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Commit 0b1acf84 authored by Jan Caron's avatar Jan Caron
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Just changed order of plotting functions, nothing serious...

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pyramid/fielddata.py
+ 158
− 158
View file @ 0b1acf84
......@@ -725,8 +725,9 @@ class VectorData(FieldData):
from .file_io.io_vectordata import save_vectordata
save_vectordata(self, filename, **kwargs)
def plot_field(self, title='Vector Field', axis=None, proj_axis='z', figsize=(9, 8),
ax_slice=None, show_mask=True, bgcolor='white', hue_mode='triadic'):
def plot_quiver(self, title='Vector Field', axis=None, proj_axis='z', figsize=(9, 8),
coloring='angle', ar_dens=1, ax_slice=None, log=False, scaled=True,
scale=1., show_mask=True, bgcolor='white', hue_mode='triadic'):
"""Plot a slice of the vector field as a quiver plot.
Parameters
......@@ -739,9 +740,23 @@ class VectorData(FieldData):
The axis, from which a slice is plotted. The default is 'z'.
figsize : tuple of floats (N=2)
Size of the plot figure.
coloring : {'angle', 'amplitude', 'uniform', matplotlib color}
Color coding mode of the arrows. Use 'full' (default), 'angle', 'amplitude', 'uniform'
(black or white, depending on `bgcolor`), or a matplotlib color keyword.
ar_dens: int, optional
Number defining the arrow density which is plotted. A higher ar_dens number skips more
arrows (a number of 2 plots every second arrow). Default is 1.
ax_slice : int, optional
The slice-index of the axis specified in `proj_axis`. Is set to the center of
`proj_axis` if not specified.
log : boolean, optional
The loratihm of the arrow length is plotted instead. This is helpful if only the
direction of the arrows is important and the amplitude varies a lot. Default is False.
scaled : boolean, optional
Normalizes the plotted arrows in respect to the highest one. Default is True.
scale: float, optional
Additional multiplicative factor scaling the arrow length. Default is 1
(no further scaling).
show_mask: boolean
Default is True. Shows the outlines of the mask slice if available.
bgcolor: {'black', 'white'}, optional
......@@ -756,7 +771,7 @@ class VectorData(FieldData):
The axis on which the graph is plotted.
"""
self._log.debug('Calling plot_field')
self._log.debug('Calling plot_quiver')
assert proj_axis == 'z' or proj_axis == 'y' or proj_axis == 'x', \
'Axis has to be x, y or z (as string).'
if ax_slice is None:
......@@ -776,24 +791,70 @@ class VectorData(FieldData):
submask = self.get_mask()[..., ax_slice]
else:
raise ValueError('{} is not a valid argument (use x, y or z)'.format(proj_axis))
# Prepare quiver (select only used arrows if ar_dens is specified):
dim_uv = u_mag.shape
vv, uu = np.indices(dim_uv) + 0.5 # shift to center of pixel
uu = uu[::ar_dens, ::ar_dens]
vv = vv[::ar_dens, ::ar_dens]
u_mag = u_mag[::ar_dens, ::ar_dens]
v_mag = v_mag[::ar_dens, ::ar_dens]
amplitudes = np.hypot(u_mag, v_mag)
angles = np.angle(u_mag + 1j * v_mag, deg=True).tolist()
# Calculate the arrow colors:
if coloring == 'angle':
self._log.debug('Encoding angles')
hue = np.arctan2(v_mag, u_mag) / (2 * np.pi)
hue[hue < 0] += 1
if hue_mode == 'triadic':
cmap = colors.hls_triadic_cmap
elif hue_mode == 'tetradic':
cmap = colors.hls_tetradic_cmap
else:
raise ValueError('Hue mode {} not understood!'.format(hue_mode))
elif coloring == 'amplitude':
self._log.debug('Encoding amplitude')
hue = amplitudes / amplitudes.max()
cmap = 'jet'
elif coloring == 'uniform':
self._log.debug('Automatic uniform color encoding')
hue = np.zeros_like(u_mag) # use black arrows!
