Python matplotlib.colors.NoNorm() Examples
The following are 30
code examples of matplotlib.colors.NoNorm().
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and go to the original project or source file by following the links above each example.
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matplotlib.colors
, or try the search function
.
.
Example #1
| Source File: colorbar.py From Computable with MIT License | 6 votes |
|
def _select_locator(self, formatter):
'''
select a suitable locator
'''
if self.boundaries is None:
if isinstance(self.norm, colors.NoNorm):
nv = len(self._values)
base = 1 + int(nv/10)
locator = ticker.IndexLocator(base=base, offset=0)
elif isinstance(self.norm, colors.BoundaryNorm):
b = self.norm.boundaries
locator = ticker.FixedLocator(b, nbins=10)
elif isinstance(self.norm, colors.LogNorm):
locator = ticker.LogLocator()
else:
locator = ticker.MaxNLocator(nbins=5)
else:
b = self._boundaries[self._inside]
locator = ticker.FixedLocator(b) #, nbins=10)
self.cbar_axis.set_major_locator(locator)
Example #2
| Source File: colorbar.py From CogAlg with MIT License | 6 votes |
|
def _select_locator(self, formatter):
'''
select a suitable locator
'''
if self.boundaries is None:
if isinstance(self.norm, colors.NoNorm):
nv = len(self._values)
base = 1 + int(nv/10)
locator = ticker.IndexLocator(base=base, offset=0)
elif isinstance(self.norm, colors.BoundaryNorm):
b = self.norm.boundaries
locator = ticker.FixedLocator(b, nbins=10)
elif isinstance(self.norm, colors.LogNorm):
locator = ticker.LogLocator()
else:
locator = ticker.MaxNLocator(nbins=5)
else:
b = self._boundaries[self._inside]
locator = ticker.FixedLocator(b)
self.cbar_axis.set_major_locator(locator)
Example #3
| Source File: colorbar.py From GraphicDesignPatternByPython with MIT License | 6 votes |
|
def _select_locator(self, formatter):
'''
select a suitable locator
'''
if self.boundaries is None:
if isinstance(self.norm, colors.NoNorm):
nv = len(self._values)
base = 1 + int(nv/10)
locator = ticker.IndexLocator(base=base, offset=0)
elif isinstance(self.norm, colors.BoundaryNorm):
b = self.norm.boundaries
locator = ticker.FixedLocator(b, nbins=10)
elif isinstance(self.norm, colors.LogNorm):
locator = ticker.LogLocator()
else:
locator = ticker.MaxNLocator(nbins=5)
else:
b = self._boundaries[self._inside]
locator = ticker.FixedLocator(b) #, nbins=10)
self.cbar_axis.set_major_locator(locator)
Example #4
| Source File: colorbar.py From matplotlib-4-abaqus with MIT License | 6 votes |
|
def _select_locator(self, formatter):
'''
select a suitable locator
'''
if self.boundaries is None:
if isinstance(self.norm, colors.NoNorm):
nv = len(self._values)
base = 1 + int(nv/10)
locator = ticker.IndexLocator(base=base, offset=0)
elif isinstance(self.norm, colors.BoundaryNorm):
b = self.norm.boundaries
locator = ticker.FixedLocator(b, nbins=10)
elif isinstance(self.norm, colors.LogNorm):
locator = ticker.LogLocator()
else:
locator = ticker.MaxNLocator(nbins=5)
else:
b = self._boundaries[self._inside]
locator = ticker.FixedLocator(b) #, nbins=10)
self.cbar_axis.set_major_locator(locator)
Example #5
| Source File: colorbar.py From GraphicDesignPatternByPython with MIT License | 6 votes |
|
def _get_ticker_locator_formatter(self):
"""
This code looks at the norm being used by the colorbar
and decides what locator and formatter to use. If ``locator`` has
already been set by hand, it just returns
``self.locator, self.formatter``.
"""
locator = self.locator
formatter = self.formatter
if locator is None:
if self.boundaries is None:
if isinstance(self.norm, colors.NoNorm):
nv = len(self._values)
base = 1 + int(nv / 10)
locator = ticker.IndexLocator(base=base, offset=0)
elif isinstance(self.norm, colors.BoundaryNorm):
b = self.norm.boundaries
locator = ticker.FixedLocator(b, nbins=10)
elif isinstance(self.norm, colors.LogNorm):
locator = _ColorbarLogLocator(self)
elif isinstance(self.norm, colors.SymLogNorm):
# The subs setting here should be replaced
# by logic in the locator.
locator = ticker.SymmetricalLogLocator(
subs=np.arange(1, 10),
linthresh=self.norm.linthresh,
base=10)
else:
if mpl.rcParams['_internal.classic_mode']:
locator = ticker.MaxNLocator()
else:
locator = _ColorbarAutoLocator(self)
else:
b = self._boundaries[self._inside]
locator = ticker.FixedLocator(b, nbins=10)
_log.debug('locator: %r', locator)
return locator, formatter
Example #6
| Source File: colorbar.py From Computable with MIT License | 5 votes |
|
def _locate(self, x):
'''
Given a set of color data values, return their
corresponding colorbar data coordinates.
