Dataset

opencosmo.col(column_name)

Create a reference to a column with a given name. These references can be combined to produce new columns or express queries that operate on the values in a given dataset. For example:

import opencosmo as oc
ds = oc.open("haloproperties.hdf5")
query = oc.col("fof_halo_mass") > 1e14
px = oc.col("fof_halo_mass") * oc.col("fof_halo_com_vx")
ds = ds.with_new_columns(fof_halo_com_px = px).filter(query)

For more advanced usage, see Working with Columns

Parameters:: column_name (str)
Return type:: Column

class opencosmo.Dataset(handler, header, state, tree=None)

Parameters:

handler (DatasetHandler)
header (OpenCosmoHeader)
state (DatasetState)
tree (Optional[Tree])

property columns: list[str]

The names of the columns in this dataset.

Returns:: columns
Return type:: list[str]

property cosmology: Cosmology

The cosmology of the simulation this dataset is drawn from as an astropy.cosmology.Cosmology object.

Returns:: cosmology
Return type:: astropy.cosmology.Cosmology

property dtype: str

The data type of this dataset.

Returns:: dtype
Return type:: str

property redshift: float | tuple[float, float]

The redshift slice or range this dataset was drawn from

Returns:: redshift
Return type:: float

property region: Region

The region this dataset is contained in. If no spatial queries have been performed, this will be the entire simulation box for snapshots or the full sky for lightcones

Returns:: region
Return type:: opencosmo.spatial.Region

property simulation: SimulationParameters

The parameters of the simulation this dataset is drawn from.

Returns:: parameters
Return type:: opencosmo.parameters.SimulationParameters

property data: Table | Column

The data in the dataset. This will be an astropy.table.Table or astropy.table.Column (if there is only one column selected).

Returns:: data – The data in the dataset.
Return type:: astropy.table.Table or astropy.table.Column

bound(region, select_by=None)

Restrict the dataset to some subregion. The subregion will always be evaluated in the same units as the current dataset. For example, if the dataset is in the default “comoving” unit convention, positions are always in units of comoving Mpc. However Region objects themselves do not carry units. See Regions for details of how to construct regions.

Parameters:

region (opencosmo.spatial.Region) – The region to query.
select_by (str | None)

Returns:

dataset – The portion of the dataset inside the selected region

Return type:

opencosmo.Dataset

Raises:

ValueError – If the query region does not overlap with the region this dataset resides in
AttributeError: – If the dataset does not contain a spatial index

filter(*masks)

Filter the dataset based on some criteria. See Querying Based on Column Values for more information.

Parameters:: *masks (Mask) – The masks to apply to dataset, constructed with opencosmo.col()
Returns:: dataset – The new dataset with the masks applied.
Return type:: Dataset
Raises:: ValueError – If the given refers to columns that are not in the dataset, or the would return zero rows.

rows()

Iterate over the rows in the dataset. Rows are returned as a dictionary For performance, it is recommended to first select the columns you need to work with.

Yields:: row (dict) – A dictionary of values for each row in the dataset with units.
Return type:: Generator[dict[str, float | Quantity], None, None]

select(columns)

Create a new dataset from a subset of columns in this dataset

Parameters:: columns (str or list[str]) – The column or columns to select.
Returns:: dataset – The new dataset with only the selected columns.
Return type:: Dataset
Raises:: ValueError – If any of the given columns are not in the dataset.

take(n, at='random')

Create a new dataset from some number of rows from this dataset.

Can take the first n rows, the last n rows, or n random rows depending on the value of ‘at’.

Parameters:

n (int) – The number of rows to take.
at (str) – Where to take the rows from. One of “start”, “end”, or “random”. The default is “random”.

Returns:

dataset – The new dataset with only the selected rows.

Return type:

Dataset

Raises:

ValueError – If n is negative or greater than the number of rows in the dataset, or if ‘at’ is invalid.

take_range(start, end)

Create a new dataset from a row range in this dataset.

Parameters:

start (int) – The first row to get.
end (int) – The last row to get.

Returns:

table – The table with only the rows from start to end.

Return type:

astropy.table.Table

Raises:

ValueError – If start or end are negative or greater than the length of the dataset or if end is greater than start.

with_new_columns(**new_columns)

Create a new dataset with additional columns. These new columns can be derived from columns already in the dataset, or a numpy array. When a column is derived from other columns, it will behave appropriately under unit transformations. See Creating New Columns for examples.

Parameters:

columns (**)
new_columns (DerivedColumn)

Returns:

dataset – This dataset with the columns added

Return type:

opencosmo.Dataset

with_units(convention)

Create a new dataset from this one with a different unit convention.

Parameters:: convention (str) – The unit convention to use. One of “physical”, “comoving”, “scalefree”, or “unitless”.
Returns:: dataset – The new dataset with the requested unit convention.
Return type:: Dataset

collect()

Given a dataset that was originally opend with opencosmo.open, return a dataset that is in-memory as though it was read with opencosmo.read.

This is useful if you have a very large dataset on disk, and you want to filter it down and then close the file.

For example:

import opencosmo as oc
with oc.open("path/to/file.hdf5") as file:
    ds = file.(ds["sod_halo_mass"] > 0)
    ds = ds.select(["sod_halo_mass", "sod_halo_radius"])
    ds = ds.collect()

The selected data will now be in memory, and the file will be closed.

If working in an MPI context, all ranks will recieve the same data.

Return type:: Dataset