Data object methods§

Ozzy is implemented according to xarray's accessor model¹. All the ozzy functionality built on top of xarray data objects (Datasets or DataArrays) can therefore be accessed via

DataArrayDataset

xarray.DataArray.ozzy.<method>

xarray.Dataset.ozzy.<method>

Example

Saving a Dataset object:

import ozzy as oz
ds = oz.Dataset()
ds.ozzy.save('test.h5')
#  -> Saved file "test.h5" 
# -> 'save' took: 0:00:00.212650

Note for developers

Ozzy's custom methods are defined in two accessor classes:

ozzy.accessors.OzzyDataset
ozzy.accessors.OzzyDataArray

Strictly speaking, the path to each method should be for example ozzy.accessors.[OzzyDataset|OzzyDataArray].<method>. However, this documentation page presents the methods as if they were under xarray.[Dataset|DataArray].ozzy.<method>, which is effectively how the user can access them.

The methods in each accessor class can access the actual data object via <data_obj>.ozzy._obj. This is only relevant when defining new methods in the accessor classes.

Example

import xarray as xr
import ozzy as oz
ds = xr.Dataset()
assert ds == ds.ozzy._obj
# True

Methods§

xarray.DataArray.ozzyxarray.Dataset.ozzy

Bases: *mixins

coord_to_physical_distance §

coord_to_physical_distance(
    coord,
    n0,
    units="m",
    new_coord=None,
    new_label=None,
    set_as_default=True,
)

Convert a coordinate to physical units based on the plasma density \(n_0\).

This function calculates the skin depth based on the provided n0 value and scales the specified coordinate coord by the skin depth. The scaled coordinate is assigned a new name (new_coord or a default name) and added to the dataset as a new coordinate. The new coordinate can also be assigned a custom label (new_label).

Parameters:

Name	Type	Description	Default
`coord` §	`str`	Name of coordinate to convert.	required
`n0` §	`float`	Value for the plasma electron density used to calculate the skin depth, in \(\mathrm{cm}^{-3}\).	required
`new_coord` §	`str`	The name to assign to the new coordinate. If not provided, a default name is generated based on `coord` and `units`.	`None`
`new_label` §	`str`	The label (`"long_name"` attribute) to assign to the new coordinate. If not provided, the label of `coord` is used, if available.	`None`
`units` §	`str`	The physical units for the new coordinate. Must be either `"m"` for meters or `"cm"` for centimeters.	`'m'`
`set_as_default` §	`bool`	If `True`, the new coordinate is set as the default coordinate for the corresponding dimension, replacing `coord`.	`True`

Returns:

Type	Description
`DataArray`	A new DataArray with the additional converted coordinate.

Examples:

Converting normalized time units to propagation distance

import ozzy as oz
da = oz.DataArray([3,4,5], coords={'t': [0,1,2]}, dims='t')
da_m = da.ozzy.coord_to_physical_distance('t', 1e18, new_coord='z') # z in m
da_cm = da.ozzy.coord_to_physical_distance('t', 1e18, units='cm', new_coord='z') # z in cm

Convert \(r\) coordinate to centimeters with new label

import ozzy as oz
import numpy as np

da = oz.DataArray({'var': np.random.rand(5, 10)},
                coords={'x2': np.linspace(0, 1, 10)})
n0 = 1e17  # cm^-3
da_new = da.ozzy.coord_to_physical_distance('x2', n0, new_coord='r', units='cm')

fft §

fft(axes=None, dims=None, **kwargs)

Calculate the Fast Fourier Transform (FFT) of a DataArray along specified dimensions.

Warning

This method has not been thoroughly checked for accuracy yet. Please double-check your results with a different FFT function.

Parameters:

Name	Type	Description	Default
`axes` §	`list[int]`	The integer indices of the axes to take FFT along.	`None`
`dims` §	`list[str]`	Dimensions along which to compute the FFT. If provided, this takes precedence over `axes`.	`None`
`**kwargs` §		Additional keyword arguments passed to `numpy.fft.fftn`.	`{}`

Returns:

Type	Description
`DataArray`	The FFT result as a new DataArray.

Examples:

1D FFT

import ozzy as oz
import numpy as np

# Create a 1D DataArray
x = np.linspace(0, 10, 100)
da = oz.DataArray(np.sin(2 * np.pi * x), coords=[x], dims=['x'], pic_data_type='grid')

# Compute the 1D FFT
da_fft = da.ozzy.fft(dims=['x'])
# -> 'fft' took: 0:00:00.085525

2D FFT

import ozzy as oz
import numpy as np

# Create a 2D DataArray
x = np.linspace(0, 10, 100)
y = np.linspace(0, 5, 50)
X, Y = np.meshgrid(x, y)
da = oz.DataArray(np.sin(2 * np.pi * X) * np.cos(2 * np.pi * Y),
        coords=[y, x], dims=['y', 'x'], pic_data_type='grid')

# Compute the 2D FFT
da_fft = da.ozzy.fft(dims=['x', 'y'])
# -> 'fft' took: 0:00:00.006278

save §

save(path)

Save data object to an HDF5 (default) or NetCDF file.

Parameters:

Name	Type	Description	Default
`path` §	`str`	The path to save the file to. Specify the file ending as `'.h5'` for HDF5 or `'.nc'` for NetCDF.	required

Examples:

Save empty DataArray

import ozzy as oz
ds = oz.DataArray()
ds.ozzy.save('empty_file.h5')
#  -> Saved file "empty_file.h5"
# -> 'save' took: 0:00:00.197806

Bases: *mixins

coord_to_physical_distance §

coord_to_physical_distance(
    coord,
    n0,
    units="m",
    new_coord=None,
    new_label=None,
    set_as_default=True,
)

Convert a coordinate to physical units based on the plasma density \(n_0\).

