<tr>
<th> Bytes </th>
<td> 288 B </td>
<td> 288 B </td>
</tr>
<tr>
<th> Shape </th>
<td> (6, 6) </td>
<td> (6, 6) </td>
</tr>
<tr>
<th> Dask graph </th>
<td colspan="2"> 1 chunks in 2 graph layers </td>
</tr>
<tr>
<th> Data type </th>
<td colspan="2"> int64 numpy.ndarray </td>
</tr>
</tbody>
</table>
</td>
<td>
<svg width="170" height="170" style="stroke:rgb(0,0,0);stroke-width:1" >
6
6
a.chunks
((6,), (6,))
Rechunk
a = a.rechunk({0: 2, 1: 3})
a
<tr>
<th> Bytes </th>
<td> 288 B </td>
<td> 48 B </td>
</tr>
<tr>
<th> Shape </th>
<td> (6, 6) </td>
<td> (2, 3) </td>
</tr>
<tr>
<th> Dask graph </th>
<td colspan="2"> 6 chunks in 3 graph layers </td>
</tr>
<tr>
<th> Data type </th>
<td colspan="2"> int64 numpy.ndarray </td>
</tr>
</tbody>
</table>
</td>
<td>
<svg width="170" height="170" style="stroke:rgb(0,0,0);stroke-width:1" >
6
6
b = a[a > 2]
b
<tr>
<th> Bytes </th>
<td> unknown </td>
<td> unknown </td>
</tr>
<tr>
<th> Shape </th>
<td> (nan,) </td>
<td> (nan,) </td>
</tr>
<tr>
<th> Dask graph </th>
<td colspan="2"> 6 chunks in 10 graph layers </td>
</tr>
<tr>
<th> Data type </th>
<td colspan="2"> int64 numpy.ndarray </td>
</tr>
</tbody>
</table>
</td>
<td>
</td>
</tr>
b.compute_chunk_sizes()
<tr>
<th> Bytes </th>
<td> 264 B </td>
<td> 48 B </td>
</tr>
<tr>
<th> Shape </th>
<td> (33,) </td>
<td> (6,) </td>
</tr>
<tr>
<th> Dask graph </th>
<td colspan="2"> 6 chunks in 10 graph layers </td>
</tr>
<tr>
<th> Data type </th>
<td colspan="2"> int64 numpy.ndarray </td>
</tr>
</tbody>
</table>
</td>
<td>
<svg width="170" height="81" style="stroke:rgb(0,0,0);stroke-width:1" >
33
1
Reshape
a = da.arange(3*4).reshape((3, 4)).rechunk(((2, 1), (2, 2)))
a
<tr>
<th> Bytes </th>
<td> 96 B </td>
<td> 32 B </td>
</tr>
<tr>
<th> Shape </th>
<td> (3, 4) </td>
<td> (2, 2) </td>
</tr>
<tr>
<th> Dask graph </th>
<td colspan="2"> 4 chunks in 3 graph layers </td>
</tr>
<tr>
<th> Data type </th>
<td colspan="2"> int64 numpy.ndarray </td>
</tr>
</tbody>
</table>
</td>
<td>
<svg width="170" height="140" style="stroke:rgb(0,0,0);stroke-width:1" >
4
3
a.compute()
array([[ 0, 1, 2, 3],
[ 4, 5, 6, 7],
[ 8, 9, 10, 11]])
a.reshape((3, 2, 2)).compute()
array([[[ 0, 1],
[ 2, 3]],
[[ 4, 5],
[ 6, 7]],
[[ 8, 9],
[10, 11]]])
a.reshape(12) # does the merge of the chunks
<tr>
<th> Bytes </th>
<td> 96 B </td>
<td> 32 B </td>
</tr>
<tr>
<th> Shape </th>
<td> (12,) </td>
<td> (4,) </td>
</tr>
<tr>
<th> Dask graph </th>
<td colspan="2"> 3 chunks in 5 graph layers </td>
</tr>
<tr>
<th> Data type </th>
<td colspan="2"> int64 numpy.ndarray </td>
</tr>
</tbody>
</table>
</td>
<td>
<svg width="170" height="87" style="stroke:rgb(0,0,0);stroke-width:1" >
12
1
a.reshape(12, merge_chunks=False) # does not merge but converts the slow-axis to of chunk (1,)
<tr>
<th> Bytes </th>
<td> 96 B </td>
<td> 16 B </td>
</tr>
<tr>
<th> Shape </th>
<td> (12,) </td>
<td> (2,) </td>
</tr>
<tr>
<th> Dask graph </th>
<td colspan="2"> 6 chunks in 5 graph layers </td>
</tr>
<tr>
<th> Data type </th>
<td colspan="2"> int64 numpy.ndarray </td>
</tr>
</tbody>
</table>
</td>
<td>
<svg width="170" height="87" style="stroke:rgb(0,0,0);stroke-width:1" >
12
1
da.ones((3, 4), chunks=(2, 2)).rechunk(((1, 1, 1), (2, 2)))
<tr>
<th> Bytes </th>
<td> 96 B </td>
<td> 16 B </td>
</tr>
<tr>
<th> Shape </th>
<td> (3, 4) </td>
<td> (1, 2) </td>
</tr>
<tr>
<th> Dask graph </th>
<td colspan="2"> 6 chunks in 2 graph layers </td>
</tr>
<tr>
<th> Data type </th>
<td colspan="2"> float64 numpy.ndarray </td>
</tr>
</tbody>
</table>
</td>
<td>
<svg width="170" height="140" style="stroke:rgb(0,0,0);stroke-width:1" >
4
3
da.from_array(np.arange(24).reshape(2, 3, 4), chunks=((2,), (2, 1), (2, 2)))
<tr>
<th> Bytes </th>
<td> 192 B </td>
<td> 64 B </td>
</tr>
<tr>
<th> Shape </th>
<td> (2, 3, 4) </td>
<td> (2, 2, 2) </td>
</tr>
<tr>
<th> Dask graph </th>
<td colspan="2"> 4 chunks in 1 graph layer </td>
</tr>
<tr>
<th> Data type </th>
<td colspan="2"> int64 numpy.ndarray </td>
</tr>
</tbody>
</table>
</td>
<td>
<svg width="215" height="175" style="stroke:rgb(0,0,0);stroke-width:1" >
4
3
2
|
|
|
|
|
|
|
|
|