def __init__(self,
vars,
title=None,
limits={},
cmap=None,
colorbar='vertical',
axes=None,
figaspect='auto',
**kwlimits):
"""
Creates a `Matplotlib2DGridViewer`.
:Parameters:
vars
A `CellVariable` object.
title
displayed at the top of the `Viewer` window
limits : dict
a (deprecated) alternative to limit keyword arguments
cmap
The colormap. Defaults to `matplotlib.cm.jet`
xmin, xmax, ymin, ymax, datamin, datamax
displayed range of data. Any limit set to
a (default) value of `None` will autoscale.
colorbar
plot a colorbar in specified orientation if not `None`
axes
if not `None`, `vars` will be plotted into this Matplotlib `Axes` object
figaspect
desired aspect ratio of figure. If arg is a number, use that aspect
ratio. If arg is 'auto', the aspect ratio will be determined from
the Variable's mesh.
"""
kwlimits.update(limits)
AbstractMatplotlib2DViewer.__init__(self,
vars=vars,
title=title,
cmap=cmap,
colorbar=colorbar,
axes=axes,
figaspect=figaspect,
**kwlimits)
xmin, ymin = self.vars[0].mesh.extents['min']
xmax, ymax = self.vars[0].mesh.extents['max']
self.image = self.axes.imshow(self._data,
extent=(xmin, xmax, ymin, ymax),
vmin=0,
vmax=1,
cmap=self.cmap)
self.axes.set_xlim(xmin=self._getLimit('xmin'),
xmax=self._getLimit('xmax'))
self.axes.set_ylim(ymin=self._getLimit('ymin'),
ymax=self._getLimit('ymax'))
if title is None:
self.axes.set_title(self.vars[0].name)
def __init__(self, vars, title=None, scale=None, sparsity=None, log=False, limits={}, axes=None, figaspect='auto', **kwlimits):
"""Creates a `Matplotlib2DViewer`.
:Parameters:
vars
a rank-1 `CellVariable` or `FaceVariable` object.
title
displayed at the top of the `Viewer` window
scale
if not `None`, scale all arrow lengths by this value
sparsity
if not `None`, then this number of arrows will be
randomly chosen (weighted by the cell volume or face area)
log
if `True`, arrow length goes at the base-10 logarithm of the magnitude
limits : dict
a (deprecated) alternative to limit keyword arguments
xmin, xmax, ymin, ymax, datamin, datamax
displayed range of data. Any limit set to
a (default) value of `None` will autoscale.
axes
if not `None`, `vars` will be plotted into this Matplotlib `Axes` object
figaspect
desired aspect ratio of figure. If arg is a number, use that aspect
ratio. If arg is 'auto', the aspect ratio will be determined from
the Variable's mesh.
"""
kwlimits.update(limits)
AbstractMatplotlib2DViewer.__init__(self, vars=vars, title=title, axes=axes, figaspect=figaspect, **kwlimits)
self.quiver(sparsity=sparsity, scale=scale)
self.log = log
self._plot()
def __init__(self, vars, title=None, limits={}, cmap=None, colorbar='vertical', axes=None, figaspect='auto', **kwlimits):
"""Creates a `Matplotlib2DViewer`.
:Parameters:
vars
a `CellVariable` object.
title
displayed at the top of the `Viewer` window
limits : dict
a (deprecated) alternative to limit keyword arguments
xmin, xmax, ymin, ymax, datamin, datamax
displayed range of data. Any limit set to
a (default) value of `None` will autoscale.
cmap
the colormap. Defaults to `matplotlib.cm.jet`
colorbar
plot a colorbar in specified orientation if not `None`
axes
if not `None`, `vars` will be plotted into this Matplotlib `Axes` object
figaspect
desired aspect ratio of figure. If arg is a number, use that aspect
ratio. If arg is 'auto', the aspect ratio will be determined from
the Variable's mesh.
"""
kwlimits.update(limits)
AbstractMatplotlib2DViewer.__init__(self, vars=vars, title=title,
cmap=cmap, colorbar=colorbar, axes=axes, figaspect=figaspect,
**kwlimits)
self._plot()
def __init__(self, vars, title=None, limits={}, cmap=None, colorbar='vertical', axes=None, figaspect='auto', **kwlimits):
"""Creates a `Matplotlib2DViewer`.
:Parameters:
vars
a `CellVariable` object.
title
displayed at the top of the `Viewer` window
limits : dict
a (deprecated) alternative to limit keyword arguments
xmin, xmax, ymin, ymax, datamin, datamax
displayed range of data. Any limit set to
a (default) value of `None` will autoscale.
cmap
the colormap. Defaults to `matplotlib.cm.jet`
colorbar
plot a colorbar in specified orientation if not `None`
axes
if not `None`, `vars` will be plotted into this Matplotlib `Axes` object
figaspect
desired aspect ratio of figure. If arg is a number, use that aspect
ratio. If arg is 'auto', the aspect ratio will be determined from
the Variable's mesh.
"""
kwlimits.update(limits)
AbstractMatplotlib2DViewer.__init__(self, vars=vars, title=title,
cmap=cmap, colorbar=colorbar, axes=axes, figaspect=figaspect,
**kwlimits)
self._plot()
def __init__(self,
vars,
title=None,
limits={},
cmap=None,
colorbar='vertical',
axes=None,
number=10,
levels=None,
figaspect='auto',
**kwlimits):
"""Creates a `Matplotlib2DContourViewer`.
:Parameters:
vars
a `CellVariable` object.
title
displayed at the top of the `Viewer` window
limits : dict
a (deprecated) alternative to limit keyword arguments
xmin, xmax, ymin, ymax, datamin, datamax
displayed range of data. Any limit set to
a (default) value of `None` will autoscale.
cmap
the colormap. Defaults to `matplotlib.cm.jet`
colorbar
plot a colorbar in specified orientation if not `None`
axes
if not `None`, `vars` will be plotted into this Matplotlib `Axes` object
number
contour `number` automatically-chosen levels
*levels* [level0, level1, ..., leveln]
A list of floating point numbers indicating the level
curves to draw; eg to draw just the zero contour pass
``levels=[0]``
figaspect
desired aspect ratio of figure. If arg is a number, use that aspect
ratio. If arg is 'auto', the aspect ratio will be determined from
the Variable's mesh.
