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FacetFlowNetwork.pyx
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FacetFlowNetwork.pyx
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import sys
import numpy as np
from libc.stdlib cimport free
__version__ = '0.1'
__author__ = 'Aljoscha Rheinwalt'
__author_email__ = '[email protected]'
cdef extern from "main.c":
void network(unsigned int *net, double *w, double *a, double *d, double *p, double *t,
const unsigned int *tri, const double *x, const double *y, const double *z,
const unsigned int m, const unsigned int n)
void tunnel(unsigned int *net, double *w, double *a,
const unsigned int *tri, const double *x, const double *y, const double *z,
const unsigned int m, const unsigned int n, const unsigned int ml)
void linkthroughput(double *ltp,
const unsigned int *net, const double *w, const double *a,
const unsigned int m)
unsigned int * upstreamnetwork(const unsigned int *net,
const unsigned int m, unsigned int *l)
class ffn:
"""
Facet flow network class
"""
def __init__(self, x = None, y = None, z = None,
tri = None, fname = None,
tunneling = True, tunnelmaxlen = 100):
cdef double[:] xv, yv, zv
cdef unsigned int[:,:] tv
cdef unsigned int[:,:] nv
cdef unsigned int m, n
cdef double[:,:] wv, av
cdef double[:] dv, pv, sv
if fname is None:
if tri is None:
print('Delaunay triangulation ..')
from scipy.spatial import Delaunay
tri = Delaunay(np.transpose((x-np.mean(x), y-np.mean(y))),
qhull_options = 'Qt').simplices.astype('uint32')
if x is None:
sys.exit('error: fname and x are both not specified!')
self.x = np.array(x)
self.y = np.array(y)
self.z = np.array(z)
self.tri = tri
self.tunneled = False
self.ltp = None
self.m = tri.shape[0]
self.n = len(x)
self.net = np.zeros((self.m, 2), dtype = 'uint32')
self.w = np.zeros((self.m, 2), dtype = 'float64')
self.a = np.zeros((self.m, 2), dtype = 'float64')
self.d = np.zeros(self.m, dtype = 'float64')
self.aspect = np.zeros(self.m, dtype = 'float64')
self.slope = np.zeros(self.m, dtype = 'float64')
m = self.m
n = self.n
nv = self.net
wv = self.w
av = self.a
dv = self.d
pv = self.aspect
sv = self.slope
xv = self.x
yv = self.y
zv = self.z
tv = self.tri
print('FFN construction ..')
network(&nv[0,0], &wv[0,0], &av[0,0], &dv[0], &pv[0], &sv[0],
&tv[0,0], &xv[0], &yv[0], &zv[0], m, n)
self.aspect *= 180 / np.pi
self.slope *= 180 / np.pi
if tunneling:
print('resolving sinks by tunneling ..')
tunnel(&nv[0,0], &wv[0,0], &av[0,0],
&tv[0,0], &xv[0], &yv[0], &zv[0],
m, n, tunnelmaxlen)
self.tunneled = True
else:
from h5py import File
f = File(fname, 'r')
self.x = f['x'][:]
self.y = f['y'][:]
self.z = f['z'][:]
self.tri = f['tri'][:]
self.net = f['net'][:]
self.w = f['w'][:]
self.a = f['a'][:]
self.d = f['d'][:]
self.aspect = f['aspect'][:]
self.slope = f['slope'][:]
flags = f['flags'][:]
self.tunneled = bool(flags[0])
self.ltp = None
self.m = self.tri.shape[0]
self.n = self.x.shape[0]
def __str__(self):
return 'ffn(%.1e points, %.1e facets)' % (self.n, self.m)
def tunneling(self, tunnelmaxlen = 100):
"""
Tunnel flow out of sinks
"""
cdef double[:] xv, yv, zv
cdef unsigned int[:,:] tv
cdef unsigned int[:,:] nv
cdef unsigned int m, n
cdef double[:,:] wv, av
if not self.tunneled:
m = self.m
n = self.n
nv = self.net
wv = self.w
av = self.a
xv = self.x
yv = self.y
zv = self.z
tv = self.tri
tunnel(&nv[0,0], &wv[0,0], &av[0,0],
&tv[0,0], &xv[0], &yv[0], &zv[0],
m, n, tunnelmaxlen)
self.tunneled = True
def upstream(self):
"""
Returns the FFN in reverse, links point upstream
"""
cdef unsigned int[:] rv
cdef unsigned int[:,:] nv
cdef unsigned int m, l
cdef unsigned int *p
cdef unsigned int[:] pv
nv = self.net
m = self.m
p = upstreamnetwork(&nv[0,0], m, &l)
pv = <unsigned int[:l]> p
r = np.asarray(pv).copy()
free(p)
return r
def sca(self):
"""
Specific catchment area
"""
cdef double[:,:] lv, wv, av
