacecolor())
color = gtk.gdk.Color(*[int(val * 65535) for val in rgba[:3]])
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
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
"""
colorup = mcolors.to_rgba(colorup, alpha)
colordown = mcolors.to_rgba(colordown, 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,),
)
ax.add_collection(barCollection)
corners = (0, 0), (len(bars), max(volumes))
ax.update_datalim(corners)
ax.autoscale_view()
# add these last
return barCollection
def __init__(self, fontsize=14, labelcolor='black', bgcolor='yellow',
alpha=1.0):
self.fontsize = fontsize
self.labelcolor = labelcolor
self.bgcolor = bgcolor
self.alpha = alpha
self.labelcolor_rgb = colors.to_rgba(labelcolor)[:3]
self.bgcolor_rgb = colors.to_rgba(bgcolor)[:3]
def test_legend_edgecolor():
get_func = 'get_edgecolor'
rcparam = 'legend.edgecolor'
test_values = [({rcparam: 'r'},
mcolors.to_rgba('r')),
({rcparam: 'inherit', 'axes.edgecolor': 'r'},
mcolors.to_rgba('r')),
({rcparam: 'g', 'axes.facecolor': 'r'},
mcolors.to_rgba('g'))]
for rc_dict, target in test_values:
yield _legend_rcparam_helper, rc_dict, target, get_func
def test_conversions():
# to_rgba_array("none") returns a (0, 4) array.
assert_array_equal(mcolors.to_rgba_array("none"), np.zeros((0, 4)))
# alpha is properly set.
assert_equal(mcolors.to_rgba((1, 1, 1), .5), (1, 1, 1, .5))
# builtin round differs between py2 and py3.
assert_equal(mcolors.to_hex((.7, .7, .7)), "#b2b2b2")
# hex roundtrip.
hex_color = "#1234abcd"
assert_equal(mcolors.to_hex(mcolors.to_rgba(hex_color), keep_alpha=True),
hex_color)
def test_failed_conversions():
with pytest.raises(ValueError):
mcolors.to_rgba('5')
with pytest.raises(ValueError):
mcolors.to_rgba('-1')
with pytest.raises(ValueError):
mcolors.to_rgba('nan')
with pytest.raises(ValueError):
mcolors.to_rgba('unknown_color')
with pytest.raises(ValueError):
# Gray must be a string to distinguish 3-4 grays from RGB or RGBA.
mcolors.to_rgba(0.4)
def convert_colors_string_to_tuple(cols):
for col in cols:
if isinstance(col, str):
if col in BASE_COLORS:
yield to_rgba(BASE_COLORS[col])
elif col in CSS4_COLORS:
yield to_rgba(CSS4_COLORS[col])
else:
raise ValueError("Color specifier {} "
"not recognized".format(col))
else:
yield to_rgba(col)
def draw_3d(verts, ymin, ymax, line_at_zero=True, colors=True):
'''Given verts as a list of plots, each plot being a list
of (x, y) vertices, generate a 3-d figure where each plot
is shown as a translucent polygon.
If line_at_zero, a line will be drawn through the zero point
of each plot, otherwise the baseline will be at the bottom of
the plot regardless of where the zero line is.
'''
# add_collection3d() wants a collection of closed polygons;
# each polygon needs a base and won't generate it automatically.
# So for each subplot, add a base at ymin.
if line_at_zero:
zeroline = 0
else:
zeroline = ymin
for p in verts:
p.insert(0, (p[0][0], zeroline))
p.append((p[-1][0], zeroline))
if colors:
# All the matplotlib color sampling examples I can find,
# like cm.rainbow/linspace, make adjacent colors similar,
# the exact opposite of what most people would want.
# So cycle hue manually.
hue = 0
huejump = .27
facecolors = []
edgecolors = []
for v in verts:
hue = (hue + huejump) % 1
c = mcolors.hsv_to_rgb([hue, 1, 1])
# random.uniform(.8, 1),
# random.uniform(.7, 1)])
edgecolors.append(c)
# Make the facecolor translucent:
facecolors.append(mcolors.to_rgba(c, alpha=.7))
else:
facecolors = (1, 1, 1, .8)
edgecolors = (0, 0, 1, 1)
poly = PolyCollection(verts,
facecolors=facecolors, edgecolors=edgecolors)
zs = range(len(data))
# zs = range(len(data)-1, -1, -1)
fig = plt.figure()
ax = fig.add_subplot(1,1,1, projection='3d')
plt.tight_layout(pad=2.0, w_pad=10.0, h_pad=3.0)
ax.add_collection3d(poly, zs=zs, zdir='y')
ax.set_xlabel('X')
ax.set_ylabel('Y')
ax.set_zlabel('Z')
ax.set_xlim3d(0, len(data[1]))
ax.set_ylim3d(-1, len(data))
ax.set_zlim3d(ymin, ymax)
def make_image(self, renderer, magnification=1.0, unsampled=False):
if self._A is None:
raise RuntimeError('You must first set the image array')
if unsampled:
raise ValueError('unsampled not supported on PColorImage')
fc = self.axes.patch.get_facecolor()
bg = mcolors.to_rgba(fc, 0)
bg = (np.array(bg)*255).astype(np.uint8)
l, b, r, t = self.axes.bbox.extents
width = (np.round(r) + 0.5) - (np.round(l) - 0.5)
height = (np.round(t) + 0.5) - (np.round(b) - 0.5)
# The extra cast-to-int is only needed for python2
width = int(np.round(width * magnification))
height = int(np.round(height * magnification))
if self._rgbacache is None:
A = self.to_rgba(self._A, bytes=True)
self._rgbacache = A
if self._A.ndim == 2:
self.is_grayscale = self.cmap.is_gray()
else:
A = self._rgbacache
vl = self.axes.viewLim
im = _image.pcolor2(self._Ax, self._Ay, A,
height,
width,
(vl.x0, vl.x1, vl.y0, vl.y1),
bg)
return im, l, b, IdentityTransform()
def __init__(self, offset=(2, -2),
shadow_color='k', alpha=0.3, rho=0.3, **kwargs):
"""
Parameters
----------
offset : pair of floats
The offset to apply to the path, in points.
shadow_color : color
The shadow color. Default is black.
A value of ``None`` takes the original artist's color
with a scale factor of `rho`.
alpha : float
The alpha transparency of the created shadow patch.
Default is 0.3.
rho : float
A scale factor to apply to the rgbFace color if `shadow_rgbFace`
is ``None``. Default is 0.3.
**kwargs
Extra keywords are stored and passed through to
:meth:`AbstractPathEffect._update_gc`.
"""
super().__init__(offset)
if shadow_color is None:
self._shadow_color = shadow_color
else:
self._shadow_color = mcolors.to_rgba(shadow_color)
self._alpha = alpha
self._rho = rho
#: The dictionary of keywords to update the graphics collection with.
self._gc = kwargs
def print_jpg(self, filename_or_obj, *args, **kwargs):
"""
Supported kwargs:
*quality*: The image quality, on a scale from 1 (worst) to
95 (best). The default is 95, if not given in the
matplotlibrc file in the savefig.jpeg_quality parameter.
Values above 95 should be avoided; 100 completely
disables the JPEG quantization stage.
*optimize*: If present, indicates that the encoder should
make an extra pass over the image in order to select
optimal encoder settings.
*progressive*: If present, indicates that this image
should be stored as a progressive JPEG file.
