def __init__(self, value, parent=None):
QtWidgets.QGridLayout.__init__(self)
font = tuple_to_qfont(value)
assert font is not None
# Font family
self.family = QtWidgets.QFontComboBox(parent)
self.family.setCurrentFont(font)
self.addWidget(self.family, 0, 0, 1, -1)
# Font size
self.size = QtWidgets.QComboBox(parent)
self.size.setEditable(True)
sizelist = list(xrange(6, 12)) + list(xrange(12, 30, 2)) + [36, 48, 72]
size = font.pointSize()
if size not in sizelist:
sizelist.append(size)
sizelist.sort()
self.size.addItems([str(s) for s in sizelist])
self.size.setCurrentIndex(sizelist.index(size))
self.addWidget(self.size, 1, 0)
# Italic or not
self.italic = QtWidgets.QCheckBox(self.tr("Italic"), parent)
self.italic.setChecked(font.italic())
self.addWidget(self.italic, 1, 1)
# Bold or not
self.bold = QtWidgets.QCheckBox(self.tr("Bold"), parent)
self.bold.setChecked(font.bold())
self.addWidget(self.bold, 1, 2)
def add_lines(self, levels, colors, linewidths, erase=True):
'''
Draw lines on the colorbar.
*colors* and *linewidths* must be scalars or
sequences the same length as *levels*.
Set *erase* to False to add lines without first
removing any previously added lines.
'''
y = self._locate(levels)
igood = (y < 1.001) & (y > -0.001)
y = y[igood]
if cbook.iterable(colors):
colors = np.asarray(colors)[igood]
if cbook.iterable(linewidths):
linewidths = np.asarray(linewidths)[igood]
N = len(y)
x = np.array([0.0, 1.0])
X, Y = np.meshgrid(x, y)
if self.orientation == 'vertical':
xy = [list(zip(X[i], Y[i])) for i in xrange(N)]
else:
xy = [list(zip(Y[i], X[i])) for i in xrange(N)]
col = collections.LineCollection(xy, linewidths=linewidths)
if erase and self.lines:
for lc in self.lines:
lc.remove()
self.lines = []
self.lines.append(col)
col.set_color(colors)
self.ax.add_collection(col)
self.stale = True
def test_simple():
fig = plt.figure()
# un-comment to debug
# recursive_pickle(fig)
pickle.dump(fig, BytesIO(), pickle.HIGHEST_PROTOCOL)
ax = plt.subplot(121)
pickle.dump(ax, BytesIO(), pickle.HIGHEST_PROTOCOL)
ax = plt.axes(projection='polar')
plt.plot(list(xrange(10)), label='foobar')
plt.legend()
# Uncomment to debug any unpicklable objects. This is slow so is not
# uncommented by default.
# recursive_pickle(fig)
pickle.dump(ax, BytesIO(), pickle.HIGHEST_PROTOCOL)
# ax = plt.subplot(121, projection='hammer')
# recursive_pickle(ax, 'figure')
# pickle.dump(ax, BytesIO(), pickle.HIGHEST_PROTOCOL)
plt.figure()
plt.bar(left=list(xrange(10)), height=list(xrange(10)))
pickle.dump(plt.gca(), BytesIO(), pickle.HIGHEST_PROTOCOL)
fig = plt.figure()
ax = plt.axes()
plt.plot(list(xrange(10)))
ax.set_yscale('log')
pickle.dump(fig, BytesIO(), pickle.HIGHEST_PROTOCOL)
def add_lines(self, levels, colors, linewidths, erase=True):
'''
Draw lines on the colorbar.
*colors* and *linewidths* must be scalars or
sequences the same length as *levels*.
Set *erase* to False to add lines without first
removing any previously added lines.
'''
y = self._locate(levels)
igood = (y < 1.001) & (y > -0.001)
y = y[igood]
if cbook.iterable(colors):
colors = np.asarray(colors)[igood]
if cbook.iterable(linewidths):
linewidths = np.asarray(linewidths)[igood]
N = len(y)
x = np.array([0.0, 1.0])
X, Y = np.meshgrid(x, y)
if self.orientation == 'vertical':
xy = [list(zip(X[i], Y[i])) for i in xrange(N)]
else:
xy = [list(zip(Y[i], X[i])) for i in xrange(N)]
col = collections.LineCollection(xy, linewidths=linewidths)
if erase and self.lines:
for lc in self.lines:
lc.remove()
self.lines = []
self.lines.append(col)
col.set_color(colors)
self.ax.add_collection(col)
self.stale = True
def test_simple():
fig = plt.figure()
# un-comment to debug
# recursive_pickle(fig)
pickle.dump(fig, BytesIO(), pickle.HIGHEST_PROTOCOL)
ax = plt.subplot(121)
pickle.dump(ax, BytesIO(), pickle.HIGHEST_PROTOCOL)
ax = plt.axes(projection='polar')
plt.plot(list(xrange(10)), label='foobar')
plt.legend()
