def _init_figure(self, x, y, z, triangles, figsize, figlims):
"""
Initialize the figure by plotting the surface without any overlay.
x: x coordinates of vertices (V x 1 numpy array)
y: y coordinates of vertices (V x 1 numpy array)
z: z coordinates of vertices (V x 1 numpy array)
triangles: triangle specifications (T x 3 numpy array, where
T=#triangles)
figsize: 2x1 list of integers
figlims: 3x2 list of integers
"""
self.fig = p3.figure(width=figsize[0], height=figsize[1])
self.fig.camera_fov = 1
self.fig.style = {
"axes": {
"color": "black",
"label": {"color": "black"},
"ticklabel": {"color": "black"},
"visible": False,
},
"background-color": "white",
"box": {"visible": False},
}
self.fig.xlim = (figlims[0][0], figlims[0][1])
self.fig.ylim = (figlims[1][0], figlims[1][1])
self.fig.zlim = (figlims[2][0], figlims[2][1])
# draw the tetrahedron
p3.plot_trisurf(
x, y, z, triangles=triangles, color=np.ones((len(x), 3))
)
def plot_group_brain_activation(self, radius=12.5, freq_range=(40, 200), clim=None, cmap='RdBu_r'):
"""
Plots brain surface based on the mean activity across the group. Uses ipyvolume to plot
Parameters
----------
radius: float
Maximum distance between an electrode and a vertex to be counted as data for that vertex
freq_range: list
2 element list defining minimum and maximum frequncies to average.
clim: float
Maximum/minumum value of the colormap. Will be centered at zero. If not given, will use the maximum absolute
value of the data.
cmap: str
matplotlib colormap to use
Returns
-------
left hemisphere and right hemisphere activation maps
"""
# load brain mesh
l_coords, l_faces, r_coords, r_faces = self.load_brain_mesh()
# compute mean activation
l_vert_mean, r_vert_mean = self.compute_surface_map(radius, freq_range)
# define colormap range
if clim is None:
clim = np.max([np.nanmax(np.abs(l_vert_mean)), np.nanmax(np.abs(r_vert_mean))])
c_norm = plt.Normalize(vmin=-clim, vmax=clim)
c_mappable = cmx.ScalarMappable(norm=c_norm, cmap=plt.get_cmap(cmap))
# compute surface colors for left
valid_l_inds = ~np.isnan(l_vert_mean)
l_colors = np.full((l_vert_mean.shape[0], 4), 0.)
l_colors[valid_l_inds] = c_mappable.to_rgba(l_vert_mean[valid_l_inds])
# and right
valid_r_inds = ~np.isnan(r_vert_mean)
r_colors = np.full((r_vert_mean.shape[0], 4), 0.)
r_colors[valid_r_inds] = c_mappable.to_rgba(r_vert_mean[valid_r_inds])
# plot it!
fig = p3.figure(width=800, height=800, lighting=True)
brain_l = p3.plot_trisurf(l_coords[:, 0], l_coords[:, 1], l_coords[:, 2], triangles=l_faces, color=l_colors)
brain_r = p3.plot_trisurf(r_coords[:, 0], r_coords[:, 1], r_coords[:, 2], triangles=r_faces, color=r_colors)
# turn off axis and make square
ipv.squarelim()
ipv.style.box_off()
ipv.style.axes_off()
p3.show()
return fig
def plot(self,
plot_mesh=True,
plot_voxels=True,
width=800,
height=600,
voxel_count_offset=0,
voxel_limit=None,
use_centroids_instead=False,
scaling=1,
mesh_color='red',
**kwargs):
"""
This method needs better documentation.
:param **kwargs: Is used in ipyvolume.pylab.scatter() or ipyvolume.pylab.plot() depending on whether use_centroids_instead
is set to true or not.