cmap = 'gray' if bgcolor == 'white' else 'Greys'
else:
self._log.debug('Automatic uniform color encoding')
hue = np.zeros_like(u_mag) # use black arrows!
cmap = ListedColormap([coloring])
# If no axis is specified, a new figure is created:
if axis is None:
self._log.debug('axis is None')
fig = plt.figure(figsize=figsize)
axis = fig.add_subplot(1, 1, 1)
axis.set_aspect('equal')
# Plot the field:
dim_uv = u_mag.shape
hue = np.arctan2(v_mag, u_mag) / (2 * np.pi) # Hue according to angle!
hue[hue < 0] += 1 # Shift negative values!
luminance = 0.5 * submask # Luminance according to mask!
if bgcolor == 'white': # Invert luminance:
luminance = 1 - luminance
saturation = np.hypot(u_mag, v_mag) # Saturation according to amplitude!
saturation /= saturation.max()
rgb = colors.rgb_from_hls(hue, luminance, saturation, mode=hue_mode)
axis.imshow(Image.fromarray(rgb), origin='lower', interpolation='none',
extent=(0, dim_uv[1], 0, dim_uv[0]))
# Take the logarithm of the arrows to clearly show directions (if specified):
if log and np.any(amplitudes): # If the slice is empty, skip!
cutoff = 10
amp = np.round(amplitudes, decimals=cutoff)
min_value = amp[np.nonzero(amp)].min()
u_mag = np.round(u_mag, decimals=cutoff) / min_value
u_mag = np.log10(np.abs(u_mag) + 1) * np.sign(u_mag)
v_mag = np.round(v_mag, decimals=cutoff) / min_value
v_mag = np.log10(np.abs(v_mag) + 1) * np.sign(v_mag)
amplitudes = np.hypot(u_mag, v_mag) # Recalculate (used if scaled)!
# Scale the amplitude of the arrows to the highest one (if specified):
if scaled:
u_mag /= amplitudes.max() + 1E-30
v_mag /= amplitudes.max() + 1E-30
im = axis.quiver(uu, vv, u_mag, v_mag, hue, cmap=cmap, clim=(0, 1), angles=angles,
pivot='middle', units='xy', scale_units='xy', scale=scale / ar_dens,
minlength=0.05, width=1*ar_dens, headlength=2, headaxislength=2,
headwidth=2, minshaft=2)
if coloring == 'amplitude':
fig = plt.gcf()
fig.subplots_adjust(right=0.8)
cbar_ax = fig.add_axes([0.82, 0.15, 0.02, 0.7])
cbar = fig.colorbar(im, cax=cbar_ax)
cbar.ax.tick_params(labelsize=14)
cbar_title = u'amplitude'
cbar.set_label(cbar_title, fontsize=15)
# Change background color:
axis.set_axis_bgcolor(bgcolor)
# Show mask:
......@@ -820,9 +881,8 @@ class VectorData(FieldData):
# Return plotting axis:
return axis
def plot_streamline(self, title='Vector Field', axis=None, proj_axis='z', figsize=(9, 8),
coloring='angle', ax_slice=None, density=2, linewidth=2,
show_mask=True, bgcolor='white', hue_mode='triadic'):
def plot_field(self, title='Vector Field', axis=None, proj_axis='z', figsize=(9, 8),
ax_slice=None, show_mask=True, bgcolor='white', hue_mode='triadic'):
"""Plot a slice of the vector field as a quiver plot.