# Uncomment to debug any unpicklable objects. This is slow so is not
# uncommented by default.
# recursive_pickle(fig)
pickle.dump(ax, BytesIO(), pickle.HIGHEST_PROTOCOL)
# ax = plt.subplot(121, projection='hammer')
# recursive_pickle(ax, 'figure')
# pickle.dump(ax, BytesIO(), pickle.HIGHEST_PROTOCOL)
plt.figure()
plt.bar(left=list(xrange(10)), height=list(xrange(10)))
pickle.dump(plt.gca(), BytesIO(), pickle.HIGHEST_PROTOCOL)
fig = plt.figure()
ax = plt.axes()
plt.plot(list(xrange(10)))
ax.set_yscale('log')
pickle.dump(fig, BytesIO(), pickle.HIGHEST_PROTOCOL)
def test_fancy():
# using subplot triggers some offsetbox functionality untested elsewhere
plt.subplot(121)
plt.scatter(list(xrange(10)), list(xrange(10, 0, -1)), label='XX\nXX')
plt.plot([5] * 10, 'o--', label='XX')
plt.errorbar(list(xrange(10)), list(xrange(10)), xerr=0.5, yerr=0.5, label='XX')
plt.legend(loc="center left", bbox_to_anchor=[1.0, 0.5],
ncol=2, shadow=True, title="My legend", numpoints=1)
def test_various_labels():
# tests all sorts of label types
fig = plt.figure()
ax = fig.add_subplot(121)
ax.plot(list(xrange(4)), 'o', label=1)
ax.plot(np.linspace(4, 4.1), 'o', label='D\xe9velopp\xe9s')
ax.plot(list(xrange(4, 1, -1)), 'o', label='__nolegend__')
ax.legend(numpoints=1, loc=0)
def test_various_labels():
# tests all sorts of label types
fig = plt.figure()
ax = fig.add_subplot(121)
ax.plot(list(xrange(4)), 'o', label=1)
ax.plot(np.linspace(4, 4.1), 'o', label='D\xe9velopp\xe9s')
ax.plot(list(xrange(4, 1, -1)), 'o', label='__nolegend__')
ax.legend(numpoints=1, loc=0)
def _edges(self, X, Y):
'''
Return the separator line segments; helper for _add_solids.
'''
N = X.shape[0]
# Using the non-array form of these line segments is much
# simpler than making them into arrays.
if self.orientation == 'vertical':
return [list(zip(X[i], Y[i])) for i in xrange(1, N - 1)]
else:
return [list(zip(Y[i], X[i])) for i in xrange(1, N - 1)]
def _edges(self, X, Y):
'''
Return the separator line segments; helper for _add_solids.
'''
N = X.shape[0]
# Using the non-array form of these line segments is much
# simpler than making them into arrays.
if self.orientation == 'vertical':
return [list(zip(X[i], Y[i])) for i in xrange(1, N - 1)]
else:
return [list(zip(Y[i], X[i])) for i in xrange(1, N - 1)]
def test_rc():
# using subplot triggers some offsetbox functionality untested elsewhere
fig = plt.figure()
ax = plt.subplot(121)
ax.scatter(list(xrange(10)), list(xrange(10, 0, -1)), label='three')
ax.legend(loc="center left", bbox_to_anchor=[1.0, 0.5], title="My legend")
mpl.rcParams['legend.scatterpoints'] = 1
fig = plt.figure()
ax = plt.subplot(121)
ax.scatter(list(xrange(10)), list(xrange(10, 0, -1)), label='one')
ax.legend(loc="center left", bbox_to_anchor=[1.0, 0.5], title="My legend")
def test_rc():
# using subplot triggers some offsetbox functionality untested elsewhere
fig = plt.figure()
ax = plt.subplot(121)
ax.scatter(list(xrange(10)), list(xrange(10, 0, -1)), label='three')
ax.legend(loc="center left", bbox_to_anchor=[1.0, 0.5],
title="My legend")
mpl.rcParams['legend.scatterpoints'] = 1
fig = plt.figure()
ax = plt.subplot(121)
ax.scatter(list(xrange(10)), list(xrange(10, 0, -1)), label='one')
ax.legend(loc="center left", bbox_to_anchor=[1.0, 0.5],
title="My legend")
def _add_solids(self, X, Y, C):
"""
Draw the colors using :class:`~matplotlib.patches.Patch`;
optionally add separators.
"""
# Save, set, and restore hold state to keep pcolor from
# clearing the axes. Ordinarily this will not be needed,
# since the axes object should already have hold set.
_hold = self.ax.ishold()
self.ax.hold(True)
kw = {
'alpha': self.alpha,
}
n_segments = len(C)
# ensure there are sufficent hatches
hatches = self.mappable.hatches * n_segments
patches = []
for i in xrange(len(X) - 1):
val = C[i][0]
hatch = hatches[i]
xy = np.array([[X[i][0], Y[i][0]], [X[i][1], Y[i][0]],
[X[i + 1][1], Y[i + 1][0]],
[X[i + 1][0], Y[i + 1][1]]])
if self.orientation == 'horizontal':
# if horizontal swap the xs and ys
xy = xy[..., ::-1]
patch = mpatches.PathPatch(mpath.Path(xy),
facecolor=self.cmap(self.norm(val)),
hatch=hatch,
linewidth=0,
antialiased=False,
**kw)
self.ax.add_patch(patch)
patches.append(patch)
if self.solids_patches:
for solid in self.solids_patches:
solid.remove()
self.solids_patches = patches
if self.dividers is not None:
self.dividers.remove()
self.dividers = None
if self.drawedges:
self.dividers = collections.LineCollection(
self._edges(X, Y),
colors=(mpl.rcParams['axes.edgecolor'], ),
linewidths=(0.5 * mpl.rcParams['axes.linewidth'], ))
self.ax.add_collection(self.dividers)
self.ax.hold(_hold)
def updatePlot(self):
w, h = self.canvas.get_width_height()
# Remove all previous images
for i in xrange(self.image_.representations().count()):
self.image_.removeRepresentation_(
self.image_.representations().objectAtIndex_(i))
self.image_.setSize_((w, h))
brep = NSBitmapImageRep.alloc(
).initWithBitmapDataPlanes_pixelsWide_pixelsHigh_bitsPerSample_samplesPerPixel_hasAlpha_isPlanar_colorSpaceName_bytesPerRow_bitsPerPixel_(
(self.canvas.buffer_rgba(), '', '', '', ''), # Image data
w, # width
h, # height
8, # bits per pixel
4, # components per pixel
True, # has alpha?