"""
if plot_mesh:
if self.triangles is None:
raise ValueError(
"There is no triangle data stored for this mesh!")
else:
fig = p3.figure(width=width, height=height)
p3.plot_trisurf(*self.vertices.T * scaling,
self.triangles,
color=mesh_color)
if plot_voxels:
self.voxels.plot(
width=width,
height=height,
voxel_count_offset=voxel_count_offset,
voxel_limit=voxel_limit,
use_centroids_instead=use_centroids_instead,
ipyvol_fig=fig,
scaling=scaling,
**kwargs)
else:
p3.squarelim()
p3.show()
elif plot_voxels:
try:
fig = p3.figure(width=width, height=height)
self.voxels.plot(width=width,
height=height,
voxel_count_offset=voxel_count_offset,
voxel_limit=voxel_limit,
use_centroids_instead=use_centroids_instead,
ipyvol_fig=fig,
scaling=scaling,
**kwargs)
except AttributeError:
raise AttributeError(
"This object does not have a Voxels object, you must initialize the voxel mesh with a Voxels object or run voxelize() to generate new voxels."
)
def _create_mesh(points,
color="black",
solid=False,
lines=True,
triangle_indices=None,
line_indices=None):
""" create an ipyvolume mesh from a number of points
Parameters
----------
points: list
[[x, y, z], ...]
color: str
solid: bool
triangle_indices: list or None
[[i, j, k], ...], if None then computed from scipy.spatial.qhull.ConvexHull triangles
line_indices: list or None
[[i, j], ...], if None then computed from scipy.spatial.qhull.ConvexHull triangles
Returns
-------
"""
x, y, z = np.array(points).T
try:
hull = ConvexHull(points)
except:
hull = ConvexHull(points, qhull_options='QJ')
if triangle_indices is None:
triangle_indices = hull.simplices.tolist()
if line_indices is None:
line_indices = []
for i, j, k in hull.simplices.tolist():
line_indices += [[i, j], [i, k], [j, k]]
mesh = p3.plot_trisurf(x,
y,
z,
triangles=triangle_indices,
lines=line_indices,
color=color)
if not solid:
mesh.visible_faces = False
if not lines:
mesh.visible_lines = False
return mesh
def showSurface(surface,overlay=None,frame=0,newfigure=True,colormap='summer',figsize=np.array([300,400]),
figlims=np.array([[-75,75],[-75,75],[-75,75]])):
'''
Displays a surface mesh in gifti or FreeSurfer (FS) surface format with/without an overlay inside
Jupyter notebook for interactive visualization.
Parameters
----------
surface: str, gifti opject
Path to surface file in gifti or FS surface format or an already loaded gifti object of surface
overlay: str, gifti opject
Path to overlay file in gifti or FS annot or anaotimcal (.curv,.sulc,.thickness) format or an already loaded
gifti object of overlay, default None
frame: int
indice of the frame (timepoint or functional data frame) to show
newfigure: bool
Create a new figure else prints into the last figure (in order to visualize both hemispheres in
one plot), default True
colormap: string
A matplotlib colormap, default summer
figsize: ndarray
Size of the figure to display, default [600,600]
figLims: ndarray
x,y and z limits of the axes, default [[-100,100],[-100,100],[-100,100]])
'''
if isinstance(surface,str):
if not os.path.exists(surface):
error('File does not exist, please provide a valid file path to a gifti or FreeSurfer file.')
filename, file_extension = os.path.splitext(surface)
if file_extension is '.gii':
surface = nb.load(surface)
else:
fsgeometry = nb.freesurfer.read_geometry(surface)
x,y,z = fsgeometry[0].T
vertex_edges=fsgeometry[1]
if isinstance(surface,nb.gifti.gifti.GiftiImage):
try:
vertex_spatial=surface.darrays[0]
vertex_edges=surface.darrays[1]
x, y, z = vertex_spatial.data.T
except:
raise ValueError('Please provide a valid gifti file.')
if not isinstance(frame,int):
ValueError('Please provide a valid integer frame index.')
if isinstance(overlay,list):
if frame>len(overlay) or frame < 0:
error('Frame index out of bounds, please provide a valid frame index.')
overlay = overlay[frame]
if isinstance(overlay,str):
if not os.path.exists(overlay):
error('File does not exist, please provide a valid file path to a gifti or FreeSurfer file.')