"""
buf, size = self.print_to_buffer()
if kwargs.pop("dryrun", False):
return
# The image is "pasted" onto a white background image to safely
# handle any transparency
image = Image.frombuffer('RGBA', size, buf, 'raw', 'RGBA', 0, 1)
rgba = mcolors.to_rgba(rcParams.get('savefig.facecolor', 'white'))
color = tuple([int(x * 255.0) for x in rgba[:3]])
background = Image.new('RGB', size, color)
background.paste(image, image)
options = restrict_dict(kwargs, ['quality', 'optimize',
'progressive'])
if 'quality' not in options:
options['quality'] = rcParams['savefig.jpeg_quality']
return background.save(filename_or_obj, format='jpeg', **options)
def get_color_range(n):
colors = mcolors.TABLEAU_COLORS
by_hsv = sorted((tuple(mcolors.rgb_to_hsv(mcolors.to_rgba(color)[:3])), name)
for name, color in colors.items())
sorted_names = [name for hsv, name in by_hsv]
delta = int(len(sorted_names)/n)
return sorted_names[::delta]
def test_conversions():
# to_rgba_array("none") returns a (0, 4) array.
assert_array_equal(mcolors.to_rgba_array("none"), np.zeros((0, 4)))
# a list of grayscale levels, not a single color.
assert_array_equal(
mcolors.to_rgba_array([".2", ".5", ".8"]),
np.vstack([mcolors.to_rgba(c) for c in [".2", ".5", ".8"]]))
# alpha is properly set.
assert mcolors.to_rgba((1, 1, 1), .5) == (1, 1, 1, .5)
assert mcolors.to_rgba(".1", .5) == (.1, .1, .1, .5)
# builtin round differs between py2 and py3.
assert mcolors.to_hex((.7, .7, .7)) == "#b2b2b2"
# hex roundtrip.
hex_color = "#1234abcd"
assert mcolors.to_hex(mcolors.to_rgba(hex_color), keep_alpha=True) == \
hex_color
def test_conversions_masked():
x1 = np.ma.array(['k', 'b'], mask=[True, False])
x2 = np.ma.array([[0, 0, 0, 1], [0, 0, 1, 1]])
x2[0] = np.ma.masked
assert mcolors.to_rgba(x1[0]) == (0, 0, 0, 0)
assert_array_equal(mcolors.to_rgba_array(x1),
[[0, 0, 0, 0], [0, 0, 1, 1]])
assert_array_equal(mcolors.to_rgba_array(x2), mcolors.to_rgba_array(x1))
def to_qcolor(color):
"""Create a QColor from a matplotlib color"""
qcolor = QtGui.QColor()
try:
rgba = mcolors.to_rgba(color)
except ValueError:
warnings.warn('Ignoring invalid color %r' % color)
return qcolor # return invalid QColor
qcolor.setRgbF(*rgba)
return qcolor
def print_jpg(self, filename_or_obj, *args, dryrun=False,
pil_kwargs=None, **kwargs):
"""
Write the figure to a JPEG file.
Parameters
----------
filename_or_obj : str or PathLike or file-like object
The file to write to.
Other Parameters
----------------
quality : int
The image quality, on a scale from 1 (worst) to 100 (best).
The default is :rc:`savefig.jpeg_quality`. Values above
95 should be avoided; 100 completely disables the JPEG
quantization stage.
optimize : bool
If present, indicates that the encoder should
make an extra pass over the image in order to select
optimal encoder settings.
progressive : bool
If present, indicates that this image
should be stored as a progressive JPEG file.
pil_kwargs : dict, optional
Additional keyword arguments that are passed to
`PIL.Image.save` when saving the figure. These take precedence
over *quality*, *optimize* and *progressive*.
"""
buf, size = self.print_to_buffer()
if dryrun:
return
# The image is "pasted" onto a white background image to safely
# handle any transparency
image = Image.frombuffer('RGBA', size, buf, 'raw', 'RGBA', 0, 1)
rgba = mcolors.to_rgba(rcParams['savefig.facecolor'])
color = tuple([int(x * 255) for x in rgba[:3]])
background = Image.new('RGB', size, color)
background.paste(image, image)
if pil_kwargs is None:
pil_kwargs = {}
for k in ["quality", "optimize", "progressive"]:
if k in kwargs:
pil_kwargs.setdefault(k, kwargs[k])
pil_kwargs.setdefault("quality", rcParams["savefig.jpeg_quality"])
pil_kwargs.setdefault("dpi", (self.figure.dpi, self.figure.dpi))
return background.save(
filename_or_obj, format='jpeg', **pil_kwargs)
def plot_colortable(colors, title, sort_colors=True, emptycols=0):
cell_width = 212
cell_height = 22
swatch_width = 48
margin = 12
topmargin = 40
# Sort colors by hue, saturation, value and name.
by_hsv = ((tuple(mcolors.rgb_to_hsv(mcolors.to_rgba(color)[:3])), name)
for name, color in colors.items())
if sort_colors is True:
by_hsv = sorted(by_hsv)
names = [name for hsv, name in by_hsv]
n = len(names)
ncols = 4 - emptycols
nrows = n // ncols + int(n % ncols > 0)
width = cell_width * 4 + 2 * margin
height = cell_height * nrows + margin + topmargin
dpi = 72
fig, ax = plt.subplots(figsize=(width / dpi, height / dpi), dpi=dpi)
fig.subplots_adjust(margin/width, margin/height,
(width-margin)/width, (height-topmargin)/height)
ax.set_xlim(0, cell_width * 4)
ax.set_ylim(cell_height * (nrows-0.5), -cell_height/2.)
ax.yaxis.set_visible(False)
ax.xaxis.set_visible(False)
ax.set_axis_off()
ax.set_title(title, fontsize=24, loc="left", pad=10)
for i, name in enumerate(names):
row = i % nrows
col = i // nrows
y = row * cell_height
swatch_start_x = cell_width * col
swatch_end_x = cell_width * col + swatch_width
text_pos_x = cell_width * col + swatch_width + 7
ax.text(text_pos_x, y, name, fontsize=14,
horizontalalignment='left',
verticalalignment='center')
ax.hlines(y, swatch_start_x, swatch_end_x,
color=colors[name], linewidth=18)
return fig
def print_jpg(self, filename_or_obj, *args, dryrun=False, **kwargs):
"""
Write the figure to a JPEG file.
Parameters
----------
filename_or_obj : str or PathLike or file-like object
The file to write to.
Other Parameters
----------------
quality : int
The image quality, on a scale from 1 (worst) to 100 (best).
The default is :rc:`savefig.jpeg_quality`. Values above
95 should be avoided; 100 completely disables the JPEG
quantization stage.
optimize : bool
If present, indicates that the encoder should
make an extra pass over the image in order to select
optimal encoder settings.
progressive : bool
If present, indicates that this image
should be stored as a progressive JPEG file.
"""
buf, size = self.print_to_buffer()
if dryrun:
return
# The image is "pasted" onto a white background image to safely
# handle any transparency
image = Image.frombuffer('RGBA', size, buf, 'raw', 'RGBA', 0, 1)
rgba = mcolors.to_rgba(rcParams['savefig.facecolor'])
color = tuple([int(x * 255) for x in rgba[:3]])
background = Image.new('RGB', size, color)
background.paste(image, image)
options = {k: kwargs[k]
for k in ['quality', 'optimize', 'progressive', 'dpi']
if k in kwargs}
options.setdefault('quality', rcParams['savefig.jpeg_quality'])
if 'dpi' in options:
# Set the same dpi in both x and y directions
options['dpi'] = (options['dpi'], options['dpi'])
return background.save(filename_or_obj, format='jpeg', **options)
def get_colors(c, num):
"""Stretch the color argument to provide the required number num"""
if type(c) == type("string"):
c = mcolors.to_rgba(c)
if iscolor(c):
return [c] * num
if len(c) == num:
return c
elif iscolor(c):
return [c] * num
elif len(c) == 0: #if edgecolor or facecolor is specified as 'none'
return [[0,0,0,0]] * num
elif iscolor(c[0]):
return [c[0]] * num
else:
raise ValueError('unknown color format %s' % c)
def display_frame_coords(data, coords, window, vectors=None, ref=None, outlines=None,
**kws):
from grafico.imagine import ImageDisplay #FITSCubeDisplay,
from obstools.aps import ApertureCollection
fig, ax = plt.subplots()
imd = ImageDisplay(ax, data)
imd.connect()
aps = ApertureCollection(coords=coords[:,::-1], radii=7,
ec='darkorange', lw=1, ls='--',
**kws)
aps.axadd(ax)
aps.annotate(color='orangered', size='small')
if window:
from matplotlib.patches import Rectangle
from matplotlib.collections import PatchCollection
llc = coords[:,::-1] - window/2
patches = [Rectangle(coo-window/2, window, window) for coo in llc]
rcol = PatchCollection(patches, edgecolor='r', facecolor='none',
lw=1, linestyle=':')
ax.add_collection(rcol)
if outlines is not None:
from matplotlib.colors import to_rgba
overlay = np.empty(data.shape+(4,))
overlay[...] = to_rgba('0.8', 0)
overlay[...,-1][~outlines.mask] = 1
ax.hold(True)
ax.imshow(overlay)
if vectors is not None:
if ref is None:
'cannot plot vectors without reference star'
Y, X = coords[ref]
V, U = vectors.T
ax.quiver(X, Y, U, V, color='r', scale_units='xy', scale=1, alpha=0.6)
return fig
def __init__(self, offset=(2, -2),
shadow_rgbFace=None, alpha=None,
rho=0.3, **kwargs):
"""
Parameters
----------
offset : pair of floats
The offset of the shadow in points.
shadow_rgbFace : color
The shadow color.
alpha : float
The alpha transparency of the created shadow patch.
