def run_cupy(lat2, lon2):
import cupy as cp
# Allocate temporary arrays
size = len(lat2)
a = cp.empty(size, dtype='float64')
dlat = cp.empty(size, dtype='float64')
dlon = cp.empty(size, dtype='float64')
# Transfer inputs to the GPU
lat2 = cp.array(lat2)
lon2 = cp.array(lon2)
# Begin computation
lat1 = 0.70984286
lon1 = 1.23892197
MILES_CONST = 3959.0
cp.subtract(lat2, lat1, out=dlat)
cp.subtract(lon2, lon1, out=dlon)
# dlat = sin(dlat / 2.0) ** 2.0
cp.divide(dlat, 2.0, out=dlat)
cp.sin(dlat, out=dlat)
cp.multiply(dlat, dlat, out=dlat)
# a = cos(lat1) * cos(lat2)
lat1_cos = math.cos(lat1)
cp.cos(lat2, out=a)
cp.multiply(a, lat1_cos, out=a)
# a = a + sin(dlon / 2.0) ** 2.0
cp.divide(dlon, 2.0, out=dlon)
cp.sin(dlon, out=dlon)
cp.multiply(dlon, dlon, out=dlon)
cp.multiply(a, dlon, out=a)
cp.add(dlat, a, out=a)
c = a
cp.sqrt(a, out=a)
cp.arcsin(a, out=a)
cp.multiply(a, 2.0, out=c)
mi = c
cp.multiply(c, MILES_CONST, out=mi)
# Transfer outputs back to CPU
a = cp.asnumpy(a)
return a
def geodistance_cp(lng1, lat1, lng2, lat2):
lng1, lat1, lng2, lat2 = map(cp.radians, [lng1, lat1, lng2, lat2])
dlon = lng2 - lng1
dlat = lat2 - lat1
a = cp.sin(dlat / 2)**2 + cp.cos(lat1) * cp.cos(lat2) * cp.sin(dlon / 2)**2
dis = 2 * cp.arcsin(cp.sqrt(a)) * 6371 * 1000
return dis
def HaversineLocal(busMatrix, lineMatrix, haversine=True):
MatrizOnibus = cp.copy(busMatrix)
MatrizLinhas = cp.copy(lineMatrix)
MatrizLinhas = cp.dsplit(MatrizLinhas, 2)
MatrizOnibus = cp.dsplit(MatrizOnibus, 2)
infVector = cp.squeeze(cp.sum(cp.isnan(MatrizLinhas[0]), axis=1), axis=-1)
MatrizLinhas[0] = cp.expand_dims(MatrizLinhas[0], axis=-1)
MatrizLinhas[1] = cp.expand_dims(MatrizLinhas[1], axis=-1)
MatrizOnibus[0] = cp.expand_dims(MatrizOnibus[0], axis=-1)
MatrizOnibus[1] = cp.expand_dims(MatrizOnibus[1], axis=-1)
MatrizOnibus[0] *= cp.pi / 180
MatrizOnibus[1] *= cp.pi / 180
MatrizLinhas[1] = cp.transpose(MatrizLinhas[1], [2, 3, 0, 1]) * cp.pi / 180
MatrizLinhas[0] = cp.transpose(MatrizLinhas[0], [2, 3, 0, 1]) * cp.pi / 180
# Haversine or euclidian, based on <haversine>
if haversine:
results = 1000*2*6371.0088*cp.arcsin(
cp.sqrt(
(cp.sin((MatrizOnibus[0] - MatrizLinhas[0])*0.5)**2 + \
cp.cos(MatrizOnibus[0])* cp.cos(MatrizLinhas[0]) * cp.sin((MatrizOnibus[1] - MatrizLinhas[1])*0.5)**2)
))
else:
results = cp.sqrt((MatrizOnibus[0] - MatrizLinhas[0])**2 +
(MatrizOnibus[1] - MatrizLinhas[1])**2)
return results, infVector
def asin(x: Array, /) -> Array:
"""
Array API compatible wrapper for :py:func:`np.arcsin <numpy.arcsin>`.
See its docstring for more information.
"""
if x.dtype not in _floating_dtypes:
raise TypeError("Only floating-point dtypes are allowed in asin")
return Array._new(np.arcsin(x._array))
def Algorithm(MO, ML, TOLERANCE, detectionPercentage=None, haversine=True):
# global to analise
global resultsMin
#
MatrizOnibus = cp.copy(MO)
MatrizLinhas = cp.copy(ML)
MatrizLinhas = cp.dsplit(MatrizLinhas, 2)
MatrizOnibus = cp.dsplit(MatrizOnibus, 2)
infVector = cp.squeeze(cp.sum(cp.isnan(MatrizLinhas[0]), axis=1), axis=-1)
MatrizLinhas[0] = cp.expand_dims(MatrizLinhas[0], axis=-1)
MatrizLinhas[1] = cp.expand_dims(MatrizLinhas[1], axis=-1)
MatrizOnibus[0] = cp.expand_dims(MatrizOnibus[0], axis=-1)
MatrizOnibus[1] = cp.expand_dims(MatrizOnibus[1], axis=-1)
MatrizOnibus[0] *= cp.pi / 180
MatrizOnibus[1] *= cp.pi / 180
MatrizLinhas[1] = cp.transpose(MatrizLinhas[1], [2, 3, 0, 1]) * cp.pi / 180
MatrizLinhas[0] = cp.transpose(MatrizLinhas[0], [2, 3, 0, 1]) * cp.pi / 180
# Haversine or euclidian, based on <haversine>
if haversine:
results = 1000*2*6371.0088*cp.arcsin(
cp.sqrt(
(cp.sin((MatrizOnibus[0] - MatrizLinhas[0])*0.5)**2 + \
cp.cos(MatrizOnibus[0])* cp.cos(MatrizLinhas[0]) * cp.sin((MatrizOnibus[1] - MatrizLinhas[1])*0.5)**2)
))
else:
results = cp.sqrt((MatrizOnibus[0] - MatrizLinhas[0])**2 +
(MatrizOnibus[1] - MatrizLinhas[1])**2)
# Matriz D^[min]
resultsMin = cp.nanmin(results, axis=1)
#resultsGroup = cp.pad(resultsMin,[(0,0),(0,0),(0,resultsMin.shape[2]%groupSize)],constant_values=cp.NaN)
#resultsGroup = cp.reshape(resultsGroup,(resultsGroup.shape[0],resultsGroup.shape[1],-1,groupSize))
sizeLine = resultsMin.shape[2]
#below = cp.sum(resultsMin,axis=2)
below = cp.sum(resultsMin < TOLERANCE, axis=2)
resultsPerc = below / (sizeLine - infVector)
if detectionPercentage:
return resultsPerc > cp.array(detectionPercentage)
return resultsPerc
def arccsc__(z):
return cp.arcsin(1.0/z)