Python matplotlib.colors.PowerNorm() Examples
The following are 20
code examples of matplotlib.colors.PowerNorm().
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Example #1
| Source File: Demo_Matplotlib_Browser_Paned.py From PySimpleGUI with GNU Lesser General Public License v3.0 | 6 votes |
|
def ExploringNormalizations():
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import numpy as np
from numpy.random import multivariate_normal
data = np.vstack([
multivariate_normal([10, 10], [[3, 2], [2, 3]], size=100000),
multivariate_normal([30, 20], [[2, 3], [1, 3]], size=1000)
])
gammas = [0.8, 0.5, 0.3]
fig, axes = plt.subplots(nrows=2, ncols=2)
axes[0, 0].set_title('Linear normalization')
axes[0, 0].hist2d(data[:, 0], data[:, 1], bins=100)
for ax, gamma in zip(axes.flat[1:], gammas):
ax.set_title(r'Power law $(\gamma=%1.1f)$' % gamma)
ax.hist2d(data[:, 0], data[:, 1],
bins=100, norm=mcolors.PowerNorm(gamma))
fig.tight_layout()
return fig
Example #2
| Source File: Demo_Matplotlib_Browser.py From PySimpleGUI with GNU Lesser General Public License v3.0 | 6 votes |
|
def ExploringNormalizations():
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import numpy as np
from numpy.random import multivariate_normal
data = np.vstack([
multivariate_normal([10, 10], [[3, 2], [2, 3]], size=100000),
multivariate_normal([30, 20], [[2, 3], [1, 3]], size=1000)
])
gammas = [0.8, 0.5, 0.3]
fig, axes = plt.subplots(nrows=2, ncols=2)
axes[0, 0].set_title('Linear normalization')
axes[0, 0].hist2d(data[:, 0], data[:, 1], bins=100)
for ax, gamma in zip(axes.flat[1:], gammas):
ax.set_title(r'Power law $(\gamma=%1.1f)$' % gamma)
ax.hist2d(data[:, 0], data[:, 1],
bins=100, norm=mcolors.PowerNorm(gamma))
fig.tight_layout()
return fig
Example #3
| Source File: Demo_Matplotlib_Browser_Paned.py From PySimpleGUI with GNU Lesser General Public License v3.0 | 6 votes |
|
def ExploringNormalizations():
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import numpy as np
from numpy.random import multivariate_normal
data = np.vstack([
multivariate_normal([10, 10], [[3, 2], [2, 3]], size=100000),
multivariate_normal([30, 20], [[2, 3], [1, 3]], size=1000)
])
gammas = [0.8, 0.5, 0.3]
fig, axes = plt.subplots(nrows=2, ncols=2)
axes[0, 0].set_title('Linear normalization')
axes[0, 0].hist2d(data[:, 0], data[:, 1], bins=100)
for ax, gamma in zip(axes.flat[1:], gammas):
ax.set_title(r'Power law $(\gamma=%1.1f)$' % gamma)
ax.hist2d(data[:, 0], data[:, 1],
bins=100, norm=mcolors.PowerNorm(gamma))
fig.tight_layout()
return fig
Example #4
| Source File: Demo_Matplotlib_Browser.py From PySimpleGUI with GNU Lesser General Public License v3.0 | 6 votes |
|
def ExploringNormalizations():
import matplotlib.pyplot as plt
import matplotlib.colors as mcolors
import numpy as np
from numpy.random import multivariate_normal
data = np.vstack([
multivariate_normal([10, 10], [[3, 2], [2, 3]], size=100000),
multivariate_normal([30, 20], [[2, 3], [1, 3]], size=1000)
])
gammas = [0.8, 0.5, 0.3]
fig, axes = plt.subplots(nrows=2, ncols=2)
axes[0, 0].set_title('Linear normalization')
axes[0, 0].hist2d(data[:, 0], data[:, 1], bins=100)
for ax, gamma in zip(axes.flat[1:], gammas):
ax.set_title(r'Power law $(\gamma=%1.1f)$' % gamma)
ax.hist2d(data[:, 0], data[:, 1],
bins=100, norm=mcolors.PowerNorm(gamma))
fig.tight_layout()
return fig
Example #5
| Source File: test_colors.py From twitter-stock-recommendation with MIT License | 5 votes |
|
def test_ndarray_subclass_norm(recwarn):
