def plotDensity(self, axes=None, hist=True, bins=100, color='b'):
"""Plots the mesh density in x/y/z-direction.
``hist=True`` is recommended, since otherwise plots generally appear fairly
noisy.
.. note:: If no ``axes`` are given or they do not have the necessary size,
will create new ones.
.. image:: ../imgs/pyfrp_mesh/density_plot.png
Keyword Args:
axes (list): List of ``matplotlib.axes``.
hist (bool): Summarize densities in bins.
bins (int): Number of bins used for hist.
color (str): Color of plot.
Returns:
list: List of ``matplotlib.axes``.
"""
x, y, z = self.getCellCenters()
volSortedByX, xSorted = pyfrp_misc_module.sortListsWithKey(
self.mesh.getCellVolumes(), x)
volSortedByY, ySorted = pyfrp_misc_module.sortListsWithKey(
self.mesh.getCellVolumes(), y)
volSortedByZ, zSorted = pyfrp_misc_module.sortListsWithKey(
self.mesh.getCellVolumes(), z)
if axes == None:
fig, axes = pyfrp_plot_module.makeSubplot(
[1, 3], titles=["Density(x)", "Density(y)", "Density(z)"])
else:
if len(axes) < 3:
printWarning(
"axes do not have right have, will create new ones.")
fig, axes = pyfrp_plot_module.makeSubplot(
[1, 3], titles=["Density(x)", "Density(y)", "Density(z)"])
if hist:
xSorted, volSortedByX = pyfrp_misc_module.simpleHist(
xSorted, volSortedByX, bins)
ySorted, volSortedByY = pyfrp_misc_module.simpleHist(
ySorted, volSortedByY, bins)
zSorted, volSortedByZ = pyfrp_misc_module.simpleHist(
zSorted, volSortedByZ, bins)
axes[0].plot(xSorted, volSortedByX, color=color)
axes[1].plot(ySorted, volSortedByY, color=color)
axes[2].plot(zSorted, volSortedByZ, color=color)
for ax in axes:
pyfrp_plot_module.redraw(ax)
return axes
def plotDensity(self,axes=None,hist=True,bins=100,color='b'):
"""Plots the mesh density in x/y/z-direction.
``hist=True`` is recommended, since otherwise plots generally appear fairly
noisy.
.. note:: If no ``axes`` are given or they do not have the necessary size,
will create new ones.
.. image:: ../imgs/pyfrp_mesh/density_plot.png
Keyword Args:
axes (list): List of ``matplotlib.axes``.
hist (bool): Summarize densities in bins.
bins (int): Number of bins used for hist.
color (str): Color of plot.
Returns:
list: List of ``matplotlib.axes``.
"""
x,y,z=self.getCellCenters()
volSortedByX,xSorted=pyfrp_misc_module.sortListsWithKey(self.mesh.getCellVolumes(),x)
volSortedByY,ySorted=pyfrp_misc_module.sortListsWithKey(self.mesh.getCellVolumes(),y)
volSortedByZ,zSorted=pyfrp_misc_module.sortListsWithKey(self.mesh.getCellVolumes(),z)
if axes==None:
fig,axes = pyfrp_plot_module.makeSubplot([1,3],titles=["Density(x)","Density(y)","Density(z)"])
else:
if len(axes)<3:
printWarning("axes do not have right have, will create new ones.")
