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

0.260, 0.853, ..., 0.398, 0.899

Values:
array([[0.26033909, 0.85252177, 0.01754777, 0.58943346, 0.82274093],
       [0.23873476, 0.03352047, 0.46069816, 0.48064053, 0.39939353],
       [0.54723983, 0.50138113, 0.43964187, 0.45008749, 0.38475728],
       [0.24076066, 0.1691947 , 0.88384524, 0.39818341, 0.89947434]])

[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

0.260, 0.853, ..., 0.398, 0.899

Values:
array([[0.26033909, 0.85252177, 0.01754777, 0.58943346, 0.82274093],
       [0.23873476, 0.03352047, 0.46069816, 0.48064053, 0.39939353],
       [0.54723983, 0.50138113, 0.43964187, 0.45008749, 0.38475728],
       [0.24076066, 0.1691947 , 0.88384524, 0.39818341, 0.89947434]])

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])

[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)  [0.260339, 0.852522, ..., 0.398183, 0.899474]  [0.00234395, 0.199229, ..., 0.245593, 0.814644]
  b                         float64  [dimensionless]  (y, x)  [0.260339, 0.852522, ..., 0.398183, 0.899474]  [0.00234395, 0.199229, ..., 0.245593, 0.814644]
  c                         float64  [dimensionless]  (y)  [0.0175478, 0.460698, 0.439642, 0.883845]  [0.907213, 0.0639422, 0.171607, 0.330912]
    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, 0.728385, ..., 0.489766, 1.10635]  [0.00709231, 0.30496, ..., 0.371558, 1.23247]
  b                         float64            [m/s]  (y, x)  [0, 0.728385, ..., 0.489766, 1.10635]  [0.00709231, 0.30496, ..., 0.371558, 1.23247]
  c                         float64            [m/s]  (y)  [-0.298633, 0.273015, 0.54076, 1.08713]  [1.37607, 0.188091, 0.259624, 0.500637]
    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/simon/code/scipp/scipp/.tox/docs/lib/python3.8/site-packages/scipp/__init__.py'>