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pixelization_routines.pyx
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pixelization_routines.pyx
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# distutils: include_dirs = LIB_DIR
# distutils: extra_compile_args = CPP14_FLAG OMP_ARGS
# distutils: extra_link_args = CPP14_FLAG OMP_ARGS
# distutils: language = c++
# distutils: libraries = STD_LIBS
# distutils: sources = yt/utilities/lib/pixelization_constants.cpp
"""
Pixelization routines
"""
import numpy as np
cimport cython
cimport libc.math as math
cimport numpy as np
from cython.view cimport array as cvarray
from yt.utilities.lib.fp_utils cimport (
any_float,
fabs,
fmax,
fmin,
i64max,
i64min,
iclip,
)
from yt.utilities.exceptions import YTElementTypeNotRecognized, YTPixelizeError
from cpython.exc cimport PyErr_CheckSignals
from cython.parallel cimport parallel, prange
from libc.stdlib cimport free, malloc
from yt.geometry.particle_deposit cimport get_kernel_func, kernel_func
from yt.utilities.lib.element_mappings cimport (
ElementSampler,
P1Sampler1D,
P1Sampler2D,
P1Sampler3D,
Q1Sampler2D,
Q1Sampler3D,
Q2Sampler2D,
S2Sampler3D,
T2Sampler2D,
Tet2Sampler3D,
W1Sampler3D,
)
from .vec3_ops cimport cross, dot, subtract
from yt.funcs import get_pbar
from yt.utilities.lib.bounded_priority_queue cimport BoundedPriorityQueue
from yt.utilities.lib.cykdtree.kdtree cimport KDTree, PyKDTree
from yt.utilities.lib.particle_kdtree_tools cimport (
axes_range,
find_neighbors,
set_axes_range,
)
cdef int TABLE_NVALS=512
cdef extern from "pixelization_constants.hpp":
enum:
MAX_NUM_FACES
int HEX_IND
int HEX_NF
np.uint8_t hex_face_defs[MAX_NUM_FACES][2][2]
int TETRA_IND
int TETRA_NF
np.uint8_t tetra_face_defs[MAX_NUM_FACES][2][2]
int WEDGE_IND
int WEDGE_NF
np.uint8_t wedge_face_defs[MAX_NUM_FACES][2][2]
@cython.cdivision(True)
@cython.boundscheck(False)
@cython.wraparound(False)
def pixelize_cartesian(np.float64_t[:,:] buff,
any_float[:] px,
any_float[:] py,
any_float[:] pdx,
any_float[:] pdy,
any_float[:] data,
bounds,
int antialias = 1,
period = None,
int check_period = 1,
np.float64_t line_width = 0.0,
*,
int return_mask = 0,
):
cdef np.float64_t x_min, x_max, y_min, y_max
cdef np.float64_t period_x = 0.0, period_y = 0.0
cdef np.float64_t width, height, px_dx, px_dy, ipx_dx, ipx_dy
cdef np.float64_t ld_x, ld_y, cx, cy
cdef int i, j, p, xi, yi
cdef int lc, lr, rc, rr
cdef np.float64_t lypx, rypx, lxpx, rxpx, overlap1, overlap2
# These are the temp vars we get from the arrays
cdef np.float64_t oxsp, oysp, xsp, ysp, dxsp, dysp, dsp
# Some periodicity helpers
cdef int xiter[2]
cdef int yiter[2]
cdef np.float64_t xiterv[2]
cdef np.float64_t yiterv[2]
cdef np.ndarray[np.uint8_t, ndim=2] mask_arr = np.zeros_like(buff, dtype="uint8")
cdef np.uint8_t[:, :] mask = mask_arr
if period is not None:
period_x = period[0]
period_y = period[1]
x_min = bounds[0]
x_max = bounds[1]
y_min = bounds[2]
y_max = bounds[3]
width = x_max - x_min
height = y_max - y_min
px_dx = width / (<np.float64_t> buff.shape[1])
px_dy = height / (<np.float64_t> buff.shape[0])
ipx_dx = 1.0 / px_dx
ipx_dy = 1.0 / px_dy
if px.shape[0] != py.shape[0] or \
px.shape[0] != pdx.shape[0] or \
px.shape[0] != pdy.shape[0] or \
px.shape[0] != data.shape[0]:
raise YTPixelizeError("Arrays are not of correct shape.")