"""
kwlimits.update(limits)
AbstractMatplotlib2DViewer.__init__(self,
vars=vars,
title=title,
cmap=cmap,
colorbar=colorbar,
axes=axes,
figaspect=figaspect,
**kwlimits)
self.number = number
self.levels = levels
self._plot()
def __init__(self,
vars,
title=None,
limits={},
cmap=None,
colorbar='vertical',
axes=None,
figaspect='auto',
**kwlimits):
"""Creates a `Matplotlib2DGridViewer`.
Parameters
----------
vars : ~fipy.variables.cellVariable.CellVariable
the `Variable` to display.
title : str, optional
displayed at the top of the `Viewer` window
limits : dict, optional
a (deprecated) alternative to limit keyword arguments
cmap : ~matplotlib.colors.Colormap, optional
the :class:`~matplotlib.colors.Colormap`.
Defaults to `matplotlib.cm.jet`
float xmin, xmax, ymin, ymax, datamin, datamax : float, optional
displayed range of data. Any limit set to
a (default) value of `None` will autoscale.
colorbar : bool, optional
plot a color bar in specified orientation if not `None`
axes : ~matplotlib.axes.Axes, optional
if not `None`, `vars` will be plotted into this Matplotlib `Axes` object
figaspect : float, optional
desired aspect ratio of figure. If arg is a number, use that aspect
ratio. If arg is `auto`, the aspect ratio will be determined from
the Variable's mesh.
"""
kwlimits.update(limits)
AbstractMatplotlib2DViewer.__init__(self,
vars=vars,
title=title,
cmap=cmap,
colorbar=colorbar,
axes=axes,
figaspect=figaspect,
**kwlimits)
self.axes.set_xlim(xmin=self._getLimit('xmin'),
xmax=self._getLimit('xmax'))
self.axes.set_ylim(ymin=self._getLimit('ymin'),
ymax=self._getLimit('ymax'))
if title is None:
self.axes.set_title(self.vars[0].name)
def __init__(self,
vars,
title=None,
scale=None,
sparsity=None,
log=False,
limits={},
axes=None,
figaspect='auto',
**kwlimits):
"""Creates a `Matplotlib2DViewer`.
:Parameters:
vars
a rank-1 `CellVariable` or `FaceVariable` object.
title
displayed at the top of the `Viewer` window
scale
if not `None`, scale all arrow lengths by this value
sparsity
if not `None`, then this number of arrows will be
randomly chosen (weighted by the cell volume or face area)
log
if `True`, arrow length goes at the base-10 logarithm of the magnitude
limits : dict
a (deprecated) alternative to limit keyword arguments
xmin, xmax, ymin, ymax, datamin, datamax
displayed range of data. Any limit set to
a (default) value of `None` will autoscale.
axes
if not `None`, `vars` will be plotted into this Matplotlib `Axes` object
figaspect
desired aspect ratio of figure. If arg is a number, use that aspect
ratio. If arg is 'auto', the aspect ratio will be determined from
the Variable's mesh.
"""
kwlimits.update(limits)
AbstractMatplotlib2DViewer.__init__(self,
vars=vars,
title=title,
axes=axes,
figaspect=figaspect,
**kwlimits)
self.quiver(sparsity=sparsity, scale=scale)
self.log = log
self._plot()
def __init__(self, vars, title=None, limits={}, cmap=None, colorbar='vertical', axes=None, figaspect='auto', **kwlimits):
"""
Creates a `Matplotlib2DGridViewer`.
:Parameters:
vars
A `CellVariable` object.
title
displayed at the top of the `Viewer` window
limits : dict
a (deprecated) alternative to limit keyword arguments
cmap
The colormap. Defaults to `matplotlib.cm.jet`
xmin, xmax, ymin, ymax, datamin, datamax
displayed range of data. Any limit set to
a (default) value of `None` will autoscale.
colorbar
plot a colorbar in specified orientation if not `None`
axes
if not `None`, `vars` will be plotted into this Matplotlib `Axes` object
figaspect
desired aspect ratio of figure. If arg is a number, use that aspect
ratio. If arg is 'auto', the aspect ratio will be determined from
the Variable's mesh.
"""
kwlimits.update(limits)
AbstractMatplotlib2DViewer.__init__(self, vars=vars, title=title,
cmap=cmap, colorbar=colorbar, axes=axes, figaspect=figaspect,
**kwlimits)
xmin, ymin = self.vars[0].mesh.extents['min']
xmax, ymax = self.vars[0].mesh.extents['max']
self.image = self.axes.imshow(self._data,
extent=(xmin, xmax, ymin, ymax),
vmin=0, vmax=1,
cmap=self.cmap)
self.axes.set_xlim(xmin=self._getLimit('xmin'),
xmax=self._getLimit('xmax'))
self.axes.set_ylim(ymin=self._getLimit('ymin'),
ymax=self._getLimit('ymax'))
if title is None:
self.axes.set_title(self.vars[0].name)
def __init__(self, vars, title=None, limits={}, cmap=None, colorbar='vertical', axes=None, number=10, levels=None, figaspect='auto', **kwlimits):
"""Creates a `Matplotlib2DContourViewer`.
Parameters
----------
vars : ~fipy.variables.cellVariable.CellVariable
`Variable` to display
title : str, optional
displayed at the top of the `Viewer` window
limits : dict, optional
a (deprecated) alternative to limit keyword arguments
float xmin, xmax, ymin, ymax, datamin, datamax : float, optional
displayed range of data. Any limit set to
a (default) value of `None` will autoscale.
cmap : ~matplotlib.colors.Colormap, optional
the Colormap.
