def check_tan():
x = create_input_for_trigonometric_ops(
[-np.pi / 6, -np.pi / 4, 0, np.pi / 4, np.pi / 6])
y = nd.tan(x)
# expected ouput for indices=(0, 1, -3, -2, -1) after applying tan()
expected_output = [-.577, -1, 0, 1, .577]
assert_correctness_of_trigonometric_ops(y, expected_output)
def rnn(inputs, state, params):
# inputs和output都是num_steps个形状为(batch_size,vocab_size)
output = []
W_xh, W_hh, b_h, W_hq, b_q = params
H, = state # 只有第一个有参数H,见上,shape=(batch_size, num_hiddens)
for X in inputs: # 遍历num_steps个
H = nd.tan(nd.dot(X, W_xh) + nd.dot(H, W_hh) + b_h) # 计算隐藏状态,并作为返回值保存
Y = nd.dot(H, W_hq) + b_q
output.append(Y) # 追加
return output, (H, )
def lstm_rnn(self, inputs, h, c, temperature=1.0):
outputs = []
for X in inputs:
# if not X.shape[0] == 77:
# continue
X = nd.one_hot(X, 60)
#print("X.shape",X.shape,self.Wxg.shape,self.Whg.shape,h.shape)
g = nd.tanh(nd.dot(X, self.Wxg) + nd.dot(h, self.Whg) + self.bg)
i = nd.sigmoid(nd.dot(X, self.Wxi) + nd.dot(h, self.Whi) + self.bi)
f = nd.sigmoid(nd.dot(X, self.Wxf) + nd.dot(h, self.Whf) + self.bf)
o = nd.sigmoid(nd.dot(X, self.Wxo) + nd.dot(h, self.Who) + self.bo)
c = f * c + i * g
h = o * nd.tan(c)
yhat_linear = nd.dot(h, self.Why) + self.by
yhat = self.softmax(yhat_linear, temperature=temperature)
#yhat = mx.ndarray.softmax(yhat_linear,temperature=temperature)
outputs.append(yhat)
return (outputs, h, c)
def tan(x):
return nd.tan(x)
def flowdr(dem_fill,NoData,rows,cols,ctx,switch):
ingrid = np.indices((rows, cols))
ingrid[0] # row indices
ingrid[1] # column indices
ingridxmx=nd.array(ingrid[1],ctx[0]).reshape((1,1,rows, cols))
ingridymx=nd.array(ingrid[0],ctx[0]).reshape((1,1,rows, cols))
dem_fillmx=nd.array(dem_fill,ctx[0])
demmx=dem_fillmx.reshape((1,1,rows, cols))
res=1
l=[0,1,2,3,4,5,6,7,0]
direct=[1,2,4,8,16,32,64,128]
direct_d=[[1,3],[2,6],[4,12],[8,24],[16,48],[32,96],[64,192],[128,129]]
weight=[None]*8
weight1=[None]*8
convx=[None]*8
convy=[None]*8
convz=[None]*8
runlen=[1,ma.pow(2,0.5),1,ma.pow(2,0.5),1,ma.pow(2,0.5),1,ma.pow(2,0.5)]*res
n = [[[] for x in range(3)] for x in range(8)]#create list to store normal vectors for each facet
s = [None]*8
d = [None]*8
weight[0] = nd.array([[0, 0, 0], [0, 1, -1], [0, 0, 0]], gpu(0))
weight[1] = nd.array([[0, 0, -1], [0, 1, 0], [0, 0, 0]], gpu(0))
weight[2] = nd.array([[0, -1, 0], [0, 1, 0], [0, 0, 0]], gpu(0))
weight[3] = nd.array([[-1, 0, 0], [0, 1, 0], [0, 0, 0]], gpu(0))
weight[4] = nd.array([[0, 0, 0], [-1, 1, 0], [0, 0, 0]], gpu(0))
weight[5] = nd.array([[0, 0, 0], [0, 1, 0], [-1, 0, 0]], gpu(0))
weight[6] = nd.array([[0, 0, 0], [0, 1, 0], [0, -1, 0]], gpu(0))