filename, file_extension = os.path.splitext(overlay)
if file_extension is '.gii':
overlay = nb.load(overlay)
elif (file_extension in ('.annot','')):
annot = nb.freesurfer.read_annot(overlay)
activation = annot[0]
elif (file_extension in ('.curv','.thickness','.sulc')):
activation = nb.freesurfer.read_morph_data(overlay)
if isinstance(overlay,nb.gifti.gifti.GiftiImage):
try:
activation=overlay.darrays[0].data
except:
raise ValueError('Please provide a valid gifti file')
if newfigure:
fig = p3.figure(width=figsize[0], height=figsize[1])
fig.camera_fov = 1
fig.style = {'axes': {'color': 'black',
'label': {'color': 'black'},
'ticklabel': {'color': 'black'},
'visible': False},
'background-color': 'white',
'box': {'visible': False}}
fig.xlim = (figlims[0][0], figlims[0][1])
fig.ylim = (figlims[1][0], figlims[1][1])
fig.zlim = (figlims[2][0], figlims[2][1])
# plot surface
if overlay is None:
p3.plot_trisurf(x, y, z, triangles=vertex_edges.data)
else:
my_color = plt.cm.get_cmap(colormap)
colors=my_color((activation-min(activation))/(max(activation)-min(activation)))
p3.plot_trisurf(x, y, z, triangles=vertex_edges.data, color=colors[:,:3])
if newfigure:
p3.show()
def plot_group_brain_activation(self, radius=12.5, freq_range=(40, 200), clim=None, cmap='RdBu_r', n_perms=100,
min_n=5, res_key='t-stat'):
"""
Plots brain surface based on the mean activity across the group. Uses ipyvolume to plot
Parameters
----------
radius: float
Maximum distance between an electrode and a vertex to be counted as data for that vertex
freq_range: list
2 element list defining minimum and maximum frequncies to average.
clim: float
Maximum/minumum value of the colormap. Will be centered at zero. If not given, will use the maximum absolute
value of the data.
cmap: str
matplotlib colormap to use
n_perms: int
Number of permutations to do when computing our t-statistic significance thresholds
min_n: int
Vertices with less than this number of subjects will be plotted in gray, regardless of the significance val
res_key: str
Column name of dataframe to use as the metric
Returns
-------
left hemisphere and right hemisphere activation maps
"""
# load brain mesh
l_coords, l_faces, r_coords, r_faces = self.load_brain_mesh()
# compute mean activation. First get vertex x subject arrays
l_vert_vals, r_vert_vals = self.compute_surface_map(radius, freq_range, res_key)
# we will actually be plotting t-statistics, so compute those
l_ts, l_ps = ttest_1samp(l_vert_vals, 0, axis=1, nan_policy='omit')
r_ts, r_ps = ttest_1samp(r_vert_vals, 0, axis=1, nan_policy='omit')
# not let's compute our significance thresholds via non-parametric permutation procedure
sig_thresh = self.compute_permute_dist_par(l_vert_vals, r_vert_vals, n_perms=n_perms)
# define colormap range
if clim is None:
clim = np.max([np.nanmax(np.abs(l_ts)), np.nanmax(np.abs(r_ts))])
c_norm = plt.Normalize(vmin=-clim, vmax=clim)
c_mappable = cmx.ScalarMappable(norm=c_norm, cmap=plt.get_cmap(cmap))
# compute surface colors for left
valid_l_inds = ~np.isnan(l_ts) & (np.sum(np.isnan(l_vert_vals), axis=1) >= min_n)
l_colors = np.full((l_ts.shape[0], 4), 0.)
l_colors[valid_l_inds] = c_mappable.to_rgba(l_ts[valid_l_inds])
# and right
valid_r_inds = ~np.isnan(r_ts) & (np.sum(np.isnan(r_vert_vals), axis=1) >= min_n)
r_colors = np.full((r_ts.shape[0], 4), 0.)
r_colors[valid_r_inds] = c_mappable.to_rgba(r_ts[valid_r_inds])
# lastly, mask out vertices that do not meet our significance thresh
sig_l = (l_ts < sig_thresh[0]) | (l_ts > sig_thresh[1])
sig_r = (r_ts < sig_thresh[0]) | (r_ts > sig_thresh[1])
l_colors[~sig_l] = [.7, .7, .7, 0.]
r_colors[~sig_r] = [.7, .7, .7, 0.]
