ajustador.vartype

class ajustador.vartype.vartype(x, dev=0)

A number with an uncertainty (σ)

>>> x = vartype(123, 5)
>>> y = vartype(8, 1)
>>> x + y
vartype(131.0, 5.1)
>>> x - y
vartype(115.0, 5.1)
>>> y / 3
vartype(2.67, 0.33)
>>> y < y
False
>>> print(y)
8±1
>>> print(x)
123±5

positive

Check if the number is greater than 3σ

>>> x = vartype(4, 1)
>>> x.positive
True
>>> y = x - vartype(3)
>>> y
vartype(1.0, 1.0)
>>> y.positive
False

negative

Check if the number is smaller than -3σ

>>> x = vartype(-4, 1)
>>> x.negative
True

classmethod average(vect)

Calculate a weighted average of an array

>>> items = [vartype(x, 1 + x/10) for x in range(5)]
>>> X = vartype.array(items)
>>> print(X)
rec.array([(0, 1.0), (1, 1.1), (2, 1.2), (3, 1.3), (4, 1.4)],
          dtype=[('x', '<i8'), ('dev', '<f8')])
>>> vartype.average(X)
vartype(1.66, 0.53)

classmethod array(items)

Create an array of vartypes

The array is a numpy structured array with .x and .dev attributes.

>>> items = [vartype(x, 1 + x/10) for x in range(5)]
>>> X = vartype.array(items)
>>> X
rec.array([(0, 1.0), (1, 1.1), (2, 1.2), (3, 1.3), (4, 1.4)],
          dtype=[('x', '<i8'), ('dev', '<f8')])
>>> X.x
array([0, 1, 2, 3, 4])
>>> X.dev
array([ 1. ,  1.1,  1.2,  1.3,  1.4])

classmethod format_array(array, prefix='')

nan = vartype(nan, nan)

ajustador.vartype.array_mean(data)

ajustador.vartype.array_diff(wave, n=1)

ajustador.vartype.array_sub(reca, recb)

Return the difference of two arrays

The uncertainty is calculated in the usual way.

ajustador.vartype.array_rms(rec)

Return the rms of an array

\[\mathrm{rms} = \sqrt{\sum_i (x_i / \sigma_i)^2}\]