agora.io.writer.Writer

class Writer(filename, flag=None, compression='gzip')

Bases: BridgeH5

Class to transform data into compatible structures.

Attributes

cell_tree

meta_h5

Return metadata, defining it if necessary.

Methods

close()	Close the h5 file.
find_ids(existing, new)	Compare two tuple sets and return the intersection and difference (elements in the 'new' set not in 'existing').
get_consecutives(tree, nstepsback)	Receives a sorted tree and returns the keys of consecutive elements.
get_existing_ids(f, paths)	Fetch indices and convert them to a (nentries, nlevels) ndarray.
get_info_tree([fields])	Return traps, time points and labels for this position in the form of a tree in the hierarchy determined by the argument fields.
write(path[, data, meta, overwrite])	Write data and metadata to a particular path in the h5 file.
write_arraylike(f, path, data, **kwargs)	Write an iterable.
write_dset(f, path, data)	Write data in different ways depending on its type to an open h5 file.
write_index(f, path, pd_index, **kwargs)	Write a multi-index dataframe.
write_meta(f, path, attr, data)	Write metadata to an open h5 file.
write_pd(f, path, df, **kwargs)	Write a dataframe.

get_npairs
get_npairs_over_time

Initialise write.

Parameters

filename: str

Name of file to write into

flag: str, default=None

Flag to pass to the default file reader. If None the file remains closed.

compression: str, default=”gzip”

Compression method passed on to h5py writing functions (only used for dataframes and other array-like data).

Attributes

cell_tree

meta_h5

Return metadata, defining it if necessary.

Methods

close()	Close the h5 file.
find_ids(existing, new)	Compare two tuple sets and return the intersection and difference (elements in the 'new' set not in 'existing').
get_consecutives(tree, nstepsback)	Receives a sorted tree and returns the keys of consecutive elements.
get_existing_ids(f, paths)	Fetch indices and convert them to a (nentries, nlevels) ndarray.
get_info_tree([fields])	Return traps, time points and labels for this position in the form of a tree in the hierarchy determined by the argument fields.
write(path[, data, meta, overwrite])	Write data and metadata to a particular path in the h5 file.
write_arraylike(f, path, data, **kwargs)	Write an iterable.
write_dset(f, path, data)	Write data in different ways depending on its type to an open h5 file.
write_index(f, path, pd_index, **kwargs)	Write a multi-index dataframe.
write_meta(f, path, attr, data)	Write metadata to an open h5 file.
write_pd(f, path, df, **kwargs)	Write a dataframe.

get_npairs
get_npairs_over_time

__init__(filename, flag=None, compression='gzip')

Initialise write.

Parameters

filename: str

Name of file to write into

flag: str, default=None

Flag to pass to the default file reader. If None the file remains closed.

compression: str, default=”gzip”

Compression method passed on to h5py writing functions (only used for dataframes and other array-like data).

Methods

__init__(filename[, flag, compression])	Initialise write.
close()	Close the h5 file.
find_ids(existing, new)	Compare two tuple sets and return the intersection and difference (elements in the 'new' set not in 'existing').
get_consecutives(tree, nstepsback)	Receives a sorted tree and returns the keys of consecutive elements.
get_existing_ids(f, paths)	Fetch indices and convert them to a (nentries, nlevels) ndarray.
get_info_tree([fields])	Return traps, time points and labels for this position in the form of a tree in the hierarchy determined by the argument fields.
get_npairs([nstepsback, tree])
get_npairs_over_time([nstepsback])
write(path[, data, meta, overwrite])	Write data and metadata to a particular path in the h5 file.
write_arraylike(f, path, data, **kwargs)	Write an iterable.
write_dset(f, path, data)	Write data in different ways depending on its type to an open h5 file.
write_index(f, path, pd_index, **kwargs)	Write a multi-index dataframe.
write_meta(f, path, attr, data)	Write metadata to an open h5 file.
write_pd(f, path, df, **kwargs)	Write a dataframe.

Attributes

cell_tree
meta_h5	Return metadata, defining it if necessary.

close()

Close the h5 file.

static find_ids(existing, new)

Compare two tuple sets and return the intersection and difference (elements in the ‘new’ set not in ‘existing’).

static get_consecutives(tree, nstepsback)

Receives a sorted tree and returns the keys of consecutive elements.

static get_existing_ids(f, paths)

Fetch indices and convert them to a (nentries, nlevels) ndarray.

get_info_tree(fields=('trap', 'timepoint', 'cell_label'))

Return traps, time points and labels for this position in the form of a tree in the hierarchy determined by the argument fields.

Note that it is compressed to non-empty elements and timepoints.

Default hierarchy is: - trap - time point - cell label

This function currently produces trees of depth 3, but it can easily be extended for deeper trees if needed (e.g. considering groups, chambers and/or positions).

Parameters

fields: list of strs

Fields to fetch from ‘cell_info’ inside the h5 file.

Returns

Nested dictionary where keys (or branches) are the upper levels and the leaves are the last element of :fields:.

property meta_h5: Dict[str, Any]

Return metadata, defining it if necessary.

Return type

Dict[str, Any]

write(path, data=None, meta={}, overwrite=None)

Write data and metadata to a particular path in the h5 file.

Parameters

pathstr

Path inside h5 file into which to write.

dataIterable, optional

metadict, optional

overwrite: str, optional

static write_arraylike(f, path, data, **kwargs)

Write an iterable.

write_dset(f, path, data)

Write data in different ways depending on its type to an open h5 file.

static write_index(f, path, pd_index, **kwargs)

Write a multi-index dataframe.

write_meta(f, path, attr, data)

Write metadata to an open h5 file.

write_pd(f, path, df, **kwargs)

Write a dataframe.