In [1]:
import numpy as np

# create an array of normally-distributed random numbers
a = np.random.randn(1000)
In [2]:
import dask.array as da

# create a dask array from the above array
a2 = da.from_array(a, chunks=200)

# multiply this array by a factor
b2 = a2 * 4

# find the minimum value
b2_min = b2.min()
print(b2_min)
dask.array<atop-9f..., shape=(), dtype=float64, chunksize=()>
In [3]:
%matplotlib inline

from dask.dot import dot_graph
dot_graph(b2_min.dask)
Out[3]:
In [4]:
b2_min.dask
Out[4]:
{('from-array-a9d179ffe200871f33b844d80bc1d769',
  4): (<function dask.array.core.getarray>, 'from-array-a9d179ffe200871f33b844d80bc1d769', (slice(800, 1000, None),)),
 ('elemwise-0999dfe8b9f21b1fafba707314f33b3d',
  2): (<function dask.array.core.partial_by_order.<locals>.f>, ('from-array-a9d179ffe200871f33b844d80bc1d769',
   2)),
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