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'))
def autoscale(self):
'Try to choose the view limits intelligently'
self.verify_intervals()
vmin, vmax = self.dataInterval.get_bounds()
if vmax < vmin:
vmin, vmax = vmax, vmin
if vmax * vmin > 0:
raise ValueError(
"The interval must range from negative to positive values")
if vmax == self.threshold:
vmax += 1
if vmin == -self.threshold:
vmin -= 1
exponent, remainder = divmod(numerix.log10(vmax - vmin), 1)
if remainder < 0.5:
exponent -= 1
scale = 10**(-exponent)
vmin = numerix.floor(scale * vmin) / scale
vmax = numerix.ceil(scale * vmax) / scale
return nonsingular(vmin, vmax)
def autoscale(self):
'Try to choose the view limits intelligently'
self.verify_intervals()
vmin, vmax = self.dataInterval.get_bounds()
if vmax<vmin:
vmin, vmax = vmax, vmin
if vmax * vmin > 0:
raise ValueError("The interval must range from negative to positive values")
if vmax == self.threshold:
vmax += 1
if vmin == -self.threshold:
vmin -= 1
exponent, remainder = divmod(numerix.log10(vmax - vmin), 1)
if remainder < 0.5:
exponent -= 1
scale = 10**(-exponent)
vmin = numerix.floor(scale*vmin) / scale
vmax = numerix.ceil(scale*vmax) / scale
return nonsingular(vmin, vmax)
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'))
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'))
def signed_to_logs(v):
return (numerix.where(v > 0, numerix.log10(v), numerix.nan),
numerix.where(v < 0, numerix.log10(-v), numerix.nan))
def log10(self):
return self._UnaryOperatorVariable(lambda a: numerix.log10(a))
def signed_to_logs(v):
return (numerix.where(v > 0, numerix.log10(v), numerix.nan),
numerix.where(v < 0, numerix.log10(-v), numerix.nan))
def plot(self, matrix, RHSvector, log='auto'):
import tempfile
import os
(f, mtxName) = tempfile.mkstemp(suffix='.mtx')
matrix.exportMmf(mtxName)
mtx = mmio.mmread(mtxName)
## f.close()
os.remove(mtxName)
pylab.ion()
c = mtx.tocoo()
y = c.row
x = c.col
z = c.data
b = RHSvector
if len(z) == 0:
y = zeros((1,))
x = zeros((1,))
z = zeros((1,))
zPlus = where(z > 0, log10(z), nan)
zMinus = where(z < 0, log10(-z), nan)
bPlus = where(b > 0, log10(b), nan)
bMinus = where(b < 0, log10(-b), nan)
if (log == True
or (log == 'auto'
and (max(zPlus) - min(zPlus) > 2
or max(zMinus) - min(zMinus) > 2
or max(bPlus) - min(bPlus) > 2
or max(bMinus) - min(bMinus) > 2))):
log = True
else:
log = False
if log:
zMin = nanmin((nanmin(zPlus), nanmin(zMinus), nanmin(bPlus), nanmin(bMinus)))
zMax = nanmax((nanmax(zPlus), nanmax(zMinus), nanmax(bPlus), nanmax(bMinus)))
## zThreshold = 0.5 # (zMax - zMin) / 5.
zMin -= 0.5
numdec = math.floor(zMax)-math.ceil(zMin)
if numdec < 0:
zMax += 0.5
zPlus -= zMin
zMinus -= zMin
bPlus -= zMin
bMinus -= zMin
zRange = zMax - zMin
if zRange == 0:
zRange = nanmax(zPlus) + 1
z = where(z > 0, zPlus, -zMinus)
z = where(isnan(z), 0., z)
b = where(b > 0, bPlus, -bMinus)
b = where(isnan(b), 0., b)
fmt = SignedLogFormatter(threshold=zMin)
loc = SignedLogLocator(threshold=zMin)
else:
zRange = max(max(abs(z)), max(abs(b)))
if zRange == 0:
zRange = 1
fmt = None
loc = None
N = matrix._getShape()[0]
saveSize = pylab.rcParams['figure.figsize']
size = pylab.rcParams['figure.dpi'] **2 * saveSize[0] * saveSize[1] / N**2
pylab.ioff()
pylab.figure(self.id)
pylab.clf()
pylab.delaxes()
ax1 = pylab.axes([self.margin, self.margin, self.width, self.width])
Mscat = pylab.scatter(x, y, c=z,
vmin=-zRange, vmax=zRange, edgecolors='none',
cmap=pylab.get_cmap('RdBu'), marker='s', s=size)
ax2 = pylab.axes([self.width + self.margin, self.margin, (self.width / self.aspect) / N, self.width],
sharey=ax1)
bscat = pylab.scatter(zeros((N,)), arange(N), c=b,
vmin=-zRange, vmax=zRange, edgecolors='none',
cmap=pylab.get_cmap('RdBu'), marker='s', s=size)
pylab.setp((ax2.get_xticklabels(),
ax2.get_yticklabels(),
ax2.get_xticklines(),
ax2.get_yticklines()), visible=False)
pylab.axes(ax1)
pylab.axis([-0.5, N - 0.5, N - 0.5, -0.5])
pylab.colorbar(format=fmt, ticks=loc)
pylab.title(self.title)
pylab.draw()