xiter[0] = yiter[0] = 0
xiterv[0] = yiterv[0] = 0.0
# Here's a basic outline of what we're going to do here. The xiter and
# yiter variables govern whether or not we should check periodicity -- are
# we both close enough to the edge that it would be important *and* are we
# periodic?
#
# The other variables are all either pixel positions or data positions.
# Pixel positions will vary regularly from the left edge of the window to
# the right edge of the window; px_dx and px_dy are the dx (cell width, not
# half-width). ipx_dx and ipx_dy are the inverse, for quick math.
#
# The values in xsp, dxsp, x_min and their y counterparts, are the
# data-space coordinates, and are related to the data fed in. We make some
# modifications for periodicity.
#
# Inside the finest loop, we compute the "left column" (lc) and "lower row"
# (lr) and then iterate up to "right column" (rc) and "uppeR row" (rr),
# depositing into them the data value. Overlap computes the relative
# overlap of a data value with a pixel.
#
# NOTE ON ROWS AND COLUMNS:
#
# The way that images are plotting in matplotlib is somewhat different
# from what most might expect. The first axis of the array plotted is
# what varies along the x axis. So for instance, if you supply
# origin='lower' and plot the results of an mgrid operation, at a fixed
# 'y' value you will see the results of that array held constant in the
# first dimension. Here is some example code:
#
# import matplotlib.pyplot as plt
# import numpy as np
# x, y = np.mgrid[0:1:100j,0:1:100j]
# plt.imshow(x, interpolation='nearest', origin='lower')
# plt.imshow(y, interpolation='nearest', origin='lower')
#
# The values in the image:
# lower left: arr[0,0]
# lower right: arr[0,-1]
# upper left: arr[-1,0]
# upper right: arr[-1,-1]
#
# So what we want here is to fill an array such that we fill:
# first axis : y_min .. y_max
# second axis: x_min .. x_max
with nogil:
for p in range(px.shape[0]):
xiter[1] = yiter[1] = 999
xiterv[1] = yiterv[1] = 0.0
oxsp = px[p]
oysp = py[p]
dxsp = pdx[p]
dysp = pdy[p]
dsp = data[p]
if check_period == 1:
if (oxsp - dxsp < x_min):
xiter[1] = +1
xiterv[1] = period_x
elif (oxsp + dxsp > x_max):
xiter[1] = -1
xiterv[1] = -period_x
if (oysp - dysp < y_min):
yiter[1] = +1
yiterv[1] = period_y
elif (oysp + dysp > y_max):
yiter[1] = -1
yiterv[1] = -period_y
overlap1 = overlap2 = 1.0
for xi in range(2):
if xiter[xi] == 999: continue
xsp = oxsp + xiterv[xi]
if (xsp + dxsp < x_min) or (xsp - dxsp > x_max): continue
for yi in range(2):
if yiter[yi] == 999: continue
ysp = oysp + yiterv[yi]
if (ysp + dysp < y_min) or (ysp - dysp > y_max): continue
lc = <int> fmax(((xsp-dxsp-x_min)*ipx_dx),0)
lr = <int> fmax(((ysp-dysp-y_min)*ipx_dy),0)
# NOTE: This is a different way of doing it than in the C
# routines. In C, we were implicitly casting the
# initialization to int, but *not* the conditional, which
# was allowed an extra value:
# for(j=lc;j<rc;j++)
# here, when assigning lc (double) to j (int) it got
# truncated, but no similar truncation was done in the
# comparison of j to rc (double). So give ourselves a
# bonus row and bonus column here.
rc = <int> fmin(((xsp+dxsp-x_min)*ipx_dx + 1), buff.shape[1])
rr = <int> fmin(((ysp+dysp-y_min)*ipx_dy + 1), buff.shape[0])
# Note that we're iterating here over *y* in the i
# direction. See the note above about this.
for i in range(lr, rr):
lypx = px_dy * i + y_min
rypx = px_dy * (i+1) + y_min
if antialias == 1:
overlap2 = ((fmin(rypx, ysp+dysp)
- fmax(lypx, (ysp-dysp)))*ipx_dy)
if overlap2 < 0.0: continue
for j in range(lc, rc):
lxpx = px_dx * j + x_min
rxpx = px_dx * (j+1) + x_min
if line_width > 0:
# Here, we figure out if we're within
# line_width*px_dx of the cell edge
# Midpoint of x:
cx = (rxpx+lxpx)*0.5
ld_x = fmin(fabs(cx - (xsp+dxsp)),
fabs(cx - (xsp-dxsp)))
ld_x *= ipx_dx
# Midpoint of y:
cy = (rypx+lypx)*0.5
ld_y = fmin(fabs(cy - (ysp+dysp)),
fabs(cy - (ysp-dysp)))
ld_y *= ipx_dy
if ld_x <= line_width or ld_y <= line_width:
buff[i,j] = 1.0
mask[i,j] = 1
elif antialias == 1:
overlap1 = ((fmin(rxpx, xsp+dxsp)
- fmax(lxpx, (xsp-dxsp)))*ipx_dx)