fig,axes = pyfrp_plot_module.makeSubplot([1,3],titles=["Density(x)","Density(y)","Density(z)"])
if hist:
xSorted,volSortedByX=pyfrp_misc_module.simpleHist(xSorted,volSortedByX,bins)
ySorted,volSortedByY=pyfrp_misc_module.simpleHist(ySorted,volSortedByY,bins)
zSorted,volSortedByZ=pyfrp_misc_module.simpleHist(zSorted,volSortedByZ,bins)
axes[0].plot(xSorted,volSortedByX,color=color)
axes[1].plot(ySorted,volSortedByY,color=color)
axes[2].plot(zSorted,volSortedByZ,color=color)
for ax in axes:
pyfrp_plot_module.redraw(ax)
return axes
def compareICInterpolation(self,axes=None,roi=None): """Shows initial image, its interpolation, the resulting initial condition and its interpolation back onto an image. See also :py:func:`showICimg`, :py:func:`showInterpolatedICImg`, :py:func:`showIC`, :py:func:`showInterpolatedIC`. Will create new axes if necessary. .. warning:: Some images might be flipped due to plotting functions. Will be fixed in future version. .. image:: ../imgs/pyfrp_simulation/ICcompare.png Keyword Args: roi (pyfrp.subclasses.pyfrp_ROI.ROI): A PyFRAP ROI. axes (matplotlib.axes): List of axes of length 4. Returns: list: List of axes. """ if axes==None: fig,axes = pyfrp_plot_module.makeSubplot([2,2],titles=["Original Image","Interpolated Image","IC","Reinterpolated IC"],sup="simulation") self.showICimg(ax=axes[0]) self.showInterpolatedICImg(ax=axes[1]) self.showIC(ax=axes[2],roi=roi) self.showInterpolatedIC(ax=axes[3],roi=roi) for ax in axes: pyfrp_plot_module.redraw(ax) return axes
def showInterpolatedICImg(self,ax=None): """Shows interpolation of initial condition image. See also :py:func:`computeInterpolatedICImg`. .. image:: ../imgs/pyfrp_simulation/showInterpolatedICimg.png Keyword Args: ax (matplotlib.axes): Axes to be used for plotting. Returns: matplotlib.axes: Axes used for plotting. """ if ax==None: fig,axes = pyfrp_plot_module.makeSubplot([1,1],titles=["Interpolated Image"],sup="simulation") ax=axes[0] xInt, yInt, f=self.computeInterpolatedICImg() #print np.shape(xInt) #raw_input() X,Y=np.meshgrid(xInt,yInt) imgInt=np.zeros(np.shape(X)) for i in range(np.shape(X)[0]): for j in range(np.shape(Y)[0]): imgInt[i,j]=f(X[i,j],Y[i,j]) ax.imshow(imgInt) return ax
def showInterpolatedIC(self,ax=None,roi=None): """Shows ICs interpolated back onto 2D image. If ``roi`` is specified, will only interpolate nodes of this ROI. See also :py:func:`computeInterpolatedIC`. .. image:: ../imgs/pyfrp_simulation/showInterpolatedIC.png Keyword Args: roi (pyfrp.subclasses.pyfrp_ROI.ROI): A PyFRAP ROI. ax (matplotlib.axes): Axes to be used for plotting. Returns: matplotlib.axes: Axes used for plotting. """ X,Y,interpIC=self.computeInterpolatedIC(roi=roi) if ax==None: fig,axes = pyfrp_plot_module.makeSubplot([1,1],titles=["Interpolated IC"],sup="simulation") ax=axes[0] ax.imshow(interpIC) return ax
def showInterpolatedICImg(self, ax=None):
"""Shows interpolation of initial condition image.
See also :py:func:`computeInterpolatedICImg`.
.. image:: ../imgs/pyfrp_simulation/showInterpolatedICimg.png
Keyword Args:
ax (matplotlib.axes): Axes to be used for plotting.
Returns:
matplotlib.axes: Axes used for plotting.
"""
if ax == None:
fig, axes = pyfrp_plot_module.makeSubplot(
[1, 1], titles=["Interpolated Image"], sup="simulation")
ax = axes[0]
xInt, yInt, f = self.computeInterpolatedICImg()
#print np.shape(xInt)
#raw_input()
X, Y = np.meshgrid(xInt, yInt)
imgInt = np.zeros(np.shape(X))
for i in range(np.shape(X)[0]):
for j in range(np.shape(Y)[0]):
imgInt[i, j] = f(X[i, j], Y[i, j])
ax.imshow(imgInt)
return ax
def showInterpolatedIC(self, ax=None, roi=None):
"""Shows ICs interpolated back onto 2D image.