Defaults to `matplotlib.cm.jet`
colorbar : bool, optional
plot a color bar in specified orientation if not `None`
axes : ~matplotlib.axes.Axes, optional
if not `None`, `vars` will be plotted into this Matplotlib `Axes` object
number : int, optional
contour `number` automatically-chosen levels
levels : :obj:`list` of :obj:`float`, optional
A list of numbers indicating the level
curves to draw; e.g. to draw just the zero contour pass
``levels=[0]``
figaspect : float
desired aspect ratio of figure. If arg is a number, use that aspect
ratio. If arg is `auto`, the aspect ratio will be determined from
the Variable's mesh.
"""
kwlimits.update(limits)
AbstractMatplotlib2DViewer.__init__(self, vars=vars, title=title,
cmap=cmap, colorbar=colorbar, axes=axes,
figaspect=figaspect,
**kwlimits)
self.number = number
self.levels = levels
self._plot()
def _getSuitableVars(self, vars):
from fipy.meshes.mesh2D import Mesh2D
from fipy.variables.cellVariable import CellVariable
vars = [var for var in AbstractMatplotlib2DViewer._getSuitableVars(self, vars) \
if ((isinstance(var.mesh, Mesh2D) and isinstance(var, CellVariable))
and var.rank == 0)]
if len(vars) == 0:
from fipy.viewers import MeshDimensionError
raise MeshDimensionError("Matplotlib2DViewer can only display a rank-0, 2D CellVariable")
# this viewer can only display one variable
return [vars[0]]
def _getSuitableVars(self, vars):
from fipy.meshes.mesh2D import Mesh2D
from fipy.variables.cellVariable import CellVariable
vars = [var for var in AbstractMatplotlib2DViewer._getSuitableVars(self, vars) \
if ((isinstance(var.mesh, Mesh2D) and isinstance(var, CellVariable))
and var.rank == 0)]
if len(vars) == 0:
from fipy.viewers import MeshDimensionError
raise MeshDimensionError, "Matplotlib2DViewer can only display a rank-0, 2D CellVariable"
# this viewer can only display one variable
return [vars[0]]
def _getSuitableVars(self, vars):
from fipy.meshes.uniformGrid2D import UniformGrid2D
from fipy.variables.cellVariable import CellVariable
vars = [var for var in AbstractMatplotlib2DViewer._getSuitableVars(self, vars) \
if (isinstance(var.mesh, UniformGrid2D) and isinstance(var, CellVariable)
and var.rank == 0)]
if len(vars) == 0:
from fipy.viewers import MeshDimensionError
raise MeshDimensionError, "Matplotlib2DGridViewer can only display a rank-0 CellVariable with a UniformGrid2D mesh"
# this viewer can only display one variable
return [vars[0]]
def _getSuitableVars(self, vars):
from fipy.meshes.uniformGrid2D import UniformGrid2D
from fipy.variables.cellVariable import CellVariable
vars = [var for var in AbstractMatplotlib2DViewer._getSuitableVars(self, vars) \
if (isinstance(var.mesh, UniformGrid2D) and isinstance(var, CellVariable)
and var.rank == 0)]
if len(vars) == 0:
from fipy.viewers import MeshDimensionError
raise MeshDimensionError("Matplotlib2DGridViewer can only display a rank-0 CellVariable with a UniformGrid2D mesh")
# this viewer can only display one variable
return [vars[0]]
def __init__(self, vars, title=None, limits={}, cmap=None, colorbar='vertical', axes=None, number=10, levels=None, figaspect='auto', **kwlimits):
"""Creates a `Matplotlib2DContourViewer`.
:Parameters:
vars
a `CellVariable` object.
title
displayed at the top of the `Viewer` window
limits : dict
a (deprecated) alternative to limit keyword arguments
xmin, xmax, ymin, ymax, datamin, datamax
displayed range of data. Any limit set to
a (default) value of `None` will autoscale.
cmap
the colormap. Defaults to `matplotlib.cm.jet`
colorbar
plot a colorbar in specified orientation if not `None`
axes
if not `None`, `vars` will be plotted into this Matplotlib `Axes` object
number
contour `number` automatically-chosen levels
*levels* [level0, level1, ..., leveln]
A list of floating point numbers indicating the level
curves to draw; e.g. to draw just the zero contour pass
``levels=[0]``
figaspect
desired aspect ratio of figure. If arg is a number, use that aspect
ratio. If arg is 'auto', the aspect ratio will be determined from
the Variable's mesh.