cdef unsigned int[:,:] nv
cdef unsigned int m
if self.ltp is None:
self.ltp = np.zeros((self.m, 2), dtype = 'float64')
lv = self.ltp
nv = self.net
wv = self.w
av = self.a
m = self.m
linkthroughput(&lv[0,0], &nv[0,0], &wv[0,0], &av[0,0], m)
a = self.ltp.sum(axis = 1)
return a / self.d
def tda(self):
"""
Total drainage area
"""
cdef double[:,:] lv, wv, av
cdef unsigned int[:,:] nv
cdef unsigned int m
if self.ltp is None:
self.ltp = np.zeros((self.m, 2), dtype = 'float64')
lv = self.ltp
nv = self.net
wv = self.w
av = self.a
m = self.m
linkthroughput(&lv[0,0], &nv[0,0], &wv[0,0], &av[0,0], m)
a = self.ltp.sum(axis = 1)
return a
def fatp(self, var):
"""
Facet values averaged to point values
"""
cdef unsigned int i, k
cdef double[::] rv
cdef double[:] vv
cdef unsigned int[::] cv
cdef unsigned int[:, :] tv
assert len(var) == self.m, '%i facets and %i values' % (self.m, len(var))
r = np.zeros(self.n, dtype = 'float64')
c = np.zeros(self.n, dtype = 'uint32')
rv = r
cv = c
tv = self.tri
vv = var
for i in range(self.m):
for k in range(3):
rv[tv[i, k]] += vv[i]
cv[tv[i, k]] += 1
return r / c
def fmtp(self, var):
"""
Facet values maximum to point values
"""
cdef unsigned int i, k
cdef double[::] rv
cdef double[:] vv
cdef unsigned int[:, :] tv
assert len(var) == self.m, '%i facets and %i values' % (self.m, len(var))
r = np.zeros(self.n, dtype = 'float64')
rv = r
tv = self.tri
vv = var
for i in range(self.m):
for k in range(3):
if vv[i] > rv[tv[i, k]]:
rv[tv[i, k]] = vv[i]
return r
def facet_centroids(self):
"""
Return the 3D centroids of the facets
"""
return np.transpose((self.x[self.tri].mean(1),
self.y[self.tri].mean(1),
self.z[self.tri].mean(1)))
def save(self, fname, compr = 'gzip', copts = 9):
"""
Save FFN to a compressed HDF file
"""
import os
from h5py import File
fn, fe = os.path.splitext(fname)
f = File('%s.hdf' % fn, 'w')
f.create_dataset('x', data = self.x, compression = compr, compression_opts = copts)
f.create_dataset('y', data = self.y, compression = compr, compression_opts = copts)
f.create_dataset('z', data = self.z, compression = compr, compression_opts = copts)
f.create_dataset('tri', data = self.tri, compression = compr, compression_opts = copts)
f.create_dataset('net', data = self.net, compression = compr, compression_opts = copts)
f.create_dataset('w', data = self.w, compression = compr, compression_opts = copts)
f.create_dataset('a', data = self.a, compression = compr, compression_opts = copts)
f.create_dataset('d', data = self.d, compression = compr, compression_opts = copts)
f.create_dataset('aspect', data = self.aspect, compression = compr, compression_opts = copts)
f.create_dataset('slope', data = self.slope, compression = compr, compression_opts = copts)
flags = np.zeros(2, dtype = 'int')
if self.tunneled:
flags[0] = 1
f.create_dataset('flags', data = flags, compression = compr, compression_opts = copts)
f.close()
def export(self, fname, var, pnts = None, cmap = None):
"""
Export point cloud to LAS file with RGB colors according to var
"""
import laspy
import os
from subprocess import call
if cmap is None:
from matplotlib.cm import magma_r
cmap = magma_r
fn, fe = os.path.splitext(fname)
v = var - np.min(var)
v /= v.max()
rgb = cmap(v)
rgb = rgb[:, :3]
rgb *= 65535
rgb = rgb.astype('uint')
header = laspy.header.Header()
header.data_format_id = 2
f = laspy.file.File('%s.las' % fn, mode = 'w', header = header)
f.header.scale = [0.001, 0.001, 0.001]
if pnts is None:
f.header.offset = [self.x.min(), self.y.min(), self.z.min()]
f.x = self.x
f.y = self.y
f.z = self.z
else:
f.header.offset = [pnts[:,0].min(), pnts[:,1].min(), pnts[:,2].min()]
f.x = pnts[:, 0]
f.y = pnts[:, 1]
f.z = pnts[:, 2]
f.set_red(rgb[:, 0])
f.set_green(rgb[:, 1])
f.set_blue(rgb[:, 2])
f.close()
try:
r = call(['laszip', '%s.las' % fn])
if not r:
r = call(['rm', '%s.las' % fn])
except:
pass