Default is 0.3.
http://matplotlib.1069221.n5.nabble.com/path-effects-question-td27630.html
rho : float
A scale factor to apply to the rgbFace color if `shadow_rgbFace`
is not specified. Default is 0.3.
**kwargs
Extra keywords are stored and passed through to
:meth:`AbstractPathEffect._update_gc`.
"""
super(SimplePatchShadow, self).__init__(offset)
if shadow_rgbFace is None:
self._shadow_rgbFace = shadow_rgbFace
else:
self._shadow_rgbFace = mcolors.to_rgba(shadow_rgbFace)
if alpha is None:
alpha = 0.3
self._alpha = alpha
self._rho = rho
#: The dictionary of keywords to update the graphics collection with.
self._gc = kwargs
#: The offset transform object. The offset isn't calculated yet
#: as we don't know how big the figure will be in pixels.
self._offset_tran = mtransforms.Affine2D()
def pastel(colour, weight=2.4):
""" Convert colour into a nice pastel shade"""
rgb = np.asarray(mcolors.to_rgba(colour)[:3])
# 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 figure_edit(axes, parent=None):
"""Edit matplotlib figure options"""
sep = (None, None) # separator
# Get / General
xmin, xmax = map(float, axes.get_xlim())
ymin, ymax = map(float, axes.get_ylim())
general = [('Title', axes.get_title()),
sep,
(None, "<b>X-Axis</b>"),
('Min', xmin), ('Max', xmax),
('Label', axes.get_xlabel()),
('Scale', [axes.get_xscale(), 'linear', 'log']),
sep,
(None, "<b>Y-Axis</b>"),
('Min', ymin), ('Max', ymax),
('Label', axes.get_ylabel()),
('Scale', [axes.get_yscale(), 'linear', 'log']),
sep,
('(Re-)Generate automatic legend', False),
]
# Save the unit data
xconverter = axes.xaxis.converter
yconverter = axes.yaxis.converter
xunits = axes.xaxis.get_units()
yunits = axes.yaxis.get_units()
# Sorting for default labels (_lineXXX, _imageXXX).
def cmp_key(label):
match = re.match(r"(_line|_image)(\d+)", label)
if match:
return match.group(1), int(match.group(2))
else:
return label, 0
# Get / Curves
linedict = {}
for line in axes.get_lines():
label = line.get_label()
if label == '_nolegend_':
continue
linedict[label] = line
curves = []
def prepare_data(d, init):
"""Prepare entry for FormLayout.
`d` is a mapping of shorthands to style names (a single style may
have multiple shorthands, in particular the shorthands `None`,
`"None"`, `"none"` and `""` are synonyms); `init` is one shorthand
of the initial style.
This function returns an list suitable for initializing a
FormLayout combobox, namely `[initial_name, (shorthand,
style_name), (shorthand, style_name), ...]`.
"""
# Drop duplicate shorthands from dict (by overwriting them during
# the dict comprehension).
name2short = {name: short for short, name in d.items()}
# Convert back to {shorthand: name}.
short2name = {short: name for name, short in name2short.items()}
# Find the kept shorthand for the style specified by init.
canonical_init = name2short[d[init]]
# Sort by representation and prepend the initial value.
return ([canonical_init] +
sorted(short2name.items(),
key=lambda short_and_name: short_and_name[1]))
curvelabels = sorted(linedict, key=cmp_key)
for label in curvelabels:
line = linedict[label]
color = mcolors.to_hex(
mcolors.to_rgba(line.get_color(), line.get_alpha()),
keep_alpha=True)
ec = mcolors.to_hex(line.get_markeredgecolor(), keep_alpha=True)
fc = mcolors.to_hex(line.get_markerfacecolor(), keep_alpha=True)
curvedata = [
('Label', label),
sep,
(None, '<b>Line</b>'),
('Line style', prepare_data(LINESTYLES, line.get_linestyle())),
('Draw style', prepare_data(DRAWSTYLES, line.get_drawstyle())),
('Width', line.get_linewidth()),
('Color (RGBA)', color),
sep,
(None, '<b>Marker</b>'),
('Style', prepare_data(MARKERS, line.get_marker())),
('Size', line.get_markersize()),
('Face color (RGBA)', fc),
('Edge color (RGBA)', ec)]
curves.append([curvedata, label, ""])
# Is there a curve displayed?
has_curve = bool(curves)
# Get / Images
imagedict = {}
for image in axes.get_images():
label = image.get_label()
if label == '_nolegend_':
continue
imagedict[label] = image
imagelabels = sorted(imagedict, key=cmp_key)
images = []
cmaps = [(cmap, name) for name, cmap in sorted(cm.cmap_d.items())]
for label in imagelabels:
image = imagedict[label]
cmap = image.get_cmap()
if cmap not in cm.cmap_d.values():
cmaps = [(cmap, cmap.name)] + cmaps
low, high = image.get_clim()
imagedata = [
('Label', label),
('Colormap', [cmap.name] + cmaps),
('Min. value', low),
('Max. value', high),
('Interpolation',
[image.get_interpolation()]
+ [(name, name) for name in sorted(image.iterpnames)])]
images.append([imagedata, label, ""])
# Is there an image displayed?
has_image = bool(images)
datalist = [(general, "Axes", "")]
if curves:
datalist.append((curves, "Curves", ""))
if images:
datalist.append((images, "Images", ""))
def apply_callback(data):
"""This function will be called to apply changes"""
general = data.pop(0)
curves = data.pop(0) if has_curve else []
images = data.pop(0) if has_image else []
if data:
raise ValueError("Unexpected field")
# Set / General
(title, xmin, xmax, xlabel, xscale, ymin, ymax, ylabel, yscale,
generate_legend) = general
if axes.get_xscale() != xscale:
axes.set_xscale(xscale)
if axes.get_yscale() != yscale:
axes.set_yscale(yscale)
axes.set_title(title)
axes.set_xlim(xmin, xmax)
axes.set_xlabel(xlabel)
axes.set_ylim(ymin, ymax)
axes.set_ylabel(ylabel)
# Restore the unit data
axes.xaxis.converter = xconverter
axes.yaxis.converter = yconverter
axes.xaxis.set_units(xunits)
axes.yaxis.set_units(yunits)
axes.xaxis._update_axisinfo()
axes.yaxis._update_axisinfo()
# Set / Curves
for index, curve in enumerate(curves):
line = linedict[curvelabels[index]]
(label, linestyle, drawstyle, linewidth, color, marker, markersize,
markerfacecolor, markeredgecolor) = curve
line.set_label(label)
line.set_linestyle(linestyle)
line.set_drawstyle(drawstyle)
line.set_linewidth(linewidth)
rgba = mcolors.to_rgba(color)
line.set_alpha(None)
line.set_color(rgba)
if marker is not 'none':
line.set_marker(marker)
line.set_markersize(markersize)
line.set_markerfacecolor(markerfacecolor)
line.set_markeredgecolor(markeredgecolor)
# Set / Images
for index, image_settings in enumerate(images):
image = imagedict[imagelabels[index]]
label, cmap, low, high, interpolation = image_settings
image.set_label(label)
image.set_cmap(cm.get_cmap(cmap))
image.set_clim(*sorted([low, high]))
image.set_interpolation(interpolation)
# re-generate legend, if checkbox is checked
if generate_legend:
draggable = None
ncol = 1
if axes.legend_ is not None:
old_legend = axes.get_legend()
draggable = old_legend._draggable is not None
ncol = old_legend._ncol
new_legend = axes.legend(ncol=ncol)
if new_legend:
new_legend.draggable(draggable)
# Redraw
figure = axes.get_figure()
figure.canvas.draw()
data = formlayout.fedit(datalist, title="Figure options", parent=parent,
icon=get_icon('qt4_editor_options.svg'),
apply=apply_callback)
if data is not None:
apply_callback(data)
def print_jpg(self,
filename_or_obj,
*args,
dryrun=False,
pil_kwargs=None,
**kwargs):
"""
Write the figure to a JPEG file.