Parameters
......@@ -835,19 +895,9 @@ class VectorData(FieldData):
The axis, from which a slice is plotted. The default is 'z'.
figsize : tuple of floats (N=2)
Size of the plot figure.
coloring : {'angle', 'amplitude', 'uniform'}
Color coding mode of the arrows. Use 'full' (default), 'angle', 'amplitude' or
'uniform'.
ax_slice : int, optional
The slice-index of the axis specified in `proj_axis`. Is set to the center of
`proj_axis` if not specified.
density : float or 2-tuple, optional
Controls the closeness of streamlines. When density = 1, the domain is divided into a
30x30 grid—density linearly scales this grid. Each cebll in the grid can have, at most,
one traversing streamline. For different densities in each direction, use
[density_x, density_y].
linewidth : numeric or 2d array, optional
Vary linewidth when given a 2d array with the same shape as velocities.
show_mask: boolean
Default is True. Shows the outlines of the mask slice if available.
bgcolor: {'black', 'white'}, optional
......@@ -862,7 +912,7 @@ class VectorData(FieldData):
The axis on which the graph is plotted.
"""
self._log.debug('Calling plot_quiver')
self._log.debug('Calling plot_field')
assert proj_axis == 'z' or proj_axis == 'y' or proj_axis == 'x', \
'Axis has to be x, y or z (as string).'
if ax_slice is None:
......@@ -882,51 +932,24 @@ class VectorData(FieldData):
submask = self.get_mask()[..., ax_slice]
else:
raise ValueError('{} is not a valid argument (use x, y or z)'.format(proj_axis))
# Prepare quiver (select only used arrows if ar_dens is specified):
dim_uv = u_mag.shape
uu = np.arange(dim_uv[1]) + 0.5 # shift to center of pixel
vv = np.arange(dim_uv[0]) + 0.5 # shift to center of pixel
u_mag, v_mag = self.get_slice(ax_slice, proj_axis)
# v_mag = np.ma.array(v_mag, mask=submask)
amplitudes = np.hypot(u_mag, v_mag)
# Calculate the arrow colors:
if coloring == 'angle':
self._log.debug('Encoding angles')
color = np.arctan2(v_mag, u_mag) / (2 * np.pi)
color[color < 0] += 1
if hue_mode == 'triadic':
cmap = colors.hls_triadic_cmap
elif hue_mode == 'tetradic':
cmap = colors.hls_tetradic_cmap
else:
raise ValueError('Hue mode {} not understood!'.format(hue_mode))
elif coloring == 'amplitude':
self._log.debug('Encoding amplitude')
color = amplitudes / amplitudes.max()
cmap = 'jet'
elif coloring == 'uniform':
self._log.debug('No color encoding')
color = np.zeros_like(u_mag) # use black arrows!
cmap = 'gray' if bgcolor == 'white' else 'Greys'
else:
raise AttributeError("Invalid coloring mode! Use 'angles', 'amplitude' or 'uniform'!")
# If no axis is specified, a new figure is created:
if axis is None:
self._log.debug('axis is None')
fig = plt.figure(figsize=figsize)
axis = fig.add_subplot(1, 1, 1)
axis.set_aspect('equal')
# Plot the streamlines:
im = plt.streamplot(uu, vv, u_mag, v_mag, density=density, linewidth=linewidth,
color=color, cmap=cmap)
if coloring == 'amplitude':
fig = plt.gcf()
fig.subplots_adjust(right=0.8)
cbar_ax = fig.add_axes([0.82, 0.15, 0.02, 0.7])
cbar = fig.colorbar(im.lines, cax=cbar_ax)
cbar.ax.tick_params(labelsize=14)
cbar_title = u'amplitude'
cbar.set_label(cbar_title, fontsize=15)
# Plot the field:
dim_uv = u_mag.shape
hue = np.arctan2(v_mag, u_mag) / (2 * np.pi) # Hue according to angle!
hue[hue < 0] += 1 # Shift negative values!
luminance = 0.5 * submask # Luminance according to mask!
if bgcolor == 'white': # Invert luminance:
luminance = 1 - luminance
saturation = np.hypot(u_mag, v_mag) # Saturation according to amplitude!