False, # is planar?
NSCalibratedRGBColorSpace, # color space
w * 4, # row bytes
32) # bits per pixel
self.image_.addRepresentation_(brep)
self.setNeedsDisplay_(True)
def set_vertices_and_codes(self, vertices, codes):
offset = 1.0 / self.num_rows
shape_vertices = self.shape_vertices * offset * self.size
if not self.filled:
inner_vertices = shape_vertices[::-1] * 0.9
shape_codes = self.shape_codes
shape_size = len(shape_vertices)
cursor = 0
for row in xrange(self.num_rows + 1):
if row % 2 == 0:
cols = np.linspace(0.0, 1.0, self.num_rows + 1, True)
else:
cols = np.linspace(offset / 2.0, 1.0 - offset / 2.0,
self.num_rows, True)
row_pos = row * offset
for col_pos in cols:
vertices[cursor:cursor + shape_size] = (shape_vertices +
(col_pos, row_pos))
codes[cursor:cursor + shape_size] = shape_codes
cursor += shape_size
if not self.filled:
vertices[cursor:cursor + shape_size] = (inner_vertices +
(col_pos, row_pos))
codes[cursor:cursor + shape_size] = shape_codes
cursor += shape_size
def __init__(self, filename):
matplotlib.verbose.report('opening tfm file ' + filename, 'debug')
with open(filename, 'rb') as file:
header1 = file.read(24)
lh, bc, ec, nw, nh, nd = \
struct.unpack(str('!6H'), header1[2:14])
matplotlib.verbose.report(
'lh=%d, bc=%d, ec=%d, nw=%d, nh=%d, nd=%d' %
(lh, bc, ec, nw, nh, nd), 'debug')
header2 = file.read(4 * lh)
self.checksum, self.design_size = \
struct.unpack(str('!2I'), header2[:8])
# there is also encoding information etc.
char_info = file.read(4 * (ec - bc + 1))
widths = file.read(4 * nw)
heights = file.read(4 * nh)
depths = file.read(4 * nd)
self.width, self.height, self.depth = {}, {}, {}
widths, heights, depths = \
[struct.unpack(str('!%dI') % (len(x)/4), x)
for x in (widths, heights, depths)]
for idx, char in enumerate(xrange(bc, ec + 1)):
byte0 = ord(char_info[4 * idx])
byte1 = ord(char_info[4 * idx + 1])
self.width[char] = _fix2comp(widths[byte0])
self.height[char] = _fix2comp(heights[byte1 >> 4])
self.depth[char] = _fix2comp(depths[byte1 & 0xf])
def test_bbox_inches_tight():
#: Test that a figure saved using bbox_inches='tight' is clipped correctly
data = [[66386, 174296, 75131, 577908, 32015],
[58230, 381139, 78045, 99308, 160454],
[89135, 80552, 152558, 497981, 603535],
[78415, 81858, 150656, 193263, 69638],
[139361, 331509, 343164, 781380, 52269]]
colLabels = rowLabels = [''] * 5
rows = len(data)
ind = np.arange(len(colLabels)) + 0.3 # the x locations for the groups
cellText = []
width = 0.4 # the width of the bars
yoff = np.array([0.0] * len(colLabels))
# the bottom values for stacked bar chart
fig, ax = plt.subplots(1, 1)
for row in xrange(rows):
plt.bar(ind, data[row], width, bottom=yoff)
yoff = yoff + data[row]
cellText.append([''])
plt.xticks([])
plt.legend([''] * 5, loc=(1.2, 0.2))
# Add a table at the bottom of the axes
cellText.reverse()
the_table = plt.table(cellText=cellText,
rowLabels=rowLabels,
colLabels=colLabels,
loc='bottom')
def _get_anchored_bbox(self, loc, bbox, parentbbox, renderer):
"""
Place the *bbox* inside the *parentbbox* according to a given
location code. Return the (x,y) coordinate of the bbox.
- loc: a location code in range(1, 11).
This corresponds to the possible values for self._loc, excluding
"best".
- bbox: bbox to be placed, display coodinate units.
- parentbbox: a parent box which will contain the bbox. In
display coordinates.
