# Quantity¶

The `Quantity`

object is meant to represent a value that has some unit
associated with the number.

## Creating Quantity instances¶

`Quantity`

objects are normally created through multiplication with
`Unit`

objects. For example, to create a `Quantity`

to represent 15 m/s:

```
>>> import astropy.units as u
>>> 15 * u.m / u.s
<Quantity 15. m / s>
```

This extends as expected to division by a unit, or using Numpy arrays or Python sequences:

```
>>> 1.25 / u.s
<Quantity 1.25 1 / s>
>>> [1, 2, 3] * u.m
<Quantity [1., 2., 3.] m>
>>> import numpy as np
>>> np.array([1, 2, 3]) * u.m
<Quantity [1., 2., 3.] m>
```

You can also create instances using the `Quantity`

constructor directly, by
specifying a value and unit:

```
>>> u.Quantity(15, u.m / u.s)
<Quantity 15. m / s>
```

The constructor gives a few more options. In particular, it allows one to
merge sequences of `Quantity`

objects (as long as all of their units are
equivalent), and to parse simple strings (which may help, e.g., to parse
configuration files, etc.):

```
>>> qlst = [60 * u.s, 1 * u.min]
>>> u.Quantity(qlst, u.minute)
<Quantity [1., 1.] min>
>>> u.Quantity('15 m/s')
<Quantity 15. m / s>
```

The current unit and value can be accessed via the
`unit`

and
`value`

attributes:

```
>>> q = 2.5 * u.m / u.s
>>> q.unit
Unit("m / s")
>>> q.value
2.5
```

Note

`Quantity`

objects are converted to float by default. Furthermore,
any data passed in are copied, which for large arrays may not be
optimal. As discussed further below. one can instead obtain a
`view`

by passing `copy=False`

to `Quantity`

or use
the `<<`

operator.

## Converting to different units¶

`Quantity`

objects can be converted to different units using the
`to()`

method:

```
>>> q = 2.3 * u.m / u.s
>>> q.to(u.km / u.h)
<Quantity 8.28 km / h>
```

For convenience, the `si`

and
`cgs`

attributes can be used to
convert the `Quantity`

to base S.I. or c.g.s units:

```
>>> q = 2.4 * u.m / u.s
>>> q.si
<Quantity 2.4 m / s>
>>> q.cgs
<Quantity 240. cm / s>
```

If you want the value of the quantity in a different unit, you can use
`to_value()`

as a short-cut:

```
>>> q = 2.5 * u.m
>>> q.to_value(u.cm)
250.0
```

Note

You could get the value in `cm`

also using `q.to(u.cm).value`

.
The difference is that `to_value()`

does
no conversion if the unit is already the correct one, instead just
returning an `view()`

of the data (just as if you
had done `q.value`

). In contrast,
`to()`

always returns a copy (which also
means it is slower for the case where no conversion is necessary).
As discussed further below,
one can avoid the copy if the unit is already correct by using the
`<<`

operator.

## Comparing quantities¶

`Quantity`

objects can be compared as follows:

```
>>> from astropy import units as u
>>> u.allclose([1, 2] * u.m, [100, 200] * u.cm)
True
>>> u.isclose([1, 2] * u.m, [100, 20] * u.cm)
array([ True, False])
```

## Plotting quantities¶

`Quantity`

objects can be conveniently plotted using matplotlib - see
Plotting quantities for more details.

## Arithmetic¶

### Addition and Subtraction¶

Addition or subtraction between `Quantity`

objects is supported when their
units are equivalent. When the units are equal, the resulting object has the
same unit:

```
>>> 11 * u.s + 30 * u.s
<Quantity 41. s>
>>> 30 * u.s - 11 * u.s
<Quantity 19. s>
```

If the units are equivalent, but not equal (e.g. kilometer and meter), the
resulting object **has units of the object on the left**:

```
>>> 1100.1 * u.m + 13.5 * u.km
<Quantity 14600.1 m>
>>> 13.5 * u.km + 1100.1 * u.m
<Quantity 14.6001 km>
>>> 1100.1 * u.m - 13.5 * u.km
<Quantity -12399.9 m>
>>> 13.5 * u.km - 1100.1 * u.m
<Quantity 12.3999 km>
```

Addition and subtraction are not supported between `Quantity`

objects and basic
numeric types:

```
>>> 13.5 * u.km + 19.412
Traceback (most recent call last):
...
UnitConversionError: Can only apply 'add' function to dimensionless
quantities when other argument is not a quantity (unless the
latter is all zero/infinity/nan)
```

except for dimensionless quantities (see Dimensionless quantities).

