Note
Go to the end to download the full example code
Iterative Data Write¶
This example demonstrate how to iteratively write data arrays with applications to writing large arrays without loading all data into memory and streaming data write.
Introduction¶
What is Iterative Data Write?¶
In the typical write process, datasets are created and written as a whole. In contrast, iterative data write refers to the writing of the contents of a dataset in an incremental, iterative fashion.
Why Iterative Data Write?¶
The possible applications for iterative data write are broad. Here we list a few typical applications for iterative data write in practice.
Large data arrays A central challenge when dealing with large data arrays is that it is often not feasible to load all of the data into memory. Using an iterative data write process allows us to avoid this problem by writing the data one-subblock-at-a-time, so that we only need to hold a small subset of the array in memory at any given time.
Data streaming In the context of streaming data we are faced with several issues: 1) data is not available in-memory but arrives in subblocks as the stream progresses 2) caching the data of a stream in-memory is often prohibitively expensive and volatile 3) the total size of the data is often unknown ahead of time. Iterative data write allows us to address issues 1) and 2) by enabling us to save data to a file incrementally as it arrives from the data stream. Issue 3) is addressed in the HDF5 storage backend via support for chunking, enabling the creation of resizable arrays.
Data generators Data generators are in many ways similar to data streams only that the data is typically being generated locally and programmatically rather than from an external data source.
Sparse data arrays In order to reduce storage size of sparse arrays a challenge is that while the data array (e.g., a matrix) may be large, only a few values are set. To avoid storage overhead for storing the full array we can employ (in HDF5) a combination of chunking, compression, and and iterative data write to significantly reduce storage cost for sparse data.
Iterating Over Data Arrays¶
In PyNWB the process of iterating over large data arrays is implemented via the concept of
DataChunk
and AbstractDataChunkIterator
.
DataChunk
is a simple data structure used to describe a subset of a larger data array (i.e., a data chunk), consisting of:DataChunk.data
: the array with the data value(s) of the chunk andDataChunk.selection
: the NumPy index tuple describing the location of the chunk in the whole array.
AbstractDataChunkIterator
then defines a class for iterating over large data arrays one-DataChunk
-at-a-time.DataChunkIterator
is a specific implementation of anAbstractDataChunkIterator
that accepts any iterable and assumes that we iterate over the first dimension of the data array.DataChunkIterator
also supports buffered read, i.e., multiple values from the input iterator can be combined to a single chunk. This is useful for buffered I/O operations, e.g., to improve performance by accumulating data in memory and writing larger blocks at once.GenericDataChunkIterator
is a semi-abstract version of aAbstractDataChunkIterator
that automatically handles the selection of buffer regions and resolves communication of compatible chunk regions. Users specify chunk and buffer shapes or sizes and the iterator will manage how to break the data up for write. For further details, see the GenericDataChunkIterator tutorial.
Iterative Data Write: API¶
On the front end, all a user needs to do is to create or wrap their data in a
AbstractDataChunkIterator
. The I/O backend (e.g.,
HDF5IO
or NWBHDF5IO
) then
implements the iterative processing of the data chunk iterators. PyNWB also provides with
DataChunkIterator
a specific implementation of a data chunk iterator
which we can use to wrap common iterable types (e.g., generators, lists, or numpy arrays).
For more advanced use cases we then need to implement our own derived class of
AbstractDataChunkIterator
.
Tip
Currently the HDF5 I/O backend of PyNWB (HDF5IO
,
NWBHDF5IO
) processes iterative data writes one-dataset-at-a-time. This means, that
while you may have an arbitrary number of iterative data writes, the write is performed in order.
In the future we may use a queuing process to enable the simultaneous processing of multiple iterative writes at
the same time.