'''
if isinstance(self.norm, (colors.NoNorm, colors.BoundaryNorm)):
b = self._boundaries
xn = x
else:
# Do calculations using normalized coordinates so
# as to make the interpolation more accurate.
b = self.norm(self._boundaries, clip=False).filled()
xn = self.norm(x, clip=False).filled()
# The rest is linear interpolation with extrapolation at ends.
y = self._y
N = len(b)
ii = np.searchsorted(b, xn)
i0 = ii - 1
itop = (ii == N)
ibot = (ii == 0)
i0[itop] -= 1
ii[itop] -= 1
i0[ibot] += 1
ii[ibot] += 1
#db = b[ii] - b[i0]
db = np.take(b, ii) - np.take(b, i0)
#dy = y[ii] - y[i0]
dy = np.take(y, ii) - np.take(y, i0)
z = np.take(y, i0) + (xn - np.take(b, i0)) * dy / db
return z
Example #7
| Source File: colorbar.py From ImageFusion with MIT License | 5 votes |
|
def _locate(self, x):
'''
Given a set of color data values, return their
corresponding colorbar data coordinates.
'''
if isinstance(self.norm, (colors.NoNorm, colors.BoundaryNorm)):
b = self._boundaries
xn = x
else:
# Do calculations using normalized coordinates so
# as to make the interpolation more accurate.
b = self.norm(self._boundaries, clip=False).filled()
xn = self.norm(x, clip=False).filled()
# The rest is linear interpolation with extrapolation at ends.
ii = np.searchsorted(b, xn)
i0 = ii - 1
itop = (ii == len(b))
ibot = (ii == 0)
i0[itop] -= 1
ii[itop] -= 1
i0[ibot] += 1
ii[ibot] += 1
db = np.take(b, ii) - np.take(b, i0)
y = self._y
dy = np.take(y, ii) - np.take(y, i0)
z = np.take(y, i0) + (xn - np.take(b, i0)) * dy / db
return z
Example #8
| Source File: colorbar.py From coffeegrindsize with MIT License | 5 votes |
|
def _get_ticker_locator_formatter(self):
"""
This code looks at the norm being used by the colorbar
and decides what locator and formatter to use. If ``locator`` has
already been set by hand, it just returns
``self.locator, self.formatter``.
"""
locator = self.locator
formatter = self.formatter
if locator is None:
if self.boundaries is None:
if isinstance(self.norm, colors.NoNorm):
nv = len(self._values)
base = 1 + int(nv / 10)
locator = ticker.IndexLocator(base=base, offset=0)
elif isinstance(self.norm, colors.BoundaryNorm):
b = self.norm.boundaries
locator = ticker.FixedLocator(b, nbins=10)
elif isinstance(self.norm, colors.LogNorm):
locator = _ColorbarLogLocator(self)
elif isinstance(self.norm, colors.SymLogNorm):
# The subs setting here should be replaced
# by logic in the locator.
locator = ticker.SymmetricalLogLocator(
subs=np.arange(1, 10),
linthresh=self.norm.linthresh,
base=10)
else:
if mpl.rcParams['_internal.classic_mode']:
locator = ticker.MaxNLocator()
else:
locator = _ColorbarAutoLocator(self)
else:
b = self._boundaries[self._inside]
locator = ticker.FixedLocator(b, nbins=10)
_log.debug('locator: %r', locator)
return locator, formatter
Example #9
| Source File: colorbar.py From coffeegrindsize with MIT License | 5 votes |
|
def _ticker(self, locator, formatter):
'''
Return the sequence of ticks (colorbar data locations),
ticklabels (strings), and the corresponding offset string.