# Emulate an ndarray subclass that handles units
# which objects when adding or subtracting with other
# arrays. See #6622 and #8696
class MyArray(np.ndarray):
def __isub__(self, other):
raise RuntimeError
def __add__(self, other):
raise RuntimeError
data = np.arange(-10, 10, 1, dtype=float)
data.shape = (10, 2)
mydata = data.view(MyArray)
for norm in [mcolors.Normalize(), mcolors.LogNorm(),
mcolors.SymLogNorm(3, vmax=5, linscale=1),
mcolors.Normalize(vmin=mydata.min(), vmax=mydata.max()),
mcolors.SymLogNorm(3, vmin=mydata.min(), vmax=mydata.max()),
mcolors.PowerNorm(1)]:
assert_array_equal(norm(mydata), norm(data))
fig, ax = plt.subplots()
ax.imshow(mydata, norm=norm)
fig.canvas.draw()
if isinstance(norm, mcolors.PowerNorm):
assert len(recwarn) == 1
warn = recwarn.pop(UserWarning)
assert ('Power-law scaling on negative values is ill-defined'
in str(warn.message))
else:
assert len(recwarn) == 0
recwarn.clear()
Example #6
| Source File: test_colors.py From twitter-stock-recommendation with MIT License | 5 votes |
|
def test_PowerNorm():
a = np.array([0, 0.5, 1, 1.5], dtype=float)
pnorm = mcolors.PowerNorm(1)
norm = mcolors.Normalize()
assert_array_almost_equal(norm(a), pnorm(a))
a = np.array([-0.5, 0, 2, 4, 8], dtype=float)
expected = [0, 0, 1/16, 1/4, 1]
pnorm = mcolors.PowerNorm(2, vmin=0, vmax=8)
assert_array_almost_equal(pnorm(a), expected)
assert pnorm(a[0]) == expected[0]
assert pnorm(a[2]) == expected[2]
assert_array_almost_equal(a[1:], pnorm.inverse(pnorm(a))[1:])
# Clip = True
a = np.array([-0.5, 0, 1, 8, 16], dtype=float)
expected = [0, 0, 0, 1, 1]
pnorm = mcolors.PowerNorm(2, vmin=2, vmax=8, clip=True)
assert_array_almost_equal(pnorm(a), expected)
assert pnorm(a[0]) == expected[0]
assert pnorm(a[-1]) == expected[-1]
# Clip = True at call time
a = np.array([-0.5, 0, 1, 8, 16], dtype=float)
expected = [0, 0, 0, 1, 1]
pnorm = mcolors.PowerNorm(2, vmin=2, vmax=8, clip=False)
assert_array_almost_equal(pnorm(a, clip=True), expected)
assert pnorm(a[0], clip=True) == expected[0]
assert pnorm(a[-1], clip=True) == expected[-1]
Example #7
| Source File: test_colorbar.py From coffeegrindsize with MIT License | 5 votes |
|
def test_colorbar_powernorm_extension():
# Test that colorbar with powernorm is extended correctly
f, ax = plt.subplots()
cb = ColorbarBase(ax, norm=PowerNorm(gamma=0.5, vmin=0.0, vmax=1.0),
orientation='vertical', extend='both')
assert cb._values[0] >= 0.0
Example #8
| Source File: test_colors.py From coffeegrindsize with MIT License | 5 votes |
|
def test_ndarray_subclass_norm(recwarn):
# Emulate an ndarray subclass that handles units
# which objects when adding or subtracting with other
# arrays. See #6622 and #8696
class MyArray(np.ndarray):
def __isub__(self, other):
raise RuntimeError
def __add__(self, other):
raise RuntimeError
data = np.arange(-10, 10, 1, dtype=float)
data.shape = (10, 2)
mydata = data.view(MyArray)
for norm in [mcolors.Normalize(), mcolors.LogNorm(),
mcolors.SymLogNorm(3, vmax=5, linscale=1),
mcolors.Normalize(vmin=mydata.min(), vmax=mydata.max()),
mcolors.SymLogNorm(3, vmin=mydata.min(), vmax=mydata.max()),
mcolors.PowerNorm(1)]:
assert_array_equal(norm(mydata), norm(data))
fig, ax = plt.subplots()
ax.imshow(mydata, norm=norm)
fig.canvas.draw()
assert len(recwarn) == 0
recwarn.clear()
Example #9
| Source File: test_colors.py From coffeegrindsize with MIT License | 5 votes |
|
def test_PowerNorm_translation_invariance():
a = np.array([0, 1/2, 1], dtype=float)
expected = [0, 1/8, 1]
pnorm = mcolors.PowerNorm(vmin=0, vmax=1, gamma=3)