Preparations:¶
The data write in our examples really does not change. We, therefore, here create a simple helper function first to write a simple NWBFile containing a single timeseries to avoid repetition of the same code and to allow us to focus on the important parts of this tutorial.
from datetime import datetime
from uuid import uuid4
from dateutil.tz import tzlocal
from pynwb import NWBHDF5IO, NWBFile, TimeSeries
def write_test_file(filename, data, close_io=True):
"""
Simple helper function to write an NWBFile with a single timeseries containing data
:param filename: String with the name of the output file
:param data: The data of the timeseries
:param close_io: Close and destroy the NWBHDF5IO object used for writing (default=True)
:returns: None if close_io==True otherwise return NWBHDF5IO object used for write
"""
# Create a test NWBfile
start_time = datetime(2017, 4, 3, 11, tzinfo=tzlocal())
nwbfile = NWBFile(
session_description="demonstrate iterative write",
identifier=str(uuid4()),
session_start_time=start_time,
)
# Create our time series
test_ts = TimeSeries(
name="synthetic_timeseries",
data=data,
unit="n/a",
rate=1.0,
)
nwbfile.add_acquisition(test_ts)
# Write the data to file
io = NWBHDF5IO(filename, "w")
io.write(nwbfile)
if close_io:
io.close()
del io
io = None
return io
Example: Write Data from Generators and Streams¶
Here we use a simple data generator but PyNWB does not make any assumptions about what happens inside the generator. Instead of creating data programmatically, you may hence, e.g., receive data from an acquisition system (or other source). We can use the same approach to write streaming data.
Step 1: Define the data generator¶
from math import pi, sin
from random import random
import numpy as np
def iter_sin(chunk_length=10, max_chunks=100):
"""
Generator creating a random number of chunks (but at most max_chunks) of length chunk_length containing
random samples of sin([0, 2pi]).
"""
x = 0
num_chunks = 0
while x < 0.5 and num_chunks < max_chunks:
val = np.asarray([sin(random() * 2 * pi) for i in range(chunk_length)])
x = random()
num_chunks += 1
yield val
return
Step 2: Wrap the generator in a DataChunkIterator¶
from hdmf.data_utils import DataChunkIterator
data = DataChunkIterator(data=iter_sin(10))
Step 3: Write the data as usual¶
Here we use our wrapped generator to create the data for a synthetic time series.
write_test_file(filename="basic_iterwrite_example.nwb", data=data)
Discussion¶
Note, here we don’t actually know how long our timeseries will be.
print(
"maxshape=%s, recommended_data_shape=%s, dtype=%s"
% (str(data.maxshape), str(data.recommended_data_shape()), str(data.dtype))
)
maxshape=(None, 10), recommended_data_shape=(1, 10), dtype=float64
As we can see DataChunkIterator
automatically recommends
in its maxshape
that the first dimensions of our array should be unlimited (None
) and the second
dimension should be 10
(i.e., the length of our chunk. Since DataChunkIterator
has no way of knowing the minimum size of the array it automatically recommends the size of the first
chunk as the minimum size (i.e, (1, 10)
) and also infers the data type automatically from the first chunk.
To further customize this behavior we may also define the maxshape
, dtype
, and buffer_size
when
we create the DataChunkIterator
.
Tip
We here used DataChunkIterator
to conveniently wrap our data stream.
DataChunkIterator
assumes that our generator yields in consecutive order
a single complete element along the first dimension of our array (i.e., iterate over the first
axis and yield one-element-at-a-time). This behavior is useful in many practical cases. However, if
this strategy does not match our needs, then using GenericDataChunkIterator
or implementing your own derived AbstractDataChunkIterator
may be more
appropriate. We show an example of how to implement your own AbstractDataChunkIterator
next. See the GenericDataChunkIterator tutorial as
part of the HDMF documentation for details on how to use GenericDataChunkIterator
.
Example: Optimizing Sparse Data Array I/O and Storage¶
Step 1: Create a data chunk iterator for our sparse matrix¶
from hdmf.data_utils import AbstractDataChunkIterator, DataChunk
class SparseMatrixIterator(AbstractDataChunkIterator):
def __init__(self, shape, num_chunks, chunk_shape):
"""
:param shape: 2D tuple with the shape of the matrix
:param num_chunks: Number of data chunks to be created
:param chunk_shape: The shape of each chunk to be created
:return:
"""
self.shape, self.num_chunks, self.chunk_shape = shape, num_chunks, chunk_shape
self.__chunks_created = 0
def __iter__(self):
return self
def __next__(self):
"""
Return in each iteration a fully occupied data chunk of self.chunk_shape values at a random
location within the matrix. Chunks are non-overlapping. REMEMBER: h5py does not support all
the fancy indexing that numpy does so we need to make sure our selection can be
handled by the backend.
