我嘗試使用 Python 3.7 確定二維陣列中的鄰居。
陣列是這樣的：
array([[ 1., 2., nan, nan, 5.],
[nan, 2., nan, 5., nan],
[nan, 2., 4., nan, 6.],
[nan, nan, nan, 5., 5.],
[nan, nan, nan, nan, nan],
[ 1., 2., 4., nan, nan],
[ 1., 2., nan, nan, 4.],
[nan, 4., nan, nan, 5.]])
首先，需要確定最大值并且位置是（2,4），然后我想獲得沒有nan值的鄰居。代碼如下：
test_arr = np.array([[1,2,np.nan,np.nan,5],
[np.nan,2,np.nan,5,np.nan],
[np.nan,2,4,np.nan,6],
[np.nan,np.nan,np.nan,5,5],
[np.nan,np.nan,np.nan,np.nan,np.nan],
[1,2,4,np.nan,np.nan],
[1,2,np.nan,np.nan,5],
[np.nan,4,np.nan,np.nan,6]])
row = test_arr.shape[0]
col = test_arr.shape[1]
temp_amatrix = np.matrix(test_arr)
p = np.argwhere(test_arr == np.nanmax(temp_amatrix)).flatten()[0]
q = np.argwhere(test_arr == np.nanmax(temp_amatrix)).flatten()[1]
def neighbours(test_arr_in,countnotnan_in):
for r in range(len(np.where(countnotnan_in==1)[0])):
x = np.where(countnotnan_in == 1)[0][r]
y = np.where(countnotnan_in == 1)[1][r]
indexlist = [[x-1,y-1],[x-1,y],[x-1,y 1],[x,y-1],[x,y 1],[x 1,y-1],[x 1,y],[x 1,y 1]]
for c in indexlist:
if 0 <= c[0] < row and 0 <= c[1] < col:
if np.isnan(test_arr_in[c[0],c[1]]) == False:
countnotnan_in[c[0],c[1]] = 1
return countnotnan_in
countnotnan = np.zeros_like(test_arr)
countnotnan[p][q] = 1
notnan_arr = neighbours(test_arr,countnotnan)
結果是：
notnan_arr = array([[ 0, 0, 0, 0, 0],
[ 0, 0, 0, 1, 0],
[ 0, 0, 0, 0, 1],
[ 0, 0, 0, 1, 1],
[ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0]])
是的，沒關系。實際上，我想使用新的“1”值來確定它們的鄰居，即確定 (1,3)、(3,3) 和 (3,4) 位置的鄰居。重復此程序，直到所有值從 (2,4) 位置開始的位置都被識別。結果將是這樣的：
notnan_arr = array([[ 1, 1, 0, 0, 1],
[ 0, 1, 0, 1, 0],
[ 0, 1, 1, 0, 1],
[ 0, 0, 0, 1, 1],
[ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0],
[ 0, 0, 0, 0, 0]])
接下來，我要確定其余未識別值的最大值以重復該程序，直到識別出具有此二維陣列值的所有鄰居的位置。最后的結果應該是這樣的：
notnan_arr = array([[ 1, 1, 0, 0, 1],
[ 0, 1, 0, 1, 0],
[ 0, 1, 1, 0, 1],
[ 0, 0, 0, 1, 1],
[ 0, 0, 0, 0, 0],
[ 1, 1, 1, 0, 0],
[ 1, 1, 0, 0, 1],
[ 0, 1, 0, 0, 1]])
My mother tongue is not English, if anyone has any question about the description. Please tell me, I will try my best to state it. And if anyone can help me, I would be very grateful.