"""
kwlimits.update(limits)
AbstractMatplotlib2DViewer.__init__(self, vars=vars, title=title,
cmap=cmap, colorbar=colorbar, axes=axes,
figaspect=figaspect,
**kwlimits)
self.number = number
self.levels = levels
self._plot()
def _getLimit(self, key, default=None):
limit = AbstractMatplotlib2DViewer._getLimit(self, key, default=default)
if limit is None:
X, Y = self.vars[0].mesh.faceCenters
if 'xmin' in key:
limit = float(min(X))
elif 'ymin' in key:
limit = float(min(Y))
if 'xmax' in key:
limit = float(max(X))
elif 'ymax' in key:
limit = float(max(Y))
return limit
def _getLimit(self, key, default=None):
limit = AbstractMatplotlib2DViewer._getLimit(self, key, default=default)
if limit is None:
X, Y = self.vars[0].mesh.faceCenters
if 'xmin' in key:
limit = float(min(X))
elif 'ymin' in key:
limit = float(min(Y))
if 'xmax' in key:
limit = float(max(X))
elif 'ymax' in key:
limit = float(max(Y))
return limit
def _getSuitableVars(self, vars):
from fipy.meshes.grid2D import Grid2D
from fipy.meshes.uniformGrid2D import UniformGrid2D
from fipy.variables.cellVariable import CellVariable
vars = [var for var in AbstractMatplotlib2DViewer._getSuitableVars(self, vars) \
if ((isinstance(var.mesh, Grid2D)
or isinstance(var.mesh, UniformGrid2D))
and isinstance(var, CellVariable))]
if len(vars) == 0:
from fipy.viewers import MeshDimensionError
raise MeshDimensionError, "The mesh must be a Grid2D instance"
# this viewer can only display one variable
return [vars[0]]
def _getSuitableVars(self, vars):
from fipy.meshes.grid2D import Grid2D
from fipy.meshes.uniformGrid2D import UniformGrid2D
from fipy.variables.cellVariable import CellVariable
vars = [var for var in AbstractMatplotlib2DViewer._getSuitableVars(self, vars) \
if ((isinstance(var.mesh, Grid2D)
or isinstance(var.mesh, UniformGrid2D))
and isinstance(var, CellVariable))]
if len(vars) == 0:
from fipy.viewers import MeshDimensionError
raise MeshDimensionError, "The mesh must be a Grid2D instance"
# this viewer can only display one variable
return [vars[0]]
def _getSuitableVars(self, vars):
from fipy.meshes.mesh2D import Mesh2D
from fipy.meshes.uniformGrid2D import UniformGrid2D
vars = [var for var in AbstractMatplotlib2DViewer._getSuitableVars(self, vars) \
if ((isinstance(var.mesh, Mesh2D)
or isinstance(var.mesh, UniformGrid2D))\
and (isinstance(var, FaceVariable) \
or isinstance(var, CellVariable)) and var.rank == 1)]
if len(vars) == 0:
from fipy.viewers import MeshDimensionError
raise MeshDimensionError("The mesh must be a Mesh2D instance")
# this viewer can only display one variable
return [vars[0]]
def __init__(self,
vars,
title=None,
log=False,
limits={},
axes=None,
figaspect='auto',
density=1,
linewidth=None,
color=None,
cmap=None,
norm=None,
arrowsize=1,
arrowstyle='-|>',
minlength=0.1,
**kwlimits):
"""Creates a `MatplotlibStreamViewer`.
:Parameters:
vars
a rank-1 `CellVariable` or `FaceVariable` object.
title
displayed at the top of the `Viewer` window
log
if `True`, arrow length goes at the base-10 logarithm of the magnitude
limits : dict
a (deprecated) alternative to limit keyword arguments
xmin, xmax, ymin, ymax, datamin, datamax
displayed range of data. Any limit set to
a (default) value of `None` will autoscale.
axes
if not `None`, `vars` will be plotted into this Matplotlib `Axes` object
figaspect
desired aspect ratio of figure. If arg is a number, use that aspect
ratio. If arg is 'auto', the aspect ratio will be determined from
the Variable's mesh.
*density* : float or 2-tuple
Controls the closeness of streamlines. When `density = 1`, the domain
is divided into a 25x25 grid---*density* linearly scales this grid.
Each cell in the grid can have, at most, one traversing streamline.
For different densities in each direction, use [density_x, density_y].
linewidth : Numeric or rank-0 `MeshVariable`
vary linewidth when given a `CellVariable` or `FaceVariable` of same
type as vars.
*color* : matplotlib color code, or rank-0 `MeshVariable`
Streamline color. When given an array with the type as vars,
*color* values are converted to colors using *cmap*.
*cmap* : :class:`~matplotlib.colors.Colormap`
Colormap used to plot streamlines and arrows. Only necessary when using
an `MeshVariable` input for *color*.
*norm* : :class:`~matplotlib.colors.Normalize`
Normalize object used to scale luminance data to 0, 1. If None, stretch
(min, max) to (0, 1). Only necessary when *color* is an `MeshVariable`.
*arrowsize* : float
Factor scale arrow size.
*arrowstyle* : str
Arrow style specification.
See :class:`~matplotlib.patches.FancyArrowPatch`.
*minlength* : float
Minimum length of streamline in axes coordinates.
"""
kwlimits.update(limits)
AbstractMatplotlib2DViewer.__init__(self,
vars=vars,
title=title,
axes=axes,
figaspect=figaspect,
**kwlimits)
self.log = log
self.kwargs = dict(density=density,
cmap=cmap,
norm=norm,
arrowsize=arrowsize,
arrowstyle=arrowstyle,
minlength=minlength)
self.linewidth = linewidth
self.color = color
self._stream = None
self._plot()
class MatplotlibStreamViewer(AbstractMatplotlib2DViewer):
"""Displays a stream plot of a 2D rank-1 `CellVariable` or
`FaceVariable` object using Matplotlib_
One issue is that this `Viewer` relies on `scipy.interpolate.griddata`,
which interpolates on the convex hull of the data. The results is that
streams are plotted across any concavities in the mesh.
Another issue is that it does not seem possible to remove the streams
without calling `cla()`, which means that different set of streams cannot be
overlaid.
.. _Matplotlib: http://matplotlib.sourceforge.net/
"""
__doc__ += AbstractMatplotlib2DViewer._test2Dvector(
viewer="MatplotlibStreamViewer")
__doc__ += AbstractMatplotlib2DViewer._test2DvectorIrregular(
viewer="MatplotlibStreamViewer")
def __init__(self,
vars,
title=None,
log=False,
limits={},
axes=None,
figaspect='auto',
density=1,
linewidth=None,
color=None,
cmap=None,
norm=None,
arrowsize=1,
arrowstyle='-|>',
minlength=0.1,
**kwlimits):
"""Creates a `MatplotlibStreamViewer`.
:Parameters:
vars
a rank-1 `CellVariable` or `FaceVariable` object.
title
displayed at the top of the `Viewer` window
log
if `True`, arrow length goes at the base-10 logarithm of the magnitude
limits : dict
a (deprecated) alternative to limit keyword arguments
xmin, xmax, ymin, ymax, datamin, datamax
displayed range of data. Any limit set to
a (default) value of `None` will autoscale.
axes
if not `None`, `vars` will be plotted into this Matplotlib `Axes` object
figaspect
desired aspect ratio of figure. If arg is a number, use that aspect
ratio. If arg is 'auto', the aspect ratio will be determined from
the Variable's mesh.