if overlap1 < 0.0: continue
# This next line is not commented out because
# it's an oddity; we actually want to skip
# depositing if the overlap is zero, and that's
# how it used to work when we were more
# conservative about the iteration indices.
# This will reduce artifacts if we ever move to
# compositing instead of replacing bitmaps.
if overlap1 * overlap2 < 1.e-6: continue
# make sure pixel value is not a NaN before incrementing it
if buff[i,j] != buff[i,j]: buff[i,j] = 0.0
buff[i,j] += (dsp * overlap1) * overlap2
mask[i,j] = 1
else:
buff[i,j] = dsp
mask[i,j] = 1
if return_mask:
return mask_arr.astype("bool")
@cython.cdivision(True)
@cython.boundscheck(False)
@cython.wraparound(False)
def pixelize_cartesian_nodal(np.float64_t[:,:] buff,
np.float64_t[:] px,
np.float64_t[:] py,
np.float64_t[:] pz,
np.float64_t[:] pdx,
np.float64_t[:] pdy,
np.float64_t[:] pdz,
np.float64_t[:, :] data,
np.float64_t coord,
bounds,
int antialias = 1,
period = None,
int check_period = 1,
*,
int return_mask = 0,
):
cdef np.float64_t x_min, x_max, y_min, y_max
cdef np.float64_t period_x = 0.0, period_y = 0.0
cdef np.float64_t width, height, px_dx, px_dy, ipx_dx, ipx_dy
cdef np.float64_t cx, cy, cz
cdef int i, j, p, xi, yi
cdef int lc, lr, rc, rr
cdef np.float64_t lypx, rypx, lxpx, rxpx, overlap1, overlap2
# These are the temp vars we get from the arrays
cdef np.float64_t oxsp, oysp, ozsp
cdef np.float64_t xsp, ysp, zsp
cdef np.float64_t dxsp, dysp, dzsp
# Some periodicity helpers
cdef int xiter[2]
cdef int yiter[2]
cdef int ii, jj, kk, ind
cdef np.float64_t xiterv[2]
cdef np.float64_t yiterv[2]
if period is not None:
period_x = period[0]
period_y = period[1]
x_min = bounds[0]
x_max = bounds[1]
y_min = bounds[2]
y_max = bounds[3]
width = x_max - x_min
height = y_max - y_min
px_dx = width / (<np.float64_t> buff.shape[1])
px_dy = height / (<np.float64_t> buff.shape[0])
ipx_dx = 1.0 / px_dx
ipx_dy = 1.0 / px_dy
if px.shape[0] != py.shape[0] or \
px.shape[0] != pz.shape[0] or \
px.shape[0] != pdx.shape[0] or \
px.shape[0] != pdy.shape[0] or \
px.shape[0] != pdz.shape[0] or \
px.shape[0] != data.shape[0]:
raise YTPixelizeError("Arrays are not of correct shape.")
xiter[0] = yiter[0] = 0
xiterv[0] = yiterv[0] = 0.0
# Here's a basic outline of what we're going to do here. The xiter and
# yiter variables govern whether or not we should check periodicity -- are
# we both close enough to the edge that it would be important *and* are we
# periodic?
#
# The other variables are all either pixel positions or data positions.
# Pixel positions will vary regularly from the left edge of the window to
# the right edge of the window; px_dx and px_dy are the dx (cell width, not
# half-width). ipx_dx and ipx_dy are the inverse, for quick math.
#
# The values in xsp, dxsp, x_min and their y counterparts, are the
# data-space coordinates, and are related to the data fed in. We make some
# modifications for periodicity.
#
# Inside the finest loop, we compute the "left column" (lc) and "lower row"
# (lr) and then iterate up to "right column" (rc) and "uppeR row" (rr),
# depositing into them the data value. Overlap computes the relative
# overlap of a data value with a pixel.
#
# NOTE ON ROWS AND COLUMNS:
#
# The way that images are plotting in matplotlib is somewhat different
# from what most might expect. The first axis of the array plotted is
# what varies along the x axis. So for instance, if you supply
# origin='lower' and plot the results of an mgrid operation, at a fixed
# 'y' value you will see the results of that array held constant in the
# first dimension. Here is some example code:
#
# import matplotlib.pyplot as plt
# import numpy as np
# x, y = np.mgrid[0:1:100j,0:1:100j]
# plt.imshow(x, interpolation='nearest', origin='lower')
# plt.imshow(y, interpolation='nearest', origin='lower')
#
# The values in the image:
# lower left: arr[0,0]
# lower right: arr[0,-1]
# upper left: arr[-1,0]
# upper right: arr[-1,-1]
#
# So what we want here is to fill an array such that we fill:
# first axis : y_min .. y_max
# second axis: x_min .. x_max
cdef np.ndarray[np.uint8_t, ndim=2] mask_arr = np.zeros_like(buff, dtype="uint8")
cdef np.uint8_t[:, :] mask = mask_arr
with nogil:
for p in range(px.shape[0]):
xiter[1] = yiter[1] = 999
xiterv[1] = yiterv[1] = 0.0
oxsp = px[p]
oysp = py[p]
ozsp = pz[p]
dxsp = pdx[p]
dysp = pdy[p]
dzsp = pdz[p]
if check_period == 1:
if (oxsp - dxsp < x_min):
xiter[1] = +1
xiterv[1] = period_x
elif (oxsp + dxsp > x_max):
xiter[1] = -1
xiterv[1] = -period_x
if (oysp - dysp < y_min):
yiter[1] = +1
yiterv[1] = period_y
elif (oysp + dysp > y_max):
yiter[1] = -1
yiterv[1] = -period_y
overlap1 = overlap2 = 1.0
zsp = ozsp
for xi in range(2):
if xiter[xi] == 999: continue
xsp = oxsp + xiterv[xi]
if (xsp + dxsp < x_min) or (xsp - dxsp > x_max): continue
for yi in range(2):
if yiter[yi] == 999: continue
ysp = oysp + yiterv[yi]
if (ysp + dysp < y_min) or (ysp - dysp > y_max): continue
lc = <int> fmax(((xsp-dxsp-x_min)*ipx_dx),0)
lr = <int> fmax(((ysp-dysp-y_min)*ipx_dy),0)