Parameters
----------
filename_or_obj : str or path-like or file-like
The file to write to.
Other Parameters
----------------
quality : int, default: :rc:`savefig.jpeg_quality`
The image quality, on a scale from 1 (worst) to 95 (best).
Values above 95 should be avoided; 100 disables portions of
the JPEG compression algorithm, and results in large files
with hardly any gain in image quality. This parameter is
deprecated.
optimize : bool, default: False
Whether the encoder should make an extra pass over the image
in order to select optimal encoder settings. This parameter is
deprecated.
progressive : bool, default: False
Whether the image should be stored as a progressive JPEG file.
This parameter is deprecated.
pil_kwargs : dict, optional
Additional keyword arguments that are passed to
`PIL.Image.Image.save` when saving the figure. These take
precedence over *quality*, *optimize* and *progressive*.
"""
# Remove transparency by alpha-blending on an assumed white background.
r, g, b, a = mcolors.to_rgba(self.figure.get_facecolor())
try:
self.figure.set_facecolor(a * np.array([r, g, b]) + 1 - a)
FigureCanvasAgg.draw(self)
finally:
self.figure.set_facecolor((r, g, b, a))
if dryrun:
return
if pil_kwargs is None:
pil_kwargs = {}
for k in ["quality", "optimize", "progressive"]:
if k in kwargs:
pil_kwargs.setdefault(k, kwargs[k])
if "quality" not in pil_kwargs:
quality = pil_kwargs["quality"] = \
dict.__getitem__(mpl.rcParams, "savefig.jpeg_quality")
if quality not in [0, 75, 95]: # default qualities.
cbook.warn_deprecated(
"3.3",
name="savefig.jpeg_quality",
obj_type="rcParam",
addendum="Set the quality using "
"`pil_kwargs={'quality': ...}`; the future default "
"quality will be 75, matching the default of Pillow and "
"libjpeg.")
pil_kwargs.setdefault("dpi", (self.figure.dpi, self.figure.dpi))
# Drop alpha channel now.
return (Image.fromarray(np.asarray(self.buffer_rgba())[..., :3]).save(
filename_or_obj, format='jpeg', **pil_kwargs))
# If --metrics was used, add space and a subplot for the system metrics
if has_system_metrics:
figh += 4
nrows += 1
# Create the plot
fig, axes = plt.subplots(nrows=nrows, sharex=True)
axes = [axes] if nrows == 1 else axes
# Add the benchmarks to the plot
for i, benchmark_name in enumerate(benchmark_names):
ax = axes[i] if is_detailed else axes[0]
color = default_colors[i % len(default_colors)]
name_to_color[benchmark_name] = color
single_run_color = [colors.to_rgba(lighten_color(color, 0.7), 0.5)]
# Filter the runs that belong to this benchmark item
filtered_df = run_durations_df[run_durations_df["name"] == benchmark_name]
# Plot runs into combined graph
for success in [True, False]:
data = filtered_df[filtered_df["success"] == success]
if not data.empty:
data.plot(
ax=ax,
kind="scatter",
x="begin",
y="duration",
color=single_run_color,
figsize=(figw, figh),
def gray_from_float_rgba():
return mcolors.to_rgba(0.4)
def plot_pcl_prediction(df,
axis,
probabilities_col=None,
colors=None,
xcol="x",
ycol="y",
zcol="z",
use_plotly=False,
inv_z=True):
"""Plots the pointcloud of a dataframe with interpolated coloring,
dependent on probability estimates of corresponding classes.
:param df: dataframe containing points and probability estimates.
:type df: pandas.DataFrame
:param axis: The matplotlib-axis the pointcloud should be plotted on
:type axis: matplotlib.axes.Axes
:param probabilities_col: Columns with probability estimates,
defaults to None
:type probabilities_col: list, optional
:param colors: Colors associates with probability estimates,
defaults to None
:type colors: list, optional
:param xcol: Column containing x-position, defaults to "x"
:type xcol: str, optional
:param ycol: Column containing y-position, defaults to "y"
:type ycol: str, optional
:param zcol: Column containing z-position, defaults to "z"
:type zcol: str, optional
:param use_plotly: Whether plotly should be used for plotting or not
:type use_plotly: bool, optional
:param inv_z: Whether the z-axis should be inverted.
:type inv_z: bool, optional
"""
if probabilities_col is None:
probabilities_col = [col for col in df.columns if "pred_" in col]
if colors is None:
colors = [
"blue", "orange", "green", "red", "purple", "brown", "pink",
"gray", "olive", "cyan"
][:len(probabilities_col)]
probarr = np.array(df[probabilities_col])
# linear interpolation between colors
rgba_colors = np.array([to_rgba(color) for color in colors])
colorsarr = np.zeros(shape=(df.shape[0], 4))
for idx, col in enumerate(rgba_colors):
colorsarr += (probarr[:, idx] * np.array([col] * df.shape[0]).T).T
colorsarr = (colorsarr - np.min(colorsarr)) / (np.max(colorsarr) -
np.min(colorsarr))
if use_plotly:
layout = go.Layout(scene=dict(aspectmode="data"), showlegend=True)
fig = go.Figure(layout=layout)
predicted = np.argmax(df[probabilities_col].to_numpy(), axis=1)
for idx, colname in enumerate(probabilities_col):
selected = df[predicted == idx]
selectedcolors = colorsarr[predicted == idx, :]
fig.add_trace(
go.Scatter3d(x=selected[xcol],
y=selected[ycol],
z=(-1 if inv_z else 1) * selected[zcol],
opacity=1,
mode='markers',
marker=dict(size=1,
color=selectedcolors,
opacity=1),
name=colname))
fig.show()
else:
# initialize 3d-plot
axis.scatter(df[xcol],
df[ycol], (-1 if inv_z else 1) * df[zcol],
color=colorsarr,
marker=".",
s=0.7)
for color, classname in zip(colors, probabilities_col):
axis.add_line(
plt.Line2D([0], [0], color=color, label=classname, lw=4))
legend = axis.legend(loc="lower left", title="Classes")
axis.add_artist(legend)
axis.grid(False)
axis.set_axis_off()
axis.set_xlim3d(-60, 60)
axis.set_ylim3d(-60, 60)
axis.set_zlim3d(-60, 60)
axis.view_init(elev=40., azim=-120.)
axis.set_xlabel('X')
axis.set_ylabel('Y')
axis.set_zlabel('Z')
mean_occ_df = occ_df.groupby('program').mean()
std_occ_df = occ_df.groupby('program').std()
fig = plt.figure(figsize=(7,5.4))
ax = plt.subplot(111)
ax.spines["right"].set_visible(False)
ax.spines["top"].set_visible(False)
for mean, std in zip(mean_occ_df.iterrows(), std_occ_df.iterrows()):
ax.errorbar(x=mean[1]['Occupancy'],
y=mean[1]['B_factor'],
xerr=std[1]['Occupancy'],
yerr=std[1]['B_factor'],
color=cols[mean[0]],
fmt='o',
ecolor=to_rgba(cols[mean[0]], 0.4),
markersize=8,
label=mean[0].replace('_',' ')
)
# Dashed joining lines
ax.plot([mean_occ_df.loc['buster', 'Occupancy'],
mean_occ_df.loc['buster_superposed', 'Occupancy']],
[mean_occ_df.loc['buster', 'B_factor'],
mean_occ_df.loc['buster_superposed', 'B_factor']],
linestyle='dashed',
color=to_rgba('forestgreen', 0.5))
ax.plot([mean_occ_df.loc['refmac', 'Occupancy'],
mean_occ_df.loc['refmac_superposed', 'Occupancy']],
[mean_occ_df.loc['refmac', 'B_factor'],
def edge_color(self):
if self.show_edge:
return to_rgba(self.text_color, self.alpha)
else:
return None
segs = np.zeros((50, 100, 2), float)
segs[:, :, 1] = ys
segs[:, :, 0] = x
# Mask some values to test masked array support:
segs = np.ma.masked_where((segs > 50) & (segs < 60), segs)
# We need to set the plot limits.
ax = plt.axes()
ax.set_xlim(x.min(), x.max())
ax.set_ylim(ys.min(), ys.max())
