See the documentation for instructions on how to use scipy.sparse.dia_matrix.
dia_matrix(data, offsets), shape=(M,N))
where the data [k,:] stores the diagnostic entries for diagnostic offsets [k]
The following examples are shown along with the description that :I'm not sure how offset works on a data array of three rows and four columns to make it four rows and four columns.I would appreciate it if you could let me know
data=np.array([1,2,3,4]]).repeat(3,axis=0)
offsets = np.array ([0,-1,2])
dia_matrix ((data, offsets), shape=(4,4)).tearray()
array([1,0,3,0],
[1, 2, 0, 4],
[0, 2, 3, 0],
[0, 0, 3, 4]])
shape=(4,4) is the reason why it becomes 4 rows and 4 columns.
「 shape=(m,n) for "m row n column".
import numpy as np
from scipy.sparse import dia_matrix
data = np.array ([1, 2, 3, 4], [10, 20, 30, 40], [100, 200, 300, 400])
print("data=")
print(data)
offsets = np.array ([0,-1,2])
dm=dia_matrix(data, offsets), shape=(4,4)).tearray()
print("dm=")
print(dm)
Results
data=
[[ 1 2 3 4]
[ 10 20 30 40]
[100 200 300 400]]
dm =
[[ 1 0 300 0]
[ 10 2 0 400]
[ 0 20 3 0]
[ 0 0 30 4]]
If you tried shape=(3,5), the results would be as follows:
dm=
[[ 1 0 300 0 0]
[ 10 2 0 400 0]
[ 0 20 3 0 0]]
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