# NOTE: This is a different way of doing it than in the C
# routines. In C, we were implicitly casting the
# initialization to int, but *not* the conditional, which
# was allowed an extra value:
# for(j=lc;j<rc;j++)
# here, when assigning lc (double) to j (int) it got
# truncated, but no similar truncation was done in the
# comparison of j to rc (double). So give ourselves a
# bonus row and bonus column here.
rc = <int> fmin(((xsp+dxsp-x_min)*ipx_dx + 1), buff.shape[1])
rr = <int> fmin(((ysp+dysp-y_min)*ipx_dy + 1), buff.shape[0])
# Note that we're iterating here over *y* in the i
# direction. See the note above about this.
for i in range(lr, rr):
lypx = px_dy * i + y_min
rypx = px_dy * (i+1) + y_min
for j in range(lc, rc):
lxpx = px_dx * j + x_min
rxpx = px_dx * (j+1) + x_min
cx = (rxpx+lxpx)*0.5
cy = (rypx+lypx)*0.5
cz = coord
ii = <int> (cx - xsp + dxsp)
jj = <int> (cy - ysp + dysp)
kk = <int> (cz - zsp + dzsp)
ind = 4*ii + 2*jj + kk
buff[i,j] = data[p, ind]
mask[i,j] = 1
if return_mask:
return mask_arr.astype("bool")
@cython.cdivision(True)
@cython.boundscheck(False)
@cython.wraparound(False)
def pixelize_off_axis_cartesian(
np.float64_t[:,:] buff,
np.float64_t[:] x,
np.float64_t[:] y,
np.float64_t[:] z,
np.float64_t[:] px,
np.float64_t[:] py,
np.float64_t[:] pdx,
np.float64_t[:] pdy,
np.float64_t[:] pdz,
np.float64_t[:] center,
np.float64_t[:,:] inv_mat,
np.int64_t[:] indices,
np.float64_t[:] data,
bounds,
*,
int return_mask=0,
):
cdef np.float64_t x_min, x_max, y_min, y_max
cdef np.float64_t width, height, px_dx, px_dy, ipx_dx, ipx_dy, md
cdef int i, j, p, ip
cdef int lc, lr, rc, rr
# These are the temp vars we get from the arrays
cdef np.float64_t xsp, ysp, zsp, dxsp, dysp, dzsp, dsp
cdef np.float64_t pxsp, pysp, cxpx, cypx, cx, cy, cz
# Some periodicity helpers
cdef np.ndarray[np.int64_t, ndim=2] mask
x_min = bounds[0]
x_max = bounds[1]
y_min = bounds[2]
y_max = bounds[3]
width = x_max - x_min
height = y_max - y_min
px_dx = width / (<np.float64_t> buff.shape[1])
px_dy = height / (<np.float64_t> buff.shape[0])
ipx_dx = 1.0 / px_dx
ipx_dy = 1.0 / px_dy
if px.shape[0] != py.shape[0] or \
px.shape[0] != pdx.shape[0] or \
px.shape[0] != pdy.shape[0] or \
px.shape[0] != pdz.shape[0] or \
px.shape[0] != indices.shape[0] or \
px.shape[0] != data.shape[0]:
raise YTPixelizeError("Arrays are not of correct shape.")
