The Quantity object is meant to represent a value that has some unit associated with the number.
>>> import astropy.units as u >>> 15 * u.m / u.s <Quantity 15.0 m / s>
Quantity objects are converted to float by default.
As another example:
>>> 1.25 / u.s <Quantity 1.25 1 / s>
You can also create instances using the Quantity constructor directly, by specifying a value and unit:
>>> u.Quantity(15, u.m / u.s) <Quantity 15.0 m / s>
Quantity objects can also be created automatically from Numpy arrays or Python sequences:
>>> [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>
>>> qlst = [60 * u.s, 1 * u.min] >>> u.Quantity(qlst, u.minute) <Quantity [ 1., 1.] min>
>>> q = 2.5 * u.m / u.s >>> q.unit Unit("m / s") >>> q.value 2.5
>>> q = 2.3 * u.m / u.s >>> q.to(u.km / u.h) <Quantity 8.2... km / h>
>>> q = 2.4 * u.m / u.s >>> q.si <Quantity 2... m / s> >>> q.cgs <Quantity 240.0 cm / s>
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 41.0 s> >>> 30 * u.s - 11 * u.s <Quantity 19.0 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 14.600... km> >>> 1100.1 * u.m - 13.5 * u.km <Quantity -12399.9 m> >>> 13.5 * u.km - 1100.1 * u.m <Quantity 12.399... km>
Addition and subtraction is not supported between Quantity objects and basic numeric types:
>>> 13.5 * u.km + 19.412 Traceback (most recent call last): ... UnitsError: 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 is 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 20.0 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 15433.0... 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 0.341950972... cm kg m / (ms s)> >>> (15. * u.kg * 32. * u.cm * 15 * u.m / (11. * u.s * 1914.15 * u.ms)).decompose() <Quantity 3.41950972... kg m2 / s2>
Quantity objects are actually full Numpy arrays (the Quantity object class inherits from and extends the numpy.ndarray class), and we have tried to ensure that most Numpy functions behave properly with units:
>>> q = np.array([1., 2., 3., 4.]) * u.m / u.s >>> np.mean(q) <Quantity 2.5 m / s> >>> np.std(q) <Quantity 1.118033... m / s>
including functions that only accept specific units such as angles:
>>> q = 30. * u.deg >>> np.sin(q) <Quantity 0.4999999...>
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 for more details).
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 1.00...>
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 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
Note that scaled dimensionless quantities such as m / km also do not work:
>>> float(3. * u.m / (4. * u.km)) Traceback (most recent call last): ... TypeError: Only dimensionless scalar quantities can be converted to Python scalars
If you want to simplify e.g. dimensionless quantities to their true dimensionless value, then you can make use of the decompose() method:
>>> q = 3. * u.m / (4. * u.km) >>> q <Quantity 0.75 m / km> >>> q.decompose() <Quantity 0.00075...> >>> float(q.decompose()) 0.00075...
>>> int(6. * u.m / (2. * u.m)) 3
Since Quantity objects are Numpy arrays, we are not able to ensure that only dimensionless quantities are converted to Numpy arrays:
>>> np.array([1, 2, 3] * u.m) array([ 1., 2., 3.])
Similarly, while most numpy functions work properly, a few 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 and scipy.integrate.odeint.