This function calculates the skin depth based on the provided n0 value and scales the specified coordinate coord by the skin depth. The scaled coordinate is assigned a new name (new_coord or a default name) and added to the dataset as a new coordinate. The new coordinate can also be assigned a custom label (new_label).

Parameters:

Name	Type	Description	Default
`coord` §	`str`	Name of coordinate to convert.	required
`n0` §	`float`	Value for the plasma electron density used to calculate the skin depth, in \(\mathrm{cm}^{-3}\).	required
`new_coord` §	`str`	The name to assign to the new coordinate. If not provided, a default name is generated based on `coord` and `units`.	`None`
`new_label` §	`str`	The label (`"long_name"` attribute) to assign to the new coordinate. If not provided, the label of `coord` is used, if available.	`None`
`units` §	`str`	The physical units for the new coordinate. Must be either `"m"` for meters or `"cm"` for centimeters.	`'m'`
`set_as_default` §	`bool`	If `True`, the new coordinate is set as the default coordinate for the corresponding dimension, replacing `coord`.	`True`

Returns:

Type	Description
`Dataset`	A new Dataset with the additional converted coordinate.

Examples:

Converting normalized time units to propagation distance

import ozzy as oz
ds = oz.Dataset(data_vars={'var1': [3,4,5]}, coords={'t': [0,1,2]}, dims='t')
ds_m = ds.ozzy.coord_to_physical_distance('t', 1e18, new_coord='z') # z in m
ds_cm = ds.ozzy.coord_to_physical_distance('t', 1e18, units='cm', new_coord='z') # z in cm

Convert \(r\) coordinate to centimeters with new label

import ozzy as oz
import numpy as np

ds = oz.Dataset({'var': np.random.rand(5, 10)},
                coords={'x2': np.linspace(0, 1, 10)})
n0 = 1e17  # cm^-3
ds_new = ds.ozzy.coord_to_physical_distance('x2', n0, new_coord='r', units='cm')

fft §

fft(data_var, axes=None, dims=None, **kwargs)

Calculate the Fast Fourier Transform (FFT) of a variable in a Dataset along specified dimensions. Take FFT of variable in Dataset along specified axes.

Warning

This method has not been thoroughly checked for accuracy yet. Please double-check your results with a different FFT function.

Parameters:

Name	Type	Description	Default
`data_var` §	`str`	The data variable to take FFT of.	required
`axes` §	`list[int]`	The integer indices of the axes to take FFT along.	`None`
`dims` §	`list[str]`	Dimensions along which to compute the FFT. If provided, this takes precedence over `axes`.	`None`
`**kwargs` §		Additional keyword arguments passed to `numpy.fft.fftn`.	`{}`

Returns:

Type	Description
`DataArray`	The FFT result as a new DataArray.

Examples:

1D FFT

import ozzy as oz
import numpy as np

# Create a 1D variable in a [Dataset][xarray.Dataset]
x = np.linspace(0, 10, 100)
da = oz.Dataset(data_vars = {'f_x' : np.sin(2 * np.pi * x)}, coords=[x], dims=['x'], pic_data_type='grid')

# Compute the 1D FFT
da_fft = da.ozzy.fft('f_x', dims=['x'])
# -> 'fft' took: 0:00:00.085525

2D FFT

import ozzy as oz
import numpy as np

# Create a 2D DataArray
x = np.linspace(0, 10, 100)
y = np.linspace(0, 5, 50)
X, Y = np.meshgrid(x, y)
da = oz.Dataset(data_vars = {'f_xy': np.sin(2 * np.pi * X) * np.cos(2 * np.pi * Y)},
        coords=[y, x], dims=['y', 'x'], pic_data_type='grid')

# Compute the 2D FFT
da_fft = da.ozzy.fft(dims=['x', 'y'])
# -> 'fft' took: 0:00:00.006278

save §

save(path)

Save data object to an HDF5 (default) or NetCDF file.

Parameters:

Name	Type	Description	Default
`path` §	`str`	The path to save the file to. Specify the file ending as `'.h5'` for HDF5 or `'.nc'` for NetCDF.	required

Examples:

Save empty Dataset

import ozzy as oz
ds = oz.Dataset()
ds.ozzy.save('empty_file.h5')
#  -> Saved file "empty_file.h5"
# -> 'save' took: 0:00:00.197806

See Extending xarray using accessors ↩

Data object methods§

Methods§

coord_to_physical_distance §

`coord` §

`n0` §

`new_coord` §

`new_label` §

`units` §

`set_as_default` §

fft §

`axes` §

`dims` §

`**kwargs` §

save §

`path` §

coord_to_physical_distance §

`coord` §

`n0` §

`new_coord` §

`new_label` §

`units` §

`set_as_default` §

fft §

`data_var` §

`axes` §

`dims` §

`**kwargs` §

save §

`path` §

Data object methods§

Methods§

coord_to_physical_distance §

coord §

n0 §

new_coord §

new_label §

units §

set_as_default §

fft §

axes §

dims §

**kwargs §

save §

path §

coord_to_physical_distance §

coord §

n0 §

new_coord §

new_label §

units §

set_as_default §

fft §

data_var §

axes §

dims §

**kwargs §

save §

path §

`coord` §

`n0` §

`new_coord` §

`new_label` §

`units` §

`set_as_default` §

`axes` §

`dims` §

`**kwargs` §

`path` §

`coord` §

`n0` §

`new_coord` §

`new_label` §

`units` §

`set_as_default` §

`data_var` §

`axes` §

`dims` §

`**kwargs` §

`path` §