mask = np.zeros((buff.shape[0], buff.shape[1]), "int64")
with nogil:
for ip in range(indices.shape[0]):
p = indices[ip]
xsp = x[p]
ysp = y[p]
zsp = z[p]
pxsp = px[p]
pysp = py[p]
dxsp = pdx[p]
dysp = pdy[p]
dzsp = pdz[p]
dsp = data[p]
# Any point we want to plot is at most this far from the center
md = 2.0 * math.sqrt(dxsp*dxsp + dysp*dysp + dzsp*dzsp)
if pxsp + md < x_min or \
pxsp - md > x_max or \
pysp + md < y_min or \
pysp - md > y_max:
continue
lc = <int> fmax(((pxsp - md - x_min)*ipx_dx),0)
lr = <int> fmax(((pysp - md - y_min)*ipx_dy),0)
rc = <int> fmin(((pxsp + md - x_min)*ipx_dx + 1), buff.shape[1])
rr = <int> fmin(((pysp + md - y_min)*ipx_dy + 1), buff.shape[0])
for i in range(lr, rr):
cypx = px_dy * (i + 0.5) + y_min
for j in range(lc, rc):
cxpx = px_dx * (j + 0.5) + x_min
cx = inv_mat[0,0]*cxpx + inv_mat[0,1]*cypx + center[0]
cy = inv_mat[1,0]*cxpx + inv_mat[1,1]*cypx + center[1]
cz = inv_mat[2,0]*cxpx + inv_mat[2,1]*cypx + center[2]
if fabs(xsp - cx) * 0.99 > dxsp or \
fabs(ysp - cy) * 0.99 > dysp or \
fabs(zsp - cz) * 0.99 > dzsp:
continue
mask[i, j] += 1
# make sure pixel value is not a NaN before incrementing it
if buff[i,j] != buff[i,j]: buff[i,j] = 0.0
buff[i, j] += dsp
for i in range(buff.shape[0]):
for j in range(buff.shape[1]):
if mask[i,j] == 0: continue
buff[i,j] /= mask[i,j]
if return_mask:
return mask!=0
@cython.cdivision(True)
@cython.boundscheck(False)
@cython.wraparound(False)
def pixelize_cylinder(np.float64_t[:,:] buff,
np.float64_t[:] radius,
np.float64_t[:] dradius,
np.float64_t[:] theta,
np.float64_t[:] dtheta,
np.float64_t[:] field,
extents,
*,
int return_mask=0,
):
cdef np.float64_t x, y, dx, dy, r0, theta0
cdef np.float64_t rmin, rmax, tmin, tmax, x0, y0, x1, y1, xp, yp
cdef np.float64_t r_i, theta_i, dr_i, dtheta_i
cdef np.float64_t r_inc, theta_inc
cdef np.float64_t costheta, sintheta
cdef int i, i1, pi, pj
cdef int imin, imax
imin = np.asarray(radius).argmin()
imax = np.asarray(radius).argmax()
rmin = radius[imin] - dradius[imin]
rmax = radius[imax] + dradius[imax]
imin = np.asarray(theta).argmin()
imax = np.asarray(theta).argmax()
tmin = theta[imin] - dtheta[imin]
tmax = theta[imax] + dtheta[imax]
cdef np.ndarray[np.uint8_t, ndim=2] mask_arr = np.zeros_like(buff, dtype="uint8")
cdef np.uint8_t[:, :] mask = mask_arr
x0, x1, y0, y1 = extents
dx = (x1 - x0) / buff.shape[0]
dy = (y1 - y0) / buff.shape[1]
cdef np.float64_t rbounds[2]
cdef np.float64_t prbounds[2]
cdef np.float64_t ptbounds[2]
cdef np.float64_t corners[8]
# Find our min and max r
corners[0] = x0*x0+y0*y0
corners[1] = x1*x1+y0*y0
corners[2] = x0*x0+y1*y1
corners[3] = x1*x1+y1*y1
corners[4] = x0*x0
corners[5] = x1*x1
corners[6] = y0*y0
corners[7] = y1*y1
rbounds[0] = rbounds[1] = corners[0]
for i in range(8):
rbounds[0] = fmin(rbounds[0], corners[i])
rbounds[1] = fmax(rbounds[1], corners[i])
rbounds[0] = math.sqrt(rbounds[0])
rbounds[1] = math.sqrt(rbounds[1])
