nbodykit.io.binary¶
Functions
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The default method to determine the size of the binary file |
Classes
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A file object to handle the reading of columns of data from a binary file. |
- class nbodykit.io.binary.BinaryFile(path, dtype, offsets=None, header_size=0, size=None)[source]¶
A file object to handle the reading of columns of data from a binary file.
Warning
This assumes the data is stored in a column-major format
- Parameters
path (str) – the name of the binary file to load
dtype (numpy.dtype or list of tuples) – the dtypes of the columns to load; this should be either a
numpy.dtype
or be able to be converted to one via anumpy.dtype()
calloffsets (dict, optional) – a dictionay specifying the byte offsets of each column in the binary file; if not supplied, the offsets are inferred from the dtype size of each column, assuming a fixed header size, and contiguous storage
header_size (int, optional) – the size of the header in bytes
size (int, optional) – the number of objects in the binary file; if not provided, the value is inferred from the dtype and the total size of the file in bytes
- Attributes
Methods
asarray
()Return a view of the file, where the fields of the structured array are stacked in columns of a single numpy array
get_dask
(column[, blocksize])Return the specified column as a dask array, which delays the explicit reading of the data until
dask.compute()
is calledkeys
()Aliased function to return
columns
read
(columns, start, stop[, step])Read the specified column(s) over the given range
- __getitem__(s)¶
This function provides numpy-like array indexing of the file object.
It supports:
integer, slice-indexing similar to arrays
string indexing using column names in
keys()
array-like indexing using integer lists or boolean arrays
Note
If a single column is being returned, a numpy array holding the data is returned, rather than a structured array with only a single field.
- asarray()¶
Return a view of the file, where the fields of the structured array are stacked in columns of a single numpy array
Examples
Start with a file object with three named columns,
ra
,dec
, andz
>>> ff.dtype dtype([('ra', '<f4'), ('dec', '<f4'), ('z', '<f4')]) >>> ff.shape (1000,) >>> ff.columns ['ra', 'dec', 'z'] >>> ff[:3] array([(235.63442993164062, 59.39099884033203, 0.6225500106811523), (140.36181640625, -1.162310004234314, 0.5026500225067139), (129.96627807617188, 45.970130920410156, 0.4990200102329254)], dtype=(numpy.record, [('ra', '<f4'), ('dec', '<f4'), ('z', '<f4')]))
Select a subset of columns and switch the ordering and convert output to a single numpy array
>>> x = ff[['dec', 'ra']].asarray() >>> x.dtype dtype('float32') >>> x.shape (1000, 2) >>> x.columns ['dec', 'ra'] >>> x[:3] array([[ 59.39099884, 235.63442993], [ -1.16231 , 140.36181641], [ 45.97013092, 129.96627808]], dtype=float32)
Now, select only the first column (
dec
)>>> dec = x[:,0] >>> dec[:3] array([ 59.39099884, -1.16231 , 45.97013092], dtype=float32)
- Returns
a file object that will return a numpy array with the columns representing the fields
- Return type
- property columns¶
A list of the names of the columns in the file.
This defaults to the named fields in the file’s
dtype
attribute, but differ from this if a view of the file has been returned withasarray()
- property dtype¶
A
numpy.dtype
object holding the data types of each column in the file.
- get_dask(column, blocksize=None)¶
Return the specified column as a dask array, which delays the explicit reading of the data until
dask.compute()
is calledThe dask array is chunked into blocks of size blocksize
- Parameters
- Returns
the dask array holding the column, which computes the necessary functions to read the data, but delays evaluating until the user specifies
- Return type
- property ncol¶
The number of data columns in the file.
- read(columns, start, stop, step=1)[source]¶
Read the specified column(s) over the given range
‘start’ and ‘stop’ should be between 0 and
size
, which is the total size of the binary file (in particles)- Parameters
- Returns
structured array holding the requested columns over the specified range of rows
- Return type
numpy.array
- property shape¶
The shape of the file, which defaults to
(size, )
Multiple dimensions can be introduced into the shape if a view of the file has been returned with
asarray()
- property size¶
The size of the file, i.e., number of rows
- nbodykit.io.binary.getsize(filename, header_size, rowsize)[source]¶
The default method to determine the size of the binary file
The “size” is defined as the number of rows, where each row has of size of rowsize in bytes.
Notes
This assumes the input file is not compressed
This function does not depend on the layout of the binary file, i.e., if the data is formatted in actual rows or not
- Raises
ValueError : – If the function determines a fractional number of rows
- Parameters