If ``roi`` is specified, will only interpolate nodes of this ROI.
See also :py:func:`computeInterpolatedIC`.
.. image:: ../imgs/pyfrp_simulation/showInterpolatedIC.png
Keyword Args:
roi (pyfrp.subclasses.pyfrp_ROI.ROI): A PyFRAP ROI.
ax (matplotlib.axes): Axes to be used for plotting.
Returns:
matplotlib.axes: Axes used for plotting.
"""
X, Y, interpIC = self.computeInterpolatedIC(roi=roi)
if ax == None:
fig, axes = pyfrp_plot_module.makeSubplot(
[1, 1], titles=["Interpolated IC"], sup="simulation")
ax = axes[0]
ax.imshow(interpIC)
return ax
def plotSolStack(self,phi,ROIs,withGeometry=True,vmin=None,vmax=None,ax=None,colorbar=False): """Plots a stack of the solution variable in a given list of ROIs. Will automatically compute the direction in which ROI lies in the 3D space and reduce the ROI into this plane for contour plot. If ``vmin=None`` or ``vmax=None``, will compute overall maximum and minimum values over all ROIs. Args: phi (fipy.CellVariable): Simulation solution variable (or numpy array). ROIs (list): List of :py:class:`pyfrp.subclasses.pyfrp_ROI.ROI` objects. Keyword Args: withGeometry (bool): Show geometry inside plot. vmin (float): Overall minimum value to be displayed in plot. vmax (float): Overall maximum value to be displayed in plot. ax (matplotlib.axes): Axes used for plotting. colorbar (bool): Display color bar. Returns: matplotlib.axes: Axes used for plotting. """ if ax==None: fig,axes = pyfrp_plot_module.makeSubplot([1,1],titles=["Simulation IC stack"],proj=['3d']) ax=axes[0] #Plot geometry if withGeometry: #self.embryo.geometry.updateGeoFile() ax=self.embryo.geometry.plotGeometry(ax=ax) #Find vmin/vmax over all ROIs vminNew=[] vmaxNew=[] for r in ROIs: vminNew.append(min(phi[r.meshIdx])) vmaxNew.append(max(phi[r.meshIdx])) if vmin==None: vmin=min(vminNew) if vmax==None: vmax=min(vmaxNew) for r in ROIs: plane=r.getMaxExtendPlane() zs=r.getPlaneMidCoordinate() zdir=r.getOrthogonal2Plane() ax=r.plotSolutionVariable(phi,ax=ax,vmin=vmin,vmax=vmax,plane=plane,zs=zs,zdir=zdir,colorbar=colorbar) return ax
def compareICInterpolation(self, axes=None, roi=None):
"""Shows initial image, its interpolation, the resulting initial
condition and its interpolation back onto an image.
See also :py:func:`showICimg`, :py:func:`showInterpolatedICImg`,
:py:func:`showIC`, :py:func:`showInterpolatedIC`.
Will create new axes if necessary.
.. warning:: Some images might be flipped due to plotting functions. Will be fixed in future version.
.. image:: ../imgs/pyfrp_simulation/ICcompare.png
Keyword Args:
roi (pyfrp.subclasses.pyfrp_ROI.ROI): A PyFRAP ROI.
axes (matplotlib.axes): List of axes of length 4.
Returns:
list: List of axes.