'''
if isinstance(self.norm, colors.NoNorm) and self.boundaries is None:
intv = self._values[0], self._values[-1]
else:
intv = self.vmin, self.vmax
locator.create_dummy_axis(minpos=intv[0])
formatter.create_dummy_axis(minpos=intv[0])
locator.set_view_interval(*intv)
locator.set_data_interval(*intv)
formatter.set_view_interval(*intv)
formatter.set_data_interval(*intv)
b = np.array(locator())
if isinstance(locator, ticker.LogLocator):
eps = 1e-10
b = b[(b <= intv[1] * (1 + eps)) & (b >= intv[0] * (1 - eps))]
else:
eps = (intv[1] - intv[0]) * 1e-10
b = b[(b <= intv[1] + eps) & (b >= intv[0] - eps)]
self._manual_tick_data_values = b
ticks = self._locate(b)
formatter.set_locs(b)
ticklabels = [formatter(t, i) for i, t in enumerate(b)]
offset_string = formatter.get_offset()
return ticks, ticklabels, offset_string
Example #10
| Source File: colorbar.py From python3_ios with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def _locate(self, x):
'''
Given a set of color data values, return their
corresponding colorbar data coordinates.
'''
if isinstance(self.norm, (colors.NoNorm, colors.BoundaryNorm)):
b = self._boundaries
xn = x
else:
# Do calculations using normalized coordinates so
# as to make the interpolation more accurate.
b = self.norm(self._boundaries, clip=False).filled()
xn = self.norm(x, clip=False).filled()
# The rest is linear interpolation with extrapolation at ends.
ii = np.searchsorted(b, xn)
i0 = ii - 1
itop = (ii == len(b))
ibot = (ii == 0)
i0[itop] -= 1
ii[itop] -= 1
i0[ibot] += 1
ii[ibot] += 1
db = np.take(b, ii) - np.take(b, i0)
y = self._y
dy = np.take(y, ii) - np.take(y, i0)
z = np.take(y, i0) + (xn - np.take(b, i0)) * dy / db
return z
Example #11
| Source File: colorbar.py From python3_ios with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def _ticker(self, locator, formatter):
'''
Return the sequence of ticks (colorbar data locations),
ticklabels (strings), and the corresponding offset string.
'''
if isinstance(self.norm, colors.NoNorm) and self.boundaries is None:
intv = self._values[0], self._values[-1]
else:
intv = self.vmin, self.vmax
locator.create_dummy_axis(minpos=intv[0])
formatter.create_dummy_axis(minpos=intv[0])
locator.set_view_interval(*intv)
locator.set_data_interval(*intv)
formatter.set_view_interval(*intv)
formatter.set_data_interval(*intv)
b = np.array(locator())
if isinstance(locator, ticker.LogLocator):
eps = 1e-10
b = b[(b <= intv[1] * (1 + eps)) & (b >= intv[0] * (1 - eps))]
else:
eps = (intv[1] - intv[0]) * 1e-10
b = b[(b <= intv[1] + eps) & (b >= intv[0] - eps)]
self._manual_tick_data_values = b
ticks = self._locate(b)
formatter.set_locs(b)
ticklabels = [formatter(t, i) for i, t in enumerate(b)]
offset_string = formatter.get_offset()
return ticks, ticklabels, offset_string
Example #12
| Source File: colorbar.py From python3_ios with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def _get_ticker_locator_formatter(self):
"""
This code looks at the norm being used by the colorbar
and decides what locator and formatter to use. If ``locator`` has
already been set by hand, it just returns
``self.locator, self.formatter``.
"""
locator = self.locator
formatter = self.formatter
if locator is None:
if self.boundaries is None:
if isinstance(self.norm, colors.NoNorm):
nv = len(self._values)
base = 1 + int(nv / 10)
locator = ticker.IndexLocator(base=base, offset=0)
elif isinstance(self.norm, colors.BoundaryNorm):
b = self.norm.boundaries
locator = ticker.FixedLocator(b, nbins=10)
elif isinstance(self.norm, colors.LogNorm):
locator = _ColorbarLogLocator(self)
elif isinstance(self.norm, colors.SymLogNorm):
# The subs setting here should be replaced
# by logic in the locator.
locator = ticker.SymmetricalLogLocator(
subs=np.arange(1, 10),
linthresh=self.norm.linthresh,
base=10)
else:
if mpl.rcParams['_internal.classic_mode']:
locator = ticker.MaxNLocator()
else:
locator = _ColorbarAutoLocator(self)
else:
b = self._boundaries[self._inside]
locator = ticker.FixedLocator(b, nbins=10)
_log.debug('locator: %r', locator)
return locator, formatter
Example #13
| Source File: spypylab.py From spectral with MIT License | 5 votes |
|
def show_classes(self):
'''Show the class values.'''