"""
if self.__chunks_created < self.num_chunks:
data = np.random.rand(np.prod(self.chunk_shape)).reshape(self.chunk_shape)
xmin = (
np.random.randint(0, int(self.shape[0] / self.chunk_shape[0]), 1)[0]
* self.chunk_shape[0]
)
xmax = xmin + self.chunk_shape[0]
ymin = (
np.random.randint(0, int(self.shape[1] / self.chunk_shape[1]), 1)[0]
* self.chunk_shape[1]
)
ymax = ymin + self.chunk_shape[1]
self.__chunks_created += 1
return DataChunk(data=data, selection=np.s_[xmin:xmax, ymin:ymax])
else:
raise StopIteration
next = __next__
def recommended_chunk_shape(self):
# Here we can optionally recommend what a good chunking could be.
return self.chunk_shape
def recommended_data_shape(self):
# We know the full size of the array. In cases where we don't know the full size
# this should be the minimum size.
return self.shape
@property
def dtype(self):
# The data type of our array
return np.dtype(float)
@property
def maxshape(self):
# We know the full shape of the array. If we don't know the size of a dimension
# beforehand we can set the dimension to None instead
return self.shape
Step 2: Instantiate our sparse matrix¶
# Setting for our random sparse matrix
xsize = 1000000
ysize = 1000000
num_chunks = 1000
chunk_shape = (10, 10)
num_values = num_chunks * np.prod(chunk_shape)
# Create our sparse matrix data.
data = SparseMatrixIterator(
shape=(xsize, ysize), num_chunks=num_chunks, chunk_shape=chunk_shape
)
In order to also enable compression and other advanced HDF5 dataset I/O features we can then also
wrap our data via H5DataIO
.
from hdmf.backends.hdf5.h5_utils import H5DataIO
matrix2 = SparseMatrixIterator(
shape=(xsize, ysize), num_chunks=num_chunks, chunk_shape=chunk_shape
)
data2 = H5DataIO(data=matrix2, compression="gzip", compression_opts=4)
We can now also customize the chunking, fill value, and other settings
from hdmf.backends.hdf5.h5_utils import H5DataIO
# Increase the chunk size and add compression
matrix3 = SparseMatrixIterator(
shape=(xsize, ysize), num_chunks=num_chunks, chunk_shape=chunk_shape
)
data3 = H5DataIO(data=matrix3, chunks=(100, 100), fillvalue=np.nan)
# Increase the chunk size and add compression
matrix4 = SparseMatrixIterator(
shape=(xsize, ysize), num_chunks=num_chunks, chunk_shape=chunk_shape
)
data4 = H5DataIO(
data=matrix4,
compression="gzip",
compression_opts=4,
chunks=(100, 100),
fillvalue=np.nan,
)
Step 3: Write the data as usual¶
Here we simply use our SparseMatrixIterator
as input for our TimeSeries
write_test_file(filename="basic_sparse_iterwrite_example.nwb", data=data)
write_test_file(filename="basic_sparse_iterwrite_compressed_example.nwb", data=data2)
write_test_file(filename="basic_sparse_iterwrite_largechunks_example.nwb", data=data3)
write_test_file(
filename="basic_sparse_iterwrite_largechunks_compressed_example.nwb", data=data4
)
Check the results¶
Now lets check out the size of our data file and compare it against the expected full size of our matrix
import os
expected_size = xsize * ysize * 8 # This is the full size of our matrix in bytes
occupied_size = num_values * 8 # Number of non-zero values in out matrix
file_size = os.stat(
"basic_sparse_iterwrite_example.nwb"
).st_size # Real size of the file
file_size_compressed = os.stat("basic_sparse_iterwrite_compressed_example.nwb").st_size
file_size_largechunks = os.stat(
"basic_sparse_iterwrite_largechunks_example.nwb"
).st_size
file_size_largechunks_compressed = os.stat(
"basic_sparse_iterwrite_largechunks_compressed_example.nwb"
).st_size
mbfactor = 1000.0 * 1000 # Factor used to convert to MegaBytes
print("1) Sparse Matrix Size:")
print(" Expected Size : %.2f MB" % (expected_size / mbfactor))
print(" Occupied Size : %.5f MB" % (occupied_size / mbfactor))
print("2) NWB HDF5 file (no compression):")
print(" File Size : %.2f MB" % (file_size / mbfactor))
print(" Reduction : %.2f x" % (expected_size / file_size))
print("3) NWB HDF5 file (with GZIP compression):")
print(" File Size : %.5f MB" % (file_size_compressed / mbfactor))
print(" Reduction : %.2f x" % (expected_size / file_size_compressed))
print("4) NWB HDF5 file (large chunks):")
print(" File Size : %.5f MB" % (file_size_largechunks / mbfactor))