*density* : float or 2-tuple
Controls the closeness of streamlines. When `density = 1`, the domain
is divided into a 25x25 grid---*density* linearly scales this grid.
Each cell in the grid can have, at most, one traversing streamline.
For different densities in each direction, use [density_x, density_y].
linewidth : Numeric or rank-0 `MeshVariable`
vary linewidth when given a `CellVariable` or `FaceVariable` of same
type as vars.
*color* : matplotlib color code, or rank-0 `MeshVariable`
Streamline color. When given an array with the type as vars,
*color* values are converted to colors using *cmap*.
*cmap* : :class:`~matplotlib.colors.Colormap`
Colormap used to plot streamlines and arrows. Only necessary when using
an `MeshVariable` input for *color*.
*norm* : :class:`~matplotlib.colors.Normalize`
Normalize object used to scale luminance data to 0, 1. If None, stretch
(min, max) to (0, 1). Only necessary when *color* is an `MeshVariable`.
*arrowsize* : float
Factor scale arrow size.
*arrowstyle* : str
Arrow style specification.
See :class:`~matplotlib.patches.FancyArrowPatch`.
*minlength* : float
Minimum length of streamline in axes coordinates.
"""
kwlimits.update(limits)
AbstractMatplotlib2DViewer.__init__(self,
vars=vars,
title=title,
axes=axes,
figaspect=figaspect,
**kwlimits)
self.log = log
self.kwargs = dict(density=density,
cmap=cmap,
norm=norm,
arrowsize=arrowsize,
arrowstyle=arrowstyle,
minlength=minlength)
self.linewidth = linewidth
self.color = color
self._stream = None
self._plot()
def _getSuitableVars(self, vars):
from fipy.meshes.mesh2D import Mesh2D
from fipy.meshes.uniformGrid2D import UniformGrid2D
vars = [var for var in AbstractMatplotlib2DViewer._getSuitableVars(self, vars) \
if ((isinstance(var.mesh, Mesh2D)
or isinstance(var.mesh, UniformGrid2D))\
and (isinstance(var, FaceVariable) \
or isinstance(var, CellVariable)) and var.rank == 1)]
if len(vars) == 0:
from fipy.viewers import MeshDimensionError
raise MeshDimensionError("The mesh must be a Mesh2D instance")
# this viewer can only display one variable
return [vars[0]]
def _plot(self):
from scipy.interpolate import griddata
var = self.vars[0]
mesh = var.mesh
xmin, ymin = mesh.extents['min']
xmax, ymax = mesh.extents['max']
N = 100
X = numerix.linspace(xmin, xmax, N)
Y = numerix.linspace(ymin, ymax, N)
grid_x, grid_y = numerix.mgrid[xmin:xmax:N * 1j, ymin:ymax:N * 1j]
if isinstance(var, FaceVariable):
C = mesh.faceCenters
elif isinstance(var, CellVariable):
C = mesh.cellCenters
U = griddata(C.value.T, var.value[0], (grid_x, grid_y), method='cubic')
V = griddata(C.value.T, var.value[1], (grid_x, grid_y), method='cubic')
lw = self.linewidth
if isinstance(lw, (FaceVariable, CellVariable)):
lw = griddata(C.value.T,
lw.value, (grid_x, grid_y),
method='cubic')
color = self.color
if isinstance(color, (FaceVariable, CellVariable)):
color = griddata(C.value.T,
color.value, (grid_x, grid_y),
method='cubic',
fill_value=color.min())
U = U.T
V = V.T
ang = numerix.arctan2(V, U)
mag = numerix.sqrt(U**2 + V**2)
datamin, datamax = self._autoscale(vars=(mag, ),
datamin=self._getLimit('datamin'),
datamax=self._getLimit('datamax'))
mag = numerix.where(mag > datamax, numerix.nan, mag)
mag = numerix.where(mag < datamin, numerix.nan, mag)
if self.log:
mag = numerix.log10(mag)
U = mag * numerix.cos(ang)
V = mag * numerix.sin(ang)
# if self._stream is not None:
# # the following doesn't work, nor does it help to `add_collection` first
# # self._stream.arrows.remove()
# self._stream.lines.remove()
self.axes.cla()
self._stream = self.axes.streamplot(X,
Y,
U,
V,
linewidth=lw,
color=color,
**self.kwargs)
self.axes.set_xlim(xmin=self._getLimit('xmin'),
xmax=self._getLimit('xmax'))
self.axes.set_ylim(ymin=self._getLimit('ymin'),
ymax=self._getLimit('ymax'))
class Matplotlib2DGridViewer(AbstractMatplotlib2DViewer):
"""
Displays an image plot of a 2D `CellVariable` object using Matplotlib_.
.. _Matplotlib: http://matplotlib.sourceforge.net/
"""
__doc__ += AbstractMatplotlib2DViewer._test2D(
viewer="Matplotlib2DGridViewer")
def __init__(self,
vars,
title=None,
limits={},
cmap=None,
colorbar='vertical',
axes=None,
figaspect='auto',
**kwlimits):
"""
Creates a `Matplotlib2DGridViewer`.
:Parameters:
vars
A `CellVariable` object.
title
displayed at the top of the `Viewer` window
limits : dict
a (deprecated) alternative to limit keyword arguments
cmap
The colormap. Defaults to `matplotlib.cm.jet`
xmin, xmax, ymin, ymax, datamin, datamax
displayed range of data. Any limit set to
a (default) value of `None` will autoscale.
colorbar
plot a colorbar in specified orientation if not `None`
axes
if not `None`, `vars` will be plotted into this Matplotlib `Axes` object
figaspect
desired aspect ratio of figure. If arg is a number, use that aspect
ratio. If arg is 'auto', the aspect ratio will be determined from
the Variable's mesh.