"""
if axes == None:
fig, axes = pyfrp_plot_module.makeSubplot([2, 2],
titles=[
"Original Image",
"Interpolated Image",
"IC",
"Reinterpolated IC"
],
sup="simulation")
self.showICimg(ax=axes[0])
self.showInterpolatedICImg(ax=axes[1])
self.showIC(ax=axes[2], roi=roi)
self.showInterpolatedIC(ax=axes[3], roi=roi)
for ax in axes:
pyfrp_plot_module.redraw(ax)
return axes
def showICimg(self,ax=None,typ='contour',colorbar=True,scale=True,nlevels=25,vmin=None,vmax=None):
"""Plots image used for initial condition either as contour or surface plot.
.. image:: ../imgs/pyfrp_simulation/showICimg.png
Keyword Args:
ax (matplotlib.axes): Axes used for plotting.
scale (bool): Equal axis.
vmin (float): Overall minimum value to be displayed in plot.
vmax (float): Overall maximum value to be displayed in plot.
nlevels (int): Number of contour levels to display.
Returns:
matplotlib.axes: Axes used for plotting.
"""
#Check of entered plot type makes sense
if typ not in ['contour','surface']:
printError("Unknown plot type "+ typ)
return ax
if self.ICimg!=None:
if vmin==None:
vmin=min(self.ICimg.flatten())
if vmax==None:
vmax=max(self.ICimg.flatten())
levels=np.linspace(vmin,1.01*vmax,nlevels)
if ax==None:
if typ=='surface':
fig,axes = pyfrp_plot_module.makeSubplot([1,1],proj=['3d'],sup="",tight=False)
else:
fig,axes = pyfrp_plot_module.makeSubplot([1,1],sup="",tight=False)
ax=axes[0]
res=self.ICimg.shape[0]
if 'quad' in self.embryo.analysis.process.keys():
X,Y=np.meshgrid(np.arange(res,2*res),np.arange(res,2*res))
else:
X,Y=np.meshgrid(np.arange(res),np.arange(res))
if typ=='contour':
plt_ICs=ax.contourf(X,Y,self.ICimg,levels=levels,vmin=vmin,vmax=vmax)
if scale:
plt.axis('equal')
elif typ=='surface':
plt_ICs=ax.plot_surface(X,Y,self.ICimg,cmap='jet',vmin=vmin,vmax=vmax)
if colorbar:
cb=plt.colorbar(plt_ICs,orientation='horizontal',pad=0.05,shrink=0.9)
plt.draw()
return ax
else:
printWarning("ICimg is not analyzed yet. Run data analysis first.")
return None
def showIC(self,ax=None,roi=None,nlevels=25,vmin=None,vmax=None,typ='contour',scale=True):
"""Plots initial conditions applied to mesh in 2D or 3D.
If ``roi`` is given, will only plot initial conditions for nodes inside ROI, else
will plot initial condition for all nodes in mesh.
.. note:: Simulation needs to be run first before this plotting function
can be used.
Example:
>>> simulation.plotIC(typ='contour')
will produce the following:
.. image:: ../imgs/pyfrp_simulation/showIC.png
See also :py:func:`pyfrp.modules.pyfrp_plot_module.plotSolutionVariable` and :py:func:`pyfrp.subclasses.pyfrp_ROI.plotSolutionVariable`.
Keyword Args:
roi (pyfrp.subclasses.pyfrp_ROI.ROI): A PyFRAP ROI object.
vmin (float): Overall minimum value to be displayed in plot.
vmax (float): Overall maximum value to be displayed in plot.
ax (matplotlib.axes): Axes used for plotting.
nlevels (int): Number of contour levels to display.
typ (str): Typ of plot.
scale (bool): Equal axis in case of contour plot.
Returns:
matplotlib.axes: Axes used for plotting.
