# NDData¶

## Overview¶

`NDData`

is based on `numpy.ndarray`

-like `data`

with
additional meta attributes:

`meta`

, for general metadata`unit`

, representing the physical unit of the data`uncertainty`

for the uncertainty of the data`mask`

, indicating invalid points in the data`wcs`

, representing the relationship between the data grid and world coordinates

Each of these attributes can be set during initialization or directly on the
instance. Only the `data`

cannot be directly set after creating the instance.

## Data¶

The data is the base of `NDData`

and required to be
`numpy.ndarray`

-like. It’s the only property that is required to create an
instance and it cannot be directly set on the instance.

For example:

```
>>> import numpy as np
>>> from astropy.nddata import NDData
>>> array = np.array([[0, 1, 0], [1, 0, 1], [0, 1, 0]])
>>> ndd = NDData(array)
>>> ndd
NDData([[0, 1, 0],
[1, 0, 1],
[0, 1, 0]])
```

and can be accessed by the `data`

attribute:

```
>>> ndd.data
array([[0, 1, 0],
[1, 0, 1],
[0, 1, 0]])
```

as already mentioned it is not possible to set the data directly. So
`ndd.data = np.arange(9)`

will raise an Exception. But the data can be
modified in place:

```
>>> ndd.data[1,1] = 100
>>> ndd.data
array([[ 0, 1, 0],
[ 1, 100, 1],
[ 0, 1, 0]])
```

### Data during initialization¶

During initialization it is possible to provide data that it’s not a
`numpy.ndarray`

but convertible to one. For example passing a `list`

containing
numerical values:

```
>>> alist = [1, 2, 3, 4]
>>> ndd = NDData(alist)
>>> ndd.data # data will be a numpy-array:
array([1, 2, 3, 4])
```

Nested `list`

or `tuple`

are possible, but if these contain non-numerical
values the conversion might fail.

Besides input that is convertible to such an array you can use the `data`

parameter to pass implicit additional information. For example if the data is
another `NDData`

-object it implicitly uses it’s properties:

```
>>> ndd = NDData(ndd, unit = 'm')
>>> ndd2 = NDData(ndd)
>>> ndd2.data # It has the same data as ndd
array([1, 2, 3, 4])
>>> ndd2.unit # but it also has the same unit as ndd
Unit("m")
```

another possibility is to use a `Quantity`

as `data`

parameter:

```
>>> import astropy.units as u
>>> quantity = np.ones(3) * u.cm # this will create a Quantity
>>> ndd3 = NDData(quantity)
>>> ndd3.data
array([1., 1., 1.])
>>> ndd3.unit
Unit("cm")
```

or a `numpy.ma.MaskedArray`

:

```
>>> masked_array = np.ma.array([5,10,15], mask=[False, True, False])
>>> ndd4 = NDData(masked_array)
>>> ndd4.data
array([ 5, 10, 15])
>>> ndd4.mask
array([False, True, False]...)
```

If such an implicitly passed property conflicts with an explicit parameter, the explicit parameter will be used and an info-message will be issued:

```
>>> quantity = np.ones(3) * u.cm
>>> ndd6 = NDData(quantity, unit='m')
INFO: overwriting Quantity's current unit with specified unit. [astropy.nddata.nddata]
>>> ndd6.data
array([1., 1., 1.])
>>> ndd6.unit
Unit("m")
```

The unit of the `Quantity`

is being ignored and the unit is set
to the explicitly passed one.

It might be possible to pass other classes as `data`

parameter as long as
they have the properties `shape`

, `dtype`

, `__getitem__`

and
`__array__`

.

The purpose of this mechanism is to allow considerable flexibility in the objects used to store the data while providing a useful default (numpy array).

## Mask¶

The `mask`

is being used to indicate if data points are valid or invalid.
`NDData`

doesn’t restrict this mask in any way but it is
expected to follow the `numpy.ma.MaskedArray`

convention that the mask:

- returns
`True`

for data points that are considered**invalid**. - returns
`False`

for those points that are**valid**.

One possibility is to create a mask by using numpy’s comparison operators:

```
>>> array = np.array([0, 1, 4, 0, 2])
>>> mask = array == 0 # Mask points containing 0
>>> mask
array([ True, False, False, True, False]...)
>>> other_mask = array > 1 # Mask points with a value greater than 1
>>> other_mask
array([False, False, True, False, True]...)
```

and initialize the `NDData`

instance using the `mask`

parameter:

```
>>> ndd = NDData(array, mask=mask)
>>> ndd.mask
array([ True, False, False, True, False]...)
```

or by replacing the mask:

```
>>> ndd.mask = other_mask
>>> ndd.mask
array([False, False, True, False, True]...)
```

There is no requirement that the mask actually be a numpy array; for example, a
function which evaluates a mask value as needed is acceptable as long as it
follows the convention that `True`

indicates a value that should be ignored.