"""
assert loc in range(1, 11) # called only internally
BEST, UR, UL, LL, LR, R, CL, CR, LC, UC, C = list(xrange(11))
anchor_coefs = {UR: "NE",
UL: "NW",
LL: "SW",
LR: "SE",
R: "E",
CL: "W",
CR: "E",
LC: "S",
UC: "N",
C: "C"}
c = anchor_coefs[loc]
fontsize = renderer.points_to_pixels(self._fontsize)
container = parentbbox.padded(-(self.borderaxespad) * fontsize)
anchored_box = bbox.anchored(c, container=container)
return anchored_box.x0, anchored_box.y0
def test_line_extents_affine(self):
ax = plt.axes()
offset = mtrans.Affine2D().translate(10, 10)
plt.plot(list(xrange(10)), transform=offset + ax.transData)
expeted_data_lim = np.array([[0., 0.], [9., 9.]]) + 10
np.testing.assert_array_almost_equal(ax.dataLim.get_points(),
expeted_data_lim)
def test_bbox_inches_tight():
#: Test that a figure saved using bbox_inches='tight' is clipped correctly
data = [[ 66386, 174296, 75131, 577908, 32015],
[ 58230, 381139, 78045, 99308, 160454],
[ 89135, 80552, 152558, 497981, 603535],
[ 78415, 81858, 150656, 193263, 69638],
[139361, 331509, 343164, 781380, 52269]]
colLabels = rowLabels = [''] * 5
rows = len(data)
ind = np.arange(len(colLabels)) + 0.3 # the x locations for the groups
cellText = []
width = 0.4 # the width of the bars
yoff = np.array([0.0] * len(colLabels))
# the bottom values for stacked bar chart
fig, ax = plt.subplots(1, 1)
for row in xrange(rows):
plt.bar(ind, data[row], width, bottom=yoff)
yoff = yoff + data[row]
cellText.append([''])
plt.xticks([])
plt.legend([''] * 5, loc=(1.2, 0.2))
# Add a table at the bottom of the axes
cellText.reverse()
the_table = plt.table(cellText=cellText,
rowLabels=rowLabels,
colLabels=colLabels, loc='bottom')
def test_line_extents_affine(self):
ax = plt.axes()
offset = mtrans.Affine2D().translate(10, 10)
plt.plot(list(xrange(10)), transform=offset + ax.transData)
expeted_data_lim = np.array([[0., 0.], [9., 9.]]) + 10
np.testing.assert_array_almost_equal(ax.dataLim.get_points(),
expeted_data_lim)
def __init__(self, filename):
matplotlib.verbose.report('opening tfm file ' + filename, 'debug')
with open(filename, 'rb') as file:
header1 = file.read(24)
lh, bc, ec, nw, nh, nd = \
struct.unpack(str('!6H'), header1[2:14])
matplotlib.verbose.report(
'lh=%d, bc=%d, ec=%d, nw=%d, nh=%d, nd=%d' % (
lh, bc, ec, nw, nh, nd), 'debug')
header2 = file.read(4*lh)
self.checksum, self.design_size = \
struct.unpack(str('!2I'), header2[:8])
# there is also encoding information etc.
char_info = file.read(4*(ec-bc+1))
widths = file.read(4*nw)
heights = file.read(4*nh)
depths = file.read(4*nd)
self.width, self.height, self.depth = {}, {}, {}
widths, heights, depths = \
[struct.unpack(str('!%dI') % (len(x)/4), x)
for x in (widths, heights, depths)]
for idx, char in enumerate(xrange(bc, ec+1)):
byte0 = ord(char_info[4*idx])
byte1 = ord(char_info[4*idx+1])
self.width[char] = _fix2comp(widths[byte0])
self.height[char] = _fix2comp(heights[byte1 >> 4])
self.depth[char] = _fix2comp(depths[byte1 & 0xf])
def set_vertices_and_codes(self, vertices, codes):
offset = 1.0 / self.num_rows
shape_vertices = self.shape_vertices * offset * self.size
if not self.filled:
inner_vertices = shape_vertices[::-1] * 0.9
shape_codes = self.shape_codes
shape_size = len(shape_vertices)
cursor = 0
for row in xrange(self.num_rows + 1):
if row % 2 == 0:
cols = np.linspace(0.0, 1.0, self.num_rows + 1, True)
else:
cols = np.linspace(offset / 2.0, 1.0 - offset / 2.0,
self.num_rows, True)
row_pos = row * offset
for col_pos in cols:
vertices[cursor:cursor + shape_size] = (shape_vertices +
(col_pos, row_pos))
codes[cursor:cursor + shape_size] = shape_codes
cursor += shape_size
if not self.filled:
vertices[cursor:cursor + shape_size] = (inner_vertices +
(col_pos, row_pos))
codes[cursor:cursor + shape_size] = shape_codes
cursor += shape_size
def test_bbox_inches_tight_clipping():
# tests bbox clipping on scatter points, and path clipping on a patch
# to generate an appropriately tight bbox
plt.scatter(list(xrange(10)), list(xrange(10)))
ax = plt.gca()
ax.set_xlim([0, 5])
ax.set_ylim([0, 5])
# make a massive rectangle and clip it with a path
patch = mpatches.Rectangle([-50, -50], 100, 100,
transform=ax.transData,
facecolor='blue', alpha=0.5)
path = mpath.Path.unit_regular_star(5).deepcopy()
path.vertices *= 0.25
patch.set_clip_path(path, transform=ax.transAxes)
plt.gcf().artists.append(patch)
def test_1d_plots():
x_range = slice(0.25, 9.75, 20j)
x = np.mgrid[x_range]
ax = plt.gca()
for y in xrange(2, 10, 2):
plt.plot(x, ref_interpolator[x_range, y:y:1j])
ax.set_xticks([])
ax.set_yticks([])
def test_1d_plots():
x_range = slice(0.25,9.75,20j)
x = np.mgrid[x_range]
ax = plt.gca()
for y in xrange(2,10,2):
plt.plot(x, ref_interpolator[x_range,y:y:1j])
ax.set_xticks([])
ax.set_yticks([])
def test_bbox_inches_tight_clipping():
# tests bbox clipping on scatter points, and path clipping on a patch
# to generate an appropriately tight bbox
plt.scatter(list(xrange(10)), list(xrange(10)))
ax = plt.gca()
ax.set_xlim([0, 5])
ax.set_ylim([0, 5])
# make a massive rectangle and clip it with a path
patch = mpatches.Rectangle([-50, -50], 100, 100,
transform=ax.transData,
facecolor='blue', alpha=0.5)
path = mpath.Path.unit_regular_star(5).deepcopy()
path.vertices *= 0.25
patch.set_clip_path(path, transform=ax.transAxes)
plt.gcf().artists.append(patch)
def add_lines(self, levels, colors, linewidths):
'''
Draw lines on the colorbar. It deletes preexisting lines.