saturation /= saturation.max()
rgb = colors.rgb_from_hls(hue, luminance, saturation, mode=hue_mode)
axis.imshow(Image.fromarray(rgb), origin='lower', interpolation='none',
extent=(0, dim_uv[1], 0, dim_uv[0]))
# Change background color:
axis.set_axis_bgcolor(bgcolor)
# Show mask:
......@@ -953,10 +976,10 @@ class VectorData(FieldData):
# Return plotting axis:
return axis
def plot_quiver(self, title='Vector Field', axis=None, proj_axis='z', figsize=(9, 8),
coloring='angle', ar_dens=1, ax_slice=None, log=False, scaled=True,
scale=1., show_mask=True, bgcolor='white', hue_mode='triadic'):
"""Plot a slice of the vector field as a quiver plot.
def plot_quiver_field(self, title='Vector Field', axis=None, proj_axis='z',
figsize=(9, 8), ar_dens=1, ax_slice=None, show_mask=True,
bgcolor='white', hue_mode='triadic'):
"""Plot the vector field as a field plot with uniformly colored arrows overlayed.
Parameters
----------
......@@ -968,23 +991,61 @@ class VectorData(FieldData):
The axis, from which a slice is plotted. The default is 'z'.
figsize : tuple of floats (N=2)
Size of the plot figure.
coloring : {'angle', 'amplitude', 'uniform', matplotlib color}
Color coding mode of the arrows. Use 'full' (default), 'angle', 'amplitude', 'uniform'
(black or white, depending on `bgcolor`), or a matplotlib color keyword.
ar_dens: int, optional
Number defining the arrow density which is plotted. A higher ar_dens number skips more
arrows (a number of 2 plots every second arrow). Default is 1.
ax_slice : int, optional
The slice-index of the axis specified in `proj_axis`. Is set to the center of
`proj_axis` if not specified.
log : boolean, optional
The loratihm of the arrow length is plotted instead. This is helpful if only the
direction of the arrows is important and the amplitude varies a lot. Default is False.
scaled : boolean, optional
Normalizes the plotted arrows in respect to the highest one. Default is True.
scale: float, optional
Additional multiplicative factor scaling the arrow length. Default is 1
(no further scaling).
show_mask: boolean
Default is True. Shows the outlines of the mask slice if available.
bgcolor: {'black', 'white'}, optional
Determines the background color of the plot.
hue_mode : {'triadic', 'tetradic'}
Optional string for determining the hue scheme. Use either a triadic or tetradic
scheme (see the according colormaps for more information).
Returns
-------
axis: :class:`~matplotlib.axes.AxesSubplot`
The axis on which the graph is plotted.
"""
axis = self.plot_field(title=title, axis=axis, proj_axis=proj_axis, figsize=figsize,
ax_slice=ax_slice, show_mask=show_mask, bgcolor=bgcolor,
hue_mode=hue_mode)
self.plot_quiver(axis=axis, proj_axis=proj_axis, figsize=figsize, coloring='uniform',
ar_dens=ar_dens, ax_slice=ax_slice, show_mask=show_mask, bgcolor=bgcolor)
return axis
def plot_streamline(self, title='Vector Field', axis=None, proj_axis='z', figsize=(9, 8),
coloring='angle', ax_slice=None, density=2, linewidth=2,
show_mask=True, bgcolor='white', hue_mode='triadic'):
"""Plot a slice of the vector field as a quiver plot.