assert_array_almost_equal(pnorm(a), expected)
pnorm = mcolors.PowerNorm(vmin=-2, vmax=-1, gamma=3)
assert_array_almost_equal(pnorm(a - 2), expected)
Example #10
| Source File: test_colors.py From coffeegrindsize with MIT License | 5 votes |
|
def test_PowerNorm():
a = np.array([0, 0.5, 1, 1.5], dtype=float)
pnorm = mcolors.PowerNorm(1)
norm = mcolors.Normalize()
assert_array_almost_equal(norm(a), pnorm(a))
a = np.array([-0.5, 0, 2, 4, 8], dtype=float)
expected = [0, 0, 1/16, 1/4, 1]
pnorm = mcolors.PowerNorm(2, vmin=0, vmax=8)
assert_array_almost_equal(pnorm(a), expected)
assert pnorm(a[0]) == expected[0]
assert pnorm(a[2]) == expected[2]
assert_array_almost_equal(a[1:], pnorm.inverse(pnorm(a))[1:])
# Clip = True
a = np.array([-0.5, 0, 1, 8, 16], dtype=float)
expected = [0, 0, 0, 1, 1]
pnorm = mcolors.PowerNorm(2, vmin=2, vmax=8, clip=True)
assert_array_almost_equal(pnorm(a), expected)
assert pnorm(a[0]) == expected[0]
assert pnorm(a[-1]) == expected[-1]
# Clip = True at call time
a = np.array([-0.5, 0, 1, 8, 16], dtype=float)
expected = [0, 0, 0, 1, 1]
pnorm = mcolors.PowerNorm(2, vmin=2, vmax=8, clip=False)
assert_array_almost_equal(pnorm(a, clip=True), expected)
assert pnorm(a[0], clip=True) == expected[0]
assert pnorm(a[-1], clip=True) == expected[-1]
Example #11
| Source File: cmap.py From artview with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def update_colormap(self):
'''Get colormap from GUI.'''
self.cmap['lock'] = self.lock_box.isChecked()
idx = self.norm_type.currentIndex()
self.cmap['vmin'] = float(self.ent_vmin.text())
self.cmap['vmax'] = float(self.ent_vmax.text())
if idx == 0:
self.cmap['norm'] = None
elif idx == 1:
self.cmap['norm'] = colors.Normalize(vmin=self.cmap['vmin'],
vmax=self.cmap['vmax'])
elif idx == 2:
self.cmap['norm'] = colors.LogNorm(vmin=self.cmap['vmin'],
vmax=self.cmap['vmax'])
elif idx == 3:
self.cmap['norm'] = colors.SymLogNorm(
linthresh=float(self.ent_linthresh.text()),
linscale=float(self.ent_linscale.text()),
vmin=self.cmap['vmin'],
vmax=self.cmap['vmax'])
elif idx == 4:
self.cmap['norm'] = colors.PowerNorm(
gamma=float(self.ent_gamma.text()),
vmin=self.cmap['vmin'],
vmax=self.cmap['vmax'])
elif idx == 5:
bounds = self.get_bounds()
self.cmap['norm'] = colors.BoundaryNorm(bounds,
ncolors=256)
self.plot()
Example #12
| Source File: cmap.py From artview with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def plot(self):
'''Replot the colorbar.'''
if self.cmap is None:
return
self.ax.cla()
self.cax.cla()
cmap = self.cmap
if 'norm' not in cmap or cmap['norm'] is None:
self.norm_type.setCurrentIndex(0)
else:
norm_name = cmap['norm'].__class__.__name__
if norm_name == 'Normalize':
self.norm_type.setCurrentIndex(1)
elif norm_name == 'LogNorm':
self.norm_type.setCurrentIndex(2)
elif norm_name == 'SymLogNorm':
self.norm_type.setCurrentIndex(3)
elif norm_name == 'PowerNorm':
self.norm_type.setCurrentIndex(4)
elif norm_name == 'BoundaryNorm':
self.norm_type.setCurrentIndex(5)
if cmap is not None:
if 'norm' in cmap:
norm = cmap['norm']
else:
norm = None
im = self.ax.imshow(gradient, aspect='auto', cmap=cmap['cmap'],
vmin=cmap['vmin'], vmax=cmap['vmax'],
norm=norm)
plt.colorbar(im, cax=self.cax)
self.canvas.draw()
Example #13
| Source File: test_colors.py From ImageFusion with MIT License | 5 votes |
|
def test_PowerNorm():
a = np.array([0, 0.5, 1, 1.5], dtype=np.float)
pnorm = mcolors.PowerNorm(1)
norm = mcolors.Normalize()
assert_array_almost_equal(norm(a), pnorm(a))
a = np.array([-0.5, 0, 2, 4, 8], dtype=np.float)
