def calculateCv(self, las):
x, y = self.tileCentre(parseHeader(las)) # calculate plot centre
pid = multiprocessing.current_process()._identity[0]
tempDirectoryName = 'lidar.processing.' + str(np.random.mtrand.RandomState(pid).randint(0, 9999999)) + '.tmp'
tempDirectory = os.path.join(tempfile.gettempdir(), tempDirectoryName)
las = lasIO(las, tmpDir=tempDirectory, keepTemp=False).all().asArray()
self.plot_dictionary[x, y] = las['z'].std() / las['z'].mean()
def readLAS(self, lasF):
# read las file and send to different processes
pid = multiprocessing.current_process()._identity[0]
tempDirectoryName = 'lidar.processing.' + str(
np.random.mtrand.RandomState(pid).randint(0, 9999999)) + '.tmp'
tempDirectory = os.path.join(tempfile.gettempdir(), tempDirectoryName)
las = lasIO(lasF, tmpDir=tempDirectory)
X, Y = self.tileCentre(las)
if 'point_only' in self.metrics.keys():
self.metrics['point_only'][X, Y] = 1
else:
las = las.all().asArray()
if 'height' in self.metrics.keys():
self.metrics['height'][X, Y] = np.percentile(las['z'], 95)
if len(
set(self.metrics.keys()).intersection([
'layers', 'canopyDensity', 'underDensity', 'pgap',
'baseHeight'
])) > 0:
self.calculateStructure(las, X, Y, lasF)
def __init__(self, las, ppsm):
if isinstance(las, str):
las = lasIO(las).all().asArray()
self.ppsm = ppsm
self.las = las
self.xy = 1. / np.sqrt(ppsm)
self.xmin = np.round(self.las['x'].min())
self.xmax = np.round(self.las['x'].max())
self.ymin = np.round(self.las['y'].min())
self.ymax = np.round(self.las['y'].max())
def fromLAS(self, las, threshold=2.0, top_threshold=99, z_scale=False):
self.threshold = threshold
if isinstance(las, str):
lasRAW = lasIO(las, tmpDir=self.tempDir,
keepTemp=False).all().asArray() # reads las file
if lasRAW['rtn_tot'].max() == 0:
lasRAW['rtn_tot'][:] = 1
self.las = lasRAW[
lasRAW['rtn_tot'] >
0] # inds lines where rtn_tot == 0 and removes them
self.z_scale = parseHeader(las)['zscale']
elif isinstance(las, np.ndarray):
self.las = las
if not z_scale:
self.z_scale = .01
else:
raise Exception(
'input needs to path to LAS file or a LAS file array')
self.z = self.las['z']
self.zw = self.las['rtn_tot']
self.lenLAS = len(self.las)
rtn_weight = np.around(1. / self.las['rtn_tot'], decimals=2)
self.total = np.sum(rtn_weight) # sum of weighted returns
# removes ground and outliers
idx = [(self.z > threshold) &
(self.z < np.percentile(self.z, top_threshold))]
self.z = self.z[idx] # ...from z
self.zw = rtn_weight[idx] # ... and from weighted heights
self._create_bins()
return self
processingList = self.l[maxProcess *
listI:(maxProcess * listI) +
maxProcess]
else:
processingList = self.l[maxProcess * listI:]
for j, las in enumerate(
processingList): # limits number of images run at once
p = multiprocessing.Process(target=self.chp, args=(las, ))
jobs.append(p)
p.start()
for proc in jobs:
proc.join()
listI += 1
self.bsCHPmutiple = dict(self.chp_dictionary)
if __name__ == '__main__':
path = '/Users/phil/ALS/WC/spl/tile_20/WC1_5m_TILES/383475.0_5828910.0.las'
las = lasIO(path).all().asArray()
# plot = 'PE2744N2556'
# las_path = os.path.join(path, plot + '.las')
# las = CanopyComplexity().fromSample(las['z'], las['rtn_tot']).CHP('model')
las = CanopyComplexity().fromLAS(las).CHP('model')
chp = CanopyComplexity().fromLAS(las.simulateCloud()).CHP()
print chp.zw