Parameters
----------
title : string, optional
The title for the plot.
axis : :class:`~matplotlib.axes.AxesSubplot`, optional
Axis on which the graph is plotted. Creates a new figure if none is specified.
proj_axis : {'z', 'y', 'x'}, optional
The axis, from which a slice is plotted. The default is 'z'.
figsize : tuple of floats (N=2)
Size of the plot figure.
coloring : {'angle', 'amplitude', 'uniform'}
Color coding mode of the arrows. Use 'full' (default), 'angle', 'amplitude' or
'uniform'.
ax_slice : int, optional
The slice-index of the axis specified in `proj_axis`. Is set to the center of
`proj_axis` if not specified.
density : float or 2-tuple, optional
Controls the closeness of streamlines. When density = 1, the domain is divided into a
30x30 grid—density linearly scales this grid. Each cebll in the grid can have, at most,
one traversing streamline. For different densities in each direction, use
[density_x, density_y].
linewidth : numeric or 2d array, optional
Vary linewidth when given a 2d array with the same shape as velocities.
show_mask: boolean
Default is True. Shows the outlines of the mask slice if available.
bgcolor: {'black', 'white'}, optional
......@@ -1021,18 +1082,16 @@ class VectorData(FieldData):
raise ValueError('{} is not a valid argument (use x, y or z)'.format(proj_axis))
# Prepare quiver (select only used arrows if ar_dens is specified):
dim_uv = u_mag.shape
vv, uu = np.indices(dim_uv) + 0.5 # shift to center of pixel
uu = uu[::ar_dens, ::ar_dens]
vv = vv[::ar_dens, ::ar_dens]
u_mag = u_mag[::ar_dens, ::ar_dens]
v_mag = v_mag[::ar_dens, ::ar_dens]
uu = np.arange(dim_uv[1]) + 0.5 # shift to center of pixel
vv = np.arange(dim_uv[0]) + 0.5 # shift to center of pixel
u_mag, v_mag = self.get_slice(ax_slice, proj_axis)
# v_mag = np.ma.array(v_mag, mask=submask)
amplitudes = np.hypot(u_mag, v_mag)
angles = np.angle(u_mag + 1j * v_mag, deg=True).tolist()
# Calculate the arrow colors:
if coloring == 'angle':
self._log.debug('Encoding angles')
hue = np.arctan2(v_mag, u_mag) / (2 * np.pi)
hue[hue < 0] += 1
color = np.arctan2(v_mag, u_mag) / (2 * np.pi)
color[color < 0] += 1
if hue_mode == 'triadic':
cmap = colors.hls_triadic_cmap
elif hue_mode == 'tetradic':
......@@ -1041,45 +1100,28 @@ class VectorData(FieldData):
raise ValueError('Hue mode {} not understood!'.format(hue_mode))
elif coloring == 'amplitude':
self._log.debug('Encoding amplitude')
hue = amplitudes / amplitudes.max()
color = amplitudes / amplitudes.max()
cmap = 'jet'
elif coloring == 'uniform':
self._log.debug('Automatic uniform color encoding')
hue = np.zeros_like(u_mag) # use black arrows!
self._log.debug('No color encoding')
color = np.zeros_like(u_mag) # use black arrows!
cmap = 'gray' if bgcolor == 'white' else 'Greys'
else:
self._log.debug('Automatic uniform color encoding')
hue = np.zeros_like(u_mag) # use black arrows!
cmap = ListedColormap([coloring])
raise AttributeError("Invalid coloring mode! Use 'angles', 'amplitude' or 'uniform'!")
# If no axis is specified, a new figure is created:
if axis is None:
self._log.debug('axis is None')
fig = plt.figure(figsize=figsize)
axis = fig.add_subplot(1, 1, 1)
axis.set_aspect('equal')
# Take the logarithm of the arrows to clearly show directions (if specified):
if log and np.any(amplitudes): # If the slice is empty, skip!
cutoff = 10
amp = np.round(amplitudes, decimals=cutoff)
min_value = amp[np.nonzero(amp)].min()
u_mag = np.round(u_mag, decimals=cutoff) / min_value
u_mag = np.log10(np.abs(u_mag) + 1) * np.sign(u_mag)
v_mag = np.round(v_mag, decimals=cutoff) / min_value
v_mag = np.log10(np.abs(v_mag) + 1) * np.sign(v_mag)
amplitudes = np.hypot(u_mag, v_mag) # Recalculate (used if scaled)!