uj5u.com熱心網友回復：
def neighbours(test_arr_in,fstnotnan_in,count_in,countused_in):
for r in range(len(np.where(fstnotnan_in==1)[0])):
x = np.where(count_in == 1)[0][r]
y = np.where(count_in == 1)[1][r]
if np.isnan(countused_in[x][y]) == True:
continue
else:
countused_in[x,y] = np.nan
indexlist = [[x-1,y-1],[x-1,y],[x-1,y 1],[x,y-1],[x,y 1],[x 1,y-1],[x 1,y],[x 1,y 1]]
for c in indexlist:
if 0 <= c[0] < row and 0 <= c[1] < col:
if np.isnan(test_arr_in[c[0],c[1]]) == False:
fstnotnan_in[c[0],c[1]] = 1
count_in = np.where(fstnotnan_in == 1,1.,0)
return fstnotnan_in,count_in,countused_in
test_arr = np.array([[1,2,np.nan,np.nan,5],
[np.nan,2,np.nan,5,np.nan],
[np.nan,2,4,np.nan,6],
[np.nan,np.nan,np.nan,5,5],
[np.nan,np.nan,np.nan,np.nan,np.nan],
[1,2,4,np.nan,np.nan],
[1,2,np.nan,np.nan,5],
[np.nan,4,np.nan,np.nan,6]])
row = test_arr.shape[0]
col = test_arr.shape[1]
temp_amatrix = np.matrix(test_arr)
p = np.argwhere(test_arr == np.nanmax(temp_amatrix)).flatten()[0]
q = np.argwhere(test_arr == np.nanmax(temp_amatrix)).flatten()[1]
countused = np.zeros_like(test_arr)
fstnotnan = np.zeros_like(test_arr)
count = np.full((row,col),np.nan)
fstnotnan[p,q] = 1
count[p,q] = 1
一步一步，第二個結果就完成了。
fstnotnan_out,count_out,countused_out = neighbours(test_arr,fstnotnan,count,countused)
fstnotnan_out1,count_out1,countused_out1 = neighbours(test_arr,fstnotnan_out,count_out,countused_out)
fstnotnan_out2,count_out2,countused_out2 = neighbours(test_arr,fstnotnan_out1,count_out1,countused_out1)
fstnotnan_out3,count_out3,countused_out3 = neighbours(test_arr,fstnotnan_out2,count_out2,countused_out2)
fstnotnan_out4,count_out4,countused_out4 = neighbours(test_arr,fstnotnan_out3,count_out3,countused_out3)
然而，這只是一個例子。我的資料集有一萬多行和列。所以我想遞回地完成目標。我嘗試代碼如下：
def neighbours(test_arr_in,fstnotnan_in,count_in,countused_in):
if (np.where(fstnotnan_in== 1)[0].shape[0] == np.where(np.isnan(countused_in))[0].shape[0]) == True:
return fstnotnan_in,count_in,countused_in
else:
for r in range(len(np.where(fstnotnan_in==1)[0])):
x = np.where(count_in == 1)[0][r]
y = np.where(count_in == 1)[1][r]
if np.isnan(countused_in[x][y]) == True:
continue
else:
countused_in[x,y] = np.nan
indexlist = [[x-1,y-1],[x-1,y],[x-1,y 1],[x,y-1],[x,y 1],[x 1,y-1],[x 1,y],[x 1,y 1]]
for c in indexlist:
if 0 <= c[0] < row and 0 <= c[1] < col:
if np.isnan(test_arr_in[c[0],c[1]]) == False:
fstnotnan_in[c[0],c[1]] = 1
count_in = np.where(fstnotnan_in == 1,1.,0)
return neighbours(test_arr_in,fstnotnan_in,count_in,countused_in)
沒關系。我知道了！
uj5u.com熱心網友回復：
我完成了。代碼如下：
def sechmax(arr_in):
temp_amatrix = np.matrix(arr_in)
r_max = np.argwhere(arr_in == np.nanmax(temp_amatrix)).flatten()[0]
c_max = np.argwhere(arr_in == np.nanmax(temp_amatrix)).flatten()[1]
return r_max,c_max
def neighbours(arr_in,fstnotnan_in,countused_in):
if (np.where(fstnotnan_in == 1)[0].shape[0] == np.where(np.isnan(countused_in))[0].shape[0]):
n_arr_in = np.where(countused_in == 0,arr_in,np.nan)
if np.isnan(n_arr_in).all():
return fstnotnan_in,countused_in
else:
r_max,c_max = sechmax(n_arr_in)
fstnotnan_in[r_max,c_max] = 1
return neighbours(n_arr_in,fstnotnan_in,countused_in)
else:
count = fstnotnan_in.copy()
for r in range(len(np.where(fstnotnan_in==1)[0])):
x = np.where(count == 1)[0][r]
y = np.where(count == 1)[1][r]
if np.isnan(countused_in[x][y]):
continue
else:
countused_in[x,y] = np.nan
indexlist = [[x-1,y-1],[x-1,y],[x-1,y 1],[x,y-1],[x,y 1],[x 1,y-1],[x 1,y],[x 1,y 1]]
for c in indexlist:
if 0 <= c[0] < row and 0 <= c[1] < col:
if np.isnan(arr_in[c[0],c[1]]) == False:
fstnotnan_in[c[0],c[1]] = 1
return neighbours(arr_in,fstnotnan_in,countused_in)
test_arr = np.array([[1,2,np.nan,np.nan,5],
[np.nan,2,np.nan,5,np.nan],
[np.nan,2,4,np.nan,6],
[np.nan,np.nan,np.nan,5,5],
[np.nan,np.nan,np.nan,np.nan,np.nan],
[1,2,4,np.nan,np.nan],
[1,2,np.nan,np.nan,5],
[np.nan,4,np.nan,np.nan,6]])
row = test_arr.shape[0]
col = test_arr.shape[1]
r_max,c_max = sechmax(test_arr)
countused = np.zeros_like(test_arr)
fstnotnan = np.zeros_like(test_arr)
fstnotnan[r_max,c_max] = 1
fstnotnan_out,countused_out = neighbours(test_arr,fstnotnan,countused)
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