print(" Reduction : %.2f x" % (expected_size / file_size_largechunks))
print("5) NWB HDF5 file (large chunks with compression):")
print(" File Size : %.5f MB" % (file_size_largechunks_compressed / mbfactor))
print(" Reduction : %.2f x" % (expected_size / file_size_largechunks_compressed))
1) Sparse Matrix Size:
Expected Size : 8000000.00 MB
Occupied Size : 0.80000 MB
2) NWB HDF5 file (no compression):
File Size : 1.05 MB
Reduction : 7629278.12 x
3) NWB HDF5 file (with GZIP compression):
File Size : 1.05812 MB
Reduction : 7560543.41 x
4) NWB HDF5 file (large chunks):
File Size : 80.24192 MB
Reduction : 99698.51 x
5) NWB HDF5 file (large chunks with compression):
File Size : 1.31525 MB
Reduction : 6082503.07 x
Discussion¶
1) vs 2): While the full matrix would have a size of
8TB
the HDF5 file is only0.88MB
. This is roughly the same as the real occupied size of0.8MB
. When using chunking, HDF5 does not allocate the full dataset but only allocates chunks that actually contain data. In (2) the size of our chunks align perfectly with the occupied chunks of our sparse matrix, hence, only the minimal amount of storage needs to be allocated. A slight overhead (here 0.08MB) is expected because our file contains also the additional objects from the NWBFile, plus some overhead for managing all the HDF5 metadata for all objects.3) vs 2): Adding compression does not yield any improvement here. This is expected, because, again we selected the chunking here in a way that we already allocated the minimum amount of storage to represent our data and lossless compression of random data is not efficient.
4) vs 2): When we increase our chunk size to
(100,100)
(i.e.,100x
larger than the chunks produced by our matrix generator) we observe an accordingly roughly100x
increase in file size. This is expected since our chunks now do not align perfectly with the occupied data and each occupied chunk is allocated fully.5) vs 4): When using compression for the larger chunks we see a significant reduction in file size (
1.14MB
vs.80MB
). This is because the allocated chunks now contain in addition to the random values large areas of constant fill values, which compress easily.
Advantages:
We only need to hold one
DataChunk
in memory at any given timeOnly the data chunks in the HDF5 file that contain non-default values are ever being allocated
The overall size of our file is reduced significantly
Reduced I/O load
On read, users can use the array as usual
Tip
With great power comes great responsibility ! I/O and storage cost will depend, among other factors,
on the chunk size, compression options, and the write pattern, i.e., the number and structure of the
DataChunk
objects written. For example, using (1,1)
chunks and writing them
one value at a time would result in poor I/O performance in most practical cases, because of the large number of
chunks and large number of small I/O operations required.
Tip
A word of caution, while this approach helps optimize storage, the in-memory representation on read is still a dense numpy array. This behavior is convenient for many user interactions with the data but can be problematic with regard to performance/memory when accessing large data subsets.
io = NWBHDF5IO('basic_sparse_iterwrite_example.nwb', 'r')
nwbfile = io.read()
data = nwbfile.get_acquisition('synthetic_timeseries').data # <-- PyNWB does lazy load; no problem
subset = data[10:100, 10:100] # <-- Loading a subset is fine too
alldata = data[:] # <-- !!!! This would load the complete (1000000 x 1000000) array !!!!
Tip
As we have seen here, our data chunk iterator may produce chunks in arbitrary order and locations within the array. In the case of the HDF5 I/O backend we need to take care that the selection we yield can be understood by h5py.
Example: Convert large binary data arrays¶
When converting large data files, a typical problem is that it is often too expensive to load all the data into memory. This example is very similar to the data generator example only that instead of generating data on-the-fly in-memory we are loading data from a file one-chunk-at-a-time in our generator.