'''
del self.lines
N = len(levels)
x = np.array([1.0, 2.0])
X, Y = np.meshgrid(x,levels)
if self.orientation == 'vertical':
xy = [list(zip(X[i], Y[i])) for i in xrange(N)]
else:
xy = [list(zip(Y[i], X[i])) for i in xrange(N)]
col = collections.LineCollection(xy, linewidths=linewidths,
)
self.lines = col
col.set_color(colors)
self.ax.add_collection(col)
def _add_solids(self, X, Y, C):
"""
Draw the colors using :class:`~matplotlib.patches.Patch`;
optionally add separators.
"""
# Save, set, and restore hold state to keep pcolor from
# clearing the axes. Ordinarily this will not be needed,
# since the axes object should already have hold set.
_hold = self.ax.ishold()
self.ax.hold(True)
kw = {'alpha': self.alpha, }
n_segments = len(C)
# ensure there are sufficent hatches
hatches = self.mappable.hatches * n_segments
patches = []
for i in xrange(len(X) - 1):
val = C[i][0]
hatch = hatches[i]
xy = np.array([[X[i][0], Y[i][0]],
[X[i][1], Y[i][0]],
[X[i + 1][1], Y[i + 1][0]],
[X[i + 1][0], Y[i + 1][1]]])
if self.orientation == 'horizontal':
# if horizontal swap the xs and ys
xy = xy[..., ::-1]
patch = mpatches.PathPatch(mpath.Path(xy),
facecolor=self.cmap(self.norm(val)),
hatch=hatch, linewidth=0,
antialiased=False, **kw)
self.ax.add_patch(patch)
patches.append(patch)
if self.solids_patches:
for solid in self.solids_patches:
solid.remove()
self.solids_patches = patches
if self.dividers is not None:
self.dividers.remove()
self.dividers = None
if self.drawedges:
self.dividers = collections.LineCollection(self._edges(X, Y),
colors=(mpl.rcParams['axes.edgecolor'],),
linewidths=(0.5 * mpl.rcParams['axes.linewidth'],))
self.ax.add_collection(self.dividers)
self.ax.hold(_hold)
def _update_positions(self, renderer):
# called from renderer to allow more precise estimates of
# widths and heights with get_window_extent
# Do any auto width setting
for col in self._autoColumns:
self._auto_set_column_width(col, renderer)
if self._autoFontsize:
self._auto_set_font_size(renderer)
# Align all the cells
self._do_cell_alignment()
bbox = self._get_grid_bbox(renderer)
l, b, w, h = bbox.bounds
if self._bbox is not None:
# Position according to bbox
rl, rb, rw, rh = self._bbox
self.scale(rw / w, rh / h)
ox = rl - l
oy = rb - b
self._do_cell_alignment()
else:
# Position using loc
(BEST, UR, UL, LL, LR, CL, CR, LC, UC, C,
TR, TL, BL, BR, R, L, T, B) = list(xrange(len(self.codes)))
# defaults for center
ox = (0.5 - w / 2) - l
oy = (0.5 - h / 2) - b
if self._loc in (UL, LL, CL): # left
ox = self.AXESPAD - l
if self._loc in (BEST, UR, LR, R, CR): # right
ox = 1 - (l + w + self.AXESPAD)
if self._loc in (BEST, UR, UL, UC): # upper
oy = 1 - (b + h + self.AXESPAD)
if self._loc in (LL, LR, LC): # lower
oy = self.AXESPAD - b
if self._loc in (LC, UC, C): # center x
ox = (0.5 - w / 2) - l
if self._loc in (CL, CR, C): # center y
oy = (0.5 - h / 2) - b
if self._loc in (TL, BL, L): # out left
ox = - (l + w)
if self._loc in (TR, BR, R): # out right
ox = 1.0 - l
if self._loc in (TR, TL, T): # out top
oy = 1.0 - b
if self._loc in (BL, BR, B): # out bottom
oy = - (b + h)
self._offset(ox, oy)
def _update_positions(self, renderer):
# called from renderer to allow more precise estimates of
# widths and heights with get_window_extent
# Do any auto width setting
for col in self._autoColumns:
self._auto_set_column_width(col, renderer)
if self._autoFontsize:
self._auto_set_font_size(renderer)
# Align all the cells
self._do_cell_alignment()
bbox = self._get_grid_bbox(renderer)
l, b, w, h = bbox.bounds
if self._bbox is not None:
# Position according to bbox
rl, rb, rw, rh = self._bbox
self.scale(rw / w, rh / h)
ox = rl - l
oy = rb - b
self._do_cell_alignment()
else:
# Position using loc
(BEST, UR, UL, LL, LR, CL, CR, LC, UC, C, TR, TL, BL, BR, R, L, T,
B) = list(xrange(len(self.codes)))
# defaults for center
ox = (0.5 - w / 2) - l
oy = (0.5 - h / 2) - b
if self._loc in (UL, LL, CL): # left
ox = self.AXESPAD - l
if self._loc in (BEST, UR, LR, R, CR): # right
ox = 1 - (l + w + self.AXESPAD)
if self._loc in (BEST, UR, UL, UC): # upper
oy = 1 - (b + h + self.AXESPAD)
if self._loc in (LL, LR, LC): # lower
oy = self.AXESPAD - b
if self._loc in (LC, UC, C): # center x
ox = (0.5 - w / 2) - l
if self._loc in (CL, CR, C): # center y
oy = (0.5 - h / 2) - b