weight[7] = nd.array([[0, 0, 0], [0, 1, 0], [0, 0, -1]], gpu(0))
weight1[0] = nd.array([[0, 0, 0], [0, 1, -10], [0, 0, 0]], gpu(0))
weight1[1] = nd.array([[0, 0, -10], [0, 1, 0], [0, 0, 0]], gpu(0))
weight1[2] = nd.array([[0, -10, 0], [0, 1, 0], [0, 0, 0]], gpu(0))
weight1[3] = nd.array([[-10, 0, 0], [0, 1, 0], [0, 0, 0]], gpu(0))
weight1[4] = nd.array([[0, 0, 0], [-10, 1, 0], [0, 0, 0]], gpu(0))
weight1[5] = nd.array([[0, 0, 0], [0, 1, 0], [-10, 0, 0]], gpu(0))
weight1[6] = nd.array([[0, 0, 0], [0, 1, 0], [0, -10, 0]], gpu(0))
weight1[7] = nd.array([[0, 0, 0], [0, 1, 0], [0, 0, -10]], gpu(0))
d0=nd.zeros((rows, cols),ctx[0],dtype='float32')
dd=nd.zeros((rows, cols),ctx[0],dtype='float32')
d_flat=nd.zeros((rows, cols),ctx[0],dtype='float32')
flat=nd.zeros((rows, cols),ctx[0],dtype='float32')
dep=nd.zeros((rows, cols),ctx[0],dtype='float32')
high=nd.zeros((rows, cols),ctx[0],dtype='float32')
fd=nd.zeros((rows, cols),ctx[0],dtype='float32')-999
d_compact=nd.zeros((rows, cols),ctx[0],dtype='float32')-1
for i in range(0,8):
w=weight[i].reshape((1, 1, 3, 3))
convz[i] = nd.Convolution(data=demmx, weight=w, kernel=(3,3), no_bias=True, num_filter=1,pad=(1,1),cudnn_tune='off')
convz[i]=convz[i][0,0,:,:]
if switch==1 or 3:
convx[i] = nd.Convolution(data=ingridxmx, weight=w, kernel=(3,3), no_bias=True, num_filter=1,pad=(1,1),cudnn_tune='off')
convy[i] = nd.Convolution(data=ingridymx, weight=w, kernel=(3,3), no_bias=True, num_filter=1,pad=(1,1),cudnn_tune='off')
convx[i]=convx[i][0,0,:,:]
convy[i]=convy[i][0,0,:,:]
if switch==1 or 3:
for p in range(0,8):#8 facets from N-NE clockwise
l0=l[p]
l1=l[p+1]
d[l0]=d0-999#Nodata value
dmax=d0-999
smax=d0-999
n[l0][0]= convz[l0]*convy[l1]-convz[l1]*convy[l0]#nx
n[l0][1]= convz[l0]*convx[l1]-convz[l1]*convx[l0]#ny
n[l0][2]= convy[l0]*convx[l1]-convy[l1]*convx[l0]#nz
#make boolean mask to determine direction d and slope s
d[l0]=nd.where(condition=((n[l0][0]==0)*(n[l0][1]>=0)),x=d0,y=d[l0])
d[l0]=nd.where(condition=((n[l0][0]==0)*(n[l0][1])<0),x=d0+ma.pi,y=d[l0])
d[l0]=nd.where(condition=(n[l0][0]>0),x=ma.pi/2-nd.arctan(n[l0][1]/n[l0][0]),y=d[l0])
d[l0]=nd.where(condition=(n[l0][0]<0),x=3*ma.pi/2-nd.arctan(n[l0][1]/n[l0][0]),y=d[l0])
d[l0]=nd.where(condition=((convz[l0]<=0)*(convz[l1]<=0)),x=dmax,y=d[l0])
s[l0]=-nd.tan(nd.arccos(n[l0][2]/(nd.sqrt(nd.square(n[l0][0])+nd.square(n[l0][1])+nd.square(n[l0][2])))))#slope of the triangular facet
s[l0]=nd.where(condition=((convz[l0]<=0)*(convz[l1]<=0)),x=smax,y=s[l0])
#Modify the scenario when the steepest slope is outside the 45 range of each facet
