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)
| 1156 | |
| 1157 | @array_function_dispatch(_tile_dispatcher) |
| 1158 | def tile(A, reps): |
| 1159 | """ |
| 1160 | Construct an array by repeating A the number of times given by reps. |
| 1161 | |
| 1162 | If `reps` has length ``d``, the result will have dimension of |
| 1163 | ``max(d, A.ndim)``. |
| 1164 | |
| 1165 | If ``A.ndim < d``, `A` is promoted to be d-dimensional by prepending new |
| 1166 | axes. So a shape (3,) array is promoted to (1, 3) for 2-D replication, |
| 1167 | or shape (1, 1, 3) for 3-D replication. If this is not the desired |
| 1168 | behavior, promote `A` to d-dimensions manually before calling this |
| 1169 | function. |
| 1170 | |
| 1171 | If ``A.ndim > d``, `reps` is promoted to `A`.ndim by prepending 1's to it. |
| 1172 | Thus for an `A` of shape (2, 3, 4, 5), a `reps` of (2, 2) is treated as |
| 1173 | (1, 1, 2, 2). |
| 1174 | |
| 1175 | Note : Although tile may be used for broadcasting, it is strongly |
| 1176 | recommended to use numpy's broadcasting operations and functions. |
| 1177 | |
| 1178 | Parameters |
| 1179 | ---------- |
| 1180 | A : array_like |
| 1181 | The input array. |
| 1182 | reps : array_like |
| 1183 | The number of repetitions of `A` along each axis. |
| 1184 | |
| 1185 | Returns |
| 1186 | ------- |
| 1187 | c : ndarray |
| 1188 | The tiled output array. |
| 1189 | |
| 1190 | See Also |
| 1191 | -------- |
| 1192 | repeat : Repeat elements of an array. |
| 1193 | broadcast_to : Broadcast an array to a new shape |
| 1194 | |
| 1195 | Examples |
| 1196 | -------- |
| 1197 | >>> import numpy as np |
| 1198 | >>> a = np.array([0, 1, 2]) |
| 1199 | >>> np.tile(a, 2) |
| 1200 | array([0, 1, 2, 0, 1, 2]) |
| 1201 | >>> np.tile(a, (2, 2)) |
| 1202 | array([[0, 1, 2, 0, 1, 2], |
| 1203 | [0, 1, 2, 0, 1, 2]]) |
| 1204 | >>> np.tile(a, (2, 1, 2)) |
| 1205 | array([[[0, 1, 2, 0, 1, 2]], |
| 1206 | [[0, 1, 2, 0, 1, 2]]]) |
| 1207 | |
| 1208 | >>> b = np.array([[1, 2], [3, 4]]) |
| 1209 | >>> np.tile(b, 2) |
| 1210 | array([[1, 2, 1, 2], |
| 1211 | [3, 4, 3, 4]]) |
| 1212 | >>> np.tile(b, (2, 1)) |
| 1213 | array([[1, 2], |
| 1214 | [3, 4], |
| 1215 | [1, 2], |
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