import matplotlib.pyplot as plt
from matplotlib.colors import ListedColormap, NoNorm
from spectral import get_rgb
if self.class_axes is not None:
msg = 'ImageView.show_classes should only be called once.'
warnings.warn(UserWarning(msg))
return
elif self.classes is None:
raise Exception('Unable to display classes: class array not set.')
cm = ListedColormap(np.array(self.class_colors) / 255.)
self._update_class_rgb()
kwargs = self.imshow_class_kwargs.copy()
kwargs.update({'cmap': cm, 'vmin': 0, 'norm': NoNorm(),
'interpolation': self._interpolation})
if self.axes is not None:
# A figure has already been created for the view. Make it current.
plt.figure(self.axes.figure.number)
self.class_axes = plt.imshow(self.class_rgb, **kwargs)
if self.axes is None:
self.axes = self.class_axes.axes
self.class_axes.set_zorder(1)
if self.display_mode == 'overlay':
self.class_axes.set_alpha(self._class_alpha)
else:
self.class_axes.set_alpha(1)
#self.class_axes.axes.set_axis_bgcolor('black')
Example #14
| Source File: colorbar.py From coffeegrindsize with MIT License | 5 votes |
|
def _locate(self, x):
'''
Given a set of color data values, return their
corresponding colorbar data coordinates.
'''
if isinstance(self.norm, (colors.NoNorm, colors.BoundaryNorm)):
b = self._boundaries
xn = x
else:
# Do calculations using normalized coordinates so
# as to make the interpolation more accurate.
b = self.norm(self._boundaries, clip=False).filled()
xn = self.norm(x, clip=False).filled()
# The rest is linear interpolation with extrapolation at ends.
ii = np.searchsorted(b, xn)
i0 = ii - 1
itop = (ii == len(b))
ibot = (ii == 0)
i0[itop] -= 1
ii[itop] -= 1
i0[ibot] += 1
ii[ibot] += 1
db = np.take(b, ii) - np.take(b, i0)
y = self._y
dy = np.take(y, ii) - np.take(y, i0)
z = np.take(y, i0) + (xn - np.take(b, i0)) * dy / db
return z
Example #15
| Source File: colorbar.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
def _locate(self, x):
'''
Given a set of color data values, return their
corresponding colorbar data coordinates.
'''
if isinstance(self.norm, (colors.NoNorm, colors.BoundaryNorm)):
b = self._boundaries
xn = x
else:
# Do calculations using normalized coordinates so
# as to make the interpolation more accurate.
b = self.norm(self._boundaries, clip=False).filled()
xn = self.norm(x, clip=False).filled()
# The rest is linear interpolation with extrapolation at ends.
ii = np.searchsorted(b, xn)
i0 = ii - 1
itop = (ii == len(b))
ibot = (ii == 0)
i0[itop] -= 1
ii[itop] -= 1
i0[ibot] += 1
ii[ibot] += 1
db = np.take(b, ii) - np.take(b, i0)
y = self._y
dy = np.take(y, ii) - np.take(y, i0)
z = np.take(y, i0) + (xn - np.take(b, i0)) * dy / db
return z
Example #16
| Source File: colorbar.py From GraphicDesignPatternByPython with MIT License | 5 votes |
|
def _ticker(self, locator, formatter):
'''
Return the sequence of ticks (colorbar data locations),
ticklabels (strings), and the corresponding offset string.
'''
if isinstance(self.norm, colors.NoNorm) and self.boundaries is None:
intv = self._values[0], self._values[-1]
else:
intv = self.vmin, self.vmax
locator.create_dummy_axis(minpos=intv[0])
formatter.create_dummy_axis(minpos=intv[0])
locator.set_view_interval(*intv)
locator.set_data_interval(*intv)
formatter.set_view_interval(*intv)
formatter.set_data_interval(*intv)
b = np.array(locator())
if isinstance(locator, ticker.LogLocator):
eps = 1e-10
b = b[(b <= intv[1] * (1 + eps)) & (b >= intv[0] * (1 - eps))]
else:
eps = (intv[1] - intv[0]) * 1e-10
b = b[(b <= intv[1] + eps) & (b >= intv[0] - eps)]
self._tick_data_values = b
ticks = self._locate(b)
formatter.set_locs(b)
ticklabels = [formatter(t, i) for i, t in enumerate(b)]
offset_string = formatter.get_offset()
return ticks, ticklabels, offset_string
Example #17
| Source File: colorbar.py From neural-network-animation with MIT License | 5 votes |
|
def _locate(self, x):
'''
Given a set of color data values, return their
corresponding colorbar data coordinates.