"""
kwlimits.update(limits)
AbstractMatplotlib2DViewer.__init__(self,
vars=vars,
title=title,
cmap=cmap,
colorbar=colorbar,
axes=axes,
figaspect=figaspect,
**kwlimits)
self.image = self.axes.imshow(self._data,
extent=(self._getLimit('xmin'),
self._getLimit('xmax'),
self._getLimit('ymin'),
self._getLimit('ymax')),
vmin=0,
vmax=1,
cmap=self.cmap)
if title is None:
self.axes.set_title(self.vars[0].name)
def _getLimit(self, key, default=None):
limit = AbstractMatplotlib2DViewer._getLimit(self,
key,
default=default)
if limit is None:
X, Y = self.vars[0].mesh.faceCenters
if 'xmin' in key:
limit = float(min(X))
elif 'ymin' in key:
limit = float(min(Y))
if 'xmax' in key:
limit = float(max(X))
elif 'ymax' in key:
limit = float(max(Y))
return limit
def _getSuitableVars(self, vars):
from fipy.meshes.uniformGrid2D import UniformGrid2D
from fipy.variables.cellVariable import CellVariable
vars = [var for var in AbstractMatplotlib2DViewer._getSuitableVars(self, vars) \
if (isinstance(var.mesh, UniformGrid2D) and isinstance(var, CellVariable)
and var.rank == 0)]
if len(vars) == 0:
from fipy.viewers import MeshDimensionError
raise MeshDimensionError, "Matplotlib2DGridViewer can only display a rank-0 CellVariable with a UniformGrid2D mesh"
# this viewer can only display one variable
return [vars[0]]
@getsetDeprecated
def _getData(self):
return self._data
@property
def _data(self):
from fipy.tools.numerix import array, reshape
return reshape(array(self.vars[0]),
self.vars[0].mesh.shape[::-1])[::-1]
def _plot(self):
self.norm.vmin = self._getLimit(('datamin', 'zmin'))
self.norm.vmax = self._getLimit(('datamax', 'zmax'))
self.image.set_data(self.norm(self._data))
if self.colorbar is not None:
self.colorbar.plot()
class Matplotlib2DContourViewer(AbstractMatplotlib2DViewer):
"""Displays a contour plot of a 2D `CellVariable` object.
The `Matplotlib2DContourViewer` plots a 2D `CellVariable` using Matplotlib_.
.. _Matplotlib: http://matplotlib.sourceforge.net/
"""
__doc__ += AbstractMatplotlib2DViewer._test2D(viewer="Matplotlib2DContourViewer")
def __init__(self, vars, title=None, limits={}, cmap=None, colorbar='vertical', axes=None, number=10, levels=None, figaspect='auto', **kwlimits):
"""Creates a `Matplotlib2DContourViewer`.
Parameters
----------
vars : ~fipy.variables.cellVariable.CellVariable
`Variable` to display
title : str, optional
displayed at the top of the `Viewer` window
limits : dict, optional
a (deprecated) alternative to limit keyword arguments
float xmin, xmax, ymin, ymax, datamin, datamax : float, optional
displayed range of data. Any limit set to
a (default) value of `None` will autoscale.
cmap : ~matplotlib.colors.Colormap, optional
the Colormap.
Defaults to `matplotlib.cm.jet`
colorbar : bool, optional
plot a color bar in specified orientation if not `None`
axes : ~matplotlib.axes.Axes, optional
if not `None`, `vars` will be plotted into this Matplotlib `Axes` object
number : int, optional
contour `number` automatically-chosen levels
levels : :obj:`list` of :obj:`float`, optional
A list of numbers indicating the level
curves to draw; e.g. to draw just the zero contour pass
``levels=[0]``
figaspect : float
desired aspect ratio of figure. If arg is a number, use that aspect
ratio. If arg is `auto`, the aspect ratio will be determined from
the Variable's mesh.
"""
kwlimits.update(limits)
AbstractMatplotlib2DViewer.__init__(self, vars=vars, title=title,
cmap=cmap, colorbar=colorbar, axes=axes,
figaspect=figaspect,
**kwlimits)
self.number = number
self.levels = levels
self._plot()
def _getSuitableVars(self, vars):
from fipy.meshes.mesh2D import Mesh2D
from fipy.variables.cellVariable import CellVariable
vars = [var for var in AbstractMatplotlib2DViewer._getSuitableVars(self, vars) \
if ((isinstance(var.mesh, Mesh2D) and isinstance(var, CellVariable))
and var.rank == 0)]
if len(vars) == 0:
from fipy.viewers import MeshDimensionError
raise MeshDimensionError("Matplotlib2DViewer can only display a rank-0, 2D CellVariable")
# this viewer can only display one variable
return [vars[0]]
def _plot(self):
## plt.clf()
## ## Added garbage collection since matplotlib objects seem to hang
## ## around and accumulate.
## import gc
## gc.collect()
mesh = self.vars[0].mesh
x, y = mesh.cellCenters
z = self.vars[0].value
xmin, ymin = mesh.extents['min']
xmax, ymax = mesh.extents['max']
from matplotlib.mlab import griddata
xi = numerix.linspace(xmin, xmax, 1000)
yi = numerix.linspace(ymin, ymax, 1000)
# grid the data.
zi = griddata(x, y, z, xi, yi, interp='linear')
if hasattr(self, "_contourSet"):
for collection in self._contourSet.collections:
try:
ix = self.axes.collections.index(collection)
except ValueError as e:
ix = None
if ix is not None:
del self.axes.collections[ix]
zmin, zmax = self._autoscale(vars=self.vars,
datamin=self._getLimit(('datamin', 'zmin')),
datamax=self._getLimit(('datamax', 'zmax')))
self.norm.vmin = zmin
self.norm.vmax = zmax
if self.levels is not None:
levels = self.levels
else:
levels = numerix.arange(self.number + 1) * (zmax - zmin) / self.number + zmin
self._contourSet = self.axes.contour(xi, yi, zi, levels=levels, cmap=self.cmap)
self.axes.set_xlim(xmin=self._getLimit('xmin'),
xmax=self._getLimit('xmax'))
self.axes.set_ylim(ymin=self._getLimit('ymin'),
ymax=self._getLimit('ymax'))
if self.colorbar is not None:
self.colorbar.plot()
def __init__(self,
vars,
title=None,
log=False,
limits={},
axes=None,
figaspect='auto',
density=1,
linewidth=None,
color=None,
cmap=None,
norm=None,
arrowsize=1,
arrowstyle='-|>',
minlength=0.1,
**kwlimits):
"""Creates a `MatplotlibStreamViewer`.