expected = [0, 0, 1/16, 1/4, 1]
pnorm = mcolors.PowerNorm(2, vmin=0, vmax=8)
assert_array_almost_equal(pnorm(a), expected)
assert_equal(pnorm(a[0]), expected[0])
assert_equal(pnorm(a[2]), expected[2])
assert_array_almost_equal(a[1:], pnorm.inverse(pnorm(a))[1:])
# Clip = True
a = np.array([-0.5, 0, 1, 8, 16], dtype=np.float)
expected = [0, 0, 0, 1, 1]
pnorm = mcolors.PowerNorm(2, vmin=2, vmax=8, clip=True)
assert_array_almost_equal(pnorm(a), expected)
assert_equal(pnorm(a[0]), expected[0])
assert_equal(pnorm(a[-1]), expected[-1])
# Clip = True at call time
a = np.array([-0.5, 0, 1, 8, 16], dtype=np.float)
expected = [0, 0, 0, 1, 1]
pnorm = mcolors.PowerNorm(2, vmin=2, vmax=8, clip=False)
assert_array_almost_equal(pnorm(a, clip=True), expected)
assert_equal(pnorm(a[0], clip=True), expected[0])
assert_equal(pnorm(a[-1], clip=True), expected[-1])
Example #14
| Source File: analysisfigure.py From ray-optics with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def __init__(self, fig, gs, ray_list,
user_scale_value=0.1, scale_type='fit',
yaxis_ticks_position='left', dsp_typ='hist2d',
**kwargs):
self.fig = fig
self.fig.subplots.append(self)
self.gs = gs
self.ray_list = ray_list
self.dsp_typ = dsp_typ
if 'title' in kwargs:
self.title = kwargs.pop('title', None)
if 'norm' in kwargs:
self.norm = kwargs.pop('norm', None)
else:
gamma = kwargs.pop('gamma', 0.5)
vmax = kwargs.get('vmax') if 'vmax' in kwargs else None
self.norm = PowerNorm(gamma, vmin=0., vmax=vmax)
self.plot_kwargs = kwargs
self.user_scale_value = user_scale_value
self.scale_type = scale_type
self.yaxis_ticks_position = yaxis_ticks_position
self.update_data()
Example #15
| Source File: test_colorbar.py From python3_ios with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def test_colorbar_powernorm_extension():
# Test that colorbar with powernorm is extended correctly
f, ax = plt.subplots()
cb = ColorbarBase(ax, norm=PowerNorm(gamma=0.5, vmin=0.0, vmax=1.0),
orientation='vertical', extend='both')
assert cb._values[0] >= 0.0
Example #16
| Source File: test_colors.py From python3_ios with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def test_ndarray_subclass_norm(recwarn):
# Emulate an ndarray subclass that handles units
# which objects when adding or subtracting with other
# arrays. See #6622 and #8696
class MyArray(np.ndarray):
def __isub__(self, other):
raise RuntimeError
def __add__(self, other):
raise RuntimeError
data = np.arange(-10, 10, 1, dtype=float)
data.shape = (10, 2)
mydata = data.view(MyArray)
for norm in [mcolors.Normalize(), mcolors.LogNorm(),
mcolors.SymLogNorm(3, vmax=5, linscale=1),
mcolors.Normalize(vmin=mydata.min(), vmax=mydata.max()),
mcolors.SymLogNorm(3, vmin=mydata.min(), vmax=mydata.max()),
mcolors.PowerNorm(1)]:
assert_array_equal(norm(mydata), norm(data))
fig, ax = plt.subplots()
ax.imshow(mydata, norm=norm)
fig.canvas.draw()
assert len(recwarn) == 0
recwarn.clear()
Example #17
| Source File: test_colors.py From python3_ios with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def test_PowerNorm_translation_invariance():
a = np.array([0, 1/2, 1], dtype=float)
expected = [0, 1/8, 1]
pnorm = mcolors.PowerNorm(vmin=0, vmax=1, gamma=3)
assert_array_almost_equal(pnorm(a), expected)
pnorm = mcolors.PowerNorm(vmin=-2, vmax=-1, gamma=3)
assert_array_almost_equal(pnorm(a - 2), expected)
Example #18
| Source File: test_colors.py From python3_ios with BSD 3-Clause "New" or "Revised" License | 5 votes |
|
def test_PowerNorm():
a = np.array([0, 0.5, 1, 1.5], dtype=float)