'''
if isinstance(self.norm, (colors.NoNorm, colors.BoundaryNorm)):
b = self._boundaries
xn = x
else:
# Do calculations using normalized coordinates so
# as to make the interpolation more accurate.
b = self.norm(self._boundaries, clip=False).filled()
xn = self.norm(x, clip=False).filled()
# The rest is linear interpolation with extrapolation at ends.
ii = np.searchsorted(b, xn)
i0 = ii - 1
itop = (ii == len(b))
ibot = (ii == 0)
i0[itop] -= 1
ii[itop] -= 1
i0[ibot] += 1
ii[ibot] += 1
db = np.take(b, ii) - np.take(b, i0)
y = self._y
dy = np.take(y, ii) - np.take(y, i0)
z = np.take(y, i0) + (xn - np.take(b, i0)) * dy / db
return z
Example #18
| Source File: colorbar.py From CogAlg with MIT License | 5 votes |
|
def _ticker(self, locator, formatter):
'''
Return the sequence of ticks (colorbar data locations),
ticklabels (strings), and the corresponding offset string.
'''
if isinstance(self.norm, colors.NoNorm) and self.boundaries is None:
intv = self._values[0], self._values[-1]
else:
intv = self.vmin, self.vmax
locator.create_dummy_axis(minpos=intv[0])
formatter.create_dummy_axis(minpos=intv[0])
locator.set_view_interval(*intv)
locator.set_data_interval(*intv)
formatter.set_view_interval(*intv)
formatter.set_data_interval(*intv)
b = np.array(locator())
if isinstance(locator, ticker.LogLocator):
eps = 1e-10
b = b[(b <= intv[1] * (1 + eps)) & (b >= intv[0] * (1 - eps))]
else:
eps = (intv[1] - intv[0]) * 1e-10
b = b[(b <= intv[1] + eps) & (b >= intv[0] - eps)]
self._manual_tick_data_values = b
ticks = self._locate(b)
ticklabels = formatter.format_ticks(b)
offset_string = formatter.get_offset()
return ticks, ticklabels, offset_string
Example #19
| Source File: colorbar.py From CogAlg with MIT License | 5 votes |
|
def _locate(self, x):
'''
Given a set of color data values, return their
corresponding colorbar data coordinates.
'''
if isinstance(self.norm, (colors.NoNorm, colors.BoundaryNorm)):
b = self._boundaries
xn = x
else:
# Do calculations using normalized coordinates so
# as to make the interpolation more accurate.
b = self.norm(self._boundaries, clip=False).filled()
xn = self.norm(x, clip=False).filled()
bunique = b
yunique = self._y
# trim extra b values at beginning and end if they are
# not unique. These are here for extended colorbars, and are not
# wanted for the interpolation.
if b[0] == b[1]:
bunique = bunique[1:]
yunique = yunique[1:]
if b[-1] == b[-2]:
bunique = bunique[:-1]
yunique = yunique[:-1]
z = np.interp(xn, bunique, yunique)
return z
Example #20
| Source File: colorbar.py From matplotlib-4-abaqus with MIT License | 5 votes |
|
def _locate(self, x):
'''
Given a set of color data values, return their
corresponding colorbar data coordinates.
'''
if isinstance(self.norm, (colors.NoNorm, colors.BoundaryNorm)):
b = self._boundaries
xn = x
else:
# Do calculations using normalized coordinates so
# as to make the interpolation more accurate.
b = self.norm(self._boundaries, clip=False).filled()
xn = self.norm(x, clip=False).filled()
# The rest is linear interpolation with extrapolation at ends.
y = self._y
N = len(b)
ii = np.searchsorted(b, xn)
i0 = ii - 1
itop = (ii == N)
ibot = (ii == 0)
i0[itop] -= 1
ii[itop] -= 1
i0[ibot] += 1
ii[ibot] += 1
#db = b[ii] - b[i0]
db = np.take(b, ii) - np.take(b, i0)
#dy = y[ii] - y[i0]
dy = np.take(y, ii) - np.take(y, i0)
z = np.take(y, i0) + (xn - np.take(b, i0)) * dy / db
return z
Example #21
| Source File: colorbar.py From Mastering-Elasticsearch-7.0 with MIT License | 5 votes |
|
def _locate(self, x):
'''
Given a set of color data values, return their
corresponding colorbar data coordinates.
