# Uncertainties in arrays¶

## The unumpy package¶

This package contains:

1. utilities that help with the creation and manipulation of NumPy arrays and matrices of numbers with uncertainties;

2. generalizations of multiple NumPy functions so that they also work with arrays that contain numbers with uncertainties.

While basic operations on arrays that contain numbers with uncertainties can be performed without it, the `unumpy` package is useful for more advanced uses.

Operations on arrays (including their cosine, etc.) can thus be performed transparently.

These features can be made available with

```>>> from uncertainties import unumpy
```

### Creation and manipulation of arrays and matrices¶

#### Arrays¶

Arrays of numbers with uncertainties can be built from values and uncertainties:

```>>> arr = unumpy.uarray([1, 2], [0.01, 0.002])
>>> print arr
[1.0+/-0.01 2.0+/-0.002]
```

NumPy arrays of numbers with uncertainties can also be built directly through NumPy, thanks to NumPy’s support of arrays of arbitrary objects:

```>>> arr = numpy.array([ufloat(1, 0.1), ufloat(2, 0.002)])
```

#### Matrices¶

Matrices of numbers with uncertainties are best created in one of two ways. The first way is similar to using `uarray()`:

```>>> mat = unumpy.umatrix([1, 2], [0.01, 0.002])
```

Matrices can also be built by converting arrays of numbers with uncertainties into matrices through the `unumpy.matrix` class:

```>>> mat = unumpy.matrix(arr)
```

`unumpy.matrix` objects behave like `numpy.matrix` objects of numbers with uncertainties, but with better support for some operations (such as matrix inversion). For instance, regular NumPy matrices cannot be inverted, if they contain numbers with uncertainties (i.e., `numpy.matrix([[ufloat(…), …]]).I` does not work). This is why the `unumpy.matrix` class is provided: both the inverse and the pseudo-inverse of a matrix can be calculated in the usual way: if `mat` is a `unumpy.matrix`,

```>>> print mat.I
```

does calculate the inverse or pseudo-inverse of `mat` with uncertainties.

#### Uncertainties and nominal values¶

Nominal values and uncertainties in arrays (and matrices) can be directly accessed (through functions that work on pure float arrays too):

```>>> unumpy.nominal_values(arr)
array([ 1.,  2.])
>>> unumpy.std_devs(mat)
matrix([[ 0.1  ,  0.002]])
```

### Mathematical functions¶

This module defines uncertainty-aware mathematical functions that generalize those from `uncertainties.umath` so that they work on NumPy arrays of numbers with uncertainties instead of just scalars:

```>>> print unumpy.cos(arr)  # Cosine of each array element
```

NumPy’s function names are used, and not those from the `math` module (for instance, `unumpy.arccos()` is defined, like in NumPy, and is not named `acos()` like in the `math` module).

The definition of the mathematical quantities calculated by these functions is available in the documentation for `uncertainties.umath` (which is accessible through `help()` or `pydoc`).

#### NaN testing and NaN-aware operations¶

One particular function pertains to NaN testing: `unumpy.isnan()`. It returns true for each NaN nominal value (and false otherwise).

Since NaN±1 is not (the scalar) NaN, functions like `numpy.nanmean()` do not skip such values. This is where `unumpy.isnan()` is useful, as it can be used for masking out numbers with a NaN nominal value:

```>>> nan = float("nan")
>>> arr = numpy.array([nan, uncertainties.ufloat(nan, 1), uncertainties.ufloat(1, nan), 2])
>>> arr
array([nan, nan+/-1.0, 1.0+/-nan, 2], dtype=object)
>>> arr[~unumpy.isnan(arr)].mean()
1.5+/-nan
```

or equivalently, by using masked arrays:

```>>> masked_arr = numpy.ma.array(arr, mask=unumpy.isnan(arr))
1.5+/-nan
```

In this case the uncertainty is NaN as it should be, because one of the numbers does have an undefined uncertainty, which makes the final uncertainty undefined (but the average is well defined). In general, uncertainties are not NaN and one obtains the mean of the non-NaN values.

## Storing arrays in text format¶

Arrays of numbers with uncertainties can be directly pickled, saved to file and read from a file. Pickling has the advantage of preserving correlations between errors.

Storing instead arrays in text format loses correlations between errors but has the advantage of being both computer- and human-readable. This can be done through NumPy’s `savetxt()` and `loadtxt()`.

Writing the array to file can be done by asking NumPy to use the representation of numbers with uncertainties (instead of the default float conversion):

```>>> numpy.savetxt('arr.txt', arr, fmt='%r')
```

This produces a file arr.txt that contains a text representation of the array:

```1.0+/-0.01
2.0+/-0.002
```

The file can then be read back by instructing NumPy to convert all the columns with `uncertainties.ufloat_fromstr()`. The number `num_cols` of columns in the input file (1, in our example) must be determined in advance, because NumPy requires a converter for each column separately. For Python 2:

```>>> converters = dict.fromkeys(range(num_cols), uncertainties.ufloat_fromstr)
```

For Python 3, since `numpy.loadtxt()` passes bytes to converters, they must first be converted into a string:

```>>> converters = dict.fromkeys(
range(num_cols),
lambda col_bytes: uncertainties.ufloat_fromstr(col_bytes.decode("latin1")))
```

(Latin 1 appears to in fact be the encoding used in `numpy.savetxt()` [as of NumPy 1.12]. This encoding seems to be the one hardcoded in `numpy.compat.asbytes()`.)

The array can then be loaded:

```>>> arr = numpy.loadtxt('arr.txt', converters=converters, dtype=object)
```

The `unumpy.ulinalg` module contains more uncertainty-aware functions for arrays that contain numbers with uncertainties.
It currently offers generalizations of two functions from `numpy.linalg` that work on arrays (or matrices) that contain numbers with uncertainties, the matrix inverse and pseudo-inverse:
```>>> unumpy.ulinalg.inv([[ufloat(2, 0.1)]])