# -*- coding: utf-8 -*-
"""
gyroid.util
===============
:copyright: (c) 2012 by Yi-Xin Liu
:license: BSD, see LICENSE for more details.
"""
import numpy as np
import scipy.io
import matplotlib.pyplot as plt
from matplotlib import colors
from mayavi import mlab
__all__ = ["render_structure_1d","render_structure_2d","render_structure_3d"]
[docs]def render_structure_1d(basis,Na,c,
save_data=True,data_name="struct1d.mat",
save_img=True,show_img=True,
img_name="struct1d.png",
**kwargs):
"""
basis - An instance of Basis class
Na - Number of grids to discretes the side a of the unit cell.
c - coefficients for each basis function.
"""
struct = basis.generate_structure(Na,c)
a = 1.0 * basis.shape.h[0,0]
rx = np.array([a*i/Na for i in np.arange(Na)])
if save_data:
scipy.io.savemat(data_name,{"rx":rx,"struct":struct})
if save_img or show_img:
plt.plot(rx,struct,**kwargs)
if save_img:
plt.savefig(img_name)
if show_img:
plt.show()
return rx,struct
[docs]def render_structure_2d(basis,Na,Nb,c,
save_data=True,data_name="struct2d.mat",
save_img=True,show_img=True,
img_name="struct2d.png",
levels=None,cmap=None,
**kwargs):
"""
basis - An instance of Basis class
Na,Nb - Number of grids to discrete the side a of the unit cell.
c - coefficients for each basis function.
"""
struct = basis.generate_structure((Na,Nb),c)
rx = np.zeros((Na,Nb))
ry = np.zeros((Na,Nb))
for (i,j) in np.ndindex(Na,Nb):
x = (1.0*np.array([i,j])) / (Na,Nb)
rx[i,j],ry[i,j] = np.dot(x,basis.shape.h)
if save_data:
scipy.io.savemat(data_name,{"rx":rx,"ry":ry,"struct":struct})
if save_img or show_img:
dx = rx.max() - rx.min()
dy = ry.max() - ry.min()
w,h = plt.figaspect(float(dy/dx)) # float is must
# No frame, white background, w/h aspect ratio figure
fig = plt.figure(figsize=(w,h),frameon=False,
dpi=80,facecolor='w')
# full figure subplot, no border, no axes
ax = fig.add_axes([0,0,1,1],frameon=False,axisbg='w')
# no ticks
ax.get_xaxis().set_visible(False)
ax.get_yaxis().set_visible(False)
# Default: there are 256 contour levels
if levels is None:
step = (struct.max() - struct.min()) / 256
levels = np.arange(struct.min(),struct.max()+step,step)
# Default: colormap is monochromatic red
if cmap is None:
clr = np.zeros((256,3))
for i in np.arange(256):
clr[i,0] = i / 255.0
cmap = colors.ListedColormap(clr)
# actual plot
ax.contourf(rx,ry,struct,levels=levels,
cmap=cmap,antialiased=False,**kwargs)
if save_img:
plt.savefig(img_name)
if show_img:
plt.show()
return rx,ry,struct
[docs]def render_structure_3d(basis,Na,Nb,Nc,c,
save_data=True,data_name="struct3d.mat",
save_img=True,show_img=True,
img_name="struct3d.png",
levels=None,cmap=None,
**kwargs):
"""
basis - An instance of Basis class
Na,Nb,Nc - Number of grids to discrete the side a of the unit cell.
c - coefficients for each basis function.
NOTE: the best way to view 3D volume data is: first save the data to mat, and let Matlab (C) render the volume data.
"""
struct = basis.generate_structure((Na,Nb,Nc),c)
rx = np.zeros((Na,Nb,Nc))
ry = np.zeros((Na,Nb,Nc))
rz = np.zeros((Na,Nb,Nc))
for (i,j,k) in np.ndindex(Na,Nb,Nc):
x = (1.0*np.array([i,j,k])) / (Na,Nb,Nc)
rx[i,j,k],ry[i,j,k],rz[i,j,k] = np.dot(x,basis.shape.h)
if save_data:
scipy.io.savemat(data_name,
{"rx":rx,"ry":ry,"rz":rz,"struct":struct})
if save_img or show_img:
mlab.contour3d(rx,ry,rz,struct,**kwargs)
if save_img:
mlab.savefig(img_name)
if show_img:
plt.show()
return rx,ry,rz,struct