'''
if isinstance(self.norm, (colors.NoNorm, colors.BoundaryNorm)):
b = self._boundaries
xn = x
else:
# Do calculations using normalized coordinates so
# as to make the interpolation more accurate.
b = self.norm(self._boundaries, clip=False).filled()
xn = self.norm(x, clip=False).filled()
bunique = b
yunique = self._y
# trim extra b values at beginning and end if they are
# not unique. These are here for extended colorbars, and are not
# wanted for the interpolation.
if b[0] == b[1]:
bunique = bunique[1:]
yunique = yunique[1:]
if b[-1] == b[-2]:
bunique = bunique[:-1]
yunique = yunique[:-1]
z = np.interp(xn, bunique, yunique)
return z
Example #22
| Source File: colorbar.py From Mastering-Elasticsearch-7.0 with MIT License | 5 votes |
|
def _ticker(self, locator, formatter):
'''
Return the sequence of ticks (colorbar data locations),
ticklabels (strings), and the corresponding offset string.
'''
if isinstance(self.norm, colors.NoNorm) and self.boundaries is None:
intv = self._values[0], self._values[-1]
else:
intv = self.vmin, self.vmax
locator.create_dummy_axis(minpos=intv[0])
formatter.create_dummy_axis(minpos=intv[0])
locator.set_view_interval(*intv)
locator.set_data_interval(*intv)
formatter.set_view_interval(*intv)
formatter.set_data_interval(*intv)
b = np.array(locator())
if isinstance(locator, ticker.LogLocator):
eps = 1e-10
b = b[(b <= intv[1] * (1 + eps)) & (b >= intv[0] * (1 - eps))]
else:
eps = (intv[1] - intv[0]) * 1e-10
b = b[(b <= intv[1] + eps) & (b >= intv[0] - eps)]
self._manual_tick_data_values = b
ticks = self._locate(b)
ticklabels = formatter.format_ticks(b)
offset_string = formatter.get_offset()
return ticks, ticklabels, offset_string
Example #23
| Source File: evaluation.py From pycodesuggest with MIT License | 5 votes |
|
def plot_heatmap(ax, data, x_labels, y_labels, rotate=0):
heatmap = ax.pcolor(data, cmap=plt.cm.Blues, norm=NoNorm())
# put the major ticks at the middle of each cell
ax.set_xticks(np.arange(data.shape[1])+0.5, minor=False)
ax.set_yticks(np.arange(data.shape[0])+0.5, minor=False)
ax.xaxis.tick_top()
ax.set_xticklabels(x_labels, minor=False, rotation=rotate)
ax.set_yticklabels(y_labels, minor=False)
Example #24
| Source File: colorbar.py From twitter-stock-recommendation with MIT License | 5 votes |
|
def _locate(self, x):
'''
Given a set of color data values, return their
corresponding colorbar data coordinates.
'''
if isinstance(self.norm, (colors.NoNorm, colors.BoundaryNorm)):
b = self._boundaries
xn = x
else:
# Do calculations using normalized coordinates so
# as to make the interpolation more accurate.
b = self.norm(self._boundaries, clip=False).filled()
xn = self.norm(x, clip=False).filled()
# The rest is linear interpolation with extrapolation at ends.
ii = np.searchsorted(b, xn)
i0 = ii - 1
itop = (ii == len(b))
ibot = (ii == 0)
i0[itop] -= 1
ii[itop] -= 1
i0[ibot] += 1
ii[ibot] += 1
db = np.take(b, ii) - np.take(b, i0)
y = self._y
dy = np.take(y, ii) - np.take(y, i0)
z = np.take(y, i0) + (xn - np.take(b, i0)) * dy / db
return z
Example #25
| Source File: contour.py From coffeegrindsize with MIT License | 4 votes |
|
def _process_colors(self):
"""
Color argument processing for contouring.
Note that we base the color mapping on the contour levels
and layers, not on the actual range of the Z values. This
means we don't have to worry about bad values in Z, and we
always have the full dynamic range available for the selected
levels.
The color is based on the midpoint of the layer, except for
extended end layers. By default, the norm vmin and vmax
are the extreme values of the non-extended levels. Hence,
the layer color extremes are not the extreme values of
the colormap itself, but approach those values as the number
of levels increases. An advantage of this scheme is that
line contours, when added to filled contours, take on
colors that are consistent with those of the filled regions;
for example, a contour line on the boundary between two
regions will have a color intermediate between those
of the regions.