if self._loc in (TL, BL, L): # out left
ox = -(l + w)
if self._loc in (TR, BR, R): # out right
ox = 1.0 - l
if self._loc in (TR, TL, T): # out top
oy = 1.0 - b
if self._loc in (BL, BR, B): # out bottom
oy = -(b + h)
self._offset(ox, oy)
def check_shared(results, f, axs):
"""
results is a 4 x 4 x 2 matrix of boolean values where
if [i, j, 0] == True, X axis for subplots i and j should be shared
if [i, j, 1] == False, Y axis for subplots i and j should not be shared
"""
shared_str = ['x', 'y']
shared = [axs[0]._shared_x_axes, axs[0]._shared_y_axes]
#shared = {
# 'x': a1._shared_x_axes,
# 'y': a1._shared_y_axes,
# }
tostr = lambda r: "not " if r else ""
for i1 in xrange(len(axs)):
for i2 in xrange(i1 + 1, len(axs)):
for i3 in xrange(len(shared)):
assert shared[i3].joined(axs[i1], axs[i2]) == \
results[i1, i2, i3], \
"axes %i and %i incorrectly %ssharing %s axis" % \
(i1, i2, tostr(results[i1, i2, i3]), shared_str[i3])
def add_lines(self, levels, colors, linewidths):
'''
Draw lines on the colorbar. It deletes preexisting lines.
'''
del self.lines
N = len(levels)
x = np.array([1.0, 2.0])
X, Y = np.meshgrid(x, levels)
if self.orientation == 'vertical':
xy = [list(zip(X[i], Y[i])) for i in xrange(N)]
else:
xy = [list(zip(Y[i], X[i])) for i in xrange(N)]
col = collections.LineCollection(
xy,
linewidths=linewidths,
)
self.lines = col
col.set_color(colors)
self.ax.add_collection(col)
def check_shared(results, f, axs):
"""
results is a 4 x 4 x 2 matrix of boolean values where
if [i, j, 0] == True, X axis for subplots i and j should be shared
if [i, j, 1] == False, Y axis for subplots i and j should not be shared
"""
shared_str = ['x', 'y']
shared = [axs[0]._shared_x_axes, axs[0]._shared_y_axes]
#shared = {
# 'x': a1._shared_x_axes,
# 'y': a1._shared_y_axes,
# }
tostr = lambda r: "not " if r else ""
for i1 in xrange(len(axs)):
for i2 in xrange(i1 + 1, len(axs)):
for i3 in xrange(len(shared)):
assert shared[i3].joined(axs[i1], axs[i2]) == \
results[i1, i2, i3], \
"axes %i and %i incorrectly %ssharing %s axis" % \
(i1, i2, tostr(results[i1, i2, i3]), shared_str[i3])
def test_line_extents_for_non_affine_transData(self):
ax = plt.axes(projection="polar")
# add 10 to the radius of the data
offset = mtrans.Affine2D().translate(0, 10)
plt.plot(list(xrange(10)), transform=offset + ax.transData)
# the data lim of a polar plot is stored in coordinates
# before a transData transformation, hence the data limits
# are not what is being shown on the actual plot.
expeted_data_lim = np.array([[0.0, 0.0], [9.0, 9.0]]) + [0, 10]
np.testing.assert_array_almost_equal(ax.dataLim.get_points(), expeted_data_lim)
def __init__(self, bymonthday=None, interval=1, tz=None):
"""
Mark every day in *bymonthday*; *bymonthday* can be an int or
sequence.
Default is to tick every day of the month: ``bymonthday=range(1,32)``
"""
if bymonthday is None:
bymonthday = list(xrange(1, 32))
o = rrulewrapper(DAILY, bymonthday=bymonthday,
interval=interval, **self.hms0d)
RRuleLocator.__init__(self, o, tz)
def __init__(self, scale, tfm, texname, vf):
if six.PY3 and isinstance(texname, bytes):
texname = texname.decode('ascii')
self._scale, self._tfm, self.texname, self._vf = \
scale, tfm, texname, vf
self.size = scale * (72.0 / (72.27 * 2**16))
try:
nchars = max(six.iterkeys(tfm.width)) + 1
except ValueError:
nchars = 0
self.widths = [(1000*tfm.width.get(char, 0)) >> 20
for char in xrange(nchars)]
def test_figure():
# named figure support
fig = plt.figure('today')
ax = fig.add_subplot(111)
ax.set_title(fig.get_label())
ax.plot(list(xrange(5)))
# plot red line in a different figure.
plt.figure('tomorrow')
plt.plot([0, 1], [1, 0], 'r')
# Return to the original; make sure the red line is not there.
plt.figure('today')
plt.close('tomorrow')
def __init__(self, scale, tfm, texname, vf):
if six.PY3 and isinstance(texname, bytes):
texname = texname.decode('ascii')
self._scale, self._tfm, self.texname, self._vf = \
scale, tfm, texname, vf
self.size = scale * (72.0 / (72.27 * 2**16))
try:
nchars = max(six.iterkeys(tfm.width)) + 1
except ValueError:
nchars = 0
self.widths = [(1000 * tfm.width.get(char, 0)) >> 20
for char in xrange(nchars)]
def test_figure():
# named figure support
fig = plt.figure('today')
ax = fig.add_subplot(111)
ax.set_title(fig.get_label())
ax.plot(list(xrange(5)))
