def sample_vectors(vec, mask=None, narrows=800, x0=None, x1=None):
"""Samples a vector field for plotting purposes.
:param vec: Vector to sample.
:type vec: 2D vector of 2D :py:class:`~.RegData`
:param mask: Optional mask for the data.
:type mask: :py:class:`~.RegData` or numpy array
:param narrows: Desired number of arrows.
:type narrows: int
:param x0: Sample region lower left coordinate. Default: use data
coordinate range.
:type x0: length-2 float list/array or None
:param x1: Sample region upper right coordinate. Default: use data
coordinate range.
:type x1: length-2 float list/array or None
The results of this function are
cd : length-2 tuple of lists or arrays
x- and y-coordinates of the samples.
d : length-2 tuple of lists or arrays
x- and y-components of sampled vectors.
vmax : float
Maximum length of all sampled vectors.
l : float
Average distance between sampled vectors.
:returns: A tuple (cd,d,vmax,l)
"""
if (x0 is None):
x0=vec[0].x0()
#
if (x1 is None):
x1=vec[0].x1()
#
s = x1-x0
a = s[0]*s[1]
l = math.sqrt(a/narrows)
n = (s/l).astype(np.int32)
sg = grid_data.RegGeom(n, x0, x1=x1)
sv = [v.sample(sg, order=1) for v in vec]
d = [v.data for v in sv]
cd = sv[0].coords2d()
if (mask is not None):
m = np.logical_not(mask.sample(sg, order=0).data)
cd = [cd[0][m],cd[1][m]]
d = [d[0][m],d[1][m]]
#
a = np.sqrt(d[0]**2 + d[1]**2)
vmax = a.max() #max(np.abs(d[0]).max(),np.abs(d[1]).max())
return cd, d, vmax, l
def get_geometry_xz_xy(sd, opt):
def full_domain():
xmax = float(sd.initial_params.coordbase.xmax)
ymax = float(sd.initial_params.coordbase.ymax)
zmax = float(sd.initial_params.coordbase.zmax)
return (np.array([xmax, ymax]), np.array([xmax,zmax]))
#
def manual_domain():
return (np.array([opt.xmax, opt.ymax]),
np.array([opt.xmax, opt.zmax]))
#
dd = {'full':full_domain,
'manual':manual_domain}
exy,exz = dd[opt.domain]()
sampres = int(opt.xres*0.8)
sampresxy = [sampres, int(sampres*exy[1]/exy[0])]
sampresxz = [sampres, int(sampres*0.5*exz[1]/exz[0])]
grxy = grid_data.RegGeom(sampresxy, -exy, x1=exy)
grxz = grid_data.RegGeom(sampresxz, [-exz[0],0], x1=exz)
geom = [grxz, grxy]
return geom
def get_geometry_xz(sd, opt):
def full_domain():
xmax = float(sd.initial_params.coordbase.xmax)
zmax = float(sd.initial_params.coordbase.zmax)
return xmax, zmax
#
def manual_domain():
return (opt.xmax, opt.zmax)
#
sampres = int(opt.xres*0.8)
sampres = [sampres, sampres]
dd = {'full':full_domain,
'manual':manual_domain}
x1 = np.array(dd[opt.domain]())
geom = grid_data.RegGeom(sampres, -x1, x1=x1)
return geom
#!/usr/bin/env python """This demonstrates the volume rendering of Cactus data using VTK.""" import numpy as np from postcactus import grid_data as gd from postcactus import viz_vtk as viz from math import * geom = gd.RegGeom([200,210,180], [-100.0]*3, x1=[100.]*3) x,y,z = geom.coords1d() x3,y3,z3 = x[:,None, None], y[None, :, None], z[None, None, :] d = np.sqrt(x3**2 + y3**2) r = np.sqrt(d**2 + z3**2) phi = np.arctan2(x3, y3) th = np.arctan2(z3,d) k = 2*pi/50. l = 70. data = (l/(l+r)) * np.cos(phi-k*r) * np.cos(th)**2 data2 = (l/(l+r)) * np.cos(phi-k*r) * np.sin(th)**2 data = gd.RegData(geom.x0(), geom.dx(), data) data2 = gd.RegData(geom.x0(), geom.dx(), data2) renderer = viz.make_renderer(bgcolor='k') vmax = data.max() vmin = data.min() vmin,vmax,numlbl = viz.align_range_decimal(vmin,vmax) otf = viz.OpacityMap(vmin=vmin, vmax=vmax) otf.add_steps([-0.5, 0.5], [0.1]*2, [.1]*2, color=['r','g'])
from postcactus import visualize as viz
from postcactus import grid_data as gd
# Sim dir:
sd = SimDir("<simulation directory here>")
# Func to plot:
func = 'rho'
minf = -9
maxf = 0
# Options:
dolog = 1
AMRboundaries = 1
cm = viz.get_color_map('viridis')
# Grid where to plot:
g = gd.RegGeom([400, 400], [-10, -10], x1=[10, 10])
# Get iterations and times, and loop
iters = sd.grid.xy.get_iters(func)
times = sd.grid.xy.get_times(func)
i = 0
for it in iters:
print("Plotting it = ", it)
plt.clf()
# Get data
func_plot = sd.grid.xy.read(func, it, geom=g, order=1)
if dolog != 0:
func_plot = func_plot.log10()
if AMRboundaries != 0:
rlvl = sd.grid.xy.read(func, it, geom=g, order=1, level_fill=True)
lvl = -0.5 + np.arange(0, 6)