"""
self.monochrome = self.cmap.monochrome
if self.colors is not None:
# Generate integers for direct indexing.
i0, i1 = 0, len(self.levels)
if self.filled:
i1 -= 1
# Out of range indices for over and under:
if self.extend in ('both', 'min'):
i0 -= 1
if self.extend in ('both', 'max'):
i1 += 1
self.cvalues = list(range(i0, i1))
self.set_norm(mcolors.NoNorm())
else:
self.cvalues = self.layers
self.set_array(self.levels)
self.autoscale_None()
if self.extend in ('both', 'max', 'min'):
self.norm.clip = False
# self.tcolors are set by the "changed" method
Example #26
| Source File: colorbar.py From CogAlg with MIT License | 4 votes |
|
def _get_ticker_locator_formatter(self):
"""
This code looks at the norm being used by the colorbar
and decides what locator and formatter to use. If ``locator`` has
already been set by hand, it just returns
``self.locator, self.formatter``.
"""
locator = self.locator
formatter = self.formatter
if locator is None:
if self.boundaries is None:
if isinstance(self.norm, colors.NoNorm):
nv = len(self._values)
base = 1 + int(nv / 10)
locator = ticker.IndexLocator(base=base, offset=0)
elif isinstance(self.norm, colors.BoundaryNorm):
b = self.norm.boundaries
locator = ticker.FixedLocator(b, nbins=10)
elif isinstance(self.norm, colors.LogNorm):
locator = _ColorbarLogLocator(self)
elif isinstance(self.norm, colors.SymLogNorm):
# The subs setting here should be replaced
# by logic in the locator.
locator = ticker.SymmetricalLogLocator(
subs=np.arange(1, 10),
linthresh=self.norm.linthresh,
base=10)
else:
if mpl.rcParams['_internal.classic_mode']:
locator = ticker.MaxNLocator()
else:
locator = _ColorbarAutoLocator(self)
else:
b = self._boundaries[self._inside]
locator = ticker.FixedLocator(b, nbins=10)
if formatter is None:
if isinstance(self.norm, colors.LogNorm):
formatter = ticker.LogFormatterSciNotation()
elif isinstance(self.norm, colors.SymLogNorm):
formatter = ticker.LogFormatterSciNotation(
linthresh=self.norm.linthresh)
else:
formatter = ticker.ScalarFormatter()
else:
formatter = self.formatter
self.locator = locator
self.formatter = formatter
_log.debug('locator: %r', locator)
return locator, formatter
Example #27
| Source File: contour.py From CogAlg with MIT License | 4 votes |
|
def _process_colors(self):
"""
Color argument processing for contouring.
Note that we base the color mapping on the contour levels
and layers, not on the actual range of the Z values. This
means we don't have to worry about bad values in Z, and we
always have the full dynamic range available for the selected
levels.
The color is based on the midpoint of the layer, except for
extended end layers. By default, the norm vmin and vmax
are the extreme values of the non-extended levels. Hence,
the layer color extremes are not the extreme values of
the colormap itself, but approach those values as the number
of levels increases. An advantage of this scheme is that
line contours, when added to filled contours, take on
colors that are consistent with those of the filled regions;
for example, a contour line on the boundary between two
regions will have a color intermediate between those
of the regions.
"""
self.monochrome = self.cmap.monochrome
if self.colors is not None:
# Generate integers for direct indexing.
i0, i1 = 0, len(self.levels)
if self.filled:
i1 -= 1
# Out of range indices for over and under:
if self.extend in ('both', 'min'):
i0 -= 1
if self.extend in ('both', 'max'):
i1 += 1
self.cvalues = list(range(i0, i1))
self.set_norm(mcolors.NoNorm())
else:
self.cvalues = self.layers
self.set_array(self.levels)
self.autoscale_None()
if self.extend in ('both', 'max', 'min'):
self.norm.clip = False
# self.tcolors are set by the "changed" method
Example #28
| Source File: colorbar.py From twitter-stock-recommendation with MIT License | 4 votes |
|
def _ticker(self):
'''
Return the sequence of ticks (colorbar data locations),
ticklabels (strings), and the corresponding offset string.