# Scale the amplitude of the arrows to the highest one (if specified):
if scaled:
u_mag /= amplitudes.max() + 1E-30
v_mag /= amplitudes.max() + 1E-30
im = axis.quiver(uu, vv, u_mag, v_mag, hue, cmap=cmap, clim=(0, 1), angles=angles,
pivot='middle', units='xy', scale_units='xy', scale=scale / ar_dens,
minlength=0.05, width=1*ar_dens, headlength=2, headaxislength=2,
headwidth=2, minshaft=2)
# Plot the streamlines:
im = plt.streamplot(uu, vv, u_mag, v_mag, density=density, linewidth=linewidth,
color=color, cmap=cmap)
if coloring == 'amplitude':
fig = plt.gcf()
fig.subplots_adjust(right=0.8)
cbar_ax = fig.add_axes([0.82, 0.15, 0.02, 0.7])
cbar = fig.colorbar(im, cax=cbar_ax)
cbar = fig.colorbar(im.lines, cax=cbar_ax)
cbar.ax.tick_params(labelsize=14)
cbar_title = u'amplitude'
cbar.set_label(cbar_title, fontsize=15)
......@@ -1109,48 +1151,6 @@ class VectorData(FieldData):
# Return plotting axis:
return axis
def plot_quiver_field(self, title='Vector Field', axis=None, proj_axis='z',
figsize=(9, 8), ar_dens=1, ax_slice=None, show_mask=True,
bgcolor='white', hue_mode='triadic'):
"""Plot the vector field as a field plot with uniformly colored arrows overlayed.
Parameters
----------
title : string, optional
The title for the plot.
axis : :class:`~matplotlib.axes.AxesSubplot`, optional
Axis on which the graph is plotted. Creates a new figure if none is specified.
proj_axis : {'z', 'y', 'x'}, optional
The axis, from which a slice is plotted. The default is 'z'.
figsize : tuple of floats (N=2)
Size of the plot figure.
ar_dens: int, optional
Number defining the arrow density which is plotted. A higher ar_dens number skips more
arrows (a number of 2 plots every second arrow). Default is 1.
ax_slice : int, optional
The slice-index of the axis specified in `proj_axis`. Is set to the center of
`proj_axis` if not specified.
show_mask: boolean
Default is True. Shows the outlines of the mask slice if available.
bgcolor: {'black', 'white'}, optional
Determines the background color of the plot.
hue_mode : {'triadic', 'tetradic'}
Optional string for determining the hue scheme. Use either a triadic or tetradic
scheme (see the according colormaps for more information).
Returns
-------
axis: :class:`~matplotlib.axes.AxesSubplot`
The axis on which the graph is plotted.
"""
axis = self.plot_field(title=title, axis=axis, proj_axis=proj_axis, figsize=figsize,
ax_slice=ax_slice, show_mask=show_mask, bgcolor=bgcolor,
hue_mode=hue_mode)
self.plot_quiver(axis=axis, proj_axis=proj_axis, figsize=figsize, coloring='uniform',
ar_dens=ar_dens, ax_slice=ax_slice, show_mask=show_mask, bgcolor=bgcolor)
return axis
def plot_quiver3d(self, title='Vector Field', limit=None, cmap='jet', mode='2darrow',
coloring='full', ar_dens=1, opacity=1.0, hue_mode='triadic'):
"""Plot the vector field as 3D-vectors in a quiverplot.
......
pyramid/phasemap.py
+ 45
− 44
View file @ 0b1acf84
......@@ -658,50 +658,6 @@ class PhaseMap(object):
# Return plotting axis:
return axis
def plot_phase3d(self, title='Phase Map', unit='rad', cmap='RdBu'):
"""Display the phasemap as a 3D surface with contourplots.