# colors is sequence of rgba tuples
# linestyle is a string or dash tuple. Legal string values are
# solid|dashed|dashdot|dotted. The dash tuple is (offset, onoffseq)
# where onoffseq is an even length tuple of on and off ink in points.
# If linestyle is omitted, 'solid' is used
# See matplotlib.collections.LineCollection for more information
colors = [
mcolors.to_rgba(c)
for c in plt.rcParams['axes.prop_cycle'].by_key()['color']
]
line_segments = LineCollection(segs,
linewidths=(0.5, 1, 1.5, 2),
colors=colors,
linestyle='solid')
ax.add_collection(line_segments)
ax.set_title('Line collection with masked arrays')
plt.show()
#!/usr/bin/env python
import nilearn
from nilearn import plotting
from nilearn import datasets
from nilearn import surface
from nilearn import image
import matplotlib.pyplot as plt
from matplotlib.colors import to_rgba
import numpy as np
VOIS = ['Action', 'LTM', 'Perception', 'Procedural', 'WM']
COLS = [list(to_rgba(x)) for x in \
["#cc00cc", "#ff9900", "#ff3333", "#00cc33", "#00ccff"]]
def load_vois(task):
f = open("%s/vois.txt" % task)
vois = {}
for line in f.readlines():
name, coords = line.split()[:2]
xyz = [int(x) for x in coords.split(",")]
vois[name] = xyz
return vois
def load_individual_coords(task):
vdict = {}
for voi in VOIS:
def _map_scalar(self, x, alpha=None, bytes=False):
y = to_rgba(BLUE, alpha) if x < 0.5 else to_rgba(RED, alpha)
if bytes:
y = np.floor(y * 255.).astype("int")
return y
theta = np.linspace(0, 2*np.pi, nverts)
xx = r * np.sin(theta)
yy = r * np.cos(theta)
spiral = list(zip(xx, yy))
# Make some offsets
# Fixing random state for reproducibility
rs = np.random.RandomState(19680801)
xo = rs.randn(npts)
yo = rs.randn(npts)
xyo = list(zip(xo, yo))
# Make a list of colors cycling through the default series.
colors = [colors.to_rgba(c)
for c in plt.rcParams['axes.prop_cycle'].by_key()['color']]
fig, axes = plt.subplots(2, 2)
fig.subplots_adjust(top=0.92, left=0.07, right=0.97,
hspace=0.3, wspace=0.3)
((ax1, ax2), (ax3, ax4)) = axes # unpack the axes
col = collections.LineCollection([spiral], offsets=xyo,
transOffset=ax1.transData)
trans = fig.dpi_scale_trans + transforms.Affine2D().scale(1.0/72.0)
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.
def plot_results(hosts_filename="output_hosts_0.csv",
event_filename="output_events_0.csv"):
"""Animate a particular epidemic simulation."""
state_colours = {
"S": colors.to_rgba("green"),
"E": colors.to_rgba("cyan"),
"C": colors.to_rgba("blue"),
"D": colors.to_rgba("orange"),
"I": colors.to_rgba("red"),
"R": colors.to_rgba("grey"),
"Culled": colors.to_rgba("black"),
}
hosts_df = pd.read_csv(hosts_filename)
events_df = pd.read_csv(event_filename)
for i, x in enumerate(hosts_df.columns):
if "timeEnter" in x:
host_start_idx = i
break
host_points = np.zeros(len(hosts_df),
dtype=[('position', float, 2),
('colour', float, 4)])
for index, host in hosts_df.iterrows():
host_points['position'][index] = (host['posX'], host['posY'])
for name, timeEnter in host[host_start_idx:].iteritems():
if 0 in eval(timeEnter):
state = name[9:]
host_points['colour'][index] = state_colours[state]
initial_colours = host_points['colour'][:]
frame_rate = 30
animation_length = 5
frame_interval = max(events_df['time']) / (animation_length * frame_rate)
nframes = animation_length * frame_rate + 1
fig = plt.figure(figsize=(7, 7))
ax = fig.add_axes([0, 0, 1, 1], frameon=False)
ax.set_xlim(min(host_points['position'][:, 0]),
max(host_points['position'][:, 0]))
ax.set_xticks([])
ax.set_ylim(min(host_points['position'][:, 1]),
max(host_points['position'][:, 1]))
ax.set_yticks([])
scat = ax.scatter(host_points['position'][:, 0],
host_points['position'][:, 1],
c=host_points['colour'])
time_text = ax.text(0.02,
0.95,
'',
transform=ax.transAxes,
weight="bold",
fontsize=12,
bbox=dict(facecolor='white', alpha=0.6))
global start_from_idx
start_from_idx = 0
def init():
global start_from_idx
start_from_idx = 0
host_points['colour'] = initial_colours
new_time = 0
scat.set_color(host_points['colour'])
time_text.set_text('time = %.3f' % new_time)
return scat, time_text
def update(frame_number):
new_time = frame_number * frame_interval
global start_from_idx
row = events_df.iloc[start_from_idx]
while row['time'] <= new_time:
hostID = row['hostID']
new_state = row['newState']
host_points['colour'][hostID] = state_colours[new_state]
start_from_idx += 1
if start_from_idx >= len(events_df):
break
row = events_df.iloc[start_from_idx]
scat.set_color(host_points['colour'])
time_text.set_text('time = %.3f' % new_time)
return scat, time_text
animation = FuncAnimation(fig,
update,
interval=1000 / frame_rate,
frames=nframes,
blit=True,
repeat=False,
init_func=init)
plt.show()
where=monthly_sum != 0))
# define heatmaps for displaying monthly issue mix data
cm_greens = cm.get_cmap('Greens', 15) # green = good (list reduction)
cm_reds = cm.get_cmap('Reds', 15) # red = bad (list growth)
cm_greens.set_over(
'#FFFF00') # use yellow as default out of range green colour
cm_reds.set_over('#FF00FF') # use purple as default out of range red colour
# constrauct the colorbar from the heatmaps
pos_cmap = cm.get_cmap('Greens_r', 15)
neg_cmap = cm.get_cmap('Reds', 15)
newcolors = np.vstack(
(pos_cmap(np.linspace(0, 0.7, 15)), neg_cmap(np.linspace(0.3, 1, 15))))
combined_cmap = ListedColormap(newcolors, name='GreenRed')
neutral = np.array(to_rgba('skyblue'))
newcolors[
14:16, :] = neutral # define neutral colour for balanced mix of issues
for color in newcolors: # set transparency level (same as for plots)
color[3] = 0.75
# set monthly time block separation space
bar_spacing = 0.1
# figure settings
#plt.style.use('seaborn-whitegrid')
fig, ax = plt.subplots(3, 1, figsize=(15, 10), constrained_layout=False)
plt.subplots_adjust(left=None,
bottom=None,
right=None,
top=None,
with _api.suppress_matplotlib_deprecation_warning():
with mpl.rc_context():
_copy = mpl.rcParams.copy()
for key in _copy:
mpl.rcParams[key] = _copy[key]
with mpl.rc_context():
copy.deepcopy(mpl.rcParams)
with pytest.raises(ValueError):
validate_bool(None)
with pytest.raises(ValueError):
with mpl.rc_context():
mpl.rcParams['svg.fonttype'] = True
legend_color_tests = [
('face', {'color': 'r'}, mcolors.to_rgba('r')),
('face', {'color': 'inherit', 'axes.facecolor': 'r'},
mcolors.to_rgba('r')),
('face', {'color': 'g', 'axes.facecolor': 'r'}, mcolors.to_rgba('g')),
('edge', {'color': 'r'}, mcolors.to_rgba('r')),
('edge', {'color': 'inherit', 'axes.edgecolor': 'r'},
mcolors.to_rgba('r')),
('edge', {'color': 'g', 'axes.facecolor': 'r'}, mcolors.to_rgba('g'))
]
legend_color_test_ids = [
'same facecolor',
'inherited facecolor',
'different facecolor',
'same edgecolor',
'inherited edgecolor',
'different facecolor',
from PIL import Image, ImageTk
try:
box_icon = Image.open(r"icons\box_icon.png")
except:
import matplotlib as mpl
import matplotlib.pyplot as plt
import numpy as np
import tkinter as tk
from matplotlib.colors import to_rgba
from itertools import product, combinations
import mpl_toolkits.mplot3d.art3d as art3d
mpl.style.use("fast")
fig = plt.figure(figsize=(6, 6))
bgc = to_rgba("#F0F0F0")
fig.patch.set_facecolor("black")
fig.patch._alpha = 0.0
canvas = fig.canvas
canvas.draw()
ax = fig.add_subplot(
projection="3d",
facecolor=bgc,
)
ax.view_init(19, -62)
a = 0.96
xl, yl, zl = (-a, a), (-a, a), (-a, a)
ax.set(xlim=xl, ylim=yl, zlim=zl)
ax.patch._alpha = 0.0
ax.set_position([0, 0, 1, 1])
children = []
def visualize_accuracy(accr_dict, path, record_epoch):
'''
Plot classification accuracy graph. It contains graph from several experimental versions to compare.