# If we include the origin in either direction, we need to have radius of
# zero as our lower bound.
if x0 < 0 and x1 > 0:
rbounds[0] = 0.0
if y0 < 0 and y1 > 0:
rbounds[0] = 0.0
r_inc = 0.5 * fmin(dx, dy)
for i in range(radius.shape[0]):
r0 = radius[i]
theta0 = theta[i]
dr_i = dradius[i]
dtheta_i = dtheta[i]
# Skip out early if we're offsides, for zoomed in plots
if r0 + dr_i < rbounds[0] or r0 - dr_i > rbounds[1]:
continue
theta_i = theta0 - dtheta_i
theta_inc = r_inc / (r0 + dr_i)
while theta_i < theta0 + dtheta_i:
r_i = r0 - dr_i
costheta = math.cos(theta_i)
sintheta = math.sin(theta_i)
while r_i < r0 + dr_i:
if rmax <= r_i:
r_i += r_inc
continue
y = r_i * costheta
x = r_i * sintheta
pi = <int>((x - x0)/dx)
pj = <int>((y - y0)/dy)
if pi >= 0 and pi < buff.shape[0] and \
pj >= 0 and pj < buff.shape[1]:
# we got a pixel that intersects the grid cell
# now check that this pixel doesn't go beyond the data domain
xp = x0 + pi*dx
yp = y0 + pj*dy
corners[0] = xp*xp + yp*yp
corners[1] = xp*xp + (yp+dy)**2
corners[2] = (xp+dx)**2 + yp*yp
corners[3] = (xp+dx)**2 + (yp+dy)**2
prbounds[0] = prbounds[1] = corners[3]
for i1 in range(3):
prbounds[0] = fmin(prbounds[0], corners[i1])
prbounds[1] = fmax(prbounds[1], corners[i1])
prbounds[0] = math.sqrt(prbounds[0])
prbounds[1] = math.sqrt(prbounds[1])
corners[0] = math.atan2(xp, yp)
corners[1] = math.atan2(xp, yp+dy)
corners[2] = math.atan2(xp+dx, yp)
corners[3] = math.atan2(xp+dx, yp+dy)
ptbounds[0] = ptbounds[1] = corners[3]
for i1 in range(3):
ptbounds[0] = fmin(ptbounds[0], corners[i1])
ptbounds[1] = fmax(ptbounds[1], corners[i1])
# shift to a [0, PI] interval
ptbounds[0] = ptbounds[0] % (2*np.pi)
ptbounds[1] = ptbounds[1] % (2*np.pi)
if prbounds[0] >= rmin and prbounds[1] <= rmax and \
ptbounds[0] >= tmin and ptbounds[1] <= tmax:
buff[pi, pj] = field[i]
mask[pi, pj] = 1
r_i += r_inc
theta_i += theta_inc
if return_mask:
return mask_arr.astype("bool")
cdef int aitoff_Lambda_btheta_to_xy(np.float64_t Lambda, np.float64_t btheta,
np.float64_t *x, np.float64_t *y) except -1:
cdef np.float64_t z = math.sqrt(1 + math.cos(btheta) * math.cos(Lambda / 2.0))
x[0] = 2.0 * math.cos(btheta) * math.sin(Lambda / 2.0) / z
y[0] = math.sin(btheta) / z
return 0
@cython.cdivision(True)
@cython.boundscheck(False)
@cython.wraparound(False)
def pixelize_aitoff(np.float64_t[:] azimuth,
np.float64_t[:] dazimuth,
np.float64_t[:] colatitude,
np.float64_t[:] dcolatitude,
buff_size,
np.float64_t[:] field,
bounds, # this is a 4-tuple
input_img = None,
np.float64_t azimuth_offset = 0.0,
np.float64_t colatitude_offset = 0.0,
*,
int return_mask = 0
):
# http://paulbourke.net/geometry/transformationprojection/
# (Lambda) longitude is -PI to PI (longitude = azimuth - PI)
# (btheta) latitude is -PI/2 to PI/2 (latitude = PI/2 - colatitude)
#
# z^2 = 1 + cos(latitude) cos(longitude/2)
# x = cos(latitude) sin(longitude/2) / z
# y = sin(latitude) / z
cdef np.ndarray[np.float64_t, ndim=2] img
cdef int i, j, nf, fi
cdef np.float64_t x, y, z, zb
cdef np.float64_t dx, dy, xw, yw
cdef np.float64_t Lambda0, btheta0, Lambda_p, dLambda_p, btheta_p, dbtheta_p
cdef np.float64_t PI = np.pi
cdef np.float64_t s2 = math.sqrt(2.0)
cdef np.float64_t xmax, ymax, xmin, ymin
nf = field.shape[0]
if input_img is None:
img = np.zeros((buff_size[0], buff_size[1]))
img[:] = np.nan
else:
img = input_img
cdef np.ndarray[np.uint8_t, ndim=2] mask_arr = np.ones_like(img, dtype="uint8")
cdef np.uint8_t[:, :] mask = mask_arr
# Okay, here's our strategy. We compute the bounds in x and y, which will
# be a rectangle, and then for each x, y position we check to see if it's
# within our Lambda. This will cost *more* computations of the
# (x,y)->(Lambda,btheta) calculation, but because we no longer have to search
# through the Lambda, btheta arrays, it should be faster.
xw = bounds[1] - bounds[0]
yw = bounds[3] - bounds[2]
dx = xw / (img.shape[0] - 1)
dy = yw / (img.shape[1] - 1)
x = y = 0
for fi in range(nf):
Lambda_p = (azimuth[fi] + azimuth_offset) - PI
dLambda_p = dazimuth[fi]
btheta_p = PI/2.0 - (colatitude[fi] + colatitude_offset)
dbtheta_p = dcolatitude[fi]
# Four transformations
aitoff_Lambda_btheta_to_xy(Lambda_p - dLambda_p, btheta_p - dbtheta_p, &x, &y)
xmin = x
xmax = x
ymin = y
ymax = y
aitoff_Lambda_btheta_to_xy(Lambda_p - dLambda_p, btheta_p + dbtheta_p, &x, &y)
xmin = fmin(xmin, x)
xmax = fmax(xmax, x)
ymin = fmin(ymin, y)
ymax = fmax(ymax, y)
aitoff_Lambda_btheta_to_xy(Lambda_p + dLambda_p, btheta_p - dbtheta_p, &x, &y)
xmin = fmin(xmin, x)
xmax = fmax(xmax, x)
ymin = fmin(ymin, y)
ymax = fmax(ymax, y)
aitoff_Lambda_btheta_to_xy(Lambda_p + dLambda_p, btheta_p + dbtheta_p, &x, &y)
xmin = fmin(xmin, x)
xmax = fmax(xmax, x)
ymin = fmin(ymin, y)
ymax = fmax(ymax, y)