# plot it!
fig = p3.figure(width=800, height=800, lighting=True)
brain_l = p3.plot_trisurf(l_coords[:, 0], l_coords[:, 1], l_coords[:, 2], triangles=l_faces, color=l_colors)
brain_r = p3.plot_trisurf(r_coords[:, 0], r_coords[:, 1], r_coords[:, 2], triangles=r_faces, color=r_colors)
# turn off axis and make square
ipv.squarelim()
ipv.style.box_off()
ipv.style.axes_off()
p3.show()
return fig
def plot_group_brain_activation(self, radius=12.5, freq_range=(40, 200), clim=None, cmap='RdBu_r', n_perms=100,
min_n=5):
"""
Plots brain surface based on the mean activity across the group. Uses ipyvolume to plot
Parameters
----------
radius: float
Maximum distance between an electrode and a vertex to be counted as data for that vertex
freq_range: list
2 element list defining minimum and maximum frequncies to average.
clim: float
Maximum/minumum value of the colormap. Will be centered at zero. If not given, will use the maximum absolute
value of the data.
cmap: str
matplotlib colormap to use
n_perms: int
Number of permutations to do when computing our t-statistic significance thresholds
min_n: int
Vertices with less than this number of subjects will be plotted in gray, regardless of the significance val
Returns
-------
left hemisphere and right hemisphere activation maps
"""
# load brain mesh
l_coords, l_faces, r_coords, r_faces = self.load_brain_mesh()
# compute mean activation. First get vertex x subject arrays
l_vert_vals, r_vert_vals = self.compute_surface_map(radius, freq_range)
# we will actually be plotting t-statistics, so compute those
l_ts, l_ps = ttest_1samp(l_vert_vals, 0, axis=1, nan_policy='omit')
r_ts, r_ps = ttest_1samp(r_vert_vals, 0, axis=1, nan_policy='omit')
# not let's compute our significance thresholds via non-parametric permutation procedure
sig_thresh = self.compute_permute_dist_par(l_vert_vals, r_vert_vals, n_perms=n_perms)
# define colormap range
if clim is None:
clim = np.max([np.nanmax(np.abs(l_ts)), np.nanmax(np.abs(r_ts))])
c_norm = plt.Normalize(vmin=-clim, vmax=clim)
c_mappable = cmx.ScalarMappable(norm=c_norm, cmap=plt.get_cmap(cmap))
# compute surface colors for left
valid_l_inds = ~np.isnan(l_ts) & (np.sum(np.isnan(l_vert_vals), axis=1) >= min_n)
l_colors = np.full((l_ts.shape[0], 4), 0.)
l_colors[valid_l_inds] = c_mappable.to_rgba(l_ts[valid_l_inds])
# and right
valid_r_inds = ~np.isnan(r_ts) & (np.sum(np.isnan(r_vert_vals), axis=1) >= min_n)
r_colors = np.full((r_ts.shape[0], 4), 0.)
r_colors[valid_r_inds] = c_mappable.to_rgba(r_ts[valid_r_inds])
# lastly, mask out vertices that do not meet our significance thresh
sig_l = (l_ts < sig_thresh[0]) | (l_ts > sig_thresh[1])
sig_r = (r_ts < sig_thresh[0]) | (r_ts > sig_thresh[1])
l_colors[~sig_l] = [.7, .7, .7, 0.]
r_colors[~sig_r] = [.7, .7, .7, 0.]