### Multiplication and Division¶

Multiplication and division are supported between `Quantity`

objects with any
units, and with numeric types. For these operations between objects with
equivalent units, the **resulting object has composite units**:

```
>>> 1.1 * u.m * 140.3 * u.cm
<Quantity 154.33 cm m>
>>> 140.3 * u.cm * 1.1 * u.m
<Quantity 154.33 cm m>
>>> 1. * u.m / (20. * u.cm)
<Quantity 0.05 m / cm>
>>> 20. * u.cm / (1. * u.m)
<Quantity 20. cm / m>
```

For multiplication, you can change how to represent the resulting object by
using the `to()`

method:

```
>>> (1.1 * u.m * 140.3 * u.cm).to(u.m**2)
<Quantity 1.5433 m2>
>>> (1.1 * u.m * 140.3 * u.cm).to(u.cm**2)
<Quantity 15433. cm2>
```

For division, if the units are equivalent, you may want to make the resulting
object dimensionless by reducing the units. To do this, use the
`decompose()`

method:

```
>>> (20. * u.cm / (1. * u.m)).decompose()
<Quantity 0.2>
```

This method is also useful for more complicated arithmetic:

```
>>> 15. * u.kg * 32. * u.cm * 15 * u.m / (11. * u.s * 1914.15 * u.ms)
<Quantity 0.34195097 cm kg m / (ms s)>
>>> (15. * u.kg * 32. * u.cm * 15 * u.m / (11. * u.s * 1914.15 * u.ms)).decompose()
<Quantity 3.41950973 kg m2 / s2>
```

## Numpy functions¶

`Quantity`

objects are actually full Numpy arrays (the `Quantity`

class
inherits from and extends `numpy.ndarray`

), and we have tried to ensure
that Numpy functions behave properly with quantities:

```
>>> q = np.array([1., 2., 3., 4.]) * u.m / u.s
>>> np.mean(q)
<Quantity 2.5 m / s>
>>> np.std(q)
<Quantity 1.11803399 m / s>
```

This includes functions that only accept specific units such as angles:

```
>>> q = 30. * u.deg
>>> np.sin(q)
<Quantity 0.5>
```

or dimensionless quantities:

```
>>> from astropy.constants import h, k_B
>>> nu = 3 * u.GHz
>>> T = 30 * u.K
>>> np.exp(-h * nu / (k_B * T))
<Quantity 0.99521225>
```

(see Dimensionless quantities below for more details).

Note

With numpy versions older than 1.17, a number of mostly
non-arithmetic functions have known issues,
either ignoring the unit (e.g., `np.dot`

) or not reinitializing it
properly (e.g., `np.hstack`

). This propagates to more complex
functions such as `np.linalg.norm`

.

Support for functions from other packages, such as `scipy`

, is
more incomplete (contributions to improve this welcomed!).

## Dimensionless quantities¶

Dimensionless quantities have the characteristic that if they are
added or subtracted from a Python scalar or unitless `ndarray`

,
or if they are passed to a Numpy function that takes dimensionless
quantities, the units are simplified so that the quantity is
dimensionless and scale-free. For example:

```
>>> 1. + 1. * u.m / u.km
<Quantity 1.001>
```

which is different from:

```
>>> 1. + (1. * u.m / u.km).value
2.0
```

In the latter case, the result is `2.0`

because the unit of ```
(1. * u.m /
u.km)
```

is not scale-free by default:

```
>>> q = (1. * u.m / u.km)
>>> q.unit
Unit("m / km")
>>> q.unit.decompose()
Unit(dimensionless with a scale of 0.001)
```

However, when combining with a non-quantity object, the unit is automatically decomposed to be scale-free, giving the expected result.

This also occurs when passing dimensionless quantities to functions that take dimensionless quantities:

```
>>> nu = 3 * u.GHz
>>> T = 30 * u.K
>>> np.exp(- h * nu / (k_B * T))
<Quantity 0.99521225>
```

The result is independent from the units the different quantities were specified in:

```
>>> nu = 3.e9 * u.Hz
>>> T = 30 * u.K
>>> np.exp(- h * nu / (k_B * T))
<Quantity 0.99521225>
```

## Converting to plain Python scalars¶

Converting `Quantity`

objects does not work for non-dimensionless quantities:

```
>>> float(3. * u.m)
Traceback (most recent call last):
...
TypeError: only dimensionless scalar quantities can be converted
to Python scalars
```

Instead, only dimensionless values can be converted to plain Python scalars:

```
>>> float(3. * u.m / (4. * u.m))
0.75
>>> float(3. * u.km / (4. * u.m))
750.0
>>> int(6. * u.km / (2. * u.m))
3000
```

## Functions that accept Quantities¶

Validation of quantity arguments to functions can lead to many repetitions
of the same checking code. A decorator is provided which verifies that certain
arguments to a function are `Quantity`

objects and that the units
are compatible with a desired unit or physical type.

The decorator does not convert the input quantity to the desired unit, say
arcseconds to degrees in the example below, it merely checks that such a
conversion is possible, thus verifying that the `Quantity`

argument can be used in calculations.

The decorator `quantity_input`

accepts keyword arguments to
specify which arguments should be validated and what unit they are expected to
be compatible with:

```
>>> @u.quantity_input(myarg=u.deg)
... def myfunction(myarg):
... return myarg.unit
```