Create example data¶
import numpy as np
# Create the test data
datashape = (100, 10) # This is not really large, but we just want to show how it works
num_values = np.prod(datashape)
arrdata = np.arange(num_values).reshape(datashape)
# Write the test data to disk
temp = np.memmap(
"basic_sparse_iterwrite_testdata.npy", dtype="float64", mode="w+", shape=datashape
)
temp[:] = arrdata
del temp # Flush to disk
Step 1: Create a generator for our array¶
Note, we here use a generator for simplicity but we could equally well also implement our own
AbstractDataChunkIterator
or use GenericDataChunkIterator
.
def iter_largearray(filename, shape, dtype="float64"):
"""
Generator reading [chunk_size, :] elements from our array in each iteration.
"""
for i in range(shape[0]):
# Open the file and read the next chunk
newfp = np.memmap(filename, dtype=dtype, mode="r", shape=shape)
curr_data = newfp[i : (i + 1), ...][0]
del newfp # Reopen the file in each iterator to prevent accumulation of data in memory
yield curr_data
return
Step 2: Wrap the generator in a DataChunkIterator¶
from hdmf.data_utils import DataChunkIterator
data = DataChunkIterator(
data=iter_largearray(
filename="basic_sparse_iterwrite_testdata.npy", shape=datashape
),
maxshape=datashape,
buffer_size=10,
) # Buffer 10 elements into a chunk, i.e., create chunks of shape (10,10)
Step 3: Write the data as usual¶
write_test_file(filename="basic_sparse_iterwrite_largearray.nwb", data=data)
Tip
Again, if we want to explicitly control how our data will be chunked (compressed etc.)
in the HDF5 file then we need to wrap our DataChunkIterator
using H5DataIO
Discussion¶
Let’s verify that our data was written correctly
# Read the NWB file
from pynwb import NWBHDF5IO # noqa: F811
with NWBHDF5IO("basic_sparse_iterwrite_largearray.nwb", "r") as io:
nwbfile = io.read()
data = nwbfile.get_acquisition("synthetic_timeseries").data
# Compare all the data values of our two arrays
data_match = np.all(arrdata == data[:]) # Don't do this for very large arrays!
# Print result message
if data_match:
print("Success: All data values match")
else:
print("ERROR: Mismatch between data")
Success: All data values match
Example: Convert arrays stored in multiple files¶
In practice, data from recording devices may be distributed across many files, e.g., one file per time range
or one file per recording channel. Using iterative data write provides an elegant solution to this problem
as it allows us to process large arrays one-subarray-at-a-time. To make things more interesting we’ll show
this for the case where each recording channel (i.e., the second dimension of our TimeSeries
) is broken up
across files.
Create example data¶
import numpy as np
# Create the test data
num_channels = 10
num_steps = 100
channel_files = [
"basic_sparse_iterwrite_testdata_channel_%i.npy" % i for i in range(num_channels)
]
for f in channel_files:
temp = np.memmap(f, dtype="float64", mode="w+", shape=(num_steps,))
temp[:] = np.arange(num_steps, dtype="float64")
del temp # Flush to disk
Step 1: Create a data chunk iterator for our multifile array¶
from hdmf.data_utils import AbstractDataChunkIterator, DataChunk # noqa: F811
class MultiFileArrayIterator(AbstractDataChunkIterator):
def __init__(self, channel_files, num_steps):
"""
:param channel_files: List of files with the channels
:param num_steps: Number of timesteps per channel
:return:
"""
self.shape = (num_steps, len(channel_files))
self.channel_files = channel_files
self.num_steps = num_steps
self.__curr_index = 0
def __iter__(self):
return self
def __next__(self):
"""
Return in each iteration the data from a single file
"""
if self.__curr_index < len(channel_files):
newfp = np.memmap(
channel_files[self.__curr_index],
dtype="float64",
mode="r",
shape=(self.num_steps,),
)
curr_data = newfp[:]
i = self.__curr_index
self.__curr_index += 1
del newfp
return DataChunk(data=curr_data, selection=np.s_[:, i])
else:
raise StopIteration
next = __next__
def recommended_chunk_shape(self):
return None # Use autochunking
def recommended_data_shape(self):
return self.shape
@property
def dtype(self):
return np.dtype("float64")
@property
def maxshape(self):
return self.shape
Step 2: Instantiate our multi file iterator¶
data = MultiFileArrayIterator(channel_files, num_steps)
Step 3: Write the data as usual¶
write_test_file(filename="basic_sparse_iterwrite_multifile.nwb", data=data)
Discussion¶
That’s it ;-)
Tip
Common mistakes that will result in errors on write:
The size of a
DataChunk
does not match the selection.The selection for the
DataChunk
is not supported by h5py (e.g., unordered lists etc.)