# plot it!
fig = p3.figure(width=800, height=800, lighting=True)
brain_l = p3.plot_trisurf(l_coords[:, 0], l_coords[:, 1], l_coords[:, 2], triangles=l_faces, color=l_colors)
brain_r = p3.plot_trisurf(r_coords[:, 0], r_coords[:, 1], r_coords[:, 2], triangles=r_faces, color=r_colors)
# turn off axis and make square
ipv.squarelim()
ipv.style.box_off()
ipv.style.axes_off()
p3.show()
return fig
def fermi3D(procar,
outcar,
bands=-1,
scale=1,
mode="plain",
st=False,
**kwargs):
"""
This function plots 3d fermi surface
list of acceptable kwargs :
plotting_package
nprocess
face_colors
arrow_colors
arrow_spin
atom
orbital
spin
"""
welcome()
# Initilizing the arguments :
if "plotting_package" in kwargs:
plotting_package = kwargs["plotting_package"]
else:
plotting_package = "mayavi"
if "nprocess" in kwargs:
nprocess = kwargs["nprocess"]
else:
nprocess = 2
if "face_colors" in kwargs:
face_colors = kwargs["face_colors"]
else:
face_colors = None
if "cmap" in kwargs:
cmap = kwargs["cmap"]
else:
cmap = "jet"
if "atoms" in kwargs:
atoms = kwargs["atoms"]
else:
atoms = [-1] # project all atoms
if "orbitals" in kwargs:
orbitals = kwargs["orbitals"]
else:
orbitals = [-1]
if "spin" in kwargs:
spin = kwargs["spin"]
else:
spin = [0]
if "mask_points" in kwargs:
mask_points = kwargs["mask_points"]
else:
mask_points = 1
if "energy" in kwargs:
energy = kwargs["energy"]
else:
energy = 0
if "transparent" in kwargs:
transparent = kwargs["transparent"]
else:
transparent = False
if "arrow_projection" in kwargs:
arrow_projection = kwargs["arrow_projection"]
else:
arrow_projection = 2
if plotting_package == "mayavi":
try:
from mayavi import mlab
from tvtk.api import tvtk
except:
print(
"You have selected mayavi as plottin package. please install mayavi or choose a different package"
)
return
elif plotting_package == "plotly":
try:
import plotly.plotly as py
import plotly.figure_factory as ff
import plotly.graph_objs as go
cmap = mpl.cm.get_cmap(cmap)
figs = []
except:
print(
"You have selected plotly as plottin package. please install plotly or choose a different package"
)
return
elif plotting_package == "matplotlib":
try:
import matplotlib.pylab as plt
from mpl_toolkits.mplot3d.art3d import Poly3DCollection
except:
print(
"You have selected matplotlib as plotting package. please install matplotlib or choose a different package"
)
return
elif plotting_package == "ipyvolume":
try:
import ipyvolume.pylab as ipv
except:
print(
"You have selected ipyvolume as plotting package. please install ipyvolume or choose a different package"
)
return
permissive = False
# get fermi from outcar
outcarparser = UtilsProcar()
e_fermi = outcarparser.FermiOutcar(outcar)
print("Fermi=", e_fermi)
e_fermi += energy
# get reciprocal lattice from outcar
recLat = outcarparser.RecLatOutcar(outcar)
# parsing the Procar file
procarFile = ProcarParser()
procarFile.readFile(procar, permissive)
poly = get_wigner_seitz(recLat)
# plot brilliouin zone
if plotting_package == "mayavi":
brillouin_point = []
brillouin_faces = []
point_count = 0
for iface in poly:
single_face = []
for ipoint in iface:
single_face.append(point_count)
brillouin_point.append(list(ipoint))
point_count += 1
brillouin_faces.append(single_face)
polydata_br = tvtk.PolyData(points=brillouin_point,
polys=brillouin_faces)
mlab.pipeline.surface(
polydata_br,
representation="wireframe",
color=(0, 0, 0),
line_width=4,
name="BRZ",
)
elif plotting_package == "plotly":
for iface in poly:
iface = np.pad(iface, ((0, 1), (0, 0)), "wrap")
x, y, z = iface[:, 0], iface[:, 1], iface[:, 2]
plane = go.Scatter3d(x=x,
y=y,
z=z,
mode="lines",