pnorm = mcolors.PowerNorm(1)
norm = mcolors.Normalize()
assert_array_almost_equal(norm(a), pnorm(a))
a = np.array([-0.5, 0, 2, 4, 8], dtype=float)
expected = [0, 0, 1/16, 1/4, 1]
pnorm = mcolors.PowerNorm(2, vmin=0, vmax=8)
assert_array_almost_equal(pnorm(a), expected)
assert pnorm(a[0]) == expected[0]
assert pnorm(a[2]) == expected[2]
assert_array_almost_equal(a[1:], pnorm.inverse(pnorm(a))[1:])
# Clip = True
a = np.array([-0.5, 0, 1, 8, 16], dtype=float)
expected = [0, 0, 0, 1, 1]
pnorm = mcolors.PowerNorm(2, vmin=2, vmax=8, clip=True)
assert_array_almost_equal(pnorm(a), expected)
assert pnorm(a[0]) == expected[0]
assert pnorm(a[-1]) == expected[-1]
# Clip = True at call time
a = np.array([-0.5, 0, 1, 8, 16], dtype=float)
expected = [0, 0, 0, 1, 1]
pnorm = mcolors.PowerNorm(2, vmin=2, vmax=8, clip=False)
assert_array_almost_equal(pnorm(a, clip=True), expected)
assert pnorm(a[0], clip=True) == expected[0]
assert pnorm(a[-1], clip=True) == expected[-1]
Example #19
| Source File: test_colors.py From neural-network-animation with MIT License | 5 votes |
|
def test_PowerNorm():
a = np.array([0, 0.5, 1, 1.5], dtype=np.float)
pnorm = mcolors.PowerNorm(1)
norm = mcolors.Normalize()
assert_array_almost_equal(norm(a), pnorm(a))
a = np.array([-0.5, 0, 2, 4, 8], dtype=np.float)
expected = [0, 0, 1/16, 1/4, 1]
pnorm = mcolors.PowerNorm(2, vmin=0, vmax=8)
assert_array_almost_equal(pnorm(a), expected)
assert_equal(pnorm(a[0]), expected[0])
assert_equal(pnorm(a[2]), expected[2])
assert_array_almost_equal(a[1:], pnorm.inverse(pnorm(a))[1:])
# Clip = True
a = np.array([-0.5, 0, 1, 8, 16], dtype=np.float)
expected = [0, 0, 0, 1, 1]
pnorm = mcolors.PowerNorm(2, vmin=2, vmax=8, clip=True)
assert_array_almost_equal(pnorm(a), expected)
assert_equal(pnorm(a[0]), expected[0])
assert_equal(pnorm(a[-1]), expected[-1])
# Clip = True at call time
a = np.array([-0.5, 0, 1, 8, 16], dtype=np.float)
expected = [0, 0, 0, 1, 1]
pnorm = mcolors.PowerNorm(2, vmin=2, vmax=8, clip=False)
assert_array_almost_equal(pnorm(a, clip=True), expected)
assert_equal(pnorm(a[0], clip=True), expected[0])
assert_equal(pnorm(a[-1], clip=True), expected[-1])
Example #20
| Source File: drapeplot.py From LSDMappingTools with MIT License | 4 votes |
|
def make_drape_plot(self):
"""Creates a matplotlib Axes object with the drape map."""
# but what if fig XOR ax is None??
# if fig is None and ax is None:
# self.fig, self.ax = plt.subplots()
# else:
# self.fig=fig, self.ax=ax
# Plot the background
self.im_background = self.ax.imshow(self.Background.Hillshade,
self.Background.colourmap,
extent=self.Background.extents,
interpolation="nearest", vmax=450)
self._num_drapes += 1
self._drape_list.append(self.im_background)
if self._show_background_colourbar:
# Plot the background image colour bar
self._generic_colourbar_plotter(self.im_background, "Elevation (m)")
# Plot the drape (overlay data) on top.
# Should be separate function really...
if not self.HideDrape:
self.im = self.ax.imshow(self.Drape._RasterArray,
self._drape_colourmap,
extent=self.Drape.extents,
interpolation="nearest",
vmin=self._vmin, vmax=self._vmax,
norm=self._colourbar_normalisation,
alpha=self._drape_alpha
)
#norm=_mcolors.PowerNorm(gamma=0.2))
self._drape_list.append(self.im)
self._num_drapes += 1
# Add the colourbar for the drape
self._generic_colourbar_plotter(self.im, self._colourbar_label)
# Add a title
self._set_subplot_autolabel()
self._set_axis_labels(self._xaxis_label, self._yaxis_label)
#return self.fig, self.ax