Parameters
----------
title : string, optional
The title of the plot. The default is 'Phase Map'.
unit: {'rad', 'mrad', 'µrad'}, optional
The plotting unit of the phase map. The phase is scaled accordingly before plotting.
cmap : string, optional
The :class:`~matplotlib.colors.Colormap` which is used for the plot as a string.
The default is 'RdBu'.
Returns
-------
axis: :class:`~matplotlib.axes.AxesSubplot`
The axis on which the graph is plotted.
"""
self._log.debug('Calling plot_phase3d')
# Take units into consideration:
phase = self.phase * self.UNITDICT[unit]
# Create figure and axis:
fig = plt.figure()
axis = Axes3D(fig)
# Plot surface and contours:
vv, uu = np.indices(self.dim_uv)
axis.plot_surface(uu, vv, phase, rstride=4, cstride=4, alpha=0.7, cmap=cmap,
linewidth=0, antialiased=False)
axis.contourf(uu, vv, phase, 15, zdir='z', offset=np.min(phase), cmap=cmap)
axis.set_title(title)
axis.view_init(45, -135)
axis.set_xlabel('u-axis [px]')
axis.set_ylabel('v-axis [px]')
axis.set_zlabel('phase shift [{}]'.format(unit))
if self.dim_uv[0] >= self.dim_uv[1]:
u_bin, v_bin = np.max((2, np.floor(9 * self.dim_uv[1] / self.dim_uv[0]))), 9
else:
u_bin, v_bin = 9, np.max((2, np.floor(9 * self.dim_uv[0] / self.dim_uv[1])))
axis.xaxis.set_major_locator(MaxNLocator(nbins=u_bin, integer=True))
axis.yaxis.set_major_locator(MaxNLocator(nbins=v_bin, integer=True))
# Return plotting axis:
return axis
def plot_holo(self, title=None, gain='auto', axis=None, hue_mode='triadic',
interpolation='none', figsize=(8, 8)):
"""Display the color coded holography image.
......@@ -862,3 +818,48 @@ class PhaseMap(object):
# Return the plotting axes:
return phase_axis, holo_axis
def plot_phase3d(self, title='Phase Map', unit='rad', cmap='RdBu'):
"""Display the phasemap as a 3D surface with contourplots.
Parameters
----------
title : string, optional
The title of the plot. The default is 'Phase Map'.
unit: {'rad', 'mrad', 'µrad'}, optional
The plotting unit of the phase map. The phase is scaled accordingly before plotting.
cmap : string, optional
The :class:`~matplotlib.colors.Colormap` which is used for the plot as a string.
The default is 'RdBu'.
Returns
-------
axis: :class:`~matplotlib.axes.AxesSubplot`
The axis on which the graph is plotted.
"""
self._log.debug('Calling plot_phase3d')
# Take units into consideration:
phase = self.phase * self.UNITDICT[unit]
# Create figure and axis:
fig = plt.figure()
axis = Axes3D(fig)
# Plot surface and contours:
vv, uu = np.indices(self.dim_uv)
axis.plot_surface(uu, vv, phase, rstride=4, cstride=4, alpha=0.7, cmap=cmap,
linewidth=0, antialiased=False)
axis.contourf(uu, vv, phase, 15, zdir='z', offset=np.min(phase), cmap=cmap)
axis.set_title(title)
axis.view_init(45, -135)
axis.set_xlabel('u-axis [px]')
axis.set_ylabel('v-axis [px]')
axis.set_zlabel('phase shift [{}]'.format(unit))
if self.dim_uv[0] >= self.dim_uv[1]:
u_bin, v_bin = np.max((2, np.floor(9 * self.dim_uv[1] / self.dim_uv[0]))), 9
else:
u_bin, v_bin = 9, np.max((2, np.floor(9 * self.dim_uv[0] / self.dim_uv[1])))
axis.xaxis.set_major_locator(MaxNLocator(nbins=u_bin, integer=True))
axis.yaxis.set_major_locator(MaxNLocator(nbins=v_bin, integer=True))
# Return plotting axis:
return axis
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