Args:
accr_dict (dict) : Classification accuracy. Each key denotes experimental version and each value contains
'test_num' lists. For example, if test_num is three,
accr_dict['version_1] == [[accr_history_1], [accr_history_2], [accr_history_3]]
accr_dict['version_2] == [[accr_history_1], [accr_history_2], [accr_history_3]]
accr_dict['version_3] == [[accr_history_1], [accr_history_2], [accr_history_3]]
tst_dloader (Dataloader) : Data loader for test set
Return:
classification accuracy
'''
colors = dict(mcolors.BASE_COLORS, **mcolors.CSS4_COLORS)
# Sort colors by hue, saturation, value and name.
by_hsv = sorted(
(tuple(mcolors.rgb_to_hsv(mcolors.to_rgba(color)[:3])), name)
for name, color in colors.items())
sorted_names = [name for hsv, name in by_hsv]
# Selected color
color_names = ['steelblue', 'mediumseagreen', 'coral']
plt.switch_backend('agg')
plt.figure(figsize=(12, 6))
total_len = len(accr_dict[list(accr_dict.keys())[0]][0])
x = range(0, record_epoch * total_len, record_epoch)
for i, (exp_version, accr_hists) in enumerate(accr_dict.items()):
max_accr = 0
color_val = colors[color_names[i]]
# Find the one maximum accuracy among all tests in an experiment version
# This value will be represented as a maximum accuracy of current experiment version
for hist in accr_hists:
cur_max_accr = max(hist)
max_accr = cur_max_accr if cur_max_accr > max_accr else max_accr
# plot
for j, hist in enumerate(accr_hists):
cur_max_accr = max(hist)
max_accr = cur_max_accr if cur_max_accr > max_accr else max_accr
label = 'M' + str(i + 1) + ': ' + str(
round(max_accr, 6) * 100) + '%'
if j == 0:
plt.plot(x, hist, color_val, label=label)
else:
plt.plot(x, hist, color_val)
plt.xlabel('Epoch')
plt.ylabel('Accr')
plt.legend(loc=4)
plt.grid(True)
plt.tight_layout()
path = os.path.join(path, 'accr_model_compare.png')
plt.savefig(path)
plt.close()
def snap_plot(nets, size_scale=1 / 10, dims=(10, 10), savefigs=False):
'''
'''
last_net = nets[-1]
last_props = get_nodes_by_type(last_net, 'proposal')
M = len(last_props)
last_parts = get_nodes_by_type(last_net, 'participant')
N = len(last_parts)
pos = {}
for ind in range(N):
i = last_parts[ind]
pos[i] = np.array([0, 2 * ind - N])
for ind in range(M):
j = last_props[ind]
pos[j] = np.array([1, 2 * N / M * ind - N])
if savefigs:
counter = 0
length = 10
for net in nets:
edges = get_edges_by_type(net, 'support')
max_tok = np.max([net.edges[e]['tokens'] for e in edges])
E = len(edges)
net_props = get_nodes_by_type(net, 'proposal')
net_parts = get_nodes_by_type(net, 'participant')
net_node_label = {}
num_nodes = len([node for node in net.nodes])
node_color = np.empty((num_nodes, 4))
node_size = np.empty(num_nodes)
edge_color = np.empty((E, 4))
cm = plt.get_cmap('Reds')
cNorm = colors.Normalize(vmin=0, vmax=max_tok)
scalarMap = cmx.ScalarMappable(norm=cNorm, cmap=cm)
net_cand = [
j for j in net_props if net.nodes[j]['status'] == 'candidate'
]
for j in net_props:
node_size[j] = net.nodes[j]['funds_requested'] * size_scale
if net.nodes[j]['status'] == "candidate":
node_color[j] = colors.to_rgba('blue')
trigger = net.nodes[j]['trigger']
conviction = net.nodes[j]['conviction']
percent_of_trigger = " " + str(
int(100 * conviction / trigger)) + '%'
net_node_label[j] = str(percent_of_trigger)
elif net.nodes[j]['status'] == "active":
node_color[j] = colors.to_rgba('orange')
net_node_label[j] = ''
elif net.nodes[j]['status'] == "completed":
node_color[j] = colors.to_rgba('green')
net_node_label[j] = ''
elif net.nodes[j]['status'] == "failed":
node_color[j] = colors.to_rgba('gray')
net_node_label[j] = ''
elif net.nodes[j]['status'] == "killed":
node_color[j] = colors.to_rgba('black')
net_node_label[j] = ''
for i in net_parts:
node_size[i] = net.nodes[i]['holdings'] * size_scale / 10
node_color[i] = colors.to_rgba('red')
net_node_label[i] = ''
included_edges = []
for ind in range(E):
e = edges[ind]
tokens = net.edges[e]['tokens']
edge_color[ind] = scalarMap.to_rgba(tokens)
if e[1] in net_cand:
included_edges.append(e)
iE = len(included_edges)
included_edge_color = np.empty((iE, 4))
for ind in range(iE):
e = included_edges[ind]
tokens = net.edges[e]['tokens']
included_edge_color[ind] = scalarMap.to_rgba(tokens)
else:
plt.figure(figsize=dims)
nx.draw(net,
pos=pos,
node_size=node_size,
node_color=node_color,
edge_color=included_edge_color,
edgelist=included_edges,
labels=net_node_label)
plt.title('Tokens Staked by Partipants to Proposals')
plt.tight_layout()
plt.axis('on')
plt.xticks([])
plt.yticks([])
if savefigs:
plt.savefig('images/snap/' + str(counter) + '.png',
bbox_inches='tight')
counter = counter + 1
plt.show()
For more information on colors in matplotlib see
* the :doc:`/tutorials/colors/colors` tutorial;
* the `matplotlib.colors` API;
* the :doc:`/gallery/color/color_demo`.
"""
import matplotlib.pyplot as plt
from matplotlib import colors as mcolors
colors = dict(mcolors.BASE_COLORS, **mcolors.CSS4_COLORS)
# Sort colors by hue, saturation, value and name.
by_hsv = sorted((tuple(mcolors.rgb_to_hsv(mcolors.to_rgba(color)[:3])), name)
for name, color in colors.items())
sorted_names = [name for hsv, name in by_hsv]
n = len(sorted_names)
ncols = 4
nrows = n // ncols + 1
fig, ax = plt.subplots(figsize=(8, 5))
# Get height and width
X, Y = fig.get_dpi() * fig.get_size_inches()
h = Y / (nrows + 1)
w = X / ncols
for i, name in enumerate(sorted_names):
def get_rgb(x):
rgba = to_rgba(x)
return rgba[:3]
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
"""
colorup = mcolors.to_rgba(colorup, alpha)
colordown = mcolors.to_rgba(colordown, 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, ),
)
ax.add_collection(barCollection)
corners = (0, 0), (len(bars), max(volumes))
ax.update_datalim(corners)
ax.autoscale_view()
# add these last
return barCollection
def has_alpha(x):
return to_rgba(x) != to_rgba(x, 1)
def cc(arg):
return mcolors.to_rgba(arg, alpha=0.6)
def _is_transparent(color):
"""Determine transparency from alpha."""
rgba = colors.to_rgba(color)
return rgba[3] < .5
def cc(color, alpha=0.3):
return mcolors.to_rgba(color, alpha=alpha)
def candlestick2_ohlc(ax,
opens,
highs,
lows,
closes,
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.