Parameters
----------
vars : ~fipy.variables.cellVariable.CellVariable or ~fipy.variables.faceVariable.FaceVariable
rank-1 `Variable` to display
title : str, optional
displayed at the top of the `Viewer` window
log : bool, optional
if `True`, arrow length goes at the base-10 logarithm of the magnitude
limits : dict, optional
a (deprecated) alternative to limit keyword arguments
float xmin, xmax, ymin, ymax, datamin, datamax : float
displayed range of data. Any limit set to
a (default) value of `None` will autoscale.
axes : ~matplotlib.axes.Axes, optional
if not `None`, `vars` will be plotted into this Matplotlib `Axes` object
figaspect : float, optional
desired aspect ratio of figure. If arg is a number, use that aspect
ratio. If arg is `auto`, the aspect ratio will be determined from
the Variable's mesh.
density : float or (float, float), optional
Controls the closeness of streamlines. When ``density = 1``,
the domain is divided into a 30x30 grid. *density* linearly
scales this grid. Each cell in the grid can have, at most, one
traversing streamline. For different densities in each
direction, use a tuple (density_x, density_y).
linewidth : array_like or ~fipy.variables.cellVariable.CellVariable or ~fipy.variables.faceVariable.FaceVariable, optional
The width of the stream lines. With a rank-0 `CellVariable` or
`FaceVariable` the line width can be varied across the grid.
The MeshVariable must have the same type and be defined on
the same `Mesh` as *vars*.
color : str or ~fipy.variables.cellVariable.CellVariable or ~fipy.variables.faceVariable.FaceVariable, optional
The streamline color as a matplotlib color code or a field of
numbers. If given a rank-0 `CellVariable` or `FaceVariable`,
its values are converted to colors using *cmap* and *norm*.
The MeshVariable must have the same type and be defined on the
same `Mesh` as *vars*.
cmap : ~matplotlib.colors.Colormap, optional
Colormap used to plot streamlines and arrows. This is only
used if *color* is a MeshVariable.
norm : ~matplotlib.colors.Normalize, optional
Normalize object used to scale luminance data to 0, 1. If
``None``, stretch (min, max) to (0, 1). Only necessary when
*color* is a MeshVariable.
arrowsize : float, optional
Scaling factor for the arrow size.
arrowstyle : str, optional
Arrow style specification.
See `~matplotlib.patches.FancyArrowPatch`.
minlength : float, optional
Minimum length of streamline in axes coordinates.
"""
kwlimits.update(limits)
AbstractMatplotlib2DViewer.__init__(self,
vars=vars,
title=title,
axes=axes,
figaspect=figaspect,
**kwlimits)
self.log = log
self.kwargs = dict(density=density,
cmap=cmap,
norm=norm,
arrowsize=arrowsize,
arrowstyle=arrowstyle,
minlength=minlength)
self.linewidth = linewidth
self.color = color
self._stream = None
self._plot()
class MatplotlibVectorViewer(AbstractMatplotlib2DViewer):
"""Displays a vector plot of a 2D rank-1 `CellVariable` or
`FaceVariable` object using Matplotlib_
.. _Matplotlib: http://matplotlib.sourceforge.net/
"""
__doc__ += AbstractMatplotlib2DViewer._test2Dvector(
viewer="MatplotlibVectorViewer")
__doc__ += """
>>> for sparsity in numerix.arange(5000, 0, -500):
... viewer.quiver(sparsity=sparsity)
... viewer.plot()
>>> viewer._promptForOpinion()
"""
__doc__ += AbstractMatplotlib2DViewer._test2DvectorIrregular(
viewer="MatplotlibVectorViewer")
def __init__(self,
vars,
title=None,
scale=None,
sparsity=None,
log=False,
limits={},
axes=None,
figaspect='auto',
**kwlimits):
"""Creates a `Matplotlib2DViewer`.
:Parameters:
vars
a rank-1 `CellVariable` or `FaceVariable` object.
title
displayed at the top of the `Viewer` window
scale
if not `None`, scale all arrow lengths by this value
sparsity
if not `None`, then this number of arrows will be
randomly chosen (weighted by the cell volume or face area)
log
if `True`, arrow length goes at the base-10 logarithm of the magnitude
limits : dict
a (deprecated) alternative to limit keyword arguments
xmin, xmax, ymin, ymax, datamin, datamax
displayed range of data. Any limit set to
a (default) value of `None` will autoscale.
axes
if not `None`, `vars` will be plotted into this Matplotlib `Axes` object
figaspect
desired aspect ratio of figure. If arg is a number, use that aspect
ratio. If arg is 'auto', the aspect ratio will be determined from
the Variable's mesh.