# plot red line in a different figure.
plt.figure('tomorrow')
plt.plot([0, 1], [1, 0], 'r')
# Return to the original; make sure the red line is not there.
plt.figure('today')
plt.close('tomorrow')
def test_line_extents_for_non_affine_transData(self):
ax = plt.axes(projection='polar')
# add 10 to the radius of the data
offset = mtrans.Affine2D().translate(0, 10)
plt.plot(list(xrange(10)), transform=offset + ax.transData)
# the data lim of a polar plot is stored in coordinates
# before a transData transformation, hence the data limits
# are not what is being shown on the actual plot.
expeted_data_lim = np.array([[0., 0.], [9., 9.]]) + [0, 10]
np.testing.assert_array_almost_equal(ax.dataLim.get_points(),
expeted_data_lim)
def __init__(self, bysecond=None, interval=1, tz=None):
"""
Mark every second in *bysecond*; *bysecond* can be an int or
sequence. Default is to tick every second: ``bysecond = range(60)``
*interval* is the interval between each iteration. For
example, if ``interval=2``, mark every second occurrence.
"""
if bysecond is None:
bysecond = list(xrange(60))
rule = rrulewrapper(SECONDLY, bysecond=bysecond, interval=interval)
RRuleLocator.__init__(self, rule, tz)
def __init__(self, byhour=None, interval=1, tz=None):
"""
Mark every hour in *byhour*; *byhour* can be an int or sequence.
Default is to tick every hour: ``byhour=range(24)``
*interval* is the interval between each iteration. For
example, if ``interval=2``, mark every second occurrence.
"""
if byhour is None:
byhour = list(xrange(24))
rule = rrulewrapper(HOURLY, byhour=byhour, interval=interval,
byminute=0, bysecond=0)
RRuleLocator.__init__(self, rule, tz)
def plot_vo(tri, colors=None):
import matplotlib as mpl
from matplotlib import pylab as pl
if colors is None:
colors = [(0, 1, 0, 0.2)]
lc = mpl.collections.LineCollection(np.array(
[(tri.circumcenters[i], tri.circumcenters[j])
for i in xrange(len(tri.circumcenters))
for j in tri.triangle_neighbors[i] if j != -1]),
colors=colors)
ax = pl.gca()
ax.add_collection(lc)
pl.draw_if_interactive()
def __init__(self, bymonth=None, bymonthday=1, interval=1, tz=None):
"""
Mark every month in *bymonth*; *bymonth* can be an int or
sequence. Default is ``range(1,13)``, i.e. every month.
*interval* is the interval between each iteration. For
example, if ``interval=2``, mark every second occurance.
"""
if bymonth is None:
bymonth = list(xrange(1, 13))
o = rrulewrapper(MONTHLY, bymonth=bymonth, bymonthday=bymonthday,
interval=interval, **self.hms0d)
RRuleLocator.__init__(self, o, tz)
def __init__(self, bysecond=None, interval=1, tz=None):
"""
Mark every second in *bysecond*; *bysecond* can be an int or
sequence. Default is to tick every second: ``bysecond = range(60)``
*interval* is the interval between each iteration. For
example, if ``interval=2``, mark every second occurrence.
"""
if bysecond is None:
bysecond = list(xrange(60))
rule = rrulewrapper(SECONDLY, bysecond=bysecond, interval=interval)
RRuleLocator.__init__(self, rule, tz)
def __init__(self, bymonthday=None, interval=1, tz=None):
"""
Mark every day in *bymonthday*; *bymonthday* can be an int or
sequence.
Default is to tick every day of the month: ``bymonthday=range(1,32)``
"""
if bymonthday is None:
bymonthday = list(xrange(1, 32))
o = rrulewrapper(DAILY,
bymonthday=bymonthday,
interval=interval,
**self.hms0d)
RRuleLocator.__init__(self, o, tz)
def test_external_transform_api():
class ScaledBy(object):
def __init__(self, scale_factor):
self._scale_factor = scale_factor
def _as_mpl_transform(self, axes):
return mtrans.Affine2D().scale(self._scale_factor) + axes.transData
ax = plt.axes()
line, = plt.plot(list(xrange(10)), transform=ScaledBy(10))
ax.set_xlim(0, 100)
ax.set_ylim(0, 100)
# assert that the top transform of the line is the scale transform.
np.testing.assert_allclose(line.get_transform()._a.get_matrix(), mtrans.Affine2D().scale(10).get_matrix())
def __init__(self,
fig,
func,
frames=None,
init_func=None,
fargs=None,
save_count=None,
**kwargs):
if fargs:
self._args = fargs
else:
self._args = ()
self._func = func
# Amount of framedata to keep around for saving movies. This is only
# used if we don't know how many frames there will be: in the case
# of no generator or in the case of a callable.
self.save_count = save_count
# Set up a function that creates a new iterable when needed. If nothing
# is passed in for frames, just use itertools.count, which will just
# keep counting from 0. A callable passed in for frames is assumed to
# be a generator. An iterable will be used as is, and anything else
# will be treated as a number of frames.
if frames is None:
self._iter_gen = itertools.count
elif six.callable(frames):
self._iter_gen = frames
elif iterable(frames):
self._iter_gen = lambda: iter(frames)
if hasattr(frames, '__len__'):
self.save_count = len(frames)
else:
self._iter_gen = lambda: xrange(frames).__iter__()