NOTE: this code assumes if any value open, low, high, close is
missing they all are missing
Parameters
----------
ax : `Axes`
an Axes instance to plot to
opens : sequence
sequence of opening values
highs : sequence
sequence of high values
lows : sequence
sequence of low values
closes : sequence
sequence of closing values
width : int
size of open and close ticks 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 : tuple
(lineCollection, barCollection)
"""
_check_input(opens, highs, lows, closes)
delta = width / 2.
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]
colorup = mcolors.to_rgba(colorup, alpha)
colordown = mcolors.to_rgba(colordown, alpha)
colord = {True: colorup, False: colordown}
colors = [
colord[open < close] for open, close in zip(opens, closes)
if open != -1 and close != -1
]
useAA = 0, # use tuple here
lw = 0.5, # and here
rangeCollection = LineCollection(
rangeSegments,
colors=colors,
linewidths=lw,
antialiaseds=useAA,
)
barCollection = PolyCollection(
barVerts,
facecolors=colors,
edgecolors=colors,
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(rangeCollection)
ax.add_collection(barCollection)
return rangeCollection, barCollection
#!/usr/bin/python3
# coding: utf-8
# https://stackoverflow.com/questions/22408237/named-colors-in-matplotlib
import matplotlib.pyplot as plt
from matplotlib import colors as mcolors
colors = dict(mcolors.BASE_COLORS, **mcolors.CSS4_COLORS)
# Sort colors by hue, saturation, value and name.
by_hsv = sorted((tuple(mcolors.rgb_to_hsv(mcolors.to_rgba(color)[:3])), name)
for name, color in colors.items())
sorted_names = [name for hsv, name in by_hsv]
n = len(sorted_names)
ncols = 4
nrows = n // ncols
fig, ax = plt.subplots(figsize=(12, 10))
# Get height and width
X, Y = fig.get_dpi() * fig.get_size_inches()
h = Y / (nrows + 1)
w = X / ncols
for i, name in enumerate(sorted_names):
row = i % nrows
col = i // nrows
y = Y - (row * h) - h
def cc(arg):
'''
Shorthand to convert 'named' colors to rgba format at 60% opacity.
'''
return mcolors.to_rgba(arg, alpha=0.6)
def plot_positions_scatter(goal_object, runs_dir, img_dir, filename):
"""
:param goal_object
:param runs_dir:
:param img_dir:
:param filename:
"""
dataset_states = load_dataset(runs_dir)
dataset_states = dataset_states[[
'goal_position', 'goal_angle', 'position', 'initial_position',
'initial_angle'
]]
fig, axes = plt.subplots(ncols=2,
figsize=(9.8, 4.8),
constrained_layout=True,
sharey='row')
plt.ylim(-220, 220)
# initial positions
step_states = dataset_states.where(dataset_states.step == 0, drop=True)
x, y = unpack(step_states.initial_position, 'axis')
label = 'initial positions'
goal_position = step_states.goal_position[0]
goal_angle = step_states.goal_angle[0]
radius = 8.5
axes[0].scatter(x,
y,
alpha=0.1,
label=label,
marker='o',
s=(radius * np.pi)**2 / 5,
facecolor=colors.to_rgba('tab:blue', alpha=0.1),
edgecolor='none')
axes[0].set_ylabel('y axis', fontsize=11)
draw_docking_station(axes[0], goal_object)
draw_marxbot(axes[0], goal_position, goal_angle, label='goal position')
axes[0].set_xlim(-250, 250)
axes[0].set_aspect('equal')
axes[0].set_xlabel('x axis', fontsize=11)
axes[0].legend()
# final positions
step_states = dataset_states.groupby("run").map(
lambda x: x.isel(sample=[-1]))
x, y = unpack(step_states.position, 'axis')
goal_position = step_states.goal_position[0]
goal_angle = step_states.goal_angle[0]
axes[1].scatter(x,
y,
label='final positions',
marker='o',
s=(radius * np.pi)**2 / 5,
facecolor=colors.to_rgba('tab:blue', alpha=0.1),
edgecolor='none')
draw_docking_station(axes[1], goal_object)
draw_marxbot(axes[1], goal_position, goal_angle, label='goal position')
axes[1].set_xlim(-250, 250)
axes[1].set_aspect('equal')
axes[1].set_xlabel('x axis', fontsize=11)
axes[1].legend()
save_visualisation(filename, img_dir)
def test_to_rgba(self):
self.assertEqual(str(colors.to_rgba(self.color)), self.expectedRGBA)
def plot_surf_contours(surf_mesh, roi_map, axes=None, figure=None, levels=None,
labels=None, colors=None, legend=False, cmap='tab20',
title=None, output_file=None, **kwargs):
"""Plotting contours of ROIs on a surface, optionally over a statistical map.
Parameters
----------
surf_mesh : str or list of two numpy.ndarray
Surface mesh geometry, can be a file (valid formats are
.gii or Freesurfer specific files such as .orig, .pial,
.sphere, .white, .inflated) or
a list of two Numpy arrays, the first containing the x-y-z coordinates
of the mesh vertices, the second containing the indices
(into coords) of the mesh faces.
roi_map : str or numpy.ndarray or list of numpy.ndarray
ROI map to be displayed on the surface mesh, can be a file
(valid formats are .gii, .mgz, .nii, .nii.gz, or Freesurfer specific
files such as .annot or .label), or
a Numpy array with a value for each vertex of the surf_mesh.
The value at each vertex one inside the ROI and zero inside ROI, or an
integer giving the label number for atlases.
axes : instance of matplotlib axes, None, optional
The axes instance to plot to. The projection must be '3d' (e.g.,
`figure, axes = plt.subplots(subplot_kw={'projection': '3d'})`,
where axes should be passed.).
If None, uses axes from figure if available, else creates new axes.
figure : instance of matplotlib figure, None, optional
The figure instance to plot to.
If None, uses figure of axes if available, else creates a new figure.
levels : list of integers, or None, optional
A list of indices of the regions that are to be outlined.
Every index needs to correspond to one index in roi_map.
If None, all regions in roi_map are used.
labels : list of strings or None, or None, optional
A list of labels for the individual regions of interest.
Provide None as list entry to skip showing the label of that region.
If None no labels are used.
colors : list of matplotlib color names or RGBA values, or None, optional
Colors to be used.
legend : boolean, optional
Whether to plot a legend of region's labels. Default=False.
cmap : matplotlib colormap, str or colormap object, optional
To use for plotting of the contours. Either a string
which is a name of a matplotlib colormap, or a matplotlib
colormap object. Default='tab20'.
title : str, optional
Figure title.
output_file : str, or None, optional
The name of an image file to export plot to. Valid extensions
are .png, .pdf, .svg. If output_file is not None, the plot
is saved to a file, and the display is closed.
See Also
--------
nilearn.datasets.fetch_surf_fsaverage : For surface data object to be
used as background map for this plotting function.
nilearn.plotting.plot_surf_stat_map : for plotting statistical maps on
brain surfaces.
nilearn.surface.vol_to_surf : For info on the generation of surfaces.
"""
if figure is None and axes is None:
figure = plot_surf(surf_mesh, **kwargs)
axes = figure.axes[0]
if figure is None:
figure = axes.get_figure()
if axes is None:
axes = figure.axes[0]
if axes.name != '3d':
raise ValueError('Axes must be 3D.')