line=dict(color="black", width=4))
figs.append(plane)
elif plotting_package == "matplotlib":
fig = plt.figure()
ax = fig.add_subplot(111, projection="3d")
brillouin_zone = Poly3DCollection(poly,
facecolors=["None"] * len(poly),
alpha=1,
linewidth=4)
brillouin_zone.set_edgecolor("k")
ax.add_collection3d(brillouin_zone, zs=0, zdir="z")
br_points = []
for iface in poly:
for ipoint in iface:
br_points.append(ipoint)
br_points = np.unique(br_points, axis=0)
print("Number of bands: %d" % procarFile.bandsCount)
print("Number of koints %d" % procarFile.kpointsCount)
print("Number of ions: %d" % procarFile.ionsCount)
print("Number of orbitals: %d" % procarFile.orbitalCount)
print("Number of spins: %d" % procarFile.ispin)
# selecting the data
data = ProcarSelect(procarFile, deepCopy=True)
if bands == -1:
bands = range(data.bands.shape[1])
kvector = data.kpoints
kmax = np.max(kvector)
kmin = np.min(kvector)
if abs(kmax) != abs(kmin):
print("The mesh provided is gamma center, symmetrizing data")
print("For a better fermi surface, use a non-gamma centered k-mesh")
data = symmetrize(data)
kvector = data.kpoints
kvector_red = data.kpoints.copy()
kvector_cart = np.dot(kvector_red, recLat)
# This section finds points that are outside of the 1st BZ and and creates those points in the 1st BZ
kvector_cart, kvector_red, has_points_out = bring_pnts_to_BZ(
recLat, kvector_cart, kvector_red, br_points)
# has_points_out = False
# getting the mesh grid in each dirrection
kx_red = np.unique(kvector_red[:, 0])
ky_red = np.unique(kvector_red[:, 1])
kz_red = np.unique(kvector_red[:, 2])
# getting the lengths between kpoints in each direction
klength_x = np.abs(kx_red[-1] - kx_red[-2])
klength_y = np.abs(ky_red[-1] - ky_red[-2])
klength_z = np.abs(kz_red[-1] - kz_red[-2])
klengths = [klength_x, klength_y, klength_z]
# getting number of kpoints in each direction with the addition of kpoints needed to sample the 1st BZ fully (in reduced)
nkx_red = kx_red.shape[0]
nky_red = ky_red.shape[0]
nkz_red = kz_red.shape[0]
# getting numner of kpoints in each direction provided by vasp
nkx_orig = np.unique(kvector[:, 0]).shape[0]
nky_orig = np.unique(kvector[:, 1]).shape[0]
nkz_orig = np.unique(kvector[:, 2]).shape[0]
# Amount of kpoints needed to add on to fully sample 1st BZ
padding_x = (nkx_red - nkx_orig) // 2
padding_y = (nky_red - nky_orig) // 2
padding_z = (nkz_red - nkz_orig) // 2
if mode == "parametric":
data.selectIspin(spin)
data.selectAtoms(atoms, fortran=False)
data.selectOrbital(orbitals)
elif mode == "external":
if "color_file" in kwargs:
rf = open(kwargs["color_file"])
lines = rf.readlines()
counter = 0
color_kvector = []
color_eigen = []
for iline in lines:
if counter < 2:
if "band" in iline:
counter += 1
continue
temp = [float(x) for x in iline.split()]
color_kvector.append([temp[0], temp[1], temp[2]])
counter = -1
for iline in lines:
if "band" in iline:
counter += 1
iband = int(iline.split()[-1])
color_eigen.append([])
continue
color_eigen[counter].append(float(iline.split()[-1]))
rf.close()
color_kvector = np.array(color_kvector)
color_kvector_red = color_kvector.copy()
color_kvector_cart = np.dot(color_kvector, recLat)
if has_points_out:
color_kvector_cart, color_kvector_red, temp = bring_pnts_to_BZ(
recLat, color_kvector_cart, color_kvector_red, br_points)
else:
print(
"mode selected was external, but no color_file name was provided"
)
return
if st:
dataX = ProcarSelect(procarFile, deepCopy=True)
dataY = ProcarSelect(procarFile, deepCopy=True)
dataZ = ProcarSelect(procarFile, deepCopy=True)
dataX.kpoints = data.kpoints
dataY.kpoints = data.kpoints