"""
if self.IC!=None:
if ax==None:
if typ=='surface':
fig,axes = pyfrp_plot_module.makeSubplot([1,1],titles=["IC"],proj=['3d'],sup="",tight=False)
else:
fig,axes = pyfrp_plot_module.makeSubplot([1,1],titles=["IC"],sup="",tight=False)
ax=axes[0]
if roi==None:
x,y,z=self.mesh.getCellCenters()
if vmin==None:
vmin=min(self.IC)
if vmax==None:
vmax=max(self.IC)
levels=np.linspace(vmin,1.01*vmax,nlevels)
if typ=='contour':
ax.tricontourf(x,y,self.IC,vmin=vmin,vmax=vmax,levels=levels)
if scale:
ax.autoscale(enable=True, axis='both', tight=True)
elif typ=='surface':
ax.plot_trisurf(x,y,self.IC,cmap='jet',vmin=vmin,vmax=vmax)
else:
printError("Unknown plot type "+ typ)
ax.get_figure().canvas.draw()
else:
ax=roi.plotSolutionVariable(self.IC,ax=ax,nlevels=nlevels,vmin=vmin,vmax=vmax,typ=typ)
return ax
else:
printWarning("IC is not generated yet. Run simulation first.")
return None
def plotCellCenters(self,ax=None,proj=None,color='k',indicateHeight=False,s=5.,roi=None): """Plots location of cell centers of mesh. .. note:: If no ``ax`` are given will create new ones. If ``proj=[3d]``, will create 3D scatter plot, otherwise project cell centers in 2D. Example: Create figure >>> fig,axes = pyfrp_plot_module.makeSubplot([2,2],titles=['2D','2D indicate','3D','3D indicate'],proj=[None,None,'3d','3d']) Plot in 4 different ways >>> mesh.plotCellCenters(ax=axes[0],s=1.) >>> mesh.plotCellCenters(ax=axes[1],indicateHeight=True,s=5.) >>> mesh.plotCellCenters(ax=axes[2],s=3.) >>> mesh.plotCellCenters(ax=axes[3],indicateHeight=True,s=3.) .. image:: ../imgs/pyfrp_mesh/plotCellCenters.png Keyword Args: ax (matplotlib.axes): Axes to plot in. proj (list): List of projections. color (str): Color of mesh nodes. indicateHeight (bool): Indicate height by color. s (float): Size of marker. roi (pyfrp.subclasses.pyfrp_ROI): ROI. Returns: matplotlib.axes: Matplotlib axes. """ if ax==None: fig,axes = pyfrp_plot_module.makeSubplot([1,1],titles=["Cell Centers"],proj=proj) ax=axes[0] x,y,z = self.getCellCenters() if roi!=None: x=x[roi.meshIdx] y=y[roi.meshIdx] z=z[roi.meshIdx] if pyfrp_plot_module.is3DAxes(ax): if indicateHeight: #color=cm.jet() ax.scatter(x,y,z,c=z,s=s) else: ax.scatter(x,y,z,c=color,s=s) else: if indicateHeight: ax.scatter(x,y,c=z,s=s) else: ax.scatter(x,y,c=color,s=s) pyfrp_plot_module.redraw(ax) return ax
def showICimg(self,
ax=None,
typ='contour',
colorbar=True,
scale=True,
nlevels=25,
vmin=None,
vmax=None):
"""Plots image used for initial condition either as contour or surface plot.
.. image:: ../imgs/pyfrp_simulation/showICimg.png
Keyword Args:
ax (matplotlib.axes): Axes used for plotting.
scale (bool): Equal axis.
vmin (float): Overall minimum value to be displayed in plot.
vmax (float): Overall maximum value to be displayed in plot.
nlevels (int): Number of contour levels to display.
Returns:
matplotlib.axes: Axes used for plotting.