self.save_count = frames
# If we're passed in and using the default, set it to 100.
if self.save_count is None:
self.save_count = 100
self._init_func = init_func
# Needs to be initialized so the draw functions work without checking
self._save_seq = []
TimedAnimation.__init__(self, fig, **kwargs)
# Need to reset the saved seq, since right now it will contain data
# for a single frame from init, which is not what we want.
self._save_seq = []
def decorate(method):
get_args = [_arg_mapping[x] for x in args]
@wraps(method)
def wrapper(self, byte):
if state is not None and self.state != state:
raise ValueError("state precondition failed")
return method(self, *[f(self, byte-min) for f in get_args])
if max is None:
table[min] = wrapper
else:
for i in xrange(min, max+1):
assert table[i] is None
table[i] = wrapper
return wrapper
def plot_cc(tri, edgecolor=None):
import matplotlib as mpl
from matplotlib import pylab as pl
if edgecolor is None:
edgecolor = (0, 0, 1, 0.2)
dxy = (np.array([(tri.x[i], tri.y[i]) for i, j, k in tri.triangle_nodes])
- tri.circumcenters)
r = np.hypot(dxy[:, 0], dxy[:, 1])
ax = pl.gca()
for i in xrange(len(r)):
p = mpl.patches.Circle(tri.circumcenters[i], r[i],
resolution=100, edgecolor=edgecolor,
facecolor=(1, 1, 1, 0), linewidth=0.2)
ax.add_patch(p)
pl.draw_if_interactive()
def test_external_transform_api():
class ScaledBy(object):
def __init__(self, scale_factor):
self._scale_factor = scale_factor
def _as_mpl_transform(self, axes):
return mtrans.Affine2D().scale(self._scale_factor) + axes.transData
ax = plt.axes()
line, = plt.plot(list(xrange(10)), transform=ScaledBy(10))
ax.set_xlim(0, 100)
ax.set_ylim(0, 100)
# assert that the top transform of the line is the scale transform.
np.testing.assert_allclose(line.get_transform()._a.get_matrix(),
mtrans.Affine2D().scale(10).get_matrix())
def test_bbox_inches_tight_suptile_legend():
plt.plot(list(xrange(10)), label='a straight line')
plt.legend(bbox_to_anchor=(0.9, 1), loc=2, )
plt.title('Axis title')
plt.suptitle('Figure title')
# put an extra long y tick on to see that the bbox is accounted for
def y_formatter(y, pos):
if int(y) == 4:
return 'The number 4'
else:
return str(y)
plt.gca().yaxis.set_major_formatter(FuncFormatter(y_formatter))
plt.xlabel('X axis')
def test_bbox_inches_tight_suptile_legend():
plt.plot(list(xrange(10)), label='a straight line')
plt.legend(bbox_to_anchor=(0.9, 1), loc=2, )
plt.title('Axis title')
plt.suptitle('Figure title')
# put an extra long y tick on to see that the bbox is accounted for
def y_formatter(y, pos):
if int(y) == 4:
return 'The number 4'
else:
return str(y)
plt.gca().yaxis.set_major_formatter(FuncFormatter(y_formatter))
plt.xlabel('X axis')
def __init__(self, byhour=None, interval=1, tz=None):
"""
Mark every hour in *byhour*; *byhour* can be an int or sequence.
Default is to tick every hour: ``byhour=range(24)``
*interval* is the interval between each iteration. For
example, if ``interval=2``, mark every second occurrence.
"""
if byhour is None:
byhour = list(xrange(24))
rule = rrulewrapper(HOURLY,
byhour=byhour,
interval=interval,
byminute=0,
bysecond=0)
RRuleLocator.__init__(self, rule, tz)
def __init__(self, bymonth=None, bymonthday=1, interval=1, tz=None):
"""
Mark every month in *bymonth*; *bymonth* can be an int or
sequence. Default is ``range(1,13)``, i.e. every month.
*interval* is the interval between each iteration. For
example, if ``interval=2``, mark every second occurance.
"""
if bymonth is None:
bymonth = list(xrange(1, 13))
o = rrulewrapper(MONTHLY,
bymonth=bymonth,
bymonthday=bymonthday,
interval=interval,
**self.hms0d)
RRuleLocator.__init__(self, o, tz)
def test_non_affine_caching():
class AssertingNonAffineTransform(mtrans.Transform):
"""
This transform raises an assertion error when called when it
shouldn't be and self.raise_on_transform is True.
"""
input_dims = output_dims = 2
is_affine = False
def __init__(self, *args, **kwargs):
mtrans.Transform.__init__(self, *args, **kwargs)
self.raise_on_transform = False
self.underlying_transform = mtrans.Affine2D().scale(10, 10)
def transform_path_non_affine(self, path):
if self.raise_on_transform:
assert False, ('Invalidated affine part of transform '
'unnecessarily.')
return self.underlying_transform.transform_path(path)
transform_path = transform_path_non_affine
def transform_non_affine(self, path):
if self.raise_on_transform:
assert False, ('Invalidated affine part of transform '
'unnecessarily.')
return self.underlying_transform.transform(path)
transform = transform_non_affine
my_trans = AssertingNonAffineTransform()
ax = plt.axes()
plt.plot(list(xrange(10)), transform=my_trans + ax.transData)
plt.draw()
# enable the transform to raise an exception if it's non-affine transform
# method is triggered again.
my_trans.raise_on_transform = True
ax.transAxes.invalidate()
plt.draw()