# special cases where the projection of the cell isn't
# bounded by the rectangle (in image space) that bounds its corners.
# Note that performance may take a serious hit here. The overarching algorithm
# is optimized for cells with small angular width.
if xmin * xmax < 0.0:
# on the central meridian
aitoff_Lambda_btheta_to_xy(0.0, btheta_p - dbtheta_p, &x, &y)
ymin = fmin(ymin, y)
ymax = fmax(ymax, y)
aitoff_Lambda_btheta_to_xy(0.0, btheta_p + dbtheta_p, &x, &y)
ymin = fmin(ymin, y)
ymax = fmax(ymax, y)
if ymin * ymax < 0.0:
# on the equator
aitoff_Lambda_btheta_to_xy(Lambda_p - dLambda_p, 0.0, &x, &y)
xmin = fmin(xmin, x)
xmax = fmax(xmax, x)
aitoff_Lambda_btheta_to_xy(Lambda_p + dLambda_p, 0.0, &x, &y)
xmin = fmin(xmin, x)
xmax = fmax(xmax, x)
# Now we have the (projected rectangular) bounds.
# Shift into normalized image coords
xmin = (xmin - bounds[0])
xmax = (xmax - bounds[0])
ymin = (ymin - bounds[2])
ymax = (ymax - bounds[2])
# Finally, select a rectangular region in image space
# that fully contains the projected data point.
# We'll reject image pixels in that rectangle that are
# not actually intersecting the data point as we go.
x0 = <int> (xmin / dx)
x1 = <int> (xmax / dx) + 1
y0 = <int> (ymin / dy)
y1 = <int> (ymax / dy) + 1
for i in range(x0, x1):
x = (bounds[0] + i * dx) / 2.0
for j in range(y0, y1):
y = (bounds[2] + j * dy)
zb = (x*x + y*y - 1.0)
if zb > 0: continue
z = (1.0 - 0.5*x*x - 0.5*y*y)
z = math.sqrt(z)
# Longitude
Lambda0 = 2.0*math.atan(z*x*s2/(2.0*z*z-1.0))
# Latitude
# We shift it into co-latitude
btheta0 = math.asin(z*y*s2)
# Now we just need to figure out which pixel contributes.
# We do not have a fast search.
if not (Lambda_p - dLambda_p <= Lambda0 <= Lambda_p + dLambda_p):
continue
if not (btheta_p - dbtheta_p <= btheta0 <= btheta_p + dbtheta_p):
continue
img[i, j] = field[fi]
mask[i, j] = 1
if return_mask:
return img, mask_arr.astype("bool")
else:
return img
# This function accepts a set of vertices (for a polyhedron) that are
# assumed to be in order for bottom, then top, in the same clockwise or
# counterclockwise direction (i.e., like points 1-8 in Figure 4 of the ExodusII
# manual). It will then either *match* or *fill* the results. If it is
# matching, it will early terminate with a 0 or final-terminate with a 1 if the
# results match. Otherwise, it will fill the signs with -1's and 1's to show
# the sign of the dot product of the point with the cross product of the face.
cdef int check_face_dot(int nvertices,
np.float64_t point[3],
np.float64_t **vertices,
np.int8_t *signs,
int match):
# Because of how we are doing this, we do not *care* what the signs are or
# how the faces are ordered, we only care if they match between the point
# and the centroid.
# So, let's compute these vectors. See above where these are written out
# for ease of use.
cdef np.float64_t vec1[3]
cdef np.float64_t vec2[3]
cdef np.float64_t cp_vec[3]
cdef np.float64_t npoint[3]
cdef np.float64_t dp
cdef np.uint8_t faces[MAX_NUM_FACES][2][2]
cdef np.uint8_t nf
if nvertices == 4:
faces = tetra_face_defs
nf = TETRA_NF
elif nvertices == 6:
faces = wedge_face_defs
nf = WEDGE_NF
elif nvertices == 8:
faces = hex_face_defs
nf = HEX_NF
else:
return -1
cdef int n, vi1a, vi1b, vi2a, vi2b
for n in range(nf):
vi1a = faces[n][0][0]
vi1b = faces[n][0][1]
vi2a = faces[n][1][0]
vi2b = faces[n][1][1]
# Shared vertex is vi1a and vi2a
subtract(vertices[vi1b], vertices[vi1a], vec1)
subtract(vertices[vi2b], vertices[vi2a], vec2)
subtract(point, vertices[vi1b], npoint)
cross(vec1, vec2, cp_vec)
dp = dot(cp_vec, npoint)
if match == 0:
if dp < 0:
signs[n] = -1
else:
signs[n] = 1
else:
if dp <= 0 and signs[n] < 0:
continue
elif dp >= 0 and signs[n] > 0:
continue
else: # mismatch!