```
>>> myfunction(100*u.arcsec)
Unit("arcsec")
```

It is also possible to instead specify the physical type of the desired unit:

```
>>> @u.quantity_input(myarg='angle')
... def myfunction(myarg):
... return myarg.unit
```

```
>>> myfunction(100*u.arcsec)
Unit("arcsec")
```

Optionally `None`

keyword arguments are also supported; for such cases, the
input is only checked when a value other than `None`

is passed:

```
>>> @u.quantity_input(a='length', b='angle')
... def myfunction(a, b=None):
... return a, b
```

```
>>> myfunction(1.*u.km)
(<Quantity 1. km>, None)
>>> myfunction(1.*u.km, 1*u.deg)
(<Quantity 1. km>, <Quantity 1. deg>)
```

Alternatively, you can use the annotations syntax to provide the units:

```
>>> @u.quantity_input
... def myfunction(myarg: u.arcsec):
... return myarg.unit
```

```
>>> myfunction(100*u.arcsec)
Unit("arcsec")
```

You can also annotate for different types in non-unit expecting arguments:

```
>>> @u.quantity_input
... def myfunction(myarg: u.arcsec, nice_string: str):
... return myarg.unit, nice_string
>>> myfunction(100*u.arcsec, "a nice string")
(Unit("arcsec"), 'a nice string')
```

You can define a return decoration, to which the return value will be converted, i.e.:

```
>>> @u.quantity_input
... def myfunction(myarg: u.arcsec) -> u.deg:
... return myarg*1000
>>> myfunction(100*u.arcsec)
<Quantity 27.77777778 deg>
```

This both checks that the return value of your function is consistent with what you expect and makes it much neater to display the results of the function.

The decorator also supports specifying a list of valid equivalent units or physical types for functions that should accept inputs with multiple valid units:

```
>>> @u.quantity_input(a=['length', 'speed'])
... def myfunction(a):
... return a.unit
```

```
>>> myfunction(1.*u.km)
Unit("km")
>>> myfunction(1.*u.km/u.s)
Unit("km / s")
```

## Representing vectors with units¶

`Quantity`

objects can, like numpy arrays, be used to represent vectors or
matrices by assigning specific dimensions to represent the coordinates or
matrix elements, but that implies tracking those dimensions carefully. For
vectors, one can use instead the representations underlying coordinates, which
allow one to use representations other than cartesian (such as spherical or
cylindrical), as well as simple vector arithmetic. For details, see
Using and Designing Coordinate Representations.

## Creating and converting quantities without copies¶

When creating a `Quantity`

using multiplication with a unit, a copy of the
underlying data is made. This can be avoided by passing on `copy=False`

in
the initializer:

```
>>> a = np.arange(5.)
>>> q = u.Quantity(a, u.m, copy=False)
>>> q
<Quantity [0., 1., 2., 3., 4.] m>
>>> np.may_share_memory(a, q)
True
>>> a[0] = -1.
>>> q
<Quantity [-1., 1., 2., 3., 4.] m>
```

This may be particularly useful in functions which do not change their input;
it also ensures that if a user passes in a `Quantity`

with units of length,
it will be converted to meters.

As a shortcut, one can “shift” to the requested unit using the `<<`

operator:

```
>>> q = a << u.m
>>> np.may_share_memory(a, q)
True
>>> q
<Quantity [-1., 1., 2., 3., 4.] m>
```

The operator works identically to the initialization with `copy=False`

mentioned above:

```
>>> q << u.cm
<Quantity [-100., 100., 200., 300., 400.] cm>
```

It can also be used for in-place conversion:

```
>>> q <<= u.cm
>>> q
<Quantity [-100., 100., 200., 300., 400.] cm>
>>> a
array([-100., 100., 200., 300., 400.])
```

## Subclassing Quantity¶

To subclass `Quantity`

, one generally proceeds as one would when subclassing
`ndarray`

, i.e., one typically needs to override `__new__`

(rather than `__init__`

) and uses the `numpy.ndarray.__array_finalize__`

method to update attributes. For details, see the numpy documentation on
subclassing. For
examples, one can look at `Quantity`

itself, where, e.g., the
`astropy.units.Quantity.__array_finalize__`

method is used to pass on the
`unit`

, at `Angle`

, where strings are parsed
as angles in the `astropy.coordinates.Angle.__new__`

method and at
`Longitude`

, where the
`astropy.coordinates.Longitude.__array_finalize__`

method is used to pass
on the angle at which longitudes wrap.

Another method that is meant to be overridden by subclasses, one specific to
`Quantity`

, is `astropy.units.Quantity.__quantity_subclass__`

. This is
called to decide which type of subclass to return, based on the unit of the
quantity that is to be created. It is used, e.g., in
`Angle`

to return a `Quantity`

if a calculation
returns a unit other than an angular one. The implementation of this is via
`SpecificTypeQuantity`

, which more generally allows one
to construct `Quantity`

subclasses that have methods that are useful only for
a specific physical type.