## Unit¶

The `unit`

represents the unit of the data values. It is required to be
`Unit`

-like or a string that can be converted to such a
`Unit`

:

```
>>> import astropy.units as u
>>> ndd = NDData([1, 2, 3, 4], unit="meter") # using a string
>>> ndd.unit
Unit("m")
```

- ..note::
- Setting the
`unit`

on an instance is not possible.

## Uncertainties¶

The `uncertainty`

represents an arbitrary representation of the error of the
data values. To indicate which kind of uncertainty representation is used the
`uncertainty`

should have an `uncertainty_type`

property. If no such
property is found it will be wrapped inside a
`UnknownUncertainty`

.

The `uncertainty_type`

should follow the `StdDevUncertainty`

convention that it returns a short string like `"std"`

for an uncertainty
given in standard deviation. Other examples are
`VarianceUncertainty`

and `InverseVariance`

.

Like the other properties the `uncertainty`

can be set during
initialization:

```
>>> from astropy.nddata import StdDevUncertainty
>>> array = np.array([10, 7, 12, 22])
>>> uncert = StdDevUncertainty(np.sqrt(array))
>>> ndd = NDData(array, uncertainty=uncert)
>>> ndd.uncertainty
StdDevUncertainty([3.16227766, 2.64575131, 3.46410162, 4.69041576])
```

or on the instance directly:

```
>>> other_uncert = StdDevUncertainty([2,2,2,2])
>>> ndd.uncertainty = other_uncert
>>> ndd.uncertainty
StdDevUncertainty([2, 2, 2, 2])
```

but it will print an info message if there is no `uncertainty_type`

:

```
>>> ndd.uncertainty = np.array([5, 1, 2, 10])
INFO: uncertainty should have attribute uncertainty_type. [astropy.nddata.nddata]
>>> ndd.uncertainty
UnknownUncertainty([ 5, 1, 2, 10])
```

## Meta-data¶

The `meta`

property contains all further meta information that don’t fit
any other property.

If given it must be `dict`

-like:

```
>>> ndd = NDData([1,2,3], meta={'observer': 'myself'})
>>> ndd.meta
{'observer': 'myself'}
```

`dict`

-like means it must be a mapping from some keys to some values. This
also includes `Header`

objects:

```
>>> from astropy.io import fits
>>> header = fits.Header()
>>> header['observer'] = 'Edwin Hubble'
>>> ndd = NDData(np.zeros([10, 10]), meta=header)
>>> ndd.meta['observer']
'Edwin Hubble'
```

If the `meta`

isn’t provided or explicitly set to `None`

it will default to
an empty `collections.OrderedDict`

:

```
>>> ndd.meta = None
>>> ndd.meta
OrderedDict()
>>> ndd = NDData([1,2,3])
>>> ndd.meta
OrderedDict()
```

The `meta`

object therefore supports adding or updating these values:

```
>>> ndd.meta['exposure_time'] = 340.
>>> ndd.meta['filter'] = 'J'
```

Elements of the meta-data dictionary can be set to any valid Python object:

```
>>> ndd.meta['history'] = ['calibrated', 'aligned', 'flat-fielded']
```

## Initialization with copy¶

The default way to create an `NDData`

instance is to try saving
the parameters as references to the original rather than as copy. Sometimes
this is not possible because the internal mechanics don’t allow for this. For
example if the `data`

is a `list`

then during initialization this is copied
while converting to a `ndarray`

. But it is also possible to enforce
copies during initialization by setting the `copy`

parameter to `True`

:

```
>>> array = np.array([1, 2, 3, 4])
>>> ndd = NDData(array)
>>> ndd.data[2] = 10
>>> array[2] # Original array has changed
10
>>> ndd2 = NDData(array, copy=True)
>>> ndd2.data[2] = 3
>>> array[2] # Original array hasn't changed.
10
```

## Converting NDData to other classes¶

There is limited to support to convert a `NDData`

instance to
other classes. In the process some properties might be lost.

```
>>> data = np.array([1, 2, 3, 4])
>>> mask = np.array([True, False, False, True])
>>> unit = 'm'
>>> ndd = NDData(data, mask=mask, unit=unit)
```

`numpy.ndarray`

¶

Converting the `data`

to an array:

```
>>> array = np.asarray(ndd.data)
>>> array
array([1, 2, 3, 4])
```

Though using `np.asarray`

is not required in most cases it will ensure that
the result is always a `numpy.ndarray`

`numpy.ma.MaskedArray`

¶

Converting the `data`

and `mask`

to a MaskedArray:

```
>>> masked_array = np.ma.array(ndd.data, mask=ndd.mask)
>>> masked_array
masked_array(data=[--, 2, 3, --],
mask=[ True, False, False, True],
fill_value=999999)
```