# test if axes contains Poly3DCollection, if not initialize surface
if not axes.collections or not isinstance(axes.collections[0],
Poly3DCollection):
_ = plot_surf(surf_mesh, axes=axes, **kwargs)
coords, faces = load_surf_mesh(surf_mesh)
roi = load_surf_data(roi_map)
if levels is None:
levels = np.unique(roi_map)
if colors is None:
n_levels = len(levels)
vmax = n_levels
cmap = get_cmap(cmap)
norm = Normalize(vmin=0, vmax=vmax)
colors = [cmap(norm(color_i)) for color_i in range(vmax)]
else:
try:
colors = [to_rgba(color, alpha=1.) for color in colors]
except ValueError:
raise ValueError('All elements of colors need to be either a'
' matplotlib color string or RGBA values.')
if labels is None:
labels = [None] * len(levels)
if not (len(labels) == len(levels) and len(colors) == len(labels)):
raise ValueError('Levels, labels, and colors '
'argument need to be either the same length or None.')
patch_list = []
for level, color, label in zip(levels, colors, labels):
roi_indices = np.where(roi == level)[0]
faces_outside = _get_faces_on_edge(faces, roi_indices)
# Fix: Matplotlib version 3.3.2 to 3.3.3
# Attribute _facecolors3d changed to _facecolor3d in
# matplotlib version 3.3.3
try:
axes.collections[0]._facecolors3d[faces_outside] = color
except AttributeError:
axes.collections[0]._facecolor3d[faces_outside] = color
if label and legend:
patch_list.append(Patch(color=color, label=label))
# plot legend only if indicated and labels provided
if legend and np.any([lbl is not None for lbl in labels]):
figure.legend(handles=patch_list)
if title:
figure.suptitle(title)
# save figure if output file is given
if output_file is not None:
figure.savefig(output_file)
plt.close(figure)
else:
return figure
mfc='blue')
line3 = axesList[3].plot(f,
z,
'o',
ls='-',
color='blue',
alpha=alpha,
ms=3,
markevery=.05,
mec='None',
mfc='red')
# Plot fifth graph:
# Create RGBA tuples for lines
lcolor1 = colors.to_rgba('red', alpha=alpha)
lcolor2 = colors.to_rgba('limegreen', alpha=alpha)
lcolor3 = colors.to_rgba('blue', alpha=alpha)
# Create RGBA tuples for markers
mcolor1 = colors.to_rgba('limegreen', alpha=1.0)
mcolor2 = colors.to_rgba('blue', alpha=0.4)
mcolor3 = colors.to_rgba('red', alpha=0.15)
# Plot x, y, and z lines and markers- the lines have the same
# transparency level, but the markers vary in alpha value
line1 = axesList[4].plot(f,
x,
'o',
ls='-',
color=lcolor1,
def test_default_color(self):
assert self.state.frame_color == settings.BACKGROUND_COLOR
assert self.state.edge_color == to_rgba(settings.FOREGROUND_COLOR, self.state.alpha)
with mpl.rc_context():
_copy = mpl.rcParams.copy()
for key in _copy:
mpl.rcParams[key] = _copy[key]
with mpl.rc_context():
copy.deepcopy(mpl.rcParams)
with pytest.raises(ValueError):
validate_bool(None)
with pytest.raises(ValueError):
with mpl.rc_context():
mpl.rcParams['svg.fonttype'] = True
legend_color_tests = [('face', {
'color': 'r'
}, mcolors.to_rgba('r')),
('face', {
'color': 'inherit',
'axes.facecolor': 'r'
}, mcolors.to_rgba('r')),
('face', {
'color': 'g',
'axes.facecolor': 'r'
}, mcolors.to_rgba('g')),
('edge', {
'color': 'r'
}, mcolors.to_rgba('r')),
('edge', {
'color': 'inherit',
'axes.edgecolor': 'r'
}, mcolors.to_rgba('r')),
# ----------------------------------------------------------------------------
# Title: Scientific Visualisation - Python & Matplotlib
# Author: Nicolas P. Rougier
# License: BSD
# ----------------------------------------------------------------------------
import numpy as np
import matplotlib.pyplot as plt
import matplotlib.colors as colors
from matplotlib.ticker import NullFormatter
X = np.random.uniform(0, 1, 1000)
Y = np.random.uniform(0, 1, 1000)
S = np.sqrt((X - 0.5)**2 + (Y - 0.5)**2)
C = np.ones((1000, 4)) * colors.to_rgba("C0")
C[S > 0.5] = colors.to_rgba("C1")
figure = plt.figure(figsize=(8, 4))
# Linear scale
ax = plt.subplot(1, 2, 1, xlim=(0, 1), ylim=(0, 1))
ax.scatter(X, Y, s=25, facecolor=C, edgecolor="none", alpha=0.5)
T = np.linspace(0, 2 * np.pi, 100)
X2 = 0.5 + 0.5 * np.cos(T)
Y2 = 0.5 + 0.5 * np.sin(T)
ax.plot(X2, Y2, color="black", linestyle="--", linewidth=0.75)
#
ax = plt.subplot(1, 2, 2, xlim=(0.01, 0.99), ylim=(0.01, 0.99))
ax.set_xscale("logit")
ax.set_yscale("logit")
ax.scatter(X, Y, s=25, facecolor=C, edgecolor="none", alpha=0.5)
def apply_callback(data):
"""This function will be called to apply changes"""
general = data.pop(0)
curves = data.pop(0) if has_curve else []
images = data.pop(0) if has_image else []
if data:
raise ValueError("Unexpected field")
# Set / General
(title, xmin, xmax, xlabel, xscale, ymin, ymax, ylabel, yscale,
generate_legend) = general
if axes.get_xscale() != xscale:
axes.set_xscale(xscale)
if axes.get_yscale() != yscale:
axes.set_yscale(yscale)
axes.set_title(title)
axes.set_xlim(xmin, xmax)
axes.set_xlabel(xlabel)
axes.set_ylim(ymin, ymax)
axes.set_ylabel(ylabel)
# Restore the unit data
axes.xaxis.converter = xconverter
axes.yaxis.converter = yconverter
axes.xaxis.set_units(xunits)
axes.yaxis.set_units(yunits)
axes.xaxis._update_axisinfo()
axes.yaxis._update_axisinfo()
# Set / Curves
for index, curve in enumerate(curves):
line = linedict[curvelabels[index]]
(label, linestyle, drawstyle, linewidth, color, marker, markersize,
markerfacecolor, markeredgecolor) = curve
line.set_label(label)
line.set_linestyle(linestyle)
line.set_drawstyle(drawstyle)
line.set_linewidth(linewidth)
rgba = mcolors.to_rgba(color)
line.set_alpha(None)
line.set_color(rgba)
if marker is not 'none':
line.set_marker(marker)
line.set_markersize(markersize)
line.set_markerfacecolor(markerfacecolor)
line.set_markeredgecolor(markeredgecolor)
# Set / Images
for index, image_settings in enumerate(images):
image = imagedict[imagelabels[index]]
label, cmap, low, high, interpolation = image_settings
image.set_label(label)
image.set_cmap(cm.get_cmap(cmap))
image.set_clim(*sorted([low, high]))
image.set_interpolation(interpolation)
# re-generate legend, if checkbox is checked
if generate_legend:
draggable = None
ncol = 1
if axes.legend_ is not None:
old_legend = axes.get_legend()
draggable = old_legend._draggable is not None
ncol = old_legend._ncol
new_legend = axes.legend(ncol=ncol)
if new_legend:
new_legend.draggable(draggable)
# Redraw
figure = axes.get_figure()
figure.canvas.draw()
def cc(arg, alpha=.6):
"""
Shorthand to convert 'named' colors to rgba format with opacity.
"""
return mcolors.to_rgba(arg, alpha=alpha)
_deep_copy = copy.deepcopy(mpl.rcParams)
# real test is that this does not raise
assert validate_bool_maybe_none(None) is None
assert validate_bool_maybe_none("none") is None
with pytest.raises(ValueError):
validate_bool_maybe_none("blah")
with pytest.raises(ValueError):
validate_bool(None)
with pytest.raises(ValueError):
with mpl.rc_context():
mpl.rcParams['svg.fonttype'] = True
legend_color_tests = [
('face', {'color': 'r'}, mcolors.to_rgba('r')),
('face', {'color': 'inherit', 'axes.facecolor': 'r'},
mcolors.to_rgba('r')),
('face', {'color': 'g', 'axes.facecolor': 'r'}, mcolors.to_rgba('g')),
('edge', {'color': 'r'}, mcolors.to_rgba('r')),
('edge', {'color': 'inherit', 'axes.edgecolor': 'r'},
mcolors.to_rgba('r')),
('edge', {'color': 'g', 'axes.facecolor': 'r'}, mcolors.to_rgba('g'))
]
legend_color_test_ids = [
'same facecolor',
'inherited facecolor',
'different facecolor',
'same edgecolor',
'inherited edgecolor',
'different facecolor',
def test_2d_to_rgba():
color = np.array([0.1, 0.2, 0.3])
rgba_1d = mcolors.to_rgba(color.reshape(-1))
rgba_2d = mcolors.to_rgba(color.reshape((1, -1)))
assert rgba_1d == rgba_2d