Quick start#

This section provides a quick introduction to scipp. For in depth explanations refer to the sections in the user guide.

[1]:

import numpy as np
import scipp as sc

We start by creating some variables:

[2]:

var = sc.Variable(dims=['y', 'x'], values=np.random.rand(4,5))
sc.show(var)

Type the name of a variable at the end of a cell to generate an HTML respresentation:

[3]:

var

[3]:

scipp.Variable (416 Bytes)

(y: 4, x: 5)

float64

𝟙

0.651, 0.860, ..., 0.736, 0.327

Values:
array([[0.65098239, 0.86044099, 0.43594134, 0.11731904, 0.80448348],
       [0.76675464, 0.04289888, 0.04183491, 0.93360158, 0.53752035],
       [0.28852665, 0.66897157, 0.00459487, 0.96690272, 0.71799225],
       [0.20426842, 0.87737024, 0.82512391, 0.73618549, 0.32716409]])

[4]:

x = sc.Variable(dims=['x'], values=np.arange(5), unit=sc.units.m)
y = sc.Variable(dims=['y'], values=np.arange(4), unit=sc.units.m)

We combine the variables into a data array:

[5]:

array = sc.DataArray(
    data=var,
    coords={'x': x, 'y': y})
sc.show(array)
array

[5]:

scipp.DataArray (1.48 KB)

Dimensions:
- y: 4
- x: 5
Coordinates: (2)
- x
  (x)
  int64
  m
  0, 1, 2, 3, 4
```
Values:
array([0, 1, 2, 3, 4])
```
- y
  (y)
  int64
  m
  0, 1, 2, 3
```
Values:
array([0, 1, 2, 3])
```

Data: (1)

(y, x)

float64

𝟙

0.651, 0.860, ..., 0.736, 0.327

Values:
array([[0.65098239, 0.86044099, 0.43594134, 0.11731904, 0.80448348],
       [0.76675464, 0.04289888, 0.04183491, 0.93360158, 0.53752035],
       [0.28852665, 0.66897157, 0.00459487, 0.96690272, 0.71799225],
       [0.20426842, 0.87737024, 0.82512391, 0.73618549, 0.32716409]])

Variables can have uncertainties. Scipp stores these as variances (the square of the standard deviation):

[6]:

array.variances = np.square(np.random.rand(4,5))
sc.show(array)

We create a dataset:

[7]:

dataset = sc.Dataset(
    data={'a': var},
    coords={'x': x, 'y': y, 'aux': x})
dataset['b'] = array
dataset['scalar'] = 1.23 * (sc.units.m / sc.units.s)
sc.show(dataset)

We can slice variables, data arrays, and datasets using a dimension label and an index or a slice object like i:j:

[8]:

dataset['c'] = dataset['b']['x', 2]
sc.show(dataset)
dataset

We can also generate table representations (only 0-D and 1-D) and plots:

[9]:

sc.table(dataset['y', 2])

[9]:

		a	b
Coordinates		Data	Data
aux [m]	x [m]	[𝟙]	[𝟙]
0	0	0.289±0.178	0.289±0.178
1	1	0.669±0.061	0.669±0.061
2	2	0.005±0.896	0.005±0.896
3	3	0.967±0.347	0.967±0.347
4	4	0.718±0.538	0.718±0.538

[10]:

sc.plot(dataset)

[10]:

Arithmetic operations can be combined with slicing and handle propagation of uncertainties and units:

[11]:

print(dataset)

<scipp.Dataset>
Dimensions: Sizes[y:4, x:5, ]
Coordinates:
  aux                         int64              [m]  (x)  [0, 1, ..., 3, 4]
  x                           int64              [m]  (x)  [0, 1, ..., 3, 4]
  y                           int64              [m]  (y)  [0, 1, 2, 3]
Data:
  a                         float64  [dimensionless]  (y, x)  [0.650982, 0.860441, ..., 0.736185, 0.327164]  [0.0708914, 0.491559, ..., 0.0564866, 0.37522]
  b                         float64  [dimensionless]  (y, x)  [0.650982, 0.860441, ..., 0.736185, 0.327164]  [0.0708914, 0.491559, ..., 0.0564866, 0.37522]
  c                         float64  [dimensionless]  (y)  [0.435941, 0.0418349, 0.00459487, 0.825124]  [0.136394, 0.0423904, 0.801926, 0.658205]
    Attributes:
        aux                         int64              [m]  ()  [2]
        x                           int64              [m]  ()  [2]
  scalar                    float64            [m/s]  ()  [1.23]

[12]:

dataset['b']['y', 0:2] -= dataset['y', 0:2]['a']['x', 0]
dataset['b'] *= dataset['scalar']
print(dataset)

<scipp.Dataset>
Dimensions: Sizes[y:4, x:5, ]
Coordinates:
  aux                         int64              [m]  (x)  [0, 1, ..., 3, 4]
  x                           int64              [m]  (x)  [0, 1, ..., 3, 4]
  y                           int64              [m]  (y)  [0, 1, 2, 3]
Data:
  a                         float64            [m/s]  (y, x)  [0, 0.257634, ..., 0.905508, 0.402412]  [0.214503, 0.850931, ..., 0.0854585, 0.56767]
  b                         float64            [m/s]  (y, x)  [0, 0.257634, ..., 0.905508, 0.402412]  [0.214503, 0.850931, ..., 0.0854585, 0.56767]
  c                         float64            [m/s]  (y)  [-0.2645, -0.891651, 0.00565169, 1.0149]  [0.313602, 0.218286, 1.21323, 0.995798]
    Attributes:
        aux                         int64              [m]  ()  [2]
        x                           int64              [m]  ()  [2]
  scalar                    float64            [m/s]  ()  [1.23]

Finally, type the imported name of the scipp module at the end of a cell for a list of all current scipp objects (variables, data arrays, datasets). Click on entries to expand nested sections:

[13]:

sc

[13]:

<module 'scipp' from '/home/runner/work/scipp/scipp/.tox/docs/lib/python3.8/site-packages/scipp/__init__.py'>