"""
kwlimits.update(limits)
AbstractMatplotlib2DViewer.__init__(self,
vars=vars,
title=title,
axes=axes,
figaspect=figaspect,
**kwlimits)
self.quiver(sparsity=sparsity, scale=scale)
self.log = log
self._plot()
def quiver(self, sparsity=None, scale=None):
var = self.vars[0]
mesh = var.mesh
if isinstance(var, FaceVariable):
N = mesh.numberOfFaces
X, Y = mesh.faceCenters
elif isinstance(var, CellVariable):
N = mesh.numberOfCells
X, Y = mesh.cellCenters
if sparsity is not None and N > sparsity:
XYrand = numerix.random.random((2, sparsity))
XYrand = numerix.array([
[min(X)], [min(Y)]
]) + XYrand * numerix.array([[max(X) - min(X)], [max(Y) - min(Y)]])
self.indices = numerix.nearest(numerix.array([X, Y]), XYrand)
else:
self.indices = numerix.arange(N)
X = numerix.take(X, self.indices)
Y = numerix.take(Y, self.indices)
U = V = numerix.ones(X.shape, 'l')
if hasattr(self, "_quiver"):
self._quiver.remove()
self._quiver = self.axes.quiver(X,
Y,
U,
V,
scale=scale,
pivot='middle')
def _getSuitableVars(self, vars):
from fipy.meshes.mesh2D import Mesh2D
from fipy.meshes.uniformGrid2D import UniformGrid2D
vars = [var for var in AbstractMatplotlib2DViewer._getSuitableVars(self, vars) \
if ((isinstance(var.mesh, Mesh2D)
or isinstance(var.mesh, UniformGrid2D))\
and (isinstance(var, FaceVariable) \
or isinstance(var, CellVariable)) and var.rank == 1)]
if len(vars) == 0:
from fipy.viewers import MeshDimensionError
raise MeshDimensionError, "The mesh must be a Mesh2D instance"
# this viewer can only display one variable
return [vars[0]]
def _plot(self):
var = self.vars[0]
mesh = var.mesh
U, V = var.numericValue
U = numerix.take(U, self.indices)
V = numerix.take(V, self.indices)
ang = numerix.arctan2(V, U)
mag = numerix.sqrt(U**2 + V**2)
datamin, datamax = self._autoscale(vars=(mag, ),
datamin=self._getLimit('datamin'),
datamax=self._getLimit('datamax'))
mag = numerix.where(mag > datamax, datamax, mag)
mag = numerix.ma.masked_array(mag, mag < datamin)
if self.log:
mag = numerix.log10(mag)
mag = numerix.ma.masked_array(mag, numerix.isnan(mag))
U = mag * numerix.cos(ang)
V = mag * numerix.sin(ang)
self._quiver.set_UVC(U, V)
self.axes.set_xlim(xmin=self._getLimit('xmin'),
xmax=self._getLimit('xmax'))
self.axes.set_ylim(ymin=self._getLimit('ymin'),
ymax=self._getLimit('ymax'))
class Matplotlib2DGridContourViewer(AbstractMatplotlib2DViewer):
"""Displays a contour plot of a 2D `CellVariable` object.
The `Matplotlib2DGridContourViewer` plots a 2D `CellVariable` using Matplotlib_.
.. _Matplotlib: http://matplotlib.sourceforge.net/
"""
__doc__ += AbstractMatplotlib2DViewer._test2D(
viewer="Matplotlib2DGridContourViewer")
def __init__(self,
vars,
title=None,
limits={},
cmap=None,
colorbar='vertical',
axes=None,
figaspect='auto',
**kwlimits):
"""Creates a `Matplotlib2DViewer`.
:Parameters:
vars
a `CellVariable` object.
title
displayed at the top of the `Viewer` window
limits : dict
a (deprecated) alternative to limit keyword arguments
xmin, xmax, ymin, ymax, datamin, datamax
displayed range of data. Any limit set to
a (default) value of `None` will autoscale.
cmap
the colormap. Defaults to `matplotlib.cm.jet`
colorbar
plot a colorbar in specified orientation if not `None`
axes
if not `None`, `vars` will be plotted into this Matplotlib `Axes` object
figaspect
desired aspect ratio of figure. If arg is a number, use that aspect
ratio. If arg is 'auto', the aspect ratio will be determined from
the Variable's mesh.
"""
kwlimits.update(limits)
AbstractMatplotlib2DViewer.__init__(self,
vars=vars,
title=title,
cmap=cmap,
colorbar=colorbar,
axes=axes,
figaspect=figaspect,
**kwlimits)
self._plot()
def _getSuitableVars(self, vars):
from fipy.meshes.grid2D import Grid2D
from fipy.meshes.uniformGrid2D import UniformGrid2D
from fipy.variables.cellVariable import CellVariable
vars = [var for var in AbstractMatplotlib2DViewer._getSuitableVars(self, vars) \
if ((isinstance(var.mesh, Grid2D)
or isinstance(var.mesh, UniformGrid2D))
and isinstance(var, CellVariable))]
if len(vars) == 0:
from fipy.viewers import MeshDimensionError
raise MeshDimensionError, "The mesh must be a Grid2D instance"
# this viewer can only display one variable
return [vars[0]]
def _plot(self):
## pylab.clf()
## ## Added garbage collection since matplotlib objects seem to hang
## ## around and accumulate.
## import gc
## gc.collect()
mesh = self.vars[0].mesh
shape = mesh.shape
X, Y = mesh.cellCenters
Z = self.vars[0].value
X, Y, Z = [v.reshape(shape, order="FORTRAN") for v in (X, Y, Z)]
zmin, zmax = self._autoscale(vars=self.vars,
datamin=self._getLimit(
('datamin', 'zmin')),
datamax=self._getLimit(
('datamax', 'zmax')))
self.norm.vmin = zmin
self.norm.vmax = zmax
numberOfContours = 10
smallNumber = 1e-7
diff = zmax - zmin
if diff < smallNumber:
V = numerix.arange(numberOfContours +
1) * smallNumber / numberOfContours + zmin
else:
V = numerix.arange(numberOfContours +
1) * diff / numberOfContours + zmin
self.axes.contourf(X, Y, Z, V, cmap=self.cmap)
self.axes.set_xlim(xmin=self._getLimit('xmin'),
xmax=self._getLimit('xmax'))
self.axes.set_ylim(ymin=self._getLimit('ymin'),
ymax=self._getLimit('ymax'))
if self.colorbar is not None:
self.colorbar.plot()