"""
#Check of entered plot type makes sense
if typ not in ['contour', 'surface']:
printError("Unknown plot type " + typ)
return ax
if self.ICimg != None:
if vmin == None:
vmin = min(self.ICimg.flatten())
if vmax == None:
vmax = max(self.ICimg.flatten())
levels = np.linspace(vmin, 1.01 * vmax, nlevels)
if ax == None:
if typ == 'surface':
fig, axes = pyfrp_plot_module.makeSubplot([1, 1],
proj=['3d'],
sup="",
tight=False)
else:
fig, axes = pyfrp_plot_module.makeSubplot([1, 1],
sup="",
tight=False)
ax = axes[0]
res = self.ICimg.shape[0]
if 'quad' in self.embryo.analysis.process.keys():
X, Y = np.meshgrid(np.arange(res, 2 * res),
np.arange(res, 2 * res))
else:
X, Y = np.meshgrid(np.arange(res), np.arange(res))
if typ == 'contour':
plt_ICs = ax.contourf(X,
Y,
self.ICimg,
levels=levels,
vmin=vmin,
vmax=vmax)
if scale:
plt.axis('equal')
elif typ == 'surface':
plt_ICs = ax.plot_surface(X,
Y,
self.ICimg,
cmap='jet',
vmin=vmin,
vmax=vmax)
if colorbar:
cb = plt.colorbar(plt_ICs,
orientation='horizontal',
pad=0.05,
shrink=0.9)
plt.draw()
return ax
else:
printWarning("ICimg is not analyzed yet. Run data analysis first.")
return None
def showIC(self,
ax=None,
roi=None,
nlevels=25,
vmin=None,
vmax=None,
typ='contour',
scale=True):
"""Plots initial conditions applied to mesh in 2D or 3D.
If ``roi`` is given, will only plot initial conditions for nodes inside ROI, else
will plot initial condition for all nodes in mesh.
.. note:: Simulation needs to be run first before this plotting function
can be used.
Example:
>>> simulation.plotIC(typ='contour')
will produce the following:
.. image:: ../imgs/pyfrp_simulation/showIC.png
See also :py:func:`pyfrp.modules.pyfrp_plot_module.plotSolutionVariable` and :py:func:`pyfrp.subclasses.pyfrp_ROI.plotSolutionVariable`.
Keyword Args:
roi (pyfrp.subclasses.pyfrp_ROI.ROI): A PyFRAP ROI object.
vmin (float): Overall minimum value to be displayed in plot.
vmax (float): Overall maximum value to be displayed in plot.
ax (matplotlib.axes): Axes used for plotting.
nlevels (int): Number of contour levels to display.
typ (str): Typ of plot.
scale (bool): Equal axis in case of contour plot.
Returns:
matplotlib.axes: Axes used for plotting.
"""
if self.IC != None:
if ax == None:
if typ == 'surface':
fig, axes = pyfrp_plot_module.makeSubplot([1, 1],
titles=["IC"],
proj=['3d'],
sup="",
tight=False)
else:
fig, axes = pyfrp_plot_module.makeSubplot([1, 1],
titles=["IC"],
sup="",
tight=False)
ax = axes[0]
if roi == None:
x, y, z = self.mesh.getCellCenters()
if vmin == None:
vmin = min(self.IC)
if vmax == None:
vmax = max(self.IC)
levels = np.linspace(vmin, 1.01 * vmax, nlevels)
if typ == 'contour':
ax.tricontourf(x,
y,
self.IC,
vmin=vmin,
vmax=vmax,
levels=levels)
if scale:
ax.autoscale(enable=True, axis='both', tight=True)
elif typ == 'surface':
ax.plot_trisurf(x,
y,
self.IC,
cmap='jet',
vmin=vmin,
vmax=vmax)
else:
printError("Unknown plot type " + typ)
ax.get_figure().canvas.draw()
else:
ax = roi.plotSolutionVariable(self.IC,
ax=ax,
nlevels=nlevels,
vmin=vmin,
vmax=vmax,
typ=typ)
return ax
else:
printWarning("IC is not generated yet. Run simulation first.")
return None
def plotSolStack(self,
phi,
ROIs,
withGeometry=True,
vmin=None,
vmax=None,
ax=None,
colorbar=False):
"""Plots a stack of the solution variable in a given list of ROIs.