dataZ.kpoints = data.kpoints
dataX.spd = data.spd
dataY.spd = data.spd
dataZ.spd = data.spd
dataX.bands = data.bands
dataY.bands = data.bands
dataZ.bands = data.bands
dataX.selectIspin([1])
dataY.selectIspin([2])
dataZ.selectIspin([3])
dataX.selectAtoms(atoms, fortran=False)
dataY.selectAtoms(atoms, fortran=False)
dataZ.selectAtoms(atoms, fortran=False)
dataX.selectOrbital(orbitals)
dataY.selectOrbital(orbitals)
dataZ.selectOrbital(orbitals)
ic = 0
for iband in bands:
print("Plotting band %d" % iband)
eigen = data.bands[:, iband]
# mapping the eigen values on the mesh grid to a matrix
mapped_func, kpoint_matrix = mapping_func(kvector, eigen)
# adding the points from the 2nd BZ to 1st BZ to fully sample the BZ. Check np.pad("wrap") for more information
mapped_func = np.pad(
mapped_func,
((padding_x, padding_x), (padding_y, padding_y),
(padding_z, padding_z)),
"wrap",
)
# Fourier interpolate the mapped function E(x,y,z)
surf_equation = fft_interpolate(mapped_func, scale)
# after the FFT we loose the center of the BZ, using numpy roll we bring back the center of the BZ
surf_equation = np.roll(surf_equation, (scale) // 2, axis=[0, 1, 2])
try:
# creating the isosurface if possible
verts, faces, normals, values = measure.marching_cubes_lewiner(
surf_equation, e_fermi)
except:
print("No isosurface for this band")
continue
# the vertices provided are scaled and shifted to start from zero
# we center them to zero, and rescale them to fit the real BZ by multiplying by the klength in each direction
for ix in range(3):
verts[:, ix] -= verts[:, ix].min()
verts[:, ix] -= (verts[:, ix].max() - verts[:, ix].min()) / 2
verts[:, ix] *= klengths[ix] / scale
# the vertices need to be transformed to reciprocal spcae from recuded space, to find the points that are
# in 2nd BZ, to be removed
verts = np.dot(verts, recLat)
# identifying the points in 2nd BZ and removing them
if has_points_out:
args = []
for ivert in range(len(verts)):
args.append([br_points, verts[ivert]])
p = Pool(nprocess)
results = np.array(p.map(is_outside, args))
p.close()
out_verts = np.arange(0, len(results))[results]
new_faces = []
# outs_bool_mat = np.zeros(shape=faces.shape,dtype=np.bool)
for iface in faces:
remove = False
for ivert in iface:
if ivert in out_verts:
remove = True
continue
if not remove:
new_faces.append(iface)
faces = np.array(new_faces)
print("done removing")
# At this point we have the plain Fermi surface, we can color the surface depending on the projection
# We create the center of faces by averaging coordinates of corners
if mode == "parametric":
character = data.spd[:, iband]
centers = np.zeros(shape=(len(faces), 3))
for iface in range(len(faces)):
centers[iface, 0:3] = np.average(verts[faces[iface]], axis=0)
colors = interpolate.griddata(kvector_cart,
character,
centers,
method="nearest")
elif mode == "external":
character = np.array(color_eigen[ic])
ic += 1
centers = np.zeros(shape=(len(faces), 3))
for iface in range(len(faces)):
centers[iface, 0:3] = np.average(verts[faces[iface]], axis=0)
colors = interpolate.griddata(color_kvector_cart,
character,
centers,
method="nearest")
if st:
projection_x = dataX.spd[:, iband]
projection_y = dataY.spd[:, iband]
projection_z = dataZ.spd[:, iband]
verts_spin, faces_spin, normals, values = measure.marching_cubes_lewiner(
mapped_func, e_fermi)
for ix in range(3):
verts_spin[:, ix] -= verts_spin[:, ix].min()
verts_spin[:, ix] -= (verts_spin[:, ix].max() -
verts_spin[:, ix].min()) / 2
verts_spin[:, ix] *= klengths[ix]
verts_spin = np.dot(verts_spin, recLat)
if has_points_out:
args = []
for ivert in range(len(verts_spin)):