dmax=nd.where(condition=((convz[l0]/runlen[l0]>=convz[l1]/runlen[l0])*(convz[l0]>0)),x=d0+ma.pi*l0/4,y=dmax)
dmax=nd.where(condition=((convz[l0]/runlen[l0]<convz[l1]/runlen[l0])*(convz[l1]>0)),x=d0+ma.pi*(l0+1)/4,y=dmax)
smax=nd.where(condition=((convz[l0]>=convz[l1])*(convz[l0]>0)),x=convz[l0]/runlen[l0],y=smax)
smax=nd.where(condition=((convz[l0]<convz[l1])*(convz[l1]>0)),x=convz[l1]/runlen[l1],y=smax)
d[l0]=nd.where(condition=((d[l0]<ma.pi*l0/4)+(d[l0]>ma.pi*l1/4)),x=dmax,y=d[l0])
s[l0]=nd.where(condition=((d[l0]<ma.pi*l0/4)+(d[l0]>ma.pi*l1/4)),x=smax,y=s[l0])
if switch==1:
#flat and depressions indicator grid
flat=(convz[l0]==0)+flat
dep=(convz[l0]<0)+dep
high=(convz[l0]>0)+high
for q in range(0,8):#check if the 45 degree range angles need to be maintaied, otherwise delete (set to NoData)
l0=l[q]
l1=l[q+1]
l2=l[q-1]
dmax=d0-999
if q==0:
dmax=nd.where(condition=(d[0]==d[1]),x=d[0],y=dmax)
dmax=nd.where(condition=(d[0]==d[7]),x=d[0],y=dmax)
d[0]=nd.where(condition=((d[0]==ma.pi*l0/4)+(d[0]==ma.pi*l1/4)),x=dmax,y=d[0])
else:
dmax=nd.where(condition=(d[l0]==d[l1]),x=d[l0],y=dmax)
dmax=nd.where(condition=(d[l0]==d[l2]),x=d[l0],y=dmax)
d[l0]=nd.where(condition=((d[l0]==ma.pi*l0/4)+(d[l0]==ma.pi*l1/4)),x=dmax,y=d[l0])
#Check if flat or surface depression area. then lable with -1 or -10 respectively
if switch==1:
fd=nd.where(condition=(flat==8),x=d0-2,y=fd)#flats
fd=nd.where(condition=(dep>=1)*(high==0),x=d0-3,y=fd)#high edge
high_zero=nd.where(condition=(high==0),x=d0+1,y=d0)
for j in range (0,8):
if switch==1 or switch==2:
d_flat=nd.where(condition=(convz[j]==0),x=d0+direct[j],y=d0)+d_flat
if switch==1:
flat_near=nd.where(condition=(convz[j]==0),x=d0+5,y=d0)
dd1=high_zero+flat_near
w=weight1[j].reshape((1, 1, 3, 3))
dd1=dd1.reshape((1,1,rows, cols))
conv_near= nd.Convolution(data=dd1, weight=w, kernel=(3,3), no_bias=True, num_filter=1,pad=(1,1),cudnn_tune='off')
conv_near= conv_near[0,0,:,:]
dd=nd.where(condition=(conv_near==-5)+(conv_near==-59)+(conv_near==-54)+(conv_near==-4),x=d0+1,y=d0)+dd
if switch==1 or switch==3:
d_compact=nd.where(condition=(d[j]==ma.pi*j/4),x=d0+direct_d[j][0],y=d_compact)
d_compact=nd.where(condition=(d[j]>j*ma.pi/4)*(d[j]<(j+1)*ma.pi/4),x=d0+direct_d[j][1],y=d_compact)
if switch==1 or switch==3:
d_compact=nd.where(condition=(dem_fillmx==d0+NoData),x=d0-999,y=d_compact)#NoData
if switch==1:
fd=nd.where(condition=(dd>=1)*(high>=1),x=d0-1,y=fd)#low edge
fd=nd.where(condition=(dep==8),x=d0-10,y=fd)#lowest points in depressions
return (fd.asnumpy(),d_compact.asnumpy(),d_flat.asnumpy())
if switch==2:
return (d_flat.asnumpy())
if switch==3:
return (d_compact.asnumpy())