Other common mistakes:
Choosing inappropriate chunk sizes. This typically means bad performance with regard to I/O and/or storage cost.
Using auto chunking without supplying a good recommended_data_shape. h5py auto chunking can only make a good guess of what the chunking should be if it (at least roughly) knows what the shape of the array will be.
Trying to wrap a data generator using the default
DataChunkIterator
when the generator does not comply with the assumptions of the default implementation (i.e., yield individual, complete elements along the first dimension of the array one-at-a-time). Depending on the generator, this may or may not result in an error on write, but the array you are generating will probably end up at least not having the intended shape.The shape of the chunks returned by the
DataChunkIterator
do not match the shape of the chunks of the target HDF5 dataset. This can result in slow I/O performance, for example, when each chunk of an HDF5 dataset needs to be updated multiple times on write. For example, when using compression this would mean that HDF5 may have to read, decompress, update, compress, and write a particular chunk each time it is being updated.
Alternative Approach: User-defined dataset write¶
In the above cases we used the built-in capabilities of PyNWB to perform iterative data write. To gain more fine-grained control of the write process we can alternatively use PyNWB to setup the full structure of our NWB file and then update select datasets afterwards. This approach is useful, e.g., in context of parallel write and any time we need to optimize write patterns.
Step 1: Initially allocate the data as empty¶
from hdmf.backends.hdf5.h5_utils import H5DataIO
# Use H5DataIO to specify how to setup the dataset in the file
dataio = H5DataIO(
shape=(0, 10), # Initial shape. If the shape is known then set to full shape
dtype=np.dtype("float"), # dtype of the dataset
maxshape=(None, 10), # Make the time dimension resizable
chunks=(131072, 2), # Use 2MB chunks
compression="gzip", # Enable GZip compression
compression_opts=4, # GZip aggression
shuffle=True, # Enable shuffle filter
fillvalue=np.nan, # Use NAN as fillvalue
)
# Write a test NWB file with our dataset and keep the NWB file (i.e., the NWBHDF5IO object) open
io = write_test_file(
filename="basic_alternative_custom_write.nwb", data=dataio, close_io=False
)
Step 2: Get the dataset(s) to be updated¶
# Let's check what the data looks like before we write
print(
"Before write: Shape= %s, Chunks= %s, Maxshape=%s"
% (
str(dataio.dataset.shape),
str(dataio.dataset.chunks),
str(dataio.dataset.maxshape),
)
)
# Allocate space. Only needed if we didn't set the initial shape large enough
dataio.dataset.resize((8, 10))
# Write 1s in timesteps 0-2
dataio.dataset[0:3, :] = 1
# Write 2s in timesteps 3-5
# NOTE: timesteps 6 and 7 are not being initialized
dataio.dataset[3:6, :] = 2
# Close the file
io.close()
Before write: Shape= (0, 10), Chunks= (131072, 2), Maxshape=(None, 10)
Check the results¶
from pynwb import NWBHDF5IO # noqa
io = NWBHDF5IO("basic_alternative_custom_write.nwb", mode="r")
nwbfile = io.read()
dataset = nwbfile.get_acquisition("synthetic_timeseries").data
print(
"After write: Shape= %s, Chunks= %s, Maxshape=%s"
% (str(dataset.shape), str(dataset.chunks), str(dataset.maxshape))
)
print(dataset[:])
io.close()
After write: Shape= (8, 10), Chunks= (131072, 2), Maxshape=(None, 10)
[[ 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
[ 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
[ 1. 1. 1. 1. 1. 1. 1. 1. 1. 1.]
[ 2. 2. 2. 2. 2. 2. 2. 2. 2. 2.]
[ 2. 2. 2. 2. 2. 2. 2. 2. 2. 2.]
[ 2. 2. 2. 2. 2. 2. 2. 2. 2. 2.]
[nan nan nan nan nan nan nan nan nan nan]
[nan nan nan nan nan nan nan nan nan nan]]
We allocated our data to be shape=(8, 10)
but we only wrote data to the first 6 rows of the
array. As expected, we therefore, see our fillvalue
of nan
in the last two rows of the data.
Total running time of the script: (0 minutes 8.670 seconds)