'''
locator = self.locator
formatter = self.formatter
if locator is None:
if self.boundaries is None:
if isinstance(self.norm, colors.NoNorm):
nv = len(self._values)
base = 1 + int(nv / 10)
locator = ticker.IndexLocator(base=base, offset=0)
elif isinstance(self.norm, colors.BoundaryNorm):
b = self.norm.boundaries
locator = ticker.FixedLocator(b, nbins=10)
elif isinstance(self.norm, colors.LogNorm):
locator = ticker.LogLocator(subs='all')
elif isinstance(self.norm, colors.SymLogNorm):
# The subs setting here should be replaced
# by logic in the locator.
locator = ticker.SymmetricalLogLocator(
subs=np.arange(1, 10),
linthresh=self.norm.linthresh,
base=10)
else:
if mpl.rcParams['_internal.classic_mode']:
locator = ticker.MaxNLocator()
else:
locator = ticker.AutoLocator()
else:
b = self._boundaries[self._inside]
locator = ticker.FixedLocator(b, nbins=10)
if isinstance(self.norm, colors.NoNorm) and self.boundaries is None:
intv = self._values[0], self._values[-1]
else:
intv = self.vmin, self.vmax
locator.create_dummy_axis(minpos=intv[0])
formatter.create_dummy_axis(minpos=intv[0])
locator.set_view_interval(*intv)
locator.set_data_interval(*intv)
formatter.set_view_interval(*intv)
formatter.set_data_interval(*intv)
b = np.array(locator())
if isinstance(locator, ticker.LogLocator):
eps = 1e-10
b = b[(b <= intv[1] * (1 + eps)) & (b >= intv[0] * (1 - eps))]
else:
eps = (intv[1] - intv[0]) * 1e-10
b = b[(b <= intv[1] + eps) & (b >= intv[0] - eps)]
self._tick_data_values = b
ticks = self._locate(b)
formatter.set_locs(b)
ticklabels = [formatter(t, i) for i, t in enumerate(b)]
offset_string = formatter.get_offset()
return ticks, ticklabels, offset_string
Example #29
| Source File: colorbar.py From Computable with MIT License | 4 votes |
|
def _ticker(self):
'''
Return two sequences: ticks (colorbar data locations)
and ticklabels (strings).
'''
locator = self.locator
formatter = self.formatter
if locator is None:
if self.boundaries is None:
if isinstance(self.norm, colors.NoNorm):
nv = len(self._values)
base = 1 + int(nv / 10)
locator = ticker.IndexLocator(base=base, offset=0)
elif isinstance(self.norm, colors.BoundaryNorm):
b = self.norm.boundaries
locator = ticker.FixedLocator(b, nbins=10)
elif isinstance(self.norm, colors.LogNorm):
locator = ticker.LogLocator()
else:
locator = ticker.MaxNLocator()
else:
b = self._boundaries[self._inside]
locator = ticker.FixedLocator(b, nbins=10)
if isinstance(self.norm, colors.NoNorm):
intv = self._values[0], self._values[-1]
else:
intv = self.vmin, self.vmax
locator.create_dummy_axis(minpos=intv[0])
formatter.create_dummy_axis(minpos=intv[0])
locator.set_view_interval(*intv)
locator.set_data_interval(*intv)
formatter.set_view_interval(*intv)
formatter.set_data_interval(*intv)
b = np.array(locator())
ticks = self._locate(b)
inrange = (ticks > -0.001) & (ticks < 1.001)
ticks = ticks[inrange]
b = b[inrange]
formatter.set_locs(b)
ticklabels = [formatter(t, i) for i, t in enumerate(b)]
offset_string = formatter.get_offset()
return ticks, ticklabels, offset_string
Example #30
| Source File: contour.py From twitter-stock-recommendation with MIT License | 4 votes |
|
def _process_colors(self):
"""
Color argument processing for contouring.
Note that we base the color mapping on the contour levels
and layers, not on the actual range of the Z values. This
means we don't have to worry about bad values in Z, and we
always have the full dynamic range available for the selected
levels.
The color is based on the midpoint of the layer, except for
extended end layers. By default, the norm vmin and vmax
are the extreme values of the non-extended levels. Hence,
the layer color extremes are not the extreme values of
the colormap itself, but approach those values as the number
of levels increases. An advantage of this scheme is that
line contours, when added to filled contours, take on
colors that are consistent with those of the filled regions;
for example, a contour line on the boundary between two
regions will have a color intermediate between those
of the regions.
"""
self.monochrome = self.cmap.monochrome
if self.colors is not None:
# Generate integers for direct indexing.
i0, i1 = 0, len(self.levels)
if self.filled:
i1 -= 1
# Out of range indices for over and under:
if self.extend in ('both', 'min'):
i0 -= 1
if self.extend in ('both', 'max'):
i1 += 1
self.cvalues = list(range(i0, i1))
self.set_norm(colors.NoNorm())
else:
self.cvalues = self.layers
self.set_array(self.levels)
self.autoscale_None()
if self.extend in ('both', 'max', 'min'):
self.norm.clip = False
# self.tcolors are set by the "changed" method