args.append([br_points, verts_spin[ivert]])
p = Pool(nprocess)
results = np.array(p.map(is_outside, args))
p.close()
out_verts = np.arange(0, len(results))[results]
new_faces = []
for iface in faces_spin:
remove = False
for ivert in iface:
if ivert in out_verts:
remove = True
continue
if not remove:
new_faces.append(iface)
faces_spin = np.array(new_faces)
centers = np.zeros(shape=(len(faces_spin), 3))
for iface in range(len(faces_spin)):
centers[iface, 0:3] = np.average(verts_spin[faces_spin[iface]],
axis=0)
colors1 = interpolate.griddata(kvector_cart,
projection_x,
centers,
method="linear")
colors2 = interpolate.griddata(kvector_cart,
projection_y,
centers,
method="linear")
colors3 = interpolate.griddata(kvector_cart,
projection_z,
centers,
method="linear")
spin_arrows = np.vstack((colors1, colors2, colors3)).T
if plotting_package == "mayavi":
polydata = tvtk.PolyData(points=verts, polys=faces)
if face_colors != None:
mlab.pipeline.surface(
polydata,
representation="surface",
color=face_colors[ic],
opacity=1,
name="band-" + str(iband),
)
ic += 1
else:
if mode == "plain":
if not (transparent):
s = mlab.pipeline.surface(
polydata,
representation="surface",
color=(0, 0.5, 1),
opacity=1,
name="band-" + str(iband),
)
elif mode == "parametric" or mode == "external":
polydata.cell_data.scalars = colors
polydata.cell_data.scalars.name = "celldata"
mlab.pipeline.surface(polydata,
vmin=0,
vmax=colors.max(),
colormap=cmap)
cb = mlab.colorbar(orientation="vertical")
if st:
x, y, z = list(zip(*centers))
u, v, w = list(zip(*spin_arrows))
pnts = mlab.quiver3d(
x,
y,
z,
u,
v,
w,
line_width=5,
mode="arrow",
resolution=25,
reset_zoom=False,
name="spin-" + str(iband),
mask_points=mask_points,
scalars=spin_arrows[:, arrow_projection],
vmin=-1,
vmax=1,
colormap=cmap,
)
pnts.glyph.color_mode = "color_by_scalar"
pnts.glyph.glyph_source.glyph_source.shaft_radius = 0.05
pnts.glyph.glyph_source.glyph_source.tip_radius = 0.1
elif plotting_package == "plotly":
if mode == "plain":
if not (transparent):
x, y, z = zip(*verts)
fig = ff.create_trisurf(
x=x,
y=y,
z=z,
plot_edges=False,
simplices=faces,
title="band-%d" % ic,
)
figs.append(fig["data"][0])
elif mode == "parametric" or mode == "external":
face_colors = cmap(colors)
colormap = [
"rgb(%i,%i,%i)" % (x[0], x[1], x[2])
for x in (face_colors * 255).round()
]
x, y, z = zip(*verts)
fig = ff.create_trisurf(
x=x,
y=y,
z=z,
plot_edges=False,
colormap=colormap,
simplices=faces,
show_colorbar=True,
title="band-%d" % ic,
)
figs.append(fig["data"][0])
elif plotting_package == "matplotlib":
if mode == "plain":
x, y, z = zip(*verts)
ax.plot_trisurf(x,
y,
faces,
z,
linewidth=0.2,
antialiased=True)
elif mode == "parametric" or mode == "external":
print(
"coloring the faces is not implemented in matplot lib, please use another plotting package.we recomend mayavi."
)
elif plotting_package == "ipyvolume":
if mode == "plain":
ipv.figure()
ipv.plot_trisurf(verts[:, 0],
verts[:, 1],
verts[:, 2],
triangles=faces)
elif mode == "paramteric" or mode == "external":
face_colors = cmap(colors)
colormap = [
"rgb(%i,%i,%i)" % (x[0], x[1], x[2])
for x in (face_colors * 255).round()
]
ipv.figure()
ipv.plot_trisurf(verts[:, 0],
verts[:, 1],
verts[:, 2],
triangles=faces,
color=cmap)
if plotting_package == "mayavi":
mlab.colorbar(orientation="vertical") # ,label_fmt='%.1f')
mlab.show()
elif plotting_package == "plotly":
layout = go.Layout(showlegend=False)
fig = go.Figure(data=figs, layout=layout)
py.iplot(fig)
elif plotting_package == "matplotlib":
plt.show()
elif plotting_package == "ipyvolume":
ipv.show()
return