Will automatically compute the direction in which ROI lies in the 3D space and
reduce the ROI into this plane for contour plot.
If ``vmin=None`` or ``vmax=None``, will compute overall maximum and minimum values
over all ROIs.
Args:
phi (fipy.CellVariable): Simulation solution variable (or numpy array).
ROIs (list): List of :py:class:`pyfrp.subclasses.pyfrp_ROI.ROI` objects.
Keyword Args:
withGeometry (bool): Show geometry inside plot.
vmin (float): Overall minimum value to be displayed in plot.
vmax (float): Overall maximum value to be displayed in plot.
ax (matplotlib.axes): Axes used for plotting.
colorbar (bool): Display color bar.
Returns:
matplotlib.axes: Axes used for plotting.
"""
if ax == None:
fig, axes = pyfrp_plot_module.makeSubplot(
[1, 1], titles=["Simulation IC stack"], proj=['3d'])
ax = axes[0]
#Plot geometry
if withGeometry:
#self.embryo.geometry.updateGeoFile()
ax = self.embryo.geometry.plotGeometry(ax=ax)
#Find vmin/vmax over all ROIs
vminNew = []
vmaxNew = []
for r in ROIs:
vminNew.append(min(phi[r.meshIdx]))
vmaxNew.append(max(phi[r.meshIdx]))
if vmin == None:
vmin = min(vminNew)
if vmax == None:
vmax = min(vmaxNew)
for r in ROIs:
plane = r.getMaxExtendPlane()
zs = r.getPlaneMidCoordinate()
zdir = r.getOrthogonal2Plane()
ax = r.plotSolutionVariable(phi,
ax=ax,
vmin=vmin,
vmax=vmax,
plane=plane,
zs=zs,
zdir=zdir,
colorbar=colorbar)
return ax
def plotCellCenters(self,
ax=None,
proj=None,
color='k',
indicateHeight=False,
s=5.,
roi=None):
"""Plots location of cell centers of mesh.
.. note:: If no ``ax`` are given will create new ones.
If ``proj=[3d]``, will create 3D scatter plot, otherwise project cell centers in
2D.
Example:
Create figure
>>> fig,axes = pyfrp_plot_module.makeSubplot([2,2],titles=['2D','2D indicate','3D','3D indicate'],proj=[None,None,'3d','3d'])
Plot in 4 different ways
>>> mesh.plotCellCenters(ax=axes[0],s=1.)
>>> mesh.plotCellCenters(ax=axes[1],indicateHeight=True,s=5.)
>>> mesh.plotCellCenters(ax=axes[2],s=3.)
>>> mesh.plotCellCenters(ax=axes[3],indicateHeight=True,s=3.)
.. image:: ../imgs/pyfrp_mesh/plotCellCenters.png
Keyword Args:
ax (matplotlib.axes): Axes to plot in.
proj (list): List of projections.
color (str): Color of mesh nodes.
indicateHeight (bool): Indicate height by color.
s (float): Size of marker.
roi (pyfrp.subclasses.pyfrp_ROI): ROI.
Returns:
matplotlib.axes: Matplotlib axes.
"""
if ax == None:
fig, axes = pyfrp_plot_module.makeSubplot([1, 1],
titles=["Cell Centers"],
proj=proj)
ax = axes[0]
x, y, z = self.getCellCenters()
if roi != None:
x = x[roi.meshIdx]
y = y[roi.meshIdx]
z = z[roi.meshIdx]
if pyfrp_plot_module.is3DAxes(ax):
if indicateHeight:
#color=cm.jet()
ax.scatter(x, y, z, c=z, s=s)
else:
ax.scatter(x, y, z, c=color, s=s)
else:
if indicateHeight:
ax.scatter(x, y, c=z, s=s)
else:
ax.scatter(x, y, c=color, s=s)
pyfrp_plot_module.redraw(ax)
return ax
