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 (160 Bytes)

(y: 4, x: 5)

float64

𝟙

0.084, 0.954, ..., 0.914, 0.922

Values:
array([[0.08414328, 0.95409003, 0.91556799, 0.60699822, 0.68800363],
       [0.19432712, 0.46310178, 0.11965925, 0.0197561 , 0.6597149 ],
       [0.88885832, 0.46207903, 0.15058648, 0.04347002, 0.84134894],
       [0.18437389, 0.44649044, 0.74775448, 0.91441335, 0.92230531]])

[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 (232 Bytes)

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

0.084, 0.954, ..., 0.914, 0.922

Values:
array([[0.08414328, 0.95409003, 0.91556799, 0.60699822, 0.68800363],
       [0.19432712, 0.46310178, 0.11965925, 0.0197561 , 0.6597149 ],
       [0.88885832, 0.46207903, 0.15058648, 0.04347002, 0.84134894],
       [0.18437389, 0.44649044, 0.74775448, 0.91441335, 0.92230531]])

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	0.889±0.245	0.889±0.245
1	1	0.462±0.566	0.462±0.566
2	2	0.151±0.543	0.151±0.543
3	3	0.043±0.731	0.043±0.731
4	4	0.841±0.359	0.841±0.359

[10]:

sc.plot(dataset)

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)  [0.0841433, 0.95409, ..., 0.914413, 0.922305]  [0.376714, 0.0226097, ..., 0.762636, 0.210029]
  b                         float64  [dimensionless]  (y, x)  [0.0841433, 0.95409, ..., 0.914413, 0.922305]  [0.376714, 0.0226097, ..., 0.762636, 0.210029]
  c                         float64  [dimensionless]  (y)  [0.915568, 0.119659, 0.150586, 0.747754]  [0.559779, 0.0821339, 0.2945, 0.0204735]
    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, 1.07003, ..., 1.12473, 1.13444]  [1.13986, 0.604138, ..., 1.15379, 0.317753]
  b                         float64            [m/s]  (y, x)  [0, 1.07003, ..., 1.12473, 1.13444]  [1.13986, 0.604138, ..., 1.15379, 0.317753]
  c                         float64            [m/s]  (y)  [1.02265, -0.0918415, 0.185221, 0.919738]  [1.41682, 0.25547, 0.445548, 0.0309743]
    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'>