Construct an array by repeating A the number of times given by reps. If `reps` has length ``d``, the result will have dimension of ``max(d, A.ndim)``. If ``A.ndim < d``, `A` is promoted to be d-dimensional by prepending new axes. So a shape (3,) array is promoted to (1, 3) for
(A, reps)
| 1184 | |
| 1185 | @array_function_dispatch(_tile_dispatcher) |
| 1186 | def tile(A, reps): |
| 1187 | """ |
| 1188 | Construct an array by repeating A the number of times given by reps. |
| 1189 | |
| 1190 | If `reps` has length ``d``, the result will have dimension of |
| 1191 | ``max(d, A.ndim)``. |
| 1192 | |
| 1193 | If ``A.ndim < d``, `A` is promoted to be d-dimensional by prepending new |
| 1194 | axes. So a shape (3,) array is promoted to (1, 3) for 2-D replication, |
| 1195 | or shape (1, 1, 3) for 3-D replication. If this is not the desired |
| 1196 | behavior, promote `A` to d-dimensions manually before calling this |
| 1197 | function. |
| 1198 | |
| 1199 | If ``A.ndim > d``, `reps` is promoted to `A`.ndim by pre-pending 1's to it. |
| 1200 | Thus for an `A` of shape (2, 3, 4, 5), a `reps` of (2, 2) is treated as |
| 1201 | (1, 1, 2, 2). |
| 1202 | |
| 1203 | Note : Although tile may be used for broadcasting, it is strongly |
| 1204 | recommended to use numpy's broadcasting operations and functions. |
| 1205 | |
| 1206 | Parameters |
| 1207 | ---------- |
| 1208 | A : array_like |
| 1209 | The input array. |
| 1210 | reps : array_like |
| 1211 | The number of repetitions of `A` along each axis. |
| 1212 | |
| 1213 | Returns |
| 1214 | ------- |
| 1215 | c : ndarray |
| 1216 | The tiled output array. |
| 1217 | |
| 1218 | See Also |
| 1219 | -------- |
| 1220 | repeat : Repeat elements of an array. |
| 1221 | broadcast_to : Broadcast an array to a new shape |
| 1222 | |
| 1223 | Examples |
| 1224 | -------- |
| 1225 | >>> a = np.array([0, 1, 2]) |
| 1226 | >>> np.tile(a, 2) |
| 1227 | array([0, 1, 2, 0, 1, 2]) |
| 1228 | >>> np.tile(a, (2, 2)) |
| 1229 | array([[0, 1, 2, 0, 1, 2], |
| 1230 | [0, 1, 2, 0, 1, 2]]) |
| 1231 | >>> np.tile(a, (2, 1, 2)) |
| 1232 | array([[[0, 1, 2, 0, 1, 2]], |
| 1233 | [[0, 1, 2, 0, 1, 2]]]) |
| 1234 | |
| 1235 | >>> b = np.array([[1, 2], [3, 4]]) |
| 1236 | >>> np.tile(b, 2) |
| 1237 | array([[1, 2, 1, 2], |
| 1238 | [3, 4, 3, 4]]) |
| 1239 | >>> np.tile(b, (2, 1)) |
| 1240 | array([[1, 2], |
| 1241 | [3, 4], |
| 1242 | [1, 2], |
| 1243 | [3, 4]]) |