return 0
return 1
def pixelize_element_mesh(np.ndarray[np.float64_t, ndim=2] coords,
np.ndarray[np.int64_t, ndim=2] conn,
buff_size,
np.ndarray[np.float64_t, ndim=2] field,
extents,
int index_offset = 0,
*,
return_mask=False,
):
cdef np.ndarray[np.float64_t, ndim=3] img
img = np.zeros(buff_size, dtype="float64")
img[:] = np.nan
cdef np.ndarray[np.uint8_t, ndim=3] mask_arr = np.ones_like(img, dtype="uint8")
cdef np.uint8_t[:, :, :] mask = mask_arr
# Two steps:
# 1. Is image point within the mesh bounding box?
# 2. Is image point within the mesh element?
# Second is more intensive. It will convert the element vertices to the
# mapped coordinate system, and check whether the result in in-bounds or not
# Note that we have to have a pseudo-3D pixel buffer. One dimension will
# always be 1.
cdef np.float64_t pLE[3]
cdef np.float64_t pRE[3]
cdef np.float64_t LE[3]
cdef np.float64_t RE[3]
cdef int use
cdef np.int64_t n, i, pi, pj, pk, ci, cj
cdef np.int64_t pstart[3]
cdef np.int64_t pend[3]
cdef np.float64_t ppoint[3]
cdef np.float64_t idds[3]
cdef np.float64_t dds[3]
cdef np.float64_t *vertices
cdef np.float64_t *field_vals
cdef int nvertices = conn.shape[1]
cdef int ndim = coords.shape[1]
cdef int num_field_vals = field.shape[1]
cdef double[4] mapped_coord
cdef ElementSampler sampler
# Pick the right sampler and allocate storage for the mapped coordinate
if ndim == 3 and nvertices == 4:
sampler = P1Sampler3D()
elif ndim == 3 and nvertices == 6:
sampler = W1Sampler3D()
elif ndim == 3 and nvertices == 8:
sampler = Q1Sampler3D()
elif ndim == 3 and nvertices == 20:
sampler = S2Sampler3D()
elif ndim == 2 and nvertices == 3:
sampler = P1Sampler2D()
elif ndim == 1 and nvertices == 2:
sampler = P1Sampler1D()
elif ndim == 2 and nvertices == 4:
sampler = Q1Sampler2D()
elif ndim == 2 and nvertices == 9:
sampler = Q2Sampler2D()
elif ndim == 2 and nvertices == 6:
sampler = T2Sampler2D()
elif ndim == 3 and nvertices == 10:
sampler = Tet2Sampler3D()
else:
raise YTElementTypeNotRecognized(ndim, nvertices)
# if we are in 2D land, the 1 cell thick dimension had better be 'z'
if ndim == 2:
if buff_size[2] != 1:
raise RuntimeError("Slices of 2D datasets must be "
"perpendicular to the 'z' direction.")
# allocate temporary storage
vertices = <np.float64_t *> malloc(ndim * sizeof(np.float64_t) * nvertices)
field_vals = <np.float64_t *> malloc(sizeof(np.float64_t) * num_field_vals)
# fill the image bounds and pixel size information here
for i in range(ndim):
pLE[i] = extents[i][0]
pRE[i] = extents[i][1]
dds[i] = (pRE[i] - pLE[i])/buff_size[i]
if dds[i] == 0.0:
idds[i] = 0.0
else:
idds[i] = 1.0 / dds[i]
with cython.boundscheck(False):
for ci in range(conn.shape[0]):
# Fill the vertices
LE[0] = LE[1] = LE[2] = 1e60
RE[0] = RE[1] = RE[2] = -1e60
for n in range(num_field_vals):
field_vals[n] = field[ci, n]
for n in range(nvertices):
cj = conn[ci, n] - index_offset
for i in range(ndim):
vertices[ndim*n + i] = coords[cj, i]
LE[i] = fmin(LE[i], vertices[ndim*n+i])
RE[i] = fmax(RE[i], vertices[ndim*n+i])
use = 1
for i in range(ndim):
if RE[i] < pLE[i] or LE[i] >= pRE[i]:
use = 0
break
pstart[i] = i64max(<np.int64_t> ((LE[i] - pLE[i])*idds[i]) - 1, 0)
pend[i] = i64min(<np.int64_t> ((RE[i] - pLE[i])*idds[i]) + 1, img.shape[i]-1)
# override for the low-dimensional case